diff --git a/.github/dependabot.yml b/.github/dependabot.yml
new file mode 100644
index 000000000..74b70987b
--- /dev/null
+++ b/.github/dependabot.yml
@@ -0,0 +1,19 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
+---
+version: 2
+updates:
+  # When the Dependabot ignore-manifests issue is fixed, we can just ignore the
+  # /lessons directory only:
+  # https://github.com/dependabot/dependabot-core/issues/4364
+  - package-ecosystem: "pip"
+    directory: "/scripts"
+    schedule:
+      interval: "weekly"
+  - package-ecosystem: "github-actions"
+    directory: "/.github"
+    schedule:
+      interval: "weekly"
diff --git a/.github/workflows/jekyll-gh-pages.yml b/.github/workflows/jekyll-gh-pages.yml
new file mode 100644
index 000000000..9b5ff989c
--- /dev/null
+++ b/.github/workflows/jekyll-gh-pages.yml
@@ -0,0 +1,64 @@
+---
+name: Deploy Jekyll with GitHub Pages dependencies preinstalled
+
+on:
+  push:
+    branches: ["gh-pages"]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
+# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
+concurrency:
+  group: "pages"
+  cancel-in-progress: false
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      # Generate event posts by reading them from the UofTCoders/Events issues.
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: 3.12
+      - name: Install pipenv
+        run: pip install pipenv
+      - name: Publish events
+        working-directory: scripts
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          pipenv install
+          pipenv run publish_events
+
+      - name: Setup Pages
+        uses: actions/configure-pages@v4
+      - name: Build with Jekyll
+        uses: actions/jekyll-build-pages@v1
+        with:
+          source: ./
+          destination: ./_site
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+
+  deploy:
+    permissions:
+      contents: read
+      pages: write
+      id-token: write
+
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    runs-on: ubuntu-latest
+    needs: build
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4
diff --git a/.gitignore b/.gitignore
index c2487f29a..803b49eb0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,9 @@
+_site/
+
+# Google calendar secret key
 client_secret.json
+
+.Rproj.user
+_emails/
+.Rbuildignore
+
diff --git a/.here b/.here
new file mode 100644
index 000000000..e69de29bb
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 000000000..a17266918
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,138 @@
+---
+layout: page
+title: Contributing to UofT Coders
+---
+Welcome to the Contributing guideline for UofT Coders. Thanks for taking the time to contribute! 
+
+The following is a set of guidelines for contributing to the UofT Coders community, whether it be by teaching a lesson, fixing the website, helping to plan and organize our various events, or taking on a leadership role.
+
+### Table of Contents
+
+1. [About UofT Coders](#about-uoft-coders)
+    - [Code of Conduct](#code-of-conduct)
+
+2. [How You Can Contribute](#how-you-can-contribute)
+    - [Leading a Lesson](#leading-a-lesson)
+        - [Creating the Content](#creating-the-content)
+        - [Teaching in Class](#teaching-in-class)
+    - [Fixing and Updating the Website](#fixing-and-updating-the-website)
+    - [Other Ways to Get Involved](#other-ways-to-get-involved)
+
+-----
+# About UofT Coders
+
+UofT Coders was formed to share and learn about coding techniques and
+best practices for computing and analysis in research. We hold weekly sessions
+in the format of code-alongs, coworking sessions or journal clubs/discussions.
+To see our previous and upcoming events, you can visit our [Events repo](https://github.com/UofTCoders/Events/issues) or [website](https://uoftcoders.github.io/studyGroup/#events).
+To learn more about our group, you can read our [constitution](https://github.com/UofTCoders/council/blob/master/Constitution.md).
+
+## Code of Conduct
+
+We adhere to a [Code of Conduct](https://github.com/UofTCoders/studyGroup/blob/gh-pages/codeOfConduct.md)
+and by participating, you agree to also uphold this code. Please report any 
+unacceptable behaviour to uoftcoders@gmail.com. If you feel that a member of 
+the executive team has been violating the Code of Conduct, please email 
+groups.officer@utoronto.ca to report.
+
+-----
+# How You Can Contribute
+
+## Leading a Lesson
+
+The Mozilla Study Group
+handbook [**here**](https://mozillascience.github.io/studyGroupHandbook/lessons.html#reuse)
+and [**here**](https://mozillascience.github.io/studyGroupHandbook/event-types.html#workalong)
+has several very good points about making a lesson. This section summarizes bits
+of the handbook, but also adds pieces that are missing from it. Check out the
+[lesson bank too](https://github.com/mozillascience/studyGroupLessons/issues).
+
+### Creating the Content
+
++ **Minimal use of slides**: The most effective sessions are ones that are very
+hands-on. We strongly encourage [*live-coding*](#more-on-live-coding) as a 
+teaching method; it's best to keep slides to a minimum or avoid them completely.
++ **Keep in mind beginners**: Make few assumptions about the knowledge of the audience,
+unless specified that this is an intermediate level lesson, requiring prior knowledge.
+Keep it simple. Don't attempt to cover too much material in a single session.
++ **Use Built-in Datasets**: Use built-in sample datasets instead of requiring
+attendees to download files. 
++ **Code Review**: The lesson code is posted to GitHub along with a pull request to
+[UofTCoders/studyGroup](https://github.com/UofTCoders/studyGroup) repository at
+least 1 full day prior to the lesson date to allow for review.
+See [Submitting a Pull Request](https://uoftcoders.github.io/studyGroup/pull-request/)
+
+#### More on Live-Coding
+
+Live-coding is a hands-on method of teaching coding to a group in which the instructor 
+shares their screen with the group and types all commands on their computer while the 
+group follows along. Live-coding is a very effective teaching technique: it forces the 
+instructor to go slowly and ensures that participants get to try out every command 
+being used. It allows learners to experience common errors themselves and debug them 
+in a supportive environment, to explore variations on material as they go, and to 
+immediately check their understanding by trying things hands-on. 
+
+Live-coding is a technique used by [Software Carpentry](https://software-carpentry.org/about/). 
+Software Carpentry has lots of great resources explaining the why and how of live-coding:
++ [10 tips and tricks for instructing and teaching by means of live coding](https://software-carpentry.org/blog/2016/04/tips-tricks-live-coding.html)
++ The Software Carpentry [instructor training manual](http://carpentries.github.io/instructor-training/) 
+includes many resources about programming education.
+
+To see live-coding in action in the UofT Coders group, watch a portion of the 
+[Intro to R livestream](https://www.youtube.com/watch?v=b74V54VFL98) taught by 
+[Ahmed](https://github.com/aays), or look over the code that was covered in an 
+[R lesson](https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/r/intro/Intro_R_Sept21.R) 
+created by [Luke](https://github.com/lwjohnst86). 
+
+### Teaching in Class
+
++ **Arrive early**: Come 10 minutes before the lesson starts to set up.
++ **Introduce yourself**: Start by introducing yourself and perhaps why you're teaching this lesson.
++ **Stay on time**: Keep mindful of the time, lessons are 50-60 minutes long.
++ **Start from the very beginning**: Briefly explain all aspects of the what you are doing when live-coding including:
+    - show how to open the program or IDE (e.g. RStudio/Jupyter Notebook/Shell or Terminal/etc)
+    - how to run code (e.g. press `Shift+Enter` in the Jupyter Notebook)
+    - if this is an intro lesson, explain the concept of an IDE or shell
+    - importing modules and packages such as  `import numpy as np` or `library(dplyr)`
+
++ **Live-coding**: Use of slides is minimal, majority of lesson involves writing the code WITH the audience during the lesson
++ **Stay on topic**: There is only one hour, if a question arises that is off-topic, you can always suggest discussing afterwards.
++ **Check in with participants**: We distribute coloured sticky notes to each 
+participant so that they can flag problems with one colour and success with 
+another colour. Use these indicators to stay aware of your pacing and where the group is at, 
+and/or verbally ask how everyone is doing and leave time for questions.
+
+-----
+
+## Fixing and updating the website
+
+There are two ways of fixing or adding to the website, either by:
+
+- Creating an [Issue](https://github.com/UofTCoders/studyGroup/issues/new)
+describing the problem or enhancement. This is technically not doing anything
+yourself, just recommending something to be done.
+- Submitting a Pull Request from a clone of this repo. This way takes a bit more work and requires knowledge of Git and likely HTML. But we
+ would appreciate any help! No harm in giving it a try! That's a beauty of using Git, it's hard to mess up and break something.
+
+If you want to view the website before submitting a Pull Request to make sure
+your changes are as you expect, you'll need to:
+
+- Install Jekyll by following these [instructions](https://help.github.com/articles/setting-up-your-github-pages-site-locally-with-jekyll/).
+- To build the site locally, run `jekyll serve`.
+- The built site can also be viewed at your forked version
+(`https:://yourusername.github.io/studyGroup`).
+
+----
+
+## Other Ways to Get Involved
+
+### Helping Out at Our Events
+
+We hold various sessions that incorporate code-alongs, and having the help of more advanced users to help out the beginners is very much appreciated.
+- If you see a session topic that is more beginner than your current level, we highly encourage you to attend anyway and help answer questions or provide more one-on-one support during lessons.
+- You can also lend a hand at our co-working sessions to people who have come to work on their own projects.
+- We do a call for helpers for all our Software Carpentry workshops, and we definitely wouldn't be able to run these without the help of volunteers like you!
+
+### Taking On A Leadership Role
+
+The planning and organization of our the group and our various events are done by our executive council. We hold elections every April to elect the executive council members, however, we are always open to suggestions on new roles and positions for interested members of the UofT Coders community who wish to take on more a leadership role. To read more about these positions, please see our [Council Roles](https://github.com/UofTCoders/council/blob/master/Council_Roles.md) document.
diff --git a/DESCRIPTION b/DESCRIPTION
new file mode 100644
index 000000000..0f9b8fe0b
--- /dev/null
+++ b/DESCRIPTION
@@ -0,0 +1,22 @@
+Package: studyGroup
+Version: 0.0.0.9000
+Title: Website for UofTCoders
+Description: Content for studyGroup website.
+Encoding: UTF-8
+LazyData: true
+ByteCompile: true
+Imports: 
+    yaml,
+    here,
+    purrr,
+    dplyr,
+    stringr,
+    lubridate,
+    glue,
+    fs,
+    gh,
+    usethis,
+    assertr,
+    readr
+URL: https://github.com/SaraMati/studyGroup
+BugReports: https://github.com/SaraMati/studyGroup/issues
diff --git a/ISSUE_TEMPLATE.md b/ISSUE_TEMPLATE.md
new file mode 100644
index 000000000..255fc754d
--- /dev/null
+++ b/ISSUE_TEMPLATE.md
@@ -0,0 +1,13 @@
+<!-- 
+If you're posting an event, volunteer, job or other
+opportunity to the UofT Coders Study Group, please address 
+all of the following points in your post:
+
+- Your Name, or Organization Name
+- Contact information
+- Type of Post: Event, Volunteer or Job Opportunity, or other (please specify)
+- Details of issue/event/opportunity
+- Why you think UofT Coders can help or would be interested?
+
+Thanks for sharing with the community! 
+-->
diff --git a/README.md b/README.md
index 1797c14b5..3920139bc 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,8 @@
 Mozilla Study Groups
 ============
 
+[![Join the chat at https://gitter.im/UofTCoders/Lobby](https://badges.gitter.im/UofTCoders/studyGroup.svg)](https://gitter.im/UofTCoders/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+
 Welcome to [Mozilla Science Lab](https://www.mozillascience.org/)'s Study Group project! From here, we'll set you up with everything you need to start your own study group.
 
 ### Wait What's a 'Mozilla Study Group'?
@@ -11,12 +13,14 @@ Mozilla Study Groups are fun, informal meetups of your friends and colleagues fr
 
 Welcome to our Mozilla Study Group! A few things to do & know now that you're here:
 
- - **Watch this repo:** up in the top right, there's a button that says 'Watch'; click it, and set yourself to 'Watching'. This will send you email notifications of new discussions; if you don't want email, but would like an alert just on GitHub, change the setting in Settings -> Notification Center (Settings is the little cog in the top right).
+ - **Watch the [Events](https://github.com/UofTCoders/Events) repo and/or this repo:** up in the top right, there's a button that says 'Watch'; click it, and set yourself to 'Watching'. This will send you email notifications of new discussions. If you only want to be notified about events (and not any other discussion topics), watch just the [Events](https://github.com/UofTCoders/Events) repo. If you want to stay updated on all group doings, watch this repo AND the Events repo. If you don't want email, but would like an alert just on GitHub, change the setting in Settings -> Notification Center (Settings is the little cog in the top right).
  - **Check out the issue tracker:** click on 'issues' in the sidebar on the right; this is where all the conversations this study groups is having live. Use this space to ask questions, request events, make suggestions, or just say hi.
  - **Read the code of conduct:** this Study Group is for everyone - we abide by a [set of rules](https://www.mozillascience.org/code-of-conduct/) that require everyone be treated with respect. Help us make a space where everyone feels welcome, and we'll all have a better time!
  - **Add yourself to the website:** If you'd like to appear on the website under the 'Who we Are' section, have a look at the `_data/members.yml`; send us a pull request with an entry for yourself, or open an issue and we'll do it for you.
 
-## For Organizers
+## For Organizers/Instructors
+
+Check out the [`CONTRIBUTING` file](https://github.com/UofTCoders/studyGroup/blob/gh-pages/CONTRIBUTING.md) or [the page on the site](https://uoftcoders.github.io/studyGroup/CONTRIBUTING) for more details.
 
 ### It's Broken, I Need Help
 
@@ -70,7 +74,7 @@ When you're ready to list a new event for your Study Group, follow these steps,
 
     where `YYYY-MM-DD` is the date of your event, and `word` is anything you want.
  4. **Cut and paste the following into your new file:**
- 
+
     ```
     ---
     title: Study Group Meetup
@@ -106,7 +110,7 @@ Your website includes a gallery of participants in your Study Group; adding peop
 
 ```
 - name: their human name
-  affiliation: school, lab, department, business....
+  degree: school, lab, department, business....
   github: their GitHub handle
   interests:
     - list one to three
diff --git a/_config.yml b/_config.yml
index e401d5126..b3650094f 100644
--- a/_config.yml
+++ b/_config.yml
@@ -1,9 +1,10 @@
 #Setup ~~~
 #Follow these 2 steps to set up your site.
+exclude: [".Rproj.user"]
 
 #Step 1: give your study group a name and a short description:
-title: Our Study Group
-description: "A study group."
+title: University of Toronto Coders
+description: "We are a group of students and researchers dedicated to learning about and sharing scientific coding techniques and knowledge in an effort to improve scientific research."
 
 #Step 2: press the green 'Commit Changes' button at the bottom of this page.
 
@@ -16,9 +17,9 @@ description: "A study group."
 # Optional Configuration
 # None of these things need to be changed - but feel free!
 
-titleBackground: "https://s3-us-west-2.amazonaws.com/webmaker-kits/learning%402x.jpg"
-headlineSmall: "Welcome To Our Study Group!"
+headlineSmall: "Welcome To The U of T Coders Group!"
 headlineMain: "Let's Work Together"
+avatar: "img/logos/logo_icon_dark.png"
 
 # Setup Google Calendar
 # Usage:
@@ -26,10 +27,24 @@ headlineMain: "Let's Work Together"
 # 2. change 'USER' in the example urls below to the username of the google account you made for your calendar
 # 3. set calendar_on: True.
 
-calendar_on: False
-calendar_embed_url: "https://www.google.com/calendar/embed?src=USER@gmail.com"
-calendar_ical_url: "https://www.google.com/calendar/ical/USER@gmail.com/public/basic.ics"
+calendar_on: True
+calendar_embed_url: "https://www.google.com/calendar/embed?src=uoftcoders@gmail.com"
+calendar_ical_url: "https://www.google.com/calendar/ical/uoftcoders@gmail.com/public/basic.ics"
 
+# ===============================
+# Categories for sorting lessons.
+# ===============================
+
+languages:
+  - python
+  - r
+  - git
+  - misc
+
+levels:
+  - beginner
+  - intermediate
+  - advanced
 
 #========================================
 # No touching anything below this line :)
@@ -43,3 +58,4 @@ color:
 # Build settings
 markdown: kramdown
 permalink: pretty
+future: true
diff --git a/_data/admin.yml b/_data/admin.yml
new file mode 100644
index 000000000..12272fc1a
--- /dev/null
+++ b/_data/admin.yml
@@ -0,0 +1,65 @@
+---
+- name: Yeshoda Harry-Paul
+  position: President
+  degree: PhD, Cell and Systems Biology
+  github: YeshodaHP
+  interests:
+    - R
+    - Python
+    - Bash
+
+- name: Margot Lautens
+  position: Co-working Coordinator
+  degree: PhD, Molecular Genetics
+  github: margot-l
+  interests:
+    - R
+    - Python
+
+- name: Sophie Breitbart
+  position: RGASC Liaison
+  degree: PhD, Ecology and Evolutionary Biology
+  github: sbreitbart
+  interests:
+    - R
+    - Git
+    - Markdown
+    - Visualization
+
+- name: Elliott Sales de Andrade
+  position: Technical Admin
+  degree: PhD, Physics
+  github: QuLogic
+  interests:
+    - Fortran
+    - Python
+    - Visualization
+
+- name: Vicki Zhang
+  position: Admin
+  degree: PhD, Ecology and Evolutionary Biology
+  github: vickimzhang
+  interests:
+    - R
+    - Python
+
+- name: Madeleine Oman
+  position: Treasurer
+  degree: PhD, Ecology and Evolutionary Biology
+  github: MadeleineOman
+  interests:
+    - Python
+    - R
+    - Git
+    - Bash
+    - Bioinformatic workflows
+    - Machine learning
+
+- name: Denise (Denny) Maranga
+  position: Marketing Coordinator
+  degree: PhD, Ecology and Evolutionary Biology
+  github: souceira
+  interests:
+    - R
+    - Python
+    - Git
diff --git a/_data/coffee-code.yml b/_data/coffee-code.yml
new file mode 100644
index 000000000..6e06b9e9d
--- /dev/null
+++ b/_data/coffee-code.yml
@@ -0,0 +1,92 @@
+
+# Date of the event in YYYY-MM-DD format
+- date: 2019-01-15
+  # Start and end time for event in 24 hour HH:MM format
+  start_time: "9:00"
+  end_time: "11:00"
+  # Where the event will take place
+  location: Second Cup at Grad Room
+  # The URL for the location
+  location_url: "http://map.utoronto.ca/marker/grad_room_second_cup-313"
+  # Only one label that will be put into the GitHub Issue
+  gh_labels: "co-working"
+
+
+# Date of the event in YYYY-MM-DD format
+- date: 2019-01-29
+  # Start and end time for event in 24 hour HH:MM format
+  start_time: "9:00"
+  end_time: "11:00"
+  # Where the event will take place
+  location: Second Cup at Grad Room
+  # The URL for the location
+  location_url: "http://map.utoronto.ca/marker/grad_room_second_cup-313"
+  # Only one label that will be put into the GitHub Issue
+  gh_labels: "co-working"
+
+# Date of the event in YYYY-MM-DD format
+- date: 2019-02-12
+  # Start and end time for event in 24 hour HH:MM format
+  start_time: "9:00"
+  end_time: "11:00"
+  # Where the event will take place
+  location: Second Cup at Grad Room
+  # The URL for the location
+  location_url: "http://map.utoronto.ca/marker/grad_room_second_cup-313"
+  # Only one label that will be put into the GitHub Issue
+  gh_labels: "co-working"
+
+
+# Date of the event in YYYY-MM-DD format
+- date: 2019-02-26
+  # Start and end time for event in 24 hour HH:MM format
+  start_time: "9:00"
+  end_time: "11:00"
+  # Where the event will take place
+  location: Second Cup at Grad Room
+  # The URL for the location
+  location_url: "http://map.utoronto.ca/marker/grad_room_second_cup-313"
+  # Only one label that will be put into the GitHub Issue
+  gh_labels: "co-working"
+
+
+
+# Date of the event in YYYY-MM-DD format
+- date: 2019-03-12
+  # Start and end time for event in 24 hour HH:MM format
+  start_time: "9:00"
+  end_time: "11:00"
+  # Where the event will take place
+  location: Second Cup at Grad Room
+  # The URL for the location
+  location_url: "http://map.utoronto.ca/marker/grad_room_second_cup-313"
+  # Only one label that will be put into the GitHub Issue
+  gh_labels: "co-working"
+
+
+
+# Date of the event in YYYY-MM-DD format
+- date: 2019-03-26
+  # Start and end time for event in 24 hour HH:MM format
+  start_time: "9:00"
+  end_time: "11:00"
+  # Where the event will take place
+  location: Second Cup at Grad Room
+  # The URL for the location
+  location_url: "http://map.utoronto.ca/marker/grad_room_second_cup-313"
+  # Only one label that will be put into the GitHub Issue
+  gh_labels: "co-working"
+
+
+
+# Date of the event in YYYY-MM-DD format
+- date: 2019-04-09
+  # Start and end time for event in 24 hour HH:MM format
+  start_time: "9:00"
+  end_time: "11:00"
+  # Where the event will take place
+  location: Second Cup at Grad Room
+  # The URL for the location
+  location_url: "http://map.utoronto.ca/marker/grad_room_second_cup-313"
+  # Only one label that will be put into the GitHub Issue
+  gh_labels: "co-working"
diff --git a/_data/contributors.yml b/_data/contributors.yml
new file mode 100644
index 000000000..3af42b8a2
--- /dev/null
+++ b/_data/contributors.yml
@@ -0,0 +1,136 @@
+---
+- name: Madeleine Bonsma
+  degree: Physics
+  github: mbonsma
+
+- name: Lina Tran
+  degree: PhD, Physiology
+  github: linamnt
+  interests:
+    - Python
+    - R
+
+- name: Ahmed Hasan
+  degree: PhD, Biology
+  github: aays
+  interests:
+    - R
+    - Python
+    - LaTeX
+
+- name: Sara Mahallati
+  degree: PhD, Biomedical Engineering
+  github: SaraMati
+  interests:
+    - MATLAB
+    - LaTeX
+    - Python
+
+- name: James Santangelo
+  degree: PhD, Biology
+  github: James-S-Santangelo
+  interests:
+    - R
+
+- name: Lindsay Coome
+  degree: Psychology
+  github: lcoome
+
+- name: Nil Sahin
+  degree: Molecular Genetics
+  github: nsahin
+
+- name: Frances Wong
+  degree: Faculty, Biology
+  github: FrancesWong
+  interests:
+    - R
+    - Git
+    - Markdown
+
+- name: Haidy Giratallah
+  degree: PhD, Pharmacology
+  github: HaidyGiratallah
+  interests:
+    - Python
+
+- name: Amin Banihashemi
+  degree: PhD, IMS
+  github: ab2347
+  interests:
+    - Python
+    - R
+
+- name: Tara Henechowicz
+  degree: PhD, Neuroscience
+  github: tarahenechowicz
+  interests:
+    - R
+    - Python
+
+- name: Kathy Chung
+  degree: Digital Humanities
+  github: chungkky
+
+- name: Zoe Francis
+  github: zoelynnfrancis
+
+- name: Derek Howard
+  github: derekhoward
+  interests:
+    - Python
+
+- name: Serena Jeblee
+  degree: Computer Science
+  github: sjeblee
+
+- name: Luke W Johnston
+  degree: PhD, Nutritional Sciences
+  github: lwjohnst86
+  interests:
+    - R
+
+- name: John Ladan
+  degree: MMath, PhD Physics
+  github: jladan
+  interests:
+    - Python
+    - Julia
+    - Javascript
+
+- name: Abigail Cabunoc Mayes
+  degree: Mozilla Science Lab
+  github: acabunoc
+
+- name: Sarah Meister
+  github: thesarahmeister
+
+- name: Ian Dennis Miller
+  degree: PhD, Psychology
+  github: iandennismiller
+  interests:
+    - Python
+    - R
+    - Social Network Analysis
+    - LaTeX
+    - Flask
+
+- name: Dano Morrison
+  github: jdpigeon
+
+- name: Joel Östblom
+  degree: PhD, Biomedical Engineering
+  github: joelostblom
+  interests:
+    - Python
+    - Linux
+
+- name: Raul Samayoa
+  github: ralle123
+
+- name: Brian Sutherland
+  github: bksutherland
+
+- name: Dawn Walker
+  degree: MI, iSchool
+  github: dcwalk
diff --git a/_data/events.yml b/_data/events.yml
new file mode 100644
index 000000000..ef63b2348
--- /dev/null
+++ b/_data/events.yml
@@ -0,0 +1,104 @@
+  # Key is the part of the filename in _posts/
+- key: py-data-vis
+  # Title of event that will show up as the GH Issue title
+  title: "Data visualization in Python"
+  # Explanation of event as a paragraph form
+  description: "An introduction to exploring and visualizing your data in Python."
+  # Date of the event in YYYY-MM-DD format
+  date: 2023-02-01
+  # Start and end time for event in 24 hour HH:MM format
+  start_time: "18:00"
+  end_time: "19:00"
+  # Where the event will take place
+  location: Discord
+  # The URL for the location
+  location_url: "https://github.com/UofTCoders/Events/issues/328"
+  # Packages to install, a space separating each package
+  # (e.g. "dplyr tidyr" or "Pandas>=0.21 Seaborn")
+  packages: ""
+  youtube_link: ""
+  skill_level: Beginner
+  # Beginner Intermediate Advanced
+  # R or Python
+  program_language: Python
+  # Only one label that will be put into the GitHub Issue
+  gh_labels: code-along
+
+  # Key is the part of the filename in _posts/
+- key: intro-bash
+  # Title of event that will show up as the GH Issue title
+  title: "Introduction to Bash"
+  # Explanation of event as a paragraph form
+  description: "A brief overview of working at the command line in Unix environments."
+  # Date of the event in YYYY-MM-DD format
+  date: 2023-02-15
+  # Start and end time for event in 24 hour HH:MM format
+  start_time: "18:00"
+  end_time: "19:00"
+  # Where the event will take place
+  location: Discord
+  # The URL for the location
+  location_url: "https://github.com/UofTCoders/Events/issues/329"
+  # Packages to install, a space separating each package
+  # (e.g. "dplyr tidyr" or "Pandas>=0.21 Seaborn")
+  packages: ""
+  youtube_link: ""
+  skill_level: Beginner
+  # Beginner Intermediate Advanced
+  # R or Python
+  program_language: Bash
+  # Only one label that will be put into the GitHub Issue
+  gh_labels: code-along
+
+  # Key is the part of the filename in _posts/
+- key: python-numpy-pandas
+  # Title of event that will show up as the GH Issue title
+  title: "Python: Data processing with numpy and pandas"
+  # Explanation of event as a paragraph form
+  description: "A code-along demonstrating two popular libraries for data processing in Python."
+  # Date of the event in YYYY-MM-DD format
+  date: 2023-03-01
+  # Start and end time for event in 24 hour HH:MM format
+  start_time: "18:00"
+  end_time: "19:00"
+  # Where the event will take place
+  location: TBA in-person + Discord
+  # The URL for the location
+  location_url: "https://github.com/UofTCoders/Events/issues/330"
+  # Packages to install, a space separating each package
+  # (e.g. "dplyr tidyr" or "Pandas>=0.21 Seaborn")
+  packages: ""
+  youtube_link: ""
+  skill_level: Intermediate
+  # Beginner Intermediate Advanced
+  # R or Python
+  program_language: Python
+  # Only one label that will be put into the GitHub Issue
+  gh_labels: code-along
+
+  # Key is the part of the filename in _posts/
+- key: python-intro-ml
+  # Title of event that will show up as the GH Issue title
+  title: "Python: Intro to machine learning"
+  # Explanation of event as a paragraph form
+  description: "A tutorial covering basic ML topics in Python."
+  # Date of the event in YYYY-MM-DD format
+  date: 2023-03-15
+  # Start and end time for event in 24 hour HH:MM format
+  start_time: "18:00"
+  end_time: "19:00"
+  # Where the event will take place
+  location: TBA in-person + Discord
+  # The URL for the location
+  location_url: "https://github.com/UofTCoders/Events/issues/331"
+  # Packages to install, a space separating each package
+  # (e.g. "dplyr tidyr" or "Pandas>=0.21 Seaborn")
+  packages: ""
+  youtube_link: ""
+  skill_level: Intermediate
+  # Beginner Intermediate Advanced
+  # R or Python
+  program_language: Python
+  # Only one label that will be put into the GitHub Issue
+  gh_labels: code-along
+
diff --git a/_data/members.yml b/_data/members.yml
deleted file mode 100644
index db3855296..000000000
--- a/_data/members.yml
+++ /dev/null
@@ -1,6 +0,0 @@
-- name: Bill Mills
-  affiliation: Mozilla Science Lab
-  github: BillMills
-  interests:
-    - Programming
-    - Open Science
diff --git a/_includes/about.html b/_includes/about.html
deleted file mode 100644
index f6ca89e7c..000000000
--- a/_includes/about.html
+++ /dev/null
@@ -1,80 +0,0 @@
-<!-- About Section -->
-    <section id="about">
-        <div class="container">
-            <div class="row">
-                <div class="col-lg-12 text-center">
-                    <h2 class="section-heading">About</h2>
-                    <h3 class="section-subheading text-muted">Lorem ipsum dolor sit amet consectetur.</h3>
-                </div>
-            </div>
-            <div class="row">
-                <div class="col-lg-12">
-                    <ul class="timeline">
-                        <li>
-                            <div class="timeline-image">
-                                <img class="img-circle img-responsive" src="img/about/1.jpg" alt="">
-                            </div>
-                            <div class="timeline-panel">
-                                <div class="timeline-heading">
-                                    <h4>2009-2011</h4>
-                                    <h4 class="subheading">Our Humble Beginnings</h4>
-                                </div>
-                                <div class="timeline-body">
-                                    <p class="text-muted">Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sunt ut voluptatum eius sapiente, totam reiciendis temporibus qui quibusdam, recusandae sit vero unde, sed, incidunt et ea quo dolore laudantium consectetur!</p>
-                                </div>
-                            </div>
-                        </li>
-                        <li class="timeline-inverted">
-                            <div class="timeline-image">
-                                <img class="img-circle img-responsive" src="img/about/2.jpg" alt="">
-                            </div>
-                            <div class="timeline-panel">
-                                <div class="timeline-heading">
-                                    <h4>March 2011</h4>
-                                    <h4 class="subheading">An Agency is Born</h4>
-                                </div>
-                                <div class="timeline-body">
-                                    <p class="text-muted">Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sunt ut voluptatum eius sapiente, totam reiciendis temporibus qui quibusdam, recusandae sit vero unde, sed, incidunt et ea quo dolore laudantium consectetur!</p>
-                                </div>
-                            </div>
-                        </li>
-                        <li>
-                            <div class="timeline-image">
-                                <img class="img-circle img-responsive" src="img/about/3.jpg" alt="">
-                            </div>
-                            <div class="timeline-panel">
-                                <div class="timeline-heading">
-                                    <h4>December 2012</h4>
-                                    <h4 class="subheading">Transition to Full Service</h4>
-                                </div>
-                                <div class="timeline-body">
-                                    <p class="text-muted">Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sunt ut voluptatum eius sapiente, totam reiciendis temporibus qui quibusdam, recusandae sit vero unde, sed, incidunt et ea quo dolore laudantium consectetur!</p>
-                                </div>
-                            </div>
-                        </li>
-                        <li class="timeline-inverted">
-                            <div class="timeline-image">
-                                <img class="img-circle img-responsive" src="img/about/4.jpg" alt="">
-                            </div>
-                            <div class="timeline-panel">
-                                <div class="timeline-heading">
-                                    <h4>July 2014</h4>
-                                    <h4 class="subheading">Phase Two Expansion</h4>
-                                </div>
-                                <div class="timeline-body">
-                                    <p class="text-muted">Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sunt ut voluptatum eius sapiente, totam reiciendis temporibus qui quibusdam, recusandae sit vero unde, sed, incidunt et ea quo dolore laudantium consectetur!</p>
-                                </div>
-                            </div>
-                        </li>
-                        <li class="timeline-inverted">
-                            <div class="timeline-image">
-                                <h4>Be Part
-                                    <br>Of Our
-                                    <br>Story!</h4>
-                            </div>
-                        </li>
-                    </ul>
-                </div>
-            </div>
-        </div>
-    </section>
\ No newline at end of file
diff --git a/_includes/clients.html b/_includes/clients.html
deleted file mode 100644
index e77797f06..000000000
--- a/_includes/clients.html
+++ /dev/null
@@ -1,13 +0,0 @@
-<!-- Clients Aside -->
-    <aside class="clients">
-        <div class="container">
-            <div class="row">
-                <div class="col-md-3 col-sm-6">
-                    <a href="#">
-                        <img src="img/ScienceLabSticker.ai.png" class="img-responsive img-centered" alt="">
-                    </a>
-                </div>
-
-            </div>
-        </div>
-    </aside>
diff --git a/_includes/come-say-hi.html b/_includes/come-say-hi.html
new file mode 100644
index 000000000..b9dd5774a
--- /dev/null
+++ b/_includes/come-say-hi.html
@@ -0,0 +1,35 @@
+<section id="come-say-hi">
+    <div class="container">
+        <div class="row">
+            <div class="col-lg-12 text-center">
+                <h2 class="section-heading">Come Say Hi</h2>
+                <h3 class="section-subheading text-muted">We use GitHub to get coding help, talk about events and share files. Join us there!</h3>
+            </div>
+        </div>
+        <div class="row">
+            <div class="col-lg-12 text-center">
+                <a href="{{ site.github.issues_url }}" class="btn btn-xl">Talk To Us</a>
+            </div>
+        </div>
+        <div class="row">
+            <div class="col-lg-12 text-center">
+                <h3 class="section-subheading text-muted">We also use Google groups to send out emails of our events and weekly sessions.</h3>
+            </div>
+        </div>
+        <div class="row">
+            <div class="col-lg-12 text-center">
+                <a href="https://groups.google.com/forum/#!forum/uoftcoders" class="btn btn-xl">Subscribe to our email list</a>
+            </div>
+        </div>
+        <div class="row">
+            <div class="col-lg-12 text-center">
+                <h3 class="section-subheading text-muted">We use Discord for chatting between virtual events and for co-working sessions.</h3>
+            </div>
+        </div>
+        <div class="row">
+            <div class="col-lg-12 text-center">
+                <a href="https://discord.gg/ud984rW9vK" class="btn btn-xl">Join our Discord</a>
+            </div>
+        </div>
+    </div>
+</section>
diff --git a/_includes/contact.html b/_includes/contact.html
deleted file mode 100644
index 10b33b820..000000000
--- a/_includes/contact.html
+++ /dev/null
@@ -1,15 +0,0 @@
-<section id="contact">
-        <div class="container">
-            <div class="row">
-                <div class="col-lg-12 text-center">
-                    <h2 class="section-heading">Come Say Hi</h2>
-                    <h3 class="section-subheading text-muted">We use GitHub to get coding help, talk about events and share files. Join us there!</h3>
-                </div>
-            </div>
-            <div class="row">
-                  <div class="col-lg-12 text-center">
-                      <a href="{{ site.github.issues_url }}" class="btn btn-xl">Talk To Us</a>
-                  </div>
-            </div>
-        </div>
-    </section>
diff --git a/_includes/css/agency.css b/_includes/css/agency.css
index c237aa9da..35c15956f 100644
--- a/_includes/css/agency.css
+++ b/_includes/css/agency.css
@@ -62,12 +62,16 @@ ul.events{
 }
 
 .eventSub{
-  color: #553982;
+  color: #696969;
   margin-left:2em;
-  font-style:italic;
+  /* font-style:italic; */
   margin-bottom: 1em;
 }
 
+section#come-say-hi h3.section-subheading {
+color:#FFFFFF;
+}
+
 .eventTitle{
   font-size:150%;
 }
@@ -191,7 +195,7 @@ fieldset[disabled] .btn-xl.active {
 }
 
 .navbar-default .navbar-brand {
-    font-family: "Kaushan Script","Helvetica Neue",Helvetica,Arial,cursive;
+    font-family: 'Pacifico', "Helvetica Neue",Helvetica,Arial,cursive;
     color: #{{ site.color.primary }};
 }
 
@@ -257,7 +261,7 @@ fieldset[disabled] .btn-xl.active {
     }
 
     .navbar-default .navbar-brand {
-        font-size: 2em;
+        font-size: 2.5em;
         -webkit-transition: all .3s;
         -moz-transition: all .3s;
         transition: all .3s;
@@ -273,7 +277,7 @@ fieldset[disabled] .btn-xl.active {
     }
 
     .navbar-default.navbar-shrink .navbar-brand {
-        font-size: 1.5em;
+        font-size: 1.75em;
     }
 }
 
@@ -281,7 +285,7 @@ header {
     text-align: center;
     color: #fff;
     background-attachment: scroll;
-    background-image: url({{ site.titleBackground }});
+    background-image: url(img/header1.jpg);
     background-position: center center;
     background-repeat: none;
     -webkit-background-size: cover;
@@ -361,24 +365,24 @@ section h3.section-subheading {
     }
 }
 
-.service-heading {
+.what-we-do-heading {
     margin: 15px 0;
     text-transform: none;
 }
 
-#portfolio .portfolio-item {
+#what-we-do .what-we-do-item {
     right: 0;
     margin: 0 0 15px;
 }
 
-#portfolio .portfolio-item .portfolio-link {
+#what-we-do .what-we-do-item .what-we-do-link {
     display: block;
     position: relative;
     margin: 0 auto;
     max-width: 400px;
 }
 
-#portfolio .portfolio-item .portfolio-link .portfolio-hover {
+#what-we-do .what-we-do-item .what-we-do-link .what-we-do-hover {
     position: absolute;
     width: 100%;
     height: 100%;
@@ -390,11 +394,11 @@ section h3.section-subheading {
     background: rgba({{ site.color.primary | hex_to_rgb | join: ',' }}, .9);
 }
 
-#portfolio .portfolio-item .portfolio-link .portfolio-hover:hover {
+#what-we-do .what-we-do-item .what-we-do-link .what-we-do-hover:hover {
     opacity: 1;
 }
 
-#portfolio .portfolio-item .portfolio-link .portfolio-hover .portfolio-hover-content {
+#what-we-do .what-we-do-item .what-we-do-link .what-we-do-hover .what-we-do-hover-content {
     position: absolute;
     top: 50%;
     width: 100%;
@@ -405,16 +409,16 @@ section h3.section-subheading {
     color: #fff;
 }
 
-#portfolio .portfolio-item .portfolio-link .portfolio-hover .portfolio-hover-content i {
+#what-we-do .what-we-do-item .what-we-do-link .what-we-do-hover .what-we-do-hover-content i {
     margin-top: -12px;
 }
 
-#portfolio .portfolio-item .portfolio-link .portfolio-hover .portfolio-hover-content h3,
-#portfolio .portfolio-item .portfolio-link .portfolio-hover .portfolio-hover-content h4 {
+#what-we-do .what-we-do-item .what-we-do-link .what-we-do-hover .what-we-do-hover-content h3,
+#what-we-do .what-we-do-item .what-we-do-link .what-we-do-hover .what-we-do-hover-content h4 {
     margin: 0;
 }
 
-#portfolio .portfolio-item .portfolio-caption {
+#what-we-do .what-we-do-item .what-we-do-caption {
     margin: 0 auto;
     padding: 25px;
     max-width: 400px;
@@ -422,24 +426,24 @@ section h3.section-subheading {
     background-color: #fff;
 }
 
-#portfolio .portfolio-item .portfolio-caption h4 {
+#what-we-do .what-we-do-item .what-we-do-caption h4 {
     margin: 0;
     text-transform: none;
 }
 
-#portfolio .portfolio-item .portfolio-caption p {
+#what-we-do .what-we-do-item .what-we-do-caption p {
     margin: 0;
     font-family: "Droid Serif","Helvetica Neue",Helvetica,Arial,sans-serif;
     font-size: 16px;
     font-style: italic;
 }
 
-#portfolio * {
+#what-we-do * {
     z-index: 2;
 }
 
 @media(min-width:767px) {
-    #portfolio .portfolio-item {
+    #what-we-do .what-we-do-item {
         margin: 0 0 30px;
     }
 }
@@ -664,72 +668,68 @@ section h3.section-subheading {
     margin-top: 0;
 }
 
-aside.clients img {
-    margin: 50px auto;
-}
-
-section#contact {
+section#come-say-hi {
     background-color: #222;
-    background-image: url(img/map-image.png);
+    background-image: url(img/newbackground.png);
     background-position: center;
     background-repeat: no-repeat;
 }
 
-section#contact .section-heading {
+section#come-say-hi .section-heading {
     color: #fff;
 }
 
-section#contact .form-group {
+section#come-say-hi .form-group {
     margin-bottom: 25px;
 }
 
-section#contact .form-group input,
-section#contact .form-group textarea {
+section#come-say-hi .form-group input,
+section#come-say-hi .form-group textarea {
     padding: 20px;
 }
 
-section#contact .form-group input.form-control {
+section#come-say-hi .form-group input.form-control {
     height: auto;
 }
 
-section#contact .form-group textarea.form-control {
+section#come-say-hi .form-group textarea.form-control {
     height: 236px;
 }
 
-section#contact .form-control:focus {
+section#come-say-hi .form-control:focus {
     border-color: #{{ site.color.primary }};
     box-shadow: none;
 }
 
-section#contact::-webkit-input-placeholder {
+section#come-say-hi::-webkit-input-placeholder {
     text-transform: uppercase;
     font-family: Montserrat,"Helvetica Neue",Helvetica,Arial,sans-serif;
     font-weight: 700;
     color: #bbb;
 }
 
-section#contact:-moz-placeholder {
+section#come-say-hi:-moz-placeholder {
     text-transform: uppercase;
     font-family: Montserrat,"Helvetica Neue",Helvetica,Arial,sans-serif;
     font-weight: 700;
     color: #bbb;
 }
 
-section#contact::-moz-placeholder {
+section#come-say-hi::-moz-placeholder {
     text-transform: uppercase;
     font-family: Montserrat,"Helvetica Neue",Helvetica,Arial,sans-serif;
     font-weight: 700;
     color: #bbb;
 }
 
-section#contact:-ms-input-placeholder {
+section#come-say-hi:-ms-input-placeholder {
     text-transform: uppercase;
     font-family: Montserrat,"Helvetica Neue",Helvetica,Arial,sans-serif;
     font-weight: 700;
     color: #bbb;
 }
 
-section#contact .text-danger {
+section#come-say-hi .text-danger {
     color: #e74c3c;
 }
 
@@ -785,7 +785,7 @@ ul.social-buttons li a:active {
     outline: 0;
 }
 
-.portfolio-modal .modal-content {
+.what-we-do-modal .modal-content {
     padding: 100px 0;
     min-height: 100%;
     border: 0;
@@ -796,32 +796,32 @@ ul.social-buttons li a:active {
     box-shadow: none;
 }
 
-.portfolio-modal .modal-content h2 {
+.what-we-do-modal .modal-content h2 {
     margin-bottom: 15px;
     font-size: 3em;
 }
 
-.portfolio-modal .modal-content p {
+.what-we-do-modal .modal-content p {
     margin-bottom: 30px;
 }
 
-.portfolio-modal .modal-content p.item-intro {
+.what-we-do-modal .modal-content p.item-intro {
     margin: 20px 0 30px;
     font-family: "Droid Serif","Helvetica Neue",Helvetica,Arial,sans-serif;
     font-size: 16px;
     font-style: italic;
 }
 
-.portfolio-modal .modal-content ul.list-inline {
+.what-we-do-modal .modal-content ul.list-inline {
     margin-top: 0;
     margin-bottom: 30px;
 }
 
-.portfolio-modal .modal-content img {
+.what-we-do-modal .modal-content img {
     margin-bottom: 30px;
 }
 
-.portfolio-modal .close-modal {
+.what-we-do-modal .close-modal {
     position: absolute;
     top: 25px;
     right: 25px;
@@ -831,11 +831,11 @@ ul.social-buttons li a:active {
     cursor: pointer;
 }
 
-.portfolio-modal .close-modal:hover {
+.what-we-do-modal .close-modal:hover {
     opacity: .3;
 }
 
-.portfolio-modal .close-modal .lr {
+.what-we-do-modal .close-modal .lr {
     z-index: 1051;
     width: 1px;
     height: 75px;
@@ -846,7 +846,7 @@ ul.social-buttons li a:active {
     transform: rotate(45deg);
 }
 
-.portfolio-modal .close-modal .lr .rl {
+.what-we-do-modal .close-modal .lr .rl {
     z-index: 1052;
     width: 1px;
     height: 75px;
diff --git a/_includes/css/custom.css b/_includes/css/custom.css
index f76136c5d..3dd9456f8 100644
--- a/_includes/css/custom.css
+++ b/_includes/css/custom.css
@@ -4,3 +4,54 @@
 .team-member i.fa {
  font-size: 110px;
 }
+.col-xs-offset-1half,.col-sm-offset-1half,.col-md-offset-1half,.col-lg-offset-1half{
+ margin-left:12.5%
+}
+/* Ensure body is below navbar */
+body.no-header {
+ padding-top: 100px;
+}
+body.no-header nav.navbar {
+ background-color: #222;
+}
+/* Sticky footer: http://getbootstrap.com/examples/sticky-footer-navbar/ */
+html.sticky-footer {
+ position: relative;
+ min-height: 100%;
+}
+body.sticky-footer {
+ /* Margin bottom by footer height */
+ margin-bottom: 100px;
+}
+body.sticky-footer footer {
+ position: absolute;
+ bottom: 0;
+ width: 100%;
+ /* Set the fixed height of the footer here */
+ height: 100px;
+}
+
+.site-avatar {
+  float: left;
+  width: 60px;
+  height: auto;
+  margin-right: 15px;
+
+  @include mobile {
+    float: none;
+    display: block;
+    margin: 0 auto;
+  }
+
+  img {
+    border-radius: 5px;
+  }
+}
+
+section#come-say-hi {
+ padding: 100px 0;
+}
+section#come-say-hi h3.section-subheading {
+ margin-bottom: 20px;
+ margin-top: 40px;
+}
diff --git a/_includes/portfolio_grid.html b/_includes/events.html
similarity index 57%
rename from _includes/portfolio_grid.html
rename to _includes/events.html
index 465f5656b..ed76255be 100644
--- a/_includes/portfolio_grid.html
+++ b/_includes/events.html
@@ -1,10 +1,22 @@
-<!-- Portfolio Grid Section -->
-    <section id="portfolio" class="bg-light-gray">
+<!-- Events Grid Section -->
+    <section id="events" class="bg-light-gray">
         <div class="container">
             <div class="row">
                 <div class="col-lg-12 text-center">
                     <h2 class="section-heading">Events</h2>
-                    <h3 class="section-subheading text-muted">Our upcoming and recent events:</h3>
+                    <h3 class="section-subheading text-muted">
+                      Unless otherwise stated, all our events run from <b>5:30-7 PM, and are open to everyone </b>
+                      (i.e. no registration necessary, just show up!) and are held in
+                      CDRS (Collaborative Digital Research Space, MN 3230) at the
+                      <a href="https://maps.app.goo.gl/TXY3fA2w1ub3SwYQ6">Maanjiwe
+                        Nendamowinan building</a>.
+                      The CDRS is at the north end of the third level. Once you go
+                      through the main entrance of MN, take the elevator to the third
+                      floor, turn left, and go all the way down the hall. The door
+                      should be open 10-15 minutes before the lesson.
+
+                      A list of all previous events and sessions can be found on the <a href="{{ site.github.url | replace:'http:','https:' }}/past-events/">"Past Events" page</a>.
+                    </h3>
                 </div>
             </div>
             <div class="col-lg-12">
@@ -20,8 +32,8 @@ <h3 class="section-subheading text-muted">Our upcoming and recent events:</h3>
                 {% if postyear > nowyear or postday >= nowday and postyear >= nowyear %}
                   {% assign isEvent = 1 %}
                   <li>
-                    <a class='eventTitle' href="{{ post.link }}">{{ post.title }}, {{ post.location }}, {{ post.date | date: "%-d %B %Y"}}, {{post.startTime | date:"%r"}} </a>
-                    <div class='eventSub'>{{ post.text }}</div>
+                      <a class='eventTitle' href="{{ post.link }}">{{ post.title }} </a>
+                      <div class='eventSub'><strong>{{ post.date | date: "%-d %B %Y"}}, {{ post.startTime | date:"%I:%M %p" }}, {{ post.location }}</strong><br>{{ post.text }}</div>
                   </li>
                 {% endif %}
               {% endfor %}
@@ -30,8 +42,8 @@ <h3 class="section-subheading text-muted">Our upcoming and recent events:</h3>
               {% else %}
               <div class="row">
                     <div class="col-lg-12 text-center">
-                        <a href="{{ site.github.issues_url }}" class="btn btn-xl eventSuggest">Suggest an Event</a>
-                        <a href="http://mozillascience.github.io/studyGroupHandbook/event-types.html#workalong" class="btn btn-xl eventSuggest">How to Lead a Study Group Session</a>
+                        <a href="{{ site.github.owner_url }}/Events/issues" class="btn btn-xl eventSuggest">Suggest an Event</a>
+                        <a href="{{ site.github.url }}/CONTRIBUTING" class="btn btn-xl eventSuggest">How to Lead a Session</a>
                     </div>
               </div>
               {% endif %}
@@ -43,10 +55,10 @@ <h3 class="section-subheading text-muted">Our upcoming and recent events:</h3>
             <div class="col-lg-12">
             {% endif %}
               <h3 class="section-heading eventInstructions">Want to be notified of our upcoming events?</h3>
-              <h3 class="section-subheading text-muted eventSubinstructions">Head over to <a href="{{ site.github.repository_url }}">GitHub</a> and watch our repository, like this:</h3>
-              <figure>
-                <img src="https://help.github.com/assets/images/help/notifications/watcher_picker.gif"></img>
-                <figcaption>Look in the top right-hand corner of <a href="{{ site.github.repository_url }}">our repo</a>, and click 'Watching.' <br>You can undo this at any time in the same place.</figcaption>
+              <h3 class="section-subheading text-muted eventSubinstructions">Head over to <a href="{{ site.github.owner_url }}/Events">GitHub</a> and watch our repository, like this:</h3>
+              <figure class="text-center">
+                <img src="img/watch.png" alt="A screenshot of the Watch settings in the top-right of the GitHub Events repo.">
+                <figcaption>Look in the top right-hand corner of <a href="{{ site.github.owner_url }}/Events">our repo</a>, click 'Watch' and pick 'All Activity'<br>You can undo this at any time in the same place.</figcaption>
               </figure>
             </div>
             {% if site.calendar_on %}
diff --git a/_includes/footer.html b/_includes/footer.html
index b77f16a12..15ac2e2db 100644
--- a/_includes/footer.html
+++ b/_includes/footer.html
@@ -3,7 +3,7 @@
       <div class="row">
         <div class="col-lg-12">
           <a href="https://www.mozillascience.org">
-            <img src="img/ScienceLabSticker.ai.png" class="img-responsive img-centered" alt="">
+            <img src="{{ site.github.url | replace:'http:','https:' }}/img/ScienceLabSticker.ai.png" class="img-responsive img-centered" alt="">
           </a>
           <a href="{{ site.github.repository_url }}/blob/gh-pages/codeOfConduct.md">Study Group Code of Conduct</a>
         </div>
diff --git a/_includes/friends-and-resources.html b/_includes/friends-and-resources.html
new file mode 100644
index 000000000..9524dda33
--- /dev/null
+++ b/_includes/friends-and-resources.html
@@ -0,0 +1,53 @@
+<section id="friends-and-resources" class="bg-light-gray">
+  <div class="container">
+    <div class="row">
+      <div class="col-lg-12 text-center">
+        <h2 class="section-heading">Friends and Resources</h2>
+        <h3 class="section-subheading text-muted">Groups and resources for coding and research</h3>
+      </div>
+    </div>
+    <div class="col-lg-12">
+      <ul>
+	    <li>
+          <a class='eventTitle' href="https://software-carpentry.org/lessons/">Software Carpentry</a>
+          <div class='eventSub'>Introductory lessons on various topics in computing for research.</div>
+        </li>
+		<li>
+          <a class='eventTitle' href="http://internetresearchnetwork.org/">Internet Research Network</a>
+          <div class='eventSub'>An interdisciplinary network of researchers at UofT studying how the rise of the Internet is changing society, politics and the economy — and vice versa.</div>
+        </li>
+		<li>
+          <a class='eventTitle' href="https://twitter.com/uoftacm">The University of Toronto Association for Computer Machinery</a>
+          <div class='eventSub'>The University of Toronto Association for Computer Machinery Student Chapter, affiliated with the Faculty of Information iSchool. They also host professional and research skills workshops open to people from outside iSchool. </div>
+        </li>
+        <li>
+          <a class='eventTitle' href="https://camh-scwg.github.io/lrn2compute/">Scientific Computing Fundamentals at CAMH</a>
+          <div class='eventSub'>This group occasionally has an intensive several days of training in computing skills, for the most part open to UofT graduate students.</div>
+        </li>
+        <li>
+          <a class='eventTitle' href="https://mdl.library.utoronto.ca/technology/workshops-training/workshop-schedule">Map and Data Library Workshops</a>
+          <div class='eventSub'>The Map &amp; Data Library hosts workshops during the academic year, providing introductions to mapmaking in ArcGIS and QGIS, cartography, use of statistical software packages, data visualization, and creating and finding data.</div>
+        </li>	      
+	<li>
+          <a class='eventTitle' href="http://libcal.library.utoronto.ca/calendar/libraryworkshops/">St. George Library Workshops</a>
+          <div class='eventSub'>This site (LibCal) often has workshops that teach computing and coding skills. Check often!</div>
+        </li>
+        <li>
+          <a class='eventTitle'
+             href="https://www.sgs.utoronto.ca/currentstudents/Pages/Research-Related-Skills-Offerings.aspx">Graduate Professional Skills program</a>
+          <div class='eventSub'>Like the LibCal workshops, this program often has workshops that teach computing and coding skills.</div>
+        </li>
+	<li>
+	 <a class='eventTitle'
+	    href="http://machinelearning.sitey.me/">University of Toronto Machine Learning Club</a>
+	 <div class='eventSub'>Join the Machine Learning Club to work through machine learning tutorials. No pre-requisite knowledge of machine learning is required. The club will aim to encourage students to pursue and also share their interests in the field of data science and machine learning. </div>
+      	</li>
+        <li>
+          <a class='eventTitle'
+             href="https://www.facebook.com/groups/free.code.camp.to/">freeCodeCamp Toronto</a>
+          <div class='eventSub'>Weekly meetups for community members to work on coding projects together plus technical workshops and guest speakers.</div>
+        </li>
+      </ul>
+    </div>
+  </div>
+</section>
diff --git a/_includes/head.html b/_includes/head.html
index b6e24ebb7..1fef977ed 100644
--- a/_includes/head.html
+++ b/_includes/head.html
@@ -13,10 +13,11 @@
     <!-- Custom Fonts -->
     <link rel="stylesheet" href="{{ site.github.url | replace:'http:','https:' }}/css/font-awesome/css/font-awesome.min.css">
     <link href="https://fonts.googleapis.com/css?family=Montserrat:400,700" rel="stylesheet" type="text/css">
-    <link href='https://fonts.googleapis.com/css?family=Kaushan+Script' rel='stylesheet' type='text/css'>
     <link href="https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic" rel="stylesheet" type="text/css">
     <link href='https://fonts.googleapis.com/css?family=Droid+Serif:400,700,400italic,700italic' rel='stylesheet' type='text/css'>
     <link href='https://fonts.googleapis.com/css?family=Roboto+Slab:400,100,300,700' rel='stylesheet' type='text/css'>
+    <link href="https://fonts.googleapis.com/css?family=Pacifico" rel="stylesheet">
+    <link rel="shortcut icon" type="image/png" href="{{ site.github.url }}/img/logos/logo_icon_dark.png">
 
     <!-- HTML5 Shim and Respond.js IE8 support of HTML5 elements and media queries -->
     <!-- WARNING: Respond.js doesn't work if you view the page via file:// -->
diff --git a/_includes/header.html b/_includes/header.html
index bbd33ad60..8f00e5716 100644
--- a/_includes/header.html
+++ b/_includes/header.html
@@ -1,38 +1,4 @@
-<!-- Navigation -->
-    <nav class="navbar navbar-default navbar-fixed-top">
-        <div class="container">
-            <!-- Brand and toggle get grouped for better mobile display -->
-            <div class="navbar-header page-scroll">
-                <button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#bs-example-navbar-collapse-1">
-                    <span class="sr-only">Toggle navigation</span>
-                    <span class="icon-bar"></span>
-                    <span class="icon-bar"></span>
-                    <span class="icon-bar"></span>
-                </button>
-                <a class="navbar-brand page-scroll" href="#page-top">{{site.title}}</a>
-            </div>
-
-            <!-- Collect the nav links, forms, and other content for toggling -->
-            <div class="collapse navbar-collapse" id="bs-example-navbar-collapse-1">
-                <ul class="nav navbar-nav navbar-right">
-                    <li class="hidden">
-                        <a href="#page-top"></a>
-                    </li>
-                    <li>
-                        <a class="page-scroll" href="#services">What We Do</a>
-                    </li>
-                    <li>
-                        <a class="page-scroll" href="#portfolio">Events</a>
-                    </li>
-                    <li>
-                        <a class="page-scroll" href="#contact">Contact</a>
-                    </li>
-                </ul>
-            </div>
-            <!-- /.navbar-collapse -->
-        </div>
-        <!-- /.container-fluid -->
-    </nav>
+    {% include navbar.html %}
 
     <!-- Header -->
     <header>
@@ -40,7 +6,7 @@
             <div class="intro-text">
                 <div class="intro-lead-in">{{ site.headlineSmall }}</div>
                 <div class="intro-heading">{{ site.headlineMain }}</div>
-                <a href="#services" class="page-scroll btn btn-xl">Tell Me More</a>
+                <a href="#what-we-do" class="page-scroll btn btn-xl">Tell Me More</a>
             </div>
         </div>
     </header>
diff --git a/_includes/js.html b/_includes/js.html
index df30ee404..03318ae8c 100644
--- a/_includes/js.html
+++ b/_includes/js.html
@@ -9,9 +9,5 @@
     <script src="{{ site.github.url | replace:'http:','https:' }}/js/classie.js"></script>
     <script src="{{ site.github.url | replace:'http:','https:' }}/js/cbpAnimatedHeader.js"></script>
 
-    <!-- Contact Form JavaScript -->
-    <script src="{{ site.github.url | replace:'http:','https:' }}/js/jqBootstrapValidation.js"></script>
-    <script src="{{ site.github.url | replace:'http:','https:' }}/js/contact_me.js"></script>
-
     <!-- Custom Theme JavaScript -->
     <script src="{{ site.github.url | replace:'http:','https:' }}/js/agency.js"></script>
diff --git a/_includes/modals.html b/_includes/modals.html
deleted file mode 100644
index 7002f6dcf..000000000
--- a/_includes/modals.html
+++ /dev/null
@@ -1,42 +0,0 @@
- <!-- Portfolio Modals -->
- {% for post in site.posts %}
-    <div class="portfolio-modal modal fade" id="portfolioModal{{ post.modal-id }}" tabindex="-1" role="dialog" aria-hidden="true">
-        <div class="modal-content">
-            <div class="close-modal" data-dismiss="modal">
-                <div class="lr">
-                    <div class="rl">
-                    </div>
-                </div>
-            </div>
-            <div class="container">
-                <div class="row">
-                    <div class="col-lg-8 col-lg-offset-2">
-                        <div class="modal-body">
-                            <h2>{{ post.title }}</h2>
-                            <hr class="star-primary">
-                            {% if post.img %}
-                              <img src="img/portfolio/{{ post.img }}" class="img-responsive img-centered" alt="{{ post.alt }}">
-                            {% endif %}
-                            <p>{{ post.description }}</p>
-                            <ul class="list-inline item-details">
-                                <li>Client:
-                                    <strong><a href="http://startbootstrap.com">{{ post.client }}</a>
-                                    </strong>
-                                </li>
-                                <li>Date:
-                                    <strong><a href="http://startbootstrap.com">{{ post.project-date }}</a>
-                                    </strong>
-                                </li>
-                                <li>Service:
-                                    <strong><a href="http://startbootstrap.com">{{ post.category }}</a>
-                                    </strong>
-                                </li>
-                            </ul>
-                            <button type="button" class="btn btn-default" data-dismiss="modal"><i class="fa fa-times"></i> Close</button>
-                        </div>
-                    </div>
-                </div>
-            </div>
-        </div>
-    </div>
-{% endfor %}
diff --git a/_includes/navbar.html b/_includes/navbar.html
new file mode 100644
index 000000000..c6a7bf3f9
--- /dev/null
+++ b/_includes/navbar.html
@@ -0,0 +1,40 @@
+<!-- Navigation -->
+    <nav class="navbar navbar-default navbar-fixed-top">
+        <div class="container">
+            <!-- Brand and toggle get grouped for better mobile display -->
+            <div class="navbar-header page-scroll">
+              <a href="{{ site.github.url | replace:'http:','https:' }}/"><img class="site-avatar" src="{{ site.github.url | replace:'http:','https:' }}/{{ site.avatar }}" /></a>
+                <button type="button" class="navbar-toggle" data-toggle="collapse" data-target="#bs-example-navbar-collapse-1">
+                    <span class="sr-only">Toggle navigation</span>
+                    <span class="icon-bar"></span>
+                    <span class="icon-bar"></span>
+                    <span class="icon-bar"></span>
+                </button>
+                <a class="navbar-brand page-scroll" href="#page-top">{{site.title}}</a>
+            </div>
+
+            <!-- Collect the nav links, forms, and other content for toggling -->
+            <div class="collapse navbar-collapse" id="bs-example-navbar-collapse-1">
+                <ul class="nav navbar-nav navbar-right">
+                    <li class="hidden">
+                        <a href="#page-top"></a>
+                    </li>
+                    <li>
+                        <a class="page-scroll" href="{{ site.github.url | replace:'http:','https:' }}/#what-we-do">What We Do</a>
+                    </li>
+                    <li>
+                        <a class="page-scroll" href="{{ site.github.url | replace:'http:','https:' }}/#events">Events</a>
+                    </li>
+                    <li>
+                        <a class="page-scroll" href="{{ site.github.url | replace:'http:','https:' }}/#come-say-hi">Contact</a>
+                    </li>
+                    <li>
+                        <a class="page-scroll" href="{{ site.github.url | replace:'http:','https:' }}/lessons/">Lesson Content</a>
+                    </li>
+                </ul>
+            </div>
+            <!-- /.navbar-collapse -->
+        </div>
+        <!-- /.container-fluid -->
+    </nav>
+
diff --git a/_includes/past-events.html b/_includes/past-events.html
new file mode 100644
index 000000000..722078837
--- /dev/null
+++ b/_includes/past-events.html
@@ -0,0 +1,36 @@
+<!-- Events Grid Section -->
+    <section id="events" class="bg-light-gray">
+        <div class="container">
+            <div class="row">
+                <div class="col-lg-12 text-center">
+                    <h2 class="section-heading">Past events</h2>
+                    <h3 class="section-subheading text-muted">
+                        This is a list of past UofTCoders events or sessions.
+                    </h3>
+                </div>
+            </div>
+            <div class="col-lg-12">
+              <ul class='events'>
+              {% assign isEvent = 0 %}
+              {% for post in site.posts %}
+                {% capture nowyear %}{{'now' | date: '%Y'}}{% endcapture %}
+                {% capture postyear %}{{post.date | date: '%Y'}}{% endcapture %}
+                {% capture nowday %}{{'now' | date: '%j'}}{% endcapture %}
+                {% capture postday %}{{post.date | date: '%j'}}{% endcapture %}
+                {% assign postday = postday | plus: 0 %}
+                {% assign nowday = nowday | minus: 2 %}
+                {% if postyear < nowyear or postday <= nowday and postyear <= nowyear %}
+                  {% assign isEvent = 1 %}
+                  <li>
+                      <a class='eventTitle' href="{{ post.link }}">{{ post.title }} </a>
+                      <div class='eventSub'><strong>{{ post.date | date: "%-d %B %Y"}}, {{ post.startTime | date:"%I:%M %p" }}, {{ post.location }}</strong><br>{{ post.text }}</div>
+                  </li>
+                {% endif %}
+              {% endfor %}
+              {% if isEvent == 0 %}
+                <a class='eventTitle' href="{{ site.github.issues_url }}">Nothing yet! Propose an event here.</a>
+              {% endif %}
+              </ul>
+            </div>
+        </div>
+    </section>
diff --git a/_includes/team.html b/_includes/team.html
deleted file mode 100644
index 011031f77..000000000
--- a/_includes/team.html
+++ /dev/null
@@ -1,46 +0,0 @@
-<!-- Team Section -->
-    <section id="team" class="bg-light-gray">
-        <div class="container">
-            <div class="row">
-                <div class="col-lg-12 text-center">
-                    <h2 class="section-heading">Who We Are</h2>
-                    <h3 class="section-subheading text-muted">
-                        We're scientists, students and researchers who want to work with you!
-                    </h3>
-                </div>
-            </div>
-            <div class="row">
-                {% for member in site.data.members %}
-                {% assign loopindex = forloop.index0 | modulo: 4 %}
-                {% if loopindex == 0 %}
-                {% if forloop.first == false %}
-            </div>
-                {% if forloop.last == false %}
-            <div class="row">
-                {% endif %}
-                {% endif %}
-                {% endif %}
-                <div class="col-sm-3">
-                    <div class="team-member">
-                        {% if member.github %}
-                        <img src="https://avatars.githubusercontent.com/{{ member.github }}?s=96" class="img-responsive img-circle" alt="">
-                        <h4><a href="https://github.com/{{ member.github }}">{{ member.name }}</a></h4>
-                        {% else %}
-                        <i class="fa fa-user"></i>
-                        <h4>{{ member.name }}</h4>
-                        {% endif %}
-                        <p class="text-muted"><i>{{ member.affiliation }}</i></p>
-                        {% if member.interests %}
-                        Interests:
-                        <ul class="list-inline text-muted">
-                            {% for interest in member.interests %}
-                            <li class="fa fa-angle-right">&nbsp;{{ interest }}</li>
-                            {% endfor %}
-                        </ul>
-                        {% endif %}
-                    </div>
-                </div>
-                {% endfor %}
-            </div>
-        </div>
-    </section>
diff --git a/_includes/services.html b/_includes/what-we-do.html
similarity index 86%
rename from _includes/services.html
rename to _includes/what-we-do.html
index 41acd23e2..81ffc911d 100644
--- a/_includes/services.html
+++ b/_includes/what-we-do.html
@@ -1,5 +1,5 @@
-<!-- Services Section -->
-    <section id="services">
+<!-- What-We-Do Section -->
+    <section id="what-we-do">
         <div class="container">
             <div class="row">
                 <div class="col-lg-12 text-center">
@@ -13,7 +13,7 @@ <h3 class="section-subheading text-muted">and you can too!</h3>
                         <i class="fa fa-circle fa-stack-2x text-primary"></i>
                         <i class="fa fa-comment fa-stack-1x fa-inverse"></i>
                     </span>
-                    <h4 class="service-heading">Skill Sharing</h4>
+                    <h4 class="what-we-do-heading">Skill Sharing</h4>
                     <p class="text-muted">Share your favorite coding skills and tools with your friends and colleagues in friendly, no-pressure work-alongs.</p>
                 </div>
                 <div class="col-md-4">
@@ -21,7 +21,7 @@ <h4 class="service-heading">Skill Sharing</h4>
                         <i class="fa fa-circle fa-stack-2x text-primary"></i>
                         <i class="fa fa-laptop fa-stack-1x fa-inverse"></i>
                     </span>
-                    <h4 class="service-heading">Co-Working</h4>
+                    <h4 class="what-we-do-heading">Co-Working</h4>
                     <p class="text-muted">Get together to work on your coding projects, help each other out and share your work.</p>
                 </div>
                 <div class="col-md-4">
@@ -29,7 +29,7 @@ <h4 class="service-heading">Co-Working</h4>
                         <i class="fa fa-circle fa-stack-2x text-primary"></i>
                         <i class="fa fa-group fa-stack-1x fa-inverse"></i>
                     </span>
-                    <h4 class="service-heading">Community Building</h4>
+                    <h4 class="what-we-do-heading">Community Building</h4>
                     <p class="text-muted">Meet new people in your field, organization or community - and find out what we can do when we work together.</p>
                 </div>
             </div>
diff --git a/_includes/who-we-are.html b/_includes/who-we-are.html
new file mode 100644
index 000000000..406b1da38
--- /dev/null
+++ b/_includes/who-we-are.html
@@ -0,0 +1,83 @@
+<!-- Who We Are Section -->
+    <section id="who-we-are" class="bg-light-gray">
+        <div class="container">
+            <div class="row">
+                <div class="col-lg-12 text-center">
+                    <h2 class="section-heading">Who We Are</h2>
+                    <h3 class="section-subheading text-muted">
+                        We are a diverse group hailing from multiple departments across the University and beyond.
+                    </h3>
+                </div>
+            </div>
+            <h2 class="section-subheading text-center">Executive council</h2>
+            {% for member in site.data.admin %}
+            {% assign loopindex = forloop.index0 | modulo: 3 %}
+            {% if loopindex == 0 %}
+            {% if forloop.first == false %}
+            </div>
+            {% endif %}
+            <div class="row">
+                <div class="col-sm-3 col-sm-offset-1half">
+            {% else %}
+                <div class="col-sm-3">
+            {% endif %}
+                    <div class="team-member">
+                        {% if member.github %}
+                        <img src="https://avatars.githubusercontent.com/{{ member.github }}?s=96" class="img-responsive img-circle" alt="">
+                        <h4><a href="https://github.com/{{ member.github }}">{{ member.name }}</a></h4>
+                        {% else %}
+                        <i class="fa fa-user"></i>
+                        <h4>{{ member.name }}</h4>
+                        {% endif %}
+                        <p class="text-muted">{{ member.position }}</p>
+                        <p class="text-muted"><i>{{ member.degree }}</i></p>
+                        {% if member.interests %}
+                        Interests:
+                        <ul class="list-inline text-muted">
+                            {% for interest in member.interests %}
+                            <li class="fa fa-angle-right">&nbsp;{{ interest }}</li>
+                            {% endfor %}
+                        </ul>
+                        {% endif %}
+                    </div>
+                </div>
+            {% if forloop.last == true %}
+            </div>
+            {% endif %}
+            {% endfor %}
+            <h2 class="section-subheading text-center">Contributors</h2>
+            <div class="row">
+                {% for member in site.data.contributors %}
+                {% assign loopindex = forloop.index0 | modulo: 4 %}
+                {% if loopindex == 0 %}
+                {% if forloop.first == false %}
+            </div>
+                {% if forloop.last == false %}
+            <div class="row">
+                {% endif %}
+                {% endif %}
+                {% endif %}
+                <div class="col-sm-3">
+                    <div class="team-member">
+                        {% if member.github %}
+                        <img src="https://avatars.githubusercontent.com/{{ member.github }}?s=96" class="img-responsive img-circle" alt="">
+                        <h4><a href="https://github.com/{{ member.github }}">{{ member.name }}</a></h4>
+                        {% else %}
+                        <i class="fa fa-user"></i>
+                        <h4>{{ member.name }}</h4>
+                        {% endif %}
+                        <p class="text-muted"><i>{{ member.degree }}</i></p>
+                        {% if member.interests %}
+                        Interests:
+                        <ul class="list-inline text-muted">
+                            {% for interest in member.interests %}
+                            <li class="fa fa-angle-right">&nbsp;{{ interest }}</li>
+                            {% endfor %}
+                        </ul>
+                        {% endif %}
+                    </div>
+                </div>
+                {% endfor %}
+            </div>
+        </div>
+    </section>
diff --git a/_layouts/default.html b/_layouts/default.html
index d16cc334e..38f1f922c 100644
--- a/_layouts/default.html
+++ b/_layouts/default.html
@@ -7,10 +7,11 @@
     <body id="page-top" class="index">
 
     {% include header.html %}
-    {% include services.html %}
-    {% include portfolio_grid.html %}
-    {% include contact.html %}
-    {% include team.html %}
+    {% include what-we-do.html %}
+    {% include events.html %}
+    {% include come-say-hi.html %}
+    {% include who-we-are.html %}
+    {% include friends-and-resources.html %}
     {% include footer.html %}
     {% include js.html %}
 
diff --git a/_layouts/page.html b/_layouts/page.html
new file mode 100644
index 000000000..3f11135d1
--- /dev/null
+++ b/_layouts/page.html
@@ -0,0 +1,21 @@
+<!DOCTYPE html>
+<html class="sticky-footer">
+
+  {% include head.html %}
+
+  <body id="page-top" class="index no-header sticky-footer">
+    {% include navbar.html %}
+
+    <div class="content container">
+      <div class="page">
+        <h1 class="page-title">{{ page.title }}</h1>
+
+        <hr>
+
+        {{ content }}
+      </div>
+    </div>
+    {% include footer.html %}
+    {% include js.html %}
+  </body>
+</html>
diff --git a/_posts/2015-06-11-HackyHour.markdown b/_posts/2015-06-11-HackyHour.markdown
new file mode 100644
index 000000000..d00351e08
--- /dev/null
+++ b/_posts/2015-06-11-HackyHour.markdown
@@ -0,0 +1,8 @@
+---
+title: Study Group Meetup
+text: A discussion on what the commmunity would like to see happen at future Study Group meetings
+location: Sammy's Multicultural Eatery & Bar, Hart House
+link: https://github.com/mbonsma/studyGroup/issues/1
+date: 2015-06-11
+
+---
diff --git a/_posts/2015-06-18-CodeAlong.markdown b/_posts/2015-06-18-CodeAlong.markdown
new file mode 100644
index 000000000..5ceb4c2f0
--- /dev/null
+++ b/_posts/2015-06-18-CodeAlong.markdown
@@ -0,0 +1,7 @@
+---
+title: Introduction to Git
+text: Introduction to Git code-along session
+location: MP 408
+link: https://github.com/mbonsma/studyGroup/issues/5
+date: 2015-06-18
+---
diff --git a/_posts/2015-06-25-Coworking.markdown b/_posts/2015-06-25-Coworking.markdown
new file mode 100644
index 000000000..c358d76a9
--- /dev/null
+++ b/_posts/2015-06-25-Coworking.markdown
@@ -0,0 +1,7 @@
+---
+title: Co-working session
+text: Casual coding with research peers
+location: MP 408
+link: https://github.com/UofTCoders/Events/issues/1
+date: 2015-06-25
+---
diff --git a/_posts/2015-07-09-IntroToPython.markdown b/_posts/2015-07-09-IntroToPython.markdown
new file mode 100644
index 000000000..5108743f9
--- /dev/null
+++ b/_posts/2015-07-09-IntroToPython.markdown
@@ -0,0 +1,7 @@
+---
+title: Introduction to Python 
+text: A beginner-friendly code along on the Python programming language
+location: MP 408
+link: https://github.com/UofTCoders/Events/issues/3
+date: 2015-07-09
+---
diff --git a/_posts/2015-07-16-Brainstorm.markdown b/_posts/2015-07-16-Brainstorm.markdown
new file mode 100644
index 000000000..f5c3cb467
--- /dev/null
+++ b/_posts/2015-07-16-Brainstorm.markdown
@@ -0,0 +1,7 @@
+---
+title: Brainstorming session
+text: Discuss the great programming challenges of your field
+location: MP 408
+link: https://github.com/UofTCoders/Events/issues/2
+date: 2015-07-16
+---
diff --git a/_posts/2015-07-23-IntroR.markdown b/_posts/2015-07-23-IntroR.markdown
new file mode 100644
index 000000000..cae1c8de6
--- /dev/null
+++ b/_posts/2015-07-23-IntroR.markdown
@@ -0,0 +1,7 @@
+---
+title: Introduction to R via data wrangling and reproducible documents
+text: Learn the basics of R with an emphasis on using knitr and dplyr
+location: MP 408
+link: https://github.com/UofTCoders/Events/issues/4
+date: 2015-07-23
+---
diff --git a/_posts/2015-07-30-IntroShiny.markdown b/_posts/2015-07-30-IntroShiny.markdown
new file mode 100644
index 000000000..01887327a
--- /dev/null
+++ b/_posts/2015-07-30-IntroShiny.markdown
@@ -0,0 +1,7 @@
+---
+title: Introduction to Shiny R
+text: Learn the basics of Shiny through an interactive tutorial
+location: MP 408
+link: https://github.com/UofTCoders/Events/issues/5
+date: 2015-07-30
+---
diff --git a/_posts/2015-08-06-PythonPlotting.markdown b/_posts/2015-08-06-PythonPlotting.markdown
new file mode 100644
index 000000000..6cff517a1
--- /dev/null
+++ b/_posts/2015-08-06-PythonPlotting.markdown
@@ -0,0 +1,7 @@
+---
+title: Plotting with Python and Matplotlib
+text: A beginner's guide to plotting in Python with a focus on animations and reproducible figures
+location: McLennan Physical Laboratories, room 408
+link: https://github.com/UofTCoders/Events/issues/6
+date: 2015-08-06
+---
diff --git a/_posts/2015-08-13-PlottingCoworking.markdown b/_posts/2015-08-13-PlottingCoworking.markdown
new file mode 100644
index 000000000..a8489fd98
--- /dev/null
+++ b/_posts/2015-08-13-PlottingCoworking.markdown
@@ -0,0 +1,7 @@
+---
+title: 'Coworking session: plotting in Python and R'
+text: Discuss the making of plots in Python and R with peers
+location: McLennan Physical Laboratories, room 408
+link: https://github.com/UofTCoders/Events/issues/7
+date: 2015-08-13
+---
diff --git a/_posts/2015-08-20-CodingLightning1.markdown b/_posts/2015-08-20-CodingLightning1.markdown
new file mode 100644
index 000000000..ae27dd2cf
--- /dev/null
+++ b/_posts/2015-08-20-CodingLightning1.markdown
@@ -0,0 +1,7 @@
+---
+title: 'Coding lightning demos'
+text: Short demos, including IPython notebook and Biopython
+location: McLennan Physical Laboratories, room 408
+link: https://github.com/UofTCoders/Events/issues/8
+date: 2015-08-20
+---
diff --git a/_posts/2015-08-27-PythonUnitTesting.markdown b/_posts/2015-08-27-PythonUnitTesting.markdown
new file mode 100644
index 000000000..b18689024
--- /dev/null
+++ b/_posts/2015-08-27-PythonUnitTesting.markdown
@@ -0,0 +1,7 @@
+---
+title: 'Intro to unit testing in Python'
+text: Learn about unit testing in Python and what it's good for.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/10
+date: 2015-08-27
+---
diff --git a/_posts/2015-09-03-PlanTheNewYear.markdown b/_posts/2015-09-03-PlanTheNewYear.markdown
new file mode 100644
index 000000000..0f57724a3
--- /dev/null
+++ b/_posts/2015-09-03-PlanTheNewYear.markdown
@@ -0,0 +1,7 @@
+---
+title: 'Hacky Hour - planning the new school year'
+text: Join the discussion on what the study group should be up to in the new term
+location: GSU pub
+link: https://github.com/UofTCoders/Events/issues/9
+date: 2015-09-03
+---
diff --git a/_posts/2015-09-10-CoworkingReal.markdown b/_posts/2015-09-10-CoworkingReal.markdown
new file mode 100644
index 000000000..021600b2b
--- /dev/null
+++ b/_posts/2015-09-10-CoworkingReal.markdown
@@ -0,0 +1,7 @@
+---
+title: Coworking session (with real work!)
+text: Bring your projects and problems and work with peers, get help with questions, and show off your progress.
+location: MP 408
+link: https://github.com/UofTCoders/Events/issues/11
+date: 2015-09-10
+---
diff --git a/_posts/2015-09-17-WelcomeHackyHour.markdown b/_posts/2015-09-17-WelcomeHackyHour.markdown
new file mode 100644
index 000000000..813d70a72
--- /dev/null
+++ b/_posts/2015-09-17-WelcomeHackyHour.markdown
@@ -0,0 +1,7 @@
+---
+title: Welcome Hacky Hour
+text: Celebrate the start of term and meet peers who code. 
+location: GSU Pub, 16 Bancroft Ave
+link: https://github.com/UofTCoders/Events/issues/12
+date: 2015-09-17
+---
diff --git a/_posts/2015-09-24-IntroToGit2.markdown b/_posts/2015-09-24-IntroToGit2.markdown
new file mode 100644
index 000000000..f3a775a94
--- /dev/null
+++ b/_posts/2015-09-24-IntroToGit2.markdown
@@ -0,0 +1,7 @@
+---
+title: Introduction to Git
+text: Introduction to Git code-along session - no experience necessary!
+location: MP 408
+link: https://github.com/UofTCoders/Events/issues/13
+date: 2015-09-23
+---
diff --git a/_posts/2015-10-01-MoreOnGit.markdown b/_posts/2015-10-01-MoreOnGit.markdown
new file mode 100644
index 000000000..e2dd8c47b
--- /dev/null
+++ b/_posts/2015-10-01-MoreOnGit.markdown
@@ -0,0 +1,9 @@
+---
+title: More on Git
+text: More about version control and collaboration using Git, building on knowledge of basic Git commands.
+location: MP 408
+link: https://github.com/UofTCoders/Events/issues/14
+date: 2015-10-01
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2015-10-08-Coworking4.markdown b/_posts/2015-10-08-Coworking4.markdown
new file mode 100644
index 000000000..91b17eb56
--- /dev/null
+++ b/_posts/2015-10-08-Coworking4.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking session
+text: Bring your projects and problems and work with peers, get help with questions, and show off your progress.
+location: MP 408 (Mclennan Physical Labs)
+link: https://github.com/UofTCoders/Events/issues/15
+date: 2015-10-08
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2015-10-15-BasicStats.markdown b/_posts/2015-10-15-BasicStats.markdown
new file mode 100644
index 000000000..1e76ee95b
--- /dev/null
+++ b/_posts/2015-10-15-BasicStats.markdown
@@ -0,0 +1,9 @@
+---
+title: Basic Statistics
+text: A primer on basic statistical concepts - no prior knowledge necessary.
+location: MP 408 (Mclennan Physical Labs)
+link: https://github.com/UofTCoders/Events/issues/16
+date: 2015-10-15
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2015-10-22-IntroToPython2.markdown b/_posts/2015-10-22-IntroToPython2.markdown
new file mode 100644
index 000000000..d69c7df1f
--- /dev/null
+++ b/_posts/2015-10-22-IntroToPython2.markdown
@@ -0,0 +1,9 @@
+---
+title: Introduction to Python
+text: A beginner-friendly code-along introducing the basics of coding in Python.
+location: MP 408 (Mclennan Physical Labs)
+link: https://github.com/UofTCoders/Events/issues/19
+date: 2015-10-22
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2015-10-29-Coworking5.markdown b/_posts/2015-10-29-Coworking5.markdown
new file mode 100644
index 000000000..dcf4a484f
--- /dev/null
+++ b/_posts/2015-10-29-Coworking5.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking session
+text: Bring your projects and problems and work with peers, get help with questions, and show off your progress.
+location: MP 408 (Mclennan Physical Labs)
+link: https://github.com/UofTCoders/Events/issues/17
+date: 2015-10-29
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2015-11-05-CodingLightning2.markdown b/_posts/2015-11-05-CodingLightning2.markdown
new file mode 100644
index 000000000..8bc16f5de
--- /dev/null
+++ b/_posts/2015-11-05-CodingLightning2.markdown
@@ -0,0 +1,9 @@
+---
+title: Python Coding Lightning Demos
+text: Short demos from a variety of people of cool things you can do in Python
+location: MP 408 (Mclennan Physical Labs)
+link: https://github.com/UofTCoders/Events/issues/20
+date: 2015-11-05
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2015-11-12-Coworking6.markdown b/_posts/2015-11-12-Coworking6.markdown
new file mode 100644
index 000000000..871155b1e
--- /dev/null
+++ b/_posts/2015-11-12-Coworking6.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking session
+text: Bring your projects and problems and work with peers, get help with questions, and show off your progress.
+location: MP 408 (Mclennan Physical Labs)
+link: https://github.com/UofTCoders/Events/issues/21
+date: 2015-11-12
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2015-11-19-IntroR_2.markdown b/_posts/2015-11-19-IntroR_2.markdown
new file mode 100644
index 000000000..17e24d146
--- /dev/null
+++ b/_posts/2015-11-19-IntroR_2.markdown
@@ -0,0 +1,9 @@
+---
+title: Introduction to R via data wrangling and reproducible documents
+text: Learn the basics of R with an emphasis on using knitr and dplyr
+location: MP 408
+link: https://github.com/UofTCoders/Events/issues/18
+date: 2015-11-19
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2015-11-26-ggplot.markdown b/_posts/2015-11-26-ggplot.markdown
new file mode 100644
index 000000000..da5e8bd0c
--- /dev/null
+++ b/_posts/2015-11-26-ggplot.markdown
@@ -0,0 +1,9 @@
+---
+title: Introduction to ggplot2
+text: A brief introduction to the ggplot2 package and making simple graphs
+location: MP 408
+link: https://github.com/UofTCoders/Events/issues/22
+date: 2015-11-26
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2015-12-03-Coworking7.markdown b/_posts/2015-12-03-Coworking7.markdown
new file mode 100644
index 000000000..026edf760
--- /dev/null
+++ b/_posts/2015-12-03-Coworking7.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking session
+text: Bring your projects and problems and work with peers, get help with questions, and show off your progress.
+location: MP 408 (Mclennan Physical Labs)
+link: https://github.com/UofTCoders/Events/issues/23
+date: 2015-12-03
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2015-12-10-UnixPipelines.markdown b/_posts/2015-12-10-UnixPipelines.markdown
new file mode 100644
index 000000000..9091357ab
--- /dev/null
+++ b/_posts/2015-12-10-UnixPipelines.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Unix Pipelines
+text: An introduction to Unix pipelines and Make for data science
+location: MP 408
+link: https://github.com/UofTCoders/Events/issues/24
+date: 2015-12-10
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2015-12-17-EndOfTermParty.markdown b/_posts/2015-12-17-EndOfTermParty.markdown
new file mode 100644
index 000000000..2288334ff
--- /dev/null
+++ b/_posts/2015-12-17-EndOfTermParty.markdown
@@ -0,0 +1,9 @@
+---
+title: End-of-term party
+text: An informal gathering to relax at the end of Fall 2015.
+location: GSU Pub, 16 Bancroft Ave
+link: https://github.com/UofTCoders/Events/issues/25
+date: 2015-12-17
+startTime: '18:00'
+endTime: '21:00'
+---
diff --git a/_posts/2016-01-14-IntroToRuby.markdown b/_posts/2016-01-14-IntroToRuby.markdown
new file mode 100644
index 000000000..ab23ab7ed
--- /dev/null
+++ b/_posts/2016-01-14-IntroToRuby.markdown
@@ -0,0 +1,9 @@
+---
+title: Introduction to Ruby
+text: Beginner-friendly basics of the programming language Ruby
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/26
+date: 2016-01-14
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2016-01-21-PythonClasses.markdown b/_posts/2016-01-21-PythonClasses.markdown
new file mode 100644
index 000000000..962189e8d
--- /dev/null
+++ b/_posts/2016-01-21-PythonClasses.markdown
@@ -0,0 +1,9 @@
+---
+title: The "Class" Structure in Python
+text: Creating and using the "class" structure in Python
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/28
+date: 2016-01-21
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2016-01-28-Coworking8.markdown b/_posts/2016-01-28-Coworking8.markdown
new file mode 100644
index 000000000..8340a9ef8
--- /dev/null
+++ b/_posts/2016-01-28-Coworking8.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking session
+text: Bring your projects and problems and work with peers, get help with questions, and show off your progress.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/27
+date: 2016-01-28
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2016-02-04-DebuggingRStudio.markdown b/_posts/2016-02-04-DebuggingRStudio.markdown
new file mode 100644
index 000000000..11f90da59
--- /dev/null
+++ b/_posts/2016-02-04-DebuggingRStudio.markdown
@@ -0,0 +1,9 @@
+---
+title: Debugging in R Studio
+text: Introduction to the debugging tools built into RStudio
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/30
+date: 2016-02-04
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2016-02-11-ForLoopsR.markdown b/_posts/2016-02-11-ForLoopsR.markdown
new file mode 100644
index 000000000..bebae34b6
--- /dev/null
+++ b/_posts/2016-02-11-ForLoopsR.markdown
@@ -0,0 +1,9 @@
+---
+title: High Performance Loops in R
+text: Learn the how and when to apply for loops in R
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/31
+date: 2016-02-11
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2016-02-18-Coworking9.markdown b/_posts/2016-02-18-Coworking9.markdown
new file mode 100644
index 000000000..36dc676dd
--- /dev/null
+++ b/_posts/2016-02-18-Coworking9.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking session
+text: Bring your projects and problems and work with peers, get help with questions, and show off your progress.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/32
+date: 2016-02-18
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2016-02-25-PackagesPython.markdown b/_posts/2016-02-25-PackagesPython.markdown
new file mode 100644
index 000000000..3715d4af6
--- /dev/null
+++ b/_posts/2016-02-25-PackagesPython.markdown
@@ -0,0 +1,9 @@
+---
+title: Building Packages in Python
+text: Learn how to build packages in Python for reusable code.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/34
+date: 2016-02-25
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2016-03-03-Coworking10.markdown b/_posts/2016-03-03-Coworking10.markdown
new file mode 100644
index 000000000..f98b8060a
--- /dev/null
+++ b/_posts/2016-03-03-Coworking10.markdown
@@ -0,0 +1,9 @@
+---
+title: Co-Working Session
+text: Bring your projects and problems and work with peers, get help with questions, and show off your progress.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/35
+date: 2016-03-03
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2016-03-10-IntroLaTeX.markdown b/_posts/2016-03-10-IntroLaTeX.markdown
new file mode 100644
index 000000000..47f35dd74
--- /dev/null
+++ b/_posts/2016-03-10-IntroLaTeX.markdown
@@ -0,0 +1,9 @@
+---
+title: Introduction to LaTeX
+text: Learn about what LaTeX is and what it is used for and how to make your first LaTeX document.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/33
+date: 2016-03-10
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-03-17-CodeReview.markdown b/_posts/2016-03-17-CodeReview.markdown
new file mode 100644
index 000000000..e80e067b2
--- /dev/null
+++ b/_posts/2016-03-17-CodeReview.markdown
@@ -0,0 +1,9 @@
+---
+title: Code Review and Reproducible Computational Research
+text: A discussion on reproducible computational research, and reviewing each other's analysis scripts together using principles of good code review.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/36
+date: 2016-03-17
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-03-24-Coworking11.markdown b/_posts/2016-03-24-Coworking11.markdown
new file mode 100644
index 000000000..e9479043c
--- /dev/null
+++ b/_posts/2016-03-24-Coworking11.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Bring a project to work on alongside peers. It can be research, homework, open science, or just something fun you're working on, and it's okay if it's you're just starting out. Everyone is welcome, and bring your friends!
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/37
+date: 2016-03-24
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2016-03-31-GitBranch.markdown b/_posts/2016-03-31-GitBranch.markdown
new file mode 100644
index 000000000..421671ea5
--- /dev/null
+++ b/_posts/2016-03-31-GitBranch.markdown
@@ -0,0 +1,9 @@
+---
+title: Using Branches in Git
+text: Come to this code-along session to learn about how, why, and when to use branches in Git! 
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/39
+date: 2016-03-31
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-04-07-Coworking12.markdown b/_posts/2016-04-07-Coworking12.markdown
new file mode 100644
index 000000000..17eac36e4
--- /dev/null
+++ b/_posts/2016-04-07-Coworking12.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: "Come with a problem and get feedback on what tools, packages and strategies you can use to solve it! Beginners welcome and bring your friends!"
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/41
+date: 2016-04-07
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2016-04-14-GitHubPages.markdown b/_posts/2016-04-14-GitHubPages.markdown
new file mode 100644
index 000000000..08e04c5c5
--- /dev/null
+++ b/_posts/2016-04-14-GitHubPages.markdown
@@ -0,0 +1,9 @@
+---
+title: Web design with Jekyll/Github Pages
+text: Come to this code-along session for a basic introduction to using GitHub Pages and Jekyll to host a web page!
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/42
+date: 2016-04-14
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-04-21-ElectionCoworking.markdown b/_posts/2016-04-21-ElectionCoworking.markdown
new file mode 100644
index 000000000..c95095383
--- /dev/null
+++ b/_posts/2016-04-21-ElectionCoworking.markdown
@@ -0,0 +1,9 @@
+---
+title: Elections and Coworking Session
+text: Participate in the elections by voting or nominating yourself for a position on the exec council; followed by pair-programming co-working session.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/44
+date: 2016-04-21
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2016-04-25-SwcRUnixGit.markdown b/_posts/2016-04-25-SwcRUnixGit.markdown
new file mode 100644
index 000000000..50ec847a3
--- /dev/null
+++ b/_posts/2016-04-25-SwcRUnixGit.markdown
@@ -0,0 +1,9 @@
+---
+title: Software Carpentry Workshop (2 days) - R, Unix Shell and Git
+text: "Join us for a hands-on two-day Software Carpentry workshop covering core small research team skills: programming with R, automating tasks with Unix Shell, and version control with Git. Workshop costs $20, click to see the event page for more details and registration!"
+location: BL224, Claude T. Bissell Building, 140 St. George Street
+link: https://uoftcoders.github.io/2016-04-25-utoronto/
+date: 2016-04-25
+startTime: '09:00'
+endTime: '16:00'
+---
diff --git a/_posts/2016-04-28-PandasPython.markdown b/_posts/2016-04-28-PandasPython.markdown
new file mode 100644
index 000000000..47bf3e847
--- /dev/null
+++ b/_posts/2016-04-28-PandasPython.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Pandas in Python
+text: Learn about the data structures and analysis tools provided by Pandas in Python
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/45
+date: 2016-04-28
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-05-05-Coworking14.markdown b/_posts/2016-05-05-Coworking14.markdown
new file mode 100644
index 000000000..4197f900e
--- /dev/null
+++ b/_posts/2016-05-05-Coworking14.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Bring a project to work on or something you want to discuss with the group!
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/48
+date: 2016-05-05
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2016-05-09-SwcPythonUnixGit.markdown b/_posts/2016-05-09-SwcPythonUnixGit.markdown
new file mode 100644
index 000000000..b95584e25
--- /dev/null
+++ b/_posts/2016-05-09-SwcPythonUnixGit.markdown
@@ -0,0 +1,9 @@
+---
+title: Software Carpentry Workshop (2 days) - Python, Unix Shell and Git
+text: "Join us for a hands-on two-day Software Carpentry workshop covering core small research team skills: building programs with Python, automating tasks with Unix Shell, and version control with Git. Workshop costs $20, click to see the event page for more details and registration!"
+location: BL224, Claude T. Bissell Building, 140 St. George Street
+link: https://uoftcoders.github.io/2016-05-09-utoronto/
+date: 2016-05-09
+startTime: '09:00'
+endTime: '16:00'
+---
diff --git a/_posts/2016-05-12-SpringSocial.markdown b/_posts/2016-05-12-SpringSocial.markdown
new file mode 100644
index 000000000..bbe1b9dd6
--- /dev/null
+++ b/_posts/2016-05-12-SpringSocial.markdown
@@ -0,0 +1,9 @@
+---
+title: Hacky Hour Social
+text: Mingle and get to know your fellow coders!
+location: GSU Pub, second floor
+link: https://github.com/UofTCoders/Events/issues/46
+date: 2016-05-12
+startTime: '18:10'
+endTime: '21:00'
+---
diff --git a/_posts/2016-05-19-VersionControl.markdown b/_posts/2016-05-19-VersionControl.markdown
new file mode 100644
index 000000000..2f797c76b
--- /dev/null
+++ b/_posts/2016-05-19-VersionControl.markdown
@@ -0,0 +1,9 @@
+---
+title: The Use and Philosophy of Version Control
+text: Learn how and why to use version control with your projects!
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/47
+date: 2016-05-19
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-05-26-IntroR.markdown b/_posts/2016-05-26-IntroR.markdown
new file mode 100644
index 000000000..6a0c889ae
--- /dev/null
+++ b/_posts/2016-05-26-IntroR.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to R
+text: Learn the basics of programming and data wrangling in R
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/49
+date: 2016-05-26
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-05-26-SwcRUnixGit.markdown b/_posts/2016-05-26-SwcRUnixGit.markdown
new file mode 100644
index 000000000..93d3bc714
--- /dev/null
+++ b/_posts/2016-05-26-SwcRUnixGit.markdown
@@ -0,0 +1,11 @@
+---
+title: Software Carpentry Workshop (2 days) - R, Unix Shell and Git - for people who identify as female
+text: "Join us for a hands-on two-day Software Carpentry workshop covering core small research team skills: 
+programming with R, automating tasks with Unix Shell, and version control with Git. 
+Workshop costs $20 and is open to people who identify as female. Click to see the event page for more details and registration!"
+location: BL224, Claude T. Bissell Building, 140 St. George Street
+link: https://uoftcoders.github.io/2016-05-26-utoronto/
+date: 2016-05-26
+startTime: '09:00'
+endTime: '16:00'
+---
diff --git a/_posts/2016-06-02-GlobalSprint.markdown b/_posts/2016-06-02-GlobalSprint.markdown
new file mode 100644
index 000000000..0eb85d0ed
--- /dev/null
+++ b/_posts/2016-06-02-GlobalSprint.markdown
@@ -0,0 +1,9 @@
+---
+title: Mozilla Science Lab Global Sprint
+text: Collaborate on open science projects!
+location: Mozilla Toronto office, 366 Adelaide St. W.
+link: https://github.com/UofTCoders/Events/issues/50
+date: 2016-06-02
+startTime: '09:00'
+endTime: '17:00'
+---
diff --git a/_posts/2016-06-09-IntroPython.markdown b/_posts/2016-06-09-IntroPython.markdown
new file mode 100644
index 000000000..f6e5cecb4
--- /dev/null
+++ b/_posts/2016-06-09-IntroPython.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Python
+text: Learn the basics of programming in Python
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/51
+date: 2016-06-09
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-06-16-IntroSQL.markdown b/_posts/2016-06-16-IntroSQL.markdown
new file mode 100644
index 000000000..c2a5c19df
--- /dev/null
+++ b/_posts/2016-06-16-IntroSQL.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to SQL
+text: Learn about how to use SQL to work with data and databases
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/53
+date: 2016-06-16
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-06-23-AcademicWorkflows.markdown b/_posts/2016-06-23-AcademicWorkflows.markdown
new file mode 100644
index 000000000..34fb46fb4
--- /dev/null
+++ b/_posts/2016-06-23-AcademicWorkflows.markdown
@@ -0,0 +1,9 @@
+---
+title: Lightning Demos - Academic Workflows
+text: See how your colleagues use different tools and techniques in their academic workflows.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/54
+date: 2016-06-23
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-07-07-Coworking15.markdown b/_posts/2016-07-07-Coworking15.markdown
new file mode 100644
index 000000000..d9c56baa0
--- /dev/null
+++ b/_posts/2016-07-07-Coworking15.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Come work on a project and get advice on tools and strategies.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/55
+date: 2016-07-07
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2016-07-14-DebuggingPython.markdown b/_posts/2016-07-14-DebuggingPython.markdown
new file mode 100644
index 000000000..dc915391c
--- /dev/null
+++ b/_posts/2016-07-14-DebuggingPython.markdown
@@ -0,0 +1,9 @@
+---
+title: Debugging in Python
+text: Learn to use the debugging tool for your Python code
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/56
+date: 2016-07-14
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-07-21-ggplot2R.markdown b/_posts/2016-07-21-ggplot2R.markdown
new file mode 100644
index 000000000..92f2acbe9
--- /dev/null
+++ b/_posts/2016-07-21-ggplot2R.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to ggplot2 in R
+text: Basics of ggplot2 R package and simple graph making
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/57
+date: 2016-07-21
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-07-28-Coworking16.markdown b/_posts/2016-07-28-Coworking16.markdown
new file mode 100644
index 000000000..45671cbdf
--- /dev/null
+++ b/_posts/2016-07-28-Coworking16.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Work on a project and get suggestions and help from your peers
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/59
+date: 2016-07-28
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2016-08-04-VizDemos.markdown b/_posts/2016-08-04-VizDemos.markdown
new file mode 100644
index 000000000..c2193d997
--- /dev/null
+++ b/_posts/2016-08-04-VizDemos.markdown
@@ -0,0 +1,9 @@
+---
+title: Lightning Demos Data Visualization + Pub Night
+text: Quick demos showing how we visualize our data, followed by a social at GSU Pub.
+location: MP408 + GSU Pub
+link: https://github.com/UofTCoders/Events/issues/58
+date: 2016-08-04
+startTime: '18:10'
+endTime: '21:00'
+---
diff --git a/_posts/2016-08-11-Coworking17.markdown b/_posts/2016-08-11-Coworking17.markdown
new file mode 100644
index 000000000..b7a00ab17
--- /dev/null
+++ b/_posts/2016-08-11-Coworking17.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Work on a project with the advice and suggestions of your peers
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/59
+date: 2016-08-11
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2016-08-18-PythonClasses.markdown b/_posts/2016-08-18-PythonClasses.markdown
new file mode 100644
index 000000000..1025ac700
--- /dev/null
+++ b/_posts/2016-08-18-PythonClasses.markdown
@@ -0,0 +1,9 @@
+---
+title: Classes in Python
+text: Intro to object oriented programming and the class structure in Python
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/61
+date: 2016-08-18
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-08-25-PythonLargeDatasets.markdown b/_posts/2016-08-25-PythonLargeDatasets.markdown
new file mode 100644
index 000000000..2d739a4b4
--- /dev/null
+++ b/_posts/2016-08-25-PythonLargeDatasets.markdown
@@ -0,0 +1,9 @@
+---
+title: Large Datasets in Python
+text: How to work with large datasets in Python
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/60
+date: 2016-08-25
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-09-07-HackyHour.markdown b/_posts/2016-09-07-HackyHour.markdown
new file mode 100644
index 000000000..0e0b9d0c3
--- /dev/null
+++ b/_posts/2016-09-07-HackyHour.markdown
@@ -0,0 +1,9 @@
+---
+title: Welcome Hacky Hour 
+text: Social pub night to get to know other UofT Coders' members
+location: GSU Pub Main Floor
+link: https://github.com/UofTCoders/Events/issues/63
+date: 2016-09-07
+startTime: '18:00'
+endTime: '21:00'
+---
diff --git a/_posts/2016-09-14-IntroToPython.markdown b/_posts/2016-09-14-IntroToPython.markdown
new file mode 100644
index 000000000..c5d275840
--- /dev/null
+++ b/_posts/2016-09-14-IntroToPython.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Python
+text: A brief introduction to Python programming
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/64
+date: 2016-09-14
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-09-21-IntroToR.markdown b/_posts/2016-09-21-IntroToR.markdown
new file mode 100644
index 000000000..e98567ca9
--- /dev/null
+++ b/_posts/2016-09-21-IntroToR.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to R
+text: A brief introduction to R programming.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/65
+date: 2016-09-21
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-09-28-GitCoworking18.markdown b/_posts/2016-09-28-GitCoworking18.markdown
new file mode 100644
index 000000000..236cf50d1
--- /dev/null
+++ b/_posts/2016-09-28-GitCoworking18.markdown
@@ -0,0 +1,9 @@
+---
+title: Git-Themed Coworking Session 
+text: Get help and advice about using Git with your code
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/66
+date: 2016-09-28
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2016-10-05-MappingPython.markdown b/_posts/2016-10-05-MappingPython.markdown
new file mode 100644
index 000000000..46f2c5f1a
--- /dev/null
+++ b/_posts/2016-10-05-MappingPython.markdown
@@ -0,0 +1,9 @@
+---
+title: Mapping in Python
+text: An overview of cartography and mapping methods in Python
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/76
+date: 2016-10-05
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-10-12-PackagesR.markdown b/_posts/2016-10-12-PackagesR.markdown
new file mode 100644
index 000000000..a2f86ee52
--- /dev/null
+++ b/_posts/2016-10-12-PackagesR.markdown
@@ -0,0 +1,9 @@
+---
+title: Making Packages in R
+text: Package your R code for reuse
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/68
+date: 2016-10-12
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-10-19-Coworking19.markdown b/_posts/2016-10-19-Coworking19.markdown
new file mode 100644
index 000000000..fd900b170
--- /dev/null
+++ b/_posts/2016-10-19-Coworking19.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Work on your code with your colleagues
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/67
+date: 2016-10-19
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2016-10-26-IntroBash.markdown b/_posts/2016-10-26-IntroBash.markdown
new file mode 100644
index 000000000..af6815927
--- /dev/null
+++ b/_posts/2016-10-26-IntroBash.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Bash
+text: Basic shell scripting in Bash
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/69
+date: 2016-10-26
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-11-02-IntroSQL.markdown b/_posts/2016-11-02-IntroSQL.markdown
new file mode 100644
index 000000000..07218c036
--- /dev/null
+++ b/_posts/2016-11-02-IntroSQL.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to SQL and Databases
+text: Learn about databases and working with them in SQL
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/70
+date: 2016-11-02
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-11-09-IntroLaTeX.markdown b/_posts/2016-11-09-IntroLaTeX.markdown
new file mode 100644
index 000000000..9e623d163
--- /dev/null
+++ b/_posts/2016-11-09-IntroLaTeX.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to LaTeX
+text: How to make documents in LaTeX
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/71
+date: 2016-11-09
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-11-16-CareersDiscussion.markdown b/_posts/2016-11-16-CareersDiscussion.markdown
new file mode 100644
index 000000000..a390fb341
--- /dev/null
+++ b/_posts/2016-11-16-CareersDiscussion.markdown
@@ -0,0 +1,9 @@
+---
+title: Careers Outside Academia Discussion
+text: Discussion about opportunities outside academia
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/72
+date: 2016-11-16
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2016-11-23-JekyllGithubPages.markdown b/_posts/2016-11-23-JekyllGithubPages.markdown
new file mode 100644
index 000000000..28182c217
--- /dev/null
+++ b/_posts/2016-11-23-JekyllGithubPages.markdown
@@ -0,0 +1,9 @@
+---
+title: Web Design with Jekyll and Github Pages
+text: Learn how to set up a website with Jekyll and Github Pages
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/74
+date: 2016-11-23
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-11-30-PandocMarkdown.markdown b/_posts/2016-11-30-PandocMarkdown.markdown
new file mode 100644
index 000000000..91c9e029c
--- /dev/null
+++ b/_posts/2016-11-30-PandocMarkdown.markdown
@@ -0,0 +1,9 @@
+---
+title: Pandoc and Markdown
+text: Make your documents in markdown and convert between document types with Pandoc 
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/73
+date: 2016-11-30
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2016-12-07-Coworking20.markdown b/_posts/2016-12-07-Coworking20.markdown
new file mode 100644
index 000000000..4441c1c46
--- /dev/null
+++ b/_posts/2016-12-07-Coworking20.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Work on your projects with the help of your colleagues
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/75
+date: 2016-12-07
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2016-12-08-HackUofTCoders.markdown b/_posts/2016-12-08-HackUofTCoders.markdown
new file mode 100644
index 000000000..9da081900
--- /dev/null
+++ b/_posts/2016-12-08-HackUofTCoders.markdown
@@ -0,0 +1,9 @@
+---
+title: Hack UofT Coders
+text: Join us for an afternoon at the pub working on the website together!
+location: GSU Pub (Main Floor)
+link: https://github.com/UofTCoders/Events/issues/77
+date: 2016-12-08
+startTime: '15:00'
+endTime: '21:00'
+---
diff --git a/_posts/2017-01-11-IntroGit.markdown b/_posts/2017-01-11-IntroGit.markdown
new file mode 100644
index 000000000..1e8ebf1be
--- /dev/null
+++ b/_posts/2017-01-11-IntroGit.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Git
+text: Learn about the use and philosophy of version control with Git
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/79
+date: 2017-01-11
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-01-18-IntroPython.markdown b/_posts/2017-01-18-IntroPython.markdown
new file mode 100644
index 000000000..45009bf63
--- /dev/null
+++ b/_posts/2017-01-18-IntroPython.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Python
+text: A brief introduction to programming in Python
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/80
+date: 2017-01-18
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-01-25-Coworking21.markdown b/_posts/2017-01-25-Coworking21.markdown
new file mode 100644
index 000000000..568c27010
--- /dev/null
+++ b/_posts/2017-01-25-Coworking21.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Come work on a project and get advice on tools and strategies.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/81
+date: 2017-01-25
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2017-02-01-CodeReview.md b/_posts/2017-02-01-CodeReview.md
new file mode 100644
index 000000000..d2625d59e
--- /dev/null
+++ b/_posts/2017-02-01-CodeReview.md
@@ -0,0 +1,9 @@
+---
+title: Code Review
+text: Learn about code review and then group code review will begin.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/82
+date: 2017-02-01
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2017-02-08-Coworking22.markdown b/_posts/2017-02-08-Coworking22.markdown
new file mode 100644
index 000000000..1f5db6e44
--- /dev/null
+++ b/_posts/2017-02-08-Coworking22.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Come work on a project and get advice on tools and strategies.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/85
+date: 2017-02-08
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2017-02-15-ggplot2.markdown b/_posts/2017-02-15-ggplot2.markdown
new file mode 100644
index 000000000..59f8229ad
--- /dev/null
+++ b/_posts/2017-02-15-ggplot2.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to ggplot2 in R
+text: ggplot2 is a great plotting package in R, learn the basics in this lesson!
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/84
+date: 2017-02-15
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-02-22-collaborative-github.markdown b/_posts/2017-02-22-collaborative-github.markdown
new file mode 100644
index 000000000..75832ae22
--- /dev/null
+++ b/_posts/2017-02-22-collaborative-github.markdown
@@ -0,0 +1,9 @@
+---
+title: Collaborating with Github
+text: Learn how to use github to code collaboratively
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/83
+date: 2017-02-22
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-03-01-open-data.markdown b/_posts/2017-03-01-open-data.markdown
new file mode 100644
index 000000000..8f000ffd0
--- /dev/null
+++ b/_posts/2017-03-01-open-data.markdown
@@ -0,0 +1,9 @@
+---
+title: Opening Up Your Data
+text: Learn the principles of open data and how you can contribute!
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/86
+date: 2017-03-01
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-03-08-ld-data-visualization.markdown b/_posts/2017-03-08-ld-data-visualization.markdown
new file mode 100644
index 000000000..543693c09
--- /dev/null
+++ b/_posts/2017-03-08-ld-data-visualization.markdown
@@ -0,0 +1,9 @@
+---
+title: Lightning Demos Data Visualization
+text: Quick demos on data visualizations people have made or tools and packages to use.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/87
+date: 2017-03-08
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-03-15-intro-sql.markdown b/_posts/2017-03-15-intro-sql.markdown
new file mode 100644
index 000000000..2f40b6eb3
--- /dev/null
+++ b/_posts/2017-03-15-intro-sql.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to SQL
+text: Learn about databases and SQL
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/88
+date: 2017-03-15
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-03-22-coworking23.markdown b/_posts/2017-03-22-coworking23.markdown
new file mode 100644
index 000000000..82e63287d
--- /dev/null
+++ b/_posts/2017-03-22-coworking23.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Work together on your coding projects.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/89
+date: 2017-03-22
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2017-03-29-intermediate-R.markdown b/_posts/2017-03-29-intermediate-R.markdown
new file mode 100644
index 000000000..ac3bce611
--- /dev/null
+++ b/_posts/2017-03-29-intermediate-R.markdown
@@ -0,0 +1,9 @@
+---
+title: Intermediate Topics in R
+text: Come learn a bit more about the R language beyond an intro programming lesson.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/90
+date: 2017-03-29
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-04-05-intermediate-Python.markdown b/_posts/2017-04-05-intermediate-Python.markdown
new file mode 100644
index 000000000..9868eb7dd
--- /dev/null
+++ b/_posts/2017-04-05-intermediate-Python.markdown
@@ -0,0 +1,9 @@
+---
+title: Intermediate Topics in Python
+text: Come learn a bit more about the Python language beyond an intro programming lesson.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/91
+date: 2017-04-05
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-04-12-ld-academic-workflows.markdown b/_posts/2017-04-12-ld-academic-workflows.markdown
new file mode 100644
index 000000000..70524345d
--- /dev/null
+++ b/_posts/2017-04-12-ld-academic-workflows.markdown
@@ -0,0 +1,9 @@
+---
+title: Lightning Demos - Academic Workflows
+text: See how others analyze, visualize and communicate their research in their workflows.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/92
+date: 2017-04-12
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-04-19-coworking24.markdown b/_posts/2017-04-19-coworking24.markdown
new file mode 100644
index 000000000..cd9e624f2
--- /dev/null
+++ b/_posts/2017-04-19-coworking24.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Work together on your coding projects. Beginners welcome.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/95
+date: 2017-04-19
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2017-04-26-elections-hacky-hour.markdown b/_posts/2017-04-26-elections-hacky-hour.markdown
new file mode 100644
index 000000000..e12e06686
--- /dev/null
+++ b/_posts/2017-04-26-elections-hacky-hour.markdown
@@ -0,0 +1,9 @@
+---
+title: Elections + Hacky Hour
+text: Annual exec elections followed by pub night at GSU pub.
+location: MP408 + GSU Pub
+link: https://github.com/UofTCoders/Events/issues/96
+date: 2017-04-26
+startTime: '18:10'
+endTime: '22:00'
+---
diff --git a/_posts/2017-05-03-careers-outside-academia.markdown b/_posts/2017-05-03-careers-outside-academia.markdown
new file mode 100644
index 000000000..fe76f82b3
--- /dev/null
+++ b/_posts/2017-05-03-careers-outside-academia.markdown
@@ -0,0 +1,9 @@
+---
+title: Careers Outside Academia Panel
+text: Are you or do you know someone who started in research but took opportunities outside academia? Or are you interested in the different paths after a graduate research program? Come to this session where we will have a discussion on these topics.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/97
+date: 2017-05-03
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-05-10-ld-large-datasets.markdown b/_posts/2017-05-10-ld-large-datasets.markdown
new file mode 100644
index 000000000..eb69bf420
--- /dev/null
+++ b/_posts/2017-05-10-ld-large-datasets.markdown
@@ -0,0 +1,9 @@
+---
+title: Lightning Demos - Working with Very Large Datasets
+text: Do you know a tool or package for working with very large datasets (on the order or GB/TB)? Or want to learn more? Come see our quick (10 min or less) demos on this topic.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/98
+date: 2017-05-10
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-05-17-coworking25.markdown b/_posts/2017-05-17-coworking25.markdown
new file mode 100644
index 000000000..f142e06eb
--- /dev/null
+++ b/_posts/2017-05-17-coworking25.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Get together to work on your coding projects. Beginners welcome!
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/99
+date: 2017-05-17
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2017-05-24-htmlcssjs.markdown b/_posts/2017-05-24-htmlcssjs.markdown
new file mode 100644
index 000000000..599c3d9bb
--- /dev/null
+++ b/_posts/2017-05-24-htmlcssjs.markdown
@@ -0,0 +1,9 @@
+---
+title: HTML/JS/CSS
+text: Learn about web development using HTML, CSS and Javascript
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/100
+date: 2017-05-24
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-05-31-intro-bash.markdown b/_posts/2017-05-31-intro-bash.markdown
new file mode 100644
index 000000000..3774dccbe
--- /dev/null
+++ b/_posts/2017-05-31-intro-bash.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Bash
+text: Basic shell scripting in Bash
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/101
+date: 2017-05-31
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-06-07-scikit-learn.markdown b/_posts/2017-06-07-scikit-learn.markdown
new file mode 100644
index 000000000..3db8ac775
--- /dev/null
+++ b/_posts/2017-06-07-scikit-learn.markdown
@@ -0,0 +1,9 @@
+---
+title: Python scikit-learn for Machine Learning
+text: Overview of using Python's scikit-learn for machine learning
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/102
+date: 2017-06-07
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-06-14-coworking26.markdown b/_posts/2017-06-14-coworking26.markdown
new file mode 100644
index 000000000..2be830f93
--- /dev/null
+++ b/_posts/2017-06-14-coworking26.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session - Unit Testing/Pair Programming
+text: Work on your projects together, with a focus on learning to unit test your code. Beginners welcome!
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/103
+date: 2017-06-14
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2017-06-21-git-continuous-testing.markdown b/_posts/2017-06-21-git-continuous-testing.markdown
new file mode 100644
index 000000000..9f7979ef8
--- /dev/null
+++ b/_posts/2017-06-21-git-continuous-testing.markdown
@@ -0,0 +1,9 @@
+---
+title: Continuous Integration and Testing via Github
+text: Using Github, travis CI for continuous integration of projects
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/104
+date: 2017-06-21
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-06-28-ld-data-visualization.markdown b/_posts/2017-06-28-ld-data-visualization.markdown
new file mode 100644
index 000000000..ae512c738
--- /dev/null
+++ b/_posts/2017-06-28-ld-data-visualization.markdown
@@ -0,0 +1,9 @@
+---
+title: Lightning Demos - Data Visualization
+text: Demonstrations of some cool tools and techniques for visualizing data
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/105
+date: 2017-06-28
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-07-05-intermediate-sql.markdown b/_posts/2017-07-05-intermediate-sql.markdown
new file mode 100644
index 000000000..140f629b0
--- /dev/null
+++ b/_posts/2017-07-05-intermediate-sql.markdown
@@ -0,0 +1,9 @@
+---
+title: Intermediate SQL
+text: Intermediate topics in SQL and databases using mySQL
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/106
+date: 2017-07-05
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-07-12-coworking27-packages.markdown b/_posts/2017-07-12-coworking27-packages.markdown
new file mode 100644
index 000000000..32b756587
--- /dev/null
+++ b/_posts/2017-07-12-coworking27-packages.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session on Making Packages
+text: Work together to package up your code for reuse in R or Python.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/107
+date: 2017-07-12
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2017-07-19-r-dplyr-magrittr.markdown b/_posts/2017-07-19-r-dplyr-magrittr.markdown
new file mode 100644
index 000000000..b77abc49b
--- /dev/null
+++ b/_posts/2017-07-19-r-dplyr-magrittr.markdown
@@ -0,0 +1,9 @@
+---
+title: R - dplyr and magrittr
+text: Learn these tools for data manipulation in R.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/108
+date: 2017-07-19
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-07-26-python-parallel.markdown b/_posts/2017-07-26-python-parallel.markdown
new file mode 100644
index 000000000..11c1aa705
--- /dev/null
+++ b/_posts/2017-07-26-python-parallel.markdown
@@ -0,0 +1,9 @@
+---
+title: Parallel Processing in Python
+text: Learn about Python packages for multiprocessing
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/109
+date: 2017-07-26
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-08-02-collaborative-github.markdown b/_posts/2017-08-02-collaborative-github.markdown
new file mode 100644
index 000000000..52d2d9b8a
--- /dev/null
+++ b/_posts/2017-08-02-collaborative-github.markdown
@@ -0,0 +1,9 @@
+---
+title: Collaborating with others using GitHub
+text: Learn how to use GitHub on collaborative projects
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/110
+date: 2017-08-02
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-08-09-coworking28.markdown b/_posts/2017-08-09-coworking28.markdown
new file mode 100644
index 000000000..24a507911
--- /dev/null
+++ b/_posts/2017-08-09-coworking28.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session 
+text: Work together to package up your code for reuse in R or Python.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/111
+date: 2017-08-09
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2017-08-16-R-resampling.markdown b/_posts/2017-08-16-R-resampling.markdown
new file mode 100644
index 000000000..1b9652b17
--- /dev/null
+++ b/_posts/2017-08-16-R-resampling.markdown
@@ -0,0 +1,9 @@
+---
+title: R - Resampling Techniques (Bootstrapping & Permutations)
+text: Learn about common resampling methods in R.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/112
+date: 2017-08-16
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-09-14-intro-r.markdown b/_posts/2017-09-14-intro-r.markdown
new file mode 100644
index 000000000..f604c108f
--- /dev/null
+++ b/_posts/2017-09-14-intro-r.markdown
@@ -0,0 +1,9 @@
+---
+title: Introduction to R
+text: Learn the basics of programming in R.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/116
+date: 2017-09-14
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-09-21-social.markdown b/_posts/2017-09-21-social.markdown
new file mode 100644
index 000000000..6bc1390c2
--- /dev/null
+++ b/_posts/2017-09-21-social.markdown
@@ -0,0 +1,9 @@
+---
+title: Hacky Hour Social
+text: Look for the UofT Coders posters to find where we're seated!
+location: Red Lounge (College/Spadina)
+link: https://github.com/UofTCoders/Events/issues/117
+date: 2017-09-21
+startTime: '18:00'
+endTime: '21:00'
+---
diff --git a/_posts/2017-09-28-intro-python.markdown b/_posts/2017-09-28-intro-python.markdown
new file mode 100644
index 000000000..147b6ed1d
--- /dev/null
+++ b/_posts/2017-09-28-intro-python.markdown
@@ -0,0 +1,9 @@
+---
+title: Introduction to Python
+text: Learn the basics of how to program in Python
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/118
+date: 2017-09-28
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-10-03-coworking.markdown b/_posts/2017-10-03-coworking.markdown
new file mode 100644
index 000000000..5de20e770
--- /dev/null
+++ b/_posts/2017-10-03-coworking.markdown
@@ -0,0 +1,9 @@
+---
+title: Tuesday Coworking Session
+text: Work through any of your coding problems in a friendly and collaborative environment.
+location: Hart House Reading Room (1st Floor)
+link: https://github.com/UofTCoders/Events/issues/126
+date: 2017-10-03
+startTime: '16:00'
+endTime: '18:00'
+---
diff --git a/_posts/2017-10-05-intro-git.markdown b/_posts/2017-10-05-intro-git.markdown
new file mode 100644
index 000000000..86bb4265d
--- /dev/null
+++ b/_posts/2017-10-05-intro-git.markdown
@@ -0,0 +1,9 @@
+---
+title: Introduction to Version Control with Git/Github
+text: Learn how to use version control with your code using Git and Github.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/119
+date: 2017-10-05
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-10-12-coworking-git.markdown b/_posts/2017-10-12-coworking-git.markdown
new file mode 100644
index 000000000..04abef46e
--- /dev/null
+++ b/_posts/2017-10-12-coworking-git.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session - Git and Github
+text: Work through any of your questions about Git and Github in an informal session.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/120
+date: 2017-10-12
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2017-10-17-coworking.markdown b/_posts/2017-10-17-coworking.markdown
new file mode 100644
index 000000000..3d2d16e2a
--- /dev/null
+++ b/_posts/2017-10-17-coworking.markdown
@@ -0,0 +1,9 @@
+---
+title: Tuesday Coworking Session
+text: Work through any of your coding problems in a friendly and collaborative environment.
+location: Hart House Reading Room (1st Floor)
+link: https://github.com/UofTCoders/Events/issues/126
+date: 2017-10-17
+startTime: '16:00'
+endTime: '18:00'
+---
diff --git a/_posts/2017-10-19-machine-learning.markdown b/_posts/2017-10-19-machine-learning.markdown
new file mode 100644
index 000000000..4e949c32b
--- /dev/null
+++ b/_posts/2017-10-19-machine-learning.markdown
@@ -0,0 +1,9 @@
+---
+title: Introduction to Machine Learning
+text: Machine learning crosses the boundaries of discipline and is found everywhere these days from our Netflix recommendations to how banks catch fraudulent transactions. This session will be an overview of important concepts behind machine learning and what it actually entails in more a lecture style session.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/121
+date: 2017-10-19
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-10-26-scikit-learn.markdown b/_posts/2017-10-26-scikit-learn.markdown
new file mode 100644
index 000000000..a2d9458e1
--- /dev/null
+++ b/_posts/2017-10-26-scikit-learn.markdown
@@ -0,0 +1,9 @@
+---
+title: Python's scikit-learn for Machine Learning
+text: Get an overview of how to use Python's scikit-learn package to implement common machine learning algorithms for problems like regression, and classification.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/122
+date: 2017-10-26
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-10-31-coworking.markdown b/_posts/2017-10-31-coworking.markdown
new file mode 100644
index 000000000..32780e1ae
--- /dev/null
+++ b/_posts/2017-10-31-coworking.markdown
@@ -0,0 +1,9 @@
+---
+title: Tuesday Coworking Session
+text: Work through any of your coding problems in a friendly and collaborative environment.
+location: Hart House Reading Room (1st Floor)
+link: https://github.com/UofTCoders/Events/issues/126
+date: 2017-10-31
+startTime: '16:00'
+endTime: '18:00'
+---
diff --git a/_posts/2017-11-02-intro-to-natural-language-processing.markdown b/_posts/2017-11-02-intro-to-natural-language-processing.markdown
new file mode 100644
index 000000000..0d082487b
--- /dev/null
+++ b/_posts/2017-11-02-intro-to-natural-language-processing.markdown
@@ -0,0 +1,9 @@
+---
+title: Introduction to Natural Language Processing
+text: Natural language processing (NLP) is its own field of machine learning focused on building systems that can understand language. While it is a very broad field, this session will be focused on the NLTK package in Python to tackle some basic NLP problems.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/123
+date: 2017-11-02
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-11-09-pair-programing-coding-challenges.markdown b/_posts/2017-11-09-pair-programing-coding-challenges.markdown
new file mode 100644
index 000000000..627e33fdc
--- /dev/null
+++ b/_posts/2017-11-09-pair-programing-coding-challenges.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session - Pair Programming Coding Challenges
+text: Come work on your general programming skills (whether you're a complete beginner or have been cdoing for years) by doing some coding challenges in the style of Pair Programming. Or if you'd prefer, come by with some of your own code to work on and if you need help, you can always ask for help from the other community members.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/124
+date: 2017-11-09
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2017-11-14-coworking.markdown b/_posts/2017-11-14-coworking.markdown
new file mode 100644
index 000000000..ccdf2e26f
--- /dev/null
+++ b/_posts/2017-11-14-coworking.markdown
@@ -0,0 +1,9 @@
+---
+title: Tuesday Coworking Session
+text: Work through any of your coding problems in a friendly and collaborative environment.
+location: Hart House Reading Room (1st Floor)
+link: https://github.com/UofTCoders/Events/issues/126
+date: 2017-11-14
+startTime: '16:00'
+endTime: '18:00'
+---
diff --git a/_posts/2017-11-16-Intro-Bash.markdown b/_posts/2017-11-16-Intro-Bash.markdown
new file mode 100644
index 000000000..11cd431c8
--- /dev/null
+++ b/_posts/2017-11-16-Intro-Bash.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Bash
+text:  Basic shell scripting in Bash
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/130
+date: 2017-11-16
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-11-23-Gadgets-and-hardware-Intro-Programming-Small-Devices.markdown b/_posts/2017-11-23-Gadgets-and-hardware-Intro-Programming-Small-Devices.markdown
new file mode 100644
index 000000000..621784b6c
--- /dev/null
+++ b/_posts/2017-11-23-Gadgets-and-hardware-Intro-Programming-Small-Devices.markdown
@@ -0,0 +1,9 @@
+---
+title: "Gadgets and hardware: Introduction to Programming Small Devices"
+text:  We will be introduced to the Arduino open source microcontroller development system and build a device using the Adafruit ESP8266 Feather Huzzah [https://www.adafruit.com/product/2821], a postage stamp sized 80MHz WiFi computer with stackable peripherals (called 'Wings'), that uses 1/2 Watt.
+location: BL520
+link: https://github.com/UofTCoders/Events/issues/128
+date: 2017-11-23
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-11-28-coworking.markdown b/_posts/2017-11-28-coworking.markdown
new file mode 100644
index 000000000..5a0c226ba
--- /dev/null
+++ b/_posts/2017-11-28-coworking.markdown
@@ -0,0 +1,9 @@
+---
+title: Tuesday Coworking Session
+text: Work through any of your coding problems in a friendly and collaborative environment.
+location: Hart House Reading Room (1st Floor)
+link: https://github.com/UofTCoders/Events/issues/126
+date: 2017-11-28
+startTime: '16:00'
+endTime: '18:00'
+---
diff --git a/_posts/2017-11-30-Intro-quantum-computing.markdown b/_posts/2017-11-30-Intro-quantum-computing.markdown
new file mode 100644
index 000000000..e1c3abcdc
--- /dev/null
+++ b/_posts/2017-11-30-Intro-quantum-computing.markdown
@@ -0,0 +1,9 @@
+---
+title: Introduction to Topics in Quantum Computing
+text:  We'll go over some basic concepts of quantum computing, including qubits, quantum gates and some algorithms. We’ll also test out IBM’s 5-qubit quantum processor using their online interface.
+location: MP408
+link: https://github.com/UofTCoders/Events/issues/127
+date: 2017-11-30
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2017-12-07-hacky-hour.markdown b/_posts/2017-12-07-hacky-hour.markdown
new file mode 100644
index 000000000..814d1b2ce
--- /dev/null
+++ b/_posts/2017-12-07-hacky-hour.markdown
@@ -0,0 +1,9 @@
+---
+title: Hacky Hour Social
+text:  Come meet the UofT Coders community members in a less formal setting. Everyone is welcome even if you've never come to a session before.
+location: GSU Pub
+link: https://github.com/UofTCoders/Events/issues/129
+date: 2017-12-07
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-01-11-IntroR.markdown b/_posts/2018-01-11-IntroR.markdown
new file mode 100644
index 000000000..e5397e182
--- /dev/null
+++ b/_posts/2018-01-11-IntroR.markdown
@@ -0,0 +1,9 @@
+---
+title: "Introduction to R"
+text:  A brief introduction to R programming. No experience is necessary to attend this lesson.
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/131
+date: 2018-01-11
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-01-18-Intro-Git.markdown b/_posts/2018-01-18-Intro-Git.markdown
new file mode 100644
index 000000000..8a068ea4f
--- /dev/null
+++ b/_posts/2018-01-18-Intro-Git.markdown
@@ -0,0 +1,9 @@
+---
+title: "Introduction to Git and the Philosophy of Version Control"
+text:  Learn how to use version control for your project using Git and Github
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/133
+date: 2018-01-18
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-01-18-SwcRUnixGit.markdown b/_posts/2018-01-18-SwcRUnixGit.markdown
new file mode 100644
index 000000000..250eb771a
--- /dev/null
+++ b/_posts/2018-01-18-SwcRUnixGit.markdown
@@ -0,0 +1,11 @@
+---
+title: Software Carpentry Workshop (2 days) - R, Unix Shell and Git
+text: "Join us for a hands-on two-day Software Carpentry workshop covering core small research team skills: 
+programming with R, automating tasks with Unix Shell, and version control with Git. 
+Workshop costs $25 and is open to everyone. Click to see the event page for more details and registration!"
+location: MADLab, Gerstein Science Information Centre, Room B112
+link: https://uoftcoders.github.io/2018-01-18-utoronto/
+date: 2018-01-18
+startTime: '09:00'
+endTime: '16:00'
+---
diff --git a/_posts/2018-01-25-intro-python.markdown b/_posts/2018-01-25-intro-python.markdown
new file mode 100644
index 000000000..ab6e27dea
--- /dev/null
+++ b/_posts/2018-01-25-intro-python.markdown
@@ -0,0 +1,9 @@
+---
+title: "Introduction to Data Analysis with Python"
+text:  The very basics of data analysis using the programming language Python with a beginner-friendly code-along.
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/134
+date: 2018-01-25
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-02-01-ML-for-CompBio-R.markdown b/_posts/2018-02-01-ML-for-CompBio-R.markdown
new file mode 100644
index 000000000..94d110321
--- /dev/null
+++ b/_posts/2018-02-01-ML-for-CompBio-R.markdown
@@ -0,0 +1,9 @@
+---
+title: "Machine Learning for Computational Biology using R"
+text:  Learning about machine learning good practices in computational biology and tips to avoid common mistakes by a code-along application of k-nearest neighbors (k-NN) to a cancer sample dataset, using R.
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/135
+date: 2018-02-01
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-02-08-ImageProcessing-python.markdown b/_posts/2018-02-08-ImageProcessing-python.markdown
new file mode 100644
index 000000000..ac1d7e6d2
--- /dev/null
+++ b/_posts/2018-02-08-ImageProcessing-python.markdown
@@ -0,0 +1,9 @@
+---
+title: Image Processing in Python
+text:  This is an introductory tutorial on image processing using Python packages. We will do a code along to understand image data types, manipulate and prepare images for analysis such as image segmentation. 
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/137
+date: 2018-02-08
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-02-15-ggplot2R.markdown b/_posts/2018-02-15-ggplot2R.markdown
new file mode 100644
index 000000000..476ce7e0e
--- /dev/null
+++ b/_posts/2018-02-15-ggplot2R.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to ggplot2 in R
+text: ggplot2 is a great plotting package in R, learn the basics in this lesson!
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/136
+date: 2018-02-15
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-02-22-graphic-design.markdown b/_posts/2018-02-22-graphic-design.markdown
new file mode 100644
index 000000000..12397b5b5
--- /dev/null
+++ b/_posts/2018-02-22-graphic-design.markdown
@@ -0,0 +1,9 @@
+---
+title: Open Source Tools for Graphic Design
+text: Learn to use the open source software Inkscape.
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/138
+date: 2018-02-22
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-03-01-IntermediateR.markdown b/_posts/2018-03-01-IntermediateR.markdown
new file mode 100644
index 000000000..8aec17046
--- /dev/null
+++ b/_posts/2018-03-01-IntermediateR.markdown
@@ -0,0 +1,9 @@
+---
+title: Intermediate Topics in R
+text: More intermediate level topics in R 
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/143
+date: 2018-03-01
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-03-06-coffeeandcode.markdown b/_posts/2018-03-06-coffeeandcode.markdown
new file mode 100644
index 000000000..86a03f4b7
--- /dev/null
+++ b/_posts/2018-03-06-coffeeandcode.markdown
@@ -0,0 +1,9 @@
+---
+title: Coffee and Code
+text:  Informal "Coffee and Code" meet-ups 
+location: Second Cup at Grad House
+link: https://github.com/UofTCoders/Events/issues/141
+date: 2018-03-06
+startTime: '09:00'
+endTime: '11:00'
+---
diff --git a/_posts/2018-03-08-blockchain.markdown b/_posts/2018-03-08-blockchain.markdown
new file mode 100644
index 000000000..7199c00b6
--- /dev/null
+++ b/_posts/2018-03-08-blockchain.markdown
@@ -0,0 +1,9 @@
+---
+title: What is Blockchain
+text: Learn about cryptocurrencies and blockchain. 
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/139
+date: 2018-03-08
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-03-15-3dprinting.markdown b/_posts/2018-03-15-3dprinting.markdown
new file mode 100644
index 000000000..559860ebc
--- /dev/null
+++ b/_posts/2018-03-15-3dprinting.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to 3D Printing (MADLab Certification)
+text: Learn how to safely operate Gerstein Library's Makerbot Replicator 2 3D printers 
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/140
+date: 2018-03-15
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-03-22-neuralnetworkspython.markdown b/_posts/2018-03-22-neuralnetworkspython.markdown
new file mode 100644
index 000000000..523ec7c1d
--- /dev/null
+++ b/_posts/2018-03-22-neuralnetworkspython.markdown
@@ -0,0 +1,9 @@
+---
+title: Neural Networks in Python
+text: Learn to build neural networks in Python using Tensorflow and Keras
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/142
+date: 2018-03-22
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-03-29-academicworkflows.markdown b/_posts/2018-03-29-academicworkflows.markdown
new file mode 100644
index 000000000..ab703cc0e
--- /dev/null
+++ b/_posts/2018-03-29-academicworkflows.markdown
@@ -0,0 +1,9 @@
+---
+title: Academic Workflows
+text: Learn about tools for reproducible research and maintaining a digital lab notebook using Jupyter Notebook.  
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/145
+date: 2018-03-29
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-04-05-interactiveplotting.markdown b/_posts/2018-04-05-interactiveplotting.markdown
new file mode 100644
index 000000000..e308b719d
--- /dev/null
+++ b/_posts/2018-04-05-interactiveplotting.markdown
@@ -0,0 +1,9 @@
+---
+title: Interactive Plotting in Python and R
+text: Make interactive plots in both R (with Shiny) and Python (with Pandas and Altair). 
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/146
+date: 2018-04-05
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2018-04-10-coffeeandcode.markdown b/_posts/2018-04-10-coffeeandcode.markdown
new file mode 100644
index 000000000..5ecf78bad
--- /dev/null
+++ b/_posts/2018-04-10-coffeeandcode.markdown
@@ -0,0 +1,9 @@
+---
+title: Coffee and Code
+text: A casual co-working session - bring your laptop and whatever you're working on!
+location: Second Cup at Grad House
+link: https://github.com/UofTCoders/Events/issues/144
+date: 2018-04-10
+startTime: '09:00'
+endTime: '11:00'
+---
diff --git a/_posts/2018-04-12-coworking.markdown b/_posts/2018-04-12-coworking.markdown
new file mode 100644
index 000000000..73de15e40
--- /dev/null
+++ b/_posts/2018-04-12-coworking.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Work through any of your coding problems in a friendly and collaborative environment.
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/148
+date: 2018-04-12
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2018-04-13-bugbbq.markdown b/_posts/2018-04-13-bugbbq.markdown
new file mode 100644
index 000000000..7393c0ce1
--- /dev/null
+++ b/_posts/2018-04-13-bugbbq.markdown
@@ -0,0 +1,9 @@
+---
+title: Software Carpentry Bug BBQ
+text: Help improve the Software Carpentry lessons, drink coffee, and meet your coding peers!
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/147
+date: 2018-04-13
+startTime: '09:00'
+endTime: '14:00'
+---
diff --git a/_posts/2018-04-19-coworking.markdown b/_posts/2018-04-19-coworking.markdown
new file mode 100644
index 000000000..799cd1847
--- /dev/null
+++ b/_posts/2018-04-19-coworking.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Work through any of your coding problems in a friendly and collaborative environment.
+location: MADLAB at Gerstein
+link: https://github.com/UofTCoders/Events/issues/149
+date: 2018-04-19
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2018-04-26-elections.markdown b/_posts/2018-04-26-elections.markdown
new file mode 100644
index 000000000..0c97afd06
--- /dev/null
+++ b/_posts/2018-04-26-elections.markdown
@@ -0,0 +1,9 @@
+---
+title: Elections + Pub Night
+text: Vote and/or run in the executive elections and celebrate afterwards at the pub.
+location: MADLAB at Gerstein, O'Grady's Pub
+link: https://github.com/UofTCoders/Events/issues/150
+date: 2018-04-26
+startTime: '18:00'
+endTime: '22:00'
+---
diff --git a/_posts/2018-05-01-coffeeandcode.markdown b/_posts/2018-05-01-coffeeandcode.markdown
new file mode 100644
index 000000000..1fb72e8dd
--- /dev/null
+++ b/_posts/2018-05-01-coffeeandcode.markdown
@@ -0,0 +1,9 @@
+---
+title: Coffee and Code
+text: A casual co-working session - bring your laptop and whatever you're working on!
+location: Second Cup at Grad House
+link: https://github.com/UofTCoders/Events/issues/151
+date: 2018-05-01
+startTime: '09:00'
+endTime: '11:00'
+---
diff --git a/_posts/2018-05-03-coworking.markdown b/_posts/2018-05-03-coworking.markdown
new file mode 100644
index 000000000..dbd3fd6aa
--- /dev/null
+++ b/_posts/2018-05-03-coworking.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking Session
+text: Work through any of your coding problems in a friendly and collaborative environment.
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/152
+date: 2018-05-03
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2018-05-10-LD-R.markdown b/_posts/2018-05-10-LD-R.markdown
new file mode 100644
index 000000000..d1653af8f
--- /dev/null
+++ b/_posts/2018-05-10-LD-R.markdown
@@ -0,0 +1,9 @@
+---
+title: Lightning Demos - R
+text: Demonstrate what you've created using R, or demo a tool or package that you've found useful.
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/154
+date: 2018-05-10
+startTime: '18:00'
+endTime: '19:00'
+---
diff --git a/_posts/2018-05-10-mozsprint.markdown b/_posts/2018-05-10-mozsprint.markdown
new file mode 100644
index 000000000..21a171894
--- /dev/null
+++ b/_posts/2018-05-10-mozsprint.markdown
@@ -0,0 +1,11 @@
+---
+title: Mozilla Global Sprint
+text: A fun two-day hackathon (May 10-11) where people from all over the world work on projects to promote internet health.
+location: Mozilla Toronto Office
+link: https://github.com/UofTCoders/Events/issues/153
+date: 2018-05-10
+startTime: '09:00'
+endTime: '17:00'
+---
+
+
diff --git a/_posts/2018-05-17-intro-r.markdown b/_posts/2018-05-17-intro-r.markdown
new file mode 100644
index 000000000..e802b0e57
--- /dev/null
+++ b/_posts/2018-05-17-intro-r.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to R
+text: An introduction to programming in R and using RStudio.
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/155
+date: 2018-05-17
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2018-05-24-intro-stats-r.markdown b/_posts/2018-05-24-intro-stats-r.markdown
new file mode 100644
index 000000000..14b352ca8
--- /dev/null
+++ b/_posts/2018-05-24-intro-stats-r.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Statistics in R
+text: Learn how to use R for your statistical analyses.
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/156
+date: 2018-05-24
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2018-05-31-intermediate-stats-r.markdown b/_posts/2018-05-31-intermediate-stats-r.markdown
new file mode 100644
index 000000000..a42c7ee3d
--- /dev/null
+++ b/_posts/2018-05-31-intermediate-stats-r.markdown
@@ -0,0 +1,9 @@
+---
+title: Intermediate Statistics in R
+text: Learn how to use R for your statistical analyses.
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/157
+date: 2018-05-31
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2018-06-05-coffeeandcode.markdown b/_posts/2018-06-05-coffeeandcode.markdown
new file mode 100644
index 000000000..682604d5f
--- /dev/null
+++ b/_posts/2018-06-05-coffeeandcode.markdown
@@ -0,0 +1,9 @@
+---
+title: Coffee and Code
+text: A casual co-working session - bring your laptop and whatever you're working on!
+location: Second Cup at Grad House
+link: https://github.com/UofTCoders/Events/issues/173
+date: 2018-06-05
+startTime: '09:00'
+endTime: '11:00'
+---
diff --git a/_posts/2018-06-07-lightningdemo-editor.markdown b/_posts/2018-06-07-lightningdemo-editor.markdown
new file mode 100644
index 000000000..076ee50bd
--- /dev/null
+++ b/_posts/2018-06-07-lightningdemo-editor.markdown
@@ -0,0 +1,9 @@
+---
+title: Lightning Demos - Your favourite text editor
+text: Show off your favourite text editor and why you love using it over the others whether it's Vim, Sublime, Atom, etc.
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/160
+date: 2018-06-07
+startTime: '18:00'
+endTime: '19:30'
+---
diff --git a/_posts/2018-06-14-intro-bash.markdown b/_posts/2018-06-14-intro-bash.markdown
new file mode 100644
index 000000000..bf61592b8
--- /dev/null
+++ b/_posts/2018-06-14-intro-bash.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Bash
+text: Basic shell scripting in Bash
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/159
+date: 2018-06-14
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-06-18-DC-python.markdown b/_posts/2018-06-18-DC-python.markdown
new file mode 100644
index 000000000..69969d9a2
--- /dev/null
+++ b/_posts/2018-06-18-DC-python.markdown
@@ -0,0 +1,11 @@
+---
+title: Data Carpentry Workshop (2 days) - Data analysis and visualization in Python
+text: "Join us for a hands-on two-day Data Carpentry workshop covering core small research team skills
+including data organization and cleaning, data analysis, and data visualization. 
+Workshop costs $30 and is open to everyone. Click to see the event page for more details and registration!"
+location: MADLab, Gerstein Science Information Centre, Room B112
+link: https://uoftcoders.github.io/2018-06-18-utoronto/
+date: 2018-06-18
+startTime: '09:00'
+endTime: '16:30'
+---
diff --git a/_posts/2018-06-21-intro-latex.markdown b/_posts/2018-06-21-intro-latex.markdown
new file mode 100644
index 000000000..4cc787080
--- /dev/null
+++ b/_posts/2018-06-21-intro-latex.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to LaTeX
+text: We will introduce LaTeX and the basics of creating scientific documents with it. 
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/158
+date: 2018-06-21
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-06-26-coffeeandcode.markdown b/_posts/2018-06-26-coffeeandcode.markdown
new file mode 100644
index 000000000..b8048c41d
--- /dev/null
+++ b/_posts/2018-06-26-coffeeandcode.markdown
@@ -0,0 +1,9 @@
+---
+title: Coffee and Code
+text: A casual co-working session - bring your laptop and whatever you're working on!
+location: Second Cup at Grad House
+link: https://github.com/UofTCoders/Events/issues/175
+date: 2018-06-26
+startTime: '09:00'
+endTime: '11:00'
+---
diff --git a/_posts/2018-06-28-rmarkdown-pandoc.markdown b/_posts/2018-06-28-rmarkdown-pandoc.markdown
new file mode 100644
index 000000000..6c38bcb3c
--- /dev/null
+++ b/_posts/2018-06-28-rmarkdown-pandoc.markdown
@@ -0,0 +1,9 @@
+---
+title: RMarkdown, Markdown, and Pandoc
+text: "Learn how to use RMarkdown, knitR, and Markdown, Pandoc to convert between file formats: PDF, html, Word, etc. "
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/161
+date: 2018-06-28
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-07-05-gadgets-hardware.markdown b/_posts/2018-07-05-gadgets-hardware.markdown
new file mode 100644
index 000000000..78718b3f2
--- /dev/null
+++ b/_posts/2018-07-05-gadgets-hardware.markdown
@@ -0,0 +1,9 @@
+---
+title: Gadgets and Hardware
+text: Programming small devices
+location: BL520 (Claude T Bissell Building, 5th Floor)
+link: https://github.com/UofTCoders/Events/issues/163
+date: 2018-07-05
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2018-07-10-coffeeandcode.markdown b/_posts/2018-07-10-coffeeandcode.markdown
new file mode 100644
index 000000000..b77f28cae
--- /dev/null
+++ b/_posts/2018-07-10-coffeeandcode.markdown
@@ -0,0 +1,9 @@
+---
+title: Coffee and Code
+text: A casual co-working session - bring your laptop and whatever you're working on!
+location: Second Cup at Grad House
+link: https://github.com/UofTCoders/Events/issues/176
+date: 2018-07-10
+startTime: '09:00'
+endTime: '11:00'
+---
diff --git a/_posts/2018-07-12-DC-python.markdown b/_posts/2018-07-12-DC-python.markdown
new file mode 100644
index 000000000..f518d8da5
--- /dev/null
+++ b/_posts/2018-07-12-DC-python.markdown
@@ -0,0 +1,11 @@
+---
+title: Data Carpentry Workshop (2 days) - Data analysis and visualization in Python
+text: "Join us for a hands-on two-day Data Carpentry workshop covering core small research team skills
+including data organization and cleaning, data analysis, and data visualization. 
+Workshop costs $30 and is open to everyone. Click to see the event page for more details and registration!"
+location: MADLab, Gerstein Science Information Centre, Room B112
+link: https://uoftcoders.github.io/2018-07-12-utoronto/
+date: 2018-07-12
+startTime: '09:00'
+endTime: '16:30'
+---
diff --git a/_posts/2018-07-12-coworking-hardware.markdown b/_posts/2018-07-12-coworking-hardware.markdown
new file mode 100644
index 000000000..e5f4de8cb
--- /dev/null
+++ b/_posts/2018-07-12-coworking-hardware.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking - Programming Gadgets
+text:  Applying what we learned in the previous session and programming our devices
+location: BL520 (Claude T Bissell Building, 5th Floor)
+link: https://github.com/UofTCoders/Events/issues/164
+date: 2018-07-12
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-07-19-Intro-python.markdown b/_posts/2018-07-19-Intro-python.markdown
new file mode 100644
index 000000000..99287b852
--- /dev/null
+++ b/_posts/2018-07-19-Intro-python.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Python
+text:  The basics of the programming language Python with a beginner-friendly code-along
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/165
+date: 2018-07-19
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-07-24-coffee-and-code.md b/_posts/2018-07-24-coffee-and-code.md
new file mode 100644
index 000000000..0aec46a09
--- /dev/null
+++ b/_posts/2018-07-24-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/178"
+date: "2018-07-24"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2018-07-26-statistical-testing-python.markdown b/_posts/2018-07-26-statistical-testing-python.markdown
new file mode 100644
index 000000000..433e59d38
--- /dev/null
+++ b/_posts/2018-07-26-statistical-testing-python.markdown
@@ -0,0 +1,9 @@
+---
+title: Measures of association and statistical testing of hypotheses in Python
+text:  Introduction to  statistical testing of hypotheses in Python using SciPy
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/166
+date: 2018-07-26
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2018-08-02-web-scraping.markdown b/_posts/2018-08-02-web-scraping.markdown
new file mode 100644
index 000000000..2b3afbd8e
--- /dev/null
+++ b/_posts/2018-08-02-web-scraping.markdown
@@ -0,0 +1,9 @@
+---
+title: Web Scraping
+text:  
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/167
+date: 2018-08-02
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-08-07-coffee-and-code.md b/_posts/2018-08-07-coffee-and-code.md
new file mode 100644
index 000000000..debc1918b
--- /dev/null
+++ b/_posts/2018-08-07-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/179"
+date: "2018-08-07"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2018-08-09-coworking-python.markdown b/_posts/2018-08-09-coworking-python.markdown
new file mode 100644
index 000000000..f623de3d6
--- /dev/null
+++ b/_posts/2018-08-09-coworking-python.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking - Python
+text:  Python-themed coworking session
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/168
+date: 2018-08-09
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-08-16-intro-git.markdown b/_posts/2018-08-16-intro-git.markdown
new file mode 100644
index 000000000..a45c45f0f
--- /dev/null
+++ b/_posts/2018-08-16-intro-git.markdown
@@ -0,0 +1,9 @@
+---
+title: Intro to Git
+text:  Introduction to Git version control 
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/169
+date: 2018-08-16
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-08-23-open-source-project-101.markdown b/_posts/2018-08-23-open-source-project-101.markdown
new file mode 100644
index 000000000..cbdf68d6f
--- /dev/null
+++ b/_posts/2018-08-23-open-source-project-101.markdown
@@ -0,0 +1,9 @@
+---
+title: Contributing to an Open Source Project 101
+text:  Learn how to contribute to an open source project 
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/170
+date: 2018-08-23
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-08-30-virtualization-docker-sphinx.markdown b/_posts/2018-08-30-virtualization-docker-sphinx.markdown
new file mode 100644
index 000000000..65b53b525
--- /dev/null
+++ b/_posts/2018-08-30-virtualization-docker-sphinx.markdown
@@ -0,0 +1,9 @@
+---
+title: Virtualization vs. Docker Images, Sphinx for Documentation
+text:   
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/171
+date: 2018-08-30
+startTime: '18:10'
+endTime: '19:30'
+---
diff --git a/_posts/2018-09-04-coffee-and-code.md b/_posts/2018-09-04-coffee-and-code.md
new file mode 100644
index 000000000..1c925a3eb
--- /dev/null
+++ b/_posts/2018-09-04-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/181"
+date: "2018-09-04"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2018-09-06-coworking-bugs-pull-reqs.markdown b/_posts/2018-09-06-coworking-bugs-pull-reqs.markdown
new file mode 100644
index 000000000..4424beb6e
--- /dev/null
+++ b/_posts/2018-09-06-coworking-bugs-pull-reqs.markdown
@@ -0,0 +1,9 @@
+---
+title: Coworking - Bugs and Pull Requests
+text: A coworking session to practice contributing to open source projects 
+location: MADLab at Gerstein
+link: https://github.com/UofTCoders/Events/issues/172
+date: 2018-09-06
+startTime: '18:10'
+endTime: '19:00'
+---
diff --git a/_posts/2018-09-10-DC-python.markdown b/_posts/2018-09-10-DC-python.markdown
new file mode 100644
index 000000000..bce0222aa
--- /dev/null
+++ b/_posts/2018-09-10-DC-python.markdown
@@ -0,0 +1,11 @@
+---
+title: Data Carpentry Workshop (2 days) - Data analysis and visualization in Python
+text: "Join us for a hands-on two-day Data Carpentry workshop covering core small research team skills
+including data organization and cleaning, data analysis, and data visualization. 
+Workshop costs $25 and is open to everyone. Click to see the event page for more details and registration!"
+location: MADLab, Gerstein Science Information Centre, Room B112
+link: https://uoftcoders.github.io/2018-09-10-utoronto/
+date: 2018-09-10
+startTime: '09:00'
+endTime: '16:30'
+---
diff --git a/_posts/2018-09-13-social.md b/_posts/2018-09-13-social.md
new file mode 100644
index 000000000..081e67cb1
--- /dev/null
+++ b/_posts/2018-09-13-social.md
@@ -0,0 +1,9 @@
+---
+title: "UofT Coders Social- September 13"
+text: "We're having a social at the GSU Pub to welcome new and returning members with the start of the new academic year. Join us and get to know the exec and fellow members of the UofT Coders community in a less formal environment. You do not need to have attended an event previously to attend. Everyone is welcome."
+location: "GSU pub"
+link: "https://github.com/UofTCoders/Events/issues/182"
+date: "2018-09-13"
+startTime: "18:10"
+endTime: "21:00"
+---
diff --git a/_posts/2018-09-18-coffee-and-code.md b/_posts/2018-09-18-coffee-and-code.md
new file mode 100644
index 000000000..203b64059
--- /dev/null
+++ b/_posts/2018-09-18-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/183"
+date: "2018-09-18"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2018-09-20-coworking.md b/_posts/2018-09-20-coworking.md
new file mode 100644
index 000000000..9390de276
--- /dev/null
+++ b/_posts/2018-09-20-coworking.md
@@ -0,0 +1,10 @@
+---
+title: "Coworking"
+text: "Co-working sessions are meetings where you can work on projects and discuss strategies and tools to solve your coding problems with the help of other coding group members. Even if you're just starting out, this is a great place to get started.
+"
+location: "MADLab at Gerstein Science Information Centre"
+link: "https://github.com/UofTCoders/Events/issues/186"
+date: "2018-09-20"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2018-09-27-demo-reproducible-documents.md b/_posts/2018-09-27-demo-reproducible-documents.md
new file mode 100644
index 000000000..71e56157a
--- /dev/null
+++ b/_posts/2018-09-27-demo-reproducible-documents.md
@@ -0,0 +1,10 @@
+---
+title: "Lightning Demos: reproducible documents (reports, papers and thesis)"
+text: "What is your approach to creating reproducible documents? how do you use the capabilities in R, Python, Latex, pandoc, etc. to integrate your data analysis with writing your reports and other manuscripts? Come to this session and show your peers what is your workflow for creating reproducible documents or hear from others how they do it.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/187"
+date: "2018-09-27"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2018-10-02-coffee-and-code.md b/_posts/2018-10-02-coffee-and-code.md
new file mode 100644
index 000000000..f1c7817c0
--- /dev/null
+++ b/_posts/2018-10-02-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/184"
+date: "2018-10-02"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2018-10-04-git-intro.md b/_posts/2018-10-04-git-intro.md
new file mode 100644
index 000000000..df0575c52
--- /dev/null
+++ b/_posts/2018-10-04-git-intro.md
@@ -0,0 +1,10 @@
+---
+title: "Intro to Version Control with Git and GitHub"
+text: "Learn how to take advantage of version control for your projects and why it can be useful. We will be focusing on Git, a popular piece of software that does version control. This session is geared towards complete beginners - if you've never touched Git before, this is for you!
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/196"
+date: "2018-10-04"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2018-10-11-git-intermediate.md b/_posts/2018-10-11-git-intermediate.md
new file mode 100644
index 000000000..3bb3de9ce
--- /dev/null
+++ b/_posts/2018-10-11-git-intermediate.md
@@ -0,0 +1,10 @@
+---
+title: "Intermediate Topics in Git and GitHub"
+text: "In this session, we will build on the material presented at last week's Intro to Git and will discuss more advanced ways to use Git in collaboration with others. The material will be aimed at an audience that has some familiarity with basic Git commands, but by no means do you need to be experienced or an expert. If you attended last week's session or have used Git before or have looked over the material from last week, you should be well-prepared.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/195"
+date: "2018-10-11"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2018-10-18-R-intro.md b/_posts/2018-10-18-R-intro.md
new file mode 100644
index 000000000..40df62e38
--- /dev/null
+++ b/_posts/2018-10-18-R-intro.md
@@ -0,0 +1,9 @@
+---
+title: "Intro R and Tidyverse"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/197"
+date: "2018-10-18"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2018-10-25-python-intro.md b/_posts/2018-10-25-python-intro.md
new file mode 100644
index 000000000..57894e067
--- /dev/null
+++ b/_posts/2018-10-25-python-intro.md
@@ -0,0 +1,9 @@
+---
+title: "Intro Python with Numpy and Pandas"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/198"
+date: "2018-10-25"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2018-10-30-coffee-and-code.md b/_posts/2018-10-30-coffee-and-code.md
new file mode 100644
index 000000000..2993f23e6
--- /dev/null
+++ b/_posts/2018-10-30-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/206"
+date: "2018-10-30"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2018-11-01-R-plotting.md b/_posts/2018-11-01-R-plotting.md
new file mode 100644
index 000000000..3b85d462d
--- /dev/null
+++ b/_posts/2018-11-01-R-plotting.md
@@ -0,0 +1,9 @@
+---
+title: "R: Plotting with theme of Heatmaps"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/199"
+date: "2018-11-01"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2018-11-08-python-plotting.md b/_posts/2018-11-08-python-plotting.md
new file mode 100644
index 000000000..c05d020ed
--- /dev/null
+++ b/_posts/2018-11-08-python-plotting.md
@@ -0,0 +1,9 @@
+---
+title: "Python: Plotting with Seaborn and Matplotlib"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/200"
+date: "2018-11-08"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2018-11-13-coffee-and-code.md b/_posts/2018-11-13-coffee-and-code.md
new file mode 100644
index 000000000..31449de12
--- /dev/null
+++ b/_posts/2018-11-13-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/207"
+date: "2018-11-13"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2018-11-15-R-data-wrangling.md b/_posts/2018-11-15-R-data-wrangling.md
new file mode 100644
index 000000000..cc151d5dc
--- /dev/null
+++ b/_posts/2018-11-15-R-data-wrangling.md
@@ -0,0 +1,9 @@
+---
+title: "R: Magrittr/Dplyr/Tidyr for data wrangling"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/201"
+date: "2018-11-15"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2018-11-22-python-machine-learning.md b/_posts/2018-11-22-python-machine-learning.md
new file mode 100644
index 000000000..f386f7ed6
--- /dev/null
+++ b/_posts/2018-11-22-python-machine-learning.md
@@ -0,0 +1,9 @@
+---
+title: "Python: Machine Learning with scikit-learn"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/202"
+date: "2018-11-22"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2018-11-27-coffee-and-code.md b/_posts/2018-11-27-coffee-and-code.md
new file mode 100644
index 000000000..e8da7015a
--- /dev/null
+++ b/_posts/2018-11-27-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/208"
+date: "2018-11-27"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2018-11-29-R-stats.md b/_posts/2018-11-29-R-stats.md
new file mode 100644
index 000000000..2b1ed13f9
--- /dev/null
+++ b/_posts/2018-11-29-R-stats.md
@@ -0,0 +1,9 @@
+---
+title: "R: Statistical Methods"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/203"
+date: "2018-11-29"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2018-12-06-python-stats.md b/_posts/2018-12-06-python-stats.md
new file mode 100644
index 000000000..efaeef70d
--- /dev/null
+++ b/_posts/2018-12-06-python-stats.md
@@ -0,0 +1,9 @@
+---
+title: "Python: Statistical Methods"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/204"
+date: "2018-12-06"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2018-12-11-coffee-and-code.md b/_posts/2018-12-11-coffee-and-code.md
new file mode 100644
index 000000000..946ffddc8
--- /dev/null
+++ b/_posts/2018-12-11-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/209"
+date: "2018-12-11"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2019-01-10-intro-python.md b/_posts/2019-01-10-intro-python.md
new file mode 100644
index 000000000..940876848
--- /dev/null
+++ b/_posts/2019-01-10-intro-python.md
@@ -0,0 +1,10 @@
+---
+title: "Intro to Python"
+text: "The basics of data analysis using the programming language Python with a beginner-friendly participatory live-coding.
+"
+location: "MADLab"
+link: "NA"
+date: "2019-01-10"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-01-15-coffee-and-code.md b/_posts/2019-01-15-coffee-and-code.md
new file mode 100644
index 000000000..1a44243f7
--- /dev/null
+++ b/_posts/2019-01-15-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/211"
+date: "2019-01-15"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2019-01-17-Intro-R.md b/_posts/2019-01-17-Intro-R.md
new file mode 100644
index 000000000..6029d2326
--- /dev/null
+++ b/_posts/2019-01-17-Intro-R.md
@@ -0,0 +1,10 @@
+---
+title: "Intro to R and Tidyverse"
+text: "The basics of using R and the Tidyverse for working with data, specifically: - Intro to R/navigating RStudio - Intro to Tidyverse - pipe operators - Basic data wrangling with dplyr and tidyr packages
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/219"
+date: "2019-01-17"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-01-24-Intro-Bash.md b/_posts/2019-01-24-Intro-Bash.md
new file mode 100644
index 000000000..5303b593a
--- /dev/null
+++ b/_posts/2019-01-24-Intro-Bash.md
@@ -0,0 +1,10 @@
+---
+title: "Intro to Bash/Shell Scripting"
+text: "This introduction to BASH aims to make participants capable of working from the command line in Unix environments. The focus of this lesson will be to learn common file system tasks and to use built-in tools to make your work more efficient (and fun!). This is a beginner level workshop, so no previous knowledge is required. A few intermediate level concept will be introduced, but we will not go into scripting in BASH.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/220"
+date: "2019-01-24"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-01-29-coffee-and-code.md b/_posts/2019-01-29-coffee-and-code.md
new file mode 100644
index 000000000..3f9ca0cab
--- /dev/null
+++ b/_posts/2019-01-29-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/212"
+date: "2019-01-29"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2019-01-31-inkscape.md b/_posts/2019-01-31-inkscape.md
new file mode 100644
index 000000000..240e4b700
--- /dev/null
+++ b/_posts/2019-01-31-inkscape.md
@@ -0,0 +1,11 @@
+---
+title: "Inkscape - An Open-Source Alternative to Adobe Illustrator"
+text: "We will learn to use Inkscape, a cross-platform open source graphic design software.
+We will cover: - why use vector graphics instead of rasterized, - basic interface features of Inkscape - paths/shapes - strokes/fills - groups/layers/alignment - resizing drawing region and exporting - importing from other sources (e.g. plots generated in R or Python)
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/221"
+date: "2019-01-31"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-02-07-R-reshape2.md b/_posts/2019-02-07-R-reshape2.md
new file mode 100644
index 000000000..32708beac
--- /dev/null
+++ b/_posts/2019-02-07-R-reshape2.md
@@ -0,0 +1,9 @@
+---
+title: "Intro to TidyR and Reshape2"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/222"
+date: "2019-02-07"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-02-12-coffee-and-code.md b/_posts/2019-02-12-coffee-and-code.md
new file mode 100644
index 000000000..4efb45db8
--- /dev/null
+++ b/_posts/2019-02-12-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/213"
+date: "2019-02-12"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2019-02-14-python-imageprocessing.md b/_posts/2019-02-14-python-imageprocessing.md
new file mode 100644
index 000000000..77238604c
--- /dev/null
+++ b/_posts/2019-02-14-python-imageprocessing.md
@@ -0,0 +1,10 @@
+---
+title: "Image Processing in Python"
+text: "This is an introductory tutorial on image processing using Python packages. We will understand image data types, manipulate and prepare images for analysis such as image segmentation.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/223"
+date: "2019-02-14"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-02-21-R-shinyapps.md b/_posts/2019-02-21-R-shinyapps.md
new file mode 100644
index 000000000..ef5131fc1
--- /dev/null
+++ b/_posts/2019-02-21-R-shinyapps.md
@@ -0,0 +1,9 @@
+---
+title: "R: Interactive Plotting with Shinny Apps"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/224"
+date: "2019-02-21"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-02-26-coffee-and-code.md b/_posts/2019-02-26-coffee-and-code.md
new file mode 100644
index 000000000..d252e73af
--- /dev/null
+++ b/_posts/2019-02-26-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/214"
+date: "2019-02-26"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2019-02-28-deeplearning-tensorflow.md b/_posts/2019-02-28-deeplearning-tensorflow.md
new file mode 100644
index 000000000..f9cb88c95
--- /dev/null
+++ b/_posts/2019-02-28-deeplearning-tensorflow.md
@@ -0,0 +1,9 @@
+---
+title: "Co-working Session"
+text: "Work on your code/research in a friendly and welcoming environment."
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/225"
+date: "2019-02-28"
+startTime: "18:10"
+endTime: "19:30"
+---
diff --git a/_posts/2019-03-07-git-testing.md b/_posts/2019-03-07-git-testing.md
new file mode 100644
index 000000000..b1a36f79e
--- /dev/null
+++ b/_posts/2019-03-07-git-testing.md
@@ -0,0 +1,9 @@
+---
+title: "Git Continuous Integration and Testing"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/226"
+date: "2019-03-07"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-03-12-coffee-and-code.md b/_posts/2019-03-12-coffee-and-code.md
new file mode 100644
index 000000000..1d53875cd
--- /dev/null
+++ b/_posts/2019-03-12-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/215"
+date: "2019-03-12"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2019-03-14-software-defined-radio.md b/_posts/2019-03-14-software-defined-radio.md
new file mode 100644
index 000000000..1342fe7b7
--- /dev/null
+++ b/_posts/2019-03-14-software-defined-radio.md
@@ -0,0 +1,9 @@
+---
+title: "Information on the Air - Software Defined Radio"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/227"
+date: "2019-03-14"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-03-21-Regression-R.md b/_posts/2019-03-21-Regression-R.md
new file mode 100644
index 000000000..ca6477d74
--- /dev/null
+++ b/_posts/2019-03-21-Regression-R.md
@@ -0,0 +1,9 @@
+---
+title: "Regression Models in R"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/228"
+date: "2019-03-21"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-03-26-coffee-and-code.md b/_posts/2019-03-26-coffee-and-code.md
new file mode 100644
index 000000000..932af1a4d
--- /dev/null
+++ b/_posts/2019-03-26-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/216"
+date: "2019-03-26"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2019-03-28-HTML.md b/_posts/2019-03-28-HTML.md
new file mode 100644
index 000000000..8a642ae37
--- /dev/null
+++ b/_posts/2019-03-28-HTML.md
@@ -0,0 +1,9 @@
+---
+title: "HTML and Web Design"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/229"
+date: "2019-03-28"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-04-04-ggplot2.md b/_posts/2019-04-04-ggplot2.md
new file mode 100644
index 000000000..5dcdb4512
--- /dev/null
+++ b/_posts/2019-04-04-ggplot2.md
@@ -0,0 +1,9 @@
+---
+title: "Intro to ggplot2 in R"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/230"
+date: "2019-04-04"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-04-09-coffee-and-code.md b/_posts/2019-04-09-coffee-and-code.md
new file mode 100644
index 000000000..7ebf3fb04
--- /dev/null
+++ b/_posts/2019-04-09-coffee-and-code.md
@@ -0,0 +1,9 @@
+---
+title: "Coffee and Code"
+text: "A casual co-working session - bring your laptop and whatever you're working on!"
+location: "Second Cup at Grad Room"
+link: "https://github.com/UofTCoders/Events/issues/217"
+date: "2019-04-09"
+startTime: "09:00"
+endTime: "11:00"
+---
diff --git a/_posts/2019-04-11-co-working.md b/_posts/2019-04-11-co-working.md
new file mode 100644
index 000000000..2b4d83296
--- /dev/null
+++ b/_posts/2019-04-11-co-working.md
@@ -0,0 +1,9 @@
+---
+title: "co-working"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/232"
+date: "2019-04-11"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-04-18-Deeplearning-pytorch.md b/_posts/2019-04-18-Deeplearning-pytorch.md
new file mode 100644
index 000000000..4df8b11ed
--- /dev/null
+++ b/_posts/2019-04-18-Deeplearning-pytorch.md
@@ -0,0 +1,9 @@
+---
+title: "Deep Learning with PyTorch"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/231"
+date: "2019-04-18"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-04-25-social.md b/_posts/2019-04-25-social.md
new file mode 100644
index 000000000..4361c22ef
--- /dev/null
+++ b/_posts/2019-04-25-social.md
@@ -0,0 +1,9 @@
+---
+title: "End of Term Social"
+text: ""
+location: "TBA"
+link: "https://github.com/UofTCoders/Events/issues/233"
+date: "2019-04-25"
+startTime: "19:00"
+endTime: "21:00"
+---
diff --git a/_posts/2019-05-23-Intro-R.md b/_posts/2019-05-23-Intro-R.md
new file mode 100644
index 000000000..5a9b26a29
--- /dev/null
+++ b/_posts/2019-05-23-Intro-R.md
@@ -0,0 +1,10 @@
+---
+title: "Intro to R and Tidyverse"
+text: "The basics of using R and the Tidyverse for working with data, specifically: - Intro to R/navigating RStudio - Intro to Tidyverse - pipe operators - Basic data wrangling with dplyr and tidyr packages
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/235"
+date: "2019-05-23"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-05-30-r-ggplot.md b/_posts/2019-05-30-r-ggplot.md
new file mode 100644
index 000000000..2f57048fc
--- /dev/null
+++ b/_posts/2019-05-30-r-ggplot.md
@@ -0,0 +1,9 @@
+---
+title: "Data visualization in R with ggplot2"
+text: "The fundamentals of visualizing data in R using the ggplot2 package."
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/236"
+date: "2019-05-30"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-06-06-r-statistics.md b/_posts/2019-06-06-r-statistics.md
new file mode 100644
index 000000000..a89766bf7
--- /dev/null
+++ b/_posts/2019-06-06-r-statistics.md
@@ -0,0 +1,10 @@
+---
+title: "Statistics in R"
+text: "Building from our previous lesson, we will go over more advanced statistical analyses using the R programming language, and especially cases where your data may be a little bit messy or you might be working with repeated-measures data (also known as hierarchical data or within-subject data). We'll assume that you are familiar with basic linear regression (e.g. testing if x predicts y) and statistics basics (e.g. how to interpret a p-value), but we will do a brief review and explain everything else as we go!
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/237"
+date: "2019-06-06"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-06-13-intro-python.md b/_posts/2019-06-13-intro-python.md
new file mode 100644
index 000000000..8d28b8e4a
--- /dev/null
+++ b/_posts/2019-06-13-intro-python.md
@@ -0,0 +1,10 @@
+---
+title: "Intro to Python"
+text: "Introduction to the programming language Python with a beginner-friendly participatory live-coding.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/238"
+date: "2019-06-13"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-06-20-data-analysis-workflow.md b/_posts/2019-06-20-data-analysis-workflow.md
new file mode 100644
index 000000000..a3267f48e
--- /dev/null
+++ b/_posts/2019-06-20-data-analysis-workflow.md
@@ -0,0 +1,10 @@
+---
+title: "Designing data analysis workflows"
+text: "How to make data analysis workflows in the programming language Python with a beginner-friendly participatory live-coding.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/239"
+date: "2019-06-20"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-06-27-oop-python.md b/_posts/2019-06-27-oop-python.md
new file mode 100644
index 000000000..1f33aaaaf
--- /dev/null
+++ b/_posts/2019-06-27-oop-python.md
@@ -0,0 +1,10 @@
+---
+title: "Object-oriented programming in Python"
+text: "Understanding and using object-oriented programming in the programming language Python with a beginner-friendly participatory live-coding.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/240"
+date: "2019-06-27"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-07-04-coworking.md b/_posts/2019-07-04-coworking.md
new file mode 100644
index 000000000..8662594ff
--- /dev/null
+++ b/_posts/2019-07-04-coworking.md
@@ -0,0 +1,10 @@
+---
+title: "Co-working"
+text: "Join us to work on some code in good company. Bring something to work on, ask and discuss with your fellow coders.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/254"
+date: "2019-07-04"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-07-11-bash-intro.md b/_posts/2019-07-11-bash-intro.md
new file mode 100644
index 000000000..09a0c8554
--- /dev/null
+++ b/_posts/2019-07-11-bash-intro.md
@@ -0,0 +1,10 @@
+---
+title: "Intro to Bash and shell scripting"
+text: "This introduction to BASH aims to make participants capable of working from the command line in Unix environments. The focus of this lesson will be to learn common file system tasks and to use built-in tools to make your work more efficient (and fun!). This is a beginner level workshop, so no previous knowledge is required. A few intermediate level concept will be introduced, but we will not go into scripting in BASH.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/242"
+date: "2019-07-11"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-07-18-git-intro.md b/_posts/2019-07-18-git-intro.md
new file mode 100644
index 000000000..9c7dc5a3e
--- /dev/null
+++ b/_posts/2019-07-18-git-intro.md
@@ -0,0 +1,10 @@
+---
+title: "Intro to Git"
+text: "Learn how to take advantage of version control for your projects and why it can be useful. We will be focusing on Git, a popular piece of software that does version control. This session is geared towards complete beginners - if you've never touched Git before, this is for you!
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/243"
+date: "2019-07-18"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-07-25-journal-club.md b/_posts/2019-07-25-journal-club.md
new file mode 100644
index 000000000..445542bd6
--- /dev/null
+++ b/_posts/2019-07-25-journal-club.md
@@ -0,0 +1,10 @@
+---
+title: "Journal Club - academic workflows"
+text: "The basics of data analysis using the programming language Python with a beginner-friendly participatory live-coding.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/255"
+date: "2019-07-25"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-08-01-collaborating-github.md b/_posts/2019-08-01-collaborating-github.md
new file mode 100644
index 000000000..74cd3d5fe
--- /dev/null
+++ b/_posts/2019-08-01-collaborating-github.md
@@ -0,0 +1,10 @@
+---
+title: "Collaborating on GitHub"
+text: "Git and Github are tools not only for version control of your own code, but are widely used for collaborative team coding! Learn more about how you can use Git collaboratively on a project, the genereal workflow as well as details about the step-by-step process.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/256"
+date: "2019-08-01"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-08-08-machine-learning.md b/_posts/2019-08-08-machine-learning.md
new file mode 100644
index 000000000..5e056a6fd
--- /dev/null
+++ b/_posts/2019-08-08-machine-learning.md
@@ -0,0 +1,9 @@
+---
+title: "Machine learning: introduction to topics"
+text: ""
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/257"
+date: "2019-08-08"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-08-15-image-processing-python.md b/_posts/2019-08-15-image-processing-python.md
new file mode 100644
index 000000000..2edbcb5a5
--- /dev/null
+++ b/_posts/2019-08-15-image-processing-python.md
@@ -0,0 +1,10 @@
+---
+title: "Image processing in Python"
+text: "This is an introductory tutorial on image processing using Python packages. We will understand image data types, manipulate and prepare images for analysis such as image segmentation.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/258"
+date: "2019-08-15"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-08-22-coworking.md b/_posts/2019-08-22-coworking.md
new file mode 100644
index 000000000..3fb924416
--- /dev/null
+++ b/_posts/2019-08-22-coworking.md
@@ -0,0 +1,10 @@
+---
+title: "Co-working"
+text: "Join us to work on some code in good company. Bring something to work on, ask and discuss with your fellow coders.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/259"
+date: "2019-08-22"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-09-26-python-intro.md b/_posts/2019-09-26-python-intro.md
new file mode 100644
index 000000000..e80dd33f4
--- /dev/null
+++ b/_posts/2019-09-26-python-intro.md
@@ -0,0 +1,10 @@
+---
+title: "Intro to Python"
+text: "Introduction to the programming language Python with a beginner-friendly participatory live-coding.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/262"
+date: "2019-09-26"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-10-03-SWC-python.md b/_posts/2019-10-03-SWC-python.md
new file mode 100644
index 000000000..3b3d37ce2
--- /dev/null
+++ b/_posts/2019-10-03-SWC-python.md
@@ -0,0 +1,11 @@
+---
+title: Software Carpentry Workshop (2 days) - Python/Unix Shell/Git
+text: "Join us for a hands-on two-day Software Carpentry workshop covering core small research team skills
+including programming with Python, automating tasks with Unix Shell, and version control with Git. 
+Workshop costs $25 and is open to everyone. Click to see the event page for more details and registration!"
+location: MADLab, Gerstein Science Information Centre, Room B112
+link: https://uoftcoders.github.io/2019-10-03-utoronto/
+date: 2019-10-03
+startTime: '09:00'
+endTime: '16:30'
+---
diff --git a/_posts/2019-10-03-r-intro.md b/_posts/2019-10-03-r-intro.md
new file mode 100644
index 000000000..07342ca19
--- /dev/null
+++ b/_posts/2019-10-03-r-intro.md
@@ -0,0 +1,10 @@
+---
+title: "Intro to R"
+text: "The basics of using R and the Tidyverse for working with data, specifically: - Intro to R/navigating RStudio - Intro to Tidyverse - pipe operators - Basic data wrangling with dplyr and tidyr packages
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/271"
+date: "2019-10-03"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-10-10-bash-intro.md b/_posts/2019-10-10-bash-intro.md
new file mode 100644
index 000000000..d015f9240
--- /dev/null
+++ b/_posts/2019-10-10-bash-intro.md
@@ -0,0 +1,10 @@
+---
+title: "Intro to Bash and shell scripting"
+text: "This introduction to BASH aims to make participants capable of working from the command line in Unix environments. The focus of this lesson will be to learn common file system tasks and to use built-in tools to make your work more efficient (and fun!). This is a beginner level workshop, so no previous knowledge is required. A few intermediate level concept will be introduced, but we will not go into scripting in BASH.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/263"
+date: "2019-10-10"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-10-17-git-intro.md b/_posts/2019-10-17-git-intro.md
new file mode 100644
index 000000000..5dd83fcc1
--- /dev/null
+++ b/_posts/2019-10-17-git-intro.md
@@ -0,0 +1,10 @@
+---
+title: "Intro to Git"
+text: "Learn how to take advantage of version control for your projects and why it can be useful. We will be focusing on Git, a popular piece of software that does version control. This session is geared towards complete beginners - if you've never touched Git before, this is for you!
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/264"
+date: "2019-10-17"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-10-24-reproducible-programming-bp.md b/_posts/2019-10-24-reproducible-programming-bp.md
new file mode 100644
index 000000000..6cf7488c6
--- /dev/null
+++ b/_posts/2019-10-24-reproducible-programming-bp.md
@@ -0,0 +1,10 @@
+---
+title: "Best practices for reproducible programming"
+text: "People are talking a lot these days about research being  'open' and 'reproducible'. It all sounds great, but HOW do we do it?  How can we apply these great ideals to our actual work? This week we'll discuss reproducible computational research,  guided by [Ten simple rules for computational research](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285) and Mozilla's [code review guidelines](https://mozillascience.github.io/codeReview/review.html)
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/265"
+date: "2019-10-24"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-10-31-code-review.md b/_posts/2019-10-31-code-review.md
new file mode 100644
index 000000000..24c291578
--- /dev/null
+++ b/_posts/2019-10-31-code-review.md
@@ -0,0 +1,10 @@
+---
+title: "Code Review"
+text: "We will have a brief overview of code review (looking over code for mistakes,  optimization etc. to increase the overall quality of the code),  why you should do it, and how to do it. Then we will jump in and  look at some of the attendees' code in pairs or groups.  If you have some code that is currently being developed,  we encourage you to bring it to the session for code review.  Don't worry if you haven't fully optimized it yet,  that's why we're holding this session :)  Or if you'd prefer you can take part as just a reviewer  instead and see how others write code and help them check for errors too!
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/266"
+date: "2019-10-31"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-11-07-python-unit-tests.md b/_posts/2019-11-07-python-unit-tests.md
new file mode 100644
index 000000000..ee49f3a55
--- /dev/null
+++ b/_posts/2019-11-07-python-unit-tests.md
@@ -0,0 +1,10 @@
+---
+title: "Python Unit Tests"
+text: "Want to be sure your code works as intended? Say hello to unit testing! Unit tests are an automated way to check your code for bugs. Not only that but developing unit tests will improve your code all on its own.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/267"
+date: "2019-11-07"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-11-14-python-statistics.md b/_posts/2019-11-14-python-statistics.md
new file mode 100644
index 000000000..d163811d7
--- /dev/null
+++ b/_posts/2019-11-14-python-statistics.md
@@ -0,0 +1,12 @@
+---
+title: "Intro to Statistics in Python"
+text: "Statistics is an important cornerstone to data analysis.
+ Our introduction to doing statistics in Python lesson 
+ will go over basic descriptive and inferential statistics
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/268"
+date: "2019-11-14"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-11-21-r-statistics.md b/_posts/2019-11-21-r-statistics.md
new file mode 100644
index 000000000..e995952dd
--- /dev/null
+++ b/_posts/2019-11-21-r-statistics.md
@@ -0,0 +1,10 @@
+---
+title: "Statistics in R"
+text: "Building from our previous lesson, we will go over more advanced statistical analyses using the R programming language, and especially cases where your data may be a little bit messy or you might be working with repeated-measures data (also known as hierarchical data or within-subject data). We'll assume that you are familiar with basic linear regression (e.g. testing if x predicts y) and statistics basics (e.g. how to interpret a p-value), but we will do a brief review and explain everything else as we go!
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/269"
+date: "2019-11-21"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2019-11-28-r-ggplot.md b/_posts/2019-11-28-r-ggplot.md
new file mode 100644
index 000000000..f8f1c6b8c
--- /dev/null
+++ b/_posts/2019-11-28-r-ggplot.md
@@ -0,0 +1,10 @@
+---
+title: "Data visualization in R with ggplot2"
+text: "The fundamentals of visualizing data in R using the ggplot2 package.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/270"
+date: "2019-11-28"
+startTime: "18:10"
+endTime: "19:00"
+---
diff --git a/_posts/2020-02-06-reproducible-rstudio.md b/_posts/2020-02-06-reproducible-rstudio.md
new file mode 100644
index 000000000..3f8acf2d4
--- /dev/null
+++ b/_posts/2020-02-06-reproducible-rstudio.md
@@ -0,0 +1,10 @@
+---
+title: "Reproducible Science in RStudio"
+text: "An overview of the RStudio IDE highlighting features to make your work better documented and more reproducible.   
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/280"
+date: "2020-02-06"
+startTime: "18:10"
+endTime: "20:00"
+---
diff --git a/_posts/2020-02-27-supervised-ml-python.md b/_posts/2020-02-27-supervised-ml-python.md
new file mode 100644
index 000000000..33cd2e58d
--- /dev/null
+++ b/_posts/2020-02-27-supervised-ml-python.md
@@ -0,0 +1,10 @@
+---
+title: "Supervised machine learning in Python"
+text: "An introduction to supervised machine learning in Python.
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/281"
+date: "2020-02-27"
+startTime: "18:10"
+endTime: "20:00"
+---
diff --git a/_posts/2020-03-12-git-ci-python.md b/_posts/2020-03-12-git-ci-python.md
new file mode 100644
index 000000000..44a4aa1e4
--- /dev/null
+++ b/_posts/2020-03-12-git-ci-python.md
@@ -0,0 +1,10 @@
+---
+title: "Git: continuous integration with examples in Python"
+text: "An overview of setting up continuous integration in a Git repo with worked examples in Python. 
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/283"
+date: "2020-03-12"
+startTime: "18:10"
+endTime: "20:00"
+---
diff --git a/_posts/2020-03-26-programming-tidyverse.md b/_posts/2020-03-26-programming-tidyverse.md
new file mode 100644
index 000000000..c2a384ea0
--- /dev/null
+++ b/_posts/2020-03-26-programming-tidyverse.md
@@ -0,0 +1,10 @@
+---
+title: "Programming and Data Wrangling in R with the Tidyverse"
+text: "Exploring how to develop basic software for data wrangling with the Tidyverse suite of packages. 
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/282"
+date: "2020-03-26"
+startTime: "18:10"
+endTime: "20:00"
+---
diff --git a/_posts/2020-04-09-writing-latex-pandoc.md b/_posts/2020-04-09-writing-latex-pandoc.md
new file mode 100644
index 000000000..1769bc0c8
--- /dev/null
+++ b/_posts/2020-04-09-writing-latex-pandoc.md
@@ -0,0 +1,10 @@
+---
+title: "Scientific writing with LaTeX and Pandoc"
+text: "An introduction to writing scientific and technical documents with LaTeX and managing document formats with Pandoc. 
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/284"
+date: "2020-04-09"
+startTime: "18:10"
+endTime: "20:00"
+---
diff --git a/_posts/2020-04-23-image-processing.md b/_posts/2020-04-23-image-processing.md
new file mode 100644
index 000000000..3829e60e6
--- /dev/null
+++ b/_posts/2020-04-23-image-processing.md
@@ -0,0 +1,10 @@
+---
+title: "Image Processing"
+text: "An introduction to Image Processing in Python. 
+"
+location: "MADLab"
+link: "https://github.com/UofTCoders/Events/issues/285"
+date: "2020-04-23"
+startTime: "18:10"
+endTime: "20:00"
+---
diff --git a/_posts/2020-10-22-lightning-demos-general.md b/_posts/2020-10-22-lightning-demos-general.md
new file mode 100644
index 000000000..98ce444dd
--- /dev/null
+++ b/_posts/2020-10-22-lightning-demos-general.md
@@ -0,0 +1,10 @@
+---
+title: "Lightning demos + welcome back"
+text: "Short coding demos. Anyone is welcome to present!   
+"
+location: "Zoom"
+link: "NA"
+date: "2020-10-22"
+startTime: "19:00"
+endTime: "20:00"
+---
diff --git a/_posts/2020-10-29-reproducible-rstudio.md b/_posts/2020-10-29-reproducible-rstudio.md
new file mode 100644
index 000000000..eb984a317
--- /dev/null
+++ b/_posts/2020-10-29-reproducible-rstudio.md
@@ -0,0 +1,10 @@
+---
+title: "Reproducible Science in RStudio"
+text: "An overview of the RStudio IDE highlighting features to make your work better documented and more reproducible.   
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/287"
+date: "2020-10-29"
+startTime: "19:00"
+endTime: "20:00"
+---
diff --git a/_posts/2020-11-05-lightning-demos-python.md b/_posts/2020-11-05-lightning-demos-python.md
new file mode 100644
index 000000000..a1f62129d
--- /dev/null
+++ b/_posts/2020-11-05-lightning-demos-python.md
@@ -0,0 +1,10 @@
+---
+title: "Lightning demos: Python"
+text: "Short coding demos - this time themed specifically around Python. Anyone is welcome to present!   
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/288"
+date: "2020-11-05"
+startTime: "19:00"
+endTime: "20:00"
+---
diff --git a/_posts/2020-11-12-programming-tidyverse.md b/_posts/2020-11-12-programming-tidyverse.md
new file mode 100644
index 000000000..65c6b6cee
--- /dev/null
+++ b/_posts/2020-11-12-programming-tidyverse.md
@@ -0,0 +1,10 @@
+---
+title: "Programming and Data Wrangling in R with the Tidyverse"
+text: "Exploring how to develop basic software for data wrangling with the tidyverse suite of packages. 
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/289"
+date: "2020-11-12"
+startTime: "19:00"
+endTime: "20:00"
+---
diff --git a/_posts/2020-11-19-lightning-demos-r.md b/_posts/2020-11-19-lightning-demos-r.md
new file mode 100644
index 000000000..7407aeb61
--- /dev/null
+++ b/_posts/2020-11-19-lightning-demos-r.md
@@ -0,0 +1,10 @@
+---
+title: "Lightning demos: R"
+text: "Short coding demos - this time themed specifically around R. Anyone is welcome to present!   
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/290"
+date: "2020-11-19"
+startTime: "19:00"
+endTime: "20:00"
+---
diff --git a/_posts/2020-11-26-writing-markdown-pandoc.md b/_posts/2020-11-26-writing-markdown-pandoc.md
new file mode 100644
index 000000000..a6bcf82f5
--- /dev/null
+++ b/_posts/2020-11-26-writing-markdown-pandoc.md
@@ -0,0 +1,10 @@
+---
+title: "Scientific writing with Markdown and Pandoc"
+text: "An introduction to writing scientific and technical documents with Markdown and managing document formats with Pandoc. 
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/291"
+date: "2020-11-26"
+startTime: "19:00"
+endTime: "20:00"
+---
diff --git a/_posts/2020-12-03-lightning-demos-data-viz.md b/_posts/2020-12-03-lightning-demos-data-viz.md
new file mode 100644
index 000000000..08b174c99
--- /dev/null
+++ b/_posts/2020-12-03-lightning-demos-data-viz.md
@@ -0,0 +1,10 @@
+---
+title: "Lightning demos: Data visualization"
+text: "Short coding demos - this time themed specifically around data visualization with any language. Anyone is welcome to present!   
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/292"
+date: "2020-12-03"
+startTime: "19:00"
+endTime: "20:00"
+---
diff --git a/_posts/2021-01-13-intro-python.md b/_posts/2021-01-13-intro-python.md
new file mode 100644
index 000000000..b4b527eec
--- /dev/null
+++ b/_posts/2021-01-13-intro-python.md
@@ -0,0 +1,10 @@
+---
+title: "Introduction to Python"
+text: "A brief introduction to Python for beginners.
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/293"
+date: "2021-01-13"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-01-20-intro-r.md b/_posts/2021-01-20-intro-r.md
new file mode 100644
index 000000000..fe271f962
--- /dev/null
+++ b/_posts/2021-01-20-intro-r.md
@@ -0,0 +1,10 @@
+---
+title: "Introduction to R"
+text: "A brief introduction to R for beginners.
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/294"
+date: "2021-01-20"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-01-27-intro-git.md b/_posts/2021-01-27-intro-git.md
new file mode 100644
index 000000000..0e0674c9e
--- /dev/null
+++ b/_posts/2021-01-27-intro-git.md
@@ -0,0 +1,10 @@
+---
+title: "Introduction to Git"
+text: "A brief introduction to version control with Git for beginners.
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/295"
+date: "2021-01-27"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-02-03-python-scikit-learn.md b/_posts/2021-02-03-python-scikit-learn.md
new file mode 100644
index 000000000..654c20771
--- /dev/null
+++ b/_posts/2021-02-03-python-scikit-learn.md
@@ -0,0 +1,10 @@
+---
+title: "Python: scikit-learn"
+text: "An introduction to using scikit-learn in Python.
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/296"
+date: "2021-02-03"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-02-10-r-stat-methods.md b/_posts/2021-02-10-r-stat-methods.md
new file mode 100644
index 000000000..2c38dc401
--- /dev/null
+++ b/_posts/2021-02-10-r-stat-methods.md
@@ -0,0 +1,10 @@
+---
+title: "R: Statistical methods"
+text: "A beginner-oriented overview of implementing statistical tests and models in R.
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/297"
+date: "2021-02-10"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-02-24-lightning-demos.md b/_posts/2021-02-24-lightning-demos.md
new file mode 100644
index 000000000..4a9694c51
--- /dev/null
+++ b/_posts/2021-02-24-lightning-demos.md
@@ -0,0 +1,10 @@
+---
+title: "Lightning demos"
+text: "Short coding demos - anyone is welcome to present!
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/298"
+date: "2021-02-24"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-03-03-git-collab.md b/_posts/2021-03-03-git-collab.md
new file mode 100644
index 000000000..91d4b8889
--- /dev/null
+++ b/_posts/2021-03-03-git-collab.md
@@ -0,0 +1,10 @@
+---
+title: "Collaborating with Git and GitHub"
+text: "An overview of how to collaborate on projects using Git/GitHub.
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/299"
+date: "2021-03-03"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-03-10-intro-latex.md b/_posts/2021-03-10-intro-latex.md
new file mode 100644
index 000000000..2b4821f0a
--- /dev/null
+++ b/_posts/2021-03-10-intro-latex.md
@@ -0,0 +1,10 @@
+---
+title: "Introduction to LaTeX"
+text: "An introduction to preparing documents with LaTeX.
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/300"
+date: "2021-03-10"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-03-17-git-adv-topics.md b/_posts/2021-03-17-git-adv-topics.md
new file mode 100644
index 000000000..d4bdbbd09
--- /dev/null
+++ b/_posts/2021-03-17-git-adv-topics.md
@@ -0,0 +1,10 @@
+---
+title: "Git: advanced topics"
+text: "Advanced topics in Git - specific topics TBA.
+"
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/301"
+date: "2021-03-17"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-10-06-intro-python.md b/_posts/2021-10-06-intro-python.md
new file mode 100644
index 000000000..5bb16865c
--- /dev/null
+++ b/_posts/2021-10-06-intro-python.md
@@ -0,0 +1,10 @@
+---
+title: "Introduction to Python"
+text: "A brief introduction to Python for beginners.
+"
+location: "UofT Coders Discord server"
+link: "https://github.com/UofTCoders/Events/issues/304"
+date: "2021-10-06"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-10-13-intro-r.md b/_posts/2021-10-13-intro-r.md
new file mode 100644
index 000000000..fbbdf74d9
--- /dev/null
+++ b/_posts/2021-10-13-intro-r.md
@@ -0,0 +1,10 @@
+---
+title: "Introduction to R"
+text: "A brief introduction to R for beginners.
+"
+location: "UofT Coders Discord server"
+link: "https://github.com/UofTCoders/Events/issues/305"
+date: "2021-10-13"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-10-20-intro-git.md b/_posts/2021-10-20-intro-git.md
new file mode 100644
index 000000000..751ad34ca
--- /dev/null
+++ b/_posts/2021-10-20-intro-git.md
@@ -0,0 +1,10 @@
+---
+title: "Introduction to Git"
+text: "A brief introduction to version control with Git for beginners.
+"
+location: "UofT Coders Discord server"
+link: "https://github.com/UofTCoders/Events/issues/306"
+date: "2021-10-20"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-10-27-r-data-visualization.md b/_posts/2021-10-27-r-data-visualization.md
new file mode 100644
index 000000000..4f7a003d5
--- /dev/null
+++ b/_posts/2021-10-27-r-data-visualization.md
@@ -0,0 +1,10 @@
+---
+title: "R: Data visualization with ggplot2"
+text: "A beginner-oriented overview of plotting in R with ggplot2.
+"
+location: "UofT Coders Discord server"
+link: "https://github.com/UofTCoders/Events/issues/307"
+date: "2021-10-27"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-11-03-python-scikit-learn.md b/_posts/2021-11-03-python-scikit-learn.md
new file mode 100644
index 000000000..a82a5fea7
--- /dev/null
+++ b/_posts/2021-11-03-python-scikit-learn.md
@@ -0,0 +1,10 @@
+---
+title: "Python: Machine learning with scikit-learn"
+text: "An introduction to using scikit-learn in Python.
+"
+location: "UofT Coders Discord server"
+link: "https://github.com/UofTCoders/Events/issues/308"
+date: "2021-11-03"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-11-17-pandoc-markdown.md b/_posts/2021-11-17-pandoc-markdown.md
new file mode 100644
index 000000000..b9a5a43c4
--- /dev/null
+++ b/_posts/2021-11-17-pandoc-markdown.md
@@ -0,0 +1,10 @@
+---
+title: "Pandoc and Markdown"
+text: "An introduction to preparing documents with Pandoc and Markdown.
+"
+location: "UofT Coders Discord server"
+link: "https://github.com/UofTCoders/Events/issues/309"
+date: "2021-11-17"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-11-24-lightning-demos.md b/_posts/2021-11-24-lightning-demos.md
new file mode 100644
index 000000000..0ffed90e7
--- /dev/null
+++ b/_posts/2021-11-24-lightning-demos.md
@@ -0,0 +1,10 @@
+---
+title: "Lightning demos"
+text: "Short coding demos - anyone is welcome to present!
+"
+location: "UofT Coders Discord server"
+link: "https://github.com/UofTCoders/Events/issues/310"
+date: "2021-11-24"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-12-01-r-blogdown.md b/_posts/2021-12-01-r-blogdown.md
new file mode 100644
index 000000000..52c9aea9f
--- /dev/null
+++ b/_posts/2021-12-01-r-blogdown.md
@@ -0,0 +1,10 @@
+---
+title: "Creating websites with RStudio and blogdown"
+text: "An overview of how to create personal and professional websites using blogdown in RStudio.
+"
+location: "UofT Coders Discord server"
+link: "https://github.com/UofTCoders/Events/issues/311"
+date: "2021-12-01"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2021-winter-co-working.md b/_posts/2021-winter-co-working.md
new file mode 100644
index 000000000..6a39e659f
--- /dev/null
+++ b/_posts/2021-winter-co-working.md
@@ -0,0 +1,10 @@
+---
+title: "Weekly co-working"
+text: "An informal co-working session - come hang out with us while
+        working on code or whatever else you want to get done."
+location: "Zoom"
+link: "https://github.com/UofTCoders/Events/issues/302"
+date: "NA"
+startTime: "13:00"
+endTime: "14:00"
+---
diff --git a/_posts/2022-02-09-clustering-r.md b/_posts/2022-02-09-clustering-r.md
new file mode 100644
index 000000000..a7dabb782
--- /dev/null
+++ b/_posts/2022-02-09-clustering-r.md
@@ -0,0 +1,9 @@
+---
+title: "Clustering with R"
+text: "An introduction to clustering methods with R."
+location: "UofT Coders Discord server"
+link: "https://github.com/UofTCoders/Events/issues/312"
+date: "2022-02-09"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2022-03-02-supervised-learning-r.md b/_posts/2022-03-02-supervised-learning-r.md
new file mode 100644
index 000000000..54222edc8
--- /dev/null
+++ b/_posts/2022-03-02-supervised-learning-r.md
@@ -0,0 +1,9 @@
+---
+title: "Supervised learning with R"
+text: "A walkthrough of a supervised learning workflow in R."
+location: "UofT Coders Discord server"
+link: "https://github.com/UofTCoders/Events/issues/313"
+date: "2022-03-02"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2022-03-16-r-tidyverse-functions.md b/_posts/2022-03-16-r-tidyverse-functions.md
new file mode 100644
index 000000000..2d4d42589
--- /dev/null
+++ b/_posts/2022-03-16-r-tidyverse-functions.md
@@ -0,0 +1,9 @@
+---
+title: "R: Writing functions with the tidyverse"
+text: "An exploration of writing functions with tidyverse syntax in R."
+location: "UofT Coders Discord server"
+link: "https://github.com/UofTCoders/Events/issues/314"
+date: "2022-03-16"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2022-03-30-epidemic-modelling.md b/_posts/2022-03-30-epidemic-modelling.md
new file mode 100644
index 000000000..152912ad8
--- /dev/null
+++ b/_posts/2022-03-30-epidemic-modelling.md
@@ -0,0 +1,9 @@
+---
+title: "Epidemic modelling"
+text: "A brief overview on modelling epidemics with code."
+location: "UofT Coders Discord server"
+link: "https://github.com/UofTCoders/Events/issues/315"
+date: "2022-03-30"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2022-10-19-co-working.md b/_posts/2022-10-19-co-working.md
new file mode 100644
index 000000000..e0493850b
--- /dev/null
+++ b/_posts/2022-10-19-co-working.md
@@ -0,0 +1,9 @@
+---
+title: "Co-working (UTSG + online)"
+text: "A friendly co-working session. Bring yourself and something to work on!"
+location: "MADLab, Room B112, Gerstein Library"
+link: "https://github.com/UofTCoders/Events/issues/321"
+date: "2022-10-19"
+startTime: "15:00"
+endTime: "17:00"
+---
diff --git a/_posts/2022-10-19-python-pandas.md b/_posts/2022-10-19-python-pandas.md
new file mode 100644
index 000000000..4d447ed16
--- /dev/null
+++ b/_posts/2022-10-19-python-pandas.md
@@ -0,0 +1,9 @@
+---
+title: "Python: Pandas"
+text: "An introduction to working with data frames in Python using Pandas."
+location: "MADLab, Room B112 Gerstein Library"
+link: "https://github.com/UofTCoders/Events/issues/316"
+date: "2022-10-19"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2022-10-28-co-working.md b/_posts/2022-10-28-co-working.md
new file mode 100644
index 000000000..041e212ea
--- /dev/null
+++ b/_posts/2022-10-28-co-working.md
@@ -0,0 +1,9 @@
+---
+title: "Co-working (UTM + online)"
+text: "A friendly co-working session. Bring yourself and something to work on!"
+location: "Graduate Lounge, William G. Davis Building"
+link: "https://github.com/UofTCoders/Events/issues/322"
+date: "2022-10-28"
+startTime: "10:00"
+endTime: "12:00"
+---
diff --git a/_posts/2022-11-02-co-working.md b/_posts/2022-11-02-co-working.md
new file mode 100644
index 000000000..46fd88d44
--- /dev/null
+++ b/_posts/2022-11-02-co-working.md
@@ -0,0 +1,9 @@
+---
+title: "Co-working (UTSG + online)"
+text: "A friendly co-working session. Bring yourself and something to work on!"
+location: "MADLab, Room B112, Gerstein Library"
+link: "https://github.com/UofTCoders/Events/issues/323"
+date: "2022-11-02"
+startTime: "15:00"
+endTime: "17:00"
+---
diff --git a/_posts/2022-11-02-r-data-viz.md b/_posts/2022-11-02-r-data-viz.md
new file mode 100644
index 000000000..c96a9fc13
--- /dev/null
+++ b/_posts/2022-11-02-r-data-viz.md
@@ -0,0 +1,9 @@
+---
+title: "Data visualization with R"
+text: "A crash course in making plots in R using the ggplot2 library."
+location: "University of Toronto Mississauga (Room TBA)"
+link: "https://github.com/UofTCoders/Events/issues/317"
+date: "2022-11-02"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2022-11-11-co-working.md b/_posts/2022-11-11-co-working.md
new file mode 100644
index 000000000..35a0f34d0
--- /dev/null
+++ b/_posts/2022-11-11-co-working.md
@@ -0,0 +1,9 @@
+---
+title: "Co-working (UTM + online)"
+text: "A friendly co-working session. Bring yourself and something to work on!"
+location: "Graduate Lounge, William G. Davis Building"
+link: "https://github.com/UofTCoders/Events/issues/324"
+date: "2022-11-11"
+startTime: "10:00"
+endTime: "12:00"
+---
diff --git a/_posts/2022-11-16-co-working.md b/_posts/2022-11-16-co-working.md
new file mode 100644
index 000000000..79069d7c9
--- /dev/null
+++ b/_posts/2022-11-16-co-working.md
@@ -0,0 +1,9 @@
+---
+title: "Co-working (UTSG + online)"
+text: "A friendly co-working session. Bring yourself and something to work on!"
+location: "MADLab, Room B112, Gerstein Library"
+link: "https://github.com/UofTCoders/Events/issues/325"
+date: "2022-11-16"
+startTime: "15:00"
+endTime: "17:00"
+---
diff --git a/_posts/2022-11-16-r-sql.md b/_posts/2022-11-16-r-sql.md
new file mode 100644
index 000000000..b778c57f0
--- /dev/null
+++ b/_posts/2022-11-16-r-sql.md
@@ -0,0 +1,9 @@
+---
+title: "SQL with R"
+text: "A lesson covering working with SQL databases entirely using R."
+location: "MADLab, Room B112, Gerstein Library"
+link: "https://github.com/UofTCoders/Events/issues/318"
+date: "2022-11-16"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2022-11-30-co-working.md b/_posts/2022-11-30-co-working.md
new file mode 100644
index 000000000..9d779872d
--- /dev/null
+++ b/_posts/2022-11-30-co-working.md
@@ -0,0 +1,9 @@
+---
+title: "Co-working (UTSG + online)"
+text: "A friendly co-working session. Bring yourself and something to work on!"
+location: "MADLab, Room B112, Gerstein Library"
+link: "https://github.com/UofTCoders/Events/issues/326"
+date: "2022-11-30"
+startTime: "15:00"
+endTime: "17:00"
+---
diff --git a/_posts/2022-11-30-r-functions-tidyverse.md b/_posts/2022-11-30-r-functions-tidyverse.md
new file mode 100644
index 000000000..e9945f118
--- /dev/null
+++ b/_posts/2022-11-30-r-functions-tidyverse.md
@@ -0,0 +1,9 @@
+---
+title: "R: Writing functions with the tidyverse"
+text: "A lesson covering how to write flexible functions using the tidyverse in R."
+location: "MADLab, Room B112, Gerstein Library"
+link: "https://github.com/UofTCoders/Events/issues/319"
+date: "2022-11-30"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2022-12-09-co-working.md b/_posts/2022-12-09-co-working.md
new file mode 100644
index 000000000..4c88b6585
--- /dev/null
+++ b/_posts/2022-12-09-co-working.md
@@ -0,0 +1,9 @@
+---
+title: "Co-working (UTM + online)"
+text: "A friendly co-working session. Bring yourself and something to work on!"
+location: "Graduate Lounge, William G. Davis Building"
+link: "https://github.com/UofTCoders/Events/issues/327"
+date: "2022-12-09"
+startTime: "10:00"
+endTime: "12:00"
+---
diff --git a/_posts/2023-02-01-py-data-vis.md b/_posts/2023-02-01-py-data-vis.md
new file mode 100644
index 000000000..0a3b86cf9
--- /dev/null
+++ b/_posts/2023-02-01-py-data-vis.md
@@ -0,0 +1,9 @@
+---
+title: "Data visualization in Python"
+text: "An introduction to exploring and visualizing your data in Python."
+location: "Discord"
+link: "https://github.com/UofTCoders/Events/issues/328"
+date: "2023-02-01"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2023-02-15-intro-bash.md b/_posts/2023-02-15-intro-bash.md
new file mode 100644
index 000000000..49264e52b
--- /dev/null
+++ b/_posts/2023-02-15-intro-bash.md
@@ -0,0 +1,9 @@
+---
+title: "Introduction to Bash"
+text: "A brief overview of working at the command line in Unix environments."
+location: "Discord"
+link: "https://github.com/UofTCoders/Events/issues/329"
+date: "2023-02-15"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2023-03-01-python-numpy-pandas.md b/_posts/2023-03-01-python-numpy-pandas.md
new file mode 100644
index 000000000..5fd3e0cbf
--- /dev/null
+++ b/_posts/2023-03-01-python-numpy-pandas.md
@@ -0,0 +1,9 @@
+---
+title: "Python: Data processing with numpy and pandas"
+text: "A code-along demonstrating two popular libraries for data processing in Python."
+location: "TBA in-person + Discord"
+link: "https://github.com/UofTCoders/Events/issues/330"
+date: "2023-03-01"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2023-03-15-python-intro-ml.md b/_posts/2023-03-15-python-intro-ml.md
new file mode 100644
index 000000000..29735d9cf
--- /dev/null
+++ b/_posts/2023-03-15-python-intro-ml.md
@@ -0,0 +1,9 @@
+---
+title: "Python: Intro to machine learning"
+text: "A tutorial covering basic ML topics in Python."
+location: "TBA in-person + Discord"
+link: "https://github.com/UofTCoders/Events/issues/331"
+date: "2023-03-15"
+startTime: "18:00"
+endTime: "19:00"
+---
diff --git a/_posts/2023-03-29-social.md b/_posts/2023-03-29-social.md
new file mode 100644
index 000000000..85e3e379a
--- /dev/null
+++ b/_posts/2023-03-29-social.md
@@ -0,0 +1,9 @@
+---
+title: "End of Term Social"
+text: ""
+location: "GSU Pub"
+link: "https://github.com/UofTCoders/Events/issues/332"
+date: "2023-03-29"
+startTime: "18:00"
+endTime: "20:00"
+---
diff --git a/_posts/2077-01-01-myEvent.markdown b/_posts/2077-01-01-myEvent.markdown
deleted file mode 100644
index 40f904497..000000000
--- a/_posts/2077-01-01-myEvent.markdown
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Make Your First Event
-text: In order to make your first event, follow the instructions here.
-location: Your Location
-link: https://github.com/mozillascience/studyGroup#how-to-launch-a-new-event
-date: 2077-01-01
-startTime: '20:00'
-endTime: '21:00'
-
----
diff --git a/codeOfConduct.md b/codeOfConduct.md
index 45356d833..5dcdc2f92 100644
--- a/codeOfConduct.md
+++ b/codeOfConduct.md
@@ -1,25 +1,16 @@
-# Code of Conduct
-
-Study Group events are community events intended for networking and collaboration as well as learning. We value the participation of every member of the scientific community and want all attendees to have an enjoyable and fulfilling experience. Accordingly, all attendees are expected to show respect and courtesy to other attendees throughout the event and in interactions online associated with Study Group.
+Study Group events are community events intended for networking and collaboration as well as learning. We value the participation of every member of the scientific community and want all attendees to have an enjoyable and fulfilling experience. Accordingly, all attendees are expected to show respect and courtesy to other attendees throughout the events and in interactions online associated with Study Group.
 
 To make clear what is expected, everyone taking part in Study Group events and discussions—instructors, helpers, organizers, and learners—is required to conform to the following Code of Conduct. Organizers will enforce this code throughout events, but you may also contact us privately; all communication will be treated as confidential.
 
-### The Short Version
-
- - Study Group is dedicated to providing a harassment-free learning experience for everyone, regardless of gender, sexual orientation, disability, physical appearance, body size, race, religion, or choice of text editor. We do not tolerate harassment of participants in any form.
- - All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate for any event.
- - Be kind to others. Do not insult or put down other attendees.
- - Behave professionally. Remember that harassment and sexist, racist, or exclusionary jokes are not appropriate.
- - Attendees violating these rules may be asked to leave the event at the sole discretion of the event organizers without a refund of any charge that may have been levied.
+## Code of Conduct
 
-Thank you for helping make this a welcoming, friendly event for all.
-
-### The Longer Version
+We are dedicated to providing a harassment-free learning experience for everyone, regardless of gender, sexual orientation, disability, physical appearance, body size, race, religion, choice of operating system, or choice of text editor. We do not tolerate harassment of participants in any form.
 
 Harassment includes offensive verbal comments related to gender, sexual orientation, disability, physical appearance, body size, race, religion, sexual images in public spaces, deliberate intimidation, stalking, following, harassing photography or recording, sustained disruption of talks or other events, inappropriate physical contact, and unwelcome sexual attention.
+Participants asked to stop any harassing behaviour are expected to comply immediately.
 
-Participants asked to stop any harassing behavior are expected to comply immediately.
+Be kind to others. Be aware of your words, and use language that is positive and community-building. All communication should be appropriate for a professional audience including people of many different backgrounds. Remember that sexist, racist, and other exclusionary jokes can be offensive to those around you.
 
-Be careful in the words that you choose. Remember that sexist, racist, and other exclusionary jokes can be offensive to those around you.
+If a participant engages in behaviour that violates this code of conduct, the organizers may take any action they deem appropriate, including warning the offender or expulsion from the event.
 
-If a participant engages in behavior that violates this code of conduct, the organizers may take any action they deem appropriate, including warning the offender or expulsion from the event with no refund of any fee that may have been levied.
\ No newline at end of file
+Thank you for helping make this a welcoming, friendly event for all.
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diff --git a/img/portfolio/roundicons-thumbnail.png b/img/portfolio/roundicons-thumbnail.png
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diff --git a/img/portfolio/startup-framework-thumbnail.png b/img/portfolio/startup-framework-thumbnail.png
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diff --git a/img/portfolio/treehouse-thumbnail.png b/img/portfolio/treehouse-thumbnail.png
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diff --git a/img/team/1.jpg b/img/team/1.jpg
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diff --git a/img/team/3.jpg b/img/team/3.jpg
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diff --git a/img/watch.png b/img/watch.png
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diff --git a/js/contact_me.js b/js/contact_me.js
deleted file mode 100644
index 056706762..000000000
--- a/js/contact_me.js
+++ /dev/null
@@ -1,70 +0,0 @@
-$(function() {
-
-    $("input,textarea").jqBootstrapValidation({
-        preventSubmit: true,
-        submitError: function($form, event, errors) {
-            // additional error messages or events
-        },
-        submitSuccess: function($form, event) {
-            event.preventDefault(); // prevent default submit behaviour
-            // get values from FORM
-            var name = $("input#name").val();
-            var email = $("input#email").val();
-            var phone = $("input#phone").val();
-            var message = $("textarea#message").val();
-            var firstName = name; // For Success/Failure Message
-            // Check for white space in name for Success/Fail message
-            if (firstName.indexOf(' ') >= 0) {
-                firstName = name.split(' ').slice(0, -1).join(' ');
-            }
-            $.ajax({
-                url: "././mail/contact_me.php",
-                type: "POST",
-                data: {
-                    name: name,
-                    phone: phone,
-                    email: email,
-                    message: message
-                },
-                cache: false,
-                success: function() {
-                    // Success message
-                    $('#success').html("<div class='alert alert-success'>");
-                    $('#success > .alert-success').html("<button type='button' class='close' data-dismiss='alert' aria-hidden='true'>&times;")
-                        .append("</button>");
-                    $('#success > .alert-success')
-                        .append("<strong>Your message has been sent. </strong>");
-                    $('#success > .alert-success')
-                        .append('</div>');
-
-                    //clear all fields
-                    $('#contactForm').trigger("reset");
-                },
-                error: function() {
-                    // Fail message
-                    $('#success').html("<div class='alert alert-danger'>");
-                    $('#success > .alert-danger').html("<button type='button' class='close' data-dismiss='alert' aria-hidden='true'>&times;")
-                        .append("</button>");
-                    $('#success > .alert-danger').append("<strong>Sorry " + firstName + ", it seems that my mail server is not responding. Please try again later!");
-                    $('#success > .alert-danger').append('</div>');
-                    //clear all fields
-                    $('#contactForm').trigger("reset");
-                },
-            })
-        },
-        filter: function() {
-            return $(this).is(":visible");
-        },
-    });
-
-    $("a[data-toggle=\"tab\"]").click(function(e) {
-        e.preventDefault();
-        $(this).tab("show");
-    });
-});
-
-
-/*When clicking on Full hide fail/success boxes */
-$('#name').focus(function() {
-    $('#success').html('');
-});
diff --git a/js/jqBootstrapValidation.js b/js/jqBootstrapValidation.js
deleted file mode 100644
index 29cbb083e..000000000
--- a/js/jqBootstrapValidation.js
+++ /dev/null
@@ -1,912 +0,0 @@
-/* jqBootstrapValidation
- * A plugin for automating validation on Twitter Bootstrap formatted forms.
- *
- * v1.3.6
- *
- * License: MIT <http://opensource.org/licenses/mit-license.php> - see LICENSE file
- *
- * http://ReactiveRaven.github.com/jqBootstrapValidation/
- */
-
-(function( $ ){
-
-	var createdElements = [];
-
-	var defaults = {
-		options: {
-			prependExistingHelpBlock: false,
-			sniffHtml: true, // sniff for 'required', 'maxlength', etc
-			preventSubmit: true, // stop the form submit event from firing if validation fails
-			submitError: false, // function called if there is an error when trying to submit
-			submitSuccess: false, // function called just before a successful submit event is sent to the server
-            semanticallyStrict: false, // set to true to tidy up generated HTML output
-			autoAdd: {
-				helpBlocks: true
-			},
-            filter: function () {
-                // return $(this).is(":visible"); // only validate elements you can see
-                return true; // validate everything
-            }
-		},
-    methods: {
-      init : function( options ) {
-
-        var settings = $.extend(true, {}, defaults);
-
-        settings.options = $.extend(true, settings.options, options);
-
-        var $siblingElements = this;
-
-        var uniqueForms = $.unique(
-          $siblingElements.map( function () {
-            return $(this).parents("form")[0];
-          }).toArray()
-        );
-
-        $(uniqueForms).bind("submit", function (e) {
-          var $form = $(this);
-          var warningsFound = 0;
-          var $inputs = $form.find("input,textarea,select").not("[type=submit],[type=image]").filter(settings.options.filter);
-          $inputs.trigger("submit.validation").trigger("validationLostFocus.validation");
-
-          $inputs.each(function (i, el) {
-            var $this = $(el),
-              $controlGroup = $this.parents(".control-group").first();
-            if (
-              $controlGroup.hasClass("warning")
-            ) {
-              $controlGroup.removeClass("warning").addClass("error");
-              warningsFound++;
-            }
-          });
-
-          $inputs.trigger("validationLostFocus.validation");
-
-          if (warningsFound) {
-            if (settings.options.preventSubmit) {
-              e.preventDefault();
-            }
-            $form.addClass("error");
-            if ($.isFunction(settings.options.submitError)) {
-              settings.options.submitError($form, e, $inputs.jqBootstrapValidation("collectErrors", true));
-            }
-          } else {
-            $form.removeClass("error");
-            if ($.isFunction(settings.options.submitSuccess)) {
-              settings.options.submitSuccess($form, e);
-            }
-          }
-        });
-
-        return this.each(function(){
-
-          // Get references to everything we're interested in
-          var $this = $(this),
-            $controlGroup = $this.parents(".control-group").first(),
-            $helpBlock = $controlGroup.find(".help-block").first(),
-            $form = $this.parents("form").first(),
-            validatorNames = [];
-
-          // create message container if not exists
-          if (!$helpBlock.length && settings.options.autoAdd && settings.options.autoAdd.helpBlocks) {
-              $helpBlock = $('<div class="help-block" />');
-              $controlGroup.find('.controls').append($helpBlock);
-							createdElements.push($helpBlock[0]);
-          }
-
-          // =============================================================
-          //                                     SNIFF HTML FOR VALIDATORS
-          // =============================================================
-
-          // *snort sniff snuffle*
-
-          if (settings.options.sniffHtml) {
-            var message = "";
-            // ---------------------------------------------------------
-            //                                                   PATTERN
-            // ---------------------------------------------------------
-            if ($this.attr("pattern") !== undefined) {
-              message = "Not in the expected format<!-- data-validation-pattern-message to override -->";
-              if ($this.data("validationPatternMessage")) {
-                message = $this.data("validationPatternMessage");
-              }
-              $this.data("validationPatternMessage", message);
-              $this.data("validationPatternRegex", $this.attr("pattern"));
-            }
-            // ---------------------------------------------------------
-            //                                                       MAX
-            // ---------------------------------------------------------
-            if ($this.attr("max") !== undefined || $this.attr("aria-valuemax") !== undefined) {
-              var max = ($this.attr("max") !== undefined ? $this.attr("max") : $this.attr("aria-valuemax"));
-              message = "Too high: Maximum of '" + max + "'<!-- data-validation-max-message to override -->";
-              if ($this.data("validationMaxMessage")) {
-                message = $this.data("validationMaxMessage");
-              }
-              $this.data("validationMaxMessage", message);
-              $this.data("validationMaxMax", max);
-            }
-            // ---------------------------------------------------------
-            //                                                       MIN
-            // ---------------------------------------------------------
-            if ($this.attr("min") !== undefined || $this.attr("aria-valuemin") !== undefined) {
-              var min = ($this.attr("min") !== undefined ? $this.attr("min") : $this.attr("aria-valuemin"));
-              message = "Too low: Minimum of '" + min + "'<!-- data-validation-min-message to override -->";
-              if ($this.data("validationMinMessage")) {
-                message = $this.data("validationMinMessage");
-              }
-              $this.data("validationMinMessage", message);
-              $this.data("validationMinMin", min);
-            }
-            // ---------------------------------------------------------
-            //                                                 MAXLENGTH
-            // ---------------------------------------------------------
-            if ($this.attr("maxlength") !== undefined) {
-              message = "Too long: Maximum of '" + $this.attr("maxlength") + "' characters<!-- data-validation-maxlength-message to override -->";
-              if ($this.data("validationMaxlengthMessage")) {
-                message = $this.data("validationMaxlengthMessage");
-              }
-              $this.data("validationMaxlengthMessage", message);
-              $this.data("validationMaxlengthMaxlength", $this.attr("maxlength"));
-            }
-            // ---------------------------------------------------------
-            //                                                 MINLENGTH
-            // ---------------------------------------------------------
-            if ($this.attr("minlength") !== undefined) {
-              message = "Too short: Minimum of '" + $this.attr("minlength") + "' characters<!-- data-validation-minlength-message to override -->";
-              if ($this.data("validationMinlengthMessage")) {
-                message = $this.data("validationMinlengthMessage");
-              }
-              $this.data("validationMinlengthMessage", message);
-              $this.data("validationMinlengthMinlength", $this.attr("minlength"));
-            }
-            // ---------------------------------------------------------
-            //                                                  REQUIRED
-            // ---------------------------------------------------------
-            if ($this.attr("required") !== undefined || $this.attr("aria-required") !== undefined) {
-              message = settings.builtInValidators.required.message;
-              if ($this.data("validationRequiredMessage")) {
-                message = $this.data("validationRequiredMessage");
-              }
-              $this.data("validationRequiredMessage", message);
-            }
-            // ---------------------------------------------------------
-            //                                                    NUMBER
-            // ---------------------------------------------------------
-            if ($this.attr("type") !== undefined && $this.attr("type").toLowerCase() === "number") {
-              message = settings.builtInValidators.number.message;
-              if ($this.data("validationNumberMessage")) {
-                message = $this.data("validationNumberMessage");
-              }
-              $this.data("validationNumberMessage", message);
-            }
-            // ---------------------------------------------------------
-            //                                                     EMAIL
-            // ---------------------------------------------------------
-            if ($this.attr("type") !== undefined && $this.attr("type").toLowerCase() === "email") {
-              message = "Not a valid email address<!-- data-validator-validemail-message to override -->";
-              if ($this.data("validationValidemailMessage")) {
-                message = $this.data("validationValidemailMessage");
-              } else if ($this.data("validationEmailMessage")) {
-                message = $this.data("validationEmailMessage");
-              }
-              $this.data("validationValidemailMessage", message);
-            }
-            // ---------------------------------------------------------
-            //                                                MINCHECKED
-            // ---------------------------------------------------------
-            if ($this.attr("minchecked") !== undefined) {
-              message = "Not enough options checked; Minimum of '" + $this.attr("minchecked") + "' required<!-- data-validation-minchecked-message to override -->";
-              if ($this.data("validationMincheckedMessage")) {
-                message = $this.data("validationMincheckedMessage");
-              }
-              $this.data("validationMincheckedMessage", message);
-              $this.data("validationMincheckedMinchecked", $this.attr("minchecked"));
-            }
-            // ---------------------------------------------------------
-            //                                                MAXCHECKED
-            // ---------------------------------------------------------
-            if ($this.attr("maxchecked") !== undefined) {
-              message = "Too many options checked; Maximum of '" + $this.attr("maxchecked") + "' required<!-- data-validation-maxchecked-message to override -->";
-              if ($this.data("validationMaxcheckedMessage")) {
-                message = $this.data("validationMaxcheckedMessage");
-              }
-              $this.data("validationMaxcheckedMessage", message);
-              $this.data("validationMaxcheckedMaxchecked", $this.attr("maxchecked"));
-            }
-          }
-
-          // =============================================================
-          //                                       COLLECT VALIDATOR NAMES
-          // =============================================================
-
-          // Get named validators
-          if ($this.data("validation") !== undefined) {
-            validatorNames = $this.data("validation").split(",");
-          }
-
-          // Get extra ones defined on the element's data attributes
-          $.each($this.data(), function (i, el) {
-            var parts = i.replace(/([A-Z])/g, ",$1").split(",");
-            if (parts[0] === "validation" && parts[1]) {
-              validatorNames.push(parts[1]);
-            }
-          });
-
-          // =============================================================
-          //                                     NORMALISE VALIDATOR NAMES
-          // =============================================================
-
-          var validatorNamesToInspect = validatorNames;
-          var newValidatorNamesToInspect = [];
-
-          do // repeatedly expand 'shortcut' validators into their real validators
-          {
-            // Uppercase only the first letter of each name
-            $.each(validatorNames, function (i, el) {
-              validatorNames[i] = formatValidatorName(el);
-            });
-
-            // Remove duplicate validator names
-            validatorNames = $.unique(validatorNames);
-
-            // Pull out the new validator names from each shortcut
-            newValidatorNamesToInspect = [];
-            $.each(validatorNamesToInspect, function(i, el) {
-              if ($this.data("validation" + el + "Shortcut") !== undefined) {
-                // Are these custom validators?
-                // Pull them out!
-                $.each($this.data("validation" + el + "Shortcut").split(","), function(i2, el2) {
-                  newValidatorNamesToInspect.push(el2);
-                });
-              } else if (settings.builtInValidators[el.toLowerCase()]) {
-                // Is this a recognised built-in?
-                // Pull it out!
-                var validator = settings.builtInValidators[el.toLowerCase()];
-                if (validator.type.toLowerCase() === "shortcut") {
-                  $.each(validator.shortcut.split(","), function (i, el) {
-                    el = formatValidatorName(el);
-                    newValidatorNamesToInspect.push(el);
-                    validatorNames.push(el);
-                  });
-                }
-              }
-            });
-
-            validatorNamesToInspect = newValidatorNamesToInspect;
-
-          } while (validatorNamesToInspect.length > 0)
-
-          // =============================================================
-          //                                       SET UP VALIDATOR ARRAYS
-          // =============================================================
-
-          var validators = {};
-
-          $.each(validatorNames, function (i, el) {
-            // Set up the 'override' message
-            var message = $this.data("validation" + el + "Message");
-            var hasOverrideMessage = (message !== undefined);
-            var foundValidator = false;
-            message =
-              (
-                message
-                  ? message
-                  : "'" + el + "' validation failed <!-- Add attribute 'data-validation-" + el.toLowerCase() + "-message' to input to change this message -->"
-              )
-            ;
-
-            $.each(
-              settings.validatorTypes,
-              function (validatorType, validatorTemplate) {
-                if (validators[validatorType] === undefined) {
-                  validators[validatorType] = [];
-                }
-                if (!foundValidator && $this.data("validation" + el + formatValidatorName(validatorTemplate.name)) !== undefined) {
-                  validators[validatorType].push(
-                    $.extend(
-                      true,
-                      {
-                        name: formatValidatorName(validatorTemplate.name),
-                        message: message
-                      },
-                      validatorTemplate.init($this, el)
-                    )
-                  );
-                  foundValidator = true;
-                }
-              }
-            );
-
-            if (!foundValidator && settings.builtInValidators[el.toLowerCase()]) {
-
-              var validator = $.extend(true, {}, settings.builtInValidators[el.toLowerCase()]);
-              if (hasOverrideMessage) {
-                validator.message = message;
-              }
-              var validatorType = validator.type.toLowerCase();
-
-              if (validatorType === "shortcut") {
-                foundValidator = true;
-              } else {
-                $.each(
-                  settings.validatorTypes,
-                  function (validatorTemplateType, validatorTemplate) {
-                    if (validators[validatorTemplateType] === undefined) {
-                      validators[validatorTemplateType] = [];
-                    }
-                    if (!foundValidator && validatorType === validatorTemplateType.toLowerCase()) {
-                      $this.data("validation" + el + formatValidatorName(validatorTemplate.name), validator[validatorTemplate.name.toLowerCase()]);
-                      validators[validatorType].push(
-                        $.extend(
-                          validator,
-                          validatorTemplate.init($this, el)
-                        )
-                      );
-                      foundValidator = true;
-                    }
-                  }
-                );
-              }
-            }
-
-            if (! foundValidator) {
-              $.error("Cannot find validation info for '" + el + "'");
-            }
-          });
-
-          // =============================================================
-          //                                         STORE FALLBACK VALUES
-          // =============================================================
-
-          $helpBlock.data(
-            "original-contents",
-            (
-              $helpBlock.data("original-contents")
-                ? $helpBlock.data("original-contents")
-                : $helpBlock.html()
-            )
-          );
-
-          $helpBlock.data(
-            "original-role",
-            (
-              $helpBlock.data("original-role")
-                ? $helpBlock.data("original-role")
-                : $helpBlock.attr("role")
-            )
-          );
-
-          $controlGroup.data(
-            "original-classes",
-            (
-              $controlGroup.data("original-clases")
-                ? $controlGroup.data("original-classes")
-                : $controlGroup.attr("class")
-            )
-          );
-
-          $this.data(
-            "original-aria-invalid",
-            (
-              $this.data("original-aria-invalid")
-                ? $this.data("original-aria-invalid")
-                : $this.attr("aria-invalid")
-            )
-          );
-
-          // =============================================================
-          //                                                    VALIDATION
-          // =============================================================
-
-          $this.bind(
-            "validation.validation",
-            function (event, params) {
-
-              var value = getValue($this);
-
-              // Get a list of the errors to apply
-              var errorsFound = [];
-
-              $.each(validators, function (validatorType, validatorTypeArray) {
-                if (value || value.length || (params && params.includeEmpty) || (!!settings.validatorTypes[validatorType].blockSubmit && params && !!params.submitting)) {
-                  $.each(validatorTypeArray, function (i, validator) {
-                    if (settings.validatorTypes[validatorType].validate($this, value, validator)) {
-                      errorsFound.push(validator.message);
-                    }
-                  });
-                }
-              });
-
-              return errorsFound;
-            }
-          );
-
-          $this.bind(
-            "getValidators.validation",
-            function () {
-              return validators;
-            }
-          );
-
-          // =============================================================
-          //                                             WATCH FOR CHANGES
-          // =============================================================
-          $this.bind(
-            "submit.validation",
-            function () {
-              return $this.triggerHandler("change.validation", {submitting: true});
-            }
-          );
-          $this.bind(
-            [
-              "keyup",
-              "focus",
-              "blur",
-              "click",
-              "keydown",
-              "keypress",
-              "change"
-            ].join(".validation ") + ".validation",
-            function (e, params) {
-
-              var value = getValue($this);
-
-              var errorsFound = [];
-
-              $controlGroup.find("input,textarea,select").each(function (i, el) {
-                var oldCount = errorsFound.length;
-                $.each($(el).triggerHandler("validation.validation", params), function (j, message) {
-                  errorsFound.push(message);
-                });
-                if (errorsFound.length > oldCount) {
-                  $(el).attr("aria-invalid", "true");
-                } else {
-                  var original = $this.data("original-aria-invalid");
-                  $(el).attr("aria-invalid", (original !== undefined ? original : false));
-                }
-              });
-
-              $form.find("input,select,textarea").not($this).not("[name=\"" + $this.attr("name") + "\"]").trigger("validationLostFocus.validation");
-
-              errorsFound = $.unique(errorsFound.sort());
-
-              // Were there any errors?
-              if (errorsFound.length) {
-                // Better flag it up as a warning.
-                $controlGroup.removeClass("success error").addClass("warning");
-
-                // How many errors did we find?
-                if (settings.options.semanticallyStrict && errorsFound.length === 1) {
-                  // Only one? Being strict? Just output it.
-                  $helpBlock.html(errorsFound[0] + 
-                    ( settings.options.prependExistingHelpBlock ? $helpBlock.data("original-contents") : "" ));
-                } else {
-                  // Multiple? Being sloppy? Glue them together into an UL.
-                  $helpBlock.html("<ul role=\"alert\"><li>" + errorsFound.join("</li><li>") + "</li></ul>" +
-                    ( settings.options.prependExistingHelpBlock ? $helpBlock.data("original-contents") : "" ));
-                }
-              } else {
-                $controlGroup.removeClass("warning error success");
-                if (value.length > 0) {
-                  $controlGroup.addClass("success");
-                }
-                $helpBlock.html($helpBlock.data("original-contents"));
-              }
-
-              if (e.type === "blur") {
-                $controlGroup.removeClass("success");
-              }
-            }
-          );
-          $this.bind("validationLostFocus.validation", function () {
-            $controlGroup.removeClass("success");
-          });
-        });
-      },
-      destroy : function( ) {
-
-        return this.each(
-          function() {
-
-            var
-              $this = $(this),
-              $controlGroup = $this.parents(".control-group").first(),
-              $helpBlock = $controlGroup.find(".help-block").first();
-
-            // remove our events
-            $this.unbind('.validation'); // events are namespaced.
-            // reset help text
-            $helpBlock.html($helpBlock.data("original-contents"));
-            // reset classes
-            $controlGroup.attr("class", $controlGroup.data("original-classes"));
-            // reset aria
-            $this.attr("aria-invalid", $this.data("original-aria-invalid"));
-            // reset role
-            $helpBlock.attr("role", $this.data("original-role"));
-						// remove all elements we created
-						if (createdElements.indexOf($helpBlock[0]) > -1) {
-							$helpBlock.remove();
-						}
-
-          }
-        );
-
-      },
-      collectErrors : function(includeEmpty) {
-
-        var errorMessages = {};
-        this.each(function (i, el) {
-          var $el = $(el);
-          var name = $el.attr("name");
-          var errors = $el.triggerHandler("validation.validation", {includeEmpty: true});
-          errorMessages[name] = $.extend(true, errors, errorMessages[name]);
-        });
-
-        $.each(errorMessages, function (i, el) {
-          if (el.length === 0) {
-            delete errorMessages[i];
-          }
-        });
-
-        return errorMessages;
-
-      },
-      hasErrors: function() {
-
-        var errorMessages = [];
-
-        this.each(function (i, el) {
-          errorMessages = errorMessages.concat(
-            $(el).triggerHandler("getValidators.validation") ? $(el).triggerHandler("validation.validation", {submitting: true}) : []
-          );
-        });
-
-        return (errorMessages.length > 0);
-      },
-      override : function (newDefaults) {
-        defaults = $.extend(true, defaults, newDefaults);
-      }
-    },
-		validatorTypes: {
-      callback: {
-        name: "callback",
-        init: function ($this, name) {
-          return {
-            validatorName: name,
-            callback: $this.data("validation" + name + "Callback"),
-            lastValue: $this.val(),
-            lastValid: true,
-            lastFinished: true
-          };
-        },
-        validate: function ($this, value, validator) {
-          if (validator.lastValue === value && validator.lastFinished) {
-            return !validator.lastValid;
-          }
-
-          if (validator.lastFinished === true)
-          {
-            validator.lastValue = value;
-            validator.lastValid = true;
-            validator.lastFinished = false;
-
-            var rrjqbvValidator = validator;
-            var rrjqbvThis = $this;
-            executeFunctionByName(
-              validator.callback,
-              window,
-              $this,
-              value,
-              function (data) {
-                if (rrjqbvValidator.lastValue === data.value) {
-                  rrjqbvValidator.lastValid = data.valid;
-                  if (data.message) {
-                    rrjqbvValidator.message = data.message;
-                  }
-                  rrjqbvValidator.lastFinished = true;
-                  rrjqbvThis.data("validation" + rrjqbvValidator.validatorName + "Message", rrjqbvValidator.message);
-                  // Timeout is set to avoid problems with the events being considered 'already fired'
-                  setTimeout(function () {
-                    rrjqbvThis.trigger("change.validation");
-                  }, 1); // doesn't need a long timeout, just long enough for the event bubble to burst
-                }
-              }
-            );
-          }
-
-          return false;
-
-        }
-      },
-      ajax: {
-        name: "ajax",
-        init: function ($this, name) {
-          return {
-            validatorName: name,
-            url: $this.data("validation" + name + "Ajax"),
-            lastValue: $this.val(),
-            lastValid: true,
-            lastFinished: true
-          };
-        },
-        validate: function ($this, value, validator) {
-          if (""+validator.lastValue === ""+value && validator.lastFinished === true) {
-            return validator.lastValid === false;
-          }
-
-          if (validator.lastFinished === true)
-          {
-            validator.lastValue = value;
-            validator.lastValid = true;
-            validator.lastFinished = false;
-            $.ajax({
-              url: validator.url,
-              data: "value=" + value + "&field=" + $this.attr("name"),
-              dataType: "json",
-              success: function (data) {
-                if (""+validator.lastValue === ""+data.value) {
-                  validator.lastValid = !!(data.valid);
-                  if (data.message) {
-                    validator.message = data.message;
-                  }
-                  validator.lastFinished = true;
-                  $this.data("validation" + validator.validatorName + "Message", validator.message);
-                  // Timeout is set to avoid problems with the events being considered 'already fired'
-                  setTimeout(function () {
-                    $this.trigger("change.validation");
-                  }, 1); // doesn't need a long timeout, just long enough for the event bubble to burst
-                }
-              },
-              failure: function () {
-                validator.lastValid = true;
-                validator.message = "ajax call failed";
-                validator.lastFinished = true;
-                $this.data("validation" + validator.validatorName + "Message", validator.message);
-                // Timeout is set to avoid problems with the events being considered 'already fired'
-                setTimeout(function () {
-                  $this.trigger("change.validation");
-                }, 1); // doesn't need a long timeout, just long enough for the event bubble to burst
-              }
-            });
-          }
-
-          return false;
-
-        }
-      },
-			regex: {
-				name: "regex",
-				init: function ($this, name) {
-					return {regex: regexFromString($this.data("validation" + name + "Regex"))};
-				},
-				validate: function ($this, value, validator) {
-					return (!validator.regex.test(value) && ! validator.negative)
-						|| (validator.regex.test(value) && validator.negative);
-				}
-			},
-			required: {
-				name: "required",
-				init: function ($this, name) {
-					return {};
-				},
-				validate: function ($this, value, validator) {
-					return !!(value.length === 0  && ! validator.negative)
-						|| !!(value.length > 0 && validator.negative);
-				},
-        blockSubmit: true
-			},
-			match: {
-				name: "match",
-				init: function ($this, name) {
-					var element = $this.parents("form").first().find("[name=\"" + $this.data("validation" + name + "Match") + "\"]").first();
-					element.bind("validation.validation", function () {
-						$this.trigger("change.validation", {submitting: true});
-					});
-					return {"element": element};
-				},
-				validate: function ($this, value, validator) {
-					return (value !== validator.element.val() && ! validator.negative)
-						|| (value === validator.element.val() && validator.negative);
-				},
-        blockSubmit: true
-			},
-			max: {
-				name: "max",
-				init: function ($this, name) {
-					return {max: $this.data("validation" + name + "Max")};
-				},
-				validate: function ($this, value, validator) {
-					return (parseFloat(value, 10) > parseFloat(validator.max, 10) && ! validator.negative)
-						|| (parseFloat(value, 10) <= parseFloat(validator.max, 10) && validator.negative);
-				}
-			},
-			min: {
-				name: "min",
-				init: function ($this, name) {
-					return {min: $this.data("validation" + name + "Min")};
-				},
-				validate: function ($this, value, validator) {
-					return (parseFloat(value) < parseFloat(validator.min) && ! validator.negative)
-						|| (parseFloat(value) >= parseFloat(validator.min) && validator.negative);
-				}
-			},
-			maxlength: {
-				name: "maxlength",
-				init: function ($this, name) {
-					return {maxlength: $this.data("validation" + name + "Maxlength")};
-				},
-				validate: function ($this, value, validator) {
-					return ((value.length > validator.maxlength) && ! validator.negative)
-						|| ((value.length <= validator.maxlength) && validator.negative);
-				}
-			},
-			minlength: {
-				name: "minlength",
-				init: function ($this, name) {
-					return {minlength: $this.data("validation" + name + "Minlength")};
-				},
-				validate: function ($this, value, validator) {
-					return ((value.length < validator.minlength) && ! validator.negative)
-						|| ((value.length >= validator.minlength) && validator.negative);
-				}
-			},
-			maxchecked: {
-				name: "maxchecked",
-				init: function ($this, name) {
-					var elements = $this.parents("form").first().find("[name=\"" + $this.attr("name") + "\"]");
-					elements.bind("click.validation", function () {
-						$this.trigger("change.validation", {includeEmpty: true});
-					});
-					return {maxchecked: $this.data("validation" + name + "Maxchecked"), elements: elements};
-				},
-				validate: function ($this, value, validator) {
-					return (validator.elements.filter(":checked").length > validator.maxchecked && ! validator.negative)
-						|| (validator.elements.filter(":checked").length <= validator.maxchecked && validator.negative);
-				},
-        blockSubmit: true
-			},
-			minchecked: {
-				name: "minchecked",
-				init: function ($this, name) {
-					var elements = $this.parents("form").first().find("[name=\"" + $this.attr("name") + "\"]");
-					elements.bind("click.validation", function () {
-						$this.trigger("change.validation", {includeEmpty: true});
-					});
-					return {minchecked: $this.data("validation" + name + "Minchecked"), elements: elements};
-				},
-				validate: function ($this, value, validator) {
-					return (validator.elements.filter(":checked").length < validator.minchecked && ! validator.negative)
-						|| (validator.elements.filter(":checked").length >= validator.minchecked && validator.negative);
-				},
-        blockSubmit: true
-			}
-		},
-		builtInValidators: {
-			email: {
-				name: "Email",
-				type: "shortcut",
-				shortcut: "validemail"
-			},
-			validemail: {
-				name: "Validemail",
-				type: "regex",
-				regex: "[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\\.[A-Za-z]{2,4}",
-				message: "Not a valid email address<!-- data-validator-validemail-message to override -->"
-			},
-			passwordagain: {
-				name: "Passwordagain",
-				type: "match",
-				match: "password",
-				message: "Does not match the given password<!-- data-validator-paswordagain-message to override -->"
-			},
-			positive: {
-				name: "Positive",
-				type: "shortcut",
-				shortcut: "number,positivenumber"
-			},
-			negative: {
-				name: "Negative",
-				type: "shortcut",
-				shortcut: "number,negativenumber"
-			},
-			number: {
-				name: "Number",
-				type: "regex",
-				regex: "([+-]?\\\d+(\\\.\\\d*)?([eE][+-]?[0-9]+)?)?",
-				message: "Must be a number<!-- data-validator-number-message to override -->"
-			},
-			integer: {
-				name: "Integer",
-				type: "regex",
-				regex: "[+-]?\\\d+",
-				message: "No decimal places allowed<!-- data-validator-integer-message to override -->"
-			},
-			positivenumber: {
-				name: "Positivenumber",
-				type: "min",
-				min: 0,
-				message: "Must be a positive number<!-- data-validator-positivenumber-message to override -->"
-			},
-			negativenumber: {
-				name: "Negativenumber",
-				type: "max",
-				max: 0,
-				message: "Must be a negative number<!-- data-validator-negativenumber-message to override -->"
-			},
-			required: {
-				name: "Required",
-				type: "required",
-				message: "This is required<!-- data-validator-required-message to override -->"
-			},
-			checkone: {
-				name: "Checkone",
-				type: "minchecked",
-				minchecked: 1,
-				message: "Check at least one option<!-- data-validation-checkone-message to override -->"
-			}
-		}
-	};
-
-	var formatValidatorName = function (name) {
-		return name
-			.toLowerCase()
-			.replace(
-				/(^|\s)([a-z])/g ,
-				function(m,p1,p2) {
-					return p1+p2.toUpperCase();
-				}
-			)
-		;
-	};
-
-	var getValue = function ($this) {
-		// Extract the value we're talking about
-		var value = $this.val();
-		var type = $this.attr("type");
-		if (type === "checkbox") {
-			value = ($this.is(":checked") ? value : "");
-		}
-		if (type === "radio") {
-			value = ($('input[name="' + $this.attr("name") + '"]:checked').length > 0 ? value : "");
-		}
-		return value;
-	};
-
-  function regexFromString(inputstring) {
-		return new RegExp("^" + inputstring + "$");
-	}
-
-  /**
-   * Thanks to Jason Bunting via StackOverflow.com
-   *
-   * http://stackoverflow.com/questions/359788/how-to-execute-a-javascript-function-when-i-have-its-name-as-a-string#answer-359910
-   * Short link: http://tinyurl.com/executeFunctionByName
-  **/
-  function executeFunctionByName(functionName, context /*, args*/) {
-    var args = Array.prototype.slice.call(arguments).splice(2);
-    var namespaces = functionName.split(".");
-    var func = namespaces.pop();
-    for(var i = 0; i < namespaces.length; i++) {
-      context = context[namespaces[i]];
-    }
-    return context[func].apply(this, args);
-  }
-
-	$.fn.jqBootstrapValidation = function( method ) {
-
-		if ( defaults.methods[method] ) {
-			return defaults.methods[method].apply( this, Array.prototype.slice.call( arguments, 1 ));
-		} else if ( typeof method === 'object' || ! method ) {
-			return defaults.methods.init.apply( this, arguments );
-		} else {
-		$.error( 'Method ' +  method + ' does not exist on jQuery.jqBootstrapValidation' );
-			return null;
-		}
-
-	};
-
-  $.jqBootstrapValidation = function (options) {
-    $(":input").not("[type=image],[type=submit]").jqBootstrapValidation.apply(this,arguments);
-  };
-
-})( jQuery );
diff --git a/knitpost.R b/knitpost.R
new file mode 100755
index 000000000..d42522664
--- /dev/null
+++ b/knitpost.R
@@ -0,0 +1,30 @@
+#!/usr/bin/Rscript --vanilla
+#
+# Convert a Rmd file to a md file for Jekyll
+#
+# Usage:
+#   -arg1 The Rmd file to convert to md for Jekyll use.
+#
+# Example:
+#   cd lesson/lesson_name/
+#   Rscript ../../knitpost.R lesson.Rmd
+#
+# Change `visible: false` to `visible: true` in the generated lesson.md file.
+#
+
+KnitPost <- function(input) {
+    outfile <- rename_to_post(input)
+    knitr::opts_knit$set(base.url = '../')
+    knitr::opts_chunk$set(fig.cap = "center")
+    knitr::render_markdown()
+    knitr::knit(input, outfile, envir = parent.frame())
+}
+
+rename_to_post <- function(draft)
+    paste0(sub(".Rmd$", ".md", draft))
+
+args <- commandArgs(trailingOnly = TRUE)
+if (length(args) > 1)
+    stop('Please pass only one argument: the file.')
+
+KnitPost(args[1])
diff --git a/lessons/git/advanced/beamercolorthemesolarized.sty b/lessons/git/advanced/beamercolorthemesolarized.sty
new file mode 100644
index 000000000..5d5874c71
--- /dev/null
+++ b/lessons/git/advanced/beamercolorthemesolarized.sty
@@ -0,0 +1,156 @@
+% Beamer Color Theme using the Solarized Palette,
+% http://ethanschoonover.com/solarized.
+%
+% Copyright 2012 Jeffrey B. Arnold
+%
+% This program is free software: you can redistribute it and/or modify
+% it under the terms of the GNU General Public License as published by
+% the Free Software Foundation, either version 3 of the License, or
+% (at your option) any later version.
+%
+% This program is distributed in the hope that it will be useful,
+% but WITHOUT ANY WARRANTY; without even the implied warranty of
+% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+% GNU General Public License for more details.
+%
+% You should have received a copy of the GNU General Public License
+% along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+\ProvidesPackage{beamercolorthemesolarized}[2013/10/11 1.0.1 Solarized color theme for beamer]
+\RequirePackage{etoolbox}
+\RequirePackage{kvoptions}
+
+%% This is ugly. First time using options and conditionals in LaTeX
+\SetupKeyvalOptions{
+  family=solarized,
+  prefix=solarized@,
+}
+\DeclareBoolOption[false]{dark}
+\DeclareComplementaryOption{light}{dark}
+\DeclareStringOption[yellow]{accent}[yellow]
+\ProcessKeyvalOptions*
+
+% Solarized palette
+\definecolor{solarizedBase03}{HTML}{002B36}
+\definecolor{solarizedBase02}{HTML}{073642}
+\definecolor{solarizedBase01}{HTML}{586e75}
+\definecolor{solarizedBase00}{HTML}{657b83}
+\definecolor{solarizedBase0}{HTML}{839496}
+\definecolor{solarizedBase1}{HTML}{93a1a1}
+\definecolor{solarizedBase2}{HTML}{EEE8D5}
+\definecolor{solarizedBase3}{HTML}{FDF6E3}
+\definecolor{solarizedYellow}{HTML}{B58900}
+\definecolor{solarizedOrange}{HTML}{CB4B16}
+\definecolor{solarizedRed}{HTML}{DC322F}
+\definecolor{solarizedMagenta}{HTML}{D33682}
+\definecolor{solarizedViolet}{HTML}{6C71C4}
+\definecolor{solarizedBlue}{HTML}{268BD2}
+\definecolor{solarizedCyan}{HTML}{2AA198}
+\definecolor{solarizedGreen}{HTML}{859900}
+
+% Set Accent color
+% Ugly. Should be done with a switch
+\ifdefstring{\solarized@accent}{yellow}{
+  \colorlet{solarizedAccent}{solarizedYellow}
+}{}
+\ifdefstring{\solarized@accent}{orange}{
+  \colorlet{solarizedAccent}{solarizedOrange}
+}{}
+\ifdefstring{\solarized@accent}{red}{
+  \colorlet{solarizedAccent}{solarizedRed}
+}{}
+\ifdefstring{\solarized@accent}{magenta}{
+  \colorlet{solarizedAccent}{solarizedMagenta}
+}{}
+\ifdefstring{\solarized@accent}{violet}{
+  \colorlet{solarizedAccent}{solarizedViolet}
+}{}
+\ifdefstring{\solarized@accent}{blue}{
+  \colorlet{solarizedAccent}{solarizedBlue}
+}{}
+\ifdefstring{\solarized@accent}{cyan}{
+  \colorlet{solarizedAccent}{solarizedCyan}
+}{}
+\ifdefstring{\solarized@accent}{green}{
+  \colorlet{solarizedAccent}{solarizedGreen}
+}{}
+
+%% Set base colors for dark or light versions
+%% Dark
+% Switch between light and dark themes using the method in the CSS
+% stylesheet http://ethanschoonover.com/solarized
+\ifboolexpe{ bool {solarized@dark}}{
+  \colorlet{solarizedRebase03}{solarizedBase03}
+  \colorlet{solarizedRebase02}{solarizedBase02}
+  \colorlet{solarizedRebase01}{solarizedBase01}
+  \colorlet{solarizedRebase00}{solarizedBase00}
+  \colorlet{solarizedRebase0}{solarizedBase0}
+  \colorlet{solarizedRebase1}{solarizedBase1}
+  \colorlet{solarizedRebase2}{solarizedBase2}
+  \colorlet{solarizedRebase3}{solarizedBase3}
+}{
+  %% Light
+  \colorlet{solarizedRebase03}{solarizedBase3}
+  \colorlet{solarizedRebase02}{solarizedBase2}
+  \colorlet{solarizedRebase01}{solarizedBase1}
+  \colorlet{solarizedRebase00}{solarizedBase0}
+  \colorlet{solarizedRebase0}{solarizedBase00}
+  \colorlet{solarizedRebase1}{solarizedBase01}
+  \colorlet{solarizedRebase2}{solarizedBase02}
+  \colorlet{solarizedRebase3}{solarizedBase03}
+}
+
+\mode<presentation>
+
+\setbeamercolor{normal text}{fg=solarizedRebase0, bg=solarizedRebase03}
+\setbeamercolor{alerted text}{fg=solarizedAccent}
+% based css pre element
+\setbeamercolor{example text}{fg=solarizedRebase1, bg=solarizedRebase02}
+
+% Header and footer from CSS
+\setbeamercolor{footline}{bg=solarizedRebase02,fg=solarizedRebase01}
+\setbeamercolor{headline}{bg=solarizedRebase01,fg=solarizedRebase1}
+
+% Titles
+\setbeamercolor*{titlelike}{fg=solarizedAccent}
+\setbeamercolor*{frametitle}{fg=solarizedAccent}
+\setbeamercolor*{title}{fg=solarizedAccent}
+
+% Structure elements use css style for header
+\setbeamercolor*{structure}{bg=solarizedRebase01, fg=solarizedRebase1}
+
+% Do not mess with subtle colors in palette. I don't like it.
+\setbeamercolor*{palette primary}{bg=solarizedRebase01, fg=solarizedRebase1}
+\setbeamercolor*{palette secondary}{bg=solarizedRebase01, fg=solarizedRebase1}
+\setbeamercolor*{palette tertiary}{bg=solarizedRebase01, fg=solarizedRebase1}
+\setbeamercolor*{palette quaternary}{bg=solarizedRebase01, fg=solarizedRebase1}
+
+% Make Blocks slightly lighter/darker
+\setbeamercolor{block title}{fg=solarizedAccent, bg=solarizedRebase02}
+%\setbeamercolor{block title alerted}{}
+%\setbeamercolor{block title example}{}
+
+\setbeamercolor{block body}{parent=normal text, bg=solarizedRebase02}
+% \setbeamercolor{block body alerted}{}
+% \setbeamercolor{block body example}{}
+
+% same as footline
+% Set Sidebar and footline to use the css style for footer
+\setbeamercolor*{sidebar}{parent=headline}
+\setbeamercolor*{palette sidebar primary}{fg=solarizedRebase01, fg=solarizedRebase1}
+\setbeamercolor*{palette sidebar secondary}{fg=solarizedRebase01, fg=solarizedRebase1}
+\setbeamercolor*{palette sidebar tertiary}{fg=solarizedRebase01, fg=solarizedRebase1}
+\setbeamercolor*{palette sidebar quaternary}{fg=solarizedRebase01, fg=solarizedRebase1}
+
+% border-color for headings
+\setbeamercolor{separation line}{fg=solarizedRebase0}
+\setbeamercolor{fine separation line}{fg=solarizedRebase0}
+
+\setbeamercolor*{section in sidebar shaded}{parent=palette sidebar primary}
+% a.hover.navlink in CSS
+\setbeamercolor*{section in sidebar}{parent=palette sidebar primary, fg=solarizedRebase02}
+\setbeamercolor*{subsection in sidebar}{parent=section in sidebar}
+\setbeamercolor*{subsection in sidebar shaded}{parent=section in sidebar shaded}
+
+\mode
+<all>
diff --git a/lessons/git/advanced/lesson.md b/lessons/git/advanced/lesson.md
new file mode 100644
index 000000000..6f842732a
--- /dev/null
+++ b/lessons/git/advanced/lesson.md
@@ -0,0 +1,25 @@
+---
+layout: page
+title: 'An Intermediate Look at Git + GitHub'
+visible: true
+tags:
+  - git
+  - advanced
+---
+
+ - **Authors**: Elliott Sales de Andrade
+ - **Research field**: Seismology
+ - **Lesson topic**: Version control (Git)
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/git/advanced>
+
+## Recap: ##
+
+* Configure your git client (`git config user.name` + `user.email`)
+* Create a git repository (`git init`)
+* Start tracking a file with git (`git add`)
+* Commit changes to the history (`git commit`)
+* Check what's going on (`git status`)
+* Compare a file with the one in the history (`git diff`)
+* Look into your history (`git log`)
+
+Please follow along in the [slides](../slides.pdf) for the remaining information.
diff --git a/lessons/git/advanced/slides.pdf b/lessons/git/advanced/slides.pdf
new file mode 100644
index 000000000..a9508cc9c
Binary files /dev/null and b/lessons/git/advanced/slides.pdf differ
diff --git a/lessons/git/advanced/slides.tex b/lessons/git/advanced/slides.tex
new file mode 100644
index 000000000..c787ff3d4
--- /dev/null
+++ b/lessons/git/advanced/slides.tex
@@ -0,0 +1,339 @@
+\documentclass{beamer}
+
+\usepackage[utf8]{inputenc}
+\usepackage[T1]{fontenc}
+\usepackage{lmodern,tgadventor}
+\usepackage{textcomp}
+\usepackage{tikz}
+\usetikzlibrary{backgrounds,decorations.pathreplacing,decorations.text,fadings,trees}
+
+\useinnertheme{rectangles}
+\usecolortheme[accent=blue]{solarized}
+\setbeamercolor{description item}{fg=solarizedAccent}
+\beamertemplatenavigationsymbolsempty
+\setbeamertemplate{footline}[frame number]
+
+% Shell highlighting
+\newcommand{\command}[1]{\textcolor{solarizedAccent}{\$}
+                         \textcolor{solarizedRebase2}{#1}}
+\newcommand{\comment}[1]{\textit{\# #1}}
+\newcommand{\hiRed}[1]{\textcolor{solarizedRed}{#1}}
+\newcommand{\hiGreen}[1]{\textcolor{solarizedGreen}{#1}}
+\newcommand{\hiBlue}[1]{\textcolor{solarizedBlue}{#1}}
+\newcommand{\hiYellow}[1]{\textcolor{solarizedYellow}{#1}}
+
+% DAG commands
+\newcommand\commit[2]{\node[commit] (#1) {};%
+                      \node[clabel] (#1 label) at (#1) {\texttt{#1}: #2};}
+\newcommand\ghost[1]{\coordinate (#1);}
+\newcommand\connect[2]{\path[connections] (#1) to[out=90,in=-90] (#2);}
+\newcommand\gittag[1]{%
+ \begin{tikzpicture}[baseline]
+  \node[fill=solarizedRebase02, rounded corners, inner sep=1pt, anchor=base] {
+    \color{solarizedYellow}\vphantom{|}#1
+   };
+ \end{tikzpicture}%
+}
+\newcommand\branch[1]{%
+ \begin{tikzpicture}[baseline]
+  \node[fill=solarizedRebase02, rounded corners, inner sep=1pt, anchor=base] {
+    \color{solarizedRed}\vphantom{|}#1
+   };
+ \end{tikzpicture}%
+}
+
+% https://tex.stackexchange.com/questions/55806/tikzpicture-in-beamer/55827#55827
+\tikzset{
+  invisible/.style={opacity=0},
+  visible on/.style={alt=#1{}{invisible}},
+  alt/.code args={<#1>#2#3}{%
+    \alt<#1>{\pgfkeysalso{#2}}{\pgfkeysalso{#3}}
+  },
+}
+
+\author{U of T Scientific Coders}
+\title{An Intermediate Look at Git + GitHub}
+\institute{University of Toronto}
+\date{October 1, 2015}
+\titlegraphic{%
+ \begin{tikzpicture}[
+    commit/.style={draw, circle, fill=solarizedViolet, inner sep=0pt,
+                   minimum size=5pt},
+    clabel/.style={right, outer sep=1em},
+    connections/.style={draw, solarizedViolet}
+  ]
+  \useasboundingbox (0,1em) rectangle (10.7,6.5em); % Mysteriously chosen to fit...
+  \matrix [column sep={1em,between origins}, row sep=\lineskip,
+           ampersand replacement=\&]%
+  {
+   \commit{fd7a4e4}{\branch{gh-pages} Create 2015-10-08-Coworking4.markd\dots} \& \& \\
+   \commit{c3cb768}{Merge pull request \#41 from mbonsma/gh-pages} \& \& \\
+   \& \commit{e2764b7}{Incorporated PR comments into Biopython/less\dots} \& \\
+   \& \commit{d212bdd}{Merge remote-tracking branch `upstream/gh-p\dots} \& \\
+   \commit{85791b7}{Added start and end time to event post} \& \& \\
+   \& \commit{b83b3e0}{Merge remote-tracking branch `upstream/gh-p\dots} \& \\
+   \commit{0b7d78c}{Create 2015-10-01-MoreObGit.markdown} \& \& \\
+   \commit{c188bb0}{Merge pull request \#48 from lwjohnst86/gh-pages} \& \& \\
+   \ghost{ghost00} \& \ghost{ghost01} \& \\
+  };
+  % Left branch
+  \connect{c3cb768}{fd7a4e4};
+  \connect{85791b7}{c3cb768};
+  \connect{0b7d78c}{85791b7};
+  \connect{c188bb0}{0b7d78c};
+  \connect{ghost00}{c188bb0};
+  % Upper right branch
+  \connect{e2764b7}{c3cb768};
+  \connect{d212bdd}{e2764b7};
+  \connect{85791b7}{d212bdd};
+  % Lower right branch
+  \connect{b83b3e0}{d212bdd};
+  \connect{0b7d78c}{b83b3e0};
+  \connect{ghost01}{b83b3e0};
+  \fill [color=solarizedRebase03, path fading=north] (-1,-1.5) rectangle (16,0);
+ \end{tikzpicture}%
+}
+
+\AtBeginSection[]{\frame{\sectionpage}}
+
+\begin{document}
+
+\begin{frame}[plain,noframenumbering]
+ \titlepage
+\end{frame}
+
+\begin{frame}
+ \frametitle{Outline}
+ \tableofcontents
+\end{frame}
+
+\section{Review}
+
+\begin{frame}
+ \frametitle{Review}
+
+ \begin{itemize}
+  \item Configure your git client (\texttt{git config user.name} + \texttt{user.email})
+  \item Create a git repository (\texttt{git init})
+  \pause
+  \begin{tikzpicture}[%
+    remember picture,
+    show background rectangle,
+    background rectangle/.style={draw, visible on=<2->},
+    visible on=<2->
+   ]
+   \begin{scope}[
+     every node/.style={anchor=west, minimum height=1em,
+                        font={\tiny\ttfamily}, inner sep=2.5pt},
+     every edge/.style={thick},
+     root/.style={},
+     grow via three points={one child at (0.25,-0.75em) and
+                            two children at (0.25,-0.75em) and (0.25,-1.25em)},
+     edge from parent path={(\tikzparentnode.south) |- (\tikzchildnode.west)}
+    ]
+    \node[anchor=west, font={\scriptsize}, align=center]
+     at (0,0) (work) {Working\\Directory};
+    \node [root, anchor=north] at (work.south) (work root) {my\_project/}
+     child { node (git folder) {.git/} }
+     child { node (first folder) {foo/} }
+     child { node {bar/} }
+     child { node {baz.txt} }
+     child { node {qux.txt} }
+     child { node (last folder) {\dots} };
+   \end{scope}
+   \node[anchor=north west, font={\scriptsize}, xshift=4em]
+    at (work.north east) (repository) {Repository};
+   \begin{scope}[
+     every node/.style={anchor=west, minimum height=1em,
+                        font={\tiny\ttfamily}, inner sep=2.5pt},
+     every edge/.style={thick},
+     root/.style={},
+     grow via three points={one child at (0.25,-0.75em) and
+                            two children at (0.25,-0.75em) and (0.25,-1.25em)},
+     edge from parent path={(\tikzparentnode.south) |- (\tikzchildnode.west)}
+    ]
+    \node[anchor=north, font={\scriptsize}, align=center]
+     at (repository.south) (index) {Index/\\Staging Area};
+    \node<3->[root, anchor=north, align=center]
+     at (index.south) (snapshot) {\color{solarizedAccent} (Snapshot)}
+     child { node {foo/} }
+     child { node {baz.txt} }
+     child { node {\dots} };
+   \end{scope}
+   \begin{scope}[
+     commit/.style={draw, rectangle, rounded corners, fill=solarizedRebase02,
+                    inner sep=1pt, minimum height=0.75em, minimum width=1em,
+                    font={\tiny\sffamily}},
+     connections/.style={draw}
+    ]
+    \node[anchor=west, font={\scriptsize}, align=center, xshift=1em] at (index.east)
+     (history) {History};
+    \matrix at (history.south)
+      [column sep={1em,between origins}, row sep={1em,between origins},
+       ampersand replacement=\&, yshift=-2.5em]%
+    {
+     \node[commit,visible on=<4->] (10) {\color{solarizedAccent} 10}; \\
+     \node[commit] (9) {9}; \\
+     \node[commit] (8) {8}; \\
+     \ghost{7}; \\
+    };
+    \path[connections, visible on=<4->] (9) -- (10);
+    \connect{8}{9};
+    \path[connections, densely dotted] (7) -- (8);
+   \end{scope}
+   \begin{scope}[
+     path/.style={->},
+     label/.style={decorate, decoration={text along path, text align=center,
+                                         text color=solarizedRebase0,
+                                         raise=1pt}}
+    ]
+    \draw[path] (git folder.east) to[out=0,in=180] (repository.west);
+    \draw (index.north west) rectangle (7.south -| history.east);
+    \draw<3->[decorate, decoration={brace, raise=0.75em, aspect=0.45}]
+      (first folder.east) |- (last folder.east) node[near start] (mid selection) {};
+    \draw<3->[path] (mid selection.east) + (0.6em,0) to[out=0,in=180] (snapshot.west);
+    \draw<4->[path] (snapshot.east) to[out=0,in=180] (10.west);
+   \end{scope}
+  \end{tikzpicture}
+  \pause
+  \item Start tracking a file with git (\texttt{git add})
+  \pause
+  \item Commit changes to the history (\texttt{git commit})
+  \pause
+  \item Check what's going on (\texttt{git status})
+  \item Compare a file with the one in the history (\texttt{git diff})
+  \item Look into your history (\texttt{git log})
+ \end{itemize}
+\end{frame}
+
+\section{Viewing History}
+
+\begin{frame}
+ \frametitle{Viewing History}
+
+ Viewing the log allows you to ``see'' history:
+ \begin{itemize}
+  \item \texttt{git log}
+  \pause
+  \item \texttt{git log \alert{<start>..<end>}}
+  \item \texttt{git log \alert{-{}- <file>}}
+  \item \texttt{git log \alert{-{}-oneline}}
+  \item \texttt{git log \alert{-{}-graph}}
+  \item \texttt{git log -{}-graph \alert{-{}-decorate}} \\
+ \end{itemize}
+ \pause
+ \begin{itemize}
+  \item \texttt{git blame <file>}
+ \end{itemize}
+ \pause
+ \begin{itemize}
+  \item \texttt{gitk} + \texttt{gitg} + other viewers
+ \end{itemize}
+\end{frame}
+
+\section{Branching}
+
+\begin{frame}
+ \frametitle{Branches}
+
+ What are branches?
+ \begin{itemize}[<+->]
+  \item Divergent commits (two commits with the same parent) could be
+        considered ``virtual'' branches
+  \item Branches are simply a \alert{named pointer} to a commit
+  \item Branches automatically \emph{move forward} as commits are made
+ \end{itemize}
+ \pause[\thebeamerpauses]
+ Why use them?
+ \pause
+ \begin{itemize}[<+->]
+  \item They're cheap! Just pointers. No heavy changes, e.g., an extra
+        directory in svn.
+  \item To keep experimental work apart
+  \item To separate trials
+  \item To ease collaboration
+ \end{itemize}
+\end{frame}
+
+\begin{frame}
+ \frametitle{Branches}
+
+ Managing branches:
+ \begin{itemize}
+  \item \texttt{git branch <name> [commit]}
+  \item \texttt{git branch -d <name>}
+  \item \texttt{git branch [-l]}
+ \end{itemize}
+
+ \pause
+ Switching branches:
+ \begin{itemize}
+  \item \texttt{git checkout <branch name>}
+  \item \texttt{git checkout -b <branch name> [commit]} --- Create and switch
+        in one go
+ \end{itemize}
+
+ \pause
+ Merging branches:
+ \begin{itemize}
+  \item \texttt{git merge <other branch name>}
+  \item \texttt{git merge \alert{-{}-ff-only} <other branch name>}
+  \item \texttt{git merge \alert{-{}-no-ff} <other branch name>}
+ \end{itemize}
+\end{frame}
+
+\section{Collaborating with Others}
+
+\begin{frame}
+ \frametitle{Clones and Remotes}
+
+ Clones are complete copies of a repository's history (i.e., excluding the
+ index and working directory)
+ \pause
+ \begin{itemize}
+  \item \texttt{git clone <URI>}
+ \end{itemize}
+
+ \pause
+ Remotes are \emph{pointers} to other clones
+ \pause
+ \begin{itemize}
+  \item \texttt{git remote [-v]}
+  \item \texttt{git remote add <name> <URI>}
+  \item \texttt{git remote rm <name>}
+  \item Local branches can \emph{track} remote branches\\
+        \texttt{git branch -u <remote branch> <local branch>}
+ \end{itemize}
+
+ \pause
+ \emph{You} are responsible for syncing
+ \begin{itemize}
+  \item \texttt{git push [<remote>] [<branch>]}
+  \item \texttt{git fetch [<remote>]}
+  \item \texttt{git pull [<remote>]} --- \texttt{fetch} + \texttt{merge}
+ \end{itemize}
+\end{frame}
+
+\begin{frame}[plain]
+ \vfill
+ \begin{center}
+  \LARGE \color{solarizedAccent} GitHub Example
+ \end{center}
+ \vfill
+\end{frame}
+
+\appendix
+
+\section{Advanced Topics}
+
+\begin{frame}
+ \frametitle{Advanced Topics}
+
+ \begin{itemize}[<+->]
+  \item \texttt{git add \alert{-{}-patch}}
+  \item \texttt{git rebase}
+  \item Ignoring unwanted files
+ \end{itemize}
+\end{frame}
+
+\end{document}
diff --git a/lessons/git/branches/lesson.md b/lessons/git/branches/lesson.md
new file mode 100644
index 000000000..78d38e33d
--- /dev/null
+++ b/lessons/git/branches/lesson.md
@@ -0,0 +1,168 @@
+---
+layout: page
+title: 'Using branches in Git'
+visible: true
+tags:
+  - git
+  - intermediate
+---
+
+# Why and how branches are used in Git
+
+ - **Authors**: Luke Johnston
+ - **Research field**: Nutritional and diabetes epidemiology
+ - **Lesson topic**: Git branches
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/git/branches>
+ - **Lesson video stream**: <https://www.youtube.com/watch?v=FwxbMxXHb0c>
+
+## Preface: ##
+
+Version control systems like Git help manage changes to files. Sometimes, you'll
+want (or need) to make some 'feature' or 'patches' to a collaborative research
+project. Or maybe you want to make some experimental changes to your code, but
+don't want to touch your main code. This, and more, is where branches come into
+play.
+
+In this code-along we'll go over what branches are, and how and why you would use
+them.
+
+### Pre-requisites:
+
+* Obviously, have [Git installed](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
+* Make sure to [configure your git](http://codeasmanuscript.org/lessons/git/cheatsheet/)
+
+
+## What are Git branches
+
+In very simple terms, git branches are individual projects within a git
+repository. Different branches within a repository can have completely different
+files and folders, or it could have everything the same except for some lines of
+code in a file.
+
+Let's use a few real world examples (at least that I've used before, others may
+have used them differently):
+
+- Pretend you submitted a research article to a journal and they want you to
+revise it based on some reviewers comments. There are several ways to deal with
+the comments, so instead of changing your main manuscript, you create a
+`revision` branch in your manuscript git repository. In that branch you make the
+changes to your manuscript in response to the reviewers. Once you are satisfied,
+you merge the branch into the `master` branch and resubmit the article.
+- Imagine you have a dataset that multiple people work off of but that is also
+often updated with more data. You think you found a problem with the dataset,
+but aren't sure. So you create a new branch `fixing` to fix the problems without
+messing with the master dataset. After you confirm the problem is real and that
+you have the solution, you submit a pull request of the `fixing` branch to be
+merged with the `master` branch.
+- What is often the case in software development, a bug or missing feature in
+the software gets identified. Because the software is already in production use
+(fairly stable, other people rely on it, etc), you can't just make changes to
+the main software code. So a `hotfix` or `feature` branch is created to address
+these problems, which will eventually get merged in with the `master` branch for
+the next version of the software. This ensures that other people's code isn't
+broken everytime a bug gets fixed.
+
+There are many uses of branches in Git. The nice (and very powerful) thing about
+Git is the fact that branches are very cheap compared to other version control
+systems. By cheap, I mean they don't take up much disk space, it's
+computationally easy to move between branches, and it's (relatively) easy to
+merge branches together. This is because of how Git represents branches, since
+they are simply *pointers* or an individual commit. *That's it.* Just a
+pointer... Git commit history is a 
+[directed acyclic graph](https://en.wikipedia.org/wiki/Directed_acyclic_graph), 
+which means that every single commit always has a 'parent' commit (the
+previous commit in the history, or multiple parents when a merge happens), and
+any individual commit can have multiple 'children'. This history can be traced
+back through the 'lineage' or 'ancestry'. The branch just gives a name to each
+'lineage' when a commit has multiple children.
+
+When you merge two branches together, the commit histories get merged together
+as well. Which means that all the changes you made in each branch gets combined
+back into a single lineage, rather than two. This makes it easier to work
+collaboratively on a project, since each individual could work on their own
+branches, without dealing with the messiness that could come from working all on
+one branch.
+
+## Commands used with branches
+
+Branches are best understood visually. So let's first start with using 
+[this website](https://onlywei.github.io/explain-git-with-d3/) to see what the 
+`branch`, `checkout`, and `merge` commands are doing.
+
+After we've tried that, let's do it locally (on your own computer). Here is a
+sequence of commands to try out:
+
+    cd ~/Desktop
+    mkdir git-branches
+    cd git-branches
+    git init # start a repo
+    git add .
+    git commit -m "First commit" # make the first commit
+    git branch testBranch # create branch
+    git checkout testBranch # move to branch
+    ## can also do git checkout -b testBranch
+    echo "Some text" > file.txt 
+    git add file.txt
+    git commit -m "Added a file with text"
+    git checkout master
+    echo "Text in another file" > new-file.txt
+    git add new-file.txt
+    git commit -m "Added another file"
+    git log --graph --oneline --decorate --all
+    # This command is long, so shorten it using aliases
+    git config --global alias.lg 'log --graph --oneline --decorate --all'
+    git merge testBranch
+    git lg
+    git branch -d testBranch # delete the branch
+    
+## Using branches for pull requests 
+
+I mentioned this already, but branches are best used when doing a 
+[pull request](https://help.github.com/articles/using-pull-requests/)
+(unless the pull request is very small or few people work on the repository).
+
+The steps to take would be:
+
+1. Fork a repository on GitHub
+2. Clone it onto your computer
+3. Make a branch and move to it: `git checkout -b fixingBranch`
+4. Make changes to the files
+5. Commit the changes to the history
+6. Push the branch up to your forked version: `git push origin fixingBranch`
+7. On GitHub, submit a Pull Request of your `fixingBranch`
+8. Once the pull request is merged, 
+[delete](https://github.com/blog/1377-create-and-delete-branches) 
+the `fixingBranch` on your forked repo on GitHub and on your computer 
+(`git checkout master && git pull upstream master && git branch -d fixingBranch`)
+
+# Resources: #
+
+If you have any questions, often one of the best places to start is either  
+[StackOverflow](https://stackoverflow.com/questions/tagged/git) or Google (which
+more likely links to StackOverflow).
+
+## Glossary: ##
+
+* `cd` - change directory
+* directory - the same thing as a folder
+* `mkdir` - make a directory
+* `echo` - print a message to the screen or to a file if `>` (redirect) is
+present.
+* `git init` - start or initialize a git repository
+* `git add` - put a file into the staging area, so that git starts
+  tracking it
+* `git commit` - send files in the staging/index area into the history
+  (the git repository)
+* `git log --graph --oneline --decorate --all` - view the commit history in the
+git repository and the branches, with each commit as one line.
+* `git branch` - An individual line of commit history that contains files that may
+differ from other branches.
+* `git checkout` - A way to move across the git commits and branches.
+* `git merge` - Combine a branch into the current checked out branch (i.e. the
+branch you are on).
+
+## Links: ##
+
+* [Interactive, visual tutorial on branching](https://pcottle.github.io/learnGitBranching/)
+* [Brief explanation of branching](https://www.atlassian.com/git/tutorials/using-branches/git-branch)
+
diff --git a/lessons/git/ci-github-python/lesson.md b/lessons/git/ci-github-python/lesson.md
new file mode 100644
index 000000000..9195789b3
--- /dev/null
+++ b/lessons/git/ci-github-python/lesson.md
@@ -0,0 +1,16 @@
+---
+layout: page
+title: Continuous integration in Git with examples in Python
+visible: true
+tags:
+  - python
+  - git
+  - intermediate
+---
+
+ - **Authors**: Jesse Knight
+ - **Lesson topic**: Git: continuous integration with worked examples in Python
+ - **Lesson content URL**: https://github.com/jessexknight/mwe-pytest-travis-cov
+ 
+ See [lesson repo](https://github.com/jessexknight/mwe-pytest-travis-cov) for material.
+
diff --git a/lessons/git/ci-github/lesson.md b/lessons/git/ci-github/lesson.md
new file mode 100644
index 000000000..f274584e8
--- /dev/null
+++ b/lessons/git/ci-github/lesson.md
@@ -0,0 +1,220 @@
+---
+layout: page
+title: Continuous integration on GitHub
+visible: true
+tags:
+  # languages
+  - git
+  # levels
+  - intermediate
+---
+
+ - **Authors**: Luke Johnston ([@lwjohnst86](https://github.com/lwjohnst86))
+ - **Research field**: Diabetes epidemiology
+ - **Lesson topic**: Using continuous integration within GitHub
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/git/ci-github>
+
+## What is Continuous Integration (CI)?
+
+[CI](https://en.wikipedia.org/wiki/Continuous_integration) historically is a
+system/workflow/practice of merging a codebase that developers work on
+individually into a main stream, usually several times a day. This ensures that
+there are few to no conflicts within the code base. However, CI presently is
+often used in the context of automated builds of a git repository to ensure that
+everything runs/tests/builds as expected in a clean, new environment (aka on a
+server). For this reason, I will only refer to CI in the context of git repos.
+
+Automated CI services are available on GitHub through [Travis CI](https://travis-ci.org/).
+Travis is a service independent (as a company) of GitHub, however they currently
+only work with GitHub. There is the `.org` version of Travis for public GitHub
+repos and a `.com` version for private repos. There are other CI's available on
+GitLab or BitBucket (services similar to GitHub). Through Travis, you can get
+your git repo and associated code/files/documents (really, any thing that is
+command based, including things such as pandoc, rmarkdown, or website build tools)
+to be built, tested, or whatever else you want it to do!
+
+The main use of CI is for automated testing of your code in a separate environment,
+to ensure that any new additions to the code don't cause any breaks or conflicts.
+Other common uses are to build websites that first require code or documents to
+be generated before pushing to the live website.
+
+## Why should you use it?
+
+There are [many reasons](https://about.gitlab.com/2015/02/03/7-reasons-why-you-should-be-using-ci/)
+to use CI, especially if your codebase is large and complex, when multiple people 
+work on the code, when it is relied on by others, or if it is for production
+(industry or commercial) purposes. If your code is small or simple or a one-time 
+thing, it is often not necessary to use CI. BUT! Using CI will make sure that your 
+code is robust and more likely to be accurate and correct... which is especially
+important in most if not all scientific fields.
+
+## Travis and the `.travis.yml` file
+
+As I mentioned, you use Travis for GitHub. In order to use Travis, you need to
+sign up and follow the [getting started](https://docs.travis-ci.com/user/getting-started/)
+steps. Travis has many supported [languages](https://docs.travis-ci.com/),
+including [R](https://docs.travis-ci.com/user/languages/r/) and 
+[Python](https://docs.travis-ci.com/user/languages/python/). 
+
+The two main things you need for Travis are to include a `.travis.yml` file in
+your GitHub repo and link Travis to it via the [Travis website](https://travis-ci.org/).
+
+### Example using R and `.travis.yml` file
+
+```
+language: r
+cache: packages # to reduce the build time for dependent packages
+r:
+    - release
+    - devel
+script:
+    # If building packages
+    - R CMD build .
+    - R CMD check *.tar.gz
+    # If running from an R or shell file
+    - Rscript test.R
+    # or:
+    - sh ./deploy.sh
+```
+
+Package dependencies are by default searched for in the 
+[`DESCRIPTION`](http://r-pkgs.had.co.nz/description.html) file. Otherwise,
+specific packages can be installed by including this in the `.travis.yml` file:
+
+```
+r_packages:
+    - "packagename"
+```
+
+If you are creating packages and want to use Travis to test your packages, use
+the R command `devtools::use_travis()` to get you started.
+
+Building websites via R Markdown is relatively straight forward. You first need
+to create a `deploy.sh` file of some sort (see 
+[this file](https://github.com/codeasmanuscript/rworkshops/blob/master/deploy.sh) as
+an example) and include Travis commands to push to a `gh-pages` branch (see
+[this file](https://github.com/codeasmanuscript/rworkshops/blob/master/.travis.yml) as
+an example). For a more detailed example and walkthrough, see 
+[this Gist](https://gist.github.com/willprice/e07efd73fb7f13f917ea.html).
+
+Let's work through a (very simple and silly) example. Create three files: `.travis.yml`, 
+`function.R`, `test.R`, and `DESCRIPTION` (this file is needed for
+R with travis). (As a side note, as with above, I will not be covering formal
+unit testing in this lesson).
+
+**`.travis.yml`**
+
+```
+language: r
+cache: packages
+# build in two different OS
+r:
+    - release
+os:
+    - linux
+    - osx
+script:
+    - Rscript test.R
+```
+
+**`function.R`**
+
+```r
+add_nums <- function(num1, num2) {
+    num1 + num2
+}
+
+random_half_split <- function(data) {
+    size <- nrow(data) / 2
+    dplyr::sample_n(data, size)
+}
+```
+
+**`test.R`**
+
+```r
+library(testthat)
+source("function.R")
+expect_equal(add_nums(2, 2), 4)
+actual_size <- nrow(random_half_split(iris))
+expect_equal(actual_size, 75)
+```
+
+**`DESCRIPTION`**
+
+```
+Package: testing_ci_r
+Type: Package
+Title: Testing out
+Version: 0.0.0.9000
+Authors@R: person("Luke", "Johnston", email = "lwjohnst@gmail.com",
+    role = c("aut", "cre"))
+Description: Testing around
+LazyData: TRUE
+# packages to install
+Imports:
+    dplyr,
+    testthat
+RoxygenNote: 6.0.1
+```
+
+### Example Python `.travis.yml` file
+
+```
+language: python
+python:
+    - "3.6"
+# install dependencies
+install:
+    - pip install -r requirements.txt # if you put your dependencies in a file
+script: pytest
+```
+
+I am not nearly as familiar with Python, however, the same things that apply to R
+also apply to Python. You (optionally) need to install dependencies and use the `script` option
+to specify what commands or file to run the tests with.
+
+As with R, let's do a simple and silly example. Create the three files: `.travis.yml`, 
+`function.py`, and `test_function.py`. (As a side note, as with above, I will not be covering 
+formal unit testing in this lesson).
+
+**`.travis.yml`**
+
+```
+language: python
+python:
+    - 3.6
+script: pytest
+```
+
+**`function.py`**
+
+```python
+def add_nums(num1, num2):
+    added = num1 + num2
+    return added
+```
+
+**`test_function.py`**
+
+```python
+import function
+
+def test_add_nums():
+    added = function.add_nums(2, 2)
+    assert added == 4
+```
+
+## Lesson flow and other remarks
+
+In the live version of the lesson, I'll cover simple examples of using Travis
+for R and Python. Since I am more familiar with R, I'll be covering R examples
+in more detail. In general, the lesson will go over setting up Travis, creating
+the `.travis.yml` file, getting Travis to run, and seeing the results for the 
+CI build.
+
+There are so many things you can do with Travis (or any CI for that matter)! 
+Simple reasons are pretty straight forward, though there is a learning curve. 
+More complex uses are, well, more complex to set up. In the end, setting up Travis/CI
+can save you time and reduce your workload as it automates many of the repetitive 
+tasks you many do when creating code or code-dependent documents.
diff --git a/lessons/git/collaboration/lesson-AH.md b/lessons/git/collaboration/lesson-AH.md
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,229 @@
+---
+layout: page
+title: Collaborating on GitHub
+visible: true
+tags:
+  # languages
+  - git
+  # levels
+  - intermediate
+---
+<!-- change visible to true if you want it on the site -->
+<!-- remove any tags listed above that are not relevant -->
+
+ - **Authors**: Ahmed Hasan; Frances Wong
+ - **Research field**: Genomics; Physiology
+ - **Lesson topic**: Collaborating on GitHub
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/git/collaboration>
+ - **Lesson video stream**: n/a
+
+# What are Git and GitHub?
+
+## Git
+
+Git is a version control system that allows users to track changes in files.
+Git is used via the command line on your local computer. Once a given folder
+has been designated as a Git _repository_ (or 'repo' for short), Git will start
+looking for changes in files in that folder. Git is quite flexible: we can pick
+and choose which files we would like to track in a repository. 
+
+## GitHub
+
+GitHub is a website that hosts Git repositories. Repositories are quite
+flexible: a Git repo could be any of:
+
+- Fully self contained software
+- Research projects, containing scripts and logs
+- Websites
+- Educational content (e.g. material for one or more workshops)
+- Just a place for you to keep a random collection of scripts!
+
+Although repos can be created on GitHub and maintained there, it is much more
+common for users to also have local copies of their repos as well and sync the
+two as changes happen on either end. GitHub accounts are free to use.
+
+GitHub also comes equipped with several additional features. Each repo has an
+Issues page, for instance, where owners, collaborators, and other users can
+submit feature requests, point out bugs, or simply have discussions pertaining
+to the repo in some form. Moreover, GitHub also allows for _forking_ repos,
+which is when users create a copy of a given repo that's associated with their
+own GitHub accounts. Finally, GitHub features a full Pull Requests tab
+associated with every repo that centralizes any proposed changes to the
+contents of the repo.  We will explore both forks and pull requests below;
+these are very important features for collaboration! 
+
+
+# Solo GitHub workflow - the minimal case
+
+Many researchers use GitHub repos simply as a place to keep all their scripts.
+New scripts and changes to existing to scripts are both _committed_ to the repo
+(saved changes are referred to as 'commits') and there tends to be a single
+linear commit history. 
+
+To accomplish this, it's common to have a local repo that one works on and
+commits changes in, after which those changes are pushed to GitHub using
+command line Git commands. 
+
+Let's quickly review a few core commands:
+
+```bash
+# getting started with a repo
+git init # initializing a repo at the command line
+git clone [url] # creating a copy of an existing repo
+
+# linking a repo to GitHub
+git remote # listing any remotes - eg the repo on GitHub
+git remote add origin [url] # add link to repo at specific url
+
+# committing (saving) changes to files
+git add [file] # telling git to track changes in a file
+git commit # saving those changes
+```
+
+# Collaborative work flows
+
+## Branches and pull requests
+
+However, collaborative workflows (and even solo software development) is often
+not done this way. Both make use of what are known as _branches_: a snapshot of
+the repo at a given time that has an independent commit history. Branches can
+be thought of as copies of a repo that can then be worked on without actually
+affecting the 'main' repo itself. This 'main' repo is itself a branch, and one
+that is typically called `master` or `main` by default.
+
+A branch can be created and switched to using `git checkout`:
+
+```bash
+git checkout -b patch-1
+# Switched to new branch 'patch-1'
+```
+
+after which branches can be switched between using `git checkout`:
+
+```bash
+git checkout main # switches back to main branch
+```
+
+Branches can be listed using `git branch`:
+
+```bash
+git branch
+git branch -v # for more details on branches
+```
+
+Why not work in `master`/`main` directly? By working in branches instead, users
+are free to make whatever changes they would like without fear of breaking the
+existing codebase, or causing some kind of similar problem. Instead, once one
+is done making changes in and ideally testing the changes in a given branch, a
+_pull request_ (PR) can be submitted, essentially requesting for the changes in the
+branch to be merged with `master`/`main`.
+
+A pull request functions as a means of merging the new commit history of a
+given branch to the `master`/`main` branch. This is the core concept behind
+collaborating in Git: collaborators working in branches and then submitting
+pull requests to have their changes incorporated into the codebase. 
+
+There are two separate workflows by which this is done, each with a specific
+set of circumstances in which they are the appropriate choice: the 'shared
+repository' model as well as the 'fork and pull' model. We will touch on both
+before demonstrating the latter.
+
+## Shared repository
+
+The shared repository model is usually (but not always) put into practice on
+smaller projects with smaller amounts of collaborators. The key requirement for
+a shared repo model is that _all contributors have write access to the repo_,
+which is not always the case (e.g. when contributing to a large open source
+project). 
+
+The shared repository model involves working _directly in the main repository_.
+Branches are still used to keep proposed changes separate, and then pull
+requests are submitted once a collaborator wishes for their changes to be
+merged into `master`/`main`.  The `master`/`main` branch is still never written
+to directly -- any changes must go through the PR workflow (detailed further below)
+
+We won't be demoing this today, especially since much of this workflow is covered
+in the more extensive fork and pull workflow regardless. 
+
+Here's the full workflow, for reference:
+
+1. Collaborator creates a branch on the main repo
+2. Collaborator makes changes in the branch
+3. Collaborator submits a PR, requesting changes from the branch to be merged into `master`/`main`
+4. Repo owner reviews PR and requests further changes if needed.
+5. If further changes are requested, collaborator makes them *in the branch*.
+6. When PR is good to go, owner merges PR into `master`/`main`
+7. The branch is deleted. 
+
+This model is good for beginners to Git collaboration, since there is no need
+to sync anything after changes have been merged. 
+
+## Fork and pull model
+
+In this model, collaborators _fork_ the repo: creating entirely separate copies of
+the repo altogether. It is common practice to also _clone_ (make a local copy of)
+the fork, especially for one of the two methods of syncing forks we'll learn below. 
+
+Upon cloning the repo, the main repository is also added as a _remote_ to the fork.
+This will be used to sync the fork later:
+
+```bash
+git remote add upstream [main repo url]
+```
+
+Within their fork, a collaborator creates a new branch and adds in their proposed changes.
+Once the branch is ready, a pull request is submitted _from the branch of the fork_ to
+`master`/`main` _on the main repo_. This sort of 'cross-repo' pull request is only possible
+between forks, and since anyone can make a fork without necessarily having write access to a repo,
+this is the much more commonly used model of collaboration on GitHub.
+
+Once a PR is submitted, repo owners can use the pull request dialog on GitHub to
+request further changes if needed. If this is the case, the collaborator can continue making
+changes in their branch; GitHub will automatically update the PR with any new changes.
+
+Once the PR is ready, the owner will merge the PR into `master`/`main`. The changes have been
+merged! All that remains is for the collaborator to sync their fork: since the collaborator
+worked on a branch, the fork's `master`/`main` branch has not been updated at all. 
+
+Forks can be updated in two different ways - first, on the command line:
+
+```bash
+# run in the repo dir
+git checkout master # or main - this will switch back out of the experimental branch
+git fetch upstream/master # fetches the updated codebase from the main repo
+git merge upstream/master # merges the updates from the main repo
+git push origin master # sends the updates to the *fork* 
+```
+
+However, it can also be done via GitHub:
+
+1. Head to your fork of the repo.
+2. Click on 'Pull request' in the bar above the file list.
+3. Set the Base Repository to the fork, and the base to `master`/`main`.
+4. Set the Head Repository to the main repo, and the base to `master`/`main`. 
+5. Create the pull request and merge it! 
+
+This can be quite confusing at first glance! But it's important to sync the
+fork like so, since it updates it for any further changes if needed. It's good
+practice to do this both after changes are merged and before starting on a
+fresh branch. It is also entirely possible to simply delete and remake the fork
+instead, but that can be tiresome if you find yourself collaborating on the
+same repo for a long period of time.
+
+We'll now be demoing the fork and pull model for the remainder of the lesson!
+
+## Merge conflicts
+
+Merge conflicts can unfortunately arise during this process. A _merge conflict_
+happens `master`/`main` on the main repo is updated *after* being forked, and
+the changes a collaborator submits a PR for conflict with the current state of
+the main repo (e.g. if the same line is modified differently on the main repo
+than on the PR). Git won't know which of the changes to stick with, and will
+ask for the conflict to be manually resolved.
+
+We won't be demoing merge conflicts today given time constraints, but a guide
+on how to resolve them can be found [here][merge-conf]. The best way to avoid
+merge conflicts, however, is to make sure that your fork remains synced! 
+
+[merge-conf]: https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/resolving-a-merge-conflict-on-github
+
diff --git a/lessons/git/collaboration/lesson.md b/lessons/git/collaboration/lesson.md
new file mode 100644
index 000000000..4297db4ae
--- /dev/null
+++ b/lessons/git/collaboration/lesson.md
@@ -0,0 +1,488 @@
+---
+layout: page
+title: Collaborating on GitHub
+visible: true
+tags:
+  # languages
+  - git
+  # levels
+  - beginner
+---
+<!-- change visible to true if you want it on the site -->
+<!-- remove any tags listed above that are not relevant -->
+
+ - **Authors**: Serena Jeblee; Kathy Chung
+ - **Research field**: Computational Linguistics; Post-Doc in Digital Humanities
+ - **Lesson topic**: Collaborating on GitHub
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/git/collaboration>
+ - **Lesson video stream**: <https://www.youtube.com/watch?v=vrftiQcqIYM>
+
+## Collaborating with Git
+Kathy Chung, 2017-08-02
+
+#### QUESTIONS TO ASK PARTICIPANTS TO GAUGE KNOWLEDGE LEVEL
+1. How familiar are you with the unix command line? How often do you work with it?
+2. Have you used git before?
+2. On a scale of 1-5 (1=novice/beginner, 5=expert) what kind of git user are you?  (how often do you use git in your work? once a week, once a day, once a month)
+3. Have you used GitHub before?
+4. Have you collaborated on a project on GitHub before?
+
+### Git vs GitHub
+#### Git
+- Git is the command line version control system (VCS) software which works on your local computer.
+- Git was created by Linus Torvalds in 2005 for development of the Linux kernel, with other kernel developers contributing to its initial development.
+- You need Git to use GitHub. You can use Git locally without GitHub.
+
+#### GitHub
+- GitHub is an internet hosting service for git repositories. Public repos are free; private repos are paid.
+- As a shared space for repos, it allows you to do collaborative work.
+
+
+### Two Common Collaborative Work Flows
+#### Shared Repository Model
+- For small projects where you are basically in the same physical space (e.g. lab with offices near each other).
+- Be careful! You are cloning the main repository.
+- Everyone has push and pull access to the central repo, so be careful and:
+  - Never commit to the master directly.
+  - Always do your work on a different branch from master.
+
+#### Basic Shared Repository Workflow
+- update your local repo with `git pull origin master`,
+- create a working branch with `git checkout -b MyNewBranch`
+- make your changes on your branch and stage them with `git add`,
+- commit your changes locally with `git commit -m "description of your commit"`, and
+- upload the changes (including your new branch) to GitHub with `git push origin MyNewBranch`
+- Go to the main repo on GitHub where you should now see your new branch
+- click on your branch name
+- click on "Pull Request" button (URC)
+- click on "Send Pull Request"
+
+
+#### Fork and Pull Model
+- This is the model used by U of T Coders on its own website and repos.
+- The "owner"/"Project Leader" of the upstream repo assigns rights to "Collaborators"
+- Collaborators do not have push access to main (upstream) repo
+- Project Lead accepts Pull Requests (PRs) from collaborators, reviews them, then merges them into main repo.
+
+
+#### Fork and Pull Workflow  
+- to be demonstrated during lesson
+
+
+### Some Git Terminology/Jargon
+#### Repos and Branches
+
+Term | Description
+----- | ------------
+Origin (repo) | Your remote repo (on Github); it is the "origin" for your local copy. Either it is a repo you created yourself or it is a fork of someone else's GitHub repo.
+Upstream (repo)| The main repo (on GitHub) from which you forked your GiHub repo.
+Local (repo) | The repo on your local computer.
+Master | The main branch (version) of your repo.
+
+
+#### Basic Commands/Actions
+
+Term | Explanation
+---| ---
+Fork | Make a copy of someone else's GitHub repo in your own GitHub account.
+Clone | Make a copy of the your GitHub repo on your local computer.  In CLI: 'git clone' copies a remote repo to create a local repo with a remote called `origin` automatically set up.
+Pull | You incorporate changes into your repo.
+Add | Adding snapshots of your changes to the "Staging"  area.
+Commit | Takes the files as they are in your staging area and stores a snap shot of your files (changes) permanently in your Git directory.
+Push | You "push" your files (changes) to the remote repo.
+Merge | Incorporate changes into the branch you are on.
+Pull Request | Term used in collaboration. You "issue a pull request" to the owner of the upstream repo asking them to pull your changes into their repo (accept your work).
+
+
+
+### Configuring git global settings on your local computer
+You only have to do this once; global settings apply to all your git repos.  
+Note: Any Local settings you initiate within individual git repositories will over ride global settings.  
+- username
+- email
+- display colours
+- preferred text editor
+
+
+Command line git syntax is: ```git verb```
+
+>```$ git config --global user.name "Albert Einstein"```  
+>```$ git config --global user.email   "bertie@worlduniversity.edu"```  
+>```$ git config --global color.ui "auto"```  
+
+Use your own name and email address instead of Einstein's. This user name and email will be associated with your subsequent Git activity, which means that any changes pushed to a Git host server will include this information.
+
+For this lesson, we will be interacting with GitHub and so the email address used should be the same as the one used when setting up your GitHub account. If you are concerned about privacy, please review GitHub’s instructions for keeping your email address private. If you elect to use a private email address with GitHub, then use that same email address for the ```user.email``` value, e.g. ```username@users.noreply.github.com``` replacing ```username``` with your GitHub one. You can change the email address later on by using the ```git config``` command again.
+
+Set up your favorite text editor (default editor):
+
+>```$ git config --global core.editor "nano -w"```  
+>```$ git config --global core.editor "atom --wait"```  
+
+See https://swcarpentry.github.io/git-novice/02-setup/ for settings for other text editors
+
+------------------------------------------------------
+## Exercise 1
+### Two Person Collaboration via the CLI - Shared Repo Workflow (without branches)
+This exercise is based on the SWC Git Novice lesson https://swcarpentry.github.io/git-novice/08-collab/
+
+One of you will be the "Owner" and one of you will be the "Collaborator."
+
+#### A. Owner gives collaborator access to their repo.
+1. Go to your GitHub repo
+2. Add a file called "tenlines.txt" and put the text from the etherpad into the file. Commit your changes.
+2. Click on **Settings** tab.
+3. Click **Collaborators**
+4. Enter collaborataors username
+
+#### B. Collaborator clones Owner's Repo
+1. Go to https://github.com/notifications and accept access to Owner's repo.
+2. On the CLI, clone the owner's repo but issuing the commmand:  
+```$ git clone URL-of-Origin-Repo Directory-Address-of-Local-Repo```  
+
+#### C. Collaborator works on clone of Owner's Repo
+Go to your cloned repo:  
+```$ cd ~/.../yourClone```  
+
+Open editor and revise working file:   
+```atom tenlines.txt```  
+
+Commit your changes to your local repo:  
+```$ git add tenlines.txt```  
+```$ git commit -m "your commit message"```  
+
+Push your changes to the Owner's repo on GitHub:  
+```$ git push origin master```  
+
+#### D. Owner review and accepts changes from Collaborator
+Look at Owner's GitHub repo and see new commit(s) from Collaborator.  
+
+Download (pull) Collaborators changes to Owner's local repo:  
+```$ git pull origin master```  
+
+---------------------------------  
+## Exercise 2
+### Dealing with Merge Conflicts
+This section is based on the SWC Git Novice lesson https://swcarpentry.github.io/git-novice/09-conflict/  
+
+When two or more people work on the same files, conflicts are bound to occur. Version control will help notify us when there are conflicts. It will be up to the humans to sort out which changes to retain.  
+
+The file "tenlines.txt" currently looks like this:  
+```$ cat tenlinestxt```  
+
+Let's say **Person A** adds a line to the file and review:  
+```$ atom tenlines.txt```  
+```$ cat tenlines.txt```  
+
+and pushes changes to GitHub:  
+```$ git add tenlines.txt```  
+```$ git commit -m "added a line in local copy and pushed to remote"```  
+```$ git push origin master```  
+
+Now, **Person B** modifies her local file without first updating it (pulling the repo) from GitHub:  
+Add a line to the file and review:  
+```$ atom tenlines.txt```  
+```$ cat tenlines.txt```  
+
+Commit the changes locally:  
+```$ git add tenlines.txt```  
+```$ git commit -m "added a line in local copy"```  
+
+But when we push, Git will not allow this because there were changes to the same line in the two files:  
+```$ git push origin master```  
+
+To resolve the conflict, you need to **pull** the changes from GitHub, **merge** them into your local copy, and then **push** it back to GitHub  
+```$ git pull origin master```  
+
+Git tells us there is a conflict and tells you the file it's in.  
+Let's look at the file:  
+```cat tenlines.txt```  
+
+Git has put some new info in our file:  
+
+```<<<<<<<<<<<<< HEAD```  
+```our text```  
+```========```  
+```Other persons's text```  
+```>>>>>>>>```  
+
+You need to open your text editor and make the changes which is the accepted version by you and your collaborator:  
+```atom tenfiles.txt```  
+
+Then, to finish mergining, you need to **add**, **commit**, and **push** your changes back to GitHub:  
+```$ git add tenlines.txt```  
+
+You can verify the status of your repo first, then commit and push:  
+```$ git status```  
+```$ git commit -m "Merged changes from GitHub"```  
+```$ git push origin master```
+
+Git keeps track that a conflict has been resolved and what was merged into what so when Person A who made the first changes pulls from GitHub, she doesn't have to fix things and merge again.  
+
+Person A pull and sees new version of the file:  
+```$ git pull origina master```  
+```$ cat tenlines.txt```  
+
+---------------------------------
+
+## Exercise 3
+### Solo Practise via the GitHub GUI
+This exercise is based on the 10 mins GitHub "Hello World" tutorial
+https://guides.github.com/activities/hello-world/
+
+#### A. Create a Remote Repo in your GitHub Account
+
+  1. In URC, click **+**, then select **New repository**
+  2. Name your repository ```Kathy's Project```.
+  3. Write a short description.
+  4. Select **Initialize this repository with a README**.
+
+#### B. Create a Branch
+
+1. Click drop down at top of file list that says **branch: master**.
+2. Type a branch name, `readme-edits`, into the new branch text box.
+3. Select the blue **Create branch** box or hit "Enter" on your keyboard. Notice you are now on the code view of your `readme-edits` branch, which is a copy of `master`.
+
+#### C. Make and commit changes
+You are now on your `readme-edits` branch.  
+
+  1. Select the `README.md` file.
+  2. Click on the pencil icon (URC) of the file view.
+  3. Edit the file. Write something about yourself and your project.
+  4. Write a commit message at bottom of screen.
+  5. Click the **Commit changes** button.
+
+#### D. Open a Pull Request (PR)
+
+1. Click on the **Pull Request** TAB, which takes you to the PR page
+2. Click on the green **New Pull Request** button.
+3. Select the branch you made, `readme-edits`, to compare with the original, `master`.
+4. Review your work.
+5. Name your pull request and give it a brief description. You can use @mention in your description to ask for feedback by specific persons.
+6. Click on the green **Create Pull Request** button.
+
+#### E. Merge your Pull Request
+You will merge your  `readme-edits` branch into your `master` branch.  
+
+1. Click on the green **Merge pull request** button.
+2. Click **Confirm merge**.
+3. You can now delete the branch
+
+--------------------------------
+
+## Exercise 4
+### Two Person Practise via GitHub GUI  
+This exercise is based on the Mozilla Science WOW lesson on GitHub for Collaborating on Open Projects   
+http://mozillascience.github.io/working-open-workshop/github_for_collaboration/  
+
+One of you is the Project Lead. The other will be the Contributor.
+
+#### A. Project Lead: Create a Remote Repo in your GitHub Account
+
+  1. In URC, click **+**, then select **New repository**
+  2. Name your repository ```Kathy's Project```.
+  3. Write a short description.
+  4. Select **Initialize this repository with a README**.\
+  5. Give your partner the URL to your repo.
+
+#### B. Project Lead: Add a File
+1. Click **New file**
+2. Give the file a name.
+3. Put some content in the file.
+4. Write a commit message at bottom of screen.
+5. Click the **Commit changes** button.
+
+#### C. Project Lead: Make a Label
+1. Click on **Issues** tab.
+2. Click on the **Labels** box.
+3. Add a label called ".............."
+
+#### D. Project Lead: Make an Issue
+1. Click on **New Issue** button.
+2. Give it a title and brief description. You can use @mention in your description to ask for specific feedback.
+3. Give it a label.
+
+#### E. Contributor: Comment on an Issue
+1. Go to the PL's repo.
+2. Click on the **issues** button to see all the issues.
+3. Select the one you wish to respond to.
+4. Make a comment, introduce yourself and volunteer to work on the issue.
+
+#### F. Project Lead: Reply to a comment
+1. From your own repo, select the commented issue.
+2. Write a reply.
+3. Assign the issue to yourself so you can monitor it and answer questions, etc.
+
+#### H. Contributor: Fork and Branch
+1. Go to your own GitHub account.
+2. Find the Lead's repo and fork it.
+3. Create a branch in your forked repo (see previous Solo practise for instructions)
+4. Give the branch a name which indicates the feature or change you're working on.
+
+#### I. Contributor: Do Work and Commit
+1. Make changes in this branch.
+2. When you are done, write a commit message, and click on the **Commit changes** button.
+
+#### J. Contributor: Make a Pull Request to the Upstream Repo
+1. Select **Pull Request** tab.
+2. Click on **New Pull Request**
+3. Make sure you've selected the correct branch where you've made your edits.
+4. Name your pull request and provide a brief description (you might wish to include a reference to the related issue #).
+5. Click on the green **Create Pull Request** button.
+
+#### K. Project Lead: Review the Pull Request
+1. Review changes made by contributor.
+2. You can send comments back and forth to discuss the work. Thank your contributor.
+
+#### L. Project Lead: Merge the changes
+1. Click on the **Merge pull request** button.
+2. Close the issue; additional remarks and thanks.
+
+----------------------------------
+
+## Miscellaneous Tasks
+#### Create a Local Git Repo (via CLI)
+1. Go to the directory which you wish to make into a Git repo: `$ cd ~/GitRepos/MyProject`  
+2. Initialize the directory as a Git repo: `git init`.
+3. Confirm that it worked by looking for the hidden `.git` file in your directory: `ls -a`
+
+
+#### Forking Someone Else's Repo (via GitHub website)
+1. Go to someone else's repo and click on the **fork** button on the URC.
+2. Wait while GitHub does its work.
+3. Check that you now have a new repo in your GitHub account.
+
+
+#### Cloning a Repo onto Your Local computer (via CLI)
+```git clone URL-of-Origin-Repo Directory-Address-of-Local-Repo```  
+e.g.  
+```git clone https://github.com/chungkky/2017-08-02_Collaborating-with-Git.git ~/GitHubRepros_KC/2017-08-02_Collaborating-with-Git```  
+
+--------------
+## Userful Resources
+* Git Cheat Sheet (CLI) https://services.github.com/resources/cheatsheets/
+* Visualizing Git Concepts with D3 https://onlywei.github.io/explain-git-with-d3/
+* Collaborative Development Models/Workflows https://help.github.com/articles/about-collaborative-development-models/
+* SWC's Version Control with Git Lesson (CLI) https://swcarpentry.github.io/git-novice/
+* Mozilla Science Working Open Workshop (WOW) lesson GitHub for Collaborating on Open Projects https://mozillascience.github.io/working-open-workshop/github_for_collaboration/
+* The Pro Git Book https://git-scm.com/book/en/v2
+* Atlassian has several good tutorials on Git:
+  * Index of All Tutuorials https://www.atlassian.com/git/tutorials  
+  * Getting Started https://www.atlassian.com/git/tutorials/setting-up-a-repository
+  * Collaborating https://www.atlassian.com/git/tutorials/syncing
+  * Git Workflows https://www.atlassian.com/git/tutorials/comparing-workflows
+
+
+---------------
+
+## Collaborating with Git
+**Serena Jeblee, 2017-02-22**   
+
+## Overview
+
+- A brief review of basic git usage
+- Some helpful git commands
+- Help! I've made a mistake! (aka how to unstage changes and undo commits)
+- Merge conflicts
+- Branches
+- Forking and Pull Requests
+
+If we have time:
+- Stashing changes
+- Rebasing
+
+## Whirlwind Overview of GitHub
+- What is GitHub?
+- What is version control?
+    - version control helps you collaborate on code especially when your changes might overlap
+    - master repo vs local repos
+    - master - shared version stored on GitHub or a server
+    - local - your local copy that you can play with and make changes to, only seen by you until you push your changes
+- How to contribute code
+~~~
+git clone [repo-url]
+git pull # syncs latest version from the master repo to your local repo
+[edit the code]
+git add [file] # stages code for commit (git remove does not unstage, it stages a deletion, we'll get to unstaging in a bit)
+git commit # create a record of the changes you've made
+git push # sync your local changes to the master repo
+~~~
+- Fork and pull request if you can't push directly
+- Some rules for good commits
+    - commit should represent one change or a small set of related changes (like a bug fix or new feature)
+    - all committed code should be functional and tested
+    - make sure the commit message is descriptive
+
+## Get Started
+- Create helloworld.sh
+~~~
+echo "Fancy code will go here"
+echo "More fancy code"
+~~~
+- Run it and commit it
+
+## Help! I messed up and now nothing works!
+- Add junk code to hello.sh and run it
+- Undo the changes to a file
+~~~
+git checkout -- [file]
+~~~
+- Unstage the changes, but keep them
+~~~
+git reset --soft HEAD
+~~~
+- Undo the changes and go back x commits
+~~~
+git reset --hard HEAD~x
+~~~
+
+## Oops, I didn't mean to commit that
+- Add the junk code back and commit it
+- Undo a local commit
+~~~
+git reset --soft HEAD~1 # keeps changes, but undoes the commit
+~~~
+- Pushed commits - do a revert commit
+~~~
+git revert [commit-hash]
+~~~
+
+## The Dreaded Merge Conflict
+- Clone a new copy of your repo
+- Change a line and commit it
+- Go back to your original repo
+- Change same line to "My version is better!"
+- Commit it
+~~~
+git commit
+git pull
+~~~
+- Resolve the merge conflict, delete >>> <<< lines, add and commit
+
+## Branches
+- Branches allow you to have different commit histories in the same repo
+- Useful for feature work
+- For more info see: <https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/git/branches/lesson.md>
+
+## Forking and Pull Requests
+- Used when you don't have direct push access to a repo
+- Fork: creates your own version of the repo that you can commit to
+- When you're finished committing all of your changes, create a pull request
+- Pull Request: tells the main repo that you have some commits you would like to merge, and allows others to review them first
+
+## Reviewing Code
+- Look for:
+  - errors (typos, potential infinite loops, concurrency issues, etc)
+  - overly complicated code
+  - unclear variable names
+  - missing comments or documentation
+- Provide constructive suggestions for how to fix it
+
+## Updating the Pull Request
+- Try to address all comments, if you're not going to change something, say why not
+- Make a new commit and push it to the branch your pull request is on
+
+## Merging the Pull Request
+- Requires push access to the repo
+- If there are conflicts, you'll have to check out the pull request and resolve them, otherwise you can merge online
+- For more info on how to merge a pull request, see <https://help.github.com/articles/merging-a-pull-request/>
diff --git a/lessons/git/intro/2024_Git_Reproducibility.ipynb b/lessons/git/intro/2024_Git_Reproducibility.ipynb
new file mode 100644
index 000000000..b08f8f099
--- /dev/null
+++ b/lessons/git/intro/2024_Git_Reproducibility.ipynb
@@ -0,0 +1,895 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reproducibility, the BASH console and GIT\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Reproducibility: What is it, What does it mean, Why it's important\n",
+    "\n",
+    "The concept of reproducibility is just that \\- **is your work reproducible**? Reproduction of methods, analyses and results is important not only in scientific research but also in day\\-to\\-day activities. It is a major factor that determines if **research results are consistent and valid** If your work is reproducible, then **valid experiments and results build authentic discoveries that are more reliable** in your field and help develop robust literature for future research studies. While reproducibility ensures that your work is accurate and reliable, it can also **encourage community involvement and participation**. _**Remember, if your foundation is shaky then so is all the work built upon it**_\n",
+    "\n",
+    "<p align=\"center\"> \n",
+    "    <img src=\"./Figures/Reproducibility.jpeg\"   width=\"596.667px\"  height=\"459.661px\" /> \n",
+    "</p>\n",
+    "\n",
+    "There are different fields of reproducibility :\n",
+    "\n",
+    "1. **Scientific** Reproducibility \\- **Can your results be reproduced from your methods?**\n",
+    "2. **Computational** Reproducibility \\- **Can your results be reproduced from your code?**\n",
+    "3. **Statistical** Reproducibility \\- **Are your methods robust enough to reproduce your results?**\n",
+    "\n",
+    "While talk of reproducibility is all good and dandy, it may not be available to everyone. There are significant barriers to reproducibility including:\n",
+    "\n",
+    "- **Costs**\n",
+    "- The structure of dissemination does not currently fully support reproducibility\n",
+    "- **Lack of training and incentives**\n",
+    "- **Takes time, held to a higher standard**\n",
+    "- Privacy and security\n",
+    "- Differences between fields\n",
+    "\n",
+    "Your job as a scientist and a researcher is to try your best to ensure that your work is reproducible. **If the ability is beyond your means, do not hesitate to say so.** Science is about being open and transparent\n",
+    "\n",
+    "There are many tools and conventions to help with your reproducibility journey. These include having a proper workflow and file organization system, as well as GIT for version control\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Workflows\n",
+    "\n",
+    "One way to ensure reproducibility is to have a well\\-organized project workflow and management. A common way to organize your files is by **project i.e. a project\\-oriented workflow**. Here, each project that you are working on is **self\\-contained in its own folder**, with its **different data types in different folders**. I.e. your raw data should be in a different folder than your processed data, documents and files should be in another folder etc. \n",
+    "\n",
+    "<p align=\"center\"> \n",
+    "    <img src=\"./Figures/File Organization.png\"   width=\"596.667px\"  height=\"459.661px\" /> \n",
+    "    <br>\n",
+    "    <em> Note the differences between the first pane and the other two. The first pane, different file types are in the same folder which makes it difficult to find and filter through projects. In the second pane, all the projects are sorted into their own respective folders and with their own subdirectories based on their analysis or function. </em>\n",
+    "</p>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## File Paths\n",
+    "\n",
+    "Having a default file structure can also help with your own mental organization of your file paths. There are two types of file paths: **absolute and relative**. \n",
+    "\n",
+    "### Absolute File Paths\n",
+    "\n",
+    "Absolute file paths **start at your root file and mention every directory before arriving at your file**. For example, if I wanted to get the absolute file path of the CSV in the above figure, it would look something like this: `/Users/yeshoda/Desktop/Projects/Salt Tolerance/Chlamy sample - Sheet1.csv`, Where `Chlamy sample - Sheet1.csv` is the name of my file, which is located in the `Salt Tolerance/` folder. This folder is in the `Projects/` folder which is on my `Desktop/` \\(also a folder\\), within my user folder \\(`yeshoda/`\\), within the`Users/` folder in the **root** \\(`/`\\). The / **denotes a directory \\(also known as a folder\\)**. Absolute file paths are useful when trying to find files you're looking for, however, **they are sensitive to breaking**. For example, if I moved `Projects/` to `Documents/` rather than `Desktop/` my path will **no longer work and will cause an error**. \n",
+    "\n",
+    "### Relative File Paths\n",
+    "\n",
+    "Relative file paths are **relative to the current directory that you are working in**. Your current directory will be noted with a ./ and the parent directory will be denoted with ../ For example if I am in the `Figures/` directory and wish to go to the `References/` directory I can type: `cd ../References/.` Here I'm using the `cd` command which stands for **change directory**, and then specify I want to move into my parent folder Salt Tolerance \\(`../`\\) and then into `References/.` The benefit of using absolute paths is that now if I moved `Projects/` to `Documents/` rather than `Desktop/` **my path still works**. \n",
+    "\n",
+    "**There is no right or wrong file path format**! **Choose the one that works for you and for the type of project that you have.** If you know the hierarchy of your folders, relative paths might serve better. However, if you do not know the hierarchy but know where your file is in general then an absolute path might serve better. \n",
+    "\n",
+    "<p align=\"center\"> \n",
+    "    <img src=\"./Figures/File_Paths.jpeg\"   width=\"596.667px\"  height=\"459.661px\" /> \n",
+    "    <br>\n",
+    "    <em> devopsschool </em>\n",
+    "</p>\n",
+    "\n",
+    "Just like variable names, **it helps to name your folders something meaningful.** Folders called `1/` and `2/` do not do much to help you when you need to find your files.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BASH - Quick Recap\n",
+    "\n",
+    "Before we dive into GIT, let's refresh ourselves with some commonly used BASH commands. BASH is the typical Unix shell and command language for your computers. You can use BASH on your terminals. For your note\\-keeping, we can use BASH within a Python notebook! We just need to tell Python, hey! this cell is just going to run BASH \\- don't run me with Python! **We can do this by using a magic command denoted with** **`%%`**`. By running %%bash`, we're telling Python to run this entire cell in BASH. **It must be the first line in the cell or it will not work**. Let's try it out:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "UsageError: %%bash is a cell magic, but the cell body is empty.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash # denotes the start of a magic cell"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Amazing! Now let's go ahead and play with some commonly used commands:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/princess/Downloads/10_reproducibility\n",
+      "BINF5503_Week10_Class\n",
+      "Figures\n",
+      "Master_week10_module5_Reproducibility.html.html\n",
+      "Master_week10_module5_Reproducibility.html.ipynb\n",
+      "Master_week10_module5_Reproducibility.html.pdf\n",
+      "STUDENT_week10_module5_Reproducibility.html.ipynb\n",
+      "STUDENT_week10_module5_Reproducibility.html_Master.ipynb\n",
+      "UofTCoders_git_lesson_old\n",
+      "UofT_Coders_Master_Reproducibility.ipynb\n",
+      "week10_git_lesson\n",
+      "total 3208\n",
+      "drwxr-xr-x@ 14 princess  staff      448 14 Nov 16:01 BINF5503_Week10_Class\n",
+      "drwxr-xr-x@ 15 princess  staff      480 14 Nov 16:42 Figures\n",
+      "-rw-rw-r--@  1 princess  staff    83583 13 Mar  2024 Master_week10_module5_Reproducibility.html.html\n",
+      "-rw-rw-r--@  1 princess  staff    35238  2 Apr  2024 Master_week10_module5_Reproducibility.html.ipynb\n",
+      "-rw-rw-r--@  1 princess  staff  1397902 13 Mar  2024 Master_week10_module5_Reproducibility.html.pdf\n",
+      "-rw-rw-r--@  1 princess  staff    29500  2 Apr  2024 STUDENT_week10_module5_Reproducibility.html.ipynb\n",
+      "-rw-rw-r--@  1 princess  staff    24648 20 Mar  2024 STUDENT_week10_module5_Reproducibility.html_Master.ipynb\n",
+      "drwxr-xr-x   7 princess  staff      224 14 Nov 16:58 UofTCoders_git_lesson_old\n",
+      "-rw-r--r--   1 princess  staff    54711 14 Nov 16:59 UofT_Coders_Master_Reproducibility.ipynb\n",
+      "drwxr-xr-x@  6 princess  staff      192 14 Nov 16:01 week10_git_lesson\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "hello.txt: No such file or directory\n",
+      "cat: hello.txt: No such file or directory\n",
+      "mv: rename hello.txt to ../hello.txt: No such file or directory\n",
+      "mv: rename ../hello.txt to ./hello_there.txt: No such file or directory\n",
+      "cp: hello_there.txt: No such file or directory\n",
+      "rm: hello_there.txt: No such file or directory\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "pwd # path to working directory. Gives you the absolute file path of where you are\n",
+    "ls # list. Will list all of the files in your directory\n",
+    "ls -l # long list. Gives the long list of all the files in the directory\n",
+    "mkdir temp/ # make directory. This makes a new folder\n",
+    "cd temp/ # change directory. Will move \"you\" into the folder/directory you specify\n",
+    "# vim hello.txt # open the vim text editor and make a note\n",
+    "# hit i to go into insert mode, and start typing. Once done, hit esc, :wq to save and quit\n",
+    "less hello.txt # view the file contents\n",
+    "cat hello.txt # prints the contents of the file to standard out\n",
+    "mv hello.txt ../ # move hello.txt into the parent directory\n",
+    "mv ../hello.txt ./hello_there.txt # move hello.txt back to the current directory and rename it to hello_there.txt\n",
+    "cp hello_there.txt hello_there_copy.txt # copy. Make a copy of the file\n",
+    "rm hello_there.txt # remove. Delete the file\n",
+    "\n",
+    "cd ../ # go to parent directory\n",
+    "rm -r temp/ #delete the temp folder and all it's contents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Git\n",
+    "\n",
+    "Now for the program of the hour! Git is a version control system that can be used to track file changes. While most commonly thought to be used for coding files only, **it can also be used for any file** including documents, figures, presentations etc. It's a great way to edit your files but also be able to **revert to a previous version if the need arises**. Used in tandem with **GitHub**, it can be a great way **to easily collaborate on projects with others**. It is important to note that you can use Git without having a GitHub account, however you cannot use GitHub without Git. \n",
+    "\n",
+    "<p align=\"center\"> \n",
+    "    <img src=\"./Figures/Version_control.jpeg\"   width=\"400px\"  height=\"300px\" /> \n",
+    "</p>\n",
+    "\n",
+    "### Configure git global settings on your local computer\n",
+    "\n",
+    "Before we start we need to tell git who we are so that we know who is making the files. We can do this using the `git config` commands\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "\n",
+    "# first tell git your email. Use the email you have when you set up your github account\n",
+    "git config --global user.email \"y.harrypaul@yahoo.com\"\n",
+    "\n",
+    "# Next your name\n",
+    "git config --global user.name \"Yeshoda Harry-Paul\"\n",
+    "\n",
+    "# Change the name of the branch from master to main\n",
+    "git config --global init.defaultBranch main"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Local git repo\n",
+    "\n",
+    "Let's make a local git repository. A repository **\\(or repo for short\\)** is a storage area for a project containing all the files for the project and the history of the changes made to it. **This repo will be on your local computer.** \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/princess/Downloads/10_reproducibility/UofTCoders_git_lesson\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash \n",
+    "# Make a new folder that we want to use:\n",
+    "mkdir UofTCoders_git_lesson\n",
+    "cd UofTCoders_git_lesson\n",
+    "pwd\n",
+    "ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initialized empty Git repository in /Users/princess/Downloads/10_reproducibility/UofTCoders_git_lesson/.git/\n",
+      "total 0\n",
+      "drwxr-xr-x   3 princess  staff   96 14 Nov 17:00 .\n",
+      "drwxr-xr-x@ 23 princess  staff  736 14 Nov 17:00 ..\n",
+      "drwxr-xr-x   9 princess  staff  288 14 Nov 17:00 .git\n",
+      "total 24\n",
+      "drwxr-xr-x   9 princess  staff  288 14 Nov 17:00 .\n",
+      "drwxr-xr-x   3 princess  staff   96 14 Nov 17:00 ..\n",
+      "-rw-r--r--   1 princess  staff   23 14 Nov 17:00 HEAD\n",
+      "-rw-r--r--   1 princess  staff  137 14 Nov 17:00 config\n",
+      "-rw-r--r--   1 princess  staff   73 14 Nov 17:00 description\n",
+      "drwxr-xr-x  14 princess  staff  448 14 Nov 17:00 hooks\n",
+      "drwxr-xr-x   3 princess  staff   96 14 Nov 17:00 info\n",
+      "drwxr-xr-x   4 princess  staff  128 14 Nov 17:00 objects\n",
+      "drwxr-xr-x   4 princess  staff  128 14 Nov 17:00 refs\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash \n",
+    "# Initialize the git repository\n",
+    "cd UofTCoders_git_lesson\n",
+    "git init\n",
+    "ls -la # we see the new hidden .git folder\n",
+    "ls -la .git"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first_file.txt\n",
+      "On branch master\n",
+      "\n",
+      "No commits yet\n",
+      "\n",
+      "Untracked files:\n",
+      "  (use \"git add <file>...\" to include in what will be committed)\n",
+      "\tfirst_file.txt\n",
+      "\n",
+      "nothing added to commit but untracked files present (use \"git add\" to track)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "cd UofTCoders_git_lesson\n",
+    "# Now that we have our git repo, lets add a file to it\n",
+    "touch first_file.txt\n",
+    "ls\n",
+    "\n",
+    "# Let's see how git is doing by checking it's status\n",
+    "git status\n",
+    "\n",
+    "# git has noticed some untracked changes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hold your horses! The staging environment, the commit and you\n",
+    "\n",
+    "Before we get going on our git train, it's important to differentiate between your working directory, the staging environment and a commit. Commits are r**ecords of the changes you've last made since a commit**. These are what allow you to jump between the different stages of your project. However, git does not commit on its own, **you need to tell git what you want to commit**. To do this we first need to **add the file to the staging area** via the command `git add`. Once you've made a nice package of files in the staging area, you can then **commit your changes to your repo** in the `.git` repository. Let's see what this looks like\n",
+    "\n",
+    "<p align=\"center\"> \n",
+    "    <img src=\"./Figures/git_staging_area.png\"   width=\"700px\"  height=\"400px\" /> \n",
+    "</p>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "On branch master\n",
+      "\n",
+      "No commits yet\n",
+      "\n",
+      "Changes to be committed:\n",
+      "  (use \"git rm --cached <file>...\" to unstage)\n",
+      "\tnew file:   first_file.txt\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "cd UofTCoders_git_lesson\n",
+    "\n",
+    "# Now let's add our file to the staging environment\n",
+    "git add first_file.txt\n",
+    "git status # We see now that our file isn't committed but it is about to be"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[master (root-commit) 5061b3f] Created empty file first_file.txt\n",
+      " 1 file changed, 0 insertions(+), 0 deletions(-)\n",
+      " create mode 100644 first_file.txt\n",
+      "On branch master\n",
+      "nothing to commit, working tree clean\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "cd UofTCoders_git_lesson\n",
+    "\n",
+    "# Now let's commit our file. We have the option to also add a message using the -m flag. It is always beneficial to write something useful so that when you are looking back between your versions it is easy to find what you're looking for \n",
+    "git commit -m \"Created empty file first_file.txt\" first_file.txt\n",
+    "git status # We see now that our file isn't committed but it is about to be"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "cd UofTCoders_git_lesson\n",
+    "\n",
+    "# Great! You just created your first commit! Let's see what happens when we edit our file to add some information to it!\n",
+    "\n",
+    "#edit file in vim\n",
+    "\n",
+    "# git status #see that the file has been modified\n",
+    "\n",
+    "# git diff first_file.txt # what was different from the last commit?\n",
+    "\n",
+    "# git add first_file.txt #stage our changes\n",
+    "# git status \n",
+    "\n",
+    "# git commit -m \"Added some filler text to file\" \n",
+    "# git status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "commit 5061b3f9de53b181a14f495c6f7ee45c07e2d763\n",
+      "Author: Yeshoda Harry-Paul <y.harrypaul@yahoo.com>\n",
+      "Date:   Thu Nov 14 17:01:02 2024 -0500\n",
+      "\n",
+      "    Created empty file first_file.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "cd UofTCoders_git_lesson\n",
+    "\n",
+    "#let's see what we've done so far\n",
+    "git log"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Branching in Git\n",
+    "\n",
+    "Branching is a great way to work on a new idea, bug fixes or developing features **without affecting your** **`main`** **branch**. They are designed to be **temporary** and to facilitate edits and trials without breaking your original codebase. They allow you to keep your original `main` branch as is. For example, say I had a website and I wanted to make a new webpage. I can branch off of my main branch, create my webpage and then merge once it is completed. This way my website still stays functional as I work on my new webpage. \n",
+    "\n",
+    "<p align=\"center\"> \n",
+    "    <img src=\"./Figures/branches.png\"   width=\"700px\"  height=\"400px\" /> \n",
+    "</p>\n",
+    "\n",
+    "There are two phases of branch creation: 1**\\) Making a branch and 2\\) Moving to that branch**. Both of these steps can be combined into one but let us take it one at a time. First, let's make a new branch called new\\_files. Just like commits and variable names, it helps to have meaningful branch names so that you know what that branch is meant to be working on \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "commit 5061b3f9de53b181a14f495c6f7ee45c07e2d763\n",
+      "Author: Yeshoda Harry-Paul <y.harrypaul@yahoo.com>\n",
+      "Date:   Thu Nov 14 17:01:02 2024 -0500\n",
+      "\n",
+      "    Created empty file first_file.txt\n",
+      "On branch master\n",
+      "nothing to commit, working tree clean\n",
+      "* master\n",
+      "  new_branch\n",
+      "  master\n",
+      "* new_branch\n",
+      "commit 5061b3f9de53b181a14f495c6f7ee45c07e2d763\n",
+      "Author: Yeshoda Harry-Paul <y.harrypaul@yahoo.com>\n",
+      "Date:   Thu Nov 14 17:01:02 2024 -0500\n",
+      "\n",
+      "    Created empty file first_file.txt\n",
+      "On branch new_branch\n",
+      "nothing to commit, working tree clean\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Switched to branch 'new_branch'\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "cd UofTCoders_git_lesson\n",
+    "\n",
+    "git branch new_branch\n",
+    "git log\n",
+    "git status # notice that while we made a branch our status has not changed\n",
+    "git branch --list\n",
+    "\n",
+    "# move to that branch\n",
+    "git checkout new_branch\n",
+    "git branch --list\n",
+    "\n",
+    "git log\n",
+    "git status # now our branch has changed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "On branch new_branch\n",
+      "Untracked files:\n",
+      "  (use \"git add <file>...\" to include in what will be committed)\n",
+      "\tsecond_file.txt\n",
+      "\n",
+      "nothing added to commit but untracked files present (use \"git add\" to track)\n",
+      "On branch new_branch\n",
+      "Untracked files:\n",
+      "\tsecond_file.txt\n",
+      "\n",
+      "nothing added to commit but untracked files present\n",
+      "[new_branch 9a7d541] adding second file\n",
+      " 1 file changed, 0 insertions(+), 0 deletions(-)\n",
+      " create mode 100644 second_file.txt\n",
+      "first_file.txt\n",
+      "* master\n",
+      "  new_branch\n",
+      "Updating 5061b3f..9a7d541\n",
+      "Fast-forward\n",
+      " second_file.txt | 0\n",
+      " 1 file changed, 0 insertions(+), 0 deletions(-)\n",
+      " create mode 100644 second_file.txt\n",
+      "Deleted branch new_branch (was 9a7d541).\n",
+      "On branch master\n",
+      "nothing to commit, working tree clean\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Switched to branch 'master'\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "cd UofTCoders_git_lesson\n",
+    "\n",
+    "# Let's make some changes to our files\n",
+    "\n",
+    "# edit first_file.txt\n",
+    "\n",
+    "cp first_file.txt second_file.txt # make a copy called second_file.txt\n",
+    "git status\n",
+    "\n",
+    "# edit the second_file.txt\n",
+    "\n",
+    "git commit -a  -m \"Added another line of text about the weather to file\" #Notice how it only added changes and committed for the first_file.txt as git was watching it\n",
+    "\n",
+    "# Let's add and commit the other files\n",
+    "git add * # the * will add all the files in the current folder at once, rather than having to type them all individually\n",
+    "git commit -m \"adding second file\"\n",
+    "\n",
+    "# now let's merge our branch back to the main one\n",
+    "# First head back to the main branch\n",
+    "git checkout master\n",
+    "ls\n",
+    "\n",
+    "git branch --list\n",
+    "\n",
+    "# Merge your branch\n",
+    "git merge new_branch\n",
+    "\n",
+    "# Delete the old branch since there's no use for it anymore \n",
+    "git branch -d new_branch\n",
+    "\n",
+    "git status"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Going back to a previous commit\n",
+    "\n",
+    "Sometimes we had versions of our code that worked better or documents that were worded more eloquently, but we no longer have them as we've modified the same file. With Git, **as long as the file was committed** we can revert to that version. Let's play around with it:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[master 3168bd8] adding temporary file to delete\n",
+      " 1 file changed, 0 insertions(+), 0 deletions(-)\n",
+      " create mode 100644 temp.txt\n",
+      "first_file.txt\n",
+      "second_file.txt\n",
+      "On branch master\n",
+      "Changes not staged for commit:\n",
+      "  (use \"git add/rm <file>...\" to update what will be committed)\n",
+      "  (use \"git restore <file>...\" to discard changes in working directory)\n",
+      "\tdeleted:    temp.txt\n",
+      "\n",
+      "no changes added to commit (use \"git add\" and/or \"git commit -a\")\n",
+      "On branch master\n",
+      "nothing to commit, working tree clean\n",
+      "[master b3f5db5] added temp2.txt\n",
+      " 1 file changed, 0 insertions(+), 0 deletions(-)\n",
+      " create mode 100644 temp2.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "cd UofTCoders_git_lesson\n",
+    "\n",
+    "# Make a temporary file and commit it\n",
+    "touch temp.txt\n",
+    "git add temp.txt\n",
+    "git commit -m \"adding temporary file to delete\"\n",
+    "\n",
+    "# Remove the file and update git\n",
+    "rm temp.txt\n",
+    "ls # Check that our file is deleted\n",
+    "git status # while we deleted the file locally, it is still in our repo\n",
+    "# git rm temp.txt # delete from git repo\n",
+    "# git restore --staged temp.txt # can pull our file back if didn't commit\n",
+    "git restore temp.txt\n",
+    "git commit -m \"deleted temporary file\"\n",
+    "\n",
+    "# Make some minor changes\n",
+    "touch temp2.txt\n",
+    "git add temp2.txt \n",
+    "git commit -m \"added temp2.txt\"\n",
+    "\n",
+    "## Revert back to the previous commit\n",
+    "# Get the commit ID\n",
+    "# git log --oneline\n",
+    "# git revert --no-commit 79c4a61..HEAD #use the --no-commit flag to avoid an automatic commit \n",
+    "# ls\n",
+    "# git commit -m \"reverting back to other commit with temp.txt\"\n",
+    "\n",
+    "## Revert but keep changes, can also do with the one above ^ however this one will delete the commits following the one you want to go to\n",
+    "# git reset --soft 8e4a7e0\n",
+    "# git restore --staged <file>\n",
+    "# git restore <file>\n",
+    "# git commit -m \"message\"\n",
+    "# git log"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Integrating GitHub\n",
+    "\n",
+    "Now that we have some of the basics under our belt let's push something to GitHub. First we need to create an access token to be able to push our work to a repository on GitHub. \n",
+    "\n",
+    "Click on your profile picture on Github and Navigate to `Settings`. Once there scroll all the way down to `Developer settings`. Next navigate to `Personal Access Tokens` > `Tokens Classic` > `Generate New Token`. Select all the permissions you would like this key to be able to access. Once you generate your key **KEEP IT SOMEWHERE SAFE**. You will not be able to view it again. \n",
+    "\n",
+    "<p align=\"center\"> \n",
+    "    <img src=\"./Figures/Personal Access Tokens.png\"   width=\"300px\"  height=\"300px\" /> \n",
+    "</p>\n",
+    "\n",
+    "\n",
+    "Now that we have our key we can create a new repo on GitHub. We can do this by logging in, pressing the plus button (create new) and selecting \"New Repository\"\n",
+    "\n",
+    "<p align=\"center\"> \n",
+    "    <img src=\"./Figures/GitHub Repo.png\"   width=\"700px\"  height=\"400px\" /> \n",
+    "</p>\n",
+    "\n",
+    "Next we can go ahead and name our repository and add a brief description. Once that is done go ahead and press \"Create repository\". \n",
+    "\n",
+    "<p align=\"center\"> \n",
+    "    <img src=\"./Figures/Naming_repo.png\"   width=\"700px\"  height=\"400px\" /> \n",
+    "</p>\n",
+    "\n",
+    "GitHub gives us the option of pushing an existing repo from the commandline. Let's go ahead and push our week10_git_lesson folder to our GitHub:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "cd UofTCoders_git_lesson\n",
+    "\n",
+    "git remote add origin https://github.com/YeshodaHP/UofTCoders_Git_Lesson_2024.git\n",
+    "git branch -M main\n",
+    "git push -u origin main\n",
+    "\n",
+    "#This will prompt you to input your username and password (Now using token)\n",
+    "# To generate a token go to settings > developer settings > Personal access tokens > Generate new token"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Great! Now we can see our files and commits on our GitHub! Note that now when we want to place our files onto GitHub (the remote directory), we now have to `push` it there. Let's try creating a file and pushing it to our GitHub\n",
+    "\n",
+    "<p align=\"center\"> \n",
+    "    <img src=\"./Figures/git_workflow.png\"   width=\"700px\"  height=\"400px\" /> \n",
+    "</p>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You should now be able to see your new file in your repo!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "cd week10_git_lesson\n",
+    "\n",
+    "#Make a new file, add and commit\n",
+    "vim github.txt\n",
+    "git add github.txt \n",
+    "git commit -m \"Adding new github.txt file\"\n",
+    "\n",
+    "# now push it to GitHub\n",
+    "git push "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Collaborative Repos\n",
+    "Great! Now that we've pushed files to GitHub let's try to use it in a collaborative way. There are two common collaborative workflows:\n",
+    "1. The Shared Repository Workflow\n",
+    "\t- Everyone works on their own branch and then merges to the main\n",
+    "2. The Fork and Pull Workflow\n",
+    "\t- The project owner can assign rights to \"collaborators\" which do not have push access to the main branch. The project lead will accept pull requests (PRs), review and accept changes made\n",
+    "    \n",
+    "Let's see how the shared repo workflow looks!\n",
+    "\n",
+    "### Shared Repo\n",
+    "Let's try creating a file with your name as the file name, containing something fun about you. Let's push it to Yeshoda's GitHub. First we will need to clone her repo located at: https://github.com/YeshodaHP/UofTCoders_Git_Lesson_2024.git. Next create a branch with your name, create your file, commit and push!\n",
+    "\n",
+    "Remember to always pull before you start to do your work so that you're on the most recent branch!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%bash\n",
+    "git clone https://github.com/YeshodaHP/UofTCoders_Git_Lesson_2024.git #clone the repo\n",
+    "\n",
+    "git fetch #fetch new work done by others - it does not merge with your branch\n",
+    "\n",
+    "git merge #merge the work you've done with others\n",
+    "\n",
+    "git pull # will pull all the online work and merge it with your local work. Make sure you commit your own work before pulling \n",
+    "\n",
+    "#Create a branch with your name as the branch and add a new file with your name and something fun about you!\n",
+    "git branch Yeshoda_HP\n",
+    "git checkout Yeshoda_HP\n",
+    "#vim Yeshoda_HP.txt\n",
+    "git add Yeshoda_HP.txt\n",
+    "git commit -m \"Yeshoda is adding her file\"\n",
+    "git push --set-upstream origin Yeshoda\n",
+    "\n",
+    "#go back to the main branch and delete your branch\n",
+    "git checkout main\n",
+    "git branch -d Yeshoda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Recap\n",
+    "Amazing Job! So far today we learned about :\n",
+    "- Reproducibility and why it is important\n",
+    "- File hierarchies\n",
+    "- The BASH console and some common commands\n",
+    "- An intro to Git\n",
+    "\t- Where we learned to make a local repository \n",
+    "    - How to commit changes\n",
+    "    - Create branches \n",
+    "    - How to revert to previous commits\n",
+    " - GitHub:\n",
+    " \t- How to connect and clone\n",
+    "    - How to collaborate with other users. \n",
+    "    \n",
+    "We did a lot of Git today, and we know there is a lot of jargon surrounding git so here is a breif recap:\n",
+    "\n",
+    "| Term      | Description |\n",
+    "| ----------- | ----------- |\n",
+    "| Add | Adding your changes to the “Staging” area|\n",
+    "| Branch | A parallel version of a repository where changes can be made that do not affect the main repository. New branches are often used to test changes or new ideas, which can later be merged with the base branch|\n",
+    "| Checkout | Moving from one branch to another|\n",
+    "| Clone | Make a copy of the your GitHub repo on your local computer|\n",
+    "| Commit | Takes the files in your staging area and store their changes permanently in your Git directory|\n",
+    "| Head | Head is a reference or pointer of the latest commit on your branch, there can only be one head at a time. You can move the head to a different commit by using checkout - this is known as a detatched head |\n",
+    "| Merge | Incorporate changes into the branch you are on|\n",
+    "| Pull | A combination of fetch and merge. This fetches the files from the remote repo and merges them with your local files|\n",
+    "| Pull Request | Term used in collaboration. You “issue a pull request” to the owner of the upstream repo asking them to pull your changes into their repo (accept your work)|\n",
+    "| Push | Sync your file changes to the remote repo on GitHub|\n",
+    "\n",
+    "As always if you have any questions feel free to email both Yeshoda and Vicki! Happy coding, see you next week!\n"
+   ]
+  }
+ ],
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+---
+layout: page
+title: '(Brief) Introduction to Git + GitHub'
+visible: true
+tags:
+  - git
+  - beginner
+---
+
+ - **Authors**: Luke Johnston
+ - **Research field**: Nutritional and diabetes epidemiology
+ - **Lesson topic**: Version control (Git)
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/git/intro/>
+ - **Intro to Git slides (2015)**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/git/intro/slides.Rmd>
+ - **Philosophy of Git slides (2016)**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/git/intro/philosophy_of_git.pdf>
+ - **Lesson video stream (2016)**: <https://www.youtube.com/watch?v=JbUeTXP05XQ>
+ - **Intro to Git slides (2017)**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/git/intro/slides-2017.Rmd>
+
+## Preface: ##
+
+Version control is a system that manages changes to a file or files.
+These changes are kept as logs in a history, with detailed information
+on what file(s) was changed, what was changed within the file, who
+changed it, and a message on why the change was made.  This is
+extremely useful, especially when working in teams or for yourself 6
+months in the future (because you *will* forget things)!
+
+To understand how incredibly powerful version control is, think about
+these questions: How many files of different versions of a manuscript
+or thesis do you have laying around after getting feedback from your
+supervisor or co-authors? Have you ever wanted to experiment with your
+code or your manuscript and need to make a new file so that the
+original is not touched? Have you ever deleted something and wish you
+hadn't? Have you ever forgotten what you were doing on a project?  All
+these problems are fixed by using version control (git)!
+
+We are going to go over a typical workflow.  This could be either a
+solo workflow or a collaborative workflow.
+
+## Checklist: ##
+
+* Configure your git
+* Create a folder and create a git repository (`git init`; the saved
+  history of the folder and files) in that folder
+* Create a file and track it with git (`git add`), saving it to the
+  history (`git commit`)
+* Write a short bio in the file
+* Check what's going on in the folder (`git status`)
+* Compare the file with the one in the history (`git diff`)
+* Add the tracked file to the 'staging' area (`git add`; this is an
+  area *before* going into the history)
+* Save the file in the history (`git commit`)
+* Look into your history (`git log`)
+
+(If we have time):
+
+* Create a GitHub account and create a repository
+* Set the URL of the new GitHub repository to your repository on your
+  computer (`git remote`; the command is usually provided from GitHub)
+* Upload your repository on your computer (called local repository)
+  up to your GitHub repository (called the remote repository; use `git
+  push`)
+* Download from the remote to the local repository (`git pull`)
+
+**Tips**:
+
+* Make use of TAB-completion in the terminal!
+* Up arrow on the terminal goes to the previous command you entered.
+
+## Lesson topics and commands ##
+
+For configuring your git, follow the "Initial setup" I've put
+together:
+
+* http://codeasmanuscript.org/lessons/git/cheatsheet/
+
+Create a folder and create a git repository (which is the stored
+history) in that folder. (Note: `##` is a comment, not a command).
+
+    cd ~/Desktop ## Move to your desktop
+    mkdir playing ## Create a folder (aka directory)
+    cd playing
+    git init ## Create the repository (init = initialize)
+
+Now, create a file, get git to track it, and save it to the history.
+
+    touch bio.txt ## Command to create a file called bio.txt
+    ls ## Check that you created the file, ls = list files
+    git add bio.txt ## Track the file
+    ## Save the file to the history with a message (-m)
+    git commit -m "Initial commit"
+
+Now, open the `bio.txt` file and add:
+
+* Your name
+* Your program and year
+* Your progamming language/statistical language of choice
+
+Then:
+
+    git status ## Check the activity
+    git diff bio.txt ## Compare to the one in the history
+    git add bio.txt ## This sends it to the staging area
+    git commit -m "Added my bio" ## This sends it to the history
+
+For a description on what the different stages are (working directory,
+staging area, and committed history) see the below links:
+
+* Description: https://git-scm.com/book/en/v2/Getting-Started-Git-Basics
+* Image: https://git-scm.com/book/en/v2/book/01-introduction/images/areas.png
+
+Then, to see what has happened in your history:
+
+    git log ## View the log of your history
+
+*If we have time*, we'll create a GitHub account, create a GitHub
+repository (a remote repository), and upload the repository on your
+computer (called the local repository) onto the remote repository
+(GitHub):
+
+    git remote add origin https://github.com/yourusername/playing.git
+    git push origin master
+    git pull
+
+Now you know about a typical workflow!  There are **lots** of commands
+and options in git, you really can do almost anything.  *However*,
+these are the basic tools that are used most frequently.  If you have
+any questions, please check out
+[StackOverflow](https://stackoverflow.com/questions/tagged/git) for
+*literally* any question on or about Git!!  Or just google it! Google
+usually shows answers from StackOverflow.
+
+# Resources: #
+
+## Glossary: ##
+
+* `cd` - change directory
+* directory - the same thing as a folder
+* `ls` - list the files and folders in a folder
+* `touch` - create an empty file
+* repository - the saved history of a folder and files, used by git.
+* `init` - start or initialize a git repository
+* `add` - put a file into the staging area, so that git starts
+  tracking it
+* staging/index area - where files are stored before going into the
+  history
+* `commit` - send files in the staging/index area into the history
+  (the git repository)
+* `status` - check the status of the folder and the git repository
+* `diff` - compare a file to the a file in the history
+* `log` - view the commit history in the git repository
+
+## Links: ##
+
+Here are a few great resources (excluding StackOverflow) to use if you
+ever need help:
+
+* [Hands-on tutorial, with web-based terminal](https://try.github.io/levels/1/challenges/1)
+* [Official git documentation](https://git-scm.com/doc)
+* [Simpler first-steps guide](https://rogerdudler.github.io/git-guide/)
+* [Reference pages for all git commands](http://gitref.org/)
+* [Interactive, visual tutorial on branching](https://pcottle.github.io/learnGitBranching/)
+
diff --git a/lessons/git/intro/philosophy_of_git.pdf b/lessons/git/intro/philosophy_of_git.pdf
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+## <- this is a comment
+
+## Configure your local git setup. This only needs to be done once.
+git config --global user.name "Your Name"
+git config --global user.email "you@some.domain"
+git config --global color.ui "auto"
+git config --global core.editor "your_editor"
+git config --global push.default current
+
+## List all the configurations completed
+git config --list
+
+## cd = change directory (move to another folder)
+cd ~/Desktop
+
+## mkdir = make directory (create a folder)
+mkdir practice2/
+
+## First, fork the 'sandbox' repo from the UofTCoders group.  Forking
+## just means copy over into your own account.
+
+## clone = copy the repo from GitHub to your computer
+git clone https://github.com/your-account-name/sandbox
+cd sandbox
+
+## Those with internet problems can do this: create a folder called
+## sandbox, move into that folder, and create an empty git repo (init)
+## init = initialize a git repo (start an empty git repo in that folder)
+mkdir sandbox && cd sandbox && git init
+
+## ls = list files and folders in the current location
+ls
+
+## pwd = print working directory (see what your current location is)
+pwd
+
+## touch = create a new text file
+touch your-name-bios.txt
+
+## Edit the file and add stuff to it
+
+## See the content of the file
+cat your-name-bios.txt
+
+## add = track and put the file into the git staging area (staging
+## just means ready to be saved into the history, but not actually
+## saved)
+git add your-name-bios.txt
+
+## commit = save everything in the staging area into the history (the
+## git repo).  -m = the commit message to be included in the history.
+## Try to be descriptive with this message.
+git commit -m "Added my bio"
+
+## log = check the saved history
+git log
+
+## Make some edits to the bio file
+
+## status = check what is happening in non-saved files.  This shows
+## things that have been modified or are untracked.
+git status
+
+## diff = compare the contents of the current file with the
+## (essentially) previous commit.
+git diff your-name-bios.txt
+
+## When you add a filename to the commit command, git will save that
+## file directly into the history. This bypasses the staging area.
+git commit your-name-bios.txt -m "Added a line to my bio"
+
+## push = upload your git repo to GitHub, basically syncing it.
+git push
+
+## remote add = set another GitHub to download from.  This remote is
+## called 'upstream'.  Having this remote allows you to keep
+## up-to-date with the original repo.
+git remote add upstream https://github.com/UofTCoders/sandbox
+
+## -v = verbose (show a detailed description of your remote
+## -locations/urls)
+git remote -v
+
+## pull = download.  This syncs your local repo with the original
+## repo.
+git pull upstream master
+
+## Sync your local repo with your own forked repo on your GitHub
+## account (not the original).
+git push
+
+## Make edits and commit to the history.
+git commit your-name-bios.txt -m "Added line on type of analysis"
+
+## Upload and sync with your forked repo.
+git push
+
+## Check your history of the saved commits.
+git log
+
+## checkout = go back, forward, or around to different commits (or
+## branches) in your commit history.  c1361 is the (completely!!)
+## unique commit hashtag (you can see the hash when you do git log; no
+## one will have the same hash!). -- = anything after this is a file.
+## So basically, this command will take the file from c1361 and bring
+## it into your current location... basically reviving a file from the
+## dead!
+git checkout c1361 -- your-name-bios.txt
+
+## You can confirm that the file is different from before!
+cat your-name-bios.txt
+
diff --git a/lessons/git/intro/practice-code-2016-05-19.sh b/lessons/git/intro/practice-code-2016-05-19.sh
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index 000000000..fae39b9dd
--- /dev/null
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+## <- this is a comment
+
+## general command line tips
+## use tab completion: when you start typing something, hitting 'tab' will finish it for you.
+## use the 'up' arrow to cycle through past commands (to avoid re-typing)
+## for Git commands, typing git <command> --help will show a help page
+
+## Configure your local git setup. This only needs to be done once. Only the first two are essential.
+git config --global user.name "Your Name"
+git config --global user.email "you@some.domain"
+git config --global color.ui "auto" 
+git config --global core.editor "your_editor"
+
+## List all the configurations completed
+git config --list
+
+## cd = change directory (move to another folder)
+## note: "cd .." moves you back one folder
+cd ~/Documents
+
+## mkdir = make directory (create a folder)
+mkdir GitHub
+
+## move to the GitHub folder
+cd GitHub
+
+## First, fork the 'studyGroup' repo from the UofTCoders group.  Forking
+## just means copy over into your own account. Click "fork" in the top right corner.
+## url: https://github.com/UofTCoders/studyGroup
+
+## clone = copy the repo from GitHub to your computer
+## In the terminal (still in the GitHub folder), type:
+git clone https://github.com/your-account-name/studyGroup
+cd studyGroup
+
+## Those with internet problems can do this: create a folder called
+## sandbox, move into that folder, and create an empty git repo (init)
+## git init = initialize a git repo (start an empty git repo in that folder)
+mkdir sandbox && cd sandbox && git init
+
+## ls = list files and folders in the current location
+ls
+
+## pwd = print working directory (see what your current location is)
+pwd
+
+## git status = check the status of your folder - see if there are any changes.
+git status
+
+## move into the _data folder, see what's there
+cd _data
+ls
+
+## cat = prints to the screen the contents of a file (also used to join files together)
+cat members.yml
+
+## Add yourself to the members list!
+## typing "nano filename" opens the file in the editor nano. 
+## you can use any editor you like, such as atom, notepad, vim, etc.
+nano members.yml
+
+## git status = check the status of your repository
+## you'll see a changed file
+git status
+
+## add = track and put the file into the git staging area (staging
+## just means ready to be saved into the history, but not actually
+## saved)
+git add members.yml
+git status ## just to see what happened
+
+## commit = save everything in the staging area into the history (the
+## git repo).  -m = the commit message to be included in the history.
+## Try to be descriptive with this message.
+git commit -m "Added myself to the members list"
+
+## log = check the saved history
+git log
+git status ## should be all clean again
+
+## Make some more edits to the members file
+nano members.yml
+git status
+
+## diff = compare the contents of the current file with the
+## (essentially) previous commit.
+git diff members.yml
+
+## When you add a filename to the commit command, git will save that
+## file directly into the history. This bypasses the staging area.
+git commit members.yml -m "Added a line to my bio"
+
+## Check your history of the saved commits.
+git log
+
+## Viewing history: git checkout
+
+## checkout = go back, forward, or around to different commits (or
+## branches) in your commit history.  c1361 is the (completely!!)
+## unique commit hashtag (you can see the hash when you do git log; no
+## one will have the same hash!). 
+
+## open a file browser and look at the _members.yml file. 
+## run the command below (with the right commit id) and then look at the file again.
+git checkout c1361 ## this takes you back in time to the state at this commit id. 
+
+## you are now in the legendary DETACHED HEAD state.
+## If you make any changes and commit them here, you'll need to
+## create a branch so that you can find them back later. 
+## Otherwise they'll eventually disappear forever.
+## git checkout --help has details about this. 
+
+## -- = anything after this is a file.
+## So basically, this command will take the file from c1361 and bring
+## it into your current location... reviving a file from the
+## dead! This takes ONLY the file members.yml, and no other changes. 
+git checkout c1361 -- members.yml
+
+git status ## will show the members.yml file as modified, 
+## since now you have a different version than the last commit.
+
+## If you want to go back to the state after the latest commit:
+git checkout -- members.yml 
+## since we didn't specify a commit, this assumes the latest commit, and give you that version
+
+## If you like the old version better:
+git commit -m "I'm keeping the old version"
+
+## push = upload your git repo to GitHub, basically syncing it.
+git push
+
+## remote add = set another GitHub to download from.  This remote is
+## called 'upstream'.  Having this remote allows you to keep
+## up-to-date with the original repo.
+git remote add upstream https://github.com/UofTCoders/studyGroup
+
+## -v = verbose (show a detailed description of your remote
+## -locations/urls)
+git remote -v
+
+## pull = download.  This syncs your local repo with the original
+## repo.
+git pull upstream master
+
+## Sync your local repo with your own forked repo on your GitHub
+## account (not the original).
+git push
+
diff --git a/lessons/git/intro/practice-code-2017-10-05.sh b/lessons/git/intro/practice-code-2017-10-05.sh
new file mode 100644
index 000000000..51965ea40
--- /dev/null
+++ b/lessons/git/intro/practice-code-2017-10-05.sh
@@ -0,0 +1,103 @@
+## <- this is a comment
+
+## general command line tips
+## use tab completion: when you start typing something, hitting 'tab' will finish it for you.
+## use the 'up' arrow to cycle through past commands (to avoid re-typing)
+## for Git commands, typing git <command> --help will show a help page
+
+## Configure your local git setup. This only needs to be done once. Only the first two are essential.
+git config --global user.name "Your Name"
+git config --global user.email "you@some.domain"
+
+## other optional configurations
+git config --global color.ui "auto" 
+
+## setup an editor for text files (if you need one)
+## you can use any editor you like, such as atom, notepad, vim, etc.
+## more help on setting up editors
+## https://help.github.com/articles/associating-text-editors-with-git/
+git config --global core.editor "your_editor"
+
+## List all the configurations completed
+git config --list
+
+## cd = change directory (move to another folder)
+## note: "cd .." moves you back one folder
+cd ~/Documents
+## for windows users with full path
+cd C:/Users/user_name/Documents
+
+## mkdir = make directory (create a folder)
+mkdir git-training
+
+## move to the new folder
+cd git-training
+
+## convert folder to git repo
+git init
+
+## check remote configuration of new git repo
+git remote -v
+
+## setup new remote for empty remote
+git remote add origin https://github.com/UofTCoders/git-training.git
+
+## check remote configuration again
+git remote -v
+
+## once the text file is created and saved as my_cred.txt in new repo
+## check file status
+git status
+
+## let us add this new file to the git index
+git add my_cred.txt
+
+## check changed file status again
+git status
+
+## commit new file to be tracked by git
+git commit -m "initial commit with my info"
+
+## if remote is set and you have an internet connection
+## push changes to remote repo
+git push -u origin master
+
+## create another repo on github to test cloning/copying function of git
+## let us call this repo sandbox-2017
+cd ~/Documents
+
+## clone using 
+git clone https://github.com/UofTCoders/sandbox-2017.git
+cd sandbox-2017
+
+## see all files in directory
+ls
+
+## open file example_file.md in notepad or any other text editor of your choice
+## you can do so from the command line using
+notepad example_file.md
+
+## after saving and closing file add file to git index and commit
+git add example_file.md
+git commit -m "add one new line to file"
+
+## using diff to visualize differences between commits
+git diff example_file.md
+
+## check all the commits made to this repo
+git log
+
+## get ore info a cleaner format
+git log --online
+
+## chaneck remote version
+git remote -v
+
+## push changes to remote repo
+git push origin 
+
+
+
+
+
+
diff --git a/lessons/git/intro/slides-2017.Rmd b/lessons/git/intro/slides-2017.Rmd
new file mode 100644
index 000000000..094bcb2f5
--- /dev/null
+++ b/lessons/git/intro/slides-2017.Rmd
@@ -0,0 +1,307 @@
+---
+title: "Intro to Git"
+author: "Shyam Srinivasan"
+date: "October 5, 2017"
+output: beamer_presentation
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = FALSE)
+```
+
+# Learning expectations
+
+This won't even touch the surface of git... But! Hopefully, you:
+
+- Recognize the power of using version control
+- Know the basic tools to get started using git
+- Where to go for help
+
+But! No expectation to actually start using it :)
+
+# What is Git?
+![](https://imgs.xkcd.com/comics/git.png)
+
+# Getting started with Git and GitHub
+
+Create an account on GitHub
+
+* You set your username, email and password here
+
+## Windows/MacOS
+https://desktop.github.com/
+
+* Make sure you install posh-git along with GitHub
+
+## Linux
+
+* Download and install Git for Ubuntu/Debian/Mint Linux
+`sudo apt-get install git`
+
+* Download and install Git for Fedora Linux
+`sudo dnf install git`
+
+# Configuring your setup:
+
+```bash
+git config --global user.name "Your Name"
+git config --global user.email "you@some.domain"
+```
+
+Optional Configurations
+```bash
+git config --global color.ui "auto" 
+```
+
+Setup Editor for Text Files
+
+```bash
+git config --global core.editor "your_editor"
+git config --list # To confirm
+```
+
+# Creating a Repo on Github
+The cloud (GitHub) serves as an online repo where files are accessible 24x7
+
+* Go to [GitHub](https://github.com/)
+
+* Create a new repo by pressing the `New Repository` button
+
+* Let us keep the name short and self explanatory and call it 'git-training' 
+
+* We will call this the `remote` repo from here on
+
+# Creating a Git Repo
+* change directory (move to another folder) using 
+```bash 
+cd
+```
+* to move back one folder use
+```bash 
+cd ..
+```
+* to make a new directory
+```bash
+mkdir
+```
+
+# Let us create a `local` Repo
+```bash
+cd ~/Documents
+# for windows users with full path
+cd C:/Users/user_name/Documents
+# create a new folder 
+# use the same name as your remote repo on GitHub
+mkdir git-training
+# move to the new folder
+cd git-training
+```
+## Convert folder to **git** repo
+```bash
+git init
+```
+## check your folder to find git configuration files/folders
+This folder is now a git repo (short for repository)
+
+# Using GitHub with Git
+If you have GitHub (on Windows/MacOS), you can add the repository you cloned/created to it and visualize the changes
+
+Remember, you installed this earlier?
+
+# Configuring your repo for use with GitHub on the cloud
+In order to use this local repo with the one you created earlier we need to set the remote configuration
+```bash
+git remote -v
+```
+will tell you what is the remote corresponding to your local repo
+
+If it is empty, you can setup a remote using 
+```bash
+git remote add remote_name remote_url
+```
+
+Before you do this, you need to have an account on GitHub and a repo on it
+
+# Configuring your repo for use with GitHub on the cloud
+* Copy the repo url from the repo creation page and use it in
+```bash
+git remote add origin 
+https://github.com/UofTCoders/git-training.git
+```
+
+* Check your remote configuration again
+```bash
+git remote -v
+```
+
+# Adding new files to a repo
+* create a text file with
+    + your name
+    + program
+    + research area
+* save the file as `my_cred` in the git repo you just created
+
+# Visualize changes to files
+You can see the changes you made to this file in GitHub
+\begin{block}
+{New files} on GitHub can be seen with a \textcolor{green}{green '+'} to their right 
+\end{block}
+
+This indicates the file is new and is `untracked` by git
+
+\begin{block}
+{Old modified files} can be seen with a \textcolor{gray}{gray circle} to their right
+\end{block}
+
+This indicates the file is already `tracked` and there are `uncommitted` changes
+```bash
+git status
+``` 
+can give information on `tracked`, `untracked` and `indexed` files
+
+# Adding Files to git index
+* add the file to the git index
+```bash
+git add my_cred.txt
+```
+* check file status again 
+```bash
+git status
+```
+Notice the file has changed from \textcolor{red}{red} to \textcolor{green}{green}
+
+* let us `commit` this change to repo history 
+```bash
+git commit -m "initial commit with my info"
+```
+* Now your file is `tracked` by git
+
+# `push` to your new repo on GitHub
+You can move all your **committed** changes to the remote repo
+```bash
+git push origin
+```
+Since this is the first time we are doing this, we need to 
+establish a link between your local repo and remote repo. 
+
+you can do this using
+```bash
+git push -u origin master
+```
+Make sure your `remote` is set before you do this
+
+# Cloning an Existing `git` repo
+We can copy or clone existing repositories from GitHub using
+```bash
+git clone remote_repo_url
+```
+Let us do this for the [sandbox repo](https://github.com/UofTCoders/sandbox-2017.git) I have created
+
+Select the green `Clone or Download` button and select ` copy to clipboard`
+
+In your shell/bash move to a directory of your choice and clone the repo
+```bash
+cd ~/Documents
+# if you are using git posh (in powershell) 
+# use the right click to paste the copied url
+git clone 
+https://github.com/UofTCoders/sandbox-2017.git
+```
+
+# Modify a existing file and commit changes
+Go to the directory containing the example file
+```bash
+cd sandbox-2017
+# see all files in the directory
+ls
+```
+Open file `example_file.md` in any text editor
+
+To open files in bash/shell
+```bash
+editor_name file_name
+notepad example_file.md
+```
+add a new line to this file starting with an asterisk(*)
+
+save file and close
+
+# Add changes to index and commit changes to repo
+We will use the previously used commands to `add` and `commit` files
+```bash
+git add example_file.md
+git commit -m "add one new line to file"
+```
+
+# Visualize changes to file
+You can use
+```bash
+git diff example_file.md
+```
+in the command line
+
+Note the file has to be `tracked` and 
+the changes have to be `committed` to use `git diff`
+
+You can also visualize all commits to the repo using
+```bash
+git log
+```
+
+commit information can be obtained in a cleaner format using 
+```bash
+git log --online
+```
+
+# `push` commits to the remote repo
+Let us first check the `remote` configuration
+```bash
+git remote -v
+```
+Notice that the remote is already set
+
+```bash
+git push origin
+```
+will push your `local` changes to the `remote` repo
+
+# Discarding changes in your repo
+Discard \textcolor{red}{unstaged} \textcolor{gray}{Tracked} files using
+```bash
+git checkout .
+```
+Discard \textcolor{red}{unstaged} unTracked files using
+```bash
+git clean -f
+```
+Use this command with caution:
+
+Discard \textcolor{green}{staged} and \textcolor{red}{unstaged} Tracked changes using
+```bash
+git reset --hard
+```
+
+# Summary of Commands covered today
+```bash
+git init
+git clone
+git config
+git remote
+git status
+git diff
+git add
+git commit
+git push
+git pull
+git checkout
+git clean
+```
+
+
+
+
+
+
+
+
+
diff --git a/lessons/git/intro/slides-2017.pdf b/lessons/git/intro/slides-2017.pdf
new file mode 100644
index 000000000..bf5fd32cd
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diff --git a/lessons/git/intro/slides.Rmd b/lessons/git/intro/slides.Rmd
new file mode 100644
index 000000000..0749aa6b7
--- /dev/null
+++ b/lessons/git/intro/slides.Rmd
@@ -0,0 +1,46 @@
+---
+title: "Let's git started :)"
+author: "Luke Johnston"
+date: "`r format(Sys.Date(), '%B %d, %Y')`"
+output: slidy_presentation
+---
+
+## Learning expectations
+
+This won't even touch the surface of git... But! Hopefully, you:
+
+- Recognize the power of using version control
+- Know the basic tools to get started using git
+- Where to go for help
+
+But! No expectation to actually start using it :)
+
+## Configuring your setup:
+
+```bash
+git config --global user.name "Your Name"
+git config --global user.email "you@some.domain"
+git config --global color.ui "auto" 
+git config --global core.editor "your_editor"
+git config --list # To confirm
+```
+
+## 4 main concepts (and ~7 commands)
+
+- **Start repository**: `git init`, `git clone` (GitHub)
+- **Check activity**: `git status`, `git log`, `git diff`
+- **Save to history**: `git add`, `git commit`
+- **Move through the history**: `git checkout`, `git branch` (may be covered)
+
+## Different areas in Git
+
+![](https://git-scm.com/book/en/v2/book/01-introduction/images/areas.png)
+
+## 4 areas to remember
+
+Using `git status` shows much of this information already.
+
+- Untracked files: Use `git add`
+- Tracked but modified: Use `git add` or `git commit filename`
+- Staged but not committed: Use `git commit`
+- Committed (in the repository): Use `git checkout` to access
diff --git a/lessons/git/intro/slides.html b/lessons/git/intro/slides.html
new file mode 100644
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+<?xml version="1.0" encoding="utf-8" ?>
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
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+<head>
+  <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+  <meta http-equiv="Content-Style-Type" content="text/css" />
+  <meta name="generator" content="pandoc" />
+  <meta name="author" content="Luke Johnston" />
+  <meta name="date" content="2018-01-17" />
+  <title>Let’s git started :)</title>
+  <style type="text/css">code{white-space: pre;}</style>
+  <style type="text/css">
+div.sourceCode { overflow-x: auto; }
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+code > span.al { color: #ff0000; font-weight: bold; } /* Alert */
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+code > span.er { color: #ff0000; font-weight: bold; } /* Error */
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   http://www.w3.org/Consortium/Legal/copyright-documents
   http://www.w3.org/Consortium/Legal/copyright-software

   Defines single name "w3c_slidy" in global namespace
   Adds event handlers without trampling on any others
*/

// the slidy object implementation
var w3c_slidy = {
  // classify which kind of browser we're running under
  ns_pos: (typeof window.pageYOffset!='undefined'),
  khtml: ((navigator.userAgent).indexOf("KHTML") >= 0 ? true : false),
  opera: ((navigator.userAgent).indexOf("Opera") >= 0 ? true : false),
  ipad: ((navigator.userAgent).indexOf("iPad") >= 0 ? true : false),
  iphone: ((navigator.userAgent).indexOf("iPhone") >= 0 ? true : false),
  android: ((navigator.userAgent).indexOf("Android") >= 0 ? true : false),
  ie: (typeof document.all != "undefined" && !this.opera),
  ie6: (!this.ns_pos && navigator.userAgent.indexOf("MSIE 6") != -1),
  ie7: (!this.ns_pos && navigator.userAgent.indexOf("MSIE 7") != -1),
  ie8: (!this.ns_pos && navigator.userAgent.indexOf("MSIE 8") != -1),
  ie9: (!this.ns_pos && navigator.userAgent.indexOf("MSIE 9") != -1),

  // data for swipe and double tap detection on touch screens
  last_tap: 0,
  prev_tap: 0,
  start_x: 0,
  start_y: 0,
  delta_x: 0,
  delta_y: 0,

  // are we running as XHTML? (doesn't work on Opera)
  is_xhtml: /xml/.test(document.contentType),

  slide_number: 0, // integer slide count: 0, 1, 2, ...
  slide_number_element: null, // element containing slide number
  slides: [], // set to array of slide div's
  notes: [], // set to array of handout div's
  backgrounds: [], // set to array of background div's
  observers: [], // list of observer functions
  toolbar: null, // element containing toolbar
  title: null, // document title
  last_shown: null, // last incrementally shown item
  eos: null,  // span element for end of slide indicator
  toc: null, // table of contents
  outline: null, // outline element with the focus
  selected_text_len: 0, // length of drag selection on document
  view_all: 0,  // 1 to view all slides + handouts
  want_toolbar: true,  // user preference to show/hide toolbar
  mouse_click_enabled: true, // enables left click for next slide
  scroll_hack: 0, // IE work around for position: fixed
  disable_slide_click: false,  // used by clicked anchors

  lang: "en", // updated to language specified by html file

  help_anchor: null, // used for keyboard focus hack in showToolbar()
  help_page: "http://www.w3.org/Talks/Tools/Slidy2/help/help.html",
  help_text: "Navigate with mouse click, space bar, Cursor Left/Right, " +
             "or Pg Up and Pg Dn. Use S and B to change font size.",

  size_index: 0,
  size_adjustment: 0,
  sizes:  new Array("10pt", "12pt", "14pt", "16pt", "18pt", "20pt",
                    "22pt", "24pt", "26pt", "28pt", "30pt", "32pt"),

  // needed for efficient resizing
  last_width: 0,
  last_height: 0,


  // Needed for cross browser support for relative width/height on
  // object elements. The work around is to save width/height attributes
  // and then to recompute absolute width/height dimensions on resizing
   objects: [],

  // attach initialiation event handlers
  set_up: function () {
    var init = function() { w3c_slidy.init(); };
    if (typeof window.addEventListener != "undefined")
      window.addEventListener("load", init, false);
    else
      window.attachEvent("onload", init);
  },

  hide_slides: function () {
    if (document.body && !w3c_slidy.initialized)
      document.body.style.visibility = "hidden";
    else
      setTimeout(w3c_slidy.hide_slides, 50);
  },

  // hack to persuade IE to compute correct document height
  // as needed for simulating fixed positioning of toolbar
  ie_hack: function () {
    window.resizeBy(0,-1);
    window.resizeBy(0, 1);
  },

  init: function () {
    //alert("slidy starting test 10");
    document.body.style.visibility = "visible";
    this.init_localization();
    this.add_toolbar();
    this.wrap_implicit_slides();
    this.collect_slides();
    this.collect_notes();
    this.collect_backgrounds();
    this.objects = document.body.getElementsByTagName("object");
    this.patch_anchors();
    this.slide_number = this.find_slide_number(location.href);
    window.offscreenbuffering = true;
    this.size_adjustment = this.find_size_adjust();
    this.time_left = this.find_duration();
    this.hide_image_toolbar();  // suppress IE image toolbar popup
    this.init_outliner();  // activate fold/unfold support
    this.title = document.title;
    this.keyboardless = (this.ipad||this.iphone||this.android);

    if (this.keyboardless)
    {
      w3c_slidy.remove_class(w3c_slidy.toolbar, "hidden")
      this.want_toolbar = 0;
    }

    // work around for opera bug
    this.is_xhtml = (document.body.tagName == "BODY" ? false : true);

    if (this.slides.length > 0)
    {
      var slide = this.slides[this.slide_number];
   
      if (this.slide_number > 0)
      {
        this.set_visibility_all_incremental("visible");
        this.last_shown = this.previous_incremental_item(null);
        this.set_eos_status(true);
      }
      else
      {
        this.last_shown = null;
        this.set_visibility_all_incremental("hidden");
        this.set_eos_status(!this.next_incremental_item(this.last_shown));
      }

      this.set_location();
      this.add_class(this.slides[0], "first-slide");
      w3c_slidy.show_slide(slide);
    }

    this.toc = this.table_of_contents();

    this.add_initial_prompt();

    // bind event handlers without interfering with custom page scripts
    // Tap events behave too weirdly to support clicks reliably on
    // iPhone and iPad, so exclude these from click handler

    if (!this.keyboardless)
    {
      this.add_listener(document.body, "click", this.mouse_button_click);
      this.add_listener(document.body, "mousedown", this.mouse_button_down);
    }

    this.add_listener(document, "keydown", this.key_down);
    this.add_listener(document, "keypress", this.key_press);
    this.add_listener(window, "resize", this.resized);
    this.add_listener(window, "scroll", this.scrolled);
    this.add_listener(window, "unload", this.unloaded);

    this.add_listener(document, "gesturechange", function ()
    {
      return false;
    });

    this.attach_touch_handers(this.slides);

    // this seems to be a debugging hack
    //if (!document.body.onclick)
    //  document.body.onclick = function () { };

    this.single_slide_view();

    //this.set_location();

    this.resized();

    if (this.ie7)
      setTimeout(w3c_slidy.ie_hack, 100);

    this.show_toolbar();

    // for back button detection
    setInterval(function () { w3c_slidy.check_location(); }, 200);
    w3c_slidy.initialized = true;
  },

  // create div element with links to each slide
  table_of_contents: function () {
    var toc = this.create_element("div");
    this.add_class(toc, "slidy_toc hidden");
    //toc.setAttribute("tabindex", "0");

    var heading = this.create_element("div");
    this.add_class(heading, "toc-heading");
    heading.innerHTML = this.localize("Table of Contents");

    toc.appendChild(heading);
    var previous = null;

    for (var i = 0; i < this.slides.length; ++i)
    {
      var title = this.has_class(this.slides[i], "title");
      var num = document.createTextNode((i + 1) + ". ");

      toc.appendChild(num);

      var a = this.create_element("a");
      a.setAttribute("href", "#(" + (i+1) + ")");

      if (title)
        this.add_class(a, "titleslide");

      var name = document.createTextNode(this.slide_name(i));
      a.appendChild(name);
      a.onclick = w3c_slidy.toc_click;
      a.onkeydown = w3c_slidy.toc_key_down;
      a.previous = previous;

      if (previous)
        previous.next = a;

      toc.appendChild(a);

      if (i == 0)
        toc.first = a;

      if (i < this.slides.length - 1)
      {
        var br = this.create_element("br");
        toc.appendChild(br);
      }

      previous = a;
    }

    toc.focus = function () {
      if (this.first)
        this.first.focus();
    }

    toc.onmouseup = w3c_slidy.mouse_button_up;

    toc.onclick = function (e) {
      e||(e=window.event);

      if (w3c_slidy.selected_text_len <= 0)
         w3c_slidy.hide_table_of_contents(true);

      w3c_slidy.stop_propagation(e);
    
      if (e.cancel != undefined)
        e.cancel = true;
      
      if (e.returnValue != undefined)
        e.returnValue = false;
      
      return false;
    };

    document.body.insertBefore(toc, document.body.firstChild);
    return toc;
  },

  is_shown_toc: function () {
    return !w3c_slidy.has_class(w3c_slidy.toc, "hidden");
  },

  show_table_of_contents: function () {
    w3c_slidy.remove_class(w3c_slidy.toc, "hidden");
    var toc = w3c_slidy.toc;
    toc.focus();

    if (w3c_slidy.ie7 && w3c_slidy.slide_number == 0)
      setTimeout(w3c_slidy.ie_hack, 100);
  },

  hide_table_of_contents: function (focus) {
    w3c_slidy.add_class(w3c_slidy.toc, "hidden");

    if (focus && !w3c_slidy.opera &&
        !w3c_slidy.has_class(w3c_slidy.toc, "hidden"))
      w3c_slidy.set_focus();
  },

  toggle_table_of_contents: function () {
    if (w3c_slidy.is_shown_toc())
      w3c_slidy.hide_table_of_contents(true);
    else
      w3c_slidy.show_table_of_contents();
  },

  // called on clicking toc entry
  toc_click: function (e) {
    if (!e)
      e = window.event;

    var target = w3c_slidy.get_target(e);

    if (target && target.nodeType == 1)
    {
      var uri = target.getAttribute("href");

      if (uri)
      {
        //alert("going to " + uri);
        var slide = w3c_slidy.slides[w3c_slidy.slide_number];
        w3c_slidy.hide_slide(slide);
        w3c_slidy.slide_number = w3c_slidy.find_slide_number(uri);
        slide = w3c_slidy.slides[w3c_slidy.slide_number];
        w3c_slidy.last_shown = null;
        w3c_slidy.set_location();
        w3c_slidy.set_visibility_all_incremental("hidden");
        w3c_slidy.set_eos_status(!w3c_slidy.next_incremental_item(w3c_slidy.last_shown));
        w3c_slidy.show_slide(slide);
        //target.focus();

        try
        {
          if (!w3c_slidy.opera)
            w3c_slidy.set_focus();
        }
        catch (e)
        {
        }
      }
    }

    w3c_slidy.hide_table_of_contents(true);
    if (w3c_slidy.ie7) w3c_slidy.ie_hack();
    w3c_slidy.stop_propagation(e);
    return w3c_slidy.cancel(e);
  },

  // called onkeydown for toc entry
  toc_key_down: function (event) {
    var key;

    if (!event)
      var event = window.event;

    // kludge around NS/IE differences 
    if (window.event)
      key = window.event.keyCode;
    else if (event.which)
      key = event.which;
    else
      return true; // Yikes! unknown browser

    // ignore event if key value is zero
    // as for alt on Opera and Konqueror
    if (!key)
      return true;

    // check for concurrent control/command/alt key
    // but are these only present on mouse events?

    if (event.ctrlKey || event.altKey)
      return true;

    if (key == 13)
    {
      var uri = this.getAttribute("href");

      if (uri)
      {
        //alert("going to " + uri);
       var slide = w3c_slidy.slides[w3c_slidy.slide_number];
        w3c_slidy.hide_slide(slide);
        w3c_slidy.slide_number = w3c_slidy.find_slide_number(uri);
        slide = w3c_slidy.slides[w3c_slidy.slide_number];
        w3c_slidy.last_shown = null;
        w3c_slidy.set_location();
        w3c_slidy.set_visibility_all_incremental("hidden");
        w3c_slidy.set_eos_status(!w3c_slidy.next_incremental_item(w3c_slidy.last_shown));
        w3c_slidy.show_slide(slide);
        //target.focus();

        try
        {
          if (!w3c_slidy.opera)
            w3c_slidy.set_focus();
        }
        catch (e)
        {
        }
      }

      w3c_slidy.hide_table_of_contents(true);

      if (self.ie7)
       w3c_slidy.ie_hack();

      return w3c_slidy.cancel(event);
    }

    if (key == 40 && this.next)
    {
      this.next.focus();
      return w3c_slidy.cancel(event);
    }

    if (key == 38 && this.previous)
    {
      this.previous.focus();
      return w3c_slidy.cancel(event);
    }

    return true;
  },

  touchstart: function (e)
  {
    // a double touch often starts with a
    // single touch due to fingers touching
    // down at slightly different times
    // thus avoid calling preventDefault here
    this.prev_tap = this.last_tap;
    this.last_tap = (new Date).getTime();

    var tap_delay = this.last_tap - this.prev_tap;

    if (tap_delay <= 200)
    {
      // double tap
    }

    var touch = e.touches[0];

    this.pageX = touch.pageX;
    this.pageY = touch.pageY;
    this.screenX = touch.screenX;
    this.screenY = touch.screenY;
    this.clientX = touch.clientX;
    this.clientY = touch.clientY;

    this.delta_x = this.delta_y = 0;
  },

  touchmove: function (e)
  {
    // override native gestures for single touch
    if (e.touches.length > 1)
      return;

    e.preventDefault();
    var touch = e.touches[0];
    this.delta_x = touch.pageX - this.pageX;
    this.delta_y = touch.pageY - this.pageY;
  },

  touchend: function (e)
  {
    // default behavior for multi-touch
    if (e.touches.length > 1)
      return;

    var delay = (new Date).getTime() - this.last_tap;
    var dx = this.delta_x;
    var dy = this.delta_y;
    var abs_dx = Math.abs(dx);
    var abs_dy = Math.abs(dy);

    if (delay < 500 && (abs_dx > 100 || abs_dy > 100))
    {
      if (abs_dx > 0.5 * abs_dy)
      {
        e.preventDefault();

        if (dx < 0)
          w3c_slidy.next_slide(true);
        else
          w3c_slidy.previous_slide(true);
      }
      else if (abs_dy > 2 * abs_dx)
      {
        e.preventDefault();
        w3c_slidy.toggle_table_of_contents();
      }
    }
  },

  // ### OBSOLETE ###
  before_print: function () {
    this.show_all_slides();
    this.hide_toolbar();
    alert("before print");
  },

  // ### OBSOLETE ###
  after_print: function () {
    if (!this.view_all)
    {
      this.single_slide_view();
      this.show_toolbar();
    }
    alert("after print");
  },

  // ### OBSOLETE ###
  print_slides: function () {
    this.before_print();
    window.print();
    this.after_print();
  },

  // ### OBSOLETE ?? ###
  toggle_view: function () {
    if (this.view_all)
    {
      this.single_slide_view();
      this.show_toolbar();
      this.view_all = 0;
    }
    else
    {
      this.show_all_slides();
      this.hide_toolbar();
      this.view_all = 1;
    }
  },

  // prepare for printing  ### OBSOLETE ###
  show_all_slides: function () {
    this.remove_class(document.body, "single_slide");
    this.set_visibility_all_incremental("visible");
  },

  // restore after printing  ### OBSOLETE ###
  single_slide_view: function () {
    this.add_class(document.body, "single_slide");
    this.set_visibility_all_incremental("visible");
    this.last_shown = this.previous_incremental_item(null);
  },

  // suppress IE's image toolbar pop up
  hide_image_toolbar: function () {
    if (!this.ns_pos)
    {
      var images = document.getElementsByTagName("IMG");

      for (var i = 0; i < images.length; ++i)
        images[i].setAttribute("galleryimg", "no");
    }
  },

  unloaded: function (e) {
    //alert("unloaded");
  },

  // Safari and Konqueror don't yet support getComputedStyle()
  // and they always reload page when location.href is updated
  is_KHTML: function () {
    var agent = navigator.userAgent;
    return (agent.indexOf("KHTML") >= 0 ? true : false);
  },

  // find slide name from first h1 element
  // default to document title + slide number
  slide_name: function (index) {
    var name = null;
    var slide = this.slides[index];

    var heading = this.find_heading(slide);

    if (heading)
      name = this.extract_text(heading);

    if (!name)
      name = this.title + "(" + (index + 1) + ")";

    name.replace(/\&/g, "&amp;");
    name.replace(/\</g, "&lt;");
    name.replace(/\>/g, "&gt;");

    return name;
  },

  // find first h1 element in DOM tree
  find_heading: function (node) {
    if (!node || node.nodeType != 1)
      return null;

    if (node.nodeName == "H1" || node.nodeName == "h1")
      return node;

    var child = node.firstChild;

    while (child)
    {
      node = this.find_heading(child);

      if (node)
        return node;

      child = child.nextSibling;
    }

    return null;
  },

  // recursively extract text from DOM tree
  extract_text: function (node) {
    if (!node)
      return "";

    // text nodes
    if (node.nodeType == 3)
      return node.nodeValue;

    // elements
    if (node.nodeType == 1)
    {
      node = node.firstChild;
      var text = "";

      while (node)
      {
        text = text + this.extract_text(node);
        node = node.nextSibling;
      }

      return text;
    }

    return "";
  },

  // find copyright text from meta element
  find_copyright: function () {
    var name, content;
    var meta = document.getElementsByTagName("meta");

    for (var i = 0; i < meta.length; ++i)
    {
      name = meta[i].getAttribute("name");
      content = meta[i].getAttribute("content");

      if (name == "copyright")
        return content;
    }

    return null;
  },

  find_size_adjust: function () {
    var name, content, offset;
    var meta = document.getElementsByTagName("meta");

    for (var i = 0; i < meta.length; ++i)
    {
      name = meta[i].getAttribute("name");
      content = meta[i].getAttribute("content");

      if (name == "font-size-adjustment")
        return 1 * content;
    }

    return 1;
  },

  // <meta name="duration" content="20" />  for 20 minutes
  find_duration: function () {
    var name, content, offset;
    var meta = document.getElementsByTagName("meta");

    for (var i = 0; i < meta.length; ++i)
    {
      name = meta[i].getAttribute("name");
      content = meta[i].getAttribute("content");

      if (name == "duration")
        return 60000 * content;
    }

    return null;
  },

  replace_by_non_breaking_space: function (str) {
    for (var i = 0; i < str.length; ++i)
      str[i] = 160;
  },

  // ### CHECK ME ### is use of "li" okay for text/html?
  // for XHTML do we also need to specify namespace?
  init_outliner: function () {
    var items = document.getElementsByTagName("li");

    for (var i = 0; i < items.length; ++i)
    {
      var target = items[i];

      if (!this.has_class(target.parentNode, "outline"))
        continue;

      target.onclick = this.outline_click;
/* ### more work needed for IE6
      if (!this.ns_pos)
      {
        target.onmouseover = this.hover_outline;
        target.onmouseout = this.unhover_outline;
      }
*/
      if (this.foldable(target))
      {
        target.foldable = true;
        target.onfocus = function () {w3c_slidy.outline = this;};
        target.onblur = function () {w3c_slidy.outline = null;};

        if (!target.getAttribute("tabindex"))
          target.setAttribute("tabindex", "0");

        if (this.has_class(target, "expand"))
          this.unfold(target);
        else
          this.fold(target);
      }
      else
      {
        this.add_class(target, "nofold");
        target.visible = true;
        target.foldable = false;
      }
    }
  },

  foldable: function (item) {
    if (!item || item.nodeType != 1)
      return false;

    var node = item.firstChild;

    while (node)
    {
      if (node.nodeType == 1 && this.is_block(node))
        return true;

      node = node.nextSibling;
    }

    return false;
  },

  // ### CHECK ME ### switch to add/remove "hidden" class
  fold: function (item) {
    if (item)
    {
      this.remove_class(item, "unfolded");
      this.add_class(item, "folded");
    }

    var node = item ? item.firstChild : null;

    while (node)
    {
      if (node.nodeType == 1 && this.is_block(node)) // element
      {
         w3c_slidy.add_class(node, "hidden");
      }

      node = node.nextSibling;
    }

    item.visible = false;
  },

  // ### CHECK ME ### switch to add/remove "hidden" class
  unfold: function (item) {
    if (item)
    {
      this.add_class(item, "unfolded");
      this.remove_class(item, "folded");
    }

    var node = item ? item.firstChild : null;

    while (node)
    {
      if (node.nodeType == 1 && this.is_block(node)) // element
      {
        w3c_slidy.remove_class(node, "hidden");
      }

      node = node.nextSibling;
    }

    item.visible = true;
  },

  outline_click: function (e) {
    if (!e)
      e = window.event;

    var rightclick = false;
    var target = w3c_slidy.get_target(e);

    while (target && target.visible == undefined)
      target = target.parentNode;

    if (!target)
      return true;

    if (e.which)
      rightclick = (e.which == 3);
    else if (e.button)
      rightclick = (e.button == 2);

    if (!rightclick && target.visible != undefined)
    {
      if (target.foldable)
      {
        if (target.visible)
          w3c_slidy.fold(target);
        else
          w3c_slidy.unfold(target);
      }

      w3c_slidy.stop_propagation(e);
      e.cancel = true;
      e.returnValue = false;
    }

    return false;
  },

  add_initial_prompt: function () {
    var prompt = this.create_element("div");
    prompt.setAttribute("class", "initial_prompt");

    var p1 = this.create_element("p");
    prompt.appendChild(p1);
    p1.setAttribute("class", "help");

    if (this.keyboardless)
      p1.innerHTML = "swipe left to move to next slide";
    else
      p1.innerHTML = "Space, Right Arrow or swipe left to move to " +
                     "next slide, click help below for more details";

    this.add_listener(prompt, "click", function (e) {
      document.body.removeChild(prompt);
      w3c_slidy.stop_propagation(e);
    
      if (e.cancel != undefined)
        e.cancel = true;
      
      if (e.returnValue != undefined)
        e.returnValue = false;
      
      return false;
    });

    document.body.appendChild(prompt);
    this.initial_prompt = prompt;
    setTimeout(function() {document.body.removeChild(prompt);}, 5000);
  },

  add_toolbar: function () {
    var counter, page;

     this.toolbar = this.create_element("div");
     this.toolbar.setAttribute("class", "toolbar");

     // a reasonably behaved browser
     if (this.ns_pos || !this.ie6)
     {
       var right = this.create_element("div");
       right.setAttribute("style", "float: right; text-align: right");

       counter = this.create_element("span")
       counter.innerHTML = this.localize("slide") + " n/m";
       right.appendChild(counter);
       this.toolbar.appendChild(right);

       var left = this.create_element("div");
       left.setAttribute("style", "text-align: left");

       // global end of slide indicator
       this.eos = this.create_element("span");
       this.eos.innerHTML = "* ";
       left.appendChild(this.eos);

       var help = this.create_element("a");
       help.setAttribute("href", this.help_page);
       help.setAttribute("title", this.localize(this.help_text));
       help.innerHTML = this.localize("help?");
       left.appendChild(help);
       help.style.display="none"; 
       this.help_anchor = help;  // save for focus hack

       var gap1 = document.createTextNode(" ");
       left.appendChild(gap1);

       var contents = this.create_element("a");
       contents.setAttribute("href", "javascript:w3c_slidy.toggle_table_of_contents()");
       contents.setAttribute("title", this.localize("table of contents"));
       contents.innerHTML = this.localize("Contents");
       left.appendChild(contents);

       var gap2 = document.createTextNode(" ");
       left.appendChild(gap2);

       var copyright = this.find_copyright();

       if (copyright)
       {
         var span = this.create_element("span");
         span.className = "copyright";
         span.innerHTML = copyright;
         left.appendChild(span);
       }

       this.toolbar.setAttribute("tabindex", "0");
       this.toolbar.appendChild(left);
     }
     else // IE6 so need to work around its poor CSS support
     {
       this.toolbar.style.position = (this.ie7 ? "fixed" : "absolute");
       this.toolbar.style.zIndex = "200";
       this.toolbar.style.width = "99.9%";
       this.toolbar.style.height = "1.2em";
       this.toolbar.style.top = "auto";
       this.toolbar.style.bottom = "0";
       this.toolbar.style.left = "0";
       this.toolbar.style.right = "0";
       this.toolbar.style.textAlign = "left";
       this.toolbar.style.fontSize = "60%";
       this.toolbar.style.color = "red";
       this.toolbar.borderWidth = 0;
       this.toolbar.className = "toolbar";
       this.toolbar.style.background = "rgb(240,240,240)";

       // would like to have help text left aligned
       // and page counter right aligned, floating
       // div's don't work, so instead use nested
       // absolutely positioned div's.

       var sp = this.create_element("span");
       sp.innerHTML = "&nbsp;&nbsp;*&nbsp;";
       this.toolbar.appendChild(sp);
       this.eos = sp;  // end of slide indicator

       var help = this.create_element("a");
       help.setAttribute("href", this.help_page);
       help.setAttribute("title", this.localize(this.help_text));
       help.innerHTML = this.localize("help?");
       this.toolbar.appendChild(help);
       this.help_anchor = help;  // save for focus hack

       var gap1 = document.createTextNode(" ");
       this.toolbar.appendChild(gap1);

       var contents = this.create_element("a");
       contents.setAttribute("href", "javascript:toggleTableOfContents()");
       contents.setAttribute("title", this.localize("table of contents".localize));
       contents.innerHTML = this.localize("contents?");
       this.toolbar.appendChild(contents);

       var gap2 = document.createTextNode(" ");
       this.toolbar.appendChild(gap2);

       var copyright = this.find_copyright();

       if (copyright)
       {
         var span = this.create_element("span");
         span.innerHTML = copyright;
         span.style.color = "black";
         span.style.marginLeft = "0.5em";
         this.toolbar.appendChild(span);
       }

       counter = this.create_element("div")
       counter.style.position = "absolute";
       counter.style.width = "auto"; //"20%";
       counter.style.height = "1.2em";
       counter.style.top = "auto";
       counter.style.bottom = 0;
       counter.style.right = "0";
       counter.style.textAlign = "right";
       counter.style.color = "red";
       counter.style.background = "rgb(240,240,240)";

       counter.innerHTML = this.localize("slide") + " n/m";
       this.toolbar.appendChild(counter);
     }

     // ensure that click isn't passed through to the page
     this.toolbar.onclick =
         function (e) {
           if (!e)
             e = window.event;

           var target = e.target;

           if (!target && e.srcElement)
             target = e.srcElement;

           // work around Safari bug
           if (target && target.nodeType == 3)
             target = target.parentNode;

           w3c_slidy.stop_propagation(e);

           if (target && target.nodeName.toLowerCase() != "a")
             w3c_slidy.mouse_button_click(e);
         };

     this.slide_number_element = counter;
     this.set_eos_status(false);
     document.body.appendChild(this.toolbar);
  },

  // wysiwyg editors make it hard to use div elements
  // e.g. amaya loses the div when you copy and paste
  // this function wraps div elements around implicit
  // slides which start with an h1 element and continue
  // up to the next heading or div element
  wrap_implicit_slides: function () {
    var i, heading, node, next, div;
    var headings = document.getElementsByTagName("h1");

    if (!headings)
      return;

    for (i = 0; i < headings.length; ++i)
    {
      heading = headings[i];

      if (heading.parentNode != document.body)
        continue;

      node = heading.nextSibling;

      div = document.createElement("div");
      this.add_class(div, "slide");
      document.body.replaceChild(div, heading);
      div.appendChild(heading);

      while (node)
      {
        if (node.nodeType == 1) // an element
        {
           if (node.nodeName == "H1" || node.nodeName == "h1")
             break;

           if (node.nodeName == "DIV" || node.nodeName == "div")
           {
             if (this.has_class(node, "slide"))
               break;

             if (this.has_class(node, "handout"))
               break;
           }
        }

        next = node.nextSibling;
        node = document.body.removeChild(node);
        div.appendChild(node);
        node = next;
      } 
    }
  },

  attach_touch_handers: function(slides)
  {
    var i, slide;

    for (i = 0; i < slides.length; ++i)
    {
      slide = slides[i];
      this.add_listener(slide, "touchstart", this.touchstart);
      this.add_listener(slide, "touchmove", this.touchmove);
      this.add_listener(slide, "touchend", this.touchend);
    }
  },

// return new array of all slides
  collect_slides: function () {
    var slides = new Array();
    var divs = document.body.getElementsByTagName("div");

    for (var i = 0; i < divs.length; ++i)
    {
      div = divs.item(i);

      if (this.has_class(div, "slide"))
      {
        // add slide to collection
        slides[slides.length] = div;

        // hide each slide as it is found
        this.add_class(div, "hidden");

        // add dummy <br/> at end for scrolling hack
        var node1 = document.createElement("br");
        div.appendChild(node1);
        var node2 = document.createElement("br");
        div.appendChild(node2);
      }
      else if (this.has_class(div, "background"))
      {  // work around for Firefox SVG reload bug
        // which otherwise replaces 1st SVG graphic with 2nd
        div.style.display = "block";
      }
    }

    this.slides = slides;
  },

  // return new array of all <div class="handout">
  collect_notes: function () {
    var notes = new Array();
    var divs = document.body.getElementsByTagName("div");

    for (var i = 0; i < divs.length; ++i)
    {
      div = divs.item(i);

      if (this.has_class(div, "handout"))
      {
        // add note to collection
        notes[notes.length] = div;

        // and hide it
        this.add_class(div, "hidden");
      }
    }

    this.notes = notes;
  },

  // return new array of all <div class="background">
  // including named backgrounds e.g. class="background titlepage"
  collect_backgrounds: function () {
    var backgrounds = new Array();
    var divs = document.body.getElementsByTagName("div");

    for (var i = 0; i < divs.length; ++i)
    {
      div = divs.item(i);

      if (this.has_class(div, "background"))
      {
        // add background to collection
        backgrounds[backgrounds.length] = div;

        // and hide it
        this.add_class(div, "hidden");
      }
    }

    this.backgrounds = backgrounds;
  },

  // set click handlers on all anchors
  patch_anchors: function () {
    var self = w3c_slidy;
    var handler = function (event) {
      // compare this.href with location.href
      // for link to another slide in this doc

      if (self.page_address(this.href) == self.page_address(location.href))
      {
        // yes, so find new slide number
        var newslidenum = self.find_slide_number(this.href);

        if (newslidenum != self.slide_number)
        {
          var slide = self.slides[self.slide_number];
          self.hide_slide(slide);
          self.slide_number = newslidenum;
          slide = self.slides[self.slide_number];
          self.show_slide(slide);
          self.set_location();
        }
      }
      else
        w3c_slidy.stop_propagation(event);

//      else if (this.target == null)
//        location.href = this.href;

      this.blur();
      self.disable_slide_click = true;
    };

    var anchors = document.body.getElementsByTagName("a");

    for (var i = 0; i < anchors.length; ++i)
    {
      if (window.addEventListener)
        anchors[i].addEventListener("click", handler, false);
      else
        anchors[i].attachEvent("onclick", handler);
    }
  },

  // ### CHECK ME ### see which functions are invoked via setTimeout
  // either directly or indirectly for use of w3c_slidy vs this
  show_slide_number: function () {
    var timer = w3c_slidy.get_timer();
    w3c_slidy.slide_number_element.innerHTML = timer + w3c_slidy.localize("slide") + " " +
           (w3c_slidy.slide_number + 1) + "/" + w3c_slidy.slides.length;
  },

  // every 200mS check if the location has been changed as a
  // result of the user activating the Back button/menu item
  // doesn't work for Opera < 9.5
  check_location: function () {
    var hash = location.hash;

    if (w3c_slidy.slide_number > 0 && (hash == "" || hash == "#"))
      w3c_slidy.goto_slide(0);
    else if (hash.length > 2 && hash != "#("+(w3c_slidy.slide_number+1)+")")
    {
      var num = parseInt(location.hash.substr(2));

      if (!isNaN(num))
        w3c_slidy.goto_slide(num-1);
    }

    if (w3c_slidy.time_left && w3c_slidy.slide_number > 0)
    {
      w3c_slidy.show_slide_number();

      if (w3c_slidy.time_left > 0)
        w3c_slidy.time_left -= 200;
    } 
  },

  get_timer: function () {
    var timer = "";
    if (w3c_slidy.time_left)
    {
      var mins, secs;
      secs = Math.floor(w3c_slidy.time_left/1000);
      mins = Math.floor(secs / 60);
      secs = secs % 60;
      timer = (mins ? mins+"m" : "") + secs + "s ";
    }

    return timer;
  },

  // this doesn't push location onto history stack for IE
  // for which a hidden iframe hack is needed: load page into
  // the iframe with script that set's parent's location.hash
  // but that won't work for standalone use unless we can
  // create the page dynamically via a javascript: URL
  // ### use history.pushState if available
  set_location: function () {
     var uri = w3c_slidy.page_address(location.href);
     var hash = "#(" + (w3c_slidy.slide_number+1) + ")";

     if (w3c_slidy.slide_number >= 0)
       uri = uri + hash;

     if (typeof(history.pushState) != "undefined" && location.protocol !== "file:")
     {
       document.title = w3c_slidy.title + " (" + (w3c_slidy.slide_number+1) + ")";
       history.pushState(0, document.title, hash);
       w3c_slidy.show_slide_number();
       w3c_slidy.notify_observers();
       return;
     }

     if (w3c_slidy.ie && (w3c_slidy.ie6 || w3c_slidy.ie7))
       w3c_slidy.push_hash(hash);

     if (uri != location.href) // && !khtml
        location.href = uri;

     if (this.khtml)
        hash = "(" + (w3c_slidy.slide_number+1) + ")";

     if (!this.ie && location.hash != hash && location.hash != "")
       location.hash = hash;

     document.title = w3c_slidy.title + " (" + (w3c_slidy.slide_number+1) + ")";
     w3c_slidy.show_slide_number();
     w3c_slidy.notify_observers();
  },

  notify_observers: function ()
  {
    var slide = this.slides[this.slide_number];

    for (var i = 0; i < this.observers.length; ++i)
      this.observers[i](this.slide_number+1, this.find_heading(slide).innerText, location.href);
  },

  add_observer: function (observer)
  {
    for (var i = 0; i < this.observers.length; ++i)
    {
      if (observer == this.observers[i])
        return;
    }

    this.observers.push(observer);
  },

  remove_observer: function (o)
  {
    for (var i = 0; i < this.observers.length; ++i)
    {
      if (observer == this.observers[i])
      {
        this.observers.splice(i,1);
        break;
      }
    }
  },

  page_address: function (uri) {
    var i = uri.indexOf("#");

    if (i < 0)
      i = uri.indexOf("%23");

    // check if anchor is entire page

    if (i < 0)
      return uri;  // yes

    return uri.substr(0, i);
  },

  // only used for IE6 and IE7
  on_frame_loaded: function (hash) {
    location.hash = hash;
    var uri = w3c_slidy.page_address(location.href);
    location.href = uri + hash;
  },

  // history hack with thanks to Bertrand Le Roy
  push_hash: function (hash) {
    if (hash == "") hash = "#(1)";
      window.location.hash = hash;

    var doc = document.getElementById("historyFrame").contentWindow.document;
    doc.open("javascript:'<html></html>'");
    doc.write("<html><head><script type=\"text/javascript\">window.parent.w3c_slidy.on_frame_loaded('"+
      (hash) + "');</script></head><body>hello mum</body></html>");
      doc.close();
  },

  // find current slide based upon location
  // first find target anchor and then look
  // for associated div element enclosing it
  // finally map that to slide number
  find_slide_number: function (uri) {
    // first get anchor from page location

    var i = uri.indexOf("#");

    // check if anchor is entire page
    if (i < 0)
      return 0;  // yes

    var anchor = unescape(uri.substr(i+1));

    // now use anchor as XML ID to find target
    var target = document.getElementById(anchor);

    if (!target)
    {
      // does anchor look like "(2)" for slide 2 ??
      // where first slide is (1)
      var re = /\((\d)+\)/;

      if (anchor.match(re))
      {
        var num = parseInt(anchor.substring(1, anchor.length-1));

        if (num > this.slides.length)
          num = 1;

        if (--num < 0)
          num = 0;

        return num;
      }

      // accept [2] for backwards compatibility
      re = /\[(\d)+\]/;

      if (anchor.match(re))
      {
         var num = parseInt(anchor.substring(1, anchor.length-1));

         if (num > this.slides.length)
            num = 1;

         if (--num < 0)
            num = 0;

         return num;
      }

      // oh dear unknown anchor
      return 0;
    }

    // search for enclosing slide

    while (true)
    {
      // browser coerces html elements to uppercase!
      if (target.nodeName.toLowerCase() == "div" &&
            this.has_class(target, "slide"))
      {
        // found the slide element
        break;
      }

      // otherwise try parent element if any

      target = target.parentNode;

      if (!target)
      {
        return 0;   // no luck!
      }
    };

    for (i = 0; i < slides.length; ++i)
    {
      if (slides[i] == target)
        return i;  // success
    }

    // oh dear still no luck
    return 0;
  },

  previous_slide: function (incremental) {
    if (!w3c_slidy.view_all)
    {
      var slide;

      if ((incremental || w3c_slidy.slide_number == 0) && w3c_slidy.last_shown != null)
      {
        w3c_slidy.last_shown = w3c_slidy.hide_previous_item(w3c_slidy.last_shown);
        w3c_slidy.set_eos_status(false);
      }
      else if (w3c_slidy.slide_number > 0)
      {
        slide = w3c_slidy.slides[w3c_slidy.slide_number];
        w3c_slidy.hide_slide(slide);

        w3c_slidy.slide_number = w3c_slidy.slide_number - 1;
        slide = w3c_slidy.slides[w3c_slidy.slide_number];
        w3c_slidy.set_visibility_all_incremental("visible");
        w3c_slidy.last_shown = w3c_slidy.previous_incremental_item(null);
        w3c_slidy.set_eos_status(true);
        w3c_slidy.show_slide(slide);
      }

      w3c_slidy.set_location();

      if (!w3c_slidy.ns_pos)
        w3c_slidy.refresh_toolbar(200);
    }
  },

  next_slide: function (incremental) {
    if (!w3c_slidy.view_all)
    {
      var slide, last = w3c_slidy.last_shown;

      if (incremental || w3c_slidy.slide_number == w3c_slidy.slides.length - 1)
         w3c_slidy.last_shown = w3c_slidy.reveal_next_item(w3c_slidy.last_shown);

      if ((!incremental || w3c_slidy.last_shown == null) &&
             w3c_slidy.slide_number < w3c_slidy.slides.length - 1)
      {
         slide = w3c_slidy.slides[w3c_slidy.slide_number];
         w3c_slidy.hide_slide(slide);

         w3c_slidy.slide_number = w3c_slidy.slide_number + 1;
         slide = w3c_slidy.slides[w3c_slidy.slide_number];
         w3c_slidy.last_shown = null;
         w3c_slidy.set_visibility_all_incremental("hidden");
         w3c_slidy.show_slide(slide);
      }
      else if (!w3c_slidy.last_shown)
      {
         if (last && incremental)
           w3c_slidy.last_shown = last;
      }

      w3c_slidy.set_location();

      w3c_slidy.set_eos_status(!w3c_slidy.next_incremental_item(w3c_slidy.last_shown));

      if (!w3c_slidy.ns_pos)
         w3c_slidy.refresh_toolbar(200);
     }
  },

  // to first slide with nothing revealed
  // i.e. state at start of presentation
  first_slide: function () {
     if (!w3c_slidy.view_all)
     {
       var slide;

       if (w3c_slidy.slide_number != 0)
       {
         slide = w3c_slidy.slides[w3c_slidy.slide_number];
         w3c_slidy.hide_slide(slide);

         w3c_slidy.slide_number = 0;
         slide = w3c_slidy.slides[w3c_slidy.slide_number];
         w3c_slidy.last_shown = null;
         w3c_slidy.set_visibility_all_incremental("hidden");
         w3c_slidy.show_slide(slide);
       }

       w3c_slidy.set_eos_status(
         !w3c_slidy.next_incremental_item(w3c_slidy.last_shown));
       w3c_slidy.set_location();
     }
  },

  // goto last slide with everything revealed
  // i.e. state at end of presentation
  last_slide: function () {
    if (!w3c_slidy.view_all)
    {
      var slide;

      w3c_slidy.last_shown = null; //revealNextItem(lastShown);

      if (w3c_slidy.last_shown == null &&
          w3c_slidy.slide_number < w3c_slidy.slides.length - 1)
      {
         slide = w3c_slidy.slides[w3c_slidy.slide_number];
         w3c_slidy.hide_slide(slide);
         w3c_slidy.slide_number = w3c_slidy.slides.length - 1;
         slide = w3c_slidy.slides[w3c_slidy.slide_number];
         w3c_slidy.set_visibility_all_incremental("visible");
         w3c_slidy.last_shown = w3c_slidy.previous_incremental_item(null);

         w3c_slidy.show_slide(slide);
      }
      else
      {
         w3c_slidy.set_visibility_all_incremental("visible");
         w3c_slidy.last_shown = w3c_slidy.previous_incremental_item(null);
      }

      w3c_slidy.set_eos_status(true);
      w3c_slidy.set_location();
    }
  },


  // ### check this and consider add/remove class
  set_eos_status: function (state) {
    if (this.eos)
      this.eos.style.color = (state ? "rgb(240,240,240)" : "red");
  },

  // first slide is 0
  goto_slide: function (num) {
    //alert("going to slide " + (num+1));
    var slide = w3c_slidy.slides[w3c_slidy.slide_number];
    w3c_slidy.hide_slide(slide);
    w3c_slidy.slide_number = num;
    slide = w3c_slidy.slides[w3c_slidy.slide_number];
    w3c_slidy.last_shown = null;
    w3c_slidy.set_visibility_all_incremental("hidden");
    w3c_slidy.set_eos_status(!w3c_slidy.next_incremental_item(w3c_slidy.last_shown));
    document.title = w3c_slidy.title + " (" + (w3c_slidy.slide_number+1) + ")";
    w3c_slidy.show_slide(slide);
    w3c_slidy.show_slide_number();
  },


  show_slide: function (slide) {
    this.sync_background(slide);
    this.remove_class(slide, "hidden");

    // work around IE9 object rendering bug
    setTimeout("window.scrollTo(0,0);", 1);
  },

  hide_slide: function (slide) {
    this.add_class(slide, "hidden");
  },

  set_focus: function (element)
  {
    if (element)
      element.focus();
    else
    {
      w3c_slidy.help_anchor.focus();

      setTimeout(function() {
        w3c_slidy.help_anchor.blur();
      }, 1);
    }
  },

  // show just the backgrounds pertinent to this slide
  // when slide background-color is transparent
  // this should now work with rgba color values
  sync_background: function (slide) {
    var background;
    var bgColor;

    if (slide.currentStyle)
      bgColor = slide.currentStyle["backgroundColor"];
    else if (document.defaultView)
    {
      var styles = document.defaultView.getComputedStyle(slide,null);

      if (styles)
        bgColor = styles.getPropertyValue("background-color");
      else // broken implementation probably due Safari or Konqueror
      {
        //alert("defective implementation of getComputedStyle()");
        bgColor = "transparent";
      }
    }
    else
      bgColor == "transparent";

    if (bgColor == "transparent" ||
        bgColor.indexOf("rgba") >= 0 ||
        bgColor.indexOf("opacity") >= 0)
    {
      var slideClass = this.get_class_list(slide);

      for (var i = 0; i < this.backgrounds.length; i++)
      {
        background = this.backgrounds[i];

        var bgClass = this.get_class_list(background);

        if (this.matching_background(slideClass, bgClass))
          this.remove_class(background, "hidden");
        else
          this.add_class(background, "hidden");
      }
    }
    else // forcibly hide all backgrounds
      this.hide_backgrounds();
  },

  hide_backgrounds: function () {
    for (var i = 0; i < this.backgrounds.length; i++)
    {
      background = this.backgrounds[i];
      this.add_class(background, "hidden");
    }
  },

  // compare classes for slide and background
  matching_background: function (slideClass, bgClass) {
    var i, count, pattern, result;

    // define pattern as regular expression
    pattern = /\w+/g;

    // check background class names
    result = bgClass.match(pattern);

    for (i = count = 0; i < result.length; i++)
    {
      if (result[i] == "hidden")
        continue;

      if (result[i] == "background")
	continue;

      ++count;
    }

    if (count == 0)  // default match
      return true;

    // check for matches and place result in array
    result = slideClass.match(pattern);

    // now check if desired name is present for background
    for (i = count = 0; i < result.length; i++)
    {
      if (result[i] == "hidden")
        continue;

      if (this.has_token(bgClass, result[i]))
        return true;
    }

    return false;
  },

  resized: function () {
     var width = 0;

     if ( typeof( window.innerWidth ) == 'number' )
       width = window.innerWidth;  // Non IE browser
     else if (document.documentElement && document.documentElement.clientWidth)
       width = document.documentElement.clientWidth;  // IE6
     else if (document.body && document.body.clientWidth)
       width = document.body.clientWidth; // IE4

     var height = 0;

     if ( typeof( window.innerHeight ) == 'number' )
       height = window.innerHeight;  // Non IE browser
     else if (document.documentElement && document.documentElement.clientHeight)
       height = document.documentElement.clientHeight;  // IE6
     else if (document.body && document.body.clientHeight)
       height = document.body.clientHeight; // IE4

     if (height && (width/height > 1.05*1024/768))
     {
       width = height * 1024.0/768;
     }

     // IE fires onresize even when only font size is changed!
     // so we do a check to avoid blocking < and > actions
     if (width != w3c_slidy.last_width || height != w3c_slidy.last_height)
     {
       if (width >= 1100)
         w3c_slidy.size_index = 5;    // 4
       else if (width >= 1000)
         w3c_slidy.size_index = 4;    // 3
       else if (width >= 800)
         w3c_slidy.size_index = 3;    // 2
       else if (width >= 600)
         w3c_slidy.size_index = 2;    // 1
       else if (width)
         w3c_slidy.size_index = 0;

       // add in font size adjustment from meta element e.g.
       // <meta name="font-size-adjustment" content="-2" />
       // useful when slides have too much content ;-)

       if (0 <= w3c_slidy.size_index + w3c_slidy.size_adjustment &&
             w3c_slidy.size_index + w3c_slidy.size_adjustment < w3c_slidy.sizes.length)
         w3c_slidy.size_index = w3c_slidy.size_index + w3c_slidy.size_adjustment;

       // enables cross browser use of relative width/height
       // on object elements for use with SVG and Flash media
       w3c_slidy.adjust_object_dimensions(width, height);

       if (document.body.style.fontSize != w3c_slidy.sizes[w3c_slidy.size_index])
       {
         document.body.style.fontSize = w3c_slidy.sizes[w3c_slidy.size_index];
       }

       w3c_slidy.last_width = width;
       w3c_slidy.last_height = height;

       // force reflow to work around Mozilla bug
       if (w3c_slidy.ns_pos)
       {
         var slide = w3c_slidy.slides[w3c_slidy.slide_number];
         w3c_slidy.hide_slide(slide);
         w3c_slidy.show_slide(slide);
       }

       // force correct positioning of toolbar
       w3c_slidy.refresh_toolbar(200);
     }
  },

  scrolled: function () {
    if (w3c_slidy.toolbar && !w3c_slidy.ns_pos && !w3c_slidy.ie7)
    {
      w3c_slidy.hack_offset = w3c_slidy.scroll_x_offset();
      // hide toolbar
      w3c_slidy.toolbar.style.display = "none";

      // make it reappear later
      if (w3c_slidy.scrollhack == 0 && !w3c_slidy.view_all)
      {
        setTimeout(function () {w3c_slidy.show_toolbar(); }, 1000);
        w3c_slidy.scrollhack = 1;
      }
    }
  },

  hide_toolbar: function () {
    w3c_slidy.add_class(w3c_slidy.toolbar, "hidden");
    window.focus();
  },

  // used to ensure IE refreshes toolbar in correct position
  refresh_toolbar: function (interval) {
    if (!w3c_slidy.ns_pos && !w3c_slidy.ie7)
    {
      w3c_slidy.hide_toolbar();
      setTimeout(function () {w3c_slidy.show_toolbar();}, interval);
    }
  },

  // restores toolbar after short delay
  show_toolbar: function () {
    if (w3c_slidy.want_toolbar)
    {
      w3c_slidy.toolbar.style.display = "block";

      if (!w3c_slidy.ns_pos)
      {
        // adjust position to allow for scrolling
        var xoffset = w3c_slidy.scroll_x_offset();
        w3c_slidy.toolbar.style.left = xoffset;
        w3c_slidy.toolbar.style.right = xoffset;

        // determine vertical scroll offset
        //var yoffset = scrollYOffset();

        // bottom is doc height - window height - scroll offset
        //var bottom = documentHeight() - lastHeight - yoffset

        //if (yoffset > 0 || documentHeight() > lastHeight)
        //   bottom += 16;  // allow for height of scrollbar

        w3c_slidy.toolbar.style.bottom = 0; //bottom;
      }

      w3c_slidy.remove_class(w3c_slidy.toolbar, "hidden");
    }

    w3c_slidy.scrollhack = 0;


    // set the keyboard focus to the help link on the
    // toolbar to ensure that document has the focus
    // IE doesn't always work with window.focus()
    // and this hack has benefit of Enter for help

    try
    {
      if (!w3c_slidy.opera)
        w3c_slidy.set_focus();
    }
    catch (e)
    {
    }
  },

// invoked via F key
  toggle_toolbar: function () {
    if (!w3c_slidy.view_all)
    {
      if (w3c_slidy.has_class(w3c_slidy.toolbar, "hidden"))
      {
        w3c_slidy.remove_class(w3c_slidy.toolbar, "hidden")
        w3c_slidy.want_toolbar = 1;
      }
      else
      {
        w3c_slidy.add_class(w3c_slidy.toolbar, "hidden")
        w3c_slidy.want_toolbar = 0;
      }
    }
  },

  scroll_x_offset: function () {
    if (window.pageXOffset)
      return self.pageXOffset;

    if (document.documentElement && 
             document.documentElement.scrollLeft)
      return document.documentElement.scrollLeft;

    if (document.body)
      return document.body.scrollLeft;

    return 0;
  },

  scroll_y_offset: function () {
    if (window.pageYOffset)
      return self.pageYOffset;

    if (document.documentElement && 
             document.documentElement.scrollTop)
      return document.documentElement.scrollTop;

    if (document.body)
      return document.body.scrollTop;

    return 0;
  },

  // looking for a way to determine height of slide content
  // the slide itself is set to the height of the window
  optimize_font_size: function () {
    var slide = w3c_slidy.slides[w3c_slidy.slide_number];

    //var dh = documentHeight(); //getDocHeight(document);
    var dh = slide.scrollHeight;
    var wh = getWindowHeight();
    var u = 100 * dh / wh;

    alert("window utilization = " + u + "% (doc "
      + dh + " win " + wh + ")");
  },

  // from document object
  get_doc_height: function (doc) {
    if (!doc)
      doc = document;

    if (doc && doc.body && doc.body.offsetHeight)
      return doc.body.offsetHeight;  // ns/gecko syntax

    if (doc && doc.body && doc.body.scrollHeight)
      return doc.body.scrollHeight;

    alert("couldn't determine document height");
  },

  get_window_height: function () {
    if ( typeof( window.innerHeight ) == 'number' )
      return window.innerHeight;  // Non IE browser

    if (document.documentElement && document.documentElement.clientHeight)
      return document.documentElement.clientHeight;  // IE6

    if (document.body && document.body.clientHeight)
      return document.body.clientHeight; // IE4
  },

  document_height: function () {
    var sh, oh;

    sh = document.body.scrollHeight;
    oh = document.body.offsetHeight;

    if (sh && oh)
    {
      return (sh > oh ? sh : oh);
    }

    // no idea!
    return 0;
  },

  smaller: function () {
    if (w3c_slidy.size_index > 0)
    {
      --w3c_slidy.size_index;
    }

    w3c_slidy.toolbar.style.display = "none";
    document.body.style.fontSize = w3c_slidy.sizes[w3c_slidy.size_index];
    var slide = w3c_slidy.slides[w3c_slidy.slide_number];
    w3c_slidy.hide_slide(slide);
    w3c_slidy.show_slide(slide);
    setTimeout(function () {w3c_slidy.show_toolbar(); }, 50);
  },

  bigger: function () {
    if (w3c_slidy.size_index < w3c_slidy.sizes.length - 1)
    {
      ++w3c_slidy.size_index;
    }

    w3c_slidy.toolbar.style.display = "none";
    document.body.style.fontSize = w3c_slidy.sizes[w3c_slidy.size_index];
    var slide = w3c_slidy.slides[w3c_slidy.slide_number];
    w3c_slidy.hide_slide(slide);
    w3c_slidy.show_slide(slide);
    setTimeout(function () {w3c_slidy.show_toolbar(); }, 50);
  },

  // enables cross browser use of relative width/height
  // on object elements for use with SVG and Flash media
  // with thanks to Ivan Herman for the suggestion
  adjust_object_dimensions: function (width, height) {
    for( var i = 0; i < w3c_slidy.objects.length; i++ )
    {
      var obj = this.objects[i];
      var mimeType = obj.getAttribute("type");

      if (mimeType == "image/svg+xml" || mimeType == "application/x-shockwave-flash")
      {
        if ( !obj.initialWidth ) 
          obj.initialWidth = obj.getAttribute("width");

        if ( !obj.initialHeight ) 
          obj.initialHeight = obj.getAttribute("height");

        if ( obj.initialWidth && obj.initialWidth.charAt(obj.initialWidth.length-1) == "%" )
        {
          var w = parseInt(obj.initialWidth.slice(0, obj.initialWidth.length-1));
          var newW = width * (w/100.0);
          obj.setAttribute("width",newW);
        }

        if ( obj.initialHeight &&
             obj.initialHeight.charAt(obj.initialHeight.length-1) == "%" )
        {
          var h = parseInt(obj.initialHeight.slice(0, obj.initialHeight.length-1));
          var newH = height * (h/100.0);
          obj.setAttribute("height", newH);
        }
      }
    }
  },

  // needed for Opera to inhibit default behavior
  // since Opera delivers keyPress even if keyDown
  // was cancelled
  key_press: function (event) {
    if (!event)
      event = window.event;

    if (!w3c_slidy.key_wanted)
      return w3c_slidy.cancel(event);

    return true;
  },

  //  See e.g. http://www.quirksmode.org/js/events/keys.html for keycodes
  key_down: function (event) {
    var key, target, tag;

    w3c_slidy.key_wanted = true;

    if (!event)
      event = window.event;

    // kludge around NS/IE differences 
    if (window.event)
    {
      key = window.event.keyCode;
      target = window.event.srcElement;
    }
    else if (event.which)
    {
      key = event.which;
      target = event.target;
    }
    else
      return true; // Yikes! unknown browser

    // ignore event if key value is zero
    // as for alt on Opera and Konqueror
    if (!key)
       return true;

    // avoid interfering with keystroke
    // behavior for non-slidy chrome elements
    if (!w3c_slidy.slidy_chrome(target) &&
        w3c_slidy.special_element(target))
      return true;

    // check for concurrent control/command/alt key
    // but are these only present on mouse events?

    if (event.ctrlKey || event.altKey || event.metaKey)
       return true;

    // dismiss table of contents if visible
    if (w3c_slidy.is_shown_toc() && key != 9 && key != 16 && key != 38 && key != 40)
    {
      w3c_slidy.hide_table_of_contents(true);

      if (key == 27 || key == 84 || key == 67)
        return w3c_slidy.cancel(event);
    }

    if (key == 34) // Page Down
    {
      if (w3c_slidy.view_all)
        return true;

      w3c_slidy.next_slide(false);
      return w3c_slidy.cancel(event);
    }
    else if (key == 33) // Page Up
    {
      if (w3c_slidy.view_all)
        return true;

      w3c_slidy.previous_slide(false);
      return w3c_slidy.cancel(event);
    }
    else if (key == 32) // space bar
    {
      w3c_slidy.next_slide(true);
      return w3c_slidy.cancel(event);
    }
    else if (key == 37) // Left arrow
    {
      w3c_slidy.previous_slide(!event.shiftKey);
      return w3c_slidy.cancel(event);
    }
    else if (key == 36) // Home
    {
      w3c_slidy.first_slide();
      return w3c_slidy.cancel(event);
    }
    else if (key == 35) // End
    {
      w3c_slidy.last_slide();
      return w3c_slidy.cancel(event);
    }
    else if (key == 39) // Right arrow
    {
      w3c_slidy.next_slide(!event.shiftKey);
      return w3c_slidy.cancel(event);
    }
    else if (key == 13) // Enter
    {
      if (w3c_slidy.outline)
      {
        if (w3c_slidy.outline.visible)
          w3c_slidy.fold(w3c_slidy.outline);
        else
          w3c_slidy.unfold(w3c_slidy.outline);
          
       return w3c_slidy.cancel(event);
      }
    }
    else if (key == 188)  // < for smaller fonts
    {
      w3c_slidy.smaller();
      return w3c_slidy.cancel(event);
    }
    else if (key == 190)  // > for larger fonts
    {
      w3c_slidy.bigger();
      return w3c_slidy.cancel(event);
    }
    else if (key == 189 || key == 109)  // - for smaller fonts
    {
      w3c_slidy.smaller();
      return w3c_slidy.cancel(event);
    }
    else if (key == 187 || key == 191 || key == 107)  // = +  for larger fonts
    {
      w3c_slidy.bigger();
      return w3c_slidy.cancel(event);
    }
    else if (key == 83)  // S for smaller fonts
    {
      w3c_slidy.smaller();
      return w3c_slidy.cancel(event);
    }
    else if (key == 66)  // B for larger fonts
    {
      w3c_slidy.bigger();
      return w3c_slidy.cancel(event);
    }
    else if (key == 90)  // Z for last slide
    {
      w3c_slidy.last_slide();
      return w3c_slidy.cancel(event);
    }
    else if (key == 70)  // F for toggle toolbar
    {
      w3c_slidy.toggle_toolbar();
      return w3c_slidy.cancel(event);
    }
    else if (key == 65)  // A for toggle view single/all slides
    {
      w3c_slidy.toggle_view();
      return w3c_slidy.cancel(event);
    }
    else if (key == 75)  // toggle action of left click for next page
    {
      w3c_slidy.mouse_click_enabled = !w3c_slidy.mouse_click_enabled;
      var alert_msg = (w3c_slidy.mouse_click_enabled ?
                "enabled" : "disabled") +  " mouse click advance";

      alert(w3c_slidy.localize(alert_msg));
      return w3c_slidy.cancel(event);
    }
    else if (key == 84 || key == 67)  // T or C for table of contents
    {
      if (w3c_slidy.toc)
        w3c_slidy.toggle_table_of_contents();

      return w3c_slidy.cancel(event);
    }
    else if (key == 72) // H for help
    {
      window.location = w3c_slidy.help_page;
      return w3c_slidy.cancel(event);
    }
    //else alert("key code is "+ key);

    return true;
  },

  // safe for both text/html and application/xhtml+xml
  create_element: function (name) {
    if (this.xhtml && (typeof document.createElementNS != 'undefined'))
      return document.createElementNS("http://www.w3.org/1999/xhtml", name)

    return document.createElement(name);
  },

  get_element_style: function (elem, IEStyleProp, CSSStyleProp) {
    if (elem.currentStyle)
    {
      return elem.currentStyle[IEStyleProp];
    }
    else if (window.getComputedStyle)
    {
      var compStyle = window.getComputedStyle(elem, "");
      return compStyle.getPropertyValue(CSSStyleProp);
    }
    return "";
  },

  // the string str is a whitespace separated list of tokens
  // test if str contains a particular token, e.g. "slide"
  has_token: function (str, token) {
    if (str)
    {
      // define pattern as regular expression
      var pattern = /\w+/g;

      // check for matches
      // place result in array
      var result = str.match(pattern);

      // now check if desired token is present
      for (var i = 0; i < result.length; i++)
      {
        if (result[i] == token)
          return true;
      }
    }

    return false;
  },

  get_class_list: function (element) {
    if (typeof element.className != 'undefined')
      return element.className;

    return element.getAttribute("class");
  },

  has_class: function (element, name) {
    if (element.nodeType != 1)
      return false;

    var regexp = new RegExp("(^| )" + name + "\W*");

    if (typeof element.className != 'undefined')
      return regexp.test(element.className);

    return regexp.test(element.getAttribute("class"));
  },

  remove_class: function (element, name) {
    var regexp = new RegExp("(^| )" + name + "\W*");
    var clsval = "";

    if (typeof element.className != 'undefined')
    {
      clsval = element.className;

      if (clsval)
      {
        clsval = clsval.replace(regexp, "");
        element.className = clsval;
      }
    }
    else
    {
      clsval = element.getAttribute("class");

      if (clsval)
      {
        clsval = clsval.replace(regexp, "");
        element.setAttribute("class", clsval);
      }
    }
  },

  add_class: function (element, name) {
    if (!this.has_class(element, name))
    {
      if (typeof element.className != 'undefined')
        element.className += " " + name;
      else
      {
        var clsval = element.getAttribute("class");
        clsval = clsval ? clsval + " " + name : name;
        element.setAttribute("class", clsval);
      }
    }
  },

  // HTML elements that can be used with class="incremental"
  // note that you can also put the class on containers like
  // up, ol, dl, and div to make their contents appear
  // incrementally. Upper case is used since this is what
  // browsers report for HTML node names (text/html).
  incremental_elements: null,
  okay_for_incremental: function (name) {
    if (!this.incremental_elements)
    {
      var inclist = new Array();
      inclist["p"] = true;
      inclist["pre"] = true;
      inclist["li"] = true;
      inclist["blockquote"] = true;
      inclist["dt"] = true;
      inclist["dd"] = true;
      inclist["h2"] = true;
      inclist["h3"] = true;
      inclist["h4"] = true;
      inclist["h5"] = true;
      inclist["h6"] = true;
      inclist["span"] = true;
      inclist["address"] = true;
      inclist["table"] = true;
      inclist["tr"] = true;
      inclist["th"] = true;
      inclist["td"] = true;
      inclist["img"] = true;
      inclist["object"] = true;
      this.incremental_elements = inclist;
    }
    return this.incremental_elements[name.toLowerCase()];
  },

  next_incremental_item: function (node) {
    var br = this.is_xhtml ? "br" : "BR";
    var slide = w3c_slidy.slides[w3c_slidy.slide_number];

    for (;;)
    {
      node = w3c_slidy.next_node(slide, node);

      if (node == null || node.parentNode == null)
        break;

      if (node.nodeType == 1)  // ELEMENT
      {
        if (node.nodeName == br)
          continue;

        if (w3c_slidy.has_class(node, "incremental")
             && w3c_slidy.okay_for_incremental(node.nodeName))
          return node;

        if (w3c_slidy.has_class(node.parentNode, "incremental")
             && !w3c_slidy.has_class(node, "non-incremental"))
          return node;
      }
    }

    return node;
  },

  previous_incremental_item: function (node) {
    var br = this.is_xhtml ? "br" : "BR";
    var slide = w3c_slidy.slides[w3c_slidy.slide_number];

    for (;;)
    {
      node = w3c_slidy.previous_node(slide, node);

      if (node == null || node.parentNode == null)
        break;

      if (node.nodeType == 1)
      {
        if (node.nodeName == br)
          continue;

        if (w3c_slidy.has_class(node, "incremental")
             && w3c_slidy.okay_for_incremental(node.nodeName))
          return node;

        if (w3c_slidy.has_class(node.parentNode, "incremental")
             && !w3c_slidy.has_class(node, "non-incremental"))
          return node;
      }
    }

    return node;
  },

  // set visibility for all elements on current slide with
  // a parent element with attribute class="incremental"
  set_visibility_all_incremental: function (value) {
    var node = this.next_incremental_item(null);

    if (value == "hidden")
    {
      while (node)
      {
        w3c_slidy.add_class(node, "invisible");
        node = w3c_slidy.next_incremental_item(node);
      }
    }
    else // value == "visible"
    {
      while (node)
      {
        w3c_slidy.remove_class(node, "invisible");
        node = w3c_slidy.next_incremental_item(node);
      }
    }
  },

  // reveal the next hidden item on the slide
  // node is null or the node that was last revealed
  reveal_next_item: function (node) {
    node = w3c_slidy.next_incremental_item(node);

    if (node && node.nodeType == 1)  // an element
      w3c_slidy.remove_class(node, "invisible");

    return node;
  },

  // exact inverse of revealNextItem(node)
  hide_previous_item: function (node) {
    if (node && node.nodeType == 1)  // an element
      w3c_slidy.add_class(node, "invisible");

    return this.previous_incremental_item(node);
  },

  // left to right traversal of root's content
  next_node: function (root, node) {
    if (node == null)
      return root.firstChild;

    if (node.firstChild)
      return node.firstChild;

    if (node.nextSibling)
      return node.nextSibling;

    for (;;)
    {
      node = node.parentNode;

      if (!node || node == root)
        break;

      if (node && node.nextSibling)
        return node.nextSibling;
    }

    return null;
  },

  // right to left traversal of root's content
  previous_node: function (root, node) {
    if (node == null)
    {
      node = root.lastChild;

      if (node)
      {
        while (node.lastChild)
          node = node.lastChild;
      }

      return node;
    }

    if (node.previousSibling)
    {
      node = node.previousSibling;

      while (node.lastChild)
        node = node.lastChild;

      return node;
    }

    if (node.parentNode != root)
      return node.parentNode;

    return null;
  },

  previous_sibling_element: function (el) {
    el = el.previousSibling;

    while (el && el.nodeType != 1)
      el = el.previousSibling;

    return el;
  },

  next_sibling_element: function (el) {
    el = el.nextSibling;

    while (el && el.nodeType != 1)
      el = el.nextSibling;

    return el;
  },

  first_child_element: function (el) {
    var node;

    for (node = el.firstChild; node; node = node.nextSibling)
    {
      if (node.nodeType == 1)
        break;
    }

    return node;
  },

  first_tag: function (element, tag) {
    var node;

    if (!this.is_xhtml)
      tag = tag.toUpperCase();

    for (node = element.firstChild; node; node = node.nextSibling)
    {
      if (node.nodeType == 1 && node.nodeName == tag)
        break;
    }

    return node;
  },

  hide_selection: function () {
    if (window.getSelection) // Firefox, Chromium, Safari, Opera
    {
      var selection = window.getSelection();

      if (selection.rangeCount > 0)
      {
        var range = selection.getRangeAt(0);
        range.collapse (false);
      }
    }
    else // Internet Explorer
    {
      var textRange = document.selection.createRange ();
      textRange.collapse (false);
    }
  },

  get_selected_text: function () {
    try
    {
      if (window.getSelection)
        return window.getSelection().toString();

      if (document.getSelection)
        return document.getSelection().toString();

      if (document.selection)
        return document.selection.createRange().text;
    }
    catch (e)
    {
    }

    return "";
  },

  // make note of length of selected text
  // as this evaluates to zero in click event
  mouse_button_up: function (e) {
    w3c_slidy.selected_text_len = w3c_slidy.get_selected_text().length;
  },

  mouse_button_down: function (e) {
    w3c_slidy.selected_text_len = w3c_slidy.get_selected_text().length;
    w3c_slidy.mouse_x = e.clientX;
    w3c_slidy.mouse_y = e.clientY;
  },

  // right mouse button click is reserved for context menus
  // it is more reliable to detect rightclick than leftclick
  mouse_button_click: function (e) {
    if (!e)
      var e = window.event;

    if (Math.abs(e.clientX -w3c_slidy.mouse_x) +
        Math.abs(e.clientY -w3c_slidy.mouse_y) > 10)
      return true;

    if (w3c_slidy.selected_text_len > 0)
      return true;

    var rightclick = false;
    var leftclick = false;
    var middleclick = false;
    var target;

    if (!e)
      var e = window.event;

    if (e.target)
      target = e.target;
    else if (e.srcElement)
      target = e.srcElement;

    // work around Safari bug
    if (target.nodeType == 3)
      target = target.parentNode;

    if (e.which) // all browsers except IE
    {
      leftclick = (e.which == 1);
      middleclick = (e.which == 2);
      rightclick = (e.which == 3);
    }
    else if (e.button)
    {
      // Konqueror gives 1 for left, 4 for middle
      // IE6 gives 0 for left and not 1 as I expected

      if (e.button == 4)
        middleclick = true;

      // all browsers agree on 2 for right button
      rightclick = (e.button == 2);
    }
    else
      leftclick = true;

    if (w3c_slidy.selected_text_len > 0)
    {
      w3c_slidy.stop_propagation(e);
      e.cancel = true;
      e.returnValue = false;
      return false;
    }

    // dismiss table of contents
    w3c_slidy.hide_table_of_contents(false);

    // check if target is something that probably want's clicks
    // e.g. a, embed, object, input, textarea, select, option
    var tag = target.nodeName.toLowerCase();

    if (w3c_slidy.mouse_click_enabled && leftclick &&
        !w3c_slidy.special_element(target) &&
        !target.onclick)
    {
      w3c_slidy.next_slide(true);
      w3c_slidy.stop_propagation(e);
      e.cancel = true;
      e.returnValue = false;
      return false;
    }

    return true;
  },

  special_element: function (element) {
    if (this.has_class(element, "non-interactive"))
      return false;

    var tag = element.nodeName.toLowerCase();

    return element.onkeydown ||
      element.onclick ||
      tag == "a" ||
      tag == "embed" ||
      tag == "object" ||
      tag == "video" ||
      tag == "audio" ||
      tag == "svg" ||
      tag == "canvas" ||
      tag == "input" ||
      tag == "textarea" ||
      tag == "select" ||
      tag == "option";
  },

  slidy_chrome: function (el) {
    while (el)
    {
      if (el == w3c_slidy.toc ||
          el == w3c_slidy.toolbar ||
          w3c_slidy.has_class(el, "outline"))
        return true;

      el = el.parentNode;
    }

    return false;
  },

  get_key: function (e)
  {
    var key;

    // kludge around NS/IE differences 
    if (typeof window.event != "undefined")
      key = window.event.keyCode;
    else if (e.which)
      key = e.which;

    return key;
  },

  get_target: function (e) {
    var target;

    if (!e)
      e = window.event;

    if (e.target)
      target = e.target;
    else if (e.srcElement)
      target = e.srcElement;

    if (target.nodeType != 1)
      target = target.parentNode;

    return target;
  },

  // does display property provide correct defaults?
  is_block: function (elem) {
    var tag = elem.nodeName.toLowerCase();

    return tag == "ol" || tag == "ul" || tag == "p" || tag == "dl" ||
           tag == "li" || tag == "table" || tag == "pre" ||
           tag == "h1" || tag == "h2" || tag == "h3" ||
           tag == "h4" || tag == "h5" || tag == "h6" ||
           tag == "blockquote" || tag == "address"; 
  },

  add_listener: function (element, event, handler) {
    if (window.addEventListener)
      element.addEventListener(event, handler, false);
    else
      element.attachEvent("on"+event, handler);
  },

  // used to prevent event propagation from field controls
  stop_propagation: function (event) {
    event = event ? event : window.event;
    event.cancelBubble = true;  // for IE

    if (event.stopPropagation)
      event.stopPropagation();

    return true;
  },

  cancel: function (event) {
    if (event)
    {
       event.cancel = true;
       event.returnValue = false;

      if (event.preventDefault)
        event.preventDefault();
    }

    w3c_slidy.key_wanted = false;
    return false;
  },

// for each language define an associative array
// and also the help text which is longer

  strings_es: {
    "slide":"pág.",
    "help?":"Ayuda",
    "contents?":"Índice",
    "table of contents":"tabla de contenidos",
    "Table of Contents":"Tabla de Contenidos",
    "restart presentation":"Reiniciar presentación",
    "restart?":"Inicio"
  },
  help_es:
    "Utilice el ratón, barra espaciadora, teclas Izda/Dcha, " +
    "o Re pág y Av pág. Use S y B para cambiar el tamaño de fuente.",

  strings_ca: {
    "slide":"pàg..",
    "help?":"Ajuda",
    "contents?":"Índex",
    "table of contents":"taula de continguts",
    "Table of Contents":"Taula de Continguts",
    "restart presentation":"Reiniciar presentació",
    "restart?":"Inici"
  },
  help_ca:
    "Utilitzi el ratolí, barra espaiadora, tecles Esq./Dta. " +
    "o Re pàg y Av pàg. Usi S i B per canviar grandària de font.",

  strings_cs: {
    "slide":"snímek",
    "help?":"nápověda",
    "contents?":"obsah",
    "table of contents":"obsah prezentace",
    "Table of Contents":"Obsah prezentace",
    "restart presentation":"znovu spustit prezentaci",
    "restart?":"restart"
  },
  help_cs:
    "Prezentaci můžete procházet pomocí kliknutí myši, mezerníku, " +
    "šipek vlevo a vpravo nebo kláves PageUp a PageDown. Písmo se " +
    "dá zvětšit a zmenšit pomocí kláves B a S.",

  strings_nl: {
    "slide":"pagina",
    "help?":"Help?",
    "contents?":"Inhoud?",
    "table of contents":"inhoudsopgave",
    "Table of Contents":"Inhoudsopgave",
    "restart presentation":"herstart presentatie",
    "restart?":"Herstart?"
  },
  help_nl:
     "Navigeer d.m.v. het muis, spatiebar, Links/Rechts toetsen, " +
     "of PgUp en PgDn. Gebruik S en B om de karaktergrootte te veranderen.",

  strings_de: {
    "slide":"Seite",
    "help?":"Hilfe",
    "contents?":"Übersicht",
    "table of contents":"Inhaltsverzeichnis",
    "Table of Contents":"Inhaltsverzeichnis",
    "restart presentation":"Präsentation neu starten",
    "restart?":"Neustart"
  },
  help_de:
    "Benutzen Sie die Maus, Leerschlag, die Cursortasten links/rechts oder " +
    "Page up/Page Down zum Wechseln der Seiten und S und B für die Schriftgrösse.",

  strings_pl: {
    "slide":"slajd",
    "help?":"pomoc?",
    "contents?":"spis treści?",
    "table of contents":"spis treści",
    "Table of Contents":"Spis Treści",
    "restart presentation":"Restartuj prezentację",
    "restart?":"restart?"
  },
  help_pl:
    "Zmieniaj slajdy klikając myszą, naciskając spację, strzałki lewo/prawo" +
    "lub PgUp / PgDn. Użyj klawiszy S i B, aby zmienić rozmiar czczionki.",

  strings_fr: {
    "slide":"page",
    "help?":"Aide",
    "contents?":"Index",
    "table of contents":"table des matières",
    "Table of Contents":"Table des matières",
    "restart presentation":"Recommencer l'exposé",
    "restart?":"Début"
  },
  help_fr:
    "Naviguez avec la souris, la barre d'espace, les flèches " +
    "gauche/droite ou les touches Pg Up, Pg Dn. Utilisez " +
    "les touches S et B pour modifier la taille de la police.",

  strings_hu: {
    "slide":"oldal",
    "help?":"segítség",
    "contents?":"tartalom",
    "table of contents":"tartalomjegyzék",
    "Table of Contents":"Tartalomjegyzék",
    "restart presentation":"bemutató újraindítása",
    "restart?":"újraindítás"
  },
  help_hu:
    "Az oldalak közti lépkedéshez kattintson az egérrel, vagy " +
    "használja a szóköz, a bal, vagy a jobb nyíl, illetve a Page Down, " +
    "Page Up billentyűket. Az S és a B billentyűkkel változtathatja " +
    "a szöveg méretét.",

  strings_it: {
    "slide":"pag.",
    "help?":"Aiuto",
    "contents?":"Indice",
    "table of contents":"indice",
    "Table of Contents":"Indice",
    "restart presentation":"Ricominciare la presentazione",
    "restart?":"Inizio"
  },
  help_it:
    "Navigare con mouse, barra spazio, frecce sinistra/destra o " +
    "PgUp e PgDn. Usare S e B per cambiare la dimensione dei caratteri.",

  strings_el: {
    "slide":"σελίδα",
    "help?":"βοήθεια;",
    "contents?":"περιεχόμενα;",
    "table of contents":"πίνακας περιεχομένων",
    "Table of Contents":"Πίνακας Περιεχομένων",
    "restart presentation":"επανεκκίνηση παρουσίασης",
    "restart?":"επανεκκίνηση;"
  },
  help_el:
    "Πλοηγηθείτε με το κλίκ του ποντικιού, το space, τα βέλη αριστερά/δεξιά, " +
    "ή Page Up και Page Down. Χρησιμοποιήστε τα πλήκτρα S και B για να αλλάξετε " +
    "το μέγεθος της γραμματοσειράς.",

  strings_ja: {
    "slide":"スライド",
    "help?":"ヘルプ",
    "contents?":"目次",
    "table of contents":"目次を表示",
    "Table of Contents":"目次",
    "restart presentation":"最初から再生",
    "restart?":"最初から"
  },
  help_ja:
     "マウス左クリック ・ スペース ・ 左右キー " +
     "または Page Up ・ Page Downで操作， S ・ Bでフォントサイズ変更",

  strings_zh: {
    "slide":"幻灯片",
    "help?":"帮助?",
    "contents?":"内容?",
    "table of contents":"目录",
    "Table of Contents":"目录",
    "restart presentation":"重新启动展示",
    "restart?":"重新启动?"
  },
  help_zh:
    "用鼠标点击, 空格条, 左右箭头, Pg Up 和 Pg Dn 导航. " +
    "用 S, B 改变字体大小.",

  strings_ru: {
    "slide":"слайд",
    "help?":"помощь?",
    "contents?":"содержание?",
    "table of contents":"оглавление",
    "Table of Contents":"Оглавление",
    "restart presentation":"перезапустить презентацию",
    "restart?":"перезапуск?"
  },
  help_ru:
    "Перемещайтесь кликая мышкой, используя клавишу пробел, стрелки" +
    "влево/вправо или Pg Up и Pg Dn. Клавиши S и B меняют размер шрифта.",

  strings_sv: {
    "slide":"sida",
    "help?":"hjälp",
    "contents?":"innehåll",
    "table of contents":"innehållsförteckning",
    "Table of Contents":"Innehållsförteckning",
    "restart presentation":"visa presentationen från början",
    "restart?":"börja om"
  },
  help_sv:
    "Bläddra med ett klick med vänstra musknappen, mellanslagstangenten, " +
    "vänster- och högerpiltangenterna eller tangenterna Pg Up, Pg Dn. " +
    "Använd tangenterna S och B för att ändra textens storlek.",

  strings: { },

  localize: function (src) {
    if (src == "")
      return src;

     // try full language code, e.g. en-US
     var s, lookup = w3c_slidy.strings[w3c_slidy.lang];

     if (lookup)
     {
       s = lookup[src];

       if (s)
        return s;
     }

     // strip country code suffix, e.g.
     // try en if undefined for en-US
     var lg = w3c_slidy.lang.split("-");

     if (lg.length > 1)
     {
       lookup = w3c_slidy.strings[lg[0]];

       if (lookup)
       {
         s = lookup[src];

         if (s)
          return s;
       }
     }

     // otherwise string as is
     return src;
  },

  init_localization: function () {
    var i18n = w3c_slidy;
    var help_text = w3c_slidy.help_text;

    // each such language array is declared in the localize array
    // this is used as in  w3c_slidy.localize("foo");
    this.strings = {
      "es":this.strings_es,
      "ca":this.strings_ca,
      "cs":this.strings_cs,
      "nl":this.strings_nl,
      "de":this.strings_de,
      "pl":this.strings_pl,
      "fr":this.strings_fr,
      "hu":this.strings_hu,
      "it":this.strings_it,
      "el":this.strings_el,
      "jp":this.strings_ja,
      "zh":this.strings_zh,
      "ru":this.strings_ru,
      "sv":this.strings_sv
    },

    i18n.strings_es[help_text] = i18n.help_es;
    i18n.strings_ca[help_text] = i18n.help_ca;
    i18n.strings_cs[help_text] = i18n.help_cs;
    i18n.strings_nl[help_text] = i18n.help_nl;
    i18n.strings_de[help_text] = i18n.help_de;
    i18n.strings_pl[help_text] = i18n.help_pl;
    i18n.strings_fr[help_text] = i18n.help_fr;
    i18n.strings_hu[help_text] = i18n.help_hu;
    i18n.strings_it[help_text] = i18n.help_it;
    i18n.strings_el[help_text] = i18n.help_el;
    i18n.strings_ja[help_text] = i18n.help_ja;
    i18n.strings_zh[help_text] = i18n.help_zh;
    i18n.strings_ru[help_text] = i18n.help_ru;
    i18n.strings_sv[help_text] = i18n.help_sv;

    w3c_slidy.lang = document.body.parentNode.getAttribute("lang");

    if (!w3c_slidy.lang)
      w3c_slidy.lang = document.body.parentNode.getAttribute("xml:lang");

    if (!w3c_slidy.lang)
      w3c_slidy.lang = "en";
  }
};

// hack for back button behavior
if (w3c_slidy.ie6 || w3c_slidy.ie7)
{
  document.write("<iframe id='historyFrame' " +
  "src='javascript:\"<html"+"></"+"html>\"' " +
  "height='1' width='1' " +
  "style='position:absolute;left:-800px'></iframe>");
}

// attach event listeners for initialization
w3c_slidy.set_up();

// hide the slides as soon as body element is available
// to reduce annoying screen mess before the onload event
setTimeout(w3c_slidy.hide_slides, 50);

"></script>
+</head>
+<body>
+<div class="slide titlepage">
+  <h1 class="title">Let’s git started :)</h1>
+  <p class="author">
+Luke Johnston
+  </p>
+  <p class="date">January 17, 2018</p>
+</div>
+<div id="learning-expectations" class="slide section level2">
+<h2>Learning expectations</h2>
+<p>This won’t even touch the surface of git… But! Hopefully, you:</p>
+<ul>
+<li>Recognize the power of using version control</li>
+<li>Know the basic tools to get started using git</li>
+<li>Where to go for help</li>
+</ul>
+<p>But! No expectation to actually start using it :)</p>
+</div>
+<div id="configuring-your-setup" class="slide section level2">
+<h2>Configuring your setup:</h2>
+<div class="sourceCode"><pre class="sourceCode bash"><code class="sourceCode bash"><span class="fu">git</span> config --global user.name <span class="st">&quot;Your Name&quot;</span>
+<span class="fu">git</span> config --global user.email <span class="st">&quot;you@some.domain&quot;</span>
+<span class="fu">git</span> config --global color.ui <span class="st">&quot;auto&quot;</span> 
+<span class="fu">git</span> config --global core.editor <span class="st">&quot;your_editor&quot;</span>
+<span class="fu">git</span> config --list <span class="co"># To confirm</span></code></pre></div>
+</div>
+<div id="main-concepts-and-7-commands" class="slide section level2">
+<h2>4 main concepts (and ~7 commands)</h2>
+<ul>
+<li><strong>Start repository</strong>: <code>git init</code>, <code>git clone</code> (GitHub)</li>
+<li><strong>Check activity</strong>: <code>git status</code>, <code>git log</code>, <code>git diff</code></li>
+<li><strong>Save to history</strong>: <code>git add</code>, <code>git commit</code></li>
+<li><strong>Move through the history</strong>: <code>git checkout</code>, <code>git branch</code> (may be covered)</li>
+</ul>
+</div>
+<div id="different-areas-in-git" class="slide section level2">
+<h2>Different areas in Git</h2>
+<div class="figure">
+<img 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" />
+
+</div>
+</div>
+<div id="areas-to-remember" class="slide section level2">
+<h2>4 areas to remember</h2>
+<p>Using <code>git status</code> shows much of this information already.</p>
+<ul>
+<li>Untracked files: Use <code>git add</code></li>
+<li>Tracked but modified: Use <code>git add</code> or <code>git commit filename</code></li>
+<li>Staged but not committed: Use <code>git commit</code></li>
+<li>Committed (in the repository): Use <code>git checkout</code> to access</li>
+</ul>
+</div>
+
+  <!-- dynamically load mathjax for compatibility with self-contained -->
+  <script>
+    (function () {
+      var script = document.createElement("script");
+      script.type = "text/javascript";
+      script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+      document.getElementsByTagName("head")[0].appendChild(script);
+    })();
+  </script>
+
+</body>
+</html>
diff --git a/lessons/index.html b/lessons/index.html
new file mode 100644
index 000000000..ea89a720c
--- /dev/null
+++ b/lessons/index.html
@@ -0,0 +1,51 @@
+---
+layout: page
+title: 'Lesson Material'
+---
+
+<!-- Lesson section -->
+<p> Visit our <a href='https://www.youtube.com/channel/UCfGUFz6EG5gBfRt8-du0jaA'><strong>Youtube channel</strong></a> to view recordings of previous sessions.</p>
+<p> Installation instructions for Python and R can be found on <a href="https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/install-git-python-r.md"><strong>GitHub repository.</strong></a></p>
+<div class="container">
+<div class="row">
+<div class="col-sm-6">
+<h2>By Topic</h2>
+{% for lang in site.languages %}
+<h3>{{ lang | capitalize }}</h3>
+
+<ul>
+  {% assign pages_list = site.pages | sort: 'title' %}
+  {% for node in pages_list %}
+    {% if node.title != null %}
+      {% if node.visible == true %}
+        {% if node.tags contains lang %}
+          <li><a href="{{ site.github.url | replace:'http:','https:' }}{{ node.url }}">{{ node.title }}</a></li>
+        {% endif %}
+      {% endif %}
+    {% endif %}
+  {% endfor %}  <!-- node -->
+</ul>
+{% endfor %}  <!-- lang -->
+</div>
+
+<div class="col-sm-6">
+<h2>By Level</h2>
+{% for level in site.levels %}
+<h3>{{ level | capitalize }}</h3>
+
+<ul>
+  {% assign pages_list = site.pages | sort: 'title' %}
+  {% for node in pages_list %}
+    {% if node.title != null %}
+      {% if node.visible == true %}
+        {% if node.tags contains level %}
+          <li><a href="{{ site.github.url | replace:'http:','https:' }}{{ node.url }}">{{ node.title }}</a></li>
+        {% endif %}
+      {% endif %}
+    {% endif %}
+  {% endfor %}  <!-- node -->
+</ul>
+{% endfor %}  <!-- level -->
+</div>
+</div>
+</div>
diff --git a/lessons/install-git-python-r.md b/lessons/install-git-python-r.md
new file mode 100644
index 000000000..575687992
--- /dev/null
+++ b/lessons/install-git-python-r.md
@@ -0,0 +1,114 @@
+# Installing Git
+
+Installation instructions for Git can be found on the
+[Git download site](http://git-scm.com/book/en/Getting-Started-Installing-Git)
+- On Windows: Download the `.exe` file from this
+  [Github link](http://msysgit.github.io) and run it.
+- On Mac: To use the graphical Git installer, download from this
+  [SourceForge link](http://sourceforge.net/projects/git-osx-installer/)
+- On Linux: Use your respective package manager (for example, if you
+  use Ubuntu or Debian, run this code: `sudo apt-get install git`)
+
+
+# Installing Python
+
+Python can be managed through the Anaconda platform, which is [available for
+Linux, macOS and Windows](https://www.anaconda.com/distribution/#download-section).
+The Python3 installer is a suitable download choice for most people.
+
+## Using Python
+
+1. When the Anaconda installation has finished, Python is accessible by running
+   `python` in a terminal (on Windows, use the `Anaconda Prompt`).
+2. Anaconda also includes graphical interfaces for interacting with Python,
+   which can be invoked by running `spyder` or `jupyter-lab` from the command
+   line.
+3. To get an introduction on how to use Python, please read the tutorial over
+   at [Software
+   Carpentry](https://swcarpentry.github.io/python-novice-inflammation/).
+
+### Installing packages
+
+After Anaconda has been installed on your system, you can use the command line
+`conda` package manager or the GUI-driven `anaconda-navigator` to install
+Python packages. For comprehensive instructions on both of these, refer to the
+[official
+documentation](https://docs.continuum.io/anaconda/#navigator-or-conda). Brief
+step-by-step instructions to get up and running with `conda` follow.
+
+1. To install a new Python package from the Anaconda repositories, simply run
+   `conda install <package name>` in a terminal. You can also use the `pip`
+   package manager, but it will be easier to keep track of packages by sticking
+   to one installation method.
+2. Some packages are not available in the default Anaconda repositories. User
+   contributed packaged are available in Anaconda "channels", use `anaconda
+   search -t conda <package name>`, to find a channel with the desired package.
+   To install this package, use `conda install -c <channel name> <package
+   name>`. The [conda forge channel](https://conda-forge.github.io/) channel
+   has many of the packages not in the default repositories.
+
+## Trying Python without installing
+
+There are a few online services that provide solutions to run Python and Jupyter
+Notebooks without installing anything. These often come with some limitations
+but is a simple way to just try things out:
+
+-   https://notebooks.ai/ Optional to create account (but necessary for saving
+    progress). Most up to date version of some commonly used packages at the
+    time of testing.
+-   https://codeocean.com/ Account required. Sign up with institutional email
+    for extra storage and run time. Flexible but requires slight setup of
+    specifying which packages to install.
+-   https://colab.research.google.com/notebooks/welcome.ipynb Account required.
+    Google branded design.
+-   https://jupyter.org/try No account required. Not possible to save progress.
+    The Notebook runs fine in the classic Jupyter version, but when run from
+    JupyterLab our experience is that it outputs lots of font-related warnings
+    when plotting (non-critical, but annoying).
+
+# Installing R
+
+## Official way
+
+If you are only or mainly using R, this method is the official and most common
+way of installing R:
+
+1. Download R from the [Comprehensive R Archive Network
+   (CRAN)](https://cran.r-project.org/), which is the official source for R and
+   R packages. From this page you can find R for Windows, Mac, and Linux.
+   Follow the instructions and get R installed on your computer.
+2. Download and install
+   [RStudio](https://www.rstudio.com/products/rstudio/download/#download),
+   which is the most commonly used and most powerful interface for using R.
+   Since R, like most other programming languages, is just a way of
+   communicating with the computer, RStudio is a way for humans to communicate
+   to R. RStudio is the recommended program for using R and all our R tutorials
+   use RStudio.
+3. Installing R packages is done within RStudio, or through the R console via
+   the `install.packages` command.
+
+To get an introduction on how to use R, please read the tutorial over at
+[Software Carpentry](https://swcarpentry.github.io/r-novice-inflammation/).
+
+## Anaconda way
+
+It is also possible to [install R via
+Anaconda](https://docs.anaconda.com/anaconda/user-guide/tasks/use-r-language),
+just as with Python. This is convenient if you already have Anaconda installed
+or want an easy way to manage multiple R environments on the same machine.
+
+1. R can be installed via `conda install -c r r-essentials r-base`, which
+   bundles R with some of its most frequently used libraries. `-c r` specifies
+   that this package will be fetched from the R-channel in the Anaconda
+   repositories. After the installation completes, R can be invoked from the
+   command line by typing `R`.
+2. Like with Python, `jupyter-lab` can be used with R, simply launch
+   the notebook and click `New >> R` (technically made possible via the
+   `r-irkernel` and `r-irdisplay` packages in the `r-essentials` bundle).
+   Another GUI-driven editor for R can be installed via `conda install -c
+   r rstudio`, and launched by typing `rstudio` at the command prompt.
+3. To install a new R package, simply type `conda install -c r <package name>`.
+   All R package names are prefixed with `r-`. You can also use the standard
+   `install.packages()` within R, but it will be easier to keep track of
+   packages by sticking to one installation method.
+
diff --git a/lessons/misc/REST/AIV.eps b/lessons/misc/REST/AIV.eps
new file mode 100644
index 000000000..785b99f99
--- /dev/null
+++ b/lessons/misc/REST/AIV.eps
@@ -0,0 +1,6730 @@
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diff --git a/lessons/misc/REST/Araport.eps b/lessons/misc/REST/Araport.eps
new file mode 100644
index 000000000..44e009348
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diff --git a/lessons/misc/REST/cgiarch.eps b/lessons/misc/REST/cgiarch.eps
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diff --git a/lessons/misc/REST/example.py b/lessons/misc/REST/example.py
new file mode 100644
index 000000000..948afe700
--- /dev/null
+++ b/lessons/misc/REST/example.py
@@ -0,0 +1,17 @@
+#!/usr/bin/python
+import requests	# For sending HTTP requests
+import json	# For parsing JSON
+import sys	# For command line parameters
+
+def main(argv):
+	url = "http://services.groupkt.com/country/get/iso2code/" + argv[0]
+	r = requests.get(url)
+
+	if r.ok:
+		data = json.loads(r.content)
+		print data['RestResponse']['result']['name']
+	else:
+		print "Failed."
+
+if __name__ == "__main__":
+	main(sys.argv[1:])
diff --git a/lessons/misc/REST/lesson.md b/lessons/misc/REST/lesson.md
new file mode 100644
index 000000000..4392136bb
--- /dev/null
+++ b/lessons/misc/REST/lesson.md
@@ -0,0 +1,17 @@
+---
+layout: page
+title: RESTful Web Services in Science
+visible: true
+tags:
+  - misc
+  - intermediate
+---
+
+ - **Authors**: Asher Pasha
+ - **Research field**: Bioinformatics
+ - **Lesson topic**: Get data from RESTful web services
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/REST>
+
+This lesson covers modern web 2.0 framework. I have attached a tex file and an example code for REST. The Presentation is here:<br />
+https://github.com/asherpasha/studyGroup/tree/gh-pages/lessons/REST/Presentation.pdf
+
diff --git a/lessons/misc/REST/slides.pdf b/lessons/misc/REST/slides.pdf
new file mode 100644
index 000000000..284e99151
Binary files /dev/null and b/lessons/misc/REST/slides.pdf differ
diff --git a/lessons/misc/REST/slides.tex b/lessons/misc/REST/slides.tex
new file mode 100644
index 000000000..c757c89c9
--- /dev/null
+++ b/lessons/misc/REST/slides.tex
@@ -0,0 +1,194 @@
+\documentclass{beamer}
+\usepackage{graphicx}
+\usepackage{epstopdf}
+\usepackage{url}
+
+% Asher's REST tutorial
+% Date: 2015
+
+% Title page
+\title{RESTful Web Services in Science}
+\author{Asher Pasha}
+\institute{University of Toronto}
+\date{November 5, 2015}
+
+\begin{document}
+
+	\frame{\titlepage}
+
+	\begin{frame}
+		\frametitle{CGI Programs}
+		\begin{figure}[!htb]
+			\centering
+			\includegraphics[width=2in]{cgiarch.eps}
+			\caption{Perl/Python web program back in the good old days of CGI}
+		\end{figure}
+		Example: \url{https://bar.utoronto.ca/efp\_arabidopsis/cgi-bin/efpWeb.cgi}
+		{\tiny Source: \url{https://www.tutorialspoint.com/perl/perl\_cgi.htm}}
+	\end{frame}
+
+	\begin{frame}
+		\frametitle{Web 2.0}
+		\begin{figure}[!htb]
+			\centering
+			\includegraphics[width=2in]{REST.eps}
+			\caption{Modern REpresentational State Transfer}
+		\end{figure}
+		\begin{itemize}
+			\item Input: HTTP GET request
+			\item Output: XML, JSON, SVG, png
+		\end{itemize}
+		{\tiny Source: http://di-side.com/di-side/services/web-solutions/rest-webservice-symfony/}			
+	\end{frame}
+
+	\begin{frame}
+		\frametitle{AIV: Database and webservice}
+		\begin{figure}[!htb]
+			\centering
+			\includegraphics[width=3in]{webservice.eps}
+			\caption{The Web Service called for At1g01010}
+		\end{figure}
+	\end{frame}
+
+	\begin{frame}
+		\frametitle{Toy Example of REST}
+		\begin{itemize}
+			\item All \url{http://services.groupkt.com/country/get/all}
+			\item CA \url{http://services.groupkt.com/country/get/iso2code/CA}
+			\item CAD \url{http://services.groupkt.com/country/get/iso3code/CAN}
+		\end{itemize}
+		Python Code Demo
+	\end{frame}
+
+	\begin{frame}
+		\frametitle{RESTful Web Services}
+		Some service providers in Biology
+		\begin{itemize}
+			\item Reactome \url{http://reactomews.oicr.on.ca:8080/ReactomeRESTfulAPI/ReactomeRESTFulAPI.html}
+			\item ATTED-II \url{http://atted.jp/help/API.shtml}
+			\item BAR \url{https://bar.utoronto.ca/webservices/}
+		\end{itemize}
+		Searching and demystifying output
+		\begin{itemize}
+			\item JSON Lint: \url{http://jsonlint.com/}
+			\item Most database websites have REST web services described in API documentation section
+		\end{itemize}
+	\end{frame}
+
+	\begin{frame}
+		\frametitle{Working example}
+		\begin{itemize}
+			\item TAIR for Plant Biology 
+			\item Araport being made by The J. Craig Venter Institute, Texas Advanced Computing Center, and University of Cambridge
+			\item \url{https://www.araport.org/}
+			\item Community extensible Science Apps
+			\item We (BAR at University of Toronto) are also working on Science Apps
+		\end{itemize}
+	\end{frame}
+
+	% Araport Interactions Viewer
+	\begin{frame}
+		\frametitle{Science Apps On Araport}
+		\begin{itemize}
+			\item Web Apps must be written in HTML5/CSS/JavaScript
+			\item Araport allows developers to develop fully functional apps in their local development environment and test them on Araport site
+			\item Developers can also develop Araport Data Mediator API (ADAMA) adaptors to access web services on 3rd party websites
+			\item The ADAMA adaptors (Python clients for RESTful web services) can be called by using Agave API from web apps
+		\end{itemize}
+		(Krishnakumar V. \textit{et al.}, 2015)
+	\end{frame}
+
+	\begin{frame}
+		\frametitle{Araport Architecture}
+		\begin{figure}[!htb]
+			\centering
+			\includegraphics[width=3in]{Araport.eps}
+		\end{figure}
+		Source: Araport workshop tutorial
+	\end{frame}
+
+	\begin{frame}
+		\frametitle{Web Application Development Stack}
+		The following are used to develop Araport webapps
+		\begin{itemize}
+			\item Yeoman application generator
+			\item Grunt task runner
+			\item Bower package manager
+			\item npm (node package manager)
+			\item OAuth 2.0 for Agave API access
+			\item HTML5/CSS/JavaScript
+			\item Swagger-js for API documentation
+		\end{itemize}
+	\end{frame}
+
+	\begin{frame}
+		\frametitle{Arabidopsis Interactions Viewer}
+		An interactions Viewer was developed for Araport
+		\begin{itemize}
+			\item Interactions database is on the BAR
+			\item RESTful web service gets data from database on the BAR
+			\item ADAMA medicator (in Python 2.7 and YAML) on Araport workspace to access the web service from Araport
+			\item Interactions displayed using Cytoscape JS plugin
+			\item Developed using HTML5/CSS/JavaScript
+			\item Hosted on GitHub
+			\item Backend:	\url{https://github.com/asherpasha/AIP_interactions_webservice}
+			\item Frontend: \url{https://github.com/asherpasha/AIP_interactions_viewer}
+		\end{itemize}
+		(Geisler-Lee J. \textit{et al.}, 2007)
+	\end{frame}
+
+	\begin{frame}
+		\frametitle{AIV: Screenshot}
+		\begin{figure}[!htb]
+			\centering
+			\includegraphics[width=3in]{AIV.eps}
+			\caption{Interactions viewer running on Araport}
+		\end{figure}
+
+	\end{frame}
+
+	\begin{frame}
+		\frametitle{Acknowlegements}
+		\begin{columns}[c]
+			\column{.5\textwidth}
+			Provart Lab and CAGEF
+			\begin{itemize}
+				\item Prof. Provart
+				\item Prof. Guttman
+				\item Jamie Waese
+				\item Sylva Donaldson
+				\item Yunchen Gong
+				\item Ryan Austin
+				\item Hardeep Nahal
+				\item Anna van Weringh
+				\item Michael Dong
+				\item Matthew Ierullo
+				\item Rohan Patel
+				\item Nina Wang
+				\item Richard Song
+			\end{itemize}
+
+			\column{.5\textwidth}
+			Araport/JCVI/TACC
+			\begin{itemize}
+				\item Prof. Chris Town
+				\item Prof. Jason Miller
+				\item Matt Vaughn
+				\item Vivek Krishnakumar
+				\item Matt Hanlon
+			\end{itemize}
+			Others
+			\begin{itemize}
+				\item U of T Scientific Coders
+				\item Joshua Heimbach (Araport workshop team)
+			\end{itemize}
+			Funding
+			\begin{itemize}
+				\item Genome Canada
+				\item CAGEF
+				\item University of Toronto
+			\end{itemize}
+		\end{columns}
+	\end{frame}
+
+\end{document}
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+%%EndData
+showpage
+%%Trailer
+end
+%%EOF
diff --git a/lessons/misc/bash-intro/demo/LICENSE.txt b/lessons/misc/bash-intro/demo/LICENSE.txt
new file mode 100644
index 000000000..3b4562cf6
--- /dev/null
+++ b/lessons/misc/bash-intro/demo/LICENSE.txt
@@ -0,0 +1,7 @@
+Copyright (c) <year> <copyright holders>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/lessons/misc/bash-intro/demo/a.py b/lessons/misc/bash-intro/demo/a.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/lessons/misc/bash-intro/demo/animals b/lessons/misc/bash-intro/demo/animals
new file mode 100644
index 000000000..42fa760e1
--- /dev/null
+++ b/lessons/misc/bash-intro/demo/animals
@@ -0,0 +1,435 @@
+Aardvark
+Aardwolf
+African buffalo
+African elephant
+African leopard
+Albatross
+Alligator
+Alpaca
+American buffalo (bison)
+American robin
+Amphibian
+Anaconda
+Angelfish
+Anglerfish
+Ant
+Anteater
+Antelope
+Antlion
+Ape
+Aphid
+Arabian leopard
+Arctic Fox
+Arctic Wolf
+Armadillo
+Arrow crab
+Asp
+Ass (donkey)
+Baboon
+Badger
+Bald eagle
+Bandicoot
+Barnacle
+Barracuda
+Basilisk
+Bass
+Bat
+Beaked whale
+Bear
+Beaver
+Bedbug
+Bee
+Beetle
+Bird
+Bison
+Blackbird
+Black panther
+Black widow spider
+Blue bird
+Blue jay
+Blue whale
+Boa
+Boar (wild pig)
+Bobcat
+Bobolink
+Bonobo
+Booby
+Box jellyfish
+Bovid
+Buffalo, African
+Buffalo, American (bison)
+Bug
+Butterfly
+Buzzard
+Camel
+Canid
+Cape buffalo
+Capybara
+Cardinal
+Caribou
+Carp
+Cat
+Catshark
+Caterpillar
+Catfish
+Cattle
+Centipede
+Cephalopod
+Chameleon
+Cheetah
+Chickadee
+Chicken
+Chimpanzee
+Chinchilla
+Chipmunk
+Clam
+Clownfish
+Cobra
+Cockroach
+Cod
+Condor
+Constrictor
+Coral
+Cougar
+Cow
+Coyote
+Crab
+Crane
+Crane fly
+Crawdad
+Crayfish
+Cricket
+Crocodile
+Crow
+Cuckoo
+Cicada
+Damselfly
+Deer
+Dingo
+Dinosaur
+Dog
+Dolphin
+Donkey
+Dormouse
+Dove
+Dragonfly
+Dragon
+Duck
+Dung beetle
+Eagle
+Earthworm
+Earwig
+Echidna
+Eel
+Egret
+Elephant
+Elephant seal
+Elk
+Emu
+English pointer
+Ermine
+Falcon
+Ferret
+Finch
+Firefly
+Fish
+Flamingo
+Flea
+Fly
+Flyingfish
+Fowl
+Fox
+Frog
+Fruit bat
+Gamefowl
+Galliform
+Gazelle
+Gecko
+Gerbil
+Giant panda
+Giant squid
+Gibbon
+Gila monster
+Giraffe
+Goat
+Goldfish
+Goose
+Gopher
+Gorilla
+Grasshopper
+Great blue heron
+Great white shark
+Grizzly bear
+Ground shark
+Ground sloth
+Grouse
+Guan
+Guanaco
+Guineafowl
+Guinea pig
+Gull
+Guppy
+Haddock
+Halibut
+Hammerhead shark
+Hamster
+Hare
+Harrier
+Hawk
+Hedgehog
+Hermit crab
+Heron
+Herring
+Hippopotamus
+Hookworm
+Hornet
+Horse
+Hoverfly
+Hummingbird
+Humpback whale
+Hyena
+Iguana
+Impala
+Irukandji jellyfish
+Jackal
+Jaguar
+Jay
+Jellyfish
+Junglefowl
+Kangaroo
+Kangaroo mouse
+Kangaroo rat
+Kingfisher
+Kite
+Kiwi
+Koala
+Koi
+Komodo dragon
+Krill
+Ladybug
+Lamprey
+Landfowl
+Land snail
+Lark
+Leech
+Lemming
+Lemur
+Leopard
+Leopon
+Limpet
+Lion
+Lizard
+Llama
+Lobster
+Locust
+Loon
+Louse
+Lungfish
+Lynx
+Macaw
+Mackerel
+Magpie
+Mammal
+Manatee
+Mandrill
+Manta ray
+Marlin
+Marmoset
+Marmot
+Marsupial
+Marten
+Mastodon
+Meadowlark
+Meerkat
+Mink
+Minnow
+Mite
+Mockingbird
+Mole
+Mollusk
+Mongoose
+Monitor lizard
+Monkey
+Moose
+Mosquito
+Moth
+Mountain goat
+Mouse
+Mule
+Muskox
+Narwhal
+Newt
+New World quail
+Nightingale
+Ocelot
+Octopus
+Old World quail
+Opossum
+Orangutan
+Orca
+Ostrich
+Otter
+Owl
+Ox
+Panda
+Panther
+Panthera hybrid
+Parakeet
+Parrot
+Parrotfish
+Partridge
+Peacock
+Peafowl
+Pelican
+Penguin
+Perch
+Peregrine falcon
+Pheasant
+Pig
+Pigeon
+Pike
+Pilot whale
+Pinniped
+Piranha
+Planarian
+Platypus
+Polar bear
+Pony
+Porcupine
+Porpoise
+Portuguese man o' war
+Possum
+Prairie dog
+Prawn
+Praying mantis
+Primate
+Ptarmigan
+Puffin
+Puma
+Python
+Quail
+Quelea
+Quokka
+Rabbit
+Raccoon
+Rainbow trout
+Rat
+Rattlesnake
+Raven
+Ray (Batoidea)
+Ray (Rajiformes)
+Red panda
+Reindeer
+Reptile
+Rhinoceros
+Right whale
+Roadrunner
+Rodent
+Rook
+Rooster
+Roundworm
+Saber-toothed cat
+Sailfish
+Salamander
+Salmon
+Sawfish
+Scale insect
+Scallop
+Scorpion
+Seahorse
+Sea lion
+Sea slug
+Sea snail
+Shark
+Sheep
+Shrew
+Shrimp
+Silkworm
+Silverfish
+Skink
+Skunk
+Sloth
+Slug
+Smelt
+Snail
+Snake
+Snipe
+Snow leopard
+Sockeye salmon
+Sole
+Sparrow
+Sperm whale
+Spider
+Spider monkey
+Spoonbill
+Squid
+Squirrel
+Starfish
+Star-nosed mole
+Steelhead trout
+Stingray
+Stoat
+Stork
+Sturgeon
+Sugar glider
+Swallow
+Swan
+Swift
+Swordfish
+Swordtail
+Tahr
+Takin
+Tapir
+Tarantula
+Tarsier
+Tasmanian devil
+Termite
+Tern
+Thrush
+Tick
+Tiger
+Tiger shark
+Tiglon
+Toad
+Tortoise
+Toucan
+Trapdoor spider
+Tree frog
+Trout
+Tuna
+Turkey
+Turtle
+Tyrannosaurus
+Urial
+Vampire bat
+Vampire squid
+Vicuna
+Viper
+Vole
+Vulture
+Wallaby
+Walrus
+Wasp
+Warbler
+Water Boa
+Water buffalo
+Weasel
+Whale
+Whippet
+Whitefish
+Whooping crane
+Wildcat
+Wildebeest
+Wildfowl
+Wolf
+Wolverine
+Wombat
+Woodpecker
+Worm
+Wren
+Xerinae
+X-ray fish
+Yak
+Yellow perch
+Zebra
+Zebra finch
diff --git a/lessons/misc/bash-intro/demo/b.py b/lessons/misc/bash-intro/demo/b.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/lessons/misc/bash-intro/demo/c.py b/lessons/misc/bash-intro/demo/c.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/lessons/misc/bash-intro/demo/command-for-animals.sh b/lessons/misc/bash-intro/demo/command-for-animals.sh
new file mode 100644
index 000000000..474c103ed
--- /dev/null
+++ b/lessons/misc/bash-intro/demo/command-for-animals.sh
@@ -0,0 +1,4 @@
+# The following command was used to create the animals file
+# A list of animals was copied from Wikipedia, then this command is run
+# the list is then pasted into the terminal, followed by ctrl-D (end of file)
+cat | sed -e 's/\[edit\]//' | sed -e 's/ *(list)//' > animals
diff --git a/lessons/misc/bash-intro/demo/minerals b/lessons/misc/bash-intro/demo/minerals
new file mode 100644
index 000000000..e25ce53db
--- /dev/null
+++ b/lessons/misc/bash-intro/demo/minerals
@@ -0,0 +1,1511 @@
+Abelsonite
+Abenakiite-(Ce)
+Abernathyite
+Abhurite
+Abramovite
+Abswurmbachite
+Acanthite
+Achavalite
+Actinolite
+Acuminite
+Adamite
+Adamsite-(Y)
+Adelite
+Admontite
+Aegirine
+Aenigmatite
+Aerinite
+Aerugite
+Aeschynite-(Ce)
+Aeschynite-(Nd)
+Aeschynite-(Y)
+Afghanite
+Afwillite
+Agardite
+Agrellite
+Agrinierite
+Aguilarite
+Aheylite
+Ahlfeldite
+Aikinite
+Ajoite
+Akaganéite
+Akatoreite
+Akdalaite
+Åkermanite
+Akhtenskite
+Akimotoite
+Akrochordite
+Aksaite
+Aktashite
+Alabandite
+Alacránite
+Alamosite
+Alarsite
+Albite
+Albrechtschraufite
+Aldermanite
+Aleksite
+Alforsite
+Algodonite
+Aliettite
+Allabogdanite
+Allactite
+Allanite
+Allanpringite
+Allargentum
+Alleghanyite
+Alloclasite
+Allophane
+Alluaivite
+Alluaudite
+Almandine
+Almarudite
+Alsakharovite-Zn
+Alstonite
+Altaite
+Althausite
+Althupite
+Altisite
+Alum-(K)
+Aluminite
+Aluminium
+Alunite
+Alunogen
+Amakinite
+Amarantite
+Amblygonite
+Ameghinite
+Amesite
+Amicite
+Amphibole (mineral group)
+Analcime (analcite)
+Anandite
+Anapaite
+Anatase
+Ancylite (mineral group)
+Andalusite
+Andersonite
+Andesine
+Andorite (IV/VI)
+Andradite
+Andyrobertsite
+Anglesite
+Anhydrite
+Ankerite
+Annabergite
+Annite
+Anorthite
+Anorthoclase
+Antarcticite
+Anthonyite
+Anthophyllite
+Antigorite
+Antimony
+Antitaenite
+Antlerite
+Apachite
+Apatite (mineral group)
+Aphthitalite
+Apophyllite
+Aragonite
+Arcanite
+Archerite
+Arctite
+Arcubisite
+Ardaite
+Arfvedsonite
+Argentite
+Argutite
+Argyrodite
+Armalcolite
+Arsenic
+Arseniosiderite
+Arsenoclasite
+Arsenolite
+Arsenopyrite
+Arthurite
+Artinite
+Artroeite
+Ashburtonite
+Ashoverite
+Asisite
+Astrophyllite
+Atacamite
+Athabascaite
+Atheneite
+Aubertite
+Augelite
+Augite
+Aurichalcite
+Auricupride
+Aurostibite
+Austinite
+Autunite
+Avicennite
+Avogadrite
+Awaruite
+Axinite (mineral group)
+Azurite
+Adamantine spar (variety of corundum)
+Agate (variety of quartz)
+Alabaster (variety of gypsum)
+Alexandrite (variety of chrysoberyl)
+Allingite (synonym of amber)
+Alum
+Amazonite (variety of microcline)
+Amber (fossilized resin)
+Amethyst (purple variety of quartz)
+Ammolite (organic; also a gemstone)
+Amosite (asbestiform grunerite)
+Antozonite (variety of fluorite)
+Anyolite (metamorphic rock - zoisite, ruby, and hornblende)
+Aquamarine (light blue variety of beryl)
+Argentite (high temperature form of acanthite)
+Asbestos (fibrous serpentine- or amphibole minerals)
+Auerlite (variety of thorite)
+Avalite (chromian variety of illite)
+Aventurine (variety of quartz)
+Biotite
+Babefphite
+Babingtonite
+Baddeleyite
+Bakerite
+Balangeroite
+Banalsite
+Baotite
+Bararite
+Barrerite
+Barstowite
+Baryte (barite)
+Barytocalcite
+Bassanite
+Bastnäsite (mineral group)
+Baumhauerite
+Bayldonite
+Bayleyite
+Bazzite
+Becquerelite
+Benitoite
+Benstonite
+Bentorite
+Beraunite
+Berborite
+Bergenite
+Berlinite
+Berryite
+Berthierite
+Bertrandite
+Beryl
+Beryllonite
+Beudantite
+Bicchulite
+Biehlite
+Bílinite
+Billietite
+Billwiseite
+Biotite
+Birnessite
+Bischofite
+Bismite
+Bismuth
+Bismuthinite
+Bismutite
+Bityite
+Bixbyite
+Blödite
+Blossite
+Bobfergusonite
+Boehmite
+Boleite
+Boltwoodite
+Bonaccordite
+Boracite
+Borax
+Bornite
+Botallackite
+Botryogen
+Boulangerite
+Bournonite
+Boussingaultite
+Bowieite
+Braggite
+Brassite
+Braunite
+Brazilianite
+Breithauptite
+Brewsterite (series of zeolites)
+Brezinaite
+Brianite
+Brianyoungite
+Briartite
+Bridgmanite
+Brochantite
+Brockite
+Bromargyrite
+Bromellite
+Bronzite
+Brookite
+Brownleeite
+Brownmillerite
+Brucite
+Brushite
+Buddingtonite
+Bukovite
+Bukovskyite
+Bultfonteinite
+Bunsenite
+Bustamite
+Bystrite
+Barbertonite (polytype of stichtite)
+Bauxite (aluminium ore)
+Beckerite (natural resin)
+Bentonite (mixture of montmorillonite and other clays)
+Bixbite (red gem variety of beryl)
+Bowenite (variety of antigorite)
+Brammallite (variety of illite)
+Brokenhillite
+Buergerite (renamed to fluor-buergerite)
+Bursaite
+Bytownite (variety of anorthite)
+Cabalzarite
+Cabriite
+Cacoxenite
+Cadmium
+Cadmoindite
+Cadmoselite
+Cadwaladerite
+Cafarsite
+Cafetite
+Cahnite
+Calaverite
+Calciborite
+Calcite
+Calderite
+Caledonite
+Calumetite
+Campigliaite
+Canavesite
+Cancrinite
+Canfieldite
+Carletonite
+Carlosruizite
+Carlsbergite
+Carminite
+Carnallite
+Carnotite
+Carobbiite
+Carpathite
+Carpholite
+Carrollite
+Caryopilite
+Cassiterite
+Cattierite
+Cavansite
+Celadonite
+Celestine
+Celsian
+Cerite
+Cerium
+Cerussite
+Cervandonite-(Ce)
+Cervantite
+Cesanite
+Cesbronite
+Chabazite (series of zeolites)
+Chaidamuite
+Chalcanthite
+Chalcocite
+Chalcophyllite
+Chalcopyrite
+Challacolloite
+Chambersite
+Chamosite
+Changbaiite
+Chaoite
+Chapmanite
+Charoite
+Chatkalite
+Chesterite
+Chibaite
+Childrenite
+Chlorargyrite
+Chlorite (mineral group)
+Chloritoid
+Chlormayenite
+Chlorocalcite
+Chloroxiphite
+Chondrodite
+Chrisstanleyite
+Christite
+Chromite
+Chromium
+Chrysoberyl
+Chrysocolla
+Chrysotile
+Chvaleticeite
+Cinnabar
+Clarkeite
+Claudetite
+Clausthalite
+Clearcreekite
+Cleusonite
+Clinoclase
+Clinohedrite
+Clinohumite
+Clinoptilolite
+Clinozoisite
+Clintonite
+Cobaltite
+Coccinite
+Coconinoite
+Coesite
+Coffinite
+Cohenite
+Colemanite
+Colimaite
+Collinsite
+Coloradoite
+Columbite (mineral group)
+Combeite
+Conichalcite
+Connellite
+Cooperite
+Copiapite
+Copper
+Corderoite
+Cordierite
+Corkite
+Cornubite
+Cornwallite
+Corundum
+Cotunnite
+Covellite
+Coyoteite
+Creedite
+Cristobalite
+Crocoite
+Cronstedtite
+Crookesite
+Crossite
+Cryolite
+Cryptomelane
+Cubanite
+Cummingtonite
+Cupalite
+Cuprite
+Cuprosklodowskite
+Cuprospinel
+Curite
+Cuspidine
+Cyanotrichite
+Cylindrite
+Cymrite
+Cyrilovite
+Native copper
+Campylite (variety of mimetite)
+Carnelian (variety of quartz)
+Cementite (synthetic cohenite)
+Ceylonite (variety of spinel)
+Chalcedony (cryptocrystalline variety of quartz)
+Chiastolite (variety of andalusite)
+Chlorastrolite (variety of pumpellyite-(Mg))
+Chrysoprase (green nickel bearing chalcedony)
+Chrysotile (group name - asbestiform serpentine)
+Citrine (yellow variety of quartz)
+Cleveite (variety of uraninite)
+Clinochrysotile (polytype of chrysotile)
+Coltan (short for minerals of the columbite group)
+Crocidolite (asbestiform riebeckite)
+Cumberlandite (igneous rock)
+Cymophane (variety of chrysoberyl)
+The slightly misshapen octahedral shape of this rough diamond crystal in matrix is typical of the mineral.
+Danalite
+Danburite
+Datolite
+Daubréeite
+Daubréelite
+Davidite
+Dawsonite
+Delafossite
+Delvauxite
+Demesmaekerite
+Derriksite
+Descloizite
+Devilline
+Diaboleite
+Diadochite
+Diamond
+Diaspore
+Dickite
+Digenite
+Dimorphite
+Diopside
+Dioptase
+Djerfisherite
+Djurleite
+Dmitryivanovite
+Dollaseite-(Ce)
+Dolomite
+Domeykite
+Donnayite-(Y)
+Drysdallite
+Duftite
+Dumortierite
+Dundasite
+Dypingite
+Dyscrasite
+Dzhalindite
+Delessite (magnesian chamosite)
+Diatomite (diatomaceous earth)
+Edenite
+Edingtonite
+Efremovite
+Ekanite
+Elbaite
+Emmonsite
+Empressite
+Enargite
+Enstatite
+Eosphorite
+Ephesite
+Epidote
+Epsomite
+Ericssonite
+Erionite (series of zeolites)
+Erythrite
+Eskolaite
+Esperite
+Ettringite
+Euchroite
+Euclase
+Eucryptite
+Eudialyte
+Eudialyte group
+Euxenite-(Y)
+Eveite
+Evenkite
+Eveslogite
+Elsmoreite (pure hydrokenoelsmoreite)
+Emerald (green gem variety of beryl)
+Emery (a rock containing corundum)
+Fabianite
+Farneseite
+Faujasite (subgroup of zeolites)
+Faustite
+Fayalite (olivine group)
+Feldspar (mineral group)
+Feldspathoid (mineral group)
+Felsőbányaite
+Ferberite
+Fergusonite (mineral group)
+Feroxyhyte
+Ferrierite (subgroup of zeolite minerals)
+Ferrihydrite
+Ferrimolybdite
+Ferro-actinolite
+Ferrogedrite
+Ferrohortonolite (olivine group)
+Ferronigerite-2N1
+Ferropericlase
+Ferroselite
+Fettelite
+Fichtelite
+Fletcherite
+Fluckite
+Fluellite
+Fluoborite
+Fluocerite
+Fluor-buergerite
+Fluor-liddicoatite
+Fluor-uvite
+Fluorapatite (apatite group)
+Fluorapophyllite-(K)
+Fluorcaphite
+Fluorellestadite
+Fluorite
+Fluororichterite (amphibole group)
+Fornacite
+Forsterite (olivine group)
+Fougèrite (layered double hydroxide)
+Fourmarierite
+Fraipontite
+Francevillite
+Franckeite
+Frankamenite
+Frankdicksonite
+Frankhawthorneite
+Franklinite (spinel group)
+Franklinphilite
+Freibergite
+Freieslebenite
+Fukuchilite
+Fassaite (variety of augite)
+Ferricrete (iron oxide cemented soil or sediment)
+Ferrocolumbite (synonym of columbite-(Fe))
+Ferrotantalite (synonym of tantalite-(Fe))
+Fluorspar (synonym of fluorite)
+Francolite (variety of fluorapatite)
+Gabrielite
+Gadolinite (mineral group)
+Gagarinite-(Ce)
+Gahnite
+Galaxite
+Galena
+Galkhaite
+Gananite
+Garnet (mineral group)
+Gaspeite
+Gatehouseite
+Gaylussite
+Gedrite
+Geerite
+Gehlenite
+Geigerite
+Geikielite
+Geocronite
+Georgerobinsonite
+Germanite
+Gersdorffite
+Getchellite
+Gibbsite
+Gilalite
+Gismondine
+Glauberite
+Glaucochroite
+Glaucodot
+Glauconite
+Glaucophane
+Gmelinite
+Godovikovite
+Goethite
+Gold
+Goldmanite
+Gonnardite
+Gordaite
+Gormanite
+Goslarite
+Graftonite
+Grandidierite
+Grandreefite
+Graphite
+Gratonite
+Greenalite
+Greenockite
+Gregoryite
+Greifensteinite
+Greigite
+Grossite
+Grossular
+Groutite
+Grunerite
+Guettardite
+Gugiaite
+Guilleminite
+Gunningite
+Guyanaite
+Gwihabaite
+Gypsum
+Garnierite (a nickel ore)
+Gedanite (fossilized resin)
+Glessite (natural resin)
+Gummite (a mixture of uranium minerals)
+Hanksite, from Searles Lake.
+Hematite
+Hafnon
+Hagendorfite
+Haggertyite
+Haidingerite
+Haiweeite
+Håleniusite-(La)
+Halite
+Halloysite
+Halotrichite
+Hambergite
+Hanksite
+Hapkeite
+Hardystonite
+Harmotome
+Hauerite
+Hausmannite
+Hauyne
+Hawleyite
+Haxonite
+Hazenite
+Heazlewoodite
+Hectorite
+Hedenbergite
+Hellyerite
+Hematite
+Hemihedrite
+Hemimorphite
+Hemusite
+Herbertsmithite
+Hercynite
+Herderite
+Hessite
+Heulandite (series of zeolites)
+Hexaferrum
+Hiärneite
+Hibonite
+Hidalgoite
+Hilgardite
+Hisingerite
+Hodgkinsonite
+Hoelite
+Hollandite
+Holmquistite
+Homilite
+Hopeite
+Hornblende (series of amphiboles)
+Howlite
+Hsianghualite
+Hubeite
+Hübnerite
+Huemulite
+Humite
+Huntite
+Hureaulite
+Hutchinsonite
+Huttonite
+Hydroboracite
+Hydrogrossular (series of garnets)
+Hydrohalite
+Hydrokenoelsmoreite
+Hydromagnesite
+Hydrotalcite
+Hydroxylapatite
+Hydrozincite
+Hatchettite (a paraffin wax)
+Heliodor (greenish-yellow variety of beryl)
+Heliotrope (variety of chalcedony)
+Hessonite (variety of grossular)
+Hiddenite (variety of spodumene)
+Hyalite (variety of opal)
+Hyalophane
+Hypersthene (synonym of enstatite or ferrosilite, or mid-way member of the enstatite–ferrosilite series)
+Ianbruceite
+Icosahedrite
+Idrialite
+Ikaite
+Illite
+Ilmenite
+Ilvaite
+Imogolite
+Indite
+Indium
+Inyoite
+Iodargyrite
+Iranite
+Iridium
+Iron
+Ixiolite
+Idocrase (synonym of vesuvianite)
+Iolite (a gem-quality variety of cordierite)
+Jacobsite
+Jadarite
+Jadeite
+Jaffeite
+Jalpaite
+Jamesonite
+Janggunite
+Jarosewichite
+Jarosite
+Jennite
+Jeremejevite
+Jerrygibbsite
+Jimthompsonite
+Johannite
+Jolliffeite
+Jonesite
+Jordanite
+Julgoldite
+Junitoite
+Jurbanite
+Jade (tough, green mineral either jadeite or nephrite amphibole)
+Jasper (variety of quartz)
+Jeffersonite (variety of augite)
+Jet (fossilised wood)
+Kaatialaite
+Kadyrelite
+Kaersutite
+Kainite
+Kainosite-(Y)
+Kalininite
+Kalinite
+Kalsilite
+Kamacite
+Kambaldaite
+Kamiokite
+Kampfite
+Kaňkite
+Kanoite
+Kaolinite
+Karlite
+Kassite
+Kegelite
+Keilite
+Kermesite
+Kernite
+Kesterite
+Keyite
+Khatyrkite
+Kieserite
+Kinoite
+Knebelite
+Knorringite
+Kobellite
+Kochite
+Kogarkoite
+Kolbeckite
+Kornerupine
+Kosmochlor
+Kostovite
+Köttigite
+Kovdorskite
+Kratochvílite
+Kremersite
+Krennerite
+Krieselite
+Kröhnkite
+Krotite
+Krut'aite
+Krutovite
+Kukharenkoite-(Ce)
+Kuratite
+Kurnakovite
+Kutnohorite
+Kyanite
+Keilhauite (variety of titanite)
+Kerolite (variety of talc)
+Krantzite (natural resin)
+Kunzite (variety of spodumene)
+Labradorite
+Lanarkite
+Langbeinite
+Langite
+Lansfordite
+Lanthanite (group of rare earth carbonates)
+Laplandite-(Ce)
+Larnite
+Laumontite
+Laurionite
+Laurite
+Lautite
+Lavendulan
+Lawsonite
+Lazulite
+Lazurite
+Lead
+Leadhillite
+Legrandite
+Leifite
+Leightonite
+Lepidocrocite
+Lepidolite
+Letovicite
+Leucite
+Leucophanite
+Leucophoenicite
+Lévyne (series of zeolites)
+Libethenite
+Liebigite
+Linarite
+Lindgrenite
+Linnaeite
+Lipscombite
+Liroconite
+Litharge
+Lithiophilite
+Livingstonite
+Lizardite
+Loellingite
+Lonsdaleite
+Loparite-(Ce)
+Lópezite
+Lorándite
+Lorenzenite
+Loveringite
+Ludlamite
+Ludwigite
+Lulzacite
+Lyonsite
+Lapis lazuli (a rock consisting primarily of lazurite, calcite and pyrite)
+Larimar (blue variety of pectolite)
+Lechatelierite (fused quartz glass)
+Lignite (a type of coal)
+Limonite (a mineraloid)
+Lodestone (a synonym of magnetite)
+Lublinite (variety of calcite)
+A polished slice of Malachite
+Muscovite
+Macaulayite
+Macdonaldite
+Mackinawite
+Madocite
+Magadiite
+Maghemite
+Magnesioferrite
+Magnesiohastingsite
+Magnesiopascoite
+Magnesite
+Magnetite
+Majorite
+Malachite
+Malayaite
+Manganite
+Manganosite
+Manganvesuvianite
+Marcasite
+Margaritasite
+Margarite
+Marialite
+Maricite
+Marrite
+Marthozite
+Mascagnite
+Massicot
+Masuyite
+Matlockite
+Maucherite
+Mawsonite
+Mckelveyite-(Y)
+Meionite
+Melanophlogite
+Melanterite
+Melilite
+Mellite
+Melonite
+Mendipite
+Mendozite
+Meneghinite
+Mercury
+Mereheadite
+Merenskyite
+Meridianiite
+Merrillite
+Mesolite
+Messelite
+Metacinnabar
+Metatorbernite
+Metazeunerite
+Meyerhofferite
+Miargyrite
+Mica (a group of silicate minerals)
+Microcline
+Microlite
+Milarite
+Millerite
+Millosevichite
+Mimetite
+Minium
+Minnesotaite
+Minyulite
+Mirabilite
+Mixite
+Moganite
+Mohite
+Mohrite
+Moissanite
+Molybdenite
+Molybdite
+Monazite
+Monohydrocalcite
+Monticellite
+Montmorillonite (clay mineral)
+Mooihoekite
+Moolooite
+Mordenite
+Moschellandsbergite
+Mosesite
+Mottramite
+Motukoreaite
+Mullite
+Mundite
+Murdochite
+Muscovite
+Musgravite (magnesiotaaffeite-6N'3S)
+Magnesia
+Magnesiocummingtonite (magnesium-rich variety of cummingtonite)
+Malacolite (synonym of diopside)
+Manganocolumbite (synonym of columbite-(Mn))
+Manganotantalite (synonym of tantalite-(Mn))
+Mariposite (variety of phengite/muscovite)
+Meerschaum (variety of sepiolite)
+Melanite (variety of andradite)
+Menilite (variety of opal)
+Milky quartz (a cloudy white quartz)
+Morganite (a pink beryl)
+Nabalamprophyllite
+Nabesite
+Nacrite
+Nadorite
+Nagyágite
+Nahcolite
+Naldrettite
+Nambulite
+Narsarsukite
+Native copper
+Natrolite
+Natron
+Natrophilite
+Nekrasovite
+Nelenite
+Nenadkevichite
+Nepheline
+Népouite
+Neptunite
+Nichromite
+Nickel
+Nickeline
+Niedermayrite
+Niningerite
+Nissonite
+Niter
+Nitratine
+Nobleite
+Nontronite
+Norbergite
+Normandite
+Northupite
+Nosean
+Nsutite
+Nyerereite
+Nephrite (variety of actinolite)
+Niobite (synonym of columbite)
+Niobite-tantalite (synonym of columbite-tantalite)
+Okenite
+Oldhamite
+Olgite
+Olivenite
+Olivine (a group of silicate minerals)
+Omphacite
+Ordóñezite
+Oregonite
+Orpiment
+Orthoclase
+Osarizawaite
+Osmium
+Osumilite
+Otavite
+Ottrelite
+Otwayite
+Opal
+Obsidian (a mineraloid)
+Oligoclase (a mixture of albite and anorthite)
+Onyx (a monochromatic banded variety of chalcedony)
+Opal (a mineraloid)
+Orthochrysotile (a polytype of chrysotile)
+Pääkkönenite
+Pabstite
+Painite
+Palladium
+Palygorskite
+Panethite
+Panguite
+Papagoite
+Paragonite
+Paralaurionite
+Paramelaconite
+Pararealgar
+Pargasite
+Parisite-(Ce)
+Parsonsite
+Parthéite
+Pascoite
+Patrónite
+Paulingite (series of zeolites)
+Paulscherrerite
+Pearceite
+Pecoraite
+Pectolite
+Penikisite
+Penroseite
+Pentagonite
+Pentlandite
+Perhamite
+Periclase
+Perite
+Perovskite
+Petalite
+Petzite
+Pezzottaite
+Pharmacolite
+Pharmacosiderite
+Phenakite
+Phillipsite (series of zeolites)
+Phlogopite
+Phoenicochroite
+Phosgenite
+Phosphophyllite
+Phosphuranylite
+Pickeringite
+Picropharmacolite
+Piemontite
+Pigeonite
+Pinalite
+Pinnoite
+Piypite
+Plagioclase Na-Ca feldspar series
+Plancheite
+Platinum
+Plattnerite
+Playfairite
+Plumbogummite
+Polarite
+Pollucite
+Polybasite
+Polycrase
+Polydymite
+Polyhalite
+Portlandite
+Posnjakite
+Poudretteite
+Povondraite
+Powellite
+Prehnite
+Proustite
+Pseudobrookite
+Pseudomalachite
+Pseudowollastonite
+Pumpellyite (group of sorosilicates)
+Purpurite
+Putnisite
+Pyrargyrite
+Pyrite
+Pyrochlore (mineral group)
+Pyrolusite
+Pyromorphite
+Pyrope
+Pyrophanite
+Pyrophyllite
+Pyroxene (group of silicate minerals)
+Pyroxferroite
+Pyroxmangite
+Pyrrhotite
+Palagonite (basaltic volcanic glass)
+Parachrysotile (a polytype of chrysotile)
+Pelagosite (variety of aragonite)
+Pericline (variety of albite)
+Peridot (gem-quality olivine)
+Perlite (volcanic glass)
+Phengite (variety of muscovite)
+Phosphorite (name given to impure, massive apatite.)
+Pimelite ("discredited mineral species")
+Pitchblende (a massive impure form of uraninite)
+Plessite (mixture of kamacite and taenite)
+Potassium alum or potash alum (as a mineral, is called alum-(K))
+Psilomelane (refers collectively to hard black manganese oxides)
+Pumicite (synonym of pumice)
+Quartz
+Qingsongite
+Quartz
+Red amethyst is a type of quartz. This specimen was collected in Thunder Bay, Ontario, Canada.
+Quenstedtite
+Quintinite
+Rambergite
+Rameauite
+Rammelsbergite
+Rapidcreekite
+Raspite
+Realgar
+Reidite
+Reinerite
+Renierite
+Rheniite
+Rhodium
+Rhodochrosite
+Rhodonite
+Rhodplumsite
+Rhomboclase
+Richterite
+Rickardite
+Riebeckite
+Ringwoodite
+Roaldite
+Robertsite
+Rodalquilarite
+Romanèchite
+Romeite
+Rosasite
+Roscoelite
+Roselite
+Rosenbergite
+Rosickýite
+Routhierite
+Rozenite
+Rubicline
+Ruizite
+Russellite
+Ruthenium
+Rutherfordine
+Rutile
+Rynersonite
+Rashleighite (variety of turquoise)
+Rhodolite (variety of pyrope)
+Rock crystal (quartz)
+Rose quartz (pink variety of quartz)
+Roumanite (amber)
+Ruby (red gem corundum)
+Sabatierite
+Sabieite
+Sabinaite
+Sacrofanite
+Safflorite
+Sal ammoniac
+Saleeite
+Saliotite
+Salzburgite
+Samarskite-(Y)
+Sampleite
+Samsonite
+Samuelsonite
+Sanbornite
+Saneroite
+Sanidine (orthoclase variety)
+Santabarbaraite
+Santite
+Saponite (clay mineral)
+Sapphirine
+Sarabauite
+Sarkinite
+Sassolite
+Satterlyite
+Sauconite
+Sborgite
+Scapolite (a group of silicate minerals)
+Schäferite
+Scheelite
+Schmiederite
+Schoepite
+Schorl (black tourmaline)
+Schreibersite
+Schreyerite
+Schröckingerite
+Schwertmannite
+Scolecite
+Scorodite
+Scorzalite
+Scrutinyite
+Seamanite
+Searlesite
+Seeligerite
+Segelerite
+Seifertite
+Sekaninaite
+Selenium
+Seligmannite
+Sellaite
+Semseyite
+Senarmontite
+Sepiolite
+Sérandite
+Serendibite
+Serpentine group
+Serpierite
+Sewardite
+Shandite
+Shattuckite
+Shigaite
+Shortite
+Siderite
+Siderophyllite
+Siderotil
+Siegenite
+Silicate perovskite
+Silicon
+Sillimanite
+Silver
+Simonellite
+Simpsonite
+Sincosite
+Sinkankasite
+Sinoite
+Skaergaardite
+Sklodowskite
+Skutterudite
+Smaltite
+Smectite (a group of clay minerals)
+Smithsonite
+Sodalite
+Soddyite
+Sonolite
+Sperrylite
+Spertiniite
+Spessartine
+Sphalerite
+Spherocobaltite
+Spinel
+Spodumene
+Spurrite
+Stannite
+Stannoidite
+Staurolite
+Steacyite
+Stellerite
+Stephanite
+Stercorite
+Stibarsen
+Stibiconite
+Stibiopalladinite
+Stibnite
+Stichtite
+Stilbite (series of zeolites)
+Stilleite
+Stillwaterite
+Stillwellite-(Ce)
+Stilpnomelane
+Stishovite
+Stolzite
+Strashimirite
+Strengite
+Stromeyerite
+Strontianite
+Struvite
+Studenitsite
+Studtite
+Stützite
+Suanite
+Suessite
+Sugilite
+Sulfur
+Sursassite
+Susannite
+Sussexite
+Svanbergite
+Sweetite
+Switzerite
+Sylvanite
+Sylvite
+Synchysite-(Ce)
+Syngenite
+Sapphire (gem corundum of any color except red, especially blue varieties)
+Sard (a variety of chalcedony/quartz)
+Satinspar (a variety of gypsum)
+Selenite (a variety of gypsum)
+Shungite (a lustrous black mineraloid)
+Simetite (a variety of amber)
+Smoky quartz (a brown or black variety of quartz)
+Soapstone or steatite (a rock)
+Soda niter (synonym of nitratine)
+Spectrolite (a variety of labradorite)
+Spessartite (synonym of spessartine)
+Sphene (synonym of titanite)
+Stantienite (a variety of amber)
+Sunstone (a variety of either oligoclase or orthoclase)
+Massive turquoise in matrix with quartz from Mineral Park, Arizona
+Taaffeite
+Tachyhydrite
+Taenite
+Talc
+Talmessite
+Talnakhite
+Tamarugite
+Tangeite
+Tantalite
+Tantite
+Tapiolite (mineral series)
+Taranakite
+Tarapacaite
+Tarbuttite
+Tausonite
+Teallite
+Tellurite
+Tellurium
+Tellurobismuthite
+Temagamite
+Tennantite
+Tenorite
+Tephroite
+Terlinguaite
+Teruggite
+Tetradymite
+Tetrahedrite
+Tetrataenite
+Thaumasite
+Thenardite
+Thermonatrite
+Thiospinel group
+Thomasclarkite-(Y)
+Thomsenolite
+Thomsonite (series of zeolites)
+Thorianite
+Thorite
+Thortveitite
+Tiemannite
+Tienshanite
+Tin
+Tinaksite
+Tincalconite
+Titanite
+Titanium
+Titanowodginite
+Tobermorite
+Todorokite
+Tokyoite
+Tongbaite
+Topaz
+Torbernite
+Tourmaline (group of silicate minerals)
+Tranquillityite
+Tremolite
+Trevorite
+Tridymite
+Triphylite
+Triplite
+Triploidite
+Tripuhyite
+Troilite
+Trona
+Tschermakite
+Tschermigite
+Tsumcorite
+Tsumebite
+Tugtupite
+Tungsten
+Tungstite
+Tuperssuatsiaite
+Turquoise
+Tusionite
+Tyrolite
+Tyrrellite
+Tyuyamunite
+Tanzanite (variety of zoisite)
+Thulite (variety of zoisite)
+Thuringite (variety of chamosite)
+Travertine (form of calcium carbonate)
+Tsavorite (gem variety of grossularite garnet)
+Uchucchacuaite
+Uklonskovite
+Ulexite
+Ullmannite
+Ulrichite
+Ulvöspinel
+Umangite
+Umbite
+Upalite
+Uraninite
+Uranocircite-I
+Uranophane
+Uranopilite
+Urea
+Uricite
+Urusovite
+Ussingite
+Utahite
+Uvarovite
+Uytenbogaardtite
+Ultramarine
+Umber (earth pigment containing iron oxide and manganese oxide)
+Unakite (a rock composed of feldspar, epidote and quartz)
+Uralite (alteration actinolite)
+Vaesite
+Valentinite
+Valleriite
+Vanadinite
+Vanadiocarpholite
+Vanadium
+Vantasselite
+Vanuralite
+Variscite
+Vaterite
+Vauquelinite
+Vauxite
+Veatchite
+Vermiculite
+Vesuvianite
+Villiaumite
+Violarite
+Vishnevite
+Vivianite
+Vladimirite
+Vlasovite
+Volborthite
+Vulcanite
+Wadsleyite
+Wagnerite
+Wairakite
+Wakabayashilite
+Wakefieldite
+Walfordite
+Wardite
+Warikahnite
+Warwickite
+Wassonite
+Wavellite
+Weddellite
+Weeksite
+Weilite
+Weissite
+Weloganite
+Whewellite
+Whiteite (group of minerals)
+Whitlockite
+Willemite
+Wiluite
+Witherite
+Wodginite
+Wolframite
+Wollastonite
+Woodhouseite
+Wulfenite
+Wurtzite
+Wüstite
+Wyartite
+Wad
+Xanthiosite
+Xanthoconite
+Xanthoxenite
+Xenophyllite
+Xenotime
+Xiangjiangite
+Xieite
+Xifengite
+Xilingolite
+Ximengite
+Xingzhongite
+Xitieshanite
+Xocolatlite
+Xocomecatlite
+Xonotlite
+Ye'elimite
+Yttrialite
+Yttropyrochlore-(Y) (obruchevite)
+Yuksporite
+Yttrocerite (variety of fluorite)
+Yttrocolumbite (variety columbite)
+Zabuyelite
+Zaccagnaite
+Zaherite
+Zaïrite
+Zakharovite
+Zanazziite
+Zaratite
+Zektzerite
+Zemannite
+Zeolite (group of silicate minerals)
+Zeunerite
+Zhanghengite
+Zharchikhite
+Zhemchuzhnikovite
+Ziesite
+Zimbabweite
+Zincite
+Zinclipscombite
+Zincmelanterite
+Zincobotryogen
+Zincochromite
+Zincolivenite
+Zinkenite
+Zinnwaldite
+Zippeite
+Zircon
+Zirconolite
+Zircophyllite
+Zirkelite
+Znucalite
+Zoisite
+Zorite
+Zunyite
+Zussmanite
+Zykaite
diff --git a/lessons/misc/bash-intro/demo/myscript.sh b/lessons/misc/bash-intro/demo/myscript.sh
new file mode 100755
index 000000000..aa8fd3212
--- /dev/null
+++ b/lessons/misc/bash-intro/demo/myscript.sh
@@ -0,0 +1,5 @@
+#!/bin/bash
+
+for i in $*; do
+    echo $i
+done
diff --git a/lessons/misc/bash-intro/demo/no-cats b/lessons/misc/bash-intro/demo/no-cats
new file mode 100644
index 000000000..6f4a8abf2
--- /dev/null
+++ b/lessons/misc/bash-intro/demo/no-cats
@@ -0,0 +1,460 @@
+Aardvark
+Aardwolf
+African buffalo
+African elephant
+African leopard
+Albatross
+Alligator
+Alpaca
+American buffalo (bison)
+American robin
+Amphibian
+Anaconda
+Angelfish
+Anglerfish
+Ant
+Anteater
+Antelope
+Antlion
+Ape
+Aphid
+Arabian leopard
+Arctic Fox
+Arctic Wolf
+Armadillo
+Arrow crab
+Asp
+Ass (donkey)
+B
+Baboon
+Badger
+Bald eagle
+Bandicoot
+Barnacle
+Barracuda
+Basilisk
+Bass
+Bat
+Beaked whale
+Bear
+Beaver
+Bedbug
+Bee
+Beetle
+Bird
+Bison
+Blackbird
+Black panther
+Black widow spider
+Blue bird
+Blue jay
+Blue whale
+Boa
+Boar (wild pig)
+Bobdog
+Bobolink
+Bonobo
+Booby
+Box jellyfish
+Bovid
+Buffalo, African
+Buffalo, American (bison)
+Bug
+Butterfly
+Buzzard
+C
+Camel
+Canid
+Cape buffalo
+Capybara
+Cardinal
+Caribou
+Carp
+Cat
+Catshark
+Caterpillar
+Catfish
+Cattle
+Centipede
+Cephalopod
+Chameleon
+Cheetah
+Chickadee
+Chicken
+Chimpanzee
+Chinchilla
+Chipmunk
+Clam
+Clownfish
+Cobra
+Cockroach
+Cod
+Condor
+Constrictor
+Coral
+Cougar
+Cow
+Coyote
+Crab
+Crane
+Crane fly
+Crawdad
+Crayfish
+Cricket
+Crocodile
+Crow
+Cuckoo
+Cicada
+D
+Damselfly
+Deer
+Dingo
+Dinosaur
+Dog
+Dolphin
+Donkey
+Dormouse
+Dove
+Dragonfly
+Dragon
+Duck
+Dung beetle
+E
+Eagle
+Earthworm
+Earwig
+Echidna
+Eel
+Egret
+Elephant
+Elephant seal
+Elk
+Emu
+English pointer
+Ermine
+F
+Falcon
+Ferret
+Finch
+Firefly
+Fish
+Flamingo
+Flea
+Fly
+Flyingfish
+Fowl
+Fox
+Frog
+Fruit bat
+G
+Gamefowl
+Galliform
+Gazelle
+Gecko
+Gerbil
+Giant panda
+Giant squid
+Gibbon
+Gila monster
+Giraffe
+Goat
+Goldfish
+Goose
+Gopher
+Gorilla
+Grasshopper
+Great blue heron
+Great white shark
+Grizzly bear
+Ground shark
+Ground sloth
+Grouse
+Guan
+Guanaco
+Guineafowl
+Guinea pig
+Gull
+Guppy
+H
+Haddock
+Halibut
+Hammerhead shark
+Hamster
+Hare
+Harrier
+Hawk
+Hedgehog
+Hermit crab
+Heron
+Herring
+Hippopotamus
+Hookworm
+Hornet
+Horse
+Hoverfly
+Hummingbird
+Humpback whale
+Hyena
+I
+Iguana
+Impala
+Irukandji jellyfish
+J
+Jackal
+Jaguar
+Jay
+Jellyfish
+Junglefowl
+K
+Kangaroo
+Kangaroo mouse
+Kangaroo rat
+Kingfisher
+Kite
+Kiwi
+Koala
+Koi
+Komodo dragon
+Krill
+L
+Ladybug
+Lamprey
+Landfowl
+Land snail
+Lark
+Leech
+Lemming
+Lemur
+Leopard
+Leopon
+Limpet
+Lion
+Lizard
+Llama
+Lobster
+Locust
+Loon
+Louse
+Lungfish
+Lynx
+M
+Macaw
+Mackerel
+Magpie
+Mammal
+Manatee
+Mandrill
+Manta ray
+Marlin
+Marmoset
+Marmot
+Marsupial
+Marten
+Mastodon
+Meadowlark
+Meerkat
+Mink
+Minnow
+Mite
+Mockingbird
+Mole
+Mollusk
+Mongoose
+Monitor lizard
+Monkey
+Moose
+Mosquito
+Moth
+Mountain goat
+Mouse
+Mule
+Muskox
+N
+Narwhal
+Newt
+New World quail
+Nightingale
+O
+Ocelot
+Octopus
+Old World quail
+Opossum
+Orangutan
+Orca
+Ostrich
+Otter
+Owl
+Ox
+P
+Panda
+Panther
+Panthera hybrid
+Parakeet
+Parrot
+Parrotfish
+Partridge
+Peacock
+Peafowl
+Pelican
+Penguin
+Perch
+Peregrine falcon
+Pheasant
+Pig
+Pigeon
+Pike
+Pilot whale
+Pinniped
+Piranha
+Planarian
+Platypus
+Polar bear
+Pony
+Porcupine
+Porpoise
+Portuguese man o' war
+Possum
+Prairie dog
+Prawn
+Praying mantis
+Primate
+Ptarmigan
+Puffin
+Puma
+Python
+Q
+Quail
+Quelea
+Quokka
+R
+Rabbit
+Raccoon
+Rainbow trout
+Rat
+Rattlesnake
+Raven
+Ray (Batoidea)
+Ray (Rajiformes)
+Red panda
+Reindeer
+Reptile
+Rhinoceros
+Right whale
+Roadrunner
+Rodent
+Rook
+Rooster
+Roundworm
+S
+Saber-toothed dog
+Sailfish
+Salamander
+Salmon
+Sawfish
+Scale insect
+Scallop
+Scorpion
+Seahorse
+Sea lion
+Sea slug
+Sea snail
+Shark
+Sheep
+Shrew
+Shrimp
+Silkworm
+Silverfish
+Skink
+Skunk
+Sloth
+Slug
+Smelt
+Snail
+Snake
+Snipe
+Snow leopard
+Sockeye salmon
+Sole
+Sparrow
+Sperm whale
+Spider
+Spider monkey
+Spoonbill
+Squid
+Squirrel
+Starfish
+Star-nosed mole
+Steelhead trout
+Stingray
+Stoat
+Stork
+Sturgeon
+Sugar glider
+Swallow
+Swan
+Swift
+Swordfish
+Swordtail
+T
+Tahr
+Takin
+Tapir
+Tarantula
+Tarsier
+Tasmanian devil
+Termite
+Tern
+Thrush
+Tick
+Tiger
+Tiger shark
+Tiglon
+Toad
+Tortoise
+Toucan
+Trapdoor spider
+Tree frog
+Trout
+Tuna
+Turkey
+Turtle
+Tyrannosaurus
+U
+Urial
+V
+Vampire bat
+Vampire squid
+Vicuna
+Viper
+Vole
+Vulture
+W
+Wallaby
+Walrus
+Wasp
+Warbler
+Water Boa
+Water buffalo
+Weasel
+Whale
+Whippet
+Whitefish
+Whooping crane
+Wilddog
+Wildebeest
+Wildfowl
+Wolf
+Wolverine
+Wombat
+Woodpecker
+Worm
+Wren
+X
+Xerinae
+X-ray fish
+Y
+Yak
+Yellow perch
+Z
+Zebra
+Zebra finch
diff --git a/lessons/misc/bash-intro/demo/number-of-fish b/lessons/misc/bash-intro/demo/number-of-fish
new file mode 100644
index 000000000..5dc22c4c5
--- /dev/null
+++ b/lessons/misc/bash-intro/demo/number-of-fish
@@ -0,0 +1 @@
+      20
diff --git a/lessons/misc/bash-intro/demo/some-file b/lessons/misc/bash-intro/demo/some-file
new file mode 100644
index 000000000..df46a3bee
--- /dev/null
+++ b/lessons/misc/bash-intro/demo/some-file
@@ -0,0 +1 @@
+this is some file
diff --git a/lessons/misc/bash-intro/demo/src/my_script.py b/lessons/misc/bash-intro/demo/src/my_script.py
new file mode 100644
index 000000000..0ab9637cd
--- /dev/null
+++ b/lessons/misc/bash-intro/demo/src/my_script.py
@@ -0,0 +1 @@
+print('hello world!')
diff --git a/lessons/misc/bash-intro/lesson.md b/lessons/misc/bash-intro/lesson.md
new file mode 100644
index 000000000..ddf8ee19f
--- /dev/null
+++ b/lessons/misc/bash-intro/lesson.md
@@ -0,0 +1,330 @@
+---
+layout: page
+title: Introduction to Bash
+visible: true
+tags:
+  - misc
+  - beginner
+---
+
+ - **Authors**: John Ladan
+ - **Research field**: Physics
+ - **Lesson topic**: Introduction to shells and the command-line with bash.
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/bash-intro/>
+
+In this tutorial, we cover the basics of interacting with a computer using Bash (the Bourne Again Shell). You may have heard the terms "command line", "terminal", "console", "shell", "interactive prompt", "git-bash", etc. To be imprecise, they all refer to using the keyboard to control your computer by typing commands.
+
+**Preparation for this lesson**: To follow along with this lesson, you will need to have `bash` installed. For Linux and OSX users, `bash` is included as part of the operating system. In OSX, it can be accessed with `terminal`, but I recommend [iTerm2](https://www.iterm2.com/), because it has many more features that make it consistent with the Linux experience.
+
+For Windows users, there are many options. Many people will use `git bash`, which is packaged with [git](https://git-scm.com/) (a common version control system). Others might use [Cygwin](https://www.cygwin.com/). As of August 2016, the Windows Subsystem for Linux (WSL) provides access to most command line tools provided by Ubuntu. See [here](https://msdn.microsoft.com/en-us/commandline/wsl/install_guide) for instructions to enable/install the WSL.
+
+# Why use a command line?
+
+The command line is a very precise and powerful way to interact with a computer. Because it is a scripting language, you can automate and perform complex tasks without much overhead. 
+
+Shells are particularly well suited to interacting with files and running programs -- possibly your own! Not having a graphical interface turns out to be a strength, because it makes remote computing simple and reliable.
+
+At first, typing in commands will feel a bit cumbersome, but like learning any language the only way to get better is with practice. Lucky for us, bash is based on English with a very simplified grammar.
+
+# Example shells
+
+In this lesson, we're using bash, but most shells support all of the features covered here. Sometimes with different syntax. Some of the more modern shells include:
+- Bash
+- Zsh
+- Fish (the Friendly Interactive Shell)
+- rc   ("run commands" - the Plan 9 shell)
+- Powershell (the better Windows shell)
+
+Of the above, Powershell is the most different. The others are all based on the *Bourne shell*, but with many additions to make it easier to work with.
+
+# Bash command syntax
+
+The basic unit for a command line interpreter (CLI) is a *command*. This is a line that starts with a *commmand name* followed (optionally) by *arguments*. Examples of commands are,
+
+```
+ls
+ls -a
+date
+man date
+cd Documents
+rm unwanted_file
+vim hello.txt
+sed -e 's/cat/dog/g' pets-file
+echo 'hello world'
+find . -not -name '*.py' -type f -exec rm {} +
+which python
+cal
+```
+
+Try them out and see what they do.
+
+Some arguments activate/deactivate options. Usually they are prefixed by a dash `-`. For example, `ls -a` lists **a**ll files, even hidden ones. Sometimes they are longer than one character, in which case they *usually* have two dashes e.g. `--help`.
+
+The command line options (aka flags or switches) can usually be found in the man pages. Often there will be a `-h` or `--help` flag to display common usage.
+
+# Common commands/tasks
+
+There are many commands available -- any executable or built-in command. The following are some that I use more often.
+
+- ls (-alhS...)
+- cd (-)
+- cp
+- mv
+- mkdir
+- rm (-r)
+- head/tail (-nf)
+- grep
+- man
+
+Many command names are the first two consonants of what they do: **c**o**p**y, **l**i**s**t, **m**o**v**e, **r**e**m**ove. Others are acronyms/abbreviations: **c**hange **d**irectory, **man**ual, **s**tream **ed**itor.
+
+## Explanations
+
+To list files, use `ls`. Common flags include `-a` for all files, `-l` for the full details (size, date, permissions...), `-h` for "human-readable" file sizes,  `-S` to sort by file size.
+
+Change directories with `cd`. On many systems, `cd` with no arguments changes to your home directory (`cd ~`), and `cd -` changes to your previous directory.
+
+Copy and move files with `cp` and `mv`. The last argument is the new filename/directory. E.g. `cp a.py b.py c/` copies files `a.py` and `b.py` into the `c` directory. **Warning** `cp` and `mv` will overwrite files without warning you.
+
+Remove files with `rm`. The files cannot be recovered easily, and you won't be asked for confirmation. Directories won't be removed unless you use `-r` (for **r**ecursive.
+
+Probing contents of files with `head`, `tail`, `grep`. `head -n 10` shows the first 10 lines, and `tail` shows the last few lines in a file. To search for every line that contains a string, use `grep`. E.g. `grep pony animals.txt` finds the string "pony" in the file `animals.txt`.
+
+Strangely enough, "pony" isn't in the Wikipedia list of animals by common name. Instead, try `grep cat animals`.
+
+Finally, `man` provides manual pages for most commands that you can use from bash. Syntax: `man <command-name>`.
+
+# Files
+
+Many commands end up using files, so it's good to have an idea of how to name them. Files are specified in arguments with a *pathname*.
+
+    [directory-name/]filename
+
+Except for a few cases, the pathname is relative to the *current working directory*. So if you are in your home directory, `Desktop/my_script.py` refers to the `my_script.py` file in your own Desktop folder, not anyone else's. If you're in your `Desktop` folder, then `Desktop/my_script.py` refers to a file inside the Desktop folder inside your Desktop folder (which probably isn't there), but `my_script.py` refers to the previous file.
+
+Try it out: from your home directory, run the following commands (`touch` creates a file):
+
+```
+mkdir tmp
+touch tmp/file_a
+ls
+```
+
+In the list of files, you should see the `tmp` directory, but no `file_a`. Now execute
+
+```
+ls tmp
+```
+
+you should see the files in the `tmp` directory (`file_a`). Now try
+
+```
+cat tmp/file_a
+cd tmp
+cat tmp/file_a
+```
+
+What went wrong?
+
+
+## Absolute paths
+
+Sometimes, you'll need to access files from another location in the file hierarchy. With relative paths, you can use `..` to access the parent directory, so `../../src/include/` goes up two directories, then down through `src` and `include`. This can get cumbersome if you're not sure exactly how many times you need to go up. 
+
+Instead, there are absolute paths. On Unix systems, there is one root node for the file-system: `/` (compare this with `C:\` in Windows). All files are in subdirectories of root. Any pathname starting with `/` is an absolute path starting from the root.
+
+Try out `ls /`
+
+Another special location is your home directory. The short-form is `~/`. On a Mac, this would be `/Users/username/`. You can also short-form any user's home-directory with `~username/`.
+
+Try `cat ~/.bashrc`.
+
+Most of the time working with scripts, you should use relative paths, because one cannot assume the state of another's file system.
+
+
+# Globbing
+
+If you want to pass more than one filename as an argument, you can use *globbing*. The special patterns for globbing are:
+
+- `*` Match any string of arbitrary length
+- `?` Match any single character
+- `[]` Match any of the characters within square brackets.
+
+Try moving all python files into the `src` directory. How about copying every file with `a`?
+
+# Getting more powerful
+
+Some true strengths of the command line come from combining commands. This is the "Unix philosophy" of having many small programs that perform their jobs well. These units can be combined to perform more complicated tasks.
+
+# Pipes (stdin/out)
+
+Commands can be strung together with pipes `|`. Those of you using R may have seen the concept in the `dplyr` package. Commands typically have an input (stdin) and an output (stdout). The pipe attaches the output of one command into the input of another.
+
+This is easier to see with commands like `cat` and `head`. Try these out using our demo directory:
+
+```
+head animals
+cat animals
+cat animals | head
+ls | head -n2
+head
+grep 'fish' animals | wc -l
+```
+
+Can you figure out how each of these commands behave from the above?
+
+You can also redirect output into a file with `>` or `>>`. The former over-writes the file, while `>>` appends to the file if it exists. For example,
+
+```
+grep 'fish' animals > list-of-fish
+```
+
+Will create a new file with a list of fish. Files can also be used for `stdin` using the `<` character. The above command would be
+
+```
+grep fish < animals > list-of-fish
+```
+
+Or, with pipes,
+
+```
+grep fish < animals | wc -l > number-of-fish
+```
+
+Note that `<` appears after the first command, because each line must start witha  command. What would happen if you tried `animals > grep fish`?
+
+Another stream worth mentioning is `stderr`, which is how most errors or warnings are reported. This can be accessed with `2>`. For example, so save compiler errors for later, try
+
+   gcc some-code.c -o my-program 2> log-file
+
+
+## Usage examples
+
+If the output of a command is very long, use `less`:
+
+    long-output-command | less
+
+You can then scroll up and down and quit less by typing `q`.
+
+Joining (concatenating) files with `cat`.
+
+    cat file1 file2 > both-files
+
+Appending a line to a file
+
+    echo "put this at the end" >> some-file
+
+Search and replace in a file
+
+    sed -e 's/cat/dog/g' < animals > no-cats
+
+## Multiple commands in a line
+
+You can also run multiple commands without piping their inputs/outputs together. There are a few ways characters for this:
+
+- `;` separates commands like they are on separate lines
+- `&&` does the next command if the previous one succeeds
+- `||` does the next command if the previous one fails
+- `&` runs the previous command in the "background"
+
+# Variables
+
+Bash happens to be a full scripting language (aka bash script). As part of that are variables. They can be assigned with `=`. Try the following:
+
+    myOne=1
+    myName=John
+    mySubject = Physics
+
+What happened in the last example? In bash, there cannot be a space between the variable name and `=`. Now let's try accessing our variables.
+
+    echo myName
+
+Why doesn't that work? Bash has no way of knowing whether `myName` is a variablename or string-literal, so it always assumes a string. To access variables, we use `$varname`.
+
+    echo $myName
+    echo $myOne
+    echo $mySubject
+
+Note that if a variable isn't defined, then it simply returns an empty string. Everything in bash is a string. Even numbers are strings which are cast to ints for arithmetic.
+
+There are special variables called *environment variables* which are used by bash and other programs to affect behaviour. One of the most important ones is `$PATH`:
+
+    echo $PATH
+
+The path is a list of all directories to search for commands. To add a directory to your path,
+
+    export PATH="${HOME}/.local/bin:${PATH}"
+
+That adds the `~/.local/bin` directory to my path, so any programs installed there are now first-class commands in this bash session.
+
+All defined environment variables can be found with the `env` command. For example,
+
+    `env | sed 's/=.*$//'`
+
+will print all defined variable names.
+
+# Loops
+
+Repetitive tasks call for loops! Bash has loops:
+
+```
+for i in 1 2 3 4 5; do
+    echo The value of i is $i
+done
+```
+
+The basic syntax is 
+
+```
+for variable_name in a_list; do
+    [commands]
+done
+```
+
+The list is just like an argument list to a command: each item is a string, separated by spaces. Try using different values in the for loop, or putting quotes around different items.
+
+One common task I have is performing a command on each file in a directory. For example, consider adding a license preamble to every sourcefile in your project. If the license is in `LICENSE.txt`, then
+
+```
+for fn in *.py; do
+    cat LICENSE.txt $fn > tmpfile
+    mv tmpfile $fn
+done
+rm tmpfile
+```
+
+Unfortunately, there isn't a simple, universal way to prepend a file without a temporary file.
+
+# Running scripts
+
+Scripts are files that contain lists of commands, very much like scripts in Python, Ruby, R, Perl, etc. They can be run in much the same way.
+
+    bash myscript.sh
+
+Another trick is to use the "shebang" syntax at the beginning of a file
+
+```
+#!/bin/bash
+
+echo "The first argument is $1"
+
+echo "All of the arguments are"
+for i in $*; do
+    echo $i
+done
+```
+
+If the file is made executable (`chmod u+x myscript.sh`), then it can be executed with `./myscript.sh`. You can take it one step further and insert it in your path.
+
+The shebang syntax can be used for any interpreted language, like for Python:
+
+    #!/usr/bin/env python
+
+# Wrapping up
+
+At first, using the command line might seem cumbersome, and like a lot to remember. But this is just like learning any language: at first you will be limited by your vocabulary, but with time and practice you'll be able to do much more.
+
+A tools you may want to look into when working with more files/strings are:
+ - Regular expressions. Used in tools like `grep` and `sed` are great for searching in strings/files.
+ - `find` can do some really powerful things when working with files.
+ - `xargs` creates/executes commands built from `stdin`
diff --git a/lessons/misc/careers-outside-academia/lesson.md b/lessons/misc/careers-outside-academia/lesson.md
new file mode 100644
index 000000000..1a5080011
--- /dev/null
+++ b/lessons/misc/careers-outside-academia/lesson.md
@@ -0,0 +1,41 @@
+---
+layout: page
+title: Careers Outside Academia
+visible: true
+tags:
+  - misc
+---
+
+ - **Authors**: UofT Coders
+ - **Research field**: Science
+ - **Lesson topic**: Discussion on experiences outside an academic career path
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/careers-outside-academia>
+
+This is a list of notes and resources that were brought up during our discussion on careers outside academia.
+I did my best to find appropriate links, but some things might not be accurate. Feel free to add to or modify this list!
+
+## Hackathons, coding groups, and places to find them
+* [Civic Tech Toronto](http://civictech.ca/)
+* [Meetup.com](https://www.meetup.com/)
+* [Hacking Health](https://hackinghealth.ca/city/toronto-canada/)
+* Mozilla Global Sprint ([2016 website](https://science.mozilla.org/programs/events/global-sprint-2016)) - annually in June
+* [NeurotechX](http://neurotechx.com/)
+
+## Professional development, courses, summer schools etc.
+* MBA courses at UofT - possibly free for graduate students if taken during registered terms
+* [Graduate Professional Skills Program](https://www.sgs.utoronto.ca/currentstudents/Pages/Professional-Development.aspx) at UofT
+* [RADIANT neurotechnology summer school](https://www.dal.ca/dept/radiant-create.html) - Dalhousie University
+* Sunnybrook lecture series on medical devices - schedule [here](http://sunnybrook.ca/calendar/?c=4&d=11/17/2016)
+* [Mozilla Open Science mentorship program](https://github.com/MozillaFoundation/mentorship-program#mentorship-program)
+* [HackerYou](https://github.com/MozillaFoundation/mentorship-program#mentorship-program)
+* [Bitmaker](https://bitmaker.co/)
+* [Lighthouse Labs](https://lighthouselabs.ca/)
+* [Insight Data Science Fellowship](http://insightdatascience.com/)
+* Second degree programs in computer science - [UBC](https://www.cs.ubc.ca/students/undergrad/programs/second-degree), [McMaster University](http://academiccalendars.romcmaster.ca/preview_program.php?catoid=7&poid=3965&returnto=563), and  [University of Alberta](https://www.ualberta.ca/computing-science/undergraduate-studies/programs-and-admissions) were mentioned
+
+## Ways to "get yourself out there"
+* Make a LinkedIn profile and keep it current
+* Make a blog - post code, write about your research, etc.
+* Contribute to open science on GitHub - [some ideas from Mozilla Science Lab](https://science.mozilla.org/projects)
+* Recruiting firms, such as [Randstad](https://www.randstad.ca/)
+* [DevHub](https://www.meetup.com/Devhub/)
diff --git a/lessons/misc/code-review/lesson.md b/lessons/misc/code-review/lesson.md
new file mode 100644
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--- /dev/null
+++ b/lessons/misc/code-review/lesson.md
@@ -0,0 +1,66 @@
+---
+layout: page
+title: 'Code Review Overview'
+visible: true
+tags:
+  - misc
+  - beginner
+---
+
+## Why Do Code Review? ##
+
+Code review is the process of having your code read by another programmer (or yourself; or better, in pairs) in order to:
+- catch bugs
+- making sure it is clear to someone who might take up the project after you (i.e. improve the documentation) 
+- ensure efficiency/simplicity
+- that all cases have been considered and appropriately addressed
+- many other reasons!
+
+Not only do we want to catch any bugs in code, but code review aims to make sure that there is a standard set of rules for code that you want to uphold within a lab, or collaborative group, allowing for consistent and higher quality code. 
+
+Ideally you would want there to be good communication about what the standards are, and whether they are being met throughout the entire process from conception of the idea to the finished code. This involves sitting down and thinking carefully about the design of the project, and defining the standards of the code along with frequent code review as it is being written. 
+
+If you often pair up to review eachother's code, you learn new tips and tricks, and you may find alternative approaches to the same task, and also to write a coherent script pipeline.  This is particularly important if you each code different parts of the project.  
+
+Many studies have found code review to be effective not only to improve the code, but for knowledge transfer and team awareness (see [here](https://dl.acm.org/citation.cfm?id=314753) and [here](https://dl.acm.org/citation.cfm?id=2486882) for examples).
+
+
+## Tips on Conducting Code Review
+
+- Review code frequently and early on. This way you don't have to sit through huge chunks of code, and that way the code is still fresh in your mind.
+- Review no more than 300-500 lines of code per hour, any more and you risk not taking the time to understand each line because you feel rushed to finish.
+- Focus on the right things such as clarity and function. Differences in formatting and style should be addressed but leave that to linters and other tools designed to look for those types of mistakes.
+- Use checklists if this will be a regular thing in your lab or group (which it should be!) that everyone refers to as they write the code and should be somewhat specific for your purposes, however many general items will be common to most lists. This might include:
+  - are functions broken down into the simplest components as possible?
+  - is the code efficient? is there a faster way to go about computing something?
+  - is the current documentation (docstrings, comments) clear? If you did not have the writer explain it to you, would you understand?
+  - are there edge cases that haven't been considered? You can discuss how to address these cases if they exist.
+  - Is the code repetitive? If so, perhaps break it down to a function that can be reused.
+  - Does the file structure, naming conventions match with the predetermined standards?
+- Make sure that everyone is clear on what the final goal of the code is so feedback can attest to these goals as well instead of only addressing superficial bugs
+- The process is not truly complete until the comments and suggestions made have all been addressed either by fixing the bugs (or restructuring the code if need be) and confirming that changes are sufficient to meet the standards agreed upon. This can take time and doesn't have to occur in just one sitting, but will save time in the long run!
+- be nice, be objective, be collaborative. 
+
+## Ways to Code Review
+
+- Review your code yourself using your own checklist based off a general checklist including  common mistakes optimized for your project
+- Review code with one, or if possible, many people at once as you walk through the code and give context while they make suggestions and context
+- Another way to conduct code review includes distributing the code and having many people go over it on their own time or during a designated time, and then you go over the comments afterwards, making sure there is a process in place to then confirm fixes have been made
+
+## Review using Github 
+Code review is of course built on good version control. So, by now, you probably use Github (Gitlab) or other resources to do version control and share the work with collaborators. You can furthere utilize Github for reviewing each other's code. Using Github, the code review happens between the first pull request by the original author, before it is merged:
+- The author (or the project manager) can manually request a review on the pending PR. You can also configure projects to *require* a review before a pull request can be merged. 
+- The reviewer can make general or line-by-line comments
+- Further discussion can continue under each comment thread  
+- make changes to the code as per the decisions reached in comments
+- make the merge (whether the author of the code, or the reviewer does the merge should be communicated between members of the project prior to the review process)
+
+
+
+
+## Resources
+https://mozillascience.github.io/codeReview/intro.html (and other links therein)  
+https://blog.fogcreek.com/effective-code-reviews-9-tips-from-a-converted-skeptic/
+
+https://the-turing-way.netlify.com/reviewing/reviewing.html
+
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new file mode 100644
index 000000000..a052ca1c3
--- /dev/null
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diff --git a/lessons/misc/inkscape-intro/demos/phage_demo.svg b/lessons/misc/inkscape-intro/demos/phage_demo.svg
new file mode 100644
index 000000000..b311507aa
--- /dev/null
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diff --git a/lessons/misc/inkscape-intro/demos/phage_flow_demo.svg b/lessons/misc/inkscape-intro/demos/phage_flow_demo.svg
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diff --git a/lessons/misc/inkscape-intro/lesson.md b/lessons/misc/inkscape-intro/lesson.md
new file mode 100644
index 000000000..0c6553d99
--- /dev/null
+++ b/lessons/misc/inkscape-intro/lesson.md
@@ -0,0 +1,161 @@
+---
+layout: page
+title: 'Intro to Inkscape for graphic design'
+visible: true
+tags:
+  - misc
+  - beginner
+---
+
+# Vector Graphics vs. Rasterized (Bitmap) Graphics
+
+In general, diagrams are usually best made in a *vector* format, while photo-realistic images or paintings are better served with *bitmaps*. A rasterisation is the conversion of a vector-graphics format to bitmap. This has to be done to display it (in editors, browsers, print, etc.), but preferably as a last step.
+
+## Features
+
+<style>
+table.content-tab {margin: 1em; width:100%;}
+.content-tab td, .content-tab th {padding:1ex; border: .1ex solid black;}
+.content-tab th:first-child {border: none;}
+.content-tab td:first-child {text-align: right;
+</style>
+<table class="content-tab">
+<tr><th></th> <th>Vector </th><th> Bitmap </th></tr>
+<tr><td>data type   </td><td>shapes</td><td> pixels </td></tr>
+<tr><td>stored as   </td><td>object and parameters </td><td> arrays of colours </td></tr>
+<tr><td>editing   </td><td> lossless editing </td><td> loss due to rasterization (e.g. aliasing) </td></tr>
+<tr><td>uses   </td><td> diagrams </td><td> photos </td></tr>
+</table>
+
+## Example
+
+![Vector vs. Bitmap](./vector_bitmap.svg)
+
+Notice how at its regular size, all copies look the same for screen/printing resolutions lower than 96dpi. The difference is noticeable once we try to zoom in, or view it at higher resolutions.
+
+# Closed/Proprietary vs. Open Source
+
+There are all sorts of philosophical reasons for opening source code and toolsets, and others for opening up content created. When it comes down to practice, the reasons are basically:
+- **it's free**;
+- you can open and change it in the future;
+- other people can make new work from yours.
+
+Inkscape is the most mature, and most cross-platform vector graphics program.
+
+# Interface Tour
+
+The interface of inkscape tends to be quite crowded, so it can be overwhelming when you first open the application. In fact, it's so crowded, I don't think it will help much to include a screenshot.
+
+Instead, here are the important things to notice (in order of importance):
+- Tool palette, typically on the left
+- Tool-specific options, typically top-left
+- Tool tips (e.g. modifier keys), at the bottom
+- General toolbar (save, copy, group, **show panels**), maybe top or right
+- Transform/Format/etc. panels, typically hidden, but on the right when shown
+- Snaps, typically bottom or right
+- Colour palette, at the bottom
+
+# Keyboard and mouse
+The other tricky part is that it's optimized for keyboard and mouse. Most tools have different behaviour when a modifier key is held down. For example, with the selector tool, shift+click adds to the selection, Alt+Drag does a path-selection instead of rectangle, and shift+alt+drag adds to the current selection with a path-selection.
+**Note**: if something is already selected, alt+drag moves the object.
+
+Right-clicks and middle-clicks will also have different behaviours.
+
+A notable modifier key is *Ctrl* (^). This will often force more "regular" shapes/orientations. For example, horizontal/vertical lines and moves, 45-degree rotation, and aspect-ratio locked scaling.
+
+Often, *Shift* is used as a secondary modifier for opposite behaviour. Examples,
+- ctrl+z is undo, shift+ctrl+z is redo
+- ctrl+g groups objects, shift+ctrl+g ungroups 
+
+## Notable hotkeys
+
+Most hotkeys can be seen in the menus, or hovering over buttons on the interface. The following are ones that I use frequently.
+
+- Escape will change tools and/or deselect. Keep pressing it until you're back at the selection tool with nothing selected.
+- The number row handles zooming 
+  - `=`, `-` zoom in and out
+  - `5` zooms to fit document
+  - `1` goes 1:1
+  - back-tick (`) goes to the previous zoom (like undo) 
+- ctrl-z for undo
+- ctrl-d for duplicate
+- alt-d for clone (like duplicate, but the new object is linked to the original)
+- ctrl-g for group
+- ctrl-c for copy
+- shift-ctrl-c for past style (stroke and fill)
+
+
+# Demo Tutorials
+
+Everything else is probably best learned through using it. Here are some samples of what we'll make in the tutorial.
+
+<style>
+div.demo-table {width: 90%; display:flex; flex-flow: row wrap; justify-content: center; align-items: stretch;}
+div.demo-table>div {width: 40%;}
+div.demo-table>div img {max-width: 100%}
+</style>
+<div class="demo-table">
+<div class="left">
+<img alt="flow chart" src="../demos/flow_chart.svg">
+</div>
+<div class="right">
+Demonstrates rectangles, transforms, lines, text, clone, and gradients
+</div>
+
+<div class="left">
+<img alt="phages (group and clone)" src="../demos/phage_demo.svg">
+</div>
+<div class="right">
+Demonstrates polygons, rounded rectangles, elipses, object->path, grouping,
+spraying clones, arranging objects.
+</div>
+
+<div class="left">
+<img alt="equations" src="../demos/phage_flow_demo.svg">
+</div>
+<div class="right">
+Demonstrates polygons, rounded rectangles, elipses, object->path, grouping, clones, arrows, importing PDFs.
+</div>
+
+<div class="left">
+<img alt="lattice" src="../demos/lattice_demo.svg">
+</div>
+<div class="right">
+Demonstrates circles, tiled clones, arrange, layers, lines, paste formatting, transform panel.
+</div>
+
+<div class="left">
+<img alt="beaker drawing" src="../demos/beaker_demo.svg">
+</div>
+<div class="right">
+Demonstrates rounded rectangles, object->path, bezier curves, path offsets, gradients, clipping, transparency, layers, spraying clones
+</div>
+
+<div class="left">
+<img alt="dna diagram" src="../demos/dna_demo.svg">
+</div>
+<div class="right">
+Demonstrates extensions (render -> plot function), cutting/joining paths, breaking/combining paths, cloning, z-order, rounded rectangles, transformations.
+</div>
+
+</div>
+
+
+
+
+## Python extensions
+
+There may be a problem related to python versions when running some extensions. If an extension raises a syntax error, be sure to have Python 2.7 installed, and then specify the interpreter in `~/.config/inkscape/preferences.xml`
+
+On my system (Mac OSX), Python 2.7 was located at `/usr/bin/python2.7`.
+The interpreter option will likely not be there initially.
+
+```
+  <group
+     id="extensions"
+     python-interpreter="/usr/bin/python2.7"
+     ...
+  >
+```
+
+The `plot function` extension also depended on the `lxml` module, which can be installed with `pip` (`pip2` if you have multiple versions of Python).
diff --git a/lessons/misc/inkscape-intro/vector_bitmap.svg b/lessons/misc/inkscape-intro/vector_bitmap.svg
new file mode 100644
index 000000000..de85c84e2
--- /dev/null
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+   version="1.1"
+   id="svg8"
+   inkscape:version="0.92.2 (5c3e80d, 2017-08-06)"
+   sodipodi:docname="vector_bitmap.svg"
+   inkscape:export-filename="/Users/jladan/Documents/src/studyGroup/lessons/misc/inkscape-intro/bitmap_sample_300.png"
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+   inkscape:export-ydpi="299.85275">
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+       inkscape:collect="always"
+       id="linearGradient883">
+      <stop
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+         offset="0"
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diff --git a/lessons/misc/jekyll-ghpages/lesson.md b/lessons/misc/jekyll-ghpages/lesson.md
new file mode 100644
index 000000000..7391dcfa0
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+++ b/lessons/misc/jekyll-ghpages/lesson.md
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+---
+layout: page
+title: 'Web design with Jekyll/GitHub Pages'
+visible: true
+tags:
+  - misc
+  - beginner
+---
+
+- **Authors**: Dawn Walker [@dcwalk](https://github.com/dcwalk)
+- **Research field**: Information Studies (Information System Design)
+- **Lesson topic**: Introduction to using GitHub Pages and Jekyll for website design
+- **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/jekyll-ghpages>
+- **Lesson video stream**: <https://www.youtube.com/watch?v=oCCwtyH3SL4>
+
+## Preface
+
+This is a basic introduction to using GitHub Pages and Jekyll to host a web page. GitHub Pages are a feature of GitHub that allows users to host websites for individuals, projects, or organizations right from their repositories! The engine GitHub uses to accomplish this is Jekyll, a ["simple, static, blog-aware, site generator"](https://jekyllrb.com/), but it also supports regular HTML content.
+
+In this code-along we'll cover what GitHub Pages are, why you might want to use them, and how to do so both online and locally with some jekyll basics!
+
+### Pre-requisites
+
+- GitHub account (if you don't already have one go to [github.com/join](https://github.com/join))
+- git installed, and configured on your machine ([git-scm.com](https://git-scm.com/downloads))
+- comfort with material covered in UofT Coders lessons **[(Brief) Introduction to Git + GitHub](https://uoftcoders.github.io/studyGroup/lessons/git/intro/lesson/)**, **[An Intermediate Look at Git + GitHub](https://uoftcoders.github.io/studyGroup/lessons/git/advanced/lesson/)**, and **[Using Branches in Git](https://uoftcoders.github.io/studyGroup/lessons/git/branches/lesson/)**
+
+### Bluebird Conditions (AKA would be great to have!)
+
+- jekyll and its requirements installed on your machine ([jekyllrb.com/docs/installation](https://jekyllrb.com/docs/installation/))
+  - ruby ([ruby-lang.org/en/documentation/installation](https://www.ruby-lang.org/en/documentation/installation/))
+  - rubygems ([rubygems.org/pages/download](https://rubygems.org/pages/download))
+
+- some familiarity with **[markdown](https://help.github.com/articles/basic-writing-and-formatting-syntax/)** and **[HTML](https://developer.mozilla.org/en-US/docs/Web/Guide/HTML/Introduction)**
+
+<div class="alert alert-dismissible alert-info">
+  <i class="fa fa-info-circle"></i>&nbsp;&nbsp;&nbsp;<strong>Running Jekyll on Windows</strong><br />Unfortunately Windows is not officially supported, however there are instructions for <a href="https://jekyllrb.com/docs/windows/#installation" target="_blank"><!--_ -->Jekyll on Windows</a>, but it might be finicky.
+</div>
+
+## Lesson Overview
+
+The lesson will cover:
+
+- What GitHub Pages are, why you might want to use them
+- Creating a GitHub Pages site
+  - Online: using the Automatic Page Generator
+  - Locally: using Git and Jekyll
+- Jekyll Basics
+
+## Why (and What) are GitHub Pages
+
+Github Pages are a feature of GitHub that allows users to host static websites for individuals, projects, or organizations from a repository
+
+#### Static vs. Dynamic Websites
+
+- **Dynamic** ones have pages that are generated in real-time
+- **Static** ones have pages that are converted (or written directly) as static HTML.
+
+- Does this mean it is also _static vs. 'interactive'_? No, doesn't have to be the case!
+
+#### Reasons to Use
+
+- Free!
+- Static sites are more secure(ish*)
+- Manage all the different content for your project(s) in the same place
+- ... others?
+
+#### Examples
+
+- **Github Developer** [developer.github.com](https://developer.github.com/)  [`github.com/github/developer.github.com`](https://github.com/github/developer.github.com)
+- **Open Knowledge Foundation: Open Data Handbook** [opendatahandbook.org/guide](http://opendatahandbook.org/guide/en/)  [`github.com/okfn/opendatahandbook`](https://github.com/okfn/opendatahandbook)
+- **2048** [gabrielecirulli.github.io/2048/](https://gabrielecirulli.github.io/2048/) [`github.com/gabrielecirulli/2048/`](https://github.com/gabrielecirulli/2048/)
+
+#### Naming Conventions
+
+<table class="table table-striped table-hover ">
+  <thead>
+    <tr>
+      <th align="left">Type of GitHub Pages site</th>
+      <th align="left">Pages default domain &amp; host location on GitHub</th>
+      <th align="left">Publishing branch</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td align="left">User Pages site</td>
+      <td align="left"><code>username.github.io</code></td>
+      <td align="left">master</td>
+    </tr>
+    <tr>
+      <td align="left">Organization Pages site</td>
+      <td align="left"><code>orgname.github.io</code></td>
+      <td align="left">master</td>
+    </tr>
+    <tr>
+      <td align="left">Project Pages site owned by a <strong>user account</strong></td>
+      <td align="left"><code>username.github.io/projectname</code></td>
+      <td align="left">gh-pages</td>
+    </tr>
+    <tr>
+      <td align="left">Project Pages site owned by an <strong>organization</strong></td>
+      <td align="left"><code>orgname.github.io/projectname</code></td>
+      <td align="left">gh-pages</td>
+    </tr>
+  </tbody>
+</table>
+
+## Online Editing: Automatic Page Generator
+
+1. Create a new repository ([github.com/new](https://github.com/new))
+  - There are 4 types of GitHub Pages site (in the table above and on **[GitHub](https://help.github.com/articles/user-organization-and-project-pages/)**)
+  - **following naming conventions is really important!** `.github.io` for user and organizations
+2. Navigate to **Settings** and click **Automatic Page Generator**  
+Or just go to generated_pages url for your repository:  
+`https://github.com/<username>/<repository>/generated_pages/new`
+  - Make some edits to the content
+  - Select a theme/layout
+  - Publish!
+3. We have a website!  
+`<username/orgname>.github.io/<projectname>/`
+  - Let's check it out
+4. Make an update
+5. Check out the commit history
+
+## Working Locally: Git and Jekyll
+
+1. First, we need to make sure we have everything installed
+
+        $ git --version
+        git version X.X.X
+        $ jekyll -v
+        jekyll X.X.X
+
+2. We'll have to create (or choose) a repo for a project
+
+        $ git init first-website
+        Initialized empty Git repository in <path/to/current/directory>/first-website/.git/
+
+_Note: There are a few extra steps if you want a project page for an existing repository! Please see: [help.github.com/articles/creating-project-pages-manually](https://help.github.com/articles/creating-project-pages-manually/)_
+
+#### Jekyll Basics
+
+Jekyll is a package (ruby gem) that makes a `jekyll` executable available from your command line.
+
+There aren't too many commands you need to get going: `jekyll new` and `jekyll serve`   
+and another one: `jekyll build` but `serve` calls build, so the command is not often needed
+
+let's test them out:
+
+~~~
+$ jekyll new first-website
+New jekyll site installed in <path/to/current/directory>/first-website.
+~~~
+
+So we have a project, let's check out what's there!
+
+~~~
+$ cd first-website
+$ ls -lFA
+$ git status
+~~~
+
+Lots of files. Before we dig in let's try to get our website built
+
+~~~
+$ jekyll serve
+... <lots of details including file paths>
+Server address: http://127.0.0.1:4000/
+Server running... press ctrl-c to stop.
+~~~
+
+...and check it out in the browser.
+
+Why don't we try to make some changes and see how it looks?
+Let's edit `index.html`, see how it updates in the browser automatically
+
+If we want to make more substantial edits to the site name, our contact details we'll have to modify `_config.yml`, but it won't update in the browser until we re-serve the page
+
+#### Anatomy of a Jekyll Project
+
+Before doing more let's take a look at all those files and folders in our directory again. [Jekyll](https://jekyllrb.com/docs/structure/) docs and the `tree` command can do a better job than me...
+
+~~~
+$ ls -lFA
+$ tree
+~~~
+
+A clumsy equation for jekyll:   
+`_*` files and folders * `*.html/markdown/md/textile` = your static site (in `_site`)
+
+#### Front matter & Liquid Tags
+
+In order for those `*.html/markdown/md/textile` files to be transformed into working static pages, jekyll makes use of:
+
+**YAML Front Matter** (<strike>Yet Another Markup Language</strike> YAML Ain't Markup Language), once again I'll lean on [jekyll](https://jekyllrb.com/docs/frontmatter/) docs
+
+~~~
+$ cat about.md
+---
+layout: page
+title: About
+permalink: /about/
+---
+...
+~~~
+
+and **[Liquid](https://shopify.github.io/liquid/) Filters and Tags** (A templating Language for handling dynamic content), thrice those handy [jekyll](https://jekyllrb.com/docs/templates/) docs
+
+{% raw %}
+~~~
+$ cat about.md
+---
+...
+{% include icon-github.html username="jglovier" %}
+...
+~~~
+{% endraw %}
+
+I hope that is enough to start, there are lots more fiddly bits, but the docs and additional resources (below), as well as google, have lots to offer!
+
+#### Making your site live
+
+`<movie training montage>`
+
+Okay, we've got a great site, spent some time on the content and now we want everyone on the internet to see it too.
+
+Before we get this up on GitHub we should make sure that the repo is set up to not commit the static version of the site. GitHub does not need it to host your site, as they use jekyll to generate sites on their end too:
+
+~~~
+$ git status
+...
+.gitignore
+...
+$ <cat/touch> .gitignore
+~~~
+
+Github helpfully publishes all sorts of template .gitignores, the [jekyll](https://github.com/github/gitignore/blob/master/Jekyll.gitignore) one only has three items...
+
+While on GitHub let's set up our repo, remember the naming conventions above: `<username/orgname>.github.io/<projectname>/`
+
+now, to commit our changes...
+
+~~~
+$ git checkout -b gh-pages
+$ git add .
+$ git commit -m '<some meaningful commit message>'
+~~~
+
+and push them to GitHub...
+
+~~~
+$ git remote add origin <repo address>
+$ git push --set-upstream origin gh-pages
+~~~
+
+success! (hopefully...?)
+
+#### Using Themes
+
+Many times you won't want to build up your site from scratch, instead you'll continue the lofty tradition of standing on the shoulders of giants and use a pre-made **theme**:
+
+[jekyllthemes.org](http://jekyllthemes.org/)  
+
+If you are feeling overwhelmed <span class="label label-warning">so many</span> I selected a couple:
+
+- [jekyllthemes.org/themes/twenty/](http://jekyllthemes.org/themes/twenty/)  
+- [jekyllthemes.org/themes/read-only/](http://jekyllthemes.org/themes/read-only/)  
+- [jekyllthemes.org/themes/end2end/](http://jekyllthemes.org/themes/end2end/)  
+
+#### And more...
+
+- [Custom Domains (help.github.com on CNAME records)](https://help.github.com/articles/custom-domain-redirects-for-github-pages-sites/)
+- Securing your Site with SSL! [Not easy unfortunately (github/issues)](https://github.com/isaacs/github/issues/156)
+- ... others?
+
+## Resources
+
+- [GitHub Pages](https://pages.github.com/)
+  - [Creating Pages with the automatic generator](https://help.github.com/articles/creating-pages-with-the-automatic-generator/)
+  - [Creating Project Pages manually](https://help.github.com/articles/creating-project-pages-manually/)
+
+- [Jekyll](https://jekyllrb.com/docs/installation/)
+- [Liquid](https://shopify.github.io/liquid/)
+- [jekyllthemes.org](http://jekyllthemes.org/)  
+- Icon Font Kit [Font Awesome](https://fortawesome.github.io/Font-Awesome/)
+- Popular Framework [Bootstrap](https://getbootstrap.com/)
+- [GitBook](https://www.gitbook.com/), open source book format using Git and Markdown
+- [StaticGen](https://www.staticgen.com/), ranking of Open-Source Static Site Generators (there are <span class="label label-warning">so many</span>&nbsp;)
+
+### Glossary
+
+- a **dynamic** website "contains web pages... that are generated in real-time. These pages include Web scripting code, such as PHP..." (from [techterms](https://techterms.com/))
+- **"static site generator"** tools that help with the creation of static websites by converting input (usually written in a markup language like **markdown**) into static HTML through the use of templates.
+- a **static** website "contains web pages with fixed content. Each page... displays the same information to every visitor" (from [techterms](https://techterms.com/))
diff --git a/lessons/misc/latex-intro/.gitignore b/lessons/misc/latex-intro/.gitignore
new file mode 100644
index 000000000..32f436fa9
--- /dev/null
+++ b/lessons/misc/latex-intro/.gitignore
@@ -0,0 +1,19 @@
+# LaTeX stuff
+*.aux
+*.backup
+*.bbl
+*.blg
+*.idx
+*.ilg
+*.ind
+*.ist
+*.lof
+*.log
+*.lot
+*.nav
+*.out
+*.pdf
+*.snm
+*.synctex.gz
+*.toc
+*.vrb
\ No newline at end of file
diff --git a/lessons/misc/latex-intro/Intro-to-Latex-Sara/MyLibrary.bib b/lessons/misc/latex-intro/Intro-to-Latex-Sara/MyLibrary.bib
new file mode 100644
index 000000000..377f23eb3
--- /dev/null
+++ b/lessons/misc/latex-intro/Intro-to-Latex-Sara/MyLibrary.bib
@@ -0,0 +1,8153 @@
+
+@article{Womelsdorf2011,
+  title = {Deciphering the Attentional Search Engine of the Brain},
+  volume = {15},
+  issn = {1364-6613, 1879-307X},
+  doi = {10.1016/j.tics.2011.08.001},
+  language = {English},
+  number = {10},
+  journal = {Trends in Cognitive Sciences},
+  author = {Womelsdorf, Thilo},
+  month = oct,
+  year = {2011},
+  pages = {451-452},
+  file = {C:\\Users\\saram\\Zotero\\storage\\IQG2WRAG\\Womelsdorf 2011 Deciphering the attentional search engine of the brain.pdf}
+}
+
+@article{Vinck2012,
+  title = {Improved Measures of Phase-Coupling between Spikes and the {{Local Field Potential}}},
+  volume = {33},
+  issn = {0929-5313, 1573-6873},
+  doi = {10.1007/s10827-011-0374-4},
+  language = {en},
+  number = {1},
+  journal = {Journal of Computational Neuroscience},
+  author = {Vinck, Martin and Battaglia, Francesco Paolo and Womelsdorf, Thilo and Pennartz, Cyriel},
+  month = aug,
+  year = {2012},
+  pages = {53-75},
+  file = {C:\\Users\\saram\\Zotero\\storage\\9IMS3U3R\\Vinck 2012 Improved measures of phase-coupling between spikes and the Local Field Potential.pdf}
+}
+
+@article{Womelsdorf2014,
+  title = {Dynamic Circuit Motifs Underlying Rhythmic Gain Control, Gating and Integration},
+  volume = {17},
+  issn = {1097-6256, 1546-1726},
+  doi = {10.1038/nn.3764},
+  number = {8},
+  journal = {Nature Neuroscience},
+  author = {Womelsdorf, Thilo and Valiante, Taufik A and Sahin, Ned T and Miller, Kai J and Tiesinga, Paul},
+  month = jul,
+  year = {2014},
+  pages = {1031-1039},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SEVU6VJ6\\Womelsdorf 2014 Dynamic circuit motifs underlying rhythmic gain control, gating and integration.pdf}
+}
+
+@article{Oemisch2015,
+  title = {Interareal {{Spike}}-{{Train Correlations}} of {{Anterior Cingulate}} and {{Dorsal Prefrontal Cortex}} during {{Attention Shifts}}},
+  volume = {35},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.1262-15.2015},
+  language = {en},
+  number = {38},
+  journal = {Journal of Neuroscience},
+  author = {Oemisch, M. and Westendorff, S. and Everling, S. and Womelsdorf, T.},
+  month = sep,
+  year = {2015},
+  pages = {13076-13089},
+  file = {C:\\Users\\saram\\Zotero\\storage\\F69ZN5FW\\Oemisch 2015 Interareal Spike-Train Correlations of Anterior Cingulate and Dorsal Prefrontal.pdf}
+}
+
+@article{Womelsdorf2006,
+  title = {Gamma-Band Synchronization in Visual Cortex Predicts Speed of Change Detection},
+  volume = {439},
+  issn = {0028-0836, 1476-4679},
+  doi = {10.1038/nature04258},
+  number = {7077},
+  journal = {Nature},
+  author = {Womelsdorf, Thilo and Fries, Pascal and Mitra, Partha P. and Desimone, Robert},
+  month = feb,
+  year = {2006},
+  pages = {733-736},
+  file = {C:\\Users\\saram\\Zotero\\storage\\RED32TGH\\Womelsdorf 2006 Gamma-band synchronization in visual cortex predicts speed of change detection.pdf}
+}
+
+@article{Womelsdorf2007a,
+  title = {Modulation of {{Neuronal Interactions Through Neuronal Synchronization}}},
+  volume = {316},
+  issn = {0036-8075, 1095-9203},
+  doi = {10.1126/science.1139597},
+  language = {en},
+  number = {5831},
+  journal = {Science},
+  author = {Womelsdorf, T. and Schoffelen, J.-M. and Oostenveld, R. and Singer, W. and Desimone, R. and Engel, A. K. and Fries, P.},
+  month = jun,
+  year = {2007},
+  pages = {1609-1612},
+  file = {C:\\Users\\saram\\Zotero\\storage\\C3UBGKTV\\Womelsdorf 2007 Modulation of Neuronal Interactions Through Neuronal Synchronization.pdf}
+}
+
+@article{Womelsdorf2007,
+  title = {The Role of Neuronal Synchronization in Selective Attention},
+  volume = {17},
+  issn = {09594388},
+  doi = {10.1016/j.conb.2007.02.002},
+  language = {en},
+  number = {2},
+  journal = {Current Opinion in Neurobiology},
+  author = {Womelsdorf, Thilo and Fries, Pascal},
+  month = apr,
+  year = {2007},
+  pages = {154-160},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6R69VFXU\\Womelsdorf and Fries - 2007 - The role of neuronal synchronization in selective .pdf}
+}
+
+@article{Womelsdorf2012,
+  title = {Orientation Selectivity and Noise Correlation in Awake Monkey Area {{V1}} Are Modulated by the Gamma Cycle},
+  volume = {109},
+  number = {11},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Womelsdorf, Thilo and Lima, Bruss and Vinck, Martin and Oostenveld, Robert and Singer, Wolf and Neuenschwander, Sergio and Fries, Pascal},
+  year = {2012},
+  pages = {4302--4307},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FWQR9URX\\Womelsdorf 2012 Orientation selectivity and noise correlation in awake monkey area V1 are.pdf}
+}
+
+@article{Womelsdorf2014a,
+  title = {Burst {{Firing Synchronizes Prefrontal}} and {{Anterior Cingulate Cortex}} during {{Attentional Control}}},
+  volume = {24},
+  issn = {09609822},
+  doi = {10.1016/j.cub.2014.09.046},
+  language = {en},
+  number = {22},
+  journal = {Current Biology},
+  author = {Womelsdorf, Thilo and Ardid, Salva and Everling, Stefan and Valiante, Taufik A.},
+  month = nov,
+  year = {2014},
+  pages = {2613-2621},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QVUQQJD2\\Womelsdorf 2014 Burst Firing Synchronizes Prefrontal and Anterior Cingulate Cortex during.pdf;C:\\Users\\saram\\Zotero\\storage\\X3U65XA5\\Womelsdorf 2014 Burst Firing Synchronizes Prefrontal and Anterior Cingulate Cortex during (SM).pdf}
+}
+
+@article{Jadi2014,
+  title = {Cortical Oscillations Arise from Contextual Interactions That Regulate Sparse Coding},
+  volume = {111},
+  number = {18},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Jadi, Monika P. and Sejnowski, Terrence J.},
+  year = {2014},
+  pages = {6780--6785},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4QU622XR\\Jadi and Sejnowski - 2014 - Cortical oscillations arise from contextual intera.pdf}
+}
+
+@article{Druckmann2013,
+  title = {A {{Hierarchical Structure}} of {{Cortical Interneuron Electrical Diversity Revealed}} by {{Automated Statistical Analysis}}},
+  volume = {23},
+  issn = {1047-3211, 1460-2199},
+  doi = {10.1093/cercor/bhs290},
+  language = {en},
+  number = {12},
+  journal = {Cerebral Cortex},
+  author = {Druckmann, Shaul and Hill, Sean and Sch{\"u}rmann, Felix and Markram, Henry and Segev, Idan},
+  month = jan,
+  year = {2013},
+  pages = {2994-3006},
+  file = {C:\\Users\\saram\\Zotero\\storage\\P9IDQ7F4\\Druckmann 2013 A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed.pdf},
+  pmid = {22989582}
+}
+
+@article{Schlingloff2014,
+  title = {Mechanisms of {{Sharp Wave Initiation}} and {{Ripple Generation}}},
+  volume = {34},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.0867-14.2014},
+  language = {en},
+  number = {34},
+  journal = {Journal of Neuroscience},
+  author = {Schlingloff, D. and Kali, S. and Freund, T. F. and Hajos, N. and Gulyas, A. I.},
+  month = aug,
+  year = {2014},
+  pages = {11385-11398},
+  file = {C:\\Users\\saram\\Zotero\\storage\\RD3UA9BM\\Schlingloff 2014 Mechanisms of Sharp Wave Initiation and Ripple Generation.pdf}
+}
+
+@article{Abasolo2015,
+  title = {Lempel-{{Ziv}} Complexity of Cortical Activity during Sleep and Waking in Rats},
+  volume = {113},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00575.2014},
+  language = {en},
+  number = {7},
+  journal = {Journal of Neurophysiology},
+  author = {Ab{\'a}solo, Daniel and Simons, Samantha and {Morgado da Silva}, Rita and Tononi, Giulio and Vyazovskiy, Vladyslav V.},
+  month = apr,
+  year = {2015},
+  pages = {2742-2752},
+  file = {C:\\Users\\saram\\Zotero\\storage\\946ZCPJT\\Abasolo 2015 Lempel-Ziv complexity of cortical activity during sleep and waking in rats.pdf}
+}
+
+@article{Aertsen1989,
+  title = {Dynamics of Neuronal Firing Correlation: Modulation of" Effective Connectivity"},
+  volume = {61},
+  shorttitle = {Dynamics of Neuronal Firing Correlation},
+  number = {5},
+  journal = {Journal of neurophysiology},
+  author = {Aertsen, A. M. and Gerstein, G. L. and Habib, M. K. and Palm, G.},
+  year = {1989},
+  pages = {900--917},
+  file = {C:\\Users\\saram\\Zotero\\storage\\MWJIR8WE\\Aertsen 1989 Dynamics of neuronal firing correlation - modulation of effective connectivity.pdf}
+}
+
+@article{Dossi2014,
+  title = {Multi-Electrode {{Array Recordings}} of {{Human Epileptic Postoperative Cortical Tissue}}},
+  issn = {1940-087X},
+  doi = {10.3791/51870},
+  abstract = {Epilepsy, affecting about 1\% of the population, comprises a group of neurological disorders characterized by the periodic occurrence of seizures, which disrupt normal brain function. Despite treatment with currently available antiepileptic drugs targeting neuronal functions, one third of patients with epilepsy are pharmacoresistant. In this condition, surgical resection of the brain area generating seizures remains the only alternative treatment. Studying human epileptic tissues has contributed to understand new epileptogenic mechanisms during the last 10 years. Indeed, these tissues generate spontaneous interictal epileptic discharges as well as pharmacologically-induced ictal events which can be recorded with classical electrophysiology techniques. Remarkably, multi-electrode arrays (MEAs), which are microfabricated devices embedding an array of spatially arranged microelectrodes, provide the unique opportunity to simultaneously stimulate and record field potentials, as well as action potentials of multiple neurons from different areas of the tissue. Thus MEAs recordings offer an excellent approach to study the spatio-temporal patterns of spontaneous interictal and evoked seizure-like events and the mechanisms underlying seizure onset and propagation. Here we describe how to prepare human cortical slices from surgically resected tissue and to record with MEAs interictal and ictal-like events ex vivo.},
+  number = {92},
+  journal = {Journal of Visualized Experiments : JoVE},
+  author = {Dossi, Elena and Blauwblomme, Thomas and Nabbout, Rima and Huberfeld, Gilles and Rouach, Nathalie},
+  month = oct,
+  year = {2014},
+  file = {C:\\Users\\saram\\Zotero\\storage\\RE8CX2B8\\Dossi et al. - 2014 - Multi-electrode Array Recordings of Human Epilepti.pdf},
+  pmid = {25407747},
+  pmcid = {PMC4353385}
+}
+
+@article{Mazzoni2015,
+  title = {Computing the {{Local Field Potential}} ({{LFP}}) from {{Integrate}}-and-{{Fire Network Models}}},
+  volume = {11},
+  issn = {1553-7358},
+  doi = {10.1371/journal.pcbi.1004584},
+  abstract = {Author Summary Leaky integrate-and-fire (LIF) networks are often used to model neural network activity. The spike trains they produce, however, cannot be directly compared to the local field potentials (LFPs) that are measured by low-pass filtering the potential recorded from extracellular electrodes. This is because LFPs are generated by neurons with spatial extensions, while LIF networks typically consist of point neurons. In order to still be able to approximately predict LFPs from LIF network simulations, we here explore simple proxies for computing LFPs based on standard output from LIF network simulations. Predictions from the various LFP proxies were compared with ``ground-truth'' LFPs computed by means of well-established volume conduction theory where synaptic currents corresponding to the LIF network simulation were injected into populations of multi-compartmental neurons with realistic morphologies. We found that a simple weighted sum of the LIF synaptic currents with a single universally applicable set of weights excellently capture the time course of the LFP signal when the LFP predominantly is generated by a single population of pyramidal cells. Our study therefore provides a simple formula by which the LFP signal can be estimated directly from the LIF network activity, providing a missing quantitative link between simple neural models and LFP measures in vivo.},
+  number = {12},
+  journal = {PLOS Computational Biology},
+  author = {Mazzoni, Alberto and Lind{\'e}n, Henrik and Cuntz, Hermann and Lansner, Anders and Panzeri, Stefano and Einevoll, Gaute T.},
+  month = dec,
+  year = {2015},
+  keywords = {Synapses,Neurons,Neural networks,Gamma-aminobutyric acid,Neuronal dendrites,Interneurons,Neuronal morphology,simulated,Action potentials},
+  pages = {e1004584},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FQ6J75I9\\Mazzoni 2015 Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models.pdf}
+}
+
+@article{Ray2010,
+  title = {Differences in Gamma Frequencies across Visual Cortex Restrict Their Possible Use in Computation},
+  volume = {67},
+  issn = {0896-6273},
+  doi = {10.1016/j.neuron.2010.08.004},
+  abstract = {Neuronal oscillations in the gamma band (30-80 Hz) have been suggested to play a central role in feature binding or establishing channels for neural communication. For these functions, the gamma rhythm frequency must be consistent across neural assemblies encoding the features of a stimulus. Here we test the dependence of gamma frequency on stimulus contrast in V1 cortex of awake behaving macaques and show that gamma frequency increases monotonically with contrast. Changes in stimulus contrast over time leads to a reliable gamma frequency modulation on a fast timescale. Further, large stimuli whose contrast varies across space generate gamma rhythms at significantly different frequencies in simultaneously recorded neuronal assemblies separated by as little as 400 $\mu$m, making the gamma rhythm a poor candidate for binding or communication, at least in V1. Instead, our results suggest that the gamma rhythm arises from local interactions between excitation and inhibition.},
+  number = {5},
+  journal = {Neuron},
+  author = {Ray, Supratim and Maunsell, John H.R.},
+  month = sep,
+  year = {2010},
+  pages = {885-896},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QGWFS9C9\\Ray_Maunsell 2010 Differences in gamma frequencies across visual cortex restrict their possible.pdf},
+  pmid = {20826318},
+  pmcid = {PMC3001273}
+}
+
+@inproceedings{Parasuram2015,
+  title = {Exploiting Point Source Approximation on Detailed Neuronal Models to Reconstruct Single Neuron Electric Field and Population {{LFP}}},
+  doi = {10.1109/IJCNN.2015.7280607},
+  abstract = {Extracellular electrodes record local field potential as an average response from the neurons within the vicinity of the electrode. Here, we used neuronal models and point source approximation techniques to study the compartmental contribution of single neuron LFP and the attenuation properties of extracellular medium. Cable compartmental contribution of single neuron LFP was estimated by computing electric potential generated by localized ion channels. We simulated the electric potential generated from axon-hillock region contributed significantly to the single neuron extracellular field. Models of cerebellar granule neuron and L5 pyramidal neuron were used to study single neuron extracellular field potentials. Attenuation properties of the extracellular medium were studied via the granule cell model. A computational model of a rat Crus-IIa cerebellar granular layer, built with detailed anatomical and physiological properties allowed reconstructing population LFP. As with single neurons, the same technique was able to reconstruct the T and C waves of evoked postsynaptic in vivo LFP trace. In addition to role of attenuation on the width of signals, plasticity was simulated via modifications of intrinsic properties of underlying neurons and population LFP validated experimental data correlating network function to underlying single neuron activity.},
+  booktitle = {2015 {{International Joint Conference}} on {{Neural Networks}} ({{IJCNN}})},
+  author = {Parasuram, H. and Nair, B. and Naldi, G. and D'Angelo, E. and Diwakar, S.},
+  month = jul,
+  year = {2015},
+  keywords = {simulated,cellular biophysics,medical signal processing,neurophysiology,L5 pyramidal neuron,attenuation property,axon-hillock region,cable compartmental contribution,cerebellar granule neuron,computational model,electric potential,evoked postsynaptic in vivo LFP trace,extracellular electrode,extracellular medium,local field potential,localized ion channel,neuron extracellular field potential,neuronal model,plasticity,point source approximation technique,population LFP,rat Crus-IIa cerebellar granular layer,single neuron LFP,single neuron activity,single neuron electric field,Attenuation,Biological system modeling,Conductivity,Convergence,Physiology,Computational Neuroscience,L5 neuron,Point Source Approximation},
+  pages = {1-7},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ADKW3KIG\\Parasuram 2015 Exploiting point source approximation on detailed neuronal models to.pdf}
+}
+
+@article{Banerjee2012,
+  title = {Parametric Models to Relate Spike Train and {{LFP}} Dynamics with Neural Information Processing},
+  volume = {6},
+  issn = {1662-5188},
+  doi = {10.3389/fncom.2012.00051},
+  abstract = {Spike trains and local field potentials (LFPs) resulting from extracellular current flows provide a substrate for neural information processing. Understanding the neural code from simultaneous spike-field recordings and subsequent decoding of information processing events will have widespread applications. One way to demonstrate an understanding of the neural code, with particular advantages for the development of applications, is to formulate a parametric statistical model of neural activity and its covariates. Here, we propose a set of parametric spike-field models (unified models) that can be used with existing decoding algorithms to reveal the timing of task or stimulus specific processing. Our proposed unified modeling framework captures the effects of two important features of information processing: time-varying stimulus-driven inputs and ongoing background activity that occurs even in the absence of environmental inputs. We have applied this framework for decoding neural latencies in simulated and experimentally recorded spike-field sessions obtained from the lateral intraparietal area (LIP) of awake, behaving monkeys performing cued look-and-reach movements to spatial targets. Using both simulated and experimental data, we find that estimates of trial-by-trial parameters are not significantly affected by the presence of ongoing background activity. However, including background activity in the unified model improves goodness of fit for predicting individual spiking events. Uncovering the relationship between the model parameters and the timing of movements offers new ways to test hypotheses about the relationship between neural activity and behavior. We obtained significant spike-field onset time correlations from single trials using a previously published data set where significantly strong correlation was only obtained through trial averaging. We also found that unified models extracted a stronger relationship between neural response latency and trial-by-trial behavioral performance than existing models of neural information processing. Our results highlight the utility of the unified modeling framework for characterizing spike-LFP recordings obtained during behavioral performance.},
+  journal = {Frontiers in Computational Neuroscience},
+  author = {Banerjee, Arpan and Dean, Heather L. and Pesaran, Bijan},
+  month = jul,
+  year = {2012},
+  keywords = {simulated},
+  file = {C:\\Users\\saram\\Zotero\\storage\\AE52RTV9\\Banerjee 2012 Parametric models to relate spike train and LFP dynamics with neural.pdf},
+  pmid = {22837745},
+  pmcid = {PMC3403111}
+}
+
+@article{Suffczynski2014,
+  title = {Afferent Inputs to Cortical Fast-Spiking Interneurons Organize Pyramidal Cell Network Oscillations at High-Gamma Frequencies (60\textendash{}200 {{Hz}})},
+  volume = {112},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00844.2013},
+  abstract = {High-gamma activity, ranging in frequency between $\sim$60 Hz and 200 Hz, has been observed in local field potential, electrocorticography, EEG and magnetoencephalography signals during cortical activation, in a variety of functional brain systems. The origin of these signals is yet unknown. Using computational modeling, we show that a cortical network model receiving thalamic input generates high-gamma responses comparable to those observed in local field potential recorded in monkey somatosensory cortex during vibrotactile stimulation. These high-gamma oscillations appear to be mediated mostly by an excited population of inhibitory fast-spiking interneurons firing at high-gamma frequencies and pacing excitatory regular-spiking pyramidal cells, which fire at lower rates but in phase with the population rhythm. The physiological correlates of high-gamma activity, in this model of local cortical circuits, appear to be similar to those proposed for hippocampal ripples generated by subsets of interneurons that regulate the discharge of principal cells.},
+  language = {en},
+  number = {11},
+  journal = {Journal of Neurophysiology},
+  author = {Suffczynski, Piotr and Crone, Nathan E. and Franaszczuk, Piotr J.},
+  month = dec,
+  year = {2014},
+  pages = {3001-3011},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PWSR49DU\\Suffczynski 2014 Afferent inputs to cortical fast-spiking interneurons organize pyramidal cell.pdf},
+  pmid = {25210164}
+}
+
+@article{Anderson2007,
+  title = {Studies of Stimulus Parameters for Seizure Disruption Using Neural Network Simulations},
+  volume = {97},
+  issn = {0340-1200},
+  doi = {10.1007/s00422-007-0166-0},
+  abstract = {A large scale neural network simulation with realistic cortical architecture has been undertaken to investigate the effects of external electrical stimulation on the propagation and evolution of ongoing seizure activity. This is an effort to explore the parameter space of stimulation variables to uncover promising avenues of research for this therapeutic modality. The model consists of an approximately 800 $\mu$m \texttimes{} 800 $\mu$m region of simulated cortex, and includes seven neuron classes organized by cortical layer, inhibitory or excitatory properties, and electrophysiological characteristics. The cell dynamics are governed by a modified version of the Hodgkin-Huxley equations in single compartment format. Axonal connections are patterned after histological data and published models of local cortical wiring. Stimulation induced action potentials take place at the axon initial segments, according to threshold requirements on the applied electric field distribution. Stimulation induced action potentials in horizontal axonal branches are also separately simulated. The calculations are performed on a 16 node distributed 32-bit processor system. Clear differences in seizure evolution are presented for stimulated versus the undisturbed rhythmic activity. Data is provided for frequency dependent stimulation effects demonstrating a plateau effect of stimulation efficacy as the applied frequency is increased from 60 Hz to 200 Hz. Timing of the stimulation with respect to the underlying rhythmic activity demonstrates a phase dependent sensitivity. Electrode height and position effects are also presented. Using a dipole stimulation electrode arrangement, clear orientation effects of the dipole with respect to the model connectivity is also demonstrated. A sensitivity analysis of these results as a function of the stimulation threshold is also provided.},
+  number = {2},
+  journal = {Biological cybernetics},
+  author = {Anderson, William S. and Kudela, Pawel and Cho, Ryan J. and Bergey, Gregory K. and Franaszczuk, Piotr},
+  month = aug,
+  year = {2007},
+  keywords = {simulated},
+  pages = {173-194},
+  file = {C:\\Users\\saram\\Zotero\\storage\\C92V77SJ\\Anderson 2007 Studies of stimulus parameters for seizure disruption using neural network.pdf},
+  pmid = {17619199},
+  pmcid = {PMC2738632}
+}
+
+@article{Regalia2016,
+  title = {A {{Framework}} for the {{Comparative Assessment}} of {{Neuronal Spike Sorting Algorithms}} towards {{More Accurate Off}}-{{Line}} and {{On}}-{{Line Microelectrode Arrays Data Analysis}}},
+  volume = {2016},
+  issn = {1687-5265},
+  doi = {10.1155/2016/8416237},
+  abstract = {Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting ``building blocks'' into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.},
+  language = {en},
+  journal = {Computational Intelligence and Neuroscience},
+  author = {Regalia, Giulia and Coelli, Stefania and Biffi, Emilia and Ferrigno, Giancarlo and Pedrocchi, Alessandra},
+  month = apr,
+  year = {2016},
+  keywords = {clustering},
+  pages = {e8416237},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DUM38HU2\\Regalia 2016 A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms.pdf;C:\\Users\\saram\\Zotero\\storage\\XZ7B6XHQ\\Regalia 2016 A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrod.pdf}
+}
+
+@article{Eyal2016,
+  title = {Unique Membrane Properties and Enhanced Signal Processing in Human Neocortical Neurons},
+  volume = {5},
+  copyright = {\textcopyright{} 2016, Eyal et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.},
+  issn = {2050-084X},
+  doi = {10.7554/eLife.16553},
+  abstract = {Models and experiments reveal that human L2/3 pyramidal neurons have distinctively low specific membrane capacitance which might have a significant impact on signal processing in human neocortex.},
+  language = {en},
+  journal = {eLife},
+  author = {Eyal, Guy and Verhoog, Matthijs B. and Testa-Silva, Guilherme and Deitcher, Yair and Lodder, Johannes C. and Benavides-Piccione, Ruth and Morales, Juan and DeFelipe, Javier and de Kock, Christiaan PJ and Mansvelder, Huibert D. and Segev, Idan},
+  month = oct,
+  year = {2016},
+  keywords = {compartmental modeling,human neurons,neurons computation,membrane properties,Mouse,DrV suggestion,Journal Club,human},
+  pages = {e16553},
+  file = {C:\\Users\\saram\\Zotero\\storage\\66S54Q3J\\Eyal 2016 Unique membrane properties and enhanced signal processing in human neocortical.pdf},
+  pmid = {27710767}
+}
+
+@article{Heuschkel2002,
+  title = {A Three-Dimensional Multi-Electrode Array for Multi-Site Stimulation and Recording in Acute Brain Slices},
+  volume = {114},
+  issn = {0165-0270},
+  doi = {10.1016/S0165-0270(01)00514-3},
+  abstract = {Several multi-electrode array devices integrating planar metal electrodes were designed in the past 30 years for extracellular stimulation and recording from cultured neuronal cells and organotypic brain slices. However, these devices are not well suited for recordings from acute brain slice preparations due to a dead cell layer at the tissue slice border that appears during the cutting procedure. To overcome this problem, we propose the use of protruding 3D electrodes, i.e. tip-shaped electrodes, allowing tissue penetration in order to get closer to living neurons in the tissue slice. In this paper, we describe the design and fabrication of planar and 3D protruding multi-electrode arrays. The electrical differences between planar and 3D protruding electrode configuration were simulated and verified experimentally. Finally, a comparison between the planar and 3D protruding electrode configuration was realized by stimulation and recording from acute rat hippocampus slices. The results show that larger signal amplitudes in the millivolt range can be obtained with the 3D electrode devices. Spikes corresponding to single cell activity could be monitored in the hippocampus CA3 and CA1 region using 3D electrodes.},
+  number = {2},
+  journal = {Journal of Neuroscience Methods},
+  author = {Heuschkel, Marc Olivier and Fejtl, Michael and Raggenbass, Mario and Bertrand, Daniel and Renaud, Philippe},
+  month = mar,
+  year = {2002},
+  keywords = {Multi-electrode array,Protruding electrodes,Tip-shaped electrodes,Simulation,Acute slice,Rat hippocampus,Electrical stimulation,Extracellular recording,MEA,spontaneous activity,Laura},
+  pages = {135-148},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2T9HIH7J\\Heuschkel 2002 A three-dimensional multi-electrode array for multi-site stimulation and.pdf;C:\\Users\\saram\\Zotero\\storage\\JQIN6KJ3\\S0165027001005143.html}
+}
+
+@article{DelgadoRuz2014,
+  title = {Localising and Classifying Neurons from High Density {{MEA}} Recordings},
+  volume = {233},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2014.05.037},
+  abstract = {Neuronal microcircuits are formed of a myriad of spatially and functionally specific cell classes. Despite the importance of the spatial component in the characterisation of neural circuits, it has not received the attention it deserves. While multi-electrodes are widely used in the study of microcircuits, the spatial information available from them remains largely unexploited for analysis beyond spike sorting. Here we show how the spatial pattern of the extracellular signal is determined by both the electrophysiology and morphology of neurons. Starting from known current source models for the generation of the extracellular potential, we use the spatial pattern observed across a multi-electrode array to localise and classify neurons into putative morphological classes. We evaluated the localisation and classification models with low fitting errors in simulated data. When applying them to recorded data we found correspondence between localisation statistics and expected recording radius and found evidence to support the separation into putative morphological classes. While existing localisation methods do not hold for the recording distances expected on multi-electrode recordings (under 60 $\mu$m), classification methods have been limited to the temporal component by either characterising spike shape or firing patterns. We show here how the information available from extracellular recordings can be used to localise and classify neurons based on the spatial pattern seen by multi-electrode arrays. Together they can improve current characterisation and classification of neurons based on complementary criteria such us firing pattern and functional characterisation.},
+  journal = {Journal of Neuroscience Methods},
+  author = {Delgado Ruz, Isabel and Schultz, Simon R.},
+  month = aug,
+  year = {2014},
+  keywords = {simulated,Extracellular recording,MEA,Laura,Multi-electrode,Neuron classification,Neuron localisation,Morphology},
+  pages = {115-128},
+  file = {C:\\Users\\saram\\Zotero\\storage\\C9P5IF9X\\Delgado Ruz_Schultz 2014 Localising and classifying neurons from high density MEA recordings.pdf}
+}
+
+@article{Steidl2006,
+  title = {The Adult Rat Hippocampal Slice Revisited with Multi-Electrode Arrays},
+  volume = {1096},
+  issn = {0006-8993},
+  doi = {10.1016/j.brainres.2006.04.034},
+  abstract = {The multi-electrode arrays (MEA) technology for the recording of brain slices is available for more than 10 years. However, despite its relative simplicity, this recording technique is not widely used in academic or pharmaceutical research laboratories. We illustrate here that MEA provide multiple possibilities to investigate some network physiological properties as well as to evaluate the pharmacological effects of compounds. We first document that MEA allow to trigger and to record conventional FP which are inhibited by the block of action potential propagation (with 500 nM TTX). FP recorded with MEA are sensitive to ionic substitutions, to ionotropic glutamate receptor antagonists (CNQX or NBQX) and to energetic failure. Second, we illustrate that different "classical" protocols (paired-pulse, LTP, chemical LTD), revealing synaptic plasticity mechanisms, could be performed. Third, we document that MEA allow spatial and temporal discriminations for the effects of known pharmacological compounds such as competitive antagonist (gabazine, bicuculline) and allosteric modulators (steroids) of GABA(A) receptors. In conclusion, we illustrate that MEA recordings of adult rat hippocampal slices constitute a powerful and sensitive system to evaluate the effect of molecules on basic synaptic propagation/transmission and on synaptic plasticity processes.},
+  language = {eng},
+  number = {1},
+  journal = {Brain Research},
+  author = {Steidl, Esther-Marie and Neveu, Estelle and Bertrand, Daniel and Buisson, Bruno},
+  month = jun,
+  year = {2006},
+  keywords = {Animals,Electrodes,MEA,Laura,Data Interpretation; Statistical,Electric Stimulation,Electrophysiology,GABA Antagonists,In Vitro Techniques,Ion Channels,Long-Term Potentiation,Methoxyhydroxyphenylglycol,Neurotransmitter Agents,Pyridazines,Rats,Rats; Sprague-Dawley,Receptor; Metabotropic Glutamate 5,Receptors; Metabotropic Glutamate,Receptors; Presynaptic,Tetrodotoxin,acute slice,hippocampus},
+  pages = {70-84},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TCMACF5U\\Steidl 2006 The adult rat hippocampal slice revisited with multi-electrode arrays.pdf},
+  pmid = {16716268}
+}
+
+@article{Egert2002,
+  title = {Two-Dimensional Monitoring of Spiking Networks in Acute Brain Slices},
+  volume = {142},
+  issn = {0014-4819},
+  doi = {10.1007/s00221-001-0932-5},
+  abstract = {To understand spatiotemporally coordinated activity in neural networks and interaction between different areas or layers in brain tissue, simultaneous multisite recording is a prerequisite. For in vitro studies pursuing these goals, substrate integrated, planar microelectrode arrays (MEAs) have been developed to monitor spikes and local field potentials. Here we report for the first time recordings of single-unit spike activity with MEAs in acute slice preparations of the rat cerebellum. We compare these recordings to results of conventional techniques, and discuss the recording conditions in view of the equivalent circuits commonly used. Simultaneous recordings with tungsten microelectrodes and MEAs verified that recording characteristics and signal-to-noise ratios of MEA electrodes were comparable to those of conventional extracellular electrodes. Spike shapes were identical on both electrodes. We found no detectable overlap between spike signals recorded at neighboring MEA electrodes (200 microm spacing). Neuronal spike activity was detected with MEA electrodes at distances of up to 100 microm from the site of spike generation. We conclude that extracellular recording of independent single-unit spike activity with MEAs is indeed suitable to monitor network activity in acute slices, making MEAs an exceptionally useful tool for the assessment of fast network dynamics in acute slices.},
+  language = {eng},
+  number = {2},
+  journal = {Experimental Brain Research},
+  author = {Egert, Ulrich and Heck, D. and Aertsen, A.},
+  month = jan,
+  year = {2002},
+  keywords = {Neurons,Animals,Electronics; Medical,Nerve Net,Neural Pathways,MEA,Laura,Electrophysiology,Rats,Rats; Sprague-Dawley,acute slice,Artifacts,Cerebellum,Microelectrodes,Organ Culture Techniques,Tungsten,Action potentials},
+  pages = {268-274},
+  file = {C:\\Users\\saram\\Zotero\\storage\\NI5SJ939\\Egert_2001.pdf},
+  pmid = {11807580}
+}
+
+@article{Killian2016a,
+  title = {A {{Device}} for {{Long}}-{{Term Perfusion}}, {{Imaging}}, and {{Electrical Interfacing}} of {{Brain Tissue In}} Vitro},
+  volume = {10},
+  issn = {1662-453X},
+  doi = {10.3389/fnins.2016.00135},
+  abstract = {Distributed microelectrode array (MEA) recordings from consistent, viable, $\geq$ 500 $\mathrm{\mu}$m thick tissue preparations over time periods from days to weeks may aid in studying a wide range of problems in neurobiology that require in vivo-like organotypic morphology. Existing tools for electrically interfacing with organotypic slices do not address necrosis that inevitably occurs within thick slices with limited diffusion of nutrients and gas, and limited removal of waste. We developed an integrated device that enables long-term maintenance of thick, functionally active, brain tissue models using interstitial perfusion and distributed recordings from thick sections of explanted tissue on a perforated multi-electrode array. This novel device allows for automated culturing, in situ imaging, and extracellular multi-electrode interfacing with brain slices, 3 D cell cultures, and potentially other tissue culture models. The device is economical, easy to assemble, and integrable with standard electrophysiology tools. We found that convective perfusion through the culture thickness provided a functional benefit to the preparations as firing rates were generally higher in perfused cultures compared to their respective unperfused controls. This work is a step towards the development of integrated tools for days-long experiments with more consistent, healthier, thicker, and functionally more active tissue cultures with built-in distributed electrophysiological recording and stimulation functionality. The results may be useful for the study of normal processes, pathological conditions, and drug screening strategies currently hindered by the limitations of acute (a few hours long) brain slice preparations.},
+  language = {English},
+  journal = {Frontiers in Neuroscience},
+  author = {Killian, Nathaniel J. and Vernekar, Varadraj N. and Potter, Steve M. and Vukasinovic, Jelena},
+  year = {2016},
+  keywords = {MEA,perforated microelectrode array,brain slice,three-dimensional culture,Neuron,culture},
+  file = {C:\\Users\\saram\\Zotero\\storage\\MN3DQFW2\\Killian 2016 A Device for Long-Term Perfusion, Imaging, and Electrical Interfacing of Brain.pdf}
+}
+
+@article{Aru2015,
+  series = {SI: Brain rhythms and dynamic coordination},
+  title = {Untangling Cross-Frequency Coupling in Neuroscience},
+  volume = {31},
+  issn = {0959-4388},
+  doi = {10.1016/j.conb.2014.08.002},
+  abstract = {Cross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pathological brain function, the standard CFC analysis and physiological interpretation come with fundamental problems. For example, apparent CFC can appear because of spectral correlations due to common non-stationarities that may arise in the total absence of interactions between neural frequency components. To provide a road map towards an improved mechanistic understanding of CFC, we organize the available and potential novel statistical/modeling approaches according to their biophysical interpretability. While we do not provide solutions for all the problems described, we provide a list of practical recommendations to avoid common errors and to enhance the interpretability of CFC analysis.},
+  journal = {Current Opinion in Neurobiology},
+  author = {Aru, Juhan and Aru, Jaan and Priesemann, Viola and Wibral, Michael and Lana, Luiz and Pipa, Gordon and Singer, Wolf and Vicente, Raul},
+  month = apr,
+  year = {2015},
+  pages = {51-61},
+  file = {C:\\Users\\saram\\Zotero\\storage\\IAAQHD35\\mmc1.doc;C:\\Users\\saram\\Zotero\\storage\\WP58EHN7\\Aru 2015 Untangling cross-frequency coupling in neuroscience.pdf}
+}
+
+@article{Friston2015,
+  series = {SI: Brain rhythms and dynamic coordination},
+  title = {{{LFP}} and Oscillations\textemdash{}what Do They Tell Us?},
+  volume = {31},
+  issn = {0959-4388},
+  doi = {10.1016/j.conb.2014.05.004},
+  abstract = {This review surveys recent trends in the use of local field potentials\textemdash{}and their non-invasive counterparts\textemdash{}to address the principles of functional brain architectures. In particular, we treat oscillations as the (observable) signature of context-sensitive changes in synaptic efficacy that underlie coordinated dynamics and message-passing in the brain. This rich source of information is now being exploited by various procedures\textemdash{}like dynamic causal modelling\textemdash{}to test hypotheses about neuronal circuits in health and disease. Furthermore, the roles played by neuromodulatory mechanisms can be addressed directly through their effects on oscillatory phenomena. These neuromodulatory or gain control processes are central to many theories of normal brain function (e.g. attention) and the pathophysiology of several neuropsychiatric conditions (e.g. Parkinson's disease).},
+  journal = {Current Opinion in Neurobiology},
+  author = {Friston, Karl J and Bastos, Andr{\'e} M and Pinotsis, Dimitris and Litvak, Vladimir},
+  month = apr,
+  year = {2015},
+  pages = {1-6},
+  file = {C:\\Users\\saram\\Zotero\\storage\\7Z65DQGR\\Friston et al.  2015  LFP and oscillations—what do they tell us.pdf}
+}
+
+@article{Principe2015,
+  series = {SI: Brain rhythms and dynamic coordination},
+  title = {Representing and Decomposing Neural Potential Signals},
+  volume = {31},
+  issn = {0959-4388},
+  doi = {10.1016/j.conb.2014.07.023},
+  abstract = {This paper reviews methodologies for analyzing neural potentials via frequency, time\textendash{}frequency, or wavelet representations, and adaptive models that estimate the signal's spatial or temporal structure. The fundamental assumptions of each method are discussed. In particular, the Fourier transform is contrasted with overcomplete representations, which are able to precisely delineate the timing and/or frequency of neural events. Finally, a novel approach that combines overcomplete representations with adaptive signal models is presented. This approach describes a continuous signal as a linear combination of reoccurring waveforms, referred to as phasic events, which are often associated with neural processing. The new methodology automatically learns the reoccurring waveforms and quantifies the neural potentials by the set of amplitudes and timings.},
+  journal = {Current Opinion in Neurobiology},
+  author = {Principe, Jose C and Brockmeier, Austin J},
+  month = apr,
+  year = {2015},
+  pages = {13-17},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SK6DMWF5\\Principe and Brockmeier  2015  Representing and decomposing neural potential signals.pdf}
+}
+
+@article{Nakhnikian2016,
+  title = {A Novel Cross-Frequency Coupling Detection Method Using the Generalized {{Morse}} Wavelets},
+  volume = {269},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2016.04.019},
+  abstract = {Background
+Cross-frequency coupling (CFC) occurs when non-identical frequency components entrain one another. A ubiquitous example from neuroscience is low frequency phase to high frequency amplitude coupling in electrophysiological signals. Seminal work by Canolty revealed CFC in human ECoG data. Established methods band-pass the data into component frequencies then convert the band-passed signals into the analytic representation, from which we infer the instantaneous amplitude and phase of each component. Though powerful, such methods resolve signals with respect to time and frequency without addressing the multiresolution problem.
+New method
+We build upon the ground-breaking work of Canolty and others and derive a wavelet-based CFC detection algorithm that efficiently searches a range of frequencies using a sequence of filters with optimal trade-off between time and frequency resolution. We validate our method using simulated data and analyze CFC within and between the primary motor cortex and dorsal striatum of rats under ketamine-xylazine anesthesia.
+Results
+Our method detects the correct CFC in simulated data and reveals CFC between frequency bands that were previously shown to participate in corticostriatal effective connectivity.
+Comparison with existing methods
+Other CFC detection methods address the need to increase bandwidth when analyzing high frequency components but none to date permit rigorous bandwidth selection with no a priori knowledge of underlying CFC. Our method is thus particularly useful for exploratory studies.
+Conclusions
+The method developed here permits rigorous and efficient exploration of a hypothesis space and is particularly useful when the frequencies participating in CFC are unknown.},
+  journal = {Journal of Neuroscience Methods},
+  author = {Nakhnikian, A. and Ito, S. and Dwiel, L. L. and Grasse, L. M. and Rebec, G. V. and Lauridsen, L. N. and Beggs, J. M.},
+  month = aug,
+  year = {2016},
+  keywords = {Cross-frequency coupling,Generalized Morse wavelets,In vivo electrophysiology,Anesthesia,Signal processing},
+  pages = {61-73},
+  file = {C:\\Users\\saram\\Zotero\\storage\\EU5DCWEB\\Nakhnikian et al.  2016  A novel cross-frequency coupling detection method using the generalized Morse wavelets.pdf}
+}
+
+@article{Cole2017,
+  title = {Brain {{Oscillations}} and the {{Importance}} of {{Waveform Shape}}},
+  volume = {21},
+  issn = {1364-6613},
+  doi = {10.1016/j.tics.2016.12.008},
+  abstract = {Oscillations are a prevalent feature of brain recordings. They are believed to play key roles in neural communication and computation. Current analysis methods for studying neural oscillations often implicitly assume that the oscillations are sinusoidal. While these approaches have proven fruitful, we show here that there are numerous instances in which neural oscillations are nonsinusoidal. We highlight approaches to characterize nonsinusoidal features and account for them in traditional spectral analysis. Instead of being a nuisance, we discuss how these nonsinusoidal features may provide crucial and so far overlooked physiological information related to neural communication, computation, and cognition.},
+  number = {2},
+  journal = {Trends in Cognitive Sciences},
+  author = {Cole, Scott R. and Voytek, Bradley},
+  month = feb,
+  year = {2017},
+  keywords = {oscillation,nonsinusoidal,waveform,shape,phase–amplitude coupling},
+  pages = {137-149},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KRSTITDQ\\Cole and Voytek  2017  Brain Oscillations and the Importance of Waveform Shape.pdf}
+}
+
+@article{Tort2010,
+  title = {Measuring {{Phase}}-{{Amplitude Coupling Between Neuronal Oscillations}} of {{Different Frequencies}}},
+  volume = {104},
+  issn = {0022-3077},
+  doi = {10.1152/jn.00106.2010},
+  abstract = {Neuronal oscillations of different frequencies can interact in several ways. There has been particular interest in the modulation of the amplitude of high-frequency oscillations by the phase of low-frequency oscillations, since recent evidence suggests a functional role for this type of cross-frequency coupling (CFC). Phase-amplitude coupling has been reported in continuous electrophysiological signals obtained from the brain at both local and macroscopic levels. In the present work, we present a new measure for assessing phase-amplitude CFC. This measure is defined as an adaptation of the Kullback\textendash{}Leibler distance\textemdash{}a function that is used to infer the distance between two distributions\textemdash{}and calculates how much an empirical amplitude distribution-like function over phase bins deviates from the uniform distribution. We show that a CFC measure defined this way is well suited for assessing the intensity of phase-amplitude coupling. We also review seven other CFC measures; we show that, by some performance benchmarks, our measure is especially attractive for this task. We also discuss some technical aspects related to the measure, such as the length of the epochs used for these analyses and the utility of surrogate control analyses. Finally, we apply the measure and a related CFC tool to actual hippocampal recordings obtained from freely moving rats and show, for the first time, that the CA3 and CA1 regions present different CFC characteristics.},
+  number = {2},
+  journal = {Journal of Neurophysiology},
+  author = {Tort, Adriano B. L. and Komorowski, Robert and Eichenbaum, Howard and Kopell, Nancy},
+  month = aug,
+  year = {2010},
+  pages = {1195-1210},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JT5DZ7Q9\\Tort 2010-Measuring phase-amplitude coupling between neuronal oscillations of different frequencies (1).pdf},
+  pmid = {20463205},
+  pmcid = {PMC2941206}
+}
+
+@article{Voloh2015,
+  title = {Theta\textendash{}gamma Coordination between Anterior Cingulate and Prefrontal Cortex Indexes Correct Attention Shifts},
+  volume = {112},
+  issn = {0027-8424},
+  doi = {10.1073/pnas.1500438112},
+  abstract = {During flexible goal-directed behavior, our frontal cortex coordinates goal-relevant information from widely distributed neuronal systems to prioritize the relevant over irrelevant information. This coordination may be realized by entraining multiple distributed systems with slow activity oscillation to phase-align their local fast oscillatory activity. We analyzed this spatially distributed oscillatory coupling in macaques during actual attentional stimulus selection. We identified that successful\textemdash{}but not failed\textemdash{}selection of relevant information followed the coupling of slow and fast frequencies at narrow oscillation phases, with interareal anatomical specificity, and based on a reset mechanism for the slow oscillation. These findings suggest a specific functional and mechanistic solution to the fundamental coordination problem in primate brains., Anterior cingulate and lateral prefrontal cortex (ACC/PFC) are believed to coordinate activity to flexibly prioritize the processing of goal-relevant over irrelevant information. This between-area coordination may be realized by common low-frequency excitability changes synchronizing segregated high-frequency activations. We tested this coordination hypothesis by recording in macaque ACC/PFC during the covert utilization of attention cues. We found robust increases of 5\textendash{}10 Hz (theta) to 35\textendash{}55 Hz (gamma) phase\textendash{}amplitude correlation between ACC and PFC during successful attention shifts but not before errors. Cortical sites providing theta phases (i) showed a prominent cue-induced phase reset, (ii) were more likely in ACC than PFC, and (iii) hosted neurons with burst firing events that synchronized to distant gamma activity. These findings suggest that interareal theta\textendash{}gamma correlations could follow mechanistically from a cue-triggered reactivation of rule memory that synchronizes theta across ACC/PFC.},
+  number = {27},
+  journal = {Proceedings of the National Academy of Sciences of the United States of America},
+  author = {Voloh, Benjamin and Valiante, Taufik A. and Everling, Stefan and Womelsdorf, Thilo},
+  month = jul,
+  year = {2015},
+  pages = {8457-8462},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SJGJ23SX\\Voloh et al.  2015  Theta–gamma coordination between anterior cingulate and prefrontal cortex indexes correct attention shifts.pdf},
+  pmid = {26100868},
+  pmcid = {PMC4500211}
+}
+
+@inproceedings{Paparrizos2015,
+  address = {New York, NY, USA},
+  series = {SIGMOD '15},
+  title = {K-{{Shape}}: {{Efficient}} and {{Accurate Clustering}} of {{Time Series}}},
+  isbn = {978-1-4503-2758-9},
+  shorttitle = {K-{{Shape}}},
+  doi = {10.1145/2723372.2737793},
+  abstract = {The proliferation and ubiquity of temporal data across many disciplines has generated substantial interest in the analysis and mining of time series. Clustering is one of the most popular data mining methods, not only due to its exploratory power, but also as a preprocessing step or subroutine for other techniques. In this paper, we present k-Shape, a novel algorithm for time-series clustering. k-Shape relies on a scalable iterative refinement procedure, which creates homogeneous and well-separated clusters. As its distance measure, k-Shape uses a normalized version of the cross-correlation measure in order to consider the shapes of time series while comparing them. Based on the properties of that distance measure, we develop a method to compute cluster centroids, which are used in every iteration to update the assignment of time series to clusters. To demonstrate the robustness of k-Shape, we perform an extensive experimental evaluation of our approach against partitional, hierarchical, and spectral clustering methods, with combinations of the most competitive distance measures. k-Shape outperforms all scalable approaches in terms of accuracy. Furthermore, k-Shape also outperforms all non-scalable (and hence impractical) combinations, with one exception that achieves similar accuracy results. However, unlike k-Shape, this combination requires tuning of its distance measure and is two orders of magnitude slower than k-Shape. Overall, k-Shape emerges as a domain-independent, highly accurate, and highly efficient clustering approach for time series with broad applications.},
+  booktitle = {Proceedings of the 2015 {{ACM SIGMOD International Conference}} on {{Management}} of {{Data}}},
+  publisher = {{ACM}},
+  author = {Paparrizos, John and Gravano, Luis},
+  year = {2015},
+  keywords = {clustering,centroid computation,distance measure,time-series data},
+  pages = {1855--1870},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QUAQEZNC\\Paparrizos_Gravano 2015 k-Shape - Efficient and Accurate Clustering of Time Series.pdf}
+}
+
+@article{Scheffer-Teixeira2016,
+  title = {On Cross-Frequency Phase-Phase Coupling between Theta and Gamma Oscillations in the Hippocampus},
+  volume = {5},
+  copyright = {\textcopyright{} 2016, Scheffer-Teixeira et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.},
+  issn = {2050-084X},
+  doi = {10.7554/eLife.20515},
+  abstract = {Phase-locking of hippocampal theta and gamma waves has been proposed to support memory formation, but an analysis using robust statistical methods finds no convincing evidence for the phenomenon.},
+  language = {en},
+  journal = {eLife},
+  author = {Scheffer-Teixeira, Robson and Tort, Adriano BL},
+  month = dec,
+  year = {2016},
+  keywords = {local field potential,Electrophysiology,Cross-frequency coupling,brain rhythms,neuronal oscillations,Rat},
+  pages = {e20515},
+  file = {C:\\Users\\saram\\Zotero\\storage\\T73HJJ46\\Scheffer-Teixeira and Tort  2016  On cross-frequency phase-phase coupling between theta and gamma oscillations in the hippocampus.pdf},
+  pmid = {27925581}
+}
+
+@book{Bezdek2013,
+  title = {Pattern {{Recognition}} with {{Fuzzy Objective Function Algorithms}}},
+  isbn = {978-1-4757-0450-1},
+  abstract = {The fuzzy set was conceived as a result of an attempt to come to grips with the problem of pattern recognition in the context of imprecisely defined categories. In such cases, the belonging of an object to a class is a matter of degree, as is the question of whether or not a group of objects form a cluster. A pioneering application of the theory of fuzzy sets to cluster analysis was made in 1969 by Ruspini. It was not until 1973, however, when the appearance of the work by Dunn and Bezdek on the Fuzzy ISODATA (or fuzzy c-means) algorithms became a landmark in the theory of cluster analysis, that the relevance of the theory of fuzzy sets to cluster analysis and pattern recognition became clearly established. Since then, the theory of fuzzy clustering has developed rapidly and fruitfully, with the author of the present monograph contributing a major share of what we know today. In their seminal work, Bezdek and Dunn have introduced the basic idea of determining the fuzzy clusters by minimizing an appropriately defined functional, and have derived iterative algorithms for computing the membership functions for the clusters in question. The important issue of convergence of such algorithms has become much better understood as a result of recent work which is described in the monograph.},
+  language = {en},
+  publisher = {{Springer Science \& Business Media}},
+  author = {Bezdek, James C.},
+  month = mar,
+  year = {2013},
+  keywords = {Mathematics / General,Computers / Computer Science}
+}
+
+@book{Theodoridis2009,
+  address = {London},
+  title = {Pattern Recognition},
+  isbn = {978-1-59749-272-0},
+  publisher = {{Academic Press}},
+  author = {Theodoridis, Sergios.},
+  editor = {Koutroumbas, Konstantinos.},
+  year = {2009},
+  keywords = {Pattern recognition systems.,Reconnaissance des formes (Informatique),Patroonherkenning.|2gtt}
+}
+
+@article{Biffi2010,
+  title = {Development and {{Validation}} of a {{Spike Detection}} and {{Classification Algorithm Aimed}} at {{Implementation}} on {{Hardware Devices}}},
+  volume = {2010},
+  issn = {1687-5265},
+  doi = {10.1155/2010/659050},
+  abstract = {Neurons cultured in vitro on MicroElectrode Array (MEA) devices connect to each other, forming a network. To study electrophysiological activity and long term plasticity effects, long period recording and spike sorter methods are needed. Therefore, on-line and real time analysis, optimization of memory use and data transmission rate improvement become necessary. We developed an algorithm for amplitude-threshold spikes detection, whose performances were verified with (a) statistical analysis on both simulated and real signal and (b) Big O Notation. Moreover, we developed a PCA-hierarchical classifier, evaluated on simulated and real signal. Finally we proposed a spike detection hardware design on FPGA, whose feasibility was verified in terms of CLBs number, memory occupation and temporal requirements; once realized, it will be able to execute on-line detection and real time waveform analysis, reducing data storage problems.},
+  language = {en},
+  journal = {Computational Intelligence and Neuroscience},
+  author = {Biffi, E. and Ghezzi, D. and Pedrocchi, A. and Ferrigno, G.},
+  month = mar,
+  year = {2010},
+  pages = {e659050},
+  file = {C:\\Users\\saram\\Zotero\\storage\\7IE85UD6\\Biffi 2010 Development and Validation of a Spike Detection and Classification Algorithm.pdf},
+  pmid = {20300592}
+}
+
+@article{Bargmann2013,
+  title = {From the Connectome to Brain Function},
+  volume = {10},
+  copyright = {\textcopyright{} 2013 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1548-7091},
+  doi = {10.1038/nmeth.2451},
+  abstract = {In this Historical Perspective, we ask what information is needed beyond connectivity diagrams to understand the function of nervous systems. Informed by invertebrate circuits whose connectivities are known, we highlight the importance of neuronal dynamics and neuromodulation, and the existence of parallel circuits. The vertebrate retina has these features in common with invertebrate circuits, suggesting that they are general across animals. Comparisons across these systems suggest approaches to study the functional organization of large circuits based on existing knowledge of small circuits.},
+  language = {en},
+  number = {6},
+  journal = {Nature Methods},
+  author = {Bargmann, Cornelia I. and Marder, Eve},
+  month = jun,
+  year = {2013},
+  keywords = {neurophysiology,Neuroscience},
+  pages = {483-490},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8KZENNTT\\Bargmann, 2013, From the connectome to brain function.pdf}
+}
+
+@article{Lein2007,
+  title = {Genome-Wide Atlas of Gene Expression in the Adult Mouse Brain},
+  volume = {445},
+  copyright = {\textcopyright{} 2006 Nature Publishing Group},
+  issn = {0028-0836},
+  doi = {10.1038/nature05453},
+  abstract = {Molecular approaches to understanding the functional circuitry of the nervous system promise new insights into the relationship between genes, brain and behaviour. The cellular diversity of the brain necessitates a cellular resolution approach towards understanding the functional genomics of the nervous system. We describe here an anatomically comprehensive digital atlas containing the expression patterns of 20,000 genes in the adult mouse brain. Data were generated using automated high-throughput procedures for in situ hybridization and data acquisition, and are publicly accessible online. Newly developed image-based informatics tools allow global genome-scale structural analysis and cross-correlation, as well as identification of regionally enriched genes. Unbiased fine-resolution analysis has identified highly specific cellular markers as well as extensive evidence of cellular heterogeneity not evident in classical neuroanatomical atlases. This highly standardized atlas provides an open, primary data resource for a wide variety of further studies concerning brain organization and function.},
+  language = {en},
+  number = {7124},
+  journal = {Nature},
+  author = {Lein, Ed S. and Hawrylycz, Michael J. and Ao, Nancy and Ayres, Mikael and Bensinger, Amy and Bernard, Amy and Boe, Andrew F. and Boguski, Mark S. and Brockway, Kevin S. and Byrnes, Emi J. and Chen, Lin and Chen, Li and Chen, Tsuey-Ming and Chi Chin, Mei and Chong, Jimmy and Crook, Brian E. and Czaplinska, Aneta and Dang, Chinh N. and Datta, Suvro and Dee, Nick R. and Desaki, Aimee L. and Desta, Tsega and Diep, Ellen and Dolbeare, Tim A. and Donelan, Matthew J. and Dong, Hong-Wei and Dougherty, Jennifer G. and Duncan, Ben J. and Ebbert, Amanda J. and Eichele, Gregor and Estin, Lili K. and Faber, Casey and Facer, Benjamin A. and Fields, Rick and Fischer, Shanna R. and Fliss, Tim P. and Frensley, Cliff and Gates, Sabrina N. and Glattfelder, Katie J. and Halverson, Kevin R. and Hart, Matthew R. and Hohmann, John G. and Howell, Maureen P. and Jeung, Darren P. and Johnson, Rebecca A. and Karr, Patrick T. and Kawal, Reena and Kidney, Jolene M. and Knapik, Rachel H. and Kuan, Chihchau L. and Lake, James H. and Laramee, Annabel R. and Larsen, Kirk D. and Lau, Christopher and Lemon, Tracy A. and Liang, Agnes J. and Liu, Ying and Luong, Lon T. and Michaels, Jesse and Morgan, Judith J. and Morgan, Rebecca J. and Mortrud, Marty T. and Mosqueda, Nerick F. and Ng, Lydia L. and Ng, Randy and Orta, Geralyn J. and Overly, Caroline C. and Pak, Tu H. and Parry, Sheana E. and Pathak, Sayan D. and Pearson, Owen C. and Puchalski, Ralph B. and Riley, Zackery L. and Rockett, Hannah R. and Rowland, Stephen A. and Royall, Joshua J. and Ruiz, Marcos J. and Sarno, Nadia R. and Schaffnit, Katherine and Shapovalova, Nadiya V. and Sivisay, Taz and Slaughterbeck, Clifford R. and Smith, Simon C. and Smith, Kimberly A. and Smith, Bryan I. and Sodt, Andy J. and Stewart, Nick N. and Stumpf, Kenda-Ruth and Sunkin, Susan M. and Sutram, Madhavi and Tam, Angelene and Teemer, Carey D. and Thaller, Christina and Thompson, Carol L. and Varnam, Lee R. and Visel, Axel and Whitlock, Ray M. and Wohnoutka, Paul E. and Wolkey, Crissa K. and Wong, Victoria Y. and Wood, Matthew and Yaylaoglu, Murat B. and Young, Rob C. and Youngstrom, Brian L. and Feng Yuan, Xu and Zhang, Bin and Zwingman, Theresa A. and Jones, Allan R.},
+  month = jan,
+  year = {2007},
+  keywords = {Laminar,layers},
+  pages = {168-176},
+  file = {C:\\Users\\saram\\Zotero\\storage\\34G3DJS6\\Lein 2007 Genome-wide atlas of gene expression in the adult mouse brain.pdf}
+}
+
+@article{Baker2011,
+  title = {Microscopy: {{Bright}} Light, Better Labels},
+  volume = {478},
+  copyright = {\textcopyright{} 2011 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {0028-0836},
+  shorttitle = {Microscopy},
+  doi = {10.1038/478137a},
+  abstract = {The tiniest structures in cells can be seen only using sophisticated instrumentation and informatics, but what biologists really need are improved fluorescent probes.},
+  language = {en},
+  number = {7367},
+  journal = {Nature},
+  author = {Baker, Monya},
+  month = oct,
+  year = {2011},
+  keywords = {Biophysics,Physical Sciences,Cell biology,Biological Sciences},
+  pages = {137-142},
+  file = {C:\\Users\\saram\\Zotero\\storage\\XUACETXA\\478137a.pdf}
+}
+
+@article{Osten2013,
+  title = {Mapping Brain Circuitry with a Light Microscope},
+  volume = {10},
+  copyright = {\textcopyright{} 2013 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1548-7091},
+  doi = {10.1038/nmeth.2477},
+  abstract = {The beginning of the 21st century has seen a renaissance in light microscopy and anatomical tract tracing that together are rapidly advancing our understanding of the form and function of neuronal circuits. The introduction of instruments for automated imaging of whole mouse brains, new cell type\textendash{}specific and trans-synaptic tracers, and computational methods for handling the whole-brain data sets has opened the door to neuroanatomical studies at an unprecedented scale. We present an overview of the present state and future opportunities in charting long-range and local connectivity in the entire mouse brain and in linking brain circuits to function.},
+  language = {en},
+  number = {6},
+  journal = {Nature Methods},
+  author = {Osten, Pavel and Margrie, Troy W.},
+  month = jun,
+  year = {2013},
+  keywords = {neurophysiology,Neuroscience,Light-sheet microscopy,Fluorescence imaging},
+  pages = {515-523},
+  file = {C:\\Users\\saram\\Zotero\\storage\\EXX3XRMR\\Osten Margrie 2013 Mapping brain circuitry with a light microscope2.pdf}
+}
+
+@article{Garcia-Lopez2010,
+  title = {The {{Histological Slides}} and {{Drawings}} of {{Cajal}}},
+  volume = {4},
+  issn = {1662-5129},
+  doi = {10.3389/neuro.05.009.2010},
+  abstract = {Ram{\'o}n y Cajal's studies in the field of neuroscience provoked a radical change in the course of its history. For this reason he is considered as the father of modern neuroscience. Some of his original preparations are housed at the Cajal Museum (Cajal Institute, CSIC, Madrid, Spain). In this article, we catalogue and analyse more than 4,500 of Cajal's histological preparations, the same preparations he used during his scientific career. Furthermore, we catalogued Cajal's original correspondence, both manuscripts and personal letters, drawings and plates. This is the first time anyone has compiled an account of Cajal's enormous scientific production, offering some curious insights into his work and his legacy.},
+  journal = {Frontiers in Neuroanatomy},
+  author = {Garcia-Lopez, Pablo and Garcia-Marin, Virginia and Freire, Miguel},
+  month = mar,
+  year = {2010},
+  file = {C:\\Users\\saram\\Zotero\\storage\\GTS7AV74\\Garcia-Lopez 2010 The Histological Slides and Drawings of Cajal.pdf},
+  pmid = {20339483},
+  pmcid = {PMC2845060}
+}
+
+@article{DeFelipe1992,
+  title = {Santiago {{Ram{\'o}n}} y {{Cajal}} and Methods in Neurohistology},
+  volume = {15},
+  issn = {0166-2236},
+  doi = {10.1016/0166-2236(92)90057-F},
+  abstract = {Controversy, misunderstanding or uninformed opinion abound over the extent to which the great Spanish neurohistologist, Santiago Ram{\'o}n y Cajal, specified his staining methods in his analytical papers, the methods by which he analysed and presented his data, and the microscopes available to him. In this paper, we have attempted to outline the information on these points that we have been able to obtain from a detailed examination of his writings and a study of the evidence remaining in the Cajal Museum in Madrid.},
+  number = {7},
+  journal = {Trends in Neurosciences},
+  author = {DeFelipe, Javier and Jones, Edward G.},
+  month = jul,
+  year = {1992},
+  pages = {237-246},
+  file = {C:\\Users\\saram\\Zotero\\storage\\UAVDKNPH\\DeFelipe_Jones 1992 Santiago Ramon y Cajal and methods in neurohistology.pdf}
+}
+
+@article{Akemann2010,
+  title = {Imaging Brain Electric Signals with Genetically Targeted Voltage-Sensitive Fluorescent Proteins},
+  volume = {7},
+  copyright = {\textcopyright{} 2010 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1548-7091},
+  doi = {10.1038/nmeth.1479},
+  abstract = {Cortical information processing relies on synaptic interactions between diverse classes of neurons with distinct electrophysiological and connection properties. Uncovering the operational principles of these elaborate circuits requires the probing of electrical activity from selected populations of defined neurons. Here we show that genetically encoded voltage-sensitive fluorescent proteins (VSFPs) provide an optical voltage report from targeted neurons in culture, acute brain slices and living mice. By expressing VSFPs in pyramidal cells of mouse somatosensory cortex, we also demonstrate that these probes can report cortical electrical responses to single sensory stimuli in vivo. These protein-based voltage probes will facilitate the analysis of cortical circuits in genetically defined cell populations and are hence a valuable addition to the optogenetic toolbox.
+View full text},
+  language = {en},
+  number = {8},
+  journal = {Nature Methods},
+  author = {Akemann, Walther and Mutoh, Hiroki and Perron, Am{\'e}lie and Rossier, Jean and Kn{\"o}pfel, Thomas},
+  month = aug,
+  year = {2010},
+  keywords = {Electrophysiology,Brain imaging,Fluorescent proteins},
+  pages = {643-649},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5X7QBS69\\Akemann et al.  2010  Imaging brain electric signals with genetically targeted voltage-sensitive fluorescent proteins.pdf}
+}
+
+@article{Ahrens2013,
+  title = {Whole-Brain Functional Imaging at Cellular Resolution Using Light-Sheet Microscopy},
+  volume = {10},
+  copyright = {\textcopyright{} 2013 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1548-7091},
+  doi = {10.1038/nmeth.2434},
+  abstract = {Brain function relies on communication between large populations of neurons across multiple brain areas, a full understanding of which would require knowledge of the time-varying activity of all neurons in the central nervous system. Here we use light-sheet microscopy to record activity, reported through the genetically encoded calcium indicator GCaMP5G, from the entire volume of the brain of the larval zebrafish in vivo at 0.8 Hz, capturing more than 80\% of all neurons at single-cell resolution. Demonstrating how this technique can be used to reveal functionally defined circuits across the brain, we identify two populations of neurons with correlated activity patterns. One circuit consists of hindbrain neurons functionally coupled to spinal cord neuropil. The other consists of an anatomically symmetric population in the anterior hindbrain, with activity in the left and right halves oscillating in antiphase, on a timescale of 20 s, and coupled to equally slow oscillations in the inferior olive.
+View full text},
+  language = {en},
+  number = {5},
+  journal = {Nature Methods},
+  author = {Ahrens, Misha B. and Orger, Michael B. and Robson, Drew N. and Li, Jennifer M. and Keller, Philipp J.},
+  month = may,
+  year = {2013},
+  keywords = {Computational Neuroscience,Light-sheet microscopy,Brain imaging},
+  pages = {413-420},
+  file = {C:\\Users\\saram\\Zotero\\storage\\VTT58TGB\\Ahrens et al.  2013  Whole-brain functional imaging at cellular resolution using light-sheet microscopy.pdf}
+}
+
+@article{Davidson2003,
+  title = {Viral Vectors for Gene Delivery to the Nervous System},
+  volume = {4},
+  copyright = {\textcopyright{} 2003 Nature Publishing Group},
+  issn = {1471-003X},
+  doi = {10.1038/nrn1104},
+  abstract = {Our ability to manipulate the genetic constitution of the nervous system has come of age with various technologies, including virus vectors that can efficiently deliver genes to neurons and other neural cells in vitro and in vivo. These vectors allow us to monitor neurobiological functions, replace, correct, express or block expression of target genes, tag cells for fate determination, and change the physiological state of specific cell populations. The available vectors differ in their suitability for different applications, which depends on factors such as the size of the transgene, route of delivery, tropism, duration and regulation of gene expression, and side effects.},
+  language = {en},
+  number = {5},
+  journal = {Nature Reviews Neuroscience},
+  author = {Davidson, Beverly L. and Breakefield, Xandra O.},
+  month = may,
+  year = {2003},
+  pages = {353-364},
+  file = {C:\\Users\\saram\\Zotero\\storage\\F5CX2XXU\\Davidson and Breakefield  2003  Viral vectors for gene delivery to the nervous system.pdf}
+}
+
+@article{Oheim2001,
+  title = {Two-Photon Microscopy in Brain Tissue: Parameters Influencing the Imaging Depth},
+  volume = {111},
+  issn = {0165-0270},
+  shorttitle = {Two-Photon Microscopy in Brain Tissue},
+  doi = {10.1016/S0165-0270(01)00438-1},
+  abstract = {Light scattering by tissue limits the imaging depth of two-photon microscopy and its use for functional brain imaging in vivo. We investigate the influence of scattering on both fluorescence excitation and collection, and identify tissue and instrument parameters that limit the imaging depth in the brain. (i) In brain slices, we measured that the scattering length at $\lambda$=800 nm is a factor 2 higher in juvenile cortical tissue (P14\textendash{}P18) than in adult tissue (P90). (ii) In a detection geometry typical for in vivo imaging, we show that the collected fraction of fluorescence drops at large depths, and that it is proportional to the square of the effective angular acceptance of the detection optics. Matching the angular acceptance of the microscope to that of the objective lens can result in a gain of $\sim$3 in collection efficiency at large depths (\&gt;500 $\mu$m). A low-magnification (20\texttimes), high-numerical aperture objective (0.95) further increases fluorescence collection by a factor of $\sim$10 compared with a standard 60\texttimes\textendash{}63\texttimes{} objective without compromising the resolution. This improvement should allow fluorescence measurements related to neuronal or vascular brain activity at \&gt;100 $\mu$m deeper than with standard objectives.},
+  number = {1},
+  journal = {Journal of Neuroscience Methods},
+  author = {Oheim, Martin and Beaurepaire, Emmanuel and Chaigneau, Emmanuelle and Mertz, Jerome and Charpak, Serge},
+  month = oct,
+  year = {2001},
+  keywords = {Fluorescence,Two-photon,Brain slices,In vivo,Monte Carlo,Scattering media},
+  pages = {29-37},
+  file = {C:\\Users\\saram\\Zotero\\storage\\U6EG4PIF\\Oheim et al.  2001  Two-photon microscopy in brain tissue parameters influencing the imaging depth.pdf}
+}
+
+@article{Helmchen2005,
+  title = {Deep Tissue Two-Photon Microscopy},
+  volume = {2},
+  copyright = {Copyright Nature Publishing Group Dec 2005},
+  issn = {15487091},
+  doi = {http://dx.doi.org.myaccess.library.utoronto.ca/10.1038/nmeth818},
+  abstract = {With few exceptions biological tissues strongly scatter light, making high-resolution deep imaging impossible for traditional-including confocal-fluorescence microscopy. Nonlinear optical microscopy, in particular two photon-excited fluorescence microscopy, has overcome this limitation, providing large depth penetration mainly because even multiply scattered signal photons can be assigned to their origin as the result of localized nonlinear signal generation. Two-photon microscopy thus allows cellular imaging several hundred microns deep in various organs of living animals. Here we review fundamental concepts of nonlinear microscopy and discuss conditions relevant for achieving large imaging depths in intact tissue.},
+  language = {English},
+  number = {12},
+  journal = {Nature Methods; New York},
+  author = {Helmchen, Fritjof and Denk, Winfried},
+  month = dec,
+  year = {2005},
+  keywords = {Sciences: Comprehensive Works,Chemistry},
+  pages = {932-40},
+  file = {C:\\Users\\saram\\Zotero\\storage\\WFHIKBE8\\Helmchen and Denk  2005  Deep tissue two-photon microscopy.pdf}
+}
+
+@article{Oh2014,
+  title = {A Mesoscale Connectome of the Mouse Brain},
+  volume = {508},
+  copyright = {\textcopyright{} 2014 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {0028-0836},
+  doi = {10.1038/nature13186},
+  abstract = {Comprehensive knowledge of the brain's wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.
+View full text},
+  language = {en},
+  number = {7495},
+  journal = {Nature},
+  author = {Oh, Seung Wook and Harris, Julie A. and Ng, Lydia and Winslow, Brent and Cain, Nicholas and Mihalas, Stefan and Wang, Quanxin and Lau, Chris and Kuan, Leonard and Henry, Alex M. and Mortrud, Marty T. and Ouellette, Benjamin and Nguyen, Thuc Nghi and Sorensen, Staci A. and Slaughterbeck, Clifford R. and Wakeman, Wayne and Li, Yang and Feng, David and Ho, Anh and Nicholas, Eric and Hirokawa, Karla E. and Bohn, Phillip and Joines, Kevin M. and Peng, Hanchuan and Hawrylycz, Michael J. and Phillips, John W. and Hohmann, John G. and Wohnoutka, Paul and Gerfen, Charles R. and Koch, Christof and Bernard, Amy and Dang, Chinh and Jones, Allan R. and Zeng, Hongkui},
+  month = apr,
+  year = {2014},
+  keywords = {Mouse,Neuroscience,3-D reconstruction},
+  pages = {207-214},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JWQSRT4R\\Oh et al.  2014  A mesoscale connectome of the mouse brain.pdf}
+}
+
+@article{Yang2014,
+  title = {Single-{{Cell Phenotyping}} within {{Transparent Intact Tissue}} through {{Whole}}-{{Body Clearing}}},
+  volume = {158},
+  issn = {0092-8674, 1097-4172},
+  doi = {10.1016/j.cell.2014.07.017},
+  abstract = {Understanding the structure-function relationships at cellular, circuit, and organ-wide scale requires 3D anatomical and phenotypical maps, currently unavailable for many organs across species. At the root of this knowledge gap is the absence of a method that enables whole-organ imaging. Herein, we present techniques for tissue clearing in which whole organs and bodies are rendered macromolecule-permeable and optically transparent, thereby exposing their cellular structure with intact connectivity. We describe PACT (passive clarity technique), a protocol for passive tissue clearing and immunostaining of intact organs; RIMS (refractive index matching solution), a mounting media for imaging thick tissue; and PARS (perfusion-assisted agent release in situ), a method for whole-body clearing and immunolabeling. We show that in rodents PACT, RIMS, and PARS are compatible with endogenous-fluorescence, immunohistochemistry, RNA single-molecule FISH, long-term storage, and microscopy with cellular and subcellular resolution. These methods are applicable for high-resolution, high-content mapping and phenotyping of normal and pathological elements within intact organs and bodies.},
+  language = {English},
+  number = {4},
+  journal = {Cell},
+  author = {Yang, Bin and Treweek, Jennifer B. and Kulkarni, Rajan P. and Deverman, Benjamin E. and Chen, Chun-Kan and Lubeck, Eric and Shah, Sheel and Cai, Long and Gradinaru, Viviana},
+  month = aug,
+  year = {2014},
+  pages = {945-958},
+  file = {C:\\Users\\saram\\Zotero\\storage\\UP79WE4E\\Yang Gradinaru 2014 Single-Cell Phenotyping within Transparent Intact Tissue through whole body clearing.pdf},
+  pmid = {25088144}
+}
+
+@article{Marx2014,
+  title = {Microscopy: Seeing through Tissue},
+  volume = {11},
+  copyright = {\textcopyright{} 2014 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1548-7091},
+  shorttitle = {Microscopy},
+  doi = {10.1038/nmeth.3181},
+  abstract = {Tissue samples scatter light and shut out a deeper gaze. Tissue-clearing agents have changed that, but scientists must consider optimizing the optics when using these methods.},
+  language = {en},
+  number = {12},
+  journal = {Nature Methods},
+  author = {Marx, Vivien},
+  month = dec,
+  year = {2014},
+  keywords = {Fluorescence imaging,Cellular neuroscience,Technology},
+  pages = {1209-1214},
+  file = {C:\\Users\\saram\\Zotero\\storage\\UE4PQDRU\\Marx 2014 Microscopy seeing through tissue.pdf}
+}
+
+@article{Marx2016,
+  title = {Optimizing Probes to Image Cleared Tissue},
+  volume = {13},
+  copyright = {\textcopyright{} 2016 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1548-7091},
+  doi = {10.1038/nmeth.3774},
+  abstract = {Probes and imaging finesse for a clearer view of tissue.},
+  language = {en},
+  number = {3},
+  journal = {Nature Methods},
+  author = {Marx, Vivien},
+  month = mar,
+  year = {2016},
+  keywords = {Light-sheet microscopy,Technology,Confocal microscopy,Sensors and probes},
+  pages = {205-209},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8UP5FREI\\Marx 2016 Optimizing probes to image cleared tissue.pdf}
+}
+
+@article{Azaripour2016,
+  title = {A Survey of Clearing Techniques for {{3D}} Imaging of Tissues with Special Reference to Connective Tissue},
+  volume = {51},
+  issn = {0079-6336},
+  doi = {10.1016/j.proghi.2016.04.001},
+  abstract = {For 3-dimensional (3D) imaging of a tissue, 3 methodological steps are essential and their successful application depends on specific characteristics of the type of tissue. The steps are 1$^\circ$ clearing of the opaque tissue to render it transparent for microscopy, 2$^\circ$ fluorescence labeling of the tissues and 3$^\circ$ 3D imaging. In the past decades, new methodologies were introduced for the clearing steps with their specific advantages and disadvantages. Most clearing techniques have been applied to the central nervous system and other organs that contain relatively low amounts of connective tissue including extracellular matrix. However, tissues that contain large amounts of extracellular matrix such as dermis in skin or gingiva are difficult to clear. The present survey lists methodologies that are available for clearing of tissues for 3D imaging. We report here that the BABB method using a mixture of benzyl alcohol and benzyl benzoate and iDISCO using dibenzylether (DBE) are the most successful methods for clearing connective tissue-rich gingiva and dermis of skin for 3D histochemistry and imaging of fluorescence using light-sheet microscopy.},
+  number = {2},
+  journal = {Progress in Histochemistry and Cytochemistry},
+  author = {Azaripour, Adriano and Lagerweij, Tonny and Scharfbillig, Christina and Jadczak, Anna Elisabeth and Willershausen, Brita and Van Noorden, Cornelis J. F.},
+  month = aug,
+  year = {2016},
+  keywords = {3D histochemistry,3D imaging,Gingiva,Skin,Connective tissue,Extracellular matrix,Light-sheet microscopy,Clearing},
+  pages = {9-23},
+  file = {C:\\Users\\saram\\Zotero\\storage\\C2HTNBU8\\Azaripour 2016 A survey of clearing techniques for 3D imaging of tissues with special reference to connective tissue.pdf}
+}
+
+@article{Epp2015,
+  title = {Optimization of {{CLARITY}} for {{Clearing Whole}}-{{Brain}} and {{Other Intact Organs}}(1,2,3)},
+  volume = {2},
+  doi = {10.1523/ENEURO.0022-15.2015},
+  abstract = {The development, refinement, and use of techniques that allow high-throughput imaging of whole brains with cellular resolution will help us understand the complex functions of the brain. Such techniques are crucial for the analysis of complete neuronal morphology-anatomical and functional-connectivity, and repeated molecular phenotyping. CLARITY is a recently introduced technique that produces structurally intact, yet optically transparent tissue, which may be labeled and imaged without sectioning. However, the utility of this technique depends on several procedural variables during the process in which the light-scattering lipids in a tissue are replaced by a transparent hydrogel matrix. Here, we systematically varied a number of factors (including temperature, hydrogel composition, and polymerization conditions) to provide an optimized, highly replicable CLARITY procedure for clearing mouse brains. We found that for these preparations optimal tissue clearing requires electrophoresis (and cannot be achieved with passive clearing alone) for 5 d with a combination of 37 and 55$^\circ$C temperature. Although this protocol is optimized for brains, we also show that it can be used to clear and analyze a variety of organs. Brain or other tissue prepared using this protocol is suitable for high-throughput imaging with confocal or single-plane illumination microscopy.},
+  language = {eng},
+  number = {3},
+  journal = {eNeuro},
+  author = {Epp, Jonathan R. and Niibori, Yosuke and Liz Hsiang, Hwa-Lin and Mercaldo, Valentina and Deisseroth, Karl and Josselyn, Sheena A. and Frankland, Paul W.},
+  year = {2015 May-Jun},
+  keywords = {3D imaging,CLARITY,light sheet microscopy,neuron morphology,tissue clearing,whole-brain imaging},
+  file = {C:\\Users\\saram\\Zotero\\storage\\W4UHSHM7\\Epp 2015 Optimization of CLARITY for Clearing Whole-Brain and Other Intact Organs.pdf},
+  pmid = {26464982},
+  pmcid = {PMC4586927}
+}
+
+@article{Susaki2016,
+  title = {Whole-Body and {{Whole}}-{{Organ Clearing}} and {{Imaging Techniques}} with {{Single}}-{{Cell Resolution}}: {{Toward Organism}}-{{Level Systems Biology}} in {{Mammals}}},
+  volume = {23},
+  issn = {2451-9456, 2451-9448},
+  shorttitle = {Whole-Body and {{Whole}}-{{Organ Clearing}} and {{Imaging Techniques}} with {{Single}}-{{Cell Resolution}}},
+  doi = {10.1016/j.chembiol.2015.11.009},
+  abstract = {Some of the most powerful methods to obtain organism-level systems biology information
+are based on using optical probes and methods to peer into organs, tissues, and organisms.
+Susaki and Ueda review the state of the art in the field, focusing on chemistry-based
+whole-body and whole-organ clearing techniques.},
+  language = {English},
+  number = {1},
+  journal = {Cell Chemical Biology},
+  author = {Susaki, Etsuo A. and Ueda, Hiroki R.},
+  month = jan,
+  year = {2016},
+  pages = {137-157},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ZGHEIVJX\\Susaki and Ueda  2016  Whole-body and Whole-Organ Clearing and Imaging Techniques with Single-Cell Resolution Toward Organism-Level Systems Biology in Mammals.pdf}
+}
+
+@article{Mormann2017,
+  title = {Scene-Selective Coding by Single Neurons in the Human Parahippocampal Cortex},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.1608159113},
+  abstract = {Imaging, electrophysiological, and lesion studies have shown a relationship between the parahippocampal cortex (PHC) and the processing of spatial scenes. Our present knowledge of PHC, however, is restricted to the macroscopic properties and dynamics of bulk tissue; the behavior and selectivity of single parahippocampal neurons remains largely unknown. In this study, we analyzed responses from 630 parahippocampal neurons in 24 neurosurgical patients during visual stimulus presentation. We found a spatially clustered subpopulation of scene-selective units with an associated event-related field potential. These units form a population code that is more distributed for scenes than for other stimulus categories, and less sparse than elsewhere in the medial temporal lobe. Our electrophysiological findings provide insight into how individual units give rise to the population response observed with functional imaging in the parahippocampal place area.},
+  language = {en},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Mormann, Florian and Kornblith, Simon and Cerf, Moran and Ison, Matias J. and Kraskov, Alexander and Tran, Michelle and Knieling, Simeon and Quiroga, Rodrigo Quian and Koch, Christof and Fried, Itzhak},
+  month = jan,
+  year = {2017},
+  keywords = {Electrophysiology,single units,scene selectivity,population code},
+  pages = {201608159},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PWKDCUNX\\Mormann 2017 Scene-selective coding by single neurons in the human parahippocampal cortex.pdf},
+  pmid = {28096381}
+}
+
+@article{Chalk,
+  title = {Neural Oscillations as a Signature of Efficient Coding in the Presence of Synaptic Delays},
+  volume = {5},
+  issn = {2050-084X},
+  doi = {10.7554/eLife.13824},
+  abstract = {Cortical networks exhibit 'global oscillations', in which neural spike times are entrained to an underlying oscillatory rhythm, but where individual neurons fire irregularly, on only a fraction of cycles. While the network dynamics underlying global oscillations have been well characterised, their function is debated. Here, we show that such global oscillations are a direct consequence of optimal efficient coding in spiking networks with synaptic delays and noise. To avoid firing unnecessary spikes, neurons need to share information about the network state. Ideally, membrane potentials should be strongly correlated and reflect a 'prediction error' while the spikes themselves are uncorrelated and occur rarely. We show that the most efficient representation is when: (i) spike times are entrained to a global Gamma rhythm (implying a consistent representation of the error); but (ii) few neurons fire on each cycle (implying high efficiency), while (iii) excitation and inhibition are tightly balanced. This suggests that cortical networks exhibiting such dynamics are tuned to achieve a maximally efficient population code., DOI:
+http://dx.doi.org/10.7554/eLife.13824.001},
+  journal = {eLife},
+  author = {Chalk, Matthew and Gutkin, Boris and Den{\`e}ve, Sophie},
+  file = {C:\\Users\\saram\\Zotero\\storage\\J6NXPRAU\\Chalk et al. - Neural oscillations as a signature of efficient co.pdf},
+  pmid = {27383272},
+  pmcid = {PMC4959845}
+}
+
+@article{Axmacher2010,
+  title = {Cross-Frequency Coupling Supports Multi-Item Working Memory in the Human Hippocampus},
+  volume = {107},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.0911531107},
+  abstract = {Recent findings indicate that the hippocampus supports not only long-term memory encoding but also plays a role in working memory (WM) maintenance of multiple items; however, the neural mechanism underlying multi-item maintenance is still unclear. Theoretical work suggests that multiple items are being maintained by neural assemblies synchronized in the gamma frequency range (25\textendash{}100 Hz) that are locked to consecutive phase ranges of oscillatory activity in the theta frequency range (4\textendash{}8 Hz). Indeed, cross-frequency coupling of the amplitude of high-frequency activity to the phase of slower oscillations has been described both in animals and in humans, but has never been linked to a theoretical model of a cognitive process. Here we used intracranial EEG recordings in human epilepsy patients to test pivotal predictions from theoretical work. First, we show that simultaneous maintenance of multiple items in WM is accompanied by cross-frequency coupling of oscillatory activity in the hippocampus, which is recruited during multi-item WM. Second, maintenance of an increasing number of items is associated with modulation of beta/gamma amplitude with theta band activity of lower frequency, consistent with the idea that longer cycles are required for an increased number of representations by gamma cycles. This effect cannot be explained by a difference in theta or beta/gamma power. Third, we describe how the precision of cross-frequency coupling predicts individual WM performance. These data support the idea that working memory in humans depends on a neural code using phase information.},
+  language = {en},
+  number = {7},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Axmacher, Nikolai and Henseler, Melanie M. and Jensen, Ole and Weinreich, Ilona and Elger, Christian E. and Fell, Juergen},
+  month = feb,
+  year = {2010},
+  pages = {3228-3233},
+  file = {C:\\Users\\saram\\Zotero\\storage\\VD7TP57E\\Axmacher et al_2010_Cross-frequency coupling supports multi-item working memory in the human.pdf},
+  pmid = {20133762}
+}
+
+@article{Teeter2017,
+  title = {Generalized {{Leaky Integrate}}-{{And}}-{{Fire Models Classify Multiple Neuron Types}}},
+  copyright = {\textcopyright{} 2017, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
+  doi = {10.1101/104703},
+  abstract = {In the mammalian neocortex, there is a high diversity of neuronal types. To facilitate construction of system models with multiple cell types, we generate a database of point models associated with the Allen Cell Types Database. We construct a series of generalized integrate-and-fire (GLIF) models of increasing complexity aiming to reproduce the spiking behaviors of the recorded neurons. We test the performance of these GLIF models on data from 771 neurons from 14 transgenic lines, with increasing model performance for more complex models. To answer how complex a model needs to be to reproduce the number of electrophysiological cell types, we perform unsupervised clustering on the parameters extracted from these models. The number of clusters is smaller for individual model types, but when combining all GLIF parameters 18 clusters are obtained, while 11 clusters are obtained using 16 electrophysiological features. Therefore, these low dimensional models have the capacity to characterize the diversity of cell types without the need for a priori defined features.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Teeter, Corinne and Iyer, Ramakrishnan and Menon, Vilas and Gouwens, Nathan and Feng, David and Berg, Jim and Cain, Nicholas and Koch, Christof and Mihalas, Stefan},
+  month = jan,
+  year = {2017},
+  pages = {104703},
+  file = {C:\\Users\\saram\\Zotero\\storage\\AHTXK27V\\Teeter et al_2017_Generalized Leaky Integrate-And-Fire Models Classify Multiple Neuron Types.pdf}
+}
+
+@article{Shimamoto2013,
+  title = {Subthalamic {{Nucleus Neurons Are Synchronized}} to {{Primary Motor Cortex Local Field Potentials}} in {{Parkinson}}'s {{Disease}}},
+  volume = {33},
+  copyright = {Copyright \textcopyright{} 2013 the authors 0270-6474/13/337220-14\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.4676-12.2013},
+  abstract = {In Parkinson's disease (PD), striatal dopamine denervation results in a cascade of abnormalities in the single-unit activity of downstream basal ganglia nuclei that include increased firing rate, altered firing patterns, and increased oscillatory activity. However, the effects of these abnormalities on cortical function are poorly understood. Here, in humans undergoing deep brain stimulator implantation surgery, we use the novel technique of subdural electrocorticography in combination with subthalamic nucleus (STN) single-unit recording to study basal ganglia\textendash{}cortex interactions at the millisecond time scale. We show that in patients with PD, STN spiking is synchronized with primary motor cortex (M1) local field potentials in two distinct patterns: first, STN spikes are phase-synchronized with M1 rhythms in the theta, alpha, or beta (4\textendash{}30 Hz) bands. Second, STN spikes are synchronized with M1 gamma activity over a broad spectral range (50\textendash{}200 Hz). The amplitude of STN spike-synchronized gamma activity in M1 is itself rhythmically modulated by the phase of a lower-frequency rhythm (phase-amplitude coupling), such that ``waves'' of phase-synchronized gamma activity precede the occurrence of STN spikes. We show the disease specificity of these phenomena in PD, by comparison with STN-M1 paired recordings performed in a group of patients with a different disorder, primary craniocervical dystonia. Our findings support a model of the basal ganglia-thalamocortical loop in PD in which gamma activity in primary motor cortex, modulated by the phase of low-frequency rhythms, drives STN unit discharge.},
+  language = {en},
+  number = {17},
+  journal = {Journal of Neuroscience},
+  author = {Shimamoto, Shoichi A. and Ryapolova-Webb, Elena S. and Ostrem, Jill L. and Galifianakis, Nicholas B. and Miller, Kai J. and Starr, Philip A.},
+  month = apr,
+  year = {2013},
+  pages = {7220-7233},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Z7E2IIXE\\Shimamoto et al_2013_Subthalamic Nucleus Neurons Are Synchronized to Primary Motor Cortex Local.pdf},
+  pmid = {23616531}
+}
+
+@article{Lisman1995,
+  title = {Storage of 7 +/- 2 Short-Term Memories in Oscillatory Subcycles},
+  volume = {267},
+  copyright = {\textcopyright{} 1995},
+  issn = {0036-8075, 1095-9203},
+  doi = {10.1126/science.7878473},
+  abstract = {Psychophysical measurements indicate that human subjects can store approximately seven short-term memories. Physiological studies suggest that short-term memories are stored by patterns of neuronal activity. Here it is shown that activity patterns associated with multiple memories can be stored in a single neural network that exhibits nested oscillations similar to those recorded from the brain. Each memory is stored in a different high-frequency ("40 hertz") subcycle of a low-frequency oscillation. Memory patterns repeat on each low-frequency (5 to 12 hertz) oscillation, a repetition that relies on activity-dependent changes in membrane excitability rather than reverberatory circuits. This work suggests that brain oscillations are a timing mechanism for controlling the serial processing of short-term memories.},
+  language = {en},
+  number = {5203},
+  journal = {Science},
+  author = {Lisman, J. E. and Idiart, M. A.},
+  month = mar,
+  year = {1995},
+  pages = {1512-1515},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ESTPICE3\\Lisman_Idiart_1995_Storage of 7 +-- 2 short-term memories in oscillatory subcycles.pdf},
+  pmid = {7878473}
+}
+
+@article{Bragin1995a,
+  title = {Gamma (40-100 {{Hz}}) Oscillation in the Hippocampus of the Behaving Rat},
+  volume = {15},
+  copyright = {\textcopyright{} 1995 by Society for Neuroscience},
+  issn = {0270-6474, 1529-2401},
+  abstract = {The cellular generation and spatial distribution of gamma frequency (40\textendash{} 100 Hz) activity was examined in the hippocampus of the awake rat. Field potentials and unit activity were recorded by multiple site silicon probes (5- and 16-site shanks) and wire electrode arrays. Gamma waves were highly coherent along the long axis of the dentate hilus, but average coherence decreased rapidly in the CA3 and CA1 directions. Analysis of short epochs revealed large fluctuations in coherence values between the dentate and CA1 gamma waves. Current source density analysis revealed large sinks and sources in the dentate gyrus with spatial distribution similar to the dipoles evoked by stimulation of the perforant path. The frequency changes of gamma and theta waves positively correlated (40\textendash{}100 Hz and 5\textendash{}10 Hz, respectively). Putative interneurons in the dentate gyrus discharged at gamma frequency and were phase-locked to the ascending part of the gamma waves recorded from the hilus. Following bilateral lesion of the entorhinal cortex the power and frequency of hilar gamma activity significantly decreased or disappeared. Instead, a large amplitude but slower gamma pattern (25\textendash{}50 Hz) emerged in the CA3-CA1 network. We suggest that gamma oscillation emerges from an interaction between intrinsic oscillatory properties of interneurons and the network properties of the dentate gyrus. We also hypothesize that under physiological conditions the hilar gamma oscillation may be entrained by the entorhinal rhythm and that gamma oscillation in the CA3-CA1 circuitry is suppressed by either the hilar region or the entorhinal cortex.},
+  language = {en},
+  number = {1},
+  journal = {Journal of Neuroscience},
+  author = {Bragin, A. and Jando, G. and Nadasdy, Z. and Hetke, J. and Wise, K. and Buzsaki, G.},
+  month = jan,
+  year = {1995},
+  pages = {47-60},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ZINI29DJ\\Bragin et al_1995_Gamma (40-100 Hz) oscillation in the hippocampus of the behaving rat.pdf},
+  pmid = {7823151}
+}
+
+@article{Bragin1995,
+  title = {Gamma (40-100 {{Hz}}) Oscillation in the Hippocampus of the Behaving Rat},
+  volume = {15},
+  copyright = {\textcopyright{} 1995 by Society for Neuroscience},
+  issn = {0270-6474, 1529-2401},
+  abstract = {The cellular generation and spatial distribution of gamma frequency (40\textendash{} 100 Hz) activity was examined in the hippocampus of the awake rat. Field potentials and unit activity were recorded by multiple site silicon probes (5- and 16-site shanks) and wire electrode arrays. Gamma waves were highly coherent along the long axis of the dentate hilus, but average coherence decreased rapidly in the CA3 and CA1 directions. Analysis of short epochs revealed large fluctuations in coherence values between the dentate and CA1 gamma waves. Current source density analysis revealed large sinks and sources in the dentate gyrus with spatial distribution similar to the dipoles evoked by stimulation of the perforant path. The frequency changes of gamma and theta waves positively correlated (40\textendash{}100 Hz and 5\textendash{}10 Hz, respectively). Putative interneurons in the dentate gyrus discharged at gamma frequency and were phase-locked to the ascending part of the gamma waves recorded from the hilus. Following bilateral lesion of the entorhinal cortex the power and frequency of hilar gamma activity significantly decreased or disappeared. Instead, a large amplitude but slower gamma pattern (25\textendash{}50 Hz) emerged in the CA3-CA1 network. We suggest that gamma oscillation emerges from an interaction between intrinsic oscillatory properties of interneurons and the network properties of the dentate gyrus. We also hypothesize that under physiological conditions the hilar gamma oscillation may be entrained by the entorhinal rhythm and that gamma oscillation in the CA3-CA1 circuitry is suppressed by either the hilar region or the entorhinal cortex.},
+  language = {en},
+  number = {1},
+  journal = {Journal of Neuroscience},
+  author = {Bragin, A. and Jando, G. and Nadasdy, Z. and Hetke, J. and Wise, K. and Buzsaki, G.},
+  month = jan,
+  year = {1995},
+  pages = {47-60},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ZJIHTAGT\\Bragin et al_1995_Gamma (40-100 Hz) oscillation in the hippocampus of the behaving rat.pdf},
+  pmid = {7823151}
+}
+
+@article{Lopez-Azcarate2010,
+  title = {Coupling between {{Beta}} and {{High}}-{{Frequency Activity}} in the {{Human Subthalamic Nucleus May Be}} a {{Pathophysiological Mechanism}} in {{Parkinson}}'s {{Disease}}},
+  volume = {30},
+  copyright = {Copyright \textcopyright{} 2010 the authors 0270-6474/10/306667-11\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.5459-09.2010},
+  abstract = {In Parkinson's disease (PD), the oscillatory activity recorded from the basal ganglia shows dopamine-dependent changes. In the ``off'' parkinsonian motor state, there is prominent activity in the beta band (12\textendash{}30 Hz) that is mostly attenuated after dopaminergic therapy (``on'' medication state). The on state is also characterized by activity in the gamma (60\textendash{}80 Hz) and high-frequency (300 Hz) bands that is modulated by movement. We recorded local field potentials from a group of 15 PD patients (three females) treated with bilateral deep brain stimulation of the subthalamic nucleus, using a high sampling rate (2 kHz) and filters suitable to study high-frequency activity (0.3\textendash{}1000 Hz). We observed high-frequency oscillations (HFOs) in both the off and on motor states. In the off state, the amplitude of the HFOs was coupled to the phase of the abnormal beta activity. The beta-coupled HFOs showed little or even negative movement-related changes in amplitude. Moreover, the degree of movement-related modulation of the HFOs correlated negatively with the rigidity/bradykinesia scores. In the on motor state, the HFOs were liberated from this beta coupling, and they displayed marked movement-related amplitude modulation. Cross-frequency interactions between the phase of slow activities and the amplitude of fast frequencies have been attributed an important role in information processing in cortical structures. Our findings suggest that nonlinear coupling between frequencies may not only be a physiological mechanism (as shown previously) but also that it may participate in the pathophysiology of parkinsonism.},
+  language = {en},
+  number = {19},
+  journal = {Journal of Neuroscience},
+  author = {L{\'o}pez-Azc{\'a}rate, Jon and Tainta, Mikel and Rodr{\'\i}guez-Oroz, Mar{\'\i}a C. and Valencia, Miguel and Gonz{\'a}lez, Rafael and Guridi, Jorge and Iriarte, Jorge and Obeso, Jos{\'e} A. and Artieda, Julio and Alegre, Manuel},
+  month = may,
+  year = {2010},
+  pages = {6667-6677},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ZW3I29AX\\López-Azcárate et al_2010_Coupling between Beta and High-Frequency Activity in the Human Subthalamic.pdf},
+  pmid = {20463229}
+}
+
+@article{Tort2009,
+  title = {Theta\textendash{}gamma Coupling Increases during the Learning of Item\textendash{}context Associations},
+  volume = {106},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.0911331106},
+  abstract = {Phase-amplitude cross-frequency coupling (CFC) between theta (4\textendash{}12 Hz) and gamma (30\textendash{}100 Hz) oscillations occurs frequently in the hippocampus. However, it still remains unclear whether theta\textendash{}gamma coupling has any functional significance. To address this issue, we studied CFC in local field potential oscillations recorded from the CA3 region of the dorsal hippocampus of rats as they learned to associate items with their spatial context. During the course of learning, the amplitude of the low gamma subband (30\textendash{}60 Hz) became more strongly modulated by theta phase in CA3, and higher levels of theta\textendash{}gamma modulation were maintained throughout overtraining sessions. Furthermore, the strength of theta\textendash{}gamma coupling was directly correlated with the increase in performance accuracy during learning sessions. These findings suggest a role for hippocampal theta\textendash{}gamma coupling in memory recall.},
+  language = {en},
+  number = {49},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Tort, Adriano B. L. and Komorowski, Robert W. and Manns, Joseph R. and Kopell, Nancy J. and Eichenbaum, Howard},
+  month = aug,
+  year = {2009},
+  keywords = {local field potential,brain rhythms,associative memory},
+  pages = {20942-20947},
+  file = {C:\\Users\\saram\\Zotero\\storage\\F5924JSX\\Tort et al_2009_Theta–gamma coupling increases during the learning of item–context associations.pdf},
+  pmid = {19934062}
+}
+
+@article{Cohen2017,
+  title = {Multivariate Cross-Frequency Coupling via Generalized Eigendecomposition},
+  volume = {6},
+  copyright = {\textcopyright{} 2017, Cohen et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.},
+  issn = {2050-084X},
+  doi = {10.7554/eLife.21792},
+  abstract = {A novel methodological framework for cross-frequency coupling and spike-field coherence in multichannel (LFP, EEG, MEG) electrophysiology data reveals new manifestations of coupling dynamics.},
+  language = {en},
+  journal = {eLife},
+  author = {Cohen, Michael X.},
+  month = jan,
+  year = {2017},
+  keywords = {dimensionality reduction,Mouse,Cross-frequency coupling,Rat,Rhesus macaque,neural oscillations,source separation,spike-field coherence,human},
+  pages = {e21792},
+  file = {C:\\Users\\saram\\Zotero\\storage\\3BDPRITP\\elife-21792-fig1-figsupp3-v1.jpg;C:\\Users\\saram\\Zotero\\storage\\4XKA5FV6\\elife-21792-fig1-figsupp1-v1.jpg;C:\\Users\\saram\\Zotero\\storage\\F42Q9A58\\elife-21792-fig10-figsupp1-v1.jpg;C:\\Users\\saram\\Zotero\\storage\\F7MGUH33\\elife-21792-fig5-figsupp1-v1.jpg;C:\\Users\\saram\\Zotero\\storage\\JQ4VTN9C\\elife-21792-fig1-figsupp2-v1.jpg;C:\\Users\\saram\\Zotero\\storage\\NFRTIUEQ\\elife-21792-fig3-figsupp1-v1.jpg;C:\\Users\\saram\\Zotero\\storage\\R7GZIQB7\\Cohen_2017_Multivariate cross-frequency coupling via generalized eigendecomposition.pdf},
+  pmid = {28117662}
+}
+
+@article{Kramer2008,
+  title = {Sharp Edge Artifacts and Spurious Coupling in {{EEG}} Frequency Comodulation Measures},
+  volume = {170},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2008.01.020},
+  abstract = {Recent electroencephalogram (EEG), electrocorticogram (ECoG), and local field potential (LFP) observations suggest that distinct frequency bands interact. Numerous measures have been proposed to analyze such interactions, including the amplitude envelope modulation of high frequency activity by the phase or signal of low frequency activity. In this short communication, we describe how abrupt increases or decreases in voltage data may produce spurious coupling in these measures and suggest techniques to detect these effects.},
+  number = {2},
+  journal = {Journal of Neuroscience Methods},
+  author = {Kramer, Mark A. and Tort, Adriano B. L. and Kopell, Nancy J.},
+  month = may,
+  year = {2008},
+  keywords = {Frequency comodulation,Nesting,Hilbert transform,gamma,theta},
+  pages = {352-357},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4S7RF3Z9\\Kramer 2008-Sharp edge artifacts and spurious coupling in EEG frequency comodulation measures.pdf}
+}
+
+@article{Shirvalkar2010,
+  title = {Bidirectional Changes to Hippocampal Theta\textendash{}gamma Comodulation Predict Memory for Recent Spatial Episodes},
+  volume = {107},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.0911184107},
+  abstract = {Episodic memory requires the hippocampus, which is thought to bind cortical inputs into conjunctive codes. Local field potentials (LFPs) reflect dendritic and synaptic oscillations whose temporal structure may coordinate cellular mechanisms of plasticity and memory. We now report that single-trial spatial memory performance in rats was predicted by the power comodulation of theta (4\textendash{}10 Hz) and low gamma (30\textendash{}50 Hz) rhythms in the hippocampus. Theta\textendash{}gamma comodulation (TGC) was prominent during successful memory retrieval but was weak when memory failed or was unavailable during spatial exploration in sample trials. Muscimol infusion into medial septum reduced the probability of TGC and successful memory retrieval. In contrast, patterned electrical stimulation of the fimbria-fornix increased TGC in amnestic animals and partially rescued memory performance in the water maze. The results suggest that TGC accompanies memory retrieval in the hippocampus and that patterned brain stimulation may inform therapeutic strategies for cognitive disorders.},
+  language = {en},
+  number = {15},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Shirvalkar, Prasad R. and Rapp, Peter R. and Shapiro, Matthew L.},
+  month = apr,
+  year = {2010},
+  keywords = {medial septum,theta-burst stimulation,fimbria-fornix,retrieval,hippocampus},
+  pages = {7054-7059},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6ANGVFB6\\Shirvalkar 2010 Bidirectional changes to hippocampal theta–gamma comodulation predict memory.pdf},
+  pmid = {20351262}
+}
+
+@article{Pal1997,
+  title = {Cluster Validation Using Graph Theoretic Concepts},
+  volume = {30},
+  issn = {00313203},
+  doi = {10.1016/S0031-3203(96)00127-6},
+  language = {en},
+  number = {6},
+  journal = {Pattern Recognition},
+  author = {Pal, N.R. and Biswas, J.},
+  month = jun,
+  year = {1997},
+  pages = {847-857},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6NVDUBWI\\Pal_Biswas 1997 Cluster validation using graph theoretic concepts.pdf}
+}
+
+@misc{zotero-372,
+  title = {Some {{New Indexes Of Cluster Validity}} - {{Systems}}, {{Man}} and {{Cybernetics}}, {{Part B}}, {{IEEE Transactions}} on - Ieee\_{{Bezdek}}\_00678624\_cluster Validity.Pdf},
+  howpublished = {http://ece.ut.ac.ir/classpages/F83/Fuzzysets/Papers/Clustering/ieee\_Bezdek\_00678624\_cluster\%20validity.pdf},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Q2XSIRVT\\Some New Indexes Of Cluster Validity - Systems, Man and Cybernetics, Part B,.pdf}
+}
+
+@article{Dickey2009a,
+  title = {Single-Unit Stability Using Chronically Implanted Multielectrode Arrays},
+  volume = {102},
+  issn = {0022-3077},
+  doi = {10.1152/jn.90920.2008},
+  abstract = {The use of chronic intracortical multielectrode arrays has become increasingly prevalent in neurophysiological experiments. However, it is not obvious whether neuronal signals obtained over multiple recording sessions come from the same or different neurons. Here, we develop a criterion to assess single-unit stability by measuring the similarity of 1) average spike waveforms and 2) interspike interval histograms (ISIHs). Neuronal activity was recorded from four Utah arrays implanted in primary motor and premotor cortices in three rhesus macaque monkeys during 10 recording sessions over a 15- to 17-day period. A unit was defined as stable through a given day if the stability criterion was satisfied on all recordings leading up to that day. We found that 57\% of the original units were stable through 7 days, 43\% were stable through 10 days, and 39\% were stable through 15 days. Moreover, stable units were more likely to remain stable in subsequent recording sessions (i.e., 89\% of the neurons that were stable through four sessions remained stable on the fifth). Using both waveform and ISIH data instead of just waveforms improved performance by reducing the number of false positives. We also demonstrate that this method can be used to track neurons across days, even during adaptation to a visuomotor rotation. Identifying a stable subset of neurons should allow the study of long-term learning effects across days and has practical implications for pooling of behavioral data across days and for increasing the effectiveness of brain-machine interfaces.},
+  language = {eng},
+  number = {2},
+  journal = {Journal of Neurophysiology},
+  author = {Dickey, Adam S. and Suminski, Aaron and Amit, Yali and Hatsopoulos, Nicholas G.},
+  month = aug,
+  year = {2009},
+  keywords = {Neurons,Animals,Macaca mulatta,Time Factors,Electrophysiology,Algorithms,Adaptation; Psychological,Electrodes; Implanted,Frontal Lobe,Motor Activity,Motor Cortex,Action potentials},
+  pages = {1331-1339},
+  file = {C:\\Users\\saram\\Zotero\\storage\\WS4NTJPM\\Dickey et al_2009_Single-unit stability using chronically implanted multielectrode arrays.pdf},
+  pmid = {19535480},
+  pmcid = {PMC2724357}
+}
+
+@article{Telenczuk2017,
+  title = {Local Field Potentials Primarily Reflect Inhibitory Neuron Activity in Human and Monkey Cortex},
+  volume = {7},
+  copyright = {\textcopyright{} 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.},
+  issn = {2045-2322},
+  doi = {10.1038/srep40211},
+  abstract = {The local field potential (LFP) is generated by large populations of neurons, but unitary contribution of spiking neurons to LFP is not well characterised.},
+  language = {en},
+  journal = {Scientific Reports},
+  author = {Tele{\'n}czuk, Bartosz and Dehghani, Nima and Quyen, Michel Le Van and Cash, Sydney S. and Halgren, Eric and Hatsopoulos, Nicholas G. and Destexhe, Alain},
+  month = jan,
+  year = {2017},
+  keywords = {citing Dickey},
+  pages = {40211},
+  file = {C:\\Users\\saram\\Zotero\\storage\\NA4SC8NU\\Telenczuk 2017 Local field potentials primarily reflect inhibitory neuron activity in human.pdf;C:\\Users\\saram\\Zotero\\storage\\QFWUDWAS\\Teleńczuk et al_2017_Local field potentials primarily reflect inhibitory neuron activity in human.pdf}
+}
+
+@article{Williamson2016,
+  title = {Scaling {{Properties}} of {{Dimensionality Reduction}} for {{Neural Populations}} and {{Network Models}}},
+  volume = {12},
+  issn = {1553-7358},
+  doi = {10.1371/journal.pcbi.1005141},
+  abstract = {Author Summary We seek to understand how billions of neurons in the brain work together to give rise to everyday brain function. In most current experimental settings, we can only record from tens of neurons for a few hours at a time. A major question in systems neuroscience is whether our interpretation of how neurons interact would change if we monitor orders of magnitude more neurons and for substantially more time. In this study, we use realistic networks of model neurons, which allow us to analyze the activity from as many model neurons as we want for as long as we want. For these models, we found that we can identify the salient interactions among neurons and interpret their activity meaningfully within the range of neurons and recording time available in current experiments. Furthermore, we studied how the neural activity from the models reflects how the neurons are connected. These results help to guide the interpretation of analyses using populations of neurons in the context of the larger network to understand brain function.},
+  number = {12},
+  journal = {PLOS Computational Biology},
+  author = {Williamson, Ryan C. and Cowley, Benjamin R. and Litwin-Kumar, Ashok and Doiron, Brent and Kohn, Adam and Smith, Matthew A. and Yu, Byron M.},
+  month = dec,
+  year = {2016},
+  keywords = {Neurons,Neural networks,Network analysis,Single neuron function,Covariance,Macaque,Eigenvectors,Action potentials},
+  pages = {e1005141},
+  file = {C:\\Users\\saram\\Zotero\\storage\\RWU2GCB9\\Williamson et al.  2016  Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models.pdf}
+}
+
+@article{Noda2014,
+  title = {Amplitude and Phase-Locking Adaptation of Neural Oscillation in the Rat Auditory Cortex in Response to Tone Sequence},
+  volume = {79},
+  issn = {0168-0102},
+  doi = {10.1016/j.neures.2013.11.002},
+  abstract = {Sensory adaptation allows stimulus sensitivity to be dynamically modulated according to stimulus statistics and plays pivotal roles in efficient neural computation. Here, it is hypothesized that in the auditory cortex, phase locking of local field potentials (LFPs) to test tones exhibits an adaptation property, i.e., phase-locking adaptation, which is distinct from the amplitude adaptation of oscillatory components. Series of alternating tone sequences were applied in which the inter-tone interval (ITI) and frequency difference ($\Delta$F) between successive tones were varied. Then, adaptation was characterized by the temporal evolution of the band-specific amplitude and phase locking evoked by the test tones. Differences as well as similarities were revealed between amplitude and phase-locking adaptations. First, both amplitude and phase-locking adaptations were enhanced by short ITIs and small $\Delta$Fs. Second, the amplitude adaptation was more effective in a higher frequency band, while the phase-locking adaptation was more effective in a lower frequency band. Third, as with the adaptation of multiunit activities (MUAs), the amplitude adaptation occurred mainly within a second, while the phase-locking showed multi-second adaptation specifically in the gamma band for short ITI and small $\Delta$F conditions. Fourth, the amplitude adaptation and phase-locking adaptation were co-modulated in a within-second time scale, while this co-modulation was not observed in a multi-second time scale. These findings suggest that the amplitude and phase-locking adaptations have different mechanisms and functions. The phase-locking adaptation is likely to play more crucial roles in encoding a temporal structure of stimulus than the amplitude adaptation.},
+  journal = {Neuroscience Research},
+  author = {Noda, Takahiro and Kanzaki, Ryohei and Takahashi, Hirokazu},
+  month = feb,
+  year = {2014},
+  keywords = {local field potential,Rat,Auditory cortex,Inter-trial phase coherence,Multi-second adaptation,Auditory stream segregation},
+  pages = {52-60},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FI4ZE5JM\\Noda et al.  2014  Amplitude and phase-locking adaptation of neural oscillation in the rat auditory cortex in response to tone sequence.pdf}
+}
+
+@article{Gratiy2017,
+  title = {From {{Maxwell}}'s Equations to the Theory of Current-Source Density Analysis},
+  issn = {1460-9568},
+  doi = {10.1111/ejn.13534},
+  abstract = {Despite the widespread use of current-source density (CSD) analysis of extracellular potential recordings in the brain, the physical mechanisms responsible for the generation of the signal are still debated. While the extracellular potential is thought to be exclusively generated by the transmembrane currents, recent studies suggest that extracellular diffusive, advective and displacement currents\textemdash{}traditionally neglected\textemdash{}may also contribute considerably towards extracellular potential recordings. Here, we first justify the application of the electro-quasistatic approximation of Maxwell's equations to describe the electromagnetic field of physiological origin. Subsequently, we perform spatial averaging of currents in neural tissue to arrive at the notion of the CSD and derive an equation relating it to the extracellular potential. We show that, in general, the extracellular potential is determined by the CSD of membrane currents as well as the gradients of the putative extracellular diffusion current. The diffusion current can contribute significantly to the extracellular potential at frequencies less than a few Hertz; in which case it must be subtracted to obtain correct CSD estimates. We also show that the advective and displacement currents in the extracellular space are negligible for physiological frequencies while, within cellular membrane, displacement current contributes toward the CSD as a capacitive current. Taken together, these findings elucidate the relationship between electric currents and the extracellular potential in brain tissue and form the necessary foundation for the analysis of extracellular recordings. This article is protected by copyright. All rights reserved.},
+  language = {en},
+  journal = {European Journal of Neuroscience},
+  author = {Gratiy, Sergey L. and Halnes, Geir and Denman, Daniel and Hawrylycz, Michael J. and Koch, Christof and Einevoll, Gaute T. and Anastassiou, Costas A.},
+  month = feb,
+  year = {2017},
+  keywords = {Electrical stimulation,electrical conductivity,extracellular recordings,field potentials,current transfer},
+  pages = {n/a-n/a},
+  file = {C:\\Users\\saram\\Zotero\\storage\\HIVG5CII\\Gratiy et al.  2017  From Maxwell's equations to the theory of current-source density analysis.pdf}
+}
+
+@article{Noda2017,
+  title = {Stimulus {{Phase Locking}} of {{Cortical Oscillations}} for {{Rhythmic Tone Sequences}} in {{Rats}}},
+  volume = {11},
+  issn = {1662-5110},
+  doi = {10.3389/fncir.2017.00002},
+  abstract = {Humans can detect regular patterns very rapidly (i.e., within a few cycles) without any special attention to the acoustic environment. This suggests that human sensory systems are equipped with a powerful mechanism for the automatic prediction of forthcoming stimuli in order to detect regularity. It has recently been hypothesized that the neural basis of sensory predictions exist not only for what happens (predictive coding), but also for when a particular stimulus occurs (predictive timing). We here hypothesized that the phase of neural oscillations is critical in predictive timing and that these oscillations are modulated in a band-specific manner when acoustic patterns become predictable, i.e., regular. A high-density microelectrode array (10 x 10 within 4 x 4 mm2) characterized spatial patterns of band-specific oscillations when a random tone sequence was switched to a regular tone sequence. Increasing the regularity of tone sequence enhanced the phase locking in a band-specific manner, irrespective of the type of regular sound pattern: gamma-band phase locking increased immediately after the transition from random to regular sequences, while beta-band phase locking gradually evolved with time after the transition. The amplitude of the tone-evoked response, on the other hand, increased with frequency separation with respect to the prior tone, suggesting that the evoked-response amplitude encodes the sequence information on a local scale, i.e., the local order of tones. In terms of a spatial pattern, the phase-locking modulation spread widely over the auditory cortex, while the amplitude modulation was locally confined around the activation foci. Thus, our data suggest that oscillatory phase, rather than amplitude, plays a more important role in neuronal detection of tone sequence regularity, which is closely related to predictive timing. Furthermore, their band-specific contributions may support the recent theories that the gamma oscillations encode bottom-up prediction error, whereas the beta oscillations are involved in top-down prediction.},
+  language = {English},
+  journal = {Frontiers in Neural Circuits},
+  author = {Noda, Takahiro and Amemiya, Tomoki and Shiramatsu, Tomoyo I. and Takahashi, Hirokazu},
+  year = {2017},
+  keywords = {Rat,Auditory cortex,Regular tone sequences,high-density microelectrode array,oscillatory phase locking},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FF7MGFZ5\\Noda et al_2017_Stimulus Phase Locking of Cortical Oscillations for Rhythmic Tone Sequences in.pdf}
+}
+
+@inproceedings{Havens2008,
+  title = {Dunn's Cluster Validity Index as a Contrast Measure of {{VAT}} Images},
+  doi = {10.1109/ICPR.2008.4761772},
+  abstract = {This paper addresses the relationship between the visual assessment of cluster tendency (VAT) algorithm and Dunnpsilas cluster validity index. We present an analytical comparison in conjunction with numerical examples to demonstrate that the effectiveness of VAT in showing cluster tendency is directly related to Dunnpsilas index. This analysis is important to understanding the underlying theory of VAT and VAT-based algorithms and, more generally, other algorithms that are based on, or similar to, Primpsilas Algorithm.},
+  booktitle = {2008 19th {{International Conference}} on {{Pattern Recognition}}},
+  author = {Havens, T. C. and Bezdek, J. C. and Keller, J. M. and Popescu, M.},
+  month = dec,
+  year = {2008},
+  keywords = {image processing,pattern clustering,Dunns cluster validity index,VAT image,visual assessment of cluster tendency,Algorithm design and analysis,Clustering algorithms,Couplings,Electric variables measurement,Feedback,Matrix converters,Partitioning algorithms,Pixel},
+  pages = {1-4},
+  file = {C:\\Users\\saram\\Zotero\\storage\\AF45H87M\\Havens 2008 Dunn's cluster validity index as a contrast measure of VAT images.pdf}
+}
+
+@article{Kumar2016,
+  title = {A {{Hybrid Approach}} to {{Clustering}} in {{Big Data}}},
+  volume = {46},
+  issn = {2168-2267},
+  doi = {10.1109/TCYB.2015.2477416},
+  abstract = {Clustering of big data has received much attention recently. In this paper, we present a new clusiVAT algorithm and compare it with four other popular data clustering algorithms. Three of the four comparison methods are based on the well known, classical batch k-means model. Specifically, we use k-means, single pass k-means, online k-means, and clustering using representatives (CURE) for numerical comparisons. clusiVAT is based on sampling the data, imaging the reordered distance matrix to estimate the number of clusters in the data visually, clustering the samples using a relative of single linkage (SL), and then noniteratively extending the labels to the rest of the data-set using the nearest prototype rule. Previous work has established that clusiVAT produces true SL clusters in compact-separated data. We have performed experiments to show that k-means and its modified algorithms suffer from initialization issues that cause many failures. On the other hand, clusiVAT needs no initialization, and almost always finds partitions that accurately match ground truth labels in labeled data. CURE also finds SL type partitions but is much slower than the other four algorithms. In our experiments, clusiVAT proves to be the fastest and most accurate of the five algorithms; e.g., it recovers 97\% of the ground truth labels in the real world KDD-99 cup data (4292637 samples in 41 dimensions) in 76 s.},
+  number = {10},
+  journal = {IEEE Transactions on Cybernetics},
+  author = {Kumar, D. and Bezdek, J. C. and Palaniswami, M. and Rajasegarar, S. and Leckie, C. and Havens, T. C.},
+  month = oct,
+  year = {2016},
+  keywords = {pattern clustering,Algorithm design and analysis,Clustering algorithms,Partitioning algorithms,Big Data,visual databases,CURE,SL type partitioning,batch k-means model,clusiVAT algorithm,clustering-using representatives,data clustering algorithm,data sampling,distance matrix,ground truth labels,hybrid approach,nearest prototype rule,noniterative data labelling,online-k-means,real world KDD-99 cup data,single linkage,single-pass k-means,Cybernetics,Image edge detection,Indexes,Big data cluster analysis,Internet of things,cluster tendency assessment,data analytics},
+  pages = {2372-2385}
+}
+
+@article{Fahiman2016,
+  archivePrefix = {arXiv},
+  eprinttype = {arxiv},
+  eprint = {1608.01072},
+  primaryClass = {cs},
+  title = {Fuzzy C-{{Shape}}: {{A}} New Algorithm for Clustering Finite Time Series Waveforms},
+  shorttitle = {Fuzzy C-{{Shape}}},
+  abstract = {The existence of large volumes of time series data in many applications has motivated data miners to investigate specialized methods for mining time series data. Clustering is a popular data mining method due to its powerful exploratory nature and its usefulness as a preprocessing step for other data mining techniques. This article develops two novel clustering algorithms for time series data that are extensions of a crisp c-shapes algorithm. The two new algorithms are heuristic derivatives of fuzzy c-means (FCM). Fuzzy c-Shapes plus (FCS+) replaces the inner product norm in the FCM model with a shape-based distance function. Fuzzy c-Shapes double plus (FCS++) uses the shape-based distance, and also replaces the FCM cluster centers with shape-extracted prototypes. Numerical experiments on 48 real time series data sets show that the two new algorithms outperform state-of-the-art shape-based clustering algorithms in terms of accuracy and efficiency. Four external cluster validity indices (the Rand index, Adjusted Rand Index, Variation of Information, and Normalized Mutual Information) are used to match candidate partitions generated by each of the studied algorithms. All four indices agree that for these finite waveform data sets, FCS++ gives a small improvement over FCS+, and in turn, FCS+ is better than the original crisp c-shapes method. Finally, we apply two tests of statistical significance to the three algorithms. The Wilcoxon and Friedman statistics both rank the three algorithms in exactly the same way as the four cluster validity indices.},
+  journal = {arXiv:1608.01072 [cs]},
+  author = {Fahiman, Fateme and Bezdek, Jame C. and Erfani, Sarah M. and Leckie, Christopher and Palaniswami, Marimuthu},
+  month = aug,
+  year = {2016},
+  keywords = {Computer Science - Learning},
+  file = {C:\\Users\\saram\\Zotero\\storage\\G2RJVH8Q\\Fahiman et al.  2016  Fuzzy c-Shape A new algorithm for clustering finite time series waveforms.pdf}
+}
+
+@article{Shapcott2016,
+  title = {Correlated Activity of Cortical Neurons Survives Extensive Removal of Feedforward Sensory Input},
+  volume = {6},
+  copyright = {\textcopyright{} 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.},
+  issn = {2045-2322},
+  doi = {10.1038/srep34886},
+  abstract = {A fundamental property of brain function is that the spiking activity of cortical neurons is variable and that some of this variability is correlated between neurons.},
+  language = {en},
+  journal = {Scientific Reports},
+  author = {Shapcott, Katharine A. and Schmiedt, Joscha T. and Saunders, Richard C. and Maier, Alexander and Leopold, David A. and Schmid, Michael C.},
+  month = oct,
+  year = {2016},
+  keywords = {Klusters,Lynn Hazan},
+  pages = {34886},
+  file = {C:\\Users\\saram\\Zotero\\storage\\UDM3I2DF\\srep34886.pdf}
+}
+
+@inproceedings{Choi2005,
+  title = {A {{New Overlapping Resolution Method}} for {{Multi}}-Channel {{Spike Sorting}}},
+  doi = {10.1109/CNE.2005.1419717},
+  abstract = {Overlapping is one of the serious problems in the spike sorting of multi-channel recordings. Although techniques to resolve this problem have been developed, there still remains much room for improvement. We present in this paper a new overlapping resolution method which is simple and can easily be incorporated in the existing spike sorting systems. The proposed method combines spike sorting algorithms with the linear projections, such that each projection eliminates single-neuronal activity. Simulations show that our method successfully resolves overlapping in real neural data},
+  booktitle = {Conference {{Proceedings}}. 2nd {{International IEEE EMBS Conference}} on {{Neural Engineering}}, 2005.},
+  author = {Choi, Joon Hwan and Kim, Dohoon and Kim, Taejeong},
+  month = mar,
+  year = {2005},
+  keywords = {Neurons,medical signal processing,neurophysiology,Electrodes,shape,source separation,bioelectric phenomena,signal resolution,multi-channel spike sorting,overlapping resolution method,single-neuronal activity elimination,Blind source separation,Feature extraction,Independent component analysis,Sorting,principal component analysis},
+  pages = {683-686},
+  file = {C:\\Users\\saram\\Zotero\\storage\\BU488QXS\\Choi et al.  2005  A New Overlapping Resolution Method for Multi-channel Spike Sorting.pdf}
+}
+
+@article{Hazan2006,
+  title = {Klusters, {{NeuroScope}}, {{NDManager}}: {{A}} Free Software Suite for Neurophysiological Data Processing and Visualization},
+  volume = {155},
+  issn = {0165-0270},
+  shorttitle = {Klusters, {{NeuroScope}}, {{NDManager}}},
+  doi = {10.1016/j.jneumeth.2006.01.017},
+  abstract = {Recent technological advances now allow for simultaneous recording of large populations of anatomically distributed neurons in behaving animals. The free software package described here was designed to help neurophysiologists process and view recorded data in an efficient and user-friendly manner. This package consists of several well-integrated applications, including NeuroScope (http://neuroscope.sourceforge.net), an advanced viewer for electrophysiological and behavioral data with limited editing capabilities, Klusters (http://klusters.sourceforge.net), a graphical cluster cutting application for manual and semi-automatic spike sorting, NDManager (http://ndmanager.sourceforge.net), an experimental parameter and data processing manager. All of these programs are distributed under the GNU General Public License (GPL, see http://www.gnu.org/licenses/gpl.html), which gives its users legal permission to copy, distribute and/or modify the software. Also included are extensive user manuals and sample data, as well as source code and documentation.},
+  number = {2},
+  journal = {Journal of Neuroscience Methods},
+  author = {Hazan, Lynn and Zugaro, Micha{\"e}l and Buzs{\'a}ki, Gy{\"o}rgy},
+  month = sep,
+  year = {2006},
+  keywords = {Spike sorting,Cluster cutting,Data mining,Unit activity,Local field potentials},
+  pages = {207-216},
+  file = {C:\\Users\\saram\\Zotero\\storage\\AATHJQ8E\\Hazan et al.  2006  Klusters, NeuroScope, NDManager A free software suite for neurophysiological data processing and visualization.pdf}
+}
+
+@article{Friston2011,
+  title = {Functional and {{Effective Connectivity}}: {{A Review}}},
+  volume = {1},
+  issn = {2158-0014},
+  shorttitle = {Functional and {{Effective Connectivity}}},
+  doi = {10.1089/brain.2011.0008},
+  abstract = {Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.},
+  number = {1},
+  journal = {Brain Connectivity},
+  author = {Friston, Karl J.},
+  month = jan,
+  year = {2011},
+  pages = {13-36},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4DIUFW56\\Friston_2011_Functional and Effective Connectivity.pdf}
+}
+
+@article{2016,
+  title = {Correlations and {{Neuronal Population Information}}},
+  volume = {39},
+  doi = {10.1146/annurev-neuro-070815-013851},
+  abstract = {Brain function involves the activity of neuronal populations. Much recent effort has been devoted to measuring the activity of neuronal populations in different parts of the brain under various experimental conditions. Population activity patterns contain rich structure, yet many studies have focused on measuring pairwise relationships between members of a larger population\textemdash{}termed noise correlations. Here we review recent progress in understanding how these correlations affect population information, how information should be quantified, and what mechanisms may give rise to correlations. As population coding theory has improved, it has made clear that some forms of correlation are more important for information than others. We argue that this is a critical lesson for those interested in neuronal population responses more generally: Descriptions of population responses should be motivated by and linked to well-specified function. Within this context, we offer suggestions of where current theoretical frameworks fall short.},
+  number = {1},
+  journal = {Annual Review of Neuroscience},
+  year = {2016},
+  pages = {237-256},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ET3DBU5V\\2016_Correlations and Neuronal Population Information.pdf},
+  pmid = {27145916}
+}
+
+@article{Blankertz2008,
+  title = {Optimizing {{Spatial}} Filters for {{Robust EEG Single}}-{{Trial Analysis}}},
+  volume = {25},
+  issn = {1053-5888},
+  doi = {10.1109/MSP.2008.4408441},
+  abstract = {Due to the volume conduction multichannel electroencephalogram (EEG) recordings give a rather blurred image of brain activity. Therefore spatial filters are extremely useful in single-trial analysis in order to improve the signal-to-noise ratio. There are powerful methods from machine learning and signal processing that permit the optimization of spatio-temporal filters for each subject in a data dependent fashion beyond the fixed filters based on the sensor geometry, e.g., Laplacians. Here we elucidate the theoretical background of the common spatial pattern (CSP) algorithm, a popular method in brain-computer interface (BCD research. Apart from reviewing several variants of the basic algorithm, we reveal tricks of the trade for achieving a powerful CSP performance, briefly elaborate on theoretical aspects of CSP, and demonstrate the application of CSP-type preprocessing in our studies of the Berlin BCI (BBCI) project.},
+  number = {1},
+  journal = {IEEE Signal Processing Magazine},
+  author = {Blankertz, B. and Tomioka, R. and Lemm, S. and Kawanabe, M. and r Muller, K.},
+  year = {2008},
+  keywords = {medical signal processing,neurophysiology,Signal processing,learning (artificial intelligence),spatial filters,user interfaces,Berlin BCI project,brain activity,brain-computer interface,common spatial pattern algorithm,electroencephalogram,machine learning,robust EEG analysis,signal-to-noise ratio,single-trial analysis,spatiotemporal filters,volume conduction multichannel EEG,Brain,Geometry,Optimization methods,Robustness,Signal analysis,Signal processing algorithms,Signal to noise ratio,Electroencephalography},
+  pages = {41-56},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JHVA63X8\\Blankertz 2008 Optimizing Spatial filters for Robust EEG Single-Trial Analysis.pdf}
+}
+
+@article{Lisman2013,
+  title = {The {{Theta}}-{{Gamma Neural Code}}},
+  volume = {77},
+  issn = {0896-6273},
+  doi = {10.1016/j.neuron.2013.03.007},
+  abstract = {Theta and gamma frequency oscillations occur in the same brain regions and interact with each other, a process called cross-frequency coupling. Here, we review evidence for the following hypothesis: that the dual oscillations form a code for representing multiple items in an ordered way. This form of coding has been most clearly demonstrated in the hippocampus, where different spatial information is represented in different gamma subcycles of a theta cycle. Other experiments have tested the functional importance of oscillations and their coupling. These involve correlation of oscillatory properties with memory states, correlation with memory performance, and effects of disrupting oscillations on memory. Recent work suggests that this coding scheme coordinates communication between brain regions and is involved in sensory as well as memory processes.},
+  number = {6},
+  journal = {Neuron},
+  author = {Lisman, John E. and Jensen, Ole},
+  month = mar,
+  year = {2013},
+  pages = {1002-1016},
+  file = {C:\\Users\\saram\\Zotero\\storage\\P5XNBT7N\\Lisman and Jensen  2013  The Theta-Gamma Neural Code.pdf}
+}
+
+@article{Parra2004,
+  title = {Blind {{Source Separation}} via {{Generalized Eigenvalue Decomposition}}},
+  volume = {4},
+  issn = {1532-4435},
+  doi = {10.1162/jmlr.2003.4.7-8.1261},
+  abstract = {In this short note we highlight the fact that linear blind source separation can be formulated as a generalized eigenvalue decomposition under the assumptions of non-Gaussian, non-stationary, or non-white independent sources. The solution for the unmixing matrix is given by the generalized eigenvectors that simultaneously diagonalize the covariance matrix of the observations and an additional symmetric matrix whose form depends upon the particular assumptions. The method critically determines the mixture coefficients and is therefore not robust to estimation errors. However it provides a rather general and unified solution that summarizes the conditions for successful blind source separation. To demonstrate the method, which can be implemented in two lines of matlab code, we present results for artificial mixtures of speech and real mixtures of electroencephalography (EEG) data, showing that the same sources are recovered under the various assumptions.},
+  number = {7-8},
+  journal = {J. Mach. Learn. Res.},
+  author = {Parra, Lucas and Sajda, Paul},
+  month = oct,
+  year = {2004},
+  keywords = {Blind source separation,generalized eigenvalue decomposition,non-Gaussian,non-stationary,non-white},
+  pages = {1261--1269},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2FIXKZZP\\tutorial-bss.pdf;C:\\Users\\saram\\Zotero\\storage\\CSDFWRB7\\parra03a.pdf}
+}
+
+@article{Parra2005,
+  title = {Recipes for the Linear Analysis of {{EEG}}},
+  volume = {28},
+  issn = {1053-8119},
+  doi = {10.1016/j.neuroimage.2005.05.032},
+  abstract = {In this paper, we describe a simple set of ``recipes'' for the analysis of high spatial density EEG. We focus on a linear integration of multiple channels for extracting individual components without making any spatial or anatomical modeling assumptions, instead requiring particular statistical properties such as maximum difference, maximum power, or statistical independence. We demonstrate how corresponding algorithms, for example, linear discriminant analysis, principal component analysis and independent component analysis, can be used to remove eye-motion artifacts, extract strong evoked responses, and decompose temporally overlapping components. The general approach is shown to be consistent with the underlying physics of EEG, which specifies a linear mixing model of the underlying neural and non-neural current sources.},
+  number = {2},
+  journal = {NeuroImage},
+  author = {Parra, Lucas C. and Spence, Clay D. and Gerson, Adam D. and Sajda, Paul},
+  month = nov,
+  year = {2005},
+  keywords = {generalized eigenvalue decomposition,Electroencephalography (EEG),Linear integration,Source estimation,Brain computer interfaces},
+  pages = {326-341},
+  file = {C:\\Users\\saram\\Zotero\\storage\\74J7SCST\\Parra et al.  2005  Recipes for the linear analysis of EEG.pdf}
+}
+
+@article{Keshtkaran2017,
+  title = {Noise-Robust Unsupervised Spike Sorting Based on Discriminative Subspace Learning with Outlier Handling},
+  issn = {1741-2552},
+  doi = {10.1088/1741-2552/aa6089},
+  abstract = {Abstract Objective: Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a projection subspace which is not necessarily the most discriminative one. Therefore, the clusters which appear inherently separable in some discriminative subspace may overlap if projected using conventional feature extraction approaches leading to a poor sorting accuracy especially when the noise level is high. In this paper, we propose a noise robust and unsupervised spike sorting algorithm based on learning discriminative spike features for clustering. Method: The proposed algorithm uses discriminative subspace learning to extract low dimensional and most discriminative features from the spike waveforms, and perform clustering with automatic detection of the number of the clusters. The core part of the algorithm involves iterative subspace selection using linear discriminant analysis (LDA) and clustering using Gaussian mixture model (GMM) with outlier detection. A statistical test in the discriminative subspace is proposed to automatically detect the number of the clusters. Main results: Comparative results on publicly available simulated and real in-vivo datasets demonstrate that our algorithm achieves substantially improved cluster distinction leading to higher sorting accuracy and more reliable detection of clusters which are highly overlapping and not detectable using conventional feature extraction techniques such PCA or wavelets. Significance: By providing more accurate information about the activity of more number of individual neurons with high robustness to neural noise and outliers, the proposed unsupervised spike sorting algorithm facilitates more detailed and accurate analysis of single- and multi-unit activities in neuroscience and brain machine interface studies.},
+  language = {en},
+  journal = {Journal of Neural Engineering},
+  author = {Keshtkaran, Mohammad Reza and Yang, Zhi},
+  year = {2017},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4U2D6I7N\\Keshtkaran_Yang 2017 Noise-robust unsupervised spike sorting based on discriminative subspace.pdf}
+}
+
+@misc{zotero-440,
+  title = {Sarela05a.Dvi - Sarela05a.Pdf},
+  howpublished = {http://www.jmlr.org/papers/volume6/sarela05a/sarela05a.pdf},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DZV7TVQV\\sarela05a.html}
+}
+
+@article{Sarela2005,
+  title = {Denoising {{Source Separation}}},
+  volume = {6},
+  issn = {ISSN 1533-7928},
+  number = {Mar},
+  journal = {Journal of Machine Learning Research},
+  author = {S{\"a}rel{\"a}, Jaakko and Valpola, Harri},
+  year = {2005},
+  pages = {233-272},
+  file = {C:\\Users\\saram\\Zotero\\storage\\D9HVMSQ4\\Sarela_Valpola 2005 Denoising Source Separation.pdf}
+}
+
+@article{Haufe2014,
+  title = {On the Interpretation of Weight Vectors of Linear Models in Multivariate Neuroimaging},
+  volume = {87},
+  issn = {1053-8119},
+  doi = {10.1016/j.neuroimage.2013.10.067},
+  abstract = {The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain\textendash{}computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses.},
+  journal = {NeuroImage},
+  author = {Haufe, Stefan and Meinecke, Frank and G{\"o}rgen, Kai and D{\"a}hne, Sven and Haynes, John-Dylan and Blankertz, Benjamin and Bie{\ss}mann, Felix},
+  month = feb,
+  year = {2014},
+  keywords = {decoding,Neuroimaging,Multivariate,Univariate,fMRI,EEG,Forward/backward models,Generative/discriminative models,Encoding,Activation patterns,Extraction filters,Interpretability,Regularization,Sparsity},
+  pages = {96-110},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6JDPVNC9\\Haufe et al.  2014  On the interpretation of weight vectors of linear models in multivariate neuroimaging.pdf}
+}
+
+@article{Bassett2017,
+  title = {Network Neuroscience},
+  volume = {20},
+  copyright = {\textcopyright{} 2017 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1097-6256},
+  doi = {10.1038/nn.4502},
+  abstract = {Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.},
+  language = {en},
+  number = {3},
+  journal = {Nature Neuroscience},
+  author = {Bassett, Danielle S. and Sporns, Olaf},
+  month = mar,
+  year = {2017},
+  keywords = {Cognitive neuroscience,Network models},
+  pages = {353-364},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FM39RBRC\\Bassett_Sporns_2017_Network neuroscience.pdf}
+}
+
+@article{Benjamin2017,
+  title = {Modern Machine Learning Far Outperforms {{GLMs}} at Predicting Spikes},
+  copyright = {\textcopyright{} 2017, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
+  doi = {10.1101/111450},
+  abstract = {Neuroscience has long focused on finding encoding models that effectively ask 'what predicts neural spiking?' and generalized linear models (GLMs) are a typical approach. Modern machine learning techniques have the potential to perform better. Here we directly compared GLMs to three leading methods: feedforward neural networks, gradient boosted trees, and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from reaching kinematics, and in rat hippocampal cells from open field location and orientation. In general, the modern methods produced far better spike predictions and were less sensitive to the preprocessing of features. XGBoost and the ensemble were the best-performing methods and worked well even on neural data with very low spike rates. This overall performance suggests that tuning curves built with GLMs are at times inaccurate and can be easily improved upon. Our publicly shared code uses standard packages and can be quickly applied to other datasets. Encoding models built with machine learning techniques more accurately predict spikes and can offer meaningful benchmarks for simpler models.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Benjamin, Ari S. and Fernandes, Hugo L. and Tomlinson, Tucker and Ramkumar, Pavan and VerSteeg, Chris and Miller, Lee and Kording, Konrad Paul},
+  month = feb,
+  year = {2017},
+  pages = {111450},
+  file = {C:\\Users\\saram\\Zotero\\storage\\S9GMTJ5P\\Benjamin et al.  2017  Modern machine learning far outperforms GLMs at predicting spikes.pdf}
+}
+
+@article{Einevoll2013,
+  title = {Modelling and Analysis of Local Field Potentials for Studying the Function of Cortical Circuits},
+  volume = {14},
+  copyright = {\textcopyright{} 2013 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1471-003X},
+  doi = {10.1038/nrn3599},
+  abstract = {The past decade has witnessed a renewed interest in cortical local field potentials (LFPs) \textemdash{} that is, extracellularly recorded potentials with frequencies of up to \textasciitilde{}500 Hz. This is due to both the advent of multielectrodes, which has enabled recording of LFPs at tens to hundreds of sites simultaneously, and the insight that LFPs offer a unique window into key integrative synaptic processes in cortical populations. However, owing to its numerous potential neural sources, the LFP is more difficult to interpret than are spikes. Careful mathematical modelling and analysis are needed to take full advantage of the opportunities that this signal offers in understanding signal processing in cortical circuits and, ultimately, the neural basis of perception and cognition.},
+  language = {en},
+  number = {11},
+  journal = {Nature Reviews Neuroscience},
+  author = {Einevoll, Gaute T. and Kayser, Christoph and Logothetis, Nikos K. and Panzeri, Stefano},
+  month = nov,
+  year = {2013},
+  keywords = {Computational Neuroscience,Electrophysiology,Computational models,Sensory systems},
+  pages = {770-785},
+  file = {C:\\Users\\saram\\Zotero\\storage\\F7R37UKU\\Einevoll et al.  2013  Modelling and analysis of local field potentials for studying the function of cortical circuits.pdf}
+}
+
+@article{Marblestone2016,
+  title = {Toward an {{Integration}} of {{Deep Learning}} and {{Neuroscience}}},
+  volume = {10},
+  issn = {1662-5188},
+  doi = {10.3389/fncom.2016.00094},
+  abstract = {Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.},
+  language = {English},
+  journal = {Frontiers in Computational Neuroscience},
+  author = {Marblestone, Adam H. and Wayne, Greg and Kording, Konrad P.},
+  year = {2016},
+  keywords = {Neural networks,Cost functions,Cognitive Architecture,Developmental Bootstrapping,unsupervised learning,memory systems,reinforcement learning},
+  file = {C:\\Users\\saram\\Zotero\\storage\\NCRD6VU4\\Marblestone et al.  2016  Toward an Integration of Deep Learning and Neuroscience.pdf}
+}
+
+@misc{zotero-458,
+  title = {How to {{Describe Neuronal Activity}}: {{Spikes}}, {{Rates}}, or {{Assemblies}}? - 850-How-to-Describe-Neuronal-Activity-Spikes-Rates-or-Assemblies.Pdf},
+  howpublished = {https://papers.nips.cc/paper/850-how-to-describe-neuronal-activity-spikes-rates-or-assemblies.pdf},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ZQDE99RP\\850-how-to-describe-neuronal-activity-spikes-rates-or-assemblies.html}
+}
+
+@article{Gong2015,
+  title = {High-Speed Recording of Neural Spikes in Awake Mice and Flies with a Fluorescent Voltage Sensor},
+  copyright = {Copyright \textcopyright{} 2015, American Association for the Advancement of Science},
+  issn = {0036-8075, 1095-9203},
+  doi = {10.1126/science.aab0810},
+  abstract = {Genetically encoded voltage indicators (GEVIs) are a promising technology for fluorescence readout of millisecond-scale neuronal dynamics. Prior GEVIs had insufficient signaling speed and dynamic range to resolve action potentials in live animals. We coupled fast voltage-sensing domains from a rhodopsin protein to bright fluorophores via resonance energy transfer. The resulting GEVIs are sufficiently bright and fast to report neuronal action potentials and membrane voltage dynamics in awake mice and flies, resolving fast spike trains with 0.2-millisecond timing precision at spike detection error rates orders of magnitude better than prior GEVIs. In vivo imaging revealed sensory-evoked responses, including somatic spiking, dendritic dynamics, and intracellular voltage propagation. These results empower in vivo optical studies of neuronal electrophysiology and coding and motivate further advancements in high-speed microscopy.},
+  language = {en},
+  journal = {Science},
+  author = {Gong, Yiyang and Huang, Cheng and Li, Jin Zhong and Grewe, Benjamin F. and Zhang, Yanping and Eismann, Stephan and Schnitzer, Mark J.},
+  month = nov,
+  year = {2015},
+  pages = {aab0810},
+  file = {C:\\Users\\saram\\Zotero\\storage\\E49WZE6A\\Gong et al_2015_High-speed recording of neural spikes in awake mice and flies with a.pdf},
+  pmid = {26586188}
+}
+
+@article{Kandel1993,
+  title = {Cerebellar Neuronal Activity Correlates with Spike and Wave {{EEG}} Patterns in the Rat},
+  volume = {16},
+  issn = {0920-1211},
+  abstract = {In this study we investigated the involvement of the cerebellum in high voltage spike-and-wave spindles, a rodent model of petit mal epilepsy. High voltage spindles, recorded epidurally from the sensorimotor neocortex, were correlated with single or multiple unit activity in the cerebellar cortex and deep cerebellar nuclei. The majority of neurons or neuronal groups in the cerebellum (77.9\%) fired rhythmically and phase-locked with the high voltage spindles, either during the spike (43.2\%; n = 41) or during the wave (34.7\%; n = 33) component of the high voltage spindle. Tremor of the head and neck musculature, recorded with an accelero-meter, occurred during the high voltage spindle in approximately half of the rats. When present, rhythmic movement occurred predominantly during the wave phase of the high voltage spindle. The remaining half of the rats did not show tremor during high voltage spindles but, nevertheless, had cerebellar units that burst during the spike or wave phase of the high voltage spindle. These latter results demonstrate that phase-locked bursting of cerebellar units during high voltage spindle is independent of rhythmic movement. The findings suggest that rhythmic output from the cerebellum may contribute to the maintenance of generalized petit mal seizures.},
+  language = {eng},
+  number = {1},
+  journal = {Epilepsy Research},
+  author = {Kandel, A. and Buzs{\'a}ki, G.},
+  month = sep,
+  year = {1993},
+  keywords = {Neurons,Animals,Rats,Cerebellum,Motor Activity,Female,Male,Rats; Inbred BN,Rats; Inbred F344,Rats; Inbred Strains,Electroencephalography},
+  pages = {1-9},
+  pmid = {8243435}
+}
+
+@article{Burns2010,
+  title = {Comparisons of the {{Dynamics}} of {{Local Field Potential}} and {{Multiunit Activity Signals}} in {{Macaque Visual Cortex}}},
+  volume = {30},
+  copyright = {Copyright \textcopyright{} 2010 the authors 0270-6474/10/3013739-11\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.0743-10.2010},
+  abstract = {The local field potential (LFP) and multiunit activity (MUA) are extracellularly recorded signals that describe local neuronal network dynamics. In our experiments, the LFP and MUA, recorded from the same electrode in macaque primary visual cortex V1 in response to drifting grating visual stimuli, were evaluated on coarse timescales ($\sim$1\textendash{}5 s) and fine timescales ($<$0.1 s). On coarse timescales, MUA and the LFP both produced sustained visual responses to optimal and non-optimal oriented visual stimuli. The sustainedness of the two signals across the population of recording sites was correlated (correlation coefficient, $\sim$0.4). At most recording sites, the MUA was at least as sustained as the LFP and significantly more sustained for optimal orientations. In previous literature, the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging studies was found to be more strongly correlated with the LFP than with the MUA as a result of the lack of sustained response in the MUA signal. Because we found that MUA was as sustained as the LFP, MUA may also be correlated with BOLD. On fine timescales, we computed the coherence between the LFP and MUA over the frequency range 10\textendash{}150 Hz. The LFP and MUA were weakly but significantly coherent ($\sim$0.14) in the gamma band (20\textendash{}90 Hz). The amount of gamma-band coherence was correlated with the power in the gamma band of the LFP. The data were consistent with the proposal that the LFP and MUA are generated in a noisy, resonant cortical network.},
+  language = {en},
+  number = {41},
+  journal = {Journal of Neuroscience},
+  author = {Burns, Samuel P. and Xing, Dajun and Shapley, Robert M.},
+  month = oct,
+  year = {2010},
+  pages = {13739-13749},
+  file = {C:\\Users\\saram\\Zotero\\storage\\RKFVRSKW\\Burns et al_2010_Comparisons of the Dynamics of Local Field Potential and Multiunit Activity.pdf},
+  pmid = {20943914}
+}
+
+@article{Zerlaut2016,
+  title = {Heterogeneous Firing Rate Response of Mouse Layer {{V}} Pyramidal Neurons in the Fluctuation-Driven Regime},
+  volume = {594},
+  issn = {1469-7793},
+  doi = {10.1113/JP272317},
+  abstract = {KEY POINTS: We recreated in vitro the fluctuation-driven regime observed at the soma during asynchronous network activity in vivo and we studied the firing rate response as a function of the properties of the membrane potential fluctuations. We provide a simple analytical template that captures the firing response of both pyramidal neurons and various theoretical models. We found a strong heterogeneity in the firing rate response of layer V pyramidal neurons: in particular, individual neurons differ not only in their mean excitability level, but also in their sensitivity to fluctuations. Theoretical modelling suggest that this observed heterogeneity might arise from various expression levels of the following biophysical properties: sodium inactivation, density of sodium channels and spike frequency adaptation.
+ABSTRACT: Characterizing the input-output properties of neocortical neurons is of crucial importance for understanding the properties emerging at the network level. In the regime of low-rate irregular firing (such as in the awake state), determining those properties for neocortical cells remains, however, both experimentally and theoretically challenging. Here, we studied this problem using a combination of theoretical modelling and in vitro experiments. We first identified, theoretically, three somatic variables that describe the dynamical state at the soma in this fluctuation-driven regime: the mean, standard deviation and time constant of the membrane potential fluctuations. Next, we characterized the firing rate response of individual layer V pyramidal cells in this three-dimensional space by means of perforated-patch recordings and dynamic clamp in the visual cortex of juvenile mice in vitro. We found that individual neurons strongly differ not only in terms of their excitability, but also, and unexpectedly, in their sensitivities to fluctuations. Finally, using theoretical modelling, we attempted to reproduce these results. The model predicts that heterogeneous levels of biophysical properties such as sodium inactivation, sharpness of sodium activation and spike frequency adaptation account for the observed diversity of firing rate responses. Because the firing rate response will determine population rate dynamics during asynchronous neocortical activity, our results show that cortical populations are functionally strongly inhomogeneous in young mouse visual cortex, which should have important consequences on the strategies of cortical computation at early stages of sensory processing.},
+  language = {eng},
+  number = {13},
+  journal = {The Journal of Physiology},
+  author = {Zerlaut, Y. and Tele{\'n}czuk, B. and Deleuze, C. and Bal, T. and Ouanounou, G. and Destexhe, A.},
+  month = jul,
+  year = {2016},
+  pages = {3791-3808},
+  file = {C:\\Users\\saram\\Zotero\\storage\\GH48DE8Q\\Zerlaut et al_2016_Heterogeneous firing rate response of mouse layer V pyramidal neurons in the.pdf},
+  pmid = {27146816},
+  pmcid = {PMC4929333}
+}
+
+@article{Meij2012,
+  title = {Phase\textendash{}{{Amplitude Coupling}} in {{Human Electrocorticography Is Spatially Distributed}} and {{Phase Diverse}}},
+  volume = {32},
+  copyright = {Copyright \textcopyright{} 2012 the authors 0270-6474/12/320111-13\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.4816-11.2012},
+  abstract = {Spatially distributed phase-amplitude coupling (PAC) is a possible mechanism for selectively routing information through neuronal networks. If so, two key properties determine its selectivity and flexibility, phase diversity over space, and frequency diversity. To investigate these issues, we analyzed 42 human electrocorticographic recordings from 27 patients performing a working memory task. We demonstrate that (1) spatially distributed PAC occurred at distances $>$10 cm, (2) involved diverse preferred coupling phases, and (3) involved diverse frequencies. Using a novel technique [N-way decomposition based on the PARAFAC (for Parallel Factor analysis) model], we demonstrate that (4) these diverse phases originated mainly from the phase-providing oscillations. With these properties, PAC can be the backbone of a mechanism that is able to separate spatially distributed networks operating in parallel.},
+  language = {en},
+  number = {1},
+  journal = {Journal of Neuroscience},
+  author = {van der Meij, Roemer and Kahana, Michael and Maris, Eric},
+  month = jan,
+  year = {2012},
+  pages = {111-123},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6JAKKZAR\\Meij et al.  2012  Phase–Amplitude Coupling in Human Electrocorticography Is Spatially Distributed and Phase Diverse.pdf},
+  pmid = {22219274}
+}
+
+@article{Maris2011b,
+  title = {Spatially Distributed Patterns of Oscillatory Coupling between High-Frequency Amplitudes and Low-Frequency Phases in Human {{iEEG}}},
+  volume = {54},
+  issn = {1095-9572},
+  doi = {10.1016/j.neuroimage.2010.09.029},
+  abstract = {Spatially distributed coherent oscillations provide temporal windows of excitability that allow for interactions between distinct neuronal groups. It has been hypothesized that this mechanism for neuronal communication is realized by bursts of high-frequency oscillations that are phase-coupled to a low-frequency spatially distributed coupling oscillation. This mechanism requires multiple physiologically different interacting sources, one generating the low-frequency coupling oscillation and the others generating phase-coupled high-frequency oscillations. Using human intracranial EEG (iEEG) data, we provide evidence for multiple oscillatory patterns, as characterized on the basis of their spatial maps (topographies) and their frequency spectra. In fact, we show that the spatial maps for the coupling oscillations are much more widespread than the ones for the associated phase-coupled bursts. Second, in the majority of the patterns of phase-amplitude coupling (PAC), phase-coupled bursts of high-frequency activity are synchronized across brain areas. Third and last, working memory operations affect the PAC strength in a heterogeneous way: in some PAC patterns, working memory operations increase their strength, whereas in others they decrease it.},
+  language = {eng},
+  number = {2},
+  journal = {NeuroImage},
+  author = {Maris, Eric and {van Vugt}, Marieke and Kahana, Michael},
+  month = jan,
+  year = {2011},
+  keywords = {Brain,Brain Mapping,Humans,Signal Processing; Computer-Assisted,Electroencephalography},
+  pages = {836-850},
+  pmid = {20851192}
+}
+
+@article{Maris2011a,
+  title = {Spatially Distributed Patterns of Oscillatory Coupling between High-Frequency Amplitudes and Low-Frequency Phases in Human {{iEEG}}},
+  volume = {54},
+  issn = {1053-8119},
+  doi = {10.1016/j.neuroimage.2010.09.029},
+  abstract = {Spatially distributed coherent oscillations provide temporal windows of excitability that allow for interactions between distinct neuronal groups. It has been hypothesized that this mechanism for neuronal communication is realized by bursts of high-frequency oscillations that are phase-coupled to a low-frequency spatially distributed coupling oscillation. This mechanism requires multiple physiologically different interacting sources, one generating the low-frequency coupling oscillation and the others generating phase-coupled high-frequency oscillations. Using human intracranial EEG (iEEG) data, we provide evidence for multiple oscillatory patterns, as characterized on the basis of their spatial maps (topographies) and their frequency spectra. In fact, we show that the spatial maps for the coupling oscillations are much more widespread than the ones for the associated phase-coupled bursts. Second, in the majority of the patterns of phase-amplitude coupling (PAC), phase-coupled bursts of high-frequency activity are synchronized across brain areas. Third and last, working memory operations affect the PAC strength in a heterogeneous way: in some PAC patterns, working memory operations increase their strength, whereas in others they decrease it.},
+  number = {2},
+  journal = {NeuroImage},
+  author = {Maris, Eric and {van Vugt}, Marieke and Kahana, Michael},
+  month = jan,
+  year = {2011},
+  pages = {836-850},
+  file = {C:\\Users\\saram\\Zotero\\storage\\9V73UED4\\Maris et al.  2011  Spatially distributed patterns of oscillatory coupling between high-frequency amplitudes and low-frequency phases in human iEEG.pdf}
+}
+
+@article{Lundstrom2008,
+  title = {Fractional Differentiation by Neocortical Pyramidal Neurons},
+  volume = {11},
+  copyright = {\textcopyright{} 2008 Nature Publishing Group},
+  issn = {1097-6256},
+  doi = {10.1038/nn.2212},
+  abstract = {Neural systems adapt to changes in stimulus statistics. However, it is not known how stimuli with complex temporal dynamics drive the dynamics of adaptation and the resulting firing rate. For single neurons, it has often been assumed that adaptation has a single time scale. We found that single rat neocortical pyramidal neurons adapt with a time scale that depends on the time scale of changes in stimulus statistics. This multiple time scale adaptation is consistent with fractional order differentiation, such that the neuron's firing rate is a fractional derivative of slowly varying stimulus parameters. Biophysically, even though neuronal fractional differentiation effectively yields adaptation with many time scales, we found that its implementation required only a few properly balanced known adaptive mechanisms. Fractional differentiation provides single neurons with a fundamental and general computation that can contribute to efficient information processing, stimulus anticipation and frequency-independent phase shifts of oscillatory neuronal firing.},
+  language = {en},
+  number = {11},
+  journal = {Nature Neuroscience},
+  author = {Lundstrom, Brian N. and Higgs, Matthew H. and Spain, William J. and Fairhall, Adrienne L.},
+  month = nov,
+  year = {2008},
+  pages = {1335-1342},
+  file = {C:\\Users\\saram\\Zotero\\storage\\WEQ9NIZ5\\Lundstrom 2008 Fractional differentiation by neocortical pyramidal neurons.pdf}
+}
+
+@article{Teka2014,
+  title = {Neuronal {{Spike Timing Adaptation Described}} with a {{Fractional Leaky Integrate}}-and-{{Fire Model}}},
+  volume = {10},
+  issn = {1553-7358},
+  doi = {10.1371/journal.pcbi.1003526},
+  abstract = {Author Summary Spike adaptation is a property of most neurons. When spike time adaptation occurs over multiple time scales, the dynamics can be described by a power-law. We study the computational properties of a leaky integrate-and-fire model with power-law adaptation. Instead of explicitly modeling the adaptation process by the contribution of slowly changing conductances, we use a fractional temporal derivative framework. The exponent of the fractional derivative represents the degree of adaptation of the membrane voltage, where 1 is the normal leaky integrator while values less than 1 produce increasing correlations in the voltage trace. The temporal correlation is interpreted as a memory trace that depends on the value of the fractional derivative. We identify the memory trace in the fractional model as the sum of the instantaneous differentiation weighted by a function that depends on the fractional exponent, and it provides non-local information to the incoming stimulus. The spiking dynamics of the fractional leaky integrate-and-fire model show memory dependence that can result in downward or upward spike adaptation. Our model provides a framework for understanding how long-range membrane voltage correlations affect spiking dynamics and information integration in neurons.},
+  number = {3},
+  journal = {PLOS Computational Biology},
+  author = {Teka, Wondimu and Marinov, Toma M. and Santamaria, Fidel},
+  month = mar,
+  year = {2014},
+  keywords = {Neurons,Memory,Membrane potential,Neuronal differentiation,Signal filtering,Simulation and modeling,Sine waves,Action potentials},
+  pages = {e1003526},
+  file = {C:\\Users\\saram\\Zotero\\storage\\HEWUQJ39\\Teka 2014 Neuronal Spike Timing Adaptation Described with a Fractional Leaky.pdf}
+}
+
+@book{Bezdek2017,
+  address = {Sarasota, FL},
+  title = {A {{Primer}} on {{Cluster Analysis}}: 4 {{Basic Methods}} That (Usually) {{Work}}},
+  isbn = {978-1-5069-0275-3},
+  shorttitle = {A {{Primer}} on {{Cluster Analysis}}},
+  abstract = {What's in this book? This book is a step backwards, to four classical methods for clustering in small, static data sets, which have all withstood the tests of time. The youngest of the four methods is now more than 40 years old:  Gaussian Mixture Decomposition (GMD, 1898) Hard c-means (HCM, 1956, often called "k-means") Fuzzy c-means (FCM, 1973, reduces to HCM in a certain limit)  SAHN Clustering (principally single linkage (SL, 1909))~~~~~~~~  The dates shown are the first known writing (to me, anyway) about these four models. There are many different algorithms that attempt to optimize the first three models, which all define good clusters as part of extrema of optimization problems defined by their objective functions. The SAHN models are deterministic, and operate in a very different way.  The expansion of cluster analysis into every corner of our modern lives (big data, social networks, streaming video, wireless sensor networks, ... the list is endless) still rests on the foundation provided by these four models. I am (with apologies to Marvel Comics) very comfortable in calling HCM, FCM, GMD and SL the fantastic four.  As in many branches of information technology, this is a vast topic. The overall picture in clustering is quite overwhelming, so any attempt to swim at the deep end of the pool in even a very specialized subfield requires a lot of training. But we all start out at the shallow end (or at least that's where we should start!), and this book is aimed squarely at teaching toddlers not to be afraid of the water. With the exception of Chapter 10, there is not a section of this book that, if explored in real depth, cannot be expanded into its own volume. So, if your needs are for an in-depth treatment of all the latest developments in any topic in this volume, the best I can do \textendash{} what I will try to do anyway - is lead you to the pool, and show you where to jump in. If you are a graduate student, professor, or professional in computational science or engineering, you may already know more than this book contains, so it won't be very useful to you. My hope is that this volume will be useful to the real novice, who thinks that clustering might be a good thing to try, but who knows very little about it. To close this part of the introduction, I repeat a cautionary statement made by Marriott (1974) about the dangers of believing what a computer tells you about clusters your data:  ``If the results disagree with informed opinion, do not admit a simple logical interpretation, and do not show up clearly in a graphical presentation, they are probably wrong. There is no magic about numerical methods, and many ways in which they can break down. They are a valuable aid to the interpretation of data, not sausage machines automatically transforming bodies of numbers into packets of scientific fact.''  \mbox{}Jim received the PhD in Applied Mathematics from Cornell University in 1973. Jim is past president of NAFIPS (North American Fuzzy Information Processing Society), IFSA (International Fuzzy Systems Association) and the IEEE CIS (Computational Intelligence Society): founding editor the Int'l. Jo. Approximate Reasoning and the IEEE Transactions on Fuzzy Systems: Life fellow of the IEEE and IFSA; and a recipient of the IEEE 3rd Millennium, CIS Fuzzy Systems Pioneer, and technical field award Rosenblatt medals, and the IPMU Kempe de Feret Award. Jim retired in 2007, and will be coming to a university near you soon (especially if there is fishing nearby).  Keywords: Cluster Analysis, Hard Clustering, K-Means, Fuzzy Clustering, Fuzzy C-Means, Probabilistic Clustering, Gaussian Mixture Decomposition, Single Linkage Clustering, Alternating Optimization, Clustering In Big Data},
+  language = {English},
+  publisher = {{First Edition Design Publishing}},
+  author = {Bezdek, James C.},
+  month = feb,
+  year = {2017}
+}
+
+@article{Perez-Alcazar2016,
+  title = {Human {{Cerebrospinal Fluid Promotes Neuronal Viability}} and {{Activity}} of {{Hippocampal Neuronal Circuits In Vitro}}},
+  volume = {10},
+  issn = {1662-5102},
+  doi = {10.3389/fncel.2016.00054},
+  abstract = {For decades it has been hypothesized that molecules within the cerebrospinal fluid (CSF) diffuse into the brain parenchyma and influence the function of neurons. However, the functional consequences of CSF on neuronal circuits are largely unexplored and unknown. A major reason for this is the absence of appropriate neuronal in vitro model systems, and it is uncertain if neurons cultured in pure CSF survive and preserve electrophysiological functionality in vitro. In this article we present an approach to address how human CSF (hCSF) influences neuronal circuits in vitro. We validate our approach by comparing the morphology, viability, and electrophysiological function of single neurons and at the network level in rat organotypic slice and primary neuronal cultures cultivated either in hCSF or in defined standard culture media. Our results demonstrate that rodent hippocampal slices and primary neurons cultured in hCSF maintain neuronal morphology and preserve synaptic transmission. Importantly, we show that hCSF increases neuronal viability and the number of electrophysiologically active neurons in comparison to the culture media. In summary, our data indicate that hCSF represents a physiological environment for neurons in vitro and a superior culture condition compared to the defined standard media. Moreover, this experimental approach paves the way to assess the functional consequences of CSF on neuronal circuits as well as suggesting a novel strategy for CNS disease modeling.},
+  language = {English},
+  journal = {Frontiers in Cellular Neuroscience},
+  author = {Perez-Alcazar, Marta and Culley, Georgia and Lyckenvik, Tim and Mobarrez, Kristoffer and Bjorefeldt, Andreas and Wasling, Pontus and Seth, Henrik and Asztely, Frederik and Harrer, Andrea and Iglseder, Bernhard and Aigner, Ludwig and Hanse, Eric and Illes, Sebastian},
+  year = {2016},
+  keywords = {Human cerebrospinal fluid,hippocampal neuronal function,hippocampal neuronal survival,Organotypic hippocampal slice cultures,hippocampal neuronal cultures,multi-electrode array technology},
+  file = {C:\\Users\\saram\\Zotero\\storage\\VVR7AVZZ\\Perez-Alcazar et al.  2016  Human Cerebrospinal Fluid Promotes Neuronal Viability and Activity of Hippocampal Neuronal Circuits In Vitro.pdf}
+}
+
+@article{Nigam2016,
+  title = {Rich-{{Club Organization}} in {{Effective Connectivity}} among {{Cortical Neurons}}},
+  volume = {36},
+  copyright = {Copyright \textcopyright{} 2016 Nigam et al.. This article is freely available online through the J Neurosci Author Open Choice option.},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.2177-15.2016},
+  abstract = {The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 $\mu$m spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a ``rich club.'' We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory.
+SIGNIFICANCE STATEMENT Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several hundred at a time) with such high temporal resolution (so we can know the direction of communication between neurons) for mapping networks within cortex. We found that information was not transferred equally through all neurons. Instead, $\sim$70\% of the information passed through only 20\% of the neurons. Network models suggest that this highly concentrated pattern of information transfer would be both efficient and robust to damage. Therefore, this work may help in understanding how the cortex processes information and responds to neurodegenerative diseases.},
+  language = {en},
+  number = {3},
+  journal = {Journal of Neuroscience},
+  author = {Nigam, Sunny and Shimono, Masanori and Ito, Shinya and Yeh, Fang-Chin and Timme, Nicholas and Myroshnychenko, Maxym and Lapish, Christopher C. and Tosi, Zachary and Hottowy, Pawel and Smith, Wesley C. and Masmanidis, Sotiris C. and Litke, Alan M. and Sporns, Olaf and Beggs, John M.},
+  month = jan,
+  year = {2016},
+  keywords = {effective connectivity,information transfer,microcircuits,rich club,transfer entropy},
+  pages = {670-684},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TN2QXRFE\\Nigam et al.  2016  Rich-Club Organization in Effective Connectivity among Cortical Neurons.pdf},
+  pmid = {26791200}
+}
+
+@article{Obien2015,
+  title = {Revealing Neuronal Function through Microelectrode Array Recordings},
+  volume = {8},
+  issn = {1662-4548},
+  doi = {10.3389/fnins.2014.00423},
+  abstract = {Microelectrode arrays and microprobes have been widely utilized to measure neuronal activity, both in vitro and in vivo. The key advantage is the capability to record and stimulate neurons at multiple sites simultaneously. However, unlike the single-cell or single-channel resolution of intracellular recording, microelectrodes detect signals from all possible sources around every sensor. Here, we review the current understanding of microelectrode signals and the techniques for analyzing them. We introduce the ongoing advancements in microelectrode technology, with focus on achieving higher resolution and quality of recordings by means of monolithic integration with on-chip circuitry. We show how recent advanced microelectrode array measurement methods facilitate the understanding of single neurons as well as network function.},
+  journal = {Frontiers in Neuroscience},
+  author = {Obien, Marie Engelene J. and Deligkaris, Kosmas and Bullmann, Torsten and Bakkum, Douglas J. and Frey, Urs},
+  month = jan,
+  year = {2015},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TCVFWJCX\\Obien et al.  2015  Revealing neuronal function through microelectrode array recordings.pdf},
+  pmid = {25610364},
+  pmcid = {PMC4285113}
+}
+
+@article{Spira2013,
+  title = {Multi-Electrode Array Technologies for Neuroscience and Cardiology},
+  volume = {8},
+  copyright = {\textcopyright{} 2013 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1748-3387},
+  doi = {10.1038/nnano.2012.265},
+  abstract = {At present, the prime methodology for studying neuronal circuit-connectivity, physiology and pathology under in vitro or in vivo conditions is by using substrate-integrated microelectrode arrays. Although this methodology permits simultaneous, cell-non-invasive, long-term recordings of extracellular field potentials generated by action potentials, it is 'blind' to subthreshold synaptic potentials generated by single cells. On the other hand, intracellular recordings of the full electrophysiological repertoire (subthreshold synaptic potentials, membrane oscillations and action potentials) are, at present, obtained only by sharp or patch microelectrodes. These, however, are limited to single cells at a time and for short durations. Recently a number of laboratories began to merge the advantages of extracellular microelectrode arrays and intracellular microelectrodes. This Review describes the novel approaches, identifying their strengths and limitations from the point of view of the end users \textemdash{} with the intention to help steer the bioengineering efforts towards the needs of brain-circuit research.},
+  language = {en},
+  number = {2},
+  journal = {Nature Nanotechnology},
+  author = {Spira, Micha E. and Hai, Aviad},
+  month = feb,
+  year = {2013},
+  keywords = {Nanomedicine,Nanosensors},
+  pages = {83-94},
+  file = {C:\\Users\\saram\\Zotero\\storage\\326DSNS2\\Spira and Hai  2013  Multi-electrode array technologies for neuroscience and cardiology.pdf}
+}
+
+@article{Shew2010,
+  title = {Simultaneous Multi-Electrode Array Recording and Two-Photon Calcium Imaging of Neural Activity},
+  volume = {192},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2010.07.023},
+  abstract = {A complete understanding of how brain circuits function will require measurement techniques which monitor large scale network activity simultaneously with the activity of local neural populations at a small scale. Here we present a useful step towards achieving this aim: simultaneous two-photon calcium imaging and multi-electrode array (MEA) recordings. The primary challenge of this method is removing an electrical artifact from the MEA signals that is caused by the imaging laser. Here we show that artifact removal can be achieved with a simple filtering scheme. As a demonstration of this technique we compare large-scale local field potential signals to single-neuron activity in a small-scale group of cells recorded from rat acute slices under two conditions: suppressed vs. intact inhibitory interactions between neurons.},
+  number = {1},
+  journal = {Journal of neuroscience methods},
+  author = {Shew, Woodrow L. and Bellay, Timothy and Plenz, Dietmar},
+  month = sep,
+  year = {2010},
+  pages = {75-82},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PFIVY4WH\\Shew et al.  2010  Simultaneous multi-electrode array recording and two-photon calcium imaging of neural activity.pdf;C:\\Users\\saram\\Zotero\\storage\\VTSX4FJG\\tileshop.fcgi.jpg},
+  pmid = {20659501},
+  pmcid = {PMC2934901}
+}
+
+@article{Bjorefeldt2015,
+  title = {Human Cerebrospinal Fluid Increases the Excitability of Pyramidal Neurons in the in Vitro Brain Slice},
+  volume = {593},
+  issn = {1469-7793},
+  doi = {10.1113/jphysiol.2014.284711},
+  abstract = {Key points
+
+* The cerebrospinal fluid contains numerous neuromodulators at ambient levels but whether, and how, they affect the activity of central neurons is unknown.
+
+* This study provides experimental evidence that human cerebrospinal fluid (hCSF) increases the excitability of hippocampal and neocortical pyramidal neurons.
+
+* Hippocampal CA1 pyramidal neurons in hCSF displayed lowered firing thresholds, depolarized resting membrane potentials and reduced input resistance, mimicking properties of pyramidal neurons recorded in vivo.
+
+* The excitability-increasing effect of hCSF on CA1 pyramidal neurons was entirely occluded by intracellular application of GTP$\gamma$S, suggesting that neuromodulatory effects were mediated by G-protein coupled receptors.
+
+* These results indicate that the CSF promotes spontaneous excitatory neuronal activity, and may help to explain observed differences in the activity of pyramidal neurons recorded in vivo and in vitro.
+
+Abstract
+
+The composition of brain extracellular fluid is shaped by a continuous exchange of substances between the cerebrospinal fluid (CSF) and interstitial fluid. The CSF is known to contain a wide range of endogenous neuromodulatory substances, but their collective influence on neuronal activity has been poorly investigated. We show here that replacing artificial CSF (aCSF), routinely used for perfusion of brain slices in vitro, with human CSF (hCSF) powerfully boosts spontaneous firing of CA1, CA3 and layer 5 pyramidal neurons in the rat brain slice. CA1 pyramidal neurons in hCSF display lowered firing thresholds, more depolarized resting membrane potentials and reduced input resistance, mimicking properties of pyramidal neurons recorded in vivo. The increased excitability of CA1 pyramidal neurons was completely occluded by intracellular application of GTP$\gamma$S, suggesting that endogenous neuromodulators in hCSF act on G-protein coupled receptors to enhance excitability. We found no increase in spontaneous inhibitory synaptic transmission by hCSF, indicating a differential effect on glutamatergic and GABAergic neurons. Our findings highlight a previously unknown function of the CSF in promoting spontaneous excitatory activity, and may help to explain differences observed in the activity of pyramidal neurons recorded in vivo and in vitro.},
+  language = {en},
+  number = {1},
+  journal = {The Journal of Physiology},
+  author = {Bjorefeldt, Andreas and Andreasson, Ulf and Daborg, Jonny and Riebe, Ilse and Wasling, Pontus and Zetterberg, Henrik and Hanse, Eric},
+  month = jan,
+  year = {2015},
+  pages = {231-243},
+  file = {C:\\Users\\saram\\Zotero\\storage\\J97NWV4M\\Bjorefeldt et al.  2015  Human cerebrospinal fluid increases the excitability of pyramidal neurons in the in vitro brain slice.pdf}
+}
+
+@misc{zotero-524,
+  title = {Wiley: {{Pattern Classification}}, 2nd {{Edition}} - {{Richard O}}. {{Duda}}, {{Peter E}}. {{Hart}}, {{David G}}. {{Stork}}},
+  shorttitle = {Wiley},
+  howpublished = {http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471056693.html}
+}
+
+@book{Duda2012,
+  title = {Pattern Classification},
+  publisher = {{John Wiley \& Sons}},
+  author = {Duda, Richard O. and Hart, Peter E. and Stork, David G.},
+  year = {2012},
+  file = {C:\\Users\\saram\\Zotero\\storage\\RWHCSR25\\Duda et al.  2012  Pattern classification.pdf}
+}
+
+@book{Jain1988,
+  address = {Englewood Cliffs, N.J},
+  title = {Algorithms for {{Clustering Data}}},
+  isbn = {978-0-13-022278-7},
+  language = {English},
+  publisher = {{Prentice Hall College Div}},
+  author = {Jain, Anil K. and Dubes, Richard C.},
+  month = mar,
+  year = {1988},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8RU4UKH2\\Clustering_Jain_Dubes.pdf}
+}
+
+@article{Dubes1979,
+  title = {Validity Studies in Clustering Methodologies},
+  volume = {11},
+  issn = {0031-3203},
+  doi = {10.1016/0031-3203(79)90034-7},
+  abstract = {Clustering algorithms tend to generate clusters even when applied to random data. This paper provides a semi-tutorial review of the state-of-the-art in cluster validity, or the verification of results from clustering algorithms. The paper covers ways of measuring clustering tendency, the fit of hierarchical and partitional structures and indices of compactness and isolation for individual clusters. Included are structural criteria for validating clusters and the factors involved in choosing criteria, according to which the literature of cluster validity is classified. An application to speaker identification demonstrates several indices. The development of new clustering techniques and the wide availability of clustering programs necessitates vigorous research in cluster validity.},
+  number = {4},
+  journal = {Pattern Recognition},
+  author = {Dubes, Richard and Jain, Anil K},
+  month = jan,
+  year = {1979},
+  keywords = {clustering,Cluster validity,Hierarchical structure,Clustering tendency,Compactness,Isolation,Intrinsic dimensionality,Global fit},
+  pages = {235-254},
+  file = {C:\\Users\\saram\\Zotero\\storage\\F6STSPZE\\Dubes and Jain  1979  Validity studies in clustering methodologies.pdf}
+}
+
+@article{Milligan1985,
+  title = {An Examination of Procedures for Determining the Number of Clusters in a Data Set},
+  volume = {50},
+  issn = {0033-3123, 1860-0980},
+  doi = {10.1007/BF02294245},
+  abstract = {A Monte Carlo evaluation of 30 procedures for determining the number of clusters was conducted on artificial data sets which contained either 2, 3, 4, or 5 distinct nonoverlapping clusters. To provide a variety of clustering solutions, the data sets were analyzed by four hierarchical clustering methods. External criterion measures indicated excellent recovery of the true cluster structure by the methods at the correct hierarchy level. Thus, the clustering present in the data was quite strong. The simulation results for the stopping rules revealed a wide range in their ability to determine the correct number of clusters in the data. Several procedures worked fairly well, whereas others performed rather poorly. Thus, the latter group of rules would appear to have little validity, particularly for data sets containing distinct clusters. Applied researchers are urged to select one or more of the better criteria. However, users are cautioned that the performance of some of the criteria may be data dependent.},
+  language = {en},
+  number = {2},
+  journal = {Psychometrika},
+  author = {Milligan, Glenn W. and Cooper, Martha C.},
+  month = jun,
+  year = {1985},
+  pages = {159-179},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2RE3GTMB\\Milligan and Cooper  1985  An examination of procedures for determining the number of clusters in a data set.pdf}
+}
+
+@article{Gurrutxaga2011,
+  title = {Towards a Standard Methodology to Evaluate Internal Cluster Validity Indices},
+  volume = {32},
+  issn = {0167-8655},
+  doi = {10.1016/j.patrec.2010.11.006},
+  abstract = {The evaluation and comparison of internal cluster validity indices is a critical problem in the clustering area. The methodology used in most of the evaluations assumes that the clustering algorithms work correctly. We propose an alternative methodology that does not make this often false assumption. We compared 7 internal cluster validity indices with both methodologies and concluded that the results obtained with the proposed methodology are more representative of the actual capabilities of the compared indices.},
+  number = {3},
+  journal = {Pattern Recognition Letters},
+  author = {Gurrutxaga, Ibai and Muguerza, Javier and Arbelaitz, Olatz and P{\'e}rez, Jes{\'u}s M. and Mart{\'\i}n, Jos{\'e} I.},
+  month = feb,
+  year = {2011},
+  keywords = {unsupervised learning,Cluster validation,Cluster validity index},
+  pages = {505-515},
+  file = {C:\\Users\\saram\\Zotero\\storage\\A2ZMTVUM\\Gurrutxaga et al.  2011  Towards a standard methodology to evaluate internal cluster validity indices.pdf}
+}
+
+@article{Dimitriadou2002,
+  title = {An Examination of Indexes for Determining the Number of Clusters in Binary Data Sets},
+  volume = {67},
+  issn = {0033-3123, 1860-0980},
+  doi = {10.1007/BF02294713},
+  abstract = {The problem of choosing the correct number of clusters is as old as cluster analysis itself. A number of authors have suggested various indexes to facilitate this crucial decision. One of the most extensive comparative studies of indexes was conducted by Milligan and Cooper (1985). The present piece of work pursues the same goal under different conditions. In contrast to Milligan and Cooper's work, the emphasis here is on high-dimensional empirical binary data. Binary artificial data sets are constructed to reflect features typically encountered in real-world data situations in the field of marketing research. The simulation includes 162 binary data sets that are clustered by two different algorithms and lead to recommendations on the number of clusters for each index under consideration. Index results are evaluated and their performance is compared and analyzed.},
+  language = {en},
+  number = {1},
+  journal = {Psychometrika},
+  author = {Dimitriadou, Evgenia and Dolni{\v c}ar, Sara and Weingessel, Andreas},
+  month = mar,
+  year = {2002},
+  pages = {137-159},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KN5HJEZ7\\Dimitriadou et al.  2002  An examination of indexes for determining the number of clusters in binary data sets.pdf}
+}
+
+@article{Vinh2010,
+  title = {Information {{Theoretic Measures}} for {{Clusterings Comparison}}: {{Variants}}, {{Properties}}, {{Normalization}} and {{Correction}} for {{Chance}}},
+  volume = {11},
+  issn = {1532-4435},
+  shorttitle = {Information {{Theoretic Measures}} for {{Clusterings Comparison}}},
+  abstract = {Information theoretic measures form a fundamental class of measures for comparing clusterings, and have recently received increasing interest. Nevertheless, a number of questions concerning their properties and inter-relationships remain unresolved. In this paper, we perform an organized study of information theoretic measures for clustering comparison, including several existing popular measures in the literature, as well as some newly proposed ones. We discuss and prove their important properties, such as the metric property and the normalization property. We then highlight to the clustering community the importance of correcting information theoretic measures for chance, especially when the data size is small compared to the number of clusters present therein. Of the available information theoretic based measures, we advocate the normalized information distance (NID) as a general measure of choice, for it possesses concurrently several important properties, such as being both a metric and a normalized measure, admitting an exact analytical adjusted-for-chance form, and using the nominal [0,1] range better than other normalized variants.},
+  journal = {J. Mach. Learn. Res.},
+  author = {Vinh, Nguyen Xuan and Epps, Julien and Bailey, James},
+  month = dec,
+  year = {2010},
+  pages = {2837--2854},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5QBJHSAQ\\Vinh et al.  2010  Information Theoretic Measures for Clusterings Comparison Variants, Properties, Normalization and Correction for Chance.pdf}
+}
+
+@article{Arbelaitz2013,
+  title = {An Extensive Comparative Study of Cluster Validity Indices},
+  volume = {46},
+  issn = {0031-3203},
+  doi = {10.1016/j.patcog.2012.07.021},
+  abstract = {The validation of the results obtained by clustering algorithms is a fundamental part of the clustering process. The most used approaches for cluster validation are based on internal cluster validity indices. Although many indices have been proposed, there is no recent extensive comparative study of their performance. In this paper we show the results of an experimental work that compares 30 cluster validity indices in many different environments with different characteristics. These results can serve as a guideline for selecting the most suitable index for each possible application and provide a deep insight into the performance differences between the currently available indices.},
+  number = {1},
+  journal = {Pattern Recognition},
+  author = {Arbelaitz, Olatz and Gurrutxaga, Ibai and Muguerza, Javier and P{\'e}rez, Jes{\'u}s M. and Perona, I{\~n}igo},
+  month = jan,
+  year = {2013},
+  keywords = {Cluster validity index,Crisp clustering,Comparative analysis},
+  pages = {243-256},
+  file = {C:\\Users\\saram\\Zotero\\storage\\S88J8N5U\\Arbelaitz et al.  2013  An extensive comparative study of cluster validity indices.pdf}
+}
+
+@article{Dunn1973,
+  title = {A {{Fuzzy Relative}} of the {{ISODATA Process}} and {{Its Use}} in {{Detecting Compact Well}}-{{Separated Clusters}}},
+  volume = {3},
+  issn = {0022-0280},
+  doi = {10.1080/01969727308546046},
+  abstract = {Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space. In both cases, the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the least squared error criterion function. In the first case, the range of T consists largely of ordinary (i.e. non-fuzzy) partitions of X and the associated iteration scheme is essentially the well known ISODATA process of Ball and Hall. However, in the second case, the range of T consists mainly of fuzzy partitions and the associated algorithm is new; when X consists of k compact well separated (CWS) clusters, Xi , this algorithm generates a limiting partition with membership functions which closely approximate the characteristic functions of the clusters Xi . However, when X is not the union of k CWS clusters, the limiting partition is truly fuzzy in the sense that the values of its component membership functions differ substantially from 0 or 1 over certain regions of X. Thus, unlike ISODATA, the ``fuzzy'' algorithm signals the presence or absence of CWS clusters in X. Furthermore, the fuzzy algorithm seems significantly less prone to the ``cluster-splitting'' tendency of ISODATA and may also be less easily diverted to uninteresting locally optimal partitions. Finally, for data sets X consisting of dense CWS clusters embedded in a diffuse background of strays, the structure of X is accurately reflected in the limiting partition generated by the fuzzy algorithm. Mathematical arguments and numerical results are offered in support of the foregoing assertions.},
+  number = {3},
+  journal = {Journal of Cybernetics},
+  author = {Dunn, J. C.},
+  month = jan,
+  year = {1973},
+  pages = {32-57},
+  file = {C:\\Users\\saram\\Zotero\\storage\\EFXRN22J\\Dunn  1973  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters.pdf}
+}
+
+@article{Bezdek1998a,
+  title = {Some {{New Indexes}} of {{Cluster Validity}}},
+  volume = {28},
+  issn = {1083-4419},
+  doi = {10.1109/3477.678624},
+  abstract = {We review two clustering algorithms (hard c-means and single linkage) and three indexes of crisp cluster validity (Hubert's statistics, the Davies-Bouldin index, and Dunn's index). We illustrate two deficiencies of Dunn's index which make it overly sensitive to noisy clusters and propose several generalizations of it that are not as brittle to outliers in the clusters. Our numerical examples show that the standard measure of interset distance (the minimum distance between points in a pair of sets) is the worst (least reliable) measure upon which to base cluster validation indexes when the clusters are expected to form volumetric clouds. Experimental results also suggest that intercluster separation plays a more important role in cluster validation than cluster diameter. Our simulations show that while Dunn's original index has operational flaws, the concept it embodies provides a rich paradigm for validation of partitions that have cloud-like clusters. Five of our generalized Dunn's indexes provide the best validation results for the simulations presented},
+  number = {3},
+  journal = {Trans. Sys. Man Cyber. Part B},
+  author = {Bezdek, J. C. and Pal, N. R.},
+  month = jun,
+  year = {1998},
+  pages = {301--315}
+}
+
+@article{Bezdek1998,
+  title = {Some New Indexes of Cluster Validity},
+  volume = {28},
+  issn = {1083-4419},
+  doi = {10.1109/3477.678624},
+  abstract = {We review two clustering algorithms (hard c-means and single linkage) and three indexes of crisp cluster validity (Hubert's statistics, the Davies-Bouldin index, and Dunn's index). We illustrate two deficiencies of Dunn's index which make it overly sensitive to noisy clusters and propose several generalizations of it that are not as brittle to outliers in the clusters. Our numerical examples show that the standard measure of interset distance (the minimum distance between points in a pair of sets) is the worst (least reliable) measure upon which to base cluster validation indexes when the clusters are expected to form volumetric clouds. Experimental results also suggest that intercluster separation plays a more important role in cluster validation than cluster diameter. Our simulations show that while Dunn's original index has operational flaws, the concept it embodies provides a rich paradigm for validation of partitions that have cloud-like clusters. Five of our generalized Dunn's indexes provide the best validation results for the simulations presented},
+  number = {3},
+  journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
+  author = {Bezdek, J. C. and Pal, N. R.},
+  month = jun,
+  year = {1998},
+  keywords = {Clustering algorithms,Couplings,Partitioning algorithms,Cybernetics,Indexes,Cluster validity,man-machine systems,Davies-Bouldin index,Dunn's index,volumetric clouds,Clouds,Computer science,Fuzzy logic,Machine intelligence,Measurement standards,Statistics,Volume measurement},
+  pages = {301-315},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SRB2C3B6\\Bezdek_Pal 1998 Some new indexes of cluster validity.pdf}
+}
+
+@article{Morris2008,
+  title = {A {{Survey}} of {{Vision}}-{{Based Trajectory Learning}} and {{Analysis}} for {{Surveillance}}},
+  volume = {18},
+  issn = {1051-8215},
+  doi = {10.1109/TCSVT.2008.927109},
+  abstract = {This paper presents a survey of trajectory-based activity analysis for visual surveillance. It describes techniques that use trajectory data to define a general set of activities that are applicable to a wide range of scenes and environments. Events of interest are detected by building a generic topographical scene description from underlying motion structure as observed over time. The scene topology is automatically learned and is distinguished by points of interest and motion characterized by activity paths. The methods we review are intended for real-time surveillance through definition of a diverse set of events for further analysis triggering, including virtual fencing, speed profiling, behavior classification, anomaly detection, and object interaction.},
+  number = {8},
+  journal = {IEEE Transactions on Circuits and Systems for Video Technology},
+  author = {Morris, B. T. and Trivedi, M. M.},
+  month = aug,
+  year = {2008},
+  keywords = {image motion analysis,surveillance,anomaly detection,behavior classification,motion structure,object interaction,speed profiling,surveillance analysis,virtual fencing,vision-based trajectory learning,Event Detection,Motion Analysis,Situational Awareness,Statistical Learning},
+  pages = {1114-1127},
+  file = {C:\\Users\\saram\\Zotero\\storage\\7K9BGI7I\\Morris_Trivedi 2008 A Survey of Vision-Based Trajectory Learning and Analysis for Surveillance.pdf}
+}
+
+@article{Franke2012,
+  title = {High-Density Microelectrode Array Recordings and Real-Time Spike Sorting for Closed-Loop Experiments: An Emerging Technology to Study Neural Plasticity},
+  volume = {6},
+  issn = {1662-5110},
+  shorttitle = {High-Density Microelectrode Array Recordings and Real-Time Spike Sorting for Closed-Loop Experiments},
+  doi = {10.3389/fncir.2012.00105},
+  abstract = {Understanding plasticity of neural networks is a key to comprehending their development and function. A powerful technique to study neural plasticity includes recording and control of pre- and postsynaptic neural activity, e.g., by using simultaneous intracellular recording and stimulation of several neurons. Intracellular recording is, however, a demanding technique and has its limitations in that only a small number of neurons can be stimulated and recorded from at the same time. Extracellular techniques offer the possibility to simultaneously record from larger numbers of neurons with relative ease, at the expenses of increased efforts to sort out single neuronal activities from the recorded mixture, which is a time consuming and error prone step, referred to as spike sorting. In this mini-review, we describe recent technological developments in two separate fields, namely CMOS-based high-density microelectrode arrays, which also allow for extracellular stimulation of neurons, and real-time spike sorting. We argue that these techniques, when combined, will provide a powerful tool to study plasticity in neural networks consisting of several thousand neurons in vitro.},
+  language = {English},
+  journal = {Frontiers in Neural Circuits},
+  author = {Franke, Felix and J{\"a}ckel, David and Dragas, Jelena and M{\"u}ller, Jan and Radivojevic, Milos and Bakkum, Douglas and Hierlemann, Andreas},
+  year = {2012},
+  keywords = {Spike sorting,closed-loop,Real-time,multielectrode arrays,neural cultures},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8XI6NVQW\\fncir-06-00105.pdf}
+}
+
+@article{Mohan2015,
+  title = {Dendritic and {{Axonal Architecture}} of {{Individual Pyramidal Neurons}} across {{Layers}} of {{Adult Human Neocortex}}},
+  volume = {25},
+  issn = {1047-3211},
+  doi = {10.1093/cercor/bhv188},
+  number = {12},
+  journal = {Cerebral Cortex},
+  author = {Mohan, Hemanth and Verhoog, Matthijs B. and Doreswamy, Keerthi K. and Eyal, Guy and Aardse, Romy and Lodder, Brendan N. and Goriounova, Natalia A. and Asamoah, Boateng and Brakspear, B. and Clementine, A. B. and Groot, Colin and {van der Sluis}, Sophie and Testa-Silva, Guilherme and Obermayer, Joshua and Boudewijns, Zimbo S. R. M. and Narayanan, Rajeevan T. and Baayen, Johannes C. and Segev, Idan and Mansvelder, Huibert D. and Kock, De and P.j, Christiaan},
+  month = dec,
+  year = {2015},
+  pages = {4839-4853},
+  file = {C:\\Users\\saram\\Zotero\\storage\\UCZRAF4E\\Mohan et al_2015_Dendritic and Axonal Architecture of Individual Pyramidal Neurons across Layers.pdf}
+}
+
+@article{Cohen,
+  title = {Where {{Does EEG Come From}} and {{What Does It Mean}}?},
+  issn = {0166-2236},
+  doi = {10.1016/j.tins.2017.02.004},
+  abstract = {Electroencephalography (EEG) has been instrumental in making discoveries about cognition, brain function, and dysfunction. However, where do EEG signals come from and what do they mean? The purpose of this paper is to argue that we know shockingly little about the answer to this question, to highlight what we do know, how important the answers are, and how modern neuroscience technologies that allow us to measure and manipulate neural circuits with high spatiotemporal accuracy might finally bring us some answers. Neural oscillations are perhaps the best feature of EEG to use as anchors because oscillations are observed and are studied at multiple spatiotemporal scales of the brain, in multiple species, and are widely implicated in cognition and in neural computations.},
+  journal = {Trends in Neurosciences},
+  author = {Cohen, Michael X},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FD9TXSCE\\Cohen  Where Does EEG Come From and What Does It Mean.pdf}
+}
+
+@misc{zotero-577,
+  title = {Chp\%{{3A10}}.1007\%{{2F978}}-3-319-26450-9\_11.Pdf},
+  howpublished = {http://download.springer.com/static/pdf/582/chp\%253A10.1007\%252F978-3-319-26450-9\_11.pdf?originUrl=http\%3A\%2F\%2Flink.springer.com\%2Fchapter\%2F10.1007\%2F978-3-319-26450-9\_11\&token2=exp=1490130433\textasciitilde{}acl=\%2Fstatic\%2Fpdf\%2F582\%2Fchp\%25253A10.1007\%25252F978-3-319-26450-9\_11.pdf\%3ForiginUrl\%3Dhttp\%253A\%252F\%252Flink.springer.com\%252Fchapter\%252F10.1007\%252F978-3-319-26450-9\_11*\textasciitilde{}hmac=cb4eb1cd749c07822eb1e48bfb60f839f1535bd943db67a2643ec2d60dc0c0cb},
+  file = {C:\\Users\\saram\\Zotero\\storage\\3ICMQEP3\\chp%3A10.1007%2F978-3-319-26450-9_11.html}
+}
+
+@incollection{Valdes2016,
+  series = {Studies in Computational Intelligence},
+  title = {Visualization of {{Handwritten Signatures Based}} on {{Haptic Information}}},
+  copyright = {\textcopyright{}2016 Springer International Publishing Switzerland},
+  isbn = {978-3-319-26448-6 978-3-319-26450-9},
+  abstract = {The problem of user authentication is a crucial component of many solutions related to defense and security. The identification and verification of users allows the implementation of technologies and services oriented to the intended user and to prevent misuse by illegitimate users. It has become an essential part of many systems and it is used in several applications, particularly in the military. The handwritten signature is an element intrinsically endowed with specificity related to an individual and it has been used extensively as a key element in identification/authentication. Haptic technologies allow the use of additional information like kinesthetic and tactile feedback from the user, thus providing new sources of biometric information that can be incorporated within the process in addition to the traditional image-based sources. While work had been done on using haptic information for the analysis of handwritten signatures, most efforts have been oriented to the direct use of machine learning techniques for identification/verification. Comparatively fewer targeted information visualization and understanding the internal structure of the data. Here a variety of techniques are used for obtaining representations of the data in low dimensional spaces amenable to visual inspection (two and three dimensions). The approach is unsupervised, although for illustration and comparison purposes, class information is used as qualitative reference. Estimations of the intrinsic dimension for the haptic data are obtained which shows that low dimensional subspaces contains most of the data structure. Implicit and explicit mappings techniques transforming the original high dimensional data to low dimensional spaces are considered. They include linear and nonlinear, classical and computational intelligence based methods: Principal Components, Sammon mapping, Isomap, Locally Linear Embedding, Spectral Embedding, t-Distributed Stochastic Neighbour Embedding, Generative Topographic Mapping, Neuroscale and Genetic Programming. They provided insight about common and specific characteristics found in haptic signatures, their within/among subjects variability and the important role of certain types of haptic variables. The results obtained suggest ways how to design new representations for identification and verification procedures using tactile devices.},
+  language = {en},
+  number = {621},
+  booktitle = {Recent {{Advances}} in {{Computational Intelligence}} in {{Defense}} and {{Security}}},
+  publisher = {{Springer International Publishing}},
+  author = {Vald{\'e}s, Julio J. and Alsulaiman, Fawaz A. and Saddik, Abdulmotaleb El},
+  editor = {Abielmona, Rami and Falcon, Rafael and Zincir-Heywood, Nur and Abbass, Hussein A.},
+  year = {2016},
+  keywords = {dimensionality reduction,Computational Intelligence,Artificial Intelligence (incl. Robotics),Security Science and Technology,Intrinsic dimension,Manifolds,Nonlinear transformations,Implicit/explicit mappings,Haptics,User authentication},
+  pages = {277-307},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FI7DXJI4\\Valdés et al.  2016  Visualization of Handwritten Signatures Based on Haptic Information.pdf},
+  doi = {10.1007/978-3-319-26450-9_11}
+}
+
+@inproceedings{Bezdek2002,
+  title = {{{VAT}}: A Tool for Visual Assessment of (Cluster) Tendency},
+  volume = {3},
+  shorttitle = {{{VAT}}},
+  doi = {10.1109/IJCNN.2002.1007487},
+  abstract = {A method is given for visually assessing the cluster tendency of a set of Objects O = o1, . . . ,on when they are represented either as object vectors or by numerical pairwise dissimilarity values. The objects are reordered and the reordered matrix of pair wise object dissimilarities is displayed as an intensity image. Clusters are indicated by dark blocks of pixels along the diagonal},
+  booktitle = {Proceedings of the 2002 {{International Joint Conference}} on {{Neural Networks}}, 2002. {{IJCNN}} '02},
+  author = {Bezdek, J. C. and Hathaway, R. J.},
+  year = {2002},
+  keywords = {pattern clustering,Clustering algorithms,Pixel,Computer science,data analysis,data visualisation,neural nets,VAT,cluster tendency visual assessment tool,intensity image,numerical pairwise dissimilarity values,object vectors,pair wise object dissimilarities,reordered matrix,Clustering methods,Digital images,Mathematical model,Mathematics,Pattern recognition,Symmetric matrices},
+  pages = {2225-2230},
+  file = {C:\\Users\\saram\\Zotero\\storage\\XHQA2KQH\\Bezdek and Hathaway  2002  VAT a tool for visual assessment of (cluster) tendency.pdf}
+}
+
+@article{Havens2012,
+  title = {An {{Efficient Formulation}} of the {{Improved Visual Assessment}} of {{Cluster Tendency}} ({{iVAT}}) {{Algorithm}}},
+  volume = {24},
+  issn = {1041-4347},
+  doi = {10.1109/TKDE.2011.33},
+  abstract = {The VAT algorithm is a visual method for determining the possible number of clusters in, or the cluster tendency of a set of objects. The improved VAT (iVAT) algorithm uses a graph-theoretic distance transform to improve the effectiveness of the VAT algorithm for ``tough'' cases where VAT fails to accurately show the cluster tendency. In this paper, we present an efficient formulation of the iVAT algorithm which reduces the computational complexity of the iVAT algorithm from O(N3) to O(N2). We also prove a direct relationship between the VAT image and the iVAT image produced by our efficient formulation. We conclude with three examples displaying clustering tendencies in three of the Karypis data sets that illustrate the improvement offered by the iVAT transformation. We also provide a comparison of iVAT images to those produced by the Reverse Cuthill-Mckee (RCM) algorithm; our examples suggest that iVAT is superior to the RCM method of display.},
+  number = {5},
+  journal = {IEEE Transactions on Knowledge and Data Engineering},
+  author = {Havens, T. C. and Bezdek, J. C.},
+  month = may,
+  year = {2012},
+  keywords = {clustering,pattern clustering,VAT image,Algorithm design and analysis,Clustering algorithms,Couplings,Partitioning algorithms,computational complexity,graph theory,transforms,Karypis data set,O(N2) complexity,O(N3) complexity,graph-theoretic distance transform,iVAT algorithm,iVAT image,improved visual assessment of cluster tendency,reverse Cuthill-Mckee algorithm,visual method,Complexity theory,Visualization,VAT.,cluster tendency},
+  pages = {813-822},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TBZASDBN\\Havens and Bezdek  2012  An Efficient Formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm.pdf}
+}
+
+@article{Weinstein2008,
+  title = {A {{Postgenomic Visual Icon}}},
+  volume = {319},
+  copyright = {\textcopyright{} 2008 American Association for the Advancement of Science},
+  issn = {0036-8075, 1095-9203},
+  doi = {10.1126/science.1151888},
+  abstract = {A decade of experience in visualizing large-scale genotypic and phenotypic data as heat maps has illuminated the strengths and limitations of the approach.
+A decade of experience in visualizing large-scale genotypic and phenotypic data as heat maps has illuminated the strengths and limitations of the approach.
+A decade of experience in visualizing large-scale genotypic and phenotypic data as heat maps has illuminated the strengths and limitations of the approach.},
+  language = {en},
+  number = {5871},
+  journal = {Science},
+  author = {Weinstein, John N.},
+  month = mar,
+  year = {2008},
+  pages = {1772-1773},
+  file = {C:\\Users\\saram\\Zotero\\storage\\74VSCNBP\\Weinstein  2008  A Postgenomic Visual Icon.pdf},
+  pmid = {18369130}
+}
+
+@article{Wilkinson2009,
+  title = {The {{History}} of the {{Cluster Heat Map}}},
+  volume = {63},
+  issn = {0003-1305},
+  doi = {10.1198/tas.2009.0033},
+  abstract = {The cluster heat map is an ingenious display that simultaneously reveals row and column hierarchical cluster structure in a data matrix. It consists of a rectangular tiling, with each tile shaded on a color scale to represent the value of the corresponding element of the data matrix. The rows (columns) of the tiling are ordered such that similar rows (columns) are near each other. On the vertical and horizontal margins of the tiling are hierarchical cluster trees. This cluster heat map is a synthesis of several different graphic displays developed by statisticians over more than a century. We locate the earliest sources of this display in late 19th century publications, and trace a diverse 20th century statistical literature that provided a foundation for this most widely used of all bioinformatics displays.},
+  number = {2},
+  journal = {The American Statistician},
+  author = {Wilkinson, Leland and Friendly, Michael},
+  month = may,
+  year = {2009},
+  keywords = {Visualization,Cluster analysis,Heatmap,Microarray},
+  pages = {179-184},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8PAG8J4X\\Wilkinson and Friendly  2009  The History of the Cluster Heat Map.pdf}
+}
+
+@article{Prim1957,
+  title = {Shortest {{Connection Networks And Some Generalizations}}},
+  volume = {36},
+  issn = {1538-7305},
+  doi = {10.1002/j.1538-7305.1957.tb01515.x},
+  abstract = {The basic problem considered is that of interconnecting a given set of terminals with a shortest possible network of direct links. Simple and practical procedures are given for solving this problem both graphically and computationally. It develops that these procedures also provide solutions for a much broader class of problems, containing other examples of practical interest.},
+  language = {en},
+  number = {6},
+  journal = {Bell System Technical Journal},
+  author = {Prim, R. C.},
+  month = nov,
+  year = {1957},
+  pages = {1389-1401}
+}
+
+@article{Havens2009,
+  title = {Is {{VAT}} Really Single Linkage in Disguise?},
+  volume = {55},
+  issn = {1012-2443, 1573-7470},
+  doi = {10.1007/s10472-009-9157-2},
+  abstract = {This paper addresses the relationship between the Visual Assessment of cluster Tendency (VAT) algorithm and single linkage hierarchical clustering. We present an analytical comparison of the two algorithms in conjunction with numerical examples to show that VAT reordering of dissimilarity data is directly related to the clusters produced by single linkage hierarchical clustering. This analysis is important to understanding the underlying theory of VAT and, more generally, other algorithms that are based on VAT-ordered dissimilarity data.},
+  language = {en},
+  number = {3-4},
+  journal = {Annals of Mathematics and Artificial Intelligence},
+  author = {Havens, Timothy C. and Bezdek, James C. and Keller, James M. and Popescu, Mihail and Huband, Jacalyn M.},
+  month = apr,
+  year = {2009},
+  pages = {237},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4WH3PAQ8\\Havens et al.  2009  Is VAT really single linkage in disguise.pdf}
+}
+
+@inproceedings{Havens2013,
+  title = {Scalable Single Linkage Hierarchical Clustering for Big Data},
+  doi = {10.1109/ISSNIP.2013.6529823},
+  abstract = {Personal computing technologies are everywhere; hence, there are an abundance of staggeringly large data sets-the Library of Congress has stored over 160 terabytes of web data and it is estimated that Facebook alone logs nearly a petabyte of data per day. Thus, there is a pertinent need for systems by which one can elucidate the similarity and dissimilarity among and between groups in these big data sets. Clustering is one way to find these groups. In this paper, we extend the scalable Visual Assessment of Tendency (sVAT) algorithm to return single-linkage partitions of big data sets. The sVAT algorithm is designed to provide visual evidence of the number of clusters in unloadable (big) data sets. The extension we describe for sVAT enables it to also then efficiently return the data partition as indicated by the visual evidence. The computational complexity and storage requirements of sVAT are (usually) significantly less than the O(n2) requirement of the classic single-linkage hierarchical algorithm. We show that sVAT is a scalable instantiation of single-linkage clustering for data sets that contain c compact-separated clusters, where c $\ll$ n; n is the number of objects. For data sets that do not contain compact-separated clusters, we show that sVAT produces a good approximation of single-linkage partitions. Experimental results are presented for both synthetic and real data sets.},
+  booktitle = {2013 {{IEEE Eighth International Conference}} on {{Intelligent Sensors}}, {{Sensor Networks}} and {{Information Processing}}},
+  author = {Havens, T. C. and Bezdek, J. C. and Palaniswami, M.},
+  month = apr,
+  year = {2013},
+  keywords = {pattern clustering,Algorithm design and analysis,Clustering algorithms,Partitioning algorithms,Big Data,computational complexity,Visualization,personal computing,storage management,Facebook,Library of Congress,Web data,big data sets,compact-separated clusters,large data sets,personal computing technologies,sVAT algorithm,scalable single linkage hierarchical clustering,scalable visual assessment of tendency algorithm,single-linkage partitions,storage requirements,Approximation algorithms,Vectors},
+  pages = {396-401},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Q957GNBZ\\Havens et al.  2013  Scalable single linkage hierarchical clustering for big data.pdf}
+}
+
+@article{Maaten2008,
+  title = {Visualizing {{Data}} Using T-{{SNE}}},
+  volume = {9},
+  issn = {ISSN 1533-7928},
+  number = {Nov},
+  journal = {Journal of Machine Learning Research},
+  author = {van der Maaten, Laurens and Hinton, Geoffrey},
+  year = {2008},
+  pages = {2579-2605},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DVGDGTM2\\Maaten and Hinton  2008  Visualizing Data using t-SNE.pdf}
+}
+
+@article{Bartho2004,
+  title = {Characterization of Neocortical Principal Cells and Interneurons by Network Interactions and Extracellular Features},
+  volume = {92},
+  issn = {0022-3077},
+  doi = {10.1152/jn.01170.2003},
+  abstract = {Most neuronal interactions in the cortex occur within local circuits. Because principal cells and GABAergic interneurons contribute differently to cortical operations, their experimental identification and separation is of utmost important. We used 64-site two-dimensional silicon probes for high-density recording of local neurons in layer 5 of the somatosensory and prefrontal cortices of the rat. Multiple-site monitoring of units allowed for the determination of their two-dimensional spatial position in the brain. Of the approximately 60,000 cell pairs recorded, 0.2\% showed robust short-term interactions. Units with significant, short-latency ($<$3 ms) peaks following their action potentials in their cross-correlograms were characterized as putative excitatory (pyramidal) cells. Units with significant suppression of spiking of their partners were regarded as putative GABAergic interneurons. A portion of the putative interneurons was reciprocally connected with pyramidal cells. Neurons physiologically identified as inhibitory and excitatory cells were used as templates for classification of all recorded neurons. Of the several parameters tested, the duration of the unfiltered (1 Hz to 5 kHz) spike provided the most reliable clustering of the population. High-density parallel recordings of neuronal activity, determination of their physical location and their classification into pyramidal and interneuron classes provide the necessary tools for local circuit analysis.},
+  language = {eng},
+  number = {1},
+  journal = {Journal of Neurophysiology},
+  author = {Barth{\'o}, Peter and Hirase, Hajime and Monconduit, Lena{\"\i}c and Zugaro, Michael and Harris, Kenneth D. and Buzs{\'a}ki, Gy{\"o}rgy},
+  month = jul,
+  year = {2004},
+  keywords = {Interneurons,Animals,Nerve Net,Rats,Rats; Sprague-Dawley,Extracellular Fluid,Neocortex,Neural Inhibition,Action potentials},
+  pages = {600-608},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FINUX526\\Bartho, Buzsaki, 2004, Characterization of Neocortical Principal Cells and Interneurons by Network Interactions and Extracellular Features.pdf},
+  pmid = {15056678}
+}
+
+@article{VidalRuiz1985,
+  title = {Is the {{DTW}} ``Distance'' Really a Metric? {{An}} Algorithm Reducing the Number of {{DTW}} Comparisons in Isolated Word Recognition},
+  volume = {4},
+  issn = {0167-6393},
+  shorttitle = {Is the {{DTW}} ``Distance'' Really a Metric?},
+  doi = {10.1016/0167-6393(85)90058-5},
+  abstract = {In general, the inter-word dissimilarity measure supplied by Dynamic Time Warping algorithms can not be assumed to be a metric because it does not fully satisfy all the required properties (the triangle inequality in particular). In this paper, however, empirical evidence of loose satisfaction of these properties with real speech will be presented, allowing the assumption of a ``loose metric space'' structure in the set of parametric representations of words in a given vocabulary. Based on this structure, a search algorithm will be introduced which eliminates the need of computing the DTW-distance between the test word and many of the prototypes in the dictionary for Isolated Word Recognition. Experiments with vocabularies of different characteristics, have proved that the algorithm finds the nearest prototype by performing DTW computations with an average of only 30\% of the words in the dictionary. This figure has been observed to decrease with increasing dictionary size, and also if the test word is close to the corresponding prototype (``well uttered'' word). Das sich aus der Anwendung des dynamischen Zeitverzerrungsalgorithmus (DTW0 ergebende Un{\"a}hnlichkeitsma$\beta$ zwischen W{\"o}rten kann im allgemeinen nicht als Metrik betrachtet werden, da es nicht alle erforderlichen Eigenschaften besitzt (insbesondere die Eigenschaft der Dreiecksungleichung). In diesem Beitrag wird jedoch eine ann{\"a}hernde Erf{\"u}llung dieser Eigenschaften im nat{\"u}rlichen Sprachsignal empirisch nachgewiesen. Die erlaubt die Annahme einer ``losen'' metrischen Struktur innerhalb des Raumes der parametrischen Darstellungen der W{\"o}rter eines Wortschatzes f{\"u}r ein Einzelworterkennungssystem. Gest{\"u}tzt auf diese Struktur, wird ein Suchalgorithms eingef{\"u}hrt, welcher die Berechnung der DTW-Distanz zwischen dem Testwort und vielen Prototypen im Lexikon {\"u}berfl{\"u}ssig macht. Versuche mit Wortverzeichnissen unterschiedlicher Charakteristika zeigen, da$\beta$ der Algorithms den besten Prototyp schon nach Berechnung der DTW-Distanz von durchschnittlich 30\% des Gesamtinventars findet. Diese Zahl nimmit bei steigendes Lexikonumfang sowie zunehmender {\"A}hnlichkeit des Testwortes mit einem entsprechenden Prototyp (bewu$\beta$t ``korrekt'' ausgesprochenes Wort) ab. G{\'e}n{\'e}ralement, la mesure de dissimilarit{\'e} inter-mots fournie par les algorithmes de ``distorsion temporelle dynamique'' (DTW) ne peut sevir de m{\'e}trique, car elle ne satisfait pas {\`a} toutes les propri{\'e}tes requises (l'in{\'e}gralit{\'e} triangulaire en particulier). Dans cet article, cependant, l'{\'e}vidence empirique selon laquelle la parole naturelle satisfait approximativement {\`a} ces propri{\'e}t{\'e}s, est mise en lumi{\`e}re; ce fait autorise l'hypoth{\`e}se d'une structure ``d'espace m{\'e}trique vague'' dans l'ensemble des repr{\'e}sentations param{\'e}triques des mots dans un vocabulaire donn{\'e}. Bas{\'e} sur cette structure, un algorithme de recherche dans cet espace est introduit, algorithme qui en Reconnaissance de Mots Isol{\'e}s, {\'e}limine la n{\'e}cessit{\'e} de calculer la distance-DTW entre le mot-test et un grand nombre de mots prototypes du dictionnaire. Des exp{\'e}riences men{\'e}es sur des vocabulaires de caract{\'e}ristiques diff{\'e}rentes ont d{\'e}montr{\'e} que cet algorithme d{\'e}termine le prototype le plus proche en effectuant les op{\'e}rations de ``distortion temporelle dynamique'' (DTW) sur une moyenne de 30\% seulement des mots du dictionnaire. Il a {\'e}t{\'e} observ{\'e} que ce chiffre tend {\`a} d{\'e}cro{\^\i}tre avec l'accroissement de la taille du dictionnaire, et aussi dans le cas o{\`u} le mot est proche du prototype correspondant (mot ``beien prononc{\'e}'').},
+  number = {4},
+  journal = {Speech Communication},
+  author = {Vidal Ruiz, Enrique and Casacuberta Nolla, Francisco and Rulot Segovia, Hector},
+  month = dec,
+  year = {1985},
+  keywords = {Isolated word recognition,dynamic time warping,triangle inequality,metric spaces,nearest neighbours,search algorithms,computational geometry},
+  pages = {333-344},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SKTB9Z3R\\Vidal Ruiz et al.  1985  Is the DTW “distance” really a metric An algorithm reducing the number of DTW comparisons in isolated word recognition.pdf}
+}
+
+@article{Gower1969,
+  title = {Minimum {{Spanning Trees}} and {{Single Linkage Cluster Analysis}}},
+  volume = {18},
+  issn = {0035-9254},
+  doi = {10.2307/2346439},
+  abstract = {Minimum spanning trees (MST) and single linkage cluster analysis (SLCA) are explained and it is shown that all the information required for the SLCA of a set of points is contained in their MST. Known algorithms for finding the MST are discussed. They are efficient even when there are very many points; this makes a SLCA practicable when other methods of cluster analysis are not. The relevant computing procedures are published in the Algorithm section of the same issue of Applied Statistics. The use of the MST in the interpretation of vector diagrams arising in multivariate analysis is illustrated by an example.},
+  number = {1},
+  journal = {Journal of the Royal Statistical Society. Series C (Applied Statistics)},
+  author = {Gower, J. C. and Ross, G. J. S.},
+  year = {1969},
+  pages = {54-64}
+}
+
+@article{Kumar2017,
+  title = {A Visual-Numeric Approach to Clustering and Anomaly Detection for Trajectory Data},
+  volume = {33},
+  issn = {0178-2789, 1432-2315},
+  doi = {10.1007/s00371-015-1192-x},
+  abstract = {This paper proposes a novel application of Visual Assessment of Tendency (VAT)-based hierarchical clustering algorithms (VAT, iVAT, and clusiVAT) for trajectory analysis. We introduce a new clustering based anomaly detection framework named iVAT+ and clusiVAT+ and use it for trajectory anomaly detection. This approach is based on partitioning the VAT-generated Minimum Spanning Tree based on an efficient thresholding scheme. The trajectories are classified as normal or anomalous based on the number of paths in the clusters. On synthetic datasets with fixed and variable numbers of clusters and anomalies, we achieve 98 \% classification accuracy. Our two-stage clusiVAT method is applied to 26,039 trajectories of vehicles and pedestrians from a parking lot scene from the real life MIT trajectories dataset. The first stage clusters the trajectories ignoring directionality. The second stage divides the clusters obtained from the first stage by considering trajectory direction. We show that our novel two-stage clusiVAT approach can produce natural and informative trajectory clusters on this real life dataset while finding representative anomalies.},
+  language = {en},
+  number = {3},
+  journal = {The Visual Computer},
+  author = {Kumar, Dheeraj and Bezdek, James C. and Rajasegarar, Sutharshan and Leckie, Christopher and Palaniswami, Marimuthu},
+  month = mar,
+  year = {2017},
+  pages = {265-281},
+  file = {C:\\Users\\saram\\Zotero\\storage\\MV4DR2J8\\Kumar et al.  2017  A visual-numeric approach to clustering and anomaly detection for trajectory data.pdf}
+}
+
+@article{Gerstein2000,
+  title = {Cross-Correlation Measures of Unresolved Multi-Neuron Recordings},
+  volume = {100},
+  issn = {0165-0270},
+  doi = {10.1016/S0165-0270(00)00226-0},
+  abstract = {An increasing number of laboratories are studying population properties of the nervous system using data where the spike activity of more than one neuron is recorded on each electrode and where, accidentally or deliberately, these activities are not resolved into single unit spike trains. We have previously examined the consequences for measurement of cross-correlation between two such electrodes in the limited case where all individual distant (between electrode) correlations are the same and all individual close (on a single electrode) correlations are the same [Bedenbaugh, P.H., and Gerstein, G.L. (1997). Multiunit normalized cross correlation differs from the average single-unit normalized correlation. Neural Computation 9, 1265\textendash{}1275]. Here, we lift these unrealistic restrictions to allow all values of individual correlation, and examine explicitly the cases of two or three unresolved neurons on each electrode. In these situations, the cross-correlation coefficient measured between the electrodes is a linear sum of the distant correlations, divided by a non-linear function of the close correlations. We then examine in detail the case of a single direct distant correlation and take account of all relevant indirect correlations. The measured interelectrode correlation shows a reduction of this actual distant correlation by a non-linear function of the close correlations on each electrode over most of their possible values. Finally, we examine the consequences of poor waveform sorting for correlation measures; here a supposedly isolated spike train is contaminated by some fraction of the activity of another train, a situation that unfortunately is all too common in experiments. All these distortions become far more serious in the more realistic situation of dynamic firing rates and correlations. This paper is intended as a cautionary note for those who want to draw inferences about neuronal organization and/or coding or representation by using cross-correlation analysis of unresolved recordings.},
+  number = {1\textendash{}2},
+  journal = {Journal of Neuroscience Methods},
+  author = {Gerstein, George L.},
+  month = jul,
+  year = {2000},
+  keywords = {Multi-neuron,Extracellular spikes,Waveform sorting,Cross-correlation},
+  pages = {41-51},
+  file = {C:\\Users\\saram\\Zotero\\storage\\IAMTHH5S\\Gerstein  2000  Cross-correlation measures of unresolved multi-neuron recordings.pdf}
+}
+
+@article{Ventura2012,
+  title = {Accurately Estimating Neuronal Correlation Requires a New Spike-Sorting Paradigm},
+  volume = {109},
+  issn = {0027-8424},
+  doi = {10.1073/pnas.1115236109},
+  abstract = {Neurophysiology is increasingly focused on identifying coincident activity among neurons. Strong inferences about neural computation are made from the results of such studies, so it is important that these results be accurate. However, the preliminary step in the analysis of such data, the assignment of spike waveforms to individual neurons (``spike-sorting''), makes a critical assumption which undermines the analysis: that spikes, and hence neurons, are independent. We show that this assumption guarantees that coincident spiking estimates such as correlation coefficients are biased. We also show how to eliminate this bias. Our solution involves sorting spikes jointly, which contrasts with the current practice of sorting spikes independently of other spikes. This new ``ensemble sorting'' yields unbiased estimates of coincident spiking, and permits more data to be analyzed with confidence, improving the quality and quantity of neurophysiological inferences. These results should be of interest outside the context of neuronal correlations studies. Indeed, simultaneous recording of many neurons has become the rule rather than the exception in experiments, so it is essential to spike sort correctly if we are to make valid inferences about any properties of, and relationships between, neurons.},
+  number = {19},
+  journal = {Proceedings of the National Academy of Sciences of the United States of America},
+  author = {Ventura, Val{\'e}rie and Gerkin, Richard C.},
+  month = may,
+  year = {2012},
+  pages = {7230-7235},
+  file = {C:\\Users\\saram\\Zotero\\storage\\65PGE8ZS\\Ventura and Gerkin  2012  Accurately estimating neuronal correlation requires a new spike-sorting paradigm.pdf},
+  pmid = {22529350},
+  pmcid = {PMC3358902}
+}
+
+@article{Grun2009a,
+  title = {Data-{{Driven Significance Estimation}} for {{Precise Spike Correlation}}},
+  volume = {101},
+  issn = {0022-3077},
+  doi = {10.1152/jn.00093.2008},
+  abstract = {The mechanisms underlying neuronal coding and, in particular, the role of temporal spike coordination are hotly debated. However, this debate is often confounded by an implicit discussion about the use of appropriate analysis methods. To avoid incorrect interpretation of data, the analysis of simultaneous spike trains for precise spike correlation needs to be properly adjusted to the features of the experimental spike trains. In particular, nonstationarity of the firing of individual neurons in time or across trials, a spike train structure deviating from Poisson, or a co-occurrence of such features in parallel spike trains are potent generators of false positives. Problems can be avoided by including these features in the null hypothesis of the significance test. In this context, the use of surrogate data becomes increasingly important, because the complexity of the data typically prevents analytical solutions. This review provides an overview of the potential obstacles in the correlation analysis of parallel spike data and possible routes to overcome them. The discussion is illustrated at every stage of the argument by referring to a specific analysis tool (the Unitary Events method). The conclusions, however, are of a general nature and hold for other analysis techniques. Thorough testing and calibration of analysis tools and the impact of potentially erroneous preprocessing stages are emphasized.},
+  number = {3},
+  journal = {Journal of Neurophysiology},
+  author = {Gr{\"u}n, Sonja},
+  month = mar,
+  year = {2009},
+  pages = {1126-1140},
+  pmid = {19129298},
+  pmcid = {PMC2666402}
+}
+
+@article{Grun2009,
+  title = {Data-{{Driven Significance Estimation}} for {{Precise Spike Correlation}}},
+  volume = {101},
+  copyright = {Copyright \textcopyright{} 2009 the American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00093.2008},
+  abstract = {The mechanisms underlying neuronal coding and, in particular, the role of temporal spike coordination are hotly debated. However, this debate is often confounded by an implicit discussion about the use of appropriate analysis methods. To avoid incorrect interpretation of data, the analysis of simultaneous spike trains for precise spike correlation needs to be properly adjusted to the features of the experimental spike trains. In particular, nonstationarity of the firing of individual neurons in time or across trials, a spike train structure deviating from Poisson, or a co-occurrence of such features in parallel spike trains are potent generators of false positives. Problems can be avoided by including these features in the null hypothesis of the significance test. In this context, the use of surrogate data becomes increasingly important, because the complexity of the data typically prevents analytical solutions. This review provides an overview of the potential obstacles in the correlation analysis of parallel spike data and possible routes to overcome them. The discussion is illustrated at every stage of the argument by referring to a specific analysis tool (the Unitary Events method). The conclusions, however, are of a general nature and hold for other analysis techniques. Thorough testing and calibration of analysis tools and the impact of potentially erroneous preprocessing stages are emphasized.},
+  language = {en},
+  number = {3},
+  journal = {Journal of Neurophysiology},
+  author = {Gr{\"u}n, Sonja},
+  month = mar,
+  year = {2009},
+  pages = {1126-1140},
+  file = {C:\\Users\\saram\\Zotero\\storage\\GQBXIR2M\\Grün  2009  Data-Driven Significance Estimation for Precise Spike Correlation.pdf},
+  pmid = {19129298}
+}
+
+@article{Kaneko2007,
+  title = {Tracking {{Spike}}-{{Amplitude Changes}} to {{Improve}} the {{Quality}} of {{Multineuronal Data Analysis}}},
+  volume = {54},
+  issn = {0018-9294},
+  doi = {10.1109/TBME.2006.886934},
+  abstract = {During extracellular electrophysiological recording experiments, the waveform of neuronal spikes recorded from a single neuron often changes. These spike-waveform changes make single-neuron identification difficult, particularly when the activities of multiple neurons are simultaneously recorded with a multichannel microelectrode, such as a tetrode or a heptode. We have developed a tracking method of individual neurons despite their changing spike amplitudes. The method is based on a bottom-up hierarchical clustering algorithm that tracks each neuron's spike cluster during temporally overlapping clustering periods. We evaluated this method by comparing spike sorting with and without cluster tracking of an identical series of multineuronal spikes recorded from monkey area-TE neurons responding to a set of visual stimuli. According to Shannon's information theory, errors in spike-amplitude tracking reduce the expected value of the amount of information about a stimulus set that is transferred by the spike train of a cluster. In this study, cluster tracking significantly increased the expected value of the amount of information transferred by a spike train (p$<$0.01). Additionally, the stability of the stimulus preference and that of the cross-correlation between clusters improved significantly (p$<$0.000001). We conclude that cluster tracking improves the quality of multineuronal data analysis},
+  number = {2},
+  journal = {IEEE Transactions on Biomedical Engineering},
+  author = {Kaneko, H. and Tamura, H. and Suzuki, S. S.},
+  month = feb,
+  year = {2007},
+  keywords = {Neurons,Animals,cellular biophysics,medical signal processing,neurophysiology,Electrodes,Nerve Net,Electrophysiology,Microelectrodes,Algorithms,bioelectric phenomena,Spike sorting,Humans,data analysis,information theory,statistical analysis,Shannon information theory,bottom-up hierarchical clustering algorithm,cross correlation,extracellular electrophysiological recording,heptode,monkey area-TE neurons,multichannel microelectrode,multineuronal data analysis,neuronal spikes,spike train,spike-amplitude changes,spike-waveform changes,temporally overlapping clustering periods,tetrode,Biomedical engineering,Disk recording,Extracellular,microelectrode recording,single-unit recording,spike discrimination,spike sorter,vision,Diagnosis; Computer-Assisted,Discriminant Analysis,Evoked Potentials; Visual,Macaca,Pattern Recognition; Automated,Temporal Lobe,Action potentials,Electroencephalography},
+  pages = {262-272},
+  file = {C:\\Users\\saram\\Zotero\\storage\\EIFJC27D\\Kaneko et al.  2007  Tracking Spike-Amplitude Changes to Improve the Quality of Multineuronal Data Analysis.pdf}
+}
+
+@article{Cao2016,
+  title = {A Real-Time Spike Classification Method Based on Dynamic Time Warping for Extracellular Enteric Neural Recording with Large Waveform Variability},
+  volume = {261},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2015.12.006},
+  abstract = {Background
+Computationally efficient spike recognition methods are required for real-time analysis of extracellular neural recordings. The enteric nervous system (ENS) is important to human health but less well-understood with few appropriate spike recognition algorithms due to large waveform variability.
+New method
+Here we present a method based on dynamic time warping (DTW) with high tolerance to variability in time and magnitude. Adaptive temporal gridding for ``fastDTW'' in similarity calculation significantly reduces the computational cost. The automated threshold selection allows for real-time classification for extracellular recordings.
+Results
+Our method is first evaluated on synthesized data at different noise levels, improving both classification accuracy and computational complexity over the conventional cross-correlation based template-matching method (CCTM) and PCA + k-means clustering without time warping. Our method is then applied to analyze the mouse enteric neural recording with mechanical and chemical stimuli. Successful classification of biphasic and monophasic spikes is achieved even when the spike variability is larger than millisecond in width and millivolt in magnitude.
+Comparison with existing method(s)
+In comparison with conventional template matching and clustering methods, the fastDTW method is computationally efficient with high tolerance to waveform variability.
+Conclusions
+We have developed an adaptive fastDTW algorithm for real-time spike classification of ENS recording with large waveform variability against colony motility, ambient changes and cellular heterogeneity.},
+  journal = {Journal of Neuroscience Methods},
+  author = {Cao, Yingqiu and Rakhilin, Nikolai and Gordon, Philip H. and Shen, Xiling and Kan, Edwin C.},
+  month = mar,
+  year = {2016},
+  keywords = {dynamic time warping,Enteric nervous system,FET sensors,Extracellular action potentials},
+  pages = {97-109},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KHNIRJ87\\1-s2.0-S016502701500446X-main.pdf}
+}
+
+@article{Kim2007a,
+  title = {Automatic Spike Detection Based on Adaptive Template Matching for Extracellular Neural Recordings},
+  volume = {165},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2007.05.033},
+  abstract = {Recordings of extracellular neural activity are used in many clinical applications and scientific studies. In most cases, these signals are analyzed as a point process, and a spike detection algorithm is required to estimate the times at which action potentials occurred. Recordings from high-density microelectrode arrays (MEAs) and low-impedance microelectrodes often have a low signal-to-noise ratio (SNR \&lt; 10) and contain action potentials from more than one neuron. We describe a new detection algorithm based on template matching that only requires the user to specify the minimum and maximum firing rates of the neurons. The algorithm iteratively estimates the morphology of the most prominent action potentials. It is able to achieve a sensitivity of \&gt;90\% with a false positive rate of \&lt;5 Hz in recordings with an estimated SNR = 3, and it performs better than an optimal threshold detector in recordings with an estimated SNR \&gt; 2.5.},
+  number = {2},
+  journal = {Journal of Neuroscience Methods},
+  author = {Kim, Sunghan and McNames, James},
+  month = sep,
+  year = {2007},
+  keywords = {spike train,spike discrimination,Spike detection,Extracellular neuronal recordings,Action potential,Matched filter,Eigenfilter,Threshold selection},
+  pages = {165-174},
+  file = {C:\\Users\\saram\\Zotero\\storage\\VGIUJCQF\\Kim and McNames  2007  Automatic spike detection based on adaptive template matching for extracellular neural recordings.pdf}
+}
+
+@article{Quirk1999,
+  title = {Interaction between Spike Waveform Classification and Temporal Sequence Detection},
+  volume = {94},
+  issn = {0165-0270},
+  doi = {10.1016/S0165-0270(99)00124-7},
+  abstract = {In vivo extracellular recordings have allowed researchers to study the response properties of neurons to behaviorally relevant stimuli. In this paper we use multiple tetrode recordings from the hippocampus of the freely behaving rat to show that the action potential amplitude of a given cell can vary in a systematic and activity dependent manner over behaviorally relevant time scales. Since the discrimination algorithms used by experimenters to isolate cells from extracellular recordings are based on differences in waveforms, we show how these systematic changes in waveform shape can lead to non-random errors in single cell isolation. We further demonstrate that these non-random errors can lead to apparent temporal ordering effects between neurons in the absence of any specific temporal relationship. A firm understanding of these naturally occurring physiological changes is therefore critical for the evaluation of higher order phenomena such as the temporally correlated firing of ensembles of neurons.},
+  number = {1},
+  journal = {Journal of Neuroscience Methods},
+  author = {Quirk, Michael C and Wilson, Matthew A},
+  month = dec,
+  year = {1999},
+  keywords = {Spike waveform classification,Temporal sequence detection,Interaction},
+  pages = {41-52},
+  file = {C:\\Users\\saram\\Zotero\\storage\\GN7N3PHB\\Quirk and Wilson  1999  Interaction between spike waveform classification and temporal sequence detection.pdf}
+}
+
+@article{Pouzat2002,
+  title = {Using Noise Signature to Optimize Spike-Sorting and to Assess Neuronal Classification Quality},
+  volume = {122},
+  issn = {0165-0270},
+  doi = {10.1016/S0165-0270(02)00276-5},
+  abstract = {We have developed a simple and expandable procedure for classification and validation of extracellular data based on a probabilistic model of data generation. This approach relies on an empirical characterization of the recording noise. We first use this noise characterization to optimize the clustering of recorded events into putative neurons. As a second step, we use the noise model again to assess the quality of each cluster by comparing the within-cluster variability to that of the noise. This second step can be performed independently of the clustering algorithm used, and it provides the user with quantitative as well as visual tests of the quality of the classification.},
+  number = {1},
+  journal = {Journal of Neuroscience Methods},
+  author = {Pouzat, Christophe and Mazor, Ofer and Laurent, Gilles},
+  month = dec,
+  year = {2002},
+  keywords = {Multi-electrode,tetrode,Data clustering,Expectation-maximization,Antennal lobe,Locust,Sampling jitter},
+  pages = {43-57},
+  file = {C:\\Users\\saram\\Zotero\\storage\\UGC5J8AG\\Pouzat et al.  2002  Using noise signature to optimize spike-sorting and to assess neuronal classification quality.pdf}
+}
+
+@inproceedings{Sato2007,
+  title = {Fast {{Automatic Template Matching}} for {{Spike Sorting Based}} on {{Davies}}-{{Bouldin Validation Indices}}},
+  doi = {10.1109/IEMBS.2007.4353010},
+  abstract = {The present study introduces an approach to detecting and classifying the extracellular action potentials of neurons, a process usually referred to as spike sorting. Our approach is based on template matching which is an optimal filter under Gaussian noise. However, this is usually expensive in terms of computational time, and constructing appropriate templates can be also problematic. Despite its theoretical consistency, only a few algorithms have been proposed to efficiently solve this problem. To speed up the filter, it is important to curtail the matching process when the distance between the template and waveform exceeds some threshold. We approach this aspect of the problem using Davies-Bouldin validation indices (DBVIs), which are a function of the ratio of the sum of within-cluster scatter to between-cluster separation to prioritize point-by-point calculation. The templates are also constructed automatically by combining principle component analysis (PCA) and k-means clustering. This matching process performed well, with a shorter computational time and fewer incorrect classifications than other ordering methods.},
+  booktitle = {2007 29th {{Annual International Conference}} of the {{IEEE Engineering}} in {{Medicine}} and {{Biology Society}}},
+  author = {Sato, T. and Suzuki, T. and Mabuchi, K.},
+  month = aug,
+  year = {2007},
+  keywords = {Neurons,Animals,medical signal processing,neurophysiology,Rats,Algorithms,Sorting,Spike sorting,Extracellular,Diagnosis; Computer-Assisted,Pattern Recognition; Automated,Gaussian noise,pattern classification,pattern matching,Davies-Bouldin validation index,automatic template matching,k-means clustering,optimal filter,principle component analysis,Computational efficiency,Detection algorithms,Matched filters,Nonlinear filters,Scattering,Artificial Intelligence,Action potentials,principal component analysis,Electroencephalography,hippocampus},
+  pages = {3200-3203},
+  file = {C:\\Users\\saram\\Zotero\\storage\\7AAPBRJV\\Sato et al.  2007  Fast Automatic Template Matching for Spike Sorting Based on Davies-Bouldin Validation Indices.pdf}
+}
+
+@article{Franke2015c,
+  title = {Bayes Optimal Template Matching for Spike Sorting - Combining Fisher Discriminant Analysis with Optimal Filtering},
+  volume = {38},
+  issn = {1573-6873},
+  doi = {10.1007/s10827-015-0547-7},
+  abstract = {Spike sorting, i.e., the separation of the firing activity of different neurons from extracellular measurements, is a crucial but often error-prone step in the analysis of neuronal responses. Usually, three different problems have to be solved: the detection of spikes in the extracellular recordings, the estimation of the number of neurons and their prototypical (template) spike waveforms, and the assignment of individual spikes to those putative neurons. If the template spike waveforms are known, template matching can be used to solve the detection and classification problem. Here, we show that for the colored Gaussian noise case the optimal template matching is given by a form of linear filtering, which can be derived via linear discriminant analysis. This provides a Bayesian interpretation for the well-known matched filter output. Moreover, with this approach it is possible to compute a spike detection threshold analytically. The method can be implemented by a linear filter bank derived from the templates, and can be used for online spike sorting of multielectrode recordings. It may also be applicable to detection and classification problems of transient signals in general. Its application significantly decreases the error rate on two publicly available spike-sorting benchmark data sets in comparison to state-of-the-art template matching procedures. Finally, we explore the possibility to resolve overlapping spikes using the template matching outputs and show that they can be resolved with high accuracy.},
+  language = {eng},
+  number = {3},
+  journal = {Journal of Computational Neuroscience},
+  author = {Franke, Felix and Quian Quiroga, Rodrigo and Hierlemann, Andreas and Obermayer, Klaus},
+  month = jun,
+  year = {2015},
+  keywords = {Neurons,Algorithms,Models; Neurological,Reproducibility of Results,Discriminant Analysis,Bayes Theorem,Benchmarking,Neural Networks (Computer),Normal Distribution,Action potentials},
+  pages = {439-459},
+  pmid = {25652689},
+  pmcid = {PMC4420847}
+}
+
+@article{Navajas2014,
+  title = {Minimum Requirements for Accurate and Efficient Real-Time on-Chip Spike Sorting},
+  volume = {230},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2014.04.018},
+  abstract = {Background
+Extracellular recordings are performed by inserting electrodes in the brain, relaying the signals to external power-demanding devices, where spikes are detected and sorted in order to identify the firing activity of different putative neurons. A main caveat of these recordings is the necessity of wires passing through the scalp and skin in order to connect intracortical electrodes to external amplifiers. The aim of this paper is to evaluate the feasibility of an implantable platform (i.e. a chip) with the capability to wirelessly transmit the neural signals and perform real-time on-site spike sorting.
+New method
+We computationally modelled a two-stage implementation for online, robust, and efficient spike sorting. In the first stage, spikes are detected on-chip and streamed to an external computer where mean templates are created and sent back to the chip. In the second stage, spikes are sorted in real-time through template matching.
+Results
+We evaluated this procedure using realistic simulations of extracellular recordings and describe a set of specifications that optimise performance while keeping to a minimum the signal requirements and the complexity of the calculations.
+Comparison with existing methods
+A key bottleneck for the development of long-term BMIs is to find an inexpensive method for real-time spike sorting. Here, we simulated a solution to this problem that uses both offline and online processing of the data.
+Conclusions
+Hardware implementations of this method therefore enable low-power long-term wireless transmission of multiple site extracellular recordings, with application to wireless BMIs or closed-loop stimulation designs.},
+  journal = {Journal of Neuroscience Methods},
+  author = {Navajas, Joaquin and Barsakcioglu, Deren Y. and Eftekhar, Amir and Jackson, Andrew and Constandinou, Timothy G. and Quian Quiroga, Rodrigo},
+  month = jun,
+  year = {2014},
+  keywords = {extracellular recordings,Spike sorting,Real-time,BMIs,On-chip,On-line,Template matching},
+  pages = {51-64},
+  file = {C:\\Users\\saram\\Zotero\\storage\\NDI86AKI\\Navajas et al.  2014  Minimum requirements for accurate and efficient real-time on-chip spike sorting.pdf}
+}
+
+@article{Franke2015b,
+  title = {Bayes Optimal Template Matching for Spike Sorting \textendash{} Combining Fisher Discriminant Analysis with Optimal Filtering},
+  volume = {38},
+  issn = {0929-5313, 1573-6873},
+  doi = {10.1007/s10827-015-0547-7},
+  abstract = {Spike sorting, i.e., the separation of the firing activity of different neurons from extracellular measurements, is a crucial but often error-prone step in the analysis of neuronal responses. Usually, three different problems have to be solved: the detection of spikes in the extracellular recordings, the estimation of the number of neurons and their prototypical (template) spike waveforms, and the assignment of individual spikes to those putative neurons. If the template spike waveforms are known, template matching can be used to solve the detection and classification problem. Here, we show that for the colored Gaussian noise case the optimal template matching is given by a form of linear filtering, which can be derived via linear discriminant analysis. This provides a Bayesian interpretation for the well-known matched filter output. Moreover, with this approach it is possible to compute a spike detection threshold analytically. The method can be implemented by a linear filter bank derived from the templates, and can be used for online spike sorting of multielectrode recordings. It may also be applicable to detection and classification problems of transient signals in general. Its application significantly decreases the error rate on two publicly available spike-sorting benchmark data sets in comparison to state-of-the-art template matching procedures. Finally, we explore the possibility to resolve overlapping spikes using the template matching outputs and show that they can be resolved with high accuracy.},
+  language = {en},
+  number = {3},
+  journal = {Journal of Computational Neuroscience},
+  author = {Franke, Felix and Quiroga, Rodrigo Quian and Hierlemann, Andreas and Obermayer, Klaus},
+  month = jun,
+  year = {2015},
+  pages = {439-459},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QC2KJJZ8\\Franke et al.  2015  Bayes optimal template matching for spike sorting – combining fisher discriminant analysis with optimal filtering.pdf}
+}
+
+@article{Franke2015a,
+  title = {Spike Sorting of Synchronous Spikes from Local Neuron Ensembles},
+  volume = {114},
+  issn = {1522-1598},
+  doi = {10.1152/jn.00993.2014},
+  abstract = {Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons.},
+  language = {eng},
+  number = {4},
+  journal = {Journal of Neurophysiology},
+  author = {Franke, Felix and Pr{\"o}pper, Robert and Alle, Henrik and Meier, Philipp and Geiger, J{\"o}rg R. P. and Obermayer, Klaus and Munk, Matthias H. J.},
+  month = oct,
+  year = {2015},
+  keywords = {Neurons,Animals,Electric Stimulation,Spike sorting,Signal Processing; Computer-Assisted,Models; Neurological,Macaca,Computer Simulation,Template matching,Memory; Short-Term,Neuropsychological Tests,Patch-Clamp Techniques,Prefrontal Cortex,Rats; Wistar,Tissue Culture Techniques,Visual Perception,overlapping spikes,simultaneous intracellular and extracellular recordings,synchronous spikes,Action potentials,hippocampus},
+  pages = {2535-2549},
+  pmid = {26289473},
+  pmcid = {PMC4725215}
+}
+
+@article{Franke2015,
+  title = {Spike Sorting of Synchronous Spikes from Local Neuron Ensembles},
+  volume = {114},
+  copyright = {Copyright \textcopyright{} 2015 the American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00993.2014},
+  abstract = {Synchronous spike discharge of cortical neurons is thought to be a fingerprint of neuronal cooperativity. Because neighboring neurons are more densely connected to one another than neurons that are located further apart, near-synchronous spike discharge can be expected to be prevalent and it might provide an important basis for cortical computations. Using microelectrodes to record local groups of neurons does not allow for the reliable separation of synchronous spikes from different cells, because available spike sorting algorithms cannot correctly resolve the temporally overlapping waveforms. We show that high spike sorting performance of in vivo recordings, including overlapping spikes, can be achieved with a recently developed filter-based template matching procedure. Using tetrodes with a three-dimensional structure, we demonstrate with simulated data and ground truth in vitro data, obtained by dual intracellular recording of two neurons located next to a tetrode, that the spike sorting of synchronous spikes can be as successful as the spike sorting of nonoverlapping spikes and that the spatial information provided by multielectrodes greatly reduces the error rates. We apply the method to tetrode recordings from the prefrontal cortex of behaving primates, and we show that overlapping spikes can be identified and assigned to individual neurons to study synchronous activity in local groups of neurons.},
+  language = {en},
+  number = {4},
+  journal = {Journal of Neurophysiology},
+  author = {Franke, Felix and Pr{\"o}pper, Robert and Alle, Henrik and Meier, Philipp and Geiger, J{\"o}rg R. P. and Obermayer, Klaus and Munk, Matthias H. J.},
+  month = oct,
+  year = {2015},
+  pages = {2535-2549},
+  file = {C:\\Users\\saram\\Zotero\\storage\\P2IF494S\\Franke et al.  2015  Spike sorting of synchronous spikes from local neuron ensembles.pdf},
+  pmid = {26289473}
+}
+
+@article{Lefebvre,
+  title = {Recent Progress in Multi-Electrode Spike Sorting Methods},
+  issn = {0928-4257},
+  doi = {10.1016/j.jphysparis.2017.02.005},
+  abstract = {In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms.},
+  journal = {Journal of Physiology-Paris},
+  author = {Lefebvre, Baptiste and Yger, Pierre and Marre, Olivier},
+  keywords = {Multi-electrode array,Electrophysiology,Signal processing,Spike sorting,Template matching,Microelectrode array},
+  file = {C:\\Users\\saram\\Zotero\\storage\\GTUEJNZG\\Lefebvre et al.  Recent progress in multi-electrode spike sorting methods.pdf}
+}
+
+@inproceedings{Kim2004,
+  title = {Action Potential Detection with Automatic Template Matching},
+  volume = {1},
+  doi = {10.1109/IEMBS.2004.1403085},
+  abstract = {Detecting action potentials has an important role in analyzing extracellular neuronal recordings. Current algorithms require subjective tuning by a user in the form of user-specified parameters. This work describes a fully automatic template-matching spike detection algorithm that does not require any tuning. This algorithm is robust to noise and performs better than an optimum threshold detection algorithm.},
+  booktitle = {The 26th {{Annual International Conference}} of the {{IEEE Engineering}} in {{Medicine}} and {{Biology Society}}},
+  author = {Kim, S. and McNames, J. and Burchiel, K.},
+  month = sep,
+  year = {2004},
+  keywords = {medical signal processing,neurophysiology,Microelectrodes,Signal processing algorithms,Extracellular,Extracellular neuronal recordings,Action potential,Detection algorithms,bioelectric potentials,medical signal detection,action potential detection,automatic template-matching spike detection algorithm,optimum threshold detection algorithm,user-specified parameters,Background noise,Detectors,Frequency,Inspection,Libraries,Sampling methods,globus pallidus internus (GPi),microelectrode recording (MER),stereotactic deep brain stimulation (DBS) surgery,subthalamus nucleus (STN)},
+  pages = {41-44},
+  file = {C:\\Users\\saram\\Zotero\\storage\\MZQAI2DG\\Kim et al.  2004  Action potential detection with automatic template matching.pdf}
+}
+
+@article{Kim2007,
+  title = {Automatic Spike Detection Based on Adaptive Template Matching for Extracellular Neural Recordings},
+  volume = {165},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2007.05.033},
+  abstract = {Recordings of extracellular neural activity are used in many clinical applications and scientific studies. In most cases, these signals are analyzed as a point process, and a spike detection algorithm is required to estimate the times at which action potentials occurred. Recordings from high-density microelectrode arrays (MEAs) and low-impedance microelectrodes often have a low signal-to-noise ratio (SNR \&lt; 10) and contain action potentials from more than one neuron. We describe a new detection algorithm based on template matching that only requires the user to specify the minimum and maximum firing rates of the neurons. The algorithm iteratively estimates the morphology of the most prominent action potentials. It is able to achieve a sensitivity of \&gt;90\% with a false positive rate of \&lt;5 Hz in recordings with an estimated SNR = 3, and it performs better than an optimal threshold detector in recordings with an estimated SNR \&gt; 2.5.},
+  number = {2},
+  journal = {Journal of Neuroscience Methods},
+  author = {Kim, Sunghan and McNames, James},
+  month = sep,
+  year = {2007},
+  keywords = {spike train,spike discrimination,Spike detection,Extracellular neuronal recordings,Action potential,Matched filter,Eigenfilter,Threshold selection},
+  pages = {165-174},
+  file = {C:\\Users\\saram\\Zotero\\storage\\WADN3FMC\\Kim and McNames  2007  Automatic spike detection based on adaptive template matching for extracellular neural recordings.pdf}
+}
+
+@article{Rutishauser2006,
+  title = {Online Detection and Sorting of Extracellularly Recorded Action Potentials in Human Medial Temporal Lobe Recordings, in Vivo},
+  volume = {154},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2005.12.033},
+  abstract = {Understanding the function of complex cortical circuits requires the simultaneous recording of action potentials from many neurons in awake and behaving animals. Practically, this can be achieved by extracellularly recording from multiple brain sites using single wire electrodes. However, in densely packed neural structures such as the human hippocampus, a single electrode can record the activity of multiple neurons. Thus, analytic techniques that differentiate action potentials of different neurons are required. Offline spike sorting approaches are currently used to detect and sort action potentials after finishing the experiment. Because the opportunities to record from the human brain are relatively rare, it is desirable to analyze large numbers of simultaneous recordings quickly using online sorting and detection algorithms. In this way, the experiment can be optimized for the particular response properties of the recorded neurons. Here we present and evaluate a method that is capable of detecting and sorting extracellular single-wire recordings in realtime. We demonstrate the utility of the method by applying it to an extensive data set we acquired from chronically implanted depth electrodes in the hippocampus of human epilepsy patients. This dataset is particularly challenging because it was recorded in a noisy clinical environment. This method will allow the development of ``closed-loop'' experiments, which immediately adapt the experimental stimuli and/or tasks to the neural response observed.},
+  number = {1\textendash{}2},
+  journal = {Journal of Neuroscience Methods},
+  author = {Rutishauser, Ueli and Schuman, Erin M. and Mamelak, Adam N.},
+  month = jun,
+  year = {2006},
+  keywords = {Online sorting,Human hippocampus,Extracellular single-unit recordings},
+  pages = {204-224},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JWBPZZTU\\Rutishauser, 2006, Online detection and sorting of extracellularly recorded action potentials in human medical temporal lobe recordings in vivo.pdf}
+}
+
+@article{Sweeney-Reed2014,
+  title = {Corticothalamic Phase Synchrony and Cross-Frequency Coupling Predict Human Memory Formation},
+  volume = {3},
+  copyright = {\textcopyright{} 2014, Sweeney-Reed et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.},
+  issn = {2050-084X},
+  doi = {10.7554/eLife.05352},
+  abstract = {Direct electrophysiological evidence demonstrates a role for the anterior thalamic nucleus in human memory formation.},
+  language = {en},
+  journal = {eLife},
+  author = {Sweeney-Reed, Catherine M. and Zaehle, Tino and Voges, Juergen and Schmitt, Friedhelm C. and Buentjen, Lars and Kopitzki, Klaus and Esslinger, Christine and Hinrichs, Hermann and Heinze, Hans-Jochen and Knight, Robert T. and Richardson-Klavehn, Alan},
+  month = dec,
+  year = {2014},
+  keywords = {Cross-frequency coupling,Memory,thalamus,synchrony,Suhan,human},
+  pages = {e05352},
+  file = {C:\\Users\\saram\\Zotero\\storage\\E9HRRKH6\\elife-05352-fig3-figsupp2-v2.jpg;C:\\Users\\saram\\Zotero\\storage\\EV65NF28\\Sweeney-Reed 2014 Corticothalamic phase synchrony and cross-frequency coupling predict human.pdf},
+  pmid = {25535839}
+}
+
+@article{Srinivas2017,
+  title = {The {{Dendrites}} of {{CA2}} and {{CA1 Pyramidal Neurons Differentially Regulate Information Flow}} in the {{Cortico}}-{{Hippocampal Circuit}}},
+  volume = {37},
+  copyright = {Copyright \textcopyright{} 2017 the authors 0270-6474/17/373276-18\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.2219-16.2017},
+  abstract = {The impact of a given neuronal pathway depends on the number of synapses it makes with its postsynaptic target, the strength of each individual synapse, and the integrative properties of the postsynaptic dendrites. Here we explore the cellular and synaptic mechanisms responsible for the differential excitatory drive from the entorhinal cortical pathway onto mouse CA2 compared with CA1 pyramidal neurons (PNs). Although both types of neurons receive direct input from entorhinal cortex onto their distal dendrites, these inputs produce a 5- to 6-fold larger EPSP at the soma of CA2 compared with CA1 PNs, which is sufficient to drive action potential output from CA2 but not CA1. Experimental and computational approaches reveal that dendritic propagation is more efficient in CA2 than CA1 as a result of differences in dendritic morphology and dendritic expression of the hyperpolarization-activated cation current (Ih). Furthermore, there are three times as many cortical inputs onto CA2 compared with CA1 PN distal dendrites. Using a computational model, we demonstrate that the differences in dendritic properties of CA2 compared with CA1 PNs are necessary to enable the CA2 PNs to generate their characteristically large EPSPs in response to their cortical inputs; in contrast, CA1 dendritic properties limit the size of the EPSPs they generate, even to a similar number of cortical inputs. Thus, the matching of dendritic integrative properties with the density of innervation is crucial for the differential processing of information from the direct cortical inputs by CA2 compared with CA1 PNs.
+SIGNIFICANCE STATEMENT Recent discoveries have shown that the long-neglected hippocampal CA2 region has distinct synaptic properties and plays a prominent role in social memory and schizophrenia. This study addresses the puzzling finding that the direct entorhinal cortical inputs to hippocampus, which target the very distal pyramidal neuron dendrites, provide an unusually strong excitatory drive at the soma of CA2 pyramidal neurons, with EPSPs that are 5\textendash{}6 times larger than those in CA1 pyramidal neurons. We here elucidate synaptic and dendritic mechanisms that account quantitatively for the marked difference in EPSP size. Our findings further demonstrate the general importance of fine-tuning the integrative properties of neuronal dendrites to their density of synaptic innervation.},
+  language = {en},
+  number = {12},
+  journal = {Journal of Neuroscience},
+  author = {Srinivas, Kalyan V. and Buss, Eric W. and Sun, Qian and Santoro, Bina and Takahashi, Hiroto and Nicholson, Daniel A. and Siegelbaum, Steven A.},
+  month = mar,
+  year = {2017},
+  keywords = {CA2 pyramidal neuron,dendrites,glutamate uncaging,passive properties,two-photon imaging},
+  pages = {3276-3293},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QHTGSJUF\\Srinivas et al.  2017  The Dendrites of CA2 and CA1 Pyramidal Neurons Differentially Regulate Information Flow in the Cortico-Hippocampal Circuit.pdf},
+  pmid = {28213444}
+}
+
+@inproceedings{Wachman2009,
+  title = {Kernels for {{Periodic Time Series Arising}} in {{Astronomy}}},
+  doi = {10.1007/978-3-642-04174-7_32},
+  abstract = {We present a method for applying machine learning algorithms to the automatic classification of astronomy star surveys using time series of star brightness. Currently such classification requires a large amount of domain expert time. We show that a combination of phase invariant similarity and explicit features extracted from the time series provide domain expert level classification. To facilitate this application, we investigate the cross-correlation as a general phase invariant similarity function for time series. We establish several theoretical properties of cross-correlation showing that it is intuitively appealing and algorithmically tractable, but not positive semidefinite, and therefore not generally applicable with kernel methods. As a solution we introduce a positive semidefinite similarity function with the same intuitive appeal as cross-correlation. An experimental evaluation in the astronomy domain as well as several other data sets demonstrates the performance of the kernel and related similarity functions.},
+  language = {en},
+  booktitle = {Machine {{Learning}} and {{Knowledge Discovery}} in {{Databases}}},
+  publisher = {{Springer, Berlin, Heidelberg}},
+  author = {Wachman, Gabriel and Khardon, Roni and Protopapas, Pavlos and Alcock, Charles R.},
+  month = sep,
+  year = {2009},
+  pages = {489-505},
+  file = {C:\\Users\\saram\\Zotero\\storage\\WTCDPP49\\Wachman et al.  2009  Kernels for Periodic Time Series Arising in Astronomy.pdf}
+}
+
+@article{Maris2011,
+  title = {Spatially Distributed Patterns of Oscillatory Coupling between High-Frequency Amplitudes and Low-Frequency Phases in Human {{iEEG}}},
+  volume = {54},
+  issn = {1053-8119},
+  doi = {10.1016/j.neuroimage.2010.09.029},
+  abstract = {Spatially distributed coherent oscillations provide temporal windows of excitability that allow for interactions between distinct neuronal groups. It has been hypothesized that this mechanism for neuronal communication is realized by bursts of high-frequency oscillations that are phase-coupled to a low-frequency spatially distributed coupling oscillation. This mechanism requires multiple physiologically different interacting sources, one generating the low-frequency coupling oscillation and the others generating phase-coupled high-frequency oscillations. Using human intracranial EEG (iEEG) data, we provide evidence for multiple oscillatory patterns, as characterized on the basis of their spatial maps (topographies) and their frequency spectra. In fact, we show that the spatial maps for the coupling oscillations are much more widespread than the ones for the associated phase-coupled bursts. Second, in the majority of the patterns of phase-amplitude coupling (PAC), phase-coupled bursts of high-frequency activity are synchronized across brain areas. Third and last, working memory operations affect the PAC strength in a heterogeneous way: in some PAC patterns, working memory operations increase their strength, whereas in others they decrease it.},
+  number = {2},
+  journal = {NeuroImage},
+  author = {Maris, Eric and {van Vugt}, Marieke and Kahana, Michael},
+  month = jan,
+  year = {2011},
+  pages = {836-850},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QSDZSK36\\Maris et al.  2011  Spatially distributed patterns of oscillatory coupling between high-frequency amplitudes and low-frequency phases in human iEEG.pdf}
+}
+
+@article{Albouy2017,
+  title = {Selective {{Entrainment}} of {{Theta Oscillations}} in the {{Dorsal Stream Causally Enhances Auditory Working Memory Performance}}},
+  volume = {94},
+  issn = {0896-6273},
+  doi = {10.1016/j.neuron.2017.03.015},
+  abstract = {Summary
+The implication of the dorsal stream in manipulating auditory information in working memory has been recently established. However, the oscillatory dynamics within this network and its causal relationship with behavior remain undefined. Using simultaneous MEG/EEG, we show that theta oscillations in the dorsal stream predict participants' manipulation abilities during memory retention in a task requiring the comparison of two patterns differing in temporal order. We investigated the causal relationship between brain oscillations and behavior by applying theta-rhythmic TMS combined with EEG over the MEG-identified target (left intraparietal sulcus) during the silent interval between the two stimuli. Rhythmic TMS entrained theta oscillation and boosted participants' accuracy. TMS-induced oscillatory entrainment scaled with behavioral enhancement, and both gains varied with participants' baseline abilities. These effects were not seen for a melody-comparison control task and were not observed for arrhythmic TMS. These data establish theta activity in the dorsal stream as causally related to memory manipulation.
+Video Abstract},
+  number = {1},
+  journal = {Neuron},
+  author = {Albouy, Philippe and Weiss, Aur{\'e}lien and Baillet, Sylvain and Zatorre, Robert J.},
+  month = apr,
+  year = {2017},
+  keywords = {EEG,Memory,behavior,MEG,theta oscillation,brain networks,auditory processing,rhythmic transcranial magnetic stimulation},
+  pages = {193-206.e5},
+  file = {C:\\Users\\saram\\Zotero\\storage\\7DTTEMB7\\JCApril2017.pdf;C:\\Users\\saram\\Zotero\\storage\\BNAZHT4J\\mmc2.pdf;C:\\Users\\saram\\Zotero\\storage\\BXV3TCVD\\Albouy_et_al_2017.xlsx;C:\\Users\\saram\\Zotero\\storage\\FBT647H6\\mmc1.pdf;C:\\Users\\saram\\Zotero\\storage\\X7765HEK\\mmc3.mp4}
+}
+
+@article{Graebenitz2017,
+  title = {Directional Spread of Activity in Synaptic Networks of the Human Lateral Amygdala},
+  volume = {349},
+  issn = {0306-4522},
+  doi = {10.1016/j.neuroscience.2017.03.009},
+  abstract = {Spontaneous epileptiform activity has previously been observed in lateral amygdala (LA) slices derived from patients with intractable-temporal lobe epilepsy. The present study aimed to characterize intranuclear LA synaptic connectivity and to test the hypothesis that differences in the spread of flow of neuronal activity may relate to spontaneous epileptiform activity occurrence.
+Electrical activity was evoked through electrical microstimulation in acute human brain slices containing the LA, signals were recorded as local field potentials combined with fast optical imaging of voltage-sensitive dye fluorescence. Sites of stimulation and recording were systematically varied. Following recordings, slices were anatomically reconstructed using two-dimensional unitary slices as a reference for coronal and parasagittal planes. Local spatial patterns and spread of activity were assessed by incorporating the coordinates of electrical and optical recording sites into the respective unitary slice.
+A preferential directional spread of evoked electrical signals was observed from ventral to dorsal, rostral to caudal and medial to lateral regions in the LA. No differences in spread of evoked activity were observed between spontaneously and non-spontaneously active LA slices, i.e. basic properties of evoked synaptic responses were similar in the two functional types of LA slices, including input\textendash{}output relationship, and paired-pulse depression.
+These results indicate a directed propagation of synaptic signals within the human LA in spontaneously active epileptic slices. We suggest that the lack of differences in local and in systemic information processing has to be found in confined epileptiform circuits within the amygdala likely involving well-known ``epileptic neurons''.},
+  journal = {Neuroscience},
+  author = {Graebenitz, St{\'e}phanie and Cerina, Manuela and Lesting, J{\"o}rg and Kedo, Olga and Gorji, Ali and Pannek, Heinz and Hans, Volkmar and Zilles, Karl and Pape, Hans-Christian and Speckmann, Erwin-Josef},
+  month = may,
+  year = {2017},
+  keywords = {field potentials,lateral amygdala,living human brain slices,temporal lobe epilepsy,voltage-sensitive dyes},
+  pages = {330-340},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5VG4GETI\\Graebenitz et al_2017_Directional spread of activity in synaptic networks of the human lateral.pdf}
+}
+
+@article{Graebenitz2011,
+  title = {Interictal-like Network Activity and Receptor Expression in the Epileptic Human Lateral Amygdala},
+  volume = {134},
+  issn = {0006-8950},
+  doi = {10.1093/brain/awr202},
+  number = {10},
+  journal = {Brain},
+  author = {Graebenitz, St{\'e}phanie and Kedo, Olga and Speckmann, Erwin-Josef and Gorji, Ali and Panneck, Heinz and Hans, Volkmar and Palomero-Gallagher, Nicola and Schleicher, Axel and Zilles, Karl and Pape, Hans-Christian},
+  month = oct,
+  year = {2011},
+  pages = {2929-2947},
+  file = {C:\\Users\\saram\\Zotero\\storage\\G2JDB6S8\\Graebenitz et al_2011_Interictal-like network activity and receptor expression in the epileptic human.pdf}
+}
+
+@article{Chen2016,
+  title = {Membrane Potential Resonance Frequency Directly Influences Network Frequency through Electrical Coupling},
+  volume = {116},
+  copyright = {Copyright \textcopyright{} 2016 the American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00361.2016},
+  abstract = {Oscillatory networks often include neurons with membrane potential resonance, exhibiting a peak in the voltage amplitude as a function of current input at a nonzero (resonance) frequency (fres). Although fres has been correlated to the network frequency (fnet) in a variety of systems, a causal relationship between the two has not been established. We examine the hypothesis that combinations of biophysical parameters that shift fres, without changing other attributes of the impedance profile, also shift fnet in the same direction. We test this hypothesis, computationally and experimentally, in an electrically coupled network consisting of intrinsic oscillator (O) and resonator (R) neurons. We use a two-cell model of such a network to show that increasing fres of R directly increases fnet and that this effect becomes more prominent if the amplitude of resonance is increased. Notably, the effect of fres on fnet is independent of the parameters that define the oscillator or the combination of parameters in R that produce the shift in fres, as long as this combination produces the same impedance vs. frequency relationship. We use the dynamic clamp technique to experimentally verify the model predictions by connecting a model resonator to the pacemaker pyloric dilator neurons of the crab Cancer borealis pyloric network using electrical synapses and show that the pyloric network frequency can be shifted by changing fres in the resonator. Our results provide compelling evidence that fres and resonance amplitude strongly influence fnet, and therefore, modulators may target these attributes to modify rhythmic activity.},
+  language = {en},
+  number = {4},
+  journal = {Journal of Neurophysiology},
+  author = {Chen, Yinbo and Li, Xinping and Rotstein, Horacio G. and Nadim, Farzan},
+  month = oct,
+  year = {2016},
+  pages = {1554-1563},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TNXZ2F26\\Chen et al.  2016  Membrane potential resonance frequency directly influences network frequency through electrical coupling.pdf},
+  pmid = {27385799}
+}
+
+@article{Ventura2009,
+  title = {Traditional Waveform Based Spike Sorting Yields Biased Rate Code Estimates},
+  volume = {106},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.0901771106},
+  abstract = {Much of neuroscience has to do with relating neural activity and behavior or environment. One common measure of this relationship is the firing rates of neurons as functions of behavioral or environmental parameters, often called tuning functions and receptive fields. Firing rates are estimated from the spike trains of neurons recorded by electrodes implanted in the brain. Individual neurons' spike trains are not typically readily available, because the signal collected at an electrode is often a mixture of activities from different neurons and noise. Extracting individual neurons' spike trains from voltage signals, which is known as spike sorting, is one of the most important data analysis problems in neuroscience, because it has to be undertaken prior to any analysis of neurophysiological data in which more than one neuron is believed to be recorded on a single electrode. All current spike-sorting methods consist of clustering the characteristic spike waveforms of neurons. The sequence of first spike sorting based on waveforms, then estimating tuning functions, has long been the accepted way to proceed. Here, we argue that the covariates that modulate tuning functions also contain information about spike identities, and that if tuning information is ignored for spike sorting, the resulting tuning function estimates are biased and inconsistent, unless spikes can be classified with perfect accuracy. This means, for example, that the commonly used peristimulus time histogram is a biased estimate of the firing rate of a neuron that is not perfectly isolated. We further argue that the correct conceptual way to view the problem out is to note that spike sorting provides information about rate estimation and vice versa, so that the two relationships should be considered simultaneously rather than sequentially. Indeed we show that when spike sorting and tuning-curve estimation are performed in parallel, unbiased estimates of tuning curves can be recovered even from imperfectly sorted neurons.},
+  language = {en},
+  number = {17},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Ventura, Val{\'e}rie},
+  month = apr,
+  year = {2009},
+  keywords = {clustering,Encoding,inconsistent,tuning information,tuning properties},
+  pages = {6921-6926},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SDEE4IMI\\PNAS-2009-Ventura-6921-6.pdf},
+  pmid = {19372379}
+}
+
+@article{Ventura2009a,
+  title = {Automatic {{Spike Sorting Using Tuning Information}}},
+  volume = {21},
+  issn = {0899-7667},
+  doi = {10.1162/neco.2009.12-07-669},
+  abstract = {Current spike sorting methods focus on clustering neurons' characteristic spike waveforms. The resulting spike-sorted data are typically used to estimate how covariates of interest modulate the firing rates of neurons. However, when these covariates do modulate the firing rates, they provide information about spikes' identities, which thus far have been ignored for the purpose of spike sorting. This letter describes a novel approach to spike sorting, which incorporates both waveform information and tuning information obtained from the modulation of firing rates. Because it efficiently uses all the available information, this spike sorter yields lower spike misclassification rates than traditional automatic spike sorters. This theoretical result is verified empirically on several examples. The proposed method does not require additional assumptions; only its implementation is different. It essentially consists of performing spike sorting and tuning estimation simultaneously rather than sequentially, as is currently done. We used an expectation-maximization maximum likelihood algorithm to implement the new spike sorter. We present the general form of this algorithm and provide a detailed implementable version under the assumptions that neurons are independent and spike according to Poisson processes. Finally, we uncover a systematic flaw of spike sorting based on waveform information only.},
+  number = {9},
+  journal = {Neural computation},
+  author = {Ventura, Val{\'e}rie},
+  month = sep,
+  year = {2009},
+  pages = {2466-2501},
+  file = {C:\\Users\\saram\\Zotero\\storage\\P5Z8B797\\Ventura_2009_Automatic Spike Sorting Using Tuning Information.pdf},
+  pmid = {19548802},
+  pmcid = {PMC4167425}
+}
+
+@misc{zotero-720,
+  title = {Frontiers | {{Modeling}} Realistic Extracellular Recordings of Neuronal Populations for the Purpose of Evaluating Automatic Spike-Sorting Algorithms},
+  abstract = {Simultaneous recordings of thousands of extracellular spikes are now possible, using silicon-based recording devices with large numbers of electrode contacts, such as high-density multi-electrode arrays (MEA) or multi-shank laminar electrodes. With the associated increase of data amounts and complexity in such recordings, manual spike sorting is not viable. Hence, there is a dire need for validated automated spike sorting methods, able to correctly resolve spikes of single neurons, as recently reviewed [1]. \\
+ \\
+Automated spike sorting methods should ideally be validated against test data with known ground truth, where spiking activity of all neurons in the neuronal population is known. Such details of the underlying activity can only to some extent be acquired experimentally. One remedy is model-based simulation of extracellular recordings, as electrode position-dependent spike shapes (Figure 1a) can conveniently be modeled in a biophysically realistic way using a recently released simulation tool, LFPy (http://compneuro.umb.no/LFPy). LFPy implements a forward modeling scheme for extracellular potentials [2] in Python integrated with NEURON [3]. \\
+ \\
+Test data for arbitrary electrode layouts, neuron models, noise content and spike time correlations can be produced at wish, and test data mimicking tetrode and polytrode recordings in cortex and hippocampus with realistic noise features will be presented. Additionally, finite element methods (FEM) are employed to generate test data for cases where significant effects from inhomogeneous extracellular media are present, as in recordings from cell cultures or retinal slice recordings using MEAs. \\
+In order to facilitate usage of benchmark test data for evaluating spike sorting algorithms (Figure 1b), an algorithm evaluation website has been set up on http://www.g-node.org/spike \\
+ \\
+Acknowledgements: \\
+Supported by the Research Council of Norway (NevroNor, eScience, Notur), NIH (CRCNS) and INCF (G-Node, Norwegian Node). \\
+ \\
+References: \\
+1. Einevoll GT, Franke F, Hagen E, Pouzat C, Harris KD: Towards reliable spike-train recordings from thousands of neurons with multielectrodes. Curr Opin Neurobiol 2011, 22:1\textendash{}7. DOI:10.1016/j.conb.2011.10.001 \\
+2. Holt GR, Koch C. Electrical interactions via the extracellular potential near cell bodies. J Comp Neurosci 1999, 6:169-184. DOI:10.1023/A:1008832702585 \\
+3. Hines ML, Davison AP, Muller E: Neuron and Python. Front Neuroinformatics 2009, 3:1-12. DOI:10.3389/neuro.11.001.2009},
+  howpublished = {http://www.frontiersin.org/10.3389/conf.fninf.2014.08.00016/event\_abstract}
+}
+
+@article{Vinci2016,
+  title = {Separating {{Spike Count Correlation}} from {{Firing Rate Correlation}}},
+  volume = {28},
+  doi = {10.1162/NECO_a_00831},
+  number = {5},
+  journal = {Neural Computation},
+  author = {Vinci, Giuseppe and Ventura, Val{\'e}rie and Smith, Matthew A. and Kass, Robert E.},
+  month = may,
+  year = {2016},
+  pages = {849-881},
+  file = {C:\\Users\\saram\\Zotero\\storage\\NBX3BVSC\\Vinci et al_2016_Separating Spike Count Correlation from Firing Rate Correlation.pdf}
+}
+
+@article{Valerie2008,
+  title = {Spike {{Train Decoding Without Spike Sorting}}},
+  volume = {20},
+  issn = {0899-7667},
+  doi = {10.1162/neco.2008.02-07-478},
+  abstract = {We propose a novel paradigm for spike train decoding, which avoids entirely spike sorting based on waveform measurements. This paradigm directly uses the spike train collected at recording electrodes from thresholding the bandpassed voltage signal. Our approach is a paradigm, not an algorithm, since it can be used with any of the current decoding algorithms, such as population vector or likelihood-based algorithms. Based on analytical results and an extensive simulation study, we show that our paradigm is comparable to, and sometimes more efficient than, the traditional approach based on well-isolated neurons and that it remains efficient even when all electrodes are severely corrupted by noise, a situation that would render spike sorting particularly difficult. Our paradigm will also save time and computational effort, both of which are crucially important for successful operation of real-time brain-machine interfaces. Indeed, in place of the lengthy spike-sorting task of the traditional approach, it involves an exact expectation EM algorithm that is fast enough that it could also be left to run during decoding to capture potential slow changes in the states of the neurons.},
+  number = {4},
+  journal = {Neural computation},
+  author = {Val{\'e}rie, Ventura},
+  month = apr,
+  year = {2008},
+  pages = {923-963},
+  file = {C:\\Users\\saram\\Zotero\\storage\\59E2EB57\\SS.pdf;C:\\Users\\saram\\Zotero\\storage\\82C4QTJP\\neco.2008.02-07-478.pdf;C:\\Users\\saram\\Zotero\\storage\\JUMQ3676\\Valérie_2008_Spike Train Decoding Without Spike Sorting.pdf},
+  pmid = {18085990},
+  pmcid = {PMC3124143}
+}
+
+@article{Martinez2009,
+  title = {Realistic Simulation of Extracellular Recordings},
+  volume = {184},
+  issn = {01650270},
+  doi = {10.1016/j.jneumeth.2009.08.017},
+  abstract = {Abstract In this paper we present an efficient method to generate realistic simulations of extracellular recordings. The method uses a hybrid and computationally simple approach, where the features of the background noise arise naturally from its biophysical process of generation. The generated data resemble the characteristics of real recordings, as quantified by the amplitude and frequency distributions. Moreover, we reproduce real features that are specially challenging for the analysis of extracellular data, such as the presence of sparse firing neurons and multi-unit activity. We compare our simulations with real recordings from the human medial temporal lobe and exemplify their use for testing spike detection and sorting algorithms. These results show that this technique provides an optimal scenario for generating realistic simulations of extracellular recordings.},
+  number = {2},
+  journal = {Journal of Neuroscience Methods},
+  author = {Martinez, Juan and Pedreira, Carlos and Ison, Matias J. and Quian Quiroga, Rodrigo},
+  year = {2009},
+  keywords = {extracellular recordings,Spike sorting,Spike detection,Simulations},
+  pages = {285-293},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8UQRFGPE\\6132448390865293191.doc;C:\\Users\\saram\\Zotero\\storage\\Z5QZ456B\\2009_-_Juan_Martinez_-_Realisticsimulationofextracellularrecordings[retrieved_2017-04-18].pdf}
+}
+
+@article{Amarasingham2015,
+  title = {Ambiguity and Nonidentifiability in the Statistical Analysis of Neural Codes},
+  volume = {112},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.1506400112},
+  abstract = {Many experimental studies of neural coding rely on a statistical interpretation of the theoretical notion of the rate at which a neuron fires spikes. For example, neuroscientists often ask, ``Does a population of neurons exhibit more synchronous spiking than one would expect from the covariability of their instantaneous firing rates?'' For another example, ``How much of a neuron's observed spiking variability is caused by the variability of its instantaneous firing rate, and how much is caused by spike timing variability?'' However, a neuron's theoretical firing rate is not necessarily well-defined. Consequently, neuroscientific questions involving the theoretical firing rate do not have a meaning in isolation but can only be interpreted in light of additional statistical modeling choices. Ignoring this ambiguity can lead to inconsistent reasoning or wayward conclusions. We illustrate these issues with examples drawn from the neural-coding literature.},
+  language = {en},
+  number = {20},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Amarasingham, Asohan and Geman, Stuart and Harrison, Matthew T.},
+  month = may,
+  year = {2015},
+  keywords = {doubly stochastic,spike count variability,spike timing,temporal coding,trial-to-trial variability},
+  pages = {6455-6460},
+  file = {C:\\Users\\saram\\Zotero\\storage\\UV5W4ADT\\PNAS-2015-Amarasingham-6455-60.pdf},
+  pmid = {25934918}
+}
+
+@article{Kass2006,
+  title = {Spike {{Count Correlation Increases}} with {{Length}} of {{Time Interval}} in the {{Presence}} of {{Trial}}-to-{{Trial Variation}}},
+  volume = {18},
+  issn = {0899-7667},
+  doi = {10.1162/neco.2006.18.11.2583},
+  number = {11},
+  journal = {Neural Computation},
+  author = {Kass, Robert E. and Ventura, Val{\'e}rie},
+  month = sep,
+  year = {2006},
+  pages = {2583-2591},
+  file = {C:\\Users\\saram\\Zotero\\storage\\RIJJTZRJ\\neco%2E2006%2E18%2E11%2E2583.pdf;C:\\Users\\saram\\Zotero\\storage\\TSRECPPQ\\Kass_Ventura_2006_Spike Count Correlation Increases with Length of Time Interval in the Presence.pdf}
+}
+
+@article{Ventura2005,
+  title = {Trial-to-{{Trial Variability}} and {{Its Effect}} on {{Time}}-{{Varying Dependency Between Two Neurons}}},
+  volume = {94},
+  copyright = {Copyright \textcopyright{} 2005 by the American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00644.2004},
+  abstract = {The joint peristimulus time histogram (JPSTH) and cross-correlogram provide a visual representation of correlated activity for a pair of neurons, and the way this activity may increase or decrease over time. In a companion paper we showed how a Bootstrap evaluation of the peaks in the smoothed diagonals of the JPSTH may be used to establish the likely validity of apparent time-varying correlation. As noted in earlier studies by Brody and Ben-Shaul et al., trial-to-trial variation can confound correlation and synchrony effects. In this paper we elaborate on that observation, and present a method of estimating the time-dependent trial-to-trial variation in spike trains that may exceed the natural variation displayed by Poisson and non-Poisson point processes. The statistical problem is somewhat subtle because relatively few spikes per trial are available for estimating a firing-rate function that fluctuates over time. The method developed here decomposes the spike-train variability into a stimulus-related component and a trial-specific component, allowing many degrees of freedom to characterize the former while assuming a small number suffices to characterize the latter. The Bootstrap significance test of the companion paper is then modified to accommodate these general excitability effects. This methodology allows an investigator to assess whether excitability effects are constant or time-varying, and whether they are shared by two neurons. In data from two V1 neurons we find that highly statistically significant evidence of dependency disappears after adjustment for time-varying trial-to-trial variation.},
+  language = {en},
+  number = {4},
+  journal = {Journal of Neurophysiology},
+  author = {Ventura, Val{\'e}rie and Cai, Can and Kass, Robert E.},
+  month = oct,
+  year = {2005},
+  pages = {2928-2939},
+  file = {C:\\Users\\saram\\Zotero\\storage\\G8MSFZEF\\Ventura et al_2005_Trial-to-Trial Variability and Its Effect on Time-Varying Dependency Between.pdf},
+  pmid = {16160096}
+}
+
+@article{Kass2005,
+  title = {Statistical {{Issues}} in the {{Analysis}} of {{Neuronal Data}}},
+  volume = {94},
+  copyright = {Copyright \textcopyright{} 2005 by the American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00648.2004},
+  abstract = {Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews well-established statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data.},
+  language = {en},
+  number = {1},
+  journal = {Journal of Neurophysiology},
+  author = {Kass, Robert E. and Ventura, Val{\'e}rie and Brown, Emery N.},
+  month = jul,
+  year = {2005},
+  pages = {8-25},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DFZ4EZ83\\Kass et al_2005_Statistical Issues in the Analysis of Neuronal Data.pdf},
+  pmid = {15985692}
+}
+
+@article{Todorova2014,
+  title = {To Sort or Not to Sort: The Impact of Spike-Sorting on Neural Decoding Performance},
+  volume = {11},
+  issn = {1741-2560},
+  shorttitle = {To Sort or Not to Sort},
+  doi = {10.1088/1741-2560/11/5/056005},
+  abstract = {Objective
+Brain-computer interfaces (BCIs) are a promising technology for restoring motor ability to paralyzed patients: spiking-based BCIs have successfully been used in clinical trials to control multi-degree-of-freedom robotic devices. Current implementations of these devices require a lengthy spike-sorting step, which is an obstacle to moving this technology from the lab to the clinic. A viable alternative is to avoid spike-sorting, treating all threshold crossings of the voltage waveform on an electrode as coming from one putative neuron. It is not known, however, how much decoding information might be lost by ignoring spike identity.
+
+Approach
+We present a full analysis of the effects of spike-sorting schemes on decoding performance. Specifically, we compare how well two common decoders, the optimal linear estimator and the Kalman filter, reconstruct the arm movements of non-human primates performing reaching tasks, when receiving input from various sorting schemes. The schemes we tested included: using threshold crossings without spike-sorting; expertsorting discarding the noise; expert-sorting, including the noise as if it were another neuron; and automatic spike-sorting using waveform features. We also decoded from a joint statistical model for the waveforms and tuning curves, which does not involve an explicit spike-sorting step.
+
+Main results
+Discarding the threshold crossings that cannot be assigned to neurons degrades decoding: no spikes should be discarded. Decoding based on spike-sorted units outperforms decoding based on electrodes voltage crossings: spike-sorting is useful. The four waveform based spike-sorting methods tested here yield similar decoding efficiencies: a fast and simple method is competitive. Decoding using the joint waveform and tuning model shows promise but is not consistently superior.
+
+Significance
+Our results indicate that simple automated spikesorting performs as well as computationally or manually more intensive methods, which is crucial for clinical implementation of BCIs.},
+  number = {5},
+  journal = {Journal of neural engineering},
+  author = {Todorova, Sonia and Sadtler, Patrick and Batista, Aaron and Chase, Steven and Ventura, Val{\'e}rie},
+  month = oct,
+  year = {2014},
+  pages = {056005},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JJEZFIKV\\Todorova et al_2014_To sort or not to sort.pdf},
+  pmid = {25082508},
+  pmcid = {PMC4454741}
+}
+
+@misc{zotero-735,
+  title = {Frontiers | {{Modeling}} Realistic Extracellular Recordings of Neuronal Populations for the Purpose of Evaluating Automatic Spike-Sorting Algorithms},
+  abstract = {Simultaneous recordings of thousands of extracellular spikes are now possible, using silicon-based recording devices with large numbers of electrode contacts, such as high-density multi-electrode arrays (MEA) or multi-shank laminar electrodes. With the associated increase of data amounts and complexity in such recordings, manual spike sorting is not viable. Hence, there is a dire need for validated automated spike sorting methods, able to correctly resolve spikes of single neurons, as recently reviewed [1]. \\
+ \\
+Automated spike sorting methods should ideally be validated against test data with known ground truth, where spiking activity of all neurons in the neuronal population is known. Such details of the underlying activity can only to some extent be acquired experimentally. One remedy is model-based simulation of extracellular recordings, as electrode position-dependent spike shapes (Figure 1a) can conveniently be modeled in a biophysically realistic way using a recently released simulation tool, LFPy (http://compneuro.umb.no/LFPy). LFPy implements a forward modeling scheme for extracellular potentials [2] in Python integrated with NEURON [3]. \\
+ \\
+Test data for arbitrary electrode layouts, neuron models, noise content and spike time correlations can be produced at wish, and test data mimicking tetrode and polytrode recordings in cortex and hippocampus with realistic noise features will be presented. Additionally, finite element methods (FEM) are employed to generate test data for cases where significant effects from inhomogeneous extracellular media are present, as in recordings from cell cultures or retinal slice recordings using MEAs. \\
+In order to facilitate usage of benchmark test data for evaluating spike sorting algorithms (Figure 1b), an algorithm evaluation website has been set up on http://www.g-node.org/spike \\
+ \\
+Acknowledgements: \\
+Supported by the Research Council of Norway (NevroNor, eScience, Notur), NIH (CRCNS) and INCF (G-Node, Norwegian Node). \\
+ \\
+References: \\
+1. Einevoll GT, Franke F, Hagen E, Pouzat C, Harris KD: Towards reliable spike-train recordings from thousands of neurons with multielectrodes. Curr Opin Neurobiol 2011, 22:1\textendash{}7. DOI:10.1016/j.conb.2011.10.001 \\
+2. Holt GR, Koch C. Electrical interactions via the extracellular potential near cell bodies. J Comp Neurosci 1999, 6:169-184. DOI:10.1023/A:1008832702585 \\
+3. Hines ML, Davison AP, Muller E: Neuron and Python. Front Neuroinformatics 2009, 3:1-12. DOI:10.3389/neuro.11.001.2009},
+  howpublished = {http://www.frontiersin.org/10.3389/conf.fninf.2014.08.00016/event\_abstract}
+}
+
+@article{Davies1979,
+  title = {A Cluster Separation Measure},
+  volume = {1},
+  issn = {0162-8828},
+  abstract = {A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm.},
+  language = {eng},
+  number = {2},
+  journal = {IEEE transactions on pattern analysis and machine intelligence},
+  author = {Davies, D. L. and Bouldin, D. W.},
+  month = feb,
+  year = {1979},
+  pages = {224-227},
+  pmid = {21868852}
+}
+
+@article{Hagen2017,
+  title = {Focal Local Field Potential ({{LFP}}) Signature of the Single-Axon Monosynaptic Thalamocortical Connection},
+  copyright = {Copyright \textcopyright{} 2017 Hagen et al.. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.2715-16.2017},
+  abstract = {Recent years have seen a resurgence in use of the extracellularly recorded local field potential (LFP) to investigate neural network activity. To probe monosynaptic thalamic activation of cortical postsynaptic target cells, so called spike-trigger-averaged LFP (stLFP) signatures have been measured. In these experiments the cortical LFP is measured by means of multielectrodes covering several cortical lamina and averaged on spontaneous spikes of thalamocortical (TC) cells. Using a well-established forward-modeling scheme, we investigate the biophysical origin of this stLFP signature with simultaneous synaptic activation of cortical layer 4 neurons, mimicking the effect of a single afferent spike from a single TC neuron. Constrained by previously measured intracellular responses of the main postsynaptic target cell types and with biologically plausible assumptions regarding the spatial distribution of thalamic synaptic inputs into layer 4, the model predicted characteristic contributions to monosynaptic stLFP signatures both for the regular spiking (RS) excitatory neurons and the fast spiking (FS) inhibitory interneurons. In particular, the FS cells generated stLFP signatures of shorter temporal duration than the RS cells. Added together, a sum of the stLFP signatures of these two principal synaptic targets of thalamocortical cells were observed to resemble experimentally measured stLFP signatures. Outside the volume targeted by TC afferents the resulting postsynaptic LFP signals were found to be sharply attenuated. This implies that such stLFP signatures provide a very local measure of thalamocortical synaptic activation, and that newly developed inverse CSD-estimation methods are needed for precise assessment of the underlying spatiotemporal current-source density (CSD) profiles.
+SIGNIFICANCE STATEMENT
+Despite its long history and prevalent use, the proper interpretation of the extracellularly recorded local field potential (LFP) is still not fully established. Here we investigate by means of biophysical modeling the origin of the focal LFP signature of the single-axon monosynaptic thalamocortical connection as measured by spike-trigger-averaging of cortical LFPs on spontaneous spikes of thalamocortical neurons. We find that this LFP signature is well accounted for by a model assuming thalamic projections to two cortical layer-4 cell populations: one excitatory (putatively regular-spiking cells) and one inhibitory (putatively fast-spiking cells). The LFP signature is observed to decay sharply outside the cortical region receiving the thalamocortical projection, implying that it indeed provides a very local measure of thalamocortical synaptic activation.},
+  language = {en},
+  journal = {Journal of Neuroscience},
+  author = {Hagen, Espen and Fossum, Janne C. and Pettersen, Klas H. and Alonso, Jose-Manuel and Swadlow, Harvey A. and Einevolla, Gaute T.},
+  month = apr,
+  year = {2017},
+  pages = {2715-16},
+  file = {C:\\Users\\saram\\Zotero\\storage\\N2JE8RQD\\Hagen et al_2017_Focal local field potential (LFP) signature of the single-axon monosynaptic.pdf},
+  pmid = {28432143}
+}
+
+@article{Niediek2016,
+  title = {Reliable {{Analysis}} of {{Single}}-{{Unit Recordings}} from the {{Human Brain}} under {{Noisy Conditions}}: {{Tracking Neurons}} over {{Hours}}},
+  volume = {11},
+  shorttitle = {Reliable {{Analysis}} of {{Single}}-{{Unit Recordings}} from the {{Human Brain}} under {{Noisy Conditions}}},
+  number = {12},
+  journal = {PloS one},
+  author = {Niediek, Johannes and Bostr{\"o}m, Jan and Elger, Christian E. and Mormann, Florian},
+  year = {2016},
+  pages = {e0166598},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2NU98GMQ\\journal.pone.0166598.s003.pdf;C:\\Users\\saram\\Zotero\\storage\\98W7F47B\\journal.pone.0166598.s004.pdf;C:\\Users\\saram\\Zotero\\storage\\GXGBQ729\\Niediek 2016-Reliable Analysis of Single-Unit Recordings from the Human Brain under Noisy Conditions - Tracking Neurons over Hours.pdf;C:\\Users\\saram\\Zotero\\storage\\ITSUMZ6Z\\journal.pone.0166598.s002.pdf;C:\\Users\\saram\\Zotero\\storage\\J7263GQ9\\journal.pone.0166598.s001.pdf}
+}
+
+@article{Kreuz2013b,
+  title = {Monitoring Spike Train Synchrony},
+  volume = {109},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00873.2012},
+  language = {en},
+  number = {5},
+  journal = {Journal of Neurophysiology},
+  author = {Kreuz, T. and Chicharro, D. and Houghton, C. and Andrzejak, R. G. and Mormann, F.},
+  month = mar,
+  year = {2013},
+  pages = {1457-1472},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ASICRF2N\\Kreuz 2013 - Monitoring spike train synchrony.pdf}
+}
+
+@article{Pedreira2012,
+  title = {How Many Neurons Can We See with Current Spike Sorting Algorithms?},
+  volume = {211},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2012.07.010},
+  abstract = {Recent studies highlighted the disagreement between the typical number of neurons observed with extracellular recordings and the ones to be expected based on anatomical and physiological considerations. This disagreement has been mainly attributed to the presence of sparsely firing neurons. However, it is also possible that this is due to limitations of the spike sorting algorithms used to process the data. To address this issue, we used realistic simulations of extracellular recordings and found a relatively poor spike sorting performance for simulations containing a large number of neurons. In fact, the number of correctly identified neurons for single-channel recordings showed an asymptotic behavior saturating at about 8\textendash{}10 units, when up to 20 units were present in the data. This performance was significantly poorer for neurons with low firing rates, as these units were twice more likely to be missed than the ones with high firing rates in simulations containing many neurons. These results uncover one of the main reasons for the relatively low number of neurons found in extracellular recording and also stress the importance of further developments of spike sorting algorithms.},
+  number = {1},
+  journal = {Journal of Neuroscience Methods},
+  author = {Pedreira, Carlos and Martinez, Juan and Ison, Matias J. and Quian Quiroga, Rodrigo},
+  year = {2012},
+  keywords = {extracellular recordings,Sparse neurons,Spike-sorting},
+  pages = {58-65},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PFTDEXGT\\Pedreira et al_2012_How many neurons can we see with current spike sorting algorithms.pdf}
+}
+
+@article{Knieling2015,
+  title = {An {{Unsupervised Online Spike}}-{{Sorting Framework}}},
+  volume = {26},
+  issn = {0129-0657},
+  doi = {10.1142/S0129065715500422},
+  abstract = {Extracellular neuronal microelectrode recordings can include action potentials from multiple neurons. To separate spikes from different neurons, they can be sorted according to their shape, a procedure referred to as spike-sorting. Several algorithms have been reported to solve this task. However, when clustering outcomes are unsatisfactory, most of them are difficult to adjust to achieve the desired results. We present an online spike-sorting framework that uses feature normalization and weighting to maximize the distinctiveness between different spike shapes. Furthermore, multiple criteria are applied to either facilitate or prevent cluster fusion, thereby enabling experimenters to fine-tune the sorting process. We compare our method to established unsupervised offline (Wave\_Clus (WC)) and online (OSort (OS)) algorithms by examining their performance in sorting various test datasets using two different scoring systems (AMI and the Adamos metric). Furthermore, we evaluate sorting capabilities on intra-operative recordings using established quality metrics. Compared to WC and OS, our algorithm achieved comparable or higher scores on average and produced more convincing sorting results for intra-operative datasets. Thus, the presented framework is suitable for both online and offline analysis and could substantially improve the quality of microelectrode-based data evaluation for research and clinical application.},
+  number = {05},
+  journal = {International Journal of Neural Systems},
+  author = {Knieling, Simeon and Sridharan, Kousik S. and Belardinelli, Paolo and Naros, Georgios and Weiss, Daniel and Mormann, Florian and Gharabaghi, Alireza},
+  year = {2015},
+  pages = {1550042},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Z7DSA93E\\Knieling et al_2015_An Unsupervised Online Spike-Sorting Framework.pdf}
+}
+
+@article{Rey2015,
+  series = {Advances in electrophysiological data analysis},
+  title = {Past, Present and Future of Spike Sorting Techniques},
+  volume = {119, Part B},
+  issn = {0361-9230},
+  doi = {10.1016/j.brainresbull.2015.04.007},
+  abstract = {Spike sorting is a crucial step to extract information from extracellular recordings. With new recording opportunities provided by the development of new electrodes that allow monitoring hundreds of neurons simultaneously, the scenario for the new generation of algorithms is both exciting and challenging. However, this will require a new approach to the problem and the development of a common reference framework to quickly assess the performance of new algorithms. In this work, we review the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms. We conclude by proposing a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future.},
+  journal = {Brain Research Bulletin},
+  author = {Rey, Hernan Gonzalo and Pedreira, Carlos and Quian Quiroga, Rodrigo},
+  year = {2015},
+  keywords = {extracellular recordings,Spike sorting,Modeling,Multielectrode recordings,On-chip applications},
+  pages = {106-117},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8IXEBD52\\Rey et al_2015_Past, present and future of spike sorting techniques.pdf}
+}
+
+@article{Friedman2015a,
+  title = {A Multistage Mathematical Approach to Automated Clustering of High-Dimensional Noisy Data},
+  volume = {112},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.1503940112},
+  abstract = {A critical problem faced in many scientific fields is the adequate separation of data derived from individual sources. Often, such datasets require analysis of multiple features in a highly multidimensional space, with overlap of features and sources. The datasets generated by simultaneous recording from hundreds of neurons emitting phasic action potentials have produced the challenge of separating the recorded signals into independent data subsets (clusters) corresponding to individual signal-generating neurons. Mathematical methods have been developed over the past three decades to achieve such spike clustering, but a complete solution with fully automated cluster identification has not been achieved. We propose here a fully automated mathematical approach that identifies clusters in multidimensional space through recursion, which combats the multidimensionality of the data. Recursion is paired with an approach to dimensional evaluation, in which each dimension of a dataset is examined for its informational importance for clustering. The dimensions offering greater informational importance are given added weight during recursive clustering. To combat strong background activity, our algorithm takes an iterative approach of data filtering according to a signal-to-noise ratio metric. The algorithm finds cluster cores, which are thereafter expanded to include complete clusters. This mathematical approach can be extended from its prototype context of spike sorting to other datasets that suffer from high dimensionality and background activity.},
+  language = {en},
+  number = {14},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Friedman, Alexander and Keselman, Michael D. and Gibb, Leif G. and Graybiel, Ann M.},
+  month = jul,
+  year = {2015},
+  keywords = {Spike sorting,curse of dimensionality,dimension reduction,dimensional evaluation,dimensional selection},
+  pages = {4477-4482},
+  file = {C:\\Users\\saram\\Zotero\\storage\\43QBZSF3\\Friedman et al_2015_A multistage mathematical approach to automated clustering of high-dimensional.pdf},
+  pmid = {25831512}
+}
+
+@article{Einevoll2012,
+  series = {Neurotechnology},
+  title = {Towards Reliable Spike-Train Recordings from Thousands of Neurons with Multielectrodes},
+  volume = {22},
+  issn = {0959-4388},
+  doi = {10.1016/j.conb.2011.10.001},
+  abstract = {The new generation of silicon-based multielectrodes comprising hundreds or more electrode contacts offers unprecedented possibilities for simultaneous recordings of spike trains from thousands of neurons. Such data will not only be invaluable for finding out how neural networks in the brain work, but will likely be important also for neural prosthesis applications. This opportunity can only be realized if efficient, accurate and validated methods for automatic spike sorting are provided. In this review we describe some of the challenges that must be met to achieve this goal, and in particular argue for the critical need of realistic model data to be used as ground truth in the validation of spike-sorting algorithms.},
+  number = {1},
+  journal = {Current Opinion in Neurobiology},
+  author = {Einevoll, Gaute T and Franke, Felix and Hagen, Espen and Pouzat, Christophe and Harris, Kenneth D},
+  month = feb,
+  year = {2012},
+  pages = {11-17},
+  file = {C:\\Users\\saram\\Zotero\\storage\\736BPGES\\Einevoll et al.  2012  Towards reliable spike-train recordings from thousands of neurons with multielectrodes.pdf}
+}
+
+@article{Henze2000,
+  title = {Intracellular {{Features Predicted}} by {{Extracellular Recordings}} in the {{Hippocampus In Vivo}}},
+  volume = {84},
+  copyright = {Copyright \textcopyright{} 2000 The American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  abstract = {Multichannel tetrode array recording in awake behaving animals provides a powerful method to record the activity of large numbers of neurons. The power of this method could be extended if further information concerning the intracellular state of the neurons could be extracted from the extracellularly recorded signals. Toward this end, we have simultaneously recorded intracellular and extracellular signals from hippocampal CA1 pyramidal cells and interneurons in the anesthetized rat. We found that several intracellular parameters can be deduced from extracellular spike waveforms. The width of the intracellular action potential is defined precisely by distinct points on the extracellular spike. Amplitude changes of the intracellular action potential are reflected by changes in the amplitude of the initial negative phase of the extracellular spike, and these amplitude changes are dependent on the state of the network. In addition, intracellular recordings from dendrites with simultaneous extracellular recordings from the soma indicate that, on average, action potentials are initiated in the perisomatic region and propagate to the dendrites at 1.68 m/s. Finally we determined that a tetrode in hippocampal area CA1 theoretically should be able to record electrical signals from $\sim$1,000 neurons. Of these, 60\textendash{}100 neurons should generate spikes of sufficient amplitude to be detectable from the noise and to allow for their separation using current spatial clustering methods. This theoretical maximum is in contrast to the approximately six units that are usually detected per tetrode. From this, we conclude that a large percentage of hippocampal CA1 pyramidal cells are silent in any given behavioral condition.},
+  language = {en},
+  number = {1},
+  journal = {Journal of Neurophysiology},
+  author = {Henze, Darrell A. and Borhegyi, Zsolt and Csicsvari, Jozsef and Mamiya, Akira and Harris, Kenneth D. and Buzs{\'a}ki, Gy{\"o}rgy},
+  month = jul,
+  year = {2000},
+  keywords = {MEA},
+  pages = {390-400},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ATWSSPX8\\Henze, Buzsaki, 2000, Intracellular features predicted by extracellular recordings in the hippocampus InVivo.pdf},
+  pmid = {10899213}
+}
+
+@article{Friedman2015,
+  title = {A Multistage Mathematical Approach to Automated Clustering of High-Dimensional Noisy Data},
+  volume = {112},
+  number = {14},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Friedman, Alexander and Keselman, Michael D. and Gibb, Leif G. and Graybiel, Ann M.},
+  year = {2015},
+  pages = {4477--4482},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5222EGTF\\Friedman, 2015, A multistage mathematical approach to automated clustering of high dimensional noisy data- SM.pdf;C:\\Users\\saram\\Zotero\\storage\\VIA8E5EU\\Friedman, 2015, A multistage mathematical approach to automated clustering of high dimensional noisy data.pdf}
+}
+
+@article{Harris2000,
+  title = {Accuracy of {{Tetrode Spike Separation}} as {{Determined}} by {{Simultaneous Intracellular}} and {{Extracellular Measurements}}},
+  volume = {84},
+  copyright = {Copyright \textcopyright{} 2000 The American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  abstract = {Simultaneous recording from large numbers of neurons is a prerequisite for understanding their cooperative behavior. Various recording techniques and spike separation methods are being used toward this goal. However, the error rates involved in spike separation have not yet been quantified. We studied the separation reliability of ``tetrode'' (4-wire electrode)-recorded spikes by monitoring simultaneously from the same cell intracellularly with a glass pipette and extracellularly with a tetrode. With manual spike sorting, we found a trade-off between Type I and Type II errors, with errors typically ranging from 0 to 30\% depending on the amplitude and firing pattern of the cell, the similarity of the waveshapes of neighboring neurons, and the experience of the operator. Performance using only a single wire was markedly lower, indicating the advantages of multiple-site monitoring techniques over single-wire recordings. For tetrode recordings, error rates were increased by burst activity and during periods of cellular synchrony. The lowest possible separation error rates were estimated by a search for the best ellipsoidal cluster shape. Human operator performance was significantly below the estimated optimum. Investigation of error distributions indicated that suboptimal performance was caused by inability of the operators to mark cluster boundaries accurately in a high-dimensional feature space. We therefore hypothesized that automatic spike-sorting algorithms have the potential to significantly lower error rates. Implementation of a semi-automatic classification system confirms this suggestion, reducing errors close to the estimated optimum, in the range 0\textendash{}8\%.},
+  language = {en},
+  number = {1},
+  journal = {Journal of Neurophysiology},
+  author = {Harris, Kenneth D. and Henze, Darrell A. and Csicsvari, Jozsef and Hirase, Hajime and Buzs{\'a}ki, Gy{\"o}rgy},
+  month = jul,
+  year = {2000},
+  pages = {401-414},
+  file = {C:\\Users\\saram\\Zotero\\storage\\G99J9GEK\\Harris et al.  2000  Accuracy of Tetrode Spike Separation as Determined by Simultaneous Intracellular and Extracellular Measurements.pdf},
+  pmid = {10899214}
+}
+
+@article{Lebedev2017a,
+  title = {Brain-{{Machine Interfaces}}: {{From Basic Science}} to {{Neuroprostheses}} and {{Neurorehabilitation}}},
+  volume = {97},
+  copyright = {Copyright \textcopyright{} 2017 the American Physiological Society},
+  issn = {0031-9333, 1522-1210},
+  shorttitle = {Brain-{{Machine Interfaces}}},
+  doi = {10.1152/physrev.00027.2016},
+  abstract = {Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.},
+  language = {en},
+  number = {2},
+  journal = {Physiological Reviews},
+  author = {Lebedev, Mikhail A. and Nicolelis, Miguel A. L.},
+  month = apr,
+  year = {2017},
+  pages = {767-837},
+  pmid = {28275048}
+}
+
+@article{Hartmann2016,
+  title = {Embedding a {{Panoramic Representation}} of {{Infrared Light}} in the {{Adult Rat Somatosensory Cortex}} through a {{Sensory Neuroprosthesis}}},
+  volume = {36},
+  copyright = {Copyright \textcopyright{} 2016 the authors 0270-6474/16/362407-19\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.3285-15.2016},
+  abstract = {Can the adult brain assimilate a novel, topographically organized, sensory modality into its perceptual repertoire? To test this, we implemented a microstimulation-based neuroprosthesis that rats used to discriminate among infrared (IR) light sources. This system continuously relayed information from four IR sensors that were distributed to provide a panoramic view of IR sources, into primary somatosensory cortex (S1). Rats learned to discriminate the location of IR sources in $<$4 d. Animals in which IR information was delivered in spatial register with whisker topography learned the task more quickly. Further, in animals that had learned to use the prosthesis, altering the topographic mapping from IR sensor to stimulating electrode had immediate deleterious effects on discrimination performance. Multielectrode recordings revealed that S1 neurons had multimodal (tactile/IR) receptive fields, with clear preferences for those stimuli most likely to be delivered during the task. Neuronal populations predicted, with high accuracy, which stimulation pattern was present in small (75 ms) time windows. Surprisingly, when identical microstimulation patterns were delivered during an unrelated task, cortical activity in S1 was strongly suppressed. Overall, these results show that the adult mammalian neocortex can readily absorb completely new information sources into its representational repertoire, and use this information in the production of adaptive behaviors.
+SIGNIFICANCE STATEMENT Understanding the potential for plasticity in the adult brain is a key goal for basic neuroscience and modern rehabilitative medicine. Our study examines one dimension of this challenge: how malleable is sensory processing in adult mammals? We implemented a panoramic infrared (IR) sensory prosthetic system in rats; it consisted of four IR sensors equally spaced around the circumference of the head of the rat. Each sensor was coupled to a microstimulating electrode placed in the somatosensory cortex of the rat. Within days, rats learned to use the prosthesis to track down items associated with IR light in their environment. This is quite promising clinically, as the largest demand for sensory prosthetic devices is in adults whose brains are already fully developed.},
+  language = {en},
+  number = {8},
+  journal = {Journal of Neuroscience},
+  author = {Hartmann, Konstantin and Thomson, Eric E. and Zea, Ivan and Yun, Richy and Mullen, Peter and Canarick, Jay and Huh, Albert and Nicolelis, Miguel A. L.},
+  month = feb,
+  year = {2016},
+  keywords = {Rat,barrel cortex,sensory prosthetic,whisker system},
+  pages = {2406-2424},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Z68ECG7V\\Hartmann et al_2016_Embedding a Panoramic Representation of Infrared Light in the Adult Rat.pdf},
+  pmid = {26911689}
+}
+
+@article{Lebedev2017,
+  title = {Brain-{{Machine Interfaces}}: {{From Basic Science}} to {{Neuroprostheses}} and {{Neurorehabilitation}}},
+  volume = {97},
+  copyright = {Copyright \textcopyright{} 2017 the American Physiological Society},
+  issn = {0031-9333, 1522-1210},
+  shorttitle = {Brain-{{Machine Interfaces}}},
+  doi = {10.1152/physrev.00027.2016},
+  abstract = {Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.},
+  language = {en},
+  number = {2},
+  journal = {Physiological Reviews},
+  author = {Lebedev, Mikhail A. and Nicolelis, Miguel A. L.},
+  month = apr,
+  year = {2017},
+  pages = {767-837},
+  pmid = {28275048}
+}
+
+@article{Pais-Vieira2016,
+  title = {A {{Closed Loop Brain}}-Machine {{Interface}} for {{Epilepsy Control Using Dorsal Column Electrical Stimulation}}},
+  volume = {6},
+  issn = {2045-2322},
+  doi = {10.1038/srep32814},
+  abstract = {Although electrical neurostimulation has been proposed as an alternative treatment for drug-resistant cases of epilepsy, current procedures such as deep brain stimulation, vagus, and trigeminal nerve stimulation are effective only in a fraction of the patients. Here we demonstrate a closed loop brain-machine interface that delivers electrical stimulation to the dorsal column (DCS) of the spinal cord to suppress epileptic seizures. Rats were implanted with cortical recording microelectrodes and spinal cord stimulating electrodes, and then injected with pentylenetetrazole to induce seizures. Seizures were detected in real time from cortical local field potentials, after which DCS was applied. This method decreased seizure episode frequency by 44\% and seizure duration by 38\%. We argue that the therapeutic effect of DCS is related to modulation of cortical theta waves, and propose that this closed-loop interface has the potential to become an effective and semi-invasive treatment for refractory epilepsy and other neurological disorders.},
+  journal = {Scientific Reports},
+  author = {Pais-Vieira, Miguel and Yadav, Amol P. and Moreira, Derek and Guggenmos, David and Santos, Am{\'\i}lcar and Lebedev, Mikhail and Nicolelis, Miguel A. L.},
+  month = sep,
+  year = {2016},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6AX29ZF7\\Pais-Vieira et al_2016_A Closed Loop Brain-machine Interface for Epilepsy Control Using Dorsal Column.pdf},
+  pmid = {27605389},
+  pmcid = {PMC5015048}
+}
+
+@incollection{Wiest2008,
+  address = {Boca Raton (FL)},
+  edition = {2nd},
+  series = {Frontiers in Neuroscience},
+  title = {Multielectrode {{Recordings}} in the {{Somatosensory System}}},
+  copyright = {Copyright \textcopyright{} 2008, Taylor \& Francis Group, LLC.},
+  isbn = {978-0-8493-7046-5},
+  lccn = {NBK3894},
+  abstract = {A fundamental goal in systems neuroscience is to explain animal behavior in terms of the dynamics of neural ensembles. Multielectrode techniques greatly facilitate the approach toward this goal. Aside from the fact that each experiment provides a higher yield of data as compared to single-site recordings, some questions simply cannot be addressed using only one electrode at a time. For example, only multisite recordings can determine whether different neurons respond independently to stimuli, or covary from trial to trial. The purpose of this chapter is to review methods used in multielectrode studies of the rat somatosensory system, with an emphasis on the whisker system. We present a basic toolbox of methods we have used to probe the functions of populations of somatosensory neurons in a behavioral context. The basic toolbox includes techniques for applying controlled whisker stimuli, behavioral training in tactile discrimination tasks, multielectrode recordings, reversibly inactivating specific brain areas, and analysis of the ensemble neural data. These methods have already revealed fundamental properties of the somatosensory system that would have been difficult or impossible to uncover using single-electrode recordings. For example, cortical (Zhu and Connors, 1999; Ghazanfar and Nicolelis, 2001; Diamond et al., 1992; Ghazanfar et al., 2000; Schubert et al., 2001) and thalamic (Armstrong-James and Fox, 1987; Nicolelis and Chapin, 1994) neurons have large multiwhisker receptive fields that are dynamic over poststimulus time (Nicolelis and Chapin, 1994; Ghazanfar and Nicolelis, 1999; Ghazanfar et al., 2000). These data, together with observations of supralinear summation of multiwhisker inputs (Ghazanfar and Nicolelis, 1997; Shimegi, 2000), suggest that tactile receptive field dynamics function to integrate time-varying multiwhisker inputs (Ghazanfar and Nicolelis, 2001). For example, analysis of multineuron response data revealed additional stimulus-coding properties of somatosensory ensembles. S1 ensembles code stimulus location in single-neuron temporal patterns and the relative response latencies of their neurons, but not in single-trial covariations among the neurons (Nicolelis et al., 1998; Ghazanfar et al., 2000). In S2 of the primate, on the other hand, single-trial covariations among multiple neurons did contribute significantly to coding the location of a punctate stimulus (Nicolelis et al., 1998; Ghazanfar et al., 2000). Even in S1, the contribution of coordinated firing may increase with greater stimulus complexity, because multiple whisker stimuli lead to a higher prevalence of synchronous responses between neurons in the infragranular layers of S1 than in other layers (Zhang and Alloway, 2005). Combining methods for inactivating specific neural inputs with ensemble recordings led to the further conclusion that spatiotemporal RF properties of somatosensory neurons arise not only from intrinsic local properties of neurons and their neighbor connections, but rather from interactions among multiple levels of the somatosensory system. For example, recording thalamic tactile responses in the presence and absence of cortical feedback revealed that corticofugal projections contributed to both the short- and long-latency components of ventral posterior medial nucleus (VPM) responses (Krupa et al., 1999; Ghazanfar et al., 2001). These interlevel interactions were reflected in simultaneous recordings in trigeminal areas in brain stem, thalamus, and cortex, which revealed widespread oscillatory synchronization of neural firing (Nicolelis et al., 1995). The correlated activity remains even after transection of the facial nerve, which suggests that such synchronous activity is generated centrally. Although the high coherence among large populations of neurons associated with this oscillatory 7\textendash{}12 Hz brain state suggested absence seizures to a number of authors (Marescaux et al., 1992; Shaw et al., 2006; Shaw, 2007), a direct test showed that rats respond robustly to mild tactile stimulation during bouts of 7\textendash{}12 Hz oscillations in S1, contradicting the absence interpretation (Wiest and Nicolelis, 2003). Thus, widespread synchronized neural firing need not preclude perception; in fact, it can enhance aspects of sensory representation (Fontanini and Katz, 2006) as well as long-term plasticity (Erchova and Diamond, 2004). These demonstrations of fast interactions among neurons distributed across the somatosensory maps at multiple processing stages were paralleled by demonstrations of a tight coupling between the two hemispheres of S1 (Shuler et al., 2001; Wiest et al., 2005). This cross-talk challenges the classical conception of the S1 barrel cortex as an encoder for exclusively contralateral whisker activity and suggests the potential importance of bilateral interactions in S1 for whisker-guided discriminations (Krupa, 2001b; Shuler et al., 2002). Multielectrode recordings in different layers of S1 while rats performed a bilateral whisker-guided discrimination revealed that a feed-forward model of tactile signal processing cannot explain S1 response properties (Krupa et al., 2004). For example, firing rate modulations that began before tactile stimulation clearly could not be explained in terms of bottom-up propagation of a stimulus signal. Rather, other inputs to S1 must contribute to shaping the task-related responses. Similarly, tactile responses were found to vary significantly in different spontaneously occurring behavioral states (Nelson, 1996; Fanselow and Nicolelis, 1999; Moore et al., 1999; Nicolelis and Fanselow, 2002; Castro-Alamancos, 2004; Moore, 2004). These data collectively suggest that widely distributed neurons coordinate their activities on millisecond time scales, and that the functional connectivity among them can be quickly adjusted in different behavioral contexts. The preceding examples are meant to indicate the range of results that have already been achieved using multielectrode arrays (MEAs). In the following sections we present specific methods developed in the past 15 years. The examples have been selected to represent methods from each major phase of a typical study, from electrode design and surgical implantation, through ensemble recording involving somatosensory stimulation, behavioral monitoring, and reversible inactivation of specific brain areas, to analysis of the recorded many-neuron data.},
+  language = {eng},
+  booktitle = {Methods for {{Neural Ensemble Recordings}}},
+  publisher = {{CRC Press/Taylor \& Francis}},
+  author = {Wiest, Michael and Thomson, Eric and Meloy, Jim},
+  editor = {Nicolelis, Miguel AL},
+  year = {2008},
+  pmid = {21204443}
+}
+
+@article{Ravignani2017,
+  title = {Interdisciplinary Debate: {{Agree}} on Definitions of Synchrony},
+  volume = {545},
+  copyright = {\textcopyright{} 2017 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {0028-0836},
+  shorttitle = {Interdisciplinary Debate},
+  doi = {10.1038/545158c},
+  language = {en},
+  number = {7653},
+  journal = {Nature},
+  author = {Ravignani, Andrea},
+  month = may,
+  year = {2017},
+  keywords = {Research data,Communication},
+  pages = {158-158},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QKWIMIEN\\Ravignani  2017  Interdisciplinary debate Agree on definitions of synchrony.pdf}
+}
+
+@article{Csicsvari1999,
+  title = {Oscillatory {{Coupling}} of {{Hippocampal Pyramidal Cells}} and {{Interneurons}} in the {{Behaving Rat}}},
+  volume = {19},
+  copyright = {Copyright \textcopyright{} 1999 Society for Neuroscience},
+  issn = {0270-6474, 1529-2401},
+  abstract = {We examined whether excitation and inhibition are balanced in hippocampal cortical networks. Extracellular field and single-unit activity were recorded by multiple tetrodes and multisite silicon probes to reveal the timing of the activity of hippocampal CA1 pyramidal cells and classes of interneurons during theta waves and sharp wave burst (SPW)-associated field ripples. The somatic and dendritic inhibition of pyramidal cells was deduced from the activity of interneurons in the pyramidal layer [int(p)] and in the alveus and st. oriens [int(a/o)], respectively. Int(p) and int(a/o) discharged an average of 60 and 20$^\circ$ before the population discharge of pyramidal cells during the theta cycle, respectively. SPW ripples were associated with a 2.5-fold net increase of excitation. The discharge frequency of int(a/o) increased, decreased (``anti-SPW'' cells), or did not change (``SPW-independent'' cells) during SPW, suggesting that not all interneurons are innervated by pyramidal cells. Int(p) either fired together with (unimodal cells) or both before and after (bimodal cells) the pyramidal cell burst. During fast-ripple oscillation, the activity of interneurons in both the int(p) and int(a/o) groups lagged the maximum discharge probability of pyramidal neurons by 1\textendash{}2 msec. Network state changes, as reflected by field activity, covaried with changes in the spike train dynamics of single cells and their interactions. Summed activity of parallel-recorded interneurons, but not of pyramidal cells, reliably predicted theta cycles, whereas the reverse was true for the ripple cycles of SPWs. We suggest that network-driven excitability changes provide temporal windows of opportunity for single pyramidal cells to suppress, enable, or facilitate selective synaptic inputs.},
+  language = {en},
+  number = {1},
+  journal = {Journal of Neuroscience},
+  author = {Csicsvari, Jozsef and Hirase, Hajime and Czurk{\'o}, Andr{\'a}s and Mamiya, Akira and Buzs{\'a}ki, Gy{\"o}rgy},
+  month = jan,
+  year = {1999},
+  keywords = {oscillation,EEG,temporal coding,sharp waves,inhibition,theta},
+  pages = {274-287},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Q23DEVRV\\Csicsvari et al.  1999  Oscillatory Coupling of Hippocampal Pyramidal Cells and Interneurons in the Behaving Rat.pdf},
+  pmid = {9870957}
+}
+
+@article{Harris2016,
+  title = {Improving Data Quality in Neuronal Population Recordings},
+  volume = {19},
+  copyright = {\textcopyright{} 2016 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1097-6256},
+  doi = {10.1038/nn.4365},
+  abstract = {Understanding how the brain operates requires understanding how large sets of neurons function together. Modern recording technology makes it possible to simultaneously record the activity of hundreds of neurons, and technological developments will soon allow recording of thousands or tens of thousands. As with all experimental techniques, these methods are subject to confounds that complicate the interpretation of such recordings, and could lead to erroneous scientific conclusions. Here we discuss methods for assessing and improving the quality of data from these techniques and outline likely future directions in this field.},
+  language = {en},
+  number = {9},
+  journal = {Nature Neuroscience},
+  author = {Harris, Kenneth D. and Quiroga, Rodrigo Quian and Freeman, Jeremy and Smith, Spencer L.},
+  month = sep,
+  year = {2016},
+  keywords = {Electrophysiology,Optical imaging},
+  pages = {1165-1174},
+  file = {C:\\Users\\saram\\Zotero\\storage\\CM7GXDZR\\Harris et al.  2016  Improving data quality in neuronal population recordings.pdf}
+}
+
+@misc{zotero-827,
+  title = {Untuned {{But Not Irrelevant}}: {{A Role For Untuned Neurons In Sensory Information Coding}} - 134379.Full.Pdf},
+  howpublished = {http://biorxiv.org/content/biorxiv/early/2017/05/04/134379.full.pdf},
+  file = {C:\\Users\\saram\\Zotero\\storage\\K8GH3ABA\\134379.full.html}
+}
+
+@article{Chapeton2017,
+  title = {Stable Functional Networks Exhibit Consistent Timing in the Human Brain},
+  volume = {140},
+  issn = {1460-2156},
+  doi = {10.1093/brain/aww337},
+  abstract = {Despite many advances in the study of large-scale human functional networks, the question of timing, stability, and direction of communication between cortical regions has not been fully addressed. At the cellular level, neuronal communication occurs through axons and dendrites, and the time required for such communication is well defined and preserved. At larger spatial scales, however, the relationship between timing, direction, and communication between brain regions is less clear. Here, we use a measure of effective connectivity to identify connections between brain regions that exhibit communication with consistent timing. We hypothesized that if two brain regions are communicating, then knowledge of the activity in one region should allow an external observer to better predict activity in the other region, and that such communication involves a consistent time delay. We examine this question using intracranial electroencephalography captured from nine human participants with medically refractory epilepsy. We use a coupling measure based on time-lagged mutual information to identify effective connections between brain regions that exhibit a statistically significant increase in average mutual information at a consistent time delay. These identified connections result in sparse, directed functional networks that are stable over minutes, hours, and days. Notably, the time delays associated with these connections are also highly preserved over multiple time scales. We characterize the anatomic locations of these connections, and find that the propagation of activity exhibits a preferred posterior to anterior temporal lobe direction, consistent across participants. Moreover, networks constructed from connections that reliably exhibit consistent timing between anatomic regions demonstrate features of a small-world architecture, with many reliable connections between anatomically neighbouring regions and few long range connections. Together, our results demonstrate that cortical regions exhibit functional relationships with well-defined and consistent timing, and the stability of these relationships over multiple time scales suggests that these stable pathways may be reliably and repeatedly used for large-scale cortical communication.},
+  language = {eng},
+  number = {3},
+  journal = {Brain: A Journal of Neurology},
+  author = {Chapeton, Julio I. and Inati, Sara K. and Zaghloul, Kareem A.},
+  month = mar,
+  year = {2017},
+  keywords = {Neural Pathways,Brain,EEG,Female,Male,Brain Mapping,Humans,Models; Neurological,Temporal Lobe,Adult,Evoked Potentials,Reaction Time,clinical neurophysiology,intracranial EEG,neuroanatomy,Electroencephalography,epilepsy},
+  pages = {628-640},
+  file = {C:\\Users\\saram\\Zotero\\storage\\BA6JNEG3\\Chapeton 2017-Stable functional networks exhibit consistent timing in the human brain.pdf},
+  pmid = {28364547}
+}
+
+@article{Stutzki2014,
+  title = {Inflammatory Stimulation Preserves Physiological Properties of Retinal Ganglion Cells after Optic Nerve Injury},
+  volume = {8},
+  issn = {1662-5102},
+  doi = {10.3389/fncel.2014.00038},
+  abstract = {Axonal injury in the optic nerve is associated with retinal ganglion cell (RGC) degeneration and irreversible loss of vision. However, inflammatory stimulation (IS) by intravitreal injection of Pam3Cys transforms RGCs into an active regenerative state enabling these neurons to survive injury and to regenerate axons into the injured optic nerve. Although morphological changes have been well studied, the functional correlates of RGCs transformed either into a de- or regenerating state at a sub-cellular level remain unclear. In the current study, we investigated the signal propagation in single intraretinal axons as well as characteristic activity features of RGCs in a naive, a degenerative or a regenerative state in ex vivo retinae one week after either optic nerve cut alone (ONC) or additional inflammatory stimulation (ONC+IS). Recordings of single RGCs using high-density microelectrode arrays demonstrate that the mean intraretinal axonal conduction velocity significantly decreased within the first week after ONC. In contrast, when ONC was accompanied by regenerative Pam3Cys treatment the mean intraretinal velocity was undistinguishable from control RGCs, indicating a protective effect on the proximal axon. Spontaneous RGC activity decreased for the two most numerous RGC types (ON- and OFF-sustained cells) within one post-operative week, but did not significantly increase in RGCs after inflammatory stimulation. The analysis of light-induced activity revealed that RGCs in ONC animals respond on average later and with fewer spikes than control RGCs. IS significantly improved the responsiveness of the two studied RGC types. These results show that the transformation into a regenerative state by IS preserves, at least transiently, the physiological functional properties of injured RGCs.},
+  language = {English},
+  journal = {Frontiers in Cellular Neuroscience},
+  author = {Stutzki, Henrike and Leibig, Christian and Andreadaki, Anastasia and Fischer, Dietmar and Zeck, G{\"u}nther},
+  year = {2014},
+  keywords = {Microelectrode array,Retinal Ganglion Cells,Optic Nerve Injuries,Neuroregeneration,inflammatory stimulation,axonal imaging},
+  file = {C:\\Users\\saram\\Zotero\\storage\\RRW3GUJD\\fncel-08-00038.pdf}
+}
+
+@article{Magri2009a,
+  title = {A Toolbox for the Fast Information Analysis of Multiple-Site {{LFP}}, {{EEG}} and Spike Train Recordings},
+  volume = {10},
+  issn = {1471-2202},
+  doi = {10.1186/1471-2202-10-81},
+  abstract = {Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses.},
+  journal = {BMC Neuroscience},
+  author = {Magri, Cesare and Whittingstall, Kevin and Singh, Vanessa and Logothetis, Nikos K. and Panzeri, Stefano},
+  year = {2009},
+  pages = {81},
+  file = {C:\\Users\\saram\\Zotero\\storage\\26SHAXJ9\\Magri et al.  2009  A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings.pdf}
+}
+
+@article{Magri2009,
+  title = {A Toolbox for the Fast Information Analysis of Multiple-Site {{LFP}}, {{EEG}} and Spike Train Recordings},
+  volume = {10},
+  issn = {1471-2202},
+  doi = {10.1186/1471-2202-10-81},
+  abstract = {Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses.},
+  journal = {BMC Neuroscience},
+  author = {Magri, Cesare and Whittingstall, Kevin and Singh, Vanessa and Logothetis, Nikos K. and Panzeri, Stefano},
+  year = {2009},
+  pages = {81},
+  file = {C:\\Users\\saram\\Zotero\\storage\\GEPV3H6G\\Magri et al.  2009  A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings.pdf}
+}
+
+@article{Dell2002a,
+  title = {Sample Size Determination},
+  volume = {43},
+  issn = {1084-2020},
+  abstract = {Scientists who use animals in research must justify the number of animals to be used, and committees that review proposals to use animals in research must review this justification to ensure the appropriateness of the number of animals to be used. This article discusses when the number of animals to be used can best be estimated from previous experience and when a simple power and sample size calculation should be performed. Even complicated experimental designs requiring sophisticated statistical models for analysis can usually be simplified to a single key or critical question so that simple formulae can be used to estimate the required sample size. Approaches to sample size estimation for various types of hypotheses are described, and equations are provided in the Appendix. Several web sites are cited for more information and for performing actual calculations},
+  language = {eng},
+  number = {4},
+  journal = {ILAR journal},
+  author = {Dell, Ralph B. and Holleran, Steve and Ramakrishnan, Rajasekhar},
+  year = {2002},
+  keywords = {Animals,Data Interpretation; Statistical,Models; Statistical,Pilot Projects,Research Design,Sample Size},
+  pages = {207-213},
+  pmid = {12391396},
+  pmcid = {PMC3275906}
+}
+
+@article{Dell2002,
+  title = {Sample {{Size Determination}}},
+  volume = {43},
+  issn = {1084-2020},
+  abstract = {Scientists who use animals in research must justify the number of animals to be used, and committees that review proposals to use animals in research must review this justification to ensure the appropriateness of the number of animals to be used. This article discusses when the number of animals to be used can best be estimated from previous experience and when a simple power and sample size calculation should be performed. Even complicated experimental designs requiring sophisticated statistical models for analysis can usually be simplified to a single key or critical question so that simple formulae can be used to estimate the required sample size. Approaches to sample size estimation for various types of hypotheses are described, and equations are provided in the . Several web sites are cited for more information and for performing actual calculations.},
+  number = {4},
+  journal = {Ilar Journal},
+  author = {Dell, Ralph B. and Holleran, Steve and Ramakrishnan, Rajasekhar},
+  year = {2002},
+  pages = {207-213},
+  file = {C:\\Users\\saram\\Zotero\\storage\\IFCN7DP8\\Dell et al.  2002  Sample Size Determination.pdf},
+  pmid = {12391396},
+  pmcid = {PMC3275906}
+}
+
+@article{Kabat2012,
+  title = {Focal Cortical Dysplasia \textendash{} Review},
+  volume = {77},
+  issn = {1733-134X},
+  abstract = {Focal cortical dysplasia is a malformation of cortical development, which is the most common cause of medically refractory epilepsy in the pediatric population and the second/third most common etiology of medically intractable seizures in adults., Both genetic and acquired factors are involved in the pathogenesis of cortical dysplasia. Numerous classifications of the complex structural abnormalities of focal cortical dysplasia have been proposed \textendash{} from Taylor et al. in 1971 to the last modification of Palmini classification made by Blumcke in 2011. In general, three types of cortical dysplasia are recognized., Type I focal cortical dysplasia with mild symptomatic expression and late onset, is more often seen in adults, with changes present in the temporal lobe., Clinical symptoms are more severe in type II of cortical dysplasia usually seen in children. In this type, more extensive changes occur outside the temporal lobe with predilection for the frontal lobes., New type III is one of the above dysplasias with associated another principal lesion as hippocampal sclerosis, tumor, vascular malformation or acquired pathology during early life., Brain MRI imaging shows abnormalities in the majority of type II dysplasias and in only some of type I cortical dysplasias., The most common findings on MRI imaging include: focal cortical thickening or thinning, areas of focal brain atrophy, blurring of the gray-white junction, increased signal on T2- and FLAIR-weighted images in the gray and subcortical white matter often tapering toward the ventricle. On the basis of the MRI findings, it is possible to differentiate between type I and type II cortical dysplasia. A complete resection of the epileptogenic zone is required for seizure-free life. MRI imaging is very helpful to identify those patients who are likely to benefit from surgical treatment in a group of patients with drug-resistant epilepsy., However, in type I cortical dysplasia, MR imaging is often normal, and also in both types the lesion seen on MRI may be smaller than the seizure-generating region seen in the EEG. The abnormalities may also involve vital for life brain parts, where curative surgery will not be an option. Therefore, other diagnostic imaging techniques such as FDG PET, MEG, DTI and intra-cranial EEG are widely used to establish the diagnosis and to decide on management., With advances in both genetics and neuroimaging, we may develop a better understanding of patients with drug-resistant epilepsy, which will help us to provide more successful pharmacological and/or surgical treatment in the future.},
+  number = {2},
+  journal = {Polish Journal of Radiology},
+  author = {Kabat, Joanna and Kr{\'o}l, Przemys{\l}aw},
+  year = {2012},
+  pages = {35-43},
+  file = {C:\\Users\\saram\\Zotero\\storage\\MR494JE7\\Kabat and Król  2012  Focal cortical dysplasia – review.pdf},
+  pmid = {22844307},
+  pmcid = {PMC3403799}
+}
+
+@article{Eriksson2001,
+  title = {Diffusion Tensor Imaging in Patients with Epilepsy and Malformations of Cortical Development},
+  volume = {124},
+  issn = {0006-8950},
+  abstract = {Malformations of cortical development (MCD) are a common cause of epilepsy. Studies of structural MRIs and PET data in patients with MCD have found widespread changes outside the visually identified lesions. The aim of this study was to investigate diffusion changes interictally in patients with MCD and to test the hypothesis that MCD would be associated with widespread abnormalities of diffusion. We used diffusion tensor imaging (DTI) and statistical parametric mapping (SPM) to compare objectively tissue organization in 22 patients with partial seizures and MCD, with 30 control subjects. Whole-brain DTI was acquired using echo planar imaging. Rotationally invariant anisotropy and diffusivity maps were calculated and, after normalization to Talairach space, each patient was compared with the group of control subjects using SPM. Areas of reduced anisotropy were found in 17 patients and areas of increased diffusivity in 10. Two patients had areas of increased anisotropy. There were no patients with reduced diffusivity. Areas of increased diffusivity were in general more extensive than areas of reduced anisotropy. Changes in tissue beyond the MCD, that appeared normal on conventional MRI, were found in six patients for anisotropy and nine patients for diffusivity. Both measurements showed widespread changes in tissue beyond the MCD, i.e. adding information to conventional MRI. Increased or abnormally located grey matter or pathological white matter with abnormal myelination or ectopic neurones could cause reduced anisotropy. Increased diffusivity could be caused by a defect of neurogenesis or cell loss resulting in increased extracellular space. The widespread nature of abnormalities should be considered if surgical treatment is contemplated.},
+  language = {eng},
+  number = {Pt 3},
+  journal = {Brain: A Journal of Neurology},
+  author = {Eriksson, S. H. and Rugg-Gunn, F. J. and Symms, M. R. and Barker, G. J. and Duncan, J. S.},
+  month = mar,
+  year = {2001},
+  keywords = {Female,Male,Brain Mapping,Humans,Adult,Middle Aged,Anisotropy,Diffusion,Magnetic Resonance Imaging,Cerebral cortex,epilepsy},
+  pages = {617-626},
+  pmid = {11222460}
+}
+
+@article{Yan2012,
+  title = {Chronic {{Temporal Lobe Epilepsy Is Associated}} with {{Enhanced Alzheimer}}-{{Like Neuropathology}} in 3\texttimes{}{{Tg}}-{{AD Mice}}},
+  volume = {7},
+  issn = {1932-6203},
+  doi = {10.1371/journal.pone.0048782},
+  abstract = {The comorbidity between epilepsy and Alzheimer's disease (AD) is a topic of growing interest. Senile plaques and tauopathy are found in epileptic human temporal lobe structures, and individuals with AD have an increased incidence of spontaneous seizures. However, why and how epilepsy is associated with enhanced AD-like pathology remains unknown. We have recently shown $\beta$-secretase-1 (BACE1) elevation associated with aberrant limbic axonal sprouting in epileptic CD1 mice. Here we sought to explore whether BACE1 upregulation affected the development of Alzheimer-type neuropathology in mice expressing mutant human APP, presenilin and tau proteins, the triple transgenic model of AD (3\texttimes{}Tg-AD). 3\texttimes{}Tg-AD mice were treated with pilocarpine or saline (i.p.) at 6\textendash{}8 months of age. Immunoreactivity (IR) for BACE1, $\beta$-amyloid (A$\beta$) and phosphorylated tau (p-tau) was subsequently examined at 9, 11 or 14 months of age. Recurrent convulsive seizures, as well as mossy fiber sprouting and neuronal death in the hippocampus and limbic cortex, were observed in all epileptic mice. Neuritic plaques composed of BACE1-labeled swollen/sprouting axons and extracellular A$\beta$IR were seen in the hippocampal formation, amygdala and piriform cortices of 9 month-old epileptic, but not control, 3\texttimes{}Tg-AD mice. Densities of plaque-associated BACE1 and A$\beta$IR were elevated in epileptic versus control mice at 11 and 14 months of age. p-Tau IR was increased in dentate granule cells and mossy fibers in epileptic mice relative to controls at all time points examined. Thus, pilocarpine-induced chronic epilepsy was associated with accelerated and enhanced neuritic plaque formation and altered intraneuronal p-tau expression in temporal lobe structures in 3\texttimes{}Tg-AD mice, with these pathologies occurring in regions showing neuronal death and axonal dystrophy.},
+  number = {11},
+  journal = {PLOS ONE},
+  author = {Yan, Xiao-Xin and Cai, Yan and Shelton, Jarod and Deng, Si-Hao and Luo, Xue-Gang and Oddo, Salvatore and LaFerla, Frank M. and Cai, Huaibin and Rose, Gregory M. and Patrylo, Peter R.},
+  month = nov,
+  year = {2012},
+  keywords = {H,i,p,o,c,a,m,l,s,y,f,b,e,r,g,d,T,N,v,z,u},
+  pages = {e48782},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KJ9GQH4C\\Yan et al.  2012  Chronic Temporal Lobe Epilepsy Is Associated with Enhanced Alzheimer-Like Neuropathology in 3×Tg-AD Mice.pdf}
+}
+
+@article{Singh2015,
+  title = {{{MR Microscopy}} for {{3D Identification}} of {{Cortical Tubers}}, {{White Matter}} "{{Microtubers}}" and {{Radial Migration Lines}} in {{Ex Vivo Pediatric TSC}} with {{Epilepsy}}},
+  volume = {29},
+  issn = {0892-6638, 1530-6860},
+  abstract = {We compared 7T MRI to optical microscopy for the detection of abnormalities in two pediatric TSC + AED-refractory resection specimens. 3D reconstruction of microscopy slides is challenging because there are non-uniform shrinkage artifacts and deformations. Nevertheless, 2D optical microscopy of stained blocks enables visualization of sub-cellular structures ($<$10 um). At 100 um resolution, 3D MRI enables visualization of cellular layers. Thus the 2D stained blocks provide a 'ground truth' for validating the features detected via 7T protocols designed to produce optimal T1w-, T2w-, and diffusion-weighted imaging contrast. After MR acquisition, the resection en bloc specimens were sectioned and stained with/for H\&E, LFB, NFP, Smi31, and GFAP. The 7T images were reconstructed into a 3D volume and manually matched to the 2D histology slides. We show that (1) both 7T MRI and optical microscopy are sufficient to visualize cortical tubers and aberrant features in the white matter, (2) a "microtuber" in 2D stained slices is coincident with radial migration lines (RMLs) in 7T imaging and (3) diffuse abnormalities persist beyond the tuber in TSC.
+
+This preliminary study demonstrates the feasibility of MR microscopy for 3D characterization of abnormalities in two pediatric TSC cases. Hence, MRI may help identify treatment response biomarkers in TSC with epilepsy. Additionally, via histology we discovered a "microtuber" in the white matter of the optic radiation, which is coincident with RMLs in MRI. Next, we will pursue 3D reconstruction of microscopy slides and higher spatial resolution MRI via limited field-of-view or super-resolution reconstruction.},
+  language = {en},
+  number = {1 Supplement},
+  journal = {The FASEB Journal},
+  author = {Singh, Jolene and Peters, Jurriaan and Madsen, Joseph and Afacan, Onur and Scherrer, Benoit and Sahin, Mustafa and Lidov, Hart and Warfield, Simon},
+  month = jan,
+  year = {2015},
+  pages = {149.5}
+}
+
+@article{Miki2015,
+  title = {An Autopsy Case of Refractory Epilepsy Due to Unilateral Polymicrogyria in a 65-Year-Old Man: {{Histogenesis}} of Four-Layered Polymicrogyric Cortex},
+  volume = {35},
+  issn = {1440-1789},
+  shorttitle = {An Autopsy Case of Refractory Epilepsy Due to Unilateral Polymicrogyria in a 65-Year-Old Man},
+  doi = {10.1111/neup.12219},
+  abstract = {Polymicrogyria (PMG) is a complex malformation accompanied with abnormal cortical laminations (two-, four- or six-layered cortex), and horizontally oriented myelinated fiber layer is evident in four-layered PMG in older individuals. Here we report a 65-year-old man with refractory epilepsy, in whom postmortem examination revealed four-layered PMG in the left hemisphere. We carried out a histological and immunohistochemical study to clarify the nature of the myelinated band in four-layered PMG cortex. The myelinated band contained no or only few neurons. GFAP- and aquaporin-1-immunoreactive astrocytes were found in the subcortical white matter as well as in the myelinated band. By contrast, GFAP- and aquaporin-1-immunoreactive astrocytes were not found in the band of Baillarger. These findings suggest that the myelinated band in PMG cortex is not the band of Baillarger and that the immunohistochemical characteristics of astrocytes in the myelinated band in PMG cortex are similar to those of the subcortical white matter. Previous studies suggest that PMG is not a cell migration disorder but a post-migration malformation. Taken together, it is likely that the myelinated band in PMG cortex represents abnormality of axonal growth or myelination after migration has terminated.},
+  language = {eng},
+  number = {6},
+  journal = {Neuropathology: Official Journal of the Japanese Society of Neuropathology},
+  author = {Miki, Yasuo and Tanji, Kunikazu and Mori, Fumiaki and Sakamoto, Namiko and Wakabayashi, Koichi},
+  month = dec,
+  year = {2015},
+  keywords = {Male,Humans,Aged,Autopsy,Drug Resistant Epilepsy,Functional Laterality,Immunohistochemistry,Myelin Sheath,Polymicrogyria,aquaporin-1,cortical lamina,four-layered cortex,myelinated layer},
+  pages = {569-574},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5J6SAVB7\\2015_-_Yasuo_Miki_-_Anautopsycaseofrefractoryepilepsyduetounilateralpo[retrieved_2017-06-06].pdf},
+  pmid = {26094545}
+}
+
+@article{Marucci2013,
+  title = {Relationship between Focal Cortical Dysplasia and Epilepsy-Associated Low-Grade Tumors: An Immunohistochemical Study},
+  volume = {121},
+  issn = {1600-0463},
+  shorttitle = {Relationship between Focal Cortical Dysplasia and Epilepsy-Associated Low-Grade Tumors},
+  doi = {10.1111/j.1600-0463.2012.02938.x},
+  abstract = {We retrospectively analyzed 29 seizure-associated temporal lobe low-grade tumors to evaluate the utility of CD34 and bcl-2 expression in clarifying the relationship of these tumors with different classes of focal cortical dysplasia (FCD). CD34 immunostained 75\% of gangliogliomas (GG) and 60\% of pleomorphic xanthoastrocytomas. FCD type IIIb [i.e. abnormal cortical layering associated with a glioneuronal tumor, according to the new International League Against Epilepsy (ILAE) classification] presented CD34-immunopositive cells in 2/9 (22.2\%) cases, whereas FCD type II in 6/7 (85.7\%) cases, a difference statistically significant (p = 0.0117). Bcl-2 immunostained 9/12 (75\%) gangliogliomas and 2/3 (66.6\%) gangliocytomas. The cases of FCD type IIIb resulted negative for Bcl-2, whereas 4/7 cases (57.1\%) of FCD type II showed immunopositive cells. These differences in Bcl-2 expression between FCD type IIIb and FCD type II resulted statistically significant (p = 0.0088). Abnormal cortical layering, overall, represents the kind of FCD more commonly associated with seizure-related low-grade tumors, whereas FCD type II is more frequently associated with GG. The profile of CD34 and Bcl-2 expression exhibited by GG is more similar to that observed in FCD type II. Such immunoprofile suggests the existence of a common pathogenesis linking glioneuronal tumors and FCD type II.},
+  language = {eng},
+  number = {1},
+  journal = {APMIS: acta pathologica, microbiologica, et immunologica Scandinavica},
+  author = {Marucci, Gianluca and Martinoni, Matteo and Giulioni, Marco},
+  month = jan,
+  year = {2013},
+  keywords = {Female,Male,Humans,Adolescent,Adult,Child,Middle Aged,Immunohistochemistry,Antigens; CD34,Brain Diseases,Brain Neoplasms,Chi-Square Distribution,Child; Preschool,Malformations of Cortical Development,Malformations of Cortical Development; Group I,Neoplasm Grading,Proto-Oncogene Proteins c-bcl-2,Retrospective Studies,Young Adult,epilepsy},
+  pages = {22-29},
+  file = {C:\\Users\\saram\\Zotero\\storage\\P56ISXQQ\\2013_-_Gianluca_Marucci_-_Relationshipbetweenfocalcorticaldysplasiaandepilep[retrieved_2017-06-06].pdf},
+  pmid = {23030838}
+}
+
+@article{Marucci,
+  title = {Relationship between Focal Cortical Dysplasia and Epilepsy-associated Low-grade Tumors: An Immunohistochemical Study},
+  volume = {121},
+  issn = {09034641},
+  shorttitle = {Relationship between Focal Cortical Dysplasia and Epilepsy-associated Low-grade Tumors},
+  number = {1},
+  author = {Marucci, Gianluca},
+  pages = {22-29}
+}
+
+@article{Wong2009,
+  title = {Cortical Reorganization Following Anterior Temporal Lobectomy in Patients with Temporal Lobe Epilepsy},
+  volume = {73},
+  issn = {1526-632X},
+  doi = {10.1212/WNL.0b013e3181b2a48e},
+  abstract = {BACKGROUND: Functional MRI was used to study the impact of temporal lobe epilepsy (TLE) and anterior temporal lobectomy (ATL) on the cortical language network in patients with medically refractory TLE.
+METHODS: Nineteen patients with medically refractory TLE and 11 healthy control subjects were enrolled in this study. Ten patients underwent left ATL (mean age 35.2 +/- 3.8 years), and 9 underwent right ATL (mean age 35.9 +/- 2.6 years). The subjects silently generated verbs in response to a series of visually presented nouns inside the scanner. Correlation analysis was performed between the subjects' performance on the clinical language tests and their neural response in the a priori cortical regions.
+RESULTS: Preoperative data revealed that the patients with TLE showed increased neural activity in the right inferior frontal gyri (IFG) and middle frontal gyri (MFG). The right TLE patients demonstrated strong correlation between their language performance and the level of cortical activation within the typical language areas. However, such a correlation was absent in the left TLE patients. After the ATL surgery, the left TLE patients showed reduced activation in the left MFG and right IFG, whereas no difference was observed in the right TLE patients. In the right TLE patients, the correlation between language performance and neural response shifted from the typical language areas to the anterior cingulate cortex.
+CONCLUSION: This study demonstrates that the cortical language network is affected differently by the left and right temporal lobe epilepsy and is reorganized after anterior temporal lobectomy.},
+  language = {eng},
+  number = {7},
+  journal = {Neurology},
+  author = {Wong, S. W. H. and Jong, L. and Bandur, D. and Bihari, F. and Yen, Y.-F. and Takahashi, A. M. and Lee, D. H. and Steven, D. A. and Parrent, A. G. and Pigott, S. E. and Mirsattari, S. M.},
+  month = aug,
+  year = {2009},
+  keywords = {Nerve Net,Neural Pathways,Frontal Lobe,Female,Male,Brain Mapping,Humans,Temporal Lobe,Adult,Magnetic Resonance Imaging,Functional Laterality,Epilepsy; Temporal Lobe,Gyrus Cinguli,Language Disorders,Language Tests,Neuronal Plasticity,Neurosurgical Procedures,Recovery of Function,Speech,Speech Perception,Treatment Outcome},
+  pages = {518-525},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SSZ2K25X\\00006114-200908180-00007.pdf},
+  pmid = {19687453},
+  pmcid = {PMC2730795}
+}
+
+@article{Das2012,
+  title = {Hippocampal Tissue of Patients with Refractory Temporal Lobe Epilepsy Is Associated with Astrocyte Activation, Inflammation, and Altered Expression of Channels and Receptors},
+  volume = {220},
+  issn = {0306-4522},
+  doi = {10.1016/j.neuroscience.2012.06.002},
+  abstract = {Temporal lobe epilepsy (TLE) is the most common form of focal epilepsy. Previous research has demonstrated several trends in human tissue that, undoubtedly, contribute to the development and progression of TLE. In this study we examined resected human hippocampus tissue for a variety of changes including gliosis that might contribute to the development and presentation of TLE. The study subjects consisted of six TLE patients and three sudden-death controls. Clinicopathological characteristics were evaluated by H\&amp;E staining. Immunohistological staining and Western blotting methods were used to analyze the samples. Neuronal hypertrophy was observed in resected epileptic tissue. Immunohistological staining demonstrated that activation of astrocytes was significantly increased in epileptic tissue as compared to corresponding regions of the control group. The Western blot data also showed increased CX43 and AQP4 in the hippocampus and downregulation of Kir4.1, $\alpha$-syntrophin, and dystrophin, the key constituents of AQP4 multi-molecular complex. These tissues also demonstrated changes in inflammatory factors (COX-2, TGF-$\beta$, NF-$\kappa$B) suggesting that these molecules may play an important role in TLE pathogenesis. In addition we detected increases in metabotropic glutamate receptor (mGluR) 2/3, mGluR5 and kainic acid receptor subunits KA1 (Grik4) and KA2 (Grik5) in patients' hippocampi. We noted increased expression of the $\alpha$1c subunit comprising class C L-type Ca2+ channels and calpain expression in these tissues, suggesting that these subunits might have an integral role in TLE pathogenesis. These changes found in the resected tissue suggest that they may contribute to TLE and that the kainic acid receptor (KAR) and deregulation of GluR2 receptor may play an important role in TLE development and disease course. This study identifies alterations in number of commonly studied molecular targets associated with astrogliosis, cellular hypertrophy, water homeostasis, inflammation, and modulation of excitatory neurotransmission in hippocampal tissues from TLE patients.},
+  journal = {Neuroscience},
+  author = {Das, A. and Wallace IV, G. C. and Holmes, C. and McDowell, M. L. and Smith, J. A. and Marshall, J. D. and Bonilha, L. and Edwards, J. C. and Glazier, S. S. and Ray, S. K. and Banik, N. L.},
+  month = sep,
+  year = {2012},
+  keywords = {temporal lobe epilepsy,gliosis,inflammatory factors,aquaporin,expression of channels and receptors},
+  pages = {237-246},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Q2NUP53U\\1-s2.0-S0303846713003624-main.pdf}
+}
+
+@article{Dhiman2013,
+  title = {Outcome of Lesionectomy in Medically Refractory Epilepsy Due to Non-Mesial Temporal Sclerosis (Non-{{MTS}}) Lesions},
+  volume = {115},
+  issn = {0303-8467},
+  doi = {10.1016/j.clineuro.2013.09.008},
+  abstract = {Objectives
+To analyze the seizure outcome of lesionectomy for refractory epilepsy secondary to non-mesial temporal sclerosis (non-MTS) lesions.
+Methods
+Sixty-eight patients with non-MTS lesions (M:F = 42:26; age at onset: 11.7 $\pm$ 9.6 years; age at surgery: 21.1 $\pm$ 9.4 years), who underwent lesionectomy for refractory epilepsy were analyzed. The age at onset, frequency/type of seizure, MRI findings, video-EEG, histopathology and Engel's grading at 1 year/last follow up were recorded.
+Results
+The duration of epilepsy at surgery was 9.9 $\pm$ 6.9 years. The location of lesions were: temporal: 41 (60.3\%); frontal: 21 (30.9\%); parietal: 6 (8.8\%). The type of lesionectomies performed were temporal 41 (60.3\%), extra-temporal: 25 (36.8\%), temporo-frontal and temporo-parietal: 1 (1.5\%) patient each. The histopathological diagnosis were neoplastic: 32 (47.1\%), cortical dysplasia: 19 (27.9\%), other focal lesions: 17 (25\%). At mean follow up of 2.9 $\pm$ 2.1 years (median: 2.6 years), outcome was \textendash{} Engel's class I: 43 (63.2\%), IIa: 14 (20.6\%), III: 7 (10.3\%), IV: 4 (5.9\%). Good seizure control (Engel's class I/IIa) was achieved in 57 (83.8\%) patients. The good prognostic markers included temporal seizures, extended lesionectomy and AEDs after surgery while poor prognostic marker was gliotic lesion on histopathology.
+Conclusion
+Following lesionectomy due to non-MTS lesions, seizure freedom (Engel I) was noted in about 63.2\% of patients, which is comparable to other series and reiterates the effectiveness of lesionectomy for seizure control.},
+  number = {12},
+  journal = {Clinical Neurology and Neurosurgery},
+  author = {Dhiman, Vikas and Rao, Sudhanva and Sinha, Sanjib and Arimappamagan, Arivazhagan and Mahadevan, Anita and Bharath, Rose Dawn and Saini, Jitender and Jamuna, R. and Keshav Kumar, J. and Rao, Shobhini L. and Chandramouli, Bangalore A. and Satishchandra, Parthasarathy and Shankar, Susarla K.},
+  month = dec,
+  year = {2013},
+  keywords = {Surgery,Lesionectomy,Outcome,Refractory,epilepsy},
+  pages = {2445-2453},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5RD66535\\Dhiman et al.  2013  Outcome of lesionectomy in medically refractory epilepsy due to non-mesial temporal sclerosis (non-MTS) lesions.pdf}
+}
+
+@article{Pozzorini2015,
+  title = {Automated {{High}}-{{Throughput Characterization}} of {{Single Neurons}} by {{Means}} of {{Simplified Spiking Models}}},
+  volume = {11},
+  issn = {1553-7358},
+  doi = {10.1371/journal.pcbi.1004275},
+  abstract = {Author Summary Large-scale, high-throughput data acquisition is revolutionizing the field of neuroscience. Single-neuron electrophysiology is moving from the situation where a highly skilled experimentalist can patch a few cells per day, to a situation where robots will collect large amounts of data. To take advantage of this quantity of data, this technological advance requires a paradigm shift in the experimental design and analysis. Presently, most single-neuron experimental studies rely on old protocols\textemdash{}such as injections of steps and ramps of current\textemdash{}that rarely inform theoreticians and modelers interested in emergent properties of the brain. Here, we describe an efficient protocol for high-throughput in vitro electrophysiology as well as a set of mathematical tools that neuroscientists can use to directly translate experimental data into realistic spiking neuron models. The efficiency of the proposed method makes it suitable for high-throughput data analysis, allowing for the generation of a standardized database of realistic single-neuron models.},
+  number = {6},
+  journal = {PLOS Computational Biology},
+  author = {Pozzorini, Christian and Mensi, Skander and Hagens, Olivier and Naud, Richard and Koch, Christof and Gerstner, Wulfram},
+  month = jun,
+  year = {2015},
+  keywords = {i,p,o,c,a,m,l,s,f,b,e,r,g,d,T,N,u},
+  pages = {e1004275},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DKXVJQDZ\\Pozzorini et al_2015_Automated High-Throughput Characterization of Single Neurons by Means of.pdf}
+}
+
+@article{Naud2015,
+  title = {Counting on Dis-Inhibition: A Circuit Motif for Interval Counting and Selectivity in the Anuran Auditory System},
+  volume = {114},
+  copyright = {Copyright \textcopyright{} 2015 the American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  shorttitle = {Counting on Dis-Inhibition},
+  doi = {10.1152/jn.00138.2015},
+  abstract = {Information can be encoded in the temporal patterning of spikes. How the brain reads these patterns is of general importance and represents one of the greatest challenges in neuroscience. We addressed this issue in relation to temporal pattern recognition in the anuran auditory system. Many species of anurans perform mating decisions based on the temporal structure of advertisement calls. One important temporal feature is the number of sound pulses that occur with a species-specific interpulse interval. Neurons representing this pulse count have been recorded in the anuran inferior colliculus, but the mechanisms underlying their temporal selectivity are incompletely understood. Here, we construct a parsimonious model that can explain the key dynamical features of these cells with biologically plausible elements. We demonstrate that interval counting arises naturally when combining interval-selective inhibition with pulse-per-pulse excitation having both fast- and slow-conductance synapses. Interval-dependent inhibition is modeled here by a simple architecture based on known physiology of afferent nuclei. Finally, we consider simple implementations of previously proposed mechanistic explanations for these counting neurons and show that they do not account for all experimental observations. Our results demonstrate that tens of millisecond-range temporal selectivities can arise from simple connectivity motifs of inhibitory neurons, without recourse to internal clocks, spike-frequency adaptation, or appreciable short-term plasticity.},
+  language = {en},
+  number = {5},
+  journal = {Journal of Neurophysiology},
+  author = {Naud, Richard and Houtman, Dave and Rose, Gary J. and Longtin, Andr{\'e}},
+  month = nov,
+  year = {2015},
+  pages = {2804-2815},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DXIS76AS\\Naud et al_2015_Counting on dis-inhibition.pdf},
+  pmid = {26334004}
+}
+
+@article{Gal2017,
+  title = {Rich Cell-Type-Specific Network Topology in Neocortical Microcircuitry},
+  volume = {advance online publication},
+  copyright = {\textcopyright{} 2017 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1097-6256},
+  doi = {10.1038/nn.4576},
+  abstract = {Uncovering structural regularities and architectural topologies of cortical circuitry is vital for understanding neural computations. Recently, an experimentally constrained algorithm generated a dense network reconstruction of a \textasciitilde{}0.3-mm3 volume from juvenile rat somatosensory neocortex, comprising \textasciitilde{}31,000 cells and \textasciitilde{}36 million synapses. Using this reconstruction, we found a small-world topology with an average of 2.5 synapses separating any two cells and multiple cell-type-specific wiring features. Amounts of excitatory and inhibitory innervations varied across cells, yet pyramidal neurons maintained relatively constant excitation/inhibition ratios. The circuit contained highly connected hub neurons belonging to a small subset of cell types and forming an interconnected cell-type-specific rich club. Certain three-neuron motifs were overrepresented, matching recent experimental results. Cell-type-specific network properties were even more striking when synaptic strength and sign were considered in generating a functional topology. Our systematic approach enables interpretation of microconnectomics 'big data' and provides several experimentally testable predictions.},
+  language = {en},
+  journal = {Nature Neuroscience},
+  author = {Gal, Eyal and London, Michael and Globerson, Amir and Ramaswamy, Srikanth and Reimann, Michael W. and Muller, Eilif and Markram, Henry and Segev, Idan},
+  month = jun,
+  year = {2017},
+  keywords = {Network models,Research data,Neural circuits},
+  file = {C:\\Users\\saram\\Zotero\\storage\\286DGNJ6\\Gal et al.  2017  Rich cell-type-specific network topology in neocortical microcircuitry.pdf;C:\\Users\\saram\\Zotero\\storage\\J8C9DZF6\\nn.4576-S2.pdf;C:\\Users\\saram\\Zotero\\storage\\XWJ3ZN89\\nn.4576-S1.pdf}
+}
+
+@article{Pons2010a,
+  title = {Relating Structural and Functional Anomalous Connectivity in the Aging Brain via Neural Mass Modeling},
+  volume = {52},
+  issn = {1095-9572},
+  doi = {10.1016/j.neuroimage.2009.12.105},
+  abstract = {The structural changes that arise as the brain ages influence its functionality. In many cases, the anatomical degradation simply leads to normal aging. In others, the neurodegeneration is large enough to cause neurological disorders (e.g. Alzheimer's disease). Structure and function can be both currently measured using noninvasive techniques, such as magnetic resonance imaging (MRI) and electroencephalography (EEG) respectively. However, a full theoretical scheme linking structural and functional degradation is still lacking. Here we present a neural mass model that aims to bridge both levels of description and that reproduces experimentally observed multichannel EEG recordings of alpha rhythm in young subjects, healthy elderly subjects, and patients with mild cognitive impairment. We focus our attention in the dominant frequency of the signals at different electrodes and in the correlation between specific electrode pairs, measured via the phase-lag index. Our model allows us to study the influence of different structural connectivity pathways, independently of each other, on the normal and aberrantly aging brain. In particular, we study in detail the effect of the thalamic input on specific cortical regions, the long-range connectivity between cortical regions, and the short-range coupling within the same cortical area. Once the influence of each type of connectivity is determined, we characterize the regions of parameter space compatible with the EEG recordings of the populations under study. Our results show that the different types of connectivity must be fine-tuned to maintain the brain in a healthy functioning state independently of its age and brain condition.},
+  language = {eng},
+  number = {3},
+  journal = {NeuroImage},
+  author = {Pons, A. J. and Cantero, Jose L. and Atienza, Mercedes and Garcia-Ojalvo, Jordi},
+  month = sep,
+  year = {2010},
+  keywords = {Neural Pathways,Brain,Female,Male,Brain Mapping,Humans,Signal Processing; Computer-Assisted,Models; Neurological,Aged,Young Adult,Aging,Cognition Disorders,Electroencephalography},
+  pages = {848-861},
+  pmid = {20056154}
+}
+
+@article{Pons2010,
+  series = {Computational Models of the Brain},
+  title = {Relating Structural and Functional Anomalous Connectivity in the Aging Brain via Neural Mass Modeling},
+  volume = {52},
+  issn = {1053-8119},
+  doi = {10.1016/j.neuroimage.2009.12.105},
+  abstract = {The structural changes that arise as the brain ages influence its functionality. In many cases, the anatomical degradation simply leads to normal aging. In others, the neurodegeneration is large enough to cause neurological disorders (e.g. Alzheimer's disease). Structure and function can be both currently measured using noninvasive techniques, such as magnetic resonance imaging (MRI) and electroencephalography (EEG) respectively. However, a full theoretical scheme linking structural and functional degradation is still lacking. Here we present a neural mass model that aims to bridge both levels of description and that reproduces experimentally observed multichannel EEG recordings of alpha rhythm in young subjects, healthy elderly subjects, and patients with mild cognitive impairment. We focus our attention in the dominant frequency of the signals at different electrodes and in the correlation between specific electrode pairs, measured via the phase-lag index. Our model allows us to study the influence of different structural connectivity pathways, independently of each other, on the normal and aberrantly aging brain. In particular, we study in detail the effect of the thalamic input on specific cortical regions, the long-range connectivity between cortical regions, and the short-range coupling within the same cortical area. Once the influence of each type of connectivity is determined, we characterize the regions of parameter space compatible with the EEG recordings of the populations under study. Our results show that the different types of connectivity must be fine-tuned to maintain the brain in a healthy functioning state independently of its age and brain condition.},
+  number = {3},
+  journal = {NeuroImage},
+  author = {Pons, A. J. and Cantero, Jose L. and Atienza, Mercedes and Garcia-Ojalvo, Jordi},
+  month = sep,
+  year = {2010},
+  keywords = {EEG modeling,Neural mass models,Brain connectivity,Alzheimer's disease,Mild cognitive impairment},
+  pages = {848-861},
+  file = {C:\\Users\\saram\\Zotero\\storage\\HIPI9DCK\\Pons et al.  2010  Relating structural and functional anomalous connectivity in the aging brain via neural mass modeling.pdf}
+}
+
+@article{Gao2015,
+  series = {Large-Scale Recording Technology (32)},
+  title = {On Simplicity and Complexity in the Brave New World of Large-Scale Neuroscience},
+  volume = {32},
+  issn = {0959-4388},
+  doi = {10.1016/j.conb.2015.04.003},
+  abstract = {Technological advances have dramatically expanded our ability to probe multi-neuronal dynamics and connectivity in the brain. However, our ability to extract a simple conceptual understanding from complex data is increasingly hampered by the lack of theoretically principled data analytic procedures, as well as theoretical frameworks for how circuit connectivity and dynamics can conspire to generate emergent behavioral and cognitive functions. We review and outline potential avenues for progress, including new theories of high dimensional data analysis, the need to analyze complex artificial networks, and methods for analyzing entire spaces of circuit models, rather than one model at a time. Such interplay between experiments, data analysis and theory will be indispensable in catalyzing conceptual advances in the age of large-scale neuroscience.},
+  journal = {Current Opinion in Neurobiology},
+  author = {Gao, Peiran and Ganguli, Surya},
+  month = jun,
+  year = {2015},
+  pages = {148-155},
+  file = {C:\\Users\\saram\\Zotero\\storage\\EK4NTVRT\\Gao and Ganguli  2015  On simplicity and complexity in the brave new world of large-scale neuroscience.pdf}
+}
+
+@article{Roudi2015,
+  series = {Large-Scale Recording Technology (32)},
+  title = {Multi-Neuronal Activity and Functional Connectivity in Cell Assemblies},
+  volume = {32},
+  issn = {0959-4388},
+  doi = {10.1016/j.conb.2014.10.011},
+  abstract = {Our ability to collect large amounts of data from many cells has been paralleled by the development of powerful statistical models for extracting information from this data. Here we discuss how the activity of cell assemblies can be analyzed using these models, focusing on the generalized linear models and the maximum entropy models and describing a number of recent studies that employ these tools for analyzing multi-neuronal activity. We show results from simulations comparing inferred functional connectivity, pairwise correlations and the real synaptic connections in simulated networks demonstrating the power of statistical models in inferring functional connectivity. Further development of network reconstruction techniques based on statistical models should lead to more powerful methods of understanding functional anatomy of cell assemblies.},
+  journal = {Current Opinion in Neurobiology},
+  author = {Roudi, Yasser and Dunn, Benjamin and Hertz, John},
+  month = jun,
+  year = {2015},
+  pages = {38-44},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FSERRBX6\\Roudi et al.  2015  Multi-neuronal activity and functional connectivity in cell assemblies.pdf}
+}
+
+@article{Lewis2015,
+  series = {Large-Scale Recording Technology (32)},
+  title = {Recording of Brain Activity across Spatial Scales},
+  volume = {32},
+  issn = {0959-4388},
+  doi = {10.1016/j.conb.2014.12.007},
+  abstract = {Brain activity reveals exquisite coordination across spatial scales, from local microcircuits to brain-wide networks. Understanding how the brain represents, transforms and communicates information requires simultaneous recordings from distributed nodes of whole brain networks with single-cell resolution. Realizing multi-site recordings from communicating populations is hampered by the need to isolate clusters of interacting cells, often on a day-to-day basis. Chronic implantation of multi-electrode arrays allows long-term tracking of activity. Lithography on thin films provides a means to produce arrays of variable resolution, a high degree of flexibility, and minimal tissue displacement. Sequential application of surface arrays to monitor activity across brain-wide networks and subsequent implantation of laminar arrays to target specific populations enables continual refinement of spatial scale while maintaining coverage.},
+  journal = {Current Opinion in Neurobiology},
+  author = {Lewis, CM and Bosman, CA and Fries, P},
+  month = jun,
+  year = {2015},
+  pages = {68-77},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4TB9ZN73\\Lewis et al.  2015  Recording of brain activity across spatial scales.pdf}
+}
+
+@article{Mondragon-Gonzalez2017,
+  title = {Bio-Inspired Benchmark Generator for Extracellular Multi-Unit Recordings},
+  volume = {7},
+  copyright = {2017 Nature Publishing Group},
+  issn = {2045-2322},
+  doi = {10.1038/srep43253},
+  abstract = {Article},
+  language = {en},
+  journal = {Scientific Reports},
+  author = {Mondrag{\'o}n-Gonz{\'a}lez, Sirenia Lizbeth and Burgui{\`e}re, Eric},
+  month = feb,
+  year = {2017},
+  pages = {srep43253},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ARBZAI2S\\Mondragón-González and Burguière  2017  Bio-inspired benchmark generator for extracellular multi-unit recordings.pdf}
+}
+
+@article{Lachaux1999,
+  title = {Measuring Phase Synchrony in Brain Signals},
+  volume = {8},
+  issn = {1097-0193},
+  doi = {10.1002/(SICI)1097-0193(1999)8:4<194::AID-HBM4>3.0.CO;2-C},
+  abstract = {This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution ($<$100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large-scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long-scale effects do reflect cognitive processing, short-scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony. Hum Brain Mapping 8:194\textendash{}208, 1999. \textcopyright{} 1999 Wiley-Liss, Inc.},
+  language = {en},
+  number = {4},
+  journal = {Human Brain Mapping},
+  author = {Lachaux, Jean-Philippe and Rodriguez, Eugenio and Martinerie, Jacques and Varela, Francisco J.},
+  month = jan,
+  year = {1999},
+  keywords = {EEG,neural synchrony,phase-locking,coherence,EcoG,gamma-band,deblurring,epilepsy},
+  pages = {194-208},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8QZ926XX\\Lachaux 1999 Measuring phase synchrony in brain signals.pdf}
+}
+
+@article{Tallon-Baudry1996,
+  title = {Stimulus {{Specificity}} of {{Phase}}-{{Locked}} and {{Non}}-{{Phase}}-{{Locked}} 40\&nbsp;{{Hz Visual Responses}} in {{Human}}},
+  volume = {16},
+  copyright = {Copyright \textcopyright{} 1996 Society for Neuroscience},
+  issn = {0270-6474, 1529-2401},
+  abstract = {Considerable interest has been raised by non-phase-locked episodes of synchronization in the gamma-band (30\textendash{}60 Hz). One of their putative roles in the visual modality is feature-binding. We tested the stimulus specificity of high-frequency oscillations in humans using three types of visual stimuli: two coherent stimuli (a Kanizsa and a real triangle) and a noncoherent stimulus (``no-triangle stimulus''). The task of the subject was to count the occurrences of a curved illusory triangle. A time\textendash{}frequency analysis of single-trial EEG data recorded from eight human subjects was performed to characterize phase-locked as well as non-phase-locked high-frequency activities.
+We found an early phase-locked 40 Hz component, maximal at electrodes Cz\textendash{}C4, which does not vary with stimulation type. We describe a second 40 Hz component, appearing around 280 msec, that is not phase-locked to stimulus onset. This component is stronger in response to a coherent triangle, whether real or illusory: it could reflect, therefore, a mechanism of feature binding based on high-frequency synchronization. Because both the illusory and the real triangle are more target-like, it could also correspond to an oscillatory mechanism for testing the match between stimulus and target. At the same latencies, the low-frequency evoked response components phase-locked to stimulus onset behave differently, suggesting that low- and high-frequency activities have different functional roles.},
+  language = {en},
+  number = {13},
+  journal = {Journal of Neuroscience},
+  author = {Tallon-Baudry, Catherine and Bertrand, Olivier and Delpuech, Claude and Pernier, Jacques},
+  month = jul,
+  year = {1996},
+  keywords = {EEG,vision,oscillations,Evoked Potentials,gamma-band,40 Hz,feature binding,synchronization,human},
+  pages = {4240-4249},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PDKPESIJ\\Tallon-Baudry et al.  1996  Stimulus Specificity of Phase-Locked and Non-Phase-Locked 40&nbsp\;Hz Visual Responses in Human.pdf},
+  pmid = {8753885}
+}
+
+@article{Carter1987,
+  title = {Coherence and Time Delay Estimation},
+  volume = {75},
+  issn = {0018-9219},
+  doi = {10.1109/PROC.1987.13723},
+  abstract = {This paper presents a tutorial review of work in coherence and time delay estimation. A review of coherence research and development is presented. A derivation of the ML estimator for time delay is presented together with an interpretation of that estimator as a special member of a class of generalized cross correlators. The performance of the estimator is given for both high and low signal-to-noise ratio cases. The proposed correlator is implemented and stimulated with synthetic data. The results are compared with performance predictions and found to be in good agreement.},
+  number = {2},
+  journal = {Proceedings of the IEEE},
+  author = {Carter, G. C.},
+  month = feb,
+  year = {1987},
+  keywords = {Signal processing,Signal to noise ratio,Statistics,Matched filters,Nonlinear filters,Frequency,Correlators,Delay effects,Delay estimation,Random processes},
+  pages = {236-255},
+  file = {C:\\Users\\saram\\Zotero\\storage\\AIQCX52X\\Carter  1987  Coherence and time delay estimation.pdf}
+}
+
+@article{WarrenLiao2005,
+  title = {Clustering of {{Time Series Data}}-a {{Survey}}},
+  volume = {38},
+  issn = {0031-3203},
+  doi = {10.1016/j.patcog.2005.01.025},
+  abstract = {Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this review will serve as the steppingstone for those interested in advancing this area of research.},
+  number = {11},
+  journal = {Pattern Recogn.},
+  author = {Warren Liao, T.},
+  month = nov,
+  year = {2005},
+  keywords = {clustering,distance measure,Data mining,Time series data},
+  pages = {1857--1874},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TZ27PGTC\\2005 PR Liao Time Series.pdf}
+}
+
+@article{Ewell2015a,
+  title = {Brain {{State Is}} a {{Major Factor}} in {{Preseizure Hippocampal Network Activity}} and {{Influences Success}} of {{Seizure Intervention}}},
+  volume = {35},
+  copyright = {Copyright \textcopyright{} 2015 the authors 0270-6474/15/3515635-14\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.5112-14.2015},
+  abstract = {Neural dynamics preceding seizures are of interest because they may shed light on mechanisms of seizure generation and could be predictive. In healthy animals, hippocampal network activity is shaped by behavioral brain state and, in epilepsy, seizures selectively emerge during specific brain states. To determine the degree to which changes in network dynamics before seizure are pathological or reflect ongoing fluctuations in brain state, dorsal hippocampal neurons were recorded during spontaneous seizures in a rat model of temporal lobe epilepsy. Seizures emerged from all brain states, but with a greater likelihood after REM sleep, potentially due to an observed increase in baseline excitability during periods of REM compared with other brains states also characterized by sustained theta oscillations. When comparing the firing patterns of the same neurons across brain states associated with and without seizures, activity dynamics before seizures followed patterns typical of the ongoing brain state, or brain state transitions, and did not differ until the onset of the electrographic seizure. Next, we tested whether disparate activity patterns during distinct brain states would influence the effectiveness of optogenetic curtailment of hippocampal seizures in a mouse model of temporal lobe epilepsy. Optogenetic curtailment was significantly more effective for seizures preceded by non-theta states compared with seizures that emerged from theta states. Our results indicate that consideration of behavioral brain state preceding a seizure is important for the appropriate interpretation of network dynamics leading up to a seizure and for designing effective seizure intervention.
+SIGNIFICANCE STATEMENT Hippocampal single-unit activity is strongly shaped by behavioral brain state, yet this relationship has been largely ignored when studying activity dynamics before spontaneous seizures in medial temporal lobe epilepsy. In light of the increased attention on using single-unit activity for the prediction of seizure onset and closed-loop seizure intervention, we show a need for monitoring brain state to interpret correctly whether changes in neural activity before seizure onset is pathological or normal. Moreover, we also find that the brain state preceding a seizure determines the success of therapeutic interventions to curtail seizure duration. Together, these findings suggest that seizure prediction and intervention will be more successful if tailored for the specific brain states from which seizures emerge.},
+  language = {en},
+  number = {47},
+  journal = {Journal of Neuroscience},
+  author = {Ewell, Laura A. and Liang, Liang and Armstrong, Caren and Solt{\'e}sz, Ivan and Leutgeb, Stefan and Leutgeb, Jill K.},
+  month = nov,
+  year = {2015},
+  keywords = {temporal lobe epilepsy,brain state,interneuron,REM sleep,seizure prediction,hippocampus},
+  pages = {15635-15648},
+  file = {C:\\Users\\saram\\Zotero\\storage\\D67EQK7B\\Ewell et al.  2015  Brain State Is a Major Factor in Preseizure Hippocampal Network Activity and Influences Success of Seizure Intervention.pdf},
+  pmid = {26609157}
+}
+
+@article{Ewell2015,
+  title = {Brain {{State Is}} a {{Major Factor}} in {{Preseizure Hippocampal Network Activity}} and {{Influences Success}} of {{Seizure Intervention}}},
+  volume = {35},
+  copyright = {Copyright \textcopyright{} 2015 the authors 0270-6474/15/3515635-14\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.5112-14.2015},
+  abstract = {Neural dynamics preceding seizures are of interest because they may shed light on mechanisms of seizure generation and could be predictive. In healthy animals, hippocampal network activity is shaped by behavioral brain state and, in epilepsy, seizures selectively emerge during specific brain states. To determine the degree to which changes in network dynamics before seizure are pathological or reflect ongoing fluctuations in brain state, dorsal hippocampal neurons were recorded during spontaneous seizures in a rat model of temporal lobe epilepsy. Seizures emerged from all brain states, but with a greater likelihood after REM sleep, potentially due to an observed increase in baseline excitability during periods of REM compared with other brains states also characterized by sustained theta oscillations. When comparing the firing patterns of the same neurons across brain states associated with and without seizures, activity dynamics before seizures followed patterns typical of the ongoing brain state, or brain state transitions, and did not differ until the onset of the electrographic seizure. Next, we tested whether disparate activity patterns during distinct brain states would influence the effectiveness of optogenetic curtailment of hippocampal seizures in a mouse model of temporal lobe epilepsy. Optogenetic curtailment was significantly more effective for seizures preceded by non-theta states compared with seizures that emerged from theta states. Our results indicate that consideration of behavioral brain state preceding a seizure is important for the appropriate interpretation of network dynamics leading up to a seizure and for designing effective seizure intervention.
+SIGNIFICANCE STATEMENT Hippocampal single-unit activity is strongly shaped by behavioral brain state, yet this relationship has been largely ignored when studying activity dynamics before spontaneous seizures in medial temporal lobe epilepsy. In light of the increased attention on using single-unit activity for the prediction of seizure onset and closed-loop seizure intervention, we show a need for monitoring brain state to interpret correctly whether changes in neural activity before seizure onset is pathological or normal. Moreover, we also find that the brain state preceding a seizure determines the success of therapeutic interventions to curtail seizure duration. Together, these findings suggest that seizure prediction and intervention will be more successful if tailored for the specific brain states from which seizures emerge.},
+  language = {en},
+  number = {47},
+  journal = {Journal of Neuroscience},
+  author = {Ewell, Laura A. and Liang, Liang and Armstrong, Caren and Solt{\'e}sz, Ivan and Leutgeb, Stefan and Leutgeb, Jill K.},
+  month = nov,
+  year = {2015},
+  keywords = {temporal lobe epilepsy,brain state,interneuron,REM sleep,seizure prediction,Valiante,hippocampus},
+  pages = {15635-15648},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PW2CM2KI\\Ewell 2015-Brain State Is a Major Factor in Preseizure Hippocampal network activity and influences success of seizure intervention.pdf},
+  pmid = {26609157}
+}
+
+@article{Canolty2010,
+  title = {The Functional Role of Cross-Frequency Coupling},
+  volume = {14},
+  issn = {1364-6613},
+  doi = {10.1016/j.tics.2010.09.001},
+  abstract = {Recent studies suggest that cross-frequency coupling (CFC) may serve a functional role in neuronal computation, communication, and learning. In particular, the strength of phase-amplitude CFC differs across brain areas in a task-relevant manner, changes quickly in response to sensory, motor, and cognitive events, and correlates with performance in learning tasks. Importantly, while high-frequency brain activity reflects local domains of cortical processing, low-frequency brain rhythms are dynamically entrained across distributed brain regions by both external sensory input and internal cognitive events. CFC may thus serve as a mechanism to transfer information from large-scale brain networks operating at behavioral timescales to the fast, local cortical processing required for effective computation and synaptic modification, thus integrating functional systems across multiple spatiotemporal scales.},
+  number = {11},
+  journal = {Trends in Cognitive Sciences},
+  author = {Canolty, Ryan T. and Knight, Robert T.},
+  month = nov,
+  year = {2010},
+  pages = {506-515},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PM5WKZKR\\Canolty 2010-The functional role of cross-frequency coupling.pdf},
+  pmid = {20932795},
+  pmcid = {PMC3359652}
+}
+
+@article{Kass2016,
+  title = {Ten {{Simple Rules}} for {{Effective Statistical Practice}}},
+  volume = {12},
+  issn = {1553-7358},
+  doi = {10.1371/journal.pcbi.1004961},
+  number = {6},
+  journal = {PLOS Computational Biology},
+  author = {Kass, Robert E. and Caffo, Brian S. and Davidian, Marie and Meng, Xiao-Li and Yu, Bin and Reid, Nancy},
+  month = jun,
+  year = {2016},
+  keywords = {Neurons,Statistical data,Statistical models,Reproducibility,Probability distribution,Experimental design,Statistical methods,Statistical signal processing},
+  pages = {e1004961},
+  file = {C:\\Users\\saram\\Zotero\\storage\\NBHMUPFK\\Kass et al.  2016  Ten Simple Rules for Effective Statistical Practice.pdf}
+}
+
+@article{Shadlen1998,
+  title = {The {{Variable Discharge}} of {{Cortical Neurons}}: {{Implications}} for {{Connectivity}}, {{Computation}}, and {{Information Coding}}},
+  volume = {18},
+  copyright = {Copyright \textcopyright{} 1998 Society for Neuroscience},
+  issn = {0270-6474, 1529-2401},
+  shorttitle = {The {{Variable Discharge}} of {{Cortical Neurons}}},
+  abstract = {Cortical neurons exhibit tremendous variability in the number and temporal distribution of spikes in their discharge patterns. Furthermore, this variability appears to be conserved over large regions of the cerebral cortex, suggesting that it is neither reduced nor expanded from stage to stage within a processing pathway. To investigate the principles underlying such statistical homogeneity, we have analyzed a model of synaptic integration incorporating a highly simplified integrate and fire mechanism with decay. We analyzed a ``high-input regime'' in which neurons receive hundreds of excitatory synaptic inputs during each interspike interval. To produce a graded response in this regime, the neuron must balance excitation with inhibition. We find that a simple integrate and fire mechanism with balanced excitation and inhibition produces a highly variable interspike interval, consistent with experimental data. Detailed information about the temporal pattern of synaptic inputs cannot be recovered from the pattern of output spikes, and we infer that cortical neurons are unlikely to transmit information in the temporal pattern of spike discharge. Rather, we suggest that quantities are represented as rate codes in ensembles of 50\textendash{}100 neurons. These column-like ensembles tolerate large fractions of common synaptic input and yet covary only weakly in their spike discharge. We find that an ensemble of 100 neurons provides a reliable estimate of rate in just one interspike interval (10\textendash{}50 msec). Finally, we derived an expression for the variance of the neural spike count that leads to a stable propagation of signal and noise in networks of neurons\textemdash{}that is, conditions that do not impose an accumulation or diminution of noise. The solution implies that single neurons perform simple algebra resembling averaging, and that more sophisticated computations arise by virtue of the anatomical convergence of novel combinations of inputs to the cortical column from external sources.},
+  language = {en},
+  number = {10},
+  journal = {Journal of Neuroscience},
+  author = {Shadlen, Michael N. and Newsome, William T.},
+  month = may,
+  year = {1998},
+  keywords = {temporal coding,noise,rate code,correlation,interspike interval,spike count variance,response variability,visual cortex,synaptic integration,neural model},
+  pages = {3870-3896},
+  file = {C:\\Users\\saram\\Zotero\\storage\\NTH7GVHH\\Shadlen and Newsome  1998  The Variable Discharge of Cortical Neurons Implications for Connectivity, Computation, and Information Coding.pdf},
+  pmid = {9570816}
+}
+
+@article{Stein2005,
+  title = {Neuronal Variability: Noise or Part of the Signal?},
+  volume = {6},
+  copyright = {\textcopyright{} 2005 Nature Publishing Group},
+  issn = {1471-003X},
+  shorttitle = {Neuronal Variability},
+  doi = {10.1038/nrn1668},
+  abstract = {Sensory, motor and cortical neurons fire impulses or spikes at a regular, but slowly declining, rate in response to a constant current stimulus. Yet, the intervals between spikes often vary randomly during behaviour. Is this variation an unavoidable effect of generating spikes by sensory or synaptic processes ('neural noise') or is it an important part of the 'signal' that is transmitted to other neurons? Here, we mainly discuss this question in relation to sensory and motor processes, as the signals are best identified in such systems, although we also touch on central processes.},
+  language = {en},
+  number = {5},
+  journal = {Nature Reviews Neuroscience},
+  author = {Stein, Richard B. and Gossen, E. Roderich and Jones, Kelvin E.},
+  month = may,
+  year = {2005},
+  pages = {389-397},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ZX8ZQJJZ\\Stein et al.  2005  Neuronal variability noise or part of the signal.pdf}
+}
+
+@article{Jacobs2009,
+  title = {Ruling out and Ruling in Neural Codes},
+  volume = {106},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.0900573106},
+  abstract = {The subject of neural coding has generated much debate. A key issue is whether the nervous system uses coarse or fine coding. Each has different strengths and weaknesses and, therefore, different implications for how the brain computes. For example, the strength of coarse coding is that it is robust to fluctuations in spike arrival times; downstream neurons do not have to keep track of the details of the spike train. The weakness, though, is that individual cells cannot carry much information, so downstream neurons have to pool signals across cells and/or time to obtain enough information to represent the sensory world and guide behavior. In contrast, with fine coding, individual cells can carry much more information, but downstream neurons have to resolve spike train structure to obtain it. Here, we set up a strategy to determine which codes are viable, and we apply it to the retina as a model system. We recorded from all the retinal output cells an animal uses to solve a task, evaluated the cells' spike trains for as long as the animal evaluates them, and used optimal, i.e., Bayesian, decoding. This approach makes it possible to obtain an upper bound on the performance of codes and thus eliminate those that are insufficient, that is, those that cannot account for behavioral performance. Our results show that standard coarse coding (spike count coding) is insufficient; finer, more information-rich codes are necessary.},
+  language = {en},
+  number = {14},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Jacobs, Adam L. and Fridman, Gene and Douglas, Robert M. and Alam, Nazia M. and Latham, Peter E. and Prusky, Glen T. and Nirenberg, Sheila},
+  month = jul,
+  year = {2009},
+  keywords = {Bayesian,ganglion cells,ideal observer,neural coding,population coding},
+  pages = {5936-5941},
+  file = {C:\\Users\\saram\\Zotero\\storage\\GEXXXQFE\\Jacobs et al.  2009  Ruling out and ruling in neural codes.pdf;C:\\Users\\saram\\Zotero\\storage\\SZQKPS5G\\PNAS-2009-Jacobs-5936-41.pdf},
+  pmid = {19297621}
+}
+
+@article{Borst1999,
+  title = {Information Theory and Neural Coding},
+  volume = {2},
+  copyright = {\textcopyright{} 1999 Nature Publishing Group},
+  issn = {1097-6256},
+  doi = {10.1038/14731},
+  abstract = {Information theory quantifies how much information a neural response carries about the stimulus. This can be compared to the information transferred in particular models of the stimulus-response function and to maximum possible information transfer. Such comparisons are crucial because they validate assumptions present in any neurophysiological analysis. Here we review information-theory basics before demonstrating its use in neural coding. We show how to use information theory to validate simple stimulus-response models of neural coding of dynamic stimuli. Because these models require specification of spike timing precision, they can reveal which time scales contain information in neural coding. This approach shows that dynamic stimuli can be encoded efficiently by single neurons and that each spike contributes to information transmission. We argue, however, that the data obtained so far do not suggest a temporal code, in which the placement of spikes relative to each other yields additional information.},
+  language = {en},
+  number = {11},
+  journal = {Nature Neuroscience},
+  author = {Borst, Alexander and Theunissen, Fr{\'e}d{\'e}ric E.},
+  month = nov,
+  year = {1999},
+  pages = {947-957},
+  file = {C:\\Users\\saram\\Zotero\\storage\\CFMAJZX3\\Borst and Theunissen  1999  Information theory and neural coding.pdf}
+}
+
+@article{Hsu2004,
+  title = {Quantifying Variability in Neural Responses and Its Application for the Validation of Model Predictions},
+  volume = {15},
+  issn = {0954-898X},
+  abstract = {A rate code assumes that a neuron's response is completely characterized by its time-varying mean firing rate. This assumption has successfully described neural responses in many systems. The noise in rate coding neurons can be quantified by the coherence function or the correlation coefficient between the neuron's deterministic time-varying mean rate and noise corrupted single spike trains. Because of the finite data size, the mean rate cannot be known exactly and must be approximated. We introduce novel unbiased estimators for the measures of coherence and correlation which are based on the extrapolation of the signal to noise ratio in the neural response to infinite data size. We then describe the application of these estimates to the validation of the class of stimulus-response models that assume that the mean firing rate captures all the information embedded in the neural response. We explain how these quantifiers can be used to separate response prediction errors that are due to inaccurate model assumptions from errors due to noise inherent in neuronal spike trains.},
+  language = {eng},
+  number = {2},
+  journal = {Network (Bristol, England)},
+  author = {Hsu, Anne and Borst, Alexander and Theunissen, Fr{\'e}d{\'e}ric E.},
+  month = may,
+  year = {2004},
+  keywords = {Neurons,Time Factors,Models; Neurological,Computer Simulation,Neural Networks (Computer),noise,Linear Models,Predictive Value of Tests,Action potentials},
+  pages = {91-109},
+  file = {C:\\Users\\saram\\Zotero\\storage\\D9EXXH5V\\Hsu_and_Borst_et_al_2004.pdf},
+  pmid = {15214701}
+}
+
+@inproceedings{Sahani2002,
+  address = {Cambridge, MA, USA},
+  series = {NIPS'02},
+  title = {How {{Linear Are Auditory Cortical Responses}}?},
+  abstract = {By comparison to some other sensory cortices, the functional properties of cells in the primary auditory cortex are not yet well understood. Recent attempts to obtain a generalized description of auditory cortical responses have often relied upon characterization of the spectrotemporal receptive field (STRF), which amounts to a model of the stimulus-response function (SRF) that is linear in the spectrogram of the stimulus. How well can such a model account for neural responses at the very first stages of auditory cortical processing? To answer this question, we develop a novel methodology for evaluating the fraction of stimulus-related response power in a population that can be captured by a given type of SRF model. We use this technique to show that, in the thalamo-recipient layers of primary auditory cortex, STRF models account for no more than 40\% of the stimulus-related power in neural responses.},
+  booktitle = {Proceedings of the 15th {{International Conference}} on {{Neural Information Processing Systems}}},
+  publisher = {{MIT Press}},
+  author = {Sahani, Maneesh and Linden, Jennifer F.},
+  year = {2002},
+  pages = {125--132},
+  file = {C:\\Users\\saram\\Zotero\\storage\\T8TIX35M\\Sahani_and_Linden_2002.pdf}
+}
+
+@article{Martens2014,
+  title = {Separating Burst from Background Spikes in Multichannel Neuronal Recordings Using Return Map Analysis},
+  volume = {24},
+  issn = {0129-0657},
+  doi = {10.1142/S0129065714500129},
+  abstract = {We propose a preprocessing method to separate coherent neuronal network activity, referred to as ``bursts'', from background spikes. High background activity in neuronal recordings reduces the effectiveness of currently available burst detection methods. For long-term, stationary recordings, burst and background spikes have a bimodal ISI distribution which makes it easy to select the threshold to separate burst and background spikes. Finite, nonstationary recordings lead to noisy ISIs for which the bimodality is not that clear. We introduce a preprocessing method to separate burst from background spikes to improve burst detection reliability because it efficiently uses both single and multichannel activity. The method is tested using a stochastic model constrained by data available in the literature and recordings from primary cortical neurons cultured on multielectrode arrays. The separation between burst and background spikes is obtained using the interspike interval return map. The cutoff threshold is the key parameter to separate the burst and background spikes. We compare two methods for selecting the threshold. The 2-step method, in which threshold selection is based on fixed heuristics. The iterative method, in which the optimal cutoff threshold is directly estimated from the data. The proposed preprocessing method significantly increases the reliability of several established burst detection algorithms, both for simulated and real recordings. The preprocessing method makes it possible to study the effects of diseases or pharmacological manipulations, because it can deal efficiently with nonstationarity in the data.},
+  language = {eng},
+  number = {4},
+  journal = {International Journal of Neural Systems},
+  author = {Martens, M. B. and Chiappalone, M. and Schubert, D. and Tiesinga, P. H. E.},
+  month = jun,
+  year = {2014},
+  keywords = {Neurons,Animals,Time Factors,Nerve Net,Humans,Models; Neurological,Action potentials,ROC Curve,Signal Detection; Psychological,Stochastic Processes},
+  pages = {1450012},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KENGRHWY\\Martens 2014-SEPARATING BURST FROM BACKGROUND SPIKES IN MULTICHANNEL NEURONAL RECORDINGS USING RETURN MAP ANALYSIS.pdf},
+  pmid = {24812717}
+}
+
+@article{Kreuz2013a,
+  title = {Monitoring Spike Train Synchrony},
+  volume = {109},
+  copyright = {Copyright \textcopyright{} 2013 the American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00873.2012},
+  abstract = {Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.},
+  language = {en},
+  number = {5},
+  journal = {Journal of Neurophysiology},
+  author = {Kreuz, Thomas and Chicharro, Daniel and Houghton, Conor and Andrzejak, Ralph G. and Mormann, Florian},
+  month = mar,
+  year = {2013},
+  pages = {1457-1472},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SWVRBM7P\\Kreuz 2013 - Monitoring spike train synchrony.pdf},
+  pmid = {23221419}
+}
+
+@article{Fisher2013,
+  title = {A Modeling Framework for Deriving the Structural and Functional Architecture of a Short-Term Memory Microcircuit},
+  volume = {79},
+  issn = {0896-6273},
+  doi = {10.1016/j.neuron.2013.06.041},
+  abstract = {Although many studies have identified neural correlates of memory, relatively little is known about the circuit properties connecting single-neuron physiology to behavior. Here we developed a modeling framework to bridge this gap and identify circuit interactions capable of maintaining short-term memory. Unlike typical studies that construct a phenomenological model and test whether it reproduces select aspects of neuronal data, we directly fit the synaptic connectivity of an oculomotor memory circuit to a broad range of anatomical, electrophysiological, and behavioral data. Simultaneous fits to all data, combined with sensitivity analyses, revealed complementary roles of synaptic and neuronal-recruitment thresholds in providing the nonlinear interactions required to generate the observed circuit behavior. This work introduces a new methodology for identifying the cellular and synaptic mechanisms underlying short-term memory, and demonstrates how the anatomical structure of a circuit may belie its functional organization.},
+  number = {5},
+  journal = {Neuron},
+  author = {Fisher, Dimitry and Olasagasti, Itsaso and Tank, David W. and Aksay, Emre R.F. and Goldman, Mark S.},
+  month = sep,
+  year = {2013},
+  pages = {987-1000},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QAKYNHKY\\Fisher et al. - 2013 - A modeling framework for deriving the structural a.pdf},
+  pmid = {24012010},
+  pmcid = {PMC3768012}
+}
+
+@article{Torre2016,
+  title = {Synchronous {{Spike Patterns}} in {{Macaque Motor Cortex}} during an {{Instructed}}-{{Delay Reach}}-to-{{Grasp Task}}},
+  volume = {36},
+  copyright = {Copyright \textcopyright{} 2016 Torre, et al.. This article is freely available online through the J Neurosci Author Open Choice option.},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.4375-15.2016},
+  abstract = {The computational role of spike time synchronization at millisecond precision among neurons in the cerebral cortex is hotly debated. Studies performed on data of limited size provided experimental evidence that low-order correlations occur in relation to behavior. Advances in electrophysiological technology to record from hundreds of neurons simultaneously provide the opportunity to observe coordinated spiking activity of larger populations of cells. We recently published a method that combines data mining and statistical evaluation to search for significant patterns of synchronous spikes in massively parallel spike trains (Torre et al., 2013). The method solves the computational and multiple testing problems raised by the high dimensionality of the data. In the current study, we used our method on simultaneous recordings from two macaque monkeys engaged in an instructed-delay reach-to-grasp task to determine the emergence of spike synchronization in relation to behavior. We found a multitude of synchronous spike patterns aligned in both monkeys along a preferential mediolateral orientation in brain space. The occurrence of the patterns is highly specific to behavior, indicating that different behaviors are associated with the synchronization of different groups of neurons (``cell assemblies''). However, pooled patterns that overlap in neuronal composition exhibit no specificity, suggesting that exclusive cell assemblies become active during different behaviors, but can recruit partly identical neurons. These findings are consistent across multiple recording sessions analyzed across the two monkeys.
+SIGNIFICANCE STATEMENT Neurons in the brain communicate via electrical impulses called spikes. How spikes are coordinated to process information is still largely unknown. Synchronous spikes are effective in triggering a spike emission in receiving neurons and have been shown to occur in relation to behavior in a number of studies on simultaneous recordings of few neurons. We recently published a method to extend this type of investigation to larger data. Here, we apply it to simultaneous recordings of hundreds of neurons from the motor cortex of macaque monkeys performing a motor task. Our analysis reveals groups of neurons selectively synchronizing their activity in relation to behavior, which sheds new light on the role of synchrony in information processing in the cerebral cortex.},
+  language = {en},
+  number = {32},
+  journal = {Journal of Neuroscience},
+  author = {Torre, Emiliano and Quaglio, Pietro and Denker, Michael and Brochier, Thomas and Riehle, Alexa and Gr{\"u}n, Sonja},
+  month = aug,
+  year = {2016},
+  keywords = {Motor Cortex,temporal coding,cell assemblies,massively parallel spike trains,spike synchrony},
+  pages = {8329-8340},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Z3CJJEFD\\Torre et al_2016_Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay.pdf},
+  pmid = {27511007}
+}
+
+@article{Martens2013,
+  title = {Separating Burst from Background Spikes in Multichannel Neuronal Recordings Using Return Map Analysis},
+  volume = {24},
+  issn = {0129-0657},
+  doi = {10.1142/S0129065714500129},
+  abstract = {We propose a preprocessing method to separate coherent neuronal network activity, referred to as "bursts", from background spikes. High background activity in neuronal recordings reduces the effectiveness of currently available burst detection methods. For long-term, stationary recordings, burst and background spikes have a bimodal ISI distribution which makes it easy to select the threshold to separate burst and background spikes. Finite, nonstationary recordings lead to noisy ISIs for which the bimodality is not that clear. We introduce a preprocessing method to separate burst from background spikes to improve burst detection reliability because it efficiently uses both single and multichannel activity. The method is tested using a stochastic model constrained by data available in the literature and recordings from primary cortical neurons cultured on multielectrode arrays. The separation between burst and background spikes is obtained using the interspike interval return map. The cutoff threshold is the key parameter to separate the burst and background spikes. We compare two methods for selecting the threshold. (1) The 2-step method, in which threshold selection is based on fixed heuristics. (2) The iterative method, in which the optimal cutoff threshold is directly estimated from the data. The proposed preprocessing method significantly increases the reliability of several established burst detection algorithms, both for simulated and real recordings. The preprocessing method makes it possible to study the effects of diseases or pharmacological manipulations, because it can deal efficiently with nonstationarity in the data.},
+  number = {04},
+  journal = {International Journal of Neural Systems},
+  author = {Martens, M. B. and Chiappalone, M. and Schubert, D. and Tiesinga, P. H. E.},
+  month = dec,
+  year = {2013},
+  pages = {1450012}
+}
+
+@article{Rajalingham2015,
+  title = {Comparison of {{Object Recognition Behavior}} in {{Human}} and {{Monkey}}},
+  volume = {35},
+  copyright = {Copyright \textcopyright{} 2015 the authors 0270-6474/15/3512127-10\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.0573-15.2015},
+  abstract = {Although the rhesus monkey is used widely as an animal model of human visual processing, it is not known whether invariant visual object recognition behavior is quantitatively comparable across monkeys and humans. To address this question, we systematically compared the core object recognition behavior of two monkeys with that of human subjects. To test true object recognition behavior (rather than image matching), we generated several thousand naturalistic synthetic images of 24 basic-level objects with high variation in viewing parameters and image background. Monkeys were trained to perform binary object recognition tasks on a match-to-sample paradigm. Data from 605 human subjects performing the same tasks on Mechanical Turk were aggregated to characterize ``pooled human'' object recognition behavior, as well as 33 separate Mechanical Turk subjects to characterize individual human subject behavior. Our results show that monkeys learn each new object in a few days, after which they not only match mean human performance but show a pattern of object confusion that is highly correlated with pooled human confusion patterns and is statistically indistinguishable from individual human subjects. Importantly, this shared human and monkey pattern of 3D object confusion is not shared with low-level visual representations (pixels, V1+; models of the retina and primary visual cortex) but is shared with a state-of-the-art computer vision feature representation. Together, these results are consistent with the hypothesis that rhesus monkeys and humans share a common neural shape representation that directly supports object perception.
+SIGNIFICANCE STATEMENT To date, several mammalian species have shown promise as animal models for studying the neural mechanisms underlying high-level visual processing in humans. In light of this diversity, making tight comparisons between nonhuman and human primates is particularly critical in determining the best use of nonhuman primates to further the goal of the field of translating knowledge gained from animal models to humans. To the best of our knowledge, this study is the first systematic attempt at comparing a high-level visual behavior of humans and macaque monkeys.},
+  language = {en},
+  number = {35},
+  journal = {Journal of Neuroscience},
+  author = {Rajalingham, Rishi and Schmidt, Kailyn and DiCarlo, James J.},
+  month = sep,
+  year = {2015},
+  keywords = {vision,human,monkey,object recognition},
+  pages = {12127-12136},
+  file = {C:\\Users\\saram\\Zotero\\storage\\IFKRAKXV\\Rajalingham et al. - 2015 - Comparison of Object Recognition Behavior in Human.pdf},
+  pmid = {26338324}
+}
+
+@article{Kreuz2013,
+  title = {Monitoring Spike Train Synchrony},
+  volume = {109},
+  copyright = {Copyright \textcopyright{} 2013 the American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00873.2012},
+  abstract = {Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.},
+  language = {en},
+  number = {5},
+  journal = {Journal of Neurophysiology},
+  author = {Kreuz, Thomas and Chicharro, Daniel and Houghton, Conor and Andrzejak, Ralph G. and Mormann, Florian},
+  month = mar,
+  year = {2013},
+  pages = {1457-1472},
+  file = {C:\\Users\\saram\\Zotero\\storage\\LETNR24Y\\Kreuz et al. - 2013 - Monitoring spike train synchrony.pdf},
+  pmid = {23221419}
+}
+
+@incollection{Lee2008,
+  title = {Sparse Deep Belief Net Model for Visual Area {{V2}}},
+  booktitle = {Advances in {{Neural Information Processing Systems}} 20},
+  publisher = {{Curran Associates, Inc.}},
+  author = {Lee, Honglak and Ekanadham, Chaitanya and Ng, Andrew Y.},
+  editor = {Platt, J. C. and Koller, D. and Singer, Y. and Roweis, S. T.},
+  year = {2008},
+  pages = {873--880},
+  file = {C:\\Users\\saram\\Zotero\\storage\\E4NRBFGA\\Lee et al. - 2008 - Sparse deep belief net model for visual area V2.pdf}
+}
+
+@article{Schmitz2015,
+  title = {Application of {{Parallel Factor Analysis}} ({{PARAFAC}}) to Electrophysiological Data},
+  volume = {8},
+  issn = {1662-5196},
+  doi = {10.3389/fninf.2014.00084},
+  abstract = {The identification of important features in multi-electrode recordings requires the decomposition of data in order to disclose relevant features and to offer a clear graphical representation. This can be a demanding task. Parallel Factor Analysis (PARAFAC; Hitchcock, 1927; Carrol and Chang, 1970; Harshman, 1970) is a method to decompose multi-dimensional arrays in order to focus on the features of interest, and provides a distinct illustration of the results. We applied PARAFAC to analyse spatio-temporal patterns in the functional connectivity between neurons, as revealed in their spike trains recorded in cat primary visual cortex (area 18). During these recordings we reversibly deactivated feedback connections from higher visual areas in the pMS (posterior middle suprasylvian) cortex in order to study the impact of these top-down signals. Cross correlation was computed for every possible pair of the 16 electrodes in the electrode array. PARAFAC was then used to reveal the effects of time, stimulus, and deactivation condition on the correlation patterns. Our results show that PARAFAC is able to reliably extract changes in correlation strength for different experimental conditions and display the relevant features. Thus, PARAFAC proves to be well-suited for the use in the context of electrophysiological (action potential) recordings.},
+  journal = {Frontiers in Neuroinformatics},
+  author = {Schmitz, S. Katharina and Hasselbach, Philipp P. and Ebisch, Boris and Klein, Anja and Pipa, Gordon and Galuske, Ralf A. W.},
+  month = jan,
+  year = {2015},
+  keywords = {Valiante},
+  file = {C:\\Users\\saram\\Zotero\\storage\\C44T4H9X\\Schmitz et al. - 2015 - Application of Parallel Factor Analysis (PARAFAC) .pdf},
+  pmid = {25688205},
+  pmcid = {PMC4311613}
+}
+
+@article{Runyan2017,
+  title = {Distinct Timescales of Population Coding across Cortex},
+  volume = {advance online publication},
+  copyright = {\textcopyright{} 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.},
+  issn = {0028-0836},
+  doi = {10.1038/nature23020},
+  abstract = {The cortex represents information across widely varying timescales. For instance, sensory cortex encodes stimuli that fluctuate over few tens of milliseconds, whereas in association cortex behavioural choices can require the maintenance of information over seconds. However, it remains poorly understood whether diverse timescales result mostly from features intrinsic to individual neurons or from neuronal population activity. This question remains unanswered, because the timescales of coding in populations of neurons have not been studied extensively, and population codes have not been compared systematically across cortical regions. Here we show that population codes can be essential to achieve long coding timescales. Furthermore, we find that the properties of population codes differ between sensory and association cortices. We compared coding for sensory stimuli and behavioural choices in auditory cortex and posterior parietal cortex as mice performed a sound localization task. Auditory stimulus information was stronger in auditory cortex than in posterior parietal cortex, and both regions contained choice information. Although auditory cortex and posterior parietal cortex coded information by tiling in time neurons that were transiently informative for approximately 200 milliseconds, the areas had major differences in functional coupling between neurons, measured as activity correlations that could not be explained by task events. Coupling among posterior parietal cortex neurons was strong and extended over long time lags, whereas coupling among auditory cortex neurons was weak and short-lived. Stronger coupling in posterior parietal cortex led to a population code with long timescales and a representation of choice that remained consistent for approximately 1 second. In contrast, auditory cortex had a code with rapid fluctuations in stimulus and choice information over hundreds of milliseconds. Our results reveal that population codes differ across cortex and that coupling is a variable property of cortical populations that affects the timescale of information coding and the accuracy of behaviour.},
+  language = {en},
+  journal = {Nature},
+  author = {Runyan, Caroline A. and Piasini, Eugenio and Panzeri, Stefano and Harvey, Christopher D.},
+  month = jul,
+  year = {2017}
+}
+
+@misc{al,
+  title = {Application of {{Parallel Factor Analysis}} ({{PARAFAC}}) to Electrophysiological Data. - {{PubMed}} - {{NCBI}}},
+  abstract = {Front Neuroinform. 2015 Jan 30;8:84. doi: 10.3389/fninf.2014.00084. eCollection 2014.},
+  howpublished = {https://www.ncbi.nlm.nih.gov/pubmed/25688205},
+  author = {{al}, et, Schmitz SK}
+}
+
+@inproceedings{Sahani2003,
+  title = {Evidence Optimization Techniques for Estimating Stimulus-Response Functions},
+  booktitle = {Advances in Neural Information Processing Systems},
+  author = {Sahani, Maneesh and Linden, Jennifer F.},
+  year = {2003},
+  pages = {317--324},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FD3BZ22N\\nips02-evidence.pdf}
+}
+
+@article{Katz2016b,
+  title = {Dissociated Functional Significance of Decision-Related Activity in the Primate Dorsal Stream},
+  volume = {535},
+  copyright = {Copyright Nature Publishing Group Jul 14, 2016},
+  issn = {00280836},
+  doi = {http://dx.doi.org.myaccess.library.utoronto.ca/10.1038/nature18617},
+  abstract = {During decision making, neurons in multiple brain regions exhibit responses that are correlated with decisions. However, it remains uncertain whether or not various forms of decision-related activity are causally related to decision making. Here we address this question by recording and reversibly inactivating the lateral intraparietal (LIP) and middle temporal (MT) areas of rhesus macaques performing a motion direction discrimination task. Neurons in area LIP exhibited firing rate patterns that directly resembled the evidence accumulation process posited to govern decision making, with strong correlations between their response fluctuations and the animal's choices. Neurons in area MT, in contrast, exhibited weak correlations between their response fluctuations and choices, and had firing rate patterns consistent with their sensory role in motion encoding. The behavioural impact of pharmacological inactivation of each area was inversely related to their degree of decision-related activity: while inactivation of neurons in MT profoundly impaired psychophysical performance, inactivation in LIP had no measurable impact on decision-making performance, despite having silenced the very clusters that exhibited strong decision-related activity. Although LIP inactivation did not impair psychophysical behaviour, it did influence spatial selection and oculomotor metrics in a free-choice control task. The absence of an effect on perceptual decision making was stable over trials and sessions and was robust to changes in stimulus type and task geometry, arguing against several forms of compensation. Thus, decision-related signals in LIP do not appear to be critical for computing perceptual decisions, and may instead reflect secondary processes. Our findings highlight a dissociation between decision correlation and causation, showing that strong neuron-decision correlations do not necessarily offer direct access to the neural computations underlying decisions.},
+  language = {English},
+  number = {7611},
+  journal = {Nature; London},
+  author = {Katz, Leor N. and Yates, Jacob L. and Pillow, Jonathan W. and Huk, Alexander C.},
+  month = jul,
+  year = {2016},
+  keywords = {Neurons,Sciences: Comprehensive Works,Brain,Cognition \& reasoning,Decision making,Environmental Studies,Primates,Studies},
+  pages = {285-288K},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KXETQTVW\\Katz et al. - 2016 - Dissociated functional significance of decision-re.pdf}
+}
+
+@article{Levakova2017,
+  title = {Accuracy of Rate Coding: {{When}} Shorter Time Window and Higher Spontaneous Activity Help},
+  volume = {95},
+  shorttitle = {Accuracy of Rate Coding},
+  doi = {10.1103/PhysRevE.95.022310},
+  abstract = {It is widely accepted that neuronal firing rates contain a significant amount of information about the stimulus intensity. Nevertheless, theoretical studies on the coding accuracy inferred from the exact spike counting distributions are rare. We present an analysis based on the number of observed spikes assuming the stochastic perfect integrate-and-fire model with a change point, representing the stimulus onset, for which we calculate the corresponding Fisher information to investigate the accuracy of rate coding. We analyze the effect of changing the duration of the time window and the influence of several parameters of the model, in particular the level of the presynaptic spontaneous activity and the level of random fluctuation of the membrane potential, which can be interpreted as noise of the system. The results show that the Fisher information is nonmonotonic with respect to the length of the observation period. This counterintuitive result is caused by the discrete nature of the count of spikes. We observe also that the signal can be enhanced by noise, since the Fisher information is nonmonotonic with respect to the level of spontaneous activity and, in some cases, also with respect to the level of fluctuation of the membrane potential.},
+  number = {2},
+  journal = {Physical Review E},
+  author = {Levakova, Marie and Tamborrino, Massimiliano and Kostal, Lubomir and Lansky, Petr},
+  month = feb,
+  year = {2017},
+  pages = {022310}
+}
+
+@article{Lonardoni2017,
+  title = {Recurrently Connected and Localized Neuronal Communities Initiate Coordinated Spontaneous Activity in Neuronal Networks},
+  volume = {13},
+  issn = {1553-7358},
+  doi = {10.1371/journal.pcbi.1005672},
+  abstract = {Author summary Coordinated spontaneous spiking activity is fundamental for the normal formation of brain circuits during development. However, how ensembles of neurons generate these events remains unclear. To address this question, in the present study, we investigated the network properties that might be required to a neuronal system for the generation of these spontaneous waves of spikes. We performed our study on spontaneously active neuronal cell cultures using high-resolution electrical recordings and a computational network model developed to reproduce our experimental data both quantitatively and qualitatively. Through the analysis of both experimental and simulated data, we found that network bursts are initiated in regions of the network, or ``functional communities'', characterized by particular local connectivity properties. We also found that these regions can amplify the background asynchronous spiking activity preceding a network burst and, in this way, can give rise to coordinated spiking events. As a whole, our results suggest the presence of functional communities of neurons in a developing neuronal system that might naturally emerge by following simple constraints on distance-based connectivity. These regions are most likely required for the generation of the spontaneous coordinated activity that can drive activity-dependent circuit formation.},
+  number = {7},
+  journal = {PLOS Computational Biology},
+  author = {Lonardoni, Davide and Amin, Hayder and Marco, Stefano Di and Maccione, Alessandro and Berdondini, Luca and Nieus, Thierry},
+  month = jul,
+  year = {2017},
+  keywords = {Neurons,Neural networks,Gamma-aminobutyric acid,Network analysis,Simulation and modeling,Action potentials,Graphs,Network motifs},
+  pages = {e1005672},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8B59XS27\\Lonardoni et al. - 2017 - Recurrently connected and localized neuronal commu.pdf}
+}
+
+@article{Kass2011,
+  title = {{{ASSESSMENT OF SYNCHRONY IN MULTIPLE NEURAL SPIKE TRAINS USING LOGLINEAR POINT PROCESS MODELS}}},
+  volume = {5},
+  issn = {1932-6157},
+  abstract = {Neural spike trains, which are sequences of very brief jumps in voltage across the cell membrane, were one of the motivating applications for the development of point process methodology. Early work required the assumption of stationarity, but contemporary experiments often use time-varying stimuli and produce time-varying neural responses. More recently, many statistical methods have been developed for nonstationary neural point process data. There has also been much interest in identifying synchrony, meaning events across two or more neurons that are nearly simultaneous at the time scale of the recordings. A natural statistical approach is to discretize time, using short time bins, and to introduce loglinear models for dependency among neurons, but previous use of loglinear modeling technology has assumed stationarity. We introduce a succinct yet powerful class of time-varying loglinear models by (a) allowing individual-neuron effects (main effects) to involve time-varying intensities; (b) also allowing the individual-neuron effects to involve autocovariation effects (history effects) due to past spiking, (c) assuming excess synchrony effects (interaction effects) do not depend on history, and (d) assuming all effects vary smoothly across time. Using data from the primary visual cortex of an anesthetized monkey, we give two examples in which the rate of synchronous spiking cannot be explained by stimulusrelated changes in individual-neuron effects. In one example, the excess synchrony disappears when slow-wave "up" states are taken into account as history effects, while in the second example it does not. Standard point process theory explicitly rules out synchronous events. To justify our use of continuous-time methodology, we introduce a framework that incorporates synchronous events and provides continuous-time loglinear point process approximations to discrete-time loglinear models.},
+  number = {2B},
+  journal = {The Annals of Applied Statistics},
+  author = {Kass, Robert E. and Kelly, Ryan C. and Loh, Wei-Liem},
+  year = {2011},
+  pages = {1262-1292},
+  file = {C:\\Users\\saram\\Zotero\\storage\\YUYAEBNP\\23024851.pdf}
+}
+
+@misc{zotero-1058,
+  title = {{{AraiPLoSCompBio}}.Pdf},
+  howpublished = {http://www.stat.cmu.edu/\textasciitilde{}kass/papers/AraiPLoSCompBio.pdf},
+  file = {C:\\Users\\saram\\Zotero\\storage\\W4V7MWC8\\InferOsc2015rev4.pdf;C:\\Users\\saram\\Zotero\\storage\\8PM5XGWQ\\AraiPLoSCompBio.html}
+}
+
+@article{Bullmann2017,
+  title = {Network {{Analysis Of High}}-{{Density Microelectrode Recordings}}},
+  copyright = {\textcopyright{} 2017, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
+  doi = {10.1101/139436},
+  abstract = {A high-density microelectrode arrays (HDMEA) with 3,150 electrodes per square millimetre was used to capture neuronal activity across various scales, including axons, dendrites, and networks. We present a new method for high-throughput segmentation of axons based on the spatial smoothness of signal delays. Comparison with both ground truth and receiver operator characteristics shows that the new segmentation method outperforms previous methods based on the signal-amplitude-to-noise ratio. Structural and functional neuronal network connectivity were reconstructed using a common extension of ``Peter's rule'' and a inter-spike histogram method, respectively. Approximately one third of these connections are putative chemical synapses. We evaluated the spike patterns but did not find evidence for ``polychronisation'' (non-synchronous but precisely timed spike sequences). The developed framework can be used to investigate the relationship between the topology of neuronal connections and emerging temporal spike patterns observed in dissociated neuronal cultures.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Bullmann, Torsten and Radivojevic, Milos and Huber, Stefan T. and Deligkaris, Kosmas and Hierlemann, Andreas and Frey, Urs},
+  month = may,
+  year = {2017},
+  pages = {139436},
+  file = {C:\\Users\\saram\\Zotero\\storage\\GF85I2NZ\\Bullmann et al.  2017  Network Analysis Of High-Density Microelectrode Recordings.pdf}
+}
+
+@article{Schwartz2006,
+  title = {Spike-Triggered Neural Characterization},
+  volume = {6},
+  issn = {1534-7362},
+  doi = {10.1167/6.4.13},
+  number = {4},
+  journal = {Journal of Vision},
+  author = {Schwartz, Odelia and Pillow, Jonathan W. and Rust, Nicole C. and Simoncelli, Eero P.},
+  month = feb,
+  year = {2006},
+  pages = {13-13},
+  file = {C:\\Users\\saram\\Zotero\\storage\\23BIXZHX\\Schwartz et al. - 2006 - Spike-triggered neural characterization.pdf}
+}
+
+@article{Pillow2006,
+  title = {Dimensionality Reduction in Neural Models: {{An}} Information-Theoretic Generalization of Spike-Triggered Average and Covariance Analysis},
+  volume = {6},
+  issn = {1534-7362},
+  shorttitle = {Dimensionality Reduction in Neural Models},
+  doi = {10.1167/6.4.9},
+  number = {4},
+  journal = {Journal of Vision},
+  author = {Pillow, Jonathan W. and Simoncelli, Eero P.},
+  month = feb,
+  year = {2006},
+  pages = {9-9},
+  file = {C:\\Users\\saram\\Zotero\\storage\\LFM2XYK8\\Pillow and Simoncelli - 2006 - Dimensionality reduction in neural models An info.pdf}
+}
+
+@article{Rust2005,
+  title = {Spatiotemporal {{Elements}} of {{Macaque V1 Receptive Fields}}},
+  volume = {46},
+  issn = {0896-6273},
+  doi = {10.1016/j.neuron.2005.05.021},
+  abstract = {Neurons in primary visual cortex (V1) are commonly classified as simple or complex based upon their sensitivity to the sign of stimulus contrast. The responses of both cell types can be described by a general model in which the outputs of a set of linear filters are nonlinearly combined. We estimated the model for a population of V1 neurons by analyzing the mean and covariance of the spatiotemporal distribution of random bar stimuli that were associated with spikes. This analysis reveals an unsuspected richness of neuronal computation within V1. Specifically, simple and complex cell responses are best described using more linear filters than the one or two found in standard models. Many filters revealed by the model contribute suppressive signals that appear to have a predominantly divisive influence on neuronal firing. Suppressive signals are especially potent in direction-selective cells, where they reduce responses to stimuli moving in the nonpreferred direction.},
+  number = {6},
+  journal = {Neuron},
+  author = {Rust, Nicole C. and Schwartz, Odelia and Movshon, J. Anthony and Simoncelli, Eero P.},
+  month = jun,
+  year = {2005},
+  pages = {945-956},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DYLREG49\\Rust et al. - 2005 - Spatiotemporal Elements of Macaque V1 Receptive Fi.pdf}
+}
+
+@article{Sharpee2002,
+  archivePrefix = {arXiv},
+  eprinttype = {arxiv},
+  eprint = {physics/0212110},
+  title = {Analyzing Neural Responses to Natural Signals: {{Maximally}} Informative Dimensions},
+  shorttitle = {Analyzing Neural Responses to Natural Signals},
+  abstract = {We propose a method that allows for a rigorous statistical analysis of neural responses to natural stimuli which are non-Gaussian and exhibit strong correlations. We have in mind a model in which neurons are selective for a small number of stimulus dimensions out of a high dimensional stimulus space, but within this subspace the responses can be arbitrarily nonlinear. Existing analysis methods are based on correlation functions between stimuli and responses, but these methods are guaranteed to work only in the case of Gaussian stimulus ensembles. As an alternative to correlation functions, we maximize the mutual information between the neural responses and projections of the stimulus onto low dimensional subspaces. The procedure can be done iteratively by increasing the dimensionality of this subspace. Those dimensions that allow the recovery of all of the information between spikes and the full unprojected stimuli describe the relevant subspace. If the dimensionality of the relevant subspace indeed is small, it becomes feasible to map the neuron's input-output function even under fully natural stimulus conditions. These ideas are illustrated in simulations on model visual and auditory neurons responding to natural scenes and sounds, respectively.},
+  journal = {arXiv:physics/0212110},
+  author = {Sharpee, Tatyana and Rust, Nicole C. and Bialek, William},
+  month = dec,
+  year = {2002},
+  keywords = {Physics - Biological Physics,Physics - Data Analysis; Statistics and Probability,Quantitative Biology - Neurons and Cognition},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6Q63LTSB\\sharpee+al_03a.pdf;C:\\Users\\saram\\Zotero\\storage\\DI22FCIG\\Sharpee et al. - 2002 - Analyzing neural responses to natural signals Max.pdf;C:\\Users\\saram\\Zotero\\storage\\RB4EGFYR\\sharpee+al_04.pdf}
+}
+
+@article{Truccolo2005,
+  title = {A {{Point Process Framework}} for {{Relating Neural Spiking Activity}} to {{Spiking History}}, {{Neural Ensemble}}, and {{Extrinsic Covariate Effects}}},
+  volume = {93},
+  copyright = {Copyright \textcopyright{} 2005 by the American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00697.2004},
+  abstract = {Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.},
+  language = {en},
+  number = {2},
+  journal = {Journal of Neurophysiology},
+  author = {Truccolo, Wilson and Eden, Uri T. and Fellows, Matthew R. and Donoghue, John P. and Brown, Emery N.},
+  month = feb,
+  year = {2005},
+  pages = {1074-1089},
+  file = {C:\\Users\\saram\\Zotero\\storage\\C9JCIH58\\Truccolo et al. - 2005 - A Point Process Framework for Relating Neural Spik.pdf},
+  pmid = {15356183}
+}
+
+@article{Williamson2015,
+  title = {The {{Equivalence}} of {{Information}}-{{Theoretic}} and {{Likelihood}}-{{Based Methods}} for {{Neural Dimensionality Reduction}}},
+  volume = {11},
+  issn = {1553-734X},
+  doi = {10.1371/journal.pcbi.1004141},
+  abstract = {Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as ``single-spike information'' to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex., A popular approach to the neural coding problem is to identify a low-dimensional linear projection of the stimulus space that preserves the aspects of the stimulus that affect a neuron's probability of spiking. Previous work has focused on both information-theoretic and likelihood-based estimators for finding such projections. Here, we show that these two approaches are in fact equivalent. We show that maximally informative dimensions (MID), a popular information-theoretic method for dimensionality reduction, is identical to the maximum-likelihood estimator for a particular linear-nonlinear encoding model with Poisson spiking. One implication of this equivalence is that MID may not find the information-theoretically optimal stimulus projection when spiking is non-Poisson, which we illustrate with a few simple examples. Using these insights, we propose novel dimensionality-reduction methods that incorporate non-Poisson spiking, and suggest new parametrizations that allow for tractable estimation of high-dimensional subspaces.},
+  number = {4},
+  journal = {PLoS Computational Biology},
+  author = {Williamson, Ross S. and Sahani, Maneesh and Pillow, Jonathan W.},
+  month = apr,
+  year = {2015},
+  file = {C:\\Users\\saram\\Zotero\\storage\\B9K8P27Q\\Williamson et al.  2015  The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction.pdf},
+  pmid = {25831448},
+  pmcid = {PMC4382343}
+}
+
+@article{Brenner2000,
+  title = {Synergy in a Neural Code},
+  volume = {12},
+  issn = {0899-7667},
+  abstract = {We show that the information carried by compound events in neural spike trains-patterns of spikes across time or across a population of cells-can be measured, independent of assumptions about what these patterns might represent. By comparing the information carried by a compound pattern with the information carried independently by its parts, we directly measure the synergy among these parts. We illustrate the use of these methods by applying them to experiments on the motion-sensitive neuron H1 of the fly's visual system, where we confirm that two spikes close together in time carry far more than twice the information carried by a single spike. We analyze the sources of this synergy and provide evidence that pairs of spikes close together in time may be especially important patterns in the code of H1.},
+  language = {eng},
+  number = {7},
+  journal = {Neural Computation},
+  author = {Brenner, N. and Strong, S. P. and Koberle, R. and Bialek, W. and {de Ruyter van Steveninck}, R. R.},
+  month = jul,
+  year = {2000},
+  keywords = {Animals,Models; Neurological,Action potentials,Diptera,Neurons; Afferent,Pattern Recognition; Visual,Visual Pathways},
+  pages = {1531-1552},
+  file = {C:\\Users\\saram\\Zotero\\storage\\F5SVC682\\brenner+al_00b.pdf},
+  pmid = {10935917}
+}
+
+@misc{zotero-1085,
+  title = {Entropy and {{Information}} in {{Neural Spike Trains}} -- {{PhysRevLett}}.80.197.Pdf},
+  howpublished = {http://www.cnd.mcgill.ca/\textasciitilde{}ivan/neuro\%20information\%20theory/Entropy\%20and\%20Information\%20in\%20Neural\%20Spike\%20Trains\%20--\%20PhysRevLett.80.197.pdf},
+  file = {C:\\Users\\saram\\Zotero\\storage\\X2WA6MUQ\\Entropy and Information in Neural Spike Trains -- PhysRevLett.80.197.html}
+}
+
+@article{Strong1996,
+  archivePrefix = {arXiv},
+  eprinttype = {arxiv},
+  eprint = {cond-mat/9603127},
+  title = {Entropy and {{Information}} in {{Neural Spike Trains}}},
+  abstract = {The nervous system represents time-dependent signals in sequences of discrete action potentials or spikes, all spikes are identical so that information is carried only in the spike arrival times. We show how to quantify this information, in bits, free from any assumptions about which features of the spike train or input waveform are most important. We apply this approach to the analysis of experiments on a motion-sensitive neuron in the fly visual system.},
+  journal = {arXiv:cond-mat/9603127},
+  author = {Strong, S. P. and Koberle, Roland and {van Steveninck}, Rob R. de Ruyter and Bialek, William},
+  month = mar,
+  year = {1996},
+  keywords = {Quantitative Biology - Neurons and Cognition,Condensed Matter},
+  file = {C:\\Users\\saram\\Zotero\\storage\\W79WDIGX\\Strong et al.  1996  Entropy and Information in Neural Spike Trains.pdf}
+}
+
+@misc{zotero-1091,
+  title = {{{PSB}} - {{Abstract}}},
+  howpublished = {http://psb.stanford.edu/psb-online/proceedings/psb98/abstracts/p621.html},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TUUQW3V2\\p621.html}
+}
+
+@article{Strong1998,
+  title = {On the Application of Information Theory to Neural Spike Trains},
+  issn = {2335-6936},
+  abstract = {The nervous system represents time-dependent signals in sequences of discrete action potentials or spikes are identical so that information is carried only in the spike arrival times. We show how to quantify this information, in bits, free from any assumptions about which features of the spike train or input waveform are most important. We apply this approach to the analysis of experiments on a variety of systems, including some where we confront severe sampling problems, and discuss some to the results obtained and hopes for future extensions.},
+  language = {eng},
+  journal = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
+  author = {Strong, S. P. and {de Ruyter van Steveninck}, R. R. and Bialek, W. and Koberle, R.},
+  year = {1998},
+  keywords = {Neurons,Animals,Brain,Humans,Models; Neurological,information theory,Visual Perception,Entropy,Diptera,Optic Nerve,Probability,Visual Fields},
+  pages = {621-632},
+  file = {C:\\Users\\saram\\Zotero\\storage\\H7FXJAPH\\strong+al_98c.pdf},
+  pmid = {9697217}
+}
+
+@article{Karimipanah2017,
+  title = {Criticality Predicts Maximum Irregularity in Recurrent Networks of Excitatory Nodes},
+  volume = {12},
+  issn = {1932-6203},
+  doi = {10.1371/journal.pone.0182501},
+  abstract = {A rigorous understanding of brain dynamics and function requires a conceptual bridge between multiple levels of organization, including neural spiking and network-level population activity. Mounting evidence suggests that neural networks of cerebral cortex operate at a critical regime, which is defined as a transition point between two phases of short lasting and chaotic activity. However, despite the fact that criticality brings about certain functional advantages for information processing, its supporting evidence is still far from conclusive, as it has been mostly based on power law scaling of size and durations of cascades of activity. Moreover, to what degree such hypothesis could explain some fundamental features of neural activity is still largely unknown. One of the most prevalent features of cortical activity in vivo is known to be spike irregularity of spike trains, which is measured in terms of the coefficient of variation (CV) larger than one. Here, using a minimal computational model of excitatory nodes, we show that irregular spiking (CV $>$ 1) naturally emerges in a recurrent network operating at criticality. More importantly, we show that even at the presence of other sources of spike irregularity, being at criticality maximizes the mean coefficient of variation of neurons, thereby maximizing their spike irregularity. Furthermore, we also show that such a maximized irregularity results in maximum correlation between neuronal firing rates and their corresponding spike irregularity (measured in terms of CV). On the one hand, using a model in the universality class of directed percolation, we propose new hallmarks of criticality at single-unit level, which could be applicable to any network of excitable nodes. On the other hand, given the controversy of the neural criticality hypothesis, we discuss the limitation of this approach to neural systems and to what degree they support the criticality hypothesis in real neural networks. Finally, we discuss the limitations of applying our results to real networks and to what degree they support the criticality hypothesis.},
+  number = {8},
+  journal = {PLOS ONE},
+  author = {Karimipanah, Yahya and Ma, Zhengyu and Wessel, Ralf},
+  month = aug,
+  year = {2017},
+  keywords = {Neurons,Neural networks,Network analysis,Single neuron function,Action potentials,Cerebral cortex,Eigenvalues,Neuronal tuning,Valiante suggested},
+  pages = {e0182501},
+  file = {C:\\Users\\saram\\Zotero\\storage\\WAMANXVR\\Karimipanah et al. - 2017 - Criticality predicts maximum irregularity in recur.pdf}
+}
+
+@article{Islam2014a,
+  title = {Artifact Characterization and Removal for in Vivo Neural Recording},
+  volume = {226},
+  issn = {1872-678X},
+  doi = {10.1016/j.jneumeth.2014.01.027},
+  abstract = {BACKGROUND: In vivo neural recordings are often corrupted by different artifacts, especially in a less-constrained recording environment. Due to limited understanding of the artifacts appeared in the in vivo neural data, it is more challenging to identify artifacts from neural signal components compared with other applications. The objective of this work is to analyze artifact characteristics and to develop an algorithm for automatic artifact detection and removal without distorting the signals of interest.
+NEW METHOD: The proposed algorithm for artifact detection and removal is based on the stationary wavelet transform with selected frequency bands of neural signals. The selection of frequency bands is based on the spectrum characteristics of in vivo neural data. Further, to make the proposed algorithm robust under different recording conditions, a modified universal-threshold value is proposed.
+RESULTS: Extensive simulations have been performed to evaluate the performance of the proposed algorithm in terms of both amount of artifact removal and amount of distortion to neural signals. The quantitative results reveal that the algorithm is quite robust for different artifact types and artifact-to-signal ratio.
+COMPARISON WITH EXISTING METHODS: Both real and synthesized data have been used for testing the proposed algorithm in comparison with other artifact removal algorithms (e.g., ICA, wICA, wCCA, EMD-ICA, and EMD-CCA) found in the literature. Comparative testing results suggest that the proposed algorithm performs better than the available algorithms.
+CONCLUSION: Our work is expected to be useful for future research on in vivo neural signal processing and eventually to develop a real-time neural interface for advanced neuroscience and behavioral experiments.},
+  language = {eng},
+  journal = {Journal of Neuroscience Methods},
+  author = {Islam, Md Kafiul and Rastegarnia, Amir and Nguyen, Anh Tuan and Yang, Zhi},
+  month = apr,
+  year = {2014},
+  keywords = {Neurons,Animals,Time Factors,Electrophysiology,Rats,Artifacts,Algorithms,Signal Processing; Computer-Assisted,Models; Neurological,Computer Simulation,Action potentials,ROC Curve,Wavelet Analysis,Artifact characterization,In vivo neural recording,Neural signal spectra,Wavelet transform,hippocampus},
+  pages = {110-123},
+  pmid = {24512692}
+}
+
+@article{Islam2014,
+  title = {Artifact Characterization and Removal for in Vivo Neural Recording},
+  volume = {226},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2014.01.027},
+  abstract = {In vivo neural recordings are often corrupted by different artifacts, especially in a less-constrained recording environment. Due to limited understanding of the artifacts appeared in the in vivo neural data, it is more challenging to identify artifacts from neural signal components compared with other applications. The objective of this work is to analyze artifact characteristics and to develop an algorithm for automatic artifact detection and removal without distorting the signals of interest. The proposed algorithm for artifact detection and removal is based on the stationary wavelet transform with selected frequency bands of neural signals. The selection of frequency bands is based on the spectrum characteristics of in vivo neural data. Further, to make the proposed algorithm robust under different recording conditions, a modified universal-threshold value is proposed. Extensive simulations have been performed to evaluate the performance of the proposed algorithm in terms of both amount of artifact removal and amount of distortion to neural signals. The quantitative results reveal that the algorithm is quite robust for different artifact types and artifact-to-signal ratio. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other artifact removal algorithms (e.g. ICA, wICA, wCCA, EMD-ICA, and EMD-CCA) found in the literature. Comparative testing results suggest that the proposed algorithm performs better than the available algorithms. Our work is expected to be useful for future research on in vivo neural signal processing and eventually to develop a real-time neural interface for advanced neuroscience and behavioral experiments.},
+  journal = {Journal of Neuroscience Methods},
+  author = {Islam, Md Kafiul and Rastegarnia, Amir and Nguyen, Anh Tuan and Yang, Zhi},
+  month = apr,
+  year = {2014},
+  keywords = {Artifact characterization,Neural signal spectra,Wavelet transform,neural recording},
+  pages = {110-123},
+  file = {C:\\Users\\saram\\Zotero\\storage\\9VB6DJN8\\Islam et al.  2014  Artifact characterization and removal for in vivo neural recording.pdf}
+}
+
+@article{Newman2013,
+  title = {Cholinergic {{Blockade Reduces Theta}}-{{Gamma Phase Amplitude Coupling}} and {{Speed Modulation}} of {{Theta Frequency Consistent}} with {{Behavioral Effects}} on {{Encoding}}},
+  volume = {33},
+  issn = {0270-6474},
+  doi = {10.1523/JNEUROSCI.2586-13.2013},
+  abstract = {Large-scale neural activation dynamics in the hippocampal-entorhinal circuit local field potential, observable as theta and gamma rhythms and coupling between these rhythms, is predictive of encoding success. Behavioral studies show that systemic administration of muscarinic acetylcholine receptor antagonists selectively impairs encoding, suggesting that they may also disrupt the coupling between the theta and gamma bands. Here, we tested the hypothesis that muscarinic antagonists selectively disrupt coupling between theta and gamma. Specifically, we characterized the effects of systemically administered scopolamine on movement-induced theta and gamma rhythms recorded in the superficial layers of the medial entorhinal cortex (MEC) of freely moving rats. We report the novel result that gamma power at the peak of theta was most reduced following muscarinic blockade, significantly shifting the phase of maximal gamma power to occur at later phases of theta. We also characterize the existence of multiple distinct gamma bands in the superficial layers of the MEC. Further, we observed that theta frequency was significantly less modulated by movement speed following muscarinic blockade. Finally, the slope relating speed to theta frequency, a correlate of familiarity with a testing enclosure, increased significantly less between the preinjection and recovery trials when scopolamine was administered during the intervening injection session than when saline was administered, suggesting that scopolamine reduced encoding of the testing enclosure. These data are consistent with computational models suggesting that encoding and retrieval occur during the peak and trough of theta, respectively, and support the theory that acetylcholine regulates the balance between encoding versus retrieval.},
+  number = {50},
+  journal = {The Journal of Neuroscience},
+  author = {Newman, Ehren L. and Gillet, Shea N. and Climer, Jason R. and Hasselmo, Michael E.},
+  month = dec,
+  year = {2013},
+  keywords = {Suhan sent},
+  pages = {19635-19646},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SSI3W9KS\\ScopolamineThetaGamma.pdf},
+  pmid = {24336727},
+  pmcid = {PMC3858632}
+}
+
+@article{Zhang2016,
+  title = {Impaired Theta-Gamma Coupling in {{APP}}-Deficient Mice},
+  volume = {6},
+  issn = {2045-2322},
+  doi = {10.1038/srep21948},
+  abstract = {Amyloid precursor protein (APP) is critically involved in the pathophysiology of Alzheimer's disease, but its physiological functions remain elusive. Importantly, APP knockout (APP-KO) mice exhibit cognitive deficits, suggesting that APP plays a role at the neuronal network level. To investigate this possibility, we recorded local field potentials (LFPs) from the posterior parietal cortex, dorsal hippocampus and lateral prefrontal cortex of freely moving APP-KO mice. Spectral analyses showed that network oscillations within the theta- and gamma-frequency bands were not different between APP-KO and wild-type mice. Surprisingly, however, while gamma amplitude coupled to theta phase in all recorded regions of wild-type animals, in APP-KO mice theta-gamma coupling was strongly diminished in recordings from the parietal cortex and hippocampus, but not in LFPs recorded from the prefrontal cortex. Thus, lack of APP reduces oscillatory coupling in LFP recordings from specific brain regions, despite not affecting the amplitude of the oscillations. Together, our findings reveal reduced cross-frequency coupling as a functional marker of APP deficiency at the network level.},
+  language = {eng},
+  journal = {Scientific Reports},
+  author = {Zhang, Xiaomin and Zhong, Wewei and Branka{\v c}k, Jurij and Weyer, Sascha W. and M{\"u}ller, Ulrike C. and Tort, Adriano B. L. and Draguhn, Andreas},
+  month = feb,
+  year = {2016},
+  keywords = {Animals,Nerve Net,Prefrontal Cortex,Amyloid beta-Protein Precursor,Cognitive Dysfunction,Gamma Rhythm,Mice,Mice; Inbred C57BL,Mice; Knockout,Parietal Lobe,Stereotaxic Techniques,Theta Rhythm,hippocampus},
+  pages = {21948},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8HU9WK2T\\srep21948-s1.pdf;C:\\Users\\saram\\Zotero\\storage\\Z9UWTMW9\\ThetaGamma_ScReports_2016.pdf},
+  pmid = {26905287},
+  pmcid = {PMC4764939}
+}
+
+@article{Shirvalkar2010a,
+  title = {Bidirectional Changes to Hippocampal Theta-Gamma Comodulation Predict Memory for Recent Spatial Episodes},
+  volume = {107},
+  issn = {1091-6490},
+  doi = {10.1073/pnas.0911184107},
+  abstract = {Episodic memory requires the hippocampus, which is thought to bind cortical inputs into conjunctive codes. Local field potentials (LFPs) reflect dendritic and synaptic oscillations whose temporal structure may coordinate cellular mechanisms of plasticity and memory. We now report that single-trial spatial memory performance in rats was predicted by the power comodulation of theta (4-10 Hz) and low gamma (30-50 Hz) rhythms in the hippocampus. Theta-gamma comodulation (TGC) was prominent during successful memory retrieval but was weak when memory failed or was unavailable during spatial exploration in sample trials. Muscimol infusion into medial septum reduced the probability of TGC and successful memory retrieval. In contrast, patterned electrical stimulation of the fimbria-fornix increased TGC in amnestic animals and partially rescued memory performance in the water maze. The results suggest that TGC accompanies memory retrieval in the hippocampus and that patterned brain stimulation may inform therapeutic strategies for cognitive disorders.},
+  language = {eng},
+  number = {15},
+  journal = {Proceedings of the National Academy of Sciences of the United States of America},
+  author = {Shirvalkar, Prasad R. and Rapp, Peter R. and Shapiro, Matthew L.},
+  month = apr,
+  year = {2010},
+  keywords = {Animals,Electrodes,Electrophysiology,Rats,Brain,Male,Memory,Cognition Disorders,Behavior; Animal,Fornix; Brain,Maze Learning,Muscimol,Rats; Long-Evans,Septum of Brain,Suhan sent,hippocampus},
+  pages = {7054-7059},
+  file = {C:\\Users\\saram\\Zotero\\storage\\UIURQ346\\OK-2010_Shirvalkar Supp Infor.pdf;C:\\Users\\saram\\Zotero\\storage\\WEEQ2BDC\\OK-2010_Shirvalkar.pdf},
+  pmid = {20351262},
+  pmcid = {PMC2872428}
+}
+
+@article{Mokri2017,
+  title = {Sorting {{Overlapping Spike Waveforms}} from {{Electrode}} and {{Tetrode Recordings}}},
+  volume = {11},
+  issn = {1662-5196},
+  doi = {10.3389/fninf.2017.00053},
+  abstract = {One of the outstanding problems in the sorting of neuronal spike trains is the resolution of overlapping spikes. Resolving these spikes can significantly improve a range of analyses, such as response variability, correlation, and latency. In this paper, we describe a partially automated method that is capable of resolving overlapping spikes. After constructing template waveforms for well-isolated and distinct single units, we generated pair-wise combinations of those templates at all possible time shifts from each other. Subsequently, overlapping waveforms were identified by cluster analysis, and then assigned to their respective single-unit combinations. We examined the performance of this method using simulated data from an earlier study, and found that we were able to resolve an average of 83\% of the overlapping waveforms across various signal-to-noise ratios, an improvement of approximately 32\% over the results reported in the earlier study. When applied to additional simulated data sets generated from single-electrode and tetrode recordings, we were able to resolve 91\% of the overlapping waveforms with a false positive rate of 0.19\% for single-electrode data, and 95\% of the overlapping waveforms with a false positive rate of 0.27\% for tetrode data. We also applied our method to electrode and tetrode data recorded from the primary visual cortex, and the results obtained for these datasets suggest that our method provides an efficient means of sorting overlapping waveforms. This method can easily be added as an extra step to commonly used spike sorting methods, such as KlustaKwik and MClust software packages, and can be applied to datasets that have already been sorted using these methods.},
+  language = {English},
+  journal = {Frontiers in Neuroinformatics},
+  author = {Mokri, Yasamin and Salazar, Rodrigo F. and Goodell, Baldwin and Baker, Jonathan and Gray, Charles M. and Yen, Shih-Cheng},
+  year = {2017},
+  keywords = {Electrophysiology,Spike sorting,tetrode,visual cortex,Overlapping Waveforms},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FDPVNR8N\\fninf-11-00053.pdf}
+}
+
+@article{Henze2009,
+  title = {Simultaneous Intracellular and Extracellular Recordings from Hippocampus Region {{CA1}} of Anesthetized Rats},
+  doi = {http://dx.doi.org/10.6080/K02Z13FP},
+  author = {Henze, Darrell A. and Harris, Kenneth D and Borhegyi, Zsolt and Csicsvari, Jozsef and Mamiya, Akira and Hirase, Hajime and Sirota, A. and Buzsaki, G.},
+  year = {2009},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JQWS8ZA3\\hc-1.html}
+}
+
+@article{Ardid2015,
+  title = {Mapping of {{Functionally Characterized Cell Classes}} onto {{Canonical Circuit Operations}} in {{Primate Prefrontal Cortex}}},
+  volume = {35},
+  copyright = {Copyright \textcopyright{} 2015 the authors 0270-6474/15/352975-17\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.2700-14.2015},
+  abstract = {Microcircuits are composed of multiple cell classes that likely serve unique circuit operations. But how cell classes map onto circuit functions is largely unknown, particularly for primate prefrontal cortex during actual goal-directed behavior. One difficulty in this quest is to reliably distinguish cell classes in extracellular recordings of action potentials. Here we surmount this issue and report that spike shape and neural firing variability provide reliable markers to segregate seven functional classes of prefrontal cells in macaques engaged in an attention task. We delineate an unbiased clustering protocol that identifies four broad spiking (BS) putative pyramidal cell classes and three narrow spiking (NS) putative inhibitory cell classes dissociated by how sparse, bursty, or regular they fire. We speculate that these functional classes map onto canonical circuit functions. First, two BS classes show sparse, bursty firing, and phase synchronize their spiking to 3\textendash{}7 Hz (theta) and 12\textendash{}20 Hz (beta) frequency bands of the local field potential (LFP). These properties make cells flexibly responsive to network activation at varying frequencies. Second, one NS and two BS cell classes show regular firing and higher rate with only marginal synchronization preference. These properties are akin to setting tonically the excitation and inhibition balance. Finally, two NS classes fired irregularly and synchronized to either theta or beta LFP fluctuations, tuning them potentially to frequency-specific subnetworks. These results suggest that a limited set of functional cell classes emerges in macaque prefrontal cortex (PFC) during attentional engagement to not only represent information, but to subserve basic circuit operations.},
+  language = {en},
+  number = {7},
+  journal = {Journal of Neuroscience},
+  author = {Ardid, Salva and Vinck, Martin and Kaping, Daniel and Marquez, Susanna and Everling, Stefan and Womelsdorf, Thilo},
+  month = feb,
+  year = {2015},
+  keywords = {clustering,synchronization,anterior cingulate cortex,cell types,nonhuman primates,variability},
+  pages = {2975-2991},
+  file = {C:\\Users\\saram\\Zotero\\storage\\3WJ8P8UC\\Ardid et al.  2015  Mapping of Functionally Characterized Cell Classes onto Canonical Circuit Operations in Primate Prefrontal Cortex.pdf},
+  pmid = {25698735}
+}
+
+@article{Quiroga2004,
+  title = {Unsupervised {{Spike Detection}} and {{Sorting}} with {{Wavelets}} and {{Superparamagnetic Clustering}}},
+  volume = {16},
+  issn = {0899-7667},
+  doi = {10.1162/089976604774201631},
+  number = {8},
+  journal = {Neural Computation},
+  author = {Quiroga, R. Quian and Nadasdy, Z. and Ben-Shaul, Y.},
+  month = aug,
+  year = {2004},
+  pages = {1661-1687},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6MMQB5CE\\Quiroga, 2004, unsupervised Spike_sorting.pdf;C:\\Users\\saram\\Zotero\\storage\\C7AX56N6\\Spike_Sorting_Tutorial.ppt;C:\\Users\\saram\\Zotero\\storage\\GBZ2JAIA\\wave_clus_intro.pdf;C:\\Users\\saram\\Zotero\\storage\\N63IEX2K\\Spike Sorting Talk.ppt}
+}
+
+@article{Hill2011,
+  title = {Quality Metrics to Accompany Spike Sorting of Extracellular Signals},
+  volume = {31},
+  issn = {0270-6474},
+  doi = {10.1523/JNEUROSCI.0971-11.2011},
+  number = {24},
+  journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
+  author = {Hill, Daniel N. and Mehta, Samar B. and Kleinfeld, David},
+  month = jun,
+  year = {2011},
+  pages = {8699-8705},
+  file = {C:\\Users\\saram\\Zotero\\storage\\32MQSI22\\Hill 2011-Quality Metrics to Accompany Spike Sorting of Extracellular Signals.pdf;C:\\Users\\saram\\Zotero\\storage\\AP9RV9UJ\\Hill et al.  2011  Quality metrics to accompany spike sorting of extracellular signals.pdf},
+  pmid = {21677152},
+  pmcid = {PMC3123734}
+}
+
+@article{Suzuki2017,
+  title = {Dendritic Calcium Spikes Are Clearly Detectable at the Cortical Surface},
+  volume = {8},
+  copyright = {2017 The Author(s)},
+  issn = {2041-1723},
+  doi = {10.1038/s41467-017-00282-4},
+  abstract = {Cortical surface recording techniques such as EEG and ECoG are widely used for measuring brain activity. The prevailing assumption is that surface potentials primarily reflect synaptic activity, although non-synaptic events may also contribute. Here we show that dendritic calcium spikes occurring in pyramidal neurons (that we showed previously are cognitively relevant) are clearly detectable in cortical surface potentials. To show this we developed an optogenetic, non-synaptic approach to evoke dendritic calcium spikes in vivo. We found that optogenetically evoked calcium spikes were easily detectable and had an unexpected waveform near the cortical surface. Sensory-evoked dendritic calcium spikes were also clearly detectable with amplitudes that matched the contribution of synaptic input. These results reveal how dendritic calcium spikes appear at the cortical surface and their significant impact on surface potentials, suggesting that long-standing surface recording data may contain information about dendritic activity that is relevant to behavior and cognitive function. Surface EEG recordings are thought to primarily detect synaptic activity. Here the authors devise an optogenetic method to evoke dendritic calcium spikes in layer 5 pyramidal cells of the rat somatosensory cortex, and report that optogenetically evoked, as well as sensory-evoked dendritic calcium spikes make a significant contribution to surface EEG recordings.},
+  language = {En},
+  number = {1},
+  journal = {Nature Communications},
+  author = {Suzuki, Mototaka and Larkum, Matthew E.},
+  month = aug,
+  year = {2017},
+  pages = {276},
+  file = {C:\\Users\\saram\\Zotero\\storage\\YCD6AM79\\Suzuki and Larkum - 2017 - Dendritic calcium spikes are clearly detectable at.pdf}
+}
+
+@article{Pillow2017,
+  title = {Is Population Activity More than the Sum of Its Parts?},
+  volume = {20},
+  copyright = {\textcopyright{} 2017 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1097-6256},
+  doi = {10.1038/nn.4627},
+  abstract = {A study introduces innovative ways to test whether neural population activity exhibits structure above and beyond that of its basic components.},
+  language = {en},
+  number = {9},
+  journal = {Nature Neuroscience},
+  author = {Pillow, Jonathan W. and Aoi, Mikio C.},
+  month = sep,
+  year = {2017},
+  keywords = {Network models,Dynamical systems},
+  pages = {1196-1198},
+  file = {C:\\Users\\saram\\Zotero\\storage\\VM93CLKY\\Pillow and Aoi - 2017 - Is population activity more than the sum of its pa.pdf}
+}
+
+@article{Brown2004,
+  title = {Multiple Neural Spike Train Data Analysis: State-of-the-Art and Future Challenges},
+  volume = {7},
+  copyright = {\textcopyright{} 2004 Nature Publishing Group},
+  issn = {1097-6256},
+  shorttitle = {Multiple Neural Spike Train Data Analysis},
+  doi = {10.1038/nn1228},
+  abstract = {Multiple electrodes are now a standard tool in neuroscience research that make it possible to study the simultaneous activity of several neurons in a given brain region or across different regions. The data from multi-electrode studies present important analysis challenges that must be resolved for optimal use of these neurophysiological measurements to answer questions about how the brain works. Here we review statistical methods for the analysis of multiple neural spike-train data and discuss future challenges for methodology research.},
+  language = {en},
+  number = {5},
+  journal = {Nature Neuroscience},
+  author = {Brown, Emery N. and Kass, Robert E. and Mitra, Partha P.},
+  month = may,
+  year = {2004},
+  pages = {456-461},
+  file = {C:\\Users\\saram\\Zotero\\storage\\M8YCBQY5\\Brown et al. - 2004 - Multiple neural spike train data analysis state-o.pdf}
+}
+
+@article{Jackel2017,
+  title = {Combination of {{High}}-Density {{Microelectrode Array}} and {{Patch Clamp Recordings}} to {{Enable Studies}} of {{Multisynaptic Integration}}},
+  volume = {7},
+  copyright = {2017 The Author(s)},
+  issn = {2045-2322},
+  doi = {10.1038/s41598-017-00981-4},
+  abstract = {We present a novel, all-electric approach to record and to precisely control the activity of tens of individual presynaptic neurons. The method allows for parallel mapping of the efficacy of multiple synapses and of the resulting dynamics of postsynaptic neurons in a cortical culture. For the measurements, we combine an extracellular high-density microelectrode array, featuring 11'000 electrodes for extracellular recording and stimulation, with intracellular patch-clamp recording. We are able to identify the contributions of individual presynaptic neurons - including inhibitory and excitatory synaptic inputs - to postsynaptic potentials, which enables us to study dendritic integration. Since the electrical stimuli can be controlled at microsecond resolution, our method enables to evoke action potentials at tens of presynaptic cells in precisely orchestrated sequences of high reliability and minimum jitter. We demonstrate the potential of this method by evoking short- and long-term synaptic plasticity through manipulation of multiple synaptic inputs to a specific neuron.},
+  language = {En},
+  number = {1},
+  journal = {Scientific Reports},
+  author = {J{\"a}ckel, David and Bakkum, Douglas J. and Russell, Thomas L. and M{\"u}ller, Jan and Radivojevic, Milos and Frey, Urs and Franke, Felix and Hierlemann, Andreas},
+  month = apr,
+  year = {2017},
+  pages = {978},
+  file = {C:\\Users\\saram\\Zotero\\storage\\EBB3C6B6\\Jäckel et al. - 2017 - Combination of High-density Microelectrode Array a.pdf}
+}
+
+@article{Shein-Idelson2017,
+  title = {Large-Scale Mapping of Cortical Synaptic Projections with Extracellular Electrode Arrays},
+  volume = {14},
+  copyright = {\textcopyright{} 2017 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1548-7091},
+  doi = {10.1038/nmeth.4393},
+  abstract = {Understanding circuit computation in the nervous system requires sampling activity over large neural populations and maximizing the number of features that can be extracted. By combining planar arrays of extracellular electrodes with the three-layered cortex of turtles, we show that synaptic signals induced along individual axons as well as action potentials can be easily captured. Two types of information can be extracted from these signals, the neuronal subtype (inhibitory or excitatory)\textemdash{}whose identification is more reliable than with traditional measures such as action potential width\textemdash{}and a (partial) spatial map of functional axonal projections from individual neurons. Because our approach is algorithmic, it can be carried out in parallel on hundreds of simultaneously recorded neurons. Combining our approach with soma triangulation, we reveal an axonal projection bias among a population of pyramidal neurons in turtle cortex and confirm this bias through anatomical reconstructions.},
+  language = {en},
+  number = {9},
+  journal = {Nature Methods},
+  author = {Shein-Idelson, Mark and Pammer, Lorenz and Hemberger, Mike and Laurent, Gilles},
+  month = sep,
+  year = {2017},
+  keywords = {Extracellular recording,Cellular neuroscience},
+  pages = {882-890},
+  file = {C:\\Users\\saram\\Zotero\\storage\\H2CXPQFC\\Shein-Idelson et al. - 2017 - Large-scale mapping of cortical synaptic projectio.pdf}
+}
+
+@article{Buzsaki2004,
+  title = {Large-Scale Recording of Neuronal Ensembles},
+  volume = {7},
+  copyright = {\textcopyright{} 2004 Nature Publishing Group},
+  issn = {1097-6256},
+  doi = {10.1038/nn1233},
+  abstract = {How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron\textendash{}electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.},
+  language = {en},
+  number = {5},
+  journal = {Nature Neuroscience},
+  author = {Buzs{\'a}ki, Gy{\"o}rgy},
+  month = may,
+  year = {2004},
+  pages = {446-451},
+  file = {C:\\Users\\saram\\Zotero\\storage\\I46DS77E\\Buzsaki, 2004, large-scale recording of neuronal ensembles.pdf;C:\\Users\\saram\\Zotero\\storage\\ZGQRKUR8\\Buzsáki  2004  Large-scale recording of neuronal ensembles.pdf}
+}
+
+@article{Buzsaki2012,
+  title = {The Origin of Extracellular Fields and Currents \textemdash{} {{EEG}}, {{ECoG}}, {{LFP}} and Spikes},
+  volume = {13},
+  copyright = {\textcopyright{} 2012 Nature Publishing Group},
+  issn = {1471-003X},
+  doi = {10.1038/nrn3241},
+  abstract = {Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources \textemdash{} including Na+ and Ca2+ spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations \textemdash{} can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.},
+  language = {en},
+  number = {6},
+  journal = {Nature Reviews Neuroscience},
+  author = {Buzs{\'a}ki, Gy{\"o}rgy and Anastassiou, Costas A. and Koch, Christof},
+  month = jun,
+  year = {2012},
+  pages = {407-420},
+  file = {C:\\Users\\saram\\Zotero\\storage\\7D6R2VUT\\Buzsáki et al.  2012  The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes.pdf;C:\\Users\\saram\\Zotero\\storage\\KMPKDNHT\\Buzsaki, 2012, the origin of extracellular fields and currents.pdf}
+}
+
+@article{Dimitriadis2016,
+  title = {T-{{SNE}} Visualization of Large-Scale Neural Recordings.},
+  copyright = {\textcopyright{} 2016, Posted by Cold Spring Harbor Laboratory Press. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
+  doi = {10.1101/087395},
+  abstract = {Electrophysiology is entering the era of 'Big Data'. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, i.e. single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention (Rey 2015, Rossant 2016), but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grow exponentially. Here we introduce the t-student stochastic neighbor embedding (t-sne) dimensionality reduction method (Van der Maaten 2008) as a visualization tool in the spike sorting process. T-sne embeds the n-dimensional extracellular spikes (n = number of features by which each spike is decomposed) into a low (usually two) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets both from hybrid (Rossant 2016) and paired juxtacellular/extracellular recordings (Neto 2016). We have released a graphical user interface (gui) written in python as a tool for the manual clustering of the t-sne embedded spikes and as a tool for an informed overview and fast manual curation of results from other clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Dimitriadis, George and Neto, Joana and Kampff, Adam},
+  month = nov,
+  year = {2016},
+  pages = {087395},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8V33CTCE\\087395-3.mpg;C:\\Users\\saram\\Zotero\\storage\\GRTB583H\\087395-2.mpg;C:\\Users\\saram\\Zotero\\storage\\PSJCXB2R\\Dimitriadis et al.  2016  T-SNE visualization of large-scale neural recordings..pdf;C:\\Users\\saram\\Zotero\\storage\\Z8RJCWG2\\087395-1.pdf}
+}
+
+@article{Vargas-Irwin2015a,
+  title = {Spike {{Train SIMilarity Space}} ({{SSIMS}}): A Frame-Work for Single Neuron and Ensemble Data Analysis},
+  volume = {27},
+  issn = {0899-7667},
+  shorttitle = {Spike {{Train SIMilarity Space}} ({{SSIMS}})},
+  doi = {10.1162/NECO_a_00684},
+  abstract = {Increased emphasis on circuit level activity in the brain makes it necessary to have methods to visualize and evaluate large scale ensemble activity, beyond that revealed by raster-histograms or pairwise correlations. We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how Spike train SIMilarity Space (SSIMS) analysis captures the relationship between goal directions for an 8-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models.},
+  number = {1},
+  journal = {Neural computation},
+  author = {Vargas-Irwin, Carlos E. and Brandman, David M. and Zimmermann, Jonas B. and Donoghue, John P. and Black, Michael J.},
+  month = jan,
+  year = {2015},
+  pages = {1-31},
+  file = {C:\\Users\\saram\\Zotero\\storage\\IJ653QT2\\Vargas-Irwin et al.  2015  Spike Train SIMilarity Space (SSIMS) a frame-work for single neuron and ensemble data analysis.pdf},
+  pmid = {25380335},
+  pmcid = {PMC4276520}
+}
+
+@article{Maaten2014,
+  title = {Accelerating T-{{SNE}} Using {{Tree}}-{{Based Algorithms}}},
+  volume = {15},
+  journal = {Journal of Machine Learning Research},
+  author = {van der Maaten, Laurens},
+  year = {2014},
+  pages = {3221-3245},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2XCPU7JM\\Maaten  2014  Accelerating t-SNE using Tree-Based Algorithms.pdf}
+}
+
+@article{Tort2008,
+  title = {Dynamic Cross-Frequency Couplings of Local Field Potential Oscillations in Rat Striatum and Hippocampus during Performance of a {{T}}-Maze Task},
+  volume = {105},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.0810524105},
+  abstract = {Oscillatory rhythms in different frequency ranges mark different behavioral states and are thought to provide distinct temporal windows that coherently bind cooperating neuronal assemblies. However, the rhythms in different bands can also interact with each other, suggesting the possibility of higher-order representations of brain states by such rhythmic activity. To explore this possibility, we analyzed local field potential oscillations recorded simultaneously from the striatum and the hippocampus. As rats performed a task requiring active navigation and decision making, the amplitudes of multiple high-frequency oscillations were dynamically modulated in task-dependent patterns by the phase of cooccurring theta-band oscillations both within and across these structures, particularly during decision-making behavioral epochs. Moreover, the modulation patterns uncovered distinctions among both high- and low-frequency subbands. Cross-frequency coupling of multiple neuronal rhythms could be a general mechanism used by the brain to perform network-level dynamical computations underlying voluntary behavior.},
+  language = {en},
+  number = {51},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Tort, Adriano B. L. and Kramer, Mark A. and Thorn, Catherine and Gibson, Daniel J. and Kubota, Yasuo and Graybiel, Ann M. and Kopell, Nancy J.},
+  month = dec,
+  year = {2008},
+  keywords = {amplitude modulation,gamma,theta},
+  pages = {20517-20522},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FPSV54FD\\0810524105SI.pdf;C:\\Users\\saram\\Zotero\\storage\\KUESLKDP\\Tort et al. - 2008 - Dynamic cross-frequency couplings of local field p.pdf},
+  pmid = {19074268}
+}
+
+@article{Hill2012,
+  title = {Statistical Connectivity Provides a Sufficient Foundation for Specific Functional Connectivity in Neocortical Neural Microcircuits},
+  volume = {109},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.1202128109},
+  abstract = {It is well-established that synapse formation involves highly selective chemospecific mechanisms, but how neuron arbors are positioned before synapse formation remains unclear. Using 3D reconstructions of 298 neocortical cells of different types (including nest basket, small basket, large basket, bitufted, pyramidal, and Martinotti cells), we constructed a structural model of a cortical microcircuit, in which cells of different types were independently and randomly placed. We compared the positions of physical appositions resulting from the incidental overlap of axonal and dendritic arbors in the model (statistical structural connectivity) with the positions of putative functional synapses (functional synaptic connectivity) in 90 synaptic connections reconstructed from cortical slice preparations. Overall, we found that statistical connectivity predicted an average of 74 $\pm$ 2.7\% (mean $\pm$ SEM) synapse location distributions for nine types of cortical connections. This finding suggests that chemospecific attractive and repulsive mechanisms generally do not result in pairwise-specific connectivity. In some cases, however, the predicted distributions do not match precisely, indicating that chemospecific steering and aligning of the arbors may occur for some types of connections. This finding suggests that random alignment of axonal and dendritic arbors provides a sufficient foundation for specific functional connectivity to emerge in local neural microcircuits.},
+  language = {en},
+  number = {42},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Hill, Sean L. and Wang, Yun and Riachi, Imad and Sch{\"u}rmann, Felix and Markram, Henry},
+  month = oct,
+  year = {2012},
+  keywords = {Neocortex,connectome,microcircuitry,neuronal connectivity},
+  pages = {E2885-E2894},
+  file = {C:\\Users\\saram\\Zotero\\storage\\B64HB35U\\PNAS-2012-Hill-E2885-94.pdf;C:\\Users\\saram\\Zotero\\storage\\QW8MSURW\\Hill et al.  2012  Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits.pdf},
+  pmid = {22991468}
+}
+
+@article{Reimann2013,
+  title = {A {{Biophysically Detailed Model}} of {{Neocortical Local Field Potentials Predicts}} the {{Critical Role}} of {{Active Membrane Currents}}},
+  volume = {79},
+  issn = {0896-6273},
+  doi = {10.1016/j.neuron.2013.05.023},
+  abstract = {Summary
+Brain activity generates extracellular voltage fluctuations recorded as local field potentials (LFPs). It is known that the relevant microvariables, the ionic currents across membranes, jointly generate the macrovariables, the extracellular voltage, but neither the detailed biophysical knowledge nor the required computational power have been available to model these processes. We simulated the LFP in a model of the rodent neocortical column composed of $>$12,000 reconstructed, multicompartmental, and spiking cortical layer 4 and 5 pyramidal neurons and basket cells, including five million dendritic and somatic compartments with voltage- and ion-dependent currents, realistic connectivity, and probabilistic AMPA, NMDA, and GABA synapses. We found that, depending on a number of factors, the LFP reflects local and cross-layer processing. Active currents dominate the generation of LFPs, not synaptic ones. Spike-related currents impact the LFP not only at higher frequencies but below 50~Hz. This work calls for re-evaluating the genesis of LFPs.},
+  number = {2},
+  journal = {Neuron},
+  author = {Reimann, Michael W. and Anastassiou, Costas A. and Perin, Rodrigo and Hill, Sean L. and Markram, Henry and Koch, Christof},
+  month = jul,
+  year = {2013},
+  pages = {375-390},
+  file = {C:\\Users\\saram\\Zotero\\storage\\3E86UA85\\Reimann et al.  2013  A Biophysically Detailed Model of Neocortical Local Field Potentials Predicts the Critical Role of Active Membrane Currents.pdf}
+}
+
+@article{Koralek2012,
+  title = {Corticostriatal Plasticity Is Necessary for Learning Intentional Neuroprosthetic Skills},
+  volume = {483},
+  copyright = {\textcopyright{} 2012 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {0028-0836},
+  doi = {10.1038/nature10845},
+  abstract = {The ability to learn new skills and perfect them with practice applies not only to physical skills but also to abstract skills, like motor planning or neuroprosthetic actions. Although plasticity in corticostriatal circuits has been implicated in learning physical skills, it remains unclear if similar circuits or processes are required for abstract skill learning. Here we use a novel behavioural task in rodents to investigate the role of corticostriatal plasticity in abstract skill learning. Rodents learned to control the pitch of an auditory cursor to reach one of two targets by modulating activity in primary motor cortex irrespective of physical movement. Degradation of the relation between action and outcome, as well as sensory-specific devaluation and omission tests, demonstrate that these learned neuroprosthetic actions are intentional and goal-directed, rather than habitual. Striatal neurons change their activity with learning, with more neurons modulating their activity in relation to target-reaching as learning progresses. Concomitantly, strong relations between the activity of neurons in motor cortex and the striatum emerge. Specific deletion of striatal NMDA receptors impairs the development of this corticostriatal plasticity, and disrupts the ability to learn neuroprosthetic skills. These results suggest that corticostriatal plasticity is necessary for abstract skill learning, and that neuroprosthetic movements capitalize on the neural circuitry involved in natural motor learning.},
+  language = {en},
+  number = {7389},
+  journal = {Nature},
+  author = {Koralek, Aaron C. and Jin, Xin and Long Ii, John D. and Costa, Rui M. and Carmena, Jose M.},
+  month = mar,
+  year = {2012},
+  keywords = {Physiology,Neuroscience,Animal behaviour},
+  pages = {331-335},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KPERU7RJ\\Koralek et al. - 2012 - Corticostriatal plasticity is necessary for learni.pdf}
+}
+
+@article{Koralek2013,
+  title = {Temporally {{Precise Cell}}-{{Specific Coherence Develops}} in {{Corticostriatal Networks}} during {{Learning}}},
+  volume = {79},
+  issn = {0896-6273},
+  doi = {10.1016/j.neuron.2013.06.047},
+  abstract = {Summary
+It has been postulated that selective temporal coordination between neurons and development of functional neuronal assemblies are fundamental for brain function and behavior. Still, there is little evidence that functionally relevant coordination emerges preferentially in neuronal assemblies directly controlling behavioral output. We investigated coherence between primary motor cortex and the dorsal striatum as rats learn an abstract operant task. Striking coherence developed between these regions during learning. Interestingly, coherence was selectively increased in cells controlling behavioral output relative to adjacent cells. Furthermore, the temporal offset of these interactions aligned closely with corticostriatal conduction delays, demonstrating highly precise timing. Spikes from either region were followed by a consistent phase in the other, suggesting that network feedback reinforces coherence. Together, these results demonstrate that temporally precise coherence develops during learning specifically in output-relevant neuronal populations and further suggest that correlations in oscillatory activity serve to synchronize widespread brain networks to produce behavior.},
+  number = {5},
+  journal = {Neuron},
+  author = {Koralek, Aaron C. and Costa, Rui M. and Carmena, Jose M.},
+  month = sep,
+  year = {2013},
+  pages = {865-872},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FXEZD4JL\\Koralek et al. - 2013 - Temporally Precise Cell-Specific Coherence Develop.pdf}
+}
+
+@article{Canolty2012,
+  title = {Task-{{Dependent Changes}} in {{Cross}}-{{Level Coupling}} between {{Single Neurons}} and {{Oscillatory Activity}} in {{Multiscale Networks}}},
+  volume = {8},
+  issn = {1553-7358},
+  doi = {10.1371/journal.pcbi.1002809},
+  abstract = {Author Summary How is the functional role of a particular neuron established within an ensemble? The concept of a neural tuning curve \textendash{} the mapping from input variables such as movement direction to output firing rate \textendash{} has proven useful in investigating neural function. However, prior work shows that tuning curves are not fixed but may be remapped as a function of task demands \textendash{} presumably via high-level mechanisms of cognitive control. How is this accomplished? Brain rhythms may play a causal role in this process, but the coupling of single cells to network activity remains poorly understood. We investigated the coupling between rhythmic beta activity and spiking as macaques performed two different tasks. This coupling can be described in terms of a function that maps oscillatory amplitude and phase to instantaneous spike rate. Similarly to direction tuning, this ``internal'' tuning curve also exhibits task-dependent changes. We characterize these changes across a large ensemble of simultaneously-recorded cells, and consider some of the neuro-computational implications presented by cross-level coupling between single cells and large-scale networks. In particular, relative to the slow time-scale of behavior, the observed beta-to-rate mappings may prove useful for modulating winner-take-all dynamics on intermediate time-scales and relative spike timing on fast time-scales.},
+  number = {12},
+  journal = {PLOS Computational Biology},
+  author = {Canolty, Ryan T. and Ganguly, Karunesh and Carmena, Jose M.},
+  month = dec,
+  year = {2012},
+  keywords = {Neurons,Neural networks,Single neuron function,Signal filtering,Action potentials,Neuronal tuning,Monkeys,Motor neurons},
+  pages = {e1002809},
+  file = {C:\\Users\\saram\\Zotero\\storage\\I95VUNDL\\Canolty et al. - 2012 - Task-Dependent Changes in Cross-Level Coupling bet.pdf}
+}
+
+@article{Canolty2012a,
+  title = {Detecting Event-Related Changes of Multivariate Phase Coupling in Dynamic Brain Networks},
+  volume = {107},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00610.2011},
+  abstract = {Oscillatory phase coupling within large-scale brain networks is a topic of increasing interest within systems, cognitive, and theoretical neuroscience. Evidence shows that brain rhythms play a role in controlling neuronal excitability and response modulation (Haider B, McCormick D. Neuron 62: 171\textendash{}189, 2009) and regulate the efficacy of communication between cortical regions (Fries P. Trends Cogn Sci 9: 474\textendash{}480, 2005) and distinct spatiotemporal scales (Canolty RT, Knight RT. Trends Cogn Sci 14: 506\textendash{}515, 2010). In this view, anatomically connected brain areas form the scaffolding upon which neuronal oscillations rapidly create and dissolve transient functional networks (Lakatos P, Karmos G, Mehta A, Ulbert I, Schroeder C. Science 320: 110\textendash{}113, 2008). Importantly, testing these hypotheses requires methods designed to accurately reflect dynamic changes in multivariate phase coupling within brain networks. Unfortunately, phase coupling between neurophysiological signals is commonly investigated using suboptimal techniques. Here we describe how a recently developed probabilistic model, phase coupling estimation (PCE; Cadieu C, Koepsell K Neural Comput 44: 3107\textendash{}3126, 2010), can be used to investigate changes in multivariate phase coupling, and we detail the advantages of this model over the commonly employed phase-locking value (PLV; Lachaux JP, Rodriguez E, Martinerie J, Varela F. Human Brain Map 8: 194\textendash{}208, 1999). We show that the N-dimensional PCE is a natural generalization of the inherently bivariate PLV. Using simulations, we show that PCE accurately captures both direct and indirect (network mediated) coupling between network elements in situations where PLV produces erroneous results. We present empirical results on recordings from humans and nonhuman primates and show that the PCE-estimated coupling values are different from those using the bivariate PLV. Critically on these empirical recordings, PCE output tends to be sparser than the PLVs, indicating fewer significant interactions and perhaps a more parsimonious description of the data. Finally, the physical interpretation of PCE parameters is straightforward: the PCE parameters correspond to interaction terms in a network of coupled oscillators. Forward modeling of a network of coupled oscillators with parameters estimated by PCE generates synthetic data with statistical characteristics identical to empirical signals. Given these advantages over the PLV, PCE is a useful tool for investigating multivariate phase coupling in distributed brain networks.},
+  language = {en},
+  number = {7},
+  journal = {Journal of Neurophysiology},
+  author = {Canolty, Ryan T. and Cadieu, Charles F. and Koepsell, Kilian and Ganguly, Karunesh and Knight, Robert T. and Carmena, Jose M.},
+  month = apr,
+  year = {2012},
+  pages = {2020-2031},
+  file = {C:\\Users\\saram\\Zotero\\storage\\RZ8DU8VS\\Canolty et al. - 2012 - Detecting event-related changes of multivariate ph.pdf},
+  pmid = {22236706}
+}
+
+@article{Miyakoshi2013,
+  title = {Automated Detection of Cross-Frequency Coupling in the Electrocorticogram for Clinical Inspection},
+  volume = {2013},
+  issn = {1557-170X},
+  doi = {10.1109/EMBC.2013.6610242},
+  abstract = {We developed a toolbox for detecting high-frequency oscillations and evaluating cross-frequency phase-amplitude coupling in electrocorticographic (ECoG) data with optimal parameters. Here we demonstrate use of the toolbox using simulated and realistic ECoG data. The results confirmed its potential usefulness for clinical research or practice. The tools have been released as a Phase-Amplitude Coupling Toolbox (PACT) plug-in for EEGLAB, an open software environment for electrophysiological data analysis (sccn.ucsd.edu/eeglab).},
+  journal = {Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference},
+  author = {Miyakoshi, Makoto and Delorme, Arnaud and Mullen, Tim and Kojima, Katsuaki and Makeig, Scott and Asano, Eishi},
+  year = {2013},
+  pages = {3282-3285},
+  file = {C:\\Users\\saram\\Zotero\\storage\\92ARHHTZ\\Miyakoshi et al.  2013  Automated detection of cross-frequency coupling in the electrocorticogram for clinical inspection.pdf},
+  pmid = {24110429},
+  pmcid = {PMC4104944}
+}
+
+@article{Zhou2009,
+  title = {{{MATLAB}} Toolbox for Functional Connectivity},
+  volume = {47},
+  issn = {1053-8119},
+  doi = {10.1016/j.neuroimage.2009.05.089},
+  abstract = {The term ``functional connectivity'' is used to denote correlations in activation among spatially-distinct brain regions, either in a resting state or when processing external stimuli. Functional connectivity has been extensively evaluated with several functional neuroimaging methods, particularly PET and fMRI. Yet these relationships have been quantified using very different measures and the extent to which they index the same constructs is unclear. We have implemented a variety of these functional connectivity measures in a new freely available MATLAB toolbox. These measures are categorized into two groups: whole time-series and trial-based approaches. We evaluate these measures via simulations with different patterns of functional connectivity and provide recommendations for their use. We also apply these measures to a previously published fMRI data set (Siegle, G.J., Thompson, W., Carter, C.S., Steinhauer, S.R., Thase, M.E., 2007. Increased amygdala and decreased dorsolateral prefrontal BOLD responses in unipolar depression: related and independent features. Biol. Psychiatry 610 (2), 198\textendash{}209) in which activity in dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (DLPFC) was evaluated in 32 control subjects during a digit sorting task. Though all implemented measures demonstrate functional connectivity between dACC and DLPFC activity during event-related tasks, different participants appeared to display qualitatively different relationships.},
+  number = {4},
+  journal = {NeuroImage},
+  author = {Zhou, Dongli and Thompson, Wesley K. and Siegle, Greg},
+  month = oct,
+  year = {2009},
+  pages = {1590-1607},
+  file = {C:\\Users\\saram\\Zotero\\storage\\XS8I76XP\\Zhou et al.  2009  MATLAB toolbox for functional connectivity.pdf}
+}
+
+@article{Meng2017,
+  title = {Sublaminar Subdivision of Mouse Auditory Cortex Layer 2/3 Based on Functional Translaminar Connections},
+  copyright = {Copyright \textcopyright{} 2017 the authors},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.1361-17.2017},
+  abstract = {The cerebral cortex is subdivided into 6 layers based on morphological features. The supragranular layers 2/3 (L2/3) contain morphologically and genetically diverse populations of neurons, suggesting the existence of discrete classes of cells. In primates and carnivores L2/3 can be subdivided morphologically, but cytoarchitectonic divisions are less clear in rodents. Nevertheless, discrete classes of cells could exist based on their computational requirement, which might be linked to their associated functional microcircuits. Through in vitro slice recordings coupled with laser scanning photostimulation we investigated if L2/3 of male mouse auditory cortex contains discrete subpopulations of cells with specific functional microcircuits. We use hierarchical clustering on the laminar connection patterns to reveal the existence of multiple distinct classes of L2/3 neurons. The classes of L2/3 neurons are distinguished by the pattern of their laminar and columnar inputs from within A1 and their location within L2/3. Cells in superficial L2 show more extensive columnar integration than deeper L3 cells. Moreover, L3 cells receive more translaminar input from L4. In vivo imaging in awake mice revealed that L2 cells had higher bandwidth than L3 cells \textemdash{} consistent with the laminar differences in columnar integration. These results suggest that similar to higher mammals, rodent L2/3 is not a homogenous layer but contains several parallel microcircuits.
+SIGNIFICANCE STATEMENT
+Layer 2/3 of auditory cortex is functionally diverse. We investigated whether L2/3 cells form classes based on their functional connectivity. We used in vitro whole cell patch-clamp recordings with laser-scanning photostimulation (LSPS) and performed unsupervised clustering on the resulting excitatory and inhibitory connection patterns. Cells within each class were located in different sublaminae. Superficial cells showed wider integration along the tonotopic axis and the amount of L4 input varied with sublaminar location. To identify if sensory responses varied with sublaminar location, we performed in vivo Ca2+-imaging and found that L2 cells were less frequency selective than L3 cells. Our results show that the diversity of receptive fields in L2/3 is likely due to diversity in the underlying functional circuits.},
+  language = {en},
+  journal = {Journal of Neuroscience},
+  author = {Meng, Xiangying and Winkowski, Daniel E. and Kao, Joseph P. Y. and Kanold, Patrick O.},
+  month = sep,
+  year = {2017},
+  pages = {1361-17},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ZZMLAIXP\\Meng et al.  2017  Sublaminar subdivision of mouse auditory cortex layer 23 based on functional translaminar connections.pdf},
+  pmid = {28931571}
+}
+
+@misc{zotero-1212,
+  title = {Visual {{Exploration}} of {{Performance Monitoring Time Series}} with {{MDS}} and T-{{SNE}}},
+  howpublished = {http://mabrek.github.io/blog/multivariate-mds-tsne/},
+  file = {C:\\Users\\saram\\Zotero\\storage\\G5IWI59A\\multivariate-mds-tsne.html}
+}
+
+@article{Vargas-Irwin2015,
+  title = {Spike {{Train SIMilarity Space}} ({{SSIMS}}): A Frame-Work for Single Neuron and Ensemble Data Analysis},
+  volume = {27},
+  issn = {0899-7667},
+  shorttitle = {Spike {{Train SIMilarity Space}} ({{SSIMS}})},
+  doi = {10.1162/NECO_a_00684},
+  abstract = {Increased emphasis on circuit level activity in the brain makes it necessary to have methods to visualize and evaluate large scale ensemble activity, beyond that revealed by raster-histograms or pairwise correlations. We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how Spike train SIMilarity Space (SSIMS) analysis captures the relationship between goal directions for an 8-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models.},
+  number = {1},
+  journal = {Neural computation},
+  author = {Vargas-Irwin, Carlos E. and Brandman, David M. and Zimmermann, Jonas B. and Donoghue, John P. and Black, Michael J.},
+  month = jan,
+  year = {2015},
+  pages = {1-31},
+  file = {C:\\Users\\saram\\Zotero\\storage\\74DAD6T4\\Vargas-Irwin et al.  2015  Spike Train SIMilarity Space (SSIMS) a frame-work for single neuron and ensemble data analysis.pdf},
+  pmid = {25380335},
+  pmcid = {PMC4276520}
+}
+
+@article{Jensen2007,
+  title = {Cross-Frequency Coupling between Neuronal Oscillations},
+  volume = {11},
+  issn = {1364-6613},
+  doi = {10.1016/j.tics.2007.05.003},
+  abstract = {Electrophysiological recordings in animals, including humans, are modulated by oscillatory activities in several frequency bands. Little is known about how oscillations in various frequency bands interact. Recent findings from the human neocortex show that the power of fast gamma oscillations (30\textendash{}150 Hz) is modulated by the phase of slower theta oscillations (5\textendash{}8 Hz). Given that this coupling reflects a specific interplay between large ensembles of neurons, it is likely to have profound implications for neuronal processing.},
+  number = {7},
+  journal = {Trends in Cognitive Sciences},
+  author = {Jensen, Ole and Colgin, Laura L.},
+  month = jul,
+  year = {2007},
+  pages = {267-269},
+  file = {C:\\Users\\saram\\Zotero\\storage\\85UGC249\\Jensen and Colgin  2007  Cross-frequency coupling between neuronal oscillations.pdf}
+}
+
+@article{Canolty2006,
+  title = {High {{Gamma Power Is Phase}}-{{Locked}} to {{Theta Oscillations}} in {{Human Neocortex}}},
+  volume = {313},
+  issn = {0036-8075},
+  doi = {10.1126/science.1128115},
+  abstract = {We observed robust coupling between the high- and low-frequency bands of ongoing electrical activity in the human brain. In particular, the phase of the low-frequency theta (4 to 8 hertz) rhythm modulates power in the high gamma (80 to 150 hertz) band of the electrocorticogram, with stronger modulation occurring at higher theta amplitudes. Furthermore, different behavioral tasks evoke distinct patterns of theta/high gamma coupling across the cortex. The results indicate that transient coupling between low- and high-frequency brain rhythms coordinates activity in distributed cortical areas, providing a mechanism for effective communication during cognitive processing in humans.},
+  number = {5793},
+  journal = {Science (New York, N.Y.)},
+  author = {Canolty, R. T. and Edwards, E. and Dalal, S. S. and Soltani, M. and Nagarajan, S. S. and Kirsch, H. E. and Berger, M. S. and Barbaro, N. M. and Knight, R. T.},
+  month = sep,
+  year = {2006},
+  pages = {1626-1628},
+  file = {C:\\Users\\saram\\Zotero\\storage\\AK46A9SZ\\NIHMS14964-supplement-Supplementary_.pdf;C:\\Users\\saram\\Zotero\\storage\\URATP29S\\Canolty et al.  2006  High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex.pdf},
+  pmid = {16973878},
+  pmcid = {PMC2628289}
+}
+
+@article{Penny2008,
+  title = {Testing for Nested Oscillation},
+  volume = {174},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2008.06.035},
+  abstract = {Nested oscillation occurs when the amplitude of a faster rhythm is coupled to the phase of a slower rhythm. It has been proposed to underlie the discrete nature of perception and the capacity of working memory and is a phenomenon observable in human brain imaging data. This paper compares three published methods for detecting nested oscillation and a fourth method proposed in this paper. These are: (i) the modulation index, (ii) the phase-locking value (PLV), (iii) the envelope-to-signal correlation (ESC) and (iv) a general linear model (GLM) measure derived from ESC. We applied the methods to electrocorticographic (ECoG) data recorded during a working-memory task and to data from a simulated hippocampal interneuron network. Further simulations were then made to address the dependence of each measure on signal to noise level, coupling phase, epoch length, sample rate, signal nonstationarity, and multi-phasic coupling. Our overall conclusion is that the GLM measure is the best all-round approach for detecting nested oscillation.},
+  number = {1},
+  journal = {Journal of Neuroscience Methods},
+  author = {Penny, W.D. and Duzel, E. and Miller, K.J. and Ojemann, J.G.},
+  month = sep,
+  year = {2008},
+  pages = {50-61},
+  pmid = {18674562},
+  pmcid = {PMC2675174}
+}
+
+@misc{zotero-1240,
+  title = {Phase-Clustering Bias in Phase\textendash{}amplitude Cross-Frequency Coupling and Its Removal - {{ScienceDirect}}},
+  howpublished = {http://www.sciencedirect.com/science/article/pii/S016502701500268X?via\%3Dihub},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5X2AETKI\\1-s2.0-S016502701500268X-main.pdf;C:\\Users\\saram\\Zotero\\storage\\TSJDJMVV\\S016502701500268X.html}
+}
+
+@misc{zotero-1243,
+  title = {The Functional Role of Cross-Frequency Coupling},
+  howpublished = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3359652/},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5KSPJMU4\\nihms235872.pdf;C:\\Users\\saram\\Zotero\\storage\\GB4BUG75\\PMC3359652.html}
+}
+
+@article{Jirsa2014,
+  title = {On the Nature of Seizure Dynamics},
+  volume = {137},
+  issn = {1460-2156},
+  doi = {10.1093/brain/awu133},
+  abstract = {Seizures can occur spontaneously and in a recurrent manner, which defines epilepsy; or they can be induced in a normal brain under a variety of conditions in most neuronal networks and species from flies to humans. Such universality raises the possibility that invariant properties exist that characterize seizures under different physiological and pathological conditions. Here, we analysed seizure dynamics mathematically and established a taxonomy of seizures based on first principles. For the predominant seizure class we developed a generic model called Epileptor. As an experimental model system, we used ictal-like discharges induced in vitro in mouse hippocampi. We show that only five state variables linked by integral-differential equations are sufficient to describe the onset, time course and offset of ictal-like discharges as well as their recurrence. Two state variables are responsible for generating rapid discharges (fast time scale), two for spike and wave events (intermediate time scale) and one for the control of time course, including the alternation between 'normal' and ictal periods (slow time scale). We propose that normal and ictal activities coexist: a separatrix acts as a barrier (or seizure threshold) between these states. Seizure onset is reached upon the collision of normal brain trajectories with the separatrix. We show theoretically and experimentally how a system can be pushed toward seizure under a wide variety of conditions. Within our experimental model, the onset and offset of ictal-like discharges are well-defined mathematical events: a saddle-node and homoclinic bifurcation, respectively. These bifurcations necessitate a baseline shift at onset and a logarithmic scaling of interspike intervals at offset. These predictions were not only confirmed in our in vitro experiments, but also for focal seizures recorded in different syndromes, brain regions and species (humans and zebrafish). Finally, we identified several possible biophysical parameters contributing to the five state variables in our model system. We show that these parameters apply to specific experimental conditions and propose that there exists a wide array of possible biophysical mechanisms for seizure genesis, while preserving central invariant properties. Epileptor and the seizure taxonomy will guide future modeling and translational research by identifying universal rules governing the initiation and termination of seizures and predicting the conditions necessary for those transitions.},
+  language = {eng},
+  number = {Pt 8},
+  journal = {Brain: A Journal of Neurology},
+  author = {Jirsa, Viktor K. and Stacey, William C. and Quilichini, Pascale P. and Ivanov, Anton I. and Bernard, Christophe},
+  month = aug,
+  year = {2014},
+  keywords = {Animals,Microelectrodes,EEG,Humans,Models; Neurological,Reproducibility of Results,Mice,Electroencephalography,Electrophysiological Phenomena,Nonlinear Dynamics,Seizures,Zebrafish,bifurcation,epilepsy,modelling,non-linear dynamics,hippocampus},
+  pages = {2210-2230},
+  pmid = {24919973},
+  pmcid = {PMC4107736}
+}
+
+@article{Mercer2017,
+  title = {Cornu {{Ammonis Regions}}\textendash{}{{Antecedents}} of {{Cortical Layers}}?},
+  volume = {11},
+  issn = {1662-5129},
+  doi = {10.3389/fnana.2017.00083},
+  abstract = {Studying neocortex and hippocampus in parallel, we are struck by the similarities. All three to four layered allocortices and the six layered mammalian neocortex arise in the pallium. All receive and integrate multiple cortical and subcortical inputs, provide multiple outputs and include an array of neuronal classes. During development, each cell positions itself to sample appropriate local and distant inputs and to innervate appropriate targets. Simpler cortices had already solved the need to transform multiple coincident inputs into serviceable outputs before neocortex appeared in mammals. Why then do phylogenetically more recent cortices need multiple pyramidal cell layers? A simple answer is that more neurones can compute more complex functions. The dentate gyrus and hippocampal CA regions - which might be seen as hippocampal antecedents of neocortical layers - lie side by side, albeit around a tight bend. Were the millions of cells of rat neocortex arranged in like fashion, the surface area of the CA pyramidal cell layers would be some forty times larger. Even if evolution had managed to fold this immense sheet into the space available, the distances between neurones that needed to be synaptically connected would be huge and to maintain the speed of information transfer, massive, myelinated fibre tracts would be needed. How much more practical to stack the `cells that fire and wire together' into narrow columns, while retaining the mechanisms underlying the extraordinary precision with which circuits form. This demonstrably efficient arrangement presents us with challenges, however, not the least being to categorise the baffling array of neuronal subtypes in each of five `pyramidal layers'. If we imagine the puzzle posed by this bewildering jumble of apical dendrites, basal dendrites and axons, from many different pyramidal and interneuronal classes, that is encountered by a late-arriving interneurone insinuating itself into a functional circuit, we can perhaps begin to understand why definitive classification is such a challenge. Here, we summarize and compare the development of these two cortices, the properties of their neurones, the circuits they form and the ordered, unidirectional flow of information from one hippocampal region, or one neocortical layer, to another.},
+  language = {English},
+  journal = {Frontiers in Neuroanatomy},
+  author = {Mercer, Audrey and Thomson, Alex M.},
+  year = {2017},
+  keywords = {Neocortex,Interneurones,Neocortical Columns,Pyramidal Cells,development,neuronal circuitry,hippocampus},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8CRDVUSD\\fnana-11-00083.pdf}
+}
+
+@article{Hill2016,
+  title = {How Do We Know What We Know? {{Discovering}} Neuroscience Data Sets through Minimal Metadata},
+  volume = {17},
+  copyright = {\textcopyright{} 2016 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1471-003X},
+  shorttitle = {How Do We Know What We Know?},
+  doi = {10.1038/nrn.2016.134},
+  abstract = {With the emergence of brain research initiatives around the world, the need for standards to facilitate neuroscience data sharing is growing. A crucial first step will be to establish a minimal metadata standard that allows the discovery of and access to similar or related data sets. This will be a key element of an open neuroscience-data ecosystem in which data is valued, preserved and reused.},
+  language = {en},
+  number = {12},
+  journal = {Nature Reviews Neuroscience},
+  author = {Hill, Sean L.},
+  month = dec,
+  year = {2016},
+  keywords = {Neuroscience,Research data,Bioinformatics,Imaging},
+  pages = {735-736},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DY4PA9NQ\\Hill - 2016 - How do we know what we know Discovering neuroscie.pdf}
+}
+
+@article{Bandrowski2016,
+  title = {The {{Resource Identification Initiative}}: {{A}} Cultural Shift in Publishing},
+  volume = {524},
+  issn = {1096-9861},
+  shorttitle = {The {{Resource Identification Initiative}}},
+  doi = {10.1002/cne.23913},
+  abstract = {A central tenet in support of research reproducibility is the ability to uniquely identify research resources, i.e., reagents, tools, and materials that are used to perform experiments. However, current reporting practices for research resources are insufficient to identify the exact resources that are reported or to answer basic questions such as ``How did other studies use resource X?'' To address this issue, the Resource Identification Initiative was launched as a pilot project to improve the reporting standards for research resources in the Methods sections of articles and thereby improve identifiability and scientific reproducibility. The pilot engaged over 25 biomedical journal editors from most major publishers, as well as scientists and funding officials. Authors were asked to include Research Resource Identifiers (RRIDs) in their articles prior to publication for three resource types: antibodies, model organisms, and tools (i.e., software and databases). RRIDs are assigned by an authoritative database, for example, a model organism database for each type of resource. To make it easier for authors to obtain RRIDs, resources were aggregated from the appropriate databases and their RRIDs made available in a central Web portal (http://scicrunch.org/resources). RRIDs meet three key criteria: they are machine-readable, free to generate and access, and are consistent across publishers and journals. The pilot was launched in February of 2014 and over 300 articles have appeared that report RRIDs. The number of journals participating has expanded from the original 25 to more than 40, with RRIDs appearing in 62 different journals to date. Here we present an overview of the pilot project and its outcomes to date. We show that authors are able to identify resources and are supportive of the goals of the project. Identifiability of the resources post-pilot showed a dramatic improvement for all three resource types, suggesting that the project has had a significant impact on identifiability of research resources. J. Comp. Neurol. 524:8\textendash{}22, 2016. \textcopyright{} 2015 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.},
+  language = {en},
+  number = {1},
+  journal = {Journal of Comparative Neurology},
+  author = {Bandrowski, Anita and Brush, Matthew and Grethe, Jeffery S. and Haendel, Melissa A and Kennedy, David N. and Hill, Sean and Hof, Patrick R. and Martone, Maryann E. and Pols, Maaike and Tan, Serena C. and Washington, Nicole and Zudilova-Seinstra, Elena and Vasilevsky, Nicole},
+  month = jan,
+  year = {2016},
+  keywords = {Resource Identification Initiative,identifiability,research resources},
+  pages = {8-22},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JA4DX9YU\\Bandrowski et al. - 2016 - The Resource Identification Initiative A cultural.pdf}
+}
+
+@article{DeFelipe2016,
+  title = {Comments and {{General Discussion}} on ``{{The Anatomical Problem Posed}} by {{Brain Complexity}} and {{Size}}: {{A Potential Solution}}''},
+  volume = {10},
+  issn = {1662-5129},
+  shorttitle = {Comments and {{General Discussion}} on ``{{The Anatomical Problem Posed}} by {{Brain Complexity}} and {{Size}}},
+  doi = {10.3389/fnana.2016.00060},
+  journal = {Frontiers in Neuroanatomy},
+  author = {DeFelipe, Javier and Douglas, Rodney J. and Hill, Sean L. and Lein, Ed S. and Martin, Kevan A. C. and Rockland, Kathleen S. and Segev, Idan and Shepherd, Gordon M. and Tam{\'a}s, G{\'a}bor},
+  month = jun,
+  year = {2016},
+  file = {C:\\Users\\saram\\Zotero\\storage\\753YXQK4\\DeFelipe et al. - 2016 - Comments and General Discussion on “The Anatomical.pdf},
+  pmid = {27375436},
+  pmcid = {PMC4901047}
+}
+
+@misc{zotero-1259,
+  title = {Information Processing with Population Codes : {{Article}} : {{Nature Reviews Neuroscience}}},
+  howpublished = {http://www.nature.com.myaccess.library.utoronto.ca/nrn/journal/v1/n2/full/nrn1100\_125a.html?foxtrotcallback=true},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6DKE29MV\\nrn1100_125a.pdf;C:\\Users\\saram\\Zotero\\storage\\SGPXFINR\\nrn1100_125a.html}
+}
+
+@article{English2017,
+  title = {Pyramidal {{Cell}}-{{Interneuron Circuit Architecture}} and {{Dynamics}} in {{Hippocampal Networks}}},
+  volume = {96},
+  issn = {0896-6273},
+  doi = {10.1016/j.neuron.2017.09.033},
+  language = {English},
+  number = {2},
+  journal = {Neuron},
+  author = {English, Daniel Fine and McKenzie, Sam and Evans, Talfan and Kim, Kanghwan and Yoon, Euisik and Buzs{\'a}ki, Gy{\"o}rgy},
+  month = oct,
+  year = {2017},
+  keywords = {synchrony,interneuron,cell assemblies,circuits,cooperativity,hippocampus,lognormal,pyramidal cell,short-term plasticity,spike transmission},
+  pages = {505-520.e7},
+  file = {C:\\Users\\saram\\Zotero\\storage\\P48HAU39\\English et al.  2017  Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks.pdf},
+  pmid = {29024669}
+}
+
+@article{Dickey2009,
+  title = {Single-{{Unit Stability Using Chronically Implanted Multielectrode Arrays}}},
+  volume = {102},
+  copyright = {Copyright \textcopyright{} 2009 the American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.90920.2008},
+  abstract = {The use of chronic intracortical multielectrode arrays has become increasingly prevalent in neurophysiological experiments. However, it is not obvious whether neuronal signals obtained over multiple recording sessions come from the same or different neurons. Here, we develop a criterion to assess single-unit stability by measuring the similarity of 1) average spike waveforms and 2) interspike interval histograms (ISIHs). Neuronal activity was recorded from four Utah arrays implanted in primary motor and premotor cortices in three rhesus macaque monkeys during 10 recording sessions over a 15- to 17-day period. A unit was defined as stable through a given day if the stability criterion was satisfied on all recordings leading up to that day. We found that 57\% of the original units were stable through 7 days, 43\% were stable through 10 days, and 39\% were stable through 15 days. Moreover, stable units were more likely to remain stable in subsequent recording sessions (i.e., 89\% of the neurons that were stable through four sessions remained stable on the fifth). Using both waveform and ISIH data instead of just waveforms improved performance by reducing the number of false positives. We also demonstrate that this method can be used to track neurons across days, even during adaptation to a visuomotor rotation. Identifying a stable subset of neurons should allow the study of long-term learning effects across days and has practical implications for pooling of behavioral data across days and for increasing the effectiveness of brain\textendash{}machine interfaces.},
+  language = {en},
+  number = {2},
+  journal = {Journal of Neurophysiology},
+  author = {Dickey, Adam S. and Suminski, Aaron and Amit, Yali and Hatsopoulos, Nicholas G.},
+  month = aug,
+  year = {2009},
+  pages = {1331-1339},
+  file = {C:\\Users\\saram\\Zotero\\storage\\P7ZITHJR\\Dickey et al.  2009  Single-Unit Stability Using Chronically Implanted Multielectrode Arrays.pdf},
+  pmid = {19535480}
+}
+
+@article{Dehghani2012,
+  title = {Avalanche {{Analysis}} from {{Multielectrode Ensemble Recordings}} in {{Cat}}, {{Monkey}}, and {{Human Cerebral Cortex}} during {{Wakefulness}} and {{Sleep}}},
+  volume = {3},
+  issn = {1664-042X},
+  doi = {10.3389/fphys.2012.00302},
+  abstract = {Self-organized critical states are found in many natural systems, from earthquakes to forest fires, they have also been observed in neural systems, particularly, in neuronal cultures. However, the presence of critical states in the awake brain remains controversial. Here, we compared avalanche analyses performed on different in vivo preparations during wakefulness, slow-wave sleep, and REM sleep, using high density electrode arrays in cat motor cortex (96 electrodes), monkey motor cortex and premotor cortex and human temporal cortex (96 electrodes) in epileptic patients. In neuronal avalanches defined from units (up to 160 single units), the size of avalanches never clearly scaled as power-law, but rather scaled exponentially or displayed intermediate scaling. We also analyzed the dynamics of local field potentials (LFPs) and in particular LFP negative peaks (nLFPs) among the different electrodes (up to 96 sites in temporal cortex or up to 128 sites in adjacent motor and premotor cortices). In this case, the avalanches defined from nLFPs displayed power-law scaling in double logarithmic representations, as reported previously in monkey. However, avalanche defined as positive LFP (pLFP) peaks, which are less directly related to neuronal firing, also displayed apparent power-law scaling. Closer examination of this scaling using the more reliable cumulative distribution function (CDF) and other rigorous statistical measures, did not confirm power-law scaling. The same pattern was seen for cats, monkey, and human, as well as for different brain states of wakefulness and sleep. We also tested other alternative distributions. Multiple exponential fitting yielded optimal fits of the avalanche dynamics with bi-exponential distributions. Collectively, these results show no clear evidence for power-law scaling or self-organized critical states in the awake and sleeping brain of mammals, from cat to man.},
+  journal = {Frontiers in Physiology},
+  author = {Dehghani, Nima and Hatsopoulos, Nicholas G. and Haga, Zach D. and Parker, Rebecca A. and Greger, Bradley and Halgren, Eric and Cash, Sydney S. and Destexhe, Alain},
+  month = aug,
+  year = {2012},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JE4SV9IM\\Dehghani et al.  2012  Avalanche Analysis from Multielectrode Ensemble Recordings in Cat, Monkey, and Human Cerebral Cortex during Wakefulness and Sleep.pdf},
+  pmid = {22934053},
+  pmcid = {PMC3429073}
+}
+
+@article{Truccolo2011,
+  title = {Single-Neuron Dynamics in Human Focal Epilepsy},
+  volume = {14},
+  copyright = {\textcopyright{} 2011 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1097-6256},
+  doi = {10.1038/nn.2782},
+  abstract = {Epileptic seizures are traditionally characterized as the ultimate expression of monolithic, hypersynchronous neuronal activity arising from unbalanced runaway excitation. Here we report the first examination of spike train patterns in large ensembles of single neurons during seizures in persons with epilepsy. Contrary to the traditional view, neuronal spiking activity during seizure initiation and spread was highly heterogeneous, not hypersynchronous, suggesting complex interactions among different neuronal groups even at the spatial scale of small cortical patches. In contrast to earlier stages, seizure termination is a nearly homogenous phenomenon followed by an almost complete cessation of spiking across recorded neuronal ensembles. Notably, even neurons outside the region of seizure onset showed significant changes in activity minutes before the seizure. These findings suggest a revision of current thinking about seizure mechanisms and point to the possibility of seizure prevention based on spiking activity in neocortical neurons.},
+  language = {en},
+  number = {5},
+  journal = {Nature Neuroscience},
+  author = {Truccolo, Wilson and Donoghue, Jacob A. and Hochberg, Leigh R. and Eskandar, Emad N. and Madsen, Joseph R. and Anderson, William S. and Brown, Emery N. and Halgren, Eric and Cash, Sydney S.},
+  month = may,
+  year = {2011},
+  keywords = {epilepsy,Neurological models,Synaptic plasticity},
+  pages = {635-641},
+  file = {C:\\Users\\saram\\Zotero\\storage\\9P3WNBWK\\Truccolo et al.  2011  Single-neuron dynamics in human focal epilepsy.pdf}
+}
+
+@article{Hattori2015,
+  title = {Robust Hippocampal Responsivity during Retrieval of Consolidated Associative Memory},
+  volume = {25},
+  issn = {1098-1063},
+  doi = {10.1002/hipo.22401},
+  abstract = {A contentious point in memory research is whether or not the hippocampus plays a time-limited role in the consolidation of declarative memories. A widely held view is that declarative memories are initially encoded in the hippocampus, then transferred to the neocortex for long-term storage. Alternate views argue instead that the hippocampus continues to play a role in remote memory recall. These competing theories are largely based on human amnesic and animal lesion/inactivation studies. However, in vivo electrophysiological evidence supporting these views is scarce. Given that other studies examining the role of the hippocampus in remote memory retrieval using lesion and imaging techniques in human and animal models have provided mixed results, it would be particularly useful to gain insight at the in vivo electrophysiological level. Here we report hippocampal single-neuron and theta activity recorded longitudinally during acquisition and remote retrieval of trace eyeblink conditioning. Results from conditioned rabbits were compared to those obtained from yoked pseudo-conditioned control rabbits. Results reveal continued learning-specific hippocampal activity one month after initial acquisition of the task. Our findings yield insight into the normal physiological responses of the hippocampus during memory processes and provide compelling in vivo electrophysiological evidence that the hippocampus is involved in both acquisition and retrieval of consolidated memories. \textcopyright{} 2014 The Authors Hippocampus Published by Wiley Periodicals, Inc.},
+  language = {en},
+  number = {5},
+  journal = {Hippocampus},
+  author = {Hattori, Shoai and Chen, Lillian and Weiss, Craig and Disterhoft, John F.},
+  month = may,
+  year = {2015},
+  keywords = {In vivo electrophysiology,hippocampus,rabbits,trace eyeblink conditioning,consolidation},
+  pages = {655-669},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2IC8PMG4\\Hattori et al.  2015  Robust hippocampal responsivity during retrieval of consolidated associative memory.pdf}
+}
+
+@article{Iaccarino2016,
+  title = {Gamma Frequency Entrainment Attenuates Amyloid Load and Modifies Microglia},
+  volume = {540},
+  copyright = {\textcopyright{} 2016 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.},
+  issn = {0028-0836},
+  doi = {10.1038/nature20587},
+  abstract = {Changes in gamma oscillations (20\textendash{}50 Hz) have been observed in several neurological disorders. However, the relationship between gamma oscillations and cellular pathologies is unclear. Here we show reduced, behaviourally driven gamma oscillations before the onset of plaque formation or cognitive decline in a mouse model of Alzheimer's disease. Optogenetically driving fast-spiking parvalbumin-positive (FS-PV)-interneurons at gamma (40 Hz), but not other frequencies, reduces levels of amyloid-$\beta$ (A$\beta$)1\textendash{}40 and A$\beta$ 1\textendash{}42 isoforms. Gene expression profiling revealed induction of genes associated with morphological transformation of microglia, and histological analysis confirmed increased microglia co-localization with A$\beta$. Subsequently, we designed a non-invasive 40 Hz light-flickering regime that reduced A$\beta$1\textendash{}40 and A$\beta$1\textendash{}42 levels in the visual cortex of pre-depositing mice and mitigated plaque load in aged, depositing mice. Our findings uncover a previously unappreciated function of gamma rhythms in recruiting both neuronal and glial responses to attenuate Alzheimer's-disease-associated pathology.},
+  language = {en},
+  number = {7632},
+  journal = {Nature},
+  author = {Iaccarino, Hannah F. and Singer, Annabelle C. and Martorell, Anthony J. and Rudenko, Andrii and Gao, Fan and Gillingham, Tyler Z. and Mathys, Hansruedi and Seo, Jinsoo and Kritskiy, Oleg and Abdurrob, Fatema and Adaikkan, Chinnakkaruppan and Canter, Rebecca G. and Rueda, Richard and Brown, Emery N. and Boyden, Edward S. and Tsai, Li-Huei},
+  month = dec,
+  year = {2016},
+  keywords = {Cellular neuroscience,Alzheimer's disease,Molecular neuroscience},
+  pages = {230-235},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PBSWXDUM\\Iaccarino et al.  2016  Gamma frequency entrainment attenuates amyloid load and modifies microglia.pdf}
+}
+
+@article{Hubert1985,
+  title = {Comparing Partitions},
+  volume = {2},
+  issn = {0176-4268, 1432-1343},
+  doi = {10.1007/BF01908075},
+  abstract = {The problem of comparing two different partitions of a finite set of objects reappears continually in the clustering literature. We begin by reviewing a well-known measure of partition correspondence often attributed to Rand (1971), discuss the issue of correcting this index for chance, and note that a recent normalization strategy developed by Morey and Agresti (1984) and adopted by others (e.g., Miligan and Cooper 1985) is based on an incorrect assumption. Then, the general problem of comparing partitions is approached indirectly by assessing the congruence of two proximity matrices using a simple cross-product measure. They are generated from corresponding partitions using various scoring rules. Special cases derivable include traditionally familiar statistics and/or ones tailored to weight certain object pairs differentially. Finally, we propose a measure based on the comparison of object triples having the advantage of a probabilistic interpretation in addition to being corrected for chance (i.e., assuming a constant value under a reasonable null hypothesis) and bounded between $\pm$1.},
+  language = {en},
+  number = {1},
+  journal = {Journal of Classification},
+  author = {Hubert, Lawrence and Arabie, Phipps},
+  month = dec,
+  year = {1985},
+  pages = {193-218},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PPWCJPPH\\Hubert and Arabie  1985  Comparing partitions.pdf}
+}
+
+@incollection{Mahallati2018,
+  series = {Studies in Computational Intelligence},
+  title = {Interpreting {{Cluster Structure}} in {{Waveform Data}} with {{Visual Assessment}} and {{Dunn}}'s {{Index}}},
+  copyright = {All rights reserved},
+  isbn = {978-3-319-67788-0 978-3-319-67789-7},
+  abstract = {Dunn's index was introduced in 1974 as a way to define and identify a ``best'' crisp partition on n objects represented by either unlabeled feature vectors or dissimilarity matrix data. This article examines the intimate relationship that exists between Dunn's index, single linkage clustering, and a visual method called iVAT for estimating the number of clusters in the input data. The relationship of Dunn's index to iVAT and single linkage in the labeled data case affords a means to better understand the utility of these three companion methods when data are crisply clustered in the unlabeled case (the real case). Numerical examples using simulated waveform data drawn from the field of neuroscience illustrate the natural compatibility of Dunn's index with iVAT and single linkage. A second aim of this note is to study customizing the three methods by changing the distance measure from Euclidean distance to one that may be more appropriate for assessing the validity of crisp clusters of finite sets of waveform data. We present numerical examples that support our assertion that when used collectively, the three methods afford a useful approach to evaluation of crisp clusters in unlabeled waveform data.},
+  language = {en},
+  booktitle = {Frontiers in {{Computational Intelligence}}},
+  publisher = {{Springer, Cham}},
+  author = {Mahallati, Sara and Bezdek, James C. and Kumar, Dheeraj and Popovic, Milos R. and Valiante, Taufik A.},
+  year = {2018},
+  pages = {73-101},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TKHIH5UX\\Mahallati et al.  2018  Interpreting Cluster Structure in Waveform Data with Visual Assessment and Dunn’s Index.pdf},
+  doi = {10.1007/978-3-319-67789-7_6}
+}
+
+@article{Middleton2016,
+  title = {Silencing {{CA3}} Disrupts Temporal Coding in the {{CA1}} Ensemble},
+  volume = {19},
+  copyright = {2016 Nature Publishing Group},
+  issn = {1546-1726},
+  doi = {10.1038/nn.4311},
+  abstract = {The circuit mechanisms underlying temporal coding in hippocampal area CA1 are poorly understood. The authors demonstrate that genetically removing CA3 input to CA1 disrupts temporally compressed ensemble-wide theta sequences in CA1 while sparing single-cell place coding, suggesting a crucial role for CA3 input in organizing the ensemble code for space.$<$/p$>$},
+  language = {en},
+  number = {7},
+  journal = {Nature Neuroscience},
+  author = {Middleton, Steven J. and McHugh, Thomas J.},
+  month = may,
+  year = {2016},
+  pages = {nn.4311},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KJVZ37MB\\Middleton and McHugh  2016  Silencing CA3 disrupts temporal coding in the CA1 ensemble.pdf}
+}
+
+@article{Schneidman2006,
+  title = {Weak Pairwise Correlations Imply Strongly Correlated Network States in a Neural Population},
+  volume = {440},
+  copyright = {\textcopyright{} 2006 Nature Publishing Group},
+  issn = {0028-0836},
+  doi = {10.1038/nature04701},
+  abstract = {Biological networks have so many possible states that exhaustive sampling is impossible. Successful analysis thus depends on simplifying hypotheses, but experiments on many systems hint that complicated, higher-order interactions among large groups of elements have an important role. Here we show, in the vertebrate retina, that weak correlations between pairs of neurons coexist with strongly collective behaviour in the responses of ten or more neurons. We find that this collective behaviour is described quantitatively by models that capture the observed pairwise correlations but assume no higher-order interactions. These maximum entropy models are equivalent to Ising models, and predict that larger networks are completely dominated by correlation effects. This suggests that the neural code has associative or error-correcting properties, and we provide preliminary evidence for such behaviour. As a first test for the generality of these ideas, we show that similar results are obtained from networks of cultured cortical neurons.},
+  language = {en},
+  number = {7087},
+  journal = {Nature},
+  author = {Schneidman, Elad and Berry, Michael J. and Segev, Ronen and Bialek, William},
+  month = apr,
+  year = {2006},
+  pages = {1007-1012},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4EKCFAQU\\Schneidman 2006-Weak pairwise correlations imply strongly correlated network states in a neural population.pdf}
+}
+
+@article{Padmanabhan2010,
+  title = {Intrinsic Biophysical Diversity Decorrelates Neuronal Firing While Increasing Information Content},
+  volume = {13},
+  copyright = {\textcopyright{} 2010 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
+  issn = {1097-6256},
+  doi = {10.1038/nn.2630},
+  abstract = {Although examples of variation and diversity exist throughout the nervous system, their importance remains a source of debate. Even neurons of the same molecular type have notable intrinsic differences. Largely unknown, however, is the degree to which these differences impair or assist neural coding. We examined the outputs from a single type of neuron, the mitral cells of the mouse olfactory bulb, to identical stimuli and found that each cell's spiking response was dictated by its unique biophysical fingerprint. Using this intrinsic heterogeneity, diverse populations were able to code for twofold more information than their homogeneous counterparts. In addition, biophysical variability alone reduced pair-wise output spike correlations to low levels. Our results indicate that intrinsic neuronal diversity is important for neural coding and is not simply the result of biological imprecision.},
+  language = {en},
+  number = {10},
+  journal = {Nature Neuroscience},
+  author = {Padmanabhan, Krishnan and Urban, Nathaniel N.},
+  month = oct,
+  year = {2010},
+  keywords = {neurophysiology,Sensory systems,Neural encoding},
+  pages = {1276-1282},
+  file = {C:\\Users\\saram\\Zotero\\storage\\B86BQI9B\\Padmanabhan and Urban  2010  Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content.pdf;C:\\Users\\saram\\Zotero\\storage\\RCIXMSJP\\nn.2630-S1.pdf}
+}
+
+@article{Spiegler2016,
+  title = {Selective {{Activation}} of {{Resting}}-{{State Networks}} Following {{Focal Stimulation}} in a {{Connectome}}-{{Based Network Model}} of the {{Human Brain}}},
+  volume = {3},
+  issn = {2373-2822},
+  doi = {10.1523/ENEURO.0068-16.2016},
+  abstract = {When the brain is stimulated, for example, by sensory inputs or goal-oriented tasks, the brain initially responds with activities in specific areas. The subsequent pattern formation of functional networks is constrained by the structural connectivity (SC) of the brain. The extent to which information is processed over short- or long-range SC is unclear. Whole-brain models based on long-range axonal connections, for example, can partly describe measured functional connectivity dynamics at rest. Here, we study the effect of SC on the network response to stimulation. We use a human whole-brain network model comprising long- and short-range connections. We systematically activate each cortical or thalamic area, and investigate the network response as a function of its short- and long-range SC. We show that when the brain is operating at the edge of criticality, stimulation causes a cascade of network recruitments, collapsing onto a smaller space that is partly constrained by SC. We found both short- and long-range SC essential to reproduce experimental results. In particular, the stimulation of specific areas results in the activation of one or more resting-state networks. We suggest that the stimulus-induced brain activity, which may indicate information and cognitive processing, follows specific routes imposed by structural networks explaining the emergence of functional networks. We provide a lookup table linking stimulation targets and functional network activations, which potentially can be useful in diagnostics and treatments with brain stimulation.},
+  number = {5},
+  journal = {eNeuro},
+  author = {Spiegler, Andreas and Hansen, Enrique C. A. and Bernard, Christophe and McIntosh, Anthony R. and Jirsa, Viktor K.},
+  month = oct,
+  year = {2016},
+  file = {C:\\Users\\saram\\Zotero\\storage\\XEJ9VS9M\\Spiegler et al. - 2016 - Selective Activation of Resting-State Networks fol.pdf},
+  pmid = {27752540},
+  pmcid = {PMC5052665}
+}
+
+@article{Rivera-Borroto2012,
+  title = {Dunn's Index for Cluster Tendency Assessment of Pharmacological Data Sets},
+  volume = {90},
+  issn = {0008-4212},
+  doi = {10.1139/y2012-002},
+  abstract = {Cluster tendency assessment is an important stage in cluster analysis. In this sense, a group of promising techniques named visual assessment of tendency (VAT) has emerged in the literature. The presence of clusters can be detected easily through the direct observation of a dark blocks structure along the main diagonal of the intensity image. Alternatively, if the Dunn's index for a single linkage partition is greater than 1, then it is a good indication of the blocklike structure. In this report, the Dunn's index is applied as a novel measure of tendency on 8 pharmacological data sets, represented by machine-learning-selected molecular descriptors. In all cases, observed values are less than 1, thus indicating a weak tendency for data to form compact clusters. Other results suggest that there is an increasing relationship between the Dunn's index as a measure of cluster separability and the classification accuracy of various cluster algorithms tested on the same data sets.},
+  number = {4},
+  journal = {Canadian Journal of Physiology and Pharmacology},
+  author = {Rivera-Borroto, Oscar Miguel and Rabassa-Guti{\'e}rrez, M{\'o}nica and Grau-{\'A}balo, Ricardo del Coraz{\'o}n and Marrero-Ponce, Yovani and {Garc{\'\i}a-de la Vega}, Jos{\'e} Manuel},
+  month = mar,
+  year = {2012},
+  keywords = {cluster tendency,Cluster analysis,VAT techniques,Dunn’s index,pharmacological data sets,clusters overlap,classification accuracy},
+  pages = {425-433},
+  file = {C:\\Users\\saram\\Zotero\\storage\\B9MIDV2W\\Rivera-Borroto et al.  2012  Dunn’s index for cluster tendency assessment of pharmacological data sets.pdf;C:\\Users\\saram\\Zotero\\storage\\FRPX8FIH\\y2012-002.pdf}
+}
+
+@inproceedings{Schaffer2017,
+  title = {{{FPGA}}-Based Real-Time Multichannel Neural Dataset Generation},
+  doi = {10.1109/ECCTD.2017.8093235},
+  abstract = {Miniaturized voltage sensors (electrodes) implanted into the brain tissue are capable of recording the brief electrical impulses (spikes) of neurons located close to the electrode sites. To investigate the activity of individual neurons and discriminate spikes generated by different neurons a technique called spike sorting can be applied on the recorded data. However, the performance of current spike sorting methods is challenged by multichannel neural data recorded with high-density, highchannel count silicon probes developed recently. Our group started to develop an FPGA-based solution to accelerate the clustering of spikes detected in high-channel count neural recordings. It is a crucial step of the development to validate the performance of the clustering algorithm. This can be achieved by using ground truth datasets where the exact time of spikes fired by different single units are known. In this paper we present an FPGA-based architecture for real-time generation of multichannel hybrid ground truth datasets, which will be used for the validation of our FPGA-based clustering algorithm.},
+  booktitle = {2017 {{European Conference}} on {{Circuit Theory}} and {{Design}} ({{ECCTD}})},
+  author = {Sch{\"a}ffer, L. and Nagy, Z. and Kincses, Z. and Fi{\'a}th, R.},
+  month = sep,
+  year = {2017},
+  keywords = {Neurons,Electrodes,Sorting,Clocks,Field programmable gate arrays,Generators,Real-time systems},
+  pages = {1-4},
+  file = {C:\\Users\\saram\\Zotero\\storage\\G8SICTB8\\Schäffer et al.  2017  FPGA-based real-time multichannel neural dataset generation.pdf}
+}
+
+@article{Morishima2011,
+  title = {Highly {{Differentiated Projection}}-{{Specific Cortical Subnetworks}}},
+  volume = {31},
+  copyright = {Copyright \textcopyright{} 2011 the authors 0270-6474/11/3110380-12\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.0772-11.2011},
+  abstract = {Pyramidal cells in the neocortex are differentiated into several subgroups based on their extracortical projection targets. However, little is known regarding the relative intracortical connectivity of pyramidal neurons specialized for these specific output channels. We used paired recordings and quantitative morphological analysis to reveal distinct synaptic transmission properties, connection patterns, and morphological differentiation correlated with heterogeneous thalamic input to two different groups of pyramidal cells residing in layer 5 (L5) of rat frontal cortex. Retrograde tracers were used to label two projection subtypes in L5: crossed-corticostriatal (CCS) cells projecting to both sides of the striatum, and corticopontine (CPn) cells projecting to the ipsilateral pons. Although CPn/CPn and CCS/CCS pairs had similar connection probabilities, CPn/CPn pairs exhibited greater reciprocal connectivity, stronger unitary synaptic transmission, and more facilitation of paired-pulse responses. These synaptic characteristics were strongly correlated to the projection subtype of the presynaptic neuron. CPn and CCS cells were further differentiated according to their somatic position (L5a and L5b, the latter denser thalamic afferent fibers) and their dendritic/axonal arborizations. Together, our data demonstrate that the pyramidal projection system is segregated into different output channels according to subcortical target and thalamic input, and that information flow within and between these channels is selectively organized.},
+  language = {en},
+  number = {28},
+  journal = {Journal of Neuroscience},
+  author = {Morishima, Mieko and Morita, Kenji and Kubota, Yoshiyuki and Kawaguchi, Yasuo},
+  month = jul,
+  year = {2011},
+  pages = {10380-10391},
+  file = {C:\\Users\\saram\\Zotero\\storage\\GJ2462TW\\Morishima et al. - 2011 - Highly Differentiated Projection-Specific Cortical.pdf},
+  pmid = {21753015}
+}
+
+@article{Otsuka2008,
+  title = {Firing-{{Pattern}}-{{Dependent Specificity}} of {{Cortical Excitatory Feed}}-{{Forward Subnetworks}}},
+  volume = {28},
+  copyright = {Copyright \textcopyright{} 2008 Society for Neuroscience 0270-6474/08/2811186-10\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.1921-08.2008},
+  abstract = {The cortical circuit includes networks of highly interconnected pyramidal neurons. Here, we investigated whether pyramidal cells form subnetworks depending on pyramidal subtypes. We classified layer V (L5) pyramidal cells in rat frontal cortex into three physiological subtypes based on the presence (SA-d type) or absence (SA type) of an initial burst in neurons displaying slowly adapting spike trains, or fast spike frequency adaptation (FA type) against current pulse injections. Pyramidal cells projecting to the particular subcortical areas were correlated with the physiological subtypes. Focal glutamate stimulation of a L2/3 pyramidal cell induced EPSCs in SA and SA-d cells more frequently than in FA cells. FA cells in upper L5 received more inputs from the upper L2/3, and those in lower L5 received inputs from cells in lower L2/3, suggesting topographic interlaminar projections to FA cells. Dual recordings from L5 pyramidal cells revealed that common input probability that two L5 cells share inputs from a L2/3 cell was high in cell pairs of the same subtypes, compared with those in different subtype pairs. Furthermore, the common input probability was highly selective when cell pairs of the same subtypes, but not different subtypes, had connections between them. Our results suggest that L2/3 pyramidal cells selectively innervate L5 cells, depending on their firing subtypes.},
+  language = {en},
+  number = {44},
+  journal = {Journal of Neuroscience},
+  author = {Otsuka, Takeshi and Kawaguchi, Yasuo},
+  month = oct,
+  year = {2008},
+  keywords = {pyramidal cell,connection specificity,cortex,extracortical projection,firing pattern,interlaminar connection},
+  pages = {11186-11195},
+  pmid = {18971461}
+}
+
+@article{Morishima2017,
+  title = {Segregated {{Excitatory}}\textendash{}{{Inhibitory Recurrent Subnetworks}} in {{Layer}} 5 of the {{Rat Frontal Cortex}}},
+  volume = {27},
+  issn = {1047-3211},
+  doi = {10.1093/cercor/bhx276},
+  abstract = {A prominent feature of neocortical pyramidal cells (PCs) is their numerous projections to diverse brain areas. In layer 5 (L5) of the rat frontal cortex, there are 2 major subtypes of PCs that differ in their long-range axonal projections, corticopontine (CPn) cells and crossed corticostriatal (CCS) cells. The outputs of these L5 PCs can be regulated by feedback inhibition from neighboring cortical GABAergic cells. Two major subtypes of GABAergic cells are parvalbumin (PV)-positive and somatostatin (SOM)-positive cells. PV cells have a fast-spiking (FS) firing pattern, while SOM cells have a low threshold spike (LTS) and regular spiking. In this study, we found that the 2 PC subtypes in L5 selectively make recurrent connections with LTS cells. The connection patterns correlated with the morphological and physiological diversity of LTS cells. LTS cells with high input resistance (Ri) exhibited more compact dendrites and more rebound spikes than LTS cells with low Ri, which had vertically elongated dendrites. LTS subgroups differently inhibited the PC subtypes, although FS cells made nonselective connections with both projection subtypes. These results demonstrate a novel recurrent network of inhibitory and projection-specific excitatory neurons within the neocortex.},
+  number = {12},
+  journal = {Cerebral Cortex},
+  author = {Morishima, Mieko and Kobayashi, Kenta and Kato, Shigeki and Kobayashi, Kazuto and Kawaguchi, Yasuo},
+  month = dec,
+  year = {2017},
+  pages = {5846-5857},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KDDQ4KCX\\bhx276.pdf}
+}
+
+@article{Morishima2006,
+  title = {Recurrent {{Connection Patterns}} of {{Corticostriatal Pyramidal Cells}} in {{Frontal Cortex}}},
+  volume = {26},
+  copyright = {Copyright \textcopyright{} 2006 Society for Neuroscience 0270-6474/06/264394-12\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.0252-06.2006},
+  abstract = {Corticostriatal pyramidal cells are heterogeneous in the frontal cortex. Here, we show that subpopulations of corticostriatal neurons in the rat frontal cortex are selectively connected with each other based on their subcortical targets. Using paired recordings of retrogradely labeled cells, we investigated the synaptic connectivity between two projection cell types: those projecting to the pons [corticopontine (CPn) cell], often with collaterals to the striatum, and those projecting to both sides of the striatum but not to the pons [crossed corticostriatal (CCS) cell]. The two types were morphologically differentiated in regard to their apical tufts. The dendritic morphologies of CCS cells were correlated with their somatic depth within the cortex. CCS cells had reciprocal synaptic connections with each other and also provided synaptic input to CPn cells. However, connections from CPn to CCS cells were rarely found, even in pairs showing CCS to CPn connectivity. Additionally, CCS cells preferentially innervated the basal dendrites of other CCS cells but made contacts onto both the basal and apical dendrites of CPn cells. The amplitude of synaptic responses was to some extent correlated with the contact site number. Ratios of the EPSC amplitude to the contact number tended to be larger in the CCS to CCS connection. Therefore, our data demonstrate that these two types of corticostriatal cells distinct in their dendritic morphologies show directional and domain-dependent preferences in their synaptic connectivity.},
+  language = {en},
+  number = {16},
+  journal = {Journal of Neuroscience},
+  author = {Morishima, Mieko and Kawaguchi, Yasuo},
+  month = apr,
+  year = {2006},
+  keywords = {pyramidal cell,apical dendrite,frontal cortex,pons,postsynaptic current,striatum},
+  pages = {4394-4405},
+  file = {C:\\Users\\saram\\Zotero\\storage\\CYPJ2TT7\\Morishima and Kawaguchi - 2006 - Recurrent Connection Patterns of Corticostriatal P.pdf},
+  pmid = {16624959}
+}
+
+@article{Kim2016,
+  title = {Synaptic {{Organization}} of the {{Neuronal Circuits}} of the {{Claustrum}}},
+  volume = {36},
+  copyright = {Copyright \textcopyright{} 2016 the authors 0270-6474/16/360773-12\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.3643-15.2016},
+  abstract = {The claustrum, a poorly understood subcortical structure located between the cortex and the striatum, forms widespread connections with almost all cortical areas, but the cellular organization of claustral circuits remains largely unknown. Based primarily on anatomical data, it has been proposed that the claustrum integrates activity across sensory modalities. However, the extent to which the synaptic organization of claustral circuits supports this integration is unclear. Here, we used paired whole-cell recordings and optogenetic approaches in mouse brain slices to determine the cellular organization of the claustrum. We found that unitary synaptic connections among claustrocortical (ClaC) neurons were rare. In contrast, parvalbumin-positive (PV) inhibitory interneurons were highly interconnected with both chemical and electrical synapses. In addition, ClaC neurons and PV interneurons formed frequent synaptic connections. As suggested by anatomical data, we found that corticoclaustral afferents formed monosynaptic connections onto both ClaC neurons and PV interneurons. However, the responses to cortical input were comparatively stronger in PV interneurons. Consistent with this overall circuit organization, activation of corticoclaustral afferents generated monosynaptic excitatory responses as well as disynaptic inhibitory responses in ClaC neurons. These data indicate that recurrent excitatory circuits within the claustrum alone are unlikely to integrate across multiple sensory modalities. Rather, this cellular organization is typical of circuits sensitive to correlated inputs. Although single ClaC neurons may integrate corticoclaustral input from different cortical regions, these results are consistent with more recent proposals implicating the claustrum in detecting sensory novelty or in amplifying correlated cortical inputs to coordinate the activity of functionally related cortical regions.
+SIGNIFICANCE STATEMENT The function of the claustrum, a brain nucleus found in mammals, remains poorly understood. It has been proposed, based primarily on anatomical data, that claustral circuits play an integrative role and contribute to multimodal sensory integration. Here we show that the principal neurons of the claustrum, claustrocortical (ClaC) projection neurons, rarely form synaptic connections with one another and are unlikely to contribute to broad integration within the claustrum. We show that, although single ClaC neurons may integrate corticoclaustral inputs carrying information for different sensory modalities, the synaptic organization of ClaC neurons, local parvalbumin-positive interneurons within the claustrum, and cortical afferents is also consistent with recent proposals that the claustrum plays a role in detecting salient stimuli or amplifying correlated cortical inputs.},
+  language = {en},
+  number = {3},
+  journal = {Journal of Neuroscience},
+  author = {Kim, Juhyun and Matney, Chanel J. and Roth, Richard H. and Brown, Solange P.},
+  month = jan,
+  year = {2016},
+  keywords = {cortex,channelrhodopsin-2,claustrocortical,claustrum,parvalbumin},
+  pages = {773-784},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8DBH3LIK\\Kim et al. - 2016 - Synaptic Organization of the Neuronal Circuits of .pdf},
+  pmid = {26791208}
+}
+
+@article{Brown2009a,
+  title = {Cell-Type Identity: A Key to Unlocking the Function of Neocortical Circuits},
+  volume = {19},
+  issn = {0959-4388},
+  shorttitle = {Cell-Type Identity},
+  doi = {10.1016/j.conb.2009.07.011},
+  abstract = {A central tenet of neuroscience is that the precise patterns of connectivity among neurons in a given brain area underlie its function. However, assigning any aspect of perception or behavior to the wiring of local circuits has been challenging. Here, we review recent work in sensory neocortex that demonstrates the power of identifying specific cell types when investigating the functional organization of brain circuits. These studies indicate that knowing the identity of both the pre- and postsynaptic cell type is key when analyzing neocortical circuits. Furthermore, identifying the circuit organization of particular cell types in the neocortex allows the recording and manipulation of each cell type's activity and the direct testing of its functional role in perception and behavior.},
+  number = {4},
+  journal = {Current opinion in neurobiology},
+  author = {Brown, Solange P. and Hestrin, Shaul},
+  month = aug,
+  year = {2009},
+  pages = {415-421},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KM4TW4Y7\\Brown and Hestrin - 2009 - Cell-type identity a key to unlocking the functio.pdf},
+  pmid = {19674891},
+  pmcid = {PMC2739254}
+}
+
+@article{Brown2009,
+  title = {Intracortical Circuits of Pyramidal Neurons Reflect Their Long-Range Axonal Targets},
+  volume = {457},
+  copyright = {2009 Nature Publishing Group},
+  issn = {1476-4687},
+  doi = {10.1038/nature07658},
+  abstract = {Using quadruple whole-cell recordings, this study tested for synaptic connections among heterogeneous populations of cortical excitatory cells (pyramidal neurons), and found highly connected local circuits of neurons, each reflecting the neurons' long-range projections to a different brain region. The findings illustrate a diversity of dense cortical microcircuitry associated with the variety of information streams that produce our repertoire of behaviours and internal states.$<$/p$>$},
+  language = {En},
+  number = {7233},
+  journal = {Nature},
+  author = {Brown, Solange P. and Hestrin, Shaul},
+  month = feb,
+  year = {2009},
+  pages = {1133},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6MASQF3K\\Brown and Hestrin - 2009 - Intracortical circuits of pyramidal neurons reflec.pdf}
+}
+
+@article{Lein2011,
+  title = {Acute {{Hippocampal Slice Preparation}} and {{Hippocampal Slice Cultures}}},
+  volume = {758},
+  issn = {1064-3745},
+  doi = {10.1007/978-1-61779-170-3_8},
+  abstract = {A major advantage of hippocampal slice preparations is that the cytoarchitecture and synaptic circuits of the hippocampus are largely retained. In neurotoxicology research, organotypic hippocampal slices have mostly been used as acute ex vivo preparations for investigating the effects of neurotoxic chemicals on synaptic function. More recently, hippocampal slice cultures, which can be maintained for several weeks to several months in vitro, have been employed to study how neurotoxic chemicals influence the structural and functional plasticity in hippocampal neurons. This chapter provides protocols for preparing hippocampal slices to be used acutely for electrophysiological measurements using glass microelectrodes or microelectrode arrays or to be cultured for morphometric assessments of individual neurons labeled using biolistics.},
+  journal = {Methods in molecular biology (Clifton, N.J.)},
+  author = {Lein, Pamela J. and Barnhart, Christopher D. and Pessah, Isaac N.},
+  year = {2011},
+  pages = {115-134},
+  file = {C:\\Users\\saram\\Zotero\\storage\\F4MJR2PT\\Lein et al. - 2011 - Acute Hippocampal Slice Preparation and Hippocampa.pdf},
+  pmid = {21815062},
+  pmcid = {PMC3499947}
+}
+
+@article{Buskila2014,
+  title = {Extending the Viability of Acute Brain Slices},
+  volume = {4},
+  copyright = {2014 Nature Publishing Group},
+  issn = {2045-2322},
+  doi = {10.1038/srep05309},
+  abstract = {Extending the viability of acute brain slices},
+  language = {En},
+  journal = {Scientific Reports},
+  author = {Buskila, Yossi and Breen, Paul P. and Tapson, Jonathan and van Schaik, Andr{\'e} and Barton, Matthew and Morley, John W.},
+  month = jun,
+  year = {2014},
+  pages = {5309},
+  file = {C:\\Users\\saram\\Zotero\\storage\\MT2WB7VC\\Buskila et al. - 2014 - Extending the viability of acute brain slices.pdf}
+}
+
+@article{Tang2016,
+  title = {In {{Vitro Assessment Reveals Parameters}}-{{Dependent Modulation}} on {{Excitability}} and {{Functional Connectivity}} of {{Cerebellar Slice}} by {{Repetitive Transcranial Magnetic Stimulation}}},
+  volume = {6},
+  issn = {2045-2322},
+  doi = {10.1038/srep23420},
+  abstract = {Repetitive transcranial magnetic stimulation (rTMS) is an increasingly common technique used to selectively modify neural excitability and plasticity. There is still controversy concerning the cortical response to rTMS of different frequencies. In this study, a novel in vitro paradigm utilizing the Multi-Electrodes Array (MEA) system and acute cerebellar slicing is described. In a controllable environment that comprises perfusion, incubation, recording and stimulation modules, the spontaneous single-unit spiking activity in response to rTMS of different frequencies and powers was directly measured and analyzed. Investigation using this in vitro paradigm revealed frequency-dependent modulation upon the excitability and functional connectivity of cerebellar slices. The 1-Hz rTMS sessions induced short-term inhibition or lagged inhibition, whereas 20-Hz sessions induced excitation. The level of modulation is influenced by the value of power. However the long-term response fluctuated without persistent direction. The choice of evaluation method may also interfere with the interpretation of modulation direction. Furthermore, both short-term and long-term functional connectivity was strengthened by 1-Hz rTMS and weakened by 20-Hz rTMS.},
+  journal = {Scientific Reports},
+  author = {Tang, Rongyu and Zhang, Guanghao and Weng, Xiechuan and Han, Yao and Lang, Yiran and Zhao, Yuwei and Zhao, Xiaobo and Wang, Kun and Lin, Qiuxia and Wang, Changyong},
+  month = mar,
+  year = {2016},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6EZTYLGV\\Tang et al. - 2016 - In Vitro Assessment Reveals Parameters-Dependent M.pdf},
+  pmid = {27000527},
+  pmcid = {PMC4802318}
+}
+
+@article{Killian2016,
+  title = {A {{Device}} for {{Long}}-{{Term Perfusion}}, {{Imaging}}, and {{Electrical Interfacing}} of {{Brain Tissue In}} Vitro},
+  volume = {10},
+  issn = {1662-4548},
+  doi = {10.3389/fnins.2016.00135},
+  abstract = {Distributed microelectrode array (MEA) recordings from consistent, viable, $\geq$500 $\mu$m thick tissue preparations over time periods from days to weeks may aid in studying a wide range of problems in neurobiology that require in vivo-like organotypic morphology. Existing tools for electrically interfacing with organotypic slices do not address necrosis that inevitably occurs within thick slices with limited diffusion of nutrients and gas, and limited removal of waste. We developed an integrated device that enables long-term maintenance of thick, functionally active, brain tissue models using interstitial perfusion and distributed recordings from thick sections of explanted tissue on a perforated multi-electrode array. This novel device allows for automated culturing, in situ imaging, and extracellular multi-electrode interfacing with brain slices, 3-D cell cultures, and potentially other tissue culture models. The device is economical, easy to assemble, and integrable with standard electrophysiology tools. We found that convective perfusion through the culture thickness provided a functional benefit to the preparations as firing rates were generally higher in perfused cultures compared to their respective unperfused controls. This work is a step toward the development of integrated tools for days-long experiments with more consistent, healthier, thicker, and functionally more active tissue cultures with built-in distributed electrophysiological recording and stimulation functionality. The results may be useful for the study of normal processes, pathological conditions, and drug screening strategies currently hindered by the limitations of acute (a few hours long) brain slice preparations.},
+  journal = {Frontiers in Neuroscience},
+  author = {Killian, Nathaniel J. and Vernekar, Varadraj N. and Potter, Steve M. and Vukasinovic, Jelena},
+  month = mar,
+  year = {2016},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8EV6F796\\Killian et al. - 2016 - A Device for Long-Term Perfusion, Imaging, and Ele.pdf},
+  pmid = {27065793},
+  pmcid = {PMC4815559}
+}
+
+@article{Huang2013,
+  title = {Physiological Temperature during Brain Slicing Enhances the Quality of Acute Slice Preparations},
+  volume = {7},
+  issn = {1662-5102},
+  doi = {10.3389/fncel.2013.00048},
+  abstract = {We demonstrate that brain dissection and slicing using solutions warmed to near-physiological temperature (\textasciitilde{} +34$^\circ$C), greatly enhance slice quality without affecting intrinsic electrophysiological properties of the neurons. Improved slice quality is seen not only when using young ($<$1 month), but also mature ($>$2.5 month) mice. This allows easy in vitro patch-clamp experimentation using adult deep cerebellar nuclear slices, which until now have been considered very difficult. As proof of the concept, we compare intrinsic properties of cerebellar nuclear neurons in juvenile ($<$1 month) and adult (up to 7 months) mice, and confirm that no significant developmental changes occur after the fourth postnatal week. The enhanced quality of brain slices from old animals facilitates experimentation on age-related disorders as well as optogenetic studies requiring long transfection periods.},
+  journal = {Frontiers in Cellular Neuroscience},
+  author = {Huang, Shiwei and Uusisaari, Marylka Y.},
+  month = apr,
+  year = {2013},
+  file = {C:\\Users\\saram\\Zotero\\storage\\N2VZUJT5\\Huang and Uusisaari - 2013 - Physiological temperature during brain slicing enh.pdf},
+  pmid = {23630465},
+  pmcid = {PMC3632751}
+}
+
+@article{Kim2014,
+  title = {Layer 6 {{Corticothalamic Neurons Activate}} a {{Cortical Output Layer}}, {{Layer}} 5a},
+  volume = {34},
+  issn = {0270-6474},
+  doi = {10.1523/JNEUROSCI.1325-14.2014},
+  abstract = {Layer 6 corticothalamic neurons are thought to modulate incoming sensory information via their intracortical axons targeting the major thalamorecipient layer of the neocortex, layer 4, and via their long-range feedback projections to primary sensory thalamic nuclei. However, anatomical reconstructions of individual layer 6 corticothalamic (L6 CT) neurons include examples with axonal processes ramifying within layer 5, and the relative input of the overall population of L6 CT neurons to layers 4 and 5 is not well understood. We compared the synaptic impact of L6 CT cells on neurons in layers 4 and 5. We found that the axons of L6 CT neurons densely ramified within layer 5a in both visual and somatosensory cortices of the mouse. Optogenetic activation of corticothalamic neurons generated large EPSPs in pyramidal neurons in layer 5a. In contrast, excitatory neurons in layer 4 exhibited weak excitation or disynaptic inhibition. Fast-spiking parvalbumin-positive cells in both layer 5a and layer 4 were also strongly activated by L6 CT neurons. The overall effect of L6 CT activation was to suppress layer 4 while eliciting action potentials in layer 5a pyramidal neurons. Together, our data indicate that L6 CT neurons strongly activate an output layer of the cortex.},
+  number = {29},
+  journal = {The Journal of Neuroscience},
+  author = {Kim, Juhyun and Matney, Chanel J. and Blankenship, Aaron and Hestrin, Shaul and Brown, Solange P.},
+  month = jul,
+  year = {2014},
+  pages = {9656-9664},
+  file = {C:\\Users\\saram\\Zotero\\storage\\CPE5DDZH\\Kim et al. - 2014 - Layer 6 Corticothalamic Neurons Activate a Cortica.pdf},
+  pmid = {25031405},
+  pmcid = {PMC4099543}
+}
+
+@article{Vendramin2010,
+  title = {Relative Clustering Validity Criteria: {{A}} Comparative Overview},
+  volume = {3},
+  issn = {1932-1872},
+  shorttitle = {Relative Clustering Validity Criteria},
+  doi = {10.1002/sam.10080},
+  abstract = {Many different relative clustering validity criteria exist that are very useful in practice as quantitative measures for evaluating the quality of data partitions, and new criteria have still been proposed from time to time. These criteria are endowed with particular features that may make each of them able to outperform others in specific classes of problems. In addition, they may have completely different computational requirements. Then, it is a hard task for the user to choose a specific criterion when he or she faces such a variety of possibilities. For this reason, a relevant issue within the field of clustering analysis consists of comparing the performances of existing validity criteria and, eventually, that of a new criterion to be proposed. In spite of this, the comparison paradigm traditionally adopted in the literature is subject to some conceptual limitations. The present paper describes an alternative, possibly complementary methodology for comparing clustering validity criteria and uses it to make an extensive comparison of the performances of 40 criteria over a collection of 962,928 partitions derived from five well-known clustering algorithms and 1080 different data sets of a given class of interest. A detailed review of the relative criteria under investigation is also provided that includes an original comparative asymptotic analysis of their computational complexities. This work is intended to be a complement of the classic study reported in 1985 by Milligan and Cooper as well as a thorough extension of a preliminary paper by the authors themselves. Copyright \textcopyright{} 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3: 209-235, 2010},
+  language = {en},
+  number = {4},
+  journal = {Statistical Analysis and Data Mining},
+  author = {Vendramin, Lucas and Campello, Ricardo J. G. B. and Hruschka, Eduardo R.},
+  month = aug,
+  year = {2010},
+  keywords = {clustering,relative criteria,validation},
+  pages = {209-235},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4QITU2LF\\d3b55fb94827c4df45e9fc67c55a7d90d00b.pdf}
+}
+
+@article{Csicsvari2003,
+  title = {Massively {{Parallel Recording}} of {{Unit}} and {{Local Field Potentials With Silicon}}-{{Based Electrodes}}},
+  volume = {90},
+  copyright = {Copyright \textcopyright{} 2003 by the American Physiological Society},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00116.2003},
+  abstract = {Parallel recording of neuronal activity in the behaving animal is a prerequisite for our understanding of neuronal representation and storage of information. Here we describe the development of micro-machined silicon microelectrode arrays for unit and local field recordings. The two-dimensional probes with 96 or 64 recording sites provided high-density recording of unit and field activity with minimal tissue displacement or damage. The on-chip active circuit eliminated movement and other artifacts and greatly reduced the weight of the headgear. The precise geometry of the recording tips allowed for the estimation of the spatial location of the recorded neurons and for high-resolution estimation of extracellular current source density. Action potentials could be simultaneously recorded from the soma and dendrites of the same neurons. Silicon technology is a promising approach for high-density, high-resolution sampling of neuronal activity in both basic research and prosthetic devices.},
+  language = {en},
+  number = {2},
+  journal = {Journal of Neurophysiology},
+  author = {Csicsvari, Jozsef and Henze, Darrell A. and Jamieson, Brian and Harris, Kenneth D. and Sirota, Anton and Barth{\'o}, P{\'e}ter and Wise, Kensall D. and Buzs{\'a}ki, Gy{\"o}rgy},
+  month = aug,
+  year = {2003},
+  pages = {1314-1323},
+  file = {C:\\Users\\saram\\Zotero\\storage\\LKB5HRYR\\1314.full.pdf},
+  pmid = {12904510}
+}
+
+@article{Csicsvari1998,
+  title = {Reliability and {{State Dependence}} of {{Pyramidal Cell}}\textendash{}{{Interneuron Synapses}} in the {{Hippocampus}}: An {{Ensemble Approach}} in the {{Behaving Rat}}},
+  volume = {21},
+  issn = {0896-6273},
+  shorttitle = {Reliability and {{State Dependence}} of {{Pyramidal Cell}}\textendash{}{{Interneuron Synapses}} in the {{Hippocampus}}},
+  doi = {10.1016/S0896-6273(00)80525-5},
+  abstract = {Spike transmission probability between pyramidal cells and interneurons in the CA1 pyramidal layer was investigated in the behaving rat by the simultaneous recording of neuronal ensembles. Population synchrony was strongest during sharp wave (SPW) bursts. However, the increase was three times larger for pyramidal cells than for interneurons. The contribution of single pyramidal cells to the discharge of interneurons was often large (up to 0.6 probability), as assessed by the presence of significant ($<$3 ms) peaks in the cross-correlogram. Complex-spike bursts were more effective than single spikes. Single cell contribution was higher between SPW bursts than during SPWs or theta activity. Hence, single pyramidal cells can reliably discharge interneurons, and the probability of spike transmission is behavior dependent.},
+  number = {1},
+  journal = {Neuron},
+  author = {Csicsvari, Jozsef and Hirase, Hajime and Czurko, Andras and Buzs{\'a}ki, Gy{\"o}rgy},
+  month = jul,
+  year = {1998},
+  pages = {179-189},
+  file = {C:\\Users\\saram\\Zotero\\storage\\EGK8KR7Y\\Csicsvari et al.  1998  Reliability and State Dependence of Pyramidal Cell–Interneuron Synapses in the Hippocampus an Ensemble Approach in the Behaving Rat.pdf}
+}
+
+@article{Meyer2017,
+  title = {The Effect of Single Pyramidal Neuron Firing across and within Layers in Mouse {{V1}}},
+  copyright = {\textcopyright{} 2017, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
+  doi = {10.1101/232603},
+  abstract = {The influence of cortical cell spiking activity on nearby cells has been studied extensively in vitro. Less is known, however, about the impact of single cell firing on local cortical networks in vivo. In a pioneering study, Kwan et al. (Kwan et al., 2012) reported that in mouse layer 2/3 (L2/3), under anesthesia, stimulating a single pyramidal cell recruits \textasciitilde{}1.7\% of neighboring pyramidal units. Here we employ two-photon calcium imaging, in conjunction with single-cell patch clamp stimulation, to probe, in both the awake and lightly anesthetized states, how i) activating single L2/3 pyramidal neurons recruits neighboring units within L2/3 and from layer 4 (L4) to L2/3, and whether ii) activating single pyramidal neurons changes population activity in local circuit. To do this, it was essential to develop an algorithm capable of quantifying how sensitive the calcium signal is at detecting effectively recruited units (followers). This algorithm allowed us to estimate the chance of detecting a follower as a function of the probability that an epoch of stimulation elicits one extra action potential (AP) in the follower cell. Using this approach, we found only a small fraction ($<$0.75\%) of L2/3 cells to be significantly activated within a radius of \textasciitilde{}200 microns from a stimulated neighboring L2/3 pyramidal cell. This fraction did not change significantly in the awake versus the lightly anesthetized state, nor when stimulating L2/3 versus underlying L4 pyramidal neurons. These numbers are in general agreement with, though lower than, the percentage of neighboring cells (2.1\%) reported by Kwan et al. to be activated upon stimulating single L2/3 pyramidal neurons under anesthesia (Kwan et al., 2012). Interestingly, despite the small number of individual units found to be reliably driven, we did observe a modest but significant elevation in aggregate population responses compared to sham stimulation. This underscores the distributed impact that single cell stimulation has on neighboring microcircuit responses, revealing only a small minority of relatively strongly connected partners.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Meyer, Jochen F. and Golshani, Peyman and Smirnakis, Stelios},
+  month = dec,
+  year = {2017},
+  pages = {232603},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FPWH2CXG\\Meyer et al.  2017  The effect of single pyramidal neuron firing across and within layers in mouse V1.pdf}
+}
+
+@article{Sirota2008,
+  title = {Entrainment of Neocortical Neurons and Gamma Oscillations by the Hippocampal Theta Rhythm},
+  volume = {60},
+  issn = {0896-6273},
+  doi = {10.1016/j.neuron.2008.09.014},
+  abstract = {Although it has been tacitly assumed that the hippocampus exerts an influence on neocortical networks, the mechanisms of this process are not well understood. We examined whether and how hippocampal theta oscillations affect neocortical assembly patterns by recording populations of single cells and transient gamma oscillations in multiple cortical regions, including the somatosensory area and prefrontal cortex in behaving rats and mice. Laminar analysis of neocortical gamma bursts revealed multiple gamma oscillators of varying frequency and location, which were spatially confined and synchronized local groups of neurons. A significant fraction of putative pyramidal cells and interneurons as well as localized gamma oscillations in all recorded neocortical areas were phase-biased by the hippocampal theta rhythm. We hypothesize that temporal coordination of neocortical gamma oscillators by hippocampal theta is a mechanism by which information contained in spatially widespread neocortical assemblies can be synchronously transferred to the associative networks of the hippocampus.},
+  number = {4},
+  journal = {Neuron},
+  author = {Sirota, Anton and Montgomery, Sean and Fujisawa, Shigeyoshi and Isomura, Yoshikazu and Zugaro, Michael and Buzs{\'a}ki, Gy{\"o}rgy},
+  month = nov,
+  year = {2008},
+  pages = {683-697},
+  file = {C:\\Users\\saram\\Zotero\\storage\\AFG9NN2V\\Sirota et al.  2008  Entrainment of neocortical neurons and gamma oscillations by the hippocampal theta rhythm.pdf;C:\\Users\\saram\\Zotero\\storage\\QUZ2ZRZ3\\NIHMS83122-supplement-01.pdf},
+  pmid = {19038224},
+  pmcid = {PMC2640228}
+}
+
+@article{Stark2009,
+  title = {Unbiased Estimation of Precise Temporal Correlations between Spike Trains},
+  volume = {179},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2008.12.029},
+  abstract = {A key issue in systems neuroscience is the contribution of precise temporal inter-neuronal interactions to information processing in the brain, and the main analytical tool used for studying pair-wise interactions is the cross-correlation histogram (CCH). Although simple to generate, a CCH is influenced by multiple factors in addition to precise temporal correlations between two spike trains, thus complicating its interpretation. A Monte-Carlo-based technique, the jittering method, has been suggested to isolate the contribution of precise temporal interactions to neural information processing. Here, we show that jittering spike trains is equivalent to convolving the CCH derived from the original trains with a finite window and using a Poisson distribution to estimate probabilities. Both procedures over-fit the original spike trains and therefore the resulting statistical tests are biased and have low power. We devise an alternative method, based on convolving the CCH with a partially hollowed window, and illustrate its utility using artificial and real spike trains. The modified convolution method is unbiased, has high power, and is computationally fast. We recommend caution in the use of the jittering method and in the interpretation of results based on it, and suggest using the modified convolution method for detecting precise temporal correlations between spike trains.},
+  number = {1},
+  journal = {Journal of Neuroscience Methods},
+  author = {Stark, Eran and Abeles, Moshe},
+  month = apr,
+  year = {2009},
+  keywords = {Correlation analysis,Extra-cellular recordings,Jittering,Neuronal interactions,Poisson distribution,Resampling methods,Spike trains,Temporal precision},
+  pages = {90-100},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ADYAP6WJ\\Stark and Abeles  2009  Unbiased estimation of precise temporal correlations between spike trains.pdf}
+}
+
+@article{Constantinidis2002,
+  title = {Correlated {{Discharges Among Putative Pyramidal Neurons}} and {{Interneurons}} in the {{Primate Prefrontal Cortex}}},
+  volume = {88},
+  issn = {0022-3077},
+  doi = {10.1152/jn.00188.2002},
+  abstract = {Neurophysiological recordings have revealed that the discharges of nearby cortical cells are positively correlated in time scales that range from millisecond synchronization of action potentials to much slower firing rate co-variations, evident in rates averaged over hundreds of milliseconds. The presence of correlated firing can offer insights into the patterns of connectivity between neurons; however, few models of population coding have taken account of the neuronal diversity present in cerebral cortex, notably a distinction between inhibitory and excitatory cells. We addressed this question in the monkey dorsolateral prefrontal cortex by recording neuronal activity from multiple micro-electrodes, typically spaced 0.2\textendash{}0.3 mm apart. Putative excitatory and inhibitory neurons were distinguished based on their action potential waveform and baseline discharge rate. We tested each pair of simultaneously recorded neurons for presence of significant cross-correlation peaks and measured the correlation of their averaged firing rates in successive trials. When observed, cross-correlation peaks were centered attime 0, indicating synchronous firing consistent with two neurons receiving common input. Discharges in pairs of putative inhibitory interneurons were found to be significantly more strongly correlated than in pairs of putative excitatory cells. The degree of correlated firing was also higher for neurons with similar spatial receptive fields and neurons active in the same epochs of the behavioral task. These factors were important in predicting the strength of both short time scale ($<$5 ms) correlations and of trial-to-trial discharge rate covariations. Correlated firing was only marginally accounted for by motor and behavioral variations between trials. Our findings suggest that nearby inhibitory neurons are more tightly synchronized than excitatory ones and account for much of the correlated discharges commonly observed in undifferentiated cortical networks. In contrast, the discharge of pyramidal neurons, the sole projection cells of the cerebral cortex, appears largely independent, suggesting that correlated firing may be a property confined within local circuits and only to a lesser degree propagated to distant cortical areas and modules.},
+  number = {6},
+  journal = {Journal of Neurophysiology},
+  author = {Constantinidis, Christos and Goldman-Rakic, Patricia S.},
+  month = dec,
+  year = {2002},
+  pages = {3487-3497},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KVPIYQ5M\\Constantinidis and Goldman-Rakic  2002  Correlated Discharges Among Putative Pyramidal Neurons and Interneurons in the Primate Prefrontal Cortex.pdf}
+}
+
+@article{Constantinidis2001,
+  title = {Coding {{Specificity}} in {{Cortical Microcircuits}}: {{A Multiple}}-{{Electrode Analysis}} of {{Primate Prefrontal Cortex}}},
+  volume = {21},
+  copyright = {Copyright \textcopyright{} 2001 Society for Neuroscience},
+  issn = {0270-6474, 1529-2401},
+  shorttitle = {Coding {{Specificity}} in {{Cortical Microcircuits}}},
+  abstract = {Neurons with directional specificities are active in the prefrontal cortex (PFC) during tasks that require spatial working memory. Although the coordination of neuronal activity in PFC is thought to be maintained by a network of recurrent connections, direct physiological evidence regarding such networks is sparse. To gain insight into the functional organization of the working memory system in vivo, we recorded simultaneously from multiple neurons spaced 0.2\textendash{}1 mm apart in monkeys performing an oculomotor delayed response task. We used cross-correlation analysis and characterized the effective connectivity between neurons in relation to their spatial and temporal response properties. The majority of narrow ($<$5 msec) cross-correlation peaks indicated common input and were most often observed between pairs of neurons within 0.3 mm of each other. Neurons recorded at these distances represented the full range of spatial locations, suggesting that the entire visual hemifield is represented in modules of corresponding dimensions. Nearby neurons could be activated in any epoch of the behavioral task (stimulus presentation, delay, response). The incidence and strength of cross-correlation, however, was highest among cells sharing similar spatial tuning and similar temporal profiles of activation across task epochs. The dependence of correlated discharge on the functional properties of neurons was observed both when we analyzed firing from the task period as well as from baseline fixation. Our results suggest that the coding specificity of individual neurons extends to the local circuits of which they are part.},
+  language = {en},
+  number = {10},
+  journal = {Journal of Neuroscience},
+  author = {Constantinidis, Christos and Franowicz, Matthew N. and Goldman-Rakic, Patricia S.},
+  month = may,
+  year = {2001},
+  keywords = {cross-correlation,learning and memory,prefrontal cortex,primate,saccade,working memory},
+  pages = {3646-3655},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6HCJYEUP\\Constantinidis et al.  2001  Coding Specificity in Cortical Microcircuits A Multiple-Electrode Analysis of Primate Prefrontal Cortex.pdf},
+  pmid = {11331394}
+}
+
+@article{Armananzas2015,
+  title = {Towards {{Automatic Classification}} of {{Neurons}}},
+  volume = {38},
+  issn = {0166-2236},
+  doi = {10.1016/j.tins.2015.02.004},
+  abstract = {The classification of neurons into types has been much debated since the inception of modern neuroscience. Recent experimental advances are accelerating the pace of data collection. The resulting information growth of morphological, physiological, and molecular properties encourages efforts to automate neuronal classification by powerful machine learning techniques. We review state-of-the-art analysis approaches and availability of suitable data and resources, highlighting prominent challenges and opportunities. The effective solution of the neuronal classification problem will require continuous development of computational methods, high-throughput data production, and systematic metadata organization to enable cross-lab integration.},
+  number = {5},
+  journal = {Trends in neurosciences},
+  author = {Arma{\~n}anzas, Rub{\'e}n and Ascoli, Giorgio A.},
+  month = may,
+  year = {2015},
+  pages = {307-318},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2XISU7YY\\Armañanzas and Ascoli  2015  Towards Automatic Classification of Neurons.pdf;C:\\Users\\saram\\Zotero\\storage\\67CSJ8WF\\Ascoli, 2015, Towards the automatic classification of neurons.pdf},
+  pmid = {25765323},
+  pmcid = {PMC4417416}
+}
+
+@incollection{Minxha2018,
+  address = {New York, NY},
+  title = {Surgical and {{Electrophysiological Techniques}} for {{Single}}-{{Neuron Recordings}} in {{Human Epilepsy Patients}}},
+  volume = {134},
+  isbn = {978-1-4939-7548-8 978-1-4939-7549-5},
+  booktitle = {Extracellular {{Recording Approaches}}},
+  publisher = {{Springer New York}},
+  author = {Minxha, Juri and Mamelak, Adam N. and Rutishauser, Ueli},
+  editor = {Sillitoe, Roy V.},
+  year = {2018},
+  pages = {267-293},
+  file = {C:\\Users\\saram\\Zotero\\storage\\V4VEPF45\\10.1007978-1-4939-7549-5_14.pdf},
+  doi = {10.1007/978-1-4939-7549-5_14}
+}
+
+@article{Fournier2016,
+  title = {Consensus-{{Based Sorting}} of {{Neuronal Spike Waveforms}}},
+  volume = {11},
+  issn = {1932-6203},
+  doi = {10.1371/journal.pone.0160494},
+  abstract = {Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained ``ground truth'' data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data.},
+  number = {8},
+  journal = {PLOS ONE},
+  author = {Fournier, Julien and Mueller, Christian M. and Shein-Idelson, Mark and Hemberger, Mike and Laurent, Gilles},
+  month = aug,
+  year = {2016},
+  keywords = {Neurons,Algorithms,Action potentials,Support vector machines,Distance measurement,Electrode recording,Tetrodes,Turtles},
+  pages = {e0160494},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PQEKPHGK\\Fournier et al.  2016  Consensus-Based Sorting of Neuronal Spike Waveforms.pdf}
+}
+
+@article{Vega-Pons2011,
+  title = {A {{SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS}}},
+  volume = {25},
+  issn = {0218-0014, 1793-6381},
+  doi = {10.1142/S0218001411008683},
+  language = {en},
+  number = {03},
+  journal = {International Journal of Pattern Recognition and Artificial Intelligence},
+  author = {Vega-Pons, Sandro and Ruiz-Shulcloper, Jos{\'e}},
+  month = may,
+  year = {2011},
+  pages = {337-372},
+  file = {C:\\Users\\saram\\Zotero\\storage\\32YL33TR\\7d01fb2634b6160a96bbdd73f918ed3859cb.pdf}
+}
+
+@article{Tang2008,
+  title = {A {{Maximum Entropy Model Applied}} to {{Spatial}} and {{Temporal Correlations}} from {{Cortical Networks In Vitro}}},
+  volume = {28},
+  copyright = {Copyright \textcopyright{} 2008 Society for Neuroscience 0270-6474/08/280505-14\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.3359-07.2008},
+  abstract = {Multineuron firing patterns are often observed, yet are predicted to be rare by models that assume independent firing. To explain these correlated network states, two groups recently applied a second-order maximum entropy model that used only observed firing rates and pairwise interactions as parameters (Schneidman et al., 2006; Shlens et al., 2006). Interestingly, with these minimal assumptions they predicted 90\textendash{}99\% of network correlations. If generally applicable, this approach could vastly simplify analyses of complex networks. However, this initial work was done largely on retinal tissue, and its applicability to cortical circuits is mostly unknown. This work also did not address the temporal evolution of correlated states. To investigate these issues, we applied the model to multielectrode data containing spontaneous spikes or local field potentials from cortical slices and cultures. The model worked slightly less well in cortex than in retina, accounting for 88 $\pm$ 7\% (mean $\pm$ SD) of network correlations. In addition, in 8 of 13 preparations, the observed sequences of correlated states were significantly longer than predicted by concatenating states from the model. This suggested that temporal dependencies are a common feature of cortical network activity, and should be considered in future models. We found a significant relationship between strong pairwise temporal correlations and observed sequence length, suggesting that pairwise temporal correlations may allow the model to be extended into the temporal domain. We conclude that although a second-order maximum entropy model successfully predicts correlated states in cortical networks, it should be extended to account for temporal correlations observed between states.},
+  language = {en},
+  number = {2},
+  journal = {Journal of Neuroscience},
+  author = {Tang, Aonan and Jackson, David and Hobbs, Jon and Chen, Wei and Smith, Jodi L. and Patel, Hema and Prieto, Anita and Petrusca, Dumitru and Grivich, Matthew I. and Sher, Alexander and Hottowy, Pawel and Dabrowski, Wladyslaw and Litke, Alan M. and Beggs, John M.},
+  month = jan,
+  year = {2008},
+  keywords = {local field potential,culture,human tissue,microelectrode array,neuronal avalanche,slice},
+  pages = {505-518},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PPRCSIG9\\Tang et al. - 2008 - A Maximum Entropy Model Applied to Spatial and Tem.pdf;C:\\Users\\saram\\Zotero\\storage\\Q8X2DXIE\\Tang 2008 - A Maximum Entropy Model Applied to Spatial and Temporal Correlations from Cortical Networks In Vitro.pdf;C:\\Users\\saram\\Zotero\\storage\\V5RJPJZR\\Tang et al. - 2008 - A Maximum Entropy Model Applied to Spatial and Tem.pdf},
+  pmid = {18184793}
+}
+
+@article{Ohiorhenuan2010,
+  title = {Sparse Coding and High-Order Correlations in Fine-Scale Cortical Networks},
+  volume = {466},
+  issn = {0028-0836, 1476-4687},
+  doi = {10.1038/nature09178},
+  language = {en},
+  number = {7306},
+  journal = {Nature},
+  author = {Ohiorhenuan, Ifije E. and Mechler, Ferenc and Purpura, Keith P. and Schmid, Anita M. and Hu, Qin and Victor, Jonathan D.},
+  month = jul,
+  year = {2010},
+  pages = {617-621},
+  file = {C:\\Users\\saram\\Zotero\\storage\\EZDL5BKA\\Ohiorhenuen 2010-Sparse coding and high-order correlations in fine-scale cortical networks.pdf}
+}
+
+@article{Tass1998,
+  title = {Detection of n:M {{Phase Locking}} from {{Noisy Data}}: {{Application}} to {{Magnetoencephalography}}},
+  volume = {81},
+  shorttitle = {Detection of \$$\backslash$mathit\{n\}},
+  doi = {10.1103/PhysRevLett.81.3291},
+  abstract = {We use the concept of phase synchronization for the analysis of noisy nonstationary bivariate data. Phase synchronization is understood in a statistical sense as an existence of preferred values of the phase difference, and two techniques are proposed for a reliable detection of synchronous epochs. These methods are applied to magnetoencephalograms and records of muscle activity of a Parkinsonian patient. We reveal that the temporal evolution of the peripheral tremor rhythms directly reflects the time course of the synchronization of abnormal activity between cortical motor areas.},
+  number = {15},
+  journal = {Physical Review Letters},
+  author = {Tass, P. and Rosenblum, M. G. and Weule, J. and Kurths, J. and Pikovsky, A. and Volkmann, J. and Schnitzler, A. and Freund, H.-J.},
+  month = oct,
+  year = {1998},
+  pages = {3291-3294},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TXZ49KEZ\\prl_81_3291.pdf}
+}
+
+@article{Dupret2013,
+  title = {Dynamic {{Reconfiguration}} of {{Hippocampal Interneuron Circuits}} during {{Spatial Learning}}},
+  volume = {78},
+  issn = {0896-6273},
+  doi = {10.1016/j.neuron.2013.01.033},
+  abstract = {Summary
+In the hippocampus, cell assemblies forming mnemonic representations of space are thought to arise as a result of changes in functional connections of pyramidal cells. We have found that CA1 interneuron circuits are also reconfigured during goal-oriented spatial learning through modification of inputs from pyramidal cells. As learning progressed, new pyramidal assemblies expressed in theta cycles alternated with previously established ones, and eventually overtook them. The firing patterns of interneurons developed a relationship to new, learning-related assemblies: some interneurons associated their activity with new pyramidal assemblies while some others dissociated from them. These firing associations were explained by changes in the weight of monosynaptic inputs received by interneurons from new pyramidal assemblies, as these predicted the associational changes. Spatial learning thus engages circuit modifications in the hippocampus that incorporate a redistribution of inhibitory activity that might assist in the segregation of competing pyramidal cell assembly patterns in space and time.},
+  number = {1},
+  journal = {Neuron},
+  author = {Dupret, David and O'Neill, Joseph and Csicsvari, Jozsef},
+  month = apr,
+  year = {2013},
+  pages = {166-180},
+  file = {C:\\Users\\saram\\Zotero\\storage\\W9KVXBRE\\Dupret et al. - 2013 - Dynamic Reconfiguration of Hippocampal Interneuron.pdf}
+}
+
+@article{Trouche2016,
+  title = {Recoding a Cocaine-Place Memory Engram to a Neutral Engram in the Hippocampus},
+  volume = {19},
+  copyright = {2016 Nature Publishing Group},
+  issn = {1546-1726},
+  doi = {10.1038/nn.4250},
+  abstract = {Subsets of hippocampal neurons store map-like representations of experienced environments. The authors optogenetically silenced a neuronal population active in an environment and saw an alternative map emerge. In a cocaine-paired environment, this approach neutralized drug-place preference, implicating recoding of spatial memory engrams as strategy for alleviating maladaptive behaviors.$<$/p$>$},
+  language = {En},
+  number = {4},
+  journal = {Nature Neuroscience},
+  author = {Trouche, St{\'e}phanie and Perestenko, Pavel V. and van de Ven, Gido M. and Bratley, Claire T. and McNamara, Colin G. and Campo-Urriza, Natalia and Black, S. Lucas and Reijmers, Leon G. and Dupret, David},
+  month = apr,
+  year = {2016},
+  pages = {564},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DWYNR5I3\\Trouche et al. - 2016 - Recoding a cocaine-place memory engram to a neutra.pdf}
+}
+
+@article{Pinault1996,
+  title = {A Novel Single-Cell Staining Procedure Performed in Vivo under Electrophysiological Control: Morpho-Functional Features of Juxtacellularly Labeled Thalamic Cells and Other Central Neurons with Biocytin or {{Neurobiotin}}},
+  volume = {65},
+  issn = {0165-0270},
+  shorttitle = {A Novel Single-Cell Staining Procedure Performed in Vivo under Electrophysiological Control},
+  doi = {10.1016/0165-0270(95)00144-1},
+  abstract = {We describe a novel and very effective single-cell labeling method with unique advantages for revealing the axonal and dendritic fields of any extracellularly recorded neuron. This procedure involves the use of fine glass micro-pipettes (tip diameter: $\approx$ 1 $\mu$m), which contain biocytin or Neurobiotin dissolved in a salt solution, for the simultaneous juxtacellular recording and tracer iontophoresis. Once a neuron is well-isolated and identified, low intensity ($<$ 10 nA) positive-current pulses are injected by way of the micro-electrode such as to modulate its firing. Juxtacellular tracer iontophoresis may last as long as the cell electrophysiologically remains in good health, while determining some of its physiological properties. Control experiments, including the selective killing of previously injected cells, provide convincing evidence that it is the stained unit that was recorded and `tickled' by the juxtamembranous iontophoretic pulses. Electrophysiological and histochemical data further show that neuronal filling could occur during an electrically induced, transient, physical micro-damage of a somatic or dendritic membrane patch. This simple, single-cell staining method has been used to label several types of rat brain neurons, including projection neurons and interneurons. Its success rate ($>$ 86\%) far exceeds that obtained by direct intracellular injections of tracers as shown by the labeling of a large sample of 100 individual cells (from 115 attempts) in the thalamic reticular (Rt) nucleus of 33 rats. We thereby demonstrate that Rt cells project to restricted regions of a single thalamic nucleus, including anterior thalamic nuclei, and that the thalamus and Rt complex have reciprocal connections. The juxtacellular procedure thus represents an ideal directed single-cell labeling tool for determination of functional properties, for subsequent identification, for delineation of overall neuronal architecture and for tracing neuronal pathways, provided care is taken to avoid the possible drawbacks and pitfalls that are illustrated and discussed in the present paper.},
+  number = {2},
+  journal = {Journal of Neuroscience Methods},
+  author = {Pinault, Didier},
+  month = apr,
+  year = {1996},
+  keywords = {Cerebellar cortical neuron,Iontophoresis,Limbic thalamus,Neuronal architecture and physiology,Pretecto-olivary neuron,Single-cell tracing and mapping study,Thalamic network,Thalamic reticular nucleus neuron},
+  pages = {113-136},
+  file = {C:\\Users\\saram\\Zotero\\storage\\G7GKXFJW\\Pinault - 1996 - A novel single-cell staining procedure performed i.pdf}
+}
+
+@article{Muthmann2015,
+  title = {Spike {{Detection}} for {{Large Neural Populations Using High Density Multielectrode Arrays}}},
+  volume = {9},
+  issn = {1662-5196},
+  doi = {10.3389/fninf.2015.00028},
+  abstract = {An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data, and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4,096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale microelectrode arrays can be reliably exploited to characterize activity from large neural populations and brain circuits.},
+  language = {English},
+  journal = {Frontiers in Neuroinformatics},
+  author = {Muthmann, Jens-Oliver and Amin, Hayder and Sernagor, Evelyne and Maccione, Alessandro and Panas, Dagmara and Berdondini, Luca and Bhalla, Upinder S. and Hennig, Matthias H.},
+  year = {2015},
+  keywords = {microelectrode array,correlations,cultured neurons,Retina,Spike Detection},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5F5TPX5C\\Muthmann et al.  2015  Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays.pdf}
+}
+
+@article{Gansel2012,
+  title = {Detecting {{Multineuronal Temporal Patterns}} in {{Parallel Spike Trains}}},
+  volume = {6},
+  issn = {1662-5196},
+  doi = {10.3389/fninf.2012.00018},
+  abstract = {We present a non-parametric and computationally efficient method that detects spatiotemporal firing patterns and pattern sequences in parallel spike trains and tests whether the observed numbers of repeating patterns and sequences on a given timescale are significantly different from those expected by chance. The method is generally applicable and uncovers coordinated activity with arbitrary precision by comparing it to appropriate surrogate data. The analysis of coherent patterns of spatially and temporally distributed spiking activity on various timescales enables the immediate tracking of diverse qualities of coordinated firing related to neuronal state changes and information processing. We apply the method to simulated data and multineuronal recordings from rat visual cortex and show that it reliably discriminates between data sets with random pattern occurrences and with additional exactly repeating spatiotemporal patterns and pattern sequences. Multineuronal cortical spiking activity appears to be precisely coordinated and exhibits a sequential organization beyond the cell assembly concept.},
+  language = {English},
+  journal = {Frontiers in Neuroinformatics},
+  author = {Gansel, Kai S. and Singer, Wolf},
+  year = {2012},
+  keywords = {cell assembly,phase sequence,rat visual cortex,spike pattern,synfire braid,synfire chain},
+  file = {C:\\Users\\saram\\Zotero\\storage\\XBAXAKKR\\Gansel and Singer  2012  Detecting Multineuronal Temporal Patterns in Parallel Spike Trains.pdf}
+}
+
+@article{Hilgen2017,
+  title = {Unsupervised {{Spike Sorting}} for {{Large}}-{{Scale}}, {{High}}-{{Density Multielectrode Arrays}}},
+  volume = {18},
+  issn = {2211-1247},
+  doi = {10.1016/j.celrep.2017.02.038},
+  abstract = {\subsection{Summary$<$/h2$>$
+
+We present a method for automated spike sorting for recordings with high-density, large-scale multielectrode arrays. Exploiting the dense sampling of single neurons by multiple electrodes, an efficient, low-dimensional representation of detected spikes consisting of estimated spatial spike locations and dominant spike shape features is exploited for fast and reliable clustering into single units. Millions of events can be sorted in minutes, and the method is parallelized and scales better than quadratically with the number of detected spikes. Performance is demonstrated using recordings with a 4,096-channel array and validated using anatomical imaging, optogenetic stimulation, and model-based quality control. A comparison with semi-automated, shape-based spike sorting exposes significant limitations of conventional methods. Our approach demonstrates that it is feasible to reliably isolate the activity of up to thousands of neurons and that dense, multi-channel probes substantially aid reliable spike sorting.$<$/p$>$
+
+}},
+  language = {English},
+  number = {10},
+  journal = {Cell Reports},
+  author = {Hilgen, Gerrit and Sorbaro, Martino and Pirmoradian, Sahar and Muthmann, Jens-Oliver and Kepiro, Ibolya Edit and Ullo, Simona and Ramirez, Cesar Juarez and Encinas, Albert Puente and Maccione, Alessandro and Berdondini, Luca and Murino, Vittorio and Sona, Diego and Zanacchi, Francesca Cella and Sernagor, Evelyne and Hennig, Matthias Helge},
+  month = mar,
+  year = {2017},
+  pages = {2521-2532},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QZTKZS34\\Hilgen et al. - 2017 - Unsupervised Spike Sorting for Large-Scale, High-D.pdf},
+  pmid = {28273464, 28273464}
+}
+
+@article{Ohki2005,
+  title = {Functional Imaging with Cellular Resolution Reveals Precise Micro-Architecture in Visual Cortex},
+  volume = {433},
+  copyright = {2005 Nature Publishing Group},
+  issn = {1476-4687},
+  doi = {10.1038/nature03274},
+  abstract = {The human brain contains about a billion cells. How are these cells organized on macroscopic and microscopic scales? It has long been known that the different parts of the brain process distinct types of sensory information. With past low-resolution imaging techniques, groups of cells, such as those responding only to visual stimuli of a specific orientation, were shown to be loosely clustered into separate regions. New imaging technology  \emph{in vivo} two-photon calcium imaging  now reveals that these regions form exquisitely ordered three-dimensional maps that are precise on the scale of individual brain cells. This implies that the brain is much more organized than previously thought, with pure functional compartments on a microscopic scale.$<$/p$>$},
+  language = {En},
+  number = {7026},
+  journal = {Nature},
+  author = {Ohki, Kenichi and Chung, Sooyoung and Ch'ng, Yeang H. and Kara, Prakash and Reid, R. Clay},
+  month = feb,
+  year = {2005},
+  pages = {597},
+  file = {C:\\Users\\saram\\Zotero\\storage\\V34QLWJA\\Ohki et al. - 2005 - Functional imaging with cellular resolution reveal.pdf}
+}
+
+@article{Marre2012,
+  title = {Mapping a {{Complete Neural Population}} in the {{Retina}}},
+  volume = {32},
+  copyright = {Copyright \textcopyright{} 2012 the authors 0270-6474/12/3214859-15\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.0723-12.2012},
+  abstract = {Recording simultaneously from essentially all of the relevant neurons in a local circuit is crucial to understand how they collectively represent information. Here we show that the combination of a large, dense multielectrode array and a novel, mostly automated spike-sorting algorithm allowed us to record simultaneously from a highly overlapping population of $>$200 ganglion cells in the salamander retina. By combining these methods with labeling and imaging, we showed that up to 95\% of the ganglion cells over the area of the array were recorded. By measuring the coverage of visual space by the receptive fields of the recorded cells, we concluded that our technique captured a neural population that forms an essentially complete representation of a region of visual space. This completeness allowed us to determine the spatial layout of different cell types as well as identify a novel group of ganglion cells that responded reliably to a set of naturalistic and artificial stimuli but had no measurable receptive field. Thus, our method allows unprecedented access to the complete neural representation of visual information, a crucial step for the understanding of population coding in sensory systems.},
+  language = {en},
+  number = {43},
+  journal = {Journal of Neuroscience},
+  author = {Marre, Olivier and Amodei, Dario and Deshmukh, Nikhil and Sadeghi, Kolia and Soo, Frederick and Holy, Timothy E. and Berry, Michael J.},
+  month = oct,
+  year = {2012},
+  pages = {14859-14873},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2QB4GMAK\\Marre et al. - 2012 - Mapping a Complete Neural Population in the Retina.pdf},
+  pmid = {23100409}
+}
+
+@article{Yger2016,
+  title = {Fast and Accurate Spike Sorting in Vitro and in Vivo for up to Thousands of Electrodes},
+  copyright = {\textcopyright{} 2016, Published by Cold Spring Harbor Laboratory Press. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
+  doi = {10.1101/067843},
+  abstract = {Understanding how assemblies of neurons encode information requires recording large populations of cells in the brain. In recent years, multi-electrode arrays and large silicon probes have been developed to record simultaneously from hundreds or thousands of electrodes packed with a high density. However, these new devices challenge the classical way to do spike sorting. Here we developed a new method to solve these issues, based on a highly automated algorithm to extract spikes from extracellular data, and show that this algorithm reached near optimal performance both in vitro and in vivo. The algorithm is composed of two main steps: 1) a "template-finding" phase to extract the cell templates, i.e. the pattern of activity evoked over many electrodes when one neuron fires an action potential; 2) a "template-matching" phase where the templates were matched to the raw data to find the location of the spikes. The manual intervention by the user was reduced to the minimal, and the time spent on manual curation did not scale with the number of electrodes. We tested our algorithm with large-scale data from in vitro and in vivo recordings, from 32 to 4225 electrodes. We performed simultaneous extracellular and patch recordings to obtain "ground truth" data, i.e. cases where the solution to the sorting problem is at least partially known. The performance of our algorithm was always close to the best expected performance. We thus provide a general solution to sort spikes from large-scale extracellular recordings.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Yger, Pierre and Spampinato, Giulia L. B. and Esposito, Elric and Lefebvre, Baptiste and Deny, Stephane and Gardella, Christophe and Stimberg, Marcel and Jetter, Florian and Zeck, Guenther and Picaud, Serge and Duebel, Jens and Marre, Olivier},
+  month = aug,
+  year = {2016},
+  pages = {067843},
+  file = {C:\\Users\\saram\\Zotero\\storage\\UG4RDILV\\Yger et al.  2016  Fast and accurate spike sorting in vitro and in vivo for up to thousands of electrodes.pdf}
+}
+
+@article{Chicco2017,
+  title = {Ten Quick Tips for Machine Learning in Computational Biology},
+  volume = {10},
+  issn = {1756-0381},
+  doi = {10.1186/s13040-017-0155-3},
+  abstract = {Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experience to run a data mining project effectively, and therefore can follow incorrect practices, that may lead to common mistakes or over-optimistic results. With this review, we present ten quick tips to take advantage of machine learning in any computational biology context, by avoiding some common errors that we observed hundreds of times in multiple bioinformatics projects. We believe our ten suggestions can strongly help any machine learning practitioner to carry on a successful project in computational biology and related sciences.},
+  journal = {BioData Mining},
+  author = {Chicco, Davide},
+  month = dec,
+  year = {2017},
+  file = {C:\\Users\\saram\\Zotero\\storage\\UBGCB3BP\\Chicco  2017  Ten quick tips for machine learning in computational biology.pdf},
+  pmid = {29234465},
+  pmcid = {PMC5721660}
+}
+
+@article{Sohl-Dickstein2009,
+  archivePrefix = {arXiv},
+  eprinttype = {arxiv},
+  eprint = {0906.4779},
+  primaryClass = {physics, stat},
+  title = {Minimum {{Probability Flow Learning}}},
+  abstract = {Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function and its derivatives. Here we propose a new parameter estimation technique that does not require computing an intractable normalization factor or sampling from the equilibrium distribution of the model. This is achieved by establishing dynamics that would transform the observed data distribution into the model distribution, and then setting as the objective the minimization of the KL divergence between the data distribution and the distribution produced by running the dynamics for an infinitesimal time. Score matching, minimum velocity learning, and certain forms of contrastive divergence are shown to be special cases of this learning technique. We demonstrate parameter estimation in Ising models, deep belief networks and an independent component analysis model of natural scenes. In the Ising model case, current state of the art techniques are outperformed by at least an order of magnitude in learning time, with lower error in recovered coupling parameters.},
+  journal = {arXiv:0906.4779 [physics, stat]},
+  author = {Sohl-Dickstein, Jascha and Battaglino, Peter and DeWeese, Michael R.},
+  month = jun,
+  year = {2009},
+  keywords = {Computer Science - Learning,Physics - Data Analysis; Statistics and Probability,Statistics - Machine Learning},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Z9RZGPC8\\Sohl-Dickstein et al. - 2009 - Minimum Probability Flow Learning.pdf}
+}
+
+@article{Klivans2017,
+  archivePrefix = {arXiv},
+  eprinttype = {arxiv},
+  eprint = {1706.06274},
+  primaryClass = {cs, math, stat},
+  title = {Learning {{Graphical Models Using Multiplicative Weights}}},
+  abstract = {We give a simple, multiplicative-weight update algorithm for learning undirected graphical models or Markov random fields (MRFs). The approach is new, and for the well-studied case of Ising models or Boltzmann machines, we obtain an algorithm that uses a nearly optimal number of samples and has quadratic running time (up to logarithmic factors), subsuming and improving on all prior work. Additionally, we give the first efficient algorithm for learning Ising models over general alphabets. Our main application is an algorithm for learning the structure of t-wise MRFs with nearly-optimal sample complexity (up to polynomial losses in necessary terms that depend on the weights) and running time that is \$n\^\{O(t)\}\$. In addition, given \$n\^\{O(t)\}\$ samples, we can also learn the parameters of the model and generate a hypothesis that is close in statistical distance to the true MRF. All prior work runs in time \$n\^\{$\backslash$Omega(d)\}\$ for graphs of bounded degree d and does not generate a hypothesis close in statistical distance even for t=3. We observe that our runtime has the correct dependence on n and t assuming the hardness of learning sparse parities with noise. Our algorithm--the Sparsitron-- is easy to implement (has only one parameter) and holds in the on-line setting. Its analysis applies a regret bound from Freund and Schapire's classic Hedge algorithm. It also gives the first solution to the problem of learning sparse Generalized Linear Models (GLMs).},
+  journal = {arXiv:1706.06274 [cs, math, stat]},
+  author = {Klivans, Adam and Meka, Raghu},
+  month = jun,
+  year = {2017},
+  keywords = {Computer Science - Learning,Computer Science - Data Structures and Algorithms,Mathematics - Statistics Theory},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QLZAXYCL\\Klivans and Meka  2017  Learning Graphical Models Using Multiplicative Weights.pdf}
+}
+
+@article{Crawford2016,
+  archivePrefix = {arXiv},
+  eprinttype = {arxiv},
+  eprint = {1612.05695},
+  primaryClass = {quant-ph},
+  title = {Reinforcement {{Learning Using Quantum Boltzmann Machines}}},
+  abstract = {We investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks. We associate a transverse field Ising spin Hamiltonian with a layout of qubits similar to that of a deep Boltzmann machine (DBM) and use simulated quantum annealing (SQA) to numerically simulate quantum sampling from this system. We design a reinforcement learning algorithm in which the set of visible nodes representing the states and actions of an optimal policy are the first and last layers of the deep network. In absence of a transverse field, our simulations show that DBMs train more effectively than restricted Boltzmann machines (RBM) with the same number of weights. Since sampling from Boltzmann distributions of a DBM is not classically feasible, this is evidence of advantage of a non-Turing sampling oracle. We then develop a framework for training the network as a quantum Boltzmann machine (QBM) in the presence of a significant transverse field for reinforcement learning. This further improves the reinforcement learning method using DBMs.},
+  journal = {arXiv:1612.05695 [quant-ph]},
+  author = {Crawford, Daniel and Levit, Anna and Ghadermarzy, Navid and Oberoi, Jaspreet S. and Ronagh, Pooya},
+  month = dec,
+  year = {2016},
+  keywords = {Computer Science - Learning,Computer Science - Artificial Intelligence,Computer Science - Neural and Evolutionary Computing,Mathematics - Optimization and Control,Quantum Physics},
+  file = {C:\\Users\\saram\\Zotero\\storage\\R9GHYVU2\\Crawford et al.  2016  Reinforcement Learning Using Quantum Boltzmann Machines.pdf}
+}
+
+@article{Sallans2004,
+  title = {Reinforcement Learning with Factored States and Actions},
+  volume = {5},
+  number = {Aug},
+  journal = {Journal of Machine Learning Research},
+  author = {Sallans, Brian and Hinton, Geoffrey E.},
+  year = {2004},
+  pages = {1063--1088},
+  file = {C:\\Users\\saram\\Zotero\\storage\\BKQJNP7D\\sallans04a.pdf}
+}
+
+@article{Levit2017,
+  archivePrefix = {arXiv},
+  eprinttype = {arxiv},
+  eprint = {1706.00074},
+  primaryClass = {quant-ph},
+  title = {Free Energy-Based Reinforcement Learning Using a Quantum Processor},
+  abstract = {Recent theoretical and experimental results suggest the possibility of using current and near-future quantum hardware in challenging sampling tasks. In this paper, we introduce free energy-based reinforcement learning (FERL) as an application of quantum hardware. We propose a method for processing a quantum annealer's measured qubit spin configurations in approximating the free energy of a quantum Boltzmann machine (QBM). We then apply this method to perform reinforcement learning on the grid-world problem using the D-Wave 2000Q quantum annealer. The experimental results show that our technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.},
+  journal = {arXiv:1706.00074 [quant-ph]},
+  author = {Levit, Anna and Crawford, Daniel and Ghadermarzy, Navid and Oberoi, Jaspreet S. and Zahedinejad, Ehsan and Ronagh, Pooya},
+  month = may,
+  year = {2017},
+  keywords = {Computer Science - Learning,Computer Science - Artificial Intelligence,Computer Science - Neural and Evolutionary Computing,Mathematics - Optimization and Control,Quantum Physics},
+  file = {C:\\Users\\saram\\Zotero\\storage\\9ZVTLADK\\Levit et al.  2017  Free energy-based reinforcement learning using a quantum processor.pdf}
+}
+
+@article{Hamilton2013,
+  title = {Optogenetic {{Activation}} of an {{Inhibitory Network Enhances Feed}}-{{Forward Functional Connectivity}} in {{Auditory Cortex}}},
+  volume = {80},
+  issn = {0896-6273},
+  doi = {10.1016/j.neuron.2013.08.017},
+  abstract = {The mammalian neocortex is a highly interconnected network of different types of neurons organized into both layers and columns. Overlaid on this structural organization is a pattern of functional connectivity that can be rapidly and flexibly altered during behavior. Parvalbumin-positive (PV) inhibitory neurons, which are implicated in cortical oscillations and can change neuronal selectivity, may play a pivotal role in these dynamic changes. We found that optogenetic activation of PV neurons in the auditory cortex enhanced feed-forward functional connectivity in the putative thalamorecipient circuit and in cortical columnar circuits. In contrast, PV stimulation induced no change in connectivity between sites in the same layers. The activity of PV neurons may thus serve as a gating mechanism to enhance feed-forward, but not lateral or feedback, information flow in cortical circuits. Functionally, it may preferentially enhance the contribution of bottom-up sensory inputs to perception.},
+  number = {4},
+  journal = {Neuron},
+  author = {Hamilton, Liberty S. and Sohl-Dickstein, Jascha and Huth, Alexander G. and Carels, Vanessa M. and Deisseroth, Karl and Bao, Shaowen},
+  month = nov,
+  year = {2013},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TB3AH3VK\\Hamilton et al. - 2013 - Optogenetic Activation of an Inhibitory Network En.pdf;C:\\Users\\saram\\Zotero\\storage\\WUF8VMDK\\mmc1.pdf},
+  pmid = {24267655},
+  pmcid = {PMC3841078}
+}
+
+@article{Marre2009,
+  title = {Prediction of {{Spatiotemporal Patterns}} of {{Neural Activity}} from {{Pairwise Correlations}}},
+  volume = {102},
+  doi = {10.1103/PhysRevLett.102.138101},
+  abstract = {We designed a model-based analysis to predict the occurrence of population patterns in distributed spiking activity. Using a maximum entropy principle with a Markovian assumption, we obtain a model that accounts for both spatial and temporal pairwise correlations among neurons. This model is tested on data generated with a Glauber spin-glass system and is shown to correctly predict the occurrence probabilities of spatiotemporal patterns significantly better than Ising models only based on spatial correlations. This increase of predictability was also observed on experimental data recorded in parietal cortex during slow-wave sleep. This approach can also be used to generate surrogates that reproduce the spatial and temporal correlations of a given data set.},
+  number = {13},
+  journal = {Physical Review Letters},
+  author = {Marre, O. and El Boustani, S. and Fr{\'e}gnac, Y. and Destexhe, A.},
+  month = apr,
+  year = {2009},
+  pages = {138101}
+}
+
+@article{Runfeldt2014,
+  title = {Acetylcholine Functionally Reorganizes Neocortical Microcircuits},
+  volume = {112},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00071.2014},
+  language = {en},
+  number = {5},
+  journal = {Journal of Neurophysiology},
+  author = {Runfeldt, Melissa J. and Sadovsky, Alexander J. and MacLean, Jason N.},
+  month = sep,
+  year = {2014},
+  pages = {1205-1216},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KANMXKSY\\Runfeldt 2014 - Acetylcholine functionally reorganizes neocortical microcircuits.pdf}
+}
+
+@article{Tasic2016,
+  title = {Adult Mouse Cortical Cell Taxonomy Revealed by Single Cell Transcriptomics},
+  volume = {19},
+  copyright = {2016 Nature Publishing Group},
+  issn = {1546-1726},
+  doi = {10.1038/nn.4216},
+  abstract = {Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. We constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing. We identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types. We also analyzed cell type\textendash{}specific mRNA processing and characterized genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we found that some of our transcriptomic cell types displayed specific and differential electrophysiological and axon projection properties, thereby confirming that the single-cell transcriptomic signatures can be associated with specific cellular properties.},
+  language = {en},
+  number = {2},
+  journal = {Nature Neuroscience},
+  author = {Tasic, Bosiljka and Menon, Vilas and Nguyen, Thuc Nghi and Kim, Tae Kyung and Jarsky, Tim and Yao, Zizhen and Levi, Boaz and Gray, Lucas T. and Sorensen, Staci A. and Dolbeare, Tim and Bertagnolli, Darren and Goldy, Jeff and Shapovalova, Nadiya and Parry, Sheana and Lee, Changkyu and Smith, Kimberly and Bernard, Amy and Madisen, Linda and Sunkin, Susan M. and Hawrylycz, Michael and Koch, Christof and Zeng, Hongkui},
+  month = feb,
+  year = {2016},
+  pages = {335-346}
+}
+
+@article{Euler2014,
+  title = {Retinal Bipolar Cells: Elementary Building Blocks of Vision},
+  volume = {15},
+  copyright = {2014 Nature Publishing Group},
+  issn = {1471-0048},
+  shorttitle = {Retinal Bipolar Cells},
+  doi = {10.1038/nrn3783},
+  abstract = {Retinal bipolar cells are the first 'projection neurons' of the vertebrate visual system \textemdash{} all of the information needed for vision is relayed by this intraretinal connection. Each of the at least 13 distinct types of bipolar cells systematically transforms the photoreceptor input in a different way, thereby generating specific channels that encode stimulus properties, such as polarity, contrast, temporal profile and chromatic composition. As a result, bipolar cell output signals represent elementary 'building blocks' from which the microcircuits of the inner retina derive a feature-oriented description of the visual world.},
+  language = {en},
+  number = {8},
+  journal = {Nature Reviews Neuroscience},
+  author = {Euler, Thomas and Haverkamp, Silke and Schubert, Timm and Baden, Tom},
+  month = aug,
+  year = {2014},
+  pages = {507-519},
+  file = {C:\\Users\\saram\\Zotero\\storage\\XHKYWJC5\\Euler et al. - 2014 - Retinal bipolar cells elementary building blocks .pdf}
+}
+
+@article{Shekhar2016,
+  title = {Comprehensive {{Classification}} of {{Retinal Bipolar Neurons}} by {{Single}}-{{Cell Transcriptomics}}},
+  volume = {166},
+  issn = {0092-8674, 1097-4172},
+  doi = {10.1016/j.cell.2016.07.054},
+  abstract = {\subsection{Summary$<$/h2$>$
+
+Patterns of gene expression can be used to characterize and classify neuronal types. It is challenging, however, to generate taxonomies that fulfill the essential criteria of being comprehensive, harmonizing with conventional classification schemes, and lacking superfluous subdivisions of genuine types. To address these challenges, we used massively parallel single-cell RNA profiling and optimized computational methods on a heterogeneous class of neurons, mouse retinal bipolar cells (BCs). From a population of $\sim$25,000 BCs, we derived a molecular classification that identified 15 types, including all types observed previously and two novel types, one of which has a non-canonical morphology and position. We validated the classification scheme and identified dozens of novel markers using methods that match molecular expression to cell morphology. This work provides a systematic methodology for achieving comprehensive molecular classification of neurons, identifies novel neuronal types, and uncovers transcriptional differences that distinguish types within a class.$<$/p$>$
+
+}},
+  language = {English},
+  number = {5},
+  journal = {Cell},
+  author = {Shekhar, Karthik and Lapan, Sylvain W. and Whitney, Irene E. and Tran, Nicholas M. and Macosko, Evan Z. and Kowalczyk, Monika and Adiconis, Xian and Levin, Joshua Z. and Nemesh, James and Goldman, Melissa and McCarroll, Steven A. and Cepko, Constance L. and Regev, Aviv and Sanes, Joshua R.},
+  month = aug,
+  year = {2016},
+  pages = {1308-1323.e30},
+  file = {C:\\Users\\saram\\Zotero\\storage\\9GNW8EHT\\Shekhar et al. - 2016 - Comprehensive Classification of Retinal Bipolar Ne.pdf},
+  pmid = {27565351, 27565351}
+}
+
+@article{Lankarany2017,
+  title = {Estimating {{Excitatory}} and {{Inhibitory Synaptic Conductances}} from {{Spike Trains}} Using a {{Recursive Bayesian Approach}}},
+  copyright = {\textcopyright{} 2017, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
+  doi = {10.1101/170878},
+  abstract = {Inference of excitatory and inhibitory synaptic conductances (SCs) from the spike trains is poorly addressed in the literature due to the complexity of the problem. As recent technological advancements make recording spikes from multiple (neighbor) neurons of a behaving animal (in some rare cases from humans) possible, this paper tackles the problem of estimating SCs solely from the recorded spike trains. Given an ensemble of spikes corresponding to population of neighbor neurons, we aim to infer the average excitatory and inhibitory SCs underlying the shared neural activity. In this paper, we extended our previously established Kalman filtering (KF) based algorithm to incorporate the voltage-to-spike nonlinearity (mapping from membrane potential to spike rate). Having estimated the instantaneous spike rate using optimal linear filtering (Gaussian kernel), our proposed algorithm uses KF followed by expectation maximization (EM) algorithm in a recursive fashion to infer the average SCs. As the dynamics of SCs and membrane potential is included in our model, the proposed algorithm, unlike other related works, considers different sources of stochasticity, i.e., the variabilities of SCs, membrane potential, and spikes. Moreover, it is worth mentioning that our algorithm is blind to the external stimulus, and it performs only based on observed spikes. We validate the accuracy and practicality of our technique through simulation studies where leaky integrate and fire (LIF) model is used to generate spikes. We show that the estimated SCs can precisely track the original ones. Moreover, we show that the performance of our algorithm can be further improved given enough number of trials (spikes). As a rule of thumb, 50 trials of neurons with the average firing rate of 5 Hz can guarantee the accuracy of our proposed algorithm.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Lankarany, Milad},
+  month = jul,
+  year = {2017},
+  pages = {170878},
+  file = {C:\\Users\\saram\\Zotero\\storage\\G5C6XTW7\\Lankarany - 2017 - Estimating Excitatory and Inhibitory Synaptic Cond.pdf}
+}
+
+@article{Lankarany2013,
+  title = {Inferring Trial-to-Trial Excitatory and Inhibitory Synaptic Inputs from Membrane Potential Using {{Gaussian}} Mixture {{Kalman}} Filtering},
+  volume = {7},
+  issn = {1662-5188},
+  doi = {10.3389/fncom.2013.00109},
+  abstract = {Time-varying excitatory and inhibitory synaptic inputs govern activity of neurons and process information in the brain. The importance of trial-to-trial fluctuations of synaptic inputs has recently been investigated in neuroscience. Such fluctuations are ignored in the most conventional techniques because they are removed when trials are averaged during linear regression techniques. Here, we propose a novel recursive algorithm based on Gaussian mixture Kalman filtering for estimating time-varying excitatory and inhibitory synaptic inputs from single trials of noisy membrane potential in current clamp recordings. The Kalman filtering is followed by an expectation maximization algorithm to infer the statistical parameters (time-varying mean and variance) of the synaptic inputs in a non-parametric manner. As our proposed algorithm is repeated recursively, the inferred parameters of the mixtures are used to initiate the next iteration. Unlike other recent algorithms, our algorithm does not assume an a priori distribution from which the synaptic inputs are generated. Instead, the algorithm recursively estimates such a distribution by fitting a Gaussian mixture model. The performance of the proposed algorithms is compared to a previously proposed PF-based algorithm (Paninski et al., 2012) with several illustrative examples, assuming that the distribution of synaptic input is unknown. If noise is small, the performance of our algorithms is similar to that of the previous one. However, if noise is large, they can significantly outperform the previous proposal. These promising results suggest that our algorithm is a robust and efficient technique for estimating time varying excitatory and inhibitory synaptic conductances from single trials of membrane potential recordings.},
+  language = {English},
+  journal = {Frontiers in Computational Neuroscience},
+  author = {Lankarany, Milad and Zhu, Wei Ping and Swamy, Srikanta M. N. and Toyoizumi, Taro},
+  year = {2013},
+  keywords = {Excitatory and inhibitory synaptic inputs,expectation maximization,Gaussian mixture Kalman filtering,Kalman filter,Recursive Algorithm,synaptic conductance}
+}
+
+@article{Huys2009,
+  title = {Smoothing of, and {{Parameter Estimation}} from, {{Noisy Biophysical Recordings}}},
+  volume = {5},
+  issn = {1553-7358},
+  doi = {10.1371/journal.pcbi.1000379},
+  abstract = {Author Summary Cellular imaging techniques are maturing at a great pace, but are still plagued by high levels of noise. Here, we present two methods for smoothing individual, noisy traces. The first method fits a full, biophysically accurate description of the cell under study to the noisy data. This allows both smoothing of the data and inference of biophysically relevant parameters such as the density of (active) channels, input resistance, intercompartmental conductances, and noise levels; it does, however, depend on knowledge of active channel kinetics. The second method achieves smoothing of noisy traces by fitting arbitrary kinetics in a non-parametric manner. Both techniques can additionally be used to infer unobserved variables, for instance voltage from calcium concentration. This paper gives a detailed account of the methods and should allow for straightforward modification and inclusion of additional measurements.},
+  language = {en},
+  number = {5},
+  journal = {PLOS Computational Biology},
+  author = {Huys, Quentin J. M. and Paninski, Liam},
+  month = may,
+  year = {2009},
+  keywords = {Algorithms,Biophysics,Membrane potential,Gaussian noise,Action potentials,Dynamical systems,Imaging techniques,Monte Carlo method},
+  pages = {e1000379},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4BH5VEJZ\\Huys and Paninski - 2009 - Smoothing of, and Parameter Estimation from, Noisy.pdf}
+}
+
+@article{Trenholm2014,
+  title = {Nonlinear Dendritic Integration of Electrical and Chemical Synaptic Inputs Drives Fine-Scale Correlations},
+  volume = {17},
+  copyright = {2014 Nature Publishing Group},
+  issn = {1546-1726},
+  doi = {10.1038/nn.3851},
+  abstract = {Throughout the CNS, gap junction\textendash{}mediated electrical signals synchronize neural activity on millisecond timescales via cooperative interactions with chemical synapses. However, gap junction\textendash{}mediated synchrony has rarely been studied in the context of varying spatiotemporal patterns of electrical and chemical synaptic activity. Thus, the mechanism underlying fine-scale synchrony and its relationship to neural coding remain unclear. We examined spike synchrony in pairs of genetically identified, electrically coupled ganglion cells in mouse retina. We found that coincident electrical and chemical synaptic inputs, but not electrical inputs alone, elicited synchronized dendritic spikes in subregions of coupled dendritic trees. The resulting nonlinear integration produced fine-scale synchrony in the cells' spike output, specifically for light stimuli driving input to the regions of dendritic overlap. In addition, the strength of synchrony varied inversely with spike rate. Together, these features may allow synchronized activity to encode information about the spatial distribution of light that is ambiguous on the basis of spike rate alone.},
+  language = {en},
+  number = {12},
+  journal = {Nature Neuroscience},
+  author = {Trenholm, Stuart and McLaughlin, Amanda J. and Schwab, David J. and Turner, Maxwell H. and Smith, Robert G. and Rieke, Fred and Awatramani, Gautam B.},
+  month = dec,
+  year = {2014},
+  pages = {1759-1766}
+}
+
+@article{Ito2011,
+  title = {Extending {{Transfer Entropy Improves Identification}} of {{Effective Connectivity}} in a {{Spiking Cortical Network Model}}},
+  volume = {6},
+  issn = {1932-6203},
+  doi = {10.1371/journal.pone.0027431},
+  abstract = {Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36\% of true connections when evaluated at a false positive rate of 1\%. For extended versions of TE, this dramatically improved to 73\% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85\% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in $\sim$5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons.},
+  language = {en},
+  number = {11},
+  journal = {PLOS ONE},
+  author = {Ito, Shinya and Hansen, Michael E. and Heiland, Randy and Lumsdaine, Andrew and Litke, Alan M. and Beggs, John M.},
+  month = nov,
+  year = {2011},
+  keywords = {Synapses,Neurons,Neural networks,Algorithms,Network analysis,Action potentials,Entropy,Peak values},
+  pages = {e27431},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5IAZ54UU\\Izhik_100_0.mat;C:\\Users\\saram\\Zotero\\storage\\JWGJP87Q\\Ito et al.  2011  Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model.pdf;C:\\Users\\saram\\Zotero\\storage\\KLVFUCNU\\Code_S1.M;C:\\Users\\saram\\Zotero\\storage\\XBIPXVP7\\te_matlab_0.5.zip;C:\\Users\\saram\\Zotero\\storage\\XE2ZNX6B\\Document_S1.PDF}
+}
+
+@article{Izhikevich2006,
+  title = {Polychronization: {{Computation}} with {{Spikes}}},
+  volume = {18},
+  issn = {0899-7667},
+  shorttitle = {Polychronization},
+  doi = {10.1162/089976606775093882},
+  abstract = {We present a minimal spiking network that can polychronize, that is, exhibit reproducible time-locked but not synchronous firing patterns with millisecond precision, as in synfire braids. The network consists of cortical spiking neurons with axonal conduction delays and spike-timing-dependent plasticity (STDP); a ready-to-use MATLAB code is included. It exhibits sleeplike oscillations, gamma (40 Hz) rhythms, conversion of firing rates to spike timings, and other interesting regimes. Due to the interplay between the delays and STDP, the spiking neurons spontaneously self-organize into groups and generate patterns of stereotypical polychronous activity. To our surprise, the number of coexisting polychronous groups far exceeds the number of neurons in the network, resulting in an unprecedented memory capacity of the system. We speculate on the significance of polychrony to the theory of neuronal group selection (TNGS, neural Darwinism), cognitive neural computations, binding and gamma rhythm, mechanisms of attention, and consciousness as ``attention to memories.''},
+  number = {2},
+  journal = {Neural Computation},
+  author = {Izhikevich, Eugene M.},
+  month = feb,
+  year = {2006},
+  pages = {245-282},
+  file = {C:\\Users\\saram\\Zotero\\storage\\7ZVKQT8Q\\izhik1011.mov;C:\\Users\\saram\\Zotero\\storage\\SBHYE8V3\\Izhikevich  2006  Polychronization Computation with Spikes.pdf}
+}
+
+@article{Ito2014,
+  title = {Large-{{Scale}}, {{High}}-{{Resolution Multielectrode}}-{{Array Recording Depicts Functional Network Differences}} of {{Cortical}} and {{Hippocampal Cultures}}},
+  volume = {9},
+  issn = {1932-6203},
+  doi = {10.1371/journal.pone.0105324},
+  abstract = {Understanding the detailed circuitry of functioning neuronal networks is one of the major goals of neuroscience. Recent improvements in neuronal recording techniques have made it possible to record the spiking activity from hundreds of neurons simultaneously with sub-millisecond temporal resolution. Here we used a 512-channel multielectrode array system to record the activity from hundreds of neurons in organotypic cultures of cortico-hippocampal brain slices from mice. To probe the network structure, we employed a wavelet transform of the cross-correlogram to categorize the functional connectivity in different frequency ranges. With this method we directly compare, for the first time, in any preparation, the neuronal network structures of cortex and hippocampus, on the scale of hundreds of neurons, with sub-millisecond time resolution. Among the three frequency ranges that we investigated, the lower two frequency ranges (gamma (30\textendash{}80 Hz) and beta (12\textendash{}30 Hz) range) showed similar network structure between cortex and hippocampus, but there were many significant differences between these structures in the high frequency range (100\textendash{}1000 Hz). The high frequency networks in cortex showed short tailed degree-distributions, shorter decay length of connectivity density, smaller clustering coefficients, and positive assortativity. Our results suggest that our method can characterize frequency dependent differences of network architecture from different brain regions. Crucially, because these differences between brain regions require millisecond temporal scales to be observed and characterized, these results underscore the importance of high temporal resolution recordings for the understanding of functional networks in neuronal systems.},
+  language = {en},
+  number = {8},
+  journal = {PLOS ONE},
+  author = {Ito, Shinya and Yeh, Fang-Chin and Hiolski, Emma and Rydygier, Przemyslaw and Gunning, Deborah E. and Hottowy, Pawel and Timme, Nicholas and Litke, Alan M. and Beggs, John M.},
+  month = aug,
+  year = {2014},
+  pages = {e105324},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TWARWQZI\\Ito et al.  2014  Large-Scale, High-Resolution Multielectrode-Array Recording Depicts Functional Network Differences of Cortical and Hippocampal Cultures.pdf}
+}
+
+@phdthesis{Ito2013,
+  address = {United States -- Indiana},
+  type = {Ph.{{D}}.},
+  title = {Functional Connectivity in Networks of Hundreds of Cortical Neurons},
+  copyright = {Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.},
+  language = {English},
+  school = {Indiana University},
+  author = {Ito, Shinya},
+  year = {2013},
+  file = {C:\\Users\\saram\\Zotero\\storage\\CRVD6JJW\\Ito  2013  Functional connectivity in networks of hundreds of cortical neurons.pdf}
+}
+
+@article{Pillow2008,
+  title = {Spatio-Temporal Correlations and Visual Signalling in a Complete Neuronal Population},
+  volume = {454},
+  copyright = {2008 Nature Publishing Group},
+  issn = {1476-4687},
+  doi = {10.1038/nature07140},
+  abstract = {Statistical dependencies in the responses of sensory neurons govern both the amount of stimulus information conveyed and the means by which downstream neurons can extract it. Although a variety of measurements indicate the existence of such dependencies1,2,3, their origin and importance for neural coding are poorly understood. Here we analyse the functional significance of correlated firing in a complete population of macaque parasol retinal ganglion cells using a model of multi-neuron spike responses4,5. The model, with parameters fit directly to physiological data, simultaneously captures both the stimulus dependence and detailed spatio-temporal correlations in population responses, and provides two insights into the structure of the neural code. First, neural encoding at the population level is less noisy than one would expect from the variability of individual neurons: spike times are more precise, and can be predicted more accurately when the spiking of neighbouring neurons is taken into account. Second, correlations provide additional sensory information: optimal, model-based decoding that exploits the response correlation structure extracts 20\% more information about the visual scene than decoding under the assumption of independence, and preserves 40\% more visual information than optimal linear decoding6. This model-based approach reveals the role of correlated activity in the retinal coding of visual stimuli, and provides a general framework for understanding the importance of correlated activity in populations of neurons.},
+  language = {en},
+  number = {7207},
+  journal = {Nature},
+  author = {Pillow, Jonathan W. and Shlens, Jonathon and Paninski, Liam and Sher, Alexander and Litke, Alan M. and Chichilnisky, E. J. and Simoncelli, Eero P.},
+  month = aug,
+  year = {2008},
+  pages = {995-999},
+  file = {C:\\Users\\saram\\Zotero\\storage\\72DCWEW2\\Pillow et al. - 2008 - Spatio-temporal correlations and visual signalling.pdf;C:\\Users\\saram\\Zotero\\storage\\YDWLXZ5J\\Pillow et al. - 2008 - Spatio-temporal correlations and visual signalling.pdf}
+}
+
+@article{Latimer2018,
+  title = {Inferring Synaptic Inputs from Spikes with a Conductance-Based Neural Encoding Model},
+  copyright = {\textcopyright{} 2018, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
+  doi = {10.1101/281089},
+  abstract = {A popular approach to the study of information processing in the nervous system is to characterize neural responses in terms of a cascade of linear and nonlinear stages: a linear filter to describe the neuron's stimulus integration properties, followed by a rectifying nonlinearity to convert filter output to spike rate. However, real neurons integrate stimuli via the modulation of nonlinear excitatory and inhibitory synaptic conductances. Here we introduce a biophysically inspired point process model with conductance-based inputs. The model provides a novel interpretation of the popular Poisson generalized linear model (GLM) as a special kind of conductance-based model, where excitatory and inhibitory conductances are modulated in a "push-pull" manner so that total conductance remains constant. We relax this constraint to obtain a more general and flexible "conductance-based encoding model" (CBEM), which can exhibit stimulus-dependent fluctuations in gain and dynamics. We fit the model to spike trains of macaque retinal ganglion cells and show that, remarkably, we can accurately infer underlying inhibitory and excitatory conductances, using comparisons to intracellularly measured conductances. Using extracellular data, we corroborate the intracellular finding that synaptic excitation temporally precedes inhibition in retina. We show that the CBEM outperforms the classic GLM at predicting retinal ganglion cell responses to full-field stimuli, generalizes better across contrast levels, and captures inhibition-dependent response properties to spatially structured stimuli. The CBEM provides a powerful tool for gaining insights into the intracellular variables governing spiking, and forges an important link between extracellular characterization methods and biophysically detailed response models.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Latimer, Kenneth and Rieke, Fred and Pillow, Jonathan W.},
+  month = mar,
+  year = {2018},
+  pages = {281089},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Q8VHLEQ5\\Latimer et al. - 2018 - Inferring synaptic inputs from spikes with a condu.pdf}
+}
+
+@article{Rathore,
+  title = {Approximate {{Cluster Heat Maps}} of {{Large High}}-{{Dimensional Data}}},
+  abstract = {The problem of determining whether clusters are present in numerical data (tendency assessment) is an important first step of cluster analysis. One tool for cluster tendency assessment is the visual assessment of tendency (VAT) algorithm. VAT and improved VAT (iVAT) produce an image that provides visual evidence about the number of clusters to seek in the original dataset. These methods have been successful in determining potential cluster structure in various datasets, but they can be computationally expensive for datasets with a very large number of samples. A scalable version of iVAT called siVAT approximates iVAT images, but siVAT can be computationally expensive for big datasets. In this article, we introduce a modification of siVAT called siVAT+ which approximates cluster heat maps for large volumes of high dimensional data much more rapidly than siVAT. We compare siVAT+ with siVAT on six large, high dimensional datasets. Experimental results confirm that siVAT+ obtains images similar to siVAT images in a few seconds, and is 8 - 55 times faster than siVAT.},
+  author = {Rathore, Punit and Bezdek, James C and Kumar, Dheeraj and Rajasegarar, Sutharshan and Palaniswami, Marimuthu},
+  pages = {6},
+  file = {C:\\Users\\saram\\Zotero\\storage\\H7PA4ISY\\Rathore et al.  Approximate Cluster Heat Maps of Large High-Dimensional Data.pdf}
+}
+
+@article{Richardet2015,
+  title = {Large-Scale Extraction of Brain Connectivity from the Neuroscientific Literature},
+  volume = {31},
+  issn = {1367-4803},
+  doi = {10.1093/bioinformatics/btv025},
+  abstract = {Motivation: In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and the integration of such data into computational models. A key example of this is metascale brain connectivity, where results are not reported in a normalized repository. Instead, these experimental results are published in natural language, scattered among individual scientific publications. This lack of normalization and centralization hinders the large-scale integration of brain connectivity results. In this article, we present text-mining models to extract and aggregate brain connectivity results from 13.2 million PubMed abstracts and 630\,216 full-text publications related to neuroscience. The brain regions are identified with three different named entity recognizers (NERs) and then normalized against two atlases: the Allen Brain Atlas (ABA) and the atlas from the Brain Architecture Management System (BAMS). We then use three different extractors to assess inter-region connectivity.Results: NERs and connectivity extractors are evaluated against a manually annotated corpus. The complete in litero extraction models are also evaluated against in~vivo connectivity data from ABA with an estimated precision of 78\%. The resulting database contains over 4 million brain region mentions and over 100\,000 (ABA) and 122\,000 (BAMS) potential brain region connections. This database drastically accelerates connectivity literature review, by providing a centralized repository of connectivity data to neuroscientists.Availability and implementation: The resulting models are publicly available at github.com/BlueBrain/bluima.Contact:renaud.richardet@epfl.chSupplementary information:Supplementary data are available at Bioinformatics online.},
+  language = {en},
+  number = {10},
+  journal = {Bioinformatics},
+  author = {Richardet, Renaud and Chappelier, Jean-C{\'e}dric and Telefont, Martin and Hill, Sean},
+  month = may,
+  year = {2015},
+  pages = {1640-1647},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4ACJ6JGB\\Richardet et al.  2015  Large-scale extraction of brain connectivity from the neuroscientific literature.pdf}
+}
+
+@article{Marblestone2013,
+  title = {Physical Principles for Scalable Neural Recording},
+  volume = {7},
+  issn = {1662-5188},
+  doi = {10.3389/fncom.2013.00137},
+  abstract = {Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power\textendash{}bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices.},
+  journal = {Frontiers in Computational Neuroscience},
+  author = {Marblestone, Adam H. and Zamft, Bradley M. and Maguire, Yael G. and Shapiro, Mikhail G. and Cybulski, Thaddeus R. and Glaser, Joshua I. and Amodei, Dario and Stranges, P. Benjamin and Kalhor, Reza and Dalrymple, David A. and Seo, Dongjin and Alon, Elad and Maharbiz, Michel M. and Carmena, Jose M. and Rabaey, Jan M. and Boyden, Edward S. and Church, George M. and Kording, Konrad P.},
+  year = {2013},
+  file = {C:\\Users\\saram\\Zotero\\storage\\53YRRX3E\\Marblestone et al. - 2013 - Physical principles for scalable neural recording.pdf}
+}
+
+@article{Choi2010,
+  title = {Quantifying {{Time}}-{{Varying Multiunit Neural Activity Using Entropy}}-{{Based Measures}}},
+  volume = {57},
+  issn = {0018-9294},
+  doi = {10.1109/TBME.2010.2049266},
+  abstract = {Modern microelectrode arrays make it possible to simultaneously record population neural activity. However, methods to analyze multiunit activity (MUA), which reflects the aggregate spiking activity of a population of neurons, have remained underdeveloped in comparison to those used for studying single unit activity (SUA). In scenarios where SUA is hard to record and maintain or is not representative of brain's response, MUA is informative in deciphering the brain's complex time-varying response to stimuli or to clinical insults. Here, we present two quantitative methods of analysis of the time-varying dynamics of MUA without spike detection. These methods are based on the multiresolution discrete wavelet transform (DWT) of an envelope of MUA (eMUA) followed by information theoretic measures: multiresolution entropy (MRE) and the multiresolution Kullback\textendash{}Leibler distance (MRKLD).We test the proposed quantifiers on both simulated and experimental MUA recorded from rodent cortex in an experimental model of global hypoxic\textendash{}ischemic brain injury. First, our results validate the use of the eMUA as an alternative to detecting and analyzing transient and complex spike activity. Second, the MRE and MRKLD are shown to respond to dynamic changes due to the brain's response to global injury and to identify the transient changes in the MUA.},
+  number = {11},
+  journal = {IEEE transactions on bio-medical engineering},
+  author = {Choi, Young-Seok and Koenig, Matthew A. and Jia, Xiaofeng and Thakor, Nitish V.},
+  month = nov,
+  year = {2010},
+  file = {C:\\Users\\saram\\Zotero\\storage\\9NW8F7E5\\Choi et al. - 2010 - Quantifying Time-Varying Multiunit Neural Activity.pdf},
+  pmid = {20460201},
+  pmcid = {PMC3111013}
+}
+
+@article{Blanche2006,
+  title = {Nyquist Interpolation Improves Neuron Yield in Multiunit Recordings},
+  volume = {155},
+  issn = {01650270},
+  doi = {10.1016/j.jneumeth.2005.12.031},
+  abstract = {Multiunit electrodes, in particular tetrodes and polytrodes, are able to isolate action potentials from many neurons simultaneously. However, inaccuracies in the post-acquisition reconstruction of recorded spike waveforms can affect the reliability of spike detection and sorting. Here we show that bandlimited interpolation with sample-and-hold delay correction reduces waveform variability, leading to improved reliability of threshold-based event detection and improved spike sorting accuracy. Interpolation of continuously acquired data is, however, computationally expensive. A cost-benefit analysis was made of varying sampling rates from 12.5 kHz (no interpolation) to 100 kHz (eight times oversampling, with respect to the Nyquist frequency), taking into consideration the final application of the data. For most purposes, including spike sorting, sample rates below 25 kHz with bandlimited interpolation to 50 kHz were ideal, with negligible gains above this rate. A practical benefit, especially for large electrode arrays, is that the bandwidth and storage requirements can be greatly reduced by using data acquisition rates at or slightly above the Nyquist frequency.},
+  language = {en},
+  number = {1},
+  journal = {Journal of Neuroscience Methods},
+  author = {Blanche, Timothy J. and Swindale, Nicholas V.},
+  month = jul,
+  year = {2006},
+  pages = {81-91},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ZFTNDSNQ\\Blanche and Swindale - 2006 - Nyquist interpolation improves neuron yield in mul.pdf}
+}
+
+@misc{zotero-1530,
+  title = {Mail - Sara.Mahallati@mail.Utoronto.Ca},
+  howpublished = {https://outlook.office.com/owa/?realm=mail.utoronto.ca\&path=/mail/search}
+}
+
+@article{Cohen2000,
+  title = {Contributions of Intrinsic and Synaptic Activities to the Generation of Neuronal Discharges in in Vitro Hippocampus},
+  volume = {524},
+  issn = {0022-3751},
+  doi = {10.1111/j.1469-7793.2000.00485.x},
+  abstract = {Extracellular and intracellular records were made from guinea-pig hippocampal slices to examine the contributions of intrinsic cellular properties and synaptic events to the generation of neuronal activity. Extracellular signals were filtered to pass action potentials, which could be detected within a distance of about 80 $\mu$m from a discharging cell.Spontaneous action potentials were invariably detected in records from the stratum pyramidale of CA3 region. Blocking excitatory synaptic transmission with NBQX and APV reduced their frequency by 23 $\pm$ 35 \%. Suppressing synaptic inhibition, while excitation was already blocked, increased the rate of spike discharge to 177 $\pm$ 71 \% of its control value.Most action potentials recorded intracellularly from CA3 pyramidal cells were initiated in the absence of a detectable synaptic event. In contrast, most action potentials generated by inhibitory cells located close to stratum pyramidale were preceded by an EPSP.In 31 simultaneous recordings, intracellular pyramidal cell action potentials appeared consistently to initiate extracellular spikes with a mean latency of 2$\cdot$2 $\pm$ 1$\cdot$0 ms. Single inhibitory cell action potentials could initiate a reduction in the frequency of extracellular spikes of duration 10\textendash{}30 ms.Some identified extracellular spikes (n = 9) consistently preceded intracellularly recorded IPSPs. IPSPs were initiated monosynaptically with latencies of 0$\cdot$9\textendash{}1$\cdot$5 ms. In reciprocal interactions, single pyramidal cell action potentials could trigger the discharge of an identified unit that in turn appeared to initiate an IPSP in the same pyramidal cell.These data suggest that intrinsic cellular mechanisms underly much of the spontaneous activity of pyramidal cells of the CA3 region of the hippocampus in vitro. Both synaptic inhibition and a strong excitation of inhibitory cells by pyramidal cells act to reduce population activity.},
+  number = {Pt 2},
+  journal = {The Journal of Physiology},
+  author = {Cohen, Ivan and Miles, Richard},
+  month = apr,
+  year = {2000},
+  pages = {485-502},
+  file = {C:\\Users\\saram\\Zotero\\storage\\UKP865X6\\Cohen and Miles - 2000 - Contributions of intrinsic and synaptic activities.pdf},
+  pmid = {10766928},
+  pmcid = {PMC2269886}
+}
+
+@article{Adams2017,
+  title = {Hetereogeneity in {{Neuronal Intrinsic Properties}}: {{A Possible Mechanism}} for {{Hub}}-{{Like Properties}} of the {{Rat Anterior Cingulate Cortex}} during {{Network Activity}}},
+  volume = {4},
+  copyright = {Copyright \textcopyright{} 2017 Adams et al.. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.},
+  issn = {2373-2822},
+  shorttitle = {Hetereogeneity in {{Neuronal Intrinsic Properties}}},
+  doi = {10.1523/ENEURO.0313-16.2017},
+  abstract = {The anterior cingulate cortex (ACC) is vital for a range of brain functions requiring cognitive control and has highly divergent inputs and outputs, thus manifesting as a hub in connectomic analyses. Studies show diverse functional interactions within the ACC are associated with network oscillations in the $\beta$ (20\textendash{}30 Hz) and $\gamma$ (30-80 Hz) frequency range. Oscillations permit dynamic routing of information within cortex, a function that depends on bandpass filter\textendash{}like behavior to selectively respond to specific inputs. However, a putative hub region such as ACC needs to be able to combine inputs from multiple sources rather than select a single input at the expense of others. To address this potential functional dichotomy, we modeled local ACC network dynamics in the rat in vitro. Modal peak oscillation frequencies in the $\beta$- and $\gamma$-frequency band corresponded to GABAAergic synaptic kinetics as seen in other regions; however, the intrinsic properties of ACC principal neurons were highly diverse. Computational modeling predicted that this neuronal response diversity broadened the bandwidth for filtering rhythmic inputs and supported combination\textemdash{}rather than selection\textemdash{}of different frequencies within the canonical $\gamma$ and $\beta$ electroencephalograph bands. These findings suggest that oscillating neuronal populations can support either response selection (routing) or combination, depending on the interplay between the kinetics of synaptic inhibition and the degree of heterogeneity of principal cell intrinsic conductances.},
+  language = {en},
+  number = {1},
+  journal = {eNeuro},
+  author = {Adams, Natalie E. and Sherfey, Jason S. and Kopell, Nancy J. and Whittington, Miles A. and LeBeau, Fiona E. N.},
+  month = jan,
+  year = {2017},
+  keywords = {anterior cingulate,dynamic routing,intrinsic properties,modeling,network oscillations},
+  pages = {ENEURO.0313-16.2017},
+  file = {C:\\Users\\saram\\Zotero\\storage\\7WPBP9AL\\Adams et al.  2017  Hetereogeneity in Neuronal Intrinsic Properties A Possible Mechanism for Hub-Like Properties of the Rat Anterior Cingulate Cortex during Network Activity.pdf}
+}
+
+@article{Pachitariu2016,
+  title = {Kilosort: Realtime Spike-Sorting for Extracellular Electrophysiology with Hundreds of Channels},
+  copyright = {\textcopyright{} 2016, Published by Cold Spring Harbor Laboratory Press. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
+  shorttitle = {Kilosort},
+  doi = {10.1101/061481},
+  abstract = {Advances in silicon probe technology mean that in vivo electrophysiological recordings from hundreds of channels will soon become commonplace. To interpret these recordings we need fast, scalable and accurate methods for spike sorting, whose output requires minimal time for manual curation. Here we introduce Kilosort, a spike sorting framework that meets these criteria, and show that it allows rapid and accurate sorting of large-scale in vivo data. Kilosort models the recorded voltage as a sum of template waveforms triggered on the spike times, allowing overlapping spikes to be identified and resolved. Rapid processing is achieved thanks to a novel low-dimensional approximation for the spatiotemporal distribution of each template, and to batch-based optimization on GPUs. A novel post-clustering merging step based on the continuity of the templates substantially reduces the requirement for subsequent manual curation operations. We compare Kilosort to an established algorithm on data obtained from 384-channel electrodes, and show superior performance, at much reduced processing times. Data from 384-channel electrode arrays can be processed in approximately realtime. Kilosort is an important step towards fully automated spike sorting of multichannel electrode recordings, and is freely available github.com/cortex-lab/Kilosort.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Pachitariu, Marius and Steinmetz, Nicholas and Kadir, Shabnam and Carandini, Matteo and Harris, Kenneth D.},
+  month = jun,
+  year = {2016},
+  pages = {061481},
+  file = {C:\\Users\\saram\\Zotero\\storage\\IHYXSCFJ\\Pachitariu et al. - 2016 - Kilosort realtime spike-sorting for extracellular.pdf}
+}
+
+@article{Rossant2016,
+  title = {Spike Sorting for Large, Dense Electrode Arrays},
+  volume = {19},
+  copyright = {2016 Nature Publishing Group},
+  issn = {1546-1726},
+  doi = {10.1038/nn.4268},
+  abstract = {Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5\%.},
+  language = {en},
+  number = {4},
+  journal = {Nature Neuroscience},
+  author = {Rossant, Cyrille and Kadir, Shabnam N. and Goodman, Dan F. M. and Schulman, John and Hunter, Maximilian L. D. and Saleem, Aman B. and Grosmark, Andres and Belluscio, Mariano and Denfield, George H. and Ecker, Alexander S. and Tolias, Andreas S. and Solomon, Samuel and Buzs{\'a}ki, Gy{\"o}rgy and Carandini, Matteo and Harris, Kenneth D.},
+  month = apr,
+  year = {2016},
+  pages = {634-641},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FTRZKWGD\\Rossant et al. - 2016 - Spike sorting for large, dense electrode arrays.pdf}
+}
+
+@article{Jorgenson2015,
+  title = {The {{BRAIN Initiative}}: Developing Technology to Catalyse Neuroscience Discovery},
+  volume = {370},
+  issn = {0962-8436},
+  shorttitle = {The {{BRAIN Initiative}}},
+  doi = {10.1098/rstb.2014.0164},
+  abstract = {The evolution of the field of neuroscience has been propelled by the advent of novel technological capabilities, and the pace at which these capabilities are being developed has accelerated dramatically in the past decade. Capitalizing on this momentum, the United States launched the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative to develop and apply new tools and technologies for revolutionizing our understanding of the brain. In this article, we review the scientific vision for this initiative set forth by the National Institutes of Health and discuss its implications for the future of neuroscience research. Particular emphasis is given to its potential impact on the mapping and study of neural circuits, and how this knowledge will transform our understanding of the complexity of the human brain and its diverse array of behaviours, perceptions, thoughts and emotions.},
+  number = {1668},
+  journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
+  author = {Jorgenson, Lyric A. and Newsome, William T. and Anderson, David J. and Bargmann, Cornelia I. and Brown, Emery N. and Deisseroth, Karl and Donoghue, John P. and Hudson, Kathy L. and Ling, Geoffrey S. F. and MacLeish, Peter R. and Marder, Eve and Normann, Richard A. and Sanes, Joshua R. and Schnitzer, Mark J. and Sejnowski, Terrence J. and Tank, David W. and Tsien, Roger Y. and Ugurbil, Kamil and Wingfield, John C.},
+  month = may,
+  year = {2015},
+  file = {C:\\Users\\saram\\Zotero\\storage\\H95ECH8W\\Jorgenson et al. - 2015 - The BRAIN Initiative developing technology to cat.pdf},
+  pmid = {25823863},
+  pmcid = {PMC4387507}
+}
+
+@article{Jun2017,
+  title = {Fully Integrated Silicon Probes for High-Density Recording of Neural Activity},
+  volume = {551},
+  copyright = {2017 Nature Publishing Group},
+  issn = {1476-4687},
+  doi = {10.1038/nature24636},
+  abstract = {Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures1,2. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging3,4,5 offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal\textendash{}oxide\textendash{}semiconductor (CMOS) processing-compatible low-impedance TiN6 sites that tile a single 10-mm long, 70 \texttimes{} 20-$\mu$m cross-section shank. The 6 \texttimes{} 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.},
+  language = {en},
+  number = {7679},
+  journal = {Nature},
+  author = {Jun, James J. and Steinmetz, Nicholas A. and Siegle, Joshua H. and Denman, Daniel J. and Bauza, Marius and Barbarits, Brian and Lee, Albert K. and Anastassiou, Costas A. and Andrei, Alexandru and Ayd{\i}n, {\c C}a{\u g}atay and Barbic, Mladen and Blanche, Timothy J. and Bonin, Vincent and Couto, Jo{\~a}o and Dutta, Barundeb and Gratiy, Sergey L. and Gutnisky, Diego A. and H{\"a}usser, Michael and Karsh, Bill and Ledochowitsch, Peter and Lopez, Carolina Mora and Mitelut, Catalin and Musa, Silke and Okun, Michael and Pachitariu, Marius and Putzeys, Jan and Rich, P. Dylan and Rossant, Cyrille and Sun, Wei-lung and Svoboda, Karel and Carandini, Matteo and Harris, Kenneth D. and Koch, Christof and O'Keefe, John and Harris, Timothy D.},
+  month = nov,
+  year = {2017},
+  pages = {232-236},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4JMYGYGX\\Jun et al. - 2017 - Fully integrated silicon probes for high-density r.pdf}
+}
+
+@article{Cohen2011,
+  title = {Measuring and Interpreting Neuronal Correlations},
+  volume = {14},
+  issn = {1097-6256},
+  doi = {10.1038/nn.2842},
+  abstract = {Mounting evidence suggests that understanding how the brain encodes information and performs computations will require studying correlations between neurons. The recent advent of recording techniques such as multielectrode arrays and two-photon imaging has made it easier to measure correlations, opening the door to detailed exploration of their properties and contributions to cortical processing. Studies to date, however, have reported discrepant findings, providing a confusing picture. Here, we briefly review these studies and conduct simulations to explore the influence of several experimental and physiological factors on correlation measurements. Differences in response strength, the time window over which spikes are counted, spike sorting conventions, and internal states can all dramatically affect measured correlations and systematically bias estimates. Given these complicating factors, we offer guidelines for interpreting correlation data and a discussion of how best to evaluate the impact of correlations on cortical processing.},
+  number = {7},
+  journal = {Nature neuroscience},
+  author = {Cohen, Marlene R. and Kohn, Adam},
+  month = jun,
+  year = {2011},
+  pages = {811-819},
+  file = {C:\\Users\\saram\\Zotero\\storage\\YR2WHVGC\\Cohen and Kohn  2011  Measuring and interpreting neuronal correlations.pdf},
+  pmid = {21709677},
+  pmcid = {PMC3586814}
+}
+
+@article{Pazienti2006,
+  title = {Robustness of the Significance of Spike Synchrony with Respect to Sorting Errors},
+  volume = {21},
+  issn = {0929-5313, 1573-6873},
+  doi = {10.1007/s10827-006-8899-7},
+  abstract = {The aim of spike sorting is to reconstruct single unit spike times from extracellular multi-unit recordings. Failure in the identification of a spike (false negative) or assignment of a spike to a wrong unit (false positive) are typical examples of sorting errors. Their influence on cross-correlation measures has been addressed and it has been shown that correlation analysis of multi-unit signals may lead to incorrect interpretations. We formulate a model to study the influence of sorting errors on the significance of synchronized spikes, and thus are able to study if and how the significance changes in case of imperfect sorting. Here we explore the case of pairwise analysis of simultaneously recorded neurons. Interestingly, a decrease in the significance is observed in the presence of false positives, as well as for false negatives. Furthermore, false negative errors reduce the significance of synchronized spikes more strongly than false positives. Thus, conservative sorting strategies have a stronger tendency to lead to a loss of the significance of synchronization. We demonstrate that a detailed understanding of sorting techniques and their possible effects on subsequent data analyses is important in order to rule out inconsistencies in the interpretation of results.},
+  language = {en},
+  number = {3},
+  journal = {Journal of Computational Neuroscience},
+  author = {Pazienti, Antonio and Gr{\"u}n, Sonja},
+  month = dec,
+  year = {2006},
+  pages = {329-342},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FJCW3VHQ\\Pazienti and Grün  2006  Robustness of the significance of spike synchrony with respect to sorting errors.pdf}
+}
+
+@article{Satuvuori2017,
+  title = {Measures of Spike Train Synchrony for Data with Multiple Time Scales},
+  volume = {287},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2017.05.028},
+  abstract = {Background
+Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time scale parametric measures, since by adapting to the local firing rate they take into account all the time scales of a given dataset.
+New method
+In data containing multiple time scales (e.g. regular spiking and bursts) one is typically less interested in the smallest time scales and a more adaptive approach is needed. Here we propose the A-ISI-distance, the A-SPIKE-distance and A-SPIKE-synchronization, which generalize the original measures by considering the local relative to the global time scales. For the A-SPIKE-distance we also introduce a rate-independent extension called the RIA-SPIKE-distance, which focuses specifically on spike timing.
+Results
+The adaptive generalizations A-ISI-distance and A-SPIKE-distance allow to disregard spike time differences that are not relevant on a more global scale. A-SPIKE-synchronization does not any longer demand an unreasonably high accuracy for spike doublets and coinciding bursts. Finally, the RIA-SPIKE-distance proves to be independent of rate ratios between spike trains.
+Comparison with existing methods
+We find that compared to the original versions the A-ISI-distance and the A-SPIKE-distance yield improvements for spike trains containing different time scales without exhibiting any unwanted side effects in other examples. A-SPIKE-synchronization matches spikes more efficiently than SPIKE-synchronization.
+Conclusions
+With these proposals we have completed the picture, since we now provide adaptive generalized measures that are sensitive to firing rate only (A-ISI-distance), to timing only (ARI-SPIKE-distance), and to both at the same time (A-SPIKE-distance).},
+  journal = {Journal of Neuroscience Methods},
+  author = {Satuvuori, Eero and Mulansky, Mario and Bozanic, Nebojsa and Malvestio, Irene and Zeldenrust, Fleur and Lenk, Kerstin and Kreuz, Thomas},
+  month = aug,
+  year = {2017},
+  keywords = {Burst,Data analysis,ISI-distance,Spike train distances,SPIKE-distance,SPIKE-synchronization,Time-scale},
+  pages = {25-38},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TMNHAY8H\\Satuvuori et al.  2017  Measures of spike train synchrony for data with multiple time scales.pdf}
+}
+
+@article{Fiscella2012,
+  title = {Recording from Defined Populations of Retinal Ganglion Cells Using a High-Density {{CMOS}}-Integrated Microelectrode Array with Real-Time Switchable Electrode Selection},
+  volume = {211},
+  issn = {0165-0270},
+  doi = {10.1016/j.jneumeth.2012.08.017},
+  abstract = {In order to understand how retinal circuits encode visual scenes, the neural activity of defined populations of retinal ganglion cells (RGCs) has to be investigated. Here we report on a method for stimulating, detecting, and subsequently targeting defined populations of RGCs. The possibility to select a distinct population of RGCs for extracellular recording enables the design of experiments that can increase our understanding of how these neurons extract precise spatio-temporal features from the visual scene, and how the brain interprets retinal signals. We used light stimulation to elicit a response from physiologically distinct types of RGCs and then utilized the dynamic-configurability capabilities of a microelectronics-based high-density microelectrode array (MEA) to record their synchronous action potentials. The layout characteristics of the MEA made it possible to stimulate and record from multiple, highly overlapping RGCs simultaneously without light-induced artifacts. The high-density of electrodes and the high signal-to-noise ratio of the MEA circuitry allowed for recording of the activity of each RGC on 14$\pm$7 electrodes. The spatial features of the electrical activity of each RGC greatly facilitated spike sorting. We were thus able to localize, identify and record from defined RGCs within a region of mouse retina. In addition, we stimulated and recorded from genetically modified RGCs to demonstrate the applicability of optogenetic methods, which introduces an additional feature to target a defined cell type. The developed methodologies can likewise be applied to other neuronal preparations including brain slices or cultured neurons.},
+  number = {1},
+  journal = {Journal of Neuroscience Methods},
+  author = {Fiscella, Michele and Farrow, Karl and Jones, Ian L. and J{\"a}ckel, David and M{\"u}ller, Jan and Frey, Urs and Bakkum, Douglas J. and Hantz, P{\'e}ter and Roska, Botond and Hierlemann, Andreas},
+  month = oct,
+  year = {2012},
+  keywords = {Extracellular recording,Spike sorting,Retina,CMOS,Light stimulation,Microelectrode arrays},
+  pages = {103-113},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ICPU8DCE\\Fiscella et al.  2012  Recording from defined populations of retinal ganglion cells using a high-density CMOS-integrated microelectrode array with real-time switchable electrode selection.pdf}
+}
+
+@article{Ness2015,
+  title = {Modelling and {{Analysis}} of {{Electrical Potentials Recorded}} in {{Microelectrode Arrays}} ({{MEAs}})},
+  volume = {13},
+  issn = {1539-2791, 1559-0089},
+  doi = {10.1007/s12021-015-9265-6},
+  language = {en},
+  number = {4},
+  journal = {Neuroinformatics},
+  author = {Ness, Torbj{\o}rn V. and Chintaluri, Chaitanya and Potworowski, Jan and {\L}\k{e}ski, Szymon and G{\l}\k{a}bska, Helena and W{\'o}jcik, Daniel K. and Einevoll, Gaute T.},
+  month = oct,
+  year = {2015},
+  pages = {403-426},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Z8X5ZEVV\\Ness et al.  2015  Modelling and Analysis of Electrical Potentials Recorded in Microelectrode Arrays (MEAs).pdf}
+}
+
+@article{Yger2018,
+  title = {A Spike Sorting Toolbox for up to Thousands of Electrodes Validated with Ground Truth Recordings in Vitro and in Vivo},
+  volume = {7},
+  copyright = {\textcopyright{} 2018 Yger et al.. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.},
+  issn = {2050-084X},
+  doi = {10.7554/eLife.34518},
+  abstract = {A spike sorting toolbox to quickly and reliably access the activity of thousands of neurons recorded with dense extracellular probes.},
+  language = {en},
+  journal = {eLife},
+  author = {Yger, Pierre and Spampinato, Giulia LB and Esposito, Elric and Lefebvre, Baptiste and Deny, St{\'e}phane and Gardella, Christophe and Stimberg, Marcel and Jetter, Florian and Zeck, Guenther and Picaud, Serge and Duebel, Jens and Marre, Olivier},
+  month = mar,
+  year = {2018},
+  pages = {e34518},
+  file = {C:\\Users\\saram\\Zotero\\storage\\M94NXM7A\\Yger et al.  2018  A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo.pdf}
+}
+
+@article{Anastassiou2015,
+  title = {Cell Type- and Activity-Dependent Extracellular Correlates of Intracellular Spiking},
+  volume = {114},
+  issn = {0022-3077},
+  doi = {10.1152/jn.00628.2014},
+  abstract = {Despite decades of extracellular action potential (EAP) recordings monitoring brain activity, the biophysical origin and inherent variability of these signals remain enigmatic. We performed whole cell patch recordings of excitatory and inhibitory neurons in rat somatosensory cortex slice while positioning a silicon probe in their vicinity to concurrently record intra- and extracellular voltages for spike frequencies under 20 Hz. We characterize biophysical events and properties (intracellular spiking, extracellular resistivity, temporal jitter, etc.) related to EAP recordings at the single-neuron level in a layer-specific manner. Notably, EAP amplitude was found to decay as the inverse of distance between the soma and the recording electrode with similar (but not identical) resistivity across layers. Furthermore, we assessed a number of EAP features and their variability with spike activity: amplitude (but not temporal) features varied substantially ($\sim$30\textendash{}50\% compared with mean) and nonmonotonically as a function of spike frequency and spike order. Such EAP variation only partly reflects intracellular somatic spike variability and points to the plethora of processes contributing to the EAP. Also, we show that the shape of the EAP waveform is qualitatively similar to the negative of the temporal derivative to the intracellular somatic voltage, as expected from theory. Finally, we tested to what extent EAPs can impact the lowpass-filtered part of extracellular recordings, the local field potential (LFP), typically associated with synaptic activity. We found that spiking of excitatory neurons can significantly impact the LFP at frequencies as low as 20 Hz. Our results question the common assertion that the LFP acts as proxy for synaptic activity.},
+  number = {1},
+  journal = {Journal of Neurophysiology},
+  author = {Anastassiou, Costas A. and Perin, Rodrigo and Buzs{\'a}ki, Gy{\"o}rgy and Markram, Henry and Koch, Christof},
+  month = may,
+  year = {2015},
+  pages = {608-623},
+  file = {C:\\Users\\saram\\Zotero\\storage\\WNW3ZJMH\\Anastassiou et al.  2015  Cell type- and activity-dependent extracellular correlates of intracellular spiking.pdf}
+}
+
+@article{Markram2015,
+  title = {Reconstruction and {{Simulation}} of {{Neocortical Microcircuitry}}},
+  volume = {163},
+  issn = {00928674},
+  doi = {10.1016/j.cell.2015.09.029},
+  language = {en},
+  number = {2},
+  journal = {Cell},
+  author = {Markram, Henry and Muller, Eilif and Ramaswamy, Srikanth and Reimann, Michael W. and Abdellah, Marwan and Sanchez, Carlos Aguado and Ailamaki, Anastasia and Alonso-Nanclares, Lidia and Antille, Nicolas and Arsever, Selim and Kahou, Guy Antoine Atenekeng and Berger, Thomas K. and Bilgili, Ahmet and Buncic, Nenad and Chalimourda, Athanassia and Chindemi, Giuseppe and Courcol, Jean-Denis and Delalondre, Fabien and Delattre, Vincent and Druckmann, Shaul and Dumusc, Raphael and Dynes, James and Eilemann, Stefan and Gal, Eyal and Gevaert, Michael Emiel and Ghobril, Jean-Pierre and Gidon, Albert and Graham, Joe W. and Gupta, Anirudh and Haenel, Valentin and Hay, Etay and Heinis, Thomas and Hernando, Juan B. and Hines, Michael and Kanari, Lida and Keller, Daniel and Kenyon, John and Khazen, Georges and Kim, Yihwa and King, James G. and Kisvarday, Zoltan and Kumbhar, Pramod and Lasserre, S{\'e}bastien and Le B{\'e}, Jean-Vincent and Magalh{\~a}es, Bruno R.C. and Merch{\'a}n-P{\'e}rez, Angel and Meystre, Julie and Morrice, Benjamin Roy and Muller, Jeffrey and Mu{\~n}oz-C{\'e}spedes, Alberto and Muralidhar, Shruti and Muthurasa, Keerthan and Nachbaur, Daniel and Newton, Taylor H. and Nolte, Max and Ovcharenko, Aleksandr and Palacios, Juan and Pastor, Luis and Perin, Rodrigo and Ranjan, Rajnish and Riachi, Imad and Rodr{\'\i}guez, Jos{\'e}-Rodrigo and Riquelme, Juan Luis and R{\"o}ssert, Christian and Sfyrakis, Konstantinos and Shi, Ying and Shillcock, Julian C. and Silberberg, Gilad and Silva, Ricardo and Tauheed, Farhan and Telefont, Martin and Toledo-Rodriguez, Maria and Tr{\"a}nkler, Thomas and Van Geit, Werner and D{\'\i}az, Jafet Villafranca and Walker, Richard and Wang, Yun and Zaninetta, Stefano M. and DeFelipe, Javier and Hill, Sean L. and Segev, Idan and Sch{\"u}rmann, Felix},
+  month = oct,
+  year = {2015},
+  pages = {456-492},
+  file = {C:\\Users\\saram\\Zotero\\storage\\NYY8ZWDW\\Markram et al.  2015  Reconstruction and Simulation of Neocortical Microcircuitry.pdf}
+}
+
+@article{Schmitzer-Torbert2005,
+  title = {Quantitative Measures of Cluster Quality for Use in Extracellular Recordings},
+  volume = {131},
+  issn = {03064522},
+  doi = {10.1016/j.neuroscience.2004.09.066},
+  abstract = {While the use of multi-channel electrodes (stereotrodes and tetrodes) has allowed for the simultaneous recording and identification of many neurons, quantitative measures of the quality of neurons in such recordings are lacking. In multi-channel recordings, each spike waveform is discriminated in a high-dimensional space, making traditional measures of unit quality inapplicable. We describe two measures of unit isolation quality, Lratio and Isolation Distance, and evaluate their performance using simulations and tetrode recordings. Both measures quantified how well separated the spikes of one cluster (putative neuron) were from other spikes recorded simultaneously on the same multichannel electrode. In simulations and tetrode recordings, both Lratio and Isolation Distance discriminated well- and poorly-separated clusters. In data sets from the rodent hippocampus in which neurons were simultaneously recorded intracellularly and extracellularly, values of Isolation Distance and Lratio were related to the correct identification of spikes. \textcopyright{} 2005 IBRO. Published by Elsevier Ltd. All rights reserved.},
+  language = {en},
+  number = {1},
+  journal = {Neuroscience},
+  author = {Schmitzer-Torbert, N. and Jackson, J. and Henze, D. and Harris, K. and Redish, A.D.},
+  month = jan,
+  year = {2005},
+  pages = {1-11},
+  file = {C:\\Users\\saram\\Zotero\\storage\\VMIWFEJZ\\Schmitzer-Torbert et al.  2005  Quantitative measures of cluster quality for use in extracellular recordings.pdf}
+}
+
+@article{Tsodyks1997,
+  title = {The Neural Code between Neocortical Pyramidal Neurons Depends on Neurotransmitter Release Probability},
+  volume = {94},
+  copyright = {Copyright \textcopyright{} 1997, The National Academy of Sciences of the USA},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.94.2.719},
+  abstract = {Although signaling between neurons is central to the functioning of the brain, we still do not understand how the code used in signaling depends on the properties of synaptic transmission. Theoretical analysis combined with patch clamp recordings from pairs of neocortical pyramidal neurons revealed that the rate of synaptic depression, which depends on the probability of neurotransmitter release, dictates the extent to which firing rate and temporal coherence of action potentials within a presynaptic population are signaled to the postsynaptic neuron. The postsynaptic response primarily reflects rates of firing when depression is slow and temporal coherence when depression is fast. A wide range of rates of synaptic depression between different pairs of pyramidal neurons was found, suggesting that the relative contribution of rate and temporal signals varies along a continuum. We conclude that by setting the rate of synaptic depression, release probability is an important factor in determining the neural code.$<$/p$>$},
+  language = {en},
+  number = {2},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Tsodyks, Misha V. and Markram, Henry},
+  month = jan,
+  year = {1997},
+  keywords = {action potential decoding,neural networks,synaptic depression,synaptic plasticity},
+  pages = {719-723},
+  file = {C:\\Users\\saram\\Zotero\\storage\\WDK5YI4W\\Tsodyks and Markram  1997  The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability.pdf},
+  pmid = {9012851}
+}
+
+@article{Dayan2001,
+  title = {{{THEORETICAL NEUROSCIENCE}}},
+  language = {en},
+  author = {Dayan, Peter and Abbott, L F},
+  year = {2001},
+  pages = {432},
+  file = {C:\\Users\\saram\\Zotero\\storage\\BLGYYBES\\Dayan and Abbott  THEORETICAL NEUROSCIENCE.pdf}
+}
+
+@article{Rullen2001,
+  title = {Rate {{Coding Versus Temporal Order Coding}}: {{What}} the {{Retinal Ganglion Cells Tell}} the {{Visual Cortex}}},
+  volume = {13},
+  issn = {0899-7667},
+  shorttitle = {Rate {{Coding Versus Temporal Order Coding}}},
+  doi = {10.1162/08997660152002852},
+  abstract = {It is often supposed that the messages sent to the visual cortex by the retinal ganglion cells are encoded by the mean firing rates observed on spike trains generated with a Poisson process. Using an information transmission approach, we evaluate the performances of two such codes, one based on the spike count and the other on the mean interspike interval, and compare the results with a rank order code, where the first ganglion cells to emit a spike are given a maximal weight. Our results show that the rate codes are far from optimal for fast information transmission and that the temporal structure of the spike train can be efficiently used to maximize the information transfer rate under conditions where each cell needs to fire only one spike.},
+  number = {6},
+  journal = {Neural Computation},
+  author = {Rullen, Rufin Van and Thorpe, Simon J.},
+  month = jun,
+  year = {2001},
+  pages = {1255-1283},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JA8FUXLC\\Rullen and Thorpe  2001  Rate Coding Versus Temporal Order Coding What the Retinal Ganglion Cells Tell the Visual Cortex.pdf}
+}
+
+@article{Huxter2003,
+  title = {Independent Rate and Temporal Coding in Hippocampal Pyramidal Cells},
+  volume = {425},
+  copyright = {2003 Nature Publishing Group},
+  issn = {1476-4687},
+  doi = {10.1038/nature02058},
+  abstract = {In the brain, hippocampal pyramidal cells use temporal1 as well as rate2 coding to signal spatial aspects of the animal's environment or behaviour. The temporal code takes the form of a phase relationship to the concurrent cycle of the hippocampal electroencephalogram theta rhythm1. These two codes could each represent a different variable3,4. However, this requires the rate and phase to vary independently, in contrast to recent suggestions5,6 that they are tightly coupled, both reflecting the amplitude of the cell's input. Here we show that the time of firing and firing rate are dissociable, and can represent two independent variables: respectively the animal's location within the place field, and its speed of movement through the field. Independent encoding of location together with actions and stimuli occurring there may help to explain the dual roles of the hippocampus in spatial and episodic memory7,8, or may indicate a more general role of the hippocampus in relational/declarative memory9,10.},
+  language = {en},
+  number = {6960},
+  journal = {Nature},
+  author = {Huxter, John and Burgess, Neil and O'Keefe, John},
+  month = oct,
+  year = {2003},
+  pages = {828-832},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5KHQWUHE\\Huxter et al.  2003  Independent rate and temporal coding in hippocampal pyramidal cells.pdf}
+}
+
+@article{Mehta2002,
+  title = {Role of Experience and Oscillations in Transforming a Rate Code into a Temporal Code},
+  volume = {417},
+  copyright = {2002 Nature Publishing Group},
+  issn = {1476-4687},
+  doi = {10.1038/nature00807},
+  abstract = {In the vast majority of brain areas, the firing rates of neurons, averaged over several hundred milliseconds to several seconds, can be strongly modulated by, and provide accurate information about, properties of their inputs. This is referred to as the rate code. However, the biophysical laws of synaptic plasticity require precise timing of spikes over short timescales ($<$10 ms)1,2. Hence it is critical to understand the physiological mechanisms that can generate precise spike timing in vivo, and the relationship between such a temporal code and a rate code. Here we propose a mechanism by which a temporal code can be generated through an interaction between an asymmetric rate code and oscillatory inhibition. Consistent with the predictions of our model, the rate3,4 and temporal5,6,7 codes of hippocampal pyramidal neurons are highly correlated. Furthermore, the temporal code becomes more robust with experience. The resulting spike timing satisfies the temporal order constraints of hebbian learning. Thus, oscillations and receptive field asymmetry may have a critical role in temporal sequence learning.},
+  language = {en},
+  number = {6890},
+  journal = {Nature},
+  author = {Mehta, M. R. and Lee, A. K. and Wilson, M. A.},
+  month = jun,
+  year = {2002},
+  pages = {741-746},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PP8EA2D2\\Mehta et al.  2002  Role of experience and oscillations in transforming a rate code into a temporal code.pdf}
+}
+
+@article{Akam2014,
+  title = {Oscillatory Multiplexing of Population Codes for Selective Communication in the Mammalian Brain},
+  volume = {15},
+  copyright = {2014 Nature Publishing Group},
+  issn = {1471-0048},
+  doi = {10.1038/nrn3668},
+  abstract = {Mammalian brains exhibit population oscillations, the structures of which vary in time and space according to behavioural state. A proposed function of these oscillations is to control the flow of signals among anatomically connected networks. However, the nature of neural coding that may support selective communication that depends on oscillations has received relatively little attention. Here, we consider the role of multiplexing, whereby multiple information streams share a common neural substrate. We suggest that multiplexing implemented through periodic modulation of firing-rate population codes enables flexible reconfiguration of effective connectivity among brain areas.},
+  language = {en},
+  number = {2},
+  journal = {Nature Reviews Neuroscience},
+  author = {Akam, Thomas and Kullmann, Dimitri M.},
+  month = feb,
+  year = {2014},
+  pages = {111-122},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PUZWNAL4\\Akam and Kullmann  2014  Oscillatory multiplexing of population codes for selective communication in the mammalian brain.pdf}
+}
+
+@article{Zuo2015,
+  title = {Complementary {{Contributions}} of {{Spike Timing}} and {{Spike Rate}} to {{Perceptual Decisions}} in {{Rat S1}} and {{S2 Cortex}}},
+  volume = {25},
+  issn = {0960-9822},
+  doi = {10.1016/j.cub.2014.11.065},
+  abstract = {Summary
+When a neuron responds to a sensory stimulus, two fundamental codes [1\textendash{}6] may transmit the information specifying stimulus identity\textemdash{}spike rate (the total number of spikes in the sequence, normalized by time) and spike timing (the~detailed millisecond-scale temporal structure of the response). To assess the functional significance of these codes, we recorded neuronal responses in primary (S1) and secondary (S2) somatosensory cortex of five rats as they used their whiskers to identify textured surfaces. From the spike trains evoked during whisker contact with the texture, we computed the information that rate and timing codes carried about texture identity and about the rat's choice. S1 and S2 spike timing carried more information about stimulus and about choice than spike rates; the conjunction of rate and timing carried more information than either code alone. Moreover, on trials when our spike-timing-decoding algorithm extracted faithful texture information, the rat was more likely to choose correctly; when our spike-timing-decoding algorithm extracted misleading texture information, the rat was more likely to err. For spike rate information, the relationship between faithfulness of the message and correct choice was significant but weaker. These results indicate that spike timing makes crucial contributions to tactile perception, complementing and surpassing those made by rate. The language by which somatosensory cortical neurons transmit information, and the readout mechanism used to produce behavior, appears to rely on multiplexed signals from spike rate and timing.},
+  number = {3},
+  journal = {Current Biology},
+  author = {Zuo, Yanfang and Safaai, Houman and Notaro, Giuseppe and Mazzoni, Alberto and Panzeri, Stefano and Diamond, Mathew E.},
+  month = feb,
+  year = {2015},
+  pages = {357-363},
+  file = {C:\\Users\\saram\\Zotero\\storage\\XQXPYFRP\\Zuo et al.  2015  Complementary Contributions of Spike Timing and Spike Rate to Perceptual Decisions in Rat S1 and S2 Cortex.pdf}
+}
+
+@article{Fukushima2018,
+  title = {Structure\textendash{}function Relationships during Segregated and Integrated Network States of Human Brain Functional Connectivity},
+  volume = {223},
+  issn = {1863-2653, 1863-2661},
+  doi = {10.1007/s00429-017-1539-3},
+  abstract = {Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.},
+  language = {en},
+  number = {3},
+  journal = {Brain Structure and Function},
+  author = {Fukushima, Makoto and Betzel, Richard F. and He, Ye and van den Heuvel, Martijn P. and Zuo, Xi-Nian and Sporns, Olaf},
+  month = apr,
+  year = {2018},
+  pages = {1091-1106},
+  file = {C:\\Users\\saram\\Zotero\\storage\\MEEC7SYR\\Fukushima et al. - 2018 - Structure–function relationships during segregated.pdf}
+}
+
+@article{Ricci-Tersenghi2012,
+  archivePrefix = {arXiv},
+  eprinttype = {arxiv},
+  eprint = {1112.4814},
+  title = {The {{Bethe}} Approximation for Solving the Inverse {{Ising}} Problem: A Comparison with Other Inference Methods},
+  volume = {2012},
+  issn = {1742-5468},
+  shorttitle = {The {{Bethe}} Approximation for Solving the Inverse {{Ising}} Problem},
+  doi = {10.1088/1742-5468/2012/08/P08015},
+  abstract = {The inverse Ising problem consists in inferring the coupling constants of an Ising model given the correlation matrix. The fastest methods for solving this problem are based on mean-field approximations, but which one performs better in the general case is still not completely clear. In the first part of this work, I summarize the formulas for several mean- field approximations and I derive new analytical expressions for the Bethe approximation, which allow to solve the inverse Ising problem without running the Susceptibility Propagation algorithm (thus avoiding the lack of convergence). In the second part, I compare the accuracy of different mean field approximations on several models (diluted ferromagnets and spin glasses) defined on random graphs and regular lattices, showing which one is in general more effective. A simple improvement over these approximations is proposed. Also a fundamental limitation is found in using methods based on TAP and Bethe approximations in presence of an external field.},
+  number = {08},
+  journal = {Journal of Statistical Mechanics: Theory and Experiment},
+  author = {Ricci-Tersenghi, Federico},
+  month = aug,
+  year = {2012},
+  keywords = {Condensed Matter - Disordered Systems and Neural Networks,Condensed Matter - Statistical Mechanics},
+  pages = {P08015},
+  file = {C:\\Users\\saram\\Zotero\\storage\\T3NWSP4I\\Ricci-Tersenghi  2012  The Bethe approximation for solving the inverse Ising problem a comparison with other inference methods.pdf}
+}
+
+@article{Nguyen2017,
+  title = {Inverse Statistical Problems: From the Inverse {{Ising}} Problem to Data Science},
+  volume = {66},
+  issn = {0001-8732, 1460-6976},
+  shorttitle = {Inverse Statistical Problems},
+  doi = {10.1080/00018732.2017.1341604},
+  language = {en},
+  number = {3},
+  journal = {Advances in Physics},
+  author = {Nguyen, H. Chau and Zecchina, Riccardo and Berg, Johannes},
+  month = jul,
+  year = {2017},
+  pages = {197-261},
+  file = {C:\\Users\\saram\\Zotero\\storage\\FPASIJER\\Nguyen et al.  2017  Inverse statistical problems from the inverse Ising problem to data science.pdf}
+}
+
+@article{Sakellariou,
+  title = {Inverse Inference in the Asymmetric {{Ising}} Model},
+  language = {en},
+  author = {Sakellariou, Jason},
+  pages = {150},
+  file = {C:\\Users\\saram\\Zotero\\storage\\EIUZ4EKL\\Sakellariou  Inverse inference in the asymmetric Ising model.pdf}
+}
+
+@article{Carracedo2013,
+  title = {A {{Neocortical Delta Rhythm Facilitates Reciprocal Interlaminar Interactions}} via {{Nested Theta Rhythms}}},
+  volume = {33},
+  copyright = {Copyright \textcopyright{} 2013 the authors 0270-6474/13/3310750-12\$15.00/0. This article is freely available online through the J Neurosci Author Open Choice option.},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.0735-13.2013},
+  abstract = {Delta oscillations (1\textendash{}4 Hz) associate with deep sleep and are implicated in memory consolidation and replay of cortical responses elicited during wake states. A potent local generator has been characterized in thalamus, and local generators in neocortex have been suggested. Here we demonstrate that isolated rat neocortex generates delta rhythms in conditions mimicking the neuromodulatory state during deep sleep (low cholinergic and dopaminergic tone). The rhythm originated in an NMDA receptor-driven network of intrinsic bursting (IB) neurons in layer 5, activating a source of GABAB receptor-mediated inhibition. In contrast, regular spiking (RS) neurons in layer 5 generated theta-frequency outputs. In layer 2/3 principal cells, outputs from IB cells associated with IPSPs, whereas those from layer 5 RS neurons related to nested bursts of theta-frequency EPSPs. Both interlaminar spike and field correlations revealed a sequence of events whereby sparse spiking in layer 2/3 was partially reflected back from layer 5 on each delta period. We suggest that these reciprocal, interlaminar interactions may represent a ``Helmholtz machine''-like process to control synaptic rescaling during deep sleep.},
+  language = {en},
+  number = {26},
+  journal = {Journal of Neuroscience},
+  author = {Carracedo, Lucy M. and Kjeldsen, Henrik and Cunnington, Leonie and Jenkins, Alastair and Schofield, Ian and Cunningham, Mark O. and Davies, Ceri H. and Traub, Roger D. and Whittington, Miles A.},
+  month = jun,
+  year = {2013},
+  pages = {10750-10761},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5NIW5GIY\\Carracedo et al.  2013  A Neocortical Delta Rhythm Facilitates Reciprocal Interlaminar Interactions via Nested Theta Rhythms.pdf},
+  pmid = {23804097}
+}
+
+@article{Shoham2006,
+  title = {How Silent Is the Brain: Is There a ``Dark Matter'' Problem in Neuroscience?},
+  volume = {192},
+  issn = {0340-7594, 1432-1351},
+  shorttitle = {How Silent Is the Brain},
+  doi = {10.1007/s00359-006-0117-6},
+  abstract = {Evidence from a variety of recording methods suggests that many areas of the brain are far more sparsely active than commonly thought. Here, we review experimental findings pointing to the existence of neurons which fire action potentials rarely or only to very specific stimuli. Because such neurons would be difficult to detect with the most common method of monitoring neural activity in vivo\textemdash{}extracellular electrode recording\textemdash{}they could be referred to as ``dark neurons,'' in analogy to the astrophysical observation that much of the matter in the universe is undetectable, or dark. In addition to discussing the evidence for largely silent neurons, we review technical advances that will ultimately answer the question: how silent is the brain?},
+  language = {en},
+  number = {8},
+  journal = {Journal of Comparative Physiology A},
+  author = {Shoham, Shy and O'Connor, Daniel H. and Segev, Ronen},
+  month = aug,
+  year = {2006},
+  pages = {777-784},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DKGQQS4L\\Shoham et al.  2006  How silent is the brain is there a “dark matter” problem in neuroscience.pdf}
+}
+
+@article{Dehghani2016,
+  title = {Dynamic {{Balance}} of {{Excitation}} and {{Inhibition}} in {{Human}} and {{Monkey Neocortex}}},
+  volume = {6},
+  issn = {2045-2322},
+  doi = {10.1038/srep23176},
+  language = {en},
+  number = {1},
+  journal = {Scientific Reports},
+  author = {Dehghani, Nima and Peyrache, Adrien and Telenczuk, Bartosz and Le Van Quyen, Michel and Halgren, Eric and Cash, Sydney S. and Hatsopoulos, Nicholas G. and Destexhe, Alain},
+  month = sep,
+  year = {2016},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2C46EWV5\\Dehghani et al.  2016  Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex.pdf;C:\\Users\\saram\\Zotero\\storage\\5GZI3UM7\\Dehghani et al.  2016  Dynamic Balance of Excitation and Inhibition in Human and Monkey Neocortex.pdf}
+}
+
+@article{Zhang2018,
+  title = {A Review on Cluster Estimation Methods and Their Application to Neural Spike Data},
+  volume = {15},
+  issn = {1741-2560, 1741-2552},
+  doi = {10.1088/1741-2552/aab385},
+  abstract = {The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons\textemdash`spike sorting'\textemdash{}is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. Given the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and challenging issue, due to the existence of background noise and the overlap and interactions among neurons in neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to processing the vast, ever-growing amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices have been comprehensively reviewed and implemented to determine the number of clusters in neural datasets. To gauge the suitability of the indices to neural spike data, and inform the selection process, we then calculated the indices by applying k-means clustering to twenty widely used synthetic neural datasets and one empirical dataset, and compared the performance of these indices against preexisting ground truth labels. The results showed that the top five validity indices work consistently well across variations in noise level, both for the synthetic datasets and the real dataset. Using these top performing indices provides strong support for the determination of the number of neural clusters, which is essential in the spike sorting process.},
+  language = {en},
+  number = {3},
+  journal = {Journal of Neural Engineering},
+  author = {Zhang, James and Nguyen, Thanh and Cogill, Steven and Bhatti, Asim and Luo, Lingkun and Yang, Samuel and Nahavandi, Saeid},
+  month = jun,
+  year = {2018},
+  pages = {031003},
+  file = {C:\\Users\\saram\\Zotero\\storage\\BUXXVSUY\\Zhang et al.  2018  A review on cluster estimation methods and their application to neural spike data.pdf}
+}
+
+@inproceedings{Nghiem2017,
+  series = {Lecture Notes in Computer Science},
+  title = {Pairwise {{Ising Model Analysis}} of {{Human Cortical Neuron Recordings}}},
+  isbn = {978-3-319-68444-4 978-3-319-68445-1},
+  doi = {10.1007/978-3-319-68445-1_30},
+  abstract = {During wakefulness and deep sleep brain states, cortical neural networks show a different behavior, with the second characterized by transients of high network activity. To investigate their impact on neuronal behavior, we apply a pairwise Ising model analysis by inferring the maximum entropy model that reproduces single and pairwise moments of the neuron's spiking activity. In this work we first review the inference algorithm introduced in Ferrari, Phys. Rev. E (2016) [1]. We then succeed in applying the algorithm to infer the model from a large ensemble of neurons recorded by multi-electrode array in human temporal cortex. We compare the Ising model performance in capturing the statistical properties of the network activity during wakefulness and deep sleep. For the latter, the pairwise model misses relevant transients of high network activity, suggesting that additional constraints are necessary to accurately model the data.},
+  language = {en},
+  booktitle = {Geometric {{Science}} of {{Information}}},
+  publisher = {{Springer, Cham}},
+  author = {Nghiem, Trang-Anh and Marre, Olivier and Destexhe, Alain and Ferrari, Ulisse},
+  month = nov,
+  year = {2017},
+  pages = {257-264},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6NSJAW4K\\Nghiem et al. - 2017 - Pairwise Ising Model Analysis of Human Cortical Ne.pdf}
+}
+
+@article{Wetzel2017,
+  title = {Machine Learning of Explicit Order Parameters: {{From}} the {{Ising}} Model to {{SU}}(2) Lattice Gauge Theory},
+  volume = {96},
+  shorttitle = {Machine Learning of Explicit Order Parameters},
+  doi = {10.1103/PhysRevB.96.184410},
+  abstract = {We present a solution to the problem of interpreting neural networks classifying phases of matter. We devise a procedure for reconstructing the decision function of an artificial neural network as a simple function of the input, provided the decision function is sufficiently symmetric. In this case one can easily deduce the quantity by which the neural network classifies the input. The method is applied to the Ising model and SU(2) lattice gauge theory. In both systems we deduce the explicit expressions of the order parameters from the decision functions of the neural networks. We assume no prior knowledge about the Hamiltonian or the order parameters except Monte Carlo\textendash{}sampled configurations.},
+  number = {18},
+  journal = {Physical Review B},
+  author = {Wetzel, Sebastian J. and Scherzer, Manuel},
+  month = nov,
+  year = {2017},
+  pages = {184410}
+}
+
+@article{Koch-Janusz2018,
+  title = {Mutual Information, Neural Networks and the Renormalization Group},
+  copyright = {2018 The Author(s)},
+  issn = {1745-2481},
+  doi = {10.1038/s41567-018-0081-4},
+  abstract = {Finding the relevant degrees of freedom of a system is a key step in any renormalization group procedure. But this can be difficult, particularly in strongly interacting systems. A machine-learning algorithm proves adept at identifying them for us.},
+  language = {en},
+  journal = {Nature Physics},
+  author = {Koch-Janusz, Maciej and Ringel, Zohar},
+  month = mar,
+  year = {2018},
+  pages = {1},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JUUJ6GNX\\Koch-Janusz and Ringel - 2018 - Mutual information, neural networks and the renorm.pdf}
+}
+
+@article{Kadirvelu2017,
+  title = {Inferring Structural Connectivity Using {{Ising}} Couplings in Models of Neuronal Networks},
+  volume = {7},
+  issn = {2045-2322},
+  doi = {10.1038/s41598-017-05462-2},
+  abstract = {Functional connectivity metrics have been widely used to infer the underlying structural connectivity in neuronal networks. Maximum entropy based Ising models have been suggested to discount the effect of indirect interactions and give good results in inferring the true anatomical connections. However, no benchmarking is currently available to assess the performance of Ising couplings against other functional connectivity metrics in the microscopic scale of neuronal networks through a wide set of network conditions and network structures. In this paper, we study the performance of the Ising model couplings to infer the synaptic connectivity in in silico networks of neurons and compare its performance against partial and cross-correlations~for different correlation levels, firing rates, network sizes, network densities, and topologies. Our results show that the relative performance amongst the three functional connectivity metrics depends primarily on the network correlation levels. Ising couplings detected the most structural links at very weak network correlation levels, and partial correlations outperformed Ising couplings and cross-correlations at strong correlation levels. The result was consistent across varying firing rates, network sizes, and topologies. The findings of this paper serve as a guide in choosing the right functional connectivity tool to reconstruct the structural connectivity.},
+  journal = {Scientific Reports},
+  author = {Kadirvelu, Balasundaram and Hayashi, Yoshikatsu and Nasuto, Slawomir J.},
+  month = aug,
+  year = {2017},
+  file = {C:\\Users\\saram\\Zotero\\storage\\CQGGQ6N9\\Kadirvelu et al. - 2017 - Inferring structural connectivity using Ising coup.pdf},
+  pmid = {28811468},
+  pmcid = {PMC5557813}
+}
+
+@article{Tkacik2006,
+  archivePrefix = {arXiv},
+  eprinttype = {arxiv},
+  eprint = {q-bio/0611072},
+  title = {Ising Models for Networks of Real Neurons},
+  abstract = {Ising models with pairwise interactions are the least structured, or maximum-entropy, probability distributions that exactly reproduce measured pairwise correlations between spins. Here we use this equivalence to construct Ising models that describe the correlated spiking activity of populations of 40 neurons in the retina, and show that pairwise interactions account for observed higher-order correlations. By first finding a representative ensemble for observed networks we can create synthetic networks of 120 neurons, and find that with increasing size the networks operate closer to a critical point and start exhibiting collective behaviors reminiscent of spin glasses.},
+  journal = {arXiv:q-bio/0611072},
+  author = {Tkacik, Gasper and Schneidman, Elad and Berry II, Michael J. and Bialek, William},
+  month = nov,
+  year = {2006},
+  keywords = {Quantitative Biology - Neurons and Cognition,Condensed Matter - Disordered Systems and Neural Networks},
+  file = {C:\\Users\\saram\\Zotero\\storage\\IS2RKS35\\Tkacik et al. - 2006 - Ising models for networks of real neurons.pdf}
+}
+
+@article{Yu2008,
+  title = {A {{Small World}} of {{Neuronal Synchrony}}},
+  volume = {18},
+  issn = {1047-3211},
+  doi = {10.1093/cercor/bhn047},
+  abstract = {A small-world network has been suggested to be an efficient solution for achieving both modular and global processing\textemdash{}a property highly desirable for brain computations. Here, we investigated functional networks of cortical neurons using correlation analysis to identify functional connectivity. To reconstruct the interaction network, we applied the Ising model based on the principle of maximum entropy. This allowed us to assess the interactions by measuring pairwise correlations and to assess the strength of coupling from the degree of synchrony. Visual responses were recorded in visual cortex of anesthetized cats, simultaneously from up to 24 neurons. First, pairwise correlations captured most of the patterns in the population's activity and, therefore, provided a reliable basis for the reconstruction of the interaction networks. Second, and most importantly, the resulting networks had small-world properties; the average path lengths were as short as in simulated random networks, but the clustering coefficients were larger. Neurons differed considerably with respect to the number and strength of interactions, suggesting the existence of ``hubs'' in the network. Notably, there was no evidence for scale-free properties. These results suggest that cortical networks are optimized for the coexistence of local and global computations: feature detection and feature integration or binding.},
+  language = {en},
+  number = {12},
+  journal = {Cerebral Cortex},
+  author = {Yu, Shan and Huang, Debin and Singer, Wolf and Nikoli{\'c}, Danko},
+  month = dec,
+  year = {2008},
+  pages = {2891-2901},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4DK2ILFA\\Yu et al. - 2008 - A Small World of Neuronal Synchrony.pdf}
+}
+
+@article{Pernice2013,
+  title = {Reconstruction of Sparse Connectivity in Neural Networks from Spike Train Covariances},
+  volume = {2013},
+  issn = {1742-5468},
+  doi = {10.1088/1742-5468/2013/03/P03008},
+  abstract = {The inference of causation from correlation is in general highly problematic. Correspondingly, it is difficult to infer the existence of physical synaptic connections between neurons from correlations in their activity. Covariances in neural spike trains and their relation to network structure have been the subject of intense research, both experimentally and theoretically. The influence of recurrent connections on covariances can be characterized directly in linear models, where connectivity in the network is described by a matrix of linear coupling kernels. However, as indirect connections also give rise to covariances, the inverse problem of inferring network structure from covariances can generally not be solved unambiguously.},
+  language = {en},
+  number = {03},
+  journal = {Journal of Statistical Mechanics: Theory and Experiment},
+  author = {Pernice, Volker and Rotter, Stefan},
+  month = mar,
+  year = {2013},
+  pages = {P03008},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JXAXRNUZ\\Pernice and Rotter - 2013 - Reconstruction of sparse connectivity in neural ne.pdf}
+}
+
+@article{Barton2013,
+  title = {Ising Models for Neural Activity Inferred via Selective Cluster Expansion: Structural and Coding Properties},
+  volume = {2013},
+  issn = {1742-5468},
+  shorttitle = {Ising Models for Neural Activity Inferred via Selective Cluster Expansion},
+  doi = {10.1088/1742-5468/2013/03/P03002},
+  abstract = {We describe the Selective Cluster Expansion (SCE) of the entropy, a method for inferring an Ising model which describes the correlated activity of populations of neurons [1, 2]. We re-analyze data obtained from multielectrode recordings performed in vitro on the retina and in vivo on the prefrontal cortex. Recorded population sizes N range from N = 37 to N = 117 neurons. We compare the SCE method with the simplest mean field methods (corresponding to a Gaussian model) and with regularizations which favor sparse networks (L1 norm) or penalize large couplings (L2 norm). The network of the strongest interactions inferred via mean field methods generally agree with those obtained from SCE. Reconstruction of the sampled moments of the distributions, corresponding to neuron spiking frequencies and pairwise correlations, and the prediction of higher moments including three-cell correlations and multi-neuron firing frequencies, is more difficult than determining the large-scale structure of the interaction network, and apart from a cortical recording in which the measured correlation indices are small, these goals are achieved with the SCE but not with mean field approaches. We also find differences in the inferred structure of retinal and cortical networks: inferred interactions tend to be more irregular and sparse for cortical data than for retinal data. This result may reflect the structure of the recording. As a consequence the SCE is more effective for retinal data when expanding the entropy with respect to a mean field reference S -SMF, while expansions of the entropy S alone perform better for cortical data.},
+  language = {en},
+  number = {03},
+  journal = {Journal of Statistical Mechanics: Theory and Experiment},
+  author = {Barton, John and Cocco, Simona},
+  month = mar,
+  year = {2013},
+  pages = {P03002},
+  file = {C:\\Users\\saram\\Zotero\\storage\\W7MDIHIM\\Barton and Cocco - 2013 - Ising models for neural activity inferred via sele.pdf}
+}
+
+@article{Kusmartsev,
+  title = {Ising Model \textendash{} {{An}} Analysis, from Opinions to Neuronal States.},
+  abstract = {Here we have developed a mathematical model of a random neuron network with two types of neurons: inhibitory and excitatory. Every neuron was modelled as a functional cell with three states, parallel to hyperpolarised, neutral and depolarised states in vivo. These either induce a signal or not into their postsynaptic partners. First a system including just one network was simulated numerically using the software developed in Python.},
+  language = {en},
+  author = {Kusmartsev, Vassili F and Zhang, W and Kusmartseva, A and Balanov, A and Janson, N and Kusmartsev, F V},
+  pages = {16},
+  file = {C:\\Users\\saram\\Zotero\\storage\\D97KR83D\\Kusmartsev et al. - Ising model – An analysis, from opinions to neuron.pdf}
+}
+
+@article{Capone2015,
+  title = {Inferring {{Synaptic Structure}} in {{Presence}} of {{Neural Interaction Time Scales}}},
+  volume = {10},
+  issn = {1932-6203},
+  doi = {10.1371/journal.pone.0118412},
+  abstract = {Biological networks display a variety of activity patterns reflecting a web of interactions that is complex both in space and time. Yet inference methods have mainly focused on reconstructing, from the network's activity, the spatial structure, by assuming equilibrium conditions or, more recently, a probabilistic dynamics with a single arbitrary time-step. Here we show that, under this latter assumption, the inference procedure fails to reconstruct the synaptic matrix of a network of integrate-and-fire neurons when the chosen time scale of interaction does not closely match the synaptic delay or when no single time scale for the interaction can be identified; such failure, moreover, exposes a distinctive bias of the inference method that can lead to infer as inhibitory the excitatory synapses with interaction time scales longer than the model's time-step. We therefore introduce a new two-step method, that first infers through cross-correlation profiles the delay-structure of the network and then reconstructs the synaptic matrix, and successfully test it on networks with different topologies and in different activity regimes. Although step one is able to accurately recover the delay-structure of the network, thus getting rid of any a priori guess about the time scales of the interaction, the inference method introduces nonetheless an arbitrary time scale, the time-bin dt used to binarize the spike trains. We therefore analytically and numerically study how the choice of dt affects the inference in our network model, finding that the relationship between the inferred couplings and the real synaptic efficacies, albeit being quadratic in both cases, depends critically on dt for the excitatory synapses only, whilst being basically independent of it for the inhibitory ones.},
+  language = {en},
+  number = {3},
+  journal = {PLOS ONE},
+  author = {Capone, Cristiano and Filosa, Carla and Gigante, Guido and Ricci-Tersenghi, Federico and Giudice, Paolo Del},
+  month = mar,
+  year = {2015},
+  keywords = {Synapses,Neurons,Neural networks,Membrane potential,Probability distribution,Action potentials,Decision making,Depression},
+  pages = {e0118412},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DLC2YEWT\\Capone et al. - 2015 - Inferring Synaptic Structure in Presence of Neural.pdf}
+}
+
+@article{Battaglia,
+  title = {Memory Consolidation in Rats},
+  language = {en},
+  author = {Battaglia, Peyrache},
+  pages = {28},
+  file = {C:\\Users\\saram\\Zotero\\storage\\27W238QB\\Battaglia - Memory consolidation in rats.pdf}
+}
+
+@article{Hennig,
+  title = {Maximum {{Entropy Models}} in {{Neuroscience}}},
+  language = {en},
+  author = {Hennig, Matthias},
+  pages = {26},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ILFH4ITN\\Hennig - Maximum Entropy Models in Neuroscience.pdf}
+}
+
+@article{Roudi2009,
+  title = {Ising Model for Neural Data: {{Model}} Quality and Approximate Methods for Extracting Functional Connectivity},
+  volume = {79},
+  issn = {1539-3755, 1550-2376},
+  shorttitle = {Ising Model for Neural Data},
+  doi = {10.1103/PhysRevE.79.051915},
+  language = {en},
+  number = {5},
+  journal = {Physical Review E},
+  author = {Roudi, Yasser and Tyrcha, Joanna and Hertz, John},
+  month = may,
+  year = {2009},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SLX2QLB4\\Roudi et al.  2009  Ising model for neural data Model quality and approximate methods for extracting functional connectivity.pdf}
+}
+
+@article{Roudi2009c,
+  title = {Statistical Physics of Pairwise Probability Models},
+  volume = {3},
+  issn = {1662-5188},
+  doi = {10.3389/neuro.10.022.2009},
+  abstract = {Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of data: knowledge of the means and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying and using pairwise models. We build on our previous work on the subject and study the relation between different methods for fitting these models and evaluating their quality. In particular, using data from simulated cortical networks we study how the quality of various approximate methods for inferring the parameters in a pairwise model depends on the time bin chosen for binning the data. We also study the effect of the size of the time bin on the model quality itself, again using simulated data. We show that using finer time bins increases the quality of the pairwise model. We offer new ways of deriving the expressions reported in our previous work for assessing the quality of pairwise models.},
+  language = {English},
+  journal = {Frontiers in Computational Neuroscience},
+  author = {Roudi, Yasser and Aurell, Erik and Hertz, John A.},
+  year = {2009},
+  keywords = {inference,inverse ising problem,maximum entropy distribution,pairwise models},
+  file = {C:\\Users\\saram\\Zotero\\storage\\GTD4M2WZ\\Roudi et al.  2009  Statistical physics of pairwise probability models.pdf}
+}
+
+@article{Hertz2010,
+  title = {Inferring Network Connectivity Using Kinetic {{Ising}} Models},
+  volume = {11},
+  issn = {1471-2202},
+  doi = {10.1186/1471-2202-11-S1-P51},
+  number = {1},
+  journal = {BMC Neuroscience},
+  author = {Hertz, John A. and Roudi, Yasser and Thorning, Andreas and Tyrcha, Joanna and Aurell, Erik and Zeng, Hong-Li},
+  month = jul,
+  year = {2010},
+  keywords = {Approximate Formula,Connection Strength,Firing Rate,Functional Connectivity,Ising Model},
+  pages = {P51},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PT6MA6VL\\Hertz et al.  2010  Inferring network connectivity using kinetic Ising models.pdf}
+}
+
+@article{Tyrcha2011,
+  title = {Network Inference from Non-Stationary Spike Trains},
+  volume = {12},
+  issn = {1471-2202},
+  doi = {10.1186/1471-2202-12-S1-P150},
+  language = {en},
+  number = {Suppl 1},
+  journal = {BMC Neuroscience},
+  author = {Tyrcha, Joanna and Roudi, Yasser and Hertz, John},
+  year = {2011},
+  pages = {P150},
+  file = {C:\\Users\\saram\\Zotero\\storage\\EFTUCCGZ\\Tyrcha et al.  2011  Network inference from non-stationary spike trains.pdf}
+}
+
+@article{Hertz2011,
+  archivePrefix = {arXiv},
+  eprinttype = {arxiv},
+  eprint = {1106.1752},
+  primaryClass = {cond-mat, q-bio},
+  title = {Ising {{Models}} for {{Inferring Network Structure From Spike Data}}},
+  abstract = {Now that spike trains from many neurons can be recorded simultaneously, there is a need for methods to decode these data to learn about the networks that these neurons are part of. One approach to this problem is to adjust the parameters of a simple model network to make its spike trains resemble the data as much as possible. The connections in the model network can then give us an idea of how the real neurons that generated the data are connected and how they influence each other. In this chapter we describe how to do this for the simplest kind of model: an Ising network. We derive algorithms for finding the best model connection strengths for fitting a given data set, as well as faster approximate algorithms based on mean field theory. We test the performance of these algorithms on data from model networks and experiments.},
+  journal = {arXiv:1106.1752 [cond-mat, q-bio]},
+  author = {Hertz, John and Roudi, Yasser and Tyrcha, Joanna},
+  month = jun,
+  year = {2011},
+  keywords = {Condensed Matter - Disordered Systems and Neural Networks,Quantitative Biology - Quantitative Methods},
+  file = {C:\\Users\\saram\\Zotero\\storage\\GESAR7UG\\Hertz et al.  2011  Ising Models for Inferring Network Structure From Spike Data.pdf}
+}
+
+@book{Quiroga2013,
+  title = {Principles of {{Neural Coding}}},
+  isbn = {978-1-4398-5331-3},
+  abstract = {Understanding how populations of neurons encode information is the challenge faced by researchers in the field of neural coding. Focusing on the many mysteries and marvels of the mind has prompted a prominent team of experts in the field to put their heads together and fire up a book on the subject. Simply titled Principles of Neural Coding, this book covers the complexities of this discipline. It centers on some of the major developments in this area and presents a complete assessment of how neurons in the brain encode information. The book collaborators contribute various chapters that describe results in different systems (visual, auditory, somatosensory perception, etc.) and different species (monkeys, rats, humans, etc). Concentrating on the recording and analysis of the firing of single and multiple neurons, and the analysis and recording of other integrative measures of network activity and network states\textemdash{}such as local field potentials or current source densities\textemdash{}is the basis of the introductory chapters.   Provides a comprehensive and interdisciplinary approach   Describes topics of interest to a wide range of researchers  The book then moves forward with the description of the principles of neural coding for different functions and in different species and concludes with theoretical and modeling works describing how information processing functions are implemented. The text not only contains the most important experimental findings, but gives an overview of the main methodological aspects for studying neural coding. In addition, the book describes alternative approaches based on simulations with neural networks and in silico modeling in this highly interdisciplinary topic. It can serve as an important reference to students and professionals.},
+  language = {en},
+  publisher = {{CRC Press}},
+  author = {Quiroga, Rodrigo Quian and Panzeri, Stefano},
+  month = may,
+  year = {2013},
+  keywords = {Medical / Biotechnology,Science / Life Sciences / Neuroscience,Technology \& Engineering / Biomedical}
+}
+
+@article{Zeng2013,
+  title = {Maximum {{Likelihood Reconstruction}} for {{Ising Models}} with {{Asynchronous Updates}}},
+  volume = {110},
+  doi = {10.1103/PhysRevLett.110.210601},
+  abstract = {We describe how the couplings in an asynchronous kinetic Ising model can be inferred. We consider two cases: one in which we know both the spin history and the update times and one in which we know only the spin history. For the first case, we show that one can average over all possible choices of update times to obtain a learning rule that depends only on spin correlations and can also be derived from the equations of motion for the correlations. For the second case, the same rule can be derived within a further decoupling approximation. We study all methods numerically for fully asymmetric Sherrington-Kirkpatrick models, varying the data length, system size, temperature, and external field. Good convergence is observed in accordance with the theoretical expectations.},
+  number = {21},
+  journal = {Physical Review Letters},
+  author = {Zeng, Hong-Li and Alava, Mikko and Aurell, Erik and Hertz, John and Roudi, Yasser},
+  month = may,
+  year = {2013},
+  pages = {210601},
+  file = {C:\\Users\\saram\\Zotero\\storage\\IFHG7AYC\\Zeng et al.  2013  Maximum Likelihood Reconstruction for Ising Models with Asynchronous Updates.pdf}
+}
+
+@article{Roudi2015a,
+  series = {Large-Scale Recording Technology (32)},
+  title = {Multi-Neuronal Activity and Functional Connectivity in Cell Assemblies},
+  volume = {32},
+  issn = {0959-4388},
+  doi = {10.1016/j.conb.2014.10.011},
+  abstract = {Our ability to collect large amounts of data from many cells has been paralleled by the development of powerful statistical models for extracting information from this data. Here we discuss how the activity of cell assemblies can be analyzed using these models, focusing on the generalized linear models and the maximum entropy models and describing a number of recent studies that employ these tools for analyzing multi-neuronal activity. We show results from simulations comparing inferred functional connectivity, pairwise correlations and the real synaptic connections in simulated networks demonstrating the power of statistical models in inferring functional connectivity. Further development of network reconstruction techniques based on statistical models should lead to more powerful methods of understanding functional anatomy of cell assemblies.},
+  journal = {Current Opinion in Neurobiology},
+  author = {Roudi, Yasser and Dunn, Benjamin and Hertz, John},
+  month = jun,
+  year = {2015},
+  pages = {38-44},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SRD8ZV8H\\Roudi et al.  2015  Multi-neuronal activity and functional connectivity in cell assemblies.pdf}
+}
+
+@article{Roudi2009d,
+  title = {Pairwise {{Maximum Entropy Models}} for {{Studying Large Biological Systems}}: {{When They Can Work}} and {{When They Can}}'t},
+  volume = {5},
+  issn = {1553-7358},
+  shorttitle = {Pairwise {{Maximum Entropy Models}} for {{Studying Large Biological Systems}}},
+  doi = {10.1371/journal.pcbi.1000380},
+  abstract = {One of the most critical problems we face in the study of biological systems is building accurate statistical descriptions of them. This problem has been particularly challenging because biological systems typically contain large numbers of interacting elements, which precludes the use of standard brute force approaches. Recently, though, several groups have reported that there may be an alternate strategy. The reports show that reliable statistical models can be built without knowledge of all the interactions in a system; instead, pairwise interactions can suffice. These findings, however, are based on the analysis of small subsystems. Here, we ask whether the observations will generalize to systems of realistic size, that is, whether pairwise models will provide reliable descriptions of true biological systems. Our results show that, in most cases, they will not. The reason is that there is a crossover in the predictive power of pairwise models: If the size of the subsystem is below the crossover point, then the results have no predictive power for large systems. If the size is above the crossover point, then the results may have predictive power. This work thus provides a general framework for determining the extent to which pairwise models can be used to predict the behavior of large biological systems. Applied to neural data, the size of most systems studied so far is below the crossover point.},
+  language = {en},
+  number = {5},
+  journal = {PLOS Computational Biology},
+  author = {Roudi, Yasser and Nirenberg, Sheila and Latham, Peter E.},
+  month = may,
+  year = {2009},
+  keywords = {Neurons,Neural networks,Probability distribution,Entropy,Distance measurement,Extrapolation,Information entropy,Perturbation (geology)},
+  pages = {e1000380},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8F3CWX8U\\Roudi et al.  2009  Pairwise Maximum Entropy Models for Studying Large Biological Systems When They Can Work and When They Can't.pdf}
+}
+
+@article{Tateno2005,
+  title = {Spatio-Temporal Cholinergic Modulation in Cultured Networks of Rat Cortical Neurons: {{Spontaneous}} Activity},
+  volume = {134},
+  issn = {03064522},
+  shorttitle = {Spatio-Temporal Cholinergic Modulation in Cultured Networks of Rat Cortical Neurons},
+  doi = {10.1016/j.neuroscience.2005.04.049},
+  abstract = {Activation of the cholinergic innervation of the cortex has been implicated in sensory processing, learning, and memory. At the cellular level, acetylcholine both increases excitability and depresses synaptic transmission, and its effects on network firing are hard to predict. We studied the effects of carbachol, a cholinergic agonist, on network firing in cultures of rat cortical neurons, using electrode arrays to monitor the activity of large numbers of neurons simultaneously. These cultures show stable spontaneous synchronized burst firing which propagates through dense synaptic connections. Carbachol (10 \textendash{}50 ␮M), acting through muscarinic receptors, was found to induce a switch to asynchronous single-spike firing and to result in a loss of regularity and fragmentation of the burst structure. To obtain a quantitative measure of cholinergic actions on cortical networks, we applied a cluster Poisson-process model to sets of paralleled spike-trains in the presence and absence of carbachol. This revealed that the time series can be well-characterized by such a simple model, consistent with the observed 1/f b-like spectra (0.04$<$b$<$2.08). After applying higher concentrations of carbachol the property of the spectra shifted toward a Poisson-process (white) spectrum. These results indicate that cholinergic neurotransmitters have a strong and reliable desynchronizing action on cortical neural activity. \textcopyright{} 2005 Published by Elsevier Ltd on behalf of IBRO.},
+  language = {en},
+  number = {2},
+  journal = {Neuroscience},
+  author = {Tateno, T. and Jimbo, Y. and Robinson, H.P.C.},
+  month = jan,
+  year = {2005},
+  pages = {425-437},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SSFSUC4H\\Tateno et al.  2005  Spatio-temporal cholinergic modulation in cultured networks of rat cortical neurons Spontaneous activity.pdf}
+}
+
+@article{Shlens2006,
+  title = {The {{Structure}} of {{Multi}}-{{Neuron Firing Patterns}} in {{Primate Retina}}},
+  volume = {26},
+  copyright = {Copyright \textcopyright{} 2006 Society for Neuroscience 0270-6474/06/268254-13\$15.00/0},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.1282-06.2006},
+  abstract = {Current understanding of many neural circuits is limited by our ability to explore the vast number of potential interactions between different cells. We present a new approach that dramatically reduces the complexity of this problem. Large-scale multi-electrode recordings were used to measure electrical activity in nearly complete, regularly spaced mosaics of several hundred ON and OFF parasol retinal ganglion cells in macaque monkey retina. Parasol cells exhibited substantial pairwise correlations, as has been observed in other species, indicating functional connectivity. However, pairwise measurements alone are insufficient to determine the prevalence of multi-neuron firing patterns, which would be predicted from widely diverging common inputs and have been hypothesized to convey distinct visual messages to the brain. The number of possible multi-neuron firing patterns is far too large to study exhaustively, but this problem may be circumvented if two simple rules of connectivity can be established: (1) multi-cell firing patterns arise from multiple pairwise interactions, and (2) interactions are limited to adjacent cells in the mosaic. Using maximum entropy methods from statistical mechanics, we show that pairwise and adjacent interactions accurately accounted for the structure and prevalence of multi-neuron firing patterns, explaining $\sim$98\% of the departures from statistical independence in parasol cells and $\sim$99\% of the departures that were reproducible in repeated measurements. This approach provides a way to define limits on the complexity of network interactions and thus may be relevant for probing the function of many neural circuits.},
+  language = {en},
+  number = {32},
+  journal = {Journal of Neuroscience},
+  author = {Shlens, Jonathon and Field, Greg D. and Gauthier, Jeffrey L. and Grivich, Matthew I. and Petrusca, Dumitru and Sher, Alexander and Litke, Alan M. and Chichilnisky, E. J.},
+  month = aug,
+  year = {2006},
+  keywords = {information theory,vision,synchrony,neural coding,correlated variability,retinal ganglion cell},
+  pages = {8254-8266},
+  file = {C:\\Users\\saram\\Zotero\\storage\\57K2XGMQ\\Shlens et al.  2006  The Structure of Multi-Neuron Firing Patterns in Primate Retina.pdf},
+  pmid = {16899720}
+}
+
+@article{Tavoni2016a,
+  title = {Neural Assemblies Revealed by Inferred Connectivity-Based Models of Prefrontal Cortex Recordings},
+  volume = {41},
+  issn = {0929-5313, 1573-6873},
+  doi = {10.1007/s10827-016-0617-5},
+  abstract = {We present two graphical model-based approaches to analyse the distribution of neural activities in the prefrontal cortex of behaving rats. The first method aims at identifying cell assemblies, groups of synchronously activating neurons possibly representing the units of neural coding and memory. A graphical (Ising) model distribution of snapshots of the neural activities, with an effective connectivity matrix reproducing the correlation statistics, is inferred from multi-electrode recordings, and then simulated in the presence of a virtual external drive, favoring high activity (multi-neuron) configurations. As the drive increases groups of neurons may activate together, and reveal the existence of cell assemblies. The identified groups are then showed to strongly coactivate in the neural spiking data and to be highly specific of the inferred connectivity network, which offers a sparse representation of the correlation pattern across neural cells. The second method relies on the inference of a Generalized Linear Model, in which spiking events are integrated over time by neurons through an effective connectivity matrix. The functional connectivity matrices inferred with the two approaches are compared. Sampling of the inferred GLM distribution allows us to study the spatio-temporal patterns of activation of neurons within the identified cell assemblies, particularly their activation order: the prevalence of one order with respect to the others is weak and reflects the neuron average firing rates and the strength of the largest effective connections. Other properties of the identified cell assemblies (spatial distribution of coactivation events and firing rates of coactivating neurons) are discussed.},
+  language = {en},
+  number = {3},
+  journal = {Journal of Computational Neuroscience},
+  author = {Tavoni, G. and Cocco, S. and Monasson, R.},
+  month = dec,
+  year = {2016},
+  pages = {269-293},
+  file = {C:\\Users\\saram\\Zotero\\storage\\EXUR4C4L\\Tavoni et al.  2016  Neural assemblies revealed by inferred connectivity-based models of prefrontal cortex recordings.pdf}
+}
+
+@article{Ferrari2016b,
+  title = {Learning Maximum Entropy Models from Finite-Size Data Sets: {{A}} Fast Data-Driven Algorithm Allows Sampling from the Posterior Distribution},
+  volume = {94},
+  issn = {2470-0045, 2470-0053},
+  shorttitle = {Learning Maximum Entropy Models from Finite-Size Data Sets},
+  doi = {10.1103/PhysRevE.94.023301},
+  language = {en},
+  number = {2},
+  journal = {Physical Review E},
+  author = {Ferrari, Ulisse},
+  month = aug,
+  year = {2016},
+  file = {C:\\Users\\saram\\Zotero\\storage\\9853T9BM\\Ferrari  2016  Learning maximum entropy models from finite-size data sets A fast data-driven algorithm allows sampling from the posterior distribution.pdf}
+}
+
+@article{Poli2016a,
+  title = {From Functional to Structural Connectivity Using Partial Correlation in Neuronal Assemblies},
+  volume = {13},
+  issn = {1741-2560, 1741-2552},
+  doi = {10.1088/1741-2560/13/2/026023},
+  abstract = {Objective. Our goal is to re-introduce an optimized version of the partial correlation to infer structural connections from functional-effective ones in dissociated neuronal cultures coupled to microelectrode arrays. Approach. We first validate our partialization procedure on in silico networks, mimicking different experimental conditions (i.e., different connectivity degrees and number of nodes) and comparing the partial correlation's performance with two gold-standard methods: cross-correlation and transfer entropy. Afterwards, to infer the structural connections in in vitro neuronal networks where the ground truth is unknown, we propose a thresholding heuristic approach. Then, to validate whether the partialization process correctly reconstructs macroscopic features of the network structure, we extract a modularity index from segregated in silico and in vitro models. Finally, as a case study, we apply our partialization procedure to analyze connectivity and topology on spontaneous developing and electrically stimulated in vitro cultures. Main results. In simulated networks, partial correlation outperforms cross-correlation and transfer entropy at low and medium connectivity degrees, not only in relatively small (60 nodes) but also in larger (120\textendash{}240 nodes) assemblies. Furthermore, partial correlation correctly identifies interconnected neuronal sub-populations and allows one to derive network topology in in vitro cortical networks. Significance. Our results support the idea that partial correlation is a good method for connectivity studies and can be applied to derive topological and structural features of neuronal assemblies.},
+  language = {en},
+  number = {2},
+  journal = {Journal of Neural Engineering},
+  author = {Poli, Daniele and Pastore, Vito Paolo and Martinoia, Sergio and Massobrio, Paolo},
+  month = apr,
+  year = {2016},
+  pages = {026023},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Z4G7FI67\\Poli et al.  2016  From functional to structural connectivity using partial correlation in neuronal assemblies.pdf}
+}
+
+@article{Lei2017,
+  title = {Ground Truth Bias in External Cluster Validity Indices},
+  volume = {65},
+  issn = {0031-3203},
+  doi = {10.1016/j.patcog.2016.12.003},
+  abstract = {External cluster validity indices (CVIs) are used to quantify the quality of a clustering by comparing the similarity between the clustering and a ground truth partition. However, some external CVIs show a biased behavior when selecting the most similar clustering. Users may consequently be misguided by such results. Recognizing and understanding the bias behavior of CVIs is therefore crucial.
+It has been noticed that, some external CVIs exhibit a preferential bias towards a larger or smaller number of clusters which is monotonic (directly or inversely) in the number of clusters in candidate partitions. This type of bias is caused by the functional form of the CVI model. For example, the popular Rand Index (RI) exhibits a monotone increasing (NCinc) bias, while the Jaccard Index (JI) index suffers from a monotone decreasing (NCdec) bias. This type of bias has been previously recognized in the literature.
+In this work, we identify a new type of bias arising from the distribution of the ground truth (reference) partition against which candidate partitions are compared. We call this new type of bias ground truth (GT) bias. This type of bias occurs if a change in the reference partition causes a change in the bias status (e.g., NCinc, NCdec) of a CVI. For example, NCinc bias in the RI can be changed to NCdec bias by skewing the distribution of clusters in the ground truth partition. It is important for users to be aware of this new type of biased behavior, since it may affect the interpretations of CVI results.
+The objective of this article is to study the empirical and theoretical implications of GT bias. To the best of our knowledge, this is the first extensive study of such a property for external CVIs. Our computational experiments show that 5 of 26 pair-counting based CVIs studied in this paper, which are all functions of the RI, exhibit GT bias. Following the numerical examples, we provide a theoretical analysis of GT bias based on the relationship between the RI and quadratic entropy. Specifically, we prove that the quadratic entropy of the ground truth partition provides a computable test which predicts the NC bias status of the RI.},
+  journal = {Pattern Recognition},
+  author = {Lei, Yang and Bezdek, James C. and Romano, Simone and Vinh, Nguyen Xuan and Chan, Jeffrey and Bailey, James},
+  month = may,
+  year = {2017},
+  keywords = {External cluster validity indices,Ground truth bias,Quadratic entropy,Rand index},
+  pages = {58-70},
+  file = {C:\\Users\\saram\\Zotero\\storage\\PSNKIWYI\\Lei et al.  2017  Ground truth bias in external cluster validity indices.pdf}
+}
+
+@article{Seeman2018,
+  title = {Sparse Recurrent Excitatory Connectivity in the Microcircuit of the Adult Mouse and Human Cortex},
+  copyright = {\textcopyright{} 2018, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
+  doi = {10.1101/292706},
+  abstract = {Understanding cortical function will require a detailed knowledge of local circuit properties. Many prior studies have addressed this problem; however, generating a comprehensive description of cortical networks requires a large-scale, systematic approach. To that end, the Allen Institute for Brain Science is engaged in a pipeline project using multipatch electrophysiology, supplemented with 2-photon optogenetics, to characterize local connectivity and synaptic signaling between major classes of neurons in the adult mouse primary visual cortex and neurosurgical samples from human cortex. We focus on producing results that are detailed enough for the generation of computational models and enable rigorous comparison with future studies. Here we report our examination of the intralaminar connectivity within each of several classes of excitatory neurons. We find that connections are sparse but present among all excitatory cell types and layers we sampled, with the most sparse connections in layers 5 and 6. Almost all synapses in mouse exhibited short-term depression with similar dynamics. Synaptic signaling between a subset of layer 2/3 neurons, however, exhibited facilitation. These results contribute to a body of evidence describing recurrent excitatory connectivity as a conserved feature of cortical microcircuits.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Seeman, Stephanie C. and Campagnola, Luke and Davoudian, Pasha A. and Hoggarth, Alex and Hage, Travis A. and Bosma-Moody, Alice and Baker, Christopher A. and Lee, Jung H. and Mihalas, Stefan and Teeter, Corinne and Ko, Andrew L. and Ojemann, Jeffrey G. and Gwinn, Ryder P. and Silbergeld, Daniel L. and Cobbs, Charles and Phillips, John and Lein, Ed and Murphy, Gabe and Koch, Christof and Zeng, Hongkui and Jarsky, Tim},
+  month = apr,
+  year = {2018},
+  pages = {292706},
+  file = {C:\\Users\\saram\\Zotero\\storage\\VEWUAYY2\\Seeman et al.  2018  Sparse recurrent excitatory connectivity in the microcircuit of the adult mouse and human cortex.pdf}
+}
+
+@article{Yadollahi2014,
+  title = {Acoustic {{Estimation}} of {{Neck Fluid Volume}}},
+  volume = {42},
+  copyright = {All rights reserved},
+  issn = {0090-6964, 1573-9686},
+  doi = {10.1007/s10439-014-1083-8},
+  abstract = {Recently we showed that fluid accumulation in the neck can narrow the upper airway (UA) and increase its collapsibility, which may exacerbate obstructive sleep apnea (OSA). However, the available methods for measuring neck fluid volume (NFV) are inconvenient and expensive. Narrowing of the UA due to fluid accumulation could change acoustic characteristics of respiratory sounds. In this study, we developed a novel approach for non-invasive estimation of NFV from acoustic measurements. Twenty-eight healthy subjects lay awake and supine for 90 min while NFV and tracheal sounds were measured simultaneously using bioimpedance and a microphone, respectively. Sets of tracheal sound features were calculated in time and frequency domains and were reduced using methods based on regression and minimum-redundancy-maximum-relevance. The resulting feature sets were applied to a multi-linear regression and a mixture-density neural network to estimate NFV. Our results show very small relative estimation errors of 1.25 and 3.23\%, based on the regression and neural network methods, respectively. These results support the practical application of this technology in diagnosing fluid accumulation in the neck and its possible contributions to the pathogenesis of OSA.},
+  language = {en},
+  number = {10},
+  journal = {Annals of Biomedical Engineering},
+  author = {Yadollahi, A. and Rudzicz, F. and Mahallati, S. and Coimbra, M. and Bradley, T. D.},
+  month = oct,
+  year = {2014},
+  pages = {2132-2142},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4FF4NS88\\Yadollahi et al.  2014  Acoustic Estimation of Neck Fluid Volume.pdf}
+}
+
+@article{Mahallati2016,
+  title = {Multisegment {{Kinematics}} of the {{Spinal Column}}: {{Soft Tissue Artifacts Assessment}}},
+  volume = {138},
+  copyright = {All rights reserved},
+  issn = {0148-0731},
+  shorttitle = {Multisegment {{Kinematics}} of the {{Spinal Column}}},
+  doi = {10.1115/1.4033545},
+  number = {7},
+  journal = {Journal of Biomechanical Engineering},
+  author = {Mahallati, Sara and Rouhani, Hossein and Preuss, Richard and Masani, Kei and Popovic, Milos R.},
+  month = jul,
+  year = {2016},
+  pages = {071003},
+  file = {C:\\Users\\saram\\Zotero\\storage\\HTSYWVDD\\Mahallati et al.  2016  Multisegment Kinematics of the Spinal Column Soft Tissue Artifacts Assessment.pdf}
+}
+
+@article{Rouhani2015,
+  title = {Sensitivity of {{Intersegmental Angles}} of the {{Spinal Column}} to {{Errors Due}} to {{Marker Misplacement}}},
+  volume = {137},
+  copyright = {All rights reserved},
+  issn = {0148-0731},
+  doi = {10.1115/1.4030406},
+  abstract = {The ranges of angular motion measured using multisegmented spinal column models are typically small, meaning that minor experimental errors can potentially affect the reliability of these measures. This study aimed to investigate the sensitivity of the 3D intersegmental angles, measured using a multisegmented spinal column model, to errors due to marker misplacement. Eleven healthy subjects performed trunk bending in five directions. Six cameras recorded the trajectory of 22 markers, representing seven spinal column segments. Misplacement error for each marker was modeled as a Gaussian function with a standard deviation of 6\,mm, and constrained to a maximum value of 12\,mm in each coordinate across the skin. The sensitivity of 3D intersegmental angles to these marker misplacement errors, added to the measured data, was evaluated. The errors in sagittal plane motions resulting from marker misplacement were small (RMS error less than 3.2\,deg and relative error in the angular range less than 15\%) during the five trunk bending direction. The errors in the frontal and transverse plane motions, induced by marker misplacement, however, were large (RMS error up to 10.2\,deg and relative error in the range up to 58\%), especially during trunk bending in anterior, anterior-left, and anterior-right directions, and were often comparable in size to the intersubject variability for those motions. The induced errors in the frontal and transverse plane motions tended to be the greatest at the intersegmental levels in the lower lumbar region. These observations questioned reliability of angle measures in the frontal and transverse planes particularly in the lower lumbar region during trunk bending in anterior direction, and thus did not recommend interpreting these measures for clinical evaluation and decision-making.},
+  number = {7},
+  journal = {Journal of Biomechanical Engineering},
+  author = {Rouhani, Hossein and Mahallati, Sara and Preuss, Richard and Masani, Kei and Popovic, Milos R.},
+  month = jul,
+  year = {2015},
+  pages = {074502-074502-6},
+  file = {C:\\Users\\saram\\Zotero\\storage\\NH6SR8JS\\Rouhani et al.  2015  Sensitivity of Intersegmental Angles of the Spinal Column to Errors Due to Marker Misplacement.pdf}
+}
+
+@article{Mahallati2018a,
+  title = {Cluster {{Tendency Assessment}} in {{Neuronal Spike Data}}},
+  copyright = {\textcopyright{} 2018, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
+  doi = {10.1101/285064},
+  abstract = {Sorting spikes from extracellular recording into clusters associated with distinct single units (putative neurons) is a fundamental step in analyzing neuronal populations. Such spike sorting is intrinsically unsupervised, as the number of neurons are not known a priori. Clustering can be performed using either the full dimensional waveforms or on low-dimensional representations obtained by dimensionality reduction or feature extraction methods. In this paper, we evaluate the utility of several methods for providing lower dimensional visualization of the cluster structure and on subsequent spike clustering. Experimental results are conducted on two datasets with ground truth labels. We also introduce a visualization technique called improved visual assessment of cluster tendency (iVAT) to estimate possible cluster structures in data without the need for dimensionality reduction. In data with a relatively small number of clusters, iVAT is beneficial in estimating the number of clusters to inform the initialization of clustering algorithms. With larger numbers of clusters, iVAT gives a useful estimate of the coarse cluster structure but sometimes fails to indicate the presumptive number of clusters. We show that noise associated with recording extracellular neuronal potentials can disrupt computational clustering schemes, highlighting the benefit of probabilistic clustering models. Results show that t-Distributed Stochastic Neighbor Embedding (t-SNE) feature extraction provides representations of the data that yield more accurate clustering than low-dimensional representations obtained with four other methods. Moreover, the clustering obtained using t-SNE representation outperforms clustering within the other four feature spaces and also with algorithms operating directly within the input dimensional space.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Mahallati, Sara and Bezdek, James C. and Popovic, Milos R. and Valiante, Taufik},
+  month = apr,
+  year = {2018},
+  pages = {285064},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2V8E7GG5\\Mahallati et al. - 2018 - Cluster Tendency Assessment in Neuronal Spike Data.pdf}
+}
+
+@incollection{Wibral2014,
+  series = {Understanding Complex Systems},
+  title = {Transfer {{Entropy}} in {{Neuroscience}}},
+  isbn = {978-3-642-54473-6 978-3-642-54474-3},
+  abstract = {Information transfer is a key component of information processing, next to information storage and modification. Information transfer can be measured by a variety of directed informationmeasures of which transfer entropy is themost popular, andmost principled one. This chapter presents the basic concepts behind transfer entropy in an intuitive fashion, including graphical depictions of the key concepts. It also includes a special section devoted to the correct interpretation of the measure, especially with respect to concepts of causality. The chapter also provides an overview of estimation techniques for transfer entropy and pointers to popular open source toolboxes. It also introduces recent extensions of transfer entropy that serve to estimate delays involved in information transfer in a network. By touching upon alternative measures of information transfer, such as Massey's directed information transfer and Runge's momentary information transfer, it may serve as a frame of reference for more specialised treatments and as an overview over the field of studies in information transfer in general.},
+  language = {en},
+  booktitle = {Directed {{Information Measures}} in {{Neuroscience}}},
+  publisher = {{Springer, Berlin, Heidelberg}},
+  author = {Wibral, Michael and Vicente, Raul and Lindner, Michael},
+  year = {2014},
+  pages = {3-36},
+  file = {C:\\Users\\saram\\Zotero\\storage\\XZLCMQ98\\Wibral et al.  2014  Transfer Entropy in Neuroscience.pdf},
+  doi = {10.1007/978-3-642-54474-3_1}
+}
+
+@article{Kutzing2011,
+  title = {Measurement of {{Synchronous Activity}} by {{Microelectrode Arrays Uncovers Differential Effects}} of {{Sublethal}} and {{Lethal Glutamate Concentrations}} on {{Cortical Neurons}}},
+  volume = {39},
+  issn = {0090-6964, 1573-9686},
+  doi = {10.1007/s10439-011-0319-0},
+  abstract = {We grew cultures of rat cortical cells on microelectrode arrays to investigate the effects of glutamate-mediated neurotoxicity as a model of traumatic brain injury. Treatment with two different concentrations of glutamate, 175 and 250 $\mu$M, led to different outcomes. Cultures treated with 250 $\mu$M glutamate suffered a loss in overall activity that was not seen in cultures treated with 175 $\mu$M glutamate. An analysis of the changes in the synchronization of action potential firing between electrodes, however, revealed a loss of synchronization in subsets of electrode pairs treated with both the higher and lower concentrations of glutamate. We found that this loss of action potential synchronization was dependent on the initial amount of synchronization prior to injury. Finally, our data suggest that the synchronization of electrical activity as well as the susceptibility to loss of firing synchrony is independent of the distance between neurons in a network.},
+  language = {en},
+  number = {8},
+  journal = {Annals of Biomedical Engineering},
+  author = {Kutzing, Melinda K. and Luo, Vincent and Firestein, Bonnie L.},
+  month = aug,
+  year = {2011},
+  pages = {2252-2262},
+  file = {C:\\Users\\saram\\Zotero\\storage\\BUUEPYUL\\Kutzing et al.  2011  Measurement of Synchronous Activity by Microelectrode Arrays Uncovers Differential Effects of Sublethal and Lethal Glutamate Concentrations on Cortical Neurons.pdf}
+}
+
+@article{Bonzano2006,
+  title = {Effects of {{NMDA}} and Non-{{NMDA}} Receptors Antagonists on the Dynamic Behavior of Cultured Cortical Networks},
+  volume = {69},
+  issn = {09252312},
+  doi = {10.1016/j.neucom.2005.11.014},
+  abstract = {Non-conventional electrophysiological techniques allow screening the activity of nerve cells in spontaneous and stimulus-evoked conditions. Activity patterns in cultured networks of cortical neurons from rat embryos were investigated at different days in vitro (DIV), by a multi-site recording technique based on planar microelectrode arrays (MEAs). Transitions in neuronal dynamics of the networks were induced by administering antagonists of NMDA and non-NMDA receptors. Neurochemical effects on synaptic transmission were evaluated analyzing the network activity by using ad hoc developed algorithms. A decrease in excitatory synaptic transmission corresponded to a decrease in spiking and bursting activity with a loss of synchronization.},
+  language = {en},
+  number = {16-18},
+  journal = {Neurocomputing},
+  author = {Bonzano, L. and Bove, M. and Martinoia, S.},
+  month = oct,
+  year = {2006},
+  pages = {1897-1903},
+  file = {C:\\Users\\saram\\Zotero\\storage\\22IPJUKM\\Bonzano et al.  2006  Effects of NMDA and non-NMDA receptors antagonists on the dynamic behavior of cultured cortical networks.pdf}
+}
+
+@article{Suresh2016,
+  title = {Network Burst Activity in Hippocampal Neuronal Cultures: The Role of Synaptic and Intrinsic Currents},
+  volume = {115},
+  issn = {0022-3077},
+  shorttitle = {Network Burst Activity in Hippocampal Neuronal Cultures},
+  doi = {10.1152/jn.00995.2015},
+  abstract = {The goal of this work was to define the contributions of intrinsic and synaptic mechanisms toward spontaneous network-wide bursting activity, observed in dissociated rat hippocampal cell cultures. This network behavior is typically characterized by short-duration bursts, separated by order of magnitude longer interburst intervals. We hypothesize that while short-timescale synaptic processes modulate spectro-temporal intraburst properties and network-wide burst propagation, much longer timescales of intrinsic membrane properties such as persistent sodium (Nap) currents govern burst onset during interburst intervals. To test this, we used synaptic receptor antagonists picrotoxin, 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX), and 3-(2-carboxypiperazine-4-yl)propyl-1-phosphonate (CPP) to selectively block GABAA, AMPA, and NMDA receptors and riluzole to selectively block Nap channels. We systematically compared intracellular activity (recorded with patch clamp) and network activity (recorded with multielectrode arrays) in eight different synaptic connectivity conditions: GABAA + NMDA + AMPA, NMDA + AMPA, GABAA + AMPA, GABAA + NMDA, AMPA, NMDA, GABAA, and all receptors blocked. Furthermore, we used mixed-effects modeling to quantify the aforementioned independent and interactive synaptic receptor contributions toward spectro-temporal burst properties including intraburst spike rate, burst activity index, burst duration, power in the local field potential, network connectivity, and transmission delays. We found that blocking intrinsic Nap currents completely abolished bursting activity, demonstrating their critical role in burst onset within the network. On the other hand, blocking different combinations of synaptic receptors revealed that spectro-temporal burst properties are uniquely associated with synaptic functionality and that excitatory connectivity is necessary for the presence of network-wide bursting. In addition to confirming the critical contribution of direct excitatory effects, mixed-effects modeling also revealed distinct combined (nonlinear) contributions of excitatory and inhibitory synaptic activity to network bursting properties.},
+  number = {6},
+  journal = {Journal of Neurophysiology},
+  author = {Suresh, Jyothsna and Radojicic, Mihailo and Pesce, Lorenzo L. and Bhansali, Anita and Wang, Janice and Tryba, Andrew K. and Marks, Jeremy D. and {van Drongelen}, Wim},
+  month = mar,
+  year = {2016},
+  pages = {3073-3089},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6XVC52LW\\Suresh et al.  2016  Network burst activity in hippocampal neuronal cultures the role of synaptic and intrinsic currents.pdf}
+}
+
+@article{Cocco2009,
+  title = {Neuronal Couplings between Retinal Ganglion Cells Inferred by Efficient Inverse Statistical Physics Methods},
+  volume = {106},
+  copyright = {\textcopyright{} 2009},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.0906705106},
+  abstract = {Complexity of neural systems often makes impracticable explicit measurements of all interactions between their constituents. Inverse statistical physics approaches, which infer effective couplings between neurons from their spiking activity, have been so far hindered by their computational complexity. Here, we present 2 complementary, computationally efficient inverse algorithms based on the Ising and ``leaky integrate-and-fire'' models. We apply those algorithms to reanalyze multielectrode recordings in the salamander retina in darkness and under random visual stimulus. We find strong positive couplings between nearby ganglion cells common to both stimuli, whereas long-range couplings appear under random stimulus only. The uncertainty on the inferred couplings due to limitations in the recordings (duration, small area covered on the retina) is discussed. Our methods will allow real-time evaluation of couplings for large assemblies of neurons.},
+  language = {en},
+  number = {33},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Cocco, Simona and Leibler, Stanislas and Monasson, R{\'e}mi},
+  month = aug,
+  year = {2009},
+  keywords = {inference and inverse problems,multielectrode recordings,neural couplings,from Ferrari2016},
+  pages = {14058-14062},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4WCQPUVY\\Cocco et al.  2009  Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods.pdf},
+  pmid = {19666487}
+}
+
+@article{Mora2015,
+  title = {Dynamical {{Criticality}} in the {{Collective Activity}} of a {{Population}} of {{Retinal Neurons}}},
+  volume = {114},
+  issn = {0031-9007, 1079-7114},
+  doi = {10.1103/PhysRevLett.114.078105},
+  language = {en},
+  number = {7},
+  journal = {Physical Review Letters},
+  author = {Mora, Thierry and Deny, St{\'e}phane and Marre, Olivier},
+  month = feb,
+  year = {2015},
+  keywords = {from Ferrari2016},
+  file = {C:\\Users\\saram\\Zotero\\storage\\WT2EAN4H\\Mora et al.  2015  Dynamical Criticality in the Collective Activity of a Population of Retinal Neurons.pdf}
+}
+
+@article{Tavoni2015,
+  title = {Inferred {{Model}} of the {{Prefrontal Cortex Activity Unveils Cell Assemblies}} and {{Memory Replay}}},
+  copyright = {\textcopyright{} 2015, Published by Cold Spring Harbor Laboratory Press. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
+  doi = {10.1101/028316},
+  abstract = {Cell assemblies are thought to be the units of information representation in the brain, yet their detection from experimental data is arduous. Here, we propose to infer effective coupling networks and model distributions for the activity of simultaneously recorded neurons in prefrontal cortex, during the performance of a decision-making task, and during preceding and following sleep epochs. Our approach, inspired from statistical physics, allows us to define putative cell assemblies as the groups of co-activated neurons in the models of the three recorded epochs. It reveals the existence of task-related changes of the effective couplings between the sleep epochs. The assemblies which strongly coactivate during the task epoch are found to replay during subsequent sleep, in correspondence to the changes of the inferred network. Across sessions, a variety of different network scenarios is observed, providing insight in cell assembly formation and replay.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Tavoni, Gaia and Ferrari, Ulisse and Battaglia, Francesco Paolo and Cocco, Simona and Monasson, R{\'e}mi},
+  month = oct,
+  year = {2015},
+  keywords = {from Ferrari2016},
+  pages = {028316},
+  file = {C:\\Users\\saram\\Zotero\\storage\\7AER27QW\\Tavoni et al.  2015  Inferred Model of the Prefrontal Cortex Activity Unveils Cell Assemblies and Memory Replay.pdf}
+}
+
+@article{Peyrache2009,
+  title = {Replay of Rule-Learning Related Neural Patterns in the Prefrontal Cortex during Sleep},
+  volume = {12},
+  copyright = {2009 Nature Publishing Group},
+  issn = {1546-1726},
+  doi = {10.1038/nn.2337},
+  abstract = {Slow-wave sleep (SWS) is important for memory consolidation. During sleep, neural patterns reflecting previously acquired information are replayed. One possible reason for this is that such replay exchanges information between hippocampus and neocortex, supporting consolidation. We recorded neuron ensembles in the rat medial prefrontal cortex (mPFC) to study memory trace reactivation during SWS following learning and execution of cross-modal strategy shifts. In general, reactivation of learning-related patterns occurred in distinct, highly synchronized transient bouts, mostly simultaneous with hippocampal sharp wave/ripple complexes (SPWRs), when hippocampal ensemble reactivation and cortico-hippocampal interaction is enhanced. During sleep following learning of a new rule, mPFC neural patterns that appeared during response selection replayed prominently, coincident with hippocampal SPWRs. This was learning dependent, as the patterns appeared only after rule acquisition. Therefore, learning, or the resulting reliable reward, influenced which patterns were most strongly encoded and successively reactivated in the hippocampal/prefrontal network.},
+  language = {en},
+  number = {7},
+  journal = {Nature Neuroscience},
+  author = {Peyrache, Adrien and Khamassi, Mehdi and Benchenane, Karim and Wiener, Sidney I. and Battaglia, Francesco P.},
+  month = jul,
+  year = {2009},
+  pages = {919-926},
+  file = {C:\\Users\\saram\\Zotero\\storage\\6NQI3AB2\\Peyrache et al.  2009  Replay of rule-learning related neural patterns in the prefrontal cortex during sleep.pdf}
+}
+
+@incollection{Gilbert2018,
+  address = {San Diego},
+  title = {Chapter 3 - {{The Allen Brain Atlas}}: {{Toward Understanding Brain Behavior}} and {{Function Through Data Acquisition}}, {{Visualization}}, {{Analysis}}, and {{Integration}}},
+  isbn = {978-0-12-804078-2},
+  shorttitle = {Chapter 3 - {{The Allen Brain Atlas}}},
+  abstract = {The Allen Institute for Brain Science was created in 2003 to produce standardized, systematic surveys of gene expression across development in mouse, macaque, and human brains. While the high-throughput method used to collect these data was in itself innovative, publishing the data along with informatics tools designed to visualize and analyze these data in an open science framework created a resource used by neuroscientists globally. Since 2010 the Allen Institute for Brain Science has focused on producing publicly available resources designed to answer more complex questions aimed at addressing brain connectivity and function. This chapter is a review of the data and informatics resources available to the public, as well as a discourse on the power of integrating data modalities to understand and discover fundamental brain properties.},
+  booktitle = {Molecular-{{Genetic}} and {{Statistical Techniques}} for {{Behavioral}} and {{Neural Research}}},
+  publisher = {{Academic Press}},
+  author = {Gilbert, Terri L. and Ng, Lydia},
+  editor = {Gerlai, Robert T.},
+  year = {2018},
+  keywords = {Allen Brain Atlas,Brain observatory,Connectivity,Human brain,Mouse brain,Neuroinformatics},
+  pages = {51-72},
+  file = {C:\\Users\\saram\\Zotero\\storage\\R7H249VU\\Gilbert and Ng - 2018 - Chapter 3 - The Allen Brain Atlas Toward Understa.pdf},
+  doi = {10.1016/B978-0-12-804078-2.00003-9}
+}
+
+@article{Ganmor2011,
+  title = {Sparse Low-Order Interaction Network Underlies a Highly Correlated and Learnable Neural Population Code},
+  volume = {108},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.1019641108},
+  language = {en},
+  number = {23},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Ganmor, E. and Segev, R. and Schneidman, E.},
+  month = jun,
+  year = {2011},
+  pages = {9679-9684},
+  file = {C:\\Users\\saram\\Zotero\\storage\\XBH4P4C3\\Ganmor et al.  2011  Sparse low-order interaction network underlies a highly correlated and learnable neural population code.pdf}
+}
+
+@article{Izhikevich2004,
+  title = {Which {{Model}} to {{Use}} for {{Cortical Spiking Neurons}}?},
+  volume = {15},
+  issn = {1045-9227},
+  doi = {10.1109/TNN.2004.832719},
+  abstract = {We discuss the biological plausibility and computational efficiency of some of the most useful models of spiking and bursting neurons. We compare their applicability to large-scale simulations of cortical neural networks.},
+  language = {en},
+  number = {5},
+  journal = {IEEE Transactions on Neural Networks},
+  author = {Izhikevich, E.M.},
+  month = sep,
+  year = {2004},
+  pages = {1063-1070},
+  file = {C:\\Users\\saram\\Zotero\\storage\\43HIT4AK\\Izhikevich  2004  Which Model to Use for Cortical Spiking Neurons.pdf}
+}
+
+@book{Cajal1988,
+  address = {New York},
+  edition = {1 edition},
+  title = {Cajal on the {{Cerebral Cortex}}: {{An Annotated Translation}} of the {{Complete Writings}}},
+  isbn = {978-0-19-505280-0},
+  shorttitle = {Cajal on the {{Cerebral Cortex}}},
+  abstract = {This is the first English-language publication of the complete works of the great Spanish neurohistologist, Santiago Ramon y Cajal, on the cerebral cortex. The new translations include all Cajal's very early contributions on the cortex of small mammals, relevant chapters from his definitive textbook, and all his great works on the human cerebral cortex made at the peak of his career. The book also presents Cajal's surveys of cortical structure dating from his later years. It is extensively annotated, and the editors have verified and completed all Cajal's references. Special introductory chapters review the state of knowledge during each period covered, and the work concludes with an extensive essay on modern cortical neurohistology in which the quality and lasting significance of Cajal's contributions are highlighted.},
+  language = {English},
+  publisher = {{Oxford University Press}},
+  author = {y Cajal, Santiago Ramon and DeFelipe, Javier},
+  editor = {Jones, Edward G.},
+  month = dec,
+  year = {1988}
+}
+
+@article{Hodgkin1952,
+  title = {A Quantitative Description of Membrane Current and Its Application to Conduction and Excitation in Nerve},
+  volume = {117},
+  issn = {0022-3751},
+  number = {4},
+  journal = {The Journal of Physiology},
+  author = {Hodgkin, A. L. and Huxley, A. F.},
+  month = aug,
+  year = {1952},
+  pages = {500-544},
+  file = {C:\\Users\\saram\\Zotero\\storage\\MDSDNEIG\\Hodgkin and Huxley  1952  A quantitative description of membrane current and its application to conduction and excitation in nerve.pdf},
+  pmid = {12991237},
+  pmcid = {PMC1392413}
+}
+
+@book{Hebb1949,
+  title = {The {{Organization}} of {{Behavior}}: {{A Neuropsychological Theory}}},
+  isbn = {978-0-471-36727-7},
+  shorttitle = {The {{Organization}} of {{Behavior}}},
+  language = {en},
+  publisher = {{Wiley}},
+  author = {Hebb, Donald Olding},
+  month = jan,
+  year = {1949},
+  file = {C:\\Users\\saram\\Zotero\\storage\\9YVZWS4L\\The_Organization_of_Behavior-Donald_O._Hebb.pdf}
+}
+
+@article{Hill2005,
+  title = {Molecular Insights into Human Brain Evolution},
+  volume = {437},
+  copyright = {2005 Nature Publishing Group},
+  issn = {1476-4687},
+  doi = {10.1038/nature04103},
+  abstract = {Rapidly advancing knowledge of genome structure and sequence enables new means for the analysis of specific DNA changes associated with the differences between the human brain and that of other mammals. Recent studies implicate evolutionary changes in messenger RNA and protein expression levels, as well as DNA changes that alter amino acid sequences. We can anticipate having a systematic catalogue of DNA changes in the lineage leading to humans, but an ongoing challenge will be relating these changes to the anatomical and functional differences between our brain and that of our ancient and more recent ancestors.},
+  language = {en},
+  number = {7055},
+  journal = {Nature},
+  author = {Hill, Robert Sean and Walsh, Christopher A.},
+  month = sep,
+  year = {2005},
+  pages = {64-67},
+  file = {C:\\Users\\saram\\Zotero\\storage\\P6HP7L2E\\Hill and Walsh  2005  Molecular insights into human brain evolution.pdf}
+}
+
+@article{Geschwind2012,
+  title = {Neuroscience: {{Genes}} and Human Brain Evolution},
+  volume = {486},
+  copyright = {2012 Nature Publishing Group},
+  issn = {1476-4687},
+  shorttitle = {Neuroscience},
+  doi = {10.1038/nature11380},
+  abstract = {Several genes were duplicated during human evolution. It seems that one such duplication gave rise to a gene that may have helped to make human brains bigger and more adaptable than those of our ancestors.},
+  language = {en},
+  number = {7404},
+  journal = {Nature},
+  author = {Geschwind, Daniel H. and Konopka, Genevieve},
+  month = jun,
+  year = {2012},
+  pages = {481-482},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TTAC8JXF\\Geschwind and Konopka  2012  Neuroscience Genes and human brain evolution.pdf}
+}
+
+@article{Ryan2009,
+  title = {The Origin and Evolution of Synapses},
+  volume = {10},
+  copyright = {2009 Nature Publishing Group},
+  issn = {1471-0048},
+  doi = {10.1038/nrn2717},
+  abstract = {See more Darwin-related content in our Nature Publishing Group collection.Understanding the evolutionary origins of behaviour is a central aim in the study of biology and may lead to insights into human disorders. Synaptic transmission is observed in a wide range of invertebrate and vertebrate organisms and underlies their behaviour. Proteomic studies of the molecular components of the highly complex mammalian postsynaptic machinery point to an ancestral molecular machinery in unicellular organisms \textemdash{} the protosynapse \textemdash{} that existed before the evolution of metazoans and neurons, and hence challenges existing views on the origins of the brain. The phylogeny of the molecular components of the synapse provides a new model for studying synapse diversity and complexity, and their implications for brain evolution.},
+  language = {en},
+  number = {10},
+  journal = {Nature Reviews Neuroscience},
+  author = {Ryan, Tom{\'a}s J. and Grant, Seth G. N.},
+  month = oct,
+  year = {2009},
+  pages = {701-712},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QEYG2H6V\\Ryan and Grant  2009  The origin and evolution of synapses.pdf}
+}
+
+@book{Economo2008,
+  edition = {1st English ed},
+  title = {Atlas of Cytoarchitectonics of the Adult Human Cerebral Cortex},
+  isbn = {978-3-8055-8289-6},
+  abstract = {In 1 library. x, 182 p. : chiefly ill. (some col.) ; 49 cm. Cerebral cortex -- Atlases.},
+  language = {English},
+  publisher = {{Basil ; New York : Karger}},
+  author = {von Economo, Constantin Freiherr and Koskinas, Georg N and Triarhou, Lazaros Constantinos},
+  year = {2008}
+}
+
+@article{Nimchinsky1999,
+  title = {A Neuronal Morphologic Type Unique to Humans and Great Apes},
+  volume = {96},
+  copyright = {Copyright \textcopyright{} 1999, The National Academy of Sciences},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.96.9.5268},
+  abstract = {We report the existence and distribution of an unusual type of projection neuron, a large, spindle-shaped cell, in layer Vb of the anterior cingulate cortex of pongids and hominids. These spindle cells were not observed in any other primate species or any other mammalian taxa, and their volume was correlated with brain volume residuals, a measure of encephalization in higher primates. These observations are of particular interest when considering primate neocortical evolution, as they reveal possible adaptive changes and functional modifications over the last 15\textendash{}20 million years in the anterior cingulate cortex, a region that plays a major role in the regulation of many aspects of autonomic function and of certain cognitive processes. That in humans these unique neurons have been shown previously to be severely affected in the degenerative process of Alzheimer's disease suggests that some of the differential neuronal susceptibility that occurs in the human brain in the course of age-related dementing illnesses may have appeared only recently during primate evolution.},
+  language = {en},
+  number = {9},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Nimchinsky, Esther A. and Gilissen, Emmanuel and Allman, John M. and Perl, Daniel P. and Erwin, Joseph M. and Hof, Patrick R.},
+  month = apr,
+  year = {1999},
+  pages = {5268-5273},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DM2J9HLH\\Nimchinsky et al.  1999  A neuronal morphologic type unique to humans and great apes.pdf},
+  pmid = {10220455}
+}
+
+@article{Wang2015,
+  title = {A {{Subtype}} of {{Inhibitory Interneuron}} with {{Intrinsic Persistent Activity}} in {{Human}} and {{Monkey Neocortex}}},
+  volume = {10},
+  issn = {22111247},
+  doi = {10.1016/j.celrep.2015.02.018},
+  abstract = {A critical step in understanding the neural basis of human cognitive functions is to identify neuronal types in the neocortex. In this study, we performed whole-cell recording from human cortical slices and found a distinct subpopulation of neurons with intrinsic persistent activity that could be triggered by single action potentials (APs) but terminated by bursts of APs. This persistent activity was associated with a depolarizing plateau potential induced by the activation of a persistent Na+ current. Singlecell RT-PCR revealed that these neurons were inhibitory interneurons. This type of neuron was found in different cortical regions, including temporal, frontal, occipital, and parietal cortices in human and also in frontal and temporal lobes of nonhuman primate but not in rat cortical tissues, suggesting that it could be unique to primates. The characteristic persistent activity in these inhibitory interneurons may contribute to the regulation of pyramidal cell activity and participate in cortical processing.},
+  language = {en},
+  number = {9},
+  journal = {Cell Reports},
+  author = {Wang, Bo and Yin, Luping and Zou, Xiaolong and Ye, Min and Liu, Yaping and He, Ting and Deng, Suixin and Jiang, Yanbo and Zheng, Rui and Wang, Yun and Yang, Mingpo and Lu, Haidong and Wu, Si and Shu, Yousheng},
+  month = mar,
+  year = {2015},
+  pages = {1450-1458},
+  file = {C:\\Users\\saram\\Zotero\\storage\\4W5W7DDL\\Wang et al.  2015  A Subtype of Inhibitory Interneuron with Intrinsic Persistent Activity in Human and Monkey Neocortex.pdf}
+}
+
+@article{Oberheim2009,
+  title = {Uniquely Hominid Features of Adult Human Astrocytes},
+  volume = {29},
+  issn = {0270-6474},
+  doi = {10.1523/JNEUROSCI.4707-08.2009},
+  abstract = {Defining the microanatomic differences between the human brain and that of other mammals is key to understanding its unique computational power. While much effort has been devoted to comparative studies of neurons, astrocytes have received far less attention. We report here that protoplasmic astrocytes in human neocortex are 2.6 fold larger in diameter and extend 10-fold more GFAP+ primary processes than their rodent counterparts. In cortical slices prepared from acutely resected surgical tissue, protoplasmic astrocytes propagate Ca2+ waves with a speed of 36 $\mathrm{\mu}$m/second, approximately 4-fold faster than rodent. Human astrocytes also transiently increase cystosolic Ca2+ in response to glutamatergic and purinergic receptor agonists. The human neocortex also harbors several anatomically-defined subclasses of astrocytes not represented in rodents. These include a population of astrocytes that reside in layers 5\textendash{}6 and extend long fibers characterized by regularly spaced varicosities. Another specialized type of astrocyte, the interlaminar astrocyte, abundantly populates the superficial cortical layers and extends long processes without varicosities to cortical layers 3 and 4. Human fibrous astrocytes resemble their rodent counterpart, but are larger in diameter. Thus, human cortical astrocytes are both larger, and structurally both more complex and more diverse, than those of rodents. On this basis, we posit that this astrocytic complexity has permitted the increased functional competence of the adult human brain.},
+  number = {10},
+  journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
+  author = {Oberheim, Nancy Ann and Takano, Takahiro and Han, Xiaoning and He, Wei and Lin, Jane HC and Wang, Fushun and Xu, Qiwu and Wyatt, Jeffrey D. and Pilcher, Webster and Ojemann, Jeffrey G and Ransom, Bruce R and Goldman, Steven A and Nedergaard, Maiken},
+  month = mar,
+  year = {2009},
+  pages = {3276},
+  file = {C:\\Users\\saram\\Zotero\\storage\\8HGDE6XP\\Oberheim et al.  2009  Uniquely hominid features of adult human astrocytes.pdf},
+  pmid = {19279265},
+  pmcid = {PMC2819812}
+}
+
+@article{Testa-Silva2014,
+  title = {High {{Bandwidth Synaptic Communication}} and {{Frequency Tracking}} in {{Human Neocortex}}},
+  volume = {12},
+  issn = {1545-7885},
+  doi = {10.1371/journal.pbio.1002007},
+  abstract = {Because of fast recovery from synaptic depression and fast-initiated action potentials, neuronal information transfer can have a substantially higher bandwidth in human neocortical circuits than in those of rodents.},
+  language = {en},
+  number = {11},
+  journal = {PLOS Biology},
+  author = {Testa-Silva, Guilherme and Verhoog, Matthijs B. and Linaro, Daniele and de Kock, Christiaan P. J. and Baayen, Johannes C. and Meredith, Rhiannon M. and Zeeuw, Chris I. De and Giugliano, Michele and Mansvelder, Huibert D.},
+  month = nov,
+  year = {2014},
+  keywords = {Action potentials,Adults,Depression,Excitatory postsynaptic potentials,Membrane potential,Neurons,Rodents,Synapses},
+  pages = {e1002007},
+  file = {C:\\Users\\saram\\Zotero\\storage\\NXLRPUFZ\\Testa-Silva et al.  2014  High Bandwidth Synaptic Communication and Frequency Tracking in Human Neocortex.pdf}
+}
+
+@article{DeFelipe2011,
+  title = {The {{Evolution}} of the {{Brain}}, the {{Human Nature}} of {{Cortical Circuits}}, and {{Intellectual Creativity}}},
+  volume = {5},
+  issn = {1662-5129},
+  doi = {10.3389/fnana.2011.00029},
+  abstract = {The tremendous expansion and the differentiation of the neocortex constitute two major events in the evolution of the mammalian brain. The increase in size and complexity of our brains opened the way to a spectacular development of cognitive and mental skills. This expansion during evolution facilitated the addition of microcircuits with a similar basic structure, which increased the complexity of the human brain and contributed to its uniqueness. However, fundamental differences even exist between distinct mammalian species. Here, we shall discuss the issue of our humanity from a neurobiological and historical perspective.},
+  journal = {Frontiers in Neuroanatomy},
+  author = {DeFelipe, Javier},
+  month = may,
+  year = {2011},
+  file = {C:\\Users\\saram\\Zotero\\storage\\AHB3BV7G\\DeFelipe  2011  The Evolution of the Brain, the Human Nature of Cortical Circuits, and Intellectual Creativity.pdf},
+  pmid = {21647212},
+  pmcid = {PMC3098448}
+}
+
+@article{Florez2015,
+  title = {In {{Vitro Recordings}} of {{Human Neocortical Oscillations}}},
+  volume = {25},
+  issn = {1460-2199, 1047-3211},
+  doi = {10.1093/cercor/bht235},
+  language = {en},
+  number = {3},
+  journal = {Cerebral Cortex},
+  author = {Florez, C.M. and McGinn, R.J. and Lukankin, V. and Marwa, I. and Sugumar, S. and Dian, J. and Hazrati, L.-N. and Carlen, P.L. and Zhang, L. and Valiante, T.A.},
+  month = mar,
+  year = {2015},
+  pages = {578-597},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QJ5IZY6C\\Florez et al.  2015  In Vitro Recordings of Human Neocortical Oscillations.pdf}
+}
+
+@article{Migliore2005,
+  title = {An Integrated Approach to Classifying Neuronal Phenotypes},
+  volume = {6},
+  copyright = {2005 Nature Publishing Group},
+  issn = {1471-0048},
+  doi = {10.1038/nrn1769},
+  abstract = {Characterizing the functional phenotypes of neurons is essential for understanding how genotypes can be related to the neural basis of behaviour. Traditional classifications of neurons by single features (such as morphology or firing behaviour) are increasingly inadequate for reflecting functional phenotypes, as they do not integrate functions across different neuronal types. Here, we describe a set of rules for identifying and predicting functional phenotypes that combine morphology, intrinsic ion channel species and their distributions in dendrites, and functional properties. This more comprehensive neuronal classification should be an improvement on traditional classifications for relating genotype to functional phenotype.},
+  language = {en},
+  number = {10},
+  journal = {Nature Reviews Neuroscience},
+  author = {Migliore, Michele and Shepherd, Gordon M.},
+  month = oct,
+  year = {2005},
+  pages = {810-818},
+  file = {C:\\Users\\saram\\Zotero\\storage\\YF8NJLKW\\Migliore and Shepherd  2005  An integrated approach to classifying neuronal phenotypes.pdf}
+}
+
+@book{Buzsaki2006,
+  title = {Rhythms of the {{Brain}}},
+  isbn = {978-0-19-530106-9},
+  language = {en},
+  publisher = {{Oxford University Press}},
+  author = {Buzs{\'a}ki, Gy{\"o}rgy},
+  month = oct,
+  year = {2006},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2BI79KYM\\Buzsáki  2006  Rhythms of the Brain.pdf},
+  doi = {10.1093/acprof:oso/9780195301069.001.0001}
+}
+
+@article{Gilbert1975,
+  title = {The Projections of Cells in Different Layers of the Cat's Visual Cortex},
+  volume = {163},
+  copyright = {Copyright \textcopyright{} 1975 The Wistar Institute of Anatomy and Biology},
+  issn = {1096-9861},
+  doi = {10.1002/cne.901630106},
+  abstract = {The projection of cells in different layers of several cortical visual areas in the cat were studied using the method of retrograde transport of horseradish peroxidase. Injections of the enzyme were made through a recording micropipette, making it possible to localize the injection site by physiological criteria. We found that layer VI cells projected to the lateral geniculate nucleus, while a distinct population of cells in layer V projected to the superior colliculus. Cells in layers II and III were the major sources of ipsilateral cortico-cortical connections. This pattern of projection was consistent from one visual area to another. Pyramidal cells appeared to be the source of cortico-geniculate, cortico-collicular and cortico-cortical projections. The proportion of cells within a layer that terminated in a given site varied from layer to layer: apparently all of the large pyramids in layer V had terminals in the superior colliculus, about half of the pyramids in layer VI had terminals in the lateral geniculate nucleus, while only a small proportion of the pyramids in layers II and III had terminals in any single cortical area. The results indicated a remarkable specificity in the projections of the cortical layers. The cortical connections of the different cell types in layers A and A1 of the lateral geniculate nucleus were also examined: the cells that projected to area 17 were much more numerous and were on the average smaller than those that projected to area 18. Projections to the cortex were also found from the pulvinar, the medial interlaminar nucleus and the posterior nucleus. Direct connections were observed to the lateral geniculate nucleus from several midbrain reticular nuclei. Finally, projections were found to the superior colliculus from the zona incerta, the reticular nucleus of the thalamus and the ventral lateral geniculate nucleus.},
+  language = {en},
+  number = {1},
+  journal = {Journal of Comparative Neurology},
+  author = {Gilbert, C. D. and Kelly, J. P.},
+  month = sep,
+  year = {1975},
+  pages = {81-105},
+  file = {C:\\Users\\saram\\Zotero\\storage\\W2XL2WAY\\Gilbert and Kelly  1975  The projections of cells in different layers of the cat's visual cortex.pdf}
+}
+
+@article{Lund1975,
+  title = {The Origin of Efferent Pathways from the Primary Visual Cortex, Area 17, of the Macaque Monkey as Shown by Retrograde Transport of Horseradish Peroxidase},
+  volume = {164},
+  copyright = {Copyright \textcopyright{} 1975 The Wistar Institute Press},
+  issn = {1096-9861},
+  doi = {10.1002/cne.901640303},
+  abstract = {The retrograde transport of horseradish peroxidase has been used to identify efferent cells in area 17 of the macaque. Cells projecting to the lateral geniculate nucleus are small to medium sized pyramidal neurons with somata in lamina 6 and the adjacent white matter. The projection to the parvocellular division arises preferentially from the upper half of lamina 6, while that to the magnocellular division arises preferentially from the lower part of the lamina. The projection to both superior colliculus and inferior pulvinar arises from all sizes of pyramidal neurons lying in lamina 5B (Lund and Boothe, '75); at least the largest pyramidal neurons of lamina 5B send collateral axon branches to both destinations. Injections with extensive spread of horseradish peroxidase show that many cells of lamina 4B and the large pyramidal neurons of upper lamina 6 also project extrinsically but their terminal sites have not been identified. Other studies have indicated that cells of laminae 2 and 3 project to areas 18 and 19. Therefore every lamina of the visual cortex, with the exception of those receiving a direct thalamic input, contains cells projecting extrinsically. Further, each lamina projects to a different destination and from Golgi studies can be shown to contain cells with specific patterns of dendritic branching which relate to the distribution of thalamic afferents and to the patterns of intracortical connections. These findings emphasise the significance of the horizontal organisation of the cortex with relation to the flow of information through it and contrast with the current concept of columnar organisation shown in physiological studies.},
+  language = {en},
+  number = {3},
+  journal = {Journal of Comparative Neurology},
+  author = {Lund, Jennifer S. and Lund, Raymond D. and Hendrickson, Anita E. and Bunt, Ann H. and Fuchs, Albert F.},
+  month = dec,
+  year = {1975},
+  pages = {287-303},
+  file = {C:\\Users\\saram\\Zotero\\storage\\WVHIG2EV\\Lund et al.  1975  The origin of efferent pathways from the primary visual cortex, area 17, of the macaque monkey as shown by retrograde transport of horseradish peroxidase.pdf}
+}
+
+@article{Felleman1991,
+  title = {Distributed {{Hierarchical Processing}} in the {{Primate Cerebral Cortex}}},
+  volume = {1},
+  issn = {1047-3211, 1460-2199},
+  doi = {10.1093/cercor/1.1.1},
+  language = {en},
+  number = {1},
+  journal = {Cerebral Cortex},
+  author = {Felleman, D. J. and Van Essen, D. C.},
+  month = jan,
+  year = {1991},
+  pages = {1-47},
+  file = {C:\\Users\\saram\\Zotero\\storage\\F22SLGKG\\Felleman and Van Essen  1991  Distributed Hierarchical Processing in the Primate Cerebral Cortex.pdf}
+}
+
+@article{Hubel1974,
+  title = {Sequence Regularity and Geometry of Orientation Columns in the Monkey Striate Cortex},
+  volume = {158},
+  issn = {0021-9967, 1096-9861},
+  doi = {10.1002/cne.901580304},
+  abstract = {The striate cortex of the macaque monkey is subdivided into two independent and overlapping systems of columns termed ``orientation columns'' and ``ocular dominance columns.'' The present paper is concerned with the orientation columns, particularly their geometry and the relationship between successive columns. The arrangement of the columns is highly ordered; in the great majority of oblique or tangential microelectrode penetrations the preferred orientations of cells changed systematically with electrode position, in a clockwise or counterclockwise direction. Graphs of orientation vs. electrode track distance were virtually straight lines over distances of up to several millimeters; such orderly sequences were often terminated by sudden changes in the direction of orientation shifts, from clockwise to counterclockwise or back. The orientations at which these reversals occurred were quite unpredictable. Total rotations of 18\% 360'' were frequently seen between reversals. In tangential or almost tangential penetrations orientation shifts occurred almost every time the electrode was moved forward, indicating that the columns were either not discrete or had a thickness of less than 25-50 p, the smallest order of distance that our methods could resolve. In penetrations that were almost perpendicular to the surface, the graphs of orientation vs. track distance were relatively flatter, as expected if the surfaces of constant orientation are perpendicular to the cortical surface. S t e p wise changes in orientation of about 10'' could sometimes be seen in perpendicular penetrations, each orientation persisting through several clear advances of the electrode, suggesting a set of discrete columnar subdivisions. The possibility of some kind of continuous variation in orientation with horizontal distance along the cortex was not, however, completely ruled out. Occasionaliy a highly ordered sequence was broken by an abrupt large shift in orientation of up to 90''. Shifts in ocular dominance occurred roughly every 0.25-0.5 mm and were independent of orientation shifts. In multiple parallel penetrations spaced closer than about 250 p the slopes of the orientation vs. track distance curves were almost the same; reconstruction of these penetrations indicated that the regions of constant orientation are parallel sheets. On crossing perpendicular to these sheets, a total orientation shift of 180'' took place over a distance of 0.51.0 mm. Column thickness, size of shifts in orientation, and the rate of change of orientation with distance along the cortex seemed to be independent of eccentricity, at least between 2 '' and 15''from the fovea. A few penetrations made in area 17 of the cat and in area 18 of the monkey showed similar orderly sequences of receptive-field orientation shifts.},
+  language = {en},
+  number = {3},
+  journal = {The Journal of Comparative Neurology},
+  author = {Hubel, David H. and Wiesel, Torsten N.},
+  month = dec,
+  year = {1974},
+  pages = {267-293},
+  file = {C:\\Users\\saram\\Zotero\\storage\\E8FKB7JD\\Hubel and Wiesel  1974  Sequence regularity and geometry of orientation columns in the monkey striate cortex.pdf}
+}
+
+@article{Hubel1962,
+  title = {Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex},
+  volume = {160},
+  issn = {0022-3751},
+  abstract = {Images
+null},
+  number = {1},
+  journal = {The Journal of Physiology},
+  author = {Hubel, D. H. and Wiesel, T. N.},
+  month = jan,
+  year = {1962},
+  pages = {106-154.2},
+  file = {C:\\Users\\saram\\Zotero\\storage\\G5WAKY4A\\Hubel and Wiesel  1962  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.pdf;C:\\Users\\saram\\Zotero\\storage\\WDVR6BR7\\Hubel  Weisel 1962 Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.pdf},
+  pmid = {14449617},
+  pmcid = {PMC1359523}
+}
+
+@article{Mooser2004,
+  title = {A Morphological Basis for Orientation Tuning in Primary Visual Cortex},
+  volume = {7},
+  copyright = {2004 Nature Publishing Group},
+  issn = {1546-1726},
+  doi = {10.1038/nn1287},
+  abstract = {Feedforward connections are thought to be important in the generation of orientation-selective responses in visual cortex by establishing a bias in the sampling of information from regions of visual space that lie along a neuron's axis of preferred orientation. It remains unclear, however, which structural elements\textemdash{}dendrites or axons\textemdash{}are ultimately responsible for conveying this sampling bias. To explore this question, we have examined the spatial arrangement of feedforward axonal connections that link non-oriented neurons in layer 4 and orientation-selective neurons in layer 2/3 of visual cortex in the tree shrew. Target sites of labeled boutons in layer 2/3 resulting from focal injections of biocytin in layer 4 show an orientation-specific axial bias that is sufficient to confer orientation tuning to layer 2/3 neurons. We conclude that the anisotropic arrangement of axon terminals is the principal source of the orientation bias contributed by feedforward connections.},
+  language = {en},
+  number = {8},
+  journal = {Nature Neuroscience},
+  author = {Mooser, Fran{\c c}ois and Bosking, William H. and Fitzpatrick, David},
+  month = aug,
+  year = {2004},
+  pages = {872-879},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TBYHSB4F\\Mooser et al.  2004  A morphological basis for orientation tuning in primary visual cortex.pdf}
+}
+
+@article{Mountcastle2003,
+  title = {Introduction},
+  volume = {13},
+  issn = {1047-3211},
+  doi = {10.1093/cercor/13.1.2},
+  abstract = {Every neuroscientist has a list of those he considers the most important unsolved problems in brain science. Surely high on the list of many will be that consid},
+  language = {en},
+  number = {1},
+  journal = {Cerebral Cortex},
+  author = {Mountcastle, Vernon B.},
+  month = jan,
+  year = {2003},
+  pages = {2-4}
+}
+
+@article{Mountcastle1997,
+  title = {The Columnar Organization of the Neocortex},
+  volume = {120},
+  issn = {14602156},
+  doi = {10.1093/brain/120.4.701},
+  language = {en},
+  number = {4},
+  journal = {Brain},
+  author = {Mountcastle, V.},
+  month = apr,
+  year = {1997},
+  pages = {701-722},
+  file = {C:\\Users\\saram\\Zotero\\storage\\62LQU3QV\\Mountcastle  1997  The columnar organization of the neocortex.pdf}
+}
+
+@article{Larkum2013,
+  title = {A Cellular Mechanism for Cortical Associations: An Organizing Principle for the Cerebral Cortex},
+  volume = {36},
+  issn = {01662236},
+  shorttitle = {A Cellular Mechanism for Cortical Associations},
+  doi = {10.1016/j.tins.2012.11.006},
+  language = {en},
+  number = {3},
+  journal = {Trends in Neurosciences},
+  author = {Larkum, Matthew},
+  month = mar,
+  year = {2013},
+  pages = {141-151},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JS5V4EC2\\Larkum  2013  A cellular mechanism for cortical associations an organizing principle for the cerebral cortex.pdf}
+}
+
+@article{Grillner2005,
+  title = {Microcircuits in Action \textendash{} from {{CPGs}} to Neocortex},
+  volume = {28},
+  issn = {01662236},
+  doi = {10.1016/j.tins.2005.08.003},
+  language = {en},
+  number = {10},
+  journal = {Trends in Neurosciences},
+  author = {Grillner, Sten and Markram, Henry and De Schutter, Erik and Silberberg, Gilad and LeBeau, Fiona E.N.},
+  month = oct,
+  year = {2005},
+  pages = {525-533},
+  file = {C:\\Users\\saram\\Zotero\\storage\\LMMD4A8Z\\Grillner et al.  2005  Microcircuits in action – from CPGs to neocortex.pdf}
+}
+
+@article{McCormick1985,
+  title = {Comparative Electrophysiology of Pyramidal and Sparsely Spiny Stellate Neurons of the Neocortex},
+  volume = {54},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.1985.54.4.782},
+  language = {en},
+  number = {4},
+  journal = {Journal of Neurophysiology},
+  author = {McCormick, D. A. and Connors, B. W. and Lighthall, J. W. and Prince, D. A.},
+  month = oct,
+  year = {1985},
+  pages = {782-806},
+  file = {C:\\Users\\saram\\Zotero\\storage\\QRX5AUZP\\McCormick et al.  1985  Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex.pdf}
+}
+
+@article{DeFelipe1992a,
+  title = {The Pyramidal Neuron of the Cerebral Cortex: {{Morphological}} and Chemical Characteristics of the Synaptic Inputs},
+  volume = {39},
+  issn = {03010082},
+  shorttitle = {The Pyramidal Neuron of the Cerebral Cortex},
+  doi = {10.1016/0301-0082(92)90015-7},
+  language = {en},
+  number = {6},
+  journal = {Progress in Neurobiology},
+  author = {DeFelipe, Javier and Fari{\~n}as, Isabel},
+  month = dec,
+  year = {1992},
+  pages = {563-607},
+  file = {C:\\Users\\saram\\Zotero\\storage\\JUIPEV9M\\DeFelipe and Fariñas  1992  The pyramidal neuron of the cerebral cortex Morphological and chemical characteristics of the synaptic inputs.pdf}
+}
+
+@article{Molnar2006,
+  title = {Towards the Classification of Subpopulations of Layer {{V}} Pyramidal Projection Neurons},
+  volume = {55},
+  issn = {01680102},
+  doi = {10.1016/j.neures.2006.02.008},
+  abstract = {The nature of cerebral cortical circuitry has been increasingly clarified by markers for the identification of precise cell types with specific morphology, connectivity and distinct physiological properties. Molecular markers are not only helpful in dissecting cortical circuitry, but also give insight into the mechanisms of cortical neuronal specification and differentiation. The two principal neuronal types of the cerebral cortex are the pyramidal and GABAergic cells. Pyramidal cells are excitatory and project to distant targets, while GABAergic neurons are mostly inhibitory non-pyramidal interneurons. Reliable markers for specific subtypes of interneurons are available and have been employed in the classification and functional analysis of cortical circuitry. Until recently, cortical pyramidal neurons have been considered a homogeneous class of cells. This concept is now changing as the powerful tools of molecular biology and genetics identify molecular tags for subtypes of pyramidal cells such as: Otx-1 [Frantz, G.D., Bohner, A.P., Akers, R.M., McConnell, S.K., 1994. Regulation of the POU domain gene SCIP during cerebral cortical development. J. Neurosci. 14, 472\textendash{}485; Weimann, J.M., Zhang, Y.A., Levin, M.E., Devine, W.P., Brulet, P., McConnell, S.K., 1999. Cortical neurons require Otx1 for the refinement of exuberant axonal projections to subcortical targets. Neuron 24, 819\textendash{}831]; SMI-32, N200 and FNP-7 [Voelker, C.C., Garin, N., Taylor, J.S., Gahwiler, B.H., Hornung, J.P., Molna\textasciiacute{}r, Z., 2004. Selective neurofilament (SMI-32, FNP-7 and N200) expression in subpopulations of layer V pyramidal neurons in vivo and in vitro. Cereb. Cortex 14, 1276\textendash{}1286]; ER81 [Hevner, R.F., Daza, R.A., Rubenstein, J.L., Stunnenberg, H., Olavarria, J.F., Englund, C., 2003. Beyond laminar fate: toward a molecular classification of cortical projection/pyramidal neurons. Dev. Neurosci. 25 (2\textendash{}4), 139\textendash{}151; Yoneshima, H., Yamasaki, S., Voelker, C., Molna\textasciiacute{}r, Z., Christophe, E., Audinat, E., Takemoto, M., Tsuji, S., Fujita, I., Yamamoto, N., 2006. ER81 is expressed in a subpopulation of layer 5 projection neurons in rodent cerebral cortices. Neuroscience, 137, 401\textendash{}412]; Lmo4 [Bulchand, S., Subramanian, L., Tole, S., 2003. Dynamic spatiotemporal expression of LIM genes and cofactors in the embryonic and postnatal cerebral cortex. Dev. Dyn. 226, 460\textendash{}469; Arlotta, P., Molyneaux, B.J., Chen, J., Inoue, J., Kominami, R., Macklis, J.D., 2005. Neuronal subtype-specific genes that control corticospinal motor neuron development in vivo. Neuron 45 (2), 207\textendash{}221]; CTIP2 [Arlotta, P., Molyneaux, B.J., Chen, J., Inoue, J., Kominami, R., Macklis, J.D., 2005. Neuronal subtype-specific genes that control corticospinal motor neuron development in vivo. Neuron 45 (2), 207\textendash{}221]; Fez1 [Molyneaux, B.J., Arlotta, P., Hirata, T., Hibi, M., Macklis, J.D., 2005. Fez1 is required for the birth and specification of corticospinal motor neurons. Neuron 47 (6), 817\textendash{}831; Chen, B., Schaevitz, L.R., McConnell, S.K., 2005. Fez1 regulates the differentiation and axon targeting of layer 5 subcortical projection neurons in cerebral cortex. Proc. Natl. Acad. Sci. U.S.A. 102 (47), 17184\textendash{}17189]. These genes outline the numerous subtypes of pyramidal cells and are increasingly refining our previous classifications. They also indicate specific developmental programs operate in cell fate decisions. This review will describe the progress made on the correlation of these markers to each other within a specific subtype of layer V neurons with identified, stereotypic projections. Further work is needed to link these data with observations on somatodendritic morphology and physiological properties. The integrated molecular, anatomical and physiological characterisation of pyramidal neurons will lead to a much better appreciation of functional cortical circuits.},
+  language = {en},
+  number = {2},
+  journal = {Neuroscience Research},
+  author = {Moln{\'a}r, Zolt{\'a}n and Cheung, Amanda F.P.},
+  month = jun,
+  year = {2006},
+  pages = {105-115},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KUPN8DHH\\Molnár and Cheung  2006  Towards the classification of subpopulations of layer V pyramidal projection neurons.pdf}
+}
+
+@article{DeFelipe2002,
+  title = {Microstructure of the Neocortex: Comparative Aspects},
+  volume = {31},
+  issn = {0300-4864},
+  shorttitle = {Microstructure of the Neocortex},
+  abstract = {The appearance of the neocortex, its expansion, and its differentiation in mammals, represents one of the principal episodes in the evolution of the vertebrate brain. One of the fundamental questions in neuroscience is what is special about the neocortex of humans and how does it differ from that of other species? It is clear that distinct cortical areas show important differences within both the same and different species, and this has led to some researchers emphasizing the similarities whereas others focus on the differences. In general, despite of the large number of different elements that contribute to neocortical circuits, it is thought that neocortical neurons are organized into multiple, small repeating microcircuits, based around pyramidal cells and their input-output connections. These inputs originate from extrinsic afferent systems, excitatory glutamatergic spiny cells (which include other pyramidal cells and spiny stellate cells), and inhibitory GABAergic interneurons. The problem is that the neuronal elements that make up the basic microcircuit are differentiated into subtypes, some of which are lacking or highly modified in different cortical areas or species. Furthermore, the number of neurons contained in a discrete vertical cylinder of cortical tissue varies across species. Additionally, it has been shown that the neuropil in different cortical areas of the human, rat and mouse has a characteristic layer specific synaptology. These variations most likely reflect functional differences in the specific cortical circuits. The laminar specific similarities between cortical areas and between species, with respect to the percentage, length and density of excitatory and inhibitory synapses, and to the number of synapses per neuron, might be considered as the basic cortical building bricks. In turn, the differences probably indicate the evolutionary adaptation of excitatory and inhibitory circuits to particular functions.},
+  language = {eng},
+  number = {3-5},
+  journal = {Journal of Neurocytology},
+  author = {DeFelipe, Javier and Alonso-Nanclares, Lidia and Arellano, Jon I.},
+  year = {2002},
+  keywords = {Animals,Biological Evolution,Cell Count,Humans,Microscopy; Electron,Neocortex,Neuropil,Species Specificity,Synapses},
+  pages = {299-316},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TG4LLDSK\\ARELLANO  Microstructure of the neocortex Comparative aspects.pdf},
+  pmid = {12815249}
+}
+
+@article{Ascoli2008,
+  title = {Petilla Terminology: Nomenclature of Features of {{GABAergic}} Interneurons of the Cerebral Cortex},
+  volume = {9},
+  issn = {1471-0048},
+  shorttitle = {Petilla Terminology},
+  doi = {10.1038/nrn2402},
+  abstract = {Neuroscience produces a vast amount of data from an enormous diversity of neurons. A neuronal classification system is essential to organize such data and the knowledge that is derived from them. Classification depends on the unequivocal identification of the features that distinguish one type of neuron from another. The problems inherent in this are particularly acute when studying cortical interneurons. To tackle this, we convened a representative group of researchers to agree on a set of terms to describe the anatomical, physiological and molecular features of GABAergic interneurons of the cerebral cortex. The resulting terminology might provide a stepping stone towards a future classification of these complex and heterogeneous cells. Consistent adoption will be important for the success of such an initiative, and we also encourage the active involvement of the broader scientific community in the dynamic evolution of this project.},
+  language = {eng},
+  number = {7},
+  journal = {Nature Reviews. Neuroscience},
+  author = {Ascoli, Giorgio A. and Alonso-Nanclares, Lidia and Anderson, Stewart A. and Barrionuevo, German and Benavides-Piccione, Ruth and Burkhalter, Andreas and Buzs{\'a}ki, Gy{\"o}rgy and Cauli, Bruno and Defelipe, Javier and Fair{\'e}n, Alfonso and Feldmeyer, Dirk and Fishell, Gord and Fregnac, Yves and Freund, Tamas F. and Gardner, Daniel and Gardner, Esther P. and Goldberg, Jesse H. and Helmstaedter, Moritz and Hestrin, Shaul and Karube, Fuyuki and Kisv{\'a}rday, Zolt{\'a}n F. and Lambolez, Bertrand and Lewis, David A. and Marin, Oscar and Markram, Henry and Mu{\~n}oz, Alberto and Packer, Adam and Petersen, Carl C. H. and Rockland, Kathleen S. and Rossier, Jean and Rudy, Bernardo and Somogyi, Peter and Staiger, Jochen F. and Tamas, Gabor and Thomson, Alex M. and Toledo-Rodriguez, Maria and Wang, Yun and West, David C. and Yuste, Rafael},
+  month = jul,
+  year = {2008},
+  keywords = {Action Potentials,Axons,Cerebral Cortex,gamma-Aminobutyric Acid,Humans,Interneurons,Synapses},
+  pages = {557-568},
+  file = {C:\\Users\\saram\\Zotero\\storage\\HNSMFS5B\\Ascoli, 2008, Petilla terminology nomenclature of features of GABAergic interneurons of the cerebral cortex.pdf},
+  pmid = {18568015},
+  pmcid = {PMC2868386}
+}
+
+@article{Buzsaki2002,
+  title = {Theta {{Oscillations}} in the {{Hippocampus}}},
+  volume = {33},
+  issn = {08966273},
+  doi = {10.1016/S0896-6273(02)00586-X},
+  language = {en},
+  number = {3},
+  journal = {Neuron},
+  author = {Buzs{\'a}ki, Gy{\"o}rgy},
+  month = jan,
+  year = {2002},
+  pages = {325-340},
+  file = {C:\\Users\\saram\\Zotero\\storage\\SV6SBYCA\\Buzsáki  2002  Theta Oscillations in the Hippocampus.pdf}
+}
+
+@article{Buzsaki2004a,
+  title = {Neuronal {{Oscillations}} in {{Cortical Networks}}},
+  volume = {304},
+  issn = {0036-8075, 1095-9203},
+  doi = {10.1126/science.1099745},
+  language = {en},
+  number = {5679},
+  journal = {Science},
+  author = {Buzsaki, G.},
+  month = jun,
+  year = {2004},
+  pages = {1926-1929},
+  file = {C:\\Users\\saram\\Zotero\\storage\\WH82WCWM\\Buzsaki  2004  Neuronal Oscillations in Cortical Networks.pdf}
+}
+
+@article{Markram2004,
+  title = {Interneurons of the Neocortical Inhibitory System},
+  volume = {5},
+  issn = {1471-003X, 1471-0048},
+  doi = {10.1038/nrn1519},
+  abstract = {Mammals adapt to a rapidly changing world because of the sophisticated cognitive functions that are supported by the neocortex. The neocortex, which forms almost 80\% of the human brain, seems to have arisen from repeated duplication of a stereotypical microcircuit template with subtle specializations for different brain regions and species. The quest to unravel the blueprint of this template started more than a century ago and has revealed an immensely intricate design. The largest obstacle is the daunting variety of inhibitory interneurons that are found in the circuit. This review focuses on the organizing principles that govern the diversity of inhibitory interneurons and their circuits.},
+  language = {en},
+  number = {10},
+  journal = {Nature Reviews Neuroscience},
+  author = {Markram, Henry and Toledo-Rodriguez, Maria and Wang, Yun and Gupta, Anirudh and Silberberg, Gilad and Wu, Caizhi},
+  month = oct,
+  year = {2004},
+  pages = {793-807},
+  file = {C:\\Users\\saram\\Zotero\\storage\\VFT7D2VD\\Markram et al.  2004  Interneurons of the neocortical inhibitory system.pdf}
+}
+
+@article{Connors1990a,
+  title = {Intrinsic Firing Patterns of Diverse Neocortical Neurons},
+  volume = {13},
+  issn = {0166-2236},
+  doi = {10.1016/0166-2236(90)90185-D},
+  abstract = {Neurons of the neocortex differ dramatically in the patterns of action potentials they generate in response to current steps. Regular-spiking cells adapt strongly during maintained stimuli, whereas fast-spiking cells can sustain very high firing frequencies with little or no adaptation. Instrinsically bursting cells generate clusters of spikes (bursts), either singly or repetitively. These physiological distinctions have morphological correlates. RS and IB cells can be either pyramidal neurons or spiny stellate cells, and thus constitute the excitatory cells of the cortex. FS cells are smooth or sparsely spiny non-pyramidal cells, and are likely to be GABAergic inhibitory interneurons. The different firing properties of neurons in neocortex contribute significantly to its network behavior.},
+  number = {3},
+  journal = {Trends in Neurosciences},
+  author = {Connors, Barry W. and Gutnick, Michael J.},
+  month = mar,
+  year = {1990},
+  pages = {99-104},
+  file = {C:\\Users\\saram\\Zotero\\storage\\VPB4XY4V\\Connors and Gutnick  1990  Intrinsic firing patterns of diverse neocortical neurons.pdf}
+}
+
+@article{Steriade1998,
+  title = {Dynamic {{Properties}} of {{Corticothalamic Neurons}} and {{Local Cortical Interneurons Generating Fast Rhythmic}} (30\textendash{}40 {{Hz}}) {{Spike Bursts}}},
+  volume = {79},
+  issn = {0022-3077},
+  doi = {10.1152/jn.1998.79.1.483},
+  abstract = {Steriade, Mircea, Igor Timofeev, Niklaus D{\"u}rm{\"u}ller, and Fran{\c c}ois Grenier. Dynamic properties of corticothalamic neurons and local cortical interneurons generating fast rhythmic (30\textendash{}40 Hz) spike-bursts. J. Neurophysiol. 79: 483\textendash{}490, 1998. Fast spontaneous oscillations (mainly 30\textendash{}40 Hz) characterize cortical and thalamic neuronal networks during behavioral states of increased vigilance and depend on cell depolarization under the influence of ascending activating systems. We investigated, by means of intracellular recording and staining in vivo, the properties of fast-oscillating cortical neurons from cat's motor and association areas, some projecting to the thalamus, others with locally arborizing axons. At a given level of depolarization, 28\% of our neuronal sample discharged high-frequency spike bursts (300\textendash{}600 Hz) that recurred rhythmically between 20 and 50 Hz. Such fast rhythmic bursting neurons have been found in both superficial and deep cortical layers. Slight changes in membrane potential as well as synaptic activity in thalamocortical networks dramatically altered the discharge patterns, from single spikes to rhythmic spike-bursts, and eventually to fast tonic firing without frequency adaptation. Thus our data challenge the conventional idea that sharply defined, invariant features and distinct locations in certain cortical layers characterize some neocortical cell-classes. We demonstrate that the distinctions between intrinsic electrophysiological properties of neocortical neurons are much more labile than conventionally thought. The present results, which indicate that corticothalamic neurons discharge fast rhythmic spike bursts mainly at 30\textendash{}40 Hz, suggest that this activity results in integrated fast oscillations within corticothalamic networks.},
+  number = {1},
+  journal = {Journal of Neurophysiology},
+  author = {Steriade, Mircea and Timofeev, Igor and D{\"u}rm{\"u}ller, Niklaus and Grenier, Fran{\c c}ois},
+  month = jan,
+  year = {1998},
+  pages = {483-490},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2QB2AF6M\\Steriade et al.  1998  Dynamic Properties of Corticothalamic Neurons and Local Cortical Interneurons Generating Fast Rhythmic (30–40 Hz) Spike Bursts.pdf}
+}
+
+@article{Steriade2004,
+  title = {Neocortical Cell Classes Are Flexible Entities},
+  volume = {5},
+  issn = {1471-003X, 1471-0048},
+  doi = {10.1038/nrn1325},
+  language = {en},
+  number = {2},
+  journal = {Nature Reviews Neuroscience},
+  author = {Steriade, Mircea},
+  month = feb,
+  year = {2004},
+  pages = {121-134},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ZYDL8Y6S\\Steriade  2004  Neocortical cell classes are flexible entities.pdf}
+}
+
+@article{Steriade2001,
+  title = {Impact of {{Network Activities}} on {{Neuronal Properties}} in {{Corticothalamic Systems}}},
+  volume = {86},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.2001.86.1.1},
+  language = {en},
+  number = {1},
+  journal = {Journal of Neurophysiology},
+  author = {Steriade, M.},
+  month = jul,
+  year = {2001},
+  pages = {1-39},
+  file = {C:\\Users\\saram\\Zotero\\storage\\7DX44ULZ\\Steriade  2001  Impact of Network Activities on Neuronal Properties in Corticothalamic Systems.pdf}
+}
+
+@article{Steriade1978,
+  title = {Cell Classification and Changes in Neuronal Activity with Shifts in Vigilance State},
+  volume = {1},
+  issn = {0140-525X, 1469-1825},
+  doi = {10.1017/S0140525X00076482},
+  abstract = {Knowledge of the input-output characteristics of various neuronal types is a necessary first step toward an understanding of cellular events related to waking and sleep. In spite of the oversimplification involved, the dichotomy in terms of type I (long-axoned, output) neurons and type II (short-axoned, local) interneurons is helpful in functionally delineating the neuronal circuits involved in the genesis and epiphenomena of waking and sleep states. The possibility is envisaged that cortical interneurons, which are particularly related to higher neuronal activity and have been found in previous experiments to be more active during sleep than during wakefulness, might be involved in complex integrative processes occurring during certain sleep stages. Electrophysiological criteria for the identification of output cells and interneurons are developed, with emphasis on various possibilities and difficulties involved in recognizing interneurons of the mammalian brain. The high-frequency repetitive activity of interneurons is discussed, together with various possibilities of error to be avoided when interpreting data from bursting cells. Data first show opposite changes in spontaneous and evoked discharges of identified output cells versus putative interneurons recorded from motor and parietal association cortical areas in behaving monkeys and cats during wakefulness (W) compared to sleep with synchronized EEG activity (S): significantly increased rates of spontaneous firing, enhanced antidromic or synaptic responsiveness, associated with shorter periods of inhibition in type I (pyramidal tract, cortico-thalamic and cortico-pontine) cells during W versus significantly decreased frequencies of spontaneous discharge and depression of synaptically elicited reponses of type II cells during W compared to S. These findings are partly explained on the basis of recent iontophoretic studies showing that acetylcholine, viewed as a synaptic transmitter of the arousal system, excites output-type neurons and inhibits high-frequency bursting cells. Comparing W and S to the deepest stage of sleep with desynchronized EEG activity (D) in type I and type II cells revealed that: (a) the increased firing rates of output cells in D, over those in W and S, is substantially due to a tonic excitation during this state, and rapid eye movements (RE Ms) only contribute to the further increase of discharge frequencies; (b) in contrast, the increased rates of discharge in interneurons during D is entirely ascribable to REM-related firing. On the basis of experiments reporting that increased duration of D has beneficial effects upon retention of information acquired during W, the suggestion is made that increased firing rates of association cortical interneurons during REM epochs of D sleep are an important factor in maintaining the soundness of a memory trace.},
+  language = {en},
+  number = {03},
+  journal = {Behavioral and Brain Sciences},
+  author = {Steriade, M.},
+  month = sep,
+  year = {1978},
+  pages = {511},
+  file = {C:\\Users\\saram\\Zotero\\storage\\Z6J2Q95Z\\Steriade  1978  Cell classification and changes in neuronal activity with shifts in vigilance state.pdf}
+}
+
+@article{LeVanQuyen2008,
+  title = {Cell {{Type}}-{{Specific Firing}} during {{Ripple Oscillations}} in the {{Hippocampal Formation}} of {{Humans}}},
+  volume = {28},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.0437-08.2008},
+  language = {en},
+  number = {24},
+  journal = {Journal of Neuroscience},
+  author = {Le Van Quyen, M. and Bragin, A. and Staba, R. and Crepon, B. and Wilson, C. L. and Engel, J.},
+  month = jun,
+  year = {2008},
+  pages = {6104-6110},
+  file = {C:\\Users\\saram\\Zotero\\storage\\TETM2B28\\Le Van Quyen et al.  2008  Cell Type-Specific Firing during Ripple Oscillations in the Hippocampal Formation of Humans.pdf}
+}
+
+@article{Peyrache2012,
+  title = {Spatiotemporal Dynamics of Neocortical Excitation and Inhibition during Human Sleep},
+  volume = {109},
+  issn = {0027-8424, 1091-6490},
+  doi = {10.1073/pnas.1109895109},
+  language = {en},
+  number = {5},
+  journal = {Proceedings of the National Academy of Sciences},
+  author = {Peyrache, A. and Dehghani, N. and Eskandar, E. N. and Madsen, J. R. and Anderson, W. S. and Donoghue, J. A. and Hochberg, L. R. and Halgren, E. and Cash, S. S. and Destexhe, A.},
+  month = jan,
+  year = {2012},
+  pages = {1731-1736},
+  file = {C:\\Users\\saram\\Zotero\\storage\\K6GKKL25\\Peyrache et al.  2012  Spatiotemporal dynamics of neocortical excitation and inhibition during human sleep.pdf}
+}
+
+@article{Einevoll2007,
+  title = {Laminar {{Population Analysis}}: {{Estimating Firing Rates}} and {{Evoked Synaptic Activity From Multielectrode Recordings}} in {{Rat Barrel Cortex}}},
+  volume = {97},
+  issn = {0022-3077, 1522-1598},
+  shorttitle = {Laminar {{Population Analysis}}},
+  doi = {10.1152/jn.00845.2006},
+  language = {en},
+  number = {3},
+  journal = {Journal of Neurophysiology},
+  author = {Einevoll, Gaute T. and Pettersen, Klas H. and Devor, Anna and Ulbert, Istvan and Halgren, Eric and Dale, Anders M.},
+  month = mar,
+  year = {2007},
+  pages = {2174-2190},
+  file = {C:\\Users\\saram\\Zotero\\storage\\ZIPK3AHT\\Einevoll et al.  2007  Laminar Population Analysis Estimating Firing Rates and Evoked Synaptic Activity From Multielectrode Recordings in Rat Barrel Cortex.pdf}
+}
+
+@article{Dougherty2015,
+  title = {Ongoing {{Alpha Activity}} in {{V1 Regulates Visually Driven Spiking Responses}}},
+  issn = {1047-3211, 1460-2199},
+  doi = {10.1093/cercor/bhv304},
+  abstract = {The interlaminar connections in the primate primary visual cortex (V1) are well described, as is the presence of ongoing alpharange (7\textendash{}14 Hz) fluctuations in this area. Less well understood is how these interlaminar connections and ongoing fluctuations contribute to the regulation of visual spiking responses. Here, we investigate the relationship between alpha fluctuations and spiking responses to visual stimuli across cortical layers. Using laminar probes in macaque V1, we show that neural firing couples with the phase of alpha fluctuations, and that magnitude of this coupling is particularly pronounced during visual stimulation. The strongest modulation of spiking activity was observed in layers 2/3. Alpha-spike coupling and current source density analysis pointed to an infragranular origin of the alpha fluctuations. Taken together, these results indicate that ongoing infragranular alpha-range fluctuations in V1 play a role in regulating columnar visual activity.},
+  language = {en},
+  journal = {Cerebral Cortex},
+  author = {Dougherty, Kacie and Cox, Michele A. and Ninomiya, Taihei and Leopold, David A. and Maier, Alexander},
+  month = dec,
+  year = {2015},
+  pages = {bhv304},
+  file = {C:\\Users\\saram\\Zotero\\storage\\KQ55QT42\\Dougherty et al.  2015  Ongoing Alpha Activity in V1 Regulates Visually Driven Spiking Responses.pdf}
+}
+
+@article{Chong2011,
+  title = {Synaptic Dysfunction in Hippocampus of Transgenic Mouse Models of {{Alzheimer}}'s Disease: {{A}} Multi-Electrode Array Study},
+  volume = {44},
+  issn = {09699961},
+  shorttitle = {Synaptic Dysfunction in Hippocampus of Transgenic Mouse Models of {{Alzheimer}}'s Disease},
+  doi = {10.1016/j.nbd.2011.07.006},
+  abstract = {APP.V717I and Tau.P301L transgenic mice develop Alzheimer's disease pathology comprising important aspects of human disease including increased levels of amyloid peptides, cognitive and motor impairment, amyloid plaques and neurofibrillary tangles. The combined model, APP.V717I \texttimes{} Tau.P301L bigenic mice (biAT mice) exhibit aggravated amyloid and tau pathology with severe cognitive and behavioral defects. In the present study, we investigated early changes in synaptic function in the CA1 and CA3 regions of acute hippocampal slices of young APP.V717I, Tau.P301L and biAT transgenic animals. We have used planar multielectrode arrays (MEA) and improved methods for simultaneous multi-site recordings from two hippocampal sub-regions. In the CA1 region, long-term potentiation (LTP) was severely impaired in all transgenic animals when compared with age-matched wild-type controls, while basal synaptic transmission and paired-pulse facilitation were minimally affected. In the CA3 region, LTP was normal in Tau.P301L and APP.V717I but clearly impaired in biAT mice. Surprisingly, frequency facilitation in CA3 was significantly enhanced in Tau. P301L mice, while not affected in APP.V717I mice and depressed in biAT mice. The findings demonstrate important synaptic changes that differ considerably in the hippocampal sub-regions already at young age, well before the typical amyloid or tau pathology is evident.},
+  language = {en},
+  number = {3},
+  journal = {Neurobiology of Disease},
+  author = {Chong, Seon-Ah and Benilova, Iryna and Shaban, Hamdy and De Strooper, Bart and Devijver, Herman and Moechars, Dieder and Eberle, Wolfgang and Bartic, Carmen and Van Leuven, Fred and Callewaert, Geert},
+  month = dec,
+  year = {2011},
+  pages = {284-291},
+  file = {C:\\Users\\saram\\Zotero\\storage\\2RAVZ6AY\\Chong et al.  2011  Synaptic dysfunction in hippocampus of transgenic mouse models of Alzheimer's disease A multi-electrode array study.pdf}
+}
+
+@article{Gafurov2013,
+  title = {{{GABAergic}} Transmission Facilitates Ictogenesis and Synchrony between {{CA3}}, Hilus, and Dentate Gyrus in Slices from Epileptic Rats},
+  volume = {110},
+  issn = {0022-3077, 1522-1598},
+  doi = {10.1152/jn.00679.2012},
+  language = {en},
+  number = {2},
+  journal = {Journal of Neurophysiology},
+  author = {Gafurov, Boris and Bausch, Suzanne B.},
+  month = jul,
+  year = {2013},
+  pages = {441-455},
+  file = {C:\\Users\\saram\\Zotero\\storage\\DNIEAT7K\\Gafurov and Bausch  2013  GABAergic transmission facilitates ictogenesis and synchrony between CA3, hilus, and dentate gyrus in slices from epileptic rats.pdf}
+}
+
+@article{Gonzalez-Sulser2011,
+  title = {The 4-Aminopyridine in Vitro Epilepsy Model Analyzed with a Perforated Multi-Electrode Array},
+  volume = {60},
+  issn = {00283908},
+  doi = {10.1016/j.neuropharm.2010.10.007},
+  abstract = {Epileptiform discharges recorded in the 4-aminopyridine (4-AP) in vitro epilepsy model are mediated by glutamatergic and GABAergic signaling. Using a 60-channel perforated multi-electrode array (pMEA) on corticohippocampal slices from 2 to 3 week old mice we recorded interictal- and ictal-like events. When glutamatergic transmission was blocked, interictal-like events no longer initiated in the hilus or CA3/CA1 pyramidal layers but originated from the dentate gyrus granule and molecular layers. Furthermore, frequencies of interictal-like events were reduced and durations were increased in these regions while cortical discharges were completely blocked. Following GABAA receptor blockade interictal-like events no longer propagated to the dentate gyrus while their frequency in CA3 increased; in addition, ictal-like cortical events became shorter while increasing in frequency. Lastly, drugs that affect tonic and synaptic GABAergic conductance modulated the frequency, duration, initiation and propagation of interictal-like events. These findings confirm and expand on previous studies indicating that multiple synaptic mechanisms contribute to synchronize neuronal network activity in forebrain structures.},
+  language = {en},
+  number = {7-8},
+  journal = {Neuropharmacology},
+  author = {Gonzalez-Sulser, Alfredo and Wang, Jing and Motamedi, Gholam K. and Avoli, Massimo and Vicini, Stefano and Dzakpasu, Rhonda},
+  month = jun,
+  year = {2011},
+  pages = {1142-1153},
+  file = {C:\\Users\\saram\\Zotero\\storage\\5XXXNDSK\\Gonzalez-Sulser et al.  2011  The 4-aminopyridine in vitro epilepsy model analyzed with a perforated multi-electrode array.pdf}
+}
+
+@article{McGinn2014,
+  title = {Phase-{{Amplitude Coupling}} and {{Interlaminar Synchrony Are Correlated}} in {{Human Neocortex}}},
+  volume = {34},
+  issn = {0270-6474, 1529-2401},
+  doi = {10.1523/JNEUROSCI.2771-14.2014},
+  language = {en},
+  number = {48},
+  journal = {Journal of Neuroscience},
+  author = {McGinn, R. J. and Valiante, T. A.},
+  month = nov,
+  year = {2014},
+  pages = {15923-15930},
+  file = {C:\\Users\\saram\\Zotero\\storage\\LBNDXJ3V\\McGinn and Valiante  2014  Phase-Amplitude Coupling and Interlaminar Synchrony Are Correlated in Human Neocortex.pdf;C:\\Users\\saram\\Zotero\\storage\\NRI86UR4\\McGinn_Ryan_J_201311_MASc_thesis.pdf}
+}
+
+@article{Kalmbach2018,
+  title = {H-Channels Contribute to Divergent Electrophysiological Properties of Supragranular Pyramidal Neurons in Human versus Mouse Cerebral Cortex},
+  copyright = {\textcopyright{} 2018, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
+  doi = {10.1101/312298},
+  abstract = {Gene expression studies suggest that differential ion channel expression contributes to differences in rodent versus human neuronal physiology. We tested whether h-channels more prominently contribute to the physiological properties of human compared to mouse supragranular pyramidal neurons. Single cell/nucleus RNA sequencing revealed ubiquitous HCN1-subunit expression in excitatory neurons in human, but not mouse supragranular layers. Using patch-clamp recordings, we found stronger h-channel-related membrane properties in supragranular pyramidal neurons in human temporal cortex, compared to mouse supragranular pyramidal neurons in temporal association area. The magnitude of these differences depended upon cortical depth and was largest in pyramidal neurons in deep L3. Additionally, pharmacologically blocking h-channels produced a larger change in membrane properties in human compared to mouse neurons. Finally, using biophysical modeling, we provided evidence that h-channels promote the transfer of theta frequencies from dendrite-to-soma in human L3 pyramidal neurons. Thus, h-channels contribute to between-species differences in a fundamental neuronal property.},
+  language = {en},
+  journal = {bioRxiv},
+  author = {Kalmbach, Brian and Buchin, Anatoly and Miller, Jeremy A. and Bakken, Trygve E. and Hodge, Rebecca D. and Chong, Peter and de Frates, Rebecca and Dai, Kael and Gwinn, Ryder P. and Cobbs, Charles and Ko, Andrew L. and Ojemann, Jeffrey G. and Silbergeld, Daniel L. and Koch, Christof and Anastassiou, Costas A. and Lein, Ed and Ting, Jonathan T.},
+  month = may,
+  year = {2018},
+  pages = {312298},
+  file = {C:\\Users\\saram\\Zotero\\storage\\YWVUA3BG\\Kalmbach et al.  2018  h-channels contribute to divergent electrophysiological properties of supragranular pyramidal neurons in human versus mouse cerebral cortex.pdf}
+}
+
+
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diff --git a/lessons/misc/latex-intro/Intro-to-Latex-Sara/intro_to_LateX.tex b/lessons/misc/latex-intro/Intro-to-Latex-Sara/intro_to_LateX.tex
new file mode 100644
index 000000000..424944ae3
--- /dev/null
+++ b/lessons/misc/latex-intro/Intro-to-Latex-Sara/intro_to_LateX.tex
@@ -0,0 +1,378 @@
+\documentclass[12pt]{article}
+\usepackage{booktabs}
+\usepackage[authoryear,sort&compress]{natbib}
+%\usepackage[numbers,sort&compress]{natbib}
+\usepackage{amsmath,amsfonts,amssymb,amsbsy}
+\usepackage{color}
+\usepackage[colorlinks=true,citecolor=cyan,linkcolor=blue,urlcolor=magenta]{hyperref}
+\usepackage{subfigure}
+\usepackage{graphicx}
+\usepackage{authblk}
+\usepackage{parskip}
+\usepackage{lineno}
+
+
+\title{Introduction to \LaTeX}
+%\author{Sara Mahallati\thanks{IBBME, University of Toronto, Canada; \texttt{t{tt{t{sara.mahallati@mail.utoronto.ca}}}
+
+\author[1,2]{Sara Mahallati\thanks{sara.mahallati@mail.utoronto.ca}}
+\author[3]{Dionne M. Aleman \thanks{This document is a modified version of the source file developed by Dr. Aleman}}
+\affil[1]{Institute of Biomaterials and Biomedical Engineering, University of Toronto, Canada}
+\affil[3]{Department of Mechanical and Industrial Engineering, University of Toronto}
+\affil[2]{Krembil Research Institute, University Health Network, Toronto, Canada}
+
+
+\date{June 21, 2018}
+
+
+% THE ACTUAL DOCUMENT
+
+\begin{document}
+
+\maketitle
+%\linenumbers
+
+\begin{abstract}
+This is a quick and dirty guide to doing most of the things most people will ever want to do with \LaTeX. This guide contains a number of examples of useful things (e.g., abstracts, arrows, tables, etc.) that may not be covered in the session today. Just look at the source \texttt{.tex} file to see how it was done (that is why there are a number of random bullets, symbols, sections, formatting styles, etc.\ throughout the guide). This document is certainly not the definitive \LaTeX\ manual; if there is something you want to do and it is not described here, look it up online. There are no limits to what can be done!  
+\end{abstract}
+
+\newpage
+
+\tableofcontents
+
+\newpage
+
+\section*{Acknowledgement} %the * is added to make it an un-numbered section 
+\addcontentsline{toc}{section}{Acknowledgement}
+This source file is mostly adopted from Dr. Dionne Aleman's tutorial at MIE department a few years back and used with her permission. 
+
+\section{What is \LaTeX ?} \label{sec:intro}
+
+From \href{https://en.wikipedia.org/wiki/TeX}{wikipedia-tex}, \TeX\ (pronounced ``tek") is a typesetting system created by Donald Knuth. Together with the METAFONT language for font description and the Computer Modern typeface, it was designed with two main goals in mind: to allow anybody to produce high-quality books using a reasonable amount of effort, and to provide a system that would give the exact same results on all computers, now and in the future. Within the typesetting system, its name is formatted as \TeX.
+
+% A message to help newbies avoid nit-picky pitfalls
+
+\textbf{The key to being a proficient \LaTeX user is simple: Stop worrying about formatting, and just trust that \LaTeX\ will do it right for you.}
+
+
+
+\subsection{Why use \LaTeX?}
+\LaTeX\ can be difficult to learn, and most people are too hopelessly attached to Microsoft's products to try something new. If Word works pretty well, why change? Here's a breakdown of some of the more obvious pros and cons of \LaTeX.
+
+\subsection{Pros and cons}
+
+\paragraph{Pros} 
+\begin{itemize}
+	\item \LaTeX\ documents are beautiful. The typesetting, spacing and kerning are all perfect, and the font (Computer Modern) is infinitely scalable, so it looks sharp at any size.
+	\item As shown in this document, you can create links to sections, tables, figures, citations, equations, bullets, URLs
+	\item moreover, it's automatic! You will never again have to redo your section numbers because you reorganized a paper, or worse, redo your citations because you added in a new reference that throws off the numbering.
+	\item You also won't have to redo your citations because the journal you are submitting your paper to wants a different citation format than what you used.
+\end{itemize}  
+
+
+For a strict comparison to Word, \TeX\ documents are just text, so they will never become bloated in file size (no matter how large your paper) or mysteriously crash and cause you to lose hours of work. \LaTeX\ won't make your pictures suddenly disappear while you tried to move the image one pixel upward. Your equations will look elegant and respectable, and not like they were written by a child. Further, equations are \textit{easy}---no more hunting for symbols and brackets in MathType. Tables are pure text, so you can automate table creation right from your data matrices in Matlab, C++, etc.
+
+
+\paragraph{Cons} 
+\begin{itemize}
+	\item \LaTeX\ has a learning curve. Unlike Word, you can't really just start using it without spending some effort to learn what is going on. That's why we have this tutorial.
+	\item Also unlike Word, \LaTeX\ won't let you (easily) do whatever you want in your document.\textbf{The driving vision behind \LaTeX\ is that \textit{it} will do your typesetting; you just do the typing.}. You are virtually not allowed to spend your time placing figures and tables exactly where you want them, because \LaTeX\ will generally put things where it thinks they go best. At first, this causes a lot of users headaches because they want to nitpick over positioning (and usually in an incorrect fashion). Eventually, you will realize that \LaTeX\ is in fact putting your floating objects in the best place they should be, and you will be happy not to have to worry about such things anymore.
+	\item Possibly the biggest drawback to \LaTeX\ is that it does not have the reviewing toolbar that Word has, which allows you and collaborators to track changes made to a document. There are some ways to get around this if you are working with non-\LaTeX\ users (one way it to copy the paragraph you want to change into Word and then track the changes made), but nothing is very elegant.
+\end{itemize}  
+
+ 
+\section{How to install \LaTeX \ and editors}
+In Section \ref{sec:intro}, we learned about \LaTeX. In Section \ref{sec:win}, we'll talk about how to get and install \LaTeX.
+
+There are many different packages and editors available. TeXWorks is included in the distribution you download. The ones listed below are just ones I have used and liked, but there are certainly many other good ones. Most packages and editors are 100\% free. Just download and install the packages and editors, and you are ready to go.
+
+\textit{PRO TIP: Install \LaTeX\ and any additional items first, then install your editor last. That way, your editor will probably auto-detect everything on its own.}
+
+
+
+
+\subsection{Mac}
+
+\begin{enumerate}
+\item \LaTeX\ package
+\begin{itemize}
+\item MacTex: \url{http://www.tug.org/mactex/}
+\end{itemize}
+\item Editor
+\begin{itemize}
+\item TeXShop: \url{http://www.uoregon.edu/~koch/texshop/}
+\end{itemize}
+\end{enumerate}
+
+\subsection{Windows OS} \label{sec:win}
+\begin{itemize}
+\item \LaTeX\ package
+\begin{itemize}
+\item MikTex: \url{http://miktex.org/}
+\end{itemize}
+\item Editor
+\begin{itemize}
+\item TeXstudio: \url{https://www.texstudio.org/}
+\end{itemize}
+\end{itemize}
+
+
+\subsection{How to use the editors}
+Once you have written your \LaTeX\ document\footnote{Look! A footnote! Did you notice that the footnote number in the text was a link to this footnote?}, you will ``typeset" (compile) your work. Your editor will automatically create a pdf document, along other auxiliary files. 
+
+\section{How to write your \LaTeX\ document}
+The rest of this document describes the basics of writing a paper in \LaTeX. This covers everything that 90\% of users will ever need, but only scratches the surface of what you can do. A famous comprehensive guide, ``The Not So Short Introduction to \LaTeX", details just about everything and can be found at
+\begin{center}\url{http://www.ctan.org/tex-archive/info/lshort/english/lshort.pdf}\end{center}
+
+
+
+\subsection{How to write mathematical equations}
+\LaTeX\ makes writing mathematical equations ridiculously easy, and the results are picture-perfect. 
+
+\subsubsection{Math inline with text}
+To use math inline with text, type the equation inline with your text, and surround it by \$$\ldots$\$. The equation will automatically be formatted to best fit in the paragraph. For example, with $\sum_{i \in S} x_i \geq 1$, the subscript for the summation is placed to the side rather than underneath.
+
+
+\subsubsection{Single equations}
+You can write a single equation one of two ways:
+\begin{enumerate}
+\item \label{longhand} \verb!\begin{equation} ... \end{equation}!
+\item \label{shorthand} \verb!$$ ... $$!
+\end{enumerate}
+where ``\ldots" represents the equation itself. Method \ref{shorthand} is essentially just shorthand for Method \ref{longhand} (look at the source document to see how these linked labels were made). For example, using Method \ref{longhand}:
+\begin{equation} 
+\label{eqn:f} F(\Theta) = \mu + \epsilon(\theta)
+\end{equation}
+yields the same result as Method \ref{shorthand}:
+$$ F(\Theta) = \mu + \epsilon(\theta) $$
+Note that Method \ref{shorthand} does not number the equation. Equation \ref{eqn:f} is numbered.
+
+\paragraph{Pro tip} When writing equations, \textbf{do not skip lines} in your source code. A skipped line in the source document is interpreted as a new paragraph, and if you skip a line before your equation, there will be too much vertical spacing before the equation in the \texttt{pdf}. Skipping lines after the equation is fine if you intend whatever content comes next to be a new paragraph.
+
+\subsubsection{Numbering equations}
+Equations are automatically numbered, but if you don't want to number an equation, either use the shorthand method or place an asterisk (\texttt{*}) after \texttt{equation} in both the begin and end statements.
+
+\begin{equation*} 
+F(\Theta) = \mu + \epsilon(\theta)
+\end{equation*}
+
+
+\subsubsection{Matrices/arrays}
+Matrices are just arrays displayed with brackets around them. For example:
+
+\begin{equation}
+\mathbf{R} = \left[ 
+\begin{array}{cc}
+a & b  \\
+c & d  \\
+e & f  \\
+\end{array} 
+\right], 
+\end{equation}
+
+\begin{equation}
+\mathbf{R} = \left( 
+\begin{array}{ccc}
+a_1 & \cdots & z_1 \\
+\vdots & \ddots & \vdots \\
+a_n & \cdots & z_n
+\end{array} 
+\right). 
+\end{equation}
+
+Dots are created using \verb!\cdots! (center dots), \verb!\vdots! (vertical dots) and \verb!\ddots! (diagonal dots). Ellipses-style dots are created by \verb!\ldots! (lower dots).
+
+Using \verb!\left(! and \verb!\right)! will create parentheses that expand to the appropriate size to fit the content inside them. The ( and ) can be replaced with any other bracketing-type symbol with the same effect. A \verb!\left! must ALWAYS be accompanied by a  \verb!\right!. To only use the left or right bracket/brace/etc., use a ``." instead of the omitted bracket/brace/etc. symbol.
+\begin{equation*}
+y_i = \left\{ \begin{array}{ll}
+1 & \mbox{if facility is built at location } i \\
+0 & \mbox{otherwise}
+\end{array} \right.
+\end{equation*}
+
+
+\subsubsection{Greek letters}
+If you can spell the names of Greek letters, you can easily use them in \LaTeX. 
+
+\paragraph{Lowercase letters}
+Lowercase Greek letters are simply $\backslash$name, where ``name" is the name of the letter in all lowercase. For example:
+\begin{eqnarray*}
+\backslash\mbox{gamma} &\rightarrow& \gamma \\
+\backslash\mbox{delta} &\rightarrow& \delta \\
+\backslash\mbox{pi} &\leftarrow& \pi \\
+\backslash\mbox{phi} &\leftarrow& \phi \\
+\end{eqnarray*}
+
+\paragraph{Uppercase letters}
+Uppercase Greek letters are simply $\backslash$Name, where ``Name" is the name of the letter in with the first letter capitalized. For example:
+\begin{eqnarray*}
+\backslash\mbox{Gamma} &\Rightarrow& \Gamma \\
+\backslash\mbox{Delta} &\Rightarrow& \Delta \\
+\backslash\mbox{Pi} &\Leftarrow& \Pi \\
+\backslash\mbox{Phi} &\Leftarrow& \Phi \\
+\end{eqnarray*}
+
+
+\subsection{Figures}
+Figures are fairly easy, and new \LaTeX\ distributions no longer mandate eps or pdf format (though eps and pdf are generally better for scaling). You can save your figures as eps files. make sure that any font in your figure (e.g., legends) is legible. This usually means that the fonts will have to be seemingly unreasonably large.
+
+\begin{figure}[tbp]
+	\centering 
+	\includegraphics[width=0.75\textwidth]{graph}
+	\caption{Graphical results} 
+	\label{fig:DVHs}
+\end{figure}
+
+We can also easily create subfigures using the \texttt{subfigure} package. We can then refer to them as Figure \ref{fig:cluster-pre} and Figure \ref{fig:cluster-post}, or refer to the whole figure set as Figure \ref{fig:cluster}.
+
+\begin{figure}[tbp]
+	\begin{center}
+		\subfigure[Contour plot of iteration $k$]{
+			\includegraphics[scale=0.4]{contour1.jpg}
+			\label{fig:cluster-pre}
+		}
+		\subfigure[Contour plot of iteration $k+1$]{
+			\includegraphics[scale=0.4]{contour2.jpg}
+			\label{fig:cluster-post}
+		}
+	\end{center}
+	\caption{Contour plots of the target at two subsequent iterations of the skeletonization algorithm.}
+	\label{fig:cluster}
+\end{figure}
+
+Look at the code to generate Figures \ref{fig:DVHs} and \ref{fig:cluster} in the \texttt{tex} file. Note that the width can be specified as a percentage of the total text width (\verb![width=0.75\textwidth]!) or as a scale of the original figure size (\verb![scale=0.4]!). 
+
+\subsection{Citations}
+Citations in \LaTeX \ are done through another package called BibTeX, which should come pre-installed with your \LaTeX\ package. Simply write the information for all the publications you want to cite in a text file with extension \texttt{.bib}, and then include that file (without extension) and a particular citation style as a bibliography at the end of your document:
+
+\begin{verbatim}
+\bibliographystyle{plain}
+\bibliography{bibfile_latex_howto}
+\end{verbatim}
+
+To reference a single publication \citep{Mahallati2018}, simply write the citation's key inside a \verb!\cite{}! command:
+\begin{verbatim}
+\cite{Mahallati2018}
+\end{verbatim}
+For multiple citations like \cite{Buzsaki2012, Peyrache2012, Mahallati2018a}:
+\begin{verbatim}
+\cite{Buzsaki2012, Peyrache2012, Anastassiou2015}
+\end{verbatim}
+Unless told otherwise, BibTeX will order your bibliography by last name of first author and then by year. Section \ref{sec:citeorder} shows how to automatically sort and compress your citations.
+
+For fancier citations (e.g., ``name, year" format), use the \verb!natbib! package and change the bibliography style from \verb!plain! to something else. Rather than make your own style, it easier to find a journal whose style you like, and then download their citation style (which is usually publicly available).
+
+\subsubsection{Sorting and compressing citations} \label{sec:citeorder}
+It is generally considered very bad form to have your citations appear in non-alphabetical or non-numerical order. But, it can be tedious to re-arrange your citations or insert new citations if you have a string of several reference. Compressing citations (e.g., [1--4,7] instead of [1,2,3,4,7]) can also be time-consuming. Fortunately, the \texttt{natbib} package will automatically take care of that.
+
+Just include the \texttt{natbib} package in your header with options to either sort or sort and compress:
+\begin{verbatim}
+\usepackage[sort]{natbib}
+\usepackage[sort&compress]{natbib}
+\end{verbatim}
+Note that compressing will only change the citation appearance if you are using numbered citations.
+
+\subsubsection{Compiling citations}
+To get your references to show up, you will have to:
+\begin{enumerate}
+	\item Typeset your document with \LaTeX: tells the editor which publications from your \texttt{.bib} file you want to cite;
+	\item Typeset your document with BibTeX: formats the citations according to your bibliography style;
+	\item Typeset your document with \LaTeX\ twice more: figures out what numbering each citation should have (usually based on last name of first author) and puts it all in your document.
+\end{enumerate}
+
+Some editors actually compiles both \LaTeX\ and BibTeX simultaneously, so you effectively have to compile the document 3-4 times to get the in-text citations updated.
+
+The same principle of multiple compilations applies to generating the table of contents and figure/table numbers, as well as to section, equation, table and figure referencing.
+
+\subsubsection{Making your citations into links}
+Your references to bibliography items and to other sections can be turned into actually hyperlinks in the document by using the \verb!hyperref! package. Nothing different needs to be done in terms of citing references or referring to sections, tables or figures; just include the package in the document header.
+
+See the source code of this document for an example of how to set colors.
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\subsection{Tables}
+Tables are a little bit of work in \LaTeX. Table \ref{tbl:vals} uses a plain layout. Alternatively, you can use the table styles defined in the \verb!booktabs! package for a more polished look as in Table \ref{tbl:fancyvals}.
+
+Although the creation of tables is not as intuitive as in, say, Microsoft Excel, the pure text format certainly has its advantages. For example, if you have a matrix of values in Matlab (or any other programming environment/language) that you want to put in a table in your document, you don't have to copy those values over by hand. Just write a quick function that will read in the matrix and print out the ampersands (\verb!&!) and endlines (\verb!\\!) appropriately. Regardless of how large your table is, you just have to call one function and your table is ready for you to paste into your \verb!.tex! file!
+
+
+\begin{table}[tbp] % t = top, b = bottom, p = own page, h = here (not recommended)
+\centering
+\begin{tabular}{|c|rr|rr|}  % l = left, c = center, r = right
+\hline
+ & \multicolumn{2}{c|}{\textbf{Obj. fn. value}} & \multicolumn{2}{c|}{\textit{Run time (hrs)}} \\
+\hline
+case &	Avg. & St. Dev. & Avg. & St. Dev. \\
+\hline\hline
+1 &	565.24 &	8.82 &	5.35 &	5.07 \\
+2 &	570.51 &	12.83 &	7.49 &	3.78 \\
+3 &	893.45 &	20.60 &	7.05 &	2.21 \\
+4 &	710.92 &	7.72 &	6.54 &	3.39 \\
+5 &	512.22 &	20.04 &	6.96 & 	3.33 \\
+6 &	799.95 &	34.07 &	3.48 &	3.40 \\
+\hline
+\end{tabular}
+\caption{Minimum objective function values and time computation time.}
+\label{tbl:vals}
+\end{table}
+
+
+\begin{table}[tbp]
+\centering
+\begin{tabular}{c @{ \hspace{1cm} } cc @{\hspace{1cm}} cc @{\hspace{0.5cm}}}
+\toprule
+ & \multicolumn{2}{l}{\textit{Obj. fn. value}} & \multicolumn{2}{c}{\textit{Run time (hrs)}} \\
+case & Average & St. Dev. & Average & St. Dev. \\ 
+\midrule
+1 &	565.24 &	8.82 &	5.35 &	5.07 \\
+2 &	570.51 &	12.83 &	7.49 &	3.78 \\
+3 &	893.45 &	20.60 &	7.05 &	2.21 \\
+4 &	710.92 &	7.72 &	6.54 &	3.39 \\
+5 &	512.22 &	20.04 &	6.96 & 	3.33 \\
+6 &	799.95 &	34.07 &	3.48 &	3.40 \\
+\bottomrule
+\end{tabular}
+\caption{A nicer layout of the table. Note the extra spacing placed between columns.}
+\label{tbl:fancyvals}
+\end{table}
+
+
+\section{\LaTeX\ instead of PowerPoint}
+\LaTeX\ can be used a presentation medium as well through a package called Beamer:
+\begin{center} \url{http://bitbucket.org/rivanvx/beamer/wiki/Home} \end{center}
+One of the great benefits of Beamer, aside from preventing the use of animations, is the dynamic presentation outline that allows your audience to see exactly where in the presentation you are at any given moment.
+
+More information on how to use Beamer can be found at the link above, but it basically boils down to putting 
+\begin{verbatim}
+\frame{
+\frametitle{Your frame title}
+...
+}
+\end{verbatim}
+around your content.
+
+
+
+
+% THE REFERENCES
+\bibliographystyle{abbrvnat}
+%\bibliographystyle{plain}
+%\bibliography{bibfile_latex_howto}
+\bibliography{MyLibrary}
+
+\newpage
+
+% THE APPENDIX
+\appendix
+
+\section{Tables of data} \label{app:tabledata}
+Look, an appendix! You can reference an appendix just like anything else. See the code in Appendix \ref{app:tabledata} for examples.
+
+\newpage
+
+\section{Figures upon figures} \label{app:figs}
+Another appendix! This one is automatically named Appendix \ref{app:figs}.
+
+
+
+\end{document}
diff --git a/lessons/misc/latex-intro/lesson-2021.md b/lessons/misc/latex-intro/lesson-2021.md
new file mode 100644
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--- /dev/null
+++ b/lessons/misc/latex-intro/lesson-2021.md
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+---
+layout: page
+title: 'Intro to LaTeX'
+visible: true
+tags:
+  - misc
+  - beginner
+---
+
+ - **Authors**: Jesse (@jessexknight)  
+ - **Lesson topic**: Introduction to LaTeX
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/latex-intro>
+
+This lesson will cover the basics of writing scientific and technical documents
+using LaTeX.
+
+Please see [here][latex-slides] for lesson material, as well as its 
+[parent repo][main-repo] for more resources on LaTeX.
+
+We will be using [Overleaf][overleaf] for this lesson. Overleaf also features
+extensive [guides and documentation][overleaf-guides] for more advanced LaTeX
+features beyond the scope of this lesson. 
+
+[latex-slides]: https://github.com/jessexknight/latex/blob/master/slides/main.pdf
+[main-repo]: https://github.com/jessexknight/latex
+[overleaf]: https://www.overleaf.com/
+[overleaf-guides]: https://www.overleaf.com/learn
diff --git a/lessons/misc/latex-intro/lesson.md b/lessons/misc/latex-intro/lesson.md
new file mode 100644
index 000000000..1842d6519
--- /dev/null
+++ b/lessons/misc/latex-intro/lesson.md
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+---
+layout: page
+title: 'Intro to LaTeX'
+visible: true
+tags:
+  - misc
+  - beginner
+---
+
+ - **Authors**: Curtis 
+ - **Research field**: Democracy and Information Systems, Sustainability and Software
+ - **Lesson topic**: Getting Started with LaTeX
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/latex-intro>
+
+## Preface: ##
+
+This is a very basic introduction to LaTeX, a markup language for document preparation. LaTeX is a popular and widely loved way of writing not only scientific and mathematical papers, but for creating beautiful documents on many types. LaTeX gives users a lot of power over their documents, but has a bit of a learning curve. This workshop is designed to give you a leg up on that!
+
+This lesson has been cobbled together from many pieces of advice from my experience and from across the LaTeX user community. 
+
+No downloads are needed for this lesson, but making an account at [ShareLaTeX](https://www.sharelatex.com) will allow you to follow along and see what's going on.
+
+## Lesson Overview: ##
+
+This lesson covers:
+
+* What is LaTeX and what it is for
+* The different parts of a TeX file
+  - The preamble
+  - Packages
+  - Common environments
+* Compiling and troubleshooting
+* Where to learn more
+
diff --git a/lessons/misc/latex-intro/tex_workshop/deck.tex b/lessons/misc/latex-intro/tex_workshop/deck.tex
new file mode 100644
index 000000000..07c5bf323
--- /dev/null
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@@ -0,0 +1,330 @@
+%----------------------------------------------------------------------------------------
+%	PACKAGES AND THEMES
+%----------------------------------------------------------------------------------------
+
+\documentclass{beamer}
+\let\Tiny=\tiny
+\mode<presentation> {
+\usetheme{Antibes}
+\usecolortheme{whale}
+}
+
+%\usepackage{ragged2e}   
+\usepackage{graphicx} % Allows including images
+\usepackage{booktabs} % Allows the use of \toprule, \midrule and \bottomrule in tables
+
+%\addtobeamertemplate{block begin}{}{\justifying} 
+
+%----------------------------------------------------------------------------------------
+%	TITLE PAGE
+%----------------------------------------------------------------------------------------
+
+\title[Intro to \LaTeX]{Introduction to \LaTeX\ for Scientific Coders} % The short title appears at the bottom of every slide, the full title is only on the title page
+
+\author{} % Your name
+\institute[UofT] % Your institution as it will appear on the bottom of every slide, may be shorthand to save space
+{
+University of Toronto \\ % Your institution for the title page
+\medskip
+\textit{curtis.mccord@mail.utoronto.ca} % Your email address
+}
+\date{March 10, 2016} % Date, can be changed to a custom date
+
+\begin{document}
+
+\begin{frame}
+\titlepage % Print the title page as the first slide
+\end{frame}
+
+\begin{frame}
+\frametitle{Overview} % Table of contents slide
+\tableofcontents
+\end{frame}
+
+%----------------------------------------------------------------------------------------
+%	PRESENTATION SLIDES
+%----------------------------------------------------------------------------------------
+\section{Introduction}
+
+\begin{frame}
+\frametitle{What you need}
+For this workshop, we won't ask you to download anything. You can do that on your own, since it depends on your operating system. 
+
+We'll be using \url{www.sharelatex.com}, a cloud-based \TeX\ editor that is alright for collaboration, but takes some of the hassle out of getting set up.
+\end{frame}
+%------------------------------------------------
+\section{What is \LaTeX?}
+
+\begin{frame}
+\frametitle{What is \LaTeX\ for?} 
+\begin{itemize}
+\item{Pronounced LAY-TEK or LAH-TEK} 
+\item{A document markup language}
+\item{\LaTeX\ is very popular in many academic disciplines- you'll probably recognize the signs of a \TeX\ document already}
+\item{\LaTeX\ is WYSINWYG - unlike WYSIWYG word processors like Open Office, Google Docs, and MS Word}
+\end{itemize}
+\end{frame}
+
+\begin{frame}
+\frametitle{What is  \LaTeX\ for?}
+\begin{itemize}
+\item{\LaTeX\ is used for document preparation}
+\item{Users write in plain, marked up text and then compile that into a .pdf}
+\item{\LaTeX\ is great for writing all kinds of documents (including references with BibTeX, doing homeworks, or making slideshows}
+\end{itemize}
+\end{frame}
+
+\begin{frame}
+\frametitle{How can \LaTeX\ help me?}
+\begin{itemize}
+\item{Write impressive documents}
+\item{Write neat math}
+\item{Make your workflow more stable, efficient, and versatile }
+\item{Use Free software!}
+\item{Never touch MS Word again-- except when people force you to.}
+\end{itemize}
+\end{frame}
+
+%------------------------------------------------
+\section{Using  \LaTeX }
+%------------------------------------------------
+
+\begin{frame}
+\frametitle{Nuts and Bolts}
+There are many files involved in generating a finalized document\dots
+\begin{figure}
+	\includegraphics[scale = 0.3]{latex_dataflow.png}
+	\caption{\tiny{\LaTeX\ inputs, outputs and supporting files from \href{https://webhost1.ust.hk/~atcwiki/cgi-bin/atcwiki/index.php?title=ATC:LaTeX}{ATC wiki}}}
+\end{figure}
+\end{frame}
+
+\begin{frame}[fragile]
+	\frametitle{The .tex file}
+	Most \LaTeX\ documents (including this one) are shaped something like this:
+		\begin{semiverbatim}
+			\\documentclass[options]\{class\}
+			\\usepackage\{package\}
+			\\begin\{document\}
+			\\tableofcontents
+			\\section\{Section Title\}
+		\-\	Some content?
+		\-\	\$Some maths?\$
+			\\end\{document\}
+		\end{semiverbatim}
+\end{frame}
+
+\begin{frame}
+	\frametitle{The .tex preamble (and some other stuff)}
+	
+	Anything between the \textbackslash documentclass command and the \textbackslash begin\{document\} command is called the preamble. Taken  inclusively, this includes a number of elements.
+	
+	\begin{description}
+		\item[documentclass:] sets automated margins, paper and font size, etc. Chances are you will use a lot of `article' class.
+		\item[usepackage:] packages add new options to your document (like libraries). I use grahpicx for include images.
+		\item[environments:] make \LaTeX\ use special formatting for figures, tables, lists, images, or maths
+	\end{description}
+\end{frame}
+
+\begin{frame}[fragile]
+	\frametitle{Lists}
+	\begin{columns}[T]
+		\begin{column}{0.5\textwidth}
+				\textbf{Itemized lists:}
+			\begin{itemize}
+				\item item A
+				\item item B
+			\end{itemize}	
+			\textbf{Enumerated lists:}
+			\begin{enumerate}
+				\item item AA
+				\item item BB
+			\end{enumerate}
+			\textbf{Description Lists:}
+			\begin{description}
+				\item[label] item AAA
+				\item[label] item BBB
+			\end{description}
+		\end{column}
+		\hspace{-50pt}
+		\vrule
+		\hspace{30pt}
+		\begin{column}{0.5\textwidth}
+			\begin{semiverbatim}
+				\begin{tiny}
+				\\begin\{itemize\}
+					\-\ \\item item A
+					\-\ \\item item B
+				\\end\{itemize\}
+				\medskip
+				
+				\\begin\{enumerate\}
+					\-\ \\item item AA
+					\-\ \\item item BB
+				\\end\{enumerate\}
+				\medskip
+
+				\\begin\{description\}
+					\-\ \\item[label] item AAA
+					\-\ \\item[label] item BBB
+				\\end\{description\}
+				\end{tiny}
+			\end{semiverbatim}
+		\end{column}
+	\end{columns}
+\end{frame}
+
+\begin{frame}[fragile]
+	\frametitle{Tables}
+	Basic \LaTeX\ tables can be a little unintuitive, but there are some great guidelines for style in the additional resources.\bigskip
+	\begin{columns}[c]
+		\begin{column}{0.45\textwidth}
+		\begin{tabular}{| l l l |}
+			\hline
+			Column 1 & Column 2 & Column 3 \\
+			\hline \hline
+			A & B & C \\
+			$\alpha$ & $\beta$ & $\gamma$ \\
+			\hline
+		\end{tabular}
+		\end{column}
+		\hspace{30pt}
+		\vrule
+		\hspace{10pt}
+		\begin{column}{0.45\textwidth}
+		\begin{semiverbatim}
+			\begin{tiny}
+				\\begin\{tabular\}\{\textbar l l l \textbar\}
+					\\hline
+					Column 1 \& Column 2 \& Column 3 \\\\
+					\\hline \\hline
+					A \& B \& C \\ \\\
+					\$\\alpha\$ \& \$\\beta\$ \& \$\\gamma\$ \\\\
+					\\hline
+				\\end\{tabular\}
+			\end{tiny}
+		\end{semiverbatim}
+		\end{column}
+	\end{columns}
+\end{frame}
+
+\begin{frame}[fragile]
+	\frametitle{Images}
+	\begin{columns}[c]
+		\begin{column}{0.5\textwidth}
+			\hspace{10pt}
+		\begin{figure}
+			\begin{centering}
+			\includegraphics[scale=0.25]{enjoy_science.jpg}
+			\label{enjoy_science}
+			\caption{Enjoy Science!}
+			\end{centering}
+		\end{figure}
+		\end{column}
+	\hspace{30pt}
+	\vrule
+	\hspace{10pt}
+		\begin{column}{0.5\textwidth}
+			\begin{tiny}
+		\begin{semiverbatim}
+				\\begin\{figure\}
+					\\begin\{centering\}
+						\\includegraphics[scale=0.25]\{enjoy\_science.jpg\}
+						\\label\{enjoy\_science\}
+						\\caption\{Enjoy Science!\}
+					\\end{centering}
+				\\end\{figure\}
+		\end{semiverbatim}
+					\end{tiny}
+		\end{column}
+	\end{columns}
+	\begin{centering}
+	\tiny{image courtesy of: \href{https://scientiasalon.wordpress.com/2014/08/25/science-is-not-a-frog/}{Scientia Salon}}
+	\end{centering}
+\end{frame}
+
+\begin{frame}
+	\frametitle{Writing Math 1}
+	
+	Math mode is called between \$ symbols. There are two kinds of math modes. In inline math mode(\$math\$), math appears like this: $f(x)= \sum_n{i=0}^{n} \frac{(a_i)}{1 + x}$ In display mode (\$\$math\$\$), math appears like this: 	$$f(x)= \sum_n{i=0}^{n} \frac{(a_i)}{1 + x}$$
+	
+	Notice the difference between the ways that sub/superscript and fractions appear.
+	\begin{semiverbatim}
+			\$f(x)= \\sum\_n\{i=0\}\^\{n\} \\frac\{(a\_i)\}\{1 + x\}\$
+	\end{semiverbatim}
+\end{frame}	
+
+\begin{frame}[fragile]
+	\frametitle{Writing Math 2}
+	Let's take a look at some equations to see how \LaTeX\ handles them! \bigskip
+\begin{columns}
+	\hspace{-20pt}
+	\begin{column}{0.57\textwidth}
+		\begin{enumerate}
+				\item ${x = \frac{- b \pm \sqrt{b^{2} - 4ac}}{2a} }$
+				\item $\oint_C {E \cdot d\ell = - \frac{d}{{dt}}} \int_S {B_n dA}$
+				\item $\emptyset = {\{u \in w | (u \in u) \land \lnot(u \in u)}\}$
+				\item $\forall x(Px \to Qx)\to\exists o(Po \to Qo)$
+		\end{enumerate}
+	\end{column}
+	\hspace{0pt}
+	\begin{column}{0.55\textwidth}
+	\begin{enumerate}
+		\item {\begin{semiverbatim}\begin{tiny}
+\$\{x = \\frac\{- b \\pm \\sqrt\{b^\{2\} - 4ac\}\}\{2a\} \}\$
+				\end{tiny} \end{semiverbatim}}
+		\item {\begin{semiverbatim}\begin{tiny}
+				\$\\oint_C \{E \\cdot d\\ell = - \\frac\{d\}\{\{d\}\}\} \dots 
+				\\int_S \{B_n dA\}\$			
+				\end{tiny}\end{semiverbatim}}
+		\item {\begin{semiverbatim}\begin{tiny}
+				\$\\emptyset = \{\\\{u \\in w \textbar (u \\in u) \\land \dots
+				\\lnot(u \\in u)\}\}\$			
+				\end{tiny}\end{semiverbatim}}	
+		\item {\begin{semiverbatim}\begin{tiny}
+				\$\\forall x(Px \\to Qx)\\to\\exists o(Po \\to Qo)\$			
+				\end{tiny}\end{semiverbatim}}
+	\end{enumerate}
+	\end{column}
+\end{columns}
+\end{frame}
+
+
+%------------------------------------------------
+\section{Learning \LaTeX}
+%------------------------------------------------
+
+\begin{frame}
+\frametitle{What tools are available to me?}
+\begin{columns}
+	\begin{column}{0.5\textwidth}
+Edittor/Compilers:
+\begin{itemize}
+	\item {\TeX Shop}
+	\item {\TeX Studio}
+	\item {Overleaf (Online)}
+	\item {Share\LaTeX\ (Online)}
+\end{itemize}
+\end{column}
+\begin{column}{0.5\textwidth}
+	Distributions:
+\begin{itemize}
+	\item MiK\TeX
+	\item \TeX Live
+	\item Max\TeX
+\end{itemize}
+\end{column}
+\end{columns}
+Or write \LaTeX\ in your emacs :p
+\end{frame}
+
+\begin{frame}
+\frametitle{Resources}
+\begin{itemize}
+	\item{\href{https://www.inf.ethz.ch/personal/markusp/teaching/guides/guide-tables.pdf} {P\" uschel's {\em Small Guide to Making Nice Tables}}}
+	\item{\href{tex.stackexchange.com}{tex.stackexchange.com}}
+	\item{Googling `Introduction to \LaTeX'}
+	\item{\href{http://texdoc.net/texmf-dist/doc/plain/gentle/gentle.pdf}{Michael Doob's {\em Gentle Introduction to} \TeX}}
+\end{itemize}
+\end{frame}
+
+\end{document}
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+---
+layout: page
+title: 'Opening up your data'
+visible: True
+tags:
+  - misc
+  - beginner
+
+---
+
+ - **Authors**: Derek Howard
+ - **Lesson topic**: A guided discussion on opening up your research data
+
+
+## What is open data?
+[“Open data is data that can be freely used, re-used and redistributed by anyone - subject only, at most, to the requirement to attribute and sharealike.”](http://opendatahandbook.org/guide/en/what-is-open-data/)
+
+More specifically, from the open data handbook and [opendefinition.org](http://opendefinition.org/od/2.1/en/):
+- Availability and Access: the data must be available as a whole and at no more than a reasonable reproduction cost, preferably by downloading over the internet. The data must also be available in a convenient and modifiable form.
+- Re-use and Redistribution: the data must be provided under terms that permit re-use and redistribution including the intermixing with other datasets.
+- Universal Participation: everyone must be able to use, re-use and redistribute - there should be no discrimination against fields of endeavour or against persons or groups. For example, ‘non-commercial’ restrictions that would prevent ‘commercial’ use, or restrictions of use for certain purposes (e.g. only in education), are not allowed.
+
+## [Why open data?](https://mozillascience.github.io/open-data-primers/1.1-what-and-why.html)
+- Maintain accountability:
+When data is open, the results or findings of that data can be more easily verified, increasing public trust in research institutions and governments.
+- Speed discovery:
+When data is open, researchers working in parallel on similar problems don’t need to duplicate work and can use each other’s data to advance knowledge across their research area.
+- Encourage collaboration across disciplines:
+When data is open, researchers can more easily find those outside their subject area doing related or relevant work; these connections and collaborations may lead to new ways of approaching and solving problems.
+- Solve a broader range of problems:
+When data is open, new users-- from academic researchers to citizen scientists to artists-- may use it to answer questions, solve problems, or create public understanding in ways that the original data collector may never have imagined.
+- Encourage public engagement with research:
+When data is open and the findings are shared in a clear and accessible way, it increases public understanding, creates opportunities for public participation, and bolsters public support of research initiatives.
+- Ensure that data is preserved:
+When data is open and widely shared, the responsibility for the long-term archiving and preservation of that data is distributed across broader group of interested users.
+
+Grant agencies care about open data:
+- Tri-agency (i.e. NSERC, CIHR, SSHRC) policy on data: [Data Management Policy](http://www.science.gc.ca/eic/site/063.nsf/eng/h_83F7624E.html?OpenDocument) and [Open Access Policy](http://www.science.gc.ca/eic/site/063.nsf/eng/h_F6765465.html?OpenDocument)
+
+## Challenges to open data
+- reference [handout](https://github.com/mozillascience/open-data-training/blob/master/Materials/Handouts/ODChallengesQI.md) from Mozilla open data training
+- ["Differential privacy"](https://www.wired.com/2016/06/apples-differential-privacy-collecting-data/) as a way to anonymize sensitive data. [More technical/not for dummies](https://robertovitillo.com/2016/07/29/differential-privacy-for-dummies/)
+
+
+## How to open your data
+- Metadata
+- Create a DATA_README
+- Use the [data reuse checklist](https://mozillascience.github.io/checklist/)
+
+
+## Resources
+https://mozillascience.github.io/open-data-primers/
+https://mozillascience.github.io/open-data-guides/
+https://mozillascience.github.io/working-open-workshop/data_reuse/
+http://opendatahandbook.org/guide/en/what-is-open-data/
diff --git a/lessons/misc/pandoc-intro/complexlatexsample.png b/lessons/misc/pandoc-intro/complexlatexsample.png
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diff --git a/lessons/misc/pandoc-intro/example_document.md b/lessons/misc/pandoc-intro/example_document.md
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+---
+title: "Learning Markdown"
+author: Lina Tran
+date: June 28, 2018
+
+# for including LaTeX packages
+header-includes:
+    - \usepackage{fullpage}
+    - \usepackage{setspace}
+    - \usepackage[section]{placeins}
+    - \doublespacing
+
+# for citations
+bibliography: library.bib
+citation-style: nature.csl
+---
+
+
+# Header 1
+## Header 2
+
+This is a test of *pandoc* **formatting**.
+
+# Unordered Lists
+
+- list one
+- list two
+- list three
+
+
+* list one
+* list two
+* list three
+
+# Ordered Lists
+
+1. ordered list one
+    1. subitem
+    2. subitem
+2. ordered list two
+5. The numbers themselves are ignored
+
+# Horizontal Rules
+
+--------------------
+
+lines
+
+---------------------
+
+# New Lines and Paragraphs
+
+This is one line with two spaces after the end.  
+This will be a new line.
+This will not be.
+
+This will also be a new line.
+
+## Block Quotes
+
+> This is a block quote. this
+> paragraph has two lines. There
+> must be a space after the `>`.
+>
+> 1. This is a list inside the block quote
+> 2. Second subitem
+
+> This is also
+possible
+
+> 1. First
+2. Second
+
+# Code Chunks
+
+```python
+num_list = [12,3 + 4, 1]
+def random_function(param):
+    return param*param
+```
+
+
+## Tables using pipe_tables
+
+`pandoc document_1.md -o document_1.pdf -f markdown+pipe_tables`
+
+| Right | Left | Default | Centered |
+|------:|:---------|---|:--------:|
+|12|12|12|12|
+|132|12|2|12|
+
+
+# Links
+## Automatic and In Line Links
+
+<http://google.com>
+<jane.doe@email.com>
+
+This is an [inline link](https:/pandoc.org)
+
+## Reference Links
+[my-label-1]: test1.pdf
+
+Here is [my link][my-label-1].
+
+## Internal Links
+
+See the section on [Code Chunks].
+
+[Code Chunks]: #new-lines
+
+
+# Images
+![picture](picture.jpg){ width=50% }
+
+
+# Citations
+
+`pandoc --filter pandoc-citeproc document_1.md -o document_1.pdf`
+
+This is a fact [@Srivastava2014]
+This fact is proven by many papers [@Joyce2013; @Omer2018; @Noor2010]
+
+# \LaTeX
+
+$H=\sum\limits_{i=1}^k |x_{i} - y_{i}|$  
+
+# Pandoc Commands
+
+For simple conversions, pandoc can infer based on file Extensions
+
+`pandoc document_1.md -o document_1.pdf`
+
+If you have a reference word document with custom styles:
+
+`pandoc document_1.md -o document_1.docx --reference-doc reference.docx
+`
+
+For using an extension:
+
+`pandoc document_1.md -o document_1.pdf -f markdown+extension_name`
+
+To use citations add ` --filter pandoc-citeproc`:
+
+pandoc document_1.md -o document_1.pdf -f markdown+pipe_tables -t pdf --filter pandoc-citeproc
+
+
+# References
diff --git a/lessons/misc/pandoc-intro/lesson.md b/lessons/misc/pandoc-intro/lesson.md
new file mode 100644
index 000000000..726ed492a
--- /dev/null
+++ b/lessons/misc/pandoc-intro/lesson.md
@@ -0,0 +1,174 @@
+---
+layout: page
+title: Intro to Pandoc and Markdown
+visible: true
+tags:
+  - misc
+  - beginner
+---
+
+ - **Authors**: Dano Morrison & Lina Tran
+ - **Research field**: Neuroscience
+ - **Lesson topic**: How to use Pandoc and Markdown for writing scientific manuscripts
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/pandoc-intro>
+
+# Intro to Pandoc
+
+Do you enjoy the spartan, academic aesthetic of LaTeX documents but not have the time to learn its unforgiving syntax? Does writing HTML documents by hand make you feel uncool? Do you enjoy automating difficult things? If so, Pandoc is what you're looking for.
+
+Pandoc is a command line tool that you can use to automatically convert files from markup format to another. With Pandoc, you can write in something easy like Markdown, Microsoft Word, or LibreOffice, and convert it something hard like:
+- HTML
+- Ebook formats
+- LaTeX
+- PDF
+- and many others
+
+# Intro to Markdown
+
+Markdown is the best way to write things for Pandoc, and probably the best way to write things for publishing to the web. If you haven't heard of it, it's a lightweight, shortened version of HTML that uses simplified tags like `* ~ - #` to format documents. It's mostly writing plain text, but with a little practice you can easily implement the most common types of text formatting, like headings, lists, links, images, etc. The veritable bible of Markdown is [Daring Fireball.](https://daringfireball.net/projects/markdown/)
+
+# The Value Proposition
+
+With Markdown and Pandoc, you can write something that looks like this:
+
+~~~
+# Introduction
+
+## The globular theory of matter
+
+### A history of globbing
+If we accept the philosophical (and now scientifically incontrovertible) position that the movement of all rotund entities are mediated by underlying interactions between globular units, then it follows that whenever a globby is stored in the world such that it can be reglobbed at a later time (ie. a glob-memory), there must be some concurrent change that occurs in the underlying structure of globby particles.
+
+Many great scientists have dedicated their lives to the study of globs and greatly advanced the globular theory of matter. In the next decade, who knows what globular secrets will be unearthed?
+~~~
+
+That can be converted into HTML like this:
+
+---
+
+# Introduction
+
+## The globular theory of matter
+
+### A history of globbing
+If we accept the philosophical (and now scientifically incontrovertible) position that the movement of all rotund entities are mediated by underlying interactions between globular units, then it follows that whenever a globby is stored in the world such that it can be reglobbed at a later time (ie. a glob-memory), there must be some concurrent change that occurs in the underlying structure of globby particles.
+
+Many great scientists have dedicated their lives to the study of globs and greatly advanced the globular theory of matter. In the next decade, who knows what globular secrets will be unearthed?
+
+---
+
+and even converted into LaTeX so it looks like this:
+
+![alt text](../simplelatexsample.png)
+
+The real power of Pandoc, however, comes when, with style templates and a little extra formatting work, you're able to produce fully typeset and personally styled documents with a markdown file and a command line.
+
+![alt text](../complexlatexsample.png)
+
+Boom! Page numbers and everything!
+
+# Getting Started
+
+- Install [Pandoc](https://pandoc.org/installing.html)
+- Install LaTeX: [TeXLive (Linux)](https://www.tug.org/texlive/), [MacTeX (Mac)](https://www.tug.org/mactex/), [MikTeX (Windows)](https://miktex.org/)
+- Install XeTex?
+
+Whether you're doing something like `sudo apt-get install pandoc && apt-get install texlive` or `brew install pandoc && brew cask install mactex`, if you can run `pandoc -v` and `pdflatex -v` in a terminal, you're good to go.
+
+# Markdown
+
+*italics*
+**bold**
+
+- one
+- two
+- three
+
+1. one
+2. two
+3. three
+
+[example link](https://example.com/)
+
+![example image](/path/to/img.jpg)
+
+refer to `code` inline with backticks
+
+```JavaScript
+console.log('or define a whole bunch of code with three back ticks')
+```
+
+# Pandoc
+
+Once Pandoc is installed, the easiest way to convert a file is to open up your terminal (in the folder where your file is) and call:
+`pandoc <filetobeconverted> -o <newfilename.xx>`
+Where `.xx` is the file extension of the new file you want
+
+By default, this will produce a 'fragment' of the file type you want. If you'd like to create a standalone document you need Pandoc to generate some code for you rather than just have it convert the Markdown markup to another language. If you want to create an HTML page with Head and Body sections or a LaTeX document with all the necessary boilerplate, simply add `-s` after the file you want to convert in your terminal command.
+
+For example,
+`pandoc sample.md -s -o sample.tex`
+will take this
+
+```Markdown
+# Just learning
+
+Pretty *cool* stuff
+
+![image](./image.jpg)
+```
+
+and produce all of this
+
+```Tex
+\documentclass[]{article}
+\usepackage{lmodern}
+\usepackage{amssymb,amsmath}
+\usepackage{ifxetex,ifluatex}
+\usepackage{fixltx2e} % provides \textsubscript
+
+....
+
+\begin{document}
+
+\section{Just learning}\label{just-learning}
+
+Pretty \emph{cool} stuff
+
+\begin{figure}[htbp]
+\centering
+\includegraphics{./image.jpg}
+\caption{image}
+\end{figure}
+
+\end{document}
+```
+
+That's sure better than writing all that stuff yourself!
+You can also go straight to pdf (if you have the right dependencies installed) with
+`pandoc sample.md -s -o sample.pdf`
+
+You may have figured out that `-o` stands for 'output' and `-s` stands for 'standalone.' There's also a lot of other pandoc command modifiers that you can take a look at any time by running `pandoc -h`
+
+You can also combine multiple input files into one output files with the regular character `*` (it's not a special pandoc feature, but pandoc supports having multiple files thrown at from the terminal).
+`-H` Will let you include other files as the header of whatever you're producing
+`-V` will let you pass variables (ie. fontsize, documentclass for LaTeX documents)
+
+[This manual](https://pandoc.org/MANUAL.pdf) will be helpful for understanding these advanced features.
+
+# Advanced: Custom LaTeX templates for Pandoc
+Chances are, if you actually are trying to publish something in LaTeX, you're going to want to put your own style on things. It's not that hard to do with Pandoc, all it takes is:
+1. saving a Pandoc LaTeX template somewhere
+2. edit that template the way you want
+3. tell pandoc to use that template in producing an output
+
+## 1. Pandoc LaTeX Templates
+These aren't just regular LaTeX templates but Pandoc-specific templates that instruct Pandoc how to convert files into LaTeX. Here's an [example of one](https://github.com/jgm/pandoc-templates/blob/master/default.latex)
+
+## 2. Editing the templates
+If you want to expand on what the template provides, you can go into it and change or add things, maybe specific fonts or packages you would like to use
+
+## 3. Use that template to generate an output
+The easiest step, simply add a `--template=yourtemplate.tex` modifier into your console command. Make sure that your template is either in your working directory or in `/.pandoc/templates` *NOTE: You have to make this directory yourself*.
+You can use variables in the template such as `fontfamily` to style your own file. Example:
+`pandoc sample.md -o sample.pdf --template=mytemplate.tex -V fontfamily=sans`
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diff --git a/lessons/misc/quantum-computing/grovers-algorthim-2-qubits.ipynb b/lessons/misc/quantum-computing/grovers-algorthim-2-qubits.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Simulating Grover's Search Algorithm with 2 Qubits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the zero and one vectors\n",
+    "Define the initial state $\\psi$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1]\n",
+      " [0]\n",
+      " [0]\n",
+      " [0]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "zero = np.matrix([[1],[0]]);\n",
+    "one = np.matrix([[0],[1]]);\n",
+    "\n",
+    "psi = np.kron(zero,zero);\n",
+    "print(psi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the gates we will use:\n",
+    "\n",
+    "$\n",
+    "\\text{Id} = \\begin{pmatrix} \n",
+    "1 & 0  \\\\\n",
+    "0 & 1  \n",
+    "\\end{pmatrix},\n",
+    "\\quad\n",
+    "X = \\begin{pmatrix} \n",
+    "0 & 1   \\\\\n",
+    "1 & 0  \n",
+    "\\end{pmatrix},\n",
+    "\\quad\n",
+    "Z = \\begin{pmatrix} \n",
+    "1 &  0 \\\\\n",
+    "0 & -1 \n",
+    "\\end{pmatrix},\n",
+    "\\quad\n",
+    "H = \\frac{1}{\\sqrt{2}}\\begin{pmatrix} \n",
+    "1 & 1   \\\\\n",
+    "1 & -1  \n",
+    "\\end{pmatrix},\n",
+    "\\quad\n",
+    "\\text{CNOT} = \\begin{pmatrix} \n",
+    "1 & 0 & 0 & 0 \\\\\n",
+    "0 & 1 & 0 & 0 \\\\  \n",
+    "0 & 0 & 0 & 1 \\\\\n",
+    "0 & 0 & 1 & 0\n",
+    "\\end{pmatrix},\n",
+    "\\quad\n",
+    "CZ = (\\text{Id} \\otimes H) \\text{ CNOT } (\\text{Id} \\otimes H) \n",
+    "$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.  0.  0.  0.]\n",
+      " [ 0.  1.  0.  0.]\n",
+      " [ 0.  0.  1.  0.]\n",
+      " [ 0.  0.  0. -1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "Id = np.matrix([[1,0],[0,1]]);\n",
+    "X = np.matrix([[0,1],[1,0]]);\n",
+    "Z = np.matrix([[1,0],[0,-1]]);\n",
+    "H = np.sqrt(0.5) * np.matrix([[1,1],[1,-1]]);\n",
+    "\n",
+    "CNOT = np.matrix([[1,0,0,0],[0,1,0,0],[0,0,0,1],[0,0,1,0]]);\n",
+    "CZ = np.kron(Id,H).dot(CNOT).dot(np.kron(Id,H));\n",
+    "print(CZ)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the oracle for Grover's algorithm  (take search answer to be \"10\")\n",
+    "\n",
+    "$\n",
+    "\\text{oracle} = \\begin{pmatrix} \n",
+    "1 & 0 & 0 & 0  \\\\\n",
+    "0 & 1 & 0 & 0 \\\\\n",
+    "0 & 0 & -1 & 0 \\\\\n",
+    "0 & 0 & 0 & 1\n",
+    "\\end{pmatrix}\n",
+    "= (Z \\otimes \\text{Id})  CZ\n",
+    "$\n",
+    "\n",
+    "Use different combinations of $Z \\otimes \\text{Id}$ to change where search answer is. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.  0.  0.  0.]\n",
+      " [ 0.  1.  0.  0.]\n",
+      " [ 0.  0. -1.  0.]\n",
+      " [ 0.  0.  0.  1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "oracle = np.kron(Z,Id).dot(CZ);\n",
+    "print(oracle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Act the H gates on the input vector and apply the oracle "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.5]\n",
+      " [ 0.5]\n",
+      " [-0.5]\n",
+      " [ 0.5]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "psi0 = np.kron(H,H).dot(psi);\n",
+    "psi1 = oracle.dot(psi0);\n",
+    "print(psi1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember that when we measure the result (\"00\", \"01\", \"10\", \"11\") is chosen randomly with probabilities given by the vector elements squared."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.25]\n",
+      " [ 0.25]\n",
+      " [ 0.25]\n",
+      " [ 0.25]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.multiply(psi1,psi1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is no difference between any of the probabilities. It's still just a 25% chance of getting the right answer. \n",
+    "\n",
+    "We need some of gates after the oracle before measuring to converge on the right answer.  \n",
+    "\n",
+    "These gates do the operation $W = \\frac{1}{2}\\begin{pmatrix} \n",
+    "-1 & 1 & 1 & 1  \\\\\n",
+    "1 & -1 & 1 & 1 \\\\\n",
+    "1 & 1 & -1 & 1 \\\\\n",
+    "1 & 1 & 1 & -1\n",
+    "\\end{pmatrix}\n",
+    "=\n",
+    "(H \\otimes H)(Z \\otimes Z) CZ (H \\otimes H)\n",
+    "$\n",
+    "\n",
+    "Notice that if the matrix W is multiplied by the vector after the oracle, W $\\frac{1}{2}\\begin{pmatrix} \n",
+    "1 \\\\\n",
+    "1 \\\\\n",
+    "-1 \\\\\n",
+    "1\n",
+    "\\end{pmatrix} \n",
+    "= \\begin{pmatrix} \n",
+    "0 \\\\\n",
+    "0 \\\\\n",
+    "1 \\\\\n",
+    "0\n",
+    "\\end{pmatrix}  $,\n",
+    "every vector element decreases, except the correct answer element which increases. This would be true if if we chose a different place for the search result originally."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-0.5  0.5  0.5  0.5]\n",
+      " [ 0.5 -0.5  0.5  0.5]\n",
+      " [ 0.5  0.5 -0.5  0.5]\n",
+      " [ 0.5  0.5  0.5 -0.5]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "W = np.kron(H,H).dot(np.kron(Z,Z)).dot(CZ).dot(np.kron(H,H));\n",
+    "print(W)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[  1.23259516e-32]\n",
+      " [  3.08148791e-33]\n",
+      " [  1.00000000e+00]\n",
+      " [  3.08148791e-33]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "psif = W.dot(psi1);\n",
+    "print(np.multiply(psif,psif))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ecb0ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = [0,1,2,3];\n",
+    "xb = [0.25,1.25,2.25,3.25];\n",
+    "labels=['00', '01', '10', '11'];\n",
+    "plt.axis([-0.5,3.5,-1.25,1.25]);\n",
+    "plt.xticks(x,labels);\n",
+    "plt.bar(x, np.ravel(psi0), 1/1.5, color=\"red\");\n",
+    "plt.bar(xb, np.ravel(np.multiply(psi0,psi0)), 1/2., color=\"blue\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA9M+lMGwYYbcAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ec82b00>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels=['00', '01', '10', '11'];\n",
+    "plt.axis([-0.5,3.5,-1.25,1.25]);\n",
+    "plt.xticks(x,labels);\n",
+    "plt.bar(x, np.ravel(psi1), 1/1.5, color=\"red\");\n",
+    "plt.bar(xb, np.ravel(np.multiply(psi1,psi1)), 1/2., color=\"blue\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ea345f8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels=['00', '01', '10', '11'];\n",
+    "plt.axis([-0.5,3.5,-1.25,1.25]);\n",
+    "plt.xticks(x,labels);\n",
+    "plt.bar(x, np.ravel(psif), 1/1.5, color=\"red\");\n",
+    "plt.bar(xb, np.ravel(np.multiply(psif,psif)), 1/2., color=\"blue\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lessons/misc/quantum-computing/lesson.md b/lessons/misc/quantum-computing/lesson.md
new file mode 100644
index 000000000..4b16e9b7b
--- /dev/null
+++ b/lessons/misc/quantum-computing/lesson.md
@@ -0,0 +1,27 @@
+---
+layout: page
+title: "Introduction to Quantum Computing"
+visible: true
+tags:
+  # languages
+  - misc
+  # levels
+  - beginner
+---
+
+<!-- change visible to true if you want it on the site -->
+<!-- remove any tags listed above that are not relevant -->
+
+ - **Authors**: Kent Bonsma-Fisher
+ - **Research field**: Physics / Quantum Information
+ - **Lesson topic**: Introduction to quantum computing
+ - **Lesson content URL**: https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/quantum-computing
+ - **Lesson video URL**: https://www.youtube.com/watch?v=lR4mZqZJyCs&
+
+This is a beginner level introduction to quantum computing. It assumes some knowledge of vectors and matrices.
+
+We go over the matrix algebra for qubits, some examples of quantum gates acting on them, and simulate them in Python. 
+
+Then we go through Grover's search algorithm in Python and try it out on the IBM quantum experience machine.
+
+See the [notebooks](https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/misc/quantum-computing/grovers-algorthim-2-qubits.ipynb) in the linked folder and [QuantumComputing_UofTcoders.pdf](https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/misc/quantum-computing/QuantumComputing_UofTcoders.phd) for the slides.
diff --git a/lessons/misc/ruby-intro/lesson.md b/lessons/misc/ruby-intro/lesson.md
new file mode 100644
index 000000000..77f94382d
--- /dev/null
+++ b/lessons/misc/ruby-intro/lesson.md
@@ -0,0 +1,597 @@
+---
+layout: page
+title: 'Introduction to Ruby'
+visible: true
+tags:
+  - misc
+  - beginner
+---
+
+- **Authors**: Jenna Blumenthal
+- **Research field**: Mechanical & Industrial Engineering
+- **Lesson topic**: Introduction to Ruby
+- **Lesson content URL**: <https://github.com/jennaleeb/ruby_workshop>
+
+This is a very basic introduction to Ruby directed for those without much programming experience. It will cover using the terminal and interative ruby shell (IRB), data types, objects/classes, methods, conditional logic, and running a program from a file in the terminal.
+
+Content source:
+---------------
+Based on the [**workshop**](https://github.com/ladieslearningcode/Ruby) developed by [Dessy Deskalov](https://twitter.com/dess_e) for [Ladies Learning Code](http://ladieslearningcode.com/)
+
+Agenda
+------
+
+-   What is Ruby?
+-   Working in the Terminal
+-   Data Types
+-   Methods
+-   Objects and Classes
+-   Writing/running a program
+-   Control Flow
+
+What is Ruby?
+=============
+
+Ruby is a programming language
+------------------------------
+
+Why do people love Ruby?
+
+-   Easy to learn (compared to C, C++, Java)
+-   Forgiving syntax
+-   Large and supportive online community
+
+Created in 1993 by Yukihiro Matsumoto, from Japan.
+
+> "I hope to see Ruby help every programmer in the world to be productive, and to enjoy programming, and to be happy. That is the primary purpose of the Ruby language."
+
+Ruby in the Command Line
+------------------------
+
+The IRB ("Interactive Ruby Shell") is an interactive command-line interpreter that can be used to test code quickly.
+
+### OS X
+
+    Applications -> Utilities -> Terminal
+
+### Windows
+
+(C:\\windows\\system32\\cmd.exe)
+    
+    Start -> All Programs -> Accessories -> Command Prompt
+
+Using IRB (Interactive Ruby)
+----------------------------
+
+Type IRB into the console
+
+    irb
+
+Then you'll see somthing like this:
+
+    ruby-1.9.2-p290 :001 >
+
+What can Ruby do?
+-----------------
+
+Try typing:
+
+    irb(main):001:0>puts "Hello World"
+        Hello World
+        => nil
+
+`puts` is the basic command to print something out in Ruby.
+`=> nill` is the result of the expression.
+
+Math:
+
+    > 1 + 1
+    => 2
+
+    > 462 * 86
+    => 39732
+
+Data Types
+==========
+
+Data Types
+----------
+
+Numbers without decimals are called `INTEGERS`. We just did some math with some integers.
+
+Letters, words, and sentences are called `STRINGS`. We tell Ruby that we are intending to use a string by wrapping it in quotes.
+
+Some other data types in Ruby:
+
+-   `binary`
+-   `boolean`
+-   `float`
+-   `decimal`
+-   ...
+
+Variables
+=========
+
+How do Variables Work?
+----------------------
+
+What if we want to remember the result of some of this math? Assign the result to a variable.
+
+    irb(main):007:0> a = 3 ** 2 
+    => 9 
+    irb(main):007:0> b = 4 ** 2
+    => 16
+    irb(main):007:0> Math.sqrt(a+b)
+    => 5.0
+        
+      
+
+We can also assign a String to a variable
+
+    irb(main):007:0> my_name = "John Doe" 
+     => "John Doe" 
+      
+
+Telling Ruby the `variable` **my\_name** points to the `string` "**John Doe**".
+
+Objects
+=======
+
+Objects, Objects, Everywhere!
+-----------------------------
+
+Everything in Ruby is an `Object`.
+
+Great, but what is an object?
+
+An `OBJECT` is an `INSTANCE` of a `CLASS`.
+
+### A what is a what of a what?
+
+<img src="https://i.imgur.com/U1PHT7H.gif" class="delayed" />
+
+Classes
+-------
+
+Before she can do something with an object, Ruby needs to know what kind of `object` she's dealing with.
+
+    irb(main):007:0> 1+1 
+    => 2 
+    irb(main):007:0> "1" + "1" 
+    => "11"
+
+### So how do classes help?
+
+`Classes` define `objects` and what can be done with them.
+
+The **Integer** `class` will have different instructions for "+" than the **String** `class`.
+
+`Objects` have an **IS A** relationship with their `classes`.
+
+Methods
+-------
+
+The **actions** you can perform on an `object` are called `methods`.
+
+Every `object` that IS A `class` can perform the methods defined by that class.
+
+    irb(main):010:0> def hi 
+    irb(main):011:1>   puts "Hello World!" 
+    irb(main):012:1> end 
+    => :hi
+
+Integer Methods
+---------------
+
+The `Integer class` has instructions for the methods **next**, **odd?**, and **even?**.
+
+You can call **next**, **odd?**, and **even?** on ALL Objects that are `Integers`
+
+String Methods
+--------------
+
+The `String class` has instructions for the methods **capitalize**, **upcase**, and **reverse**.
+
+You can call **capitalize**, **upcase**, and **reverse** on ALL `Objects` that are `Strings`.
+
+Playing with Integers
+---------------------
+
+    > 99.zero?
+    => false
+
+    > 99.odd?
+    => true
+
+    > 99.even?
+    => false
+
+Another example
+---------------
+
+    > title = "Peter Rabbit"
+    > title.reverse
+
+ | 
+ --- | ---
+What am I supposed to do with this object? | `reverse`
+Do I know how to do that action on this object? | Yes! The `String` class defines a method **reverse** and returns the string in reverse order.
+
+    =>"tibbaR reteP"
+
+Fun with strings
+----------------
+
+    > "universityoftoronto".capitalize
+    => "Universityoftoronto"
+
+    > "universityoftoronto".upcase
+    => "UNIVERSITYOFTORONTO"
+
+    > "UNIVERSITYOFTORONTO".downcase
+    => "universityoftoronto"
+
+    > "universityoftoronto".reverse
+    => "otnorotfoytisrevinu"
+
+Method Errors
+-------------
+
+    > myNumber = 123
+    > myNumber.reverse
+
+ | 
+--- | ---
+What am I supposed to do with this object? | `reverse`
+Do I know how to do that action on this object? | No. The **Integer** class does not define a method **reverse**.
+
+    => NoMethodError: undefined method `reverse' for 123:Fixnum
+        from (irb):1
+        from :0
+      
+
+Methods and Objects and Classes, Oh My!
+---------------------------------------
+
+How can I find the class of my object?
+
+    > mysteryObject = "What am I?"
+    > mysteryObject.class
+    => String
+
+How do I know what methods I can call on my object?
+
+    > mysteryObject.methods
+    => ["upcase!", "zip", "pretty_print_cycle"....]
+
+How do I know what those methods do?
+
+Look them up [**here**](https://ruby-doc.org/)
+
+Built-in Classes
+----------------
+
+Ruby comes with many built-in classes that we can use.
+
+`String`, `Integer`, `File`, `Hash`, `Array`...
+
+You can read about all the built-in objects [**here**](https://ruby-doc.org/core/)
+
+Custom Classes
+--------------
+
+What if we want to define our own class?
+
+Let's return to the `method` **hi** we wrote (and improve it)
+
+    irb(main):010:0> def hi(name) 
+    irb(main):011:1>   puts "Hello #{name}!" 
+    irb(main):012:1> end 
+    => :hi
+
+The `method` **hi** takes the **name** as a `parameter`.
+
+`#{name}` is Ruby's way of inserting something into a string
+
+    irb(main):024:0> class Greeter
+    irb(main):025:1>   def initialize(name)
+    irb(main):026:2>     @name = name
+    irb(main):027:2>   end
+    
+    irb(main):028:1>   def say_hi
+    irb(main):029:2>     puts "Hi #{@name}!"
+    irb(main):030:2>   end
+    irb(main):034:1> end
+    => nil
+      
+
+The new keyword here is `class`. This defines a new class called Greeter and a bunch of methods for that class. Also notice `@name`. This is an instance variable, and is available to all the methods of the class. As you can see it’s used by `say_hi`.
+
+Custom Classes
+--------------
+
+Lets create a **Greeter** object and use it:
+
+    irb(main):035:0> greeter = Greeter.new("Pat")
+    => #
+    irb(main):036:0> greeter.say_hi
+    Hi Pat!
+    => nil
+
+The method `new` is a unique type of method, which is predefined in the Ruby library. The new method belongs to the class methods. The `initialize` method is a special type of method, which will be executed when the `new` method of the class is called with parameters.
+
+Custom Classes: Exercise
+------------------------
+
+Let's make a class `Calculator` that has the methods `add` and `subtract`
+
+    irb(main):035:0> my_calculator = Calculator.new
+    => # 
+    irb(main):036:0> my_calculator.add(5,2)
+    7
+    => nil
+    irb(main):036:0> my_calculator.subtract(5,2)
+    3
+    => nil
+
+Custom Classes: Solution
+------------------------
+
+    irb(main):024:0> class Calculator
+    
+    irb(main):025:1>   def add(x,y)
+    irb(main):026:2>     puts x + y
+    irb(main):027:2>   end
+    
+    irb(main):028:1>   def subtract(x,y)
+    irb(main):029:2>     puts x - y
+    irb(main):030:2>   end
+    irb(main):034:1> end
+    => nil
+
+Writing a Program
+-----------------
+
+Open any text editor, add the same code, and save it as twitter.rb.
+Remember the directory you have saved it in.
+
+    tweet = "I'm writing my first program and it's pretty rad."
+    puts tweet.length
+
+To run your program, type **quit** to exit from IRB, and then type:
+
+    ruby twitter.rb
+
+Getting Input From the User
+---------------------------
+
+### The real Twitter...
+
+Asks you to type something.
+
+Tells you how many characters you're working with.
+
+So far, we've just been putting our tweet directly into the program.
+
+Asking for Input
+----------------
+
+The **puts** `method` is used for output, and the **gets** `method` is used for input. Try this:
+
+The gets `method` warns Ruby that you're about to speak.
+
+    > tweet = gets
+     all-string
+    I'm learning Ruby with #ladieslearningcode
+    
+    => "I'm learning Ruby with #ladieslearningcode\n"
+
+Wait, we didn't type \\n in our tweet. What is that?
+
+Asking for Input
+----------------
+
+The \\n is there because you hit the enter button after you typed your tweet. It represents a new line, and counts as exactly one character. To get rid of it, do this:
+
+    > tweet = gets.chomp
+     all-string
+    I'm learning Ruby with #ladieslearningcode
+    
+    => "I'm learning Ruby with #ladieslearningcode"
+
+Asking for Input
+----------------
+
+Now try this:
+
+    > tweet = gets.chomp
+     all-string
+    I'm a lady learning code with @learningcode #ladieslearningcode
+
+    => "I'm a lady learning code with @learningcode #ladieslearningcode"
+
+    > puts tweet
+     all-string
+    I'm a lady learning code with @learningcode #ladieslearningcode
+    => nil
+
+Back to your Twitter Program
+----------------------------
+
+Working with your group, modify your Twitter program to do the following:
+
+1. Ask (politely!) for a tweet from the user.
+
+2. Store the tweet in a variable, without \\n
+
+3. Output the tweet the user gave.
+
+4. Output the number of characters in the tweet.
+
+5. Output how many **more** characters the user can add until they hit 140 characters.
+
+(answers in assignments/twitter\_3.rb)
+
+Giving Output Based on Input
+----------------------------
+
+Twitter...
+
+Lets you send your tweet if it is 140 characters or less
+
+Tells you that your tweet is too long to send otherwise.
+
+Giving Output Based on Input
+----------------------------
+
+So far we know how to determine the length of the user's tweet.
+
+We don't know how to tell them whether they can or cannot send their tweet, depending on it's length.
+
+Conditional Logic
+-----------------
+
+Programming is writing out sets of simple instructions for the computer to follow.
+
+Let's break down out tweet logic into simple instructions.
+
+Conditional Logic
+-----------------
+
+We want our program to ...
+
+if the tweet is greater than 140 characters, tell the user that they cannot send their tweet
+
+if the tweet is less than or equal to 140 characters, tell the user that they can send their tweet
+
+Conditional Logic
+-----------------
+
+Logical operators
+
+-   &gt;
+-   &lt;
+-   &gt;=
+-   &lt;=
+-   ==
+
+<!-- -->
+
+    > 200 > 140
+
+    => true
+    
+    > 200 < 140
+    
+    => false
+
+Conditional Logic: IF Statements
+--------------------------------
+
+Try the code below in IRB:
+
+    >if 200 > 140 
+    >  puts true
+    >end
+
+    => true
+
+Conditional Logic: IF/ELSE Statements
+-------------------------------------
+
+Try the code below in IRB:
+
+    >if 200 > 140 
+    > puts true
+    >else
+      puts false
+    >end
+
+    => true
+
+Conditional Logic in Our Simple Twitter
+---------------------------------------
+
+    tweet = gets.chomp
+
+    if tweet.length <= 140
+      puts "Tweet your heart out!"
+    end
+
+Conditional Logic in Our Simple Twitter
+---------------------------------------
+
+    tweet = gets.chomp
+
+    if tweet.length <= 140
+      puts "Tweet your heart out!"
+    else
+      puts "Your tweet is too long!"
+    end
+
+While Loops
+-----------
+
+What if you want to be able to tell your program when to stop running? Create a new program with the following code.
+
+    puts "Hi!"
+    greeting = gets.chomp
+
+    while greeting != "bye!"
+      puts greeting
+      greeting = gets.chomp
+    end
+
+This is called a while loop. The program will run while the user inputs anything other than "bye!"
+
+Arrays
+------
+
+    [1, 2, 3, 4, 5]
+
+    [7, 13, 14, 16, 14, 48]
+
+    ["ladies", "learning", "code"]
+
+Iterating through an Array
+--------------------------
+
+     > my_numbers = [1, 2, 3, 4, 5]
+     > my_numbers.each do |i| 
+     >  puts i**2 
+     > end 
+
+Final Task
+----------
+
+-   Make an array of 5 tweets.
+-   For every tweet, check the number of characters.
+-   If the tweet is too long, give the user an error message.
+-   If the tweet is &lt; 140 characters, print the tweet.
+
+Credits
+-------
+
+Inspired by Chris Pine's **Learn to Program**
+
+![](framework/img/workshop/learn-to-program.jpg)
+
+Resources
+---------
+
+[Learn Ruby in 20 Minutes](https://www.ruby-lang.org/en/documentation/quickstart/)
+
+[why's poignent guide to Ruby](http://mislav.uniqpath.com/poignant-guide/)
+
+[Ruby Koans](http://rubykoans.com/)
+
+[Rails](http://rubyonrails.org/screencasts)
+
+[Lots more!](https://www.ruby-lang.org/en/documentation/)
+
+And finally...
+
+Thank you!
+==========
+
+[![Creative Commons License](framework/img/cc-by-nc.svg)](https://creativecommons.org/licenses/by-nc/4.0/) by [Ladies Learning Code](http://ladieslearningcode.com)
diff --git a/lessons/misc/software-defined-radio/Information in the Air - SDR.pdf b/lessons/misc/software-defined-radio/Information in the Air - SDR.pdf
new file mode 100644
index 000000000..187e09c40
Binary files /dev/null and b/lessons/misc/software-defined-radio/Information in the Air - SDR.pdf differ
diff --git a/lessons/misc/software-defined-radio/lesson.md b/lessons/misc/software-defined-radio/lesson.md
new file mode 100644
index 000000000..523de58fb
--- /dev/null
+++ b/lessons/misc/software-defined-radio/lesson.md
@@ -0,0 +1,41 @@
+---
+layout: page
+title: "Information from the Air: Software Defined Radio"
+visible: true
+tags:
+  - misc
+  - intermediate
+---
+<!-- change visible to true if you want it on the site -->
+<!-- remove any tags listed above that are not relevant -->
+
+ - **Authors**: Brian Sutherland
+ - **Research field**: Information
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/software-defined-radio/>
+
+Thanks to the mass manufacturing of digital video broadcast decoders, it's
+possible to obtain a RealTek chipset USB dongle with an antenna (somewhat like
+a sound card) for $20-$50 that allows your computer to receive, record, and
+examine significant portions of the radio frequency spectrum, e.g. 25MHz -
+1.7GHz.  More expensive transceiver devices have a wider range (1MHz - 6GHz),
+and they allow you to transmit as well.
+
+Examples -
+<https://www.nooelec.com/store/sdr/sdr-receivers.html>
+
+
+In this session you will explore the radio frequency (RF) spectrum with one of
+these dongles (bring your own or use one provided) and open source software
+<https://www.rtl-sdr.com/big-list-rtl-sdr-supported-software/>.  We'll look at
+the GNU Public Radio project, gqfx and some interesting uses folks have put
+these systems to, including pulling scientific data from satellites, hacking
+internet of things communications devices, and recording FM broadcasts.  For a
+project, by dragging and dropping filters, graphical controls, participabts
+will build a fully functioning FM tuner in a matter of minutes using GNU Public
+Radio.  Note that these devices are fully programmable in Python, making all
+kinds of interesting custom applications possible.
+
+
+Check it out:
+* <https://www.youtube.com/watch?v=mBU7eSviy_0>
+* <https://github.com/search?q=GNU+SDR>
diff --git a/lessons/misc/sql-intro/.gitignore b/lessons/misc/sql-intro/.gitignore
new file mode 100644
index 000000000..519cf0e85
--- /dev/null
+++ b/lessons/misc/sql-intro/.gitignore
@@ -0,0 +1,5 @@
+.ipynb_*
+lesson.html
+mtcars.csv
+mtcars.sqlite
+export-mtcars.csv
diff --git a/lessons/misc/sql-intro/Makefile b/lessons/misc/sql-intro/Makefile
new file mode 100644
index 000000000..4e3681ef9
--- /dev/null
+++ b/lessons/misc/sql-intro/Makefile
@@ -0,0 +1,20 @@
+lesson:
+	Rscript -e "library(rmarkdown); rmarkdown::render('lesson.md')"
+
+open:
+	open lesson.html
+
+python-notebook:
+	jupyter notebook python-sql.ipynb
+
+r-notebook:
+	jupyter notebook r-sql.ipynb
+
+requirements:
+	pip install -r requirements/requirements.txt
+	R --no-save < requirements/requirements.R
+
+mtcars-init:
+	sqlite3 mtcars.sqlite < mtcars-init.sql
+
+.PHONY: lesson open requirements python-notebook r-notebook
diff --git a/lessons/misc/sql-intro/examples.sql b/lessons/misc/sql-intro/examples.sql
new file mode 100644
index 000000000..632ceaf53
--- /dev/null
+++ b/lessons/misc/sql-intro/examples.sql
@@ -0,0 +1,98 @@
+-- launch sqlite3 with a DB file
+-- sqlite3 mtcars.sqlite
+
+---------
+-- help for sqlite
+-- .help
+
+---------
+-- export the mtcars results again
+
+.headers on
+.mode csv
+.output export-mtcars.csv
+select * from results;
+.output
+
+---------
+-- CREATE
+
+create table datatypes (
+    numbers float,
+    integers int,
+    strings varchar(32),
+    truths boolean
+);
+
+---------
+-- DROP
+
+drop table datatypes;
+
+---------
+-- SELECT * FROM TABLE
+-- get all the rows and columns from a table called 'results'
+
+select * from results;
+
+---------
+-- SELECT count() WHERE
+-- count the number of rows in the table called 'results'
+
+select count(*) from results;
+
+---------
+-- SELECT ... WHERE
+-- filter the results to include only cars with 6 cylinder engines
+
+select count(*) from results where cyl=6;
+
+---------
+-- SELECT COL AS COLNAME
+-- "rename" a column within a result set
+-- here we call 'wt' by its full name, 'weight'
+
+select wt as weight from results where weight > 5;
+
+---------
+-- INSERT ...
+-- add rows to the database, according to the schema
+-- columns are:
+--      mpg float, cyl int, disp float, hp int, drat float, wt float,
+--      qsec float, vs boolean, am boolean, gear int, carb int, name varchar(256)
+
+insert into results values (4.3,12,320.0,774,13.62,8.31,75.5,0,1,2,6,"Powell Motors Homer");
+
+-- retrieve our new row:
+select * from results where name='Powell Motors Homer';
+
+-- and the heaviest cars by weight are:
+select * from results where wt > 5;
+
+---------
+-- UPDATE ... WHERE
+-- change values in the table WHERE the filter is matched
+
+update results set cyl=16 where name='Powell Motors Homer';
+select cyl from results where name='Powell Motors Homer';
+
+---------
+-- ALTER ...
+-- change the table schema by adding or removing columns
+-- NB: sqlite3 does not allow dropping columns
+
+alter table results add column emissions float;
+
+-- check the schema now
+.schema results
+
+---------
+-- DELETE ... WHERE
+-- remove rows that match WHERE clause
+
+select count(*) from results;
+select count(*) from results where cyl=4;
+delete from results where cyl=4;
+select count(*) from results;
+
+-- SELECT ... JOIN
diff --git a/lessons/misc/sql-intro/lesson.md b/lessons/misc/sql-intro/lesson.md
new file mode 100644
index 000000000..e49e4c046
--- /dev/null
+++ b/lessons/misc/sql-intro/lesson.md
@@ -0,0 +1,84 @@
+---
+layout: page
+title: 'Introduction to Databases and SQL'
+visible: true
+tags:
+  - misc
+  - beginner
+---
+
+- **Authors**: Ian Dennis Miller
+- **Research field**: Social Psychology - Social Complexity
+- **Lesson topic**: Databases and SQL
+- **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/sql-intro>
+
+## Presentation
+
+<https://iandennismiller.github.io/databases-sql>
+
+- covers data, databases, and sql
+- contains javascript for some exercises
+
+## Notebooks
+
+The following Jupyter notebooks contain a R and Python examples:
+
+- [r-sql.ipynb](../r-sql.ipynb)
+- [python-sql.ipynb](../python-sql.ipynb)
+
+These can be run locally:
+
+    make r-notebook
+    make python-notebook
+
+## Requirements
+
+To follow along with the lesson, you will need a several R and Python libraries.  If you don't have R, Python, and sqlite3, see [Basic System Requirements](#basic-system-requirements).  For Python, consider creating a virtualenv for your workspace.
+
+Now you are ready to install the required R and Python libraries.  Inspect and run the following files:
+
+    pip install -r requirements/requirements.txt
+    R --no-save < requirements/requirements.R
+
+You can also automatically install requirements on Mac and Linux:
+
+    make requirements
+
+## Basic System Requirements
+
+This lesson makes use of the following:
+
+- Python (Jupyter)
+- R (IRkernel for Jupyter)
+- sqlite3
+
+### Mac
+
+MacOS already has Python and probably sqlite3 as well.  You need R.
+
+    brew install R
+
+### Linux
+
+You need R.  Try `apt`, `yum`, or whatever:
+
+    apt-get install r-base
+    yum install R
+
+You may need to install sqlite3.  You can test by running:
+
+    which sqlite3
+
+If it returns nothing, then it is not currently installed.  Try `apt`, `yum`, or whatever:
+
+    apt-get install sqlite3 libsqlite3-dev
+    yum install sqlite sqlite-devel
+
+### Windows
+
+You need Python, R, sqlite3, and maybe other things like a C compiler.  These links will get you started:
+
+- https://cran.r-project.org/bin/windows/base/
+- https://www.rstudio.com/products/rstudio/download/
+- https://www.python.org/downloads/windows/
+- https://sqlite.org/download.html
diff --git a/lessons/misc/sql-intro/mtcars-init.sql b/lessons/misc/sql-intro/mtcars-init.sql
new file mode 100644
index 000000000..1bad276a1
--- /dev/null
+++ b/lessons/misc/sql-intro/mtcars-init.sql
@@ -0,0 +1,27 @@
+-- launch with:
+-- sqlite3 mtcars.sqlite < mtcars-init.sql
+
+-- drop the results table to start from scratch
+drop table results;
+
+-- create results table
+create table results (
+    mpg float,
+    cyl int,
+    disp float,
+    hp int,
+    drat float,
+    wt float,
+    qsec float,
+    vs boolean,
+    am boolean,
+    gear int,
+    carb int,
+    name varchar(256)
+);
+
+-- set sqlite3 into CSV mode
+.mode csv
+
+-- import the mtcars file and store it in the results table
+.import mtcars.csv results
diff --git a/lessons/misc/sql-intro/python-sql.ipynb b/lessons/misc/sql-intro/python-sql.ipynb
new file mode 100644
index 000000000..c8cf5239c
--- /dev/null
+++ b/lessons/misc/sql-intro/python-sql.ipynb
@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to Databases and SQL\n",
+    "\n",
+    "- **Authors**: Ian Dennis Miller\n",
+    "- **Research field**: Social Psychology - Social Complexity\n",
+    "- **Lesson topic**: Databases and SQL\n",
+    "- **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/sql-intro>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Plan\n",
+    "\n",
+    "- open sqlite3 database\n",
+    "- send a query\n",
+    "- parse and print the result\n",
+    "- import from sqlite directly into pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import sqlite3\n",
+    "conn = sqlite3.connect('mtcars.sqlite')\n",
+    "c = conn.cursor()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# select cars with 4 cylinders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# create and execute our query\n",
+    "cylinders = 4\n",
+    "cursor = c.execute(\"SELECT * FROM results WHERE cyl = {0}\".format(cylinders))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# print a list of the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Datsun 710:\t22.8\n",
+      "Merc 240D:\t24.4\n",
+      "Merc 230:\t22.8\n",
+      "Fiat 128:\t32.4\n",
+      "Honda Civic:\t30.4\n",
+      "Toyota Corolla:\t33.9\n",
+      "Toyota Corona:\t21.5\n",
+      "Fiat X1-9:\t27.3\n",
+      "Porsche 914-2:\t26.0\n",
+      "Lotus Europa:\t30.4\n",
+      "Volvo 142E:\t21.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# fetch a list of the column names\n",
+    "column_names = list(map(lambda x: x[0], cursor.description))\n",
+    "\n",
+    "# fetch all rows and map column names onto them\n",
+    "rows = map(lambda x: dict(zip(column_names, x)), cursor.fetchall())\n",
+    "\n",
+    "# print the name and MPG for each result\n",
+    "for row in rows:\n",
+    "    print(\"{name}:\\t{mpg}\".format(**row))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# import into pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  carb  \\\n",
+      "0  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4     4   \n",
+      "1  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4     4   \n",
+      "2  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4     1   \n",
+      "3  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3     1   \n",
+      "4  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3     2   \n",
+      "\n",
+      "                name  \n",
+      "0          Mazda RX4  \n",
+      "1      Mazda RX4 Wag  \n",
+      "2         Datsun 710  \n",
+      "3     Hornet 4 Drive  \n",
+      "4  Hornet Sportabout  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_sql_query(\"SELECT * from results\", conn)\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/lessons/misc/sql-intro/r-sql.ipynb b/lessons/misc/sql-intro/r-sql.ipynb
new file mode 100644
index 000000000..d0c1dbec3
--- /dev/null
+++ b/lessons/misc/sql-intro/r-sql.ipynb
@@ -0,0 +1,626 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to Databases and SQL\n",
+    "\n",
+    "- **Authors**: Ian Dennis Miller\n",
+    "- **Research field**: Social Psychology - Social Complexity\n",
+    "- **Lesson topic**: Databases and SQL\n",
+    "- **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/sql-intro>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plan\n",
+    "\n",
+    "- get a data set from R: mtcars\n",
+    "- select from it using SQL (via sqldf)\n",
+    "- export it as CSV file\n",
+    "- in sqlite, create database and import CSV\n",
+    "- use RSQLite connection to select direct from database"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# get a data set from R: mtcars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data contain Motor Trends car testing results.  There are 32 cars."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "32"
+      ],
+      "text/latex": [
+       "32"
+      ],
+      "text/markdown": [
+       "32"
+      ],
+      "text/plain": [
+       "[1] 32"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "11"
+      ],
+      "text/latex": [
+       "11"
+      ],
+      "text/markdown": [
+       "11"
+      ],
+      "text/plain": [
+       "[1] 11"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(mtcars)\n",
+    "ncol(mtcars)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>Mazda RX4</th><td>21.0 </td><td>6    </td><td>160  </td><td>110  </td><td>3.90 </td><td>2.620</td><td>16.46</td><td>0    </td><td>1    </td><td>4    </td><td>4    </td></tr>\n",
+       "\t<tr><th scope=row>Mazda RX4 Wag</th><td>21.0 </td><td>6    </td><td>160  </td><td>110  </td><td>3.90 </td><td>2.875</td><td>17.02</td><td>0    </td><td>1    </td><td>4    </td><td>4    </td></tr>\n",
+       "\t<tr><th scope=row>Datsun 710</th><td>22.8 </td><td>4    </td><td>108  </td><td> 93  </td><td>3.85 </td><td>2.320</td><td>18.61</td><td>1    </td><td>1    </td><td>4    </td><td>1    </td></tr>\n",
+       "\t<tr><th scope=row>Hornet 4 Drive</th><td>21.4 </td><td>6    </td><td>258  </td><td>110  </td><td>3.08 </td><td>3.215</td><td>19.44</td><td>1    </td><td>0    </td><td>3    </td><td>1    </td></tr>\n",
+       "\t<tr><th scope=row>Hornet Sportabout</th><td>18.7 </td><td>8    </td><td>360  </td><td>175  </td><td>3.15 </td><td>3.440</td><td>17.02</td><td>0    </td><td>0    </td><td>3    </td><td>2    </td></tr>\n",
+       "\t<tr><th scope=row>Valiant</th><td>18.1 </td><td>6    </td><td>225  </td><td>105  </td><td>2.76 </td><td>3.460</td><td>20.22</td><td>1    </td><td>0    </td><td>3    </td><td>1    </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllll}\n",
+       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
+       "\\hline\n",
+       "\tMazda RX4 & 21.0  & 6     & 160   & 110   & 3.90  & 2.620 & 16.46 & 0     & 1     & 4     & 4    \\\\\n",
+       "\tMazda RX4 Wag & 21.0  & 6     & 160   & 110   & 3.90  & 2.875 & 17.02 & 0     & 1     & 4     & 4    \\\\\n",
+       "\tDatsun 710 & 22.8  & 4     & 108   &  93   & 3.85  & 2.320 & 18.61 & 1     & 1     & 4     & 1    \\\\\n",
+       "\tHornet 4 Drive & 21.4  & 6     & 258   & 110   & 3.08  & 3.215 & 19.44 & 1     & 0     & 3     & 1    \\\\\n",
+       "\tHornet Sportabout & 18.7  & 8     & 360   & 175   & 3.15  & 3.440 & 17.02 & 0     & 0     & 3     & 2    \\\\\n",
+       "\tValiant & 18.1  & 6     & 225   & 105   & 2.76  & 3.460 & 20.22 & 1     & 0     & 3     & 1    \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/plain": [
+       "                  mpg  cyl disp hp  drat wt    qsec  vs am gear carb\n",
+       "Mazda RX4         21.0 6   160  110 3.90 2.620 16.46 0  1  4    4   \n",
+       "Mazda RX4 Wag     21.0 6   160  110 3.90 2.875 17.02 0  1  4    4   \n",
+       "Datsun 710        22.8 4   108   93 3.85 2.320 18.61 1  1  4    1   \n",
+       "Hornet 4 Drive    21.4 6   258  110 3.08 3.215 19.44 1  0  3    1   \n",
+       "Hornet Sportabout 18.7 8   360  175 3.15 3.440 17.02 0  0  3    2   \n",
+       "Valiant           18.1 6   225  105 2.76 3.460 20.22 1  0  3    1   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "head(mtcars)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "      mpg             cyl             disp             hp       \n",
+       " Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  \n",
+       " 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  \n",
+       " Median :19.20   Median :6.000   Median :196.3   Median :123.0  \n",
+       " Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  \n",
+       " 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  \n",
+       " Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  \n",
+       "      drat             wt             qsec             vs        \n",
+       " Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  \n",
+       " 1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  \n",
+       " Median :3.695   Median :3.325   Median :17.71   Median :0.0000  \n",
+       " Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  \n",
+       " 3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  \n",
+       " Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  \n",
+       "       am              gear            carb      \n",
+       " Min.   :0.0000   Min.   :3.000   Min.   :1.000  \n",
+       " 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  \n",
+       " Median :0.0000   Median :4.000   Median :2.000  \n",
+       " Mean   :0.4062   Mean   :3.688   Mean   :2.812  \n",
+       " 3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  \n",
+       " Max.   :1.0000   Max.   :5.000   Max.   :8.000  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "summary(mtcars)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# use SQL to select cars with 6-cylinder engines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "library(sqldf)\n",
+    "# help(sqldf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>21.0 </td><td>6    </td><td>160.0</td><td>110  </td><td>3.90 </td><td>2.620</td><td>16.46</td><td>0    </td><td>1    </td><td>4    </td><td>4    </td></tr>\n",
+       "\t<tr><td>21.0 </td><td>6    </td><td>160.0</td><td>110  </td><td>3.90 </td><td>2.875</td><td>17.02</td><td>0    </td><td>1    </td><td>4    </td><td>4    </td></tr>\n",
+       "\t<tr><td>21.4 </td><td>6    </td><td>258.0</td><td>110  </td><td>3.08 </td><td>3.215</td><td>19.44</td><td>1    </td><td>0    </td><td>3    </td><td>1    </td></tr>\n",
+       "\t<tr><td>18.1 </td><td>6    </td><td>225.0</td><td>105  </td><td>2.76 </td><td>3.460</td><td>20.22</td><td>1    </td><td>0    </td><td>3    </td><td>1    </td></tr>\n",
+       "\t<tr><td>19.2 </td><td>6    </td><td>167.6</td><td>123  </td><td>3.92 </td><td>3.440</td><td>18.30</td><td>1    </td><td>0    </td><td>4    </td><td>4    </td></tr>\n",
+       "\t<tr><td>17.8 </td><td>6    </td><td>167.6</td><td>123  </td><td>3.92 </td><td>3.440</td><td>18.90</td><td>1    </td><td>0    </td><td>4    </td><td>4    </td></tr>\n",
+       "\t<tr><td>19.7 </td><td>6    </td><td>145.0</td><td>175  </td><td>3.62 </td><td>2.770</td><td>15.50</td><td>0    </td><td>1    </td><td>5    </td><td>6    </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllll}\n",
+       " mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
+       "\\hline\n",
+       "\t 21.0  & 6     & 160.0 & 110   & 3.90  & 2.620 & 16.46 & 0     & 1     & 4     & 4    \\\\\n",
+       "\t 21.0  & 6     & 160.0 & 110   & 3.90  & 2.875 & 17.02 & 0     & 1     & 4     & 4    \\\\\n",
+       "\t 21.4  & 6     & 258.0 & 110   & 3.08  & 3.215 & 19.44 & 1     & 0     & 3     & 1    \\\\\n",
+       "\t 18.1  & 6     & 225.0 & 105   & 2.76  & 3.460 & 20.22 & 1     & 0     & 3     & 1    \\\\\n",
+       "\t 19.2  & 6     & 167.6 & 123   & 3.92  & 3.440 & 18.30 & 1     & 0     & 4     & 4    \\\\\n",
+       "\t 17.8  & 6     & 167.6 & 123   & 3.92  & 3.440 & 18.90 & 1     & 0     & 4     & 4    \\\\\n",
+       "\t 19.7  & 6     & 145.0 & 175   & 3.62  & 2.770 & 15.50 & 0     & 1     & 5     & 6    \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/plain": [
+       "  mpg  cyl disp  hp  drat wt    qsec  vs am gear carb\n",
+       "1 21.0 6   160.0 110 3.90 2.620 16.46 0  1  4    4   \n",
+       "2 21.0 6   160.0 110 3.90 2.875 17.02 0  1  4    4   \n",
+       "3 21.4 6   258.0 110 3.08 3.215 19.44 1  0  3    1   \n",
+       "4 18.1 6   225.0 105 2.76 3.460 20.22 1  0  3    1   \n",
+       "5 19.2 6   167.6 123 3.92 3.440 18.30 1  0  4    4   \n",
+       "6 17.8 6   167.6 123 3.92 3.440 18.90 1  0  4    4   \n",
+       "7 19.7 6   145.0 175 3.62 2.770 15.50 0  1  5    6   "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sqldf('select * from mtcars where cyl=6')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## There are 7 cars matching the query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "7"
+      ],
+      "text/latex": [
+       "7"
+      ],
+      "text/markdown": [
+       "7"
+      ],
+      "text/plain": [
+       "[1] 7"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nrow(sqldf('select * from mtcars where cyl=6'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## We can obtain the same result directly with SQL...  but let's not get ahead of ourselves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>count(*)</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>7</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|l}\n",
+       " count(*)\\\\\n",
+       "\\hline\n",
+       "\t 7\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/plain": [
+       "  count(*)\n",
+       "1 7       "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sqldf('select count(*) from mtcars where cyl=6')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# write mtcars dataset to the file system"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df = mtcars\n",
+    "df$name = rownames(df) # move R's rownames into their own column\n",
+    "write.table(df, \"mtcars.csv\", quote=TRUE, row.names=FALSE, col.names=FALSE, sep=\",\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# create sqlite database in terminal\n",
+    "\n",
+    "    make mtcars-init\n",
+    "\n",
+    "That will run the following:\n",
+    "\n",
+    "    sqlite3 mtcars.sqlite < mtcars-init.sql\n",
+    "\n",
+    "That SQL file does the following:\n",
+    "\n",
+    "- drop a table called results\n",
+    "- create a table called results with columns for the mtcars data\n",
+    "- tell sqlite to load a CSV file\n",
+    "- load the CSV file into the results table"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# access sqlite from R"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "library(RSQLite)\n",
+    "\n",
+    "# connect to the database\n",
+    "db = dbConnect(SQLite(), dbname=\"mtcars.sqlite\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## list the tables that are available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "'results'"
+      ],
+      "text/latex": [
+       "'results'"
+      ],
+      "text/markdown": [
+       "'results'"
+      ],
+      "text/plain": [
+       "[1] \"results\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dbListTables(db)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspect columns in the results table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<ol class=list-inline>\n",
+       "\t<li>'mpg'</li>\n",
+       "\t<li>'cyl'</li>\n",
+       "\t<li>'disp'</li>\n",
+       "\t<li>'hp'</li>\n",
+       "\t<li>'drat'</li>\n",
+       "\t<li>'wt'</li>\n",
+       "\t<li>'qsec'</li>\n",
+       "\t<li>'vs'</li>\n",
+       "\t<li>'am'</li>\n",
+       "\t<li>'gear'</li>\n",
+       "\t<li>'carb'</li>\n",
+       "\t<li>'name'</li>\n",
+       "</ol>\n"
+      ],
+      "text/latex": [
+       "\\begin{enumerate*}\n",
+       "\\item 'mpg'\n",
+       "\\item 'cyl'\n",
+       "\\item 'disp'\n",
+       "\\item 'hp'\n",
+       "\\item 'drat'\n",
+       "\\item 'wt'\n",
+       "\\item 'qsec'\n",
+       "\\item 'vs'\n",
+       "\\item 'am'\n",
+       "\\item 'gear'\n",
+       "\\item 'carb'\n",
+       "\\item 'name'\n",
+       "\\end{enumerate*}\n"
+      ],
+      "text/markdown": [
+       "1. 'mpg'\n",
+       "2. 'cyl'\n",
+       "3. 'disp'\n",
+       "4. 'hp'\n",
+       "5. 'drat'\n",
+       "6. 'wt'\n",
+       "7. 'qsec'\n",
+       "8. 'vs'\n",
+       "9. 'am'\n",
+       "10. 'gear'\n",
+       "11. 'carb'\n",
+       "12. 'name'\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       " [1] \"mpg\"  \"cyl\"  \"disp\" \"hp\"   \"drat\" \"wt\"   \"qsec\" \"vs\"   \"am\"   \"gear\"\n",
+       "[11] \"carb\" \"name\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dbListFields(db, \"results\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# use SQL to select cars with 8-cylinder engines - directly from database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th><th scope=col>name</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>18.7               </td><td>8                  </td><td>360.0              </td><td>175                </td><td>3.15               </td><td>3.440              </td><td>17.02              </td><td>0                  </td><td>0                  </td><td>3                  </td><td>2                  </td><td>Hornet Sportabout  </td></tr>\n",
+       "\t<tr><td>14.3               </td><td>8                  </td><td>360.0              </td><td>245                </td><td>3.21               </td><td>3.570              </td><td>15.84              </td><td>0                  </td><td>0                  </td><td>3                  </td><td>4                  </td><td>Duster 360         </td></tr>\n",
+       "\t<tr><td>16.4               </td><td>8                  </td><td>275.8              </td><td>180                </td><td>3.07               </td><td>4.070              </td><td>17.40              </td><td>0                  </td><td>0                  </td><td>3                  </td><td>3                  </td><td>Merc 450SE         </td></tr>\n",
+       "\t<tr><td>17.3               </td><td>8                  </td><td>275.8              </td><td>180                </td><td>3.07               </td><td>3.730              </td><td>17.60              </td><td>0                  </td><td>0                  </td><td>3                  </td><td>3                  </td><td>Merc 450SL         </td></tr>\n",
+       "\t<tr><td>15.2               </td><td>8                  </td><td>275.8              </td><td>180                </td><td>3.07               </td><td>3.780              </td><td>18.00              </td><td>0                  </td><td>0                  </td><td>3                  </td><td>3                  </td><td>Merc 450SLC        </td></tr>\n",
+       "\t<tr><td>10.4               </td><td>8                  </td><td>472.0              </td><td>205                </td><td>2.93               </td><td>5.250              </td><td>17.98              </td><td>0                  </td><td>0                  </td><td>3                  </td><td>4                  </td><td>Cadillac Fleetwood </td></tr>\n",
+       "\t<tr><td>10.4               </td><td>8                  </td><td>460.0              </td><td>215                </td><td>3.00               </td><td>5.424              </td><td>17.82              </td><td>0                  </td><td>0                  </td><td>3                  </td><td>4                  </td><td>Lincoln Continental</td></tr>\n",
+       "\t<tr><td>14.7               </td><td>8                  </td><td>440.0              </td><td>230                </td><td>3.23               </td><td>5.345              </td><td>17.42              </td><td>0                  </td><td>0                  </td><td>3                  </td><td>4                  </td><td>Chrysler Imperial  </td></tr>\n",
+       "\t<tr><td>15.5               </td><td>8                  </td><td>318.0              </td><td>150                </td><td>2.76               </td><td>3.520              </td><td>16.87              </td><td>0                  </td><td>0                  </td><td>3                  </td><td>2                  </td><td>Dodge Challenger   </td></tr>\n",
+       "\t<tr><td>15.2               </td><td>8                  </td><td>304.0              </td><td>150                </td><td>3.15               </td><td>3.435              </td><td>17.30              </td><td>0                  </td><td>0                  </td><td>3                  </td><td>2                  </td><td>AMC Javelin        </td></tr>\n",
+       "\t<tr><td>13.3               </td><td>8                  </td><td>350.0              </td><td>245                </td><td>3.73               </td><td>3.840              </td><td>15.41              </td><td>0                  </td><td>0                  </td><td>3                  </td><td>4                  </td><td>Camaro Z28         </td></tr>\n",
+       "\t<tr><td>19.2               </td><td>8                  </td><td>400.0              </td><td>175                </td><td>3.08               </td><td>3.845              </td><td>17.05              </td><td>0                  </td><td>0                  </td><td>3                  </td><td>2                  </td><td>Pontiac Firebird   </td></tr>\n",
+       "\t<tr><td>15.8               </td><td>8                  </td><td>351.0              </td><td>264                </td><td>4.22               </td><td>3.170              </td><td>14.50              </td><td>0                  </td><td>1                  </td><td>5                  </td><td>4                  </td><td>Ford Pantera L     </td></tr>\n",
+       "\t<tr><td>15.0               </td><td>8                  </td><td>301.0              </td><td>335                </td><td>3.54               </td><td>3.570              </td><td>14.60              </td><td>0                  </td><td>1                  </td><td>5                  </td><td>8                  </td><td>Maserati Bora      </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|llllllllllll}\n",
+       " mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb & name\\\\\n",
+       "\\hline\n",
+       "\t 18.7                & 8                   & 360.0               & 175                 & 3.15                & 3.440               & 17.02               & 0                   & 0                   & 3                   & 2                   & Hornet Sportabout  \\\\\n",
+       "\t 14.3                & 8                   & 360.0               & 245                 & 3.21                & 3.570               & 15.84               & 0                   & 0                   & 3                   & 4                   & Duster 360         \\\\\n",
+       "\t 16.4                & 8                   & 275.8               & 180                 & 3.07                & 4.070               & 17.40               & 0                   & 0                   & 3                   & 3                   & Merc 450SE         \\\\\n",
+       "\t 17.3                & 8                   & 275.8               & 180                 & 3.07                & 3.730               & 17.60               & 0                   & 0                   & 3                   & 3                   & Merc 450SL         \\\\\n",
+       "\t 15.2                & 8                   & 275.8               & 180                 & 3.07                & 3.780               & 18.00               & 0                   & 0                   & 3                   & 3                   & Merc 450SLC        \\\\\n",
+       "\t 10.4                & 8                   & 472.0               & 205                 & 2.93                & 5.250               & 17.98               & 0                   & 0                   & 3                   & 4                   & Cadillac Fleetwood \\\\\n",
+       "\t 10.4                & 8                   & 460.0               & 215                 & 3.00                & 5.424               & 17.82               & 0                   & 0                   & 3                   & 4                   & Lincoln Continental\\\\\n",
+       "\t 14.7                & 8                   & 440.0               & 230                 & 3.23                & 5.345               & 17.42               & 0                   & 0                   & 3                   & 4                   & Chrysler Imperial  \\\\\n",
+       "\t 15.5                & 8                   & 318.0               & 150                 & 2.76                & 3.520               & 16.87               & 0                   & 0                   & 3                   & 2                   & Dodge Challenger   \\\\\n",
+       "\t 15.2                & 8                   & 304.0               & 150                 & 3.15                & 3.435               & 17.30               & 0                   & 0                   & 3                   & 2                   & AMC Javelin        \\\\\n",
+       "\t 13.3                & 8                   & 350.0               & 245                 & 3.73                & 3.840               & 15.41               & 0                   & 0                   & 3                   & 4                   & Camaro Z28         \\\\\n",
+       "\t 19.2                & 8                   & 400.0               & 175                 & 3.08                & 3.845               & 17.05               & 0                   & 0                   & 3                   & 2                   & Pontiac Firebird   \\\\\n",
+       "\t 15.8                & 8                   & 351.0               & 264                 & 4.22                & 3.170               & 14.50               & 0                   & 1                   & 5                   & 4                   & Ford Pantera L     \\\\\n",
+       "\t 15.0                & 8                   & 301.0               & 335                 & 3.54                & 3.570               & 14.60               & 0                   & 1                   & 5                   & 8                   & Maserati Bora      \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/plain": [
+       "   mpg  cyl disp  hp  drat wt    qsec  vs am gear carb name               \n",
+       "1  18.7 8   360.0 175 3.15 3.440 17.02 0  0  3    2    Hornet Sportabout  \n",
+       "2  14.3 8   360.0 245 3.21 3.570 15.84 0  0  3    4    Duster 360         \n",
+       "3  16.4 8   275.8 180 3.07 4.070 17.40 0  0  3    3    Merc 450SE         \n",
+       "4  17.3 8   275.8 180 3.07 3.730 17.60 0  0  3    3    Merc 450SL         \n",
+       "5  15.2 8   275.8 180 3.07 3.780 18.00 0  0  3    3    Merc 450SLC        \n",
+       "6  10.4 8   472.0 205 2.93 5.250 17.98 0  0  3    4    Cadillac Fleetwood \n",
+       "7  10.4 8   460.0 215 3.00 5.424 17.82 0  0  3    4    Lincoln Continental\n",
+       "8  14.7 8   440.0 230 3.23 5.345 17.42 0  0  3    4    Chrysler Imperial  \n",
+       "9  15.5 8   318.0 150 2.76 3.520 16.87 0  0  3    2    Dodge Challenger   \n",
+       "10 15.2 8   304.0 150 3.15 3.435 17.30 0  0  3    2    AMC Javelin        \n",
+       "11 13.3 8   350.0 245 3.73 3.840 15.41 0  0  3    4    Camaro Z28         \n",
+       "12 19.2 8   400.0 175 3.08 3.845 17.05 0  0  3    2    Pontiac Firebird   \n",
+       "13 15.8 8   351.0 264 4.22 3.170 14.50 0  1  5    4    Ford Pantera L     \n",
+       "14 15.0 8   301.0 335 3.54 3.570 14.60 0  1  5    8    Maserati Bora      "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dbGetQuery(conn = db, \"select * from results where cyl=8\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.3.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/lessons/misc/sql-intro/requirements/requirements.R b/lessons/misc/sql-intro/requirements/requirements.R
new file mode 100644
index 000000000..9675a9e56
--- /dev/null
+++ b/lessons/misc/sql-intro/requirements/requirements.R
@@ -0,0 +1,10 @@
+# https://irkernel.github.io/installation/
+
+install.packages(
+    c('repr', 'IRdisplay', 'evaluate', 'crayon', 'pbdZMQ', 'devtools', 'uuid', 'digest'),
+    repo='http://cran.utstat.utoronto.ca/'
+)
+devtools::install_github('IRkernel/IRkernel')
+IRkernel::installspec()
+
+install.packages(c('RSQLite', 'sqldf'), repo='http://cran.utstat.utoronto.ca/')
diff --git a/lessons/misc/sql-intro/requirements/requirements.txt b/lessons/misc/sql-intro/requirements/requirements.txt
new file mode 100644
index 000000000..740f782b7
--- /dev/null
+++ b/lessons/misc/sql-intro/requirements/requirements.txt
@@ -0,0 +1,3 @@
+sqlalchemy==1.1.2
+pysqlite==2.8.3
+jupyter==1.0.0
diff --git a/lessons/misc/sqlite-fts/SQLite3 FTS Workshop.pdf b/lessons/misc/sqlite-fts/SQLite3 FTS Workshop.pdf
new file mode 100644
index 000000000..6738c8f61
Binary files /dev/null and b/lessons/misc/sqlite-fts/SQLite3 FTS Workshop.pdf differ
diff --git a/lessons/misc/sqlite-fts/SQLite3+FTS+Workshop.ipynb b/lessons/misc/sqlite-fts/SQLite3+FTS+Workshop.ipynb
new file mode 100644
index 000000000..a7a2dbc29
--- /dev/null
+++ b/lessons/misc/sqlite-fts/SQLite3+FTS+Workshop.ipynb
@@ -0,0 +1,309 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import sqlite3 library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3\n",
+    "import sys\n",
+    "from glob import glob"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Connect to a database file or create an in-memory database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conn = sqlite3.connect('/users/mikespears/Desktop/mydb.db') #file-based db\n",
+    "#conn = sqlite3.connect(':memory:') # in-memory db\n",
+    "c = conn.cursor()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### SQLite basics: Create a regular (non fts) table, insert and search for values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c.execute('''DROP TABLE IF EXISTS uoftcoders''') \n",
+    "c.execute('''CREATE TABLE uoftcoders (date, title, attendance)''')\n",
+    "conn.commit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c.execute('''INSERT INTO uoftcoders VALUES (?, ?, ?)''', [\"March 7, 2019\", \"Full Text Search with SQLITE\", 12])\n",
+    "c.execute('''INSERT INTO uoftcoders VALUES (?, ?, ?)''', [\"March 14, 2019\", \"Software defined radio\", 15])\n",
+    "conn.commit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c.execute('''SELECT * FROM uoftcoders WHERE date = ?''', [\"March 7, 2019\"])\n",
+    "print(c.fetchall())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c.execute('''SELECT * FROM uoftcoders WHERE attendance > ?''', [10])\n",
+    "print(c.fetchall())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#basic pattern matching with LIKE\n",
+    "c.execute('''SELECT * FROM uoftcoders WHERE date LIKE ?''', [\"%march_%\"])\n",
+    "print(c.fetchall())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Full-Text-Search: Create a table, insert data and search for matches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c.execute('''DROP TABLE IF EXISTS files''')\n",
+    "c.execute('''CREATE VIRTUAL TABLE files USING fts4(filename, text)''')\n",
+    "#c.execute('''CREATE VIRTUAL TABLE files USING fts4(filename, text, tokenize=porter)''')\n",
+    "#c.execute('''CREATE VIRTUAL TABLE files USING fts4(filename, text, tokenize=unicode61)''')\n",
+    "conn.commit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Insert all the files in a directory (that contains only plaintext files)\n",
+    "textFileDir = \"/users/mikespears/downloads/science/\"\n",
+    "allFiles = glob(textFileDir + \"*.*\")\n",
+    "\n",
+    "for path in allFiles:\n",
+    "    with open(path) as file: \n",
+    "        try:\n",
+    "            text = file.read()\n",
+    "            c.execute('''INSERT INTO files(filename, text) VALUES(?, ?)''', [path, text])\n",
+    "            #print(path)\n",
+    "        except:\n",
+    "            e = sys.exc_info()[0]\n",
+    "            print(\"Read or insert error: %s (in %s)\" % (e, path))\n",
+    "            continue\n",
+    "    \n",
+    "    \n",
+    "conn.commit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c.execute('''SELECT COUNT(*) from files''')\n",
+    "print(c.fetchall())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#basic token search text column\n",
+    "for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ['relativity']):\n",
+    "    print(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#implicit 'AND'\n",
+    "for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ['special relativity']):\n",
+    "    print(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# OR\n",
+    "for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ['special OR relativity']):\n",
+    "    print(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#search for a phrase\n",
+    "for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ['\"special relativity\"']):\n",
+    "    print(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#exclude a token\n",
+    "for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ['relativity -special']):\n",
+    "    print(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#NEAR operator\n",
+    "for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', [\"relativity NEAR special\"]):\n",
+    "    print(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#specify a column in the search term\n",
+    "for row in c.execute('''SELECT filename FROM FILES WHERE text MATCH ?''', ['filename:faq']):\n",
+    "    print(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for row in c.execute('''SELECT snippet(files, \">>>>\", \"<<<<\", \"...\"), filename FROM FILES WHERE text MATCH ?''', ['relativity']):\n",
+    "    print (\"\\n\\n--------%s\\n\\n\" % row[1])\n",
+    "    print(row[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#get offset\n",
+    "for row in c.execute('''SELECT offsets(FILES) FROM FILES WHERE text MATCH ?''', ['filename:faq']):\n",
+    "    print(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#get matchinfo\n",
+    "for row in c.execute('''SELECT matchinfo(FILES) FROM FILES WHERE files MATCH ?''', ['filename:faq']):\n",
+    "    print(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#direct access to full-text index\n",
+    "c.execute('''DROP TABLE IF EXISTS files_terms''')\n",
+    "c.execute('''CREATE VIRTUAL TABLE files_terms USING fts4aux(files)''')\n",
+    "conn.commit()\n",
+    "for row in c.execute('''SELECT * from files_terms ORDER BY occurrences DESC'''):\n",
+    "    print(row)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conn.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lessons/misc/sqlite-fts/lesson.md b/lessons/misc/sqlite-fts/lesson.md
new file mode 100644
index 000000000..b5544c98c
--- /dev/null
+++ b/lessons/misc/sqlite-fts/lesson.md
@@ -0,0 +1,22 @@
+---
+layout: page
+title: 'Full Text Search with SQLite and Python'
+visible: true
+tags:
+  - misc
+  - advanced
+---
+
+ - **Authors**: Mike Spears
+ - **Lesson topic**: Full Text Search with SQLite and Python
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/sqlite-fts>
+ - **Lesson video stream**: <https://www.youtube.com/watch?v=vaLIzN4t_44>
+
+## Presentation
+
+<https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/sqlite-fts/SQLite3%20FTS%20Workshop.pdf>
+
+## Notebook
+
+The [provided Jupyter notebook](../SQLite3+FTS+Workshop.ipynb) contains
+examples of using SQLite and FTS using Python.
diff --git a/lessons/misc/sqlite-fts/sqliteFTS~prepared.db b/lessons/misc/sqlite-fts/sqliteFTS~prepared.db
new file mode 100644
index 000000000..71d05161e
Binary files /dev/null and b/lessons/misc/sqlite-fts/sqliteFTS~prepared.db differ
diff --git a/lessons/misc/webdev-intro/.gitignore b/lessons/misc/webdev-intro/.gitignore
new file mode 100644
index 000000000..1377554eb
--- /dev/null
+++ b/lessons/misc/webdev-intro/.gitignore
@@ -0,0 +1 @@
+*.swp
diff --git a/lessons/misc/webdev-intro/LICENSE b/lessons/misc/webdev-intro/LICENSE
new file mode 100644
index 000000000..dbbe35581
--- /dev/null
+++ b/lessons/misc/webdev-intro/LICENSE
@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
+  The GNU Affero General Public License is a free, copyleft license for
+software and other kinds of works, specifically designed to ensure
+cooperation with the community in the case of network server software.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+our General Public Licenses are intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+  Developers that use our General Public Licenses protect your rights
+with two steps: (1) assert copyright on the software, and (2) offer
+you this License which gives you legal permission to copy, distribute
+and/or modify the software.
+
+  A secondary benefit of defending all users' freedom is that
+improvements made in alternate versions of the program, if they
+receive widespread use, become available for other developers to
+incorporate.  Many developers of free software are heartened and
+encouraged by the resulting cooperation.  However, in the case of
+software used on network servers, this result may fail to come about.
+The GNU General Public License permits making a modified version and
+letting the public access it on a server without ever releasing its
+source code to the public.
+
+  The GNU Affero General Public License is designed specifically to
+ensure that, in such cases, the modified source code becomes available
+to the community.  It requires the operator of a network server to
+provide the source code of the modified version running there to the
+users of that server.  Therefore, public use of a modified version, on
+a publicly accessible server, gives the public access to the source
+code of the modified version.
+
+  An older license, called the Affero General Public License and
+published by Affero, was designed to accomplish similar goals.  This is
+a different license, not a version of the Affero GPL, but Affero has
+released a new version of the Affero GPL which permits relicensing under
+this license.
+
+  The precise terms and conditions for copying, distribution and
+modification follow.
+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
+
+  "This License" refers to version 3 of the GNU Affero General Public License.
+
+  "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
+
+  "The Program" refers to any copyrightable work licensed under this
+License.  Each licensee is addressed as "you".  "Licensees" and
+"recipients" may be individuals or organizations.
+
+  To "modify" a work means to copy from or adapt all or part of the work
+in a fashion requiring copyright permission, other than the making of an
+exact copy.  The resulting work is called a "modified version" of the
+earlier work or a work "based on" the earlier work.
+
+  A "covered work" means either the unmodified Program or a work based
+on the Program.
+
+  To "propagate" a work means to do anything with it that, without
+permission, would make you directly or secondarily liable for
+infringement under applicable copyright law, except executing it on a
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+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
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+free software which everyone can redistribute and change under these terms.
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+Also add information on how to contact you by electronic and paper mail.
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+For more information on this, and how to apply and follow the GNU AGPL, see
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diff --git a/lessons/misc/webdev-intro/app/slides-module.js b/lessons/misc/webdev-intro/app/slides-module.js
new file mode 100644
index 000000000..ea9db877e
--- /dev/null
+++ b/lessons/misc/webdev-intro/app/slides-module.js
@@ -0,0 +1,270 @@
+'use strict';
+// Separate the module scope from everything else
+
+(function() {
+/* These variables are declared in the outside scope so they behave like global
+   variables */
+
+console.log(this)
+
+var cElm, nElm, contents, layoutListener;
+// the definition of layoutListener had to be moved here so it could be exported
+layoutListener = function(keyEvent) {
+    switch(keyEvent.key) {
+        case 's':
+            toggleShowAll(cElm);
+            break;
+        case 'v':
+            toggleHorizontal(cElm);
+    }
+};
+
+function startSlides(contentElm, navElm, url) {
+    cElm = contentElm;
+    nElm = navElm;
+
+    // XMLHttpRequest is the key to "AJAX"
+    // Many libraries will provide easier function for this
+    var xlr = new XMLHttpRequest();
+    xlr.open('GET', url, true);
+    xlr.send();
+    /* The simplest event listener is just a call-back function.
+     * This is the first step to asynchronous execution */
+    xlr.addEventListener('readystatechange', function() {
+        contents = this.responseText;
+        /* I'm doing a timeout just to show callbacks and binding arguments */
+        if (this.readyState === XMLHttpRequest.DONE && this.status === 200) {
+            window.setTimeout(setup.bind(this, contentElm, navElm), 500);
+        }
+    }); /* often there will be some combo of braces, parens, and semicolons
+           to finish of statements, either because of call-backs or scope */
+    
+    /* Because these functions are defined inside `startSlides()`,
+     * they cannot be accessed outside of the current scope. 
+     * They are hoisted, so that they can be used earlier in the function. */
+    function setup(contentElm, navElm) {
+        renderContents(contentElm, this.responseText);
+        navElm.innerHTML = '';
+        genNavbar(navElm, contentElm);
+        // On loading, we determine which slide to show based on the hash
+        var h = location.hash;
+        if (h === '')
+            selectSlide(cElm.children[0]);
+        else {
+            let slideIndex = parseInt(h.slice(1)) - 1;
+            selectSlide(cElm.children[slideIndex]);
+        }
+    }
+
+    function renderContents(rootElm, contents) {
+        /* Setting the innerHTML will remove all elements there, generate
+         * a new DOM tree from the HTML, add attach it to the root element.
+         */
+        rootElm.innerHTML = '';
+        for (let elm of makeSlideElements(contents, true))
+            rootElm.appendChild(elm);
+        rootElm.children[0].classList.add('shown');
+    }
+
+    /* Handle the hotkeys for switching slides
+     * Again, we just use an anonymous function */
+    window.addEventListener('keyup', function(keyEvent) {
+        // switch-case statements are useful for handling inputs
+        switch(keyEvent.key) {
+            case 'h':
+            case 'ArrowLeft':
+                prevSlide();
+                break;
+            case 'l':
+            case ' ':
+            case 'ArrowRight':
+                nextSlide();
+                break;
+            case 'j':
+            //case 'ArrowDown':
+                selectSlide(cElm.lastElementChild);
+                break;
+            case 'k':
+            //case 'ArrowUp':
+                selectSlide(cElm.firstElementChild);
+                break;
+        }
+    }, true); /* `true` sets the order of evaluation of events (bubbling)
+                 it has no effect here, but you can be aware of it */
+
+    /* Handle hotkeys for changing the layout
+     * notice that the event listeners are both run
+     * By using a named listener, we can remove this functionality
+     * with `.removeEventListener`
+     */
+    window.addEventListener('keyup', layoutListener);
+
+    // This handles the user changing the hash on the url
+    window.onpopstate = function () {
+        var slideIndex = location.hash.slice(1)-1;
+        selectSlide(cElm.children[slideIndex]);
+    }
+
+}
+
+/* making the nav bar (actually used by the startSlides function, but hoisted from here
+ */
+function genNavbar(navElm, contentElm) {
+    // sometimes people predefine all variables at the start
+    var prevButton, nextButton;
+    /* Complicated elements can involve a lot of statements
+     * HTML5 added `<template>` objects which make it easier to define elements
+     * with html */
+    var template = document.createElement('template');
+    // a library could help with string formatting
+    var tHTML = function(np, text) {
+        return `<button id="${np}-slide-button" aria-label="${np} slide" aria-controls="content">${text}</button>`;
+    };
+    template.innerHTML = tHTML('previous', '&lt;');
+    prevButton = template.content.lastChild;
+    prevButton.onclick = prevSlide;
+    navElm.appendChild(prevButton);
+    var i = 0;
+    for (let sl of contentElm.children) {
+        template.innerHTML += `<button class="dot" aria-labelledby="slide-title-${i++}" aria-controls="content"> </button>`;
+        let marker = template.content.lastChild;
+        marker.onclick = function() { 
+            /* Note that using `var` for `sl` would break functionality due to
+             * scoping rules */
+            selectSlide(sl); 
+        };
+        navElm.appendChild(marker);
+    }
+    template.innerHTML += tHTML('next', '&gt;');
+    nextButton = template.content.lastChild;
+    nextButton.onclick = nextSlide;
+    navElm.appendChild(nextButton);
+    /* This line could be used instead of each individual appendChild,
+     * but `innerHTML +=` recreates DOM elements, removing attached event
+     * listeners
+    navElm.appendChild(document.importNode(template.content, true));
+    */
+}
+
+
+/* The rest of the functions are defined in global scope
+ * so I can access them from commandline as well.
+ */
+function prevSlide() {
+    var current = cElm.getElementsByClassName('shown')[0];
+    if (current.previousElementSibling) {
+        selectSlide(current.previousElementSibling);
+    }
+}
+function nextSlide() {
+    var current = cElm.getElementsByClassName('shown')[0];
+    if (current.nextElementSibling) {
+        selectSlide(current.nextElementSibling);
+    }
+}
+
+/* This function really does all the work
+ */
+function selectSlide(slideElm) {
+    var contentElm = slideElm.parentElement;
+    // update the classes of slides
+    for (let slide of contentElm.children)
+        slide.classList.remove('shown');
+    slideElm.classList.add('shown');
+    // update the nav bar
+    var slideIndex = Array.from(contentElm.children).indexOf(slideElm);
+    // nElm is from the outer context (bad)
+    for (let b of nElm.children)
+        b.classList.remove('shown');
+    nElm.children[1+slideIndex].classList.add('shown');
+    updateUrl(slideIndex);
+}
+
+function updateUrl(slideIndex) {
+    /* `window.history` keeps track of forward/back
+     * `replaceState` changes the current place in history, while
+     * `pushState` would add to the history chain
+     */
+    window.history.replaceState({slide: slideIndex}, 
+            'Slide change', 
+            './#'+(slideIndex+1));
+}
+
+
+/* These functions are a bit pointless, because the inside is the same as the
+ * name... but
+ * if this functionality is used in multiple places and the implementation
+ * changes, then I only need to make that change here. */
+function toggleShowAll(cElm) {
+    cElm.classList.toggle('show-all');
+}
+
+function toggleHorizontal(cElm) {
+    cElm.classList.toggle('horizontal');
+}
+
+// making the actual slide elements
+function makeSlideElements(text, addParent) {
+    var slides = splitContents(text);
+    if (addParent)
+        slides = addParentSection(slides);
+    var elms = [];
+    for (let i in slides) {
+        let elm = document.createElement('section');
+        // `marked` comes from the markdown parser import in the HTML
+        elm.innerHTML = marked(slides[i]);
+        elm.classList.add('slide');
+        // Shortcut evaluation with `||` (or) to grab the heading
+        let heading = elm.getElementsByTagName('h1')[0] ||
+                  elm.getElementsByTagName('h2')[0];
+        // git the heading an id, for `aria-labelledby`
+        heading.id = `slide-title-${i}`
+        elms.push(elm);
+    }
+    return elms;
+}
+
+
+/* Just some string-handling functions */
+function splitContents(text) {
+    // split the slides apart
+    var slides = text.split('\n#');
+
+    // handle the special cases of starting with a newline or whitespace
+    if (text.startsWith('\n'))
+        slides = slides.slice(1);
+    else
+        slides[0] = slides[0].slice(1);
+
+    // add the # character back to the start of each bit of text
+    for (let i in slides) 
+        slides[i] = '#'.concat(slides[i]);
+    return slides;
+}
+
+function addParentSection(slides) {
+    var prev = '';
+    var result = [];
+    for (let i in slides) {
+        if (slides[i].startsWith('##'))
+            result[i] = prev.concat('\n', slides[i]);
+        else {
+            prev = slides[i].split('\n')[0].slice(1).trim();
+            result[i] = slides[i];
+        }
+    }
+   return result;
+}
+
+// Choose what things to expose
+startSlides.nextSlide = nextSlide;
+startSlides.prevSlide = prevSlide;
+startSlides.layoutListener = layoutListener;
+// These don't work, because while making the module, they're still undefined
+startSlides.cElm = cElm;
+startSlides.nElm = nElm;
+
+// add startSlides function to `window` (the global scope in browser)
+this.startSlides = startSlides;
+}).call(window);
+
diff --git a/lessons/misc/webdev-intro/app/slideshow.js b/lessons/misc/webdev-intro/app/slideshow.js
new file mode 100644
index 000000000..ed27ccb4b
--- /dev/null
+++ b/lessons/misc/webdev-intro/app/slideshow.js
@@ -0,0 +1,252 @@
+'use strict'
+
+/* These variables are declared in the outside scope so they behave like global
+   variables */
+var cElm, nElm, contents, layoutListener;
+function startSlides(contentElm, navElm, url) {
+    cElm = contentElm;
+    nElm = navElm;
+
+    // XMLHttpRequest is the key to "AJAX"
+    // Many libraries will provide easier function for this
+    var xlr = new XMLHttpRequest();
+    xlr.open('GET', url, true);
+    xlr.send();
+    /* The simplest event listener is just a call-back function.
+     * This is the first step to asynchronous execution */
+    xlr.addEventListener('readystatechange', function() {
+        contents = this.responseText;
+        /* I'm doing a timeout just to show callbacks and binding arguments */
+        if (this.readyState === XMLHttpRequest.DONE && this.status === 200) {
+            window.setTimeout(setup.bind(this, contentElm, navElm), 500);
+        }
+    }); /* often there will be some combo of braces, parens, and semicolons
+           to finish of statements, either because of call-backs or scope */
+    
+    /* Because these functions are defined inside `startSlides()`,
+     * they cannot be accessed outside of the current scope. 
+     * They are hoisted, so that they can be used earlier in the function. */
+    function setup(contentElm, navElm) {
+        renderContents(contentElm, this.responseText);
+        navElm.innerHTML = '';
+        genNavbar(navElm, contentElm);
+        // On loading, we determine which slide to show based on the hash
+        var h = location.hash;
+        if (h === '')
+            selectSlide(cElm.children[0]);
+        else {
+            let slideIndex = parseInt(h.slice(1)) - 1;
+            selectSlide(cElm.children[slideIndex]);
+        }
+    }
+
+    function renderContents(rootElm, contents) {
+        /* Setting the innerHTML will remove all elements there, generate
+         * a new DOM tree from the HTML, add attach it to the root element.
+         */
+        rootElm.innerHTML = '';
+        for (let elm of makeSlideElements(contents, true))
+            rootElm.appendChild(elm);
+        rootElm.children[0].classList.add('shown');
+    }
+
+    /* Handle the hotkeys for switching slides
+     * Again, we just use an anonymous function */
+    window.addEventListener('keyup', function(keyEvent) {
+        // switch-case statements are useful for handling inputs
+        switch(keyEvent.key) {
+            case 'h':
+            case 'ArrowLeft':
+                prevSlide();
+                break;
+            case 'l':
+            case ' ':
+            case 'ArrowRight':
+                nextSlide();
+                break;
+            case 'j':
+            //case 'ArrowDown':
+                selectSlide(cElm.lastElementChild);
+                break;
+            case 'k':
+            //case 'ArrowUp':
+                selectSlide(cElm.firstElementChild);
+                break;
+        }
+    }, true); /* `true` sets the order of evaluation of events (bubbling)
+                 it has no effect here, but you can be aware of it */
+
+    // layoutListener is in the global scope so that I can demo something
+    layoutListener = function(keyEvent) {
+        switch(keyEvent.key) {
+            case 's':
+                toggleShowAll(cElm);
+                break;
+            case 'v':
+                toggleHorizontal(cElm);
+        }
+    }; // semicolons after variable assignment
+    /* Handle hotkeys for changing the layout
+     * notice that the event listeners are both run
+     * By using a named listener, we can remove this functionality
+     * with `.removeEventListener`
+     */
+    window.addEventListener('keyup', layoutListener);
+
+    // This handles the user changing the hash on the url
+    window.onpopstate = function () {
+        var slideIndex = location.hash.slice(1)-1;
+        selectSlide(cElm.children[slideIndex]);
+    }
+
+}
+
+/* making the nav bar (actually used by the startSlides function, but hoisted from here
+ */
+function genNavbar(navElm, contentElm) {
+    // sometimes people predefine all variables at the start
+    var prevButton, nextButton;
+    /* Complicated elements can involve a lot of statements
+     * HTML5 added `<template>` objects which make it easier to define elements
+     * with html */
+    var template = document.createElement('template');
+    // a library could help with string formatting
+    var tHTML = function(np, text) {
+        return `<button id="${np}-slide-button" aria-label="${np} slide" aria-controls="content">${text}</button>`;
+    };
+    template.innerHTML = tHTML('previous', '&lt;');
+    prevButton = template.content.lastChild;
+    prevButton.onclick = prevSlide;
+    navElm.appendChild(prevButton);
+    var i = 0;
+    for (let sl of contentElm.children) {
+        template.innerHTML += `<button class="dot" aria-labelledby="slide-title-${i++}" aria-controls="content"> </button>`;
+        let marker = template.content.lastChild;
+        marker.onclick = function() { 
+            /* Note that using `var` for `sl` would break functionality due to
+             * scoping rules */
+            selectSlide(sl); 
+        };
+        navElm.appendChild(marker);
+    }
+    template.innerHTML += tHTML('next', '&gt;');
+    nextButton = template.content.lastChild;
+    nextButton.onclick = nextSlide;
+    navElm.appendChild(nextButton);
+    /* This line could be used instead of each individual appendChild,
+     * but `innerHTML +=` recreates DOM elements, removing attached event
+     * listeners
+    navElm.appendChild(document.importNode(template.content, true));
+    */
+}
+
+
+/* The rest of the functions are defined in global scope
+ * so I can access them from commandline as well.
+ */
+function prevSlide() {
+    var current = cElm.getElementsByClassName('shown')[0];
+    if (current.previousElementSibling) {
+        selectSlide(current.previousElementSibling);
+    }
+}
+function nextSlide() {
+    var current = cElm.getElementsByClassName('shown')[0];
+    if (current.nextElementSibling) {
+        selectSlide(current.nextElementSibling);
+    }
+}
+
+/* This function really does all the work
+ */
+function selectSlide(slideElm) {
+    var contentElm = slideElm.parentElement;
+    // update the classes of slides
+    for (let slide of contentElm.children)
+        slide.classList.remove('shown');
+    slideElm.classList.add('shown');
+    // update the nav bar
+    var slideIndex = Array.from(contentElm.children).indexOf(slideElm);
+    // nElm is from the outer context (bad)
+    for (let b of nElm.children)
+        b.classList.remove('shown');
+    nElm.children[1+slideIndex].classList.add('shown');
+    updateUrl(slideIndex);
+}
+
+function updateUrl(slideIndex) {
+    /* `window.history` keeps track of forward/back
+     * `replaceState` changes the current place in history, while
+     * `pushState` would add to the history chain
+     */
+    window.history.replaceState({slide: slideIndex}, 
+            'Slide change', 
+            './#'+(slideIndex+1));
+}
+
+
+/* These functions are a bit pointless, because the inside is the same as the
+ * name... but
+ * if this functionality is used in multiple places and the implementation
+ * changes, then I only need to make that change here. */
+function toggleShowAll(cElm) {
+    cElm.classList.toggle('show-all');
+}
+
+function toggleHorizontal(cElm) {
+    cElm.classList.toggle('horizontal');
+}
+
+// making the actual slide elements
+function makeSlideElements(text, addParent) {
+    var slides = splitContents(text);
+    if (addParent)
+        slides = addParentSection(slides);
+    var elms = [];
+    for (let i in slides) {
+        let elm = document.createElement('section');
+        // `marked` comes from the markdown parser import in the HTML
+        elm.innerHTML = marked(slides[i]);
+        elm.classList.add('slide');
+        // Shortcut evaluation with `||` (or) to grab the heading
+        let heading = elm.getElementsByTagName('h1')[0] ||
+                  elm.getElementsByTagName('h2')[0];
+        // git the heading an id, for `aria-labelledby`
+        heading.id = `slide-title-${i}`
+        elms.push(elm);
+    }
+    return elms;
+}
+
+
+/* Just some string-handling functions */
+function splitContents(text) {
+    // split the slides apart
+    var slides = text.split('\n#');
+
+    // handle the special cases of starting with a newline or whitespace
+    if (text.startsWith('\n'))
+        slides = slides.slice(1);
+    else
+        slides[0] = slides[0].slice(1);
+
+    // add the # character back to the start of each bit of text
+    for (let i in slides) 
+        slides[i] = '#'.concat(slides[i]);
+    return slides;
+}
+
+function addParentSection(slides) {
+    var prev = '';
+    var result = [];
+    for (let i in slides) {
+        if (slides[i].startsWith('##'))
+            result[i] = prev.concat('\n', slides[i]);
+        else {
+            prev = slides[i].split('\n')[0].slice(1).trim();
+            result[i] = slides[i];
+        }
+    }
+   return result;
+}
+
diff --git a/lessons/misc/webdev-intro/contents.md b/lessons/misc/webdev-intro/contents.md
new file mode 100644
index 000000000..93755418d
--- /dev/null
+++ b/lessons/misc/webdev-intro/contents.md
@@ -0,0 +1,564 @@
+---
+layout: page
+title: 'Intro to web development with HTML/CSS/JS'
+---
+
+# Overview
+
+This is a very brief introduction to programming for the web, focused on the "front end". We'll talk about
+- how websites are structured -- the *Document Object Model* (DOM)
+- a brief overview of HTML/CSS
+- an introduction to JavaScript *using ECMAScript 2015 (ES6)*
+
+## Resources
+
+First, some resources to go more in depth after this primer.
+
+- [Mozilla Developer Network](https://developer.mozilla.org/en-US/docs/Learn) for comprehensive documentation on HTML, CSS, JS, and relevant APIs.
+- [caniuse.com](http://caniuse.com/) If you're concerned about compatibility.
+* [JSfiddle](https://jsfiddle.net/) for quickly trying and sharing snippets.
+    - There are other sites like fiddle out there.
+- [Accessibility guidelines](https://www.w3.org/WAI/eval/preliminary)
+
+## Tools
+
+You don't need much to start
+
+- Localhost simple webserver with a text editor
+  - `http-server` (installed with `npm`)
+  - `python3 -m http.server`
+- Developer tools in the browser
+- JSFiddle or the like
+
+For more serious work, you'll need
+- `node` the stand-alone JavaScript runtime
+- `npm` the package manager included with node.js
+
+
+# A website's structure
+
+A website is separated into 3 parts:
+  - content and semantics: HTML
+  - style/appearance: CSS
+  - behaviour: JavaScript
+
+## In the browser
+
+![rendering a website. Source: [MDN](https://developer.mozilla.org/en-US/docs/Learn/CSS/Introduction_to_CSS/How_CSS_works)](../img/rendering.svg)
+
+The HTML/CSS are used to create the DOM, while JavaScript interacts with it.
+
+We can inspect the DOM using *Developer tools* in any browser, by menu, or right-click > inspect.
+
+## Exploring the DOM
+
+Things to notice:
+- Tree-like structure (root is `document`)
+- Each element has 
+    - tagName
+    - classList
+    - id
+    - attributes (if applicable)
+    - rendering information (e.g. `offsetHeight`)
+    - events
+- Nodes can be elements, but also text, etc.
+    - have children and parents and siblings
+
+# HTML
+
+HyperText Markup Language
+
+- A way of adding semantics to text
+- First designed for static documents based on SGML
+  - tags for: h1 to h7, paragraphs, tables, links, emphasis
+- Now on HTML 5, fully made with the modern web in mind
+  - main, nav, section, article, header, footer, video, etc.
+
+```html
+<tag-name attribute="value">innerHTML</tag-name>
+<tag-name attribute="value" />
+<!-- comments -->
+```
+
+## [Sample HTML Document](../sample/index.html)
+
+Things to note in this document:
+
+- doctype is html
+- `<head>` contains metadata about the page, including
+  - title
+  - linked files (like css)
+  - scripts to run before the contents
+- `<body>` contains the actual page
+- Things are nested, like `section` in `body`, and `h1` in `section` and text in `h1` **like the DOM**
+
+## What are the semantics?
+
+- The purpose of text is described by the tags surrounding it *e.g.*
+  - `h1` is a heading
+  - `p` is a paragraph
+  - `ul` is an unordered list with `li` elements inside of it
+  - `a` is an "anchor" (a link, or a marker in the current document)
+  - `nav` is a navigation bar/menu
+- built in semantics can be extended with `class` or `id`
+  - classes describe properties that many elements might have
+    - an element can have multiple classes in a space-separated list
+    - ususally using lower-case with dashes like `red-box` 
+    - e.g. `<div class="hidden slide">`
+  - ids are meant to be a unique identifier for an element
+    - e.g. `<nav id='main-nav'></nav>`
+- also attributes like `href`, `alt`, or anything else
+
+## Why provide semantics?
+
+- Describes what elements are
+  - So the browser knows how to render/place them
+- Describes what actions they can do
+  - video, img, canvas, svg...
+  - links, text input, buttons...
+- [Accessibility!](https://www.w3.org/WAI/eval/preliminary)
+  - tools like screen-readers can navigate the page
+  - tabs and associated keys work as expected
+
+## HTML pre-languages
+
+Writing raw HTML with all the opening and closing tags can be be quite tedious.
+
+ - so a lot of developers use **Markdown** to write content. The text in `.md` format can be passed through a parser and compiled into HTML. This can be done with most markup languages.
+
+- Even with the content written in a nicer language, the boiler-plate and layout is often repetitive, so templating languages like [mustache](https://mustache.github.io/) are popular.
+  - especially with server-side processing
+
+
+# CSS
+
+Cascading style sheets describe the *appearance* of elements.
+
+"Cascading" refers to the way rules/declarations are applied:
+ - some declarations **cascade down the hierarchy to children**
+ - the rule with the *most specific* selector takes precedence
+    - otherwise, rules are applied in order of the CSS file
+
+Syntax:
+
+`selector { property: value; ... }`
+
+The whole statement is a "*rule*". Each `property: value;` pair is a "*declaration*".
+ 
+## Selectors
+
+Describe the elements in the DOM. There are,
+
+- Type selectors (element name)
+  - `a`, `div`, `p`, `section`, `asdf`, ...
+- Class selectors start with a dot
+  - `.class-name`, `a.class1.class2`
+- ID selectors start with a hashmark `#`
+  - `#main-nav`, `#start-button`
+- Universal selector *
+
+## Combinators
+
+Selectors can also be combined to describe where an element falls in the DOM hierarchy.
+
+- descendent ` ` (space)
+- child `>` is an immediate descendent
+- adjacent sibling `+`
+- general sibling `~`
+- same element: no space (e.g. `div.red-box` vs. `div .red-box`)
+
+I read these from right to left: the selected element matches the rightmost selector, the rest describes its place in the DOM
+Example:
+
+```css
+section > p + h1 {
+    color: blue;
+}
+```
+
+Selects the top-level heading directly adjacent to a paragraph which is the child of a section.
+
+## Other selections
+
+- Attribute selectors: i
+  - `[attr]` has the attribute `attr`, 
+  - `[attr=val]` has the attribute `attr` with the value `val`,
+  - `[attr~=val]` has the attribute `attr` containing `val` in a space-separated list.
+- Psuedo-classes preceded by a colon
+  - `div:hover` affects a div if the mouse is hovering over it
+  - `h1:first-child` only selects `h1` if it is the first child too
+  - `h1:first-of-type` selects the first `h1` of its siblings
+  - `p:nth-of-type(2n+1)` selects all odd paragraphs
+- Psuedo-elements preceded by two colons `::`
+  - Often used to add decoration ``[href^=http]::after { content: '⤴'; }``
+
+## Declarations (`property: value;`)
+
+These describe the way an element is drawn. Different elements can have their own special properties, but no error will be given if a property doesn't apply. 
+
+Common properties include:
+ - `color` : values can be like `red`, `#f00`, `#fa0000`, `rgba(255, 0, 0, .5)`
+ - `background` : as above
+ - `display` : `block`, `inline`, `block-inline`, `flex`, etc.
+ - `margin-top`: a length `0em`, `0.3rem`, `4px`, `1%`
+ - `visible`
+
+You'll have to look up a reference for all the possibilities.
+
+## Box model
+
+It's worth mentioning the box model for element sizing/spacing. 
+
+<figure>
+![box model illustration](./img/box-model-standard-small.png)
+<figcaption>source: [MDN](https://developer.mozilla.org/en-US/docs/Learn/CSS/Introduction_to_CSS/Box_model)</figcaption>
+</figure>
+
+- `margin`'s are the spaces between objects
+- `border`'s are the size of the drawn border
+- `padding` is the space between the border and the content
+- `width` and `height` describe the size of the element
+  - `box-sizing: content-box` is default where width/height does not include margins, borders or padding.
+  - `box-sizing: border-box` makes width/height include padding and borders (easier math in many cases)
+
+Sites with more complicated layout will often use 
+
+```* { box-sizing: content-box; }```, 
+
+to make the sizes consistent regardless of borders and padding.
+
+## CSS pre-languages
+
+CSS lacks some features that can be very useful/convenient, like
+ - constants
+ - nested rules for hierarchies
+ - conditional logic
+ - include statements
+
+This is good for browser performance, but a pain for developers. There are lots of pre-processors for CSS that add all these features and more
+ - [Stylus](http://stylus-lang.com/) ← *my preference*
+ - [SASS](http://sass-lang.com/) 
+ - [less](http://lesscss.org/)
+
+
+# JavaScript (ECMAScript)
+
+JavaScript is the scripting language supported by modern browsers. It's specified as ECMAScript, the latest standard being ECMAScript 2015 (aka ES6). All modern browsers support ES5.1, and most of [ES6](http://caniuse.com/#search=ES6)
+
+JavaScript is a scripting language with 
+ - C-like syntax (curly braces), 
+ - function scope and [lexical scoping](http://pierrespring.com/2010/05/11/function-scope-and-lexical-scoping/),
+ - dynamic typing,
+ - objects, and
+ - first-class functions
+
+ES6 adds
+ - syntax for classes
+ - template strings with the back tick \` (think  `"""`  in Python, but with `${expressions}`)
+ - block-level scope with `let` instead of `var`
+ - modules (not implemented in browsers as of writing)
+
+## Javascript types
+
+There are primitive types:
+- Boolean
+- null
+- undefined (*not assigned a value*)
+- Number (*floating point*)
+- String
+
+Then there are notably,
+- function (callable object)
+- Array (mutable and extendable)
+
+## Declaring variables
+
+Variables can be declared with `var` or `let`. You'll probably use `var` most. 
+
+```js
+var x = 1;
+var s = 'hello world';
+var u; // undefined
+var f = function() {
+    console.log('function f called');
+};
+var o = {name: 'some object', callMe: f, 1: 'one'}
+```
+
+`let` is for block-scope which is good for loop variables.
+
+```
+for (let i=0; i<10; i++) {
+    console.log(i)
+}
+console.log(i) // error
+
+```
+
+## Functions
+
+Can be anonymous:
+
+```js
+(function() {console.log('hello world')})()
+```
+
+Named throughout current scope ("hoisted"):
+```js
+foo() // logs "bar"
+function foo() { 
+    console.log('bar'); 
+}
+```
+
+Attached to a variable:
+
+```js
+bar() // error
+var bar = function () {
+    console.log('foo')
+}
+bar() // logs "foo"
+```
+
+## Lexical scope
+
+The *scope* describes what variables are accessible and their names. In JavaScript, the scope of a variable defined with `var` is the current function. JS also has a lexical scope, so functions inherit the scope of their parents.
+
+```js
+// example of isolating scope with a function
+var x = 10;
+(function () {
+    var x = document.createElement('section');
+    x.innerHTML = '<h1>From Demo</h1>';
+    x.className = 'slide shown';
+    document.getElementById('content').appendChild(x);
+})()
+console.log(x);
+```
+
+## More with scope rules
+
+Variables can become private.
+
+```js
+// Example of a closure
+var counter = (function() {
+    var i = 0;
+    return function () {
+        return i++;
+    };
+})();
+console.log(counter())
+console.log(counter())
+```
+
+## More with scope rules
+
+We can make factories
+
+```js
+// Bigger example of a closure
+var countBy = function(inc) {
+    var count = -inc;
+    return function() {
+        count += inc;
+        return count;
+    }
+};
+c = countBy(6);
+d = countBy(2);
+
+console.log(c())
+console.log(d())
+console.log(c())
+console.log(d())
+```
+    
+
+## Objects
+
+Object properties can be accessed with either `.` or `[]`.
+
+```js
+o.name;
+o['callMe']();
+o[1]
+```
+
+And properties can always be added/reassigned
+
+```js
+f.name = 'asdf';
+f.obj = o;
+```
+
+
+## Methods/Calling
+
+Object properties that are methods have a special variable `this` added to scope.
+
+```js
+var f = function (arg1, arg2) {
+    arg2 = arg2 || 'earth';
+    console.log(`called by ${this.name}`)
+    console.log('object', this);
+    console.log('arg1', arg1);
+    console.log('arg2', arg2);
+}
+var foo = {name: 'bar', method: f}
+```
+
+The following are equivalent
+
+```js
+foo.method('hi');
+f.bind(foo)('hi');
+f.apply(foo, ['hi');
+f.call(foo, 'hi');
+```
+
+## Conditional statements
+
+The if/else if/else statement is same as in C
+
+```js
+if (condition) {
+    codeBlock;
+}
+else if (condition2)
+    statement;
+else {
+    codeBlock;
+}
+```
+
+There is also `switch..case`, which we'll see in this app's code.
+
+## Casting in comparison
+
+There are two equalities in JavaScript
+ - `==` or `!=` perform casting when comparing values so `12 == true` is `true`
+ - `===` or `!==` don't cast, so `1 === true` is `false
+
+Casting also happens for `<`, `<=`, etc. 
+
+A logical "not" is the operator `!`, which does cast to booleans. So you can do `!!undefined` to get `true`.
+
+## Loops
+
+There are `while`, `do...while`, and `for` loops. All follow classic C syntax, but there are special forms of `for` loops
+
+```js
+var shoppingList = ['banana', 'apple', 'bread'];
+shoppingList.title = 'My Groceries';
+
+/* This will print the object's properties */
+for (let i in shoppingList)
+    console.log(i);
+
+/* and this prints the elements in the _iterable_ object */
+for (let x of shoppingList)
+    console.log(x);
+
+/* for arrays you could still do */
+for (let i=0; i<shoppingList.length; i++) {
+    console.log(shoppingList[i]);
+}
+```
+
+The `for...of` syntax behaves most like Python's `for...in` loop.
+
+## Interacting with the DOM
+
+Go over the code for this "app".
+
+Note:
+- Not organized well ("global" variables, weird scope, etc.)
+- `Element.getElementById` and `.getElementsByClassName`
+- `classList.add/remove()`
+- slide creation and navigation bar element creation
+- this [link](#10) should go to slide 10. 
+
+
+## Classes/Objects
+
+JavaScript is *prototype*-based. This is different from *class*-based, but has similar functionality.
+
+- An Object is like a key-value store,
+- A constructor is a function applied to a prototype object
+    - `var obj = new MyClass();`
+- methods/properties can be through the prototype
+    - `MyClass.prototype = {foo: function() { return 'foo';}, bar: 'bar'}`
+- or just `ownProperty`
+    - `function MyClass() { this.asdf = function() {return this.bar;}`
+- properties go up the property chain
+    - `obj.toString()` would go to `Object.prototype.toString.apply(obj)`
+
+## Prototype example
+
+```js
+function A() {
+    this.varA = 'a';
+}
+
+function B() {
+    A.call(this);
+    this.varB = 'b';
+}
+
+A.prototype = { foo: 1, bar: function(x) { return this.foo + 1;}};
+B.prototype = Object.create(A.prototype);
+B.prototype.foo = 3;
+
+a = new A();
+b = new B();
+
+a.bar(1);
+b.bar(1);
+
+B.prototype.foo = -1;
+b.bar(1);
+```
+
+## Class syntax
+
+Just a syntactic wrapping of what we covered before.
+
+```js
+
+class C extends B {
+    constructor(c) {
+        super();
+        this.foo = -1;
+        this.varC = c;
+    };
+
+    get foo() {
+        return this._foo;
+    };
+
+    set foo(b) {
+        this._foo = b;
+    };
+}
+```
+
+## Modules
+
+The current state of modules is a bit complicated because of many players coming up with many solutions. See *AMD*, *CommonJS*, *RequireJS*, *Browserify*, or home-spun versions.
+
+I won't be covering those here, because it is so complicated.
+
+However, after all of that, a module is often accessible as an *object* with the supplied functions as *properties*. (*e.g.* [`marked`](https://github.com/chjj/marked) and [`hljs`](https://github.com/isagalaev/highlight.js) in the current site).
+
+Many libraries can be installed with `npm`.
+
+## JavaScript pre-languages
+
+- [TypeScript](http://www.typescriptlang.org/) superset of ES6 that adds static (duck) typing
+- [Dart](https://www.dartlang.org/) a very C/C++ inspired language from Google
+- [CoffeeScript](http://coffeescript.org/) -- JS with "nicer" syntax
+- [PureScript](http://www.purescript.org/) inspired by Haskell
+- [comprehensive list](https://github.com/jashkenas/coffeescript/wiki/list-of-languages-that-compile-to-js)
+
diff --git a/lessons/misc/webdev-intro/css/styles.css b/lessons/misc/webdev-intro/css/styles.css
new file mode 100644
index 000000000..bf610a270
--- /dev/null
+++ b/lessons/misc/webdev-intro/css/styles.css
@@ -0,0 +1,118 @@
+body {
+    background: #112
+}
+#content {
+    display: flex;
+    flex-direction: column;
+}
+#content.horizontal {
+    flex-direction: row;
+    overflow: scroll;
+}
+section.slide {
+    font-size: 1.5em;
+    padding: 2rem;
+    background: #eef;
+    margin: 1rem;
+    margin-top: 0;
+    min-width: 50%;
+}
+#content.show-all .slide {
+    display: block;
+    margin-top: 1rem;
+}
+#content .slide {
+    display: none;
+}
+#content .slide.shown {
+    display: block;
+}
+
+section.slide > p:first-child {
+    color: #777;
+    font-size: large;
+    margin: -1rem;
+}
+section.slide h1 {
+    margin: -1rem;
+}
+
+nav {
+    display: flex;
+    flex-direction: row nowrap;
+    justify-content: center;
+    align-content: center;
+    background: #eef;
+    margin: 1rem;
+    padding: 1rem;
+    margin-bottom: 0;
+    position: relative;
+    box-shadow: 0 .2rem .2rem .2rem rgba(0,0,0,.2);
+    border-radius: .5rem .5rem 0 0;
+}
+
+nav button {
+    box-sizing: border-box;
+    height: 2em;
+    border-radius: 2em;
+    background: #333;
+    color: #eee;
+    padding-left: 1em;
+    padding-right: 1em;
+    width: auto;
+    height: 2em;
+    border: 3px solid #333;
+    line-height: 1.5;
+    margin-left: .2em;
+    margin-right: .2em;
+}
+    
+nav button.dot {
+    width: 2em;     
+    padding: 0;     
+    background-image: radial-gradient(circle, #919396, #919396 40%, #fff 50%, #fff);     
+    color: #000;     
+    border: solid 3px #fff;
+}
+
+nav button.dot.shown {
+    border-color: #0ea;
+}
+nav button.dot:hover {
+    border-color: #333;
+}
+
+img {
+    /* max-height: 12em; */
+    width: 100%;
+}
+
+figure {
+    margin: 0;
+    margin-left: 1em;
+    float: right;
+    width: 50%;
+}
+
+figcaption {
+    font-size: .7em;
+}
+
+pre {
+    margin: .5em 2em;
+    padding: .5em;
+    background: white;
+}
+
+code {
+    background: white;
+    color: #600;
+}
+pre code {
+    color: black;
+}
+
+ul ul li:last-child {
+    margin-bottom: .3em;
+}
+
diff --git a/lessons/misc/webdev-intro/img/box-model-standard-small.png b/lessons/misc/webdev-intro/img/box-model-standard-small.png
new file mode 100644
index 000000000..4f0f1bdf0
Binary files /dev/null and b/lessons/misc/webdev-intro/img/box-model-standard-small.png differ
diff --git a/lessons/misc/webdev-intro/img/rendering.svg b/lessons/misc/webdev-intro/img/rendering.svg
new file mode 100644
index 000000000..fb94c322c
--- /dev/null
+++ b/lessons/misc/webdev-intro/img/rendering.svg
@@ -0,0 +1,251 @@
+<?xml version="1.0" encoding="utf-8"?>
+<svg xmlns="http://www.w3.org/2000/svg"
+	 viewBox="0 0 635 245" style="enable-background:new 0 0 635 245;">
+<style type="text/css">
+	@font-face {
+		font-family: 'open_sanssemibold';
+		src: 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) format('truetype');
+		font-weight: normal;
+		font-style: normal;
+	}
+	@font-face {
+		font-family: 'open_sansbold';
+		src: 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) format('truetype');
+		font-weight: normal;
+		font-style: normal;
+	}
+	@font-face {
+		font-family: 'open_sansitalic';
+		src: 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+			<path class="st2" d="M109.218-128.091c0-8.41-5.078-15.227-11.34-15.227c-6.262,0-11.34,6.816-11.34,15.227
+				c0,8.408,5.078,12.441,11.34,12.441C104.14-115.65,109.218-119.683,109.218-128.091z"/>
+		</g>
+	</g>
+</pattern>
+<g>
+	<path class="st4" d="M100,105H10c-2.761,0-5-2.239-5-5V10c0-2.761,2.239-5,5-5h90c2.761,0,5,2.239,5,5v90
+		C105,102.761,102.761,105,100,105z"/>
+	<text transform="matrix(1 0 0 1 36.1523 51.322)"><tspan x="0" y="0" class="st5 st6">Load</tspan><tspan x="-3.406" y="19.2" class="st5 st6">HTML</tspan></text>
+</g>
+<g>
+	<path class="st7" d="M235,105h-90c-2.761,0-5-2.239-5-5V10c0-2.761,2.239-5,5-5h90c2.761,0,5,2.239,5,5v90
+		C240,102.761,237.761,105,235,105z"/>
+	<text transform="matrix(1 0 0 1 168.4844 51.322)"><tspan x="0" y="0" class="st5 st6">Parse</tspan><tspan x="-0.738" y="19.2" class="st5 st6">HTML</tspan></text>
+</g>
+<g>
+	<path class="st4" d="M235,240h-90c-2.761,0-5-2.239-5-5v-90c0-2.761,2.239-5,5-5h90c2.761,0,5,2.239,5,5v90
+		C240,237.761,237.761,240,235,240z"/>
+	<text transform="matrix(1 0 0 1 171.1523 186.322)"><tspan x="0" y="0" class="st5 st6">Load</tspan><tspan x="4.98" y="19.2" class="st5 st6">CSS</tspan></text>
+</g>
+<g>
+	<path class="st7" d="M370,240h-90c-2.761,0-5-2.239-5-5v-90c0-2.761,2.239-5,5-5h90c2.761,0,5,2.239,5,5v90
+		C375,237.761,372.761,240,370,240z"/>
+	<text transform="matrix(1 0 0 1 303.4844 186.322)"><tspan x="0" y="0" class="st5 st6">Parse</tspan><tspan x="7.648" y="19.2" class="st5 st6">CSS</tspan></text>
+</g>
+<g>
+	<path class="st8" d="M490,105H280c-2.761,0-5-2.239-5-5V10c0-2.761,2.239-5,5-5h210c2.761,0,5,2.239,5,5v90
+		C495,102.761,492.761,105,490,105z"/>
+	<text transform="matrix(1 0 0 1 359.4766 51.322)"><tspan x="0" y="0" class="st5 st6">Create</tspan><tspan x="-11.918" y="19.2" class="st5 st6">DOM tree</tspan></text>
+</g>
+<g>
+	<path class="st9" d="M625,105h-90c-2.761,0-5-2.239-5-5V10c0-2.761,2.239-5,5-5h90c2.761,0,5,2.239,5,5v90
+		C630,102.761,627.761,105,625,105z"/>
+	<text transform="matrix(1 0 0 1 550.7734 59.5974)" class="st10 st6">Display</text>
+</g>
+<path d="M145,53.33l-11.25-6.495c-0.773-0.446-1.727-0.446-2.5,0C130.477,47.282,130,48.107,130,49v4h-25v5h25v3.99
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+<path d="M198.16,131.25c-0.447-0.773-1.272-1.25-2.165-1.25H192v-25h-5v25h-3.995c-0.893,0-1.718,0.477-2.165,1.25
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+<path d="M453.28,110.25L446.785,99c-0.446-0.773-1.271-1.25-2.165-1.25s-1.719,0.477-2.165,1.25l-6.495,11.25
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+	c0.894,0,1.719-0.477,2.165-1.25S453.727,111.023,453.28,110.25z"/>
+<text transform="matrix(1 0 0 1 446.3633 159)"><tspan x="0" y="0" class="st11 st6">Attach style</tspan><tspan x="-9.98" y="19.2" class="st11 st6">to DOM nodes</tspan></text>
+</svg>
diff --git a/lessons/misc/webdev-intro/lesson.md b/lessons/misc/webdev-intro/lesson.md
new file mode 100644
index 000000000..bf9621145
--- /dev/null
+++ b/lessons/misc/webdev-intro/lesson.md
@@ -0,0 +1,22 @@
+---
+layout: page
+title: 'Intro to web development with HTML/CSS/JS'
+visible: true
+tags:
+  - misc
+  - beginner
+---
+
+- **Authors**: John Ladan [@jladan](https://github.com/jladan)
+- **Research field**: Physics
+- **Lesson topic**: An introduction to web development with HTML/CSS/JS
+- **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/misc/webdev-intro>
+
+# Preface
+
+This lesson is an introduction to making web applications. A little HTML and CSS are covered as base-level knowledge, but the focus should be on the Document Object Model (DOM) and JavaScript.
+
+The actual lesson contents are in [`contents.md`](../contents/index.html). 
+Part of the lesson is a demo app that reads `contents.md` and makes a slide presentation (found [*here*](../slides.html)). This app is left in an intentionally poorly designed state to demonstrate concepts like scoping. Comments are littered throughout for a novice reader.
+
+To view the demo, either run a web server in this directory (e.g. `python -m http.server`) or view the latest version at <https://jladan.github.io/webdev-basics>.
diff --git a/lessons/misc/webdev-intro/lib/marked.js b/lessons/misc/webdev-intro/lib/marked.js
new file mode 100644
index 000000000..2696019cd
--- /dev/null
+++ b/lessons/misc/webdev-intro/lib/marked.js
@@ -0,0 +1,1286 @@
+/**
+ * marked - a markdown parser
+ * Copyright (c) 2011-2014, Christopher Jeffrey. (MIT Licensed)
+ * https://github.com/chjj/marked
+ */
+
+;(function() {
+
+/**
+ * Block-Level Grammar
+ */
+
+var block = {
+  newline: /^\n+/,
+  code: /^( {4}[^\n]+\n*)+/,
+  fences: noop,
+  hr: /^( *[-*_]){3,} *(?:\n+|$)/,
+  heading: /^ *(#{1,6}) *([^\n]+?) *#* *(?:\n+|$)/,
+  nptable: noop,
+  lheading: /^([^\n]+)\n *(=|-){2,} *(?:\n+|$)/,
+  blockquote: /^( *>[^\n]+(\n(?!def)[^\n]+)*\n*)+/,
+  list: /^( *)(bull) [\s\S]+?(?:hr|def|\n{2,}(?! )(?!\1bull )\n*|\s*$)/,
+  html: /^ *(?:comment *(?:\n|\s*$)|closed *(?:\n{2,}|\s*$)|closing *(?:\n{2,}|\s*$))/,
+  def: /^ *\[([^\]]+)\]: *<?([^\s>]+)>?(?: +["(]([^\n]+)[")])? *(?:\n+|$)/,
+  table: noop,
+  paragraph: /^((?:[^\n]+\n?(?!hr|heading|lheading|blockquote|tag|def))+)\n*/,
+  text: /^[^\n]+/
+};
+
+block.bullet = /(?:[*+-]|\d+\.)/;
+block.item = /^( *)(bull) [^\n]*(?:\n(?!\1bull )[^\n]*)*/;
+block.item = replace(block.item, 'gm')
+  (/bull/g, block.bullet)
+  ();
+
+block.list = replace(block.list)
+  (/bull/g, block.bullet)
+  ('hr', '\\n+(?=\\1?(?:[-*_] *){3,}(?:\\n+|$))')
+  ('def', '\\n+(?=' + block.def.source + ')')
+  ();
+
+block.blockquote = replace(block.blockquote)
+  ('def', block.def)
+  ();
+
+block._tag = '(?!(?:'
+  + 'a|em|strong|small|s|cite|q|dfn|abbr|data|time|code'
+  + '|var|samp|kbd|sub|sup|i|b|u|mark|ruby|rt|rp|bdi|bdo'
+  + '|span|br|wbr|ins|del|img)\\b)\\w+(?!:/|[^\\w\\s@]*@)\\b';
+
+block.html = replace(block.html)
+  ('comment', /<!--[\s\S]*?-->/)
+  ('closed', /<(tag)[\s\S]+?<\/\1>/)
+  ('closing', /<tag(?:"[^"]*"|'[^']*'|[^'">])*?>/)
+  (/tag/g, block._tag)
+  ();
+
+block.paragraph = replace(block.paragraph)
+  ('hr', block.hr)
+  ('heading', block.heading)
+  ('lheading', block.lheading)
+  ('blockquote', block.blockquote)
+  ('tag', '<' + block._tag)
+  ('def', block.def)
+  ();
+
+/**
+ * Normal Block Grammar
+ */
+
+block.normal = merge({}, block);
+
+/**
+ * GFM Block Grammar
+ */
+
+block.gfm = merge({}, block.normal, {
+  fences: /^ *(`{3,}|~{3,})[ \.]*(\S+)? *\n([\s\S]*?)\s*\1 *(?:\n+|$)/,
+  paragraph: /^/,
+  heading: /^ *(#{1,6}) +([^\n]+?) *#* *(?:\n+|$)/
+});
+
+block.gfm.paragraph = replace(block.paragraph)
+  ('(?!', '(?!'
+    + block.gfm.fences.source.replace('\\1', '\\2') + '|'
+    + block.list.source.replace('\\1', '\\3') + '|')
+  ();
+
+/**
+ * GFM + Tables Block Grammar
+ */
+
+block.tables = merge({}, block.gfm, {
+  nptable: /^ *(\S.*\|.*)\n *([-:]+ *\|[-| :]*)\n((?:.*\|.*(?:\n|$))*)\n*/,
+  table: /^ *\|(.+)\n *\|( *[-:]+[-| :]*)\n((?: *\|.*(?:\n|$))*)\n*/
+});
+
+/**
+ * Block Lexer
+ */
+
+function Lexer(options) {
+  this.tokens = [];
+  this.tokens.links = {};
+  this.options = options || marked.defaults;
+  this.rules = block.normal;
+
+  if (this.options.gfm) {
+    if (this.options.tables) {
+      this.rules = block.tables;
+    } else {
+      this.rules = block.gfm;
+    }
+  }
+}
+
+/**
+ * Expose Block Rules
+ */
+
+Lexer.rules = block;
+
+/**
+ * Static Lex Method
+ */
+
+Lexer.lex = function(src, options) {
+  var lexer = new Lexer(options);
+  return lexer.lex(src);
+};
+
+/**
+ * Preprocessing
+ */
+
+Lexer.prototype.lex = function(src) {
+  src = src
+    .replace(/\r\n|\r/g, '\n')
+    .replace(/\t/g, '    ')
+    .replace(/\u00a0/g, ' ')
+    .replace(/\u2424/g, '\n');
+
+  return this.token(src, true);
+};
+
+/**
+ * Lexing
+ */
+
+Lexer.prototype.token = function(src, top, bq) {
+  var src = src.replace(/^ +$/gm, '')
+    , next
+    , loose
+    , cap
+    , bull
+    , b
+    , item
+    , space
+    , i
+    , l;
+
+  while (src) {
+    // newline
+    if (cap = this.rules.newline.exec(src)) {
+      src = src.substring(cap[0].length);
+      if (cap[0].length > 1) {
+        this.tokens.push({
+          type: 'space'
+        });
+      }
+    }
+
+    // code
+    if (cap = this.rules.code.exec(src)) {
+      src = src.substring(cap[0].length);
+      cap = cap[0].replace(/^ {4}/gm, '');
+      this.tokens.push({
+        type: 'code',
+        text: !this.options.pedantic
+          ? cap.replace(/\n+$/, '')
+          : cap
+      });
+      continue;
+    }
+
+    // fences (gfm)
+    if (cap = this.rules.fences.exec(src)) {
+      src = src.substring(cap[0].length);
+      this.tokens.push({
+        type: 'code',
+        lang: cap[2],
+        text: cap[3] || ''
+      });
+      continue;
+    }
+
+    // heading
+    if (cap = this.rules.heading.exec(src)) {
+      src = src.substring(cap[0].length);
+      this.tokens.push({
+        type: 'heading',
+        depth: cap[1].length,
+        text: cap[2]
+      });
+      continue;
+    }
+
+    // table no leading pipe (gfm)
+    if (top && (cap = this.rules.nptable.exec(src))) {
+      src = src.substring(cap[0].length);
+
+      item = {
+        type: 'table',
+        header: cap[1].replace(/^ *| *\| *$/g, '').split(/ *\| */),
+        align: cap[2].replace(/^ *|\| *$/g, '').split(/ *\| */),
+        cells: cap[3].replace(/\n$/, '').split('\n')
+      };
+
+      for (i = 0; i < item.align.length; i++) {
+        if (/^ *-+: *$/.test(item.align[i])) {
+          item.align[i] = 'right';
+        } else if (/^ *:-+: *$/.test(item.align[i])) {
+          item.align[i] = 'center';
+        } else if (/^ *:-+ *$/.test(item.align[i])) {
+          item.align[i] = 'left';
+        } else {
+          item.align[i] = null;
+        }
+      }
+
+      for (i = 0; i < item.cells.length; i++) {
+        item.cells[i] = item.cells[i].split(/ *\| */);
+      }
+
+      this.tokens.push(item);
+
+      continue;
+    }
+
+    // lheading
+    if (cap = this.rules.lheading.exec(src)) {
+      src = src.substring(cap[0].length);
+      this.tokens.push({
+        type: 'heading',
+        depth: cap[2] === '=' ? 1 : 2,
+        text: cap[1]
+      });
+      continue;
+    }
+
+    // hr
+    if (cap = this.rules.hr.exec(src)) {
+      src = src.substring(cap[0].length);
+      this.tokens.push({
+        type: 'hr'
+      });
+      continue;
+    }
+
+    // blockquote
+    if (cap = this.rules.blockquote.exec(src)) {
+      src = src.substring(cap[0].length);
+
+      this.tokens.push({
+        type: 'blockquote_start'
+      });
+
+      cap = cap[0].replace(/^ *> ?/gm, '');
+
+      // Pass `top` to keep the current
+      // "toplevel" state. This is exactly
+      // how markdown.pl works.
+      this.token(cap, top, true);
+
+      this.tokens.push({
+        type: 'blockquote_end'
+      });
+
+      continue;
+    }
+
+    // list
+    if (cap = this.rules.list.exec(src)) {
+      src = src.substring(cap[0].length);
+      bull = cap[2];
+
+      this.tokens.push({
+        type: 'list_start',
+        ordered: bull.length > 1
+      });
+
+      // Get each top-level item.
+      cap = cap[0].match(this.rules.item);
+
+      next = false;
+      l = cap.length;
+      i = 0;
+
+      for (; i < l; i++) {
+        item = cap[i];
+
+        // Remove the list item's bullet
+        // so it is seen as the next token.
+        space = item.length;
+        item = item.replace(/^ *([*+-]|\d+\.) +/, '');
+
+        // Outdent whatever the
+        // list item contains. Hacky.
+        if (~item.indexOf('\n ')) {
+          space -= item.length;
+          item = !this.options.pedantic
+            ? item.replace(new RegExp('^ {1,' + space + '}', 'gm'), '')
+            : item.replace(/^ {1,4}/gm, '');
+        }
+
+        // Determine whether the next list item belongs here.
+        // Backpedal if it does not belong in this list.
+        if (this.options.smartLists && i !== l - 1) {
+          b = block.bullet.exec(cap[i + 1])[0];
+          if (bull !== b && !(bull.length > 1 && b.length > 1)) {
+            src = cap.slice(i + 1).join('\n') + src;
+            i = l - 1;
+          }
+        }
+
+        // Determine whether item is loose or not.
+        // Use: /(^|\n)(?! )[^\n]+\n\n(?!\s*$)/
+        // for discount behavior.
+        loose = next || /\n\n(?!\s*$)/.test(item);
+        if (i !== l - 1) {
+          next = item.charAt(item.length - 1) === '\n';
+          if (!loose) loose = next;
+        }
+
+        this.tokens.push({
+          type: loose
+            ? 'loose_item_start'
+            : 'list_item_start'
+        });
+
+        // Recurse.
+        this.token(item, false, bq);
+
+        this.tokens.push({
+          type: 'list_item_end'
+        });
+      }
+
+      this.tokens.push({
+        type: 'list_end'
+      });
+
+      continue;
+    }
+
+    // html
+    if (cap = this.rules.html.exec(src)) {
+      src = src.substring(cap[0].length);
+      this.tokens.push({
+        type: this.options.sanitize
+          ? 'paragraph'
+          : 'html',
+        pre: !this.options.sanitizer
+          && (cap[1] === 'pre' || cap[1] === 'script' || cap[1] === 'style'),
+        text: cap[0]
+      });
+      continue;
+    }
+
+    // def
+    if ((!bq && top) && (cap = this.rules.def.exec(src))) {
+      src = src.substring(cap[0].length);
+      this.tokens.links[cap[1].toLowerCase()] = {
+        href: cap[2],
+        title: cap[3]
+      };
+      continue;
+    }
+
+    // table (gfm)
+    if (top && (cap = this.rules.table.exec(src))) {
+      src = src.substring(cap[0].length);
+
+      item = {
+        type: 'table',
+        header: cap[1].replace(/^ *| *\| *$/g, '').split(/ *\| */),
+        align: cap[2].replace(/^ *|\| *$/g, '').split(/ *\| */),
+        cells: cap[3].replace(/(?: *\| *)?\n$/, '').split('\n')
+      };
+
+      for (i = 0; i < item.align.length; i++) {
+        if (/^ *-+: *$/.test(item.align[i])) {
+          item.align[i] = 'right';
+        } else if (/^ *:-+: *$/.test(item.align[i])) {
+          item.align[i] = 'center';
+        } else if (/^ *:-+ *$/.test(item.align[i])) {
+          item.align[i] = 'left';
+        } else {
+          item.align[i] = null;
+        }
+      }
+
+      for (i = 0; i < item.cells.length; i++) {
+        item.cells[i] = item.cells[i]
+          .replace(/^ *\| *| *\| *$/g, '')
+          .split(/ *\| */);
+      }
+
+      this.tokens.push(item);
+
+      continue;
+    }
+
+    // top-level paragraph
+    if (top && (cap = this.rules.paragraph.exec(src))) {
+      src = src.substring(cap[0].length);
+      this.tokens.push({
+        type: 'paragraph',
+        text: cap[1].charAt(cap[1].length - 1) === '\n'
+          ? cap[1].slice(0, -1)
+          : cap[1]
+      });
+      continue;
+    }
+
+    // text
+    if (cap = this.rules.text.exec(src)) {
+      // Top-level should never reach here.
+      src = src.substring(cap[0].length);
+      this.tokens.push({
+        type: 'text',
+        text: cap[0]
+      });
+      continue;
+    }
+
+    if (src) {
+      throw new
+        Error('Infinite loop on byte: ' + src.charCodeAt(0));
+    }
+  }
+
+  return this.tokens;
+};
+
+/**
+ * Inline-Level Grammar
+ */
+
+var inline = {
+  escape: /^\\([\\`*{}\[\]()#+\-.!_>])/,
+  autolink: /^<([^ >]+(@|:\/)[^ >]+)>/,
+  url: noop,
+  tag: /^<!--[\s\S]*?-->|^<\/?\w+(?:"[^"]*"|'[^']*'|[^'">])*?>/,
+  link: /^!?\[(inside)\]\(href\)/,
+  reflink: /^!?\[(inside)\]\s*\[([^\]]*)\]/,
+  nolink: /^!?\[((?:\[[^\]]*\]|[^\[\]])*)\]/,
+  strong: /^__([\s\S]+?)__(?!_)|^\*\*([\s\S]+?)\*\*(?!\*)/,
+  em: /^\b_((?:[^_]|__)+?)_\b|^\*((?:\*\*|[\s\S])+?)\*(?!\*)/,
+  code: /^(`+)\s*([\s\S]*?[^`])\s*\1(?!`)/,
+  br: /^ {2,}\n(?!\s*$)/,
+  del: noop,
+  text: /^[\s\S]+?(?=[\\<!\[_*`]| {2,}\n|$)/
+};
+
+inline._inside = /(?:\[[^\]]*\]|[^\[\]]|\](?=[^\[]*\]))*/;
+inline._href = /\s*<?([\s\S]*?)>?(?:\s+['"]([\s\S]*?)['"])?\s*/;
+
+inline.link = replace(inline.link)
+  ('inside', inline._inside)
+  ('href', inline._href)
+  ();
+
+inline.reflink = replace(inline.reflink)
+  ('inside', inline._inside)
+  ();
+
+/**
+ * Normal Inline Grammar
+ */
+
+inline.normal = merge({}, inline);
+
+/**
+ * Pedantic Inline Grammar
+ */
+
+inline.pedantic = merge({}, inline.normal, {
+  strong: /^__(?=\S)([\s\S]*?\S)__(?!_)|^\*\*(?=\S)([\s\S]*?\S)\*\*(?!\*)/,
+  em: /^_(?=\S)([\s\S]*?\S)_(?!_)|^\*(?=\S)([\s\S]*?\S)\*(?!\*)/
+});
+
+/**
+ * GFM Inline Grammar
+ */
+
+inline.gfm = merge({}, inline.normal, {
+  escape: replace(inline.escape)('])', '~|])')(),
+  url: /^(https?:\/\/[^\s<]+[^<.,:;"')\]\s])/,
+  del: /^~~(?=\S)([\s\S]*?\S)~~/,
+  text: replace(inline.text)
+    (']|', '~]|')
+    ('|', '|https?://|')
+    ()
+});
+
+/**
+ * GFM + Line Breaks Inline Grammar
+ */
+
+inline.breaks = merge({}, inline.gfm, {
+  br: replace(inline.br)('{2,}', '*')(),
+  text: replace(inline.gfm.text)('{2,}', '*')()
+});
+
+/**
+ * Inline Lexer & Compiler
+ */
+
+function InlineLexer(links, options) {
+  this.options = options || marked.defaults;
+  this.links = links;
+  this.rules = inline.normal;
+  this.renderer = this.options.renderer || new Renderer;
+  this.renderer.options = this.options;
+
+  if (!this.links) {
+    throw new
+      Error('Tokens array requires a `links` property.');
+  }
+
+  if (this.options.gfm) {
+    if (this.options.breaks) {
+      this.rules = inline.breaks;
+    } else {
+      this.rules = inline.gfm;
+    }
+  } else if (this.options.pedantic) {
+    this.rules = inline.pedantic;
+  }
+}
+
+/**
+ * Expose Inline Rules
+ */
+
+InlineLexer.rules = inline;
+
+/**
+ * Static Lexing/Compiling Method
+ */
+
+InlineLexer.output = function(src, links, options) {
+  var inline = new InlineLexer(links, options);
+  return inline.output(src);
+};
+
+/**
+ * Lexing/Compiling
+ */
+
+InlineLexer.prototype.output = function(src) {
+  var out = ''
+    , link
+    , text
+    , href
+    , cap;
+
+  while (src) {
+    // escape
+    if (cap = this.rules.escape.exec(src)) {
+      src = src.substring(cap[0].length);
+      out += cap[1];
+      continue;
+    }
+
+    // autolink
+    if (cap = this.rules.autolink.exec(src)) {
+      src = src.substring(cap[0].length);
+      if (cap[2] === '@') {
+        text = cap[1].charAt(6) === ':'
+          ? this.mangle(cap[1].substring(7))
+          : this.mangle(cap[1]);
+        href = this.mangle('mailto:') + text;
+      } else {
+        text = escape(cap[1]);
+        href = text;
+      }
+      out += this.renderer.link(href, null, text);
+      continue;
+    }
+
+    // url (gfm)
+    if (!this.inLink && (cap = this.rules.url.exec(src))) {
+      src = src.substring(cap[0].length);
+      text = escape(cap[1]);
+      href = text;
+      out += this.renderer.link(href, null, text);
+      continue;
+    }
+
+    // tag
+    if (cap = this.rules.tag.exec(src)) {
+      if (!this.inLink && /^<a /i.test(cap[0])) {
+        this.inLink = true;
+      } else if (this.inLink && /^<\/a>/i.test(cap[0])) {
+        this.inLink = false;
+      }
+      src = src.substring(cap[0].length);
+      out += this.options.sanitize
+        ? this.options.sanitizer
+          ? this.options.sanitizer(cap[0])
+          : escape(cap[0])
+        : cap[0]
+      continue;
+    }
+
+    // link
+    if (cap = this.rules.link.exec(src)) {
+      src = src.substring(cap[0].length);
+      this.inLink = true;
+      out += this.outputLink(cap, {
+        href: cap[2],
+        title: cap[3]
+      });
+      this.inLink = false;
+      continue;
+    }
+
+    // reflink, nolink
+    if ((cap = this.rules.reflink.exec(src))
+        || (cap = this.rules.nolink.exec(src))) {
+      src = src.substring(cap[0].length);
+      link = (cap[2] || cap[1]).replace(/\s+/g, ' ');
+      link = this.links[link.toLowerCase()];
+      if (!link || !link.href) {
+        out += cap[0].charAt(0);
+        src = cap[0].substring(1) + src;
+        continue;
+      }
+      this.inLink = true;
+      out += this.outputLink(cap, link);
+      this.inLink = false;
+      continue;
+    }
+
+    // strong
+    if (cap = this.rules.strong.exec(src)) {
+      src = src.substring(cap[0].length);
+      out += this.renderer.strong(this.output(cap[2] || cap[1]));
+      continue;
+    }
+
+    // em
+    if (cap = this.rules.em.exec(src)) {
+      src = src.substring(cap[0].length);
+      out += this.renderer.em(this.output(cap[2] || cap[1]));
+      continue;
+    }
+
+    // code
+    if (cap = this.rules.code.exec(src)) {
+      src = src.substring(cap[0].length);
+      out += this.renderer.codespan(escape(cap[2], true));
+      continue;
+    }
+
+    // br
+    if (cap = this.rules.br.exec(src)) {
+      src = src.substring(cap[0].length);
+      out += this.renderer.br();
+      continue;
+    }
+
+    // del (gfm)
+    if (cap = this.rules.del.exec(src)) {
+      src = src.substring(cap[0].length);
+      out += this.renderer.del(this.output(cap[1]));
+      continue;
+    }
+
+    // text
+    if (cap = this.rules.text.exec(src)) {
+      src = src.substring(cap[0].length);
+      out += this.renderer.text(escape(this.smartypants(cap[0])));
+      continue;
+    }
+
+    if (src) {
+      throw new
+        Error('Infinite loop on byte: ' + src.charCodeAt(0));
+    }
+  }
+
+  return out;
+};
+
+/**
+ * Compile Link
+ */
+
+InlineLexer.prototype.outputLink = function(cap, link) {
+  var href = escape(link.href)
+    , title = link.title ? escape(link.title) : null;
+
+  return cap[0].charAt(0) !== '!'
+    ? this.renderer.link(href, title, this.output(cap[1]))
+    : this.renderer.image(href, title, escape(cap[1]));
+};
+
+/**
+ * Smartypants Transformations
+ */
+
+InlineLexer.prototype.smartypants = function(text) {
+  if (!this.options.smartypants) return text;
+  return text
+    // em-dashes
+    .replace(/---/g, '\u2014')
+    // en-dashes
+    .replace(/--/g, '\u2013')
+    // opening singles
+    .replace(/(^|[-\u2014/(\[{"\s])'/g, '$1\u2018')
+    // closing singles & apostrophes
+    .replace(/'/g, '\u2019')
+    // opening doubles
+    .replace(/(^|[-\u2014/(\[{\u2018\s])"/g, '$1\u201c')
+    // closing doubles
+    .replace(/"/g, '\u201d')
+    // ellipses
+    .replace(/\.{3}/g, '\u2026');
+};
+
+/**
+ * Mangle Links
+ */
+
+InlineLexer.prototype.mangle = function(text) {
+  if (!this.options.mangle) return text;
+  var out = ''
+    , l = text.length
+    , i = 0
+    , ch;
+
+  for (; i < l; i++) {
+    ch = text.charCodeAt(i);
+    if (Math.random() > 0.5) {
+      ch = 'x' + ch.toString(16);
+    }
+    out += '&#' + ch + ';';
+  }
+
+  return out;
+};
+
+/**
+ * Renderer
+ */
+
+function Renderer(options) {
+  this.options = options || {};
+}
+
+Renderer.prototype.code = function(code, lang, escaped) {
+  if (this.options.highlight) {
+    var out = this.options.highlight(code, lang);
+    if (out != null && out !== code) {
+      escaped = true;
+      code = out;
+    }
+  }
+
+  if (!lang) {
+    return '<pre><code>'
+      + (escaped ? code : escape(code, true))
+      + '\n</code></pre>';
+  }
+
+  return '<pre><code class="'
+    + this.options.langPrefix
+    + escape(lang, true)
+    + '">'
+    + (escaped ? code : escape(code, true))
+    + '\n</code></pre>\n';
+};
+
+Renderer.prototype.blockquote = function(quote) {
+  return '<blockquote>\n' + quote + '</blockquote>\n';
+};
+
+Renderer.prototype.html = function(html) {
+  return html;
+};
+
+Renderer.prototype.heading = function(text, level, raw) {
+  return '<h'
+    + level
+    + ' id="'
+    + this.options.headerPrefix
+    + raw.toLowerCase().replace(/[^\w]+/g, '-')
+    + '">'
+    + text
+    + '</h'
+    + level
+    + '>\n';
+};
+
+Renderer.prototype.hr = function() {
+  return this.options.xhtml ? '<hr/>\n' : '<hr>\n';
+};
+
+Renderer.prototype.list = function(body, ordered) {
+  var type = ordered ? 'ol' : 'ul';
+  return '<' + type + '>\n' + body + '</' + type + '>\n';
+};
+
+Renderer.prototype.listitem = function(text) {
+  return '<li>' + text + '</li>\n';
+};
+
+Renderer.prototype.paragraph = function(text) {
+  return '<p>' + text + '</p>\n';
+};
+
+Renderer.prototype.table = function(header, body) {
+  return '<table>\n'
+    + '<thead>\n'
+    + header
+    + '</thead>\n'
+    + '<tbody>\n'
+    + body
+    + '</tbody>\n'
+    + '</table>\n';
+};
+
+Renderer.prototype.tablerow = function(content) {
+  return '<tr>\n' + content + '</tr>\n';
+};
+
+Renderer.prototype.tablecell = function(content, flags) {
+  var type = flags.header ? 'th' : 'td';
+  var tag = flags.align
+    ? '<' + type + ' style="text-align:' + flags.align + '">'
+    : '<' + type + '>';
+  return tag + content + '</' + type + '>\n';
+};
+
+// span level renderer
+Renderer.prototype.strong = function(text) {
+  return '<strong>' + text + '</strong>';
+};
+
+Renderer.prototype.em = function(text) {
+  return '<em>' + text + '</em>';
+};
+
+Renderer.prototype.codespan = function(text) {
+  return '<code>' + text + '</code>';
+};
+
+Renderer.prototype.br = function() {
+  return this.options.xhtml ? '<br/>' : '<br>';
+};
+
+Renderer.prototype.del = function(text) {
+  return '<del>' + text + '</del>';
+};
+
+Renderer.prototype.link = function(href, title, text) {
+  if (this.options.sanitize) {
+    try {
+      var prot = decodeURIComponent(unescape(href))
+        .replace(/[^\w:]/g, '')
+        .toLowerCase();
+    } catch (e) {
+      return '';
+    }
+    if (prot.indexOf('javascript:') === 0 || prot.indexOf('vbscript:') === 0 || prot.indexOf('data:') === 0) {
+      return '';
+    }
+  }
+  var out = '<a href="' + href + '"';
+  if (title) {
+    out += ' title="' + title + '"';
+  }
+  out += '>' + text + '</a>';
+  return out;
+};
+
+Renderer.prototype.image = function(href, title, text) {
+  var out = '<img src="' + href + '" alt="' + text + '"';
+  if (title) {
+    out += ' title="' + title + '"';
+  }
+  out += this.options.xhtml ? '/>' : '>';
+  return out;
+};
+
+Renderer.prototype.text = function(text) {
+  return text;
+};
+
+/**
+ * Parsing & Compiling
+ */
+
+function Parser(options) {
+  this.tokens = [];
+  this.token = null;
+  this.options = options || marked.defaults;
+  this.options.renderer = this.options.renderer || new Renderer;
+  this.renderer = this.options.renderer;
+  this.renderer.options = this.options;
+}
+
+/**
+ * Static Parse Method
+ */
+
+Parser.parse = function(src, options, renderer) {
+  var parser = new Parser(options, renderer);
+  return parser.parse(src);
+};
+
+/**
+ * Parse Loop
+ */
+
+Parser.prototype.parse = function(src) {
+  this.inline = new InlineLexer(src.links, this.options, this.renderer);
+  this.tokens = src.reverse();
+
+  var out = '';
+  while (this.next()) {
+    out += this.tok();
+  }
+
+  return out;
+};
+
+/**
+ * Next Token
+ */
+
+Parser.prototype.next = function() {
+  return this.token = this.tokens.pop();
+};
+
+/**
+ * Preview Next Token
+ */
+
+Parser.prototype.peek = function() {
+  return this.tokens[this.tokens.length - 1] || 0;
+};
+
+/**
+ * Parse Text Tokens
+ */
+
+Parser.prototype.parseText = function() {
+  var body = this.token.text;
+
+  while (this.peek().type === 'text') {
+    body += '\n' + this.next().text;
+  }
+
+  return this.inline.output(body);
+};
+
+/**
+ * Parse Current Token
+ */
+
+Parser.prototype.tok = function() {
+  switch (this.token.type) {
+    case 'space': {
+      return '';
+    }
+    case 'hr': {
+      return this.renderer.hr();
+    }
+    case 'heading': {
+      return this.renderer.heading(
+        this.inline.output(this.token.text),
+        this.token.depth,
+        this.token.text);
+    }
+    case 'code': {
+      return this.renderer.code(this.token.text,
+        this.token.lang,
+        this.token.escaped);
+    }
+    case 'table': {
+      var header = ''
+        , body = ''
+        , i
+        , row
+        , cell
+        , flags
+        , j;
+
+      // header
+      cell = '';
+      for (i = 0; i < this.token.header.length; i++) {
+        flags = { header: true, align: this.token.align[i] };
+        cell += this.renderer.tablecell(
+          this.inline.output(this.token.header[i]),
+          { header: true, align: this.token.align[i] }
+        );
+      }
+      header += this.renderer.tablerow(cell);
+
+      for (i = 0; i < this.token.cells.length; i++) {
+        row = this.token.cells[i];
+
+        cell = '';
+        for (j = 0; j < row.length; j++) {
+          cell += this.renderer.tablecell(
+            this.inline.output(row[j]),
+            { header: false, align: this.token.align[j] }
+          );
+        }
+
+        body += this.renderer.tablerow(cell);
+      }
+      return this.renderer.table(header, body);
+    }
+    case 'blockquote_start': {
+      var body = '';
+
+      while (this.next().type !== 'blockquote_end') {
+        body += this.tok();
+      }
+
+      return this.renderer.blockquote(body);
+    }
+    case 'list_start': {
+      var body = ''
+        , ordered = this.token.ordered;
+
+      while (this.next().type !== 'list_end') {
+        body += this.tok();
+      }
+
+      return this.renderer.list(body, ordered);
+    }
+    case 'list_item_start': {
+      var body = '';
+
+      while (this.next().type !== 'list_item_end') {
+        body += this.token.type === 'text'
+          ? this.parseText()
+          : this.tok();
+      }
+
+      return this.renderer.listitem(body);
+    }
+    case 'loose_item_start': {
+      var body = '';
+
+      while (this.next().type !== 'list_item_end') {
+        body += this.tok();
+      }
+
+      return this.renderer.listitem(body);
+    }
+    case 'html': {
+      var html = !this.token.pre && !this.options.pedantic
+        ? this.inline.output(this.token.text)
+        : this.token.text;
+      return this.renderer.html(html);
+    }
+    case 'paragraph': {
+      return this.renderer.paragraph(this.inline.output(this.token.text));
+    }
+    case 'text': {
+      return this.renderer.paragraph(this.parseText());
+    }
+  }
+};
+
+/**
+ * Helpers
+ */
+
+function escape(html, encode) {
+  return html
+    .replace(!encode ? /&(?!#?\w+;)/g : /&/g, '&amp;')
+    .replace(/</g, '&lt;')
+    .replace(/>/g, '&gt;')
+    .replace(/"/g, '&quot;')
+    .replace(/'/g, '&#39;');
+}
+
+function unescape(html) {
+	// explicitly match decimal, hex, and named HTML entities 
+  return html.replace(/&(#(?:\d+)|(?:#x[0-9A-Fa-f]+)|(?:\w+));?/g, function(_, n) {
+    n = n.toLowerCase();
+    if (n === 'colon') return ':';
+    if (n.charAt(0) === '#') {
+      return n.charAt(1) === 'x'
+        ? String.fromCharCode(parseInt(n.substring(2), 16))
+        : String.fromCharCode(+n.substring(1));
+    }
+    return '';
+  });
+}
+
+function replace(regex, opt) {
+  regex = regex.source;
+  opt = opt || '';
+  return function self(name, val) {
+    if (!name) return new RegExp(regex, opt);
+    val = val.source || val;
+    val = val.replace(/(^|[^\[])\^/g, '$1');
+    regex = regex.replace(name, val);
+    return self;
+  };
+}
+
+function noop() {}
+noop.exec = noop;
+
+function merge(obj) {
+  var i = 1
+    , target
+    , key;
+
+  for (; i < arguments.length; i++) {
+    target = arguments[i];
+    for (key in target) {
+      if (Object.prototype.hasOwnProperty.call(target, key)) {
+        obj[key] = target[key];
+      }
+    }
+  }
+
+  return obj;
+}
+
+
+/**
+ * Marked
+ */
+
+function marked(src, opt, callback) {
+  if (callback || typeof opt === 'function') {
+    if (!callback) {
+      callback = opt;
+      opt = null;
+    }
+
+    opt = merge({}, marked.defaults, opt || {});
+
+    var highlight = opt.highlight
+      , tokens
+      , pending
+      , i = 0;
+
+    try {
+      tokens = Lexer.lex(src, opt)
+    } catch (e) {
+      return callback(e);
+    }
+
+    pending = tokens.length;
+
+    var done = function(err) {
+      if (err) {
+        opt.highlight = highlight;
+        return callback(err);
+      }
+
+      var out;
+
+      try {
+        out = Parser.parse(tokens, opt);
+      } catch (e) {
+        err = e;
+      }
+
+      opt.highlight = highlight;
+
+      return err
+        ? callback(err)
+        : callback(null, out);
+    };
+
+    if (!highlight || highlight.length < 3) {
+      return done();
+    }
+
+    delete opt.highlight;
+
+    if (!pending) return done();
+
+    for (; i < tokens.length; i++) {
+      (function(token) {
+        if (token.type !== 'code') {
+          return --pending || done();
+        }
+        return highlight(token.text, token.lang, function(err, code) {
+          if (err) return done(err);
+          if (code == null || code === token.text) {
+            return --pending || done();
+          }
+          token.text = code;
+          token.escaped = true;
+          --pending || done();
+        });
+      })(tokens[i]);
+    }
+
+    return;
+  }
+  try {
+    if (opt) opt = merge({}, marked.defaults, opt);
+    return Parser.parse(Lexer.lex(src, opt), opt);
+  } catch (e) {
+    e.message += '\nPlease report this to https://github.com/chjj/marked.';
+    if ((opt || marked.defaults).silent) {
+      return '<p>An error occured:</p><pre>'
+        + escape(e.message + '', true)
+        + '</pre>';
+    }
+    throw e;
+  }
+}
+
+/**
+ * Options
+ */
+
+marked.options =
+marked.setOptions = function(opt) {
+  merge(marked.defaults, opt);
+  return marked;
+};
+
+marked.defaults = {
+  gfm: true,
+  tables: true,
+  breaks: false,
+  pedantic: false,
+  sanitize: false,
+  sanitizer: null,
+  mangle: true,
+  smartLists: false,
+  silent: false,
+  highlight: null,
+  langPrefix: 'lang-',
+  smartypants: false,
+  headerPrefix: '',
+  renderer: new Renderer,
+  xhtml: false
+};
+
+/**
+ * Expose
+ */
+
+marked.Parser = Parser;
+marked.parser = Parser.parse;
+
+marked.Renderer = Renderer;
+
+marked.Lexer = Lexer;
+marked.lexer = Lexer.lex;
+
+marked.InlineLexer = InlineLexer;
+marked.inlineLexer = InlineLexer.output;
+
+marked.parse = marked;
+
+if (typeof module !== 'undefined' && typeof exports === 'object') {
+  module.exports = marked;
+} else if (typeof define === 'function' && define.amd) {
+  define(function() { return marked; });
+} else {
+  this.marked = marked;
+}
+
+}).call(function() {
+  return this || (typeof window !== 'undefined' ? window : global);
+}());
diff --git a/lessons/misc/webdev-intro/lib/marked.min.js b/lessons/misc/webdev-intro/lib/marked.min.js
new file mode 100644
index 000000000..555c1dc1d
--- /dev/null
+++ b/lessons/misc/webdev-intro/lib/marked.min.js
@@ -0,0 +1,6 @@
+/**
+ * marked - a markdown parser
+ * Copyright (c) 2011-2014, Christopher Jeffrey. (MIT Licensed)
+ * https://github.com/chjj/marked
+ */
+(function(){var block={newline:/^\n+/,code:/^( {4}[^\n]+\n*)+/,fences:noop,hr:/^( *[-*_]){3,} *(?:\n+|$)/,heading:/^ *(#{1,6}) *([^\n]+?) *#* *(?:\n+|$)/,nptable:noop,lheading:/^([^\n]+)\n *(=|-){2,} *(?:\n+|$)/,blockquote:/^( *>[^\n]+(\n(?!def)[^\n]+)*\n*)+/,list:/^( *)(bull) [\s\S]+?(?:hr|def|\n{2,}(?! )(?!\1bull )\n*|\s*$)/,html:/^ *(?:comment *(?:\n|\s*$)|closed *(?:\n{2,}|\s*$)|closing *(?:\n{2,}|\s*$))/,def:/^ *\[([^\]]+)\]: *<?([^\s>]+)>?(?: +["(]([^\n]+)[")])? *(?:\n+|$)/,table:noop,paragraph:/^((?:[^\n]+\n?(?!hr|heading|lheading|blockquote|tag|def))+)\n*/,text:/^[^\n]+/};block.bullet=/(?:[*+-]|\d+\.)/;block.item=/^( *)(bull) [^\n]*(?:\n(?!\1bull )[^\n]*)*/;block.item=replace(block.item,"gm")(/bull/g,block.bullet)();block.list=replace(block.list)(/bull/g,block.bullet)("hr","\\n+(?=\\1?(?:[-*_] *){3,}(?:\\n+|$))")("def","\\n+(?="+block.def.source+")")();block.blockquote=replace(block.blockquote)("def",block.def)();block._tag="(?!(?:"+"a|em|strong|small|s|cite|q|dfn|abbr|data|time|code"+"|var|samp|kbd|sub|sup|i|b|u|mark|ruby|rt|rp|bdi|bdo"+"|span|br|wbr|ins|del|img)\\b)\\w+(?!:/|[^\\w\\s@]*@)\\b";block.html=replace(block.html)("comment",/<!--[\s\S]*?-->/)("closed",/<(tag)[\s\S]+?<\/\1>/)("closing",/<tag(?:"[^"]*"|'[^']*'|[^'">])*?>/)(/tag/g,block._tag)();block.paragraph=replace(block.paragraph)("hr",block.hr)("heading",block.heading)("lheading",block.lheading)("blockquote",block.blockquote)("tag","<"+block._tag)("def",block.def)();block.normal=merge({},block);block.gfm=merge({},block.normal,{fences:/^ *(`{3,}|~{3,})[ \.]*(\S+)? *\n([\s\S]*?)\s*\1 *(?:\n+|$)/,paragraph:/^/,heading:/^ *(#{1,6}) +([^\n]+?) *#* *(?:\n+|$)/});block.gfm.paragraph=replace(block.paragraph)("(?!","(?!"+block.gfm.fences.source.replace("\\1","\\2")+"|"+block.list.source.replace("\\1","\\3")+"|")();block.tables=merge({},block.gfm,{nptable:/^ *(\S.*\|.*)\n *([-:]+ *\|[-| :]*)\n((?:.*\|.*(?:\n|$))*)\n*/,table:/^ *\|(.+)\n *\|( *[-:]+[-| :]*)\n((?: *\|.*(?:\n|$))*)\n*/});function Lexer(options){this.tokens=[];this.tokens.links={};this.options=options||marked.defaults;this.rules=block.normal;if(this.options.gfm){if(this.options.tables){this.rules=block.tables}else{this.rules=block.gfm}}}Lexer.rules=block;Lexer.lex=function(src,options){var lexer=new Lexer(options);return lexer.lex(src)};Lexer.prototype.lex=function(src){src=src.replace(/\r\n|\r/g,"\n").replace(/\t/g,"    ").replace(/\u00a0/g," ").replace(/\u2424/g,"\n");return this.token(src,true)};Lexer.prototype.token=function(src,top,bq){var src=src.replace(/^ +$/gm,""),next,loose,cap,bull,b,item,space,i,l;while(src){if(cap=this.rules.newline.exec(src)){src=src.substring(cap[0].length);if(cap[0].length>1){this.tokens.push({type:"space"})}}if(cap=this.rules.code.exec(src)){src=src.substring(cap[0].length);cap=cap[0].replace(/^ {4}/gm,"");this.tokens.push({type:"code",text:!this.options.pedantic?cap.replace(/\n+$/,""):cap});continue}if(cap=this.rules.fences.exec(src)){src=src.substring(cap[0].length);this.tokens.push({type:"code",lang:cap[2],text:cap[3]||""});continue}if(cap=this.rules.heading.exec(src)){src=src.substring(cap[0].length);this.tokens.push({type:"heading",depth:cap[1].length,text:cap[2]});continue}if(top&&(cap=this.rules.nptable.exec(src))){src=src.substring(cap[0].length);item={type:"table",header:cap[1].replace(/^ *| *\| *$/g,"").split(/ *\| */),align:cap[2].replace(/^ *|\| *$/g,"").split(/ *\| */),cells:cap[3].replace(/\n$/,"").split("\n")};for(i=0;i<item.align.length;i++){if(/^ *-+: *$/.test(item.align[i])){item.align[i]="right"}else if(/^ *:-+: *$/.test(item.align[i])){item.align[i]="center"}else if(/^ *:-+ *$/.test(item.align[i])){item.align[i]="left"}else{item.align[i]=null}}for(i=0;i<item.cells.length;i++){item.cells[i]=item.cells[i].split(/ *\| */)}this.tokens.push(item);continue}if(cap=this.rules.lheading.exec(src)){src=src.substring(cap[0].length);this.tokens.push({type:"heading",depth:cap[2]==="="?1:2,text:cap[1]});continue}if(cap=this.rules.hr.exec(src)){src=src.substring(cap[0].length);this.tokens.push({type:"hr"});continue}if(cap=this.rules.blockquote.exec(src)){src=src.substring(cap[0].length);this.tokens.push({type:"blockquote_start"});cap=cap[0].replace(/^ *> ?/gm,"");this.token(cap,top,true);this.tokens.push({type:"blockquote_end"});continue}if(cap=this.rules.list.exec(src)){src=src.substring(cap[0].length);bull=cap[2];this.tokens.push({type:"list_start",ordered:bull.length>1});cap=cap[0].match(this.rules.item);next=false;l=cap.length;i=0;for(;i<l;i++){item=cap[i];space=item.length;item=item.replace(/^ *([*+-]|\d+\.) +/,"");if(~item.indexOf("\n ")){space-=item.length;item=!this.options.pedantic?item.replace(new RegExp("^ {1,"+space+"}","gm"),""):item.replace(/^ {1,4}/gm,"")}if(this.options.smartLists&&i!==l-1){b=block.bullet.exec(cap[i+1])[0];if(bull!==b&&!(bull.length>1&&b.length>1)){src=cap.slice(i+1).join("\n")+src;i=l-1}}loose=next||/\n\n(?!\s*$)/.test(item);if(i!==l-1){next=item.charAt(item.length-1)==="\n";if(!loose)loose=next}this.tokens.push({type:loose?"loose_item_start":"list_item_start"});this.token(item,false,bq);this.tokens.push({type:"list_item_end"})}this.tokens.push({type:"list_end"});continue}if(cap=this.rules.html.exec(src)){src=src.substring(cap[0].length);this.tokens.push({type:this.options.sanitize?"paragraph":"html",pre:!this.options.sanitizer&&(cap[1]==="pre"||cap[1]==="script"||cap[1]==="style"),text:cap[0]});continue}if(!bq&&top&&(cap=this.rules.def.exec(src))){src=src.substring(cap[0].length);this.tokens.links[cap[1].toLowerCase()]={href:cap[2],title:cap[3]};continue}if(top&&(cap=this.rules.table.exec(src))){src=src.substring(cap[0].length);item={type:"table",header:cap[1].replace(/^ *| *\| *$/g,"").split(/ *\| */),align:cap[2].replace(/^ *|\| *$/g,"").split(/ *\| */),cells:cap[3].replace(/(?: *\| *)?\n$/,"").split("\n")};for(i=0;i<item.align.length;i++){if(/^ *-+: *$/.test(item.align[i])){item.align[i]="right"}else if(/^ *:-+: *$/.test(item.align[i])){item.align[i]="center"}else if(/^ *:-+ *$/.test(item.align[i])){item.align[i]="left"}else{item.align[i]=null}}for(i=0;i<item.cells.length;i++){item.cells[i]=item.cells[i].replace(/^ *\| *| *\| *$/g,"").split(/ *\| */)}this.tokens.push(item);continue}if(top&&(cap=this.rules.paragraph.exec(src))){src=src.substring(cap[0].length);this.tokens.push({type:"paragraph",text:cap[1].charAt(cap[1].length-1)==="\n"?cap[1].slice(0,-1):cap[1]});continue}if(cap=this.rules.text.exec(src)){src=src.substring(cap[0].length);this.tokens.push({type:"text",text:cap[0]});continue}if(src){throw new Error("Infinite loop on byte: "+src.charCodeAt(0))}}return this.tokens};var inline={escape:/^\\([\\`*{}\[\]()#+\-.!_>])/,autolink:/^<([^ >]+(@|:\/)[^ >]+)>/,url:noop,tag:/^<!--[\s\S]*?-->|^<\/?\w+(?:"[^"]*"|'[^']*'|[^'">])*?>/,link:/^!?\[(inside)\]\(href\)/,reflink:/^!?\[(inside)\]\s*\[([^\]]*)\]/,nolink:/^!?\[((?:\[[^\]]*\]|[^\[\]])*)\]/,strong:/^__([\s\S]+?)__(?!_)|^\*\*([\s\S]+?)\*\*(?!\*)/,em:/^\b_((?:[^_]|__)+?)_\b|^\*((?:\*\*|[\s\S])+?)\*(?!\*)/,code:/^(`+)\s*([\s\S]*?[^`])\s*\1(?!`)/,br:/^ {2,}\n(?!\s*$)/,del:noop,text:/^[\s\S]+?(?=[\\<!\[_*`]| {2,}\n|$)/};inline._inside=/(?:\[[^\]]*\]|[^\[\]]|\](?=[^\[]*\]))*/;inline._href=/\s*<?([\s\S]*?)>?(?:\s+['"]([\s\S]*?)['"])?\s*/;inline.link=replace(inline.link)("inside",inline._inside)("href",inline._href)();inline.reflink=replace(inline.reflink)("inside",inline._inside)();inline.normal=merge({},inline);inline.pedantic=merge({},inline.normal,{strong:/^__(?=\S)([\s\S]*?\S)__(?!_)|^\*\*(?=\S)([\s\S]*?\S)\*\*(?!\*)/,em:/^_(?=\S)([\s\S]*?\S)_(?!_)|^\*(?=\S)([\s\S]*?\S)\*(?!\*)/});inline.gfm=merge({},inline.normal,{escape:replace(inline.escape)("])","~|])")(),url:/^(https?:\/\/[^\s<]+[^<.,:;"')\]\s])/,del:/^~~(?=\S)([\s\S]*?\S)~~/,text:replace(inline.text)("]|","~]|")("|","|https?://|")()});inline.breaks=merge({},inline.gfm,{br:replace(inline.br)("{2,}","*")(),text:replace(inline.gfm.text)("{2,}","*")()});function InlineLexer(links,options){this.options=options||marked.defaults;this.links=links;this.rules=inline.normal;this.renderer=this.options.renderer||new Renderer;this.renderer.options=this.options;if(!this.links){throw new Error("Tokens array requires a `links` property.")}if(this.options.gfm){if(this.options.breaks){this.rules=inline.breaks}else{this.rules=inline.gfm}}else if(this.options.pedantic){this.rules=inline.pedantic}}InlineLexer.rules=inline;InlineLexer.output=function(src,links,options){var inline=new InlineLexer(links,options);return inline.output(src)};InlineLexer.prototype.output=function(src){var out="",link,text,href,cap;while(src){if(cap=this.rules.escape.exec(src)){src=src.substring(cap[0].length);out+=cap[1];continue}if(cap=this.rules.autolink.exec(src)){src=src.substring(cap[0].length);if(cap[2]==="@"){text=cap[1].charAt(6)===":"?this.mangle(cap[1].substring(7)):this.mangle(cap[1]);href=this.mangle("mailto:")+text}else{text=escape(cap[1]);href=text}out+=this.renderer.link(href,null,text);continue}if(!this.inLink&&(cap=this.rules.url.exec(src))){src=src.substring(cap[0].length);text=escape(cap[1]);href=text;out+=this.renderer.link(href,null,text);continue}if(cap=this.rules.tag.exec(src)){if(!this.inLink&&/^<a /i.test(cap[0])){this.inLink=true}else if(this.inLink&&/^<\/a>/i.test(cap[0])){this.inLink=false}src=src.substring(cap[0].length);out+=this.options.sanitize?this.options.sanitizer?this.options.sanitizer(cap[0]):escape(cap[0]):cap[0];continue}if(cap=this.rules.link.exec(src)){src=src.substring(cap[0].length);this.inLink=true;out+=this.outputLink(cap,{href:cap[2],title:cap[3]});this.inLink=false;continue}if((cap=this.rules.reflink.exec(src))||(cap=this.rules.nolink.exec(src))){src=src.substring(cap[0].length);link=(cap[2]||cap[1]).replace(/\s+/g," ");link=this.links[link.toLowerCase()];if(!link||!link.href){out+=cap[0].charAt(0);src=cap[0].substring(1)+src;continue}this.inLink=true;out+=this.outputLink(cap,link);this.inLink=false;continue}if(cap=this.rules.strong.exec(src)){src=src.substring(cap[0].length);out+=this.renderer.strong(this.output(cap[2]||cap[1]));continue}if(cap=this.rules.em.exec(src)){src=src.substring(cap[0].length);out+=this.renderer.em(this.output(cap[2]||cap[1]));continue}if(cap=this.rules.code.exec(src)){src=src.substring(cap[0].length);out+=this.renderer.codespan(escape(cap[2],true));continue}if(cap=this.rules.br.exec(src)){src=src.substring(cap[0].length);out+=this.renderer.br();continue}if(cap=this.rules.del.exec(src)){src=src.substring(cap[0].length);out+=this.renderer.del(this.output(cap[1]));continue}if(cap=this.rules.text.exec(src)){src=src.substring(cap[0].length);out+=this.renderer.text(escape(this.smartypants(cap[0])));continue}if(src){throw new Error("Infinite loop on byte: "+src.charCodeAt(0))}}return out};InlineLexer.prototype.outputLink=function(cap,link){var href=escape(link.href),title=link.title?escape(link.title):null;return cap[0].charAt(0)!=="!"?this.renderer.link(href,title,this.output(cap[1])):this.renderer.image(href,title,escape(cap[1]))};InlineLexer.prototype.smartypants=function(text){if(!this.options.smartypants)return text;return text.replace(/---/g,"—").replace(/--/g,"–").replace(/(^|[-\u2014/(\[{"\s])'/g,"$1‘").replace(/'/g,"’").replace(/(^|[-\u2014/(\[{\u2018\s])"/g,"$1“").replace(/"/g,"”").replace(/\.{3}/g,"…")};InlineLexer.prototype.mangle=function(text){if(!this.options.mangle)return text;var out="",l=text.length,i=0,ch;for(;i<l;i++){ch=text.charCodeAt(i);if(Math.random()>.5){ch="x"+ch.toString(16)}out+="&#"+ch+";"}return out};function Renderer(options){this.options=options||{}}Renderer.prototype.code=function(code,lang,escaped){if(this.options.highlight){var out=this.options.highlight(code,lang);if(out!=null&&out!==code){escaped=true;code=out}}if(!lang){return"<pre><code>"+(escaped?code:escape(code,true))+"\n</code></pre>"}return'<pre><code class="'+this.options.langPrefix+escape(lang,true)+'">'+(escaped?code:escape(code,true))+"\n</code></pre>\n"};Renderer.prototype.blockquote=function(quote){return"<blockquote>\n"+quote+"</blockquote>\n"};Renderer.prototype.html=function(html){return html};Renderer.prototype.heading=function(text,level,raw){return"<h"+level+' id="'+this.options.headerPrefix+raw.toLowerCase().replace(/[^\w]+/g,"-")+'">'+text+"</h"+level+">\n"};Renderer.prototype.hr=function(){return this.options.xhtml?"<hr/>\n":"<hr>\n"};Renderer.prototype.list=function(body,ordered){var type=ordered?"ol":"ul";return"<"+type+">\n"+body+"</"+type+">\n"};Renderer.prototype.listitem=function(text){return"<li>"+text+"</li>\n"};Renderer.prototype.paragraph=function(text){return"<p>"+text+"</p>\n"};Renderer.prototype.table=function(header,body){return"<table>\n"+"<thead>\n"+header+"</thead>\n"+"<tbody>\n"+body+"</tbody>\n"+"</table>\n"};Renderer.prototype.tablerow=function(content){return"<tr>\n"+content+"</tr>\n"};Renderer.prototype.tablecell=function(content,flags){var type=flags.header?"th":"td";var tag=flags.align?"<"+type+' style="text-align:'+flags.align+'">':"<"+type+">";return tag+content+"</"+type+">\n"};Renderer.prototype.strong=function(text){return"<strong>"+text+"</strong>"};Renderer.prototype.em=function(text){return"<em>"+text+"</em>"};Renderer.prototype.codespan=function(text){return"<code>"+text+"</code>"};Renderer.prototype.br=function(){return this.options.xhtml?"<br/>":"<br>"};Renderer.prototype.del=function(text){return"<del>"+text+"</del>"};Renderer.prototype.link=function(href,title,text){if(this.options.sanitize){try{var prot=decodeURIComponent(unescape(href)).replace(/[^\w:]/g,"").toLowerCase()}catch(e){return""}if(prot.indexOf("javascript:")===0||prot.indexOf("vbscript:")===0){return""}}var out='<a href="'+href+'"';if(title){out+=' title="'+title+'"'}out+=">"+text+"</a>";return out};Renderer.prototype.image=function(href,title,text){var out='<img src="'+href+'" alt="'+text+'"';if(title){out+=' title="'+title+'"'}out+=this.options.xhtml?"/>":">";return out};Renderer.prototype.text=function(text){return text};function Parser(options){this.tokens=[];this.token=null;this.options=options||marked.defaults;this.options.renderer=this.options.renderer||new Renderer;this.renderer=this.options.renderer;this.renderer.options=this.options}Parser.parse=function(src,options,renderer){var parser=new Parser(options,renderer);return parser.parse(src)};Parser.prototype.parse=function(src){this.inline=new InlineLexer(src.links,this.options,this.renderer);this.tokens=src.reverse();var out="";while(this.next()){out+=this.tok()}return out};Parser.prototype.next=function(){return this.token=this.tokens.pop()};Parser.prototype.peek=function(){return this.tokens[this.tokens.length-1]||0};Parser.prototype.parseText=function(){var body=this.token.text;while(this.peek().type==="text"){body+="\n"+this.next().text}return this.inline.output(body)};Parser.prototype.tok=function(){switch(this.token.type){case"space":{return""}case"hr":{return this.renderer.hr()}case"heading":{return this.renderer.heading(this.inline.output(this.token.text),this.token.depth,this.token.text)}case"code":{return this.renderer.code(this.token.text,this.token.lang,this.token.escaped)}case"table":{var header="",body="",i,row,cell,flags,j;cell="";for(i=0;i<this.token.header.length;i++){flags={header:true,align:this.token.align[i]};cell+=this.renderer.tablecell(this.inline.output(this.token.header[i]),{header:true,align:this.token.align[i]})}header+=this.renderer.tablerow(cell);for(i=0;i<this.token.cells.length;i++){row=this.token.cells[i];cell="";for(j=0;j<row.length;j++){cell+=this.renderer.tablecell(this.inline.output(row[j]),{header:false,align:this.token.align[j]})}body+=this.renderer.tablerow(cell)}return this.renderer.table(header,body)}case"blockquote_start":{var body="";while(this.next().type!=="blockquote_end"){body+=this.tok()}return this.renderer.blockquote(body)}case"list_start":{var body="",ordered=this.token.ordered;while(this.next().type!=="list_end"){body+=this.tok()}return this.renderer.list(body,ordered)}case"list_item_start":{var body="";while(this.next().type!=="list_item_end"){body+=this.token.type==="text"?this.parseText():this.tok()}return this.renderer.listitem(body)}case"loose_item_start":{var body="";while(this.next().type!=="list_item_end"){body+=this.tok()}return this.renderer.listitem(body)}case"html":{var html=!this.token.pre&&!this.options.pedantic?this.inline.output(this.token.text):this.token.text;return this.renderer.html(html)}case"paragraph":{return this.renderer.paragraph(this.inline.output(this.token.text))}case"text":{return this.renderer.paragraph(this.parseText())}}};function escape(html,encode){return html.replace(!encode?/&(?!#?\w+;)/g:/&/g,"&amp;").replace(/</g,"&lt;").replace(/>/g,"&gt;").replace(/"/g,"&quot;").replace(/'/g,"&#39;")}function unescape(html){return html.replace(/&([#\w]+);/g,function(_,n){n=n.toLowerCase();if(n==="colon")return":";if(n.charAt(0)==="#"){return n.charAt(1)==="x"?String.fromCharCode(parseInt(n.substring(2),16)):String.fromCharCode(+n.substring(1))}return""})}function replace(regex,opt){regex=regex.source;opt=opt||"";return function self(name,val){if(!name)return new 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diff --git a/lessons/misc/webdev-intro/sample/index.html b/lessons/misc/webdev-intro/sample/index.html
new file mode 100644
index 000000000..e0eac81f5
--- /dev/null
+++ b/lessons/misc/webdev-intro/sample/index.html
@@ -0,0 +1,35 @@
+<!doctype html>
+
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+
+  <title>Sample document</title>
+  <meta name="description" content="For the presentation about HTML/CSS/Javascript">
+  <meta name="author" content="John Ladan">
+
+  <link rel="stylesheet" href="css/styles.css">
+</head>
+
+<body>
+    <h1>Lorem ipsum</h1>
+    <p>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque pellentesque tellus eget purus luctus tincidunt. Sed at congue orci. Curabitur sed magna vulputate, auctor felis et, fermentum ligula. Phasellus vitae metus eget purus consectetur pellentesque. Fusce vitae hendrerit sapien, sit amet consectetur risus. Aenean tincidunt volutpat ipsum eu fermentum. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Morbi quis erat vel ex blandit finibus eu a dui. Integer quis dolor lorem. Fusce quis nunc feugiat lectus congue pharetra.</p>
+
+    <h2>This is a subsection</h2>
+    <p>Nunc vitae aliquet tortor, id eleifend diam. Donec luctus leo id lacus mattis accumsan. Vestibulum consequat neque vitae nulla tincidunt, vitae semper nisi eleifend. Suspendisse ut eros laoreet, condimentum sem ac, laoreet nisl. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nunc convallis tellus sit amet eros finibus, sed placerat ligula blandit. Vivamus tristique augue faucibus lorem luctus placerat. Fusce maximus nisi nec augue laoreet iaculis. Sed suscipit, neque eget mattis auctor, dolor metus efficitur enim, sed malesuada justo dui vel nisi. Nulla et auctor elit, a commodo purus. Nunc dictum augue elit, vel vulputate arcu tristique quis.</p>
+
+    <p>Maecenas posuere semper pulvinar. Maecenas venenatis imperdiet justo. Donec elit ex, sodales id purus ut, ultrices sagittis ex. Vivamus commodo nisl vel aliquet facilisis. Sed congue elementum eros sit amet tristique. Fusce luctus, justo eget cursus tempor, tortor erat scelerisque ligula, in iaculis felis orci eget nulla. Donec iaculis ante vel aliquam vehicula. Curabitur ac sapien a leo volutpat laoreet. Quisque sit amet ex gravida, rutrum nisi sit amet, consequat leo. Donec pharetra est odio, in convallis leo ornare sit amet. Duis et rutrum erat. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus.</p>
+
+    <h2>Subsection 2</h2>
+    <p>Nulla facilisi. Nunc maximus elementum dignissim. Etiam iaculis posuere velit in consequat. Cras sed erat a purus congue tempor non id nunc. Vestibulum gravida nibh in arcu varius maximus ac a turpis. Donec quis euismod nisi, auctor lobortis erat. Quisque consequat dignissim laoreet. Sed condimentum maximus lorem, vel ornare neque facilisis in. Maecenas dignissim pharetra neque ultricies ornare. Aenean nec ultrices justo, et ornare enim. Donec eget laoreet ex. Nulla commodo congue nulla, cursus sodales nulla. Mauris dignissim mattis orci, a condimentum tortor sagittis a. Maecenas eros erat, rhoncus sit amet molestie sit amet, faucibus et tellus. Sed in rutrum augue, interdum iaculis est. In egestas turpis in magna egestas dapibus.</p>
+
+    <p>Phasellus lobortis nunc nec facilisis venenatis. Ut tempus congue blandit. Vestibulum in elit ligula. Nullam non enim vitae leo dictum luctus. Nunc tempus at neque id posuere. Etiam ultricies, diam sit amet cursus finibus, lacus erat convallis risus, id congue nibh turpis id justo. Proin arcu ex, porttitor in risus a, egestas pharetra risus. Cras bibendum elementum aliquam. Suspendisse ultrices imperdiet venenatis. Suspendisse egestas diam ut magna hendrerit, a iaculis ex dapibus.</p>
+
+    <h1>Conclusion</h1>
+    <p>Proin mattis, lacus eu imperdiet convallis, nunc magna sodales sem, eu aliquet nisl turpis ut risus. Pellentesque non imperdiet lectus. Integer luctus consequat ante, quis ullamcorper est dictum at. Integer vulputate nisl eget velit tempor, ut venenatis elit iaculis. Pellentesque ante nulla, porttitor sit amet ipsum a, elementum finibus dui. Donec congue, turpis a gravida pellentesque, risus arcu faucibus tortor, eu pulvinar leo felis at diam. Suspendisse orci dui, facilisis ut turpis sit amet, cursus sollicitudin justo. Vestibulum ornare diam ac venenatis facilisis. Fusce sollicitudin finibus orci, luctus mattis elit tristique at. Nunc sed condimentum massa.</p>
+
+    <p>Donec faucibus tempor dui, id malesuada odio pretium quis. Phasellus iaculis quis sapien eu tincidunt. Mauris quis euismod dui. Ut at eros orci. Donec maximus interdum rhoncus. Sed interdum nunc at ante semper, a varius enim gravida. Donec sed lobortis leo. Aenean tortor mauris, consectetur vitae augue in, sagittis luctus quam.</p>
+
+</body>
+
+</html>
diff --git a/lessons/misc/webdev-intro/slides.html b/lessons/misc/webdev-intro/slides.html
new file mode 100644
index 000000000..fb4bc0c4d
--- /dev/null
+++ b/lessons/misc/webdev-intro/slides.html
@@ -0,0 +1,56 @@
+<!doctype html>
+
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+
+  <title>Intro to web development basics</title>
+  <meta name="description" content="Presentation about html/css/js basics">
+  <meta name="author" content="John Ladan">
+
+  <link rel="stylesheet" href="./css/styles.css">
+
+  <!--[if lt IE 9]>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/html5shiv/3.7.3/html5shiv.js"></script>
+  <![endif]-->
+  <script src="lib/marked.js"></script>
+  <!-- for syntax highlighting -->
+  <link rel="stylesheet" href="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.11.0/styles/default.min.css">
+  <script src="//cdnjs.cloudflare.com/ajax/libs/highlight.js/9.11.0/highlight.min.js"></script>
+</head>
+
+<body>
+  <nav id='slide-nav'>
+      <a href="#slide-1">first slide?</a>
+  </nav>
+  <div id='content' aria-live="polite">
+      <section class="shown slide">
+          <h1 id="slide-1">Meaningless header</h1>
+          <p>The actual content will show here soon. This is <em>just</em></p>
+          <ul>
+              <li>some filler content</li>
+              <li>a little HTML to demonstrate a document.</li>
+          </ul>
+          <figure>
+              <img alt="just a placeholder" src="./img/rendering.svg"/>
+              <figcaption>Source: 
+              <a href="https://developer.mozilla.org/en-US/docs/Learn/CSS/Introduction_to_CSS/How_CSS_works">MDN</a>
+              </figcaption>
+          </figure>
+      </section>
+  </div>
+</body>
+
+<script src="app/slideshow.js"></script>
+<script>
+var cElm = document.getElementById('content');
+var nElm = document.getElementById('slide-nav');
+var url = './contents.md';
+marked.setOptions({
+    highlight: function (code) {
+        return hljs.highlightAuto(code).value;
+    }
+});
+startSlides(cElm, nElm, url);
+</script>
+</html>
diff --git a/lessons/python/Seaborn/YHP_Seaborn_Jan2025.pdf b/lessons/python/Seaborn/YHP_Seaborn_Jan2025.pdf
new file mode 100644
index 000000000..253ab559c
Binary files /dev/null and b/lessons/python/Seaborn/YHP_Seaborn_Jan2025.pdf differ
diff --git a/lessons/python/Seaborn/readme b/lessons/python/Seaborn/readme
new file mode 100644
index 000000000..8b1378917
--- /dev/null
+++ b/lessons/python/Seaborn/readme
@@ -0,0 +1 @@
+
diff --git a/lessons/python/biopython/example_data/BLAST_output.xml b/lessons/python/biopython/example_data/BLAST_output.xml
new file mode 100644
index 000000000..f1ada83bd
--- /dev/null
+++ b/lessons/python/biopython/example_data/BLAST_output.xml
@@ -0,0 +1,206 @@
+<?xml version="1.0"?>
+<!DOCTYPE BlastOutput PUBLIC "-//NCBI//NCBI BlastOutput/EN" "http://www.ncbi.nlm.nih.gov/dtd/NCBI_BlastOutput.dtd">
+<BlastOutput>
+  <BlastOutput_program>blastn</BlastOutput_program>
+  <BlastOutput_version>BLASTN 2.2.30+</BlastOutput_version>
+  <BlastOutput_reference>Stephen F. Altschul, Thomas L. Madden, Alejandro A. Sch&amp;auml;ffer, Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997), &quot;Gapped BLAST and PSI-BLAST: a new generation of protein database search programs&quot;, Nucleic Acids Res. 25:3389-3402.</BlastOutput_reference>
+  <BlastOutput_db>DB_June29</BlastOutput_db>
+  <BlastOutput_query-ID>Query_1</BlastOutput_query-ID>
+  <BlastOutput_query-def>2.10</BlastOutput_query-def>
+  <BlastOutput_query-len>40</BlastOutput_query-len>
+  <BlastOutput_param>
+    <Parameters>
+      <Parameters_expect>1</Parameters_expect>
+      <Parameters_sc-match>1</Parameters_sc-match>
+      <Parameters_sc-mismatch>-3</Parameters_sc-mismatch>
+      <Parameters_gap-open>5</Parameters_gap-open>
+      <Parameters_gap-extend>2</Parameters_gap-extend>
+      <Parameters_filter>m;</Parameters_filter>
+    </Parameters>
+  </BlastOutput_param>
+<BlastOutput_iterations>
+<Iteration>
+  <Iteration_iter-num>1</Iteration_iter-num>
+  <Iteration_query-ID>Query_1</Iteration_query-ID>
+  <Iteration_query-def>2.10</Iteration_query-def>
+  <Iteration_query-len>40</Iteration_query-len>
+<Iteration_hits>
+</Iteration_hits>
+  <Iteration_stat>
+    <Statistics>
+      <Statistics_db-num>3696</Statistics_db-num>
+      <Statistics_db-len>242246886</Statistics_db-len>
+      <Statistics_hsp-len>16</Statistics_hsp-len>
+      <Statistics_eff-space>5812506000</Statistics_eff-space>
+      <Statistics_kappa>0.710602795216363</Statistics_kappa>
+      <Statistics_lambda>1.37406312246009</Statistics_lambda>
+      <Statistics_entropy>1.30724660390929</Statistics_entropy>
+    </Statistics>
+  </Iteration_stat>
+  <Iteration_message>No hits found</Iteration_message>
+</Iteration>
+<Iteration>
+  <Iteration_iter-num>2</Iteration_iter-num>
+  <Iteration_query-ID>Query_2</Iteration_query-ID>
+  <Iteration_query-def>2.100</Iteration_query-def>
+  <Iteration_query-len>44</Iteration_query-len>
+<Iteration_hits>
+</Iteration_hits>
+  <Iteration_stat>
+    <Statistics>
+      <Statistics_db-num>3696</Statistics_db-num>
+      <Statistics_db-len>242246886</Statistics_db-len>
+      <Statistics_hsp-len>17</Statistics_hsp-len>
+      <Statistics_eff-space>6538969458</Statistics_eff-space>
+      <Statistics_kappa>0.710602795216363</Statistics_kappa>
+      <Statistics_lambda>1.37406312246009</Statistics_lambda>
+      <Statistics_entropy>1.30724660390929</Statistics_entropy>
+    </Statistics>
+  </Iteration_stat>
+  <Iteration_message>No hits found</Iteration_message>
+</Iteration>
+<Iteration>
+  <Iteration_iter-num>3</Iteration_iter-num>
+  <Iteration_query-ID>Query_3</Iteration_query-ID>
+  <Iteration_query-def>2.101</Iteration_query-def>
+  <Iteration_query-len>39</Iteration_query-len>
+<Iteration_hits>
+<Hit>
+  <Hit_num>1</Hit_num>
+  <Hit_id>gnl|BL_ORD_ID|1975</Hit_id>
+  <Hit_def>ENA|JX486088|JX486088.1 Lactobacillus phage ATCC 8014-B2, complete genome. [28-FEB-2015]</Hit_def>
+  <Hit_accession>1975</Hit_accession>
+  <Hit_len>80617</Hit_len>
+  <Hit_hsps>
+    <Hsp>
+      <Hsp_num>1</Hsp_num>
+      <Hsp_bit-score>34.1929</Hsp_bit-score>
+      <Hsp_score>17</Hsp_score>
+      <Hsp_evalue>0.283655</Hsp_evalue>
+      <Hsp_query-from>5</Hsp_query-from>
+      <Hsp_query-to>21</Hsp_query-to>
+      <Hsp_hit-from>62651</Hsp_hit-from>
+      <Hsp_hit-to>62635</Hsp_hit-to>
+      <Hsp_query-frame>1</Hsp_query-frame>
+      <Hsp_hit-frame>-1</Hsp_hit-frame>
+      <Hsp_identity>17</Hsp_identity>
+      <Hsp_positive>17</Hsp_positive>
+      <Hsp_gaps>0</Hsp_gaps>
+      <Hsp_align-len>17</Hsp_align-len>
+      <Hsp_qseq>ATATTAACGTTAAACCC</Hsp_qseq>
+      <Hsp_hseq>ATATTAACGTTAAACCC</Hsp_hseq>
+      <Hsp_midline>|||||||||||||||||</Hsp_midline>
+    </Hsp>
+  </Hit_hsps>
+</Hit>
+<Hit>
+  <Hit_num>2</Hit_num>
+  <Hit_id>gnl|BL_ORD_ID|223</Hit_id>
+  <Hit_def>gi|593777468|ref|NC_023719.1| Bacillus phage G, complete genome</Hit_def>
+  <Hit_accession>223</Hit_accession>
+  <Hit_len>497513</Hit_len>
+  <Hit_hsps>
+    <Hsp>
+      <Hsp_num>1</Hsp_num>
+      <Hsp_bit-score>34.1929</Hsp_bit-score>
+      <Hsp_score>17</Hsp_score>
+      <Hsp_evalue>0.283655</Hsp_evalue>
+      <Hsp_query-from>2</Hsp_query-from>
+      <Hsp_query-to>18</Hsp_query-to>
+      <Hsp_hit-from>120558</Hsp_hit-from>
+      <Hsp_hit-to>120574</Hsp_hit-to>
+      <Hsp_query-frame>1</Hsp_query-frame>
+      <Hsp_hit-frame>1</Hsp_hit-frame>
+      <Hsp_identity>17</Hsp_identity>
+      <Hsp_positive>17</Hsp_positive>
+      <Hsp_gaps>0</Hsp_gaps>
+      <Hsp_align-len>17</Hsp_align-len>
+      <Hsp_qseq>TTGATATTAACGTTAAA</Hsp_qseq>
+      <Hsp_hseq>TTGATATTAACGTTAAA</Hsp_hseq>
+      <Hsp_midline>|||||||||||||||||</Hsp_midline>
+    </Hsp>
+  </Hit_hsps>
+</Hit>
+</Iteration_hits>
+  <Iteration_stat>
+    <Statistics>
+      <Statistics_db-num>3696</Statistics_db-num>
+      <Statistics_db-len>242246886</Statistics_db-len>
+      <Statistics_hsp-len>16</Statistics_hsp-len>
+      <Statistics_eff-space>5570318250</Statistics_eff-space>
+      <Statistics_kappa>0.710602795216363</Statistics_kappa>
+      <Statistics_lambda>1.37406312246009</Statistics_lambda>
+      <Statistics_entropy>1.30724660390929</Statistics_entropy>
+    </Statistics>
+  </Iteration_stat>
+</Iteration>
+<Iteration>
+  <Iteration_iter-num>4</Iteration_iter-num>
+  <Iteration_query-ID>Query_4</Iteration_query-ID>
+  <Iteration_query-def>2.102</Iteration_query-def>
+  <Iteration_query-len>41</Iteration_query-len>
+<Iteration_hits>
+<Hit>
+  <Hit_num>1</Hit_num>
+  <Hit_id>gnl|BL_ORD_ID|1675</Hit_id>
+  <Hit_def>ENA|KP211958|KP211958.1 Cyanophage TIM40, complete genome. [15-FEB-2015]</Hit_def>
+  <Hit_accession>1675</Hit_accession>
+  <Hit_len>188632</Hit_len>
+  <Hit_hsps>
+    <Hsp>
+      <Hsp_num>1</Hsp_num>
+      <Hsp_bit-score>34.1929</Hsp_bit-score>
+      <Hsp_score>17</Hsp_score>
+      <Hsp_evalue>0.308321</Hsp_evalue>
+      <Hsp_query-from>19</Hsp_query-from>
+      <Hsp_query-to>35</Hsp_query-to>
+      <Hsp_hit-from>131830</Hsp_hit-from>
+      <Hsp_hit-to>131846</Hsp_hit-to>
+      <Hsp_query-frame>1</Hsp_query-frame>
+      <Hsp_hit-frame>1</Hsp_hit-frame>
+      <Hsp_identity>17</Hsp_identity>
+      <Hsp_positive>17</Hsp_positive>
+      <Hsp_gaps>0</Hsp_gaps>
+      <Hsp_align-len>17</Hsp_align-len>
+      <Hsp_qseq>AGAGAAGCAATGGCGTA</Hsp_qseq>
+      <Hsp_hseq>AGAGAAGCAATGGCGTA</Hsp_hseq>
+      <Hsp_midline>|||||||||||||||||</Hsp_midline>
+    </Hsp>
+  </Hit_hsps>
+</Hit>
+</Iteration_hits>
+  <Iteration_stat>
+    <Statistics>
+      <Statistics_db-num>3696</Statistics_db-num>
+      <Statistics_db-len>242246886</Statistics_db-len>
+      <Statistics_hsp-len>16</Statistics_hsp-len>
+      <Statistics_eff-space>6054693750</Statistics_eff-space>
+      <Statistics_kappa>0.710602795216363</Statistics_kappa>
+      <Statistics_lambda>1.37406312246009</Statistics_lambda>
+      <Statistics_entropy>1.30724660390929</Statistics_entropy>
+    </Statistics>
+  </Iteration_stat>
+</Iteration>
+<Iteration>
+  <Iteration_iter-num>5</Iteration_iter-num>
+  <Iteration_query-ID>Query_5</Iteration_query-ID>
+  <Iteration_query-def>2.103</Iteration_query-def>
+  <Iteration_query-len>39</Iteration_query-len>
+<Iteration_hits>
+</Iteration_hits>
+  <Iteration_stat>
+    <Statistics>
+      <Statistics_db-num>3696</Statistics_db-num>
+      <Statistics_db-len>242246886</Statistics_db-len>
+      <Statistics_hsp-len>16</Statistics_hsp-len>
+      <Statistics_eff-space>5570318250</Statistics_eff-space>
+      <Statistics_kappa>0.710602795216363</Statistics_kappa>
+      <Statistics_lambda>1.37406312246009</Statistics_lambda>
+      <Statistics_entropy>1.30724660390929</Statistics_entropy>
+    </Statistics>
+  </Iteration_stat>
+  <Iteration_message>No hits found</Iteration_message>
+</Iteration>
+</BlastOutput_iterations>
+</BlastOutput>
+
diff --git a/lessons/python/biopython/example_data/spacers.fasta b/lessons/python/biopython/example_data/spacers.fasta
new file mode 100644
index 000000000..eadefc1ca
--- /dev/null
+++ b/lessons/python/biopython/example_data/spacers.fasta
@@ -0,0 +1,15 @@
+>2.10
+
+AACATGATAAGGCTTTACTTAGAAAGTTAGAGAAAAAATT
+>2.100
+
+CAAATCGCCCCCGTTTCGCCCGCCCGTTCTCTTTCCTTTTCACC
+>2.101
+
+ATTGATATTAACGTTAAACCCCACGCCCCCAGTGAACTT
+>2.102
+
+AACGCAGAAAGATGCGTGAGAGAAGCAATGGCGTATCTAGT
+>2.103
+
+CCCCCGCTTAGTTTCTGCCACGCATACCTCTGTTCTGCC
\ No newline at end of file
diff --git a/lessons/python/biopython/lesson.md b/lessons/python/biopython/lesson.md
new file mode 100644
index 000000000..60399f870
--- /dev/null
+++ b/lessons/python/biopython/lesson.md
@@ -0,0 +1,111 @@
+---
+layout: page
+title: 'Biopython 10-minute demo'
+visible: true
+tags:
+  - python
+  - intro
+---
+
+ - **Author**: Madeleine Bonsma
+ - **Research field**: Microbial ecology - prokaryote-phage interactions
+ - **Lesson topic**: Biopython - examples
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/biopython>
+
+## Intro ##
+
+Biopython is a package designed for working with genetic sequence data in Python. 
+
+THE MOST HELPFUL resource for Biopython: http://biopython.org/DIST/docs/tutorial/Tutorial.html
+
+## Some Biopython features ##
+  
+  * The Seq object:
+```
+from Bio.Seq import Seq # sequence tools to do stuff like reverse complement
+from Bio.Alphabet import generic_dna # more sequence tools
+
+my_DNA = "GTTTTTGTACTCTCAAGATTTAAGTAACTGTACAAC"
+my_DNA = Seq(my_DNA,generic_dna) # convert string to Seq object
+my_DNA_reverse_complement = str(my_DNA.reverse_complement()) # reverse complement
+```
+
+There are also options for searching, transcription, and translation
+
+  * parsing BLAST output: 
+
+This is an example function that extracts pretty much everything from the blast records object. To see all options, use `dir(NCBIXML.parse)`, or check the help: `help(NCBIXML.parse)`
+
+```
+def parse_blast(resultfile):
+    from Bio.Blast import NCBIXML
+    result_handle = open(resultfile)
+    blast_records = NCBIXML.parse(result_handle)
+
+    count = 0
+    for blast_record in blast_records:
+        print "printing header stuff"
+        print "blast_record.query", blast_record.query
+        print "blast_record.query_letters", blast_record.query_letters
+        print "printing descriptions"        
+        print "printing descriptions"
+        for description in blast_record.descriptions:
+            print "description.title", description.title
+            print "description.score", description.score #I think this is the best score
+            print "description.e", description.e #I think this is the best e-value
+            print "num_alignments", description.num_alignments #number of alignments per query
+        print "printing alignments"
+        for alignment in blast_record.alignments:
+            print "alignment.title", alignment.title
+            print "alignment.length", alignment.length
+            print "printing hsps"
+            for hsp in alignment.hsps: #Multiple hits means multiple hsps 
+                print "hsp.align_length", hsp.align_length
+        
+                print "hsp.score", hsp.score
+                print "hsp.bits", hsp.bits
+                print "hsp.expect", hsp.expect
+                print "hsp.num_alignments", hsp.num_alignments
+                print "hsp.identities", hsp.identities
+                print "hsp.positives", hsp.positives
+                print "hsp.gaps", hsp.gaps
+                print "hsp.strand", hsp.strand
+                print "hsp.frame", hsp.frame
+                print "hsp.query", hsp.query
+                print "hsp.query_start", hsp.query_start
+                print "hsp.query_end", hsp.query_end
+                print "hsp.match", hsp.match
+                print "hsp.sbjct", hsp.sbjct
+                print "hsp.sbjct_start", hsp.sbjct_start
+                print "hsp.sbjct_end", hsp.sbjct_end
+        print "-----------------------------------"      
+
+    result_handle.close()
+    return
+
+fn = "data/BLAST_output.xml"
+# Here's what the output looks like: https://github.com/mbonsma/studyGroup/blob/gh-pages/lessons/Biopython/example_data/BLAST_output.xml
+
+parse_blast(fn)
+```
+
+  * Running BLAST locally (on your machine as opposed to over the web)
+
+Note: this won't work unless you install [blast+](https://www.ncbi.nlm.nih.gov/guide/howto/run-blast-local/) and then add the blast+ directory to your PATH.
+
+```
+from Bio.Blast.Applications import NcbiblastnCommandline
+#help(NcbiblastnCommandline)
+import os
+
+my_query = "data/spacers.fasta"
+
+database = "DB_June29"
+
+outfile = "data/BLAST_output.xml"
+
+# these parameters should be changed to meet your needs
+blastn_obj = NcbiblastnCommandline(query=my_query, db=database, evalue=1, num_alignments = 100, dust = "no", task = "blastn",reward = 1, penalty = -3, outfmt=5, out = outfile)
+
+stdout, stderr = blastn_obj()
+```
diff --git a/lessons/python/biopython/lesson.py b/lessons/python/biopython/lesson.py
new file mode 100644
index 000000000..b32cd0fa9
--- /dev/null
+++ b/lessons/python/biopython/lesson.py
@@ -0,0 +1,96 @@
+## Some cool things you can do with Biopython
+## MB - August 20, 2015
+
+## Biopython is a package designed for working with genetic sequence data in Python. 
+
+## THE MOST HELPFUL resource for Biopython: http://biopython.org/DIST/docs/tutorial/Tutorial.html
+
+# Two useful commands:
+
+# help(some_thing) - prints the help stuff
+# dir(some_thing) - list the attributes of the class to which some_thing belongs
+
+# ---------------------------- The Seq object ----------------------------
+
+from Bio.Seq import Seq # sequence tools to do stuff like reverse complement
+from Bio.Alphabet import generic_dna # more sequence tools
+
+my_DNA = "GTTTTTGTACTCTCAAGATTTAAGTAACTGTACAAC"
+
+my_DNA = Seq(my_DNA,generic_dna) # convert string to Seq object
+
+my_DNA_reverse_complement = str(my_DNA.reverse_complement()) # reverse complement
+
+# Options for searching, transcription, and translation
+
+# ------------------------- parse BLAST output ------------------------------
+
+def parse_blast(resultfile):
+    from Bio.Blast import NCBIXML
+    result_handle = open(resultfile)
+    blast_records = NCBIXML.parse(result_handle)
+
+    count = 0
+    for blast_record in blast_records:
+        print "printing header stuff"
+        print "blast_record.query", blast_record.query
+        print "blast_record.query_letters", blast_record.query_letters
+        print "printing descriptions"        
+        print "printing descriptions"
+        for description in blast_record.descriptions:
+            print "description.title", description.title
+            print "description.score", description.score #I think this is the best score
+            print "description.e", description.e #I think this is the best e-value
+            print "num_alignments", description.num_alignments #number of alignments per query
+        print "printing alignments"
+        for alignment in blast_record.alignments:
+            print "alignment.title", alignment.title
+            print "alignment.length", alignment.length
+            print "printing hsps"
+            for hsp in alignment.hsps: #Multiple hits means multiple hsps
+                print "hsp.align_length", hsp.align_length
+        
+                print "hsp.score", hsp.score
+                print "hsp.bits", hsp.bits
+                print "hsp.expect", hsp.expect
+                print "hsp.num_alignments", hsp.num_alignments
+                print "hsp.identities", hsp.identities
+                print "hsp.positives", hsp.positives
+                print "hsp.gaps", hsp.gaps
+                print "hsp.strand", hsp.strand
+                print "hsp.frame", hsp.frame
+                print "hsp.query", hsp.query
+                print "hsp.query_start", hsp.query_start
+                print "hsp.query_end", hsp.query_end
+                print "hsp.match", hsp.match
+                print "hsp.sbjct", hsp.sbjct
+                print "hsp.sbjct_start", hsp.sbjct_start
+                print "hsp.sbjct_end", hsp.sbjct_end
+        print "-----------------------------------"      
+
+    result_handle.close()
+    return
+
+fn = "example_data/BLAST_output.xml"
+
+parse_blast(fn)
+
+"""
+# ------------------------- Local BLAST -------------------------------------
+
+from Bio.Blast.Applications import NcbiblastnCommandline
+#help(NcbiblastnCommandline)
+import os
+
+my_query = "example_data/spacers.fasta"
+
+database = "DB_June29"
+
+outfile = "example_data/BLAST_output.xml"
+
+# these parameters should be changed to meet your needs
+blastn_obj = NcbiblastnCommandline(query=my_query, db=database, evalue=1, num_alignments = 100, dust = "no", task = "blastn",reward = 1, penalty = -3, outfmt=5, out = outfile)
+
+stdout, stderr = blastn_obj()
+
+"""
\ No newline at end of file
diff --git a/lessons/python/cartography/Cartography and Mapping in Python.ipynb b/lessons/python/cartography/Cartography and Mapping in Python.ipynb
new file mode 100644
index 000000000..524f6eefa
--- /dev/null
+++ b/lessons/python/cartography/Cartography and Mapping in Python.ipynb	
@@ -0,0 +1,15068 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " - **Authors**: Elliott Sales de Andrade\n",
+    " - **Research field**: Seismology\n",
+    " - **Lesson topic**: cartography, maps, projections, Python\n",
+    " - **Lesson content URL**: https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/cartography\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Default imports\n",
+    "import numpy as np\n",
+    "%matplotlib nbagg\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "We will be using Cartopy for this lesson, so let's import that.\n",
+    "The common convention is to import it as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start with a simple plot of points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fbc1ca81be0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.seed(1)\n",
+    "x = 360 * np.random.rand(100)\n",
+    "y = 180 * np.random.rand(100) - 90\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1)\n",
+    "ax.scatter(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Why pick $x$ values between 0 and 360, and $y$ values between -90 and 90?\n",
+    "Because that corresponds to the ranges for longitudes and latitudes!\n",
+    "Let's rename them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "lon = x\n",
+    "lat = y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Projections\n",
+    "\n",
+    "The first problem with mapping is deciding in what projection to create the map.\n",
+    "Some interesting properties you may want in a map are:\n",
+    "\n",
+    " * Conformal: Preserves angles locally, implying that locally shapes are not distorted.\n",
+    " * Equal Area: Areas are conserved.\n",
+    " * Compromise: Neither conformal nor equal-area, but a balance intended to reduce overall distortion.\n",
+    " * Equidistant: All distances from one (or two) points are correct. Other equidistant properties are mentioned in the notes.\n",
+    " * Gnomonic: All great circles are straight lines.\n",
+    "\n",
+    "See the [Wikipedia list of projections](https://en.wikipedia.org/wiki/List_of_map_projections#Properties)\n",
+    "for more information.\n",
+    "\n",
+    "Cartopy has [several options available](http://scitools.org.uk/cartopy/docs/latest/crs/projections.html).\n",
+    "We will start with the very straightforward [Plate Carrée](https://en.wikipedia.org/wiki/Equirectangular_projection)\n",
+    "(also known as Equirectangular or Equidistant Cylindrical.)\n",
+    "We pass an instance of the projection class to the `projection` keyword\n",
+    "argument of `Figure.add_subplot` to signify the projection in which the\n",
+    "resulting map will be made."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img src=\"data:image/png;base64,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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fbc180b5908>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.PlateCarree())\n",
+    "ax.scatter(lon, lat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding elements\n",
+    "\n",
+    "So far so good; let's add some coast lines:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fbc18013a58>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.PlateCarree())\n",
+    "ax.scatter(lon, lat)\n",
+    "ax.coastlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or how about a land/ocean image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fbc17fe08d0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.PlateCarree())\n",
+    "ax.scatter(lon, lat)\n",
+    "ax.stock_img()\n",
+    "ax.coastlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And maybe some grid lines:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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w3uDKFq1sZkwGPNgMOYpthdoNMs8k2aFwAw0CaFdMz8+ZqfXyoWv8/GTzWC5cGKDmPx4WKE3WlkYVjbbDTQ5ey3Zd2YXv7uQFJvorQvhYi5EJuqCk+0I4gOpIBrF70e+L6pwtfg98ImCNj6XiziZxWZBF5cFbRwXIk7hISFULQs9JzATBnyDiZnTZMKkmjgInz3ZhtBkyXsgeYTlj8men8I8DytTHVh1iNvhfKxMtSMsxY7QFBtCk60IMuaJqQK62Ez4RV6RQp+RGZKgUXgYnS/mgmDyi7gCPy4IfCKZZySAizTB2y6KrugMJqyi0uAnvGf4FfhFpsE3nHnS/CPTuHhjQk4IkQreOCbaMsXQHROazJiHn/0VRI+3TKAJolMh3ESxwj6r5F3kHrHIDJUIVt8XtpEBz/2X0KP/CDTlUQr3Zs+wKMX+Sw2E2jJFL4tc5PlEZsAnKpNZpNqk1yteG5GpCO05eY4jlSLfz8VzhP/5ykS6MHHwj5LEuz/3mrIJhRRZ8eXfpyAChZC2r/y7i2SCXxCQcvHppxDdSoEqeCZZuFkIS1+GFw9PK73G7Pbjij/Ju6me8Cg94dKEQBCAXGzFSSkshoQ8BCbmIpiPu3KBFxCbCQ+v9Rjj8TU8vNbBPyZTSmWRjVcGoxm6hBzlRIp7e4VJnjjZi5FFHhRRCCktSoxIyCatwlgmpQLlwTf8IglA4rFop9Fo4Bt52ckNrhBjCmFh5qJImVdhUDzGfhQGoxkGk1nhlRJEjCCUdInyHxMTfHqjRQpFJpoxy3sD3Js/q5zBN+8DD6/1stc5m0JUJOYpxJ0ihyQuRyYs+P34HP5jz1WJimwx70QyZW6cISEP2thM9Brwukq0vg5tXJZ0bFwIUXXIQRR9XECLXj1BJBotbPbpJAbNsmOt/C5dCWa5t1HtZZSLNefzwTlcIv+ORUsU8hKV54uYMyg+znMK5caFcXCSRWaC0OKhGHW4XfA+ykVpsky4ygQauy89LzTVzjyUqXbRQxmSYhfD9kI4XuFFNQmCWQrvB4qTGil/U5+Qh4DYLPjxvDG/6AwWRuS5UFIoWC5i0mUCQnZRFtMmZAIgJot78ZkHVBfLPXSx2Sy8KHr/BI+cEK7N4GIgU7xwBYjewUxx+35RSiEqiFHmjRFup4u35Z9BCmu7Dml58bxKuYjzUlt4OrxUr/NRCC5BdGVC7n3x3H8Z05auwsjpCzBt6Wql+FNvTyXu5LmhTukA0S6soccf1dTpBi7e2/c/ND8u9P0iZWJOJix9uRAXJhGieIqQRLsyr1Ep1uVpPr6RTKT5hDNB6c0nEz7qMK+rfENV3qEilK02VTqEwgso7FOUTPzJvGw6nioULIvgyNODtHHZrp0OcVn8OpcrRqdYbjj3/Mm9/9FZqggBE6CSZSkfi82WcgyFFCiZt1K8vnCHhG/EJRKAxGPBBGD4RZkbXLhYqy/U8qRaluDPQmLMAyMmRcdm8URdabaiT8jlJ3GOakYjCwUn5EEbn6NK9GXPGT3zS7g1UeXwNPkaoz2+cipgYJ4i5Q9Gx5OAhdw3Ld8PlvPlOsdLdNOLF0Q2SxN+kELOWEAcn6nF52Cm5zrxPWasWOssWpv1Yc+RFVHIP6dwW6+aYQaKIko6/roEaQAKNOYpBHygTHgFGZUCXBBzgQrBx8UTFzphMgvlXiomtGyyHBq7mBcZlGxDcAoTPHqTFQaTDXqTFTojuy+9l016LyFEnGJHiEpozfHdyD2Lwn4phVegPJdQbWLoRgrh6E3MdCYrdCabaIYkG/TcDElsX8JS2edde9iBdUfzsf5IPlan2rH+sB0bjzqw+ZgDm486sPGIHesO27EqVTg+wnuxzx2UZEGIyYIQkxmhSSxnyGC0QJeQK4aIhHwwoZBJEFtCbphvpDzUy4SKbxRLhjck5CLIaGapEtwbysKyGdDHZfFc2Dx4eK5lv83YLCnnVuaVEPKWBEHpE6kWblyMhquEWbgg1pw9b8LzXeaZRaRJxTMyYdeQKHSV/O4b4Zx72bAAzHS+H9lIsj4vEFIXBflFZWL68tU8bCfljLLxiucnx6nHt1yZ4JBuy58nz21mli3+DVCbkF6gNhci0rUQzHgEsZghic1omXBUeR+Fc0cM94teZMkz7cu9g7481UBelCN4iX1EMZjmUgA6m8oTGHHFyRPuzT2/flEZ0May30JgohkhyVaECpNEWf6fcL1jKSS5fCx3MX4374MZK9bIwsdsm0FJVpZbzVOihBCtPj7X6buWvsdsxXet5eeRIv9ddExwJ0OCUnT6kAeQeEzaaTQa+By8yCtNpcpXebWrTrjAiJVPmTLLknkJMpWzSln+jF9UhljR6x+dKeY0+fBZmrA9IQ9RuKAFxGY1+GOUBFeOkyjzj82SmZTg32hyv3zglH3GgJgs6HnSurr6S/zBRmdhrnar+PioGQ2I1plfqvLRckRBKopCRehTqmIWQsxCONe5Yk3uFZMJqCRlCFcQW6Eyz5jwXNHrZ7QgOImFNkNNeQiNuQjDgVPQ7TuGgD1Hod1zGBuNafg1OQOrI07B+w8jPH+NQPCOWATvMmHtgaP4Ne4MNsWdx/r4i1hvysSGVAvWHbJhdaoN6w7ZsCbVhlUpNqc8v3lBf0r3uUdYL/wVw8UW0SRvqTIFIDjZhp+P2PHbcTv+PGnHrwlnsfavGPyyLwFrd8dj5a4E7IhKRuCG3zFxxly8Pu4djH3zLYwcPRavvPIaXnrpZbw8ZCiGDB2GV18bgVGjx2D0mDcw9s3xGP/2u3jnvUl4e+JkvDN5OiZO88DsLxbiW+8g+P28A/q/U7HGlIu1h+xYfUgKEYck2xBssiDYZEUIP74rU5lnMshkUVSLC78FqfJQEo6+QoVqZLp4ERYeEytXI9Iwx28LDwGzyYyv4AFSV6xyr5B0zjOxoY3Jhi42R6pul4fa43LhH50lC8cqL8LqHEXnx7mg42KL/Q6zZMKDV/zHZCs8KYr8TbUJlfUJuQhM4JMjoxnBJjM/7hYW7uOTo2CjGSFJFmYm9jdUZUzIW/BZ0B+iB1rwvqvTYkKShfQWu6JQTG7Bycp8ZLGK3WThBU1KC3RxW0z/kBVAiZX6aqEpCMqYbPF4qkWsughKPq47eRO5R9g3Sir68YviHuOYTNU5ms49cld4OkM65vhvEb3a8oIqp2ImVyb3/qmLV2Tnl28ku97o4rJZ8V+SlIYkRLHEVJ8E2aSbRzHGeHzdiNOBvdZgNCMoMZcXIuUqnSWKgjt5sV0udHGqxxQFeuwxp04VYpcFafs+B0kAEo+HLARsls2E2IkqzkCEma6qXYtYAh+dJQ4OPlHOrTOkdhrpUOfKMK+GlKTrL7rrJW+bb1Qaer7wOqs0bvI13Jv1Qa8XXkdAdLqTt1B0xcvCV4ryfJ7D09gs2EcUgJIbPyBGErzqxF2tcLGWzeZm/LQG7s2dE89nrljHBwDJ8ym2KUgQwqFM5AXxkGIo90yFpTqki0qKPHxpU1YNJ0tesqBkyTsVzD12ISk2rD9sx7pDdgTGZ2LRmr/wxYpALAtch+Bt4fD5LRyDRryJp55+Bi1btnJqPtq0aVM0a95c2ZC0bVt069YdXbt2RfsOHdCiRQu4ubk5vda9WTO0bdsW3bs/hf79X8BrI17H5BlzsGCxFzYlXICOf3a90YLVKRZsOWrHH6cLseN0AXaeciD8fAGiLhVh/WEbth+zYssRC0JNeVgTcw7RF+wwXSlE1MUC7DjlQOD2KHzw0XwMHPoq2jzRDk2aNEHXbt3QpUtXdOr0JNq1b4+2bdvi+ef7Y+GiH7ByzTps2LgZGzf/il9+3Yat2/7A1t//wMaNv2DNmvVYvWY9QkJXQWcIhr+/Dss8/bBomTe+X+qJL79bjKkzZuPV10ejV+8+4vFp36kzWrVuA41Gg4lfeUFvZN7A4CRJ4GoTrQhIsCAg0QKdLCdTLwh9o1k8x4VzLSAmS8zN8gm/Iub5aeNy2G+YtzKSn5/6+BwEGfO4GGKCKDTZijDuGQlLEYQqD6snsSpHJjRkbYjESSKbHDLxKS9iyJBChopkfOk3JbTOEB7TxjLhJlbqczEWxnMuw1KYrUxlv4WVojkQluoQb69MZV7ZlYcc/H8spC/mq6ZIv4MgJ++x5GE2mKzQ8pQUsSAryarwXgvdAMJSHaK3XEgVkCZUkvdaMMn7bVMWR/F9DRM/k4P/7qXff5jTYzJLkeWVptjYeWbiojSJm4mlJejlIiVeGO9zXXqrFGHNKHXxUuNhaeG5kjeZCcEAPpaKY7MipaAxEeicRqDwDvPzLCAmG7r4bLEzgDA5FtM1ZKkxeqOQeyyJspme6xpxOkjP08blimlAWrGzQq7svotuC410XxAe06tEoXCNEKN0RjN8Iy+TACQeCyYAIy7JcsXMsn53uYo8OHUoQp6/oFV52RSCKlISYb4ygSiEWHy4cBRmk75RUshVuNB5H7yM6ctWYdSHCzB92Sp4HbzMZ6F8m3LhKTPPg5cxbdlqjPxwAaYtWw3Pg5elRO7/KH9GWdEpfm4uUhXVWgm5CIjOQK8XXod7cy5am/dBrwEjoY3NkloK8PAruzjZpYuJapAP5RcVlyFPVaGLwWiBLpHl/wlVzZ57T2HaF4sx/oNZeOPtSRj9xlt46eUh6NmzJ9zd3dGzZy+8Oe4tjBw5Cp06dYK7uzuW/LgM+w5G4OipcziXlo2MPDsKiq/BVnAVmXk2ZOfZcPFKBi6lZSE7z4asXCvSMnKQkZmLtLQsZGXlISfHgrS0TJy/cBknTp1HeHQ89KHr8NX3y/DuVA88N3CoQhy+/sE8zJn/NabPnI13Jk7BqLHjMHTYKxg4cBAGvjgQL3IbPmw4PlvwLb5btARvvjkOHTp0FIXpc/364b1338OkiZMk0eneDH6h61ByoxTVdQ9Qc+9f1NT/i7v3/kV1/b+orH2A23fvoaLmPm7X3EdZZS1u3q7GtdJKFF69BZujGPbCa9i6fedjde2fOO97bDjswO7jOVgdfhw//hqDr1buxEL9z/AM2YDQLTuxcVcktkcdwr7jmYhIu4Z1RxxiQYogHAONZnaR4RMQcbLDvekBsZJHggk+JjrDUmwIS3U45TAakuTVjSz/Vit603hqgpB2ILbayZPEpFEKc4ckWZhXzWRGaDJ7LDSZWVgKE5uipdiwktuqVAdWH3Jg1WEHE2+HHOK5HyIXUzKBxnpHWqTbRqusuIkXNplYGoJwW8xXNSkLnuS/nSCTFWsO2fDHmSL8cboQv58qwLrDdgSapNeGJtuw+XgBVqbYoE1UF1KpzGRTmlAUJReGYjW+VZFK4dzySbXvJisMwmRPlmIh5btK6RrBydIkkglku9NxDuH7YhB6aXKPlTCJkDsB1PluysiQFFHxE1rdRGfCJ/wKPPddwE/7zsNz/0V4C61uZEJSchSkuxzTJctUeI/9ozPF0Kk+LgdBRrM4qRHSNIKTrDIR1UDueIIZATFZ6PXCSOX4/cLrvBDEWcRpZaJP+yii71HEoCj+ZMVrYgTISgKQeGxED6CQuK8X8v/EvmmumjNni60slEmtghiUeeT4wOEUfhUHCLl4y2xQyDHvXSOPi16+TPG254FLih6F7kKPQpUIVAtBP/mgJQuDKcJQ8tJ9LpaX7ToOvZika4EuPgezvTdg7OxvMNvnZwQa88QcOmlG30Chgbz3n3ARUF0gtLKLjp57DH89nIeJHp9i7oKFWLh4ORb+sBSTps4URcgr46dgwU96+Iasx5rf/sL2yBREnc5D6iULzqZbcDLNjLjjl3Eu04aLGWZcynHgxBUzks7lIOacFacy7LiUZYPNUYTComsovnoDt0orUF5RhZtlFbh2sxz5hSUoKLyKwqJrMFvsOHkxCy8NH6EQQ63aPIGBQ1/Fa2PeEh97Y8J7+OzzL7Bw0Q8IDAnD+p83Y9uff+GvPfuxe+8B7N0fjn0HI/Hb739g7sef4JNPP8P6nzfhxOmzKL9zF9lmG2LijVi5Zh2W/t2g744AACAASURBVOSJLl26Kt6zsOQG6h/8N+oe/Dfq7v8X7t77F3fqHqCi5j7Kqupxs7IWNyvrUFpVj7Kqeyirvofyu/dRVn0PZdX3YSm8jrFvvoWXhw7HqDFv4N2Jk/HhzNmY89E8vPveJLw8ZCi6d38K7u7u6NKlK55//nnxvYe+MgI9ez2NVq2YR9XNzQ0dOnZE3+f64bURIzF23AS8NHQ4WrdpK75mrucqdsFyKuKxiILQkGgWhZo2NkucEGljs6CNy8FPu08w0SYWXgnbUbfYkRfxmFkOY5IVodzzJj9fBc/XykMOPjmxQ8/FSiAXW3pZ8ZDOaIE2kZku0QKt0aL0VMtEnN4ke63LFkCu/ycXgQYj89itP+LAbycLsPWEA5uOWPHLMQd+P2ZFxIVCxKYVY90hmyiug5Os+ON0ISIuFSE5+zpSc24gKfs6krOv468zBdh0LB9/pKbhcN4tRKddw+pUG/44XYjd54oQl1aCbSfyoROL0RraR0kECr9jV59hZYoNvxzPR6ir/4vCV75NYQxgn3vdYQdWp7Lq+w3J2fh1fyJCN22Hlz4UAUFh2JtwBH+fdiAsySKOK2Iub7KUKiIIJqHdkhBi1sq8yqKXMFotBrO5ye+z2z/+eURWWZzOrwHKFjpS5bWLamvZ/4Wx2j8mi193pHQZoYOC6PE0ciElF4Am6RhIucVMYOniczBL7OKwgXkUZUVtwracuha4EIgNiTt179XG/ie2IOP76Bd1hQQg8ViwNjCRlxW984RKYDEfjK8aoY3NkTxg0UoPoJAHJ4hCeYhAHjr1j3Humu7HBwZ5ArbLH78g2Fw87uS5i8zE9GWrG1ylxE/l4RMGETaQsMpJfVyWmFCvF4s2mAmhKnlD4zdmf8NCTLLQjtT7zq6YjTtdyNQXjSS514A/JrvAbD5qx66zBQhNtsFgtCIg6gqmfvotWrRsKQqIWbM/woyZs9CmTRv06tULffr0gbu7O9p37ITBw17F5I8WYNg7MzDh/WkYP+kDvDXxA0ya+QnGe3yOl14bi9CN23ElrxAFNytRebcetTW1KK+4g8rqGlRW16Lybh1ullegoPg67IVXYbU5YDFbceFKFownLuEL//UYNGoCOvXoI+5Ti5Yt0bJlKzRp0gT9nn8Bkyd/gClTpuKXX36DzVYIiyUf3373PW6U3RE9c7fv3sOd2vuoqnuAam536/7B3fp/JG9e3T/i4zX13O79i9r7/6L2/n+huu4Bau//F7N7/+Ju/T+oqvsHlbX3UVFzHxW1D1BR+wC3ax6g/O59lFffR1k18wiW3qnHrTv1uH67DlfL6lB0qwaFN2tQcOMuCm7eRXFpLUrK6nH1dj2uldei4Go5HIU3sXvPAWg0GkyZ8gG0WgO2bduBv/YcQNj2CPy634jdkYlY/+cBTJn7JZ4Z/Cpatm0HjZsbOj3VG116P4e5ut9kFdbKNj3yqvxAXuhjSDSL56pQgDXa4yueE8ZCYtr4bNnKCXmQV9GLhTp8EmJIsiHAaIXWyM7BoCQr/BKYkAtJssI//AI+CtiCj71W4dPFPpi74Dss+PZ7LAtchy+8QzFu0jRM9vgY877/CZ7B67Hyzwj8dTQLBy9fRXCSDUILIPl5H5ZixeoUK1YmN+CdUVWcuxJcn4bsUAh/tyZN8FSv3k4e2fmGrQhOsuLg5avYeiIfYckW6BLzsDLFisSsG4jPvI6krOvYdMyBv88V452532LzMTvCktmEKyDBDP94M7xj8+AXn+difxsTgtLvXsfTAITJnM4opAM8wutVpjdZMX/l3xj+znQMeGkY3N3d8eSTnfHS0OGYMPF9jBk3Hq1at0GHTk9izNRP8OM2I9YccogRBHkniGDerissRdlAOTiZh8+NFj4ByYWioMVlcYtko2d+hYDYHPHaIXgLFSs/qdulqLyLykKYbATEKt+fCUEhZ5qnN8nErjQ2856k3OQRGKFtk5BuoIjKiKkFqsl7ktT6TDwPZO2nHmZiuFdYEYqLTDGlJ0lKSSAPIPG48BDwZWWrFqEfmpD/I6tCVScZCxcSeZ/AIBMLAwUazWIukpSPlC16BIVwsFR95uzadz0LzFQ8p6HQ7cgPF7heAWDGApfhCj/5DJY3vdXGZisS35VLowmhNrPUTkXVMy6YNzcOTJLEnFApqjXKvHgqIRicbMOWozZsPWbDtuN2rD0krRqiM7Lk9F+OWBFsMkOXaMHAsZPQpElTLDKsR/TJLBy5YsOZTDtOZ+XjYnouNm7eghWe3pg1ew5eeulldOjQEe7u7o2GLD0+mof0XAcsjmIUXb0Jq6MYOZYCpGdbkJFnx7HLeUi9kIvY09n4+UAqvtZvwtxlgXh//mJ0efo5tO3UFeM+/RFTl4Qottuz30BsiTqMsmpB3D1AVf0/qKp7IIo89lcSedV1/6Cq9gHu1NxH5d17qKy5jzvcKu/eQ+Xde+L96roHkjjkdreeib679f+guvYBKmvuo7LmHir4tiprH6BSEIF376uMCcPbNf/gds2/KL/7D8qr/0EZt9KqB7hV9QA379zHraoHKK3+F+V3/8WN23Uovl6B/OJS5JeU4WJajtMxbtuxM4a8MxPTl4bhm01R8I/OkISDWADjQlDIBKCwrJ9ByCHlFxExB4lfEFkrjksIiEqHIT4HgYm5CDTmicsGGkwW7pWy4GPDdsxZ5IPpH32GUW+8hfenz8JPq37Dtz96ov+gIejdtz+6devu9HleGfMWnnthIAa+PAwLFi3HwiXLMXXGLAweMhxNmjQRnydvYC7/TK56gUrFP0LRj2rCpAi3WvH1hgi4NWnq8pxu9UR7dOndD937DsCynUcUolJvlFotBQvhYv6eoVz06Xi4Vww/O4lyq/P+NSL+9DxVY22q8J5Wxffv6vOptyF5h9ljbTp0Fj/vR8sCsf24DXEXHSi8cRtF18tgdRQhPDwSs2Z/hJYtW2L02LHw3xYDXaIFm47aseOkA78csWFVqk306Aq5kU6r8iRJhWOCIGQ5qGYpt5kvbanIM1QLQ1neoaJ4RV01rTKpcbK0aECg0YxgnvLAenAq02lCU6X+pEHJdt7HVGlOHQfkDfCFlAQ+vqv7n4r5nLzDgTwvVN58X9GuTC0G42XeSv67lDfoNxgtJACJx0YhAOWzF7HKTOhwLvRx4xVTQTyviOXv8ARsHhIKk8+Oku182xYE8oop/2hhtQBp1heUmIuQJAsv+pCJuwbEoDx/UFG8IbNGPYCyMLQk+qQQthTGlpZFY6Exno/F8/VWHWazxNX8r9BGRKi8FZZu05vY7ZWpNqxOykHAThP+TLmMvWfysTrVJg3sXBCO/2QxmjRpiuGj38L0GbPx47KfsGb9Ruw9GIuIUznYdtwOr6hMLPklElsPmhC6IwaDR41HU/dmaPVEe/ECkHjoJAoLi3HzZilKrl6HxVEIq9WGhGMX0feFgYqL45bdUYhKPono1FO4km2F2V6AW2UVKK+sxtUbpSgovo4cezG2bFPmwrk3a44mTd3R7dkX0G/YaLw64QPM/VGH1bsTYNj4J1b4GzBs+CuK10TFxqPun/+D2gf/jRoejq3mOXk19/5F7YP/4uHa/0LdA+a5kws65tnj4o579ATvYFXtA1EsClZVcx9VtQ9QeZcJxoq793C7uh63q+tRXlWPsqp6lHK7daceNyslu1FRhxsVdbh+ux5Xy+pQUlaHktJaFHMrKatDcVkdu19WK94uvFUDx7U7yHbcgrWoHMczChGyKxGduj8NjUaDaUtDeTiqATGjyHGzOP+PX0CCTFaxSlRRPc4nKkIYTPi/uIQe76/I2grliG2efMIvo0XrtniiQye899FX8AsMw9y5czFs2DAMe3UEFnn6Y+PWP7H651/wwbTp6NSpk1jgIy/8aereTCnE3NzQ4/nBePfTxTAYrVjFPX2BgtBShLobFk7yfFeXz5cdm+W7T2Je4HZ8uyka/jGZqm1alM8XvGjyfD6n76Xhvp0Nhqa5aeNz4OG9AWNmfQMP7w0sV42/l87VRNCFAHTaRnyOy/fSJeTiw59Ww715C/ToNxgjpn6GqT8YMF//G37cEoVVEaewOeoYgreF46nez2Ls9E8xatw7eO655zBs2DB4zJqNNavXICI+GVEXHAhIYJNcPfcSC8tjBquiHMLYH5aq9IwFKkSPWRHu9I/JwExhJSmvdQiIyRTzD6ViCKEllkVcgCA02Srmj65MtWPVITsbhw9LOaRCaybn1AJVdEWdp+nyNa5e37jJW1+JleEqsSjdFxrrCwUr6pWapBQLg9EK3wgSgMTjwXMAr/CZr0Wq9BPc5kZ54imz4CSruBqCuLKDLClZOFl1iWb4RaUjMCGHt5jJQaAxDyEm5sFi7Rl4r7RklmvCGuXKEoEbEn9yYdiAB9Dr4GWnHMCe/UfA++BlWQsbnp8i780ktmRhM0pB7Kqr7uRufyEvT2eSBnL5ShJbj9kQlmzF/BXBTl6JL0O2S54ePsB07tnH6XkaflHt2KkzVm/ahvmeoTys2hoajQYtW7bCgu8Ww39btPj8d+Yvw5wlWizUrsV3AWswZ1kQJnh8geb8NWyb7nh19DgcOZOGnBwzsnPzkJtnQUFBMSrv3sOt21Uovl6KkuulMBdcQ3jqGfG1/Ya8jsXbk7FwSxwGjnkP/Qa+jA4dmSBo3aYN+r8wAO+8NxFffbsQuuAwbN25F6cvpuPWnTqUV9+T7O49VNQ8ED1xlbXcK1dzH5U197lg4x47mYArr2J2u6peFHQV1fWorL4negYrqtn/y6vqUH6nDmVyq6pH2Z06lN6pw607dbhZycTezYo63Kiox42KelyvqMP123W4Vi5YPa6V1+NqeT1KypnYK7pViwIeFrZfq4K1pBLZ9ltIM19Ftv0mruQV4/djVgwdPxX9h49BYPQlll8l++1I7W2Uzc2VIU9njxNrgST1ExR6Uwbw/n8BsZniba3Mky2tXJMHv6gM7j1hk78v1x1Am/ad0L1Pf0xfEoSfkzIRcd6BqPM2RJ+z4Hi6BRdyHcgvLMKOv/ehw5NdxPOzQ6fOmDFzJt6dMg1r/4rGqrhLMMSm8yR2dTi3EW+ZkwBSvc6laLY45zm6LM5wId4aEJJyD6RCiLvaX6PVhafSBm18rsvGwtq4nAa8fM77q21gRaSGRKDBaMXyXScwY8VavDr5Yzw3fCy6PvO8YoLYvFUbtHqiPdybNce4iVPxyRJ/vDPzM/TuN0B8zsjJcxBotGBlshVrUpmTQOmFlhWkcHGkzikMli0HKReMgYm5eFr1mZ4eMBL6hFyxZVVwsk0MyYbJwrJC4YqYMy0vEHokkfaw5zQ0CXnU7bgWjXLPonpZTnGpTiF6JGssL/ZC5d5J38g0EoDEY8FyAKPSmKdKtmoGM9nFyCRLmBXc/UL5vEwoBprY4KeLz0VATAbrpZVs5UnqmfDwXIexs9jSOkGJ3EPBy/R1cdnwibjMWgU0kggs9/g1VM0rPO558DKmL1uN16d9jlff/xgjpn2O6ctXwyf8ipioLPTEEhtzcgFoMJplrR6kwUbw6gkWkGiFNiEXb89egJ+Cf4ZP2CZ87xeGr75filX7UqBLZFWSTw8Y4lLULd5xFNpEWYWfyQq/HSa0bdcBvQcOx7D3ZqP/iPFo92Q3xesW/hKHrn1eQPNWbdCshbJdS+t2HfDWR99i0JtT8Nyrb6L3wGHoP3gYXhg+GoPfnOxyP0wnLiLNUgSzoxhWqwNXsqy4aLmKC5YSnEi3IWjrPvQZ8JIUupzwIRZtNcJgsmLSN37idrr07IOxUz/Gx4t88J2XAQs99fDWr4Rv8FrEncqCrfAayqrqcbvmH5RW3cetO/dw7XYdt1qsXLcRxaVVKLxZicKbFSi4UYGC67dRcP02im9V4lppFa6XVeNmeTVult/Fzdt3cauiBuV36pgQ5KKw/E6dwsq4p495++pQWsVE362qOtxSeP7qcLOiHtcFuy3Ztdv1XADW4Wp5Ha7ermOP3a7HtYp7uFF5DyVltbAW34a5qByW4goU3boLR8FVWCwOjB73DmbO/w4HzzmwNtUqCYqGPA0uc97YeaI3WsQ+kULoTRefy6v1mWf9/e+0ir5vWlUqgzDp841MF5viCksi+kWl44PFgej6jFTMsmCZP/IKr6P46jVk5+Ri2uxP0KptO7z3lS+W7TwmPq8dF4SCef59UinaGhRmLsRQQ4835C1r8Fg2IP4a8+DJtj3lh0AEmuRiQ8rl1Rkt8E8ws/xIXn0rF0keDa1m5P1zw/ur2oeGt7EBepMNYbxoo6Fq5ECTFSuTrVh3iK23rU80Y8oPBsX31LxFS7Tv2Aldu3VD127d8Gzf5/D1khXYbLyC1ak2rD3Etu+fKDcbAhJt/Pt14VmTVT0HJ9sxbXGQuEb4HN+NLj/TXL+NCDvk4OFam9gFQfDMiR46o+SpUxQfOYmyRxWFtkY8f67EXkPbbei1kkntnmTeRsHbpyoAVJ8LgSYrfKNIABKPhyQAhYuNfN1U+UyWn3zyZrusASqr6GMnplkqs0+UVqlgjZ0znWavTw8YCUN8NoJNFvjHZMDzwEV4RaTDJzIT3pGqHk8uWgCoC0DUlb0+D/EE+kamsYIPcYmdPDEUHpqs7DkmhDEMSVax7cK6Q1YsXr8b4yfPQPv2HdC+fXsMHfYKBrw0DC8PGYImTZtinmcYfj9uw4Gzdnzw5TJ07zsAHZ/qjV4DhmLIOzMwcf4SGNb/jt8iD4m94QJNVuh4Evhfp+z444Qda1NZu4x1CVcQtCsJXuv+xOqDx7F+XxJ+/Hkf2j3ZDW5uUo6Vm5sb5i/2waLAjfAM24ItMSew85QDH3y2EK+MfhMajQZvvPU2gtZuxsQPZkCj0eDP3ftx7LIZJy7n4tBFM1IumnEu24Htf+1VXCS+/zVBkcMiXOiW7jyCj7RbGs0rDApdDXNRKWwl5chy3IS5qBzZBaWwXq1AXlE5zIVlOBibDMe1ShTeuIPCG1UouHEHBTcqUXTzDkpKq1B06w6ullbhWlkVbpRJIlCw0opalFXWouxOHcqr6nC7uk70HIpWcx+377JK39Lqe5I4FCt/mZVWCfYAt+6wXL9bd+7jRuV9XK+4h2u3mSfw2u163Ki4h6vldcjLv4mMvEJkmgthLriJi9abCPttDz5aokXHrj3EY2GIy3Ly5jl5qZwuGqyiUReXA/8Yvr5vXBZf0UZquC5U33+xaq+yel9otB6XLS65p+MVxMGqJf6YEGRtLV6b8om43xM+/RFf+q/BsJFj0XfoKHxs+F30YL86+WO079oDT/UbjF4DhmHwuKkY9q4H/KLSVR48tTfTqjoGqvw/l8epoXBsIwLxPzVBVBgt+GrDQQQn2bD6kB0rU1i/wQ1H8rEhORd+v0XgR8Ma/OSnw3eLlmDDlu3446iZh0ytGDv7G5eNhcfM+qZBT5FaKIyZ9ZBtGFWiQhZWDuTpPat5kY3QE08fmw7vHSb4/xGHEW9PZhXsnTtj9OjReOONNxW/3e8WLcHPBw9B+8serPp9D9Zs24M12/diR0wyIs5ZsOMUi3I0djz1Jiu+XH9QFDyNfabGQ7cuBJXwXP59CylN8jZAgbL78lC//FiFJjGhLAlaVwJOqr52fW49Woi4wecmObcLCpQ7H4xW+EZQFTDxeIghYDbYmmXVwNL6qGIFWLK0FJhgbAkwm2Lw1SewXD9hKTJ9Qm6D6/nO+Gk1fMIv8QahjSwF5Ur8qQpAFJXCj5AL+OHyNbxhqCyvkVe9CQnDUohbedH5WPebFAYdNAzT5n6BiXO/xqDhI9C6bTu4ubnhya7d0ff5/ujRsxdee20EPpg2A999vxhrf92BHcdyMfIDdlHt81x/NG/eHPo1mxG0dT9+/nMfDhxh38n6Q1bsOW3HzHlfY/iocXhx8BC4uzeDu3sz8f0nTvmwQcHVq9+LGDiIee1mfTRP8b92T3bFs4NfRf/X3kK/Ia+jd/9BeKp3X3R5qheeHzwMr4+fhLc9PsPg18cpXrd051FRILgKU077MQTd+7IQ0lP9BmHKN974ZEUYPlqiw9RvvTHpi+WY9PlSTPhkESZ8vBAT5i3GxAUrMH2RDkt/PoBth/MQe7kYp803UFJajRsV9bh15x5u3anH7Zr7qKx7IIaHq4QwMQ/1Vgq5fVWycPDde6i4Wy8LCd9DebUy76+0qp5V+8py/27yELAYCuaevqs8x6+otAaFN+8i/3o1HNeqYS2phLnoNixFt+G4WsFzB+/ht+27FMfvzSmzMGb6Z9DG56mEkEoAmqzQJ5r5EolZ8I+Rrf0bLSzfpm6dkSH21PSPyWSrFPCmv1LifLa47JWwRKI2VljjNE/m1Wf7JISslu86hg8WB2HAqHfw9MBX8MLrE7Bi75mHCignUZeotEcrnmhom/LHXIXJG3i+q4u4KAYs0CaYEZBgwepUG7YlX8GanRFY4q3DjDkfY+L7kzFy9FgMHPwyevXuI+Y9ujdTNkf/LGSH+F6Nee9cCxrpPmsF1PA2ZnhtQEACEywhyVZsOGLDL8fs2HbcAs+12+AVvAbe+iAs9/FD0Oq12LRjH75cvBwjx76BHj17okePnmjfoQPGj38LR48eRWFhIQoLC2G12uHvr4WPjz/8/PzRuXNnNG/eHH369MEzzzyD3r174+mnn0aLFi3wxBNPYMyYMVj0kxc2Ga9Al/gwwWb7D45LY4JJEsnrDtmwMtmKRb/GY0XIBqzw12GZtz8Weukwb3kgAtduwdaDRmxMyULkBSv2nLFh81H2+pBkIb/OhoBEK/wSrPBPsCoqsYOc9r/hauzGQ8KPKg6dP3egGEK2kQeQeGzaaTQaBESny5JQlVVM8tJ/sXt9suxkNlpgSJSJAZMVBp50LjUoNjc4A359+nzeAZ4LOGHdTqHnk0IEynMAH76upbBmZUPLso2a8aXoLdEn5InLM4Umy/qNubg4GIwWzPLZyDxtTZqgbacu6Nb3RQx5+0NMX2zAHP9f8HTf/ujd93n4Bq/F+t934VsvPaZ/8iVGvzmBhVDbtkXfvn2h0Wjw4qCX8KOXP5o0aYKnnuqB9h06oN9Lr2BlihWH0vOxNtWKrn1ekDwwn/yAk5n5OJ1hx8oNW/BExycREHERU+d9jddHjsLAgYMwbfoMdOn5DF77YB7O5RRi1qw50Gg0aNmyJV566WW88sorGDFiJEaNGo3BLw1Bnz7PokuXLmjXrh1atGiBFi1aoE2btujSpQtGjRmLZSt/g/fBi7IBV/LMyNctFi0hD9/9Eofxny3Fc8PH4NkhI9Hv1TcxYNQ7GPzmZLw8fhqGvjMDw97zwMvjp2HgmPfQd9hotG7XEU3c3fFUv0GY+JUvkjKuIjO/FAU37uDa7VpcLbuLolvME1h6pw43K2pYCPh2Dcoqa3G7ur7BIhBB/N2uVgk/3vevtOoeF5r3RMF3tbwOxaU1KL5Vg6Kbd3mO3x1YSypgKWZmLamEtbgSjmtVKLxZg5IyFs4uLq1BXkEZLmVYEZmQgvnfLkbrtk9gkW4tdp6yIu4Cu2Apzy/2m9Il5DHRJ67fmyFVqzew+oLzagwZYp+1gNgs7oln618L640KqQ6GRDOreBeXg1R/n0KLilyxMlEuppwbISuFmEL0NSTOnETewwScWgA+gniUX1SdvIhWBMRmY9GmcHy6xA/j352E7k89BY1Gg759+2LS5Cl4893JeKb/YLR7sivcZFXNbk2aoEO3Xug7bDRenTwXc/w3y3J65TmAqsbw8bmN7q/eZMXGw6ztinobTZs9g6dfeBUbo47BsOFXfPzppxg2fBj6P98PPXo8hSeeaIvu3btj1KiReGvcOEycOBEvv/wSunfvhsnvT4Rer8XBA/sQERGOgwcOwGKxIT+/CAUFRcjPL4TNZofVaoXdbkW+w4qLF88iOzsd+Q4LrFYzrJY8WC1mZGdlIC42Cj6+vujQsRMGjBgnC/c3JpRs0MbnNHBc1HmNDQtk4TitTrHCY8UaNGvREi8PHYp33pmAiRPfw4S3J+DNN8dhwIsD0apVa7i5uaF7jx4YMXI09H/GYML8nzB1/iIs9PRHyLqfsfmvfdgRdwQHT2Vjw2Gri8/ScH6fa7Hq6rnqY9OI+EtSmhAi9osmAUg8HpIHUHXiNux+Vw/AsrxAWRJuoMkqJv+GpNgxx8d5pte02TOYunSVasF2SQD6NuDpexThJ6/w/XC562XZZqxY41TpG8wb7MpzL6QqLBs2HrEh6qIdkaezsf/wJYQfPovo83nYddqOgF3J2HTUjsmffg+3Jk3QpKk7mso8dXLr2KU7WsuSsZ/s2Qfd+vTHkz2eQbMWrfDa1E9Z5R2/OH0WuhOTv9ej89N90bxVG/R84WV06/MCWrZ5AuPnLsQTPD+wTbsOeOqZ56DRaDDfZzWat2qNsdM/xczvvPGNfhPmrwjC23O+xtTPf8DUed/gramz8faMT/DhgiWY+4MPZn27AtO+9cXXAWuxwCsEL41k3r/3vvRGQHwe/OPzoE0wy/JFBbOoLvZmF01Oc6V1NMUCBLPC46Q3WvBpyA6MnD6fieNR7+LrNXuQnF6Eq2XVvFDkH1TV86phVZuX6rp/UFVzH9VcBFbXSq1jpIpfVvRRekdo+sxCv4IAvFl5Dzcq7vGq31qUlLFK35KyWhSX1qDoVg2K+N+Cm3dReLMG1pJK5BaUIcNyFRez8pGWU4CM3Hyk5xbgdLoNiWelFjCf/ujP0idUbVB0CWa2jnU0896xwo1sqbhD1mxdvgyjYlkusaclE3nfbY4Tl1iUeq1l8kbRPBSckItAoRo4LkdqxWKyyn7bUhsKYe1bQXjpjVaEJrO1caW8Yefzo1EBKBN08nPpkfIEXQi5RkUjH7e0cdliRe0snw3QxeeIBVUTJ70PgyEQu/fsg9/qLRg/4zO079wN7s1b4ukXh2PYe7Pwn/UCMAAAIABJREFU7pde+Fi/FYu3JzMPakMizsg8SQFx8gren6GNz1WEHtVeSb2RLxPIewQGmqzwi2XNiQePm4ouPZ4Wz6nnn++HLz7/FJs3rcfu3X8iKvIgTKYE5OXlIT+/APn5hSgsLEJBQSEcjnw4HA7k59vhcAhmQ0F+PvLz7SgosKGgwIbCQhuKix0oLnagsNCO4iIbrpU4cL3EhqvFNpw4fRIBqzbg/Y8+R7+XXmH5g092wZyA31iLK77vAYnstuAV/OH3JMVnFSqbR3t8gxleG+AXmyPlxRlVx6UBD5zexMbtoW9PR1P3Zuj/2lv48Ht/TJy3EC+Nm4Jhb7yLQWMmYuDYSegz+DW0bt8JbTt1wWdhfzeasrLi7xMN5642KAAfJnwfMjF5qLBkRkUgxOPCBWDaf3zyNe6qdh5s/aPT2dJosvV8e/YfAe/wK1L4VuH9U3Z+d70gucyiM+EXw0xoECrkPPlFul5L2D8mU9l7KdEsVmAZuKCVf4635i1xOUg0bdoUvZ/uhc6dO2PQoEEYNnQopk6ZDP+AAKzfl4ilO4/jwxXrFK/p3KsvBo6ZiHGf/IBpi4Pg4bUOH2l/xbzQvxCyKxGJl/MRcSGfX2gtMCSxi8gqYxZ+1IZh0U8+0K3Zgs2//YGohBQsXbcLIz/8HE8/PwhdunZD165d8faEt/HKK69i0KDBGDDgRfTo8xzaduyMJk2d+/81aeqOVu06oFP3Xuja6xm0bN0Grdq0Rc/nBuKN96bC8Ecsth3KxJ6zDmw+aoc2wQp1cYLyYu9KAEj3WbUpKywSxLVwnN/5YgWCkmz4dnMM3pizEM1atML0ZWE4m+nAzYoalN+9j9Iqlrd3q7IWt27fxbXSO7heVo3SylqxGKSimhWClFbWoqxSJvru1KH0jhTyvVWpDP/eqKjHdaHIo5wVedyoZAUe1yvqca2iHsVltcgrLIe1qAyXs/NxId2MK1lWnLmUg9TT6TCdy0XCqXScPX8ZUZHRimP97NAxquNm4avuZLAiDfm6oMLqAvG5CEzIVa24ky325GT90PiybfHC+tg5mPDZUtHzp+X5f7o4VpVvkC0XZ4jPgj4+m92WT+hk69eGJLO1ZlemOnjeryT4tAlm6BJUk4GHXvAedlFtaNJpbVjoNXaxlglFbVy2mJOscZsDtybt0Lw5a6K+dfsfuH7jJnShq9C2Qye06fAkhr03C0MmTEeAUyuZRj6fkXkZw5JtWHPIJh5Xg8kmirvgJFa8IXkkuVhKMMNn7yn4bI3AsjXb4LnuD/ht2gXdr3/Db/1W8VzalXoOeZYcXC1xoLjIhpJiGwoLrCgstKOwwI78fAfsdhscDiscDivstjw47LkoKrKguNiGoiIHiovyUVxUgOIihyj4iott3KwoLrLgWokVRUVWnMu1IiXNiiFj31WMf+MnTsak2fPx9+ELCDLmIiziJFbtS8aW+JP4PTUdvx61YMtRVuk64fOfWNsbLvB8ItLgs+8U/jqRh9hLdvxylB2TwCQbVqeykGyA8eFiKijJipUpVvywLQUT5i/Hc8PHYti7MzFu7vd4z+NTTPfwwJfffotvlnnig69XYMn2FPQaMBJNmz0DjdsUNGnaEW07dseQtz/EaI8vsWD1vobPwYcKv0fx7jW2jYa2JZlvJOUAEo/HIwrAxk76Rk5S2ewt0GSDITEXs3034o3Z32C2zwbo47MRyBeX18XlsHV/+QLhyibPLgRgtPMKHsJaxVq+bJuwTJs+IQ8BMawCeYzHV/DwXA9tXBYTHyZZ82ZZbmOQSvzpTTYs2mqERqPBkCEvIyPjEmzWPBTkW3Hm9HHs37cbO3f+iTWbf0NAyGoMGDhIHCAnT/sQ2pUbEBybhr+OpGPHkRxsOmJDWIoVu47nYetxO37YmoAf/UPwwouDFGJhrvY36I0WrEy1Yd2eBDzVuy9at31CXFJMsLdnzMPLb01F1x5P4/PPv0BAgB5LVvjhuyXL0YOvgjB43BQs3BSF8ZOmib3bBPt0iR/emvAunu3bV2wQ/eYbb6B5c5bXJDTxberujtdHjcESHwNWGzMRIopl6Ttv2AtkVjTdFb2E8VJLEnaBNiuWPZoftgst2jyByfN/wOH0fBReL0dxaRWuld/FjYoalN2pE/v73eF5gXdq7ovtX25XsWKQ0jt1uFUpCcBbFXW4cZu1fLlVWS+Gfq+VsxU/WKUv8/oxz99dOK5XwX6tCraSSlhKKmEtqUCW7RpOXzbj0LksJJ1KQ/KpK4g9mYmpH3pgzBtvYdJ0D+l7+nw5fvr7pCT+jBYxN08vfH6hFZOTCQvC58max8rW7Y4X1u/loVyh2jc+B4bEXNb4OZGtYhOouJ8rtoWRhy2F8JO6ZYXg1Xq4sJONCa68XK7Gikfy3jUgAB/Z22KFh/fPPCJRBo3mSRbGdWuJlWvXY3/CIQwe+RbatO8ED++f2VJ6Ysse9djW0P7aoIvPxso9JiSeuIBjGRb8fcaKX47y6EiSFRuPWPHLUV65a2LixWCyYssxG8bM/JJNFDt3xoAB/dG/fz/0fbYPnnmmN14c8AJ69eyBnj17IDv7Cq6W2FBSbEdJMROAxcU2lJQI960oLraipITdLim24mqJHSUldhQX27nYs6O4iHn4BNEnPLe42IrCIisumS24kGfFyWwbzuTasGPvXnz51QJ8/sVnGDt2NJ599hk83+85fhzdxHQT+RjTsWMH+Ieuwqrte7BmVyTW7E3ApshDeHH46+Jz2rdrhyef7IROnTph+PDhCND64fiZMzhw7hG8aU7eVCsmyroTyC1kTzI+8Wsgp9L7Z+cG/Q+9/jX0v8Z+Iw9zoKi3SwKQ+H9LO41GA8+DPGn3oQNpY7kLDQzsjVigSeonyISXDaHJfEF5k1laPYQvQC6sJ6xYAoh7+QJU6/LK29MIKw4EJlkRlCyFppW9qaRlgEKSbbKQFbso66LTMOjlIRg7ZhQy0s6zgbMoH8VF+bhaVIizaXn4wjMEfQYOQ5MmTeDm5gZ396bo1aunKLaaN2+Opk2bon379vCY8zHemzQFTZs2xbChw9C8RQu069gZzz3XTzFQffdrIgwmK16d8rHi8YL8fFy6dAlLFjOvZPdu3fBkj96Y+oMBV/KK8Me+KPR+cRiaNmuOth07Y+aK1fhyw0G0bNUaX3yxABER0Thz5gJmzvBA2/Yd0aJVa3y5cDG2bduOY8eO49z5i/joo7lo2rQpmjVvgS7duqNZ8xbKfVu0BIfSHNh8PJ+HstjyWHqj1UXoz1kAih4k8TYTNsKFVl4t+v2vCejQ5Sm8NnIMcnJzkGUvQcnN2yivrsPt6nrWEub2XZRX1eNWRQ1ult/lnr9a3KpgVlpZi1uVtbhZUYvr5bW4XlaLqzIrKa0Vc/2Kbt5F4c27KLhRzZZ8u1ENm1DkUVKBbPstnM8qwoXMfFgKbyEtJx9nL+ewZfBMKZg0zUNxrN79ygefhe5UCAVdPCu+0CXkipOQ4CSL2JxZn8hFWUKe6GEVVuSRVqRhKx8ECo3K5SvyGM0ITGArfhgSsnk/Tma6eBb+1cbnSIvXyzsANFSVzAU6awnl4sL3sDGkMS/d/9QavUjz+0lKMTpm1jfQuM2GRtMeGs0waDS/wc3tC7w6YgSatWyFIROmw3PfORiMwuTG5vyeDexPoMmKucG70Kp1a7RowSZQ7u7u6NHjKRiCg6BPtGLsnIXQaDT4ffcerEm1IjTuMn4OT8Zf0QnYERGLybM/wZj3Z+L341bsOW1GptWC6yUygVcsCDoLNyuK+d8S2f+V4s/Gb9sUYpEJQS78SizIL8iBPT8L+QW5KCjMQ2FRLq6WmFFUbEZJiRVXr9px9aoDV0scTGiWsO1azBkIDtbh9OmjyHfkoKTYhqJCM3JzruDC+eMI8PfC888/h6ef7oUuXTqjXbsn0Lw5ax6+b+9fOH/+JJJMcTAmxiAxIQYhIcEY8foIdO3+FMKPXWQVxrLj7J/AwsvCdxwgK9zQGa34Yu1B9B8+Bs2aNUPr1q3Rvl07dOjQHk888QSmTZ2M4cOHw81tJDSavdBokqHRXISb22xMnfsZ/j5lwaqUBoT+Q0Roo+dpY+fvw/5HApD4f0w7jUaDTeHJ2HmKiTHXM/aHDK6PaEIujC5RyBuyYuNhVoEVzN9b3V0+hAvDEKE7uoldIIOMZgQm5rH8pcQ8tuRcYi4P5fKFs5OtigaacsEXluJAaIqD/5XeSwz/yvc70YwnOnWFRqOB/45ExF4ww5ZvR5bZhhPpVmw/mIAuvfqibacuaOreDBOne2BfVCyu5JiRZ3Pg8OFU+Hh7Y+/evTh96jQiIyIx75N5+HLBl0hIMGLhwu/5RaLZ/2XvPcOkKtPt76abjAEVFQElKqiAAmYxjII5oWAcRx0FUTGAo6KSFVBRQTGBqBiOARo6h+qunNPOsWI36DijY5jRCeecD+/v/bB3Vzfq+b/n/c9Xn+t6rqreXVflevZ61r3WukmmUqiKwnXXXceEiRPZ1KKyotHiN797pAImBg4ZyqJnt+ET83izedZu28WRo45j5sXXUF1Tw/Dhh1HTvz9X3HQXS95qdXLimi3GTj2deXcsRjRKiHoR1Sxi2wUeeWw5m97+GK+Qo1wqks8XXZ1QkSlTTqw87rHHHsttt93GjTfexLRp0xkyZAhHHX00N97+e/7DK/Bup8T7nhQfRnJsbHV0Ymv7hJlWonR+aTPQp83S2j4mox5g+eiHUY6dPJ0pJ09jZ20zb7aJfOYXaY/JhJIy+e4/8cVX37Pvy2/58uu/HZj/9+0PfPXdjxXt35ff9AY+9wQ892j9Khq/X5hd7m0KX3yPve9bjOKfSUk2gZiAL5ymM67SHMpyzY2/PQD8DR4ylC2evqYIw9XfqfQE6B4A3FyN3dpmgzVNeu8xN0C9Z4PkTNMFeKrD7DXpbnlXZUWdXIl8WVEpA8t9AqKdwOgVdQ4IdGZf3eYvs7iVmKe+IPEX1oO+GWerm3v6jLvXmy3WNjlxRyvcyKOfx278Ajvyv2H8Wn6yPv3CSfWUOfPcz2cGVVX/D1VVf2fgwNFUVVWx6pUdlTiTnu/fTzWGP9MxNvfo0EwefGEHh484krVrVlAuW5imjM/XxmmnzWLStFncsXILEyb1bvTGTJhMv379GDBwIMOGDePggw+q/O/W22/HK9m0ZG32Jh0wt6/bZl+Xxb5ui+5uBwT2gj/nWFeXQVfZpFw2KJcMiiWdQlEjX1TJF1WKJY1SSadU0imWVApFlVJJx8yJ2EUFu6BQ7jIrTOGBlz3g0WEZy2WDctlwH9t2y8um+xwM57IyTfc5W32u2y6gzLGvO+eWsnN0d+V4batjtnv1vf/gRfc3tOTtDq5e/BTvfLaHj6MGL+yJ8NirH/Lih/V8EhLY4VdY/sYujj/5FA4bcRQvb3+PTz7eyWuvbWH9+rU89tgyFi78PWeddQbV1UOpqjqHqqqTqarqzVk95dRTeePTRt4KWGzpdKO5er6jTb0s7jMtPecl53jPue1/tdH5Px3/6fVf+G2tqU39CgB/Hf/WOKSqz4nqjCtu/v9RVvnfgcAeS/2KJotN7SZbPm1l0wd7Wbv5Da64+momTZrIWbPP586F97L1s1ZeeX8X2z5tYENditUtPeygAwAd3UiO1a051rTmWOuGia5zrz/blq808O5Ji1/ffmCbovXt+Uq6vBPx0jfM1eoTaOqcsFa7J6mzrv0dR4+fTHX//hx0+JFctWg5l961jGkXXkVVVRUTZpzLoKEHHXDir6qqYvWGFwmLJp1pk/a0TUzOoZg58vk8xUKeYrFAqZjj3kX38MADi+nucnbWhbzBhAnjuW/po2xpV9jQZrO8Tuf25z7mvAX3MGz4ERw28lhGTjyR6v4DnL+PGMFJJ53E3LlzOe200xg4YCCX3XCbAw6aLc6edxdDDzmMqqoq1j3/MpJVolAsUiiVKJXL5Esl9nU5s1wuUirlCQe9dHhaMQ0ZI2ezv1ygkC9i23k01WD79h1MmDDxgNd8zbz5vPphPR8FNHbH87wVsFnZZLK6T6r9mp+c2Ne0WBVwPufOZQeYQlY1Gfx27XYG9OleMmjwEBbc8xCbPgvwYUAlXyjT9cdv2f+n7/jyL47m76tvf+TP3/xYyQTsAX9ffvMjf/z6Rz7/6sdKoHO3G+tS/tKJdim6jt8e9q/wxffk9n+HUf4a0fqCpNpNOGMRTGh80JagIaywuzPFWedfTE3/Ady2+i1e3v4ec+bOpaZ/f96pbWdljzauJ2y9qU/v2RaLNU26C+a0SkzLGrfsu7bF6fu8yWPxTJMTs/R0rcBKF+T19Kp2zB0yT+7Oct3S5zju5NOYt+x5ntyVqTh/DzCU9PRere8FgSvr9QNnpR1Xn7ZcPfpNt0/pgdlzDqB7qcNkq1fn3vVv8siK1SxavoZ7nljD5b97gKtuWMB1d97Pujc+4G2vzCve3pPo/66ka/c6In9i2vofT7p9js/9/WPud6k/VVUT6D9gDFOmncmoMcdywokn8ujqZ9nuVVyJiM2KJouLfreslwHtE4HUc58Lt9Qydsp0Dh0+nKee/APdXaYD0ros1qx+iqqqKoYNG+YwwjfeweTJJ1BVVcW06VOJxIOYBZO0aZIrGLS31TN37kXcf99CiiWD5ozBmwGLQsmuMHpdXUaFAewqG5RLunOs23ncri6DUlmnVNYpFFXyJQW7KJEvKhRKKoWShlWQyBUVurodtrDLZQhLJYtyyaKr3Du7+5SW97lAtNsFoV1lk1JZp1w2HGDpPm5f8Fd2Lx9//BEHgJZ1imWdQkF1H8OkVNQolVTyBQU7J+PpaGTcuLGMHXscjz72KJvf3smRI0cxdfp0DjpoGAMHOia7kSOPZtiwoVRXVzNs2DAOOeQQBg8eTHV1NeGQp/JedXWZFWCcy8nMnHkWgwaNo7r6HgYNGseMGWeSyUS4687bGTBgABMmjOeu39/Bi9ve4aX3a3l++/s8t/VN1r/4EquefYaVa1ezactmtr3/PvXBOB/HTDa2Oc0P+n7n1vzSd7K5l63s+U47hhmz99gBzLvzXfvDRzGOP+M3fdfcXwHgr+P/ahxSVVXF4IMOYdCwg7l88cr//QL8/3GbVU0WzzbrPP78q9x0591ccNFFDD9sOIMGDeLQQw9hzOhRLFr0e7Zvf511a1Yw+9yzqaqq4sgRvY3Mf3PxRVw37zouvPACrr9+Hg88spRVm7aw4a2dbP5gN6/u8fKGR2HTZx28/GkrOwLuCbLVckBjW29sTY+QvdJLsed5NlusazHZ0GayuU1i3bv1rP2wnTV7M1x8zXzue+hh1u+Js7rZZSo/bOOGJ15i1MSTmDb7Eub+7kFuWfEaj3+S4r7XGply9tyfgcCfzi1v7yRfyju76LLt6G8qZZkc+7py7O/OsePtNyoL29xL5vLwk6vY8mkbz7aYLP8sy2/X7uDqB9eybHszV96/iqkzz8TK58kX8hQKNl6vhxNPPJEpp57O458kWPZBqPIcJk4/ndseXsn9z77B5o+aaIrIxBWbtFYgIuVIqTnico6AlCOq2CQkha17/OwIGIQlG8PKY9t5LCtHOi3S1uFHUg0CgTBz5l7CsGHDqK6u5rixY7no8mvY8mEdr3l/8t7/hDla02yxutHg/jeaOaDrjHuCf3qvzPips372fl5wyZV0JhU+//p7vv3hX/ztH//Ftz/01QC67d56wN9ffuSLr39g/1c/VABg158cZ2/pSxf8uQCw8MX35Pd/R37/d+T2fYvV9Rfsfd+S2/cNRvELUnKOqJijM22x+oX/OQT74t8uOYBRXlmvsqrJrLSx6um/XQEWPeXyns4crsbPyQXUKuXbp902cD3O3lX1fUxPA8dR1W8+/Qe6weefZfqEQvcJh96jVBjJXibw53OlWx5e3eyEv/dsrta1WLzmN3i1Q+KJbXu5Z9VLLFu/mTXPP8+UadM48sgRnH/Bucw880zOOfcsrp13NffeexeXXD6Xo47q7Rhy21Mv84LH6RH8Uwbx510e/j/AXmX2xNX0bjZ6JAr3vdbIOTfczfCjxzjPoU8f46qqKo4eP5lVTRafxC08osna95t5wWPxvMfpQftsq9Pv27lPkxmXzOfwY47Dl87QVdYpl3W6ywblsommZrjiiks5cdp0xk47gyOOGklHRyPLly9j585tdHXpdJV19nUZlPsAp2JRpVhSEU2ZDkGnWHJYtHLZoFTWKJU1CkWNcpfOvn05ymWTctmiXM6xb1+RffsKdHfnKJVMCkWdXF4lX9QolZzb9TB5XeUcJdf5WyqZlEoGpbIzHVbPuY9i0agAwnLZpFDQyBcU8gUFIydg5kWsvISZF7ELEnbemUZOQM9lUcw0jS215IuKMwsKZk7AyAnk8jK5vIRuZ7HyEqWyRrlskM/rbNi4gblz5zB8+KH89rabKJU07JxIZ2cjmpbBzsnkCwrxuI+mplpKZd0pZxdU9u3LuUDVrMx93Q5Dmc9LvPrqJhYvvoctr7xAPifT3e2ARF3L8NZbr3LTTTcwatRIjjjicI47dgxTJh/PzJmnMHv22Vxw/mymT5/K6NGjGDJkCKtWPsG2gMPmr2lyNjXPtDjh0o68o6eLlvO/LZ0WW30WK/eIPPlxjA9CBu8Ebd70OxrRzZ02L3VY7nSIkId2dPx0ffkVAP46/q+GowH8NFOJGTiQ4fuFBfZ/AICrmnrt/quaLNbXZZgwaRLHHDOS3/72JlaseJyG+k8pFlX27+vZQfaIjZ0drKIk6e4ysW2Rt956hdWrnmTZ0iWsW7eShx++n2uuuYLp06YyduxxHHHEERWzwoABAxg4cCDDhg1l+inTufTKK7lz4SKeWvsMW977iLd8GhvaemIVTDa838CSx5ezcNnjLFm1nutvv5NxEyfRr18/hg4dyqBBgxg9ZhSTJx/Peeedw0EHHcTNv1/Iqac7YuUHV67nptt+y6kzTmX2ebNZeP8DbHrnEz4IyOwIWdyw9Nn/IwDcuvVluss23WWb/V02n3fn2Ndlky9ZpA2TlqyFYNnkik7cQnNz/QGmkpuf3MzClz7m7CsWUF1Tw8CBA5kw6Xhq+vdnxvmX8IfXa/FLOaxCnmuuvcYBSXMu4antDTzxcZwFDzzJjEtuYNJp53PUuBOo6e/olGpqahg8eDCHDh/OmOPGMv3M8znxrIsZP3UWBx82gn79+jF2ynROveBy1r69m/aMTb6Yo1CwKRbyFApFSqUytl3Asiy8Xh9vbX+H+TfdxqDBg7l/xfM81+oaGX6yq+39XjnHnB7KP5cjDBw8hFuXrmFHvZ/FK17gyDHjqKqqYsPm1/j8z3/hq+8dI0iPC/gv3/+Dr777kb9839sT+M8/cfzudwFgyY11KX7xV8p//BtG+WsE8wsSShfhTJ6Mvh+j/BeM8lfIuT9id3+NVvgjev4Lcl1/YvZ5TmTO0GEHMsE1AwbyxKdpVjXqfVq3OaXdnv7aq/qEMB8Qst3cq6dc0+JsQhyzhwMCn9ojOxEvu0WWu5mB1y7d+HNx+4BxzFv2/M+A39MH9MB2NYF1faYbFr222WE1NnqcnqybO02ebbN535/hwadXMe3UUxg0eBCDhwzhpJNP4uSTT2Ty5ON58OFFSEoYM5dGNKJoVhLZiJGUA8QkH22hhl7ANfJoTp52Mvc98jBb6zr5MKqzI2TwdsjkxQ43p6+hT0zML4C+VY0WKxrM3lJcn41ebdIkrOrsCJmVjeCqJoun6w0e+zjOsve83P9mM3e/XMudL3zMHz5J83Sjc6L+3dq3GD1xCuf/5jfccutNPLT0IdY/t4FPG+ppSSvsCJk8uNVxez/40vs0p52yasEttxZLKi9seobJJ5/MkeMmc+WNt5HWVYKyQkTVyBoOICqWVPJFmWKpp0yrUXaZtbLL5BVLGrmCjJkXyRVlCiWdYsnEsiUMW0TRssiqgKap5HIWpqmi6RKmZWBZGqYlY9sqxYJBIa9VGLFi0SkXl0o6dl5CtzNodgbTFjFsAdsWMW0BKyeSz8vkcjJ2XsLMCeh2tgLkNCuNZCbJ6nEEPY5qpVGtNIbtAEQjJ6BaaSQjQVaLkVYjpNQISSWMZCZRrRRWXqBY0sjnDUxLx87Z2LZKzgV65bJGV7eJnZfIKFFELY6ZEzBzIrmcQqmoUyxqFIsahYJOPie7f6sUCirFouoyowq5gkyuIKPbWef15kQsW0I102hWBt3KuGBWroDdQtFhKXs+o2JJ4777FzJ+wjhe3rqZpzc8xy33LuGuRYu48+7fc9+S+1m5bh2b33yLDZtf4clnNvLi1leJqxpvNYY4dMRIqqocs92IEUdwwuTjmXXaTKZPn8bkKZOZMXMGC26czx+WL2fLtneo9UbZ6f1VA/jr+PfGIVVVVax4eRubPE4p5QDq+RfLvr+8+35way2LH36Eex94gJtvvYURI47gnHPOxDBESkWdrrJFoaBSKmqUizrdZZPussm+LpP9+3J8vj/vOtRs9u/LuZc2n+/Ps39fju4ui0JBp1AwkSQBQUgRT4RpbWsmkYiQTEbYtesjNm5cxwMP3Mv111/LmWeexuGHH8bw4Ydy1dVXcNfCe5h0wgkMHjyYM846jYvmXMD0GadyxTVXseXV5whH2rByAs1te3jltRcR5CialeHd99/iwgtn8+CSe7nwwvOZOHE8l19+KVs2b2Lt2pVcc82VHHGEY/QYPeoYZs8+hxmzZjJw4EAGDR7MgAEDGDd5KlcseoKopOCJxdnd2sH2T2t5Ydv7PPH8Vq6/52FOm30hY8ZP5PAjj+LY8ZOYddGVXHb7A9y45KnKSXLKmRexfGfvDvD4qac64K+mhqqqKsZOnsaAQYO5afGjqLk8zz6zmhkzTmXq1JM5evSxzDx7NtOnnczWt7axvsVkTbPNpYueYvzkk1j66B+44447uOyyy5gnh4eSAAAgAElEQVQwYQLV1dWccsHlzFv2PHes38maTyNcs/R5ppw9x2G1rr+NzfUx9iRtErpNueSUtgvuzOXyyIrOhk2bmXzydPpVV/PQtlYX3DkdY1a7peG+Wq+e5u99w4R7jCQX3PJAJfPw4OGHcdwkJyD7o48/4pu//Z3v//5f/Ombv/HFV9/zzV//WQmF/uZv/3QMIt/8nT9/+w/+5M79X/9I959/dJg/l+3LdX+LXvqarPE5Vtc3pNR9RIQi3qiCL6Hhjcp0hCU6Qhla/Gn2diZ56d1abrvvMU487Vz6DxzEEaPHM3vBQu556VNXk9pjnHB1qc2WyxI4Or8eB/SaZtN1A1sVJrACiJsNVjeolfdtRZ1ayf/r6f6xfJfAWdfd+YvB5+fccDerG3TXBay7Otreto09Jd7VDTrrmh3n+XOePM958qxvy7Gu2eSVTpXaWIKP2juZd/cSDjviCCZMGMfKlU/Q0PAJtp3Gzqcx7DiaHUXPxbELKXKFFIIeIqn4ics+YrKPhBYkoQZo8O5m/i3zuPWOm3hi1aOccdZp9OvXjyFDBjNmzChOOWUqV155CaueXcc7rSFe8FgHlJ17wN/KJpONbQav+S22B01e9ZkHGIreDujsDGkVfeoL7SarPwuz/PnN3LXkQR5628Mmj8l7YZOdEZMtTXFWPP8iO3ZuZ/YVjqln5umns3DRXVx73VWcdtpMBg8ezPDhh3LD9VfzyNIHen+nZ1zIGw1+ci5YKJU17n9gIafMOIWa/v0ZPfkUFj+1nphmINkGrVmVHUGdYknCygnkCiL5gkShqDiMYFl3waBGuUsjX5QxcgKFkkp3l0U+r2LmBVQzg6pnEZUEopRClLLIShZZSSPLApIsoCgZdENA17NYloJlSeQLqsPWFSQM22HqsnoM0UiQcUGaqMfJaFFEI4FoJJCNlAuanGnmRXQ7i6gnyKgxJD2JameQzCSa5dynYqXI6nGyeoykEiEqBYhIAXyZDmJSEMVKYRcc9tCwBAxLQtU0VE0ll1cpFBQXcOkYVhbVTCPqzvPRrAyalSFX6AF7DpgtFnTKJYdkyLssY9EF51ZOJOcylEZOIF+Q0ew0aS1CSg0jGnFUy3mdiplCc8GsZmcR9BiymcDMCeQKMo0tu7n+hmuZdPxEpp8yjcuuupwbb76Bm29dwDXXXcU5557FCZOPZ9rUkzn77DMZPXoUEydO4LFHH6Jfv37Mveoa2nwe9tZ9zNtvv87LL2/i9ddf5e23t/Hyy5tYtGghc+ZczLhx46ipqWHWrJm/AsBfx781KhrAgQMHcsVVV/FRYzufxCw2tvcGkf48rPTAuWRbW2XhG3/CiSy49Vb+48O3MYw0liViWRKGLmCZorODy8kU8yqFvELeVjANCVXNkhXiyHIKRXGmJCXJCnHS6ah7GSEjRMmKMZLZEIIcQ1BiZKQIGdmZsp5AMZIoetL5gepJPv70PR566D4WLJjHypVPEE524st6iEoBFDOJZqWQjSSSkUQxU0hGsrLIiUaishBmtRiykcTKiS6Y1Z3dc8mkkFfZvv01Pv7oXZ5Zt5KFC+9kzpzfcPZZZ1BTU0O/6poDgp/7VVcz9OBDOPzIozly9NjK8YMOPpjRY8Y4rsExo5k+63QmnDCZI48Zzc1LV/OG3+a9sM0On8r8B55i2EEH069fPw46+GAOOexwLr/jIU6cOp2Hlz3C/n159u8r0N1V4L33dlBTU8Ndd93JiqeXU1VVxb1batnSkmX88VNYsuRBSm4HgEKhiMfr5ahRxzJ7/j2VE2yPRuuuZ7ZRVVVVYWCPOnY8V952Dxs+7kTNOdrGXK5ALldkz566SmTN0UeP5KlnXuClnXXs7Mjyhs/qBUStvS2O7n7xkwNYsJ5MutVuW76VjSY3LH6MkcccQ03//lx9/Q3kixZffv0XvvnbvyrdQP769//kux/+xdffOQHQTvSL0+Jt359/oOvLv1L84jvy+77F7v4Gu/sbct3fYe/7Dr30NXLuT0SFAnGpREdM49ltu7nu/qe5fvETXHzTIk79zdWMm3oahx55DNX9+zNu+pnMuesxFm+tZ5Xr7H2mx1zU1+TialvXNBm9GsDWPhErfdi/inu9xTWK9IRnN+g8XScfCP7c1nDXLd3Yp/Vhgp54i5uWb+YZV//qyCKsikFnTbNTnnq2LcdzHXk2ePIVycTqJpP1LRobWzUee7eNS264icFDhjBz1gxee30zupZFlKJoRgxBD5PW/KQ0L0m1k7DUTkTyEJU8xJVOZ8qdJFU/CcVHVPYSk32ExU5CYidhoZNItpOmYD0f1e7k9bdfYeOL67j/wYVMP2UqNTU1TD9lGosfWsL23Xt5J2Swyt04zLr8Zqqqqhhx1NGccdZpXHbttWz8qNExhLWYvOTReKW2jcfXPcu8+fMYN35sJbKkqqqKY0Ydw8RJExhz7GhGjjya6upqJk+exPDhhzJu/FiGDBnM1q2bKJVV8kWJQknBMNN8+tl7LFx4J2PHHsfgwYM58cTJVFdXc/Y5Z1IqO+xQoajS0lrLCSf0mj8umXcjn3c77GBXWaYuqSIaIkZOpFhUKJYOnIWSQqnklITtgkRMDpLRoihGFj0nkC8p5Asapi2hGRlUPYOipTFNCd0QkLU0qp7FMAVsW8K0RCQ1iWZkUPQUkpYkqYRJqhEEPY5kJhH0BLKVIqPHXOAWRzbTyGYKPecAP8VMo9sOC2gXJMyc6JhJCjKalXEBmsMIJtUI2z54nagUICz66cy040m3Ech6iStBRCOBkXPux7IVJDWDoikoqoBhihhmFt3KIulJBC2OpCdJKRHSapSsGiOtRNDNLMWiTi6no6oCuZyCoqZcsCe7LKtCoahUGEwzJ6BYKYy8gGFnUW2HoZSMBKqVcs4LZgLBcFhNyUiS1iLIZgrNzmDks1h5kVxBRrXS6DkB2UwhurdV7AyalcXICVi25DCMapZ161Zx/PGTuOCC83n1lZccI8y+PN3dBUrFPLmcTj5vVnIcbdvCNG28Xi+rVq34FQD+Ov6tcUhVVRWe9kbu+N1tnHPOWQwcOICzzjyNDc9tYH2DyLagxfPtFhvabXeX3bOj7gWAT+2RuOj2h5ly9hwGDBrCcdPO4pXPmsloIhldIiVF0fQEghIlJYYRlBhZKUJajJDMhIglAsSSAaIJH5GUj3g6QDDVQTDRQSjVSVqKICpxIikfoVQnHbFW6oN72R3YRV1oDw2ROjpT7QQynQQynUTFAAk5hGgkyWhRUkqYmBQgo0UxLQFZjxOTA27ZIURCCRGXQ0SlAHExiKDHyagRUkqEjBYlo0ZJKhGSinMsqYTJaFHsvIRmpLGsLKaVZc3apzBtgUJBoVzSKZd0du7c1mtcGDSIMWNGc+SIIxg2bKgbFdOfsWOdRP8RI0ehWI4bT5GTPPDAIk6ccgKzZs1g+PBDGTZsKFOnnsjIo4+qxEsMHDiASLiT7dte69VOXnQhspxmX1ePYDtHV8kmk0nS3VXA63UYxCVLH2X8pBO44PzzME0N2bQol3J0lfN88h8fMnDgQG5/ctNPWnxZjD15FieeOospp55GVVUVVz/yHFPOuYRBww7m7SY/X3TnKZfyFItFcrkCmmawc+cHLF58PzNmzOSwww6jX79+zF9wE/XBFK97nT6ez7jA5Kolaxww2GxWOo701XTNf+pVhgwdysubX0YWE+QLzmL/5Vd/4uu//pMf/vFfzvyn0x3kL3/9B3/67u98/tUP7P/zD3T/yYl6sbv/glr4EqP8FUb5K5TCl6j5PyPZfySpdJNUukir3fzH3g7GnTSTAYOGMHHGuZxwxoXMmHsDF966hGsfWc/v1u9k+Wdpt1TrBCmvdw1IDvCzXAd0T0nbrOQc9uhUezSqFaDY0guIezvs9JaLe7qGPLnb6fLhgD+RJ3cLPP5pmtEnnOGAwH7n0n/AOI6dcpbjku+TP9gXZK5rsdngcWKQ1jT3cbm65dYVjRZz71jKgIEDufiSOeyu/YRSUcW0JDJCmKwSJKl4SWl+orKHqNRGINuEX2imM9OIX2gmLLURlT0k1E7CYjsR2UNE7iCh+IhJnQQFD55kK7VRH53JFvyZJiKih4jYSVTyERa9NHlrWfnMk1w05wKGDBnM9ddfwXuBLCubrEpO53U33cjEPkBr6UcJLrj8Ko455mj6969h+ilTueP3t7H5tefxx9p4aNn9jB4zigsuOo/nXn6Wt3e+zo4P3sAbasLKZ8kIQdZvWMXjTzxCoShh5wVKJYVCUSZflCiVVey8iqZnqav7lEceeYBrrr0Cf7iVfEGiqyxTKkkEol4mnnI6E2adz3HHT2Hy5OMpV3R8isNg5WTsgoyZE5CNBLKRwMxlyRUkzFwWzU6j2WkyWoSo5CcqB5DMOFZORFITiFoCQY077JwlIKlJFwSKGFYWxUgh60nyRRXDymJaIrKeIqvGyaoxBDNJ1kiQ1CKIRgLVziC4l1k9TlqLuWXdDKqZRtDipLUooulsoCUziWZnkc0kgpFAsdKodoa0FiOtRUkoYR5f8ShJJUxQ8BISvCSVCHE5TETyIZkO6NKsDJohIEoZZEVG0wVMM4thZZGNJFHBR1IO9W7a9aRbdnaen25l0TQRWRbI5y1KZd1hHcUQkp6gVHYcyF3dpgtaBVQzhajFHTLBZTyzWoyMFiUi+YjLQWQzhZUXMXLOa1StNFbOYT4lPYluZRwyQU+QUsNk9SiiHkc2ksh6EsPOUioZFIsmliWTy6nkbIV8TscydfI5i1zOdPSWBYNiQaNYMMjldHK203rPNHVURSaRiP4KAH8d/9Y4pKqqCl8sQc7WKORNMukYTz+1nCOPdMwYI44cwYQJ45k+fRpXXHUlL+7cxcsdB2poVjQ6kS5bvBYvfFjPca4r9PiZ53LXqi00pBR8koqgZzFsZzGz8iKamSGrRAmmO/GnPfjTHYQyXqKCn7DgJeWCsEjGR2u0ifZ4M23xZtqSLbQmmmhONNGZbseTbqU50URTvJHWRDP+TAcJOYygxkgpYcKCj7gaIqVG0My08yNX3R+mu+AIunPbuBwkLPoIZ71ERT9R0U9MCpBWe4BgmKQcIiGFyChRUnKElBwmLoVIq1FEPYFsJBH1OJq7OGSFCE0ttbzz7hvsfH87u3d/REvLHhJxP6qSYsmSxSxefDcdoSBGrlf3UypplEs6pZJBoaDS1FTL669vZveuj/B6W/F2tpBORRxRdpfF9OnTaGqpd1k/uxKrsK8754q78xSLNvu6crz7zjaOP34SDz20hPcbOnmvNUJKz9Ga0nll56dcfP1tDB52CJffu+JnkQQnuA606v4DOHf+QtY1m9y3+kWqqqp4++036CrnKZfzlEp5usp5SqUCimnjSRvUxVQk3aShuY0zzzqLESNGcMaZZ3HuhRezdmdrBXj0BA/3Ap9eADh68nSOGTuRWFZCsXJY+TzdXUU+/2I/f/vnf/PDP/7baQ/3j/90gqF//Bd/+eFffPX9P/niL3+n68u/YZa/QrK/QC3+2enXq3bREVWpC0j4oyKdYZEPm0IsfHQNQw4+lFmX3ciTu7K9AO4A/VkvW7m22XIAnQv8XvRYvOnVeMHjgCrHOGDxgsdmQ5tjeFj1QTsbP6hn5QcepyVbk1VhBvsCwJ4YlVVNJqsaDJ7a6+j/Vrjt3Z7cLfC02/btic8yzFv2POdcfzfXP/oCT++VDogxWdNis6oSzL6IBU+87BpTDnQfrm2xeLo2y8zz5zLymJHs2fMxxYKCbQloepyE2ElC9hKR2gkILQSEZoJCE95MA95MPX6hAV+mHm+mHm+2kYDQTEBowS+0EJLaCEltBMQ2WhP1tCcbaUs28szW55hz3TXc+vAfWPbS67zZ2MyeWIjmZAi/ECQo+gmKIV59722qa2pY8srHbPiklZVvfcDAwUO44vaFzJg1naFDhzB95qmcMutUTp52Im/s2Ewg0UZA8BDIeggJHYTFTsKSj6jsIyJ5SSpBIpKXsOglpYaIKwFicoCI5CWhBInKfhJKENlKYBVEVD2DoMaJC0FScpisGiWjRsjoURQrjV8SaEoJrN+6lYMOPYyDDh3O6WefxfHHT2RX7Ycus6dSKKnodgZBj1XWRzOXRbczpLUIohFHMuIIegzBiBGV/CSUEGHRR0j0EhZ9pLUYqplGNpIIehzTFlGMFKYtoBgpDKsXQOlmBt1y2DSHAXTWrB6wIrpMl2wk0aw0Zl5AtzKuQcMp0Vo5Bwip7nHdcjSDgssURiQ/IdFHwt1kx+QgEclHQOggLgcICl6yegzJTBKRfMTkIAklSFINoZhpVD2DpGTQDQXLlhztXkklq0aJy0F3k59AMpIYOQFJT5JVnPfAzsuYtoyiZlE0Bd1QMG2HkUzKIZJSmIyUQJQzaLqIaUuuDjBDRouSViOORlGPOeVgl+3LajGnFGxnMGwBzU6T1WIIWgzdymLmRDTLYUYNtySu2hnyJWdjny8oFEoauZxCsaiRz+mUigbFguqUrQtOyTqfV8jlJMoljX37cuRslXxOcf+nUSyYSOKvMTC/jn9vHFJVVcX02XPwBTsQVI2EpGJZGkuXPkRVVRWrVj3Jzve28fzLL3LzrbcwePBgppx2Lkvf9TpM4Cchlq5cy2WXX8oxx4ykurqaIUMOTIC/5ZYFdJcVCgWZfF5CMVPEpAAx0U8o20lI8BKVAghGgoQccn5kRhJVTxFKdeJNe/BmPJUFI6VESGsxEmqYYNZLUOgkIHhpT7biSbXRmfYg6AmyUgxRjZOQQ6TUKDExSEwKEkx1OGUOI4FopVBzWeRcmqwRJ61HSaghIpKfqOQnIviJi0EScpCUEiEs+oiIfkKCl7gYJCL4iEpBAplOQoKPqOh3b+NzdqNaHMVMIZtJzLyAnROx8xL5gqNFKRQUV9eiVxa4HmFxj7i4WHQWB4dV7BFqO7Mng8sRcjvutW43UqES3dCVwyzYGPkcXsFkb0JHMk2KRYuOcJRDDz+Sfv36cegRR1Fd059BQw9ixtzr+d3691jVYPzM3f1Uncayj2I8tlvm6UaLuXc/wUGHHMo7O96gu6tAV1eefd0FPv+8yOf7y7S3tnLM6DH0HzCQEcdO5M7179IU17ANja2vvcH5cy6nX3U1D27vrOj9+ppC+nYOWd1scf2jmxh80CG0RTL4owKxrIxu2nz13Y9896Oj+XP6/v7IX//x33z1/d95a8dO7lvyCFvffBe9+CcEcz9m+WvM0te0RFT2BmSe2VHPI+s2c/O9y5h69sX0HziIEWMmMO/RF1jtavN6y9ImKxt0VtQpbni1eUB+4d0vfMSosRMY5EZRzL1kDvc8u40HHn2Mk6ZOY/bsc1lw8y2MHz+OwYMGceihjjzgmTc+YFOHzaoK2+eWifswiJUuKvVOS7enakWe2i06GX9u1EvfTjgr67RKlIsDrh3wd6zbCq1f9T30HzieMVPOYkW92gsSm20efNvDMeMmMev0mQTD7RTyEooeQ9YjRCUPQaGVqNxCUGzCm62nM1NPfXwXH7Rs45HnN7Hg/ge5Z8Vatre348k0E5FbiMgOUAyIzYSkVvxCMyGxlZDYhjfbzAVzZlfWjupqR9s6bPgIxp1yNmdcfTuzLp3PcSdV9E8McKNABg4ayJFHj2Du5RfxUf27zLvpGq689jLuf+ge6to+IaH48QvtdGRaaUk00JxooDPTSmO8nrroHppidbQmGmlPN9OeaiapBvBm22lJNOLNthMUvcS1EJKVQtQSpKQQYaGTqOxHMhIklBBRyUdSCSIZcTJqGNVKsHtvb7/Zw484jCuvuYzm9lrMXBYrL2DlBBQriWwmSaghBD2GaMQQ9BhZPUpKDZNSQ6TUMGHJR1DsJCx6CYqdhEQvIdGHT+ggpURIyU75VtDjyFaSrBZFMZJYtoCsJ5E1h4mStASCHkfUHJZL1OMImrNeqWYKw8645c4YaTWMbCRRzCSSHkc2EmhW2jGJWBnH5GGmUKxUpXSaViPE5CBxOeiso5Iff9ZDS7yRlkQTYclHWPKR1qJk9Rh+oZOOdDtJxXmdaTWCbCRJCRFEOYOiyei6jGULmLbgAr44hi2gmmkMO+u8LjWBYqZQjBSKlkHTFRRVQpREUpkUgpQilgqhmA5oNWwBuyChqElULUuxpGHnJVQrTUz0E8w6n29GixKT/A4w1BKIahxBjVXWdslIoBgpRCOOZmUxTQnTFMkXVCxbxrYVCoUeXaJNoWBg2wqW6ZTie9Zz25awTNE9V8rs32dTLBjYlkh3l0GpqNDlngdU9VcTyK/j3xsVDeBhR49h1KSpTDj1HG5ZuoaErBNOxsnlNDq9HqadcQ7HTDqZIQcP59CjRnH3S7t4cleWAYMcsHfR1fN5973tpDNhRCnB/UsW88CSe5l/0w1seu1VFEtEccW0STmEN9nuLg4BonKQUMZLKO2lI9HmsHyJZpf1a6E52khztJ7aUC0tsUbqw3V44q0EM53EMgEyapS4GCKS9RPMOPcRzfiR1QS6kUE3s8hqCkGOI6lJJC1FVomTFWNkhBiSnCKTjZMV4yhqBkFOkJFiZJUECSmEP9NJIN1JR7IVf6aDqBQgJgZcIOjDm3b0hGHJjz/twZfxEBK8JOQgWS2GbmUw7AxWzlns7Zz4s5nLi+Tzjui7UFAcDVBRdYBgUaVUVF0g6BhpnLwsp8zcc713uuCwZFF24x66yhbdZYv9XRaKbSHZOZo6PBx29GgGuO3epp96Kn9YsY4PvAIvdzgmBQd8WZU8xOuWrOTmO+7mjrvu5Pqbb2XOFU4O4vXz59PdVaC7K0+53NNiKk+pWOSlFzdRU1PD3QsXM3/hIwwaMpSTz53L/eteZe41Cxg4ZChXLVl3AEPlODrNA1yxPWDovtcbOXzUWMZMOZX3m7wYpoqo6xS7P+ebH/7Jtz/8i+///p989+N/8ufvfmTW6eczePAEqmvuYdDgCZw6czaS/QVRscg9S1cyasIU+g8cRE3/ARwz8SSmnD2HC297kPtfb6i0V1vRN1DZnSvr1UqXmbV9yrurGvQDNkDTTj+Hu+6+ixEjRnDBBefz4gsbWbVqFbfddhtvvbUNw8zT3b2P886bzaRJE1n35oeVzMCe/tSrm0xWNmis2Kuwqt4JfV6xV2LFHpEVtaLLAso/a4G4qsHoE+ysV3pLz3/8l9tg3fD4SxWg/9uVrzJk2EHMu2UBohzFMNKYVgJJDxIWWglJrYTlVgJiE53ZOtrTe2mK7+K2RbdS038Ahx9zHMef8RtGHOtUBJ58+QX8YgNBqRGf0EhntgG/2IRPaKQ5sYfOTCMdmUa82Sbe3f0mp8ycykXX38zyz1LcsnobZ116BedeeS0X3ngnBx/uxMdceOmFbHxrA03xvTz37vMsWnYPzdE9dGSa6cy24Mu24ck005ZqpClRT3OinvZ0E43xOurje2lM1NMYr6Ml0UBj3LnelmqiPdWEJ91CY7yOzkwbKTVKUgiREkOEUh34M+0EBA8hV7som3FHwyh5ico+kmqQrB5BMGJktAg7P97GxpfW4Y20kFSCpLUIgh6rsHoZLUJMDiAYMZd9c445EpUgMTlAUHJAX1BwHicuBwiKjp45KPnQ7AyqmUJwTRsZNUpaDTuVDClAWo2QlEOk1QiCFiOrRBFcXXNPdSMmBUgqIbJ6DMV0AGRSCZHVon2eT4iUEnLLpM7zrJhE1AgxydkE+zPOWujPOu+TN9tOR7qVjnQb/qyHjnQbHZk2onLALbM6zzMq+YnLAVJSCMVMoeoOS6frIrrhaPryRa0SMVMsaVh5EdVMo1opd4OtkC/IlfVf1UVkNUNGSBBLhUkJMWSl535VJNlx/9q2U8pXtQyKmXBYYPc8JWhxoqKfzlQboWwnGT3mMoVJFNNxOeu2cMDfhYJJIW9gWTKFgkYup2EaEpYpkc9r5PMKdk7CsiQX3DoVMqcCpFJwzwPFkoFVMEjpOpJlYuZNoqn4rwDw1/FvjUOqqqq4Y+MHXHbvCuYv38Lli1fQf+BgRk6YwtnX3s6KbbXcsWwVhxxxNDcsf5XbN37I8jqtUipauGUPJ82+jKGHDOe8ebdz0yOruP6Bp7nsjoe49dF1nHHFTZUToajGSIkhfMk24nIIX9pDS7yJxlgD9bF62lKtzky00JlsozXeTFO8EU+6DU+mjYDkIyD6aM+0sjeyl7rQHpoi9YTSXpJZh9kLJD143dkRacEXaycQ7yCS8JESIkRSfkKJTvxRD4FYB+GEl2gyQCodIZGMEI+HiMYCxJNB4okgmWyMZCZERPDTFm8mmHVK1BHRKW/4s84C58t4uO3Om/swhD5CgsNuxqQAguYs9qqVdllAsQIIHVDoxCsYVtYxmbg//EJBqTCFxaJ6wMLg3MZlC11QWCioFAsOIDRyOoWiAxK7ywbdZQPFMrHyJu9/8glHHzeBSbPO45jRo3nh+We58cYbGD3qGA46+GCuuPJynlj/Es95HG3aajcId9DQg5g990pOn30hJ886m379+jFoyFDuXL2VvUmLcslmf3eerq4i3W6gdDGfY+srrzB+/HiGDTuIc845l3PnXs3wo0czcca5PLzDe0DZ8dz59/T2um00Diiz9mTiPbk7wwln/Ibzrr0Vr2CQVA0M2+Sbv/6dH/713/z1H//Fd3//T7a/+wGDB084AOgMGjSeZ1/Yyl2LlzL8iKOYt+x5HnizhVX1qmO2aLGdThp7JQdQVXrt9s7VTYb7vpgVo0TfOXvBwgNAoD8Wo7uoUyrodJVy5PM5uru66OrupvvzP9ISybLosVVUV9cw89IFlZZsPQ7WSlvDJsN1UKs8vUeqzAPaIPYJbD53/j2V0nRvALrF7PkLf9EpfO78RSzfLXDxgt8xdNhQVm58ClELUSyImLkEGc1HINtEQGwiLLcQUZoJiI20pGppTuziuTdXMXDQIJtH6+4AACAASURBVBZu3s3KRpObV29j4tTpVFVV8diah/AKDfjEBjoydbSl99Ke3ktbei8tqb0OKMw0EJQchvDuB37HmbPP5JU9ezlo+OFUV9fQf8BAps+azpSpUxgwcAAfNLxDfayWtlQDtZFd7I3upjVVT3Oyjj3R3TQm6vg4+AmfhT+jLlZLc7Ke1mQDddFadoc/oy5aS21kF7vCn7E7souGeB2tqUYaE3U0xetoiNfRkmwkJvi59pZrqIvswZtppTPTgi/bTmemlfZ0MxHJS1YPUx/b6zCGmTaiko+UGiSjhUhrIZJqkIQSIKWGyOoR0lr4AJCXUkNEJT8pLUxGC5PWQkRlP0HBAU/eTDv+rIeg0EFHupWA0EFQ7KA93YJf7CAqB0jIQYLZTkJCJ8FMB2HRiz/rwZtupy3ehCfR4vw/68WbaCOU6SQm+glkOoiIPkKCl4jgJS4FSCkhUmqImOQnIvqICF5CgqPHjEk+gkIHcdkBa0klSFaLkFZDJJQACcUpmze7wNqTacGTbqEhXseC382nJdmEP9tBRHLW0NZkE2HR54BdJUBQ6CQi+JzSuhgilvaTyoaIpr1k5SiRpJ+MGEeU04hKCkVLk5EjZNUYqpHGzkluZIwTeK1bWacaJAaJZf1kpDiikkLWE2hGBtOUsG0NTRewczK6mXV0fVqClPsZiXqcuBggKvgrDKUj90mQL6pYOYlCUcfOqa4z2Amzzuc1VC2DYQpoRgbLFrEsEdt2DJL5vEKxoJArSBh2hnxBplRy1vJSWaPcZZLPKehGlksuncNV867noqtv4BC3O1XVrwDw1/F/OQ6pqqpi+WeZA3Rey94PMX/5Zqb/5hpq+jvllTOvvZOn367j0c0fsGbbp7zoMXimpdcMsuDJVzjj6t8yYca5nHD6BUy74EqOGns8My+4tHISFNQonkRLBRSFRB9BwYsv04En1Up7opWmaAOeZKtTHjCirsss4whzpTCinETRMmTlOKKRIpTx0xiqpzFcT3u8GV/aQyDVgS/RTku0kbZECy3xZodFDDfSFG6gPdqKJ9pKR6KNzkQ7nmgrvlgHsVSQrBgnnY2STIeJxPyk0hGiCT+xbABvykNCDtGRbMOTaKUl0YQ/00FI8OLPeNjT9imBtIdApoP2ZAvtyRb8aQ/BbCdhwUtE9JFSQihmslJmsXJZND3JS5s3cs+iO3lp8wY0PYWdE8jnJdcNqPW6/3rAoQsC80Uni6tQVNyysV5p86TaOpqtkzUNAqk0C+5cxKhJJ3PE6HEMGDSEM6+7g5HHjmPspMkIpo2oqdz5yFPU9B/AmEknUVVVxZFHj+S8a25lRYPBVUvWcvDwI5xsu/79GXrIYQwYNIQBg4bQf+AgjjxuEpfcdh+fdSYoFvOUSkVUReLptc+y258mLJh8uquWOXPmcNLJU3ndo7qZbgcyfkvfCzpAr6GnLZlTwlxZrzlRKi7LNm/Z8ww/ajRvt6fRTZNy9z6+/ds/+es//otvf/xPvv3xXzy09A/U1BwIdKqr72HBrXdw+lnncuUdD7G60WBlo+YwbPWaG6wsVwCV0yVDdTP8HPBX6S/d1BPb4pauXcby6b0y1y3dWPnujxw7idsf38DGt97nw/pWdjd7WPn8y1x8/e2MOf5k+lXXcNjIY7lu6UaHWWzpLYFXMu/6tNFb2+MMdtvErXadwasbDdY0OTEn61ptHvuPaKWt4jq31eHaVpsFT7z8MwawZsBYzrn0Sg457HBOnHYiO3Z/iGAkKJYEZD1MXG6nM1NPR6ae1tRemhO1tKVqaYh9xqeBT/j9Hx7k8KNHMmrSSdy68jVGTZjE8MOHc89Dv6Uh9BGdQh0d2b14hTq82Tra03toS++hPb2H9vTeCiPYkWmgM9vAi28+w2GHD6emf43L9l3EG7t2svixRcy9+mLuf2IxdZFdNMT30JTYS318D3VxB+TVxWvZFf6Mz8KfUhvZxe6IAwAb4nuoizmgry62m4b4Hmoju9gT3c2e6G7aUg00xetoSzXQnmpkT2wPbalmWmMNvF77Bnsiu2lLNTpAMdlAXXQPnZkWRCOCL9tOfbyO1lQTvmwbYamTmOwlJntJqgESqp+EEiCjh0lpQTJamKQaJKkGKuAwLHlJaSEyWpiYEiAkeQkIHRWmsQf0+QUPnnQL/mw77ekWPOlWOtOttCdb6Ei14nUZyqjsIyr7iUg+fJl2OtNttLtAsC3WRHuimfZkC4Gsh5jsJyI5IC8ieQkIHnyZdjzJFtriTQQy7XhSLQQED53pVnzZdmKyj5jsJSR0EJd9LtANEBI78KRbqIvtpT62lz3RPdRGaqmL7WFn07vsje6lOdFARPIRlrz4su34BU+lNBwUO2lONtKWasGTaqUt2Uxnuo3GWD0t8QYaonW0x5vwJloJptoJpzuIZDoJZTuIi35ELY6spbBzGqaluHpHR3MuanEUI4Wmy45juiej0JIR5RS6KWJaIrqZQTFSqFYGxUyRViIIWpy4FHTiZ7Q4Zs5xbecKCrmchuGWdC3bAX6FguYwfTkV23byE01LwrJFNMNZ58tlZ30vl52+y93dTuZjLidj56RK9E0+LzNsmNMNadbMU3jkhe2/AsBfx781egHg/xDxssotPd37yt4DGI2qqioOPuIonqyVDtCH/eF9P/Nuvokrr72auZdfVrntaWfMxJdqJyo57tuMHiOlRvCmPDRHG/GkWunItBOSfGT0OIopIMlZorEAibSfrBRCkMOISoSUFCAh+AilPARSHsKpTsKpTrzxVnyZdrzpdjqTbU45OdaCN9VBS8wpKTdFG2kKN9IcbaY10oI32YEn3oY/2Uln3EMkEyCRCZPMhIklgsRiAcIxH8lUmFgmQDDdSXu8hfrQXjpTbUT+X/beK0qO+v72HYEyOeccTDCYaMCAsQ3GmGCCyAJEjgKEsjSaUUICkbHJWWGmU3Wq6pxzV+ocJigh8H+ds24699513z/34VfTGgH2efB5VK31W9VT6unpUXVP79p7f/euJFHrGcunk6NUT5OvJMiYcUJlmXBJIa6FSRsCGObMBBkzRqmaRqtnyesJfn3xZfvUEV1yyW9ptVUBArfXGRsXWVUjY1VaIzpj22uMjQvpeHy8RnfUtBjBunV/sbZvF/LCN94Qf7r9bo4/41zmLH2Xueu+4rXvMlz0hzt65+fM31zFrEMO45hTzuSPt/6NQw47fJ9zvcQKC98QaPKGUmdDsGtV51mT4JLJna+9KXxOx5/Ce9v8JMwWQ75Q7zFOP/tc5i3dwOvfyRx59LE8vOLdfViziXiTAV+7l3M34GuKYxPLasRY5Wsy4G1y7m//wFEnnc6yT11Uu2P89//z/+W//vf/h+//2//Ff/0f/x8fff4tM37CAE6dehynnHkOhx51HK99lxaP6W/tBZoToLPXgtHcG+Dsb1vxLXuZP1EZaAVYW4Bs4nne8fJ6Lrz+r1x4/V855fzLOOyYEzngwKn0TZnCMaeezaU338tdC97glS9jvQniNZbku28H76SpXatjeV1odG/NYXhvzeHaoNWtHBrtxdCstwDgOqsTe43S5pTzrubAaafRN+UvTDngYA48cCpnn30mn3zyNs1uibHtBtvHK4yMqGx6Z5B5Tz1A/8bFePM25LILX8nJN4GvueupJzjkyGM46uQzuHvRJq78y10cdMhBvLr8eWKqg0TVS9iQCGkuQppEWJeIGG4ihpuQJhEzPAQ1NzHTS9z0EdG9BFQJKW9Hytv5xP535i96hr9//SYhzU1Y96KU3fhKLpx5B/acjZB1LKB68BScDGWG2JLexlBmiOHMMM68wwKJEq68HX/ZjaJ6CGs+ZNWDK+/AnrXhLrqwZe1EdD8R3Y8ta8edl/DmJbamtmHP2vAWJWKGQkiT8ZU8FOoxcrUozpwTd17CU3QT1mTSlTBFK/JGbSbRmim0Zgq9lUZvTQC/FOXG3iWOpcnXE6QqgmEMawoJM0zMCBHRAsT0IHEjREwPkjDDRPUgES1AUN3rX0wYYRJGiGwtRr4Wp9xIkaslyFnALlWJkDTCpC3gFSzLBMsyUTVgAclA7/HTlQgRVSFXi1OoJ4jrQlpXWynKzRRpM0KuGiNdiRA3xfMVHksfcmkCAEoEVD9hXSFRCZOuRsnX42itDOVWmnw9TqYqwG2uFiesK/jLPqJGiLAWQC75yNeSBFQ/noIHe9aBM+fCW/TgL/nwF72EVJlcVQBRtZah0ihh1lWqdZN2V4C90e012iMG3fEKrRGDkbEataaK2cij13PotRyNtk6tUUav5yhWU5QrGYoVaxiwkRcqzniDVkcX2YE72nS7DdrtKs2WQb2l0WzpdLsm3ZEK7a5BsWqQMk1qLaMHLEfGq+zc2WTP911RjrBnVHi3d3cE4NveYGyszth4g107moyP1tBrBi8tWMCJJ57IlqHN+wHg/u0/2n6RAfxFICh3eWzDt8xd+wVX/e1RZh50KJfePKdnzB9URljxTZhfnf8rrrv+Gp557gkefPheJMVGuhAhbcbImHEq7SKlepZcJUmhIjxz6UqcUiNLvaVR1NLoRp6iliBvREnqQYqNJPVmEbNSpNHIYzYzpIwwobJCoZ6hM9qk3jSpNQxUM0fJzBBTw8S0CCkjTs4QDF7GSODL+4iUQ0QKIWLFCBktSbQYIpwPkNSiJNUYeTNDychS0nOk8jHS2RjZXBxVz5JR43gzHvwFP1LOTbCskDCjYtqtnqFYTZMzE4TLCkrRR1QNktDDxNQQwaKfYEkmVJKJqiFCZYUVry9j+ozT2VeiPI333nuDsbEq42NCFuiMGHRGTUsqFmtsXITFTvgHxybJyqPjNcLpOBf97k8cOHUahx93Cvct/3Cfad4J9u2pt21ceftcHn9zK9Nn7e3bPfuKG3jlyxhPrf0HS/oHGPRUuOOpBfzpxj/y2LzHWLZ6DRs/+oIFgxt4fv58PnPIvPDOZi647hamTp/BIUcdz29uvJu7n1m4T95aX18fBxw4lUVfRRjw7wtyesyfVwAoAcTak8CYtayWin5PnT/Onc/U6TN58JVVlGpNujv+yff//f9m13/9D0a//9+49PLrmTHjDA448GmmTj2SKVMO4I/3PcnSoQIDvkYvamawBzAn/QzvJObP37LAX6fH/onJXDGwsT4ksvpEZVu795j73hZRMStd5l6pdnLUzaTg4omhk8kAcIIZfD/e4d1o25qa3nuhtsrbYFDp8Hp4jI0RC/iFxvZ6FK014Gvyl0efY/qMGcw+aDbXXX8NX37xEVUzT6NeotspU2tmaLZy/OaSy3sXKNNnnMZ5F12MOzfMt8rnHHzYoZx20W95ePATBpUOdy54g8OOPo4tgc9IVr2EdBe+kh257CSgugjpEorqJKC5CKou/GUHQc2Fv+zEW3QQ1CTCupug5kYq2HEX7EgFB8PZYTxFJ1LBgWTtFdWNvywhq25ceQcBzU2xFSZVVQhrHvxlCUfOhi07zLb0NuzZYRw5G86cHVfejqfowldy4S4IhtCWFfd1ZG1IBSepahB7zs7m5Da8BYmh9BDOnANvUbCF3qIbR86Ov+whoHpJmkFiuoK3IBHWZLK1CMVGjHw9it5KYbRSVDtZjHYGrZXCaGcw2xmMdoZyI4naTFGsJyjU46jNJHorTaEet762QGIzSdIME1T9hDWFsK4QKPsE02bJ10GLmZRLPqJ6gFDZT7DsJ64HSZlh0maYpBEioikkzBBJI0RUU4joCqlKhIimENMDxI0QSTNEWJWJ6gGy1Si5WpSIJlOwMh1TlRApM0TWynlMmEEiugC/cTNASPPjLwl2Nqj5USwPZtwIkKlGyNWiJMwQmVqUmBEgZvk1Q5r4mVEjSLISIWYEyVXj1tCOF3/JQ0QPENYU3AUJd8GNK+ci30iitYR0rVYy6NU8lZqKbqoYlRq1eoWR0Q6dbotxy5PXaGm0R0wKZlIAvmpW+MAbBSrNIq2uLqZ5u7qYeB4R1XP1pkZnpEK1rlPU8mTVDCWzhF4vMb6jzvffj/DRR+9xw5//zLlX30SgrFPrCq9fZ8RkfHvdqvwTlXqdTpXR0VYvnHtsrE6na2LUND74ejN3Pv4iF157M1fc+rC4oL7g0v0AcP/2H20WANT/Pfj7nwRBT8jGBx1yKA88/ABmrUCrKybDzGYetZ6hWBWTVKV6RphpKymSepRCPSXymepZVDOLphcwqypGtUTWTGHLJnAVEuT1BFpVxajpmI0qZr2E2ciTLUVJF6NkignShRhlrYBR1am2KmT1NOlynIKWIqclyBspoqUQgYKMO+MhoydJlWKkynHSepKckSJvpAkXgmTKCZKFKKlCjGguRDCnEC2GSBYi2ON2/DkvzqyEr+jDXfDgybtZ994aIlZEjTfvxl/wohR8KAUfIQv4BYp+QiWFmBZEKfq495E5HHDAzyXKeU/OxWiKWAmtkaXaLtLqycbCLNwZMeiOmuL2qNFjCsfGK9h9EgcddiQX/v42lmwr/qwy65dWv6fB1OkzOPiIo7n2vmdZNFTkhY+VHmh7cOBj+vr6eP7ll3n88Ue4/vprOffcs7nkNxdz6623MGvWLK6+5mo2fDbERqXB4xu+4do5T3H6xVdxyFHH9x7nvuXvs2ZLjNcDk6RTi0Fb5Wvy8OpPe6zfzzppvQKY9XsaYlkDGY+/sYVpM2by8rtbyJoNRnbsoTX+Twq1cRI5lQ2b3ufxp1/kkSeeZ9q06Qy6Tfrd1Um+vuYkqffnsu+gv8VqeS/7t85qM1kb6LI+OMLaQIdBq6WjB/gmA1t/p+fL2+vR2+vVm6h6W9N7z00Ml+yNf5nw701Us62z/v8mGMvVSof1oVE2RMZZHx5nTXCEuWu//MX36zV3Pc4Jp57Gx1+9T9HIky3naTQ0dD1DrZlFq8XR6hHWvrniZxco06efyqK1C7hr7u2ce9lV1pBQl8FAl6ffdTB95iy+Dn6DrDqQy05k1Ymv7MRTtOMrieUvO1BUO+6iDalgw12w4cwN48wN48rb8JacuPJ2pIJY3qITX1lCLkv4Sk4UVRLHSi4U1Y27YMdfFsekgh130YG/LBHWvfgttlIqOHDl7Tjz4vEceTv2nA1n3o4tO4y35MSZt+PI2fAUnXhKLuw5O668k0DJw7JNy7BnbXiKLjxFF0rZg1x299jEkOYlqHnJ1cJEdZmA6qXUjIlQ7FYcs52k1s1QaaepdNIY7RRGO0W+FiVZCRHVFdLVMMVGnEonQ6kRJ1eLkq1FSFcjJCshEmaQTDVCSPMTUH3IZeF1dOdduPKCgZTyEh5LdrVn7Hjzbtw5F3LRg1z09lag7COqKyTMAKlKiIgui+EZI0DKDJKuhEhXQ0Q0PzFDoVCPoLUSxAyFdDVEsREjUw1TbMRE4Hc9QtwMEFQ9RHQ/qUrAYkp9BDUfIc1PSPPz+oerCWl+YkZA/PxKkLgZJFeLkqqECesycSNIVBPgVikLIBs3QwRUmYDqRy55CZT9+Ete5LIPpeQjUPYT0YNUOwX0ehq1msZs5Gh2NLRKhkpDRa8UKOs5YfeplTFrZfRqjnpLxajnqbVK6PU8xUoarZan0ihRb6l0Ryu0ujojYzW6Y3W6I3VicYW177zHXS+s4Pp75nHNHQ+zxmOg6E12bK+x/p13mD5zJiedfQHHnXom3a7B9h0tuqMieLozajI6atJsl2m2dLRKjnbboNXQ+XLLZm5/aB4XXv1HZh50CDMPPpTzrvkzf3zkFa7622Oc/7ubueOV1/cDwP3bf7QJAOj8FxKwVcC+3iqx/iUQOGiFQ89d/w0HTp3GPc8upFivIKsVqq0i+WoBvZGn3ikzMl6l1izRaJV7AZv1bpl6p0StXaLSzFNrGDSaVd7/9msOP1aAhs+Gv0Cv5nHG0jy2+kPueXUt70sBkmqJckVlZKTO6GiXsfEWO3aOMzI2illrMLp9B52RLql8nHBaIWp5/5LlKP6sH1/ai5z2IedlomqYvJlGzvjxpj24khKepAd32oM360NKu/FkvURLYb4NfMdQ2o6v5MdX9CKX/chlmRUblhG3IiYSZpioJcuEVOEZUko+fEUPnrxEWBUM4dK1i3/hA/Y0BjauJGPGyJgiq6xcz6A3slRbRbqjBt1RISWMWPlb3VGzN0wSifs55Iij+f2DL/1iX+o+KzDyc/C/T8dzl788vZybn17G6kCXQ486ls++/aYXTbNjhyij3769hWEUmT//BWbNmsWVV17O9ddfx9MrXmcgMMLbsVH63VVe+lRh0N8UDRQWmzWRdbc60GXA12TO0ncYVDo9Jq7f26TfKwDfSk9D5N+5a6yU6qycmH71Nrjl2ZVMmzmLa+c8xdwF/Tzw8kpeWP93ivUmlUabSLbCvKdf5MwLLmGlNcU7KLf3gj/vpGlfS/IVErQF/uS2AH8WEFsX7LI+2GXQkosH5bYFACeBP2sKdzKI3VfOtiRvK0ZmTVBItG9EhZw7ATbXBgTbOCFXr7ayAAf8YuhjQg5eb0nAb8e6bAx1e1O9+3Ytj/DbOx7lpnvvR6/HKWop4Xlql9FrCfJmCDkvkTRl5jx61y9eoNw1928cfdxR3ProY6wLdhhQBMPY76lx4NSpfGj/lKDmIlV14ys7iBhOEqaQgP2qF3vew3eZAFLBjqc4jKfkwFdyCLawJICjNAH6VDeK5iakufGXXYR0N8mqD0/RibfkxF0U7J+sSoR1D/6yE3/ZhSM3jFx2IRUceIoO7Llhhi0W0GEBP0fOhssCgIJldIj7Fxx4ii7ceSfugoQrY2f+wHycORvuohNP0UXc9KO3o9RGElRH4lS6cTK1AIVGCLUVxejEKbeiJCoBUtUgaWtFDJmkGUBRvSQrQTLVENVummo3TbEZI1kJEjcDRHQ/UUMhosuEdZmw5kMpeyg3Y+TrEZKVAL6Sm4DqJaz7SVeDJCoBvCU3AdWPr+TBUxSStD3nwJ6148g58BXd+Ipu/EUPgbKXmC4TLAsAGzdklJKboOolboqp7KjuJ1cLkzAVwpqPkOZDUT0ENS+K6iWs+Yhovp4XUy658RYkgpofueQhrPqI6DIRXQDAJesW4i+6UcpeFNUn9iUPSslDUPURVP2ENPE7T+wDmh+l7CWsycglDxFdQVF94u9q2UtMV4ibQYJlP6VagpwRwajn0Sop9FqKci2B3khjVDNk1DBaLYVaS5MqBckZMUqVJIlCgHVvDPDIvAd5fdNqjKoI1W52dDrdOrWGRq2pUdBSDKxeyeyDD+HIE0/nnCt+z29ve5jjzzyPK297iPteWcUJp57B9JmzOeVXF3HQIYfy+Vef8MOeDtvHqxjVLM2uRqtTIWOoxNUSel2j1VbZ9MF7/Oo3VzBtxkwu+sMd3DTvNeZt+I6lNo3lDoPlzgr9HuFPXjJU3A8A92//0SYAoF39WQTHZCCwxvI1/RIAXGJTOfPiK7ngkss564KLmXnQIdz3/EL8uSwfufxscqdEP+N4jU7XoForYNRzVFtFKq0C1XaRarNAoZIgp0XJaAUWrH+rxxb96vxzkSJRvouXOeWsczn0qGM46ZwLRfXWRb/lTw8+ywvr32HhW5/w7OAm3tzsxZE1KTXH8OUNzGaTVrtBSctQ0tMUSkmKappoLoQ/6yNaCiOlJELFIDk9RbQYxp52MpSyM5y040g5+Ta6FUfGxebYEN+ENvNddCvDGQeuvIQrL+EtePAVPARVmYCVL+bMOAhrCqlqlKghzNCOrAN33oUjJ/a+ghtP1sV5v/4N06efKiS26adx/q8vwZ/3ENOEfJytxMmYMVJGlEI1ScaMUagkMZp5Wl1NAMIxwQR2Rwyuu/EmLr7xnn3Pp/ITgPdLQPAXAOBEBdwEM/XrP9zBX++by65dogx9ItNw3JpI3rGjhSQN8fY7b3DzzTdy5Z/+yqAywpvhfb1+jy7oZ/HqDb1+3N6SO5Yk295H7u33Nuh3i+Djn64V0l4wOG/jZs67+ibOuuw6zrr8eg458lhOOufXPLNqE6+tfZuZs2bz6MbNrPLUBYCaYP8mgcy9US/CZ9iTfq3hiskDGKv9rUmsn+j03RcATsjXzUkgUPxug1bn7vqQaAOZqGhbFxwVgykWuBuc1NM7MXCyWun0QOHE9+5lD0d4L9baO2Czz3kX5+CWZ1fy62uuR8rHKJt5xkYMut0ipUqYSMmDXHARKLtYvG4B06afts8FyrTpp7Jw7Sts+HQ9sw+azfV/vo71rjCvB1ss35Zm5kGHcsTxp/DMhn+wbstW5m/cxB1PPsdF1/6B408/hyOOO5lDjjyWK2+Zw5t2O7ZCmKjpRbbk4JAuETYssGd4iJkeApqLgCahlF0EVMk6JuEvO/GVBOBzFx14S078qgt/2YW3JACip+jAabF99pwNR96GPTcsbueEn3HiexVVwl92ETW8+EouApqXiObHnh62ZGMHIc1teRZ9JCp+svUA2VoAtRXB7MZQW2HUdgStHSFXD+IvS8RMP8mqQrKiEDf9FjPpxleSLObShVx2U2xEMDpxjE6cYjNCthayQKOfoOYlavpJVsTjxKzb8YpMUPMQ0QUwcxfENLRcchPRFYKqAI5K2Ys778KZcyDlndizNjEok7VZoFhI496SkMaDqoeg6iGkey3wJp6zUnYjl90oqpuQdR9/SepNYcslLwkzSNIMka1FSJgB0tUwcUNBKXmRi27ihkLAAoZhzU9I9Vm+S5mILhM3BNgMaz7Cup+A6iWqK2RrggkNa37Cmo+oLveYxbgZJFWNoDaTFKpR1GoMvZ5AbyQp1+OUqjEKZoS8GUStx8gZYZJlhWIlSloNcOFFlzJ9wuow/XTOu/AyQvkg8VKMgpkip8eRIzauuu53HHT4kdy/8u+TEgBGeOnTAFOnz+ToU87imJPPYOasWZx33q94c9M6pGKNz+UkLw2+weBQnCeWrOb8q//IjNkHc86VN3DX/EGOX9HvZAAAIABJREFUPfFkDj7iaK5/4AVe/TbFSqnKCmelV/MoVo0VUp0VUp3F2/Y3gezf/rPt0L6+Pu58eZ0opg+O0u+pc/Wd8+j31HvgYM7Sd3l47Ze9rxdtyXHtvc+wOjDCom29qxD6+vqEtHSWmCCdMuUA+vr6+OCj92m2VV5+9Xl8/m1kKynMZobPvvuIRSsXUKynaY9qfLXtKw4/9oSeD+3aW27n2jse5KiTzmDqdJE3eMPD8/nt7XN5+l07f35iCRdcdwsHHX4UBx95HMed/iumTp/JtBmzOOSo47n+gefY5AwSU1XWrh3kmy1bMaoqabVMOBLg+fnPEc0HCOcUHCkXz7/0NMPOzYRSCr6Mn/UfbeDpRc8iFfwECgHsCQe3P/g3PvV8wbaUjaG0jRVvr2DR2oVsSw1hy9jZGt3CbffdylBiiLARoNhMMvDGcgbfXcXm+GZcOQdfeD7j7rl34cw68ORd+HIubvnbn7nxr39k8dpFDCWH+GDL+zzy/CMESj5iWpCkHubpl55gs/QVGSNGUo/w8TcfsmjFAkq1FOV6GqOR48mnHmXGzFk8/p6PfnmUuWu/3IcF6p1f977ntycV/uT8ToCG215aw5Pv2Pjr469wxJFHEAp7WLT4lV6B/PiOBouXvEos5u8BwZdefJZjTzyJVf6RXq7d7+Y8w0W/v5WTTj6J2bNn8+Djz3Df0nf3ytDuGlf97TFWuio9YHb3wk08OPCJAHlSjVe+jHPN3U9YwE8AwFueXcW8jZt7jODzf/dx42OvsdyhccNDL3HsaedwyFHH8Zsb76LfU2eVr8njb27lr8+vElO/Fvj73d1PsuCblGD+fE0eWfMZcxa/3YtfGfDWuebOefRLFQH8fE3mLHmbuWs+74G1hd+luXbOUwz62z1m8a/PD/D4pm2s8gm5dsHnIW55ajEbI2O9wZGbHl/Iy5+FenVvT741zK0vDIr3ptxmlb/FtXOeZtHWHBsiYqBj7tovmbPkXQZkwda/Lles81v7+fm1zuWiLVnOv/YvHHvy6XybylBpFxkYWMLn335ItOTho6H3eOj5J7nn2edw5myce+FFTJt+KlOmXMHUaSdw/kUX4yvaeeurt7jtwTn09fXx8JrPGVRGuPbep3n1qzi3vjDIlAMO+Nng2AknH8cxxx/DqWedTl9fH0+9/DDLNyxgzQcr8Jbs+FUnW0JfMOfRuwjqbsKGRMx08+qK5/ho85vETDepmo8vXR8yf+HjJKpevCUHAc3FIy88zHtb38GeszGcHWLjZ+t5bunTuAo2pIIdV8HOnEfv5Avfp4Q0N+6ig4H3VrB4/QLcRQfpqg9ffpjbH7gVT3YYrwUAV2xYzIpNy7DnhnEXHGwJfsn98+5BKtgJ6W6ihpcFK1/gw283UW4FqYxEscuf8/yrj+MvS6SqMomKnyfnP8q30icoqpuA6ua9r99g/rLnyNQCpGsKCVPmwcfn4ElspdSMYHRifPzlG6x9axmZWoByK0xC93Lv3DsJlVwkKzLFZojVm5by+t8HiRg+vCUXn3k/5c65dyIVnEhZO/aMjWcXP8Mbn63HlXNgywzx1ndv8dCzD7I1uZUtyS1sywxx/zMP8Pbmt3sM6YbP1vPC0ucI6T4BLstu7n3sHr5VvsRdEFPUg+/1s2jtAjzWFHWo5OOhR+dg1uN0x4tU2ynWvbGCdz5+nbguEyh7GQp9y/3z5hDXZUIWyFuwcj5//+ZtQqoPpeTmU9vfeWr+PIKaj5ghE9Z9PPHSY3zp/JhURSGkefngm7d5ZcX8nhwe1RUenHcv/rgdo5WkUA3z4adv8Ma7A2T0IOVahFDWyx/vvJuBr1288u7XPLX2Pf768FNce8ttTJ12En19bfr6BunrO50pBxzEBb+7iU/jVV5csoCPv/4HRxx1FOecfz5L/7GZG+ct3MeX++fHF3LzU8s47uRTOe/8c1m+fDEPPXQfU6dNY9YhhzFt+oxebu4JZ13I9fc/x43zFnL2Fb9nxqyDuOWpJSx36Fx1xyMsGVZZIdVY7qrxtwVvcH//xyx3VVnmqvHSFzGuvusJXtuc3w8A92//0XZoX18fCzfnrQ/SCiulCqu8wls14N/bCvAzZvAnx177LsOr3ySYKLN/9esEA/4Whx93MpffcDNn/Ppy+vr6uPfZV5jz/GJumfs0J59+GpG8Qs6Mo1ez3PewKHN/+YMtXH3HQ0ybOZtTL7ycW59fxYufKqyUKvRLFZbaVF7bXGC53RT9pxYIWCHVWTKs8uTbNm57cTVnX/F7ps2YxdV3zeMtewxf3mBLusRQMoNSrlCumpTNPMlSjHAxREKNEMwrZPQE6XKceC5MJB9CzsqkS3EypTjhXIDhlANb2oGrIOHKu7Bn7bgLEsPJYXwlD46cA2fOjlLyECh6sKeG+S72HbbMMO6CC09BxE8oJQ/BspeYESBhBkkYQWJagEDRS6DkI1yWhVen5CNUFh7CiBogpoWIqgHrtoiCCKsKCT1MsZbkgAMO4LJrfse9jz++Fwj8ktTbW6O/cE5Hf3K+u9z6wiAzZ83ik68/ZXRc5BYK/6Epsgp3iPDViX0iHeLAqVP56x23sfpLJ+ucWf56/1yOO/54SsUkH/3jA2bOnMnSDe8JOdh6ra2UKvt6ACc8f5MYwIlzvtxVY5lL7MXVsVirPAJ4DfrbPS/dan9LtGV4G0JCdtdEc4a70WvQGJiQhieWNfU74flbI7dZLbcsabjJmokIGKXTk4IH9mEum71hkgGfYBI3Wj3BE00fYpCq26t7Wy23e77IwUms6evhvVO/a4IjvB1t8trWIs9v+DtL3vs7r258i7UejVXyXrtGv6f+i9aOpXaVA6ZOY9OQi5geJWuEsUWG+Mt99zBtxgxmHnwov3/gOdwlBX/BxupNi3noiTms2LiQsOrEFvuG2x68hyOOPYGr/vZozwc48TPWBLo8tnQ5Uw44gIuvvIy/PXg7T8yfyz0P3cZNt/6eW+/5Mxu/eB1XwY637EDRnPjLDrwlh/ALWqAubEiEdIl0zUeu6SdV85KoeAgbbhJVLyFdIqgLWTisS7iLdtxFO76SA39ZyMpR00227iPf9FNsyeQaYkUMN2HDTUgXK17xkGvIJKsi5maCCfOVXEh5O7bUVtxFB7Lqwld24i0JadlfdhKveMVzrMuo7SDlVoB4xYeiSqRqfnINhWxdIVuXydYVMjV57+RzxUdA8yCXJaKGD1kVjGFI95CqyhSbQcqtEKVWkGTFT6ERIFNXiJk+IoaXgOreh7105IeRCnbksiT8jZlhnFkbzqwdZ86Op+DEV3QS1MTEtDNvZzgjGE57zoa74EAuSyiqR7B8moeg6kYuS0QML+6CQwzk5B2Wr9KJpyiRrITIVyMY7QTVVhKzGUetRzFaSYq1CNlKkFIrTrTsI1UNkawGSFQCJKshUmaQmO4nbiqUWgn0TopCPULKksMTZpC4rpCuhElVQiSrYaKGTEj1EdcVEoZC3FCImwq5aoh8NUy5EaM+kqXaSbFN+pqLr7yyB8AOOHAqhxx1HMeedg5HHH+KRTBMXLDcQF/fh/T1ifzK3/zhNs777R844czzexcyy+y6iFyyFIx+yeSa2+5n9kEH0z+4mE63ypYtX3H4UUdz46Ov8No/bCz4h53FWwu8+lWCZc4Ky12C3ev3NBj01lln2Un6PXWr27vGMmd13+WossxRY5mjxoLvcvsB4P7tP9oO7evrY8F3OYterrDSXWO1vynK7b111kyWhv/NEMi/khX/8swKzr78em56YgknnnMRU6fP5MgTTu29kXJGDCWb5KlVmzj57PM4/uRTeG7Rq6y0ZYU5XhG+sBVOgyW2Mgs253htS5HldkPQ484KSx1VljrEG2S5q2qxQA363Q2eeHMbZ19+HX1TpnDUSadz3m8u4YJLL+Giq3/HnKeeIWdqdLototkgoXyAUD5AOB8kWowQLYbJa0kypTjBjEyiECGTj6FkZYbTdobTdmw5J66ihKfs5bb7b8NVcOG0csf8ZRF/4MjZ8ZTcuMsevGUvUt7FcHoIf8mNXPYSVL3END9JXUEpugmWvEQ0GbnowZNz4S968OfdBApe5KKHYMmHUvQRLPlw51x48hKenISv4CZUlnnrHxt5ZeGLnHraycxbsIRV8ij98ij98hgDgdF9gd/E1/9KElb2ysD3LH6H6bMO4tIb7yRhaHTHxERcoysaXibkYBFHU6PeUfnoi/e5f+59zJg5g6lTp3LV1VcQDLl7HsJPPvmAWbNmMrjpbdZaYOrqO+dNklH3gsBVHnFOJ5jAfwUAV/bAnABeKz11VkhVcbHgqu6Vjy22cOK10u+uM+BpsNqqWJvw/a0PiunetUqLQV/DmhxuW0BNLDGt3Oz5+iZPEfd7Gwz6m6wLdNkQGWN9aKzH8gnZWHyICG/hqFXVNhEELSTxtVau38S52RRpstEZ5piTTuPEk09g9kGz6OvrY+EHX/Blpibk6cAIV98571++Z6+47SEOmDqNc664nrMvvYpZBx/K6RdfxQsfKSz70s3U6TMY/M5JWHcS0N3IZReKJiEV7Dz07AOcdObZ3PnaW7wdqfBFuiyea0Awkat9Jou/sDPjoEPo6+vjU8/XxKse4hUPyZqPeNVDvukjoLmIVdxETcH2uYt2FM0t9qoTRRUgMGq6ae4IUxkNUGzJZOo+qqNBKiNB1I5CvOIRgK/stHx8NuIVD/GqB1l1kqp5KbRksg2Z+liI2lgIvRtA7wQodwIUWwrmSJBSSyFTl8nVZYothaAu4mb8ZRf3PHwHiYqXVNVLrOIh25DJW0AyZnootRTUdoBiUyHfUMjU/aRrPvINuQcQ46aHgCr1gGTYcKOoEmHDg7/s6vkXvSUhdQc1Ae5CuptU1U+iIgCiuygmo+05G+6iA5c1LCMVHT2JO1YR4DKoufEWnXhLEvacDSlvx1dyEdG9SAU79tywNQgjmL+g5u6BQ1fehrsg7u8pOvAVHbiyNlx5BxFdDIckKkEK9SjFWphSPUrGCFCsRShUQxjNOJVGgmo9SXM0R72T4dF591LrpGmP59GaMcxuklIjSrYaojKSprujQHd7Dq0Roz6SoTWWpVyJkiz7SesB8tUI5UacQi1Cygz26gSjuo+o7idTDVGoR2iOZihqPu59eA7Tps/gqjvn8eJHfpbaSr0Lq4m1txnnf+yTi/mrq2/khode4pZnVnL/ig95+j0ni7cVWR3ostyh89DAR8ydv4hTTj+NSy//DZGEm/GdDWw2EdHy6IuvMJSrsdSu0++u/UTOtT6vpBrrlLbwE8ttVnkaLHNNBn41sSY+56y14Nv9AHD/9p9tAgB+mxcvMItNW27tB7wNywDfZbXc7UVOrAt2e16j/xlwWK1MmiJWRlirtHh2cBOHHX446999kxtun8OM2Qcz+9Aj6OvrY+ZsIf8eddLp9HvqLHNoLB4qsnBLgUVbiiyx6Sx3VFjhrLLMUem9SZY7xYe7eENNMEUWEPC1WDpc5KGBj7nrtTeZt3g5D74oKskWvrmRYsWkpGepNgxyapKymSOvJsiW4+S1JKlSlHQ5hivhJF4MEc+HcGfcbEsOY8vYkPJOHDkH2+LbxD69jZDqwVlwsi09hD1rQyq62JzcwtbkFr6Lb8aesTGcteEuOJGLEr6iRKDkRi5KyEU3vqKElHdgz9iwZ2yWZ1CAPCnnRMo68eRc+PISnryEL+/Gm3cTtCIfolqADe+s5oijjuS5NZt45OVXeHXdRl53xHkr0hIA5F/5An96zGJ0BNOb5vgzz+ea2x+gOaLTGtGpd0Vh/ERUzUTQdXNEw2wXyVUTfLbtY+zKNoxWgUZXY/v2Oju2i37ojz95XzCB733OaqVLv7vGoNxlzaSWjQk2sN9Tp1+qs3IS6JtYy34CBsVro2JdTVf3gkN3fdJFwuQKtQar/e29Ax9ym7WBDmvlFmv8TQZ9DQYt8NeLjJkUFbPKYv96QyuWj3C13GF9aKSXx9eLnZk06bsuOGoNfYww6G+zUqpa9W0tVvkaYkI4KM7HmkCXjaEGV958J1fe8AcSeds+MuulV162l5mYkPr/BQh85asYNz2xmNteWsMDy97lpQ3vcNEVomf3wuv+wjlX/oHNuQzuogN3wYarMIwjb+PSay7hnhcWsDrQ5dO0wbuRiph2VrrccP+TzD5oNqefdSqvblhLX18fzoyNaMVDxBRgL1aRKLX9xCoCFPrKDqSiHU/ZhafkQFbF9HCm7iNV85CueUnVPBgjCtmGH7WjUB0T4M8cCVBuy+QbfrJ1nxUzI74/UfWgdmTq4yF2/ZBgdFeU6mgQRXUSNtxkG37iFQ9mN4DWDmB0gmTqMqW2QmtHlHTNj6JKBDQ3/ryNVE0AwFzDT20sjNoJkGv40ToBjG6QUlshXfOTb8oUWzKllkK27qfQFLcLTRmtHSTXkImaHlJVH7mGTLkVIG8BSsEYyj2mMaS7iZoegrpEqRXAV3aiaBKFpoKiunDlha9RKtjxl13EKh48JQeSFasTM714Si7iFS9Rw0tId/eOB1QBCl0FB56S8FN6i048BQcBVQzW+EpikMZbdOLKCRYxYsjo7QRGU0w3l2sRjKZg/vRanHo7jd6MYTQSVOopdCNBUQ1j1OKY1Rh6LUbBCKA3Y7THMtS7KYxOErUVw2jF0BoxKq0k1VYSrRZFbwkfn9aMk6+EyVfCaM04hVqYfDVE2lRIVQIUGhHKzSh6PcKq9cs4/KijOfWCy3j2H/59AN/eGCwrfcDb4OTzrmbq9NOtbuzTRTe2t7lPRNXEtP6g0uXyW+5nygEH8Oeb/8CK/tcwm1k6YyJD8Luhz5g5cwYHHnggL2/4gOVOQ1y0Tvq7tNRZYamjwnJnlTWyaPkZ8DVZ4apY/17tkRuT134AuH/7X7VZADAnaOVJVPNkOW2lVGW502Slu8qAr2EZ0psCGP67yVJlhJX+EVYHR1iljPDauo2cf+H5TJkyhcOPOZ5jTjmLw445gWc/cHHz00vp6+tj6vQZnHzepTww8Akvf53ilW8yLNxaZPGwxlK7yVKHWMvsOkvtBsudJiucFcHsuMweCFwh1fZ+sPv2TmaultusD3R4P1TmvpcXM23GDG575DF8mRh5PUu1aVLUM0RyMtGsTDgrE8krpEtR/Bkv9riNcEHGHrezLb4NT8FJuiqqsSKqh6ThJWH4KDaCSEWXuOIuOhnODLEttZVXVr/MXXPvZOHa1/g2/p0In807sGctk3negTNrYyi1jS2prdgyw9gyw9izNhFom7UzlBrClh5mKDWElHPizjnx5l24c048Vmitv+glqin8+bYbOfOcM/ndDddw5tlnMGXKFE49/VQeff4ZNnoKDCqCGRz8KfibdA7XBEb4LNXms1SLwUCXFz4R4c5ZPY7ZKooqq3q21zeqt/KU6hmMVh6tmSOqBUmZMdFhWk2QqcR7E8ujYxU6IwZPPDOPo48/Ya+UqEx0/3b3DlJ4W5OkYGGEXvET8LfM+XM2cIIRXDnp65XSvgBwhdPgnkVvce2cp5iz9B0GvPXe9O0ai+2bGE4ZsIDdhKw7wfrtlZbF626NItpyNoTFYIeIcJlcbzciQpmDI9YEsJBsV0rVffIHhczc4fXIWE+OXxPoMvvQI5j/+lskK1G+2vo23vBXXHjJxfz2jkd+fnH279j7wAgvfRpk+syZnHXOaZxzrvDnvfz+dxx85DHMeWUVYTOEVLDjLLh41/kNM2bOZOGnDgYmLvBkATjvWvgmRxxzDF8Nv83td98kfMHXXoreVYhV3GQbXtSOQrLmxac68JQcKKrLAoFOvCURDRM23KRqHrINH2pXIVXzkW34yTX8pKpeii2Z1vYQelewgdmGj0zdh9ENUGrJhCzWMNvwk6h4iJseklUv7e1htn8fY3R3lGJLpjoapDkeorsjQqmlMLIzQmMshNYJEK960ToBym2FqOlFUSUSFQ+t7WHGdkcZ/z5GZ0eY2lgIcyRIe0eE9o4I3Z0RRnZGGd0VY2xXjNFdMTo7oozujDK6M8bozhgju6J0dkSpjoYY2RljZGeM6miIxngYrSPA6ASIzDdlQrqE2glQaIr/i0TVR2UkhDkSIlv3k6r5SNf9lNsBKqNhApobb8lFoe7D6ISQCk6iupvujmgPZKZrMgHNLUK3C3Y8RSeBsougKuGbFLHjKbpwZe34iuJ4QPVgtpLUOmnUapSSHiFdkslXglTrSYrlMJmyQkYPkDeDGPU4RS2MXk9i1BKYtRhGPUqpGiZfDVKshVEbUSrtBNVOArMj9pVWHL0Ro1ANobcSlKth6iMZqt0URjtBrZPCaMapdtOY7QSlZpTaSAqf8jWXXHkpBx1+JHcvfJMBK6t2ItVgolpxIsN2Mgics/Rdrr33GeYsfZdV3sY+g1PiNd5lXXiU397xKIcdeTRO6Ruao0U6YzqdUR2jliNvJCiaSYx6mjPPOYfFG95mqU0Vn1euKksdFZY5BPibAHUrpBqr5Tb9njpL7ca/BX77AeD+7X/VNgkATvIXTP4QnbT63XVWuiqsdJkMeOuskVs9VvAXWT+5w0tr3uKmO+7g5NPPEFO9l13D0SefyakXXM6cJe+wzGGwRha9pg8NfsIj67/m5a9SvLa5yOIhjWU2AfSW2E0BAO2m9QYxWWK9iZbYdJY7KyxzmqxwmvswPRNesAlJbsDfYtB63msDHVZ99inHnngiD7z0MvaUQlZPk9FyeDNuPEkn3pQbKenAlXTgTrpwpBwMJYdxp1040yJ3K2bKGJ0ouUaQ5liMxmiMke1JEhUf7oIDX9GFLT3EuRdezLTppzHlgKc4cOopHHH0Cbw8OJ+tyS048zZcORtDmSG2prYylN7GtvQ2y7tjw54dxp1zIOUFw+gpOLFZ992c3CJYxrxTVFwVJNx5F56ChKfgRin5erlfUtzOqo0rufjSX3P0sUdz10P38errb/CmYvTYqDWBvedw4g/nprCYgh3wtbnxgac4/sTjSaghkkaUbDVB2oyRrsQoNzJUrAJ5tZFBndRxmrLuk65E0Zs5zFae7piItHl03kPMPmg29z36MGudhR7IEaxXp8eSrQ1Y4NDftsCgAHErpPokAFhluSR6fFd59wLGHgCU6pM8f01WShVO+tVVTJ1+hnX1fwannHc1A56aeI1PyvfrTfRavsSJyJge+JNqrPI2BIOoWDKxf+Ixmtbzn5zxN8qg3BFStVSl313b+3q1gKXw/42xMTJusbGChb/2rkc47KhjWPX5d3R3Jrnvwdvo6+tj6eqFvBVt/WsA+AvHbp+/lnMuuZKdP6Z4acE8+vr6OOeSKznxlJOYNm0acjnG8jXzmTFrNjNmH8yF19/KgDyy16doPe619z7NDX+7h8qoTGN7iNpYgO6uMLXxINWxELmmn1zTR6kt9n7VQdiUrMlfMdUbNt1ETAm1I1MdC1JoCWYvWfVgdBQKLeEFDOvi+0K6hNaVyTcFMziyK8r3Pyb4/scE7R1hdu6JsWtPnNFdUcZ3Rdi9J05zPMzYLgHitltr9w8JxnZF2f59jOZ4hIDmwegGGN0Vo9AMEFQlKiMBxnZHGN8dZfePcbbviVEbC1EfCzK2O8aeH+Ps/D7Gzu9j7NgdY/v3cXbuibPz+zi79oi1e0+cPT8m2fNjkh//OXmfYPcPCXb9kGB8d4zmuACXuYafdE2A0dpYhO3fJzBHggR1CV/ZRa4ZpLE9ztjuJDt2J+juiKN3QviLDtx5B4mqH6MbZPvuOFoniKy6CWgeoqYPnxWrEza8yGUXCdNDWHfjK7vwFJ34SmJS2ZN3oZTcRDSJkObFbCQIZGyUqzGKeoRSPUallaKkhcmVZVQjSkkPktb9ZI0AaV0hbwbQ6mFK1QBJ3UtC95GtBsjXg72VrcgU60G0Vph8VSFrymQqChlDptSIUKqE0WpRzFacRieFXoui1sKUKkGKpsIjTz3MtBkzuOLWB1nm0Hoh7fu0DVkXchO1jYOT9hO39/W+72uDWu40OHDadL6RHGzfoTG+q0arrZMtx3CkhsjXkthsmzn1tFOZMuUAHl77JQs2Z1m0rcSgp8ZqX5MVUpV+qcYK114Fa8DbsD7bKr8IAHs2J2vtB4D7t/9063kA/xUAnAwEV7rrlizWZY0yEUY70d4gaPK1gS4bhwLc9fhzHHrk0T+bAjzsmBO49t6nWTJcYoXLZMlwmdc253j12wwLN+dYOqyx3G6w3GGyzG6wSqqy0lVluWNfALhkAhQ6TCEFOyo9uW/CVzHBAAog0LQM/i2r1UEY+DcEqtz/yiJmHnQwv7/rAT7ze0jrScJZP+64A3fKhT1hw562MxwfZltyiK3JIWypYaSME19WwpV1MZy10//GYlFlZfpI1WRCulfUTOUcLF2/gOkz9o3T6Os7mQMPPJrzfn0x9vQ2nHkbw5khXJYvx56z4cjaGEpvYzgzhC0ztE+bgWAVLaBoSTND6SGG0kO4ck4cOTv2jB1nzolSFkGpIVUmogWIagFWvbGC2++5lRNPPoHDDj+MS668jKuuv4Y/3vJn7pr7KCtdlZ6na5WzzLODGzj7gvM54aQT+GL4E9ErqgZJm6J0PlcVDN9Ev2ehliRfTViF8WKlrD7SbCVGsZbCbOWotYuYjTz/+Pw9fvu7K5g1exb3PfYIz65cx5otQQF6lI4FmkZ7OXxrFMHorvIKdm+5q9aTTZZP9vpJ9b1s9gQYnJCAPQ3uXvTWz3pxp04/nXuXvC0AoJUF2BvwsPICJ/K4BnytHpPY766zugcQ6z0GfaUF7NZZUu4EsF3lbbDKU+9lDvYGRywAuEbp8EZUDI0MyKMMSipPvvIsD867n0X9L/feV+5Sgo2fvMOLLz/CrFkzeOerjxm0JoD/pVd30vF7l7/P4cedxEf27+jr6+OqPwn27uBDD2XKlCkcetRxTJ02nYf6P+SZd4b+qL4bAAAgAElEQVRZJjV4PdBkkyfJl1mDNYEu8z/xcd3NN3HhZb9hxw9hursjjH0fEVJtV7E8eD7yTR/maICA7qLUljFGApQ7CvpIgHzLT77pJ9f0EzEkUjUvuYZPDG80/VTHgnR3RejuilBs+4lX3JTaMuWOTKrmRe8qaB2FHXtibN8jGLpCU+aHf8bZ/n2UkZ1hRndGGN0VYXx3jLHdAvjt3BNnx544e35MMLorQnUsRNT0kW0o5JtBEqaP9W8vYfePcXb/EOP7H+Ps+THOrj0xtu+O8sM/E/zwY4Iff4zzzx8T7Pkhzo//TPDjfyWsf4vzw4/iPnv2xPnxx8Te9c8E//wxwe7vY+I+/0yw558Jdv8QZ+cPCXZ8H2d8V4z2DrFqYxF27hHPudBUiJo+EqZCyvSxfWecnTvjtMejJCt+pLwLX8lNSPOSriqEDD9+1YdUlPAURbB2ouIlXvGSqwtZOmx4kAoO/GUx+OHK2pEKEkpZQi65iFVkMrqMUnZSaaZIFbyotTgFQ8FsxdDqETK6n4TqIV2RSVdlMlWFUiNErupncOMSMlWFXD2A2olQbIYp1IPkqwqZqkKmppCq+ElX/aQqPhKG6I7OVv2kDC9xzUdK85NSvSTLHqJFibc+GuT4k0/mpLPP46UPbXye0lgbaLNKHmGVPDopPqlrKVedXn/25EaeyWzfZIl4Mgi8b8UHHHPqWWTqFTpjRdojOpqZJlaWyVSj5I0oN978J66+9hqefnuY17aWePHzOK9tLvDcp1FWDuXYqNRZLbcY9DZ6aQarPHVL5foF0OeqMuhtMOitMehtsNrfYrmtvB8A7t/+o+3Qvr4+Xv02+xN62TKd/kRKW+VpsD601zuxRhasyFq5zaZwm3ciba6b89Q+gO+4M37FzU8u4cVPAiyxqSzcWuCVr5O8/GWc+V8kePXrjPD2DWssGtZZuE1l0TaVRUMaS2wGy+06y50GK5wG/VKFZXaDpZb8u8xhssxhstwaBpnsCVxh+S1WSOIDf8Bbp99ds4J0BZOzWm6zZEuS170F5q39mNN+fQWX/P5PxEoBlLSHtBFFznpwZZzY0wIEOjJ2nHkHw1kBzlxpO5G8n3ApwIpNq0Q2YEHCkXfgKUl4C3ZihsQzzz/ws0Ddvr5n6OtbxPQZp7L6zcX4y0Iy3preJib38nacmSEcuWGkvA1XbhhnzoaUtyGXXP8/e+/1JsdZp3+PLdtyBmMDDoAN2KxNsA14SQssLAuLYQGzLGGXZQEHrCyNcrYkJ/KCcVTWxJ7OOVRV5xymw8x0zmFGYn8Hvz/h8x481T0jw/sevJy6ruu5eqZV07JH1V33c3/vgDmqxRjRoFNDbfVhwRBO+kVn6WxwltmAyPoyRfQCFAbnRF9pWC+YQXWd+NMJNu/fzBM7nuTff/Z9xsbG2HFG4ZitxPf2/JaxsTHedfu7eXzjT7EGTchJp6i1i9tEd6jaUepNu/Ck7CgZF+GsTDgro6SdeDMu0gtBojkvvrQLb8pJLO8jkfcTzMqijzRmxZ108N9P/ZhvfPcbfPihj3L1Ndfw7Q37OWxdZSaH+h1RfSaYuAO63Eg4PfzgHOVmqcBwrbRhqAs8qM/zuX97nCve9G9zxZVP8vnvPSEYPPOiWKrRQ7SQ5EdNJMPX26/LctiQ54BuXs3wyrBH3dEfMhQ4qmoBR8BP3ZActZVUzaAY+z7rKPG8q8KL7gq/kmocd1R40VVg0+HD3HLrrTz0yY/y7z/8Otdeu56/e/BBdWN1O//0H78gvuTlyLFN3HLLzTzx9A956oXX/pKhfxMYPGorc8C0xC133M0Tz/yWW95xM498+kG++PV/4apr1vPhhz/G+P6NvDhjZN90gP/e9wyf/cqXuenmGwVIvPF6/ulfvsj69dfw+S9+kl/9bjfFlptC3UW26iC6ZMWXN5Gp2EmVbBQaLuJFG4G8CX/eRL7uoN7xUO24KTWdpMt2vFkjgYKJyKKFWsfDfNXBQt1JreOhtyxz8ZKX/rJCreOhUHOyWHfiSOlp9WQKNSfdgUK9I8aqSw039ZabpZqLYt3FUt1Ft69Qa3uotwU7WO/I1DqCwWv3FZaaHqSMVWThRbVUGk4uTL5AveWm15Pp9SR6PYluV6Lbk+gPFLpdiUFfZnlZYbmvsDxQGPQU+l2ZXkei15XpdWW6bYluR6bblei0xep2ZLodmV5PZnnFy8pFH72Bl2rLQ7YsxsTNrsxC3UVkwYKUMbJY91BtS8jzFpKLDqSkifiik3JDpt5w02jJxBdtaP0zGEMa5KSJyLyNYMaJnLChzNtxxA3Y43rCBQuxRRupoo3oog0ppceeMKALaTGE57BEddijc9gTRvzzNjxJHb6cjWjSQqrkI7Ugk1xwE8wIkObLWIjknatAb9FDcN5KJG/n1386RiBnQ06ZkJIGXLE5PEk9UsqIK6HDFddhCmuwxfS4k0aii07mK26SZQ/ZiodYwYYjOoec1GOLzvH7Uy+O7jf/+E+f5vdWH0cNCX58/BTH3zjLGX+MY/YSOy8EeWzH85z0RjnhLAuN7Ro97tHLGMKiChTXOPFVpvuJ385w3U1v54w/R6kaZamUJD0fJLsQYWbuLF/8l68zNjbGbe/5AIeNObae9bN3Ksa+2TTjkwk2nw7w1CtOxi+EOKBNic8KfU4lMjKj8fC+OXFfO6AV97ndM0l2TSfYM5PiiKnAvrcA4FvH33ioDOAaALjGdbRfZf2OmhY4YSuKZS/ynLPMs84y/6NU+JNU4I/SIn90JDmgTfO9vb/jI1/4Bo9uOMJPnj/P5pMSW07KbDutsP2Mjx3nQoxfiLBrMs6u6SS7Z1LsmU2zeybNzqkkOydVADgVZ9dUnN3TydF5u2dS7J5Nqexfmr0aFfzNqYyhCgD3qzf64VobGnzIkFNz4BbZNTnKUWL9ddfxxO90rL/+ej71pS/ymllPdD5IOCVhDZvQBrXoQjp0IZ0a8zKHPqzDEDZgCxuRo3akiAMl7mbON4chYlD7SnWkS3Zeevkw1157eePH2Nj7GRvTcOWVT/L4U98ntiBE4pG8hdiCFf+8iVjBSihnERlfkTlcKQPOpF40FYQ1GEOzmCNzIgw3pkMX1jDhm2TCN8lMYIaZwDSawKzKCs4x5Z9myj/DTGAWbUgrxsRhPXPBOWYDGnQhHWfs57jiiis4OCXzS1ee3dMRbnvPB/jHb30bb9qFnHLiSthwJmwoaRdyWrSeONWaJlfSJnpH511EcjLBrAcl7SQw7yakfi2nHKIpJSYMK3LKgTVqRh/UMen3YI6HUdIKz/3xWa6/8Qa+/+TjHNNH/2o8yhHzIgdVh92+y4TT8+xR13CsMtrQjATZ83xnx4tcdfXl/zZXXXMP39v961EczIihG1bQjdzI+dHr7BvpT1VDknb49wqht9h0FEevNWo2USNpjtuL/NJT5VmnGHUfW8PQPfDpf+Sdt7+Tl145RKnpotp2c9PNN/KvW47z819N8O0ntnLt9Tdw9JeHaHYlTp09wbvffSuHf/sCR2yVv6rxPKSNs/cPr/HT8d186wffZWxsjB/8+DE0hv/how/ex9jYGHe95908+s0vcMMN1/HBe9/HunVX8tDD9/PU1h/z+tSviGS1jB94ip8/8V1OX3iObM1BsmQlU7FRarnIVOxkqw4WGk7yNSdLTReLDSdLDSfRJQvBgolgwUS2Kowc+ZqDeNFKbMmKXwWIiaKVattDqy/THSh0B7IK1KTRSpTEOZWWm0rLQ7npHhkvogtWClUnhaqDcsNFvuqk2ZFpdCQqTY/K3Cm0uxKdnmDx0mUXnpQJf9ZCseKg15XpdCW6HQ/9ngBxnZZHPN+WVoFeV2JlxcfKio/BwMty30u/52XQU+h1FLptmW5LotcRr9FTgV+3ra6OTL8nM1AZw3ZXjKdzFSe5ioOluotOX6HRlYkvic8Lb86KO2nCHTdii8yxVHLQbMks1jws1NxIGQuOuAl33IwnZiKYshFN2fEnbCgpO/6MEzlpw54w4koZSS7ZKDVcZEtOHEkjnrSBSN6CJ23CHtPjjGtxJo0E5p1E88LtG8xYiOZtBLIWAlkLsQUn4bwVf8aML2MllLXiSxlxJfTYYzqsUR3WmA5HwoB33kwgb0WeNyOpWs1Q3kykYCGYM6NkjCjzZtwpPc6EDltcjzmiFaxmzkEw62BK+yq/efkZPvLQ/dz5nju4/a47ue3d7+Jtt9zMRx+6nzdMU3z4s19mbGyME394jt96CioAXBqxe6uhzqXLNppH1xggjznKHDYvcPW117PtVTOLlTi5Qph4RiGclHn4Ew/z4Of/mR8eepldkxE2vOJg8+sSR1ST2QFdlr2aDNvPR9hyyseW0z52XAiLOlbLAs+YhRzqiLHAIX1u9Fm2R3P5WPigPsfemdhbAPCt4286VA1g4DLL+QFVP3XCXuRFd4UX3FV+6anxvKvCrzwVnnWUOGZZ4IQxya6pGLumYuy4EGLrGR8bXnWx4RUnW1+XGD8bYO9UjD3Tgt3bNZNifCrJ+GSC8ckEO6dS7JpOsXsmza6ppGD/ppMq6EuxT5Ni32yCvbNJ9s6K2Jc9mjR7ZsUamkLEDmmtI/hyR+iIDVwzyj5sWuCQIc877rxbBHPefQ/P2ErsOOfnrg89yDd+8hOm/TKBhIIccWAJGzGEDBjCRnQhHcaoAUNYK7o2fVpiaR+eoBVfzIkScRFKedGHzGiCGmxJPUt1J4888gmuvvq9jI09roK/LzM29n9Zv/5uXv7TITotmXLFRaMu0Wn7aLe9dDo+ijUZOW3EkzZjjuqwROewREQkgymiwRSaxRzVYohoMEREmv9caJZJ/yTnvBNM+KeY9E8x5Z/mvHeSC95JznsnmfbPMOWfxhQ1oAvrmAvOYYoY0Ia0/N3H7udbjz/NS1KS4/YiP3txghve9nbkhAV/RoA4KeUQhfRq3Z07acOZEIXv/nkX8QUvyQUvsbyMf96FlLThjJtxJy3IaTtSSpTOu9SqPE1wjtnAHIaITh1bW5HTLnYe3M79H7ufj3/paxyxVhiGrx5TRd3CMFLikKEgxsD/bwBQlRDsUTcOI2nBbII7P/Qprrr6bq644kmuuvpu7vrQ37N3Ji526CoDfUCbZv9cehQnM5Qc7FOB3vD9s1+XY78+yz6t+Lt2z6bFbl6XFS4/fVbs5meS7NFk2KfNcshQ4Ncq2zd0HK4FbJ/4lx/wha99lUJdot6VqdYsjI2NsfGPBq698W3c+PZbuP7665jW/IZ2X6a/ovCb3+7krvfczvO/O8JZm24UN7Nfm+a+T36eq65ax/vedzsPPPB+vvf9r/Gdx/6J3KKOcstNMGcgOj9HteOm0nYTTms4cGwzUugCmbKd0IKZQt1BoyfR7ErUux5qHTfdgUSz66HYctHqS1Q7Hnw5I9mqnWTJQmjBRLJkodh0kigKAJivOSi2nGSrNhbqTsptN9W2G1/ORL7mpNaRaPZkWj2JRlei3Zdp9WQWag6qbQ9LDScLdSfFhotyy02x4aLacrNQc1Ksu8mWbNSaHhotD72eQqcr0+nKdHsKlYabbk+h35PptQVT1+nJFBseAgUrubKDVtNFryPT6XjodSS6LQ+dpod200O7JdGsu+l3FbodiUFfYXnZy2DgpdaW6PZkam2JdtNDryXTaUp0m9IICHabEt2WRKcl023KdBoy3aZEq+Fh0PfS6yo02jKNtgCb/pyJhZqDTk+iO5Dx5qwiQzCmwxTR4U0bCRWcmMM6tW7NgjftQEpY0Qc0aIMaDKE5TGEdStpOZtFHOuMhnHQQm3diDOpwpkyEF2wklmwE82byVbf47AlM406Z8MxbCeY92CJ6HBEd0ZyFzJKN+JKDSMFGYtFBYN6CkjLiThkJZU1EC2Kc606ZcMYNq1mocT2+nFWM2efN+HPWEQhMlYQBJ1txkFyyEV+0Ml92IGUM2OI63GkLwZyddEkmmXdhD82ixOc48buDjB/ajDOiwxPT8p3vfZ0bbrx+VEzwla99jrPB5Ch/c6irPeaoqI/lUbjzm8e/w/HwBz/+ef75yQPEF1IkswobNjzOhx/5DFeuu4pNr9rZO5tg4xseNrzsZPx8iJ2TMfbMpkZylF0zSXZNJdg5GWfLaT9bTspsfN3N+Hk/+2ZjPGfJ8QdPXkRjWZY4qM9z2JhXN5Y5njEVOKLPvAUA3zr+puPmsbExDmgSvOhc4gX7Ir91LfCcfYET1kV+48jynGWeo8Z5jhiz7JtLs2cmzq7pGNvPBdh8ysuWkzJb3pDYclJi22kv4+dC7JqIi4t7SrB3O6eS7JlOsGMyzvikeNwxER8BwVVAOFxxsSZj7JqMsHsqJkCgJsnemSR7ZpLsnU2xT9UKDkfA+0fsX1aMA9UR4L436xn1OQ4ZxAhu93SMdVddLT4Y/mszz1iL/Pu+33PlVVdx170f4jdnXkKJuQmnfDgjFswhPZ6k6L+0RrWY4wYMQS0aaZop8zn8cQfBhEQ44UUKOLBH7Jiieto9mXbLzUt/3M/t7343V111G1dc8Tjr19/NJz7+CRplF+2GRLcp02t76bUVOi2Zdkum1pDIlZ1kig6Si3ZCBRvuhA5HQnSrOuJaAQrjWkzROQyROWaD08wEp5kKTHHOe4GzynkmfVOcVS7whucsb3jOclo+zynpHLOBGSxRo2oa0WGOGvnlq8+z/tr17HtjkmP2IofVjtffnfvNqN/YlRTLHjfjTlpxJay4U3Z8GSdeNbjVFjOiDWqwRAx4UlbR56nWPUkpG9aYEUvMqFZUiRH1KfMpdCEd2qAGfUhHJKew5/gB3nvfA39FkyOceSIvUriE9+tyKqM9L8xCQ83oaOMg5AMHdMJ0cdCQY+9sku/seJHPfvdxvjP+S/bMJkcs3gGdqGQ6pBqgDunnOaTLcMSQ5ZAhK0Y4a84dxc6or39YDbDeO5dhn3ae3TMpxtXrf+eUkDocMuR5zqmCWmtxNJ46psYtfeGbj/Gpz32CQl3hT1Ov8/Dn/oEPfOQhnn7FztjYGI9v30Ku4qFQc9PuKyxf8tHpS/zs54/x/g+8h/d/6L5RHNNnv/tTPvqJh4gkJii23LT6Mo2eTG/FS2vgZanpptKRWWi4qXc9FOpOFhsukiWh1wsWzCJWpS/R7Et0BgJwdpcV2gOZasdNvSvR7EmUW4LxW2w4yNcc5Kp2MmUbgYIJb85EbMlCrGhhvmInXxNjXnGeg2zVQaXlptxy01QBT2+g0BnILNWdVFpuegMBDOsdD/W2h2rbQ7Mn0epK1FpuSnUnlbqbSs1Juy3T6Uj0ejL9nkKvK9MfjmTbApy1Gh66HQEQiw0PlbqLdsNNryWTSc2Jc5seOk1JnN/00GlJAgC2ZPo9L/2eooI6D52Wh646/hV/j8Sg56Xbkum1FDoNmU5DUl9PptuQqVRdtJsy5ZqbSt1NtyMz6HtZWQnQ7vuod700u16yNQlH0oQ9YULOOAULH7PgiduQ41ZsPg2OkB45ZiCYMKLEjLijJuwxE1LaQWJBYb7gJZ2RKSz5yS/6iKbdyDELtqhB7QYW4NIQ0hBZcOCKG5CSJnwpE/60hUjWSrRgI5S3ES3YiOasBDIW/PMmQjkL2aKdYs1DtuzCl7Ugp83MOM9gjenFWDlmUBlKA66kEXvCoLarGAmp8TmRBQuZsp1k0UaiaCdVcuHL2nDGDXgSegLzVuSYHr1/mkjBgnfejD1uwBk34o7ryRRt3Paud/Deez/Er8++xNvefhN/8KQ5osY5DTdGx/5CK7vW4FQanfeMvczXntjHBz7+efSJPK+++mvecdutfOOpPfz8l1Mc0KZ56iUrG152suN8mK1ng2w7F2L7+QjbL0TYPSM+W44YBSO4eybF7ukE28+F2HLKx8bXJTafVBi/EGTfbIwD2pTQC5oLHDFmecGxKKRY+vRbAPCt4286RBPIeS/7ZmPsm41zQBNnz7Rg9bafD7H1bJDNp7xsfN3DL1528IuX7Tz1ko2Nr7rYdsordjcTUcYnouyYjDE++ZfAbsdEnJ0q6BMrxo6JGOMTsdH3q+eLP9sxuXrO+GR8lR2cEYyhuJkLLeAB1f27XzscAQpDyAF1ifHckBXMjljAQwahu9o3m+T9D32Gr//iAM9YhTZwnybBzbfdztOHD2Lz64mlvQSjEpFUAG9SRkq6sQT12GIGTFED054pvvWDf8UeMKBE7EghGzavESnowOTXUaq76bUFC1AvOnn5fw6y6ekf8fL/HKKx5KJTleg1FHotL92msnqDaMu02xLtlkSnLVOouojmzVSaHmILVjJFB7mKk0DWjDk2hz6iGRXdzwanmQ6IOJnz3gtM+CY5JZ/jDc8ZTkrneMNzlpOes5xXJtAENUz5pzFG9DgSFtwpG//x5A9Zf+16Hnv853z1B//BXfe8F3fSgj1hwZmwqODPhCtpEZ2ccTOWmBFL1IA1ZhBF8EkzE94pZv2zzPlnmfXPjororVEjehX0nZcnmfTOMOmd5jv/+R10wRlesWrZ/KuX+Puvfpt169Zx6+13sHc2eRn4GzGBQ9BkUx21hjyHTQX2a/8SAO7RpEeSgj2zqVUjkTYrYoY0afbOptmnEWu/qsEZAsHDhhyHDFmOmgsCQOqza3SoAuTt186rzLMYCR82Coff8AN/fM37YXwywV5NhuOq0WWYLXjUKpzXG14WbN8t77qDt7/rTtZffyMPfeUxdk1Fue+Rf1xtJ/jlCXL1AJf+rLC8IlFvO3nqaVHV9qmvfZujuhgbj55g/fprsElnKDZd1LsSvWWFQsNDvOgivGAlU3EwX3FSarpo9WUGK156F70stTwU6m5SZRdLLQ+VjoeFuniNpsr25WpOSi23qtXzUut4yFTsLKoj31TJRnTRgjtjwJsTGr/wgoVQwUyp5aLWET9b60i0ejJNlWFs92T6y17aKhBsdj10+oIN7PRlig0XnZ5Mty/RU524tYabVksAup4KvrodWR27SnQ6Qn/XHurw2jLd1uo4tt2SaDXdI/Zu04Yf0W146DQ8dBsS3YZMty5AW7eh0G2Kn283VDDXUlm+hjQa+w56XpYHPsH8NRV6DVld6s83xc/X6h4aDYleV2Fl2U+t46PVC5JcdJMruYkvOFHSNnxpJ7aQkXDKSSbnJZpwkE5ZWFiSyJf9JHIeEnk3uSWJek1BSZmZUTRYQga8SQeJrJdgVE8652GhHCSR9ZDIS/hSDnxpO56EGd+8E1NwjmDKRL7oJLHgQEmbRsHLrqSIvorkrYRyduILDgplJ426h2pNIr7gJLXkJLXkwD9v5mdP/jverA1n0ogzacKZMOCI67FFtdjjWjwpPYG8MP5EFiws1pwUm25SJTuxJTvyvBVnTI8nqUfJ2pFTFoz+WZxxMSr2Zs3YkybmfNN45638/pXjXH311dz2vnvZ/cp5brjxeiYC3tEGa8T2rQV+1rV6wDVAUJ06bHnNzrqrr2Hj8f/hXXfcwee/9zj7tWl2zyT4xUs2tr7mEoDuTJDNZwJsORNky5kA286F2XEhwl5NmmO2JTXGLM2emQS7JuPsnIiycyrO9nMhNp9UxETtVRdbTomvt50Lsns6xkFdhj2TgbcA4FvH33TcPDY2xuO/07P5dYnNJ2U2n5TY+KqLp192sPEVF5tfc7PpNQ+bXpfYdsrL9rMBdpwLM34hys6JKDvOR9h+Psy28xG2n4+MwN0q0IuqAE88v/1CVH0uuuZcAfK2T8TYcSHK1jNBHt10nEe++V88uuk4286FGFfZQQEChzfy4c06yf65lKoFnBdjvaEzeC6j6gPFjXnf3Lw6Cs6ORt1Dl/DQ7XnUssjm153c+/AjvPPOO/nP8T385sIrXHAbcQSsxNNBPH4zRo8GnWcaS2AOOW7C4NOg82oweedwBUzYfSb0kgZPyEEsZ6Pflug1ZPpNiUHTS6ei0Kt66da99Bpe+i0f3aZCv7V6M1mrDep2FXo9hUrTQ7bsoNrysFh1Um56WKy5cKcNIjw2psUSEyNhbUi4iKcDwi084ZtgwjfJOeUCp+RznFMucFI6ywXvBGeVC6NaJ0NYx2xwluOvnOD9993DvQ98kD+c/Q2WuEmMaIMaTFGDWt5uwZO2Yokb1UL5OQwRLbOBWSa8E0z6JjmvTDDpneSMZ4IznhlOuac57ZnlgqLlgqLjrKxhNqBl0qflZd1pPv6Ff+SKK6/k+ptvYd1VV41AzgFdZrQzP7bmA/uIZXEUnHxUjV4ZRrDs1aTYo8moWlMhO9g1nWL3dOoyUDhkBvdqxKh3n7r2D58b5k2qjj2h3ctyaA0DOAR/+0avlRGVTyo4FCPo9IjlHlc3P7tnUmLTos+yfy7NEcsSz9iKnHCURiDwR0df48fHT3JomE9mL/Ofz7w++t289573cOzV1zn4+9/x/Y3bePQnT/KV7/8XV1xxJbe8811cccUV3Hff+3jj5GH6F720BgqltsJCw0OyaMeXNRFfNJMqO0gsCbYlXXZSaivkaxLZspP5sgCH8UUrxaabakeiUHeSrToptQT7Vmq5qLTd5OvCsdvoemj0BFgrt9yU226yVTuLDRfFpot8zUGmbKPWEUxfU3XZdgYy5baHWscjQF9PsI2dgQCDHVUTuLwiXLfdvjBRdNURb6PuEhunlmq2aHnotNy0W246bTedtmDoOk3P5e+1N61Ow0OvpQjwVxer11Do1mU6VYlOXabf8IoNXFO8l3t1hU5Dol33CFa/pa6OYAe7LZleW6Hf9dFve+m3FPotL/2ml3Zdpl6XaDUk2m3B/uUqToJZO8m8k+i8lVjOhT9pwR0xI8ft+BJOEbicksjl/cQzHuIRE8mInmzaSi7jID3vIJSTiBV8eFNOInmFbMFHflEhv+hjfknBEzPiS1oJZmwoSRNSwkQ4bWRh0Uap4iZbdBHO2fDNW/EkzdjjJixRI86kGXfKSmjezGLZSfQtBY4AACAASURBVL3mplmX6DQUalUPS2UnSyUn1aqLZksmW3YSzFlwJY04EjrcST1KxkC4YCFbdrBQ85Ap2cmW7bR6MtW2h1TZgZI1YwzPYQrPIWcsWONGpIQJJWnF4J/FFjWgpPUEc0KH6E4Yee89d/G9DVu46dZ387NDL3DLrW/n6O+Oc8y2pLrxVycJo3HwZWzgmmmD6hx+zlnigUc+wzXXXc/XnjrIPk2S7ecCPPUnO5vfUNh+LizA32k/m08H2HTKz+bTfgECz4bYNRXnsDHH/rk04xNiyrVrKsaemRQ7LkTZcSEsJmBTcXZOxNhxPsyWUz42vOpmwysuNr7q5me/1b8FAN86/qbj5rGxMTa/5mb8fIQd54Lsmoiw43xImDXORxi/EGHrmSDbz4dHa8uZEFvPXr62nQuPdjc7Lghwt30iyvYL0ctA3/iI+Vv98xEQvBBjy5kgd9z7COuuvocrrniSdVffwx33PsL2c+E1TGCKPbOZ1Zu3qgcc3rxHN/IRq7PGUaUyMmtjQg4ZxDpqWeSYygA++vShv4iwueO9d7Hzj69xwW3BKM9ilGbQuC5gCetwJSxYQzoMAT0azwxa9wxWnwGTV4PZq8cTNdKuuxm0FZbbXgYtHyvtIP1WgEE7QL/po99cvREM2l76bUUVikvqTU2iv+Ym11BjI9qqbilRtONKG3AkdVjjAgia41pRFRUW2sCZ4AzT/ik0oRmmAtNM+adElqBvkgnfFBP+Kc4oF5gOzKANibop3bDaLmbAEtNjiupENlhUhyGixRLT40mZRfdoUIMhPIcpomXaN4U1pkcX0jDtu8Czp3/DvZ/4HO983we55fb3csPb3sE773oPH37kU3zya9/lk199jPse+QLX3nATH/3iN9nwsoVb3vlu7nzP7YyNjfHF7/yQo9YSzzmLq9lel+3ai6I+0FpUcx9Vc4gqD9g3N88eTWYEAIf60xEAVFnlPTMp1Y2XZu/squN8dP3o5jlsyHNInxXd2YYshw05DujmR2zhcO3VrDKJw++HxifBAg6ZbnFt755JcdiQ51lnmRfcNV701EQ/8F8JXP/v586Nrs133Pk+rl5/Hdfd9HZue8/7efALX+Whf/4+D3z2n/jgA/fz1W98iVxBR29ZobeisNAQ+XLxJSvxJSuhvIVsycZ8xUF4wUqp5SFdto/aK1JFG5mKk6WGh3JbYqkpkVyykVyyUG5JpMtO8jUPpZZMuS1R6cgsNt2U2m6KTZcAgj2ZVl+m1Vdo9iRqHY/K8AmAWGy4KDVdtHuClez0xXi3M5CptNw0uxK9gcLyine0+gOFbl+h21PodGQ6bTFqbbfEOLfTVMFfUwDAdkto9zrNVfAnAKCkMnCSCtaGLLx4rtuQ6dQlOnWJbl2mV1fo1RW66mO/4WPQ9LHc8jNo+hg0/KNzRiyfyvB1WzL9tpdeSxEgsO2l3/bRa3jp1hT6LR+Dtk+AybaXQd/PYDlIue6mULSSKzrJFNxEky7CcTeRmIfcvJ9KKU405CQddVDIhljMuGmW/AyaMS6246x04jSqQQoFmWxeYXHJT7Hkp1L2s7gokSu4SeVdROctpPI2vEkz3pSRzKKNWtVNvaGwWPXgz9qxRg04EyIFwJ2044oaiKZNFIpOmnU3nbpgR/stP726l1ZVplmV6DYE6O31vJQaHgI5K56kAW/GyHzZLq6LtodiUwRWl5suOn2ZZleiUHOIar64HlvCgD1hROOfRuObRqNM4EvrSS1aSCzZ8OZt2BNmLBEdN918I/tefoMPferLfH/LXn664Ufc/5F7+Y0jzFF79TLn72XAbwQAi6Ng+iOqS/9FZ5HDmjDbT8vsnY2z8XUPT/3RyuY35NHId8uZAFvOBNh8OsDWMwG2nA6MwOC2cyF2TUbZp0my6aTCtnOr99htZ4Psmoyy/ZyoPd05GWPnpEqmnBf35/ELUTa+7nkLAL51/E3HzWNjY2x4zc32CxG2nwuy/VyQrWfEBbv1bJBtZ8VzO86H2X4uxPZzIbaeCbL1bIjNZ4JsORNSAWGYrefCggW8EGXHhah4TXXtuBBTn4syPnpu+BgdrUc3HWfdmxyZ666+m29ueVYdL8dVneCqg3ik69Jc/rW4aWdG4HCVBRRszkH9vBoKLBidI8NMNssiB3XzPPyVx7jxHe9k/fU38sCn/4F169Zx5bp1/N0nP8W+V89gi2gx+GYxhwyYAkYcQQ2WwBxz0hQ67xxaaYo57yy2sBZP3EylJrPcDYibRcNPv+GjVXbTqUp0a+IDs9dQGLS9dJsS/bZMvyPT70j0uxL9nliDvpdOV6Hf99LtCV1TuyuTLNpR5o140npcw+7WhH7kEDZE55hVG0f04Tlmg7PM+KeZDswwG5pFExKl8LPBGaYDM6JrNCi+14Y1owJ4XWQOU0yHVS2LH34gGyJadCFRb2dLGLHGDZijWn438RJf+eGPueqa9dxxzwf5/Ncf5Uvf/vZl4Prtt97KP/zbf/ONTUf5+S8nOeYo86lv/JAHH/k4n/7834/O+8TnPs2nv/gZnjz+68vDWtUKtWGe11FLkSPmJY5YlnjOWeFZR5kj5gXVsJERxqO1bOBMas1YeBWs7ZlNrV476jV1UJ9VwZ/I5jqkzwpGUM2sPKCbH/VUjzYlKrDcP5dmz8zQ6DSUPAw3RuLa3jOT4qh5QWgb13YC28uCpVD/n7ee9HDfI1/k0Q1H+PEzr7HjlJPxMxJXXHkln/3iZ7nzPXdw44038N3vfYVgdJJ620OlLVi7lUteKh0P5bZorkiX7ATzJgp1Ed9SbHnIVh3El0SuXrhgE4L8qoP5ioN81Ua2bKPcFHlzroQROWXGHdcjpfR4s0ZsCT3BvJVs1Um55aHVE47dVl+i0fVQ74jnOn2Zdl+m0ZVYbLjE+Hgg0x3ILF/0cvGSl+WLXqH/G+bpDRQGA4X+QM3b64tsvU5LottRaDXE6Hao0euqJotuUxbj2ebQkKGOc5uq/rap0GsOwZqkfq3QqXpWgV9NALvhe3jQVFfDx3JLvL87ZYluRRLn1hT6DYV+XWHQ9tFv+VSQ56fb9NFteukOwWTTT7/lp9/wiseOn5VBgG7XS7XqoN2S6Xb95BaczBc8ZLIKuVyYdMpHPOImFfVQygXIRFykA1ZKcRdLCTfFjMxSVqFZidCuR2nUIlRKQSqVMJVyiEJeprgkUal4KZc9LFVk8kU3papEqeSk2/RSbfjwzduxxa0oaQdSyoE/ZSWaMVMoOihVPbTU322v6aXf8DJo+OlWFZpLbgYNP4OWn0HHx/LAT6PjJbZgxzdvxJsx0OhKrFz0cumSn85AEZuOspNczU2qJNjn6KINc0yPIaxhxjuJKTyLOaLBlTKQKtkp1JxI8xYscRO2mJ5T+le5ct2VPKv1cPdH/57vb9jKTzf+J1ddfTX/ueeocOVb1kS/qDmBR1Wmb5jNeVgNej9hW+JFV4ln7Ysc0KXYelrh8f8x8dQfrWw/7WfLmSCbTvlHa/Np8bjxpI8NJ70CEJ4KsOl0gM2n/eybTbJnJs6Oc342n/Ky7WyA8fMhtp3xs+NcUCVUIuycjIr78LkQ284G2HYuxNNqI9PYWwDwreP/53Hz2NgYv3jZydYzQbacDoweN58OjHYwwx3N1rNC0yAehwAwyJazAgBuO7dmFHwhIti/86sgb7sKCndcUM9RdzxiVyPWI9/8MVdc8aZMtiue5O//9SeMT8TYObWqLbycyVlzAx8FRYsb9qhFZMjqrDGOHNTPjwDgMJT3sLEgoj8sS+zXpvnKT8e57qa3ccPbb70MtOx6/igaeQpXxIRRusDe/RvxJo24IloM/mmM/hm03hkcUT3m4CzWgJaFRacAexWFTkmmX/fRrXvVm4usMg0e2nU3nYabXlsSILC3Gg8xGCh0e5LQMXUl+n3hNmz3ZPJVB76sENhLGSPOlEEtl9eKFZlDF5plNjDNhG+CKd8kU/5ptCENcyEN2rAGTUjDdFAwhLqwBmNENApY43q1H1QsS1wttY/rMcf06MMa7Ek9vpwNa8KCNjDHz/ftuux3dtu7buUD99/LLbe9g3e+53185rGfs/56kSf3k+fOctRa4hsbj/LAZ7/C9TfeiMl9juSiCzmuZ+/Rrfz0qR/y4Mc/wtjYGJtf0v9FyPExe5kTzgrPOis856rwvLPCC+4aJ5yiceOAPjfSAO6aTrFzWkQPjV+mMU2yWzUb7Z6OqyzgcDScUcfAYh0xiX7gYc7kcBS8fy4j2MThmkmyZyYlWEYVAO6eTo1A4JAVH8oc9mnSHNJn2atJsl8ndEKH1eDpy5nPMl/5yTbu/9hHuO2dt/LAR/+OsbExvvXYP3PyzHGeObGJRk+ityx0c42uRGeg0BkoFFvD8a0bOa0nVXIRKViZr7gECGx6KLU8VNoeFhsu5ss20mo12lLdRavnYanuothwEsiakJN63Ekt8QUzxYaDQs2BktJjDGlQMmaWmu6RU7jd84wMHJ2BCFcWockC+PUHAuT1lwXjd+mSl0uXfCyv+ERjRlem1/fS7ysMBl4RzdIVTHm3I9FsuQXbpzJ7g5aPfl2MZrs18V7rNbyqjk8FXw2FnnpOry6rrLyPTlWmW5N58cQO+jUv/ZqXQd3PcjPEcjNAv+5lUBdgZ9Dw0y566JQlOhWx+nUvg4aXQdPPSlsAxG5doVOTaFc9tMpuulWZdlmiXZFo12Q6NUXIQjo+Og2JVs0tVtVDteSmkLNQyNtIp62k590kEhKZtI/FXJhM0k8iYGMx6aKekVguR1ipJOgWY/RqKaqlKNVyhFo9TrEUYWHBz1IxyGIpQKXqo173Ua/5qJQ91Ctu2g0fzYafat1HrKAQyojqtmTeSbHspt300qwrNKseOjWxmRW/ay/dqkK3rNCreVluBVju+HnhhXG6Az+ZspvgvIlo3ky57mTQl1heVvjzJR+DZRH3E8hb8MwbsSfEZ48nbUQfnsMZn8OXNZIq2VioO8iU7CSWbAQKNsxxI5rALPqAhqd3PsG9H7mfI8Y06666mgc/+3ne8c53cf3b3sEHP/4PapD82krHBQ6bhUHwgE5IMoY5fcetSxy3LnDMushBXYaNr7t5/Pcmtr/uZvz8cOQbGOn+VoGfbwQANw2/fkNhwxsy2876OTCXYsspL0+/6mHzKS87zgXYdsbPllMK284GxDh4IqoSMgG2nPax9bSfX/zJ/hYAfOv4m46bx8bGeOol+2W7lo0n/Zd9v0UFesOLe0hrD8HgEBAOQeBQE7gKBlU2cPi9qhscfj8cH287F+brG575qwzgo5uOrxktr+oMxyfi7FwTHbNrOr3GJJK+DAwO20OGYZv7tcPOYLEOmxdGN9lhYPTQJbbtpJuPf+3fLwMzDzz8II6EAaNvhkh0DuPcH2nVJCpVN0tVN864AUNwDqN/lljGhDmoxR3W02sKDdGgFaRTVaiXnNRKLvHhX5Xp1GWxS+766KmuQeEi9NDteBio7MeQAen1JAYDmZWLXpo9hXBBdIfak3qkjBF70oAhqkUbnEYbmlGjYmbRhmfQhmbQBNX6uYBoH9GFNWhCs8wEBTOoDWuQMibMMVEPZYxqxTlBEUw9qzKF0/4pMSpOmLDFdfzw6Z+Pfldf+saXOPHqcbRhHb+aOsN1N9zAp775I1VgrfbiOgTL9e3tzzM2NsY/PfrPvG7UctIwycuaCWaDHowBC9/8gfh3+Nyj37msru7N64hlSVTIOas8Y69w1FbioF6Nh5lJsXNoWJpKqBrTpLiWZi6XFuwdSQxUl69WbZpRr52j5gKHDVmOmPIcNornhuGte2dT7FZDzVcjjhLsmk6weyYpGO3JuACBKsM9PhlfBYaTcRWsJtgzHeWwXjiRD5sKaoBtmXe//37effcH+OGmrex//iA7dv+M/IKOi3/2ojX9ge5AptxykSjZaPYVal2ZpaabbEW4cr3zZpJLNoI5K7G8kUzJzkLTS2LJiW/ehjdjQUlb8GcdSEkL9rgJW1RHuemityzTXfaSqzgoVB3kKzbqHTeNrmD6ym0ZJW3EGJxDF9RQqDnpqJuY/sA7atXoLytiPD2QGSzL9Acyg4GQOwwGChcv+rh4ycfFFZmLKx6WBxJ9dfX6Et2uh05bot9X6HZkmg2XMG+0ZfpNhUHDR7/mpVNW6FRkelWFQUNRWSof/fpw+QXIa/joVWV6VS+9ipdBzY9F+wqDRoCVZojlRpCVZph+JUCv4mNQ89OvemkXPTTyTtpFD92yTL8m/u5B08+gGaBb89IpS7SKbjoV8dguSnTKMu2STLsk0a54aFZc9JoKK/0I3WaA5U6IS70Ig3aIXsfH/1lJ8n96STpVP5VFJ4WslXjKQSrtIxlVSEa9lOe9FGIueosB/reRpFUI0SyEqC4GKZUiFAphiksxKuU4xWKY4lKAxUWFesVLp+6nWfYyaIfoNgJ06kFa9SClokK1pNBuhKgUJZoVhUZRol1V6NV9DNSR73D16z76NZ/4uilG2xbLKyw1fURyFlILFqp1J52Wm5465VhZVrh40ctgRSFTtmOKabEl9JgiWrwZPcW6g3rXQ6OzutJlB+6MCX1Ejz6sY9Y3zQtvPMeNb7uZRzce44h5iSuuFDEwG3/5MoctRbGpMhU4oMuMsjuH7P0B7fyoMeiwscCz9hK/dJU4bMxxwrbI0684+emvtew5HxQOXpXM2HpWrOH9cuNJHxtP+tn4hpcNb3hV8OdlwxsKv3hN4unXJJ4xFxifiLLxdZmNbyhsPxsSJMwpP9vP+Nl6xq+mbgwZRR9bTvl4+mXHWwDwreNvOlQAaFtDWYudy9Yz4kITADAworaHzOBQ3Lr5TFBdAgQOAeAq2Ftl/obPbT83BIoC/G1VGcStZ8NsOuXn9g9+knVqJtu6q+/m9g9+ki2nA+o4OToCgkPt1DBOY62ua622681reDMfVuyIyri8qNp5U+3XEcvSKJLjiGGem97xTr7/1E9GwMYeM+MJa1jIWqiVHbz6p8Ns2vAjXn35CHJCjyGoRR/QIMUMOCM6lJieRMbIUslFZslNueSmUpFoVT2CcVCZgn7TJ24AvQCDrgiZHfQVwQCqbQODZYXBsszysrhpXrrkI1dzEi6YsSd0uNN6pIwBe0KHJaphz7M7+LefPMaeZ8cxRjToI8IpPBMQbuHpgKiRm/RNMRucYSY4jS48y2xwGkNkDkNUuIw1oeGYeErU1YUFcygq6WaZkie4/2MPjH5Hvzn/a6xxLea4HkNEw2vGl7ly3ZXsOWPhefs8xx0lTjiE4/XwGS2/+O30ZUD7iivX/YUe8+fjO7juxps4Yqv8Fc2OGuJqWRTMn+rcE0XrSXUEnFIjihKj62e4hk7z0TU0k1JDnTMjE8jwmhlWvx01rUoIDqnj4AOq+WMIAkW4uQg5HzKMO6fi7Fajj4ZGqSHoW9UIimt897SIQRpXTVW7p+Mc0GX4rxOnuP+zX+W6m97G7R/8ML/SmFi+5OPS//ppq6aJZl+m1pVo9AT4K9QEc5euSiSWXKRLHhKLEqkFBV/SRiQtI4UdOAImXCErBlmL3qth2n0BjTRBOGcU+X1NEbos+nYlBisygxUR1ZKtekiWXCzUXCSW7BgCGmzRORodD82uTL0tbt6trmjgqLR9LDb8dHsyg4Gk1qoJVi9XVnj+pRf4jw0b+egnHua89lUGy8KhPFgWQc693rBpYzjeVcRIt6GyaTWFTlkhHLZx8MVf8ZLWjFZy4IqF6DcCLDdDDOoBelUf/ZqfQT1Er+KnW/ayXAvQr/m42ApzqR3jUivGxUaE5VqIlXqYi/UwvbJCe0miuyTTK3vpV1ZXr+KlW1bolCQaBSeNnJNG3kVrwUOz4KFR8FDPu6nn3TQW3IIlbMg0627qFRfFRQfVkptKyUO9FiYRsbKQdlDJu1hph7jUitIqelnKe0glPCRjMpm4n0TIyWJKoZZRWC7HqeWDdMpx6qUYC7kgC9kg2ZTCQj5EPh+isBSkVAnSakToNWN0GmG69SC9RohOLUir5KNd8dOp+ulUfHQrfnq1IN2an3ZZoVNR6Fa89Go+ulUv/ZqQuwyafpbbfpb7PnoDP8lFO9klK+Wqk3ZDbGxFTI6HXldieSBz8aKXZldCyVrQBjUoaQPVpovuQBkxhPNVF5EFG9rQHNqQFl1Yx6xvlhdff47rb7yBL/94E0csixyzlbjmuusZGxvjn7/1NV60p4SBS2Xth6HuBw159mnSPLbzV3zu3x7ne7t+xRHDPM9acqqcKMmm1z384k829kxE2DkZG8k4dqy5n61lAIegb+MbXjae9I4A4NOvSTz5spvNp3zsm03wxB9tbHxD4enXJTa9obD5lI/Np7xsPuVjy+nVcfLw+6feYgDfOv7GQ4yA/+T4CwC46ZR/RF1vPuVXx8L+y9bwfGFzD7H5MhZwrb4vcpmJZPsaxm/rufDoZ4YgcPMpP49ueIZHvvFjvr7hGJtP+1fBo+o0Hp8U7N/4RILxySTjU0l2DUGgevPePZP5K0xghn2ay1tDDqpu4COmAs+YC6LL1TwcCyxxxCyKw591lvmH7/6ML3zzXznx0jE27n6aGWkCOaKhVnLx8YcfZv36e7jyyidZv/4eHn7440gJI3rvLL64cKrFsjbkmJFkUs/CopNC3kKrrDJ/NZmBOnYatP0sd/wMOl4GHYWeqgMc9ER22cqKl8FA3ChXVmQuXfRy8aKX3rIQ2KdKNpSsETljwBrTcv/HHuSa9Xdz5ZVPcs36u/nQRz+GxjeBLqzBEJ3DENVgjs1hic2pQE7Uz9niOqwJwfzpo1pmgzNM+ae44J1kNjDDdGCaSf80M77zeNJm4iWJfce2MzY2xn0fvpczjlPMBATraAhrmPJNoAnN8v777h6BuX/5/g/4zDe+y7/tOMbY2Bi7/3Ry5MgbVjR99rGfMTY2xoNfFtrBq6+9nk/8y/f/OvtnK6udvQLQ79Mk2TUdZcf54BoN6V+uXVPJy13maxhA4fBdBX/DnukjpgInbEs8Yxbs3+FhRZwaDyO0g4IJ3KmyfbumEyNt4PhknN1qvdPu6aR6QxHn7JpaNYlsvxBbY6gauuzF/8s+TYpDxhyHTAU+/e2fcOMtt/HrWQN//rNCf0Vh+ZKfSsdHs++n1ffRWfZR6yqU2zKOqJ5IzoE/6UYJuZi2nEXvmsEqG9HYp5hzzzBrP407dB6tdwqjfwZLaJZU0cp8xU6u5qBQc1JtuWn3JAYrCpf+7KVQd+OI67EEZyg3XSy1JJJFG674HOG8maban9vvK8Qi5/nEpx7mhrffyo93HWXOoye8GKZYNPP0lp9yxz33cu2Nb7tsAzAz8wIXL/nE+2DZu2qO6gjXb08Ff/2Gj17Ny8WOh+dfGOehz36Rq65Zzzvuuocr1wl3+cP/+HW6KthbrofoVwMMKgF6S376xQCX6lGWG0EGtSAr9RAXWxH+txXnYi3MxWqY/20mxDmVIIOqj17Jx6DiZ6UaYrnkp7uk0FmS6ZW8tBc91NN2GnkPnZKPlWaMfilAvxSkW/LSrsj0an66NcGgdVRDyv+uJLnUj9KuhwnaJ8j6dTSyMiuNKBfrEf5cjzMohmjmFAoxO1GfgYBkIBJwkEn4SIeclNNe2ksR8kkvxUyAckqhlPIxH3KykPCST8gUsgEWcj6KWZlKTqGW91LLKzQXAnRKAQaNCN1SgG4xSGXeTXNBplP00il66ZV9YizeCtFveFlpB1lpq9+3/AzafgY9H92ORLvlptXwCMNIVbC0vZYaxt2WxWfdQKbVkbDHdTjiGmotD62uTKRgpVB1UW55iCyIGCFjTIchoscQ0TPn13Dn++7kSz98ksOmBY7ZSxyzL112/Tz1wqtCAz7q8C5wyJBn/1yau/7u01x1zT1cceWTXHXNPdz1oU+xeyrO9rN+fvIrLb/4g4k9k7HRpm18MjaSO209J6RRm9Zo/1aXAH4bT3rZcNLL06/LPPWqh21n/BzRZ9j4msTGk+LPN530sukNRbB9bwKTQ/LlqZfeAoBvHX/bcfPY2BhP/NG+ZvwrNAsb1At240mxc9l0Sr3wTnrZfNKn1tgERIr5aT/bzq7RAw6B3ZuB3v/XOjsEgqqz+E0/v20tAFxzExzlB64Jnt410gOmVbdwZgT+hlouwQKqvcG6rKi8syxyVAV/h82Ll62jliWO24t8d/wF7n/4IZwxDUrGit4/y+Kikz/+7hDXXH3nZaPra655H/uOj2MOzOEIavHFjYTiBuazNuZzViJpC8mMjWrZQ6fho9vwMegEaNe9dJs+Bh0/vdbQCLK6eh0PywOFXl9ieVnm0iU/KysKf74ktFIrFxUGKwr1jpt8zcnzv9vLNdfcfdl/29XXvI/9z+/AmdLhTOkEY5gxImUMOJJaXCk9jqQOc0zUzJljOmZDs0wHptGHNXjUcwN5E4G8BW9Optb1kS678OVsGKM6dCExJp72CdfxXGgGTVjHbNjG1uee40p1JDNcV6+/ZvT1fl3mL0wPQ6fvtlMSPzryKgf12b8K/g4Z82pjh2Df9syu5u5dNmadEqPYvarmb2gA2TWdvOw6OTDK9Ftl/g6bhhuGBZ4x5dRQ6NxlOtIhu7BPOz8yguyeTqhsoHjcM5NkvybN7umEChBjaoh6bARM980KLeKOCdVMtSZCaagZ3KtJqzKGRT7z2M+44wN/R6XjRfadIVt1Ml9x4stZyJSchPM2cmUnizU3ctKIP+nCKRtw+ibJLJlZrNpI5PVEMjri87Oc+PVBvvSv3+H35/+ELqjDEpkjVLAQXbQSLliZL9soNp0CbK4oLF9UKNQ9yGkrStqCK27AHdchJ0Vwr5KYo9eV6PYUcnktH/jQfTz05W/xsS99i6uvFSzN1x/7Ji+9dJAbb7qB7zzx/7D3nmFylnX/90AgIYA0G+UGBFFEAZFiw4YFBfHGjoiiSPomwz0lCQAAIABJREFUm2x6b3TRW0HA9GSTbLK9zc7s9N57771sTwC9/8/z8vO8OK+Z3UWP/xufl7mO4zwmy25CYK5rzu/5/X1LCytf+B8ef64VmUyG23uWsQkDE5PSWHjCyNi4kVrVSLWsp1oUWZrVvJ5aQc8bb+zmq99/nCuuupbvPreZVe/I2Tmc5KUeM0u2v8wtt91GLWdhLO9iquhmIu9gsuBmuuhjPOfi4BsvsWbFs+zdtpp62sFEySGBQDfTJQ/TFQ8ztQBTBQ+TeSfjWZtYKRtjKSvVmIFawkgpoqWaNFCMaMnHtZSyOsZyVuopC5WogUrCSC1nol62Ml5zMFb3kkxa8HmGifhV+Nwj+B2D+I29FKMmxrMepvIezpW8nCt6GYtbKAQNJD0aIvZRijEHSY+WhM+Ix6Ii4jYS8ZoIufR4bRrCLgPpgIVcyEIxaqUUtVOIOciGzORDJgphI8WwkVLYTD5goBg2UYwYKEX0VGImqlET1biJStREKWqgEjdTz1qp5c3UimYqBSOFlJZK1kg5I1IOaiUDBt1xigU1+ZzQNVbywhxTK+ol8CdCumtVHbGsmkROhT2mwOAfJJxWEcuOksyPki2qqI3r0PoHGXYN0GPt4ejIIe5+4PN8/BN3sLXbz76RBC+pRL2iTCbj+ltvRyaTseyNfnYPx6RO7gi7BqLsGYry2x2vc8nC2/5FgvTt32/imRdPsvyNQTadstF20i6Yv3kmRhtrjlnmgbVVh8X+uOqgjpUHtaw8qBXgrrG/HtSx8qCOtuMm1p+0seKAhpUHxV676pCBZW+raPnAn9cwl/zhjeELAPDC9R9dV8lkMpbMA4D6OTebgVWH9Kw8oGXVQa04kfxdLW7igzpWHhT09KpDAgy2HDWx+lgj/PIDDuFjc4De0cbXs/rBRrRM65xImWa8zIkPAkDx0M2Of6VGhTPCabnprEcCgd5m/VfDBLJ5XkZgoDkG3tYbZOeA6GLcJbF/DfC3S4oU2T8S55k9b3PzJ2/DGR1C5ZWjdvSQTetYuvRXXHTRQ/PMKxdfvISnn/0pGksPGmMnbs8gkZCCaHQUraULja0HnX0Ag7GTasZEOKwiGFNSyQstTbVkpJDXUSvqqDbiKAoaSnm16B6VRsHj4wbGx3VMTemZnjYwNWlgclJPuaajUtezdMUvpPo55qw/8NzSn5EsjOKMybFGRtD6B1BIwM+blOOKD2EJD6H09KIL9KP09NJtEbrBAXsn1sgQgbQSQ1iJxjeAIypnxN1Pv62HLtNZTmra6bWepd8uHMc33/FJrrjmuibIu/OeO/n1yt9xw623NNmYK675CJ968JvzUvcbH9RbpRaOhv7tgy7gPSOC+dva5WFzl1fUv3X72SiVsDeZY2lt6nCxtdPNlrPuptGjYdTYIt0j23uD7OwLsLXHL7F/YXYNiHtk92CEvcMxdg8K+YDIkwyza1CAQ8ECSr3DDRDY6RXZXqcctJ20S602bjZ1OGg9LgTfW6TWm40dTlGzeHKOk76ZrTkfBG7p9LGjJ8jewTDP7Pk719/6SQIpOZu2Pk84O4ovocSXVOKJjWAODjNo6sAekqOyD2CyK5Hr2inV9GRLOvwJJaWanplzFjZs+A2XLpwF5vd947t06c+g8Q6iDwxjDgyz94+beXbJz/nzm9tI5kYp1XSYgkPIrWfxxIbwxIcIp0cIp4bI5BUUi2rKZS1dZ1/nxlv+izsffJgdfWF2D8W49W7h+H7gO09w9Oxb/zL63/U/L+KMibiaVEnH+KSZ6Wkr1bqBatUgYpOkCJdyXse2zcu58qpr+MpP/sDyN/vZI0/ykiLO3V+eDdB+8onvUc+5mMi7mSkHeb8e5Xw1xHQpwEMPfInLLruNBRcv4ZIF1/LQA19iPO9jIu9mquRlphZgsmJnvGxhquxkuuRjPO1gPGNnLGUWK22hLr1W00aqKSO1lIl6ykw5rqMS11NJGhkvOpgad5LJ2XE6DegNSpwOBUH3EPmEhnRQTSmhoZ43Cm1iRsdYxspUzsFY0kzOqyJiHiRk7Mc5eha/oR+3phunqguXuougTYXHqCAftBL3GAnYtPgdejIRF+mQnZhbTypgJRu0UopYqUbMjMWsjMVtTKXdjCWdTCQdTKVd1BM2yhEzExkXUxkXEykn4ykH9ZSNSspEIaGjnBKM4HjZSr1kZKJuoVo1sXHjcxTyKgo5FdWCSD2ol/XUK6I7WUhddNTrOgIpJdaoEq13AH9CQbKoY9TTjzM6SKmqxRYdYcghqizfOvtXbvrEzXzyC19h4ykb+0bi7Fck2a9Isr7diOyii/ji17/CIZWa3fIUOwZi0ng4xuvaFH/Vhvn2L3/PRRd/0IT4PPc+8iRth7Wsm2NabOxTa49LungJ7InRrdgXWw7raTmkY5XE/q06pJ+nr191WOgDN52ysWcowtK3lGJ/leRXKyTQ2ACBqw4bWSnp9J9/Q34BAF64/qOrqQGcHetKoO+gjlWHdAL8HVBLzJ+0DhuaN+iqOTfm6oYD6ohJUOANN/ERc9NBPBcYth6bdRY3dIZrjlqacTOCDWwYSj4AAudu5qddklZLYlnOuOeP8OaBv3/tDW5UxG3vC81zdzZOhzsHIuwYCPOiMsnjy7bxha8+hDEwxJBjALNngFxWz+uvb2PRovnmlUWLbmX3Sxuwe4dxuobwuYZwWrqIR4YIREYwuAcxuocxW/vIxTTUsxZs7kGSUSX1oplKTksyo8UWGKCUF2G01bKOakVHrTL7ITk+Lpi/qWkD09KamjYwc87E5JSR3z33E2Sy+eykTHYDv//DjwllFJjCQ1gjw1hCg9gjgyTyo+TLaqlT1kiuoiWZV0n9qgoCyRG8CTnepAJNYJguaxendacwBIbotXTRaTxLh/4kvbYe3uzv4Cert3HTp+9GJpNxpeSkvvn2W/jUfQ9y6WWLue6GW3nw8ad5avubbOv1N+NPtveJoNQtnZ55bRzbe0UF256hKLuHE/MA4M7ByGzwsxTjsuG0a17m3rwAcukk34hp2dLpa/67tkqtMVuajnHBAO4anAsARXbkroFwc+zcAH47+kJs6fJKLTUC/G3u8jZzvTafcbOjR0TFrDlqZMNJO5s6nGw47WRtu615+Fl7wsamDhf7hyPsH0mybzjMvqEwu/uD7B8Os28oyq7+APuGo7w4mubHa1/krge+iCs+SDSnYnLSRKGiJVfWEUiOMmrvxBUeIJZWYfP3Y3QO0DXSzlPPP8uD3/8xy19+kxOafozefn756yf4xne+xpL9b3DPI08KDdXPnsIelKOxd3HXPZ9n4cJZacE9992HPz6CMSAnmBSdtZWalrFxwQ5WpENJqaxFJpPxX5/+HGuP6sT/u/4QG0/bmv3cCy65lNvue5hvPN3CV3+2hEsWLuLiBZfwibsf4JWus/Ra+tD5FYSzeso1K9W6WcTBVPRUSlryGRUymYztr+xn97A4KKx8q8ma0N99hKBDST7uYKrg51wpxPtjMd6fiPOP6TTHD7/BZZfNZ4MuW/QJjrz9GudrYd4bT/DeeJypnIfxlJNqwkwtZ6SeMVPPWBlLWRlLmhjLmqkljVTjRmoZI2MFM+NlK+NFG2N5G/WsibGiGJNmkzr8jkF8zl5iITnx6ChezxDRkI5wxEooaCSddFDM2amWHNRLdsaKDsoZN9WMnYmCk6mch2rcRj5iI+s3kA0YSblUxO1KgqYRonYNKbeepFtP1m8i7tSR8BjJhexUYg6KYRtpn4nxtIepjI+xpIta3Ek94aQet1MOWyiHzUymXbxbDjGT9zGedDCZclKPW6klrIylbVTTFqppC/WSlVrFQiGnJZlUkM+MNsOxGxKXWklHvSLc3JWKcHgn8mq0/iF6TJ1YgwNYIyPInb3off2kCkpccQXDzn4GHX3sf3sfV117DV/43s/Y3OmSkh0C7OwLsHc4xmMrdnLTnfdx7/33cOvtN7N0UytPrW7j56vW8xdNkNcUfvbJY/x4/Wv/PoZs5V7aTtjmpWHMGiPFPtfSmIg1NPKH9dI+aZjH4LUcEWHQrccs4vdJjN7mDgcrD2ibE7VWqYVr+TvqJmvYMGleAIAXrv8/LgEA3xgWYZVHDKw6qGX526OsfEfJqgMaVh82sOawXtz4R8UNvqqhUWieUvRNxrBF0go2TzhHZo0jq5uVOGZWH7U2HwKRjm5uAr+1xy2SVtDC2oZZZM7Jq+2kg40dLmlU5hSxGnMq4jae9fxfAWCTAZzTHTwbDC00IeJVBPzu7A+xYyDM/pEEX/3RU/zoqR+h8g4xaO3FGRwilVCRiaq4/777WSTp7BYtupUH7r+fVFyJ19OP39NH0N1HJqKgWjYQS2vQOXoxeQaxmvtIBhRU0xbSEaGJacZV5LQkEyNUCjrGKgbqVT1jdT3j4wbGxnSMT+iZnBRrZsbYBH8CCBqZmjawatVTyGS3IJPdhky2VHq9hWUrfkG6pMKfGiGaVVKoiPqtQkWI+ItVLeMTBgoVNYWymnJNR76iI1PSEMwoMUfk6PwDnDV10GM6jdo7RKfpDGeNHfzl9F+4+dOf46KLL+amOz/PD5bvYGuXl8985bvcfOe9LL7yKu766qMs+2tvU+u3czAqzBOS2HrTWbd4LztFbM+ms56meWNbj4jw2TUYkbL/xHi44ejb0i1Cn0XbxlzGzD5bNyitxjh2S6eXrT0BNnfNdgU3a93mmD8a4G+/PM6eoYg08g0K80efcAcL5jDEdgmwbj4r/vzZbmsP27qFG3nDaSfrTlhpO2mXDj5W1p2wsqvHx87eINu6fOwdirD1uJKv/eAxPnff3Xz7h9/jsZ/+iP/+5ZP8vrWFFdu2sHzTOp7ftJFP3XMvP3vmZ6SKBnJVI+UxE/VxE7GcBr2rn/2Hj7PyhT/x8rHj9Oq6eW7tKq674WY+essdPPCDX7L4Q9fwpcd+hj85Stfg21x73dU8+NA9/F2p5wfLdyKTyfhJy1aWbNrMwkXzpQULF93CjhfX448rsYfkBBIKChUD5bqJ2piJTNlAMq/mnQN7WbhoEdu7PbMj9SEhtdjS6eYnG15j9YHReaaeTR1Ovvqz57n0ssVcfMmlPLvjVY4r+xjSd6N3qPH4TXgjKgo1A6WakWJZx8qWX3PDDR9lV5+f3fIk23p83Pjpe5BddBEab4j/nUzyj+ks5+pxJksh3htP8M/zef7PP2usX7eKBQvms0ELLl7C+jVLeb8e5d2yn/NFL5NpJ2NxK9WoiXLERD1roZY3UkyqhQ6uZGGsZGSiZGWiaKWeNzBeN1It6SlkddSrNup1N5mcm3BESz5pJpd1kEpZicZ0pFJaimU76ZwDb0CHx2vFajNgc+jx+Ux4bEo0Q6cZHepAO9qDQdGNRXEWj34Aq7oXi24Ap0VF1KWnHHVRjnvJBWxkAzYyfgt5v4l6yk/MbSLtt5AOWIm59MTdekJ2NVm/mYzXQM5vpBKxMJ3z8m45xHuVMNM5H1MZNzM5H/+sxzhfDDCZdTGZdzJZdDFWtFMvWcjnDOQzgpWtFY2Mlc3UywZp6amXtdRrOup1A7W6gURegzsqR+cfwpMYQeXuQ+XupdfcyWnNUY4rOxl0DNBn6qB19zoWXbaYR55pYXOnaNQRet0A27t9PPvCMT7z0Ff57989j8U/yLptq/jUXbfzkY99mGuvu5o/qUSY+6YzDla8o+Sjt9zDgktuaZoQb7jjQVYfMUngzzxnD5sFgQL8ifzcVYcMPLpsN/c/9mu+t2wXy/+uaYK/NUfNbDjlEJVvpxySEdLCuhMWdpx1sPqwXnL9in+2+ohgCJe/oxZ772EjK6V9d8mbFwDghes/u8QI+K9DtBzU0SIxfiv/rmLFOypaDulZcVDbPNWsajJ/EsXdZP9mNQstTT3grFO4ye4ds4oQ6SbbJ1YjTqZVAnxrjlnmOIUbDSN2aezrmJOO7pRiNdxNF/DGD4DBxtht3uqa7wLe2uNvjoFnRcGzeYB7BkOin7UvwJVXX8Pu/9mFytWP2jWAIzCALzRCOqYmFRrlL3/ewbLnn+LAW/uIhRTUC2bGK1bGq0Yp1sVAMqkmFBvB6h8mFpBjdw6Kn83ZKCZ1lHNGMik1pawoiq8UddSkD8paWUetrKNeE8zf+LiBySkjk5MGpqYE6JueNjA1JRjBqWkDhw7tkjbqE8hkW5HJTrBw4S387Z3tFKpq4gXR1FCu68iWNaIDtqJmbExPuaohV1IRzijxpVQE0ipCGRWJggZXXIkzpsQSHsYUHKTf3sMZYwed+nY+duP1PPTEb9lwytLU7+2RJ/jD62f42i+W8atdb7OjwZb1Bdje42fzWTebO8V72DRnnGpUAArg12Dy1p92ilBm6X3a0RtgS6e72cu7qcsnfr7ZPy2BwPZ/ZQE3SOHLmyWg2bhPNs1pAWnoRBv3xN7hGHvlMXYPSJpAyf07LwuwW9xXW7sDwkF41k1bu4VNHeLQsv6UlJN53Exbu1V6FgTrve6EjR09flECPxTlqe1vCG3ck99n18sbeG7FMzzz+5/zzLM/5luPfIkvf+U+vvXtL/G1rz/Ij578NjZPDwbPIKPmXrROOSr7MH87/mcuv3wxiy6/ko/d+mkWXfEhZDIZV33kBp5c+zLrT5h4+PGf8rOlK7nsig/x8plBAkk1nmAfX374AR79wTd4VRnm6d1/56Y775XG9l9AJqvOkz08+/zPcIblmPzDeCJyjN5BLL4BLP5BXJFh1KYTLFq0iB+2vsrOgXCzZaGh8WyM//eNpNirEC7vxuuekSQ7+oN85ivfZcGlC7n17ofYfOAEA4YOukdPMaTrxexSY3KrMXs16M0DyGQy/tbTKw4JQzG29wa46iPXc8Mdn+OptbuplhOcr8d4dyLFP8/n+cf5PP84V+DE4Tf/LQN49O3XOFfwMVPwMZXzMJ11M560UYuaKYX11BIWxvJmJmtWJupmSkUT1YqDatlBLKanUHSRzdnIF1zEE0ZcbhUOmxK7dZBAUEsm7yGacJJMu0hlvATCTmwOIw6ngUjUhs9vwuUyoBntQasZZLinnbBDQylupxy3U47aqaf8lGNuakkvtZSDqFuDUzdEIeyglvKTD9koBK0k3AYSTg0Zj4G4U0vcriHu0FDwm6hGbBTDduJOLUmXlmLIxHjKwblCgOmcj/GUi1rCzlTOy/vVKO+WgszkvMzkvUwV3UyWnYyVrdRKVopFC+mkmnxaTa1kYqxioV42isaTklYE3EsShFJFR66sxRZVMuzqQ+0bROsbwBkZYd/rW/nYTf/FJQsX8aXHf871t97G4g9dzQ9Xv8CWbt/ssyf1vn/r16u5/ENX8bEbPs7v1/yBsxYFB7re5LP3fpZLLl3I4iuvpu2kmbZ2C8veVvL7P/bQ8o6CJ1bt46EnfsNjK/cJRk7al+bm4DbIjWYixhETKw5o+fgn75/XZHX9Jx9g9WED607Y2HDKKWLRpNYsER0j9sF9AyFajwqmsPWYmdbjggVcc9QszCON0fAhAysP6nj+r0MXAOCF6z+6hAnkr4NCjHpIJwIqDwiN3wppzdMENtYhQzPUstFxuKYRHN0AdUdnKfHVR83iIToqWkVaGw+TtOk12MC1c2Jk1s3R+826iCX9XyPD7dSsEaQhiG8E+go9l2eO6WN2bZ0TBr210e7QABN9Ygmh/yy7s6PHy8LLFvPqoT+isPXi9A8TjimJx5WEQ0o2rXuemG+EZFhFLq6mkjNQTOvJxrXkElpioRFSESWpuJKwFOuQDakpxQ0UojoqaSPllJ5Sxkg+pSaXUjcr4SpFLdWijmpRKwBgRYDAsbqOyUk9U1MGxib0TE0Zxa/HdUxO6ZmZMVCrq3nwQcFOLlgg2Mn7H7ifTF5JsaohXVKTl7pXyzUxsqvURLeqP6XAFW9kbHXSazuL0t2L3j+A3j/IsKuHTmMHSlcvfdYzyJ1dHO4X2q3Pf+sH7BoMN/s1G0zOzn4Rsrpdcspu7fazqdPTBH0P/3IVa0/Oul4bztf5Y1w7bSftbOxws7VbhC5v6wkIA0h3QOrbdTe1fwL8NSKEJCAogcG2087Ze6ZZBycMGo3MSKERFWPevcNx9spj7JPH2D0ohYYPRprj3209wSZrOFeDuq3bx47eAJs6nOLDXWLVW4+Zmu06Qh5hZfVRKxtOOZpawnXHdMhkMg70deAMyXGH5biCgxQKauolg+iYLRlwheQkk6OUC6IabcPG3xNNjOALD9Gy/jnufuA+dvS4m8zpxtN2UW03GOW+7/4UmUzG/V//Bg/84BdccfV1LHnxHUxRMwrtUW6+5Qa+8OC9bHxxL692yvn6r1q46KLFyGQLpYPFeyxcdAt7XtpAPKnEHRrG7B1kwNSJwTNAPDNKvqzl4KG9fPij17Fn0MPOoQR7RlLsUaTZO5JiT5+b1lf/yrNrVvPMyhU8t66VV7pGeWV0Ni9SMMYp1h3T8+Bjv+LGOz6HNmjA6B3B7Fdg8w1icnVjcvagt/Xx6Pcf4VOfvo2jGh07h4WxaNMZB99+tg2ZTMazS5ex6X+O8P5Emv9zLs//vlfk//nfMc5NF3nogS9z2aJbWXDxEhYsuIYH7n+AibSDmbyLc0UfMwU/U1kv4yknY0k7tYSZatLIeNnM1JiV8TE7oZABi2UUnW4Am20Yp2sUo3EIq3UUu12F12skFrUTT3hJpmJoNKMYDRpG5EOolX0Y1d14LUN4TYN4LUOEnAoCLhXxwCiZmJ5cxE4+4qKWDjJVjDGWCVJNeKjEPYxngswUY0zmI0yV4tRzUcqpIOWEj3raTzFspxx1UIu7mMz6mMh6GUt5qERsVKJ28kEzeb+B8aSDf44l+OdEhul8gPGsl7Gsm3rOyXTRz1RRfD2Wd1HOWKkULGIVLWQzWuIpBbGkklRCydbNS8imlFJQt5p6TUulpiFb0uGIKnFER7BHFMjtPYx6Bxm09dI+dJhPffbTLLjkEr6/dBtPbf8b9z/6Cx5bvosNp+1s7Q2wtSfYrPkUvw5yzcdu4ne7/swD3/gGsosu4vrb7+LDN97M/d97kmVv9LP2uJEV74yy/G8jrD6kY90cEqK1aUSc1aivOWputnisOmKcAwKF7Om7S3b9+xzblXsl8GebR4o0ft+ao2Z29fppPSYcv+uOm1l33MTaY6Ym8ycIFyHNajmkZ9kFBvDC9R9ekgtYPmtPlwDgioM6lh/UzgeAh4yzItSmFkGMfdccM7P2mBjfNsBgSxP8zXmAjllY125tsn8NHeCaY5ZmRMza441xr1Qpd2I2RqYxAp4L/NadbDSDNHLdZqM+NjWdwPNHwk0gOG8EHJhlc/pC7B6IsKtfCvztD7NrMMJnvvQIv21ZwoDxDIP60ygsZ4kn1UTDowx1vkM6bKKQ0JOJqsgntcT9ctLhUZJBJfVSlKlakHrJQDSupFK2UcnbyCcM5KM6ykk9sbQGa3CYTFJLJqGknJOyzAo6AQBLWupVkf1Xr2sZH9MxPq4T4+BxHZOTBmZmjIxP6oUrUwKC5aqKg4d2sab1ad45sINKTc3EpJ7auKjZGpNGyRMTeiYnDdTGDRSqegJpBd6kgmFXF3JXD52ms3SZOtD5+lC4hClE7uym29DBc+vX8M0nf8wD33uiqbNa9c5Ic4y3W4pn2dLpZmu3n41nPWw442bDmQZQE2zdb145Q2v7nJDwE7NO8IYOtFW6T9aesNLWLswU23uDTa3etp5A0/whWEDHv1QUtp1ysW5evaBLauhoNIF4muzfjj7BAu+Vx4XxYyjCC4oku4ciYg2LFoHGyFfExsyyiTsauYBnnc2T/dpjJtYcEeOeTaedrD0mNLBrJBDYdtLB5rMu8f+rSzQZvHbqNIHYCKHoMNm8mnrNwETdJLpiMzoyaQ3FnJ5SwUi9ZMaob2dU0876vTu57uM3cP8PfsWO/pBkcIo1jU47BqJ8+b9/g0wm43tPPcuO/hD/vWYvCy+7nLu/+QRLdr/CUXkPv3pm9r3906nT3HLXl1lwyY2SZu867r7382jtXQSiSpyBAXzhQdyBfuSmDmz+ISJJOWN1LVdcsZhPfPITPN3Syqa/vMWeQyf47i9+zaWXXsqtt/0X3/newzz62Df54pc+z6WXXsLD3/wKa/bs5eUePfv7nOw6McS2diXffep3XH3ttejsQ4TjBpJpCy6/nHhGRbWip1RU43D38MUv3c89997Jm0o7O+Up9ilTLH+zTxiPrr6OK6/7GC+eHmWqnuT8RJr3p3K8N5ZiuhTm2Dt/om3l79m/vZV6xsRM3sn5vIvprJOJtItq3M5Y2sFYxkI9Y6KWNVAvGRir6anXLLjdajSjHYQDPUTCcjx+BV6fmlDATDRsxeczEU+G8HjdKBXDjAyeIeEbJRtVUY6YmMh5qBVE1dx02cv5eoRzVT8TRTuFqI2wy0DMY6Kc8FCMusiF7NRSPsayISrJABP5EFOFGOVkgFTATthpJOm3UYq5mcoFmcwGqSVc1OIOprJeptIuZnJ+prM+ZvJBzheC/KMSZiwTIOgcJh0epRDXkAsbKMStFNMOsgkTyZiRTNxAKWukkDGQSxuIxUYJRgdJ55SEkiMkknJ0usPk8xrKJTXJ3Cj5sopcxYA3oULnHUTv7cPgHUDjHWbYMYg1pOCOO2/ni9/6Jr956dSssao3yLbekPQqxTNJB/gt0jN78133sfjKq7l4wQLWvPZXHl+xi6/8+Hds6/bRetTI0jfltB7SSfVq1uZeJZa5GW/WcsTSzOJb2ZQ3meaAPwHcvvCDp/99k9UPf8O6k3YpOaMhkzKwUtIHtrXbeUmRYMMpGy2HjYL4kD7nVh0x0jJHb99yyMCKAxqWXmAAL1z/4SVGwG/KRWr5YSPLD+pYfkDL8gMaiXZuuIKNTRFqy7wTiXggxINgFg/J0fk6wMZD0hxxSULaZpXc0YbuydbU/s2yf7Orrb0x+rVLobpbxoP5AAAgAElEQVROaSQsxWGcFjlrzVy3joYz2NtkBDedFUL/bRLzt6VzdswnstnEZru509M0IGzpEhEbLyoSfPr+r7B8wzJG7T04Av24oioc/iFiHjlxj4psyEAqrCYd0ZCNackldCQjGpJBFe+dH2eiFiKX0hKIjmD3yjFbugn55KQioxSyerxxJWbvAAPmHvyB3qZYulrSUStrhfmjLIG/cV1zjY0JJnByysDklEFqaNCSr2oo17VUxwUYrI/rmJgUPzMxaWBq2sjktHidmTEyOWVkfEJPrqIhmlPiT4nqL2dMjik0jNLVg87XjzehwBMfxhOXs3Ld7+Y5NW/89L080bKP5//cJRWox5rhyFs63cKY0RznCnDWKjF9re22JtD7dxFCc1tj1kha0Y3N6BRnE+RvklZDMzgXCM5WEs51BTukA4S4r8R9454HAPfJ4+xTJNk7kmD/SII9g2F2D0UEGziSYOeA2IC29wWFllCKftncZBRd0iHGypqjRtYdt4h1wsq6ditrjwktrGDNhVu+9YSV9aed7B4IcccDX+cP61YTSCiJJpQkcyoSGRXJtIZYQkkyoSCbVpFIa0mltCSSWla0PItMJuOjt9zBo89vYfdwnJdGU+wbFqCvAQCX/qUHmUzGxQsu5hvf/iq7+7zs7Auw5oCSex95kmtvuIWP3nIHz27Zx7HeIzz6g68hk8m44667eHbtWhZedjm/W7kEl28Ad3CYYFjOgfa/sqx1KZcuXMjiK6/iuuv/i2fWbuDKD10573655RM3ce21V/O1b3yR011vEYqOEo8qiUeVpJIajPpTrGp5hs9+7lNcdNFFyGQyFl22CJlMxlcf/iKHD/8Zv19FKKInHFQSCikJRHQkM1aicQ2VsoZUapgHH/wcjzzyZfYf722OmsV/8wIWX76YT336U8xUorxbizNTCvNeLcb71TDvlYPM5DxMp53MZF2cy7mZybuZKXiZyHkYzzqYLDgZK9gopkQjxljZyMSYmVLOhNvcSzmvZ3LCwdSkj2LJSTTuwOd34nRasFp0KOW96BS9GEb7cekGyYUMjBUtjOUczFSCzFTCnKuEebca4d1qhHOlADOlAOfKfs6Vg5yvxpgpR5mqxDhXS3K+lqSeDlAM26gmPVTibsoJJ/Wsn0rSTS3jYyzjo5pwkQ2YqMaEwWMy7aYeszKecDCZ9TGV8TGdD1BLuilFzUwW7czknYznbOSjenIxLcmIkkJaTzZlJhXRkIyoSYRHScZU+EODhONDxNIqoikF2bwKh7ODp595ko/ccBO/2flXwlkz2aKaSEpOKDWCwTeAytXHsK2fHn0Hm/e1cemiRSx97XhThrFTkunsGojwwkic/fI4++Rxdg9F2TscExFNQ1EefOwprrz6Om68+UZ++PMf8rIiyB55nJ0DYZa9paTlgEaSG80mUjT2sma+7RGR69cwX8zd0z64Hl22+9+bSFbtE6YOCQCuOmykZU5I9LrjZnb1eln2tpKVB3WsO2Fr6tzXttubwdLNPfWwgaUXGMAL1394CQD4l4FZg8dhQ5MFXCmxgk33kuT0XTMH4DXyAputIVIcTPNkdHiOQ6oB9k7YmiPfhs6vAfjE38PAGqkCR2yIJskoIm2WJ6ysO2Gh7YSFdcfNrD0uOhLXSB2JayQGctZJbGbNMTF6Xn/Sxvp2CxtP22k7aaXtlF0K9GyARxHCu7HZLyxyBBvjxetuvJWfLl2K2dtHJD5EIDHCiL2baHSUiFNOKW4ln3RQSplIhdSkwhrSETGuSYb0VLM2cgkNwagSr3eAdExLveQiFlfhjShQmDoxeIbpNfQQigwxVjYxVjFSLWmplfXUKnrGqjrqUiOI6AYWcTBj43qRjTZpoD6uo1LXUqiKrtVSTUtlTMfYhKhYqo+LJP1STSuMJFMGxicEEzgzbSBeGMUQHEDt60Xl7cUUVjBk62TQ1kkgqSBVUBFMjxKWnJYymYxvPL2S3754VGL4fMKQ0S16bLf1CLC98YyLjWcESysA3xzQNyfzsfW47V8yIptRQs04IQtrjlrnfGCbm+9163GLiHqRmN/GOLjtpKvJADalBCcdzfy9BvDbeMYjTCHdYlS9Tx7jRWWK/coULyrj7JUMIHuGY+xXpNivSLGzT7AQ2yQmsiE12HTGxaYzLtafcgizR7uVtnYbmzqcrDthYe0xMeppOWyk9aiZ9e1WNpyUvt9uZVOHiz39Ia768Md5+8grBBMj5EtaauN68mUd2aKOWtXKWMnEWElPPqMmHJHz6ittXHb55fzmhSPsHo7zqiaDJujkj0o/Lwy62d4fbgLAbV0eZDIZRzqE1nBrp7OpZ9zWE2DTWRc/WLKFSy9bzENP/IbHf/6TeSDu+ts/w90PfxdnSIHR1cs7B/fO+/5zLx/j3q8/2vz6wx+5lrvv+RTd/W8TTqjIZ3XkMlrKOQPVooVUTE0pa6SY01MumsllzeRSBrzuYey2PrxuBTbrINGInlBQRySsJxgYJZkwEwspCfvk+LwaDOZuEqkhJiaMnDzx4mycjNSpfNVHb0Amk7F69Spsmn4qcRtTBR+TOTHencn7OJf3cC7nZirjYCrj5FzWxbm8i+mil7Gck7GSmcmSiIKZrNuolc2Ui07G6mHKpTDRiBe/z4bNbsRi0RMIunDYzIwO9zPccwq7tpu0X0naqyEX1lLL2jlfCfNuLcq71RjvVSOcKwU5XwpxvhTk3UqI8+Ug58oB8VoK8G49yj8m0rw3luL98TTv11O8V4vzj4kc/5wu84+pEu9PVxiv5Bmr5MklwxRTYeo5NxMZFxNZPzP5IGNxB2MxK7W4g1LYSjVm5+3X97Fm6W859tYfyYes1FMOqnErxZiJQtRAPqyjEDeRjRtIx1RkU2YioVHs7h7C0QEmpYiqmWktb7+5kcVXXMHt93+Ny6+6hi89/BBDQ39l5aZWfrV6PYcUCtxxFaHUKHv+tJ3P3PNZFl52OY+v3MPuwTB75TERuD4g4pheVaXYPRjhRWWC/YokO3oDTZf+/pEEt3z2Czy96QVeO/AyV37oCl4fGGbnQJgt3V6WvDFMqyRNahw0myHOc6RLjRasFXPA3yppNaZhTQ3gQR0fv/3+eU1WN9zxIGvmOHvXnrA22cSWw2L/XH3EwPK3lSx7Ry3SNw7paT1ubRIarcdt/2I6udAEcuH6T6+rZDIZy99W0iIBp1WHDU094Ko5tvVZEGaWqHIJBEoAsUX6OTHOlR6qY2ZhCmkaPmbt72uOGmk92nBN6QVoO2YWI7FjRiloehYENkfD7TbaTtrYcNIqmUZmnVlrmjrCWRPJXIDZ1jSS2Js9q+sl5qjRz9pwhG5shkkLXdiWLj87+kL8es8BFi2+HL2nn0B0iFpdhyumxOof5uTRP5EPG8kE9YS9SqI+FQn/KInQKAG3nFxETyllIZ3Q4veNkParSPpVhDxygiE5Fnc/Knsvarsci20Ak6uHQlpNIa2ilFNLTKBGMIAVLXUpL6tWExEbYxN66uM6auO65vi3UNWSKatI5EfJlFSUa1rKNS2VuoZKXUsoo8AeGyKSVeJPykmX1Niigyg8PYy4uum1dzHiGUDuGmDA0onW04c7LufVt/fxvR/Nbui/ffEYOwcjbO32CiesFKHTZMO6fGzocDVH9Gsl16tgv2bzHdvabfxy599pPfFBsCfW6nlrdlzTqCRsHDIaZoq1UmD43AihtZKmtCEnWH/SzoY5LOLGDpcAf10+IQWQNpO9I2KT2TMUlvR/EfaPxNkvD7N/OCzpAaMi/kXSl27p8rLpjAh+bmu3sfG0AH1iI7BIhxXx7LWdtNHWbmVHj4/NZ1xsOC3c7rv6AvxgyWau++jHiCSGyJZ1FKWojPqYkUJZTzqvoVzQM1a2UC0aKGX13P25O3j0F8+wbyTJ6+o4u4divDjkkTbQCDsGIuySxsB7hmJcfPECbrldBOXu7A+yfyTO3uEoewYjEtsSZvkbvVx25VUsWnw5Fy+4mCdXbeOR365lzWENiy6/kh8t34zarWZAdRyZTMa1H/kIv97yGnuHIvy47WVkMhl73z5AMq0mldYST6hIpbQUciL8fKwsmiPqeSvVgol60UIxayKXNlHMWUnFdAQ8gwR8w/i9Q4SCo/h9CqIRNemkiUjYQMhvIOJSEvdrsNr6SCW11Mp2lq34FZ/74sM8sXo/X/v5En6x5X948PFfIZPJeKNTwfu1MOfGkoznfUwWvYylnUzlvEzlfExmPSi6jzCVdTOTczOdcTKesVHLmJjMOpkq25kZczJWtZNKOrCYR1GpelFrBtAZBjFb1didNhxuNzaHC7PVjt6gx23TUUgZKcf01HNmpope3q2Gea8S4N2KYCHfrYQ5J4G/8+UQ71bCYlUFSHx/Is17kwXOT5aZqJeoFjMUMzEK6Rj1cpZYJEjI7yKbihHweYhFQ/h8XiwWMy63jXDQQSZup14IMpYNMp7xM5nxUovbePC+B7ls0SdYILUbPXDfg+RDRkpRE6W4kUJERzaippCxEg6riMdUON0DRML9ZDNyJif1VCoqNm76A1999PssvuIKvr9kM4+vEG7ym279BFdcdQ13PPhNbrrz88hkMv7ScZpsScQEfeFLD7L5hJLdQzH2j8Skzu3Zys79I3H2DIXZ1Rdge6+fHf2h5nP6iirNnV/+Dj9d1sJZ5VEB/M+MskuKilr6xrC0f1hnmb7Dsyzf/D7f2bXi4Oxqdv0eFpOvVUeMrDig4/sr9vDQD3/Dj9a8JD5rmp85Yu9bfcTImiON3EDh9F3y1ihrjhhY/ncR+9JyWNLOn7Cy4bRLjJCbcWomll7oAr5w/YeXiIH524gkQjez+rC+yfI1QNlaaVS19riF9e1W1ktxFSsO6purUQknWDoL646bWHfcIjF4Asy1Hp01iwgNlLkJAMX3TRLgk4CdtDE2HFENZmddu422E2bWHDM1HVSNDMGGCWXNPNBpnucobmu3NwM9Z5tFxAhyblBww4W6QXKIbu7ys/GMiyuuvo6fr1yLKzJKqaqnUNFjDgyzbdtqIh4lMYeSpFdN1C0n4dOSDKpIR1VE/SPkkwZc7mHcjmFyAR3loJF0WIc/MEwsY8DmU6Gz9aMzncXt7qOY0pBPKCnn1FTyaqoltRgFl3UCAEq5WdWaYADHJvRUx7RUx3T4UiO44sOEMqM4YnK8yRGqdR3VurYJFIMZBdbIIPH8KOGskmxZw6CjiwFHF/2OHoZcA/TZ+jitbUfr6WXU1UOH5oTI9Lta1HM9/PNl7OwPs7Xbw3apCq0B+jZ3+dh41iMYuJOzI99mtM9xK20nrWw4JT4gN3XYeeS3beJEPhcEzgN91lltTnPNz5hcPZc1lDSmDTf5xtPOOaDPyeYzIgh6W5ePLV1etnZ52dopvt7R42fXQJi9clEFuGcoIuoCB4ICCA6H2dvvYtdAkB29fnb1B9krj0ldwGIMvFGKehHxDzbWnbCwXgJ7LYcNtDbu0aMm2k5Y2dEbaIY/t0qHns99/TE+/60fYgyZqNR0lGoGihUjpYqebEFDLKUglVGRyuqIRpTkEwruuOM2br3rPnb0SbrEHi87mqyfyLdsdl4Pxnjo8ae5+TP38d+rdvLyaJL9igT7hqPs6/dyyBTmpZEgO08qufmOO8UY9vIr2XTWyXaJKfzFtr9x6aLL2PjGYcxBPR+74XpkMhlnFT28qgyx+YydSxddxto9u4il1ESiCvIZLYWMnnh0lFRcTTlvol4wc77qol6wUcyYyaWMxMI6Qt4RYkE10aCSgHeIeFRONqMgGpYTCigIh7QkY3b8HgMe2zBRr4pU2Eg8rKO/VwCAD9/8WS5ZeJtU83UbN9xxP7d/4at86vMP8f+ey/DeVIrxgoty1kY6bGA87+Rc0cu5nIfX92/mXN7HdN7JVM7NeNpFPelgPGNjuuzivckAtYILs66LRERFJCgnGlHg9qgwmTXoDTq0BiNagxmNVofdbkOt0TA6MkDKr2eibJsD8CJilcPMFIO8Ww5zvhxqfv98LcL/Tud5b7rK1ESNWrlIPBYl5PcQD/soZ+NM1fO8O1VhqpYnEw+SiIUIhUIEQyGisRjhaIRoLEw46sfltZJI+ihkA6QjZibybg78eR+X/Zts07f+tIty0kA1ZaKY0FFMG0klDUTCChLJUaKxQWo1DdNTeqam9GzY8Fs++tFr+ekflrH+71080bKXz3zle9LUYDXbevzskSf47YvHkMlkOHz9FEoqfvGrx1hwyQK2vrKZvUPiudo7HOVFZVLK3ozwiirN3uGopGuN8IIiwcujSXYPx9nePsolCxfxzpk3+eLD9/PgI98Rh56hGG3tVla8pWxWkDZYvrlNWCsOisq2FQf1HwB/+jngz/AvY+HW41bWn3SwpdPbDIBvTK7WHjPTctgo7W/mpuRjzVEzW8662XrGyfK/a8S07aiJ1cfMrD8l/qwtXX5xmJXYwGVvKS8AwAvXf3RJDOAoayWAteqQTgC9kyKeYu1xs6RTkpi1YyZWHdYLx/ABrTT+NUoZRyZxk0vgbfUhPasP62k9amTNEQOrD+tpOaxvpqULlk88RKuPGFl9WIygBWBsmEg+mLtkovWoidWHDZKBxCyBPss8RqhxUpqrMWys2eoeR7O3uG2uS/SUQ5gDmk0js/2wW7r8/OGPHSxafAW/bW0jWxC1a6m8CotrEI9TjtPQR9SpIBvQEPMoiXuVxIIKYiEF0dAoAe8QPls/MaucXEhPMqgknlShsPXRrelAa+umkNZic/WLRP2snmpBRyWvplbWCOBX1lIri87MakVDuaKhXNVQrYu/T7muI1FUE8qMYo/JGXB0MeLuIZRREMkqieVHyVc1BNMKfKkRQlkl6ZIGc3iIfkc3A85e+h29DDp66beepd98hgFbJ4PObp5Z+ot5473NnR629fibOXgNg0fD5NF2ysm69lnt3dr2OWzcCVtTgyPcd2bWnxQSgbUnhAZubltMy7+wfeZmwHgDAH4Q9DX0pWLsKsa8GzpEh+7ms242n/WyvRndIoDbjt6gCHeWun5fUiaknugQuwdCQv83KBjOXYMiqmXXYIS9QyFeGPSwqz8ojU4FA7jlrIctZz1s7HCy9riFDafsQst6ys7mDhcbTzvZ2ulhT3+ITWfcTfC39oSVzWfcLP3TGS65dCGtOzdRrOiIZfVEU0oyORXlsp7xuoVK0UwmqcXv6mPZ6iVc85GPsfytIXb0BdkxEGkaP3YNxcWSfr1nOMbe4Rj7RxK8psnwJ02Ml0cF4/n7V0/Oe6/nrk0ddglcChH+9t4g33p6Fbff8wCeuIHnlz2NTCbjnoe/w2ujEVrf7uOiiy/mp6178MRNRBOjJOIq0kkd0cgooZCCdFJDMaNnsuqgVnDg94zgdHUTDMsJBoaJBIZIJxSUilqKeQ21ioFCRk0yrsTvVeC0K3DYRwh6lZRSFjIJG8mwgZH+oyxYsIAFl1yPTPZeE9BcsvATfPbrj3H/Iz/kH1NRzk0GqFXc5ONWMhEjCb+OQtLIZNHBZMnOZNHORNHKZMnGVN7BRNbOVMHFRMHBZMlD3DdKLWeikDRSyGjIp3X4PSqcTiM6nRaTxYrVbsft8eL1+dAb9CgVw/hdBibKEc7XYpyvhJmpBDhfDfFuJcS5YpBzpSDvVaO8O5bi3bEc/5ipUa+WCQaD+Hx+ItEY0UiEbDpJNpOiXilSL2eZqmWp5eNMlNPUS2m8Ljsupw27zYzDYcPjdRKKeIjGPcSSPjx+Kw6PkUzSzuplv+PiD7RiXHzxElYte4ZKxkIxbaSQ0lDKGvGEhAa1UDCQzqgplUaZnhZyEpernauuuoLbb7+J3sEDrPjzKb7x2GPs/uN+Xu9W8KY+wh55kvu+K2QFn7z3QTYf6sEet7DzpTY+dNWVvH7mBHuGo+yVR9k9GGbPUEQ6pCTZ2Rdkj9To8Yoqw8vKKM+9fJzrrr+Rx37yOFtf3sRHPvZhNp22smsgytZuL8v+NsLKv2sEyDoiTb4kSdOKgwaWNxMwdBIA1P9bJrBhgmw7YWseKDdIkqINHS4xBej0CuPjMbGXzv18a5ASa49bRSzUcZOYvkkM5Grp5zaf9bC9L8imTm+zgm7526MXAOCF6z+6rpLJZKz4u5q2dttsDMtxM+vbLbS1W2k93oht+YD2T6q1aTlsYJWUIdhyUMuaw8Km3giLXnGooSPUSyclw7yA6AbFPq8aZ86Jam5LyGx2oKUZm7HmmNBtNBjIuSPBBgBoAsI5msO1czRns4DQLoCCVPDdcBo3cug2drhFJEhPkCfXvsTlV12DxjlMsahlYsJAMqPEZJdjUJ7Fqekh7hoh5VETtA2T9CrIxnSU4gaSUR15v5560kY1bGIsbqeatpKOqwlERzB7+okFFSjMZ4hGFGQSoxTSKso5NdWiuhkHUylqqFU01KoaKhUN2aKKclVLqaYjUxbgzhEdwhYZZMDeiSHQR76iJlNS407I8aZGSJfURHMq7DE5+uAQI+4++u3dKF296ANylM4eBqR4l1FvLyPuHtS+Xq77yLXIZDJ+93K7yPLrFyBgSwP8nfGwoUMwf8J48YERbAMINvV4tqbzbd0JS1Pn2fiA/Ndxb+P9NjcjhVqPW2g9JgF9SQ6w7oRVMNFHjDwuZXv9cPULbDzlZFuXj81nPc1IIBEALoDM7gExYtozFGW/PMau/gB7BoLs6Auwo1/S+vX4BfDtC7K9xy9pHr3s7PGypdMlwOUZV9MA0qh/E4cWIW9oO2FlgwT+9g6G2TsUZmOHk81S/+/uXj/bOp1s7fbxX3fey1XXfZRv/vQZvvaTZ/nLmbOcnzETS6joUXXTPtzLW6fb+eRn7uKaj93IT9e/yq7BGHtH0uwf9LLjhIKd75xg35tvsv2Pr7D+tTf4xmNP8JVvfo2nf/80z65axm13iBHwDTddz3MbtvKHV09y6WWLkclk3PXlb3HjrbfyuS9+lZUH1GzrEZE3m7v8kkY2TOthNQsuXcja199pAsWfbniNF0ZivDoa5fcvHOKShYvY8nY7x5VydC4FyYiKAcUpjiqGMbkUJMIqsjEVgwNHeLvrNM64CWvISCY5SqWgZbxqZLxiZKJiYrwmVYhV9JTyGuJROfGoAq9rhFzSTCVrJxE2kw4Yuefuz0iRNXcik/UgkwWRXfTf3HjrJ7jznnvIFx1EwlpSASP5kJlizEouaCIdMpIIaiilTMzU3EwXnMyUnZyvuzlfc3G+6uX98QjvT0Y5PxZiph5hsuxhomqnVnSSijtw2PQYTGaUag0KlRqD2YpKq8fqcKHR6bE77YRCQTweJ7Ggm0LKy3g5JoKpp9K8N1nkvakqE2MVEokE6UyOWDxFJpsjmcqQzuYplUrk8zlKpSIBf4BYJEitmGasnKKei1FIBshGvSSCTgqpINlEkGDARTTiJpX2ksuHcPv0OH0GDOYRXnlx+79tN3rjL7vIpnRkkioKaQ3lvJloTIknrCSVHiUYHqBSHmV62sDMtIGZaSOp1DBf+9r9XHrpJVx++WJ+8+vHmvfHjsOd7JYn2Nrt46EnnuHmu+7nmo9ez6hjGH9qlO8+/i2+/t2H2TvgFiaPkST7FEnByA/HJBlGhBX/c5aP3Hw73/rhD7ls8WKWrl1Cr+Esq7cs4467Pi3qJLs8rHhnlCV/GWLdMUFMNB22h43N0e7yg1rJACkYwOVz0jDmrpYjJtpO2tkhBcU3TBuNadKGDpcE7AQpsvaY0Ku3zplmic898bm14ZSD1uMWae+TZCwnrGzvEUByW29QEBGdPlYe0FwAgBeu/+i6SiaT0XJIJ3RKpxxsOOVgU4dw1K5rl9xRR4xNRq7liGj+WH5AywrJLbyqCQbngjjDbKxMg0I/aJhTI9d48OZnDDYo9UY9ziwINDcfitlIGWsTEDRB4jHzLAPUYP2OWZvgo3XOqasRPbPuRCNr0D4nY9DZjCWZBYJuNneJkNHtvUFuvfshHv35U8QSoxTyaqYmjISSWky6Ttz6XiI2BUmvmpBtkELEQDqqIRkcxe7sJeFSUI9bKMT1ZOIG0mEN41k7+aQes6Mbu28Eg+0s+aSaTFxJKaeSwJ9GAn9aigUVlZKGWlVLvqQmmVeRL2uIZFX400oMwSHMIdHn640PU6gI3V+mpMERG0bp7cOTGMGbVGAIDdNv60Lt6cUWGcYWVdBp6KDffBpjoB9jaACtvw+dvw9HdIhNe9dIWrEQ23tFTt7mLj+bOr1s+P/Ye6/ouOqzbV8B00JeILwkgTTSSSAJEEgjeVNIKAFC4IM39A5uclOxLRfJNiVAKAkh4C4XdauP+miapvfee1EvVjP5H17/g9+eGZnk+0449V5rr/HMEkvgGWbf+3nu+7pXiD8R9jCy+aShUGuWF4SbVrwfZ+F9JItBfvKcD3aUHtWwsVZXwC9sPDrC1nqD8INK7+VGyapQdlwELcpOGth4VMM137q1AGdddcHX+OK3f0z5SYMU9LAX8C07Wp1Udwjf3x6Zn1f6guzpdFPd6aa6K18P6Dmr8WNnWz7hbJUaP6wi8NIg/ISieUSsuAufY8mWUF5npKrZRnWHm30yLxX1QhxXNIgkcnmdmBxU1Jv4xUMv8bUf/Jjv/+oPXH/bHXzuK99g37uv8tnPX33WZO7rN/6cnW129rbbuf+5dXz12q9QUlLChRdewJe//AW+d903uOH6b3HNNZ9j7UuP8NrLW3jg/t9x/x9/ywfvV7N2jfDFXf/TX1PTE6Sm28eT+w7x0z8KTMzda3YJ4XzKKa34xYVpd4dYK//0/qdZdaFI6f7v1jfYI/MLY36nmz3dXm57oJga/+7PbueXd9xVeH7z7x9kzbatPPzM01x0yacLrx89/lfGMhomRzVM5/TMjpk5PWZnNmdiftzGVFrPTE7PzJiBqawBn6sXj6ufeEhHxKcl4BjmH+/s48ILr/m7/xUAACAASURBVKWkZA8lJTdSUnIFJSXn8Y1vXsurr1bh0vUTsSkJmOWk3VoiNiVpr46EV0PQLiftH2EibmZuzMrsmInpUSPTOT3TGQtz426mx5xMpG1kI1YSQT3pqBa3Q4tmRMOQfJjW9ja6ZDK6ZDI6u9rp7e9BpdWjNZgZVuvQmazojGYMJjNmixmbzUw6FSEajeD1B4nEkkTjKeLJNPFUllAshS8YweUNEIzECUZiBMJRgpE4bl8Au8NJOBLBH/ATCfmJR4PEoyGiYT/JeIDp8STxeBivx4nXY8btMohmEYsCi6kft3WQm2+69ax2o5tuuplwYAhHYIBgdBBfYIBQSE4wOIA/MkgmqyQU7mNyYpi5OTVzs2qmpzXkckoyaTldXe8xMPAh3/zON7no05/hi9+5kXUf9hcZjz0hvvPj33DZVVfT0HcSrUfGB8de5/LPXs6r3Rb29gZ5dSDCa4PifKXXz56eAK8OhPjit27g5tt+wS9v/zl/P/42nSMtDJtauOOOn/Pgk49R1e6lstHE8+92svGgnA2H8+vd4uQvv+LNUzDWSEi0fPNV/lq2ciBRUW+Wvgecwqu3YhJYLgm69YfVK66RxSrVPB93kxRY3NpgZlujpYA/2yJ9j5XXGSg7qaey0SxajppsbDikPCcAzx2f6LispKSEzbVqUSbfJqrPdrU62HJspGBQLT2iLsTV164ARItT3CkVugolXmDpYXUBFL1uBTR6pYF27cG836I49SuYag99rE7urBq5YuhDiAMpwi+dK71fH18BbjlR7HPcuEIYFphyJ41skaZ/W1b4AfOcwTxeZFuLnZfebeWiT3+GNVVVpJIKNm54gnRmBKepi7R/hIhDQTqgJ2jrJ+5TkAgqSYSGiUeVuOwypgJ6Mh41UZ/AxUT9CryWXrzOPuxOGU5bB1H/IOmogmxCSS45TDYpJx0fJJ0YJJMcJJuRk8sNE0kO4osNEEyIR3uwF6WjgxFXFyafjER2iFROTiwtxxXpR+PuYsDWji3Uiy3UT7+5DbWzkwFrF43KRhoVJ+kztqDzdKHzdjFgbUPv7cIf78celtFq6Ob8VatY/4GM7S3WwuSvolGc+clfgeFXCOToi4Bn6e++TGp4yeN7fnzfUwVafpkUmCj0ZkrgVNGhqaLsuJayE2LSV15npOKknsp6AxV1Im1b2WDivg2vsOpjaIZVF1zLn7a8Xpji7Vyx9t3T5RFm8w4XNVKf6O4OEYLIt33skiDWO9tc7O7wFJpMKhstkuiTwkYrPnNiQqkt+FArG8zsbHWwT+Znn8zLjlO2QgJ55ykbO07ZBDan0cL2Fhs721xiXdsbpKbLUxBHV1x5JWv2vkvlOwd4t0vDo5Wv8a3rb+CSSy7m6qs/x/GjrxPw9DMR0zOXsjKbsjGTsTOVtJIKaIl6FYwnjEylLIwlDIxGdXzuqivZXFHGnt6gBPEW7RzVnW5qZH4hgqW/N4HKcVEteQp3dXh47s0G7llbzXd/ejuPb6xkffXLbH/7Pd6UWdjdZmVns551/+zi1rsf4frb7uDFt5vZeKCf639xJ1/9zvV86dprufTyK3jhrQZKSkpoOfUek6NalqZcLE35mZ/wMZuzMT9mZ2HMxeK4m4VxO6fHbMyOGRlPaYn4B/B7hgm61eRiVmI+LTfdeAsXXVgUND/8/s0YlTLCNiUR6zAJt57RmIfplJ+Ux0DMOULIriJkU/DSM4+ScCuZTJqYm7ByesJOLmEmHjAT8VvwOI3YLSO4rDosBhUm/TDD8l46O9tpbW+mu6eBnoEOevo7UKoGGFYMMjA0QP/gIBq9EYvDjTcQxurw4PYFcPtDmGwOrE4PdpeXSCxBPJEimc6QSKUJhaOEI1EisTihcIxgOIovEMYXDOMPRfD4AvgDAQLBIG6vF5fHQygcxB/w4fP7cDqdeLxuvH43NruOZCpALO7F6TZgtasJhQ34vRr+9vYe1qx+grfe2kUoMEAyrkTn6sEX6MHl6SIZlxMIy7G4OojG+kgk+5iZUQoBOKdmalrJxISabFbOX/+6hVUXrOKGX94twke9Rbh3XgDev+lVrvnGd3FGFQxZ2/jn8Tf5zH99hld63bzcF2ZfX4gnXz3GPc+s5ue/u53rbvwBN//sFj53zReQaVtRWttoVTdyvLeWuoFGvvXd63h0wxYqG038/vnt3HzXn7lrdTVrPpQXfeyHVkz7pMHGmoPD0qBDXmDjrj2oLFSe5tutCs1UJ/WUnzQU8FHl9SY2HdNRekTNmvzQRJoqrl+x9crXpG4+pmN3u5M9Mr9kDzEJv3KdQVTHnTSwvcnKzjYX25ptbDqqPicAzx2f6LispKSEsuMadrcLAHJNt4/yE1rJGyFhXj7mgSgaYJWsO6ig9LCKjStKrUslTtHa/5voOyimgYVR+uGi6Fu3Yor4b6zBo5rCunflWnhTrVYKkBTXwiuRInmRseVEca248ZhuxTr47JYR0TpRrAorQqeLXsCtkin3mddqueCii2loO8CAoh6brZ1sXMdUykIuaCDhGSHsGCbuVhBy9xHyDeB1ybC6u0mGlEwE9ExEDGRjGkLeQULmXiI+ObmoimRgiFRomGxcSTY5RDY1RCLaTzI2QCYxRDYtJ52Wk8rIiaXkeCJ9WIN9aD29aFwyFI4u1K4uQskBYplB4hkh/rSeblSuTqzBXnyxAdSubpT2TuSOLhpV9TQqj6N2tjNka2PA1saIuxODtxtrsIcRdyd/a/gnv/rTQ1x21dVsPjrM9lN2qXlDTEnL6y1sOq4/a227EuNSEILHi2vfconDV1ZnYvX7vQVMzMajI5TXGSgt+EKLSKFNx0YkDJCWqiYLlfUiHLTl+AhlJ/RU1BvZ1mjmJ3986j/CWX/2p2fFnbvU95wXf3u6PBIA3CmhUIRI3NEm1r01UjtHVatT/LPtLrY3maWqN73klR0pYIg21Qr25cpUel4A1ki/a5vUX13VYqXqlIOKOoNUF6enoiHfaCNSyS/3Blj9TiMXXXwxH364k5tu+i7XfedrfO6qKykpKeErX76av721A7W8Hre5m6mYidm4hdNxG6fTDiajJtJeDZNxCzNZF9moidGYmbGYhYm4hamUjXvu/A33PPZc4aKcr/Lb2ydaXaq7fFR3etgt/f1UtQohXNMdYI9M+Aq3Nhj+zTd4wy/vFKK6w8WebjG9qe72USMLsKvD9W8/v63RSElJCTpTK3PjdmbHvIyn7MyPeVka8zKftbM46WRxysvClIeFCTunR23M5IxMZAyEvAp8Ljlepxy/S0ncr+Ovr+xg7fOPcegfbzCZcHM6LQTf4lSaj+YnWZ4bZ3l2jGzAxunRKIvjUWZTXrT9TYyHDczE7WRCRtJhC9mYk2TISdBlxG0zYNKrMOk1jKgU9HadwjzSg0rehEbRjM3ShtHShlLXSVd/B7LBLmxOGz19ffQNyhkYGkZrsuIJRomlsgTCMfyhKIFwDHcggi8UIxCOEYmniMRTBKMJkukMsUSKcDRGJJ4glkwTS6Zxef34gyH8gQDhSIhQJEg0ESYcC5HOJUmkY6SzCRKpKJF4BJfPjj/kIJXxEYm78IZsWN1aPF4Nfp+SaFiB3tZDMNxPINRLwN9FNjNENqsiEpUTCg+Qyw4xPT3CzLSK2VkVc7NqZmdVkgBUMDS0n/POO4/ntlaxty94tvDrLZ4PlL/BV7/7AxpkR/n1fffz2c9fw42/vY99vQF2dXr53dObuOTSS7n9nt/wf556kHsffYj/ufdePjjxHjJNA1V/e5/K1/fxyIZyvv6DW7j0iv9m9XudfOEbN3P+qmvPqmh76YMhsd6VBhprDipYs1/O6v15Dm6eizssodGKoZBSKbyRZ5FuazSz85Rd4sva2Hxcy/pDyoKfUGzMFCvCk8UmrVIJW1Z2Qs+udicbjqjY1Sq1CTVZJSqAiR0F0L2LbfX6cwLw3PGJjstKSkpYf1DO9iYLW45r2dZkYWujSQQwJH/ESgr5WmmVu+6gNOmT0r2ba9VnCcCC1+/w/2P69x+i9OKfKTIE1x85WwSu9A4WaueO5JPFZ6NfNklnfsWYr5dbyR3cciI//ZMwJCcNBR5gkQko8DBFRIxFCoTY2dHm4sbf3s+nPvUpPv+Vr/PnNaUoDDLCITkuSw8xp4ps2Ew2oGM8picVUeBzd+N0yxixnsLk7sbo7Mbp6SPkH8SmbyPhHSYWkBNx95MJKxhNqMgmhxlNKcjGpX7gpEgCj+YEEiSSHCSUHMQZ7kPv7Ubj6kTl7KDf2orB140v1kcyJ8fo78HokzHi6Ubh7BDrXq8MlbMLpasLjasDpbODQVsbCkcHalcnRl83Om8XJ3qPceef/8wFF13Mt2/9NS++28L2QsrXXIAsb6gd+VgqV3eWKM9DvcskoS2K0Q0SJsZUWJdW1JsprzOxWbqLLvo88ytg6X0/qhZoFUkMlp3QFXh7FXUG7i19+f8yAfwLO1sdYr3b4aa63cXuNge72uzsksTezjYx7dvVlp/45U87O1odVHf5qGq2sLXJIhAvJ/TFhHttMYS08r+/ol50WOcTggI542B7s8C/rKw0rJB6rSsbxZr49cEIO+qUXHPtN9i6o5SZaSMz0xZSST1xv4agXcF4QM9sxMSsX8tCzMpS2sVczEbOO8JkyMBM1MLphJ2FMR9LM1H+tZTjX2dGWZrPsTiX4c3XdvPpSy+l/MMOqleIvz09QtxVd/nZ3Smq5Kq7RYp4h2SL2NXhoabbz57eIPt6g+zp9vHcmye5e/VO7ivdy+ajcgHq7faJLugOt0gjd/upkQX4wa/v5dIr/pufPfAsWxuMPPOXE6y64AI0Q81kolZOTwRZmgqxPBlkadLP4oSLxUk7C+NOFqbcLEy4mBk1MjdmZm7MQiyoJOyT43fJ8ToVhDwjBJ0aIk4dYYeGtFdH1m9mLhtmNhtiKuVncTrHZNxLLmxnKuYi6dEzGrIyFrIwnXQzlXCRC1vxWNXY9Uq8dj1eux7jiBytahCjVo1Rq8Cik2MxDGM19eC09JAID5FKDOMPDaAydqK39qExDqPW6egfUjAwJEejN6EcMTBisKDU6LDaXTg8PqwuH95AGJvTjdsXwOsP4vEHCYQihKJxIrE44VgCbzBEIBIlEo8TioSJxCKEIyEyoymSmQSBkI9A2EciHSOaCJPMxEikIkTiPiJxH8GIk2jCiTfqoF87hFLfi8XWh8EhQ6Ztp1fbwbCpE6uvH0dgELNHhsXTg97RjS/cL9K/M0pOzwmw/IzURz49rcLjbeH3d/yMSy+9hOb+erH27f3385FdokryvPPO56bfPcifd77P7g4X6z/oKloHfnQrn7ns8sLz7//id7x6+EM++7nPc9lVX+BzX/kmX77uRu54tpzKkxoeLH/zPwKaf//ibrHhOlhswVr94aAQgHnhd3BYasWS2LgrROCGoyKlW9ViFzdEHR62NdmoqDdJk79iscLq/UNisnhg+KyWrfUr1sKbakfY3mBgwxE1ZScNbG2yFjYOYishtVR1+djRYjknAM8dn+gQIZAP+ig9ohYw2iNqKuoNEpR5pDB1yQuzdYfUBQFY+PAeUlF6aFhU2BwbYUutho35ypvCWvf/Jf7ywZBiP2LpWX8+ex2cF4qFWp2PXWRXJkzzXcL57lgR9tAXxEX5SdEqUrlC7G2VHisb8mBg0SZRUW8uVISJii8r21oc7DxlZ9PhQe5dV81lV13NpZdfyfsn9uN29TGaMJLxa8h41cQdg+RCaqZzBiayWgK+fryeHvSmFrTWNrSWUwTMMlIBDTbfAH5XD+MJDaNxFdnoMLmYglRELgRgaphcVkEmO0wqIxLAyYycYGIAS6AHracbuaOdbtMpBu1txNKDJHNy1O5u5I4OdJ4u+q3tKJ2dDDs6GbS2M+zoQG5ro9d8CrWrE427E52nC7m9lW1vVHDBRRfz5e/dzPoPe6np9lPVYitM/jafMBRJ+itTuYWJbBG5U35Seg8apP7fOuE3La8Tnpl8Sfqm4+I1wadUFliVpSusCZukZJ3wDErIouPCAyjWKEbKTuj54nd+zCoJzrrqgmv50nd+QlWzlZrOfLLXw85WOztbJYB1q0OCWEt/bnUWKqfyPsHd7Q52tTnY1mRma5NVCnno2XJspNCLvaG2aDfYKOEhdpxysLs9P3H08lp/sNBGU14vsENVLQ4pcCQq7Xa0Otl6coTb7n2Iiy66kKef+COZ2AipmIp4xIjXNMBEzM5i2s1y0sVywsFCzMZsyMSoS0POO8LppI2FpJ3TCTtzcSunc27OLI/y0Zkc//ponI/OjPP//WuarvZazj//fB564B4ODljY2R1mpyxEVXeIXd1BamSB4tkTFGKvL8Sr/UF2tznY3eXn5T6p41eqzcsLvD0yIR53dbjZ1eEWQGZZQLDb+sO8OhBhj9S4sq8/xBe+/l0qtm8k5OhlIedlaTzA0piH5VEvCyk7C2N2FjI2FjJOFqfDLEx5mBrVMTtqYWnaTzqkwWvvIeyT47YN4LQq8Dt1BBw6PEYFAauahNtA1D5CxKoi7TMxGXUxHnUyGraT9hiZirnIhpx4jXJmk26monYSbg1WdS/WkQEcRhWaoU5GhjpwmLWMKIcw64ZIx9yMKDsJWHsIOHtJBBRMjeoYzSiIRPsZ0jQyoG6nV96DyWZHb7KiM1kZkCvRmyzYHC5cHi8Gkxm90YzFZsfn8xMMhXH7/MQSCawOJ26fH68/gC8QJByLE0tlcHr9hKJRUpkUmVyKSCyEy+vE4/cSCHmwOU0k0yFCUb8kCCNEEwECYRfJjI+x6Tg6u4GW/hbUhg6U5naU5nZ0tjaGLa20K+sxOTswu7qweTvxhGT4gl1SL7lY/c7OiQngnPQ4PaNibEzBI4/exQvP/5H9Gs9/FIDV3QG21fbxZrucGlmQHa12fvm/q7n0sssKgm/VBRecNSk+/4ILueCiS/jVY+vZ2mhke5OZXa12XpE+l7f9n+f/4xbg5rsfF2LuoJK1B8W6d92BYdZIE8A86SJvc8qTKfK2lLITBqqabdR0uNnT5St8H244qmHtQSWrpWni6v1DvPThEKv3y4sTxYOKs7yH6w6JdXBFnRhE5K1J4hplpKLeSPlJHVtOaNnWaKL00NA5AXju+ESHhIHpk1KTI4Wz7KSYZJQeVhUmLnkRtnKFu/ZQMfCx8aho8BApYp2EktFSelglYV/y5POPnQdXVOsczXsrRiSUi7YgBPNJrQJ2pra4Di76/Yog6PyUT5hojcWYfp2RigYTFfWiRi4P3K2QIMD5Pth8K8TWlVDoFStgcdEWiazdHS4er/mQXW12fv9sORd/+jN4PQOkIjpmxozMZazMJu3MJK2MxXTMjJpIxlSEAwOkQsMkQ0r8jh6S7mFGQzq8jn6Cjj5GI2pyERW5mJJcZJhcTMloSkUurSSTVZBM5wXgMInMEO5oP0a/jGFnB3JHO12mFixBGf6EeF3h6mTQ3s6ArQ2Vu1MSel0M2doYtncw4u5C4+pkxNOF0ddNl76Je554nFUXXsSfNr9GTbefmm5/YTq2tdlGmQRaXpnSLXo1dWw8pi8EQMqktfruNgfbm0S6uoBNkKauD1S8I5Aw+U5pyXOTvxkQz9Ur2JEawaYs1AhKAZATeirqTWxvtlHZYOb+za/xswee5YGy19nWaKa6w82eLi+72x2Sn63YIyySwYILuL3FxrYWG9ukqW9Vq4A8b5OaQvbJ/FSdsotEs9TzWwwlSWEkSfxWNljOSkpXt9nY2ayXps3CYrCtWfzuXVLt1c42FzsaRvjvq7/I/ffdjm64nsmUnvG0jnTCis+uIWxTkvToybhHmAqZGfPoSDnUNB54m9NxC6cTFk4nLcwnrczGrMynHCyMuVk+HeXMYlw6YyzPhVieC+I09PHD73+XVatW8b3vfYdbbrmR5194mveO1/Oh0k9Nj6jE29snenVfGYiwp8PJfU+9xM233Mwdd/yG9aWrqVW7eGMwxKt9QdGV3O4urNxruv3s6w3x2mCEvwxFeW0wyiv9EV7uD7OvL8xjNfv5r89eSSQ0yEzGQtqtYjJoZDnjYSnnZSHtYCHnFOIv52VxPMDSTJTZCRuTWS0zOTOzORvjcQNJ/zAx/yBeWz8mXT92vZyoe4Rs2ELQNkLKY2Q85ibpNRNzGZiKOsn4zYwG7YyG7MTcRg787XWSbh1Zn4HxmJ1M0ELYocWm7SPgUBF0ajFpBnBb1EykA3itIxjU3Th0HYylDYxntWSTKrJpOeNjAt7uj/ShMHQiG+xFozcyqNCg1upRj+jQ6k3ojCasdgc+fwC3x4fP78cXCOAPhnA4XfiDQVxeL26PB38wSDAcJhAKEopEiMSj+IIB/EE/8VScYCSI023D47fj8TuxOsz4gm78YQ+RuB+7x4rBpscfdeEOOBgYGeJY+zE6h+vQ2DpQW7tQ2bpRWToxe3qw+vqw+3pxhwZIZuTMn1YzPa0mnZUzPSWmfjMzKikNrGZySvz57bfLuOSSizjadpzXBgL/tgLe0yv1hsvC7OkJsaVWxfmrVvGz+x7hb+3dXHzJRQXRd8P//IG7Xqzi8ZeP8Oieg1Q0mAr/n9Z0eni5L0B1l4cHy9/4ty3A+Rdcyx2rq1lzQFGY1K0/qGDjEaVEslCc1YaV766vqDOytdFMRZ2JqmY7ezq90o2Nh61NFjYd10nXSSVrD8hZs1+cL30oxOAaSQSKlfNwAYO28ahgBJad0FPVbKGi3ii4grV5S5awZeU97+cwMOeOT3oIAfjhgORVKq6tSg+rKD+e7yVdWXpd7EQUUz31WZ7ATUdErU3pETXlJwQ3UMClVcUy7bx/8NCKnuF8hdzREUpXeMYKjLcjZ5dvr+QCCoagitJDSjbVaoQAPWksTPby+I0KqUNYiD5zYeKUX++K5JZ5hdCThF+DReoQLna7bmsWPo9tBSHo4MHyN9jT7aWm081VX/kmW6p3E3IPIO88Sb+8l2zMxnTMwHTSKCC3cQ3pqJJIYIhUUEXKq2QyZGIipCPn05ANacgGVIyHtYzF1EwkR5hIa8ilFWTTw6QyQ2Ryw8RSQ6SyckKJAcz+HkbcXcjt7Qza2+k2tTDi6ULr7abH0obc3s6QvZ0ecysycyv9tnYUrk6Uzg60nm7ckT4c4V4MPhm9+nq+/cMfcM03r+elv7UWpj7VXT52tDnY3ellR5uLsjrTWaiWsyaAR7VsrNWflQAuqzNRWS+tf6X+5rI6Q4Fv9Ye1ewSXcgUP8iwBKD3fKH0pivW/eN/L8hWBedFfb2Kb9N5VtYhp3q42J7vbnNR0edjd7qCqxVYUgK3ieZXU5LGtxc7WJtEhXbghkJ5va7azp8tLTZeHjUdV3LWmhlvueYK71tSw7qBCEoB58Se6hyvqLdK63ERZvaWQlq5osBbE354uH9VS3dW+niCvD0b4/VMb+dkvfs5swsxM0sTcuJtY2IZRM4xN2U3GpWYmbGYqYGTcb2DMp2M+Zuboe68yHzUxn3GyMOpiIesUk8CMg8UxL0uzAZbmAiydDrA052dxzMnSjJ+lcS+Lo2585gGOf/gmH7yzlz8/eA+XX/YZnnriz+zqDrKjO8Te/givDISp6fZy9de+zS9+fisfvvcab7y+mx/+4Ht89rNX8NijD/Hq6/t4V2bmTZmNV3oDVMtC7O0N8/pQlNeG4uzpCfLmkJgk7u4Js7s3zA9/ez/Pr3mGXFzPwlSAuYybuYSDxbSLhbid+YSNhaybhTEfi2N+lkZ9ApkyHWAqq2du1MFs1sLMmIWprJ7pUR1zozb8riHM2j4CDh1Rl57plI+QQ0/CZyLjtzAWdZNw60l7jYwGbOQCdmIuPW+9spuYU8tM2sd8LsjSVISF0SCncx5yMRupoIGxuJOoz0wyaMNnUeM2DBF2DjMzbuT0pIvpCS1TE2pmpovr0XByCL29B5m8h56+fnr6B7DZHRgtdhxuLy5vAIvDid3lxu50Cb9fLE4kFiMYDuHx+QiGQsSSCXK5LNF4DH/ITywZIRjx4w14MNnMmGxWPH4vLp8Dq9tKJBbA5LBgtJmwuiyYnGbUJi2DOjU9ykFOdjTR1HsUX7iHSHwAT7gPX2QQta2LPn0np+SNqOz9DBk7MLm7mZ1Rkx5VEE8NkUgPE0sriWcUzM5qmJ5WMjklqioPHapm6/YXuOZLV3Owf4jdsv+8Cs77T2tkAe5dX0PJpz7Fc5VlXHzJRTzx8kEe3v4uZSd1knXJWvg+3tos3ZS3u3m5R3QD7zhl45pv/5jzV321UNH2hW/8SEzlDhRFWelhJRuPiHPNAYUoLzisZN2BYWmQIYYPW04YKK83s71FqnyUwlAVDWY21o4UQpPrDipZe0DB2gMrfIbS71p/SNSuFu1SxQaSTbUaSo8oJbZukWlbwMcc17Hmg3Mg6HPHJzskDuBg0axeq2WzlLjcfKxY37ZyNXtWSONIUQyulyZ9YkIjgM6bjhSnNSJG/+/r37WHVMU+xaN5/tGKM5/aLeBeNAIofVhV6PwtO66j4mQxLXXWxE8SglsbBXBXTJyKVXAFXlO9uZD2rchPAPMX/rzwky7825oFQ257i53tLU6qTjmpanUIn0aXhz9u3MdVX/wKLvcwX/jC5ygpKeHyz13DgaMHGE2MkIkrCQYHCAcGiASGiASGyARVjIWMzKRsZAIqkh4l2ZCWsbCWaQlAO55WCwGYVZLKDhNPDRFPDZLKyvHHhQDstbTSZTqFzNxKn7UVo7+bfqsIdMil6d+AtQ25Q0wJ9d5uXJE+3NE+nJFeLAEZA9ZWHnryfr76vR+yo80ljPrdAWq6RZH6jjYn20852H7KydZmG5uP66UV8MfA3bX5CeCKlfxJo0DE1AvkS34lXHbCIH3m1IVO52JlkqYIB1/xmP858RnQUiYxLMtP6KisN7K1wcT2ZitVLYLKv6PVwc426ORRkgAAIABJREFUJ9UdTna12tjRYpW6ih3sOOUQk78WW0HsVxbsAKYVE2JxkyBWsw62HDsbM5M3mK87qCwkzfPg8WKwyCwJPwuVTTa2Nwu+5J4uH3u7hagU4NsA+2Q+rvj8Nez/5xvEfQYCbi02dR92dR82ZQ9h0yATQSMzYTPTISNzcRvzGTvzKRvzGYcQe6NOFsaczGftzGedLORcLM0EWZ4PszTjZ3Hay+KEm8UJF0sTbpamAyyNulkcdbE46uJ01sn8qAuntoeLLrqQhx9+gL1vvEGdXM97iiDr9/dy3nnnE477ODMbZmk2ykTKReORf1C5ZS1XXF5c333ve9fx7OrVvFnbwJNlu/nsVVeJtd6q8/npbbew86/vsvtt0UmsHGxgLOVkcSrCTMLB4niAuYSD+YSTxbSbhVEvC+N+liaCLE+FWJ6JsDwbZXk+wdJ0mLkxB5MpI7NjTubGrCxM28lGtXjsQ9hNw/jtOiIuPeMxFyGHnpTfQi5oJWjTELZqGI84GQ3YSXtNJL1GUj4Ti9NxlqZjnM76mE27WRz1spR1s5D1Mhr3EA/Y8du1eM1KPOZhXIZepsf1zI67mZpQMz2pZmpSzeyM8MmNTygIxOXIdZ30KmUoRrSYbHYsdgcGiw2XP4gvGMbl9WN1uHB5vdhcLjw+H3aXC2/Aj9vnIxyLEk3ECEXDRBNR4qkY8WQMfziE0W5FbzExYtRidVrRW424vA5UBg0Wlw2ry4bOZkBl0tKjGqRZ1s6pnqOk0wNMTCpJ5+Rkx5TEUsN4wwM4/b0YnV3I9afo1zahtp0iNyYnkRliblZFbmyYUGKIEYcMZ7CX0XElmVEFuQklyZyC3OgQv/r1rfx59Rp2S5O+jwu/fPioprsYKPriN67j1jvvp6rNSWWDuLGvbLIVvNkV9SYqpeam3e1O9sq8BXzT1kYzv3p8Ezf9/mHuXF3NSx+KAEhxPSseX/pwiNUfDrJGWv/mvX/rpdCj6O4VjR/5LvFNx3SUndQXUE/rDuepGXm/oLIw8Vsj+QvXHTy7SKEw3Diyoi1L4vJWnDRQWacXv1faMJxrAjl3fNKj0ASSF39FjIq28AHMN24UUCtH//1CX4jFH83jY5QFEVl6WFUQivmJ4VlJ4pWdw0eLWJeNR0U38IbDKjYeUbG5VtTLlR3XUSat+zYXQh66gr+v7KSR8jqx4q2oM1JZZ5B4ccX6rzIpdZoXfhUNlgKGRIQ9zJIAkDx/+amPNAXa1ijhOZrF1GZn3i8moXR2ttr4/LXf5oXNm/n1L27l7idWc+PtD/DDn9zGXM7IeMZAPKbA45Phcslw+4cIe4fIBtTkQiOkvEqSXiUJ3zAxr0gDpyNDpOJy0qlhEkk50aScaGKIWHKQaEoAng2+bgZtpxiynWLE3YY7IsMb60Hv7aTXKuP91mO8uHMXT2/dxebX/8oT5VVseevv9Fo6GbA2023uQOftRO7o4Hs/+DaPbH2NGlmQapm/wOqqlgXY1eEWa/MG4Y8sb7Cw+bj+rFaO0oIAPDuMk2ctbjlplHyZeqlBRieJPlVhoisEoNQUc+Rs4b9RaoXZXCt5T49rpYo3kbDd1iiYfDtaxHuzs1V49na2WNnZYqGqycT2Zgs7pOSeEH7C37lVupiU56HgJ/Wivu2ktK6tN7O1yUZNp4f7N732Hw3md63ZI7yPeR/qiWK7zEqs0M42lwigSIxBAZ8W4m9Pb5C93W7OX3UBPb0t5CJWgmYllqF2/Np+goYBxjxaZoJG5qIWTkctLGQczI86WBhzsjDmYmHCzcKkh8VxN4vjHnFO+grr3qVpH0vTXhZydhazVhanvCyfjrA87mcp42YxZWch7WJx1MvimJfBrpOUb3yRm2+8gfPPP58//OH3PLm2lIsuvphc2sWZpRxnFtOcmU+zfDrBmcU0c5MhxhN2klErJ08c4PFHHuTKK6/gphtvoP7k+wQ8ckrXP8o3v/FVbrrxBr71ra/z3t/+wnTWy3RGqkVLuZhPu1gYd7M05mN+1MvCpJ+lWUn0zYTF41ycM/MJziykOLOQYnkmxmzWTUtzLQq7k1jMQi6qJeAexmWR4zAN4bOoSQdtRFwG4l4TMY8Bl0FB0mNgNh0i7Rwh7beS9Jk4PRridNbPTNIlatpSLmaiZqbjdsZTPuz6IcwaGTGvjphbg9PQy0RuhKmJEaYmVUxNqZme0jAjCcDZOTWTU0o8oR4Upna6FS10y3voGexHozegN1uwu9yYbTYcHq8If4SjeAJBvMEgbn8Al9dHKBollkzg8Xmx2C24vG58oSBqvZ4hlYruwQGGNWp0JgMj+hF0Ri1mh4lhnQaHx4FMKUdhUNPQ3Upt60Ei8V7GJ5SFbvHcmJJIfIhwYhiTbwiVpYNhUxcD+lPEUwPMzao4Padibk7F1JSKySkV/mg/mZwcb0yO0duH3tOLwd3NgaOvcsGFF/L49jd4uS9Y5AB+TADWyASHskbm5zePb+DWux+m8qSKbc1WcUPfaCncuFdK382VDeaiAOwWzSE72xxsbbay+p99PPNmi+TRUxVYf6v3y4UgLHj2VoZAitizddLEbiUIWnD+imy/vPjLBz3y6WIxERTotLUHh1mX9xfm2bdH1FJbiBi8bJEag/I3uBslZuBmqZyh9OA5D+C545MdUgikv+CnKxjWa6WL9bERNh4TgmvLinDFpuN6Nh4T56ZjerGuzYvII/mOQ01hald6ZAUAU0LLrD+kpPRQnoukLMA2N+Qv8lLCeNNRdSHdWXZCEnrHhTm2rNAeoSumegsio7hiLK8zioYFqZWhot7EVgnUu03y+QkRKHxoRU+gqArb2milssFaCIVsbbIVjPvbm+1UtVjZ1mgSdWhtgiH35L5DXPzpz/DHP97D979/A9sPdrLqggvp6jpOJqYgGR/CH+rD4+vF4JDh9PSRDKsIuwfJBjVE3INE3UNEnQPE/IPkkmpyKSXJ5BDBaB++SC/hWD/x9BDR1BCR1CCx1CAH22p5auMGfvfw42zYs5sXtm9nf9cxNG4Zqy64gPPOO59rf/BTPv+16/j6jT/n0sv/m2//6Gd89brrufxzV7N3/9tYAz3cee+vuPuxJ6iWBamRBQtfyHt6QmxrMouVqzRFrZTq1cQqeGVThxQCySeypYR2PpyzWUplF2r9jqhYu39Q3EBIIrB05WfiaH4iKE1/pZuCLdLat+ykQZr2Woqr31NCoO9qdVDT4WLXKRtVLRax6i1M+6R0d6OU+q4zFLheeUB6XlyW5YNDjRZ2ttq59Z7H/6PB/JZ7nii0nJSdMH6sWtAq1kennAWWXlWrkx2tLl7rC/JKr0jcvjoY4eUeP1d+/mpqP3wHt7Yfj0ZGyqFi3G9gKmjkdMwqVqJxGzMhA9NhE3NpGwvjTibTNhYnBTJlMeVmIeVkMecR070xF4sTHhYnPCyMOTmds7M0HeKjuRhnZqOcmQiwmHSyGLezlHaxmPWwOO5nYczL0rifpXE/QZ+WLZvX8utf3YZa0cWZxTQfLY/yr48m+GhZEoLLo3x0ZpwzyzmWF7PMT4dZng7w0XyU5fkIZ06HmZ/yMTfhomLzGjqHh/hoPsnCdBS3TUkuYGA2YeN0zMyUb4T5hImlnIv5jIPFnJPFMQ/LEwGWp4KcmYmwNB1iaSrI8lyUM/MJZG3HKCkp4VOf+hTnnb+K7/3st8RjdrJxLT6Hgqhfjd+uJuLUMhp1EHMb8dn0uC067JpBxgM2cn4LYaeRVMDORNLL/HiU+ayfybibjN/EbNLJ/HiE0bgXo6KDpF9LwjtCyDFMxCtnYkLNxISKqXEd0+NapsZ1TE1omJaQKbOzKiYmlcQzQ+gcncj1rfSommnrb6Wxqwm9xYzH78fj96EzmTHaHJjtTgwWK2qtDq1Bj0Y7woheh1avRatXYXNasbmcaIx6hrRqTvV1c6qvG41eh86oR2fSMaRRMKRRYnXZUOo1KI066rub6BlpwxHoIT2qJDumIJ6Rk8kpiaeV2LwDaKwdaB1d9OtaUJpPkUgPMjOrYmxCQSyjwBMdwhMZJJUdxhcfZMDSgdLeicLayZDxFHVt/+Ciiy7k2dIX2Ncb+NjUTxJ/PUFRsyjzs683yN4eP7s73FQ2mNhwREVFvZGdbQ4qGwVzNH8zX1kvfICC0emipstL1Sk7W5tsbDyq5rG9tawvVL0pC2ENMfkTZz79u1L8rT+sYsuxEdYcyE8DldLreTuUotAd/J+ZuQqJNyih1Qop4BVMQMnjvP6Qgso6HRuOqNhyTHw3FoDRUsvR+nMC8NzxCY/LSkpKuHdtNdsaBcOs7JiGH931COsOKqQWDQ1/WLePByvfFb66kwZW/6NHgvUaxUX8hJ7bn6nkf3cfKIB/n3itjtseWi16gI+KNd1tD63miVdOsPGwig2HlDxU9T6/fmJLQQiWHlLyoz88znPvtBdM9H8se4s719QUJpDrDw1z812PsPGIqjA9+sO6vTxY+W5hAvjSezJuve+pAsS5vM7E75/bxqN7D1PZIFbBL7zdzP88up5tjRYx8Wmy8j+PlvLsX5sLa+PH9hzijue3C3EorRZ+ev8zrNs/QGWTmPw9XPUP7t/0GlWnHNx67+NsbTDx43sfZWuDqO/60Z0PFVZfP7rpB/zmwSf58je+xbo1TzKeGSEaH8Lh62NH9UZqj/+VaFiO392DoqeW7WUvkQlqiHoGSUfUbNn8LP39BxjNKEin5XR0vcdf3thMZnSYaGoQR3iY62/6AasuvIjv/OS33PKHR7nyi1/jyi9+jYsvvYzv3HIbJSUlfOlb17O9STDo9vWFuG/DPr5+48+588Ud/P7ZCi685FLufOBe/rZ/D1dedSU17VbuXV/D839tKiQ/n3mjnl88vLYwzSuvN/PLR9bz9OuNwgd4RMPDOz/g109VCB+c9OV1yz1P8MLfOwtC8E/lb3PX2j3SFFnFmg8GuOqr32L1BwPibvuggjtf2s0fN78huqMPq3ju7XZu+cPjbDwiJsObake4/ekKHq0+QPlJPRV1Bp57q5lfP1YqrX7FBPD2Jzay9r02qlpEU8eTr9Ry14tVYtIrTfx+ct+TrH1fRpk07fs/lW9z7/p90o2Gjo1HVPzorkfYeEQtrXMN3HTHw5x3/hdWTAATfOq8y/jDun0Fofu7Z7fx6J4jBfH3/Dtt3PXMZqrbXbzaG2Bnq5PfPLGZLQd6OCB38EpvgDXvtvD7Z8q4+We/5PvXX4dD0cnaZx/Dp+vndNLBTMRMa+0/qP3H6yyk7MxGDIwFRlj73KNMRE0sjLooXf00tf98k/bjHzIfdzAfsxOzDlO27lkWso7CNPDvb+1B2dfE8mSQ5ZwXm6KdvVWbWci4mE86WUw62bt9E1ZlJ9MRC3NJJ8Pd9fz9nVdYXkiztJjlzFKWivJ1JMJmziyk+NdHE3R11XHi+Ad8dCbHmeUsM9MRNpS+yOnZJB8tpViai1J74K+0tx5h6XSQkpISLv2v/+LZF17gi9d8gWu/9nWSTg3v/mUngy1HmA7qWYxZsCna2Fu1gcWcnYW0lYWUlb1VG7FpOlmcCrA0HUA52MTf39pLe/NhSkpKeLVlhB/efj8XXnIpW96tZTxt5vD+1/nLvm34bUP4rHLchiGefuwhTKo+lL2n0A6287fXa2g+foCXnn2CdNCJXt5F6YtPk3TrSbj1xN1GXq3ZTl9HI0GnAaehH5upn51bS8lFTEyM6ZgeN1KzYwMmdTvTWQtTaTP9HUd4641KpifVTE+pmJhQsmHDozjdLfijvRhcHbzydhXbqsuRDXczoFTQNzTEw39+BJVWh8FiwWixsPflfbz3/j9wepzYXQ4UGhUvvPACNqcNk83MgHKYp158nqq/7OJg60n61HL21x5mzbq1dA320TUgQ2PS8dQLz/H3Q/up72ykT9PN2x+8ws49pWRGFSSySkIJJc+9+GcGVSexe2XY/b28/c897Hy1HG9sgLFJJeFYP489/QD9ulMMW9qIp4d4/8OdvH/4FXSubozePu5/+C6efuHPPPn8w9z8q99SLQtx7/o9vPBWU0H8lX7Yx++eKRMp8Z4AL/cGuePZcla/1y4sGHUGnnz5CHe9uIPtzdbCDf+P73uKdR/0UdlgYWerk0d3f8BDlW+yq80hbrqazHz3F3fz1JvNklhT8LsXd3PvptcLYY2n3mjmprseEVM6aUDxqye28GDV+wUg9CP7avnpgy8U08IHFfz0wRd5ZF9tYar3YNU/+dUTW84qTbj57sd49u02cVN7RM0fN7/JHS/tZsMRNesOKXnpgwF++LuHWP3BQKE55M7V1dxf/pa0kdPy0t+7uOWeJ86tgM8dn/i4rKSkhA2HFGxrNFHVYmXLSR01HS7JyyCmN0WUionKBpO0KpXM65KpveykSNRuOa4vpHA31Y5QdkJH+QnR6LDlWNHHt75g5i9yA9dJqJmzJ0hnN3/k+4rz/04bpTV1fqWYDxmcDXE2FXAjhfVdg5mtjeLcLq38KhvEz+S7WysaxASwosFcDAI02c4682GQ7S0OKuqMovy7RTzf1mRld4eLZ/e8T0lJCZ+59NN86ctf5jNXfoE7/vcpgsFhIjE5juAQGmsHZlcvgeAAAY+MqLefTEhJwiUn7pWTjqpxuWREIwOM51SMZlSkU8OMjSnJ5uRUv7mHK6/5Kldc/RXWvNcpVieFM8BTrxzl7tU7eGjr22ypVbK7S9R27WyX2G2yQMGD84PbH+De//0TzlAv37/xOu59fgM1Mr+oBZME4LYmizTJMxSmWptPiIq3lfie/FQ5b1zelL+blabF+d5LweISybuX/tnP+gPSiiTvlTk4LFbC0mR4w2HhLxUCUEPZseJnraLu7ClgVYu90Oqx85SNqmYLVVJd27ZmK9slr19FvVGsfE/oxaT5mFYCS2sLIZMtx0YKU+e8/WDDYRVXf/MWzr/g2oLB/Opv3sKGo5rC57Qw/WvIMySd1HS6eLnbxcEBHe/0Wnizz8U7fS5e77Ky7biCF6vf4bNXXslD992FVz/ATMTKYsrNYsbN4phPTPXGPSIJm7JyOmXldMrCwqiLxUkfi5N+ZtJuFuIOZsJWJv0GpoIm5qI2FjJulmfCLE2HWZoKsjjmZiHnYCFjZz5hZT5hYT5tYy5pZD5tZSHnYT7nZT7nYT7jYnEyyGLOx+mMh6WZCB8tZ/loWYjA5ZkIy1NhPlrOideW0ywvpTiznOHMUoozS2nxuJiU1rcRFiY9fLSU44nHxA3T6/trKSkp4ds3/JCwVcVkyMRsws5MzMZs3MbydIDFuSALY24W0pIITFoFGiZjZ3HSxeK0m8VJBxMJC1dccTl/r61jZ3eIO5/fxpVf/BqDRgOpkIa4T0XMN8Jo1ILXMoR9RIbHOICiu464S4lrZICEy4zPpCLuNhJ2GsiG7MxPxJlNOhiLWFkYDzIe9+HUDxJ0KJie0DE5rmZyfISpUQMzaTszaQuzWQezWQfTWTOTOT1TY1qmJzVMT2mYnFSJoMSkWKGmR4dJ5uRonR30j7TQ0H2Upu4jNHQco7WnHZVOi83pRmcw4vX7MVltaI0mVCNahpRK7C4nvYODvF37Pn9v/IBDHQdoHqylrreBtoEuWvu6aJS1Ud/ZTIe8n6beDk7KTtGn6UZvG6ZP1YLFLSMQV2DyDuAO9uP09xKODeIJDeCL9GP29qB19WJw9+CJ9BFODaFz96JzdZIdlTMxqSIzpsQdHcIaGKTX2EGboo7DLf/g69/5FvetqaJG9p+nf7s7PKLrty/E6wMB9nXZqag3sL3JRE2ni+p2JxV1esGubTCJ9L8U8KusN7G1Sfh+qzs97Dhlo7zBzL6eABtr1TzzRnPB17e5dqRQZ7peQsHk18KF9a0UAtl8bESscaX1cJ7xt3bFandl/Wl+yij8fopCW1bpYalE4ZAISK4rrIcVBVG5ZsXqOL8ezn9/bjw6wup/Dp4TgOeOT3QUUsD59enWRgtbG4zCjC+hVQoC8ITw1pVLgNqtTTZR/yUx8iryPa4fAy5XNhipqNOz5ZhY122qXdnkIcGeV4B+S4+uBD0XsSJ5AbhJ8v5tyVeI1RkLeJG8r6xsRRik4OOShGCF5PkTHrYVxv6Gov8vL/zy7L9KqaJL+AHzaWCxAi6ugq0CGSKJjqpTDiobjPz3F6+lpKQEWcsRSkpKeHRdGVd84UtcevlnUWhPEYwNoLV1oTG3Y3fLCPr6yERVZCMqYl45Cf8wmagGn0tGIipnNK1kPKNiLKsmm1Hw7t93cMln/osHyl4XYONuH9UrIL3VXV4h+Lq87O7wUN3lY3eXtwDi3dHqpLrLx96+MHt6Q9zwq3v501NPE0oOUL79BX7085+xsyvAvr6wYLv1BNnWbBH1enVmKhusorlDsgaUHsmnttUFFMwm6aZgc2GdIXykec9M/kt17f4h1u2Xs3a/nLX7hwSXa7+cdQfkrDs4zPpDCmkdrBTiL+8NlewBogrOSGW9UXgAm4THb3e7ix0tVqqaxWvbmywS9Fzy+q3wIm45rhOewmOaYnm75DfcVKuRbjq0hUTeJgl19Ie1e7nlnie4e+1ewdVcEf4oO2kqTP+2tTioOuViV1seM2NlT4ed3W029vUEuHf9Hi666CKu/+63efvVnYz69MxFLczHrCxmPSxNBFiajbI47mNxzM18ysZi2sl8wsbplJX5MRdLsyGWT0dZynlZTDmZi1qZSzlYyLmZz0ribyrIYs7Dwpib+bSZhZyNxaSN+aiJ0xEjCwkLp+NGTsd0grU35mZh3MPybJgzszEmg2ZilmFyXr3w3C1n+ejMmPDhTYc5s5QWAnA+xvJ0iI+WMpxZzrC8GGNpIc7ifIIzCwmW5+Msz8dYPB1hZiz4b20g1X8/QtKmIGpRMurTszQdZnk+zvxUgIVxHzMxOzMxK/M5OwujDhZSNhYzNhFomfGxNBekvGwNN9/8Qz6Qadjd5eerN9zCVV/+Ovc8swGNRUsmYiTmVhF2KvHbVViMw0Qcw2SDRuZyYSYSPrJBGwmPkZjLQNSpZyzqZjLuZirhIRdxErJr8Zrk+K0DTI1rmB43MpuzczrnZDZnZzZnYy5nZzpnZiKrY2pUx+SYhrGcivFRBVNTQvhNTeZPJVNTSrFWTQ0QTfbjDvVg8nYzrG9BpmimqauOzqFOjree5GRHE/9sOMo/6o7wfsMhjrY18LcT73Oo8whDhgYCsV5ckUG69O2833yQA12HOdbdRMtAB80DHciUPciUMrqVHQxqOtFbe3EHlQRjKnzhIeKpYaKpYZzBfkweGbZAj/D1ObsIxQfwRfvxxQaxBQew+HrwxwYw+vow+vroM3XQrWtBYZfR0HuUz155Bdf9+H+orDecLf4kD+DuTg81Mj+v9Id4uTfE7g4nVadslB6Us+mo6mzfb51k2akzSs+FZ7eszihS/afsQiw2Wtjb7WPTMQ3PvN7Illo1ZcdG2HRUXQhjrD2oYONRjQiASN9Jm46q2HhYQemhIidw7QoRuHbFujgfGhGnsigADymF6DuklPAy0s8fVBZxM/kO4nwVnVRHt+5g0Sdf8MgfHWH1P89hYM4dn+wohEAEo80gJjtSEnOr5IPL16OV15nYIl3IKhoEKqWy3kxVi03yX1goqxONEEUvnp7yE6KvteyEVvISaiUzq04KlowUpn6C6fexcEkhYLIC9ixVawm0iKEw7SuvL659xfSvOAWskEIeHz/F66azp4X1xenfyjMfCMk3OBRPuzAmN4kqr+2nnAIe3Oriup/eTklJCT++/W7Wla7j8ss+Q9lb+/nmj37JDbf9jmHzAHa/oOlrzB14fIOE/P1kIkpS/mEmEyYiXjlB9yDxsIJYWE4urWRyVMNoRslvfvsT7nlpG9XdXskzE6BG5hfw3W4//z977xkeV3mn/2txhRBCSSiBLC1AQvrSEjbJkkICIQkhCZtsQgjd2GpWtZotFwIEwoYSwJYtWVbvvdcZSdN70Yx6m6KuKbLZ3//V5//iOeeMRHZf8dbnup5LzdaMpDPn3M/9vcuRFlG5ldtgU8J3Dze5lEDenDo7uXV2jncIgPdE2htccc11DJja6B0uYeeunRws7OXHf0rgjm99mzvueZDkMz3S79xAmqTBkZsuDpxRb+lvFgBeCJiHOHhuWBE0JxaqJUF17KK5/1Qv+z4UYuz9H3az/8NuAQpP9nDgZC8HTvWScFrEDSUVDkgtNGoR/Fwi2L+0kmFh/CnXCQBYbSKn1syhSh2ZlXrB/FUayKzQC7BYKjuRBYiUw6STCgeVMXPC6ZhMQY402u7UEwy3otMpjrXLHDyn3Wb6yK4R54WypD7hVzu9pJ7pZPeeveRmJJKWvI+Cd14jOKol6Nay4tESmrYQWbATnrMSmbUSnjazMqpjbcIkRrwzZiKLDqJrXqKhcaJBJ+E5K2GfXRrnWlifEmHQK+MGFhwjLHmGCbqG2BgfITyhJzRpEFErkzrCkyOEZo2EFsxEl5xEF51EVz1E1ybYWHAwZezDP6ohuuSRmL15zoemOB+aFADw/AIXQlMCNEYXJBZwhsjGBB9tznA+Os+F6BwXorNEQuOsB2ycevtV4uLieKO4gUdeyuHT11xHR18fC85hHOo2NmatXFj2sjFjYcmjZ2lUx/KYiY1ZO+GgTTCgc0YicyaiATubax788zYeeeSH3P3lOzhZ305mjYXfZL7Fdbd+iRvv+gaJx9/ig+JifvrYz7nmuhv44j3fw+0249J14TEMMmnTMjdqZswyglPTy6hhgHHrMCvzLqbsGpy6Pqwj3VjUrcx6hlld0rLut7O+YGXdZ2HVb2DZp2VpXsPigppFn4ol/xDBhQGCCwMs+gdZCqhYXlSxFJTAn7SWZGAo5eptrIv4mNn5LizORnSWOjTmajTWGqo7z9CqPseAoRq9rRrPZBvO8Xb8iwPoPb2c6yjlg8Zmkb7fAAAgAElEQVQPOdVcyLnuUs62VdGm6qCht4lOdQv13RX06drpVNVjcPRjdfXhGO3BM9HN7MIArokerKNt9Brr6Tc30m9uxjvdg3eqk+mFHsZme9C6Ohm2t2D0dKKyttBrbqFeXUWHrp4ufSMP/eT73HXf98hr9nC0zSOBPo8CAoUZys6JtlFe7fTwepuJjAo98ad6OFSuJa1kWNKGqyQj4BDpZRoyykbE9aB4WJIHiTahQ1Um8ptdHKo2cbTFRUalnoOne8msMEgmC7WyGX3ppHT9+aA7llN7soeE08ItvL+gj49rA/dL166EMwMK+7dPYgQPSLKVhII+4gskgCiBxgMFfcSf7uflD3tEdeqpfgX8Kd+7INY6IvICpdizQjUvvdd2EQBePD7RcUVcXBwv/aNDGa0mnhWxK6klWnJrzeTVWUgv05BXayGv3kJ+k4PMKpGblt/kJLvGQlq5QeSaleoV0CXr8VJLtKKD95yG9JJhUorVypLZluSzI5KwVc7+k7PkYtVfctizzEomF48IdlI2fJRolUiX2PMQ7x8s1W35WCc5hGMOz7Qy/RbwF2MJ5UBopQqu3KCMhzNkFrBCmEQyq8z8IvkvEhCU+iAlkX9WtYkns/7Op6++lks//Rm+9LWvc+11nyOlqJ9bvnY/n7v+BmbmehiyNNGrrUVlaMZgqGN2vJd57yD+mWF80zp801rGnb1Mjw0yPtHJkl9F0D/ITx/+Nr+PT1WYPQE8xcptcJArxbZk11rJqRdVQjn1ogM3p85GTp0AgSc6vOS3jXGk1cO/fuUefvTkU4y42rj2+s/x6MuHt7EyDz+TpmwGBPgTDvH4QrWU6TgolgwEtzi8k4rUUkyQGP9vHZ/sP9XLj5/PYf/JHgX47VcAYA/xp3pJPN0fuwEUqaSx7LCi/0srHSGzQqcwfWLsqyezQidWpWT2KNMoZg9RHyeNe89K3dJnRAbYAWn8vP+UCIo9cKpPYiBlJ7Ja0efIQdAfB4CK8aPKKInTHVIosmgE+Wv3OE8fO8Wln/oUn7/+8+zdeys7drzA3j23cP+/PcD6lJXwjJ3NpXHOR2bZDE0R8TuIzLtYGzexNmZgY8pMeM5CZNFJdM3NZniC4oK3iCxYicxaCE2ZCE8ZWRzVseQ1sjRlZmnaStCjZ945gs+hYcGhYdGjJTRrIjylY2PKxLJXS8hnI7rqJbrsJrrkZnNtjHDQxcaMlfDKmATs5rhwPiCYwM1ZLmzOcyEyQ2jBTnTBxvnwlPh3kUmioTGiG5NEw9Nc2PQT3pjho/OLhJZHWZy3cNstXyA3K43DzW6+9O0f8a0fPo7LbsI10sm4vo9xXR/TZjWzFjXLXiNrMw5Wp6yEA5LLecFCxGcjMmcmMmsS4/DlMX71q8f4zve+yxPxuXzQriWnzsa///ZFrr7xFnbs3MWd9/9QOcez3ngfh0WDx6ZldtTA2389xoRtmLlRA7OjBuZGDUzZNUw5tGK5dMx69Mx79az4DSwHhlgNmFn16Vjx6Vha0LK4MMKSX81ycJigb5CgT4C/gE9FwK8i6B9gaXGA5cV+yTEsgJ8cqryy5f31tUHWtnxubXWAxeUBphb6mfGr8MwO4pnuQmVrRWXrpLK3moLW05T3l1HSU0p5fzklHdXU9dTTOtBIz0gL7UONqI3t9I60orH0YHL1Y7K3Mj7VhWeyC5OrFY2tmV59PQPmJjq0DfTp67B6O7CPdWAd66JD30y3vhaDqx2dq41uYyNNwzV06Rt5/d18du3eTeJZtYh4afVypFXKF212ifDzRjuvdHg52uzkaJONzAoNB052k3S6X7j6a0zk1VvJrjGRWaEn5dww6aUj5NYYSS4aJLFI3pipSS83kFdn41iLW6p9dJBdayGvzsKRRpswdZ0bFtVr0veJL+iLjYQlHZ7M3onRba8yrt1/KsYACnAY0/vJI+bE0/0cUPqG+xXWML6gT+QLFvTHgOUW57GsJVQA4BYgeOD0IC++exEAXjw+2XFFXFwcL/6jXWHWEuWy+hKNxJoJWj2rWk9KyYgYc1YZOFRl4HCjnWOtbrKrjSSeHSL5nIakc4KRSz4nVka5TorQkEezWgWEKZ27EmBMOrs1/Dk2JpYNIHI8zfbKN/n5ajlYqiVFigTYyuallMU0gQdlZ7C0ZO2fnPGWWhbLe8sol0eE0nhYYgDTK7ZmAkrj32oz/5n9juQ6tUhaQAs5tTYO1ci1YhZ+mfQKe/ZeytVXf4YXcv/Cn06cZsfOnbyYewyzp5MuXR09I7VojQ14R7vw2tuZcHXjm9Iy7VUxPabC5ezE4mxi0a8i6Bvkv373CPc+/EuRz1drUfL5DtVYJWepnFO4nXXK3gL+cuqEW04eA+9/v4Xdl36KH//+aXbs3MmBkz2kFKt46e+VfO4Lt5F8qpWMcg0pZXqSzwn2L152e2/Rdyrj/a1AXm73kGMTCrY75h5Leo0D8gVxCwMor0QJlCUUSGCwaFAZ/6ZJpeyZlQayqkyimqzOLPp6y7VkVOjIrNCTUa4lTTJ6pMgAUBoHJZ4eEDeBgj7iT/VxQBo9y6YUMYaOAVj5PJVbSRKLtjKAmu3j3yoTuXVWMZZvdHKkycUbPeO82jXOldfdxO//8Dv27r11W6TM3j23UFzwDpuLY2wGPZxfn2JzxUt0ycPGlIW1CSNr4wZWvTrC81aiK242N8Y5H52lseoUEZ+N9Qkja14da5Mm1mas+Ed1hJbGWJ6ysuFzsrrgYNY2RMCtY3XcyNqUhbUpg8gVnNGxsWASYdEhL+fXJlifNhEOOAkvjxENTXE+MsuFTblSzi/AX3Se6KKL0LSRyIyRiM8u2kbC42yGvESWXUSWXGwERllfdAv94PokK34Hhf94g6uu/AyF9R0knO5h195LeSnvr8w4RG3bpEnNmK6PCX0ffpeOtWkb6zN2zi+NE11yEPFZCPsdRINuIn4n4Rkjm+uTvPnmCQXgPfnHP5Lf5uVYm5ejbV4SC7r43I1CrnH3Pd/mkp07uer6L/DVf3+Yx59L4vE/v8zJmhaqW9r42wenOPLmOxw48gYHso7y62de5tGn9tFnMhEJWlgNWlgOqFgJalgN6Fj261gLGlld1LC+rCPgUxEMDLLoVxMMDrKyNITfP0DA389icIDlxUGWFgfF15ZFw8bqmni7vDLAyvIAwcUBlpYGCAbFqFiMi/vRuZoZsjTQPVJFg7qCxqE6avurKGwppGywnBZNDa2aKvqsvdT21tA90kyrqoEhSxdDlm4GTF2M2Howu3rRWruwONuwj7ZicDSjcbQwZG2kc6SaHl0dOmcLKksTRlcbakszWkcrbZpqBkyNqO2t9Jma6NA30DhUTWt/MTfceB0PPvYbcmqtPJ3/Hu/2mMWmtEG03hxr8/B69zhHW1zk1FvJrDLwwttNpBUPkV6mI7tGdO6KSYI6Ji0qHia9TENykZQ/K6UDpJZqBQBsGyW/yU5+i5vMKj1pZRrSy3VSaHM/+06KTeX+k91klI5IrF7/lpYO8do+IMeWFcTSLAQwiwG2A1JgtLgeiCmCAHKCXXz5Q0nOcrJH+ribfR92c6Ag1ke8/9RW8BdbW9nA59+5CAAvHp/suCIuLo4X3msTIzkZaG3p000rHYnp6CSjR3LxEOnlOvLqreTUWTjSaFcy8UTTho5kRfskgF/qNuClVzL40iSWLbVUR8o2EChnDQ7900pUXKVbY0U0iiNVGf3KgLNEq7CAcuuHAKKyUUSrGEBS5U7gCqMUC2JQDAWy/i9djoGpjMXBZMotIZIhJEsKFT5UZZAAoNCiJBf1ExcXxx+e/gPXfPYaTrQ4+EV8Pp++5lp+/ewLWMZ66NM10K+tx+FsZ3q8j7nxAea9KubHVIy7upny9KAz1jM+3YN3op2rr7mK/8o/RW69lUM1ZjJrLGTWWJV1aMvKkjptT7SN8kqHlyPNbsEM1tnJrrVxpNmp7Mj/dOIMl376Sr7yvUc51u7l8ZS/iueeeYK8JieHG8ykl4nxqWBtJeZvS2vMgTOqbUHh25a8w5UvfAWx+AQZFB441StpAoU2ULCAAgQmnpFHwCpJsyeYwPQyLYcqjeTUWoTuT3L9ZlYaSC3V8Fj8Me77+R957MAxkgoHFc2fPPJNON1PYoEAmPs/7ObAKfG4+6UR9IFTfQp43RbiqnT/Dm8JvtYo5o9Mafyb1+BUOnBf6Rzj9Z4JEk91csmOHex76Vl27NgeKbNjxwukHnyZ8+vTbPrdRH0uoivjbMzaWPToWJswEJqV2D+/g+i6V2jr1scEY+d3sD4tRr8rkybCC05CATfhpXECk2aiwVE2fC6WJk2s+hwEJvT43BqWPCOszxhZD1qJrI8SCXmIrrhFjdyCjc2wyPjbDE0J9i86J8bO62NcOO9jMzQlDBozRqKzZsKTJjbXPESCNsKrbiJLTiJLLqJLbiKrHi5s+tgMTbO+6GJ1ysyP/+NBfv7TH/G3djs/ezmX62+9E5NRi2ukmzF9HxPGfvxODStjJlanbIR9bkI+N+eXJwkHnER8dkJBN2FJ6xhdHmNleUoBgL/6r6c41u7lta4xEt5v4oqrPstzzz/NDddfS1b+ceJPdvCH/JM89MckvvzvP+Xam+9k5+697L38M1xz021cd+uXuOH2r3Dz1x/gaw/9gs9/8Svc9s1vo3VqhL7PP8JyQMdyQM/KopnVJQ0riyMEAkPMz3UTDAzi9/UT8A+wtKgi4O/H5+8jGBzA5xvA5x/E5x8gEBjEHxhkzjeAzz/A4pIK/6KaQEBFMDjIvK+X5WWZIexndXWAxaU+Zvy9WMY76TQ00aKuok5dTqOmlqbhKho1NTSqG6jrr6ZDXUfnSBP1/bU0DFTTb+xAZ+/G6OxBZWhi2NJMv6GWQXMTaksjQ9ZGrJ42PNNd6F3tjNiaGbI206Wto89Qz7C9lQ59PUO2ZlSWRnpNTXQMV3L/dx/g7vu/S0aViVu+/gBxcXH88o//RV6jk2Oto7zWOSryL5uFNjmzUs+L7zaTUNAvGn0ks94RaaObUWEgs0xDWplG0uqqSSsZIblQdITLuZ3Z1WbBJra4ONzo5FC1iRffbVX0yHJtWwzAiWtPeqlWyrVVkXJuWKo5FTKm+NOxcgPZxJEo1bnJuX2p54Ykt+9AbJMrmdpePtlL/KleZbKx78OefwJ4ck6unKu7/2NfuwgALx6f9LgiLi6O599pU6rYEgqHFPAn38BSFSAl55gJhk3O4zt4bphDVQayqvTk1Jg4XG8jSzJHiBetkeRz2m1jVhlEKavCSEqpnuRzolEiViW2BQRKI2J5FJwgabBkUX5ykVrKlFMrnbAHz2lIK9NLgFS0TaSWasXXZO2gEhwdGxvLIFA8txj4E5ExMQdwbMlAUBr9VosaMQEGrRJLKLKpHvjln/iXS3YQFxfHrbfdQl55Py+9XctlV1zFA488QZumkc6hOrTmZsa93cyN9zM72s+8Z5A57wDj7i6M5hbU+kq6+4vYtXs3GdUWqaHDJkBftU2smtjKrrVxrMXFXzq8HG8XrEd+m0cySYhR5NFW0WmZ3eAiu9Et6XS85LeM8uuMN4mLi+OmO+4mv3WUo61ujjRYOVSpk4C70PXJJp6twd77Tw9uu3jFwN7Wdhjpcwr4k5ek/SvoU0wgQpsX0wEdPDukOIAzJAOIYPo0ZJaLzMfUc8PccMe97NzS2HHD7feQUNBHUpEAf/EF/SScEhfnA6d6pMeXgKfMCEranf0Fopt6//8FALfq/+RcwmoL2XV28hqd5Ld6eL17gpSiXq646mr27fszxQV/Z+/e7aHSe/feQvGZtzm/Mc35oIdoYJTVcSOrHq1wvs6bxajTZ5XaPSZFkPOqh+iyi2jQSTToIup3EfY5iS65iS57iS6NsTHvZnnKxpxzmLVZG+HgKJH1CdZ8bpbGtKxN6wmvuolGJoise4jMW4gGXGyGpsW4NzrH+dAk0cVR1mYsrE9oiSy72FwbI7I6RnTZxeayi8iCnfUpM2GfjbVZA+EVN9GNMaIbY4SXR4mseDgfXWBjdYKC914n+eVneetENpfu3cuz8QdJPadm7+VXcOd9/0F1xwBjpgG8+l5mbMMseo1szDmJ+EdZmbYS8o+yMmMjvOBk3ecktDRKdMVLZNnLZnQeo6GHb37zq8TFxXHLbbdyx51f5PLLLycvN53o0ihvHjvENVdfRXJ6Jn8taeQvLTayamW5hI2cWqsI7250iLzGJifHW93k1pq44rM38OifE2nXGFnx21kN2AmvjrO66GDJP8xyUE0gMIDf108woCLg6yfoHxBg0N9PMKhiwdeHPzDAvK+fBd8AU/MDeKd7mZofZHx2gOkFFTPzvfj9fQSDfSwu9bEsVa2tLPezuDzAjL+PPmszncYW6ofqqOktocPUyLC9GZW9iSZdAy3D9dT1V9CurqdN3UC9qp7q3grahmoZMbdhdHTiGOvF7O5CbW5EY2/FMtqG0dWMe6Kd2YVehq1N9Jrq6THWM2BpZsTexIC1mU59HUO2Vozuds5V/52bv3gbN9z2JTLLNXz7V89y19e+ylXXXMmvE7PIrDaTWaElu0rPoSo96eUaEk/38uI7zeJ1eHqApLNDIquzTMdBWe5RpiGzUi+5/wUwSz03RFqJ1AVeLFIBDlUZya2zcaJ9lLwGO4eqzew/2cNL77UJ0FUQa+vYf6pf0fIlFwkjWPwZoTNOk3JnEwrlyjbBCmaWa0USQfEQmeVaEqQa1KTCQTLKBIiUa9/kfMF9krHtwKkeyVwSM428vMX0ceC0eOz9yqZzQGEgn3+n9SIAvHh8okMAwHfbRB/vVpeRMmYdFjcyBSzFmgxkpk0OWk4tES7itFINWdVGcmqM5DfayaoyklYuxrCxDl496RUmhUlLrzAqo+DEomHhJN0aJlw4rIA+OSwzURLEKqHTsitzCwBUGMJiAf5SJNCaIoUGp27RBR78mFs4s9LAoUqp/7VCdo9uNYEI8JdRZSKz2syBk50SABRrGwCUTCGy5u6ZV4vZuXuvuAl9+Wtk1ZhJKerlhtu+zD0P/ZgubQ3dQzUM6xpwOjqZG+vHP6Fidqwfu6UJo6mBxr6zFJW/wdWf+xxZdcLdm/O/AcBqG7n1dl7t8PCXroltlUuHm93kNzvJb3FxrG2U441mkv+7kJcPv8KfD6bwXGYu6e+X8Eq7i/xWL19/6Jc8+JsXeKVzXIDAZit59WYyK/XiHJG0mVujfGRd4Lad7al/XjIA/PObtYreJl6KZhCsW78C+uILekkuEi5d5e9ZPERqyYhS+5cpRb2IwGcjv0g88b8Wwj/6cr74nqcHhN5PApvx0vvxBdJbSfsnh1OL8fXgNrC7tfpOBFNLG4oKIxnS5iC7ziZYjzYvr3VPcNs3v8Mfnn6KzdA0G34H99/zAHv33CI0gHtv4f57v82G38XmopdNv4vQlInInInIvJnwlIHwhI7wjIlIwEF00c3mike0fKyOMjWqZnPNw+bqKJGgm0hAgLPospdowMukVc2YoYf1BTur0xbW/U6hzQtNElqwsLFgIrzkJLrkJLJgJTRjYnNlPBbpEpokuuRlaczEyrSV9Sk9kWUX6xM6EUAtAdDNFTeb62OsjGlZX7AQXnGzGZlmMzxJdHWUyKKL1YCD+++VfvZLhP7xM1dcyWOP/4r8llHSSlTc/eDDfPGb38Ft1TLlUDPnGGbeMcLKtI1o0EtowcWGz8XqrJ2NBSfrgVHCK17CSx4216a5sBngfGSGxcAor/4lj5bmcuqrTtLZeJaQz04k6CI0a+Pt1/K5/9++zmWX7uXJJ37Bq01mkgv7yZU0tEeaXRxudHCkwcbhBiGFOdIyyh+OfMh1t97FldfdxDslhUzPDDE6pmXJp2fJp2MxMMhiYJDlxSGWgmoW/YME/QMsBgdY8PUxt9DLvAQA53wDTM31iwDm2R7mfGrc413ML4i+XZ+/D3+gj6AEABeXBAh0zapZCPYx5GylRdtA3WAN3bpatK4WpuZ66DM3U6+upXmklsqeUhpUtbQN1VGvqqVeVUOrupbOoRq6R+oYNjfTo6lm0FCPyd2CfawD91QH7skOJuck04e7HZWthX5zI2PT3VjG+rCNddCsquI///QEu3bv4bu/iyevUYx6/+WSHaQdTuGyyy8jqaBddMcX9LL/ZDcvvtvKy+93kHCqV7QBnR4g5dyIiBQr0ShsmnDTimD4tDIthyoNJBSqSC4W4PBg8RDppcIwIox5ZsEs1oumprQyHannRnjh3TZe/EenYOYkIJh4Ro5vkbt6RfB8VpWB1BIRWyXaqKREizP929g+2aiRfFYAwuSzsW7gA/K1Y4t+MFYxJx5TvrbIADOpUEV6qYaUYjk9QWiqX7xoArl4fMJDmEDea1dKrpUbmTRqlWu85JYFoaWLMYHp5XqhryqTAoFLY47ardl7GRV68mv1HCruilX3SNo8xaxRouNgiUYBEImFokZOaMYGFWGvoNoHJQCokurDJP2VBGDlLMLks1v1giOSe3go1mqiOJzluJgYCyjaP+RaOINkItAr+XIy45dZJcDeA48/o4yAZQAo4mBiQDC7zkZWtYkb77yfHTuvJy4ujkt2XMlNdz1Adq2ZtBIVV9/wr/z2hRcZsTQwMFyN3dGGx9PNtKeX2bE+XM52nO42WofKOXU6ny/cfjtZDS5FRJ1VYyOrxkZ2nZ3DTU5e7fDyatcYx9vHyJdcd4dbxoQAW6paym9x8/ust/4pguOeB77F5Z/+FHd/7S5+8+wzPHXoOHlNTuUGmNdg5WiTnWNNFnJrjWRWaEkr00qtHqpteY7xW0HgVvC3ZdxxoKCfbz3ye0V8LY9ZEqVu6cQzA1uA4KBSi5RaolEiYDLK9ZJW1SjFwIjmjwd++fT/0djxh5jT91QvCadjUTNJ8uMUSjmAxUPKOaT8jNvA38fGvyU6ySktAGBmtej8Pdzk4kTHGHl1FuLi4rDqewj7BMhbn7JQ9N5fSYl/jrOn3mTDb5XMFy6iAYdov5gzE5nSE3Kr2bD1E3KqWHePEJ21CMZv2c3GkovHHv0RqckvUlzwJus+K5sro2yuis7c9QUHi5NGliYMhIIuNvxOQkEH0Y1JImtewksuNvw2VsY1rE3qWRnXsjFvIbI0RnR9kvORaaJr46zP2VidtuH3aIkuOdiYMxFesBAN2on6bUSCdkJLbkIBJxt+uwCUG+Ocj0yLLMCgg81lN0Wn3mTvnu0AfefOa9i7Zw+vv/kmf+1wk1k+wp7LLufk2WIWvCP4PVrCAQ8Rv4eQ3010yUN4aYLlSQvhBQfrwRgAPC+FVYsMwmk2IzOcj84RCTrYmDGxPmkgNGshGnATXfSyPGXFpGrl6quu5Mknf8N3f/2MiFFqdJLXYCe/wcqRBit5jQ7Rld3iJr9llMONDh5+Np24uDjeLj6Dd2qEpYCawGwv7gk1Af8Ai4tqfPP9+KRQd5+vD19gkKm5QWbn+pmd72FsphfXeAfuiTbGZ7rxTHYxNdPN5Kz42qyvn3lfDysrfQSWBhib7eDr37qbuLg4fvbMflSuQTqMTVT1V9KhrWVsqoulxX707g669PWobO00qKtoVNfSNlJPzUAFlQPlVPSVUtlTQl1fGb26arp1tbRrahmwNGD2iKiX8dlO3JOd2MY60DpbGbY1YnI3cTDzJe7/8SPccPOt7N57KTd/5R5e/kcz+a0ecX1qsLNz9x5uuOWLfPdnvyClROjNk4vkjbxgvMTrXGzyM8sNJEstQfu3jWoHOVAwQFLRkNKWkVqi5VCFntQyrcgDLdWQVq4ns0JPdq1VmEeqjeTWCRNJSvEwz7/dwkv/6BSbTKlfXGT1DW6TechxVXI0VMLpAfFcpTizxCK1YPyKxHUipVhMJJKLYhORrZpnWUOYWixyCPfLTVhS7WV6qWjiyq4ycqzJLhnVhsTPVDJC/MmLOYAXj092iCq4DzpJLBSCdlm7tR0IDkluW2kkXKolvUxPeukI2ZV6QbmX6jhYIjGDW0BiWpkQwB9psPPV7/2UXXsvJeHDVg6+V0FmlUlU0BWqpLxBjRIWnFSkFi/6QpVSfSNnxyVuDRpWxo2DkoYx1iWcVCxuyFuBrMJqbgGGsk4wZQsDqBhVykS8jRwZkymPfytN2wCgMH2YFfOHogOsMZNda1GMIdm1Nh4/+Bo7d8s3urC40e26hSdS/8rhJgcvvFnGzt176BhpYFBfj9nWhtHcwPxsH5PeXlyOdgz2ZtqGKikoPM51N1xHbr2VIy1u8hqdZNfalLy/VzvHON4hnL1HWr0cafOSXakhr7iR9FO1ZFeoyKmz8XjyKwrou+Vr9/L5W2+XwOkO/vXL3+DKa67h2us/x7XXXsN93/8e6SUDgtGsFSHLeQ12cmtNHGuxc7TFSUaFTgl5jjm4h7ecV+JvllCollx2g8ruOF4SUcuB0fJYXzFZSOLq5CKVuNBKPb2pJSMC/FUKgJ5TayG31ixcwNVGfp5w/H9lAB95+YhgISTQF1/QS0JBP4mSPjDlY4xxqqIv1SiB1uJ8G9lS/aYR9W+S/k80x4jzQjBIbl7tnuCuBx7igQfuJRJwCWC1Mk500UV00UV4ZZToulfUtS07hWYuaCeyYCayIPLuNlyDhEeHCHmG2fCOEPVZiSw6WV9088B9D8bcxHtv5f57HmDdZxP6wNUxkb23Ps6G30Fk2S3WopPIupfIipvoipvo8ijLXi1rU3pCkzrxXIKjrM07WJ224ndrWfQaGTMOMG8bILLiJDRjIOITDGU06CC67CQ0b2N9Xjy3yKpwKJ+PTLEZmuD8+hiRNS8H45/7Z/3jJS/wkx9+n8s/dRmH33iHw01OvvDlb3H4lddYnDCytuBibcFFwGNgbcZOaN7Jht/D+ryTsM9FOCC6gqNLHpFPGJ6WgOek9HaaiM9GaFLPxoSejRmzxJJOEV5wsaM99yYAACAASURBVLHgJv7Fp7j+hs+TVW0ip97OsRYXh6r0ZNfFXPV5jU6Oto6KWJOWUZILREZbUs4hgv4+fL4BVoO9VGrMLPhVzM0PMDPdS9CnJuhTEQiqmFsYZH6+l8mZTiZmOhmd6mJyupO1lUGCiwP4Av1Mz/Vi83bineqia6iM9wuO81LaQT53ww3s3rtXcuinsHP3Ht6urqRFW0dlfyUDpnoWFnpZXVIxOtVLp76JfnMztYPlNKprqVNV09BfTo2qkrK+MhqGqug1NNAwUInO2cSIvQmtq4XqttO8mLKP/H+8w5Ctg9Hxdtp7zrAvLYF/vfPLxMXFce0td/Fk1ju8/G6jCJBvGeVIs5ucOgu59VZ+/Ew6l+zYSeLJVg5V6jhYMkJi4YC0oRLOe7HxGyT1nIa0Uh1JRWrJeCEctYKVUyl9vLJZI6NcuP1Ty7Sklor+9wxJa55dIzTBhyQjlugItpFUOMjLH3SJTFHJxZ9QqOKAFM0iawSTpGuVqKAU2sGMcp3o7ZXizTIrdGITWiqMZUln1aSXahQDXPyZQSXOZb8EMuMlPXSM7OgnqUjNwWI12VUGTrSNkl1jJK/OwtEmB0ebnRxptJN6dvAiALx4fKLjiri4OA6c7CL5rFqhrUVQs5oECVAlFQ2RVqolo0zHoXLRqXuoIuailIM3lS7ec7FxsRIPU6LjxXfb+M+8D/nOE88qYOOnT+0jvqBbAQnJknsySemHHdrC7sWaQcQ4WL3NaRoLlxaaQRnwfXx9PE5ma5SM7H5OKZPjYvRb8gNl7aJRAYEZlbIL2MKhbeNfyQlcZZJcuOLjnFoL3/nVs/zLJR9joi55gQefeJacesEQXn/bl3nmwPMMmRox2ZvxTnQxM9uL19vOxHgXdnsbakMjA/p6brv9C/wxPZ8jzW4R8Fxv53ibh2PtXk50jHGsXYx7f7kvlRtvvom4uDh279klLtY33UR2nZ1n36zkWz/9T/7r6Blx8SwZIb1Uw7636/nuky9xwxe/ws7de9iXlcED372Pz1z5af79h98j44MKjrXY+Uurjdebhkkv14nw1So9OTXCeCH3WCZKzK44r8TvXo6FkVle2T0sg365XUYJAC9Sb8v+O3huWDEZpUsubWFIEqHc2VLzR2alntQSDZ+/495tjR033H4PB071klQo3XgK+mO9wxLTKF/c5Q5gpSGmTCflXcbOo5StrR9S9l96pYlMaVOQXWMlt8HO0dZRTrS7iYuLo66nh821cTZD00TXPETXPIQW7KzP2VibtxFedIgx6YpbALCFmOYvsmAV2XxLTsEQro4SXfNQXPTO/+Emfovz6+NE1rxE18eJrHoI+WxEgk42l91iVLzuFfq8NQ/RlVHCi27WZ81sBB3Cuet3EPK5GDcOMGFWM2FSYelrJOhWE1lxEZozEpk1CE3ispPN1VE2g06iAbvI6NvwxgBYaILompfQopuik//9T895z56bOfXOazz1n0/wk5/+hMNNTr7y3Uf46e+fY33ezpitn3m3lhmrmuUJMyGfi7VZB+HAKBsLDiIrXqKrYxIAHGczNMFmZJrzkSkBQMOTRBYdhKaNbMxZRc/xkocLoTk2V6dorSvmsk9dxu9y3iarRoD3nFqx0cupFw56sdlyKpmbeY0OcuptfPe3L3DZFVeR+cZb5BzP4vYv381DTz7L1IIan39IMID+AWn1s+Drwxfow+fvZW6hl/HZXvyBfgLBQWYX+plb6GVqrh/XeCfe6S4eeeRBPnvdtdz17R/x1f94jIefy+CJ1NfJb/Vw2ze/w28TMhm0NtE0UkPHcAVLQTUrSyo80/1o7O30GuqpU1XSNFJPVa+IhSnvr6ByoIKGoWo6dfUMGBtQWxoYm2wnIfU5du3Zw62SgeOq627k+s9fL5i+r97HY/HHyKo2xUKdWzwcaR4V7F+Ti7wGO3lNTrJqLCR82E5OrZljzTZSSzVKtma81JIRLwG8ZClTU24KUjp3JT2dHMci2jJUZJbrhVZvSxlAWqlIK0ivMJBdayGnzsKhSgN59Vbym13k1lk5VGUk4XQ/ebVmKRVAbCjlKJiXJW3gvpM9Uv6nYCyzq4ykl2lIOTcUkyYVDpJVqRdkRpGajHKtiJrZkoKQWLhlcnBGtW3jmyAByswKPTm1gkDIrDJyuNFOTr0IxM6o0JNSrLoIAC8en+i4Ii4ujviCXvGCkU7YBIlNS5aKp3OrjeTVmcmuMpBXZyGnxkR6qdy8oCG1WIxmFXateISkc9IqjumgUstFFEZKqZYns9/j83d+nbi4OP7jV78jsaBbADsFpKmlQOitDuDtxpB4OXD4jGr7knRnsqNZdBrLLOIWEHh2eNtzlkFszDksj4blaBiDEgSdUWEkXTawfGwUHHMBm8SS3Lc5dSJ774nU1/+Jidq56xYeP/gaObVWkosGiIuLIzlrP2pjAw5PF56JTiZn+5meG2BmfpCZWTUTE71YnJ08f+CPfPUbd5Nba+Bws4ujraO82jXOK53jHJcA4P73W9m5axcHjx/kdPtpigdK+O6jP+Cmu75OWolayVFUwFOVifxGYQzJb3JwrHWUXyUdV4D7wUMv8pkrryAuLo5//eJtJJ5s5ZW2UY40uclrcJJeYSStwkRWpQhojT89yL4Pe3nxHx0881YDz7/dzL4Putj3QRf7pfT9BGmEf1AC7rLsIOnskNL5K+v9ZOPHweIhyfkru7cNW9hWadWYyK6WXMAlI/zswFHue+yP/OzAMVEpV6SS2L8BZfSrtI1Izr/ks0PKTUWRCSg3Gu2WLEqDAH0VRtIrjRI7bCWrxkZmhZFfpbzOg79+nt9k/I28eis3f/Vebvvmd/D5x4gsu4muCsfqyrie0KKTzc05AVaiM0TXPQIALjoEO7fuYTM6zfnNOS5sznI+OiViVja8pCY8/7+yaQcPPEd4yU14ySUYvyWXyAn02YkE3Kz5HUTD42yGBTiKLo8SXXKJ3MHVUQE0Aw4iKx4WJ4y4h9sZM/Rh7m1geVxDZMNLJGAlPGMQz3HBQsRvJRJ0EAo6iKx62NwYFyzj+hjhFaH/Cwc9bKxMcP9939mmAbzvnm+zPGvnz3/4LQ8//GOONLt46e/V7Ny9l+cOvcKYYwinsZtZ+zAbvlEiwVE2A27CARuRxVE2l71EVzxsrk8ItjE6LWkPp0QjyZqHyLyVcMBFZMVDNDTFRxcW+eijVV5/JYdLduzg58+ncajKIDZycsampOXNrXeQW+8gp86+JVzdQW6Dg2Nto/xsXw6fufZGrr3lS3zz4V9z/S13MBvQMDc/gG+un0BALZk/Bgku9hMI9rHg72NqvgfXVDcTc/2Mz/bjnexkwT+AfaIPjb0Zs7WSm2/+PL9PzRebvgbxWj3cKBzmv0w8zqevuY7XansZtLfRqC5naVGF3z+A2dPJkKWZZlU5LSN1NA/VoDLXUzdURc1gJdX95TRrGijtE4xgs7qC7//HvXzhttt57k3RB/7LJDEx+PRnruT1TgdHWj1SmHOsfjKvUQDjw00usmpM5NYLLXJ2rY3MKhNHGh0cb3GQWalXXv8HlNfggKQB3JIgsCUK5WWpsk1u0NhfMEDy2SEOVUobdTnntUQUEYjkCTEGllMrMisN5NaL6UVevU00gxSrSC8bIa/WxJFGKynnhkguHFRMHAlbgGhSkVq5phyUQGrCmUGyq4xkVWqEMeSchuxqI0lFg8SfGZCYwwEObDHIyRMRGRzm1phIKxkmt9aiNEsdkrJ38+oFYE4t1ZF0pv8iALx4fKJD6QJOLdVIpokhRb+QXqblSIOd/CYHhyqF/T6vziJcv5V6sip1pBQLViZ2IkugrSjW+RpjBXVSmLJg0dLKtNzzyO9ET+5nruSl99qU/DSF4dtSBRcvN4NsAYIKIFT+vWqbeUR2ZcZYvyFimYLD280uW7RbB5XoGDESTpd0f+nlsUo4wQKaFePHT17IVgBgZrV845fBiGwCEdqTG++6n527buZfLnmBnbtu5vN33CvpVCyknFMTFxfH85mZdGrq6NPUoDLUoTY20jdSTY+mBpOzG4u9HYerG52+mS/dfQc/ffI/yW3y8ErnOH/pGudE5xgnOsfJb/XyZNY73HTbLVQNV3JusJS/lQtH78v/aBaB3RLLmStdILNrzBypt3K4zkh66TAHi/pIKR7kqw/9QgGBl+zYqbz/4K8Fq7tn7x7i4uK4+pqrePiXP+Pg28Wklgzx4tuNPPdmDU+/XsVzf6tn37stJBX0EH+ym+ffaeWFd9uFw/fMID94Ok2Mfc8OSbpUof9LPjsU6+GVTD4p54ZJK9WSWiL6fzOlOKKsajM5dVZx06kVDmzZrS0if7RKllhykUrRFh4o6IuFQJ9RKQHpycpjjii1gzG5gFaJNkqX3eGKDEAA/0PVRm6883527r5F/M1338pNX/o2qWfFRdzr1hDyWdmY1BEO2lmfNbGxYCW65uWC1J8b3fAK0BfyshkZZzMyxfnotNC0bc5zftPH+egcm6Exzp58Q3ITv7ONATzz/uuEg04xil1yEVmU6uTmLKzPWFmatRJeH5MA0pSolFv1ioaPyDSbG+NEQ+NE1jxE18dZ8zkIjutxq1pYHdcSDtgFQF1yEp41EZ4zEpk3El5ySKzjGJurHkJLDiIrbsKLTqIrHsKSOWV9yU3RyTdJSXieog/eZHnSxMqUldz0RB76/nc53u7haIuL32b+jcuv+iwP/9cLLHiHGLP2sThuZHnKIqJu/HZCPqsYQa96JOZvhk1p7Ly55hE/v99OeN5KJOjmfGSO//lohf/v/0VZWZxg586d/D4ph7TSYbJqrTy6L4/cOgEChStYytCst5PX4JSYP7uyjjS5xEi4Vehun3+zgs989jrG5waZmetnYaEPn68Pv7+PBV8vgWAf/kA/swt9TM31YhjtwOTpZGa+lzlfP/axbjoGSnjy9z9j1+49fOtHvyCrxsqRZpfEPooGoCPNQgryjR8+zs7de/hT9qu06JuxedvwzvSgH22nW1dH01ANDepqeg0NGEfb6NQ3UDdYSb26mvqhGurVVVT3lfHkH3/BbXfeTnq5niOtHn6X9z6f+sxV7NghkgwSjx3jSIsM/kalx3eQ22ATv5M6K4eqTQI811h5+IUsjjQ6ONZs51iLg6xqI8nFIxITJnI4RVuQqF6U++LlUelWN63snt1fMKCwdimSWzfl3DCpxUOkFg9JUV9ic5jf5FRiy4Sb205GpUGYOs70k1oyIlU+qkg+q1K6wA+eHSK5SKWMhYVRY5CkImHSOCB1BaeVCuYwvVwnOYTFpCq9VEPSWSFlySjTklaiibVhFQ5JbKWWvFoL6aUCOKaX68mUpkjZNSJWLLVUXIMOFPReBIAXj090XBEXF0f6OTXp5VJGX8kIGWU6jtRbOdpo52izg0NVBpKLh0TERoWO3BojaaWCLRTZfUOxAGAFBKpjdLcEEGW9XXqZCJjOrDDwo2cyiIuLY9eeS3n+741StU/sRRG/DeyppPHgVsCnVhi/+I8BwHgJGCoh10VbcgSV1pHt4+FkiXlKLdMpodUppToyJPevPPpVxr1b4l+eOnFWGQdny2HQNRayai3kysHL9Xapl9bA48kn+M4Tz/GrlL8q4cXZNUby6m38Ludddu7awzf+41HK2qsYNNRi9XQwYq7H4Gyle7iGvuFydKZapqf6eOv9Y1x62V725R7mcKWaI22THG52k/JhHb96OZ2rPvtZ/rDvd5SpKyhRlXL43WPExcWRWjJEapkA5Uca7ZxodZJTrSe1WMXBs4McLBog8XSv5KjTcPBkOy+9VUVCQS8HCzpJO9tLwjtVPJmUS1xcHPd++17aO8/w1ntH+NLdQkf40E8eIr/eSG6VhmN1Wv7aOcrfeif5S7ubnCqdSN8/2cPLH3Tz/DttPHHoXXE+nZYddTFX3cHiIVKVzl7pYl8sgGGGFLQs75jz6m1kVRuUHMCMSiPpFXol8zGlRKN8nyTZYS7daOTzLGmLVEBxkytSAa0UHB5rjRGgX8pcrLGSU2sjp8HOE2lvsHP39vHmzt238OvU17nxjq8ybNQQWXIT9pmJBEXrxuaKh82Ag4jfRmjJJdiqVRfno7NS44bo1RUgcEasyBTRdQ/rfgv33/MAu3ddL7FpN3Pfv93PqtzosTYmKt2W3UT9dqJ+O8vjBubsw8y5NUQ2BAsomjuEWeN8dE6AzM0Am9FZNqPzRDcmCC1YCS0IEBlesBCeNxGa0LAxoSM0rmV9WkdkY1yA2FWRJRgO2oiuyCykk/CsGGkLcOolsuRhc2WMiM/B8oSZtupCPn35pyjpt5DTLHR28R+2ccmOnVS1tzE3qiIwZmRx0oLPrSHkdxDyWQVjuuaV3MYeokGn5JZ2iR5kv1M0layO8T8frfDRR6usrM4Rn7hfMN0nW8UmrtbG068Wk1FlkvI0bUqwugwCc+sdHwtXF27h/FY3+W1eXnirhsuuuBLr+BBzc3345nrxz4vcv8WlQQJBwQTO+XqZm+/F5GnHOtaFc7yDmXkVveoSvnjnzXz5/u/x/FvV5Le4FeCngC9p5beOktfo5At3/xu3f/MBSofVjNgacE11o3G10KapodfcQstQFVpnGz2GeoYdrdQMVlIzWEa9qpKi9kJKekr53HXXkPPfb5HdJNIDrrr+C/w5JYXX/p7FZZ+6jPgPWjjS7OZwk1Mwao12Mir14nVYYyGjykhmlYXMaivpVWb+/Jdicqp1/LXDRn6Lm/xGGymlGhIloJcst3iUimq3JGlztl8CfqIndysAFIaKjHKtNBEQr1WRCjDMwbMqyZioJ7PKTH6TnUOVemXSkVdvlVIdjOx7v0PJBE0oFGAv6axa0uzFQqATJKNIzLw2oPQJ7y8YIFVKJRCTMVlHPywYxbPSNezc8DZCIqfWwokWAeYPST3lWdVm8ups5NRayKwSE6WMCsFyXgSAF49PelwRFxdH4ulewT5Vm8mtNXOk0cbRZid59RYyKrSklcZGbrITN7VkmNRSjXICb+vuVUwaKsWkIZtJkopHFM1WaomGA6d6+dEzmcQX9Euu3BFlVyQDtljIcEyTmKho/bZqAmOPd+CMiOg4cCZWIffxJhFlSTd3mQkUOi7xHNPKtDHXb6XUIVtpEDtapQdYRI2ImiKJ+ZEAorh5iN1bdq1ZiYLJq7eJ0WRdrLZNaAaFQy233sr+91v54j3f5+obvsAvn32BI++8QY+hGZ29lRFLC9bRDtwTPUzPqOgdqiTzcDxf/8adXHbZpdz1ta+wY8clXHrZXh783j0cfzODBl0NZUOVvFf1d+Li4rjmxltJL9eRVSV0cmmlI1K/7gDppSMckurUcqoNHG208dTh9xXG77LLP03Shw1klig6FGX97vk/c+VnP8unr7xa+dzP4/OV93fs3ElyYR+5NXpOtHs43uYmr85MZoWO+NMD7PugWwFhsrsuSXLeySxgaqlGYgPFSFhpb6kQUT2iecUsXTDFWDujQjIlSaNbuZdYRDkMKLETCVvA39bKwbRS6bwoi1UJiuBwqeatWv772mOtKxIwePCJ5/5X3ef3nnyRq66/iZPnion6rdK41C7A0LyVjSkDq/NWoutjRDcmiIYmia6NSeHLPi5s+kWAcmSaaMhDdN1DdNlJZMXJWtBG0Qevk3LgGQrfe42VKQORRRfRJWk0uihCosMzFjbG9KxOmAh49ATHdEQ2JjgfXRCtHud9XDjv56MLi5zf9IvHCk+IuJi1ccJLLqJr44JVDNik5y4AXchnI7zkILohxq/RFVdsHLzsEmaTVTcRv5XwnFHkBM5YCfschBfsRHwONubsrEyZufP2W3nwgXspbWkXzQ4toibuvkefxGobxGnoZtw6xLixj/CClZDfRjhoF7rIgE3oJYMuzq9NcCE8y4XIHB9F59ncmOFCeI7/OR/A5dTwk0d+yq133c2j8ceEDqvOxqFaqwhYr7aSWS0Hq0sgUAF9saxA+XN5jQ6OtgpAllNr4ZIdO6nsqmVyuldyAffiW+jFHxDsX2BR5AEu+PtwTHRh9XZgG+uhV1XKXXd/kW9974ekV+iE61jq+hYMo0dh4baCwbTSYT515TX8MfdvDFqbmVnoZcjRTLehjnZtPUOWZvTOdrSONtTWVnqN9RjcbdjH2vFOdzFgbeKxJx/lrrtv591uPfltXm686xvs2LmLXXv28MgLGWKE2iAq3PKbnUKzVm0WQfTVFjKqLWRWSwCw0sjTJwq5ZMdOhUE8WjNAZoWeg2fVZJQLE0V6mUZq9FBLCQBiYxZrxIg1B8WfHiStRFRAygBQzgBMLVaTem5YYeszKo0cbXaKjWGViIfJb7KTVSt0gM/8rY6XT/Yq8VJH6q1klGkFiDw3IplA1EqI/fYlMkHlCJcE5fo1JJkOh0R6QdEgqeeGySjTKPKSpKIh0sp0HG60k10rphc5tRZya0SKhJAdyRpnPbm15osj4IvHJz6uiIuLY98HnRw8N0J2tYnMSr3U5jEi5S8NKWYMhQ05O6SM3A5KbF3iFtZPdjvFS2O0rc0QCYVCK5giOYpTpAaP5HMa5WabeDYG1BIKY93AMusnx7xs1wRucQZ/7PFkw0fS2RHp7dCWuJjhLaNFESeSWqohrUwA3/RyMf6VwUW6VA8XC342xZy/khs4q8ZCTp1V+XxWrYWsOptwDUo3iFypyzK7zia+XmuRGEUjefVW8hoc5DUI9vUnL2Rz6ze+w+69l/GN7/+Y/y49SU1XEWp9LT0jtYyYWxk2NFLdVUKnro6/f3iM3OMptPWVMWBqZtjexqCtmV5bE5VDVZxqEEDuhbdqOFRtIvncCCmlGnKrTRyut3C43iqK2Du8nOjwcrzdw4mOMZILOrnmxlsUIPeLhKPkNdj46YvZfP0Hj/OV7z/GHff9gLi4OG771r/z4+ez+E3Wu/zkxVz2/aONa266jauu/wK3fvNBDp5VkV1j4njbKPnNDo63eThcJ7QyiYVqRZsn77Ll8U/iGSn65dywFAArgFm6FEmUUa4TgLzKSE6NWWoCkVpbynWkSaPf1BKNot1JKoo5ixPOqJRA8eSzw0pfdWyNbHEBS5sZySmeUWkip9bGmx0OcusF25vfJCJDHk9+9Z91n7tv4XfZf+eq62+iuOgkoWk9oRkdGzM61id1rE8ZWZ7QEvZZCc9bhNlDioOJzIvx5mZogs3QlGD1VqSvLTqILNoFs+a3EZ4zEZ43E16wEgk4BAhcdLHpdxKZMbM6pidgGWRlzIB/dISNRTfR8DQfnQ/y0Uer/M9Ha3x0YYUL5wMC/G3II+JZousTRNbHRfD0updoyEt01cPapInVKRMbsxbWA1aioTEi6y4iS9KIWNITbq552Vx2E5rUCZfxnIXwnImNaQOrk0bWZiyEZq0ERjW4tT088uOHeOh73+Zkv5sjrR6STvew57LL+fkz8SxOqnAZexgzDrAxbyUcdBAK2IkGHUT8dkLzdjb9o3wUmuHC+jTR1UkuhOf4f6Fpostezha9J3Rt11zHn14pIr1cJ/pmq0RdWVaNWQGAmdVWDlXHQKBYMa3vodpY9WJeg0MxiHz+jq+RcPQ449PdzM/0EZgbwLfQjz/QRyAwwGJwEL9PGEEm53pwTnTz9of5XHPt5/jmDx4jo0KrxM4cVsa+H1/b2cAfPJXI177/KEZvN75gP5NzXehGe9E5m+nU1qK2NDI204XW2YHe0crEbDczC71ML/QxOdfLoLme2+64lSee+i2vdHjIqDTy1CtF7HunnrxGO0db3dI1whvr2m10kNdgkzJSpVD8GitZ1Qa++aMn+MGv/0BS5svExcXxl6pmUktHyKu1CFOGrHOT8v8EIyc2aAekKBala1eKikkv1ZB8blgaA2vEKFYCgOmlsQrS9Aoj+Y0OYeSpNpFVY+Jos5OcOguppRr+/NcqEgr6SCwaIlfqEE8qFOxiSrGQhuTVWsQGVQajBSLQfisQjJddzCUjop1E0tfHnxnk4FkRaZN6bpgkKcIsSTKDJBfLsWsaEovEZC6tXCeBP4OYREmNW/tPdl8EgBePT3SIGJgPu0gpGVFugjJ1LXcgCi2UWglWTpFuurIGK2kLU5cggTC5j1F2Ox1QGhPEmDixUFDj8lhNAZLKmHZLvMbZj49wh6THlBlHWfOnUhijWKD1EImFEuArVCs7LnmnePCceIGKjCVJ/6dkHmoUbZxgeUxiSREwh6pl/Z8Ab8+/VSdGvlL8S6YUEJ1VY95mSsips4p4kloxKpbjSuTA4lhXrIO8erEzzazQs/8fLXzpOw+zY+cuvnz/d/ljfDwlLedoH66hZ6QGjbGBpv4yes1ttI7UM2xrQW1qolldTWFnESpbEyZvF38/+wY7du4SDGW1idxqA8dbnJxoc3Oi3cOrHR5OdArzyNE2z7Yoh9x6GwcLezjS5ORYq1vsWGssImurykRenZVnXysWH0u/n8xKkxIALncpZ1VbONwgsSPNLvKbHJxotpByVsXTr5ZJ7jiZkVMpI+EDBVvHs2qhmykTTR8Z5TqypJ7q7BozOTVmcmrEjjmz0iDiICQGMFUqgRf6nth5kSIBva2B4souXTlft8QIbakgTCsXEoHDtQZya/Rk15h4rWOU3Drx+/n8nfeLFpJLXmDnrlu46a4HSDrZxs5du3CYmlnxaVhb0LIxr2d1WsPKlIbluWFW5jUsTQ+xNq9lbUHH6rxo6AjNmwU49FsJB22EAjbCS3bx8ZyJ8KwRfV8NS141a5NaNqYNrE8ZCM9bxf9bMLExZyA0Y2TZoyU0ZyWy4mFzQ4pL2ZQZwCDno/PCPbs5z4XzAS6cD/DR+aBgCaNzXIhOsxkaY3PFRTToIOy3sTZjZnVaT2jJTnTdRXTJzuaSg+iig8jaKNFVF9GVUTYWLEQWbKxP6AhNGwhN6diY1BFwDXHyrVdIfPFpTr/74GEbPgAAIABJREFUF/weHSZVM5fu3ct/ny0np8VLfoubP79azM7deyiuq+P/Z+8to9s68+1hhZMy3+IkM6Vpp1NIimmn7XQ6ZUo6M01pmqRhM8ogc2DalCkNOGbLMsqWUcwWWmBbMoPQkLSZe++6a72f9vvh95wjOem9/w/9mrPWs0yK7Vg659ln/zZMD6ngs6kw5THgp1kXafsWRnAuMoSzk078HPLibMSHM6EhRCYcKD/2JV5/+22su/QyCAQCPP7Gh8hvdkPYGNPxCiVOFLZ58PHnjQzMOJcAQG5x1Yt5LYxRanRByD4ulvlQ2uXHI6+9jz+//S7GphSYDagQnFEiOKNCJKjBHAuJDgWoEm42pMTx0wexdt1avHEgDzkSK5k9pINsDaGgfem6AAy2D+GVvXm45+EnMBWUwzPYgmeefxq7RIcxNqOAydsFi7cdwYgC3nE5jC4Zhsa6MRtUYDqgwNBEL4Yne1Et/R5r161F8WcFONrnpSxR9vNLu/wo7fShtHMYBW1u6ojvGCQNXAMXnE/XwOw6M26+8z7sKTgIhbkZ199wDXbmFyOrtp+iWdo8yGl0ErlQoeOr1RJOUT93AhcDc1yJfT/KeUYut8GGlNNaIi0q9GyEbEJahRapFVq6Yau1QNhgR0m7lweAeU1OlHYMIkdCRQQ7v2hFeqWefvYpFbUAnVAi8ZQKe47J2TVHj/3HFdjL9wCrlo6BT8SyAylzVsfvV2mV1BCSVqlHWqWen3JwBjhuz+QlKeU6pFUbkVFn5RMmhBIH0msuAsCLx68/WA5gL1WkVZuQelrLMgFVcdZ8KspOq6DoDWF9P+ktqjgAqI1jAPU8+3eABfku6U1ln48PbY5nEOP/baxKLA78lS8dN3Mhw5xGkIBDDGByI0LO3ZtaFYsHSGPNFUuiPKpMMSDI8gw5B1lGnW0JAOTHwsxc8My7ibwBhDOD8GPgJcuBXImdGCo2pswWW5HNMVdNTsrVa3Ujr8nJRwHkNFKLxP7vZHjyb3tw270bsWrNWmx89nnszMxEkigL9z22GTfc9js8/spWfC8ph9ohhcHVBXFfNTQuGQyeLqxdR3lh/0zNQMLBz/D6+x/irY/+iX/s349CiRqFshG+LaSAaXtErS6IWl0olJKzL7/VjbxmJ3KbHMgSW5mrz4385gFkNdhZ6wVtkpliO9JqWdB3DY1Msxuofq5ENoxC1kRyuJPiY576xz5k1PYvAfKJzJ0ea93gLpg6Pvw5s5Zel5k1RgjFFuQ22iBsYJVw9RyDa0FGjZlvDkmt1PO5XhQoywVLG8gYEl9pF/czk/goGwP/WklnWkDOGZ7X7MaR7mHkNlCPaUadBW+mHsETW3ZiS/qnKGhz4V3Rt7jlt3dgIWzEfMiAxZAJZ4ImLAZMWJwxYS6ow1xAi+ishn3djIWAGfPTBizOmHB21oIzAQvOBPtxNmLFmaAZZ2ZM+GnGgjNTZoiy92FuTI+5MT3OTluxOGHG4pQZZ2ctWJzux9mAFT8HbPg5YMe/I27858IQ/v3TCJklzlEjyH//F6f9IzD4P/8dwf/8dxT/899hBhLD+O//nMS/5zw4F3XhbNCOnyNO/Bx046cQ0+EFB/DvgAs/Tdrwbzaa/feZYfzXwjCNZoMunB0z49y4BWfHzIgOarHpwYexds0GLGeO4IcfegSzfj1eeelZvPfBNpR1+VHW5aNR8OYXsG3PAUQmjfAPaDEzZMDPATfORYZwLurD/NQAZocM+PGrMiTv346T3/0Lual7ccmll+KGDXfjyb/vxr5vpTzzTsYdB6t+tCG/xYU/bUtg57ZzCROYLWG920s+5+T7t/Na3Cjp9KFY5sOWjH/h5jv+AMOQBpPTSgSmlQjPEuibC2sRCarhH5fj1MlC/OXl57B8xQo88dZ25LPMQS6IukA6CJF0iEBYHBAs5Ncw0+UN4p7H/4L7nvwLPs7OxWVXXolb7r4fl19zA7ocemjcMnSbJAhGlAhEVBgc68XETB8iERUiURVmQ0qMz8hhHuxE4dFcrLt0Hba89xY+kw+ioGMYJV1+lHRSmHxBK2nVDnX7IGpxxvp72fUhp9GBrWlHIBAIcOSbUiSm/hMrV6/G/u87kVFjpush6/hNYfmeKcxdyzlsE1hDTxIDhEmn1MisNSO7vp9u7KuMSKnQEUlRRfq/1Aod797PEpP7mMLh6RpcJhtEejUBsr3fdyGr1oTkcjWfA5h4So20Kj32n6Cg6ISTGuRJbNh7rI+YyBNK1lxEN6sZ1Ua+uCDhpAbC+n4kntItkVAlV+j5xycygiSBFRwknNIgrcrAPqbHchE22RIHssR2pFYZsff7nosA8OLxqw4aAX/XhdRKPTKq4/KYTmpYIroKyeVcA4MSyeVq7D0mR8JJFQXxVpDGIr3aGDf+1cZOWJ69iekjOCHtgZNqxMa23Ne4PlgCjXwV3Gk9A3y680wdsWDhpHJW0cPcVLFWj34G5mJxHRwYTOVcpfEAsMoc6whm470MtjLrmBO4LuYEzoobB1M1nIN/K5SQEYTPCmRNELlNbDwZ97UciYMBPsYUNjlZdIKLj56IaY5cyGsewM5PavH4m9tx9U3rcdnV1+OR1z/EloxP8cBzb2LNuktR/M0n6NI1QWFsRZdeglZFLe657y4IBAL88cF7sOF3t+HJZx/Fn/6yGfc++HtcdfWVeE9YRqHRMh+LcfCgsJ2Cbku6/MhtYuYWiQMZ9QR4RC0uFLSQljFb4uS7krMaHEivoxzIZG7sX9OP7AYHClo9KOscQmH7IERtHpR0uFHQ4qR2GcZOplaZkFh+XtxPuRaJcXV/GdUGZLFxnagl5pbLqjMjs9aMHLEFWXVmZNf100ilxsRYQDOfXcmPgk+pkcxugDhXMHdxP8C9Jk/FMdlcdmWVkcBtLQnND3Z48Hm3C8VN1ESQWUebT7bEgdxmFwravDjY7cfvH30G733wNhbndJiPajEf0WIuosFCRI/FsBHzYR0iQTWiIT3mw3oshA2YC2kxH9JjPqTHXNCAhZABC1EdFgNmRKcNiE7rsDBrwMKsEQszRizOGnFmth/nwgP4OWTD2UA/fgpaMTduwNyUEQsBExYDZvwUJhB4LuLAubkBNqr18dl5//WfU7EIlXMT+K+fxwkY/nsC/7k4iHNzTiyGzTgTtuBs1InFQD/OBZ04N+PEv2ed+GnKjjmfAdMuFX6atFH+IAOF5yasODNswE8jZkTH9Dj2dQHWnNcKsmbNevzwbRE62k7gkkvW4Z8fb8cRmQuFHT5cd9vteOWd9zEzpoTT3A2Hug3RkX78O0imksiYFY9sfAxrVq/HsmVvYfnyKyEQCLBs+bUxV/bdj6Gg1YWi9iGIWt2k62UMfmadhX/+YusXgKBkIE7zRuYRUasHJcwNnNvQj0uuuBp7i4/AMtSFwKwK4Vk1wiENIkENArMKGMxiCAQCPPXWB9j9VQvyWwaQxyJURG0e5Ld5GABcunhWUDrIbtwolubDoh+w4f7HcP36O/H33G8havNg2fIV+KK5A312KTqMEsyGFJgNqTAdVCMYViIcVSEyRyBwJqSEw98DmbkF3zd8g8uvvBy5h/JQ0E6jZtIjDkLUSrrm7Pp+pFbpmWnOiqz6fgjFdDP2ygHSArcq6rFs2TLs/eQ4DnYN45PeEeRI7KxnWx0LRebNYEREJJzgWnoY+Ks2Iofpe4nRNzAmkGkAq5gRpMpE7VP1NmIZJaQLLpJ6cFDmRvJpHfb+0IvEYz1IKed0fkrs/VGJ/cfl2HesF3uP9SHxpJr6gU8qsOu7bhZFQzVyXDg9jZwN1FZVrkVWLbmTuWi1tCr6HQ+coOzCzBoTy7uNGdAS2PuJrOs8vcYMocTBriVWpFQasOe77osA8OLxq44rBAIBdn/bSexHnE6BG78lnKBNkBoS6C3XuZpcrkFqJde5S3c2nPj1wElOtKtZsuKrdQ4wIMhpJhJOcD9PhQPHY3dU1AIRq9xJZvEAFOFBjA0H5FLZSI4HcTVLu4s5YJcS95iU8wFg9dJKOKq9s/JMIKcHJHOIPbbi8wC5juCGeDaQFjcOIX2RlcZFXGQIGxFTxhhtHlz/aMxJzArpmwaIGeB0hc0uCBvsPJh5aa8Iq9euQ+aRg1D0N6LP2ACDrRlauxQ9xhYoLa3QOtqgdbSjSdcAsU6Mv3/4BtZdeiny20dQ0D7Ij2cLpF4UdQyhuGOQWDXGjnBjbmIoHbyZJUtsR0a9FWk19DdOZvrOlEoj0mv6kddEFVplXT7GKHpQ3DGIg+02FLQNIkfiQG6jEylVMXc2D/hPx2r++OeeaX8yas18dR9nyilodVF8B68FtMR0fJwbj4VRUyC0mkZOxxU48KMcBzi90QllrMuTE3qXxwWKV1EOYGa9Ddn1NJImDamN5QLSzUJOI2k8H/gzReq4vR1YnNNgfk6Duaga4YgK0Yga0bAakbAGkYiW6sNCasxFNZiPajEX1mAuqMV8UI/5sA4LczrMB4yIzuqwEDRiIWjAQsCIhVkDFgNGnJk246cZK87OWvBTyIpzEQfOBiwITWiwGDTh3JwT5+adODfnwNmwBQuzRpwJmPFz2IFzc278e3EY5+Y8OBd14+fwAH6eG8DZeSvOLbrx05wNZyNWLEaMWAjrsRg1Yz5sxHxIj8VgPxZn+7Ew1Y+FMTMWRk34adqBs1N2LI730+80acXZyX4sThixMGFAeFKDhH3vYvl5ppnly3chMfE9zM7o0N1Vgd9tuA1/vO8eHJe04d4nX8RNt9+DyQk9Bt0KeC29GLLIMeZUY8wuxxeH87FixXUQCG6CQHA5BIK3IBBcA4Hg9JIszq0ZnxD73kKRTcJG5vTnGoAY+MtqsCNricHByRsesiQOWuwGsFDqRWmXH8WMLfu78HNceuU1aDfIMBNQIRrWYi6oxUJEj2hYiwbJl7j55uvxebcNeS2kI8xviWUO5jdT84hIyq1fBoOiNi+O9JCGN14fWCTz49pbNmDXwe/RZ5PC5JIiHFFibl4F10gPHL4ujE73YXiyF86Rbjj83TB6ZeiwNKNR34g9qTuw6bEH8HnvAP0erW66SWwdgKjZScY51vFOko+Ydm3Ptx1LDGNpn36GnNPtONw9jNIuHzFtx+RIOqVGerWJSZBiUySuLnL/cSUST1BjRla9ha+E5OQ9qRWsIYgBQe7GXyihTuAcNm052DkMobgfKRU6fPRJA4S1RiSdUjPwp2CRMzHX8d4fFRQIfawPqRU6ZFYbYjeLcfsVXxlZTiRJzPBhoLq4ci1yGmxIqTAgt8GK5Aod3y1+4CTXyqVBcrmON8fkSOxIq6YRd+Ip9cUu4IvHrz6IAfy+mx+JUgQLp7/SsGoeYgCpwFqJAz/Ksf9HOQ6cIBYwmavkqtQjkdnneebvVAzw8Z8/qYnFbTAWMHai08m0/4SKuTG1SDiuwkv7irDplffx0r4iKgHnDSlLIzq4Ez01bp3PBHKdv/GsYCozocT6W2P9xGnVFr6/OK2Gi/3gQCABiqy6fr4fOCtuxQPC2EbhQI6E0uizWXCxUOKAkI14uYaB/BYaR+W3EgDkcu049i+vmYAgF06b02DjAVmW2I7EU2q8vL8Iq9ddit/8/n48+tfXUPrdUWiszTA5W6G1SmCwt0Fta0NPfws6TBLsz9qN/9hwB7F/bW4USt0okQ2ynzeArDoT2wgZmGX/nywGeHPYSJtz3WbVWXmmlQPZ2fVW5LewnuKeERSy9pIS2RBKpA5yeEq9KJR6kVFnIaDO2mG47EbOlJFSaWSZX0Z+tJ/JRNNCsY3pfIhZFTI9UlYdFwVjZr8XudyTTsUkC/uPK3HguIJ/zRMAZDcncT2hCae0cVmSxphkgAVCx1433E2ElQcG2w9XkAi+ogbBsAahiAqhsAqBsAozIRWCYTWxMrMKTAfo40hUg7mIBtGwGnNhDeZDOlphPaIBLb2d1WIuoMN8QE9vZw1YnDHizIwJZwImnA2ZcTbUjzMhE+YDevw0149z8w6cW3Dg5zkrzoTMWAgZcSZkxk9BC84ETTgTMeNM1EJvI2YsRkz8x/MhA4HQsBbzYS3mwzrMR3SIzGqwGDJiMdSPGZ8S834DzkxacHbCioVJE+YDRhpZB8z4adqEMzMmzM0YEJ7W4Luv83+RAfzy6zxMTmkxMaaBQSXG1tdfxG9uuREpBaVYd/lVMDt1cNjboemphbmvHjLxMWx563VcesVVEAhWQyCQQCD4/9j33AmBIGeJK/uJLTtY4weNM8mBSTd6HIC/YHGAL+484M9/iQOiVi+NSrv8KJENoqSTGMuUg4dw9Otc7D/wHn78oQjhoAbzUT0czhYIBAJ83q5DXpzLmAeALR4WNu0lANbmiQOEBADz27wolQ3jU8UYRG10bonaiEEskg3joRf+hsff2o4GQzcsg50YnejB2IwK4Tk1/FN9GJvpxeiMHPIBKbQeGZQuKdTudsidUlQ3f4N1l6zFd7065DZTs01h+yBKOjwsu47dGNf3I7PWwtqSnMhrGoCw0Yn7//zGBckBGx9/BKV1HShqsWD/sT6klGvY+FbPs/D7jquxl92I7TuuwIETNJ7lRsBcNWMyG9kml2vIBVxN1/eMOisKWt3IZRphUYsLR7oHkVnfj4+ONmHX500sgFrJ4mVYzAwHBI8raB2TY+8xOQ6cUODjbzuRcFyB/T9ScsH+k2ocYO1B3Bg3o9qEwlYXMqoNfIJFaqUBBa0uJJ2OpVTEdOw6/hqTwPqBhXX9/DicjCZK7P3+IgN48fh1BxsBd1KOWp2FaSj0PNOWGLcpxqIyVDw9nXyaNArJjNpOZvR5Eldxw4lc+To3Hd82whVvx+JedHw1TiLTeR04ocaNt2/CilUbWH3XBtx4+yYqAeeE+RUxh2Yq03pwTB7H9KUwU0cqy2/7fwFATv+XGldnx73lTSHxodD1Njy9LYFpXv6XjYJtFhwAzOSMC42U+M4JfMlFTHofahZw86NgAoIsdoKBPyGLMMgW2+LG0QQyMuss2PO1FC/szsdDL/4Dq9auw2VXXYsrrrkWAoEAOmszLN5O6AekGBjtwTMvPY3nPkig0U6rCwVSD/KaaSPMb3bxIJaLweD0TvnNbpTKhnCENZAc7hlBWbcfxR1DpLmRUABzapWRH2eIWj041DOC3GYnRG1kKslrduD57ekobnehsGMI2Q126mWujTG4XO0aAUoOeMW0nek1Jt7Bnd3A6uAkZA7JbmAAsNbM5wCmVRrotckz38Qw7GfZhBwLvp8Vti+JfDilZVmTcXmBcfrRlGozUvibkX4Whk660eL2Qdyx6Wl8lJwI77gcw1NyjE4rMDIth29KDv+UAiPTCozOKDExo8RMSE0jupCKsuMiasxF1JiPajAX0SIaVmEurEY0qEI0SOAwP3cPGxlrsRDUYTFkwGLIiIWIgcbMUT3OzBlwds6EM1EDFsMGnI2YsRg2YjFiwGLYiIWQHotRAxYiBsxHtJiP6LAQIeaR+95zITXmIxrMhzUMBGoRDWowF9YjGtZjbKgXC1NGzE8bsDBjxPysEfNBPRaDBszP6DE3q8NCgN6PzGoxOdqDTRs3Ys2a9VixYhfWrFmPBzdugntQDptViqlJPSzGZnjNMmx9/QXcvuE2CAQCnBA3oVFSjief/hNWrl6NZcuW4XcPPoFHXnsPK1bdugRQCgQ3QyCojGMA1+ONlEPIlth5hl4otjIm14o/bUukc/v/Or/jFgcYRa0elHb5UNLpR1G7F0WyYTz4/BZced1NWMNpHNduwKZNGzE12YOR8R6sWrUSn9a38LpfLlcwP37FgUHuWiGSEijMb6Nz60v5AIranCjuGERhuxcF7YMo6fLjnwfLsXrdpfiXRIqXt7wAgUCAJ1/fhoExFcyD3XCNdWMyIId/qhf6wQ5IjA2w+bth9ffCOdqHNWtXo6y8CvmtbhR1kBksi2VsZompjjGDVTNybTgFbcTyX30TPVfXXHft0vioxBQUSweQcprMF9z+wpkI97H4lwMnVNj3o4KXZmTUmNjY2ciCmEkClFnbT4HLtTSCFjUPoKjNxaK2XBA1O5F2WoGPPpXgw7IKpJ5SIae+P3auM5LjfAC470diAvce60NGFRlCEk+oeNaOk4nQlIqMZomntBQ7VWngpw+8waySJFRcwkYSawuJj5hJ5TWDHABUYe/3XRcB4MXjVx1XCAQC7Pq6Axk1ZuRIHIwF1PNRLknMcZl0WofU0ySWT2TUdNJp5tJijkyO9uaCn5PKdXEgUst3O6ac1jKmkB5Hzq1YKwdX3ZZYrsOLe4uw4rz4jBWr1uPFfUX8z+W0YHxLQ415SbRMCvd+Vby547y15OumGJCsXjpK5vWAdbHKL67ZZOfRRrY5OGIO2IY43RB7nwN5BPjsS9pCcuJqpuii7uENIdwomM8aY4+nEGkbG7PEM4504c1lj82s60dyuRpvZ3+BB557EwKBAG/v2I6xqV44fN1w+HvwxLNP4E//2ANRmxuFrS7kNtl54Tnp++h75rW4kN/qRi4bPZfKBnGoewTby05gy9/eQEm9HNnHm1Ha4cHhTjdKpaQRzGuMc9/JBnFQRhrDEpkPB2X0f03+sZNcxp0+5DQ5SYdZZyMQXtPPA/SU80EgYwLTa8y0AbD8Ri4Whgt/5RzDXINIWrUxxiiejt34JLFier6A/oQKCSfUcYwgu7k5reNBYFIcI83nWlYZ425AWOWe2IFCqRf3P/s6Xnh3Oz75Mhcf7f4HjnyZA4evC94JBQbHlPBNqeCbVGJ4Qg7flALjM0rMBpUIhFQsOFiNaFSNUEiJaFSNaERFzGBYjbmwFgZdJeYYWAxF1IhwzGFYi/moFmfm9Tgzp8PinA5zYTXmQioshDVYCGuwOKfHYlSHhagGC3NazEfVcUtDgC9C3ysaIlYyElERQxmhkXV0ToeZKTlC42pEprSIzmgJ7AUNWAjqMR/UYyGkp98pqkNoRonQrBKhgAqzM304caIAiUnv4fNvCjA6ocbYmAKzAQN8PjVstiaMe/owZOrE1tf/ugRMrL/vEez9uhnCOiOK2z0oaHHg1rsfw8rVGyBYth0rVt2G1Wuvo17o5buwavV6/Obuh5FRY6JsT1bjmFXXz851G3YcbYrVP/4fIDCTgT/usXlNA9TNLSNDVZHMh4df+gcEy1ZBIPjPJQzn19/mor3rB6xduwbCGuUFAPCXgSAHBt18FZ2ozYsjvaP4Qm5HYZsLZZ3DyG8ZQIF0EId6qSLyiutvRNY3J/DwEw9i1epVECxbhut/cwf2lh5FdlkW3t3xNxz5PAdjUz0wD3dC6+2EytMNlbMdy5cvQ2FFM/JaKfsvW2yJ/Z/rbbxhLltM5jBRK9VJHuz24/38L/jnKeNkO57aup3PFr3zD/fgaH019nzXQwCPB39q7DtBDOC+40q+AeTASZIhCcX9fEUk1XeakFlrhlBspdVghUjSD1EzZexlN1ix/bNGbCs4jr1ftiDllJry+Sq1fLcwFzWz7/h5AJD/HeRIPqXCvmPkFE46RT3AnEyEayJJKmfmSbEVWfUWpvEmXWlmXT8KWweQUWOk0TCTtyScoikcpzvmo7BOxgDgx1+1XwSAF49fdZAG8BtZrAOYbX5pVUZmvKDctaRy0tsllZPLKbXSyDNwyRVcdRxzErONmAu4JJcl5R9xgZgc20fxGgY+/48LZOayBze98j6WLTsvQHfZLmx85X2eTuf+PW/q4PR9cSwgvxEzMMjp/7hFLFL82DjG2KSxsR43wot/P6OeLnRZPBPIAOCSEbCdHw1xonKqK3PGukX55WQ5gWwE3LZ0DMwBQO7xQs6N3HChDlHIQqiFjU6+vSCX6QQz6/rxfukprLn0cuzKzoDD3w3PWB+uuf5abM08ClELV1vnpJ/b6ua/Rx4b3xZ30AW9tM2BYokOL727nb+w3/HQExAIBHjrg3+itFaKog6mIZQNo6hjEKWdQzjc40dp5zDKukdxsHsEJbJhxhoO8kG/OY0OHmxzLOyFzyln5DCepwk0I4PrN5Y4kN/k5FtB6G9mo85QXgsYBwSZY48MISwcmom6uV7SBNZXShd+ej3H+qaXNogks5pBei1ZeRPMe6JvKBx7xfVYtuxjrF6zAQ88tBH93h4M+PvgGumDe6QXw5NyjE0rMBVUYpqtmZAKkzNyfH+sAAmJ7+KHH0SYme1DZE6NSFRFYDDKifrVCEU0CEXUCDNwODenxfy8DgvzeizO67Awp8bCnIYAX1SLhagO83MaLMxrMDenwcKCBgsLaiwu0sdzcxoCnXNahCJqBMMqhCNqGmVHVIhGNZiLqBGeVSISUCMaIN3iXICNqGd1iIY1iESUiM5rEAopEAzIEQkpEY1oMD+vQXRBi9EZNRw+OSZn9AhH+uF2d8PlksPpbMHEYDemh1TIS9sDgUCAnTmHkVljQKHUy8eiHO7xI/NkF/Yc/B4bn34BK1eugkAgwMEmI7akf4LNW3fg5YRSpNWYkSOx8edTrsSBrPpYdBEHBPk4qPqlQI97n9d8ssfkNDpR3DFEILCDDBObt2yHQHAZBIKTSzSOe/a9g6Tk9/Dcy88jS0LVadxNIbf4CcAvMYMtrI6u2YVDPX4cktkYACPjWVHHEA73jqKky48rr78JH+aW4rLLL8Xuw8eQeLwHL+/Nh2DZMqxYdQuWLf8Ya9YQMzk50wv3WC9MQ12oaqEc0eJmE/UPSwf56QP3tyiRUgVcaQfpEAukHhzs9kPUaMGRnmH8PesT3PvUy1h3+ZV4Z89uJH9dzV87tn70HvZ+I8V+FvNy4KQG+46rGShTMVCm5Dt5k8q1yK41kdavkpzA6VVG5IgtSKvUIb/Ziaz6fiSXq/DRv+rx8dEG7P6iGQnftiP9NOXxEdmh5mvlYvl+Kp75p59P3cP7jiux95gcmTVGMq4wyVLSaZokpFfqkddoR17UOFQAAAAgAElEQVSjnRk/SJ+c00Ah9WWyQeS3DCC7wYb0WjPyJDYqWGCylKTTnPGNS83Q8e9z+vrdX3dcBIAXj191XCEQCPDxV1LsP67gjRf7T6j5TZTT8iWxqJeMaiPymNMr5bSWGUIIeGXX9bNWBnNckDQDeeU6Js7lol8MFwBAfrNkVHhyhR4v7Sv+XxjA4qVtHhUxFogDgVyW3xJAGBc8zYO/uPEvmUTijCPVbHFaLg74sc0go96GjLgNIUsce8sDsQY77/TNbXIiR2LnmyrINOGgrLoGO3+BJ9DF2L/zgB/HGC4xYnCtJJzhJK6OLKeRjYnZ57iRkVDiwPvFJyAQCHAgKwEmN11Qth+uQFEHVUnlNbsYizjA/x6iVrro50sseOTPz/MX7nv+cBc2P7VpCRNz/wP34JJL1mFn4afIq1Mh45gEBa0DKGofRMJXEqQc/REfF3+GV/Zk4vn39+DjIyfJfMLchcJGB2+cyWpwILPejvRaK41X40BgDPjHnmu+sq3aiJRKHd2EVOqQVqVHRrWRnMH1FsYEUhYkMdQsqoGLhymnzYEbESeVx3pJk1hFHdccksTGVtxrPOW81ycHALPEdhRJvdiW+wWWr7gaAsENEAi2QiBYxOo161F2NAcufy9sQ51wj/TAM96LiVklhfPOKjAxq8TIZC82btrIjxHXsDHi5HQfAiEFMYRhNUJhFYIRHWZCKgTCNEYOhFSIzOkQimgQCGsIFM5r2eeIVQyHtJif0yE6p8H8gg6RqB7RqArzCxoEIxqEIhoEI2oE2NtQVI3ovJp9jj4OR1QIh1U0qo4QezjHWMq5sBqzs3IcO1aAAwnb8MOxfMwE+hCJqhCOqhGd12FsRg2HXwH3qApDfgXGJ02w2aVwOHtgs7dgYqQXY+4+NFcSkP54xwc4InNhz2e1SMovw8tvb8ONN96ItWvX4I7bN2DrGy9hx/tv47prrsLnPR4698RsXMkqBDNrTRCKLciqM6GA5XBmiWMAMH4tAYNsLXlMvY0PRi7p9KGgzYWybj/ezjyK5Sv/AwLBFezaxlzOPxQgKXEb/v7eWxA2u5k2eCAubJoDgu4LRsOcTpAzkP2rz4+iNgc545ucELHczSN9Yyjp8uPeJ1/ADbfehptuvRmFrK/47ezPsWz5pey1+DP/e31/LB9j03Ic+kyIK666Eo+/9i4KO4YplLrNTX87Cd28lsmGUNrupkB52RCKpHQdE9ZSx3ny0e9Q0OHHQ399+wItoEAgwGVXXo0DP/Zix9FGMh8y1mvfcRUPxHhN7kkCcHksxy+92oy0KhOyavuRXWfCgR+6kVGpQ2qFFvu+keLlPfl4+OV38dqBYmTVmJHX5ERGjZmZGPVIrtDxe+C+X2z7oEUmRhVSK0mjSJFnNNnKEdsgFLOmESZ9yeSag1gbSU6DBRm1ZqQyzTLlwNqQUUvypeQKQ1wsmpZ3RKdw+vzjSuz6SnoRAF48ftVxhUAgwM4vpTHR+48Kxm6o+Hwj7sRLr6bKrfTafqTXkEMrpYJWerUBeSxbKb2mnzbWWjMfGM3p9NKq9GxUTCxfShzYS+YZwJiuL/EkpwFczzSA63Hj7ZuQEJeaHj8G5N5PYyAv7XxTyPlgkGeTTHHsXxzgO3/xLOCFbMB7JafZeCgO+DU4eEaBzwFsYvqiBgp+zm20I6eRnKM5Ejtv9hAxAMgZPjgGgANFwkbnEr3f+aCPX3HgLy/OZCJq9SC7wYYVq1bjwccexnf1x/HS68/hgccfg6h1ACKpl+l2hnCw209sX5ef3Ludw9j8xnt4dPMmiFu+gdwkhtndg7ziJAgEAmSVCDHol8M3qsT2Pe9S5MayZVi3bi3uvPduPPnyazSq++1tSy7+Dz33GnZ+Kua7RfPYZsf9/lkNDqQxABgP6Ok5Ncf0m+xzaYzN5Wr9Mmr7+Wq4HD6L0Y4c1sKSXU+GHi4wOrVSj1QWd0QrFgodL39IilvEAnL9n0bWdkMMZUYtuYKzGxwo6xzGU3/fzSri5iAQ/B4CQRmWLfsYO3a/g/7BTliGOuEc6YHF34OhSTm8E33wjPdheFKBr777ZaPEt9/nYzqgRDCshrT9G4SiWsyGtJgIKDEd0mB8VomZoArBsAbTASUCYQ1mwhoEIzpE54w0Yg4qEQppEAlrEAprEI5oMTXdg2hURb21UWITp0O0ZiNqhKNqzIYJ/M2G1QhGtQhGNQhFtQhHNQhH1Qizx0Xn1JiZ7cOmTRuxdu0GLF/BdHAPb0Qg2IdIVI1ARAfvmAImtwwamxT2wR7YXTK4PJ0YG9PAZpNifLQXs6NqzE9ZoO6rx1VXXo7Vq1fhuuuuxZ+eeBg73tsKcfkX8Nt6cHbShjOTVnx1JB/33vt7FEk9yKg1Ib2Ozue0GjIs5DXakV1nhqjJgfxG2syzG+x4r+QUMuus/OM5kJfOVkadDem1Nv7r8Q0xuc0uFHcMoYjFKeU22nHL3Y/RGHjZM1izZj3uf+BBdHX/CIFAgKffegclskEyVfFa3xgIFMZ1EcdYwZhGWNTqRmnnEAqlHuQ1k+GhVDaEYtkQjvSNoZh1+q5ctQpv7NgNUccIimR+PPm33RAsew8CwT3snEzEsmU7sHPXVty/6QFccd2N2JL5GYpYRJSoZQA5EhvymgnslXUO4WDXEIrbPSiTDaJI6kJhG4VX33LnHyAQCPCXbR9D1OrG89vTfxEA7jvyHYT1emwrPMWzfPEMIOnv5DhwUo2U01pk1/cjv9GBjBqKW0mvMSG/yYmMSi3yJFYc6qK/9613P0ZB7ExLfvOdjyCvibp1UyuNfLtVVq2Zd+KSeZHY/ViWbczkyE3HuHM/pcLAHMZkNClqc5M+keUaptWYaVxeR32+WWIbClvdKJG62edpP+IkUVy/PaedTy7X8Gkcu76+OAK+ePy6YwkA5LVNxxW87oHTQ5DWggNXxLbwrRp8tZYJmXVmpFUZIGywIaOWNt7USj3SqwwssJcq1tKqYmOy+H7eeADItS4klWvx0r5i5gIuppDNivjHc+AvXhO2NNaF1/HxYDCmyTp/LHw+4ONWejz44wGgA5lict3+dVcuP4YVNlCMCTeiXcLOMSaQQqAd/Dg3r5kbAXMAkOV+tcRGPpzuj6JjYiBTyOn9uK+z8W9OnFEkNkKKsYr5LW786Z39/MX33YQErP/db/DsG6+jSErGjh1FX+B3d9+Fu+77A/YdPIqijkEIa/RYe8kl+OybAozPKjER1KDHIsWmR+6DQCDA6+/8DT2GdmhtzfBPyWH3dVG3qa8L6dkf4423X0Rt0zcYm5YjNWsn1qxdg2XLluG5d7bjlQPFKOwYYhmEgxTMGwdaC1rdZOSJG9GnVcc9Z9znari3LNOxxszc26QFpCYWG2tgcfAsalY9ORnTa+jfcmPhmFPeyEc6cJIGTu6QUsFFEhn4aJr0mn4G9tlYsY40m6IWF7akf4KVq3/LQFwfBIJrsHzFVSg5mgfnaC+svj5YfT0YGO2Dd0IJ95gcnvE+uMZ6sWvfO78YlbL3wDZMzCgQCKtx6F+pCER0mAqoMDajwGRQjcmAChMBFSZnVZgKqBCMaDEb0WFiVoXJGTXGxrowPSvHdFCNcESLSFSDQIjAWyiqxUyIA3QaTAY1mAioMBPSYDasxkyYAGEgokEoqkMgasR0SINgVMczhcEosYPfHyvA2rUXAtgfTxQgPKfGVFAHz5gKjuE+DIwo4RpWwO7tw7BfBf+IFiaTGNGwGVNTRoyNqDAxqoLH2o7+bjEiHjUibhWiPj0WJ8w4M2HG4pgRC34jnn/6cexJTkOG2EbXsup+Ni2gbmdhA5k/ciU25Eqs7MbWgRd35zJ9m2XJtSB+pdXEv083kln1JPcolA6iuJOabwraqH7s/mdfxbU33YZDX+TC4WnHAw/cjbv++EesXLMOCQV5EDWzlIDG2E2cMA4ALgWBbvq+DASWdZHEIq+JgEhxxyDKunw41DuKok4frrrhFggEAqQU5eFo3yAKZcQAxl6PfggEy7Fi5XW4bf2t+P0jTyK3aQBFMj+KZD7++lTcMUjVkT2jKG734GC3D8XtXhS3e1DcQSHyf/zTSxAIBLjvyb/g1f0FKG73IrvOiGtuWs8kECuwLedT3Pjbu3DTb+9EWV0L3i2rwb7vSQu4/7ia34/2HKPsvdSK2L4iauJcx/3IrreitGMQaafkKGim9pG30o6Q/vO8SdIbKYdZ0P4AsuqtKJUNERCrJVCZUmlERo0Z2fVWpFQal3TPx1IoljL9JIFiYdQ1LGGA3RRm11tY5aoV+c1OZNZbkFZjRn6TE9liui5wMWXJlQa+6pQPqy+n+LP9x5X4+CIDePH4lQeNgL/uoFBmPr2cO9kUzG6v5nUIFFipZcYLY2zUValnTloK5U2vNvJvOXCYVdcfc0Bx4O70L4+AOXBHDSUxYHf+2Jd73C+tVAYKCdjF3LzxmYAcU0RjYMYacbEddVz2X8z0kV5LDACn8/nlUS8tzgjCV8axsS0X+ZLFWgYo/iXeAOLi7+LzW5fe2XO6n3gQGG8g4VZu3MbArSUO4mYaMXPC8Z3/qsYLOzKwYuVK7MtJw5VXXQGBQIDrb7oR11x7FdZecgmuvPYGXH7l5fjTq6/h6hv+A8+/8CRsA82YCKjgm9ZAZmpF0WG6qy88mAdVvxijATWGppQYnlJifFaF2ZAaU0E1xmY1mAmqMRVQYWhSAf+0Bgm5SRQOe/Q4ijt9KOoYRmn3CAplwyiQDqKwfQilncM41DsGUZsH2WJ7LF4l/vm5YFO28DFAnDwhs66fjWloCesteCPlEB574yO8nnyIXnesBjCdMYjc4mNnGMDjNgKe7as0stEz1dRl1FpYVqQVOQ0WvpWAGzvfdOfDMYZ75c1Ytmw5jpT/iMEJOSy+Xug9MjhHemH19cA71gfvWC8Gxnpx9OvcX2QAv/o2DxOzSkwFVZgOqTE+q8XIlBwj0wqMB9SYCKgxGVBjIqDBVJDWZECLyVklxqflmA0qEQwTaJsKaBEIaTAxq8HErBIzYS2mQloe8E1zgDKoxsiMCiMzWozMaDAa0GFkVofhKRXGZtXwT6swwpzMsxENQlE1EpPexfIV5wHYFbuwP+FdBCMq+KeV0NnbYR3qg21IAZunEwOeHgz7FHC5pJic0MI/Qu8PD3VicrQXMz45Qm4FIh4FIl455ka11HQStuGnsAOjtl6sXbsGH39WT1Eh8TeNVUYKIW90IKveihyJDQXNAyhoc/M3Z0J2zmbWW5HB4n44pi+t9sLJQXqthbS+jU7mBiZnfEGbFyLpEFJP9mLFylX4pKYW72x7GQKBALc//Az+4ze/w9p1ayD67ltkiJmelweBsfo54Xmj4ZhZi3SHRR2DKJK6UST1oLDNjUOs4jHxeDeWLV+BJ557GmvWrkHmJ0dQ2OmHqM2LW3//ODPLfACBQIDlK1bg90+9irxmFwN/tArbCVCWdflQ0kmj3uIOL0pkwyjuGOKrIv9jAwXP//a+jchv9aCo3YOsemLkX95HwdAvvr8bz779IX8juuXDf+Djb3uw99tOfhK1nxkzEk+qSMJRS47/3AYC6tl1ZuQ12lEs9SC7Wousaj1ELQNIrzHi8Tf+eaGWfPkubN6yA6JmJ/KbXSiSenGoy4dD3T7kNzlQ0u5Geo0JhS0DKG4b4MmK1AojMmvM1DTFS09iUWTp1SbkiClpIL2atPGJ5Vqmc9cjtVJL7R4sJzSrntqK0qoMELW46GaEGwMzMyRnUEup0CPhBDmRd14EgBePX3kwAChDwkkNMmv7kdfoQHptPx+CuY8JYDkAyFWwJZVr+Ry25ArKaONYjzRGw3N5a9n15HbKrjMjq5bL4TPzvY3JFQb+QhwDdvolm2p6dayybSnwO5/1M8TdmcUDwPNHuyzahRsRcyNjVlVGDB+lrseDv3hxNwFADvzRiu8IjoE/FoXAAcMGOwt+dsbMII3OuB7hWLYfgT8XzwTmt7hjbGD8aoqxf8KmGEvAfT2nyXXev4sxgAWtbhS1e1DaOYyHnn+LvwjfdsddeJG5Kx9/awcEAgF+d8892H7gn0jIToBjRIGxGRUsfiUsXhlMHgUMrl60KcRQW5sxHlRjZEaJ4WklfNMqjM6qMBVUYTaoQiCkxlRAjZmQFv5pNeyjcjSo6rHxyYdxyWWX4MEnH4NIokdxpx8lXbRplXWP4EjfKMp6RlHc5UdhxxCl49fHKvoy62xMiB/TYsXAIMkX6LVAm0d6tREpFVrcfOfD/HhoJRsPZdb180w2RUuYmLuwf4kUgoudyKhh9YjVJqRXm5nLzwqh2Ebdz43EBmfWE7uYyuKIkisMeC3pEB5/czvezvwUdz36LN7YlQCbvwc2fzd6HO3odrRDMdAOu78Hdn8vbMNd8Ix248GND2HNmvWkAVyzHg9u3AjfRB/GZ5RMM6iCd1yJ8RkFJgMqjM6oMMkAoG9ajeFpNcZnVBgP6DAd0iAQ0SIY0SEQ0WEsQAzc6LQGozNaDE9pMTSpxtiMGlNBLUZm1PBMKOGZUMI7qYRnQgX3hBLWUSXMvj6Yhnth9ffBO6nC4IQCg5MqDE0pMRZQYzKownc//DID+MOPBQiEVZgMqqDzdEM90AuNvRP9rh4MjWjgHezF1LQZFnsHfMO98Pm6MTnai8C4CqFRFULDSoTcPQgP9iEypkR0Qo25cR3mxg0QZezGPQ88hCyxJW56EGNy6EbVjMxay5JmnoKWAeQ3O5lUwI7sBhtvMMqojYG98yUjGXVW5LCszEKpF2VdFAUjYuaJkk4fnn0vkYDW8hU48H077nzkWWxLSEVqxnY88NC9OCS18DeQXL2iULIUBArZuV8oHUR+K90gFrRxXdseFEo9KJR6cbh3FIUdw3i/5CQEAgEe/uvr+OjAdmx+5nEUy3woZiDw7ezP8eTfduPBv27Fa0llpBGMA39FMh8LhqfWlJIOCp8ukTHg1+pCTqMdBS1OCAQCPPX2ThR0DCO/xQWhhGJiUioMuGfzC1i2fDkEAgGuvOZqlH0mwu7ED7H52ceRfEqO3V+1I63SwDvzE06qkVFNWaJclqeoeQBZ1RpkVFOocp7EgoxyJYrbyUSX3+rGm6mHsfI8LfnKVRvw98xPUNjmQpHUg5IOArCFUg8KWl2UYyq2oqCF3k/jNHx1ZoianTQhiJ8ssQQJ0hQbkF5jYiSEnjc5JlWQbInTqBMTyOlMKbKK0wGm8LmnpFlOOa1B8mlOh6zFrosmkIvHrzyYC7gLqRUGFLYSuEipMlAFDpe6zsSwsRYPNd9TyNeqVXJNHBTDkV5Ldzf5zQMQtQwwKz7FKqQzQWzMuWnktXopldxIOJbxl1ZF2on0uPq2C8Hf+Z+LOUO5k5TYHDYaZK0e8QxfZl0s349jfzLrLLwDkNPzZNQR8OPAX4zpc14AAOOjYIQM+AkbHMiWMA1go5NGxXxTCNPxNQ0gh+n1ODdwLBD6QgBIrQXxY18Xcpo5diD2PXMZW8CZOUStXhS0uVHcTrErxR2D2H6kEu/kfYVLrryaB4MflJXj+e0ZuOam36CmTwzPlBbqASV6LDK0W6XQutrhGDNB0tuAoSk5hqeVGJmJrYmACpMBFUZmNBieUkPp6oRqsBfuSRWGJhQYGJNDN9iFJksLTvdVYdNTm3D3A39AAdtsijv9ONgzgoPdIyjupFaFIpmPB9fxbQz8aqAVbxyhHD7G7NXSa+K1xLJf2BzW483kgxCKLchpsELUaCemUGxFtthK4d/1VgjrrfyYn252rHxLTC57bkXNAxC1uJAtps9lsoaUFF6H2s8HSGeJbdia8SlWr7sUe0s+Re9ADzpsrWi3tkHjkUHjkUHhkMLolZEecLQbX36bj137t+Ho13lwjfRgaJKyA/3TCoxMq+AZkWNyVoGpgBpTQQ3GZtVwTyjhmVDAN6nAxIwK4wEtxmdVCEV1CEX1GJ7WoN+nhN2vhmOwB/5pYhK9Ywr4pw0YmlBhaFoN17gSjjEFrCMK2EaVsI7IYRmRw+STwzjch36/ArYROQbGFXBPKDEwpsDwlBKDUyqMTMnx0KaNSwDsxk0bMR0gJ/NMWA37qAJdpjZqrjE0w+XtweSMDv5RPfyjOri8XRj1d2FmRIHguBbBcSWCEyoERuWI+BWIjCoQ9SkRHdNicqAL1117Nbblfon0Grr5jL9mJFcYkFljRGqFFtnsec2ROOgcbHIir9nJ1zLy7nuxjX8tcSHxFATO3fzaIGQJAKI2Lw71jKCIVR8SAKTX8etJpfiw9BTyWwZw71Mv4e19KTAMkMbrWGsDMuvtLHj9wuo5buU2u1DUMYS8Fjc/YRC1unCkZwgFLXYUdwyhrHsEhR1DyGm049KrrsW6y6/CvZv/issuvwxHmtWkBWQ94Bcs2dJVwJpIimU+FLYPoUjmQ2mXDwVSMtcc6vbhSPcw8pscKO2mJpKcRjtyGqkb/NUDJTzDKBAI8Jf39qFbW4dHn9iIWzfchqQfWrDri1aknNbyveAHTpJBMaOOQvjzJHYUS91IOt6F7DozEk/KkXZKhTyWq5rXwq6NDTbccvejWMmY9pWr1uPWux9Fjrgf+c1O5DXFskLzm50QtThoUlBvZWDWBlGrm98vsuqtSK9h/cL1VhRL3cgSc8Hvsf2N2DwDC7KPLdr32ISgWsf09RQRk99EKQ2plUwbf5p1lp9SkduYBUZfbAK5ePzag1XBdSGrth9CiQOprNOXA39cCvt+1t0bi8DQIImrwaoy8sCKxPYWpnuykf6kfRB5TU5yuoqtrCLIwmuz+PDluLFsCqsOS66I5fulMe1ePPBLjgOAS6vgTHHfO9YGwrFAfFQDl1rfEJfvJXbwoC+e8eNcqNzod2nmnx2Pv7kdQq4mKm40nB33mGyurYPpzahCzRkzccQBQG5syzWC5Ejs2JL+CZ7YshNvpX2C7AZbHMM3sMQtyI1/4w0g3Nfymt0oaPWSJkk2THftnT4Uy4YhamMVdC0DKGz3YvPWj/Hy/kLkNVFkwf3PvYmNTz8Ly4gSansfegxSdJsbYRpWwuBSoUfbCZOzBaOzKgb+VJgKqjETVGN0RgXtYB/6BrrRapZC5uyE3NMFlbcHbRYpOhwd6HR0Yus/t+J0L7VkPPvKCzjYwXRHnX4Ud/pR2OlHIfuY0yHlMrcyD6KXAEHSaPIj3Vou1JtA16Ov/8J4aNkuPP7mR6TVbHIgv5lpNhsZiyuOPY/ZYjuy6q2x3EGJHUIWM0N9stSMkssAuFBso9dxnCOdMyFxrNGridRscdvd9+MHmRQaTxeUrg70DnRA45XBPNwF9zhnCJHDOdoHq78XVn8vLL4euMb64BqTY+/+bZgJKuD09aG+sw4/SirwRc1ptJn7YB9VYHBKjYlZNWbCBsyGNJgM6eEYUcI1qoV9qA+OoW6mP1TAO6GEc1QO77gSztE+2EbkcI4rGfBTwDKigNnXB/uoEv1+OYy+PvT75HCOKeAYU8A+Sm/dEwp4J1Xwz6gxOC7H198VYO+Bd/Hld/mYmJFjflGD6Lwa4Tktxma16Hd3wuSUQmeVwumWwTuiRZ+hBd3aeri87RgZ6UJwTI3QuBrRCQ1CEyqEJ5SIjCkQHVFiYVSPxSENyvIS8Zs77kZGvZVc4efdPGbVmskhXmPi2R9hAxmFRC0D2LxlR2wULHFQ7iYLPeYy3bL4ZeGrADk5SFH7II70jqKAVSsWSAdRzABgQfsQa/XwYvPWnXjoz6+iQ9+I5SuWI/u4mG/f+d/6hwkAulHUTt3aOY1OZIqp97a0cxCFbWRCKen0o7B9CCVdfpR2j+DDg6ex5tLLseHu32PdurXY+sHf8XnfAApkI/8n+CuSEQNf3EXnZLHMh7JuP4raB5Hf4iKtYdcQbvjN7RAIBBB9+zmKO30QtQ1i85YdONjhxd/SD0EgEOC1/Xm46Y778Od/7MBjmzdCIBBg3eVXIOGEAjs/b461RLFMQI54EDbYkNfoQEYV6eMya/tjQdTimAEvs84CodiKjGoDtqQewpNvf4w3Uw9BWG+GsIEzhNFzmSOxU21kA+m2acxPzUI5DVRpx5EC5Hx2oIB1IGfWmhkjTBMCrgYzvmiAywZdsqdVGZDX6GSEA2lP06qNvFQqsTyWRBAPAD/+WnYRAF48ftVBTSDfdiKtUs9ebFo+BDqBZZzt56qvWA3WAZZ5RFpANq6tMrG7XlabxO6S8hqtKO4YRC6LW8gWx04iAov9SwBaahzIizd3pDFHb4z9O5/t4/R7/fyJxTtAmQ4ro7Y/jtnjgpxjOXpCDrj9QpYXN1KMB37ptRa8nnIYj725Ha+nHMbe7zqXAsm4cFhhHBhZAgpZqXw8+OMz+zjw0ULjlFvuehQrV2+g8vpVG3DLXY8SCDxP+5fTFBv7xr4fAUpRqwdFUi+K22n8VCKjzaCg3RtnQHFD1DKAIqkHQokNeSzAOb95AB8erMCadetg8vVC45BDqmmBbkAGy5ABzmELxO2VGJmWYyJAI9+JIGnOxmfVMAzL0WLuQKu+Fc26VrRapWg0NKPF3Io2oxQKZzeajM2o6q5Eq7UN3zZ9h7v+cAc23LEBjzy9Gb+9+07c+Ye78fK7/8CBz05AJCXWgYArid/zWzmzTOz/ny2JdRRn1Fl58MdJAV5NLLsgamjlqvV4M+Uwchvpb0K5iE5+8xc22HmGKJttNEKxFVks/iGTOfyyxFYIGx3IbyWBfpHUiyyxNcb+sQgbirIx8zrVzHraBO7Z/AKuvOFm7D3yA6TWNrTZpOiwtaHH2Q7bSC9s/m54xrvhHOtGv68TpuFO6LxdcI72wuZuwv70vbjnwQexfMUKrFi1GmsuvRxrLrkM1//mDnwl7cDwNLGyYwEtRmd10A7J0efqgdXbC4tXBrNfDvNQD9TebthHFHBPqGDz9cI2IodjVA7HmBKWETksfloEKlVwjClg9BEdNkoAACAASURBVPVBN9QDo68XtlEFHGNKGId7YfbLMTCmgGtCBc+kCr4ZNSaCaowHVJgKqTC/qMHCohpz8xpMz6rhHe7A6JQatoEOuAZ7oHXIYBpoh2+0F/6RbkxO9GFqqg/zMzoszhgRHlUjOq5GaESO6IgK8341fLZOXHbZpdia8zXSa4wXTAzSa0xIr9RReG+1kUBgXT8fCp3XPICUkwqIWt18vWAOY/E5EJhdb2H1jhZk1/fzUw/u3C+UenGwZ4SxdC6IpIO81rWQAUChxIHdX7dh5eo1WLFyFW5/6EmkVhkpR1Ti+D9BoKjVQ9mZTU66xortKGx1o7RzGIVSD0RN/Tja40KBbBzF3eMo6iSQ9/Ar23DfUy/gx/pvcOMtN+Lhxx/Cp51musmS/e8AsIhFx5R0+XGoZxSHekYgYg0kh7qGcN/Tr2D9Xfdg6wdbcPV1VyPz08MokzmQWaMjWUe7Eymn1UirNuOZ91Ow7vKrcPWNt0IgEODV/fnIrDNhx+etSGe5eImnyYmbfFqLtCoTRM0OZNYYkF5FEWMc8OKkF5l1xNTzOl+JjeVDDlLIN0dI1Pfz53AuG/nnNTmQJbYgjSVfZNYSU1jS7qVrbqMz5rxuiumws9jeQQQH7TXZYlsstaDSxE/MyJRGUVTZdWYmPbIgvdqIzDqSlCSf1rJcXB2SytXYe0zO5wHuuggALx6/8rhCIBBgz7edVNV2Wh8Lsj1NbSDc3RcHCuMraij3iHNFka4hhQncsxscyKi18qXb+c1O5Dc74zK1LPwomEbGS0N+OQ0EP8aNi9KIBfbGGD9iH2ParEzGyJAOLBYknFa71MHHZfbxpeX1NmSyzK9YsKudOX5t/OMzavtx852PXKAbS6s2xR7HhTLHAz2mB+Ir1eIu4ML41RhrBMlrdjO36IUaljfT/sXYPhoZ5/7CymNtHVysDIW3elDc7kFJ5zCKZT6IWl1LMwebHBCKLRA12Ukkzaro/ib8Aldccy0c42roXSp0qNpgGOiCxa1Da28d7N52TDLQNx0kl+jYrBrWkV60WWSoUzeixdKGBm0TWsxtaDA2o8nShgZtI9TODuhdMvTZ29FqboXUJkWDvgFJRcnY8s8tOCBKQFppKp57/S+44uorcOvv1iP7dDttRLJhFLR5UcLGUQXSQeS3etjf18nH8GQ3OJaM6tJrSQsWb8RYuWo9br7rEbrrZxd2zrXJdSKTVtPB6zizG2hDyYorvqcOVAdym1woaPOgsH0QQokdaTXxLTXx2ZVcBiXpULMaHMgW2/Hi7lysXnsJXtmZiAZTF68HPPhNGd744D1seuY5PPin5/DWrgNIKC3D1+KTeOP997By1WrcePu9eG57FvZ8LYWwwY78Vg8KOwbxh6dexB+feRn6YTmc4wqMzOqgG+hEr6sP3fZOdPe3QePqgNrTBbW3C51OGQzDfRicUMI+IucB4MCYAlY/AULXOI2APRMqOMaUkLs60eOSQe3thmG4F2Z/HywjcuiHe2EY7oV1RA77mBKuCRW8kyqMB9SYDmkQmddgYVGHSFSL2WkFpkb6EAnrMT6uwsSUCqPjcgyNdGNsvA8T43KMj3QjPKZEyK/E/IQeiwEj5qd0iEwoMTepwdyEFlve+Cse2PwschrJxBGbJJCZh+tpjW+SyWbPJ/f85rBNX9TCbghYV7Cwwcb3TnMZnzkSRyxaiElAcpupk7ew3Yv85gEUtBMjJ5J6CRC2eSlMud6GDw5WYucXLZRPyEbI2RI7siRcx7AzNn1g1xIyfg1A1EIZgZmMoSruGMKuz8R8FNPV112LJ557Gl/36FAk8+P5HZm4Y+OTaLf2or6vFnfeeyde2voy/tXj+WXmTxZj4LnFSTJKuvxIOS7DrXf9Abfd+Xs0yOtR01eNV//+CgQCATY98wzKOt0obLUjuVLHpzGkVBnxesq/cPPdD2Bb/rfIbXKipMOLxON92P1tVyyBotKA9CrS+uVKbMiuM1HdG9f+U9vPvxXWc5INYvPyW1wokQ1TgxFrNcpusPO97tliYuqFDTZk1vUjvcbIy4XSa5kOkz2feZy8hk1pCts8/PcUShzIbRzgteHZ9ZxEIJYsQZ30ZEqLSUooBzCjjogSvi6u0kgJA6e12PN9Lx9SfVEDePH4tQcBwO+6CezFXQC5po/EUxRsy5VUczT8gZNqVl6t41lDzh7PZfil15ghbLDzesBcloyeJY4Fq3In2BIGsIpzCscAHx/qywHDSmp6oO/N9fLGWL1YMLMD8aaN8x29GczIkVFni6t4iw9yZQCQgboM9nNeTTp0gW5sxar1eC35EB9azL/l41/i79hjnbpLACBjAnMaY0xeXosbm7fsZHlx57nY3trBm0Uo/mGAZ/GIySOtX2Gbl2qoOmjcW9rlR1mXHyWdBP7yWlzIZ/Ez+S0e5DcPoKTdi4NdwxC1ONlm50TS91IsW74cZp8KtkEtmrsqMTiuR1uvBC6fDNNBigGZCWkwNKWE0tsJmbMdzdZWNJpaINE3QmqTQWbvQoOuCU3GFjSaWiE2NKNJ3wDXsAzDo73osrRBZu9Au6Md7XYZWq1StFjaILXRW4mpGW+8/yYuufQSpH9TDlGHD8WdPhzqGSWnpcwHUZsHOU3/P3vv+R5XdXYPC7DBBUOogQRCTw8hpgYeQg9gMBgMwabYELCx1aUZSaNuudAdugHbsvr0PhqVqdKMplf13g3kff6J9X64997njM3z+8JXn+s6l6QZIQvNnLPXXvcqGbFYVumSqDaSc7qGNaJUaOh1L20LYWuR5AJWdEZZ7R2V2NdbqOi+1pCiEZEuwbSbCcEGCiZIE2c3dWr84O0u3DggDx/PDS8PiyByRVdcLPw1pize/kSLS6+6Fpdf+xvc8+RzuPTKq3HxZVfi9kefx0OvFuKhVwvx+/sexxXX3SSYox2Nx2lTI2OtVYY06m2jKD3lwYXrN+Kfr+3BP557ATf96Q7k5eXhpbID8GT60JvugSvVjcSMF67hHjjT3fAM9yAw1ovMrAfubDf6M92ITPRhcLQHmVk3ktMupGZcSM644Rl2wj3shC1hgz1pgzNthyNpQ3iyDwOjxCgGxvoQm3ZheJ50hMOzLsytDmJq0Y/FlQEszLuxMu3BTLoXyzM+LE56sLzkxcKcB0sLbszP9WNuthcLk734fsSD5Uw//js7gNNzbvww78WPiz78uODD0fcVuOyKK1B80oVaUwaV2iQFiDNQoWjjcVZD0maSgwrG/vBxXxVjk+stWWLGjVKDT7U+Kb5W6SnwnZzmTPKhT+Fg9zgOOMZQa0qjgY1ra0wk1aizkHYuR2KiSUoAUABBYqD551yDXGPMoME6gkbbKLXcsPd2g3UEleoQNl56Oe765/N4/7sP8Ke//gF33b8Z7zviKDrhxvkXrMFXViN6YwZ80n4U551/Hj7q+WkW8FDPOJlLztIJTmDPFxasv3gTXty9HeaQFWqfFl0+NZ54jgLjny89DGUXVTBKEgi+EaLQ9iYb9Y//x5VFnTGJgu/cKGbtVNxQWGdIQqWNot6YlHq920Ms7knS03JgV21I46CDtQsxoKZUx4VxTHwfC+UvbQ0QiNSSdKSsM8pMGmnUsHtqtTFDGyqWUKBi7msKzs+i2pARjKA8oUACgmzD2MUIC752dRFxUsZMJKWtQyhpCaKo2c9q50iCtffLc13A546fd9AI+Otewf7xBg+edSYF3QZQ2ExnARv/crNGAQOA5BIOCG1gaXtICO6VbFdTbyazAtffcdEsZ/+kGjaW38ft9Sxeo0SYTKKMxWGOXfUZ6fyyRo7cpH7JwKFg1W0KWc3TT6X958SKMFr/7p/QjeXlPYV7n9+dc6PmrB8HdmeBvTNPHcU8yKMdakz/NwO4rfR9VBs4y0dmg3pzFvUWYvoa7GNotI+h0U7RDAe6J9DkGEcTuxnWscqmWjOBxXoWDVNvzuCAbRi15gwabCOo0SfRYM5i/9Eu5OXlwdjdAl+kF4GYEYmxIFxBLRa/H8DCqg9jix64h7thjFmgjZigDRthjJlhT1philugD5nR7lVDEzBAE9RDPaiDJqDHQNaBle99aG9/H/GJXnSnHHCmHTDFLDBFLLDELDDFLTDHrTBGLTBEzdjyr2ewdu1aVKgjMpZinJpErKPiplzBdID8hqziMTyMcVF2MaAkM3HITTo1stGQpAfk+s2kAH8VnEXuirL4F17TR65yGkFLVXbyQHJ5fqEUNE7MdBkLpX56fyM2P70Tz5d9LNhmaqOJQamJo9qQRMFxN43JOiJ4oeJT4V7nOrIqtoi9duA7/Paeh/HXx1/E0/vr8dDOfKy/eBN21R+Ff8yDgXEPXNkedKccsCbsMMYsMMYssCXtsMZtMERMMERM0EfMiEz2IjbVj8ysG86UHfqIGd0pBwxRMwxRM/vvrHCm7PCO9MAz7IR3tAfxadIRji0OYnjGhckFN8bmvJic68f8ghfzYy7MpXoxP+bGXKIHy9NezEx0Y2baifnpHizP9OP7aS9+mPDivwuD+GHFj4UFF1YXXfhx2QeD9ijWrVuHl+uOoUoXZwywFEXFkwhEZJQsO7S8IyQbKUbxsupzEfXEN1r0fkij3pRGPWPRa7j2VsflAlIQPI9IqTNn0GAbE47dCsYQ1hgzYnxL97HkWXKSSi3fROc+XmPM4GD3BOotw1Cqo/Te7qIaxCbHOAq/sWPDJZfhgRfewBcmNW667Ubcfuft+NDiwdU33IYte5RwxLvRbP0OeXl5+KA7nAv+GNA77GRVjezrBvZ8efsgLvvlr/DGvtfRnbDDGrGj3d2J/Jp92PSLS7G1qAlV2iheUn2Oso6I0L7yjRDP6aQWkRSabMOoM2dQqYmipCXADDVxFqfEgvM1MRb+LE2S5DIfLuWpNWaoicU6IvSR/DluAFR2UVyTojMCRQexb2XsZyrVcZbfStOABsswi7QZQ511BHXWUaFDFvprfVrcX3grjDRRijEjojSFUnSRYUipJv2isiuSs0EsaxuiRhDWOrLnHAA8d/zMQzCABOYGhfu2+FRAVm1FoE4CgCz1nAlUC2UAsLRlEEXNASkok+VrlbVHUKWlG2edOUvUuzp+FvgTILA9JGz3ZTw2piOMss6IyNySO3jLu3LHunIGkLOAFbKRLl2YZ1Q4yRlCsbDGWdZXLgB85ieco+ddcCW2Fh+RdQDz/D8ZuNNJII/OXACo0svy+2QAUKVLkgZwba4GsMaQpLGuOYsGC+V9NdpoR9poHyMA6CDW4YBjAk3dEwwgjaHGSNlktcxhXMccczVGApF1HAyaSBTdZEri/uffwG9uvgneeA9coR4kRtzo7O1Ad8yC2FQfhufccKbt0EfNMMQsUIeN6AoZoI0YoQkboA4boA7RaQiZoR7UQRcmIOHKODC77MV/vqxFaqoXtqQdtoQN+qgJxpgFppgV1oQNlriVQGHMDFPcit/++bfYXtqIevsEW6wm0GCXFlc5ADwzIkbOCiuZs5A7uXleo4rpAGmMniGnnjx6Ry/pAon9IxNIBdP0EBiMiPdrabssK7BD9p7viND7q4NMVOUdEdnjkdxFU2RbhkX4Nc8Ok+tin84/xP5dllvJxocqPbEXZEag17/BOoJn8+uw4ZLL4Eh60J/tJWNOygFbwg5rwgZTzApjzAJ9xAR9xARd2AhdxITulB3OtAPdKTssCRtMMQLq+ogJxqgF+qgJuqgJ+qgJprgZ1oQVfdlueEZ6EBh3YWJxAJNzfZhZHcDMCskGllc9mJ7ox+rYIBYybsxl+7CU9WBxqg/Lc/1YnXXh9Kwb3y94sTLrwo+rg1hadGF5sR+nF90IeE/i4k0X49n9dajS0TVZzrTAXEKSGyc1JP5uXJ+s6AhD2UWjumcLD0mtPiwiRqWj0WKdMUWbLusIc+SzDm4d1xjThqHROooG6wjqzFk02sdQbWIMtYZtNhhTzAFBTsWk7OQmM76pqWIxM03dE6izjjAJAX2vojOCSk0MDdZhKE7Y8MsbbsF9W19FW38XHn78Afzy2qtQ9N77uGDNWnxisMMeUBMANDjFqJfuG3R9NTjI+MFZv5dVn+GuJ57BFb+8Gg8//Ri6k3aYQhYYhszo8HThxttuxL/eeQ0HbUlUaqJ4puAQimR98IWsz13RGYFKl8RBWxYHWHd4jTGND3rHUaWJoM6UhrIrSnpkPWvtUUeYlk8yZHEtnZD3aJOoMWREu1ClNgEFkwdxIoG+PwZlV5ziWdRRWs9ah4SkqVKXQo0xiwbrKOptFHsjzDvWUdSY6Bri5jv+uipZXWgFmwhVyO9BMllRjllQE0dZe1DcB7hUgbT3JMna+9U5AHju+HkHA4C9DNzxQnsydvAuQhEAnQMAAyhgGsCi5kEUsO8RieWyyiyu5VOwOq5qXRwN1mHUGpOSbb4zIsbACtnOTYyJmWifL6LcLXmmUUMs6LIL6/+18J85JuYXJe/7lX6H3Hyv4tYhXHvrXTkVddfedjdpRf4fOr+f1vtlBPjLdfBKALDWlIVKG8fzJUdw/7Y38ULZ+6jSxlHLWIcGpusjfR+NewkEjuNA94Q4G3nDhm2UPlqk/4YaR3gIdYYAoiWLWkMShx1juOnPm3H1NVehWf8tEhNR2L0mGN1auOImOFM2mBNWONN2aCNmYv4iJqjDBnSF9NCEjQL8acIm6MNmGMJmdPo06ArooAsbYYlZ4EjZMTjag9BEHwMcBB4sCSsGR50wRs2wxm0wx60wx4mR2l30Bu647x5pLOWYZAzgCGrNw1AxI4hc0ym93hLwEyyZzEHI8xqrDVI9H7GrWUkTaMqwjEcp2odGvglhiOIArrRtiJXD066eb7Z4A045ax6h8ngpsobLI3jTQHlHGCVtFCnB2USKmDjDFd8eZhEzMcFykiSBjEE8cLypexyNjnHsOarBhk2Xoj/rQW+mB73pbjgYEDfHrTBEzDBFCQBqwwTqtWEjdBEj9BEjzHErLHErLHEbbEkbvUZRC/QRM7RhI7RhA9QhPYxRMwwxM8wMzCdmBjA84cTUSoAig1Z9WFz1YnHJi/lxDyaiPZgb82Blyo+5iR6cnnbj+0kXTs8S27ey6MLqigcrS/1YXXXj628asX7DBvzPK/tRpUsIkM/F+PKc0DNP/rfniQGKzogAhMrOiNTso41Tkw8zSNUx5p2AofR+kPd0Ux0cXasUx0LvzQqNBNBJi5ygjWeXfLohu39peKYkfV5jzFDDiJUcwIrOqOz9zjbarQEoOkMo/NqEtRetw9Z3FTAMOfDEs4/ipltvwPW33YaLL7scZZ98hc33/Q3/8/gDOOwckzR/MhZQ0RFCwdcW7P/KijUXrcOOPa+i6dNamEImWMIW6INGdHrU0Pg1KD5Qgg0Xb8B7ZjeqtAmU8PGqmho3ytpCJB1qDaLelEaTLYsD9jGUnHThoDmJY74E6vVULMAlFsrOEKrZiJ1na3IQpWSbuApZUD/9fSiAm0+gFOya5/mNPOC/6NQgKtVRFr9C8iOlOi7ujQ1ig03XTAMz8dRbZdpjPQFAeg0kTbh0sgkUzy5l9ZD896/WJ1FvTDFDSETEoRWc8MliYM4BwHPHzzsuycvLw75jfWIEXNoi1d0UyDoPCxnIK2geEMCPwKIEHHkrSBHb0ZEWMEAO45YA61ok0WuVLolqXUxiRoSDi13IImIhLoKZy/kujWn9FGewfXIQJ4wYst2z3BDCx358JKwUNwOuD4xK4zUxqpbGC4ou0o09W3QE92zdRbqxjmgu8NOfqfn7KQCYEcDvp04pCobiWWgMyTRHhjRqDGlh0KjlYylm+OCav0Y7xb0ccIxLNy77GOptY2InW2fOosaUYeNk6d+qNWVxwDqM12o/Q15eHvQ9HUhOhGHxWGDxqhEb68bgWC9sCSu0YRMMUbNgfnQRE7pC+lwAGDZAFzZDFzKhK6BHV0BHbGDYBFPECHvSKvR+lqgV1rgNuogJ5rgFoYke6CImmKIWxgjSePEb67e4eNNGPPzc86iz57oU622jpPfTyUfACSjUElugVMcpv49pU+Xh3CrBAEpNKgQECSDXGjMiHqaadWHXGFJM6xpjGkOqP1R2siD09iGUt1Gyf/EpqWNY0R4ScSJl7WFRPScfAZW3B1HWFmTgMbcJ58xebElXSDpbErsnBCutMqQZazGKgw7aCDz2RjHuePBRuIdd6E51ozvlgD1pgz1pF2DNFLNCHzay19gIbYQ+N0TNMEbN9D1xK0wx+n5D1Ax91Aw9YwD553w83JN2YHiqH9k5H8YXvJhadGNm2YOF1QGMTrkxMenCzKgHMxMeLE+78cOsHz/MDOCHaS9+WPTg+2U3Tq+4sbziRnbEhCe2PI51Gzdhm+IjVDGtl2QyizB5y9kAkEdGKbqiqOySYqOUXTGJDezkjFM8BwBWG9Ii87TGQI9VMVMID3+v1CbQYKOw5Aa2SavSJ6Fk7zv5JpcyS/mEQyZHyfk6JoBhhSaJWlMWB52TqNBwBoxvjmVAh7XTFBxtwWVXX4Mtb5fCEtBhx65tuP/he7BmzRqsWXsh7nziOeTl5eGQtk+6nhj4q9IlcNVvbsX555+Pi9ZdhPseexh9mR5YohaYwmZYIzboA0boA3roBvVo66bQ6f215fjAGUeNPoHdR1rxvjmAT1zjOOQYxQHrCLYVH8YdD/wDB4wBHHCMYe1F6yiDtLwSjcYhYuBNGTSYMxTZoqU1olIdI9ZUGLC4M5+N6w2kiW6wjlJagDEjbQhl9/dylmBR1OyHojMsZFBl7WE2wmc/h2k3iRGdYBMHkpxwAKjSp1GhS6FCG8+RI8mZXAUDfUp2H+LPV2oSZC5S04aRb0DKWoMoOUUSrIKT/nMA8Nzxs4+cEXBxSxCK9qAwdEiRMAPYf9yPfJbFRI8TDV1yahAlpwLihlrcEkB52xDK2wIoaxkQfcHFpwZQ1hpAWUcEys4wFF0R4cAq75BiW8o7wkx/RdoZ0lPFpfEcu0BIryUt6BUayajxU2MT+c6ZLrJcTZiCgQLOAHIWUAA+Ee4ay7mxiotWl4SiI0RjGV1SxDZUMDBYcVZY8U9oAQ3yU8YAGikLUDqzso9ZGTDJyhg8yg4U4wkzdYEesI8yUDhGowyrBAAlBpCNgS30M554dR/y8vLw+FMPI5gNwu62wOhpR2/Wjt6MHcaoGZqwgRb2iBGasBHasBHqkB5qBgAJBBqgDRug8evQ4u5Ae1AL9ZAehpAeprgJ5rgZxpgZmqAOxpgZ6iED1EN6wTBZGONniBILZY5ZYYya0J+x4aVXt+KP9z2Us1DxcXCjYxy15mG2KCdZXmBS6v/VyBpfBMCXTmIXYtQhrEsyABBFlZbqwlS6BGp0cdRoo6hUh1GloTEVBwc8r0zZFRWsX1kbMRo1ujjqDQnUGxJQaSg7rlITY52mUZYxF6GxMXO/Uw2jrDVHgL+zAU3hCT9dV500xqxQS8HZVWxBO2CnRavRMYE/3v84HnjmRXhH+uFM2uHKOtCb6YYj5RCjXWPULF4Tc5xecw7mDDF2ciDIRsaWuBWWhEWM8w1RM0xxG6wJO2xRO0KjffCP9iIz68bovAezq+QiT2YdGJlwY2UljJmJfqzO+fD9og//XQzgx0U/vl/04IdlD1ZXvbA5vsYVV12JW+58EEXH+1ClS0HJxn0C/LWHz4iAkYw4ZW2st1kTp5q+Dll+aEcEpS08ay4i+qQ501dtSKHWkEKDJYsafUIYQlRsDFjBRpQHbGM43DOJGlMGVfokaTdzpDByt2hUdJDLN6hcH8plLxwIUhxUkiYt/Pkz9MxKdQLFLUHUGJLIP/whLr7sSpjDTliDOgTGnDjW/hHWrl2D626+Edf97nbUaYdo/CvLA3yp6lNc/ZubYfB14BvNF3ClbehP2eFMWmFL2GAcMkPr08IQ0EPrV6Mv2IUbb7oeeXl5eEN1ELc/9AzOO+88ETJ/9bVX4/qbbhBfV6jjqDdncP75F+APd92La39zHQ46RshAwzqGq7RxVOsJ4Km0cRSd9DHwHhNgmwO2OgvdB2vNpNvj5jCVgWQdKn2KJTsQ4C5pDdKmrG0IJS2DxP6q48zJzacAI2hiGYgiEscxQfdSywjpOhmbxxlcZVeCTrW0LlVokyx1gqYNFRwMMgezSpcUI2BO0nDC5dwI+Nzxcw8BAEtaguT4bR5A8akBmT5DAoAS+JPOklODKGkZFB9LWwIoPTWAklN8tMVT9gkYckcvZ/xot0qmj7IOCoPm6eq8VYELsStY+GYFdzZyCl2Mi7lTl49/kzm6GcpnirPgXr6wS4whv1DlRe6iOq5dqo/j4l15LmClNom7nnk1l+bXJQQYVHLBNgOFZ+kCzzKEyBnAYdbbS2fNWQBQAoai59eUFTe7RksWX/ZHcbh7mIwgDqpwauDsn4U0STxUuckxhkPOSRxyTqBWH8dF69ej4WApMnMxJIYj6OrpQGC8F8aoWYz+NGEDNGEDdBEjOkN6pvMj0Mc/6iJsBDikQ2dAi66QHvoosX72pBWGqAnaiBFbXnmWjRf5aYCOMUz6CLFMxoiJ2KaoGQe/eh95eXnY+sbrKP34Sxw2D6HePoV6EVcxgQOOidyMQL0U4VGpiTGgJTHEcjG5YJs7eVxEjDVFEAtEsQ8pxsqSS7iWZQZWMANIhZqBu84QKjpDKGulrtLKjiHU6GLEHuqTgjGSwoVpTKbsZP3ajEEsawuKMXLJGQBQchWHsPnJVwQA5NcQB4Bc0N5kTaLOMorDvVO48S/34I3yCniGe+HJ9sCT7UF/phuOpEPoLp0pKxxJK7RhA8xxs2BiDZzVi0nGD0OEHjcyA48xZpZAYcKOgVEPgik7UpP9GFvwYGzehYkFao+hAPEAxmYGMb8UwuS0D+G4CYsLLvyw4MOPiz58v+zB96seaHSfYN369XhidymqDfT/p9TS+E0eMVXKzB9yAMhHH0X5aAAAIABJREFU5WXtYXqNushRWt4RyZF93PnUDpR3EACsVFN0RyXLCiRNYBL1pjTqTCkW8M50pFrJtVtvpk7eWlMWSs4Qd8VywV975CzJCYHEKMo6YijrkOuRpb7yWmOaAolF041Mu8yrLLtilCvYFUWDZRibLr8K20sPwDDUA3vYBE/WgfzKvbjwogvxdnU9DnSP46BzLAcAbtlXj1vvfBCWeD+8ww5EJvoQnuhFcLQPlrAFmgEt1H4NjCEjvFk7du7ehjvu/hueKz6Ep/fWCKD3U+c7R3Vk1rAM4+LLriSAeP1NaLBk0OQYw4d9Izhopgq1Jgtdd3XmrGAF5Xq+SuagrzUPs4QEqQGp2pgRJh1yXsdQ2hrEM/lNuOuZ1/DknloUNw+wjUEQ5Z0RMUpucoxThqpzigE/zpASAKy1DKPGmKUx7xkAXNElYwO1CbE2cEZX0rVHUaGOQtERZJs/KRu3iK3Le77qOQcAzx0/67gkLy8Pj72lYpq/Aez/th9/fWw7Hv13NTZveQ1PvtuIx9+pxdbSj1DcPIjiU368ddSEzU+/iqJmP4pOeFHWMoBHd5XhpeqvUHjSj6JTg3jtSAfuf+ldWa1bEPe/9C52vdcpFqcdDd/i8bcqUd4xRHU57SHc/ewb2POFQ4jft1d+ii0FB0WYc1lrAHdt2QlFe4CFSiewtegItld9Lhbvd7/qwT3P7RYGkApNEk+8XY3XDp4SjOJbH+vxjx0FMpdwHA/uKMSbH+vFTXXHgZN4/K0qWX5gFHc/uwt7v3IKALi96gtm/EjSwtw+hDu3vIqy9iEBBLcWH8F21ecCAO7/rg/3Pv9Wzmj4yT01eP1QqwB/73xqxsOvFUsA0DSMR14vwbufWwQAfPP9djz9bh2BPSOdf3/h3yg+4WE3vCx21B3DC+UfwhoewOf9SRy0pfHAtjdQbx0WcTAvVR7FG03H0dQ9gcO9U1Cph/A/L+9BnWUYz75bhVt+eysaD5Wi09AKh9uOZkczjhmP4bX816FnoE0bMWLn/tfwifoz0vmFDDh4/AjeqdwrwKE6bMDW17fha/tx8VjtZ3UoP1wOQ9QETdiAVn8Xnnx5C9r8aqYXNKL4YBmqP60TxoPjzlN4cdd2WKJm9KatONbfj1v/8hfc/LubcMUVv8DNv7sNd9x/P+5+5BFUaUKotU2hwTGJx3aXY9+XVsr0M6Sw6702PLmnhnbebDNwz9bdePerHvFe2l71OZ4tOiKihspag7hry06Utw+hmo2Hny99DzvqvhItESUn3fj7tt3EEGpjqNJE8fjucrys+hyF3/WhvC2Atz5S4+FXC1FtkDRkD79WhD3/MTB2MY5dh1vx5J5qxlTSKPne53Zj39dOxpqHsE15FE/vbxJgpuC4F3978hUUnvSjtD2MouYBbCk4hO2V/4GyM4RKTRz53/Xhvm1vodpARpA68zCeyW/EK9Wf4vwL1sDk7UKr7Vu8U/JvWGM29GWccCTteL1gFyx9JxGa6IE768ChE0eQX7Mf9qSNjXlN2LbrBRx3NpNjOGpGzWd1KDtULsa/HYMabN35HDSDOlhjdngSDnx+7DCOn/oAkwtuzCy5kUibsL9gJ5a/92PlxzhGZwdQV1+Mr44fxvRMHxaWPBjwNKOm6h2cPu2Bs+8bbLx4I2679zHs/dwElSGDCm0SrzY147G3qnJMZnc98zre+cwm/l7bFJ9gS36T0GgWN/ux+clXUNQ8IEDUM4WH8GLlZyg+NYjyjije/aob92zdBWVXRDC1T+2pxu5DzWgwU6/svs/NePi1IqgMKah0lBX54Cv7se9zMxqZfnTHgRN4/N9VOea3u599A3u/6EYZ022+oPwMWwoOic1nSUsAm5/aQW0YDBxuKTiE7VWfo4m1cOw/1oO7t+4SDGJZRxSPv1WFnQdOoKyD6gpfP9KGR14rwq6mY1i7bj3eqPsIb+W/gWO6L9CfceDa667BIy+8hOcKG0g7zADgAy/twfaKo9h05TX41ufHoS8bUXlECW+6Gz1xOzTeLjy/cyscESO6Uzb0Zrrx+z/diie3PYtaUxpXsWaQ+554Gm8casZTbxZg0y8uw+aHHsGuw82oN2dwyDmJ/K9tuPxXxAq+/f4JNNnH8NC/9uCR1/bjbw8/hTVr1+K6G6/DX++7B0d7o/jIPYOPerN45F//hqLVj1rmpn65+nNsK/9Q3B8r1VHctWUnlB1DqDOm0WDO4iXlh3i14Wtc//v7sObCm5B33qs47/xLcc0tm7H/WzeKmgfx+FuVePXgKXICGzMoONaNx3aX50wcHnuzHPlf29Fgp4nDawdP4Ym3q3NSJu55bjf2HeuBim3yXq6m9aOSubrL20O4c8urKG0NoLSdeo6fLTyIbYqjUHSEUdISwL8/NWPz0zux5xwDeO74mQeLgekhavnkAPZ968E1t2wWAccXrL0R19xyJ/K/8wjWr/jUAEqafSj8zoX8b/pRxMqpeXA0dxOTcYQVWsu0SnyEVdoWgqIjJKJnePQLbwYpFR8lg0hOd69o64jniPh5T2SFWnLMCabwJ7SAik4pAoZrcJSCDTyDCeyQRc/w3KYz4hjk9H7lmV/r5GcqlwGUN4HI4gR4FVytKSuAHR/1csavRoyKs7LHsoIxrNFFybTAYkxqTczUwCqphFmEOYQbbKOo1seQl5cHVUMRTv93CMMTMXRYOmAIGYTGj8CfiTR+zO3LNWH85GCPj4LVIT06h3ToDOnQyR4zxozQR+nnWOPENMlNBqaoGT0pG+wJK9wZB/pTVniyDlgSXuiiPegM9UEbceCN/Hew6dJL8da7r+D2O36PNWvX4O5/PIiSz1tQb59kuscxEsxbRsS4u9ZEdU68qYZ3OEs5XmwkzPU4TJ9X3hFCWXuQVTexjUwbNUsoOFPXGkApY8CVTMjOTSaVols4TKc6wrIGUyJQWFTL8ZFwZ4TpA+nnSxEmUlwSz0Ljo18FC0evYuG01foU9aQakqgxZlFnGcHzJYdx5XU3wxT3oi/jRH/Gie6kA7aEA46kHYHxfqye9mFkzo3QeA/6Mg5Y4hZY4xYYoqQHlGv75BEw+ogZWjYqNjFtpyVqR2jEjcSoG6PTfZhecGNmyYOlVR+WT/uw+sMg5pb8mF8aRCZrwcy8B7Pz/Zia68XpZTe+X/Jgeq4Xv//TbXhsx9vEgLHcR6UmgfIu+eg3xOJ3zjZ98Lw/aZwalZIAxHUuyQPkPeFiFKwlbVu9KcMYvoxgAVW8OlAdo5gm5hQubQuSyaeD12DKawFDMne4lJMqRsUdUXGWM2a6wTospiny5/l9S8GjTtRRYUT6sCeBHRUHsOnyq3G8zwFryABX2o6ntv0TeXl5aPz6KJqc4/jalyAg6JhEvW0UV15/C+7e8i90BV3ozzhgiVpgi5nhydrRm7HBlrCgN9ON7bu2Y92GDXjz/TaUtw3i/PMvwN/uuwcXXnQhLlizRjB/u6oP4bBzDB/0ZHDEOYom+zDKWny47rY/Ys/RLjTZR3DPM68gLy8Pmy67HPXHjiK/rgC/uOIXeLuqHE2OCRzuHsFHPTHs/8KIyu868Ukfi+ixjqLGmIWKbbQaTGk02YdxyEmRPE2Ocbyi+pTA30/EbJEhZAA1+hS9fmz82+ScwgHnlExqQlrAOsswqg0pkXbBEy/kuk3uClexwGj5JImbVETvsCYGRccQKtVhIl0YA7j3HAN47viZBwHAr5xCWPrE3vqzarEuWHsDnt7XyLR8BN5KTg2gqNnPOhopGFrKDJQZQk4F6fOWAIp4bVyrJF6nUVZIFGfz6jb5ODi3uksOxGRjOrZbFxo9dtMWGkLZBShAH9fWyOh37i7m4E+uA8wd/8qdxtIFzHuARUzD/wkAU2cDwDPiYOQawDrz8FmAr9qYPQMAZtjjmRxQyP97uWaQg0nxNXcDW4ZRa6LgY5WOAOCbe3cinO7FyFQczYZT0DLXro5HgTAgqGHgTyuAAD2vjZABRBM2wBBlWr+oiQFACQjyMbEmwrSCIQP0ESNscTNC404MjfcgMtmL0Xk3hib64B2RgZCEHZZEDzr8TlgCakQm+5CcccHqacWe/J1Yv34d3nzvFAq/1NPo2zGNJuc06rlpxDaKelMa1YYkqvQMnGulrEC51KCMhTmXtrGTmwqY3rWkZZDAWQf1fJa0BlDcws0eAyhrGaTasdYBlLcHUamOCaCn0ktj5BpRA5jMMUXx0XB5R1iMgcvaQgKUEjANo7yTrqUKdVyYryrZCJviSWKoZBE21cYMqjRRXHzZVXij+jC6kxT07B/tgSNpgzlmgX+0F8unBzCz7MfEghveESfcww50p2wwRk3QRY1CA8h1ftwYQgCRDCDGqBmGkAk9qV7Ex9wYnujF1LwbU/NuTC+4sXTaj5XTPpz+cRA//m8I07P9WFoaxOyCG/NLLkzN9+H0khuBwVbccNP1+NUtv4eiPUg9y0x8T6PfmGD/ShiY+kndX3sIFV1RKLvIPSqFwMu0vmpZbinTiFaqSY6i0sZRrUugmo+BzVnUGhKoNSSFZlTFZAA1LPC52phCaSsBQN4HndMLLbqh6fdXyMLyy9rlesGIGPmqdFRHqOyKyb5H6r3mGwF6X9LmQdEZQYM1g1/f9mc8ubsA1iE9nHELBkd78No7/8Idd9+O+uYuaCJ+fO5JC0dw/rFurN90KWq++grJaSd8Iw4ExpwYGO2GOW5Bd9IK32g31qxZg+LPW9FgH8fO+q9xxa9vhDXmxuv7XkdeXh5u+sMfUXTCDZUuDkV7AKpOL1SaIdRqB1HeHoSiYwgqbQIN1lFUdAbxwLY3cMmV1+APf38Mdz3yKDZcfDGe3LET9fYp7Dp8Er+88Vas27AB6zeux9XXXo1XC/ehSe3EEUcW1aYRqHQp1JspL5EbOQ44xvGPf71zVtB+3nlv439e+jdp+yzDqDdlUKGOkJHOMXEW+GvkLUSWEXIcW0fFZrKKJQKomK6da5FpFE2ZkXx9oJxC0rST/pRaaZSdYSg7w2L9ffccA3ju+JnHJXl5edjzhYNVvHmw+emdZwUcn3feO7jrmdeg7AyhtGUQxYzpo37CARSc8JGbsTWIMlZbwwEgdwPzgGg+DubPlbYNEVPSznt+WROJEGDngj45G8hr3fgOme+YBCvINE+i91fWACLXzijVMVkMTVTSzLCwznLx70vxMzngj+3gtha/lwMAz2QEcw0gKWYCSbGokTTLnEtL+XLyKrczmL1qY/YMxzDvA5by6arZ1zxapvYn2EO5dlACgyOotYygvD2IK399IzVEvL4D3mAvOrtboQvqoYtIAFCu/9NGzgaG2giZQoxRYoF0HBxGySXcGdKhK6SDmmnKlEcUMMVMMMVM8I90IzDWje6UDdqICc4UmRIsCRv6s06EJvrgSJFD1Z60wTXshHu4GwNjPQhO9KIvY8fYgkcwDRsv3oBLLt2E+x97FP98eQee2v4i9jUeQZODelTrLcOCZeVZbtxVyE1F9D6Uwpx5lAs54Wl3TvERQyhtCdBG6YRP0si2BlDaOojy9pBM30oAoZrly3FXdzWPFNHymqqEiAxRdsXF+7a8IyJtoNppoVB2RbC1+DAqeVaamhmwOsOyLmwpKqPamMGO2i9w4fqNONDSiYHRXoQnejE41gtnyg5X1onZJQ+mFz0YmXVhcNSJxHQ/QhO9sCSsgv0zRs2wxLhTm059xIieNMXCGCJmWON29Kb7EE47kZnow/isG6MzLiysEPv3w48D+OG/Afz4vyHMzLgwOdWH2TkPRse7MbfgwkjKhPPOOw/3PPUCqjQR1qaRQaVYPGPideIaqtIz2D/OlCq7oqjWkaGjzsQd43QKN6k2ga1Fh1HBNIXcMV6p4eAvDpU+IcB7o5kcqyoRVUJxJdzBX6VLCCkM13eVtLHYHvF1SJh5ag1pKDoYM9iRm5la0haCkrGLNcYM5RyK5zlQDOee7bQhKG4JQNkVwb6j7Vhz4TrUnzgF3YAOA8MO9Eb0uPO+O3DRuotQ8X4jTgaiqLdP4VQwhAOOCfzmj5txz1MvoKBWiWM2DdR9zejoV8MSt2Fg1InBNFWV1Wr8aOyewA1/vhtbXv83tr60BWsvvBC3/eV23H7nn3HYHEAla98o7wjj30cNeOe9k/jA6MX9z7+OOx96DP/Yug27K2vwkTOG+198K0c3eMOtN+LthkNYe9F6vFmWD0NQD0NQj8r3lPjtn27D+RecjyuuvgLPvb4D9a1GvN8zioPdUhpCg3UU25UfnxW0n5d3AfLy8vCy4hAO2tM4YCMjWZNj7CwNYIOdAOCBbgKBDXYCgFUsQ7RKI4VYczOaPOe12piRxYfJ7zURwe4TaxsUk7Z3zzGA546feVySl5eHtz+zYf93buz/zoMn3qn9SQbwyXcboGgPoqwtwBazgBRKeZK+Lu8IsaqeKHNQsdFua5CCo1sCrNKGdQczg0hpKzWQlDCwyMfB8lgEDvJK2WJXLgvWlbs2RS0cW9wIIMrjFWJnnLynMSx+pvx5AQg7ZY67nIxBDgCTeKn6SwkAMvaPA0DuFM4FgCkxBuDATzB4JmbKMHNXbho1Z2QESqBPxhwacs8q2XM84qXakM7pCSZAKQmka9kiWKNPYmflh8jLy4PadBImlwEatxr6kGTo4CYPdUgPU8wMXUQaBXJ9oJ7p+4gJ1Is4GAMDgqa4GaYYMYPWhAWNXzTAmSJjiDkugUZj1ARbwobulAN9jJ3yj/TAM9wDD2uXCE32wZm2Y2C0hz3vxMyyFz3eVgxlrEhM9aHD+AUKy9/C7rf/hXfe/ReuvfYq/PEvv0fVt1rUWkYp58+cRZ0pjQpNlHLdtDFWIB8TrEwJC16mDU2AsjCZeYrGsUEUnfQxgxSTPbTyOCTmJu2K0uLAnKRUG0eZcrUsZ1DFTCZVrM6Og48KHpMkYkN4a4VkIHmp6j9QdFD8kqKD2K6S1oBwuPKQaAoTTqHWNIyHXi3ExkuvQFNLFwZHejA670F4og++YSfGGUM3Pu/G0HgvhufdmFr2YmiiVwJ8cQv6Mw7YEzb0pOi1CIz1IDbVB0fKBkPEDHPUBnfWjVC6ByNTHmQne5GZ7sfkoger3/vwww9+/PiDD6dP+zE958Xq9wOYnevD9Ewf5uf6caBxP359w2+YOYrey5X6tBDe881iLgAMnTX+rVCTi7vOlEaTNYtD3eM4YB+lXlf2967UJFCtS+CVmi9RzRpk+OtAGX/cXZ6iaCBWFcdNGcIspE1Q245lBJWaKErbh84Gpex3LGuX/75BVOuTdK9sp81xSVsoBwBWaeJosFCrj6IzkvO8vFmJHKV0j1V2RRl7HUCVNo7H31Jg46WX46hOB2/SguB4H6KTfdhb9Dr+/sj/CPavqXsCDY5JbK88it/e+yiuvfVPWHvRepx/wRpcuG49br79Lqi++BZ/f+jvuOCCC3DISYDot3c/jF9c/Sv84vLLkZeXh72HKVqq5ti3qDUmUNzsh6IjhDzmEL7h1pvPMonc+egTePMQNZXc+/Q2PLn9GdzxwAPIy8vD7qI9MAessAcd8CRd6Iv2wBI0whQyo/GzBtz/yN+xZs0a3PqH27Bz75uobzPjgH0EDaw2kjSALGj/whtxxa9/K/7d2+68H03WBBptZJhrsI3iAO9A7p5Eg0PmlGZymgb7GGpMrAfckkWjZVhEdXGTngQAszld8VyqVNoeQXFLkF23Qyg6NSiSBN79+hwAPHf8vIMA4Oc27D/uRf5xL/Z/68I1t2zOCTi+5ubNzNk7gILjHhSe8KG0NYjCk37kn2BxMC1BVGniqDNlaaHsikDZEYKijeUAsswzygQMyjqDGfA7NYDS1pAUjtsSFF2/vBGBg0IKx+X5gVznRIupPKpFJMKL2h05mJRpY9jimQs2JXZRMCVqqtziAZ4i2V2AvWQO8JPqx6RuYL7Dq9KxijHWv1vDM/3MwyySJSuLZkkL0KYypESoLDVV/HSlnEqfIvaKx9AI5zFjHBlArOYfGRhUsTGkSp/Ei5WfYuPFF+PY8UNIjfthcBvQ6e6ELqinZoeIEdoI0/2FDTDGzNAyJpAaIAgEamQuYQ4YifnTywKEaUzYFSKAqI0QSNREjNBHzbAlrBgY64Uz3Q170g5rwoqetEOAwf5sN4JjPcjOujAw1ovEtAupWTfSsx6MLHgwu+LD6IIXqRkX0rNuTC75Mb0cwNySF1NzLhQW78KGjeux7/Dn1JxiH0OdKc10gDEoOkOS/o9pAItZNEMBD1E/SbFHhc3kiC9u9lE8RWsQZa303i9rG2KAjMeJELDkIK/GQO8LqvTjI/00ve66JIFFTSJXn8gzNDuJ4Stn2kQFO8s7aCRc2jbEcgcDLEswRNdRV0ww1NUsG/AfO/Kx8RdX4NtuGwZHezG56MXssh+zK35Mzbsxu+LH2LwLiWk3snMeZGY9GBzrhSNpgz1hQ1/GAUfSBkvcgt60HZNLHgQnehGd6sPgWA8GR3owMNKDyHA3xmbcyE73YWzejbEFDxZXffjxxwH8+OMA/vv/RTE168by9z7MLbowPdOHRNqMa391NV6v+Zi52KU6NS71ENc3B4Ft8v5lLj8JQdEVQ40hhSZrFu/3TYnA9FpzloHAhMiGrDGmRaWb/PFKTZxdN9T7TFl11MtNulL6vlpTBkecFEuk6AwxVicoVV0y7V8Ja5/gzu7yjjBU+mQuM9g2lFOjVqGOo8aUgaJLpiVsDbFWk4jQFZYzI4GyMyLC+ZWdERSc8KHOnMFdT7+CX970O3zQ2Q570IjoZB+ef/FxvLb3TZJLyDqB+ef11jHseq8NVdoY9n5mxOYnXxbA6cbb70OjYwK15gyUHQGsufAi8dy6iy/B+Recj+tvvgGHDF4UnfSjvCOMbeUf4/aHn8PW4vews+FbvFL7JV5r/AY3/eVu/O6+J1ChjuKhHfvFz7n4sqvw6CtvovzD9/CNrQ1d/e04bvoOrfYWdNhboXdpYRkyoTfphNbTCWVTGR545D6sW3cR9tco8FH/CJq6Sbu3vfIoHnjpHbxc+THqzVnc/+JbWHPhhZRHWKaESjOEBitNCw44J9HUM8WiYAgA8vdPk6yPvNExjkM9U2LCQDIbSZZTZx4hrbaY7KSF67u4dYgaSdpo80gRNQEUNw9gzxf2cwDw3PGzDtIAfulg/b7E5uV/58aTe+tw55ZX8dS+Ruz7pp9GxN95UHjSj/K2IRQ3DyD/hI+aQk5RiK1Km0CjdRgqXYLiEdQUXUHMB2UrlZwaIB0UWwxL2W60hI3GytvpBl140k+MYRvXUhFQLG+XFjWuaSnvzGUEudlDmD/OyHg7EwTyup0z2T7OHiq6YkyAL4l4c638/9eZCwzlALCatUvUsB2gFFHA9H6mbM4ol4BaioAAB3EsgJaqyHK1hPLMQYqe4dlvDEjInq/Sp1hjAfu9dBST86cHn8bWV3bg+x8GEEr1weDSwjRkgTFsFq5PXYR6frmLV8u0gTqZCYQ3QEhuYH2OIYSPjrvCsqzAsAFq2c+ScuVYplzcCkfSBgerIHNnnfCNOJGdcyE7R1lyE4te+EacSM+4Mb7gxdiiD9k5enx2xY+ppUEsrvoxv+zF4ooH337XgA0b1uHhfz6G1/Pz0dishaIrIgTZ5YxVosWVmL+Ck1I4esFJ2uQUN/tp9HvSTxFJLewxNuIisBaVhU7T311lkN4TfARcZ6KsMqlflrWUaKW4j7L2EDODBFDaMoiytiAqOiNs/Bgnt2rHEG3Cmv30Pa0BCj5m73klYwCrDWnUWShA/K+PPo8b/3wXfKMehCddmJhzYWbJi5kVP+ZW/IiM9yE948HYgg/JaTeyc24ExvrQk7KhJ+OAI2WDNW6FO9uNmWUfkjMujC16EZ/qR3TShaGxPkTH+zGz5MfYvBeTix5ML1ELyNKqDwunBzC75MPYTB8Wln2YWvRieqEf2//1FH57++04bIuxthcJAFIeaIKuXS4PkbmASTfJjDLtISi7YhTdwmJ7uP6Vh/2qDClhCKpkui2VPi26ePloj17HBKr1CdQaUqgzplHLHN6V2jjqTRl82D+FRitd46Wtg2yjGxAymRLW5FLUkpvxWNJKMSRcWvBTTKaiM4Jq1ngiGE9mcCEdqmR4KW4JiI9cwrD/uA+lbUFU6+K499mdWHvROry4vwx9CTvue+BvKGusRJ2d1cCdEQx9ZvZmvW0Mxc0e1LFmjAb7BBptIzjUM4Err7sJa9aswUu7X81h9go//hZ1piwq1TE0mJn+1ZBAvSkJlTqM0lYaE5e1+LHvWD/qTCkUf9eDeksGL5Qewc1//Ts2XHo5rrzuJmwvUuFbVzdMURMMYQN0QR26XJ3QeTUw+vRwRBzoiTlx6MuDuHjTxdhbU4l62wQOdo+hwZrF+30TeHxXKXaqPhZRNHl5eXiuuAk764/h9Yav0GjJEAhkkTUN9nEccEzgoIONllnlZo0pg1pzVrQtcYNdnWWEZbCOoc4yLAAgOdgTKO+KoqglKEWvsY1kUbMfBSd9KDjhw94vHecA4LnjZx0SADzhQ/5xDwqOe1Fw3Iv933lQcMKLwpMS8CtgOYHlbYPIP+FF/gkygJS2BlGpjqDWmIZKF0dJK3UoKjvCUHaEUN46CEVbAKUt3CXMGUFeE8ddwVyUTTfnkhba7ZS2kLmkrG0I5dwswjtSWTwM1/KdOZqVHFhSYKoAgbJ2j1xzCTeERKDojDF9ltRAwsfEZ3Z17v+2V6YBlErcK2XMYKU2CRVbaOX6vjqTlMMn5dVJXbPyOikO4FSsa1SVEyzNxwhp1iuZkv5tbkBhfxeeg8Wfq9JSqKpKm0BFVxjPlb6HS6+4Ev9dDWN6IQB/2IJgxgWjXw9DUGqCcKZs0EVZADTXAbLGBw7u5OBPPgbmj3eG9Mz8YcQ33ceZoYS6ZvUCAFqFxszKNH/2hB39WSe6Uw6EJvofqNuZAAAgAElEQVQxsejF5JIPU+ycWPQhPUsgZXjOi/QcfT6zTAzg8mk/Zhc9WPneh9PfDyAQ6EBZyS5sffZhbNi4HoXNPjb2jQozgaRrJfCXf8KP/cf92H9CMkQVnPCRNOLUADGAbUNQiDw+HmoeF6+pimeS6aR6OWIL6H3B3w9kUogKxlvZGRGbp7K2IcobZBloKl0C+7/uRkUnBbOTiYvae0paAnS9cYZbwzuCiYVutI+jwZLBhes34r1TzUjNeDG/OoCF04NY+D6IxGQv4lP9yM55MLfix+LpAcwtezA278HgaA/6st1IzroxsehBYtqNmWUfJhd9GF/0IT3Tj/SMG8kZDxLjvRiZ8yA740N6uh9TS15ML3kwtezH9PIAhqd7ML/sxcyCC6Mz/egL6bFx4wZUHutAvSWDehuN2VRnVG9xBy+/xhWdpJGs1qeERIUHzx+yZVGliaDaSBEuldoY6sxUrdZoH6PrT5dE/nd9pM9i149KR13AlWzEW62LQ6WNodaQRJ2JHPcqLQU0N9lHcbiHjAcqXQJlbZIeuliAPwkAys9iZp7jxjkJBLIQ8BYCR3Umqh8sl4Vdl4vcQ8qQK20NovgUvVeKTvokUNkaRP5xH0paA6g1pbHrSAvWb7oU9255Eb+46pf4+5PP4pWK91Cr9qLGPiU6guXdwAdEZzCBQj4ybrCN4aBzEjXGFNZetB5rLrwIW/5dgsKvbbhgzVqcd/4FePczHeotGdSZUqgzZ6DoCKFKE0N52yAKT/qg6AihzpiBoiOItz+zo86YwhHnMLYWNiIvLw+fOvpQbRrBtrL3cfWNv8Utdz6EzpAf3Sl2n0iRM1k/qIPOrYVtwAKb34h8xT5sumQTtry8DYUff/eT2YRr1q7FS6rP8O4Xdtz01/uRl5cH5Wdfo9Y6iXrrCAuapmuGwOAYYz2zYuNGMh3atFM2IUkBeA5rjTmLKiYvUHRGUHRqkBkzaWNZdHIApa2DKDrphaKdOoHf/bL7HAA8d/ysg8XAOFF4wkfA77gX+497kH+cFrHSVtL6kch9AEXNgyiXlVLTWDdIAnYGSMo7wihtCULZGaKIhfYQsSAtASaSHxSGEH4TFKOQ1iDK2kNQdoWFbqpEtlPmxhEuZuYAUGrlyLXUS+5NWUVcV5xVy8k7fqWQZwEAuQ3/DBcgjZ5za+gqNEnc89zuHOZRDgD5WSVj/+RBz3WykYCc8VGJsS3TErFxIY1/U2LUKwG/VA67Jx9Jy3+PypyvE7LvIaCp6IriibdVyMvLQ/0nX2B+wY/k6CAGo/3Q9KlhHdDAHjVDFzbAFLdAzwweZAIxykwiUgWcOkyMH2cA5S0hWqYR1IQNeP6NFwSTSMHCFtYmQQygIUYA0JG0Y3CsF6kZF1xZJzIz/Zhe8mJmhcDfzMoAZlcGMbPkx/iCF8NzHozOezC15MPkkh8TSwOYW/ZibtmHpdN+LK/6sLrqxY+nA1ha9uG666/FrobPoFSzqA42Witi70cuxiYDlV+c+Sf8KDhB498Szri1Mcd7+5DQqvICe64f4zpQrmujKJGkGC/yOJEKtUza0B4i00dXROj+eIi6ShvDfVvfQHlbAEUnvSI+oljIL0Isb45dP7KGkHrLCA73TuN39z6K+7Zsx/A8/S3HFghUD471wpVxYmisDxNLPsyu+AjIzXkQnuhHaNKF0QUfFk77sfKDH9PLfsys+JGadiE5Q8AxMeNGesaL+GQP4hO9GJ93YWzeg/lVYminFz2YWXRhedWD+UUP0uNO9HhaccEF5+Nrox4HbMTUEQOYQaUuLd7vvOeZb9hKOyi8udE6LKJXuAGn1kh6Pr5A15uzqLdk0GinztcaI7Hq9z7/Zg6zXiXvf9aSTrBGF0edIUnjexbuXWPM4EgPacJqTeS2LmuTpDBnAsGiM2rq5ICutG0Iys6o1AzBxshVmjhqTRSGTIwf01OzkXKJrDawiCU58Pu3ooOc7IXNg9j/nRdl7UOoswxj76cG/OWhZ7Du4ktw3c234Pa//REXXXQhXtz5AupPqFHd1o8HXtiFN0oUaDhlwGHnKD7oG8dH/QSAmpxsNGofFwaJ4hN9+MP9/0SdMYYm+zCKj/eg9KRLCmTvpLgTZWdUaFaVXVGKRjIkUHjchRpNBO/1TKLBksVV15NOsPKUBQ3M0V+pjuCijZtQ8Gk73MM96E7Z4Uzb0Z2iiYE5bEaHqwMtzha097ah6WgDrrr6CuTl5eG3m+/OAX+bn9iGNz/UQMH6i58r+1g899K7+1DZ5kG9OS200zXGDAOA42LkKzfoSUAwhWpm8lJq4mzTEkNZW5DMlSfPLmMoaRkk5vgUN4GccwGfO37eIXIACxhrkX/Ch/wTfuHyLW+l7sGytiGmZQpC2RESI2NFG31dxaIsVIY06eQ6hlClS7Cw1DDtXlh9nHQOyiI0CPyVCCaQ9EoEGgMiSqa4hbQ7EgCMstT0uABcVbokq/hJyyI8eO9rIlcj9H+wf/I2CM7YyMfIyq4oKmXgUgqUzq2iI9CXkjR/egkAcgEwB4DyKrdqY0Zm4EihUhMTRgQOGviNRKVP5SxMOePfHHAnuVnlADA3ukbSlhWcHMDzxYdw4fqN2KNUIZ11w+u3QdPThf64FdaIFfqQAcaoRTh+uf5PjICjUp4fgT2dNP4Ncx2gxAZqI0ZY4lYx+rUlaNxrjJphTVhFJIktYUN30o6+jBN9mW7EJ12YXvJgYtGD2RU/JhY8mF32YXrRg6lFAn3TywOYXfFjfNGHySUf5pZ9+P7HIcwuD2BmyYvFVT9++DGElRUflpbceGXn0/jnyztRwjqsS5npo7A5IHqxuQues4D5JzgApGuohLFt5a1BlLcRI8M7S/nfXA4k6L2RRJU2hmpdXOQU1hipOozHwPDGAwIyUcEMErAkyUJ5ewilbUHkHycGn19zdC3xa41y5oSxiYHAGmMGTc4pvP1xF9ZceBEMPhtG5jyYWCTwHBrrQXqqF+lZt9BVZmZdiE66kJn1YHLRS9rAOTdmV3yYPz2IsXk3UlOM/Zt2ITzpwcj8ICbmAxiZ82JueQBjs/2YXRnA3GoAi6t+LKy4sbjiw/i0C5GsA8GkGU9u+Qd+ff21qLfR+IzYk3TO+12pTojsPZUuya7ZCGqNKRb5EmMMahiV6ojEwupTqDVnxUi4wTaGA45x1FlGWINMWpxVLOCZeqCTqNYlCPyZ0qg3Uh90lY6MHwdsI0z7lYSikyJYSlqCKDwVQKEsLaGkdUiwtHyzW8wYv1LWoFTRFSWQ0EIAsLSVmj2qDUmK4GoelHSO7SHGEgYF4Cw8Re/Doma/2GTz7vfiUwHs+86NguMeKLrCePXAcdLrbbwElUc/w2ctR3HV1Vdgwwbq6V2/fh0efOgubNiwDg/+4y5ow1F85c2i3j6BRtsw6ple7lDPFBqso1BpoyhtG0SNPoFafQwNlgyqdTFUG9KiM5l0rpSrWKEOo9GcgqJjEJXqKA7bR/CFbwIf9SZw2DmBv7/wFm7Z/GBOI0eDYwI333E/nthdCGvCBUfKhu6UDf0ZO9OmWmGOmmAJa2Ec0kDv12Lt2rX45fXXY+/Xfdj7hQ1bSz7AO0cNKG8LiuzECnZN3b99bw5I3F33AV6uOop6SwbVhiyq9Sk02sfIJWwfo80826hzsx3l/8Wp7YO5tvlaxzeXPFWgqJnSNkpbybFd3j6Eam0Mpc3ecwDw3PGzDpEDyPP8CtjOQ34TITF5mJ3E5imY7qhSE2N1SHG2eGVQoY6xWAQWYaGJ0ji4MywWy8KTEgjkrkkJ/IVY8f0Qyw2kyIZiVi/HRyJijNPJ416o5q3RkkW9KQWVPiGxW8xVJdf2nT36lcbBfAQsjXtjYtSsELEdcWE4UeYAQB5KLbF+Kl0S1QYqJhcxLDlVbhk26uNjg0yOaaOSF9CzOAkO4PhNk1eIVelSouqrko93z9QlnslSaiSnspIBE6UmgRKWkfbGwRNYv+lSOL0ORJMDcHps6OrrhCFII1pbgoJ+9VGTqAUzxczUE8vy39QMAMo/asJ66KJGGGMmqMN64fjlzmGuIXQkHbCxHlpnWop8caYc6E070ZNyIDNDFWILqz4srPoxt+LDzAozLSz6Mb00gAk2Bh6e82B62Y/lHwaxtOrHzKILc0sezC/5sLjsw9KyD4srPuzP34n7/vksbQbYKI2YcKmPs/AkA4EnCfTxk4NAzhKSFjCAMhb9QmHQHDykREhtvZnGYBTWTTpAlT4pzkptPKciUdEZyXG/V3RR0wM3gdC1SgxgMfs96XeXAAYPGxZd2doEqg1p6ox2TOCGP92F3cpqzC77EBjrR3rWg/R0PzIzLkwv+zEy78XIvBeZWRcSU/1IzbiRmfUgNeNGasaN8UXSDXIAODzTh8ysH/FJD+KTLmQmXIiMODC1NIipuX5MzLsxOu9FdqoXc8t+LK6G4Y+ZEB3txtSiByXlb+LeB+9Dg5UMUzVMAyhpXiU2u9aYRoMlS+aYjggbkUtMqaKTupflm6paIzdepVBnGcYBVv3VYKXw8DpWscj1tHSfIwawlgHAWmMSVZoYqvUpHHSMo8k+inrLMKoNSShY8xHXkRKTNyTeUyJMXzb+lY90yzpCLEkhIFhD6o1NsAxWMg2I++WpgEhcKGSb6ZKWAMrbggxsDGL/Cep6J1MTsdr7vnGxJgwf7tryCjZdfjXOO/8CrL1oHdZv3IQ/3fcPvF2lQrulBYa+Tlx5Fbl7r/nVNdiy83Uc0rlwqHsEhx1p7Gr8EtWnHDhiS0DVNYSK43bU68Mo+caGdz5owXvdGRyxp3HEFke1NopDOjea9D40WdNoNKfQaMnig55hNDlGcbQ/gUP2DBkrZBl8XIv4xNtV2HjpZXh+53Oo++oTtPhscCRt6E3b0J9xoOE/dfjd7X/CBWvX4nd33Y/8Ix/iqZeewaWXXQrFf46i0ZpBnTGNRubcrdRQ5qaSRTXVGdOo0UWx9zMTLrvmOty/9RUBBqtPWVDVGUCDhZj8BtsoyXyYrrfGkEKlluREfF3gE5diJtGg13BA9voNMqMlje7L2oKo1sWQ/03vOQB47vhZBwHAY30ixqLgJGn0iKmICgavQh2FijneytuHRBBthToqSqt5XEmFOooaI2mYqtibvUITR2lrAAUnfChvG0JFVwSFJ2lB4jczbggpYzo/GgGwRaqVHpePM0oZMORAjbOAKn0a9eYUGixpVGjjEqslbwHplEBeLgCUZQwyx7GiM5YzUq5kbQrcCVjBdYdq6XO5S7BK5hCskbF+wvRhyojID671UgntH/1bSh7oy4OA1VLpeSUzEvCsOGE2kekO5SxgDvBjoFUUlqtlYLkjjMKTftRbR/D7vz+OzQ8+hnSsB96BHuidOuj9WpiiJrizDpjiZmHUMIrwXwkMasIGWOIWxgQaYIiZoI8a4Rvphp6BwL6MXTRK6CImGKMWofvTRyhHsDfTDUPUDEvcAnPcwm7sdsQn+5CZc2Fu1YfZZWL4ZlcGML3kw9SiD9OLfiQnXYhOeZCe8WBikYwMM0sezC97sbLsxfKSF0vLPiwv+zC36Mbmu/6MZ/bXoUIdkWI5mB5V6P8YE871sMXN3EzF9K6yMZuiI4zyDgodpjDmpNB3qvQE+OotwyJqos48zDLjkqxXNoFKdZSyAGX5lhW8qaSTGIryzjBK2oICdBadJId//nEfA4ADlMt5KsiiR8LUP8s2OhVMD9hgHUVT9wQefb0Qd/zPI1hY9cCT7cHwvA/Ds/3IzrowtuDF6KIfYwteZGddSM/0E/ibdiE57UFyhsbuk0teDM/0ITvVg+mVMOLj/UhM9CM95UJ2uh+j8z5MLQUxOefCwuoAFlaDWPl+ED/+bxxDCRPmV5hRZGkQ7+zbgSeeefz/Z+89w+M6631RkZBCEsImEFoILbDDJqGE3gk1oe5NDZsQIKS5qNdRL7YT2IQNCRDiIlldGmmkmdGMpGlrZtSm996rJDvhPPeee+75/rsf/u/7riWH8+Xmq+d51iPbsR3bU9bv/VWolkPks1qNiBqYHu59FQejAPqWfGid3qMOvxkXVGo/hlbDGNDQHFfHzB4659zCX0sBLErKD6wSABxbp7LwIQOZ/Qe1UcHqUIiHgHy/Joh+tQe9i/SZOLQawZnNDAZ1BBpVSwHq8xO+vG32XPAeSRkY8qRyG5No21jfH1VmbbPKLHpd9qoD6FrwMk/fjqiXaRx30g77NKsQYT/GS/j54eX4OTux1gx0NDIP2olzdjz1ghlN4w70LgfQfN6CJ/60jF8/M4Mv/uxJvOMD9x5J9jY0NOD6192EhoYGvPN970fzsy8e+W9f/8EPXuGxu+baazG6vIMTzy/hQ5+9H9dccw0aGhrwmmuuwYCGKnoGtREM66IYMyRweiON05sZtkyi2OJl15vueK/4vV//prfitre/C39cXoErtYZVxxQaGhrwH0824snnVvHVX7bgtdffiP4pPR45/ghue/Mb8azeihEuXRszGDUkMGZMYWyN/gyjujDNPKoDUKl96FPv4cctg0f+TsdGn0H/Mh2k6KASRvfcHrrnqAGgY2YPPYs+Yd3g99W2yW2mMmyxYYVtKpKf2GIEyY44PJw8Z74KAK8+XtXj1oaGBjSeszK5iCoHOmZcbOzefySlyKeyOufcImnImT/VUhB9yyG2WODBADv99DNjcveCD50zu2IruPGCHc3jTvo+8zk0TRCr1z3vpZAHB4FT5INpZ31WfDlEBoAulq5kEtgiS98tuDGsDYtiTdkPyNk8WeI9ehHA6lmQgxfiw5577pgxXPjoFvz45mO9BKCY/MTH4FUsPdjHp9y47KsliURUvXBpmCV+OcPAZQLBNirKe5WhEB4MEcwrn6Biz6Ny0YID1855Py2lHBksZ96pGTIjd8y60Dm9hddefwM02nm4PE6YJCN0jlWs7Gmg9+qo9oWldJc9KwQCWTp4LUCdfushPTReeSFkxUf1LqaIAeuhNeEdfKL7KZH41fl1WA/qYYoYofXrYIsaofOtCqZR59fBEVvHbmId3oyJvGMVO1JlknRzZYk8gAUbgjk7PGkz0mUJuaodxaqEas2Oet2Bet2BSsWGas2Kal2CWvPfuP6GG/DoH5coyT7lkgtZJ5xoHt9CC1u+OSHCHw5moSA7RdO4k8lsLBQ16xa1G3L4g4B737Jc/TKopRuGHP6g95aQ+3mIh8ll7ayepnOOAVWWtu+Y3sXXftWBpnEnjp+VZHlagMAdNCv65/hBir++hvVxjK6n8Z4Pfwo/efwJHFySEMjaUKg4kKnYESvakC7ZEC/Z4M/w9LUViZKEUNaMYNaCYM6MYNYMb2oTu7F1JEvb8Cc2EMpYsRfRIl5yIF7aQrRgRywvIZYxIVu2IZzchCesQzJngTtqQCjnQCxnQawoYVn/V9x44w0YWzCjfzVKlT2sU+1oCj6AviU/k3npvdNycQvD+hhOryfRs0BsYNecG11zLpLh1QFWh0T/xn0aSnuOrafw3eODNB22nmLMTkSWgRkI7FN7oZp3sRk/P/NyhljBeBg9LADSqmD/GseJ4SNbwTazGGyLgzhVY+3I4bcp7pWmQueWqV30LgXQoUgKN7POOO4FPHmB/fjFbfr21C5aJrYUhxUCG03jcvjgxHk7Wia20XpxByfOWvHUCyY0XpDY53sYQ7ooRtdi6F3yo3N6Cy3jVjz1Jw0+/5Mn8NVfteOJvxjxsW/9hKpa/uVNeOcHP4o77v4obrrl9bj1jeS5e999X8TDoxP49Pd+eQRA3X7ne/HLUxMYMyTwzGYaT5uyOL2ZxdhmFmMbtMDBO/iuTCF3zbvQMmGDaimAobUkPv39X6LhNa/BDTfdjLfc+R7c8Lqb0D23g2FDGgPaGD75nZ/jzXfehd8vreLe++5FQ0MDfvz443hmPYzfrQfQp6FgysiqD4MrQXTNu1kvK3mzu9g98dtP9uGt75G7A/vmHWibJk9jz4IHvezgMaSljlFeCN0976HfgzHSvPFCVrtkdYxYYApJNp2zXgWAVx+v6nFrQ0MDWsbtwkDO2+p72I2Jl53SWLUXrez02Ta1Q360BZ+YsSKmZBc9i14yw7JZnP6VMLrmPWiZIBqbryU0jcvyWfPEFtrZbFa/JsRSjXzZgDoB23ixKSs35XUOvJeNboJkruds14DGj74lH0lnXA5lElr3ggxy+cVlVt61diXA4slZ8WvUMgj75akJGZQpbvC9SwHh9+MrEyQjxVjhL/P98XLnJRn8dfMb1LxXgFdlyEXZKyjKpJdDJAkLkCp7+46yf/KWsmyYZ4spolyYZPve5SA+/uBD+Nw3vgOf1w63fxfSjhlqaQGbYROWXSvQeFdZIGSFgT9eF7MKe8yIvdQmK4vWwBAgb589vgFv1oJgzkJeP+8Knr7wOxgCazAE9TBHjLBEDXCnTYgULEiUbLBF17Ee1GM9qIMjZsRecgN7qXUEc2bkaw6kyxLiBRuqdQcqVSdyZQciOSuCOQnhnBWZsg2pkh3xvIR8WUK5JqFcsyNfsqC+L8FkOYtb/+UN+NojLeic84jXHU9SNrEbJQ8zEahyyrIvB1isFoYzce3MS9g97xMHrF7OILH1j4HVCIZ0NMc3wHsAeYpw0S9bABbIP9S94EX79C66ZvdYWIDeQ60X6eb+k96/4fhZm0gp8z+rsHqIRR45WU+HKD8GVqMYW0/j/l+cwAc//WWU6hKK+y6kSk5E8hJiBfL/+dNmBHM2hPNWJEs2RPJW+DImhHIkBwdyNnjSFvizEvwpCyI5E+IFCa7YGpJlByI5G8IZMyI5MzJlK2JZE6JZG+LZTXiiawilzYjkrEiV7QhmTEiVrPjM5+/D9x/6Ic4YCaAN6uLoZ4sKwj6hDrLXvg+dcy5Wmu3CsC6GYS0lSrkM3M4CIfw937dMrLlqmQIxQ/o4HvvDPMbWUxhdT4n6GR4c6VX70b/kE94/lZolg3k3oDaKHnUAHXNutLA+tybmJ+VsbOP4FpNod4RE38JSvi2CESQJsH2GgB8HdSo1pUdPXnCKWq1mdqA+eZ7Svu0sOdzCLTST22gap5R6KwuHUHrdwTyBDnSxvsrueTeOvWjBk3/dwLEXTDjxopldm2ibpNk21RKxdf0rYQzpYuSVVAfEv+t3T4wIcPSuu96HZwwBPGPK4JFRSt9ee+21+MT930DzuU08bcrgd2YCeyNGuWR5ZF0B/K7sJPxn32dXy0U7nnxeix93/xGdc24MGVIYYr7MwdUw7vvWj3HLbbfjWz//tfgzfvqr9+PUihNjuiCGdVFxSOtbpvot7vscWglgYCWEbz7acQTEqibX0T27jQFNCKOGBNkRVkMY1UcxpKWDAZ+E617wEaky56FWhwUfgftJmejg1T5t0zSU0HTedhUAXn28qsetDQ0N6JjaZixECAOsVb9rgY3OM9mqnbXW806/Dr4owNONUzvomN1D15xb+JWo8yiBvpUIAUC2lao8bTZy/9QFh/C+tE7tsjJbN/NMudhaiEvcpNpYI74AgzMuYYgXxvpFP2MCvVCpfVCxIEW3YpqJMym8BoVLu1yWU/HePEXgQrUUFKynirM4vKpFeII4mxeWVze45281KnxEcuULv9ETCORVExys8vmv7gXalFQCQAFQl4g55OlDJWDtUvgGRS3NFV2JnXPyaooI1szR83byggNN5zZx7XXXY3pxFi7vNgJRDxY2ZqF36TDnWIDWr8eKT8dYwFUsuVew6tPCEqFOOPL1aaBjP7bsWcWabw326DpKdTtc6U2serVY9hDzZ40YsBFcgyGggyViQLhgRShvwUZQD7VrGY6YEYHsJvzZTQSyJgSyJqQrdmQrBPSqdSdqNSdieQt8GQtcSRMCGTNyFTuKtS0k82ZkSzZUajaUqjaUKlbo1v+OW269FV986AQVQDMmtI3dcJu5cV8RBGlkr1tuaZBZFYe4eXN2vW2amEB+0BCvp2W+DhBhDCCTNzXMCrDgYz5QL6uP8YnVHVr5cAmGgNZ2HOiY3j3iSTxxga6TF5TvuW00XdxV+GkV/kS1H0O6GDqnt3DDTbdg9G9/Q7G+g3DOhnDOjnDOgmjRBk/ajJ3YBqWA82b4syYEsmaE81YEc/TfA1kr4kU7ghkLwjkz/Gkz9uJGJMt2JAtmxIsWxIs2ZCsOBJNGpMtOeCN65CtOhFObiOXNiOVNiOVNyJQtWDW+iJtvfh36/vqi7LVakTeze9k0nGopyNLz/P3qx9BqBJ2zVILNE8Gi75MFEDpZ/yOfyBvUxagWxpDCiDGN/tUwq59hAF7tx9BKiKwcai96GUhQqQPo1wQxsBoRcnPLxLbiMKEEgWw+c0JOBLceAQBknWmblH3R7dN7aL24jbG1OHoWfTh+TiIwx2ReblegUN02Y/TdVBk050bLxS0MLFNxfyv7+e1TuywE6EQXA3+N5y04cZZqwng1V9tFOrS3T+6ifXIHHdO7aL4g4eRZC1ont9By0YnO2T10zRPD2jnrwqPPqvG5//gNvvDTpzC8lsSoPoyn/nse77vnY/jiz47h1HoSZ8w5wfINKxY2eN1MnyaIxrMbGDUmj9TRcAA4wpZKjkjDxjQeOT2Bf/vMV/DHDS8G19KiumWIXf/echof/fp/4J0fvE+AuCeencGgLsFCPGTZGV2L47QxiQFNAAPLfjy9kRTv09ZxCb86c1H8+s5zS2ifot3v5gs2dM+5MKoLUHJ4JUTLMSsR4Q/smqeDyKA2xg59AfQvEwDtWfSjfcYt37tmtq8CwKuPV/W4taGhAT3zLnbCIcN5z6If7bMuEXrg27ut0/zUyczwLMhx4rydVhAmnOQVZAzWkC7OupFo+kjZQ6asgaEPQKeoqGhmDKPwwEztHWEEOQCUC5xJAuarId0LfrawwCo0GFNJgM/HOthkr14PL0lWy3Nb/JIrH3jalld2BFVJVFoAACAASURBVI4AQtUSeYaUYE7Z9acMfPC+P94If4T9W5YlLPJ6kamf//l7xN+Fs5byIgiBTRYaWJYDJBwMy57Bo9u2gglkAZfOWY9gRfgI/cnxLfSo/fjUd36OT3zp6wgH7PCHPNjybEFrW8WSU43lXQ10/jUsiy1gknQ1zNe35F6Bzq/FqncV6yEChDofSbyxIvX37SQ3yf/nXYUzboQjboQzvo6NkB7byXWYw2sUFnEtQ++nnxPKm5EsWRHJm5Gt2pGpSJT6LdtRrjqQr9jgS20ikLUgkLUgUnAiU5SQLkpI5k2o1G2o1CWkCya8533vwpd+8hixRgs8ccuXZtxondxF0zjJaCfHHexijNoFJzGA5+04eV5iN1F58aZtirPUjNVlz2cPS8/3asKUBtewtPgVHZBdrAKmm3UI8tcFBRz4ssQW2xym1P3J83YCfgrmT1zcC3hxhwrXWaCKd9z1aojJGVtP454vfRu/aGxBuuxENGdDLCchWXLCnzbBl7HCl7YglLUgmDUhV7Vi/5KEVMkKV3IT/owV4ZwNgYwFvrQF3pQJ4ZwF/qwZ0bwFhYoV1QMJmaoTvpgBiYIZ1Usu+OMGpEs2+JNGAn85E9IlC3JVGxJFCe//1/fixGlabBjQxjCojeGU1icXqbODV+9yAP3LIYwZkji9kSbJcs4tap14n2gn9z9yhpUpCaqlIPrYhNsom/rqXfKJz5XepQAGNEEMaKi8mOwcARHy6V8Ji3aE9hkKuPFEuQwAt9A4voPGccVOOm9I4KslLEjXcpEOziQJ0+F8dC0hQnYdrHyfmh3YYYRttbcze0/XvEeEhKiTkMBa56yLMYhbaJ/aRuf0NtqmtnH8nISWCfK48t+ra84tLAwDKxH6bFqkGrDOmT2cYJ2yJ1iquOkClaF3zbmZKkKHnFPGJPP1ZTG2nqb07D9bHDGkcXEngJte/3o0NDTgjve+DyMXpnBK58egIYWueTd6Ft0YYWDxSjbwjrs/goaGBvQ8PYDBtTR6NUEM6mKMzSWrBT90nfz7Oo79ZQ1dC150LdDBS7UUwLCWPKU9i7QgM2ZMsWLnqCAGRvUhPNT/F3z2P36LJ5/ToWPSic6ZXXp+zklombBjWB8lbyOrexpcDaN/OSBCS31sEnJYG0G/JoBR9vMHVkIY0cUwoo+ha3b3KgC8+nhVD2IAp7cwuBLGiC6K0bU4+pZITuhbopOwGBZXeO+oT2pHMCC8SLRxnPwlvUtBjBqSBAB1cXTM7IqbYctFOb3Wwk62Yh5OUV/QwVg9zu61sR1VLvfyVQXqZ6Mx9Y5ZF/Pk+cS6BU8Edi/4j3jo/pmUKgDhol/sMspgUOnpU4I/Yu6efG6VSVBU5DuwGsWgYtqNMxSc/RvQRoUkzH+dcruXJGku+frk6anFo39mpWTM92R7WFWF6AVkPsXuBf73U0zWLZBvkcthbXx2b9Z9ZFO1+eIOOqYcuP7Gm/C3cy/C59tGMOyB3qSBdkuP6Y05GL0WzEoaqF3LULs0ULvkEugljwbO+DqcCSNiRQsMAZ3Y/LVFjHClNmEKG/H3lRdgDFB1gzGox5JbA2fciHjRio2QHhrPCjReDbS+FdhjBoRyJqTLFsSLVtYBKCFdlpCt2FGuO5Evk3wYyUsIZS0IZyyI5qzwxI1I5M0o16wo1SV09T6O2++4E20zLgJ/onLFJa7Wi1uM7XMIFk209bNKGGVHJu+wbGU38XZW5dEx42JMGwGIfk1IpEe5xMQPYz3stdu94GX2BK/wDfYtE8PVOesWFU2tF7eZP9GJh09NkTx9hP0j8Hdy3EmSowIAdsx50b0oT8KNrCUwpI/hTe94N373/DNIFOwIp61IlRzIVxwIpjcRyFnhSzFWL7GBbMWC+qENxboNgZwEd9IEd2ID7uQGvKlNeFMkD/szZvizFqTLVqQqdoSyVkRSBpQOdlDd34I/pkeutoVY0YFowYZY3oJU0YJc1Y5k0YoPfuj9OD5yhm7aK3To6tcEMaxxY3CVAlfDWppZPG2I4Y4P3IPjqm7yMi9S8Emek6QgGE/3d13htVUtBXH8L3qc2kijR+1F94KbStPVzL+57Ef/UoB8m0sBqBa85AMUKgIVULdP770izNY0oaz92D4SAGmd3GUe6D0RFOAAjFhfChWMGRJom2GzYRe3cPK8neY92eG8dXJHbBJ3z3vROsmkYjbRefysjTU17EG16BUrIs3M53r8nESHiwnqtaRgIAWZ+leIIW2fpiLy1ovbit7XbfH6O3mBXm+P/3EZw/o4BlcjOLNJgY5hQ/IVgO8VMu6EDR++//t43799iCTaz38KN9/8OjQ0NODt73wbGhoacPMtt+Cvpm0ZALLfq+m8Cde89jp87v4v4jOf+xj+aAmjR+0j2wA7rHNlpJOFqsRUKNt/75inA3LPoo8OE5sZjK0zxpEFt7oX/RhYCTPWmQ0RzMr+9LYZF578i0EszQyuhuUVGgYAR3RhDKwyG8+8W/iC+1dCGNCQJ7FPE0LH9FUG8Orj1T1ubWhoQPfsDvkRFr3oXaI5o85ZMq52zOyhe8GL/mU/WsSupjyF1cze7I0KP1/TuJPmj9j8zcBqREgQAgAqZBCehpO9LpQCPmJun3GjY9aF9hke+tgTpvoOllKm8AYV2goW74qeO9HRNy+vePB1Dhng0cm9W8kMMq9GL+sXlMMaIeE7+urDzfLGLtv45dcAA4IcAPavysXP/NcrGUD+/xRMJWM1j3gW1TyYwspFeUn0UhD/bBeYg9cri5+7FnyC6eNpVwH6+UTenJf2RKd28LVHWvDWd78fDucmovEQXF4njJIW+q11WPasWHNuYM1jxKpXx3yBqzCH9XDG1xHImZEqW5GpWBEvWWGLGmAK67EZXiM2L2fB4y2/gTO+gWDOit3UBnR+LfZSG0iVbTCH1yhU4tfCFl2DN2NCjEmImYqEfM2OVElCtiwhU3agVNtCrmxFumSFP72JSN6KeN6GRF6CN2ZEtmJHtiyhve8krr/xdfjF6AXWI0drEp1zPlHd0ja9J16nTawChuYQOXu9xQIiTjH/1jzuQMvEFps53BMhkC7mt5Xrk0IYZNvP/cxg3sckRB4m4mlylZoWJvo1IfQsuNE1SynWrjkXOmZ20TTuEJ2bn/vxkwL8/XMASHUjJAG7BQDkr/VBXQwf+cp38JH7PopcUUKqaEc0Y0WmZEcib0UgbUYoa0UwY0EgTWGPUNaEQtWKfNUGb9oET3ID7uQmvGkzPKlNuJKbcCc3ESsQIxvK2ZAoSHBH9MhVrKy824pEdgPZqhPBtBnuuAHJEgH7dFlCsmTD177xOfyisRn9OloD4YeobnUAw1rqWevVUADjt0+TLHfdda/F07Ma8b5q52y3sklAVDkpglZLQXz9V60YNcRJ7mPPi0rtJ/ZvJYyBlTBUah9jB33C28ktGPQaktsMml8BALcF89fOrC28/aCTWTJ4ureDhd7aZwmIjhnijMkjuZdPafKQ3YnzdrTPuNCzQOwf34VuZp/bT/3dguZxp+i865zdQ/vUDk6ck4j1Y6s27VNb6JzdQy/zOMpTdttoZnvxzePyAYkHo46fo7L05oktfO3hJgrTGFN42pzD6c0s7elecQ2yJZFhYxr9qxHcevs78PXvPoDe/icwNfM0yjUJmawFVukinvtrP1R9T+G6665D38L2KyTg7zeO4fW3vQWr0jJuvuUmjJx9EX3Mq8hn2fo0YXlFRtkVq2iJ6F4MoH8lghFDUtTPDGhp/rR/OUAH/5UQuha9wiPYow5gYCWMU4YERg1JtE5uYVjH7wdhNv8YQr+G3v/EDIYwsBqhSjNtBEPaKAZXIxQgWaV7StfMzlUAePXxqh4kAS+4cGo9jVFDAsP6GNqmyRzdPktMUN+SH39Yc+DMmg+96gD6lohhkw3xsqzEDfLNE1tom95DnyaCjtk9tDFmj3uUhBl58mjrvfC2sAqWvqUgBlcjVCcj2Bi2pcpOYz1MGqNgh5+xYKEjfr5uBQvIGTVlWbMIUyh/zSvStQTMBFO3HIJKyExXyL5s0aN3if69+jVBwfoNaKOKgt+I/OsVKUYBADkjeWRzlMtLvJSWlUwvczZQnoS7cjqui8nHXQpJsWOWbdwqmd5pFxuQd6FNkZRumSLAfdMbbsPA6DB8/i34g264vA64/TvY2pMQDHsQiofh9Duw5lnHqlsPPZN6E0ULslUb0hUbMhUbIgULdlIb0HhXsRHUYzNkgBQzwp+1iFm3UM6CvdQm/DkzfFkT/BkT4gUrclUJ+aoNiYIZ0YKVAUAHYjkrknkJkbQJxdoWsiULCmUbQlkLEgUL0mUHIqkNeCM6JPMWbDim0dDQgB91PotudYBuAld2RgqfqUts7rZMkM+vlftamW+Kql+cosi85SIVqCuDSjyspFqibkheCdS77KcdVE2IXWHxmlItU1JcxVYTCJwweXieugrbJon948Z/JUt54gr5lwNAXjTM2Q55Ei6Cnw/8Dbe+8U0Ih/Qo7zuRLjqQzJuRKNgQz1sRyhII9CZNCKTZlTEhWTCjvi8hmjdjL7kBV3ITvvQmXIkN7CU34c/Qgks0b0E0Z8FOcAXxvBWF/R0Uaw7U9u3IFEnyDaRNiBasCGctCGY2EMubkCyY8L0ffBUPPXUM/dq48P7xdR0VYzCHtHH8ov95XHPNtfjOD7+DL37503jo8UeFB5ZXP3Xxcm0mA3fNM3sFY3J61EGcXk9ibD1FyyAMuBNYp8MeScCBo/7gpaB4fniJL0/1KgEgv1oY4OOeP27+57+ef4Z2zdNn4wA7MIyuJdB8kcBfKwd+jNkjhk+iINLMHtqnttHDfq/GC8RYH3vRShVd7HDdPEH2AZoEteHEWSvVHZ2zoXncgb4lP5onnCTzMs8rsYZ7TA2ScFwB/PjXlos7GGZbyyOGJJ4xZzFkSGFkPYNhI11dC258r/kMHhv5b5zZSGDYmMaXHjqGhoYGxNMGFGt21A4cqNQcKJatKJQtWN+gwuqHu0bwnC0qVkE4C/ilh47hvq//OxJFCZ/93H1o7O+EapXt8upiGDUkMbqepjqhRT87EL/yap+l913fShijRgoDdcwSsB5Zi6NfQ+xd76IPw7oY8/mFmIcwjBFDEp3zJIGf2YhjUEvLMP0aAnwc5JEaEBL71AMM+A0qein7lv1XAeDVx6t63NrQ0ADVogejazGMGBIYNaSYjOFHz6IXLRe3ceKcjbwbs9voVXtYMaYcCBG9aJzmZ+nHtqk9dMySf49LYULi5XKy6P1zCVmX+/k657kp24OBlRBG9TGMrsUxpItR2/4Kpar62eamkGNWwoxZCynSvzw56RVyqgB/RxhD/sH/yrUNpe+PAhevvDgI5Bu/dKKLCMaP2D/m/VsJK+TfsFga6GFVH5xl4L6knkUqm1UtuKFiIJfqd4KCcTjKICoqQ5Rp4EVZCm9nM3iC8Zt2sXUIN3XDiTJsjyIw4sHdn/0GvvmDHyPi3kQk7EIk5kcsGUYiFUU46kU8FUEyFcVuwIV58zLUTjU2g3pEChZEChakylZ4MyY44+vQ+YkpXPLQrvBWzIBQ1oJsyYZE3oxEwYR4wYJ4wYJ0SUKhIomFj0TRjCQDlfmaA7mKhHRJQq7sRCyzgULFgULRinzJhkjOhHTZhmzZjnjBinxZQqHixOzyn3HrG/+F/q0X/UcAoLIgnOwHu2Jdgbf2c5lMFPiOEwvIOwBbL/Kg1K54fcsMIOuqWyYwMSBYiYgAhf3ssKDiawns9Su/ptnBiN18qaqGF8rKpdUnx/8ZANw+CgDnKTDRqwljUBvDh77wIB5+4hgO9rdQre8gV7IhWbQilrcilrcglrMglDEjlDEhlCHwF8hsIprbxP6hDcWaFdEC9QNG8mYkihb4Myaky1ZK/OY24YsbEc5YkK06kSlZEUgaEclZkS3ZkC2akSpaEMmbEc2bES/YkC5JSJRs+MlD38aPH3kIXUthDGi5lSIiDmBDujhOGVP48JceRENDA3aDevznoz/FF795v2C/uceZF0N3zcs2C+675Oz/05u07NDDEvY8vNOnoc8gmZ2JsCLpECvC5547XuGye6Trr3lSBoO8kqeVe595Opux0O3Te+ia82JwNYozG2lWUB3DsD6GxnEHS/RSmrd5YguN5+3sM9zOgCW9PjpnXaTaMMXm+FkrTjCZl69AHT9rxVN/t+D4WRuaLjjRdF5C43kJTRNb6FnwommcAN4J9v/onveiV+1HB6v7ksGfvJLTMb2LodUITq2ncMaUpT6/dR7ySOELP31SlEnffvttePA7X0Pn2RW89rrrcU49gfq+A5W6HdWahEpVQqlsRbFsxeNP/BT3fPQePL2RwB/MsVcwgF/5RSPu/fJ3kCja8etHf4Rb3/B63HX3B/BnwxaG9HGMGWmqbnQ9jRED+dZ7l0PonPfiysGAtlkKbw2uhllDg4eKvnUx8vQteTG8Ss0XfLe7e5E+N1VLATw08DcMa7wY1oWZnBtmDCBj/zX8/sH3wGXiYFAbo3DlWgKqRc9VAHj18aoeDAC6MbaeFh8kI4YEhtfiGNbHcMqYRMesC8de2GS+CDZZc1GuJ2gWNxoywTczLxS/KXayyhbeZs/LntunScroFBNWdDPjp3Lee9fF5M/+5QD1MbE/25gxiX5NkK1g+NC75MeYIYFRI52Q+lcjIjTB/W3U+eU5mgBevCJQceQKsmoWpddOlliVO4+9TPrlNwX5jcxv5iz5y4MfR9K/8mA47xcUNRYLcqWMSu0X/5/+lTB61T70L/OiaYWEvCgHP66Ue8UesiL1ewQEsqCNAD4cAM6T7DyojeGxZ9W44ebX48EfPQS3VQOvdwtWyQS31wN/0ItsPoV4wo9kOoLplRnM2uawsrdC1S8xI6JFCgGsB/VYC+ig9+uhD+jgjG/Am95EvGhDsmBGOLuBWNGKTFlCpmxHnoGEdMmCdNmOdNGCTNmKYt2OfMWOTMmGfNWBVMGGXFlCoWhBvmhFMm9GIL2BaN6CdNGOUMaMVElCPGfCivHveN3NN0G1JP87dTIZqGOWTwLyfyuXYEdl+W4LTcz3xz2AXAZuZf4/7strm95ltRpeUaEkAwmyClCtSUwkxDmY4DvQYrmCpQE758ge0coAavsUAcAWFriSAZ9iYF7pAZzgtgs32ue86FEHGXsWxetvux3TM+dRLkjI5zfp37tkQa5iQ7pEoDyeMyNVsCBbsiBWoLRutmhGpW5D/YDAeqJoQaxAVyS7gXTJhGJNQrJgRTC5gXDWjEBqHd64AeHMBqI5C7yJDfhDKyhWaVoulrUgmjUhnNlEsmDCw7/5Ib70wDfRsxRmrDoBv0FdDCP6OEbW4vjIl7+NN73lLbDaJ5Ep2vBE06/whje+Ad2zWwLoy691NzrnFIdEBoZ71KRCnN5IY2gtSQcuxrpTAbSf2LjVsGB8VGo/+28Bxt556DB8cYulePn2+a5YQWqZVPqi99A6LR+SWxQMcvusmyquViOUTGZ9qyfP29Ez76F6Fy4Bs9fjsbM2dM24cIKlhJsv7oii8sYLDpx40cLKwiWcOC/h2ItWWgI5a8Xxs1Yce5FWQY6flViwxI4TZ21iB7t92oXuBS9OXiBGvFGwfzILeOK8A32LXlHkfMaUkwMfxjS++WgnXv8vt+HJE79BNmdCMKjFv33wfSJRG4qZUK9JqFUkVGoSyhUbqlUbqlUJ73jH7fjRT7+HZzfD6F+7IkRiSOOBJ3px131fQK7qgNunRnvno/j4J+/FF7/yGZw2hDBqTBD445cxjbGNDLF8DAy2z8lSsEodoG7H5SCGGDjrZS0aA8sBhWJAif/eZb4W40NDQwPe+s478bs1N0Z1AfSvRMUCFFeFeP2T8r4xoI1gUBelUuy1BHoW3FcB4NXHq3qwFPAehnS0QMBH0IeZR2PMQBH3J/5iRMvEFtpn3GJQvHliB22T2zh5TmLMB6sL4XUY4060Te6KAudmhb+Pr37wdYQeBv54F2EvK5/m/pmuBc58eDG8FseANoKRNfqgH2If+KeMSQEMh/Us3cXM4d2LfEfVJ0zevBpFLHcs+oQPULnhSzIrk3auBHwK6fcrv2gSnkDlhms/YyS5969vJSIYHaXvT8ne9S6Hj6x89C4x9m/Rzwq5PcKrSCnSoJCbehgz1C2YTrnrj4NqZeGzvIfslq9ZGQDyD72exQAtI6wlMaCN4rfPLuFtd92DN97+NvT09cG0pobVpIXdYUU4GkIqE0GhnEI07seqpMfKjg5zTg0Mfi2CeQuc8XU4YkZIsQ2YwkZsJTawlzKhqfO3iOasyFetlO7NWpAqmgUoTBUtyDAmKl20oLzvQGXfiVLdiULNiULVgVzJilzRgkzBjFzJgkyJggupskTSZcYEf2oD4fQGNqwXcN3112FYF6ViYF6Rs0ge0fYr/EAtDABe2Qt48ryd3gvjcv9fqzK4xNZ1ONPUvehj4C8sNkIHWA1M3zLrj+S9kGou9/oYcKRQyMCyXwCD1ilaEmib3EYr82R9/idPyez8ODfjO8T35UWQHbY1Sz6n3uUwfnNmArfe9mb8j6IXl2p7qNQcyJXNKNSsqNQkFMoWlCtWlNhVYHJcvmxBqWKlG/S+DYWqDdmKFamSBbmKFbVDOw4uO5HMmxCIryFbkpAtkWcwU7IhW7EhXTQjmt1ENGVApmCGP70Jb8IIb9IIb3IdW54F3HzLTXj86RfJZ8wm2sYMcZz40zwe+MVjeOsdd+Cej3wInoAGxdoWIokNASZaJmzEgCte352sEqZ73isK5VVLQYzo4/idOYNv/qad+vwWaaO5Z8Engji8tHtwNYy+ZT8z64dFWpsqtfZYbco224Xmcu+2YP2UfW8cCDazUuAWpqDwUuiWyR10L/gwYkhQcvW8Hd2s2oUStzui6691kg4ix8/aKCHMPp9PnrOj8bwdJ87Z8NTfLTj2ItW9HD9Lsu/xcxKOn7Xh2ItW+rFzdkXi3S7KxdunXehd8qN/OYiOGZdcKM3mEI+dJZbwzHoCD/y2A0+bshgVU25pfPupATQ0NODF+Qs4qG+hXLajWrShkrMgFV2Dx7WAg5qEg9oW6hUJB/t2en3VJVTrdqyuPo/3vOcO3HHHW9E2Oozfm+IYWiMgN2JM454vfxff+PmjOLxkR7kqoViV8Oe/DuLf7vkAfrceIFmVAb9hY5oxgSmMGtNi01eu+GJl4UsBIk3WEvIhXxMksoEdrjtY5ROFa+h9e90NFFwZVUt4xhhCvzYmq0Ka8JFpUNEYwaThYW0Eg6shjOojOP7c8lUAePXxqh6CAeSAaVAbIYZtLU4vfmMSvctBPPFnHdont9Ex62byF9vvnXDIE1OMAeE9Y80T26I6gzMmYn+UJdt4iIOMxy7m6fOib8mPAY1f3CS5lMm9a7wod1gXJfCqi2BQG8bgakSU6g7q4mKNRLUUYvKlD0fXNPxHpOAr1zVUatnLwwMWKgb8lNUvqqUAnvyzRgEAw0fYv/4VeiMPKN7oRwAgX3rgoG7Rix7Wn9Wz4EEPM10ry6u5tE3p4ID4+whP5IJyo/goABQTcHM+Geixq13xtZ35ojrnqSB8WJ8Qpbad8z40jztx/yNtuPGWW3HXhz+BPzz/PLJBO7a37UimU0gkw6jWcvD4d6Gz6WHY3sS8YwmOxDqMQT18WRPCeQt2ExuwhA1wxtZxVvM3bMWNyFQsSBTNCOc24UtvIJw3I1m0ktRbdSCVN6NQdaBQdSBfdaBcl1CsSShUrMhXJOSKZuTLFmSLFmTLEpJFG9JFCe7oGvzJTQRSm4hnzXj+hSG8833vpdeNhm7olL6m10j7nBdtTA7m7J8SAIoajwsOkn3HnaL0nDPd7TN7LLVOHkA+EcgDILyDk3t/SBIOy9VMDPB1z7vF8y+YJcY2tE7tomNqB+2sxqN1cgcPn5mm7W0G/o6fs7OvHABSCKSNSY0dsx6W7Izgwce68Mn7H8D/rodQKThQrUnYr9twad+BwwMJB/sS9usSqnUbqjUbyhUryhULihUCgJUadSwWqzYUKlZkSmYC6jU7opl1JLPrKNWcyFfsyFYc9O2yDemSFYmCFfG8BZmyBeGsCfawHjsxA3bja7iw8Czuuvsu3Hf/g4wZiWF4LYlT62n8rOdZNDQ04IEHv4znXxhCNGFAbX8H5eo2PLF1NDQ04Ms//E8C5sz72j4rdwHKS0A+sQRyZiONUxsZHPuLDt1s5YOHOzgbyM37AxpaHxlgJdEiVTxHgbqOaQb+2OG3jX8Wsuewc9ZNjQaKlDBfSWoW398SQZHepQBGDUkM6aJovEAMIAWQ5B7ApvEtdM15cPyslQ7oExQKaRIAUMLJ83Y89YKJmD4OAM9JBADPSYzFk+cET7LPenptkd2nc86DEUMSfSscqIShWvBiQBNEz7wHY2tx/N6SQ+v5TYxukPT75J80ApTfe99HkS85Uas4UC3ZsF+SsF9yoFqw4rDuwGF9C7WShMO6HQd1B4olKypVCfmyDfmyDbm8GX99fgBvfvMb8d0ffAOnL87hOVsMI/owXvf6N+D5mfP4xz/sKFdIPvb4lnDNNdfgvxZXMKBPMeYvyTyJaXlqzpjGsCGJobUERg0Jer0x5pX2oSOi6UGl9tOix5wXrdMu9C6F2PvWIw5pv/0j/Z3v/tSXcXotgAFtDP3aKAN+dI/g9TRDnPHTx1hiOIAn/jB75ZzeVQB49fH/60EAcMGF/tUwhtYSGF5LYGSNDLpjxgRGDAl0L3rx5J91ohS4VZFia+JFuONb4qIE2o6ovGibdglTM5+0aWeMCH9TtM8wj8uMS3z4drEqhVNrEQxrg6JsWaUOoHvey9rvXTTNsxwQoJDLpLRGQlF7IbsyICj2exnLI5LCQi6VFzauLIH+P/r/WAG2nOqSb+R0ogujf5WXfsopYrn6JXjEf8gZSjEJLdCtkgAAIABJREFUt6BkK4+uenQvHF1t4asmysobefVD3v3liTcReGBf24X0SdLvwEqEvGms6qCTL5PMedEytYtHn13GZ37wa1x/402491Ofx57NAPPmGoJBH6rVHNLpCKKxALz+bdh2zNh0GeCMmREtSijt21GsS3CnNmEOr0Hv02HNp4M9uoZk0YRQ1oQw64DLlO3IVezIlmwM/NlRqEooVCwo1Wwo1STkKzYUqhIyBRMKFRuKFTtyZRtSBQmh5AYCKRMiWSuCSQPyZTPaOn6Fz3z9mxg2JDGwGkbXvFus23QqmVIGAPnVokjDNzHPXSNL4PI+S17X0TbNprzYfnavmoeVgkfS4nw6rJ/1gPGNWS4n8gAQLwnvZGstbew9RSnTHeH1amJzi40XjnqxeDE0AQLZe9Y240YXYwAf/G0XPvPVB/D/1oMo5e2oV5w4rDtxeX8LLx1s4dKhHZcP7divczBIILBUtaLIvuarVkoEVywo7xM4T+U2EEuvo7K/hUrdjlTRjFyFAH2xYkepYkesYEMsb0a6ZEEka4I1qMXoH7rxr/fcjRtvuhmf/v7D6Fny001SHyf/liGJd93zCTx64jGUaw4USjbUqk6UK9vIZMz489+GcePrbkTLuIWtKbBlIWZ/4LYTngbuXiSAPrpGLE/Poly/08Oevx5W9Ny/HED/khe9i24CA6sR0eHYpeiT5H1w/PBL05vyZ2H7zJ6QgcWBeVIxDcY20ztYfU3vUhCn1tPoXwmhadyB9uldkchtZEl18ljv4qm/m9kh3cF82mz547zEdn8lHPu7BcfOWnH8nE0B/OTrxHlWd8RDfwyMtk7tomPOg1F27xheo4P3mCGB0+tpDOtjeMackytfjGkMrsVx65vfLoCMeUeHg307Dqp21Ao27Bcl1Is2VPNW1EsSDqoOVEtWXKrbsV+RUCxaUC5bUa2xNZ+KhGrVDo97Ed/97v24/vrr8GTjo/jmL4/hLXe+G5cv2XBw6ECxLKFYllCvb+Hr3/g8vv7gVzBmiGLYwDyARhkA8sDFoI4OGcOGJAZ0MYysJZhiRh69XvZZTxvtAbHwo1oKYoDdC/jaVte8Dz/p/QseOTON08YYBrRhAfgGtDEMMhVgSB+XwZ82ggFNCN95shd3foBqcO7+zNevAsCrj1f1uLWhoQF9arfwFQzpExjWxTBiTGFsPYWB1TDaprZw8m/rQqZomSQfU/vUrmLWbUvc9NqndtA5Q5Ut7TPyDZObmtunqbaiY04umG6bcUGlJj8TrXXIax79mgB6F/dYQbVHFDnTSZ1Vv8x70MNWPgZWQlCxse4+trNL80QUvedyLmf6OpUTaaI3MKDw4gWEDCfYQAWAOyIL89UPcYUUAJCBvxXu+wsekYDJ/6e8WBG1SCYHFF4+OdV8dB3EL1YilPIvZ/zE9i8Hgwo5uH3Wi3bFJBzfhVUtBTGki6FvhcBRJ5PIuGzWNedB56wbJ89LOPY3I2657Xbc+8nPY8eig9/rhsfrQiYdRbWcQSwexN6eGbueDWz5N2F06RDPm5AqWxHMmrGXMmEnvoEVtwZazwqMPi0SBRPyVStJv2UbsmUrUiUbMiULsmUrcmULilULMYBVGwoVG3JlK/IlK0pVB/JFC7JFKxI5K4IpEwKpTUTSG0gW6Nd99OP34lu/acGwIYXeZUpK9y8HSa5VVAe1K4By67T7CgAos4BUvSF3AcrhEXo/dM56FP4/YpNoW5QYpV61H4PK1wtPAjPgwaV9PtnHLRXECtElfIeMDWpkU3UnzjuPhEGOds/tEgBcoIT5T7r+gHfedTf+n1oQ1YIdlw9cOKza8fLBDl4+3MbLh1t46dCBS/t2XD5w4PKBQwDBas2GSs2KWt2G2r4NtX07agd2ZIpmJHNmlGpOmuqr065woSqhVLUjX5aQyFiwFdLCnzIikt2EJ7mORx//Id5w22148IkedM65MKiPY4hNwBHgSGB0PYMbbroFk9qLKFcp4FOpbyFXkJDJWXGs9df45Bc+LWql+LIQ//zhxfI8od23HMLQagTDujgGNEHhvVQtBQWD37tMN/hB5v+jm3hU9Mvx92PPopcVMHvROe8h//MsMX68Yqid1cQoQyIc/PHXl5B/p2gOrHcpiFPGJHpZKrd9iipZeC9ly0Uqf6Y+QLtg7fhzTylfSZZ6ue/vCPPHpF4GAHmxeNM4NT10zXnQNrUHlTqAM5tpJpumMbZBPrqx9QzObGbxtCmrmHXLoGWCpsz01mVYTedwWHXg0r4Dl2p2HFbsqOclHBQl1Es2HFTtqFck1IpWHJRsOChJ2C/ZsF+x43DfjsMDOw4PHDg8sKNetqFekjA/83t8/vMfw90ffC8W1f+N/X076jU79usOHOxvoVbbwuTss7j9rW/Cf20GxcrLMKt4GdKzYIeWqWO6uAB/YwayGXHvqWo5hCFtGB1zHppz1MfQx1jBU8akqHDqWaTVpa55L1omnBjRhjCsj2KYhRtJJYoIZpsKzglkPvrMpADLH/3cZ6Cac1wFgFcfr+rBAKAHfZqg8P4N62I4vZnFqfUkVGoPGs+a0XzeisZxB5ov7ohJtdapXeF/4ifUJlZ+y5vraUKL6kRUi9yw7hZLE+0ziq8ze6zfz4XueTf6lvxsxo3kMi6nvGK5gkk2nXNedM6SLEITRF6oFjxMUvaxgl1qfx9ib6puUbdytByZFz+LsmUGFpVBD7nzj0Dfr56eUqwPMA/gsgwAuc+Dd//1KmTgbjW/OPgLyQBQzabb1PzPd0WJ8z9hAbvmPQIochDTzb8uyrIwl4HlslMZLHYtUD9in4bSaMpwBC/Q7ZzlU3w0IXbyvIRjf1nDXR//Ih747veQjfrhce+hmE/g8LCMbC4Bj3cbe55N7HiMmF6fhC+1gUieOuF2Y0Y81fxL/Odvf4L2U63QudWIF83IMwkxW5GQq0goVh3IV6zIlczIVyzIlc0o1WxCYsxXJRSrduTLVmSLZhTLVhSrdvjiRkRyZqQLJmRLNmzaxvHa665Dx4Ifw4YkuuZdIoWtTE13sQNB10LgKABU7LXyOTglCOR+QN7nRgXndADqUTNfJw8MKFLdvPyVe8t6NWEm8/tEYpUAoFv21E7uiO9zRvBnA3+nNKgIf2yJVPCVW7QtF3dIBp73khytduPW29+Oh59qxkFuG+W8DfslG16ub+Mfl3z4Hy/5ibGp23Gp7sDlfQcu1e04rEuo1W2o1q2oH9iwfyChvi+htm9DsSbRUguT74oVGzIVCYWqHfmiFYm0Ee6QDr7kOtzxNdh9Gnzh/k/jzW97G068uIEBbRRD+iQG9QT+BnVkVRlaS2DYkMZb33s3mk6dQqZoRrZoQ6FkRbXqQDhlwkc/cQ9++MTxK4rtFUlbVizfOe9D71IIvzNlqK9tJYi+JR8eOXWBqQvE3PYuBTCsi2GIf56IVQme8o8Q089qs3gjAS3suNA5zzsI+ezlLlrGHeKQ3SoWQ+j5aZogVYX/2bvnvfRZvZ4iUMvYX2IKt1gxs5fZdbaZ9EsM4ElmByDfnxXHmOyrlH/FhKDi4geH5oktdEy7oFr0sy7EEJ7ZJKA3sp5mbFoGT5uzOG3K4tSmHPZ47NlFDOriePv778Fnv/xF/F/7DhyW7LhcduBy1YHLNScuV504LNpxWLJjv2TDQcWO/ZId9YINB0UbDksSLpccOCw5cFC246VLdP3jMh1IDmt2HFYkek0e2nH5kgOXLznox+sO1GtbKJbtWNG9gJtueh3+pF7AwFoGIwYGWjcyGGXXqc2s3FXI/M+825UfQga1UZwyJDC4EsZpY1Jef2Lb3qLsXRthvlDqUOzXhPD0Rpzm4bRRFiSKyMCPvZ4GViPomdvFx7/1Y9z9ma/hyT/MovPqFNzVx6t8EABc8mKQJYxGDCQBj62nMGZIoGN2D4/9UYOTL3L/yDbap3fRPEHf5vu+/NTKpSd5ts3FNlBdaGNSR4/az6oRvAIA8jmmDjbK3jnnRhdP7c570DVPoLCbedv4bmc3T/eyGyJdbupWWqGOJdpQlWe3etQBkdIl9oWAGt34fehRMH/KJRDOBPYuB6mGRSODv15NGA8+2S96AfnCR58mgn6NXPrcr1z+UKx/vBIAHmUBu/h+LweAjK082m3I5V8fVIt+RQhE/m9c2uoWv09AAEClPEwsEMnXQwrZt2NWnkZrU9yM2maIGeya9+Cx54z4j/bf4f3/di/KcS92draRz6VQr+RRKGSxvW2Hx22DtLMG/c4ywlkTInkz3HEj7v3Yx3Dta9+Ea655HNff8G585GP3IZbbRKJoRixvRqpE6d58xYZM0YR8xYpihdi/ct2Gcl32nBWrFC7IFU0o1YhlCqVM2AnrkSrakMxb8MK5U3j7u96F3lW6kfcsesVzrpTclfvJ7bNetM64hQ9QZmicAvg1s45AnsYlA/822iZ3hN2BA7luUTUiV4sIA/gKTVTJiyA+2afGwd40T4zusVWdbTEJ99VftaNx/MrFki3BJvFkPrdlqNR+jOhjOLWewuhGBk/9WYNrX/ta7G5bUUnYcVDZIhn4YBuXDrdRq2+jUrbhct2Jl/adDACSLFytWVGpWomVqUsoM2k4W7YgW6Zkb6FqR7ZoRa5gQS5vQqloRixjht2vgd5yAR/80Afw4c99Ca1T27STrItjUJ/AAJfL9AkmA1Nf3Nd+2YwPffpLiObMKBQlFCtOFEpb0Jsv4tprr8Wx51bRLmYt5dLz9hk3Omb2mIGfyqBH9HFSQKb30D3nwrce6xFsXs+iD6P6KPo1QSbTsWUHZtinBSC51FusCTHpvkfIwh4m/VLFS9vFLbmMfZLqsfjzww8QLVMEGLsXAxjRxzFmTJIdZmYPHTMEAmlxhOqHeHk5WXa2BBt87CxL+/KE7zkCf8fOWsWCSBNrduCMYiNrfGifJgDaow6QzLuRxhlTDqd4sMOYwtMm2vTlQYwhQxo9S0F85efH8clv/RBve+edqGSsuJS34zAn4VLWjsOshIOsDYcFCZeKduxnrTgo2HFQkLBfsGG/YMVBwYZLeTsuF+04LNhxUJJwWJJwqSLhpX0HXj504OVLDry078BBxY6DqiR+7FLNgct1B8pVJzIFCbmiDb985Ae4/vrr8IOf/gB/MuyK7sARHgYxUiCEHzg4wzvIQNqALoYzmxmMGEgxG9JGMKyLCWawfyWCQX0cfSvk5xtYCaNnKYj2WTdaLm5jQBvBqJ4Yf7EgxQiKIfb/GliN0qFMG6XZ1kUXOqecVwHg1cerehAAXPZRxFwbPeL/G9JF0XLRiUd/N0ebjuzkyGsvaOmA3eguMhaQr3xc3BEn69apPbTPsq1eBh46ZnZZJYtb7HEqNzl5Grhn0YfOORe6ZmnzsnPWJVjE7gUfeQDnPFAtekVnk2opSFN2y4pZngUvY3B8Athxc30PS2eJxYVF+abPk4DKQuheBv745BsBwH8W/mCgTzEH168Ahrz/j2//dqtDR759FAQGBBPIJ+uUFwcH/NsqNU8C+1/xc68EjbIn0CeYRJU6iMHVKIZ0MWJU53iVhVt4l3gYgj/P7bPknTtx1oJfDJ/FzbfcisYnfo1TY6PIZpL4X//r/0Y8EoTTaUbAvwODbRmOoAHB1AayZQue+ZMK19/wHjQ0/G80NAANDf8bN9zwbvzXc32I5GjtI1sh03e2aEamaEaubEGhYsH+oQO1AwmVfRvyFStK+06k8hYkM+uo7DuRr9qRLErYixiQq9gRy0nYCxrw7ve+C5//8WMiADWki2JYG8aIlm7eHPxydrhHHRCTTlRI7pLZv3HWBcgKoXkNDN+/5kCQ/F57bA5OTmz3LPpFwIBbBvghRU54+4+wf3xarJXtZzezupnWCYdYJWm8oNgrVhRAt09T+r5j1o2eBR/G1mI4s5nFqCFJtVBr5NV61z2fwC+Ot6PqWUM5Zcd+xYFaRcLlS7uoFCUcVpx4eX8Hl+tOXKo5cFiVcHnfjssHdpKID+04qEu4vL+Lw/1tFOsOHBzsIFdxIJM3IZndQKFIz1U8Y4Q7sobtkBbf/t5X8IkvfxUqDRWnK2U4kseiQqYbWktgdCODb/y6DR/+7OeRLVlQr22hVN5CIm3GY42/xIc+9hG0zXhfCQCn6IDaNeuCSh2CaimCjnk/XnREMbpgYUW/HkUS24++Jb+Q7vtZt+fgagQDK0EMacM0dcZaCPo0V2w6M+aP11/xRQ8eBGllU4ztU3vomHGzPfQddEzLwblOVno/rCfVpnvRh9bJbQYC6ee0TO6g9eKO+GzmLG/j+BYDgDYZAJ614viLFpJ7+YQcC4o0iQohqg1qneJNDtvonPNgSB/HqY0MxjayGDGkMbSWwqmNDE5tZNCvjaFpchs/6HgWd330U2hoaMDrXncjvv3NLyC2q8ZLBQddOScuZx24nLbjIGHDQVLCQcaG/YwNBxkCffWslYBhQcLlnB2Xcg4c5iUcFu3YL0k4LEq4VJJwqSzhpQM7LtftOKjYUS/R6/HlAyf+cejEywdOHNYk5PIWpLImZPNmSPYpfOlLn8Rb3nIb/uvCCxhcU6ySsKTyEJs17WNNDhQGieFpUxanN9IYXktgYDVC/xbrVEPWqwnje02ncO111+PnXcMY0CUxakhgZC2ObnUIjeMOdM26MKKPYnBVrg4jEEhVUAPaCAWMViMY0obRv8zS1tNXl0CuPl7dgzGAxJYN6+MYMSQwZkzh9EYCKrUPv/2jBsee1x/ZPhWnwXGFj4jNGPFy3DaR8nUzo7OLATZ525dqF1xCDpYBILEbnXMedM5QmXS7wqMjlkDmeQ+aR4y78z7BXpbAI6bRjX5NkKh4HtbgnWrspqtSsHwCSCl/TC2DQA7+ehVVL0rg94r076os5Sm7nnqXw+hhAFC1xMFfCD38Wgqh658xgoq0skrBLMh/Zj4VJwNZpXdMDrwQA8jl3q4FCr30sw+gU8akXI3CfX/sJkXMk2yi54CoZ9GPxnM23P6uD6Gh4TW49prHccMN78GHP/wp5DJJHFZL8Hi24dq1QWddhCO4hu2IHqHcJn775M9wzTWPM/BH1zXXPI5fP/EQYnkz8jU7CjU7ciUzknkzsmVKidYO7Ng/tKN+KKF6IKG8LyFfpf65bMlKnsCKDdmSDbHsBpJ5C4IJA3760IP4wEc/jt5lPzuhE2NMSV0P6+sjNrljjvyA1LfnIg/W5C5b/NhmFUh0s2xhElzLxS2xBywz5cTOdM5Q8r173k1s97yXJbwpAa9S+2QAyF6vPYuyzN8x5xYVSjz0QfUfdrSyGbomheGfWD/62jK5i54FH3rV9Nro14Qxoo9hYCWM7kUvuhd96GP7riPGNH7W+xzecPvb4Ns2oxbbwksVD146dKFedWC/YsdL+7u4XN/CpRoxLC8dbOMfh9t46cCJf1x24uVLdrx06MTLlzy4dOBBoWxDsWRBLr+JXMGEbMGETH4TqcImciUzYrkN+FMb+PcffRMPfv8bGDPEWACJapT6udyqlYvVh/QJdE1v4TWveQ3+Ov4HVGsOVEt2FIs2+GNG/OuH7sLDLZ3MM/nKw0v3PHW7/bclht/pHOhc8EO16EH79A6656j8np4fxrCzmTfxGcDCO4MsiDaoi2JIG5EBIOvw7F7wCvDHD2E8yc0/A5VXO+8CZK8bKoR2iV/PmaKueQ95TGfZa4KFQVrZshJPFLdM0iQnB4Ak+VqFDHziHPUGtk7S37tnwYu26T00XnCKYF/7jIutzuyIFHOP2o8BDsTXKTX94BO9eP2/vBENDQ2488470HjsYfid86gkzNjPSNhP27CfsuIgZcN+0or9uAUHcSv2+ZWwYj9hwUHKhnrKgoO0FYc5iUBfwY5DxgrSV/r2Qc6Gw6KEyzU7Xt534HLFgYOSHYdlO16qOfGPgy3843ALLx/Q67VSsCGXt6BUsqFUkvCpT38Y733fu/Bna5DN0JFncWSdmM0hZpHqX4lgdC1GSpkxhVPrVCQ9qI1h1MgSxGsJDGqj+NXpi8K794UHvoVhrQ+DuhjOrHmhUvvQNO5Q1LBFme0gzORjKoXnTRd9y1QDploKonVy6yoAvPp4VQ8GAH3CuDqkixKdvRZH87iEx/5rAU0X7OxG4lAYyB2iTPZoqSz1A/Jqg5apPQHweMqXQJ5LdALyguaOWZp/EyBwzi0DRPa1g/lnuhjz180kzY45r+j261kM0FalOoBhLa2aUCjEh4EVmmvi5vorE75H0r5K+VexrqFaUvr/5OUPUf6sAH//DADKBdAyAOwRJdO8C/BKSThwBAB2zHrw/ean8dl//w3+veVp0TYvJu2uBIaC+fNByW52Lyrn78iHNqyLY9SQwJA2jJ5FD5sFdMsf/AyI85sUf8665oiVfeDJAVzz2negoeFGweRdf/278Yff/x7JZAK7u3YEA7sw2JawvrsMf3od4dwmnvmTCjf8EwbwD8/1I1kwo1BzIlcxI1smT2C2bEGlLmH/0MEAoB21AwmluoRMxYZsWUKqyAIhVQfCqU34EwYk81ak8pu488634ZHRc9SzpYtDtSxvJvPaHO517BDhGA9ap/fQclGe72qa2BYMSROr3Wi64KAVBrbN2sZYv/aZPVYL42LAj4CFkgXknqoR5h1SMdDBp/tkvywdrDoYC9gy4UTLuFKCliVfntDnsmL7jBsqNYV7hhizRtVB7MCwFMKANsbkryTe8YEP4/4f/Qr/8yCJ/3kYQ73kRL1sx+WDPRxUt1Av2cnEX3PisEbS2+V9O14+dOAfl5x46dCJS/tOHNScKJasKJYtKJYsKBZMKBTNKJSprzFbtiBeMCFVNMO0NYOGhgb0T+oxyA5v/L01oo+hT8MqNFbCGNIn8J6PfBof/+xnEM2akMlbUCnZUS7asLp+Htdeey36ptbRMet+hf+vi0vxi6QCdMxsU7iMSfUE1D0UNFM8Tz0sECKCPIwR5F2OPA3cuxQQFU7diz626U2fU90LPqGEdDIlpGPOK5STNv6eE+DPLQ7CXfM+8ZnSveBD69QOvRZYKpyvz7TyrfXJHbROudB8cUdIwMfPkQzMQx8nz0toukCLHacMxFaplkLonN5F56wbgythOjjPkf2hjU16trJgYMekHZ/69k/x3ns/gdvf8hZM/f0MsuF1VNJW7GclHPx/7L1XlF1XmS5asiTLJthgusFgaLuBpoFujME2YGiwaRqcMzhHyUGWKsdduUqybINtnI1tqVS5duW8c6y8c845VZBszjn3jHHvuOO8fPfhn3OuVXLfJ79qj7GHVMqq2nutb34xZsFm1IxiyIh8QIeCX4eCV0tPnxZFvw5Fvx6bfj02/QYUfFpkfWoUfBqUgjpsJ83YTltwJmPBdsaMUtqIYtqEzaQJpTQxhVsZE3byZpwpWnA2b8VWmtjBrZQJOzkzzhQsOLtpxU6BwGEhY0Y6ZUAhb0YgMI9vfutyNHY3onU++mkmcD4s1JzOOQKAXQvMFzhNNiryo4bRMRdEB/MM3lP7FwECv/Xtf0bH0Dw6FqI4vkCLVbUDK1RrNkd+0pYpH02zzvrRNUfgsGnULnzd9UoHqnvPS8DnH5/tcUlZWRmax+1onfKwUwh5ShqVNjzz5gyOvKeSjXlLre586PuFk6xT7JRVZniXBs1rWdqOmDub8PxV9a+I+bcaBgrrBtcJRIwQ09Q85pABP9uudnXpx9dRz/aB+R4wMSQ2WlsYlcvJLhaOcAgzd+uUF62T57JzXrHKIV/94EwdLYPIgB9j/W6492lZkeenASAZx93U7SZPAE/I2D8GND8tA7tFIrh+2IZvfO967Nt/FfbseQb79l+Fb/zL9VTiy6tr5OBVlhZuOAcA8huZuKGN2mmLctqDtgkX6hnQFoyEkOppEqmObTI3jtgowTruwk9ueQhlZbegrOxfdzF5Tz99EMvLKwj4XLDbVzCg6oXWPgl3TI1QSgtbaB5XX3MN9u69RHgA//2aa7Dqn4eH1cAkc3rkSiak8gak8kZkiyYUNs3IFgn4ZYsUAImztYpgwgBPVIdV7yKMG+PY8C9CuzKB73zvO7js8itQN7RKFULTfgLlbA6Ob0Vz0CcVQVNPG3n+GPg7tSR2djnYqjy9JPoAK3qsrBJmVchntazzrZGxSfzz3zLuRvOYAy3jLijG7Oxb7kkkD2cdm6Wr5vNyDBwQ47PMAiF00//prY+gvMfKggFS3Uuj0oGuuYBI0LZNeSmcMuJgUrcbLZPkXTqmjqH8AzUuvPhz6J2Ywf8sBFBKrWGzsI58Zhm5pAnFtBnFtBmbWQu281ZsF63YKVlxZtOKM1sWBgDN2C5ZkUrrkM7okcsZUMiZkM9RACSW0SGU1BIYzOrhT2hRVlaGX996O1qn/bt9tWJK0cNkMj8u+sKleF/Zg2jajFxuCYWcFYmEDuU1T+EnP/sJGpQ21CsdrHmAASq+RDQiVSjVM6WidpAk/lpW3fKzOx9Dw/C6AISNSrtkIWHsO+8EbGW7rq2TNPHH2b/GEUm5oFCWXVZpJXVv1ojOSJvs4GwTHly+X6wYo0WQusE1wcbxAweVRa8yiwJPeq+hgjGAFPIws3QvMX9U7G/E0Y8MqOlfYfYDJ6X/B9fQMeNjQJj+HXzjvaZ/DcfmAvjSV7+BsrIyHDz4KKL2KWzGTSgkjMgnjCimzcgnjDjyzEPIh3TIB7UohnQoBQn4lfw6FDwa5L0aFHw6bHrp45xPh2JsDcWAHpsJE0pJI4pJI4opE4pJA0oZEzYzBPy2MkZspY3YzpgJ7OUsOJO1YDtpRiluRCluwE7OirMlK3aKFuzkrdjMWFDImlAqWlAsWdDQeBBlZWW46rvfwYlJE9qZrN0xHyZAN+1H66SHbFLzIbTPBdE+x9Loc1QXc4wVSXewsFLbjB9PvTKIL3/9n1BWVoZ/u+HXlPydj6BrPoj64Q1U9ligYApAy5gDzaM2NAxxS17hAAAgAElEQVSvoXGYvJ317N7GlZ7zEvD5x2d9EAAcIwm4az7E1jT8OPw3DZ460Y/yk1T+ycHf4Q/NBPpkybAXPqKLSVWPhXVQsQ40vn3KWSOZzFs7KNXC8KAI9/ZxYMhZwCYlL1LlMrKN7alSw77km2FAcphXw3BWkE72lCgmBrBxZEP4+5r5RXuKErvNzNvHwR5n7Xjil9e9nAsaK04aZIsOUulz25SHgSOnmARqGnXsLoKWyb5NQm7mP07gT8F+7q6ql7Fv/26mbO/+K3FX1UuMkfBAwf8M0UXGZvVYhU4TY/545yBnLxSjVKPTMu5A/dAqAc4RqROQfw152WkdqzThaygNSgd+d1CBCy64jIFAzuRdhRMnTmBleRlupw0q1SxmzIPwxTUIJrV0809pYQvOo6X7KA4+/wBefqMJa/5Z+JO0LxtM6ZHK65HM65HMG5DfNFPxc96ERJ4lfwtGRNI6FDfNSBes8EbVWHGO4/n6clxz4+9x2de+gc998RJc8d0fMCDvJCAxRb1tDWNuKWE9Knkkpd6/VVQwP1V5D2f+lqSNXVb9Qj68Jea/WhY7wCT/yZhsxppy/1/rpAct4y60TLpZVZG0SUu1PLv9f1wepNqXJVT3SqGT8lMWHPzrlEjmV7HC5/phOzqmfehm26fts35U9lqhGHWgdnCNWMAx6gLsmAvhRU0cx9QxXPmjn6Px2Mv4P/9XAZupDeRTy0hFdCgkTdhMm7GZNmMra8FWnj0LFgKBDADulKwo5o1Ip3XIZQzI5czIZs2Ip40IRFXI5PUIJTWIpLSIpbUIJrW4+/7f4+7HH2eeKz8z3jM5mC3sEOjy4evf/Tc82dCGYFyDZFyPQtaEaFyPn99wDR45Wk47x4w9px5SWk6hsnPunyVGjoM/vlZUN7iGw2/PoX5wjbF1a2gc2UDzGBWvn8sItk64JQlY9p5vYGCPewI58yvCPTydz7eJuU+Q1S3RNY9ZEkbsaGUJfV79wxm/apYM5wGlCrb5XNW3wpZhZEoOu45X966gnm28Hz1pRsUps1gcqTxNO8NNo060TXrRPR8k1pEd9junvXjVkMTXrvpXfPv7P8BORI+tmBmbMRMKIT3SbjWSbhUyYR0c1hGU4kbkgzrkPWoUfDqUfHqUPBoUfBpkvWrk/Rps+nTY9OlQ8GtRCBmwGTViK2FGKWFCKWFCMUU1MSUGLItJA0pxPYoRHTZjBmwljNhJmnEma8VmzIhCUMfkZAOTiS04U7JiK29FNmNCPmdBKmNANLKId9+hZZK2vxxDy1wMnQz8dcyH0TFH6kgXY/q6mDTcMR8iRn0+hGNqLhlLv6912o+GUTsebH0Xdf1LLLwUEKwifT6XUXnKhJpeK+oHV8WBQTRfjHCrjwf1g6vnAeD5x2d6iCWQjtkAuhfDOKGOQDHuxENdPTjyzjyOssSYNOptEgPg0o/TKfLoSUl+qhReQJ60k4ICQj4UDCFbBGGeptrBdcE4VfezDUwmQXRMedA87pTJkrzE1SbYGn6jpMm33WlYkveYBMMlN7bAwYucWyY8wrtDA91e4YsTW75TPrSyfkFiET3ihrRrwmdGvulKpuA29nuaBdPIWcXdbOOnmEcGAH9xz1PYs2e3V27Pnmdwwz1Pi3qa5nEpVaoQqyZU60KeMuZnHHejZcIFxagdzWM05l4/vIHGYboB8tADB3w1gzbUDdmFRNowQvU6inE3C5E4cOjNOXzhS1/D3r1fxAUXPIMDF16Jn/7kBljMVnhcLrgcNnw49C509mnYQosIpTTwJTRwx9TwJzUIJDUIp7UIpbTwJ+nn7BEV7BEVohkdYlkd8ptm5EpmhNN6RDJGeGJaJLJGpApmBFM6uKIafKh8E3c/+gD+4RtX4KtXfhc3PlKOR7tP4dBro6gfWkHzhJOFdJgfc9wjS1u7pCm4QVqMqJRPv51mN1JufxCF6BSKqhZbvFZR0UIVRxuiO5EzQQo2e0j9ch6xJ0srExL7x4M6gkkfWEd1H4U/agbWKHDCJWAWzKruI+8hzYmRD6x2cB0tk150L0ZwXBVF24wPtUMb4j1aP+wQc3BtMwF0LZCp/zvX/hqVzR34v7NOfJx2IeU3oBA1oRg3oZQyYzNpwVbagu2sFds5K7ZyBAR3ihbGBFpRyBuQy1E1SyZnRaFgRTxpgDeiouJoVukTSmkRzejw/e9/GzfdcZdg+dqmfWhh5voW1rXXxHr5/nCoCd+75jqk82aUCkso5CzYsE3hwgsvRPnbI6gbtlEP29C6KLOXCrBtokaK1xqJPV4+5Tco+e8alHbp+yN2cZhSsE3yFqYutLDKGA4Am9mBk3fCyacbpWUiLvfz1wlP8vPkMD9I2JmEzwAg6wCkxDnvE1xlu+38SX5VyRPKaotOmlF5yoTaQbpeS8X+FlEjVNO/irZJN/7U+Cp+ef8h3Fn5IuoG1tA9G8Bf9Ak0K9dw4UWfg3LkQ2xHTdgMGVH06ZBxqZFcW0BqfQFprxppjxqFiA45jxo5lwoFjwZFlwZFlwpZ9yIybhVyLhW2Q0YUAzqUQjqUIgaUogZsxozYTJiQTxiRjemRjemRi+mQi2pRjOtRDOtQDGhR9GtRCuqxEzHjTNaMnaQFmxET8l4tAcGEETvMs7qVt6KYsyCfo5WQYsGKSNKA555/EN/97pV4a2oOXYsRBuLIJ9ugtKNlyruLAWyfI+tM92KEamRUMXQsRFhqncIjCmZj2FUwvRBG51wQTaM2oQbIWX95Gb20YOU6DwDPPz7zg7aAletS+EMTQ8OIDU/9eRSVp8zitHhEAEEOAOXMIIFAfgOs7LEI2Uk6la4I+YsXsPLwQM3gOpVCi2k4GVM4sIr6Ibpx0jTaBuv1c4ngB/fNcADIwyB1bM+zSeZ34/Kn8Pnx4AQfd2flrsTKOcRMF13UXWiddO0Ge5NuSmixKajWSfb92QA6ZgLsZi6FQ9qYsZdvFO8qkmZ/v5x1FH5BWb/gPbWvYN+FuxnAffuvwr01L7M9TqdgGRRMQmye8KB1UpKg26a8aFTa0cxvWONkeic/miQ98sQwr4gRM3oMvLQyz1Mjq/VpHHXh8Hsa/MtPfom7b78VR549iDdefw1mkxk+twt2mx3jE8NQWYeg3ZiEJ66Cg4E7R0SFjfAi7BEV3HEVAgwMBpIaeOJabLBf409okS5ZEcsaEU6b4InrsBpUwR5WQWufxpR1BA8cfBD7DxzA1b+9C/fWvSpm+MSgOi/snfZTxx4L3Oxi/0ZpBq56gKVG+1ZR0UvSbyXrv5RAH/n+qk5bUd1LW7xkg1gTT7pxS+l2wf7IyoVbJz3kUZ2kQmjuReX1IXVsr1bIgqxkulpWwVTN6maqepdEKr+CrUhUMcamUUng4Zgqio5ZP+pH7CzQQwepBqVLgMDO+TCOqWO4/vaHcfMDT+D/SW1gM7yKtMeIXMSEYtyCUsSErYQZ26klnMms4ExuGdt5AoHbRTN2Slbk8yakcyTdx1IGhKNaRGNqpJMmBCI6hBNqpPJ6xDJaWJd78fhT9+HARQfwVPfbYtKxbYrAulSe7kED8+g+0PYuLv3HyxFMGFDKG5BOaXD8xUp865+volQtUwtEfQ+TL6sYYyZCTcx/J/0aLq2uiM957aDU59jAAGETC4lwQN/Crg3NbNe5RfQHco8xt104hSRMX2di6pvE950yBogOADXsOtfNlpsaRzZE7Ust6w8UYZe+lV3/30pRWWRlCyBmVPZY0ThE7HBl7wrKTy2J10xFjxU1A6s4+pEJ3/iX67Hvwquw5wKynlzxvZ/h+EIQL2riePqlXlx48efgteuwGdKTjOszoODRIrk2h8T6PFJ2BvC8BP6yjkXk3Srk3SrkPCpkfWrkg1rk/RoG5nQoRQ0UGIlRaKQYM6IQ0yMf1SEf1aEQ0hLrFzWg5Neg5NWg5NNgM6jH2cwyzqSWKGWctKIYoD9rM2HCVsaCUpZ6B3NZC2JpI1KFZaRyJiSzZhQKZjz19H348pcvxZvKQXQsRNE66WLsK0ngdNCnpZi2WbJPdS/yQmnyDfLaotaZAJrGPWie5AAwSjv2C2F0zoehGJOYZFpaYglxcV/jW/ZsKWroPAA8//hsj0vKysrQOLyKrvkguhfDOKaKoH7EhhfeV6O2j9cGsAvFSWqSF96Rk5ZdZaEicSiSwnSKrB1gI+cMBEoX1TWpjoHtkNYzL2DdMAdyzPvH1kMaRgicNLFdVD68LX/K++waR6U+N/nSB/fJcYDIK14UPCE87mIBD7eQNylBTACKGD03zc9Nkd/n4bZ3CQROedm2smQE58ZwafLHLwqhW9i3nFlsFqlCnijmsjT9OQrlBq743vXYt/9K5gG8Eld873o0j9koNTrmYKZ0AhJtky40j9rQMu6AYsyBljEHGpW0nNLMgg986aB53IXGUQd9PLJOIJABQM6gKhgr2DJBN7hGVvDNmdTn31Xhy1+7Au/+9S9I+pzYWF2DyWSF0+GE1WKGWjMO7eoYrO4peOMq2CKLWA8tYj20AFdMhbdPHoMtsghfQoNQSotQSg9PXAtfQgNvXI1ASoeN0CLcUQ28cT3M3gXYwmrYwypshLT4z7tvxWVf/xaefWMKnfMRdCxEqF9rxk/VJsyb0zYbYOyfRwC+eqU0BVivdKB2kAAgn4GjHWwCfxT4sDLJ14rq3iXUMPBVN7Amk3xtIulZz1go+Z4zBwzNE25ihydcVOMzKutsZKwQ94Bxn6xIZfNd2T7OAlH45O7a16isWrDxBEobRuzomg3ghDaG5gk36oZtItHN104aRmkRpG0mgBc1Mdxf92dc+YMf4//dDqAUXEYpvoZ0yIDNxDI241Zsxc04k1jG2ew6zmRXsJNfwnZ+CVsFC7aKFuRyJiQyRmQLZqSzRuTyJhQKS8jkVmDzLMLmn8GafRT/dfOvsG//flx946148tVxtM34aYJrxk/SKevFrFdKX6+GURdqB9bw5cv/CVd8+3s4UvsC/u3qH+CCC/birvIOtsSysWvDuZKVeHOmTP6s7GXyae8KKhirdm/9a+watsIWV9bE9apB5u3jQZ7WCTetGHFGkLG6lOx2S95BmRTM/cri8DXKZitHCAzSAg95cBuUDhxbJA9Z86hdeEGr+9dQ078u1WsNrO0CtfxAwA8wVb1WtE240DzhQu2QDRWnJcawii2PHD1pws3Pt2Pv/k8fPB9oeh3HNXG0zwaw54K9eLKuE5shYuBKXgOKXh0KHh2y9kUkl2fQ+2YX0o4FZNwqpJ2LyLgXkHOpkA9oCPyFtShEdCiEGaiLs2fCgFJEj1LUgGJML35NIaBF3qdBwa1CwbmAom0emx4NdiImnI1Y8UliDTtpqpnZirEUccKCYsqMZFyPErMtRJMGrAdUCMR18MX1SGZNiCYNqKk7iMu+8iV8pNbg2IIPtUNsvo9ZYprGXOiYDaJ12ocX1VG0zfjRrYriRU1cTMu1z4XQOu0XbHXnXAjH2eGriwVG2ljAhvu264ZtImBXw4NoXNUadaFx+DwAPP/4bA8GANfQuRBG92IYrVMeVPUtofxDPQ2T964wCUAmFZy24uhJGgcXw+CyhvgKmSRW2StVBUgnbpkkwYtouTzMZ7MG1yXDM5tn4iXGnTNetExIN07y7nDK3CVbt3DsnnlTSr6uXVNv3AvIgJ0U/mDzbxNSQa/cCyhNvdGF/d7aV4S8S6d/L9qYD4iDvw45ANzVDehlLCI9aVrKK0bmWyfdaJtyo53tjjaNbODu6hP4xd1P4u7ql6BQ2gTgax5zMEnXycAEZyeY/2iMNk5J2nZBMWoT0lXjyAaBQ6WNJRVlvkUmY/F1A77OIgANCyk88+Ys9uzZg+XpPqQCLjidLhiNZqyvrWPJaoXJOAvd2jg2/HNwx1Rwx1RwRBZhjyzCHVPhxOuN8MbVCKe1TA7WwxPTwBZRwxNTQ+2agztO0q8zpofWOYsV/zw2Imq8+LoCn7/kUlSc1KODeXD453qXh2eOpJvmSa9I4YrVFM54jtD0W82gjX1LAZAqdjASfZe8/LxvmZ4yI75cNqxnErA8lU3ePzfz/7kZS+uSfb53AwTOFDUwCZ53yEnLOhJDVd23glsPdwpQWN2/inoWHOiaC+KEOoKu+QAl8oXcuS4Ab92wA01jtJzzoiaOun4LLti7DzG3BX+P25D2mJBw61AILaEYtWArasZ2zIyP02s4m13DTm4ZO4VlbBeWsF1awk5pGfmcFemMCZmsBcXiMvJ5CwqFZfhCaqy5pvHAQ7fhi5d8EUdPmtjiB5Xv0uJCgNK54utjl3a82bNpZAO3vdCBK390Pf7wdD2qeq1onfKgYcTOZN3dMj4HQoIl5V2msl/DWdRbDncSy9a3jNp+6txrGKYC6fohSnQrRlmAh1kqWsYdULDDGH9vt056mfdYtu3MWGEu/ynG3JIUzBZ8KAC0IQJvLRMeHFsMo3PWB8WYg9W/sHQus8Wc+9rgQJBbAqr7VtEy5kDblIdeMwPr7LoshTu4D/C62x79b60nv7r/EI6pY3i060ORdPW7LCgE9Ci4NSh6tSgFDcg7NUiuzOLN4w1I2eaRdauQ8aiQ9+uR9xPrl2MgMOfXoBDRoxDVoRjXoxDToxDVIx/SohjRIx+hjwth+n15PyWJi45FbPs0+DhqxZmIFR8nV/Fx2IrtkBlbQSNKcdYxyKpnijEjsgkDcikjUikTgjEt0ikjUmkjigUrgkkj4hkzrvvZ1ahqLkfnnA9VfUuilqxmkIA493vTjFxYlEjz6hh+zW+Z8kEx4WVhywg6Zv1CRWmZ9NA60Dh/vzPGn9ma6phHlN+/zodAzj8+6+OSsrIyNAyvonuRRf7HnHj+PTVeeE+N2oFVxnTw0+ISqvuWZMZhyQsoeaAIAPLSWX7KruzlXiQpJVxxell8W8HkKS63kLwipd5oooli8C0TNM1ETJdLVrFCMmqD0oWGESfqZP12uxhAOQDkCx9iicElAKBC9i158NxU3Mzn3xgrx9OIfLOVV8y0iGoIdgOY9goA2DEbQPu0h9LXM4whnPWjbdKNdjY91cKYAwJ2VEpMyxzSgokApqxxnpviSXJyoWnMgcYxJ5OYJTDbyhhJWilwszocjyQ3j3MWioBK3dC66Ezk3XSNSgczs7sEmKkbsuGxY9R7lXWaEHA7EAxFMLeggtvlgmZxDmrNFPQrAwgmVPDEVVQDE1fBn1QjkFQjktbCm1AjmCL5N5jSwpvQwB3TYCO4gDn7DMzuGXhjajgiWuicM9C55jG7No77H70TN9xyh2D+2mYCrNOOAYm5MDoWCAS2zfjptK10iJQmJZ1tqBnglRw2xoIS+1IlWDYKXFT3LaO614qa3iXUcj8eA4A8aMC9rRwoUIpUAs7ce9o87pJZDXha3LO7iFyUCTvEpihnweWTijX9K9K/r3+VrV3QTGL7bBBd80EoRm206NNjlWpRWAkxJWU30DjqpkoKdkO7/Ns/wIuvvY7/80kSWY8FKacBSY8RxegKSmEzNmMW7MSXsJNcwtncCs4UVnC2uIpixkReq7wFqZQOyZQWWea5oqm2Rdh9syivfgKXf+NraJ0JshqNEKvViEAx7hINAATKbcyW4BQHvaYxNzrngjiuilKpPQu6NI05qZaKM2A9yyjvkabwaBNZ6srjh1Re7s2DPXWDq6jpWxJJ4CY2i9nE9qObx5xoHFolSwUD8vzrya0jbdM+dl3wChtKo9LBwiIu2YSjg4WwJABYz/a3m0ZdaJ/249hCCF0LITSMbLCKmzVUMgBYw5oXBAMtSxhXM7sNrxiqYV9zzpBymw5vcag4vYSbD3d8igHcu/9K3F39MrpVUTzc8T7KysqwZ88enE0ZUIzokXWoUPLrUQobkfNpkPWokXEvIrkxh5RjATmfhpi8kBG5oAb5gBY5nxpZrxo5txo5vxZZHwHDnE+DrHsRWfciUu5F5AJq5IMa5Hxq5Pwa5NxqFLwa7ITN+J8lJ/5nyYmdrBVnQlacCVtRcmqwGTWh4NZSx2CUAiGFpAGZuAHJuB7xuB75tBGZtBGJhAGppAH5ghVPHPwj7n/kHnTNuAkAysYLWifcbMXDh85ZnwCAJP9SFUz7XFjsQysmPDQdpwqjZZLtrLO1H2FDYjORFBxynAMAibioPd8DeP7xGR8SAFwIUdfQmBOH3pjF0Q90qBtcY3u/khzAx8Z5DcxR2T7kUSH/SnujfAdVkn+XBSCUwOGKrER6hXWVraFxmNio+hGbJHuMSP1ZzWMOtE440T7tYeWZHpnhWtrA5acmAfwYYOFSXxMLSfBalMZRF6Qk7m4gKEIWLNTBgZ/8zX1uMTT1RnlZ1U5AdISRd9CD1kknlchOS4xf+4xXunEwANgywSbs2KYxB34t5/ydxIwSgFOMuaBgnimStQngtU37d4UMFLyHkD0VbCmFAzsOkpvHpZuUmCZjyWKeYnu86yQuufRLSLtN8DhssFhXMDkxAc3iOFbM09CYlMgWDUjkdEjmdPDEVPAnNKzfT4dIWgt/gqphImktgikKhXhiaiwHFuGIqBBM6RFOG+GOauCMaLDiX8SSfxFH6p/BD669jszXs8w3xoI4rdM0sC7a/CdJnqeZO16vwfxVI9IySh2fJhyhkEAt21yt7l0WrJ8AW6IcV6po4eCvgd2IRQ3IqEuwPQq2BdzK2F+SDHlvJA8AuaQqH8a2cn8YZ3t2gUARXiA2qFHpYNNSXvIN8hQx84xV9REIJDaQ2I16JR0eOudD6FJFccN9B/Hj//gd/p6yIeM1I7ShQSmyhpTfiFzAgM2IFdtRKz7OrGAnu4Lt/CoKGSuSCR2KWTOKWSO2ihaUilYUC1YUistIpE0IRlTwhBfw0KN34A/33AHFdGhXgrJrMYLmCbfMO2wTzGztsAN1Q5Llg4OirvkguuYY4z5NzFvNwLpg+yTwxw+gq7LwxIrkgTtlZl/nJdQNrKJhcA1NShuaRx1QjJKtggrDN9AwvAYF6/zjUjB/r4raGmYd4ZaSZhYIUfDNYKVU6i6fciRLjKzrdNwtqmYUo/ygTFI+NSHYpQlIxho3iLSxAy3jbnRMe8lmMyRZAMRCybANNdz/2ruMox+ZcPl3rsVeZj3Zu/9KfO3bP8XRkxYoxt3omA/ju9f+Bj/9/X1IJMwoRHXIOFUo+nUohHTIeTXI+TXIeFXIetXkBfSoRR1M3qdG1ktBkKxLhbRjAVk3/dqsR4WMYwGpjTlkHAvIuhaRcc4jtT5L8rGHGMC8W42PUxv432f8+B8lGzaDRpxNr+BsehmlgBHFiBGlgJ7k6YgRhbAepaSRQGDCgFRCj0zKgFTahFLOjFCc+gEfeewu/Omh2/FXnYdVi9HnpX7YhrYpLzrnQ+x+QJ7ZbhVN4PH+wPbZwC7Pd/uMH8fZa7pt2g/FmAvtzB/eMumVfOLMKiCuG+LedN4DeP7x2R8iBHJcFSEz/5gLh/46jYqTJlT0LKFhmMkm3ETO/EWC4ZMxfkdPWVkNjNSFJqXSVsQNspoZkWtYn5oEACUGsLZ/BQ28B2uQ7wqzChLGfNSzhFyj0kEX5AnJx8crNBpHJXZA2sD99FOaUJOm4DgwlDZa2fdlIFBeB9M4siGYQflKiDwZLC+IbZXLvGz3tW3SzQbBPYI9bJX9Pbv8iBPcoygDpkyebZ6Q9o5bmUeRB1daGcPH/39N/P8kK8CWe5A40OM/phhzsj5FDkakX1PZY8GNDzyPG355A7JRHywmIyYnpzCh7IdrZQ6l8DqW1+fgjakRTamRyungT6gRTWuQyuuRyOngicwhkFQjmKIkcGHLiFBKC29cjUTOgETOgGSeiqCjWQMiGQOcERVW/Rrcdf9/4T/uuI9ddMmX0zK1OzRAnXJe0VvIGZX6YRsa2WuKS8D1I/R/a2VSf+u4E91zAeHXqmGbq1THQq/1GsZw8woRyQsodVJyACh/jTWztHnLlBfNEy6qEZnkYSE3O6Q4RFCHg4O6YQfz7W2IgxJnzqt7l0QtUsuEF+0zPjQMr4uaJlEXwtYiqnpljOGQHfVKJ1omKO34oiYOxfAqLvrCpXi6ohZZjxH+NQ1yQQtKoSVkfUYUIhZsRq3YjFmwlV5CKbuMZFSLZFyLzRxLXRat2CwsoVBYRjpjhjcwi3hSA19oAVf/+F/xZGMb2uci6FwI09bvHDGADUq7kKnlIJDm3QgMNo+7cWw+iE520GqZop5AzibWD0sycJXM58aBX3Wf5JOr6SOQX9W7jPrBddSeptmu5lE7FEobmkY20DrBbBMj9HEDC1LwipddDO8En/pyMwZf8gM2Kp1olvm/pAOsQ1oMYQdZnhxunWAsv9KO1gm3qHaSSvFZtyQ78FEozSeCXcfnKbTQNOYU/tIaFjBSjDpQz0J41X00Q1d5eglHPzLj5sNd+Omtj+L3z7Xj8AdGWgnpW0XHbACPdv4NX/zK17C4qkYpY0QuyICfT4Och5i9mGMW+SAreI7okQtoUIzqUAzriPnzqZFzLyJtm0PaMY+MfQFp1yIyzkWkbfNI2+aQcy4g61pAYmUayZUZ5H1alIJGbAZN+HvOh//9cQKfZNawE7HiTGoZH6eWUfSosRkyoBjQU6o4ZkQxYaRewZQRhRSVSheyRuTSRhTzJsRTRiSzFswufoQLL9yPt+e1rGPWhobhVdQyS0fHXFCoMN0LESH/ygGgYtxDB+8pHzpmAnhJE2XXflpz6WZTcgQog+iYY6BxzAkFI0IamX+9ecKDmv7l8wDw/OMzPUQNzHEVO2EPrePgaxOo6LHiCPP7VZ4miVfyy7Dk42kCeXxflDyAUuqQ+6MqhWneyuax+B7qhqhjqOxbEXUFVQwk1rLm+3r5VNzghjjpci8MP+3uKsoclU683Dv4KS+gjAWUr2LIC5QFSGKF0LtSuzIg2Drlw8/ueFT4CAXYmmLF0KwXUF4RI3Yep+W9gVKP4CbzHfQAACAASURBVG5Z2cdYQC7/7g6oKGRVMpypbJ30yhhK9n0BJpyC9VOMe6SuMs4GypZE5JNzdLPijB9J6hwo1o84UP6hARd//ovo+eANuG2r0Gp0mFAOYsUwi/WleTg25uD0zMLmm0I6Z0Uia0A0o0Uiq0O2qEe+ZMATT9+LaEaHaFqLeE4nGMBM0Yh0wYRMwYxU3oBwxoBl3xyWffNYC6nQ+ZcmXHTxxXjypT7aYZ0m9o+nfHl4QEjWXP4f98hmBJ0iNa3gDMuUD51zQfLrTHnQNR9E6zQldpvGXAS0+vgc24oUFJCXmMu62/jesvB7MT+mlDh3sc5IqR6maZzZFUalHWcecKoXe858uYT5xIbtuPbWh1lfnAedc0G0TLqpm7BPMvhXsgNaTb9Ui1I9sI76EQcUY8y0Pi/1AT775iT2X3Qx/vrRaeQCJuSCS9iMrmI7sY4zsVVsJ1axlVhCMWlFLmlEMqpFMWNGMWPGZt6CzeISSoVlZLM2uH16xJI6uIOLqKx7GgcuughVJ7Uk/bIS3e7FKNrngqKiRB4cq2ZAUM5wHmfeKpqPo5Rm22wQrdN+1A1v7OrLq+mXtRIMUNislqVpaepsRXT/XX/bw2gYWkXTyIYAfVzWr2M9pvxQyqf9GkU6mCe6WQG97HDGV0SaJ9y73mtyJYKuW1J7AWf4O2Z86GQF/nQtkA6dTWNutE350D0fQud8iJLU034hPXcv0I+1TFKVTj1bmaGpx1Walhvgqg873DPFRnQIsoN/xell5i914J9++FP87oEnsLVpRTFpRC5EMm4+qEU+psOzT/2RUrwRHfJRLQoxAwoRLYph+jgfICYw7VxAxrmAjGsRGY8KGfcCgcKNOWTsc0ivTiNuGkNqeRpFnw7bARM2fXp8nHbif23H8HFiDWcTa/h71obtkBGbfh1KIQM+zq1gM2pCMW5AKUXgr5QyoZg2YjNnQilnwlbJhO1tC4qbS0hmLLC65lBWVoa3phdQM8DWqQaW2YiBDZ3stcaT9cfVMXSpopIUPBug68lMQFzfuxfDYuKwi3UI8onDzvkQ2qfp4Nqg5GoEY5aZL/18Dcz5x2d90BTchANd8wHUDW/guffUeO6tWRw9RZUvlaxT7MhHZtoBPmVh8olVFMxyuZczCLwDkNi9JWGW52bqql5JJpPSeKuo4idzblrvW6Gy3BFWfjq0gbrhDeEDkvcFivoXDvqUu+WOxl0soNTzdm7pbwMPlMjAX6McAJ7bzydb82gY2WA1D8y7N8lDHn4B8AQInPGJGpJWWWm0BBx9aJnwniPvegQDyX2Gu5jJXU+JEeTSBM3PsRUSNj/HF00UY45dcrCYnpMBJbopSdIoB32cCawb2kBtrxUX7N2H+dlR6PV6zE5PY1J5GqFVNexL0wgEVUin9EikzcgV1xFJapHKa5Er6pEtGpDK6+GLziOc1iCW1SGR0yOa1SFVMCJdMNL+b0aHQFwNW2gejvACbKEFKOfexYUHDuD+xjfQMR9BG5s34xdezgDyRRXFuOTjbJ7wEDM3SjLMsYUwpeLng3hJExPyjVhVGbWjZZLk/KYxYlwofLEurA4097YhknzcwyrCTEpJWueHBsW4G21cImTAXTCXslLvXclgWTVIA68/Yu+V+uEN1A+son1aqiBqn/IIoCdPw0reNwKAtYM2AUCaJ7zomA3imCpKBbeLUdz4SDm+9f0fIxVeQSlixXbYirzfhKzXgELQjO2EFcW4GYWEGZtpC4ppC0pZYv5yaTNSiSXYHGqsrs1j1TGJBx+9A5d99at44qUBdMwFWVqeWLsu1pdWM7AqrhNiy5fJnVRmTmGrTvb7j6ki6OLLDdM+BnBoYaOGNRPU9K+JHd6GYTsaRmy7tm7rB1fRMGKjacmhVTQOr1Et1SCtM9T0r4jfX8euQbzGhcv08qk/xRh5epvHXSwQwt7L7HXYNOrYveLDwd8or6/yUD3PGP25XXMBWqSYC5JMz1Yo2lhZ9rFFYlI75kNUZsy8ry2TUjCtZdKLpjE3pd65DYLPz7HEc0WPRTCm3K5z9BTvwFxi6s0KGkbseOqlXuy78AD8cSt2SmYUU0Zk4ybk48SyJcNaZBN6FBK06FGI6lCI61CI6ZCPaVGIU71LLqRHNqBBPqZFPqJBLsT8fh41Ms5F5JyLyK3OIrcyi/zaLEouLTa9BuyEl3AmuoqdkAVbITN2Eis4G7dim+0LbycsKIUNKER1KKWMyCYN2MyasJkzo5gzIZfR4YMP2lFe/ijeea8TydwSXnqnC1+9/B/w0vwGqvusor+2ZmANLWMuvKiO0Ab0jB8vamJsD3g3AGye8FCR+SyVmZ/QxMj2M+km//eUB8dUUXTOB8m/Oh9C84Rbeq+ze5iCdbfWnw+BnH98xsclZWVluLf6BJrHnag8vYSHOk/ix7+7D8//TYcjJ82o6LHi1hc6cUfVK+T3O2nB069P4ae3PiLYv/KeJdz0eB3+2PIeA4hLePylQfzyj8+zzVQCgb/602E89tKg6AN8oO19/OeT9aiQeQGvve1RHHpzVrCA99W/jjvKjzMf0zoqeyz4yc0Popy11FcPrOO2o8dwb/1fRXL4hffU+NldT0oS5pgLNz/TjMeOnxaG/kOvj+PGh4/uSn/+5uFyHHp9ktWZuPH4i324+ZkWscjROObBL+55Gkc/0guA9UDre7i75hUhBzeO2HD97Y+iccRG5dFTXtxb+2c83PG+eOPX9Jrxy/sOSsXS037c+nwbnnp5UMxdPffWDG56tEq2H+zBTY9W4tk3p0QS+fETfbjluRbBICjGXbjhnqdQ/pFeANOH2t7FPTWvsLULL+qHN3Dd7Y9SnyIDgHdXv4QHm98R5dBHP9ThF3c/JYIyTeNu9vk7JaSlg69N4DcPH6WuReYz+o8Hj+CJlwZx4KKLMTfdg7+9/zccfu5ZWHXjiDm0CLjn8MLhR7CypkSptI785ipO93Xjgw/aUNoyI1cyIBJfxFOH7oMnMo9EjpY/3nq3FX2DryBVMCKVN8DunsDzRx5GumhGJGOE2WfGN771TfzkppvRtRBG+2wQz701jd8+VsnkVOreuunRSjzzxhSaxokRfPzFPtzybDNNW4060Tbtw6//+AwaBpfROU9BgieOn8T9ja+hfZYSw/VD67jutkegUNrROR9E45gTt5cfx32NbzJWex0vvLuIn935hFhsqBuy4b8ONuGRrlMi7fnM6xO46ZFyycc56cF/PlaFI+/Mkl9tyosnX+rHLc+1QjEhAdef3/0UjnygZeDCjvub3sId5ccF61fZY8G1tz6E6t4l1A9toHPGh/vr/4I/Kd6kfeGFEA69MYXrbn9MAoB9K/jtE/X4U9sHbK5uHU++MopfP3R0VyH0756swdH3F9C5EEHzhAufu+TLuPnOO5GNmpByqJHyaPH80w/AYZ1AIWxBMWrBwIev4P03OpCLm5BLmBANqPD0U/fD6ViAzb4Al2cOB597APsv3I8jb0/RdeiUHjfc+zTaZoLoWojgmCqKO4504qEO/u9bxRMvD+NXD7wgAGCj0ombHilHxftzlLBURfDs60rc9kI72mb8aBxzomZgHdfd8Tiee2eBMVxruLf+r7j96DHRx1jTt4xrb30IVT1M7lXacF/ty3io5W0olDbU9C3h8Dtz+Nkdj6K2n8q9a4c28F9PN+HhrlOkRgxt4OlX6fNHTCC9P258pBzP/HVSMO1PvdSHW59vY20A9D6/4Z6nUPGRXpS3/6n5HdxZeUJ4lOsG13DdbY+gcWgNLRNu+vo2vIrHuz9C10IYXQth1A1Y8cv7DqJrMUIhGHUUd1V049nXR4lZmvHj+bdn8F9P1lBqdT6E5gkPbny4HIdeG2eJ6XU81PEhfvdkHWr7qWC8miWBD705i0rWh3ln7au4+XCXODwcPWnG1b+7F2VlZdDbNDizacYH77dhpP/PyCVMiEe1WFkaxuFnH0QqqkUmpkMmqsOLXZWYVr6BfFyHYsoAs7YHirpDKMR0KCT0KCZ1aK4/CLPqJPI+WgqZ6XsdJxRHkV2ZRW5tDtnVOZQffBAByzh2oivYCprR995LeP/VDpxJWLEV1CHjUePZJ+9Hwr2AYsyAUtKEd99qxemel1DImpDL6HH11T/C3r2XUon9gavwk2uvxY+v/Xf84qZfonFoCXVD63jyz0r8+sEjqB/ewLH5INqmffjtY5Wo+GCRZuAWIzj0qhK3H+kSSeC2mQBuuPcgqk+b0bUQwQl1FA+2vs0WnFhP5LgDP7/jUXTN+dEx60fzhBt3VZ7AHxVvMcbfhaMfavGLu59C08jGeQB4/vGZHsQAjttRM7CCQ2/N49CrY7QmwOpdXvjIhCMfmVA3sEplzycZE9hjRcUpE0v6SoWhPDHHf6ycFeRWnqZeMgp/sMRvL0nIVMewsquxvpp7cvicHDPS1wwQAyDM63w0XTYpR6yLnYE/J/O4OCUj9IhDdiKXqjhIfmGmbc6MjZ/D/I3JvHbn1MFI35KU1yqfheMdgDMBGdW/e9KKr4XwZ/OkDwqZp3DXdvCExAZKKyKyuhpuPGcyEx8w3+UjZJU3fJaKM4cS+yeTv+UsqUx+lJgsJ+qGNvBw+we49MuXIRaywGZzYnZCCd3sIOJuE/zOeSQSOvgji8gUVhCJa7BzxoidM2Zs7piRLxmQLxkQz+oQy+gQz+qQYtu/tPqhQ6pgQLpgQjilRzBlgCemxT1PP4Mvf/VyWlpg3sdm2ZNCMF6x9iGezHvXMEKbrpyZ7WChgfsbXsWv7j+E++r/gtZpL2NLPCL9TDKbiy2kyC0K3ERvF9IyZ6K5xUBKndO/j7PFbdNULt4i5H0mX++yJvCCbonl5sESkiGpkqRj2oemUTtNlw3b0DHtgWLURj5FXvch88BJNSEksdYwFlAxTjaGthmauTrOZq7ub3wdn7/0MiwvTyHt1yEbNSPl0SHl0SPl0iDt1SMTMKAYt6KQsiAdMyCbtCARM8Mf0GLVNgGV9iP841cvwx0Hj6J5kvkzGWPN/76OuRBaJj20dMHTyv10Hahh/aFNY04cm6clhq6FyC7mpWnMyfrzbKLouYaVc1f3r4lVlnpmN2kYtqFhaB0tY060T7nROkGJzPqhNVT1mEQYpIbZWPjXvf5Tu+QOacVjxEFl0bJqJWlhyEczlIz5J7bZLTUUCEsGM/+z3e3WSZJxj6mjQsql3V4bmxiklHLzuBOt0142WxYWFUjHVFHBDLYI1YHeD9X9q3R471lCTf8KKnuWZIE9bvNZYQFB2apKH/XjffP71+DeZyuxuWXGzrYZOyUzNrMksyZiOsRjOkQiWsQjOiQiWoTCGkQjauQSeuTPeZYSOmTCc/BsjGDZeBqxwDw2Y1oUI1qWFtag4FJjM2hBKWBEKWDATtqOrcQKko5FnImuYTtixFbIhFLQgEJQi0JEj2LCiGLKjETSgHBcj1zWiL+9346LLtqddD5w4Ep874f/gseOHETd4Brqhu2st5bmHDtm/FCMOdE25cUJTQxdi8z7x55ckehgtVOdcyEcV1Oqvo2F0BrYga5l0oP2GR9OaGJom/aKOijqBHWJrfeWSS+ax+znAeD5x2d6UAp4kMDbEy+PoOqkkXYgeyyoOEXFz4c/NKH8lBnlPRZRBC2VPZPUW9NPlTFVbP+04pQFlUwm5sCwgrXPl5+yiO4tLh+cCwLlFxsuF9cNkqmep4p5pyBfPhDeHrE9bBMdglx+k0uWDUoHFEqbiNyLQt4xJytKph/jp/NmxsS1cK/WpCTFtk55cW/NKyK5yYGXKHMWIZAAe3LgJ83J0RLE7tk4KqOWpFsqbpZvBvNZN5k/SLY7TLUtXuEpJJ8Z9xu5PyVxi+/Lfk7uSWqUf1/plN2cnKgd2sDBvyhx4OKL4fHooFJrYFAvQLc4gFRQh3zQhGzGiHhGj1zBinBsHp/83YytHSO2dozIl0gCfuf9NiRyemQKBiTyeuRLJmSLRoTTWsECxjIGOHxT+MPdt+OLX/4HPPvWjABprSzh2zzhReM47fs2nvPkVT8No/TvpjSzXXi3vvn9n9PiwR5aPPjG964nO4LSzsAyGbppWtCxC+wRO+eUCltl/tLdXyuZh5TZANpn/KwQVkqPyqV97svk4I8fZJqU1D/XwGprWic9qBtaw50VL6JhaBUK1p9Z3mNG3dCGzG6xKoJXdUMb7D21JgBgg9KFpjHpoNMxFyTWaDGKjoUIvv/L3+P39/4R21kzEkEd4i4tki4dkk4tYi4tYm4VUmETwn4NkiENEmE9/L55BAJzmJ17D1/5h8tw7c33oWmc+y6lzwmXnaV+UnZw7F9jfYxSXYlizIXOGR/9ep7AXIiwQ4BsUYfVOTWxr3sN8+5RObZNdPkplDYKe4zaoBgj6fjO8m5U9Zgkj+DAunToHFxDw9A6W3thHaainJ7PubFU/phLePXapnxUJM9mI9um/UKB4Ae8Zv65YYGA1gk3WsYcaBq147iKQB0vdG8c2+3J5YsifOO4fcaHznkqIT6uiaGbScRdC2GSi+dCqO5fYddt3o9oFf5WzvxVnpb7tVfFs2ZgHfUjdjzR/SEu/uKXsBpaweaWBWe2LdgpmbFVNOHNN1oQjekQCqsRiZkQjOrhDWsQj2uRS+oQ9c5i1dyP2Yk3Uf7Cw/i3f/9X7NmzB2VlZbhg7z584ctfwb2PP4XjL7cgFlKhENWiEDVgM2HFdtqBQkCLzfgSzpRWkfVpsRk0oOTToRQ0IutSsdCJHptpE/JpIxJxPWJxPfI5I8rLH8Heved2HT6FAxcdQNWrb4u6HP66qx+2o2suCMWYtKfeuRARARB6HYZFFRWfYHxJG0fnfBid80GpD5K95ztnAzi2GEbbjA+KcbIrNcn82TxF3DxmOw8Azz8+0+OSsrIyVJ0y4vn3tTj46hiOnjSj/BQNgR85SVNvhz80ovL0Emr7V3DkpEVMX/Gd4HK2ilB5egkVp4glLD9lFmsJlb3S9iRdWGivlC+FcOB3LgDkIFBaMqBaDb54sHv5gFdwrLA36bps2UDqwKplFR8NI3YBArlHp4V15/EEcdMoK1IetbFiV4cAVXzaqYVNO7VNefBQ2zuSX4/NVvGLutgGFjUwXlbn4hLdertAoww8cq8ar5hR8Bvlp3x/UoULTx7yiwqfu5MYQPcub5nUdSgHlzx5KgUP6mXzZfLJqtZJD2oGVtHIfCkW6wSsS1aoFxexZh3DZtKCQsKMdNaMeMqITM6CZFqNs2dN2NoxYueMCdtnTIhndTjZ9yLyJSOyRSMiGSMSOQNyJSOSOQNSBRNSeSOSOT3uuvu3uOqHV6Oqx0SD6rNk9m9hc2GNY240jElez3olFYPXiZJnm6hNEaszQ3bcWfkS9v03fWd3VZ4gSXScbfayNDFn57gnT9pude4K0pDfzy2+fiItzg4A7dOUNG5mZbDN424oJtwi/CF8qSLEwoMkVMLdqLSjbdKDZmZzODYXwEMtb6N2cB3tUx40jqwL+wUvZqf326eXMIhd20DdsAMN3Hs2Rj2YrWxRpWsxgh/+6mZc++vfIJ8xIupSIxM1w7s8A495AhHbIhJuFRIBDWIhNYKBRQSCKrj8c3jjnRZ8/gufx38+dIj+L5xpn/BCMUmrLcdVEbyojqFrgfrSKnl4hf8be1fEhFvDiANt0z6c0PL1hShtt874hWWCh4D410aa1SPmrokxZi3jLjSP2lE3uIKG4XXUD6+jfnAV99a/hrqBVRFgk4q3qbe0blACf7UyACjaCkadtGDEwz+sC7B1yic8j5yFa2HMNWfxefCrdYJeCy3jLrRO+dC1QJ4+xYSbDjt8w1opeXX5eggHuHWDNK/ZOLKOxpEN+v8NraFplMriawdWmYdbqvLibCCv+arooXovxahDzBLWD1PYpXPGj85JB8rKyqC10273xztWbBfN2CqY0df3EuIxLYIRDSJxHYIJPVyBRfT2vYL77r9VdAle/IUv4pobb8G9NS+j4rQZzdN+tM1H8GD7+7j6P+/BV775z9i7bx863nwTZ0oGZNMGlDJmBJ3TCPm12NxcRilpRD6oR86rQcGrRiGgpUm5GPX/pRN6RKLESOZyRnzwwbkM4P/C3r2X4Ctf/Uc0jdpR2bcsehQbhu1oHLGjfdqP5kkKa7RyHx8Df92qGDoXI6z1gQ6mnXO0F9w5H0T7rB8dsyQhK8ap07ZzPoTuxTCrluFKhUf4llumyKesUK6fB4DnH5/pcUlZWRkO/02NJ/48jsPvLODoSRMqTplIAmYA8OgpK46etKBhiCojOJA7Ksqf+ZOk43LZOki5XDqQbZKK3yNkhJVzRsvl3YBUPUPgb03aFWbfr+pdEvUbfIGBn9R4n5lgBIeoOoKf0MWFmcm/jaNOUdPAmQK+ntEy7kDbhBNNShsamVmbgy054FIwgMCZvdYpNmQ/46c6CrZryg3AIhE46d0FAPnv4R+Lnr8JDxSTkgxN0o/0d0uhh93gUAC/cxLAImAw5pLkX1ERI/UMdrAC2/YpYiK4/49Koe2o7F3CE8d7cOlllyGR1MFmX8X0zDiiIRXyaQOyaSOiKT0i0UWkkloUi1ps75iwtWPCzhkTzn5sxsefmOGKLSKU1iCcMSGUMSNXMiNTNCFbMiHDgiC9Qy9j/4UX4uhHRnSylGjbDHlmqPbFy/4fHlngh0p1xboH75IblNLktUM2/OzOJ/7bxYMb7nkaTaNMapwPoWM2SCd0VkkkDyw1CDZR2nvlLIFCyPM+tE76pNfJJE9jO0RtSNO4S0jwUrWIC40yIE6yojTN1zLhRMukBy/O+1DTv0yv9cF1KEbWGYiig9N/956TM4K0J0vTcPUjDtQrHRRGGHOjbSaIbnUMP7rxDvzH727CzrYZQa8KqaAB3pUZ+DfmEbQtIO3XIRXVIxXXY21tBG+/rcBvbrwOn/v853HzC11oHLWjibG0Dez11jzpw7HFCLoXI+hepDWQplE7AZG+3f9W3tnXMOJA12wAx9UE/DqE78oPxYQXDaNuYVmQA0Da8qVn67iTitinfWidcKKqxyTNTQ6s0gLI4Dpq+nm347qo+allu8A1/PoybEPdkB11Q6y8d8Quq06ir1nzuFsc9Iihp87P9mkf2zyWHRIEEyzNBrZOESPLfa7yUnZeJC32gxk7LXVI2kS5ed2wTZSWl580oG5wVfi7K09bUdVD1/Sq05QELj9lQXXfKtonPXhRFaWexSkf88L58bIujj81voZ9Fx6AL7OB0rYFZ3cs2MqbUcqbsLlpQiSuRjanwd939MjldahveBqXfukS/OQP9+P591ToYGX5lF4OkIQ6T9J1xxxjoOdCuPw7P8CePXswYZzD1qYRq6uDKCsrw32HjuLjT5aRT+mQDWqQT+hQTMpSvwkTk4CNSCR0yGSN2NmyYHvbiOuuuxYHDlyJCy54Bhfs/QL2X3gAT748gIpTBlaUTgCwecyJ2gEKvsg7HrsXI+KA1K2KMQ9geFcPaddiRFQTdbLamO6FMDGzi2FWIxNmigYRCB0zfnptTPtxfDGMuv7zRdDnH5/tcUlZWRmefWcBT/55FOUfGoiZk4G/Fz4yo5wNhledtoqfF1UvfSvSKfE03/+17iqDFl1/DMzxhHA5a+MXDCADfCQry6aYREH0MqpOL0lj94z9qzi9xHZYJQDIE8L8DStNy9kEI8jlOmL62GrHuAstY1QyTUXKThrpHnOhdcJFN4sROwuT2NE4YqPG/3FJgm2RgTPFOC/7JSaOGEAfFOOSD6iNBTSERCt2gb3iY7nvUEhDsi5A+Y8JkMh+LfeSiY4/2dqJBPwILPHAC5edmxkgoRk5u5i8qh+xCzmYqlBo6uzX9x/E7ffchmzODH/AhYWFSaQiJsRCKqQTOqQzJqRTeuTyemxtG7HFAODWjgnbOyacOWvGzlkzIslFXHP9j3Fq5F34QnPIlwwobJqQLhgxPf8ODlx0Ee6teRmdCxF2svYIdo3LsrXDdrohc+A/xIqDB+2oHtxANQOAfCWDHxBuO3r8/5cBpJ5Iu/ic85UFShE7mczrEknyZjkAlyW2m2Wvk1Z242hjM37N405xs5fX8TQqOdPHGWy78JO1THrROe0RU28t407UDrKespENlqBdEXuw0vtNOoCdu4tLXjuWtGf7wHxekfoUffjDoQaUlZVBvTSDYHABOtUpHD9ejxt+dR2+evnlOHDRAXzzqqtw6Ze+hLKyMnzl61fgpgcP4fD7KjQo7QTKRRKfQO+xRZIjO+aCjB3xo3ZojbUOSP9meXFz3RCFrjrnQ+heoPJovv6imPBIC0DCt8r7H+lAR3NodLgh1t+BuoEVBgBtMtlXur7w4nDOIteIp8QAClZY5tNsHKFqGIWMkeddoDQZFmCHA7oOdMz4xWtD+HVZNRQFXCTPrjwhLre9SF5Uh7DE8ClBuXWBpgxp0aaCKUHlp3Zf8/n1vEnpQNdCGN1Mbu+YC5HHedqNsrIy3HLf3fCmTChsmfHJJ1ZsFg14791W/Oiaf8c/XH45LvvqV/G9H1+DK666ChceOIBnX/oArTM+NLMFI/5sUNqhYGsbYsqRbXq3z4Xwo5vuxD//+Ab85a0ufO7zF6OsrAyPHzmMs58sYbNgRD6pRz5lQClNHsR0Qo9swoBCklLJmzkzikUzzuxY8Pf/sYTNbTPeeb8T9/zpNuzfvw/17/Sjus9K3nMBANeFZ7aGscE1A2toVDrQucDXa8KCiaYnm4SbDaJjLsQk4JCQjDvmmH91ISQWRY6p6ONu1kxAlVRUEXN+C/j847M+LikrK8MTr07i2bfmUc7k3XI29fbCRxax9VvRQ0GO2r5lIQnwTqiK08uo7qXS1HLuG+mR6mF4gTTvBOTePz7BdLRnGUeZpHy0hzOLBATl1QNVp/n8luxmxW5cNUwaru1n/VWD66w8en33SX1oQ3bTtjNPoa+BdQAAIABJREFUjQut4w4muznZHiuT4ZjM1j7phkJJE15CAlUSMGpS0p9TdVKHtinO2ki+HX6R597BlikqYhbevwk3AaxxtvDBbgzcC9g65RNdg1wWFkXUjAHkc3iCPZT1fUmdhW5ZlYlTumkI5o8BQJncIJeCm9hNdJc0zD4PVX0r6J524/OXfAkjY68jV1qHxboC1ewYgq5FpBIapJJ6ZNIGpFJ65PIG7Jwx4cxZ6bl9xoTtM2YEgpNIJGfFruiePXtw482/wy333oF7H7oHFx44gD8cakQnn3pjYY9GBvw488t3beUXbenHNlA9YNv98+wGXtm7iq9/97pdiwdf/5frUN23ikalQ5R3K8ZcqB+iOa2GEZIO5QsfzeMu8XnnBwTu45FKgelr1z7tpd1YmXdTbAKLMJNMCh6Vwkvt0150zfpQP7SKplEnvbaH1lE7sILD7y1SUTWzRVT101QYT/9yEFgpl1bPYQHpAGVDNXvWDtnRoHShbcqPhqFlXLBvHy7Yuw9X/+KX2Ld/P77x3R/it49V4rEXT+P5d2bxQMu7eOx4Dx5qf5965QbWZMwUyfG89LiFmeTlntmmMafYF6+QhQ8km8gy6gY30DbpReesT4SsuCdU2td1CUa/ftiG2oFV1A/SFm77pBut4050THvRPE6gsH5ojT6Pg7SF++ybc9JEGi/cZiCKWD8+yUfsn9QH6BT7zXzjlQ4KtP4h7YYTQ0Q1LR6ajpzxom2SAJFCxtI3seLwpjGJ3W7gbPCYtB3Na2n4PjoVhsv3ZW3CK8hBY/kpC8o/MqK6dwXlp8xs7Yl7vpeEKlPdvyoSzPwQ1jRKO+Tf+9lN2HfhRbjt8afR9VY3XvlrE6741tdxwd59uOnhF/Bg67t4sO1d3Ha4DX9UvIGnXx1B3SAD2IOShUd8jkeIKe6YC7G93YgAVs+9OYm9+/bj4s99Hj/8xU3iuvGuegXb22ZsFs3YLJiQyRiRTRuQyxhRyBpRypqwmaH6l+0tMz752Iq//30Jn3yyjDNnl3H/A7fgptv+gNqhDVQxzycPEdUOSlJ/DQPR1exgwIF8x1wIx9QxwUhzoNfO+i1JDQoxEB2hFPdiFD+/63GUlZXh2RPvCk8rScZhdMxReXf7tA8tY+dTwOcfn+1xSVlZGR5/RYkX3tfJZt1o5o1v/HK5lk9e1Q2yBB1jCURogwG7qt4Vth28LGMBrSL9y0+R5Rz4yYqky/mqCLvYcLNxdT/9fZWnienjhuSqPi4F704M17KUX83AbvBXx4zf3JhNF2YJ1PGuO5rdcYgbrWLUzkp47btuwI0MBDYq7fjF3U8SezNGO75No45dUnCbbDOY39zamC9EyLWMoWuZkMvC/PdzACgljuUycAvzh0iJPhk7yIDg7gJkBuJkCUP5Oog88MFTiY3sY37DoZ3gDSiUdvyp9gT+6TvfQb6gRzhhg05ngHNtEenwEsKBRbr4po3IZAxI5wyC8Ttz1oyzHxPzt3PWjIrKh3H2YzOMpg/wne9cgX+//uf45R+fxw33PYsf/+4+HHpjisDfbJBSv1NeAf5qB+Wgbg3VA2uo4t/y7jixe7smwB//uRpmGajsXcVt5cfxszufwB3lx1Hdt8peE8xgP2xD3fAGtfPLeicblQ7Z50wybcvleGkhhl4TzRPU69Yy7mTAT8b+je8Gj2KakN3oKdDjQt3whjD+8yRyzeA6rrv9MWnqrX8NFb20f1shZ977VkSogpeyE0BkQHBgHTXsfcMBdP2Ig8m1BFweP96D3zxSjooeE7vRhUWaunXKg/oh1o3IWVjxrV1I8pTclzyOHCjXj9hkAIQBwJ5lIV/X9K8K/xm9X6T3gihfHqei8+YxafdZMeZEw9Aquma8aB7dgEJJRcaN/w34q+5bEdU5NQNr7CBpF9Iu+e1c0oSgSGXLejOV5BkTHkDZ66Ft2s82wv1on6bPacdsAG0zlOSmInDu8ZOmKz/93mWvkTG3eB3S64IDQAn81QxJTCXNCbKO1SEbqvtWUNFjJdXnlBnVvcuoZitPws/ds8S23ZeENEz+7iXUDKzise6TuOpHP8c//tN38Y/f+mfc8mwTjnykx3W3P8rY6FU6rLFGB/4erOxbRUXvsgCb5TLWsaZ/DfUjNro2Mjm0dcqLx4+fwk2PVgjwV1ZWhlsefgrrQTP+/rEFpaIJ6awBhf+Pvfd+j+ysz8YHcMN84wTyhmAwLy3YTgHegBtgSiAkca8kxOCGDfbuqk1v0kjaYmPAr/MmBpdd1ZGmaNTL7nq1q67R9FHXutsEElref+H+/vApz3Nm5Xyv67u/6lzXuSRr19rR6Jzn3M/9ucsvzuDXv5zBr381g1/9cgb/8e8z+M9fzuC3v5nF//2/C/j3/5xH/uwMTs7Ecen73ovHnnqBer+tDaRELpHJMKvsb1MPFRdE2KQh415hKx25gGMm6FxkQTIOfvCJHv0ZbvzGjfiX01U1k5CLm66TYGKPAdw7zu+4zOVyYd/PpujGlk7fowYASqxLPY94afS6AG8vgTJ5KPj7VuBmZqGpBuTJzStsXb2Ohxcsxm8edQwgDxybw/4aHaGIkiV8WlxqTcz+iahd2UDWC0qhuZR3a9J93HbqcW8mL9yyiMsZSFLnZyBBzIphYgqa62ZnDsqfU6tDAS3DZTQP8k7fqoSjcY/EXqyqWN00jHCMCQODKANCMp8YE0iEnb0yTg6xQFyYRwf7lDYjaYpAyeko23T+Ol3Apj3DgD/7v93xZTwxtYmPXv05BFvq8eZ/LKK6UcTS8hJycxNYzU1iuzKON1+bxi/ePIPX35jGq29O4z/+0+j+fvd7AoG/+d0Mfvf7Wfzuv2bx29/NwO9/AJ+88s8QTmfROrHDJ9W8hTMlNUl4ExLzQexeowK+JetcdnwuQJD+LscLKSikRV0YHdJPmXouuV58/EB3AEDeGBhZgLj46JQQ3tbRNc38k+DnMIP6UMY26FQcI/1Ipoxwin5nYjQR0b+HBeruXhOm3ti9aNj6DmvsazHpHnY2NrKmThhCAo7m4efm98bTlyfmjg0ckcEqWkbX9EEXG99SPaYnvoym3iVHpaA0+8jvy9OXgzdR0GsqNFBBZGgVwYEKGroWsO/FM7wWmLWkiWNgPALIEgVtPIlJ9+oAt2akiohq1hqxqy3Dqwj2L6F5kDZ4gQR3jzOD6k/kuOJvydTHifPYYgBlIylVbc71g/vItZ5NNggCSs0koGWEK+sYCJK7VxyiZVAWqe1mL6l5Rlj7kBXfJIwhmUKs5hgeCduvXwCgjLElXquxm/TXDR1zGuclUyKauCyjqZucwmT8m3VUgDZZY1N3PKvvo4mSMWaT+k5DGOw/Nus8hZxQNnKe3ckLaOLWktBAEd7+JRzOzODqa76M93/oo7jq2q9h9bVF/PY3M/jlr87g17+ewe9+M4vf/mYOv/3tLH79m1n87vdz+P1/LeDVf5/H/PoJnClP4t7v3IovfesbaOqZt+oHs8ZQGM/qJMnTt8K9yfQ6fP0rFBM1vKqsLmkCKRJGACEZRbaZ0dzikTF9/RsPNCkIbD96jEfGm3SNjK6hdXQdweSeC3jvOL/jMpfLBW/3PD3Ie6gW6sAx0/XbKHEAXdLju8Aj1WV4GFx5+/OIpAvM7nG8i+zcdNGkxUS0gLZGsM4GgR1zqOMFYf/RWex7cQb7j80pENQWko457Rj28MLi5j5WMYk4TsfoL+uk8x0LOQE9+lweRvQA8VmiccnYImG3ldIurJmO+gSQVRAbrqB5iMY9LWwIaeZqoHBGwJ9pf6AIE/pe4UyFGcSqxfpRjljLyBqNsQcriAoQ5JFwWLRnokW0o0SsbDllP61YmUDaCfTkYRJImY8+bjq41/MkLrjwQhTXR7D+VhYz8ycwc2oMiyeTqCwM4rXN43j95Zfw9hun8cYbp/Ham6fx778yDODvfz+L3//XHH7/X3O8IM/i97+fxWtvTOCDH/wAHok9hbaJbcTGtzlOgYCtGfvmiD1gBkGBTK27tcbpqsDPYsmabMDDZhEvu8flIR8aKCsQtBtonBuBsvUeFx3aP70GRlYVBNqO7wgzWeI6dgDAAXamK2PMOY1sQKHKtCWtp9Ou3+4lej96TGwHtZYQq9HUu+xgAYWlMSNhS//Ul2N2i//dRI4eTuMbFDmSyMLft4S6YzNo7F6koO2hKmJs1JHX67WBSLKkDJe40T39OTKcqQaNWT/J4Os1so5gqkjAaXyT5A8auUKSjubhKtd1WXE7iWWSfAigThj2T8bLjcyG2qyogGLZJNTmPfqSRQSSFNzrTxgmMCAtQymrBYar4WKj68TwjK4hNkbShuCAeS9M/BOF0tvATzaPRp5B14Vf71vevOm4vQhPP+lk7dG1mzfKbt1ILOvnTT1LaOjgOs+OWTR2zaGhg3SCjZ2zqD96Bo3cHNUoea9dC9akaIEnRSwh6pxjMmDOefLGX0Df/qNmKiXVcwIMD1jPBtr8L8Lbv4LI8BoCyRVcdMmleDqRxH/931n88jez+NWvZ/Gb383jV7+exy9/TYzfL/5zAaVXZzBRmMJobgyT2SFc8dHLUXf4EI+krcapfjL1ePtyCKcK8OqfZVU3K887f7JAmsixTRw+fhbtU0YP2Dqxjbaps2g/fhYHj79M0UV8tvIG6vN/d6+CwD//wvX48YkK2sY3KetychvhTHEPAO4d53UQAOxZ0KorWfBloTWVblLfRguhgEA/C5ppFyixLguqDaTvs2BYud5lZhQthrDDCQgbuxbReA4bOMeuNPr/DhydRd1RWnxkkZYxtGRWCQiwBe32yE8F7uwSFheoGkRS9qi0ZOWuUdhqIMEF3XYHcdqMZUIDRucVHSwzA0gsXfNQBbExcnjZcS8hCf517PZlLGZnylljJNaYhQcrWi1lwmWJNRRmKTxYZabAMIEGEBYdzEHAAjHiLDTgjz73sungPRdcgFvuuRmbb0xjfmMa2dxJ5GaHUJkdwkZ2HK+tHcdbL5/C26+fwZtvnsarb0zjrV+cxm9/S8Lr3//XAoO/Wfzmd7P4j9/M4te/ncETTxzAH3/wg/AnsoipfoZ0YX4ZefYx+Osyo8sGYb3+OwBYAwbtkaeMoxp7zOLv7aONgX1NyGjRAagdTHBJM93kvZWe5pbhVbQMV2jkZ7VByNifdKFlHe+LXpR+xxXjGh4osxOZNieN3QsWcBOzlLknbFAn8onG2jHwO1TECeBy65g5p4y6jxlyAaDSIe7uWSZZw5DkYLJejMG73zJm+FmSIOx2I48iZV2RDFAxYkjESjBN1Wpt45toHl41OtpMCS1DVbSNrqN9fANto6toG19HyzDF7ujYV+5nfg89vE7J+iemGWFGRTNpwHDeYvmkVpIjZxJF6+c03eTCTooL+ODkJtomNjXXL5Sh2B2KDKpqp3UwXUFooEqnhJwPmmpHAYFyzdmZoXLv1nZI031kAcG4kw2U37G03UhoNoVfr5Cekr/m7eOxbtcCb8gX0MASItFxN3bOooHzBTU9gnNiG5hEaOC6uTpLHkTA71wAeMACkBQ9No9AMo+rrv8GPvfVv0fx7DTe+CVtOP/j12fwi1+dRnLwGRTXRrGwfgKZpWH8PPEMHjrwAG6579twuVzwvJBGU89iTYqEyXYMWKxpE+fTCrHQ1E0GLH+ygOBAiWJdJrfVDCJh5e1cr9h+3ABAOUMDRc0/dLlceMzbgLsOhHDlF76I6M9eQCi1pwHcO87vuMzlcsHTu6ihtoEkaYjsh4BQ+SR4XtEexEAih9jIqgMkyo5PnYadZlzj6V2Gty9rjWuNM7HBAeDMGIrYwXktHDctIhwrYznUZMepxpHOeUfTgS1sbxATierBstZCRze1r58iN2Qh9Sdy3HmbV52VEXoX8HePhp0au4zN5rBJY7Ci+p4W6QJWBtA0VQgA9NvaO4uR8zMQtQOCVV82ZEBm85CYDEzfsDQtiEEhwhlWogeUf0eAod2X7NAJpkvw9OXQMlTF//jIx/HEs4ew8eZpjK6MY2FlHJWlYVTnhrCdncCbm9N4c+cUfvH6abz1+mm89dZpvPn2abz9SxoD/+qXJ/HrX5/Gr351EpHII4j3Hcbtd/4tLrr4Yjz4o4QZ/Y5R76k/kYM3QQ0cDXqtiV7IypS0yutv2teOa275Hm7a1466Y3MOAOhooLHHxd1ybSzT75tz1eT9Cg+UaAyssSwFCwAWHSYbu3uZdF9VxIYraB1bR2xkleJHpDlGf1/8+7RMPs1iHEqXuCxe9GbSdGGy/r7xoI+0szVg2AA8p7OzXnq7GeRIX66nl2JPgskcGrsXnKNCq13DzSHCtnnL3buMULrkaMGJDFZZP1lUdko0a/5kEeEMj4l7lliLtqDxNfKglQ5gitch3RX1p5aYZa2gebBEwG9sFbGRClpHVxHJUN5ndLCsml7KATRdwE29yyQ1sVzHX7/fhzrLNd0gvc8MegwTTD+HL2mNXmUDxfFAsgGLsIayfWIThya3OBduDcF0EdGhNRNi7ZCHVGtOKyHA+jsUfG0y5AJpZ9QTvSZmKZNFeBNFBYAejrCxwY+y4aqflvijHLzxFZVBCCNGkTlLaOicI+awcxZNXfP45kM+zXWVsgABfI3WPWhPe6hAYM4AvmMGDNogUIBiXccc3L1L2P/ccXzok1fjwkvei6u/cC3+8eH70dX/DK6/4bNwuVy49ps3Ibt+Evf+86248KKLcPUN38Dn/vZu3B14hmJ9epf5PcgpWeDtF7LAaP/EHOTtMyHRIo8Kpak1SNzLzSNrODi1TcBvaschbWmb3EHb1A4OHn8ZR06+jA98+GNo/OnP8S/PRnHDjV/AVX/5abhcLjwecqOxa24PAO4d53Vc5nK54Isv6YPdnxRGxdLU9Syrjs5k6dGiF0ySdsbdY0CelM2bkFlp6aACdbXP92bhEbG+ADEr26uBBesHhBnsXuKFYl5BILGEs2bnyKMHXUx6LE1Tl/l5dOSnDNAyaxqdP6dxK+a44omAlzJiUvPUn8N3D3aoBszEr7BQ3nqot4xScbv0AEtrhQ0AzwljVibOBNnamiJZ+EVQLhmCAh6ah1fNvzUobIJp/6BYkaKCPxkviilGnKgkYOcRcZIeGIcmNvG5b9yBux+8D2uvT2N4aQzzi0OoZkexnZ/Aq+WTeGv7NF7fOom3XmUjyOvTePOt03jj7TN48cUYXC4XLrn0UlrQ3vUu/PGHPoIb73kYjz07rgtkjNm/ULpAi2s8S+BPI4XMaQPAA8dm8aFPXYP3XPhxdvV+HB/61DV0zfBYVBke3pCoPrCbQKCJ/jAMsYx9bdaHro2CkwW0wrbtBoiWkTW0jq2jbXwDLSOrDheo/O4imTKbGsRMtKpgMDa6rhWG/iSNo4ihWVYQ84/Nz+/KeBqG3uh0RWwvIyw3j4Q9vcsUP5NYMXKKeJbMNdYGyt27rKCSTrpHff05xBicxcY2EB1ZdxiS/Cm6jmz9bCCRUwNGAwNKmUBIILzoCSMcidI+sYXYCDGr0cEKjdcHy6S1ZMZUjFjRQQLOvj5q/ghyPIufNxX0u3fqJu+JPKfXljCjXjaZ+fpWWBfKa0PK6c71c+e4mly4TlBd4CNrdB2MrXPGn2R/8jm0agFB89HohQ1QdP59iY6qHSGXHRs7W9/q0ciffM2ab2mm+52RWl5OVdC6PWEQxYzXswwPpzR8J/aCXl8S5K+sX5eJIpLr0waCB2zWT0HhrMM46Ige61qAJ76Ch3+cxE2PteAzX7sVl172fvyvr9+Ebz3sx5989FN4zwUX4GNX/QXcHafQMkGZopFMiQ0eOQWAnjjXBCYKzH6vKGEg94FuhJRBzVI+5/Aq2ieJ/YsMVtA2sYVDx01jTUxczdx9fej4Dj7z9dtw8XsvxcDcKAovn8Gzvc/gGzf/DS66+GKEe19CY+fMHgDcO87ruMzlcsHds+CoANME+74cu2ZJ++DRHaHkp1GlVGPXvGaNNXAos23KEDZA9Hie3iVrl7RiAKDQ5/zgFcBmi4PtKjm92SWhXkTK3EpiFhTDdMgOs1EiLqxuURGVS46X5nlxEK6XjQASiRLkOjRhAXUMzCBJwJ+D1RFnmOj/hk3Mi+p4HADQGgXXLtY8dorICDgjDtNVjYxp1h5gox/UTuGBij5wvZbDVUfZwgpa2jZzErDxJ4s4NLGJD3/qL/DP+x7C8dI4eqdTyBVGsVkew5s7p/HLVxbx1s403j57Gm+/dgZvvz7NcTDTeO2NaaTTP8V7LngPfvBvE3B3z6NldJMXRnPGxrcZxK7Cw3Em9boBsJzk4hTtMCzNP+xrx3t2yfX7h8fbTR6edTZ2L8IdXyEWUOUDS7Azv8RA5Osv6MPdAECrAk4BYEVHeSb8d5VZMXJ+RpW5ldaAqkbO0O+OGyLSJdLSpam5gTYj5DD1JfIa99JoP5R66LQ3Pc7szXnUMWMuvdqOuIveZTR0zRsHpAX+VFPYs4yGLvPgFgYxMlBGq5V7FhvbRGSINiD+JAVMe/vzCpClL7mpR8ABG1UYTHjiFLhtag5X0Ta+iSdOnEV0qIrWsXU0D1XQNrpK+X7Dq8z4cWC6dX2HbEd/qghPfIUyInUdkqirRZan0OuxwbS7d5kAoNX4YbvllQHUrxeVCY5y1uehiXXExpgdHZLIpzU9iQ1eU5e/bBrN5nK9hjk2a4BhD6sIDlRVQyj64oDNwPKmzo7msY1QJlamoADQx5tjyUS0ZTUUl2OMd7YUyB3na1FZaKfkwLz3CzVawXlL+yfacGmaWtDfV1P3EmvXlwigJulaaJvYxJETW/iX02uIjmzi+z9JoHl4DbGJHfzk1AYOTa6TGYi1xYb9W0EgkeeYmhV2VVuMn/TSOwDgCm2c00Xa2A1Tf/jByS1qDGFds2oDJ3fQfvwsoiNrcLlcuPSy9yPyb89iZes4vnDdZ/HXn/8L/FPkWUQyZXh6FvYA4N5xXgd1AfdndWGxO17DGRKhexNG2Kwp+n05NIkFniNh/H084rXGtI0OLSA/XHqWlB5v7JGIDqcQ3w55FSbQ3PhmFyjaIF0AdKQwzw7mmrGwAtFlx8PQLOZG/G9ne9FZcObgiXmCwaCMgkWbpwBwWJgdOkkYvOFo+qhlAGvHNpK7J2MWX4LyxwJJk3FmZ/5pPuAQmQwig2WTQSh/PsSg0zaJqL7Nbp4wekM72DiQJg2c+4UpXHjxxZgpZzC3cRLp2RFUqyfwysY0XludxquVl/D62km8vXMav3j5DN5++TTGxo7i9GwSb781jad+1ISPXfnnaB7nAvWJGvA3sa3vlT9J7HR9FzHD++3xT4dhAcjlSuzeNbd8b9dmjy/c/N1zASAzh009WaMJ7F5GU/ey6X7lh5p2S/MmQAwi/qQNku04l6oFAAnc2exUVL9u2kEk+ofAS9lic8TVTaNf6TH2CzsTzxJbwQ90+yEmbt8G3RiZB6btmjchxwQEW4cpZ8/XT2Nmx+ape9mxcZPNm68/TwYmFsPL5qdldEONNHY2nbBLauRikxfVmOV1ZBvJlDXvsm2cqrWahyqIDVfRPFxBy1AZ4YE8ooMVZQSlA1v6sqX7O8iSCmnFUABouaKNJto5TWjsprVMckVJ+2eYTWPCKKpxSrt+2dQjDKAw9bJRFCa/hVnhKINAkY20DK+idWyDw+XXFWDUAkCTMFBFMEMAUM5AjcTENjrZEwc1tySo51ryD0U/aFdtqpnE/lpcXMBOY4mHwWDdsVkj0dhPEo1aKZCMius7FrSnWIxBptHGGJjoOjcSIHdvFoF0iZo4xjdx874WPNj2r4iNVtDUNYsHnuhGqGsCT0xV4IkvmUxRPmlitajGH3+yqAyo25pkuZUBlUlZjp8LnB2bLuLQ5DaPf5n9Y1dwy+g6vnF/E+7cH0Lj0RO4/vb78T8++im8kO7EJZdcDP+z3aoF9vXtxcDsHed3XOZyuRBK5RWcRHUxWtOcJe0uZVDi56wxs/vhxb+LXMLBVN5R1eTY3ekYdskssJYT0+lClAWAjR+SGSj6D8kpFFOJlU8l4E+1JVZorLAV5/5bJvjWVMjldPcri50zPNlo5R75acbK9zI9r8r8jazpGRvdMAu7BQCN21NYOgsAskM5mCpQBVF/DuFUjjPhzL8b4vw/qXBz9heXKNOQXczBZI6aLdJ57qA1ETEaMeNgJyV2gkBw69g6/in4U3zyz/8c+e1RDC8PIzM7grPbs3htcwbb+UmcLU7i9bWX8PbOHN7YPI3TYy/A5XLhPRdcgD/8wB/jXe9+N264/X7EGPAd+PmUAYCTO/xgW4MnTno0GuvOOeMhjs1i39EZEoZ3zKGuc0EB3E3vwAD+/eNt54I/HR3XhCJ3i0RA3JHGPa5uYMfD0wpvTpdhC/kjqusjJ7hcE/rwH17T6j/tcU5T3IoYfnRUmszzWCqntXPhgTKa2R3++L+OIjayppotx8ZrF4bTYz24RPcYSkkFokg9TAuPxjBZ95St/fNruPOG2fRwh7KvP2eYVr73vX0rZCjrmENj16KuM4FEXn82aciIZEq41/cT3Hjvo7jH/xME+pcY+JUQG1mlqJeRNQXWttM6OkQhyhJ2Lr87Yf7dvTnVfso69uCT/VaMiVkzSPslWYBOs5RfWcCirhUhDgiPZCq8zq5x3RdfA9wL3Dy8qqC5ZXRDgV90iBzMrZIlx40pMQaDDrA4tOYAgf5kAXc0PYkb7nwYtzc9SdmLViqA/fp1nWOAbFfp+bhPW0KkhSn06ug4DzMelq8RWfDQUwkzKu3NoqFrgYPXjUTj8j+7BvU2CLTW8vpO4y629d3ye5LT08vpEJaj3dOXQ3CgjP0/H4fL5cIffuADuPCiC/Hud78bf/j+P8LlV1yOH01mHRpjkTv5+pYVyApLKj+bm8mQc0PnOSlDdKKs/2wbP3fKofEvlvHjg5dfDpfLhQ9fcTm/EoG0AAAgAElEQVTu/P4PyRA2RJvIQHLPBLJ3nN9BADC5grZxzhkas4IpeUGhGpottE9s6c49mqFRlIBBiQkQoEWF4/YYZcFaNA3bYI+RDEgzYNEe38ouUEXAR2dV7+E4OxaovaTTFJkbl/B8Deu36Fw4rFGfLYAWh7AvUThnQZfF8mv/XKdsjyOqRUfAovva4O7adceiLtod0u+saoG9uH6lDkyrnFh7pNlzVqC1BlYPlDQ02FRPlREaKKoLODpYZldpycTJDDl1R8IgmIYSYhIOT27hq99+FB/8yBV4oPkJ9M2ewtj8GKrlE9hamcLLlZfw5s4sXlufxlZhAtuFcZweO4qLLroQ37r/AG646V409Swr+Gud2ME3H/Q4mb/hVQRSBWWH5XdPVYWzfJrqQmMYYvbg2BxrAE2zx4c+9QW6RizWr6GrdhS8ZAGlRWWlvMoCOtsfRDxvO1uNk9xoNO0RMPVDkzM2yjpN4wznIGMFfvy5XhMlBX3BlOQPEiMdHariyPEd/N1DbhoxD1VYV2dqy0i3Z1hByTPz9efZ3VxAJLWCQP8S3D3cg2tpt9xyz1p6P3PfUrZgeKCM2Mg6Xy/OujJff94CjQvm3u+YI72YmLESpqosNFAmUDdYxhVXXY8LLvoE3vXuR3HBRR/H5X/2BQrUzgjwq+p7bM51HaU6w5NZRylSl74c3FZklCeexZf/cZ8DAAroFWbYZgAdIEqugTSDd96oGZaOx7zWlICMYmvMmBIIlE0kZQbSuFjAYUw2lfz3hEG0v38wVcRHrrwOFzDQuuDCj+PDV15HoMQxbXCGSptxtol+IqBIBhLJQBRQ6K2Jl6llCG/8p/0Os90tdYd23aDdvP8gvb89S8Yc0r2oa3Vjze/BSHnoc4kpI7ba+jpruT/2V9fgxru+h2eSz2FwLo2e9DP4f/7gfTgylkUwXdBsUb0/OAdQJkMqiWItu8bncMJEo2jKWbIg13EwXUJkcBWtE6bVpHVyB9//SR8uvPgSBH/6FEJHQvjEJ/8n/up//RUueu/78JV7HyEj1eg6YqMbaJvYRiid3wOAe8d5HZe5XC60DJZx+OTLOHLiZRw+cRaHjzvPg1Nn0SZVNRPbfBFSSr0/mUdDz1INqFpS8ObrW1EQp6Gz/BCRpg5jyDAAkDqAzail3tr5SV6gGffZYmGTVC+nHToqC4gTAJqoGNVy9GaNGNoeUVkuv4C1SGrkgo77yirOtgvfWxwawDUHADQOXQKAARVul3WBFgGy6hFTJYcbTx5CGlfjcPeSq1iaBEI1wnECIJQ/1zKyxmzJ7gJ0YQSfOL6NT/zVNbpjDf2oDVNLY1grTWC7dBI7xRN4uXIS26XjWM+P4vW1E3ilchx33PZN/X+CA+Vzxr6tE9tWxVTZ6P44GmLfizN4/AU691nB5SYvctEydizhwLE5/MPjbfjCzd/F3z/eRrly3c6/Y4M/O0ZGdKTuHmKonLKAvKNjWsTz5vdQdLCzpgVklR/k9KBvHiLWtnnYjO1CmSoCaYkCIR2o6ONMDV9Roz18iTzCqQKC/cvwxhcRSBbg7VumYNo0VRsK6ybRFcrk8xgzMlBCKJlDJJlFKJllvS4bYOJZuLvnqReXN3zuHifrYo/b/AkCpb5EXsN0JW5HNLfC7KsbuWOe3+ccO33zCjbE7NE6uo57fD/GBRd9wgEaLrjwY7jL85QaaqLCqI2sKatGfbUbDlYwnOEaQXbt+hJF1Tg39a7wemBes9EnG2BBBrG8JQMoOcCuiYcq7aIFXXcAf10XLM1wy+gGaQMHTctE86gBiwIGm/nv6uc62VnDXZ6ncMFFH695zz6O25uedGiNSX7CJrABkyvor7ne7GvQlyjClyxp44h8FOmM6GZrTSXueA7X3vbArhKNa2+9X2UAwuCpUdDBXmfVwKcyDf7o78/pyNmEj2fhSxTw3bYX8Ad//EH0zk0heSaJD37oT3Djt76OcKaozLvU+Hkcr912SJuCAZmIaR6gmKPiyyrhkOlAdKiKw8d3cOj4WRw8fhYNL57AH/3pFbh3fxNm1s7g4ce/h/d/4I/Q+MIEjhzfweGpLXwn/Ay++o8/xD+Fn0H7xAaah8p7AHDvOK+DGMCBIolPp87i0PEdtHNCedvktlrXW8Y20TK2idAAxTP4kgV4OSzVsHMLcOpj6PNAQkTkZP2Xh4bHZg0lgsIay9ZmEdquxbqOeWVtxPl5QEbAnea1SBdxU/eCjp7dupCbkbRjF1kj4jUsYJ51kEbcrTVrGt9SVkMNsT6y0JtFOza2ieZRWpQjDhagRq9jOfcC1iIc4HEMOSDLpolEmEAJBZYmE3X78etOl53OQUedXMWhSTIOZeM2FGF5ZLCKJ0/s4A8+8CcK5jpGOjGxNI7VyhReXZ3GRn4C24VJbOfH8ebaNN5ancb02FG8/wPvx2e+8ve4/8k+xwiEDB9b2gUbHarQdWbr/o7OMvg7o4yfcyOwiPquJdTbIK57ifRBXYvW100VmjKBHU4WUB70Wkcoxoo+Sydn6+ssd6C4xmXRN9dKWQ0AEuTdMrzK7k8BJVUEM3QdSedrUAKSLUZGWD9xsIZTebh7FqiXmPPxgukSvH1Za5MglWBkXAili2geLCOcMll4xuXIAn4OWad7dlHHd8LSycZJN1Jxs5HSEGVLymEz+Srk76DWCUkJ8PatqGGjhftV28Y20D6+ji/d/TDe9e4a0PDuR/Glex6xJhmctzZBAnv6fJtZMotlzYgrVhzJtvg/p6DBGV9lfk7TMV5gnbBlqrDYNNXocnCzMWutqXlLpgDKDrIUR8Cfsnr89ejwGqIM8GwHOYFCAoHNPBb+0t3fP/c9e9ejuOHOh8/RHoeYsbQNabsFwesmJGmAr7Sf6H87grJzqrWU++edGMBb6g5BOt7dPdwTbHd39zCDreYsMaEYd7IwcG5l70jP52GD44c++ee4+rqv4sMf/xhcLhe+E/nf8CdWWJ6xiZaRddL6ifExLtrGPBp5gqWdwByf1mg9S9RMZcmIxATUOr6J8EARX7/vAC665L349kMPYql4Avc/fA9cLhfcP0uhbWILreMbuOLqGyy2+xO44uob4E9k9wDg3nFex2UulwvBZA6xsS3ExrdxeGodbRPCUNFDKZwhlsmfzHE0xKK2cDR2L1J8RIcEQS+gvmtBhbiaii6ZSj0mykXiWHzxrLaQ2OBRdn92qLSdwWXiLKTf1BlEbcbJxBy6u5c0pNZESiyrMcU5As7WAEBjBPFxBVYgJTVNzKhZRg6j91pVF5/s5qNDVdzlfQpfuvv7uMvzFIKpgiOfz454sGMbggou+d/j0Z+fM+CkXUHMIrYmTUdTDADtCqmgBe4ig1XVgYpD2X5t4rpsHl5Dy/AaDk1u4X/+xV/j/X/6YXzwIx/F+PIYJhaHcHbzJZytnsBOaQqvVk7htcopvFk9hdfWphHxPYLP3XAjomPb52hgCABu8miUGj88cbo26joWsN/B/p0hBvDFGZUD1Fmsca2OTwBgowDCGpaPriM+OxdwgP89iSGy+0g9djuAFYVhgnSzyhb6EgXttxUgYJpcKmgZXiPwnZHQZxnVGxlBUHWZFQ37lT8jJqZA4ccDJUQHivD1LcGXyPNoU4wgDPj4uvH1LbMelMwVEuzcZF/3PPalcZpxyzcxMygPVsnC8zLT0xQ3ong1UigAtIN8ScdLMo15eHuXOdSaXM2hdBmx4SoOT22jbXyDw5zXcI/3R+cygBd9HPf6f4rDJ87i4BSF7LZObiM2scXauHWu51pnZnnV0rZW4U+VFQB6+wtwM/PfFDcNQsI2Ncm6YaUG6O86bYC6GMbUfW81d4jcQxy6NgDUz63e6HM/X7WApDF+2G5gm/28y7s7A3hH048s3XHVcZ2ZjW1F157Abiy0NRKWXEFjIskjIK7i/oKCQW9fXmUAl3/6WodE4/JPX0NrtkyKuhkAqrPY6LNtZk61h2yM8vat6IaiVv7gSxSw79lxXHfrfbj+9vvh71/mZqeiGvWiQ1XNNmyKr6CRf99NvVnVqArQrO8yk67GHiNxsnMBPX05zZ285qZ/wiWXvg9Xf/Yz+N8vPo3xiQ585nNX62b64MQGWkY3cY//p7te67fXH9oDgHvHeR3sAl7mHaNoYwoIpHIIpKgw3duf0xGQVPpI8LKdwSZNIHXH5jQAuqFr0fSr9ixDuhK9cclVW0E4lefqpXMZRBkn7xYPoA/5LgGkS2jsco51FQR2Ugq99Bl74uRW9vQsam6aAFLdUdayf6p9KWgotMS+hDMVfP27DQqiZHEX8GcWlMo52qWPXHkdQtwRK+NWBZMZ49jTMc0uO+8Au5CD/HV58AesP989moTZykyteYScqeGBEoGKwQpVVHGQb3SYRnENz03ggosuxjV/80384/4DGFsex0v5cWxWJvFKeQpnKydxtngSr5RPIvtSEo11D+HTV1+Jr3+vwYC+mvObD7hV3B5IUb+ttAHsOyrA7zQef+E0HnvhtAJBGgHP44CVBei8bgzId14jpEGrs4xG+1lnuP+oZSqxTExSZO/WB4+ViabsWp67gg34U+DGFWASTxIdrCI6KCNg85CP8im1YeLwtuu/IhlTMxgeKOpDOpAq4ivfOWByC7njtmWojOahCnwJ2TQYVsZUgsmDUlgMGQOvoKlrXu9fYepEoxdIFU0bgtSH8f1H4/tZp4b32Byk39sja0PfCnzxJUQHimgdWUXrcAUHJ7fQOrqK2OgqDo6vIzZSZVbk43offfTqG9A+sanxGtS1Sp8LI9Y2sc2B4hsmWmXQgDAfs1sywnTH83yKdm2FdZPWBlF/fmLNwgNlbe0QAGWz7YZ9r/DItWxpbG0ZSNWYgezTMmaFa/4fOzJG5AQCBCODZXzkqutJA/huowEMpAomW3CwNmTaNBMJC6gTD2alNUpGcxwtB/Quesiv/XMdOdb59PatwN29iFvqDuH62x/AbfWHVT7kiWd5jRam1ZlNKFq8WlmG5LVSSHWWI80MU+eOZxFIFRBTrShpo8XsIi77QLqkUWiyEZCsQ3lNFEYv64I0Thn5gLbn8OaIwqn/HJ/8y8/iZ30/w2x2GJW147jzrr/DjX/zZXj6qFaxhadFX77n0V3Z7utu+94eANw7zuu4zOVyobFzlmIkknn4U5xpl8hxwfWyNTKlB2V9V+04dkFHOJrLZ+mw7PGquL+CzCbSw4OEut64iV2QBgKtqeo2r6HRYgDloa75fg7GxwKKHCHQ2DkPd9cc6ZishUVGWrSw27oOZ/7VuXVfZgT8g38Zph5ee6zLGV1yM9/j+8muu/A73T9ysH/hQQJ/AR7/OoOhbeF6UUGpNlKkCqaejBsq/EkJJyYhekhZCvk7eQQZhIY5niKYYSYzI6xfVUfD0aEK2sY2cHvDIXziLz6Ln5+ex/DyCUxlR5FdncLZ1eN4tXwcr1RP4o3NMzg+9CIufd/7cNW1X8V1t34Xvv7sOwLA/T8bR2xsE9EhYiOaerPaF03s3xk89sJpPPbCNB57/hQee36aAeEZ0gMeIyBY13FuhZdxk88zs8e91ZaMQBgqMZjUdc5bMRNGMiDsj+0Wd4REJ0ztlzEN2WNgA7abh6pUTzbC5pAhCjRW1yy7h+3GB3GI0/crIZA2TS5yffzg6QGNUPHx/eyJky7Q179ixVNYmW9x85AUfSDdJ8vaBU4d4DllFqkVhxkd29zFMoy6jjmqb2TWb/9Rq8GHJwImcDqHSLqA1pEqIgMFhJI53O37Mb58zyO4x/djRIfKaB3fQPNwFd8OPI0b730U3w78lO6x8U3ExjcJALLAPja+iehwFbGxTTz10is4cvJlHDq+o45ZYdZCmYqOgSmbsMj6XwKADz6VqmH7OB+PnZ3NQ6toH9/E4akdtI5vKpsXylSVnTNNHdTCE85UHOBOAJ85V626N0uyUQMKbdbQ6But/+aRcXighLt5+nCn5ylQ44j5OzJ+DrP+tzaZQAxuonO2u4dF/2x3jdvgUEbHj/w0w88YE74t8UV+Zu4IsJHJjTqjnY5ib3+ee3nNpIOmHYZ9DKaKmlEoDVYCHGV02zpGevbWMepk14DsBDV+mNzCrI5wBURKvI1oE209u5qr+nLw9JuueX8ihyuu+iyu/ebNyMwNYLE6huL6JAbHnsPFF1+E1s4kTT/Gt9DK0oVdGcALP45b6w/uAcC947yOy1wuF+qPzfDiX4AZ/azAEzehnY0W09bQOe8Aesr+WRpAefAKkHP3UtyDs3B8hQ0M/KDpnsPN+w/i2lvvx83728nqL6aMXY0bMiYWobDz78hr8fQuob5jFk1dCwT+eil2RgJvjaNT3L5mV+mzdpMSGGtnegU0rJk1NDL6Hawa9o9zulpG1/Glex7ZdTf3xbu+D+0EzlT54W70hJIDqAHMNa9DQKBh/JynZp6lJT/OsIOiJaNFXjIgTXC0P1WEv39F+1XFwXr/oQ5c/N5LsS/0OHrnJjFXmcZieRqVyiR2KmT2eKVyEq9tnMb37rsDf/a5a9EsY9/JnV0z/1ontpX9ax6hekJvfx6NPUvYf3QGj784YwG/U3jsuWk6n59mYMhjYY6EkRGuyAgOHJsnFpEdw/uPzRLwO0bawn1HZ5hlZFfx0VltyBAJgsMwVKMVVTOAGkEKFvhzNoNERAfIDETzUBWxkSpaR9fRakWmtIyusxTDDhfnYOiBMiIZp7vWycaYcGrd0El7RT91t1J48gaig1V4GcA1sIvXE8/C37+CYJJ0vP5E3sm89OV4PZgjOUd/TtkxcWVKRWRDh2lvqDs6Y1U1LsDTs4RQuozmwQoiAwUcHFtHKJ1HsG+JnKvK9H0CH73qekQGCmgb36Cmi8EyWkeqiI3SKD06vKYPz9j4ljJhbWMbOHLirN6PhyY30T6xgeiwMdz4xQySMKNg2+VtAxDRyUUytBk6PLWDH516BUdOvIzW8U20jW9pPEvb2AYZ52wwP+gcBTukFhL4bE0EwpnqOcBPwGGtY1+yJuU+FjDZwoYRubbOPW33MJnRzjV/mc9lbfL153B745O44c6HcHvjkyRJsTasGpsla1Oy6KhQpDWWwL9sWCjeKK9/LhmEDgdyapfT3iAzwPT1m/pOGdl6+3IID5TROr6Fdta8H5zcRvPwmhqmRD/o689pTaonbnS/nr4VlQM0KWGxpA5g2QT6k0WE00V4u2fhcrnwRx+8HH/9tW8i9vQhuL0P4r2XXoK/ve9RRIfXEBvf5g3MtrLXDrb7wo/jI1ddDzd/L9ceANw7/n8el7lcLnh7FzUB37Qf2CBqwcGANEheWieBv4auRatGyjBxcmOogLbH+cDUsNWeZTR0LuDyT1+jEQWSBdXQuWDCmTVok8ZPUjHkcCHWgERxcDZ1LxDjZ+3obGdY7eJe6+R05GKlSw5gRqMRk90nRola7V9sdA13+36y627uTveP1GxhwJ8xmziq55JWuXyq5MijEsZCIgd83FJiV5OZBbigdWYBYQczFXIH8zg4NEBRP+F0SWulBBh+5ds/wAUXXYyLLr4EvZl+5FezmC9NYbU4he3yKby5OYfX12bw2to0RlPP48OX/ymu+qvP4rbvNyAysrk7+BvfsgDgujoPm3qzOHBszrB/z0+/88ls4GMKCM9g/1ECgvuOzloj5DPYf3QGdR3zePzFM/x1cxJAdNZL2W5EGwBKOLRUw/n6C4aRsMC6n9/nsLVRMKL/qnZEK+M6XEXbGAXXto5taL2ZCYUWRsYK9eXrNKQj/hK7hAtWlV1OH6jRwQrXtK3rZsEbp3YLb5/pPfX2mZGw6P6aepaw/4VpHDg6Q05p6dEV3ZPcrz0sGeGw9oZjMyZHkMd9wVSBx75VBBNZBBNZ3NV0BBfsYhC4vfEJAgx8zdLPXeDrc9Xcg2yUcHRmp4vUnDPAcUnpgtWPW4KP20l8HHhsenILMMaqkmrkWsc2cGjqLJ586WUcPvEy2thQd/D4WRw6cRZPnjyLQ1PbODK1xcB+3dLXVkz7y5AZ4yoYtBzhTvbQ/LdsPA1TKK0fdih81WxGNT7GfK7GLytQWtYwG6TK580S7TNQhi+Rx4d3i5dhEGj0kCY4X0PtE3liffmkPua8gkEytpV0U+vfBezJ7+4cA54YphREmhYTNzPYQY5WaZvcwUF25LZN7iA2SveCdvvGRdbAo13LTCLMojELUW+2n+8dAYGeeBbto6uo+/kE7ov9HNfdeh8+8mdX488+/yU88uNeNI9sMPO37ZAwtE2dRWxsHd8OPI2vfPsH+HbwadIHDpb2AODecV4HA8B5NHYvOJLvnTU8CyyON+XoIpq3gaHS4DLytcwUTb0rlvjeGbrsjmdx84GDuzvBDhzSfDJxueqo1q4V6s2yW0x2Y1nNkBIHoxSQq37HcnI6RgvM/omWLpgybR9BBX8kHLedcw880eNg/iTc1US/UA/kFTU6nI9ceR2NaDO2484epTjBnxg8/MkC65UKlnhfgqtX+KQdtTCIvqR5iPksx2LABojs9jOsY5HNLvywlZaYnlm87w/fj+u+dTMS0xM4kz+OpeJLWCtM4bX1Wby2Oos31ufw8uopvL52GqXZETzwnTtwyXsvRWBwzWH8sAHg93+cYOf5BjMmFXjiK2jsWsT+ozPY9+IZBXs/fO4l/PC5U/jhcy/hsedOWUDwlBkPPz9N/9/RGQvonca+F06To/jFGQfoU1PJMQoT3//CGfz9Y634ws3fxU3723VTYmvA7OtI2HRbN6pRICmpAWN9J2f7hTPSVEF6wOZhqjRrn9jGwaltDox2jvZoPF9x/O5UZ8iO0+8d6lJGxG9tFrTTOmk+Rgcp+y+YLrIeL2cBQNZQ9WYV5PoT5CaWe082ZO4eFu7Hs/D2yUZtSQFfQ8csGjrm1FzS1L2IYKpAhpS+JYTTeUQHiginC7j+jgd3jQi5/o4HrXgjY7YICcgYMEApMlhV40wtWJDYE9l0+VMla6RYYOBM4O++9g4EUiVEMxViKgcJXLZPbutY2fHg5jNmM9ujazg4sYnYyCrpvIarNYBwTce2tQygMxWgYq1DBhAG+ZQIKdX1Dll95Fa0jDiipStc24kGK/yazPjamMDWrDFxFXc0PXkOSCdzyZNaeadO6HQZ9x/uVuNSkLvMaY1h6UrSsIEScm//rnVttH7vDgezfWYqPBkRKYyRy/i5tSk6tEoRZ5M7ZIbkpprY6DpPIHKO+90OeZb/buheRBPXJvoTOXhZz6p97akSfPFltI1R//eRExStFpvY4cxdki7ElPmjtbFtyuhYWyf5z8a3cPD4WYQzxT0AuHec13GZy+VCQ8cZdvY5R71OjZ8dxWKAoWSmSYeq6PQ0M8sKea5no4Ydyik30rW33v+OWVCUKSZZd1xx1S/MS9Y8pOIitF2Bl8fY9HlWzybW/bnjWX5w88NbAKC1w7f1VLKYhAZsgXSFBN/84P6Hx5rRPGSFzlpVTi1W/Es4U8ZdnqfwpbtIhxNIFQwYsMcmHNti9//ap9Em2j3N9gjfcqhKDAFrlnTEbbUXGD1NwYp6MGNmzQRLl9A+vo6vffsRfP7GG9E1N43szipKWwVkyy/h5Y0zeHn9DF7fnMdr66fxanUGr5TP4PsP/jMuee+l+Oq9j+zC/u3ozveW/W1ondhG29QODp94GUdOvoyDUzuIjW4glC7BE19B3bE5PP7CNB577hQef+4UfWSw9/gL09jHJpHHX5jGvhdPY/+LM3j8efra/hdnsP/oDA4cnWFd2jyXys85KqYau0g7arcUXCDMdMcssWSsXSJNXF57o21tUiBpgTPRZcqmIu2MEAoxgyWarOYRGgdLDEozi9YjgxUOaybwFx4wDSHy0A0NlPH3P4johkLYEzGiqMknU0LzYBnNg2V4+7L0PVMcXMyvWTYcnt4svP1mzO2OUxSUtBwYliWvrKiHs0B1cylAsHMe7t5lfdD744toHiggwO00wVQBt9Uf3pUBvK3hiD5cRWdpZBDUimJHH/kcLGxp15NyAAtWHqDVcNFfwLceCSGYLqF1dB1t49S+0TpOna5kPKGRc/vUDoLpAr4bexaRTAltkzvsxLUYt9F1imjhCBfb8Wuil6o6FaDuXpPjaUc6OdgvBYfGKGSDOEdH+Mg6oiNWnAwzphGOJ4pkpKKQGUqOiKLmIsMsfvGu3SN5brjzYd6ksO6YtdJyTVJ7jVWfmSrw9UZMoNRT2rKJWoeyaJRNlaYZiQtolVGzbuBlQyYsJHcEh7grOjxYRev4FtomtxEdWtUWEw9f4xovo3FhtMnxxlfg7aNNj4yew2ly3Hv6VhBKrKB5qErVhXq9UMyaAL92ZiHba04FgRPb9PnkDoJ7QdB7x3kel7lcLtS9OA1/fw6RgTKimTJFsrCmTwwejnDdzprMNA3UXagRy0u37rI1OtslNLp7CTft372u69b6w6Tf6DOic5+AOh7fytcdN2q/YSy82tUp+g8TCWCDJHusqsG7uvsssxOOmD8ZMUnDR3RoVZm+ltF1FvKvapgxORC3NLxVqppkXFULALWayT4lV0seTlbzQG3fa5M9ouD3xMtOT1/C2W9Mo+KiE7Q4xixG8C2jxUOTm7jy2q/hgSY3pgpnsP7GK6islVAun8Jm+QTOVs/g1Y15vLE1hze2F3Cw1Ys/vvwKfP+nqd21f7vUIrVNnVUWpXVyWx+u7VM7aBndQDhTpoiYznk0ds6j7tgM6jtmUXd0BvtfPIP6YzM48OIZHHjxDBkOOmYpToJPD0cQ+eLLaOpaQFPXPBo7Z9HQOauA5abHW3e9Lm/a10b91/0riGRoTG5nm2l3dIKjXzRrzlkV5rN6gwPpMsIDlXNGw83DqwhnSmgeqqCFg7rt644YrjKDOWIQZUxvMyXywJSxYJQfzM3DqzgytYVAkhgLfb0p0zIiGzB/gjZfYiDx9ZFw3/QhO0+RUtBYbL85Ti0AACAASURBVEVF857eZXh6lxBMUAyNr28J4UQWwVQe/n6zYXPvGhFyLUVL1dSTyUjR189gVAOKnSN4MXkYwFdwAj8LAHo1M5HOQKqE2MgamoeriI2tk27s+Fm0T1FzTdvEFtqntvFHH/qoxnl85aY70T7B1V/j1P7QYrV7kBHFAMBgpkrmL94YGOBXNBtCB5AtnsNsBgUAZkwDjQl9N2HkDu2hg+kz1150eBXhwQp1Vg+tIjpYpWuNv8ednh+9IwMYzlQIzGVM0kBogDrmQ6mifp/wAFVUigYwmMyzWY3ajJysnlMPKa/d1j/aukbJ4CQNM1WaBrnv3I7OkjrHQIpeU+vYBqLDFMHkTwprXtAJkoQ/6zXNDKCvP68bvWCKjC7u7kW0j9Cm7PAUs/rWuLeN2b52vpZaJ2kDbDOAsfEtlRe0TZ3dawLZO877IBPI0TMIpoo4NEkRCsF0wUS6SOCy6gGXHK5KPR1fW4KjJsliFpssjZ4ZBS+hvmOOmRZnFpS7Z1kXcxl9CqsXTJIQWwCNXSYvN6sIiTVygMdatWG+AgAN81dGIFW2Hp4yZqgqCxjO8E5Z4hcEAI5RrZ7ouaIcqSIdsBHpJJVFrGa849RzWYt+LQuoQK3IIDmvBptGDrRu6s0yEDSLlY4qWQdmOj2l0snuMrXAr76uCtrGN/DXf3snbn3wUfTOL6O4vYFSdQU7m/N4bSeLt15ZwVuvLGGnegZeXwP+4LLL8J3Iv6JtN+A3WfM5n236Z0YQHROdIO/QTUPNlqnZG5GmGmJgY8OraB/fwMGJTRya2sbB4zsmJ258k6JLOMqBpAwEUHx9K7jmlu+9IzMtOlRPfJl1S0UrSmXF4Qj2JSzAkSia91zr4sq68dDAYGZgIoNkEJEGnhYBICNriDn6pdetOrA1NLPrNJwxzRP0cK+gZaiK5iFygbcMV+Dvp59DZBbBdAnRwSrJAQRc9RNjE0zS6E6kATp2TZdUsG+uUWIDJc/N07tEYvreZQQTK4iki3B3zyHYv4xgIqeB7Z5eCv8V09ktBw7h2lu/h1vrDsEbX1Edmf3eOc1QBQdDeC44pd+ByY3LO183Zyfa4cGiVyOASQHa7eMbODi1Tfc7V2e2jm/h/RYAvOkRH60JzGq3T51F++Q2R0NtIsoAUFksOS3mthbAOk/bAMSGI16vNH9QdYNVzv20GUZZi1Yt04jRpUYGq4hkyo72FHsk7O9fMRVzKmu5Fv5Ejsb6nEogveKRwQr9N8dLhQaI/ZPKStLtUV4lZWM6TTHShRtleQ0ZV/jkez9m1eTZaQ2mW9ukKYi8yOh26XUEUgVDHEj9Jq+h57iSrWePGAZl7NycKeOJyU0EU3lEh9dw+PgOtWtNnVXJgL0Oxsa30cpGENVE88/SOrGF5pE1hAdKaOo8swcA947zOogBPHpGdyuePhqTitNXmjTsXD3T1mEaFuwKLYlm0Qwkyy3VxO0bHs1RkgdlDu6eJdxSdwjX3no/bjlwyIyW+vOkV0vYfbgG6KlAmAGej0dUpPnIayuGpzeLW+sO47rbHsAtdYfQ1L2o41HSbvD3sgThNnuiQam6+7RF20br12w9hO0FMzpodte2iFtE/HaTiOiPRI8n2WTOyAN5yBqXomS31QJAbXRg5lTc2HbMjbgefRyl4E+ZqJva19cysoZvfK8eH/uLzyG5MIPsRhXFahZnd+bx6vY83nhlHm++uojvfvdu/On//CS+HfrfvOP9b5g/XgRbJnbQMr6NQLqEFv46LYTG2SmnAMQWa5GU1hoZz7VPblOf9eQ2j1621WQSzpThS1J3qQn4XeLR5Apu2te66wjylrpDnB1JGtMQh3k3D1HHrMSDCDCX2Ap9j7VT2jA4djwMtbxIO0tVW0NIU0pgr3WcfsZW7vGOjW3yw4IyJym3cU1BY8swNY60DFcR4tEvdQjnTZQMawLFNS4bn/axDTQPVfnrJWYby6zbotcaSpf54Sk9xSXNYvP1ryCYyCHIGZq+vhVEB6vw9S7C1zMHX3wRjWzUauya157hJv5dCONPDCVrJoeMYzZs6fyoNlE2TQL2BMyZuj6pe5MNoHYrp0ucjSj3E3cS92dNnRk7PAksFxEaoHqvVmZpAskcGjtOw9e/7Oh7NafZvNDY1TBYQc3ec5rBakFfLRA0gNip6ZUqSds0Umsc0a9rILwzBF4B35BhD6NDhmkOJHK4o+lJfPGuh3Fn05OQDFky3HBm5aCRJsg1HuSRr0ZRcauRP5EnhnDQ9GfX1ihGZYw9bGmueTPUzPdIdHhVpRFhMUylTXRSeKAMH7uOg6kSPWOSZBJq5hBoYZL9cvLmQHqzBTSK0UTAXyRTRstQCe3jG9pRHR1axdPTOzhojX/fKQ6r1drsxsa3EMrQdemOk752/4vTewBw7ziv4zKXy4XHnz9FbBzXbTVw3ypposgFqWXvls6vlvkTNk/iVaSQW3KYfMzGebgyRxfRpOkMlRBdW3OheWqO4OOiYwdHTlYKy6XR1ApnSHEgaDyLyz99rcOpdvmnr6XeUQGT/01gsnbyWhlcEvQsWX9fuvsRBSBaxj5sNXxI2KruwiuO7+3Q86Ts1yIAV8a1YjSwgZ8wFcZZ3SimHKs+yQiZVzS/zmuxVMYdbAW46njJziIsw9tFO9CD8SG8VMlhoTSDytY03nxzCW+9sYQ3Xl/ETTd/E3/znR++g+bP1v7toKnzDL7wD/+Ei997KT565V/C5XLhq3ffj+axLQU3JNKuAYJjWw6W0AjuN6xqLNFh0vdqHlnTiBsx3vg5mkJGuU29WTR2LuCjV1/vjGC48joaU3H4tr8/RzlwwyQDiA5VHEygHZtiA3h71OhX0GUeVCb6hVgQYZdaxzYoZkTqGicIBNrC/hZ+v26891G0ja0hOlRRQBkbXdOcSDEKiOxA2L8wRwFFh6poG11DbLiC6BBJRGLDq8xMWuCUgW9EGUcChOo+TxUoSihF71fzUBXBZB5NHWfgjy9pZ3ijtPZw3Vwjj9Y88azez0EW7tvRJbHRDbSObRIDOrLOjFqFpRxk7BC2z22ZpTz8u/YnCzyGJJbTxwYXuZc88Syuu/V+Xr+WyfGdyMOn6w69B61jwtIQMDnIIE8kIK3jBgyq4H98E9LAIwHS5vfilA6YVp8SaicCsibq57XmIKv9J8QjZu0bdzCFFY0b0oxEcZ3bf0cAq0xH2IGsLTYD9L4I+JR17oY7HqJ0AYmtUpduTqNbfP25c9hwYTFtgCpB2AJQZWzdMiKbHonDoetVkxqssbgydmx8ExLAUaOZLFppCzI1MS5xAo6kM4wMVhHNVNA6XEagf5mvpxxCyTxaR6ponyTwJ9dCmz3tsM7YhDMKxt275NDQP/7CqT0AuHec13GZy+XCYz8/4dD7Cfg7IKGtmqdmGhEkENoGgNKiod2LPPbxxg0A1Gy0ZMFkM8WzNZlQeU1uN+yU5Vysca2GWM9BC06Zxgipgj6AfIk8bv1vxOS+hHGJBS3gJd87aC1CDvDHIn0Z/Xh75hAb20QrA0Aj7jZ5Wo6at3NcvxyoyhoZowN0GjQkZHg3AGg71SQ+p7E3i8beZY7nWN4FDGY1uFS+p6NGztIYyejEnyrC0zmNCy66GI+2PY3ehRxOFGYxlj+Flc2TePvtRUxOduPTV30KLpcL97U+9/8BALfxma/dqmMzOT/2l3+NlrEtdVELAGyRqBjRVI6scS/qGrMpzt9VhMXt0sAgwIAeSlUdkcZG1tEyvKbvP7FbJdzj/ym+dM8juKPpSQTTRQUaoYESM6oECKJDVaP9SRb0mvfEVzRT0mYS/EkTkutgbKyoj/BglcfaAuzIgBAT1m90gyJixjaUEWxjCYK78wy3ClC1njSNhNL2uFkYbukoXqXx8nAFLYMlNA+W0DxURstQGS1DFUQHy3o28yiZsgxNh3SL6GI59y7EjGEwkUM0U0IwmUPDi9Pw9C7BnyD2VXq8G3mz2cRrio/D4v0sBQmkSwhlKJS8fWIL7RPk3GybIHDfOm7GqtGhNXaCGtbGHc8ilC5pDBRptgh8SwyJBFzLfeLpXcbjz05qI5I2GfVb7m+NU5KecIlSKqN9YhPtk6RhbZvaQeukJewf31KDSK3BQgKqAxJPUysnSNqyjd1Our5MTt/DuI1z+sQsEkxXdD1yrHOZikY/OQPInQ5r0yBis4xlDZMXp7vcUweeP66SHNGO6rqfyOt0R0xSYgCxgaajy9zSNoqRRVjw5mHKh5Rucdm0RFiSEx1aRSRTVbAszLFPmUCnvEAc4bJps010gQSF8LcMVdGcKcHft0wZncm8BvEfmqB+6tbxbQaAFD/TNrVjbQ62VfPcMraJYLqgoK/Rklc9/txLewBw7zivgwDgz05aLQhUfUVgbx51XNqutVhc8yY6wCbL8evsyFx2uKH8iQKnvVvZc0lTH+XtJwdVkB+6JhzUbrko6ohGnbmsc6ERQZmYDl68JNYilC7ghjse2j1O4vYHTfG59bG2vN1m/2yRsTAuURZLN2vQ6ro66yLngL+qLqB22boudDxCswOdTY9mASaktjagNmfVE1ltDNbHRnZeN+p/m+DSJq4sEj0kxcQ4QaCMxQOpElpH1/Dt0DNwud6F9//Jn+JHz/8cA8sL+D9jk7jyL6+G613vwrv4/NYDje+c+ze+jdj4NupfPIWv/nMdvnuoCzfta8Ojz2SURTXslvS4VnTBbxlZQ+s4gSAZW7UMMzjg8Wjb+BbaxjdxaHIbh4+TBsses0ldVzvn4dkPlZaRdbTyqE42BMLMBVIFNSPRZmYZvvgyWkeqCCRXEEyXHE0y5sHBYyVHW4JkmJWdOW4cLWQaHdY00DfGbOChEy/TqHtyiwwH4xsKhlq5HSM2apjQFs4ZjA2v8oh4XYFk82AZ4XQBsaEyIgNFhFI5Bn4lRAcpeDo0UEJ0iILDZSQcTOUR5viW8ADptwRwxkZWEUhkEUnn4Y0vofHYGQQTOUoH6JwnV/axWdR3zmvVZH3ngo5+ZfTqSxBL0zy8htaxTf29NA+voW1iC4ePb+PIibO6KRODxcGJDYTSNBkI9K8glMrBE1+i9SZVRIjNJ74+cw94+0kmUd+5QNOMniV6HZam1tefQ3iAdMgeZdMNex4coLWpZXSDfz/bOMQa1Bab1ebfU+vYOlpG19DKRrIwB7P7ObbE12+MNbIG1Lr5a0PivX25XXP6PP05+B0TjooFtGgdsuUp5/aHy5pG0VC1MpGg5TxXfSZv9NXQlyjoZEikQBLIH7CCnv0pp8vZgNAa1tJm9/gelV5z0sVa99IQ3UfRoTXT7S6TmFSJRsSWecuXdJIDATbfiR7WlyggnC6jdagMT+8iIjxulmdXOFPG4ePSUGPpnLm1RiQtGiE0sY3m4VUymUiuJm/WG7oW9wDg3nHex2Uulws/fPaEVY/F4I+Db+sk8qVzQcOeGyxTiA3+pEuX9H/Ljj5UEdCKHk+0FhoImizAG8/itoYjuO62B3Br/WF44llt3whaDIkyFwOmaL3W3UaggNi4lpFV3OU5twj9PRd+DLfVHzL0vzVudQBAa1dsxgympUF1KNw3KsJuk+z/TgDQGr9poHTJZA7aAahJs+OsdVlK2KinP0+5aj1LaOyaQ1P3ggnOtgJN3QIAa5zYTZxer9ExbBLxWSMmGYsHUiWEMmX4+5cdjN3lV1yBv/rC53HBhRfi+m/djKncNPY3PISPfuITCCWzaJk4S7lotsZFnHB6bjGgo4om0bwJuD44uYXDx3fw5MmzeOLkWRya3ELb2DqiQ2X4+rMIsOuQIiwqHLC8hrYxAjjtE5to57GpCMbbJzZxeGobTxzfweHjOwrqw+wGjI1t6ChMHq5eZvbcvSsGYHcTSPD1rbDezRgSZMSuek5rnHeu7MAwHkZuYPVLa7bkBloZ7KkuUL9GWWYGDJH+r31iU9tG2ie3qYJumHLpwqkcomkS75uYGM4HZJdmgPMnQ+mSRiCFGASGM+RGjg6WFRiG0gX4+7Pw9MwjmMqjsXMO3t5Fkl/0rWirUD1XwtV3moYhD+v/xLAVSIpDU5gUGucLCAwPUp5iMC3axhICyTwOjlOziKd3Ed7+PMLpElqG1xAbJgeqABDpdvUl6B5o7F5UDaJ209oAsC/HDLCEYwsYsyJ3BlfRxnpUmhCso3V0rUbPSox2bGyTR/4V1cmFByjkWhgyMiDkrfvUTEjU+CKbtlQRtzXsHqZ9W+OTjvw8RwqBaCkVDNaycE62TyQiRsLilK/Y3eW2nMVR89YnUV8WAKwZe9vgUlIStN0oUz4HEIYGyoaVVsPUeo1xap0nR2UNF5f7UA1BfSu4reEJXH/HQ7it4QmSL0lINd/bkUwFkTRJHYjIKCqg9fXnEBuu4pAAQGvsSxtglrnwtUCbzSL8iRyaepd5wrbsWGf2AODecb6HBQCdvb6OHlU7vLn2rGH/tCeVLfE0+sqZ8a84ba1gZbrx8sZJprvUa+GNZzXrTE57HCECY83QYq1HlBlBYUBaRladAcwXkZbLF19GJGNE7bIQCBNpx3G0WNECysCw87J5ZB33xZ4zjCCPIu2i9pAFAnfbLTt0WApITWq+T6uphP3LMyvIherM/jV1zMDdNcc5a2bMJWNKDzc62O0pWtlnj704OkYNKfaYUrQyqSKiA0X8r2/e6QCC/+NPP4QXji9gKDePU8UpfPnrN+DiSy7Bl2+9B3d6foSGY9NonTxrHL9TnHc1dRbfO3hUP28/fhYHp87iyEuvUFL/5BYig2VyFyZX4IsvwdO7CH9/FoFEzrDILHz3JwoWk1tEiN2MoYEiwpkSYqNraBlZRfvkNlonz+LpU2eRXnkNz8+9ghbuIw6kCnDHl9HQRVpY2Rw19Cxxj/AcM1cEFnwKynPwJ/MEYvpyCLCL1OHETlijOut+kIewmEPCg1WrnWHVOM5H1hAbIzDXyoCvVX6e8U3c3/4igeNMAS2ZAp44voO28Q00DxKjFxnIIzZURvNQBeFkDs3DVXJmZsoq1ZARt4AiZWhS4pg02YNh1YvRtRwZrJB7uD+L8EAJ7t4leHoW4Ysvazbo/qOzqNNNp9mI1nUuWJ3LBABDzP61jMjGa1Xv/5DE3qSKBiDw2NTdu4SG7gUEEnlEMyU8eWIHh09QmG4gIS0nIo2g6KoGZiB9cUoiuMv3zDnVf24bhFlB4AYA0hrSMlxF68gaosOUA3dIGifEzMSmAIk3coCrVBGRwTIauxfZaW4x/+roN7pgbTBi0HX97buHad9w58OW8cgAwEDN9Wdfj0EHWCTQZacWOMwpvFGQpiF5TfeG/tXE97COjtId8sykm8gd27Xt1Dta94u23Vh5gQoAK3rPxNj41cISitZR6QHe4KilqsOJr+tc/y4M6qevhSee0+dYIFXEkalNuLvnVPNOmYA53RAemdpGO7O/ZAASp++21r9FBivwxpfQ0DmHuo5ZXVfs4oTGniU0dC3gsZ+d2AOAe8d5HToCdmT7dZq2j0bL1SsMkTu+onR0EwukpQtR3L1U17aiTkh7pOtgOvjjO6bJe55ylKBH9MYuqb5GnWLsUhOGTkJXhSUJZ8q4y/vjc4rQJVSX8s5WWOBfQDNHtgTTFC4bTtMDLTpYUTZKzAnNo+u4y/tjdv7aDjmb7ePPBzjB394xa+yLqX0zI8OC6iDth0woTZEU9hi4vmMOTZ2zOHB0hiq3WFPlkdq7Xqu0nMGfu2eJWBaLGWzqJQ0n6ddyjhGTHZItXwumi3joyR586vNfxnsuuBB//eW/we33/wD/Fh/AyOICnkkm8N73vU8B4h984E/QOlJCdKiEQCoPf2IFgVQegVQBdzQdQZBbAUIZqqALpovKQkUGjGhbYj6MiNu4VAMJZqwSed5EFNS0II0GETYzhAd45DNYQXS4ip9Nk7asbWIT4UyJ3r+eBXh6l1B3bAYHXjiFH/7sOB57dhKP/fwkHnp6GA8/M4p9z0/D3Zs1QcoDrEPlYnlP3DhaZUMkr11if87pcR6gUartdhXTgeS0NTMDRoBwDS3DFbSMruFu71MIpQuIDBQ1ToZyBFcppHiUImUimQpaRtYQ4BBeP7fIEANHWl6z0VvS+9vbl1NQEmTAJQ/R6FCV9KKcGUg1WYvq8JWxL50EAA34m0cD9y6LTMHbt6LAXt2naTNB8HOen5d1xna7j5uNZ9Q/PA93L0XNNHEzEE00qOHFl6D80ABnCvoTeW0r8ij4I91tIFlgtrmqoEHvFd5EGtkA5Te2cx5gy+g6jYHHRbtIQLB1fAuRoVUE2ODmYZbR3ZvV+1+czGaMWlAQKJtsuTffiQG8o+lJHd0HB8z6I1IYp7O4bK3XFkji/0/zSBOmAcfWK9vg7db6I2Z6YWVmGpe2Wc/o5yyYKj57GnHOptTeQJWUsRRdq2lCsRh0K76neXjVApVm6rPbs0n04/JehDMVxAYLaOqaMxMvi/0LsVO8eXiVgqAntvTfjQxVEEwX4GHgV99JwfSUwGEydRu7l4zmvmsRP3z25B4A3DvO62ATyEmL6TMfG7sXjUZMAaD0gua4R9doEzwcuCwAUHQrDhrfyj0z4Z5lfPHOh8/dpb77UXzxru9r0Kc+5KyxVnSQgkaDAyXDjlgsneigZFQrAE1iBew8KwVq6l6krlBf/wo9DPqy8MSX4E+sMMtIjsrm0XVEmY2w4wqok9MIp0W7ElQAaMc8GHNLgGME7AXe7if2ctaUr88wIx4O5G3sWkC97Bw7F6mtRUZrHXOo65hDQ9eCdj03dS+o2L5RxgwCDHuX4e/Pc5C2yQ3UAN60aGFM/lowlccPnxnENx904zNfvw0XXnwJ3v2eC/CeCy7AR6/+LP7yKzfhM1+/DY88ndR8Rxq7mLBr+fk1w401oMFUEdFBYqjEnafjqVSJR5J0PYXSBR6dFXWBltFPZJA2EhLeHWPTjrkexBhRRCCZQ0TNDlUdzUUzJe6tXUXr6BoiAyXsf2EaDz09gkf/dQqNPcsO7ZO3L6f3hN2v6+7Nam2f6ejliIm+Ffj7c3QtDgizZX4eaWZoHqqgebCEcDqPcCqHyEAB4XSRR4gFZZQlEJiAILsj0wW+rrNU/xanrD5/P71eimXh06qJdFsgsKmXro9AqghPPItAkn5P4UwFrdyp6omvoL5jHt74MuqO0UPuADN/BP7mHPITiZ/SOkrW4UmTjT9Z0PdQw85r2DntGucc0oYuu81Imo7mre+/pCHEAmz9iYJGK5nucGO48vXnaSTNEoXwYIVjQIT5kspGAgoxdaWb2BJpCYmJEWR4lR3qJZJ1xA27L6DbrA0S4k4aYbORMKe3L4cP14Rpf/jT16rrNphiFzpXs4Wse9GwbUW9x1UGwv+OBGmT0cbarFqAVF9jomiAeZ8ds5PT02N9nWJ67OYiE4GlhhthfQfMFIW04sJummxDqe8zJMGGkgTNbNrTthHWlt9w5+768RvufAihgTJLQ4oI9C9ru4hMvEgmkiVne+8SWoRQSOVZUrAMd88CJ20sMBM+p1O4uk7TsiX1rCK/euznewBw7zi/4zKXy4V9z71kxrkc2NwgtW3dtr5vmdyivVnNj1Mg2EuVUE29yxqsqT2ju7AajnqfTAV3NO2eJn+X5ylHxl7LmMl5o8+J2fMnVkiEnuGHterxTNp+VGJZ2PmlfaqWk0zjChysHL/mJHdVco6Z9J/6+leUsTLOYWb3rIgXYvxqk/xtLZgJzlXDh9XUYWsAvX15JxvB+iR3j4wP6MEmD+56rjcTrZX5SCxhQ8e8g+WhflfD8pgHrelJltgUBfqyIEukSYZclQeeOw5f3xJaxjYQSOXgS6zAdCwXTdhtsqhAThy4pIcibQ6NsLmJoo+0fpLXRq5BYmPoozRZyBiOQpEj/OcymovyQ0CaNUQ87k/SuDjCo9ewygooj09y+cigsobWsTU0j6yisWseP3z2OB5+ZgzuniVlA6RXVzSZwp6L5sn+OwIEyTyVUzkCmRUoX0xaQVpHV7XGLZIpcXBuxbQopIzDUn9HqSIZsvqW4OUQa1/fMnx9yxzDQg7c+s551HdSFJRcMwTK+O/wpsEbN1WNgWReR8bRQXLke/tzCiLrLY3fAQv01Z4CyhqsOsoGfl0yAtP4qR7JHHWanBosxkS+34EOk29K9Zb0ZwJuPdxc5OX3XzI11T0fp1xNiVYSB3FksEpmnKkdHDlxFgenKHtSQskPTWzhyZNnqTJOI3vWNR5GImLaufkhNraJ8GCVR4i1bUdyP1ptPpwraQMzCYj2MwC/reEIrrv9QdzWcIQBboHBn5NBl9G5HeYtoEpMe351IHNbigX6iM2z2DvrNJWVOQV4plt3xfG1xt5luHvl7+bg5v/X/rnNVIk2RypNSZpnjEwsjFbcbP61XpHv/+bhNQV+Ei9zp3v3Z9PtTU8iPFiFpy9Loea6STNMqUcmK12L2ontvC7n1fhUz/FrdY5rdN4xjbNlO48/twcA947zOy5zuVzY//ypGiPHsoIAs/s040NiLXK6y5VeUOlDtbPqbGBwDk1v9Tr6k4Vz0+Svup7CNFXwvun4eI7TlkfD0jEZYhYkao+IJZPP2hEaxs4GgWVHBIC8bmWmHLVT9LBo7JyjXV3vsgmwTuQtxpRyx9QRmjTgT7Kl7E5fexRij4NlcdVwZ15APfEVzmFcNDlq3UtWDzItHvYYob5rUbtvm7oXNCy6kTWBAu7FIexmsbbs4Im9yjvGlaG00WCZjK08guk8fIkV6+FS0F26vN+RTAX+vizl0CmTR8GsOmJjzWMgmadFX1x5DAilEk1NQYNmQ2Cug6pGbrSOb+rir+xwpsosr3ETiuFBQKLNJDePrCE2Qp/7kwXse+4U9r8wbSoLe5cV4Mn14e414cZ2kKy/nwKTwwMlRAZKCKcplDaSIQeuhDk3D5bp43AVEQaA9J6VNNbC27uE9ums6AAAIABJREFUgDLxBa6+IhAtr8HNVY0kFyA2orFrEXXHZlF3bBb7j86gvmOe6vU6BBBSOkBTF7OBDGY97D4XdqOJr6GGrgVL4zfHqQOG8asFgDZIqwWANqCzr+Vzzu5FR5+5GN3M911UrbME2ysLLmYqq7O8/tis1oBRfqa5N2VqoDFU6SJNIMYp/qVtnDIp7SagltENrYaTkPJ2rj8kkxS1PkQypGU1rSRmfXD0f9vNFImCMyiamTxnkxBl78n1p+yVHTdT0+qirTBWBE0tAJT1ycNgzdOX55gpAn11x+bQpAAw6ziJYMjxyZvbXgKATXFzClNoxt4mLUK+JsDPb7GBdvi1RMZIiLRMiSJDqwimCrjL8xS+eNf3cXvDE/hwTYbsh6+8jjaag1U0dM6hdWQVIda9ambiQAVeYfz7Vhwd2I3dwqwv8MbIbE7UBGVdu/U1o+DG7sU9ALh3nPdBDCADQHGJeji3z82jXDUYSJ2TlZ8lYMZrfXQ40njRsHdF58a4MPBKl3CX5yl86e5HcJf3x2ThHzOhtuT4Wze7tndw2mpAqRopxHjidPfKLs8k8Fc0t0rAhuR5ib6JfqY8jUb7pJeYduif/4fvqO6DFmfRSBJzJaYYeejKe+vpqxGWW6enBgh64mYhFYOH7dwVNozAIOmcvPEsR1yw2L1nSYO9ZbRAVX0L+jWvAr9lHn85R1/u3hV2Zq5obqAwB7WGBll4I4NVZaUkfiPIozHR7YUHSrj2lvtI68eO0mC6iEC6yKLwvP4b1NxQcFSRiW6nWcc8ZswbG9tUCUF0eM2R52hcpFXERtfRNr7JNVPcKMBhshLKHFGHMblyY6PrODy1RX9vsIqmnmX84N+m1MXq689pfpz8Hrw8evWym1QckRSVQRsYkiBkEWQJQiCZQ2SgSKaXZI4ctokcvKzZC8jYkZnZa26+jzVd9L088SW448to6llEE9esNXYtUCcyj30bmV2rkyzQowQEDxydZXaZR7bHZok17l7iEazZOLp7s45RK4XKO9k3JyCzzs4FB/CzH4L1ypws6n83Wfoo6Sqv6zAfBejpaeudpc+8pt6ytvWo6f9l77vDoyzzrgcFBAuiviquuoti110VUFiwyypFiiIWQJogHZKQ3jtdUZHFQkkjnfQEFJWSkJDeGyDSUqf3mYT3fH/c9ZmE3fe7+DfPdd1XZiZTnpl55rnPfX7nnF9MEca++7HCKOIex7IJSykbWAzPeFIqJg569ltkv3OSd+qTXIbA9CqEZjcgLKcRkQUtpIdwLm1PKLVGDM1tQlhuE+lak1IJ1vZSaViRwJNUFdjkGuPkAgh5L3FpyKyeKNuWSn+VzB/X7jEwygAfXchy0MYBIDlPinOYcp894gRDqBgUBHJwKIFAlhfrJclkXI0i7HzhGuklYr2YuaoGfqkVhIwYKgyDf3l8PGZtiCKdTrh+vAa+KZVYv+84vBPPkDmJSkSYFpS1Ll2/7zjc6W9lA110b6TH3Dom05HAIBmnOAvOzZaSYW/ABTyw3eg2QqVSYe33v3B9iScNSuW9e2MKqRuUCKO51o9OYIyNkfVLir6bCSIEkwMCRWZT32BlFooqSr2ixRVn/eikLTcyl7ts9MmtYq41xjglK/s1eieVcnCoLKMoS6/CaUe7RTBNTnwJNuw/IToN8MymIlqyEj9mMsEoW7W5UbDFundsog5cFpHgHi86eGyUcvzcJJAogrRLKKg4zc0HjA0U7EYR7+jiJuk8GQBkLc4YAGT7x1gRVmrjYbqHhCaJgTqm75RF8KyvKGnPJBYFfqm0hJlShk00n80npVyAcV6WV7LIftS8wDp7MNcsye+r4RmBodTQEZBexbVwJLy4hvYRFc7SMNZhI6uOPkc17+kcnFGLQD5hVPE2VMFZ9Yg+0oyQrFqEZtfBI64IK77JhXc80Y96J56hfwUT55dcBv+UMqrdIxICr4RieB8qgVfCaW5I8j50Br5JpZxt3UTBltwhY1P8abjHCraXs777T9Dw4iJegiL9dY9zqccGyky4U52bGzVEEEBGgN46yvwxGYHbwUJs2H+SGzrcaVmWTXAbqVua5IYSNy2TIvQL+hRgjwAzV/DGAZsE/jYcPMUnzXX7T5FBH6d8DpfnkQEgA4EHXQGgMuVg7Y+/wS1GCQBZf23GfsqOYK/EM9wJyhZwPsnlCMmoQVR+EyLzmxFZ0EJ6A9Pe1AQASl1teFwICQ72TyNGBWZskbVy7pxxk3vUuphFWHeLfoKMhayjTKG7EzpkOYGAmTXOwD2OAD4G9jziWIVCgDYOAmNLsGHfScoQSmyhC3CUwSMBf321ggRwlhCmMU7kM5JFOHFiy6YQORhb7ggi+mSTOeT9TX0jwwYPGY057ls4W8iimAIPV5NQ8ZhC6nxnRAHRkrrHFIpjI6GEO9rdDhYJXR8HgydpKfgEX8yw+4jUjSL+d81AJ5CB7QY33gvYK6FEAQBZAr9XggjuZGwFO8kxNoyVrtjk5skiMA5Jk54UzeHL3btVPOQ28HCtotTLm2Bz5k8CffQHKEpwQssnnLYyYBARBZ6JQnvH9X0soJSWjrl7i4WvHirjAIf3DmbALFbE3bChMFTESD90ScDLJjFitBHuaiZml5+DgC0K/mIk8MdAmFRKZMwjZxmZIzuWBXSfViTKyy5v11xAtt8etETISn0MULrz15YbppfyFThrRu8nrbb90qpoXpxrhpiIu5HLKKzE6sca10uMMTNxhGbVk9iSJBpcy3PBSNp/ZH4TwnMb4JtC9Gikb205AtKqEJ7bgIh8ksIfyPo40+MvlPfrJJlzwZm1CM4guiEGOkOyGxCe24jNR5qJGSStEj4p5dh48CTW7j1KWU6SCeaXUgZ/WtYNTK+EbyIJJPZJLBF9UGmUjTfTDh46w7PpmLnKLYZ2yWAGHtpD152ClXU//U7MQPtPULb3JJ1ojlN276Ri8mGlVKbTY/q69ZxRO8llAowx3HjgJDxiRBC8u0sUFCu9rqd9xNmx3y8Tt/8U1u8v7GfIIK1QCdjksb+f4XIfGRAq7ifFXMlxV67gzy3mNNxiRBDvxljRV5vJEdxi5SxAtkgs5sYtz0NkkRlBS70huaILjiILjmqbg7PqaaswqV1Ydj18UkivcAb22GvKg7NzzEl7iEpMXEq1DCB6ygxgYhnPGRRl4HJ4JZaLBTBbDB8qxaZ4MjziSzlo9KCA0F0ChB7xZ+CRUAqPBPaYM/R1XMCfBCYJ20dA5nXZQakbktApCyc20VkLOZKIi6nklR5WEfJLrcSkD5Zh0E19DYmTPljGS8UBh2sQlEXivkhGaCXcYwvJ75guWtmc6JtcDtbmlM0rbpQkkBk9UZEp5Jm68vlW1ma7xRRi/Q/HBgDgwHZD2wiVSoWN+37jug5lcCft1sFz+8r4SoqZFURLH+E25N082GWm7+JxLlWCDWLBtoelWJXsBtJOjer82KpLdthyRxf90TJKX9Fjl7pDFaGk3JUstC5ekrNMXhkzMMNK4gz88ZOuXKZl7Ems0FDKoIrpNzZwlkEpBmZidgb4GGMnylDicX3Cm/nJoYhPVjJDR4BikeLk4u5yMpFjYdz6TIDitQQDwjRHysWA6PIiwJxPcrmS5aW6OpIhJmd4CZ0f01J5J5XS/DFRtg+i2YtBGTUITCdxJr40ticgjbh3fRPPIOhwFWkJmERMDm4xJA7HN7kUgYerEZnXiMj8JoTm1NPG8eRkzro1hOU2ICSzGiEZVQjOqEJoVi1CMqsRnFGNkKxaHrIcmVtPu5BUwiepFG4xJ+F24Dj8EhnYlABgWiXpqpFKQCAJWS7jbl3G+G2KF6zdprgiAehjCrlBg+g2T/ESEQdvtJzEApaZo9bVASvr45jGSAaEPAngoPjfhv0nsGH/CbIP+45j2powvDRjIaatCcO6nwSruF4qZzE37zpe1upnKMq6hRL4/O/g7v90n+vd1wUAbuwDAMVvSlmuLObnBWGQkg0KZ3h0Ce+ElFhG3OeHqxFA3b9Mz+zaSSgkp1HoAeUe19n1NK6HnK8EoJNTA6g2jwG0BAkAyvvnqttLkIBjvKTjTpL1g4wxLKfnzHIJHIrXVQLSUq5x3iQzifJ1BgCloQSGbMjA7wwHgLJBxNUxTMrWSqZTdN6R2vbRtnfXiyT7wHO7CP0/LNzczNATnFnLW8yxhS/XjieVwY+DQmXfe2W0WhEBs7Q8zrMeJac7Y53X/fDLAAAc2G5oG6FSqTBtZRA/UDfFFeGlGQvgGX+aa4dmu23GPL9vSKBrYinW/XAUE+cspQ2/ifj53eV+WBi+j4LECqz4Kg1vzF+vcF69uXAjVn6TyY0bSzbHYtrKQEVbtclzP4f7gd+pc7cWnwTtoU5gAv68D5WQ/Uso4WLeWW6bMS9gNy/3rv3hZ0ycvYQ7Tb2Ty/Gvz/2wIHwfB3dLt6fg1U/WSllTZ/DqJ2vx+c5U3u1gQfh+TFnmw3+g6/efwLjpC7Di62xuuvjA60tMXxsBt9giMgn++BtefPdjrPvxN86GTFsVijmbdvCJ5fNdWRg3Y4FCd/TWEk98EvIjLwd/Fp2AyfNWcZZuw8FTmDRvFRZGx/NJeV7gXry5yJNMVJTZGz9jIZbvyiLxPTFFmOP5JaatCeNlg3U//oaxUz/Bhv3HKWNZhBlrw/G+55fc9PPFNzkYP+MzynqQifCtJV74OOh7PhEu3pqEVz5eC9b5wPNQGV77dB2W7UjjEQ0LI/bjneV+PJrCL7USk95fio0//UJKJ6mV+Mj/W8xyi+YMn2dCMR55YTLW//Q7ZbYKMXtDJOYH70F4TgOi8prgHXcCr374OWmsnlWF8KwazFoTiKWbYxCaXYegw9VY+10Wpix2R2huE7YdaUJkXhOmLPHAhr15CM2qg09yGRaG7sW7y7zgl1QMr8QSBGfW4s2Pv4Bn7HGEZlbDJ6kEn/jtwvtu0Qg+TNqZ+aWcwYRZC+GXVAK/lFL4JpVg1oYIzPP7Gj5Jpdi4/zhWf5OBibM+g1fCaWyKOwWPmJN4Z6kXFob+AN9DRfCKL8SKncl47ZM18DpUDM+E09gUfxqvfrwaS7cncrb5k5Af8fYSL67N84g7jfEzFmD5rkwO0mZ77MDUVaEcpK354Re88M5HWPX9L1i37wTeXRmCd1eGYKb7Dg74ln2ZgXHTFxCtHQVlby7yxIf+/+bPuzA6HpM+XEWYCbpQmfThSiyIioPbQcIy3v3gkxh00z00WmQ0Ro0Zh7HT5mMZ3b91+05gjscOTF8TxjVNq384hhfe+Qirvz/GQdi7q0Ixy2MHB33y74OBwDcXeWJe4F7+mAVRZP9kUDfpw1VYEBXPr88LIL8PGfCNm74Ay77K5M8922MHpq4O5eBv7Q+/kt/vD7/SrjpFmLYmHO97foXpayPgHleCL77Nw0szF/HJ2SO+BFOW+uDT0H1gmXVLt6fi1U/WKkDH65+uw/Kv0ujitxqfRR7E1C8CeHSRXxphnzbu+5UYRHIasSDsB8z12kG61mTVwy+lDC+/twCb4oq4SW3mhmgarkzAzJq9RzBh1hLqrCfAjJz/DsCTXl+yPRWvfrKOA0SP+BK8+vEaLNmezAHlp6H7MGWZL1jXGs/EMrw8azFW/7uAg8APfb/FzA3RNA6mHG4HCzFu2nxiPqNgcMb6KMz1+Za8dmI5pizzxcuzFkuxMKWYsswXn4T+xFnCJfT87JEgmMVXPl6LxduSOfj7JPQnvL3Uh4PFTQln8PLMxVi1Jx+b6Dlpru83mLkhin8HbjGnMG76fHjEFlFtOvn85vnvptKQSqzek4fb73pAYUgcef9oLNkcQ9i+9Cqs+jYTby1y46RFSFYd3l7khtW7s3icz9ItcZi+KpAnBvinVWHSB8uw4YejlEktwVyfr/Heukhe0t+w/zjGTf0E6/edgEdcCfySy8n86/sNr7Ct/i4XL7/3GTb8+OsAABzYbmgboVKp4HbgONe+yZotkRJfQV2Z5SRfLJmsZvxpDlkA1UOxbLKANGUzc39WqqUdDNgIyqjlbA4T6Qdl1Qnmj2brMd0WS/xnLKIysb1codvzdmH+uHNNKoO45oex1ZUQF9P/MYMM11+IGIqNUplojueXvLzKmT+Xkip33x5U6o4YO9cfyyezEUxP6DpYP1/B1BUpGQwW3N3vEOVcWVTuxi7HnBYRQLJgW9I2caE4L7GLz1/h/KZOSd9UYdiQOzj4JpdifuBuhGfXYUtBI7YcaUZ0QQsi85sQklWHyOxaROTQdm7ZdaTRe3oV/FLL+cTqn1ZFmLl81mOzEcE5jQjKakAwjdsIyarlx6p/OslxC0irhG/SGfgln4FXQhGC08vhn1JKTuA0rNWbmim8E4kByDO+iC4YiLOWRKoUk8sJJZzJY9E1m1wG0/F5xBa6lHqo+YZm7bm7MMpuMUUKsfgGF7aMmSxmuu/grts+Q8GGMZetYHzdqfZzw8FCrD/AXoccx9NWh+HmfsJxp64KFWVjxlpTRpBd58c+N18UKlnxg/2XZuXH9H18ofh7sBAbYuhwZdzl53Nl1SXGm0f1xArX7/teu+AeJ/LdRMadYLQIAyZVFBTnoXIXM4Z03kqugH8qiyapo85g1iqsgUoUamhvY5oxmqKUUYj2aOI3KLdPk7t1eB4qh2cCc/CWSo5dick8VIpNCWVQRrmUcaApty9kZVbGCnonVSjYQS/+/ssxz+9byijS/WBMnVSCZp+jzBJer8zNBnlseZ/XFdfLXPaljO87C7ZnWYKeCSWY7bYZE+csxfseWwlzTzMDlU0GRPs25ubm+uFMMcexy4HpxLVP8jureXtJVjUj7SJJdc07kbKGtBsPixximvqN+34bAIAD2w1tI1QqFdwPHKe9Z6XkesrYkMBcmnvGS3hEexWYydpR1Soa05ODv440a2eZV1n1CM6s5z8ilukXkt3A788DUtNJo3r2miK2hQU2Szo/uq+uoI9FAnDgx0M5WXxCMTdxcM1cnAR6uFGiSAH8FK5ZqWTKXNRKfZ1LmfdA/wBw/UEZyJ3qA/7461BDCQMASpApdW1xKV2JSaxYOO9cIhgY8BN/JbevNDyo45lF3LgKyL0SRZcQ0fGFlVjEgsCPgnf3uNMISClDZE4dth5twY5jZ7HlaCui8hux+5dGbM+vQxgFfJH5TYjIb0ZYbiM9TqoU+kAe60MDwUn8D8kRDJRKbP5pFfBOKqXdKmoQnFFDetcmUxAXfxruMSd5RxgvmkFITsIE2HnRMq1HbCFxWscSHRwxZxRxbZgcocQ1fOyYcQngJlma7DiSvtMYBsBOivw7hUZPAoD9uV5dDBYbDpwiRoz9Smeta6tHDypDYK7cjXTB4xFzCuNnLOw3HHf8jAUuoEt218qA7FTf+/WjxVPepgRs8kJpQx8AxyJFRMciOdS+3yFr/hSuWuFyZQvH64cckwUmAyL8t5Cs/F34sFzQJPk6OffyfEAaDROSQxcwdGHMzody31tfljsqZQGy7h0icL+S3+YjAUbPRNHBg4MpDmLLOVhjwMk3uQK+vJNRJQeVrKzqlSTreqXL/X4WFS4A2QXAUcaSldP7lJD5/paT101ifYwr4Jfi+rkoQavc0UjugsI+Fz/p85XJCyZHCs4iHT3CchsRlttENcMNPKOW5c8GHq7hLUkDWevG7HqE5rCIoHoEHa6hUVc0tYKZxZLLOQj0ZSZKOtwPHB8AgAPbDW0jVCoVPGJOUvaEgD0WJss6a8iDuSXZ6icku4H0VWRgjgJAVrLoDxgGZpDbQhUdOmqF3i+9irssRWN04drykRhK0cBcZvrklaQS/DEHJclnE11MZHbM3QUAyo5ZBejrM4n0D+D60xj1YSwOFiondZnZ6HeyKup3ElOyfq6xMiUu14uVYM9FXK1YcffDerAOB66RL2xy86GdT/gElEYYOqZzCjpcjajcOmz7uRUReaR37dYjTdicV4/A9EosjtyHhSHfYUtePb48dhZf/tJK+mhSYXxEAQ3WzSIaviDqygtIF/1hfZLLOAgkJbNSqscpp3E0pM2fJ3XoucUWgec2cp0Xa91GolyYA5eBPGagYlpQN+aelvI0RSN3IfBmjHC/RiFZf8eZtOuzYHL7xvUSGHTVvTE9nsxG88UHHYpYlX0kwmUtzQZ0o8f39RjAaatDr/s7EHrYvsf+dRc9MS6XJZAnt6YTAeaSdkpKJHCLK+ZMNtf0xRTBPcb1d16kYP084uixL+na3ONLiMyBBZi7ABi5dZrMvjHARH4XVVJCQZXLddkkV4mAjBoCKujC2T+9Wpz7KMDhoI4+h69kmOLOVSqzCc2uR2h2PdXREhNVRE49wnMaEEr74rLzvR8FjXL7QbYo5/ubLMV5yV03pHmD6OKqeD6eb4oMIJXsJGNE5UBrxhZyFk/SHHoeKlM8zjdZ7AtrvSn39hV92Nn3IVIGxHcgwB8zF/rTmBgC6ggZwoBdSLaUVpGlJDpYZJl/Og2Oz6hBcGYN7SNPyBOeVJBdJ7SEKcQxzkPtk8vgJ7VB9Ig5NQAAB7Yb2kaoVCpsii0UII8loktgT+6hyBppE4daAwF20oEcmFHDW24FHq5GYEa1wr0rl395MCo90fFyMVvhupR4faRVpg8LaWYnj6S+bcSYkUP00hSZhrKwluXcsXZPDABeD2TJBgn5tuW7svrcxsDe2p+OY+rqMIyfsRDTVhPBfP8A7z9Pnn3HKc4g9ZnEaGzCun2nMGNdBF6a+Rmmr43AhgOnuOP4uuCPTnqeh5QBst6JZfx78KVhzfJJnQwxEQQoyv7kpBee04CIPNIUPZA6HSPymuAVfwqLQvdg8sxP8T+jHuR9g9+YswDbjzZh17EWhOc1UbdkHW+bFZRJ4l74sUTlB74pFcL9x4K7adaeyLoUEgC32GK4seBmDo5P0/ijIg7+5EWD3NVDPmbcJQC4MUZ5+b9/n4V9AKBr2VTOruOX+1lMLNuViY0HT8E7oQTB6aSNnm8yyVv0YoujuGJ4xIrAareDhVhH3b4k9Pk4B4YesUU8JPqBMeMV7cVGjRmH9ftO/NfjVyFb6Pe31T9bJwCg5MqNKxYZdAz4SYsVHxoEzM4DHvGs1R5h8piZzYO2dZOrA8QdW8pByOo9BZzZI8ySJHPggIy2HksXUhWZpSbSlSppYcSuVyl/QxS8+PAe4lXKcm+Ky+JKus6BmBytJfUaDs1pRFhuI8LzmhBRwKJoaARNdj39PZHfrV9aNXxTSDZmCM3GJF11qCyHgsJgbtirRVBGHWW4iJM+iOZvkoVZNdb/+AsFZ7Krv5ruf5UEDkXUE39PLiCUtG+j0U78fzWKWDD2OvLndD3w7SvdzlIE/NIq4ctAIAO2inZytZy1DWIkhzwoAOSMIJ0b2RzJ5k/2GZNRh5AsOkemCXImgJaQA9Kq4BlXOAAAB7Yb2kaoVCp4xhe6NMumDJ+kzWNsHgvVZREZ4mAnJeHAjDoFVc5YPF/pBCmHcMorNJYbJ2vGGLjjpUQ+JO0fA4YKl5qcdl8mYhn4hC8y7EQ4s1Lz9v83CjF+xkIXZoKM9fuOY9Rj43EzTZIngvnxJBG/D6PhymoU/h9fn+43L9US9mLDgVN44LHxPMX+5iGj8cDj47HhQKEE+JSuO9lFxyJzmK5IlEzoRERlA3LmoghXpcdLZh3Cc4njNqqgmWhk6CQU+F0ipi5YjWG33sYBHxu3j7wbb87+BDsyirmOLySnkZ9AWVnX/3A1/NKZlrCC7qtwJQv2huWLFdPSfzG/LErhxTzr0iOuH12kpJv8b8fJxv6ATIwAMv0tFP4jcLru6KsbZQBywsxFCEqvQkRuA8LzmnipKiSrHv7pVSSwNoGAHvZ340GW71fI80E9KEjkfb9jTxMX8OpQjJ+xgC9qxGJEjoXpv9wqL6TY/RkQ80kqdQHap8UxLjlyWVtCuWsFL1OmVPJKhn9aFWdPAtKrEJRRS3SfVI7gT6sL4rGkhMhAgVdSOSbMXkLLi5WidzgHc1X09mp+Dg08LDJL/Sn4YRmlPHsuXWSYKltSVsGHDt9U5e08O5U760UcFgMnrCITltuI8Fwindh8tBVbfj6LzUdbsfnnVqqtbUZ4fjOijrRwIBhR0ILIIy2IKCALroi8JkQWNCMkp1H0L6bkQDCV+oTlEr0tKYc2IrKgmba1a6RlUcI2BqTXYNIHy2glQAAnxkYGZtTyRSOrLIXRBWNkfjOiCpoRWdCM6CMtiKSXw2k8ThgDnLy1Yw137MqgkJ2fOEhMqZJAbVWf+8hZs6J7lLLvPJGXSFo/adErQJ+YXwMkVjaA9uVmwDaIAkGSQ1rNtfbBNGaKgUXfQ6cHAODAdkPbCJVKBa/4QqU5g1LXrLzLSnZyLIs4uNn9GaNXLZ2sGDvkwuTxUM4yKDUxAiTKJV1vKdVdBiByfIi3QgMoAIsny4Ki4JB36khQtlFieiHGnLm5aOF4j8oYWSMoj+sDgWlrrlMuWxOmBHCK0nM/Wr6YYpfXLFZOiDxZn7jkfJLKMGNdeJ9Ig5uH/A0z1kX0jWpguVyslVSii5jcRfjNSy4y88fBXw2CM8nJOyq/GRH5TVwgHZ7XCO9DRXj4qRcwYuRdHPC9/Ma7iNyXgYj0Uqz85jCZBChQZDqokJxG7rwLyqrnJ1PGKHgnV0r7LIJtXfuQ9j9El5X+zTJibJRAt7gsM34U5MTIwE+Ugf+/AGA/i4Q+YKqfyB7vRBKNE0yZCZZl6J9exdtWeSWegUf8aR5lsknKA/U6RI4h/9RKCp6EscsnqZxGIZXQfMoiCdBJvyP+WYn96rNwoZ/ppgRSWg06XI3gjFqE5dTDI7YQXofOcL2uzGwzbR7LfJO1dKTPs+gCE5LdwKsZIdmkDBqe24CgjBpSEs1pJJ2FaOeXUNp1iLF5ZMEjlXCTaccZ3pmoloOvYHr+JIvmeg4GZUDIABQDpzKw8KMvW4SfAAAgAElEQVSBxAGHa2i5kC6kMoScJojuYyjNq5RZvcj8Jmw52orNR1sRfbQVkQUtiMhvQUR+M/aeuoBfm7uRVdOOyPxmBGY3IDC7AcE5TdjxSyvCchux9oejCEkrxY5jZxGR34yIfCK1iKSSi4i8JgH08psRVUCeOzyfhFqH5zeT248QYMl0u6E5DRyk8ZEtQGMEfe6ogmZE0cViZAF5jqgjLfT/jWTkN5F+y7mNWLcnG9NXBSAwvZzeTkYU3d+wHJLVGUY/I+LapaQGYy0z68RfblAUJIgo59ZxVjWAhci7VMmYxEkxZ0oA0FVSJRMmvCc5ZfvIcVxF+5SzTkSEDfROKBoAgAPbDW0cALKTmABzrEm2UvzKV5tsBZUqMo/kjD9m0GDlOAVYYyBQEcyp1PBxxknKJWT3Ib1fRVs37qzj+U7kdm/OAFIWMEGUAFl8A2OC+oIBlivlcp0GkvYJJY0t6aenJRkvvfdZv4L5l977TNHWSAajSg2imEwVzy2BFfF6olWSW2zxdcX6L89cpMwJc8kCU7jokmTQLQAfK8EzgwdjBwIO1yIsuw7RtNtBcE4TDbJtxMYf8vHPqXMx5JZheH3qbDS1nMc99/wP5i9cgtorRuw5cR6BWQ0c7PUZjE2gJ+QAeowycxDTRCn7oJZzECjnpcnOQp4/ptBJKoGhqyGmDzCM6W9R0B/ocQFx/YA7xfUYJdDbyEAWA//yAoBq3TziiuGbXMHZlKDMOgSkV4vFUkoF/7140RI/b3eYVM6DskM40KinDEQ1McyklPOAaqab9YgrEQwh7wQhGYgklykL7/VIIFo9xt75pVYiipUiM6ppt54y+ltValOZHpXFefB+zrJOmfbbDc9v5qBv89FWBKSVYUHgN3hr3jI8N/F1PDzmSYx6+BGMefZFPPfqVLy3LoxkQuY1ITy3EYHpVZjruZ20qfTYCn/aCzogXWZ/6nimaXBWPW3hVo25ntsxee5yfOi9kzA5FJAGUkaMgYzADAJCgrPqEJnXiC1HW7H157PY8nMrthwlI/poCwVGhKULyajEut2ZiD7Sypm9qCMtCMslQHBzQTOCs+uJdja3EeUX9VCbnNCYnWjpMKPwnAaFZzU43qJGbdN5vP/xQgwePAQ33XQTJr/2Br7YfAABmXXwz6xHQFY9ArMI2AyhAC4kpwGfhX2POSu9MX3BSvz95Vfw5odLsXHfL+T/TBOeQ0rMoXlNiKBmrqiCFgIeCwg7GX2kFZH5zeRzy6pHYFYdL1kTfXkD/FLL4ZlQBPfYU1j/01F8sTMBT0/6F1QqFZ5/ew4CsxoQkFlHHP/ZxJUbWUAAJHmNFgoqm0lAfF4jogqasYV+btHS2ELvH1XQgtBcasLJFFo/BvAVxkZJ3hR4uIYsXNkCROphz+daanrkzCpreJDO3N5kcSBrEn2TifTG/cDvAwBwYLuhjZtAuECVi+lrOM0tU+Fy712hs6gUpVzehUO4ifsygMK4wRxXrn2CvRXOUrbCF0CP2OXLpNulsGf6l7c5ohMFCSwWpUB3PhFJpU8KgvpGDijbKfVpfK6IUFCCyelrI/plAGesi+SAxJ21M4oXAKQ/MMnAnQcDpRLg85CS99lrz7jea6+P5HEVmyQHI+v12Yf5427eCnglnsFs9y2Y9MEyvO+xFX4pJNIl8HANQrPqEEVP8CE5TcS9mN2IL/NrMHHmfAweegveeX8+ElMO48MPP8Lw4cOhUqkw+dXX0Wmwo0Nvx3e/nxedEbJZAC7VEnGpgSj/suBVBv6YsFyUr8V7cXUVugJAxffZX/isDNgVYLAY/TKH/bLD/ZX8+7td6OFctYTKxcZpDoxIn9hiUcan+sdNh87AK6mMLtIqpbgQpYvbJ7lclKJYVAUdvimV8Eoup3EfZ0RkEjNKsN9EgmgFJjo1nJHcpUSXKcs3fJLIOcEvpQKReSQE2S+1gkZilPb53Sl7vVaQsi4V5YeykiErEdLyYUhyMfzijmP6Ch8MHjIETzz5NJavWIkvv/oahzOykJd/BLGxsfAPDsVtd4zAC2/OhNu+YwjMrMHDT03E4KGP0N6wj+DhpyYiNKsWkbylGxkR+U1U7tCM8Nx6PPy0y+OenojQnHqEU6YqkrJlhElrwmYOVFops9aCUNofODi3Cbt+O4cDxxuRXliPgjMN+OcbBPx8utYXWzLL8M1v5/DjybOIP9mI3No2pFVeReyJZuw/2YLi8xpc1dmgNTuhMTlwpUuPK116tF5ow5LPV2Do0KF4b+Zs1NS3oLHlDwQGh2PkyLswdvwEROz8Dpv3xCB890F8EbgD0xZvwMSpc3H3fQ/g3vtGYdacuVi0+HN8tesbzP1wHm65ZRjGvfIWxr4yBbOXe2Dt16kIy64jIPVoK8IPl+PD9cH42+PP4LYRd+K+h0bjHxNew8S3p2PMU89h9KOP4cnnnseLr72DF96eg4ee+DuG33a7QiIyePBgPPTQwxj30gQkpWdhxJ13Yv7SFdi8ex9Cd+7BuuBtWOUfjQ9XeeO9pRsxbfF6/Gv+arz28Uq8s3gjpq/0xzzfXdiSV4e9J/8g5rI8AkBZtYK14gvNa+ZsaGQBYzWbaIqFAIFynFlwVj0icsmiI5hq+9h95ccwUMgYQX8XhpDNwbIMwDe1Cu4xJwcA4MB2QxvJATx4UuT+0ZUtoZurJMCnjB5gsRs8x42Ll4VziiShlykdWLzMK3XrkP7HyreeiSSB3ot1lpBKu4wNJE48FlNQpmACPRPLFAyH3NlDRDlIPTNpOyT2XAqHGW+ozowoSuDEnnvKMp++6frxZ7DxYBEeePwlhWD+L4+/hE3xJVJsTRmdOF3ARqwSTCoZSJGEL3oys/ZKFNjGnsYDj4/v89oeccWKbC7yGZUpWT+Xkq9vSiW8E0vx4JMTpEbpj+ChJycgIrsG0QVklR+c04SQbNK+Kjy/CeUXtHjtrXfw5HPPY3/mb/h29x7cetttmD5zDn4+UYKWi53o0NvRrrejXe/AIu8oBGU3IiSniQzaDYGvuLPquHZGdvvJXWBkZ6RcsuYgUM4aY1lobMS7AEMKtj1cWGDxHf0HZvC6EoH+bne97TTVxUnudBfwJ5dENx0ix7CH1BlhylIfeMSRll4BaaQzSkB6FXXJlyqObW7sodFP4TkknoItBtlvW/6NELnAGcWx4plQ2u+iyYeCNb+USqqvIzpS0SO6CluPNCP6aAvte0vOKYHMkZpaxb/TwPRqkiJAS6QRuQ3YXNCE6CMEiPmnV+KTkO+x0Hc7vgjcgUf+8TIHDqPHPI49++Kh1pmh1pmh0VthMNtgNBrR3tGJzm4tjvx6ClPenYahQ4di1EN/xaBBt0Kl+gwqlYkuov6Kce/Ow9h/fYCpX/jjo6A9+GJ3NjwTS+GfWYeQ3CZ84L0Tg4c+ouwoMXQ05nrvRAhnxUlbuFDaGi44p4kveFZ9l4MX352H+x8ejRF33Y0H//YIbr/jDgUIevOtt5GZnYunnn4aKpUKw4YNw6BBg6BSqTBo0CDccsst4n2PfgQvvPAiRo0ahXvvvZffPmTIELw7dRqqauuhMzmgNdqh1tuhNdpxpUONiKjNeHnCRDz/wot4/vkX8Nprb+CTTxfAy9sf6Rk56FCb0KW1or3biLYODS5c7sAvx08jInortmz7EjPem4m77robgwYNwl133Y3bbr8DN998M8a/9DJ++OkAik6fQW5ePvbu/R5bt21HWlo68gsKkJqaih07v4KnXwB+OhiH8vIqdHarYbHaYLbaYbX3wNFzDY6ea3D2XkNpRRU+XbAQ48a/hMmvvIZ//esdTJ02HR999AmWLFmKJUuXYtmy5Vi0ZBk+/nQ+3pwyFTcPHoybbroJXx87i0B6zmIAMEQGgbS0HZ7fjLD8ZoTmkdtITE8D1Tq6MIMUCAZRLd+WI02IyG3gesWIvEaE5TZIALAGAemVJJaKstnM1EayU2m3K2oSGgCAA9uNbkIDKK0wuJCegapkyfWWXKFoos1EzcxWL7usODCU+rMKx66SXeJlXwX4Eiv/vnlzUswLY63YxO4SA9M3N+oMZ7tkwOgp7ZMiK4vHOIigVU9aMvOUJr8FEQeEM1kCVZsSSuEeW4yZG6IxYfYSzNy4mYM/n+QK/p7Z+xLp/DLj2H8YqtzPk6XoK98ncTjO2hCNiXOWYLbbZngeKuGRLRw8pQhw7ie55nz4eyf72m+bpKGj8ZHPTgTnkBNoUHYDArMaEJLTiPDcRnx//ByemvAm/vXpKpy9osWdd47EOndPdBgcfUbZnzos3ZZIS75NfAXOACAbCj2qJPJmYm7mvPRNraKOQokRlEBPnzZWCcpjhA8KChXtqlxlAxIY/G8aQlcA14c9VBgeTlPXcgnV5hH2W85ilBdRchuw+WH7RCuwhBKei8kzxSQDFlvkBWYQEwFzhgZTPRwDgVziIeXOsfMDybar4M5Y9rxiIhPnDmZgYC7SzQXN2PrLWYTlNcMj9gR8ks8gJJtMlJFURxqcVYfw3AZE5jVypjkyrx7eB37Bhm37sXCDH8ZOegO33DIMDz78V/xz8isYO3YcQsKjUNvyJ9q7jTBZnLDae2F3XoPdeQ1maw9sjl44e8hw9JDbrPYetJ49jzlz5mDQoJegUo2GSjUUKtXDUKluxl13342N7p54a8o7GPP4Exg2jLDZQ4YOxbDht+LZF1/CTf31lJ27HMFU0yqPkJxGLN1+CK9/sBh/fexp3Dx4MD74dBEys3JQVHwGeQVHcKasEm2dahjNVnSp9bDYe2Cy9sBs68HVTjUqaurRfPYC2ru0aGo5h+q6Bly80oE/L7XhcGY2DiWl4MSpIpwuLsG583+ioeksauoaYbH3wGh1wmR1wmghg30uVlsPTFYH9GY7dEYCDDUGaVCwaDCTxxksThgtZL9M9Dmt9h788eclnCmtwN7vf0Tr2Qsw28j/DfT1rPZe2Bzk8++99r98XLv2v7j2v8rR03sNJvp4o8UJvdkBvdkBg9kBnckOnckOvclOb2eX7dAarNAaLOjoNqBdbUJRSRmGDh2KO0fehRcnvYmZn3tgaeSPcP+xAGHZtdjycytC80i5etvPZxGVV4/IghYESy36WK9m9nsJpeV2JlkJpnm4wZkkpopHo2XVISS7jruBeVQPjUDjur80Qbb400gd//RqeMUPuIAHthvbOAD0py5KBnYC0iq5ScNLKruy/CtluY1OsP1c90qW41qU5V8G+mQ2wjNRDvcsky5Lgasc+FHgxHtSKpPklRO6SOpnYIuBRsYoynlQIkpE5FzJ+kIWPM00jV6JZVTrWMGZCpb07woklZEplRyYsEBrFr3CnsObBZxS5kUGeLz5egIDf+Lx3skVPMg1OIOcWNjk7cdcbHK4KS3ty+45khMmJt1XPlzeb6P0Vz5cTnU+zfD8LhVTP12B6YvWwTe1Ahv3H8PIUQ/j/dUBuNqpQ2TUFgwbNgyTX30d23buwvk2LToMDnSbnDh9TgP/zAbC+uVK7B89kQZnNSA4q0GKWBDOQVlI75sm6QEZ88cZ5P6CZ8nxQDoflPUbOPtfAWC/OkEBCJVGHRkoygBQXHaPJaDPiwaaeyeWcW1ecGYtcV/mkEicwIwaib2rJswdc7/Slo3+9JgOokYHxkSQHEXR5SAwoxahOQ2kpJnXLNydOY2kTJbdgIjcRvLaGTUIobIR/7Qq2u2gWprQqGNSEsCzto8hWXWIoBPmlp9J6TMwsx7vLFiNmwcPhkqlwpNj/4nPIvfh851JWPfvXGIwKGiBb1Ix5qwNxmtTZ+O22+/A0KFD8fTTz2Du3LnYufMr1NU3wGxzwmLvgcMpwJ2ZghKDyQGT1QmzjQC93t5e9PT2wtHTC7uTABG7sxcOZy9i4xMwbNijdNFzDirVUQwd+hDiExLQe+0aHPT+BpMdf15uR2VNPXLzf8bw4cMxeMj9isXS0FtGY33Ud2JRk0061ARk1uPVj1Zi2PBbseTzFUhMSsafl67CSvfDau+Fxd5DBt1nk9VJQRsBgGY7/esyLPZePqz2XtgdvTBZHTBZHLA5emCyOGC0OBSPsTl6YbP3wGC2w2Cyw2wlAEpjsEGtt6JbZ0WX1ooOjQVdOis0BgICdSYHdCYCxAxmBwWA5PkcFHA7eq4RYOno4aCPfN7X4Owho6eXsHqM4WPfiZNet9p7ONjT0tdW663o1lrQrbNAY7BCZ7TBYHbAYnPCYnPCTo8Du6MHTroPPb3XYLNaUVh0Glu378QHH87DE08+xZnT++67H8+Pn4AZsz/AlHen4+abb8ZjTzyF+et8seGnY4g60oLA7Eb4Z9bDP6sBQZm1+Nj3K7w6bwU+9NmJwIxq0smFdSDKaVDomYNpNSOYBkeL8y7R7DJZlQ/NhfRlGvvUKnjEDuQADmw3tpEYmNgTLlopVgot7TcCxCe5HApdlaS18uTXJdaMt9upUD7GldGTAZtUgr3RwSf0Q0pwydP6KVjyT60kIdg0/4lHmvAIgEreYcIvhab20zyn0Mxakp11WLipWRmcCXcZUxIgxz245Hl5JZURkW9qJW9NpGxxJwCMXMqU2yD5pVQiIK1aApkVtHRXBt/USn7CIe64Jp7JF55HGLvIvCZEH2nheqTNR1u5Tukjny+vW9aKLGiB33cpGD78VixevBT/eP4FjH9jKv7noUfwzgcLcLypEwaTHQaTDY3Nrdi8dRueeeYZvDh2HM5fVaO53YSdv5zl2r9gOkEGZzdS4EcAoML8wXMkpVIwZaP5AkRmcVOEi5PcRxy7rp0IPA+VSzpJZeP66zGCsn6wz+hTVlYagEQUDZEnsBw7/7RKom/LFFmKQZm1CM6upzqkei4+D6Kmh81HmvHdiT+w+/h5wqAx92SecEmG5zfR+I9muP2Qj6DDFYjIrkNIZjW2/tzKRfPh+c3wjz+ObZlnEF98HnElfyI44Xe4f5uCtTtisCJ6H5ZvjcWmH/MRdbgMWwoaEU6BI88MzSJaqPDcBkRT0f2u387j38db8V1SHnwitmPFOg88P+4lPPjQwyg+U4HzF67gsyUrMOqBB3HXPfdi6C3D8PRLr+KNT1bh3ocfxbP/GIuAgECcLj4Di83BGT0GEtgE39NLQFpP7zXY7L2w2gnwICyfE1a7Eza7Ew4HAYsMbJD7OaE3WvDyhDcwbNgjuPnm5Rg27BG8POF1qHUmmG1OGMx2aPRmaA0WqHUmtHXpkJVzFE888RRGPfCw4nHjX34DJ06XwyfyS8xZ5Yvpi9Zh8tT38djTz+G++0ehoqoORosTFgbGrE5Y7GJ/LDYyzFYnGfR+HARKgzBwrkCQPNZoccBsddDncHIwbLb1cGaNAUOLzQmL3QmrjTxOY7ChQ21Gp8aCTi0BgFoKDLUGGwWCjIVj4NLJQZ48enoF4GODsLDiPdspQLQ6einj6aTg0gGtwYZOjRlXuwzo0prQrTVDo7dAZ7LBYLbDbHXA5nDC4eyB09kDh7MHPT29uHbtGh+91xjQ7OUgscfZg6tXr6KwqAixsbHYsmUrwiOicbKwFPsPxuHtt6dg8ODBGDnyLowe8xjGTpiMOQs+x5hnJ2CIi1Y0JKuWl495GVliCYVmsI6zhcytzOeRNJZJSE0hqZUDJeCB7Ya3ESqVCr6JxQg8XENjNJSteWTwJuuoODPoMhgD10dgLv1Pvg9j/zhgS5DBG0t/79saiP3fNRFefpyiz6Q8kbu2OpJy7UgwaQW128s5UCyqoYqu0ES+VzANJF69O4saFeq4k8uVmRJOWREb4J9ewyn+PsLf1CpeRmMMDzPaMHDnn1qJoMPVCMuuQ2QO0UJF5tO4hpxG4uLMIkn/kXmNiC4QjjjmimPiZnFyEiUOxsSF5jUhKLMGDz01kWsAhwwdjUee+Se+/LkZPzd2YctXe/Dq62/hUocOFbVNePivf8Mtw4bjx4OHoNFb0KUxQW+ywmCyQa3R4+Klq/jHP57H4qWf42KXCYcrr2LVnhz+2twMkiUGj2ro00WmVpmrlioE0740gNc3pQo+yVWcqVYwg8nKxUz/x1xfA4myW4pgqxV9TLm2UMokpF0l3OKEG93r0Bn4pRDAxwKzw3KbuIszmpoadvzSil2/ncO3x89j78k/sPv38/j293OIL7mI3LoO1F0x4qrOjmOnq1B3xYjKizoUndfiWHMXjreosSe7CAFf7ccqzyC8/MobuHnwYAweMgSDBg3C/9w3Crt+OoSjleexMSAS99x7P2666SaoVCrcffc9eOAvD2Lw4MEY9cBfMOaxJ/DYE0/hob+NFiXQIUMwf5UHog5kwDP6GyzeFIbF7kHY4BeOlZ5BWLHeE9Pf/wgvTZyMO+4YgXvvux9v/+tdLP38C+zctRvnL3ZAb3bA2XMNRquTMz9n/7iMgOBwzJn3KcK2fAmd3gyrQzBINsc1CpJIydHmIBO+w9kDu6NHAAwKCO1OwgZZ7T2ELaSPs9p7YKPgz2J1wGSxQ6MzIjp6Kzw2eWL/wViotSZojVZ0qg1o69LhaqcObV0GtJ6/gtGPPAqVSoXX33wbRRX12Ls/DuvcPbF3fxwOJKTi1ltvxZtvT8Gc9+di8dJlCAwORWz8IVy42Aa92Qk9ZSctFIgx8GZm4K+fwcAeK7marD20lEvZOAsrj9qgM9qgM9n4fY0Wh/Q6FCwyUGh1wkY/S3IbAV1dWgu6dVY+NAz8GWzQGe186E12GMyCYbQ6elFTUw+7o5eD8v4GY2ydPb2UYe2hn4cDepOdvo4NGr0VnWoTunUE+GkMVgL+LA4YLXaYLXZYbHbY7A7YHQ44nU44nU709FDWt4c8N//eHeRYsTsJCO3pvYbe3mu4du1/4XBeg9lGS+UWJ7rUOpSWlSM3Px97f9iHZ555lgKyv0Ol+hoqVSEGD3kIs9cGwSuhEEHpFfBPLoV/ehWCc5oQnEsBYV4TwvIaeXOF0FzSFSmYOp8DeHB0rdA+p1Vh0wADOLDd4DZCpVLBP7WcdFjIquelQBZyKtgTUR7mpcnkcs7qeSaWKoGfzCT24zhUAMHEMngdKifDBex592Ea+xn/4TEKtoyXiqWyqktpVk7QZ8JzJr7lHUx4WDbJeWLN299e7E7dXg0084qUy0KzRUYXz7yigMs1emDzkRZEH2l1uU6iH6JocGtEXhOiCwRLt4UFux5pRSRlbAjLw16D5IFFH6EBry4gj2iQmqhrVzJeMP0dA4PZDTyH70OfnZi5aA3condjd9oxrAmIxio3b7zy2htYsGgZuvR2XGzX4NjxYgwePAR33jkSp0oqodFbyYlab4bBRPQ4Z8oqMeaxx7BsxSo0txnw9mIPKQamSRrSbblEfB2c3SBFLNQrXHV9AlcPi+4AXKZADUaiRM9Ybta6S7nwESaScqnxvGSkSJaPt3LONLPLrvpM76RyDuBDKSsbmdfEj42tR5uxaVccNkTtwffJufijy4LUIycxcdJkvPra69jx1W5U1LXiREklYpPSsOOrb+AXGIKg8GjkHjuJNRs9sOu7H7BmvTtmzn4fL44dh7vuvhtDhgzBCy+8iA/mzsPmrdvxx8UraGxqxfkLF3HgwAHcc889UKlUeOzxx3EoJRNtnQZ06Sw4GHsI8YdS0K2zQq23wWDpgc7kwNUuI65qrDh7WY3cn3/Hq6++jvtHjcLkV17De+/NxKzZH2DWnLl4f+5H+HThYvgFhWLP9/vx64nTMFHGy2hVasEs9l6iKaPaMsIIEY2YUxqktCiVbukgEztj+BgY7OX3ZYMBRLvDCauNAD6L1Q673Q6rzQ6L1QaLxYbw8AgYTVboDGaodSZ0qvXoUBvQqTGhQ21EW5cR5y6pceedI6FSqTBrzodo09nQYXCg7uwlzF+4GLfccgsOxMZzJouBOgI+ern2zkIBCfvLwBkDKawcrASBTqqpc8BgpiycUQA+rdHGS6OsPErAH2ECTZQNtNgI22e1O2GxOWCxOWC22mGgGjqN3krLwKTcqqWgi7wO1dtRzR15DScHtFZ7DyIio2GXwDkDej29LoPebnMQgE6OBQJktRT8devM6NKaBQA10n0x2aA32WA0U8OIjQyb3QGrzQG7w0m+b7uTl4itdsGGcnkABYPseLI52OJAsLHs+/H08sZNN30KlSoWKtVrUKnu7hNuz8YttwzDg397FM9NehvvLvXAosh9CDpcSeKKconuljGEzJEclkvmDxZvFJBaOgAAB7Yb2igALCMHF7W6h9EViGizUyVNhhUuk6Lk2k2UgZnECvYTN+EK2mTw5grgXAGf6336AEH5vi6Ttxwd4ZtSCT+pbZm/pE8ScTiim0WI5PJig6ft5zVxfRIZIi6A5V4R4NXIm4eTcNRGhTYkNE9k5jGwE5rbCP+0Sjww5mk8N/lfeHX2Qrz72VrMWumLFaHfYFnEXsz32Yrt+bUIz2smOXrZBNSFUmNGSG4jB3NhucKByCIOQnKbEUKdiDLjFyI9NiinEcE0jiIkrwnRBc14YdJbJMR50muY9t5sPP/iWBSXVaFLa4JGb8alq10YPfoRTHlnKp557u9o79ZDoyMTaLfWjPZODa60dSEx+TDuHzUKbTobdv5ylrqAXQAg3dfQ/GaEFbQgPJ8NZTxDOHXpsTJLKNXfsG4DAek18EtTxpuwsrAcau2TUgW5X6lXkjILUSlvKO8DGBWLH+kY96FyAxJ23ECiQ/KbEUGZ2OgjLdj2cyv2FZTiyaefxagHHsCECRNx1913Y8xjj0OlUmHJ0qXYvmMn7r3vPtx000247bbb8Nzf/46p06bjk/kL8d7M2Rg6dCgGDx6Mf06ajC9WrsbmLdsQfygRRcUl0OpNsDp6YHOK0qLZ1gM9nbA7unW40tYNDS3paQxkcjeYnfw+bPIzmB3QU/eozkQu60wOaAx2dOls6Nbb0aG1ol1tRYfGim6dDRqDHSYLYVIYq2KkpT32/Oy1LnWY0R6FLOkAACAASURBVKa2Qm92SGyfAHz/aVjtPbDaxCRvtspMHyn9Wu0OWKx2GM02GExW6E1W6I0W6IwWDvbUWhO6NEZ0aYzoUBvQodajvUuPq52E/bvSocOFKxqcu6TG1BmzoVKpkJZzFOevapB99Hc8/sRTePtfU1FSUceZNQZ8TVbB+nFtn0UJTBgLxwCtxUbAChk9nMljjJ9eBoAU/GkNVmgMgq1j/9OZbBysmSxkWKwEKFmsdgr+rNAZrdAayXOoKdumpWCSPJ4NCVhSMGmzOyV2jYK/HjKcPQIMCjDeQ74bCkBNVvbeCLjUUTCr1lvogpK8r24KBnVGK/QUBBrMNhjNdhjNdvL+KKtrtJD9NVocMJrZZfJXLw2DC5CVP3ez9D3EKbSiVPM5dDR2/3sfLl3pRuv5i/jjYjsaW/7AqdOlSDiUAh//EEyZOgOjHnwYKpUKK7ceRBh1F/O/uQ0IzWtCOI0aYppc36SSAQA4sN3QxgFgH9F9FolYUDT9lpp/C7NHuejoIQ8XbaBCSygxJcp+vmXS9b4lZkVpWn7cfxzKZuX+qVUI4AaIGg7kQiUgJ7NmTAMXfbSFJtK39jsiC1oRUdCK8IIW6XNs4DQ+H7IImEcNNCGMJennk0gINrziTmLacm+sDNx+3dWkPGovahF/5hJ2/noWW38+S0u8Ldj2y1lEHmnBiq0HMGNtCAKyahGcSwBdSC5zr5HXZPtA4ikY6GrCgZJL+Pb4eew+8Qeij7YiNK8Jz0x+h6xoXVq5TZr8KmeRnnzqaWzyCcAdI0ZAbbDBQkXmZqsDOqMVnRojms9dwp0jRyIuKQ0nWjUIzyNRMnIJmgwCVtl+hue3cNDN+pqyEcVvb0E4G/nspNpIw3jrONtNFgGsmb1L79Zk1re0kouzidOVRc5U8jZdPskCHDInLCvjB9LuAZH5Tdj2cyu+Of4H9p66gJSKKzh9ToMjp2vw5Z79mPX+PAwbNgyLlq5Ae7cZNkcvrrZ3ISsrG2lp6dAZjOi5RtyQFpsTRosddmcv1BR4m60ONLecRbday0tcjMlgJTU2LPYe6E02fh8rvU2UIe2UFXEIsCG5N/VmAtZ0Rgc3AXTrCPjr1FqhNdjRoSGmgStdZrSpLejUWNGls1E9l5M7S9nj2XPqzQ5c7DCjU8sE/US/x00RNkkXp5iQxXVRFnVy8wMBEjbojIKR1lAw0U3jYTrVJrR3G9HeZcSVDj0fVzvFuNKhw6V2Hc5fUqPpj06U1V3GWjdvxW9hxIg7sXqdGzR6C98nocWjjlum95P+MvCnHA6h/7NSMGN1wMgAEh2y+5WDPz0BTGq9BWrK3rHrGj0Bd3ozYc2MZhtMZgKeGJgymAWLqKVMop5+jgazne+DieoLLTYHAX52pwLgcfDn7IHD6RRsnI2ATrPFDhMbFLgZzHb+XbHvp0NtQpeGDqr/a+828ioDAbzifcqgkJWP2fuQWUOdUbxPNQWW7Pl0RpsLu8kYVAcMJjMmTlRqRSdMeAOdGhO6tBYYzOR+HWoDLl3tRsu5S6hvuYSy2nNYs94DKpUKy3cmITibEAPhedTlfqSFa7BJb+U6BOc0wjdlgAEc2G5sIxrAlDIS2MtbazGreRVNuifDjzsrXYfEekhlYiG47weYSSViwq5USNclk0i/fWj7A39if3yShbGCtXUKoyVZVoLlzdAl1k4GeJF0LI38EWOeG4f3lnti5a5UBGXXIzSvGT7JZZiyZBPmuG/B0m0J2HjgVyz/KhWLt8QgLL8ZYQUENIUVUEaKXXYZYXSwy6H5zZyR2/ZLKz7ffggqlQoP/XU0Hnz4r7j3vvsx6oEHcOedIzFo0CD85S8PYuGiJXDz9MHGTd5Iy8rHV9/+G2s3euL9eR/j9bemYOkXqxEYEoElK9Zg+K234pFHx2DEnSPx7Ivj8fQ/xuLRJ5/FnFV+mB+yF59FHcQXu7PhlVyGgOxGfLHlAMa98jZWB0Qj+Ov9CN7+HeIzf0brVR1qLhtQ+oca+7N+h8f2n7BwYyBeeGkSXnhxHJ56+lk8+tjjuPvuezBo0CD84/kXkJt/RCq79cJkseFymwZ/XunG2Qvt2OjhhZdenogTzZ0IzW4QADCnqV8QGJLHWEvKXCpyA0UJO4yBRQ4EpSwv2tUghJWSXUrIpMVXjcLcE0CZcRGIXsPbQ/lR+YQ/7RJBepnWk7JufhP2F13AmQta/NFtwQW1FZe1NmT/fBzvTJ2OkSNHYsiQIRg7djx8/AJQUV1LmCwnGXYn0a4Jo0IPLDYHdWraoDVYYLYS7ZwM9Cw2J8xUzG+mOi6jxUEnOjJJavQWaXJzUA0VHZRd0pvsnNXhZTB7j8RACUDIGEA1Hd06GzR6Gy8f6kx2SaPm5AwiY1r4c5kcUOtt0BhskiGhhwMoswsAlMGeUX4PjB2jDI+Ogj+twULNGwRUkJKiCR3dpKTb3m3AlQ49LrcToHexTYc/r2px7pIaLRe60XCuCw3nu1B/thNVze2oae3CJbUV567o0Nh6CXVNF9CltUJrpK5YFlliYqVMwkDJTKWVsmUMEFmpAUOUYwUjZlK8Nwcv/+pNdsr8WTmwVeso8NOSsmmXxowujYkAFI2JAisLBzsCHFo4c8iHgZVcbRwAmihbyNhDm12AOzZsDgcZdjHY+7RYiWbPaJbZRDI0Bgs61SZ0qtl3Y0BHtxFdGhM61CYKBs3o1pqh1lt4eZqBWwMFsDLAY+ylEgC6gGYGAiXw57pvRgk0a3QG7D8YA3ePTdh/MB4avQV6kwPdWiP+vNKFP690orH5POqaL6C+9RLqWy9h1Xp3jBh5F9Z+k46gbNYFyeWcRyUwoTnCyBWQVj4AAAe2G9oIAEwuU2RRhTLmisZDhOQwu3q1CHmWnZesNZiso0uT2ROREeibwkCaYEYUjOL/gd3zdSnVsY4lpMF7HcKy6xGR24gtR5qx/RfSSmnz0VbaT7IRHvt+xqaDxxCSWQO/lDNY930+POIKeYkxOKcJmxKKseb7I3hrzvw+LNvcVV74PqfouizclMVueOPTNVgctAvzvLfhnx8sw70PjcaoR5/G4m0J8M9phH9OI/xyGhHCGDc6wjjzRvYjKLsRf312PJ4a/wpW+0Vi9/4EtGksUBvtOH/hCnZ9/S0eeuhh3H777bj11lsxZsxjePPNt7B8+QpERERg9+7vsGr1Wnz44Tws+3w5fj9+HGarHUWnSxATG4+4hETs238Qb771Np5+5jk8/LfRGE7ZvJH/cx9uve02rFu/EZMmTcYLL47FpFdexb333od777sfU6fPxIR/TsLIu+7CsGHD8Oprr2P58hUIDArB19/uwaGkVJw4eRqXrnbBbO2B3XmNMn8EuGj0Zlxq1+JyuxaVda2Y/t4sPDv2Zbyx0I12AGnqOxgA5MCvHwDYn2Ywp0nBHEZQZjDKZYTnN9OyeSPvV8rK93JfVFbCZ0wiyyYMyqhFdH4TvjrWin1Ff2LHsXMIo1rMtIorUBsdNPCahF5XNl3gx01i6mE0nbsErcHCgZbCCGDrIZM/Fbgb6V8WiOvsvcbdnWyyCouIUrhEmYOyrz6MMEBEz0XKhwbuFCXDaCaMj8nCJncWF9KrYOJkNo7ptvQmBwclHGiahEmAaddMVlpilgClHClidnkdxpRZbMw52xf8MTDL2Co22bNMOMKOWaDWU+ZPQ0BGl5YwS21dBHD4BYbiUrsef17V4cJVPf5sM+CPqwa0XtSh/lw3mv/UouWiHmevGPDHVSMuteug1tsUZXHyl7hjWRlWrbPBQD8Lk8XBgZ6Ffu6sJCuDPwYA2ftjn6VOAjasRMp0cl0a8t4YkFIMjTTUJnR00yEBq24tAVQMGOlNNsIYWux8/2Rgx4cL4IuIjHIBgERzaewH/OnN5Lvq1po5SG2n4Lyty4iObhPa2X5qzVDrzZz5U1N9ot5IwJ/BYhcMoALwCVaPf470fkrAx26j750uIPRUNsAkA1q9CR3dOly43I3L7VrUNp5DffMfKKxowPdH6/Dl/lQscgvEPX/5K2666Wbcee8obNiTRU0grPtIMz9vBWf30xoztwm+yWUDAHBgu6FNAMBsqkXLrqPxEnQCzBMGgGAXDRzXymXW0t6F1VxfpXRfVsKPdhbx5YCRlNUIW0f0Vt6s1JZcRQ0ogtHzlsq4zClLoiUaEZlPDBFRR1oQnFEJ77gT8Iz5FWv3ZGFR1H5MWxWIcVM/xl8efw6DhwzF4CFDMYi6GlUqFW7hAa63YNjwW3kGGfurUqlw++134O6778Gw4cNx++134K677sadd47EbbfdjuG33spzo/7x/IuY9/F8fDz/M7w4djwmTpqMTz9bgoOxCfANDMMtw4Zh/MRXMPzW2/DUs//ArMVrMXNtMD4J2QufpBJE5jchKqcGXvsKsDjyJywI/wkfB36Hx8a/hseen4BRf3kQTz79DI79XoSrXUZo9BYudDaZLdAbLVSgbuCr+6tdRlzuIGJ1g9lGTtRMZ+PooZMJyfvSGe3QG+04e/5PZOccQcv5K+jQWNBBy2Ed3UZc7TIgISkdEVFbsHvPDzjyywlc7dTRCUhMqjojcQVq2ASrM6FLY0BbpxaX27pRWlGH6TNmclfpG2+/i52ZJVj5XQ6CmCFFZvwY0OsH7PG4GKl9nBI0NvV5DlkHGcaBYXMfVpiVkdn/XEvN4ZTljcgn8oETrWp0GBzoNDhwUW1D3VUj/lRb0am3oZ2aAjoMDlzsNGBfTDw/xsqrG7gJgDkxWcyIhQrUmVnAanfCZLHTScfiUh4TE3VpRQ13eTJgRACVmPAM5v5ZF6PZDp3BCrXOQgX14n4mi50DQwZI5Mtc52Z1KsEKLZ2x8hljATnYswh2TDBayvubXMqfpj6gSLwH2fzAhlYCRt1aAooYiOjoNvJjvZ0Cow41AYEnispxpdOAyx1GXO004kqnEVe7TLjcYcTlTjM6NETj2KWzQa23Q2d08Hw8pqNkbKZaLy5rDDZoDcrvwsg/Y1EONXJQZO8DZjmIl96nWm/hwKmTsmTsLwNOYhj5aOsy4mqXkXwm3QQAysygTmL92P6aLXbO5DHHLRlOOBxORam3pqYWdoeDay/Ze+OMpUGA1m6tmTJ8bP9MAvypjfS7I//rYhEwRrGPrMyvN1kFgDPbYDCJy/Iw0P8JsEcBH30OJdizQGtgemYjujUGOvTo1uhx6Wo3TpXUobL+HJoutGN3TBruGfUght12Bx5+Zhw+8NyBdT8cRWBmjeJ8xOKvmEGPEROu574BADiw3ehGAWApb0EUmksyvKKpW5Trp2hPSqYLY2L7sNwmYWSgYZbMKctYQia25y3lUqu4Xso/rVowhSlVvIsDG0GHaxBEe8xG0DJuQFo5nn9zJkaNfhz3Pjgab81bhrFvTscDjzwh2iDddBNuu+NOPPjXRzDpjSlYsnojwnb9iJMlVdAZLDBb7Pjj4lV0a/Sw2nuh1ZtQU9eAiuo61DQ0o0ujh8nqREe3Hi1n/0BZRRV+P3EKRcWlOF1SjpNFZ3D8VDF+/f0Ujv1+CieLSnH2wlVa6hFshNFsh95Iy01GO06dLsO2nbtw7Hghdv/7R3y6cDFen/Iu/vK3MQoWcfjw4Xj0sScweszjeODBhzB06C0UrA6DSqXCl99+jzaNGR0aMzo1ZkUGlizONtBVrNZgRYfaDJ3RRhkGJy+7GMxiNavRm+kqljwnYQeM6NbSE67aJOmhREnswhUNLlxR49LVLlxu1+BKhxZtnRq0denQqdajrVOD0opqJKWkIzQsElOnzcBtt92G92a9j4z8YyirbcFlrQ11V4zIqe3A96f+xOYjrQjIaoRvZgP8s6ip5XoAUHYzu5aL85rJirpfltCFWXQBlAJIKlff7L4MPG452oqdx87i+1MXUHhOg07G9BnI3zadHaU1zfgpJhFrNnhgwj8nY9iwYRgz5jH4+geitLyaa5RISVBoJNU6outjURdqqlfr1pmpnsuMbq2SxenSmmibM1HeZSVfrYHqvUw2qHUm7D8YC3cPT+w7GIturZGXR+USoJYCJzL5WYVOzNI/cyM0duQ1DWabBADZ/5VuUb0EDglrqLwPY7h0Jva6cglUCPgZkNBJwK+bMmCsLNhNmb5OjQlt3UZcajfgUrsBF9sNuNhmwMV2Iy51mHC5kwC89m4z2tVktKkt6FCbaRAyGe1qCwV+NhfQ1/ey+EvAX7eORaiI74WBEKPEPImSff+D6/v0lj4At7M/0NdlQhsfglVjC71OyqrxaBUKkLg+j0asEHetAIAcBNodBPxJJWB22ULd1kazjZ4fBfhT6wn4I/tt5Mdzp4aysrRk3akx8u+wQ22k7B/V+lE3MAGsYghAyMq/0u0mG3QmKz12rPyYlwfTi5LflplKB4hBqFtrQluXHm2dOrT80YbTDZeQUXIWO1OP44kXJ2LIsOF4e7EH0fflS+ejXNdFbD+6Z4W8hZzrfJMHNIAD241tEgAUUSDyYGYA5rSMkJgPeUQfaUF4HokqIaXWJoTT/DLGJvKOAxm1tC0OAYohmSQEMyijBhG5DdhyhJXkmmlMSiu2/XIW8Wcuoai5DSHbv4VKpcK3//4Be3/cj5UrV2PLli1ISk5FXdN5dBvsMNucpIXQtf+F09nLw0bN1h50aMiP2ULDSdU6MxVYs04BRF/ESpY2Ry+PqSDsBJl8ujQm/LQ/Dm4envjpQBzau41Q64nY3Wh2cG1Kt84saZ6c0BoIKGOaHxsNoL14qQ1Nredx8XIHjGYHz+Cy2ntgtjjx58U2/H7yNH4/WaxI3dcZicaqU0sclmq9jU86BjNxGxLWhAW8srKanU4yZMKRGQaW6aU12gTIoCe6DrUJnRR4dmrM6NSa+cTBVuIXr3QgessOzJr9Pp5+5lkMHToUQ4cOxehHHsEbb74Nn4BgHD1ehA6DA+160Qqu0+CA2uREt9GBKzobmtpNKDynQWZ1O/afvoivfjuHqCMtvG+qiK2Rncz/mS3sCx6bpNDpfow7dDCXdkheE1+ZR+Q1ISCpCL57M7Btbzy27NiFgOAIePoG4ovV6zHng3kYN/5ljLjzTgwePBjPPfd3LFn6Ob7b8wPKK2sV7BwzN7BcOnaskAmdxG50Uf1WtwTutJT1YBOU1oVB6daaFRo3BhA6uvV46eXXMWzYo1S0/iheevl1dGoMHCgyFonoxkyKCVbLJlqjFTqDBRr6PyNnUqzQGSRHJmdRBIMkym12BZvFJm/y+hZ0asyUmROMFJuo9UzHaGB6NTakyd/EHLDkPgxgtHUZ0dZtwpVOIy63G/Bnmx6X2gkgvNxB/l7pMqG924y2bjOudpnR3m3mOXhdWiu69eR3J3LxbOiiMTnd+r5sn9ogWEDxGKFZE6DFJrSKRgFiu6TvtFsaXfQ7YqNT0sd1UOaMM5f0r+u42imYNgYAlQtLsW96F1Bopj16LVY7LWELgGim+j6TRbBsjFkjx48VnWo9ftofg43um/DT/hh0qPW0lEtKu2r63bL3q9aZoTFYJPmChS5iLfx3oKZ/tQb5uJCu69l1F6AnD6pb5b9B+l11alhZnOhbOzUWtKvJsXG1y4C2LgOKa89j5H0P4KXpn8IrvpBEhOU2ifMJZ/xcKxcSA+iqCaSXfVMGGMCB7cY2bgLpM1nmismxrwj/OrfJKxV6gHNqm7Z8CstrxjzvHXjtk1VY910W3A7+jrX/zsGSLXH42P9rLIo+CL/DtfDPrCe9QTPPYHfcYbj7BOGfr7yGwVJZtqikHDq9CWbL/2vvzaPlKKu9/yYhNwMQQHDCASKgTIIaAgoYRFS8V9/r5ToriCKKDAlkQEEZVNQrKKgM4WQ6OQlyGZwuiCDzJKMyJyQmZ+ihuoau7uqqHs8JrvV9/3jGqu4T8Mb7/u5vre9nrb26u051n+7q6qe+z9772bsrZpgyoVjlSKlZdCLrQdkhgWq9ZTwYOol3PGXKMxbUVN5JLC2B40c4fN781MXzgAPfDSeIUXDrevBRA7rKJ6nGbZS8CH410Tk/tmckbpoiqpEMoUbWBUOV5RAryl5GuyvEabO9VTdyj2VyeV2LxHGE9RYGh4RYHRxah0otQRSb5Hc1gxWDflvW2BKDnGj11NWf5dB3vVt/B7vv/hrsvPMumD5jBnbZZTZ223137LzzLthxxx1x8CHvxLe+cwlWX38THn16PZxaW+e+9esD7CcT8JIJ3H7PQ/DicWv7uBSK43CiLoYrbTy8pYbBxwr4xQMj+N4fsmHdTdIsj2GPEFSFpicXfBerwfr3G0VI+vaN+O4dG/Eftz2Lb/18LY776L9i5sxZyOVy2GPPPXHwOw/Fhz7yz/jUZz6PL5x0Cr5++lm48JJLcd3KITzw8BMo+3VEyTgqUROr11yPRUvOw+DaXyJKOqIUigx56u9ei++0V6dSa6Iai9yjurwgRrG5+KntUdLGXfc+ID2E8nu2hMOK1UM9ZSumz5iD079xppzUrINfTcxFUoXSmtat5VFUAtR4CcU+an91UdZeLiUU7dCmlaOlvF32alVxLFpaEPpVaxFDZASuqgenXitumveY9uaI16zUmghkuNHLhEuVuPrDH+8Tde+0x6qb+o3av9Mg6oiVzpEpfVOpd/Vju6WaEH9mxamYRBqxL8SqEKxhZC3iiKxJWCaHr1xpoODGMmSdCIHrJyjK+7YQVKJPePiVmeOqVtlWoqYRnFErdV7WLM+jzg9Mraw1x/zOu++TnknpZaw1UQ56x9PD582HW4ky/9NerGM84Eoo6kmP2k9PXsxzdJpE3RaT5r2nvKpS7IkFM+Z4i+9HmK6tKMdZP0wwXPCxedTF4NAvseuer4co8CzHo9tewoW3bsCFt72E7/zXenFfPr7wtpdEua1UCszGzPgmxjgKQLK9zM7lcviWFIAXy4ucOtkutr0jdk7V7zOFg3sEYDpcZq9q+u7tG3HwMSekwpmzdtoJb3zTm3HIYe/Grrvtjrfusy/esvcc7DJ7V+RyObx1733wiX//NC674hf4y/MbUYk7CBsTSNpbMbH1bzrk0O6Mo93uIkpaIgcukqKmliCMmgjrTdTqTdTipmzdZKyetPRFUwkitxKj6EYYLgQYzvtw/Lq0GMuWD/ZcPHfccQ+sWDUEJ0jEwFpp6NmimulXIlk5X14AKlEHFbuiftLVK+3sC0QlaksPRBNu2IRfFeGsmlxRaYfNRM5TV89a3UqCw+dlPD3zjkVQbaCWdFOhFsdPUPTqyDtVjDohRkoh8uUQBaeCkhvC8SOcs2hJuszFrrvivAsuwgOPPYN7Hn4SDz7xLB5/ZoMugvv32uU/u2ZScejKBRSVxlZUW1tRaWzFcKWNv/pN3PJ0GT+6cxMu/t1zOHvgDpz03eU47Yqb8M3/fDwdJrZF3q0bcNFtwsx209x96dr7sfRn67Dgwv/AZ0/5Og5652GYMmUK9pnzNpxx1kLc/8gTGCkG8MIGyoFYMVr0Yhkar2OsXMeYE6Hg1lH0E4w6NczNfBeHz5uPMaemc8uUNzWoilWOtbhlhak6WowpD4Z9YVMXykpNPOdnv7hKTn6Eh8T2HC08dwmmTk33dc7lTsXUqbv1egQTK3Sc8uRZgrBlPH1KHCoBmPVQ2iU5dPhNenJ6BEUf8atChfoCHrcyx6Opc2CV2V5Tk8/WSQuVzGpRUVNOPP7ZVdeYPEqrw0bcUgtcVLha/Q7He71/8jdte5SUKUERWKkdWnBkBEhQa2mBai+KUF4+dS6p/N+il6DkJXpscmWOnxc2UXIjDKxcgwXnLMbAyiGUvMisuLVyf9X/CWpGaGvvmc4/NJOFerOb8rIpT95PrviFCeHK1x9YsabvZGT5qiErBGxMCX/1vWb/nhXEStD61n29AjoyZWSyglCF0LUn1XodL2zCrzXlGGwW3hTKVTyzoYDnNjq46/4nsMvsXfHRk8/CpbK01aV3bMIdz5dwy+NbcOMTo1j36AhWP5rH4GMFrPxTHgMPj2HZQ6O4+oER/Py+YVx57zB+es8WXHbXZpGLfMcmfP8Pm3DBrygAyfYxO5fL4e7n8rh/U4g71wcYeryAgYfHcK08AX9x/wiuvE+cgJffvUW3EDNtwzZpL4tYtSpuP/K1C/DWg+fioKM+hKM//nmc+u3Lse6BF/HQ5iquWnMTZu20E04+5VSsGroew3kH+XINQTXBWNHFTTf/Cnf+8W489KcnkHcqukBpu7NVl0RodiZ0myaVKN1odlM5GuJHbZnMlQqtoq5qxuuHCfwwgRvE8CoJvFC48MtBgpIfy4GvoZORz164uOfiOWXqaVhwzuL0wC0H9bCuyjDIwT91EVCzf+NBqEixKMJI4n4QiVwjUUi3q/cXs1VT0iDKhC9WDq7rO7guWzEEJ2hipFRF3gnhBDFKQYIxL0HerWO0HKHoxyhVGnDCJsrVJr598fdxzPwP4A1v3AtvfstbMXPWLC0EnWor47mzxFs8nloAsb3mxcZU6PjWux7EvPcerSvt77Pv/njNHq8VHS0OOARfXnwJbnyqiP96zsUdL/q4b2OIu18K8Ntny7jlLw5u/HMJ654oYu3jRfz715dg5k47Y+rUqZjztv3woQ+fgK+dfiauHViNx/+8HiOlKoaLIbYUQmzJV7GlUMVwsYoxJ0K+XEfBjWVeWSw9MMITc811g5ie/S6mz8F1K9aIC7L03ngq/KZDnd2UqTyxSmQvuDGiUHjihEBrWqE3PcGJmlixqtcDmMvthVxuSD+eMWMOVg4O6XzCyLrg1zOmk+QbSvx10WjaIT97FWU/AZhZmZukhZ7OSa1aOWBV8Zv0pViuqEUPcnFAuaI8XFIoydy2sN4SuYkyn1CVMEktbGlPWAtZTHcI3StXd91I5yuqVdRRY1x7BZWnUPwm1eRMhIhDeb+qJ4myx25GEPoyB9ELW3ArTZQr4lxJh3HFeaY+txKB2fw+cdtE0a1hUhSbwAAAIABJREFU7uEZz9vh82WUQuTW+dYiEbfSkCK8pcPsKgRrn5u2F1fnI1pCraImnVIA9htPp8rxVC/Ksd6DG6Y/k/pcviVas9uyr6OOge1FtidS6TB6xkNaa8Ct1OGHdVkUvI6xko/Rooe8E2BL3sP6TXms35TH7Xfcg9mzd8V+BxyEw4+aj//zyc/iyRdHUK424UYduFHHqgwwDlfmDJfrIuJRiroo1rrIVzsYDlrY7Lew0WviiU0OBSDZLmbncjlsKYUIGhMImxOIWlu11VpbUW1uRbU5gUpDXGSVF6YUdVGodTFWbUsPTAsvuU2sLzew0Wvq0Nj8447H6WctwOFHHIkZM2Zg4ZJvwosncOtdD+CkL38V++63P3bf/TX40ilfwRlnLcCS887HD350GZYNrMLa62/Cf978W/zmd7fjT088g5JXk5X3Y5SDJP1j1oNV76DgyXwMT4o8ddHQ+8j75UAIPkeaGjj9qggPKW9EJWph5eDavqJqYMWQ5XkQIlDUkeqmQ0cyZBRaeULKQxjU2vBrbbhhG15V3K/UhaewFstem2rFYNsubNvPJrBo8dL+g+vCxSiUQ2weKeGZF4fxwsYxFL0IvhyInFoHxbCBvB8j78cYKUfI5XL48Ef/Bb+741789g/3YO2Nv8YfH3wcriX89GAWjaMUtlAKGnDC1qsSgN5/QwyWqi2cuXAxZs3aCYuWXoiHntyAl0Yr2FKsYbQc4fmXRvGzqwfwpje9GZf99Ocix7C5FUEygUpjK2pNca7b5/2sWbNw+ZXX4v4/PYtHn3oRd977CG7+9e+xcs0NuHb5Gly9bCXufehJjDoR8tK7VwqacIKm8diokKI8t4pejDPOPhdT+kwczjh7EcbKsc5DMwLSnJMlX5zzTtBA3k3gVZvaO6ZWP6p0A3s1tsqBUuFalTfnV2PMm3esLlw7deqbkcu9Fbnc1tR5svDcJbLeWkNOoEyITS1IUV49+//aKydVHl69aQSjvSDFzuNKib5aryfPl8WZ9YrVIG3GGyby3lT40600dBpDtd6W3p/0CteURysjvFMFlXVoUOaERVYY1/L2ZXvmqn1UKz2Vs2vnD9qmJoKVSIwDIhexCTdsyRWxWesVfrZXWVtF2MDKSTxvK4d6ysRogR01dU5e1gtonwNGwJvizSZ0bXnnak2sHOyfjrB85VDKu2k+X9KzPSvu3HCS7Rkx3CMAM+/RXCfSXlExuYvhBDGKbg1Ft4qiW0XBqeDqZStx9jmLccbZ5+CkU76CQw49LBU1Oe+Ci+RkeZIJszJrouvG4tqr7K/FkAKQbBdaAJqTbTxVp0zbK1yEg4yNeXWcf+H38IY37oXpM2Zgrze9WZ/8L24p6ue58Thu+PVtOGfpt3DaN87CZ79wMk74l49j3pHvwwEHHoQ5b9sPb3rzW7Djjjtit913x7Ef+CDOWXQennlxM0p+XRRodUWRVieIU8LNkeUMXDlQeqEIn6owqidDqX7VrBAMo3bKzR/UTE6JyuurRC2UgzoOnzcf062q74fPm4+iG8GECkzoxs6X6ck1USHfem/Yt2LlB1Xl9n75Kip/yG7GLvpldvt7AKfPwTUDg3D8WIpj5T0QRW/FcWrAq7Xh1jrIe03k/QQnfvIzmDlrFo5839H44wOP9g3zuvUunLCJMTdGKWzCq3eNBzAWlj2fvHgca274Fd579DE474KLsCnvT3quFSoNPPr0i7jxN7/HJT/4MfZ/xwE48KBDcNeDf8FfCwk2FxNsLsbYXIwx4oi8p5IXY8G5S3DYu95tzu3MAGuf+/M/8MHUgD1j5kzs9aY348CD34l3vWcucrkcDjjwYLywMY9ikKDox+b4ye4QjryvJizlIMHVk3gArxkYFIsO/AZKQUOfpyVfhPMKXoKxcqzrz5X8hi7PIlbEdrXQUyJPiUOVG1iVYTv7b26ljhWrh7Dw3CX4+ulnYvr0Oan3NkOF4eQEyy4nokLUIv9OCUMrT6xucrZUjT0hHHoXMNheF23ZEJ41cXMz4qZcafR4vYzny4gjsxK2Aa/SNJZZIetbt2qM6Gs1c9/LWmiZva2afo4I97bFxE9O+MRrt/W+XlUJPyP27PerxzdbDOnPn+jfuK9Fj/BkLThncs+bEUVN/T3ai0GU1VTdvXgb1u/7tiYSQVUtSBLj6fQZczD38PkourX0920JQFsIehXz2bKCz57oG2+mFdLN5pFaEaJKzUR+lBOhHMQo+jGGCyE2j/l4abiMLXkfG7eUsP6vBXz6s1/AHnvuic+fdAq+dOrX8PUzF+AHl12BdTf/Fnc/9ASeeWkE5aiz3VGQLSUKQLJ9aAH43w7FWSE5+9a+sD/y5+fxuzvuxYNPPIv1o842Xq//bKgUNrG5GOKGW27FRz76MeRyOXzkhI+JEg1BE0VfeF7KMiziVJr6b27YglsRK/i8ahtOpYmyftyyZtQqt85y+4fpsIEjvQjKs+P4kb54Ll81hLMWnKsTsisyR0fkqBjRpvJ9lDjM5v/YOUGBKluhE5bTpkNLDdvj0IEXmhC0XzVhnqxYLQdCrIrBVLS28iqJbHEl8tjcShNu2IYTCBFSDBp4YVMenzjxk9h5510wc9YsvOnNb8Ehhx6G4z/yUZx+1kL85OfX4t5HnkwNckpYfe6LX0oJq1wuh2nTpmHatGmYOnUqln7zAuRyOVyzfLDvuTDq1fXz9pmzLz74oRNw+ZXXYtNYBRtHAjz46PO48Td34qdXrcDSb12ET3/2i5g770jsssts5HI5fObzJ016nmXP23yQ4LlNY3jg8Wdwx32P4D9/cxuuXj6I7/3wcpx0yqn6fTy7YQR+1EW1MYEwmYAfdRHUu/BqXd0CLai1UYk6KAex/i6mTD0N06fPwXsOn4+RUh1uaLy+Kh80Srq6Bp7y6ob1thQ44qKvVt7qBQ1y1WYYiTw/u8VZZHtoZO9Udc67lbpMxO+9CKc8LX28KqmQWz/LLjB4BVMhNz9soOjWMLBiDc5euBgDK9Yg79R6vX6TegEz3qOekKktFtPetJQgrLaw4Jwl2xSDKcHXU2fPsmoTQc9zjZBLCdnAeDBVKoHT5zOqz+BZ348SQ0EqdGkVfq42sbxPGsCMGXOwYvVQRsC/gsCzwrx24Wl71bravvDcJanFHMrKQR0rVg3h7IWLsGxgECPFKkq+KM3jZkSgWdGcpL5zIwCb2munF/WoMTk1QTdeP7UwRUWK/FClAsVwvEh4+MpVlP0qhvMeXtw4iqdfGMaTz27Bk89twTMb8vjeDy/HjJkz8Zf1W8x4Ise+7RV8FIDkH812C8BJLeVJ7P8D+MFlV6ZEwJHvOxoHHHgw3rjXXnjNHnvi2OOOx9fPXIh//vgnsO9++2OHHXbAW/feB58/6cu47+E/S/HXQMETIbG8a7wktuXdGPlyLASM10DRa2jPSlF5XeQAW/TEAOtmZtN2bo2X8g6Y0NSLLw1nBhw12JqBUFstPUCaVWXpFX5qn6BmXzCa1q1KAjcDYVEneyfwKgmCahMlL8LAinSit8mNsQvAJtJjYC6WpZ5SEUJwjxaruP9Pf8FvbrsHq9behEv/4wp85bRv4Jj3fwC77DIbM2bOxKGHvQef+dxJWHr+hfjBj6/AeRdchP32f0ePCLTt7W8/AKWw2fe8GnaqOPr9x2KnnXZGLpfDP/3TP2GnnXbWAm+HHXbA617/BsyddyT+7ZOfxrlLz8e1K4dw7yNPoVRtiQlFtYW7HnwcP77iKpx/0fdx3rcvwZLzL8LZ5y7FZz7/Rbz/2OOw7377Y8bMmTKfcDpe97rXY/+3H4Aj33sU/uXjn8DJp3wVF3/vRxhcdzNGnRpGiiFKXl2cJ16sPRTqvMq7MUYdsShkuFjFVctW44yzF+GqZYMYcyIEKoSoc0ON8Nch1qStV27bISqT8yaLFodNFH3x/59bP6zPN9UtIRWCq9nff4JCuYplywdx1gJxER4rVYVnXdZ+LFk9cdVjbV4f6/Mcx0+MBQnKvpV2kbG8U+3JUZt7+Py+IlCXNpErXR0/SW/vU/LEeJMSnUNnCyz7tZ99cUvv/+r3einvYzoMmcrd02NOrMOJ6nyxTR2rkn3MpLmVJCX4/NAW2ZbYthc9WAsfzOrb9OTQrdTNSlrbYye7bYSWaFILKPRCjJTISnuOvbCBFzYMy7Qdk5+nBV7Y0OeCI9MmHJn6oCcFcozNLn4x+6TH515Pn1lQo8fgWkuKcvFce6JQ9GQUQX9X6tjLnNJaCwU3wg23/BdyuRx22mknrPnlzf/4ayoFIPkH8z8rAOtduCr810cAPv7sBrz/2OMwQ64GzsmL7SdO/BR+f+f9uPRHP8WXvnwavn3R9zG47mY89ewmIejKMcYcEw4Twm8yayDvCpFYkOKv6Ges3zZfeFjsATs7qKvQimkOLwYqOxwlqtW3tEcw5WHUYtEMPCnPg/ybCgOVLe+EGqDKgRkwi9aF1KwkTXczcKQoVLNj21TYUXmX7Iuc9q4G5piUgqY+dgWvgbzXxJgrHjuVBA8++mdct3II5yw+D5/+7Bdwwj9/DO87+hgccOBB2G233UVodcYMHHjQITjxU5/B4m9+B1desxyP/PmFbS4m+fOLm3HDr2/Fty+5FF869es47viP4G377Y+dd94FM2bOxPTp0zFt2jTsvMsueONeb8L+7zgAR7z3KJz4qc/izIWLsc+ct2H27F1x9PuPw7+e+Bn82yc/h09++gv4/ElfwdJvXYgrf3EdbvzVbXj48WcxnK+gHLbhBC2Zl9mSE4+W8IxWRG04JxAFgsthG5VI5G/VG6KPbUUu3vGqbQS1jl4VGta7cNV2mfhflTmeql2bWozQaI+jJVegZosfN2R7NrWAQS2mEPXbRJ5dUGuZMGjKy50NqaYFmBAesRYgpT6ir9jHCp7KY+zdrhbHFOWEK2W+udiW/KTvavvp0+dg2fI1r0oAGjFh/V6kkOoVgElKlNniTF34S36s//ZK5lj3xXOTzHEwZh9H8/4SuNK04AtMPrMXJunFCVZhZFvkpe73sXIQYcWqISw8dzFWrBoS4i9K518qYaXGExNSTXcPKcsxRo01jn1s5X17NXI5JY7Tt3qxStXk4FUikYpjoiMt/XlVCkGoJ8/N1IQ6K2TTE29jfs1EjJS5YVsUAJe/33pDlGuqRKLsS75UwZZ8gD/c/RC+c/Gl2HnnXfDB4z+MawZW48UtRXoAyf9KXlEAavEmbTIx108A+vE4vFcVbpvA5mIF19/8O5z8ldOQy+Xw1LN/RcGNZRmNOkadOkadCCMlY6N6ex2jToyxcoK8KwWfFIZ5N0HBFSJPeQoLrrrgyLCmbzyCyuNlP1ahNl+HjIUoE2G9tl7tmw3nqsFEF7GtWjk8ofEkmtdu6hCSGzbl/2ilkpDtFWll2RDdznuxPY+mtZVZbedWkpQAMKGnhiUAjdgTIXUhclToXJgcFGVxXKfSQtFvavHsyA4KjhSTbtiEp1YzRm24UQfDxRAPPv4MBtfdjG9d+F184sRP4e0HHIipU6fiiPcehd/dcS+efG4jfvLza/Dlr56OI957FHbZZTamTp2KfffdH8ce9yGcfMqpuPCSS7HuhlvwyGN/wZ+feRFPP7sBTz+3Afc//BRuveN+3Pjr23DNwCC+c/H3cfKXT8P3f3QFNhfqGHEaGHObyHtC0JWCFgqe+Dwi8d7UcVPdHsz9DtxQCD5zkbCOT6WFsN6RtR+FV0+nG8hwo1+1V4u3Ut+HX1U134RV6yq/0/QnbbRMDcl6w3TKUCWE/Jo5N8vWRMatNFKP+3mzSpZXvORnHluezaxgU2JHCx3XiMGCl7nvmvtaDPlCICgBddaCRX0XzZy1YJEWcWmRl/YupkWgJQyzXkgpBG2hUnAtz6YvH3tpAWiLPEcJRy8rEtMeUeMFFffL1m05iOFa5lUSbSIsmQ6rKxHmh5n72X2qtnfQiEb7Nvt6dh5hViCXfEvQWaLY3tf25rmhXHEb2m3chGANM11rzEr1bAmWhhZ3ofSMq4VNumyQyo+OTcqNKqRuW6jMKtOlttvVGVTZrkpdpHF4VVH0vujWMFqqIO9UUXJryDshNo+52LDFxYYtDh589GksPHcpDjr4nZgyZQre/o4D8LkvnozLrvgF7nnwcYw41XSUjAKQ/H/AqxaAXl169KIOytUO3FoHXpQ2v96FLz1+fjwu7svHbt0IR/G4i1LYQj5oIO83MObF2FKqYdasnXDM/A/g6ReGMepEUlA0UfJNXp9abenIELD24FmehIIbIy9rseXdWFvBTV/U1PNUsdSsx8vOD/RCu/VaW5ZpMSv6fvWb2/QKwFqcKdMSd63Bp1copoWAGfjKMsygPH5iVXFmkLNDxn2KxPaEn/vUtvKUB9IWgYFaiCCOtT7uliBMC8M2vGonZX5NlK8JpJDyah2ZU9hCyTch/KLfhFNtw6uPY9W6m3DBxd/HzFmzMG3aNLz3qGPwtW+cjSuvGsBd9z2GgltHGHcR1DrCc1ZrytzFOtwgQpRYq6aTcfi1DsphWwu8vNfEaLmJ0XIDBem1HHMbGJOP825Df05HevdKvhHFShi7mXPEHE+T9B/UlHevo9+TeuzX2vLYtHXCf1BrIUq6KLp1XLdiCAvOWSLK9fj1nolGNe6gUmvDl8JSpQmoSUa50sQNN/3OTGoCM8GxJzr2BMg28dtLLK+4EHuFlMcu6zk3orDHu+cllgcwHd5UQkFtU965Zcv7rFKdPgfLlg9aIdtMCLefAPTTAjAVXrWeq0Wbl2jhp/a7/j9/Y73nPqHvbBi8J/SdNiX40qJPhXWTlDesJ8/SEm72iuiseDMLQdKe3pS3LZsnaXlFvdAW2L2haLfSwJhTxTUDgzh74WJcI9MGbC+qEotuKKop3HjL7/QkNFW8O9ONQ223xzAdKZETaDXG2ZNvfd8eY63fTFhX4s/cmu29udbqdYNay3SN8RsoV1oo+QlGSlWMOnUUvCb8WksIYjfChs0l5MsRnnpuE9becAvOXrgERx39fsyW9W332HNPHPHe9+G2O++DyUtOO0y8zH2PApD8A5mdy+Vw0pe/iksvuwI3/fZ2fO9Hl+MT//5pfPuSH+CPDzyGqwZW42P/eiLePXce9tv/7Xjt616PmTNnYv+3vwNf+sppuHpgNR5/ej2csCVEYa0DP+rIC1EL5bCFgt/AqBtjzI0x6sYYLdcxUq5j1JW35TrG3BjPb8xj5sxZukD0DjvsgIMOfie+/8PLsXz1Otzy29tx9/2P4qlnX8Jz64fxwktjeGFjHhs2l7BpxEXejVPirVxpomx5ZXQvT7UIxHLziwtwW3pNrOLMsk6XXajZLvWgEvVrSRerBtfJ1kCdPgKvrUVjtiyEXerB3ldd0D3LM6QGwyBqydITmYHKqjVoVg+39Qpn007M5BwqD6USDUpUq9D6iBNjpCRW1I6WGxgtN8R9aSOO2Ge4JGxEe2MTHUrXQqmSLk6bdxsYK4veqyUpugZWroVX6+D5l0YxVqqg6Dcx5iYYLtVR9IRXWOUQCSGbIKgmCOstxM0JxLI9XtKa0ALQr3VQqZuSO6org1sVwlCb38KY29SW95oo+C0UvCYKXgtOpQ03NMLWLu1RidpmUuKnz0M3dR7K80yGfYOa8SIGURtOkPQUi557+HyU/FgvKFGTEXVB6uvFDpq4dmBNT75r0U6F8PqJN5P3lBJ9fdIk1L4FT+2jPIK2kJT5s1Z+XL/wbT/LO3IB03S5MGW6WJgy5tQyeXwmvGu8Uw3poUrS+wTGe5kSfdsI5Tp+gmUDgz3ew3KQCCGX9eQpcWeJPNtUaNetWGbn9GXMFme+9swllmCzyqPocLFZLWuH99X7zgrlkv6ejAhOe0/N8Sp64jOMlkK8Z+77e87VsVJVhLOtcLB4zRgDK9boMLKKSOgFHZWGLDlkohQqjK3EYqXef6Kbqr/aZ2Gd8AimPXy9jy2hmBpfVYmfjCex3hY1O+W1Iqi1UfLqKHkRRooBNo+WMZz3dXTG8esYKfh49MkX8Otb78LipedjypQp2HHHHbHHnq/F299xAD74oY/gZ1dfh+Wr1+KHl12Bcxafh1O/djq+c8mlWDl0A+5+8DFsLgTYUqxQAJLtYnYul8NnP/9FHP/hE/DWvffB/A8cj/MuuBgn/PPHMWvWTnjnoe/CeRdcjBVrbsCNv74Nv7/7ITz0+LNYve4mHP/hE/C6178BU6ZMwZQpU7Drrrvh4EMOxXHHfxgnf/mr+M4lP8DqtTfitj8+gAcfexpPvziMzYUK8n6CvJ/ghc0l3PPQU7jpN7djcN3NuOW3t+P2ux7E/Y88hQcfexp33vsILv3RT3HscR/CYe96D/beZw5m77rrpIsHTv3aGZYH0Hgb9MVJLQAJ5ErgSjqk6+ucjz4XaS3e+vX1HBfCIumimozrvK9qrNrIGQ9bIEs+qJpfqm2UCjV6Mo9MrQZVq0d1KKKPMPRDOwfG5LjUZI02ezGBCpPovqKyXqEbGlE25tQxXIowXKxjuFjHSCmWIXa5mMYVYUAnJe5aKLh1DKwYwtnnLMHyVWvh+DGCyPTH9GtG3LqVpkimrrSQ9xoYcRrIu02Uwzb8age+DLEKz1kXTqUFV3rU1HekjmMt7iJKxlFLxnvEeU2XxbHqL2Y6paTEuBLhkRHKSsyr50aJCbPq2o2y9Zf4/loib7DSghM09apedY6pY6AuOOL4dOCG4ntdsbp/4e6BVWvhykmM8LwqL7jyjBshWPBEXUIlCHtCvBmvYHa7E4jwfb/n9ix8yOTJ2qbC3dkws53DasLSaXGoPHNjThXLlq/BWQsW4zq5Ctj26pWDtBcsK5i8SnqVcD/Rl83RmyxEbKz/auO0cDOevH41+Ho8dH22uYH5W/Z/2u8jfcz6eOv89PYeEdvnef3C3G6lISba8vWuGeifo3n1skGMleNUekDRi1Pft6iP2TALKqzzwLVTWqpWS7pa08rfy+T21bNir3elci3O9PmVFuki5N0+fzeRnCjpotawJv96LOnq33O13pYpOuJzlLw6No642DTiYeOwi2fWj+Gp50fw5POjeHp9AY89vRl3P/Ak/uuOB7D2l7fgWxdchHceehgOe9e78aEPn4DPfeFknHra6fjYxz+Bww57N3bdbTfkcjm8Zo89KADJdjE7l8vhhQ1bsGGzgxc2B9hSSlAK2hgr1/HSsIv1mwoYLVZQcCrYtCWPzSMO/jri6lm5KBz7RuRyn8K0aW/EvvseiB/+x09w9sLF+Pi/noiDDzkUb3jDG1MdI6ZNm6YXfkyfPh1vecveeMcBB2HvfeZgz9e+FrNm7ZTad585b8P8D3wQ//bvn8FRxxyLt+49B1OmTNH77Lnna3HgQQfjtjvvT4Xa/KgDPxLhwEgKM1VUuV+LJtu0V65uOnMIoSaS96PGOKLGBKLGBKrxOFTXDuPNM507vGpb55Co2aWq62d7/ZSH0s+Ebk0ZmXTNv6pVKsTuRSp6U4q8MPGZm3C8Gvwwliv1WqlVe2rxie0pEiuhW1K02h7MtIVSLJWDuKfdnPDU1FGutLW4dmW4shw0MFaO4Ph13bVC1C4UphZJVOMuvKrpmmB7XVUJnHpzXD9XFclWFss+s6pQdtKaED1nW1YrLykQTd9SkWenOqrUm91UaErUbVQ9T02bs0hfTDpotMdRidpIZOmWRObnqS4JpmBzV4T16+YYLzy3t3C3yHtbrGsD2l5GFcrNu2Kio/YRYex0WsO2V6uaenl2KRRVMzNVJiWVGtFbQ8+3t1XTuaWp2n32e5nkvaltqgh2v9WxpR5BYwSbEn/Z59ih6KzgUWkkKo+xpMPYsfaS6TBppYFiOV2qpliu9Rd0/wNm5y+mBaKVj6dFVsb7Z3lM+4m/7HFRz1Wt5V4pR9PklKp8zV4hmy0tpM6fdK/njOjTPX9NeFcLP2lK8KkcwB7RZ9czVCKw0U0XJm9k9smKxpSJv4tasW0ZshbnsYhimJSXUSfGcKkuJvvVDkZLEYaLIUZKNeTLNYwWA2wZc1B0a3D8OkpeDQUnRLEcwqvUMZIv4/d33E0BSLYLkQM45sl8Ktsrpbweqs1UG1EsvEordRP5IeRy2dnfPvjxT67G8xuLGHOqKHkRCuUqiuUKCuUannp+GPc9/BT+cPfDeG79CP466mHzqIsxp4q8W0fBi+GFLRT9BjaOlPHw48/ixl/dih//5Of45vkX4idXXo21N/wad9zzEF7cNAY3qCOMGun+oo2uLpQc1EQJFD+MRX/gWgN+GKMc1DBWCpB3QpS8SOSPydV1jl9H3gkxnPeQL/rIOxWMFnwUnACOG8IPY1TrrdRgENY7KHmR9h6p0J42+dguHGsvAghkwr5eMFE1eWViEUAbtbr5jEmri4ZssSU6O3RRl4NPs93VCdUN2f5L14LTJUDk/5UCOYg6qOquBOZWCSjV1zRKRHcRJdLU7HeyYtMDK9fqRSJqJZ3yrgpLC+tKvYuSWzdetShdR6+iQjZSBKom7HYbrrgpOqRE1t+SlvhbQ7bualhtvsStFIStcbS7W1FvtDA4dD0WLVmKVWvWIYya1ozfCHElzsVijfQFqudiZT0OaumSLHaj+35dEWbMmIMVq4Z0XpQ6H9Tzvaq4SJaDhi754leb2JIPROhJCQHL8+ZMIgSzIkzlx6VCuBVzEX8lgaNEQI/o6xfCtXL6jNcuSYd3M7l7qZW8viofUzPlbJar2oEZb5dvhKISRiYUa3mmVK5bqWIJKVMFoFDubac29/D5PSJQ2aS1El/Bgm0s2PiftG3Vd5ysh+/AijW9OYdS5I0VK5bga/aUsdErjq1SRdoD2CefWae0yN+GLQB7PIBJJyUG0yJvMgFoTza7erIobHxSi5sTqEuzJ6dicqtEqersJJwJ9aboLa0mwtrxUJfRhYqIIvi1Nv46WqYAJNvF7FwEUu9RAAAMZElEQVQuh7wT6IujbnbeEt4TdcHUKw1bE1i0+DzpoTgfudzXeqrIL1pyHhqdv6HReRlJaysabXHr19oixFKuoFgOMFby4QY1RLEIRXoV0VbHCRKUKip01oVb7ciwqAgzbhpxsSXvI6wlqMnnNlqiCX0tNl4xNUsTfTnTLdhqeqYmqr6HkWgyH+k2a+J9x62XkbRfRtTcijA2zd2DSJUi6Giv2xlnLbC8iLIob914+PRqYJm3ZRehVW22VMuuRktYqyOEXqszjra0TncC3fGt6I6/jPGJl9Eef1nc3/o3dMZflt/hVrRlz2S9alSLn63SJkRv085W+R0bcWT22Wo9Fn+ryzy7WmMCYTyOSr2LhYv6t5tbeO5SHRIX7a1MOLsapz2vNXlsv3HmAj3wpRdLdGSenJmsKLFajcdRTcb1YBs1TB5gZHkHY3kuJFoMCs+c6LnblfebsiuBuaDPO+JYhFFDCs2uDiVVY9FPNt2PV3gNbS9DOtzU0edfv24YXphu0TZjxhzMm3csvEqsvRCpAryqTWHKS9nW52Qt7kCVz9BdQeT/Ti0I0hdgu/1Vo+dv9upSdQEXIb1m+oKfDX1aIVC1ItQu2qvCpm7YSIVOTV6cXBgRmPy3fgK0nyA7vJ8gy4ic7HvMhmW//o2z+nr0rnsFEaTEm7q1e5Crbir97vf8LZL9j6XZj839pp5cBFEzZab0S6bjSuZv9r6htW+YeZ4o3jxJHcGgrt97xS4+XWvi9DPO0kWp1T6mlI2pX2i3ADTdZ7YhAqWXUKe6yFZ0/bx5usVfTx9qI/RSrTsns2yLT2nZCam6n05DsbtApaND4nXGU1ZLTL/osN7B8JhHAUi2i9m5XA4FJ+jpdylCVVIEtqy+s52tGFr3y0k9gDNmzMHa629Aq/uytmZnKxqykXp7/GW0x/8m7/8Nrc5WEZJrjuscCyFGt2phErdE7bNmZ0LWQjMiNWlNoCGbsevZV0PMqpR3SotC68enhF4sZ1wqJBhnrFcYZfc1IUYvjPX/VSuAVZP3Wtwxs8dGV4rrcf2aqr6b+pztrrKtaHWUx0oeA7ldCbyWvN/uvmxs3Ny25LaW9nalzX4PibS4NSEXVFiN7WVze3tAUqHXVWuu7+u1WjW4ToemI9meToWnU+Fa3b5uHG4l1t49tX82f88W8j15flb/Vn0eW55A/T02zXdnb1+z9pd9P8vQul+mhHDSnrA8jKokS/p1VfjXnlTZ/zPObFP3q/UWBod+iUVLlmJw6HqE9WbmvYrfZUOfR+NoSmu0xaSh2R5HGMVIWl0kLfvvooag6R9tjpfp0St7uaoWcpbZ9dSUmLALDZs2ilYNOSUeq2nBlS1VpGrW9ff+GOGhBIl9X3lXV/TpbjFdelB1bTu780jm/wRZk/sVnIoRKlaR5cnaqS08d0lfgR/KlnhhvYVQtcabrMOG6qdrefFV/92adau32dvtv8Vm+6vp6pF9T2HqcStlntVOcMVqWUcwU3NPtwqMmii51Yx4yxx7XcKqv6VTDhqpos+VmvGum8LV1vHIhH71GNO0xxvbw9drcTMtHLNCst9zIuvamg47d/T7qlkTO9ObenLBWigHFIBku5idy+VQciupi8srWbXexBFHHovp0/eROYB7IZc7FTNmzMERRx6LWr1pitS2e4WOudBNaPGnLqK20DIib1wKo61I2sKjY++nhEyiRZt6r/I1pCi0E3yVOBAXx27mAqpEb1dfPM1t5oKpm96bRvFCnJjE4mrdlHlRCy7cUAxW2fIHKr+sbr2mCjWK2lcyETk2Qt0WBaogsBKOzfaELhSsxGUzc1x7BKgSm/JWCYZm26o/1xKvmzTNOTHvyLTX6ogjj0U1I1xS4scSSlkhY4sb8Z7G+1vHCONmv8/QkedHu/d8Ufs1MrZ46Xl9L+iLl5yna+9p66TP0+x5Kwo4m88g7o/r+1mzP1urMy4mAdZtpzuBzricHHSEdbrC1KSh1TF/U8Wh08fGHN+0GBXnu8pTTJrmfl2mGiiRaJsSjLpbSZK+uEWpfSbJxUr6XxSVZ1XlWertjU5qW2TdP2fRkr7f37mLlug6cyJlRJjtGa0nbbN4Soq0mhS+5nki91Mcnw5WDa7dxuRHvj+rT7P9eXsFRQf1pjR1jLNiwxLrZvtkQqHT53vr/Q7/p603n65tJhlZs4Vm337Rrf5CfRLhPpm4VwvglEhMedEb5vuZVOz1EYbpCaf9uP++9ew+1v9QDpjJrOhyFTDZPmbncjkUyz7iZqfHtvWDrtQSrF6zDgvOWYTTv3EmFp6zCKvWrEWlFouB1Rr06sqy2xvtvq/d772krdvXkm1YvwtZz/Nbmee1LLP3nez9qm2JfUHs6M8fyYtLNU4XPRU5fJNbPUnP4O0LlvYKyJW9tXrmmOv2Ye0eL06qL6hMtFa5aPZ7E8ejg6Qpes42dP6h+exJs4NqlGBwaB0WLV6KwaF1qFQT+fzs88zrxdZsWOdw9hEgiRS6jZQAnEh7wlqWcE89rytFsBFiRmz2Ckbj4e71ACpR1U55aY23VllnXJgtyDqWR1dZW4o3ta/aX4ht48Vstsf7/s9J30tnIi0sO5MI6EmFtXxuW1iz3bW+w/Txtp+XmvSlRPw29ktt62aEaea3af8W5bnZzJxbg2t681GVIDPnq3ydRie1TZ3XJre2o/fL/ubtsVCkDFgh+yOORaWWSPHTRiTHOlvkRo1tj7E942Kjk3of2fs948Y2/vaKZo1pff/2arera4m0ycbuf5QJgfX/QNQ209b//byaa9l/34plnwKQbBezc7kcNmzcjKLjGyu/GgtQLAco9TGnj/Xbb1vPK7vGJns9/dj9B9p2vO6y65bDcdPvva95fUx91r/TzGtWUuZup036er5164eTmueH8PyKtLDXAmmp/YQNLF+R2dbnufbtJH93+z2n3/OCtBUdF3PnHoPp0/fGlClfwvTpe2Pu3GNQLLm9+/d8lldn7qv4mzqW5W0c51ey65avSD0u+xWU/bDn+92WOdL6PXZsc5X1nqclaa/025tsbCj+HTaad/CeuUdnvr+jMZZ3UCr72oplNeZ5KDgeitLEfR8Fe0x0fFy7bEDvo/ZTNjJWxLXLluPMsxbg2mXLMTpW2uZYWipnxtt++072/Fc7Rjt/p2XfU7/X2NZrb2t/y669bvnf8f68Hiv8PVZ6Nea+ouX7WHa72PfV/k/LXsXn6HccNmzcTAFItovpuUlq6tFoNBqNRvtfbV4ul5uRI+S/yfScmEHQaDQajUb7/49R/BFCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBC/pfyfwFFInpGRRlprwAAAABJRU5ErkJggg==\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.gridliner.Gridliner at 0x7fbc17f49ba8>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.PlateCarree())\n",
+    "ax.scatter(lon, lat)\n",
+    "ax.stock_img()\n",
+    "ax.coastlines()\n",
+    "ax.gridlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This projection supports two arguments:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "ccrs.PlateCarree?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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OVzgClHYnPeVHQPux9xiEoDEvyRAo9Fg2bbDqoM1nJ/U0oGbLJ+I4qRlrmrh0j6NIO7a9px5Zjcv7JqbJbvNllfsPIGu/Z5CS7/eaNFiLlr69WZexl0n3cIgioGDu+21qQVS2EvPQ4tR5hImJKzsgstCw4oQHP9fxz0vIuwleQYtdhhMxZIHUIRExeeJ25DEbExdvhpPv/kJpi5fjZA92QoPRgHCuUAN554zKcSdLofuhIhi4U3mOVGHIm3MayKEkggzWhySFyjKYmeCVXizFBd79rdbyM7ikASZEDJCGAjPWYwqlKnwnFociLQ4EWlhXq9IqxORmU5EZboksRLlJkxUolvlDXNAzz2zzDvLBYXZIXls9SabwgtWLP2O7kKPiTseync3s02RHiD/xiJMazDLXjVl6DbKXThKpha8BrdtkYSwwgOt2odKL7RFfkxvsiv2hwN6s7wvpLC48FhLHkzu7eRe8FiFhy5G4bGL3VuqFnXSb8UnRkLMKbfFJO83vdHmEbKO4MJOtgLJM8RuudfGLVStCjWL34X/Nu4e00jFsSt7fdXHa5S5BD0GjUZL/95o4TMXLf17o8eg0Ygyl0jHckymgwtdF2J51CB+XylW/VbGbH85Vu4rRTyPKsRmOhBtsSHSxNIGdGn5PE+SX8AlMZnrlnaRI13QpUgGFxlhSTkIV6RfCG+ULq1A8rYrxRoLq+bxfEzmNQtLykXoHpbPGZ6Ug4gUJuS0Cq+92qufz9M6cqVQqBB7woQgC1bciuuBJMKSlGKFC+GkPD7p5R6xJO/REFUqTFIeQpPc02rkSAqLquQrXqNIf3FLhQkR75OcrzJVeozH88X75yNw92lMWboKo6d+jClLVyEo4bTqddIkO0ntcRaiMjQ5H6FcXEphZOHJ5uIzPIV5L6VwNxfToUm5zDspeQ1zJIEtfpcVvx4mAUj8KyQBGMJnpUF7crzOWEPdZrXywZgnuexF/kgwH4hC9jSfvxDo4VFU5tqoRWNzeYYshydbNZiGivy0FCHEZG+ALoPNgFneE8sHEyJJEhXGYim3SpchPx5p4SKRe7d0kteiQOWNE16xsKQcdOs/UnXR6dZ/JEITs1XePBFCkcIK6YUqAchyHW2SByvK4uaZsjq8iwiLWkiI3Ej3kKFS+KpEh8o7JS78NpXgYq+3S0JNut9kky7IwjOl53maBp4/JsSM0tMqvK1KU26vMrctIl0pLvh9fH+J30lnLILeWARdRqH8ugzFc91Eh4cwVIYpJSHoXZy5pw4YJJHnFqbmnspIi1pUysLRrvgchbjzEi4WqQzuXtMoSUB7Px7E/teZ7NApBJxeIaR1qrBzsTwxSXcXdVzYKUJwOimvs4gJJB7KZ8JXDvlK3lJlvqTVqcqXjHYPQ1vVnkvJa8lFLEv5cCk8lexxg6kEM0M2YNzMeZgZsoFtm0Xet+J9ojJZuDrSapfyM/XGEkSkF7J0k5RchKfksRzm3acU+cwKD5PkkRKCRvYWKb1EwXxMDUvOYxPVtAIpB04n8iBT8rmXKBthidkIS8rx8LopxaHw6IlwZkRaAbQpeWpPGfdsMk9SLoK8hLuFJypYuh54TzEJSvR8vfK+oMRc/hmeok+dNqMQdpLAy/fymPfcaHcRp8yD9hBn/8CC95xm47cfH7/92PgdvOe07DFUmtvrvX22e162EIhe7+fiMEJyMsheRWHLtpEAJP4dARqNBsu3H1EdzOEpykR+2bMVkVbA8kLSmOcqIlUeXIK5KzuY55WoEnmTczF16Uo2w+RC7W7hFPfcw1FTZnvN4Rk1ZbYkVEMS2bbq0gth4CJPXAykXC4r84oJ0aPLkHPVJOFgFp4gG09IL+RCrAQGkx2xWcwjYjDZmKBUiEKDwgMyLXAtWvr38Qg7TVuxVhHaLeKhWx62ki6uQszI3ixRJGAw2TA9aJ2ieIALE5PyOV7MWAxdRiHPW8mHNk0kWOdJYTcmhIphMNugM8oXfdkr6WbGYmjTC7kHs0AKAYu8rYj0QugzWFGFuF+ICmWoWHyW5Akyyt9deIMiMooRkVHMc77+bgAXuYlsAiAEo/CyatOZRWQUYVrgWmmbVN+3uUIK8T0yilWPy2KaHT9ygUcJ9Cbx/YWgZwUY0VZF+FchMJXexEiLXfK8qYRPpuy5i91bKnmn4rnnKn4vEz+xe12I38fDt3tLEZXlQlQmMxG+1Zkd0FuciDA7oDU5EGFWhG+5J1ApymKyXIjZq/YmzgrdyAWUbMK7K72XuRlzCxeLbYkwO6A1OxBudiDc7ES42QmdxQmtxYkIblGZLskjt25/GTb8Xo4vfy/Hmr1ObNzvwqbfS/HdoTJ8f6gMmw6WYuOBUqzax4RiTJYLBqsTOgv7rCirA7GZDsRlORC/14k47gkVkw8xSRNpGrp0dl9YSj6fzBVwD2CuJJpCEpkXTJtWyLz1PIdYly7ORSYq9ekFPDfOhumBa6BNYxGCUO4NChcFVVzUSTnQyXkI5mIyaA8Pi4rJ+x61cAvak+cmxBTC7W4hbv5+QYk5boLPLWzunvKSlK8Qvnnc0yk/5lW0JXk+Huom6NyfF+omsiQHQEoBpi5bzcdXRU5yehF06cXc1NEWvZGl9Ey/6/hdrH69wlRjiCJlQetuqkgQ+629CssU+fuoQtbJ+Vj26xESgMS/IkCj0WDFjqMqt7T7bEVcVFkVaJ70HFHZpUzkZV5EUVmVw2YrSXmYGfYNz+mQcy3UM8zmw8DNegCXxEsDGMv5Y/lCkaYSxUXUIVUJq0K6impGFtLlz8kodvOCyBZptiHaXCJVrhq4Z8OgECpMIJRgzPQ5aOHjJlp95mLM9Dmy90zybKkT/vVG+X2lEKXCY/du5I9yHhUXnnpJfBbx/MFCOYdKVVVX4OW3lmeVrHI3T6rAlvML5YKXEClExav5RDiPTxBY2L1IIfTk/S57lpSirICJsrRChKcVcCuUkv+bnWkrKkLFbFp8N21qAfTpLBlcqipV5EwKezviO498SpGbqU0V1crMq+UuECVPrThWMoSYLZSOETksbZM9ozw3UeQnigIIVdGJwrsVm+VUhCaFh0v2bon8Q4MIDVscbPsSjyN0+yEEbt2P5Vv2IXjrPqyxFGCNKQeGnb9j0XdGLNyYgIgf0xD+sxkxO37DhtQjWJ9+EnGpp7DSVIB4qw2x3Psmhf0tDugsDujMDi4eHXgvajML31vlUL4yZGwwMa+jdOvmBRa5j5FWB9buc+LLA6X4er8DaxJ/R9zPydiwPQMrt2YgcosR3++xIih6Lca/9iaGj3seY8aNx6inx+LJ4SMwdOhjGDbscQx7/AmMeGokxowZizHjnsG4Zydgwgsv4YWXX8XzL7+G5ydOwatTZ2HWnEWYHxqPkI3boN1+AJEWGytcyZT3PZs82lkhmNWJ+CwnVu5zIX6vi+cBF0udAZhALJT/TyuUzj1RPco8fsyLJyImwXtYcUJIYjZmhn3N8tmkcVXk+fECBkVuYPAeOedMVEiL41jk7yqLNiLSihCWku+Zi9hcjqLIP3MXtUnKMKg671iIGlXRhJf8YsmEF58La4OReY2jTCWqVJcoXhgWZSpBtMWGGKsdMRY7Yqx2xLpZjIXZB1E/SrnBcgqNXOAn5fAqUgZiMp14ZuY8r+P3uBnz1BM0s10qaBITcjYuy2O5uD5IE3ajTZ4Ai4mkm4CUjyc+PoqokSK/NXDnMRKAxL8iQKPRIHj3CVXVpjZNDlFqUwrVMxVpMPPu1hYDkJQcm8hCwaKQQLRoEDki4m/PAUi2FbtOomv/kfBV5gD2H4mg3acRlsQHvDSFKLPYEWWVL6Ys8d2uuDDxC5DRJpkyrCn/bVOJNO+VlzbVa8Rzm/UABq69y2fJ76U3KnOfbLIHiIcKWbWk7PkzcC+FavapGEgilL9paiHCU/hvkZyPEJ6fw34bbqKVRpIy70YdXhHeYVGdGZFWKB1HQjgJj114Sj70GYUwGJkgYyFCWbTqpHC6fFxJ2+GWu+M1nJKi2B6+LeEpeVIagFYqFODeG+7BEZ4ctbmFhzNKmBndBnLuJTaYiqWQr6g4Fjlk8ft4bl2m2osWpfSM8Upa5d+RVif0FtmTFpvlxPd/lOO7Q+WITDmNhfE/YEFINGLWbMTmPWasWP0DBj0xEp0f6gp/f3+PhrEtW7aEn5+f9H+LFi0QEBCALg89hE6dHkRAQIDX12k0Gvj7+SEgIADdu3XD4EGD8MzYsXj33fewPCQCaYfzEWa0se9jtmP9Pgd+OuRCSs5ZpOecQfrpCuzPr8IR2zn8cMgFy0kndhx2YK21COvSjuFw8Rnk2qtxtLgKySfKEbruJ7w89S30GzwMre5pjZZ+fniwc2d07NQJHTp0QPv296FNm7YY9vgTWLoiEGvWbcDGr77Bhi83YdO3P+D7Hzbjm2++x5dffo01azdg9ep1iI6Og05nQHBwKD5bHIg5C1fgk8+X4r05C/DipKkY+uRIPNi1O3x8feHj44v2HTrC/57W0Gg0mBP7E3QmJqyjrUzsGswOhBvtCMmwIcxoR7jJjgizU/KeRlqZYNQbi6SJgTgXtWmF0Kbks/QY7l0LTy2Q0ztMNiYe+HFpMBYjPsuJ+L1McMZlObHqN+7x5RYrji8R/jcr01WUk8Niqf1SaLK6+leaCCqK6FgYUj0hCueeKG1aIfQZxYg02RBt5uI4k3lNV3FvtNjOVb+VYaXIpeR/r9zHb93uFx0bhHda5BCL80PkvkpmUVfYh2XYoDXaVHnEqlxjK/NgS5+T6VJ5rUXu64yQDZ7jt39vzAzZIBVGKc9x5feJV/w2wvset7cUcW73rxSv21eGlb/J+ysuy8Ue2yv/H2N1SpEeVkhlQ0jCSRKAxL8iQKPRICw5mwuiEn7BK/YiHJQJrLIXSWlKb4w2lbcASMxG4M4TWL7zOAJ3nZK8R8qLuZQorRCF7ha4+xTeUCTkBiecZp4eHjrVp7NwbFwWu1jG72WDYpSJufPHzpiL6cHrEZFeqMqtcheC7qbzJgC5ibBkhJf2HOEp+eg+YJQqh6T7gFEs6bcZMekpBmXxJ/Kf9FIlrTInjYlAVZiSh1XlkEOhm3dLnkkKL5u6b6C6p5p0q7w/OQ+yh1j2LkakFSguIso+gvJtaHKeJDxVluyZtN1cEneY4tbDay28galy9XOE+N7c48BEnSIcrQwDGb0UIEhhWCdrFcOLBqQBXuGhi+MXZWWBizcTx6DOxHJNY7g38PPvTJj0wWd45tU38czzr2D0mGcw+NEh6NLlIfj4+OCRvn3x7PgJGDlyJNq0aYM2bdpApzcgMSkVfxw9iVMFNuTZXKioOgtn+RnklzhRUOzA8ZPZyM4rQLHdhcJiB3LyipCfX4ycnAIUFJSgqMiOnOx8HD12EgcPHcWO3UnQRq3Eh/M+x4RX30CPvgNV4nDcW5/hrfc/xuSp0/H8yxMxaswzGDrsCQwe/CgGDx6MQYMGY/CgwRg9ajTmLViE+fM/xahRo3DvvfcycenfCoMHP4pJk17HgAEDpPd9sMtDWP3tLzh/8TLqb9zEjVv/wfWmv3Dt5l+42vQX6hvv4PK1m7h8/Rau3LiN2vpG1Fy5hrMX61Bx5gKcZVVwlZ9FeIThX620sDR6Izb85sJP+woQt+sAFm9KxsexP2Gudi2WRa5F/Ldb8fX2NGxO3Y+UYzYkZJ/F+gNlkkdTeJcizTYmBPkkSeTshfNqU21aodSzNMpcInscM52I31fKxZBcsW0QHl+TTSrIEWkUekWKho4f28zjxbxo0RYbos025imzssr6KLMNMRYb4jLF5zoQx8PhcZkKy2Ie0PgsJ1bu5ULlN7XYEV60aIWgkiY4Zj4BV6QEyMVNvLDJwoS2sshJb7KrPMrK3Ft27rBt/ulIJTYfqcD3h8vx1e/sd9Dz4rQYqwNfHixDfJZTEvV693QEbhEZxeg+UF041H3gaOhNxW5i0SGZ3uKAwaJu9xTplsYhxnKDVd0pQEqtkFpRuaRIgGsaB48AACAASURBVNinIrVjJU9hiEjJJQFI/CsCNBoNtCm56jw5i13OAeNtFkTui/CoqIWg0uTHwhShjy9+yELIntNSoYZ8sVZWynmr4lJ7okToLzy1ABHpIgzLPGIxmcq2HzZo07gI8+/DT+I+6D5wNCLSi9ShKkU+nd5kk8JpUoWwNIuWRZuqUW8zIjE8NR/TAtdizPQ5mBa4FuGp+eoCCi/iT/mYwSR636kT31fs+FOu0uVVpnpjCeurJaqKPaxQag2h9ATKYlDtBdOmeoZfhUdAJd6SFd5C8VuKMLF43JuoT1Y+lq9+H/ff34sADPEiANV5rIUqoSsfx4WKvJ8SLPnlIBN+phJEipY/JtnjKkJDcVlyNa0k9rKU4ViWTxadpWj2LHnzZC9EmMkz5y008RRGvzwF8z9fgg8+XYq5C1egT79BkgiZMOVdLA2LQuSqjVj17TaY9v4B67Ei/HbagexCJ3JLSpFdYMPv2XZkFzpxqrgMDlcFTuQUwXy8BAey7ThyugilZVUoK6/G2Zpa2G0uVFaeRdWZ86iqPg9XaQVKy6pQWXUOTmc5CovsHmKoc89H0HfoCDw74QXpvqeeexWzZ8/Gp599juiYOGz46ht899MW7NidiB+3/Ipftu3Eth27sHrNekx+YwpmfzIX3/2wGcdOZqP2ch1Ons7FrsQURMbEYf6Cz1VeSo1Ggxu3/4PGO//DBODNv3D95l9oaLyDuuu3cOlqE65cv4XL127hyvXbuHz9Di5fv41LV2/i0tWbqL16Cydyi/DEkyPwxJMjMPaZ8Xh14uuYOm0GZr71LiY8/yIeHTIUDz74IFq2bIkePXqgU6dO0mePfnosOnXpCn//VtBoNPD19cUDHTuib/+BGDX6aYx/7nn0GzQELVu2lF4T8f0erPvNpS4K4sUtIsdQx9tGsXOtUNVHMzy1AMu2HpLOa1F4pWy6rTfLokb+HM+CouhMp+RBEhXSwssUqyiU0Zns0nEdyz1vQpCEpJUgyupESEYJzzl1ycc3P8aFaFOKH/dm4PJYK7bZu/AS50qM6BlpsWPrYSd++qMUewvP8AIquXho9V4nfv6zDIfsNbAWnMMR10WcLq/Fd4fKEJ/lQGTiUWw+UoF1v7mw7jcXVu9z4euDZdhxvALfHiyFzqToLuD2HSIyijA9eAPGzpiHGSEbmPhz89yL7xSb6cTXB8sQn+m9DZJ6v8hiWG9m32XToXLEZjIv+rf7CrBo/Q7M023Au1+EYqluJTYmHkB8pgMrM+2SF5MEIPFvCdBoNAhNYh5AMfuIzXJrniy1FbErquLUoUZ1ny6F8cfGTJsj5TBIidOKFSFUSbFevIvqhNlCaN0+Q4QURSjOYLJhevB6r278t8I2Ssnzwnsj3Pir3Fz44jHxXLF/pP1idXpWXZqa9yh6NxHClT1P3vq/iRnlMzPnqXIcozOdfJZpZ54Ci0POC0wvloohlF4wySOo6A0oCb5kubmz3F9QKdjzpKRkyVPoJXHbm2hrth2DtwRvpbhz8/KFpeQjTIjZNFF9XQhDRiH06QVS4Yky/BUpVr/gHtQovi9j9qrboSiPi9i94rd2eQ7+YkUZq5NfOFj7G2VrG4OZnzdm1kh869FyrN7LHlv+62G8MHmmHKb188OMmbMwadLr8Pf3R58+fdCjRw/4+PjggQc7Y8TosXjzw/l4bMIbmPDqZEx45XU898rrmDTzAzw79X08+uRofLvLiJOFZSg9dxl1126i8UYjLtc1oO7qDdRfa8S1xlu4cLkelWfOw1V5Fk5XGew2B07lFCJ131HMWqTD4KeeQfsHu0rbdU/r1mjVqhX8/Pzw6JChmDx5CqZMeRPbt++Gy1UJu70M8+cvwIXLV1F34zYXZTdRf+M2rjbdwdWmO7jG7frNv2Qx1/QXu6/pL0ncscfZc6413kbj7f9B463/oPHWf3CtiYu/G7dx5cZt6fbKjdu4dO0WLl+7jdqrt3D5+m3UNtzEhfomnL/ShDO1jai6eAMVNddRfv4aymuuoeriDVTXNuHM5Sacu3ITZdWXUFp5AStXr4NGo8E777yLqKhY/PzzNmzdkYBVmxPx3W4TtuxOQ8w3W/HKm++g18Bh8L/nXvj4tkSnnn3R5ZHBmLc+0atAUFeKi1ZA7NgQ3mex2syY6XPkvpqSN09ZbKSoDJc8SDxkmulCuMkBrZkdq9GZToSbHIjgHrTl237HzLCv8G5gHN75LAjvzp6HTxcuQejKr/HO4gg89fwkTJr5Pt77Iggh8V9i7a8Z2H7YhpS884jf65LGOWVINS7LidV7HViV5ZCEqkr0ed0H6n304seBKuHf7r770alLV49JyPLvMxBtdSAh+yw2HSzFqr1sAh+b6YCpsAbGgvM46qrFxgOl2HasChPemodNB0tZ8Y3ZjgiTDeEZNgSnlSDc6Flt7ynYnHJahuJ+vdmBCBPLgTVYHNCZ7dCa7NL/qtfzRvBSmytV/072/tOD12PYc5PQu98g+Pr64sHOXfD48Kfw8muTMWLU0/Dz90fnrt3x4jsLsPDnA1i1rxShyTkkAIl/RYBGo0FUSg5WZYlZiZ3PxJxS0+PYLKci4dwpC0GTOr9EMkWF1F3FYVoRD8l5CkaPClTFrfT+0rJWLMQhEunj9roQv7cUz86a7zWR95lZ8z17omU5pTCL5JqXZsLyChEiGV9YnHtivpghq8ShMhG+GRGYoQj7Wuyy58ji4G1qmMCLFB5LqV2LwkRBi1luX8KqleWcN7E/9RnFqsRlA29ozSqf5TzQcOHt5cJbCuWmyMUjwmvnrYKv2Uq/5gSgu+hTCFJvKQdSqFcq4mDClQlBOawmCmhEgYbeVKLuJee+mofIOxK5R17yj5QXt0irA5GZ7GIYl8lMCgvx1ivRFhu2/eHA+izmbezcewDubRuA2G+347fj+cgtLkWRswLZtgocPHoK6zZ+jSXLVuCNKW9i4MCBaNu2LVq0aHHXkGVE7DocL3ChyFGBqjM1sJdWwlFaiYJiB4od5ThRYMex3BL8fqIAm7an4Qv9OsxepsPr781HwAOd0bHHI3hpTiie/2iZ6n17Dx2J2KSjqKlvRE3ddVxqaELdjVuov3ELDY23ca3pNq413cHVRmbMU3cbddduoe7aTVy52oQrV5tQd+0m6q7dxOWrTbjU0ITLDey++uu3pNcpTQjHhuu3cPlqE7ebuHL9Jupu3EZ9423UcVFYd+OOyuob/8KV63dw5fpfuHztDi5dvYPaq3dwoeE2aupvoabuFs7X3UJN/W3UXvsLl2/8D6pr6lFWWQNHaTVc5eeQZszy2McduvbGiNc/wNQVa/H5t0Y5rUTy6ihFhTcR5LY8oalYGkullJJ0kRebj5DE0whLymYNmDOK5OeL1ASrAzpjEWaEbsTUucsxacY7eGLUWLzxzmzMi/wKb3wwHz37DUbnXn0REBCg+i4+vr4YPuZZPNKvP8aMewZLA0OweOkyTH1zGoYMfUz13NhMh9TGSVnk49ELVPTvNClM6fVTTaDY+TV12Ur4tvTzeky3e6AzOnZ/GH0fHw1d0km2//g5pZPa+DDvqrKF1Jp9TmiNdiZ+pc92E3zevJEeAtC9kp1NvtftY55K1es8fm8v3lE3wakzFqu+b/D6rdh+xIV9uWU4U3MJZ85fRLHNhV9+2YqXXpkIP39/TJz0Or5L3kcCkPhXBGg0GvTu3QvDHn8cH3/0EVavWY/kvX8iPKOEXcAUMxXROkKEAsQya3H7mCmbyUpiSohKt35jepX4kPPtpNwrow0Gk50LOydi+Exz5V6WZLyS2+r9ZVi9X06+NVid7GLNZ1XePIDTgzcoTuy7meMfPk/hAVLmdCiXH9srN5iN5Z4lsQ/F0mNSgYCqlYZyQFH3mhMrk0RbnVJeoNR82FSiyHFklWjRFjvLKVLsQ5FELraLCd1SXtDgQJS5hIepWId9ebm3QsRYShCX6YDBVOK9PUMzfbmU3j9vnkApzK8Ue6oQb4Hs1VSFr+UcPimMywszojOdiBXNffczT+/q/WVYxfOX4rJckvgTs309/z1X7XVipSkf4Vss2HYgD6knWZsR5fGhM9owYNQEtOvQCSOeeRFvTH8bQUHB+Orrb5CYaoL5pAOmU2VYtusk5mxIwtaULMR+txMPDx0BHx9f3NNGvjCfyC2By1WG2trLuHDhIhNxdgd+Sf8d/q3uUV0skvceRoLpAEwHTyK70IFCexkuXLyMuqs3cLamFhXV51BcWo3PFi1Vvc63ZUu09G+Frn2HYMBTz2DUy1PxSdhqrNtuhH7d91i8IgT9+w9QvSY3vxBNf/0fNN75P7hx+39YPh6367f+g6Y7/8PsNru9ces/zZgs7q423sbVxttouMGs/votyRqu30LDDaWIlIXjpYYm1DY04SK/vVDfhAt1TahR2PkrjTh3mXkAq2sbUXXhBiq5VXGvYNXFG6i8KP9fXnMdZecakO+qgb2iFkXOaixd+wv872mNVq3b4P3on6Xel+6eYCmM2UzoMyK9CNOD12PcDNaLMCKtAJFmu9yGSFSNG4ulorVIfv6KcVSXXoCZwesxdvocTAtcA21aPvSmEnz2rQktfHzRuXtvvPreAoRGrcTEiRPx5JNPYsILL2BhaCS+/3EzDHFr8OxzExAQEIAWLVqowtcajQa+fv7QKCYZPr4t0WPwcMxaFgODmXn5VmXaebqEe9NyhzReqRq8C0+X2dvznTzUuh5jZ8zFtKB1+Px7K96L/BGffWOEzljsseKNajz04q2TvHZePY92lfhqzuP3d3/rLU7ozE71RPBuYV9VDqRDaq9kUEQPtOmFmPDBEvj6+aPno8Px9PR5eO2LaLyr/x6ffZ2K+KTDWJ2wHyvWbkabdu3xyidBJACJf0WARqPBrE+X46U338WDXXtKJ/5Lc0KwKsuBtfsciFaccMqZnTIEJkSPyJkSYTPlclKivYLyucolr6J55a6o4BVh2FW/lUqVZVLFWybLt1INCG5irLlE3oiM4r8RfO4zv38qDL1vixi4oqzu68WKJcRkwehVBCq8USIMLAa4KIXHT84XUhaPMIu2OniBjChOUMzgpUHaAb3FjrCUPOZBM7KqQYOxGNFmliAebbaxFgwWG+KynFKvu4j0Qr5igOwNvGuPr7t4AJtr3iq3sSl0y/FTtmQRbXRYnz1lcYYI6cdkyQ2GhXdPdeHm92864ESM1YEXZ3zk4ZUI+sksr1BicUJv9syZE9aypR969OqN5KRUvDzrE2g0GrS+tw00Gg3u79ABQeEGfGD4lgszP7w0ewXeWaLHIv1afBa+Em8t1uOZ12bAx9dXes/Wbdpi4tSZ+ONUMewldthKSmCz2VBeVo5rTXdwqeE6Ks9eQOXZC3BVnsOvSUbptS+/NQeLfszER3Fb0X/kBDw8YDDatWMCtP1992HIkCGY9Pob+GLRYqxauwE796Qiv6QUl67eZOFdnncnCjDqG++gofEOGhpvS7f1N26j7vpNXOEev/rrt1AnvHgNsvfvMvcMCu9gPX9+Hb/vUkMTLtU3olZYQyNqG5pQW9+Ii/WNuFDfiJq6Rpy/0oiaK404f6WJWyPOXW7E2UuNOFPLby9xIVh7A5UXrqPywnWUnb+GsvPX4DxTD3tVHfId53C6uBpFZRdQ4KjGloN2dO37KJ58ZRbiTQXS+Oe9cbmiobyb0IhIL+RjkZyP3G3ASIQl57CG8sKktBg5RUa0FNKm5qH7QEVOMy8s02UUIspix4yQDfBr1Rp9hgzHrNCN+PmgDRmny5B+0oFdf9qx71QJThS54HCVInbVGtzbti07Pv1b4YEHH8LMmTMx6c2ZWLUjE4bkk4hIzeOFaHK+oc5sh87k7tly824pvV9mt33i5h2LSC/y2C8iT9t7nqBbCNldyInCEun3cfPyuYszZSGKylMoj90e43yzXr5mvrv7fV5FpWwLf8jE5IXRePylaegzbDQ6dn8Yre5lv1ULHx+0atMOrdvdp/SYkgAk/isCNDzHp2PHjujSpTO6dOmC/gMGYHZQLOItRVj3G5vlhJmcCDU6Ecpvw0xOnu/gXRSJGVs076w/eWEUDKYSzArdiGdnzcdbYRuZiOC5VsIbFZMll+XLpf7eBZ4371uk8n8uAqcHb8CY6XMwcvIHeHraHEwP3qASgerqrf/WK+htFtq8KJTyQqxiqTS55Yf3wUTOB3x9YSTP+XFJYSBlSEgMZspCF3nFBZvU/1CEo6IsfGWIjGJoU3KxijfWlcKnGYVgeUslWLmvlFUVWphnVpdegJA9pxCUcNp7yNeLuLub+GsuP1AUdkjCL61QnRLAq58jeS6rOK6kSlylgLY6EG5yICy1AC+8+R608RsRs/YrLAmLwcJFS7E18wR0JhtCkvOaDUsFpxRAa1L3vft09a9o0aIF+o14Fo+/NB2PPDEWrdvdp3rdJ19loG2HB9E64D6P9+zUvQ/Gz5qLQc9MxMNPjkOPgcMw8LHhGDh8LB59dqJnaKxdAA7nOpHnOovyqnModZahoMiJw4VVOFJchX0nirA47lt04hO79g92xcjJH2D59sMwWBwYMfEt6b269R2MCTNm4/1F4Zi7Qo8FwQaERa9G1JpNMB63o/JMDequ30Jd439w6dod1F69jbNcYJ29dAPxazaioqYOZecuo+zcZZSevQTXmVqUnqlF+bnLOHOhHmcvNuB87VVml67hwpXrqK1vVHn1LtU3Mmtgt7Xcw8e8fY242NCEC1z4XeCev5r6JtTUNaKmrgnnrnC7LNvZy004e6kJZ7gIPHOpkd13uQnn6m6ipv4Wqi5eh6PyEuyVl+CovoIztddxpvocairK0avPwwiKXAVrdjm+3KcWOupcLmUajdLLZG82GjEjeL2UUy36vU38NEJKj9GmiaKlIkwP8tJayr83pgetk8Ro0O7jeHH2crTv9JD0234eswklVTWoOX8eJ44fw2tTpsGvVWvMCP0Si3/eLz3v/i49VMeX51jnOSZ5D53eZdxzEzvTg723W5kevP7uQkkqGpFz9OT9L2/P6wsjPR0O/D10ZjvCMkoQYbIjlrde8fBUKrffKl9XVL+7t8IQ6Rjx9ph8jVjJU0b0bvtR6UFevdeBTQdcbDJusWP0lA/cxwISgMR/RYCGH0Rdu3bF+PHjVa0YNBoNPg6MROxWE+K/347V32/Hqh92YN3mHfgx/QB+OsTaBOg9vGdq01scmL16h/eZXkaR6nUifNv84OH0eG8xK/O2EH2kxQFthvdZpja9CAYL650VnynyupoTcJ6DS/OD3H8hIBWC0N3zxwYl+bPnrEtgz+HvJ1Uqmzy9s8qwFNsfdkRZ7bxvmwMGM6sWZGH6Eqm5NVuVgIWRo6xsZQTWf5CtZhBjdSA8JQ+Bu07wFQhEf0fFck0e1b9yVa+74Pu74hC5wS0P+Ur5nyVSpXQMD2/H8Z5pkqdT0aohzlqCj/VfYdSEV9C69b3o1KkTHn9iOPo+OgyDBj/KvHtf78amA078esiGMa/NxEOPDMZ9nbuj19CReOLlGXjl4xWI3LgZO6x/qgS2zmRHmNGBLX+4sOWwCyuzmNd3ddoJaH9MRfDGbYjavg9fJR/Ax7qv4d+6jepca9OmLZZrY/GZYSOWx3+DHywn8aU1H8+98TaGDh8FjUaDdz6YDV3sWowc+yw0Gg3MmQdw8LQNh7OL8dtJGzJP2PBnnhNhEZFqj+Wu42xfKC5semMJFv5gxbTAtXfNK/x1VyLs1bUoPXsFBaU1sFddQlFlLVxn62Cvvgx75SUkpGWi7Gwdys/Xo+J8A8rP1aHsXB0qa+pQfbEBlTV1OHOxHmdrG3CutgE1l66i5vI1yS5cuY7auhuorW/kYrARV641SR7Eums3eeXvTdRevYmL4rahCbVXb+LStVuovcrsYoOw27hQfxsXGm7jQj3L9ztXdxNnLzfhzCVm56/cxLkrN1Fceh55JRXIL6lASek57M8pRdSXP2PmZ1KYDYOGPo5Yi83TE+XuoXIfp8x2jJ0+12s+8tNTZ7N1eXlVcERaEWav3KGu3ueNyMdM+6SZ5sRzVR5JnZGtmNN94OPQaDS4t30HvDI3BO8Hr0TvvgMwbMJkfLJ2NwxmO7RpBej75Dh06vEIOvcZiN6PjcLQCW9g1JSPPEK8HrmNXv+2e3hJPfeT/D5jZ3jfL2NnzGtmnJWF993Eoci9nbMuAZEWJ1buZUIrNtOJtb+VIjY9Fys2/Ir5YXFYHqLFp18swu5kI7Yd4nmFVjkS8M9Mfb2Q2tg093xVzqgir9gsv04SgFnMRIeOkITjCNmSiYitVhKAxL8iQKPRYP/+31FRUYXy8irk5xdjyZJlCAvTIjAwCH5+fmjTpg169+6NXr16oWfPnujevQdatmyJjg88gPETJmBOcAxCUwvvKnaan+lt+BsR5X0GqecesdV7HQgz2rFjTxLWrV0NvV6HoLBwLAjWwRAZg80//oi3F4bCz7+3x2e/HcpyAcNNDgRnOBGS4YDOLH9GpNft+t+IvWaeb1XYP3idlIOpzIUR28cFmrI5qbLhaGyW3JdOFLyI2bLoH2YwO3jFcCFLqLbIeUcxWS7WAFcUkxhLEJ6Si+CEk9KKAJLAc1urU93Gx7tHUNznLvyU7X7CUwt4Q/JCueF0RjFvk+NgPbK46IvJcqnaLCgvTMpKw0eGjcYrb8/DuKkfYsCQYWh9773QtGiBLt16omevPujZqzfGjR2Lt2fNQuCyZdi9OxF/5Jei3+Oj4ePbEt0f7oe2bdvi5193Y+NPO5CwOwFH80okAbB2rx3jX34dI8c9h0f6D4SPjw9a8hYnrVq1wtQZbzcruB7qNxS9eb+9wKAQ1WMdu/XGI8NGsdDtYyPRs/+j6NqzD7p074XHho/CC69NxevvzkHPAUNlYXnfA8x7YPZ2QWe/9YT3F0seoD7DRuOVT/WY9EUUXvtUi5fmBOH5j5ZjwgdLMP7dhXj2nc8x4cNleOGTYLy2MBpzN6Yg2lyCrX+WI+10FRzVl3C+7iYu1N/ExfqbuHLjDq7e+g8amu6gvpFVBdffYHl+IjysDAVfudqEel44oiwYudggh30v1DdxbyDP/bsi8v6acF7p/RNhX1EFXHMNpeeuouzcVZSevQpb5RWUVFyGo+oKKmqu4cqN/+Di1TtYskwWfu3atcPE16fgpTmhHv3ovAkTvcnGlnJLyUVYcg7CU3IxZdlKj9WMWvr1xhtLV7FjPyUfwUlsFR5tGmsMrVf29kwvxMzgv/cAKov4DBYHFv2QiVfmhqDvk8+g16Mj8NiENxCRUeS5TJ/HOdOcuYdKFebhFfPu9ZO9aXfL017v1buoyqVz2y6t0Y6wDBuirHYkHjiN77clICQsHO+88w5emzgRY8aMwZChQ9H5oW7Sb+vjq86BXLL1oFv+ortToLlrgloExmc53RwS6mtXmNGB0AwmFOOynPjhkBNbjpRi26EiRKz5GtGr1iA2Jgq6iDB8++0mbP5lKxZ8/gVGjRqJbt26onv37mjVqhUJQOJfEaDRaFBQUIyysko4HE44HA44nQ6UlTpQWmrHn4cPwW4rgt1ug8NRArvdBrutGPaiXOxJ2In5XyyEX6t7MHrqx3c5KRwYO8P70jpjZ8z7BwLLc6antzABGJvpwLi3PkO7du3w9OiReOXlF/D8Sy/h5RefxzPjnkHfvv3g4+MLjaYFNJp+0GgmQ6OphEYzDEMeH45wvR6rv9yEdb8kYLPxIOItxW59rbxty9+JVW8zw7uY9e4mvILKfEq5YMMptS1Rdp1Xrj4hWpnIeX8OqSJZmd8jJZ5bHKxKViouYTmEBpMNhoxChOw5xXv95curhLj3bHQTgdK6oH+X96do+6NNc+9XKC8xpUsvgsFkk3IpYzNZfqW3i4MQOmOmsfw7Xz9/BHTsgq79H8OIiW9h5opVmLJsJe7v9BCGjRiNuHWbsPG7nzFveTgmzvwQw0eNZSHaBzvjoYdYaG38hBcw59PPoNFo0LNnD7Rp0wbjX5+JkAw7/igox/e/O1Th42mB67Avtwz7TjmxaMly9B82Akt+3o9JM97D8OEj8Piwx/HmtOm4/4GOeHWBDr/lVmDoUCbiAgIC8PiwxzF8+AiMGj0aY8aMwaBHh6Jnz17o2LEjAgIC0KpVK7Rq1Qpt27ZFly4PYeLE1xD97XaEJufKgl+kB0hpAjaV6Ywl+Hj1ToybOQ99HhuNhx9/Gv2eGo9BY17CkGcnYdiEN/D4i2/iiZen47HnJmPQmJfRe+hI+LduA79WrdFj8HBMDVyPHcfKcdJxAZXn6nD+SiPO1F5DRU0dKmrqmICru4Hzl66hhoeBL19tkopAhCCUTFH8cbFBbbUNN1HbcBMXG5jYrKm7yYo+LjWi6uJ1VF1gVn7+GkrPNsBRfQX2KmaO6jrYK6+g9GwDKmqu49yVm7jQcBtnLt2AveoKjpzIx4496Xj7vQ/h4+uLr7ftQcJxFw7myu19lMaEH/OohSfnMq9eirw8W3DCaXTrP5I1hlesZhSccFrycIsVbUKTchGmWGIxgq+SE5aUw95DkdPcbcAoaFPyPJqX6012RWFdMVuG0M2jJY1zdxF80rJ9d62YVYx9dw0NOzzCpRFSdKa5PO1mxJbC2xqekof5q7fhtY8WYvS48bjv/vvh6+uLRx8djKlvvonxL7yM7o8MRNv7O6rEnk9LPzzQrQ/6PfUcRr3xAT5a+au64MTingPoKea8CjyLA9/87uIeO6e0r/UWB6LNNhiSTyEu4QCWRK/D1Okz8NiwYejb9xF06dIFrVu3Ru/evTBmzNOYMGECXn31FfTr1xc9e/bAtGlTER8fg8TEBCQm7sHOHdtJABL/igCNRoO8vGyUljpQVuZAVVUpqqpKUV1ViuoqF6qrSlFV4UB1pQMVFXZk7c9CSFQcXp3+Dh4ZNAQ+vi1x/0M9Me/LwHr7ngAAIABJREFUVM9lehQn/0tzQpqd6ak6wHsbVJpxsYuBoFv/x9CqdRuMfPF1zF4WgfEz5+KJ8RMxZOxLeHTsK+g5cBg0LVpBowmARvMYNJqUu4a8IjJKPE9wD3d+c6LV+6Dw9zPIf+pddOCL761e30OvMM/PVA6e6io8KY/GbFd3ps9kveuirMxDGJ6Sh6DdJ9kyfmJpOKW5V/7+TaGHe4g3VCEAvTWrFj3/RCNnZXVzjFj32YtHZmWWExt/c2BDZh7Wph3HJuMxfHuAtYlY8qMV0VYnhj7zKr8otISPW2WksIf7D4SfYrm0bv2HonPvfujwUA+09G+FV+ZroTOzpbEizXbMDN2I1z434J42Abi3fQd0HzAMD/bqB79W92DaspXS+3To3BWdujCvxCfLDWjX4UGMe/MjfLg4HMvjvsany8Pxxvvz8f68RXj7w7mY9OYsTHvnI3y0YCnmLwvH7M+X460vwhEatwELQ/QYMGQYNBoNPor8jgk7t0IF6W+lN5CnEbi3dGJrHheyCtWMInl1FEWDYp2xBG9HfIeBo5+HRqPB0AlTsPwHM8y51ai6cBUX6ptw6SorGGlouoOGJrldjGjzcvXGbVwT9zXe4ZXBt3CFewIvXWV5gJeuysZCwMzLWFN/k4VzLzehuvYGE4AXWaFHRc01VNSw/n/l56+h7PxVlJ+/BlvlZazf9BPmLliEleu+wYm8UuTaqpFTUoWjeU6k7T0s/Uax32xFjNWOeCtr0ySNQya2EkV4Sh7CUnIRnsqXHxRLISoar4cm5mDqstV4+s1PMGXZKoTsyVatwCMq3ud/lSa3WVKsCaxNLUBYcg7eXLEG46bPwYzgddCm5kOXUcTbsNig8vLy3qAi1zfSbJPGMr3ZgZhMufhBPi7sbseKso2Le5GEUkzepa3KXbyJBrMdEemFvAp4HlutKaPIe/hXFXq3w2BVt1CZPGUq1qxdh/QMI6LWfIkXJs/Ave3uQ6t726HXY6Mw4rV38Oqn4fggejOW/nKAr+ZUhBmqzy7mY6nTLSVIPcarw7vqiafoESiaPBvMrF3MW9pNUj6uj48Phj02BJ8tmIvvvv0S23/dgtSUPcjKNMPlcqC8rAzl5WWorKxERUUFKioqUVFRjvLycpSVlaG0tBS5udQImvh3BGg0GhQX56GqyilbpR3nzjhwttqBY8UO7M9zYN0+h6pVRUD79hj7yhuY9ckCRKQXYENWETakHMSa3VnYmP4n1lvz8OV+lgOlNTkw4YOlqpmer18P9B4wHAmHC/H1AdbmRW9hORpaEys68Zxlec5gV2Y5oDM5MO/LFIybOQ+PPDkOIya+hZffW4C33n0P02bMwLtzFuD+B3vCx/d+aFq8j5b+vfFQv+F4x/ADJry/BCMnv4/HX5iK8e98gQXfGJsRc97E2T8RbP9vvEY9CL0we8XfP7+5ELNV/XikF2+jSITWiZ5jGWxNUrGUVVhKgRS+VYo/99Vb/rayNyUfYanM5AXk5QIPuchDrvB1XypPb7RJhUbeGqz2f+o5r2Kulb8/+vTuhU6dOmLIkCF48oknMHPGNMTHx2JTYhYW/3IIz763SPWaLn0G4rEJk/H8h0sxZXEMZoasxyzdd3g3dhs2JB5AxqkyWHIr2cyf5x5GmB1YmX4SnwbpsDhYC8Pab7Fp8zZkZB7AoqiNGPHa2+jSpz86duyEbt264ZWXX8GwYY/j0UeHYOCAgejW62HcG3A/Wvj4eHwHP39/tLnvAXR4qCc6de2BVvfcg4D7H0D3/kPx4tS3sXLXb/h+XyG2HyvHht9cCDfyxrUq4Wd3u9jLIsC9ylVUnUda7Cw/VbHPX/x4BQxmO2av2oHhr76FVve2xQexW3CqqAznLl3F5Wu3cLHhJivmqG/EhSvXcfYiKwy5yHP/RPj3Un0ju6+uUar2ZdaEizzke6FO/puFfm/i/OUmVZHHeV7gce7KTZyru4mq2huwVV6Gq/oSTuQ68Niwp9GqVR/4+MyFv39vDB46GsaDJ3H0+Gl88823qn395KT3FS1deNGUsQTa1DxEpBVIHlRpeUdFj1LlOtxiKTixDjUzuemz3liM5z9cyjx/aeL5hdK6wAbFcnGRGQXQZxQi0lQsFSPJ0QIWMYjN4tEBHhVQ/uY6IzOpgvmuk1cvk1yv+dDNjZ1/Iwa9PU/1GjnkHml1Yt6XyRj+6ix0eIB59YwWK+quXscXS5bD/57WuK9zd4x640M8+cpMXrSmXm9b7X30zEuPzXRi9T72XSIV3yvCxDoIsBAvnzhbeFNoYzGW/nIQy77ahcWrvseKdZsR+uVWRGz6FZ9r41g6Qfv7sO1ALgpsxThTXYqqSifOVLtQUe5ARYULFeWlKCsrRWlpKVwu5pRxOUvgchWjstKO6monqqpcqKwsR3FRAQlA4l8RoNFoUFSUg+oqB6qrHKjilm2z42CBA8ZsJyw5Tpy0ObF2w3p8+tl8vP/e2xg5cjgeeaQPundX5FP4+CjzEtCiRQs8+FA3LF/1HcK+3oHgbxIwbUEQxkx6C23b3y8957777kOHDh1wf4cOeObZZ6CPicWu33P+VgAaLA6Pzut6swNDx7/m9aI/ZUkMxs2Yhxl8lsk8j07ozayiufmk3bsLMu/3N+eFu9vzvc82/7tt+Sef7eX5Cg8sW/NWhJvFYvN2vlh9Ec9z4usIe/T78yIAU9QmhXnTlBc7xYoHKhNLtPG1kC0OxVJtrKdilFWZ18S+1yztN9BoNJg+fRoKC7LZYOoswe8H9mH7r7/g559/wpebvkG4PhIPdu4iHSsz3p+NuK9/QnhyDjaac/Dl3hKs28e8opv2FWPNXidmr0nAwhA92gW0Vx1nn25MgcFsR1yWEyvWbMZ9HTujbfv7PJY5e3tRBB7qNwQ9+g7C/PkLoI3QY0VQGD79fBHuu4+dH+PfXYj3ozfjyTHPoX179ee8s0SHMePGo3v3HmjRogVatGiB1ye9rjr/NBoNWrdujedfegVB0euwLssmtb9xv+C6h4T1QgCKxH5RZJBRzL2BJTBYlF4nm7Re8tRlK+HXqjU+CF6F33JKUVlzBZUX6lFZU4+qiw04f+kaLjfwfD/e76/++i1VHuDlBtb2RQjA2oYmXLjShBre8uVifZMU/hX5fmelEPANbtdRXnMVZeevwnmmHo7qOjiqr2Dl2q/h38otIuHXE/0GDMKEl1/D2OdflfbjzNCNCE44Lotmsx26jBJoU/OlfqaskbPNLQzLV/gxyk3X9UYbX/WnmK+7LtbvZa1edNL/TPBFmooRaS5hq4SYStj/3PRGthSmXvL2yZM7VUN7qzev1d+NT+7CzMtYZPY8hjxfp3zc/jfP+7tJP/tuK3Yek36bj+cvQG6JC1sTktF30FA80K03Poj/lU1g+Bjh9fPM3vMPff164o1PQ2D68zQO5jvw9QGxwgm71mzc78Cavaw9FBsj2X798oAT/Z56Di18fNClSxcMGjQA/fv1xcMP90avXj0x5NFBCAgIwMiRT6G8woEz1U5UV7tQXeXkok7xf5UDVVVM7Inr8plqF6qrXaiqcvEonQtFheQBJP4dLAScfwyusgKUV5SgotKGsooinD9rR1V1CTsIz7hw5kwZqqtLUV3twplqp2SnT/2J+PhonD51GJUVNpw940JlRQmKCk/g2LEDmDPnI/Tt+wh6dO+GTp06ol27tvDz84O/vx8OHdqHo0cOwmLJgNGYhtTUVASHBKPvgIEYOOQxrLIWI1Lk2FhY4qxWsZZqhMkheTV0ZuYJ/Dz+e/QbOAh+/v5o27YN2rcPQPv2Aehw//2Y9dZMfPL5IsxZocOiyHUI27gZP+xKxlbjfmzem421+7ytWyl/XvNrW7rPVr0MOs0MQs3OhL0MopFc8GpNDmiNDrkfV7MDe3MC1f3vfzoAs22I5f0E4xQVtzGZLA/PwJe1EoJOuYSfWFlEuT6vLp17QRTNm4WIiLI6VN6MmKxSlUm5j7w/pN7sUHmvArcdRNdu3TBzxpsocxagqqoMVVVlqK4qR3VlBX4/nodpnwaiS+9+kmDy9/fHI488LF1gHuzUCS1atEDnzp0x++M5GD1mLHx9ffHcs+NZXmDXnuisEI4t/fywbPsRRFqd6PnoCNl72LU77A4nTp06jRdeeBEajQYjnnwCPi398F7sVpSWVWH9ph/RtU9/+Pi2RIduffBh7M+YEbIe97Zpi2VLlyEjw4yTJ7Px5JPDEdChI9q0a4+FK8KwZctWHPnzKPbvP4jnn38eLVv6oaWfPzp36Qpft3D2uo1f4WRJJX468v+w955RUpX59n8JdJNRQBQDYsKAI4pizowRc8RxHETMjGNCUVITxIACipgVFXVQYtM5VVdXzifnSt2N2TEN6sy99//i839xTlU3OnN/d61567PWs6q7urqruuqc5+xnf79771IFECyqM1wl/y9UnGXAt7TR7AMZe5T5+gzey6ryPm9Eiz+t3MCQ4SOZce1NOI5N1ijQ89lXfP39j3zx7W4+9RTBX333E5//7e989tUPHvO3my++2c3nf/Nuv9nNZ3/bzSdf7eaTL3fT22/2fLGb7s//TumzHyiVS72ffl+5dXq/xej+Gqv3G2T7M2JSkbjgcOe99zNgj57khNcj7FnyHHAwl969kLvWbul3PtoeCy67lkONlmfibrG80fB67fS+hJ2KMKNsdq9VrJWWNxos9yyYlnuelSsaTTfrts5l9ZbuVFhWp7pf17mgu1yOX+xlWVeywysl/V+uKf3AeX+Q+Mt14F+uIXuWNpc2/qIn7t+yeP/iZ7963P+yuf2X1jrunPXUO1RVD+a0M87g+dVryWQFbr51FoOqh3DW9XeyYKvgXi+a+v9/9r9co3/dl74Bn28AAz2/zcGDqzlkwsFs3Pg2j+6wOPasSxlUPZjmjhZeaLdYsS3O2o+a+GBHHRu313Hymecw877HWNdh8WqnQVo3+bS3DPL6wF1Pj0VPt0lvjwf0ui16ul0msNcDfXvc9tje77qP6el2WUBN/Q0A/jb+szHK5/ORlWJYeZmeXvtXu4yebsfdnXgHXU+PQ6lkUCzp3sFsUyqZdHebdHcbdJcM97YyTe+Atypf7+p1/657wDt0l2yv39CdDz/slt6WfhSq9FTc9eI2Hl64mNWbmlnXYbLy4wALX9rICx818manytomkaVvbGbUmH2ZdOxxbNv6Ae+/v4H169eyYkUN8+Y9yJw5t3Hddddw5nkXcOLUqRx5xOGMHTumcsKfecGFrPg4yJo29zkrrKA3lzeWjZutfl5Sez7mf10E/x3w+yXo+xeLcU2DxZONFi+3iqzd3MZrjTHeDtmsav1Xi/j/xgD+7wDvX+3c+78Xy5s8c3BPdNG/5NQ/J3lli7tLXtHYF06/zGNElu7U+mX17pnesbzZYrnncfgr4UtLrlLS6h/l1z+jt8wALtiSwefzMWzUPjz1cRebYya5Qg7DdohIFs+/8jYj9hnLyH3HU1U9mBtvu5utDc0IuoVmO2zZsplFixax+ePNxKIxNmzYwHXXXcdDDz5EY2MzV199DT6fj9Fjx2Hn8yQSCU4/7TQu/P10XuiwWVxvMeWCq/oAxcRJ3L3mI8JKgZRs88TqNxk2YiQnnHc5g6qq2Gf0aKoHD+a2Bx7nwQ1+9/1otBi+z77MW/Y8slVENgpoVgFFMbh77l94c1srnYJDqVgglyuQc/LknMIejPykI49kzpw5XHPNtRx99DFUVVVx2GGHc9f9j7AlYrC+McPG9iwbIzlWNpVTU7z3vtn5dRpN/81AU5+R+4pm+xfHjMmfX2tizPiDOe/3F/NRfTuvt0rs7BJoiysEolns0md8/vUP9H7+LZ99/cOehs/f7Ha9AMvK32/6DJ8/qfgP/uj1+/UZO/9ydn+5m54vf6TwmWv2bJW+JimY1Cx/huo9nAGm7QGUp55xLi+2u15x/XvVympq11dzz1xplyE3vMxp17LDBXUGK5vd2xVNHsir11i6U3VLuQ16pby7uLYs/FArsYWLd/Q3iHYNoxfXqpXYuMXlsvOvhCB9sxz3uOe68+v1oGJF4n2OritA3++UE0CW1Jv9euT+HxWOf1fq7T+bfrE+/Ys18vRrZuPz+bhwxgwSqo5oGLz51qsMHz6c1R/W8XRL3+P37Ek0f3Gf9QsG8O/4fB/i8w2nqmp/XnppDaoq0NCwnYkTD+GMi6/ilnkrGdkvRm/cQRPx+XxUe8KrYcOGVn72zPPP0SrYtAg2m+Muk9fbY9PbbdHb486ebtObHiD0wGB3yZ2lkkGxqJMvaOSLKvmCRrGoUyzpFIo6haKGKMV/A4C/jf9ouCVgJUOpaFEqmnSXrMrsv3NxdyLugdrbY9FdMimW9AoYLM89wZ9BT49Joajx2PyHKJQ08kUNyxHpLhru3yioFIsqTk7GsiXyeZUtW/7K6NH7cMIJx7N4WQ3LXniVESNHcPLJU6murqa6uooBAwZw0EEHUF1dTVXVIIYOHcqYMaOprq5i+PBhFAsqPd1lYGrSXTIolQzv5DIpldxSd6lkUizqpJIhrrj8MgYOHMjxv5vM7ffex7p3P+StTVt46713ef2NV1i37nmefWYZzzy9nNdee4mNmz5kZyTD6nazsviU2cHl/axa+kLAy2xl34K5qN7aM0S8wdojW9O90Fjc92oThx93EkOHj9zjQnXxnPn/+0783zJ//34urLd4os6NOnqyUeeFD3fy6sc7Wfrsai68cDrHHH0U5//+9zw6/zE2bG/m7U1b2bSjnldbRZY2udnRT7W4Cu1FDW4w/ZImh5omh6Vev97yZodlzQ4rW/ti2VZ15CuG4OVouv52NpV83sY+i4Ul9RYLK6bk5bQViwXbBI47dwb7TZyEb6+9GHvQoVx+/5Ocd8tfOPasS/D5fJx44XW/soHw+Xysf+ev+NMGdTGDHTGLkGgjGQ5OLkch71As5Mk5BtdccxVPPbWC3t4CvT05ZCnJiBHDWfPSet4PGzzbavPYdpWblr3NaVfcQvWQYew74QjGTZzEXgMGsPd+BzJ8xAhOOeUULrjgAqZMmcKQIUO444FHWdXqHkOTTjmPYXuPxefzseH9TahOiXyhQLFU4pNdPXy2q8SuniK7ekp0l4qUSnk62pvxd7Sw7sXnuePuu3lt/TpMw8Jx8oiCzAsvrGP48D4PwqqqKubcdR/vbGulIWHRkc2xMWKzpMGkpmyiWwHafXOZZz+0sjXHhbfP28OGZGmjyeVza/ZQQu+733jumFfDS/UpXmg1sCyHns+/45Mvv+fzv7k9f199+6Pb++f1BZbB3+ff/MinX//IJ1/1pXr0fvkjpX5Cj7LIozzzn36Ps+s79OJXiNanxKUSwZSBPyrzRl2Eo447narqifh8I/H5qhhz8GSeXPMiU086ibFj96W+K83iOqMvzrJsseKd3yuaXBBYZuhq6ty+vmWeKXsZED7fZrGy0bVyWbAlUwF5S7yEj8Ve2sdiL/t3+q0Pep6AZfCn7NFHWOmR3al6Qh2PDew/+7dPlEFgY985srzMkDWUPetMljTYPN9u8qpfZvaiNSx7sob7Fy7jTw8v4ZJb7uay627iD/c9zEsfbGVTROf1YH87qv8HwKscM3taWf1vG8/y3ys//sSLrq9spk6+9CZWNwhsbI0zbPgITjr1NFate4lNEd37/2yWNNpMn/VIxQC/vK6WW0gW71QZe/DR+HyD8PlGMGjQvpx00ukU8iq9PRaz/nSLxwQOZq8BA/n97McYOtQFerfcfD2aEqdYMhFtCztvsGP7JqZOPYFVq1bySY9Bq2Cwut0iVyhfRy33muiBv+5ug2JRq1wnd/Xa9PZalLoNSj0GhaJKoaSRKyoUSu7XhZKOU1BwCgqanv0NAP42/qPh2cCk99hdlEompW6HUtGiUDAqgLBYNMnnNXI5hXxBRbezGI6ImRMxHAErJ1am4WTR7SyKmUK3srS07SRXUHDyCqYjottZ7JyEnZPQ7QxWTqJY0iiWDApFE11XeWLB45x99pmMGjmSBQvmUSxpmGaW1rZ6VC1NLq9QKKhEwu20ttV5J5JFLqe5J1lP3+ztcU+4UrdeAa6FgkqppNPT4+66erpNBCHG2rXPctWVlzFu37GM23csEydO4Nhjj2bayVM595yzOOecM/ndcZPZb79xjB69D488s87d1Te6aq+nW9y+kadbzEpppxxY/nKnxesBl0FctCXNO61J3g+bvBtyf/ZKwOalTrf35NUu18B0ZbPFbU+/V7mQVg8dzpARezN05N7csvyt/8Pi+++ZxV/u/pfXyTy07BlumnUbZ5x1JsOHD2P48GGMHDmCo446kkceeYA33niZmiVPMHmyyyjtO3Zs5bVddMlFXH3NVZx3/nnMnHkTDz02n6defIXn33qfl/+6g3cbQ7zZIbH6o2Ze3+nnrzH3f36mxTWbduPq+kcEerm8/QBeTaPFymaT51pN1jZmWPbWVlZvD7FoYwcXXH4NTyxeyNomyfs8DJZvbGDGPQs48IjJnDz9Cq68cx6zV77Fwu0y96yvo2rI0F+BwP5z8JAhfFTXjJXPVxjx7pLjMeR2hc3e1eOwfNli9tlnb8aMGc1VV1/F4yue4eXaIE83Wzz6QZQ/LH2DK+5fQc2mAGdc/ScuvXYmhpPHzjnk8zZbt37MAePHc+4lV7Jkp8LcVxsqr2PahVdxy8PL+PMzb7JmUyv1MY24YpPS8gRFh7jsEJUcWpI6k6ecTnX1RHx73UJV9WFMnnI6omJg2zlsK0cgEKK9M4Ru5amrb+SUU09j8ODBDBo0iCOOnMTVN/2Rt2s7ecXvtRr0Z4gqjE3ZMshg7quNlNWxZf+0ZY0Wj3+cYtS+43/1nt71aA0tMYnip1/z7e5/8vef/7vSA+j2Bv7siUHc+dnffuTTr3ez66vd/WLddlcAYMEDgPlPvyf3yffkdn2Hs+s7nN7vMLv/ht37Lbneb7BKn5NWciSUPMGUzu8vu2qP19VfbDPzibVUTLM9UVRNg1lJlCizgL/yV/TKvku8su3SBsNl6rzy7aJy6scO2bN6cW/LgO+edbUe4OtnBt3vtgwGKwxhhQn89SyzgmXQWvbLXNls8UrAYG1zlkdf/og7lzzPY8+8yMIVyzn4kIkcMvEQzjnnTKaeeipnn3MW199wLffeN4dzLziHESNH9PW7rv4razss1rableOjXC3YM8XpX1Un/gXTVylb77mBrWmwWLJT5b6X65m5aD37H3q093kN3OPzO/O621naYLE5btEhmSzf2MDaDovV7TbPt7nCjpVe9OiSBpMJk09i/0OP4pbb57D+pedxHInukkGxZJJMhpg69QTOOv989p1wBJNPPp3a2i088sj91Ndtprtbp1TS6e023OtIhfBQKRQ1VFuhQ9QpFN1KWKmkUyxplEoa+fK1p9emUDQoFk2KRZvu7jy9PXl6ehwKRYNcTsV2VJy8RrHoEhc93TalkoOi/AYAfxv/2fAAYBInL6NaaTQ7g25lUc00hplBtzIYdpZ8XsHJyzh5GdMR0K0MTl7GzokYdhbZTJE14ghGHNVKo9oZ7JxErqhi5SRUK01Wj5NUwqTVCAklREwKIplJNDuNlRPJFzQcx8SyDJxcDsdRKeRV96Ts1untsbBzIgk5SFaPYeZEdCuLbgoU8iqFgkqhoFHI6+Qcyf0+r5DLKeRyMsWSTi4v4+Qk7LyE5YgoVgrNyqCbWQQthmwkUcwUdl7CycseaJXJF1ScvEyxqHknso6dk7j0sos57bRpvPbGKzy+YiWPPPxn7p97J/fdM4f5jz7I2jXPsuHt13jp5RdZ/fxTvP7uBj6IWty1bgdVQ4Z5DMwg9t9vP3533LGcMu0kpkw5jmOOOYozTj+VWbf+gSdX1LDxww/piKd40e8qLxfWu4uruwP/v4g6fs0ILmlw2bNFnmXB4o1tjNtvHJMmHcns2bfy5JM1tLfV0dtj8uknDrt67Er5orfHoljQMPQMvT0mshTj5fVrWLRoPvPmPcCTK2qYO/duLr30YiZPPoYJEw5m9OjRDPAusGWwsc/ee3PKtJO57tqreOAv97FmzbPU1W3jw5jFmnb3Nc7fobPotY+Z9/gjPLH4cR5fsYIrb7yJAw8+GJ/Px6hRIxk2bCj77zeO008/hd8dfxz77TeOuX+Zy1FHH011dTXPrX+Zq6+5imknT+XC35/Pwice46MdO2nLGmwKq0z3Mnr/3azd8TE9JYvebptPemw+63X4tMfByJtEVJMtCZsu2SZr2pQKBts3b2T8AQdWfv/+V+q4c+UbnHjORfh8PkaOHMGkI49gyNBhnHbZjdz7Rjt16RwZ0+Ggg9zfu+fee3lhi59H3g1w410PMmX6NRw+9Sz2PfjwyoVv0KBBDBs2lLFjx3D4EUdw6rnTOeCIyf1sj6rw+c5irwEjuOn+RbzZZaE7Dk7OopizsG2bfC6PZeUxTZOO9nZee+11Lrp0BsOGDadm/fusby/nK+8ZkfVLNmdli8OTzXsefw9vDOLz+Zj99Lus/riD6/68mKEjXTHLB9t2Uuzt5avvf+a73f90s4HL4O+7H/nb9z/3M4L+2VX9fvszn/7tJ3Z97QLAYhn8ffoDPV/8iJb/kpTWS0Lppi2iEco4GKVvMLu/Jq33YJa+RDB6kMxenO4vGOZlMg8bPmKPz3vSKeezYJvIkjoPuNXKFXbZ9cbU+/r9yv115fxtzwy6psFkuZdOs6xBr0S+Ldwus3Br2RfT9QxcsE10hR+eyKZmp/bvrZDKPbSemtjtCVQryvnFOzWWNRg81WzxbJvDqvYcq9rzvB02WNls89fOKPc9/ABHHXM0gwYNZOSoUUyZ8juOO+4YJk8+mqXL5qNbCcxcioweJquHEfQIEbGDrmwb73z0WuV9mjhxAqeeOpUlyxawJRCmIaOyLWWwNWmwPuCZHu8d8nh7AAAgAElEQVR0e3OXeRWNX1rFLKm3WFRnukra+r6KycJ6m6ShU+rWCKs6K8objgabx3dozN8UY967fu59pYHZq7cw67mPmb9V5ok6l9m85J7FHHT0FM6/6CLuuP2PLHziYV5d/xyRcDO6I7M5aXHbQjcF5743OniqyS2t2nnVrWgVVe697w7OPO88hu2zLzc/uISwotKYVtmZ1ggrKnZOplBUyRVkD/ipFVbPBX0G+YJCsaRh5yUMR8ApyBS7DUrdDoaVxS4oyFoGSc0iSlksy8Q0dTRdwjRNDEPFtlQcRyOf07Ftt7qlqqnfAOBv4z8ao3w+H2khhGZlyOoxJDOJoMdJqxFEPU5GiyLocSQjiWQkUa0Mup1Fs7PuAW1nEfUEGTWGZCTR7CyKlUa3BVTTBZSikUA0EqTUCDE5SEIJE8h2EBEDyGYSO+8CMsMSMG0FTddQNXf3UyhoHktno5sZVDONqCfc57IyqFYauyBRyGsUCi7LV8jrdJfcUnU+r+DkJIrersxyRHJ5GcMR0O0MhaKK4WRJqWESShDJTKBYKQxHqABizXb/Z9GIo5jJPQDhe++/yYwrLuWwww7lxJOncs11V3LTzOu5+ZYbueLKyzj1tFM4+uhJnHjCFM4883TGjBnNKdOmMnPW7fh8Pu6c+2c6A81s3/ZXXnvtZV58cQ2vvrqeDRveYOXKlcyZczvnnnsOBx5wANXVVUyffgGXX3UVy96pY1mT7QljymzM/13ksazR5o6n3+a2uQ9x59w/c+W11zBixHD+eMtNFPIa3SWDXb05igWdUlGnu2h4PSy2BwhzfPZpnk92OXyyy+n7+hOHzz8r8PlnBT79JEepZFEq2aiqjCCkicWCtLQ2kUzFiMdCfPDBOzz5ZA333HMHV111OSefNJXhw4dzwAHjueGGa/jTnDmMP/BARo4axTnnncl5F5zD8SeeyJU33sibb79EMuXHyols3voBb254Gc1Mo5sZ1q5bxZlnnsbSpQs49tijOeaYo7h55g2sW7eaxYsXcOGF0xkxYjgDBw7k8MMmMv2C8zj2uMkMHDSI6upqBg8ZwlEnTGPmE2tRdJloMkpLRysfbNnKS2+/y+JVL3Lln+7hhNPO4sBDDmX02HEcdvRkpl54LZfPfoDLZ82tXCSPPetSFrzbWvn+hNPPZcLEiZXvj/zdVKoGD+HeZS/SLOSYM+c2zjzzdA479FCOOW4KR/9uCuecdQbvb97KqlbXimLqpTdz3Knn8sDDj3Drrbdy4YUXctBBBzJo0CAmTJqMb68L8PlS+HxFfL738PlcVfHtjyxhdV2arQkbwbT5vDeH4+TI51xvsVwuRzYrsGT5Mxxw0ASqhw5j4Xaporiu8bwAK7mo/fpAn27L9SsHur1ii2tVpl50fQX0jR43ngMmuiKbaCLKt3//ke9/+m++/u5HPv3qe7754eeKR+B3u92EkC++/Ykvv/3Znd/9o2L2XPrCNXnOf/I9Tu93WN3fkDV2Ufj07wjmZ4QyeTqiKpGUTjil0xWVCEczdIbSNPnjrH1tIzNvn8uk46YyYOAgDjjyd/x+1kPMfaXeA3Nl4YTpRS96bL6Xi10BfGXwVz7PvFL48mbb7Z+rc1W9yxpdFmvhNtcGqaKQ3yIwf7MbB7fMA5U1dbpX2tWo2amzrM6b9TrLPM/GPmWxW/Zd3mDwdIvNqvYcz3XkWdmSY1mjC0B3xONsbq5n+g1/Yujw4Zx44vGsWrWCltZaCjmBfCGL6STQ7Rh2IUWhmKVQzCIZYVJqkIQSICx2kNCCJNQuNu3cyOXXXMZdf57Dg/PvZ/Lv3OSaESOGc+ihEzj55BO4/vqreGrN87zammZFs13ZbO4J/gyeazV40W+x1u9WPBZ4AG5JvcWmqMbHMY0X2w0W1Fksrrd5+B0/99Y8x5wHHubB9xOsbHarJ2+GbJZ9HOQvS59hy46PGXegm2xz/sWXMueOWVxxxWUcf/xxVFUNYvwB+3PbrJuZc/utlfPw1Bk380JDEiuvVBi6Sy+7iFNOO4UBAwdy6JTTuPfpNwjIBmnToD6t8XrAoFSSsRyBXEEiX5DJFxS6u11GsFTSKXW7INbJyy7RUVTY1etQyOtYORHVzKDqGQQ5TlZKIEpZFFVEVjJIsoAkC8hKBt0Q0PUs+ZyBZcnI8m8A8Lfxn41RPp+PrkQ7gh5HNJNk9TiSmSKrxxGMBBnve8GIeyVfAcVKewApg5WXXEBoZV2mz8y4AM1MIZpJkmqEqBTk9Y2vEBYDdGbb6Ui34s+0EZNdwGU6gnty2AqSmkbRZWRFQTdEDDOLaYvIehLZSKKaaVJKhLQaRTbc+3Qz6+608gaqmiaX11CUDJYtYloShZK7Q8v1YzDtnIThZCsAT7XTyKbL/mlWGsVKIxkJsnqMrB5DtdIuKDbiKFYS1U5j5kScvIxsplzga6YQ9DhZzX3PFA8s246MYYrkHBVJTPHIIw9x2GGHcvmMS9j013fp6bXp7c1RLOaxbZ1czqwksdi2iePkMAyNxsY67r33HoYMGeKxFsP44+zZvN0U4a2gy07UeAvnL424fzlvffLtysI35eRpzJ4zm9odH2FZ7iJjmiKalkHTMjiOTM6RyecUco5CIa9hWwq6LiJKSVQti6Zm0XUBTc0iSWmyQgJBSJBOR8kKMbJCjLQQISNEkdQEohwjK0cR1TiSGkcxksh6ElGPIylxNrzzGnPn3sVNN17Li2ufRVHjBLLthIROdDuNnXNZasVKo5opb4OScI9jI4FgxJHMJKKeQLcz2DmZYkGnWNDdPtCiST6n8corL/DuO29QU7OA22ffynnnns0ZZ5zismtVVVQN7isPDxg4iGEj92bMfgcwevzBlftH7r03Bx50EHvttReHH3E4x5x4MhMOn8TYAw5mzsq3eK7NtY94ti7L2dfOpnrIUAYMGMDIUaMYu994Ztz1BGP3G8/TL75Ed2+OXb15errzLF60gGHDhvHYY/O4ffYsBgwYQM22DE+838neY/bluTXrKBRK5PNFCoUi72/6mOEj9+aUy2b+yt5ir4FjXLbZM7I++Kjfcd2dD/FyXRwnn6OQz5HLFXDsAuvWra9Y1hxz7GQWPrOOFe828EqrVMk37hOIuEDwjjUf9yt/7mkyvswzEj/r6j8xZswYBg8Zwpw/P0hPT4FPv/ya7378L7789scK+Ptu9z89FtDz+vvbT3zxzU988tWP9HzhKn4Ln3pl3t5vcXq+w+75DjX/JZL9BaG0TVzuJirkWP3q+9xw3wJm3vsoF900hxPPu4yJx05lxD5jqRo8lKNOvYAZc5fxwFttbv+eV+J1k3ScPqGLlzXt+iDaFRui5V45sa//scyOlsVirghqaYNrAbOoVvHAn9hvSq6R9FZ3znp6I4trVZY3us/5VKsneGp2k3/c12d7FicmTzbbPNOW49n2PE+1OhUQ/myzxnMtCn9ev40zLppBVXU1508/n/fefxvTlBDFGIoeRdTDSEaIrB4gowcIS61E5XbicgdxpYOE4ieh+EmpXSSVADG5k4jkJyi0Exb9RKVOIoKfWv9W3t/6Dq+8+SJPPbecOXfOYtKkI6iqGsRpZ5zK/Y/N59Udbazzm15+vM24Q47E5/Nx4MTDOOec07nxDzN5s6mr0qv3ZJPOwg11zJq3hIsuv5z9x4+viPZ8Ph9HHnMMEw89hIMPPpD99x/n9nAfP5mhQ4dwyMRDGD16H7Zt3UihpJIvyDh5EVVP8vY7r3DLLTey337j2HvvURw16Qh8Ph83/3EmpZJLFuQLKu+99wbjx+9feb4/PrKMz3vdMu6nPTKb46rbBuUI5AuKN8tsoEKhqFAqaRUGMKvHyBpxVNO7ZuZFrJyCYYvopoBuZFHUNIYpoWgZVD2LpgkYhoBty+imgGq4FatUOvgbAPxt/EfDBYCpDpJaBMVKozlZBCOBartALq25AEiz3QuubCQr98keUNQdAdkDfKqVQbOzpLUYaS1GUo2QUMM8vuRRgoKfkOAnpUaJy2FCoh/JTCCbLpunGQKCmEaSRVRNwDSzGFYW2UgSFTpJKmH3Qq8nEfUkghavADTDymIYMooiksu50vmsHiMhBpGNJKVui95dNj29FlbO7VfUrQySniCrRMhq0QqAyGhRQqKfqBRAMlyG0swJyGbSZRy9ErKku+BV0hPIRpKUEiarRxH1BIqRQjXc8nKpZFAsmFiWjOOoOLZCzjGwTIN83sGxTYpFk0LeoJDXyOcMHEfHsTUs0y0HKIqMqkoYhsKKFUtpbanj2muvZtq0k6mqquKiCy/gpZdf4tkmjTdDbk/dyhaHJQ02C+t+mWts8eiHMc6ZeS9HnnQ2AwdVccw5M3irKYyoi4iGRFqKYhgJND1JQggiKDGyUoS0GCGZCRFLdHkzQDTVSSzdRTjtJ5hoJ5TqICNHkbUk4ZSfcMqPP9bCzuAOtnRtoTa0ndrwDjpTbQSzfoJZP1Gxi5jYhWj0MdAxMYBoJrAsEd1IEZO7Km0ESSVMQgkTkQLe77lgPa1GyagRslqMlBIhpUa9W7ftwMnL6GYGy8pi2QLLli/EtAWcnNv7010yqKl5orLgj9t3LAcddABjx46pNIAPGjSIiRNdduG4U85Esd3e2Xisk1tuuYnJxx7NSSedyLBhrjDpuMnHsO++Y6mudkHV1BOnEA62UlOz0H2OAw7mumuvpFjss3no6XbIOTqKItDbU+Ctt15nyODBPL5wAWPH7c9ts26lkLeRTYvuokN3Kc9zzz5NVVUV89dv4qA9TNcnMmBQFdPOPp/R+45j6IiRXPHA00w8/jRGH3AIW8Iin/XmKRVzFAtFVywiqrz++pvcdttsjjvud4wYMZLq6mrumfsXWhMKG0MWz7b2AaUr/7K8Eh3YPyWkvOGYPvtRxo7bjw3vbEBXsxTyOrajs+uTbr7+4R/8/ef/5sd//A+7//E//PDTf/HND//gq+9+5pOvdrPLs3zp/vzv2D1/Q81/jlb4ArP7a7TCl2j5r5Cdz0mpvaTUHjJaLxs31bL/hMMZOnJvJk07j6NPm860y2Yy/U8Pcd28Vdy+6q8s8gycXT9DqxKj2KeCdsUvFWGXB+ZWtroq92VNtgfInL6+wH6M6PJmuyI4KL8ni3eqLPBSP8oAcOE2kQXbJRZtd++79O5FLK3TXOFNWV3dP47Nez0rmuyKSGpFk91nA+PFtz2xXWXajJupGjyEq2+4jpa2RooFlXxeIyMEEdUQSaWTlBYgKrcTk1vpEhoJCI20Z+roEpuIyK1E5FaSmp+IBwyjcjtJNUBc9hMS2+lMN7M92o4/1URXuo6o1EZE7CAmBwiLfrY2/pV5TzzI6WeeyuDBg3ngL3fyUUxmSYPFjQtedJnpBx+p9Bbuc8BE7n61lWnnTmfMmH0YMmQwp51+MvfOvYPX3n6RSLqDK66+jKOOmcQlMy7ixVefZ8P7r/L2B68QTbZj5bKEo208/cwyFi+eT7GkYOcESiWVYlEhV5AolhScnIamZdm06V3uums2t9xyI2kxRL4g0tst012SaGjewf4TDuOEi29i+N6jueyyiyn16+PT7Qx6TsH2iAXFTCIbCUwni5OXsHICmp1GtzOktTAxuYuIFEA04zh52d0MawmyagzZSGHYIrKaQtHSmKaIaQnIehJJS1Ao6hhWFtMSkfUUoYT/NwD42/iPxiifz0c01YloxL1Sr8uYmDm3z88Vd0jYeRnL2+ko5dKox3AJehxBTxCWAoTETmJykKQaJip1ERE76RI6iMtBuoQOBD2OamWIyV1EpS6SSoikGnSBo5FFUjJohoxhCuTyKsWSRlaNEpW6SMght5ysJ9DtLKqZIaO4TKCTV7AcBUnOoqgKmq5iOQq6nSUhB0nJYUQljaQIGKaEaYvkCiqalSarRUmrEWQziaDHSKlhkmoYxcqQ0WIoZgrVTGHYLmOY1WJkvfttR3Jfu5VB9xhFxUq7yq2iRs4rJziOQqGgkXM0igXD7VnMq27pOq+Tz2k4jkSxoNLb45BzNCxTdn+W1ynkLQp5k3zewrANJF3HtjVytkE45OfBB+6vgJb9x+/PEUcezgknTOGmm2fyxvZWnms1KyCwzBI+2Wyxus3ksRffZajXCzX5zIv4y5r3qUvJdIgKCTmDYWdw8lKl3zOrRAmlOwik2+jKdBDMdBDNdhLK+kkoIRJymFg2QEu0gdZ4I83xRloSjTQlGqiP19GRbqUt1UxDvJ6GWD3NiUYCmXbCQgBBjZFRo4SFThJamKwecz9rPVl53yUjgWQmXJAnh0nIISJigHDWT0QMEBE7iUldHhiMuoBRDhGXgmSVKGk5QkqJkJDCZFS371OzMki6yzCbtkA6E6K27mPeevsV3nv/LbZt30Rzcy2ZTIRkIshtt/2Rx+c/TCCZRLNdi4ZSSXd7RIuuyMi2JbZs+ZDXXnuJ2h0f0dnZTHt7I7KUoLtkIUkJjjjyCNJCwmP9bHr72SOVSjalUo5i0aG75PDUUys4/LDDWPXsSj7Y2cLHnSmypkNrSmXN629z9qXXMKh6MH9csWGPeK1rH32OsQcd5gmIhjFj7jKW1BlcNecBfD4fN910PevXv4DjGBSLOQ8I5jFsh86swc6YhmJYfPjRZo466igOPvhgzjjzLC64+DLeakpUfP+WNduufU+TzdIK8LFYslNjyIhRHHf6+Ui6gWo7qKZFT3eeXbtK/L0f8PvhZxf8fbv7H3yz+5989f3PfPa3nyh99gNm6Ssk+1O0wpcYxa9Ia7344xo7gzL+iEhHWGTjDj9/uOtBqgYP4YJbH2BxrVxh5vbsP9uTrSznai9vctOF1nfoPNvqMWye4OWZVouVzTYrm0zmvVnHcx82sPjDTp5qMSs2UcubfhGv2GRVnrem3mDRDoWF28riDsnr/5P6xb31JYVUvATL6uN+meDlrO/lTXYf01+2q2qyeOyDEEccdwJHH3sswWArhZxMzpbR9ARJ0U9S8ROR2ugSmugSGgkKDXRm6+nM1hEUG/Bn6ujI7KQjU09QbKJLbKIz20hIaiEstRIUW2hO7KQ11UBzso5Hls7j0htvYNa8BSx4+Q3eaW2mPtFFazpIWAoSkboIS0EWP1WDz+dj6cchVnzUzj3LVjOoqpo/PPokY8aMZtSokZx61hkcPukIzpt+Nu98+BpRoYOg2E5QaCcmB4hIfsJSJ1E5QETqJK2GicqdBIV20lqYhNJFQukiIvpJqy7oSmthZCuBkctimAKyliIuBElKIbJqlKwWQdBj6E6aTlGiLikwb8kSBgwcxP4TJjL5+OM57fRT8PvrPWZPrbQPCUYMpyBh5dzro2anyWhRRCOOZMQR9BiiESciBYh7ADAk+gmJnaS964jsbXydvIJquGuQYqRcYsN2SQbDymJYApqZQTMyxFKdvwHA38Z/NEb5fD6SmS5MO4tgxBENl31xxRBJJD2OqMfRLHcXU+67U82UK6CwM0hGwmVs5CBxOVg5yINCB43xehoT9YSlTiJSJxk9hmgmCImdNCUbSSphUporDFHMFCkhgiCnUDQZTZOxbMGdORFRj2M6IqqZRrcyKEYSSU2gWi4zKaspNF1FVSVkRSaVSSFIKaLJIKqZxcyJmI5IriCj6imyQpxiUa8IYKJiJ13ZdqJyAFGPExU7SWkRJC2BqMYRVPdkLZ+wspFEMOIoZhrTkjEMkVxexbJkbFuhUDDI53VKpRz5vIFtK5imhGmJlIoGxaILEixTpFBwhSq7em0cW8W2RHp7DEoFhe6iRqlkuKxmNswDjz7GtOkzEJUMhqUTE1UUTWHGDDe94M03XuLdd17jyVWrmHHF5VRVV3Py9MtZtDXL0gaLx95p4YH5jzN9+nmMGTOaqqpfW6E8t2oFn3QrlIoKjqfajooB4lIXwWw7YbGTqNSFbKVcYOUJaFQ9RTjlJ5DtoDPbTlgMuOIgPUFGi5PSInRl/QSFDgKCn7ZUC03xBvyZdtJ6jKwUQ9FTnlgoSlTsIi6HCKU7SKoRBDOJaKXQcwJKLkPWiJPWoyS1MFEpQFQKEBECxMUgcSlISokQSLWx7JnF3DL7ZpY/s4SuZBtRqYuuTAchwU9UDBAWOwmLnWT1GIIWdz9jM4mZE7AcEScvVQRE+YJCPq9QKunk8gpFr/G7LBAqFLWKIKlU1CkVXWax6H3mpbIlUbdrSdRdMukpWZ4HmFUxfTXzDmbeoSVj8nFUx3BMikWL2oZ6BlVXM3DgIEaNHcdeAwYwYvS+nH7VLO5+Yeuv/SAbLB7fKjHvwziPbVdZWGcx5cLrGVQ1mKqqAxkw4E4GDz6Mk046g+5um08/KbHhrbfYb//9GTBwEAdPPpl71u1gW1THUCVWr1nL8VOnMWzUPszfIrpWIv2jxPr5rpXtR869+T7GH34siYxIZyRLV0JA1Q2++GY33/34T77b/U+++u4nvvx2N7v/+f/xwz/+hy+/+5meL76n+/MfcHrd/j675xuMwld0Ji22BUSWv7GVvyxdzQ13PMCRJ57OgIGDOHDS8dz29Hve6+kP9Exq6nUW1yqV+/qshSyuf2wN+x1wEFVVVQwePJibbryOuas2cMe993HUMcdw0YXTue6GGxi///4MGzaMvfd2feE+qG1gdYdDjQd6y8bYK1qcPjPjskLYs4lZuE1g0TaJJbWesrdWZfGOfrGHFQWvXhGW9Gcma/qxq5X33OvtnfP8R4waux+XX3EJGSFEoaCi6DE0M0ZYbKVLaCYqNxGSGujI7qQju5P6xDY27HiZB1Y+ybV3z2Xu02t5J9BJS6aRoNRAVGkmKDbSJTYSlprpEpsIi82EpVb82UZGjuoT0VR7LSqjxh3IESefx2lXz+aE6VdzwJG/63uMx4aPGDmcvfcZxV1zb+O1D9Yx4+pLuPqGK3hg3n10xhqJy50EhFb82Raak/U0JuroSDfTkKhjR2Qb9bFamhL1tKYbaU42kFC7CIkdNCXq6ci0EJQ6iWshdEdE0pIISoyw2EFUDqBaKQ8gBkgoQQQ9SloNI5sx1rzwZOW1HjThQG68+TqC8TZMJ+tVkAQUK4lsJkmoIQ/kxRD0GFk9SkaLkFRCpNQwIbGToNhBWPQTEjsq4K9TaCelREjKYQSv+qTaLiEh6nEsD/hJWsJtj9G8Fhc9TlaLEU7+xgD+Nv6zMcrn89EZayGjRVHMFFkt6qpstSgZLeoydEqItBomo4YR9RhZLUJaDXsikQhRyb2Admba6My0Esi2ERb9BLJttKebac+0eF+30J5uoUvoIKvHiMoBolIXUdndGaWkkCsc0TNouoimi+iGgOMo5PJaxTamUHQVuLqVRbXSnrDD7b9Q9TS6mcUwFWQ1Q1ZKEkuGiGfCyEoGTRPQNAVRyqDbAratoqoZVC2DYiZJKCHicpCoGEDQ4yTkEB2pFoLZDjJ6rB84TmHYAoYtophpVzVtCxQKJvm8gWm6noY5R8MwJCxLJp9zLXRsR8I0BLcM6Qg4ecljj1SviVijUDTIFwxSuk7WNNAdE902+PP8RZWF6ZDJJ3HAEZOZNO08bql5hYSs0RmLUcjpfPTRh0w68VTGH34s1UOHsd+hR/PQe8GKrcheAwZwzay7+fCjjYhSnGCojbvvvZO5f76bG26+gdfefw/NlpDNJLqdJa1E6Ey1kZCDHtAKEsx0EEx30B5vpiXeSHOikeZYA63xJhqidTTG6tga2kZjtI7a0HZaY00E0m3EMgEyaoy4GCSSDRAVu2hPtNCVbEdWE2h6Bt0SULQ0gpJAUpOISoKsEicrxckIMUQpSToTIyPEkJU0gpQgK8URlAQpOUIg00FXpoOmaD2Tp0ylevChbubr4EM5bspJtMabiEpdhKUAgXQbnZk2gtkO4lIXGTWKbqUx7AyWI2A5AnZOxHb2nE5OJFcGhAWFQsG1JSoWNdffslBWpqsUC5p3vwsKi2VjV2+WNwSlotufWKr4cZrs6rbQHAvVcdi0eRNDh49kr71cNfU5557LytXr+Cis8WKHxVPNrr1F2dB34Q6FS2Y/xB9um8Ps22dz481/4LyLLvFK2WP36BMcPPhQ1q9/gVIxz5/nzmXYsGHMe+wJZsy8narqwUy75EYefPpVTj7zPEbsM5Zblr6+hz9eGQjW9GOuaupdUPSnle8wbO8xHH36hXzoz1DI6WQ1DatQ4pvd/+Db3f/kh5/+i+9+/C++/fG/+PL7nyumz+XSr1H8imDK4rpZ97H/hMMZMHAQVUOGcvAxJ3LsWZdw8Z1P8NCGDteupaLe3XMu2an2ixLsK6/Oez+0xwbo/Muu4vobrmf06NFcccUMXlq3hvnz5zNr1iz+uukjnHyJnp5POOigAzn11Gk8/2E9i+v7bGDKLGhNvc7iHYorAqk3XLuX7SKLt4ks9mxefh2BaPQzdtb7lMeNferrX/b0Lmu0WFJvcvm9C6kaPJh7Hr4fw0hiGGksK4WkB4mKLYSkZiJyCwGxHn+2ltb0DmqDm5hx7UUMHDSIcYccydGn/Z6RnnXP85s+oEvcSVCqJyg10JGtIyDU0ynU05DYjj/TQHumHn+2gZfeeZ4Jhx7MHx+az7wPo1z76GpOv/gyzrr8ai76412V9/byG65g3YcvUh/dzrKXl3P/E/fRmqrHLzTTnmmkM9tMW7qR5lQ9DYmdNCR20pZppCFRy87YDuoStdTFa2lK1FEXd79uSTXQmmqgNd1EXWwH/mwrSTVCLNNFUgzSEWsikGklLHYQEjsICG0oVoKQ2EFE8hOV/SSULjJ6GNGIkpC6ePO9l1m1diXRbCdJJUjaYwlFI45oxMhoEeJyF4JRBn8u8EupIRJKkJjcRVDyExQ7CAodhCU/Mdll/zqzbXSJflTbJVSyqluFEvQ4KSVMTOoiKgVIqxGScoi0EkZQ42SUaGWzHYy3/wYAfxv/0Rjl8/loC9cRkwJktDBpNURC7iIs+Alm27EWSVAAACAASURBVF22R+ggIvrp8oBdTA6Q9HZMWS1CUg2SVINE5U7q4rVsi2yjxduR7YhupylZz/V/up4uoZ2kGiImd1EXqyUodJBQgiTUkHuCCJ1EswESQhfBZDvJbBfBRCtpMUQg2kYyEyErphCkBKKSJCtHSCsRZC2JZUvYjgueiiUDOycjanFiYoCYECArJ5C1DLKRwDCzWLZELqeh6QL5goZhub6Fohon7e3eJCNBWg4TF7vIajFUM4VqpRH0OIWihpNTKBR1cjkd0xbI5RVsRyaX01A1105HM9LYjtvLYVkCpimSz7msUS7veiA6ngdiPq9S6jbo7rHIOTKOLSBkw5xxxmlcNnMWk8+5jDEHHca4CUdw55otzLhvCTc+sY7zbrmfgYOqOPDoEzhr5n0seLuBi2fO4cBJx3PDwleYvWYrT9T1CUNmPfUuh045jdHjJ3Dm9Xdy3YMrmHH3An4/62GufWA5U35/rdurNu0kRC1KSgzRFnPZ2s50Gw2xOupiddTGdtKWaaU900progl/qpXmWAP1sTr8Qjtt2VaiaoiIEqRL9lMb3cG24FZqQ9vpTLaSyHYRSLbRmWglkGqnM91Oc6ie1kgjHZFmgrF20mKUcLKTYLwdf6SFzlgboYSfcLyTdCZGIhEmGu0iGusiGg8QjQVIp6Mk0yHC2U4eXz6f6sGH7gF0qgdPZMnTC/Fn2rjltpuJSgHicpCQ0ElIcNnJkOAno7oLvmqmsHOiax9UBoSOgJMTsRwRw8pW1OWFgko+r2A7InnPp7K7pFMqaRSKqgcU+8Bgseh+7oWCRndJx3B08p7yurfbYFfJIGNY6LbJqpdeZcQ+Y5l06nTOOPscHpv3INdffy2jR+/D2HHjuOnGa9nw3gaeb3d42kuIOeHCaxm17wGccsGlnHjGuRx78hmVUrBvr6u998SdAwbcydz77qW3t4hl6CxfuoRx48YxduxYzjv3XM67+ApGjN6XKRdcxfyPEp4YwgUjZ91wl9tPV1fu/+sre9Z42bfzPggz/vBjuXLuYnYkTNKaiWbpfLf7Z3785//w/c//zXc//Rdffe9avnz5/T/44tt/UPr8B5xd35JUurloxjUcdOiR/GHhOh7a4K+weCuaHZbWqSzaLrKkVnWfs07zvPjcuazR5MkW5xfl4D4m7eBjTtwDBIayEp90mxQdhV3dOfI5h57uEoVCgd5PPqM1lOSqP92Dz+fjojser7B1Zd/AGg8QLm90hRo1da7pc7n860Yg9nn1lfOuz77hzkqP31O/KPcu95jAMuhz/weHeR/GmHbBJYzbfxzrN6xGUIOUihJWLolodBHINtAlNhCWm4goDXSJdTSnttOY2MIDC+5k3/335cENfhbXqlz98CrGTzwUn8/HmreepEPYSUCsoz1TS0t6By3pHTSnd9CSriUoNeLP1uMXGghJTVxy5e+54ZZreHbD61QPHsJeAwYyeNgwTj17GhMmHsQ+o/emNVlLXXw7Lamd7IhuZWdsG03JWlrSdWyNbKE+Ucum4CY2hzdTG9tGc6qO1nQ9O6Pb2Bz6mNrYNrZFtrI59DFbI1uoi9fSmm6gJVVPQ2xHBTQmpRAzZl7O5tBmOrMttKUb8Wdb6Mg048+2EJU7MewUm8NbqI/vpDnZQERygWBWD5PRwiTVIHElQEoNIRhR0lqErB4loQSJK0HSWpiI1ElKC5PVI6S1EHGliy6hnfZMCx0e+RESO2hNN+HPuM/bmmoiILa7PcxSgK5sOyGhg66MK6zpEtrpSLXQFK+nOdZAV6adYLaD9ngTrdEGIkIn9Z3bfwOAv43/aIzy+Xw0hWqJSH46s6340y20JhtpiTcQyLTSlmqiS2ijPdVEQGglKncSlToICm1EJT8JJUBc6aRLaKMlWU9tdDs7vLktso26eC0NiTrebXiH1nQjcaWLmBIgKLa7uy85QFjqJCC005Cspy3dTFu6mcZEPR3pFupitTTEd7Ijsp3mWD3tiSZC6TbC6XaiWT+hTDuRbAeiGkdU4li2jmkqWI7k9vcpUUQtjqgnkFURw3Q9CTUrg2kLSEoKVRfQTQHNyHj9fmlELU5SDiFqCeJSEN3KImpxDEfAdCScvELO0dySrili2bLLVOZUbNtVy9qWgO2IGF4zr6oncXIi3d265xXlGoPmcjLr16/m7rvn8MILq7Aswfu5hSBEKxekCy67invX1/HIxtCvkj/+8lYb1zz8DEd5geS+vfbisnuX8uj6j3h47bs8934drwdMVjZ7QLDe4Mq/rODkS2dy2Amnc+zp0zn+nEsYN+Fwpp0z3VW9DhiAZibxJ1voynZ47F8XIaETf7qV1mQTjdF66iI7aU+1EJEDiFYSzRKQ1CQZMUxWDCPKcUQpSVaKI+kpIpkumiONNIR30hpvpDXRRCDZRleqnaZoPa2JZprjjS6LGKqjPlxHa7yFlkgj7fEWOuKttESa6Iy1E0kEyIpx0tko6WyUSCxAKhMlEu8knu3i5lkzf5H56gKdP86+ma5MO9tbPqIr3UYo00F7spnmRAP+VAtdmTbCgp+I2ElSDiEbrrCnzAyatsvelm9dVtAVk5SZwILHCpqWqxAsFt378gUFOyeRzyte6dgtG5eKOlZOw3A0ZEunLRTkkmtvYvzhx7LP/gczdOTenD3zPgZVD+aMi6/GL9kEkhmuuPVufHvtxYFHuFYcEw47guvvnc+SepNzbrqHIV5/56DqwZVUkcHDyokyp+Lzrcbn+5bBgw/lpXUvkkzEePLZ59gWlmmMa7z29kZOPPFELr3scp5tMvrKvf1A3sPvBqmp72OulnjAZslOrWKXUlOnc+7Me5kw+SRWNmrIuo6dy/O3H37ih5//m292/5NvfvwnX/3wD7749ic+/fpHnN5v+P/Ze88oOcrz27cloUQQGBswwSInY4PBYAEmGwwYjMg5CwzCIJSlGWlmlAgCEY3BBAMKM9M5VHXOsbpi5zBBItn/tO5Z99z/Xed+vb/7oWpaGsDr3LX4qnetd3Wc7p6uqn537efZexudb8jIbYREmRN/djIP979pvcd+D7wNlpHyFNjreeNZaRlTli5TytlBKxViUOj0eu3Wjkjc8txQ73g777LruG/Ndja9v4u/eWJ84hRZuellLv3DPRx38hnYZszguNPO5cFNH5mvHzqwBN75Dks3lcCxxRKUTMWzbRI7vZjJLeEx1g4Xp8UqTolBptTJWy1hyJZQhwFXmdsefYJ58w/lsqt/izsRoN6V+eLLKpVWjlItSkz1EtN8hFUfYtlFWPHgL9rZGRvm9iVPcegRCzj/6pu5fdkgRx93PMefdDwrB5YSkhzEdS8xzUvC8JGqmIxhWPEQtYBg0giQrpjMYEz1s2rgBQ47/NDed/jYs4/x1q6/8Pjzj3Hj4htY99Jq/CUXfslN0AKC3pILv3XpzNux50ZxF5y4Cw4Ckgeh7MUnufEUnHiKTgKSB3fBiafowlVwElUFBMmHWPYRkgN4Sh5iaoiMGuUDzwc48w5CcgBfyUNEMdeosCygt3IktLDJJsoBknqEjBElX01QqiWRGxnKjTTlehq9lUdt5lAaWZRGFrmRQW5kzV7DahLFeqxQM8u9ST1KxoiTqyYsti9OUo+Yn8uIEVWCRJUgcTVEVAkRV8Mkp6pnFlNYqKZIa+aaHCsHiZVDxKQgcTlMRA4ipN0HAeDB8YPGApvNRiDtJq6EiCpBUkaUmBIkUhbIVcwdOK1HSekR1KZ5QGT0KGktQlqPkpyi62WTrhckH37Ji7vgQpB8RNUgCT1CUo9QqCVRWlnkVpZSPWXaCdSS5KtJomoQoewnqUeJayH8JR8ZI05A8uEperDnHLgKbjxFD8FyAE/B/Mw5I0a+Ekep5tBrRap1Hb2i0mxXaHVNr79622TuKk2ZVscwFa2NgjnrRaoNhWq9bAoE6nmKWoqSkaZUzaA3TNFBZ7xiNuWO6UzubdPtNmm1KzRbJnBstQzTsqZbodXWKBgaCVWj1tRotlUabaXnO/jVl20r9meMiYk6F110KXPnnmr1Yp3ChRcuYryr88Vkk5ImozVq3H33nfzil+ex/NPU/zb5Y1DsMBBocve6t7/T23fWxVdOs4cZFLssecfHdbfdxc133MmFiy7rPffu++4gpUbJGymkWha1WURrlczSakkkIgdJGXGSeoxys0Clo9Ds1FDVEmU1g1bJYtTyVOsF5EoGxchS1tMUVVM5nC5HSZZCJNQIKS1GUokSk8JEpBBxJUawJBIqCohFgVAhiJgXCReCJgAshkjJCZKlGJKWo6Rm0SolJDmLJOXIl5LIWp5NL238XgZw86sDlGtZSlON2UaKvJ4kKAWISCIJJUzWOusuGCnSWgypmkGvF9HqRdRa3urT0ZmcrDI2ZoLA7pjB5GSNiYkqExNVxieqNDoK45NVJidNv7C9e+t0p9lG1BmfsPoLJ+uMjVUY9fu58JJFnH3R5dzX/y4Pb/07a+0K8484qrd9zrnkSmbPncdJ5/yKi6++kfkHxLst+MlP2RhoMyh2eTtS5zWxxksW+N8gdNngq3P8Gb9mxswjrZaAQznznAsRpQqvvX+ARdBvrmDp4Gs8tnQFM2bM4IbHV7HRV5+WejHF8A0FO70y6+AUEPQ3pzFxfQ6V4049h5+deyFvO6Io9Tb/+T/+m//6n/+Lf/wf/81//s//h2/+6//my//4b/b+4//E6HyDXNvHwNYdnH7WuSw86zz6nBoD/oZp0uxv7PfLs95v47cAoMnMTQeAm62yqvl/tA/oD2xw1X1LOf+6xZx7+Q2ceNb5HH602Wc565BDOOGM87jk1oe5Z91brPg8Y+XstizF7/4SuDmnA8GpdJutFpjbFhk3Yw+nGD6LyZzKvd76LQA4ZQXT5zZ4sP8NLr/+BuYdeigXXXwRI/aP0FpFWhMqkxMGX+yrUmln0dsJknqAoOwmKHsISC4+8vyVm+69k3mHHc5JZ1/AAxvf5fSLruAnxx3L+pfXkdTdpCp+IqqbsGLOiOomrnuIaV5Cipu45iWkeEjoPuK6n7jm772+J2/n3V1v8MwLj7PH/zeCspuI6iMoexHLHlwFB46CnYjqQyx7CMpe/CUXe7LDDFvTnrPjKjgQy178kht30UFAcu8HjmUProITR96OKPvYnRkmJPsQZS/2vB1R8hMriwxnRrDn7PhLHjKVMFFVJCB5yRgRstUYjrwDf8mLt+gmrAhkK1Hy1TjleopyI4XcSKM00mjNLForR7mRRmlmLACYoVw3AaLcyFCoJckYMRJamLAiEtfCJPUoUSVoso5amIQaIqlHiKrmehuWBWJqiLjFDqb0CLlqnGItiVRPka+afYNpPUZSi5DUwiYxo4ZxJUYPAsCD4weNBTabDXfKTkw1d9K0ESGmiETVIIVagkItTlQRKDdTSPUEhUqMlBYiqYWRGkkSWpCYKpIxIkRUgaDsN8/YSm5CsknFRxWRhBYkV41SqMVJ6mEylShxLUhMCxJRBEJygLgWIq6FSVdipIwoWSNOyogSkHz4Sh7iaoiYGsJtAUF7zkmxkUZt5c2+CyOLWimiV2Vq9SqaUUM3dCb3jtEdb7N3X4vJvW3qTZnueAWlluuViGtNFa1WwGiWaFrq55aVcNIeM70I602VzniFTlPD0AvoegajUqDRlBibrPDNN+P84+suz61aw5mX/p7f3XY3lY4pHmh1VCYma2bm4zdj7N3bZnyswVtv7WDu3OmebXPnnszgtq1sfvdvXHX3En5+xU1ccvN92Gw2LrrxnunswvcCQZMh2Oir89Dmj3hg8H3Ov/Y25h56ONc9uqL3+KDYZcmOUY497jhu/ePNPP7kwzz19OOIcS/FcoKUFiOhRNCbppK2UDEtV9JqjLQeR2rkqbdUylqWspKlVE6QU6Ok1TBqK0erLaPrJWq1PLVWjqQaIiQFkJtFJveNU2+o1JsGil5E0jPE5TAJNUZKT5BTU5SMDCk1ji/vIywFSZZjRPIhskqKcF4knBNJKTFScoJSJY9iFJG1Ipliglw+SSGfQpaz/PyXFzJnzslmD+CchZzzi1+RUqOU6rlej025miWhhAlKAWJyiLQWIyGHCUsCQUkgJAnEyiEicpCoEiZfSVGopKi2JFpdlbGpsvB4hcmJWo8d7IzrZv/ghEFnXGdissrEZLUHCM0ystlj2J2oEo54OfXc85k9dz4/Puk0lrzhmratTVDR5d6+d7j8riX86S3nNIB/6eLHWPFpintWbGP7jtcZcMpcf98Sbrjhdyx5agkbt77Etvc+4bHlG3juxRUsXbuRcy67nmNPOYsZM2dx9AmncOHND/LHx//8nZMHm20es2afzElnX3qAhcp+wGP2sZnAzCxpHsAG9qLJmvS5NH59073MPewInt76AXq9QWPy3/j6v/6bL//9f/LFv/9ffP2f+zN/H3niTxx55FEsfnoNA96KCTCn+gwPAKBm3FqjBwAH/GZG7yaxzeZg2wR9YmdaTvfWsKnsNV+ztf81hf0ZuoOBFoNii43eaq9Hr5evKx6QhXuA2tf8Xjo9Q+hNoTHeTXV4N9maJuCYUgYPCC22WH5+26ITbI1MWJ9zv6F7n1PnslvvZfacOZxy6sk88+wSXO5hapUSFaNEpyNj1LOMjcu0uyUqnTSFWphcNUhY8RKU3bzx2avMmTuHcy67gSWvjzAU7HDF3U9zwlnn489+TqYWIKK58ZcdBGUXIQv8BRUXouwkrLgJlB2EVTei7CJQdhFRTWAYVb24i3Y8JQfeooOR3Ai+khNPyYm75MRddBBUvAiyB0H24C44CCoe5FaUXC1IQg/gl1yM5EYYyY6wK7MHe3YER34UZ96Os2CCwoDkxlty4i46Gc2NWoBxFGfBQaYSwie5+Ty1m4DkYzgzjKvgRJS9eEtu3EUXjrydQNlHWAmQqUSIKgLuvBNv0UWhFqNUN9c9tZlGa2WotHNorSyKddto56i086jNDGozi9xIU6pbYLGZQWqkKFQTKM1Mr0KWskQsEUUkqpnAz2/1L/Zul7z4S14SaoiYEiRUDhBXQ2SNKLlKjLQeIaqIZCpR0noEd8p+EAAeHD9oLLDZbPgyTtJGiEwlQsYIE9dEEppIvhpBbiSIayKFWgypHidbjVBuJMjXopTqMZJ6kJDsI6YJ5Crm34ZlPyHFT1gJELL6N7a9s4mEHiShBUlXwiT1EPlqjLQRIaqKJLQQUVUkWPYjSj4iskBKjxBSBIJyAEHyESwHECQ/AcmHIJlsY1wLo7cLKNUMSjWDUS/QbKsoeo5KTUHWCpTVAiU5R7WuUakpaLU89ZaCUS9RaZZQawWKWgqlWsCoS9SaMq2uTr0l0x2v0h6r0hmroeoF9vj8PDHwJrf9aQ2/ve1BrnvgGezFOl99UScryzy8bB3zDz+CY086hWuvvYqJyTqtrm5aBIyZIKHRkmm2dLRqkSVLHrNKlP8vNlsCm20FNtuJzJw1i8OPPpZfXvNHrnvkRS659SHOvfz3PPbqLh76/5H/O/idx7/bOP7Um07mzJ3H8pUv0GirNLsKjbaM1iiY7FglhWL57elNyQJkMUvNXTSbk40CslpEr5Sp1DSKRhp3Po5XipNX4qgVBaOuUW9V0WoSer1ARoqQKkbISxlyUopSOY9qyBgNjXQ5QVZJUtZyFJQUkpElWgoRlcO4Mm6ScpxMOUFGTpJWk+S0NHktQzgfJC0lSJfiZEoJYrkQ4ZxIvBQhlguyamAldz90FyuHVuLKuvAUvfjyHra+ucksr8gh/EUv/qIXseQnWPQTlgRCUoBwSSBSDvYAolgyQWKxkiKjxSkYpt2E0SxiNApUmiW0ep5qS7Lsg/aLSdpjOt1xc7a6Zv/nxESV8XGDj//+PnPnz+fSO56k312ZZu3xr+bKz03xwo+OX8j1T65lvdvggcH9UV339r3LIXPmsmLtGh568D4uu2wRp592KpdftojrrruGOXNm8/ubbmTHSIghj8oDG//Kb259kJ+de1GvXGxOCZvt37DZ/heHzDmFu9a80WMBN/obPDj4/jTWb1omrc8EZht8dTZYEWYbfXXuWPEKc+YfxtbhOMVKg4mv/p323n9Hqu8lW65SNrrUxv+NFeuGOGnhKbwUbLLBU+2JPKaXe6czf1PMo1li3c/+bbHMnLeEzJzuIaHJQMACfz3AZwE4C8T2QODUnHpOoN2LwZsCc0OWWGOKXTTNoM10kM2hDi+FTNA41aM4KLTYHDI9O7dFTdD38OaPv/d4PfPiqzjr/AvY5fyIsl6ipJRo1FV0LU+1nkOrpdDqceRaHLUZR2nGKDYipAyBoOzCV3JwyRW/5urb7+oJYQbFDreveJUjjz0BZ8GBIDsIlM3plxx4Snb8kgN/2U6g7EAo2/GWzOkujOLIj+AqjOAqjOIvu3AXHbiLdhMIFh34JBd+yYW35ECUXfgkJ37JRVDx4i7YEWUP3pLTBI0lJ6LsIar6en/jKthxFR04C3a8JSeOgh2Hdd9obgRvydEDh56SE7/kxlVw4Cq4SGlB+l5fx2huxOwbLDoRy14Ckge/5EYoewgp5tqVr0aIayJi2UexFkNuJJCbCfRWmmoni97KoLczaC0TBOarMVJ6iLgqkjLCFGpx9FYGuZmiUIuTr8bIVWPkKqZlTq4aJST7CVrTU3ThKZglbG/B3SM1AmUfjpwDb8GDr+BBlHyIkg+hZM6gvJ9QCWRdBwHgwfGDxgKbzYY9sZug7CNliCR0U9VVqEXJVEJE1QARJUBQ9hFSfIiyj7DiJ6kJCGWvSbuXvfiKbiKqgCB5CZZ9xDSRiBow7yv7WL15JcGyD9E6AETreaGyn5AcIKwIRFSRsCoS0cwplL3EVBFB8hLTggiW+itY9vUOvGA5gFRNklMjVJoSejWLVsugVFNojSxGtUBWjqDWMqi1LBkpjFRNI1cz5OQo5UqGvJJArRdotA0qzTKNjkan26DW0Kg3VSQtS0lKct9TSzlkzjyOO+Uszrn0Oi5b/BhHHnM8tyxZyb1PPc8RPz6O+Ycv4LyLLmHu3Lnkc2H2Tdao1XI0OzIT4xUKukxalag3FTodhZdf3cqsWT/BZrsAm+3H2GxPMXPm0Vz7yIuss6usd+r0e6rWYmeW1+5c/dq/BH5DwS6DwTFzcfueRWRqbhTHuOm5zRwyZy6PbHwLtanjKVeptCRK1aIprunIjO2t0mgpNNtKTxle7ZSpd8qmRVDTLKE3GlXe/MtrPcDgjntRK3k+8ER4uP917l79Cu8F4sSlPEqlzMR4jYnxjulxuG+MsYlx9GqN8b372PfFFyRyESIZgXgpQrQQIltOIOQFPCk3QsZPoBAgrkQpGll8aS/+jA932o0v7cOd8eLPB3BnPPjyfuJShNHoCLtTI/jLAQKSH0EKIMoifS+t61lMxNQQCS1MqBwgIgvWvhbAX/LgLbgJlwWCUoBA0UtKixJXQiSVMEk1Qs4qF2cNUziVNeLItSxawwSCU6CvbSm/W12N7pjeUxPvHvk7c+cfyuKVr0+zb9nPLH0foP++7TvGBl+dq+5byl1rdrDeqTJjxgwS+YwlPmmZedl7TX/JfC7B/fffw4IFC1i06BKuuvoqXn7vIwaDXd5IjHPp4kexzbjLOkExeyhnzHyKK+55umd2POBvcNfaNxgS29PA3wZffRrw6/dMCSAsIOhvcMktD3Logh9xzUPLeHTFBu5/oZ/Vb31GudGk3moRyxlc/bubuPzG29lgiSYGhRYD386/9e0HfoMBcw4JVnRasGOJKMw+u23hLkOCVbYW2tZz29NKt4MH9jF+Dwgc8Jv3bYmMs8kq3b4SM42kN1mij6n3M0GxGQVnKnzN190aGeel6ATbIqZ34quxLq9EOuZ3+T3H7OkXXcFDK9ag1xMUlTSdbpVuR0erppBrCYSim2wlSKDkRCx7iGt+YpoJpgKSi+H458ycNZOnNqxjKDjGgDjBULDLc++LHDJnrimGkB1kq14CZQepiou04SClOwipXlxFN7tzAu7iKH5pBK9kAcWyA79kR5RduIoO/GU3AdmDqHgJqx4CZReC7CJTDeAtOfFLJuALqR5E2U1E9SLILkTFjTM/imgBSZ/kxJ4fYTRvsYAW8HMU7LgtADiSGyFQNtlFV8GOt+TCW3TiLrjwFdy8MPg8jvwonpITb8lFTPOjtePUxpJUu0kqnSRJQ6BYj5DTRV7asYHHltzPwCvriCl+spblTUQNkDbM9TBlhMjXotS6WardLKVGnKQRIqEHiagBYppIVBWsGUAse5GbCfLVCGkjhF/yELREL5lKiGw1jF/yElLM3xxvyU2g7MOed2DP2XHmHQQkL/6SB0HyIUhe4qqAOzVyEAAeHD9oLLDZbIwkduGXXERUP2HZR1DxEtMChBVf70wpUDZp+4ji693vLpi0uSD5SOohUnqYbDVKSg+TqYRJ6UGCZR9CyUNCCxKU/dbBEbCUXYHegZLQRCKK3wKN5kETUwVy1SgpI0RUFYgofmKq0GMW43qQdCWK2spSrMSQqwm0egq1nkRtpJAqcUpGnIIeQqnHyasRUpJIuRKnoEfJaxGkSoKcFkUoxREKaZJynIQUp2xkySsJilqcj0b+zs/OOo8fn3gqS3aMTiv3PLz5I2wzZnDimefx05N+xpw5c/jNoovZ8Ob7DOcbvDkS4JHBd3h1OMjNT6/l1AsuZc78w7j4hsU89Oc1HLrgR8yaPY+ZM4/GNuNxDpl9Miec9RvWjCq9mKg+d40+95RlRI0BX51N4n6l5XeAQnB/csH3xcG98HGc4087m/MvuYxTzzqHI485gUfWbCVYzPOe3cMbQpFmV2Niokp3TKdaK6FWc1SaJYxmkUqrRKVZpKAnyCsxslKKx19Y1QN/V1x9OcFCkb/HisyeO4+fnLiQ4xaezux58znj4iu58bFlLH3pLVZs/yvPDL3G6yMhXIUKCde7VgAAIABJREFU5eY4vpxKwdCp1SvIWgFFz1GQkpTVPPF8GF/aS0yK4Ml4iJWj5NU0oXwQe8bJSNrBcGoUZ8bFZ9HdOLIuPo/t4dPwTj6L7GQ468CZd+HKu/BZbF/I8hYLygLevBtR8pOpxIhrIXwlD46cA0/BhSPvwJV34i+aP8LBUgCh4CVY8hOTg0TKIqGyQEwOmoDQSJDV46S0KMVKiqwep2Ck0BoFGh25Vx7ujOm0OhoLTz+Tax5dbbJF4vcAvu9jAIPfevyA61MlzqFgl5+ecR5Pr9nIl180mJjqN5ysMWEpkicnG+za+THbX3uJCy+8gAeffo5BsctrUROMmLFy/wObbRCbTTAZwLVv7n8/oW0lXbQOYP0s4Oep9UyOD5ym+XGNPneVu9e/zZkXX83pF13B6RddwdzDjuCMi6/i6aG3+NPqAebMncfzHyfY6K0xJLStk6HGt1jFqWmZKAdMv71NYtsUV/QEGJ1pANEs+x4I/kxmb3ovoQnYNlp9hlNijalevi3hMbaExthkAeApJrQHGMWpmLi2mSYS6vYi3TZbwpEBscvrsdZ+BfV3tnuHi295gOvueQRXIYZsFBjvanQ6ZZRajHjZh1B0IUous+xadOAu2PFJToslG8VdHGXFpuXMmTuHW+67lTdDaV4J13j2rWFmzZ7D8aefy7K/jLDp73/n2S3buPmRJznnkss5buFpHHXsCRx5zPFcdc8T7PCHGS1GiOs+ApKDkOIipLiI6l78ZSdh1UvC8BJUXAQVN2LZhVB2EdPN+wTZhV9yEii78JRMoGeCRDc+ybn/Mxfs2POj2PMjuIoO7BYQdORHESzQ55NchCyQGVY8+MsuAmUPUSWAMzNqfg9FBxHVS1DxENV8JI0AuVqQXDVIuRHB6MQpVkR+ccGFzJ2yi5pzMuf+8gJispeUIRDXA0RVP6LsMQF12YNPciGUPUiNGFo7gdpKIDVi5KphstUQMS1ASPET1wOkDNEiWAQSukDSEAgpXqKqn5Diw1M011JR8ppkiGKugUHZj6fgxJU3RS/23CiuvANHbpRdsc8OAsCD4weNBTabjaXrn8GRH8VddODMjnL7A39ELLoQJDchxcvqLSvY+EY/vpILd9HJZ+LH3PHQ7XiKLmJqEK2ZYmDTauzOjzCaKaRKlGHfxzz1/GMEyz4iSoCoJrDk+cf40PEeIdlHygjxl8928Py6pYiSh2DZS8IIcs+jdzEa2UlUCxBW/Gx7Z4iBV9YSUf3E9SAhyc/tD9yGL+ckpppU+KZX+vjLh9spGGG0RpJE3sPDT9yLXItR0gXejxe5c+kqHlr/Cku3f8hz297iqfUDXLn4QbbuFun/xMMV9y7l6BNO4exF13D/sg0IpRif7Porjy19zjRSvvI6+txVfnv3U6zeU7AAVpf7hz7itzffxo9//CMuvfQ37Nr5Eb/89cXYZsxgwY9+zKxDZjNn/qHMmDmTk8+7mOsfX829fe/w01POYs68Q7ln/Vusd2hccP0d/OLqW1m8/FVWjyo8/baXax5axjpX1ZoVrnloGU+/7aXfXWNrsMXTO0a59fnN03zBfnv306zane8tIg9t/ng/oyB22eCtcf51i6f1dp1z+e857KifmMrfQ2ZjmzGTxXfcRq0pI1cLNFslVvat4NPdf6VoGXwP+3ayYtWfef0vOzjxtLM5/OhjTP+0P97JrU8u57jTzmXGTDOz8661b7HOafDEa3tY+POLOO1Xl3PUsSfyk5NO48hjjmfGjJnMP+JITrngcq59ZDmX33oX0UKOfXs7qPUWr7y6jTfffYt9+8ZRayojvl3c/9A92FNOCkqKopxm2Yrn2f7X7TjSbuxZF++MvM8TS5/AnnKwOzXCcNbBfU8/wOs7dzCac+Atedn03iaWrFiCI2vHmTEtJe5+7C4CaRe5agxf0c2ql1ezfGg5rrwDf9HNnvhubr33FnwZJzFZIC4HWbNlNX2v9+HMOfAVPOwWP+O+R+8hpUZIq1HyRoJVfS/y7t/fJKPFyekJRryf89yLf0JvFIilAiZwfnA5z7wf7gG3JTvs3PLnTSbTZwGB3va1GMDe9hUt9s9b49LFj7HBU+0Jgk79xa8548wzGJswew8LpQRPP/MEE5YAZd++Jlu3DuBy7eHZZ57kj7ffxtL3Q1z32EoG/HVOPOs32GbMxWYzDX5PPf3nbBMbLHl9lFue22T1JpoM2eV3LWH5p2kLmNW4f+P73L7i1R7wWzMi85tbH2bNiEyfu0a/u8YdK7dz/8b3TfsWoc2z7/r46ek/59iTz+TEs8/n90/3c/MzG3li+242+ups8NV55l0f1z7yIhu8dTM9w9/kd48u57m/BtgsthgKNFny+gh/fH5TDwAOCS1+e9cSVn6WtoQfXR4c+pC71uywQGGLfpfBotseYb1DM8Gkv8kdq1/ngaG/8Up0nO2JSfpGilxx91O9aLgtoS63/nmIJ18b7n0Pyz4MccPjKyzTabMcfOOTq1n2YdgqF3d54nU7Nz23mddirenbd1e+dzzv374dfr9kHT9fdCU33HUPciVDs1lCrabo27KKJ194hDXbN/NeSuPvwkcsfug2/NIo/rIdr+Tghb4/8cYnLyEoAZaseZGZM2dy6R1PWNZQHdY7VE7+xSXf0/dp45TTf8ZFl13I8Scdj81mY+XAM0Q1BwHZweCbfazeuhxBduEru/AX7dzx4K2IpVGShpdM1ceGV1by+vtDxHQv1fEInsRn3PnIYoKqCfgc+VGeWrWETX/dhKswik9y8M7wGzy89EFcRbvF8Nl5/LmHeHfkLZN5lN288uFWVm5YiiC7cBftBGU3dz5yO7vCnxJV/biyo/TtWMfKzS8SVk3wN5zaxe0P3IpYcpCuBIhpPl7asZ4nnr6PuT2x2H9gs61iztyF9L20inRF4Ll1f2LHJ68QUjyEFC87ve/zzIuPkzIEEnqAXDXI0uVP4hA+pjaWpNQI88GuHawbfIFCPUSmIpCuCDzwxF0EUrvJ14JENR8vvTvE6q0rGM2N4MjbGU7u5pZ7/sBn0c9wWarn5UMv0vf6Ovak92DPjfKB531ueeBWPgp9fBAAHhw/aFgM4E5E2YMoexHKHoSym6DsJiC5iGq+Xi+Hu+DAaamz3EUXmUqEvBFBaybR6wm0egK1kcBopVHqCfKVMGo7TaEWJS6bdHe6EiJVDZM2QsRVP0kjiNxKYXQylBtxctUImUqYjBEho4fJV2NkjDCZatQqR/tJakESVp9iQhfJGSGK1ShSLUa9m6fayVBpxOh/92/87KzzmDV7DjabjVmz53DkMSdw7MlnctSxJzL/iCOZMXMWM2bO4uxF13Hzsxu54LrbmT13PhdcextnXnxlL6zcZrP10gQGRKuJ3JHj4t9ewbHHHcM7f3kDwyjx503bmTv/MB5Y8zLPvvIJf34/yJo9RZZ/nu1lf27w1NgkmAzFkNhig7fGOqdBn6vKOmeF9c6KCfqcFdY5KqxzVlnnsG47q6zaU+7FR6136vS5dPrdFfo9VTP6alpT+r9gkIKmdcyq3YWeYGT5p2kGhTY2m43L/3AXx59m2orcs2wjtz21nMtuvotfXnSRaeVTSaIYOS65/HJmz5nL0GicMy9cxLzDj+T0i67k9pWvsuzjGP0ugw2eCusdCit2lVhv11nvNJlNk92ssnqPxKMv7+TGp/o48ezzmX/EUVz3yHJe9hTxFjR2ZiScuQLuoka+UkWtSuSVLFEpTFwKIxYEsmqSnJwkkY+QLMUQcyJpKU6+nCKSD+FKuxhOjeIquHFZjJ635GEkNYKv5MGet+PM2xHLXsJlH/bMCDuTuxjNjeIpufEW3fhLboKSF1Hy9k4+kmqQuCISLJnGtFFFRLT6dcLlAKGSn0hZJCaHTKZQFglJAZJahHBZIKlGGPXvxGazceUN1/PUsufYLLanx3v9i1Lvd0vDY9/a3h0W3fYIP/rJsQz73IxPGKaIaUynNaYxNmF6F05M1pjcW2dissYnn37AYYcfzp333MnffFG27Amz6OprOelnJ7HkyUdZfNutLFhwBB/t3GmWUq336fdUeszXFHNmgrPaNNZvaq6ztv/UPtDnrrLR6g8cEtq9E6xBv+Wd563R76lOLyP3TJTN1oihQOOA0m+zB/w2CSYgHPDVGQo0TFuaKdZyWi/hFHN5YFm5yZZgh5eipiJ305TIQ+wwFDQFJVtCXYuRn1JGW5eBNtsilno3Ynr4vZ2o8txHSZZt/wt9b77JmtdfZ0dYY0Ngf6TbBm/tu9tb7PKnt93MmX8o74byBKUYSSXEp75PuPR31zFn3nzmzD+UW198FV85QFB2UWwKZGoBYoaXZMXHx+6/cP0df2T+EUfyh+eGeuBvSpSyOdTm+nsfYM68+fzq8su546HFPPH8Q9x2z438/parueOBW3h39C1cRbspElFcCLJZAvZK5gwqLsKqm7DqJl31UWwKpKs+MlUfUc1DquInqnmIaG7r0oO3ZMdTMoFeoGz2EMZ0D9man0I9QLEpUmgIFBsiqYqPhOElpnuJaB6Sho9CXSBTDZA2AhYr58YvuXDl7Yymd5vlZsVNoOwiUHbilRx4inYSho901U+hLvLIkru/3y7qybvJ10TLCFsgrvtJGOYMKibzGNN9CGU3cd1PUHZbDKNIuRlGaoZIGH7yNZFUJUBMN70URYvtFGTzczkLo7iLdkKKt1feduZGcFm/S76SC7Fsqql9JSfOgp2PDwLAg+MHjgU2mw1PZpiQ7DV3NmtH9EtuvEUHvpIDR3YEZ8FBXBNJVyJkq1GkRoJSNUKpGiOtihSrUfJGCLUeQ6vGMKpJmt081VaGZifDw4/dQ2eyiNpIUBvPojRNgFjpZhnbV6IzmUeuxWiO52hP5NFqCeIlH8myQLESRa4nkeomGMxa+ZVx1U9MDZCrRSnVojQn8hi1IKOBXVx6/Y3Mnnco1z++kuc/DLPOoe4PqBcPzMXtmIuOdf+qXQUuv/NJrnnwef7w7Ebu2/gez7zjZb1TY3NojDV78vS/uoPnXlzK0UcfxY03XYdq5FGVAm+/8yo2m43lm7ezakRhyF/vgb4DZ7+7yrZgh62hLkNim41e83nresCv2gN9ax0WCDxgXnzLQyZQdFXoc1XY4K2zSWix3qEy6KtZZcTv+pB936LyfcDwsjue5OxF1/H7Jes4+viFHDJ3HseefCY2m42TFp5IqZrCnYhy57JBfnTc8Zx6zs9Zt3kjQ55yzyNuo6/OeofK6uESL36WY9nnBfocOv2uCuudBuucFdY6rBK3q2r2OfoabPDUuX/j+5x49vnMmDmTY085m7MvuJDzf30BF1x+JU+vW0lel9m7d5JwOkCkECJSCBLKi0SLEaJShJycIF9OEcwGSJaiZIsJkoUIIxk7I1kHo3knzqILb9nHLffegqfkwVU0m8d9kgeXpRT0l314ZT/+sg93wcUn0U/NlgjZbwqflAApVUQseYjIAaKKgFDy4sk7CZS8+PNuQkU/gZKXkOQ3xSUlH+68C3fehStnNoD7Cx42vzbAn5c/w4IjF7D5nb+wQRin35qDByhBh4JjDPXixQ7cbmPTwaFogp/fPbaSQ+bM44p7nyVbNRifMPO7p7wwxw5IMhmfqFBpSry8Ywu//8PvmTdvHrNnz+b6319LqZzqpZr096/hiCOO4KNdn7MtaLJXly5+bL/f35QfoL/Z26YmEKz1yr5T230KAPZbcWgDFgjr99Toc1Xom9o/rL81X8ucGy2AOeCtM+Rv7O/5E9tsDXXZHGyzRWwx6K9bbRNT5d79lwNTghG/2dfX8xX01RkMNNgaMsHftsh4zyqm9zsS7PYMmjeFxg74fWlboo8u26LjvePq9ViDvo9GOXTBkSw8+UTmzp3DzFmHsN0RxC5X2BY2ge+lix/7XtA/KHY57VeXMXvufM6+7AZOPu/XzJ47n19cfSvLP8/z1Na3mX/EkbwuZIhqTgKyG6/Va2fPj3LljVdy5oWXcN/A+3yQNRgp5XrbbCjYZcin8szLb/VOeEOKnWTFS9zwkqz4SVS8yG2BiOomVfURMzyENTfukh1BceEu2fGXnQQVFxHNQ0z30NoXpTYRQukEydUD1CfC1Cci6N0Qcd2Lv+wgUHb2+v3iuod8I0Cg7CRZ8VJoBMjVBYxuiNpEGLUTRG0HUTohyq0g1bEwhXqATE2g1AyitINEdV+vpHzXQ7eRrQUoNASSFR/ZWoByK0i25idp+JBbQdR2iC2vr2XO3JO/ZRe1kIFXVxPVPBZIs+Mq2QmpHkKKm5j1Pn4LyHlKToKKh6DiwVNyIMguUpUAqYqfgOzGVRhlJDfMSG4Ed8kEuq7CKJ6SA3thlNH8CGHVQ7oaIGkEeqXm0dwIzvwo3pKTmObHX3Zhz4+wO/75QQB4cPygYTKA8Z14i07CitfqcTB9l3wlhynDz9kJqgHUZpJqO43eTlOuRdFbaYxmCrkao9nNozUTyJUYajWBbqSQylEkNUx7rEBRDlFURLRmnOZ4llonhdJKUG7G0VtxytUolXaGajONUo2hNVOUG0m0VpqcFqJgRJDqMYqVMPlKiLzFIpYaMXKVkPk59AB3/2kpcw89nHN/exMrPk3tB3viAaCo1zv3rf4567EDbRwGLTZiU6jLgNDijF8t4rDDDuXmP1zP9h1bqHVKdMYMSkqCD/72JjNnzmT23Lk8/bbLZCzcNdZbJdx1rkqP6ZsqHw34zfSCPpfBWovhW+uo9IDf9wHAVbvL+0Giy1w8+z3mwjrgrbE52DHtI6z/ZUjssi38Pf2A/6rP7IDrg2KXLUKNm5/pZ/bs2Yy6PubiK69m1uw5HHbU0dhsNubNn4/NZuPC629nMNBmg6fC2tEyq3YXWbmzyFq7xnqHQd8Uu2nN9c4pEFChz13tsUUbPHUGhRYrP89wb99b3L7iVe5b3sfiJ5Yye+5cXvv871QaBvlymnqrSrYcp6zlKKkZJDWDpGdJl2Jk5TiOhJ14KUyunMCf87M7Ncxo1o49Z849iT048g4+S+4iILlwldzsyY4wnBnGV/ayM7WbXaldfJ7cyUh6D/aCA1/JRVDyIEgeorIPoeRGlLz4iy48BSfOnGlmGyh6rPs9OHN2fAU3rqwDX8HdA37egttkDmWRUDnA039+grPPPYuHN+zg3ueWs+yVt1lvl9kojPeSXL4XwH8PIzjVA7r0vQBHHH0sty/t6+V51zoynXGd8Ykq3fGKmfXd1Wh0FaodmVItzdufvEEg6cZoS3TGDTOmcML0OOzvW8WRRx7Ju6MeBsVOzxZm0wEpG1Ns4AZvjQ0WgFt/APN34OyzgKC5bxjmiZKrYpaJPbVeqXcK9G30mtcHrJOfwYAF/oQWW4JttgTbDAXqbBKaDPhqbBL3W7pM9Q9O9fcNBlrThCsbvHWGhDZbw2NsDU+pc7s9gGfGyJk9lqaAw7ze76703mODt2YBw/39mNsjNU4+9wL++PAjjLjemVZmfejhW9kWMn9rvpcBPGA+866P6x55kcXLX+LudW/xp4GtHPmTY83y/PmLuOyOJ/HIKRz5EUuRawoojjvxOJ7e9g5bQ21ccp4P0xJDgrntfnntYmbPmc2Fv/kltz1lWgDFDTdR3Uui4idheBBkO8ZYkFTFR7LixVd24Cra8ZbN8q+ouPCXnWRqflIVs/ybrfkwxoIUmgK5mp+qBeKq4yHktkipKZA0fIQUF6Jigsdc3U9tPER7X5R//48M3X0xuvuiRHUPguwiXfMTUt3onSCVbgi1HSJfF4hqHrpfxMk3RAJlN0HFg1AYJVP1E9e9lFsClfEIasd8vtYJobaDSC2RlO7h3PMvYM5cyy5q7kLOu+BXpHUv6aofuRVEaopEVNMUO1sNIDWDFBsi+bpAwvBRqIuIsstiQt3ErJ7IciuIV3ISVj2UmuZzPEU77pLDYlNNwOyVnLiKdoKKm6Thxye5SFXMnOZA2eztTFll65HcCJ8f7AE8OH7gWGCz2XAkd+ErOgiUTXm+t+TCnbfjKTjxl5z4Sk6qrTTVdhqjkUQx4uTLQYpGGKOWQlZi5OUgWUWkqIdQqjEkJUrZiFNpZNCrCbRaDKkSoVgNU65H0ZpxKu0URjuJ1kpSaScxmgnkapSiEUZpJJArUSqdNEY7hdJIUO9kUWoxtGYStZmkUA2jt1M0O0le++A1TjztTH584ik8vPljNvXA3v5g+qFp930LAE673Zk2t0TM8szPL/ktp51+CuGkm+a4QmtModlWyStxCnoSo5EnUc4zZ+5cHn/dzppRlfWuCmsdBuscBquGZf744issWvwEd6zczkZvlYFAk7V2jbUOowf81v4L4Dd9HlgWtgCVa6rUVmGDp0qfy7AUxJZvmtD81v/8/SBwo2Be7/M1uH9ZHycuXMisWbM47qSFHHXM8Zx0zq94/sMwv77pXrO0fsghnHnJNSx5w8mLn2VZ9mmWFbtKrB6WWWfXWefQWevQWefQWOfQWe806HMa9Lkq9LsM1rsMi82s7i/r+ZvWgtu2mJsOf42WuOauB5h/2GHc/8JKEuUMJS1PpalT1nIki0GiOYFITiBeDJItx/FmPDiTDoScF3fKxa74Hlx5B0k9QFwXCEoeIrIbb36UTa+u5Y5HbmfZ0As4LH+xkdwII7lh7Hk7PsnNnswwo7kRPAUHdqtE4y86ceft2LMj7EnvYSQ7zGh2BHtulJHsCO68g+H0MI6sneH0MN6CC0/eRaDowVdw4y968BZc+IoexLyXX13yK84690x+e/VlnLjwRGbMmMFZ557NEytXsz2oMyiOsVEY+x6rn+nb851Em7+mWgwGu9zb/x4/Ou5E5HoBvVnq5XxL1Qx6s9iLPzRaJWqdMgklQkqPkTUSFKopCtWUmVs9XmF8vEJ3TOe6G67lwkWLGOgdX+3eNpsCQht9zQPEIKaYaYr56/W3OveXhKfYwKm54YDr+xnA6Rm6PaNnsc0W68Rqk9gy93dL6DF4gGJ4quQ7EDCNn/u9UyCzxpDQYkuwY5kum0KNqXLu1O/I1vA4m0LdXjTbQKBJv7ti5vxajKL5GdpssQyizaSREjabjdeG7SS1EB/ueo0R73uccNLx3Lp0IwPCt9o2/jfz4S2fMPOQ2Zx17hkcc6xp2bPsrc+YPe9Qnnz574Q1H+6iHa9kZ/D9V5g9ZzYv+/LW9jKzg4eCXa649xlOOPMXfO58g4svvQCbzcZ9D91KoeEnpntIVrxILYFszY8gOwnITkKqm4ThwVd24pMceEqjhFQ3Md1Dvu6n3BZIV/1kan7y9QCpio9SM0BjMozaEcnX/eTrfrI1P2onSLklEFJdRDXzNeK6h6ThJVf305yMMPZFjNbeKPlGAL0bwugGaU9GyNcFOnujVMfCqO0g6aqffF0gVw8QN3wEZTcJ3UN3X5SxL6JMfBmnPhFG7wZROyGakxFqExHzshvkzXf7eerZ+3jjnT5q3RDdvTE6e2N098Xp7IvR3htDbYdoTkZp742hdUJUxyMobZOZLDaEXuk6onkoNQWKDRPkTjGO1bEIyYqPmG6CyVxdQO9GEBWTfEkZXtqTcdwlJ2LZRXsyRqkhkq0FyNUEYpofUXazK/rpQQB4cPygscBms+FK7SYgm6yft+QmpPisBclNXPUglD00WhmCmVFSZT8lNYpUi1Pr5JC1GJmSH1mLI+sRsqpAQQ+RUUQKWhC1FkWphUmpPhKKn3w1RL4ikq+GTJNSXUCqhdHaMUrVEDlDJGcEKRgh5EYcvZ5EryeptJK0xvIY9SRyJYJciVDvpgnGdnHtLbcwe+58rn14GRt9tf0KyGmmrGbk0rQS8LeA3r+y3dgUGuPZvwSYN28e1WqafV81aHV09EqRnZFPydeSFIop3vrr2yz4yfHYZszguQ9CvLizyOaA2dO0akTmxLN+wyGzT2HGjKeYOWshhx+9kFuf38KqYbnH9k0Df84Ka+yGBQ6NfwEKzQV0vXM/mzJVLut3m4tkv7tCn1Njg6fCZnG6Ee104DfGem+DR1cNcv0tf2DBUWbqxM8vuZwFPz6Ocy79HQ8OfcAGX50twRYbPRXu7X+bJW86+PNHCZZ9mmfVHpl1oypr7Rpr7Dpre1NjrUPv/S9rRhWrFKybPYKuirXg19no3Z/usNE3ZSzc6JU2V27fxmFHHM6K7dtxpAIolSLlioI/68WXduFNufGknThTdrxpD860k5H0KP6s2/QALLhJGkEa4yly9RD1TpRfXXjRNAXg2b84nxHLRNZRsOMuufAUHaalhORiODuMI2+WZkayI3ye2sVweg97MntwZPcDRG/BiavgYHd6D+6CowcSd6Z2MZwZ7inpA5KZRuApuvGXvIiSn0DJhyD5GA3vZuWGFzn19FM4+dSF3Pv4w6x+4z1eDtXpFyboFyfoF8bZIIwxKE7Zw4zxRsxkwta5avzyqpv59aKLKVbNVJNCNUVGj5PSY8iNHNW2hNzIoTbyqI28CQ5rWdJ6nFwlSVqPoTULGK2ixRwaXHHV5fz4J0fz+J+X8lLAoD/QtSxXukyla0ypYwct9e7UNp4Cdetc5r7b567S56mx0WuaSZv7rbUf98DfgXFvjV6JeUhomWkcwbbFAjYZEiylr9juJYb0mD9fvecT2Httb53Novl9bT5AKTzgb1iMeqf3u7I5PN5jOPvdFfpd+jT7mynbmM2hMV5NTJp9fcIY/f4uZ1x8FQvPOoeXdrv4+p8pLll0AbNmzeSDz3awPdbqtaZ8t9/zu7Y/ly5+jAtvup+xr1Jc+7tF2Gw2zl50DT894acce9xPaOxN8uTTdzBv/nxmzzuUqx54gQFhzAKn+38Dzr70d9zwVB9qN0BlPERlPEj3iwjVCbPsKneC5BsBcnUfhUYAn2QnrLl7/X7+shNRcRHW3OQbfmoTYQqNAFmLCTQ6QfINP+mqj6jmJqK5ERUXWldEaonR5VjqAAAgAElEQVRILYHGZISv/pHkq38kae2N8o9/JvnymyTtvVEmv4zx9T+SNCdMIDj5VZy9XyeY/CrON/9MMf5lnH1fJxj/MkFI9ZKu+Ol+EUdphxAkF6VGgImvYox/GePLfyTY+3WC9t4YtfEQnS/ifPPPJHu/jLP3yziTX8bZ+1WCfV8n2PtVgi++Nq9/9U2Sr/+R4ut/pPjHv1mX/0zxzT9TfPlNkn3fJBn/Mk5jIoLRDVsA1IPcDmKMRZj8KkmpKSDKLpxFB4VGiM4XKfZ+nWbvVylakwnkVpiI5sWdGyVhCBjdMF9+nUJthxBlDxHNR7ISQFTMErQ/dzAJ5OD4YcNMAskM4yo4EKwSsDtvx110I0huBMlFRPOTKfsRJAflZoJ0wUdej1DURLRGDKUWJacJJGUvWUMgVxVJGwKlephCJUBGFxh8ZS2FWgi5HaXUiFCqhShURHIVkWxVJGMEyFQCpA0/Sc1HUveRNQKkNS8JxU9GFcjIflJlL7GSm6TsYsXAag4/6mjO/PVveWmPi62hJhuFrsWOHNCQbeV99oxdDzB0PbBx2zRy/S5DuDk0xrUPL+Oq666lPV6i3dWo1orEJYG0EaWgx0kXE5xx1plcfPO9PLHDydK/xVmxs8hzHyVZvTPLA2u3W3Ya+3tMbLaTmDnrGE446zesGlb2gz+rVLrRW2PQV2PQX2coYKYarHdVue3FV/YLRA4Egd8uq1mL54CvYWWPtnosyUCgzYBleDsgdun70MVN9z7CjBkzppWmZs6cxTELT+f6J1bT76nQ5zZYM1Jm5a4CL36WY8XnedbsKbN+VGW9Q2OdQ2Oj2yzfrXdMB4BrpkChQ2edxYpOlfumeiMPLO+Zlh71/ZmygQabxDYvhwyuvfNejjj6GG569E/sjgmklBSpUgRPwoE37caeHMWesTOSGGFPapjdyT0Mp4Zx55z4c25cORcjOTsbXlnN/8fee365Vd573wJjTKghIT0khCSkBwKp3Cc54aQDCWmkd5p77xXbBAKkQWjunj7qve291XubkWZGo941tjn3utfzJ3yeF9eWZpzkXd6y17qWNBpZHnvtPft7fdvvwDM7/8X/s/rq97H7+FbsST1yTuzWzXEdusgUuvAUtoQAhLrIFJPBCQxRLdrIlEjzhaYYD4ypTOAE06Hl+orJ4MQQKOrCIlA1GZxkKjiJIaxDp3Z/aUPTOBIWHAkxpN6TcuCKW9l2YDPfeOBr3PyWm3n7O9/OXZ+/hy98+V6+dv+3+MmjT/CMa174xRwldo2F+PWOg7zjPe/hjo9/BL17AiXjEYPos17CMwrhGZnwjEJUZfmisz4iMwrxvJ9EIUAs7xcjH9NuQjmJeN5PZi5MvhgnNRPk2b8c5yMfv4Ob33IzP3/kEdYefoa/mPxi46QmbY86lytPnrQX1Q6+ORFi0s+yW/WDDoJN+w3Lvr8hIziQgo0rzo3hCDixjtoXhgDw8L8ri1aTyYPXDphUVlI/y5EVM4XFzyHOx4PmwrBCZjA+7oCpwAHTrApCB57B5TLqI7Yif5KqPOOtcsRRZvv5ID999Pf84g8/51eP/ByNRsM7br0NW0ph5x+P8Ovf/ZAbbriOKdc0h+2lf98D+G/Y3vt+tZnbPvV5dj/3PG960xru/OK9Q0b+iiuv5OZ3vJs1117P7/40yuN/nWKvqcgx6yzPmMOcDOV40r7Aur+O8bG77uRrDz1Eo+um2PRQaXmYqTpJFO3EFuz4Zs2ECxbSJQfOtJ7ovHXI2qVLDoIFC/EFG9F5K56MkUDeTKhgITZvIzxnZbbiZLHpodh0kyjakHJGovM2Egs2/LMm0ot20ot2Wl2ZWkei1vYSmbPR7cnUWl5KDTe1lpdyw0O9LVFpSbS6yhAEtnsK5aaXxYYHb9aCb8ZKdM6JlLXy1PO7aPVkmh2Jdk+m25dpdCSqTQ/dvkK3r9DryfR7Ct2uTK+v0F/y0e0rdHoy3Z5Ct6fQ6Yjnvb5CrydWv6/Qasv0eor6foVWV6bZVai1JWpNifm6l4W6RL7iodlVaHYVMiUXnowZX85BIGel1pCpNyQWql7icw6MUT3mmAFX2kwo78CZtmJLWdBHdZhiBixxI3LO/AYAfOP4j48bNRoNrpgWd1KPOW5Ar47Escb0OGLTOFNmgjMOPCkdvlk7yVmJ9KKfmcUQ6XkvkZwdf8aMP2slVnATzNkIzthIFyXCOTvxgmD1nn/lKHLajJwy4kno8CR1yBkTnqQBT0KPKTKNPW5EypiJzrnIlT1kyxL5qky8YMcZ1+FLG7BFpwnPeThwbKf6y24VX3nw+xyxFdg0kmTf317i6OkRjjmEbLjxhIu1R45zzCWkmgErOBgIvzzSqSiA4gq5+PAAALrKfG/LH/nwJ+9isSoAYDLtJ78QJzUT4Njf/sbH7v0GGo2Gu77+AzadCbBnIsG+6Qzbx5NsOhvmzm8+zBVXXJ4y02geQ6PZwVWr3893tzwj0rG6GfbpsuzVCdC0YyLJzsm0AIEq0/Gzg68sS78rAOBAUhtIaQPf1PFBH6B9UXiiVL/UM64FnnMvcPtdX7oM9H3gzi/y4IYn2XbOxx5thp0Tcbac87PxlMTGkzKbzwTYMRpj12SKHZNpdkyk2DGRYudkml1TGfZpMyKZrMuwX69Kv9OCFRTALzv8t670BO4zXG74P6RKh4MyXiELz7NrROGYIcDP9/+FW957Ow/86rd44w78MQeBrBdXxIo+qGM6MMV0QIs2OI0xqmMyNIU2NIU+MI07bMYdd7L32QN8/1c/+LcJwMfW/pR2z8d81UN83k6+4iZf9hAu2JFzZsKzVkxxHdPhKUb8Y0wEJpiOTKNV2UG9urQhMSnBGtNhjesxRbXo1WJbQ0SLKapjLCAqHrShaaaCU+gjOiwxI4awTkwGiBowq3OwbXEL1qiJ/c8fYMPeDTyy5RG++dA3ufqaNTxlFx7Q+361GY1Gw4c+egfb9m3GnbCjpN241L5CJeMeDqP3Zz140058WTfRWYXYjIIv40LOuMjMh0jk/fgybpS0i1QhSLoQJJEP4Mt6xGi8pIOHf/kjvvHAN/jwR+/g6jVr+O2Rl9hnFRLpALgcti2qY+FEQni/QfXGrki77x34/gz5YdBpcD4PQ0+DEIh5edbvYcs8xxxFnlRHvx22iuTvoPB5UEtzwCRqZAae2b36GQ6aCsIuYRDX3i7VknHAmFe9gAL8DboGRbff4rCIer9ZhEWedpf5k7fC83JVVDNZC/x03QbedN213PvfX+CB793H1Vev5o5PfgKNRsNb3nUrD256Cl8+yK9+8z3e8563s+/wOh57+rVlJvDfAL/B97ZPZrjq6jVs+ItIkX/nu/fxxfu+wlWrr+bOL3yeg0/t5DmDiw0nZX68cQ93fvFernnTGvF3v+UmvvI/93L99dfy9W9+iXOjf2Sx5aVQE8xfctGOP28mW3aQKTmYb3hIlZwE82ZCBTPFppt6x0ut46XUdFOoOlFmTATzZqLzgnHLlp0Uai6qbS/tvszFSz4uXPRRbXuZrbhYqLuxpwy0ejKLTQ+dvkKzKzNbdlGse6m3vCxUXRRrbmbLTlpdhUrTS60l0erKVNtidfsK7b6PRldCytlwpy3YYzoWKg5Gxp6m0vTQ7cn0ejK9rky3K9PpSPSXFNodiaW+woUlRX300e8p4n1tiV5XpteR6bRkuh2FTlum05ZpNSU6bfFar6ewdMHHxUsB+kt+am2JXMlFqSHA6VzVTaRgRc6amS27WboQwDdjJbfoxpuykJh3UW4o1OoeGg2Z9IIDXWASY3gaKWUmlnMQyrnxJKwoMw6UnB17XI8r+gYAfOP4z44bNRoNoYyOxbqbdNGJO23GlTIQL1jFCKGYHldCiyttJjjjIpR1EJ1xEcpYiOVthGatBGdtxOdchGas+LMWfFkroZyVQMaEO2nAEddji+mxxfW4UiaUnIVQ3oYyY0XKmlByZqJzVuJzVsJ5K4EZM/4ZC3LGiJQ2YEsYMIWncaXM+GccRPNuwikrr55/jmf/cZh3vvsd3PbhD3LzW27mPbe+k+uuv5avfO1entQHeNP1N4gKlxHzcIrAP8vAT67wwq2c4XnELsIfRxwlNp9WuHLVVSRn/OTyYTIzAUJJL1JY4qa3vo27v/UTfv3UOR57wcqmk8qwmmKfYZY92hzfWX+UVatv43IG8ANoNNNcceWj3PvDRzhoXA6A7JrOqfKv+Hq/YZZDlsJQnroMAKrhkQELeMBU4LBF3JSOOYocdxR52l3mKVeJF/1lXpby/N1b4M+2FLunU3zttzu4+9s/5YENx/jdc5NsOOFl00mZLWd8bD0bYNv5CNtGYuwYT1wG9HZOZtg+nmL7WJId44OVYOcKQLhzMs3OoSScYfe08ADu1abZM2ACB8BX/88ytmCDDhhmOGCc4ZBlnt8/NzEEqu/98Mf48d4XuOrqq/naDx7ipMtJJBskkfVjj5jRBbWi9iWsRxfWoYvoMER0GCNG7BEzUsyBHHVy8Pg+Vl/9rwnAF186yNKSn3ZXoddV6HR9tFoytbpEpe6lWPGQXnAQm7cTyVtIztuJF2yk5u1kFpy40xYM4Sk8aROulAFzdBpzVIspPIUlKiYiWOOCSRzxjzHiH2UiOMGECgYnAhPowzrG/OOMByaZCk6jDWkxRISMrQtpmQpOow/r+eNrT3Pt9dfxV3eEv3hzbDzh4prrb+Lh9ZvxZ93IaTfupB1X0o6cdg0BoDMpRi26U3aUjItAzkMsrxCekUShe85NOOdFzjiR0048KTvOuGU5uBK3og/pGAs48WRCyGmJ9TvW8vZ3vZPfblrL0/ac8JSu2HgJT90CB1QQNkiEi5Vj18AmoNoeVlbF7FWDVHt0OZUdnB3WxByxznPYUhDLKgqhD1vmRXBE3QwN2MRB+GiPyj6v/OyBTWGPbmZYSzNg+w6YlgNX+wwzHHUs8qxUUatgFoezew9ZF7jlvbfzvg9+kCndnyk1PcwWRd/jL58a5ecHX+L+X/yOK1ddxQtjp6g1vTz/111cc83VnDaMctBWHnr1VoK/PRNB9r3wMr9cv477f/gAGo2G3Yc3c+rccd72dhHKuuMjt/H1b3yRq69ezYfveD9Xrb6KL/2fu9i06w+MGF8gOqPlsQ0/53ePfB+D/SXyNRfJoo2Zip1yy0O2LEIaCw0BBgs1FwsNN6Wmm9i8lWDBTGTOwkzFQXTOQr7qJFu2E1uwEpu34p81kyzaqLWF5Nrpi9XuybR6Es2uRLsvk1q0U21LLDY8lJoeqi0vsXkbiQU7sTkb+bKTuYqTUt1Nqmin1pJotmXKDa9g43oCwLW7Mv2+QrEp402Loudy1Um3I9PuSHTaXnpdmU5botXwiNfbEv2eTwC9jsyFC34uXvTT7/vp93x0uz76XR/dtkKnJdNpSnTbAhQOQGCnpa62+IxeV6bf99HuyDQ7AvgVKi7mVXm70ZGZrbjwz5gJzNpxp8x4EibM4WnKZTfNpkK5LpOvePDP2nElzHiSFtwxI/60nXDSRjBpwxUxEcy6sfnfGAX3xvGfHaIGJjhJqGAnuWAnMGtmoerBm7FgCo7jzVpxZ6yE815sET2OqJHYrJX0vI1k0UWs4CC14CKYs6KkTXjTJoI5M4k5G4EZC96MBXfSpE4MMWJPGJBzViJzLrwZM8G8YFN8OTPpRYfqmXCSXXSQmLeRr7jwZo04knrknJ3wrINc2Ueq4MEWnCA+Z8XiH2fvse0cfHonoRkrSlzH5750N9fd9GZWXbUajUbDj3//m6Ek9aRzubrhyUFSTy1TftJx+RSFlRLxTW9/N3999QVycyFSKYUHfvwwt37is6y57gZ2jkdZd8LLupfdbDsfYftYnB0TSRFsMBXYNhrnXR/+HFeuuhWN5g8q+LsPjeb/46rV7+eH25/hBW+e404h1R4w5jlsVmUy3SxHzAX+6CrzjLfCcUeR3dqcMNKr1TF79aJG5Ul7kT95KzztqfAnb5WnPRWedpc57lzkqG2Oo8Yk28ZibB+Ps2UkzOazAda+4uaJl5xsek1i29kgeyYS7J5Ms3Mqw86pDNvGU2wbS7FtLMn28TQ7JgQA3DGeYvt4cgj2dkyk2TudZs9Ukt1TKXZPZdinn1mWgKcEANyjzbJfP5CAVwRZVjCYogZkeQrKPsMsB81z7BgNs+ba69FoNHzuvq9yxL7I2pft3Pi2d/HrfccY8ctE0jJKzIUtasIYNmKMmNCGdKLORZVYjT4tiWwAT8iGEnXw8U9+hquvFgnA1Ve/j4988tMs1Fy8/nqApSUf3Y5MV/3F327JdNoK3baPdlOmVHZTq8u0W346LT/ttp9a0483LTrBjJFpbHEtVnUMliWmxRIWjKCYmTo9HGU1HhjnnG+UEf8YY4FxJgITnPeNMeIb47xvjKngFCO+MSwxE8aoAV1YhzlqZDowzU0338TaJw/zkpziKeccD248yvs+9mn8WQfBnBcl40LJuHCn7LiTNnX8nRhEb09YCOY8JOf95BaCxAsyoRkvUsqGM2HBk7IiZxx403aktAMp7cAaNzMd0jIV1GKOGdCGtNgSVtwJO49vfoRb3/9evvWrxzhoU68t5z9fVyUOmObYa5i9DACuBIEDC8GuacGKHxh0X6ohor06cT7t0+XYp8uyT5dmny7Nfn2WfboM+3UZ9mozIlGsX04YD4Deyo3TXsOsGljKsXNwrupmhsBv52SKHZOpITjcb8jzvFzjuLvCkWFjQHn477v1E5/jwd+vJ1+TqLQVJPkEGo2GDSckrrhyFTfc/BZuuPE6gpERWj2F3pLM2nUPc+ddH+MvLx1lTHbyrEf4Ajeflnj3R+5i1aor+cAH3s1nPvNRvvf9/+E3v/8+1YaDZk/Gn9WRLBiotr2U2x6c/jMcOL6JWE5HpuQQgYqGi3pXojFcXjp9ie6STLHpodWXqbS9SDkjhZqTzKINZcZIomil2HSRWrQRKpiZr7tYaLjIlu3M1VwsNj00uhLhOQuFmptqW6LZU2h2JeodSciwXZm5qpNS00Ox4Wau5qJYd1NqeFiou6m3JeZrbkoNL7MlB7WmRKMl0ev56HQV2h2Zft9PueGho27Kui2Fbkem05VpdBRCBTuJBQfNpodeW6bd9grQ1vTSbnhpNyVaTYlGzUO/66PTlljq+1ha8tHv+6i2JFodmWpLotHw0G0qtBsS3aYswGBDotOU6DQk8XugIdOuy3QaEs26l37PT6/ro9FWqLe8LPUUfLNm5qpOWl0hQ4fm7DhTJmxxPaaIHl/GRCDvwhTWq5OGLMhpJ1LShjGoRRuaxhjWYo0ZUTIOYnmZTNaLU9a+AQDfOP6j40aNRoNVmcQVM2GPG3Al9diTeiwxA8GCCylpxJexEcrZCGStxGdthGft+HNWYnkbsVkrwZyV0IyFxJyN2aKTQsnDbNkjyi+zVrwpE2OOU2L4dsKEnLXhSopZlY6EAWfSgDstij/DBSvROSu5kkP1mdhJLrrxz9ixx/Q4Y1oCWeE31CljxOZshPJWHEkTrqQZJW0gX3YSzJq4ctUqvnz//ew9upEPfvJTaiKwOLwJrbwZHV6xLh+xtpwc/sy3HuY7P/klpcUoj294lHfe9iG+88QBHv+7gT/81cyGVz1sORdm05kQm86G2XwuzJaRKNvHE+zTz3DQkOO7m5/i+pvfyRWrbkGj+QOrVr+fd9x+N4++aGfjaYXtoyH2aVMcsojS2gP6DE/ZZjnqKPK0a5Fjtjzbz8occyzyrLvIc84Cf/fm+aN9lsOmGY6b0xzSp9ivS7Ffn2XXVJId4zG2j8cE2HvVw/pXPWw6KbHljJ9t50JsH4myfTTB9rEk28cFe7d9PMnOiSTbxhJsHVMfRxNsG0uydTQ5fD5g/raPJ9g+Fmf7WILto1F2jMXZOZli52SSXZMpdk2m2T01kIEzIgCyUgpWmZg9quS3R/+vnsbBDNlNp+Tl+ozD/+BJ+yJf+802rrhyFbd/+h5eNY6hJDwkZ6J4IlZsESOuhFmdJGPAlhJdfVPSBOeNp/HF7ChRB4eO7uNnv3iY/cf2MSZP0uxI9FWGoNdS1F/0Mt2Gj05NplNXaNUkOg2ZXstHt6XQbsq0Wwqtpkyx4qZY9ZBccOKbsRDMmXCn9HgzRjxpA9aYFltCjL0yRKcF+xecYDw4znnfCGfk84z5xzkjn+eE9ywnpHOckUc46T3LmH8ca9yIOWYcjmPcvG8Db33bW3jRqOVpV56Nr9q54oormPaPq+OllpcjYcGTsuFJ2XGnbPgyYrC9J2nFk7IwHZhEH9LiTYn53u60FW/ahpIR4x3NcSN6VZ7WhrSctZ1GF9KhDU4x4R8jnlf42e9/yT3/cz9POv45bLV4GRN40FRYroBRvaFDz+jUYOOQURnj5dLzoSxsGJwjM+zTzaj9gcKHesAww35DjoPGGQ7osxwyzXLAOKu+PisS6Wo35b6hxJznkCoX757OqGGsDFtHxTWwfVxYH/bocjzlEnOGByGXI/ZFnnSWOGBZ4EOfvocf/OwhUkWJYy/9nVvv+Bj3fO1BfnZEFPge+uszpIpesotumj0fSxf9lGp2fvDDr/H2d9zC5/7nmxx1LLLfWuLDn/0yX3/gm8wUtJRaHpo9mXpXpnfBR7Pvp1DzsNCQyJScNHsS83U383WRuJ2tOgkXrGRLDho9iUZPMHDdCzKdJYVmT6bdl6l1vDR7EkWV+ZurOcmVHWRLDmbKIsjhmxHsX7xoZabioFBzUWy4makI1jBfdVJpeSg3vTS7Ep2+TG9Jod1XqDQ9LDY8dPsyra5EtSU8fbW2RLMrfHrVpoeFqpNK3UO17qbdFsxdtysLFr4jWLh2S6LdlGg3JJoNr9iYtSUqTYlSzUOr7qHblMmmtXRbArS1GxLtukSrIdFqeOm1FTpNmV5XbPA6LfU9Ta/4OzsynZaXblsWYLEp020qdOoynbpMuy7Rborn5YqbZl1iseKmWHHT7cgs9f28/nqYdj9AreOn3PIxU5HwZGw4khYCOQ/+tAsp6UCK25BjFtwBLe6IHjlmJJkxE06acYYN2KIm/Dk38bxMalYimfEQidneAIBvHP/RcaNGoyGYsKGknPjTdqSkldicjCk4jZLQs1D2kp534U0ZxSzejA1XyoyctRDL2wnnnaQWXMyV3bQbMtWaTG7RQ3rBRaboxJ+zUCg5+cUfHsaVMuNOm3GnzDjieqxRLY6EDiljJFywkliwEylYyZedtHo+0osOUosOMZc4YcCfMxEouHHH9Bj8k3jSFuSsKPd0pMxMyKNE59ykixKbdvye1ddcy0fv/TaHn9nGHR+/g8NqoONJZ/lyEPjPDf6qF/AygGhb4KcHX+at73wPj+3ez5tuuIkHNh5l93Sax160sfWEly3nImw8E2LDmSCbzoZZf8rPlvNRto5E2TWZ5phzkT26LHumknx301N8/ru/4YENx9g6EmX7WIKNJ/2se8XN4/9wsP6ExKMvWNl4JsC20ahI8Rqy7JyI84Xv/Zrdk3F2jEfZMR5j50ScHWNRdozH2XwuzMYzIdadVHjiZRePvWjn0ResPPI3C2tfcrL1bJCdo1F2jCfZNhpn62icbWMCzK1c4ka3DPy2Dt47Gh9+PXjvjnH1+2OXf95lMvDkIAQibub79Dn265YB4IBV2afLqdUwy8zmABQOQwDmObadC/CuD36cn+5/cVj3sW0kgEajYc+r57D5dcTSMuG4TCIbJpjy4c8oWIImHHEjlriRCe843/3JgzhDJjxhC76oC7OiwxN0YFT0LFbddFteek2FdlWiXfXSrSt0KjKdiky7ItGr++i1/HQa4mbSaQiGsN0a+IQUZkpOYnkL9ZZMasFOZt7OfNVDuujEHBfgbziKUS2LHQsISXjUP8Yp+RwnvGc4JZ/jNe9ZTkpnOauMoA1NM+YfxxTVDwHefd/+KtffeD2/3b6Bz371Pj73X1/AN+PCkbTgUsuqHQkL7pQAdO6kBUvciDlmwBo3YoubiOQ9jPnGmQ5pmfRPMOmfxBY3Y4mZRJdhWIc+rOesNMqYb5JRZYKHfvEQxtAELxqneOzYn/nwZ7/MFVdeyQc+9kkOWpc3USJFK66/gSfwiGNRDWWIWpc9K+wCu6aWz5udKiu9ayrNrqn00D6wW6umzKcy7JkWa68KFvfpxTl10DjLAeOM2FQZB0GT3HAjskeXEzYEw+xlQHC/UXy9R5tbcW2o5/1kmqOqqnDILCaWiC7EIg/v/TsajYZb3ns71970Fq578y188aHfcMBc4Jrrb0Kj0XD1Ndfy3PmzxBfD/O//Kly4KFFtOfnFL+8XqsXmg+ydCPGTdRt58803kSpYmK+5qHUkuksK+ZqX6IKLQN5KvuoiVxbgq9VTuPR6gAv/G2SxLVOoe8mWPcw1JMptL/mqi0ZXotmTKTU95CouZqtuah2J3gU/1Y6XbNlBsekmtmAlPGchNm/FmTbgz5tJLNiIzFnxz5hodIUPULCJMs2eWI2uTLun0L/go9mR6C0ptLrCd9foiORtse6m01Po9mS6PZlGW6Le9NBqSvQ6Cr22AGWd9oB9l+i0JdptmVZLEr68tsoCqnJsuynRbnlpNbx0WzLr1/6MTt1Luy42at26om7exAau0xCgrlWX6DTUz1JBXachfo5+18dS3y/Yv4ZCtyHTrcv0Gr7hn+80ZKo1L42GQq+jcOlCkErbR7MbIjrrIld0E5l1oKSthHMeHGEToaSDmbyPeNLNbM5GqeSj0oqRmfORKEjMFGU6rRDetIVxZQpr2ISSdBDLyQRjOhIZxxsA8I3jPzpu1Gg0OHxaAmqKV0qY8ScNzM3ZKJU9ZBfcJOacBGfsSGkrroQJW9yEPWFEyjlQ0mYKi06qFQ/NukyrJlOvScwtuigUHSyWXdSbEvE5O1LaKMqmE3qcST3ulOg8yiw6KFQ95GmhAX0AACAASURBVCsu0kU7CxUnL7x0gN8++jBHnt2F1jeOKaLFP+vAnbERzDqIFWTsER2OpAVvUocvayazKOGKG7FHdKy6ahU/3PlnVq+5hr+9dozVq69i/UlFePpWdv8NJN8hMCz/E/MnDOVP2hf5hzTLW9/1Xq67+RZ+tPsv7JyI88Qrbta+7GHr+SgbTwfZcDrAhtNB1p8KsPFMkA2ng2w5F2HHeILDljn2ajPsGI+zU117prNsHYmx5byoUNk5kWT7aIwtZ0NsPOnj0RfsPPaig7WvuHn8ZRfrXpNYf0Ji7aseNp6U2Xjaz8ZTMmtfcfPoCzYee8HGEy/aeeIfDta97GbjaxJbT/vZfj7CjtE4O8bibB2JsuV8lC0jYgnAlmDbaHII9IZAbjTBltGY+lps+N4BANyy4v1bR2NsHYmzdSTGtrE428aTKyTijAoCs+yZzrB7Msk+bUoNvIgb7u5BKliV9PbqsgIIanNqabS4QR8yC3/XIYtIew663v7w3CTv/sCHuO2jH+WXe4/z96nT6CQ9StRNJhfDH7JjcE+i9YxjC2pxR/UYfJNopUmsfj3uoAlvyI5emsIX9ZCZc7DUHvyyl1lq+WiXZToVH52quJH0mn46DR+9lrgZdJoy3Zay7A/qKHS7PkoNiVTRRanhYWbRwWLDS60pY46L/k1zXIctocekgsHxwBiToQnGA4INHPGPcs43yin5HGfk87zmPcN5n3jNEjfiSFowqcPitx7fxjve/Q7u+vydjFpPY0taMEb0TIemMUT1eNM25KwDKWPDlbExGZzCGNVhiOrQhrWMKKOM+EY5K48wEZjkvHyOCd95RqXTnPeeZlI5hz44wpjvLNrgGFOBUf4+8iwf/tSn0Wg0XHfTW7lSnQN9w1veftkGS4C/slqjolamDLy5tgWetIuvd02lVek3M9xArPSf7lYB4u5pcY7sXgH6BpLw7unMkNnbb5jlkLnAfqPKAA4YRJ0KAnU5FTgKBnJwru1XPby7tTm2jcbFpmjAfk+Kns+D5jx7telhd+UghPLYX3X8ZP8/+N2fRkUoRd1QfuORPUMG+zvfuZcXR17mmReO88t1T/D93z3CfQ/9AI1Gw5tveRtXXHEF99z9UWy2v/H6/w1w4fUAraUgta6PTMlJMG8jOGNktuomoU7IyFU8NPsBFls+cosuogUbqaITX9ZEvSOzdMlPpuxkpuKi2PRSbXvVsIbw/wnwJtFZUobScKXtJVG0sdBws9hwky07iM9bafWElFxte+n0FbpLCqWWl3LLQ++ConoAxeudvqL6AGUuXPTR6cp0eiJF2+nKdLoKjbqHdlNSrx/BxrWbHtotsTptwfx1mhLdIQCUh169bksRz+uSuCbrXto1L526NGTu2xWJdlWi3/DTrSt0mz66dR/dmpB4WzWPAHYtwfyLz5aG13a/G6Db9tFrKury0ahKlCseWg2ZRlPmwpKfXMmFnLaSnvMSn7ERnXURSFuxh/S4Eza8CQeZWT/xuJtiOUYi7yMYsxKQJ4lEdUTjZmIpK3LOS3w+hDNhw5dxU6wkyMx6SWflNwDgG8d/dNyo0WjwBqfIFOzISQtK2kR0xkal4qHeUJireAnNOrBFjYJBSDtFJUXMRCRtZnbeSaPmpV2T1BtigG7dT6siU694adcVeh0/3a6PYs2LL2fFkzKiZE0kF+zUO14qTS8LdS+Rgo1i1cHd96zoZFsjOtmM4WnsSSPTgQkmlTGm5BFcsSmSBTOZop1AwYErZcUa1TFmE16bvbost7z3Azzz4gG+cf9XuPO++zniqCxP+bAWL/f7ORZVgLgsUR1S04XPuIs8655j00kvm09LbDrt47EX7ax7xcOmsyG2qMybAH0BNp0JskkFgOtPBdhyLsyOsSh7plOsP+ljy/kwW85H2DoSZfO5ELsm4mw5F2bbaExIqeNCTt06ElXrVqJsPR9hy9kQm88E2HYuzNZzYbacDbF9JMLWcyG2ngsL/+FojM1nQ2wdUf/M+Sgbz4SHsvSms2E2nw2z+Vxk+DNsHREgb8toTADSkWXQJ8BefPj95e/F2ToSF1+PxNg2WCpI3D6eXAEC0+yaWvYCrvQD7tFmhtLwbm1umRHU5tQbuDDcD6piRF3MLAeNsxy2zA8ZwM898PN/GWT/kTs/xaHTI5xx2zDJU1hkLVr3CJawXsibYT3GoJFJ7wR67xT2gAmzT4vVZ0CKmWjVPfRbCkstH0stP0vNIL1miH4zSL8RoNfwDW8C/ZaPXkvcLLptiU5HptWW6PaER6nXFSxHt+ej2ZZod2Si83Y8GSPOgfUirsOS0KkVM9PoI9OiOiYwLsY/BScYC4wz5h/jvOoVHA2Mc0YZYSI4yXR4monQJPqIFn1EK1i9hBFTTI81bsAU02OK6TDH9HjSYsqAPjSNMaLFEtMz7h9Hr6aTJwNj7P7zIW77xF3ccuvt3PyO93L9m9/CO99/G5/+4pf4/Hd+xN1fe5AP3f0lVq+5hi9+/3c8+tdpVl9zLTfccJ0oFN56kMP20nDM2eW1JmoK3yqS+IMalX0G4e3bq0rCu6azKgBMs2NC3UiozODuAUM4mVYBYGaF1SC7QtrNcdCYF9KvPstB4wyHTPnLWUB17VZZ6gGTuFv9/F1TmeHmZ6vKhG9XQekBY55jzkWe9oj6l0F47J8L17+3+anhuXnLe27jmutuYM11N/L293+Eu776He78xo/56Oe/yrtuvZXfPf5ziiUrvSWF7gUfiy0PkTkb8XmxwnkrqQU7M2UngVkrC3UPmZIIJUXn7WQX7eTKLgo1N6WWxEJTIjlvJzZnodySyZScLNQlSi2Rpi23ZYpNL8Wm8OQVmx7qXcHmNXoy9a5EpS3YvnrHS70jsVB3s1Bz0+nLdJcUAQg7QmKutLxDtq9/wUf/gkjL9tTKlK4q6bZVpq/dlmnWvbQaqsTbGABAwei1G15VplWfN4U3T7BwKhPXFKCt05DoNhQh09YkunV5GeTVZDo1hV7DT6/hZ6kZoN/wi+u5LkBit66IjZ/K7rUbMr2WXwDClkKv7aPXDojPrProNf30W34BJps+el0//X6QUt1LfsHGzIKLbMFLIushmvQSiXvIZnyUFuPEo16ycRf5XIhUxEpzMUC/HudiK87FdoJqOUBhTiGX9zG/EKS4GKRcChCPmd4AgG8c/9Fxo0ajIZ02slCWmS16KNUUCnMOOg0f7XYQJWvDlrDhz7nwqsbUYMpMpmBloeylXhcXXrfpo9/ws9QI0q8HqM+76dXEDbPf8rPUD9Ds+IgWHARyJpSMkVLTy4WLPi5dCtDqKcTnbfzpb3tZs+bytOzqq9/HjqNbGZNG0Acn0AbGsSV0xBfsLNTchOds2BIWXEkT1qiB518+zM23vJUjjkVueMvb2HbgCb73q1+h0Wh45M/Tw+qXYe+fbbkOZrk7bI79xlkOWwo8J1V42lnggDHHlnN+fv83M7993sCmE7Iq9QaGa+OZIBvU52tP+tl4OsjG06EhENwznWbXRIJNpxQ2nPSx+WxQgLezIbaeFwBw22iMHeNxtp4Ls/V8mM3nBMDccj48fBSALaaCvDCbzgTZfDbE5rMhtpwTSzB9Ebaejwx9iRvPhFUwGBHrXIQtI1G2no8KgDeyghlcIf1uHwC/keXHraMrHtX3bBsVjOL2sQQ7JpJqcCT5L1Uxw0Twiq7AAYNzwDCjgkMVBKqdgnt1OTURXOCgmv48bBFdgYdtC2wfCfHRL32dNddez423vJMP3fU5Vq1axZWrVvH5r3+LA+cM2COTGP2TmEMGLGEL9uAUJv8UWlkwgXp5Aq1vCkdUhy9lo9Xyc7EXpt8MsFQP0G8EqJfctMpeOjWFbk2h1/DRb/vpqjefflum15bE6oq11PfT6QXo9Xx0uwrttjCbhwpW3GkDnrQBV9qIJaHHnjSI+aIJMTB+IjSBPjKNMapHp/YGTgYnmQxNog1Pix7B0CQT6utTIfG96bBg9vQRLfqoFnNcjy1pwp4yYU4YsCeNmGN6poKi8NqVtmBLGjHFDDx/6in+z4MPotFouP1Tn+Xeb9/PVx783mXg+l23vpev/+IxHtx4lLUvmjniWOS2T3+Brz70MDfedMPwfXd+8Qt89Vv3sevFE5fVmwzm54peTgEEByDwuLPEH12lYeJ9t1YFgWrYaMdEWgWG6eE5tVeXG8rBQrbNrvANDnx/Mxw2FzhgmOGgadD3JwDiQPrdq8uxZzo7lJT3qhsUkW5PLrPho/Ghb3b3VIZDljk16LJ4mcdYeCDF80f+PM3td93LD3Y8z4/3v8KWkSg/PXKKq65ew733fZmb33ozN7/1Zn7z2+8ys2Ci0fFSanootyQuve6n3PZQbnuYr7vJlhw40wYKVTf5qpNSS2a26iI6ZyWxYCNUcJApuZiru5mpuJirOUkvWGl0JFwpE56UmdCMAzllRk6L6RuGqI5EUfyZSkuAvZbqGax3BNMnUryK6huUmKm4KLe8dFW/34VLPi697mfpoo92T2X51M69pSVl2LUn+vR8tJoiUNFqCKDXakrLLGBDSLftpmDX2w2JTkMweoPvdZuqLNsQ7x+AwXbVuwzmqgL89esBeg2/Cvj89OsBLrTEZq5TlumUJbpVhV7NR6+u0Kv76Lf89FuB4WOn6afXUsFf3UevGaTfDIrfA60AvVaAixdC9PsByhUH7ZZMrx9idt7DzJyXmbyPubkYs7MR4mE3qaiH2lyYbNRD3G9lIe5iNupgLu2lkJVolKO0ajFqtRiVUphaJUq1GiURf8MD+Mbxnx03ajQaZmbs1Kt+yiUPtbKHRlWhXgtSq/uJ5RUiWTeZOYlU3k1+3kW74aNZU2hUvLSqXvWC8dOt+ehUFNolccH1GwGW2gF6XT9P/2kbmaKbYM5ENG9loeKg3xMdTK+/HqC/JGSCn//+R//SyXbFlY/w8K8fIjBrJr1op1h3i5BI0UGgYMcSNzPpn0QfmEbnG+Ub37+fe77xEJtOull11VV8/J7PcvO73s+aa6/n89/9lToJYG65yNVcGHaBCblIyD9HbQscdyxw1DbPAeMMa19184e/mtl2SmbHSIRNZ8NsOB1Umb8Q608FWHfSz9qTfvXRx7rB8xMKT7wms+mMn326DGtf9fL4yx42nPKx5WyQLWcDbDilsOm0ny3nw0MWb/PZIJtO+9l0OqDKywH++9fb2HRGgL0Np4NsUpnHlWvTmdCQ7duogr9NZ8NsUH/WjSoA3Hwuwubz0aFXcctIbCgPD8DelpEo20ZWvOd8RABGdW05H2XzubDwMg5ksrGBhCxuktvGVd/URPqytVwVs+z1GrA+u6ezKhOYHcp5e/U5Dhhyy6O3zAXxqFbfHLIKIPhfP36U1WvexDXX34hGo+GKK69Eo9Fw9NRpJr2juKMWjN4R9uxbh5I04gxPYwtPYwpMovdP4ooZsYamsQa0LBTddKoy3YqPdkmmV/XTrqlMQU2hW1do1zy0ah5aNTfdlkS3pXaIdSX6PZmlJeF1Eh4miX5f9JG1ujLFuhv/jBlFXc60EXtSgD9zQo85qsWgFk6P+kcZ848xHphAG55GF55GF5kWzF9wkvHgBLrINEa1a9CWMOBIGrElBRNoT4pB9c6UCXNczCR1pIwE8g6sCSvTgWke/NXDlwO9976LD338Dq674Tpu+8Rn+NwDvxh+b9NJD4dti3zr0b2864Mf41233kooYyG94MYRnGDjzkd5YvNvueFGkdzeMRq6fKKF6rU95ipz3F3mj+4KT7nKPO2pclwdw7bfMLssBU+kRfXQ8HwSASMROEqJbsrxhAgdrdhkDK7r/YYZsWGwFDhkFkzyfhX07dVl2an6C3dNpQXrNynA3QD87ZgQn79tLH6Zh3bHRIpdU2n2G3LsnkqxV59lz3SKQ+a8GMW4Yg75Yfsin7v/p9zxsQ/ztre/lQ9/5HY0Gg2/feTHTOqe59gfN4qAx5IIS9TaAnB1lhSKTYlCzUWm7MGbNrDQ8BLO28gsupiruVlsellseqm0JebrHmYrDmbLIkxXqLppdiXmqi4W6i5CsxbklB5vSk+0YGKx7qLS8qCkRe1WYNbKXN1DtS1Swq3u8mNbDXL0Lyj0lpRh2KPXF18vXfBx6ZKfS5f8LF3wi4kZHZluz6eWKPuG1SzdjkjyNhoeWk0v7ZYAdv1mgF7Np95XZHEN1n3Cu6cyed262IR1h5sxweq1KhKdqswzx7bSq/roVf30awGWGiGWGkF6VR/9mk8AwlqA1oKXVtFLuyzRLnnp1330Gz6WmkEutEL06n46NeEFblW8NBY9dKoyrZJEqyzRqsq0KwqdmuoJrks0qx6aVS/NipfKoof5vJ35goNs1kom5yausoALs1EyCT+JgI3SjEQl46UzH+RCOUV3MUGvmqZSjFEpRanVkxRLMebmAiRTnjcA4BvHf3TcqNFoyOdsdBpBmmUfS60w3UaIdi1EqxaiWFQoLyp0WjFKRYlGWaFelGiVZLo1v9j51P306gIA9mp+elX1eUPQ4v2lIJPGl4mq9TELFSftpmfIjlxYUrh4UUgER5/fydX/1Mm2Zs37+ftL+6i1vdTbXhodL7NVF8GCDUPUgDlmYso/waRvgkcOHmT1Ndfy6+Nn2XJGETf/K65g8xk/+/U5dUZudmju3qdX59LqcsNZowdNBf7oLvMXpcoBQ5Y/ukusO+HhN89q2XUuyM6JJFvOC/l08zmVSTsbZuOZ4BDwrTuxDP4EGBQAcO1rMkdt82w8G2TdCYW1r8lsGYCzUwG2ng2y+UyAredDAvCdCrD+lJ8Np/xsPhti05kgP9r38tBvuP6U8ByuOxlgvcoyrj8VYNNZAfRWAtQNZ4JsVP+uIQP4TyBwGQzGhkzg1pEo20ZiQ7l4y/no8N+//P+gAsAxIWEPwOTWoX9wkCIW/qntK6pjdgz8gVPZYUhklzo/eLdWVIAMOgP36gYj4wQDKMaGLU93GJRGP+ko8cTfDHzos18ZngMajYb/+d6DuFJGTIFpIjEtpukXaFYlSmUvCxUPctqMIaTF4J8injNjCeuQogaWWjK9hsJSI0i35qdWdFJddNOqSLQqwjTea/m40A3SbUksdf3DFGGn7aXfV9SqCUVsdroSvZ7MxUt+ehf8hPJiioJNnTziTBsxxfXoQhPoI5MYY9PoIlPoo1NoI5Now5OqT3CK6bCokZkOTzEZmmQqPIUuosWXs2CJ6zHHheyrj0wzHRaTTKbDYmrJRGAcQ1SLLWXBGp3iy9/+2hDcPfDw/fx55Hn0UT07/vIcV69Zwzcf2XNZMfFTLsGi/9fDj6PRaNi8ewMnzTpOG88zahlFF5UYc2n5yre/hUaj4XuPbVW77VaONRNjzgQjuMBxd4WnPNXhe/Yb8+zR5dg1mWb7ILA0tBekhh5TERrJDtk70Tk5YPVmhtaBAyqzf9CQ45BplkNmcT7t0y1vNnZOpETV0URqBfhLsnMyNWS4B5uc7eMJleVOqvaNhNqTmWLnRIxDxpzosbQKj+Bhe4mrrl7D7Z+4k19v28XOo3vYsfv31FsuLv2vD53573SXFIp1F6lFO62+j3pXptyWyJWdFKoOpIyJdNFOYsFNeMZIdtHGXMNHtuzGP+PAl7OiZK2EZl2EZl24khZ0QR2Vlpvekkx3yc9s2cVc1clMyc5i3UW9I1HreFlsyvgyJnSBabTBKYKzJtoqe9df8tPuitRw74LYtPeWFMH+9WX6fbVwuadw8WKAi5cCvH5R5v9e8nLpgpf+kkyvL9HtSbTbAuz1uwrdtkyj7h6GN8S1FqBX9dEpK7TLMt2KwsWGYOWWGn5xr6n56dUC9Gp++nU/3YpMr+KjWxagz6p7iX4tyIVGmKV6iKV6mF45SLckAGGv4qe54KE+66I176VTkulWxd/dbwRYaobo1sTGr1n00C5LNBc9tBclOmWF1qJMuyTTKntplN20axIX+1G6zTAX2hFe78VYakXotn38v4sZ/l8/Q68apjzvYn7WTjztIJMLkIrJpGI+FjI+5pMe6jmJ/62naebDNPJhKnMhFktxCoUIpcUk5VKCVEp6AwC+cfxHx40ajYZk2kmjHqXdiNOqRmiVQ3TrETq1MK3FAO1ygG4tRLcapFsJ0q2FaJf94uQvK3Qr4kJslxV6VT9L9SD9RpCldoClno9eP0hm3kZh0UGl6hbVGS1JTW6JyH2/L3Pxoo9izckn7ryL1Ve/jyuvfJQ1a97P3XffTbPtodOXSS66COWtGCJadGEt+rCeCWUCfWiCdQc2s3rNNfxw53McthXZPZlAo9Gw5po1PPTYBvapAFD4yJbni64cM3XYkudJ6xyHDWkhKU2nePxlF0/8w8Eudde/XfXDbVWB0IBZe+JVmW88dojPfPvnfP3Rgzz+ipd1J32sPelj7QkfT7wm8ehLbta9JrNzLMpjL7lY+5rME69JrHtNZoMK9DaeCrDxtJCTBcATX1/mLVwhOw/k5U1nBFhcf2rABIZUWTrEuhMK33z8EHd/5xd88/HDrDvhY8OZMBvOCC+g8AOuBH+xoTS8ZcjyRYaPAxC5+ZyQkQevbTkXZsvAWzgMmsSGHkORJFZB4HhKBYArQeAyE7hyDaeIqJUxYmRcnsOW/Iq5rZeDwMO2IjtH/FxxxRX8atc+EUq46QbsMQue8BTFvI12w0u/5aNd89Lv+knO2zCGtBiD03hiBjxxI/6kiVjaSH7OQargJDdrZaHopl5yC8ahrtBvqJJQQ+FCJ0ivLdPrSPR7Cv2ePGQBl5YU+n2ZpSVxs7x4KUCh5iYyZ8GR1As5OGvElTLgzYph9ua4HkfKiD1lEiPnwgPJd5zJkKiOGfWPMRWaYDI4gTE6jU6dU6yPTmOMaYUsHJxgzD+GVp1Coo1MM+obRR+e5oztNDfcJNjSt9xyM68aX8Ea12FJGDBFtRx58TA3vvlGjhmCPOvO8pSryHGXCN88OzbJz/Y+K25GKtC+ctVV/+LH/Mn6Tdz60U9xyF7596POHCUOWec5qvrnDtsX2W+YYddkgj3arLphWGb/BBOYGp5HO1ecQzsmUmqnX5b9+oFvVDDFB4x5jljnOGIpcMicHxZJDzoG92iz7JpKs3MipV7vCZVVTLF7SoDQXRMi/b59fBkI7ppMr/AILtck7Z4Un7NN/Zx9ugw/2P4nPnTPV7j6TdfyoXu+zFF9kN7FABdf99O9IGpTWn2F1pJCe0lhsellruah1HCTrigk5pxkSxKxOS+pvA8lZiWakvHHvDgCJtwhGzrPFDp5kinvebTKKPE5iyh4rrupql4+kdCVuXDJR6evMFv1klhwsVBzkVxwYApNY45MU2t5qLVldVau8Pa1exKz1SCpUph6W2Gp7xXneU/4X4NZD7uePsZ3fvkHPvnZz2L1abmg+gCXlkSRc1etWeq2hM+u2xCWCsGs++hXFTplBb9Pz/annuf4lMwph4dz/ixL9SAXGhH6tSDdigBzS7Uo/UqIbsnHUjVIr+rnQiPC6+0krzcTXGrGuVCNcKEW5lIjRq/ko7Ug0SkqdEs+emUfzTkvL//lEOsf+zkv/eUAtYKL2qyTWs5JfcZNc16iMeelUfBSy7up5T3U5rxCRq4qtOoemjUPi/N2KotOSkUPiwt+woqOdMREMWdnqRHm9VacVtFPdV4mnXSTiHpIxvwkI14KKYVSWmapFGch66cxH6UyF2V+Nkw+E2AmLROPuN4AgG8c/9Fxo0ajIRG1U5yRqeR9VPM+qnmFxlyQVjHIUiNGpxSkuximnPNQnfHQLvpoF/10SwGW6mGWWmF6dR+X2mGWWiF69SC9ZoB+K8BSLyBkr5aXZkOiWfPSqQrZrNdS6KlJq15HgMBq04snqWPHk5t4bO1PeOWVgwQyRmbLQt6IzVkJz1mxJgwYYwaMUSPGqImXtC+w5k3X8aNdf+ZJxyLHXWXWv+q67Aa0/jX3EOgtL1U+NM/xor/EYZPoDNs9nWHbSJjf/dnIY38zsXs8rv6yT6iBiNhQ+tx4Jsza12TecfvdrFp9G1dc8SirVt/GO26/m8de8bD+lJCF155QeOxlL5tO+9kzlWT9CYX1J5Uhw7f+hMKGYXo4MJSUBdsXFKDwVED1FQ4CJstAb+A7XH9KMIeb1M9Zd0LhHR+857Kf7Z0fvEd89gDIqWERwfjF/4Xp26qynQPAtxL8DZ5vGazz4vOWPyOqgr7EMvgbS7FtPMWOFSBw52SGnVPZYcpzGQBmlz2BumU255A6K/hJ6xxPWsW8VwECBQA8YlvgKXeZOz7333zv8a1sPbKVA8/uZVIewx2apF1XqJVcdJoSly7EyS94KVdkInk7et8UgaQZJW0nNetEipuIRaeZn3eSzZlplSXaVYV2VZWqVP/PUjtIv+1jqeMTo6TULsFeRxZjpvoK/b7EhYsKly76uHjJR7uvUG17yZUdBGbNKDkTtqQBV8aIK2PGljRgTeoxxqbRh/9/9t4zTM660PsfQ0LoggiKgDSP6LECIhYQFaUJKEcQ5YgiNWWzyab3RhcFRQTSs8n2Ors7O7vTe9/pvfeZLUlo5zzP/93n/+J3z+wGfF75lvu67ms2k2Qym53d+d7f2sWQrZdBW6+UIO5jzNlHh6GDfks3/ZZuxhwiUDJs72PA1ke3qZsOQwftuuN0GDrEr41ddOqPMukewhVX8+AjYlXiR3fdRqf2GF3GLnrMXQxYejiuO0anvpNzzj0bmUzGRZdcyo/v/yX3/HE537n/cWQyGa/L5exUNCbfouwci3H5l0Uq+Bs/eYBPLTqDM5acyZ1Pbjo9DLIAAO6QhwWbOxxkc6+b9Z021hw1NdO3/+qc95Z6pJCR9DrpmRKJ3r75jenG9/qO4SDPKaLskgfYPuQ/zUrQ6AbcJKXV13XM+1k3S8niNcfsrO90slGSidd12Juhp2YlknSuPi1Q1egRFH93xsSgQwAAIABJREFU+3CATT0uvvL9n3Hp1V/mL3IDs7NayjUttRkj8byOZFFPrmKkUNWTKeuJ5zWMWQewBSYwuCZxTFnoGjlCn7KDUc0gPaPH6ZnsonPsIBrbMfr1HQwauhgxd2MPjeBNKPAlFQSSShK5SfIlAQBnZvUE0pMoXYOMGDuJphSEsmo8sXEMnkGMvmHSuUnKVR3lio5R+et87Rtf4dyLLuVPe9+iVzuGIWTH6erh0cce5NIrr+bMs8497eevybSf6Rm9UHuqOsplYYNodPqVczoJAOpFsCKjZN2GJ7n+hu+yaPESLr78muZj/fCRlVQzZkoJA7WMhXLSSD1jpRTRU44amc7YBeuXMguwl7cxl3cynbFRT1qZSduZTtuoJcxUknpKMT2VhJFS1MBN376pGUI8a+nV3PitG4g55GT8KvIxPfWcg2rCTCVuphTTU0hqKaUMFFMiEFLKi+DIbM3FdNlGPmXBNHaMoGmQ9JSKetZBPW1lJu2gEjWTD+gJ2Maw6wexaIew6McIeM14TEpCDjX5iJWAW0fCaybu1pLyGfGalTj1n9TAfHL8e8cFMpmMiEtDOW4n69eTDxrIB01kvToKQSOFkIFyxEglaqIas1CNm6lETJTCRkoRE7W0jVreSjlvIp/Skk9oKKUNlNICANZLRkoFPVrNARHxT6ukN0sd1aJBKtkU24vVihZPVEEopWQqqULpGCCYnCCcmiSWnSBfUlGt6xixDzBsH6Lf0ke3rp2/Hn+TS774Ja6/5Q6eVyZ4fkKMz++Q+5HJZPzHd38qJpO6bNIP+xA75RH2jEZ4xxjk75oAzykT7B31s6HTRssBDb9/uZv/fu4oy/8xyvrjIpixthG6WOCHaz1iYeVBIz9/esfHpt7OWHIVP3tqGyv261nRlIN1LHtHw/pjFjZ2Olj2jprl+3QSc2fgqX8oaTkopOTGfSsOGFhx0MCy/TpWHDDw6HPtQvJtgL59OgEwpcdfcUBI0CslJvHnT2/7l8/t7mW7hCfw8OmhkCawW3C2fvTjwx9hAaVUceP3P+otXNNuXVAv42zKePMS3oK+wG4h+y4Efxt7G2/u88XRWweF9LtL2pdtBAi2L2ABnxuPcdvDT/PD+x5EaevCEZmgz9BDLDpJMWPEoO8ilTPhCSlx+EYwO/pRmvsYNfaiNPZicg7hcA0RiapxuAYxOIYxu8aIhJUUMkYKWT2VopF8Wkcho6NaMFLOaSjmVNLFjUb0meVUQuoqq6lW1dTrBipVDdPTOmZmdNSmdVTrWgplNdHsJNHcJMbAMIbAMJOeAcacfYzYe9D5htD5hpj0DDDhGUDjlaP2DjHh7kc1NYDS3c+Ys49hWy/D9j66TF0SOOxh3D2AMSBnKq7Am1BgiRgoVHSYQwoMgTEGLX30mbrpMXbRZeig19IjJY+H2K/Tcv+TLae9oZ919lmcfe454uNzz2fHaKyZpm/63UbFZu6yN0f47z37BTD/F+Bv64CPzVJB+MYeDxt7p1jb4TzNNiBYNuH129QtisXXdwqP3rpOd3NxZmO3h6393manXwP4bR8ONi8YdskDohRaApwNYLh9SJqnk15v6ztFZ+ctD/yBX7Q8R1u7lS29U2zqcbO+w8H6LmeTHdzQ5WzKxZu6nazvsLPmmHRBtaAnUwBFFxt6PJL1xMv1t/yUr992F8GMFqX2IK6Ygimp5mUqNok1MIo7OoE/rkTtGsbonESu7EJlPEY8o6Rc0ZHJK4mnFQRCfWzY2cYt9z7MoeFj9JnkDJp7MAVHMQVHMPlFKXQoo6RcEwsk9RlRUqz3jaO09TNk7GbE3M2oqZsJWy/WqX5KRRWlkg694TAXffYSfvDrp7nuxlvnL7DXt7J69WNcfvmlPN7awq43/8n37hQXFtWamvq0XgDAGT3TM0ZqdSOVsqhUKeV1UphDSz6tYefuNq6/+VYuvvxq7lm2nbYjeraMxHmlT8cfnl3GrT/+MdWMlXrew4mSl9m8m9mcixNFL6fKAWbzU8wVvVhUXczl3dQLNupZG9NZB3NlD/W8hbmqlxMFL9NZO/W0jem0jXde38NZHwkhLl16FW/+ZTu5kIZCSifUrZSFYkhHKaqnmjdRrzio171Uq35CYSNm8wAe6xBWYz8uUx8h2wjFiIly3Mp00s6popf3yj5KIT0Zr5aIU41bJycbthN1qkn6DNi0o/hsanw2DVNmJQ6TiqDLSCroIOE14fuEAfzk+DePC2QyGSGHmmLYRjlkohaxUI1amUk4qcXtTMftzCadVGMWCkEjtbid2ZST6YSdWtxGLWmjnDSRj+vE1VjOQr1gEeCvYqJaMZFOT7Bx4xPkMpOiLiOnp1rQUS3pqZZ0kkwmhrhdsXGMwXEMfgWO0Cjpog61ZxhrUN6c0RmyDtBv6afH2MOON17i/Isu5oaf/5odwwH2jsfZOy7G2x97UYyk//hXD7NdHmHbsAAKu8civKZN8PJkjL+pvTynCLOpx0XbMTNP/n2ER587ztN/7WPtQS1tR0zzYYeGVHpEePFaJEl25QE9N97zKJ/61EfCK596ihvu/u1pKeEGYNvc7WD3SJCn3xxn+TuaJthbtk/Div0CvC2X2LwVBw0s3z8PAL/362dYsQAALt+/0G+oY8V+nZCU92lYsU/Lt3/+sDQ/x4LzCW6467dCKpbSwSIg8vGU8KrDFlYdWvjr09PEjUqZhUCw9eg8AJwHgbYF8q8Af8JoL0z3DRawkQpuhEAaXkCRCp4HgGIeLsiuZqBnAQCUh9k+HOa58Sh3PbGOG398B/bgMKbgBGPWPlIJDcmkntVrnsAZVKJ3DuD2DGGy96JzDqMy96My9OB0yQkFxgmFlBhs/ags/Rhdo5jMvVQyJoKhSQJRpfC75kToKZfVUiloBRCUpqMKGQEAK2Ut1aqGel1Pra5ldlbH3Jye2Rkds7PCB1us6sSaQ3YCf0rBVEKB0T9EQNpnDWWUogYkJGYUle4BlK5+puJjeOJiR1XpHkTjHULh6qfX3EWfuYthWw8G/zBTiXFMoXF0vjG88XEUriFGHIP0GrtpV7fTa+pi0NrDgdF3uPDii/n0JZ9rvsnf/etf8Ivf/YrPX/GF5n3nXngxn73yOn698a/skIfYpRBevh0jEbYMeNnSP8UOeXA+db9A8t2liLNDHmFzr5uNUg3Qxj4RAhIAsFEsLs6NXU4297jY1O1iY49beP2kWqFGanzrgJ+dQ/5mbdC2waDUGxlipzzEntEIO+UBdsmDpwHDHfIQ26SaoS0DfjZ2u/jCf9zMYok5X7zkai770nfY0OFgY7d4DqsOG2hrt4rn1ONifadDYgHng1Sn9WQemweBm3um2NLnY+dwgLuf2sDXf/gzvIkx1m9+En9KiSemxJeYwBMdx+IfZdjUhT0wxoR9CINViULXTrGqI5XXEkhOUqjoOHHSxAMP/IiLLv5M8+tz269+y4BpAJ13BJ1vFFtYiTWkwBEdI1VUky6qKVS1mAKjDBu7cYZHsAWH8cZGiabHCCdHmyXMB/bt4tzzzuX79z3C9hGx5NL4d+59so3n/7z1Y9L/m51vYw0pxEpIQUNtxsjcnIVK1UClrJc69kQBcz6t5tFH7uXSL1zOD3+zjNZDWnYp4myXR/jclVc3H3Nt61NUMw6msy5OlgK8X41wquTnvWqI9+tRTlXDnKwE2bm5lXfrYeaKfqazLqZzTk5UfcyU7EyXLMwU7Mzm3dSTdqbTDlqe/m/O+EgIcdGip1j59KOU43qqSTPFmJZSTLCGtayV2bqDdMaOzaZHb1BhtSjw2EdIBpXEppRkI5MiJCn1h1aTRuayTmZSVjIeFWHzCH7DMPaJLkKWMZyqXjy6IRyqPkJ2FR7jOCmviaBDh9+qYcqqIRV0EHDoPgGAnxz/1nGBTCbDaxgj5tTyfjHA+4Ugsxkv1ZiTetxJPe6gHLKQ8+mpRK2cyHk5lfdRjzuYjtkoBgyUIyaqCQuVtIVywkQtZ6VetlIqGohGFaRTymZXU7Wgp5LXUSloqJX0lItCBihXdGRLWianRug29DJiOo4/pWbUMYB+apBMUYUrrmTcOcSIfZguQzebnlvFmWedw08fW832oQB7RkPsHA6weyzKHkWc79z3e26+/UdcefXlfOWbX+ORjS9z1+OreXzby7ymtLNXEWX7SJzNfR5aDmj57xeO88cXjtPytpINx6ysbbexRvK8NXr0Vh40SgGLBbLsISN3PfOvGcCfP7OjKdO2HjY3i6KX79ex9piVZW9PsvKAfoG3T8+z/5wQkvACELh8/wJA+C88gisO6Fkhgb9l72hYLj3uin1avvnTB5HJvnDac5PJLuMbd/yXqI85Ij6vlYcMrDxkbALBxue9UgJ6LYcsou/wsLkZLJlnAc0SQG5IytbmudBb2Hwz73BKb94Sm9LlEsxf90LwN9XsA2zIv5v6xDby5gWbwVv7BYuycyTU9AFul4fYPhzihYkE3/3Fb/nZb36LPTSK0jWCzjlENqsnEFYSCE3gCihwuoeIBEbIpSYxeuRYPaO4PQqCXiUWYxf+qUE8U4Po3XIsvnEstmGKSR21jAWNZYBCSkutYKaS0xNNa9H7RkVBbU4r+QHFokClJHyA9Wk909M6Zuf0zJ0wcOKEQfrYyOyckWxJQyw3iSkoJ5gSm6nVuvCHJfITGIMjGAIjaL1D6LxDhFLjzUWGUs1ApqQjnlMRz6nwJ8bxxBR4YmMS86dkwD5Al64TS0jBgLmHAXMfHZpjyG1dHBx6gweeWt4Efo3t5Wu+fJWo1lm8hM9d8xVu+eXjfPaKrwhrwaKnWHzmNVxx/S1s7HayqcfF5j7B6G0d8LF9OMi2QR+7x2IfCYDE2TESYWOPS9S6SOzf+i53EwCuOb5wZcZO2zEra45ZRYWQBAAbhc4Ne8AmKdDRkH93yueLw3ePRtgzFp6/byQseYPF393Y7WLbgI9ftb3E4n/xPf1g20ts7vOK0FO7lXXHbazrcEgJ+nk7xZp2G5t63LwwHmXzQIDtQ2E29njZ0h9g51CQbf1i2/iFiQQ/euhJfvjAI1hCg3iTSqZnjCRyKlIFDamCjgl7L3pXD8HEJAZXPwb7GANjB3jsTw9y2wO/puUvb9Ol6ULlGOT7P/w2Tyx7jDuf2c7XfiSqfJ7csJFgdIhJxwATLrlYZfLIsYYUeOOTRNIqdD4FVt8wuaKKfEnN9LS4WCmVNZSrOnyBfmQyGTffcQ9b+qYEuzsUYM1hLZ+Sir/PWLyE679/Jz9+bA033vUwn1q0iMVnLuU/f3gHf5P3MuoYQh+YIFnUU582U582UimL9YxSQYPb0YNMJuMvxzvFhvRYnN9uf7MJ/AJuFS7zCNWcn+mMm3eLQd6vRXi/HuG9WogPT2b43/eKfDCb4v3pBKcqQd6tRzlV8nOy5Oe9eowPZpOcLPiZTrupJC3UCgbKCROVpIUDf3/uXzKAb7+xi+milem8lZm8g1rSQr1gpZQ1korpsBv7MKiP4rR243EPYDYPEAkYicamiEcslHJOZmsO5moOTlSd1HIWTlb8zGZtzKQszKUcglRJe8n7DeT9RrJuFWHTCC71MD6TkphDQ9JjoBC0EnXpMY4PfAIAPzn+reMCmUzGlHGCiENLxKXDb50k4TaQdOtJT+kphczMpt28WwrwXinEyZyP2ZSb2ZSLD2tR3i34mEk7mck6mC04qeWtVPJm8lkDqYSYzqnkDdSKJqpFvThLeqpFLdWKllpV1AREMypc4VEmPXKsoTF0vhEmnP0MmHvo0R2lXT1At+Y4Q+YuDimP8+DTT7Nk6Vn8au0rzRmnrQNi+3NL7xRPvXiAy6+5lhf+vJ4RfSe/f/phLv/iZVx2+aV88T9vZPuQj809TlbuV/OHl7tZ/oacNQd1rG0Xcm/D37dqAdhppmmbnjwB2lYdNguf3bU3csaSqySfndj4XbFPx/rjdlGPctwuwNARC23tFrZ02Vl9WE/LQYPo7TtqFvLvPh3PvKViVcMH2Ax76IXvb79OBEYkALpyn4Zn35pk+VtKlr81QcsBHS37teL5HzJyw12PIJN9EZnsGmSyp6XbL/LtOx9phkgaIZMGoGykh1cdMi743CV2T0o8i87BBvgzi9LpZgjEKnyAEnva1m5jvZSkXNvRkPLmTfuifkMqiF6QAF54LvQAbl6w1LB1sBHmEXNf2wdFRcy24SDPjcf50rdv5qkNrWi8YwxbBrD5RohHJykkdOSSGso5M5m4jkxMTSmvJ5/W4PEM4/MM4ncNko4oKZcMFEtmtI4B9O4hLKYhYlNjlFMWkpIBvJzWUkyqKWc0xGIKKnkdtZKBWllPraqnXtNRq2mp17XMzOiZmdFx8qSBEyf0AvzN6ZtAcGZWz1RcgSc+jj+plFYV1BQratKFSbwJBcG02H3NlRrTWxqyJdEXVyiryUrrDLmSllRBjT+lxB5VoPUO02fups/Ygd6voMfYRbehk51/380lV17NojMWc92Nt/KrtS+zfSjAZ6+8jmu/+V2WLD2L79z7W1btn2S3Is6vN/6VxWde8zGAdH/rCyK53ecVYQopvLFZkjt3yEPS+ocoW9/SL/Z9N0nMnwhSiO/DhQs0C711a487WN/pYlOPR7B1vZ7m9Numj4DBbYOBJvv3nCLKrpGQVB0kJOBGAGTrgCgX39rvY9ugj1se+MPHWf1FT3HLA39gfaeT1sMm8bPiiLn5PbBrUCSLt/R62DUcZMXfernx1h/xrZtv4M4H7uLeX9/Pg7/7L55Zt5pnN21g5eY2ntq4jksvv5xVW9pI5PVkygaKNSPVaSPpog6trZs9b73Nsudf5+X24/Rr+vjl7x/l/M9cwlVfu4mv3/4Lliw9m/ueWY83McEb7+zh7LPP4p5f3E6HxcVNvxAdqL/Z+AqHVWomnOPYfXJUjmEsgTHM/jGUtn58MSWO0DiB+DjZoo5CxUilZiRfMRDLqNiw6SmuuPY6dskF8759OCgtuURY227kV20vsbbddFoB9uqDWm648yEWnbGYM88+l9Y3uzg0Poxc24vWpsI1ZcQdmiCR11Cqmkjn1Nx97+185+ZvsFchEtNtRwycd9ElXHr1lzluifA/M3E+PJHlZCXCTN7PBzMJPjyV5X8/KPPhu0XenU5TyZqoZA3MFFycqoT4cCbJ+9UwpwpeTuU9zCYd1KIWymETxaCQdGs5E6nQODfdcCNLl17FGVII8Ts33kQ+oaZeNVArGciltZSLZqpVF+msm0BIQy5hJpWyk0xY8flV/OWvW1i+4vf87Y3ncLgmcXssWK16rDY9DpcOj30SjbwDpbwDtbIPnaIH01gnXoMci2oAg6ofh3mSgE1DKeqiHPeS9dtI+6xkAnayPiMpr+kTAPjJ8W8dIgWsHiLj0VOJ2ClHnYRtaqIODRmvjmrUwomsl5mUm2rcQSVmZybt4v1SiPdLQU5k3JzMTTFXcDNbclIvWakVrZRLNtIpHdmEikrByHTZQr1kEr2ABS2lopp6TUe+pKNS1ZMvaXDFJxm29TPuGkThHGIqNoEr2M9jq1dx7oUXc+4Fn+b2B37JBZ/9HBdcchmP7t4nrQZ4m5uxWwcD3PzzB7jo4ou56OILef4fzzNu66Kj/1U+d/V/8KlFZ/D5a7/Khi4HKw/q+P2L3Sx/Y4SNHQ7WHrNLs2zzPraWw6am523FQYkhk9i3xtkAhMve0XLXszu56d5HuWfZLlYdNLL2mF1IQket4g2tXQDL1sNmdgx4WdtuYsV+PasOieqW1sNmVh0ysWyfdkEyWMi8z76jYfk+DcveVtNyQM/K/TpaDuhYvk/D8rcmWfbWJC37dSx7Ry3k6cOiSubOZ3awaPFVyGRHkck2I5MdZdHiL3LnMzuagE8URc+nhlskYNv0+h0ySs9PnI1ewdYjZtYctUoJ4Pk0cesRC22NhRAJ/IrTIfW2iT43AQLd0n2S569ZDu2Z33ldwAJu6Rfers2SVLdt6PRAj/B5BdkpD9N2WMsZixfz9sB+lA5RfmuZGsITUJCJasjFNKQTekLuMWpFC7HIJJWskXrRzHTZwHTFQKVoJJHUEI0pMXtHCPnkGKyDeL1yalk7+biWYs5ERgo3FbOiEqbSsDgUdVSKWqplUXdUn9YzM2tkZkbPiTmDxAAKADg3J8Dg7JyenFTAW6lrqU0LAFiqasiWVMRyEyTyKsp1URESyUyQK6mo1kTNTLGsJppREkyriWS1hDNqMiUd/pQad2wCd1yJJTiKwjVMh6GDdwbe5OzzzuWOJzazudc5L9GOxfjdjre49eGnefKvPQIsSVUq33/wT//S9nDz/X8Q4Qypn6/tuIM1Ukii8fVq+O029biE/69PzK2t63ILm8Ax52m+uebtAgDYqHrZIk3BifSup/naaVQGNToid49F2KOIslNK/u4YFuBv+6D/tBLozdLHv1zzrxnAe1bsYfURE+uO2Vh12MSqQ/Ne2F3DAfaMiQDSnX9ah0wm45E/PsS2vav549O/49HHH+I3v7uPH9x6Ez/44Q3c/pPvcuttN/HY47/CERhG7ZSjMAygdSnROBXsenkrixcv5pxPf4ZLrryOxUvPQiaTcenV1/PI1jdoeUfJt350Fw8/9TSfveJaXhrSE88oMdo6uea6q3h62e94eTLKfS17uOSL13HOpz/D3U9vol87gNs/yLh1CLVtAGdIjjUwhs6rwBMaxeSVo3MNonMPo/cMc/D4K5x9zln8dm8724b8zRWX+dsoO8bi7BxLsFORaM5rNip+1nXYuPpbt7D4zKV85Xs/Y09HH72TR+gYPcqIegidTYXNa8Dq13Os8y3OO+8c2vUmdowKmXnNYR1Lzjqbq75xCyue+we5YpIP6xFOVmN8cDLDB6eyfHAyywdzGf5nLst0yUa1oOdE3sn71QjvlYOcyns5mfNwIuvhRMZNPW6lEjFRCGqpxEV6eKZsoVzQ8Y83ttPS8nveees5gkEV1bqXYslBpeohk7Xg8kxgNCjQavpwuRXkih4CIRPhqJEbb7y5GSJZuvRqvn3DzfgCFqamzKhVw2g14/R3HcJrVlKMWSlGLRSCFqrxKcpRD6WIk3LMgs86jmq4g2zARi3lJ+Mzk/GaSE0ZidgmCZonPgGAnxz/1nGBTCYj5TVQDFoohW3kAmbSHh0zSScf1mJ8UE8xm/FRTbioppzUsk7mCl5mcm6qaSfVjJNCykQxa6KUM1HKm8mkdUTiCqIJJdHIOMWchnVrHyefV5HPqahVNZQqGvJlHbawEktoDHNgjDFrP+NuOcOWAbQ+BXvffJkLL72M8y76LA+uf5UHWp/nprsf4VfrXmVjr6cJALb0+yRJ0MfOYR8ymYxX29/mc1d/WQzSf/O7nH3+p/nRI8t44q+9PPn3UZ55Y4xV+7XSVNrpIYaGx23VEbGgsaLRuSedKxsTbwcNLNunF/486VZ08ImZt/UdDtqO2SQ2TUrrSmCx9bCZrb1u1h23sOKAgTVHTKw+YmT1YVNT/m34+5a9o5HAnp6W/Vq+9+CTwue3T8uyfY1brZB/94uPG57AFQf0LHtb83F28rqbWL5PR6skZ88zfUZWSQBXlElbWHXY1PRArpIAasuhBXNyRxp9gFI5dLNQev7+1Uetp5n52zr+1UqI8AQ2przmp+KmmrNem6V5uM29nvlJuKH5VPf24RA7h4NNdqd13ziLl5zJm/JBhoy9OLwjBCJK4vFJggElG9Y8SdgzRtQ3TjI0QTmjJ5/SEw9NkgqrCPnGSIYnSMWUBCLjpGIq0v4JijED+bCWcspIKamnkDZQSGlIRCeoFvVUCjoRAMmJpHulqKVaEiCwWtEyM61ldkZPra5jdtbAzKye2rSO2rRWYgSFPFyf0TIzp2d6RkexoiZfUpOvCN+W2FkVhcHlqoZiRU2hrGEqMY4tomBiaohes+gMVDj70HqH0fvkjLsG6TZ0oHD00W/uROHsZdfrm5HJZPz0t0+yRxGdX7IYmw9pbOn3NvszN/V5ua/1hQW2h5dPB0hS+vV05k4sxazrcEqTgIIV3DYUYHO/jw09HtZ2uGg77pQSs1J9UHN15vSd6rUdzvllmW63FMRwSYsyotR9+1CQHfIwe8aiAgCORdgpsYG7RiLN9O+Wfn9zdrCxUrOuw8nlX/5u0wN4xpKr+Px1N7Fin1YUtB8ySmz4vEVifYdDLI0M+nns+SPIZDKGnCbcwTHcoTGcATnFvLDClAoaamUDvrCSWFRJLqOhUtKxceMTJFITBGOj/Ndv7ubOX97DzkEXOySLw9p2kwC28jBXXP8t4b97+Fd86cZb+ewV17Di772M++wc7XyFCy+6gJ/eeSu7XtlFr26CBze9wRVf+TZLlp7NPct24AhP4PCNYHAOE0tMEIpNoHcOMaDrQu0cIpxUki3q2LjpCW646Ws8r/CxbUSAup3jSXYrEmzutLJizys8sXo5j698mlWb23hToeXFiRg7Ruf9njvHEqx4W8F/fOd2vvHj+5mc0mPwKLAFVDgD41g9/Vg8/aiMnXz9G//JrbfdTJfVxrbROHvGY6w5ouObPxFrNM+uXMHm/cOcrMb5nxMZ/ue9PP/nwyofvFfhvZkMcwUPMzk7Wzc9QzVn4FTOw4msk5M5NycLPuayPuqSz70SN1GOi96/2ZqZes3M1JQak2kc5UQvZosch1OJwSjHaBzDblfhcGoIBO0Ew1OEoyEUilE0KgVta1Z/vMf2zKv460tbiXg1RH2TZBNG8lEH2ZCLWjrAbCFKJT5FKeqiGHZSTXg5kY8wkw0zW4hSToXIx/0UYx6qSS+FsINSxEHc9YkH8JPj3zsukMlk5Hw6Tma9zCTdnMj6ebcQ4sNSkPerCbxWOYmAknxMQzZkIB0yUsq4ycTMxCMG0jEjxYyRfNpANm0kHJnAG5STzCgJJ8eJxkYp5FWo1YeolibJ5sfJFifIlXQEUloN5jL4AAAgAElEQVQ0Hjla9wA6zzCaqVHG7HKMvnF6dAOce/753HTvf/PsG3K2Doh03pbmrVTtMBRk+1Coyf5tH/RxzgUXcfb5F7D07LNZ/uYoP/nvVdzxx7XN/d61R4xifUNirk5juhYGHA4ZWXlIAD0Rxphn+xrde8v26+eLmCU5dfURM2vbbaztcEhgydQEkMsPCKC4vsPB3tEI2wd9ogBa6txrPWJhuVTj0pRkD+hZ9o6alfu0tBzQSylgvQT0GgBQI1hC6XZ5sw5GdAY++7aGnz+zgxvueZSfP72D5ft0TcZxzVGLkHQPSbL2YdPpQZDDYpmkwRK2Lkj+rl4QkBFyrzQXd1qFjGA/5wHg/Nko2m3WaHQ62SAxgc3JOEkCbvi8tkhgv7HusGM4yI6hRo9jmJ3DgeZrY4c8xGcvv5rWl//CgK6TcWM3w/rjROMqwqFJRnreIhkykY/pSUdU5JM6ot4xkkElcb+Sci7AdMlNragnGFNSLliYrrgoJEzkojqKCR2xlAate5B4VEU6NklRGpgX4E9HuaAR7F9NCoDUJCawrpVSwHpOnhQgULB9OqZnddRnxKTW7JyBSk1LfUYv/Rkd1bpIDs/M6MRjzBqo1g0Uq3qmEuP4kkpGHL2MOvrpNnbTbexEOzWIwjnAgKWHIWsPnROH+d3yJ/jxLx/k6z/4cdNntW0o8JGljgSbelxskUDa+m4367vdrD5q5bIvfYczllyFTPaQBJC+w8qDhvkQ0IJOydXSnvWaoxbWSJYAUczeKGj3s77b03xtNLamF5aSC4A4vzW9tsPZZJQbFxCN18m2wQA7hkPsHouyVxFj50iI55UJUcg8EmLXqDT5OCD8f5slYLtJ6p3cNuBja5+bX7Y+x033Psrdy3bRsl9H6yEDrYdNrD9uZ82ReQaw5ZCFdR0iAby5182zb4ywaNEZHNRZCCXGCYRHSaYmqFUNTFeN1OoWMjkTsbiKYlZPPqunVjRh0LYzrjzI8vWrOff8C7jjic2i9qiRbpeCTtuGw1z77R8gk8l4aututg76+MmjK1h85lncfP8faX3xVQ6NdPOL+29vfm3/PjzOi5NxHnv+EDKZjK6BN3H45ExY+vBHJjC6h3AH5PiCw4zq23H6hghGR0nGxe7st2/+Fk9v3sKGv73D1reOcMvP72XRGWdw/Vev4467buOue37MN755PUuXnsmd993J2pf+zAvDdrZ029h8RMna/SPc8ON7+PoNN6CxyQlF9cSTFqYCEwTiE1TLoj5JrT/G1dd8kbvvuY03dQHWD6fYq0zy641/QSaT8ZnPX8Gl136VPf123puOc2omxQcnc7xXS/BeNc771SjvF/3Y1d3MFqyczDl5N+fiRNrBTMpJOWqjnrRTS5qppU1UMmIarl7VU6samJrSMjneTdDXTzAwinNqHPfUJD6PjmjIitutJZEK4fV7UY6PMS7vIhVQ88yTv/nYktUZi55iTcuTnKxMMVOwU4zZCNh1+G1ailE3hYibtN9KOe6hnvZTSQaop/3MFWJU0yHiXhsBu4G410oh4qKenGIuGyTh0X8CAD85/q3jAplMRtwxQT1mZzrtYzrp4VTeTy4o0sAnCm5OZlxMpx0UY0aKSQO5hJpyxkg2aSYRUhMNTBIPqUjG1XgDcoIROdHUJIHYGOnsJKmMku6RI3z7J7/g4i9cRXv/IfLFSZIZBbGMEt3UEOO2PobMAwxo2unXHOeR5SuQfepTrD1mEUk9ieXZKQ/xojLBc4ooz43H2D0aFmGOoSC7RyO8MJHkP274Hpd8/hIuvuQzPLKqTZIpIjzx2jAt+zRNibJx9d7wu608ZKTlkIWVB03N5O1CYPf/PBfIwC2HTKw+amXtcbt4fGmqbcUBAy0HGrNwOjYct7K1x86Tfx9l+TtaVh0ysb5T1Eisabc1vYbzoQ8jKw4Iv+DK/Y2+QFMzVdxYGVnI/i3fbxAVNAvAZDNQ0kwlny5jN6tnmjL3vPfvtCm5Q+Ym23ea96/hhzoqmEGxDmJn7TEL6zrsojOt3craY1ZpXcHBuuN21klv5uub015uaeZLelOXNoK39ntFAlSqARFVIA42dovH2tDlYEOXk43dLrb2e3leGePCSz7PttdfQeOS4/QP446qMTgHiHgURN2TxD0qEkE18YCKZEhDOqYlHlSR8E9ycrZItegmEZkgEBrF7pbjsPaRDKpIhifJZHT4EhqM7mGGDL2oDIelwXhR/1IpCvavUhRl0PW6VgKAWqq1eS/g9KyeYkUwepmSikpNS3VaR7kmKjrEnxMs4cysAIWzcyI8MjOrp1rXkixMMpUYwx0X01+iRmQMpbMfnXeYqfg44bQSX0LB92+94bSk5o13Pcz9LXtZ8daYZO4PsbXfK4IcvQLwret0NbvsVh+z09pupeWQibuX7+GmX/yeu5fvEez2RyqDmqnwIxZWHRGvpfXSWkzrUUtzClAAS0/z32rrcEpysKMJAhtBosbcoHjdiOTtxm5PkwFslIXvVcTYMy7NzY3H2SUPsHMkxB5FjD2KmCQDB0XytyEl93ik15WTDV0uVh+1SBdABrHX3W6Rbq2sOSK8sC0H9LRISfnVRy2s63Sytd/LRZ+/gj1vvEogMUEwqiCcGCcQGccfGiMSVZDLTJJLq0ll9YSjKgKhCe685yfIZDIuu+4/eWjjX9k9nuQlVZIXxgXoayTdH970WvPrt3zFo+wadLFt0MdTf+nh+u/9jPMvFn7nx/bs46V9f+fbN34NmUzGN39wO0dG+pDJZKgtw3j9cgyOYXx+OUeO/4Xb7/wZ555/PudccBGXXHE1/9W682PJ3iuuvIwLL7yA+x64g7Hxw/gCCmLhcdJxFZmUjrGRd/jj4//FNdd+sfl3zjprKYsXn8E99/yU/v4D+HwqfH41Qd84Qf8okaieWMKKwztBsaAiFBzgqqsu47e/uZdDIwp2j0XYNiiqvS6/6hoWLVrEfXf/nLm8n1OVCKdKYd6rhHm/FOC9gpeTGTezCTtzKTsnMy5OZF2cyLiYyU5Rz9mZyduoZM1kopMUUjqqBQNzMzamKzaCzlGmyzbmZj3MzYZIpR1EE26cLhN6vQb15BhDve0YJgawqOX4TKPUUk72vfnxEMlZS6/m8D//zMminxMFHycLPk4WfbxbjfJeNcZ7M0lOVuKcLEWpJH1U406mMz7qaR/TGQ8nKxFqGR/1rJeZrI+ZjI9MwEjsEwbwk+PfPIQE7JykGnNQClmZSbqox13MJhz8by1KKeKgmrRTjlrIR4zkIgZyQR2ZkI5MzEAiPEEmaSYUmMDi6CEclVMtq5md1XHqhJYPTqh45OmnWHzmWXzlez/hrLOW8qc/3sdr+1/joWdbeGbrdtT2AQLxCYbNvSzbspHLrvky5174WR7d8U/2KGJsH/SzfTjEjuEQr6iSbBv089x4lBcmU2wb8EppPvFD/YWJJOec/2lePfwaa7au4KovXc12eYhtQwGeeG2Y1ZKs2fT4HTILWXeBTNsEVhIAFHKvOOcDGfPgT4Al4Ytra7ex7rhDKk4203rYxJp2azNksVICga0H9Sx/e5Kn/znJiv2CyRPAUby5rT5qawZQFjKOgmmzsOIdtdgCPqBvlj0v26dl+T6t6BxshjvE2Sr5ARusZaMvcKHn72MhF6kjsOWQWbA5TbN7I+XY6EI00XJQT+shI6sOGURSWPr/bJXk8LZ2K2uOmlnbbmHNERNrjphP2zlupKBbDzc2jAXb2HbMJubl2s2s77DRdsxC2/H5Uu61C5jDeTlwSkpb+tnc62bR4sX8ccer2Pxy7FMDhFNKRq19hCOThBxjlOI2Cik3ubiRuF+AwVRYRdQ3QcyvpZQykY2picQm8UzJiQSUVAtOwtFJXEEFCkMPBq8SuWkIteko9aKRetkkAUAd1ZKOWkVH7V+sgizsRqtN6yjXtE3vX6GioSCtNFRq2uZtpqSiPq1jds5AfVowgSdP6AlnJjAEhtF4B1G6+zGHlIzYehi29uBPjJPMqwinJ/AnVM035Z/9aR1P/rmLLX1TbO6bYkv/lDSP6GHLgLeZ0F7fJQD66nYbrRKr15wPbPo+raf3Ry6sElpQJ9R62Cq9xkyslFjlVZLXtu24o5kCXyft/rYdd7L6mLRJvaAWZq0EABvAb32XW4RCJDVg90iYFyeT7B1P8OJEjN1jUXaPhtk1FmWvMsFeZVLqAgw2rSSbeqfY0udlfaeD9Z0ONvV4WHPUwrrjogd0Y5eT1UdM0n63YNdbD5lYd8zKOun317Rb2NTtZtNxM4vOWMyEoQt/bJxcUUu1riNb1JHJ66hVrNSKJuoFPfmMmlBojJUrHuHCz17C8jfl7BqL8aIqhSHk4J8aDy+OutgqzR5ul4d55nWRyn3pzVe4/PJL2THkZfuQXwrb+Flz1MSPHnmWJUvP4o4/beQ/b76l+XX/7BeuZOk553HHI3/CFZrENjXIhk1PnQbylv29ny9cd33z1xdedAHf+c7XMVm7CcVVZDM6smkthbSOatFCIqwin9KTz+go5oxk0iYyCT1OhxyXQ47XM4HFPEwoqCHg1xEO6fD7JohHjUQCk0T9CjzuSfSmAaLxEWZmjKxdIwIsV37tu+yQh9na7xVg8twL2LJ5HUG7imrczlxuipmsl7nsFCeyU5zMujmVdTObsjObcnAy7eRk1smJvJtaxkGtYGS2YKdetDBbt1EpmslnLNSqQarlMJHwFB63GYtVj8mkJRhy43LZUCtGGek7jmmyh5RvgtSUmmxISzVj591ymBMFPzffdAtnLb2aM6Qi6Ztv/C6zWQ+nin5OFf28WwpwsuDj3UqID6aTvFdL8H49xQe1JB/UEnxQT/M/J0p8OFvgwxNlasUMM7Ui2USIfDJEPuZiJu0i5TN8AgA/Of6tQwBAn4l6ws1s2k09YmEmbuf/zmZIBSzUYjaKUTP5kJ5i1EAurCUVnCQfNxKLaIiFVXg8Y/h8g6RSo8zO6piZ1pFJyfnj04/ygx99j09f8nkebHuRb95+j/jhc9kVnH/x57j++3dx4WVXIZPJmAoOM6g7LnxIv/kDGzqs7FVE2DkcYOugX/SLjYTZOxblhfEoW/un2NLvYeuAj11j4ir/z5o0u8ZifOGqq9j+ymb2/HUTS88+ix0jYmHgydflUoDBIkk25tPk24ZcK/x8+o9VsCz0+TUAVoP9EyEIqyRnCtmqwYy1HTWx4oBeklglEPaOhif/McHyd7Qse1vFiv3a5uO0HrWwodPF6nbrv0whP7ztbSHbHjSyQuoNXL5Py3IJSDaTvQtA1arDpmZqWXgM9ZLMLEBa4w24UVXTesgkJF/JC7j6qFXyPRlYdVB4E1cdluTig2LDuOWATgKb4t9pgNXVRy20HbOx/rgIwLRJQRgRLLFIaWLzaWsjzdoYyf/V1mB+jjuac2DrpVLgxk6rkIzngwCber3sGA5x51Nb+MKXvobGI8flH6Y2rccaHMMyJaf94J/JhYyk/QaCbiWJgI5EQEUqosbrHCMXMVBMmIhFVESiakKeMQLuMZy2AaIhBSprHyrHAJOWMWyOEcYs3RSzIghSzKpEH6DEBFZLGuEDrGilRRANtWkh+5ZrOip1LZWahlxZQ66iJlNUkcxPUqxqKFY1lKoayjUN0ZwAepmSilBqnHxFQyA9zqhTrIMM2noZsveh8o0zaOpG5x0imBpn20ubuP5bYp1j8ZIltOyfZPd4gs19Hrb0T4mqluGgYMQGhdVC/N+K/+817RYRlDpimt+NbrfS1m7lN9vfofXox8HeR8+GvaLl0MI6ofn7Wo9YWHPcITGN82saa447hI9U6tVrO2YXzHHnvI1gU48Ab0L6DbJ7NMpuRYy9ygR7RvzsVUTZMeTjoY2vcttDf+KhDa+ytX+KnSMRtkl7w5v7vGzpm2JDp1Pa/Lay7ri9eQGz+qj0uj1kkBhBCxs6HewY9LG528XGbifrOxw8Pxbhxjt/zdX/+W0KpUniOTX5kpZ8SU2lqiOeVpHIqCjktMyWLUyXDGSTWs477xweWrmJ58bjvKqKsFcR4cUxD7sHXUL2HQqyQx5huzxC6wGxBXvplVdz7Zevk6qQwk2P4/ahALtGwvxu51ssXnImi85YzIUXXcAjrZu5b9XzPPmXbmQyGb/fsx+9R8HhDiGvXvu1b/H7vQfZMeTjvM9cJkDGpx6RAg034vKNkk7pyGS00vKNiZmSnWLKQClnpFYwUi5YSMa0VAp24mEtXtcwDusgDvsgwYAK79QEfp+CXMZGwK8h4DPgs40RD2hQ63tIpzXM1l3ce++t3ParR1nf7Wa3Isru8QSXfelrnHn2OYzpjbxb9DNXilBOOjhRCjCdcjOTFud0yoWy7yBzaadg/pJOZjN2qlkjM2kHs0UbJ6oOClkd0YiDSNiJarIHrU7F6PgAKs0odocFu9OJ1e7A5nCh0+vxWjWUswbyURW1tIXpnINT5SDvlXy8Wwowl/dz+K0/s3r5nzj4j5eZyXh4rxzkvXKIU6UA71ZCnCgJ8Pc/J0v8n/dneX+uQjzko5JLUsknSacSeN124pEgLoeVZDyKy+nAarNiMunJxj3EfZ9IwJ8c/95xgUwmw+dQMp1zMR23MR01Mx0xkvRqyKc0lGJmShEDxbiecsJIMWGgkNSTTpsJBceJxycIh4eplFXMzYky2+lpLb/65Y+54ppruOfxlaw+qOGeFbs5+/wLkclk3PPsdlHIrIjxs8dWsuTMJaRzSjzBIW665Vucd8F5LP9bB7vHIuySB3leGWf3aIQd8hAvTSaFx2tIsILPjcd4RZ1k52iU19VBVvy1nXPPO5cjA29y5XX/wW2PLGPnSIRNvR6WvzUpBRkszU4/UbDcmGqb3+1tAMBl+6T7F4DCj8rCwiNnZ1O3aPbf2O1uep1WHxbgb/UREZ5ohC5aD5vY0Olk95CPZ99Wiw1gKVW8vsMhPZaPNumNryG9rjxk5Cd/WHdacrflgG4BWBOgTADPhjRrYu1xG6uPWKTPRXx+AsAKH+DqI2bWHDFKzJxg9FYdNNJ6aCE7Z2yWYK/Yr2vet0ryRK0+apHeGBsVMQ12x8yadittR02npZ1FzYxZeu5Sr+Ahk/h3JUDaSGS3SYC6sU3cKNhd3W6TvGCOBdKgk3Vd7maR9LJ/Kliy9Cwe3/06zvAE+ZKWQlmHyTfKli0thD1KIg4lMdcEYbeCqEdFzDdJMjRJeEpBNmHA4RrB7R4nOaWm6NORDGqZmhohmjZimZpAaehFbejGZOsnl1CRiY5TzKooZdWUC2ohA0tewEpJdF+Wypomg1eqaqhO64jnJnBERwlnJrFHxnBExqjUtJSqGmrTWqp1LYn8JKbgMJHMBMG0kkxRzYC1R4A/ex/DjkHkjmHaVUdRu/qZcPbz2tFXkclknH+RKAh+aMs/2T4cYnOvYJW2DwfZ3O9r+i2bMuyxeTAmWD8h7bcdE6zYqsMmNnba+MljbULi/QgIbGmCvvlOyflb02nl4y2HF7CGzZS55BfscLD2mE2yC4gt3g1dLrHK0esRKyI9brb0TbGt38v2QT+7RqPsVcbZPRpmpzzItn43V1z/XRaf2egtvJrLv/xdNve42D0WafoAGz2E6zudrD1mY+0xAf7WHbexvsPOSulirvWQSbDeBw1sG/CJech2W/M5f/7ar3L7I8+gD1opVbQUK3pyJQOFkpZ0Tk0oriCemiSV1hAMjROaGuK8887laz/4KdsGhb91y8BUM/DR6LZsdF1uGwzwpZtu40s33crvtr7Gi0ohcze2zA+agjw/MsW6AwoWL1mCTCbj89d8WTBpUpjnZ09s5JwLLmT7kVGMXgEov3jVFagtI/xm46ucsbhR+O1qduL97fWtZFM6ouEJklEVhayBas7MyZKT2aKDfMpENmkkEtQSmFIQ9k0SDUzgnxolEhojnRwnHBoj4BsnGNAQD9sIek3YjENEPJMkg0aiQS2v/XUPS5Ys5pm3JpuJ493jcZa/Ncbnr/0qP7r/N/zfEwnerYep55yUsw5SIT2VtJ2T+SlOZT28uncjJ7Ne5rIOZjMuptMuqnGxBzxXcvLutJdC2o5R24vHMUjIN0o4oMDtnsBq06E36NDqjWgNZpQTKmw2K2qtDqVCTnxKx3TJwqlikHdLAuC9WwrxbinIibyfU8Ugp4oBThUDvFsKcaoS5IOZJB+erDA3XaZWLhCNhPFPuQgHPBRSUabLaU7Nljg5XSAV9ROPBPD7A3j9fkKhMMFwiGDITzDsRW9UfwIAPzn+reMCmUyG0aQkm7Qwm3Yzl3FTjRjJhjRUMhaKUTPlpJlqxkopJebeiiktwZiOYGCIfE5PPq+jUFRx4oSeE3PiHBj4C0uXLuErX72W1iMmnnnudX507338fvc+NnRYmibzL0h7oTfdfgdqSx/jjgEe+dNDfOGLl7O138Pe8Sg7pc3OlyYTvDCZZHOvm22DfnaORHhFneL5iRTb5SEe3vhnzr/gfJ5o+QOPL/sdV1z/LSkM4KflgFjFaACnlQf0zUDGPMgTDNqy/aJUuckCSmna+TJmwYqtkZK+6zscrJMYqS39PiEbtUshicMm1h23NaXQRlXKmqNW1nfYaTtmFusf0gZwy2HBkG3odrN9KMCGbg9txx0SO2cRgE3q3ms7aqbloIHl76hZdcjI2nYra46YWXPU3LwVgRQzKw8IyfdZySe4kMVcI5nzWw9LAO2gntWHjaw5bKT1oJ6WA7ome7nqoPj9FQck6faggZaDegEYF9THNOVk6bYhV69pyLvNmpmPBnDmC6YXJogbdTJtx+wL9oXtC9ZFTt8aXtfpFrvCPR429fn49cbXWLz0LFa9+DqliolaXU8wocTiGMXnGsOuGyDqVlEI6ol6Joh5J4gFJ4mFlIRDKrxTo1gsffgNQxTDZoLeUdJpDWPWEfrVfThcAyQiE6iMPRQTOoopHdWCnnJOTaWkpl7VUSvpqJYEACyXNBSLainBq5UWQLQEM5P4U5M4o+OMOvsZdw0QzkwQSitJF1VU6o3d4HH8KSX5ihZ3XMGgXbB+Q/YB5I4hBs3d9Oo7GTJ3M+Lo40tfubYp5X3x+q+zYyw5z/pJG7iNcMf6blHC3HbMwWopibswyNHosmw9YpJsFAbhkZPY64YsLBg/y0dAX+M0nQ4AF4C/1iPzsvKao1Y2dDrZ1DMl9nh7RNp3Y7ebrY3alj4RCNo24BeBoGHhFX5pMiG65OQBdo+EeHjDqx/rLVy85Gp+vf5Vnpc72CkPSSlgwQBu7HKxWdoDFhdsDnF2ONjU5WJjp5MNnU6293tZ3+mU+kPFRcuGTie/2/p3ZDIZfz74T07M6qiU1YTjY0QSClKZSabrdiplO/mskSnHIL975Bdcc/1XaTmkbZZnbx8OsbMZ/IhIQZAw2+QRtskj7BiJ8tJkgh2jCXaMJditiHF/yx5kMhmLFi06TdK95MprJT+1X+rNFP2H3/jRvdx054NEUhq+94Mbkclk/OIPy/jpb55E9qk7kMnORCbT0FjFeOrpR0gltEQjKoJBFZm0jmR4gtmKjXrBTjxqRqvrwT0lZ8otx+saxD81SCY9QSGvo1oxUMhqiYYncDvGcdgncdgn8bqU5KJGskk38ZCe11/bxUWfuZD1+3p5bjzGzrG4KBIfi/Ktn9zPA4+v5P97N827M37mpr2U0k5SYQMRj4ZyxsRc0UY9Z2Eu56BeNDFTsjCXdTKdtjOTcVLNmPn/2Xuv6LjqQ+1bCb3EyUlyQhoBUiCVQAIJJ500QoAQYkIIvdrYsmyruxcwEAIJHVxlyypW72160fTee9GMRt2yJdnkfb+r33fx37MlJ2et94Jb77X28owtWZY10n72U2fG7Fi0rYxFlczk7SSjwyRjCtxOBQ7HKGq1CsOoCb3RiM3uwOPxoFQqGBrsw2PVMJf3sVAICHk37+dkzsNC3s+pMR/zYz5O5/2cGg+yMBHj/y5Osjg/RcDvx+VyE43F8Xo8RMNBMqkEp+cmmRpPMzeRYTIbZ24ixcLcOEa9DpfTjl6vx2y14HQ58Aed2F2m8wDw/PGhjlUlJSWo9QMoND14bAp8thFSIRPZiIlczEo8pCIV05CMqYhHh0nHFYxnRwmGhrC4B4jFh/D6u8jnhkWR7ZzoNjs5N4rH08Y113yeSy65iE998uP84U+/k38Y1Z6wsXsgyuY6Dd/77f187tqv8q3vfgOLvw+lo5Nv3ngDv/zr0xJTKKbdXhiKsrsvxAuDYfb2+dk3GObPtW9y3Q3X86Pbf8aqj6+ifNcmGocO8/DTq7nx9j+wrd3JM2/388SrnZQfNbDxsEZe0BABCsHyrT2gkmRUwZCtPSdMsXyWHtZT2WBhu2Qyr2qys6neIldeVDbZKD8u+eWO6ARTUKeXWS95F7cYkjhmYnP9qCTZCjC0+ZiJ7R0etrS62NrhofqEky2tbiqKQFBiGCqOS8Dy6Cgb6/RU1AuPXVmdgfJ6k5Bopd7C0sM6uSpm/SEt6w9IU3H7FZQdFOB4/SEN6w8Vq2Q0Mtu5TpKLV7KeK1dJ1h/SydN0pYeL03L6c1m9I0UGtJiYNMoS+gYZIBhk1qe4vFJ8vklKjm6UwIVcD1LsGmywipUIKWkswgGCxaltdVPdbOeGH/6SKz7+SXa+vINEYpjpaS2J9AhaUz/KgWYsilZCln5iTgUeUz8x5xCpsIZ8WE8srCVmH2EqZiHr0zAZNjOZtJCJqgnHFOisHbhdffRoT+APDBAPDZFNjJBLKRjPKhnPijqYfFZJPqugMK4kn1eSyIyIAudxNfGcEn9yCJO/B1Oglx5LCyp3B/mCilROyWigF0uoj1ROSSSjwBLqR+XpYdDRSZepjWFbO1pPL0PWNnrNLfRZWxlxdjBob0fhbJe/98qPqsXyRZdX2r11C+DX7KCySZybJfAnA74VSdzi4+K83+Z68fyGj9cAACAASURBVHovyqSbjy1/DVd+bUtX+EyLYF/4ZKUbozpRLVReX0wLi8fFpY3aFidbW13UnBChoGLSt1gCvqtbFEzv7g2ybzDCzi4ve3r9bO9ws73Ly4/ue+p/7S287d7H2d7moOaEVQSMmm1yuKjY3Sk6NcWNTnm9SWYh9/T42DcYprLRQm2Lk+pmwepXN5qoaXEISfW7P+QHd97PT+5/hr93KFg8ZcATGOBYXzuHe7p46cABPvWZq/jvL32FJ146xs7eMHsG4rzQaWXbkV52vFvHi2+/zr43XmHzvpf58W9+y09u/zFrSp/kkWce4bOfE4st3/z2Dex49W+srvq7AIAXXMh3fnEPl3/s4/z4d/dQelgndWh6qVlRk/PYvjouvXIVa154Q36NPPpCHQ/UFAHzm5SUfIqSkhAXXnQ1VbtrifqG6Omr5+CwgsHRAQKeQWKBQXq7DvBudzfWiBGdR0ciOkwhq2a6oGcyp2V23Mj0hI6pgpbJgpZ8VkU03E80NIjDNkAsqKWQMhPxGwk71fzmVz/hkksu5qabv822V/ax91g7FW8f4/rv3Mgd995NesyG26Ug6taS9RkYC5vI+PQkfHpCLgX5hIFTk07m0lZOjlk5NeXg9ISdU+MOlqaDLM0EOTXpY34yyPSYk+lxM9PjdpJRGzaLFq3eyODwCH0DQyg1WjT6UUw2ByqtHpPFjNvjwWwexeeyko66GM8GOT0V4excnKW5PAtzBSYn8gSCIaLxJOFonHgiRSKVJpXJMpbLkUolyeXzuN1uvB4XuXSM6UKayXSITNRNMugg5rdRyITIxgMEfA5CAQd2x/kQyPnjwx2rSkpK0Gl7sVuHiPo1pKIWUhETsdAombiZeFhJJqHBGx0mkhghnFCRTOrw+fsJJxTk8moSiWGmplTMz+uYn9cxN6cnn1czPq4hmx2hvf01Glrf5mP/9Wku//gnufbG26iWAGDx/OTnvsSXv3YdA6Mt6Ly91O7dyLXf/C67+8LsG47z4kiCF0cSPD8QYkdvmL1DCbZ2ePj4Jz/FL377U26/4+c0DBzB7OkknR3iy1+5mt+XPs+2DjdPvNrBhvdHKJWATZH9KoK/5w4uM2NrpTDFcrhCAKENK8BMdZNd7p+rarbLxbSVUhpRTKvp5L9jZQK3WJ1SDFOU1wtZqVidsvmYmJcS7KCJqmYbtW1ueZi+QiqsrmoSLIiQqMwSC2kVM1n1Jkm6LYY7BGATRdJq1u5XUnpQI05pUaQ4OycAcLFLUMe6A5JfUPo8NkizcwIUaqUgiQDHG1aGR6SLfBHYFiVAGfytDJ7USWGTOtG9uEliO4vgY+MKJlCunqlfyQCK//8KCfxtPr5CCm4SIPDz1/+ACy76EiUlV3LBBZ/ipptuIh4bYGZahzOkYHikCZumHa+hj6hzBKe+nUxAQzKsJuZVoNI34dC1MxE2koqoSIb1hNxDTCZM5BI6tIZmLM5+2pTHyMaURAMD5NIjjGcVjI8tA8BsVsFYVkmhoCI7piSaHmFsXEkwrSCcUWIMDGAO9mP092IN9pIcUzA+qSIxJgDgoKMTS6gPV3QQjbeXTnMb/dZW7OF+HNERBm1dNCjq0Lg6MPh7ULk7GXF2MBro4d4H7uTCiy7i+YEQ2zrc0mSaR0ywnXDIoYuKRintK9kPNh0zSRJwcd5vud9RDmUct4g1G+ncJFULCVA/KlcobTo6SnWjmcr65eqgMgn8bZbCQhVSQrii3iTLvltbndS2uKhutlHbIqbf5N3edg87Jb/b7r4QLw7H2NsbYFe3l129AbFY0RPgjxV/48KLr/03BlAsl9S2ONna5qKqSazUiJsJ0V0oglHidVpWZ6SiwUJNs53t7R72DYapbbGzqd5MdbNNzNM1W0Qg7LiFb//sLq79zg+4+Td/4trv3MpXv/djtj5fzeUfW3UOO3fTb+5nR7ePTcfM/PSPj/Dp//40JSUlXH75pVxzzef55je+wvVfu5YvX/dFqjY/zr69G7njjp/w5z/fQX3dS/z61/8jQj2PPMfu/gjbu7w8sOV1bvrVvZSUlPDQnkNUNVnY2i5m8qpOiAWVHV0Btnf5+dqtP+eCC4VMvOGtVrZ2h9jS5uQL1/+ACy68hpKSa+R/63du/yM33fp9GWT+8L6nebaqivseuJcLLrxQfju9sZGJnJ7pgo6Z8VFmx00sTHqYG7ewMOVieszI7LiRuUkTswUzfncvXvcg6dgo8eAofvsw6YgZp6mPRx+6l+uuvZrLLruUz3zm0/zkp7dy6NBrOHT9JF1aog4VSaeGsE1BNmgk6dfisw6QDeqZSTtYmHZxatLObMEiPJdjoyzM+Dk17WVu3EUh6SAXt5JL6UlFbRhHDag0Wto7u+jq7qanr5/uni56B3rRm6xYHW5G1HoMJhtag/AJ2mwWzCYD+WyU3FiaQChCMp0lkR4jM5Ynnc0TT48RjqVw+YJEkxki8SSReJJEOos/FMHrDxCLxwmFw6STUXKZGGPpOJlUlPGxMEsn88zOFAgFvdht50Mg548Pd6wqKSnBbNNgc+kJR0zEY2ZiIT2puJ5AWEs6qSab0qBz9hII9RIJ9TE+piaRVGN2tRNP9DNeGOHkSa0MAGfntMxM68nnlZycVXHf40/wkY9ewI/vf5Zd/ZEVKwPLJaE///OT/O6uXzBgbmHE3kHN3kqu/vqN7B0UawRbe6KU7dvH6sf/wg9//kO+/b1v8q2bvsl3v/9tbL4e2rSNHOo5wJHBLo5rDVx+5ZU8sO0t1r0/TNkh4VcrSr3rJK+fzPbtV7F2v5K1B6Qqlf1K1u5XSkComJbVLycVpVDC5mNGEW5oslHb6hK9f3VGIbUeUEugUnyc9SsYs2LqduNRI1taHOzuC1HVZJF9TRUNZiqPi7O2xcmu7oAozm0WF77b7n1CLkfe1u5md4+fXd1+SYrVyWxd0e8nAzr5sUr8e6TtYMHgSSzfCrZz/Tk+yH87V1TkyEzhQd05rOBKxqco95XVFRO+kuRbZARXSMEr5b9yKWRQnOUrWwEMiwXU5dJSRHFxQpaDJQB4z8aXViw6DFNScgUXXHgVb7+1nbIND5MbM2Ab7aAQGyXpVZOPWgg5BkkFlWRjOtJRJbGYCoO7g8mAgZxXTyqoIhvXEfaN4LH0kgipCPuH8Di7SEWU5BIaxjNaxjMiDJJNDjOWGiGXGWE8p2R8XEU8M0IgOUwoNUw4PUIwMYwl2Icl0Isj3E92XEl2XEk8q8QZHUTnE2yfPdyPKzZMv6UNU6CfPls3R4fraVIeY9DSisHXjTnQw4CtHUuwl2hmBEeklzdaDvLRj17Azi6PYLvaBPNX2Sxk3/IGm+zzW5mULzKAm1cwgpUS6Ct28d1696OU1wufaUW98L8WbwCWT3FTJBK0OsrrTaJqpdFMdaOFiuNGNtePihqfZpuU8BU3OrUty+XOIqXsXp556/azpzfA9g4XOzrc7Ox0i8dd4s+K6yVbWh3Lxc6SB/DzX7tV3EA1WalokObn6kV90SY5yS/S7xXHLVQ329nZ5eOFgQh7+0NsaXVQK4HTPd1eMUPY4ZKri7a0uUSArT9MbbMs2XHLbbey8YV/8Px7RzgyrOPeNZVc97WvcfHFF/Hl666mtflNkmEl02kzizkPp3Ju5vMe5vMeMiEDYfcI4wkLM2NOxhNGgo5BSkpK6Gp8R+puFD83dw9ERa9jf1iEXTrc7Oz0CXWh3bu8nd0d4OE9B7nzuZ18+eYf8WztDmr27eGld99gzY4Xue3eR/njxj089Uoj3/n5XXz3l3+gbP8Qz/6jla/c/CO+dP03+fwXP8/V113H/Vtep6SkBKeznZmJUT6YC3J2LsriTIS5vJPTEy4WJrwsTQVYmHRxasLB/ISV2XEzyfAIkZCakEdDIe0kGzWRCBnxWBQEzYOMBbXEPDrcxmFywVEi1hGyIRtzhSRnThZIug0k3FoCViVh2whPP/pnkm4l8+NO5go2jhx4mfVrH+W1v+3G5zTic1txWI24rEbMBiV6rYKhgQ7a20/Q0t7EwHAz/cPtDCl6GB1Vo9OpUKgUDA0PoTGMYnV68AUjuH1BQpEY/nAMu9uHyxfE7vaRTGdJZ8fI5QtksmPEE0misQTxRJJYPEUkniAYiRGJJYgmUgTDUSLRKNFojFAkjC/gJ5lOEE9EiUTDuN1uXG4n4WgQrV5xHgCePz7UsaqkpASHW08w5sTh1eMPGoiEDaTjGtx+JcnUCOHoIMFAF/mxYaYn9SSTSmJxJeP5EWZnDZLkq13BAGqZmhZFtwcPbueCCy7ggZ37zymXXcn+7RmM8duHn+X39/6Kre/s5/u/vJMrPv5f/PLRjeweiLK13cVP71nNJz/1CX599y+457GH+c3993P3/X/gRN8B3u4eYMc/nue53c9z5zNVfPa6G7jq2utZ804/j/29jcp6k/D+1RkluVctJ2fXHlCz9n2lzACuO6iWAKEAg+sOqKXaFK0sa246NkrlcTPVTTa2t7nED/pWN5uOGSk9pGWtBPqK1SzLIZJirYwAgZukwMX2Tg+lh9TskC5o1c1ChqpqtLK1zSWxHGKndGeXn/I6jcR6iPmz3X0htrTY5ETucwe1EnOpkdm55RCLAIGCzdNLfYLFbWOd/Lk+d0DLuhWAb13xLILDFT2J61ZKwYf0/wYCBbtXZPyWpb9RqTbGIAO7/0gAH7dQXj9K+THjMlMkr46sZKGkZGjDyqWI4tKIgx/84fF/k/4aKCm5kIcf+yMabQNOZwfpmJb5vJuphJ2030DUpSDtUxPxDhANDhP09WPzdJONaZmLWJiOmSmkDET8w0StA0SCI+TiGjIRJdmoinxKSz4j2L9kfJBMcphcWkFuTDCA2ZyS9JgSX3wQV3QQo38Qo38AtacHnbeHYHKIVF5BJq/EGx/G4O9F7enGHhkglBpG6+1F4+pG6e6lSdNIo+ooek8nem83Q44O9N5ujP4eXNF+jP5uXjn8d777k5/xhetvpKbFwZZ2N9UtTinha6eyySFSvHXL0vu/d/kt+zHF16eiwUJVs4PyBitr3h6Ua2LKjhiobLRQdmT5tVVkhjcfM1J+1EB5/SjbWh1UN5kpPyZWcMqPm4WUesJOdbPw19VIpcxb5LCHSwZ+O7p87Ozys7snIN0E+dje4RHydnG5RHq+uyfA9k4vNS0O7qt4hR/96Snu2/wilQ1m2bKxqc4gJX3/rc/ymOgxLD9uprLJxgsDYfb0+qlpEb2FOzqFB7Gq0Uxti4OK46NUn3BRK5Vmb+vwsqcvyOqKF/nkVZ/j7beq+foN1/H1G65j1ceupKSkhG9/63oO79+HxdhOwNHPTNLCXNLKfMLGUsHPTMpG1q9lKmnl5LiXsZiZ8biFyZSDiYSVqbSdb1z/Zcq27Vm+sZYA4J7BGHsHY+zuj8il6Vul+cyd3QF29YTY3Rdmd1+INa93/Efn373rt7Gry8POLo9IVveH2dUXYldfmI2HRs55249/+jM8848WLrn8SrI5DaemXMxNBCikHZwc93JmMshC3sXitIfFmQALMwFOT7o4XXByasLO5JiJeEhHwKMg4FES9mkYT9iIuHQkPSrm0i5OFaIsjgeZzQRZnM7yr4UZzp6a4sz8JPmIi5OFBGemUywUomh6jlOIGBkPm/neTbecO9F24y24bEYsoxpsZgNGnZrujhacpkHUI02olc04bB2Yre2oDD30DvXQOdiBL+hjaHiEIYWKoRElozYXoViKTK5ALJkmEk8RTaTxh+OE42ki8STJTI5EJkc8nSUzliOdGSOeTBFLpkhlc6SyObyBEKFIjHAkQjwRIxILk0zHiKdi5CfGSGUTjOXTZLJJ4qk4RrP+PAA8f3yoQ6SA/UayhTCdyn561V0YTT0YnV0orN00j7TQONzMoKkbrb0bm6+XUXc3GnsXVk8P+byC+Xktp+ZFKW1xy/SktGuq1R3kllu+wWeu+hQ1LefKvisB4a+fqBD1FBdfym33Ps7Dzx9md3+Y9X97V/7h8q1bf8Cll10mP7/z0af4a+2LXHrFKj5x1Rf5zJe+ynU3/pB7y/awpdVO6WEdD+w4KC9dCECnoeyITvL7aVn7vpJn3hli7fuCBRRlyirBBhYXNiTWUEi4BjmlK/ZM/VQ22ahotCwzf+8rWfO+QjoFuCz+fcvVMsssW22zlCw8Kjruli9uPnZ1i5LaXT1Bnh+IUHNCXGTL60epOG5iU50w4O/q9olN4cPLfr1ihU0R1Ak5V6p+OaKj7LBalnbF+xTXQ/4f7J8MBM8tlC49rBfLI0VJWfr4/7mhLLoGRVr4XIBRlBiLid/lsukVj1fIh+XHBVCsbLQKebzRSlWTXS7Vrmy0cXfZvv/YdC35yMV85KMf5Ys33Mi963fQojcQi6twOfoI24fJBs1MJq1Mp6yMxVQkI8PEwkNYnZ1YvT30jzais3cQCY0wqmki5VcRDyoIefrJx7RMZHTkUyoKGTW5hIqxpIJ8RpqEK6jJ5lUksgqSYwrC6WFs4T7MwV4swV4G7O0MOToIJgcZKygxBvowB/vQentQuDrRenvQebvReHrRenuwBHtRujoZsLWjcndiC/VhCvZg8HfzVsshbvzZb7j0ilV8+2e/Z9MRlQAuzQ55bUP49VYmcg1yqXHxa1Nev1zFUwTaFQ1mUcPTKDr5yutFuXdVk10A9iP6FT5Pvfw1L1YFbZYqVTYdNUhpdcEAVkq70TXNdmn2z8WWFjs1K1K+u7p9AvC1u9je7mRru1N4/Tq9MkDcLi18FJc+tra7BBjsC1N7wiqWQ4rhDenjl634fyh+/puOjgrP3wkHOzq8VDU75I9R2ypmCVdOGlZKxdaVTU62tXn4uyrJhne7uXLVKuqO/o2ZaSuzM26ScTM++wgRh4r5mJXTCTunwqOcybpZGvMwFTIz5tEyG7eyMOZhcczDQiHA0lyCf52d5F8fzPCvD2b4YGmWZ595jKs+91ne7jWwrW8FAOwX/YdFlm93X5hdvYL1K4ZAtra75VaG5wej7Orx89dd73HXul3cu+lFak9Y2dsfFuGTLmk/WQqn7Oj28cUbvst/ffZqfvlIGTt7A/x+3S7++7Ofw2noJhu3c6oQYGkqzNJEkKXJIItTHhYmnCxMeliY9XF6ysvJCSuzBTPzBTupiIZYUEnIqyDgVhENGAh7DCR8ZrwWNSm3lrRbz2zKy0xKrGYszeaZSgbIR+3Mp/0kvUYyARNTcSeLk3H2v/nyfxQ0X3zxNezcWo1W0Y/NZMBtN+N3GnDZ9Bj07ZhNnWRiavJZHcHoECZnFwbbEBaXArVRz8CwEo3ewKjViVJrRKUzMqRQ4XB58IUiOLwBYskMNoeLUDRGKBIjGosTicaIJVKEowl8wTCeQJBgNE4mlyMSjzGWy5LKJBmfzJHKpglG/PiCHjJjCYIRH+lsnFQ2hi/gOA8Azx8f6lhVUlJCMGpncjbFgE7JicFWVPp2NLZODK4ezO5OdM4OOtUNWD1iJsjh7yYY7SMY6WFmRsPcnE5m/4pncdx+YkIM2f/wB99kdWmNkHT/FxD40oCbnXWtbO3wsrs/SlWDkZ/d++dz7i4/8pGPnPP84suu4KJLLuOesufFqHybk31DMV4cjrKjO0Blo4UHn69nY52B8mNGOaxQDDg8d0DNc/uVrD+gXl7RKBYpHxSBiNJDWmnqySB79ra2utgleY5q21xUNAr27bkDGgn8ifPZ91YAwP0q1h1Ur6icWU4VVzaYqGqyylNZxcWMmhM2Ko6PsumonppmK+XHR9l8TC+qYeoEoNoo/VrZKMIQRd+fvCIi+/t0MgCUJd+DKkoPaik/ZmRTnV5m5EpXANSVrN9zK1jAcxi+FRU1xV9LpXJteYXk8Ar5Ww6KrLjIrgB6ssR+XACBotevqkkKd0iex+qm4nKInZrmIuhbLoSulABg+XEzn/varedsIX/+a7ew7t1efvXoJi6+7HI+/cUv81ZbB/7AAGPxUfJhPbmAjpR7hEJMz1zBwskJM7HwCJHQECZrG6OODkZtbYStvWQjBjyhEVyOTiZTeiYzBvIJFYWUhmxcSS6lYiytpJBXk8+rGMsryY0ryeSUxDLDWEN9GP29jAZ66bW20W9vI51TkJ9Qofb0oHB1YQr0MuToROnuRu3pYdjRicrdhcrVyaC9HZ23m1EpQKL2dPLUpoe56JJL+OotP6fyuJFdfSG2trtk5q9YWbIyqFEs/y6rG5WLnsvrzaL/r94kL7QUewGL4K+46rHx2KgIK9UZpG5L3XIQSWKbi9+PonrIKFcWVdSLzr3KBgtVjVY56CGsDh62SUnf3ZLku7vHL7yM7W4hC7eLDr+tbcXf84i6k6JPsNvHzi4P2zvcQlpucUpl0qLbcGOdXk7ib1xRZF1x3MLWNrfMNj7fH+LlkRhb24Qlo7LJTtUJJ7WtblmmrjrhpKbFzaZ3e/jOT37DpZdewvYtzzKVs5BNaknFLQQsQ+SjVs4UwpzN+lhKu1lMuTgZtVLw6ikEDMwlbCxkXZxOu5hPOTg17uXsmQk+ODvOvz6Y5IOzE/yff83yz9f2UFJSwpbNazig9LK9L8b2/hhbemNs7Y1K1psIu/rC7JLqVPYORnlxKMyODjc7ekK8MBRl71CMPf0hdvUGpLcNs7tPAMjtUl3Qzp4ge/oj7B2I8oLkz947EGHvYIxtnW4uu/LjvLn/n0TdgyyM+zk7E+XMhJ+lQoDFrIuFCTcLeScLOQ9Lc3FOT/uYm7AyN25haTbERMqE39VPPKTE4xjC41AT9BgJu0cJO3SEHFoyASsJl4G4Q0s+bGcm6WU84qQQc5FyjzKT9DKZDOAbHeZkxs+GZx//j4m2j370GR564I94nRaMWiWq4R4msiHM+kEizgFC7n4yEQ0zBROFnIpMZoRRRwcj+k76VX3YXG5GLXZGrQ5GVFrMNgd2pxtfIIjFZmfUbMPudBMKhYhEY/hDYeLJJFaHE18wRDAcIRiOEEumSGZzeAIh/KEw2VyWfGGMeDKKL+glEA4SS4RweW1kczESqRDhWJBAxHseAJ4/PtSxqqSkhHDMRa6QRGfVc6yrgdbhBkZMrZi9w5gDCgyufjzBHgLRQXzhQcKJYcbGFCyc1jN/Uk9hQi3SvyeXgd/crI6ZWZ00bK9l29bHuHLVKjYdMwp/yr+dO/uibO+Ly/LFmlfFkPqfSyv4x+G/yaDvik98ihtu+xX3Vf6d1bVv8NjLDdQ026ltdbGzy8++4Ri7egJs7/RQ0+LgkZcaKTuoYeMRKcUrSbTPHRTev3X7VWw8opWCH8vVL8sFyUYqGyzCk9QgDOJ7egI83x9mT1+ImhYHm4+bKT2sF1LvfqWQlCUA+Ox7Cta+r5QBZjFdXFzoKDuip6LBzPZ2N+WS4XyTVJWy4YjunHqVjZJ37t6KV+WVjY11xUJlA7UtDmmVQycHMuS0swQABSMoBTkOathwWCPWSo6JWpmK46PSEkqx/qZYHL0CFB7Usu6ATpb1isCvuOggL5gcWWYCiwBw0zHTiqJqg1yJs3IjdrPkJRMrIFLhb6OFmhN2qhpXPG+WpMJmBzUS4CtWhFSfcFLVJJiu6mYnFcet3LPpJX547xPcu/llqpps7Orx89Du99nWaucHdz7A56/7GrmMklzSxOlZG4W4ibm0k/kxF1NpMycLZnJZA5HwMMngMMmwEru1jbRPwWzWg8szgMPSxkRCTyGpYzylZjymIp/SUMhqmchpyefVZMZU5MZVjBfUZPJKgskhRgO9KFyd6LzdDNjb0Xm7SOZE3YvG243S3YXW243eL1hApauTYUc7I07xWOnqZDTQgznYS5eunl/dfTsXXXIJj790XGJ9QtLurZB+KxpsbDxWLBo3ngPkN9SNUnbUtAwAJVl9S6uL6mYhF8sTbE3CA3hf1T9FD6T0GiiGizYUmV/pLDusE/u5dQZqmoTNQdxYicDHZskXKGwVwkO3pc0lMVUe0e/XG2SX5Lfb1uFhR5ePre0e+e1rW53i/doFeKxtc7NVYgKL3tld3X52dvuoaLTKhe1lR4oAcHnBRPx7pHqnNjc1LU5eHXDxYreZjceMUgm1g+oWF7UtDtHZ1+WnttVF6VsdXHrFx3jyqQcIe7qYypmYSOvIZcykwhZyITNzY0HGfEZmojbmIlaybh3H3n6F+bSD0xkHp9J2TqdszCcdnEq7WJjwc3Yhw9mlLGeXMnxwJsMHSynOLsQY6q7numtEUOKGb9zAD267lafWr+Plhl52DcTkn7t7BmPsGYzx/HCcLc0mbr37Ia7/zs3cceevqazdzK6+EFu6QzJbWGT9tknVW8vBvDgvKRK8MBwXwHEwxm+equZLN3yHTFbLTMZMVtqZP1sIcXYixGLew+KEl8Wch8W8j6XpKIvTUeanXEwX9Jwcd7Aw5SMT0hB2DeCz9WI39TGqH8JlUhGwKsmGrdg0A4z5zBRibsZjbqJOPfPZIJNxN4WIi0LURdJv5b3X9pELmHn9xe1c8m8M4CUXX8Prr+xGPdyLUdVHOmwn6rNhHR3GoW9nMmthImdgPGskkxphIq8ml1fgDvbTq2pDrdNhNNvQmayYrHYUai1qnRGD2YrD5cYfCBIORwmFI0TjCSKxGD5/gEAoRDyZIhyJEo3HiSWSBEIhAqEw8VSKUDRKNBGjMDVOLBnD5jDjD7lw+514Am6S6TDxVBhvwHkeAJ4/PtSxqqSkhI6RAQa0CuraG2joOYQv3E0iPUwoMUg4pcTg6qHX0MWJkUaGTF0MmToxunuYP6kjW1CTyipIZVXEsxpSOTXTszpRCD0jZq7m5nS8u38Xd/3xd3zt29+hpt37v0vBkhy8S/rB8z/3PclHPvpRni1/hk9+/hoe3PEOf976FuXHzctbsC1O0fXW4mJ7p48XBgUw29ktErpPvNZJ6cFi6EOEQNYUQdp+lbhQQgui4gAAIABJREFUHVBRdljDmv0qNtXpKTusYd0B6c8k4CP2Pe3UtrqpkZiJLW1uqk84pOCHbjlgsl+9fK7wGK6XWEd5b1eSXYtAcP0hFaWHpCm1Ff15G4uLGhJbcudzu+XHxRWPDUcMlB7SUCGtjogEpgQCV/r1Dmp57sBKMKcR1ThST2BFvWADxarH8oVbAEDJ21gswz68nOItPWJkZYlv6RFpeu7wuR4weXbusPgYpQfV0nycQSQ+pV5FwfyJ9ZCqJuvy7uuKCTBxWuUliOpmacO12SGeS+GB4mul+oST2laXvPda2+rmvoq/sbs3QG2zhUsuv5Id7x4lHhihr3E/aoOGXMrNbNLMbNbC9JiFsZSeTFxNLKwgG9GS9WuYidnIh/S8++ouNqx9hHde20k+pGMipWMyrWdqTM/4mJp8Vk02pyA/riKZVZDNKwmnhrGH+9H7elC7uxiwt9NrbWXA3o7e30ufrR2luwuFq5Neaxs91jYG7B2oPN1oPF0Y/b2Sj7Afc7CPTuVhrvrC5/jq93/GxkNKdvUKr9aOnoAs9W3r9FHRaJP3nZdrWpZ3nsvqTJStSPpWNNrEHJoE/ETK2iwAVL1Jek0aKDtcTIOv3M/Wyc9lkHVETKkV2cDyY2JGsaLBQlWTTQp+OKltdbK1XUi7Ozo87O7xs6NDMH3b2t3S6RFvJ31P1rS6qD7hEGez6A0sPq9pcbG3L8SuHr90s6OXy9mXU+lGGfxulorGi6XY5Y02OS1d2WSnqllMD+7pDbK7R6SN9/RH+Jsizo2/uJu7HniI+YyDubSV+Sk/6YSbUfUwTk0PhYCBU0knsxEL0xEr434jCyk7dW/u43TCysK4l8VJP4sFD6cyThZybhYnApw5GWbpZJilU+JcnPSydDIkQhXjPhzaLo69/wqv7dvKXXfczqWXXMLzu6rZ3RdhS2+U3UNxXhiOU9ts4spPfJI/3XMH+996kReer+Xzn7uKL139BZ56/C8cPPgmbw3YeH3Yy77BsOjhG4jx0kiCFxUpdvVFeVUZ4YWhKDv6Y2zvj/K5r32bl17bTT45ysJshFP5APMpF4tZL4tpN6dTThbyPhYmgiwWQiwVgpyZjbM0F2U6b2S+4GYuZ2OuYGcmb2ZuYpTTE24msiYsxn4CTgMxj4mTYyFCDgOpgIVC1Mlk0k/YoScXsFAIORgPu0j7LLz6/HbiTh0zGT+3fv82MdF2gZho+95NtxL36ZjO+okHbGQiTkJOPe7RIWIeFSenrJye9jM3Y2JmSsfcrI7ZWS0nT2pJ59XYfAP0KnvpHxyif3AIr8/HqNWB2x/EGwhjd3tx+/w4PR7iyRTxZIpEMkkkFiUYDhOLx0llM0xMjBNPJghGgqSyCaKJMJF4BIvDyqjVjD8UwB/24vQ7SWdj2D12LE4rOtP5JZDzx4c7VpWUlNA50seASsHRtveIxXuYn9eTySvIT6hJZpX4o8PYvd2YnB0Y7J30ahsZHG0mO64gllFwel7H2LgKT3QYnasPe2iIiSkNk1Ma5k5qKUxpKEwomSwM89mrr+aP1a//RxBk5fNdfSF290d46pVGLrz4Ev77i1/i9kfLqWg0semYkeoWp/SD3SEb0atbnOzo8LK3L8jObuGh29Lm4pk3+1j33gilh4p9f2rWvL/MzD37noJn3h1hzXsjAhAe1kkpWTWlMuDRCAP7cbOcehQhBRPlDaIAd/3hlYXSxeSvhrUHVgRKVky1rZxrE2siBqlc2iR1+pnY3emWweGGI8WeQCPVjWbKiylFiaXcVJxwq9PLDMu/7wjL5+FlMCg8fzrWH9RITI2GTUd0knQnwN9KL6AsB0t7w0Xv2HLCd7m7rzgtV3xeekgryrgPadlYp6f8mJHK4qSXtPBQKXn4irU6tS0OqqWpt+LaR2WjjfIG67IkLM/CiddETYtT2oeVbhJO2GXAVyOBwNpWj9gKbvews8vLrh4/P7rvSb5y84/w+5e3cj973fUohtoppA2Mp3TEIsMkogpSURXR4Aj5iI582MIt37uVSyWD+aWXXMstN99CIaZnNmthKq+jkNMwWdCTG1eTGVOSyirIjavwJYbwRAcF8LO1ifJnRzumYC89VvFY5e5ixNnJgL2dYWcnKncn5mAfvsQQofQQgdQwrmg/ak8Xt/3sFm6+/Q529EQE+OsNsas3zPYukYbd2iE+59o2t1yr8+9TgxuOGM9hAEXPooXKJofkARTVQ4KhtUo3IpKn84hgoItev7Ki5C8HjfTyn28+JgI+5UeXQaAIV4mv+9ZW57KU2yG+Tjs6nGxrc7K1zSlLv+JmTDCGNS2i9qdaYoeXH0sgsMXFzi4f1U1mOem76ejoMviTamtWhl2KwaJiRY7s82t2sqXNw45OH3ulupldPQH29IfZ3mrlwosvoWeoi5lsgGzcgcMwjFnZi1M7SNg0yEzMxlzcznTIxGzMzukxN6dSNk5nXCxkXSwWvCxOelnIOzmdd7OQc7M0G+bM6RhLcyEWZwMsTvtYmvaxNOVjaSbE0oSPxXEPC+Nu5nNuTo976Gx4j1Ufu5KHH3mQLa+8xVtDLrb1xXhwx9t84rNXE8nG+b/zMc7MJ8gmHRx+62+sf/YxLpQqXT760Y/y/e99lzVlZdS+38YvH1rP5Vd+TP4euWf1nZS99BZ/eGoDJSUlzI6byaXsnJ1PMZf2sDQd5WTSyemMh8UxH4sFPwtTYZamIpyZjYlzLs7ZhQyLM2FOTtiZTBmYLTiZn7CzMONkruDAbRvCaVES8ZiJe0aZTvvxmNTkIw4KURcxt4mM18R0wst40EE2YCXuNpD2jvLB6TFOTcaoe+8fbF7/NHXvvcKprIuliSCFdIBM1EMq5MRtUhCwqXGb+pmbNHFqKszMpJ7ZaT0z0zpmZ4TXfXJKQzChQmXuZ0g7gsXuIhAKY3O6MJpteENRIvEk0UQSu9OFx+/H7nLj9fuxOBx4/D48fj/xZIJkJkU4FiGVTZEbF/6/dC6L1e3E7LShMWrxBjwY7SZiyTBWlxWH34XVaTkPAM8fH+pYVVJSwhtH9vN+8zucPKlnYlLN9IwYpZ+Y0hJLjhBPK9C5B1Ga29Hae+nVnSAUH2T+pJZT81rm57XMzIgznh4mN67En1Rh8A6g9/Sjd3UTz2l5pqqSCy68iHX/PMGu/nNBX1H6LbJ/u/sj7Oj284M7H+DHq5+kqtHMllYn1cWVggabfMGvbrJS2+pie4dHnn3a2SWWDTYfN/PoyycoO2KQK1HW7lfJHr2Vnr3nJKB2bmVKkbFb9r5tPGpc4RMURcmiPFolv8/aA2oZCBaDJev2q85h0IqhiPJjRgm4GalptkomeQEIRemxkMiqG83yBbvI/m1cUaVSVmdkg7TssczkGGW/XZHdkD17RRm2zsiGIzpKD2mkrjYh4a0/vBwkWXfg3CSxkMlXbA4XwZ80jbXhkIayw1o21+llf1fFcbO09yvSlSsl33IpxVvZaBUesAazDHQrJVZIgBD7ii1ghwzIi34/UeBblIXFRb+meYU03CQKhWtaXGxrc7G1zcnWDiEtVtbruexjn6D6lTf50heu4uGKPVzzrVv4/QMPszhhYWrcTjajxRfsw+3pxRNWEAsoeOcfO/9TXrrkGt58dSuZ6DCZlJLcmJpsVkUioySZUZDMjBCXQiC2cB8KZxsqVxtabxvRdD/Z/DCmYA8KVy8v1x/gseodPFTzPM+98Br3b9zKxjeP02EZot/WRIe5C0uwB423myuuvJzydxrY2RdlZ29Q/j7b2ReWlmXsVDQs/59tOmZeMTO4UgIelfv+Nssg0Crt8JrPCebIazCHtJQe1EhSr6gk2nBET+lBjViNkSYFN9Xp2XREJ3UGGqWuP7O8viEKn5dZvu0dHra12tnaYqP2hAQOJWm4tsVJbYukBKwMpDSIHs2K4ybxq7zW4+SFgTBb21wC2NWb5Zul4vdMceNYAECLvDRTBH81LaKCpgj6dnT52N0XZE9fQNy8DkTYfGiICy66mE6jnfl8kIx3FNtIB0HDAHGbgqnAKHNRC/MJB6cSdhZybhYKbhYmPCxMeFmc8rEw42dx2s/ChJ/FyQBL00HOzEc5Mx9haTbI0myAxUkPC2N2Fqd8nDmV4MxUiKUxL4tpJws5H0uFAAsFPy31b7PumUf42lev4+KLL+LxR+9n9YMPcPXVX+BfSxmWFrKcXcpxdjEvScw55iajTGXcRH163n//de6567d87Mor+MXPb2Owr4GQT8Pdd/2cG7/zdb71zev51re+TuuJI8zmfJzMB1kYD7Iw5mMh6+X0hJszs1EWCgEWpkIszSc4M5/kzFyMMycTnDmZ5OypFGcXMnywlOODkxkmEg5OtDTQbfYQTPmZSJuJhRR47UqcliE8ZhXZsJ2Qw0DSbyUdtOG3avEYBpnPxci6dGRDDhIeIydzIeZzQeYyHuazXk5lvczGLcwm7EznwtgNw2iHWkgGjGRCo7hN/UzmdcxM6ZiZ0TI7q2NuRs/srACAJ+d1zM1piKeH0No76dOcoE81QHNnK0qtDr3JhNPjxepw4PIH8IcihONJApEowUgUTyCINxAkmkiQymZwedxY7GasDiv+cAidycSgSkV7fy8KrZZRqwW9UY/BbCAU9aEwaLE6rOcB4PnjQx2rSkpKeOvYu3Rr20hkhshNaBmfEMWz+YKGTE6DN6JCY+vA6O5h0NiKwtRCODHI3EktUzMa0nkN4bSKWEbJ+ISaVE5Fn6UTtbMbjbMHtaMLo7ub197cxqWXXcoDta+uqCo4tw9wZ0+Anb1B9vSH2SsZkKuaLGw4oqO62ca2Do98IRIXMeEjqmlxiTmoLuER2tnloabFSVWzg7/uPiLLr8UalCL7t+Y9JWveU0hy7TKAK7J1lcdNrN1frI3RyDNq6yXQV5SV5Sk5ufqlOCsnAb4DatYdUEkgcmUnoOSxO6hmS7OFssMaKqSLUnHyrUwCehUNktdwv1K+uC6Dr2XT+qY6PWWSr2pzcb5LKvEtOyrOjUdNK/r3BHAU0vNyT+HK8Mb6Q6IUW4RHBLAtlXsEtZQe0rBBmofbdEQ6jxalPamr75gAc+USeNh07D8XPQTLapUv5KITzi5f3GuaBPMnfk9IvTIIlDxpoiRbMIBVRQmwWZj+l2VgF1taHdQ0W9ne5Wdrh0hd/37NVj79hWv4+U9+yK9/+xueePEwl125Co+9n7GEhnRyhEBkAH+wn1FXL4HgEOvWPPS/GszXPv0A4xkd41kNmYySWHIYf7SfSGKQ1JiCRFZBLKsgPTbCG8fe4snSp/jdgw9T+9IONu6o4ISmHo2tkZKSEj79xS9z9Tdv4TPXfp3rvvsjLr7scr79k9/ymS9+ic986asc7noPZ6SPr3/rqzy9pZYdfVFpP7WYBo1S22Jn4xGtvJlc1eygqtl+jmezVGaal0Mdy0sspmWPpsw+i/nA595XiBsICQSWHtLKNgLBDOolP6ueTUcESy3qfUYl4G+RQbooenYtJ367vGxvd7Kl1S42f1uFhC8AvsTuNdnk8nR5M1hKE1dJ/X5Fu8DOLg+bjuooK9oojq5grutGz1k5KU4LVjRKDHOLiy1tHnmGbmu72A/e0x/hpZE4O3tDvDAcZ0e7nY985COoRvoImBQ4FR2knWomgiamw2ZOJWwspt0spF2cjFuYjVqYH3OwMOlheszB4nSApckAizk/C1kPi+N+zkz4WZzwsTQVYHHKz8KEh/msjcXpIP9ayAgANRNlMe1iMekUQDDvZ3EyyOJkgMXJIGemQtjMQzz26F/4/Z2/JuA1cnZpjA/O5PnXB1N8cKbA2cUxzi7l+eDsJGfPFDizmOPUbJzFKT8fnE5wZiHB2dNxFmaCTOecrHvmESwuK//f0hgnpxJkImbyYTMnU05OpxzMBI3MJ0wsFXyczrlZmvCxNBmUmL8oZ08mODMXY2lWsJBnF7O89Y8X5ODfRy+4kB/c9zTxuItC2kjQrSYZ0RJ06og6dUymvCS8ZrxWAxGvHf1QF9NRN+MBC3GvlUzIwanJBAvTac5MxZnPhZmMuziZdrI4FWc86Wd0pJ2IfZiUX08qoCPsGWZ6WsfUlIbpKQOzU0ZmJ03MTOuYkWTg+Xkd0zMa4tkRNLZOFKNt9Gma6Bru4lDTYSxOB4FwiEA4hN5kxuryYHd7sTrdaI2jGC1mtAYdBtMoRpMBvVGN02PHG/Cjt5hRmw30qRS0D/WhNRrRGrRYHRZUejUqow614bwEfP74cMeqkpIS1m5cy5BhgH79CXT2dp56ejWB2DDeuBpXoJ9XXqvl7fd344sNYw8N0tB/iEefvp/xSQ25CTUW/wAba5/j7cP7yI0ryU2o6FcdYv3mx3FFFRh9/Tz42J94pvRhrv/+j/jJA+vY3R/h6ddOcNf6Peewfz9e/TSVx/U8Pxhh31CUv+58j7s3vMAWScarPG7ilt//lbJDWhks3FO2j9W1b4ikYIeHiqMafrL6aaqaHezqCfDIi4384A9PcN+WtyV2TsODe49x231PC2lWKn7+4X1P8+DeY3I9y5+2vM1PH9os0rtSNcxNd/yFx//eJoO8uza+zG+e3SknjJ95Z5gbf7WaZ94ZEqzfQQ2/XbOTeza9ItKQhzQ8/mo73//9Q6wvrm7sV/Hzhzfzpy3vCNB1UMvDLx7nR6vXyBNbG48a+dHqtTz6UhPf/91f2N3l4bF9R/n1E9VsaxNpxo1HR/n+7x/myX92sqneRHm9hT9Vv849G56nQurFKzts4Ht3/IUNhzQCHNabuHP9Xu6teE2SbA089Y8Ovn/nX4X/UNoAvv3Rcu7f8g4bDosC6Yf2HuV//vTsCiBq4H9Wr+XhfcclT5meP+94n9sfq5JYTOElu/XuR3n2jW4BAI5b+GPVP7lz/V5RMXLcStkRPd/73YNsrNPL8u7dG17g/to3qD0h5LzS94e47d7HqTnhlKtefv1kLX/dc0hKkNp48tVWfvqXdVSfcMrnz/66gaf/0SFAYIuTR/cd45pv3yqSps0mtrR7uO3ex/ni12+S5a0f//AmvnTDt/n+7b/DG3aRS6h5+unVmFzduCNKPN4BHn/sPi666DMrGMA0F1ywinfe3I3L2U00NsgrL5fT0/WWWMgpqNAa6qiqfRKtR8ntd/+eS674GJ+59nq++ePf8rmvfovPfvkbXP7xT/LJz19LSUkJfyjby7YODz9e/TTVTSZK3x/iWz+9k//54xN8+eYf8/Grvki9aoBNVY/z6f/+L17pN7O7L8Suvgirq1/j4b0HqWgQ9TnPvtnHrXc/KjPpm49buP2xKlZvfVcGgg+/2MiP7n/unCLkH9//HI++1CgDpvu3vccvHq2g9JCG7/76ftYfUHPz7x7k8b+3SSBQwz2bX+GONTvl3ej1+xXc9Jv7WbdfIV4D9aPctX4vq2v+KQP1soMj/M8fn2Brm4udUrHyHc9s4ZEXjgj/5gk7T7/Wys8eLJUYY8Eg/uSBdTz+tyYqjhmobDDx4M79/PLxKrGWI6XJb7n7Uda8PSAzmPdWvMbvntsjAUATpQc13HzHX9hwSCvYvwYrd23Yx+raN6lp8fDKUIjNR3Tc/pc1vNTnZWuHn919IR4uq+WZV5vZOxjj2bd6+fKXr+P6r1xLzKpkImjiZNLFni2bsAy1iDBHxoOq6yiv7aliPmVlPu1goeClvPRJnnjkzyxkvCxkPLTXvc2RN15kMetlaczHTMJK6ZpHmM15OHsyzgezcY6+9yrdDe+zkHaxmPcRcyjZ/NyTLGTcLKTdnM56eGV3NQPNB1mcCHL2VBqXQ83ze7dJYG+Ms6dTPL+7Gq/HyAdnx/ng7Dg6bS9vvfkSZ8/k+dfZSf7Pv2apqiwllXCwOBngzOk0Jxreo6SkhPtX38ugI0RJSQnXXHsd4dEhTo97mE85OPT6Prrq3mRhzMlCwUPCo6R8/RMsZB0s5dws5j28/spONMMnJHYzyuYNz1BSUsILbUYe2v0eF158CT+9/2n0mk4SQQ1WQxfNda+zs7aMiEuLd3QYl3GIv95/L50N75Pw6DiZC3Pnb3/Ju6+/QiZgJRe0ELSqeeaxB4m5DITsOqYyQV7au503X93LmdMpZsfc+GzDlG14mJlJC6fGPZwa9/LqS1sY7DrMXNbBzLgZs7GeHTvWMDWlYWJKzeyslpqtT9E7/D5qWxtDhg7+9sZLPLehlL6hYYZVWhxuD48/+SQDQ0OYrBYcbjfvHzjAK6++SiKTIpqI4fK4eOjhhxhQjPD28SO82fQ2D617iuf//hIdwz3YXA6ONB3nTw+spl81fB4Anj8+1LGqpKSEXlU7No+OQV0LNk8v0bQaa2AYb3gId3CAaGIYb2SYUGIYe6AXo3cAo6cPX3yQxJgSg3cAo6ebqWk1M7NaJqe1BFNKvHElg9Zueo2tdOuaeOngG3zsU5/hoT2Hzl0C6Rc1BTu6/OzpD/P8QIR9QzFe6HVR1WxjS4udPT0+dnZ5qWwwsbXNSU2zVV6BqGwULGBVs4MtbW6p7NRDRZONPb1BSg+peXhfg+znKz9mZL3E8q0/UFz+ELJwsRy6KAVXHDdJrJ5KrnNZXglZyRouV8usO1hk+9TyAkjpIVEMXXpIw4bDUlHzgeWQiACYaklG1sjsoMxOSN6kDUdGWXdQLRiwJgvbO9xsrh9lV5ePjUeF1Lvx6Er/klWsHEjbuBWNdiob7ZQ32oTsKslgm44WN4UNVNSPUlEvqjk2S2XVZUcMos9vxblO8gGuP6RfZg3/bf2jyF5ukkGgWU4wF5nclfJicVavGDQoTrxVNkrFwM22FZNbdskHaJU9X5VNwkNY2WSTjP/O/zhrWlySD9BNZYOFLW0eqWhYdM5ta3fzx3W1oth21ZV89YZvcPFlV3D3+l3EohoSSRWeiBK9owu7b5BAYICbvnsTl1xyjVQyew0333gzqbAKv2+AXFrF5LiWybyWsayKwoSGXF5B9da1XPGJT/P5679L+VEdu/oi8rmzN8QDW9/kzrU7uL/mn9Q0W9nZK7rctnV62d7lFwzfgLhx+sINN/Fc9QZM7jauuupTrHvhFeH/64/IFSA1J2xS155ZljTFjrVphV9UJ5embzwqGLpiurcYGhFTh3ppKlHN+oNqnn1niPVSqn7dfrEtve6ASkjCEjO84ZCWjZI8vPGITi6ErqgfpbLBLM+w1bYIlm9Hp4dd3RL712IXqxstDmpaHLLUX9lokTsIy+vF67W8uEdcp5eqZgznsM4ri56XWXaJ6aw/1/tXZP+qTwi/364eHy8O+DmmGuWVDh1/H/Ly0mCEl3vslO0f4s8bd3HpZZez/smHSDi1zETtLGV9LOb8LBYCLE0EWJz0szDuZSFj53TWwamMndPjHhangyxOBzmZ87OQ+v/Ze8/wuOoD7XsMtikhJm1DEggtIbvpgRBgEzZ90wmpbEIKZA0Y27J6Ly4YUwIskACukqzee7HqFJVpmtH0GfUyMxppRm2KnDzP++X3fvifcyRv8nziq891ncuyZEtja0bnPne1sjphYsWtJ+zRszZpZnNuXGHK4mEvsSU70YCVqN9CdG6MzVkzm4tW1uf0bCyYiS052Qw4WZsfZ3PRRizkIRZ0sbnoEGxhws/lhJ/LiQCJ9RkS4Qkhvyb8bCUWScTn2UosshWf53JigcuJRbZic8RWfSTWJoku2/jbVoh/+dAHUalUFP7PGVQqFb99OompsUFWp8ysz1mJTI+xKoHG2IaPaMhBdNFCdN5MbM5MdMFKdNFCbNlGLGInFrZiN3axd+8eiju15Lf5uO97v+LOL32VFoOD4KSGaccAUw4NoRkz1pFOxrRt+Mb66W8tZ9Gjw6LuZNZmwKkfZNZhwGsdxe8bYyM0zdqclaDPSHx5gsjiBGZNJ+6xfjYiVlZCGlZCOsLBEdYWrawumFjzW1nzW4j4TYT9esJLQ0RWdETCWlZW1KyEB1lZERao+WA/84F+tJYmunX1lDWdp6rlHOUNxTR1tjGg1eGdmGJYb8Drm8BksTBqNKHW6ejXaLDYbAxoNPz57Ju8XvU2b9W/Q3V3MWXtlXQMXKKlp4PajiYqW+pouNRxFQBePd7VsU+lUlHZVklLbx1OrxrP1DB27wC+6X6mZnuZXVQz5u7C6unE6OpgxN7OsKWRqbkeXNOXsE10o3d2M2pvxTF5iUFrO73GZnpMrXSO1jPk6KFrpJY3yv7Cnutu4Evf/TmFLY4rZN/jnRMUNNs40TXB890TnOjwkt9oJbfOzLOne0iWLhjJxRpFTsysNpHfYCX14pAk9Ygf2PkNoo6hqMlGRrWJE+1uDp8f4PETJYIZKB/iwJkB5QJ24OwgKaVDPHOmXzByZ/pJK9WSdK6fg6d7ePqdHg6e2eHtO9MvSZ/SpNp59Q7pdzv8Iape1BIIHODgmb4r5GcBCAc5cKaPg/IUnbJGMqj4DYXfbzutmVQ8zJHiYUWeSy3XU9RoJatSL4VFdFca2MvERUwup82pswoQpFxAjVKP27a0l3JRJG+zq/SSOV8nBUtkqWynXLj99mFFkh6WQiHbAFDx/SngwyD5tPSKr0zeNc6oFD1waRUGyXAvAJ3Y9TVJMq9Refw7pd8M6X1ZNULelFOg2TWSV6zGLEnA4tecWgEqFODRMM7h05e4dvcebrjxRsrPv8auXbs4lJXB9Tft40O33c0ls475xT5GbZ0MjtZj91zCbevgrTeKOPTMb3n9z3m4LZ3MT2iwjbUQXNQRXFSzHNCwEtDhXxjkYNIfeP8tt/L4sdMUNtmE97XdQ1GbW5rnElaIojY3hc12Ubrb6hRni5OCJlFCfqJ7kmOdPj5y92c4UFiIa+YSv/j19/mvP/yC3BYvz3VPcrx7imMdHrKqReWKDGiU6TepC1CW/OUiaBkciY48nQL85C3og/KN0pk+nj3dJ57jp3t5Vvr1oNSxeejsgPAqmBzJAAAgAElEQVQEnh8Ur+MLIgSUKoG2zArZAyj8fTk1JiH/No2TV2cip9ZErvR+8b0SwC+jQq94ScXNik6amdORUiJ6/Y5IwSg57Zu844ZEnEPS5Nuwsjktv25SywyK9CuHhrJqx8httJPbaCe/yU5uk4MTnV4eevRJbrjhBr70hc9QWfwXFu06ll3DrPv0xBYdxJfcxMM+on47saCdzdkx4gt2YnPjbMyaiS47iK/5SGxOE/fbiS/a2JizsOm3kQh7SCy7ubw2SXzJRcxvZzNoY3PeSHTJQmzeQmxujM1pE3G/jc0ZAxtTQ0T9Y8TDTuEb3JjkcmyeoGOYidEeFu1DbK1OspUIcnlria3YvAhixOa4vOVnKzpLIuzhcnyBy4klErFpErEZ4rFFAQ43p7mcWGQj4mDSpYQRlDOvpJuwW4dntJeAZ5TL8Xm2onNsrniJhTyEJyyseE1sBMaIhcbZXBgjvmghFrSKx7s5wSOPfJ+f/Oi7DNvt5NWZed+Hb+Vj93ye7x98jhL1GIu+IabGe/GZe/DZdQzqR5lxqpl3jZBYXeTv0SU2/F5C0zZm7aPMOw3MuUwszzhYmnYw7dBj1w9gG+7CZbpEZHmEtWUzGwE7G8FxNkPjrAXNrPnHWPGPsrw4xEpwmOWghmBggGCgn0hYI51q5QyHB4lE1EQiasIRNdMLvYz7Ohgaa6BvpJa6zhJqO8t5o/QN3ig/w5tV53mh+C+8Xvk2xa3VvF75BufbS3F4WthYVzPi7qXd0MJf6t6hWlPNmdYKWgc7KG6qugoArx7v6tinUqnoGGyiU92A1aNj3DWIzdWPd6oXf1CNa6oPm7eDHmMjg2PNDNk7mJjrxzN9iTl/H9OL/ehdl9DZ2jF5e9COt6MZb6dRV0uXvpFeUytN6ho+/q+f46Gf/YnjnTt2KmWDeqswUz/f5eGlXh8vd4+TXWMi6fwA2ZWjZJSL2bDkYumO/uIQmZWj5NUalffJG56ZVWIsvrDFSU7dGMfb3WRWGzl8+hLZ1WYyKw2K9+3wBS3PnBkQ4Ot0n+LPO3imj+RiNc+c7pXA3MAV/j4Z/IneQMn/d25QAoSC6Ug6N0DSOdlzKMIlhyRZ7NnTfaJe5qwAnAfO9l3hG1RWQs5rdqSARZrysASw5PLk9HI9ObVmUkq1HCnWiMRspWEbaFXsNLCLvjx5Yk4GVWkVRtLKDRIAFOAxvUx0AqaXDys+PvlrbgdLdDtA347+vwtahf07csWiwtD2nFiFPNkmwFualPpVgGGFnPQ1KVLwFYGPKpOSCpYN/hmVRmkFxLQDKEpBEGlSLEeeFasdI7fOSnatqAcRFSKiKy6z2sj7P3o71+7Zy9cf+TWPPfYLbv/4R3ilpIwPfPR2vvLTJ9DZBrF6Ohi2tKI1NuP29uHz9OCfUTPv7icya8Bn78EzfomZiQGmJ/qZnukjHNSytKjm9ts/yp9OnuZom5OiVpeY4pL3Wpts/DLrFR7+1VP8PP1l8hos25utrS6R6G1yUNBo57nuCY51TvD1/3qW2z/9RZpNw5RW/5mbb34v+fVmHvrp49z1uXt58Mf/RdpFjfS8MInt33JRPp5cOszh4iFR2aPM9Q0pbF9a2bAyW3hErvc5r5ZWcqTXhfwaOt2rnM+e6ePw2QEOnenjyAVRN5RcLG7mUkp0IhxUPiLqZcoFEMyqMpCrJIAt5NYK1je3Tki/YiJRr/RBppaJgFGK9DjlG8UjxdvBlCSJdU+6IJZ8RO/lCKlSEfU/n7oT4Y/0KhNZtRby6gUAVM5GG3mNdl645GP/qQvceNN7cZkHuLwxy9qcjbXpMUJuPRGvns1ZK7GAQ6x5zI8TnbUQ8RjYmLESnR1nc9ZCbNlBfM1LIjpFPOQkujBONOgg6rezOW9lY8bC5pyVyKQJv2OUFe8wIdcQG5MjRKeMxGZM4vPPmonOjBBdMLMZsBAPi/BIfNVLYmOajUU781YNy1Mm4ssehQXc2pxha3Oardgsl7f8XI7NklifZCvhl/7MPPHoFH/fEh2El+MLXI7PE93wsR6ykXHkaVQqFXXtXdz/o99y66e+gMFkwO8YxqHrYG3GwuUVL9EFG2GviRWPgfDEGJsLDqIhm9gGnjcTWxwjHrSRWPPidQ1x771f4BtffwizbZTMSj0/OXKSfR/6CJ966Ltkv/I2r7x9mq9+81u8/0Mf5v4fPIZ30smEpR+fWcu0Tc+i18qUTY9jpA+PSY3PMsSq3828y4jTMIBttJ9xXQdz7iE2VsdYD9oVALgaNBEO6AX48+tYDmgJLw0R8msI+dUsL2lYCYqE8EpoBwBcGVRCkeGI6MXd3NAQjgyyGOjF6mrBaG1kyFzLqLWemu7zdOjKUJvqGB6rwT3VgdndTnBZjW1aTXH7Rd5qept3ms9R3lfJ2bZyOrXdVHY0XAWAV493dexTqVT0aBroG2pnZHwAi1vNsLmV6bk+Zhf6MTnaMdjb6BqpY3CslUFrBx3aWsa9nZjdXTineugd66J7tA6LpxujuxO1tZ2W4Xp6jM30Gpt5MuUwN978AfIbrVdsVB7r9FHYItr8n+vycrzNxUtdNo41mUgvH+bQOz3k1JjJb7BQ1GQTUqb0wz6jfITCBotImxZrSZaa/LNqzBQ02jja6iS3boyCJrtYB6gzc7zNQU6NidSyUQ5f0JFWNkpG+aiQa6V9YPk8fH5AubgdlACaHOSQJ+MOnRuQgN+2HHzonJoj5wc4LP95ZXFEksUkBvGg8vf6FebvWWmFRJaT5Tk2pa5Fkue+f+CokHpLpAJbSSZOL9dTUD/Gcx0usqsNojetxU5O7ZjSiVbUJKasUiVze1qFSfIH6kktFyxgpnRhTS0bIaN8hPQyHWmlWtIuapUFBxEiEY9BSNNDHJa8f3L/4HagYGjHBfbKrV8Z1Il6EaMSUEiVkpfymSEB24wdoC+twiAu0jvA4fYMnAQI5QqQaqPw/tUIJilbkoIfSTm1QxIWpcG59ePkVBv5yeFj7L3+Rt734Y/xyXs+wdce+hJJb7fw4Ts+yRcf/HcCgX4GjI30jdahG+vEYGwkMK9maWqY1cAYM14tgZlh3ONdzPs0uL1drAS0LAe13H33reS+9oYAgG1uabfWRnatkdv+9UF2772LXdc8xe49d3Lrpx4gt95CfrONfGXqzEF+o52T3T6OdU6Q3zjOzR/+GL9JL2LEWotKpeIHzxRewcr8IutVUsuFpJlWYVTsBUklMvjTXMnmXtBtV/qU6DgsgSgh/w8q9oeDZ/v5zn/ncfBMH8+e7lXOg6d7OXymj8Nn+kg6NyCCQSUyS6cVbL4k/2aUj5BdbSSnRrB9+fVj5NWZyK4xkF1tEMsdNSYyq/TKfm96uVwgLrF4JdsJ9ENnt5n6gxILKRjI7bqa5BIdKReHFJvDNgM4ss2cV5mkgmlROJ3fKIM/G8fb3fzsyDF2772OM2+e4vLGPH/bmCEyZSa+6CTud7E5O05ieUIka6OzxJacxBacRLwmVidMbExbiC6OEws5iK+6SWxOcPHca6IweWFc+PimzUS8RiEJT1kITBgJOIZZsA8TcI4SchsIOkfYnB9jc2qE6MI4K1490aCD+IqHeNBBbNlJYmOSxKqPzflx4uvTbG0FRABka0kKgsyzFZvlb1uL4u+FnGzFpHRwfIbYuof45ixb8Tn+thUiEffz98th1pbt+Oz9vOc9N3L+rVfJarRzy13/yg+eSGHWMYp1oJUJfR+Thn6CjlHmrFpWJ8fYWHCyOjPOZtBCbNlObFGqvpk3i7cjXhbn7dx//5f4/e9+xaOHCnjtkp2sSj1f/sFjvPeDt3DdjTfxyfu/oTzHy9susWDrx2vWMGHR8vKJAqxDPXjGtLjNWrwWHdP2UaYdenzWYWaceqZsWuY9BiIBK+GlYVaXxlkNGIgE9CwvjhJaHGY5oGNlaYiVpSEB/oJalvxaQgEtoaCalZCa8PIgYUkKltm/tTW1wgauralZX9MoM6mRiJhQjayqiaxqWV3TshwZIrisRmPrwOjpo13fxdm2c1QOVnKxt5yy/nJKuqup72mkrqPuKgC8eryrQzCAmiZG7f2MuwcZHe/BON6O3dOB2dHGqKODIWszHboaeg2NaKxtaKzt6G0tqMdaMTg66RytQz3WgtbeSZ+5hUvGZpq0tWjMjdS0/JXr33MT3/p9MicaRzny8tvkNtooaLYJCavTx5/7pjja6iS/aZyMylGe+Us76aVaiQ0Y42irk9Qy8QM+o0Lc5Yst2CGSi4XUmizXjVQYKJTG14+3OShscZBVrSelVCxuJJcOiQ3gMyKpeOCdHnKrBQh89syAxLpplHH4Q+cHRenxObkUWmboBrc7/aRlj+RisXEqSpWFP+rA6V4hj0lMyAHp4njgdK9gTiQgKH/eZ6Wk7c7f7wSBB89peCTtzySV6La3WncsNqRV6MWsXKmO3Dox15VZbSSn1kROnYmiFjsnu7zk14sC65SyUZLLRgT4Kx8lpWyU7GojaeVSYrdcdMAp1SESCJNZRjkl+r+75JIVuXg7nXwlCJSSwRWyz8qogD25bFhmCOX9WbkLMLtGSLYy0JNTw5lVRrKqTNI+7TYbmKV0Ao4pEnCOVOb9WN6bonakYZzc+nGy6wQQy5HmxHJrx/jar57m/f9yC3v37uGF8xf50YEC3nPzB3j61NvYJ3vpMTYxaGhmxNyCz9OLz96Fz3EJ/7SeaY+a2UktXk8fl9QVhIIaloNavnL/Z3lkfwpFLXbyGq3kNtjIqR/np6kv/cNu8e49d/Jo6kvkN9qVs6DRLm1EOzneJVjAx4+dYffe6/j6Y/vZe/31ZNVaSS/X8ceTZ/nQbXdytMlMQb2B1HJxCuZPtw38dpQ3y/vNcgflESmtfvi8ersWSXneDvKTlJckW4MsCQsJWJy9JF8QoCzpXD9HzgsmMF0CcfLSTk6tmdzaMYqabBQ1WcmrE7U9WVUGcmqE5zVDqnVJKx8Rvr5SHSklGo5IpeKHzgrm/ZDU5SmDv8PnBgQbuGOSLqlYp1TTiOm7nTcn20np7FqxIXy0zU1Ri4tj7W5e6Z/iROs4u/dex/nKKhKbC2ytz7A5byM67yCxPMnW+hxbkWm2Ql62ItMkIj7iYR8b0xZWJ02sTZoEQzhnIb7qIb4+weXEAi21Z4kH7KxNmlj1GVibGmN93kbQrWczPEl4ZpzNkJu1oIugx0DANcrq5BhrM1ZWp41EvKNszBsEA7jhIR6bYGt9irVpM9FlF7HIJPHoLFuxeS4ngvxta4nLW0uC+dtaIr7kYHPWTHzOTGxhnK11H1uxSRKbPuJhF/FlJxtBN+shJ5cTAf4WnWV1yUV+xmH+7Z67ae7T8LtTF9lz3Q3kvlVFwDnCxJiW2fERpkxqfPo+gi4Da7M2NqT/q/iKm3jIRjRgJx7yEF9yE50xktiY4emnn1AA3muvPMexTh/PdU1wrMPHU6/WKB/7ygMPsHvv9Xzwtrv5wjd+zE+eyuCnTybxRk0HlS3tvPzWGXJfeoOnC1/hT5lH+fHvnubRpzOwTznZWrGytuQQnX+hEdZCRsJLBtaXx1hdHmVj1cRScIilwAArS8OEljSshocJBtUsBQdZDqlFV+CyhlBIyMKrEqiLRERyOBLWsLyiYWVFTSi0UybWMOpoQWdpom+kmmZNFR2jzdQN1HChtZjqoVo6DY10G+rQOjVU9zbQN9pGbVflVQB49XhXxz6VSsWl4VZMjn5sXjVqQzNqYyODpnp0Uoef1tqMxd2OZ6YHo6sDk1OcPfpGBkzNjDg66TY2ox1vo9/URI+xie7RBloHq7jtE5/kgZ/+kSMlOj5+150SE/Eaz3V5ebF3khcu+TjZLkpqC1sd7H+9hUPvXCK9fJTsWgsZlQaOt7uENFdpILtKyEQpkkE9o2KElGI1ycVaMiQPWV6dhUKpE7CwxUF2rZn/fqONtAo9R0p028BK6vY7Uqzh2bMDgg2UwFxmxSgpJUKCTS8bUeasxHKHPB0nwN8R6UKSXibKjoX3b/AK5u/ZM/0cli6Oh872S5KzDP52TKxJPWpJ568Ef8r6xo5pNdETqFOAVupFUfWSWTmqADcR9DCQXKIho1LPsRY7R9ucPNfuEhUZlQYpCKInZUcdS1r5COkV+h0yq3GbhZOk1bRyPWmSb1BmAeUutf99KmB1Bwjcrn4RwY/UCoN43BLwS5XAn1j5MCsAV/YMZlYZlcBIRtV2rUlWtZiME+W/ohRa2Qeu0PNI8ikefPQJHkk+JcDHjs3ZnDoROMius0qysIU/nCzhxn3v48eP/Ih7v/wlshocfOcPyVx3403kvXAU13QPvSMNqPVNjDu6mJ0cxD+lITChwz+pw+fsZtbXh93WwczCAIMjNezefS2Hz/WT32glp95Cdv042fXjPPizP7Fr15WVMrt2PcVDP/sTL3R7OXVpgmNtbqkc2U5eg42iVocS9vhJ0gmue897efixZzne6eObvzuCSqUi+c0KClucHG0xSyXIozsWQDTsXI05dEGrLMfsXKwRp8Rey8y2bIGQEu/PSoDr4Ok+Dp7u49AZcR4+2yfA3wVZAtZKPYAiCJJZaZDYfgH88qTKl+wa+fu5LROnlYluzFRpw1r2Fx451y9sF6d7OXyun0Nn+gS7L70GD5/fucAjL9lot2+mrii+Ngr5t2aMvPpxCprEBu5RaQbt5b4pfn/iDDfctI9QJMBWbIHEiodYyEciPM3WyhSJjTm2ovMkAi7ifgeJtSliQTdLHj2RCRMbcxai81ZiATvxNS9bsVkSG5PEI27iQQdrU2OsTY0RnjCz6XeyHnASDU+xNGsjHnITW/YSmjKxPGvB7xkm4BplyaVjfc7MetAiMXZeYuseoos21ib1incvsTkjhTsWScRniW9OcXkrwFZikeismY1JPfEFC9FJE/EVF7GQjdiqi9iyVEWz4iYW8XB5K8jW5jQbYR/Lk0Y+edcdHHnmCV7sdPLgTx7ni1//AQvjg4wNtOEzDDBh7CdgH2ZtysrqjI1owE006CGxPEE87CYWsLMRchMNTxBbtJNYm2Fmxq6AvGMnT3C0w0dBu5ffHDvD3utvpKgwk2uuuYaq4r/y7F/b+G3BX3n418/wqQe/zQdvvYtrd+/hxps/wIc+/gluufvTfOQTn+WuL32Vz3/7Ud7/kY/z0I//C9eUidWlEVaCGsJLI6wEhwkvGVhb0bO6oicUGmZ+7hLLIQ3BgJqlgJqVZS2hJTWBwADLIQ3+gJrgkpZAUPpYSIs/qCGwpCYS0RFc1hFc0hFe0bHoHyAc3mYC19c1LIcHiKyqcc9eot3QQudwHeXdF2gcaaBZW0uzvoHWoVYqu8q5NNRIfc9VD+DV490d+1QqFQ095YxYOrF7+/FMD2L39jFsbWXU3oV9ohuruw3vdBeLwQHGPF1obC10jNahs3cwZGtlwNpGt6ERna0Tk6sTo6ON42+8yM3/8hHuuf8b5Le4+Oo3H+aBr36Za67dzf7XGsmsGpWkHSPpFcM8+3YnT7/ZrnTLJV3QKh1wyVJnWHr5MDm1JvF2mRT+uDhEdtWolAIcIb1sRDAJ9VZOdXsparGTU2chuVTH02+2Cz/fue21joNnB0mSAODh82pSSgTLcaRYS3aVkfTyUcnzpJE6z7Tk15rEdFnZEPn1Zg5LE2pHLmjIrTGKxKy0tXvgTD+Hzg9Ke8B9HDrbR9L5K0MjMgiUL0yppTqePTuwPcG246IlA8GD0gXssDRen7qj80+AvhFFIk2rMIrkb4VBXOSkBGRWtUFcaGtNPN/hokDqNsuts5BVM0ZKmX4HK2dQQFS2VKScVTNGeqWJ1HL9P1mUkAIrO4Cf+DNaCTDrFJZV9pcJb5YAiFnVJiWRmSbL0lLoRU4vp0u+P1k+TpfCH9m12x2A2crjHSOj0sjH7vkKu/fcya5dQl792Ke+InyjdVZp5s+6LQNLgDC72sBnvvZ95SL0vf/8Ki+06PlN4Vvsuf4GvvG7FAbMLfQMN6IxNjPh6WVxRsO8e5AFj5p53yA+Zzfz02oGhis5c+F5Pn7XXWQ1OqVpNhvZ9ePk1Nn4aco/YQD33slv814XG66dIjGfWzdGUZNdWp5wU9jiIKfJRU6zm8J2IQsfbXPzjccPizqbR3/L8U4vJzq9HG9zUFBvIuWi3IEn5vyUOTQJEAkguHP9ZVBZhNk+d76WBpTQhzgF23fonCgF3wZ/ogBaXgLJrNSTVSlYvvSyYX6SdIIHH/kDjySdJL1sWASSakySV1B492QZOblYKp8+2y9Anyz9nhVvJ50f5LDEAB6W2Hz59aTME5bIvYdXyr+y/y+n1iJ6IiUA+Of+aZ56pYo9193AG689R3x1injQTnzZy+XoAlubcyJgsT7F1sYsW8s+4kseIl4Dq55REXZYtBBbsBILjBNf9ZCIzkoSrZdE2E1i2Uli2UUiJM2lhcVqRnxlgvjSBEsTY8zbdYQnzWwuuYlvTLGxMkF40sj6rJHoqpt4bJL4ho940E5i2UNic1Z0+8UX2NqYJLHsITJlYtU7QjRoJRHxEot4Say6SUQ8xAMO1mYsbMyaWZ0xEA3ZSWxOEd+cJBb2EA172EoIGXk96GJ91sZIt7AfPP/yKzz9Pw3s3nsdn//OL3irx8acYwSPvo8Zq5aViTE2F13El7yEpy2szdtZnbezseBgI+Bic8VDYm2K+OoUifgC/X0N7Nsn1kfuvvtOPvuZf+XGG2+gpuxt4ktOnnniN9x95+08f7yQ8vYeXuy0k9MgWPLtU+w0n7o0wYl2F891uEkvHeTaPXv5ZcbLnFO7WA3aWQ05iK9NsxqyCyC4rCO0pGEpMMhySEi+oaCG5ZBGsH/LOvz+AULLWvzBQRYDaub8Gtwz/cz6tXhn1cz6tczO97Ho7yO0PMhKeJDIqkgQr0bEZOpccICB8TY6jW20G9qo6yun3dyK2d2Bwd1Bs76FjpFmWnR1dOmaqL/KAF493uWxT6VSUdddxqWhenpHGtGPdzBgaEBjasLq6cA11YNruhvXVBczi31MzvVg9Hajs7fTb27CN3MJ+2QfjskuBs3NdA6W8e0fflfUZiSd4GiHl4Nvd7Bnzx4ee/I3/Msd94jC2HP9HDrbx1N/aeeZv3aSdLZfLAmcVysrF2nloySXbG/QCrZNLdYCakwkFWtIkYzkqReHyKzQk14+LEBAjZkT7S7yG60UNtkEOCrRsf/NDqn4uV8BYcnFWgWEyUXPSRc05NWaya01ClB2UVSjJJdoOXx+gKzKEZIuqKX1DI3yuHOq9aSUyiyjWvITqiU5ql/5us+808N/PlXAvT/8Ld97pogDp/u3Z+EuaEgvGyGzfFha6RATWuJx6XjytSYOnt9maJTAhSKzSnUW5VKVheL3k8CSxLgJtm1YSgTryaszU9Awxok2B7l1ZjKqjKQqErAsiY1tp4irTSJBXK7f4QPc4f0rHlaAoSwhHpF6BZOl8ukjJTqlfkZmdVOk7WN5KSStbFRZQ5Hnx2RQmiqvPkiPL0vykMmdcsLzJ54PjySf+gdwdc3u23gk+QVFFs6W62Dqx8mpH5eYNhv5TXZ+nfu6AgJ/+Lv9FDQ7OPiXRvZ96CP88k9P0GtoolNTzYihBZu9i8WJQQKTGmZ9/YyPNeNxdtHUV8KxU+l89sv3k1XvoKjNdQUAzKwy87FPPbANUvfeye2f/ncKW51SYl5ay2l18FynhxOdHvJqDBx48Tz7807wZEY2T2YfI+dCM8c6PRxt8/Dxz9zHjw8d51TPpACB7XaKmsbIqjaSUjasBEGuTHSLZZArGWg1B89eeco3U0+80iDKwaUy6J0ATAA/LYfO9inALfXisCLhyjJwRvnIPwL0e+4n7aKoJkq9KAdS5B1hrfiZIX2tI+cHOXxWsI0yA3j4bD9JUmm5zACKFZ4d+9Qlw1cAQOWmqcpEVo30fGiwCfm3w8upbg/v+/Ct/DGtkER0jkTERyLoJrHiYys8QWJ9UgC28CSJlUkSSx4R9JgfI+a3Ep01E502El2wCjYt7BGfY32CRMTDjFsnkrARD/FlN9FFB4k1aTItNMGMbYQJYy/rizZWp8ys+R3EN6aJb84QDdjYCFiIhl1Crg3YWJ8xsRWZICFVumxFp4kvewl5jKxMmlmd1BNfc7M+MUo0aCO+4iS+7GJr1Utic4qNhXHWFsbYXHEKljI6TTziIbbsIhrxEl/zsb5oZXVunGjAxd133s6R1FTyWj0cOdvNbf/6Re770eOYxsxMO3TM23Us2IeJzNiIL0+wsehgfdHBut/J2oKDjSUP0YiP6IqXxIZIIW/FZvG4Rnjlz0fp6qihofo0Q/31bAbsxEJuVqfHOJaTyhc/92/s3r2b9ORDPN9iIeXCAAVNdgqaHBxrc3G0zUVR0zgFjVZOdIjk/SNJx3nfLbfx0Xs+T0VHIytBDVNTekKLBsJBA8tLGpaXtIRDQ4SXh1he0rK8pGZlWUMgMMCCv59Ffz9LSxr8SxpmF9WEQlpm5vsILA3hnuzB7x9gZq6XBX8/odAAS8sDhCODLIcHWV0dwDWvIxzuR+/upMPQTPNwE72GBvqsLUwv9KG2tlE3WEvrcD31fZU0axtp7L3KAF493t2xT6VSca71LOW9ZTQNVNKnr0VnaaZ9pJ4eUyOOqT7cM30sBHpwT1/C6GrH4u1Ca23C4WtDb2/n2795is9+7bt84Na7uHbPXj79tR+QVqqhqNVFQbON5Av9qFQqPnzbHXzrD6lC8isTE1BiFF4n9eNpSC7WkVFhkObIdvjfzom+OcHECbYtpVQnEq21IhGaUaEnq1I0/ufUmChosgswU2uiQBqQz6428adXm3nmr90ceKdX8qtpFdAnMwMHz2t49pyaA6f7SSnRSmBPTepFAWrk/d6C+jGxblAqLlKZlXpSdkxLHTqvUVg+eXM3rVjNLXffx7XShekbkYwAACAASURBVO7aPXdyy933kSH1l51odXCy0y0ATqmO7CoDOdXi35ZcouPLP/qdAqSuYDIkqfWI5AtMLRuRANqoEvoQcqqRwoYxMitGJCbNJEm6BiUgkFZhJLfWzNEGM8/XDwq2r0qkMFMujiifT/jJ9Eo6+EixTjA+xVoOn9dsgzqJPU3e6buS3pbTpSlSIjNVAiRyKlOuwUmVfy/JxkICHt3hEzQq5dCZVQIMZlUJf1l2tZmHfvrEP8irKtUXeejRJxUGUPYG5tWNKUAwv0kwBwVNDn6a/DwqlYqfpb/E0TZhMTj4VhvX33QzTx19Ge9sN4MjdVht7bjcl5hwXWJ+YgCX6xIWWzNtuirSs/+bB7/9XbIaXRS1iVRvrpQuzW+yU9Q8zuN5r/P1x57hV9mvUdTq4Finj4L2SYo6JjgqJeePtrn40f6Mf6jg+NyXv8h11+3lG9+8n4e//TW+8q3v8PP0lyloslLUYudoi52jLQ6Ot45zrMVKXq2BHIm53gkCZfAu70bvPGU2WpaG7/3BbyT7g/aKG5mkC6ID8PB5AQJTSsRrJUWagEsvHyGzUk92tYlHU1/4B4B+7Z47eOTI82RWGcis1IsbuVKdYBKLRaeg8PcNCrbv3ADJxVqFJZSVAWU7u3RY+XceUXy0Izvm30ZJKzeSXiFS81k1FrLrrOTWCw/gye4J9r9ay+7de4ivTxFdsJNY8RFfdIry5iUH8bCLxJqb+JqHeMRNQpZQ58eIzZrYdGvZsA2waVezahkkNm0msSxPvU2SnvqMBAbdgg2MeEis+bi8Mc3Gkou1gJPVOSubIRfRsJfNkACAsTUfsVUvm0sOwp4hIpN6wr4RNhYtxFZ8xCKTbEVnSKxPEZkeI+r3sDJtIB52sOG3Eg1YiYfsxAPjxPwW1gIO1gN21pfsRFecxDcm2IrNshWfJx60sbXqYTPkYH1hnPVZK5tzFlYXHBw5uJ8P/8uHaK+/yOu9bg7+tUkEZVoHWZrUE/CO8veNeeJLPqJLHhKRSRJrs6xMWdlctLO25CYW8RELe8UySXxBANfYHInoNJfjC8KHOGNmfdLAxuwYiZCHrcgU0YCHgY56brzhek7mJfPAo08KlrzdTWGzjcKGMQoaLeQ1jgswKM2O5jVYeOAnj6NSqajpaWd5aYiVkI6l+T5M7hFWQoLpCywO4l/oZ36+l2BwkKXlIabntczO9TM734N3tg/HRDfe6U6m53vwzvQwt9DH9EI/M4uDzAfUzCz0srY6QHBFg32ynd17xObyo2kvU2O00m9po07bwCVDE3ZvJ+HlQTyzg6gtrQw5L9FvbqZtqJGmweqrAPDq8a4OAQDbztGoq2XA3ELTYA2DpnpG7K2MONoYsnfwRukbPJWVwYWatxjz9uKe6OKdytP89E/P8sGPfVy0v3/+AX53/CxJZ7pEQW27h/ymcQqaBINy3/d+xXtu/gCppWLBQ9zBD0l+t0EFzGRIe7Dy3u622VwtgJkUxpA/llVlJKda2v+UPIBZVUIOzKu3UtAgKj8Km8REXEGTXXiDTvfy7Oke5WIkT7LJJczPnh1QAErSBQ2ppUMcOq8hq8qghETSyobJrTWKDrPKUakqRUdmxai086tTCpOTdjz27z1TxLX/5EL3w4PHyK4y8Fynh7x6sXd6vNUh9o1b7BQ0iL6/VKnIVgZZO037OzeAFVm4TCwhZFQayKwYIa/GKIqeKwykVhh3pIBluVgAwWMtdm6581N89BOfZv+r9eQWtwo5XeoBFAsf2/u+yVL/mlz0LJ+y5KsUDcspYbmWo3R7pzj5opggk4Gs0tEm9bbJwFCwl5LPcQcITFdCJCalMkaWgf8ZA7h7zx08mvqiAgBz663SxNfOt21iZrDJTkGTnYyKEVHJIu3AFrY4eDTleT7wsdvROAbQGJswjbdjtjSxMN/HpLsXt7Mb03grHbpqMnOf5otf+RJFrU6OtXsobHGS1yCVO7e5OdU9wQu9Uxzt8Ikt304vWeUaTlS0kHGmluNNo+Q1jvPQz/6ogL4vfOOHytvX7tnLHZ+7n2uuuRbVrt2oVHu45pr3cus995NdYyS/0UJRi0089kYLz7U7ONHhIqNSL30vJAAnscrbNxhaZb/68AWtckNz+LwAePJNUXLJ0HbZcslOwC98fykS6yefoqfRxIP/BKDv2vUUD/70j2RXi9eZSO3qJO+fKFgXIFAwfUeKNQp7vJMxzqg0KM+ZZCnZLBLOI1dYDXayf5k1Y2TViudFfqONY+0eTrQ7ueXOe/jt44/xf9YniK1MkFidJr7sIh5yEY2IMEc0ZCcWdhILO4kvO4gFrYL9W7Sy4VQT9Qyx6R1h3TtMbGFMqoGZZCu6IIG0SUkKdpFYcZPYkHZzN6eF3BuwEwu7ia96iS07ia17ia16RJgk7CHs07M+Y2Jz2kAs4iIWchNd8bE6N07IZyI8aWVyTEvEN0Is4iTqtxILjBGbNxEPOYmHnWzMjbO2KAqqo6tuEpuTbMVmSERn2FqfIL7uYyPgYGttgtiSg80FK+GZcSKzNo7mpHLD9dfz18omiloc3PDem3mxuI6VKTORBTvrATdLXhPrc3Y2Fpys+92s+91s+p1sBl3E1yaJr3hIrE2TiM6yFZtjKzbNVmyGrdis+HpTBjamDGzMW4gFnWxtzBP1u9hYcPPN/3iQ73znm2TXW8lvtPN8p4vsGiMFTaK/Mb/JTmGLk2PtbtFK0e7h98dPo1KpKHi7hFBwgEBAzWqon6oRK6thNf6AlpmZPkIBHUsB4fFb8GtYXOxneu4SU3OXmFrowzvdzfqqhtCymsDSILML/dh8l5ic7aVHc5E33srniaRneO/NN3PtbgH+vv9UNntveA8vtZvpNjZTp61HbW5iYaGX1bCWmQU1l8xtqC1tdBvqaNE1UNFVchUAXj3e1bFPpVJR3HGeBm0N6rFmtNZWevV1LAT6cHpb+c+f/Zg9113PXV/8d8Eu3Hcve6+/kevfs49PfuVb/DLrNQqapWLndi9H2zxKqW1Bs/DN5DfaKGwa5/A7nZxst5NRZRA9XcXaHT1dInCRVj5C8kUBDA9KO7Py0saBs7JsK/vnNGRWGqRkoFwNMUpGxQhpkmctu3aMHGlBoKjZxvEON9k1JgqabDx7updMaYdUvvAdONO/I+ShJun8AGkXh8muMnD4vIZciVWSL2KHL6hJKdWSViaYriPFGnJqjAIMlugUhi61dIiD58S/+b4fPf7/vNAdbRqnoMlGVo2YsctrsHK0zUVBo5XCZhu5tSbB3JUJL5S8taqwjiXbHrz0cpHozawQvWk5NQYyy0fIKBdp6fRywXiklG2zamnlxm3JuNzAH1+u4w+nKrnzC+L7f+3uPfz46UyOFMsXfHlZYXjbTyZ1Fh4p1klTcTuWQSQgeEgpHd65HDIkGfF3eAdl1qZEvmBLSU3JrK8A/8ptACh3AirVMNWi6y+9wsDH7vkK1+65Q5IY79jhAdwh/daOkd9o2wEALVcAwIJmB0flPr5mOwXNdjIrh7n+pn2kvvASamMDw6ZGJqZ6WAppcNg7mJi8hMdzCZO1nebei7x3302k/aWUY+1u6TVi52SXjxOdPp7vnuD5S5MUtjp5+Ff7+eCHP8w111zD3uv2olKpuPdrD5DX5OT3Jy9w7/f/iydfqSWzclgA+Is69v+5hru/9DVUqr2oVHtQqRpRqe5FpbqWz9/7eV6s66Ko1cmpTgcX1Gaya4wUtTjJqzORX28mr86i9DoekUqik0p2VsJI3+cLGiGtStUwMuBXCsVLpQWRYtFRmSItcqSU6iRbgWBx5S3fR46c/H8wgCelxY/RK1Y+kos1yga1LDXLp1wPs70BLFLiGVUGySu742aibJQ0ufdP3v2tMQs/aJ1VCoHYOd7hIflMJyqViupRF39bnySxMSXAyqqHeMjNetBBZMoseu3WvMRX3cRXnMSC48QWLEQD40SnDETnx4iF7MTDLrH7G3ZLwQwZAE6wFfYK717YK32dCeIbU8TXfGwEbGKOLeJha81HbM1LbN0rvmbEy3rAwdqMmY1lJ/F1H7ElB7EVHwtOAwGPCb/HjP5SAxHfMNFVN5tzJmILZuIBK/GIi63NSRIBO7GgTcjCmxMkNiUGMDpJYnOKzWUX0ZCP+No0sbCXjTkLK1MWQj4TSxNmHrr/XvY//TQ5zS5uufNT/DHrJIklN3NePcvTVuasOpYnTKxK/r/NoIfNgIvYslv8n4ZcEuidIiEBPwFAp4lH3GxOm9iYGyPqtxNfneRyLEAiMs3brz3P9ddfT9bZFiUslV9nUhh2OUl/tNUlzSW6RQixwcpnH/4+H7j1Ts6UvUlh0bPcdfcd/Cb9KJHwIIHQKMGAhkX/AHMLfSz41czM9bAY6Mcf6GfB349vro9Ffz8ryxoWg4Ms+PuZnOvDM3UJ91Q399xzO3fc9XH+7avf40vf+zXf+kMqvztxnqPtHvZ96CP8/uQF9O4umkca6R6uZiWkZXVFy9S8Bu14F/2mZtpHa2kZbqSqq/gqALx6vKtjn0ql4mLHebqMrVQOVlOvraFFV4vFXsdd99zNnZ9/kCNnuznWOcFDP/8TKpWKj//r5zja7t2x4evlaLuHwhaHWDBodZHXYCG/cZyCZjt5jTaya8c42uLgZIeDvHqzYAZKdKKSQfkhLletSNLSuR1Fy1LZrPw+mRHMqRE1IJlyibA0Y5ZRaSCz2iR9XbvSI5bfKABWUbOd7GoD6WU6Uku1FDVaONoyTkqJkJYOSl8zSZq7OnhOePy2zeijSjI4p9pIfp1BVNGUjZJXZya5RMOh84OklIpAyKEd5cjfP3DsnzKAP097kdx6i6gqkVjLwiYbmZXCgyfWKkzSPJoEfKRexKQLGmW39UjJEPmS7J1ba6KoUWJCq0aU3rX0i4KJkeevZGlVPmU5OL1KhCmSS4f4eearfOi2u1GpVPzyYDpJFwYlkDCyY1JrSPEDyoXC8iqInAg+tKNuRDkl39nOuhiZ9UvZESKRWUEZBIoC8BElGJImeQLlahixBCJCIWL9wsAjyS+IFHDKC2RUGsmp32b/8uot5NaJ2a9cxf8n2LKCJhuFTXaOtjookti/vIZxChptPP1aPSqVir+W/BmduRnnRB8T05eYWRhkdkHNvF/L5PQAb7xewDPP/JbPf+nT/PDH3yCn0cHRNhcnu3y82DvFqZ4pnuue4GS3j8fy/8q+D/4Lua/mUtp7kQv9pfzbl/6N+77zYzIrdAIAVxql54WZ3LoxjjaLx/rwL/ez65qnUKlyFWZw166HlbfvffBecio0PN/lpajVTWGLi/QqMxnVFnJq9GRXinm4A6f7eeadHp54tYn9b7Rx4J0eqeKl/wopP1UCVPL3IuXisGTTEDdG6eWjSol76sUhSfoVCe/MahN59Vayq43c+qkH2L0ToN/zFYUpTJNsIzKzKP/ckFPASecHRcVMiVZZ+UiXVoKUrkj5dVNh2NFFKYrFM6vNAvjVit4/UQ0kAgSFLQ6Od3jJqTHwgY/ezn0/fBytZ0qwcBsTxAIOVifNRCNutrYWRaFyYkF8bMUpGL6IkIW3EnPStNo8W/FZtmLTJDYnhAQc8pBY8RL3O4gFXUT9DjYWbERXPERXXMTCbnH6x4kHHSSWPawHHcSjEyRiM2KtI+wWnys6K1jBsIv4koOtjUlCk2a8I91MWTSYehvZ9FuIbfiIrTiIzpmE/09a5IgtO9kMOYhGPCL8sTFBfGOS2KqX2IqLzSUPW7FFtqLzxMJeYgEnsaCb6MoEmyEvDz/0Ffbv/xM5LR5+nf0q1990M4dfvoDXMYLT3Mu8fZjNoFgGSYTcRENWYssetiITxCNeEuvTJDan2IrPsRWfEyAwPiP2ggM2oiE3sYiXeHSOv11e5u9/X+fQAVEZ86ejb5BdYyCvYZw8KQSS12gjv8lBQbNY0clvFCxgYYtT3My1ODjW5uThX/437/3gLdz2mfv5t3//Lp//j++xujrCYkDL4nw/odAQweAgoZBg+ZZCg/iDA8ws9uGZ7WNiboBZvwbP1CWCS2rsUwOM2ttQq89w0003kvNWibToI36OFLWI1Z+v/uK/+egnP8trXSbsM2padFWsrGgJBAexT/YyYm2jTVtFl76J1qF6OtXlVwHg1eNdHftUKhX1/ZU06Opp0FRTN1BJbX8FD3/rq3zy/m8Ir1G7l0cO5XPd9dcpfoWnX2+UwJ9YLyhscYhuP+nCmFNrFndfDeNk143xw2cKeK7dwXMdTvIaLKSWjZBULGalks4NkHpxSOrj0ipyrAhPCAlYlCX3Sxu92wAws1L018mlsiklGtIvDpFeMUqmJP8da3WQKU195TfZKWqxk1VjEj7Ei1rSy0dIuagjqVhDaqmWzHIR8Egu1pFVMaIEQw6d10h9g0Pk1hg5LJnbc2uMHDzbT2alntxakVo+eG7wioLarEpRYJtcOsyhcxo+8okvX8FEffQTXyarUlSbZEu9dAVNNnJqTaL6QgI3GZUGvr8/R7qw6ZWVhtSLQ6SWDpNRPkpRo02UQdeYONbq4GiLg+RSLWnloxTUm8koGyJF7umTzP9HioekNKTE4kidgKnlYo4tSwp/pFeM8ol7v4pKpeK2T35KBDt2LoQU66RCaNlPtr0MsvN927NycnBE7p2TH8fwjh1hOa26LQ0n7wCCKQoQHJWqY/RKbU1WjVkCGWYFBGbXWJTy5+89lacEP3Kkwl8BBAUAEAyCKAEubLJT2GQjv3Fcqigxkt8g+gKfer0RlUpF0TtnRIjK2MigoQG9rYuRsWZ6h2r5whfv5brr7uSaa55i797b2LNnD79MyqWg1csLPZOc6pnk5KUJTvVOcbLbxzd+n8p9X/8GNcM1VGgrSXsxjetuuJ6simGSS7WKzC2An538egsFjVaea3fy87QXd7Bpd2+DwGuuVd7+/Nd/hEql4pprr2XXrl185NaP8p+//BXppxtJL9PxxMt1/OGFSn7/fAVP/rmeJ19p4MBfOnj6Lx3sf6ONp//arQR6vv1EpsKsyfJ+0gW1ZJWQdnjl5K98EyLN/mXXmLYXWmpM/Dz9Rb768yd5NOWU8nrIrDYqYa+UEq3w/xWLm8eDZwcURlDp8JSfH6VD0o3C9s2B3CspF4zLXX87A0D5DTYF/Bc0i4v00TY3Jzp9PPFCKTfuez+OGR/R4Dgb0waiS3ZWZ4ysL4wRX5/k8laAy1t+4ute4mseEuteErFJErFpAfqkChaxtbsoFkCWncSDDl5/qYh40EF00UYs4GBt3srmkp3okih0jq24xJzcnIWN+XFWF+1EN3wkojMkYrPEIx7iy07BJsZmSKz7xOPY8BHfmGIt4CToNTDSXsXGnInosoNY2MXWmvh7Uf8YMf8Y0RUHsVUv8Y1JEms+omEn0bBL+AHXJgRYi86S2JwhFvYQCzqJBV1s+J2sztn4zS8e4Ynf/5b8jkmOd7j53n4hc/4h63kCXjU+6wCRORtr83Y25gXTGA3aiC/ZiEc8IhUdmycRmxXgdn2C+JqHWMhJ1G8jtuwhsTnD3/8W5v/7vwnGzEPs3buXwlf/SmaVntwGKz88UEhRs53cBpvw2UppYFkCPtrqEqxgs3jfsTaX1DHo5Xinj0dTTvHJe/+d4FI/swtqFhf68fv7CUisX3BpgGBIzWJgkJmFfiy+bsa8Pcwu9jPvH8AzM0hN6zv88EcPs2fvXn74hwNkN9gVD+/RdreiAuTWj3HHZ+/nhve+j/1/rqDV0MyYux3fXB8WXzcacyPtw/U06uoYNLegMV4tgr56vLtjn0qloqL7Aq0j9ZR0F3Oxr4LKnovs2rWL/X/p5njXBAXNDnbt2sUL/5PD08lP8r5bbiOjfIiiVqdgRVrsZFWLgt6ceouo3aizkl03Tlad8FPtf7mcF9tMFLW6eaHbK0qZJQ9baqm4yGdVjipTTvIPdrG/26cAwINS39ih82pya0wSuyAuPHJVTGqpVoCjCpFaFQsg4kKZVW2isNFCliTzHXi7W5qZE716z0rbwMKzt73IkXRB8w/LHAowPKcmvXyUNIlV297u1ZJeLjERks9IZukOn9fwaMoL/Mcv9/PLrFfIqjKQWW0kt95KUZONgsZx6aIoutDkibSMSgP7XyoXfsBqqZevQswkHW+xc6zZxrE2Bzm1JpJLhySPlYHChjGyq0aldLVe2nXduf0qn3Ixrm5bbi2X/IOVenJqzNz7n79CpVLxvls+vl3nohRCDymS4TYAFNUfMqg7LEnVV4DEHbvCSSXb81w7Z8lSSgX421ktI4dGUstGyJASzkoiuFqkf7NrBeDLrRMVN7l121U2vz9Zui311lvJaxgXNTANVgqabNt+IQn45debyJdWOPIbbWRVG8mrN1PYbOf7+7PZvfc6Hnzkd9Ro+1AbG3BO9TJqbeLFV3PYe93/Yn2v/SA333wT2S+forDBTFHnDAUtLpLerOQnTz7DDe+5kdwX06gcqqFcW8Efk/dz6913CRa6ykxGlYnjrQ6e73CSV2sQU28latJKtRw63aPI3SrVr7nm2o/xkbvvI+mdDrLK1Rz4nxp+ul90BP7ssZ/RP1jGiRcz2bfvJlQqFQeSn+TVfg+FtXpebLfw2sAkb2jmeb7TRUG9iZxqI4eljr3/fqODn2W/qewEHz6vUeR/AdKHyCgflcCfJNtLHr3MapNIX9cIBrOwyUZOrZG8emm7WQJ/cudjWvmo+H7L4SB5IvG8WpKhdf/QN5m20yogs8NVJjKlbelcaeYtt16c+Q02IQm2OClqcVHY7KCw2UlRq5ujbR4Ov93GHZ/7ClWjQm6NBS3EQjYSaz62Il62lhxEA1Y2QmLfNxZxkogJluryVmAbBCbmxRmbIbHhI77iIL7sZKCrUgC8BSsbC1aiyy7iqz7i65Ni0i3iEdUuQQerUyYWHSNMj2mIrk8If96Wn8vxObHrm1jk8lZQJGkTfrbifhKxOTYXrcSWnGKFxD9O1G9lc2qUzSk9mxN61qZGSMTnxJ/fnCax6iEashGPuIhFPMTDTqLzFuLBcaIhB9GQl9iKl3hkgnjQSWTawpsvHeWzn/4UF3Vuclu9HOvw8vix01z/nn20j5pY9OoITVoITVnwu/VsLjmJBsdF1+Caj63YjACzISfxoJ1YyEk86CQWdArZd32K//P3Vf72twhz8x4e+cn32HXNNZxsk76fDTb+eOoiWTVmcqTf/28QWNDs2P79Dmn4eIeb450+fp7+Erd/5j5mFnUsLA4SWOgnuDhAKKRhJaxlKTTI8rKWxeAAi/4BrL5urBM92H2dzCxoaGh7iw9+6P3c//1fknSmi2PtLsUmpST6JQXtWIeX/AYr7/3gLTzw499QMTzCJUMd9qke9K42mrXVqG2dNGrrMbq6aBq4ygBePd7dsU+lUqExN2DxdjA530ufpYUGXQ0Pfe0+Hvj2f3Ksw0tRm5sb972P6264kd17r+OxvDdFuKPJxtEWB3kN4u45W1pSyJL61LLrxsmsMVPUbOc3macU5uFjd32CzCojKSUaMQNVoSezYlRa9NBxpFijJAflPr3tuTWRpk27OEJ2lUGqhhiRLijDpJVqyCgbVoBARpWJE+1u8hvGyKsbI7fewok2Ozn1gtn506tNHHinV5JDhzjebCOrykBqiVYJWhwp1krS8/+qxNjRKSaHRZKk9KQMApOl2ov0smFyqw2kSFNqKaVCpj7R7haj943jQvJtsAqgWmMiu0aAmMwqAwUNVnJqzVLKWU9Orfh4Xp2FgoYxChosnGh3cazVTpaUGJZBsahY0ZFeLiSxbWD1/7P31uFtnff7sNpgYSlDyius27oVkjblbt3arW2a4srcZgGzLdsyyBwo71sIOXbMtswy22JmlmyZZIslQ7jd9/ff/f7xPOdI7rrf+15v/825rueS7Zgin6PnPvfnBiXb3Zua5N5kTBos8KrXsKO03CYttn8jxN/+yUf6MQUroM+k7mjyfZMjYZSsMSCdHQszLkwVmz3H/Mzd1O2dYAgT3zcZ9DFawQQA1LLxNmT0ayQ3JDQCJr+NAI0CARmvE8BhAU9Axo88Cg5ZF3CnnVQIJun/+JRlK+qysaOlgg7ydyEMoR2ffCnAxtvvwsbb7sJbKbvx6ZGvoLAI8eEnr+H883+q+/wY927+PW6/4yZcfsVluPG223Deeefh0ss24Km/PYJva/ag39iJZlUbcvfmgMPh4KEX3kVuswYFAguKOy3IbdEio5bkV+Y2aZHfoiPNGa16FAkMuOfJF9jrbuNNN4HfKsHH++v/wzn8yjv/wMWXXIINl13OfuzlzEr27Rtuux35AiP4nUZUDniwb2yK6KoERhJrdGCUjVlhzT3HlPT8V1JtrpaygbRmkGlvoX8rArDp+U+bQfJaDcht0VNDhwZZSb3ETNdvIkFAQQ0oGnpe0OBoqhFNXszIl4x7icmDcWIX0tiQYjouZJpX+L20t7nPBQ6Hg0+7VDgbsRN9X8yJ0yEbTvltODlrwmLAitPLkzhzcoZEtByfpM0bEQrIgmRce5Iyc4tunF504cySB6fCdpwOWnHST9bpqAtn4iRm5kyMAKJTc1acnDZiacqI+JQZQZcSp45P0mq3MOnv/SFEQWcUZ0/P4cypWfq7TONUzIUzy1M4HXGQ3z9kwym/BaeDFuIIjjmJ+eLsHM6enMKZmBMnw3acZjSLSx6cDttwcs6AU2E7TgYcOBV242TAgTNhF074HZi1KXDZpZfg1RefxYhSiapBD0qFLlx5w614ZjsPbocUbqMY03Y1Jk0SnAzacJJhAZcniHkmZMOZiAtn4+P48dQcfjw9j3+fCeDsyTn8eGoe/z4bgdUsw3333Y3HH38ML+f/ixqdHOB1JPah/HYS68TrSPydmZu4ouQRMc0KrBicQPngBHZ/34c16y+EeUKMaZ8YQb8EoYAEMuPmDQAAIABJREFUwQBh/yJRKSIxOcIRKQIRCVwzY7B5h+CaHkPPQDWuvfZKPPvW+8htNVLGj0zMmDpUIqVKrNL+cez8thdr1l2AzAPtGDT0wBeSQGbrRYesBUP6bijM/TB5hiHRnesCPnf8smMDh8OBc1wIX0CMab8Ec0EJJnxjaB2tx6WXX4KXs/ajuN8LbpMK71QcQfrhIVQMTqBq2Is9I5Mo6XWgqMtGtH89djJia7eyovrCdiNKhC7c+od7sTt7O175x9PgcDjIalChtNuG8j43SnqdyG7UILdJR8KA60j3bBptxUh08krI5lJLxrZZjOmjmcTG5DZqkF1Hxq3Miz63xYiKPhf4PTaq7zKhanAcxd02cFv0+OAzAVIPjyH9mAr8Tgtym7VIOyoBr82IzGMypFSLUSF0UkMI04JAAnGTgSDjUM5t1oHbSDVsxwi4ym3UIPWojI1xSRgkFAnA2EAicXJb9OA268jG12pgq8tymoiuKruJOKQz6Saa22JAfquejGwpGGYduEn6uax6NfJa9GzNWwYFVmksIKMO3aPyFUCQuIuVrL6L5PIx5hEtG9mSXpds1mCMA1QPSAFcVj0xEKSy4E+ZBDjlK+JH0usS411WX0jBJPk+BFRkN9AMOToS5zZpSGQIrYrLo+0gjCGImIIIG8jrsJFzlAZgF1OWj0dZ60J2U7CjuNuBkl4nCjvMKOpyoKCTfA5PYGYBYEkvqR7kNqrx53cycMNv78Oq1WvwyItv4YOsNKxZe/MKBnDt2puQlr8L7dJmlOzloupLPgblbRAZ+iAx90Fk6YXC2Y82lQBFn/PA4XDAbdKgoN1EYn4atSjrtqGiz4HKfjf2j07hM9EM9o1No2p4EpXDk/jk82ZccPEGFsh9sK8ehR0mPPHmbtz79Ku464mtuOn3m4nB609b8ezucryc+xW2ZezDu3vqcOk1N2LDVRtxz1OvIqdJh+JuOypo8HTFwDiq+m1Iq5Ehu1GH9BoikWBulhgdbxrNyMxqUBM9XiMJ9s5rIeYPpgausN2Mok4LijrMKKI3PIz7l+gGtfRtEiuUQQ0mJEoqKUOSxr6wWZL1SbWG1DCWbBDitVvxP2NOlPYSjVhFvwf8HgfRgnYmAAK/h4yBqwbd4HA4qO2X4IRPj5M+HU7M6bA0pcHyjBGxSQ0BVHMmotWLuYjZw2/DmbAdZ09S88giiYk5E3PhTIzqBOMunIo4cMpvps5cG06HnTgdc+NMzI2zYSdOz1mw6NUjbJFjyWtAzKvDyZgHZ07N4t8/RPG//17G//nf4/j3j4u053ceZ05QdvBMAGcpCDx7Ygo/nPTi7Ckvzp6axtKUCQtePU7MWbAUMOHsqUmcXnbh9IITZ+JOnF0aJxrB5QmcjbtxYlqHEzM6nPFbccZvxgmfEUszRiz5LDg5b0PYo4FF3o97/vh7vP/WyxgyOFA66MUH++qxZt16vM37DCeDGnhMYkwYpVieJ2ziyYgDZyJOnIkSIHk2PI7/Pe3Hj8d9OB2bxI+n/Pg/J2ZxKupBMZ/cGF123a+x60A/uM1aFHdZkScwo7DbgXyBCbwOWwIEtttYJpCpX2T+xrxOGwsO+dSRXyJ0Yf1FG/BlYwOm5sYwNydG1C9HOEi0f9GoHLGoApGQBKGwGPMhMTwzoyitSseFF12ER/6xA0WdZnLz0OdGSZ+HNUmya2CcBYLMx/74p+fxl3dSoXKLEIpKEYqMQe2WwD45iD6VACprL2yu7nMA8Nzxi44NHA4HVmc3/EExZgMiuGdG4Z0bg9LVj33fluGCCy9A5netKBv0oqTPA36PE1VDXlQOTaBykDRt8HscqOh3I6dZSzZYOl4r6LAiv0WHsj4XNlx2Ob45shd2Tx84HA4+/rIdRZ0WlApdKOi0Ipdm3jGMWyrN9yKp/hJapyZC6lGiOSpoM1LTBa1vovErWceIji+7QY1s+gJf2e8Cv5votgo7rNgz6EFhBwk6/uirbmTVkvaNjFoZUo6IyM+rFmHHIRHSauTgNqmx64gIO4+Q5pDd1UnriJRmFFKNYB2JqWDiTwhboUBWvQrcRg0bo8Gs9Fo5YdKS+nQZ0XpyZ21Okx6ZDQRUvrevmXbpUgNILaN7UtLxGwmOzjhGarYy6bgtv1VP6vMa1dT1TMerdEybViNLev4VbAtJKgWBabXKBPis17CZiAyQZBi+ZLaODYJmImGOKRNsY/LzcCzBCqYz34v+TIYxZYKAsxqoKaA+wf5l0ciPrEZtore4xYjcVjNZNAqGJ2D0f+Qm5ZOvutgRcAErX7CgoMPCxsEwOsDiLhupKqOrmLK1BQITaxTg9zLnGgEvH33eipvuegCr167DBb+6AqtWXQHOeR9g7dqb8Nu7/gilrgO94kbI7CPoU3VCZeuD0iLEoLoLtSPHILZ0w+QdRW55Fjbecht1pVpQJNCjoo/kRVYNjmPfyASqRgjoKx+cQBkNuS3t86C4y4bsY2KU9btRMTSBUqEThZ1WUpvXZga/04KPPm1AHv2/5wkstD7PSM1HJhYoldFMtdI+D8qETnw64kHmMTne39dM/q41MtbRn0qrE3dTCUWCiSY3bXkteuS26FEgMFIm1kIY1g7C/HGbtNiWXoUt297D1pQK6iImI2UmODyzTkXPg6QooroEy544X5NihNgKQj241ORU2mVEcYcBBR0WfDbqRUGnjWgCqRmosMvOMoDvVx7BRZdcjqlpPRZDWiwHdTgRMGDZp8XirBYL82os+rWIzaqwNK/B8aAeSwE9ln0GnPSbcSpsIytqx4mIDafiDpyO2AjomzfBIOlA3KvE8owex2cNWJ41EiYwbMepoAUn/EacnDNhYUKHEzNmnF4YJ4DylA8/nA3RXt8YAXunfZQVJMzjv3+I4oczAfx4eh4/nvHhzPFxnFl0E1YxbMeyz4JFnx4nIlacOe7CmbgDZ2MOwsYtecjnLo2TbMGgHcuTWpycM+LUrAEnZvRYntQh5jUiPmnA8pwZ8Rkz+lqP4sIL1kM4PIjCvkmUD3iwNbUcF192JQYUcsw6JRg3SjBpkuF4wIZTETd+WCQaw+M+K06F3TgRGcdyZBwBrxHffrkHz2x9DqvXElf8MzuKUNxNahHz2wljz2u3olToxkdftK9kAdsJC8jrsIPXSQPYadZncbeD/ht5v7x/HJVDXtx67yN4j1uIqTkx5gIShPwShPxSRENyxCMKxCJyhINShMISBMISFPF34sorL8OHZV+joN3IpmGQ5UFJ38r1H4Cwz4MHnnsTf37lbcz4x6DU1GPLw/eBd6AeswExjONDMLr74fEKzwHAc8cvOjZwOByMe/sQjUkRjUsRjkrhD4uhcvRD5RrG27vexdp1a/FSRgVK6F1KxRAJouX32FHQYcb+US8KO0yEcRGY2Tqugg4rSrsseOLVD8HhcNA1eAh/fOIZ/OqKa5FVp0QRdVzmthiQfoyYFJg4lrSkjK+UwyIS+VAtQUYtqVsjfcBKGgyrQnajBtxGDQF/NP6BucMvE5IYlaJOKwoEJuwbnkB2owrpNTL889t+FLQaSKVULZPZJwO3SYPdR8Tk96hRoKzHjh0HRki3L3Ump9fKkVItB69Fj7QaBasdzGvWIa2GBEMzIIzNPKTsHxMPk3GMbJTcJi0xVFB3NLfFgMJOKxmtC4hpJoOCqUf+sYvq+7TIbTbQzmKmdYSEMKcckSCnQU0NHwoUtumR36pjHZXptRQo1ipo1pucrbxjtVw1iX7Y3dVyClYTmrz0pBFySk0CBKfVKpNGtz/tCU64oVnNX60SaTWJMGimGYJpGWE2bEbHxWj+GIYwq0GbAIKN5Gu4LUZwW43gtpqQ22ahFXYWOg5mdIFm/OmtNNr6YU0yg1hR0Ek3fqYer8NCRsLt1KzQbibsgsCMwnbSYVvcZQO/10naZ7oSzGJBlx3bv+rAgy+8h0uu2ojVa9fh5j9sxlt5lXgzdTd+/YdNuOrm3+Chl7djb8cYRvVd0LnGIBhthNjShwElEXuvu+ACpBflInf/Xrz0zht48+O3sD03FZ/1q8Dvn0TZoJeOlYgjv7jLiuJuG8r6XKxet6jLhoIOC6lXE5hR0eei55kVeQILq4PLZWv+9Mhs1IHbQp6zMqEbFQMemmHoRmW/HfltRjz62k7ktRhW6DZZZrcmAfbT6U1AToMa+TT+Ja9FD26TBrw2A3gUDJJWkPtXtIJsvP1+ZNTI6A0WbQNhauHqVOA2qpFWI03kTdYkXMoZx1RIqZbi77vKsfm5d/BsSgVSqmXYmlaFLdveJ73QzQaU9o3j09FJ5DZr6OuYlZ4HpAmkfGAcT3+Yg01PPIUTMT0WIiosRbRYjmixFNZiOaBDLKxANCBDLCjHQliB5YgeSyEdFvxqLPrVOB40YCmox3LYgBMxE5aCWiwHtDgeNGB5Xg8+bxcWfRos+jQ4ETDhxLwRS3M6HA8ZsejT4njIiBNBE04GzTgVYTRz4zh7aoaYSU5O44ez8/iRgsF//xDFv3+IsOuHswH8+EMUP56dw+moAyejNiyHjDgZI+PmUzEnTkfsOBOw4nTQgeNzFpwNO3AqaMPp5XH8sDSB0wErzoTsWJ7U4PSsESemtFjyqhGdUiE8rkRoQo6loAGLc0YszBvwu9/ejvJSLvaNTGLfCAE5V910Owr2f4HQrAZehxIzNhlOhZw4FXHhVNyLxYAL0SkDlubMOB6wY3nOjHf+sRVXb7wem7e+i0++aEF69SibPsHvcRI9Kd2D+D0OPP5mKm34SYBAdnUkv8/UQCYAYcXAOMoHJvD46zvxhz9tQ79Fi4nZUfh9YkSCCoRDEsSjSkRDMkxMjeGrL7Lw0OMP4vxVq/D0jlJWQ8zcOJQIPeALPQQEJgHBUnYRAFgidOPKG27Fwy++h2c+zMCadeux8Y4/4qY/PIBukwHWyWEMaATwePvOAcBzxy86NnA4HHin+hCJSRGNyxCNSxGMSGCbHMWoqR+dSgGqvq3EuvXrsf2rdpTRE7S034OSXgcKO0yUWVLSC8+IvFY9iaUQGJHdoMQTr+/A+vXrIBB+j7UXXISc2jF8OkIErylUv8PUhDHNAmk1pOOTSflPr5Eio0aO3EYNCqlGiHEUptfIafyLmjaMkPEhE+9QJnSx5fL8bjv29FuJa7BOhZQDQ8htoqPJo1LsPCLB7iMi7Dw4jB2HxpBWLUV2PWEHt38/jJ2HRNhdLcFOGhFDeonJaIsBVfnNZDSdztabkU2K6TjmNmpYkJtSLaMbIxGyMyHKOU068ARm6sA1IbM+wRBmJjEd3EYty7qkHiXayd1HJMisJb2rKdViZNcrsePgGPl4nQLZ9YQtzW5QJwVWE9YwLel9pjaLBXfVMjbwN5VleOQ0q1GKXdQcw1bB0RDhVOoyzqhLVI6lUZNARi3phc1uIFpQMpojmX4MmEvW92XR2BemFSSrXoPsBh2y6CKxHmT0T5aJiv3JymsjbuY8Ogpme40pM8g6QakZhImIIWHQVhS2myjDQB3D7RbWCVxIH4u77ZSRoDpBVnhuQ2GnFW+XHsLmZ97AxZdfjUuvuRGPvb4br3A/w+2bH8eGK6/FgbbD6JO1QqYTYkDRhmOdB7Hhkl/hkks34O77fo9f33YT/vz0o/jT04/i5ttvwnU3bsT2T6tRMkB6gkkUkxOlfS5S/TbkZYOtizpt5BptM6O4y47SHgJy89utLEjOE1jBbTEip8mADGbs36SnANCFykEPyqjzv6zfhaIuG3KatMRg02EBt1lPWnCS4n6YjEo2pLlejfxWPZkC9NhR2EEii/JadMht1uLFjD0/mwm4NbWSDTgnrB5pBSEVjUm1i9QoxtyU7DosxjW3bV7RvrNm3RU4f/XNLMC86bcP4n9GLNjbq0dem5FICdooI9xpJ7lxQieuvvl2FJdm4/SiGgtxBRaiZC1GVViOaLEQVSEalCEWViEeVmIpqkE8rMRiWI3FsBoLIQ0Ww2osxZUUGGoRm1diMaDGYkCLRb8GywEtlgN6nAxbcDJsxvGgHifCRsSnVViY12IxqMVSUIcTESNORe04HbXiVJzWuC2TkS6JTvFRM8ccrYHzkbHw6VmcXR7HqbgVSxEdliMGnIjZsBgw4EzYhtMBG84EbDgxZ8HipBZzdglO+Ew4FXHibMSJk3NmnPaZsTyuxskpHRYnNYj5FAhOShGaliPkkyDuVyLiVyEyr8LhA5W48ML1KCzhoWrQDX6/FxwOB6lFpZieEMFqGIJ2tB1RrxZnQk6cWZjC8aATZ6Lj8HvUaD36NbZuex4cDgfv76lB+QDV0vW5Ud6fOB8L6A1aQYeVSGnoNZ1Ytv+yqF6QRgDxe5yoHCSER9rBfqxeux5ZBzqg9wwhGJQhNC9FJKJANCRHMCBGS9tXWL16FbZ+lIadB0bA76EOcgoA+TQejU9XCftIgaDQTUbOdOz8SmYlrr/zblx/5z346LNWpB4axOq161CrtEDj6odQ3Q6Hp+ccADx3/KJjA4fDgc0lhD8sgS8oxnRAjBm/GFr3ILo1AvRoezCk78IzL/4Vj730Dvh9SXl/3VaUdNuQ26KnGyuN2qA9rHmtZBP+YC+bWI4LLr4YO75sQV6DFFUjk8iqV2L3YRHSa2TIbtAkzBQ1TOMH0y0qQXqNDJmUOSQgjzJsNPQ1p1FDwFijmt20cltNqOhzoaDdhAIB0f9lNyqR3ajBju+HUNCkZjeOnbSnN9l1vIuygDsPjdFGECV2V0vYbMC0ozLSTtBAmLvMY2RDymkgTGZWvRpF7WZk1CoIKKxVorTLwmYgMo7iVDpqTauRs5VyRZ1Wom9sIIHTabUKZB1jnh85HS2r2Z5dpq6OAEIp0WIdESOVxuxwG0kfcladkkZkMPl8SbVySX2pyY8pSYCQ/Z2ZNoij5GelVMvo/yHRBJKe5CxmDAFMVRcx72iQQ0FeVgMBfNnN+qR6twQQTIzudKzDk3EpZzXqEl/DNoEYSSh0C42Eoecm0wzCPjLgT0Dc4QwQTIyKCTtY0G5l3alMXExBBx0NdzFjJDutjXOw2sGCTjt935YAiPTfiruINjX1KAHnD7/0IS669Ap8VnsAA/JWjGnaoTL1QG0bxKi+F1KTECprHxTmXvRrOtAqb8GWRzfhN3ffA37/JB0hEaNCSR8Juq0cnCDMWruFgD866i3ushPNXRcBq/kCC3LbTOC2GunzrWO1nzlNehR2WGlm4QRp1elxorjXgz1CAwq7nIQNpaPlRNWaaoUxipEOMKP87Aai2cxrNYDXZqTxUVY88spHNMdwZVj6A8+/ywLAjGNUQlFDAtvTjkqx+/AYUg6NYfdhEXZTtj6lWvqz7TsczkZwOPUrWmFeSKsAt1FFMgHpyhNYwGu3oUToxq+uuBocDgfhoASLMTkW4nLEYzLEolLEo3LaGytHLKpENCxDLCxHPCrHQpQ8xkMKLITUWIyoKADUIzKvwEJQjcWQGoshDRYDaiyHtDg+r8cJvwEnAkacCBlxKmrB8aAB4Rk5FkManIxZcDJuwcmoBSeilEkM6gggXHDjzPFJ0tm75KHNJG6cWDDj9IkJnFyw4ETMhOWYDosRDZYiWixGdViIKHE8ZMKyX4/jfhOWZvRYnNZhadaEkwEblqb1OBmw4PisCcdn9Via0WBpWo3YtAqReRkCXjGCPgkCs2JEQzJEwgrEokqEgirU132BK6+4DH9+4jF826fGxZdfhUdeeAe+aRk8DhHchjF49CL4XWoExjWYtoqQuetDXHjRRbj4kstwz5Mv4IPP21DcZUHVsBclvQ4ScyV0oaTXScx0nVR/Ttl5BsATaYM5yaBoZYFfXrsVee0WsgSELSwVuogRhIazP/lOGq759e/QoZMjEJKRPuCgAgsRJaJhOfbuzcITT2zGQbGRNYcxHcTFtEaSAYJkJYNBD3nsI49VgxPYPza1wiDC73Hi/FWrUdwwDLGlH0pLL1zj5wDgueOXHRs4HA6Mjm5450bg9o1h1CqE2jUIqaMPCmcfRFYh9J5BlH+agxtvuwO8LifVNLhQOeBCcaeZCOzbzOC1Uldfmzkhru+0gddmxCVXXvsfzsPNT/4NxQIFdh8aQ9YxObLrlciqV7HjzF1HZNh5RIKdB0fx1PZibHrmLfx9Rwm4DWq2/i3jmIqCRzUBXvVKIhRv0ILbYkRxpw2FAgMK24kb+fMxD7gtenz0dQ/eraonAK6WaRtJbhqRsABw52Exdh4SIfWoBJ98N4j0o1KkHB4lLAONUiFxNIRRy23SolzoZGNXmKDc8j4XjVihujtWA6ekuYck/ib1qAwF1P3IsmzVEjpiJqAz4xjtUm3SUaOEigXN6bRhhRmb7z4iQeoREQnSPiqjNXxEl5hdr0q0slAtIAP2ktnB1Bo50mhfMlv7RYEo+blEE5lWI6ffP1mLpWYbJBhjCtMWkZXcxEDBHhPjwjxmNeqQy4LCBCvIjH5JCDRtAknqMibBv0mNIKzj1EzHj4Sp5tHNgdkkWADYbgWPbhJMUDSvjWj7CjqSeoMZMEdf8ElsiIMFfWycTBcJjSaOU3JtFAiMlHUkYDOrToXHXt+F1WvW4da778djL7yOr+u+hcrSDa21G0qjACpTD+SmXowZutGvbcfzrz2Lux77G0oHJlDa50Kp0IHyfjeKmVaZFi17TSYD2jwKeAuoFIPXRrR3eS0GYphgGj3qNeC1mcCnOWn7RiZROTRJ2A3hOMp7zagY8qJM6EKZ0AVehwXZjXpiEKpPGIMY8EfaN8jKaUw0guS3GcGjcTAv/ZdeYMIAaunoX5PQrNK0gNQjYqIbriYxUowR5b5n3vqZHuiPaVB2AmBu2fYBYT/pecSwyHmUGXo+tRyr167Dv/okCEdkiNAg4FBUBn9YinBUjmBEBn9Qgrkg+Xg0rsBCTI54RIaFiBwLYRUWwkpEAnIc+r4UGWnv4PD3ZQj5pFgIqrEQVGIxoMGSX4tlvxbLQS2Oh3U4HtZjOazFQlCFE3EDTi9acXrJipNxE5bDOiyGNVgO63AiZMBySIflqA7H40YsR3VYjuqxHNNhOWbA8ZgeC2EVFiMqLEYUWIwqsBBRYTGmQjQgx3JEi+MRI+bdYix41Vic0uKEz4SlWR0Wg1osh3SEkZzXYTmgxYJfg6hfgZBPisCMGIE5McIhKRbicvjDCkz7RAiGtZiaFEM82oCHH7gPm+/5Hd5PycS1t/8B45MGeD2jkI+2QDvSjI7aL/GXJ5/AldfdiEuu2oj3qmpR2utAWf84KgfHUd5PWDIG+BV2kZsxfredZeJJtqSFZfvZ1U6vc3p9s+BPsPLz+D0EAFYMeVEx4Eap0Ik169ajsLYHHt8YfAE54hEVFuNqLMRUaO/4F66//mo0qc3gtdMA+a6Em5zfQwOnafA0CYJeyQryhW7we134XDwNfo+Nmq1I1WTZwDhu+v1mPJdahSatHjpnPxyurnMA8Nzxi44NHA4HUmMXHDMjmA2MwTs3CsPEAASaNujHh2CeHIVtWoSOocM477zzwGu3UOrdhXwBASn5bSRfj8tkrzE0ercDZUIX+N12nL+KBEhfecMtK0Dg+1U1SK+RIK1aQlkiJQt6dlVL8c+Do7jm1k0rRjfX3XE/uJRByKxTgtuooZuIHnnNBBTlNpPolMp+EojM73agpMeGghYF3v+0DW/wDyH1wCAKWnWsu3f3YRHpAz4sYgHgzhUgcAxZdQrsOiRCCmUGmTFTKgU+mdRxS2rt1GzpfTbVKjIaP8ZAwThamc5gZuU0qJFeK6OVeGRzy23SIP2oFA+/sp24pRs0yG81smHQaZRlY0Aiw56mHiWGmpRqKVsbl8EI6Gl4djqjy2PAHjWSpLEaKiVrDkmvkSM5zoWpfmPYvxU6wWOJ6BdiDlGxuYHZVLeXWa9BdpOeraNjHxsZTZ+WDe5NHguzX9u4EjxmNZKV3Wyg/cZ61hTC/Uko9BNvplLpwk82CpYNNNPNgoC0vBY9cpu1FACaKXAzs5VT/B56p99D2D1mHFzUlYidIFmDNtZ0kmy+KOiwgdukx/v7W/DUJwX43aN/x6rVa3D5tTew14zBMwaDexgKSy+skyO4/Xe34dX8r6j2jwSdF/fYwaPmlJX6JjvLfBR22kmky8gk9o1OYd/oJPYMe1E5OI7SXif43TbkthjYyJaCDitKhG7sGZmkHcgulPePo7DDjL9+kIPSXitbq0VG8aROMKtBy8b1EEBJVhbt5mVuZLhNGgLK2y20FeQBthVk1ZqbsfGO+8l5T0fIGbQPOJ3KRXYfkWD3YRF2HRpjmT/C1Evx9D//3xnAVWtuxrMpVfTc0604n7gtxATD73Hi0mtuQN6XX8ExI4Z3XoRpvxjTflIDNjkvwZRfjGm/FLN+KQJhGfxhGcIRGaJRGWJRGeIROcIBMe7fvBnr19+CVau2Y/36W7B50yaE/VIUF+6gTKECi0EllsIaLIe1WIpqsBhVYSmmwnJcjeNxHY7HNViKanA8qsNSRENWWIPFkBpLMQ0WY2osROVYiCqxGFVjIULG0gthBeJhGfm3iBwLEQUWokrybxE1FmMaTDlHsDSvxcK8Bot+LRbmtVgIqbAU1mDBr8JCQIWloAaLfjXifiUCPjGC82LE4zLE41JEYkpMzsswMSWHXt+HyXERDLpumOVd2LL5bmy85ipwOBzI9Wp0NB/AvXffhVWrV2PV6tW46/Fn8CrvX8hrVqNM6EBJjx2lvQ6UC+0oGyD1bXsGnCjtsbHGLV67GYUdZhS0m4i2tNWIx99MY6/l/y8rt40BgCQKpmLQi/J+F8oHvbjp9/fh9dy9sE0OY2JGhFBYhnBYhkhYCom8DqtXr8KwVY88gWVFtmAxs5hooaQ2kmQwWNxLet+/FjlR3mtAeZ+L9MD3eVA5PInndpXgkquvQ6nQjrs3/yF5Hz0HAM8d/7+ODRwOB7fe9Ud8UfMlVO69H1+AAAAgAElEQVRhmMZHEAiLYfQOQeUehNQ5DJm9Hwebv8SatWvB63KivN9DLjLmDqvVBG4LcVvyBBaU0DFYef849o5MYv/IOJ7fwQWHw8GNd/4Rqf9qwkPPvgwOh4NLLrsM92zZhI++6iF38Enu013VMvx1O/9na9O2pVeSAOg6NbjNepJRR5s4CmgtFr9dj5IuCyt+f3tPA14vOoC0g8PIOqZAxjE5cuoV2HVYjN3VMuw6IiZu4yNitn5uJw2eZgBheo2UtJFQY0rGsUReYXaDGtwGNRW5a9h4i+JOG4q6rGznaYXQgbwWHfJbdEitUSCzXkNMEjUyqrNTJGI0jhKjyW7aiJJyWIS3K44h9agMOY068LvtxCzRoGbjW8jvRYBmdp2CNaBkUODHxKkwm2mix5c2bxxTJmkBGWdzAgymU/aROH0TUS1M92uym5cNhk5yAmew2khmDKhD1grDR6KWLouyg1lJIC/rJyxgNgMKG7UrQCJhDA10FGz8DwCY32bGx190rND/MaNgXvtK7RCjn+MlOYQLaHB0QVdSYDTzot7LMIFOOupldIF2FHbZkN9qYIEnt8WAFzL34aEXP8S2jL3IazXSFgoH8lpNSK0WY1t6Fa7/zR/B4XCwvfwr+AJjsHlHYPGO4MZbbsAL2Z+DTzfJok6q7xO6CQPfTswdJNyYBN8S8OfG/hEvXs3eg/fefw0V3UYUVHfisxEX9g06sKefaASLafNJUSdx0O8ZcIHf60DFwAS+HCOSkKzqYZT2EdakpM9NWNcWE7KbDMiijGxidJ+k4WSYwCZS55hHaxB5AhNym3V4IaMKD734AekDZs1B2kQFYIOGOvgZMJgAhClHSIpA6lEZdhwcozeSCUBJNIA3se9fe9v9xMy1oh+bNg01GZAvIADwutvvwtv5e6D2SGDyjsI6NQLn7BhcsyK4pyXwzssw4ZPAMyOCd16KGb8E/qAYwbAUkSgBgocPl2D9+pWva+vW3Ywjh0qhVtaTsXJMhkiUrHhEjsWIAgsxBZYX1VheUGIprsRiVIF4WIrFiByLETmW4mS0vBiXYymuwGJchoUYWYtxORZjcizGiGYxFlEgFpEhGpUiHpMjFpUhFpYhFJbhu2+LkLbzHRz6thTBGQniASUZUYdVWAgS9jAelWEhrkI4IEHIL0YkKEMkLMHSsgLRBQUmfDLMB1SYmZEgHNFieloBvUEAn1sEq7QLD2wi5/PqNcTN+7sntiL18BAK2w2o6Hehoo8AoorBCfDaTSjrsaFiyIvKgXHsG51CjdSML/q0yG8zEPkBZbILBYnr/KMvOgmoa/sZNjAZ+DHgj66iThsqBsZZw1P5oBePvvox7nr07xi1K2CdGEIsKoPXJ8KcX4RvvivCjTdtREUvKUP4abg0OyFgwSADCB1sFR2/14VPx6bwlciGkh4bqgbHwe+2obTPg32j0yjqtoPD4SCvWYVrNl51DgCeO37xsYHD4WDLCx/ioksvR+1gLXSuAUSiEgQiEjimR6F2D0Ju60dhVS42XHE1imglEk9gStwxtVlQIXSisNOGqn4XPh3xgt/rRNUQ0TLw23WoGnTh6Y/ycdu9D+OGX9+CV3Zz8Vbxv9iT+M/vZlIGLqE923Xk50c35523HQ8+/y5x/dariEapQYOidhO4jWqU9NjAazMgq06Bj77swPav2rHjX91I+74f2TViZNer2VEoUyu3u1qGXTTWhdH3MSCQvC3BjkMi5DZpsOuwGGlHJUg9KqOMmxzZdUoUtZtR3GmmzJqWGjlM4AlMqBrwgN9jB09gRk6zDkUdZupc1lKGRJXkwJXTvMCEs5bJIUytliDlsJgFdExrR1qNnGgo69S0DktJ2T7CqnIbNaxjOqOOjGKz69XgNmmQ05SIUclk+oRrFcg8JqdB0qoVMTXM1zP5f5kNBIhnJIG+ZNaPzQOsJV9H3L7k67MZ7V+Dhj4SQJcAgEmZgw26BANIwUSy8zeZJWRYP0YLmNNsAJfRBbaakEur7QgTyJhAzIkRcBILyIA+0sxipVlhtqRlZfV+ROvjpNWIiRd+hvkjzmALqz/kNutx3R0PrHC7XvebLchrNdAxMRkZk8/VYlvGHqxeux6FX38Js3cYWhuJgrj7Ly/j5Zz9iTgVGktTwOScdTP1Ux6i4evQo7hJggeeep69Bn999xbSApLLxf5OMcr6XSjr89DYFxcqBtzYOzyBysFx7BmZwp7hSVQMuLFnxIuKAaI1LKcZgcSFbWLH8Uy1YCb792SMHIlgaKI11CG3heiHee0WAj4FRBtI3Nsm0vtNQ6VJJiXN+6tTs9mARAsrRyYT5H5Uhp0Hx/C3HSXY9CyRkuw8OIa/7yzD5ufewTO7ysn1zPRQ1zNh4xrai01imYo67Xjq/SxceMnl+OcXDei3KKB2D8A5K4HJK4LNOwbHlAiOyVGM+0SY8IkwF5JgPiTBfEgKf1iKYESG1LS3cP6qla9rq1ZtR1raW4hHZQhFJIjEZAhH5QhFZYhEZIhHFIjHFVhYUGJxQUVBoHwFsFuKq8j7C3LE6eNCXIalRfJ+nGoW43ElwlEZQhEpwhEpwlEZqTQLinH/5k1Yv+4WrDp/O9avuwX3b95ETB1BBRYCKsQickSjEsQW5KQSLTCGaFiCaESOhQU54otKzATkMI+PYdavQixuhMMxArt9FEZTJ2bHR+CfkOHVbX8Dh8NBxld1KBAYUd7vRgmtR6samkDad534pOJ7PPX6x+BwOLj99ltxaMzCSpAyG7XIaTWCJyB5qXltJhRSfXgeTQBgjWBMHBS97hmwl8ssVvNJPqegw0pq4QbHUdHvRmmfGzn1CqxZdwE++awBY5YROL1j8HiHMOUT4bmtj+PdXduR02ZjpU9MjiiTJZoInnYkxsP0kfm3PcNe7Bu0oKSHsJ68dhPK+sexf2waBR1mnHf+KrxXWY3Vq1ej4GDNOQB47vhFxwYOhwNeuxkbb/s9nnvnHYxoO3B8SYJQlLxoWcaH8WFGCtasvxB//2chTcJ3Iq/NyI559w97UdlHTthK2kbB77GjctCDj78iaeWZdQrwe9247rbf/ocWkMPh4JZ7HsYHn7ZRQwYdAR+R4umfEW+vXnMzXszcS5k/Uv+V16JHgcCA3QeHkVuvJM7k6jHwmjTIa9Iiv4W2ZnRZKXOoJOaNehXSjspYc0Ny1VtKkkmDAWGZ1ARCDBBk7MlrNbKu56JOwvTltpCaNiaKJr+VNFRkNWiR12oAv4s0OfAEZnaUmcb04tYo2Ly8TGb0ekSM1CNi7Dokws5DYzQYW4yUw2JW/0SYFC2r2cuso7mCDWoUd1pQ2G4kNXkNaqKpayDdw9l0xJdNg7UZZpAxcnAbE/q9tFqar1avRkZ9QsuXSZnEzPpE3hpT2ZZeS9+u15CVVNOVSTWAbMg0HftmN+pYQMgYRDKTwB4DALPpx5LZvuxGAjqym5lHI+sIZ8Aft5XcuOT+5DFRG2dmdXmFnQkGjLSDEPcviVIxsexfYrTjoMLvBPBj2ML8dvI3ZzIeX8z8T63b6jW34MWs/eB12thYCn6PE6VCN4q7HXh2Vyk4HA6qDn2DO+68k1xD572L1Wt/jRvufJA4ITtodl1Pgoks7fMgu1aE323awl53Dz+6GdffmNDnXnDBevz2t7fiiisvRcmBw+A3S1B4tIP04fbYkf51E7K/PIQd5Z9i6z9z8ec3dyD1+242zqKkz43iHgcKOhLOyhWRMszonv6902uVeDalEvdvfQ/PplTSeCMdchq1yKpXIr1Gisw6BbLqFchpUCG/RQcejYHi0g5wxmWfVaekJid6jtbKCJt9VMpKI1KqpcSERHuCMykjzlTIpR+j5yY9lzPq1Mhq1CGn2Yg8gQXF3Q5UDU3gwRfew9oLLsKDL36AVq0WholR6NzDsHtHYXQPwDk1Atv0KKb9YviCUswExJgJSOALSuAPSfHNd0VY9zMM4MFDJYiECesXjCgxH5IiFJVjLiRDICxBJK5EOCpHICJHOCJDPK5AJK5EICxFNCZDJKLEQlyJWFyOpWU1IlEVwiExFhblCEXJCsfkCNLHaFyG2IIcwagMwagMB/8LM3n4MJ8wfnSMHY3JEQiKEYlJEYlJEYtLEYnJEFtQYHJeBrNXAue0DE7PKOb8Juj0XbDZRqA3dmPGO4wpxxi+/bQEHA4Hn+0tQZ3YhHerapFRVIGnntuGyy67FL+6+CL89o5f44M3X8Ir257Gg/ffg1qZCwWdFtLxTQPeeQITcps04DaqkdesQbnQSQiKNhPLBHJbTchq0GFr2l5seeEDPJ++F9mNehbwkWpFulpNVLvrIIx2jw37xmZQ1ufE3z7Ow1U33YEmrRHj06NYiKsQiyrw7LOPIq8kCwU94+Smv2Nl4DQBgo6Vo2EK/BKPduwbnUZlH8kwLOyysqPoz0QzKB/04uqb78DvHnkaDzz6IPLbLecA4LnjFx0bOBwOCjrM2PFNDy6+7Eqcv2o13s9Ix1xIjEFFG6644de46uY78MG+epolRkJuizotqBogIbR7hjwo73OiasCNMqEdpUInSoRuVPS72Y3l+bRylPW58PtH//azAHDH1614o/Toisq1XUekPzu6ue6OB4hJollLnL9NWpT2OJDbqESF0Im9Q+MoFbpQ1udGUZed1piRWJXSXge4LUSgzm3UIq/FgEKBkQI8Ba11I8CJAX4pSbEnDKhiHI4ZdSrktxpQ2E66hkt77Mhr0ZFWjkaiT+MJjLThg4y4SrrtKOu1sQG4mQ0advNhenHTj5FcPMbIQcwhZPz8Uv43rGGFjIXFbAbgbhqBsesI0UCRuA0SrZLboifMXp0SmXWkp7igzYBCGt2Rn9QtzLYq1GsSmywFhZl1tI6L7VxVU20fDdytX9kKwnb3MvVtSTowZiTIdLZmNhDzQXYjZfKohoxl/5JBIAv+dCsBHwMEmxIAMJsdA5uQ22phmYC3K47RsY8FeW3UjEFHkGSsREOiO21sbmVBuwmFHWY2Aqmg3cyaPRIGkJUVUwUd9Ht3WNk2El67FQ+99NHPMtwPvfTRikwyYiIhI9x8gYmwflu2YM3a68HhPAgOJx0czlmsXnsLXuF+RuQXw17sHfaicogw8nuGJnDbvQ/j7y8+B+HwEciNHbB6pXj1jefA4XBwoPFbeCYlmJmR4cm/PQYOh4NVq87HuvVrcd9DD+DeRx4Fh8PBNRuvXnHt/vX9DHz8RRsLAIvoZkd0jgQIMnrMBKDXIL1WhY23379C37vx9vtJX3WTjnZf69hqPx6VdhR1WlDUaaFVfiZSmUivLwIcVciigJAsFbIo882CvWPMNZyoKmTqGxPGJGomo7FCPIEFlf0ekrc4MIG0w8NYd9Gv8EbhvyBzSGGfHIPONQi9exCWqRHoJ4bhnCGjYcfMKMZ9RCvoC0gw6RvBps2bsG7dzTj//O1Yt+5mbNq0CXN+EYR93yESVyAQlsMXlGA+LMdsUIr5kAShiBxzQQmCETn8ETlCUSWicTXmAxIEQxKEwwpEIzJEInKEIwrMzY8gFpUiFieALxyTYT4kI4AyKkM4JkMgQsBfICJDatrb/5WZjNKRdDgqRSxOHpn82FhchmhMilBMAfe0GAbXEJTmPpjdIzDZ+mGzD2JySgqDUYhp7xACU3IszhnRUv8/WLd2LVavXoUbbrgOTz7+EFI+fgvC1gOYtYlxfNaE5VkDdn/yDp7f+ndUDHjAbdYiJ8moky8wo6jDjLwWLfidpBqT12YEr92MtytqkNtqRGaDFhuTmPZVa27BxjseQFaDDjnNJvb7MVODvDbCApKIGRfKB8bB77GhoMOMdRdejKzvW2GdHIXZMYjmls/A4XDwYmoh9o9MEIlIJzF6JXcQ/2wfcZedAkA7+D0O7BkmRi5+j51Elg1OoGJgHJ+KpsHvdRLZ1NXXY0fpHuS02c4BwHPHLzpYAMi8qP2DfwQcDgdfHjuIy6+9Efc/9yZKhB5qRXeglNLie4e9+HRsGmV9TuwbnULlAAGBFQMeEhDb58blV5GN4pntPGzdzUdeiwYph8fYjePKG2/Di1n7cdGGS3Hvo4/hjcpG7D40hpRqooVj3Ljbvx/G09uLseX5d7E1pQK5TRoU0803t0WPvBYDqgbcyDgyjJIuK3Kb1ChoN6O4y4pSoRMFnTY2BqSgneSOlfS6kNtKYmEK2k3gtRlR2eciDF2zHoXtVlJvdUyFNAr2MpN0S6QCLRFGnEkDqHObyWiKAUZ5LQbwu23gCYwo6aGi+kYdyoQuCqStNN9Oh4x6dZJejrAZmceUyGvRr6jDe+LtTFbcvptxOx4RY/cRMXnOaF1dao0iqQ6LYedUSWHLWurCJDVyRHelJb8Pk2/YqCHu7mYtid6hwC+dBX8JAMewJiTiI8EMrujwTWJWGC1YsjmAZQKb9P+xVrB/Scwgw/6xwI+uRBRMAvwRRsDCsn1/214InoAZ75JoFF47iYjhJQE14gK2EZE5ZfMSox170gjYgZJeF2X/6KKj36JuBwswC2gTyYtZn/5fGUB2hNSdGCfzux34wxPPJYGwQ9TQUAnOeZ/gkVc+xmuZJbjtzttw75Z7UXLoACoGPMg4PAwOhwPh0FHMhyXwheToUXbj8isuBYfDAZefh47RdnSL6+ELimGZJKBFb+/F7rR38I83t2JM1gB/SIY33nwWq1evwpo1q/FaThmeTalEab+H9J32echzwMRf9DhROeBBdpM28XdqMmBr6p6f1fduTa1CdrOetroQQxcxm5HrpZAF4PS57LDSiB8T2/VLHOJqYjCh7xPdaYLdY25yMlkpgoaVbuQ06cETmFHSbUNxp5WYCuiIsajLRjuCbXg+tQxr1l+Il3O/wphdDuesGGavCKaJUdimRuHxSeGcEcM1K6Lvi1gmcGpuDN8e4GNXypv45vtiTMwMIxCRYe+nWQhGVZj2E0PJXFgOX1CG2aAUvoAU8yEpghE5glElZvwSTPnE8HiEmPOPwReUIh5XIb6gRDiqQIwyhL6QAqGoAuGYAvNhBXxBKfxhwvz5I3LMheUIxRQ4eLjsZ5nJA4dLEI4RwBiiwNFHXc6hmAzxRTliCzL4IwrYpySwesVwTMvgGBfDMSGHe1wCz4QUEskxLMSNmPap4XQNY2p8FCZlJ4wiAWJOGeIOKRa8ahz36XF8To/FaTWWJrW46YaN2PP1/4BLQ/GJ0UuLrCY9uC0GejNgRmG7EYXtJjJdabfgmX8WgScwY2ta1c+7ytP2ILsp8VpBXjsMyG8j12ip0I3ywQnWeVwidGHT0y/jsVc/gnlqDDbPANatW4s/bHkY6y/agK+PfoEvhvQkjqbDxjaL8H4CAJNBIGH7yGtI5SCRWJT0OlDW60DlgIfo6MemUdRlwXnnrwKHw8F3zQdxaEx9DgCeO37RsQIAlg968eFXnVi9Zi3WrL8Qj722E6V94ygboA0DQhcBfyOTqBgkgK+kl5y0FQPjqBoi1XAl9E6Fw+Hg4Zc/RsWAG3mt5G4+u1GLh1/5BBwOB6/nVGLTE0+xn/vEu7nYdXCU6u6kVINHYh1yGjTIbdaC26wDv8uKonYjsuuVKO4kozlunQQZ1SLwBCZkN6jZTDF+jx3lA+Mo6XWhpNtO67ws7B3X3qEJ7BueQHGnBXuH3Mhu1KCsx4byXjuNsFAh45ga3EYdMUOwfbjJMSZEf5jbTJ2MzTrST1qvRnqdCln1pKkgr9UAbrOOaJla9MhuUKNU6KINFzqWNUurTQTmZtapqPFExAK8RMitjGUsU49KaYQN+RySyadg2Q4mQDqrQctWtWXWq5DVqKFdqaRnldvMtIWQcWweHWVnN2rZTTO56o3RbiVAHdlMcxo1KwFg/U9BoIYFfMlvZyXprki0iyFpJbuAk00gSQCwxbjC8cttMbEv7sT8weT/UbMHBXl5LQa8mLkPD730IbZl7ENeqyFRZyggzF1Bh41tuuF1WFHUbWeBHQvOepzg9zpY929xEjhk4l/Y+JhOO/LajLjuN1sIM3E+0QBe/5styBeYVsTLMEJyPgWXxT1OPPrKJzh/1aX0+hlJjHEvughXX3sV1q1fj8uvvR7rL1iHJ19+Desvuhivv70NU75RBCJSTAYU6FV14YPtr4HD4eDrg19iVNWEuYgCnjkJPHMSzAYJ2PCF5JgMyBEMyzAflMIxLcKUX4bX33sJG664ChkHBSgZmEJZvwdVQ16UDoyz4bZlQjf2jc2A3+tEbouRBeoPbHv/Z9nPB7a9T9lcAvyZczKvhbDshe0mFHWYUdxFnktG/5XbQqrrmDDxnEYdlWHoWecwiZ7RJs7BOjXJoazX0K5gyvgxiQZtBhpoT3q4cxrVyG8zIrdZi6wGFfIFRrxZ/B3WrL8Q3w+OYmJODJN3DDr3EJzTY7BMieCeEcEzMwbb9CicM2OY8osxOS8mY+GgFLN0RDwbEGMuJMV8WIbZoBLeORG8cyLMBGUEAAZk8AXl8IflmAvJMRdSYtYvxsy8CPMBEYIRGUIxJfxBJSIxJXxBBeaCEgQicsyFCfALRWQIRuSYC0kxF5Zj0i/DpF+BmaAcvrAKE3Ny3LvpJ8zk5k3wBcSI0HFxJE4A4FxYirmQFKGYDIGIFOGYDNMBCRSmPhg9ozB7xDA7B2F1jMA7KYHNJoRvVorpGQVstl643QPwTY/BPy5CyClGzCVFzCVGbEqOUyELTkXNOBG1QipswAXr16G8RYycBnojzrxuNGiQ12qk0S9m8ARGlPXYSUkBlWwUtFvw4Isf/NdzLaf5JwCwyUC0vh1W8HtdqBwiGtdSmt/3XvlhXHz5VTg82IdHHrkHHA4HN9/9MG644/e48KILcKi3FTmtFpYBZ8Bf8koGgYxZK7/ditJeF8r6PagY8KBc6ESZ0IH9Y1OoGp7EWyXf49JrbsCd927CmjWr8V391+cA4LnjFx0/YQDJevyNXXin4ij7PlkT5MQcnEBxDwF9JUIX9gxN0IvDicIOE8r7XeDWkM3oJe7nKBW6wGsnMTE5zXpkVIvx2yQN0vU3XocvD1fh2VeexaZn3sDuAyNs1VM6DTbmNqrBa2WAiA6lPQ4UtBDNRwG94DMODaOqn7gvi7vtZDzXRnR5pX1uSuE7UdlHSt/LhC5UCB2oGvSgcsCD4i6SHZXXZiTdqY1q1gHIa9WjqN1M8+Yo48Q2VFCWoklNzRTq/zBJZNBRZ06Tlo6xLCRzrc2Mkh5HovKMAUnHmNBmOdUu0Xq3ailtIpGy2sSsejUKBSbw2kw0vFqSBBIT42ymFovJYcuo06wYxTJGEAbw5VFmtUhgQG4TaenIbtIhmxpWWOfvT4EdHRsTAJkAd+zn/GTsm1mfDBSTWFbG2NGYPM6lTOAKowiJkOG2JPX/MqvVRLV/RNeTR8XeDPtH2Dgy/r7uNz8xYtzxALjNibw/hgksaLcmzCC0KYQJhGaq44poMDRzLq4wgnQlmMNCWjGW12bEC1n78dBLH+HFrE9Z8Jc8JvopC1jS60Bxlxk33PkgOOetZa+ndRdchCefIuPbv3yQCw6Hgwee/Ate//g1FH9eAceMBDMBGdx+JfSuQRjHZdC7RRhV90GkEWAuIsekX4LxOQkm5qSY9JNNPhCSIhSWUQZKjpmADHqvCIc7D+LuLfdg7bq1ePT5bSgV2lA+6EXFEOkkrhzyYs/IJCqHp1A+5EVJnxu8dtI08nz63v/KyiSAOwVwjeTmkbDWGipfMILXZkQhHQ3zBGZ2fE9iZXTk85u0yG3WU2BHDCQ5jVpw6bWbVa+mcTQ6cFvIuDe/jbBI/C4raQWhvchZTaRpJrtRB26LAbx2K0p6nbji+l/jw9KvYJ0ahnlyBIPmXkgcgxg298LiHYV1cgQW7zAm5kSYmBNhyi8mb8+LMTFH9IEzASl8QSnmQjK4ZiSY8ZOPT/plmKXP+6Rfhol58vzPBBSYDcoQjCoQjCoRjikxH1HBNSPF9LwEU345xn0yeOeJKWMurMCUXwbPnAQuHwH4Tp8ULp8ElmkJ9F4RdBMi6N1D+OKbEnyy60188Q0fzukxzIaIDjEYJQxgKErAni8kRSgqRSQuQzAqxVxYCo1zCArHGNS2YWhtw/DOKOB0SeDzKWBziuB2DcI7MULA34wYIa8EIbcYIdsIIq5RRKckiM3KsehTY3FOhzdefgZvvPYieAIDrQtVs0xuVr2GNH606Nmwcp6AkAKlvQ4Ud5Eax20Z/+1cq/rJTSZhpwtpNWSZ0I2qYS/KhITJJhp4N+5+8gVwOBxcdtXV4DYqcfl1N4P7xf/gpVeewouv/h2VvSb2BpLXQTuI2+0reoh5dExc0usi04EO8rpRMTiBqiGyp/J7HNg/No3S/nE8+W4G2VdTeHjqub/grXefPwcAzx2/6CAAsNPM9oiuWAPeFcCQ6f8tH/SirH8cZQMTqByaQInQiYIOC/aPTuILyQxKem00E2wCBR0kaJbQ6iY8/eZ2cDgcnHfe+UTI/nkBGjsP4LY7b8Ot92zBru8G2Y5PJoYku0FDjRQmFLWbUTXgRGbNKApaDUirFiHjyBiKqcaKbLh21gVZ2usg7QIdZpT12lHcScY6RZ1mFHWYyPhXYAK/24KsehVyW40o7XEQ95XQRVskjMhrMxHnYRMZRZULnSjutJCsM3bsRIJtiWaOxqPQxbCG3GY9cptU4DZpwG0mfb/FXRbktRrZfDwykiL5fKnVkkRbB3U3kqo8olsq6bSiuNuJnGY9dh6mwI/mBu6uTriKSbcvye9jgCwzfmWaHrgtBsqw2sjYuo04ShnwTuJmEl282UwES4OGjX1hgp+zGzTIbtIltXxoWMZ05eg3ORsuMV4njR4Mm2NgGz6SgR6XMdvQurzkwOdc6gBk9DzJwC855oXXbv2vm8O2zH2UIbSCx9TE0ZFjIQV9PJqvxwDAAhaoJeJgkiMfkke6RV2kbo4NhXz9WpsAACAASURBVO60o4Dq5wqYrECaL1jQmWzocJERUZ8LpUInXs39HL995Gk8uO3dFdq8tOpR3Pf0q7j1vkfRpeqBe14JtUeJEcMgRu0jkFh64fIbIRhpxfi8CJ45CaYCUswEpZj0E/bPF5Ji0i+DxyeD1D4IqXsUtlkp5oNy2GdEME2OocPQjQM9h3Drnbfiry88hdIBLxmbDXqxd3QKe0cmSaPC0CTKBiaQLyCbY26LAdf9ZstP9L1bCPvWZknEyDQmAH4OY6xqMVD5hx65zTrSy9xmJLpaytoVdxC2kJkGMNlwvFYjOzLMbSERIkxzUSH925Z021DSY0eBgICBvFZjIoS8UUdGkJRtzGsz48l30nHx5Veh6GAtJI4xCI296DX0QGzvh9I1CJFFCI1rAM6ZMXjnxZiaF2NiXgL3rAjjcxJ4fGJM+SWY9EvgnZfAOSmCLyDBXFCGOQq4HbMSOGfFmPCJMeuXYiaowLRfimBUidiCChN+OfReCSxeOcyuEUz55ZgNKjE+K8XEnArjPjk88zI4ZqWwTIthnBTDNCWBaUoMw6QI2gkRNONjMExKYJoUwTErgWNWAuu0BBPzUnjmCGvoC8ngC0kxE5Ak6QeliC0QXaFlWoIhnRAj2h5INF1wjYsw4xPDN2/A9IwGTs8IprxD8E+KEZ5VIjQrRsgnQXBahIhXjNiMBLEJCeIzSpjELVi3di24h3uR00wMasna4Yw6NfKbSQd8brMeBVS3W9TtIBWNXaR5J6/VgOvueGDFubbxjvuRUacmzDF9bSHnFTk/8gRmlAjd2DsyibI+N23A8qByyIsSoRtPf5yHnd90objbhmtuuRNZn/0L3cPV4HA4GDWNsdmRvJ/UzvGY1UFeC8oHxlHUbWcnDMXdduwZmUJJtxmlQjeqhidR2u/B/8Pee7+3Wd3v4yKEDEagtIy2UFbLaqHsWQoUWgIkQAghgUDCTEjiKVveK4MECKNQoBmOt/YelodkyZasvW3Le2TT9v39H+7vD69zzvM48PmJX/Nc13Mplmwn0TjPfe7XPXb+24UVl64i89Hat3D9jdefB4Dnj591rFIoFHju/TI0OCd/GgDKznrHGBqdeTR1EyO41z2BJmceteYMdjvzOOidQZU6gOUrVuKyKy5H8RE36qwjqDGm8cT69/BJ7yT+8uoWXHL5L7Cm8GNcduXVaPykAhddRCHRt9z/JLZ/46QmC+aG5SxWSUcIVToa/RQ3e1B8zE+5Ybx2TpsQVVdULUW9otW6KJrso6g2EOCrZJES1QbqE67UJ1BrTKFSR7t+ZSexBBXsYlKhi4tFgWeU1bOQX5U6RBmIjFmQ3KtSSwUfUwhheUeY6e0SYkxZpY1J2WZtw6QBbPZB1RUmAMiej23f9+Pv2+pxzU134B/bGlDaGkCtMY1S1ju8g8XY8C7UHUekjEIyifiFm7eknbR+xGYSMC3tjKBSSwafRvsoA3/0XFRoaRxW2hmWsZ90EeSGDL4gF3EAyFoeeGYbX7iL5ICPgz5Zq8e5GkA+1hNRDWpq81DJzgqW88Vdf+TuY0CDufykHmCe+ZfAIy+/g0defueno4ZefodVxcVFZRxnF6pYR7DY3csA4E83gtB98mgICeQx4GdcfL/cSVhtyqDeMoJ66ygbEeXpdI2zGqwM6szUevPgi29gXcWXqDVnoWwP4Ma7H8Gat7YiNuXFUGYAvUE7bINqhPIeBDIe9Ay5MJwyY+qEV7B/86cowHj6+ADCE154Mj0wh6xwplzoz3VjON8LW8wGR8IOV8KJV7e8io+PfAyFQoE3PngL+9yjYgPZ5JpAg2uCAcMJ1FoyqLXQc6HSxPBS8QE88vK7WFt8AGXqqOhkVWkTAtQrRag32wAIMCitAWSuSTBmlRz/fARYpU+hgn1+K3jAt4aYPZWGZQ9q42xtoFBhHj1TxVzfUhxRSIopkkUOPbFxJy5avgJ3PPoM9MFuDI50w59zoi/tgH/EhfB4NzLTfcjN9GF8vh+JyT7EJ3sRn+xFNN+D7HQfsjMebN+xCfMnPUiOuWFyt6Pd0oLvda1wJ31ITHkwOu/D7Ekfjp8JEjN4JoDcrA/pqUEkxr1IjrmRnurF6KyH2L3JPozMepGe6kN8sh+pGQ9iUx7EpryITHoQHu9DfIoYwOHxPkQm+pGc9iA57UFiim5zsx6MzA2wkbEPc6d8mD1FLODxMzQaPvtfH878x4/pE35Ect0IJq0YjFqRyTkxOReEP+qAzdOFZMaKyalunJr24dTMAM7O+XF6bgCnZwdwdtqDHyY9+N9UAP835sOWTWvx8N+eQ6kmQcH650wOyjvDKGkdgrKDcldVTA5QY0iizpzGY+veRQ1rClFpolhb9DEefmkr1hbuQwmvIeQbCXVUyHR4W06jfRQHeqdQa80yDeAoESDOcRa8Thu9Wx96Gqu37MQR3bdYvnwZ/unsp/VnUe3cj89qUxaNTDtbbUyzEOos9rkn0GDNsEq6CQqD7iYm/YWP6nDFNdfjjof+eh4Anj9+1rFKoVDgsssuweMvvoIGR/5HrN+ikwHD3e5J7HNTewCnqD/zTKNCF8Uf7robTzz7BB5/5jHc+IdbsO1gCxodYyjvDGK3LYHCYz7h3rz7mXW4/Orf4vIrf0kXjobv8e4XFhQ1s3y9liGWiTeIkrZh1JvJ4FHCKuM48OIArVxNQcvV+jiqdATu6sxZ1NtHUWVIolxDdVPUNpAiUbcxxSq6UlBpo5TN10WhtDW8SUQXF92ONSZiEXkDxNqi/bSgFO1DceuwGB+VdPAxcZiZKgjglavDqNDEqL2kK0KaqPYgVBrSOhW1DJJeriWA0vYAVdA1+7H93x7RiKJQvEkutlvuF4HNvHN351E/io6RiaagmTGGTDNYwJo6ilulKBZlZ0SA23LWlVtroEyuKja65wCIR9sQWAxLALKTCbLbZdoqmZOyVDCAcsA3LEwcys7Fv0/ZFRG78TLG9hEAjDE2lnRf3FDBDRvlmjjle8miXQj4EZiQKt6kc8ehHqwt+unasZeKDywqia+QsYYVLA+wUs/YP0MaVWxHX21IsiiY7KIRsMQAZn+UFVZllG45G8gfq7eOoNE2it1MgrHHlccexgqI96+FmjsabTlU6GKoNpFrudaUxos7G3Hj7XciOtmH4MggHIMWDMTNiI0PIz+bgdrWgukTXsye9GL6pA/zp/2YPUkgY2isH5aoC5aAFaYhK2xJBwzDJuiGjbAO2zGQ6YM2YEBnTwfMUQsONB/AVddehTv/fCce/MtDuOH3N+Ou+/6Ede9tQfm/O1HnoI1mE9MI8tzEGvbcVBlJNE/PL43UyzVxaTPQFRPvEc4EU/sQmXhIW0kmnTpzGrXmtAAABAQTYqxfoSV2kDZ7pOGlTUKMPu8WEug32kZQbUzJwsfl72HJhFSmjqPwqBe/ve1uXHPzHfjoGxPMsR5Y4zbY41a4U3bEJnoQn3AjN+NGatqNyLgL4bwLgyPdSEz2IBjtwtZtb+Om226D4oILsHTZciy/+DJctGIlbr7ncXzb60d+YQCTx/2YPjmI+dNDCOY96M32IJrrRWLUgfCEB5F8L3wjbiQmPRibH0R8vBfJKQ+SU/1Izw4gOtmP2EQ/ohP9iE/2Izc3gOS0F6HxPgyN9WB4vBfJGWL/wuN9CE/0IzHVj8zsALJzA5g44cfsKR/pE0/RCPj//j8//vM/H46f9GN03IGZ44OIp+3I5WkcHEhYMTnTj4nJHszP9WN+vg//OxHA/44P4+zMIP4zM4jTk/34YcqH/074MOBqw4oVy7H1cyPKO6UNI58UlHWGoWwbRCkzq3HpCo9wqjFlUNzsRb01hyp9nMbBenqf0IY2RrWOOqp3rGSSAv4+qtBTFuC+HvqskaxjBE2ucTQ682iwj6HOOoJKfRKv136LJRcuxQVLLsT9qzeipCNEOaKscu7/BQLrLDk0OfMkk9JSfEydKYN9TE9fpY3iy/4s6l2zaHLPotE1hXr7GG6+51E8tO798wDw/PGzjlUKhQJfa77G1ddehee21f408DsHADa6JtDUzRZy1zj2901h26fH8Ktf/wYPP/EwDD4Njtia8ciTD0OhUODJjR+i3hxHUduQ0NWVdIRQ0DyIZ96txHW33Y0PvzRij3MUO79348NvnMK1ysXbVdooqvUxVKhJ01PGND7lLLakvCuCSk1MxlbFiZl0jJFY35RmDE5S6oRleqEqfQLl6giUHcMiTkTZxU0AxBTV8IszZ3hMKfyWi/dlurHyLoqkkEaXUZaBFmbsYhiV2hh1rmpYPIw6hip9QgQx827f4pYhbPu+FzsOD+DZD3+6EeW5jxoFq1fEw5ePUeZZIe/2ZWaRHQwU8t5fnnlWwrL3KvVJlHXFUKVLoEpHC2admTLwKnRxNiKOiFgdzspIrtzwIjNHKR8X88BmNgKW179xFkfZFWG5XjExvi1jhoFSFs3AR7oisV9DWWB00aYMLxHwysBfuVYKf+XNG/KvK3T0vP/6nPHQb259ULBRnA3gwK/y3HHOIhZQnvO1+KwxSSNg3ghSJQ+LlY+ITRkRK1NvIWNVvS0nnPZNrnGKipCBTIpGiaHWECf3KpNDPP1WIW67536kZ30IZH3oCTgwEDEhlgvC6O5CasxBjM5JHxZOkcFg+oQPmZl+WKNOqL0GWGN26AaNMIet0A+bYYhYoBsywB21YCjtxEDSAVPIAlvchva+Nmyv3oFXtqxDyZ5S7KrdiceffRwrLl6BP95/D/YavWh0jtP/wZrDbuc4Gu1jBAgtOVFVJzRU+tQinad0klmDM3v8ws1d2nXWHGpMSdHBTHpN5upmekFpghAX749KfZIB7xwa7aOo1Ceoc1vWQbwIAPK6QXUMFfoUyjrDePy1D3DRipXYoPoUmlAvXAk7ehNW1ByoxuqNr+PeJ57CfU/9A+u2FaL40y9wsO17PPXiC1hy4YW44U8PYvWOJuz8t1sAmWpDAr+99S78bfMuDI4RizdxfAjxMTf6s/3oSXajO2LBYMYB/4gb/hE3XCknAvk+jM/7kJjsR2zCg9RUP7IzXsQmehGb6EdmxovYZD9yszS69Y+40ZtxwZvrRjDfi9B4L6KT/QjkexEY60Vssh+JKQ8yswPIzQ1g7pSPOYj9+N//BfDDD4M4cdyDuck+/HA2iJmZAczOD2Bm1ov8pBvTM/2YmenD9KQbZ6a8OD3uxf/mgvi/E8P470IAP8wN4D9zfpya8OL+e+/E317biioDvSZcbsLZP55PWtI2LNakCk1UjPUrdHFUsZD2OsaQ15rSUve10I7GmaRDihaqZPVyNaas0LjXGFOot7MRMIs7qrOOUCGCJo6NDYew7VsXyjUxYrB1CVToqX+Y/pySpg9sLalmG746xoiX66hNa7dzDOuUB6BQKLDkwiX41bVX44UNL+PIYBgNzgnc8/dXcf/qjecB4PnjZx2rFAoF1ANqKPco8cvf3oiGc4Beo2sSe7rz5wBAaVzc4BzH9v3fYfnyZdih2oH2/k50erug9bXjD3+ivsINNd8IowMf8fEgWGVHCHscI6gxpvBPTwaNtiyUHcMoOOyBsiNEZgl1BHXGJCrUYTRa0pT83z7MukTD4oLAO4m5xmuvizSKVSyAlzNE5WoaE6g0LLhZHUFx6xDKu8KoNqTE71NpE6g2MHbFMkKuTEsOjfYxrK/4AkuX/Ti+Y23Rxwx8LHagKlm8gEoTR7k6IsaZKm2SxdNQM0iJDDQVtwWwg2X83ff8mz85prz/hc0kaO+kxbGolfp8ufO3sCVA2YLNVC9XwHLPCljeIGUO8raPgMhL5KPwSm2UsaOSiYJnZXGnJq9mK+uU2jnIHBNhesOQAIbkzGTPAztpbCuxdhW6FMq1UlYfjX1//L1l6rgs3kUe5MoAIHt+6bWIolyTgJIxiCodAQHe+1uujmJt8QFWx7afBP5MwF3BwN3iTt2fOhmLJ9MBcu2ffAxcy40g5gxqzDkGUNJC41dvyRH4sFEDwW5nHntc49jrnsS+nins7p5AnSWDGnZBqzNnUGcZQa05g0ZbDgd6J1FnTotR9Rs1X+HXN9yI+NQgEiNeuHwGTJ8Mwt5vxNh0NxZOUxTI8TN+5Oa86Mk4YY5ZoI+YYQibYQzqYYu74Er2oMunhWHYDH3ABG3QiHZvF0YnXZhf8MIatsCZdMCetMORdMEStcESs8Iat8ESs6HTr8Ejf3sUv7z6SvzTrEWdg20ge6exxz2J3a4J1FlHUGWkvmIV+xxzBq/OnCVtFvsMlzP2j9f6cZaWN67UW7KkBTQmUWfOsAzBtAQWuYNYI20aVWra+PF2l0qWkckd6MVyyYIsr1LJGEDeHlNnHcHGmm+w4pLLcO0tf8Sfn16DlZeuwhXX/g73PbcBT28pwV82bMetDz2NVb+8BstWXIw7Hn8OWz9Rs3VBkitUGYh5evfTDly04mI8/0E5Hnj2RVx7M4Xqv3dQA1/WC0+uF86YHckZP/pzPehOu+DJudGbdSEz44U350J3yonweC8GRrqRmvYgMNaD5FQfxua96GPAzxK3wRy3wplywBq3YnDUjcGxHgyO9mBorBfhiT6MznuRmPIgO92H2VNDmDg+iPmTQ5iZ6cfJqQHMpHpwYtqHhQkPTp30YX52AMfn+jE/14/ZmR4sTPTizIgXJzJ9+M+sH2fmvDgz68EPCz78MD+AHR9swM233gaVJkLB4sbMIvCn6qRWI75mis1kZ1hMKUStIAPR1JHNMzqJGa42plhFIwtrNxAArNbT5rDSkMIeVx6NjjHUmJJoYIHnNWYCbrWWHKqNqcWvGdMa81YhFfs98t5v3v1dY8qi0U7Gkgod/Y6yrhjqLTns/M6BC5cuxbNvF2DP95/g2t9cizWvPQd1KIjNe1qwbMXF5wHg+eNnHasUCgX0A2p8/G0jrrjmOkm345TA3m7XuQCQ9DyNznGUfG/CxZdeDNW+ctgiDnR4uqDxa/Dy26/jyl//Dq9VfgWVNo51qq+EboaPCJUdVPu010Gl3rsdZDKp0idQ2DIoFuVKbQzV+gRUamLP+JiWB7RK4v+YABW0i8qj0Zkn/Yes7kcS+0aZGD2G8q4wilqGBLNVoU2IFoh66whFADDwV2cdwWPr38cFS35KN7ZVaNAk5ymxUWRikPRp5Zo4G0Un2L/j3AvMMDWCHPXhuW0NMgawVzCAqz9qZJVYPNYiIHp+eQ1bwbEAay7xs6gZahzh3b4FxwIoPCZpA8u6ogwAEjtSa6KLoYja6IoKcT4HfySQZyHN7bzeS2r44M8rB49lXbyqKc4iWuTsXFKENfOaJr6o8j9LLKH8VjrlwFvZKbFGi7SA2gReq/5GLMrS4izT9xl+gu0799TT+HdRvpfQAS7u/eRgT2QHMgDXYM2iwUrAr8ExhkbHGJqceTQ6RrG7e0KMfhsdY2xkyrp9LVnSpJozqLdkyZxkTqPemkONIYEGaw5rtldDoVAgGHcgPR6Ad9iE0bk4+oJ6zJ8ZxPwpH3JzHrhSdphiVugiZmjDJhiiZtgSVphiFuiCJnQMqKELGKELGqENGqALGmCLmHH6jA9dnZ/An+uGK+VAT9YFc9wKU8QKS8wKc9wKS9wGU9QKY8SMux64G7++7lqWL8olJ+NodI6jnkdiGNJS1zh7D1TqkyjXxgXjUiYDgFTpF19k0qkxplFjJHmH0AMK1zaxgCo2CqSNWVQwiiq2meQmMN5iUiwDf0Kr2hFh7zH5+zEKZWcYBUe8+NvWMjzw4lvYUPs9sZasmaJMHWMbkhgKjnpR1DKE0s4w1qm+WvSZqNBTpEi1KYuXCvfiDw8+hQdffBMvFe/Dn59Zh8t/dTXe+VQLz4gPQ3kfejPd6E47YUs4YIrZYIpZYUvYYY3ZYIyYYQiboA+bMTTWjcHRHiSn+xHI98AQscCRdMAQtcDITmvCCmfSDu+IG31ZJ7wjboTG+5Cc9mJsYQi5qR6Mz3kwfWIQ47N9mD/hw2y+H3PpfsyN9mMm3o2FCQ9m8i7MTLkxN+XGiZk+nJn24uzEAP67EMDpkz4sLHhwcqEPP5wYwBcHy3DpZZfh3S+MlMmpo75svpEsZRvdxbKSkMiNpFEwre8bqr4W7wmSShBTXmtKC6NPnYU77bPE8utYwLiOGnWa2Oex1pRCo30MtVaqPVXppc5flZaTCwmU/0TvMGeX5feXaxMi+6/WnCGJki6JcjVdo5qcY9iyrxkXrViJ57dV4dOOw/jlVb/EM88/ie97vVi6bPl5AHj++FnHKoVCAYO3Dd92fo6Vl12OOhsl3e/ppgWZg72mbg7+xlFvH8NLu+px/9PP4tJVq7B111uwhG0wDlvQ5dVA7dNi+Yrl2Fj1KSp1tBiu3rFb9MMWsC5YvtjudYxgt4PS1quNKXzak0elJkz5fuooqg0J1JuSqNBGieLXEEAjgMguBl1MAKyhXXO1IYVGZlqp0CWk0SITkJcxTZlKQ2xXhSaGwmN+xiyGSTTOAGCDbQT1dtZTyQDgq+UHf8QAXnjRDYwBZF2zuqQ4BSso66EVujJG+ys7h4VDt5iNNUi358eOw15ce8v9uPCiG6BQPM3K6+/HziM+1sLBo2cCpCNsGWKh0gwAMsBX0jYsex0IAJa2DqGQ9fdK2X7c8UyBu/XWHO1mNXEoO6XRb8kiZ2RIisrp4CHTfMTLGEP5OFctC2uW9XXyVg4BALXS80X9nUmpwkk2LhYXVk1c9P4uPqOy14Au8muLPpYt0kkxepSDO7lrTwS7yk6h/ZMZOWpkoK/WlBF5gDWmNOosbLxrzqDBQuCvkb23GuxjMgBItxwANjnHUG0kl2At6xquM2dQwwA6z+Dkf67UxlCrCeGOR5/BA0/8BUNpDxL5KHwRF8xeA3qiFqRn+pCf96I/54I+YoYxZoU2bII6ZIQuYoI2YoImbIQ6aEBXUA9TyArNoA6GMAEJS8xK1Wbf1sGfdcGWdMCdccEQNcMYtcAcs8IatxOrFLPAHLPAEDbhsssvw0eft6PeIQFALqrnJhjOpvFNgBTkLb3mnAXk7So8nodrAWtNGWJLGaMn9TFLIJB0pAQERYSQOiaMJiRFCFPkEPtcSP3TEYoqYvEhyq6oGAmXdcmyK9uZc5iPi9n3lbD8Tx6BVNoewvM79y76TPDxYZWBkg3q7aPUymShys1H172L626/F870ILwj/XCmHOhOu2CN22FL2GGJE7A3hM0wRMzQh03Qh03oTjvgTjthjdvhTjthihFQN0TotdNHzNBHzTCw0xq3oi/bjYERN0LjHuQXBpGfdmPm1BCmTw5i6oQPx08OYHqiDydG/ZhN92Im1YPjGQ8W8n1YmO3FyelenJrpx5k5L07NePCfk0NYmO/DyeN9ODnfD03HfqxcuQLv7vmOtMf6pND/nts0JDGC0tSB14NyvfLawn3kEGeMb52Zxum15gzqzSnUW7NosI2w9wbTn/L3gy4prf+sBavRkUeNJYtKA00PqlhTj5iQqBd3Dst7h8UappPur7fkCABasrShYWsRRdok0WgfwUefd+CyK6/Cup0qHHF24s67b8cdd92G6q/P5wCeP37esUqhUODa66/HYc3n+M3vfosnN+1Ao5MzfPKR7yQKj3pReHQAr1R8gyt+eSU+KNqKpm+aYAlZYAgY0elRQ+vXQuPX4s3tm3DN9dcTm3dsEMqOEFRd1PFZ2j4sxpM1hiSa7CMiaV3V6sU3/XHsNvqo3onp9ZQdQWbuiDPBf2wRKCBXn/Qhqzam0eQgxqTSkBLAgGfD8dDgck0cRa3UglHUMiQy5lTahBjb8QszRd/k0eAg5/N1tz2CpcukAN/f3PoQSjv5eFcaA4gxI2e52MJewcY75RpiOutNKZS2B1DWFabFrj2EXUd9YmT70SEvntvWgPte2IznPmrCjsN+7GLPY1ELLYw8OqewJYDC1iAKWgIsXDqAwlbGBB4bRBEbDdNjVJtVxCreREcvzzhURyhP0UZGAx7LIm47I1CqJZcuXTAjQhzPgRcJ5eUuXfnFPSY6eiv4Lll7rpkjIUwgKjkIZFo/nvfHwTZnKqU6uKgY1QkmUbiCU4u0OQLo/Qj8ZWRfS8xftXGxmaNG5gSu0sdRx4xGFBZNbF2DLYcG+wixfnYa93Lmb7drHLu7J1jkEmshsFMYbT1nCm0jgr2oYUCn1kzatzpTCo3mFC678ircfOvNMHm0yEwnYOoxw+E3whXQoDvlgClugzvjhC5igTZsgi5ihi5ihiZsFLeasBHasBn6sBm6oBFqvw7qQR10ISMscRtcKSe82W4M5XthS9phjllhjFlgjFnhy3XDGLXAnnDAGrfBmrDBHLfhb2ufwZo3NqDeOclclRQtRRfanJBtcADI2WH+2nLgx4E8b26oYCHdVYYUC9JmOkprjmUnspxGNhLmDFMViw4hPSFJApSMCS9h2ZfczV7UGhDNQEVMNsH1rdyUxvWqJZ0RkX9ZxtpNxCSkI4Si9hB1asvikeS91sL0pE8S02wk9rjWkqX3SfcEXindj+vvuA+9WT96Mm640y64Uk5i/eJ2mGM2mCIWGCMW6MImcRqiZugjJjGmtyXscCQdElsbpfeENmyEJmSAIWqBScbmxqYHkRm1Yeb0MCaOc/OQF8ePD2Au78V4xI3ZMQ9OTg5hYsSJ0zMenJn04PRMP04t9OPkfD9OnxrAyeN9OH68D3sbt2PFihV4oWAvKvVSSgIH1IurI6XniwNnMRUSeY8SG8hH/lWGpIj6qWMsegN7X1TqEwIoVrCNgaiDs1H2ZqMzj2pjWshHyOmbJN2fltg7JVvLzj35dapcE2fj3wy77o2S1EAdla17CfZ/DkCliWBL07+wfOUleLvuU3T47Ljrvj/hzrtvPQ8Azx8/61ilUCiw5MKlWLpsOR56kro+5e0fja5JNDonUNzsxapfXIGlS5di6UUXYdOHRl3oxAAAIABJREFUb8IWs8MSscIWdcAYNMMQMEA3aIDOr8E37Z9DoVBg22fNqDEkUGfOYMenR7DHlsJe5wgaLFlU6xJ4ccs2PLZmA5ocY6izZEWG2Vu1n9Gu3ZhCvSUjQp0rmGOLj4f5To2AIO30qtki2WAbRZ11BNVcA8hBgjomAEKFLomi1gDKWT8whcKG2fiXLuaN7OLc5BxHo4sxow7KblpffhCPvfo+Xi75BEp1FCodNyJI4yu+2ytjsSRSSTl/nMWMaMLM3UbRLOWdIcbkUQ8x7yMuODYoRrk8N7Dg2BAKWhjAY99XyO7fdWwQO5u58WNImEU4CC9sYR2prbwaK0Ddv60BKDtCKGfGlSp9EjXGKMqYyaWMVXSRizghds4qrTSClUBfTGJtNAmZxk8CeII51cies3OaPMTYj42ByzWJRQYRASTVNPpdFPAq+z3830wX19RifR8Dfec69+SBrlWGDBNwSwBQPgKWzB8Z1jxD4vNqI2nzJIcwgbZac4ZFSuSIuWZAsMmZF8CQunbHBAPER8FSDR0bKVuyqLdk8MybO6FQKBAZ9yM5HoLd70CXqx2BlA3RyT64UnZow2aYYhZYEjZifBjoU4cM0IZNAgAawhYYwhaoAwZ0DemgHjbAGLbAEDTClrARwxe1whqzwZFwENMUMSM+2QN9xAxzjEbJFgYO9xzai6UXLcV7JbtQ75xcNAqut4+i1pwV0g0OAsvYe6NMTZrQSvZek2u9eGUfN4II576ZMYEmYgVrWEtENTMEVBtoTMz1gGQ0G2ZGM9IqK9uDKGmlesaStgBKWwOUUdoVYS1HzBwl0wqWtg9DyW5LO3gNnSz8/JxebLmzmMtaVGyzKHIhzcQY73ONotacxt1PrcHTm7ajP+tBd7ob3Skn7Ak77AliXol9tUkMYITYXX3EBGPMAlPMCnPMCotgamlUr49I7J8hahGsrjFqQU/aiexkH3Jzgxhb8GFiwYPpE9RbPD7txcSkB9M5D6YnvDg9PYAzMz6cnfHj7LgXZ+e9OH3Cg9MnPThx0oNgqBMPPXIvrrz6Gry1t4USG9gaSTFTTH/Z+hMAkMVNlanJxcu1gMTi8tSCsHhdK1miQzX7LHImvsaYQo0hIQLFq7gGUJ+kOjiHtFGrMhL447Fhi4xJnZFzJCmkO5a+5sH0tMbVmLPY0z0h5AeLNsfs+6meMIR3Gg5ixSWXYkPt99D0tuPvLz51HgCeP37WsUqhUGB75Q6sWLkSV/72Jtz24BPY6xr9UR7gc1t24tEnH0K78xjaHYfRm7SjO2mDPWmHedgKnU8HY9AIvV+LYNYOW88hXHb5Fbhw6VK890kH7rz3XgHulixZgt/87jpcde01UCgUuPqGW9FgzaD4KDWI3PXQA7jj0b9hjyuPBmsWTfYc5XsxN1iFJozCY372wY+JEWo1c1PReCQnarNorMREu6YM/Q5dUjZOiqK0jYMp0gFy7U01D+C15rDHzdgKmWapwT6GOtblqGLgbtGHmO/+2EWMs4IcDFbqUwIcVWqjqDGmGeswLEAeZwALjg2isGUIOw/3S8CtJYjyzmFy/orA6CGRocgZP3rML3UMiw5UYjFK26lZpbR1UPQFF7dQvzFVwjFTiD4pFlbeikIsYZS5K1ktlyYhjee09PqotDJwx8Gf9se7ZH6/GAszEF2pk+klZSBS0lRywCixJxJbJIFNipGh91JZZ1gSZcsZQH1a9rWkAayQsYHyyBZ52TvXBXEGUDKDyM8s5BVxvEOY54rV20ZRY2GOXydJGRodY2IzQlKEHGMAeftIRmgO7/3bWigUCry7fTOCuSDsfRaYvB3ozjjQl3XCHLNCFzHBELVAH+HMn8T2qNmpDRuhjRihGdChtb8THcM6qIf1MA8bYIqZYElYYIpZoA3qYYxaoBk2QjNsgC5MAMOWIA2aMWphTJMd5pgFxrAJj/7lPvx94xY0sazARtlni1zO5Lzk2X6VemJvuEarnI/+ZRpgMvtIzl7K+kwy130UVfo4A3xJ1OhjqNFFUaEOoVIbQa0xxTY5SVRqY6gxkEmEfxY52Ks1JFBvTKLRnES1LiY2SOVdEXL4s0D10o6Q0MHy8WXRT4AYebVZSXsIBUd9AgASeImjQidpUmvNWTQ5xlBryaHBkcdV192E5z+ogHekD66kA/1ZJ3oyLjiSDtiSDlgTdpiiZvaamGFP2mCImGCKSVo/U8xKQDBODK1JsIJWGKJmmOM2eg0TxBDaow6ER3rhG+lBZsaL3KwXM6f8OH56EImMA/kpH06fjmIq34uz80M4s+DDf48H8J+FQZxd8OKHEwM4dWoAR47txspLLsb9/3gV5V0BGv3rkyjTSCHgxSJnNLiI+ZO6m6m3uVLD2opk+aElrQEy9rDXnzt+K/UJ+swYU2i0kV6Wj4arDCmRM1qhSxDYdk8w01aGAuiZGa7knLpKueSEs9PSWhSHUi1rJ1InWJVkmjbTQtISF2tfmZpkTUWtATRY09jw0U787s77YE/0osN19DwAPH/8rGOVQqGAaUiLNZvWYsXFl2Lr/nbsd4/8KAz6obVvY+2mdejP2BCd6MVw3o3gWC9sERu0gzpo/FqYwyb4sg7EJ3vw6BMP4I9PrMar5Qfx8OpXFzUUyM9Lr7wKVbowuR6tGXH/XU++iN2OMezvyWOPJYRyTRxNFmoVqbeQdqpSxzR9bNGn6BfqY6Wy9owAgDWmjBD4VrF2i3J1lBy0XREUHvWiVPT8BhmwpBHtbmceTa5x0WQgxeIQAKy15kgIrEv+2JHKNCEqjTQKVsmYSHlvrUoTRVlnUGTq8Vy9ohbu2CWt3j3/eB0FLQGUtNH4tujYIIpbBgWrx0O0KQjaL1pESPM3yAAgAcGilkGUtAZQwoBfUUtAMBRFrQGUdVB8SymLWSB3XUwAQKrHogVXpYmhgl18+SiMi/N5WDdfWDlo48yf0DtxUCjXzvAxnyaGck1UPH9iwWQ/z0e9YvTbERXjtFLxPC/OBXzgxTcXibVVDAiqdHw8nBBA8Fzjx0+5gCUGMMfc4wT4FmkCFzmEc+fExtAGpsacxbd9UXzsyqGJ6QGbWGBsvX0UdbZRYQKhKBXq4N3XPSE2Unrz98jNxZAaiULt7sTwRB8sMSv0ERMMHOyFjdCHTegKGaAJGQXo04QMgiXSR0zQDOvRGdBCHTJAHzHBnrTBkbTBGCXg+MKmNcI8wseG+oiJtGdRK8xRC4wRszi3lW+DQqHAm0WFKPvq3zjojqPOMYl6p6Q1bnKSNorcmhmm22MCfW2MfYYXM/ri89QVJaNRF4t80tJ7k1fF0aSAZwYmmRmHXKHEZPP3chQVXVQHWdo6CFVHABWdw6jRx0iSwv49FEJPsUTl6ijKWZaospOkL2WdtK6UtnPtWhBF5zBZXHt773Mb6fMmOoljQp9Km9wRNFkTqLOOosExhkt/cRUKPj8Eb64HA9keeLNu9GVccCSdsMSsMEXNcKVs6EnboQ0bYYmTFpPAn5nOmAQEjRELDBEzA4SMuWWg0BK3wzPSj0DShuR4H8bmPMjPeTA258HEArXHHD8dQH4mgPkTYUxM+TAcNWBurg9nFwbww4IPZ054cfa0F58dLMPKi1fi1fJPUWWkWsVyXQLlWglclcg00eeOgLkOU6UOo0IdZpWWiwHZ/as3MU1ghCZHmhgqNBR4TxuKJOotadSZU6jURfFyCSUBvFS0n4oGWDBzPdPtcnBYpokxfSf7+37UW05sHv35XEOatO7XsrzOsi7JICdOnn+qjrEihATKO4NYcuFSbD/YiqN9zvMA8Pzxs45VCoUCyt0lMAW0WLZ8Bd6o/QoPv/QWXlF+gsdf+xDrK77AuvLPcN/qTfjDn+9Bd8qGjt52bNiyHu64A+ZhI1wJK4pqd+Jg22foybgwPNaDa39zLf789Fo0OvMC1D2+/n28VliDv7/+Fi674go8/VYRXtheQ+Xx9hGotHHxvYWH3TjQN4U1uxrxwLMv4p7HHsWyZRfhtrvvws13P4B9rhz2defxeV8Or1V8gU1137FxcBxFzV48tu49wa5UaGN47oNqvHegHY3WHHbbcyj8txPPvavEx+4J7HGMolIbw6OvfoDNe9tReGwIKm0cm/e04O8fVNMYmFXfPf7ahyjrDIocxM1Nh7FedZDyoLQJKDtCuP/5N1DcGhB6tBcLP8aG6n8RCNIlsONQLx5+aatwGlfokvj7+9XY2HAE9eYMSjuC2LJfjSc27hCu3l3NPjy6fjs2f9yBHYc9KDg2hNfrDuHpLWXkHmwj7d99L2zGu19ZBRh8WXkQqz9qQEGzH4XH/Nh5qA/3PLseRUf6UdLiR2GzD89tq8fa0oOCcXzvKyvuf2Ez69wkAPq3d8qxsf4wylgG4zufqvH46x+R8461oDz++g5s/VTLxO9hbGo6imffrWQMHDWrPPzSFuz8dw9zbsawvvJrvFiwT+yYlR0hPPDCmyhtDwmgtrboY6yv/FoAyB3/7sFDL20VmsEydRzPvluJTU3NAgi885kOf9m4a1EW4xObduGdgwbBHm7e24pn3ylfBAAffnkrdhzqYYxtHK9Vf4O1xfsZS0js7AMvbibWgQHBl0s+wYaa7wQALDzqYe8/CfA9v70O7xzoFF9v/9qGpzcXy74nh6ffKsa2r62UCWbM4GDzd9hUVIkDPeM44B7Bvp4pPLHhQ5R3DjFjyATe3nMUG6u+xMe9U9jdPYkaYwrX3HgrHn3qSep9nU2jorYCyt2lcKRI6H+kuxkvv70OegHYjHi/Yhv2HtlPrGDEjM81/8QbOzaLEbA6ZMAbOzfjc+3XMEbNMMcs+Lj5AD6o2AZN2IjOIR20YRNeeusVHHIeFWxTzVf1KNmrhClmgzVuhzGgx7rNa3HI2oKHnvk77rzndixfvgw3/f4mPL3mBRTt24eCI314bP02Fkk1iTrrCF7cUY+tH7ehxpxFlSGND74y48k3dgm2+UevryaBN3c34+/vVYqcwEpdAo++8i4KjvTTGN6Uwaa67/ByyQExqi/rCODBF9+Asm0QyrZBFB714LkPqvFq+UFUaKKoNiRRdLQPj77yjgCM1YYU/vF+JTbvbkaNMY1qQwrbvjLhyTcLiDXXETB8YuNObP1EI7SEr9cdwt+2lgvmr6Q9hAfXvI33vjSTlrgrjPUVX2Jt0cekTTOmoVJH8dDat1ClT6DRkcfbuw9j+cqLcfD7vfCNdMM32o8Wdys2vrcBjiQZQEbnvKhoKsT36i+hjxjhStnxvek7bN71lhjxGqMWbN71Nr7WfQNjjMw7+5oP4MPK7dBHzbAlHDBHrVi/ZT007k6c+G8a4zMDONp2AF/+qwYnz/hx4rQfk7O9+GD7m0iN9yM32gdfyIB//asObe2f4MTxfvywMIB0yojnX3gCK1auxPraQ6gxZ/Dctlq8tbdNaLS3fKLF46/vFBmjxW3DeHzDR3h7v5o5gUPY1HQUz7xbSWNXFiT/4Not+Og7twBZLys/x4sFe0W4fHHLIO5fvQmlbUOoNSZRrY9jfdmneKPuX/jtbQ+xQPg3ccGSy/GbPzyIktYAhalvr8XW/W2o0NPGdeun0r+vtIOc4H/ZuBPvHjSwjWcUmxrp3ydpoqN4cM0WbPtXt9BGv910COuUn4JnXBa2DOG+1W+g8NiQkNm8sGsvXq/+BruO+lFjTOG5d5VYsvQivLmv/TwAPH/8rGOVQqGAfVgPy7AeD//lfigUClxz4124iJsblt2E625/BGUdQ1hxySq8sr0Y3pwLlqgV9pgFvhEnutM0KujLdsMSNuOhp/6KS39xFXYd6sYHXxixbPky3PHnO7Bs+XJceOGFAuTt+saEgz1p7HePYK+THIzK79T4zS13oKzVi/09efz295R1df3vf4/9zfvxbsm7uHDpRfjonwY02kfxRW+cdlGHmlHfakC1IU2snIUS2ussGTRa0miyZbGHsSh7uiewv3caB3rGKVfNlcfe7jya7Kw8XBdHYbMfVboEGq051FtHsId1mXLgx0fAjY480xmmUGfJiXBhuSGiTC2Ne6tYnhwX+5K2JwV57VW5OoLyrhAqtVEUHqP6tsJjg4t0ewXNg0y7x+vy/KjoCqGsMyRy/QoZ41d4jFzBxcf8KDziwc5DfQwQDorg6MIWSQ9YcCyIwpYgClmtW0mbZA4hjWIIJe1S3RvfpXMgKO2MJcejnOnkjmy5iJ9/reJtHrLYHqERVMvMIjI3qDCaiFiZqHAcy0czZeyUxvKSSUcwjrqkyOuSDCLyMyUzhixmAqsMaaEblca/Wdb7mxUbEs741ci0gtLjssgYQxw1phSqDXERaExtBFRK3+AYY+/DPI2GbaPY8a0NCoUCh7u+xdn/BjE6FUentRPGkEno8nRs5Ms1fvQ1Pc7BgI7drw4ZoAkZoAkb0BXSy04DDFHOJJpgiVthjJqhDRlpbBym39efccCRsGEw54Iv68DQaDccSR/0kV6ow73QhLvx19X/wK+vvw5bP9yAW/5wA1ZevALPrPkH9nUZUOeYQL09jwYHY0BtI0I7WWeh0R2vbKvkr53Qj3JWMAJlV1hkxik7hilLlEWKEPMfJIa7kx4vbacAemV7kJhsdUwWHB0hZlATYWcUtczkQ1Ez1BJUpSPWWsUYQapFHBZd4UJzzLI/Jcc86dY4i16lT4mmEx4KXWPKotaSw2OvvovbH3kGXeEwejIu9GW64Uo6YU860J1yYCjfh9NnBpGf9yI22YvejBOWuBUOFu+jj5gWafu4JtAUIxZQxx43Ra2wxR2wxhwYznkQz/VidLIH0wteTM17cPy0DydO+3Dq7BDmTvgxfzKATNaK2fkBTM/2YHymByeOe3D2uBfZESt+ceUVeF25BzXmNDP8pMRrJiKmeLqALDpMRIixaYQ0XpUc/nxtULHUB7lGr0xNLGCllnrgawwJ1JszWF/26U+mOrxUtE/S51pyUHaFUK6JMYYyvMjUQ05v7g6PQjLCMR1jZ1Sc5AKnyZeyMyR+hj/O1y0eWVahiVKPe3sQlYYMnnnjPfz69386DwDPHz/rWKVQKGAN6hEccyOY78Gdd92KC5ZcAoUiJwUcL7sR6yu+xDsHOqBQKOCPqhEYcyE80YPAWDcscSu6U3YE8m602ptxwZIlUKnDaHTmsfrDKjz4xCM4ajmCtRueh0KhwB8efBqlHUMoaRtCaWs/yjqDqFL7UNw6iKK2Yag0cex2jqPalEbpUSfuX70RKy5ZhUdXv4j7nnoaFyxZgpeK9mKPawyvqz7GL6+5BpeuuhTLVyzH9bfchHeqm7BH70W1eRSVerL673GNk5HDNY4mZx573RP4si/JIiISaLCPYH/POIntHXk02EbQZBuBSh1Bo4Ocvz8Cfyy6gkTCecYCSrVUlbokqnQ0wqzU0zi5ykQGAoobkKrFuEu4tJOExKQlomT7knYqOy9qkTqSC4UxhM6iliF2n4+BvyGUMG1f4bEhlLYPo7hlEIXH/ChkhpKdR31C78edxIXipEgYDgKL26TbkvYglKIPOASpzWPxoseB12KNjLzLlR4nwTYHezyTjS3gLPBXxIB0RdlIXRqhc6MJB3/cECL+bpkGp4w5tOUxDTyqR5iJZACwQpeSgT+ZGeQnTnmdW42JpAgC8MmaQCQAyO/PyO6XHq9nF51664gIia61yMbIvCHESmPSetsICo/Q+LeiqRzJyQAmZpM4qm+GNqBjAM8iRP36iBlaBv50AggwAMgiYLRhI4wsD9Aat0IdMqArRGCQ6wQNUTPUwi1Mo19HworYhBvDeTdCeTfGF7wIjveiP9cNb84Ba8LCHKcOdHktcMf0iE72IT7VB53jENZvfAG/uvpKfPRPPYoP2UkT6JjGHvc06p1TaHBOYbdzDPWWLKpZ4LW8tYUH7XLdqVIdW8TWcNBFhifS/pa2Bxk7F0RR6xCKWoeY6cOP0lZiBEvb/CjrHEallmvJpPq5OtYQVGfJslEhM3dpE4IF593YPCaJxyYphcSCNlIV2gTKOsNMv0YjbBozR1FpzKBCn0C1KYMd3zlx0fIV2PaVAc6kC66kE/5RN7pTDhijVvhGe3D6bADTJwYxd3IA3lw3/KMu2BMUy6OPstedAT8OAo1MG6pn7xlzzApT2IKedC8iuT6MTfZiat6LqXkvJub6sXDaj5OnfTjzwyD+839hzM17cOZMBNOz/Th+yofx2V6cON4Ps/Er/PJXv8AfHnyKPW8Zynxkm2JqTZKBv46w1KkuM30oO0JQqaNQaUjLy+UgcmOZShMXutBSDv6YHKVKFxMmoFpTCo+9+t5P57q+tIUcw1aSZpS0BcicIgd9PBdSFg1UwowpYi3s4KCWznI1AcgqFjpN38ujgaICQJarperNkrZhKDuGUdQyhEpdFJde8YvzAPD88bMOqoLr+AL9SRuCeTfWblgNheIqKBQroVDE6IOw5AM8tv5DNDnHcfGqX+CtbZuxtaIapoAO3xrb0OU3wpvrQXrWh0+P/RMXX7YKja4J1FpGcPkvr0JpbQEeevJxLF+5Er+//WY8+vxLqDQkKXi5I4Si1iGoDhlQ9/1hqNoH8Jc1r+Hev/wVz6x7Fbv27EODfQTX3yGZSBQKBe544GG8XNiIiy+9BMrdSjhidpiCBuyo2o4bbvkdFAoFfnvD9Xh9+wfYo/figHsEDY5xoaNqtI+izppDk0Ny+FbrY+L3v7e/GXucI6g1pYk5dOZFCXijcwINjnEmVp8Q8TD19lHJaMIcylW6hGhkEADAnGU9svKLFQEOZVeUCcaHmQs3KBg87t597qNG7DwygF1HffjosBc7jwzQWPjoAAE89v0lbUGo1GGUtgUEQBRsIouXoao9EqlzU0lR6zBzFgcW5QOK6rh2Mp4IgXsbu3B2SL2oPz7ZQijTuQihuzzSg4n3z9VHkq5LCnUuZ9lscqDJxdWlLJNNDkAXZzBK4GBt0f5F0QuLYnu0iXN6PFMyNzCxuRQ5kpby5XjDh8yQIQd81aasMI3IDST856vFfYtr5ap/VDGXW/TnOtsoasw57PzeLRoC6g80whN0o6u7HfphIzN7EDOnlYE1HbvQc9enXPtnjNL3yu9TywCgJmyAPWFF1QEVHEkrNGEjPFkHElM9cKcdZDpg8SLWuB3+sR6ExvvQn3XBlrCzyYEL/TkXBsd6EJroRV/GifioQ3wWly9fhquv+RX+8dJzeOmNDVj92npUf/0t9nWPMvaTsYKiSzgljF4V7HUUjE17CEVtwyJ7T66bLWkjp25JWxBFxwax6+iAAIilbaTfK2OGp3J1ROiIqWGEdIS8e7iSRcyInEEWOk0jzohg+0o7QijtlOKWVJooXiraB5U6DJWGmnjKu3i7BXPUs0ikCrZxefYdJS6/+jfYa/DAm+tDZLwX0YledKec6M92Y2zOi9kTPkwtDGBotBvpmT7EpnrhSjtgiEn6PytzAFviVnJ2x8xwJG1wpewwRiywxu3oTfchnOnFyJQHY9MejEz3Y+7EAI6f8uHsD4P44YdBnP1vEJNTfZic6sX4ZB9yow5MzfWht/sQFAoFXnivGNWGuNgsVRhoAyw3fshD5c91/SpZxmItc3DXWzIsT5XioajnmTbhawv3UbMR20Ty16OK1cDxdph1ygP/DwbwY1ETV2vO0KaaabS5eadUzlLy29YgagxJlHaQplueHcnH2mVdEdSyzZ9SlqdaKqYnPGcyDN5yUqmNCyZ5+z9N5wHg+eNnHasUDChZfR0YHu/F3i+qsXz5jVAoNkChqJIYQNUXaHRN4LkPq/H7+5/AVb+7BctWrMCSC5fiklWX48+PPobv1d/iV9dei5v/dC/lJZnSuOq6G3HL7SKvCJX7y6FQKFCti6Kw2Q9lRwi7jnjF4zf+6b4fGUVe/KgGa3Y2QKFQ4OXNr+Afr67BHff8EQqFArWf1cMyZMdA2gNPohe2sAWOhBM6nxYV+5S456E/48ILL8Qf77sHa7eXo7zdT2XejjwamcOy3jbCWL4x/P29cvH3PrXpQ6mhwTayyKlIAJB1IjvIJNLAeofrrVT31WQbQa1RcmjWsr7HGnOW7Xw5uGDGELYAFrUEKS+xI4TCliHh1C1uDaCg2Y+1JZ9hx2Evdhz2sFsvA4A+lLYFUNjsY4aOIZR1BFHSNoQCGUtI4dI+lLCYFxVz9ZawhYs3eXAWsaiVGMXCFknAXtpG7CKPjSnl1Xyy7D8O/ErEKDiyiKGjsU1UCt/9UY5gVFz0zg10VjKdIf+diyr3znHjidGzzH2sYheL16r/JQHARbmNUgvDYgDIY0bSAvhJDF5G5PzVmtMsbkQWDi3AX1qEHcu/rma3/LFKNlImAJhe1BPMNxOS6SnL3K4xPLlxO2Pqu2H3mqHpV8MQksAeB3+akAHmmFkAO64H04aNBATD9P2asEFoAY1RM41+oyYY2a09YcWefzWiN2NHd8oGZ9JGgDFqhilqEfly7rQTwXwvhvO98Obc6M91w51xIpB3w512YGisB4F8D4ZGuzF53At77xHE8t2I5l041vkZthdsxjsfvI533nsVl626BI/+9WHs1fagzjZKfcnseVdpY6g2pNkFnp4TKZCZGatEIw6vRRwSI+HCY34ySbUGyCDVJsW9cODHI0UoToRaJurMGdY6khZO40qdlCtH7FNcFjIdFaNMGjOTU/W1yq+o31wdZppb2mhxQEAboLgAgDWmLO56ag2uuv4WHLS4ERx1Y3TOA9+IG95sN1JTfTh+ehDTx70IjrmRm/Vg8sQAQhM9Qu9nilnRk3bAmbSjO+WAN9cN34gLscleeHIuGCMWWGJ2eLIeDKf7MDbtQ26iB+mpPowveHH81ADOnvXjh7M+nD7jx+SMFydO+jEx1YP8eDfm5z146601+MvTj4lQ5WpTRjS+lDHtn8SShfBT49+itiBUanr+68xpNJjTaHKMotFOYc7oem08AAAgAElEQVRcUsMD7DfW/As1xjRUWsr0K+eh4XxKYyDgXqWN4brb5N3uN7BudwJptZYsqvUJlLQFfgKUhkRuKv9zUWsA1foEitsCPxGYTwCwvCtCDLYpDWUXNc3wxyU5TVikQig7QlBpWCNK6xAKj3rPA8Dzx886VikUCtx098O4++GHoHE2IzjixL333YcLLlgGxQVPY+myG3Hd7Y+gzjqyyBVcbUxj64F21JjSeOeTDtz6wF9xwZIlZPZ47QMqqreNoLylbxGY+/3tt0ChUODBZ5/HjkMelHWGUdo+jOe31eGup17Ga1Vf47Wqr/Hu/la83fg9rrjmOvx10040OfP40xOrKUZm6VJcff1NWP/eW6j/qg7a/jZ8p/kOLZZj6LC1wtivR3fMid60G864DV09rSiu+Qj3PnQXLrt8Fd7eewx73fJRbh57umk03OjM485H/4YLLrgACoUCW3d/R4G9jjHsdk9it5vpABn7t7t7gn7eTffV2UbQ5CRtYZMzj3pLDo22ERHzQbejbDyYFeCAgj+jKG4nDZ+yI0TjJ6YTKm4ZQkEzMX7E+nmx48gAdhz2irYQYlQJuBUc7keFmmIQiluI8StuDco0g+T6VXVFUGfOotqQQjkL6i5rD0DZRj9HBpOgaBCRJ/AXtfhR2h5mQDDAtDkktub9qfyiRSCOA0BpDCy5hqOSZpIl/5eyW0nnFxPsHm9pkHqJZcCvU6oFVGmldhAO/Cp0i2+lU3Jpi++V6f8qWcsEMT2UuVdj5F3RWQbGpPw/DtrIKcoYIUPqHNewpB+UgN85uYRGuk+0jZik3EEKmk4wQJrEk28W4qprfw2T7TvkpoZg9ZnR1d8FXdAAY0wCeHoGBC1xK3TsawMzd3AWUBM2ijEwsX1GdIUM0IeNsMWtsMYtcCRt4jFdxCjlBrIR8/B4H3qybjiTDtjiVvRkXHCmKKakL+tCaLwXuVkP/GNuJKc9SEz3Y3Teh5H5Acye8mFswYfkdD9SMx5MHPdh5uQw5k4OYDTvwmsbX8CvrroSxd/qUWsfx27nGGpNSdEFXtYVAq9kU7IxWjEDgAUtxG4XMOBH7UQBFB3zofDoAJTtQSjbA1Ay566SRSGRmzQuwGU1G/9SjiClE9SauRSAoms468RZSWokIhaLgB+ZGMpkGsSyTt6lHRJglLSLw+DtJDzAnJpNkrjryTW45qbbYQr1YSDXw4KZB7FwJoCpuX7MnBjE2Fw/4lMeZGe9yM0NwJPrhjNphy1uQ1/WBXfawXIdrZg6MYDgeA8GRlzwj7oxOOKGP+dGIOnA2LQX2cle5Oc9yM15MXfSh//8Zwg//ODH//4vjqnZAZz+YRCzC/2YnOrFUFCNiy5aiorDBgLsNormUulTQt4hnxjIgRJn/nj8S7k6hhpjGnscOXzunWExQuOot41Qu4shBRXLLa01Z0TQdyXrfubNL5QBmRS90TWGBNYpP8Gjr2ylRqeuMKqNaXzMWrEqtFEUtw5Jod3tIWqGYS7lMnVMxPkoO8KoNqboPceZQTYq5v+nsq6oqIDjWsKSds4O0rWglG0QilsDrAY1JKQB731lOw8Azx8/6yAX8LE+3P7Is7jkslVo+qIe/qQFq664HH98ci3WV3yBeuvI4i7gc3qBG10ECJXtQ9RY4MijwZbDXvcEak1pLFu+HEuXLsVfN+1YBAbL2wNQaWJosKRRa0yi1phAnSmBKm0EJa1DUKkjqFSHsOPffWiw5VBrSqL4iBtNtgz+8X4FfvfHB7B85SX43e1/ws7GahhDBhhDBuiDeqj7u2AY0MHoM8AVdaE30QNb2ILihkIsW74C73yixh7XOJpceex1jqDRnsUTr76DN2u/WvRv3LLvGF6vOIj3P2unWi7XOOqsOWoGcY5jL+tnbXIRkKxhguF6W44xizRy5tlu9TYaPdfKNGEqXRJKdQyFrUHsamaRLc0+NvYdxK6jPnEWMM1fYbOPQqEZm1fQ7EfB0QEUHSMguOsojYh3HPZi15EBKNuHUdwyhJ1H6eeKWoMo6wihUhtDky2HGlMaKnUEVdooXWjaSCRPGqhBlLQOCsaQMyXFrQGUtgVQ1kGs4K5mPwFBxlZwJpEWLQoz5eJ2uW5QjGflwdmyjDd5xAePdOEBrOeyfcouag7gmiuuFVwUvK0795SYvwo5EOS6QAYAebhwjVnS7nHQV2eh+6pNfJSbYYxeSkRc8BgTXkV2bpSMpGFLsegZHj9DF69K9jNSYHWSRRWlUK2Po1wdwTU334GtZbU4e9qPWM4DU78O5qAFprBFJvrnGXBsvBuWjYAj3AQiYwsZC8g1f5qQQYBGddggRsQEGqXfpY+YWaYcjRetcRtcSQecKQdcSTv6s93w5lzIzXqQmfVgdN6LiYUBeLPdSE71Y2x+AGMLPmTnvBhfGMDMST8mjw9h4ZQfx0/5MXfCg9q67bj44hVY/coavLWrAPt1bii7IiyMnEkBeI8v07HuauaxSqSnJeDnR2lbkNhzFpNUdMyPEpYLKo81klolUoyxpZq5OrYpEOwvq57jo+AKXUI0jJSyRqSStgBKWodQ2hZAeVcYVboEKrX0WpZ1kDaxkP07KEImJN7zPMS82siMQdYsfnfnfXjo+Q3wj/kQnfBgfLYf0wsDmD7hx8LZIML5XqSmPMjP+xCf9CAx2YvAWC+8LDvQlaLGlu6UAwunB5GY6kdu1oP4ZB8SUx4Mj/UinO/HzMlBjM0NYGLBi8njA5g5OYDjp31YOO3D7HE/xqZ7MX/Sh8kFL6bm+3D/g3fh6ef/joM9pFuttWTJ1cymH9zJzTdyvCZP9IgzLWBZB4H6Ot7vbKRqxXrbCGtsGkW1iemrWbd3pT4lYnpUWnp+K1mjVJU+gRpDArXGJIF3E+kCK3Rx1BhT+KxvCk12yoEtbh1kWY4BKTSfsZJcK81bX4pag1B2RYS04KeMLKUdYVTraa2T90srO8Ki2503yBS1BFDeFaENeQc103z4L9d5AHj++FnHKoVCgQp1EHWWLNaVfYGLlq/AMxvewLLlK/DEhvfxeuVnKFEnRVhrE+8GZuwXgSiJGSR9XB573ZNodOax4zsHLliyBEuXLcMbtd9gc8N3UCgUWHnZ5ajURtBgTaPBRk6oKl0cpW2DKGgegLJjGLXGNOotabz3lR21hgQOuLMo7/Dhxa0fYtWVv0SNbQpVhgRWf1iNVVf9Bqs3vg5H0gZnyg570g57wgp9QA+9RwdnwA7nkBn98W68vHENfnX1VXhhywfYXPvlj0bOCpZPuPUTNbZ/142LV5HYdsdhrwB2ddacMIvU20fR5JpADS8V52M9xsrUWsixV2+jAN962yhqzFnB9pR1RVDUGpC1fQyisHkQZR1BFDYPoFIdIeB31IeS1kG8/6UZhQz07TzsRcFRH3YdHRDj4ILmAXx02MtMIYMoaB5EWUcAO48OkPmj2Y/S9mFUaCLUl2pM0oWojZgHVVcIZW1DKGfB0EUtgyyPcFC4gUs7gkyQHRIsrqiwY8CRGMlhlHUMQ9kZWjTCEnom5gguZ/VuKsbeLOp71cRFJlapzNH3Uw5f+v08CHgxABT9qjLH745DvYtYPw4AK2SsYIWOOmZrjKnFo14G/jizK5k8mLORsX4CLPD+WcbYVZzjIOZjZgJ57O+WXcB4LiWPDlLpKEaI9ExJqLrCeOy1bbjlzw/hh5MxzJ8KwReyIpTzwDRogHHYDAPT+/VmHIwNPMcEwnICpVHx4hGw3OjRFTJAy1ol/u06wgwli4GkOW6XdQLb4EjaYU840J1ywpVyYmisB+MLA5g47sPUCT8mj/swvuBHasaLkbkBjMwNID3rxdi8H9Mn/Zg+GcTJ0wQAT5zx4cyZQbjdh/DR9o146skHcdMtN6C4K8ZaIUiCUMxz5MTYlwxUO4/SWXBskPIy2YaquGVQuIPLOjj4k0LNq/RJYf7g/cM1JmJ7GxgQEw5wU/b/Z++9ots8z+xfjmO5pCixk7gkTp9kJs0ziRNn0u1Mmu30Mhk71XGRxd4JsFPNiZ0448SVlNgLCkEUAiQ6QIJE770XkpLt+Z+Ls9ZZZ53LfS6e930/0Mmdb4W1sChTEkUTwIf9Ps/ev82ezwHBBuxnEx2xWm5aK8tVYZx+3oiB5T30zO+I11znLNkxaBXsPzYBlKloCj1hyKD7ogXXXHsC02YrokUHqkduVI52ULviQThvQShvQbRoQ/nAherRDvJVKzJVO7yZbZjjRgQKVmRrdvhyVlQPXcjVnPQYFOnvhYt2hLIWpKtOJMtuxIoWFBpO5Ot25BsuFA92EC9soVy3oVi1IlW0YEn3Ek6cuBYvmiwY08Ywpk+TAFTx9G+Qmj9WpQ7vniWajvYs7pMHTxkW15veJS/OGxIYWNmHfD2GKUMSg4oApjYJlD5pzLLHJoLWl83i9TLIXi9c4MkUJP5kigBG1GHm44zQ+l4dxYQ2gXNbFAyUKUPoXdg9JvJIoJFI62Rcx+bgnBCKbIPCYdadnLG65MWohooJejn7dZ6mzZx5KKG4KAHccdHJQksenH7RfFUAXr29odvJlpYWtE5bMboRx7g+hV9NvYx//eI3ceK663Dnv38S//rxj+Atb30L7v/1Yxi8qEfftB7f+enP8MjQCAZmt/Dkdhp/tqYxYczi7FZTMpbR/CdNOTzyjBKf/tp9mNDFMK5LoP05Hbou2girsLyPwVXWn8mmRAMrfgyrwhhWBdH2sgVjqiCe3M5jXBvDH7ZzQqSNaDNCdJ5+3oA3XXsCOvcyjBEDjBE9jBE9DGEdNvY3sGRbxqxxFkuWJWjsa5Cf6RZf5/aPfvqY+Pvyzx5H14wVA6sBnH7Jiq8+2ImWlhb80zXX4HunZBhc2WPJTEogjulSYpVMq43oMaO/6CZlkx+62AUIJrpAqa6Opqo2KelLSWmaApCQ67zkwmfvewgdl1xC+FEIxEHTvRknehY89EY25xHw577FffLxXWRr3YU91pJAcO2+FT+65nbRv7wvVlEk/HZFHR1fmfFVcNecVGPVs0jYGm6a75zdFRfAzjmPmAo2A245Aqa5Nk8Y95vwLwNNtUjNqWIBfW0OlrAVs0DKNAOCBZ1fagS5+we/bWomkSaE/Pvg60Q5f/yaMS9N/rtjEx8m7GnyExQ/46GmNTIPkvCJ3xCb6g2yRHjzRPIf3pUSroajh/qW/bjr/ofQ0tKCM9NrqNXciKR2sBuwYmV7BfqdNVhCOij316EJaqHyb4gGEKVfI1Kfar+mSfBJGBg+/eOTQI6KWfOq8cNf/xhrXik12iz6tEGqf9MGddgMG+BKbiFStMKe2EKkYEa+7kDxwIl83YnigRvlgx2U6i7kqg4kyw6kKnbk607k6i5k6y5UGg5UD5yoHRF77uDQgVeOdpHJbuHEiWvR+7KWXl8sZNHFunwpREXg9NYZJ1pnXDg9LQnBthknTQD5xG1+Fz0Lu+hZ3GOdsgHRajMkRH1UNBCNaGIMWs1WiwwJM7gWkmwOKwGRAB5YJd8f7zsfWPUTK/P7v0LP/C7aZxxoZxuBztlddrjaR88i8wGu8pQ6dVePaokV+c73fhD3PTqAVJUEWaHhQrbmxH7WAnPECEd8C7m6E8WGE7GiHfGSHdGiDb48rYarRy5cfsWNQsOF0oELmaoT4aIF0ZIdwYIN0aITwewWgtlt5Ot2JEt2lBp2lA9dKNQcKNasqB3aUa7aEU2b8MLMOdx2+7uhcRkwoU8wTBf5YQeVUfF853gnXuPYveSHTBHEhC6BgbUAu25Qt+/4RkQIOpkyzKw2cYzraesyrKFr7xd++LvjVY/KsJjIyvgEUBHAmDqMsY04YwOGMLwew4WtHNUSbsQwtOZDzwLZXDrmJEpCJ+ts72DXOL7e7V3yinVwz+I++pf9wnfayQTcwIqPVtRrQXTNSwD+nqb2GC4g2y/toptd13sW6Ouffsl6VQBevb2h28mWlhY8/IwWp1+yEmJAGcQDrRNoaWnBez78UTxz8U84+7QMJ9/+Vrz5zTeipaUFt93+bvzHf9yJEyeuxQO/+C88a4thzJDDhD6OMR31JZ7bLtA4XhlA17ybxuyqACZ0cQyrKIIvVlts7TGiDmNgZR9j6yEMruxicMWHc4YE/ubK4hlLEE+aqYrtI5/9Cr744983NXLkMKxN4qbb7sDwUzJsRvQwRqgSyRrfxGZYB61/A5tBNdS7K9jYXcezC8+ipaUFd3z8LrS9tI3fXFjBTweeQftL21Rrtkbohd4lL7pm3fj4l75zTCT++sw0fjn+POTrCciUYVoPm/KY3MwIYjz3bY1uJOi0qwhhYJVOtvxNqYMJJV7h1tnE8Oua26HpwIoPw0r6+fQtcqO6mwlA59+hYPoWdtE642T9pXvonvNgYNkreoR753fRv7SPoVXyyZAHMUh4C2WYeZ286FnYFanijktSzZwIi7A1MecD9i5SdzEXfs1rESq55wJQSv5KJ/MwpThVUZHo428IvPmDvEHHp38SZV+qf+tdlgSgYIQxv9WQQqqNG2z2+zXXxInpDnm7hlVSvzQP8YxskABsZvvxVCPvbB1cC0CuCIh+0UFFiE37IoJ9NqSSRKAUNDnuPxxSEidyiK+FuUBsEs2cfdd+yY17HmrDietvRP/TzyORsMLh0mPVtAx7ZBP2uAWruwpoAjrBBORpXxVrhlhnNXGcA7jShHxZ8RIGRsn8fhwjow3piP3HvH+6kB7rgQ3ow3psRQ2sVkwPY9gAc2wLW9FNBHJW5OsOZOsOFA9cyFbtKDWcyNdstDqsk4ApHbiQqTmRqztROXDh8it7qB7uIl+1o3boxiuvenFw4EKjYcPdX/g0ftMnR9scdVh3L3qPBT7a2IGonU3bT880CUCGVxI1ifMe9C/S87p/xd/08w4dFxLKMOTqMIYUfsiVjEvKpsQyNeFg+lnYqY/BfftWqKKOh6AG14IYXA2wSZAHrdP2v+NzdggMEzHj+pqex7y7/Iwpjx91X8Cb334zgnknkmU7Sgcu5OouhAsWJIpmJCt2RItWRIo2hPIW+HMWRIo25OsktmNlO4oNB6qXd5Gu2JAoWRAr2hAt2uDNO5Cu7SFf20OiZEel4UK6ZEa+7kLlyIPqoRu1QzuqDTvSeSt88U2Yd1Zw57/9K7789S9g3EDtLiPaRNOBp4m7yRp/ZEpW1bnixch6hKr2VljD0LIXQ6veY4epkY0ExhiYe0xHU8BxfYpZMaTu7iFVhB4zVQTDqgjkyhDGNFFxH1FTs8uoJo4pQ4pQTJoIepcIwdLF1r0ds8ebXDpZvabwSLN1bs8SE62rfunayCaCfUteyNVh9hjvSAKQgcK5+KPn7q4IKPUtUevJqRe2rwrAq7c3dDvZ0tKCx58zof2SG6dfsuHUCxa0zdjRt7SLj3/pW7jhrW/Hm05chzddewJvfcfN+Oy934XsD2dwcX0Rc+ppXHvttazb9/34+alWnNF48awtifP6EB4/8xecV9lxzhDHUxoXhue38ORmDF3Pb6DvBQWeMkXwJ6MfsjUfntr046zCgglNAJPaCMY0MZw3JjCuT+KPphDOGZMUtDjG4SPxN2nM4csP/Bi33n4LHvj1byB//gWs+4zYjhpgjhmwGTLhlKwLt37oYzhx/Y34t6/9J6Zefgaf/cJncPv734u+GR2m9ElMGVI4s5nGsDpMqxqGJZnYiGN0PYL+5T08+tQi3vGuW/Dv91Ag5U0nTmBwwY7h1R3IGYtteD2KcW0C47ok5KoQ+ldo6kXrInrz6F320cV8boddAPgpfxedcyS2upjXrnfRQydQhnPpYqnc9os7Ym3MJ3O8I7N/2SuM5b1LZCTvXyJBOLQWZHVIJE6G12MYWA1CrgzSBEMVPtZt2n6Rr8p2xL9FQpCmfaKTc5GDqHcE6JYLWr4S6WYVTlykcd7fmCaGMU2YlbAHhcDpY92ZzdiYf8QVlJLHPjHta/YQSlMWzgYLiskf5wLy6SCf+nG8B09xD3PxJ6rcYqyLNy6t/dnEd0gVwcCKT6RFhxSMCce+9iCbPgwp+X+HBRaIT/WOeRTFdLTJJ8m//7WgmJr2LPnRNuPEdx+T45YPfAybHje8QSdMdj2Wtxax6TdgdVeF7ahesN54J6w2SKw/lV/DWkKk8IfUDUzibz2gwapXBWNELzyAav8GVL4NqPwb2IxswhA2QBPQwhzbhCaogy5EE0BLfAtbEQOiRSvyNTsqhy7UjtwoH7pQOnCj1KDVY6HuQqbiQIytg/MNFw6u7KJ24ESpbkO1YUel7kCt4USt4UK14cB3vvsVfO/37RhQRmj6x56DHbNNyJfXcTTJXsHvTnHo6WKvNbI3UPhjSBEmKwBHg6zHWPo4gpH1MD031sn6IWMJ4UFFkIWdmsMfPPlOKJnBtSB5ZNlrtW+B0vydLLRF37sUwupe9KJ36XjnrEwVZV3Rabz1pndh5LmXUGo44E6Rfy9XsyNasCJTcyJecSJVdSJesiJatCKUtyJWtiNStCFasiNVsQtRHi9akSqZka55EMrZEMhaEUqa4EsaUGjsIFe0oFi1IlN1IJ7fRvVwB+XGHpx+FaK5bRQbLnz/R9/Eg7/9ueiv5h5AaTonPe/lqjDGtXH0Lu4J5FM/AyL3r/jRx3A5khCPCOySXB3ByEYcU8YspkwUDBnXESR9TJvEkDoqWTKUIchZ9/P4RhTjG1FR63fOlMWUgQgRcnWEwcH3xOG3g1VmisdldufYVJBf6/g0sGeJbWD49mSeUuUyZRAdTBhyZmvHpV228qWVcDt77nJLTfe8B+0Xd/DIX69WwV29vbHbyZaWFjzxglmwsNovkhBsm3FgYC2AcX0K7S9s4rFn1PjVmRnc/b1f4d0f+BiuedO1xyZi191A08HP3fsttA33H/u9L/7gob/z2H3403dhWLGP3z85h8998W7x+Y985ssYXo9hTBvHhC6Bs8YMzm8XcN5ceF0Pb57umzmMahO45toTTJBdhzefvAnv/8RnYQ+uwp3S4y+z53Hiuuvwo+4LeOwZJe6677/xtnfegmW7At+47x587NOfxIgmRiZiYx5njGlcMBfw5FaSINL6OAZXfXRRV4YwtLaPM5odfP2/Cbdx83s+gJaWFjz2pxXyjugSGNFEMbi6j4ElDwZX/czgHUTfspeZyQO08l6QLiQdjPXX9brmDVoPeChAseQV69dOxurj4q9vicDR/SxVOLgWwDA78RJCICBOozLRLED4Ct6jPKZLYkgRYBOPIHoX99A27UDX7C7kigB7jrjExayHTf74WqJvaZ8xAvcYuFTytvBJIE0DfdKkjlXijWpimNRFMci7VRUS1LWPvWEK2DSf+LE0sPQ5v2gF4F93UMFxHBEhvgb4ynf1eDewqA1ThsTaXrD2NpIMzJwQyA8Sh/FjKBcu5vqXfVKjCRega6/DUIhu2mZ2XUhM/7hgPDbpY00mvPmETy5JLPvQOu1A/7IXt3/k4/jWg48iGTTBubMNlUmNdZcC6/tqOJJGaEME/t0IaiUIMLtvBGkKqA9p2SRQDU1wA2r/OlwpIwxhLTTBDbhSJqyzdLHSR40RG0EdTQK9amgCWpiim1D6NNgIaqEJaGEI62EK6xHImhEpWVA5cqF84KK15IEb+ZoD+boLhboL4ZwF/rwD0aIdmaoTpQMXCnU7SnU7jg6caNQdaBy40Gg4UK7ZcNPN78BDUxchVwWJ6bfoRfc8AZ25AGxloq+Vib7OS8z/xybdnZco8MTTlr3sdSV1CHN/Z4QCCNo4Jg0p8v6xViBuG5ApQ/R3lXwVzHmXQfE8pT5yL7oWPCzxu4N27umddkhd3ozTSQlUxohb5iKQmJVj2iSmTDnc+fUH8P1f/Q6Nyw5YYltIVpxIla1Ilm3I1RxI19zI1BxMAFoQLdoRKVgRKdoRLtqRLFO4I1E0I5w2onQ5iHDGjFDWQoKwbEem5kbp0ItChdpAGle8aBzu4n//rzB2g2pcfs2LdMWBbH0X3/jWl/FE1+8oRc8EoHydAfGbEEuDijAGVoMYVlL/7eBaiH7+awFMauM4o0+wLnLiJnJf7QhbwctUEYxo4gz2n8e4LonxzQymjDmM65LMmx1hwo8S3GOaKEaUfgyt7NHfV0dxwZzHmI7CHzJVhFA8oouYMVK5MJtrDnfQSriXYbVo1R+g5yIThRyiL1OEMLBG11TeQ989vyeurb2LewTrZ53v5Eul50jbRRceefZqCOTq7Y3dxASw7SIlRDvYGvKJF8x47G8m9Cx6MKKJYmozg6nNDM5sZsgou+ZH95wTXZfs+NW5edz1wC/x7cfH0T5txXs+ShU1N9/+frz/E3fhg//6CVz/lrfhxre9gyDOX7kPjz6jwSe/9M1jovB9n7gLbc9p8eRWBn+0FnHBUsQ5cxFntws06TPlpDYOIxeB9LFvaRfdsw4Ma2IY2Yjho5+/B9dc8ya8+W0ncct734t33PoeTBrzGGNJsQ/92xfxsc/cjRfVL+Jdt74LLS0t+OXUNJ40xSBbJ5bfhc0gJrRR9K/ss6onSvQNrNEUaWjNh6/+/FG872MfF/8PA6t+DCxTkEK25sdoEx9sWB2FTBnBwIoXMlWYEoFsnM8nZZLxd+/YarWXhS1G1yP4xu8GxOe65j0sNbbXVHZPJ1vi0IUgVwZFC0H/sg8yBWEUmn2JA6t+jG7Q+p6jEwbXgiT6LroYC5D4gm0zLGl80S08ML0CWkr/770szUaA032x1hDCkHEBubDjadbBVS8mtPFjbD4SPE3sNAHEDTSt1QgZM7QmITfkIogjYVYGm4XWWgjfekTOWgM4GiLEpgP0/fDEryQC6Wc0zOrI5KqwCADIWfhjSBnBkCLIoMEBJjKb13U82CEFQ+Ts3oyq4N8nTfzoaww2CcD+Fcknye+9yz50znnQs7iHx/68hutufDPCIRs8+y4YzFqsOzTQejUwhY0k2BjqRLgAACAASURBVIJakdSl9S+lhTfDegEE5gERlY8aQfRhHSzxTRjCOlod+9bx+NApISTXAxvQhXQwRgxQ+jdgDBugYhNC7g20xI3YTZngy26jfOhGqeFEtuZE9cCFfM2OUsONbMWBRMWN/awFuRqtict1GxqHThwcOnF05Eaj7kC9YcPBoQNPPTOAt558G1ovOtG/5BHIjZ6FPZq0M9RLB3vu8jutgFnK/hK15fAJDlkjvOhb2hPhDzlfIyrDgj84pk1gbCOOUS21v8gYFkYK/kSE8B9aCwmwL2964Gn7vsU9fOO3feia28XpaSdNJfmk8tIO2me514xPAen1wKfCE7oUzphyeOtN78LEU2fwyqtOeHMW5KsOFOoU6CjUbIiVbPDnLEiUbUiUbUhXHYjkLYgUrYgULYgULPBnTNhNbCKUMSGYtiCQNGIvpkWq5kK6totE2YV02YVEzoxM2Yp0wYZd/zqqjR34kwYkqx7E8mYkynY89YwM73nvrXhqc58CK/o0dV4zESgl4cOY0MbRt7SPIfbc75zbwagmhnOmDORK8koOrQUwJA7lVJE3wqwscnUUE4Y0zpiyuP/0KMZ1SUyxlTCF76THT66KYEQZwODSLkbUEVazF2PCPkFeQlUIvfM7wu7CD97dC/viMWmf3RX3HoZv6Zz30HVuyceEG/n7+paJcyhXhdHHksI9Ag/jEQUJnKrQMbuD1hmXEJFdLDD4+NUU8NXbG7yRAPybUaxGBphxenDFhydesuHx57Zw6vlttL1sRfu0DW0vW4iPtbQPmSqMsQ0yPo9pE+yNLyxOuDJVGHc/IE3/PnPvA+TjM6Tw/SeGpdDFTx+BbMWDP1iKOLOVPwZcJtH3OgTNZpMA3PzHWJpxQwYdM3Y8+owaP5M9Swk9HQU3+Gr2w//+Jdz2gQ/i27/4hfhevvPbNoxr/BjXJagmSBPDsIpSYXIVGYTH1sMY1UQxrA7jznt/QGxC1nHcMb2N0fUopgxpWgVvRHHGkMS4liC1Q8oQ5OoYBlb9FHhZ9VOH6VqQWFCiI1Q6EXLIcu8S+WF+d35OrI74+mi4yU/JfUqDrCOVi8zuuR3IlUzosBXnwKoP3QseDK75MbxOU9AxbZJhYfwEgp51i3AK7xbmnsOeBWILDqsilGpkMOjeRVYX19QV3LPobeo9JZo9R2xI33MYYxq6ENP3H2qC6LJKLO6n4y0La0F6Q1iTSP9CZLE33yFWzSf9Ht1/dXZWEmWcF6YgAz/3+3EBOLqRIJyPlhv+mxPfTX6+tQAGV31i2tO/GhCTO1H/p+ICkKaGI2qaFpFIDYtghwimiH5kycPIk9I0AZIEct+yDx2XXJCrI/jAp+/GT37fhmDABX9oD/t+J9QOJewpO9TedawHtCwQohGrYHWA1rjbUQP2stvQBrVQ+tQUqApqYUuY4M1ZEC5aoA/psB7Q4PzFJ7EZMcDAPH+2hBEu1jqRrtlhjhmxFdVjO6qHK2mEPbEJb9aEYN6M0qGbplIVOxqHLtQabpTqLqTLdkSKTvKn1ezIVZ1Ilx0o152oHTjQOHSiXLPh6MiBuYXzuOHGG/CzgafRv8qqs5rBz7P0RsqnK3z61zrjQuu0UySAO9jzmrP3ehbIS0gJYHru8VSqXBUhG4CGwm0jGkKS8DCQjPH5pK5i/hwOondpDwMrXgZg3yVg8Dx5f38mf55N/1w4PUN3LlZp8iSFD3oWOWfTj0EFraDPmHL41Fe/i3t//hvUDu0oNHaRr+8gUXYiWbYhUrAglDMjWrQjUbIhU7UjWbEhmDeT+CtR2teXsyKQtyFZ3kU0v4ViwwVv0oBcfQeJkh2xggXxogWZigWFmh3Jgh2J3Bb8cT3CmW3EClZka06Ec9tIFLZx+3tuQftQB/6wFccEW6+OaJq9szH2WqXXfv/KPk1Kl+gAPqlPoH/Zg4FVv0BJcV/vkDJCjSAKYm2O66jh6ZGnV3HWRMSKMV2SNhzsWilXhjCsCmJ0nXy+MkXgGNx7VJtgCW6/OBBwPyl/HCQB6BEreg6C5iE5PrkjNNa+APvLFCH0LfvQdtEtDvPtl9zoXtiT/No8wMTXynO7NKGe9+D0i1c9gFdvb+x2sqWlBZ3TNuZ5cZMgWfWh46IVp160om3ajp65HXTPudEzR3+mb2EPnZdc6F3woH3ahtaXLOhZIAjxkCIAuSoEuSqEobUAepe9+N0fFvG5B36F77VO4Kw+iiljFr8ffQp3fPyz+EHbBJ60liThx/x9fNI3upmHXF/EgCKA9hn7MXHIBeFkszh8HafwB10X8Pnv/gSjBuL3iV5fYw6j2ji+/agMn/zaA7j1Q01TvGUvzhoz1LHKzMAXtnKY0EYxrAzgKUueSPbqKMa0SbS9aMJ9rWfE3++Zd6F3YQcd0xYMrnjxpCmGUQ2JRvk6GZap1D0CmYLWsuP6NGtyCGJYFcb4BnmJehalGqhhVQRyVRDjuqQQRUOKMMa1CYxoEuSdYxOF3uYGAVY+z0vvexb3ROE9eQX3Mcw8TROGDMb1lKLrX+EXPmlCyQMq/ALVyX5NiINdduL1YmDFxwQh7zrla1ofe9OSKPc8EcyxGqOaOLU5KGkCKVcGRShn8PUir7lpgQk4GWPk8Zq2Y1M2JtZkynATqiUspm9c1HHP30iT+BvTJjGqTbIpT/RY8IMSjU28Mbbu7WfokKHXJXg5EJpPJkdUdGDi6eBB9nUGm5LREq6GPscnoKI1hU9TVwPoWfDg9Mt2/HpqGje89e2w2I3YD3hQqeeg2FqFKbiJFdcatCE9q//SSendoBaW2CbM8U2ofJQO1gW1cKVMUPu12AxtQhfUoX7kQqxso0liQAt9WI/tKDH+NIENbMc2kak7kG84YQzroPSqESyYESmaEStZECqYEcqbkau7UKg7ESnYcHDkxsHhDtJlK0J5C7xZCwJZM0oNJyoHO0gWtpGv2lCqWVA7sKNSt2Fm9hyuv+EGfK/zPAbWWG80a4rh4OeOOQ/aLzVZJpiniifuha2BTf94I04fO6jQRIquac2T5VEGAR/XJTG2ERMTQLk6SoeTVT+GmLCQKUMYXGMHN8bD7GFv/LT6c6F/ySsCKq0zLrRepDvng7ZdcqHjEgUQSADS64oOkmQnmNCn8PhfVDhx/Y14SafF5dd8iBXtSFR2EStakK45sJ+xwB03IlOxIVm2IpQ3I5CzIF62IVZ2IpC3Ily0I1tzI5I3I1GyIlq0YS9pRKbmQqZiQaJkQapiR+2yB7H8NvKNHfhjBlQOPEjkLUiVrUiVKHiSq9nw8qVzeNObrsGMyYARbVJM5HitIa/IlKmjdJhTkV9vcC2AsY04+aCXvOKwwwHzdAAkgd2/GsAgC7aN6VKYNKQFsmxkI45RbVy8/uXKECY0EQyvRzCs9EOupte/TEk4mDFtgur+lvfROdssAKUVsFSf6RFrYLrW7gs/9sCKD70LHhEMIdHvwRl9EkPKIE5PO9C9sIf2iy5RAdp+yc0g4CQK+5epDWRw1Y++RQ/G1iPonXNeFYBXb2/oRhiYlyzonttB34IH/Yu76JnfQeu0g5mQXaKObGDFR6BSRQhjmrh4E+bJrNZpByVTpwk+TE/oXQys+AVxfUQTZ76+PM5u5XHGlMPUVh5/B5neJN7g9E4Ey3teaYr4pS+jd8ZEKzhdFv1LOzRp+zvxl8eoLi28io/+RQOZKsgYfkmMbMQgU4cxzIzDck0Mj/5FjdMvmDCw5iexoQxhfCMEuTKAwTU/9QKzVQKFPWIYXAthSh/DsDqM+1un8KWfPoaeSw4MLNHKtuOSG0+8sIUJPZsQaKhFgifOhtejmNDGxYp5kgGvp/SUPhvVEKWe+k5p3TSuS2DCkMKkPkHreCZwepe9EmpiSWrioDUYiTiOlWidcRJWZtYtvDRDyjDGdSlMGDIY0yUxsOoTk5DOWakXuEOY6iVEBZmYPehibLMeJgj5OqSXCz8hBr2ioaF32U9CZ9XPILshNskMYmhN6lGVppssOMHxKYrwsame+DxbsUotHmF2b1q9NqV7RR/vPwh8EOw5ISC2PPnLkT8SyDlMBnU26Rlc42/8XAg2TyAjoglGpgpDvs6Fq9Qgwaei0l1KS/azQIswx7OpCH9zbL+0g/5VPz505xfwwEOPIBF2IByPYC+wC4NND5VLAcWOErqgAWrfumAEKryUCuYdwfqQFpqAxA7UBXXQBTeQrDhQOnRhJ71F0OfABlxJI5xJmv5tRfXwZLbgSG5CE9BA4VVjM6yFI2lAqGBGtmpDvGRBseFEoc58fzUHagcU6IgVLNQOUrAhVnIjW3YgU3YgWzaj2rChceRAKL6Bk29/G37UdQaDSslvydtohtb86F6gFpvWiySg6E4g6PaLjAE442TJ250m5Ipkb+hbpsaOAfZ4DnEANz8krEtpcZmKkr9kF6FU6yCrIOPPi/5l+rqUpKeJY8/8rsA7tTIB2LyqbrvIvudLlAimw51XrID598PXwLd+8F/Q//SzqB55kCjakSq7kKvtIJwzI5i3IZi3Il6yIlKwIFEy4/KrDhQbNuxnthHKEx4mnLMimLPAn91GomRDqGBBomRF5cCOgysO5Bs7CCQ3EcsacfSaH9HMFvI1B4JpI5IlM5JFM/I1G0oNO7I1J06cuBZPrSjFunxMl8KUxs8A2vGmphvy6J01ZnF+K08hvFWyefQKELyPJajD4rXGMU7UvR7DhD6FKVNOEClkyqB4DEfWIxhbpwICHn7j4awRDdUv9q0EBOeUT5M7ZqVgRsesR9w7eWOJmPzRe0D/spd8nQwKTSvhPZwxpNG3vI+2GSf6mceQ8175gaRrbpcdqFl6fMlLgPL5HZx6znRVAF69vaHbyZaWFvTMOsnvsuIXjLjT01SE3jXrEq0cfH0xoolhwpBG7+Ie5KowqwrbEZH1doZbaLtIxuXBtSDantdjbCOOJ815nDcXiBH4ukne69e4E8YcfveMDp/7+r245T3vQUtLC773va/hxAkSde++7Vaql/vEJ46vgtnHX0zM4ORNN+Mzd30SX/vhzzGk8BOaQRVhhe0kFvqZIbu5O5ZgwfT5wbUApgwpnDcXSLCacpg0pDGmTWBQESJvIDOIDynD7HTvZ+ZfL377tBrj+hQmDGnylmiiGGYAZpkqjEldHKMsgTa4si/1D2tJDPKwwYgminFdEm0v6KlqTxPDeVMGZzbTGFknoPSwkgzSzb2T3HdHj49HBE665z20/mIrBbkqQnV4hgzGdCn0LnrEm6EIpcxLbCuehuP9wN3sgscFXt+yH11zbtFrLH2e89lYmfrCHglAMd1rCkM0iZ1jbDxl013Bwx4hVqMmwZP5RyH6xGqOhN7jz26IKd6whpA9YxsJCeTLV78bZAgf1SZogtsEfhboFz69WwuI/x8uXAdf933LVJwPyTyETd+XaARhPsUhIXjD4usMrIXEv9HP/GTdi/voXvKKTtWeJfIDPvbnNZy4/kYYjBr4fQ5EYgFsWXXQuLS4pF+AMWDDilMDpXcdSu86FF5i+6161VD61PBktrCTNiFbs2MjsAFjRAdr3ABHwoRQwQJTdBMval6EIUSgZ1NED6VvHeaYAemqHY6EEZoABUg2ghrYE5sI5LeRr9uQKFtROiABmKuRT61xtINy3UYcwIoL4bwFsbwV8aIN/pQR6ZIFpboFtUMHfv6L7+ATn/8CWSLYxJUEsY+9Dr0EVGY92hwC3caDFZfc4g23fYbad3hgiT/fe/kUfokM/bznd2Sd0CGj65QklYuVr+TfFClwtj4eVpMXeEjBHreFPfQu7KGH1T12ze3gl2cXGLDdLaZ/XPy18TBIkwDsWwlgUCG11ExtpjG4sofrbnwLFCYlygc7iOXtSJUcKNddiOaodi+YsyKUs2A/vYV4yYyjVxy48qoD4aIT/qwF/swWfOktBHJmBLJmhAsEkg4WrCg2bMjVHYjmbQgmtKhf3sGV1/YRSOhRPNhBqupGquJAsmRFtmJF+ZASxTfccD2eWlykQxSrSxzVhDGpCWBsnYJoZw1JDKsjGFPu46Zb3oOp55+HTBkQh5/mOsmeJb9IWA82WSW4p7j1OT3OmwvoX9nH4JpPbKhG1qMYU4cwrAyS14/7//gBkb0eyacteZi5uGtOlTdXZDY3lvQu7rMA3h4NUeY9bKJMYb6zxgx6FnnnOvW1n54mrmvbRRe65z1s80ETzo5LhPbqZFPqR57VXxWAV29v6EYr4BkbBpa96LzkRBt7AnZcJBxCz7wL/UsejKgjGFIE0MmxJZeIUt/NpkNtbFVxesZJ92knTk/TKWZUHcZ//rYHk8YsnrQWccFcIG/f68Te2GYO45tZTBizmDDm0DG3g+tvuB6PPPYzjIyewrrmb6g3XIil9NjcegkvvDiB7//iZzj5zlsxrs/8nQj8jx89jH+/+y6saf6K62+4Hp2X7KKWjXtEBtakNGUzXoTf+1aD7CSZFuvpEU2M/FqKIIkGTQz9qz4KBKgobTqhTeDCVhbntnLoXvAcM4rT1CmKkfU4m+pFMc5+f0IbE1O+MS4ENwhxMMr8iN/8TTfGtfTfchW9+YxpotRdqaR08dCqH5MbYXTxXs1jFHoPoWYYbJpQGG4WBKEk44gmhq55mvRxLlrzGoT7XAToma09epm461/xMQbWPnqX9tjEb1+Y3nka+lhiVyV59Zo5dwLUvMoTr7ydQ6pCo25Wnp6NNAm/qOhlpekeS+sy4Xbvr7qkBC/r+KX1blRK/HJBuB4VXb98UtE8ARRgZ/4mtBbCAPMoyQQUOiJW00OMQyZ73Up4iPkC+c9DxhOkYhpIE1E+6eMl9ELwN62Euxe96JzdwZ33/gD/+oV7Yfe4EIn44At6YN0xweQxw+F1wLpng8FvwkZAB01AB02QhJ47ZUK4aEG+bkOhbke+bsdeZgvO5CbMcQPcKSOSZRtOdT+M/YwZ8ZIdoaIV+pAWlrgBmaodOykTTFE9jBEddtNGmiKVLUhWrKxGzEnYl5oT+ZoTtcMdFGs2ZCs2hHJmxEtWJEt2pEsO+BObKDZcKNSceOg3P8Rb33ETHv+rjnHkWCp6JSgaI7gZv2vew1a8bpyedoiVKvf7dTLuZuclFzouudA1u8Pq12gNzNPzA2tcAEZEhyyvgJOredArLNaM3CsoU1Fjy7A6gr5lwjT18+rFBQ+hZ5it4os/fVw09hwXgDtMAO4IwdGz6BMCkD/Xh9VhvPdjd+KHP74P1QM3qoc7SBbsyFUdSJXoZxotWBEtWGkamDXDl92mqd5lB3zZbQRz2/BnzfDnLAjmLfBmtuHNbCFVtiJWtCFeciBVsmM/qkWxbkPpcAflhh25sgX5ugvRvAW+1CayNSfSrMovV7PjQx++AyPPPI1RPdVmis5kFdETBhUBDLPe9B+2E4/2ttveBbNvi6Z8ipDwuYrJ7KoUjhp4Haj7W7/twVljmiWEw2LDMLpOdX2jGoI8y1VByJRB1gIihbHosEqH1K55ic3XKTYgu+zaJwXbuN2G/Nkh8Xe5lad32YchRRBnjWl0L+wK5Bfvc+c+69PTDnTNe8ibyCbZnbO7LNDkxsPPaK8KwKu3N3Q72dLSgvZpKzpnme9k2oHTL9vQ+rKNUAnTdnRcdGJUE0XnLP1+O/PMdM/ThaxnYY/+HiPrN987Z3cxwbpzJ41Z/MFaJA+eKc98fnTvnHXjv7qG8fAfFjC2mcOZrRw++m934eTJt6By4MTBZRdqDScqNRsqdRuqDQf+9jcKkvT95WWMG44HRCaNOXzqK99GZ9+jKB+48c5334yHzq9iXJ/EqC5FiWZTllJhyjB7Mz0OGOb3QQWt584wIzEXMhO6BOTqKMa1ccgVAUwwH9DIegxThhRGNyiNJlPTRefJ7TTGNmIYWvMzgUG9mOPaOK2E1wlLMKKJsmkT/T5PtJ015XB+K4cJQwoThhTObeVxZjODSUMKfcuETODTvyFlGH/etOOPWyQ8eMiAm5h5TVzbRTdb6ZInqnthD8PrlMLraUrtilRyUzq5m6MLGLm+b4kE3bAqgrGNOGRKto5bbupSXSGchkwRYlw+mpbJWZ+qmHY1Tc14GnCQtQVwX5xYpTb/HaWUuuQr3iFlRBJq6ihkfM3E1r7HAM+aOE1pVJIPaIxN/jgPkFd8vX4CKCrd2ApqUKwLpWRxc2hExsQ7nzhIgRVpeilTRaT1Nw+GrDLv3yLVnHUzc3n3ohddi/uCs9jPavS6F33omKHWgGeX1hALuxCNB+ENuBCM7GNn34FIzIdQPIKdsBubfhM2fAZoAzqYInqkKlYUGg7k6nZka3YkKjaEihas+zewHdVjK2KAPWlEtGRDtkb1bfGyDe6UCaGiBcECTY+SZRvKB05UD51IlymBmq/bUT50I1WyI1t2Ip4zo37kQaFqRalqR7RgQ6pkQb7uRjy7hUBch3TRikvLT+OaN12D3z+1giHWrsP7ZAUzkvlRe5d9zAe7i+458q32cF/rJapa67hIoo9zNvlzneONuN9SPFeZnWVMmxDrStEFzFthmp5j3P9HBxYW1mIoqJ4FD7rndhmDUPIl/qP1rxCA7MDFhb5UCRfHt37Xjzv++ePI57dRv+xGobaLTMmCdNmBVNmOWNGGcNaCQMaCSN6MSN6CcM6CTNmCgyMHYkUL9jNb8GXNJAQzW9jPmOHPmBApWhEvWZAoWrEbXkeu5kL5iMDch5ddKNWsyNesiOQtSFYoKBItbCNRsiBd2sYnP/XPGPzDWYxoKQXc/HOSsQnmuDaFB06NoKWlBa19p/C+978XZ58aJF+5MixIAAMrUqUkP3SRJ5jbLKL4gzmHc9t5DKsjLOBBjxPnBkqdwk3+YPZapdeZD32L0mqXH3iPNYHMS4xAiXZAjVYDq372XPJgcDUAuTKMSV0SI+tRnN/Ko5MdMrpmaQLYMUv2q665HZyedlAimOHC5Iog2RkuOtF+0Ynf/+WqALx6e2O3ky0tLTj1nAkdl3Zw6iUbTr1oxemXbcTGmqYQSPtFFwZWfMSmmnaI/syhtSCx7Zb20DXrFtVK/N4640Lf4h4mdQmc387hgqWAs9v5Y6neIU0SX/r2/WhpacEnPvnPePNbbsSPf/MwWi88h7e+7W2IRtWoNRyoHzhQb9hRq9lQrdtRqVpxzzf+A//+zZ/gwlb6uAeQ/fpTX38Aj3c8jMqBE1/56l14+ztO4u6vfRUyZRhnjFmc2SKszJQxhwl9GuNaClf0v65yrGc5gCEFhTgGVskHOWVIYUKfogmAOoRRtoLtZVVFMlWUmfgD6F7w4OEzz0POvCYj7OQ7/Lr7iCYmxAa/cyMzXyHzj5ObtPqd2kxjypih39uIYVQTRc+CB48/t43+pT2WYPSjd2kP3QsM3DxPKIH22R3RidrGKuIIdbAj/kwnezNsrigSaJolWov1LvvQxzyH/Wyi17dMieXzpgzOmgghNMb8jGMb5G2Us1WaTBViE88E88FxkRekqQvzUA00eXz4BFA0yYiPNEk8djHnKA4euliXoM18GitTS34u/liMaOIMXJtgnDHCQjQLPx4IGFJFxPfE67+450+uCGBodZ+lC6NsfRimHlIh/qJSAEQhCWAufIXnbzWIXtZx273oO7bqp2mgTzAWBR6ErZLf9b6P4L5HBhAJuJCKeRCO+pHJJpEvZhFNBJHKRpHOxZHOJaC0bmB9Rw1DUIdI0YxQwYxMzQZ32gRbYhMGholR+NZhiujhihsQyVuQq9iQKGwjU7EgU6EpXr7mRLFmR75qR77mQLpiRbpiRbFB4q9QsyNfdaJQdSBTNKNSd6FSsaFUtSNWNCNdsSFfdSFZsqFUdyJftePcH/vwqc98CnJ1WEz/RGf0ErdzsLrAReJO8gmOFGiiSYr4yCYv/HXQw964e9n0WqprC4rJHn89T+hTxI1joRAREmryBA4wn5poBlkmvAyJB4/o/G1eL7Zd+kcCcPdYCIQLQPk61cHd8sGPQXbhadTqbjQO9lCtO5Gv2ZEq25EuWZEq2RDJmREvmBEvmhEpmBHJbyNRNOPyFQdqh3b25y0k9kpmRAoWeszqdiQLW4hmtxHPW5GvOZGt2BDOmBDJW5EuWVEoW1CoWpEoW5Gu2JCuOFjK24bPfv5TGJzsQ78iyg5V0kFKroljQp/GGUMSN9/+Ptx+xx2IpLdxz7e+gl8/9l8YVQdYe0qAXWvo8eCvtYFm24WSQlMXLGQ34teFIeYNlKsjGF0PY0KXwKiGT/Zj4mA2yEJnfUte9LCe9C6WxOW8v+bmD34Q7mHbFkr8SrSD/mU/xrQJ9r6Tx5guibOmLNovUpiye96D9hmn+Ngz70HrtEMgjLpZQwgP3nXO7uCR/9FdFYBXb2/odrKlpQWP/dWAUy9a8cRLVjH9e+IlK5540YpWxqJqn3Gg9WU7Wmdotds158GQIojWaac4vZIApPUv4RVcGFYEcH6LTmHnzEVMNXH8xjcz+NL9P8H73v9ejI63olp1wGGZw7vf/U4R+jg8cOCgQciHat2ORsOORt2BVNqAlpYW/LRNDpk+f1z8MQH4ufv/G7/83U9Rrtlh2p7G0Mhp3HHHbfjc93+DKWOavHwmEqRTJgqlnN3KkxBRRdC34kc3ezMdUoQwzlo+JnSEAhlmNW+j6pDg4PWxP0trBCobP/1XeqF+6YGfYEoXwogmKS58PGzAL0oSboQLwAQLfaSFABzXJQl0aiK46YQ+SUXohjQmDSk8aS5Argrj1PMmZoYPHEMJ0DpXeqNpnXGh89IO2pgHqnN2h2q0WK+lhDKgNGTfsldMoTiChQsOzr3jKdURNaGCxlhw5dxWDsPrVMhOK5cgzhhSmDISuJVK4iMC+yJWbyteajDhwk9gYJoDIE2TM+bHkqubUDB8SqeW1sDN/D6++iXGX+KY948LQzlLCQvPnprzBdk6mnkRebpYpoxApgodEwvDqhD5hULbeAAAIABJREFUP5UhiR3IEo/8/2uAw6DZureftaY0dxuT4GPij8OwhfjjlXgkMif0Kfxc9iyuve4G/OzRTsRdOvh9Tpi29djz7iMQ8qNQyiKVDqFYTkOhV2DBtgzNngaagA67aRMSFRvCRQvMUQM2I3oYwnpoQzp4MtsI5LaRrTmQKVuQLZuRZMIvX3OhfLiLQs2GXNWKfM2FXMWKbNWK6pETlUM3CjUHcf/KdpRqTpTKZpSrNmRLZoSyW0iUrMhXaa2YqzmRKZnx578O42Of/BiG1SHxs+fNMX3LgaZJoAQK589n/rznlXCtzAPYyde+83xauIuuWTd6Fjw0uWaTWMLARMQhbnQjgcnNDMZ1yaaAEE2LRcJ8jcTL0Brzsq34KNnJqub6OCh4bgdds7tNK1+p7Uf4FpuA0D0LXvSyFfCwJo6BpT380zXXYH/PgVLRhlLJjHLdjlzVQivaqhWZig3pkgW5ihWFqhXJsgXJkgWlqhWVuh2HV5wo1OxIVaxIlq3IVm2IFbaRqZhRPXQiV3UgnDYhXjAjmDEimDYhkDIgU7FjP2FEIKxGpU79zYm8FYmiGbGCGcniNr789c/jwVO/Z93F/PpHeKVJXRLDSj8+8Im78KGPfgTJlBb5sh3fvP8efPzOj+OMZkes+aUWIB9rEwqw9H1IeHFH1mM4t0XbILk6zA6GEWbVCWFYHRWebN7eImeT/0HGeu1f3m96TniYR35fNCB1zTelf4UA3BPp7h4G+e5d9mFIFaHniiGNCT0NENpmnJArgmIKTUEQJ4UHX7ZjaMWPUy9Z6fPs8e+apWv1Y1cngFdvb/BGAvBZA4m7l21sBWzHEy/ZSAzOMAHI0nJ8ytezsI9hNV3Quuc9TBg6BV+LTwDPGpI4by7gu7/vP4Z6Gd/M4M5v/RfecfNNCPgUqNacaFQdOKw4cFC2IR7dQDSkxiuXd3FQc+DykRO1uh2NQwcaB07Uqna8/NIUbrr57Xj/P38U/z01iynuIzTSv3PHP38cz/xlANWGA+W6A5XDXbR1/Raf+Or9BLbmAtCYw5mtPKaMWZwx5XBmq4AJfZIaKRi4Va6iCwRfyRIrL46JjRiGFMFjb8iDq0H0LOxCpgjSxWR5Hy0tLfinf7oGU9oA89hJXjL6NVtJbJD/ZUybwPgGpYLHNBGMa2OY0CfxyB+X8Pn7fyGaUib0SUzomUDczJAwNGYwpk3g4Wd0xDJb8rFuyV10ztJko23azropPbQG5pVYF93onvOQl2XRKy5uPLHYs+ChtdXiHmQKmnCSSCPhx6dyNK0LY3CV1iDntvMY08YwtUkXvtGNOCb1SZzfyuGCKYOzWzlMbaapfJ2Zw3krAOdKiilKk8Aa4G+svPGDrVCp/zPyOqEWE6lbnvb9+kMdTdNYaX3XjH8Z/QehDyHa1NLkjjP9+HpargrT2oqZ/2UK7jFiEG4W/hhShhj7jNb1HPo8sEacP/r/58Bn1ne8wifUzcJPEn/8DXJwLUS9ppsU7Pnl1EW849b34b0f/hie/dMFWHXLMBg0sDntSKXjyBXiaByVEI56odheh86rx+qOBo7EJlJVO6V6E0Y4kiaYoptwp7fhz1nQPfgIshUHqgc2VA7siBftyJQsyFSdyLNJYK5qQ6ZiQ6ZkRuXQiYMrblQO3Sgf7KB8sINCxYZCxYocEyP5igWFxi5SZRuyFQdieTMieTPi+S089+IEPvovH8LZzTirD5TaUqglJXTM0tG95GOgXYnl1sneSNumHWLy18WwRzS52UfPwr6wKfCpNKXFY6LnW8DB2cGGp8nJihAS61+5KoyBVR8mtVFa67F/o3t+F71sOt8168aXfnZKmv413f9xI8g+m/QS1uiBJ0bx0Tvvwv99EMdrR17UD9wk9OpWNA6dyFctqNZtKNds4mO5ZkOpbkOtQduVwysOEos1GzJVK3JVCw6vOPDq/zpRrDvgjWjoMS1bkKtakCyaUag7UKhaES9sI5YyoFyzI1GyIpQxIZAxIpAxQa37G2644XqML24wGgMdrMbXQ3jsD3O4+/4HcfLmd+HL//kNpPImFOpObFvnxDBAtuLCAPMA8uf30FpQfBxYCWBgjV57k7oknrYW8K2HezFpzGFIQTWX3A4zrA6TYNfEMa6NkyBUBTGqIQzMEMP19LIVfd+CR6Czetljxjmt/HF4/Z0jtI5dQxf2qF5UT80xrTNODK36JRvCHPEgeSBocDWAJ162oWdhH20ztCLmlYWnnrsKgr56e2O3ky0tLXjkf/Q4/bIdrTMONsGTPjaT6PmUr+2iWxDNJxgzbkJHYQW5IojxjRgGV/2Y1MbxR1sR581FtL+8RavfzSx+PvyceFH/6tc/QqPuQqNqx1HVgaOqCwcVO64cunDl0I1G1YFXjly4cuRGuWpDveFAsWZDqWZHre5AMbeFyYk2vPktN+KBBx/C6OIWzm9l0DPvwjXXXINycQPVmgPVmgMHR27MLz2Nt779Jgwow5gwZjFlzGDMwMSgkXUMG3MYN6QxbkiztC+FRib0rFNyI44xHgZQhSmBuBpE96IXclWUpdKIQ9azSKfBH/Q+jZaWFnzjwVMMQ5NgfbLsDURHk71xPdXPTeiTtJ5YD+O8KYOfDf5Z/Mz+5e57MGlIMfGXElVUE4YUdWAaMxjRxPDwU0r0zu2gb5mM8DzE0T3nFkK9gz2utGZyoWOWsD0cRN15bM2xJxpJ+thqmYInNAWUKYIYXSdRJlNJq0wKcgTI96hNYFKfpMmljgIwE/ok8+BQkfuEgZm21VEx+RoQXb2hJk+gVJV2fAoYOWbEH2JrVvLfRdi0j0zhj//PupgGvp79d2wFzAVgk6AcYlNGmTIomI5Da3xaGRBv/gPs11IamKeDJW/jUDM7UPT8kuCTgNIh0fwhVpzLflGBJ6rwlqWOWLk6Qq0L2gQGlQRp77jowue//1tce90N+PSXv4mX5uZQjrng8biQyWWRSEZRb+SRzsSgc+ixvWfBilMBd9qEzbAOvtw2EhUr9jLbsEQ3sZM04cX157CfNiFftyFftyJW3Ea4sI14yYJ02UodvwduZEtmlBsulBsulOou1A6dqByQ4CjXnShWLSjXrChWrMhXbchU7MhVHPDFDQiltxDObiNVsKBn4BF89ZtfhXw9ilENefIG1wLCJtC3GhQ1ad2LPnQt7gsOWzPMV8CfGQeVd/+SxYEjYLxNwGHJA0j/dpz5fqOQq1n7Du8CVoYJZr7ikya7DODexwIq3fMekQTmq75fnl8UNpu2i246WF+kQzUJQTq49bCJU++yBIG+69s/xQ9+exr/3+U4amUHGgcOHB7Y8cqRC68cOXCZ3RuHdjQO7KjWrKjVrajWrag2bKgd2FE/pMejVLORYDxwonboQrpkQaZoQrnuQLHmQL7mRKXhRKlmR7FmR7psQ6pkRaFmRbxghiOmx15yEztxLZ55aQLvuuWd+O6vTzEeXxITBjr43fNgK21z/uu7uDh7AfmiBQdHHtQbHuhti+QFPHOWeIwr9HiK9f6ST/xMqVEoBJkyggvbeVywFPHEX7UYWPViZJ3IC4P8MKYKk2eT47hUIWINqiLiMCvqOhd30bvAD790TRfrXdau1NXUE9zJ2H3NPkHqTqeUsEwRxJQhjTPGLNouOiFTBI8Bnmk7s4PBVT9OT9sFaouautw4/bID7TMOPH4VA3P19gZvJACf1eP0tF1M8aS7A60z7mPG5PaLbgG67FvxY4KtI/mb+JQhhbNbOYxr4zi/nccFc4EmU2zV2jG3ixvf/GYhZo4O7bjcINF3uerEYcWOg7Id9ZINVw7cqFftePXIicsNJ0plK6o1G3kBDxyo1B1oNJw4OnDBZJrGPfd8DteeOIH7WyfxyXt/jO9854t45TUXylUHylUHLl/eRa5gwwc+/H7c+1ArxvS0BuZolylTDlNGCqCMahMkCA00lZpk61VaydKqR66KYGCNEqx8fUddkoSI4BemIUUYPYv7eKDtLIYWnRTu0BFLkE/7xrQ0RRjXxsnzp2OAWW0U9/6yA7e+/8NoaWnBXfc9iKGVPdY8EGOeQJr8TRqzTAimIVdH8PgzGsjWggLRIqYfvPNUpLl32fp3Vwj7HoYz6GXdw2RwlkzxdAGUphj9KwFBzZcpwzhvJIEnqPvqCAZXiffVu7SP/uV9jGhYl6ouSW+sTICNG9IY24hKa1dVlPWo8no4FgZRMgGlDAtfIOfoidSv8AIeX9mK32dsQLmaVkZ0jx6byo4wf9BwU2BkSPj2pAYWGYc3K0KsEi4seZMYxqU52SwZ1yU+4OCaJHSbfX99K0H0rQbRvxqWvIB8zdm08uTCsH8liFENAYo5OLx/LYh+JjR7lnz45dkF/Ns3foQ3nbgO9zzwY4R3t7FpNCAYCqJeLyCbjSESCyAU9mB7dxtbfiN8eRuydRcqRy5UD52IFK3YjuqhDWphCuvhSBhQalgRLZoRym+zta8D5YYLxZodtaNdmvo1nCjXrag0rKgyAVg5cCJfNqPSsKPScKFUcyBfdSKe3UY4u41kyYlQ2oBCzYIf/PBePPj47zGmz2BcF8fAqo/8dUqqyOPTUhKAzUikfVHnxcNPHaz+rbm5oW/Ji95FD2u2IfjwsJp1P6ujzCMaExP70Y04RthziNMAhlV8ChwRmKiB1SD1Cy/72OqXXkO9i3tCfDa37fBNCr8TwsaFjlnJe9az5MOAggJGn/vOz/Gzh0/j/z2Kolpy4rDhxuVDN149cuO1ox28csWJ115x4eiQhODhoR3Vug3VBoXrag0bSg2aCBZqFtSOHKgeOlGu2+ANq3BwxYNqw4l02YxCzYVUYRvFmgMHh25kay7EixYU6nbEC2ZsB9bRM/w47vjAe3Hyppvxrd91Q6aOYkxHEOhJYw6jmghOvus2dJ87j9qhG+WqHfWaA/X6LnI5Czp6H8Zt770Nk/oEuhfYNoOJXhKCJAD5c54joc5uJnF2K4+hNQqdjayzEIiaA9/ZdVoVhGx1X3AJZeqoCFv1sbRx/9IeUQ3482LJKw7C/KDQ3AXMW1q655vpCR4hUrkHfdKQRsclN/qW9il4xOoKOy650THrRt+yF489tyW11FzaQdecB63TdrTPOHDq+a2rAvDq7Q3dpAmgmPo1e/icEotqhgOh91nTwD7kyjAubOeFeDqzlceUKY8zW3mc3y4Q7uV1vb0PTc3g1ttvg293Cd6dZbxy6MKVAwdeabhwWHLgqOLAUdWOywdOHNYdOKjYcFSx43LFgaOaHUd1J1657MQrV5x49RU3Xr3ixFHdgctVJ45qDjz7rAx3fvqj+PznPwWH4xIOD5w4OnThymU36o0dlGounO5/FB/9zN0Y38wzTyJN/jjgmcRZSoi0SUMaZ40kcgUAWhXBuDaO3mUfCTEdTfVGN+I4s5nGoCJIvhIlTQgHVgPovOTCOJt4TTAv3yirp5NaJpIiHTymjeH7HWelyrwf/wbjOmommdpMYXSD/t1xQxoT+iQmjVny0a1H0LfkQfe05diaon/Fj96FPdGAwGuxOmd30cfaDnoWpfUvgZtpDUZYA6qc6lmi5GnPkg8yRRC9S/sUdBD8syCzB+wTSmHFLxoxaKLnYz8TYizKlQHGUvQJYDh/s51gOBwh5pjPr5+FG/gk8PV8QCn4IeE4uOg7LgSlFOKxQM46f4NPiEDIsJqFRdRNq19VRCqyZw0e4qOi+XtrujcFRMQEUBHC0Cr9XCjpG2zC30hCkNabQYYuCtC0r2ktTL8m3My4LsUaXbwCGk1vlAxqvOJH+yU3fn1+Ade86Vp85b6fIBlwIh7cx96+B5l0FI16Hrl8Cp69LfhCZnjjVmx59fCn9MhUbZQYzZoRzFmg2lNhw7cOQ1CLdMWM6pEd2YoV+ZqdsC5VB/JVKwo1GwpVCxN/NpTrNlQadjbZt6HScKFYsaBYtiJTsiGWsyKc3UIiv41sxYZMXo/b3nMLHhx5FhObOcjVdBCjFau/qTIv9DohyG0NnmOTmfZLvAnEJbyAXJTxA0/fsh+DLPzBgz4kBikBLFfSBInbOJrr4Ph6n/vK+pb9bPXLOYOstWfew3yAuywVyoJ1M9QDLNXBSew58pl5RZPOPQ+24rP33If/5zCORsWJ1674cVS347WjXfzvlV3875UdvHbFhVeOnHj1sguvXnbhypEDR4cOHBzaUTuw4fDIjoMjO3UuX3aiVLchV7aheriLgyMXGodu5OoulOsO1BouFCp2FCoOOILrCGdNiBfNcMf0+M53v4Tb3/8B/KDzHGSqEMb0KYwzxugUO1z3L+2ipaUFCu8+raTrbtSPdlAs2VEo2vCdH3wD33/wxwIr1cM4et2LUpOQFAiha+6ELoFJdpgeVJAoFGl/dugbVkcwpo1hdCMqraPZ4ybB6AMYZFD3/lU/ehf3GNiZtS2xIFz3gkRH4IflbuY17WDT2t5Fr9ikyBQhnNkkJmzn7A61hMztCk8qTwwTn9KJthmHsGG1X9rB6ZftaJtx4Pf/c5UDePX2xm5sAmhg8EmCjzafOvlFp2tuF72L+xhcYUBTVRh/YBO+SVMeE5tZnNkq4IKFVr7njrH+8njkT2voXfHh3be8G52nf4ErVSderbvx6gG7N9y4UnHiStWBy1U7rjRcOKo5cVhx4HLFjitVB16tuvBKzYXLNQdee8WJ/33Nhf/zqguvHrlw5cCJKzUHXdReceHyEX28ckCr5MuHO6jUnMiXrLjwpwHc/r470L7gZxO/PM5uFzC1lRe/Pr9dwJQpj8nNLHVWbkhIlnF9CvL1GM4Z0xhWR3DelD2WGh3XJSBT0qphXJugycRqEJ1zuwTD3k5TEnaD/uyohlYivKtyhPmKxjai6Pn/2XvP8DjLO11cEMqySchusqRtCmksm2QDm1CSQBrJhlBMCSVAHMBgwNhWb6NebGMIHUIAF1m9jbo0vauMRtN7702Sk+z/ZPecT+fDfT78nud5R2T/n/jqua65JBe4JGvmfe/nrme1+NaP7sB3fnInDv9xCZ2zLjz14iCOrYTQJneil4U/ehZ8OKGO45gihNYJK2rO6NE4ZMFRxmzIJrbQKScmkicguVxRPSDtnnJZo5YxC21T5OPjywoNoxVes5FNxgKyvdQxK9rZ9qlsygFZhTxe6eUjQGiTQM0odSg2jdIN996a4/j+vU/g/roTaJ2kiaauBT96OJvFQKA0kUasoChLrqhQIR+gBPA+GAB5/IUhkUDkSzXtcrcEAJlPSap+2dtd1jpVWd/i3gsARXmz9Hnz5N5kL8nArOZl3CbKnyu3gys/flAK5mCQGEMnecGmCKR2LwRYdY5dyMb1IwT+GtjNs3l8C4ff1+PAy5P49Ff+Bc8dqUYp6sKWzYpkPIDtchbJVARbWxas21QwWRcxqhzCmncR3oQW7oQOtogGZydeg8a9hHmbHONrEwikNEjkdIimCQAmcgak8iYksjrE0xrEM1rEMxoks1rkShakC0Y292ZEMqtHLKVFKqNFpmCCM6iAL6FFOEFs0x9P9+Kfv/h5yOZC6FkOo3lsQyyxiPT0JEs/T7nQPOFE45hdkoIZcKo9V1HoK1gWkoN5+r1+SAJozeM2FuxysrlLp3hNdcy4qbdzwS/sIR2sYopCH7YPAEDmqR1cFawSZ5ge7nyXUvoV/X+idPgcX6FYRfUAMfr1wxQEaZ9xo/79FVx2xUfRePw1FJOryCT1SMfVOF9YxfmyFX/etaNcMqJcMGKnaMZOkcDgdoHYwFxRj1LZgGLJgGLJiEJRj3RBj1jOhGBSg1yewnjRrAHJnBGJlA6B8DI8wWXYQwpsBlewqB/GN//tGnzjm99E4+gGMX5LIWL2F+lg3a+Isl8HcenlV6Dr7CSiaTViKT2z+hjhi6jxqas+icP9L6B2uDL1zhO3UrE8HZicOKmOEriadaFD7sDjx87QtZgFvGRTTvQukn2me94nDvx8nrNjzkfy/jRbF5nk3YObYruddxDyztPasyahsHBWtubcqlgIaWAJ/bqhDTSN2dA168VxRQQtE1tilpNfk+sG19A8vsWqY6iFgdt1jrLGBgps6vDU6xdSwBceH+7BGMAF6vdjrNCR00a2O0mdU9UDq6wFny5ix1YogXpCHccxZYz55sJ4URNnXroIehUUyGhbjKBjxo7bfnMAN/3w+/jpj25AOWrEdsyAnbgJ5YgBxYgOpbge20kjijEdygkjyikjikk9ikkdykkDtuNGbKeM9GcZAorbKQN28kbslkw4XzZht2hCIUNM4W7RhPM7ZuwUTNgtmpHLmxGKaxFP6RGKKvGz276PK674Ozzw9DPomvcJppI+EiPIWUBejNzF0r9diwGc1NAqSNsMgbz+lRDbsiRvUPdSEO1zAdHzJ5O70Txux9EzJpqum6dZuPZZt0iadjHwKOpgGMPXuxhA64SVRstnPLj7cDeOKcLoWfCiY86D3pUQehYDOMYqV+qH13Dg5WlR8szZgqNnTJIxuWLcnKfZ+Gwbn2mrr7jItU450MbM7LzwVAoeSJUMjawKhgPCptENtIyTB6px1EYS6bhNgA/OTDWO2dAwtIp/vuYmXHLp1bjoooO45NKr8flv3EhTSBN2BuZcojNQrLlULC9IU3CSL1BaAaHgBWf52mY8+NWzHaIXUPQAznrRMSMFQP7H3j+5Gy1T/PlBFpCezZMVoG9S+ihWQSrWPHhZtJizYiEazgRW9iI2T7CgAwuINDLJs3mCvteOGSoTp/TwFptqJNailhdGs591I/O2HXh9ET965DB+dPs+nA9vYmtzE6GgF/lsApl0AqurRmzZjFBb5qDYmIU1oIAvoYEjqoY1pERj1xGY/MtQOGaxFVlBLKuHn9WIBJN6JLIGxiSpkciS3JgrGpErGpApGJDMcSbQiHBCjVhSiUzBhGTeCE9UA4t7HuGUAeGUAU2tz+KmH9+KVjm9P1onbYL1rVyCqFxNaRzbQt1IZbCJEvFHmdRafcaEmgGzKIEmP6AFtWzBga+BNI3ZxCKSKCFnYSMKDUkF7TIm+1KinadK6f1UP7xBQGZ4Q+y/8tnFn/2ugQFSzviZBQAk7x/fmyXA0T7tRP9KCH1sXenB5lfw8U99BgmfFVmvHqXMKsp5M3ZKZpSLqygW15BNabFbtGC3aMZOwUhKTMGAYkGPfF6P7ZIRhYIBmbwOmSxNuSXZWksiZ0QkrkYsoUY8oUI6qUYsocPy6hgGJl/HZz73afzw7odIep3zonsxiO7FIKkd8376fN5HysVKBN/56T786IEnsBVRIZU2IJ01I5404e1Tx/GxKz+G2nM0aSh6LtlrmNZZCDA1jtHBqHvej+PKMGoH19A6vonbD8podWWM7l+9C14W9nCzr4s83d1zXnTNkdrQwWR+MS05Qd7NVlZLReEQNmM5YiV/IP+6BtdRP2xF7TkKizSw9oTaoQ12/XOwOpgQ2mdcEvs7JNUN8UlNfgipZtvVh0+bcOiUEYfe0+PQe1o8fQEAXnh8yAergVmmKgRWA1I9QPUDhxl4IGaIkk/1Q+voXiB/Rb+KJN7uZeo2OqaMons5gpYZHw4PWHBfdTeu/vrXUVVVhX/4xMfx2EN3IOFdwW7KjN2EGTtxE3YiRpSDepRCBpSjBpRiBpSiepSTBhRiDPwlDdhJGLEdN2E7aUQ5bUQxbSAQyJ67JSN2iyaUs0YU0gZsF4w4XzLjT9tmnC+ZsVswIpbQIhxTI5HSIpM1YG7+HXzrW1/HV772ZTz/R+WeVRKRVl4MomPeJ9rpexb9OKmJ4wVVFL3LIbTPuHFcFcMxJY2Nc3box48cxhUf+zj2952irr7lAFqmPThy2ojmUSt6F8g8zusiaHXCz4CgV3RTdc950D3npuTaDAHDY8oIjikj1Ds2T7UClP4Nonc5iOqzRjzBjOTcN8S9JTWM4ahmyweVKx+1zLvCu6wameTbyABew/A6WsZJOuRdXDyI0Mg2aVsm7GibdqJhaBX7jvbjxrv34+6j/SSfjG6idcJBh4kx6lMjEEihkX3Vx3HJpVejqup/o6oKqKr63/jIpV/GvpoTrOSZlegyoMRZxsrdXA4E+ZKGbJolbqddaJ+VymbbWf2LlMLmqV+W0J7zCTmvowIYVhY/tzDWr3Xa8z+Awcq5OrYQwm8oFb2FfN2A1iUcAlhUlmBXSseVyyj8ptc8QX+Pl9x2LwTQMcMDNHtvWHXDVhGGoAABsa9HTulw99E+fPO7N+N8aA3OrU0EAz7893/9L0RDfpjNanhcVsxpJ6B3LGEroEAso0UgqcF6UIH1oAJm/zK0ngU4okoEUjrYwyoEUjqEU1okMnrEUtQPl8jqEM9oUdo2o1A2IlvUI5HVIlOyIJzQIhxTIF+2IJGjZZAN3woSORP8cQPU5gl84h+uxCONPaI7smfRj555D/oWvOiY8aBn3icsAZwZbhq3C7aocYQzNMT+1QxYWLcaC4EMEitePWAhOZapHw0jNOHFQbsE3JmnjB3cBNvEQISMVfnwWiMODOrOUbigllXO1A2YUHvOjFo+UVdxCOcfG4Y3IJt0oGmU+kh75jw0UbkSRt9KGN1LIXQu+PHxT16FnpffQN62hIhDjWLWjFxaj53tDWQzRuzkV/Gn0jp2CmbsFMwo5wzYZZLweWatKReM2C2tY6e0jlTBhGJ5FYmcGbGkBqGYEsm0FsHICvzhJfiiKpids7j2X7+GOx7dD9l8BO0z7gpLDbe4MK/zYgDdS0H0q2K47uf34bYH9yOS0SKfNyOXX0MwosHt+27Dz+6+E/Wj9Drey/6RFNw8akXbtAdtch9aJhx41+jDW4tK2q2fsLEDIEm6HdMOVrVFTF8nW2bqZC0L3Qv0tXF/JwXZHIKdlw4AEpNLB2Gr+NoamIe6YdiK+qE1NFUqJGwPvHveTwMB007UDq6yPsgNqgUaWmPX5IpJQqbSHD5txKHYr+R5AAAgAElEQVT3DXj+FAHAgxem4C48PuTjyqqqKjz7BwXqBtdopmiYCmtrzppRP7SOZnZjbx61SosQQ8Qatcld6F4MCNm0c96HOx89gCuuuAJVVVX4t299A22NB+FelyMX0qEQ0qEY1qIU0qMQ0CDvV6Po06Lg06Lo16EU0KMY0qEc0SMf1KAU1qEcN6CcIPavlDKgnKRnMWlAIalDKaFHOWXAbt6I8yUTdrNmlNJGlNIGnC+Y8aeiBX8qW/CnkgXbeROKaQMSSR1SaT0yGQOicQ2u+vSn8O+3/AidS9EKEMi7BUOs/8+D7nkvW+SgZ99KWIRF+lbCoqOvfdaLOw71CO/enY8fROd8EMeW7OiZc6FmwEKJ3WXaE6YyUg+dlhcoBdwx60Hfkh89c9JIOS8x7VsOoW85UCFLU4/eCaUfsqkt7D82iCPvKKjbj0n6R4WM9AEf0QBvtV/dM3vVwObaWibsaB7blFjBYUrBNTNZmLOAjawAu2nMhvpzZnzu6zcIJu8jl16Nz33jBrogjvHU3ha7iG6KE/atDzyNiy46yMAfPS+6+CBuvvdJWl1hJa2VrJ50SrdLwGpKCoOIVHCF9EsMoBeVqd+/Sf8yAMj/7IMMIN/t5RNurVNuyKYJGDb/DRsoydUfLLAWrKUIgnBPoJMliFn9jQA0Tsn7WCH5drJVir5l8qs2jVrJu8SAOw8d1LMbFfdTNY6S77L2nAX3N7yEr177bZQ8JkT8XqxaLIiGA/jLbhkbGybYrEbM6yegdyzA4lmEK0YbsvaIEtaQAs6YEmvBFXgTKmyGqbIlkTcjmTMgntIgkCD2L5zWIFs0oFg2olA2IlcyIFsyIJrWUVl0WotklmTjZM4Af1yFQEyFQEyBm27+Dm69Yx9kLCxEHkebsBrQdreNWOZRK5onpI5OPuHFGfFq1rdXzbrVuBWiZsCCes7KcRmYHVpaJrboEDQuMYF8X7Z10i4YpLZpaZGiZYJP91mZj4wzQ2tifq5hcFV8Xun34uxf/dAGA520JtI1Tz63rjkvWieJWZLJybLQp4jgtt/V4kvfugHRTTVyTi3+kndgt7iOQsaA7YIZu6UNbOfpmridN+F8aQ3nSxZsF8z4844Z57dN2C1b8OddF3ZKVqSyeiRTGmTSWiRSGsSSasRSKkRSakRSaoSTamwGl/Gd66/F0frH8aLSJ9L8HfM+dLBS+25RrE7M4FMvDeMjH7kEwyvjyOZMSMV1yKb0sDrn8fErP4ZDJ98R15nKJHfd0AYra7bjDdUWXlpmhf0TW2gYsaJlzIbWCXottE7a2YGRe4FdFWXvbnTOesRUJ1dgOlgdDP28t9iM26YU3hrfYlvTkhIiKpl4FyDrA+SzhE1j9HPiTHEL+1pJBia/dS2TgUWARMxuru0BgM+/f0ECvvD48A+xBFJ3zoITygj6V0LomPWiiRleu2Yp7doyxootB6Xyy8ZRK/qWg6h5ZwE33v4Avvq1r+Lb//p1rMjfQdi9iELMgHLMhFLEgEJQj5xfg7xPjYJXjbxHjbxXjYJPg4JPi5JPi7JPh7xPg6xPhZxPhWJAw6RfE3bTJmL+UnoUkwaUEgwAJpk/MGfEbsGE8zkzyikDttNGYg8zRuzmTfhTyYKdvAmltBH5lBGphA65nAH5nBE64zCu+PsrcODlSWlRZCXKSqVDrLGeevj6FWH0LoeYV48KjrsWA+hlIZKepRA65ihN9rP9tQIEfvOmW9Ay5cSxZQIUjSPraJ9xUbJ43oe2GQ/6l4PoWfShb8mP3qUAMULsxt/E2u3b5G70LYfQPe/F/Q0v4pYHnsYDTS+hd5GYkIbhVTz14ihqzhjZxm/FlijbGt3TLcZuiDxV2MD8f41C2rUJObh5jNYUqNx0k91k2d9hH5vGNnHXkf7/mck72o/msU20sL5AbtzmHrj760/+zX93yaVfxv0NL6BjxikuzK1C0nVLQY8PAMK/AYB7CqDdwhTeLiRh7/8PAPzgrJfEAErzbZV+wA+ygB8AgFMSCBSMJfctCgZTCoa0VLCElSCytYIlbJO70bNAB4qeBR/aprbEz4QnFivl+voRYnYbRjfRPErp2eqzRvy66WV86ev/iqJvDT6nA0ajGQG/D8lkAtYNI9wuK+TacSyvTWMzsAxPXAVvXA1bWAlbSAFbWAFraAW28AoCST18cRWSBQsSWQ1iGQ1LlmqRKehRLJlQ3CYAmC+RDBzJ6BHLGBBKUlAkkTPCG1HB7l+iMEhwCRdffDHqzupISlwK0nIMY9p4byJV4diERE69cVbB+vHX+9GzDAAOSOnLo6eNJPuymzf3uDYM80m4TQKYE1sCWHDPa4fchd4Fn5B/Zcw/xjdleWK+cYTSvzQBZkYt2x+uGSAWUgS02LP2nJRGlk050T1PXtiuhYDw2bZM0uu9Y47sLO1yBz72yavwUG0Pzme8+GvZj3zShEKGWL1i1ox82ohyzoTtnAnbeTNKWSMpJ2VSTnbLZmwXzdgpWJBK65DKkC8zlVQjmdIgmdEgzhjdQFKNcEqDs6Ov4JJLPoLXlRvoXvCxfW16T3Uv+NEud4hgVddCAB//5FW479EHEUxqEY5rkEkbkEvp8ea7ffjUP/0juqZZAnd4c0/HXhNL/7ZMEHPeOGKhgNMI9SJSeIM6SCsBoJhlZEGQDsbcSsE7+to4gCfAbRfqBn/fUTk9XfPo/bS1ZwWkgQE/fq1sYNOETWNb4trTOuVE3dAavRYqOiG5xM/L++uGyLawlwHU4eCbSxcA4IXHh3pcWVVVhadencWR93VonbQLL1rrpB0Nw2voXw6iib3YG0atrBGdvCddsx6cVEUFyHm5rwZJvwrFpAH5pAHFjAmFlBH5kAGHn/4Ncn41sXohLQo+DUp+LXIuJfIeNQpeDco+HXIuJTIeFcopJwo+LcoJIyV8UwYUU0YUknoU0noUU3psp43YzhhQTukJ6OVM2M2asJshwFiK61GIarGbJxZwJ2/CTtZM/5+MAaWCCeWyBemsHnfuuw1VVVX42r//EJ1zPgb+SFKhdJgL/ctUsdLJwho9SyH0LZO/5ZgyympjSIKhi54PD8rexKWXEyN664NPo3spjL7lIKoHzKg5Y0THjJN68eY86J6nYfLWcSvqhyxCNmiecIhm++55H2594Gl88dqbccllV+Oiiw/iksu+gi9e+300ja7jyGk9qt9V4TDrbOQpwudPSUDwgwGfaj59NcTm3VgNDA2tbxHDN2ZD7eAq86lYRTKSn4xbJ+1oGKEQyE37fve3TN5FB3HTvt9VyCibrCJHKnauHVzH579xIy659MuMOfwyPn/NjWhkjGPbtAOdc5VbvGzLd9YjhuX/ZvVj2gPZNAtusKJmLue2z3rwg/ufYmyATwC/jlle7UFyXtu0S5R2y9iWcKtYAJFm3FoqAGArA4FSQXWFJ1GEWKR6GFEnM1UBZHnIhXsAhc9Rqh0iCdIuJq3apx1oHqWy2YYRKbBTz+bReGVMC9s55TUlR84Y8Ysnm3DTrT9FzmGE37kJn8cDr9cPi2UN4ZAHkZAPw+ohLK5NYdW/hEBSDV9CDWdUiecOP4JAUg2LfwmrgSXYo2q4Y0oCclktciUTMgU94lkdMgUjCiVKmOaKRmSLRiTzBkRSGkQzOgRiWkTTBmwFNTDZpmD3L2FWMYBPf/YqfPVb16FzIYCOWbcIY3XOuisWUxwCBNJOrp1VhmyI7j/+8XDFFjZZIFZFHyAl5C3CH1sn0sCb7GcoVf908O1ouQuySRva5U5WL+QSP7+msS1Wn7QmwKTYjWVWDH64/u6dj4keueoBmh6rGyQWt2fBh97lsJiDbB6jnWJajqE1kK6lIPqVUTx+fAAXX3Ip3lNv4S95LwrxNZTzVpQLNmQTBuRTRhTS9CxnSQreLZlxvmwmT3XZTGnhohnJlBaptBbZrA75jB7FnBHZjB7hFG0IJ9JqxLM6+BMqVFVV4WBzMx2c9wSovGxKkfpAGwaNqKqqgtmtRTJnQTZnQSG/ikxah7vv+wUefHQfmseJdeM/w7qhDTSNcHab2SUmttDEDkm8l69x1IqmUStu2vc75kGmrtKWCTvr5LRXVEC5hd2je96H9hmX8HG2Ms+ymJhjoaqW8S10yF3sIGUTh4yGipRwo7hG2vYcqGVTTnTO+Wiic3gvS9jAvsfaQWlvmHdX8jAIXduNeObtC0XQFx4f7nFlVVUVDv1RjSOnTSRFjLDyX+YVa51yoGPGg/7lEKWhhqlSpGPaiZd1CRxXx1BVVYWn99+H7agRxagB+ZAOaacScccK0iENMkEV3OvTyAU0yLmVyHuUKHp1KLlUyHtVyHiUyPlUKPk0KHk1xBAGtSiF9QQAE0YU40Zi/tJG5ON6ZGN65OI6FKNaFEIalGJ6lKM67MRN2M2YsZsyIx/QoBTSIx/SohjXUyCkZEYpa0Y6pUcpb6LEWUYPr2sWJ19qoovXm4sstUbPnqUQOmdpTovWNggYdi/SpE/3YgDHlFEBGLuXQuhhywut0y60TNjwSPtbaJnYpALkpZCQGBpGNlBzxoC6ASNaRqlAWEx/scRnywTNG7XNeNCzFMS+Iz245LKv7GXKLrsatx1owWNd71JarKLS59D7RgEEBSg8JRVBk+xFMnDd0LrwKfFpO8EejVjZHjALEDBZo3F0U5xgm8Zs2Fd97H9kAO860o8W5ktrGJG62TiYJLlyA/uqT+Dme57AvXUnUT+0znZXnWgZtwopXDZVUeQ6KwFBDvIq07pit/eD+8uzHlSf1gkfYGXoo3PWzW7kbK6N1UJUVslUsn6tUxVM4HQFEBQJYWmPmE/W0c2IA1YXA4YSAGyeYAlq5h0SIJIByna+Yzy5ha45N9qn7ZBN2iCbcqFpwi48mlymahrfYtUxBOx5tU7zhB3PvqPCD+9+FE/ufwRJhxFe2zqyiQgcdgfW19bgddmhVi3D5JqFN66CP6FCJK1BNKOBM6qEcnUE3rgKnrgSzqgCwaQGnjjtxyazWsSzWsSyOuRKBqTyBqQLJsRzJsSzeqTyBCRKOyak8ma4wsswrY/gmZqnce3NP8YnPvVpXH7FR/HtW29H55wXsmkHAYk5LwPhLpEE5ynrpnHH/7D+wWRfvrNbwYyLFZBzFml7lSeA+Q2dy4Csvqh1iieB3YxNosMCAQyqQyIWckuEcRo46OMy34CZ0p5nzSz5a8JTr82Kr6eWFT43jtnQPeelondFBJ3zXlQPmCGbcqBxdEMEX9rkHnQvBnFcFUO/MoqPffIq1Lw5gf/7XwVsJ2zIJixIhNQoJAwopU0op80oZ0woZ00o56g1YacoAcCdohmlvAGplAbZlBbZnBGZjBHRlB7e8AoyeR1CSRWCCZKDQ0kNvvPv16K6ux3trBWha57YSs78dc75hC/38o9+HB3vnIIjrEWSyb+JhA6f/fxVaP7970n5YEn5huF1NA6vo35wlR1Kme1jcgvNYyTz04TlOgs+WXHorUU0MQ8eD6S1TVdOSTJGkP0su+e9dMiccbH3PIE9DuiF3URsOm9JfZ0sINTM+v6a2OfcRsX/vJN5uRtH1sXrrI4pK1z6rWYp4hp2KOB2BZEMP23CwbcuAMALjw/3uLKqqgqH39eKQmAqRF1lgZBV9C0yr9qCn41jb6B71oPfa6hp/fm3ZnHZZZchtDmHcsiAvEeDtFOJ5KYCSesy0l4VUm4FClEdsh4lMX5uJQouDYpOBbIuBdJuBbKOFewEDSj6NCj6NShFDShG9ATs4kbkEwZkYjp6RjXIRNTIM/BXCKhR8KtRDGqwEzISC5g0oxQ2IOtSIR/QoBjR4XzRwgzPFhSzZpQLFmSyehTzJkSTemRyRtz28x/g2n+/ES0TNvQsk8dPNu1A0/imSK51MQNz12JASMLHWG1Mz0pkT4cgDx30LIXQr4hQr+ByCD2LPrRNU7KVG/hFZcS4NPnVJFKgTrTNeNC7HMKPH34GF138t165a3/4Kzz36tSeyT76aBA7zYdP85ofo3hWnzWxUAglIyWfGIG8+grpsH6YdW+xIlTRiTViFXN41WdN+NzXb8BHGJN3yaVX4wvX3EQMI5ND6lg1QtO4tGhBDfzUe1c5+yabdklSDiuapk5AB5vecrEgDZNwZzzonPWSCZ11K3awObeOGXfF33NLhnAe+pinVRYu+3fM+hjL5EHX3N4aGNEpWAH6Pvg53yWVTbuZh5HAHi8HbpNL9TQEONl/x32OjDEkSUoCiJ2zbrRO2NA2tcUqdCh13Ti8hja5W9yAGtlkXKO4UREb1c4M8Txx/MzbCnzju7egs7UBxcA6vFYL/nO7iPW1DbidTgS8bvxh4C2sB0nuDSfV8MZVcMdUCCRVJAGmNQil1PAnVPAm1HBEFHBElEgwAJgvmZAtmRBMaRFO6+GLa5HI6lngw4BAUo93Bk7i7gf34cp//Ed86V+vw38caMYTJ0fw9CuTtB09bRchnfYZL2RyT0XimoBgE9sErhsmAFgj6o6Y7PuBabXqsxYcPWNE3YCZBaRMJMUNrpFEN0qv82Z2A+d7wG0s/ds65aQDwwzflJXey5TOdrCDEqXfSeZjqxJD66g5a0L1GSO79lpoZqyiq7B6gJj39hkP+lbCOM42s3mVTx0rYucAsGuBSpaPqWL4h898Ac+9PIL/k3Phvwt+pPx6FKJGFOJGlFJGlBMmlFMmbGfM2M6aUc6SJ5Afls+XLSgVDMhndcjnTYinDHjnjz149rlHcPLlFsSTKvr5ZrQIJNUIpTS45JKP4EBdDVv8YLIqZwPnfGiTe4Sf+YZf/Qa33fcgAslV5POrKOVXsbT4Hi697FL0y40iINYyQV5WDujrh61UaTThQPOYteL6RMlqIb+OVMjFkw6xY948viV2u9tYAKtD7qQOR7mLuh2nnXToYyx5m9wlWF/ZJK9usjOW186k4S3q2Bzn8j//2UuHXKqz8qF+iA7cvJaobogqing7Q/U5AoCVgSXpNWvGM28tXwCAFx4f6kEhkHeUOHrGgpoBAgC151bRMLKOo2dMaBylUEjHnJelFZ04oYriuCqG4+oY7jzUiWu+8VWUgxrk3GqUvDoUPFokrUuIbywgubmMtEuBnEeJrFOBjH0FWZcCOZcCGbcSGZ8KuZAGOZ8ahbAWxYAGxbAOxaiBnmEdClGScvMRDT39KuSDapQiOhS9KhTdShQ9KhR9Gvwpu4bd5Cp2wibspswo+nUMSBpQzpiRT5uwU7AgnzUhmNAinl9DKKlFNm9GqWRBMKLCT277Pr5w9ZdRO7iKviUvGtkObcPIpgAMvYzho/QdTw2TbNzFKw8WAqJsuJcBwG4GAHtXwuiYcYkLRxOfiBq1UaGvSNYSC0jt9QQAH2p9FZdc9rdeuZ88VovaM8Y97AYBPir5PnzKUMEMEiDkCeGasyYxOcQXCeoG15jssi5Sd6KMlVWI1A9vCJZEmKGH11E/aMFdR/px8779uPvoMTQyebhpdB1NLEHMQS4BFBurg9lCy4Qkf0r+Oe7dcQmvHAdSJAW7mK+Ge2zcrIuRP11i4aNzhm7YnbMedM7Q73fOuhn487ObuRQO6eK/P+cV/kH+c63cG/6gV1D6nNhJqR/OQzcYuRudM07IJrfQMS0BCxnrIGtj/XJt0/Q1ds15yNQ+wVYppp2QTfIwwhZjLaUuPF4Rw6tkuGeqY8aN7nk60DUz/9hz76jwyc9+Ae++/jJSXhsSkTBWlpWIBPzwuNwYHT8L3foEVBvjcEep720ztAJ7WIGN4Aq2wgo4owoEEsQC+hPcH6iAPaKEL6FBurSKYEqHUNoAb1yHjaASrogKS1Y5xtTn8PN7bsflV3wUN9zxCB7tehddiyH2fvNSmnQxwAp7CfzxwI1UsUPPhrGK0uehDVSfI8mXB0D4DbT6rEWagWPXvbrBNdbLtyEqOhqZT5J7yfi/r4y9J9vlLnRMO9A5Q4XQlQBQTL+NbkpSIK+BYexPPet8q2czYDUD/Gu1SPONg+toHrOxFoAouuZ9aBq3sb5O7nek779thljAfmUUX7nu+7j9+V78d3ILuxErMj4jshEDSnELiiEDynEjthMW7KTWsZtbxXaOQGA5b8RO0YJsVo9UVkdhnKgK111/PS6//GpcfPFBXH751bju+usRSykRTqmxovwD7vv1f+DvrrgCR/4wy0IgxKy3seBUy7RbAPbWaTd+/mQTvn79zdB711HMa5BJqXHw4AO48cc/Qi07MNax8Afv2SMPOgEmriTwcujaCo963RDb8WXhm/rhdTSyVQ8e6OETljwgQvKvxP63y91UPcUPbYzdlXFvLu/0ZB2nMg4KJyrL21n3I7um9y740b8SRuuEjQIgfF+YpZtrmQTMv5c6Fl4SdUBM4Xn27QtTcBceH+5BIZB3VOK0WXuOLoBHThtw9IwBjSPUnN8yRdKKbNqN4yz126+MoX7QhKqqKiyNvopSQIeSR4eiW4uCV4vMlgKJtQWkNpdw7s0+pF0KpBwrSLtXkHWuIOdTIRdUIx/WIB/WkJQb0aMYpV7AYlyPYliLYkRHADCsQT6kQT6gRs6rIibRsYKCbQlFjwo7YQPOh8z4S2ITu+lV7MbN2I4ZqTomTn7EWESNEiugTqX02PAr4Aor4YlpEUvpEY7TGP0DD/4SX/zqV/GiwonGMZITyFu0Cdm0C33LdHN6URNH57wXfYoIyS6KCNsSliTgthkPehYDOKGKoXvBR4nhBb80PM4uPkIGHd1EPfu8cZx2cFsm6XTavRjAYz1/pL484QG8Gp+/5kY889YiGpiEwNO+h9lwOPeOHDljEgwhf1bzIlx2gak+a2GD56zDanAdtewjZ+6ow8wqghwkB2+J1RPeH9g8RunJ9mkaWJdNbLLeLv4kjxbf/CVZR+pzkwp9uQQjeepEvx8r5uXSYKUETFNytN1K9S8uSv4xv8+jnX9A5ywBLb7FzOffhDGcTVfxUmjeGSgqZOZ87PcqE8Uc+LEqGbkDXTNOdM260DZpQ/u0A60TNtqxnXGjbcrOACHdgLpmXWif2kL7tJ3YiWk7WsYpgNA+Qyxi1yzt31JK3I6uWTc65BKrLLaT2TYyn6HqmvOgdYqYCc6kPvXqLC666CKszEwi5liDfXMTGq0BTrsDGxsbUGnk0G7IYQsuwxNTwhZegTW4gvXAMpxRBd4+fQy28Aq8cRVCKTVCKR3cMRV8CRWcUSVCaR02AstwRlTwxXWwhVTYCqvgiiqx5lPi3268Hl+49jpUn9GLPe5OttDQvRSkuqmlIJMSyUu2R/atCEs1jBAArB8hRrqGSWjS/KGZJNYBM+rYEkP94BqaGOPNfap8s5fvAMu4/M4+b+P+MXaQkE1RcbBY/uC+ROb/a52wS6XPFZNivJez9hy97+5reEV0A5LlgqY3m8Zs6Jn34aQmhjY5hT+IqSd/Z+OYnU3CudE578cJdQw/+c0hfPtn9+G/SgGUA+soJaxIhXQoJ9dQipmxHTNhN76K8xkrdjNr2MlZsJ2zsF5AE7IZA82zFUx48612XH753oPn5Zd/CW0dz+LGm/8Nf3fFFfjB3Q/huT+qWRglLOReskRQgr2Jva9bppx4/o9KXP73H8fXvnMDnqs7hKu/8kVcetlleOLYe6gfXpOA3dDGXnDH1o2kAmbpz2uYhFo7uIb7m15lQHuN7fpukEQ7amUpYp7kpiqmjhk3umbdDPy5xIYwt420TtrRxne9+eGd119V1Dw18+lKBgC5F7dpfAv9y+Qdb592UCUQB39DkopCB2sCtrVsY5i/ho+eJaa69rT+AgC88PhQDwKAf1SjhoU7aGCc92RZqCyVVSXUsxt+9wJVv/QqIrj1gQMiBJLzaZFzKpF3qVCOGJD3apGyLiFhW8IbfU3IuBVIOVeQ8+uR82uR8yqR8xMDmPEokA9riQ0Ma5CLaJGLaJEJsD8PaZEPq5ELKJH1KpH1qZHzqlFwKFD2q/GfyU2cD1nw5+wm/hS2YNunRznAQKRPj1LYgGJQh0JEj0xch3RCj2zSiGBUjXRSj1zWhHzODGdUi1jaCF9Mh3/69Cfx1PF3UTtoYRduYrlaJxzoZruRvctUvsqfPQz4dbGZN54I7pr3o38lhK4FH9rlLnTOedHKaj64J6VlgoOiim491vPGQU/Xgh8PtbwC2bQdD7S8glsfPIj7G15E/fA6jrynQcvElrRzeoamhWjn10xbz+/rP5AMptWDat49dsYslc0OrQtWhPuoxEGBncDrRxijMbQmiqN54q2Zrca0TDrQNetB+zStKNDsm0PInHzlghcaN03svalXTr5JQQj6b6WFj73+Ob4B3MaZuj0VMG5xYb+v4UXmJfSIfdDOOQ/aZ2nZgYM/vtvMJ6MEOzhHcnM362zsnGVy8Syxe+3TW+iYtpPkOm0X6cK2aSeN0MudtFM6ZYdsksBc95xH1IrIpuwCwPIAS8esBy3jVgY07OJmRslG+rdtlxMgaWUeKb5uwD1M/HMqt7Xjwba3cdnfXYHkxgpyQQe8Xh9M5lVsbmzAbF6FXr8ArXUajtASnNEVuGNKOCIrcEYVcEQUeOHVFoRTasQyGvgTKoRSWjgjCqwFFPDF1Fh2LMIW0SCS1sGd0EHtXMKafxkbIQUOHHkMn/3il9A6uYXu5TBL0nvZ1nVQ8uOy4t42uZtmutgOd/MEyb5NbBWFL540jm6hYcQmTPWcSaljXX91g2uoP7eKesa+kedvXVS1NPEdWMEASl2TxAjRz65zhliidrkUDqlMcLeyBD8Hg40VjCCvUOI9g/VD67jjUA/qh9YEQ9g0ZkO7nBoDTijD6J73oGF4lZV6bwgvLQeCrVMudM77cUwVw4GTg/joJz6FVMKP/1P0IunQI+5UIxe0IOPXUyl/1IA/JVZxPrOBncwqdgqrVBFTtGCntIrt0ipSKR2eO/QoLv6A9eTiiw/is5+7Cl//l6+gccdqHq8AACAASURBVNSKnqUQvV+YTYZL1XttLZXvbwcahiz42f5qXP1vN+Huo/1oGbeik8248QWNWj6tNrAmDqk8JCNdl6SEN//8V4d62OKKBQ1Dq8wqsVkhDdvQPuVg05U2duiiftH2adoQ7pylaUixHc4O7DTnaJdCQVMuFtqrmHGccNDPmoHEdrkbxxRh9C76IZt2iGotsehS8ZrgT0oXU4qdVLo1tE85UDtguAAALzw+1OPKqqoqPP+eVkh8/AVHbyCzODUL78GABbJpF3qXQ+hTRHHjXY+iqqoKD97zcxTCWmTtChTcapTCBuR8amTdKqTdCiQ2l5CyL5PUG9EgH9QhF1Aj51cj51Ui41Ei6yJgl/GqkPWrkPWqkHGtIONaQdK1gqxfiVxQjZxPiaxPhSxLEJf9Ovx/JSf+WnJhO2nEbtCMvyQ2UHSoUIrokXeqUfRrUYzoUQjrUEjqkYnrkYhpEYtpkUsZkE7pEY/rkErqkcub4U3ocMuPb8R9R9pQO2iRTmYjm+jiha+zXvQueAVDQfJvAN1LYXQvUn0Mlwc75304oaLlkJ7FADMYO5iE6WZpTNb9xqor+MWyZdIlqkM656iLsIMZwvtXQmibdqL6nBlH3lWjadQqdZ0x30jtuVXB/B05w5nAvR4oDgCPMgDIlxIqpTG+b1knwCDzsDBppnFkQ3QBSr1Zko+vfdrOmC7qMeNza+1yN0vzOfd8v2LejIFAaeljLwPYJneJMACvhpFNV1a/UGq3vYK9k/r9PAxYsV4wbuhnMhBfYxFAcM5NCdR5xhAu+NE54yLGbtou5OY2BnR7FnysnNgjpsPamPzbNu0SoRP+fXTInYJJamN/Vyan74dL0R0zbgF22+Qe4SMU1RUMODeOUmim8jVGQJEzqSRnNYxu4q7DffjsP38JJZ8FYfcWAoEQZuaX4Ha5sLI4j2WFHErzIHxxBTxxJdwxJTwxBXwJ8gDGMhp44kryAyZUCKYICLpjatiDy5i3zUHjmIUnqoIrqoXetQitaxlLVjlu/tENeOi55yndyny37TNuMRvWvRhCz0pYLEjwvWk+11U/somGERvqh23CV8qBWPO4HbXMzlB7zoJ67rE7Z0bdOcb+DVWCP6p8aWIycOPIBlW/jNsqmCKe8uVA0EF1ImypR3oNugX72lrBBvJpSB7IoQMTgT8RQOFethErWiftdOheCUE2aUPtOWLtOTCoG5a6HeuGN9HK0sD9yih6loO47Iq/xzn5AvDXNFJOA1IOHRIePQqRdRSCBhQjRmzHzNhJWnA+s4bdwhrOF9aRT+lRzpmxXbQgldLi9Tfa/oYBvOzyL+E711+LH/3sZnTMSwXP/Joom3aw75EDVJsoMG9i4L112oXOeR/Nii4Fxba8bNopaqlqz62h+uwqqs+uVSS5Lew6JF2DxOGXs2QDFjSPkje2ddwmSb7Mw9fBdpxbRtfQPmljpdEVFTHMJ9w172O/52EHEH79liqduJ9WJL/HJQ9gM3tNds560bvoR99yEC0TPGRnrfA1bkrBIX5YGJNYwcYRK30fk3YcOaW7AAAvPD7U48qqqipUnzaIFxrF7dlgOpNLjp4xo3pgDYfPUKCgdnCdAgmKKDrnfbjqC19Ba8szyIc0SDsUVOsS1iHrVSHnVyPtWkHGrSQvoFuJQkiDYkSLvFeFrEeJrFuBjFOBtGMFWY8KWZ8KGa8SafsSkpuLSNvJR5iyLyJpW0Tep2HVMWrk3WrshM34644Xf/2TCwWXGrvxVfwlt4miX498QIuiV4NiQItSxIB8UEM1NQk9UjEtEnEtkgkdclkzilkTQnE9CjkjgnENvv+D63H/oXoyDrMm+sbRTXTPkwewg3Ukdi0EaDeYlUh3s/3gthmpe65r3ocTSrqodS34aSZpxoW+pSA657zoWfCTZ22a/CVchmqddELG5JNOVk9yUh1Dx5wXvUsB9C0HIJO78Nx7Whx9X4emkQ1xCq49R4BNNmFDzYCFgT8KhxytYACPVMi/HDByD2DD8AYtFTAZQgJ9kkG9hs3KNbB6hrYpu3g9NY7ydRCX+J57F7zomfegZ4H2jokRZL1pYtNW+tjyN+BPAniVxcokE7srmMCKvkBeFzMjlT5XepPaKwBhJ5+EmmVgj4O/eb/4vU75Fpv0I6aqQ+5E1zwxgu1yYvc6ZlzUe8bZQLlLLERUegy5DNs+40b7FLGmnLVsnXKIQ0TPYkCY1HltjOhBlPPgCGdVHSKg0ME6x0TZ9KS0R9wy6UD98Bp++VQrvvWd65HcUiPgsMJsWcfk9CxWlmahUUxiaukMsgUNwik14hk1ef1iSoTTasSzGkTSasb8qRFJa5gHUAlfXAWTdwU65yzsISUCSQPsQSXWPItY9y9DuTWDex66E//x6/vQvUwBqvYZek9xH23nQgDdi0EC8HKPYHepRmNDVL7wtLRsyinVF7GAUuPIBuoHpZRv/eAqGoYqmL+RDVbISzfZRgb+mketaGY2B17cLROMDzvYMA9pu5wKuYX1QO7Z++/NJuHooGMX4SfO+nDfIfe2cc9Y16wXvUtBtEzYWHVMhf9taAO1Q1ZhzeDBiNZppk4oIrjmpp/hnv0H8X/zW0h5TIg7tCjHt5CLbSDv16MYMqMcNuPPmXX8KW/FbmEDuwUrknEtChkT8mkdSgUj0iktvve97+Hyy7/MPIBfxneuvx43ff861DQ/i9ZZAn49DMj3KSJok7sqpiPJB9cwuoXGcQeaxhzM3+xC97wfxxQRdk2jfXNiwR1UAi12mzkAJCawdlACf/yAylss6tnPuXFkA63jNjSPWSGbsInDWfuUnTozR9bRPmVH64SNpZOd4pDWwewGnTMudM1J1g6u2tDh1bHnoCv2vnlh9BirkOEb3WztqW1qSzDBdWwqrpGVRfPtdNpNZwlj1mXYOeNG8/gWjpy6wABeeHy4BwOAesgmmT+FeVIah9fJKM3AhPCTsSh69VkLuhf86FsJ4ecHWnHtt7+JeEiBbICKnLNeFbJu6vRL+xSIOZeQ8SqRj2iR8SlRCGtQiGiR8TJJ17WClG0RKfsSUlvL5BV0rCC9tYTU5iKyjmVknMuIr88htb6InEeFvF+DUsCA87FN/Nf5KP4zZ8V22IzdxCr+kt9Ewa1EKaBFwa9lwRI9CnE9igk9ikk9dQqmDChk9Mim9CjkDIgkdEikTQinDDj+Siuu+syn6U04akXz6DrqR4hV6VkMCCmwdymEPkVEeJd6GACUMYanfdaL7nk/Tqqjogahe8GHvqWg8AJ2s2RY24yHJs4mbGgas7L6Dylo0DXvI/C0FKQ1kuUgWqecOPj2Cq0InDGjZZyNlLOJtxrmL+KsX81Zyx4AyGtgONu7l/WT2L+agVU0DK3vkV14VQGNoK+hedTKal74rjCrIBnbEob4ZhaqaRnfFNu+nE3h/ikun3DZd++eriQfce8OL2jlXXpt8gp2sAIEtrEJOA72WsY3RVCjjZ32JYDIvICM7aNf87SxR+y+ds640DlHfXQURmGVIDMV0hF/MrmQ/5qniTkDzGfEOkRohEJHHTMe5nN0SrUxFWwnl6H2lEizGwb9mnZMuRQpAM2EHdVnTfjmLbdj/2MPIxPxYdVkwMzMLEaHBuBZX8J2yAqteRqusAKxlBLJjAb+uBKRlIpKnrMaeKNLCCSUCCTVCCbVKG7rEc9p4IkpkczpEM/qkMhREXQ0o0U4rYM9pIAzosV3b/wWfnOkRuxvc5ldVO2wguN2Fp6RTbHvZdJJ/is+zzXuQGNFdRKFe7zomHbgxEqQFfpukc2FrRlx/129sMCsC59e4+imeM1Khn9J7qu0ILTP0s+ne94rfuYE4F1o4eERVvHUPOEU3lcB/ir64urOWQgIjNvRNk0scsu4TfQS1g1XhATOMf+g8OfahA+wZzmE46oYjv5hDpdefgWe6HkbWbceTssKsgEjyuE1pDw65EMmlCNmlKIm7KTXUMquIhnVIJXQoMTrYQpmlPImpFJGvP2Hbhx46td4/U0ZNp2z+MQ/fByvnH0bXYthWk5aDKJrkYBg84RNyNQcoNKT5PnGMbIn9C760bMYYMqKmy2FEKNIRdAb0mwl+1hb4QPkB9T6wTXUsiaL5tFNNJ4zo2lkA23MYtE0uo4OuYP8tOM2ClGxa1blLjdneIVdhD3b5E7RDUol1NKqj3Tt4gdZiQVsZe/B9mknOmdcaJ3YYr5R8pVSSp9AX+skHQa75nzoYZ5Xnig+thTAC+oYzcgNmC4AwAuPD/WgGphTOjQMr7GU1AYahtZQN0j7mGKG5gzJhxw0HD1rQeskFfO2Ttrwyc98Dm+82oh8VIdMQEVMnl+NXFiDQliLg088QPJtVINCVI98WI1iQINcmCTdjFuB1NYSUltLxPi5FUg7lpHcXEDKuoCMYwkJsxwR3SRSawtUHB0woOTT4nzcir/uxvGXlA1/Sdvwl+wmtkNGlHwaFL0a/Dm3jnLUKIBfKWVAKWVEPqlHKWtEKWtAqWBAuWxErmhGImXCRkCJ0bk/4KMf/yjqWDly4/Aq87nZ0MkAQvdCAH3LYRxXxdDLpIsethRCsh2VQnfO+9C3EmJN8170LAXQsxQQAKNnKYjOWbpp8H1cftOQscRr+6wXfUtB/ODe36FvJYTjygh6Fmj39InfT5HUe9pIE1MDJskPM0AAkHs6uZH4yFkC9ZWScS3z/1ES0SL2UbkhnU6rTG4ZZFUF3BM4uEaymaiFkeaRGsf27gVTWIR9FHu4zoq05V7/nxQIkQzke8qVK0AQXYw5SPJ8APx5hAzcOefDTXf/VoQ1ODgXH/le6Dx9pDoZJvvOecS2aeesFAYRyyFyl5T4rbiBtM+4BBCUOgolIMjrbEStDQOR5HF07Nk4bp/1irmxNsYA8i3kSma0Xe4WKwatPFTDPWpTTjz50jg+cumlMCxPwmvfgFZrwMzUBFa187CtKeGzqbFmk2PdKUc6a0Qyq0UkrUY0rUYqr0WupMPjT92PWEaDaFqDCKuDcUeVyBQNSBeNyJXMyBQMSBfNsIeVWPMtwR5Roab1WVx++eVoGNCKHdauhQA65nwUHJh2U2J0yoXmSfueKUAOAHn6kvtJ29m/Y+9iAMcUEXTNe/GCOkbvPbmLQktid1WaQawbklZuGvdsXG8xi0JFbxwrBxc2gmkX2uROegrvacVU4aRjD5vdPEFgdc9yyegmWsa38L07HkXT2BYrfvejbyVM9TCsMqSBrZrUsZ5CkXgdtqJhlOpNeGPACXUcJ9RxPNz+Fi69/ApotSvI+vRIeY0ohNexk9rCbsyKcmwN5ZgFxbgZ+ZQRId8SihkT8mkDShkjSgUzSoVV5PM2BMJmpHN6uH1LeOiRX+Ezn70K/bMW9CwFRfitdyWM7qUgGkatwitMUvUm6jgIZNVP3fNeHFsJoWfRL5Y4Ouf91KYw70PzxBZqOPhloRgekGhgHsGGobUKb+cq2y3fwo13PUrF6KPraB3fRBvzzDaNWlkgZFNMtPGDUitbeeEMn4wxglwO7pzzioR+u1gDkvzM/P3IVQzOErfJXWibstPsHFNwKvfKiamn107/ckiUfXew64Fs2oXjCmJWO2bdaBheuwAALzw+1OPKqqoqHDllkNrIh6mWgDqp1lDDpL4P9mdVMzNq4+gmuua9uKe6H//yL1/BTsmIcs6AdNyAfNyAQkqPXEKPaGAF+bgehYSePIBxLQpRLXIRNfJxDfJRLbIhLTJ+FXJRDfIxNbIhFTJeBXIeFTLOFeScCmQ3FpFdX0BubR4FmwJljw7bASO2A2bsBE0oB40oR8z4c9qK7aAexaAO2zEj1cnEtCgl9cgkdShlDNjOmVDIGlDKG7CzY8LOtgWZvAW5bStm1mbQIHsG//Lv30PtOSPJTCyx1TXjxkl1VDBAx5VR9CkqgyBh0QHYxXwxXfN+vKCOoWPGKbxlnbOUqO5dCjAgGUI7C0i0TNoF8GmdclFqdMqJk6oIZJM2HFNE0LccQOOYFc+8vYIDL46TxHvKiDoG3Ljn7yhbOzgial7WhNxLFRNrEjgcWEXNOYv4e5XBj3rG8omJIpYOrmG1DNRhtSq6toQXcGxT+Jz4LmtDBTPIJZJmJqXwSoWWCpmyVezg7gWCHBi2VgK/ihUOWUU3X+WEm0xOE2LN45vMs0dGcGkSjncDVnYEekUNCe8O5CCvQ/gKvQKA8MAJZ+s4YNjDTFbcANpYpyB5+rg87BFggm8Py6bp/y+bslM3Gb/58Ak69u/BwVLLJDELlfUwXLZqnrDj4O/H8JFLL4PXYYRSqcLKihLyiQGE1hRwrM4jEFQimdAinbEgV7IiklQjlFQgV9QhU6ClD29kCYGkCtGMBqm8ln2kkuc4A4b+mAKb/kV4owq4Y0q88W4X/v5jH8WTL42jZynMFnYC4vDEGUD+fVEZOpPwZzzM4+hE15wXJxRh9C4FcWw5iBfUMZqJYxJ4K5v06mBpfOrn2xKvSb5sRAsdBAh4UIN7sfjrUXgA2c9YNu3aMynGPYCi8JvZNypTwc3jLLAyypjLMem90jS2ieaRdbZIQZJj/1IADSMbIvkq3pODe1mwhlEbA/UuUQjdr4jghCqG3pUIvv3jO3HdL+5HJLSJXMCIcsCEhEONpFuDtEeL7ZgZpbgJxYQZ5ZQJxZQJxYwZxZwZ6aQBmaQZ6xuL2LAuw7gxgR/c+l18/qvX4Jm3ltG9SD5ZPtHXuxJG90IA9cMsoTvEOvyGNlA/TAdCDnq65iX2r18RQR+b3+yc96FhVFrX4M0EdUMk0zeMbjJ5fkuwtlSpskbhjmknZGPraBnbQMPwGhVCj6yLDkbOwPLDaDM7fDYMW3FPzXHcfM8T2FdzgloM2LpLu9yNDjkFQ6gCyC2CIPzw1VpRTdTCLTyTTmZx2UL3gh/9rFu3byVEPbLKqAgN9q+EBYDuZYxqJ9uj75r3oZfV6zQMrV4AgBceH+pxZVVVFW5/rlMsOdScNuD6Xz6Ew+9pRAXIrw714t6GVwg0DFjw1OsLuOGu/eLi0zCygVt+fQBXXvlRbBcM2C4asaofgqzpGcQiasTCaqSjOrQ0PAWd8jQyUS0yUQ3mJ17HC301KCR1yCd0yMe0OPzMb+Bcn0AhoUUhrsHI6RP4w8lW5H0q5FwKxNbm8NRD+xDVTyNlmsOOX4f3ft+OifdfRtmnR9ylgV07idrn9qMc0KDo06AU1uJkdw0Wp95CMWFAPmOAVnUWrS0HUSoYUS4ZkcsbIWt7DkrdEJL5VRg9K/j6tV/Dt2+5jXqkGAt4077fQTZiRu9SEO0zbvyu73083PqaCIF0zHnx/XufoD4pxv7d3/gSftv3Pl5QRdE2bceR99X4wf1PomPGJZi/e6t78dRLI2ifoRv4U69M4yePHkXzBGe5XPjZ/lrUvLeCznkvTqiiePLkIH5xoAWPHR/G4beXceSMGd+941EcfH0ejSPreP6UAfvqfo9fPtuF6rPMA3hKh+v+4yEcek8r0nS3P9uFexteEVLwwTfm8b07f4uaAWJGas6t4rYnm/BI9ynUnrOgdnANv31hFLc8dGgPQLzloUN4/IVRNAytoWnMhkd6TuEXB1pYVxeZ2m+8+3d45q1lkX67v/k13HX0mEgJNgyt44Y7H0PTyIa42d5b9wIekr0ppLTD76lw8z1PVABkJ25/ph37j50T3r+Dr8/iJ4/V7Fnm+Olva/DMG7NiFeTxF4bwq2c7KjaB3fjh/QdQd0YnKmAe63kXv278PZPo/ZBN2nHzvv00mzjnQ8esD/c3vIRHOv8o5q5qTuvwg/sPCLlZJnfjV8914vEXhgT4fPaNGfzst9VCDpbJpa+P7xM/cXIYdzzXIaTENrkb37/vSVSf0hIrMeXAw21v457akwL4Noxs4Ia7HkPjqJXJ4lvYV30CD7W9Lf69Dr2jxM33PMGYWDsOvjqDqosuwuCZ17BqWcXs7DwGT72KxiNPweecRyFrQTyhRXvbYRgso4inVrC9Y8Di8tt44cVaJHNaJLIahFIaPPf8b2C1TyKV1yNdMGBg5CTe+EMH0gUDUgUjYmkNnnj6QZi3ZvCZz38WB1ra8GDLq3is932q0Zlj/373HSCwN0t9f7c/04H9xwcJEE+78Mxrcvzk0SPomCX2qF8Rwc+fqEPt+0oxy3jgpRH86tkOYmgmHeia8+HWB5/G8+8pBbj7ddNruOtIPyV+x2yoG7Dge3c8gtpzFrEnvK/6BB5ue1OA6upTWvzgvgPM+0d1Qnce6sSTJ4dE/RC9/qolADvpxE8ePYKnXp4SgO/R3tP4xYFWmi9jQYGb7v4dGgf06F8JoWViCw+3vY3fyF5D/TBJ1kdOGXD9Lx/G4VN6URR8x+E+3Nf0GuqHqUP08Htq/OC+A+ic94tgzV2He/BwG7GAL58ZgWbpHFqOHEDSpULSrUY2oIes/lmYlUPIhwwoRE2YnXwTJ/rrkInrkYzrEAqo8PjjD0KpGkBL5/P4zGevwr66l3Bv7XF6zbJaq+/f+wS6F2iTuGFkjX19rwoG75m3l3HTvseZX9OBvqUA7nq+C8+8OoFjyii6FwM49PYifvLYUQq6sMPirQ8/jydeHGPMnxX7+8/i9oPtDERT2fJNd+9H9R+X0T3rRofciYdlr+PO57up/294DTWn9fjur36DmrMm4bm76+gxPNDyOppGqXrl01+5Dhdd/AlRYv/5a27EL59uxf5jA6xI3odDby3gtv01ogeyTe7GTx87ioOvzQjAv//YIH55sE0cTtunnfjhfU+i4ayO/fuE8bu+U3iw5VX0K8KCAf/BfU+gc55CPMeUUTzQ/DIe63mX2Y2CaBqx4JYHnkbjBQbwwuNDPq6sqqpCzRmDCH1Q8egaLUNUGP0lNmhdOnWyZ+PYFjqmnfj4J/8Jp870Y3vbhN2yEeW8Eds5E1JxHWJRDUJhNWIRLRIRNQIhFYIhJTJxLfIJLXLsYzZOwO98WoW4bw5O6xhMugEUIwoUoxpKCLvVVDodMKMUMCLv1WIn48T57BZSthXsBE3YCehRDhpR8GiR86uQD1H4o5gyIpbQIpzQIZHUoVwyYadsRjxrRjpnxPbuBkJpM2ZWp/Dlr30Zv278PRrHtsRkWf3wOnoXA6yg14mTmhh6V8IiANLLOgA7WVCkfcaL7oUgjquIJeya91X0RG2xtQovTmoT6GD9bHx2TEicrOy4dymEtmkHnRCX/KgbstAw+BvzqB2gUtujLOX7/Pt6tE1u4dD7BuoBZEGP6tMGZp5mk0MDBAI5q1c9QMxgDfPRNDC/keQlZNIwn9bibfV8I5p1cYm9YPZvJuavhIyzwbaHrVJ9DGcwJu3Sv1HFk3vWKg3YsikCQu1yl0i7Cs9fxRqHSMrOeASzwoGWkGfl0nB927QLv278PX74wNP4dePvxfYsZznaZ9wVEmDl1qkXsspCaCHxekRxNJ+r4/1o/NccDHJg0cXKpztmvYJxameJYdm0m0p/2fdJm8KuPfNzMhaMEfLWFMmmEpNFjNTtB9twzXXfQ9Bvgd3hxuzUGBSz55Dyr8K9OY9kQgt/RIls0YpAaB7bO3rs7BpR2jEgW9AhV9QhntUiktaQDJzRIFs0IFs0whdXIZHTIZXXI5rWIZTSIRhX4JZf/gpf+/b1aJsLQjZlF15HaWmFzfcx1lY8p6T1jZYJu0hvd86RjaJnSfKR8Z9X+4wbzSz93DnnE4yoNHG4KaTkRsb2cRM+eSWdYrpMNi39LKUJQQ86Z7iHU/qaW3hn4PTfSsFNY3bxWm5mW+st41voXwqQ95fNzXXOuNA546COwJFN6RrMPHBiDWTIKkqhiXWi12XHnA9diwEcVxML+NPfHsVVX74G8aAK2YAOmYgJCZcaSZcGsc0VJJ1qpH06FGJmFFIWxEMapGN6xKImBIJqbNjlGBl/Gf+Pvfd8j7M6t8ZFM90JJCeUhADJAdJJoQWSNx1CgNBCOAmEEkzcJI00fdTlRklIDqSAi9pImqLebVldGknTZ9QlN9nGgLGd5PcfrPfDfd9775Gd9/fBX/Vc13NJGFuWR/Psvfa6V7ns8svwrOsNFLdNU8uHMLP895W0z8DTEKexNT/v9HlIGR5cwTi2d1OvcSmnSpR1ERByMCsnVZT5tZNqPcmrGYfTF2FtNqcN1JOWr7AhhrL2aRQEIyhsSiKvegQ5u/bDWj0CWy2PjznMXuri7HUTxLz6COyvrLG8+JKb8bhlh14fRFvanFSTAo/oPVmGIu9RMqXFqMucx8il7TNUCtA+wyw9m/74+yH3eQiuYBSlHTNKX07NU4so7aR6UqdvchUArl7ndXEQdLfe+DkTTjKzpDhd8ocsxkYvuYF2XwTlHTP46bpCfONbX8cHHw3h1MlhnPxgEB+dGMShA/vx1p89mJ3rwcLiIOYW+5Ca24uFxX04erAXxw/0YjbeiOhoDXzVO/Dyi0/h87feovIFL7zoYlz/uZuwOfsFVO4qw9HZbhyf78P7C304eTiGY+m9+ODAGE5/NIEjsW58ONOHE+n9ODHTjyORLhyd3ov35/fjgyMDOH5kAAcO9GFxqQ9HDu/HBx8O4aOTwziwPICDRwdx+NgghlPdaNm/GxdddBFefafDWIBoZFneMUsjvCAFEJd2GgCwm7IAC5pTfKKjGrjXepeoI5gXRml8KGgkl+nW7nkUtabhboiz5immXIbuBjJ/bO1egKcxjqftb8LTEEVu1She/ksbLLv7sXlXP1W67RnEpl2D2LBzADl7BpFdwbEvXCRObl/6uebXjBOwq6BoDGpFGGJQOJxxZ+8ZVHWB2RUj3KtKANDUAUo8jLiIbbUhtfCKmUT0giqLjYXt0l1rrdNdmlKHZ44s3f4w1TcFYyqyoaAhRi0ZEujKjl9t+iCX5kqjx5P5r6OwKa2AYCHr6jKDtm/F5+64V43uC3jcJ9qclUDQwwYQbfLQwN2P/QAAIABJREFU48wM9y5vEPJ7PMGoipARN3IBfy1hGVaOuFXnsHGr8TczT7IpiY7NHZQ2FWqgeSLvddx021cRj+1DV/dedDQ3oK/Hh+WlPhyfGcSxIwNYOrIf738wikNHunD6zAA++ngAH57sx/EP+nHo2H787R9FWFrej+XjfZg/0otjJwZw/INBzB3eh8PH+8kIstyPkcla3PPA3bjultuQWzVC+Ypt0+q19DSmqN0jaIy0VwBbRyCmNLISPO5pTKK0Y5aDwHm0HozBFYzC4WdndQOxN46Arlu0i8aPTR4Sum7ebgbO6jCxQkda3Cqh7jH9cxJ3ttEKQrIO3SPr8kdR2JiAU0WEpGCtDeGxnK1w1IbgaYijrG0Km3b1sWZxnJ8z7Xwl8BeCpVrGq2FVi2e62su6dLj29V/4MjbYrPj42CCWpnuxFN+HA/FeHIr34mCyD4vJbhyZH8RsuhsHZ/diebEfU+kOTKXbsXvPNlx55eX48QsWw4jEsgoe527pXkA5Nx1ZqkfVoU/C5EluNAF3MIFSGft2LyqgI2HuqlGH/w5XIMaRKJTKYPWOM1AMwx3giKlABIWNlLmZ7x3HY9nlsOzpJ2lTjT6YijPcUTfBNW0TPOF5ARdcsKJm84J1uO/xl5TutrA5jWIJiW+dNrqN9eFTQsLdHBlT2JhAARtRtnTOorxrHh6Wh5gSAbuSv5AG0VY7rtqjtnP71hYGyu5gdBUArl7nda3NyspC9s59sFSPKhCYUzGk9GCbdw8QIKgchbVmHEWNdKq11YVVjt1rPfP4Y99BvPBGANd+6hocOTGIM6dHcPIEsYDvHx9A5Z4dmJnfi0OH+zB/uA+p+X1IpNvx+ttbcef3fkRv5AsuwFXX/Be++eAzeLbgbdhrx9QG8bhlG77+w8dw1bWfwVVr12JocA+OLA/g+HIfjh7ox/7hNhz/YBQnDg3icLIHh+NdOD7Vg/dne/HBgQGcWOzDsYP9WD6wH+mZvVhY2Iejh/tw8uQQPj4dwpH3xzB/ZAiDUz2ITnfgG9/7CW6/98fIqRxSzRfuQAxOXxglXG7u5oqubXsXUcYxMOXdi2oULDqx4tZpPrXNsrNtllzEzSmUtU2To7eHDCSkf0rCw4yHsD7buudR2jaFopYUXijfidyqYaz/+148t6Ua2XuGiP3bPYjNu6XqbRA5lSOw146rsvscrojbtIdAXvYe6gDO3TNoNIEMK8bXbE+gPz9kBLAy+KsUPZIww2NaH1g1wvlqrC3lz3XzgSG+rwnpWic2jZgxGTbW+jj92lXnDMRpYW1g114gBjcvti7/pGoekVO2h8GV5OUVNSfxP0V/RaHKBkyhuGUKT9vfxMVrbs1kAdbcgqdtb3C0S0K5AQU0im5QaQEl4qUpZbBamvnTmj8GCkGdEehuEP0eAZ4idsQSkEwZzKa4iVNacxbU7KAOSZYYCskni3A4dxJ5NSG88kYdLr9qLWZmexEaH8Pevf2YHGvCR0dHcPzgIA4tD2BmthvHjg/j+Pv7cPJkP05+PICPTw3g1OkhzB/Zhz/vLMfxD/px/EQ/EgcGcfBYH46eIA3gkRNDOHi0D0vLvfja12/DVx/4Key+CIemC6NKm70zqKvCqNc3mhHwbOVMOas0z9RPwloXgd0fZbbFMA5JFI4yhiTZlMVMLMfFOOU1MSQFmjWNK1Ant7h+C5tTKGlNUaSHER3ibtQ6TKktpB5niZDRfbJSDSaxMtu75/CM523Y6iZR1ppGcXMcVtatWb30fOkDl37mpK2HDlER3Y0cTMDJwEXMGZ++6Yt4Yd3zOH5sAAuRHrx/aBSpiVZMDzXhQLQXB+LdODLbiyNLvZiZ7sTUdAfCqRa4POuw5tLL8ItNZaQ9VSarlGo72to9rwCgpylB5g1V4UbMpUwAHL4I6Tf3LikNdUk7tb1IvqWD210k6Nvui1AhQR2xgZ4A5foVNlLDTX71MBy+SRX2/IT1j3DUkQSFchcnOO0iZLB/ep159BwM4EWX3IzHcrfTz60hoQ57xaxZlWpHqoqU552indyNSXg42qmgMamY6mLOiHWxdlc9r9L97o+RxtEnGsdxWCoGYfOOws6H6vV/X62CW73O76IYmF19HPZMG31eJUW9WLgUPXvPEPJrxlHclMC2rjnFbG3tnEVhUxqv7z+ArT2LeODZzbjjK7fh6AeDOH2K+iQ/ODaAEycGML/Ug2PH9uFfH/fh2PH9OLy8D48/+TN84r9uwH1P/h75VQMoaZ1CSTvXbrWRmJiClecoXqWLRqiXXHoZPnPD9Vg+3IWTJ0fQEngDWVlZCDT8CYfmerA8sxfHD/Ti/UPk+j1xaAAnDg7ixMEBvH+oH/NLvTh8pB8ffziMf/5zGB+fGsGxD0Yxe2QQe2NtuPPe7+DG2+/ES2/W8aJKwKSgIQ6bd4wS3XkD8DTEsKV7nlnAeWzpWUJp5zxKOubYRTqtXXHyb+mcV2Ln13oPYEsXiYApQmGGgSMHDfPHrV00/iXB+xxyKobwwpuNeOl1HzbvHkDungHkVgwjmwGg5Ps56yeQXzOmQryViYfv3ArqQs3m8fFmNowIkJMoGQpi5T9XqYGfbEYiSNfZgGOq2kpnl1HYqcTLCADMrwkpE5JERUjw9spmFOoKjqnMLQ+bYwoNh60OaI2hsJFuVyACZyCqwVcDbewCDsWpV9icwv1PvowLVjQeXHDhOtz/1O/ZvKMdgYVNqQzQJ20hEv8jwNLDDIkGEUkVNWECQleGQSTJG+EKB7AwImr0Gz/rczGilHBuYHEzbUjy+nkaKJ4kp3IEj2wsxpe++R0cPNSHcGQcbR17MZVsx/vLA1g+3I/Fw32Yn+/EoYO9+OCD/fjoZD8+PDmAkx8TADxzZgjpgz04eKwXi8sDmFgYxtETg1g+MYCjHwzi6IlBLC3vR+nWbKy95ho4GlJclziLotZpis5pTsPdmOKu2KQyszi5OsxaTxVvkvlnrZs0mnP0rZ3l9B4pbKIsvq2dMyjvmEVp+yykgcfhj5KRxBeBnfMqBRBKfp87oFk9j3RJK+MPBYcXNiW5sYVAvDB/4khfGc0jblN3MAp3kJ5pChBPYkfXNPJrRgnc1k3AWT+B3MohWodqjGeueiUIDCmmTRpB7NxI4WBjSEHzFMp7FvHpm76IDXmv4p9nhjET78Lh2X4khpsxFe7ETKwHy7N9OLywH4cP9GFwsAbbd1hw55134NOf+TSeLvg7HP6wAi7OYBKuBjrcbO1eQFnnnNKzuQLk3pXvL1fiajhH1OGLorx9Blt7KDmhVNX9TcPTlFLASBtoOB+vfpIrJidR1BhHCUcmbemcgaViAO4A/RytXgrWttdPwlY7qcCfzWAA7fVUu6niaWrGccNtd+OiS27GBResw0WX3Iwbb7sbtrpJBdwVC8hSkKImMiIVsVRD3isF6sBHBxCZPkhHspZvJAyXuPQKxxRDLeYUij4icGirDyNn12oV3Op1fpfKAbTVTWDzzj7k7BlkLdkgsT6VtKHn1YTg9Ec5+09cSnMoapnCtp4FbPprC7KysuAq3oTjHw3jX/8awamTQ1g+0guX5w/46je+jE9/5tP47E2fxTe/83XcfOtNuOyKK7D5vR6UdlD4a0Ez5Sy5AhG4G2IEgJgxK+FcqdKuBdiDCdz05W/h2eeeRFHRH9SouM5bhqMH9uHYgf04vNSLDw4N4MThfhw92I/lAwM4fnAAxw/1Mys5iI8/GsI//zmCU6dHceT9IcSX+lFRvQ2XXX4F1r/TgpyqUQYkBACt3nHjlD3JLMQkObVYx1LWvaiqq0o75hQAJEs/1VyJMLu4bQblXQvY2j2HLZ0UCF3eRW7G8s455QouaplCaRttOmXt0yhsSmDTrn48t70eG9/pJMa2YiiT/dszhI07B2DzhtgRPKiqkyzVYwrsWyo4LmbPkHIGZzPLp3SfrP8TOUB2Reb4N6dqDNnCChofc6vGyDVcOQIL121ZmP3L4S7W/OoxamPwhpRDWMrdSacTZh1QWHUl231RFW9S0JhCQUOc65wSalOV03pRc1JtFhQzQxurHsMnFPiTMfDjeTvOoQO6BY/n7YCb2caiZsl+SyhWSOJ+VBexGmPpLELNJDEjxCwgAaBkRrafh12mytGrnKWy+cZpA+Zb3LEFTSSAdwciKGyIwF43Dns9jbgkpkJYrk27BnDHPT/E73NzcPToIGZmE6j3NeDg/ADmpwj0LR3qw6FDvVhe7sWHH/XhQwaAcp86PYjTpwexf8yLO+/6Bpr2VmHxYDeOcgTMgWP9+MtfC3H12qvw3LYa2ujbZ3U0Bhtl7H5m+uojsDKgs6q+6Ag/i1Tzlu9lTRgHC+fXcmCu0u9FVHCuOxiHvTbELu5pAn/KdRs2AF9cTTXMlpnCJg3Q6Xsm0FfYSA0wnmBUZTRqvZ8e/bokB9AXgZ03cfl/Wzum4fSH4Q7GUNqWRn7NCLV/+COUpVkzyrl/IQX+TNmFsO26kWcceTUTigmkesUItW4EqXHja9//OS688CIcONCDVKIdTf53UOzagG9/68u44YbP4LLLL8MX/vsWXHHFZcjKysIX7rgdT6y3wFI9DKd/kli5gNHQE0ygpG0a23oWqQWEe7Pt9RNKSy7fs6kft9dHyEQja3zHHLW/tM9SHJZUQBqMv70+Qq5uXxieYBRFzQmUtU1xj2+CGj/8ZKiRjmdZW6xc/6bq1ljak68OFXTIzPeO47Hc7bjnsd/hsZztsNVS1aKLI7lktF7ItY8lisVOK3lJSdsUiptTlD9oBLOTJGhWVVfq2CZ9KJE1zq4kClEFBFWdXn0E2btXg6BXr/O71mZlZWHd2x3YvHM/JakzkDDbIQgMEHBwBkRfRZumMxBBQWMCtpoRfPZL38IVV6/Fy/kb8M6ubSgqz8UVV6/F5Vd/Ej95IR+/KfkHnnH/Bb/YVIpfuf+CdW8FkMcdsgSy5OGMwOaLwBmMM/CbU+YKuZ9yvoUrrl6Liy9Zgy98834FAk+8vw8fniD28ciRfhw53IdjywM4fpTzrI4M4oPjg/jowyGc+ngY//znCM6cGcWp06NYWu7DbV//Kr737GYlOpZ6HquwUXWUX+X0x+j7rhmnAvG2aZS0z2Lr3iXFWJIucJ6zsWZ5BEzWfnWzbvDmr92NrKwsbP5rM8q7F7GFtYSlHbPYvm8JxZyV5Q5Gkb1rP176cxtefbuDx7gjzN4NYdNu6fQdUhl+eWzcyKseo9gWZumEictRmX9jyulNZiDtAja1f9kVI9hcMYrNFRIuPcrMIgFEqYqzVIdgqRyBpXKEHeVjyK/RIbb53vGMEbGKcmBBtI2F2lbeqB3MBEpkS2EDbbyehhg3McR5HEwAsKQ5CZd/Uhe+cwiyOxiFk3WElt29KG6hnD0ni7ed/ghpAC9hDeAlt+Czt98Dhy+smB9PY4IPKjz6E4DQnFLCcAUKxVhiMICSNVigGESSClDbhQ6udkvrB2f2iaNU2D4FAA2zi4q94ZGoRFNkgJJgHHnVIbiq9uOiSy7BZDSAoyeiGBgcRUPAj3SkE4cO7MXBg/uxfLgfhw/tx/KxPh77DuLU6UF8fHoQJ0/RPT3ThP6B99RzuObyK/G9n/8Cv3jmSfz00Qdx8Zo1+HXh34nFb5tRI3RnMK4MGXlezoqrDevP1T2JPG+Yb+P/iT5X+n+lPtEfVQcGpy9KWY0tFNHjqJuEh/WPBVyPJzmUnoa41mQ2JjgHTo+AVUh3K7HzBfweVHWOXC1mdl27pLs6qM1LxS0plLenYasdVcwjjSbHsOHvXVRP551Qh08yWtGdw4DKUmOMVlewgBIKnV8bVrl7dh85oTf9rR1ZWVm47Mq1+Mq3voU1l12Gm77yHfzs1SI8v60G6//ajqedf8Hz2714fksFbLWUq6f6lrnKzcH/7WlMoqRtRuk4i1un4WqIqXpJpReuHDNahEYpQqw5jdI2akKi/EdiyDKAeFAqMsnpa/OOwROIoLg5iYKGKEpauTvbP0mhznXjsNbSYf3V/22H1Tgs5NeFVQSPRMBkMsgxZfyR8GXJJnUFxOBBEwSKhpqi6CKuLyxqSZMsoJniYTyNmpmX59PFeYGq4UhiZPi5lMghCZK21kWo19qQxNh9UeTuGVwFgKvXeV2kAdw1AEvVKLL3DCKvahi5lcMqTNPCrE+OdMZWjiCvmhid7ApiCnMqR2CtHYezPoSnXf+Lz91xJ667+b9xwxfuwKO5O5C9uw93PfIc8ri2SYKEZaGy1IwjV1gp84TLIMEVjKGodYpdfRye3JLG084/40fPW9Smk5WVhTf/UoAzp8h8cvRYP44e7cfJ9wdx8oNBinw5PoiPjg/i4w+H8O9/j+LU6THMLo/iyPsj+HvVG7hkzaV49e025NXqTYZ0RpzWr1iHMIuwJ5S2o4hBYCmPeVUuYNcCipnFlJwz6csUcPvIhiL1b/g/v34VZV3zKO9aQFnXHMq75lDSNkW9si1TuPfxF/HiH5uw4e97sdkY927aTbd0++ZUDMNaMwZL5TBsdQRWhS0Q04alyjD/VI4wiBM5wDCzd9oQlFMxRsCPMyGlXWTzHsmIHFGGIUnoz60cUexfLh8kdKZXSMU6WDlnMd9rjGmYqRFdoMPHXZu+iBqvKXelj8a8EuLqDkR4EY9ksC4OH53mnb4wRfY0xODyh1HUlIDTH2GDTxRP5L+O+598GY/nvUaOTF7wizijzdR0CVhQo0HuD5WsQInJUOMh5UTW3cSiISww+n/djUkj1y+h2ARnMJNtEkexUxgFZQiJG78W1yHQfgrE/cEzr+DeH/0Yx471YvZAFD1796O/txmHZ4cwN9WN5cN9WD7Uj8OH+3D4KAHAj08N4uNTBAIFAObk/ganTg8iEHgd1167Fg889HPc8+SruPuxF3HXI89h484+lHbSc+BpjKOgOUXgLxDjHl953iguyMIfZX0Q3Zj8mvy65MrlG+HCwp4QAAwr8G+vn1RuT5e8X8SQwY0MIi3I7JROGNmOSRS3pHlSEdb91iwrENd+JhvIOY1+6nAtaqa6wHxvSNXXWQ0D1F2PPq/q3vJqxllrS8+eYt75MGeCP1OLm++dUIYJrauNkqSgKYXitin8yvkWfviiHdb6MK1XnXMUTt9EOXf2OsrUo7DqiDqcyy2A2xmQg0VUhSA7AzFs3j2oNMV0cBxV/5b8GmIAXYG4fj74WfA0SQUiGXk8wSj19bIxwlE7hi1tadL3+ifhDtDzbK+fgJ0r/Cw1ZDC765HnYakZpzzBukmlJXb46H3i4Fo+Yo7ZWFQv/4/WDocvrA4IbjHvsRyguG0GJW1TVPfYOkW6wNY0pTg0xJWe1TyESZ2lGWQvpi3d5sNh4aoxhuUPfNvqI6sAcPU674ubQPp5kZhgQT6Bhuw9gyr2I3vPEKzecdhrJ5BXLb9Go0bFLlWNZSzQ0issm74CEZV6zChgY9Oeocyba+c27x7iFpJhbNo9QKCkahSW6lHalNumUdIwiryd7fj81+7GlZ/8NH7z8m/w//1riMKdTw7hzKkhnD41jDNnhvHxqSH869+j+Ne/x7B8YgQD6R7si3Zh5kA/vvzNr+G7T7+qKswU6OPQWCXI5ZMjdXdSDqIrGOMFgcYBpR0Ua1DSMY+SDtYIsntL3+waZnbzGz9+QoHATe92Y2sPOYMJ/E0Tk8jhqM76SeRVjyGbW1mkqm/T7mFt2lBjVmLxHLW6cDy/dhJO3yTsXt481M9GRryjalQjPzuTAczmry8Le7aAQv5ZCag0+4UlckY6hsUpbGEnsA4j1yDbjOoQdlB0geIApeYUHd0hdUruQBSOenL4Scm7sIGymZjgyhWkzcjdEEVpaxKFjTRWLmqd0q4/NiVpx29mzIvK/uNRsYDBgsYEaw4TPK4ixlIiTNzBmNYN8thYoiSECfFIzy83xDh9ETK+BONkGjJcwY7A2cBPHMLOQByWmhC2tqdwxSeuxZ6aN3D0ZAjp6SQmJicw3NuCRKgds7E2HDnUh/ePDuDQch8OLPeR7u/UIE6fGVL3qTNDOPPPYZz55xBOnR7C40/8CPd9/z68sTfF+ZhyCCLmz9NEESk2X4TXCGL/LN5xBnUh4x7XTJcBBC1e/fs0cJxUQEcxOn7NVKmKLl9EgT0BYGZ1nmzyovlyBWMoaEqhtG2a2yq4ApBlAAJeaGQvOk4zAojZwUAYDp8ew4tW0crrLjWTjKlnwqxbzKkMIbcqpGsXq0NKHiEGEBXHtKIf2FY3ydMVGglLQHFBU4oO1h1zKOsi6YqH+6rzvWOw1k3C0xCH00eRKjZ+DvO8otOl0bKAGhlxuhqSyKkcwcZdA6wpFn3wqIp+kvgau4+ifMTp6mnS1Yjkno1z5BYHbjen4KoLobQlCU+Q6iQdvkkCdLXjcPjCarIg4DmvOqRGvbrdhZ5/uy+mXOF2X0zdDn+cx67MCAYz23VEA1zMjGUJvzfKu+YhVXEupdPN1OuaDD3lWiYVQJRIJzGFuIIJGt/7Y3SoqddrXW7FahXc6nV+lwKA+ZyvREJ8WlDzvePI2TNINWJ7BmmkWDlMzSFVw8je1U+xIXuGVKUSuTt5EZKFjEfIucwiZnMVmboVwJOPwxn/Lc5VAYICMIiBC8FaN4GiNmLTXnmnGxddfDE+ON6FEx8P4fiHAzjzz1F8eGoUH348hvdPjuD9k2OYWR5Gb2of2iMdCA4HMZFux4UXXoiX/hjM6LC11lFNkK0ujIJg1EiQn6STdg11VOZ7x+HwR9UoeNveRWzpMfSAnfMUddCziK17lwgMdi+grHtR9QeXtE3j81+9W4HAx15eT+XoHdOKKSxtn0Fe1QiXkMe5FmpMgcDNe4YpE7BihLPCxkgDVsv1R7zwWvnfIxo/cXpnV46qrMd8L52kzRGwgD1tKDF/niPYtHsIG3cNYpPxcyL2YljFC+Vxfpm4f82+Yc0OS72VHg3me/Wmpp3BFKfh9IX5BE8LtYeDku3SruCbVAyMnOhlzOKRqA7e/GmMGqfsv5YkZbExo0f6pmnd6tGo69youiwBB38tT2OS+3zTxgiRNUKtU2pzK+SNrVhy8Fa4lVWUCFeJ6dqpWMb3bbYRqDuoGwnUR38UlppxfP+Zdbj2+huxtLwfcyciGB7Zh8G+Dgx1+xAfbsTB6W4cXurFsSMDOHykH4eW+/HhR8QAnjw1iDNnhnDmzDCDPwKAZ84MIRqrw8UXX4R/NNSirHMeJdyNXdCcVN+n3R9Ffm0YlpoJ5NaMZ7rIqzOZrYzbAIc6XoR0bzIqFjZQgpxlXOkOCtsTUTor1YksGsCgrvNyBWPM7CV0NiMfBopaNNNbYIz2i1pS9P5TI/ykBu+BsHYAC5Prj3LIvJijxqDjkvhwVh1Cbo0GfjJ5sNfToUgOamLEMsfC+nWZ4GeGx7esR9zSNYcyBi0O3wTc/gk11fEw0Clrm+buWx5VMyMo7J+DjToCbKz11FtMMpRRZRhTGXzeCSXrcAViFIrcMcsHIalSS7DGL0U6wbYpVaPp9o2jpDUNT5Bq2xz1k9zVPs7TDdYvr2BFNSimAGjR2OmDYwxOfxwOf5zNJtKgE1eGIGE4qe87oYBrSWtaaR9dwWhGjzVpdzUQlNdJrRvCDAbMOrkEH9riyhls9zEjyEAwZ8+qBnD1Or+LquB2Dyh62Vo3yYsFn6Z55Onyx+D0EevgCtCpVZgUh4+Es1bezAUw5teMwlI5jLyqEVgqh5QWLKdyGDkSRVIxrE69mtEaVqBi854hBSZMALjZ+D3y0eoNoaRtCtd+9gt44qWXsbjci3/9cwCnT9PIamaxFVV1f0LreCvqhhrQMR7AnyrexFPrXsa9Dz6IrKws5FYM0umWNX/afRrm0m5mpjiXS+l0qkP8ZwhclHXOqcBOAoBzHBOzgC09BATLuwkkEhik21a5XwHAz1z3KfzOXoyfvZCDb333XuTs7IHDN4mStmnDYJDkE++4CoiVUGdL1RiPVSe443cc9jpq2HD5whkaz2xm/EwTh4xmbbUTzOBRHiT1C49lMLiWKurilJ/pJsUEDrOJZIxGQnsGkcPRQgJQc+VzM2dSAUBetL2TaqMXJ6gZFyP6TJV7J5EgKneNy9r9lAvn9IVVB7GAKJcKjGatF+tcRe9V1KIF3yowmllA7V6V6jIN2JwGG+cOxtjJq40eogcsak0rVqFQsUs8FmtMqjGjgAhxR4pjWAeHa7ZB+mdVXV4wAYc/is07e5GVlQX7lnxMH+5Hf3ofYrF9SIbakRhqwsxEBw6le3BsqQ/vHxnE8nI/Dh7px7ET/Th1ahD//vcoTp8Zxekzwzh9Zggfnx7CByeH8NHHg/j9y7/ErV/5OpzNs4b8YYbGaRzrYa2LkPRDuchDGfd/BIAZejcNHIUFtCj926Rij4XBkX9/YVNKMUyy6boNEO1WrKvOWBR2q1g0Xq1pHvFzh3RzmrtcReyvsyaJuaIsz8ImbSqR+jdr7YTq+pWDs/T+SuCzHIrUoZMP1ybYM1t5CEDqAGZbfZiBFz87XunnpZGpwxemZ5xNYXnVIbXGyFhTQtbJiJHgaJuEwWzRqFyyZCV6ysrOf+oSp/o2p5/etyUcmF/M+sGCxiQ8DTGUtKYpL69jBmVtaZR3TKO4hWJ3XGyacdRPMgAMw+GjeKCVIfVmSoECyPw6KABoBs5L3aQ/rjMh/cZUQRIEOANya9es6ux1BaKZIeb87BLok0xDHXQuNZES42TGO6nPjQOdHODsXCW4ygCuXud7qS5gOdHpmAXTGcXaKz/lbtnqwrDVTvJmGlUmDmstRY7kVo4gt2JYgb/8mjH8+AUbGQo4jkR3zY4aurRRI6JkBNl79EhRs4DDGSBwswEY86pDcPoj+MNfGrH209fjyrVr8e37v4s8+zrU+d/A9TfegKysLBSXbcZguAEP/OBeXHr5lfjyAz/HnT99Gr8t28O7f3lRAAAgAElEQVQp8Xr8SKdFARq0qSjgpxbUiQzBtqeBiuLF2VbI7t6tnHdVKmOxTj0G3tKzhO37llDWOY0LLrwQYxP1cBRuxje+/TV8/VtfQ1ZWFp7bVk21Yn8oRAGDBjItEItgbgIWLotXMSp88nb6IihtnVKMn7hyZZORbD+L6v4dh72OagIt1TrIOd87oY0eVTK6Cmk2khnJHGkQYYmApWpMNZYoZrjKdByPaH2ikW+WxzpR6h8WPdhExmZP3cK6ucEVTBD7548iv2Ycj2zeqpx9Vu84nP4oHlzn0ZltwTi3T+jID9rEWd/DI2DNAFJ0iasxc9TjOJf+zhCVmyNn7SCmkWJxMzmXKQLI1BCa42aKoREAuPLvcSnWz+hJNnRH9voIXL5JXHDBBahsr8bMcj9aJzswEe5AMtSCqdFWzI13YHm2D8sL+3H8cD+WD5Oedvl4P469P0BNIB/04qOP+nD8eA9crpfw7ntF+NGP78XaT1yNzbv61Xu8uG2G2x3oUJVfS2Be9GDKQb4i4uT/DwiudMGSLtAAPvxM0vgsrPqmPQ00flWxLCwHyGBQ1aFAN7cQUE+hpCWJ0vZpFDUnVUyTgEH6eaXU86mNQCQDEOe3w8/5g/URWju9IY5LGsePX7QrRi/3HP9eMXGJ014+CsihgH4yV9lqJ+DyR+mZ5Wck47nxjiO/Zgw5lcOZr2lNCO5gQvf0cli33SevY1wzVnzQKGiMw+4nMiCHD/bETo6RqaVmnA7SDLILGpPcbTvDrRocq9NI9XBl7WmUtqZQ2pZGUXNSxWA52Y1rr6N/m4A/a90krydjSi/5w9/ZVWqBvEZ5NTQ+p+lARGkBBWTZ/TElldA6wIhylEu4dnnHLLZ2z6G4bQolbVMsA5rKaAE6qwlI3UZMjLoZIBoAMGMdkbzAALGUqwBw9Trfi0wguweUtdx0GonbSE5KojmRDkU7a+PUwsJ9wlav1nnYeJT8P6W7uNB8LKNlRBYbM0NOWCRxkQnQ26z0blpnplspRtXiRfq8Cby4oxo/f9WNL333p7jsyrX47pMv4/vPbsDaT1+Piy++GLff8yM4/GEUdyzAHYwxwDMyoWon4ayfZMaTxo5KQ+edyGAANQiZQElrGoUtaZR10umwoIlaCrbtpYzA0s4FlHBWYEnHPLb2LGLb3iU4AnHc/s27cN2Nn0Hq4AAGk/tQ+Kci3PnAD3D1p66DwzehOmKLW6YUM+EMxGGrj5AuqnpM6eistWaQsozFSBPk8k+qajZbLW0CAgBlFCfF6yKsVv9Gyezzjq8Y2YbU6XtzxYhiFS1VY8hmmYClijSDpO8c1Mwia02FqZSvJWAwv2ZcbZBKXsCfi1bTzApUUQ91k7B6J3DjbXeTq1f6PW+7G/k1ITy3pUJltLmDCdYgpVDQrFP+C1um2NwzawQXawDoPkddmcr3Mk/wfp1l5uYQXV0nJuByik1OPPJqmTIaTWjjcLK+yME5b/J3KKcpC/Ld3Cbj5H+bmGDs9RFs7ZrDZ2//Ol4pKEFsqQ8No60IhVqQnmzHXKQDh5K9OLE0iiPzvTh6oA/HjvTj8KE+HDnSj+XjA7DaXma37xXIysrCBRddhE/fdBt+8XI2LBUDKrezpJ1Cb+119KyIzk9HCulbVwuOnQV+MoCf3AZgVCxgzbgRhxLSutGVjI9kBaqIkZh67TRYlvgXYnKKW0gCUNo+g7KOGZQYB4Ji/rmJ0UeaZ4qa0+wMnWLGaI7kG6wBtBludzI7TeDXRe+d5ey1mP9mMVSIZKOSJDey5lqqQ7DVTqKgIQZ3MIqcqhEGfGyuMZh1qpcbNW567m21kyjvnENZO7VWFPJzoJ3llFNnNp2Q5jasdOOWqjG1LgggdfqjHM9EIGpr9wJKWnWeYnFLGkVNcTK8NUstYlK17ziDMdjrwvCwa1bAGU2uxtXaJa7jpwv+oaYauby22HiyJYYgp2LJ4+qjaPCUHICZQJ0XmubsQTLz0YFxSuWBFjZPoaBZNL3m85vJDspH8xaTmABDYRA1AKR7FQCuXud7aQDIOgNyHkWMOi4trlfW+Xqtr8kETWKcENH+JDNloi0cVydC86HUehfjdFsxaugEZRw8bIwtR3RrRXWIIgZYaCzjWAdHLpR3kQbvf/unUNSxgKLWNH7/pg8lHfP4U98M3thHWVwUR8FOKwY7jvow5XGxYFn+bfnmv61mPAMIUwdrDAWsG3MGYhz2TMwfGUPmVFxMeQ/pALN39lDu1pfuQKBrF6YO7MdnrvsUbr/7B3hxRzXcbFIobJ7ihYFE/e6GpM6HqtOuWclQWwlqLTXU0uH0TcJeTyadvKoRAliy4VTpqJa8mhBs3pBmOnmjotDZSbXxKndxTcgYD48qJje3Uht/cipHVfh0roqhGVFMoDDCkkeYZzCB+ubNRbG2+gBjq4uouq/Hcv9Dun/ONtWQoQCgmenHDFxR67SK7ynioGfd9pE2FnbRuCWVGcOseaLxDR2oCjkDT9zBsnkUtUwxaJgy9GWZm4H0/EqjhzODvYopAKj/Xt0F7G5IwuGPoSgYxpWf/BRsr5WiJ96Bmr4AEvEOzCbacWiuFycOjmF5oQ/HlwZw/NAgsYBH+nHoUB8OHe5D+ZYc3Hzr57Hur12w1YZQ0jGHcgZ9Cvx1LhA71jbNelJicXMqR7F5D7PEfAsA1EzWfwB91WyG4FtFolSPwVoXznQM12jTk7XOyJGs50o2M2duBYMqr5XqkZafTesUStpmUNo2RQc9ZgWLWtIqBkUMIjL+FVNAads0Cho4hojBi5NNTKK7Voca7yTyaybo9k4oUCvpCOr5YsY8v2Zcm9e4JcXqDSG3aoQP7QR68rmD26L0hOOwVI+zvGNUHbbcgRjKOMu0jKUshS1puBoS6uBhq4+oVgxhs8nIQqA0v2ZcsZD53gmq7FM1h2mUdczitX1LKkuvsJGq7wqC5Lgv4KggT2NCOYzdDQnS4hnveWvtJK9Jkzp5gNeR7AoxKBrJE3yYdNSLSzymWF/R3mXEtHBQs6dBdJ4kydjWNaP7ppt1nJPc9AzrQHhaKyjvU9aVopZpddAUtt/UF1PzTwqS9Sn6YmcgDkvV8CoAXL3O66Ig6D1DzCSIdimu3EZ2I5VcAKDDF1OfW+vCsNZHlD1dfk1l+9WQI0sWOGEH1XhPOU0Nxyk/uDmqgmxEVZDlGuMiKUbP5XGhaN0UeKklx11Jxyy29CzBE5zAw+sL8fy2ShS3zSD7vR688noFynw9sNaGdB6ZMSqx141DGinIDBKBXRYbPk1LgLEARzuPOTzcMuEMRLGlc46y/TgEuoRDrcu6F+FpSuC7j/8Ozxa8g/V/a8ft9z2I7//0h3j3vWJcefXVyN61n7PGUqpOrrBFjxooFoU2lYLGJBw+ig6wcYWW+jnWR5DnnWBXIOkW7bUh1iCtGNMajKr6GRpGjDzveAYgs9SMZzAWOVWGO1iN8keweY/+WQqwF3NQLoN6AvFjeiysdFDC/hnsSLWhE5TA6Ix2iCju/eVL/7Hf08EgSjYX0T2pFpfWafWxtGNWxcCQsSOVMQIWHY/qrg3q8ZjTH6WgYwYh0lksmX9mzpwYTqTZJMNNKh3DwbhyAIt+zeyZVWPNQAwFpsaNGZvfFb+DG275IvbHgxia3oe6gWbMpPdiaboXx5fGsDwzgOWZ/Ti20I9jS/1YXuxDc/AdJBPNOHq0H79/5Snc97OH4WlfNCQNmXdJ+yyzpUli/aqJGd5kSjgqtJ43p/JsDaACfAbjd9atTAYTme+Vas3Qy8FUDrVOaZVgx6e0MJgAkEBgSgFAGtMnqZKxOUnSAFMD2JxSpgVhiNzBmNLOyWvvCcY4asSIJOFuW1vtBE0wjEOoHDZNzeRKVsvqnTSYcDmgjcETjGFb5yzcgQhs3lDm4UlG5eZ4uXoM9voIPA0JHt3P8j2j6i8dgZg6bEngNn3PJAGyVI0aAItArtMfZVc8vTZlHbPY0r1ALtrWNAqb4ngybzvuf/IlPJn/GgoaYmpsLk0s8l73cC6mg/XnIlcSs5ilRu8j5hRBS0t4slEfYWmAPkQJ+6dbSIxooGBCHbwLmpLM6qbV6F8AneqHbp1CUcs0AUIBd00plDFo1L8nfQ4AqFlDF08YzMxPZzCO3MpVALh6nd+1NisrC7mVw+oEnJlMTs44cSDJrRfPqAKKoiFUBevMluV7Sfj74hs+Gk3W6pOaBhMTmkUSIChmBjmZcm3YSpOAdpLS2CKvJgSr/D7+O9wNCRS2pPHrwn8gKysLn7z2k7hkzRpcsmYNrlp7Fb56z33EAPqjDGS5IaB+QoFXAVQCpmRTMfU02m03qUY6dtaOlHbMZjIkHI2xde8SvvGjxzOyDLOysnD11VfhyquuxNOOP9IopCWtFuNNf2tHGWurNCsxrWJESBvIGxd/dPqjyPPSKEzMHXnsYM6tHIHTF1ZsbH7NuMryy1WAmjWBzEZoF+Z/0mex8cNgbyXeRxlDBNCr3MIhrQ0VfaLKDhTxuwCDsRUneh1PYbr8bPWRc/Z7Xsz9nq+81ahCnN2qezmlFnRaqKc45oFyHE0NoNL6NGpHsGZmOcDZx6L1Omok8QTCKvxV6X0aUzx+TjC41E7gwsY4PIEIZYv5wpR7FojAE4zAE9S1d2YriYyXZMNyNYgjNYnilin85LnNuO/BnyOx2IbGsSYEBppxYH4Qh2aHMDPZgcVYNw6ne3FsYRhH5gaw550SZGVl4aKLL8bVn7wGF19yCR561aPA3uZ3uzPYPwHK+V6K/sitHmPQp2OesvcMYePuQTIMVQwrduss5k8AIDODAvw27R7CQxvK8J1fPIeHNpSTA756JUs8rg6jotm1ZUw2tEuYQICZ95ZUon3JaixqmVJdriVtuvaviEO8xcUtLmKp7qOfUUKNEmn0GFENJAWNSXa7TmPjX9tQ1j7NgF7YQVkj5VCtI5OUPIUPpfZ6el8UN8Xh9E2wvIOel+zdg3h4YxnufvR5PLyxnGKZGFhaWDvo9NNhs5yrKYuUFCJNQJU1nLIG2OvDyPeSvju3coybeybh4kNIQWMShcz6keZvloPuZ1AQDMMTCOOzt9+Ni9ewRGPNrfjs7ffAHYxmdms3M9PI72tiIuNsyosgvzaidJ/y/nnxtXrke8c1+OO9It87YRwG4oYGUOvtnIYkgBpr6PBV3Dqtet4LWaohGbCFLWmUcrOJuVYUtpB7vFTyYI2Gq2KuPlWvszFKlufY00hA0N2QUgDQUrk6Al69zu9am5WVhbyq4bOE4to5mFAPmsN4UMSJZONkeKvButC4OFNP+L1nNyogpU63GYwSf86ATo39ROStQKHWpukxoIxNiN1S2jT+fdY6Aneehhiuuf6z2ODcgH8E/orhRBs8W/Nw85e+AlcwaphgROcWUqdLrS2jMnLrOcCshUeiEmBsrxe2h8YepeIM7pxHadc8ynsW8XjeDlxx1dV4u+4dWD2bcc21n8At37gXV6y9Bg+uc/PJMI0y7houapvBT17IU1okWUAokHRGRRGUddDiXcbtI5Jh5eSqIZPBlPG7vY5cwqKxU25i9XMQfZ6wfrqI3gSLqj1EmFyVETjMTSVat2neyjRSOaL0ggJQMwGmiMt1LIZubAkjjzdEe71UO5EGUPo9L1b9nmH84LfZkPBeqWjLHAGnlRBeGC3tAiZ9nl6ouc9WGEBj/Org6BEpohc3vdMXVv2wku/nViMvYh4Km4gJkYYT6jiOEwvVyK0oTYkM4GdqjiSjTsZKpW0z+PJ9P8GtX/kaNr3xDvyjfWgZbsNUfC9mJ7qwEO3B0cVhHF0Ywly0EzPhNux6uxQ33ngd7nv8d/jRc9nIr4+ixGD7fvKiVZk+qCKS9HBkxphUP/uNu4Y4KmhI1RZu3CUd1JnmDwF/EnxsAsBNu4Zw/Re/g4tY13nRJbfg+i9+hzrM2S1ukUgVaZsx4oPMiYa1zgCBK6J23Aag1u+HKZ0HyDE/wvAUNKUN8KiDpFW8TCCudIfuQJSjRSSXL4ntPQt46GUryjpmUNqagjuwQmfNjnhZPyV5wFYXVk0jhYEw3L5x5FePGnrdCeTsGcIN/33XWa/Z5t1DxLrXhGBlE1tp2zQKm1Pqe1f1gb6oOgRKRailOsSh87rOUQKXKWQ7oV634tYplHXOUpVoawp27wietL5+zurFJ21vqKYNOdxmhifTHqXW5bqIChSXMPkHfr0Bed7xDCZQ1msrT2ukL9nU08otmZAufh3MwPbCZn1IpHWB42raMgEgxdckVYZocdsMrdlSbiAgOwMA6tvTZBrOtN7YWjO6CgBXr/O61mZlZcFaM5aReu8y3EeqTDygtUzaNp/JDsrI0cbsn47o4I24NnNkLKYTMigImFoRDqwYHgGHEziX+9PKBgynRNLUTsJqfF1rXQSl7TP4xfoifOWbX0FruA2N+7244IIL8P1fr6fNUWqbAvEVZhiq4snUOoaVHjBDm8a6wGKm9VUpfXMK2/cuYhvnAG7tWcTLb9bj8qvWYvvb29A63ISHHvsZ/uvm27ClI403+w/h9f0HsaP3AF7rPYjt+5awo/cAduxbwo59S3it9wC29x7A9n0H1K9v37ukPpZ3L6oYmrKueaVVKWpNw+GPIDcjK0wDZkvVKJy+sPoZCOOqatp4hK8jKyS7TYfWigtZxlQyUs6uGEZ2pYRIS9zMWMY4WEbGJvsrDG/eChAobKXoTfO8E7yxS3USb/Ss7XwsdwfuffwlPJ73Ghy+MAct8wanmDgeAfPiW8wLvJhAilqNcU6z3vRF3+NqTMIp42AeDTvESMXGAwIBEW6YCev4mhV5fcr5x7okN2eHeZoF0JHurLCFXJKSk6ZAqaFPNE0k5R0zuHztJxXb/Df/LnSGOjAd78R8ohfz8b1YSuzDUqoX05E2HJneh+nJNnz9q7chKysLaz/xCRS2G+NexWpTnhxp4RKw1U0qw8fmPcME9nYOYsNODfo2SQd15YjW9FWvjIUxzSEEAB/aUIaLzqHr/PnGsoz3iEwgMg6n9XRA1Q0X9H4Rg4hoAkUHSKyqtLWQXq2kbQqFTcQMCQgsMA4ABU1p/f4SV7q5fgbkIB2BOxCFqy4Eaw3p9fJrRmGvn1Rg3+HTLScU7KzjY+z1YRQ2xFEQjMLjC8Htn1R6QNH9WSqHYPOO4ZHNW875mj28sRyS0Se6UtJThli3qUfweazvlWdUnl0rN3vkeydUiwZF7kiXOR2itnbNUd955xzc/gm4A5O4/6nf44ILV0g0LlyH+59+RTUrlbTP0VhdvccNUwTvRbb6qJoCqHDwlVMKIz9SWmJEBqCr2QwtreiDG00pgB75ylqg1gU1OaBDeQE7mxVIZABY1DqlDu5F/HsJGM6qz+mgSeuQKxjD4/mv474nqJnI7osgv3oVAK5e53etzcrKQn71CI89Mp2MOk4ingECM3KT/HHo9HTJT9J1OnY1ijMcqaZjkx164tSTFHfSnIUyYwtq+PRrCLutdUbCO2sPrapDNlNH4wrG4agdxdXXfAr3P/gTrL2GNsFX/liP4tZpNe5w+GMK7OXVirYxAguPqRVzIIYKg5GkSIcJ1VRBYakUklraMYvSznk460N44LFnsebSy+AozMfwZBN+9ON7ceUnroW1apByArkdRDbWUtYM6o5h/fnWHsoXlIaRYlWsTg61wuY06Z98EdjqJtglp80Y5ulYtDJOH411pDPY6g1lBDibhh1zLKtYnKpRZLaL8N8n4mx2MsqIOIebRehrEgOYVz2qDwLekPp7M/MCdUWYYkaMKB9bfZST9OMZ0RUqO2+FZk/GsQXGCb+obZodwDMG+DMAYLMGWqYbWL6uQ7F7MbgDcWIUAlE4A5lBxNJoIhuoQzkSCQh6DCCXGSydRJHKpWN9omFMEaex/LmihrACf5defiWah1vQPtaB6WQ3DqX7sJjch9lIJ2Yn23F0ug/HpvpRu/s1XH755bjn0d9i3dvtmeCPmb8Cia9pnaJg45px5HAbzKbdQ9iwcxAbdg5QUPjuIR0SLjcDwLMNHyYDSGDwO7947py6zu/84rkMzVfGKNhwi1vNqCe1TlDmqUO9/rGMgzGNaXXNH1XCJTgQmkCJq5F+5pT/JuAvcz11SQg5a/88gQgsVcMoaGCHOLNbtroJA+hF9TpZT5KCoqYEChu4DaNeXMQ6jUBC1mUEfPejz5/zNbvrkefVsy0HKc3qMzsv4e97VpjxuCbUWqtdyDLWLmb2vKx9BuXt0yhtS6Osg9tUWpLwBCZR0prGU/Y3cfGaWzMZwDW34GnHn9Tkg9h3dlo3kVNWxrTEAhpmN29Y7xvGlEKmBvkShl8fVmHPAgDNKZhLGnhYOqEOXYr9T6nDn65zZDaYtaECgPWv8/+TMbLoAOVum0YRg0IBle5A7Kxu8htvvwe5FatVcKvX+V0EAGtGlT5IhO0SaCujC4/RQZrRNCC3jE4C7ECURdQXU1oLSV3PLN+OKr2cja38Vs6myq8JZUR8KAbRYHfMzx3+KC+Y3CerGDxaDESM/8qf/PjWQ8/gB7/ZDHvtGByBKEqYli9oSnK2IYEIi1FpZ+ECc/l+cqp0t61mKfV4QUCt00/hsJaKIdz7k4dwyZpLcMeXvognnnwQOZbncd3nb0FWVhZu/tpdND7geBhZ+EraTTE2gTr9+Ry2ds9gS9c0m0MojNYVjMLqDcFWN65OwpL3Z1HiealnGzX0VxpY2eomuO6OtII5FSPsIgzByaL1THG1NowIyCRAaJ6+xw22cDRDC6jGzdWS5j8Ka02IwYQGgaJ/UsxgjSn215u7VF/ZVKI/B/+qkVZCC60NtyeN+9ihx+8L0wAi+W5FLZr9E/YnAwgGtSlEMYwSXxGIURxPgAwBEuwsm5GZSyZA0hnU9VIuwxRS0JxWo2qlTWKWQppHilunUMLxNR7/BP7rpi/iE5/6DL723e+jc7IT7SNBHJzbj8VUD5aS+3Ao1YdD6T4sJ3txeGoA//P0g3j42ef04cNk/roWUNo1r16nko5ZWGvHWQIwik3M/m3YOcA3MYGbdrP2r1JHuWQ0fJhmnxVa04c3lp+TzXpwfalhMhlR7ysr615VPFC9wfrxmiS6XmEKTSbILR3OzWne1DnguYkiSgpkBM96rcLmNI/w4wpA6DzBtPq68jWKm+LIrxlVhgNy2QrjNwl3IIbCxgQcdSHVgEEscoQPqpnTEKlOpIgnev0e3nTu1+zR7K1w+CLIq9VabHmd9QFuRFdycoh/XvUYrDWjcDBQdfopnLmgMYnSFpp4lHfMoKQ1jTIBgJ1z1PrRnER5J7U3lbZN46Yv30cawAvX4eI1t+BzX7pP5W8WtqRVB7ypvXXxPkVVbnTLXmBOa/K8Wm6kqzD1ZMrh1wYwNRHgA5fsiyq6hQ9T4tA1AWBBBjBMKU2oCgQ3AWOTOUo2TSQaCEpv/JPWN3DxmrNH5L/YVLYKAFev87poBOwdy8gncjVqx5ESwkrVlGkUkf93DoZwJTj8P/+z2ejSnYSdAZ/Dp4GbvW5SgT8ZOepMN80amqDP7PyUUnVb7QQc9ZpplBOxOxhDWds0P3Ak9ndwmLVY/R28Mcuimuelk2Iea0qsvIAISMlnvZEyIRiuw3wBgIEY1v85iGuvuwH3/+C7+O/bvoRLL70FF1zwCi644FJcdc31cPrH1aixpGNOC4TbDH1IK40kf/w7C439Wqk9oqA5BWcgAmcgDIc/AleA2QJu8aDQZgFZJiM3qjR62aYGS/K7xJjjDXH0j2yiVMBuqx2HpfpsBtHUa5rjOMXMyK9VafbQov58SLsSJR6magR5VSOw1Y4jr2oE1poxzTh4V8R91EofrNGbqfL3pP2DQJinMYkfPperQKC5iIuTTwHtNv0zMBfuDN3dSleweoZ0VprJoBPgi7CjMWbkjcUznyEZRTEAFFApGjVPI7FTBQxG3A1xFT9S0kZmheI2bVz5bdE7WPvp63HnAw/gD6Vb0D7ejr2RdswlO3Eg0YNDM/1YjO3FYnwv9jbtxqaNL+K/rr8Ov3L/5eyxL98/eTGfDy6zcAWpKUj6oTftFvDXjw07+7F+Z58GgrsHVSSMCuw1GBuLGMMUk0P/L3vPMGsAb2Y928247ovfwfp3+1SFpESkKMZY3ifqELlyKjGpRvWaDeJRsDCu7EotlI29SUbAUgtHTs+iZt0KIjpCYZEkBLi0haoGPY1x1fjgCtBaSfpVvusmUNAQQ3FLCnZf2GAPI8zqh1kDZ+R0GuH8trow6QGrRlgDqF+zG2+7m9ZdyTblLD1h4mnca4Tx7x5C9h4ycVkqR2D1hmCvn4S9Lgx7bQiFjTGUtU2huDlBAK8tjZLWNLZ1zaC0XSot57Glex5bumlysX3vEso7ZvC08y088KtX8SvnW+qwVdw+k6F/LjbGosSCp2BX4chclcaGkPy6CL737CZ+TSaRkdfKP2unnw6EwvCurGpTrnvO7ZTRs7uBWF53Y0prgZuMXL8VYdAiI5B14iwg2KLXHQ0IKWfyP43I7370uVUAuHqd17U2KysLttoQv1GNtoGGlBpdeLjg3G3EI6gF0ggCFc2Ezk5iYOiP4fd/DCrbvtNPgaHUMMJdjHWTauQh4n3bCsAnoE/GZHpz19oNW90EJbybeXjM4Dl8UTJFtE0rC78K/TTCRPN5nKgBp1E7JmHE1SSYls/zvZxFVSd/jnPHfBHkVQ7hms/cgGde/g2Kttuw5lI5zbUiK+tTuOjiz+Mp+x9R2jmn8gEJeIgLj8Eea0M2/LWNxoeBKAG/YJQ3BHrt8mu4UF4Yt6pR5GRUuWnwpzt+hzOy2HRcQoiZzTEUNsQYeFE+Yr5X5wTmVukOU4nzyWBuBBDW6EL7DABoAkGJb6gc4c1mmIHfKPIqhwnUqkJ5ifgIa/ZDnJ5yMDA384DWd3kaEnj1f5shPbxqrNsyxezftFaokgsAACAASURBVGJgaUPSwdB60RdzhXGAUqyfgMCzAaB8rhopONLF0xjnjuKoek9LILEeRyVUpZ0rQO7fgkZtOjBZP9OtSpl1aTzw1Mu468En8be+EdSNDqI91IyheAcWEl04kOjBwVQvDqZ7sevtbbj00kvxnR/+DN/71ToUtkz9RwC46R8dyvzhCsaQ551Q7ysBgOsZ/K1/j25hBDft5p7vCt1Fbcb/bN49iIfWl+Lbv3gOD60vxcZdg8ipHMXGXUPq1x9cX4IN7/VhIxtMdAtN5uEkn7tg80XTq7JNaR2SOzNfUb/uHgaAOgw4pUKe5fWVVhBzRO+Rm13ZVAXI05IG44AdjOPVtxopI1BiTmqJiZfgYhtXqZH+mk14GWNsncVqq6OaOXutjt96JHsb7n70d3gkeyuFULN2T6QUFgZ/KsJp95D+GancTh79esfJ0OSPwhOIoLgpQf3ZjTEUiVmmbQol7dMo7ZhBWSdpALf1LKK8i/vNeb0r7SBzHEle5lDYkkJJ+yx27FvCG/sPYNveRZR2zGW68Jv1Gm73R2GrN0BgXQQvvhFQGaGZxp+wciiXtFKrR3nnnBrxuhqSSvOp8j0bU3A36mimjINjk2m2Olt7u/LPyO/LGB0bxjIZGRe1pPGU7dwM4COby1cB4Op1XtfarKwsOOrG1anK1DOZTkL1ucqz0uJ5kzZXm53cstnxpuYKSH8w55X5wooZtNaE8Gj2Vtz7yxfwWO52Dg81DSdxOAOJs8fPhitMFkmHT0rWRRs4yflWcWzpXsAW1sxt7V5AcdsMPA0xrhSi0YmDN2Zz5CtB13kccGrG1ogpxcljZmcgjsLGBDz+MJwVFPB8/S234rYv34YLL/wpsrI2ISvrMmRleXHBhevwwK/Waa2fagnJNB4UtU7x6TGlwK+5+FuZ9ZOA6jwDUFmqRpVmRwKac1Ttmt5wc5j9y2dHs3I2s3M4v3aSw2u5XaHOCIhWmYgTakPJzHrUTAy9doaOz2AJc6vGYKsdR/aeQeRVjSK/ahi26hFY+PuS0bpiSOojsNZnssIO6f8N6JGr1KMRI6dP+x4DyBUp8DdjuPamkeHSMwCg7gTWkSvq6/MhSLqJ5XAk7nr1bMivGUYrJwM7j2Ik4qq/1nQrehpTaszoMcZUzmCcImcaEiREZ2DyWM5WXHzJGmz/eylqhjowkurDWKIPyWQXFlN7cSDZgwPJvTg03Y/v3f9t3PfIr1Hckak7XRllVNo5r+MsWmkNsbNbdNPuQWzYNcjAb78Cf+vfNUEgGUMIBA4pRjCncgybdw/huhVu3+u++G1s3DmgxssSKyOO4k0M/rTJSCJOxo0RoEQGSV8wsWl2fu+Ijm1lK0hBowYGRS0pMns1J1Q7iEgEitumKTpEAIMKEE4oFlHHbp3jkBKIqwOClV280l5hr5tEUUsapR0zKG2bVvV6udUhXr/G4agnlt7qpVBnc3pir48gu4LimPK94xztMqESDdSBrmoUuWbn+h7qc8+RWK7qMXgakihqSsHtn8Szzj/hu4+/gCdyt8IdCKusu5L2GWzvnkNRcwJbuuaoLaUpQTVv7dMoaCbjkByASzvnlbGmvIMAoHytso4ZbOmkNdHdmIKzIQFHUFe4ieTDbAQyJ0WUJUjPVXnHLLZ2zeONvoPYsW+Jx9NzKk2hrH1Gx2s16W7uzGlBWgHAjMgaQxJyrtvdmDKyXDVzSPmfxtdqokDsz91xb4YG8LN33Atr9cgqAFy9zusiAFg/zuGlsrHpN7i8YYtaprVTVtVdMQBsOBcYNJoJ1Gg4yiOMiA6q9VMQqq12HDfedtdZdV3W2vBZYE/nNK0Q7zckiRmrp9OdYhc54NnGkSwlHbMo4waOLXsXsaV7ESVt1NiRz3V2KnJBNCQcEC3mD1s9sQd5Xh0D42RAIhljtrowipsSKOuYwfq3m/Bs0bu4+St3IuuCNcjKegJZWYta8Ox8S5k6yroXUd6ziPLuBWzpmkd55zy29Sxg295FbO1eQHnXPFzBuAJ+lhqjQN4Ub4tzsmqMmT99ss+tCnEH66gB0EIqY8zKGhoCgIZLW0bgbHhRJh8j/NvsRpZaN1XLxc0pZkxPZryLhNuOwcLjXhs7jzPG68aY17xNJ+fZ75l4xkHFGdTibp31pjfw4tZpjnWY1jldrUa6f1Na6YQE+DmD2myysonDwWHQzmCc45MiarOy1hk6WQ7WPSubk+UKdh9/HXZamgHRHs6cc/opYkRYJ9q4kvjy/Q/iwosuwhVrP4GOkT6kZpMIJfZhJrkPi8k+HJkZwsFUPw5N92H3O9tx9dVX4ev3fg9P5Zah+Bxhz9RsM2fEEU0TQxmIIb8ujM17hpn908zfOe+d/YZGUJjBYTy0vvScujVhAjfsHMAGQ1+4cdegUROZWTeZtwIASji0wy+ZgGJmixuVWxqwe1bowEQCIEyr7gNOobSdwFlZxyxK26ZQ3JxUQF0ih/T7Jp5xUNCxMcTwyTRDIkvsviiKmlMEhroXVGSNrZZqG2WUba2bgL2Oxro6gotYwg3/6MWmXQNssNK92hkZg9Uj7OanoPac3QNszgopmY47EEVBMEI5fubaffvdcPrCKGhM4PWuNJy+CXoOfGE4OcuPUiYi9P7k50lLW6aMCB3JuYyjqCUJpz8MT0NM7QPC/tnZ0Wuti6h0CtH/2v0EECl3U2roFvF67xK2SWpC1wK29lBCw+u9S9jaPY/t3XMoZWCvs/mSiu0VkJaRCMC/T3rF3Y1JuI0pgbtRNLzm5CCptMNK28sH0pK2aRQ0JfCU/Y+4/+lX8JT9TdKq14VWAeDqdV6XAoCFTQnlXJJkcjMPrbB5ZeitbHyGeaRBNxrIIiYP8fNbq+Dk3CsxlEjMgdMf/Y91Xb+07MjYtEWf4c74e1MZImsZn2m9lR7reBriKGmjUURZ14Iasxa3Ua6eKxinUS9vEKIfMWNn8rwTFETsHVd5VzZvCI76sGI23cEEcitHUNZGp7odexdR1jWPgtYZfO5L954leC5qm1bgr0ycv12aDSzvXsCWnkWUdi7glT/64QpEYKkeXSHaNqv0xpBdwZ2hRjyLtKhIdIvEqGTE2Kh/56TOYzMjY5QLe1LFUljrwoo9UCHY9WEVBivjKTLo8AYjgNBrtHkY9Xp51aOwVI1wGPek0lXqU31U3cL+ifxAnJTuoHFIaUjC1cDvF451eGFHDb/HDQF2i8S/zKC0Yw4FTUk8ZX8T9z/1Cp60vqEK4UUv625MZbBEwoCbQeoysnNw5p/dp53mYm4iMM0bvS/MBxre3JjxpRozXU+lDlpBg1kU40KD1q85+Xve+JYfay67HD/5zSsIDO9Ff6QL47FeTMe6cWh6CIfSQ1ieHsGB9H4cnu7HcFc9HvrxA7jp1i+iQFo/VjCAJR1zeOWPfsVaF7fPoKg5BVt9BHnVIWbnBrBhZx82vNeHP7zbiz+8ux9/eLcX6981WMH39iuWcMN7fdi4awDfevg353Sufuvh3yoAKHmCm9lZLEHi1CQ0qpk/E/zVhzMYIhUPY+hGnf6YAmgywqMctoQCcdJSIUHHZR2z2NK1gC09C7SutBoO0OY0B7QnldnDbHKRsOHnt1arA7UjECfwUm+Mpv0yqo7AFYyioJFdw7UhpbE26zzzasbVGqgML94J7uedUIBPpBX2+kndKyytPJXDyK0YooMYs/euAI19H91Ues6g9V/mbleHFlP36lpxGBNjlAorl+xKfq7k90q8khi2hHBQAJCzaSWn1lYfxW/LK+AMxKldp5l0eEWt0yjvnMOO3gNczzmvD998l3QazHYrdTiXtU8x65vSvd0rx7YGc3d2KkDSmD5kgsGM+CjOkFSB9MZ6pCNipuCoXY2BWb3O72IN4JjRTUphl8XNAv54xCUi6OY061l0rpibFy6VnN+YVKdSAWYPrStQ6feSgO8ORNXDf98vXzh3XdcTL6mvob++vmVDNxlLqVhyZTiUZTOmBobi1im4m/hU3pRS4uSCppRyBCqWSUaszAwSeKHgaXsdOe0cPMouaKBgV2vdJHIrBlHaSun+VAM3p9Lin3b8CQ/8ah2ecf0ZpR0z2NKzRKwf3woEds6r8VsJg8FHNhbDWqvNDxbp7j3L4KFz9tQ4rGIsgxm0MJAVvV4eZ39JyHOOcjlnZh1K4n6+6I3EdMKxFfmsuRQRu62OoioIyInBJ3O0LjEUkl8md171KL/2K/4+Yf44e9Lhz2T6JNfP3WCctBuJxfawgeLn64tQ1CzVTRoESmBrUUsKn73jXly85lY9frn9HjrMGJtQZoA6f5SSeSNo1i5NORJea7Cr8m8XU4LUxomWVI+4o+pwI2ygXbptM8bdcQVWnME4yjtm8KX7foyHf/M8dg32YXgmhuRiAhPJXizNDuDA9AAOz43g8MwADqYGkB7rxpO/fBiXrFmDJ3K3npP9kxHwI5vK6L3avYBte5ewfd8Bqvpqm4Y7mEB+zTiy9wwTwHt3Pzaoj33q3sgmkY076fNNuwfw03WF53b7/qGEO6aHkWM0y1iqxrghg0CKrXYcttpxCimuCdGzyiHt+V5tMjN1xXafZo9dEuhtjoWNdU0ZApj5EalAaccsgQVDf0mH5wTcQVojPPI+ZOZPDs4PvVrAwEf30ooOsZB7hQsa4yhqSqC0NQ1H/QQ8Qb3GCah0cOWdPJcSRm6tC8NSPQZb7aSS34ihxNTUmlmgudzERKHsY3D4qM3G7h3F/Y+fe+2+95cvqK8t34uY7gTMSjKD0x9RAFGij+yKhY2p50rdwXiG9s/Oz5j8/OQ5+dkrHjgDMZR30Ii+hCOdTBlQaScdsO21Y/hd+XsoaU2htGuBMvlaTRnIFEe06Pw/5fhtSmfsS0KOmDmeZqSTaIT1x2TGREKihcy7qEW/vwpb0nDUj68CwNXrvC4CgN4xOhm16DGGpylJrGBzGkXNadWQoHUP2v0oHYdSZeXyh/k0m1SMoTMQh4djD4papmjxC0ZYAxjGL3O3nTMN/vG81zPyzzLs9mpsMKVOSkUt04r9KBARNj+QoqsiI0BMuYddzI6UcoyKuyGuNWT+qAKDOvx5giIQ2GEn2hJKvY/BUR+GtWYMb3TPwBWMYVs3aQ1Vlh9vlOXGqLe0a/5sBrCDWMotPYs0Fu5eoJ9NQwy2Ogq99gTjKGlJw1o7Qb3JhsEju3JU1WaJvi+3ckyF7eZUj2X8f9EDWlVLiJg8xtWIWHIP1bhYxrIyDvaZTIM2Y4jL284gUGrapDLQyoyhvT6i3MsOX0T1iYqec6XJQ078JjusdFU85pUTt9bZTGecrEvaZ1HKbE0xi/kleuIZ51tnZ5Rdcgsez38j44QvbF2G/tXM0VSB6RqorYyryJAZcN6bzZAV2A2mij6P8WsdywC/MhrT+XMEdrd1z+HaG29G/p/+htGpfoQX5pCankIquR+ziR4spoZwaGYUy/N0r3vpGXz+y3di8869/w/9n5FJ2b2o3r/yHtbs9RLKWNvl9EeRVzMGa80YciqGYakcRs6eIWzePYDciiFs3jWAzbsGkFMxhOxd/edwrt4FW80Y8qtHkVc1grzKIeRWDqkGodzKEa4lGybNWgXnSrKG1OWPoKiJNJhi1jJZZTMWRIUNGyBEwIYaDUtGnNqsiQ10N8RQ2JRESWsaBU1Jo/t1ihkuGtdLDZxMWVwKICS4npDYJKpUI/NISes0Xt+3iMJGYQIpqkYOANQyE1ERQ3auIXRxE5DTJ2tWDJmGOo4i8kVgqQ7pA11NiJ7f2nG4/RRebfOOoSBw7rX7ImYAberZFl1uVIFrYcQV8+0L6+fEkHCsBHpq3OvXo12Hn5lSXxR21kVLdJjTH0NpG7VxFLakUN5No97y7kWUdM5jS9c8SjtnM6o4n9uYh61ds2xSmVWRXMXtszqjz4h2cTWm4GzQLJ4GflpGoAPeORvUkDLp0PFURiB9hmFE4mT4vWLzro6AV6/zuxgAhhQAVKGmDNQk3kBHXqSo4Fx0RY0JFDYnUdBIoEt6Ss3fU9CUpP9uiKOQi9HdwSj3odKmafOGcONtd2cKXW+/B65gzMhmY6amxbhbpzmZf1qNcsVRJ2MVxUbyyZjs/1E1TnP4+PvgTDYFZFSu4ITaKJTWTDE0YcUOOHmBK25OYkf3LFyBMAqaUtjWPU/hzt2LGYHOcpd0zJMDTswfRr0bBRBPw90QgysQ4VaAcR4vUjREedc8CptTFKhsap9YD2jhOi2p2ZJGBbMAPrdaa/KsrIPUocvaxKFHwQIAx2Gr1REL0n8srRdy2rexy9vlp41HeqTFMW1nRziNt8LKASktGjYeMZu5kAKMzNYMYU1coqVqkP5MYQCTHOFhVDmZGYtturqpsCmJ+598+ZwRDN994mUd97Ii+Nk0dmSGpsc0WAvENAvIWlKLd5xiT7wTDAQ1M5rJBkZVL7XD0K05zY0mGCfw2yBRNCR6v+mOO/FS4Q5UDo5jcnYK4cQkFudGcWhhAkcPTODogRDSkR6s+8NLWHPpGu6d/k/M39nGEJ0TOI+zTU1zHFZuhJZzQ0QxP8fFHHVU0kIGgC2dNHr7testfP+ZV/GM8y0UNdPP0VoXzmiosHrJ/CANMabWVDfGSJsNsfZOfubpvTipXmfFBvr1eNFmACVTl+xpSCrAKCxdAbM4sjbRwZocwyWGZpD+v25+KOKMOa031KCyuDmFomY6aBQ1J2Cvp8gseVZcwTgKlbaO5QbSNBKg7EAXu80ldzJjpCzvT177hEl11E/A5SdTncsXRkFDHPlVQ3DVU6XhuUD6Df99Fx+Sx/nZjmaw5MogpYCg/j6cCoSarKz+GUiEj+TI6j8fV+uzHKac/iiDXj5g+iIo65hBeafkq4rbfyYDAD5f/FcUt8+q97RIcGRNLpJMTRX9pGvaMsHff7olMUOMacLWJ7Vu0NAOuhq19Eo+WmvGVgHg6nVel8oB1KdXrVkxM84o9FZT3bLgEcPGYLAhrscZTSm4Dedwwf9l7zuj66qurS8Y0/JwXvJCXhotEFJIIQFMS6ihuoAxHQzGNBd13Srpqss2NRBKAFu9XHXpqtpUq/debQdC3ktCdZesaub3Y6219z5XIu8bw391xzhDvd5zzp57rlmKu7X2I58pf745SQuCI7cVd4RvwZWrHsOdEc/Ald9uUOw8kg4o4iampp8jBwYoi6uIds9RFj1iJ5y+VtwRtgWXLV+DFcHJpNnjG0dUcTe8pb2GXqVTvSRNiRTH69R4M9bG6WtHTEk34su6EVfez8aabsSU9uCl93cjqYoDnCuG5l04zUUzlpk/b1kfj3objAwrcTRzjVFuK8IyeUzDBg+T8TAjWUw9n3SshhigTrRSEiRLI9oWHv3UW1zVjlwNzvTIUsaXDAJzCaCIIYOyBFsszKu7oEMBbQf/TVqUr7VOAn4ic4Q9bFVxGMoowXohupnqAFczosUjwK+U8sREWxNTbtYv6fHL14Ww3hG2ZU6TiNRoqRrFfM36CbMhrnb5v0lEUZiAvwAdpg4nZkbDiC1ROsi8djjyu9SoWWtvNUPpzO+Ct6wfv75uOX5740qUt9fjg952dPV2YnjoA+wd+QB/21uPjz9sxB+uvgTnXvQ7rEnJmKfxYy4LGF89Cm/lKKLLB+Es6kF0BTGBsVzhpaKNKoYtLLhscqQCK7ZiiMdzVBUW6x8gJ7z0C/N17iogd6zd16H7xDOIrbbnNCE0fRc8vLHU0g/ujBWTV2Yj3PltiCkhiYa7sItjqeici8hhZ7mhNSX3tdHukt+pIkIkgieaN7wCAKNZyE8duEYXONc0xnGwu5eBYJyfmjIormoAsWX9FDVTSgY3T6FIZzpoklLUrUbTaqTMQGJLzQhieKLhkWzIoi7qmxawyPcxGUW72GxEjSitcOe3sVO4BeFZjYgp6YU9qwGOrDpEZlFOZ2RWEza99S5u35iAS25/CLdvTKD358h9Skw03bo+rZQ6bk3tuFw3St7g61BmNF0AQJsl2QC6C7vgyKPf3+XT96ywrBaEZTcjMpsq6sI40J+62Qlseoo6EV3ai4SqYcRVjsCe04Tw9Pfh8rXweFhvaOKrRjUYrGKXcmkfA/Veo6NXJwCYzJ8a/xqMpsOIGdJRagZhwTIV0UW6Au5n4QsAcOFxnA8GgPVK3ydMn9rR8ghXU9E9aqci7iVxGEYVdioNoNYHEvC78s61aqxqMj2kVaFRhcc46a0RNHN7V03hrbfM0G2V9vIIm5lHBqNOX5uu0zGcag5ZtAu7lHmEmJtOFSugF/FOBh161+rKpxGOp7ALCeU9cOQ0UiZfXhuiC6muKbmG9CSmpo9uLKPzgkAanw3BnkuVTGEyhs1uRmhmEy5dvoaaSTKbuKuTNDqiiZJRsApV5oYOBQANNlBl/YkZhM0ZkQw6dHg36YYiOOrGzWMmic4wQQppMDsM3U+H0l6KOSMip1UFcEcV9yowS6PfDrUQmZo5YRbtaixKz4WZP6nHKdpRZ8YxiHxAsv6uuutx3cEpLt9S7YZ3+trnOW+Wwu5rUzofl4p46VAMh4BSpfszat4iA0KIdQMDPafWqBLJOGw1NgDauKB0UwIwFSA2WkMKxLXfjbXJ27HopMX4yzstqO1qQXN3HQZG3sXfP27G3z9qxEcfNuAXP78A9zu2ICFw1DvPseFVP3521c049fT/wHfPPh82mw33hETDWzGsQ80DAWDlMGL9Q5jDEqrgc8OMU95Pn1sxBG85JREQw0nXNv3dOt8tLJPC2hP8FBniLe2jDWpRDyRomzYwrfAUdsJbKpuBblXdaGbFicZUYqjs5pjUR2xaVJHebJrRL9IWE19JwDa+coilH3R9x7Mm2AKC/YP4/d1PIKGin4AkG028ZSTDEe2pWwBTvga5Hh4ZR5f0IMHfj+jiTkSXdMNT0I7Y0l54Cju1lKe4B96yfjK4FHapYGV3YTexhfmd8BR0IDK7CdFFlK8aw5v80G1vw5FNAdDhGZw5mk6h0KQhbtQbl5xmi8xGma1KGfAy0PUqRo1zLgvofy35r+HGcxLBmyenrx1RRV1w57dSmoSvnTvbKaUgIrsFS5ev4TgvAoCOvDbFkEex4z9BDIEcuRVXMYSkmlF4/YP83A2p+3Ks3MMrBwnkc5SLkoIU6QB4Bf4CGGMJrVbPXYF+212gjV0CBJV5rajbwjSGZS6YQBYex/egKrisBiOskhoFlJu3SKeiK3eWAc6EpnYZuxUtPO/iHLZ2bPhLjeo9FaesgAs6Wi2ZW2o3perpTDCqbfLEVvYilmuoqPKqVzmaBYzeEb5lHibnHKwM3aKYGkdeO0dYyEWqR5VSGSQCcRkdO/PbEV3cg9jSXjhyGhFd1M0sFoGeuPI+fRNh9i+hZhTx1caYrGpYuSljK4bgLuxAeJaOdVFNCPz2Ey/5VbBzEGv+KDttl0rt37h9F72dqsGgmbMXooAhGUkixNSR1aRqs+y5uh1BGFvJDIvk+AzVa8pjXDEsaF0Rg0BlxtHuXNkIeArFsaorB/UYWf8vzXGPfG/1OxigS21QzNEcs38i1Jecv8jMXYjlG72OeZFUf9pIOPPbcUcYFbGvDNvC47YuY+Rr3cXLjd06vtIOTFOfZDrLpe86TPpYsziYVyI6sqwssISVC5to6eQ2NEa0INHGZm1KKs749pmIfiMbqXUdeKfjA1S1v4324R34+G8NyM5+CaeffipsNhs2vF49d9xrAYDDOO2M/7SMzmw2G2665yF4/UNUa+jXLKCXNVRSeB/j585Tfhldaval9im2KIqNaO6iLjgLOtSCKI0KMaW9iC7uVWyWK78D0SU9iK8cRqx/QAGeWI72cRV0KDba6Wun8Or8DtKg5bWxJEGepxY1ftT/WzPwvlMbQ4yNrwAs+h/IuHFAg2EeP4r0II5BYmz5AMLS3iOnaVkvgyPqHXbmt1nSFSScX/UUl1LrRmxxJ7zFnYgt7UFsCQUzewpasSpiK65a9RjuingGUYUdasLj5Xull6c8Yvxz5bcjuqgLLl8bZZoWdSFk2ztU/8YjYYqV2sX93RwGbyQB0L2eI26KKDIn3k+hy3Gcc6rC1sv71eZewL1pwrPntqp2JWHyPIVd+t6U125lzrObseG1GpapEDtM9zUzMqqdpEuFnXAWUGe7l1nmuPI+JChtNrGASuJQMaSIB6mGM8OgSXOudYmOvHaLJETOJ71pNLSblmxb6yZOzCKugm6ELwDAhcdxPpbYbDaEZdYr3VxUsWZOlNutSMddqO5UC/uhQZvF6aTYPn1Tdfg6eLwgzI4WuuvcNllcuy07K9212m0BgfroVjf66JIeYhFKqKrnqlXz1OmcQFouWcTdhZ2Qqh8JFjXdgM58YgDd+Zp1cfo6kFDWg/DMekTx7tVh9IgmV/PYxxTKG6PeOLm5sCMt1j+gcgjDRbOU3cpNGzqORRy9m7hua1MqF7Sn6dL2jUaCv45/0YDSko2WpcvcNSgnDZE9R+v5nL42ddNSur58As4a3EvmI49ofXpXK5sK6USV+jL1HBZ1wVPQzhFBmh2mc0YLyCXMVTl6jbgO6e90FXZZmD9l/GC2hQBHL4/fdNizsMsCAAMDXM34I5V/yTdvh/G3Kpcus8gOdieqkXmuBtQqdojZXhkHh3LVYJgBAkONj4cbY3kxiFCOXacCgQJYXPmkwb35MQdsNht+eP6FSC0uQEFjPRIzc/Df3/8eTjjhBJxwwgk4cdEiPBj90jwA0GTtRvDki4W4/pFIPBC/HcuCN2P9qxUqEFqaVLzl5Fr0qNForw4y9g+o5yW2rB+JlUPEyFTIQTpAao4YtozcZQpBeq5+y5Qi1qKflY0kSVEcvjZLS1BkTjPs2U1I8PfBkdsMd2GXYp1Fj2YaeeYz26j7ZCFtiLWmWjs3CRD2KeCTvGMPEnikmFg1hHgTEFaQMYlyKGmzEsv3s9jSXqUdjPPT4wz5wAAAIABJREFUEVvWS0xfCV8/+a3wlnYjurgTMUUdmsVW+urL4PK1sl6bRsuewg64CztYA9uDuPI+uPJa4MptQmROE8Iz6uD2tSm5ycZtlLlI9ZIkOwlJqyf3vrExkdgiMtoNIr5ymPL+SglgpdQMY3PtKIPBAWZ6e5FcNQRXPl0j9pwWePJbEZZRp641l68VjtxmShmQ3uRcYgiDUutoY5vZqExkKvcxrw0elhGEZ7eyttPKnstGMbFqmJpLakeRXDsKs6ZTgvrjK+h3jmcjWVQRrSMyXbJLFmGu3gxKPqHogVVIvQUA6jYhp4yV+XAWdCMsYwEALjyO70EAML1O3cicgcHOFgDWbYy9ZDeiWZD5w2/5JevFnL52BfiojN1odMhuxYqQFFy+8lGsCEkhmt9wSQkzp+jxIs06qrFzYZe6gcnO2VvW/zV1OudgRUiKJWvKzGpTNH2BjIUJ2Al4deV3Iqaok0TSLK5WJpLcVnjYASwA0BTIq5tIpbAjA1QDlt9GSfxcWyWaMLOcXWJeNqXWWwGgFLenNShmMChN6rV09IsF/GVql294lg6RtfQ25wbq+YxMO8m145EsASB9I5Oxr9K0MGiS58sM1Y0plcVxCDGl1KcaI+ajom4FCKWlxQwZ19l3urfVHfAzTACotKRlfYqlkeJ5s9dTgz6D8VZ6pS597uTrQ49l2zUIzDNv/m1KwB6R28aVgo0ITd+FsPRdCgQK8xHOPa2hltBsw4mdbUTHiGM4T197KkeNz+WnXy62MHbn/eQnOO/CC7H45FNw57oNKG2pw7IVN+Kyq69EdEkXYitHKRfNcPbGVgyr1ob4ymHEVwwhzt+vuodjRJvLIDuhcghJNSPYunM3tuzcjeSaESRUDiCuvBeOvBY4fW2IKu6i57yUWKjY8j6O7xhEYtUQEqto9Camijj/AJKqhpBSPYxkjpzR4cz9iPMPKret6K+U69zMvsxsVNesdUJBz5dTRYx0qBGwqemic89qCApsilCGIza9aOaLNH80wWCWkJsoRNvsLe0lcFhOf3NC1TBiy4i5i/MPIKqgHR5fMzwFbTTyZbOdi80f1P4yn451MwUsq35jipfxFHbAXdAOR24zIjJ2wZXXQnmcGXUIz2yAI7dVaY2lFUQC5oNS6xDBjJuMbV35tFmL415fVefGoc8e3gRSJ3Ib3EVdcOW3IalyAO6CVkRkNSjTW4xElBV36dF/dotiCCM591AMLHYjXspMGyBzX5MlB1K03RRU3Ys4P8XFxFUMIra0C/fYn8FVqx/HasfzNHXi+zbdu7sUCUGaS5lacF8zr3fm5k+ZWPL0vcKRb04ROi2sn2XjU9iN8AUAuPA4zscSm82G0PQ6Y6Rl9P2ylkrYFBN0ma0HWuvQroFSgbkQduBu958JwDEL4sgjbZQsXqEZhgvYbALJabFoKHT7iJHTpcYvdPMVcTR1uNLNNbqke26dzoVLEZnTaqTvcxK/Gp11zdFvyPjSmU+l5+EZH2jNXE6zijxx5LZiM7t/k2t3I65ymDVVzP6xGN5b1ofI7EaEpFPVklnPJuxcqPT5MsBbGf68BeAFpzXQDTmtgXpUMxoQwiNjAXsC9MIN8GcugKEZDUYen+SD6Y5UPebU/ycaO+lOXA/rrCgI2xQ4aybOLSMylaqvmT9i3roIBHJnqITpxvn7VS0ejaY6latctJ4CNqOKu5VRKLasH2ZTgzAnsZzz92DcGwqMR4tA3dCbuudh/sz8LjlvFCshQnoWmttz9W5fAquljstkocJT30NERh1C0nYpDSCZEVqVKD+cgWKYBbQ3qxGy6oJV8Tgd+pop6KJQ7AIak0Zm7sK5v1pqAYI/+cVFyK7vRElrM0p2FeFnF12IM765BDfd/zDudv0J9pxG0q1K1Ev1KBJqdiO+ejceTtyuXk+o2Y3Emt1I2bkXSbW7kVQ9Ak8hMTbOvBbYsxsQkVnH7lKKAHHndygNmoj6icntgJsTCdyFHfAUdRIrVtaLhKohxFWN4pkdoyhs+StS6/cS+8iVa2FZdF0Fp+lYpJDMRmziarPg9HpunWlSY0FyBrdT9mQ2xaa4fG0WsCCsjSXQuLDbcg+VzYdsOqI5fsjLI10Cfsze+fsRV9ZD3biVQ1iTuA3x/n5EF7bBW9iGrTtGkVA5iJiSHkQVdSCqoJX6dku64chpgreUzCZuCcGX9AI2Yy1dMX9O35V3PmbRa3uKSAcdXULsldvXqmKzIrMaEZmls0E3bvuA+8PrdG8wb0KVC5vPbQ/fkyVrM5o12iovT8bp8j9k8B2e1YiQ9Hq4fO1w+lqxpXYEm3fugbuwA848HUQvmaSRuS0ISasnOUp2E5y+Dqyy/4k/z9hMWUxUbVYAyORCVDGNxOPL++AuaMNZP7tCZ4GefB5+9LMrqOO4guQMEuIt/0tHfgeiirsQmlHPcUNtBuuvwZ9sCO0cYm9OlnRtpI6x0qaZLkRmLQDAhcfxPZgB3EWaLGHtlBtJV7pZKookZiCfoxHM2ISATClhQJYHp1guAnF0iaB9WdDcXeoiZugUk2GwSmacgCpSNxP6jZuvHu11Y7VD1+lEF2tmxKzgoUw3a1q9w2CZPNysEFvShZDt7/OF3KaYTHtui6ouiiruRjwXjYvOxVPcDaevDRHZjVSzxDqaoNQ6hHBjh+TuhRgZfRLmfMv6eKPntJHCnVN1np/JDglDJCySsIqS6B+eqTV/NAYWBy8XzjMDoququhSw08ahPsXieYp64LG0xui4INEqkTHHqpkRhk4CmOMqBriCScZnfVZWhQNxXfntcOS1qGBayWBzFbTDmdfCcURdiC7uVqyUjNhiyvuxKvJZI9G/h12cgXVNEimjxzFOy4bJaJ3J0/pEGSNGGiyDu4DqsMwWiuC0OoRn1GPjtvcRnFpHYbvp9YjIolFamDSlKFd3I7U4SFsDM4PiJhbgaF6Hlko8xW634173S/jBBb/EKad9A5ffcCvuejwYbxbXoryxEQlvbLMAxF/+/ibElXUguqQTroJ2xdw589twR2gKnPnUTOFms4GLmSRXfhuiCjvh8un7i7C4Apo97FR15lE2qDOvlTYRrLGNKdHVWOq8KeriIHfKd3v1nSHucx1EdEk3InNbEJlFVYLBabuw6c138PTrtdjwWg3Wv74Da18ow2MvluOp12oRntXMIcosJ2BWmzIptTHKEvskOsv8TszpcWbQqjRhrGWOknB9NqtJ8Hicvw/eUtKf3RX5DP3PijoQXdLNmrxuHi92Ib6cImWiiihxQEL1HdyDHpGlo5zCMptw24aEecO0lwUlE6vO7JO7sAve0l64CjphZ41vOOv8wrOaEJS6i8a+2z5QWmNh/Tax/CSEe5dFrxyZ06LMKWKUcaqf2a7Cm6W/21wjwrO5iSitHsGpuxCe2aCMJuHZzVxxWU/sX14bIrMpG1X0y+HZLbh9U6JudGIDlWgHJU7IxbFUjjw9inUXdqq1hJIAArJATz4Xq+3Psa6b9N0e1sJrmQfdc+nv0W1FkWq97FBrJ8XkWGNxlPnQkEUpjX1+G8IWuoAXHsf5IBNIRp2xUHRYFzTuLhWNFQlbKYRTh4a2W05qySaTfDK909JORurX1Rb/y5avmXeXunTlo5ZxlttYxMzOTNWLyjdazUzpGiazTofCfweZWehS2UrR6m1aCIT9ISalm112vXAVdMDta1GmBU31U2yCjEViirvg8LXCntdCY5GsBoRn1nMfL93YglJ1d2kQa/WEldPxLdq1G8Lvl7dDM/Wh2jwsWX1aJyZdoNLWESFMEy8YyhVtutZ8HQbok7R7gzkw+i81IOxTvboydpWxV3RxN1y+VrgLOy2Mm8RlCECTUa0a7Rml6yZ4lJ2xbFiIuWyhpoLcZkRkNSIyt1llVsaW91GkCGvTJFA8ygB/4uxzBoz2RBZhhjyrkY3P2NHnif5PdvzMOmQ3G7EWrXDmtSEsvR5B2z9ACDNSIdzeEpJWh03b3qOPpdWrbMawjHrqSmZjj9IN8nNO7Q9N/PNbLK5V02Goo4xa8EhKOq57KBg/veJGnHDiIiw6aTEWn3Iqzvv15fjlNbfj4ptWY9ObtSp8V5zzOnexy7hnEDCy86ImlWlqcZVFrID0myp1gMG67sTtViYIMabJJkGdS7zBoxFiF5z5bXDktcBT1ElGCqU9pHy+2NJuJFYNIYmP0PQ6PPZiOda9VIGgtDqlQRYDQkhGA5x5uo1FaiKlLk2yQO0il2D2XzakDmbDZSMUzXFT3pJueIs74Slog9vXgqjCdnh4HOsu0A1J7sIuPdbmXFZPQTsishsRntWI0Iw66v/NauB2kybu0W5gU0YjgrZ/gO+df8mcnL7wTLrexRgRXdILaowZhLuIgEzQ9g8Qmd2MoO276P7EoE80xxb9caqeXkgHM4HAVgPgtaosz3AezeqIKzFDcSZmRqNxTehrQ6YdwVzz584XlrZZbValsUk6kMOzKUJJQug9RTSSlhG0sPgWiQs7q69e/fi8WaBXrX4cXu4Kjyrp4TWSAaBKVGhS8VlmlJisjXZJPDDYZDV9yjfuMbzmedS104GQ1A8WAODC47geS2w2G0JSPzDclloDJ2MsXWRPoaiRrF2S7kxztDVfR6sAvbAszmdSgvYWddy2MXHeXertm5IQkW2k9Bs6NG2hbzeMBrTQKDav2ExR79PVTKV9ageuGkaKjRgcSxSN1Q0dmlEPe3YjvCXd7CLrgvSuRohei80WEVmBLF6dujkGp9XzzbPOckhvrzCBqoVDVTM1qvcrhzBHwcjHVJ8umwhoJ9rCocxGowmDxEgeNTp97Zb/r3moka5yfPcYgKzXwtCZ/ZXkeNTuTxJ5d3MloISGcxYjh7JKFIhkPUaV9CKqyKhcshgztC5Pa/L4d+YF2Olrp4aWbNEGNSuNlJO1nQJopIVBxryOAlPm0KEMQmbFWyTrJPXRpjYuKtSWGwosES/cshCsxmn1Knw7mOvNZPRP7KD1ZYQ0trDuiZ7LFovmSa5LrUlkjSdf22Tg6VRuyMisBjz9ih9OXxtiyvvgzG9VQcNyP5CeWopC6VJNOBQyTKNaV34HRwm1wp7TDHtOExw+kRYQSKdGig44edyo9JQFnZbRJIXGd6roDdIX9ql8UuoA72CQ38PXtWaZTYY3uqSXzBjlvfCW9SIytxnrX9+JR54rpnOD8wBFKyZSDLpemlQskjYotSiAKHmmKpalgNi5qKIueEt6yJlb1ouYYkNzVyTMeKdiPc2UBGEc7bktsOewo5XHnDKWDeXKxuBUqsYjOUk9glLJpLFp2/u4bX08Ll32EG5dH4fwjHrlenayKSYiuwVR3IJk542JGDuC0+oQZI56De2xHKJPDuGXdB+rR1gmbWgVmEtvoM0q94CLqYkirRqNe6Vonc37o74e5L4Ywcx3ODN/KlPT4pzXa4+YEaNLKD0iqWYEKbW7kVRNho84P+lOk6qGsHnH7vnbgE4+F6udzyOOm26kMSSmrE9tBiSrVDv/W+bkS4pBy8wIVEHywgQa5IzTR+eHw9eO8PRdCwBw4XFcDwUAFWNhZosZ7jeTlo40hOzSZhCR04aI7Fb1eriAOwZ+wal1CM9qtbgZw7KaKcoiuw2h6U3zpsmHpDcizGAKRZOmEuNVRpzo1ISxkkxBLeAXFkmAhQoBLus3xkuaDYou7bUAP6lqCk59H95iyuZy5HdwXRPR/3afXjhCM+QGWmcAOYpKoBuoRCjQYi6GDX3zNMa5DP4ispoQvO19renLMorueZGKNHfXHOsSnt0Me06LBnc+Ha8QuDsVwCeaMXXImNwMVy7pRZRi+6idxcuOR3HKCeMqgcsq3qO4V5k2HHktNPrztcJd0K7aFKIMAK8rkaSVJtCowSMm0yhkYbO1MFsCp0PTdsHOYdcSXUFMNbOkfOgQcB0SLhsg2eREZEuvsR750qalzfhc/dyEMQMbyh211F1LICMyuxmO3Fal2RQ5QGCWY2h6nfo9lfidR08UfssAMEdrjwSwqO7YQgFYXZb3CUB3+FqNDaJm9cTUE1XUDUduC1eb0fdyF7RzHl4PO28JDNL31GCOmMh2RBVpM5CI6b1lfYivGFIsn4S/e4p0x64wzKY0IJo1gB4OoCdQSDl4JpMcVdKLuHIaxboKuxCe2Yigt95jF6u+logla+KxYrMKjLarPFMaW1PFHPf1FnQS+1jUrVi/mNIeRBV2EAAs7SX3e5ER4Gy0Ijk5106is5y+NmrjyGHNG3cdh6bTJGHT9l18XyHwt3Hb+whOq8PGt95D8PYPCBhyOkBYunT7NikQG5bVBGdem9pE2HNbGWhxqkCqoaU0xr6bLCyggETrPUzuXcEsY1EAz9gYm61EwekNhtGtTgPAtHoEpzVSx3l6o+VaUGa5gM7ykNRdc/Iz5XCxac+V346okm5E5DRxGgCN2O+KfAZXrnoMK0KSrS1VJ5+LH/3sCsRVDFCdZ/VoQJj/EJUB+DoQmdNGsU6iQczRTKRMwdTvJFExprTAYrDkKUNOMxy+9gUGcOFx3A8CgNs/UJS0w2fVIJjgz2T1InJarYCPF8EwObJaEJ7ThrCsVoRnteJ3t95P78tqMRiQVkRk6wU0JL0Rt29KxmXLH8GyTckISW9EOH99eLboKnQwsDNPgz/TrGGmqptJ6mqEyKNGYgIIBAoAlHYIVfAu7GBJL2JK++Eu7EbQ9vcRmd1E46lSWnTcRd2q4SIypwXBqbu4M7hRjTDC2IWrqtp4Fyu7Xa3rq9dOXWH0jBHfxTffqzRh9P9s5rGUZvbENEA6MY54EaDH+j4RIgs4cfh0DRRlIkoUUJcKcJbRnLBwGvhprWWsX4faSm2SuGxjlFNzrts2urRPuamd+RrQa5NPrxoZm3FEpkNdx8H0GOywtVbOoRpJ6LyUEZ7s0sM4RJbyxdqVliecGQsBihHGwmKyelrnylqmrGbNhGfpkVQEOxhldBjJMTCiOXPmtSCSgTsxKFLNpzcGoen1KnvNniuOSGJEzNo8uV7NQF17XrslAF0DZyvD6ynsUCyUBIC7CrtUu4S7sBOXLXsQrvw2YrWkeaeArslI5Q7vUgHfFr0cj/C9ZXRueHlzph3hfWTkMth6MyzaU8z5cszAeMv6WQfII32Od4oq7qaKP5YpeMsGkFxNjnN3URfsee148tUadrE2G89zk3p+Ig3phBlnJex5VBEBOWrRINbO6WtFFBsXXPlt5BDNa0VkLmnXXJxFJ7mXly57kEfilKFqz2lCeHYTwrMaEZZRrzSiDjZ5RLDkJCS9AcGpddi0/QMa1W7fhU3bPkBo2i6V1bdp+wdw5FBdXmRuK31PyQLNbtHTCQaVwYbbV0xnFkYuTYMzZUwzjuC0BiVZEQAYnNZgmM8aFejT978GBKU2UJpBqml2a1Sa5xBLvaV5TVg10LTuWIPVxWQUKhvmbB1c7WDDz/cvuNRiSPz+Ty7BiuAk/P7uJ7Da8TwSKwdIb1o1zF3uFJUkIf+JNaOIKeuHp7BbMYEmI2n9nbQ0KiKnzeoELtBOcwmVlzV6AQAuPI73scRmsyF4+/sWgarJXmj3ok7GD89mwJfThjATBGa3IiyLDnk9PIsWnuDUegZ7elysgaMVPBJTqHdIlkOAZ7ZQ6kK1k4tLokCcAReRGA9Ii2PovkoE+LEou6xPO9VYcxRtYbh6mTmqp/BQcT/ntcKd3851VLSQ2/PatIDfGOFSMPPcsXBwuqHrYpdnmPFSAEho2i5Lbp/0FAtLFJ7ZqOIOtLmjmcBEbotyoIqo3RQiy0sC0FozZnadqp5SiVZhnZ63vB9eY9wbWzGkQKFZAWWOjM0xrsvIntS1SjpGhp5P7jWVkXShdobLzVJ20XYGtFqL16pYT2GTQ9PqFPBTDRxslJGGFRlZ6bES9YyaMT3hqj2FQ5mlRk9JH5o50LmJvpZHtFJL5chrVcLxyJxmBThNLaeK7QmIgpGRsSzmYRwgrZl2yRRsVr+rOEQjOfJH10/pAG3TUBVVRIBPGa4KpAGjE1GFHXDkNLEBhMKaxUzhMo0yAuZZR0uMux7d675vDnav5A2EavbpUeNdfc32qTw55RrneJQY3pxRDy9f3yWyaaHrOaV2BPHlfUioHITD14bHX6qAI6eZzC0+NqT4aLPp9LXDnc9aPTYZeQrJMOHMbYEjtxn23GZiaHJb4M5vU9ebXeQHzC7KlCA8i9jfyBzOs8tpQVh6HQFQlitEZDXyJGEXA716ZvAa1bUdntXEm0sCZBu3EQgMTq2jl2l1tAFNq2ftIBmJQtL1JpTuTXWKdYtgo0VwIOhLrZsD9ILYiBakDv0+E9gFp5uAkBMM0vTXBc/7feqt38cwvQVnSJ2lCfz0NbNp2/uWa1RlBXLeqd28B+aSjnv5PIbEkxafi/tcLyC5ZhQpO3YjsWYUiTUjSKweQSK74nWw/4h6O9Y/CA9ryCO4pYTuCeYETBpOmA20mEU61FTGDMJ3+DoQmrYwAl54HN9DjYDlpFPJ5VJ9JA4tsawLY5TTrsCcsIJqVJutAV5kbjsi+PMjeRQVaQLA7FYFJAX8RWS3WQ4TAFoYDXWwnsJoQJA4GqHRVS9sodawkWu1WzEPolWTlzFlFC4by2G1uiqIAmbDs5rg9LWqhdKe28qaM9rNi7ja6WtHGPfrWhg95fZtUMX1EQzyIrKbWeDfrD7mUAwPLd7keCPReTjr+Wh0qPMWI5hBkpo2Hc6tK/lcbGJxKE2QzvcTUEC5ejrqQACxaaqRLlevfxDx/D+KLutDtIDqEnNc16eaHKKKe7XxoqBbAXYxGCgdTL4GpE5fh3KMktlDGkjadJWfUe0lGXnCjMr/Q5gc8/9NhooGBdJNN3awcjk2qQ2JZJGZCw+BRSsAJCDWrIBmJPekak1mixrhqnMhs0n/TpnCBIsOdG4uoPzeEVlyXml9oBh/wrOaVe6dbPLsucQ8KNYvoD7PU9hhaQYyUwJMLaFZKSmGIZFIeBT7PoC48gF4irth93UoE4jIAaJLe5FSO4rkmhFEFXfBntdKMScFnYgu6UFilQ6GVjV+fP7FV7DswD+A+KphMoswoFSd4RVDSKwewZaduxHv7yeNYQGNqIPe3MnMWwe7RSluyFPQAW9pD9y+Fq4eE6dyO7m6c5rhzBMpBZuq+JoXrW14ZiPCM5ixzahHRFajcrUGbf9AGcfCOfZJRrwyQlWbSOM8DMvQhg8ZqwaLaYKPUNbyhWc2IiJT32sEQBJ4rFcsX7AxrQjJkEzRBm1SMxMI5j0EpBkJBeaR9jVHwOeZoNIKIo1Oc9MMFwD+wjKbLddbWDZtwnTmYzux/VwvJ5uhy1bMY0g88Qn84Z6nLP3AcZUjfM/jSj9eN6g2jhpEEjhH0FXYpdZSc92KyDXWUWMao9423mdO4Oy5CyPghcfxP5bYbDYEbSMG0NQ5SY2N1v508m5EOjHlbdIERjCTR8COT9q8eQ55v8kA8hER+L5szQ4KUFRuLjVG0+YTe562zgdG0VBkg9R18ShTZQd2QSqlVOwIx4XElGv2SqrCvAwO4/wD1GRR0gOPKjTXi0eUyuUy+0qNETiDO5VTlSU1R608njECTLObEZHVRIDNAG/mOE2aKJzmSC9f6oW0U5kMABr8qcWuQDdayIKsq410pIsKti3t0yYPBn+mxi+6rE/n/Bn6vUCjjWb/DCOHGSwudXKs25PIBiVR4OfaBDMyWtXu51blzlMNKywcDzMNM8ZCojV3hvaOGTc1emLGjQBfE6xjKAaR84U3ZzVZXkrDhxoR8cdCpa6PMxyptk8v4Or3tSyAOttRLYRGjZyMpEhLK9e8HpWruCN5rkv7ODKjS+WAugq7EJnTipWhm3HFnWuxMiSFdGoFxNI6DbOQp0Q3VxAL14vo4m6489s4247YRbevDd5S6u32cBBxWCbpXt357Ygp6UFy9QiSqoc57mWADSEUjkzh0kOIK+9FfHkvYst6kFDRr8we1KxB121y1SASKmlM7CnsQFhGPULTdiGqoAPRRd3cRNOBKI5yii3tQVRRJ7wl3WrM685vV+Necgpr1i5SOe3leSJ2LSSNgFgIyzzEvEUu13pm6+rV5wbPc5jj1VAGezK6NcehSleXpqOFdDJAk2KYFag0zBfy/SRndM4hYNMAgyFz3vf14M7yfQI/J/Dz5/nZ84G/QLacwJ8pOdId5xZ5hJkJmNeBZUEp8zKAqyKfUXIWr6ou7FWtN3GVw6rmM75Kt+XEckWpMozlmYDOJF46jHVSA0DrdE5/zaa33lsAgAuP43ossdlsuOVpr9pdhGY04pLbHiBNEQO/FSEpuNv9CiQpfeMbO3H5HWvhyNcg8KbHPXgwMY2dwh1Y93wxrnkgCA5fFxy+TiwP3ow/3B+Ex54rphM9tx0PxG/HHx9zWcDi0hWPYv2rNcwwtuEu58tYFpSsgF5IWgN+d+sDVDnEX7c8JAWrXX9W4HTDX2px+cpHeQxITqubn/DgocQ0BQCf/FMJrn0wBNElnNFV3IvrHw7BUy+XqQt63bO5WLYhVo2JY/0DuGr144jM3IX4iiHElvfhwbg3cJf9WRVYHFXYgaXLH4Lb18b9mn1YFb4Fd7teVu7CDa/XYOmKR/T4OqcFf3zMhfvjt8PDI88nXijGtQ9sIjDma0NkdguuuX8jnnihCCtCNsNV0ImHkzNwy5NRRstGF664Yy2C39qBqCICc3e7XsKK4CQlyHfmtWLpsodgz26ixdrXjpUhKbjH84py0oZsewdXrnqMgpl5zHbbei/WbslSQv0Nfy7HDWvCmDGlRPwb1oRh/St+NeZduyUbtz4VYwGRV65ah6C33lGg797oV7EybIsCf/bsJlx6+wOIyG5SN8wVIZv5+aXzZv1rNbhsxSPKuOIu6MItj7vwUMJ2SF3f2mfz8fv7NvJCWofQjAZcfe8GrH02XwUw3xf7Fi46enbkAAAgAElEQVS6drke5WY24bLla/DES34FqO60v4DbNiSoxWbDW+/i4lvuxca33iOWLrsFtwclYZX9RbUIPfmyH5cue1iNpkIyGnDDWjvu875BQd3pDXhkSx5+f+8G5RgPzWjE1feux5otecoReV/MG7hxrUNrznJbsXTFI3j6lUrWpjXjLsefsCwoif+GZgRt/wC/veU+BMnoK7MJt29MokBclm48/WqNilcS5+HNT3jwcFK6kkw88UIRrn0wiE0M5Ky9YU0onnyphAOHW3HmWb/AiSf+F0444QmcuOhMfP+CS3HJbQ/gqZf9cOcTmHsk4S3c73oByTUjSK4aQlRBK65c+RC8xe1IqSJwdo/zOTwY+zoSKok5tmc34Ib7nsTm2lEkVo8guWYEyzfG4uGkVDiz6+HMacTjz+Ximvs3Iqa0F8+/+1ckVA7ij4+E4onnfYguaEVUQTvWbcnEsg0x5Pj198Nb2o0r73wUQW9Uw5XfCndhB+7xvIxbn/bCkdUAt68FYWnv45Jb70XItnfgyG6EM6cJdwQnYLX9eWrHyGvFhtersHTFGkTmNPE4twU3Pe7CA/HbVfzIuucK8Pv7Nijtbmh6A666ez0e3pyjZAX3xryB6x+xK6AWlFaPS25/CNetiVQgb2XE87hlfRyCU+sRktaA9W++g4tvvhcb3npX6YlvWR+HOyNfUE0/T71coc6/YAZ0Nzxqx30xb6gNxZotubj6nvUKKAbz7/dQcpaaUNwT/Rdc/4hdg730Blxy+0NY96dyBcZWRjyPW9fL9dFA18fN92HDW+8hmHV6t6yPx8qI59XXrPtTOS65/WELkLz+ETvuiXlDfc1DKTm46u71ivELtvz/6Jq6J+YN3PCoXQO/jEZctnwNnny5Uo1XL1vxKK8ftOkJSavHJbfdj9D0erXmLQtOwWr3n5nUaMf6V6vwH9/6njIknrT4HPznd8/FmuR0tSl+6qUSXPdgCF0f3GF9wyNh2PR6lWrLefzZXCzbGMsxVj1wFXTgijvXYtObO9UG927PK1gRskW1aUVkN+OS2x8kg46vE878LqwI2YK73a/o9e31Wixd8cgCAFx4HPdjic1mw6a33mMGsMPK3OW2IzK3QwE/s6tXNFbEwnTBmS/jOmkHMW3tnbjH84phMiEGUTGJzCZGmjugPKtWUMa8+veSWBrjZ8nP9pmBmubvosfDljJ3Se9n13BUSa8RWTKgejx1T+cQ90CSCDi+chixRo+lV9omWIcUU9rDlU59iC4mttDF7Jw4K6U311PUpYwH0ilsYft87bjH82c12lZuZ9VkQo5CrdfrUmOsqKJuFZQco7R49D4SzHcrgCdmC6XL4tw89ffJuKPCcPeWD9DIlwNwdRWbkfyvwKrupw18TswqNWH0TGOFijfJNnf3eqQbkd2i2DKtpRS2pcHCFNxpf9EY+WpnoTAjsqiaDsVA3VHIPCyf/IzwzGYViis6K+XgDWAFA49wXsQCnfNznPTZ5utktNLjLyNyxvgfmnlkZue11Qyig789XHElz+kdYVsCWJJynHTyuXjQ8yJefGcPkmtGkVQzgtjyPiT5+5DEjFu8nzR+MaWaURSzVZx/AMnVw4itHFJ6Kq9/iN+mqjmJC4oq6SHTRwllSkYVkFnCW9wBd34rOX+Lu+HKb4Mzn4Cb0ye9stRA4vK1wZFL7nPJ73PkiimHdHyR2czmsTknMqdZBy3LOZbVpDI4Q3msKlIDdU4F5OMpN6zF+EBauxXhz7Pmbq6pIsjCnOmsPQGa4az/NI1lIelGAoFiufU5rc7r9Hq9aUm3fjxETBzKfKHPewF/gcyc/hr99erlfGNcyTOdh3VX38/4fSQDNbDX3GS7VzleQni2TFXYaeuT8Ol21QMcmduhZES0iWzFipDNuPyOtVgRkkKGH7OejddBD7vXY8tJcpDA42GqSxxSsgO5F8vkSbUJGXl/sm6awc9m45Y9r9Pyuy4AwIXH8T7YBPIB9xHSjsM8VLhyYQ/chaLR0pVxZlevProtHzM/Ll+rWwnMo8vQHnYqSlzGugT+ZNTHv6uMmgq7EMWaIwnqlWDOwIvJLHJXvccF+uIUfaA2MJDTMK5iiDpJWfibUEW9ll7WDcrnRpf20oiqcojqgvzkSkyoGkJi9TASq4ZVuG1UkQRZ63GuhEtTLlu3qshSGiuJ25CwWCXgpzyxaBbLmyJ5lcvn15Essf4BLmcfUsG6cRWD/HsPKH0L6awGlN4qnltNCBxr04w0FsyXC2jNU9Q3vsBzQvqExfEmY1vJz4vIaeVYBdH2BITIGvE4JvBTujnLiEgyxJot49+QgEU7cKGzvj0X+FmAnYyH51TvBWqWtG5JQJslM9AAcoGuRqub0DxadAi4oeOVRc7c2GlzR0Ddn+Gcj2KXdVhWE65Y+ci84bg33fc4XtnRj8TKASRWDWHLzj1Iqd2NhKphdW1IBJDl4HPGU9RJnbCF1IIRzZsnT1EnHL42rlfrR2xZP6KLu+DJb4WDQ9bDONtOon/ECGXPoaYYcX+HZzYgPLMBkVmN1C6RbXV3hyutpNmoYYIoHT0ibRWqW1hGqplicqiHmX9n6vSUMzZVA8AgHn9qYwS9LyS9AZvYjRucxpsQS0wUHZFs3BBHbhiDpPDMBotcQDrBQ41NjAZfc8/rQCD2/3Puy/ezvDTD6gOqKiPEECGbOKP9xnKdBPwMM+uU5B16tEvERrslmsmpopw6FekRwRIiWVMC1wpZH8zKP3PTrRqMKo1cQDYlxZTpIHuzIclT1KOrAwv1fZHu9TpwXxuz+OB7Z/CCBnDhcZyPJTabDY6cJjW2k0YFEW4LeJBmBDnchTqXTbFGzBip8F7D4elR4LHbAgrN0F3t4jQYPYPBs/s6ldvXrGqTWBK3AZTMyjLd38rNDkZ3p+VvMsKfTf2TaD0oNmZAxVOonDuDLaTXyfwgWYJe6aM1GLT4ikEFCuMriCmMLupCbGmvcry6CjosFUq0WHbzIZo8Ye0M0FdiPidmKHO/6tmNLR/QpfTMXsb5DcZTNXfw31UmhfVaF2lx+BoZbjRu1qJ+lV4vz4scKrexXRk97ByYLLo90fLpvmW9OKhF18hB/DqgZS6SoZb3N3ztAjbvQhjIXBgLeiCzYf1+TZbFcC4IFC1gYLRM6zxALzBCwnTJG4HshqtbayfbGXgHMshUcyd1d2bbS1Qx6Zxi/QOIK+9Dsr8PD3j+NCccd9His3H1XY9j3eZUxFeNYOuOUbxZ/yGe2UkAMK5yGMm1u5FUM4o4/6DqwpXzVphAV0EHgbsi7vwu6OBFslM1vDh9raruS1zN5vliRutEihM+S2c7SmVYeHYzbQKYuTMz5BSjJO7rTK0N1fq5RgtTpnWfxkbB1NeZR5pmAr9OOyfBymbDj2Kn07UBRG1UWBMozRyh6ZIhqhltuRYisgzA+m+ug68797920xNwbgeydBaWWzY8WcxQ8wbQntc+Z1MXltlsfL71/SbrpzThvg71ekSOjiIyJ1RCBFi7zjWJQS/l2ujhw/q6W62J3br+ktcKIQVkbdJNQl3GWirrj+5HNmtMY8sHEF9BhIJMlhKrhhFT1L4AABcex/VYYrPZ4Mxr4n7KPniKe+Eq7Ia3tI9rmGhx10G7JOj3GtEe3rIBNRoVYBS46wl+6x24C3UnJo0De9SuRpjFQEDnKTQ6Y5luVyG0Uq9kxomonDodVKtBXk/ARWxevD3KgWiGCws7IQA5prRPs2EmM2aMQWPK+hDNgEvyyjyFHbjL/iyuumsdVtufRXRJN7FtFYOIq2TnMZfce4q6KCS2uFv1oEqDQXRJD0K2v6tGs1J1p2IuOONQOkZjmGlcbX8OV69+HKudz1PMRnk/Hdy64S2nMTd9Xa+FrYkpM8a/nJ8oz6tZASegVO2MjcoxBfTldb7p6oR7DVLUjj2nVTEzkm0oRhpK/peXzWr0I724X7cwyYIk73viJf8coPj/CwCtn/9/L6Dm5863GErEjDiHZdRtaQ6xNIkY7vhs0wwVYIBixk8AtzDJ1tiXHrUAkSGqD6b5Kb5iEIlVw0iqHkFc5RCiS3tw1s+uwKKTfgTbCTfAdsI3cMKJi1Rv8ENhsdhcM4S/7NqLLbWjesPkH+D6rD7ElkvHs2wm6fyRmkmSAbSp80Jc7Qq4SbsDnxuyMZDzQZsxrGHpulu5yQAYjQaoaFY6TEtTxb95Ps2xpQkALYAvYy4QnG8zse6lcoSkN8Ce00KRM4XanCVyiAgj+F0YyiBhG9PqEJy2S0W86PNbs4DhEj4urvX/C/xlNCJQtjAH9H3N5sYSzqwYbs1QmxFjdpG98ATA/FgkR4CJ9lcyNwMlDjLmdeR3Yv1rtapJQ5IFLKy3cQ2olqGAwy3TLwX4At/uVikTOpu0Bx5ON3Bz/7GAP5PNU2/L2ioGO7UJJ/lDfCXFHCVUU8Zgcu1uRBd3LADAhcdxPZbYbDbEFHchecceJNfuRmI1OelSduxGfNWwHuMJi1TWT7t3OTEr6ORMrh0lyzsLYL3+QQaPBEauXLXOyIsbYEdgnypMF5aKdju82NSMUBxE7Sg279iNZH49oZKo9fgKGUcOKk1btGSEcdyIXJRRBmhxF/Zw9EiP+rj5OZYRZrF+W4Ud8/fWjJ4AwEBmTAOwH/70cpx08nkqSf6HFy6lxouSHsUIxlUMIra8H1FFXYjhLLOo4m7EV5B7MYHDba+6a51iJKXjWG4YsTy6FUAeXdKNH1l+9nn40U8vh6e4W/XhesutekfRA5pAW25OUQzAAwGE0ovJDlfidxgIUsepkQ2nku2Nhg6zdUaK0nP1SEj1GFv0fxr8mYzN/9/RiEuXPfy17IQFLBoMjHVsa3Xefv33mu9otoC+iGwJPSfmg6J89CIYCPjCA/WxrGkiPa4U28t412DcC0g2IM+VNN1EsexBGnLiK4eQXDOCrTt303VdOYzYigEEpbyBm+9/UoE98zjzB2fhj/c+jm3v8+dzHppsKGSjEl3ai6hSPXFwcMYjuZKJAYpQzSky9g8Mzm1RbSdSdSgAMSK7WTHDOnPNep6Ez/O6RPiEzvPcSxTPvGPO/4NFnns0aN2b+lwCjUtXrIG3pAfJVUNIqhmhtomqYSRU0v3YVdDFOZNU3xiR0wKJ/BF2MiKrhUBipjCcTQooihaVzuUmmDFU6n8wD5sXOu/5zz+Lf5fQjEYr2M7Uh5Y2tFlc+5LX6pbJk8hYWC8dXdyDqCImJXhz65Ye6aIeOH06d9Lc9DsLunD5HWtBHebdTGR0KymTEBBKKlNGTUaqX7qECZEizdCZG16PEWIvhIGLD9lMESCcG6sk9ZbmpCm2XBMp8ZXDHFW0B8++sxdb396LZ97Zi60792Dzjt1Iqt0Nb2n3AgBceBzXY4nNZoPd14qI7TuQ4O9BUg0BrcTqEQaEtOunG9AQEquGkVI7iqSaUSTW7EZCzSjtSGpGkVJLIZnJrPmJVwBxiE9qeltCW5OqR5BUPaIyvwTsJdfuxuYde5BUQ5EPSdUjSOLQzYSqYSTVjGLtlgysCtuM+MoB1uSNYHPtqOX3TKiUcNhBdXj5IjdHnWrRK2MAVT4Ab5mMPgfUDSGaw2MVU6ZYtn6dM2bQ/vI5qyKewUknzxMrEPGM6jMVdo/AsF4kRcxOo+Q+Bc5UALMJ/sSgwsyjt3wAq+zPze2xXHwuVtufY4ayX4diF3OFlgJ3eowuuipnQE2RHqNLxE6Hir5xWsYsVhZQj7m1Dk2E0FLrpwwynN8XkRvQuiGjoCy9uMzt/mzRIyLDHEHHvwdooQYQ/PcArtn4PCvbEZYZ+DNbjPfJ39Cixrd2jhMJz2qCI68VUUVdit1Q2WE5RtwS52tGMHsihg414rKYtySQ2cr8maHesgFLqRmhzVYVZZolVQ8j6I0a/NcPzsF3v/9DBfhuu2cNXszbgS0lbXjq5TLEVgwjtnIEiZZg3GEVq+TlaCWRSJAOysh8lMYDMYTltM0Bv3N0jtkaNP+7I1Q9J+br1ufaAvwyrHrR+cDe145C59kkWIBU4Gg0gw5nHsXdxPJ0Ia5iiMbkHEwvNY4Rxtg7kjdJYmShCCpdhRfFAMpd2KXOI3VdZBjn7Jz/VeD/wGTx6P8oI9sozlNNqBhERFajatYJyzbPcTnPWy2GCxUEzhMeMdzFVQyp+2h85SDi/CSZ8ZYRexxfOaQ25LG8iSa9aA+iSkQLLrIfOtz8e5r3vphSIh685YP6HFVgUAfYK1Zc1RWabUYM5kp71e/jLSOtqpbX9KueYVkXE6qGkVg9zButPdjMRwqDvKSaUbzV+DF2Dn+Ogs5/IKVmFNH+IXgrhuAoWBgBLzyO77HEZrPhl0uvwaJFJ2Hx4sX45dU349HnC+AqHYC7bAAxftqNxDOgS6gaRnRZHx6OeRl3r3fh1vvW4oKLr8Tv710Pl69ZASEJzEyoHkFSjYC63Qogbtm5B1vf3ouU2lEkVo/CUzaA6HJdrxNXOYTYymHE+Adhz+9ASGY9gjN2IfitWjy8ORP/fe6FsNlsuPbhMHjK+hFdzkGwVcNI2bEHW3buwTNv7yUR+o7dikFMqh7GVt5Vbd5Jv4ccW9/ei807SLQex0aImPJBVWsmNwfr+Ldf31BKe1X6e7wwoeUUHTOfYP6qu9aRXlAct0bfrbuoR+WokQmHmhdcBRROK+yjgD9vmc4pjC7tY3ZvAFfdNf/PvnLVY3pxKBZWr4czEXuMnlbtkhbTiQA3lwEKHT5xWBvdwcYYWLGBDCydRkevjIXdBV0cUK1HmJLOr3unWwOYIOnglbpAI2A8Wzp65w8VNxP5FYic51CtNFkt0NWEc8FoWJYJLJvnAlT+ekslotGWI6Pf0MwmzoNsgye/VZk4pLFFG6L471VjrwAXvoDuApJSCFMrOZg6mJkWuoTKQWxhtj2ukpin+KphPP58Hn7z+z/ixJNOwpqnQ7Hno3/AZrMheeuL+OsXE3jm7d2I9tP1Kte9eZCbd8AwDvVrjbAybWnWUpz8KpXAyD7TVZRmXuh8Y3GdrTn3+TSBe+AxP+APTt2F2zYm4NJlD+O2jQkISt01B9xZ3uYjyPi62zcmIji1HlYtm3Uz4y7oUlpjCaBXm6WibnWd6ZzPLsWeuwo6EV3cg3j/AG2C2TlNm0sGgFlNDABlhMzgObNJXTfm+alqP3OMGsFc0urJRs1d2I1EnmDE+ft5XN3J16q1MlRrUuke4i3TkhUFmsrJnJZYM4o4P5mJtu7cA0dOA+6K3Iqr73gIP/3dVfjBuRfg++ecj/N/dQl+ff0K3O1+iWrYqkeQWDWMOP+gkjFFMbAUrXp0aS9PPwYsmaYJVZThJ8H2ch+P9RNok8+nqsIhxJQNKACZwiDu2XeYrXt7L69BuxVrl1i7G+68JkSk7lRrzjPv7KV2keoRxFYOY+uOUXgrBpFYPQJvxRBGPx3DvrEZHByfwd7Px1E79Dnq9nyJgua9CwBw4XFcjyU2mw0Pr3kUH/7tn+joHsSGTaE49bTTcP1Nt2LrK9ux9S9ZcD2/HfdHJGPZo0H4/S13YMk3/xM/Pv8C3LX6Xjy9fgNe+NNLuPzqa/GNM5Zg6XW34NJrb8bKDdEI/osfW2pH8czbe/Hiu3vxTHkbHtxgxw/O/jFOP2MJzvrxhbjsDzfi6htvw4U/+znO+fEF+PFFv8Uvf38LLr5+Bc678Bc45dRTLSOmU045BWedfQ6uvf5G5BYU47TTT8fToU48/0YGnJv/jND457DOkYTVT9uxfF0olj8Wimvuexp/fGgjbnwkDLc9HYOgZ9OQ3vgRsRvVo+qij+can7iqESTUEMuZxNU/STXEisZx5IsAQQ0MCRAmVg4isYqNFXzc9TUs3KqIZ9TNSUCgimexuGd7lAbTXWTEvHCEi6nfUyNsPr6OfbwjbIvqzlUCZSP2QzOAzAIar9s5IuGKO9ZiZegWROa0qnYOhznSFX0fj3i0A0/Cqa3dzfI5KtzZaKFRLJBaiMzxZ5ty8pnvtweCJiPwVX+ebpSZyyxZWQsRqQcm+ps6JGn9CA8EeQqotlkAZISlD7vNeF+r0WCiTR2RKny9QwEk1dwTyPwZUS5icBImw1vWr8TmCRWD2Fw7wuc3A7aKYfzJ34pf/eFWnHzq6XjgiSDkF5Xiuuuux+KTT4bNZkNIhAOfHpzE54cmkd36d9WMEFsxbEhBBpTUIKasVxmqokp62BwkpjCTBQw4fBRKHymOzYDQeAsrGhgenx0A2L8G3M8FhSb4q8P3L7gUi7gbdtHic/H9Cy5FcGrd10gBiEkMTq3D9+b5Oqpja1YbFuqJbVHnkp0DusXIIMYC1XCUb+g7+boR45e3jEK76fqnEaezsAt2n2hrtVHCEoCcG8C0qoBkrcu1+9oVgyzgU+4JCRUDSKwZRXRpL5yGlte87gT0yaYyukQ6nIeQUDmkJD8pO3aT1q1qGHH5DXBnvYfLVz6CExctwm9+ewk2BYXgz6++jjJ/JSorq5GamopIhxM2mw033PcUXHnNvBkZRnz1CBKqRph4oGiiFDWx2o0UZtmSqkeQXDOq2LfkWiIrUlgalVTL9W88GUupHUVKzSg2145iKxMI5veLqxqBt3IYMZXDeG3Xh8ipG0Rtxx5UNXTj3AuIvIjc/Ape3jmAN+o+QnbjXpS27sWuPZ+jeuATFDfvRkHbR2j9aD8+PzyFA+PT2Dc2hf/9dD8+3XcEPYN7cM+9DywAwIXHcT2W2Gw2fPyPz7Hv0CQOHJnC4aPT+OtH/4OwCDsuufQy/OY3F+M3v7kYN954E9Y8shZebzx2vFOHz/aN49Mvj+Bfn+3D3/73U3z8yX5U73wPW555Hpu3PItrr7sB3/jGN7Bo0SL813fOxOmn0+t//OPNKCgsQUdHF/z+Crz62mt44YUXUVpWhrfffhs5uXlI2fIsorxxKCoqRt/AIPbtP4Aj4xM4Mj6JyelZTM0cw/TsMUxOH0NVzdtYddfd+N0ll+Kaa6/DzTffimXLluPBBx7CunXr8Nhjj+Gxx5/AunWP4/aVq3H1DbfAZrPhrJ9cBHfZIOtriKmMr5aXo2q0LUe8vJ9F8AnVZg2QPsQFHFvWi+SaYWypHUYMC+ZPOvlcrQH86eVwF3bCW8ajXaPfVGVGGdVbAgC9Zf24fX2MxcTiLurixg1i8ZQ5pKQXzvx2/PDCpZZi8x9euBQOX5tFgOxm55qHmT/ValLYxXoZ+nn2vDb8IOD7/eDCyyhyg2NsBAC6Csjpa1a4mQyGqu6TsZ8xEpKQcZ3ZNbenWhZI0RRKcLldXOK+DmU4cfH3U5/D3/umx90Bi5WuOozkRY/CxK3jKgFZGnS2GgAkcFQ7z8HglByrmt2MzKU8yHBmAGmRN5tRrLFGwvpZM/y6FAAUcbs+v1g64B9ESjWxDQnVI4itHEFcxRCxftUjaNz7Bc499zz84cZbkbWzHU6XG6eccgoee2I9Gjr68OG/9uPTQ1P49NAUPjk4haejtqrmg7iqUcRXjxIgNDZIMj7zGJoo1QbDrny36BLNv9FIBZB4qDm5oTlSTzm3b9wCGBUbPDcuZ97RcWYzbtuYiEUBzRCLFp+D2zYmaBlApujsiE2LzGnB7Zvm/7rbNyUpdk1GqJG5Gsz+8TGXYss8hd1w+doQXdytg+3Z4CDPt+jeokt6kFw9rAC+TBI8xT1KFmBmvarzKb8LTk5YUIBPbdT4HCqSMXK3ZUoQXdyLZ3aM4tl39iK+cphH1To9giYJ9DNiSnqQUDmE2DJpY6HzbzMDLndhO1a7X8YD7hfxmCMJ3/vxz9TG/6Jf/xaFle/hiwPj+PLAOPYdnMDh8QmMjY3hX598is++2I/s/BJcfuVVOP300/GzX/0Wv71+Oa5b9TBe3EnntSu/DTc8GoHLbrsXy4MS8EDCW1j/eg2chV2I8g/CWzkMb+UwoiuGkFAzSnIGPmhjNGJMtvgcr+Jrhzc8a5/JxUXXLsO3//sH+NZ3vosfnn0eTjvtNPV3nHTSSbjv/geQlZOHs88+GzabzfLxE088ESfzBstms+HnP/8FLrroInzve9/Dt7/9bcvn3Hf/AgBceBzfY4nNZsM/P/0SR45O4+jkLCanZzE+MY3D41M4eGQS+w9PYP/hSew7RMeXByfx5cEJ7D80iYNjUzg0NoWDY9M4NDaNw+PTOHKUjqNTszg6OY09ez9CY3Mr3tyWig8//geOHJ1Rx9GpWQXoZo99hdljX2Fm9hhmZ4/h2LGvcOwret8xfv+Ro9M4fJR+zsGxKRw4Qr/jAT4OHpnil3QcGpvEgcMTOHhkAvsOjuOLgxM4cnQGvsIi2Gw2fOe/v4el19+GOzd48OTWdHiy38cLbxMQ/NN7f8Wruz7Cq3UfIblqADEV1qLv+EpyZCUwKJTcp7hKCYkeVO0g3vJ+RBtO3FURz5ABRGlLelmLwiDOMKBoUwrpYxL8A1i7JYNGveyedBf3cJSOwRDkk+vNwaOYlWFbcOWdj+GOsK2wM/ibcxTouBxpBVHOXQZsK0I2z6lJWrT4HCwPTtF5jbk6b05p0iTiRV6qRYYXJp+h/cptNxYjK9Nl1QcaYM9wGutYIcNtbLAmkbktFECd36HaL/TndaqAc7taaOdmWmoGhr53pAFSVcAyHwqUGMwjjXT5fbnt1r+R9VGu/E6VA+ZSAEl6mbsN0KR7rSVn0czHFIdtTGkf4sr7WVdLobWxfE7H+IcQXT4Ib8UQEiqH8dzOUXz7+2fhAddzaOndA5vNhrcychXoM8Ff20f7sfZZn+pHFRZd5BByKH1sab8Co6agnlhBFtPzeahDcjsU8LEEyud1wJ5rPWcsID633VI3qUfGBjs432jYOC5dPk837AlP4NLla+aARdHGRWQ3Y+mKR+b9ustXPkrPLWe6uQq6LFVgDyakKWBrz2ujkWpBJ+d/6lgos5pR9JsJEkZcMaQ0bgTW3RIAACAASURBVMrNyr3MsjEQA46OOpENQw9vKPU5JakMtIGge1a8fwDPvU0jz8SaUQSnvYeo4m6K+2FNeHLNKBIqh5BcTdrxzTtog5FQ0Q9nag2cL2zHuhAnfnPpFVi0aBEu/NnPsfSKK3HVVVfj5Vdew8f//BKffnEEh8amcXRyBhNTs5iYmsWR8WlMTM1iemYWk9MzmJw+hvGJGYxPzKC7pw85uT643FE488zv4vTTT8dZZ52NE088EUsvvwIhYRG49rrrce55P8bixYtpsnTqqfjGf5yBlBdfR9/H+xDtH0KMOoYRVU4Sh1jWtcrrsRVDuD/uDVy38n788OzzcOppp+OpoDDsfOdd1DU0oaZ2Jzp7+vH5lwdx8PA4vtx/GOMTtP6NTczg4398gs6efnz48T/wz0+/xNDIXvQODOGfn+3DXz/6H+TlF6G4tBz1jc1obW3H3z7+X/QPjmL3ng/xyef7FgDgwuO4HktsNhs++Xw/JqZmcXRyBgfHCDyNT0zh8Pgk9h+awBcHjuKLA0fx+f6j+HTfOD4/cBT7Dk1i/2FiDQ8cmQoAgDN8cR7DFB+T08dwlC/eyelj6pieoWNmlli9KeNrjvL3mJw+hqOTswz6JrGff/aXB4/ii/3jtCs8OI4DhydweHwKRydncHRyBtMzxzB77BimpmcxM/sVJqeP4dixY5iZmcHBQ0fwzrvvY/PmLVi2bDnOP/98nHTSSTjxxBPxvR/8ENdccx1Wrb4H11x7PU444QT8/Ne/w+0b4+DJb0Z0xTCi/ENwlw0ivmoESbWjSKrdjYTqUSV8l5FyvKmL4gUxRvQjYhop7UOsf5B27IVdqp9Yd/CKW1nHCnjZVUmjIVrwY7huSECbgC3Jv7KO27rhKOiCQ73f+j4Hs2fSn+zifMXLVz4676K2dMWjCrAFgjcBUwpY5XXAkSeLuGb/dN+0ZnfU5xuLfiDzEQiEzEw7pS9UukPNbjp8HYhmV6W8lL/XDGaVxH9PkY4xEo2djLQE6AroNsPPZYRp9mbLyDrSAMKaXaQj3t+P+HJpVelRGwZx6dImQEsHxH0uRo7kGjJGJVUPY8uOUTzDmtfEGr1pia8aQWLNbgQ9m4pb712L+0Ni4Szpw5MvFeGUb5yBDVtS8cnnB/HU0xtw2mmn4eZbb8cb29LxP18cIQB4aArvDH8GV9mgBn5mGwJXBcb6h3RMknKb9xn5kb28keFRsGkKKeiwMII6I1Q2DZ2WjUJgY9AczeB8INCiJbXKAG7fmPQ1DGCSRSoQxpo6ey4196wImadT9uRzca/rBcrTLOtTkTwxpX1kGCjrQ4yYDNgNK7FbsayRTqwaRnzFEGncxNhW1o+EymHWktF9SEx2Yp5LqaGxarx/APEcAh9V0ovYsn54JT6qrE9NEMRgoRIa/Pq8SqgZxda39yBlxx54invwm+tXKNbq8pvuwNqt2Vj7bB7C099VsSXh6e/i1seduPy6m3DKKafg9NNPx8UXX4yHHnoYb7zxJv728d8xPkHAaHrmGKZnv8L0LN37xyZmcPDIFA6PT2Nsgu7vx47x/d1YY2RNmZyexdGpWRwZn0JnTz9q334fe/76N0zPHMOxr77C1AytRfsPHcVHf/8nunoGkJnjw2mnnQ5/9bsY/NcRNOz9Er3/ewjdfz+Ioq5/IqlmVAE/r38IUaV9+MUfbsW3/utMhIVFoKqqGv/416fIyMqFw+lCVnYeDh6ZwNHJWRydnGXgRyTGGP+dY5Mz+nXjGJ+cxTh/3dHJWUxNH8OhsUmMHZ3G1MwxHB6bwj8++WIBAC48jutBDOBn+wi4jU/h8PgUjhydwtjENMYnpnHwCAGtf30xhk/3jeEzBoD7D03gy4MTOHB4ko4jUzg4NoWDR6ZwaHwa4xMzFrA3xS9nZhn0zdLr9DaxgAJCZZdHjOQxdeGMHZ3G/kMT+GzfGP7x2SF8tu8Ivtg/hi8PjGP/4Qm+QKYwOTWD6ZlZzMzMYnpmBrOzx/DVV1/xQa/PzBKrODlNLORXx45hamoaH//976irq8ebb76FpOQtSEp5Fh3dffjL63/B7y69DCeeeCK+9e3v4Jzzf4pfL/09lj0Whlj/ADu2RlhnQg7pxBoaJctYTRWEM/gTZ2SsfwDJNaMqcNldRCDQY8QEmBlTjoJOeEu74chvt1QI0Uiw3ahW0wHb7sIu/TnGAqvf7iS3HAMqJ79Pg0P6Xl/HAC4LSlG6NL0gdwYAuA4rKFQj2bnvj8w1i9E1oNTB4AQKKCOxW4UWC5spzryoImkm6YankNpVEvxk2EmsGERsWa8FQIneyi0jcAaWMZbnwhi/F9Jozs1RE8LEugo1gPl3DKNUIroLuxFd3GvEtXTCU9QDu69NjeclnFvMWLTBIBlCUtUwNu/YrWIjnn1nLzbv2I3NtTReS96xm9nqUURXDMPjH2KD1m7cb9+CJd/8T6xfvxFnnX02bnvgSSw+5TTcFxSN4s7/xf6D49h/cBwdnT2Iivbihz/6Ee5YdRf+uW8cnx2cxObaUaX9i+UxcGzFMAE/rsRS2aBl1pB4YZachdYNiIVxNRhqOhe1Y1yz03pErHWSxvmT06ZGxIFA0NQPzjly2hCa0YTv/+Qy1Q27aPE5pOVLb7AGEOdK4wpn9/naKYJpMUs/Fp+LH/30csRXDCChchDeMmJklYPVTyA6sWYUMeXihB1ktnZYgbmk2t2IqxpG0OsViC2nlpS48n4k1ZKGTeK8QlPfhTuvCS++uxvP7RxGyLYdWPdsDtYkb8dDCdvw2NYsBL9ZA1d+C7z+QSQxW+flEa0AzDg2tSXzuRRf3ovN2wuxKSoJjz4dhPMu+Cku+s1vsXvPh+juHcbKVXfj2985E9/6zndx0uKT8etrb8OVqx/Hf3zrTFx8xTXYsnkrBgaG1PowNXMMM7NfYXr2K0xMzap1gTbuBADHJ2cICB6dwfjkNCamZjA5NcP3eA0AJ6ZmMTk1g4mpGYxP6mnTofFpHBqbwqGxSYxPElN44PBRHDoygQOHj+KL/YdRWFyOb33r23jxz3/Bl2Mz2D8+g31jdLz9QQNiNr+IW9bZ8ft7nsSvrrkN3zvrPJz/05/jH//8DEeOzuDw2AQuv/w6nHrqj7Fo0RM49dQf4/IrrsPBw0f5dyGCZD7AJ6BvbGIGR0wAOHWMp2kzGDtKa/IRJjk++WyBAVx4HN9jic1mw78+24exiWkFso5O0sV1dGIaR45O4cCRCXxxYFwxgV8cOIovDxII3H/IBIF6/Hp4fIp2alOzmJyaRV//oLqg5Zg9ZgWBckxNE60vwO/gkUn1c/YfOopPvzyCz/eP4cuD49h36Cj2M/N3eHwKY+OTGJ+YxMTkFKampzE1NY2ZmRnMzMxgdpZA4cQU7SDH+e+cmp7B1PQspmZmMTv7lRpBT00fw9jEDA6PE7t5dHIWn3z2OVpa2lBc6sdrr7+Jiy76JU4++WT8/OLL8JT3BUSn1cKTU4eI9HfhyW9BTHEXXPmtiCnrQ1zVKFfIidNsSPUJUyMHLZYSOh3nH1RhzGZO4YZX/Igrp925qhMKEP9rAChvdyJwRGwedl8HAgXmlo8xSxeR04IfBC6GP7kM4VktBugLGNNZtFudFqbPYQGLgZ9rsIEGo2i2wDgLpB6viwO0547N5aWXGTNPcQ9iy8mJuuFVv2U8KayUGfFgaYMplWxHic3pUVVpKiCbmcKo4h4jdLlbhc6qXLLiHsT7+5FYMYjNtaNIrqbYo/jKIdWUkVA5hCQen23hDLAtO7WbXpo1Equ149Zk4fTGgwBflH8IqY1/Q0bjh3j+3b+itOdfeGJjKNY+sR5//2Q/dr5XjzPOOAM2mw3vNHbiwKFxfLH/CA6NTeDw2AS+2HcAI6N7ceaZ30Vs4mb868AEMlv+jvWvVyoNoDKBCAD0DxmtCDIC1q+L3MFku9VhhO4qltoYC+vze77xcOecc2g+Laa5wYgM0IKSnrAdoRlNWBaUhMtWPILbg5IQnN6o3OiROa3KvZvAgC2+inLcYv39uM/1Aq6550k8FPUnPLdjGC+//1e8tutDvPwemeO2N/4NJd3/RNtH+/E/+yaws6kbnR8fROtH+9Dw132oHvgUO4c+w3P57yBy61/w6CY7fvari7HopJOw+ORTYLPZcM75F2J7yU6UNg7i/ieC8B9LvolFiyiY+0dnnY1vfvM/cfIpp+AHPzoLP77gQpx/4U/x/R+dpQw9/3HGEjzlTkHUa/kIiX8BayLi8Ui4F5vcCXg8IgaPrg/FjctW4TeXXIZTTj0VZ519Dm5btgJPPL0Rb2zPwD8/P4j9h6cwNXMMB8emMD45g6npY+jsHUZQmAN3P/gItry8TYGXo5OzvNE/piY2Ewzc5H48OTVjWS+meLMuAE9ICvl6OqYxfnQKR8YncXhsAh1dfTg0NolDYxNKEvT5vsP45IuD+NdnB/Gvzw+hub0Pp55KOrw7V9+L0Y8/xScHJ9WR/MyLOOOMM3DTzbfijrvuwbonnkJcYgoKCotptDs2jYNHppCRlYtTT/2xZXN86qnnITMr92tBn5ZETauXspbJceDIBA4cnsCBIxM4Mj6FMf6cf3z65QIAXHgc12OJzWbDp599iaOT03TxTExj7OgUjYDHSAO47yABvs/3j+Pz/ePYf+j/sffe4VJV5xfwUOReihh7Lxj9iSaxUWyxRQ1qbFETE2PsDenFgkgHsWEXAekSKypS7YoNFelwG7dNP3N6n5l70fX9seuZOyTmId/3/XPP87zP9HbmnL3XXu961xtKB6UAfbZfgOMXeSqYUfX5QiumTnuUnNitlJ3bxYIAwV0/Ee1fSysBjEGBAC+H6gwt+j00K4Bq+jDsgOoT8zRtTbR+rp+HHxYQMhBYbEG+0MKBXr7QQleT8ipRfF+mSSSMJfkuBboCC+nKMV8kK7V8oRVBEOKHDZsxY8YMnHLqaejevUdZg9xYLIbKbt1x4FHH4aRzL8Old43B7Y+/iokrttOJewdvlzWR+iROWklSN6zKdyr1TLz09lG8H/HEFTs46BAt7xh7IlK+LDXJCjvKg8EosGLAUQaCo19fjxGvrsOVQ6fhjKtuxZVDHsXIV7/D/a8TIbnoJy3a+kWanJdUd5amdgWrs0Eq1thA3p8J1iV2SGiWNmPsu4TZ411kaOqMVUiXGndPXFmFi24eQVvh7eAGrIT1EJ6VLJU/mVoaPfphHR79QLIPkqyE2O3HPqyjmidWMViN6Wuq8fiHtXj8w1o8+fFOPPlJPR7/aCemfUDSsgTI1WDaB8QL7LGPdpI024d1mLKmhjN4E6h2bzwVprPLCZJAnYPA5TvoMUXSdkt+SOCdDQnMefcT3DXqEdw7/EGc9JvfYfyUx5A1QzSmNMxf/Dq6dOmCXx93PHbUNcOwQ3K+OQE8n7AmK1Z9iAMOOBDTHp+BbQkLF90yUmgAmQ5wheQFuIJqZCkgnCABQwIOt/GU49hIiEp4djyzopzSPtJMz1bax1XYytAFhpQ+Ziw4Od42CND4uniuYBJJoc79rxMj7bFLN2ECZe+mriKVppPX1GLMO5tx19TZuHX8C3jmjQ/RqIWYufhtnNanHy74w8VY8Oob2Lh9Jz796nvMe/UNPPbk0xjzyARMmPoYvt6wDXcNHIInnnkRd947CJdefgV++7uTsffee6Nbt27o1/8M/P3Gm/DcCy8hp5nYsaMa8WQK06dPR2VlJTp06IDT+/bD6o++INka08ezz8/EqjWfwPKKUK08nKAVpltEKucgZeZRm9Cw4LWlOOXU03DEEUfiggsvwtVXX4Or/3wdrvrzdbj2+hvwz1vvwLhJ0zB73qv49octXG/nhWScDimbFxZ3cX0bGU8Jg8dYPpYBKraK7AvL+BRbdnEQGFKWr9giSYlaBAgstu7iY3qQp1mrsIBCoYhCsYh8voB8voDJU6bAD/Kw3QCG7UM1HOQMFznDQ87woOg+fthcy8foUQ+OQ1LzoThFfLtxOy697E/o1q0bPl/7Nf1tZJ5g80C+ZRccn4C30fc/gE6dovKYTp3uxOjRDyBf3MVZvaBM2tehYM/2maY9T4ORHyScoMjnq3YGsH3b042kgLMagrCIMF9EEBbghwU4XgjLDWG6IUwnjLBtFgVdDg/KvlGKOsgT0MVo+kJLawT8EZavlUeh2IpCsQX5AvsO5IR2A0LZs0IPAkQDGHZAJ6WQpIDtAJYbwvYJCHS8PGEsggJdMdEVYUC+L3tfOeyS4hEGZIVeQ74kERbId1Z1C0G+iJwZIKG4aEoZyOku4kkF9U1pNCUUbNteh48//RKz57+KISMfwhnn/gG/2u8A7FVRiaFzPuATN+vEMnFVNSZTU2wODt/fzv0Yp6ym5tyraoRFzXKSVmLMlNx7+cEIGxhl9gRAk1i/N0oZwTJgrUSTJ0BeWzuP3QFCDgTl50bAZ7S6lXgibuJmr7x7BQUSzPeLXSfFONt532ZmMDuJAmjmUUmCiNaZLcSU1dWYzPYzNURmbBrrhMPA1QSq8ZzAioVWVGHI3I9w0NHH45TzLsX5196Cy28ZgmvuG4vBj87CbRNn4p9jn8Fzn9Zh6poaPPL+DoyjKVTmuzlhpXjvSaX+eqtqMGFlDSZwsFcjsX415LWriBXF+JXVGE/vn7xiG/Y7+DB03msvnHvhxbjk0svR74yzUN+cQs5wYdg+qusa0KVLF5x59u9x1TXXI6PaMCwfuuVDNTxkVRPprIqp0x7H2eeci6xTwMy1jZjIAZ8MBEm15MTVNZi0ppZKI2o54JUtNiaxKnxpP8vm7WOWin7jD9Hjm1WoM1PrB0t7jkt6TNEhgjHkP/Ker7KeUxQNSYuSNzdg7FJiWzKJdiliNlFTVldj+ge1mP5BLWYu/RgHHXwIjul1LPr1648ePXrgpN/8FrFYDGMeHouHxz6Cnj17okOHDvjVvvvitNNPx5+uuBJ/v/EmXHTxH9GxY0d069YNF/7hIgweMhRPzXgGb7y1FOt/3AjXzyMskkUqS2OGhVa6SG5BIqMip9tcmmO6eVheEV7YSgr2qDQnLOyCTSU7pluA7bXACVpgey0cJOp2AVkjREYPoZh5cp9TIIt7qsNj46Pjt9CCwBYaRSRyPrJ6CNsv8tSsHGHJZeSxAsk+sbnED+l4SyNfaEFYIIDP9fNwvBC2G8JyA1hOAMvxoVkedNODqrvI6Q4UzUZWs6FoNlKKhXTOQkqx0Jw2URfXcMSRRyMWi2Ht95tRG89hyVvLcMCBB+HGm27Djtpmnh3z6D70Q7KvvIAwkF7YggWLlpRlABe/uoQQD9Lc4eclti9socWUdK6jbJ/pkgyb4ZC5znTyfP61vDwSabUdALZve7RRAKjCCwocJNleSIAUYwCdkK9AZODn0gOYrL6K/OTMF1oEwGslly27iB6P3S4UCRsX5gll74cF+AEJ8j0I9c3Bp01sZ1SDpH9V04dm+shqLmcoLPpddcoUst/AqH9yEoX8BJIrhdnvFCeb9JsZu8k1kjRVnm/h6Qo/yMP1Q+SLu6DTwhknaIUXFqGaHlJZHbX1CWyrTWD91np8/d1mnHfZtWTVufhLbmDK/aeocfZE1kWBMjmTVlVj2ppablfDg7I942iHBWETI9JoD0gaqgek9FkbtoTrCiVm8I0NJc+V2ZcNJbelFJ2cmn6j9Lltq2sfpIwlm9jHvL2JdAigtjZj393MAR1j6BiQYAbg0ylzNp0yddNKgnh9kZiypg6T19RySwdm5xAJCvTkzhaMUZu8upa/nsXgl1fg0rsewvV3j9otG8zivPMvQG3axsy1jXjsozpMoe81bmU1pn5Qh3Erq/GXB57Enx98Gg8vJ75i4yigm0Q1phOZ1lSKiatqMH5VNV75Jo6nP63H05/WY8KqGjyyogr7HX4MOnbshL0qhMfmUUcfg779+qNnz33I97rgD7jx5ttwyml9YHpk0iu0/AQ/LMLx8jCdAF9+ux6VXbvimx82YUvKxbjl8j6qiQQDgZNpwdTUNWT/T6Vea3JMlf6XyTI4pAziI+9t5enhMbxiVfQLZ/Hgm/Q5b22UDJV/5Cw5awdGrEqYxvDHSH9Y1kJszNLNmLiStLx85rMGPPd5A+Z93YwvalQs/Xgdpj31Ai68eAAqKysxZvwUaFYehZafUFffhGXLluO995YhX2xBK3U7CAoECPz0M2DaAXTLgxcUsHV7FRzX58wYy0JwxkwCgI5fQJBv4Y8xZolkKoocmITFXbx4ghXr2X4L3KAVllsk4RWhWcThIWeGsNwCFIMU/mW0AGktgOEUoFp5mplp4c4QrBCQuUE4QQsSig/VIgvpoNBKgR5hCAOpKCKigcuTjExA9W5+vpWDP5b2JOM1GZ9NJ+DB5gjC6nnIai7SOQcpxUY65yCjkiDgz0ZSsdCY1FHblMO3G5sw4PKrIuflAQcciElTn4DjF6k+j/yGgII3m97vhQIQOl5INYC9qAawF8448wI4XsDBLAOBpBhEpHmdgGrofeFsQX5jSOe0QCJAyH1NCaUdALZve7T1jMViaIpnYTohNIsUVOhWAMOJMm3soDOdPAdWjHYPwiLyhSJh8YrkkkW+UGwTYV5iG4MC3BI2jgDPEDmd0PRZzUVGdZHVXKiGD0X3KBj0yXe2SWqaVQObTkg1H+S7MiBnSasn2xMgkNvdsBPOpkynBBIFU0je12FMoxfCdsmq03IL8MJWWF4BmumgIa5gR30S1TX12F7bjK11CdQ0Z7GxJo4zL74SBx55LIbP/zSaNmNMDmVTJq6oIm3tqDh8yupaTF5DPdYoWGFt6ggjtok63W/hxtAPRsCfXNQRBXOMJXmIsyKSlYpkp8KAHCvGkHWHQjsod6PYHfCLtmpigG/Csm2kFzQVvcsxhYJkYdRaGwEO0z6owzVDJ6H36Wfib8MmYuDMFZQFq8HwBZ/hDzePwLX3z8DtM97E0AWf4aZpC3DPS+9jIq1snLCqGhNXE/Z1wqpqTFpDQAuLKfRyknQ5cXUNZ+Smf1iHK4dMRiwWw3EnnIhDDjsCBx10MA4+5FD06NEDHTt2RK9ex+Le+wbj3kFDMHn6k3jjneV44pnncc+gYbjqz9fjoksGYOiI0Rg7fhKuv+FG7Lvvfjj4kENwwIEH4fT+Z+Gkk0/Db0/rizvHPIYbJ87GP6cvxt0vrcL9b2/CQ8t24IbRj+HMCwdg3JMvYtwzczH2iRfx9gdfImWEaNZDVCVNvLn8QzzyzDzcPPgBnNynP04+5VT0PvEk9Dr21+hJ9WPnnnc+1v+4kafnWlp3wXYDpBQT8bSGusYMrrj6z7jhxpvwQ4OGR5btkBjQaEUwB4OrGGtJo03KOOq1xkHgGqmgaqXQNXKWkKaRRaeRaKtEptHkwapblxHd55i3SRuvse9uwYTl2zF1VRWmU0b47Q0pbE46SFt5pCyiC1vwr7dx1jm/R7du3dCtWzec8/tzMWXqdNQ1NBPwRoMVObD0ZlhogReQcYSBl7DQgtZdPyNfaOXALyxI4IcK/x1fAgW2GOuYFMeRtWMUMBFnhALNygitHANdTK5DGEACBnWnAN0pQLPyMGwyZup2CMsrwAsYEGuhwI9mS6g9FwOXzDrMC1t4AQdjLTnY4zo4YR/GFtgcGPkFODRzw9KibL8Ztg/DJnOAapJ5gc0TKYWAvETWQjxDmL6dcR3VjRq271RR3aBj+84cNtVksaPJQFwLUZ92sLWqCdtq4tBt4jTBMkEW3TdOUITlFuB4Rc5IyhkvxwuwaPESjB59PxYvXkLBH81CUcDnSUSCQ8GfyHTlxf9ridBMH6ohImd4qG9KtwPA9m2Ptp6xWAz1TWkomhcFVqYPzaRpX5r6ZcDPDUi1bVgoIl8O6BVpSPdNnTaN3xfmCwhCyvSVpmJ9cpKTA96DbgccAGZUF4rmIauJ78nSv4YTkmpgJ4TtUnAWsPcsCLDnFziLKV+K+8XA43JtIWFEbZesOh0vhO2RlIPtBjBtD6puozGpIpk1sXVHPbbXNGLzlh1Y9EUN3vjwO0yc8zYuvPE+/OrAQ9ChQwccftxJGL34CwLyVgnjXN5NQbKTEYwKefyiW0dGhf4s/biiihYP0FZ07xOrEFZF+nCkSGETr/ZlwIuZ9D5E2ZMH39zEn8sYkjFvkxQsT7e9JYpQeF9glnZ+a0OEiWSp2zGc0aM9PN/fTsTz1Bh2xif1nMGb9kEdJq/YjhGvfIAHXl2Lie9vx0Ovr8PgWasx+rXvaNeWWoxfUY0Rr36FQa98hMN/fWIbpu3B5xbj4WcXlGXhDjj0SFx002BcdPMw3D7xRVw/6lH0ufxv+NWBh6DXKWdg4Ow1eHhFFcauqMKYFVUYv0owb4J9q8Wk1bWYsLIGD7+zGfsceBj6XXIN7hs7HW+8uxKqU4TqFLC9uh4Pjx2Hgw4+GN26dcPee++NE07ojQEDLsXAgffh0UcfxfPPP4/bbr8DN/zt77jn3oHYtn0HLMfDF2u/woIFi/D6m2/juedfxNlnn43eJ/0Ghx9xFLp0qUDHTp2wz34HYv8DDsTwEaNw+umno0/ffjjzrHOwzz77oNexv8aAy/6EPn37o3v37ujZsycuuWQA7r33PkyYNAUvzZyNt5cuwzfrfoCi2fDDVl7F74cEvKiGi0TWQiZn48tvN6JP3/646Jq/47x/DG+b/pUXNaXArzRWiEVP5H0oezhpNUkVTy3D6E5ZU8t9OVmFdGlqn8kkJtPOEBN5Nf42jH13K6avqcaMj+uwYF0cz3zWgIlUHvD0J/Uw/RbueZi1i3j/wy8Qi8XQs+c++PDTtaiqT8JyQ3hhUdLGCa2Yzx0WCjxjEhZa0brrZ/z0MyiL1MLHqElTpvECAa4RoxZYTB9myzoxN89ZI4dKXoK8KIawfSKFYVpoGWSyYjh2yQAZS+WysZNnQniRnwCO7Pt5YUvkfnY9kAAf01ETcC5IyQAAIABJREFUMEjZM8rwRYCfpIkj4zZLizLmL+TMn275nPlTTQ+a5XMg+PAjk5BUHDSlLTRnbDRnXTRlXNQnbWyv11AbN1GXcFCfclCbsJHIWiR7RB0tWDCga7nk+2hWnt9mmnkCCAXY86msit32Shg/WXrEUrumG9LfRPSbDOzldCno7XYA2L7t6dYzFouhtiGFNAVXiuaJIgu6uuSsF0v9+gS8BXl24DMGkLB/xWILii0EGLLLLVu20JOBvNahA5jhBNBtcQIz+l4GfRnVRVZ1oehEvEvuc6DoHkyHAED2PW1a1m+5Ib/NdIE2BZicHfQFm2dHIpQuqbaE6ktMx4dueVANFzndhma6UA0bqm4hldHw3Y9VqGtWsL0ph7e+rsVd459DZfee6P6r/XFcn3Pxj0lzMGLR5xhHLR+4ZUYkfSZpvFZWt5lUB81cRVOVZBKbsJy0pCMT2w7e1YSBR9axZMLyHZFexqT/8GZaGbs5Wn1Jiygelqw6ZI0VB3/8vtI080bc/8ZGXqQx7t0tvKp1KtVPMUbn4aU/YNSizzFywSe489ml+Puk2bjkjgdwyh+uwYFHH0/SlV0q0KFDBw7aunbtRgprKruia9duvOKRXXbs2BF79+yJfffdF5WVldh7757Yd7/9sM8++6B79+7o2rUrunTpgo6dOuGMM8/CP266GX+54W84+ZRTcd75F+LuewfhzbffxW133o299+6JfmeejYrKSvQ/82zcNewBXDd8Cv4+eT7Gv7cJT35Uixc+2oaJi9Zg0JMLcNOUefjzqCdx9O/649iTz8C+++2Hc849H+u31qE5YyGjujCdAK6fh2l7MB12/LtQdLbo8dCcsZFUHLh+gab0qP6JenQyiYZuhVBNH5u3VuPdZavQkMjRicJFOkeqHRviKl58+RVMnf44Zs6ah48++xbpnEUWT5YH1XCgmS5hHEwPmulBo8e4ollIKzqSGQ2ffP4VzjnnXBx00MHo3Lkz/v7PWzFl2Ubc/eIKPLy8CuOWs0WJDPCqyx7H4tjfEem2EGUPS96n5Dmsaw/RwrJ2XTWYtLJGmLTTYAz6FKo7nErTyxNWVuOTahWa1wLFLiJjF5AwQmSsAhRaCcqMr+uTGp566ml+HCqqRRk6UfQWFloos8cqVSnrFhRhU8sR3fKh23ShTTMvGl3A/rBhCxf7CyaMhRiDy2VOXMoSalYAx8tHnudRTbQXSixUScUpf1wGK3RhzBbHbtAiXbZEivWcyHcVTBeRzUifEfncQpvFOpP+sDAo0GOgSNE9pHMu0qqLjOYio3n0uoesLtjAtd/8iFTORSrnIa16SOc8ZDQfKdVDSvWRM/LQLGJpptsknW26BR4GtTsznDwMpwCNXjcd4kfLMkRsP7tBAV5Y4JIqxy+IuYcXdohMlCnJjliqV6MSJzYn5ui8yMkPelnX2A4A27c923rGYjHsbErz1YVqejCckLNjbiAOZo9arLAK29IoFEnVbVFKATMAGORFab7D07IhSTU7Aafx2QpHNcVqTqN6P9X0pOotlxam0AIQCvgIaI0CQHadfSa/3xOrSqEriYYhDT7skojhXWimh4xqI52zUNuYwdqtcby9rh7zvm7EiOdewyHH9ka3ffbDdaOewOTVtdFOCRLwG7+8igv7OfCTGBCW7hXMCHOjr+a6wMmrSfqRsSRCWC90YWziYyzIhOU7iP/Ye1sx7r1tvH0U603Mu5Eslc2oN1HgSHVWFAAyq5Wx1Fh2/HtbMXkFYfUmrtiBkQs+Q+8zLsSBRxyDI48/CRf/9Q6cdt4fceDhR4s2SJ06o+ev9kOv4/4PF192BYaOeghPzV6CL9dvRxgUUMgXEE+k4Lg+8sWfoJs2Nm3ehg2bt2F7TR1M24efb0UmZ6CmrgE//PAjvvjyG3z73Xp8+92P+OLr77D2q2/xyedf4bMvvsbX69YjntZhukVYXgv8kEzYrk8ZDyqiX77qIzz7/Ex8+uU6PP7Us/jLDTfi/D9cjAMPOTzCJO69d0+c0PtE9Pr18Tj0sMPRuXNnDkZjsRjeWfkR0rqPnBkgZ1CG2wq45pRV+7lSoZJq+lzrFVLwF4RFrtW1XJIOc9wApk2OTQb+dMvjXpkpqodK0bRYc9pEU0pHTUMGL86cjUGDh2HmrLmIp3LIqBYUzUYyreLb73/Ea2+8jYfGPILzL7gQFRWVuO3Oe7Dioy+wtS4OxSmiUQvxVb2BOV83Y/oHhI0d8/4OjFlWxYtPhMayPEMoaygZY8hBpJwiLq00loBhdCEls4lV4rn0tZNWkfPh2U/rMXNtI75vsqB6LchSc+uMXUDaCPHlD1vw0isLcfvdA3HKaaejc+fOOO30Ppg2/XHsqNlJjhE3zzspeWGRMFOWB9OmxQh0zDBs6mDghNDt6HhGggFDMuYwoGDSDIfpCm02K3iT5SyWK0CTPIa5PnkPl1r5MK13OQBpS5/JGMuyxXIUFLJLFqXFdKyowaTHdRT8FaTKVzYOCy22bpHzhEl7NMr05QwPWd1DPOMgkXUQzzqIZxzEsy4SiodkzkNScaHoPrJ6AMUIiI+t7kOz8siZeShGSDSOVp5rGQnoK0avu3LzgSIMakemmswGLeTfmxcfltmXXF8uRSnIZb+RA0BOiJSE6iGjeqhtSLUDwPZtj7aesVgMdY1pZDUp9Wv5hGpnrJovUr8MEDKrFcb+lUYgVRTzAhM6AHHWjQIvxgSyE4UBO2L1QrSJGhtEnSAC2iw3lNhKVgwSrWAud2m5AiyaJSclKyQxHNoBhXogqibTR5K2c4mshZlzFmLI8NF4ec5CJHMWvq9TMPyVNejStTsuvmUExrz5AyatquFFHKyqM8J68EmsSgJ7EvsRsflgE2M1b0lUOqmySkumkZu6ppYXQzxKiyAepVYlU9eQ6sXpH9ZhKu0UMJnbdWzHeIkplP3a5C4OE97fhsc+IE3RH/+IxOTVNRi/sgavfBPHsvVN+Mvt9yEWi+GNd5bjyaefw8D7BmPGjKfx/vsrsKM+AcXKI2eH+PnnnwEAra0/Ydeun/Hzz0Cx5SfoDploiAfYLmhmgGILSWORVBNJXbXQji+tu2QfR5JOI8L5FlguS+sUodt5aDaZEByvCNUghUWa6cOmFZTk/XfBDXfBD8hCJiy0YlfrT2hoTKBuZzOSGQ0urRAsUs1cGLaguTmFjz/7Ej9s2EpbFxb552tWHirtsKPbedLpxiXpNV9OIbbRShXpgirKpMi2SCb179NMF5rp0vRYQCOEaoVIZEz07XceN66tqDgahx/eC5ddfgWOO+54dOrUCV27dsVxxx+PAZdejgmTp+HbDdugOAVkbdEOTnWKsIJW6H4LUmYe1VkPK7YqmP9tHJNX12DMsh14aNkOjKPAK8oOivTwv2O92wLHqFY2WrhTFWUWVxJNJzv3xry3HffN/RS3PvEaJr2wAI8+8TTGjp+MkQ88jDvuvg9XXH0tfnfyqejWrRsqKyvRp28/3DNwEBYsWoKqmvrIf8EM6/PUusoLW7lmznTzVJ9Mxi5NBnfy2EPvYxM/G+vYc9nrosAw5Atos2S80zhTRsASGxMdOm7aXsjHX5ZBEeNyyMfFaFZEFADa5dLQNDiLRcGbovvIah5yBmE5be7OUIgswA0nRFazMXf+YgwfORpzFyyGarqSBCnkvymdI0xfKucikXXQlLaRyLocBMYzDhKKi6weIKsHyGgEAOaMgJxzJvOzzVNP2zwP0u40CgotiRFkbKBmhdAt0olKpxZJtiwzolW8hh2SxZ4pFgFtAJ8l5l2h9SOETFYjTGaaZsLSNFI5F9X17QCwfduzjWsAI8wbr7T1IgeobgVtViwEELVl1SyJQdMsD6s//Iz4L/FKXmnlq3t80OKfJzF/bPBk5s/yicSqgdmgyZ7P3ku8JnrCiWKX6KqMpWKE2JZFwCdQwyFmov36ny9Nnr1w6unnoK4hgSvvfhC9TjlTWHesqCI9JZdXYeyy7aLP5PIqPLK8Slh1SKkvxpjIth+s0veOJ19v03YrwhZGerKS93lk2TZc98AMnHP9XbjugRl45P1tkdeOX1FN08RV0c+lYvzL7h6Di24ZiVHzPsaw+Z/hvpfexy2PLsJfHnoadz27FA+/tw0PvrsN09fUYMIbX+LZea9j4LDROOXU04UPYmUlGuJZWLYHP8ij0NKCQoEtFIqS5jJP01fRtLxu+bC9fGQxwVMvPjN7Jc/NGT6yqouMalOW1obKUiqUVdDMAKs//Bw5M0Bz2ibsN620Mx3BtmhWgETGpClTYsbq+lENjyVNFCxFxNJHxL6BPM8PW2G7eSxa/C+MHH0/5i1cgpzhcUsOUVUpGBbO8nBLJiJ+V2mqNkfBnWGT41cxfNqukQCQTM6MpM/32/8AdO/eA3vt1QWxWEfEYvshFuuBWKwTOnSoRL8zz8IVV1+LR598BnHVpYa4xTZ9gHk/YKeIlR9/GUmVKk4Bil1E1i4gYxXQoAX4vtHC7K+a8OIXjXjswzphVE1BIYlqDtTapo6ryoC9tjGJvmYcPRcmrq7GpKXfYeRjL6Pv2edjry5d0KFDBxx08CE4+ZTTcOnlV+AvN9yIG2+6BfcNHo4Jkx7F/EWv4et1G6DoLkyHau/cAu90xPR5DrVXIQvKPO2QJI83LLtBUvu2FxJzbcuXihkCmhUhtz/85PPIWKaVBDs+GfjjC1kaYiFLHrfdkOqWmdyF6ugoAJQXza5Pnst0zgyccpbLj8qBZA9WGcxpZeYKVqihsLmGstOa5SOr2ZHxtLLyWPTrfz5yhiNV/NKMkc3YQY+/Z1YX6dKszuatAKs++BSqFfACQJNHgduqsPNUtULkDNbyNKQLtDy/TcAfA48Bb4jACAMyP1AAZ7J5wyPtSk3B7LHHuL7PECnrprSFlOIgmXWQyrlIKg6SCrmezrk0nU2u17UzgO3bHm4cAKpSKiKrEaCmaK5YvbEVKBPgumKQ4atQrmkgtxljpxoenpzxHAd7ZBBwudaPDQhc82AIQKga0TRJBCRSnYRacpmjBSJcGCwNNqrhRUFgG51JEPkeWV2coFnNg2L4MN085i98tY3nU0XFMXjmxVfw8uKl6NylAlcMm46xy7bjgXe3490NccxfW4PZX9ThhU+JEfBTn+zEkx/vxPQP6qi1C5kAx62oJmBxeRUeXLYDD1IG5aFlOzBm2Q5cOnACHqGgjaXYmElw6UQ6cSVpMXdE7zPRuUsv0pKqSy8c0ftMPLJsWwn7SD3o6PeYRFmZ8Suq0f1XB0Q0dj167I0jjzoGvzu1D7p27YZex/fGoUccjW7duqNjx444/v9646833oxnX5qDTVWNyNl5KHYBmktYPD8o0CrwPIIwD8shJq2q4dDUpQPNJOkz0/ZhOj5lK0TYbkBS/3Qi0y0PiuYgmbGws1lBbWOGWz4QOwgbGckSIp1z8eTTz0E1fTrIksFWNQPo1Pxct/OcobNo/2nGoOm0K47h5KVK+Tx/rWYFyOo+UjmPaJM0IhkoP9F5kuaIvC9hLEI+STEQaboFeiy7UnrXQmNSRUMih/q4iua0hnhKRTKtIqUYuPxPV0TS1QcdfAj+cPEf0bHjtYjFNiEW24FYrBmx2O3o1OlX/Lv17X8Bkrq/W/DH4olnXtwtOMxQAKm6Lby9VsLIoyrjYeU2BQ+9uw0jX/8Btz/7Lq5/ZCZunfE2Rr6+HmPfF4sRBvzGvU+CMer8mF1RxXtx3zd7DW6dMhu3jpyAq274J4497gR06NABvU88CWMfmYh167cgpVhQzYCm1chEm8jaiGdsNGdsUjSQtsiEq3pQrSggUM0AGY2kGtM5VypMI8FAlc2ZMZbhIPfr0uKULV5Vw4flhnjm2efp4iekTG50YatT8MjG4lK2jmdAJPaM6bj5Astn6co8dMvF/AWLMXLUaMxbsBiG5QofWAYAnaAsKyhncAwnOpYyaY9G097s9/PqVltkfV6Zv6ish94r8xdxLToDomxxJhZGosuH5eZFMaCXxzPPvcCLU7g+kV4yyxVxvchZPln3JxhCen5zACgY9ZwR0FapPmf8WEaNgT2ZVChN7aZVlx9LjOFLZEmKO6mQsYoUtggNYHsRSPu2pxvVAKb4yowJ0eVCjBwVpstgjwG+SPGFJ3R9bUCa4SFHizg48KPgj1HepSA0IxWCpCUanL2egUf2fUmVsCv9Fi/yXPKZ0RWoYBADyn76/HWK7iKni8t0zkJWtZDVbAweOhwdO0Zd3zt2vBN33DUQdQ0JLFj0L3Tt2g0n/e5UnHfhRfjn7XejMWMgazFRORGYK05BVBg6hUgLooxdQMrMI2XmEacWHg1aiLqcj60pFxviNtY1WnhrQwoL18Ux95tmzPmqGTPXNuKFLxrw7KcNePqTetw09ll07tKrTVP66x6YgfErqvHI8mqMW16FM66+BUf/th9+d+6luODamzHysZfx/vpG/Nhs4/5xU3DAgQfhnvuGYMmb76I5raExbUAzXFTX1uP1N9/Ghx99gm/W/YiUYnIDblYh51PdGikACuEGwkJHpzopww4ksE6DH0fktmq4UHQHik7YvSxl97Kag6zqIKM5SKtE75ZRXeQMlx7TLj++c4aHjEYG20TW4aygGLyDyApft0MYNhn4GSjk91PWh2iLQuTMECJNRFiFrB4QJtAtYO78tguHyspemDt/ccSHUgagOYN4sWX1EGmVAMqdcQ2NiRxSORupnIOmrIumtImGlIm4YiOleUjrPuI5G3cPHIL+Z56Fgw8+BEceJTSXhx9xZJvvEosdhlhsoVjUVPbCrPlL/iMA/CXBqmizdpGwg1YB85e8haOO7oVYLIZu3Xvg2ONOQM99iBfh6f3OxEOPv4SVWxV8WqPh650G1jdbWNdg4tMaFau3KVi5NYulG9N468cULrjqBnTpUoG9unTB/51wIgZcdgUGDRmGuQv/hQ3bdqI+oWNns4a6Zg11zTrqmnXUJ3Q0pkzEMzbXlCUVGylFMC5EZ2ajOW0jkXXocWOTwjk6zqRVlzBPdCzhVZ1OyNlB2yPpT9UMOChjDgYGNdon2tOQymYISGOAkek5c4bHgZScVmagi2hLRdaGSWRk2QwLzXTaLEj69z8fuuWWFMeFUnGcPN5HK3MZCGTnrWqKRT8vdJLGf6Y7HzJsZNkuGoOHjuQFigxIOjQL4IcsxFjDWqX5krUM66/LK5yDEr0irbBmiysC/EqMmPnCLk9lI2IcYPIg1SRZBZVqfBWWAtd9ZDSfFKHQSElsHglHpHlzYq5j850iz1900VLX2M4Atm97tkVSwByElQAklr6QWUCjpNiCgUJTAoClFU056b11qjNkqQs2WDAqXI40PSlK7y8Fd7JfYOl98onE2USJUZS/I2cldZEyzGpk0E8pNtKqg5mz5qGiUgZVDjp33h8DLvsT7rj7Xvzj5ttwdK9jI6zL7AVLOOArx5TwkCbKDAWCJBhQLCLnEkZF81pgBa08TNq8XPfIY6rbgiEj7m8zuHbsdCfuGjwSOzIetqRc7Mh4/HtefsXVuHvgYPQ+8ST03GcfPPncS0ibeSx64x1c+9e/4dDDDsdRRx+N2++4C4OGDMODYx7Bo48/hTlzF2Dxkjfx5tJleHfZKqzftAMpxURKMZHOEUNWtuKNpG3kRQFL42gOsppDwJ7mcODGnpejDHI65xDDVxps4FR0wtYKHZUQx7N2giIlJVbs7FJ4QTJn/gKfEJhu0KBeaZqVh2IwsXnAheeqRZhCMvkTfd+IUW3/i06d7sSIUfdTy4kCZyZYSthg6WQrhGr4SKR11DUksWHrTmypakQ8YyDnkHRr2swjoXloViw0KxY+++ZHxGIxDBo2Cis/Xot3Vn6Eha8vxVfrtyKp++jb/wJUUOPaTp2OQCx2FGKxlsh3GzLi/v8JAJRjZ1LDVX++HvvtfwCmPvYc1m1qQE2Thp0JA01pE99v3IHJ055Ajx57451lq/gxrbot0NwWGH4rzICEFbRia00DYrEYlrz5Pr74dgu+XLcZqz74Av96cxnmzF2Ml2bNxwszX8F3G6rQmDLRnLGRUBxSMJBjoD/kk3eWjkOpnEPBoY3GlIXGlIVE1kYiy5hCUqnNnpNUXCQUh49bScXhFavNGQcp1eWZBsMOeOEas5lizB0fWyWw5nCmTRp3I+MweQ5PNVLAxQBh6RhuuSHmLVhcdkEyf+HithpAaZEfqWblRQ7E0UGeN3LSnFI6HqdzIl6es6BMRqUXXp6zgLOHLI3MmFMOrpmmm1bWRqUZeVFYQsGaRjWAuiUt4iT5RqkuULNEmphVC5PIt3merOtVzQA5k+gPMxqpPiZZgGhkNU/sCz6/eZG5TNGIEwZzw1A0FzvbAWD7todbz1gshgaaAuZhsgHD5wNOFOCJlR5L87bVBrbVrggNi0/TFwFf0SpU7JrhwM0rGSgcvlLitjCaiHJgMMoCClZQBoAkvUKYJRY5w0VWcyhoIZWT8ayF2kYFVTvT2FqTwPbaOE497WxUVByDDh1uR4cOlaio6Iq//eMW3HLH3Rg4ZAQem/E8Xn9nBT79ej221DaXaKT+B4wKB4sFrtOKBH3erPltWxQxZoezMXYRNc0K7ho4BL/ad1/02LsnDjnkUGF3IX1u2srjpVcWYujI+3HHPffhr3+/CZcMuBx9+p2BE3oTM+FDDz0MHTp0wCGHHIo/DrgcD42dgNr6FBIZE3EeFhKKLVIcmocUY3o1HxmVsSuksi/DwSNZWTNhOasWZEBO46yuzycOpsXKGQLos2OPHRs5pg+kILCc5lWwdEGEqWO6oHLsIUsX6VaAV+aXn3DnLXw1MgGx92G/J2d4SOeIT1kiYyKtWKSzQc5BImOhOW0gTdPaac1DRg/QmLKxcXsjLvjDxdhrr71w9Z+vx9rvNkWOw6TuY9b8JRg8fDRuv3sgulT0anuczFsMxcrz44kfB2aIx55+AWf//lxMfWwG4qq722O1MWvhi+82YfGb72L0mHE49LDD8fvzLsQPWxtRl3Bo2KiL26hPWiQFm9Ix4NLLccPf/0HZ8bYLJHbsN6QN/Pq44yMLru49euDIo47GSb89Gaee3gexWAyXDLgcOxMqkqqHpOoiqbpIqQ6SqoNEzibXKYBjC4mE4hDASKtNUzkXCYUwg+x6WvWQMwJkKdNDgKBLQaKDphQBi4pO0sOs6IIBQZYy5po7yQxYNUlqmB1DXB5jiuNfriiVtXbyOEicHtzIOD90eHnmbejwUVJFquTGUKLRZm4NkflD8q2Tx2d5PI8UNuRcNKcM9Ol7Hl+MVFT2Qp++5yGeNiSw5LYBTqVROkaUDSPg17O6j6xGL9l1OaTH5eeT8ScgqV9dFLsouk+/Q5T1K/ed+W0Z7HHbM5LRyEpzXE5n4bUbQbdv/5OtZywWQ2M8E6k0k4ssGLgzSlaPpVVMEQbF9CMDlWb5GDZiVGQVKg9ixLld6B3iWSc6YEgDRSrn8NWSnPJl2ogosyRsZXLSd+NVeXQAy2oOSfVS1imj2khlTSSzBppTGjI5A43xLLZVN2LD1np8v2knftjSgHUbavDMi7Nxyml9sO9++6Muof7PmZJy8d8yMqVMT0Vlr91qu3IumVQ/+ep7rPx4Lb7+cet/+F1tQW3ObUFC9VDVmMHL815F/zPPRiwWw31DRlLGxUNC8ZDK+XR17COtkdRmkvp0ZVRyf1YPkKaPZ6QBOUMH+wzTXrFJT4um/lN0MmfHA/PZY9qcIcNGRUTZTDuqSTolBuDYc1ihUBTwiedwAbwVfUyzQmQ1lnIT7aL69jsf8YzF2SfGOBOvS7IgkQXzzAsznbNpGtyhCxUCEMkEFCCteogrLhI5D19+twkXXnQJKioq0GPvvXHkUUfj5NNOx2VXXIXBI0bjhVnz8Pm3G9Cn3/ltjpODDj4kAqxisRj22msvUlCy3/4YPGQ4YrEYPvj827LHyHebqvjrTuh9Ei6/4hrMmrsEjSkbO3Zm8MmXG/DqG8vx+IyZGDZqDK659q84+ZTTUFlZiU6dOmHUg49g1vwlGDLifsyav2S3msSMlUeTYmNjVT0+X7cRKz9ei1ffeg/PzZyLCVOm49LLr+TfI2OEyJp56G4RuttC+t3aBWSNPBQjD9XMU20XYXNSOR8ZPUBKDZBWAyQUHwmF9LvNaAEvGmDFBR4zRPZJwZBuh9SrjoAEi6Z5TVp4JherDR0+KlJNK6du2fPZMS8Dsv/ko9pWDuNiVjnmrbIXZs1ZwAEjBx2/IJjcIluyMC9l/NI5F5mS2/G0gZfnLMDgoSPx8pwFFPyJOUBOmWZU6vvHwJXWFmApOjm//x0YjIC8MoBSfq+cLmQiDPAxIJcu8/tEAYco4miT2ZKAn1IO7MmgWiIu2gFg+/a/2CIAUBYly1W4HMiZ0VWfZvlcx1HKrpWmYDdv24mMtBplGqyMKk4S0rtRrMBzhks/K9rqR6zOJW2XxOIIjZ8vXm9EAaqo0iIDgCiv95DIklROMmsLw1CTDPC6QzQ8adVBbVMO0x6bQSa2E0/C6k+/huK2/L8OAJn/2n8LAv/jJGoXkLXyyFh5whKVqfz805XX8El0/wMOQL8zzsL/nXAiDjr4YBx08MG49PIrcNe9Q3DRJZfiyKOORocOHfB/J5yIe+4bhvVb6pBSfSQUD/EsSYmxaEpTpoQGuz+eJZYOCYWIpBmzQm6TarmE4vAJIqOx1bTHGeOUJKBmK212nG6rqo9oRjNUZyNrAiOLB+m6ZpaAQ1N+jWAGGJPJQGhz2sDMWfMxaPAIvDhrPppTBteepUsWNQy8yprZrEbS4lmWBi9ZJDHtWrRq0CPawUYFn6z9Hm+8sxqz5i7BuEmP4h83346+/c9CBTXMPvbXx+M3vz0ZV15zLaZMfxL3DBqGo44+pg0IlOOiSy6lmta2x8z3m6vx25NPRUUFMfPuUlGBHj32RvcePRA915UAAAAgAElEQVSLxdC5c2ccdvgROOPs3+P6G27EA2MnYM7C1/DlD5sRV12yeKk4Bh07XoHOnffHYYf3wqiHHsGoh8bh3kEjcO1fbsCZZ/8eRx19DAemXbt1w8GHHIreJ56Es84+F1dc9Wfcesc9mDJ9Bt56dxWaUiaa0ybiWXLsJHMu0qrPNZYZzaPaQJeneZvSNhqSNmrjJrJ6QJ0BmAk9qYZlFbiEzfNpRa0wASbFcST7QRa/wu6DVXtu2raTa6PlxQ3T0LF0KtHBsoImMnaySCo21TKKSxZJmsJOZG00JjT06XMuKip6oSN1M+jT51w0JDQy/vHXOxzQJBUxTqcUB5mcg7TicDlG22gLjuSQK1tTilP2Mfk5IpzIOV76ONuX7Pxg9yXZpSK9Lxs/SvTmMrjLqB4vFmKRpBmM0kiV7COxHwizJwM9pYTZY8Av4spBddGk8p/Maw3xTDsAbN/2aBMA0PQjFL4ir/oMMeBEVnT0JGFpWa7DkjRZGZWlShyucWDvQarnyCAWEbrqQuugmiSNJ9J9oqiEW9WYpfeVWMpw8CqsXORQDMFEZal5aFr1iZ7LCKFRAT8xAPWhaA5SWR07mxTUNCp4f9XHGDR0JCq7dsWfr/srFr72NupT+v8nbOD/NEoAYLmU9bI1n6Jv/zMjk/8+++yDu+8djHeXf4ix46fgltvuwsQpj+PV19/DlqpmNKeJgL4pbaMxZVNg50YAXzRcNGdc4umVdZHIehz08chGwSADiCzdkmYDd4luVEyYtqRLjGpNCesWrdhjl4Jh9iWQJrEF9DGWMpLTPxkq/k7nxLnCzo2kInSqOT7ZCx0anzR0twSwyr/Vi0x+QnAu9glZ4JD9Rfavh6Y0u23iw0+/xgsz52Dg4GG47q9/wyUDLsMZZ56N40/ozQFb9+49cMppffDXv9+EB8ZOxPMvz8PmmqbdHldpM8Q3G7Zh/pK38MDYCbjx5tvw+/MuxFFH90K37t1R2bUrKioqsNdee6Fnz31w2OFHoPdJv8HZ556PM8/+PTp12gex2P6IxQ5CLDYAHTt2R7/+Z+O6v9yIm265Ew8+PBHPvTgHS99bg29+2IbGpEHYupzPTYBTqoeEQs7pVAToEUZPNYl+i3lDqmbIfeSYf5xOC3tSGnmfHNV4sQpxh1r5sE4bjl+IdL9g5sesBaYnFS8wSxLShYmASLZ4YAuI0tSuSKlGARcDgAK02dGQAEwia6MhoeHFWfNx3+ARePHl+WhIaJHHSVDgQzM1PBQRMuP1XwNACvz4Zc4Rx/FuwCGfZySwJQCauCwH0HYXpUC3FPBFwZ/DL9l3SFNAnJF+P3MeELpmCfQZpZp02YpNzHHM8kkGgI3N7QCwfduzLVIEIldosYGFTVRMp5VWHengJ6J+dtIoutdGjJ9RHVotJ1q5MaCmS3oXlpKVW8LlDJKuy5k+dJv0+HV9qVm2ZJJqSGlqVlHHDZ3tEBoNZurMNFqaXNllhZzpI3YPpAJTMQJkVAeJjIbmlIpEWkVTQkF9Uwqbq5qxrTaBzVXN+GTtOtx+5z3odeyv0alTJ5x8ymm49c578MKsefjiu01ozjn//4O8XxLuL3vetvokFvzrLZx3wUXo0qULmtNECN+UttCYMtGQNNGQNFCfEEFumzwaU4T5a0xRJpAxfxkXScWjOioGBAXoY6nkJG3xlKI6wRQNBuaZLisj6XhUM+AaHpVXgEcXBKwQgBQDSGke1ZVSz0LvwzREGY11+hDvI1eVJxWbTAZyYRJle0TLJwEECVNuc1DKv5cmUl8E6PkiNHE9owXIUCCT1UOeskyrJJ3J2Nh4ljAjSYWxYURLmDU8KIaHnBkga+VR25zDJ1//gNnzl2D46DG47IqrcEyvY9GxY0cMuPwKfL5uIz775kdMfXwGbvznbTj19L4c4J3Q+yRc/MfLcNsd92DS1Mfx2pvL8NW6jdiwaTs2bqnCpi078Nna77B0xcdY8vYyPPPSHJx59jno0OEExGJvIRb7mRcwDRw8CnHFRyLnI571kdYCcd5aeWSNEDkzj6we8pSuYuT57yf7IqAMP9WZKh71eWzhVZ9ZzacLC5enb9lxolOtJksHsuIhQ6oOZ8BQtFETLd7IfS2wvQIvOmLFA4aT54tSbiGkRdOdDPizlChjsxiTKIf83yYVtwzIEUCHFbTEMzbiWRq7uS6DQg6EpJRnaaWrDOIEyJMIgn/LEjptgCBbILHr8UwUoHFQl40CVPl2KagTryFgmYFpETbSlE1NKzYFebYIKssgizYnktoVWavStLqwXhO9f8VzVCOa/m1PAbdv/4utZywWQ01Diq/UslrJiRnRMTB2jwwgPG1BnxPPCGCYpGm5HF21vv7We7T6VwiZS9uuMTDHDTVNUkrPfJKYJ6E8CMsaLNWK3hcR6NsS6JPAn2YzcbUQ6wtAwNLDBFRkjRBJxcHOuIaGpIHmrAfNCqntiIUdtQk0p000Jg1s3FqLWXMX47Y77sZpp/dB165d0aFDBxx22OG48KJL8O2GrREbGBalQvvS24pTxOvvrPi3wIwXhViU0TNDpPUQaT1A1gyRNaQwQyhWnn+XnJVHljKAGUuwgOR2HgnNR1PWQVPWQWPGwvpt9YjFYrjtzoHYsqOJ9q8l4C2pREEZ249JyuAxIT2bfIgHG/Ffa6Z+bCTYMSWxfmzCkdNDTHAt6QOzGrVjMNgxIx8Debz9znI+WRs2jTbWD6GkXZXBos+LRhTJ1JX1J2VVoFzfJS1UGCtdqk1lIC+dszBn7kIMHT4Kc+YuRDpnRRZHrHqZAQPZYoKBGaapTOU8znrJkaFMlmKEHBxm9ZCCJxKsZVZaI4wa+18Ze5jI+cjaBcx8ZSHuGjgEHTt2RLdu3XHBHy7BoKGj8OKsBfjsq/VIqy63tGGt8DKqjUTGQFoxYDrEvJpVVytGHik1wEuzF6GiTHXo8zMXoCnjojFN2n81ZVyuK03SSCji2GMLBa4doxIBweoKPVjOYOwe2Qesmpvsj0DaNwEH/pabpwsC+v9RsJgz2o5Viu4jS/8nMr5FLUL+9cZ7ArRF0pYeB3BcElEC7BK0CjkRuU6lFFlxHiWUUgbdEexeBBQ6FOiVgDwJ6DWliKRh8JCRmDl7AZpSRhSwlYA8ARClFK78nqWXOaGpK/3slHR/koE2ac569fV3SlKyggVl4I5fz7ZNl5dGWmGZAwnwMYZPFdmESPWu5kZYP57hkq3LZM2k6kYWiKWWMOw57a3g2rc93TgAlEFblMYXVghZlTCA8QzxHuM2HJQtTFKtAwNsbGLPqC7mzF3IK3xV0+fvm1GJLUKpobPcDkkUjASI6qyi3m2qVR78RSs121ZusufLmi5dWsmXur1nNJcwhZQ5TCqkH3BTMoea+gRqG9J8lZbOkUmutjGL9Vt2Yumy1fjHP28jQvouXXDwwYfgxJN+iz9deTVmzV2E2fMWY9K0xzF4+Cjccc99mDjtcSx47S18vm4jmhQbilPEvYOH4857B+HxZ17EG++twgNjJ+Ca6/6KaU8+g4+//B6PPvUsLh5wGU45rQ96Hftr7H/AgejatStOPuU03HPfULyy8DVs2r6TFFboJM2t0Iq2LC24iCsuGjI2mrI0MjYaMxaasjYa0hYa0xaaMjY++nwdYrEYKiorEaN9b88+51w88dRzmLfgX3hn2Wp8uvY7bNhaiy07GrC1qgnbauLYUZdETUOGpEA5cBMMVjQlLwMWX6TmaJouZ4Y8hafbRKfJ0nXMf4+BvEi4ecydv5i2c6JdYEwRzBqkrS1ESI2BRbUu6xSjMUZIF2lhVlRSdmFCFyfMe4ywTi769Yt6s/Xtdx7SOTviM5aTwAMrrCHaSheNlFVtTDloynhoynhoTLv8sjHtoiHloD5JooE/lwC7VM6j/4MATQwUMMaWsIYEXM6auxiqlccPm2uR1RzEKSirTxKGJJ6x+URIJCYOFM2GarjEpJca8XphC3SnQJg6PURaddG3nyiaYdWhDUkLCcUnLKDioyHlojHtoTHtoTnroznr09/qIZELkNZY71f5vyQZgbQqJAYZDo7pAoKCOhn4RQGguC1ajxFgncyR15PMQsABJgPsCQm4pVQBVmfOXsgXSmLBIy+aSuQQCgV1XC/r0JS+gzbSCWkBFZeCvYf4zJLCBU0UXcgFD6x6Vz5W+/Q9DwmpgKOc7UuE3YukjslzecZJiiQFqSwj9UvSuTNnzS9h7+i8xsCcDOwYuJNAnnybpXaZjCSjSundMkU2rPgmnZMbK7Qt0Ckt1pErpcvtOwamq3cm2wFg+7ZHW89YLIbqnUmx4itJC2RUF81UBE00S+R6Q0LDzNnzcfe9Q3Dr7ffg7oFD8cLL89GQ1DmYbM7YER2HbOmy28o0ifKWO3jIVcPM0kNm7Bg708aywy7fhNukJq3C6FO2uhEGoAwoiG4MdKKmvmwGfS82wWc1D41JDVuqm7GlqhnbapL4cWsT1m9txo/bk9hSq6CqUcOGrQ344puNWLbiA8ycPR/3DByCE3qfiNP79MUfB1yOv/7tH7j5ljsw4NI/ofeJv0H37kR/te+++2HffffDLbfeid+fewGOOPJIDLjsKox64BGce94FqKisxBlnnYNxE6dh7sLX8MbSFVj50Vf44tsNmPXKIvzz1jtw0m9+h44dO6KiogKHHX4ETjmtDy4ecBluu/NeTJz6BBa+thQrP1qLL7/bjK01zWjMmEjpPhKqiy21zfjgs3X415vvY9GSt/HO+2uw+qMv8cU3P+KTL7/D/WPGo9+ZZ6P3ib/Bb357Mo448ij06LH3bosHHn3iWT4hkUIQi2oFLTSlLHofYQFL07skpcvSm6LxO9Nm5cwoeOMgizr8azb9H50CDNoKSrfZZC2KLzTTl8Ag8QFjoJMBlZQaIK2RYGBBBonEMkJKB1MBeKQFokm6nszdTVeEOXMX8iplVj1PquZtNCYt7Iyb2Bm3UJ+00JAUektyPnoRUK0YrIMBSzsKLzPdLkChujfFEB1JMpqH5oyNVM5FIueiPkUiowWU2Qtp31Wii0urPlRqkE1SsLI9DmuXV+BebMKPLS+11ssjp7uYu2Axho0YjTnzFiOrOeJ9JBDOzn92jmtmQNKwzLDYyVO7FFLZm6NVvgo32g6QzBEAmKNMcUolxxjbB8xLLq16MN08358aNfzWqAecZuWhUDDIFjQJKmkgmlbBzjI2Lp516H1tix/KRdkiCanYYbfFFlL1LC+QkCpTWfFDuffmn0GfN3M3/n0zZ8/nrxNVvIK9S+fE+M/nAgnsZNW2VcKl4E64RtD0bdZpAybLaREz9L3lz42G04bB43OV9D1LX19qc1MOzJXTRDJAJwNbAValx0tAbE19OwBs3/Zs6xmLxVBFAWC5FRRboSWyrD+hgybm2VTRixrHHoZY7HZU0NVfU8rgKzg+EEkDCxHNC78/5m4uV0qyNjoC8LW11RBgT+rYIAM91veRXo/0gpSAHTMQ5Waikee1fT/xOOvNKlKCik4Z06yHhrSL5iwZ2BtSNuoSFtJaiIyeR12zirrmHHbGdTSldDSlNNQ1JJHI6NyCJpnREU+pSGV16HaA2oZmrFj1EbZX70R9cxrbaxPYWJVGXcJBc9ZHY8pEVX0WW3Y0YmdTFk0JBdV1Tdhe3YiGuEp1mj5SORc1DSl88fV6vPH2csyetwjTHnsS9wwcggGXXYETepOq3krK6sVoH98uXbrwIoBjev0aJ/Q+EUcedTT23/8AVFZ2lYBdJ3TosBd67rMfrrz6OvQ/42wcetgR/PFOnTrh4EMOxamn98V3G6qQ0alpsp2HYobIWSF0p0Bbr1EQYoa8LZMwZA4jwI4YJhdhOEWYbhGaXSBhFaCaBZhukQczcdZtAgQ1qvlUrTxnchh40y2Zoctz5o4UCghAlZOBiGQyGwk7zwGOKYEcw8nTbgRFDBsxuqw32zDqzcYLRLQoS8SLYGjqW+5fHZFLUIAs7ie/h6V6CQgioI6B6ww1q62P60hkTRi2T82BicG1VdJGK6uHfN8yI2u5py77rWwfsE4MtleAQ7VyTDtn++Q+1raLXWfnLbtuewV+rtp+QerCIfqSm7SThM1NjIWZcVhogWqGvIDDlTpE2F6BGnoTc+Gs7sNyCAuoW0QjPHf+YgwZNgqz5ixEPGPRwhM3wjDGsy5lbMn/1ZR2kKD3pWiaN61GZRNyZPj/y0JiK5ketdTDLnLbE5eyzYnmQdGiFinlPpexl2wcHzx09x085Gr8ctWxu7NI4QBQdcvOQ6JwSrqkjzENoCxHYqQFB4JqGR9Z6b7dNRL4X0apDrKt563DiRLWtjJVsv+q2wFg+7aHG2kF15iKClVlPyJWqi55E816ha36FiIWK+8hVbpqakqoHPCx+6PC2KjPEW8PZ/q7D8tHKetXzriXAUPTiRpaCzAXCkDnhmWBIXe+9wqRsNklnaRY8ObxfhGWJ4JNeGSSE62KSK/KFth+ETplQnQ7j7QmGIiMSpiGpGJy9iJnhrxbhWj7xJrME0bJsD3olgvd8qCZDnK6jUzORCKtIp5UUL0zhW01CWyujqOuScHO5hziaQ2pjIrmpIrvNlTj/dWfY/nqz/HDxhpsrmrEtupG1DcraE5biGdszJqzgC4I0ojFqhGLvYvOnffHlVddi6eeeQmLX38PK9Z8jk3bdyKV0ZDNGdAtF4blwfEYY+NBt0naP5k1oWik9Z5mulA0E81JBTsbEqhrSKKhOYt4WkVz2oBqkKreZNZEJmcirRhIZHQ0xLNIpHJIKwYUzUbOcKFZHi8S0u0Q6ZwZ8blkACmdszFn7iIMGTYKs+cuRCprgbV3kq1emN6L6LmI7ov0MKb/hR3A9kJ4QR5BWEC+QFrjkUrQPHzqBReEBeo7GGLR4vLt4l6Zv4j7CZoulSbQClSW7tYsAlI8egzqdp6nVh2p36lhR/ucqqYAf6wKnnU2yUhpzawekOPRzPO0u2blkcpalEkUzBpLz7NKWbmfMevW4PjiPPLCFpi0r6vcDUUUTIg2Xz4vpCCvDfJF+Pki7wSR1RzMX/gqho0YjbkLFkPRHdKzlmp+ZV9TmYUtHWNYYVqkH7gu7HlUw0M6Z6Fvv/NKUvbnI5OzuBMBOR8DWFTrrFFttOmEyKgehMWWj4aESgqAVJfLZtqkTiNsnQQeJEZPBheyFYrMTu0uyoEyxpzJ32Pm7N0zgLKOvFS7l865JdWy4r4oWyeqajNSEQnT3EVTseT1WXqZUV00JVVy339Iu/LQ3LaM4G7mxl8SOUn3x2MP3k+O9k4g7duebqQTSHMmapvyH2Lo8FF01fcQYrG72rrIDxspuc2TkvV7Bg4Ca09UrqKp9LYiVUiVDsilnmyGHUCPMHVhG/DG2wJ5IkwpxUs8ukrYv3LhlH+eLYNB6br8GsIAhbTbQ1QPRt6HskJegTNERkm/Sd0OMXDQEA5AHb/IJ0M3IGwJ63fpBmTCJ89rgemKpuc6TX8ShoWATy//E9xwF9ygFR69VK08UjkX8bSKZEZFc1JBJmfAYj5mtBfvoCEjyjMBw0YhkbVR3ZDBzmYFimrBoCwW6QtMG7jbISym1XPzsH0BluX9TCZw2p6QakQZuLb9IrxwF5ygFW64C6bXCt0pgrVtUi2RzmSM3MBBQzg4Yf9FWrWp7iyqa2pOm5EUmKK70EwPpkNAnuuTKnU/zMML8vDDAsJ8EUFYRFhoQb7QgnyxFYXiLgSFVoSFVhRbf0JYaOUN64NCKyw3QL8zLoiYRfc/4wLSQaJk0cCCvZ5cb+V9UNljbP8YLtkfqkXa2VlekaZ/C0ipwtCYP0cClux6hM2k1++9bwgvlJCDpclVM891s+y9CABl7CFdJEl2Kuz/t31yDDhegYM+x2M9dWk/cl+0/lJNF/3PKOlve8b5UDSHA2fXF23CLOrfZ/sFzvRHWH4+PjAtcDQLMWde+ZT93PmLoVkBZyHlhSnra8vek7crc8j5zY5z1rKNZSKY9EU1/RID5miISlIpDNado4zZsQx25C4iLDNDWUL2elatnsgY6FvSwaNv3/OQyBiRnr+lkh/GurVNozoR0FXOvJoXU5QWT2htAdzd9w5qk6YtB+x29xmkc4obkST9V2H+kpD8aimxIYcmXcrR2O4D2L7t4SZ8AG0fmh1I4UevSwfenHkLd8sACq2SdAIYHhIZPbq6piyf3CNYiQwu0bSwfFsMaNH2RLqsAaJsHgN7zIjV9gvR4A3BSTCWr/R+BujkSWN3wJKBSaY5Eu3zRMqYp7Qlz0KZtZTT0fJn2F4eWc0SaS0+MQSRycSSXivAgbChCGhz9KAgws+L8MIWuBRoBIVdyLf8jLD4E/ItPyPIt5L96YT0u4eYu6A8a7Vw8RLemJ39Ds5QudF9SrSUoSioYEBNYo8st0BBLQWzPFo4kGWpQ8srwvFbKJgUqV9DAtpZzebA06Bs2Jx5u2/XxtKQbF/6+SK8kLB6YaEFQZ5dL3KfNy8Uz/H59RYEebJ//TC67/PFXbC9EItffQ2jRj+ARYv/BdsNERR2ISjsijS6l0EeY/gcvxhdRFCmj6W9TUfcz0AXey6XRkiaPHnf8//MK0Rel1Ft8r70dRZ7L3qfziqs7TyxYzJL0/eiOEcc++I8lNk+uc8r89dzKEtouXksWNS29WFlZS8sWLQETlDkv0f+H3l6mR1P0m0nEGlozlj64nNHjCqfsh8xajR5HR1DOHj1C3CDQsn1PBwvDy8oQDdtOlbl4QYFGsXIa1gK25TAYaT7jLRg1mSAUbLoloGiIgFFkZ3x2oAxMhYL/7q0YmLO3IUYMmwkZr+yEKmsGVnQMyIgYm5slgGtejQ7lFGFDYqcURIArUwLuhLg25xSyzByEiAuAWq/lASJRJv5koJ8GfD/myxVudDKvF4rud6YyLYDwPZtj7aesVgM8ZTCWw39klAN0s6qouKYiAawsrIX+vU/H6rhlKRaQ5GWLBc8BUf6BLc5wXY3cJVjEHXppC7LMEZ7vMqp4XJArux9Eqi0vbw0Kcm3pebpZZqqs+eUspRtAGqZcPYw3NIJSLovGsVIkM8uRoAzmyQN20f/MqyV6QRwgiKdcMV7yq2ybL8ofT86sdL9IxfpEPBIeqg6dHL05O/rF8Rj/Hp0wmRsjOWG9P1D7sHGtKLDd6PBGzRkJBTd4ylEoS0LYVhCXyYKDQiQZawvAUDkd8iTP0ltElDoUxNh1ycg2QuKEWNhh94v9qMI9lr+HnT/MD0bC4f/d+Lyl4b8fPm4YebG4jZJc5cNDoxbIt+ff3fpMXnRIj+/9NgkjxOQPWr0/WX/v5Gj7ifPD8Xr5GNS/l38ewRF+KH47n4ogt1euKj84mfR4iUIKAsc0tR/EFJGOC+CLRrYY8wcmu8TaV+6pf+HdMyzcyMy9rBxrWQMl8dky2GyEbZYDaRxO7rQ5OOlLKdhnU8cMZ6yzzQd6X7ps/ln8MeZf6vcbzvkqXPymQEslwT77rrtw3KJ3EW2WDJYURX7jW4I2xNhOgFseV+0+b3iM8yS38zGDkvexx4Nto/lsb1Eayrul0gKt+T93JL3KvMfxlNKOwBs3/Zo6xmLxZBI58oAlH8fmuli/sLFGDpsBO4deB+GDh+J+QsXQzPdyAD0377vfxu7PWmkk/u/Abe7DTpwkMv/3fd3aMi3y93fNsKy8ct+D00ruQFMVwyqVmTQI7flwVYMwG3f0/ZCaKaD+QsXY8TI0fRYcPj32t1vYZOXQ9OnLrtd+h+7AgBG9p2/+4gcJ7v5zmIyDPkENHd+eQZwztyF0Kinn2lL+4EBShoWnVzl4iW+yGE9hi1h/Mz6C7PnsElMdCGROoNoUa2XbDXDmGTTId/D8dgl25/ku5qOmLRtaX/wfUAnXPk4tEqfRxknMbHn+cQYWfhEgG6BA9O2wFJmrdmx0Zb9YrcjwFFiWf2wiEWLyzOACxcvEWCLRwuPsNDCWVxPAmoMmLMIGHijz7UcH2eULH769T8fhu3BD9sCPb8EBMoAkHxGIQI6f2m4QfR1ri/OEZcvigRQl18nL6LEfxUF7eXujy6+ov+nI30+P5/p8Vj6HeXzSD4WTSeMjA9MNiIv9Nj7uOxz6fu7fnQsZY+XGxPKjTcyYJUBLwfVXtv4X81nv+S5iXSuHQC2b3u09YzFYvh/2ruX5LRhAAzATDb0FjkEy+QsPUHb2yQE7tVOpt0AfgKHUBe2QRIiQ9JNOvN9M/8Yy3oYYSSyys/X32HX9GP2Q+p+yFReR8c6O2+yuoU8LVdp26vpLrJ9T6pb0oZt1YbNeCydbwrJy4e6t44Z5Yb3UZqHaY6engtzGc11Nebyc9iH6krqdkxzTlx+tU1Uv2nHdGn5KeP1Oh5vrN90aVl8rVg+9ZXfwzv7el6uTm2mPjfbOiwWD2E+vw93d1/DfH4fFouHsNnV57pRm7TPw5WxD6HpDsm9T2VNdwhtKX12PJUfh5Tq94U+436mtoV0/TF0/WHM8TL7KKd6Q15W69Dvy+37/XG8NrXNyvfn8jYfq0/bJf1EicfYVW1YLB6zz+8x7Or23Gfe/w1ps9dt9HpbteFltQ7fvv8Iy9U6bKs2mdvmjXnPs1yts7aH0PTH5Hl5K3WUvCy/Pjyv5We2ilK6Pn33rq0puw8mHjdea5I1re6HdfJ03oVtsh4OfQ1rZbqX7Jo+WmezNoV97Hwv6V53fp+X+2RVuN+4r2tjlPbWKt6Po/x6/eMHIP9kPnvjH7yLiIjIp003m82+zOCD5rPhLwgRERH5f+LHHwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAftfAAAAChSURBVAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPBJ/QVI4Yx/meWtigAAAABJRU5ErkJggg==\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.gridliner.Gridliner at 0x7fbc17f2ecc0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.PlateCarree(central_longitude=-79))\n",
+    "ax.scatter(lon, lat)\n",
+    "ax.stock_img()\n",
+    "ax.coastlines()\n",
+    "ax.gridlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Other projections\n",
+    "\n",
+    "Let's try out a different projection now, say\n",
+    "[Azimuthal Equidistant](https://en.wikipedia.org/wiki/Azimuthal_equidistant_projection).\n",
+    "It preserves angles and distances from the centre of the map.\n",
+    "We'll centre it on this building, 43.660924°N, 79.398329°W."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.gridliner.Gridliner at 0x7fbc17e92cc0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_lon = -79.398329  # East is positive.\n",
+    "to_lat = 43.660924\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.AzimuthalEquidistant(central_longitude=to_lon,\n",
+    "                                                          central_latitude=to_lat))\n",
+    "ax.scatter(lon, lat)\n",
+    "ax.stock_img()\n",
+    "ax.coastlines()\n",
+    "ax.gridlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hmm, what happened? Our points should be all over the globe. Actually, if\n",
+    "you look at the previous plot, even though we changed the central longitude,\n",
+    "the points never moved either!\n",
+    "\n",
+    "This is a common trap with Cartopy, but it's also one of its most useful\n",
+    "features. We neglected to specify the projection in which the scatter points\n",
+    "originated, thus it was plotted in the map coordinates. Map coordinates are\n",
+    "almost always specified in metres (except for Plate Carrée) and since our\n",
+    "points are in degrees, they are _extremely_ small (the circumference is\n",
+    "approximately 40,000 _km_.)\n",
+    "\n",
+    "We can specify the input projection using the `transform=` keyword argument\n",
+    "to `ax.scatter`. In this case, we use the Geodetic projection for the points.\n",
+    "This projection is basically longitude/latitude."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.gridliner.Gridliner at 0x7fbc17e63a20>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.AzimuthalEquidistant(central_longitude=to_lon,\n",
+    "                                                          central_latitude=to_lat))\n",
+    "# Here we add the transform argument and use the Geodetic projection.\n",
+    "ax.scatter(lon, lat, transform=ccrs.Geodetic())\n",
+    "ax.stock_img()\n",
+    "ax.coastlines()\n",
+    "ax.gridlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What's the difference between Plate Carrée and Geodetic?\n",
+    "Let's look at a map:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.gridliner.Gridliner at 0x7fbc17ef8630>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ff_lon = 8 + 41 / 60\n",
+    "ff_lat = 50 + 7 / 60\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.PlateCarree(central_longitude=(to_lon + ff_lon) / 2))\n",
+    "ax.plot([to_lon, ff_lon], [to_lat, ff_lat], c='b', lw=2,\n",
+    "        transform=ccrs.PlateCarree())\n",
+    "ax.plot([to_lon, ff_lon], [to_lat, ff_lat], c='r', lw=2,\n",
+    "        transform=ccrs.Geodetic())\n",
+    "ax.set_global()\n",
+    "ax.stock_img()\n",
+    "ax.coastlines()\n",
+    "ax.gridlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, Plate Carrée uses the straight-line path on a longitude/latitude basis,\n",
+    "while Geodetic uses the shortest path in a spherical sense (the\n",
+    "[geodesic](https://en.wikipedia.org/wiki/Geodesic).)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Features\n",
+    "\n",
+    "Let's focus in on Canada for a bit to see what features Cartopy can depict.\n",
+    "We'll switch to the\n",
+    "[Lambert Conformal Conic projection](https://en.wikipedia.org/wiki/Lambert_conformal_conic_projection)\n",
+    "which works well for this size and range of the map, and is recommended by StatsCan.\n",
+    "We'll use their recommended standard parallels (see wiki link for explanation) of\n",
+    "49°N and 77°N, and a central longitude of 91°52'W. The boundaries are arbitrarily\n",
+    "chosen to be 63°W to 123°W and 37°N to 75°N, and set with `ax.set_extent`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.gridliner.Gridliner at 0x7fbc17da3128>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# An arbitrary choice.\n",
+    "canada_east = -63\n",
+    "canada_west = -123\n",
+    "canada_north = 75\n",
+    "canada_south = 37\n",
+    "\n",
+    "standard_parallels = (49, 77)\n",
+    "central_longitude = -(91 + 52 / 60)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.LambertConformal(central_longitude=central_longitude,\n",
+    "                                                      standard_parallels=standard_parallels))\n",
+    "ax.set_extent([canada_west, canada_east, canada_south, canada_north])\n",
+    "ax.stock_img()\n",
+    "ax.coastlines()\n",
+    "ax.gridlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we can see two things:\n",
+    "\n",
+    "  1. The stock image and coastlines are not very high resolution.\n",
+    "  2. The coastlines only show the oceanic coast; no lakes or rivers, and certainly no countries.\n",
+    "\n",
+    "Cartopy provides various \"features\" that can provide some or all of this content\n",
+    "at varying resolutions. Some simple shortcuts to [Natural Earth data](http://www.naturalearthdata.com/)\n",
+    "are provided in the `cartopy.feature` module and can be added via `ax.add_feature`.\n",
+    "These are downloaded and cached on the fly, so there may be some issues if the\n",
+    "WiFi is being flaky in MP408."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import cartopy.feature as cfeature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fbc17d76320>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.LambertConformal(central_longitude=central_longitude,\n",
+    "                                                      standard_parallels=standard_parallels))\n",
+    "ax.set_extent([canada_west, canada_east, canada_south, canada_north])\n",
+    "ax.gridlines()\n",
+    "ax.add_feature(cfeature.OCEAN)\n",
+    "ax.add_feature(cfeature.LAND)\n",
+    "ax.add_feature(cfeature.LAKES)\n",
+    "ax.add_feature(cfeature.BORDERS)\n",
+    "ax.add_feature(cfeature.COASTLINE)\n",
+    "ax.add_feature(cfeature.RIVERS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These are all defined on the 1:110m(illion) scale, but what if we want\n",
+    "something a bit higher resolution? Well, the above were just shortcuts\n",
+    "and we can use `cfeature.NaturalEarthFeature` directly with its\n",
+    "`scale` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fbc179aa9e8>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "land_50m = cfeature.NaturalEarthFeature('physical', 'land', '50m',\n",
+    "                                        edgecolor='k',\n",
+    "                                        facecolor=cfeature.COLORS['land'])\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.LambertConformal(central_longitude=central_longitude,\n",
+    "                                                      standard_parallels=standard_parallels))\n",
+    "ax.set_extent([canada_west, canada_east, canada_south, canada_north])\n",
+    "ax.gridlines()\n",
+    "ax.add_feature(land_50m)\n",
+    "ax.add_feature(cfeature.LAKES)\n",
+    "ax.add_feature(cfeature.OCEAN)\n",
+    "ax.add_feature(cfeature.BORDERS)\n",
+    "ax.add_feature(cfeature.COASTLINE)\n",
+    "ax.add_feature(cfeature.RIVERS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's add the provinces and states (the possible names as used here\n",
+    "can be determined by visiting the link above):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fbc17984978>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "provinces_50m = cfeature.NaturalEarthFeature('cultural',\n",
+    "                                             'admin_1_states_provinces_lines',\n",
+    "                                             '50m',\n",
+    "                                             facecolor='none')\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.LambertConformal(central_longitude=central_longitude,\n",
+    "                                                      standard_parallels=standard_parallels))\n",
+    "ax.set_extent([canada_west, canada_east, canada_south, canada_north])\n",
+    "ax.gridlines()\n",
+    "ax.add_feature(cfeature.LAND)\n",
+    "ax.add_feature(cfeature.LAKES)\n",
+    "ax.add_feature(cfeature.OCEAN)\n",
+    "ax.add_feature(cfeature.BORDERS)\n",
+    "ax.add_feature(cfeature.COASTLINE)\n",
+    "ax.add_feature(cfeature.RIVERS)\n",
+    "ax.add_feature(provinces_50m)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Web Services\n",
+    "\n",
+    "Web Map Services (WMS) provide vector data as a download via some\n",
+    "web API and Web Map Tile Services provide raster data via a similar\n",
+    "interface.\n",
+    "\n",
+    "These can be added by `ax.add_wms` and `ax.add_wmts`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.slippy_image_artist.SlippyImageArtist at 0x7fbc178df780>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.LambertConformal(central_longitude=central_longitude,\n",
+    "                                                      standard_parallels=standard_parallels))\n",
+    "ax.set_extent([canada_west, canada_east, canada_south, canada_north])\n",
+    "ax.gridlines()\n",
+    "\n",
+    "ax.add_wms(wms='http://vmap0.tiles.osgeo.org/wms/vmap0',\n",
+    "           layers=['basic'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unfortunately, WMTS doesn't support reprojection in 0.14, but it will\n",
+    "in the next release 0.15, so we need to use Plate Carrée for this map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.slippy_image_artist.SlippyImageArtist at 0x7fbc15935588>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "url = 'https://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi'\n",
+    "layer = 'VIIRS_CityLights_2012'\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.PlateCarree())\n",
+    "ax.set_extent([canada_west, canada_east, canada_south, canada_north])\n",
+    "ax.gridlines()\n",
+    "ax.add_wmts(url, layer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shapefile I/O\n",
+    "\n",
+    "What about outlines that you provide yourself? You might obtain a\n",
+    "shapefile from somewhere outlining something. These can be read\n",
+    "with `cartopy.io.shapereader.Reader`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from cartopy.io.shapereader import Reader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use the data files provided with the lesson here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "data = Reader('icitw_wgs84')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7fbc1584dda0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.LambertConformal(central_longitude=central_longitude,\n",
+    "                                                      standard_parallels=standard_parallels))\n",
+    "ax.set_extent([-79.6, -79.2, 43.5, 43.9])\n",
+    "ax.gridlines()\n",
+    "\n",
+    "ax.add_geometries(data.geometries(), crs=ccrs.Geodetic(), edgecolor='k', facecolor='none')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case you do not recognize the shapes, these are wards for the city of Toronto.\n",
+    "\n",
+    "Each of those geometries is a [shapely](http://toblerity.org/shapely/) object,\n",
+    "on which we can do things like determine if points are contained. We won't\n",
+    "really go into any details of it here, though. The example below is just a\n",
+    "quick one-off to show how it might be used."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import shapely.geometry as sgeom"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ward = next(data.geometries())\n",
+    "\n",
+    "us = sgeom.Point(to_lon, to_lat)\n",
+    "ward.contains(us)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<img 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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.LambertConformal(central_longitude=central_longitude,\n",
+    "                                                      standard_parallels=standard_parallels))\n",
+    "ax.set_extent([-79.6, -79.2, 43.5, 43.9])\n",
+    "ax.gridlines()\n",
+    "\n",
+    "for ward in data.geometries():\n",
+    "    if ward.contains(us):\n",
+    "        our_ward = ward\n",
+    "        ax.add_geometries(our_ward, crs=ccrs.Geodetic(), edgecolor='k', facecolor='r')\n",
+    "    else:\n",
+    "        ax.add_geometries(ward, crs=ccrs.Geodetic(), edgecolor='k', facecolor='none')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What happened here? It turns out that the ward data is a bit buggy,\n",
+    "and each ward is _not_ a closed polygon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "our_ward.is_closed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Shapely will nicely render its contents in a notebook:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
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+      ],
+      "text/plain": [
+       "<shapely.geometry.multipolygon.MultiPolygon at 0x7fbc157a5ba8>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "our_ward"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The type can be found from the `geom_type` property:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'MultiPolygon'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "our_ward.geom_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`MultiPolygon` contains multiple polygons, but fortunately, there's just the one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(our_ward)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll construct a \"linear ring\" (basically a closed shape) from\n",
+    "the data in the first polygon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-79.42373624654661 43.62912524734518 0.04794778684534151 0.05076003808391505\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,87.30901053277427)\"><polyline fill=\"none\" stroke=\"#66cc99\" stroke-width=\"0.001015200761678301\" points=\"-79.40792017043108,43.63296978534256 -79.40844646142484,43.634350007999245 -79.40861858793687,43.63443222048306 -79.40874745593234,43.63445238779015 -79.4085234257068,43.63469162590749 -79.40826060507624,43.63492177695938 -79.40812192420424,43.635024928567304 -79.4079832664107,43.63510955500585 -79.40781543040028,43.63519679504741 -79.40761844866742,43.63528136481252 -79.40747617728933,43.6353282811657 -79.4070058047533,43.635440351359804 -79.40671593483033,43.63549030554359 -79.40668313337012,43.635495958494914 -79.40617645453104,43.63555269133913 -79.40565254422263,43.635603655869446 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+      ],
+      "text/plain": [
+       "<shapely.geometry.polygon.LinearRing at 0x7fbc1585ff98>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sgeom.LinearRing(our_ward[0].exterior.coords)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And if we use that in our plot, the fill colour will no longer \"spill\" out\n",
+    "over the rest of the map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width);\n",
+       "        canvas.attr('height', height);\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'];\n",
+       "    var y0 = fig.canvas.height - msg['y0'];\n",
+       "    var x1 = msg['x1'];\n",
+       "    var y1 = fig.canvas.height - msg['y1'];\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x;\n",
+       "    var y = canvas_pos.y;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        event.shiftKey = false;\n",
+       "        // Send a \"J\" for go to next cell\n",
+       "        event.which = 74;\n",
+       "        event.keyCode = 74;\n",
+       "        manager.command_mode();\n",
+       "        manager.handle_keydown(event);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(1, 1, 1,\n",
+    "                     projection=ccrs.LambertConformal(central_longitude=central_longitude,\n",
+    "                                                      standard_parallels=standard_parallels))\n",
+    "ax.set_extent([-79.6, -79.2, 43.5, 43.9])\n",
+    "ax.gridlines()\n",
+    "\n",
+    "for ward in data.geometries():\n",
+    "    if ward.contains(us):\n",
+    "        # Close the polygon...\n",
+    "        our_ward = [sgeom.LinearRing(ward.geoms[0].exterior.coords)]\n",
+    "        ax.add_geometries(our_ward, crs=ccrs.Geodetic(), edgecolor='k', facecolor='r')\n",
+    "    else:\n",
+    "        ax.add_geometries(ward, crs=ccrs.Geodetic(), edgecolor='k', facecolor='none')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Other links\n",
+    "\n",
+    "* [proj.4](https://trac.osgeo.org/proj/)\n",
+    "* [Geopandas](http://geopandas.org/)\n",
+    "* [Folium](https://folium.readthedocs.io/)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/lessons/python/cartography/externalize.py b/lessons/python/cartography/externalize.py
new file mode 100755
index 000000000..554168f40
--- /dev/null
+++ b/lessons/python/cartography/externalize.py
@@ -0,0 +1,48 @@
+#!/usr/bin/env python3
+# Externalize PNG images from markdown files converted by nbconvert.
+
+import argparse
+import os
+from base64 import b64decode
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('input')
+parser.add_argument('output')
+args = parser.parse_args()
+
+figure_dir = os.path.splitext(args.input)[0] + '_files'
+
+with open(args.input) as fd:
+    lesson = fd.readlines()
+
+img_count = 0
+code_block = []
+with open(args.output, 'w') as fd:
+    for line in lesson:
+        if line.startswith('<img'):
+            # Convert embedded image to external one.
+            prefix = '<img src="data:image/png;base64,'
+            suffix = '">'
+            img_count += 1
+            img = b64decode(line[len(prefix):-len(suffix)])
+            fig_path = os.path.join(figure_dir,
+                                    'figure_%d.png' % (img_count, ))
+            with open(fig_path, 'wb') as ifd:
+                ifd.write(img)
+            fd.write('![figure %d](../%s)\n' % (img_count, fig_path))
+        elif line.startswith('    <') and line.endswith('>\n'):
+            # Ignore output lines that just print repr of an object.
+            continue
+        elif line.startswith('   '):
+            # Group all lines that are part of a code block.
+            code_block.append(line[4:])
+        elif len(code_block):
+            # Wrap code block in back ticks instead of indent.
+            fd.write('```python\n')
+            fd.writelines(code_block)
+            fd.write('```\n')
+            code_block = []
+            fd.write(line)
+        else:
+            fd.write(line)
diff --git a/lessons/python/cartography/icitw_wgs84.dbf b/lessons/python/cartography/icitw_wgs84.dbf
new file mode 100644
index 000000000..72072b7db
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diff --git a/lessons/python/cartography/icitw_wgs84.prj b/lessons/python/cartography/icitw_wgs84.prj
new file mode 100644
index 000000000..f45cbadf0
--- /dev/null
+++ b/lessons/python/cartography/icitw_wgs84.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
\ No newline at end of file
diff --git a/lessons/python/cartography/icitw_wgs84.sbn b/lessons/python/cartography/icitw_wgs84.sbn
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index 000000000..c1c2624ee
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index 000000000..e48ccd63a
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diff --git a/lessons/python/cartography/icitw_wgs84.shp b/lessons/python/cartography/icitw_wgs84.shp
new file mode 100644
index 000000000..1e9599e0f
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diff --git a/lessons/python/cartography/icitw_wgs84.shx b/lessons/python/cartography/icitw_wgs84.shx
new file mode 100644
index 000000000..0c8eacd58
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diff --git a/lessons/python/cartography/icitw_wgs84.zip b/lessons/python/cartography/icitw_wgs84.zip
new file mode 100644
index 000000000..01ba570fb
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diff --git a/lessons/python/cartography/lesson.md b/lessons/python/cartography/lesson.md
new file mode 100644
index 000000000..487f8915b
--- /dev/null
+++ b/lessons/python/cartography/lesson.md
@@ -0,0 +1,769 @@
+---
+layout: page
+title: Cartography and Mapping in Python
+visible: true
+tags:
+  - python
+  - intermediate
+---
+
+ - **Authors**: Elliott Sales de Andrade
+ - **Research field**: Seismology
+ - **Lesson topic**: cartography, maps, projections, Python
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/cartography>
+
+
+
+```python
+# Default imports
+import numpy as np
+%matplotlib nbagg
+import matplotlib.pyplot as plt
+```
+
+## Setup
+We will be using Cartopy for this lesson, so let's import that.
+The common convention is to import it as follows:
+
+
+```python
+import cartopy.crs as ccrs
+```
+
+Let's start with a simple plot of points:
+
+
+```python
+np.random.seed(1)
+x = 360 * np.random.rand(100)
+y = 180 * np.random.rand(100) - 90
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1)
+ax.scatter(x, y)
+```
+
+
+
+
+
+![figure 1](../lesson_files/figure_1.png)
+
+
+
+
+
+
+
+
+Why pick $x$ values between 0 and 360, and $y$ values between -90 and 90?
+Because that corresponds to the ranges for longitudes and latitudes!
+Let's rename them.
+
+
+```python
+lon = x
+lat = y
+```
+
+## Projections
+
+The first problem with mapping is deciding in what projection to create the map.
+Some interesting properties you may want in a map are:
+
+ * Conformal: Preserves angles locally, implying that locally shapes are not distorted.
+ * Equal Area: Areas are conserved.
+ * Compromise: Neither conformal nor equal-area, but a balance intended to reduce overall distortion.
+ * Equidistant: All distances from one (or two) points are correct. Other equidistant properties are mentioned in the notes.
+ * Gnomonic: All great circles are straight lines.
+
+See the [Wikipedia list of projections](https://en.wikipedia.org/wiki/List_of_map_projections#Properties)
+for more information.
+
+Cartopy has [several options available](http://scitools.org.uk/cartopy/docs/latest/crs/projections.html).
+We will start with the very straightforward [Plate Carrée](https://en.wikipedia.org/wiki/Equirectangular_projection)
+(also known as Equirectangular or Equidistant Cylindrical.)
+We pass an instance of the projection class to the `projection` keyword
+argument of `Figure.add_subplot` to signify the projection in which the
+resulting map will be made.
+
+
+```python
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.PlateCarree())
+ax.scatter(lon, lat)
+```
+
+
+
+
+
+![figure 2](../lesson_files/figure_2.png)
+
+
+
+
+
+
+
+
+## Adding elements
+
+So far so good; let's add some coast lines:
+
+
+```python
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.PlateCarree())
+ax.scatter(lon, lat)
+ax.coastlines()
+```
+
+
+
+
+
+![figure 3](../lesson_files/figure_3.png)
+
+
+
+
+
+
+
+
+Or how about a land/ocean image:
+
+
+```python
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.PlateCarree())
+ax.scatter(lon, lat)
+ax.stock_img()
+ax.coastlines()
+```
+
+
+
+
+
+![figure 4](../lesson_files/figure_4.png)
+
+
+
+
+
+
+
+
+And maybe some grid lines:
+
+
+```python
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.PlateCarree())
+ax.scatter(lon, lat)
+ax.stock_img()
+ax.coastlines()
+ax.gridlines()
+```
+
+
+
+
+
+![figure 5](../lesson_files/figure_5.png)
+
+
+
+
+
+
+
+
+This projection supports two arguments:
+
+
+```python
+ccrs.PlateCarree?
+```
+
+
+```python
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.PlateCarree(central_longitude=-79))
+ax.scatter(lon, lat)
+ax.stock_img()
+ax.coastlines()
+ax.gridlines()
+```
+
+
+
+
+
+![figure 6](../lesson_files/figure_6.png)
+
+
+
+
+
+
+
+
+## Other projections
+
+Let's try out a different projection now, say
+[Azimuthal Equidistant](https://en.wikipedia.org/wiki/Azimuthal_equidistant_projection).
+It preserves angles and distances from the centre of the map.
+We'll centre it on this building, 43.660924°N, 79.398329°W.
+
+
+```python
+to_lon = -79.398329  # East is positive.
+to_lat = 43.660924
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.AzimuthalEquidistant(central_longitude=to_lon,
+                                                          central_latitude=to_lat))
+ax.scatter(lon, lat)
+ax.stock_img()
+ax.coastlines()
+ax.gridlines()
+```
+
+
+
+
+
+![figure 7](../lesson_files/figure_7.png)
+
+
+
+
+
+
+
+
+Hmm, what happened? Our points should be all over the globe. Actually, if
+you look at the previous plot, even though we changed the central longitude,
+the points never moved either!
+
+This is a common trap with Cartopy, but it's also one of its most useful
+features. We neglected to specify the projection in which the scatter points
+originated, thus it was plotted in the map coordinates. Map coordinates are
+almost always specified in metres (except for Plate Carrée) and since our
+points are in degrees, they are _extremely_ small (the circumference is
+approximately 40,000 _km_.)
+
+We can specify the input projection using the `transform=` keyword argument
+to `ax.scatter`. In this case, we use the Geodetic projection for the points.
+This projection is basically longitude/latitude.
+
+
+```python
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.AzimuthalEquidistant(central_longitude=to_lon,
+                                                          central_latitude=to_lat))
+# Here we add the transform argument and use the Geodetic projection.
+ax.scatter(lon, lat, transform=ccrs.Geodetic())
+ax.stock_img()
+ax.coastlines()
+ax.gridlines()
+```
+
+
+
+
+
+![figure 8](../lesson_files/figure_8.png)
+
+
+
+
+
+
+
+
+What's the difference between Plate Carrée and Geodetic?
+Let's look at a map:
+
+
+```python
+ff_lon = 8 + 41 / 60
+ff_lat = 50 + 7 / 60
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.PlateCarree(central_longitude=(to_lon + ff_lon) / 2))
+ax.plot([to_lon, ff_lon], [to_lat, ff_lat], c='b', lw=2,
+        transform=ccrs.PlateCarree())
+ax.plot([to_lon, ff_lon], [to_lat, ff_lat], c='r', lw=2,
+        transform=ccrs.Geodetic())
+ax.set_global()
+ax.stock_img()
+ax.coastlines()
+ax.gridlines()
+```
+
+
+
+
+
+![figure 9](../lesson_files/figure_9.png)
+
+
+
+
+
+
+
+
+As you can see, Plate Carrée uses the straight-line path on a longitude/latitude basis,
+while Geodetic uses the shortest path in a spherical sense (the
+[geodesic](https://en.wikipedia.org/wiki/Geodesic).)
+
+## Features
+
+Let's focus in on Canada for a bit to see what features Cartopy can depict.
+We'll switch to the
+[Lambert Conformal Conic projection](https://en.wikipedia.org/wiki/Lambert_conformal_conic_projection)
+which works well for this size and range of the map, and is recommended by StatsCan.
+We'll use their recommended standard parallels (see wiki link for explanation) of
+49°N and 77°N, and a central longitude of 91°52'W. The boundaries are arbitrarily
+chosen to be 63°W to 123°W and 37°N to 75°N, and set with `ax.set_extent`.
+
+
+```python
+# An arbitrary choice.
+canada_east = -63
+canada_west = -123
+canada_north = 75
+canada_south = 37
+
+standard_parallels = (49, 77)
+central_longitude = -(91 + 52 / 60)
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.LambertConformal(central_longitude=central_longitude,
+                                                      standard_parallels=standard_parallels))
+ax.set_extent([canada_west, canada_east, canada_south, canada_north])
+ax.stock_img()
+ax.coastlines()
+ax.gridlines()
+```
+
+
+
+
+
+![figure 10](../lesson_files/figure_10.png)
+
+
+
+
+
+
+
+
+Here we can see two things:
+
+  1. The stock image and coastlines are not very high resolution.
+  2. The coastlines only show the oceanic coast; no lakes or rivers, and certainly no countries.
+
+Cartopy provides various "features" that can provide some or all of this content
+at varying resolutions. Some simple shortcuts to [Natural Earth data](http://www.naturalearthdata.com/)
+are provided in the `cartopy.feature` module and can be added via `ax.add_feature`.
+These are downloaded and cached on the fly, so there may be some issues if the
+WiFi is being flaky in MP408.
+
+
+```python
+import cartopy.feature as cfeature
+```
+
+
+```python
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.LambertConformal(central_longitude=central_longitude,
+                                                      standard_parallels=standard_parallels))
+ax.set_extent([canada_west, canada_east, canada_south, canada_north])
+ax.gridlines()
+ax.add_feature(cfeature.OCEAN)
+ax.add_feature(cfeature.LAND)
+ax.add_feature(cfeature.LAKES)
+ax.add_feature(cfeature.BORDERS)
+ax.add_feature(cfeature.COASTLINE)
+ax.add_feature(cfeature.RIVERS)
+```
+
+
+
+
+
+![figure 11](../lesson_files/figure_11.png)
+
+
+
+
+
+
+
+
+These are all defined on the 1:110m(illion) scale, but what if we want
+something a bit higher resolution? Well, the above were just shortcuts
+and we can use `cfeature.NaturalEarthFeature` directly with its
+`scale` argument.
+
+
+```python
+land_50m = cfeature.NaturalEarthFeature('physical', 'land', '50m',
+                                        edgecolor='k',
+                                        facecolor=cfeature.COLORS['land'])
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.LambertConformal(central_longitude=central_longitude,
+                                                      standard_parallels=standard_parallels))
+ax.set_extent([canada_west, canada_east, canada_south, canada_north])
+ax.gridlines()
+ax.add_feature(land_50m)
+ax.add_feature(cfeature.LAKES)
+ax.add_feature(cfeature.OCEAN)
+ax.add_feature(cfeature.BORDERS)
+ax.add_feature(cfeature.COASTLINE)
+ax.add_feature(cfeature.RIVERS)
+```
+
+
+
+
+
+![figure 12](../lesson_files/figure_12.png)
+
+
+
+
+
+
+
+
+Now, let's add the provinces and states (the possible names as used here
+can be determined by visiting the link above):
+
+
+```python
+provinces_50m = cfeature.NaturalEarthFeature('cultural',
+                                             'admin_1_states_provinces_lines',
+                                             '50m',
+                                             facecolor='none')
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.LambertConformal(central_longitude=central_longitude,
+                                                      standard_parallels=standard_parallels))
+ax.set_extent([canada_west, canada_east, canada_south, canada_north])
+ax.gridlines()
+ax.add_feature(cfeature.LAND)
+ax.add_feature(cfeature.LAKES)
+ax.add_feature(cfeature.OCEAN)
+ax.add_feature(cfeature.BORDERS)
+ax.add_feature(cfeature.COASTLINE)
+ax.add_feature(cfeature.RIVERS)
+ax.add_feature(provinces_50m)
+```
+
+
+
+
+
+
+![figure 13](../lesson_files/figure_13.png)
+
+
+
+
+
+
+
+
+## Web Services
+
+Web Map Services (WMS) provide vector data as a download via some
+web API and Web Map Tile Services provide raster data via a similar
+interface.
+
+These can be added by `ax.add_wms` and `ax.add_wmts`.
+
+
+```python
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.LambertConformal(central_longitude=central_longitude,
+                                                      standard_parallels=standard_parallels))
+ax.set_extent([canada_west, canada_east, canada_south, canada_north])
+ax.gridlines()
+
+ax.add_wms(wms='http://vmap0.tiles.osgeo.org/wms/vmap0',
+           layers=['basic'])
+```
+
+
+
+
+
+![figure 14](../lesson_files/figure_14.png)
+
+
+
+
+
+
+
+
+Unfortunately, WMTS doesn't support reprojection in 0.14, but it will
+in the next release 0.15, so we need to use Plate Carrée for this map.
+
+
+```python
+url = 'https://map1c.vis.earthdata.nasa.gov/wmts-geo/wmts.cgi'
+layer = 'VIIRS_CityLights_2012'
+
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.PlateCarree())
+ax.set_extent([canada_west, canada_east, canada_south, canada_north])
+ax.gridlines()
+ax.add_wmts(url, layer)
+```
+
+
+
+
+
+![figure 15](../lesson_files/figure_15.png)
+
+
+
+
+
+
+
+
+## Shapefile I/O
+
+What about outlines that you provide yourself? You might obtain a
+shapefile from somewhere outlining something. These can be read
+with `cartopy.io.shapereader.Reader`.
+
+
+```python
+from cartopy.io.shapereader import Reader
+```
+
+We will use the data files provided with the lesson here:
+
+
+```python
+data = Reader('icitw_wgs84')
+```
+
+
+```python
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.LambertConformal(central_longitude=central_longitude,
+                                                      standard_parallels=standard_parallels))
+ax.set_extent([-79.6, -79.2, 43.5, 43.9])
+ax.gridlines()
+
+ax.add_geometries(data.geometries(), crs=ccrs.Geodetic(), edgecolor='k', facecolor='none')
+```
+
+
+
+
+
+![figure 16](../lesson_files/figure_16.png)
+
+
+
+
+
+
+
+
+In case you do not recognize the shapes, these are wards for the city of Toronto.
+
+Each of those geometries is a [shapely](http://toblerity.org/shapely/) object,
+on which we can do things like determine if points are contained. We won't
+really go into any details of it here, though. The example below is just a
+quick one-off to show how it might be used.
+
+
+```python
+import shapely.geometry as sgeom
+```
+
+
+```python
+ward = next(data.geometries())
+
+us = sgeom.Point(to_lon, to_lat)
+ward.contains(us)
+```
+
+
+
+
+```python
+False
+```
+
+
+
+
+```python
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.LambertConformal(central_longitude=central_longitude,
+                                                      standard_parallels=standard_parallels))
+ax.set_extent([-79.6, -79.2, 43.5, 43.9])
+ax.gridlines()
+
+for ward in data.geometries():
+    if ward.contains(us):
+        our_ward = ward
+        ax.add_geometries(our_ward, crs=ccrs.Geodetic(), edgecolor='k', facecolor='r')
+    else:
+        ax.add_geometries(ward, crs=ccrs.Geodetic(), edgecolor='k', facecolor='none')
+```
+
+
+
+
+
+![figure 17](../lesson_files/figure_17.png)
+
+
+What happened here? It turns out that the ward data is a bit buggy,
+and each ward is _not_ a closed polygon.
+
+
+```python
+our_ward.is_closed
+```
+
+
+
+
+```python
+False
+```
+
+
+
+Shapely will nicely render its contents in a notebook:
+
+
+```python
+our_ward
+```
+
+
+
+
+![svg](../lesson_files/lesson_51_0.svg)
+
+
+
+The type can be found from the `geom_type` property:
+
+
+```python
+our_ward.geom_type
+```
+
+
+
+
+```python
+'MultiPolygon'
+```
+
+
+
+`MultiPolygon` contains multiple polygons, but fortunately, there's just the one.
+
+
+```python
+len(our_ward)
+```
+
+
+
+
+```python
+1
+```
+
+
+
+We'll construct a "linear ring" (basically a closed shape) from
+the data in the first polygon.
+
+
+```python
+sgeom.LinearRing(our_ward[0].exterior.coords)
+```
+
+
+
+
+![svg](../lesson_files/lesson_57_0.svg)
+
+
+
+And if we use that in our plot, the fill colour will no longer "spill" out
+over the rest of the map.
+
+
+```python
+fig = plt.figure()
+ax = fig.add_subplot(1, 1, 1,
+                     projection=ccrs.LambertConformal(central_longitude=central_longitude,
+                                                      standard_parallels=standard_parallels))
+ax.set_extent([-79.6, -79.2, 43.5, 43.9])
+ax.gridlines()
+
+for ward in data.geometries():
+    if ward.contains(us):
+        # Close the polygon...
+        our_ward = [sgeom.LinearRing(ward.geoms[0].exterior.coords)]
+        ax.add_geometries(our_ward, crs=ccrs.Geodetic(), edgecolor='k', facecolor='r')
+    else:
+        ax.add_geometries(ward, crs=ccrs.Geodetic(), edgecolor='k', facecolor='none')
+```
+
+
+
+
+
+![figure 18](../lesson_files/figure_18.png)
+
+
+## Other links
+
+* [proj.4](https://trac.osgeo.org/proj/)
+* [Geopandas](http://geopandas.org/)
+* [Folium](https://folium.readthedocs.io/)
+
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\ No newline at end of file
diff --git a/lessons/python/classes/Classes_in_Python.ipynb b/lessons/python/classes/Classes_in_Python.ipynb
new file mode 100644
index 000000000..f7629c7ed
--- /dev/null
+++ b/lessons/python/classes/Classes_in_Python.ipynb
@@ -0,0 +1,630 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Class Structure in Python\n",
+    "\n",
+    "Lesson Page: https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/python/classes/lesson.md\n",
+    "\n",
+    "Adapted from https://www.jeffknupp.com/blog/2014/06/18/improve-your-python-python-classes-and-object-oriented-programming/\n",
+    "\n",
+    "## What is a Class?\n",
+    "\n",
+    "A class is a structure in Python that can be used as a blueprint to create objects that have \n",
+    "1. prototyped features, \"attributes\" that are variable\n",
+    "2. \"methods\" which are functions that can be applied to the object that is created, or rather, an instance of that class. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Defining a Class\n",
+    "\n",
+    "We want to define a class called *Client* in which a new instance stores a client's name, balance, and account level.\n",
+    "It will take the format of:\n",
+    "    \n",
+    "    class Client(object):\n",
+    "        def __init__(self, args[, ...])\n",
+    "            #more code\n",
+    "            \n",
+    "\"def `__init__`\" is what we use when creating classes to define how we can create a new instance of this class. \n",
+    "\n",
+    "The arguments of `__init__` are required input when creating a new instance of this class, except for 'self'. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create the Client class below\n",
+    "class Client(object):\n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        #define account level\n",
+    "        if self.balance < 5000:\n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else:\n",
+    "            self.level = \"Advanced\"\n",
+    "            \n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The **attributes** in *Client* are *name, balance* and *level*. \n",
+    "\n",
+    "**Note**: \"self.name\" and \"name\" are different variables. Here they represent the same values, but in other cases, this may lead to problems. For example, here the bank has decided to update \"self.balance\" by giving all new members a bonus $100 on top of what they're putting in the bank. Calling \"balance\" for other calculations will not have the correct value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating an Instance of a Class\n",
+    "\n",
+    "Now, lets try creating some new clients named John_Doe, and Jane_Defoe:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "John_Doe = Client(\"John Doe\", 500)\n",
+    "Jane_Defoe = Client(\"Jane Defoe\", 150000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see the attributes of John_Doe, or Jane_Defoe by calling them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'name' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-6-fcac2ded7a1c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mJane_Defoe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mJane_Defoe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbalance\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mClient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mJane_Defoe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'name' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "John_Doe.name\n",
+    "Jane_Defoe.level\n",
+    "Jane_Defoe.balance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also add, remove or modify attributes as we like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "John_Doe.email = \"jdoe23@gmail.com\"\n",
+    "John_Doe.email = \"johndoe23@gmail.com\"\n",
+    "del John_Doe.email"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'jdoe23@gmail.com'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "getattr(John_Doe, 'name')\n",
+    "setattr(John_Doe, 'email', 'jdoe23@gmail.com')\n",
+    "John_Doe.email"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also use the following instead instead of the normal statements:\n",
+    "\n",
+    "- The `getattr(obj, name[, default])` : to access the attribute of object.\n",
+    "\n",
+    "- The `hasattr(obj,name)` : to check if an attribute exists or not.\n",
+    "\n",
+    "- The `setattr(obj,name,value)` : to set an attribute. If attribute does not exist, then it would be created.\n",
+    "\n",
+    "- The `delattr(obj, name)` : to delete an attribute."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Class Attributes vs. Normal Attributes\n",
+    "\n",
+    "A class attribute is an attribute set at the class-level rather than the instance-level, such that the value of this attribute will be the same across all instances.\n",
+    "\n",
+    "For our *Client* class, we might want to set the name of the bank, and the location, which would not change from instance to instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "Client.bank = \"TD\"\n",
+    "Client.location = \"Toronto, ON\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'TD'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# try calling these attributes at the class and instance level\n",
+    "Client.bank\n",
+    "Jane_Defoe.bank\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Methods\n",
+    "\n",
+    "*Methods* are functions that can be applied (only) to instances of your class. \n",
+    "\n",
+    "For example, in the case of our 'Client' class, we may want to update a person's bank account once they withdraw or deposit money. Let's create these methods below. \n",
+    "\n",
+    "Note that each method takes 'self' as an argument along with the arguments required when calling this method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Use the Client class code above to now add methods for withdrawal and depositing of money\n",
+    "\n",
+    "# create the Client class below\n",
+    "class Client(object):\n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        #define account level\n",
+    "        if self.balance < 5000:\n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else:\n",
+    "            self.level = \"Advanced\"\n",
+    "            \n",
+    "    def deposit(self, amount):\n",
+    "        self.balance += amount\n",
+    "        return self.balance\n",
+    "    \n",
+    "    def withdraw(self, amount):\n",
+    "        if amount > self.balance:\n",
+    "            raise RuntimeError(\"Insufficient for withdrawal\")\n",
+    "        else:\n",
+    "            self.balance -= amount\n",
+    "        return self.balance\n",
+    "    \n",
+    "    \n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "300100"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Jane_Defoe.deposit(150000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### What is \"self\"? \n",
+    "`*`*not in the philosophical sense*`*`\n",
+    "\n",
+    "In the method, withdraw(self, amount), the self refers to the *instance* upon which we are applying the instructions of the method. \n",
+    "\n",
+    "When we call a method, `f(self, arg)`, on the object `x`, we use `x.f(arg)`.\n",
+    "- `x` is passed as the first argument, *self*, by default and all that is required are the other arguments that comprise the function. \n",
+    "\n",
+    "It is equivalent to calling `MyClass.f(x, arg)`.\n",
+    "Try it yourself with the Client class and one of the methods we've written."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "200100"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Try calling a method two different ways\n",
+    "John_Doe.deposit(500)\n",
+    "Client.withdraw(Jane_Defoe, 50000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Static Methods \n",
+    "\n",
+    "Static methods are methods that belong to a class but do not have access to *self* and hence don't require an instance to function (i.e. it will work on the class level as well as the instance level). \n",
+    "\n",
+    "We denote these with the line `@staticmethod` before we define our static method.\n",
+    "\n",
+    "Let's create a static method called make_money_sound() that will simply print \"Cha-ching!\" when called."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Add a static method called make_money_sound()\n",
+    "# create the Client class below\n",
+    "class Client(object):\n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        #define account level\n",
+    "        if self.balance < 5000:\n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else:\n",
+    "            self.level = \"Advanced\"\n",
+    "            \n",
+    "    @staticmethod\n",
+    "    def make_money_sound():\n",
+    "        print \"Cha-ching!\"\n",
+    "        \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cha-ching!\n"
+     ]
+    }
+   ],
+   "source": [
+    "Client.make_money_sound()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Class Methods\n",
+    "\n",
+    "A class method is a type of method that will receive the class rather than the instance as the first parameter. It is also identified similarly to a static method, with `@classmethod`.\n",
+    "\n",
+    "Create a class method called bank_location() that will print both the bank name and location when called upon the class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Add a class method called bank_location()\n",
+    "# create the Client class below\n",
+    "class Client(object):\n",
+    "    bank = \"TD\"\n",
+    "    location = \"Toronto, ON\"\n",
+    "    \n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        #define account level\n",
+    "        if self.balance < 5000:\n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else:\n",
+    "            self.level = \"Advanced\"\n",
+    "            \n",
+    "    @classmethod\n",
+    "    def bank_location(cls):\n",
+    "        return str(cls.bank + \" \" + cls.location)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'TD Toronto, ON'"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Client.bank_location()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Key Concept: Inheritance\n",
+    "\n",
+    "A 'child' class can be created from a 'parent' class, whereby the child will bring over attributes and methods that its parent has, but where new features can be created as well. \n",
+    "\n",
+    "This would be useful if you want to create multiple classes that would have some features that are kept the same between them. You would simply create a parent class of these children classes that have those maintained features.\n",
+    "\n",
+    "Imagine we want to create different types of clients but still have all the base attributes and methods found in client currently. \n",
+    "\n",
+    "For example, let's create a class called *Savings* that inherits from the *Client* class. In doing so, we do not need to write another `__init__` method as it will inherit this from its parent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create the Savings class below\n",
+    "class Savings(Client):\n",
+    "    interest_rate = 0.005\n",
+    "    \n",
+    "    def update_balance(self):\n",
+    "        self.balance += self.balance*self.interest_rate\n",
+    "        return self.balance\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# create an instance the same way as a Client but this time by calling Savings instead\n",
+    "Lina_Tran = Savings(\"Lina Tran\", 50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lina Tran\n",
+      "150\n",
+      "0.005\n"
+     ]
+    }
+   ],
+   "source": [
+    "# it now has access to the new attributes and methods in Savings...\n",
+    "print Lina_Tran.name\n",
+    "print Lina_Tran.balance\n",
+    "print Lina_Tran.interest_rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150.75"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ...as well as access to attributes and methods from the Client class as well\n",
+    "Lina_Tran.update_balance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#defining a method outside the class definition\n",
+    "def check_balance(self):\n",
+    "    return self.balance\n",
+    "\n",
+    "Client.check_balance = check_balance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "600"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "John_Doe.check_balance()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/lessons/python/classes/code_along.py b/lessons/python/classes/code_along.py
new file mode 100644
index 000000000..2889cd596
--- /dev/null
+++ b/lessons/python/classes/code_along.py
@@ -0,0 +1,92 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jan 12 17:19:42 2016
+"""
+
+class Client(object):
+    bank = "TD"
+    location = "Toronto"    
+    
+    def __init__(self, name, balance):
+        self.name = name
+        self.balance = balance + 100
+        
+        if self.balance < 5000:
+            self.level = "Basic"
+        elif self.balance < 15000:
+            self.level = "Intermediate"
+        else:
+            self.level = "Advanced"
+        
+        print "Name: %s, Opening Balance: %s" % (self.name, self.balance)
+            
+    def deposit(self, amount):
+        self.balance += amount
+        return self.balance
+    
+    def withdraw(self, amount):
+        if amount > self.balance:
+            raise RuntimeError("Insufficient Funds")
+        else: 
+            self.balance -= amount
+        return self.balance
+            
+    @staticmethod
+    def make_money_sound():
+        print "Cha-Ching!"
+        
+    @classmethod
+    def bank_location(cls):
+        print "%s %s" % (cls.bank, cls.location)
+    
+    
+John_Doe = Client("John Doe", 2000)
+Jane_Defoe = Client("Jane Defoe", 10000)
+
+John_Doe.name
+John_Doe.balance
+Jane_Defoe.level
+
+John_Doe.email = "jdoe23@gmail.com"
+John_Doe.email = "johndoe23@gmail.com"
+del John_Doe.email
+
+# try calling these attributes at the class and instance level
+Client.bank
+Jane_Defoe.location
+
+# Try calling a method two different ways
+John_Doe.withdraw(100)
+Client.withdraw(John_Doe, 100)
+
+Client.make_money_sound()
+Jane_Defoe.make_money_sound()
+
+Client.bank_location()
+
+class Savings(Client):
+    interest_rate = 0.001       
+    
+    def update_balance(self):
+        self.balance += self.balance*self.interest_rate
+        return self.balance
+
+# create an instance the same way as a Client but this time by calling Savings 
+# instead        
+Lina_Tran = Savings("Lina Tran", 50) 
+
+# it now has access to the new attributes and methods in Savings...
+Lina_Tran.update_balance()
+
+# ...as well as access to attributes and methods from the Client class
+Lina_Tran.deposit(200)
+print Lina_Tran.name
+print Lina_Tran.balance
+
+#defining a method outside the class definition
+def check_balance(self):
+    return self.balance
+
+Client.check_balance = check_balance
+
+John_Doe.check_balance()
\ No newline at end of file
diff --git a/lessons/python/classes/lesson.md b/lessons/python/classes/lesson.md
new file mode 100644
index 000000000..900e6441a
--- /dev/null
+++ b/lessons/python/classes/lesson.md
@@ -0,0 +1,40 @@
+---
+layout: page
+title: Introduction to Classes in Python
+visible: true
+tags:
+  - python
+  - intermediate
+---
+
+ - **Authors**: Lina Tran
+ - **Research field**: Neuroscience
+ - **Lesson topic**: Object Oriented Programming with Python
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/classes>
+
+## Topics Covered:
+
+- Defining a Class
+- Creating Instances of a Class
+- Attributes
+  - Class Attributes vs. Normal Attributes
+- Methods
+  - Static Methods
+  - Class Methods
+- Inheritance
+
+### What You Will Need
+
+- Python 2.7, or 3.4+ installed
+- Download the files for [this lesson](https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/classes)
+
+If you'd like to follow along in IPython Notebook, do the following:
+
+  - Install [Jupyter Notebook](https://jupyter.readthedocs.io/en/latest/install.html)
+  - Run the notebook by typing `jupyter notebook` into command line.
+  - In the web application that opens, navigate to the folder containing the files you downloaded, and open 'Classes in Python.ipynb' to follow along.
+
+Otherwise, you can follow along in your favourite IDE or command line. 
+
+All the code will be posted to "Classes in Python", and as a .py file after the lesson. 
+
diff --git a/lessons/python/cpu-multiprocessing/dask_tutorial.py b/lessons/python/cpu-multiprocessing/dask_tutorial.py
new file mode 100644
index 000000000..faf2e5164
--- /dev/null
+++ b/lessons/python/cpu-multiprocessing/dask_tutorial.py
@@ -0,0 +1,35 @@
+'''
+From: http://distributed.readthedocs.io/en/latest/joblib.html
+'''
+
+
+import distributed.joblib
+# Scikit-learn bundles joblib, so you need to import from
+# `sklearn.externals.joblib` instead of `joblib` directly
+# This is not true depending on the packaging (e.g. Fedora/Debian)
+# in this case, use the following:
+# from joblib import parallel_backend
+from sklearn.externals.joblib import parallel_backend
+from sklearn.datasets import load_digits
+from sklearn.grid_search import RandomizedSearchCV
+from sklearn.svm import SVC
+import numpy as np
+
+#load mnist digits data
+digits = load_digits()
+
+#set up the parameters to be explored
+param_space = {
+    'C': np.logspace(-6, 6, 13),
+    'gamma': np.logspace(-8, 8, 17),
+    'tol': np.logspace(-4, -1, 4),
+    'class_weight': [None, 'balanced'],
+}
+
+#create the model
+model = SVC(kernel='rbf')
+search = RandomizedSearchCV(model, param_space, cv=3, n_iter=50, verbose=10)
+
+#using sklearn's parallel_backend
+with parallel_backend('dask.distributed', scheduler_host='localhost:8888'):
+    search.fit(digits.data, digits.target)
diff --git a/lessons/python/cpu-multiprocessing/joblib-tutorial.py b/lessons/python/cpu-multiprocessing/joblib-tutorial.py
new file mode 100644
index 000000000..0867eb5ea
--- /dev/null
+++ b/lessons/python/cpu-multiprocessing/joblib-tutorial.py
@@ -0,0 +1,20 @@
+from joblib import Parallel, delayed, cpu_count
+
+
+def f(x):
+    return x * 2
+
+if __name__ == '__main__':
+    inputs = list(range(10))
+    print('Input   :', inputs)
+
+    builtin_outputs = list(map(f, inputs))
+    print('Built-in:', builtin_outputs)
+
+    #number of threads to use
+    pool_size = cpu_count() * 2
+    print("Number of processes:", pool_size)
+    outputs = Parallel(n_jobs=pool_size)(delayed(f)(x) for x in inputs)
+
+
+    print('Result:', outputs)
diff --git a/lessons/python/cpu-multiprocessing/joblib_tutorial_2_multiple_args.py b/lessons/python/cpu-multiprocessing/joblib_tutorial_2_multiple_args.py
new file mode 100644
index 000000000..b2bd87b3a
--- /dev/null
+++ b/lessons/python/cpu-multiprocessing/joblib_tutorial_2_multiple_args.py
@@ -0,0 +1,19 @@
+from joblib import Parallel, delayed, cpu_count
+
+def f(x, y):
+    return x * y
+
+if __name__ == '__main__':
+    y = 2
+    inputs = list(range(10))
+    print('Input   :', inputs)
+
+    builtin_outputs = [f(x,y) for x in inputs]
+    print('Built-in:', builtin_outputs)
+
+    #number of threads to use
+    pool_size = cpu_count()
+    print("Number of processes:", pool_size)
+    outputs = Parallel(n_jobs=pool_size)(delayed(f)(x,y) for x in inputs)
+
+    print('Pool:', outputs)
diff --git a/lessons/python/cpu-multiprocessing/joblib_tutorial_2_multiple_varying_args.py b/lessons/python/cpu-multiprocessing/joblib_tutorial_2_multiple_varying_args.py
new file mode 100644
index 000000000..acf7a0b19
--- /dev/null
+++ b/lessons/python/cpu-multiprocessing/joblib_tutorial_2_multiple_varying_args.py
@@ -0,0 +1,21 @@
+from joblib import Parallel, delayed, cpu_count
+
+def f(x, y):
+    return x * y
+
+if __name__ == '__main__':
+    y_vals = list(range(10,20))
+    x_vals = list(range(10))
+    inputs = list(zip(x_vals,y_vals))
+    print('Input   :', inputs)
+
+    builtin_outputs = list(map(f, x_vals, y_vals))
+    print('Built-in:', builtin_outputs)
+
+    #number of threads to use
+    pool_size = cpu_count()
+    print("Number of processes:", pool_size)
+    #can specify backend parameter to use threads
+    outputs = Parallel(n_jobs=pool_size)(delayed(f)(x,y) for x,y in inputs)
+
+    print('Pool:', outputs)
diff --git a/lessons/python/cpu-multiprocessing/lesson.md b/lessons/python/cpu-multiprocessing/lesson.md
new file mode 100644
index 000000000..d3ca591c6
--- /dev/null
+++ b/lessons/python/cpu-multiprocessing/lesson.md
@@ -0,0 +1,33 @@
+---
+layout: page
+title: CPU Multiprocessing in Python
+visible: true
+tags:
+  # languages
+  - python
+
+  # levels
+  - intermediate
+---
+<!-- change visible to true if you want it on the site -->
+<!-- remove any tags listed above that are not relevant -->
+
+ - **Authors**: Lina
+ - **Research field**: Physiology
+ - **Lesson topic**: CPU Multiprocessing in Python
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/cpu-multiprocessing>
+
+## Resources:
+- Lesson material adapted from [PYMOTW lesson](https://pymotw.com/3/multiprocessing/basics.html)
+- [Joblib documentation](https://pythonhosted.org/joblib/parallel.html)
+- [Introduction to Parallel/Concurrent Programming in Python](https://code.tutsplus.com/articles/introduction-to-parallel-and-concurrent-programming-in-python--cms-28612)
+
+Making use of multiple CPU cores on your machine can vastly speed up your computing. In Python, it is not technically possible to acheive true parallelism in Python due to the Global Interpretor Lock (GIL), which in Python serializes access to different threads, meaning a single thread in python can never use more than 1 CPU core (see [this](https://en.wikipedia.org/wiki/Global_interpreter_lock) for more information). However, Python has a number of packages that can help with concurrent programming by using multiple concurrent processes instead of multithreading.
+
+
+In this lesson we will go over the basics of how you might achieve concurrency in Python with brief examples using the following packages:
+- multiprocessing
+- joblib
+- dask.distributed
+
+And of course, while there are many uses for the Python package `threading` which has some high-level control over threads, we will not be going over it in this lesson.
diff --git a/lessons/python/cpu-multiprocessing/mp_tutorial_1.py b/lessons/python/cpu-multiprocessing/mp_tutorial_1.py
new file mode 100644
index 000000000..cd1ec86d6
--- /dev/null
+++ b/lessons/python/cpu-multiprocessing/mp_tutorial_1.py
@@ -0,0 +1,16 @@
+import multiprocessing
+
+def worker():
+    """
+    Worker function
+    """
+    print("worker")
+    return
+
+if __name__ == '__main__':
+    jobs = []
+    for i in range(5):
+        #target is the function we are sending to Process
+        p = multiprocessing.Process(target=worker)
+        jobs.append(p)
+        p.start()
diff --git a/lessons/python/cpu-multiprocessing/mp_tutorial_2_input.py b/lessons/python/cpu-multiprocessing/mp_tutorial_2_input.py
new file mode 100644
index 000000000..e88c5fb07
--- /dev/null
+++ b/lessons/python/cpu-multiprocessing/mp_tutorial_2_input.py
@@ -0,0 +1,18 @@
+import multiprocessing
+
+def worker(num):
+    """
+    Worker function
+    """
+    print("worker:", num)
+    return
+
+if __name__ == '__main__':
+    jobs = []
+    for i in range(5):
+        #target is the function we are sending to Process
+        #args must be a tuple otherwise it will evaluate the item itself
+        p = multiprocessing.Process(target=worker, args=(i,))
+        jobs.append(p)
+        p.start()
+        #output won't necessarily be in order depending on how they were executed
diff --git a/lessons/python/cpu-multiprocessing/mp_tutorial_3_main.py b/lessons/python/cpu-multiprocessing/mp_tutorial_3_main.py
new file mode 100644
index 000000000..d923188c5
--- /dev/null
+++ b/lessons/python/cpu-multiprocessing/mp_tutorial_3_main.py
@@ -0,0 +1,15 @@
+'''
+ Due to the way the new processes are started, the child process needs to be able to import the script containing the target function. Wrapping the main part of the application in a check for __main__ ensures that it is not run recursively in each child as the module is imported. Another approach is to import the target function from a separate script.
+'''
+import multiprocessing
+from mp_tutorial_2_input import worker
+
+if __name__ == '__main__':
+    jobs = []
+    for i in range(5):
+        #target is the function we are sending to Process
+        #args must be a tuple otherwise it will evaluate the item itself
+        p = multiprocessing.Process(target=worker, args=(i,))
+        jobs.append(p)
+        p.start()
+        #output won't necessarily be in order depending on how they were executed
diff --git a/lessons/python/cpu-multiprocessing/mp_tutorial_4_pool.py b/lessons/python/cpu-multiprocessing/mp_tutorial_4_pool.py
new file mode 100644
index 000000000..1a591feea
--- /dev/null
+++ b/lessons/python/cpu-multiprocessing/mp_tutorial_4_pool.py
@@ -0,0 +1,41 @@
+'''
+The Pool.map and Pool.apply will lock the main program until all a process is finished, which is quite useful if we want to obtain resuls in a particular order for certain applications.
+In contrast, the async variants will submit all processes at once and retrieve the results as soon as they are finished. One more difference is that we need to use the get method after the apply_async() call in order to obtain the return values of the finished processes.
+'''
+
+from multiprocessing import Pool, current_process, cpu_count
+
+
+def f(x):
+    return x * 2
+
+def start_process():
+    print('Starting', current_process().name)
+
+if __name__ == '__main__':
+    inputs = list(range(10))
+    print('Input   :', inputs)
+
+    builtin_outputs = list(map(f, inputs))
+    print('Built-in:', builtin_outputs)
+
+    #number of threads to use
+    pool_size = cpu_count()
+    print("Number of processes:", pool_size)
+
+    pool = Pool(processes=pool_size, initializer=start_process)
+    #using pool.map()
+    pool_outputs = pool.map(f, inputs)
+
+    #using pool.apply()
+    #pool_outputs = [pool.apply(f, args=(x,)) for x in inputs]
+
+    #using pool.apply_async()
+    #pool_outputs = [pool.apply_async(f, args=(x,)) for x in inputs]
+    #need to use p.get()
+    #pool_outputs = [p.get() for p in pool_outputs]
+
+    pool.close() # no more tasks
+    pool.join()  # wrap up current tasks
+
+    print('Pool:', pool_outputs)
diff --git a/lessons/python/debugging/lesson.md b/lessons/python/debugging/lesson.md
new file mode 100644
index 000000000..77a248eae
--- /dev/null
+++ b/lessons/python/debugging/lesson.md
@@ -0,0 +1,168 @@
+---
+layout: page
+title: 'Debugging in Python'
+visible: true
+tags:
+  - python
+  - intermediate
+---
+
+ - **Author**: John Ladan
+ - **Lesson topic**: Introduction to Debugging in Python
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/debugging>
+ - **Lesson video stream**: <https://www.youtube.com/watch?v=E6UfaNyLdps>
+
+## Preface: ##
+
+Debugging is really an art form. In this tutorial, we'll cover some concepts and tools you can use for debugging, but the skills need time to develop.
+
+The tutorial breaks debugging into three basic steps: testing code, finding bugs, and fixing them. Testing is a large topic on its own -- there's even a [blog/podcast about it](http://pythontesting.net/start-here/). We'll only cover some basics and the [`unittest`](https://docs.python.org/3.5/library/unittest.html) framework, so you may want to check out that blog to learn more.
+
+The bulk of this tutorial is about finding bugs. The only tools required are Python (`pdb` is included) and a text editor. However, I will give a demo using [Spyder](https://github.com/spyder-ide/spyder) to show how an IDE can help. Many other IDEs have the same (or more) features, but Spyder was convenient for me and is FOSS (free as in beer and speech).
+
+Fixing bugs is very code specific, thus not covered in detail.
+
+Sources: [wikipedia](https://en.wikipedia.org/wiki/Debugging), [Python Testing](http://pythontesting.net/), [Python Conquers The Universe (Debugging in Python)](https://pythonconquerstheuniverse.wordpress.com/category/python-debugger/), [Python Documentation](https://docs.python.org/3.5/index.html)
+
+## Outline: ##
+
+- What is debugging?
+- Hunting for bugs (testing)
+    - interactive testing
+    - unit tests
+- Finding bugs (search)
+    - stack traces
+    - print statements everywhere!
+    - using `pdb`
+    - long-term debugging (user warnings)
+    - reporting (to OS projects)
+
+## What is debugging? ##
+
+Quoting Wikipedia, "Debugging is the process of finding and resolving of defects that prevent correct operation of computer software or a system."
+In reality, it is a very large topic with many different methodologies and steps. We're going to focus on finding bugs, and talk a little about prevention.
+
+There are two forms of finding bugs: discovering their existence (testing), and searching for the cause in the code (finding). The debugging cycle typically goes
+
+    test -> find -> fix -> test -> ...
+
+## Testing ##
+
+There's a whole range of things that fall under testing, from running the program and seeing if it gives you the output you want to a full suite of unit tests run every time you commit your changes. In Python, the most common form of testing is through *interactive debugging* using the REPL.
+
+    # Demonstrate here
+
+The REPL has been a major feature of interpreted languages like Python for good reason: quick and dirty testing.
+ - Don't know how a library function behaves? Test it in the REPL.
+ - Unsure of your syntax? Check it in the REPL.
+ - Not sure how to tackle a problem? Try it step by step with some test data in the REPL.
+
+Where this form of testing falls through is consistency and automation. Every time the developer makes a change, she has to remember what to test and type it into the command line. Even with tab completion, or copy and paste, this is repetitive and tests will be missed.
+
+That's where unit tests come in. Unit tests are the smallest testable parts of a program. That smallness of them makes them easier to automate, and also (as we'll see) easier to debug. Python includes a module [`unittest`](https://docs.python.org/3.5/library/unittest.html) that can handle this work flow.
+
+As an example, let's make a function that will split a list in the middle. It will take a list of arbitrary length, and return a tuple containing (first half, middle element, last half). We'll follow a Test Driven Design (TDD) style, and create the tests first. This clearly defines the behaviour.
+
+```
+import unittest
+
+class TestSplitter(unittest.TestCase):
+
+    def test_simple_case(self):
+        self.assertTupleEqual(splitter([1, 2, 3]), ([1], 2, [3]))
+
+    # TODO: More tests (hint: length of list)
+
+if __name__ == '__main__':
+    unittest.main()
+```
+
+A good testing suite should include a few examples of normal/expected input as well as any edge cases you can think of. Let's fill in more tests now.
+
+Now, run the suite (which will get us into debugging/finding mode)
+
+    python test_splitter.py
+    # or
+    python -m unittest
+    # or
+    python -m unittest -v test_splitter
+
+(The `unittest` module has *test discovery* that will search for tests in the current directory using some rules.)
+
+First, we'll suppress the errors by actually defining the function. Next, we'll come up with a solution (and hopefully make a few mistakes along the way.
+    
+## Finding bugs ##
+
+After discovering that a bug exists, the next challenge is finding where the bug is in code. We've already seen how unit testing can help narrow it down to a function, but what if that function is more complicated, or we get an error, or the bug came up in use and not in testing?
+
+There are a few tools for this.
+
+Fist, stack traces. These are the messages the Python interpreter gives when there's an uncaught exception. At first, they may seem cryptic, but it's pretty straight-forward. From bottom to top, the trace prints:
+
+ - the error type and message (e.g. SyntaxError, IndexError, ...)
+ - [the line in which the error happened]
+ - the function in which the error happened, along with the filename and line number
+ - The filename, line number, and function that called the function with the error (and all the way up the stack)
+
+We'll see an example of this with a partially working `splitter` function.
+
+### Print debugging ###
+Sometimes the stack trace isn't enough, like when a function returns the wrong value, or you're not sure if a loop is behaving properly. The quick fall-back for this is to insert print statements wherever you're unsure of the value.
+
+
+Let's do a little debugging of a broken average-taking function.
+
+
+### Using `pdb` ###
+
+This is the more sophisticated way. You may have noticed that one annoyance with `print` is that you need to quit and restart python whenever the function definition is changed. Using `pdb`, the execution is interrupted, and we can check the values of variables whenever we like.
+
+To use `pdb`, you first need to import it, and add the line `pdb.set_trace()` where you'd like to start doing introspection. e.g.
+
+    import pdb
+
+    def average(xs):
+        pdb.set_trace()
+        length = len(xs)
+        acc = 0
+        for i in range(1, length):
+            acc = acc + xs[i]
+        return acc/length
+
+Next, when the execution is interrupted, use the commands
+
+ - `p <variable>` for printing the value of a variable
+ - `n` to execute the next line
+ - `s` to step into a function call
+ - `c` to continue execution
+ - `!<code>` to run any line of python (including changing values)
+
+Lets repeat the demo for `average()` using the debugger in Spyder (which just runs `pdb` for us).
+
+Note: sometimes print debugging is still useful, like in a loop if you don't want to stop every iteration, but want to see the values as they come up.
+
+### Helping the user ###
+
+There are a couple other problems with using `print()` for debugging:
+
+ - you may forget to remove a print statement
+ - it messes up unit tests
+ - it corrupts the useful output to the user with debugging output
+
+For these reasons, it's better to use the [`logging`](https://docs.python.org/3/howto/logging.html#logging-basic-tutorial) module.
+
+Using `logging.debug()` instead of `print()` lets you output the debugging statements to a file, instead of `stdout`. An example from the python docs,
+
+    import logging
+    logging.basicConfig(filename='example.log',level=logging.DEBUG)
+    logging.debug('This message should go to the log file')
+    logging.info('So should this')
+    logging.warning('And this, too')
+
+Creating logs this way will also automatically format the messages (which you can customize). This is a good way to provide debugging data to your users, like warnings that their data is in the wrong format or that another function is better.
+
+### A brief mention of reporting bugs ###
+
+It should also be mentioned that users can report bugs to developers. Each project likely has its own bug tracking system, but GitHub and other hosts provide issue trackers. It's a good idea to let users report bugs, and to report bugs in other projects that you use.
+
+With more practice in fixing bugs, you'll also have a better idea of what should be included in a bug report.
diff --git a/lessons/python/debugging/test_average.py b/lessons/python/debugging/test_average.py
new file mode 100644
index 000000000..f1996b738
--- /dev/null
+++ b/lessons/python/debugging/test_average.py
@@ -0,0 +1,20 @@
+import unittest
+
+def average(xs):
+    length = len(xs)
+    acc = 0
+    for i in range(1, length):
+        acc = acc + xs[i]
+    return acc / length
+
+class TestAverage(unittest.TestCase):
+
+    def test_average_of_ones(self):
+        self.assertEqual(average([1, 1, 1, 1]), 1)
+
+    @unittest.skip('will raise an error')
+    def test_average_of_nothing(self):
+        self.assertEqual(average([]), 0)
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lessons/python/debugging/test_splitter.py b/lessons/python/debugging/test_splitter.py
new file mode 100644
index 000000000..220401821
--- /dev/null
+++ b/lessons/python/debugging/test_splitter.py
@@ -0,0 +1,14 @@
+import unittest
+
+
+class TestSplitter(unittest.TestCase):
+
+    def test_simple_case(self):
+        self.assertTupleEqual(splitter([1,2,3]), ([1], 2, [3]))
+
+    # TODO: More tests
+    # (hint: length of lists, or types inside)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lessons/python/debugging/test_splitter_full.py b/lessons/python/debugging/test_splitter_full.py
new file mode 100644
index 000000000..98d4e7317
--- /dev/null
+++ b/lessons/python/debugging/test_splitter_full.py
@@ -0,0 +1,66 @@
+import unittest
+
+def splitter(the_list):
+    return splitter_e(the_list)
+
+def splitter_a(x):
+    """
+    Return nothing useful just to prevent errors
+    """
+    return ()
+
+def splitter_b(x):
+    """
+    Only work for the simple case
+    """
+    return ([1], 2, [3])
+
+def splitter_c(x):
+    """
+    A version that kinda works in python2, but not 3
+    """
+    length = len(x)
+    return ( x[:length/2], x[length/2], x[length/2+1:] )
+
+def splitter_d(x):
+    """
+    A version that kinda works in Python 3
+    """
+    length = len(x)
+    return (x[:length//2], x[length//2], x[length//2+1:] )
+
+def splitter_e(x):
+    """
+    This version should fully work
+    """
+    length = len(x)
+    if length == 0:
+        return ([], None, [])
+    elif length % 2 == 0:
+        return (x[:length//2], None, x[length//2:])
+    else:
+        return (x[:length//2], x[length//2], x[length//2+1:])
+
+class TestSplitter(unittest.TestCase):
+
+    def test_simple_case(self):
+        self.assertTupleEqual(splitter([1,2,3]), ([1], 2, [3]))
+
+    # TODO: More tests
+    # (hint: length of lists, or types inside)
+
+    def test_even_length(self):
+        self.assertTupleEqual(splitter([2,4]), ([2], None, [4]))
+
+    def test_odd_length(self):
+        self.assertTupleEqual(splitter([1,4,2,6,8]), ([1,4], 2, [6,8]))
+
+    def test_empty(self):
+        self.assertTupleEqual(splitter([]), ([], None, []))
+
+    def test_length_one(self):
+        self.assertTupleEqual(splitter(['a']), ([], 'a', []))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/lessons/python/image-processing/image_processing.ipynb b/lessons/python/image-processing/image_processing.ipynb
new file mode 100644
index 000000000..e291da9db
--- /dev/null
+++ b/lessons/python/image-processing/image_processing.ipynb
@@ -0,0 +1,1501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Image Processing in Python\n",
+    "\n",
+    "This is an introductory tutorial on image processing using Python packages. We will do a code along to understand image data types, manipulate and prepare images for analysis such as image segmentation.\n",
+    "\n",
+    "### In this tutorial, we will learn:\n",
+    "- to load images and extract basic statistics\n",
+    "- image data types\n",
+    "- image preprocessing and manipulation\n",
+    "- image segmentation\n",
+    "\n",
+    "### Useful links\n",
+    "- [skimage](http://scikit-image.org/) Sklearn's image package\n",
+    "- [OpenCV](https://opencv.org/) Open Source Computer Vision library\n",
+    "- [mahotas](https://mahotas.readthedocs.io/en/latest/) Computer Vision in Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Packages used in this tutorial\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import matplotlib\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import skimage\n",
+    "from skimage import data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load images\n",
+    "### Load example image from skimage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[156, 157, 160, ..., 152, 152, 152],\n",
+       "       [156, 157, 159, ..., 152, 152, 152],\n",
+       "       [158, 157, 156, ..., 152, 152, 152],\n",
+       "       ...,\n",
+       "       [121, 123, 126, ..., 121, 113, 111],\n",
+       "       [121, 123, 126, ..., 121, 113, 111],\n",
+       "       [121, 123, 126, ..., 121, 113, 111]], dtype=uint8)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "camera = data.camera()\n",
+    "camera"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(camera)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(512, 512)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "camera.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af9898a58>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af98c63c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(camera, 'gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image shape as array: 512 x 512\n",
+      "Number of pixels in the image: 262144\n",
+      "Range of pixels: [0, 255]\n",
+      "Average pixel value: 118.31\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Simple statistics\n",
+    "print('Image shape as array: %d x %d' % (camera.shape[0], camera.shape[1]))\n",
+    "print('Number of pixels in the image: %d' % camera.size)\n",
+    "print('Range of pixels: [%d, %d]' % (camera.min(), camera.max()))\n",
+    "print('Average pixel value: %.2f' % camera.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load your own image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'>\n",
+      "(1059, 1571, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from skimage import io\n",
+    "colony = io.imread('yeast_colony_array.jpg')\n",
+    "print(type(colony))\n",
+    "print(colony.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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f7nHPzdFzxQ5x7Yz5yqVdS3fS487+5vFat/nFRr9Zor0A7Vm53vt2m+OYu10/Kz3qFoh2ZU59Vlg/ccJKwtZ0vY84lPs4Tjp3tBpW9Fq//ZBGXxXtID1GHaTHGvPVmzzqMZEfOvnlfr2/+wcOWBa0OfmhIa/Uk+e4r7rbeI7juWs3Kept67RjlDOdoy/zJNd+x4qStn59rqMPjjane8eWRHsW7V63Tr/JWNFrnx11Fn3GpYyXe+8p6vZSJgDdOvnFmfb6Mdpsc+qzjC1Cue/XZ8NVSOfO/lohn3q0q2NBt8b2wQPrJ870m7j3fY2+89LPXPqdtPXr7/vzRNo8xgyLsoqBK+RTZ3ujRB91367tpx8TWg2cMU7FMdtNivFGYXm38vihlfwYn63Pcnze4lx91RivHmL8yY92bY+XvvHSdrX5p5Tz/Eb6lHEznzp9jedif6aUR6OvStrsWr+XfshHlefHfu3D99fytX60xUmv9TDK4Umu1wGQNsOKXPvfvip9fao7S0Ie53cV0mbXdnsdU65j6dO4akWoR+Hw9itj4Diu1kv7IcZ9wJY4Vn40XGF/FuPWcm/R172WcX3qg3uJsVzM6UWQS/8wXr+c41IeT1znLu0o13nK4Z3OfjvW/XusT5+OHce5zBvaqmiPtQ7EWtMFtEM9juelgxXIJydvzv/0Z3/PZzXXfl87bUUSuRzYekdKiEnseyzsgK3u1PMWIpkIKRVyXsCEPARbwSg5FkpOjNu4gseCtFUDND6HQs+IHZEmaE8sUkjoVYxSVXJKYD1EHr+IabEId3e2uqM5sSwhaooaSI/fTbBquCdSWql7x4FlWTjVM5s4PWU0LbgJqUOyDFZoKNZAq5D6Al1RF9QTbTOkKosv4M6iC4WM7422V1zgZT3HYkwMUeNYlOeHBTwEgpQybW9xLc1ZDwU1OCwL2iXELjPOvuOL0NU41x1y4tzimt0dWQtna+haKJpYJXGzvsYhFd58fotvGytGsspdXrmRTGkhaGLOqnHvVJWODxEhJlRNnNo73oy0d7R1ck5UKik7C07pneN6iLI4JIStgxxusFZQPSL5ALWH2NM7TRxJiSZOKZlSMqrAECVqa+RUEJRujuplgZ14eNxDjHBn0UTqTvZEzgeSKFo7BSW7sGhm0YzqgqSCXHoX7+A9BAwuYoXQBXbZsdTZfaN6iGmSeEWoiGPntITg1Q21EJZOvXLWxuP+SLNK7ftVJFNVzKJHUgmBmNpJFiKJdCMZIdyldH0Geu+01nj58iVJSwhB4njdyTh1P5N65zYt5BbifUkFzRlQlrQgXfDd8N1YdWHJJWQ+M5a84snx1EPA75CRCIgIiDtKQlUjWLNvtBaT/FIKddtZy8Lp4SVqhvTO/vgY4ltKIUiNRZ274CZXcbfXRsnK4RAT2W4h6i3LwiEvlFJYc0EBScpG50zHCDH7rhxCqNNESR1nw/2MuCKuqCf2PQT8ZS00Gk06TTqqC9Y7qs7SIRlsrVIVeu9X4bgMgS5ropvgotQu13tTSvR83e0qvKlDQWl9jwHEnWoNE8OSX9tc7522nRHrqFsEi14RAbs7JiG2oTEp3nqlOpiHMH8R5XJSBAd3vHV0iLAXEde6oiSSZJREJpEtxLQllxCdrJITiBueOo1K846LRNtLKyllci4seeW0bexmNDeqRH/RcWozWmtocsAxYgGHhjivOcT3JQu3x4UlR7+qnrAafYR5Jec2ytMoGP10JhscNI/nF8AgQa0bfd+QFGJ4740qhrtje6Ook+iYNd558Tan7YyNety7UBuct061ztk6G3FvRYSkSiGTTFlywcZkR1UxTTSErRtdYOs9+hEMyYnad44lc1xWenP21inrAVwiQOfKqpnsQrPx2UtApxrZhaNG4M7pdG8jMLLGteGIKnAZX4WUwbyhCZpGucQSa1qgCmrK+eU5Am8j2CAJWt9JONnAa2c7xdPmiRD4F8E14VZI7rjttFbZ9x260bYdyen6LKcMqkJKo65yprmThpgtmtGcMTHOewePRWHdO616zBEM6NGm6W38QG8ewdERnDFriCg5x1iikq9CvFkb84UOGK1uqDm0ilrMM8gSdWARXD3vEZQQh71VTDqSBZdG7TtIiPfdIsCNxvypi6JloSOjLgzFMI8gY8sLWwNviqQIiOYl+srJFweuQjum60L1spDL5/h3fdEpJxtTZ6EvIWwC9INeBdl6jL9JB+1PqdesPC3o2lE/5byusZiudymEmR5TIitCX18VQsecZ3la9C33sZCsdymExSFG+RCmtNp10ZgfI2hcb5X1rcrybguBquj1PZfz5bNfF/9WZFyD4nksRpNQxwKuPku0myh3OoeoePzYI/nlHkKMCuc3Mi+/MuYUYmCLks52FRnqsxBAtjfyUzkclrfOVwFpeesMwPL2PhZ9USfLOxXLQrtJ7M8TpzeV/bnw7t8Xi9nteQgrp684cnoz0W7SdfFen+UhCspV2Ij69xBIHlrUzRA1+yHE576m63Vvb5arQGZrvgqdWi2EpHEurSGup3MIQ3qu9NuVfrN8ipiTH0PYjIV/x0u6ij+HnzkhFoJce7Zga6If0lWsvIicdkhxbc/KVYxFP3VNfDmnlahr3Y1yX5Fm5PstrvOmXEVgaY4dCv1YonwpjulZQYT1E2eWdyqHnz2RzlF3l/dZifqAECAudfKqWGxrCN+uMgTMEFDTuXP4+CN9DdHMSgTM+01BH0L8Pn/ZkX4s+GgHV8HuNr1yPKe+tlCflev527M1xtse7ZYh/LpKiJQS7d+z0G8yuveraNpuM/mhUV8Lkf0i7Jf7Sn2WQvgdbSDaVYig5Z0thL/d6LeF/fUQhXTrUf5nhf0DB9ptZn9t3L8kLO9WlrfO1wDP6UM3IdQdEv1micX9EFiQEDHz/YaY056tpFOPwMI5RHtpFu3lWcGKUN7dSY87aeujrdp4NjREwr3FmuhxB/MQ7e4WPCu69auA6Umugi0SdZpf7khzpPbRBqJtX4TaqDvFhqB7CcpoNfRhG51l1F1+jPJhQ/hKQj+ma993EWwvgmza7CoM2hKCUT9ofCYJ9W6I1NVo42/S4/nXcx/Pf7S9vkZ9t2eF5ZMnyn0lbUZ+NNLZ4/eHRj517HrfX0nBKVyP354Vti+/o16e0SH06n4JwKVredptJm1Rvv0D0Y96eZqf5Jf7EKEjOKfVSY/t6bpuEn0N4fXuJ16yvrVf73G5b+SHxvL2TnqorG9Xlncr7TZH+1+UdqPx/N3G/bnUcfT5neXdim6N5UWPOrjfQKIvO30gc34jo9UpD3YN7rlGWffXMv0gpLORtijPRfRuB+H8RqKcQjDWIRCGEO/X4Bk8idSXMdRTiN1piwDJRXy1oqxv1xjj5WmszKcIWrZjBEfKfUO3fhWdr/3IGJtjjDDKyxFE2V4pyxZj22UsszzuyTmE04uIayX+fninX8ft/NjJp1fE5zEmvcqlTSPR3rVf6lOxFAJyiLAxD0mbXY+RT4Zd+t9XxveL0JwfjfXdTjvG3GR5sCHMh5BcTkM8TiHQ9vH5i2BrOf69CLaXe7ncG+lsrC+G+K+QNqevcj3GZ0P68Ic//Fm/+fPN9//ARz/ca0x6Eo6qc1gXrBuIUw4rPQknDxFX6HTbcWssJdH6Ri4LtTu9O2hGktKbDVfQOlxL4A7i4zzeKJJZc0a80XrDVHHraEoI4XrJKSNuNGuUnHHC2VJKwdwwC+db6w0hhcPFQTwPx02mLBmS0NtO0kwW4SCJ3mLh1fczTYxdhTOVlNMQ6QzMUIVVCyknfM28rGd6igVllgRjIltVUJxFwdWQFgvyM0ZrHVG9ikFtiM/LYSE3o0u4/Zp3SDFBCcEasoQLs2giqWLduC0rtm9kzyRN0EP8KccbHraNKlBxGrDerNw+u+W1N9/g7XdfIJrDbZtz3BMDQYczS1jItOakw4p7QVKmu4cbywTXzNYNlXBNa1ZqPfH89pb6eMYzeOqQOh0BiQnVkjJ724eDdAgureEIKkI2oDqZhKIgB7o5u20cblbwR9p+RnolZUWy8NB3UKWkxGYVt7Fo6Z3qLdqnxgJd3EMUdw/3Y29oN8wrLsbed1QhdadjVDOS5rhOa6ySCXlaSSJkBUHQJGx1i+iwADit1uuCvPfOsixYbaQ14aIgSm0NG+Jb0kRL0MVImrAW7Vo12oJ6iFbuRu+Nm+OBnBXbKutygFoxEyStVDf22sgl3JEh7vgQIUvUuVe6CoYQNZ5QnJQSba8UjUDDbpVulbIkEk7RKHcuBcbT6NZJSTHv1L2hQ6Az65g5SQvmwlIKh2VBeqO3xl53cMfcyUlpdad7tNlwXsR97K2xSA4RNwzGVOuQBfdOkhBTJROC+ZIRwomdVLF2psiI0qug9HCtXxyMYxAsOdOtc7w5UnvDU8JUKTglKQkD/OpgRYWSM7VXmls8I4BaSJZrPtDbhoqxXNzVQ+AtOUdb9eHaHAEJIOo/Z5KDtUopCVW76FgUV259ISu4NWrbolxlwarRNHYiJHOMjqvRxypdxFgIF/W5nUedKOfaaJKvQmDHWZcj3kGq4R7tMKWCuNNqI+dl9LFxvd1OaFLEEi4dSqYDa1mjP3On6Ip18Nrwbpg0SCH8es6gC+6JilNEwXqIwKNfT7JgBqetckhDKM/RN3evaBK8GWUpGJ3eDJNEJ7EejjhCsx2jseZMToL2TsoLjKASOCrhVK7WQ6x1pdJpCtY7i2q4tQXW9Yi7kYcz1VvHW6fYWNtI1J2b4b1jvSNDBN7qHm29x2IjeSxQ9+zsw9EboqMgKVEbIWir0r2z1xhXUkrs2w6eUVmxHlZ7d0ipk0s4u4/HleRQJNF3kLwCSnUghVv/oAk36ALJiXZvjUWMJSdEGxWni9J6Jx9W9m1nWZZwqNfGuh7oTdjPDdGESozpD4+P4Urtzt7PpAzNTmMu0eK+9cuKMKEYOSkpEQ5cr4g6KYzcdB8B2ZwicOE1ROt2pvuZUhZyTrRWyWXFUwSCZIzt2UG8U6SwpIV1zCX2XtGUOdVtjA0b5bCytQqSSMnx3tCaEcLxe+4NG2783ls4UTSFy9+MQykhdKOkUthb7Bj5zd/yTb//cz2vm7z/+KMf/ZEPp+pXB40n4fx6DhcJsZBL1VnfaqgPMaA6arDfJcrZ6KtQTk7aL3ahJ7fnxR2jNhwuQ6i1IrRDuJ9cQyyV7mh/Eh76Gj+uEu7eY7hYpEO7UdJwd9W7RD7HQu0i0IXTiScXHOGS8RLOx/2NhbT7WDzGIrvcN5a3z9ia6QelHTQCnUnYnydsLIpvPhbinFqIIDLmq+VlG+7RBe2jHk7G+k6Lxegoiychn0PIugjWaXPKQ78uwIVwkZKUfhzOw5sQdrXD45cUUov6sRLzChehHYXjJ5zyaJTHEGD2Z4nzm4mHr0w8/5v7cLBZ1OWooxB65LrIzyfDh6jNWNRK9/hbh+XdFqLV+Ft+qDx89Q3loQ9RiVHnFnPF3Wh3JVySN8v4zB6OVIu5VT/EfKgfQ3wUj0V6ebFT3zjEuuPUr+5XVFg/fo8QwuLyzhbrLEJQyS930rlhhxwCqw5xzOPepD2ExYsQll+c8azYkknnRnrc8SVf66E+L9fP2hL3xZPiJZHfPcXcTfV6fh8Calx7DifiMtqwQ35o0U4EbE3hqD0Pt2vttLuC5yjvxSmoFuXevvQ2XIAd6vPYRarNh/jRyPc1nMZtuIw9RApbwpUn5qRzQ5uFiLkkPA8B+dxpt3Gt+b6Rzu16z/oxynmpx4uQ7iXc5/kxXMKeJcTKcWxpRr8LodaTXkUqkHDXHnKIhVsnnaJexEPo163R75ZrG70Kc85VWJQ+2ulDxQ4ZxhrFh2jeb3LMFc9jF9Ztjjm2MBx4QrstpM3Y31jIj3246Dr9ZqHfZHxJ4315CF/R7vJDi+epRv916Xvq84LuUTft2YK2eBY8awjvQ9S8OGxDtBXSY4u2tZbr+T3HdejWsZvM6csP1/4oP7ZwgY9ypXOnH3Pc6xG8CiemgEK9S9S7xOHt2NWUTp3l7TPp3K71ls4hyl/a+6WfsCKQEulU6cd8FQC1O3qO/g+R67VdnLJI9CHtNpP2CKLp5Zrhej8h/p4f++i35Srtyo6vAAAgAElEQVSuQbRfbc76yRP19UP0tyNYkbanum83iVTt6gDWrYdb2CA9VrQa9bWFflC8xDMsPgQ6D1d1PybKQ+y2lg75oZIfG9KcfjueLYf6+hrj50ER9NqvhqDO9fnTEZSMZz6e13Ifeorw1K+IwXLfWe6jDqIuQ0i87PRAQyAs97FWsaIhoo6gRao2jFw8Bd1OxvaBEMov4/pYDES/SgRp9tcS6nG/IiAW40u9U+ptBJrKYzi28yncw8uLTr2L/iifjP1ZivK9NNAoEwo3H9+wNcbuch87SfKpU58l8tlot+nqgIVLAHcEbG2IuCrYOnZ7DOfrJZCqFqJqGbt1QnRXysmvY/UluIMw6hLaIYLH+7NE3pzlMeYTh7fD+b08GO2oLPcWOk2J+2w5+iRxOLxV0R5lSHtoGxeHr3bYXkusL+NB6kvsXnIVvus3/5Of1Vz7fS3a/vkf+OiHxYwFEHGSKI5TPRaD3eDcGpmCtVgpeXcED8EAobrx8vzIuq44Qu+xDR1JIfKimAgNyNmQ4Wa7bJ3ubkiSkI9yYTvtkWJBYqFWrWEenayKouZY62QN56LjuDk5J9wbSRLbvlFyuvSL4Y7TjKvTvdOskg+J7kL3TipLLLxFyTi4YVap1lnXhdp3mnVSybS6ozm2TnYMqoU7q3Vu8kLdK4lELgXJJeojZdpwp7kohtFj1YkMZ5djCIp3Y00rWYQiivbGkpTeOjbqqO8VQTGElJVTPyNJuX88s3cP82qo5LS2c//wyMNpI6XYvtmtky2ENDNDcogAIkKVHaOiCIslVJydhmZFL2KKACkEt2YdPay02lhKxrqT0HBGxR7xqH9g3SPJQRfw5qxlJYlg3egddFkwkdiOrI2UBCwmeill3DWcm2Mb/6EseA8h/3A80qyxpCHGpNha7gJ5bH2VZqiFsGhE5NZRRBJFE3QnqdMI4QxzaussI2DQ+o6mEKCaGZmC7xEBv13GFgISN+sxHHHuIRIQaRUcp/YdGx1Z6xVR6D2ucV1WxOPF1iIlhTfQlKhtD7FSGe5LQUui1p1cog20vuPeOd6sbPspxG1N5LziIlTfQTsuTpYMXclkck70XvHx3C8F0IYno0sIIaskWrMhNOsQTEK0N4tBSTSRsmC9xWupYxLPHDi1t9j6nCTE3ZLIKUW/oaAps1ujY1eRcy0JxEmqqAjmTl2UatF+vMWT09xBFHNY84IPc7VICsesh5PViC1oSVKIrCmCHkrGzKm14W7X/gfrFHdEIaOYQsNHmhJHXBBJsUVcEt07mkLozwJFc2wz77Bv8dxixmFdqa1GH6WK1U7SRCk5nI4COSVEFDcle7izo+2Gc9s6pJyjL3Cn5EgX4ELsdMgZzYnNwjlbh1C0u0UZ3WndKXnlphygN6TkCMAlJXunCJBAFF4+3KND3JWcoDku0c/QoaRwD7oo0iWUP3e8GdYq5h0RRrsJN3q4T2wIe05KEeBx8eg7tVDrjqrQetRXSkKvZ8oSfbr1SP9hFm7nvlUWLSFiuuJmI8gS6VnWfAR69CkSTsuURpoIIuBYSqFVYz0cObczOWUO41gigkoIft4ND/subY/nuZRweO9WMTpZJIKRKogKdQRBNSdcBHCyZnTNNKsIULICQm39Wk+xQ2MsSIBaTyw5saQ0tupH+qJFE5qcRsdyi/EuK3U/oxJionWnYZiE8/d02kP4l+hXrHdKTrTWuDkeOT28xKogZDwl8iFTNLYFZon7VXJhKbFw2PZI76MpIRLBJo2tHrjvHG9uMOtIKmy9sxxK9PcK57ZBFoxIixBBz0pKEZDJFq+pKDd5GcEh5VxPiIJbtMVcCqfzhpYFxFiWwr5v5KyIhl+31jb2njW6xc6DRkdTBMuyRGqepRRyylBH3xQed0SJtqTCcV3BZQSTIxVFLomlZLZ6wpLHLiJRau+YC9/6m6Zo+8XAf/aDP/JhJNIjcHGz9hD90Fh8rG8PAaiHkHsRJtSgPBjl0Th+7HEsiod4+7TOjq2ezcnDYerKdbGfmpP3Ee0gRND1nUY/jG3FRiwQQ5d42mq/h4jrSZ4WyYdY8PY1Ugb0w8XJNgSDo8aiqoYoYKuiNbYP2zLEsUMItiGCxSJ5f5YojxaiQA7Bk6xXhw4SKRI8CecPFspDH05QHQtLu7qf8tmuQlcImrFojLQ94eCU7tTnOZydQ5Bot2kIrbHQjAV3lLEvyuGtWLzffryzvAyX1WUIzGfn9qcrx0/6+GyIP/2YQxRoT8IsIkOo8Ksbz7NQXrYnMf2oI/wsaLMhUMaiuz4LIecqWG4NQYbwV2jPC55DtNNm1NdWbI0ypC228YbQxBBQNeYyPG2TlyF6pnOnvnGMRXFz9jdW8mMNZ+AxheDZHek27utwZWe9Or20+TV9Qr+J9hvCZcNu19iS/HKn35XrFn0vsc0/PTZsTWNR7mxfdouMZ+n8wUgvl2qsNTyF2KQ9BMeL+zadKkg4NoUQgJAQ7dNjDRF4pFkoj3FOxvmWdyO1X37o8dx84hyiUjf2N48s72zYmq6pN8RC9Jbhvu03BRzsmK+pJELUkHAWDlH24nquz8L1qHtc02U7vueRLmSIXiGUXtygY32zRzvIj/3q5veiITgdhuPz3CAr+X5D9xCUXTXqBLAlYSXqND1W8v1Ge76+kq4ihL+LICfm16BLX8PhHqJvOG4vApnnENovwmgItj7chU/iLBrO1oswqluInBfndXqs0T/udq2TfszXviydL07MMOvsb0TqiuuzUkMk6zfhVNYen0ejj7M14SVdd0S4yjUQAKAtnKWX50m47BJQlnc2tEN67KDK8rJf0z2kU6PfLuxvPom0aesRDDkk+jGNgI5w/NhDpJO87Fa71BHRZttt9BcXcQ2Rq0CrzZ+CCN1HIEXGUBHrrxDSEu0uj74r7lV52cJB/RiubDuE+Ntu8zh2HOIinl4EYuEpgHcJCtmaqK8NN/kiV9fsJWDBSMvQj9Hn1meZ5a1I+7K/vow2pcNpH9cvY+dqGuVtNxFIXO77aAfR51yCivmxXwX8i8B9cRfnk420O1He/Bj/F+cadInzhpDc7koIpmOrfhrip+UQRKO9WojQL/s1HVGqEdS7uJsPP3ei30bANlLhjFQdZ+P0Zub4Vo9xZ4j44UR+pX9xqDdKvYvf1xf9mkKD4S61kq4i//ZmGbtalOVFo91l+hJtZnm3IYC2uEfldEmjFG29rxG8tBKO5Mvv67sxbwnhXmlrpF/wFO1sv02s933MQ2QECOwSryTvEZBOZ8MvOw3WmOvUWx3CPFeHr9oQ+on6OL+eroGlepMop3BN11tlfdGRMX4DlEcnVedf/m2/7gtftP3In/1zH0Y1BAg6eFTqUg7UvUYEDmVNirROTgeyxlbenJYQwySTPBalHitLrDs5ZZaiuO2x7TXFIhVzNCU0haOruZOTxALaLs+a0VolrSu1dzTnq6POL/liVam9Rk7C1lAdW2R9x9xYDwd6GyLXWIyLhLu0NUgFWtsoqcQCzCFBuDwJ63VKid49nD4jZ+7eKquurLlgtcfi77K1uRPO0ZTxbSf7mOgmAYtylJzIOcMr9d165ZALVg11iRx9GiIGCuaGW+R6NYvcqI/7hi1CFUNKiEZ7a6iGWJ4UBKNIODbDWZzJSbk5HjjKEPwOC5KVve1oifQFJgourCnqYu8VJVyo65JwGo6xlETfGymUD/bHnWVdw0HZI+9mEkW8k8Z9NmuYJ9b1QKuQzBDNlOPKaY973foWYq1H2gQAdIigWqLzN2VNhd4ruSS2usWEszXcibpXsGLY7sORJvS9htO4hBigYx+/O5SSsb4Nh3NM7teS6cNZnHO4XdF4bxZlKYmUNRyEHrk9ZbQHaw0tQus93N8egmerBmum5Bgwl8txJZymApSUKKpIyTQL0XxdVpZ1pddG653DWpAkkT8yKSJGWleMsf3dnZwKj9sZwgcGOSNSoDoHWVk1Y9UxhbIeqebXnKWdmEiZg/cek+NloXlnaxupRI7SJZeRJoMQQD0CJWk47CQ7e60xoFxSTGg4jBVl31sIrvR4/gWWvNB7xUZeYQH2uqNLuFb3fSenlbKsiCbMK0tKiIUjNucULrsuoBlzIZcMrZOHAAyxjhNiy3zKKdykFoKQCtA7ySJI5aohmlq4A3LKgNOGiyCLsKaCpsTeG6Us18CFY9w9u412GrsFwz1oo22lTNJCrR3zEJiEjLiHK3C4DUtONHq0YU2IhTCZ1TFxnAgUVEIoj5QLgkrkAc6LjrRO0ccdSsZa5+HhniVnVCQCIVYRiZQJroq5kVR59uw1Ui48PG5QYiGsEmJkyplt39CRk3tsjhzCFiyqLDmuyazH7oXhMF4lXK4pK9t+xt1Y1+gT8whsFR0imUZ6FO/h9t57pFaptUUdto7kTBr9p2MhgnqjiJJcOW+NU+uU29sInjiIWQjBDiN2h5To85Mn1KNfVA0Xd0oSovoQ+UvJJIl86+WwwBDKb48rD4+Pwz0v4JGWxVTJS6TaUITkY0cFgttTsIcR0HCPABreKerc5YKOoE+SCCCYNdp2JolwOC5Y3cnDmZtKijzSHosZXWLHyU0qUa8iIZa7U7LSeo2AjjXKslDyEhP/0S/nEUTtOK1VSoLztqMSz1KENp3aNsoSZUBi50PbjWU9ROClR8DUWqWk9PRMNovFwBj3Y06xhsOMmMQnGClXCjsdSWn0F0LdKnd3t/RaUZz7d97iUCKf8GUnQM6F3pxu47OSMDoJyEkjVcPYndH2iqUUz6oI6ZBGWpUY23DoW+e43FL3PYI5tUf7K0u0FTp73Ub6mM63/qZvmaLtFwEf+eP/w4eF4UIdC0PXWKCUxxA5bFH2Z7F7K9yvsdDsS+S26wcdjsQnR9Zl8bzfhuh2+f91e/hFTL1snTwMIc0urs9IOWDL0yL2ks5AhlPJc+QJRMK56ilSN+TNSdXYn+erE0eM2H32yhbgi8vnmiu2OX1NT9u6R3qAtPvYehpO3fKiUl9fqc/zk2tquJbSPtx2ORautj5tow8Bw2k3GVtHbzLE7bRFLkPtIQhod+qzdL0nF8Gv3aXhaDTWnzuRajib0KiD/LJezxfOaaXd6lUojfyPifMHF85vJJYHo4+0FflluGfTqaN7iAr1eWwVvrxmJdrCxTnd7sIZ5SXmQvlloz0r1zJfcpmiOtypT3l76+vrVSxBYtv9+skTnlIIYEOszQ/16maKrbFydaXtr8U97sfE8vY2XJwhTPZjgqTX/K4isbDO53A0X3KietKrQGdDMHPVEMmShnN6CO/98CSItdsSjuy7SAlwdRDWmN8gjNzMOkSa0Q5FSOcauRZvYjy+1NlFjHYNobDdxHN3yS/cRlqLcJtW9g+sMEQpzzq22odw3u4ixU8/JA4/e4rnsMbYNbbfUV8v7K9ltHJ1pOaXO16Ge3G3sT27D+MG1+emvH26upLb8+UqiF2EUt0NX3TsUFPS/TnGz0Megl4IdFaE/LLF2N/s6nrsdytpC2ejX1IlvKjXY+v9Cb9Zr4JquHmXELo9rtuGWCsMUXQNUeWSIiBe4JrWw5Zop8glaDCCOUMIvTzbF/GxH8MUc0l3YGuiPg83Y37Zrtv7hbi3pw8dKS/ivRJLq6e2dcyjv4s0CPVZuYp8yGh7I89qOttVgIexe2Hk2r4EXPJDCF+R+3mkGzEPl7Q99Yv76wvanfVnHmjP1yEkEjmg8yinxb2xQ+bhK1a8JA4/8zjakVwFWFuU5d3og9JjexLEh4Db7vI152uqhi3DSSoS2/glRL5y3yIQ81qOwNRBxzORnkTYYx7jgZNf1nCBP0S/Xh7iOzTCpXvJ+3xxSof7cn2rsn7yTL8tpFO831WuuYAFrg7u/HLH1nCJX1zrnuWa4uRyL/tNOJzzY7+er7xsnL6kcPOzlXqXIoesxy4EbZH64dXdJe1m3MvOUzAPYkeWQX5ZR3tQHj+0hpg9xqhw4BrpZNiq7G+M+ht5by/v0ZFe4yLOnt8skNK1j0p7iLyxjV/Jm9NuL+mQRr/8GO+xEURNWwRSD+8MYX0E2WwR8mN8/jIeX8TpcLcqOoKuqTntoCF0wNVlLMNl21dhvxvaiD6lWkh7zCGWl09zFiSE0ccvzaQW57z7fzf212PcTs3RCv2o1xzCaKQ1SM2vqaAk8v5FQPNsaA1nrhWhLxpCb3vK5Z935/x6ZhmB73wO1229SaPuYXkZorA4n7VoK5dF6fuR3/jt3+nejZtlpdLGl644RQtrWWjbDsQiPGsi9DMLAY2OdCGV2FLUxIeDdaHvabjZKgfNY/twuJdSC8dda5HLUnKmS6N3RyUWsKIe+SWH446xvRrAamwl1hLCbXxh1ShDaxQyMnJEGheXX5Q76cK2xZeCeGoh9soCZoglNE5B7Z115O4sI9l+d8NTCdcvihNO0MjrquESvnzpi8WXJW37GWUsMEkhEPbIW2Ye28xba09ftiI5Ui6gtLZHjjGxEFKqczwe2bdI1C85kS9fEFQb1TtLjm3tp/MDB4kt1m+8/hrVKi8eXlDWhcNaOJTM3d0dP/7jPwG6ICm2nVv4TsMVmgpb3a+OM1BuBWRJVBrSldYM8SG2qbLklfP58fqlMU+O6j4cnxm8gYXQnkoBX+LLyOojuYwvM6qCahrbSUPsz1IQddq2sRwPnPdtfKlXDKiHssQiX0ZuWIkynPcNXSMnc6/niPq4DrF0Yd/GIJKVfWssOWMWwsOyLEN8ElqP9AHugjUPcUUU9UpTI6XEZi06I2vXPKVtr+S8jMh6ptuZ4/GWdtpDFNVwbDG+sEw0gzsHD/dYJ4StZh0nrnXVzOlsT/kTl/EFUJecnB5f0nZcV5pBFSEnZxu5XlOJZPdJnJuycjqdYvBPiebt6k6XlKhuMdHaWzhWP+0LgTRBNh9fdja+ZEgKFt7kmHDRcVJ8kZRVknvkrG47t4dbOs7WKjbSdKSSqVvn5ubAvm/QI+dU0hAel5H3WFLBNCLfeCPlzLnHl41t28ZaFh5bpQsjKNI4iI70BrGzQMxHH9Pxkee5OZ+S01haoxLPdq3OUsYXS4yVYE/C/8fem8RKtuTnfb+Yzphz5p3r3hre1K/exNcDm4PIFlskW6RWsiXCMgzbMGDAa6+8M2DAXnvljeWFDRhaiPTGFihKpGWSgppkd7PZTfabh3o13/nmfKaI8CJOZjUFGyIMQ6QNnk2hbmXdPHkyTsSJ7//9f59qJTajNFVVI1UIbtzMEVpBUZWI1g3bNCGgTcvQ9q6cQkmDlAZLg4k1lS1oyoJk434lBHupOA3ORa1QjUQJSOOIdbUOwXPe0c0zautonGjXwVBYcD4E8MUGXGOp6oK802O+WoIKDmVrBV0haKSnDr5iEmXA+e34KWtH4yGOY8qmCO5rG55+tAVPCHb0ksD8xVG7MojUskVo2GB90trgy4pht0MnS1tWkWQ6m1FbSdnetw7PxsBrbPjejNH4utrOnd4HJ78TgcnauOBaNhi8q0mUwTVQlHNUmoaiSBPWPqkVsQpufuHDGDdpTG0blKsxrQztvQ8NABakCW7mTeBa4BbXQbhUocAQWv4hEmFcitaxIVS4ZrGQHIzHJKkmjlKaxjGd33Axm7Wu7rYIogK6QCoRuOPWksYpCs1qVaCjNjysqVqGenDQa6WQ7ZyktaRcrkh0go8i1k0VRNMowltLWZYkUcASOV8xHo/J4gScoigqqrJh3lSYJGW9XiJF4LNiG4yATmTo9/t0e0Oqpuby/Iqz+QznQ/GnaVwbZtc6rwDpJUaDt00Qq3XgYjfCbkV9gUJJT6Qlk3GXvclOKG4h+ezhE67nKyrCg2qsdVu8C8GZAEdHB+SJJjaGVVkyWxRc3UxpvMZ5Re3qgEowMYKwsa2qilhHVEUZ5jEhidOEyjYgFA5DrASuqfB1Tb+bM+h38LamqWFZllxP54g4YCgCk94icO1zgOcf/w//UPw/foD76+P/M8cv/tx/7XGechIHh1nbstrkmqort2xbZwRNHATRzbFhv264bLplG25DYSTbUDNdBFfJJowjhHA4nA4dGKoOTMONC2jjxg1BY+H96s7GUebbtk+BUxBPLVWvDQpdu+3Gcuvuql6cs4uCi2cTHBR4fu38KUMYCbQO275GrwKLcOuqbcVIaN2K7cZ0w9XbhIJB4BZu/r5h+G5E6434HJiGfsuVtbHCtszgTRiaaJERwnmKoSZavOAANklwHAUmnw2OaSC+qqgGBhtLZrcVau3pPrHUmaTsC6qeoJh4jn7vRWjaJhjuhTjuMdOSphMF1ydQ7KWEcCy3DQpCEIQ1HZy50U39IkCtdZSK5kXo2ebYBIFB2CibeRNEgJYFuPkZhPbaHw9TqruK6OZFYBACyqHZjskNn9dLQXRT/1+HNYngijWL9hr4ENDVdOPtdWjyFgdggpi9CfsSTbhHNk4yVbjt2Np83k2IkSwCTmCDvJKVoxybP/e9txXrrfgHUA2j4Hyv/Nblu2EI110deLIbHMgGR+DCeW24wMXYoNdhbDSpIprW0LqJN6LpeqxCG7AL99QmSGgTTqVa9643gSO8ERk3QVn/+s8RvGD5tp9LlvYFj7dshfr258VejKw80bTehsW5WKEXFcVu+iLYD7bXv+7o7RyzGSvb+/sm3N/RtKbqmyBYVw31KNuGs23Oa4tv+bFQovB9+m2o0YaluwnU0ouKphvx44es2lbyVvTbONM3P9+EJUY3dcvnltvx32yQE9GLcC9ZhdZuswhO9mqgt/PY5rVbsXcTHjlQJDcWM7fIyrE8jNsOAbedf8KcGoIOq24QBs3CstrRdJ5W27EoS8vqKNmOHTxbBvFGPItmgRVc9zRm1jLRN8FfSZhftyGHLeN6s8b867xnm4TCyey2Zr0jtniV/KlHr30I49oUryq3DUQT1m9F1oDcaQXRMsxfqgzXD+fD653fzhM/Pk+ptd2yjuueDuNDsMU0BCyB2nJpN7zXTfjlhp2++ZzbtbFdCzcYiDoPLfabkLBNYafqKS7fVFQ9h8stopKkTxWDT+yWs67KcA6b67m5luXYYI0gvajDHAnbsMnNOAldBoEr38SCeOYoBuF3be59mwTXsZk3AWPS3hc3L2vKYTh/MxOYpSc/ddsQsU2xFILTdr0jWB062C1xS032wDD60G6vw6ZQ+gJ7Eeb/Om3n1nY9lU3g5W7W/82zRp1Lpi+DeHWBlJ48qVh+e0L3oW+RA1B1Nm5/ttf68h1BM66JOhXVdULyXNN94LedQKZd0zac/g3XtuyG4iaEdbbsqSAOE54Bqq7advvMjxTLo7YTpBTEl9B/0GyfK348/C6MI/7CQWT63/ySv7wjacOYGldipKFWYbcrCG4hLxyR0q04FezV3kuE8NiqQadRu4F1WOeCw1Vp5jaksTtq1tbiXRAKyrpEWoVwQSAyxlA2DZYgbDkbnKFlVaO0QNKenw9hLSCDI8h7XO1Bh4Cxq9mUbreHjlOaeYluKoS1eB1KXFEUUbmw0ZJKYJQLKc9eQG2J0oTaVhRNjRaSJIpYLOZ0Op0QZiLLEKCCp6rqEBrlQXmDlqCso25KXO2IkgRE+P9ahXbtNDY4qSiqkkgHMTSOFeumCO6qOKeuS7yQlI1DioBisL7eupqMDC3XQgQmp8Jxc3OJjhOENkgX3LauCeKS9TUmDm7LbqdDVVV4Z7m6nDHY7bK6uibOO1RVg1WKSEl8VdHUAq1BRQJFHDauccR8uaDX67JeB7FUSYFQmtqVZDIKKfDS4WRwa3kfHHZV7RBa0yBwzuOdwFCRSg3W0rgC4hB05oOyRaZDaI8WMrwPAlfWmDi4J8uiRClJ3dREsaSpLFVV4ZQk1m1QnAgp9N5bysq1QnJgVTXW020RBkaF9PCq8sRCEEuJlClNUyFsjXcljdNoE9O4gHcwUiJkjXaKZVGQdHOEcEjr8E6jRQgg8hZik2KtpXYVSjbEJqGua5wOYjtWhnAlJwLOoWxRFFrjqoo07yCcp7KWum4QQiEkoVU40qybCiUhiRS2dqQKYqMwWY/Z1TVxnAUMSL1sx79peaIOq8DbNSIOOIvNDOdli6Fo3ZBKR0hMK2Z56qbCKJDStcUFGbARhEp/6aqt+zxREdo2NHWFt83WMU8bmNY0DdJobN2Q510clvV6gY4y6rIg15pl7UmjIHy4GlCBCetscJTjGmSehPEgBcLVpKYNKRQEvqh1SAGNBLwjlkGsGw+7aKnAW2azWXAzlmFR06rBOo81HkeMERF5FrNcLomUItZQeyhc1TqSBV4ZEAZbO6z2ZLEkj2IUlmjQYTqdM6srsjSmKGsq65A+zIW2bh3s0jPp5Ki4y3K5ZL2cU5Y1jTDoKCHwCsIim2lNp5MzHg2obYOgYT5bMl2vKZYzsiwLzmw8tvEkcRCr9wY9JqMudV1zM1vQiw0Pnp3THezgSsu6XhF5iVAgGsXhwZgkSukkMWVjmS9LHj97Tr1cYpTB69A1kQiFJTBd4yyiKAqM0qHo4MM9KFTA6HhCKGHTWCbjAYe7Y7I4CmuOhtPnmrOLGWXlkDoKrGEfqqlSGaBmECfkozG9bsrZ83OqYkVZWFwc4b3B2RqnPIlW1DasK0p6jiYH1LXl6WJGZEPYlhQKW3i8DA5oIwICI0KSqZQkkqzWJY0vUSqiwFI3JbGKt+KiszWaUPjUArrdDtY5bmZzvA+BewHHEPDlINkZdLlzNCLJI9armihK6KYR69k114sSEWnkpmCiFUW5Ik10KApYy3K2IO92cI1jXYfPq0Rw3UYyCgWAxqISw97OhGGWMb+6YjEtsKuG2hjK9Zqo08XIgPFII9gf73Nrb4yONaCxDUxnK66++BxrBWiD85ZVWdBPY3ZHA14+OgidOFpTVYpJegAPBOezWSh06bA+iKYhiWLKqqCXdhBiTRTF4DWXl1NUkmCdRGhJXVt0HDp4jk926TtnHWYAACAASURBVOQpvU4IAjUW4rsnPHj8lEfn1wCUZdjkSikxvub2rSMmkwmJlnTTiGVpWfVrbs4XOGupsUjtUVqjRIXwEd4FTq+oLf3+EFvVrEO8HzExVgZRufSCOFLsjbp0sohuHpHFQ8Dx+OIaaRQX11OiNGG5WmKSmLBL0XhX/dt61Pvr4y/52AgYqnTbzWkI9gpYAnzbFhiJ7YZjI4ap0m25rxuBVdZuKwA0WXBhpleh3djGknjmtqKsNWGDFM8De9AmbThXGjaVNn7xniGspe3FaDdAQWgJYWTdj6asb3UDu6/wNOFR7YXgm8vgyiUISnUmtzxRaPm8K0d0Exxb5dCQPitYHyStWBuEQa8E6sfTxWOJTUKgiFkF7qvthl+aPl0Hkap2FGMTQkluGppcYWYNdU8TX9bbNtNNW3Y0fRFutBEvfjzdfrOxtJGg96DYhhEFruOL8KHQogk4WO8KzDJs0gefVsxuRyRXIYE7WoQNdd1RCBcEq01CuI1lSBIfxCSPptiTjOSyfhG+JMN5FztREB86EmH1nxsneh4Y6XpeI/NoK6CUo+BG2XB81apCuPCzYieIYoFv2yaQW0+dBR5jNGud0suGumtQhcUs7DZFfPOdBuGtFZUi0eIRBLIIotSGf+llcMQ1vZiqr4MosnbbEB61btul121oVh6SyptEkJ7XW5EjhBR5XBrEkk0IWAifalonZigGyLpFDbRjElox31ncpg2+TZD3UhAt5TY53Su2ImF0ucZFIahJlZZiqKm6giY1dJ5Zyp5qRZ/wXl7LbajSFnEg/7xusMUD2I1b2vxYynwQMJuWM6va0Cqvxfbz6FkZwrekpB4mAb9QvPh9GxGr6Qbx2iuQZUOxlyIsxBdrbGpCoNNeRHpaUY7Nj933L4SzTbtxEAXdNoStHEX4jckhj7di8ma8VX2NKhw3L2lsIDCQPwtuaTOvX2AVyvY61RaXa+Z3c9LzmiZXlL0QFhXdtGK93bTKsxUXi4lhNWk53rkhf+LpPi4pdiKimyaEQOrgsAxhXGG+unlZ4YwiuTTEN47YBgH2xwsfXgnWY816V7C4Y1FrhZeS9EySPwnzWbETteJlGG+hmCO4el1RvlTg15r0sWR5kHD4e3NWtzJkrUku64BHacfG+duauudwOxUsDPFpxN736iBstnNTcH+HcdhkgREaTy1NFpiqwv3YPFoGYbHJgnP4+tWI67ctZlCSZyWxaTj/cEL/I0E8a93bG9G3df1LHLMTTTEWFPuW3kcKswzhT9KG8arnVUCGtKxia8J8ffZuii5g508q6q5uxzZbcXEjCHsZilHrsaLsC5KrFvVjBPE0oGiqfhCQ0e26kAQR0saC1X7gpncfBnNT3QnXwsZyW/C8eUXS/8kzDjtTHs2GjNIVD3ZHrG9yel80bEI7N4WUaF5TjcKca02Yg9Y7Bmkhvqq3rvEQqqeos7D+WSO4fl1QHzeoU0Vy0fJlV45o2lB3grvfzC3FSHN5X8NPzMjj8ExY1oabZx36nztUm5MnrCe9aJidxNy8Bl/62c8oG00/XnO+7sA9OOOI4ccWp+QWSeGloOwFd+ziIMzVTRauR/8zh40Eqt6wfB1WhQLlxVccelLw2t4543jJJFrwo19c8PF3brPzvdaIsgj3sVNBSL74Scfu3QsOO1O+OnjID+dHPDgZ0TzewSzD53ftuLCR2BZsqjwIuPOjMIcm1+Hfyl64broI/OGqq7h+U1LuWKKdFQfDGQCPfnCA15rhRzVVR5GdVVvGfriB+Qsff6VFW+8KhIoQ1lA58EKEhkbv0DoEIgkUUoV2V+uLbcu1iiTrpsBZ0DoiiyOcVfiVZSeNKZoap8IG1TaS0jYkKkUkmxbO4EF1eLDBQZamGbaukITwm8CzC23G0gu0VtTCYq0LHNTGEccx3azTtilCmpngSjIKaR1KgKs8WdbBuYq0o6jnJZFqmbY/luAcqSi8viiYdEdI4WmcpxGdNiSpIookxkNdW9IYXOOD+8/kxKpBaMW6XNHNMso63IAVDlF7Yi/RXhCZiDhSDLo9OkYxX5YsnaNsaqQJbjiPovEKJQRRpIhM4K5aVzLaGXB+fk6iYpx3SGpQMXUZWKzSS2gkqe4Sp5pIWsbdmIvpkkhFrGYVS++QUiF1DE6wWC7JIs3Ld49IUs3NdMrNYo2WirKpGA66XF9ek7XXusGTJAmygXG/Sx5HrOcLZlSsy8D3xARuqJcC42XANUjJYDBkkHWYzmYU9YrFek0eZRRlg5KKZbUi1zGJVgivmIx26GSGKNYUxYrnV1csy4ooyYPbSxmqsiZLMpqmQnrYO9hjNOyTxIbTi3MeP32OlDFly6CtmoayLEljQ1GU3Lt9yGgwREnHqlhweTVlsWoo1xUm0Vi3wmiPbVKsdOyPR/SHPfppirAN0+k1T56fslqVFFohoxhrRQi3cWuyJA/oC2fB1mgjkcbQWCjrkjROyJykikIgkooM48mYvfGIJI5RWnN1dcXF1SVnF1fI2IRihwpt3HGakPYzjg8PyIxiYDLqsuLZ+SkPnz1mbRukt9R1iUw7OCEoigKdRhglePnkiEQrlquG5/NzbpZTaq8DcLxaY0RwGNqmITE9skTR72VUdcFiXlCtKyrZAtqlCWKfbfB1RTfOSHJNJ0uxruLs9BIvwMo0JLevGzwhqKcoVnTynMZ6hv0+R5MJsQ5tyI8fPmRWrKgjT+EcSRsg5b3CrSv2xiN2Rn20dEipef7sgqRcs64bFlUZwOkt/qHyNa++cpfD0TAIemXB5dWMp6fPsa7CNhrfSLIoovY1lQNtYqT1HO/uIUXNYnqFbWoiBHVtkSZhvV6iZYSJYa834qU7+2jpySJD3TiW65L3P/2M9aokQmNdKCjU1RqtNSe3xgz6HQb9nEhLysJzdT7jweNHzIsG56Gy4bXaC3aOBxzv7ZMKT54mTIsl68kup6enVFXFehWcnaUNxQCjNMdHe5wc7BFHEoWgqiqWyyVFUXG+mCGTGG8BFVzdJ7cn7O/0MCam08upqorr2QpnLc9On+JkRexjKtewFjVeQ6xj+t2UV18+AWC9XvLk9IqyLCnacDMPNNWKNE64e7xPGkUMexlaC8qy5PjWHsPhkD/94GOWZQX4wKc1BttUHB3ucXK4R+RqOnnMpJvy/OaKRw/PEFKzdhIvJLqB2gVkwK39PUaDPqmAJEkZnp/z+RcPaRAsiwIRSZT19CJD6UNA32jQ56XjY/JEMJ1e8/DJBatijVSwWjf4xoa1oqrpJAaJ49VXTujkMYNOTrWwLNdrvv/DD/AihHVWTYUVgldvHzEa5IxHPbyr6KYdjInJco31x3zw8UOWVU3dlHhvUFqH4K51zb2TI453d1EIbm5uePLkGVEjmdVBNK2LMrTTNoLXbh2RJQkHuwMSrWmGHZ6fXyKfOi5XBSbLWCwXREohI8l4MuL23RM6sQzICBTWrej1xzx89ATbQFU3NE1FlmhSKdkdDuh0sq2but/vEgnDetWwuLxGxzlFUyG0wmrPdHbF4eE+h3sjJpMOaZQgrefTj5/z8OkZTkZbXr7CE8cJu5MdUu3JE4NB4xGYPOe1POfRs98nTjJK61kWa/JOwuHhIYcH+3RSjbcVSarojnMuLq959eU9fvDeB8TJMAQgSkG1qkE1JFnG7u4OB+Mx0tWcPb/kwwdfEJsIE2maskQbRe0sWZZycHdCLDyHvQFGaMrGEiU5nz94zGKhqYuK2ERgZeheEB7jzb/tR76/Pv6yjtZRtBGtNn/fMOE26dFuw9Rs3UVNElxEyVWzZeKtxwrZBNfT1ZdiopnfumVkDcl5cH9uHF+q8G3L+8ZB5iiGahsQJlwQdnUT3K6udfSYlQvBwXVwyZQ9xfpWt+WkBjfqJt3ZGfAt/249Dm3jxUCh154qEaiW7bc56m5oUZa15+a1DKdBlRJduC3Koe7qIEK05yubICBVnSDibAJMVreyrfPSLILYVnc1TSKoOjFVV+BOgrMsPfNkF60Y2TrQhBVbN17d1dRte6qqLbO7kuFHlmpgts41EFtn2MbFVAwldQeazDO7K+h/GrAL6ZUlOS23oVEA6bOachLz+G9qmr4lfazoPJbErYN4+fKQ7idzyt0sMF3X4f29EtzcU5RjTzQV2GeC9OqFIi5cKAzYTLesRsnsWLM88mTPBPFUkp01FHsZ0XWFixXJaUmxF1P2g2hy87KknDhcZlFzxfA9Q3ZuqTsx8VUQ2PQ8nP9GBLx+XVLcLkm7Jc1HXXb/OKSHR3OL9uE7MdOKahQR3dQ8+UbK+naNiBrEVUTngSE7c5g5bZu73f6pSsv52wmrA489BCFr5NOE8Q8E6aUluirwRiHLZuvcKyfxlqULniZVyNbBa24KqnG6dZZuWqWn9zSL1yribkkUNSy+6NP9TDJ+v9o6OW1qgijVlRQnmqsvW6JhwRsHz7guM56+d8DOdw3pVesUXQaRkAbM1QqbdGgyyZNvSFyvQV8ahu9B74sStazD56hbRqj1W+FvNdEsDwV6BdmZI1q4F65l1yKtWtF7eRBR9gXFJHCYRx80rbPbb0Pj8B69tJirFcVhF1VYZndiLt/xuI6GBkbfV3SfNCE48KagHiTtGAuT2eUbEfOXLT5yoB2d92OKfof0ypI+W4Xu1DzaBjc9+kXD+M0gll2XGQ8e7NL7M4OqdXBrOs96YjArh162rlYtOP3JBKcgPfeoWmwd2V4JoqsKl4SArOkbCcu/sSSKG476U6ZlwvlVF34rIb1qtsFLG6emjSXPv66ojiqOD8+YpAtOV12efjFm8oea/HnLB2+D5mwsmb4C+bsXvNmb8WbvKd++uMvptMvVR12ipSG+qkM9dhbwIXUWc/4VwcmXH3OQzeiZguf3u3x+PeZiNmT0fhHGRzstVn3N6dckyevXjJKS+8NTTosu7w33ObMZOz8UW8ehnkviizV4T9XPWdySPPumBRx6qhn/QBHPgpgs2syFeFVTThJufrpk0F/x04cPGOgVD9cjRj+x4sHxCP2/dsnOW2G8RX6o0nL6lYTyK0vyrOTN8Tmf3x1z/cGIne8FcS1uCwouMVuH8vm7AnF7xai/5LAz5U+O77H3r0IXSXpabp3Qi6NoW2Sc3lW4n51yMrzmk+c7xN/LSa6C4zW9bLaBX3ptWe5HOC04+2bFaDLn5/cf8nwdrm/068NQ4BBBRFUlPPpFRXp7yreO3qd2iq8OHrJnpny/d5vfrO+jVwnZeYNeNluXfCh2OJ58IyJ/9xJMw9MnI3o/jEhySfdxuXVw48P6/uiXFH5U8Y3XPuZOesl1k/Gbn9znRnXofxYKgdnzIGA7I5m+pIi/dsUr43Mm8ZLaKeZNjNsVPPjOq8GtvHSoItwj5UAgX57z7uARKxuRqYpvjGYcm0v+i6/8OxTn3dahHb4bVXm6jyrO3o1ZvlXw5p2n3OtckMmKf/RHX2f0PY1sBPnziqqvsUZQdSUHr5xy3L3h7e4T9swUi2Q3mnH0jSk/+t5bVF1BNIfssmK1G3H1ludL9x/xE4PH1F7xWvKMb3Te53+OfoZ/9s0O+/9LTNkLAWTCs3XVlj3J9X1Bfv8aoxxXH4/o/fMg0DZpGC/hecRTjAT+/pzdzpr/9O6/ZN/c8KDa4XfTV/neH71Cehb4tpuCom4LvH9OwP03HH+lRVutTHA/ekdkDKJx1K5BC0snNvRGE0xbZby+vma5LgNfrmWxaqGwPji3FII8StkZDoGSyjacXt6A9VuBwTtHWTYh7ChJiKQgy2PyNNkKB04JrA3hULULzFHvBMjAzdPCszsZMVIJ62rNcjnndF2ipMIrT1W7Fp+QoEUQOwfdHmkWI6VjtZ6zQrMuqtA66y1lFdrSAy/Xc7y/T7/XI01jyvWCB4+eY71nvg6irESTxgnFak2SJOTdnIPxDpF21FjOL89YzavgJEVQ1Q25ztsAE0mkBHduHZLmKbmGxbzg9PSc0+srnNRU3uBdaLmOlUYJz/5wzGDQo5MmLFYzxoOcD977HOEltrRIFbixtqhQWtPrdjk63KfXNSjpqIolSSfn4aPnWCHRomWDNiWZSZmMJ+zvDJn0c6JIMhl0OL284fzikov5HFsGPENoPV/Sz/soIXnt3kv00xQtPeUgZbFe8dEnX2DR+NrgbBMcbLUjTQy3D3YZj3qkynB0a5ebmysePHzGbFVjvMQJHwRo6xDacGt/n+FgwGSQg3BY20VoeHJ+waqqWqE+RemY6+mMfidhf3fC0dGEPE0RrkLu5OTpIR9+8BlgAltQBOyCc44vvfYqBzsDoliTRZrza0tkFJ98+pBIB5aoaBx1VUMU0e/3Obm1TyePSZXElyX9zi6ehi8engWHiaswKgT4KSdJvEIpyf7hHiYSzJdTrmaLMKZMC133IWTP25qdvR2ODw7JI0mkDVIL4miHuim5vr5unZbBjW5rSdZLOTk8YDzoEBlJpgy21Dgx5mZ6STltiNMUX1TYskJqRTfNMFpw7+SIg0GPLFKARj+rMWcVV6saqRSrYo3VEVhLrA17kz4v3T4kTQzL+ZSHj5/xZDYj6aSsaxeKP22qfaYMO+M+R4dBJKxWC7QSXF0tWFYh2M55gdSaui7pJh1sXfOlV19l0M/ZHfTBNaxWK4Tb4bF4zvl8jbWORjmkMkgBnTxjd6fP4c6QPDWsVgWp0Tx6esH06VOyrMOyWaOVplyvef2NVzjYm9CNFRrodvrUOBqxw8WHnwUMigxt4tZZIpOwWsx4/Z03yFPFqNfh+vKUq+mMx4/PkEKxLGuiNMXWjv6ox/7uDt1OipGWSBsa5zCJ4dbBIY8ePWG+LImjhKoO4UhpGjMZD+n3cmIj0AoiLdB0WRd9ytMLqtZtL4VAS8XOzoQo0uRGImRDJ4/QSiJ2Ojx93tDtpVzPV0RJwnw+4417r7G3NyFJJYmKkRKSPMZLx2jQZbpYYqWjpsKolGGSc+vwgG7HkKYZtS0xJgv856pmtU6Zr0vWRRmCtlzAboz7HU6O9phMhi0ioEdVVTx6OieJMhrnqOrgOE1jxcH+DrZaYVRwq3bzlCYSJElGJ4+p65qmakiSgCvQUjHsden3OyS+AuEYToZYo3jy4FkI1nMS7xt0FCFqGAx69HsZWRox6KbUjePkaJ+bmxsePz9FRxFN44niiLJqKF3JwWTMwaTPeJxihKfb2aeyjkePnzG7WoSgLe9CSJ1zuKZhZ3fEqBda5WMlKamJE8Ot40OePr+gKApUpFmXS/IsZdjr470lSSKaOswDeRqRJylJkuCI8FVJ0TQ0vsbjORiP2R13mAw7KKXQCmbTKTdnZ2iT0dQ1kQhFv6vLc9598y6Dboe0F6GUIfIJyWpJ3s15djOjpgpohmJF01h2R0OGnRwjLHFisA14GdNJRxzs7vD89DJwopOE9WrOqJdy584t6vWaKAqFU2M0Sgq6/Q5pJ2VdNYE5b0XAhlCzuzekP8gZ9rooKYmEYjLucXp5Rdk0GCWRomXGCehkEUYFxrMWgQtM40lSw6Df5ep6ilMRaZ4xW1yzu/sGk90JTTlHy5zSVviqYDwccXO2oJvlrJrwPLMuajKpca4kSzW7O30m/RxbLjH7I56dnWK9pnY2ICEaS9VU5FmfbpoQK4E0EiMjTC5Z1DeMx0MePX6O0RHWg8ejWsSHd3910Vl/ffy/e2zcrbJ2wVXVtr7bRLDclaz3BU0SNjOdh5L0wiE0W/bt5v9v2nrXE8H85cAtV0vJ8AMASRO/aL+MZrZ1LwXmbXksKMYevRQklx7ZBGfSxu27cfihaF20kuvXBOvbNWqqiC8l4/ddmyJPwAd4cCq0gta5YHlLUPUdXnuia0F8LYmv/TZEJpq/CHfxUnD6VUl1UJP2C4plxOAPYmQNnWdN2NSrEICS3FiKgWI9Uczu18i8wa01+aeG+MoDwc1n5g3rvWgbIFTngsuvN8T9gqPRlMeXA1Yf5gzfb1PFly9czZsN69UbiuKopruzYHneYXlHcOufCYRii5bYtOPaWLI4UNy8XTPYn9MzDbNVwkWvw+53QpCZSxSi8eh5aEOfvRZz87pneO+SQVpQ3NU8+3CX3ieK4SqkcVfjlCaTpDcli9sZNhI8+mVBcjCjG1fMFymro4Rbv8O2rX+DTthgAZ5/XcGdJeP+Et6G0ydDxn+o6Txtti3P0CIYOoqzdwT1cdjUAyzriAfyEPEDRXYRQr3WoxgVS/JPrlndHXDxlkG9PeX14TVaOh5oy5NRl6Pfbge+aJ3AWiIsfPGrCfn9K/ayNS/3Lvi9By8xy2LSC0WTq63woZdBOJqfxMy/UrA7mXHSu6Zxkul+ykN3xP4fSWQdoVbBjSrsi5AaZ+D6vselgvhU0f3C03kGXiVbVqdoHLJ0XL+SYN+dc280ZSddkKqaB/mKx+tD+l8Ed6mSYst4XY8l119puHPnjJ10wevd50yblOo1xezJPvFcUPckaeuw9VqwPu7RZJKnf0NwdP+Ul/oXAPxu/AZNGtP/PLRVx+cFsgrvWfc0l28Y9M9ecbs34+H1kOn3+ux+31IOg0gobHApuziEst28IhFvzxh2VkxXKedRn+4Xnuy8aR35eose2BRgvvjbMe5WwU/f+xyAz2cjnooJXmpGH5TIoglFjTaccLmnmb3a8Nb9h7zZe8oH8z0+6u+y+OM+vYcNxW5KcrrCJgozLfn017oM7l/y1vgZA7NiOFjxG0XMdTVk+LHbYlA27elNrogvSh7+akw0WvHurcf8yZMjbh7mTL6viIwgOS1pugZZOea3NNP7lrs714yTJbvxgps6pR8XfPLuLXa+p+g8DYKUmYcwufVIYe+teXXvgsN8St+s2U/ndEzFZ9NjonnbMdC6uptE0Ly8ZpAWvNl7ipGWNwbP6UYln0lP84Mu9UFE79MlTTcmfnjFxd/t0HntmpPONXfSSzJVcjc9p/GKD+4M6T4OHF49D67A+R3N6J0z3ho/49X8OWdVj51ozjhe8rvNK8yvYvJTF8IQW86oN5qblzSrr6z4+u2HVFZRWMPnqzvsfScUe8J71GGMDBV/67X3mDcxO9GclY346cGnXNRd7nSu+P2dL2NWQRStBmaL+1geO760f86dzhUAhwdT/vdG4X4wCPxU66HlG3slmJ0Y7GHJwXDOz+x+zkXV4avvfsJ7T15j7ztlEIMLS90N4rKZN5y/kzC/X/Erx5+yE8352ugL/qfVT6P/OKLztGmRCAFHJ+swf9+8Irl3fM7P73zC3fiMz6NdDtMZv/nugPEPBcmNxUWC+LLGjy1fO3xI7RQvJWdc2RyL5N3OF3x3dEzRS7fF0OiqQK0cwmrOfyLFvbLkW7c+oKNKfte8wuenx/QeBU6uXluaPAj7vU/m3Px9wdsHT3klO6OvV9yKrviD7h0uxhnRD23A6/TCeqUKy/IVz68cPmAnmnMSXTJ3CbVX/Ez2Mf/Rm68wfE/QNGFtT64bZncV//lbv8OzasBRfM20ydjXU3qy4OWdCz4f9Ihv/Jb7bk3Yly1eqTnev+ZbOz+iK9ecmCu+fe8ulx8dEc2DsOlavA3A/eEpO9GciZmzo2esXLDJ300v+INjSf9BCF8rxobO44L1v1/w7+7/MVc2Z6SWfFbtUHvFr43+iD/snrDcy0iuQlE1uaxZT4JjuRgL9CszfvH4Q5Y25sOkYPqjI2QNeu1p4nA+Zhlee2/nkqNsSi5LjtSUo3TKd/Ud0jtz+F6XOhWoii0TWcLWlf8XOf5Ki7ZOBEEqUp56XSGjlEjCuD/k8GiHLDVoVeKdYNSPeH4G52eX4C1KBedYTIRbNbzy5n3SVBAZRxx1aJqGYd7l6elzrqcFWIE2JbptS+4lHY6P9tkdZiBLpI5YVyXPnpzx4PFT0Aap4tDu7z3e16RpzBv3X6Xf7+OqAt+sWcxj0ueS56eXRKrLwhasyyrw/6zl9Tfuk2cJaawoijVC9Hl+ccHDR8+CcFkHJq0QEqM19+/f4XDcpRcZqnKJtR3G45d48MUjlo9naKFYNxbfOAa9Lv1uj4PJkKO9MVpZVuWCyUgxPZvx/ucP8LUkTTqs1yUgyOOcl+/dZjhM6eWaaj2ll3YZJgJJw6ePz4iyLuumwiQGz5p+r8/twwnD/gBEzaAbsV6vSd58iUePn3N2vsQ3NU6F4KzGN3zp/m1G/ZD46pzFZjEqW4Gq+OTzIFCgU4wvg/vuYJ+9nT55FNiHtnYoU5HEgkffeUJn2Ed7w2q6IFIS1hXvfuVtBt0EI0AJR0VCtjAYdYdPP3vCslgQJwneKUQi2NsZcvvkgK4i4B/wdPNdlNH84IfvEWlN4STOSRofMch7HBwN6XYM3SgG5ViXjsOTEV7X/MkPPkablGJaMW9KJoMh3Uzw0r1D8qR1Z4kYbTx5J+b8POfTJ0/p9Y6YL9bkeZ+Ihju3DoijELy2KufknS7dLlRVxfvvfYLKepSVp5v28EazPx5yeDTGVws6UrFOY7wT7FR73FwveTydksc96qoV04Xi+NYRR8cDJnmGwLOcX/Pk7IYfffSIRiWBf9s4kiRD47mzf8T+zgThFwjviLIMvVhy7+4tLq/PmU2XSLKQrG7hYDLm9uEB+GVgRauQYj/pTbiYLrlYfMH1ssZWNVkvQyFYL5Z86Z0vcbi3y+4gA2cRwvNadhsj4Oajz7CNRaICV1pGHO/vc+dkn4PdPsI7Rrmhl8VoHB+fnqFNDs5TN5Yo1hzvjTg4OmA8zEiNRvoecZqAfET9ZErtBEYJqCukitBe05QNR3sTOnnCoN+lKhb0+inaWJyouf7wIV2TYlUDrgAheOnkZXbGPQb9jEhLuv2IONHoRPDF0wfUxRIpoVxVuKrh9ZfvoPAo6bGuxuIYj4ekacoPfvQe2khqJyibGmE0VEvu3D2g34/Z252QKks/v824rJlNF1xO5yQqxTUST80g73L7+DzQTgAAIABJREFU1jHO36CVJo4iIgupUqA0Z5cXzMuG0tUYAVWx4JV7t7hz5zbr+Q1xFAL5siwligy35B5Pzy9pGksUCWxh2Tkasj/cQ4sK4WownlSnmMjT6ya89/GH3MwXJKZDtW7oxhmT/pBxf4TxdSgQaIn0lslowOx6yRcPHrOaFaRpcF7ePjykN+qjRUNRFGR5wGQkOqfqL5lOc6azNUIq0jzi5uqaXi/izvEBO8MusRFIJVAq5fBgwOXVGbOixANaQd0sGXZHpJmm8hCbIMRbDALIEoWwDXVTIuOYqmnQxtPLIjrdmMauUJFCmwxnDXcPunzUecDNaoUUJakQ5CY8KKQRnBzvU6ym6EjSiIY86zEYdrm+vsYnKatVQ1E2gCfJDMcHOwy7OUls6cZ5CDo7qijLksXCsa5WeOHRWqDTUDCcjHsMhjm9XheAPJfMF0uGow5nZ0/JkoTC1ri6wShDHMc0zQqEQiWBj2tUhIoTYq24Ws8xUUpQUwTlas7townjUQKmIc4SdqIepT3h0ekV1bogiTMcaxbzGbv7Y0wsGQwy4lgFVEIUk3S6IG9AuFD0pCJNY9JehyQ1CGeJYolrCiKZgMxCAKCoqFZLiGJq5xFE9Po5z8+eM+im4CRxEtNUK0SSYo3HKsHSLSGSWL+mtpooS9k5mjCIE+JIIpTEAPnIMBjFXD+/3oaflcWaUXeAFB4hVHCc+8DWVzpiuV4gNcSxwcuEunFEOsbahqv5ORrIs4xIp2EeqGtMnLGuajCBNR9HQYD3a8d42CWLNIKaLA286Dg3XFw1IC1JFNPUjmG3SzfJqGvL7mAH7Sy1CLz8PE+ZTZd415DpDpUNRVIZCdZ1iVHR/82T2V8f/387NinvdUcFkcxB3VHc3FMs3yroD1ZkcYV1kovbHYr3MgYftxzCKLjDql4I6bn4hZJOf82ttGA/nzEtUz49mRD/aUb3oYMytJM3SWDcLg4U0y+XvPvSQwAiZXk0H/D8vV32v91iXTZ803aDVwwl9a/e8OXdZ1RO0TjJJ1cTnvUHDN+HJoPs3IUW/ijwDa9/ecnRaMq97iUPF6Fr6f0PbzH+rqJJJNKGFBkvg7vx8a84vn7/Y35u+DFPyiGzJuWzkzEff+c22YWgiSXJdXAYz25HLA8FzRtL/sGXvs/QLHlvccjDu0MePB+jfyshdoE7mFwGLuzqpZirny959eiMn5p8ztNiwNfHD/jkaIc/0a9y6/9oqDuK+KoOvEYLi7uKzlcv+NbBZwBM91MezEc8+ntD0j/OGL9ft2FBgY+o1w77SzN+5ehzJJ7Saeb9mA/NLmcMuPUv3JZ/2OTBOXz9bsPrrz7hjf4zhnrFwsb8oW74rLfD/rdL1gdZmzL+gum5+vtTvrH/mEm0oKNKTqsePxge8igZs/MvNdl58+cYkZeva46++oR3Rk/o6zXTJmU5fsbvRK+R/7qmGGniacAcqLVldmIw92e8MTnjq4MwTj5e7VK/oXjq9hj9oxUuMcTXNdHjGxZvTFjsK+KfveCl4SW5rjhJr+iZgtnwms8/ucet356zOu6QPl1S7GXUueT4a084yGacpFd8MN/jcDQlmlg+Wd3i9m+G8WemJatbGcLC1RuCv/36e9RO8bXe57y3OqRJZzx7ucf8SY+9xyuKvbR1X4YW8Isve+689ZT/+PD7GGH5k8UJ//S9+5j/zaALueU9VsMohPJ8/Zpfu/sDjLAY2XBsrvgD8xLdny55/OAu3Sfhd4siCPY3bzj+zk/8EIBUVUg8u2bO3zn8Ef/93QmDTwS9T5fIylLuZjgTXKEPv5Vy8uYz/sGt7wCgcEx+bsGvJ1+l97BlV5vNWFGcfs3Q/8kz/pO7/wqFo9gz/O7uq/wwfpXjf160AUwGvQiuvbOvSPL7V3zj1ifcz56SiIp/mP8c5xxgViHwsMmDJFH344A4WVvGb5/z+uiUbw3/jEf1iJ8ffsRvmC/zsT2i/0CxPu69YAIDFz9X8da9J3xz8gHH5oqf7X7Eb6bv8IfRbfy3u+13ITHTEtE4vvULf0xPr+mokoWNWdiYbx59zIe9PVb/+BCbKPSywdyUW87ws5/L6R5c83fv/pDXk6d8c/QB3z55ie8+fwvzmacaRi1z27O8BT/z7odUTtE3BbfTCw5jTZ0reAfOPzoO817L2NZLy+XfUvxnb/8+Hy33uZVcc91kvJE/YS+asX7HUP7pPrrwAQlSWq5fjfjlVz5goFcYGa7F6/lTdqI5PzX8nN/of5Pew4ZqlGDmNeXJCHFvyTdufcKumVN6Te00mSr5e3vf5b95eQf/7RQzLSknKenTJc9/qs/P7DxmoFd8vt7hjfwJtQ8C4+lxl4+f3qb7OIzBchSRfzZldbtH+ZMLfuHOp9xKrslUSV+t+W/fHLH6vEv3cbUNRVPLmvlJyqv5c56UQ06iS3JZMnMpRljeyJ/w+/LLbQu7RC8tNlEsDjVyZ82iislVyVCvKL3mv7z/T/iv/sV/QO9RKExU45TVTsAflAPBv/fWd/lsNWGoV9zUGW90n/Hdo5dZfRHCntMLtriLaqCZf7ng3tEFd9NzvpF/QFfUnL/d5Z8ufoLsrO02cS0mp8UFlS8V/PzOJ/xi988A+FrykN9fv8xv33qV5qMOq4kmmQbTVZQ4TtIrLqsOAH215lk14CC6ITU18w4MPrMtTzc4y820Yva1gp88eQTAvfiMvcMp/91bGXx32HJpQxdG+qzg6s0eeXLJL43e46XojIf1iIFacdiZcsEkrOdxKE4VI00xjIi7CySeo+iawhuOTRDGH9VjXOwxy3DfbViwq33H//jFT/Ezu6HIcjc+49J2MKJhWUfIGrKLMEZl5dEEpvi9l57z8zufcCc6x2DZUwteH5zyW0cH9L7wW0Z9NLfM7hpi2WCkZeUiHtVjVi6ir9b8cH6E14Fniw/nVXcNZd3wT87foh8VvNN9xLG5IpMln1a79JKS9Y3bziFVP+BARO1Y3LHcHcy2Y/DT2YS6Kxh+1PLdB8GdO7utKfYs0zLhPzz8NiO14Intc9l0eKf7iB+aA4SDYiQDimHptnOvzf7iUuxfadHWN2EDJJDoVLOuGiItGU4GHO2NsHaNNgpnIctDu7pycHp+Q7kuQMd4Ken0I7rDhEE/xtazwLIkJGVng1v/J3tv8qvZlt5pPavb3deeLs6J5mbce/Nm46y0K22XcWJXFS5wSRaSRQEeIUACwT+AGDFK5jAoBoiSUAmQigEUNhYGA24qy047nf3tb8RtIuKcOP3Xd7tdDYN1MhJPkEeFJXuPYhD3xvd9e+1m/d73fR7+2R99h/HeMbu6RTtPLjUP79/j4f0DeibQtlHokhpF7/F9bq7PkaliU1UIadBSk4icx/cf8Oj4gLbeYYqAbRLGxX3AczO7pbJbZEhIhEFayf0HJ5yc3Me7GiUCOjV4b7kn71E3jtOz28jHtWBk7CQcFj0e3n/AcnaNSRKkFwTleeutt7i4XVK3FqsKtDIsNzVf/uJP8fBkhBE1CkmhCpTRjN8Yc3Y7gcozXa/pD0Z4H9jf36c/KBgOMqxrSIoc11my8YBknpP2U6SPmAq8JtUFD+4/5vGbj9ms5qSJxnuNSSTDcZ9NVXI72xKURxiDdYH9gyP29/cxmjvUhAc8SQpZcszV5ZTlsqLTHUVvRFWvefjoiPvH+6wXl5GxCgzTIb3BHo9vF1zd3GC0oigKyt2W8XGP45N9bLcjMR46QSJy9oeKrq3JEkvdaASKtulQKqWXFBzt92ltFHYJb9Eu4dGjB3zw4RPWqx26NyB4D6EhSRSjwYB7RwO2mwVSaoxRDJIhg96Ww6MRbStpbCBPDLuuYS/boxjvk9HgbYcQjkJnKNOnKAp6SQbeoo3EuYZi3EMnApU4QnAM+im1i8KkJNN0rsFj0YOC2nlC1SIl9Popm1UDQqOdx6QKlMVngUGXsikja8WHlMJo9g8H3DvYxxDlVL3hMTerLdpA07W0bYtSGlxN5zoSE9DKYxuPlJ6uKsE39LMcV1V03tL5JjIyrUUk0NqSVBIFP8Jjg8c6T5InrFcLzGgPp6AsS/ZHY1btBusCUmqkMhhtsK6hdh0qTe6KJYHCJLRtS9daBkWPPE3oZwm27TCForUJ+wdD1MtT8qzPDkfoPE3TcXR0xOFwSD/XaAGpNtz6BbnOKP0tTmiUSWidJZOC+XrBaw8eovBkWqBoEMLjCAQZSLICaz1SBFQQCCGpy11EPeh98jtchVKSYX9AFzxJKtiUFcGMyDODyhSr9ZYiTzEqsncDHSEIdu0OpSPXVApJEAp8QApJ3Wx5+NoJSgbwkChJTwnuP3zE5e3bmEwTOkeea7pmR1WvSLQn4OiqAF6gTUJTrTFYTLB44amCZ29/j8vJlPH5Kft7A7wCESSt7UiShHK7o2trvPV4l9BsStLXMzblAqMDudEkd4WvEARd3dC2dbyQhUdriReeZTkn3Sj2BzlpL04cCCGobcu63CASST9NqauOXj9nW26Y7tYMCkNiNJ2NUj/rPB5FY91dAKbYbdf0ioxe0afXK0gzQ15IvI9CKWEDhc6oXI0XUOPI8gHbquTmekm/l1K2Aa0lqJogFZvNDmskjYqiKqMTlA6UTU1ZlhT5iF3tyKWl3MxZBUFjS6yrUXfM5LKuEEIxkgmT2Zp+P2XXrNAmZTK/ZLFa0gaHq2t23pFJiQmBpqppmg7fi5Zq5z1eeqp6hxAusm11lOs1tiNISZIXlF38fKJUZCZh11R4IMjIWa+qLVJolA5UTcX15JaDwzGN82gi89l2Dda23Exv0WaAdQ4USBkIdHgZqJuGYW8ItiMIS2dLDg9GzLclXduhMKSD2EFeNRWbzYJgExKT060qEqlYbdaQJNEK7QMqwG4xoz45oPI1XRswWtAEoPPUlWNdttjgybQGPC7peHl1TT5IMIlACYn39pVU8uLqnM1uidZxEqM/TGlby265wLiW2XTF4PgQWzuWwbGpSiaLOUoocAGhA3kv43Y6YbpcMcg0vSxHK4GtHY3o2Gw2rHYVwhicdaAEvazH7e0EbY4Y9gvKqibRBiU86/WWl7c3pEZThhZvHanQmFRTBcnNZIHRKWE0pMgzlruW7a7C5ECQdG2DLhLmuyVmq3gk9lnNpoz7BUFGntzt7QTnoPUdwtZY4bECtDToO3nlXx9/NY4f8yxj12zs/GGg2b0m+LWvfMi6yxib6lXo98ngkHlywN5HgXTj7v4fsLunuHc05+vHL9jZFCMdj4s5qbbcjvv0/ssR68cJxcRhM0G1r1j9TMevfuUJXxuccdHskUrLa/mC6XjCu0++SlDQu4mFLZvE7tTFL3T8p1/8FhfNHveSNQvb4187fMI/4m/DkzHpMuBVZBB6LZh9VfHrb71P5RKGumK8V1K5hOYtzdn6Icff/bFQJMp1APZPVvxnD/8Pfmf9N7mXrEmlxfcFw1+uefHOF8mW7hXjdHjasvr7lt/48o+4l6wZyJqv9C/pqZa/e/Qpv/mDXwEUex9tqO4XCBfYvA73Dtd8/fA5s7bPg2zJyuY8Lub8II+hq/+x2Z44prn+ass//NLv8MebL/F6NqVMEw6TLX/r4IzfvPlFDj7iJ92glWf5puFvP3zOUbIhFZbuDgR8kq35qH/C4v3XXo2/N0cZ2azjF7/6gv/kwf/F72++SnEHLPzFgxf81OiGP/47P8/R2zW2UNRHCdmkZfO5lH/zjXeZtX0epotXa+pX72/4rfpnaId7ZCtxh95weCNoDjy/8fCHXDR7ABjhGJuSf/0rH/Cj7Gv0zusoMfJRlNz1BF89vuI/PP5jvr37AoVqOEy29PZbzu/tsfzygGQXeYz2cEB207B63OPzowWPizmVS5AEvtK/YqRL/uH+G9QnPQB8Frv91p/THCcVQ1PThTie/Lw6pHKGjwsXBVSJxOWGZBnD+iAVX+uf8e3V5wHY0yX3kyV/yBeQNlA+KijOy7u1ZbC5JH+84d95+F1OzJKxLPmXsuf8fvIlbJaSbCxm070Ss+nSM85rDs2GaTegEA3XdgTAz45fctW+gS4jJ9cODKpyIOG26fMgX706FwtbsOwKRGHpnbXYfoJsHeltyfoLA4oXNbIpSKQjEy0nesXc9Tkt9yHxeHMX5BwlmE3sxquPBT89XPD1/BlzV3Cstlw0e3z/8ePYUbynKW4j7iBZdcjP1/zqa0/5ev8zDtSWN82af6K/Tjv25NPulaxLb1ra/YzB0yU3v7zP0HS8ns8YqApjHSuXY4MkFPYOgSFwacSRJMsOsc4YmpqvpBckwjGWFX9v9BHTtsflYBzPR4jd4t4Ivj95jddHcw6THVo6rFd4BC8WexzeCc+CFgQfrx1vJGYd+Pe/8G0GMr7DPjRz/uYg4Zt/44vc+9EdH9kTRXOV4Gyzx2G+xQbJbTt8tVamZYHLxauiTDJvWb+Zc/Cu4L99+Mv87KNzGq8xwnHR7PEwXXCzHHBUxbFq2Xn0usH2U96ZPWCU1rzZn/EgXfLR7gEewbTpYXY/6eRzWWQJ24uCP+m9ya88+ITXsykf7R6gpeNTf0x7UxBUoLpfRB7uSY9sIvjm6Vu8dTTlONtw2w2RBBb0aJzGbKMw0BaK7KaiOelTHmke7N/yKFvw1fycnU8i1k4Gmn1BPo+fJVnEc55PAv/Ti5/nS/u3vMcj9nS8djyCP5h+GdmAWdtXoj1vBPncM1+ktGPFB6v7vNmf8cn6iG+Fz5NsAqqOwjVVO/KZI0jYPpT87tlX+MLBhHc3D9lPSn7n5VfpvVQkW4vsAun1lm6/wPZ0xNtsDZ1TGOFYuoJSOD5bH4IM6Dv+t2rumOshsrTFPOGj7QlGOL6UXfG9bp/SpdG/4iGfx/uhTxXNKuF/+exr/N3XPuOsPWBP7+L56wZsm5SDjyw2jxxc4E606AlOc7kd8Xf2PonnF8muSmnfUgxVEkPuRNCN4p9v1gV/tv48bih5aOZcd2NO8g2fXkmiPIYoJ0wExdQz36Q82x4wbXscJjteqBjunpb7ZNcK3VjqURTUpyvH0Q/gcrTPB2lN3zQ0XpNKS+kTXrz3gONrj00lxU1LeZyQbB3ZTcUo2/F/X32Zf+ULT5i5Pr+z/Vxc058I3B2pK0io9xT7TyzffPkWXzy85adHl+zpHYuux2l1yNPpPXoX4RV6AaA80thPBpymLT9zdMUH24cssh59VfPt+Zuc/+gBgwHks9hZ3wwUNgexEIyeKJ5lx/ggeGs44NOrI45uo+gUwNx1246eW2xhOPiZkj9YfoVf23vv1fP2f375czSdoX+HR9JNQFcem8tXeJO/6PGXOrT1Io7r2eAgaLwQ6Cwl6xdopcBLtJQ4PMIL7j88pmwbXt7MSIqCYBVBQG+UMRgVOFdiUo2WBmvjaG7YNQwGfequROkU6zyt75CJASEwRiJ0jvcOETwyUxwc7fPi8hpEihTxJ1w2NV8cD1B5iqRBCR83ztbRHw5IspSqtGglyUwWeaJpjpSSPOvRNSVBxhBKSMv+wZBPn52iVUZiDFpIdKoZF31sWyNlZPX61jPu95kvNne2ckPZtmSZYbXbYEzslBLeon5sx9Y5SgfSPGWxXpJlGXVdY62jN+hHo7cukBiEDATvKIZ9TBLDnn6ekklJ1XUkQSKlRghFv9+nrnZ0XUeWFuhc4fFxw44koKjLitde+xxSaZSWCN/inMWHaJtHSowxZDl3wVdAasN8viSEhiIN0TQvPFXbYJIM66JMqGsdQgj6vQFJlkUmbJbgaKMRG6jahiRL8UHSegfWgRBx9H3Yo3OBzlmMiGZz4RXL9Zr7949puymdj7KrH4eRznnWq1hqEsKhpKeqK5JUU20rvOrTBZBS0HYdZVlTbSuECfR7Oa6ryYxhtpgihMDbQNuVyCzDC4sNlrKpSNOUpi4JRuBCDJl2VQVSRcmQAGQMXvI04+rimkGvTycD1jts1b5iGHddh9YaqQVt4xiOR2gjcLal38tp2h1WSJwMIEFrR2cdQid0tiU1hsVuQ1KkJMKh8aAErfNsVlNaC/ZO+mVDoLUdi+WS0SBjPCiQUmBSRdM4OttR2walBHVdoU2CTnI26x1SQV23NLaL4ZSGtmtw3rOrK3Sm6RpPW9YkqUKkmqap8MLS2gYhobMeJ8BLQWMdso2j8r3+iO1uhTKKsiwZDEZ03tJsS7yAzkUZmdYJbWsxJqW1LcNhn7LaUjUNahcQMkOYQNt1tLZhvlihkwylU5qmjJ2AUtAEx6rakZSKXpZSdw2bXRM7Wnclw70TyjaycZNMs9qskXKIzA2pVmzKkvWm5PZ2ipAJnb17cIs4IhWcZ7vdstru0BoyI+lJg7UdnW/IshTnBQSJR7KtG25Wc8a9PsOehuAR3rPdVHRdR900ICUhgDGK7XYbMTJ5ihPQ2opERM5v27Zsyx3Wtzip6dqO8f6Y2+kV+yd98lSilcI1LUanLJdLql1F5yxJUrBrYudl62FT1ZjNiixVbModWkDZWryHm8UKk2Ss6hYhNU3bsljOqOsH9DKFV4HWdcgQi0qbbc1iuUEnKRJFng1wtsU5wXq9RJGTpENC8LStwzuJVgbndgQpyNMcgsfWDcvlCu97jMaRmSuNZbft2GzKiM1xDilULLI5x263ZT5fcHiwR+cdomnpHCxWCzZViTY5NoCU8u66hcV2ieMB63LD4V6f5XJNwLDebqNA0QeMECiiwBAP1XZHlSeUtYFEYFuL9ZKmszjfYW0AJTFaYEyKazvquqZp4mbcDBRt56jrGPqtNyXCZAilSZKExWqJSfcpq5o8S/HOYa3Fdp7ZYkOvP6KsHCEIpI4dp71+n+W2IhsUbBuLkQHfRbxQ17koNyR2sLduQ2cl8/WaEBzHeg/VVBg0F5fX1HWDt47OO9If33O94OZ6jnU1Dx8cU9UNiY5y0tlszXK1IityrLV4CSbLabqa6XSLkZrRaETdOLyr6ULNrqyxAVznSNIoefRBMDo84MmzZ5ycnHCzXDEe7rHdtiyWO6raxQA2ADhc6+m843Y6g8MDbKgjPkRlLFY7zi5uWVUVg8EAjMBbiyBjvSrJ8w1SalJt0EqxWiwQxImZqrOEJMVJgZPgbEDolKq2nF/PKBtLqg0X1y9pHCgpkUJEPIjvMFqyXq6YzNYMsxwhKnSW4J2jdXA5nSHSnNr7+P4hNT4EhPMoPH99/NU4Ilfy/yUVsR6XScKwY3y3cT5ItqxsxOn8e29+l/9q8a+SfE/T9iWJi3KZ+kDwcwdX7O7GW/dM3Ez9/PiM7/jXmT04eiUX03XA7CzouM5O9AojYueMEY5CtfzJG3D/T+9M5Hd81uGZZff3WgayIpUDBrLGGMe0G/DW/pTnvT2ypcOmcRRdtYGu7ylUy5fzK266EV1QdEGhhUc+3qG/GTv7un5EKbhU8IsnZ8xdQaEa9tWW0qX82vhdfmv280gbxSbpJHYyFadrslzwejZF4RmrkrnrcZRsGOmSrgfDU0+7n5EsO1TV0R4UNJ1mT+9IhcVIS+cVP1O85J/mv4AuA+U9/WrUXPUkQnuU8Hy9/xlP6/vcdgO+kN9woLb8lvtFCLyyiZt5yc0vHJBIS1/VGOFY2YLGawoZ3wVtDs2ewRgZOZ1G8KPzR/wX4df4qcE1JliaoPl0d8RhukPV4SeiOiko76fYHM6qfb5Q3LKyBebO+vZ0e8w4r9nagNn8pBNSdx5/z3HbDZm0A46SDTuXIgl85+Yxm5+WmF2UBSHFXTdhoLQJ39z8FBBD3r5q+Gx7SFa0ZIvY5f1j9qra1GTzgpfrPbT0fG14ztalPMrm/Pbt1+5s7R6zca+kYbqG09UeD/I1L6s9uuyO3RgEZq6B7s8JY4QP2LHjHz//JX764IqrdszCFixsQb1LyLhDjBTmjrXbsr2fkScdG5/xS3rOM7vPLEhsFzf3LhPoXQy4denoBpqX5wf8Qf5lTvINjY/7zet6yD+fvkW+iyxYiJI0vWkoXuZ8eP+E5MSRq457yZrzeo9ZU8Da4FOPmcfOZFcYepcNwSjMRjAtC54396hDwkW7x7ZLUTMTR5/LKMvqBjqyUTdx1P2lHWNw3Lg+a5sTrMSs6hhuzkqakz7ZxYYkkTzdHPN3Bk+5tiM+a+/ReoWqxd13V3EUv5+gNx3l4yH53HOzGvAd/fqrTuOLZo9llZGdprisi93Pm8iFFZ1D7wTv3t7n97Kv8vO9F3zsT/j26vN897PX+eJ5ye5zPbJJi2w9Xd9wczUmUY581PG5ZM735o/57PYQPukRpH2FwuBuhFm4QO/W80ezLzBKal7PZ/yt3jMWtge1otkTr2SJsgvkk8D5x/eYPyz4yr1roI8WnncXD9lVKYPVT4RqrtD0rjtcJqnTjtImXDUj7qcrrpoRV82IbpqjqygjU6Vl+0af8VO4eTykHWqGSc3OxWLOn96+wXTV514V6PqS/CbyxM3Wkt9KpvmYp4Njfjh/jVFScbrao3OK0RNFl3sGZ3UstqxaRi8EZz+X0uxpKme4rMek0qKl4+Vkj/HLn+AHmsMoblRt4Oxmn+/o13l0MschuWj3sFZC+pMJj3Y/duyrBm4vRlgn+drxBdf1kFx1fLw84up2zL2pR1hP0PLVuShuKnqnA3gDVk3Gmdxj1WTcvNzj8bWN3eq1v+N9x+Li8NSx+Lrmw9sTfuW1T/nOzWN8gOIqvJLIuV6CvwvtAfRKcrvq88H4IQAL22PbJeitRHb21XVoC4UtInrGbCQ35YB3ecixWXHR7HFa7tNNcwYX9lWh1PYU2UtDlSc8Xd2jGyhKk7DsClZdxux0j/1DST6PUtCgYnhdH+ckF4rpoMf3169zmGxjMXKek1W8em52ucRsOkwJ8jTnD/0XsW9Jeuo1jpIN/+eHX2F/FTn1ehenInQTpYX9pwkftY/42b/xnGfbA47zDbOmx/unDzg8D9S3NPhSAAAgAElEQVSjODEjXcAWUTKaP0v4OL/H54+nPBOHtE6xaArSmUQ3FtlGzIhuorB6+7jg9A+/CF/e8t9c/wp/f/9DfrB6zOXLA06Wfz6AVV1Al57m4yEfA6s2Z5REfMi3L1+n/sE+By9rdvfjmpIu7q/ya8F8POYj5bnX2zI2Jd+afB4tPGYlyBY+svXbiG5QbSx+pkvP+G3D+eIBLwbH7L2jSNcuClKVoEtA2sj+71163nv2kNvjFUY6xrqk9AmrMse+PyQoR7aM2CbheYVJ8smfF0D+fx3qG9/4xl/4L/+LPv773/rtb7RCIkWsxrSuw1nP4XDAfp6jZCCIBGs7vPeU5Ybzi0uWmwYhU4RQsfspUeRFQZJppIKubZEK2tZTdy3nV1ekaUETYjdoEIoiLRiNhgTvCMHj6eispaXj+dkF26pDqgQjFV3TMUoKxr2cfqoJziIwSOFZrzes1muq2rGrYnjZBhA6RRnNYJQhpUMG8L7G2xbnGq6ur6lqD0LhXZQZFf2MPDd0XY1WUZS2rUo25YbVessnHz9nPDqI0iERsQN7oz5SBlrb4JHYAEjJZDnh8nZK5VpUMAilEVJSbStGh2MS7SMOYFvjkbRdy83t9E7u4mKl0gd0YijyAoLDBYcM0WxuXeD84oz1akdTBzwKJxRl3dLrFxzs7UNwCJXcBbuw3VbsypbTs3OEEgRhcGUDPjAa9MnTlPjFYrjZtZbZbMnZ+S0+SKyXBGFBeZqyiuPnRqG9BKVpu4bgO15eTVjvOpoQw2aUpj/uM1tOGQ/7GBK6dosLHdWuYr6YcXszp7MSJeNWtkNTVjv2Rz2c9ZhMU5cVvrPMpmsuT29oETSlixtpFMF7QtvS6w3o9cfUdYPzNU3ZsV6XnF/coPSIzgncHRNxV65JigRtNDYEvBe4pmO5LPno41OUyQlex9BIGhbrOFrd6/eRSNBQNiW7asf5xS3z6QrhU6SQKDROQNPU3DsaI4IFJdk2FdP5gk8/O6NpAq0FKQzWgjCKqm5BG4RJSYoeq6omyMB0ueR2vmW+rKLVUypkYtBJhsfTOgeJQEtJWZW4pmSzqTh9cYH3ksxoIGCcJC0KlNKs1ku2my1FkbPeVGx2G85vprw4u8DLjM5LfBc7j1ViqEMsXJjcULYN692O6XrH+WTGqrV4pembAY31mDSjtQHdSxB01E3NZrfhajrn/GZCFxJcG9AhQXiNDB5tNHXT0rouSqRMSucc1zdTprM5ZxdLOpdifYO/4xIH6al3W4SUjMZ9FuWSbW2ZzVZcXk1pO0FjiUWQRBAC1O2OxnYgDduyYbEuOb+8Zb7YUruASXqAjlI4IUiMQUpN0stwBDpbY7vAzWTGx5+9oOwcQqSxMNVZyralrBtMokiUoq06ghTMlmvefv9j6tbdvaNKTPAkaYIPgaYt0UohhWRbtljn+fSzcy4upyyWW/ZHhwQnMZkiyMB2WyFlikkERitmsym7bcn5xYzOScrWEwAfosBrtVpGDqsQJEkUwW22NecX18y3LRbNprRYachVDrajqsp4XYaA6zpa61itVjz58BM6C8iMruvo6TTeuzwIJSMGQ3t224Zy0/H+p2cstxVSJ4QAGov0Fployl2JNA5kS2M3NCXMJite3E6odi0aiVQGIQVaJSRKUVctu03NZrslWMXzT05Z7Gq6INhV3d11EcVzAolKEs4uLkhNn9lsxWpd88mnp1Slo3Oxmzv1EpVopFHkec56tcFbGI3G3Ezm7LYNFxe3XF3PsF7iPSTGYLuGXh4D57LeojKJSlPWmx27yjFb7Xj66XM6BFoXWOeRCNbrDdZ5siyn3NV4ISnrmpvbHZ89P2e3a5Da4ILDmLhGsrTHdLrCB03bBZqy43oy5Wa6ZrWpUbLAOYcLDbowBKlYbnesNw1OZ6zKhucvr/jo+cv4/LOOzCRIBDrRQELpGqpdi0lTmsqz2dZcXs24uprRtp6gJNZ1GCPpnMfaQNs55vMNdeOYb7ZMVzuefnxKWXusj+shhIBHoESCC5LFtmG6KKlay3pnubiacnZ+A9LEjbVWdJ1FekeWZGxWO26WW7zQzJZbLi4mXJzdsqssymS4u/qaCiCRVF1D1dQs52uqqma2WPL8bMqz82tWu5LWd9ggkSajazuEiEXfuon3vMl0yny7o/GgTBo5+0KgpUB5T5HndNax2VXoJMMJzeXNhG3rePrpJeumo2wCMssJWRZxTD5ytlKl+bf/wa//5/8/vf799fEv8Pgn//U/+4betgQdwyOzblAtLN5KefO1GwaqpvGGRdejcglPtics3zlk8DIWbJHiVZD5Yq9gVNRoGbhthyTScdmMudyN4Ec9un7c2MVOISgPDPn9kloktMFQ+YTbdshVPWL+nXsUt1Hs0xUSUwW2DxXVQ8/jgyUegRSBTFr+ZP55Prw+wa9S8tmdCXsXP58zisl9g9CCRDoar6lcws6lTD64R7KKCAVlI7e33pd8Nu7xmb/HMGno0Pxw9Tn+dPkWH9zc5+D3A+vHhqQSkdNq4fqNHhfJkMtuzDbkTNsBAcH/9vJnkO8VZAuPTwXIWNA2a8P8gUT1Ag+yJc+rI2pveNkc8OyTB+idehX+JJvY0VvuGd5J7jMNQ1JpqVzCtBvwj5/8y4ibFLONsjXVevTNkvrBkPWb4IXCSM/OpTTB8O7iIS8XY4bfNZHr6uKmWARYPEwwoxaLwqHY2YxZ2+O7p48Zf/8uHL3r9BNE2ct7o326QjE0NVIELuo9Wq958vZjimtBuonFZRGgPE4oXmg+fq3PKKuZtH3WXcbzzQHXZweMnkqSXSAY8SpEyGeOp28OubJDhlnDabVP6VN+cPY55HsDdClI15aQxNBIuoBEcXVU0NuvOC0PWNuM0+aQj26OKd5Ocaki2Xlk4/BpNHpPBkMu8px1m7NzKdsu5cnlCQ9+V0YLu43oiaAF5ukFYXDA9FDT6bi/+WR9xCeLI8SPhoyfx4aQoAXhTnSXbOHmy4qtzmi05rubz/O/Xn6N8K09knUg2cZRbl3FNZ/OKpzq8UIN8QW8PXlISco7Lx7hP+kzfuZ+wgs2Ep9phJM0Zc5zOYAUPt4cs+1SPp0cMfheivSSbi9FIHGFoh0ZfGoYXDjqWZ8nhyO+P3vM+7MHTN455v63PdJFOZjsYieaTyRmI3meD1n1c97ZfY5vrd7i26dv0Hsnp/+yjTzJ+wXJusMVCWUz4GVaMEsHPK1O+MHiMc8+esDhj8DsfOy0zVQUImqJyxTp0tJUPS7CgEWeMmkH/PNPvkj3bMDJd2LHr67i5wpaIh0kW8Va5uj7NX84+RLvrB7x/ulDeu9lJKUkWVuSyRY3SBFBILeGxa7Pmejz4fyE6/MDeu9kDJ8H0uUdXzkEgpIEI7F9TdCCjwdjrro+V92IGz/mdz/7Cgd/kFJM4rt0PI+efGZJlpp1nlL2BJNqQBs0H58d8+CfJqSrn3BzXa7o+vEaq8oe52lOqxUfzU9YtAWf/MkbHLwjGH66YfVWlEfbTCI8qGnCQqfUmWSQNnzr5edZzPrkbxdREjfpXgVFNlcMziw2SXgp+iS9jk2bMZ0MMe/0GT+3mMqTnq9RVUe3nwMCtTKcp32mPmdlc27qAe9dP2Dw+z1MGRACzM5RHWl0E991WpkyHyi2Oufd9SM+Xh3D/74fsQV3zwCXRxGi15AsFCudMZE5t7sB17sB8+f7HPyZIV/ENRK0BCHwqcAODOkqsJ0P2N30uaRAfmdE7yw+L/LrBpcr1N0aCRJ8Ihh+T7MY5nx6dkJ7VdD7ZkE+j3I7VTu6cYpPIiu63jP0rgKNy7APLL/37Kf4aHFC+d4+B+/HTltp74LIylEeGdKNI13ARTJglWq+P3+dT1ZHPH9+zKPfE5iduwuUY7d4/9LhbMp0pDlb7rMKOS+W+zz/5AEnfyQoJhGDoppYOJM20Iw0e584Kl/wSXXAp9UBT5++xuiJZnDhIrqoiSGku1sngzOPuUn4yB3x8c0xT777Jq/9rgcigqjdix3MNhUgRHyWLhXPigGzbY/T6QHzDw4Zvq9JtpFXbmpPl0uSnUdXHlMK0rOE2+2Iy5eHTJ/to/+gTzYPmMrTDtQr+ahP42ccnHmSZykv1kf84fwtpu/d4+h7McyULgpPTRXZ9s1QUdyCeZpx7YdMTvc5e+8B5t2MfBrZ4aqLoas38Rzmc49ZKpqLPlfLMR+ePcT92Qj3pwMGFw6z8+i7cF9XITaiDSXZ2iMt7H3sGJxK0k3sXNZlvFfbLBY7632JrgLJrWaW5pzbAW8/eZMn83uMfr+gdxXIp10sEvpYNOTuPtEMFf/xv/VLf6F37b/Uoe3/+Ju//Q0lBcF6ohNVkAbJMO0xyPMoJUvA2tituVyvKauW+WpHlhfYziKFpKpKjg6PSRNF27Q4V+ODY7XcMpnOWCx3tJ2PYqbOoQjgHP1+htEBcNjQ0TlHaz1XtzOqusWYjLbt6NqOxCQE4egNUrI8pbUlZbPFe8f17Zz5YguYGN7didJ8aBgOcqQC76IEpWstq82WyWTFZtvEbixlcCFEY/1+n9GoR6olTVOhlGK93jJfrNlsapyLwV7wUd6WZAapBFmWolWg6xqc7ZhNZly8vCY4iUQjANtZnPCoVDDe68dwW8K2qbiZLpitNmzLls56ys7ecX0lXsL+3pggAoQW6y1127HdlsyXazbrGpTCS0lmYnAyGmSkuQa3o21quvZuhHS1ZrUtsShaK1AyYiySVJFlCVJF8VLbNMyWc1brDcv1DussytxVVoTBdQ0ez3g8ovEtdVNj25b5ZsXN9YxyWxO8om4b3I85Zs6SJDrKWJRHBk9TtZy/vGC9LXFeEHB4IQnCU27XGA1ZngCBtrUs5ksuLq/Z1R1V26FUghcBIQNGGOhaTBK/kxQeYxTz1ZKb6Zzpck3bERmtzpL3erRdw3Ixp8gz9sYHTKe3NG3H7XTOprZ4oVBSx3ELqUiNpqlr+nmOSQ0ei/OeyWTGZLqmsR4RYreukMTrY7Nl1M/RxtBZR9M0LNYbridzqqbDNyCDwpiULliM1jRtDQi6ur7r0NwwnS6ZTqa0XYvUKc45vA93Ib+nrWsybVBe4DrLZLZkOlkxmS5RKqexDodECGh8gwuOLFH44Cm3W+qmYjqbczuZ0VlHQNLWHXmaYEVAKMVmtyVIGB8MWZclQmvOr264vZlhLVFM6AU2eDyOLNF46+OVKRWL5Zrb6ZLFakfbxZFzcScWAo/UIKSg6yybzYqu7Sh3Fbc3U2bLDXXtkTJ2qDtr4yh9CJg0ZTKf0nlP8IHlfMtiuWU2WRJkZGBmWULXtSg8bdNQViXeeaq64fr6hvUmdio6D1IorI/4GE0sZAQkrWsBz6BXcHs55ep6wraq8FLjrMcBUgtUCPjg8W1kx+62FdP5nMl0Qdl0dJ3DmBSUjOdRSoRSBBvYbUskkt2uZjqds5iXdNbTG4xZrjaYxBCIQpe6brCdw3Yddd2w2ZScnl6w2VS0nUMIifAi8ruDQypoqobVfEFVVlxfTbi4nlI3HcFJmqaN4jtAK4EyxGJJ8DR1je1gs9lxeXnDZlsSUFgkyqg4kp8a6qahbStscDRVxXq14/p2wfXlBC0NXWdJkxQhNNYFEAIrAmUVu6O1KZjNNpyeX9BUFocgKEPVNkglI4M10dRdy2q7YburuLi8QQjNtqqp6g6dJLFDnihIMCJ2iibGsJjOmC9WLJZbrPV0xCBfK40IgJAIIUjTjPViiQCur29Zr3ds1iWrTUlrA9IkBFS8jvIezjZobXDO0rqWsmpQKmE+W3J1dY0PkiAkPjh8cCgpSHWCtw7btjRNy2q95ur6luvbBVXXkuUFPgSk0bjgCYF4HzWCpq5YTG5ZzmcsV2uatqOsWjo6XLCvih5tZ0lkSvCSH37vhwyHe5R1w64q6Zyl1+vhbZyiqKoarTUeS1OXlFXF9e2EyWxGWXXxPq0i+0oaTescygiMUiglcETkw2qzZrXZ0LpAQCClwjtH8AHvLELGzaMLjtBafNsyuZmyK0vaeAHivKAuGxKTIvB0ncVohesc5WaDbRpWuwp/F240tiMEEHfUgeACQt3hPHCstlvWmy2N87HLXcRgVwpFlmfgAlmWx2IcirquydLIlG7KiriafJQ1edBB0jQdRqe0tmO2XHIxvcUJyeX5DVXZ4bzA6ITO2ojFyDVKgQgOSeA3/sG/8deh7V+B43/47/74Gz7RSBdiYVxJ2pFh90ChjltumwGjpGbZFWxtypPLY1gaRs8dzTAakV+Nqn5R0CtabusBmy6jDoZ3rh+y/HSfwWk0TdtcIl2I8iAhmRwp8swCgmVXMG8LbqsB9YdDsoXHFjJa6XeOrq+wQbM5VHyuWHBe7/PB9gGNN1w/vcfwRfxOPw7vkCCc4Oa+xmnJ1qdsbMa8Lfjo+oT0s5T+tUPfbZ5VE1ANLB5Lvnxyw8NswWl1SE+3vDe5T3U6pLiVqCZuJkUA29e0Pc3UJAwHNQPTMm17rG3OJ6cnHLx91/Gj4yZUVw7VQZOm8Khh0g5IleP9+X1++Onr9F5oercxdM4nbRS+aQleUr5uaYOmQ7Nsc852e8xmA4ozTf/SxaErG3D7PUwFLx4VpL0Oh+KqGjJr+zybHNBc9uld/LjjORrVgxK0Pc16GAv+6y7nuhry9PQEeZExOA/o2uNTFYMsLVFtwIeE6qHjshpxujtg2eZ8ePaAwVNDPotdWMm8QbYOGSS6DdwOelxRoEzAKM9k20e/3aN/FTv0fhwwCRcwt2ua4ZjlSLITCdOyx5OL++TvFmSzQD73r0JVhMD1DCJAVxiudI7TMMha3nn5EPG0x/gzT7J1eC0jk3zfYLaO4Zlnctjj+NGCT5+fsCxz0qc5vRuPvBOJiZhvEEZ90rVncy9llwu2LqW2muWn+4w+IUrtZAxrY4gnSacVi0d9VpnmqouFjIuXh4w/EOTzn4SP7q6b2RWGZBvw0jCp+7RSMpmMyJ+ljD7jLuSVqMYhW4eqLRKFKaEaGNZGU3WG288OMc8y9p9aXCpJVg7Zxs+XrCyqcbRjjXCCelFQbTPUs4y9J3GMV7jIz2wPUnQdw+jeeYmXGdNHks9uj3BS0r69x8ETG0OsxuIzjaodsnO044RgNVd5hjaezy6O6H2cMDq16G2HaixBSVwe33OCEpE9W0N2K7lph1wuxvQ+Thg+h3QVg22kQNXx34LY6bj/UceL9h4Ln1Ge98leGsbP3KvQvdvLUaUlJJJkG8jm0LoUv04YfygZXDpM6WOHphaoOv5b7i5Uli4gK00dErJ7FR98+hrZhzm927hOdO1QbTw33sSuS7xiYwuqWc76xYj+p5p8FoNnl0lAoJr43yMBLzFTw1TntNMc+6xP/yyOY9f3UoYvIjdY3BWCs5VHtJp1k/NyMybcZhx+V9O/8ZhdLGQjYkdzLDxI0k0gv1RsVn38kx77b0uSdUB6SNYWN0gJRt8FpYJ05QneYLcp6zZjd9On+CCjf+2RPoqZfBJDr6AF6aLD1JKmzTjb7TG9HtN+NOTo3RaXRZlaN4i/qa48gng/ShcSP8nYphrxvOD4u4F0HSWIIkA6rUDF38wnkmze0X/ZUUzg4IcerxT9G0s67+K9KuLKEQFcITFl7DwevAyMngWGpx5T+VdTCj6NxZ+74IlmqBi8bAhaUz0ZYq4SxCSld0EsMKnYOakaT7Nn0G3ApfHa1ztJu8jZaE04K9j7QCItSH834WJjcNkMFbIDXyf4nWF71Ud/WLD/fhR8tUMVg0gtojTUBVQHXV9iNtA/FaiLhP65INnEgNCUsSNbhBh6JhtL149c34f/6AOaN+6TzQPFrcWUnurQoOJjmGzhIvKhDaQbj14ahh8qBh8r0oVgcGlRXYgNh1qQbD0uldgihpi6CaRL6F8EBhdxDcoQ8SLKhrvvDoi7gmnr734TyfBTEZnC27iPVF0gW7qIJBDxN7f53WdbCFQt6F96pIvnLFs5pIvPcxHisxdxd69ooHcR6F1BsrnriPexezlIQTOKTUPNMIoNCYJ05WhGiq4nSdaxyCL83XVKnMxJNgGbC8wuMHwGvXcTsIq9dyX53KGbGOqbrSOdVgStIgZHgNeS/+g3/mKhrYgbx7+cx6//u/9BSKTChUDbtmRFTmg60jTnwb09jk8OCXaFMYa6rpnMF6zWW5oOtruKJMvpqgYjE0ajAQ8f3aM/yPCUWGu5uZ6zWdV0XaC1Hqk1Ogi8dRhjKHopjx4cUvQiS7OsO6aTJdP5HO6CMqTAuig+UxJef3DMYNhDiZbOOybTBZPLKVqleC9xQdD8P+y926s1W3re9xvHOsw51+E77t6nPrhbVstIEcEJsZFIkH0lDAq+dMCQm/wFCeTSN8GQ/yE2JPhGYEIEIRcxcUIgAZNgWbbVUndLVnfvw7e/4zrMOatqHHPxjqr17bu+i4R6wWb1t3qtOWtWjTGqxvM+7+8pwoo0Gp49fcwHT5+xGx3TfISk+OKLF7x8/Q5remgbrlwLxnkudp5HFyPfeHItgUpRXEzv3t0SMoBGWccSpP1pmk988OwpHzx7wuArYz9w8/Ydb9684zS1VtplwY0j0zyjrSKWzEfPn3F9PbDzIy/fvmFeEufzzOk04YwnxEw1IghQ4DuffMTF3jN2YJTmdJ752c8+J6VCmBXKCtPVK9v4wPDNTz/k+qrHakPNhS++eMFXr98Ri2HJhSVLO7AuGe8t1MyTR5f0fY9Sinfv3nGeF3LR5FxBW2IogmcYHEZVLvY7hr3j0cWB4909x3nh9uaMto5piVQF6MqSImM3MrgeZwqXe9kIz8tCqZ6QCue4YK1lSZGiIOdIZzSXl5d0RjOdztSUccPIaV5wWHLOLLkQc6Y3Fq8N1Wh2+4G+t1inuLu74/58RmnPqSbirPDeYnPFWSPiFBXnDP3gmZd7bu8ntN4DCmst82nB+x5lNDkGhrEjxzN97yk5knOlFkVKiZgLWmsJBKsiCqS8cLHv6bqOqhWn0z1hKShjYYJcC8pZgkqUlCWBvURqY5qGnOh7CQKqqlAyKGWlLwGNUhVrFCVGdp0nlDNVQVUOlON8nuj8wBIDRRVyzvTW4rUwNK21Mi87z3mZ8d5jsdRcmeMZZRzKaGJODIPDmoq3HW/evMGg6PueKYqju0wJtx+IOeOUOMoG4zBGgYoitGjH3NpkFBVnLGmJVCOtnIPvsFYTl4D3noJimSOlyDjMOWOMIeWAtQbTGXJMdL0hLglyQStPKZWiEG5tMWgSSoF3hkIlZ+kiqNqQC9QKWlkK4mi3RuMweDQBcKMhpoX5fOKy35NrZc6Z3IoKXeeouWAqKC8OcIPi0O+5P99ibE/IItwbZwXXogrWGKy1EhpQCiks9H1PKplSO47HI+M4omoVXtV8xlphPB9PNzx+9Jzj8Ujf9+QsDNY13E4phbGWaT6x348yfxve5HC45PW7t9Ra8XbH/fGWw+FAjLH9nYzlwXdAkdeuoLVlCYlMJZVCKJlOGYwzKApWaUKYGX2H6TzHaabGdh2UEe5uDnTeyr3eKlKMdF0HWTo0lKrYaggUtDWEUlE5MVrPcTrT9b1gVEKmKshZinXKaFJKLGlhGAZiyHhrUUXGTC6lITnc1/6mlEIpBa0MWmuc8cTpxG7sUUWRycScMN4xzTNoQyoFVTPeOrwqpKixVv6rVVAwShlqaa1jSsm5tQbnHCUmrDHUmiklUa1FG0PMRdjfIONCyfFpKl5bmGWO5prQTlPRzFEKftZ3xJDw3ksbGhVnLTUItiXWCLpSsrx2ihFvNGEpjONIKYVIYvQdcwxorVlCwBqD0pZUMmhDSRIU8Ohyz3QvrvI5zg0j1FFKaUUZEcFDSPS+gyLrTUqJOSzsu4FUFCHOaK0JJWKcoyqFrYrBOFKI+M6Rs/SXl/a6SYNaCqpokrXiBtJiTrQZssoojNwbrYwLY4zMbWuloyRK+KbCyPp3PqOtRxtIKTF2XnAXxhJTYXAWckIVhXGOnDNucKRayEqzxEBf5bVRsr4aK991KZQUcNaQjOZ3//F/9/P3bf3i6y/s12/9rX9Y496iY8XdR8KVEy7fwfD631PYX7qnFMVuWLi9H7E/Hhi/kE3P7kUkHiTALI2a+48sN78W2T09k5ImBkv3RwPDy4qdwJ8KxSDhX0k2OvMjxfnXJx5dnZiC4/RuYPhTz9WPG2t2bVGdZTOcneKr/wjsswnrMsvs8D8euPyRoBdW52h3J0GoxSneft9y/P7C1aMTt7cjALt/0/PoB4nUrwnQssnNneb4oeX+W+B+Rfj/8+zwfziy/5kIzwBVNXEOERJe//qBd79SKZeJ3fXE+fM9+z8z9K/FjeXvJUimfxNbmE7iq78+cvqkkB9Hxh92+Dvo3xbGlxJA5o4JKugWtvT5f7Jj+qDAdcC6THrb8/j/1ZhFjqtqxfDVQjw4qhHB4eV/CN0nR4YuUIrm9G+vuf4BuKng7zLuZiHvHMVp4l6qSjffNYQLaU3d/1R4fyZWdGtTteeMPQqLsjjF/UeWcKU4fTPTvTL0r+DwuaSq928e0ALuZmZ5NjJfW9KgOH6kcEfoWnuuPxX6V4E8GNzNgsoFPSfS1cD9pz3h8NDKGgfN+CpJO/JScHcJcw7Eq554EE7o+akhXCpyJ+3P+y/FCdm/OKFOM+VioPSOtLPkTpM7aScOFwp/V0WsMWsLv8bfRDm3WkSa+ZHFRGkT1qlujmXbRKDiNMWKmFCcwsyV44eWcGhcy9dFxNdWzNNBVLjV2bqGTVGljdqeRGB191meT1Pdjk8mC6RBjuX81OJPIrquc6h7NROvuq+d2/hoJB5EzMm9iEJx1AyvI3FvSb2IH927JGKqVthTYnnsSJ0m7hRXP5opXrNc2+166yjhYvaUSDtLuDDMV5rSGJWHz/LGNEXL66adCL1K2EPbeyalmRYAACAASURBVLhzIexlzfD3RZxqsLFwzSzBVCtbNFxa7Pl9/qccTzVKhDknrsu4E4euWR7Okw7iJK9WoVJFJXF4ptEQ9gZ/FAHJnQv+JnL/aY+Jle5dQoeCvV+IV728TyuGFCtt0McPDPsXmewU7lTwt41hXB+uXe7l2HSqmLmyXEoruY6V/sWZ5elI1SJsu/tE7qUlf/hy5uaXRnZfJZZL0woGD6+zuky7N5HzB8I4na8M3V3h+A3D9Q8XVK7Eg2X47J7pYwluQ0lAVTUwPRJx3IT2elbR3WcJlZoy9j5IkFyn2+fRuGNhemwoTjG+TKgiW0NV5HzbUyJcuq/NkXAQBEJ3KyiW3KsNZ+LuZQ7PTz3DlzPx0hP3jfda5Vqu49TMGXczEx4PG/t5dWCu97u0s1LEUCLcrbzg4uTarUiY8zO3teq7s9wnurfynGnOUTq8Do64N8LS7QW3o0oL6mrjcB2TtoV3pVGjY93mmW6iZnHi6HT3sa0/rZOgHdu6VoddK55ZOafd7VoMkBC9FWmioyAMVJYwUHd+j+Xey3mP+4frpZMc9/RYxkgxcr/L/Xpu2xxsTvGb7zj6d3JPX0X2OEqB1ix1E0bdUURQkOcAOxe628zpucPOa5FB4Y6J3BnZJ3Sa6ZGgGOJOoSPbe6+c2Nqu34pdKE6eFVKvtvE6vkrb9SlexkAaZF2x7ZpWI/eW4Y3cv6SYW5geW7q7vN3/5sduew7IXt4jjho7V+msuE0s13Y7PyszXxUoRsTkOIjA/X/97n/5cz1r/7kWbf/Tv/+fV1cMoSRqzXQXe8I0o33H2Bkuh55nl8Kuu7m54TRN3N7fM+6vWEKiUHHFYFSH7xRVBfrB0e89MWTu3p7IWTbpoFiKYrk/cRh6EWRJPHl6ifOKEIX3d7w7cXF5Tc5ZNkFUaUm0hrzMHMYerxWHw4HTeWYOmXB/T9+P1KyZVSHVRCQxmA6rRdXfDRalFOfTQlgmvBtRyhBTJZZEImE6j8oJbyxPHl1I++q7GzKVZVkYdwfuzxOlKk5hRhuD1wprZAO7GzqWaWbX9eQaicWQqghqU1jAaGqA3WFPXAIpHxn6njkmdNF43xOWhFWaqkUgq6rirMdR8E4xWESoDBllFNo6jvdJKhdZuLrD4EghMA57xkcHrg8DP/6jHzD2e05zJJsOpZO0haaAsiIsUSoa4UAWZEMfUoZqqFoxzRIuE0LAeQWl0DnDo/3I8XxiGDpCCCxV+KRYS0kRQyWmREmVwY84BcYWUiloA0rtSKWQciDXQjd03J9OKCVzbNz1qByoGUyBpCoVjU4FrS1TjChtyWlh7CSZ/WKUlPDTfELlhPM9sSh0LRQcuQqL1SkNpeCMCK620xidKFkRk8dox/3pSO874UVqg1HgvUXlhG3J41lLcvyyLCgrP5MWdM8yJ7QzdEY3xqbClsLxNKGswxRZlBaVKFWTY2K3G4lpoR8G5iCrp9aQq7hQLUWcios4NrUWQSjXwugtsS6kPKO0pVRDKoh4YCzTnOj7nrosjF64qTFGLi6uuDvdYb2IoipVRjegusp0FFZxLBmrM4oi2A+l0E6EirgKRMWQbSXkRKetBBgmQy4LSieqEQFuzq2NAYf3PfP5hPeWTMJoqTBabaTQETPTOTCOezKZkmUeKF2bILQwjiOoQq2ZnCu7fmAOiVIqRneUWLFWgSpoJbiAKQqYqKJJKWO0jI1SEsZqCcSrls54lhpRThyc3jnyHAgpgnWclplh7ChhwRov4V55oXc9RjlUqKCF7VqboIqpIg5aRU25/UwesmS8RezQMc2ZXERR9tYR55lx6IjzQr+3TPORkg3e9RhjiDETY8Zai0ExlUzXeZYY6IzGWhH7nXOclnspWBnD+VxEdEY2bKYoFJGkFJf7A9M0UXXFoOi6jtOUWGLAeS+BLiHRdZ68zFhtyCEy7nqmklDWEabSxDaL0Y5KhJowRqGA0pjQ1ij6YeB0muThj9zcyA6TK4OyDH3P+XxGaY3yltM8sT8cmBbhyXadI1UZ8wqDcVLQyTGwlISjiaDaQszgDEUrQgjshx3n84y3HZbVEevIZOYwYXrPEgKlFaSM0pAyrio6f5C5WjJhSUwp4HwnN9yV2WYsaE2uBesNKUZUBWcgK00CqqqUUsm1IvAbhfcdKi/oBD5buW8bhe0t8zyTS8E4i0oiiFZEoOyGnpwzpEjIiXHsiXFBqx6nRcAsNRGDIBic65jSzNXuQC2Z4+lEtRpbICuINVOVxmkDOeOtJiwVo0BpEfkBliXKfTrPpCqYDo0hh8TlMHKczvi+xznL/XHB9Q6TKlllspbgwRIiB9uRFjBG4QwkJVaZkJMI+8mQK2TjKKaSSsQozU5bEglrOpYU0UYKxlrLtffek3OmCj+EqsB5z2kWxvk0Tez3O06nM67z5FSFWa9g8LJOVGifTV4zRxGDey8O6lxFtM9NGKbI9Sq6Mp1mfu+f/ONfiLZ/Cb7+49/+b2saNe4oYkm4tBIaZRXTY8vpI8XpewGCZvjM0r+pXPwscX5i6e4fNmfZK+JONo7hQhEvKmZS7L6sjV8o79fdZdxd4vRht4lJN9815KFiz4rxy8ruqyTJ4BHsXDaWXbGygTp+6Ik7mJ4p+jdIe/mpsFyIANLfZsxcvyYuVQ3TE/nev5XfDzv5tzuXzV23Cg5hb7j5rmzixhfCufPHwvRIs/9SzlX/1blt1r20oGYJPunuM+cn4p5y59KcvEjIG3IfnZ532HPBzJnlkaN7F7fNuDuK00mVKsJJfeAlhp1muZJNojvSBBzYfSWcRHeftjAouxSmR4b5keL0SeGj/6MyPdKMr/MmEJq5bCLWytcrVph9OkvQyipUqwzd2yCi8/1CHqXDLV5Ybr7jGF8W5muFO0F3nzee7Spg2VNCxczypCeNmtxcWcU8bLjNXMX9eWkZPztTtRzT8rQXUaVt4s0snQXVyAa9exNF1Fgyy5MOfxM5ftxRrGJ8lYQfOWoRS5p4ZxZpzU+71ampN8ElO/n8toX5jJ+dCY/6jXNavCbuLdVAamEypnE6/W3ajrsaRdwb/G2iOE04NCEX2cSPL1MTxvT2tyqJW3V+PmDPmeXa0r1r7+vEXWtOkTyK2KXnTB7tdh3NIs651W1XtYg9Zs5QxE3oX52Ij0bhUbeQMXNOnD7utzA4HcomkFUN/RsRcleHaNXiNKuqCXh3Ihit53QVMdNFx3LtyE5tYqIq4I4Z/1bYqdUZ4sHh386kg98E2eI0uRfh054L3euZ5WkvjsNYtmMUdvDC8nTc5pgOhempo7sVETx3Rr73up1LReo0fTu3KgsnuHgt4m3MlE7GRjxYwsHg70XIpIroK/xSGefdiyPh6a69h4hs/u28FTfWMbEVY5r4XJyWQLLy8LNqxPnnztKd0L+RwDY5Foc9JeanXhifl4b+XRKhf2+FG34um3hXnKJ7GwiXDn8bCZduOwdxkPmxnu/u5XkTnasRN+sqgN5/0jG8lWMvXrEcDOOrhLsNgpJoYns8SBEw9wodKtNjy/C2rU/HtBUbitfb36zr7ips5l4xX8rrr+dFB+Hals6wPHLMV+JkXPmww1eB8wee/m3C3S7Ey24b82i1zVkd69eO+f3Ch8oVe78wfzDSvZq3tT13muXQHJ63meLVNlfX9UDWECP3obaO+lOhfyNFuPe/1rGwnqdVWJciVG2FBOk20EtG5ULp7CbCFqcIO70VZlIvrtD1Oq7XbF17w6XFLDJ+7CkzP3HbWriKq6t4mUZNHDT9u8zdp1LQ2X0lhaI0yPHZc/t3+1xrQSO3e21p59TfZ04fWMZXeftM6xp292nH7qtE3Mv77b8IpNGIU3lZnxtknh8/9NilihA7PLCj12Jlau5ms8g5s1Mmd4bzU4udC6nXdPfyXKNT3b6vordqcqgqD3MiXBj6N5HTB34rpOooa0QaDfO1+Vqhjia66yDzf7k0XxOtdUNVAKRO3r9/m/jn/+t//RdftP3tv/f36toaGUKgdz2kglEKtEepyul8y67r6LqOhLjAQoqsDkRiRlVJH6y6okxj1hnh3VZVKEU3QUn+3lRpsXfeEGMAleg6ESlKRhw9Rdy/qutIKaEQJ6gpVcKvckV7JyJZlQWFJoaUnCEntPNExCW66xw5iiCbc8b7jrgEchUxrKhKqhOg2wZMftcpCZbq+p7TPDGOI6fThLHyIJVSkdAyY9BFHFq1FopCRMicMUYRS0VZQ4wRXRFBymo5r6lglYhQRdEcvUgri1GUmPBV0TmP15WwTGBls22MbB5TfM/dVAslQt+PKJ2pOcvnLosIXV42+c5YOY8VqrFQKks4se8GrNMsVQJwclKgxNEpgljGOweIYKZixPSWrCTAi9JcRUrEA9MCq5zxm5PR6kolo5WgI3LOpEUCa9wwEJrotIQJ5xzW603EzVW+z2HBGMuyiENXJTBUOmu31nk7iJstVYilUqK46pRxxBzQStE7TzxNOGdYUmB/ObBMQdhVS2Q39MScSLWgrJHrmCOUytB1GKWJORDjIg7HnCFbtBLGs3UtAE8pxBWrIBeWJM431ZyjKWU67alNfK0KjAWlKiDjOiwJ53ppmVZK5mqu6OYm9d5LkJMuTLE0AT4jGU5KiiUFvHWQkoQ7aUXI7YHKaUllXSKPLi9YloV5nnC98CgzFaMKTos7OaWCtV6cpqm5ypQl1EXGuJP2otXdVkug1iqhTVRSyBhjpRDgHhyuWkuYUs3QWUeqlRgy1mlKESxEZztyqhhjMFoT0oJ2ajvXUQDTaC1z2HlNKjKuapVrojXNXelEcNEGSkTpijWVJbc5luSBZE18zznTaTnubBUYqBGcESFIKUXWIggZY6BkFMJARVsqGaXkOGsUhIIxBqtM6zJRFCJoT6HKvDGGjLik0xy4PFywLDNdL1iFZY4trHDCKgkdLKWgqyaWiPWWWhJKO0JOWCvFNKogGlCFXEKbn9LqVJMUBc5hwfXCiKUUnJJiVAxFnJdG2s2VkvuC1YaUIspACEEC/bKI1Kvr1Ag2i1Vp0EbQCbVKW3vVFacdS8PmuJbm2itLbqR5YyTkULueog2m4SyWacZ7J9ddG3JKG8u3KNkApRAYfUeoEZQhNSeu1xarpEPCGEOmYpUWPIPWWG+kOwMZg7F1CCglhZ/ed5ynI7vxwDRJsGBVMMUg5yOLYKiX5jzNBWctuSZClsLYKjwClKpaUOKMs93mzk05o6zBd5bj6UShstsJV1cpGd81y/hb50TNwoHOtZBjxpmWlZoz2gtCoreD3NNLwTi5p8UYxTmcFrTTZJUxSpzkOWcMqjnTe0CY6N57Up0l5K1WSoFCbfMvYhseIyuF9Y4wL5LcqiXWMtdC7zvSdGbwDqrcY5QWx6zWml57pnmhGks20lHiOyvntF3TXAvaWVQbdzmK2Ftq2uZzqVWCO9vnlvWikwJhc8uqolBVY9q8zTmSjJEiaxFnc61KnLeII1kpRWjfc6502qJ0lXE7T/zTf/Lf/0K0/Uvw9bd/87+p0/NONrLHxHItDpLcwr+qhvFl5PzMEQ7SYqjTg+N0FaxWV5eqUujVWV5Dlbo5iKpWm6utONWCUhRuas66gzjYdJZNTdUPmzIzixsKZIM/X2l0FNHOH+WZZN146SQbPnhwLK0CjokPG7W4ExadiXVzv6wOolUoiAcjYS5T2USK6bF8XwPSVuFzFZeKkc+9vt7q4F2da2tolvyNIRzEoZN62XjqKBxRoG3aEyoX0qET8WgvyIj1/y9ebW63NY3eLFLcDlduc3mFvd42ttWAv5HEd7OUxtuTc+ZuFqYPB1Kn8fe5tf8+BIqt7t90aG6jJpqsx7EK9KrI5njdQK+uy/XnwlqU/11bK6yZHzbF7j5trtu8c6TBbGNgvb7+7UzpLPZmogzNSddZ4kUTjjpN6tV2Ley5oQhWoe8YKM4QriUALY0Gfxs5f6PD3+UtjGleRfbGnF2vI8DypN9Yh/Yk4pRaA2OtMDvTYDaXoZwc+ebu5DPK3MjoORMv/ddcn6tjUz532UK7VtfdKhitrt5VQNnE7OYaXcXq9TrH9fo1PWkdc6so6W5m7r53wN8X/NuZvHPbtS+duE+LF8FpFejcfZJr6jX2Xs5t6U0TePWGvVgFHx0rZsrb7+ed2xyu67mjihBoFjm/uTebIzJcum1+Ff/gyFSlbq5guQ5KhO6dQ89ZGLqVr7lqq1Wb4CLnRY55LYSsoul6vnUoxIPFHdP2/9FCxXQoWxFhFdzW11yF2VVMXIsRq0i8jof33Zk6SuBjcXIcesmYKXL61h5/mwiXVlAW94nzc093m8n9gzC4roNrcWAVUNPwICauop4OZRuz61hIo6Z/HTemKsjPJDivbGFcqwiae/M1MdLdp4c1wT2Ite+P7XUMr6iCdbymncXdBuzdTN55qtGEa78d77q2bk5qr7frlnZmm9c61m2OrM5qM2UpWhwfQv2K18SD2da/dV3Lvbhr5fNpccQvMl/t6b15PIsjvn8VmJ53dG9jc7NX/Ftxlpv7mXzopZBz1QoDQysCNPf9VkRE7mnhwuDv8oM7dxCHcdWKsNfsXgRUqkzPvbzGe47m4tTm6Kau41AKZO8Lyus4WS7ls+gsQmmxCjdJocw25/A6roqT8/O+e3U75p3GLnVzEKsmDq/jcP0M61r9/phcBdBwZTFzZb6Wn5soRgY7y/svF5r+JjdHvawtqxN5dbOapbbCyTrW6/YZtnMc61aE0plNwF4LhOu8We916/PMWuhbCzmqyuvHUWNb0cwd87bGh0sr66ETx/P/+T/9Vz/Xs/afa6bt7/6P//M/oGpqqjjTt9ZBSwHuT+8Y9wOgqTmTc6FsArRBKU3MmVwqqVayAtN1nKeEMR5KFTdVQRxDueCNgizVWOd8a42vONcTo4SZdL6nFIQniSNrZGNZArVm+s4TUyTkQiWSc2zcOtEVU4oYbdBVNRaiwmsDrR0ypIg1liUF5rhgHGglLdpxSaA02hpSlZ+FmHDWAYqSCzFEESAQYYtSsRis0cL+1UpcVEVcOMY6EcqUhqxEVNBG0uS1EXcfCo0lhoRrrFKl5AZTc6V3HTmu10ZRVWWOcQumSSGLiKeNCNzKYqwlowipEFNooUsGbR3n8xFtFaAwFZaUwCiMsfS2lxTtKg/9urVt55wRaR60VtSSZNdQEfeps8L8q5qUkzwr1VVEV1hjxeJeClVVYiwYrPA4cxLXmrPC+KTgvDgIjZXAm1oSqipKrpzmQC4V6ztKlWOy2uCdRyuoObPrB6Z5EedaAaUMpShQRhZZo7BGUWthXmZ2fgAqxmsJvlEKVVvxoQgosRThw1WQFLNamyBSyDEKR1eLk9G6jqoUrtNoVchzxhsn4hyGrCrOdlA15/OCMVpc4QgjzliNc5YQ58bmVFREZNQIk1Er4exmpA3ZaOGkFelfRxtxCKQgTmyjJaytNFdnVVquNdLG67wnp7yxQGsRt7CEB60O7NpE9goYrHbMsVLQUNsDUw1oJ0nrIo42AahUej+iiiItEU3GWBGfvHeEkkFprPOgDFo5VNWk0trEvSWXTK1aWDe6kmqkqIyV7BFinIVJqRTVVGxniSWhWsFJa4N4F6GihPOrRKjuvCWkgPMi/hjjSDWTUsFYi1GGkgsli8uZAt5LoQQKBo2xMh6M1egCTms0WQTSClDJKeCMpqDICZQxOCdt8aWsfHERbjOFoioZ4SSnlFC1Mg4DS1ioBYzqSCHjnQRQJYq4G6rMLaNFlDXaiAiZKtYaisqMvqfkgsZIwa1KIaWWtDGKQkx03pNioncebx1hSWgjPG+KFkQFMv5rVqSSUFoxNUQBKHLJjDvBMygtLthSC8o0rp52lFKF2VsSNRdUSqScsc7SOS/BFtaQi2zMrbdyrbOsoaSKErl/E/9RqrX0K5QqaJWpVPZ9DyWilYi9a8FL0GSZGBeWHOn7jlgK2hhSLcSUUEoeHjRKBP2qKEWjtWHJCWUNWluM1oLKKOLIFfOPJiQRz0vDfNQQUKbN6Vwx7dhLrWQtIvRgpPAYQqAbB2IO5BLI8Yypcj4qlSWFhiQQdrNWBlU1nXH0iIAdQqTzHpSwVbVR5AzaWlJG7mPWyH22FjpTiXPkMI7EFDEgwqx1hJjxzqOwgnoohVQSlcZ4VoYYEhXwzkOFbKoUMgv0ztDFwmikWKDXVtmSULVgFFAruSoqInCbCp1xlCVB75iL8Ju988RpEoZ8CdIRYColJpwCnYX7JQUvg9YyNrSzpCzdH846Usp473FYypLotKO3hnye5ZnIGLJ2co9XkM8THsXFMHDoPZ3SGGWFd7tEtKkonSnyQERJmbHr+Lu/84sgsr8MX//od//vf6CkztTCVGRzY1Jl/GJmfuJlzS+yETGxoqra2kndWTika1tgcYrhdaQ2Zmj2Cl1AR9kUrZuadcPW3eaGSpDwGDsXwqFtXucHoTc1pp0qbA40f5TOC7vIBr+2G5SdZQMpjh6Zy2k0oBTZi+CZek13LHQ3aQudqVqwAaBa8ItsuFYW7xpYZid57lEV/F0UU0anSb2IuybIs8j63sWrr7EVqzciPDXRxi7CNqxG4c6VtJPjlop3c/9cdZsL0CQ5f927KGzLLKxc1XiVZpY1t3QGEwruKA7I7lb2EdUo+leLXKPSmIc3QT6H04RrtyVpo0TwyoPeRBL52YPIKw/V4mTTDVO0iTg8bNRLY2qapaCLMFOLE8Hazg/cSnFXifjVvZ6oncWcoxS3tAgD/ZdHES17h4oZjBLUwYWntsCX81PL8CZiYnNCafXQrlqa269xL/1bcXAqpB3cHfNDeFH7HRDBTTcHnDAZK9WvQrmIXdVqSm+E0drEybXNNrWwKRE4JIgJFN2rM8Xbjampqjgw8yBChoS/0QSXxtlpi3w1IjZK269u51zGTW0FP3OOUixvbF4d8lZk0fGhyLGKW6uopou8/pZ43gROlMIEYZEWr+nexe3aq00M1RRvHhyDzWW3XEnh3k6N/doQB+ngsPfS1STBZOo9dq2Ilrk3jf/armMVoU34oKaFCeXtHKoqr2XOSZ6z2hxZizKqVOztjF4SOlfipcfdBdLeyWs6LRzqOUvgm9fyd2uyqFLbNaVC9e0zN+G6Wk3auzaH2jhSSri/o0XFIkxfrShjwyilBzEXLczP1XGpQ8FMrVjwdBS3Z4Hq5HymvTjs7Zyl+DMXzFSoTuZv8etnkHOpM8yPGjN1HUtNMNXbGEeYqAdxLYYrSxxNa2Vfed2yBooI+16XgFZ0r2fC407GWigsTzzuJHzkdW1Ay3VOo7gXV3F45Ujbs4TapUsvxXOn2/gTdICCr7FMV7OcjnK9oYnB0MaUzI/zB2Jy265tEwprwyO4Y8bfJcJVc6a29dWeZB+i2t+a8IALqEbh79IWSreeczSUtvbrqjH3s+yHk8x1VdnWWzSUTjdxVdYWf5tYHjnMIsWReDDt/lswgYe5X5H1vBWm3F1sa5kiHMQBakPFHbOIyXUtouomZqrtfq5KCwssleUgAY6nD6yw7BuOKI7iQI97MVqt93wz1+25obTxqQrye0qeCdwpN1e5JRw08yNB1VT7wLtdzyMKbCsKpkH4ueEg509V6G5kjse92fAobpLiSfYynuOgN+xBsfI8UI0I0sKwrsKg7RV2LsSdEXdxrF87d+szkwkP65pZ5PePHxrOH2ji4QHR4E6in9R2vVWp6ATTY8N/8Tt/4+d61rY/zy/9//bVOhByqmQCVStiTfjeYZXh/nSLdwMGTSyZzvnNDZtKFoeps8JOrNIK6HvHEmbZIDmHSkgAmU7kOYlgVSHpuLUoznNoLYuwzLJQVqXQSovLTysGW7CdZZ4lHMz2liWcKLVg7EhNmXicwUqCbSgVbSumFkxp1VjAWEXMeXMgllIoIC4a49AayKW5vRyuM8Q54b3BKAnRSlRU1Yz9QG3tljlVed8YUVoEtNUZilab200BtDZ55xwKWE4Lg3UY7YlL20Q36abXlrQIfzFk2cR6VzG2kmPCaQeqkkvGFMTNlxLVGbLK4opC0XtPLRBSxFhPrrIpMNrjOqhGnHFOd6Rc0abiK+Li1GByplQlglatpBREdESEv5gLqrmLjOmaWCdczGanExGlub689+hiySpRTaWYylwS1mjIM6pEut6gdcVWhbfCplRKsW+p8DEIemF1FYI4IHOQwCFU4yUZTQ4FZz25ZEoWx6dzToJxQiSpIE7V3nG8uWE37HHOcjwe6cYRpRxaV0rOlCSCR+qMBIHFSO8c83KWsWmt/LwW0rTgDYzdICKv7ZjCgmrheLUqRu9EIG2O2FwLRllKlRuSNkbEmxTE3aYFaaKrFsdjlTG1EEmnE96LK7aWhMKw2+1YzjMlFcZhZFKBlDO6ueyEvQtSwBHB2FgpXtQq1TZVKsoITqK04o7TwiYVfmrFOXGoKxQhzKCsiIFK4bShs4bltFB0xXlPLYmSpIAzTROJStePTLM4IFUW1/Zh2DMtZ3SBOWe09lir0QqGQYocs5KHQW8b+oLEHKMEmSRLZwcqitraobUpKK0p2omLcREXunWGXDVVW1JjBfvOoVCoBFqJMDuXIkIecm6sNZArKke8lsTt6hwhLPSDJx2l1XqKCc2q365rhMGYTE4L1UtwkamFmkW4yjlg3NiC/Dyk2IozkkLv8FSV8FiOccYMjjlNGBTKQCTieiH5LyXjrKfkhNOF4+mGvjswxwBKWOMlK2o1It5qhTWVkjLXu4O4FowmOmFJi3NaMyongmEIeC/t+MZaRiuc73maqFXmU98NnM9nhv2OECeUVgzWMziLV4C3kgatmwP/9p6dG5nO4miNS8B6R8kRm+HQd9zcnalZ4wdPKJGpLlgrAYZWa3H0a42xkMKC0RrnK3MIUD2xibqXhwtOxztSFPxN7zzzPLM/XIrbVIHXjtPpJIKwMqXeegAAIABJREFUaU7WImt8rYXdOIKSsUiUe0xOScIBEPau3+9ZUpa2W1VxphOn7DAKxufmSHSmhZYVtJeiU9/ut1prLi729M6iTSWeJuYlseRC70ZqToBh7AcMhpqht57Bez48HAgh8OWbr5DOEnmPgqYkqVRbK9iJvg4ceofLAVdmlNGY3qOqpqRCQmNNB1VEb28Nh8fXFCUFn/N55u7ujotxx5RmYlzwXrAzOc9Yoxic5enVJcYIa/3L12+wSCEozgtj58lJOnW0geV45mK35/rqitPpnmPJ7K0nLBlNaTiISEkV44Qv/PR6h61yPm5u7lhCZl4ygbi5kHOBzndMy8LlxQWX+z3XwyBhlu/uJXhxpzjPAVKVkNDmwn70+Anj0PP48kK4zCny9vbM7d2Ru1yaq17c9rUqtHc0s+Evvv6SfG2OkaVsnMF4kBCa/ReBeDCbK2S5EHFVXDplc0yu/zZr6Ma9uDhL63TY+HbNzWIWhNWYxBUj4UIiNnV3Dzzb4sDfRHS0wkK9VPS3edsMrYzL3Mnmc2NZNndLseIEyp3aXjN3akujFsee/FebULI6D1dhZm2rjzu9OYRWB9n81G/O4TV4yh3T5izcnJWwsWxXZ5m0Rctm2t8m8iO7OZ2EqSh/l68dZpaWc3cvf592ZhNf0qhJrI5ZRdVmczSurjkV6sYrdsdE6Yw4JntxS+bRts+VSaPDTulr7MRi2NrLpZW7yjE1l5ZuCIuq2cRltYmbD+dgdTaunNaVL7n+rnt7pvQNfKogXXQiaPRm4ydXo5g/OkjL/ylSOvs1t97qRHTt2NfrurrK3m89jgeLrWBj3kT6uNcMX5yYP9iRRs345UK4FKZltQoSqFSIl76FbRX0nAnXHv8uUDpxUetYMOeIOUfSwTM9WV3s0s4uDE0Zk8vT8WtOLnGPihhMqSJEN2xC6QSF4O9nijNNoBIxyt9G9BxJh25ztFatmJ8P4oLNgkPo3ioRcl1zyjfBbr1GIjT5zbX6cGxqaysvXm+czNVBmnZ2czLb+yC5Wq4xTkfDcmE23vQ6jlSGtBOBXk+RdDXg3pwJT3fbMZ2fOfp3skb4t3Fz8K5oAR0r/kZcxWkn/9ah4N6eqU4E4HjZkCzvicpy3JZ42eFuF3Hajg8s4ZWRmpuLW1iuMmf8TWrzWZ5Hy/DQdbCObZXZOhjMXDh9sH4OtRU91lb53BlxyOe64UNWUd2cH+ZsOgjWag1JW1vcqWyOy9yZzYVejdrcpgD+NjVHvrzP7kVsCA4ptGyCaa4tkEl4uypXbr/tt3nmzrJuredzfuo3xEfct3PiFPOzgfMTS38rLfXDFxPhUU/36kx4PGDvxeAWLi3zlSbsfFurH9aHizlvrtXcmw39oGMh9YrjNxwXP02by9IdM/7NtK0luQWqCZZBxLnSWu27O7kO673j/tOO8VWim1PrhpCixOkD39ymhrj3DF8tMrZhYxOv13V6Ltco7B5cpyZK98l6v42HA+42bPM0DiLoTc9XjE9p99q8uep1FM7x2uJ//NCxXCqqBXuu+DsJbgvXfut4WJ50G091uVSEK8XbX9XYc8fjfy2q+NoNI10q0uqfe0PxsIyG0weaPLC5rzdHaqzY5QE5omMlPtLcPtfobKgKuneVw+eZ44eO/ibLvXSvUU4E3dxr5mvF3Xflfm9Pisf/WpAPKsu1np+4DZlQjTwj3H9kOH4K/RvF8FJz+sDjj8LfXQu/qnX8pF7x7mNF6SrhujB+bnD3D2glKts9Ilxa+reJ+088x48U0ycJNWT8Tyzjl1C1YXzdcDVWRPU0aG6+41iuYf72QreT4tP5pzvGLzV80eZ5re+5nR+edX6erz/XeIS/85/9/eqspdaMKqrxNi0xt7CNlFBlAWVEMLReWgezuBwhQZUovJoyWimGfsc0H9nv99zc3NANPcsyUVTBaSeOx1wZupF5nlHKgErEWth1PSlnwrLIxqdx5krJGCctms7JJK0xUzGUKsFC4mLSTPncWJUSpBKmI33bEEvwTGPkNpFvFVZVVqACtECajLiTcs70vmOeZ6yStsxxEKTBGloDCKtTaWmh15pURLR03mCUZp4K1gpSwBlLXCYymb47sISEQri9VdHEXYNRilr01t5KqfSD43S8ERdXw0hQxaGVsqALbOdRxkibLAZjpW3XWfkdcZ1ZcWzmgnGaEjXeWKx2KFU5xxntNUuKGO3RVRyjpCib65q3YCynFeRCcqCLMCRjFoeUtMQL8885J85l5zAoahKnJO1mWkpBKU2OcWv5ttU0p2Zm7AdyiSQaEFsZcokY7Qgp4rueZZ7x1okov0TheK1YDu2JOaGNGDVzzmgvAVw2K0JcGEfZ8FelKWhqBacFa+FtJ3PFaKYwY5zDGUNvHUplcoXTPAlnU4mgq5wh5ZlR+W08HcYdcxThdG2GEzerOFzXFvMQApfjXkJ7cuB0nABFwZKsOGENFV00mEqiCEqCitcKlQshFZLWxJSxxghrq43/UhPOKVQx1CDCgjGuOexyC4My1JgxXjLVd9ZurdnOG1KUQLtUIFYZ295bQpzICXwTtjUKXSu7TjAj1oqznFIIpWK6HnRt6ATHMs0YDRe7Hbc3N1jTYYyiUChKEcWiirUaauG0LAy2p1OqFTEKyjuOYcZoRwwBq3QLdQv0TjAGzloJ9judhPu6LGAMRWkOQ8cyp60d1FvN1eWe3SAhaS9eviaC8ERDxDk5vykJv1Jrz9PrC/Z9x+PLA3fnM6/e3PLq/k7QGdqgdGEO0uqtlOb6cs/zx4+4OuzRtXBcJn7y5efcn2ILSpP28hgjOWfGwXHZ9zx9cs047igZXr58yf3tmSUGsIYpzRjj5Nzajv3Y8+mHz2We1MTxfuHFly+ZcuY0T2AtxmjBSyhH18F3PnzOYex4cnWBpvDV61v++E9+SsqZ2Pissp6K69Zbx/Onj7m83BGnGV00P/nZT2XN117muRIEi/MFu1S+881PefzsSsQ/V7i/uefFi9e8eHdPsXZrPe/7nlIDKSz86l/7ZXqr2PuR0zFwNx/50Z9+gR0kYDJFcbLqWtEUnl9f8sHzp1wOHWnJ3N3d8/kXLziHzEkrkgbjLDpluWfUyrc/+Zj90DMMA9M08eVnnzOnzLtpoWpNUq3tHRgHz753fOfTDzHGobHc3px4++o1r29uG69W3PBVgXGa3mm+/cnHHIaBofekFIjnxB/9yb9jTplYNSmD6S01yr3ie59+zGHnZZwoRVgSn714ycs3N5xmcXZP82kLRHt8ecHHH32Dx9fXOG+Awts3d7x9d8fnX7xkCpFihbftlKGS+eTDb/DRs0d0ncPbjvvjxMu37/jss8+Yc6TrR8LmzE90SvGrv/Q9fGe5OuxxznE+B/74R3/KF69e4/zIlGaWMuPVgKmJb378lOfX1zy7uqK3jqVEPvvqFS/f3vHi1TtBjyhHVZpd54jTke988hEfPHrExThwTok3d0c+++wFb04TKClEKeS4VJ7ZHwb+2l/9JXFq54TWnpt3J/7wT36CaggFrStzmqEadruRv/Ltj7jcd4zWo3JAuZ6ffv6SF6/fcZozU1woWkmgYVr43ne+zfXFgV1v8M7ggbu7wHlR/P4f/AFZaUpzuZQizx2Jyu/9D//o52rZ+sXXX+yv3/qtf1hXnuIacAMtbKMFz6yigm8tfirXbdP8/tfa6j5fG/p3mfMzy8VPFsKl3fh/aWc3lMJ8bVvYDNt7Tk8ddhZUQ7hscyavQpLaWha3nzfRZ21ZXBmVEhQTNl7nGgqytqCu4h88tIevnxN4T8hdk8GFK5p7aZecnrgtIGjFJKxtzquga6cHHu0q8qznbBWCzZJZrp1wUJv7RxU2cXcVP0UslmNbLg3jS2HLvs/KlCCfhmNozNH3BeQVn2Dmh3bm90WHlYmZWtjM8Fo4u+42bC7G7fed8HPXYKxVEFqFfHHH5U3oXcWyFXuQdiJQr+LvNoaayKuDtL6vAVnrGJgeW3F2vx8etbZ/n7IUDO6k6LC2S28t+e+xddegoU3UTpU8GOx9ZHnSfY2nuB63Pcvrr23G/m3YMAHSTizOq+FV3Jzo2+eZhbW7sg1PH1i6u7K1Bq+ffXW4okQgcfeJ+0+7jfM4vkptfIpQvLbe18bJXfm2udeEvRyTP5avoQXW99raz8eHNngRZOQav8+cVaVuYvH01G0hQmv4jr/P4khtCIK4f8AGxIN7L9xJHNDjqySt0asjriEI1nUkjXIdi9fcf+y5+OlCGszWgq0yG/ogt3bq7sWR+Hjc1o2V29y/EBezOS3ixj547L0E6a1Cfu40w1fLxn0FUDFz/njc5iaIS/DuE8PyuJI9Eup3LhtPdJ0HdsqkQebM279qmZ9U9LdPxBcjFz/WPPrjsDn+VlTJKsDefstx88sV+9EZYwrT65En/8Kwe5G2wloaZDyuDOf7jw2336uU6whJs/+RY/iq0q1hiW/jtpak0XB6bnjz1zNqTGhbKK96rn6g6N9VxpciOK1IExBu9ee/aUnPA9//1pf0JvIvf/RNPvhnFrvUbS1b18F1/L37vmb+RkTNBmzlyb/Q9LcPHNrVNZpGCWH74jcUw7fuSUlzGBdevzqw/0HH438Tv1aIClduY5f+6d91cBF5/PjIm7d7zIuOj/+3tAWYrUWkNRjwza84jt8PPP/GDefguH9x4PpfGfq38tn1nB8c7+18ffEblvg44a8Wwk3HxR86utvKxZ9JgJuZIvGyoxphwZ+faea/ecS5jDOZmy8vGH9iufpxxs51u2+t6IL5yvDyb1b80zPPLo+cguM0dez+2Z7utm586vcxBz/7WwaeLXz3G6/obOLNNPLy959z9UMYX2VSr+jfpq11/+avOG5/PfCdb77kcX8C4Pd/9jH1JyNP/lWlf5c3dMdaUPnqP3CYX7vl0e7Mk+HID18/Y/nhBU//pbB9w0HjTmW7N4WD5sXvBIYx8OsffM6T7sgP757x7/75t3j6B2lj5brbQDo4ilN8+Tcs6peO/O1v/5Dvjl/xZ/MTfu/f/hr9D3se/2F+6PCosna4c+HL31B8/Mtf8e8/+Rk/vHvGH/30Aw6/33P147TdS9YicDVwemaIv33D490ZrSqdSfzwy2c8/l8GuVfPEubW38gYPj+1vPrNyPNv3PCty7dYVXA687///ve5+IFleF3kfLWis46Vz35L031y5JPrG54OR77R3/L/vPmUL99d8Oif7jb00fv3OTOXn5tp++faaausoqpCrdLCS1HNDeIIc8TWyuAtfhhJKRGV8PXCeZKka52hihjZWyeCxXRi13uON+84DAO5KnrfUWrCuo7pLE6sFCL7fhBnZFk4391T0OLO6gc5wJbeDNKO3fmR0tAB1/uBm/v5/2PvTX4lS9P7vOebzhjzdOebcw1ZQ1c3q7rFZkM0RUqWIE+wLQOytTK0MLw2YBta+H8wDHjhlW3YgBZaWDYoURIlSBTZHJo9VHd1dVd1ZmVl5p2HuDGe+fu8+OLeorSwtDBgQuZZFaryVkbciDhxzu/9vc9DbFoUdeHXuiUIFOui9M2kuqHXHlDnnosqlb/ZUg40AVUNGHxzuK4xMsRWljBMsLXnF0olNwzcwMtdpKCq/QotgHT41e4iR2lDYHw4srY1bsOUREgviBGassg2LECBEob2RigUBJbCOkrrGX9COIqmIdaJL6rWlk4rIYkD2pGiKhuW6xV5UaKD6I6PaaLIIyCEQEpBy4Rk+ZpIKYpiTdxqk+W5by0JhdQSgSAMIpzza+ZBEBJoRU6GsJLGQdU4FI1nVgmB2hjKtdR0A402UCooV2sfQjcCqxqquiI0ATi1YQorjFLEJiBbrtAB1E5ghaQphUdrSDYBlaWTpAgH3V5CGGisrTk5O6FuoKw8k9ZuVvirskTiA2pX1nRaLWpXUda+3dXU3jhunUNrQ24zZNNA0zBs9wnNgLJas16VnkcoNUXdoCPh5WJyM7ZxDbuTMe12m2Evpqkci9WM1yeXSCupnee+RtqwKgvYCHFaOubJ08doJVgsfFi0yHJQGofwq/vW2+yttbRaCTvDEe1WghCO46Mj1uuGuhLUdY7D83Uz4fmk5XrF3sEOaRAQa0m1ynhxfERpJTKQmzV40Agf3taOINQoKRiOBjjnyPKSVZajav9aSa0w2lC6GonFaEk7TRmNByRJxHy+5Ga2YrlYs6hLGmvJy4JAKXQgqOsCo0O2JyNaSYyoKy6nN6yyAuE8WiWMU+q6ZpWtiKOAfJVzcHBApBXjYZfzs4DFPGO5KrG2wm5S90pAUxRIgW8XN4JWmmACmF7PEGWNLBsfwulww7CVqMYPGUTVEIcRWmt6Ucj1fIGRGqsCbON8YCsURoKtKzrtDvu7I1ppRBLERKHh8mrKxWpFGBjKukZKTRBIijKj326xt7dDHCgi1dDpjhj1epTPn5GtfQvdmIDZYk0rjAmN5t7ODoNOisJLoaLYYLTg2ctj5ouVxyRU9QYZ4xj2uox7HYaDFkYalDK04oDTl+e8Oj1G4DEzfngDnVbE7s4Wg06bQGvKMicIAoRo+MXzl3TShJssQ2o/rFRSMBx0mEwGBFqgtEUJx2CQcLia8PzlK4IgohIezWIbS1nX3NubMOq1SVsJQb9Hvl4j3Q4vXx1R1pK8KBEmwFJTVQ33Dw7p9VuEYUASGyQNSRCireRmlbFuPP5FCEFV5hgjePTgkEE/JY0MLrcIHSMzcFV5h7xQUmNri5GQhBH721uMhj2krXABtNKYoih4fX7uhzg6osgyEh2QZxlvvvmEQatFtxUilKKVGIxteH1yQtZo1mXlvyOwCNeQRil7OxMG3dQPigi8tNBVnE+vkNpQNxuBVVVhhGJ7b5dur0USGGKjAIWNKiY3PX7xxWuiqEdta2azGe12i0Aqeu2UdqKJQt8Uz4OKcTNiucqoi5zlYkncSrlZzUijkPFkwHDcJ9COQHn2Mr0OxgQcHZ2QRjGzMkNqQ6w1SRQx7KX02oGfjCtNR0ZI1efk6AjtFE1RIpWmsgVaCQb9Lr12TJJEmNBgrSVNQ/b3dzm9vsTRUDclrXaHJqtQDg4PdgjQhGGIEBCrkPGozyovSIKQla1wwiGspVxnbA377G+PCQNNEGqEU8RxSrGquFofe9FaYKDyk3qk5N69+7TbKQpBJ+l6oaQTBKFhebNCBYasXKGlQitNqjWDbkK3kyAa61ngwjDaGjBdLLmeL5FCoY1hvV7Qbafsbk0IjETYHKMFoVC0WwZlLGESscgypIpoaGiazXei/LO89v83x2b9/nZdHDaXEsobzptIkrcFVSoougKd+cDVLOqvwjh8wFRswhJdeHt9cl6z3A1QpaPomztLdnTtVzt1YVlt+RBOVY7oPEMODE0oqG/bloC+XdUuPdfvlo87u6fovGooU3lnMTeNb/mFl379X1aO+b3AN4B70t+YTb8KeG8t2n9SMCIadycdujWIq8JRtRVqc8PtDeKbsFR/JYVpjKTcCNnuQoY/Iau55Tta41mzVUuz2pLotaRqSYJ5g15UyHKzfltbymG8CQFhuaspBoLVdoTOHOl5cxcGuw0Kvk7UHRvQGslqyxBf13fhcD7QRNfccTdvBVZlx4ekdSSpUqjSgHBucdLzI9W6RtyFt77pq9f+PbLY9wxLvXKYldv83EaAta6pur495xtmmjrxXOLoxt5xDUUDuqzvQrjbgGq5G+IkrHYFdcshGsHgJ56VaJZu05jmbpjgNhxdHCz3gs2fszRtz168fVxVZDA3uccNADdPIsp2gFk54muPG3BS+TZc9BVP9/a4+EZKtuUo7xe4ymHODePv+8/RbVBU9jTRWXH3fFZbhvN/q0KGOZyHDH+kaZ18Jai7beq5DcphvR1w/a6gmlQgHe2fhERXntGoi68asHcNxEXJyS+l5ANH3WuQmWTr9z1i4ZYlesscBb+pWKVeJDh/4MOd8AbiS4sygrod3DXCb3mxVSLJtgTLh46g74PI9MgQn2vSU98WDW48x5NY3Q0LLt8z5COLk47qmSG+tHfBRdXRqNzeybyCm4rTP5dQtaF8nLH4LCY+d6Tn9o5nC6DWJbKQfoOzHYHzAU0dC7pfblbUlfQhnI7vmKp3jFkpyAaaJoT5QULveX3XFlZVcxfY3nJElzuK+QcFg9GCe90pP+g+oPVMM/i5Pzfd8kirlm/Fz++HrN7P6XbXTFpL1OSSs4ctFqsh0Y1vplexJH2xJNtNKduK649qDg4viXVFP1pz3O7yOu7jfiekdVzfbUXUiUIDN481i8cNw3tTOlFOakpejPvcfNpj8sfcYWNum6+LPcXNe5atw2sG8ZrrLGGdFNzIDrv/FBb7IZ0XOcK4O8zD9LEhfGPGJMlomYJUl9w/vODlRzvs/nOoWvJuUCUaz+Q++WVN83DN7mDOMF5zsuhwoXoMv6f8OWLWbNbkLXoNJ9/W6L0Vg3TNo+4lsaq47KV8PzygeBURX301bLltDr/+tZDu4ZQH/Stqp1DScqlbCKuRJXePX9QOmwjWI836g4yP7r8kkA21k5SDSz5ePiKY3/J8JeYmp5gkBBcrXv7bfey9jDe2LlHSUgw1X8Qj3I8iohvPrrVG3Z1Ps5Fk/n7Jt/ZfUzaKRFccJRkvxITBz+VmuObumLp1Yrh+R5DsLNjrznjUuQQgawy/88Y77P+ThrynSC4syYs52UHHc9D31twfXXPYmrIfTXkRDpk+TsgvO6jCN6jzgab9xYp8K2b+2PLw3jnjeMl2NGdiFnAAP4sn8KMu67Gm/arwz2GgKAaC4kHOt7ePqJ2krQueTk75XDU0n/Q9s7janO8KLyecP1A83rngSeeCcbCgsJp3uie8/KCP/WnLIwXyhmwnumNR979xwSBecy++xIiGp8kxxw+7/PHVk404092xnYOV5fotxVvvv2AvmbEXTjHdhodPr/jN5Qf0fuGb7bdbDZuuBNcf1Xw0OifVJd/pfs5Z1QXgNL1Hem5pjCCa+udQh4JsJLh/eMEvj77AyIbKKiqnOHhwweWrHXrPNxK3SBJdlCwOQw7fPWEcL5HCMQqX3I8uqQaKWFect1u0Tpo7/q8q7N1w6V/3+FMd2lorsU6ikFihN7INiZJwuLtPNw5odzRSSi4vr7m6mjJbLFC2RuuQxhU0pQYbYLXgwf6EnUFCVlX0ej1evXrF8fExzhry0uHqklQFuNoRJQlVkTNotWgPh0Qy4BfPX2KBbL1GaY3eNCY9Ny/HFhmHOwdsb2+j3Jr5esX5xQ3LTLMuakyssStvm5bW0UhJnVWMRyN29yY4saAsLK9fH3EzWyFFhPNmKn8DpQtE3RAEiu2tHaIowgnL5cUN89XScwJx1MqfoMuqQLmAKEh44/Gb9Nspy+WSuqo4Oj1hvqpRKiYrSxAOS3EnG9rZO2S7NyLpCObzG66uLnh5coUwMXUBURJSNSW2XOOE5Je/8QEaRxiAFZa8KPj8+TOumxxHjnMhOgxZrzOMVERS0e0P2Bm1N+vfOV8cHbNaZigHCIXatIrrrGRrf5fQaHbGKZ1uC2trphfnXJ5dcnKZIYWkARqhcEISE1CUGe9/7V3S0BIrwbrMuJ7d8OWrcySSyvnnWlYWLR1GKnqdNju7QzpJgrM189mSz559QWMFBBvBl9NU1BglUC3H3s6ETquNx1da+pMWL1+fcnk5Bw15UaDCxKMQpKDX7vDW4SHKWQqbcz2b8ursHEyIa9xdEC1MQGAUg1bK4909BJbYCLJ8xcXlNZ+9OkUFIaX0CI3K1iQiIE1SDna26XUTkqgkMgnZUhNJzeuTS5ZZ6d/Hpb+ITEzM03vbjEd9TOAItKAXKQbtmNdHZ7w+u9o0cxXxRmw0ngzZ2hrTbwUEWoKrSKNDrqYznj//gtzWWK1Zrgu6rTZKOd578oj90ZgoNgShZLGYMdwe8b0ffUKZlb5FX1sa5xub7XaL/a0B43HXtxVbHa6nM5794gVX6zVBkJDnGVr4UE9IONzdoj/o0G5FSCkYd2NWgw6nJ+e8ujzn6vqGMOl5PqZUSAmPnuyyPRrimoJxe5vpYsCr0ws+f3nEIstpWYlyjkQq6vmKt958ws7ugG47RtHQa28xm+d8+slLAhGwqH3DkabwvFjniLRhMOnz5I371HlGkW1x/OUx9jxHb5imldWIqiFCka9mHB7uMxx0SeOEoigwqeHqZsZyXeLw/OUoiMhXC7YnIx7e36bX9SvpjXZsTYbERrGaL1it1wRBSF7WxHGICRLeevMJvW5CFGtEUxFGhtV8weOdPs9enDAtHQUCHSbUruCNhw/Y6vdJI4VyNaUoSLptaldyb3fCjz/5GVobHIq6KImjiAcPHtGLA7TMUaIhMJo0bJMmAS9PXuGagMZZjFFU1Zqt7UP2dibEEmIdsG4UAkWv1+Ls7Ix16cWOWQVREGA07G1v0e11kK5G6YA4kLgsp7cD3dUNZ6dTTDtBbaRwcWx4+OAQIRuMcxjhiFoxcRixWC55fXqFUJKqtoSRoakKDg8mdFsRRZ0TBwFaaMJOlzRK+eyLV5RVRaE069WCUEn2h9vc2zqgFSiUwbdgK8ve1j6f/vQFjbWsyiVGGSoFN9Mpj3/pA0Y7HYx0mCilri04QbcccjK/RmYVyoJShlQaCirefOchq/kMJSBKAtZFzmR3hEJw+vEntJI2pXA0dYNwJffv7TPst9G6oWk85GarPyBUih/9+BOCWGCMl27SOLrdPk/uPyaUIIOGUAdgBU1Ys3OguLieMVtlviWqAhbTax48fZPJ7gRbLDBxSOMc3V6bpN1hPl8wX5wRhiHrLKcXpayWNxzsbpFE2qM7EBgU4aBDGMYoIX2QGBpqCXOXszXe4c23HjC9vCSJDWWZ0+6k9NI2z9stjl5fEKQxdV0SBhGnZ8d88PQpaRQTbCSdQRRgjGKVFwwGPU6OL4iSiDLN3xBSAAAgAElEQVTLCZQmVIbJ1pBsXZLT0IojlJMkCWwNJnz+7DWEBlEVgEYIGA4GdAdDnGtAK2JqQhlz3W1jXisQiqIskcISy5jKSkajEWEkoaypixItNGkSEkaWG5nj0JjIoz+qJqM/nhBHCmcLBP56SCvJsvGDhKDB32g3DZGTBEBiFM7WxEqz6cghccShwRjhb3idw+E82kWBujWO/tnxb/whaoeufaNNbMKs25v7029pip2a3mSGUZarL/q0Xiharzeylw0W4VbCIxvHybcF208vmM1a7O+c8MMfPaT/Y98SDWeeQbuemLtVTZ3B/L4kn1jsX4kY/zPPFYymzV2wag3UG/mWrB2n31QEb80JTcXr0y6tzxVOKeKrhirx8pj1XoRVXxmlZ08k4t051gpuMkP7hxHtV81d2/Nf/KVAlQqu39FUbd8Q7TyTtE4anPwTQjXr2502kBQdydGvOwb7U64v21Ao+j8ytE48yiG63jQk8eFtHQmu3lPw1pKd/pyXZwP0i4jtP3A+JM0VZU97idqm7Xn5N9fEQcVBuqRoNBfLlNk/7dP94raN7Nc0b4VBRVex3JMs3/HrmsUqYPDdgOTyK8HNrRxK5Zarp4a6Be7pgje2LihqzedHE8LnEcOfNJiN9VuWFmMd+dAQzBu+/A8d7eENO+0FR7MuN1+2mfyhvEM9iEhhFj5UrBPF/FBz825NujVnbQX5Wcreb8s7GdBtu0+tSpo0oAlg+rWGdGvFIMlQwnF00KP1xzH9zzZNzds28IbjOD80LP7iCmMK1ssQ8zJk63sbzq69fQ09V7NuB9w8Crj58zlCOvqdNSfzBP2LmMPfyrChf/yi8k3SYhyxHmtuvlGyu3fNg841j9ILfr7c4g87Txj8IPC29cYRXt3KykJe/lW4/+iYr4drtuMFz3ZGvDrssfxhh8n3K4+1qCz50PMzp28a5m/WjPavGMRrAM4HLS5f9Nj5555pLGpHcDQlfzDEGsGz/7jN5INT3m7NmIRLPr7e5eZBhPjfOpiNPFCvG3TpV8lXuyEX35DIR0uGnRVP+2d89+g+ze926T3zzevgpvKojw33+fJDS+tgzkejcwLZEN+v+PnNhNc/24Lva3qflR4dsRHcWSM5+lVN5+1LjJX8lcOf8k+fPubiB1sc/CNL+HpGuduhCRXlJEXmDV/+1QTzdMbXtk5o64KXO32enY3QvxVTpt40f4cXMJ4TWvQ1s/uK4FcvqYqA198ISH8cIat4w6jchPoSyp7xwfAvxywf1ASDnHIZ0MQB3WcNyalf5b9tdQY3NZfvRay/veK93TMOkymrJuDhk1O+7A7ILmOSi5qqJQmnfjBSJQHXfynjrZ1z7reukTjuxZf8KDzg9361xeB3QrpflARzj1/QWcOr31C898Yr3u0c01IFx0WPt7dP+bvlu1x9pGj9H9w1J3XWUAwM+Z9b8sHOCd0gJ1Ulu+EN73aO+Ul/l+kf3fO8100LWK8bZm8q3n33Sx62LnmaHPPx8gAtG2bbMT/82XtE1z6QCq4yql7kP7Pvl3xr64RYVaSq5O30mMoqjve6LPZajH6SbxrLHmmQDRT7f+4ILSz9aM0oWHGYTDnuTPnp+WMm37d3bfxbmVz/Gxd8OH7FcdbhSXLOyCy437/gu+0n/J//+FcJll4uFpwtqHsJZx+F9D485+3B2d1j+jTZ5ZsPn/E//NZf88PA4qvnnTy/4fWvDfml+y8ZhSvGwYKrsgXAx7s59ifxndjPBrFvdd4Yvvmrn/KLmxGpKThMp3w2n/DNRy/4vjlA/9iw3gkxS980dlKw+FbG1w9fMwjWzKsIIxv+xv4f8A+Tp5z/nYdULfXVpgWw2NO8+53P2Y3nxKrkfuRD21md8OnTLea/GNN5VVN2FE52iF/NOf7rA3794WdkjWEcLFg2Ib/W+5SnrWP+x7PfID3xuIn4smLxICW+qHj3g1e82T5jL5ySyJJtfcPD8Jzfjx/xe8FHXs65Ef61X5fcvBXwt775m/zW1Tu83T7lOO/xUe8FvzH8lP9+7z8g/b6lavnNjbKr6fzRES//kwlv907ZC6cA7AfXbJsbPunv8Op+l/HHpd/OWHoGbtGW/LXDH/DZapsvsjG/1HpBT615u33Kyzf7hP944D/jt9swArIHJd8ZPqPa/Mu34hMeBef87v4DqqQHaIKFH55kA43JLA8fnLEbzwC4blJGZsE73RNete6jXvrv8tsNHpNZVgfwYfuayikMDS1VMDILPtZ7lH1L2fKDLp177FCVCt7rXFI7yZPknERtMhZZ8rB1xXF8/w6X4rdHpEcOtf7FbaX/p0P+q//I/3eHl2VVOOdv1KVtUE1NLwiY9BJGg5huOyA0lp2dPv1ezGjYRgpHHEbYRpOmKVW9ZmtrTK/XI44No3EXRMVg2GZre0BRrRGmxuI8o7Cumd8s6PV6bO2O2NvaZtCLee9rD2n3BSZ1JL2QzK4p65wsX6FkQBQmTLYGGG0Zd1NGvZD93T6BqnBNwXI599NN4cHwTdMw2Rlx7/4uUSwYtPt00pB7O7u0EoOj9BeH1Jub7IY4bDEejNmb9NmdpOyOWoyHhn5XUWVztHJQW+q8INSGbpqyvzVh3O3RjkLeODxkZ2fM4d42YSDIVtPNqmaDEQZtHakJ2JsMSfuaIITBIGJve0i3HZAvLjGyQtqGqmqoasdoawKqoTvq0um0aCWGfjtgb3vIoNvafKlainWOlg6cpdtKub+/w86wTSeEfifh4e4Og266YWEKqtobwAMdYIuKg50d0rZGG0e3HbF7b5fh3pisnhOoGi0d0kDtalRTc393h9G4S7fXIu6E9HttRuMurW5KXq6w0q/8C6vBaera0ekm7Ozt0u6F7O0P6A5D9vYnKAFhUxOpBleVJIFBOcGDrXvs9rfpp23SMKadpAy6XbaHYy/VKvEG8/nag8GVZjweoSJNZ9im30nZGQ7YHg2om5yq8UK7piqRtiFbreh2OmxvDWi1De1hStIK6E96VLbybWvrUBa08/KmKNIMxz3ixGCCBCsgjCN2dgf0uymCEoVAO0mStimqJa1+m7TdIohCwiQmbiX0ej163b5vBltJ5RyZsxTOkfZaDEZ9Ot2EVksTd2LCVDPa6hMkIRbfiG8nbayFap0zHg+JktCv2tuGVhLSTgPGgz6uqWlqaJzE6JjQRMxv5ty/d0AniUmSGGUEaSvwrVvpsBsBWKMkVmtq62gPh8RpB2MMUaAJjGQ47NLqtlgsFgSmhXAKV0OV5wRKcri3TzfUtAODEo4kCtgZjrFNg3GCqqqphUKZAKGg22/R7bU9YzWOMEGL/mBC1Iq4nF0gG0FYKUxtUM6gTYwwEePxmET7ZuN40iVpxzhtyWxGJhsqWVHXJUJahJGMJtv0en2SNGLQazGZ9ABLUxcY4ZudWb6kVjU6FLRaLVpJgJKQEjDotLl374CqWNM4yyrLwCimszlaJcQthYnAUSNDReNqup0BUdylrGtcU2KrAm0c2lhG/RZRqgGL0pY4CtA49rd36LVbYAW29E1SozVaN2wNOiAdSgcEYQeLIkhSojilN+xRiwyhHbUtqaqKg719WklKIIRnKNMQGImzlm4nZTZboIMYIw1FniGlo92JCQKPlpDCUVtLGkfEJvSsVu1oygpchRQNSlu67Zg6XxNGGoxFhpC2I/qDDkW+BFcS6oYmW5CGESoKCNOENGrhkDhb+/OMEuhQ+3XHpqSbRiSxJk4DokD5/1Y3CCUwylGxYmu7Q2MzhNM0jaAuG8JAMx4OMTpCmQgtNNJBGkeEGowWxKZN1VQoBOt8Racz4Ga29EKqMIC6ITERUkrqzcpdbaCmRgZsEDY12Ir14gZNgxYW21Q44TE0twO0RtS+Ra0kVZljbYmoBdY2NK7ChCGWCq31RlQpUAhwDVZaz5OPDUo5lGio64JQ+e2EVbnECoijkDDQdEc9lvkSJwW18Ox5YRx14RETLhBkTUVZNyhn0U6wLiqOj06JTICrapIwoWpKapdT5Gs2Lk1wkjzPub+3R5lnSKORRuKoqcqcolzR2BzlGgItCVSAJsDVDc3mPSmFxaAoshJsQxgGrLIlUWCQVUUQRF5qqr1cbLVaUFUFTZFRliVlU2ObCvKKVIDYCBStlqAkVzfXrIrc4wmUYF2tWWVLmlygRUIgDU2RYyJFkLSYrZdUWLIqQ4aKrLIsigxnjEdbSIFQCi38BsNivWBdZX7jR4AVsC4LKtuwmM9RVlOuKmxZI+pbQaP9U351+GfH/5uHLOo/wazkjgE7P9TYJ2sePjjjg60jxumSB2+fsNq33Dz2QUw28I20bGzQ64brtxR6b81B+4Zv3/sCgO7hjJu3HMHiq/ag2nDw2kc1y31B9lbO/feOuX94wfLfXbDe9s2+bODZn2bZ+BZMKMi7Cvl4SSsq+HcOP+HhgzOyD9ZUqQ9fk7PSt3k2LURVWG7eFLQ+vGTUXvHh/it2tm5YfbRmPZZ3a6qe4edD6GygmT2B4P0bDt89Ye+dM+aPLfN7tyxF/wFRuW+NLvYNV1+3HDy84O3hOf/Vt/4B7739kumHFUVbEV3Xdw3KOvFhdzaSBO/fsDeYMY6XvHNwQvLelMW+Z1HWifIm9VWNWlVMn/jm0EdbL/nW4AXv9Y/51s5L5u8XzB7ou5vaYP4VtmK5J6m+ueBX3nzGG1sX3N+/ZPorBbMHvjVqjbeuW7PhgDYQff2avcGMjsn59ug5f/ntn1I8yImuKqqW9CvfWvg1fCM4+XbAG/dPeX9ywqPOJR/tvqTz8Ib1ZCOnqqyXP23a+6qwrHccb7/1mjdG5/yNN/6IZGfJxQc+5K062rN3raOYJNhAcvPLBe89fclHuy95o3fB+4Nj3jk4Yf5WteFp+vsqWfhmdNVSzB6DMQ1PJ6c82TvHPJ1z+Y7erPCXd4GcNYrgKmO9K/gLTz5jdzjj3dEJo96S4qBErcq7NrINNU1iEI0fODy8d85B+4ZRuGTdBBzEU568dcTy0IcMNvDyqWIUYhY18WjN270ztuMF++GUR51L3t85JtuvfUPRenZpdFURzCryoWPr8JoPxke83z/iUeeSQbqm//DaC/020qPicHDXMjaPF+y1ZgSyQcuGdwcnvDU6Z/qG2nwuPO+3TjVl15C+zjn46Ig3ti540Lkm1QWH/SnL+81dexlucSEWnTWYScZOe8EoXPEovWA3uuEv7/wUOc5pnVTUbc//veNjtxSH3zjiG5Mj3hyd01KF/7ueTj2yohWiVhUq9zgNpKDcqXh36wSAcbCgH615undK2RH0P114YV7X0CTB3QDJCVg+rrnXndJJcj548Ipi4M8D0XVNeF2i1vVGVOa3CZYPa3q7c7Z6C+4fXrB6Ut6dF+tWgNNi83MVTQh7oxu+3ntFR2d8rf2Kv7z9Cf/5u99Fbzjf8Zn/+fbzJXUk6HfWDMI1lVXEqmTZRPxa/2ck7QKz+grBYSNFHSvShzMOEx94dfWat9NjRmbBf/n4nzHa86HTbfBTJ4o6FPzFhz8HuAtsC6fZC6ccJlPmB2rTGN+gU1YVb37wkq/3XrEXTlFYtGzYC6dUVrHac7Rf5jSRokkCzKzAGsFgMmc7mrMTzohVyWXV5mudV4y6Sy81C9Xd6reXWsF3xs9YVv5zARCrkve6xxQHpWfhAmXXh4TriSE2FffiS97rHlM532y8blp09XrDZPXv9/WDHsUopOw6dlszuibDIjCiYRwsOKu73LzFXQv0bouil5A8njEKV6SqoK9XxKrk660vaac5dQzFwHiMSCiJrxpWhwk/PN1jEK8Zhn5w8kbnnFhV1KXaIBo8buaW/3x7/Hw2oWcyuiYjdwbr/Hdr0fYbFarwA4E6hmmRUFiNEQ1rGzCrE7bMjKqRNKHH21gl7lA8shYEsqZrMlqqoKUKrpsWD4MLsJBc1MjKD9bKtmRxEPL8akjlFNM6ZdbEKGG5alrMqhhh8diCZe0Z8JEknAr+py++w73kmsoq3mkdcV52uKzamAUblI9/vuG04uLXD3BrTSJLEllSWc1Z1eVFOSarDdZ4vEj5J9nwpeO87BDKmnGw4GU5BOAwuOLiuk3e80OAMv2K8Uwp+Xixx0XZZtlEvC4HvKwGVI3/3luPPRbm9noD4PmXEz692aa0vq/68fKAT2Y7XgYZScqN/LOK/T8nJ5KrIuXZckQoaj5fT/ju7BG1kx7ZWm9495F/TVonDb+YjzhedVk2Iesm5JOVf4wfX+9iAzALP8hz0g9Lxb80K/5XHX+qm7bKNsRBTJ6VlOREQYgrSt9mHfYQIiMONS72rMf7B9ucnF1ycXVDuV6hEbi6wpYlO8Mhe1sDsDMaWaONIor66AA+e/6MOOxA1YDSmCjAVYLxZMQbTw5YLC+RSUCkYp6YPf7wjz9mnTXUhSOOUqQw2Kqm2+lyuDOmqUocJT3dot83XJ6fsFg7lG5hmxKcpSkbbG053Ntie9wBV1KWOVHUoZ0mzFdXzBYX1C5CKuP5qLljNNlhezJguxsgdENRV9w/3CG9CDh+eUKVrYmjFJQhLwuidsqDe4e0Wo6mqSnrFSZybO30+OlnPyOKAs/KEzFFXtPREQ/uHzIap94ULizWgdaSw3t7rIuSyjqqqkQrQe2g3Um5d7iHrXMkElXVNMa3HuezJccnZwRxj8o60jRhtVizuzXhwe6Aps5w1jN2g7BF2jKc//4PkCZACuV5mggGky6H9yesZ+c0zovYwjDkjTfe4Hvf/zFSOVxRY61CSUVGTWfcot+JKQsQrkHLgGHYZTScc3J6hHLGN2gxWAlN5VBhQpqElFlGkWWMu30UIS++eIUzbaqiIkwMrq7JViuGwz6tdkhTZZ5/7CAyAXEUYBSs8wKnAsIk8dZSaXn86B6B9bxlhSKK2+Su4bOXRwRRCE2JdJKm1DRVzu72gLJa0u54Rm+7P8AkNVEUUZQZRnvhnFEGpyytYYvJ1pDZbIqKPOpCNAbRzAjbEnvq14kbVyKbkvViTjtN0VpidMRiMSeMI2osnUGbPF/T7qbQVNTOss5nxKEgTQyiXKO0t9s3uqHTTgi0wmY1SSqwtaWRNUI4GhpqI9CiocEbPLWAVhphI4krHaHWQI0QljjRmDRGO40SltpZ+uMR14sCGQVIEVBbSekamrIhCjWHj+5R5zm2ynE0GA01gnbXs3e1DijqGikFWgdorclWGVJbkjigLHOqyhInIaYpUVpRCkFFTdVY6saSVZaqKTFaUpc1dVNR1Y7VckYUGNjgTEIVUAlJVjpqMsrGs7EBiqqkM+hRPX9BiaR2EKkAWzeAIQraFEWDTR1OOIqmoMirDZvW4MqGQAkqE+GEYbUuyG1FVtZEQlBUa6xUFGXN0tbIuIUWgqZxtHtdlJG4RlCXjiRSPix2DTWwzAvyskSbiNpZ8nVBEivm6xVpEmBdRWQCwtriVEO+WrLMFzhKtBaAl0sVyzWL+bW/aI9jClcijaYqV2RNydHFKUqHntFsHa044dWrl+xuT4iUI7AOJyxWKILIcLPOkIHBVhVShQRCESmYn1/RUh7GGGlFIwUZDTfLa2rrJZJSc8fqLBrH8dkp3XbCfL6k1Uoo6oab6Rk3izlxGmGdoMgLOh0v+BJWcHVxSSdJcU2NNILlYk3pKirraKxEbkQbRV1ycnlKb9JhHKVgISszf47IQ1azJaEOqcqGMq8xocIkXU6Ozz0aIJDI2Mv6siznZr6krBoaUSMl1JQEkaag4HJ2jRn2adYFSRRhneViuuJytiBKOrgCWjrCCE0VSq4ursE1DPoJq6IkUFAUl3z5+gQZKqbZnCCMwTVILbm+mbLO19BAK0kRlRfGracVi8ulF7hJhwQiqRl1h9xM11zP5iShhSCEukEYyXQx4/L6ChUYhJUgHYWrKfOcy+trnLQM222MUiyKjDxruJ6vyOsKZxRhGNOUa7RyrBcLpvMpobSkYUBRZOS1pcpy1kWOjjz+IFSSqoEsXzGdzbjOu4SNJlIgESxXDeenM64vlkgReFmgFFhvdOHz50dsDXtEWiJtQ2NDpjdzrm6mZEWJDgKqqiHWAZkTvDw7R0WSnfGEsrRIJXh9+iXn0ym5aajwaJlIBoiqpmpqXrx6RRQ/YFbOGHa7ZFnG1fWcdZahVYxtIApbNFmGtZYVJacXN/T6HapFRlA7iqbm4mLB1XIJRpHVpccvCAkq4Oj4jMGwS1GuabUSlus1WoUcX12QFbkfbjelRysFhrquqP/l5uGfHf/GHjbUFEPfJPOMUoOwjumHFX/18c/Q0q8rVh3Fi3zE8KMVf/TpQ8rPPA6gjtVGlmOp31nx19/6YwBmdUzXZBymU/4oPMT8oyF5X4EvoFDFEhHC+nHJf/vNv893Z4+IVcVeOuOTP78N/2uX+LpBZQ3F0HjhR+1Y7Qr+i3d+h1mdYBH8+fEvMJOG//n5XyA98zeDt+HBrYik/Y0r/rMHfwjAUdHnUXrBxbDNb51+QPv1n+CwOlBFw/yRIXp7yn/04EcY0fC66LP90ZzvDe8x/sGG9zv0IWo49ezdX/3op3zYecGsTjgq+gzDFV9/8iXnf+/hHQ8XvARosR9y88sF/97B5ySyxMiGdRPQMTm/+2GL6DrFrO0GPaBQQLbl+K/f/m1yZ4hERVevmdUJ//77P+I3Tz9i/IOaOvUMymwrJD4rWL3X8Lfe+4csbORv1quUcbzk2WAIn3bvgqvgxrNrl4eK/+7Nf8SP1wesmhCLYD+c8t988+/zt/+Xv4JVnrcraocNPbc437b8xtanHBV9jGjo6TXd/Yy/+/BDxj/ywfBtE1s0G6GSNrzTPeEk7/K66PMb937O75iHiN/rfhX0db3UyFyteetwztd7r5jVMWazJ3uYTPl5b0KdxESXlRf5bBAMTsLXv/MZk3AJQKpLDltTfnvxJvrvF3ciJ6c901YUFeLdOWd5m/cHx5wXLd4bntANc6r+BDMrqFv+8dSpF7blY8d/uveH/JPpW3R1RihqjKxRcoeq5TZBln/uVguCswWPxwt6es0kmPP7Nw85iKdck9DeXmCmgmwvBTyX1pzNqLohj3uXGNncBTPzJOJbwxf8vfQ7Hp3R88KxW2FWXSlmRUw3zFjVIUY2dE1OMbJ3OJPb3xMCykHAJFrRNTmpLphVMX9p8lN+1t3GGgNGoDOBWVTIoqYcRPzNd36XaZWytgGVVUyCOdM65WA8pQm37gSB1khsLKhDwXTeJlQ1b3TOOS56XBcJO50566SNDbyMSi9L9BJkViFnfaZ5Qj9ac1MnzMuIWRERX1rKQQT4pmnV0eh1Q3BdonKNXBumRQLA2bpNtVWh19ZL0bKKauA3HwHKnkYtJVWjaJzgetFCLDWqsjSpl/ZVLf//F4EkvnRcLFr8PNniYXLJi3xEaTWv1z2Ss3IjLPNr8tluijUe5TWvInaiGTdVwqoJuSpbrC4SRjPPvdVZg5nmWJOwOG3zst+nqMdctNrsR1MsgmerMVdXLVobxjN4wVI4b/j4ehclLd0g53XRJ5A1F2WbZ/MRW99be151USOsItuNOfrhIdm7hr10xk40o7aKdROyE81oveJOoue0b6EXHcn1aZfTQQeAh8klyybk89WE46MBY4/cvxt6OQnBwvG3f/4Nnm6f8r2rQ97pnfIq6/PxyS7BUUA+9FsTt+dDk1mkcPzmybt8OHxJ5RSJKvg8e8Rp3rkLxW+Z2mZRM/iJ5Id7B+iH/vzyU7l7954PrwRly7dZbxnadSLJf5rwk3iHXpSRqJLKKX6W7XBz3GG85q4drrPGt8znDfnzNtdPC7La8LhzSdYYfv/FfcJnEUW/QVgoWx7JoTOHfiH4Efu8c3DCp7MtxvGS46zL9/74CfvG0n2ee3TIZkDafdFwMvW/27BXY6Q/x/14vc/1eYfxzH3Fi+8p6qc90teO3zl+yL3uFPxpAyMa/q/F12g/V1hjvxrkLSxmZbl63uYf2Lf4lYMvGAcLfrd6g+frET94vc/2jR8alV2NLL0cLLx2nL3q8/N0i3c7x7zIR7xc9ZkWCdG1vXu9y7bEKM+DTV5q/vG9N5ikSw6TKbEq+eF8nxe/2GL7hZeEWe1fm9tA9e9895vce+OUvXRGrCpmdczvnjxEv4iIrz372mTuDqcz+iPFH+hHfPT2c86LFoFs+N9/9iF8nhLOaszaXytkG864XlsGf2D4su3b+t84THk573P25YDJtaMOBbKGvKdQpUM2jujS8eOfHTDamzEtEsbxktNVh1fPx/T9PJroprnDNgC8/PEO4eGSy3XCk8Eln1+PSIOKo0+2GEwd64nBZP57vTQCXXim+b/u8ac6tE1UgG0sgQ6ogojlKqMdppSuokGjVULtvCyosgKTaNCOWjQIpVDec0NstA8kqoIoEFS1RErftlFBiNQKZIiLCvKmpspLxp0JURxTFDmJjjDK+tAiTkjjFuvSEiiLqy1VUyECSaMlOlassxVpGJHVBULV1AYaClSjUPgWy2q1oJOMSKIUJTRNWSCdxJYW6SRKhjQ1xIEgK9YESZsgVjS2oN/toSnIi8JbpWVB5QqSXoLUIYv5CpQGJZCtgMI5VGlRDpwTlFWJcmB0SFkLAhlQWgtScNOsWDdrAjPC1gVIv6JrAomkpqlLVJBgbY2WEQSwWMxYL2/QWFCSQEOVV1zNVyzLilZvQJ6XSGko6wIrDCWSm2KJkg2tUFLXFbLMkaLBNiVSOFQU0zSCxlrKuuDi8pjQWcI4pqpLVssV6/Uarb2BW9XQ4G3nVjWsqwxpK/J8jVEWozWuluSriiRu09SKhgorCkQF0lpsmbGarTFK0tSOslgxvbxGSYFzDUpKbFlT24ZWEnF0/IrBoEtsNNoIGpuRrxouLi4QzhIZzaJqqLDoQLHMlrx89YJRr4U2EmcLiizj+PIEtG9BmSDE1QKBII76vDy6Yr/XQ+smeFQAACAASURBVIqIoiyZT2fUFUROEmpN1liMVEhhyazg9fkl85slRZZhMFRNgy0qVouM+WVG4xxNXVE7CAND0m1zdHnC7tYEVeMnedYyn99wfHpG3EpxriHQmkLUtFoJFxfn9FqJD/yswglNNi+4mWbk65IgihFCAjWukTjpOD65oBIFrTCkHWny5YKqqZleXxLWnkssJKytQ0qHszXPXr7gwe4WwtUIKbk8PuXy/AItJNfzOWm7S13WaCNRRvDFq5eEuqEVbVqXCK6nK66upogNW9jamv+bvTfrtS1L07Oe0c1mdXvvc/ZpIs6JyIwkK6uyClyFUZWRACNZSEYIyQgL0QrEj4Dr5AoJ/gASEnBhGYNUkq2SkBACUS6XoXA2UZWREZERkdGcZvfNamc3Oi6+udY5CTflG1PGtaRQnFDss/ZcY44x5hrP937vO6RMPSnZNDs+/+Yl33q6YNMrnIXNtuPl6yuUrSFGShUpJgV9yLTGcLa5wq08p8fH5CEy9Inlck2z6ymKGTlnCqcJMWBQDG2PtRV+27CyCmUS2mlenL1mFwbmxRyyxSqN0hYTM332vH79mpSe4Jwh+p5vXnyJRTOEDkVNyCWqV6A8PsHN+R3myZRWK2aVY7VreH15LcrLEA4hi1Erlss7vvjqJd/+1jv0MVOXir7vaZsV5zcv8Hh0qFG6pCwN6MDVzQ2mSBzPK7oOuhiIxnCzWfHNyxdUiwXbTUukYVJWWGd48fqC4+MZwUhQnk4D22bDzfkGnQ1W1+LlqyxGl7x6fUFKmW+9+5i27fEqEXY9t7cr7tYbptMpbd9hXcQqxfp6yXVdMplbCXK0NaTM8u6eV+cX9F0gJ0MewJiSnAxtGzm/WtF2geOp4361xPeBq+sbzi7OMbYSG5fFlCYnsjH8yccf8/jRsQQFoihcZjdELi9uuN8uKaoZISLBJEXBuun54tVrquoDLIFhGOh3A7fLc+43G0I2RFPiXMGQe5TWvLy5xE7g+GiGTx7QbDctL19c0ewiuTJS6LEWZRXEyM3FOWm7YT6f0vieQUdenF1ycX6NcVOitqJqrw39xvPq6pKWyGANOSaK7Dk7u6AdIql6SNNvmWSLTZY0ZFLy/PDHP+a3f+c36bYrnDEoEuu7HWdnt7TRQ1USEtAHiqJiu9zx4sULHj56wLSG0jrUEPj5p1/y+nZN0AVF4UjR44xhMXvI67MbvFIQFdN6Rtd1XJ3fcnu3IURDNArftFTW4JRj2AZevb6GqJlPSrJ2XC9vuL2+4r7b4eoJKmdQGaMTWikuri5ROXJyKgF0Xd+yW3V8+vlXVOUCZQybtsFYJWHayvD112fshsTJRDGbzGhXHX/yyc8I0aCmFX0IlGXJzntSjsRO8cXHX/OiOOOd588IXcftesumHaQVKkQmtRTZ0JnaVvSbHT//+TdoDeeTJbc3d6AcqZiQggdtGGKHjRlTSEL8i1e33N0HyjKzcBVn1+dsO0XTBQyFhDhqRQyRsqy4vNqy3g4cL0ruNy3OVpxdvODmZok2BYFIyJFsDDvfY+2fvl3rz1//+L+6R4V4zU5FRVqfNzTPJ6hGs4sFR7rlxs8BuBlmnJYSYGK7fFAThlLRPyhQuuN2mPHdySXXw5xCB0IyVDYQDsn0UG4Cpgncf39COe/5ujvl12dnPLEr/mD1q3xwcsvXpyfUt6JkFa/TNPofwgOz5WfbZ/zW/AWf7N7F6ojpJKhJVEkCF+vLnu3zig+Ob5nrjk2qmJiBJhYUOpCdgIDuxFLdiyLQTzXKw1965wWnbsPn7RM0ma0vya2hOxXF0/SsP4xhMnC2O+IT8y4L2+Kz4bKdM3M9vha47ecGt5ET3uLrjuv7kmflPU0sD+9zWm5RNmG8fA7b5IOqt75U/LR5zokVtdfz4o6rtOD3X32X6kbRPCkoV6L2ctuI8pHcFPwvd7/O3Pb89uIr7v2UQkecSYf08T1Y1H3EtIq/df47PJuseF7dcz3M+WT1hPPlgseVWDq4Rkno1Qjq3X3BRA+82J3wsGx4VGwIyeA26pD4vQ/6Yuw0VAl+tnqHB6W0DzfBcffqmJkTBaGEDIFpI927c7756Ijz9xe8s1izKDqaUHC+WZB/MSWrxHBsmbxqsFqg2eS854c/+S4PP7hnVvbEpLnbTZj+tAJ6VEjEiT3YJPjTGepHNT//LU31zHOxW/Dx7ilDb3ly4hiOLOWtlxA9A27lefxjw9/7V36F82bBabllSJY2Ol7cnzC50Ggf0D5iGvF2HZ7M+fSjB4Rf18xcTxcdbSz4+PoJ/UfHDA8HCTybO9yyZ3h2wvwLw4fPnvHsaMVdNSFlxYv1CR9ePGO6zqJoNW8F8qWM/WjKZ9+1PDjd8P2HV3yxPmXdlUxfaYYjsdvIGqqrluQMKiZ+8ve/x/f/0ldYNcHqxN85+03KLytCmTj6fEv3pKboIrGW4KD/6sO/zHTW8d0HNxQmsgwT/t7rD9h9dcQzkw5FHLsZ6E8rpucDze8f8fPfLvjy+iHTuufuasHxjwuOlNhH+KPi4Olc3GYe/onis/pd3v3ghq/TA9ZNRfd6xrdvZZ2qlPEzh22TBGZtBoaqwHSKb84fonTGmMTJDx3FxQ3de0dANdo8CPAsNomHf6y4CwvO6znawzt/lMXr+i1l+CGcsc10X8753Hlebo759ZNLfnz1jM1PH/KeGTDdGNQYEroyLF54rv/glC9+p+BqN+P9xT0XuwVXqxmLjx226UbLCYU/FhA9/9zyU/ecd5/d8WJ7wovtCW1wvPzqEaf/l6E/km6CfQBZqDVXP35KeNaz7Use1A2lDXx5+5DuyznfdgOhNmNavSI6xcMPFS/jO9T/nOfF5oRNV9L2jvj1jOdfBdpHjupOPLFjpZmdeZpfFPzJybtMq4EmCPT/9OwJRx8WFNt4ULzvAxOrdUB9POcnq4onz+75Py6+xa4tSV/MePTHo4foaONS3wZUgpvfe87me5H1+yXOJB7Uj3lxf8Lw8wXvf9XjZ3YMpZLOgNnrgfB3Sz5dPGZSeK7bGbe7Cbuvjnj6jQR+mT4Rpha38WTtePLDxFnxhItnLctOMorObo94+CPD5CZIyBcSwraHnotfKG7DI8KTgfPpgqF1LH5YcfrTToD4+GzaPS0oNpHHP9YslzU/3b0HWfHVJDD9qOJkKc8bd7Whe/RwhIUZHeHx36y5/A+kKDCreoxOnF8es/iooFpKwc/2Egg+TDXTy8jVj0758IMZ3zw44XTS8JmWwK/nP2wxrSfMi0P45/Zdy8M/hmU35w/5gPdOllxtZ2w+P2b+lcb0A3pIVDsp3Ppa4XaZkw8tP+U9vn5yQkqa/osF86/g0S+2xKkbOyjMqASG048C1/oxl+894OU7x6w3Nfm25Fv/c6Q/EvXr7NxLV8VoR/Do/9Sc377L1++d8uB0w3I15Z2/XfBOK57UxToyLAyT6zj6WWse/13LFz/6HsvfSOheMf9KxsT0Ee0V7ak7hKN1J0Y6ff63GdkoPvrZr3L0ZeIpoENC7/1yt5FQi6LX9PDgR5bdi1NWs8zXTwfmH5U8vsrirTwGIvqJBJv2c8PxpzBcLLh/lvijV8dQRfxHJc8/C4eQ0L3X+uRqXJf/EC/zgx/84B/qL/yjfP2P/8Pv/kDnjFYB34uybFoXnBzPeHgyBTqUSmQGCmtZrXfsmoarq3u8B2UUyjg2zY5nz94FPNaANoYQB9brO3bbHZeXd2gMoFHZoLRjXtXUtaUswKhI51vaPrBabbm+XbLd9Rw/OGXT7EBryJG6qKgKhysNOiU2XUvfe26Wd6xWDaWdo80IKHWJK6ccHc1ADdhS0/Ydg88s1xvOLu9p+oStKvoYwShSMlhnmR9PCHi0gaQT623H/f2GzbYnZ40yFpU1tRP4N5mUlIW0IGcF3vfc3i65vdsQs2M3dGgj4NYZRV0WLI4qUvKQEylGuqblmxcXdH0m5wKlLO0QSFqjyTx+9JDBe9CwbVuGIXO3WfH65SWVm+CTwqcAVrHdNTw8OqG0Bms1KEVOmV3b0my3XN6tKVxFipkuRFxV0YaGkwfHEk4UPZGBza5hu2s4e3VJ5SqGoIlJAs+SDxQGTh6c4LNCWdhsG7qh4/r2ntu7Jdk6ehQJR1EVZCVQVquesqzo2oZ26Hl9fkWIGR81KUNWGmMqUspoHXGVYzKv8CnS+8TVzT2392tScvQxkXWJcgofg5RclaIsC5wxbNsd98s1q3WDoiBGCeHaPzVS6NFhYD4tKErLZrdmt+t4dX5O4yVYKmvFECNRaTSgQqCeig1BVAkfEr5rOb9ZcnO3wpQFQwponUnBo3KkNAajrKhmnWHd9tyvt7w6O0PlkhBkHoQg8LbdrqmregyJUkQ/sOt23NzfcrddkW1NnyI4J6FLiNL2aDqXn+89GsNy2XJ5tSJFQ9ASbJGzEhWrH1jMKoYhokj43rPdbnk5gj20JaOJEUpn6bsts0nBYjYlh0RK0A2Bi6sb7u5XbJtAIBM1aKVJKVOUBZv1Cl0UpKTZNT33q5a71YaMJaZMjpCjJIBEFDFqUh8hKnwfOLu6ZrnZ0cZMNEqS24mgjITolRWESIyBmBNd6Lm6vudmuQYtYWoqJ2KIhCSqW20NWQVW6yW7bc/93S0RSz9AtiU+RbRW4DLKZFKOMtdUJivFcrflxdkl6/WONmhAo9WYaGssvm+J2WOcxTrLdr2l7yM3t0tubzeQFFkbcgykrLG2oOsT2hbkpLEhEUPgdrXi4uqe+/se30estjhXErIHMt4njC2wrmQYOlKG+/s1F+e3xJRJQZ6ohTN4P+BzwPtOQv0wZBJ3N7fc3VwRQqYfegpnIWnZd62jGyJKO5RxtH3Prmm5urrk7m6LT5mgLEErYpY15Wzm+v4GYxw5ZromsLzb8PrVBW3n6X3GuJIYxTMsRVFIDoArK+pJxa7zXF/esFp27JqehCIrJbM8SnhM6Ad8TKioaTvPcrPmbt3StAM5KxQBY8BpjdXSJrPbNZTlhM4Hrq+WXF3f0mxb8SgPMi8whpQVVhdkHxk6T0Zzv95xebPkdrVF6wqFpXQlfd8Sgmc6naAwrNYbUkyEPjC0A7tty2q1ZQgBazV+aLCFwTqDsQV9P7DabGg7Dwk265b7mxXbYSBh6IeIUhmj9Zhm5AneMwwD26ahaQe+evGCq9USU5V0UYosDrARfBgY+p6+8XTdwP1yzc3VHU03sNq27ELAliVGgdYKpRRDSvis2KxatvcN6+WSZtXQbjOzao7GoLTCWkPWBdpVZCzbfmDbtNwt1zS7gcvbLUlZVKEZhoGMFPeGCGiHsortpme3bTk7v+L11R05O6pqQrvrqYoachK/OGXAOFxVkJSR37PekL0o67XWkDNVUZBUJpKIMWPthGbVEqOm6xLeQ/AJ7z1WGRRx7HpRWOuIgxSq75d3bNcdy/sNzW4g54xF00YpftZGisQhBHKErmlZrTb0XeD6YikFDS1etkppfAYjQmoMAgD+7b/+b/5n/8i+8P356/+z13/7t/7wB9mqMbwpEWaO5tSyez/xG8/PSFlTmEBE87jY8Pevv8P6bsrxzyUxHC3QYfK65fovOra6YFp6Ku2591N+cv2ci7sFs59bgR+yYMBomieW3ZFGzTO1DXzVPeaL9SO+uHhM/bljeuHZvC//VglQiu7E8OHDRyzKntJEfnz3HhftgubLBbPXkmadrMK1mWzFZuHrZzVbW7FwPZ9un3LRHfGj8/coPqqpbxOh0pRr2btNyITKcPak4DwcU5lAnyyfXD+FVzWTi4xO+8+g6B86VFK8elgxnfUsw4SQDXf9lI+/eZfplxbjM/VFD0bh1gNx6uhOHNt39aho1QzZ8s3uAet/8EgCcYRNUN5Janq2httfU5y1x0Sl+cn6fc7aI159c8rpT2BYCBy2jZxAi9f3rH/1iLu5pS48g3KiKls+4Xo5Z/qJEz9CnynuO+LEUa40L98vmU86LvsFm1Dy5fUp3c2E0w8j/ZEVX2GfR4CeGeaO5bc19/0EoxMf3j7n5faE9MWU488HVAa36tEh4ReiVo2F5aWdY2aB8+0R56sj6j+pMT24RpROOohvshkSwRWsKsfipGHV11xvZyy/OGHxpUJHUZLpKPPD7jwoRdIFd9MCXQeubo5IL6ZMXyPF8eFNJ4HKoEIiFY7NI42ZRl5fHzPcVcw+rKhvRXGoMtiNHyGe+Lv+8YNTgoN1rLnpZtx2U5Y/e8jRF5IIX2wj2RmMF6jTLQouiorsYOI8H12/w+blEY9+LPPJNmn0Fw7EqaO6z9ydVPSzDFqx8yUXd0ekX8w4+loS24tlQDGGtsWMDobdu5pBa1ZeREeXrx9w9KnGhIzt5P6pBHFiMV1g+7zirF2wNo5lV3N5c8TJjw3Gg1IC4fSQ8EeOYuVZP62pn+4I2dJFx+vdEauPT5l9o5lcRWwnQVnZanSUgtDsLNAVFUN0dHc1k5eW46/CG8uGmFFJxtl0ERM0RMcNE7ZdSfmzCdNXUN0nzJAprztMQBaJUoT5GDwUNMk71MZSf1bw4DOPyrLfZKMwQ8J2SdLinUJnmL3OlHea+QuwQ6bYRHTM2K1nn+ydjQA2uzWs04RVKvny7DH2R3OmZ1DdC/DLVlK/YqWxu4gOmm1Zsas0r29OWG9r9FcTjr7MqHH+6TFYLVaGapnx1nGXK2KZaYPl8puHHH1sOfom4Frxo44TI+F9gGk1Q2FpSs2qrWmjY/tqwfEnGjvIGlEZKRw1GdcmTGt4MZ/SBkdMmvBiyvxrje2h2ETCTOw0VB73oRXsTMVGO67XMy7vFpQ/rzn6OoqSuZV2f9sluR8Tw8mnA74qWeaKdlOhzyoe/SRR3QWKtXiBm15+3gwJ14FuDPeTknrRc353RPzFjMmZYnIVRsA5/jOuYdtB00+51yXL5ZT0asLshWZykw52CtlKMSdWEpZW3mp2M8tqO6H7YsHks4L6LsteNYZP7vc3gdAK2yhystiXJfNPDSefD2SnBbgvLMVa7k3z2IFSzF8HcnLoXkNrqK9h8Spgd5FcOdwuiiXG2F5fbBLqrMavKu4p2V7NqF44JlcZHaBYRRi95vf3xHQK1TqG6wnXyzn9H53w8BPxp3f3Hal2xIklVvI8LLYJt1WETcXy5RF8WVPeK+avI/WFeHcnp8lWnunFNmE7KK8M4aYmX1SUd4rqPtM+LUX1rBShkmtKVqFTZnKdKK8N+Zsas3ScfJoxXjogim06WHu4ncxflKK+BndrsT+dcPKh7BvdsaW6CwzHo8ZUjwVDGEPQoLpWLL6S767lOpKs5HX0R/JsM17WdCwVk5uE9lCsM0WTcG2SQNFKj3ZBiqwkiNP2wmuOvo7U11C9ttQ3cu2x1FR3nmQ1/YkhOo3t0ujbn5megd0aFp8Z3PbNGgcJ0MvjuGUn8+w//vf+hT/Vd22V85/dFri//u/8hzknRSSjC2nzTikxrSd8/9fep5pkJoUlIwfXq/M7bm5XbBqPj/JQTSNlP314wrOnjzialRQOhhBY3q+5u91we78hZ0UIYQzAMqSQeHh6zK989zna9CidWS0bLs4uObu8ZTo7os+KvvdoJanhxjjee/aYhydTThdHrFb33N7ecnV1S4iKGKTN2jlH3wWstTx8MOfd5yccTSxD8HR95JuXF7TdQARSlDCupmup6xprKia1451nC05PH7K8vefm6p7bm3t0luvotEL5jE1QmAnzkwnPP3iIUQlyxPuWL3/xmvXOkzFEVTAET1YKqzWV03z7+Ts8f+eUtt2R4sByueb88o6sLLs+ERMo7cg5MvQN3/un3qOwluOjkqKwtG3P5599zWbXURZzclb0aUBpCTnBex4/esC3338fkzq0yixXGz778hc4d4ylYt01eCcgMmfPfFLzneePcc6hDbx8cclms0Gh8SniBwVaYYxB5Ypds+E3/unvMq0UrlDEkLm4uuPm5p6cDEMSZWtKSHJsDih6jqZzTo7mhLYnpsB205KVIXmIZFEGmgKfPMXEEImclBWTSc1ut2PIieVqgysq+t6TVIEpNcRE9gN1WcmBOifq6YRd17JrOkLSKGOIMWKtlfmuM4XRFDlSVRUpeu6Wdzx68oTr+xVFVdP1PTFmiqJg6Dxaw2RacXIyx2ZPbSvatuVmuRY/nSAA2WgJ0CuKgqpwVJOS00cPSCnRDw33N0v6fsAPipgTxpWE5MWvVIkFwKwqmU4qnLN0Q0/T9QQPXUoSpKRE1a4QT+bZtMZpxXw2wXeR9bYhIxts242m3ZUoqbWGaS3t884ZQgjiOZ00VVWJei0mnC2JyVM6TYo9VVXw5NEpAEOIrNdbtrsWHWUv8SGRzBh4pC1VKbYIaIVzhiHK/A6jiq5td7K+NRhboLXsK6XR5BjISpMwDD4SVEaNJjUuhtE5XWO1E//TQjOkgV3TYK145MYYUaMKXmExxgADSkXpCAiKOO4xMQEp4wpDDD0xDyicWD1kRcYL3CktMUPKiqbpgCTvmxRFUZBjorKGrCBGjyosfohobVFBgJF2jhgjJmlyjpRlScyRodnx7jtPiKlnudrRhUhS4m9baEMyiSF7bM7YakpZGULy+H5AKUPTdMzLBU27pSgsoOn7nklVo4wmpYGitESfMFra3FNKdF2HMRI0GMdQMa0N2SR0TlRVQR6tKUCT/UAXIqquGIYBo8C5RPYZXZbgo6hmi4L1ektVTUSJHWWN2/FeD6HH1gVD6MkpiEevsqQIRRYbGu2sQHDjMEqj4kDlKpQyKKuIMeJ9z5CjKIJjhhjkQQ7YMcXYmvFaqwLvPTFGXAqEBNFZktdoa9BaY8jEYRAAaktijNyvVyidOVqcoJSASJ2hcFbC4eqSpKAfdjgnY9vttkwXR3S9J4SEqWtylDF3OeOcQRMwpexnIQQ5/IympzkqIBNzYlpKMGjOEd8HtJXnaeN7YgLjSppO2vFjN1Abh7WGwTeklNCuQAXx/u5yZBd2aCvPzv31FlZ8ewFoGiaTCQRP9AmUJZFQ1oiHr85SbIky7kMaUCoTgkDUolzQNj3GKnJOWKtRJILWWGXJ2aNDAutICRKRYRBrmhSSFE6AoDxaGAZGWVmrIcs8TokQxApB7pvCWotvA1U1QSmFHyJJyV5gjMyXlMY1a6QQmWOirmv6psFaS1EUbLdb3HRG1/RYbQg245R8BjO2nWnr6FIgxyRr1Fl2fU/WBmMcQ4r0vewbtZUOBZUh5sTf/u//u/9HVvqfv/7/+PqX/tp/mcVXUZSX2ShUzDSPLHd/teVo3vCto3tCNty2E84/f8T8S8PkQg49b/te3v+KY/0XBp68szykkl++PqH+xrH4Sg41ohYSpZuOmdV3DNVfvqGwAaMyry5OmHxS8ehDT/PIUm4kwT2NB+TkFJe/bRi+3fNb337JxxdPiV/POPoMTA9myLgmHQLJYqVZfsew+2c6nj+9Z9lWbNc1i39QUS7zoR1Sh0x1PdA/KAiVon2oWf5G4uEH99y8PmL6lePoF1HGSEtrZNZyYPVTTfNUMfzmDusEmvad4+gPK2bn0kKpfcZtPKRMqgz9iePin9e885sXXK1m8n3/dc2Dj0TFOrkOB5Wfihm7GXjxr84Jk0x63lFPBnarikf/e8Hs3DMsBLLs0+7TeLi9+76j/50tzkWciSzPFjz7XxW+1sQSZmdBDpKj5+723YLLfzFBFVEmM/9JxeRC1JmuHUE94wHXQHXZ89VfmxAeBnQdSF4z+6Tk+Atpt937+GmfRlECoBWb9ys239KYHvQAkyv5LqSDABmV5CuUbaRNWfvM6tuW/kRR3WbcLrP4uhMV3bJHxUQq5FmqYqI/rehOxA+xP1FUN1nGtBsT6weBZPtWcD8Xr8VhIWExx78YuPu1kgef9vi5oVgG8YKdO+zGk62me1Sw/pYhljAcZYqV4viLKJ/Bi8dsHEPM/NzRHxn6I8X6OwIZ3EYxf5Eo1wLb9ZAEZHXiORtLgVq7J47uVBEqKNZQ3SVsL0FkupP5lq1ASd1LMruvFc1TjW3yYQ5mwyEYrXtUiOrWQPNIxjfU6rCGbJ/ojgyzc38Isdonnmuf6Y4N99+X22l3iul5ZnoZDunotoni1er0IRSv2Mrc8RNNsY3iNTpaHBR3g0CpQh9UjirCMJeitkrjGtoG+cz75+/0TdNwLMX/M5SKYpeoLntSJeF0enizlrKR3wOMwXWM1ijyM3s7j/1n3v/dWBlipQ5jGUpR2WmfKW/kDJNKM16XqFv7o9FLeLQNkDFX49wW6wzTp4NCdZjLe7pN5OqfLdEeFi/jIXQu1gY/M4ex2Kth+7nGdrL3ZQPV9cDu3ZL6xuNnMkZuE+hOxf9eR8THc8jEYlR8hjyq9dVhPPYBdCoJ5O7Gz2O70fIki0VMNgq78aRSwgb363e/VwxTzey1hFDtnxlqtHpIVuG2SeDiJh58UsUnNx3W3/7eyBwR0NYdj+GDetz724TbRMLkjcdu1gLdw2grESopcEWncI0UMcJEi+fvWPw4+Fg7UVTGSvZM4zPzLzZko9h9ayb74lbGfJgZWTfHZuwoifhaH8a1eWSplhHTvbnf2sva8hNNsozFnrfa5sctc68aNX2keVyQ7LiHtDKHolPUt+EwPuVVQ/d0iunFyme/JsRiQqDsMNVUy0h12ZKdOfg4y5i7w/0DaE5lDu0LXnr0KX/bw9d48YQtxvEwYzfOMBNv+v282sNz2yZCLeOQxt+l45t13p+4w/WCeMjv104cP4Px+TCPbZ8Oa2ofYuqaNM4R5F7HN/Na7jWH99/vOe0DQ7lJhFLhp4rppQSclqsosLiT+ZK1XAcIsK5Gi4mw98W9lqJUcgq3Ddh1D0rRn9ZiTaRlbv3+//Sf/qm+a/+Zhrb/xr/172ejHUPI0g6qFM4V5OR5590HPH5yTPJb6qqk3w2cn1+y2Q0EDEPoEh5hQAAAIABJREFUIQsw8t5TT0qmdcV7z94hqUhKisuLW1bLhhiD+MwVctjqvIRBKaV4//33mS9qSpe4ODvHR9jsOtouUFgH2QKKXdNhtWM2KZhMS5xTmJzYrNdkNN0QMM4SQiClgPeZejonZ5hPKx4uCkxVcHO7ZNd6gahZgtEASJmMFo87MrOJwdWa3XqDDWOlJyfafhDApCQAhyTzYL5wLBbHkDy7jag1TTEhag3K0g0dyhhSCFhtmNQli7qmqi3tbkvXetreo60bD6AZsiHkgaKwBN8zmVQ8efSA9eqenBSbzRZXTIhBYY2hG1owmpQCTsth3pSKuatQKXNzf0d5NKNpIHW9wJFCgE9IkWlVUhmppBKRILQYcYVh8NL2sH/5HgiZk4VDGSgrSwzQxyBeg0qCx9ooYV4pS6iOeOU6VIoCWXPEGEcIaYRa46FBjW1+WkJlbEoMw8B0WoOxtH2H0proAaUJeEpbQgxYpXHGQkwMMWGtpR8GcJY4JBKZRKQoChrf4ZzDpoRzjnFvoG17TFkxBA8Gkk8YbdFZqvHaGpSzhNhgtYOuwRYVXTegtHxOlTVKCehWSlG4mhBblBrwfcNsesT6fsfR0QkhRTbNjrIsCSHgbClQwQqAgETqxzmbAz5rstLkBDkbNFlgPZmykJClnLNA8DHYyaAgRUIAEPWOvDe4qqQfAVU/BIxx+HYQ0KOkcFMXBcVoJYDW9EOLcZpJvWDX9SPMg5wUyhnSENF7H9RxH8xWE/NYlCCRgSH15BFeGQQeK2cxWlNaQ2y9qFxRst+kiDEaqyHGLJ7XXYe1FmU1iYgxhm7oKYoK770UilLC6AJjFH3XYQxoJd6aaMhRvJwL44he7A6sLQjJS5CglXXgqhKrDJ0fUGYMFSKRcsBqRdv2lNUE7RVVJXPITRx97/E+AHkE0wGjNXGQ63POUGiDRrFpO0pboowVOA8MoSOSMVahjSHFSE6KlAeOThYMQ4dSBj+IuiDnTIiRshZY6lsJT9RGrteYkhAGGauuwyhL7weqckJRWrquI/iErQRITiYT8ZvVVjxgtfiHJuRgrbOmsA7lPVEnnFZopUhZMcREs+uY1RPImZTE+iapTFkXRJ1QBuqyou97wiAwsNCGIUWyQoBu1uQMRSlBUCF0VNWMtpV24mEII6hGFNwKAZBGC8jzYnmTkWdXzIkiSWBiGyPOFMScDtA2BAGBhZN5pLUmgXwp6CVs6mg6I/pAqS0xCwysasswDDS+Z1pP8D4SQ5aCmBbYa4xBW4tRClKQDgYEGrd+IMRBLD3GggZewKgoNxU5BSnIKEVIcjjyMaKzQGeV38wzbWTfDSGRR0V4kzymMBQpEUdwD7L/WiWHuJSSjKGHwolHedM3mLpExUip7WGuGWNIORMQ8B7CANmilDk87zMC8rMtUcpCDNJtkmROlmU9/lnRtq2E32kpaqkMNisBt8ZAVlLUmRSsd2uqeorVBlIixojOiYwmhIjSdrz/nkk1ZbfbyDrKAm1jSrh6QvC9FC68wHNnHCFFUlay36vMJBtSHu9XlPnV5ww5Uqa9GkLjlRSnk1YMw4C1hsJackxoEkZZfvdv/Nd/Dm3/CXj9y//af5H3h4p9WJQcujPXf8HRfK+HoCmPOvpdwexnJZNLUezt0+izEQVJ98DSnmru/2KAqFBRsfjUMH8dDwekfvSZK1fiBZg1XP6Oo3sSUNNA/UmF7WBykajuI372JhWvvhyItRGI9UARa0gW6qs8qlySJDr3AiBsk+hOpA1+99Sw+SCRXWb+paG6yRS7NAZSvaW8HEGC9pndE0uoFfVNEjXReEiubj1Zq8PBe//aPbXs3pExrG4zkxsBA8a/CWvJWsBRrAztQ8PuHY1fCPArVplqKYXl/QEUxCbAzyVEqz113H3fMLmQzzw7D4cWzVDKwRLewIBYyoG+eSqHzJPPAv1CQna0l/ePpXg46i7SPSnpjt6c1l2TDhDc7dIvfV67k+9Vq+9U4o86l3ErNjK2aVRvF3eD2CIECVwig5+/CU+Ttmr5WQlG0wJ14p5YyAF3r15sH8n3/X3A2x6ymcbjj8vDZ9q33+7bXov1ODbD6O/qI2FeUFzviNNSlG8jQNm/fywlsR5GRW5pDu3yUkgQb9V9q6sE+PgD0BAoOEIBrfAzjW0lGMo2kd1Tx+z1wOa9Etslpmc9w5GT95yYA1TYAyc9WiHswajAryznRKsPSrThyI5BQQIpGMHnAYD0b25kLMdn9FuFjmIln/1t+Kpipn/oDpAEGL1rFe1DM0LxEeaNn30PwPoTixkLNvsi0b6tnAx208u6mEqw2B7MpkIffDaLlT98TtMMpMKSKjMGyumDWlXCp8Q2objrCPNCvJRHCLgHw241/NL93F8bWaCr+LOqA6jZBwnuoWCoFOUq/hK80l58vsubDn9ckqw6AOv9+O7V8HuIvG+xT4V0BgxT6RbYF2OyEjsaQKD1+LP7z7y3Ktm8Xx2AmdhgyHXtwTh5LILM7WEexNFbMzp1ALZuLS3yw0z2k/383u8/KouV3h6878MGBexqwtQcgFSYSNu8juC2kfKqoX139G4O+bDO/dwe/HC7E0O5Toc56mdGLF/2cx0O9yjUsp66Y/HYBoHTfj4WcBIHP9j9+txbhOzBsvb5AMLLu4EwtW9A/bg3JqfxM3ModOy9TN1qwOx6mm8tDv6ue5DfHRspHlwPtE9KbJMwvcyd5ARQ76F4LMc9YgTqycj+Y7p4mNP7e+Yn8h04Kw4FjT0EBQ57aLLqoCDdw89+bkQVOg5jdeNJhXy2/TN6vw72ViLa54O9xjCTz7f/e1krhrk+zDUpAIAd753p3oDz/XUegP6oGD6svfF3D3N9sCwo7zxhag7FIBSEWkIHY6Heuscwe9WNwW5jUGgvn1mlLABZv7n/+/1KQvPGvXWQ+WrbPVTPB0BrunQA1aaLdKfu8N/7sX07XC9rDt9B9uvDbnpi7QhzJ+t+LAr8wd/5T/7xh7b/+r/7H+U8HqCtLUT9pAEC1mWcKagnojDKObPdNKJi9QIuq6Km73sJroqZqqoIMVOUZlQSBUgCNYxVdF13AL3WiqonDh5TygHZOUMK+/HSuLJgvdqC0ZgRYmkk7MeZjKstve8w2qGyJvQeYxM+eZQW2FLYmpw0Og+E0OGqKWH8PMaqN2orW+JDBmNHtZlUpI2Sw1jOGWmqH0GtchSupOs3qBEuoC3GKgn+GVU9SltCgkIbQpTDZFkVhKFHa40rDCmJL2TKAylqSl0yRDkUDkpCiWKMdF3H8eIIreQQWGrN0HUkNMmLslgpUaYGDYlMVoZKW9LgwY0t6Dmjgxxqo07kFKS9OiaskfAoH3psFqymrGGz3VLWE3GqyJkUFSSDM1pO9IBSAiW6PpJRh3uZUAxxoHAVpc0MQ4c1ApsKV4kq1Th8kFZnVEkfB4zKTKaiBBWVlZHrGb1Tt9st1jqsmZDwowJN4IH3XpSeVhS4Sin64DHOkKJAzm2z4/hoTggepURxXZRykEop0QdPVU7o+x5jLH3wODd6ow2B0hXSpu8MMSV8CmgDcfAC/IwT5akf0FaueVI4Ygg4BDgqZVAH6+tMO/QUVUnXepTRlIUFMtokohfwGmMUD8poKYxGKYfKSX53lLWprRK4NgxoDMYourZFK0vSkMfQKD2CHeNELWecJqckABOL7wesdWirCTGhQ6IoRCHqCkMfvMDzGPFR5qBRFpIAn3JMqIpIi/RuaJhPpvihp1SKEAU2+9RjlRW/38gBKEciKozwKCuU0uKpnAKKBMqRk/h6piQ+llnJz+ok8yGPjhgRgVEpiVJUQK48ALTi4MdrsznAsRAyGAgpYq0h+JbCTYhRwJ5WlkggkUSxbCzGOFLK4q+pSymkZEUfW4yT4oq1BW0/UBROlKCMbdQ5om1BzJlAxoygyqJo4oAdxz7HjM6iTC/LAkzEB9kzYlQUMBZwEiEzAqZM4SQxmiTXOmQve3ZRECNYrQn9QFFqIgIzjZakB4VBZfnynJOAtBACykhIWA6ZnEB72dcEVAlozyYTBwGmPqpxnUoRT6lRhYFAc6NBB0NOiaikBb9tW5wrBJKj8cljFBilGXsF8KHFlcVhvuBKYpKQvpzzYW9QSoLhclZoZbBWCkbGOFCWnOWTK6XQ5ENrEWiZf0mR/QBOS0BgTuSkQCWKrNFGEUJHSBGso8+RwjnUAGZUHg8hSUFIZ1xhZY9DYZyVvR9Djm/tYT6SssEaI8+vIuO9pygKCuekcyRGrDaYEbZi9EFpXiDPz2yNFBm07HGkTAwDhdFoHNZakhcLkEIbPBGlDV4pYlZopNAm21WGlDBJ2p2UNThnpaCrknR9aCkY7UG5Gp9dIelR8S5WNqRE30oBbRiLGFVRElIkjs9fq8fCi5I9XYB0wqsBW5UMSeaSjpoiy3qKSuZr7wcm9Yxm21I6I7YJ2si4W+kQ2BfbQggUTsB3jKNvpVYEJZYxNkpRaIhSdEpKMwCltkyUJYYBsiGrjNJR9nmMFDAQqO+MhZT53b/53/w5tP0n4PVX/sp/nlXMvwTUYrUHUKIu6o8V2svBaHoZRvggB9/+yFCu5fCuYqY/sdg2MYy+k/rtQ+AIBkCUKfsgk/2hJ5bqoEzav0KtmJ3JszC5NxBoDwR8rSjXiViqA9wQP9g0tpCKUmsPv0yXDgczsnjy2iYd1IR7oJIK/ZbialwK+Q0E2L/8zBwgnZ+P4zaC2r3Cdg/29p6MehCv3n0AWpjoEWAwtrhnhoURBZfmEBCnfaa469h8MCVZKHaJfm4OCiqBwQISTS9+jnuoMxzbg/IM+CVltbTLyyH9bfWeBKGpA7yqz3aERfn/gqph8pYkS3FQJb39M9onzKYjziuGI4fbBAlt8ukQ9rSHhPuDvF33pMLSn5ZyDaM6ScV8gH312U68ViuDCvngt5sKjd2Fg9Jzf21uHX7p3pZna5rvHB9UXPvr2cOKPcBym7HteNlLSNX42itP92O6/526F+gYR6homoFsBAR0j2vx2pyYw/x4G14UdwNh7ijuutHvtDysmbdVYvv3DfPyALaT05g+Hv6cDdKSPQKo4v4NQFdjV+oe9sbSYHpRB6uUD5DM7Dxx4sReoIsHMLdfg8UqHNSsphlIlTsoWVXIAgzhMI+qix3tsym2iQxH9qDIs43AQVEqj2rX2rxRwI5zeX/vtU+HQpOEKLnD3rG/9sNcTRkVMroPxIk7vH8q9CGY7G2v5liZQ/FqD6L3AM10ET93mP4NHDvYU/RRWtJLffgc7anMFx05wPDifiBVBnfbEOcVyWliZd4C/RyUyKnQxEoTak114+Ua+ngAaXpIhzG2TTzciz241EHe5zC/52/mb5iIB/a+MGR6aRff75t6GK1K9mrE8dr2Psr7axQFpDpA72zEImG/n+z/334O2F2Q+zOCvz3A3t/Lg6fsXvFbaYq7gTixh5/dg/hU6AOUtY1AUdQbCLhfD/tr2/9O08kela0SKN3EN/N2nDOHdfSmdniYf/sC1h6K7p8LfmZIhoOnLoBb9oR5cXieDDNDsY3YXTzsofsQtLeLqAcQPhaSRBH/5vllm0iYmoOa9U0HwZvx3Fs97MH0fhz2cwOkkLWHo6HUo12JqLFdKwKJvSp5Xwx6+7WHo1mrg5J+P1/e/n/7f+/n3H68hyOZq8UqHOZNLEWhb/t06L7YP4/2haZ9QWD/rNrbWyQrHTAqy7NQJdnLu0cF1fWAX9g3YZHjs2U/Nw+Fjrk9PB/3sNmMnQ37MXbbcNgzzKYjHNV0j4o3RbH8ZvzfXjv7oirwp4a2f6Y9bf/G7/3eDxJZDt1pgBzIyaOVJoSM95Gu83if6PuA0UZgxHjgDr0nDZHCFYBm8ImYBVCkmOg7jzaKTCalsR3RikdoHIO5kkKOxcogWhukhT0rfPRkrQkq41XG54grCrRRqLHt2BrL0Pc4W4ASRZexBYOP5PGQh8roJIfDlMTnQinxU9RKo9X+TJ7RNoPKxJhQKLSx9D6S0ESj6Hyk1BU5yaG5LGpy3ms3pYU1ATknUpbDoFUSlqSNxhQOrRQxRXIWBXKKalQMyjh6RmWZyihliQlyzDhbkAbIBIbQkkMgxEQyAo9QWg6IWRGRz6iMI/qENU4gz6iGDBr5eRTKFOTRvzQoSEqhtCYhYAYlKd1KI9AiC5wxWpOzJ2dRqXZDR9t1ciDNCUwk5Uw2hpQ0xmX6IMB28B47wqmkFD5Bdo6YE1bLe6ss6q6cQJEPfrq+96SYsEY8YpXS45Mjo5SM/f6eKi3wKeQk6lil0UERs8ci0KEyjhgGSidKvBhFDRlTRmuFQR+qV4Ek99Fo8Uwky30yGmImeQGGxlgSltJqgg8UtsAosFZ8PkMSC4CMwik5xLH/jhwiGiittP6mnAg+YZwW32VdYTGYArSYgmLI5BAxtkChGIaAUkkEtdbSBXBlLWA/aRRK5oQuMFaTkyfngNiFirdw6SqxqtAaQsQkRcqJmBVKW3rvZU5myIi62Wgp2GglqmSlIBeGpBXZCNSPviP3HYUV31A/RAwO5d7M1TzaTORkyRGIisI4TBKvO4NG77+Fj5A9K5nTZE2za5nP5nRdP653JX7Eo/JSQtzGuaI1JgpQK22BNuYA+TDS1u0KRwpR1nv0GA1hCGLpoAtUFphX2om0YmdFTB7Ge5iTR2mpyAYdiAmqshIFOqLg1cagbUHvPUOOGGvQWS66zxHtDE3fj57PhkimGAssWilcNuhkMEmjIjhTYDDEJCrHvW+nsYaYIaZxTWlLUlp8WIPHWCsFn4wUgZJHoXFG46Ms7SEETDLorPE+YpwVsKtAF4re91STiqg8PkYwmjCudeUcEdAY2V8HL+AuQwyRytVEHwEjvrwqo61lYFRbpgxJUTpHDHF0yFCjl/o495yjjV5Us0Y2A20NYHjw8CHb+zVFYVFktDZjmJwfn1WZoigJMaCNlWIbiLJdaaKKKKMwKaJCj0ba/kNK+BRF3WwrtLFYkyBFnFZkEj4K4NcGtM6i3kz/N3tv8mtbmp55/b5udbs/zT3N7aOPyMzIxk063SQYVwnRGIEKJFAxgBn8Fx4y9gQGSAghpAIECFEuyRRV2FWUqbLLZaoyIyOjy4i8cbtzT392t5qvYfCuvU/kBMwIl8k9idC9+56z11pfs7/nfd7f02EzAynifcBoS6YVSidiioLUSCJwNiGBtSjrIDM0bUeQLUvwINb0zF/R5n2U+4wxGLcZwznRR3KlBMMipSestlgsXUp4CyY3+NbLOIiyD2SZI8VI61tAbdfXpEHpRFIBax0qGpzJqNdr2qYTFrzWqBaGWgp1mXP9PglWiQCeYuzXEGQPVlquJ2YYbTHKYa24nZ21GBvwvsPpjJTkGWllhHWskUJOTOSugJioSmGjKyvieCRinHypM0rCGTNrhGccEiZpxtUQozOiVzhkDZROkICS5gWG1oH3WAN5nuF9I3O9S2TGEIOn88LONWiskc/47/3b/+afi7P189c/36//6j//e7+j29Dz4vqDYRA3m12Lmza/EedktojETFhycpDvWW6hbwcGshsvB/0g78luvHT09C3CupOD+tYBqMC0ceuWMT4J3kOB6eSgqKKInm7psYtODu694Ljh1WWLvtUSYTJuA7lS7xRCbVs1jZd1CS3u8w3TVyCTt85I08QtR8/deHSQQ3h2WdNNMnHeLkV4Umnjakz9/dm0dEdMf5B2c09yuhfThOMojEZ5v3BAFaBwy9CL3ml7j3RIhIEIUzoKf1HaPUVYSaZngrbSdWXa2H+xUNIGXcrvNm3q2aFxy0OFW0F66yoy4poyrXQYYEXsQAvaQoVEcnrLmQyVJrvy5OcNoXIinMRbNILuAim3uOuGWMh/Q9kL2V3CrvxW+IqFJWWGZLTwNbf3IW1dqrpL8r4enbF99b93w6ZMRmPrgFn391cpkhPhMxUOuHWCthMpOpiezWmaQLJ6K/ToAGbtSc70KAN5vqov6AO9syoSC4vuRUuzFldvykwvzoGtw7YtXQ7wG16l3N+YGbpJthUE7Sr0blER52OuibndjtfQiy0h14L8mndboThZTXbRECtLKG1/rtSCDzCKmPVCbUzbcb9xQKp0Oydi3ovQ/XuyG4+vLNrLPI2lFaE0shXyRLD72TFsWhGduqG4wd0qblveTSNuVVkz+rVhUyAYmF7Y0sTMCG8Y5MwcboVmlSB7tWR1ryK/6rZCYnKmHxNqO2Y2693GvdpNbB+Yl7ZnHx3S9jrlnrB9/tFpklMkI/e/G1lc74LWTUCpfs6FtF1z7KoT1+W0QHXi4LYLwW6Ia6/DzhuS653ASu51Mor8dCXPNBOhzw/d1g3qB2Z7LSgRZWXOx+11b9Y00z/HW1G871BY344zOV/Ke0WI1biV/LlbiGiarMLOxbW84a0mq8luOtqJw3TyM1EKsxbntqA8ZB4lJ8WQbuL6OSfhgpt1Snf9emQ17kaKH6E0KMSxvxlXCvrCgjwTNOTntczh/rvUZo28et0xeNHhh07uR49DMPWGDZ3wfZFN9kLBGugoxTXT3u4Nmz0j9GKqXQXcOopL2ongrlT/mZPsZTp8dd+Rz/zVvw+5kSJg7+RMVq4HFPm1l/2s31fdTYdtbpEOoVC3a3x7+3w3Y1b7RDsS5ms7kgKdW4swGraBk2nrVLW1/KxkpKjRDWScu4WX0N9+fMieKnurL2QN2hTw3I3funvRgmRxdZK1PW2EWEXsu1e23xH6vdt08hmkuKvF0RukQBgyha2FTb/5HpGMrClwK153vTC83rPYdS8eL8OWnw63zGA/MIRSHL8xUyyOLCHrQ+8Ls+Ury/UIXqQ+qkBJMboZG9zG+bwOUgDx8v8yP+V9bhX5D/79X/9zfdf+Cy3a/hf/zd/4HaMVeFGxje5dUSmilYgXMQr/0/cuLhEz5EAVYkAZQ9N1/fs0IYl4ZjQSUpLS9iBprZUwmf5ntMFjMgckko4o5JBW120vDGs84hjteseQVRKAYpShqWuUQpyhrcfHiMsddbPCZjkoI+64EHBKE1Ok86EvfsjhPEVZ7LVW5CbDKEP0QQSnBM5YQuexRsQXYx0qRqwVV3JM7daFq5Ii9e4lZ1z//wrfdeAT1jli5wm+xcSIsop63WFtLvfUi6hCCH2oChCjtLkmyLMMFUUM1sb1FS5DwEIUd6t10qJri4yooasbcZoh7qnQuxFDaiiMJaZeLYStm04jreJEKGxG6FqskoO7QaGTwiQRwXTqUCh8DCgNZVmSYiAzRtq6U0LrRFVagl+JCzFFXJ71Dkk5bEcEP6FT7HmuDdqA71ratsZYi9bCo82ck4O6s/jQoo0m0qJURClxdceQiMQ+zEwCakzvnBrYAizkpUMrQ9u15DYXl6A1IlwZQ2LzPBIpJkzuxEEeAilEEZYBYsKoJOKlzvDKyZebJG1Gne8w2lHkluVyDVqjlEWh5dAfQy/OKoJOaCdidPQeZxNKbrS41JVBGU29WqIddG0UAII2oCzRy8FQKy3t4cjvQikRS31L0JC03B/j5VoyDJnORJj2sgj7CNpogkqSfK7ky5gxIiiJYB7x0aM1IsoBUvLScv9Tom7WaKXJXEbXNYBBuQyfFLWPtMkTtIT5SGeCpLJrrdBGCj7ZxrEXEkb115dEaBWncN+GjaGtO8pyQFM3ZL0AG6K4EEXMkn+zWes23QYb7EWzrrdrnZeZIzxL7xmWIxIB27Od40bg9x15XlIv1ygDykRspok6QopUyoqw17XozJK6iE6gU6JtvYSpAZ2PVHlOjsH4vuihoEuJLgass+gUMDqSO7Ud37nNRFAPCW0MylhZPkR3xrr+y6UXNE1SETQUZUHdiVNeRHdNiiIWboTezrdYEDHTSHhameXCsNJ99VTLvUhJHLVVVRKadS+MihhbliUxRNpmg76RL1nGWBH8+sCmGALOWGJMWCWfW22qMFGE4cJZ1usFeZnh2xaVEgYrRaUuoJMiKMFfOKXQyaNiwBnp6qAOGKUwpF4kFOFBabXFDxht6HxHVhQQW4yWtXyDHdEEnHL4lCSwLXo6DUkp6dyInhgVNiHBblF8u1oZFH0HSn9NRit0kkOpRUILk5L3d22L1eJKjinIjhg6VIDMOEqbs9nHQ1PjTN8B4SGzVu6NAh86ceQrCMnj+jUs5RKuufYdUSsao4jJ07U1RZFRlRnOJsJ6iUqRGDqczSUYLUFmczKjcdagosckRWFyhsVQnOLGknzEIwXZGBO5FXetEh2d5fyGwlmUUaSIFG4UZNYRfEeMCmM0WgcKq9mdDHFWAhJL5wh1QBtH1zQoLXt4Zq24sSPSkRACsa1RIWJUwugESdzU3bqmKHJ81+GsI4aGKrMczmZUucE3a3HBB0swqi8OSdFHERhUOZrIeFAwGVQ4k2jrGqMUXYy0oUNZTdIR6wxd6FAa/t2/9m/9XLT9/8Hrv/7dv/07Md84r0TMSlZvRdno5EAWSr0NHtsKrG3cHl6zaxE1xQUm3w3FIWZ6h0zPTyz0NphDBeG8JtdzKzdf94wS11Dvjt0ENNlFi649sZDU6pgpCWhRtwcg2ztp88tO3JdRPtOm3Xzjytk4haTdnK2g0Y5NnwzdO++0OMY2wqtpAikzoOSAGfLbA9xG4NiIRqHUIuamrzh0SrM9SMdMBJnsohYhLabt/ZQDqr51YCoR1duxuXXu2I2rYyO89v8uV9t7rTtxpH31udn+s5g2/owDd/sMegFf9+L5RkCJfSvvJqwmZvpWeNCCN0BBu5Ojohx8bS3iXHKadq9At0n4iV0kDCThuxvZW4F72ZCs6UOygghCdcQu/PZ+iVAluIhQ2e1BWHsREFDcPpevCD7ANvxofUdMBN1YBLpsLi3kGyeYqeNWOBAxthdSc7P9rBtx4WcEkd7JZmq/FUtVArPuSJmwjItXTe/c6MfdRgxuY8/TEH1VAAAgAElEQVT0FaEo5v24K0W83gyFjSCVXXVSuFj6rfAIfZGiH8863LrqUOLAlCCnuBXUkxOB0g97XmpfJAER9unvuVnLnFQhkTLdzwGZC5tnYFadCKcb8bmfG9llTTJGntsqbK/friP5lcfOG3E+9iKLgu19SE7wAX5o++chQkqywpeNmbwn9p9JRWFAt3uViMpDJ9fahK2wqHxvfOgF5U0Y2Eb4y66/Umxah9s1rI6sDnqjUa77Zy7P0daBbuzIrrrbcVjaraBV74oD1zb9uIj0LmcRI7tpLmf7pTgCu0km196jB+yyw6w7YuHA9gLz2G0LLCLE/ezasCmeRSdrFaovePTrlQLamSO7aFAoQmW3TvSNCzM6hVt6Wf98ohuKaNfM7PbniAipt0KvaaM4DlsJotuIsc1uhvYJN28Jw2y7D20wFboXtrWXIpP2EHMR1KRw0Bc2euRGcd6JcFvfOqdlDPQCbi94+oHdcp/9QPeBVSKebYRAuY/6tphn5Pm6hZexB8RMnNa6F+TlZwtPWMVEdtlgl53sOwOLW0r41cZRCtyuR6oXGPtntSnmJaOIubhFN/uTXQu2BC3Pd7OHJqPwQ0s9c8TstmjnK70NCOtGcs7eFBlQvWu/f1/INDrKGp6ftxivyG4kANOtIu3EUO8YCehqv/p8zG1xYGjoBoKW2bhr27FmfUdvXeQ69K71fr1rRyK8b4Te4lVDO+3drUHmmw6JbmAwreyNm72nHWtuHmtCpehGimZm0G1fYF1uXOXiLt4gELqeQ2s6mfsxU1uxNlnVO/9lz/IDGQ/1juHiPU2zI6F3G1f4RhQXPIXsGet9KSYvjgyrI0U3MGQrGb92FXE33XZ9CoUR/rRW/Id//c8XRGb/n9/y/93L9KFMubXCy0QmYpaVNI246IzRtG3AqAyDYtmu8caglQNjcCqKm0s8SlgNXVejsgwQl23bt3yv1w3KaAm/SR1F5ghtJwtILu4/QqK0/SITkIOsUmS6ojNrYgj4dYDCkFWFBHW1nWw+PmK0olQWnzy5dsSUsFrT+YDTVhrstSQ+W2v6cBYRpduuQZYYaUMlRFIIoJE2WyB3GT6saZuA7ZENIKJR9AFtRAxqg6eLIoga7SDrnXsqorXBh0TXeVyWC9sQRUi9m1n3yISUsE5+ng4K39aoaLCFo/ELCeQxmgxxzialWfs1QWlsbAFQookTUi/cao1KknodkiZTaetGTkbRIeKm94kYDTURsDhl8amjlfBfrJUv6m2XGBWCcyjLMU2zRiVDUweyPjE805q4bnHKAYaoIrETPmXbSatpZS2dF8+X9x6rMxFabSLvBZAQW7Iip+vDvbrO92FGmzZ2TQgabTOsE6E7KsS15b20p8ZIw1p4nF3EtwqrHW1MgjuIHqMLlILMiMssOUXoIrEW3qFJkGXCHk4xUpW5YDqwqNjhtKZpAiZXRCJFlhFCS2gNRakpjKNedSSQgKWAjH2tSH24WPASMuSMoesCysn1BiIxLjFVjjYaa8U56oy08wedKFyGD40EGDlDmxI+ipBlTU7sxBntY89lMtLu3TZrbFmCU3RdwKeux5gksjIjtB3OKgyyIPq2pSwy4R9Hj8UQUyRpA0lcaIXN0ERya9ifTbm4OGPthcUZcYTUUOYVSotz3Xeeqqqol0uKTAlGA+FRBq/wKYijkoQy0lZulQjcqhfEiiIjBI/LMzo8XRdx1pCQVmUA3bvlrTF94NB8W4AqigEpJcG5WIczfdCSLbi5vmA8qLBGsaqX5M7gY8BZEcWrQUbbNvLMvCb6ltxodK7QymGygsvVYuskb9tAWRRElfBBGLfNesGoLCDTNG1LR0QbA10ic6Z3IXf4BHQJY1RfKMogBdrgyVyB977ncip011eazSa1oBW+qveMipwQ2ToBfV+oq+sVWZahUiRzhjLLGQ+Ez3q1blARmlVHMh4b2fKps2qITpFBXjAuh/jQsliusdqK2Og0bYpkVpFCi3IKMNAF9nenOO1QvuF6vqbtBDXShUieWVrfotC0PjAej8kNPNzfQ+vEYrHmZrkiJEPjvRQVtCLGgElSMR+WFeOq5MFsly4Gzs7Puby+weUlAUNSCmMFLxB6sb5drSlyg9NSJLDO0XUdymbEoKRY0+9TBQpnFFnsUF7WuLwoWK0bjJW5nkxf2W8lrE/WuprpdIfpeJfl/JLF4obLpiVpQxsTxoL2gRgDZVlJyFjpODo4RsXEYj7nbN2iTEEiUTdSRCAm2rphNC4Z5jlvvPlY8C3JcHl+wcvTM4w3tKEj1xnBS9ts4SzD0Yg3Ht2jsIY8JdbLmuubBU+fn9CFNSmrSBqir+XAmRRH+wdMpyOqqiJGRVfP+OizT8EiaCEUa6AIUoyxSnN89w733n+PzCQWN0tenV7w9PQGksFHWderImPdLpmOBhzd2WVvd0LmhOl9c7Pg5YtLXp7fUGjDqi/aNtGDb9mdVBzu7TIeDRlUGXXdcnZ6wWdfnpI5R4OGzBIU2MyQaHj48D57syGTakjXtjzSR3z82XNenZ4Dec/CN9JJECNZlnH/wQG70zFd3ZK5Y16+fMXV1Q2X8yWFzWi6IAcShexVmxTXn7/+0r9SZtGtx4/zLZcQJGl806ooXM/QB/1oypMGXYuLD4CB23IZVZTDnF16Kdggh5oNZzC/aHvBVYSBdpZt20KBrfut3r1t4d0cZrvRYMvxzK47wG3RB27Rt4T3oSzNrsPUadu+HgrhEYrzbMO5k7ZbCRYSUcEt/LblORR6+/nhts22G4tIYJfSLv1VNqz2t9gBNw8imMI2oOi2pVYCp+yixQ+zPthLb1ERSqteGEzb9uLo1JaHumG0RjGKbtELKibyc79tAwduHaBtRAW1FfHkmSVxAW3QCWnDRFSYXsjKL+Xv2kque9O6KygGaRFdHGdkSyUcx56rmV17uqHFLeS/KqRtcJTu4tYBtWmF9Xs5tpL3mXW4xXTkmjixPRPyFqfQjXqsQh8othHGlY9bocAPzLaV26zEqah8JJsLZ1l7cf+FUriZ7VgQZVsxog+UUpEtCzdmmphZupHZsk7Xe04c3tt/V+Gumu0476bF1qHazjLBimy4zpvWXHM7XzbjLTph87pV3LZw29Zv+brRaVR//b7SWyTHpq14y95ceBH8nOmxFH0xor4dYyqA60OSwIoYXItIuOH/6jYSBnYbZmSaSD2T52SXX0EP9NeQjNoKMe3EcvmWYfy5tHJv8Bm68bS75bZt3qw76v0B+WVHM7P4XKG9xfcOQtukbfu5oEC+Mo/7dvZ2p+hbvuU6zKolVJmItz0DOThxcG/CvcoXgqPYIDFUTCLAIoFnppGfN3zWsribEXJFeRFo+xCwDe6i3s9wi4Bd9i3uPuHHpl8XIGSWyU/WIlRqcak2e6UULWoZQ9m1zCmA/GaTOt+HnPVMTBVu2++/yjt1q7idcyK2h37epe2834wzX8nnWt2rMM0tU3qDgnBXNWqUy7o30NQzCV20tWH4paxF7qreClEb5EE7dMLz3RcBSzeG6pW4qlU0JFNg5932Wr7KFb54J8eXgIbBs0i2vGVAd2MruA8t4WWLezntSDH/nrDSizPF4EXEtEl4yxuHfHcb/rW8Y1gdKs5+SaHXivFnivFPvbBLGxGdo5P5YBopmmwQNV2/f4gbVT7XpjiUoqzh3czhKwk23OxnzUhTnXkpNjRq2wmQTM+s7jms8/uG5d1Ifq4pTxPFRdsXIZotBkKFRLPjpPi0Y7n4mtyr4kwz/VQ6/FRM5Atx+27m/+I4Y3nHcvP9NbGTdco+tex8IC5Xt/C9s18WjXbqWB4YLr7fMByv2R2sOLkeUb8YsPtn4lbddGdsMEjRKS6+Dv5uw2y2QAXD9Tpj/HcqwJD120x+0aGmmwBEzen7FvOtFZPqlBdnE7KPSw7+VObeho+73nMUF56bBzlXX/ccPz5jt1zho+azV3u0Hw7Z+SCKmN9jRDYc86vXHVfvRbKDFXd3rjldDFj+ZMLdvydFiM37TI9MUBFeftegXlvy9uErruoS80uRkz+4y85Ht2x0mUhSyOoGitNfgPLRNV3tcFXDyYdTBk8Vky862llGdt39DApkc6//PK+/0KLthrebQNpboQ8WCzhnwHt811GUGb5p8EiYS+sT1kJA0yXhINK1hBgpi4LkFcr37agWWp+EU2czCi0hUhDJ8oIYoBxUtK0EGWmlCJ2E59R1Lc4xrdGhYWAz2uglBdFquk6EySIX8dcUDlpxyVY6I2pDXTfYzOI1rNuGKq9QJGnZtYaOSAieum0k3TxEjNWYJPiCFBRFUbKqa/KypKk7MmMwSgvH1xSs1w0pRDoUmETsOpKKWH3LRzX6tgIkzNWWqqyEqZsCzUYwtkYCoazEFuZFxWq1wvXO49G4YrGaQx94Y7JcQsKUFuezlVRu+vA3tAWtMUrSsRUSZCX8zAQpSDCRAmMzou8ksMgNiUoRtUJph7Y9WyhFlNF4PDEminKIj9LO0MznND5gc0vmxBXmnMPYjNQ7jm1MFJkjKjnodjHQtJEYO7KkCUHhjaMLgSx3KC0tw9PhAKUS6/Ua3wb0RsztQ2yszrFK0elA9B2ZdmTa0HQNKemeP5sklMx3PZ80obXD2RxntYhUVmFyi9KB5aoTc5+y6MwSWo9zvUsgRsZVidHQdY0wYSMErVGxF8eSptXigo1IkFNVOoZ5yaK44fxqQegP8hhF10VsH8AVtfAfM2MZVwOSgfl8DUqxbDxaBdqmQWvLuoaahiLL2ZlMyZymzMdcnJ/S+UDbBWxSFFVFGzyNdiLsGcPACX90VA4wWrFuE6fnFzgkoT6LQJAAIWEjB3TUHBzuY7XCx46T8wsyndP6hti7A0mwCUvam044OjpAo3jt4SHPnr/g9OKSdasw2G2AWekyXD7m4vyCd959ndwlilwTGs/ZxTWvzpZYm1FHT1Aa7ZLwmV2SVv2+9dinQF7mzOuOpKM4TbUB36D6L1Slk4JNngnHMhsOqUOHUZaurYlR+KcmK4hehMPMKu4dHDMZlrgIz09e4kNg2YiYLsUgKcpkShN8w+uP7zMeVFRFzs1iwc1iQZtE1G3bmsIVqODBR6osE6bdoGA4KpjkJTfLJWeXV5igqIO0DKWYKF0lxQhdU5Y5O9OxzKeQOL+8YNUGohJsgVE9EzgEMIlhWfD40esYJaiDF69Oubq8IdfCO7Wu5zdbizUGlVfs7+0wGQwYDkqST7jzS56dnOG0Biec8JB6FE675nB/j0FuObyzD1FSlX/4zz4hQ8uzCJE2dlLkUqBVYrI75tHjY7KYyIg8f3XB2fk5y84TojiSI4E2Bap8RArw1ttvUrqIMRDayNX5nA9+8hNQ8kU8htS7vAuijnIdZcm0kr/fmRR8+mli0bZ0xoDStF0jIr0zOOvInOPe4QGWiF97XpycUmUOjyLoiA6K1KNbMmMolWJ3OmJ3vEPXdTx/dU7wCY9FK0MwSQIpgcZ3xOR5792H7IxmlHnJsnLUwyHrL58QtCJojTUyryyJul3y+oNjdqYDBuUQ53IuzwqW6wV121KHKKKl7zBRU5YlO5Mxuzsz9mdTMq0wZExKEfbPr+ZEl9G20g3jlMKpxHQ4YDoeklsDvmMyrBgMcm6urrlZL7mpV3QqUWYlbVfjsoK7RztUg4IyL0g+cWkVk9mUJ8+ekuUlXYyMygHX19cUuePhw7tMZhVVbrBacefBXZIPLJYNN33bU4yROtRE5ZnOxkwnA4ZViVERExT5eMzQDjg7/wBPwvVjCiCqwGw6Zmc2Zm93xno553B/D4Ph9HxB65WEkqKJ3hNDS1EW3Ds+wNmI1YkiL/Gh43B/zOn1Jb5tSElRIN0NXerY3RmyMxszqkooKmLbcbS/h0mRs9NLgtJgM0FR+EBmLKivMCp//vrL/Uq3Yt3mMB+d7gXOXoxZB5qZk1bwuneczmti4dCN3zIvQQ4yzc7tQXzDGnRzv+WCNjNHfiEHsm4g7aP1VOOWkgAtjMhEM5Z06414lYxive/EveRFpNqEdTUTaff8appzuytOrby+TX3OL7o+QV1t+bV26cWhc1nTzQoJCak0oW+VVBFxSPYsPHfzs0E3G8eXirfhXKoLvYDttizJr7qtolPYZaS+U25b/d218EajtRKS1IuW7chQnnVbAX15oKlO+96h3o28ZettWKqb59HdFmA2AiKKrcAuwrq+bX93qg9QCVvBW8Qw4QeqTBH9hgkpwms7tSKkWcFUZDci8G/E4E0gml0naQkvjbj0fOqZiUmc2h1bjqZdi+DtR4JZMHXi5oETYeYyYhrVC/l2G5YVig2LN2zZtO1Qk9/0YvtEktFjYciuezboWty8wkeWVt5uoAkTEdSqV+3PBGrRyPXIn8Hi2BIysGsR40ybxN2nIbqiH1+34WqrPUuzo1jvJ8pTx/RTv+UdbsZiKMyt868XyxbH4vAdvJQxVJ52fWGgIzpNdr4mu1C0OwVXrzn8QNFOFKMvwK4TbgGhdMLn7ZPM7VzhRxmrOxm+VKwOlbCSr2HnIwlksjH1DFQRMdvpJkRHcf6uJeagGwno64aZcJg3bFPY3qfLtzKuvh5JxuO+d8P5D2ZMPoHyQkRK0zt7m11HV+ZMPlnw0399TDeMhJEYDYY/0ex86CXs7Tpsw6I2QWQbR90mXCuODdUz6WCJmSX0YXHbwlDvFG2H0p3SDipJgO/ZypuQsNAHmCUjQXInv+CojwOp8Ix/mGHqRHkWxeWapBgUrdpyb5/+Zkl95HHTFeFlSXmiyZYSTOXmHe1OsWWm1vvZNsF+va9YHUeql5bppwqTa7LL24C3Zqd3ELeJ9Y5l/lDhBwmztow/N5Tn/rbI0DNON89leeh49auJJK4GJh9Yxl9KkUuCATPhTleZBGFpuHzLUB8H3N6aJip8MeDOn8Ut49nUAd14iIbMJ87eL6l3IbwrYby1Tkz/5oB2oPG5pgDsvNsylaM1zO85rr9bU1Qt46rm/If7jH5iqM4iupMgQt1J8aTeF0TN/DdX7EyWlK5j3TlOns24/3u3IvBm7kpbP9y8AfF4zZtHpwD89MGM+PdHVK8iNsTtfqUbTxiIANsNDa++o4kZ6Naw+wOZy7aW+R57tzpa0Q0t6x3D/LGieVyT1pbpDxRNa2Sd7LnTuhNmc3btUT7xxW8X6IcLDiYLTi7GrG4yiivBtuj2NhwrFMJRf/qbOe3DhtnOgnHR8MXTPYqzjPwmkl/KPJHCnqLeddw80qwfdfzaoy84Kq4ZmoY/OHqTl/Vdxp8n9MRKyF5poA9kXN5TPD4+46i6AeB7e5/zp7MHfPnyAYMXMHzusUtPs+t6RJFl8o1zHk/PeWNwSpcMf//l61wfDxj9sZcgxlVkeZQxeiLBYSe/rInHa96YXDPJ1/w79/8Jvxv/JW5Oc4YvwnYfy6+lULG8l5ge3/De7ISBbZjYNXera36yu8vis7t9t80mYFDG/eIBTB5c81fuf8Rniz2+//hT/qb9OotPdrErcIsei9Hvd/XUsPPNUyZ5zdjVPKguuepKnry7Q/o42/KQ26mMfbsKzB9D/mDBW3uvZLwHx4v3EnM3ZfbJbbHIrDp0E2Rv+3/x+ost2kaFQjiwkpYtbFViZDKaMMgzxsMBKbas10uulyuWXcfAKlLy0DtFUpJwl3E1xKAwGRJIEltU0PhGbTmSnW8g1yRjWXctu5MJOrbk2tH6yPVygc5K5u1CEuRtH0iTPHgobM54XDEcO0LoODs7o+08xihJybZGEqZTAA/aO0Ko8d2ag909bDIs2pZhUeJVwCXhxY7KIcFHCAlrFeNxxcDleO85uTwnTwm/DJSuoPOJ1ArvpPGeLHNEBZlP0uJuFFo7up4taJSERxlr+/AjEcd92wnOABE6DQrfeQqbbR286/maKs+4M51yZ2fGoMpxWWK1qnl1esmriwt0UkSd0XaCB4BICOC0tP+nzgsyQIMPEZdltFFChEQMsOJwtI7Hh3cYDwpS8Dx5+ox127HuQEdPEyPYhLJS8Uldy/37d7FodvfGXC8uOHn5jMWqJSmLr0UiCxjaVFNkhgd3drh/97APNAs06zUvT6959vyEtucjGiXOSt92DAYFdw6OuXu4K4LFesliseDJszMRL42EzBijUDaj0g5nFLu7O+xPJmS54eXZGc9OXlEH6DpplyN5YdMmRYotRweHVINDdndGnJyccH19SUqJ1VrQFlELI3GxmjMsSt589JCd8Yi8sFiTmF9d8+TpCTfzJVEVKGdYtnNsnoFvOdyfcrB/h+PpLkZ5buoLDlcNT569ZL6s6XzEFRUpJHG/G8uDBw/YmxYUVoPqWM7XPH91ApdSLEFldL6hGhfML6/Y29/jtQdHlJnGpI4HhxNWq4Yffvgp2Iz5zRyTZWRJnmepFa8dHTEdjxiPHKjAdVNTOPjyy6cM8wGNb0X49wGTWcbDijcePaQYGrSOpOAZjzOeP3uFrz3WOuqYSJ3C2sjR3V0eHd9jMCzIM4tJgckbD3jxsuBHH3+CNZp5n+4+b1v2ZpZ3v/6Q4zsDBnlGpjW+Sxzs7oD/kHnT4D10yaOTInOarlsz251xuLvP/mDE2te8PLsgvVwTMbQkuj7cCQJOGzITODzeZ28yY1BVXN8s+OL5U04vr7AuAZYYIMQVSkemsxnHB3u8fv+IZnlDjmNnNubk1Rmff/mMrmvFDR3WGKUxKnF894DHD+4xGZWEsGRvf8B8OSF+2nE9XxA6JMytTlidkduS46NddiZDSpPI85xV7RkXFT95+pzxYMiibjHO0NGBjRTG8vjhXXGoOkXTeXZmOS+eX/Dy1ZW0tCpBQChgOhxy9+iA/dGQ0bDCNzUPj/Y5eXXGhx9+RNN5UIbKZXgfCW3k7nSPN+8/QBtPXhW4oNiZjdAq8fFPPgMzwUQnwVApMqpKHt89YFA4xsMKpSJdE/jG1x7z4vkJX55e4LXCuowImKi5f/+Ae3f2GA0sOgZKmzE7OODJZz/lxz95Ks4kEl3QuGFBWWge33/A7u4AoyLWJFILw8mAi/WC06sV81acqdoaVusb9u/MeHhvl9LI+qK1ZtiVxBY+efIl9aIhGodRBusMJkV2y4IHdw+ZTsdoDa2HO/u7/NkPPyAVOav5ApcP+kyujlW75tFbr3PvYEZhHTHA3sGUFydnfPL5K+rO47TwW10ShvCD433efHyf1HQMcsud4R1Wy5b1asXnz58zzCt89MIXN4FmveDx60fotma6M2W1XPPa6w9ofcMXT74k9U5gW1aolFgs5nzt+E3u7M+wNpFpRYZG7Q95wz3k8h//U2JQZJmlaTqywjHbm/DeN94iV+LqrgYDQoSd4YjRySU//egcN5lhVaKOHUXuuP/oLjtHe6QQpAvEKh7v7tCsas5fndI0QToU6jWlk66E+4d3cLkn15qiKIih4bXXH7BoOpYvTri5vmE0LUS4XS544/4xw8oJIxKF1TAaj6gHkcmg4HoxF6xN9GRlQW4GvP7oLoPckUJNVQoveG86YlTkvLy5plQWRYAgoX87kymDskLRYKOwvckyzH7Bi2cvaNs1OItxBhMzYrviwe6EybAgtcLXJTcU1kCY8eHHX1BWFUlZ5usFLsuE+53+/NX/n7/++X5tDrR23kg7aG63h4v5vYJ6T7E6FMEou3IMnieq00A6HN4GnXzl5yzuZr1w4vClwm1cUj1zURxTcftvigvP5ZtZ396rcEvF6KknGRg+F8FsE4ykuyQFpYlheayp9xK604w/k1Cu4NQ2edvnG2Zlj8lpE6YOnH+tIBRQvkosDzNsk1ADKXiv7w62jlOfa/kduwlTK3Z+HLYO22bXCWKhv/7sJm2dpN1InIZ6w41ciji9ua8hF0FOxFbdt70KdiEWhpALyqGdZdv25OLSs95zXL6tad9as78zZ5DXPLue0Hw8ZvoxFH0YjlO3otTGabgJjtkIN7Z33rl51zvqNknZEpp0+uuO5kBE4N1/bCmuIsX5RiT0/QOXz6/byMkvGmIG5tGC9sWA6Y8d1angA0wdbp/dXNxGJ7/sKL59gTXCDb9YlJgPB9z5My+c3rYXmnpRdbWfc/W2Inv/iirrOJtXdGcle3+i+wRwLSJ371xc74kjbv5Y0b6xpqxa2h+P2f8zOfiblUd3feBc3zauApx/Q9HNFAcPzzn9aI/qhUZFR3nWbccrWlG8XFEfVnz5W4b8wZzZcMXQtXxxtkP2p0OqFz1jWGnK027rGD77RsHNO4F33vuSoWv40atDnrxTsfOnluGL0IcK2S1Xtp1YTn5Jo15bMhutAHh5MaL8YQnQMxvBrDzNfkX55Q2Xb46Z/9qK2XjF1HWsv+N4dTXg4H+WMLPBc3Hs+VFOMop6x/Ly1xPV3Tnv3XkJwA9fHvFyPObwT1rWR8U2FR0g5IrVnuHm+2smoyuciYSoeXq4w84PxAneFYbsSpy0sXfR5b98wbdm5xyXN4ztmhcHE/7w/psc/q0MXygGvSu1erri8t0RX/wbI+zXr3k8u+SgnHPdFly+VXHOXaqXEVNL0SMYsxXurx/lXL2b2H39glc3FdmPKlQsZF4s/Nb5CuKc7QaKy/egeO2GB7NLPn25T/nHA2Yfdbcu+3DrsL954Lj4RuK3fuWf8mw14W51zacP9vjpx4dkf9QzNBV9qJ60ZZ9+q2DyK6/4rf0vWYaM7qHhw/M7XPk9hs+EmbwJcwuFphkZLr4B+uGSvcmCN6s5P72e8fxwh+P/XbO8V1C9bCVo7ysFq1e/Grj/2inTYs2r5ZCTh1NGP8rY+0HfwdfdBmvdPHBcfDNy9Pop39x9zkVbMfp2w//22Zvs/H5JPpefW+857Fp406++nbH3/Rc4EziqbtjLFzy/P+Gf2bd48PtiUhNxWAojy+Ocm+/JWvWd/adoEmftgP/zt++ifjTi4E86CaLqAwRDbmXGboIAACAASURBVDj5RYf51jXf2ntFZgJvD09YHX7Gf/+Db5P9L5KrEwq17Ziop4bT73m+++BLRrZh6lZ0yXC9c8I/+uk3mH4SGTwXV3qoLOVZx8U7Oa/9wpe8Pj5jalfk2vP+LOd/Cu+j/7Aiv2YrdIcyJzrF1RuWxS+seePolNx6ztcVZ28O2f/vSlSE4lQKHCokVBfIzxpe/GrFzjdfcTy8pg6OxeOcZz864N7flTDJMHDSXVDJHvDyeyXf/fUf4ZPm/dEzqvsNHyzu8gdX73P8DwKro3z7LEBCGu989xJnAt/f/5QPF4f8i9/5hP9y9Rvs/0ONitKZEHIJI6xeNDz71wzffv0JU7fmbn7JfXeBOwj87d+A5ZNjwQn0HTbd0DJ/qPnmX/0x98tL1iHj/eGXXIeSf+Xwh/zunXsc/1ErBblS5ns7tZz9QuSvHX9ClwxD0+C05z95+3/gP1r8dVafD8nnUhSzTaLZyUgG7n37ObN8xXF5wxvVCauQ8x9/5w/5z87+KuWZYvC0ZnW3QPtEdVHz9V/7km9OnuFUYGJX7JoFvzb4mE9Gh/ynd+8xeCbdoklJUbgdKd751Z/w9fFzumR4Z3TCveyCf/X+B/y3B/8COx8E6pmhzzYjFIr5Q8V3pmc4HZi4NY+KMwDGb9f84RvfwS3jdj0M/b56+J2XfHf/C7pkuOPmADwdzvg/eIT+vWpbZKmerggD0xe/vppw93//+gst2mbB9JUnAbqXeUEbWjLt2JtOGE8GFKWlbWuqkSVeaBYvTlBaHEmFySVhvPOQEs5EDg6mzEaH1PWKV6dXXNxcg4pkxZC69pjMYoDFcsnR7j73D2bsDwbUoeF6dUN1YbherfB1gmBJRFKKDPIKjeHuwT7TWcVwrAkhMh1lfPL5F7RdIqCJKki4VSOMwEk1YTDZ4c3HB9SLa1KAP/3RZwxHI66Wa5zJhAkahNlojeLgzg737tzBup5xk1kur69YrjyhbRgMx9RpTpZl+AjLmwXj8RifPMpq6CAQwHuM0hRFuU32Xq2W0pasNZW11MsVVZ737ttOBGedSBiUcjSxJrM5R4d7jIcDSgfGwnggKenNuub67BVY8DZRJtDa0npwLsM3LVXmWNdL8r51bNOWqTZcP0BnlsP9Ca/dGVOUJUHDsDA8f3nGp18+p6uG4tg1lq7tSChGg4rJuGR/MsGogLYDxqPX+eBHH3F9vcDZARErbeg+MRqUHB0eMM5zXOGo2xXlOCMay6vTC3yMwq7UBuslIOlgd4+DO3cYVwWkmnFVoVLL3aMdfvzpC6JykAmE/fTVObuTKXuHOxwf7TEclGQ2kGe7OJX48ItnFHlBEyIkj1FOuJSZY29/SjVwWBOZ7RY4N+bi6hrva1w+IstylusVLiuxRcnu4Q5Kwah0dH7B7mxKG1o6Ok5Pr8kHY4yO4s61hnt37rAzGzMYCHpiOpgyKAMX5zesl2va3jlto8K4gvGw5OjODmUeUUmg8IPBPslozs9/RFd3pKIU7uqqZX8yYVqW7M1G+LYj0yWYjlE15HT/is++eEY5m7KuO+FixoTWsHtnyrDKiKFlNBhis5L1bstitWR+UwvXyCeStaxWK77+ztuUg5yqcGgjTE6lNOtFw/XNGh8TNs9olacJkYPjI7KRE75r8phMQOHT2ZiqdKzriMOSZTmNX5N8x8HhPvvjIUoLXsRmCpc8+8eHnH34IZ5CxklUdF1DSonJaMre7g7j3FAES6Lj5OU5PgS6LmCzrA/70xRFhs4cb777NvOLU5RJjMYFe82M+XxO3Ts6Q1K9+1RT5op79/bo/ELCmHygHFSMZ1PCF0/oPBgDee5o1jWJyMHBPi63JAIRz6CcoJLhYHePl89PyPMBvg34JAGNna85vLMvTukMYtMxKi3N7pDq1HB1eY3NC0LXoR3Mb65555e/xeHhAXkUF3llPCZq4o7n4nQu7m8VMXmOjp5qUPDg7jGGDuM0ZT5Eobl394hPPv+Cee3RVhGaQGEdmdWUg4JyVBFTi9OGhKcyBbPRQNi21tDWrbDQbYTGs78zQacWG2qMMQyGQxQFq2XN04sLvBf0CUDqPPf299mZDFBdQ14MCHSE0HBw7w4/fvKEJnjyrJSgOit88+O7+xi/Js9zFILlKIoCbQ1tvcLGDFVkaBWJrWc2qBhUJQRPXvSV21xxZ3+Xjz/9nGowYL5uiMrifEsbanYPH7KzMxU2r1K4AobjMdUXX7BMEeuA6DEmw0bLcDJgNptROodzwvU+ODygjfDlyRzPiqgRjm6K2NIxHJUY1WFzB8Fics2doxk/fZlD6NAo6nWLLnIyp9kvd3EYxpMdYldTZRnEjrJwqBRxydA0kS6sIAZi6Lh7fIjGY2JLjJG1X1LNRqibOZ1KBOWJUeMyxXxxzlsPjxnnFaldSfCZEmFmMCxIRlNWQ5aNhM1lypC6jslovA0F1LlgWJrQklWOrmsYDGbM1zXJQjUoaOs5rrI4rRgMC3wdsa4kKENMgbpuGUwGwhsOkelgJjwwV9B2NT6B1TneN0StAQ0pQxmFT54MMMaiXSlIYR+oihIfQZmcLMtwSTEPa8oiQznF4maJyQ0m06xu1uwMx6zaJZUu6JqWTOVkei0OaadJpiEbGubrOeWoJKWA1h1dUkSbsE4KCpHEum3JipLQrBiWBbG7dan8/PWX++VH+c+44ppdh5tLcMb1G9AdtOSjhnbtWM80KmQMTm6dge3USYDNNu1dcfVOwtxf0d7kDD9xjD8XgawbikMo9InfxbM5Z7+4w+W3Pa+9fsLpYsD5ixHNjmPwLDLoOYWbJPvVvgiQ599M2LsL7u9eUXvL87s73PkDR7aU9nOt1W3omVPc3LfUu4q975/RzAfEqJj8jYrlHU3+REQTFcUp6oKkUF++q3HfumSWdRgdOS0OGD7RVGfyO1b7luIq0A6lDb04aVjeK/qW7Z4t2ovfMbfb+xQKtT3kJyMCY34dWO85QqZwa3lP0sKFTz0vuBso4tcWvH3njNeG54ztml/ZC/yP+n2Wixmjp70jqQ60OyKcu7kEEZk6sd41FJeBdtSHG/YtpyKKbxzWiouvKY6+95y7g2tW3vHjvQOWPxxz7++0W2dyzLQ4ihMsj3Pao453X3suQqQ9ZHlosH9rwPhJg69u8QSxMCwPLem9OV/bf8levuDJcoYf3/BBOKL7KN+G5shYEn7v5bsK9daC9/ZPAPjO/lP+rnqT828OGPz+rZs4ZprhD16yfO+AqzcM6us3vL17wUE557PBHs+KAx79Xtq2qW7+TTLSVs7jFcezOUPXcnN/waKsGH+u0LWwc7uRoThpCFWGLzXlozlaR765+5yzZsAv3X/CP1i9jlnn7HzU9Xxk3QtzhutvtTx6cMrXJi+46iq+dfiMi1nFF08eUVwq3CL1jmFNGBoWR4bBexccjW+wOuKj5o3Xzvij+BqTz5w8j4Sw0OvA1ddnLO8lfvHhE27agoNShAP24B+++w3u/kFNs+vIz7tbvIJVzB5d8ObOGW0w/OL0CRNX879ev8v1qWP4ItxiG0KiOOt4/tuwO17xeHpOpgNjV/NPdOTqep/RU3Cb1vp5h7tqqN+OHFRrJllNE0V6KE3Hw+NzrneOya96EXVoyK774LS3V/yVhx/her7DxK3ZzVd88cYBs4/7QJ+NO7l3xa6OYfbaBd/af8bJaMQH8Yj0Y8GvbLoBNhzSZmKIFn7j1z/gh2dHWB25v3/J568VVC8FXbHhCwtzOdFMFe+8/1POm4qDco5PmkejC04ORph2sBVr24kV92TULN9uuJvXRBTr4Hh/9AynA3/w9oS9H0I7sX0Hg7jT3ToyfveSaVnz2uicdXC8u/uKP16UrHcqxk86wVu0ERyUX97w5F+e8dZbzzkeXDNxa94enfB/sfcmv5Zt933fZ3W7Of3tb/X1+o6k+CQ+QRIpW3YoRXJgR0kkQHYQAUaQcZAMMmaC/A9BBmngdHAcBEogOIljWbQkSpRI8fF1fK9eU+3tT9/ubjUZrFOX1ExDIdIFalSoW+fss/faZ3/X9/f5fN/c5bE9pP9IXYv+njeRq13B197+nLauaauKVwcXGOE4eGPJ//Ptr2PO3XbaIY6a+46g7gfudqfUXrGfrrBe8Ub3gu/duU+1m2yZu+4vBFHvvPCEXDUM9IaOqni78wQtPN+ZvhKb+kWDT59PLATUV+f8/K3HGOm4nU5ZuZS1S/nZlx/z+F+8SrLY4ktqj0gjSuadtx7S1RW3sylGOJqgeCW/5Lv6yySriD0JUkdkgIPVXfhbg7g58TyIa4Lib7/4OT/4/a9Q7Ck6Zy4iLdoRxbC8r/jSnXOOsiVGOvbSNW/uXPKD3a/QGvnrBnfcjDNsjhLcnZK7vSn76ZpJ3eI/vPVH/OfFv0U5GKCqNAbDJm6m2VxR7gd6pmSg4+ZMV5b8R4ff5l8O3orrlBIks+ZaNjd9s8dRNubndh5RBc0bnfieRL8myIwmFyRzj96Gi6JxfPPNz0ilpacLNi7lQ3ubt1uP+T3xGrr4ccvUthT52Zr1N7v84k4MYAHU1gzzTv4Q4SPCKL+sIzaiEzdWsxuxVV17TUtVbFzKF/Uh3VaFKVps9hWd83jvKPY0ydqzk27YSze83XnCxLXZN0vmLkfaKF0rj9JrXvzyhTYt4IX0ipN6j1HTZU+tmLgOG/9jRnIQEZ/StMEnkKmGjU+oveat9ikT10YSsK1A05J0zhvKHU2Q0L6o8YmiZ0oezA/51eMzPlrd4q3OKaOqjW0HmrYkWfotbzqGwxfDAefdPjZIWrJmWHcpnKGV1ti8i2wC6aS+xsxsbqTXG7N/mZ+/0iKy/+l//z+/BYFUxdZcWRco77l5cIN7t2+yN2iTGmhnKe08o9fTKOUZj+ZILym9xVqHCJJuO+O1V+9x83iPLBNkmWHQ7+OcpdiKxdIsx3tP3TQc9Ae8eOc2927s0e1IklSR5Rm9fovHTx9jpMbJlOAdSmlqX5G1Ut586yV6vZQsjWOrhwcHcXz6ckSiEvxWIiOFQSrJS3ducudoh6wt6e+0SFsJhwcdTs+eEryMD6xEiVIwhju3b/Dyvdsc7QxoZxqjJP1+jpYwnU4I3lE3NU4okFGElXfarIo1wijq4AkixBagNmip8LXfihJEBDNvEQOVawhGxRF/FShtZEHZrZ268Q2tJL7XL7/5Gq08RRsBkvi62hnKSK6mV2Argg9IG0PSVCeUmw33b9/hlfu3OTjYYbGeUzc1HkmwHuksOggSmdBtd3ntlZc5PhiQ5pGXmqYJGhiPZzjbkMiM4IhYgrLg1Rdf4LWXXkBKj5Kws9sjTQzzxZLhcAKtDGlMtAQWBS/du8fXvvQWjW/QWmKMwMgEKQXawNXVBVJplExorAMc7/zMT5FnhjyTpFqTSMPuQZ91XXJ5NQIcITiMEnR7A1xZ8+brr7K724vtW+9J05Rur8eT03NimTq2x4L3FOuSo+MD7t85JkkkGk+eGg53DxkOp6zXq8hqFYIkT5kuF9y9d5cvv/kKibSkQqF0SpIGert9zi8uQQSKykLQ4Bzdds4v/Pw7oOMujlKSTVWwN9hFJwnnF5cEKSmbBik163LNa6++xJ1bR6QyYIhhoJaedqvFbDZlMpkgt7Z3qeN18vobr8aQLUsxSpEkmqyVM5rNWK02NMEjpCKRKQRIM8NPfekNoKGVGvCQasNgMGA8HjNfxNAva7Vp6oambnjhhbscHu4RqNEJSOkwiSYIxbOzE5TWBKWpyxIJvPHyfe7fvY23DakxaKWRQpK3WlycXrCcr5EiRSIxWtPJM+7dukWSapQUeBvZuEorirJmOplT2xARHdaS5S0uLi/4xW98g8QoaldiEsdg0GWxXDIfz6IgSQgsAqkFZVVx49YBnW5OKzOs1yt6nRQfApcX53gfECLBB4ND4HzDjVv7HB0cgK9JUo1JNA7P4dEhH376I0rncdKgdI71UYa3f7TPYHdAXZd08u625W64HI5ZLFdIk1NWNTKFpimoqzWvv3af3EgyKdCZRCpN6RoeP3uGkiqGcD62DJWQHB8e0O60ECqA9kgcSirWZcXF1RQLNEJgg6S2Dbduxs0f7yvSVgY+4ERkdl9djJiMl+R5bHxb4djYkp2DXY4P96LESzjyNMqtrLM8efoUqZIo5tLgbImSkps3DkgzTZKmSKOoq5qmgfPhJZuyiSGgUNeA+eOb+3HjzyhSpfGuIdUZa1vz2aOnCG0oQoMxBlcWHOz02el1MUYgpUNpscXcWE5OzvFeUDeR4620xODp9/sc7e4gEAR85FcHwXA+YbyaM1rPYzBcNySZQipJp5Ozu7dD4zeoRGJ9zWa+5OnTx9FkKyRaR0SNTAXrasWtOzdiWJgahFSMR1M2m4ZnJ5dAQAZP42owgmq9ITWKVitHysherp3jbDTm9GqIReJcg5SBNEuQWuGahoOdPYTSKCMoyzWJEYyGY+arFbP56pq72+l2UEpidBTNSekRIpC3OizXM9arDacXI1zlSZQkAInOEa7GKE8iQSuJCJ6qLtgUBWenl4wnY0ySRM5vqNFBkaqUNJVoLfBU1E1JYxsefPoZ602JtRIpFUaDdY7gHf1Bn51Bl/V6SQiB2tecPBuxmNeUtqGq64i40IbSNiR5SlAeozXSOYK3rIua8bjg02fPCEpjBQgtSARsihVSKfq7XSrXUNkKby2z6ZTldMZ4OUHnCaVrsE2Dc56qKuj2u6SpoQmO0MByUTCbr7iazqmIgkZqh0cynS24efsWDoVMMjbW4YSk8ZKLqxGrZcQXIRV11ZDoFKUSnGv4rd/4GxHZX4ef/+G//YNvAdR9QzBbgVeqGH7F0P7amK/cOWW3veF4Z8HB7orqyDHKOgw+t1HgNKtRhQUpWd9MGX2z5q03n7HfWdPqVthbNQvbIV3EJmjdU1sRjWX6Zo/hLzi++faP+OnBMwatEtlxFMeO9p8kkblZRXGSTxTJOjL6bv3KCW8eXHC3PeUgX/N37j7gD7lL/3OoO1F4ErZN16AEV1933H77ghvtBV87fMpeZ8PZaxr1XitKlzaRgRgFZYHLdxKOv3HKb9x7lzf7F3TTmvRmwaO0R+uZRHow24BYEEVZ1V5C66ICGduozwUztptshSpsiygRufBcmpUso9TGbHxEOUybKEsrIjJBbxzlvqHuCf7Br3yPN7rnDMyGgOTALPn5/Ue819qHz9rx93qiZMdB01OkU8v5L6TUv7Rk/KokudAkqx8/bAYtcHnkL26OFLt/94LfvvNd7uYTtArsdTY8y1roJ/lWXBPvjU0/Bhon39T8B9/4DonytHTD1w8f0u9UfHFxzOCTIqKotLyWVZ39kuaf/Rv/FSdulx29YS9ds5+sqbThad5l52N/LZV6Lqw6/Ecn3OgteKUz5E425Tid83eOP+WPlvfp/kjzXBLmWoryTg9VB+a/VvC128/IdYMSgVvtOZ3DNevv7aJLH/m0dWTKJuOSyZdy7n/tjP18TS+puNFd8I37n/Hhk/u0hn7LLgbXVuQPLhl+fYff/ua/ZpBX3EpndHTFYbrk5cMhnz24QxCSbOqu5XrrG4b/+B/+Lk5qBqagq0s+XRzx94/f58P2HvKjeC4m04qQKPKLgtNfVfw7b73LndaU/WTNndaU3WTDzd0Fn17covdFFXEamY7CKheofnnFIC94sTvmKF1ymCx5MR/yR89eoTWUsSEmxbXUq+4q/t7f+z4ByeudGIrfz0e8enzFu89eoHPqrsM7s4kM5uk3BN+8/4DKGwamwEjHTlbwUPTY/bMoa3rO4QxGoX9txX/5yv/BPLS5k022DUfHS50R737+Iu1Lf32t2pakOFDc/ukLDrIVXV2x8QlGOLq65OPlIclJQrLaovMKR9M3pH/6Keo/CQyykmHZYSct+KmjM340ukX3WZSRIQWqiOtWOm2YfDlhcxi435/wcLLHVw9OOXFd0gcJqiFKn2wUIMk6MPmy4o1XTggIbmQL9pMVc5vzm3d+wEf/16sk02rbEo+sYJ8qZi9LXrx5xfmmz1cHJ0xsm6N0yfuf3ad9IfAqBlPPRUhm5Uh/ecmL3TEv5CMGpiBVliebHTrfTSLiZLt++EQSUs30LUl3b4MQAYTASEeuLY+nuww+iu3nyAp2mEXD6O0Ec1RSOsOtfI4NipXPmDVtHj68Sf+LKrYCQxTFZcOK1f2El149Yy9d09cFr+fnSBF4bPcwf5ZFBnYVxXGq9vhUMvuK5yhfcZQsaamKk2qXYd1h+MkB2USAiiKq5+H76EuSQmr6acmdbMLapdzNxnwwu4X4/TZBCrJRfc0Xnr1mcLcbBmlBrhraumLlMh4Wh5y9f4xwkmTtr4+VbUnWNwVvvvwMJQJ1MKxcRl8X/O6zL+Eftdn9pIhis8pj24qgBZsjxeDOglHZoW1q5nXGF/N99PdaUfxVxd//XICVX5aMflrjjMDo+Jz4z8+/xHTaYf/78R4Q9BbBEQTJrKYeJDzdzZAG9pI1Z/WA//rx3yK830XWAl1HeVndjzxpXQfOX9E4LWnrmi82B9xM5/z50/vkZ4rekzqy5TeW4kZKSBTvdg9pWgKtAlIEXsvP+YPF63w+28f8MEM10HQiz9W1DV5pvt89pJs37Jo1Wngeloe8v7nL449vMvjc4XKJzSUuEbhUsOgkFD1JPynZ+JSzasDQ9vj0918iH4WthE9gM4nZ8sA/u9XhZ2885r3lHSqfcF4P+F8//hnMRUKy4HoDJegoKP2kd8ADs88gK8llg0Xxg9V9/t/TN+j8qxTpQtw83W7G5ePAp8kux8czTjcDnpW7FD7lO5cvUly1GTz018gVXcUph2Qt+eB4QL9dMqy7NEHxg+ldHpwf0fo0IVnG1/Nc/Nb9bM7w7RZXTQuh4bP5IUubMatanH98RD6USB+vT72ssb1ki8kI/ON/9JcTkf2VDm3/x//lf/tWlqTUBCyKkCRIL7lz9yZ7B32sW6F0QGgwqSJtd9iUNaPJDC8EyiRIldA0njvHN7h/+w5GS6QKJMbQyTo4ZxlNJjTOUdUxWETEcef9nR77By0kNanJCF7QabVZTBfUpUO4+MUleIEWhk7e4rUX7pAnkc+aGo21JSAZjSe46OOhri1KZdQOXn7jJQ6Pd2ITVMUWoFKS5XrDeLaklbapigqlE6RQ3LxxwOHBLnkKjgqhAkmqQSoenZyhkoTGS4wIeFtv8QqW4C1BRrFbpqMUKjgfwxWTRQWLMSit2VQlSI/YWu9DCAgpopDaQ13XKCHJTcJiPuXWjSMGgzZSAVoQfEOWpHjpWW4qzmZTmgYSlbOuK6TJkEqyWa95/a3X2dtrY/JAp9Xi8vISlbRwW1GZUpK6suzuHXDjxiFSNehEo43CSY9QmkePHmPSFAhRVmQUnsDt2zfpdVpIYem0Esp6gwiwXq1ZrQtw4G1AppqGQKvXZm9/AN4i8eAtSZA0tWMyW7GcWIRIYuNWSZSWvHTvJlkqSU0MmoUTlN6yXBecXY2pXAzwShyruibPc9qdnJv7OxGR4T21rVgsl5xcXiGNobEBrXT8ow3FZsnezUE8xxNFWRcUVcGjp6dYAZvtiDUEqsrRyRL2uzkyOBIlsKGmbCo2ZcN0smA6WyGkIk2jXG25XtLd6SBUIDMaZx3WW4r1hvlyzsXlGCsjW9lGOTHdtiZPBFLEpndtG2rrWBeWs4sRReFQWRcfDEmaU5crer0OnW6K1gpnIyt5XRWcXVwyno6pQ4gbDT7EDRcR6O+20JlBKPAiUFUVo9mU86tL1mUJUkVRWpC0sowkycjynG63TWMbfICqdlxcjBhNF0iVAilSaOym4c7RPjTxfA7e09QNLgQWyzlPnpxSNR50SuMdVnikhCzPSDQoKUi1pHIb1mXJyemQ6XSBswGlFSbN8c7S6/VZrKdknQyTabxSnF6NubpaMC8btEoRQWKEjK29IBAqpdPpUK5XDAY7LFYbRuMV51dTrEpo8Kg8spfD8xa+MKSJxtWOqrYgDY+fnTAezlE6xUuLbTa0UkNwIJyllRhkcCglWJclo+mUxWLDbL5AmpQg1FZi2MRwPjMkeUphG5yHgOLkfMhktqISCpGkuMaRpCkgCdojCbSzBHzkZZ6P55ycnjJfVXgkKAsikCaCxXpGkmj6/Q51U1LbkrJumC7mPHpyRtruYoOnwUU2KB4bGjrdHBssRmsqa1mtSr54esp4vqRxCo9ECkWqDGma0en2qawlzVPWZUFdNYwvx1yMplQuYKRAYFEqUG6WeDxSaaSSeOEpbYUPgmePnjAezQlBIK1FhcgjXs6nJEaTpAbnA955IGF4OWKxWLNcFAShkCE2yovQsCnWJEmUgCnhcXXDajlnPF1wdnaJSts0tY3nifJUdU3ta6x37O3tsF5tqBvLxw8+p649GysQQYJtIuPWhTia72Cnv4NWmqasOH12weXVmKqwlFVB2s4RoUEJT6fToygqBoN+tH/7QFl5RsMJJydXBCJOR0mN2t5fiqLABk/V1JHV6zWnpyNOLq5YbApsEvEpJlFICd55bFUjhSLPNMjAZD5jMp7z5PEZs7mLLGrVgJLYxmODp64trW6H0npqH1hsai6uRjw9G+OMIcjI6NVCIpViNh+zN+jQ2IbGwnLZMBoOuRjNaazEKoNMFF40SBVZ1sF5qiq2c0OQzNcFFxdDJpMZm7rEpClpq4Mta7RJqas1wimk1FTOEmTC5XTFk/NL1kWFtY7MpCgkde1JkozxdEaWt/BOIKRmeDXmbDzhyegSl6SooJEywXpJkrYJQrPalFSNZl2sWW5K1qXls5NzZqsSL+NETuM3OAKtVkZRrKm2wlWZJqznBZenI0bjJVXd4H0UrwniWhAIhAC/9Ru//jeh7V+Dn3/y3/zBt+pB5BeqIo6SoiTDr0levnfJrMrpmBopAnvphuP25+GeAQAAIABJREFUks9n+/S+EKg6BmzBSPS64epnM776zhfspRsyZdnP1rwxuORDt0/rc43ZeJKFjYFlgMlbBvHChl++9TGZbLiTTqiF4fXBJe+N7qE3wE/YvH0Sg8Vf+ca7vNE+pw6a2+mUqW1zITq4h+3IlXQhsvySKKtp/nbBr999n7auyZSlbwrSxPFweMTgs4ZiX5MuLD6JI82TLwu++uJTXssvWLgWWnqOsiVD2cJ8J9/+XrdtkW6DTP/coh6QpaXeSa4lLcFEG7iqAsj4PrKrDdFgIqLR3m/N5YJt4FUTEhVbx882jH4mwdwuMdITENRe82p2SRES/nxyF/1+HmVVHUV+XhC0IhhBOiy4+jX46t0TTO64PDB0Hyh8KlH1tmGtY5i3uK+4+/oFmXHsqA27es3UtXFK0ny3H2U5IvJzg4rs28nbgptHMxJpeafzkM/LIyTw8eyIfGKiZLSOrTK9aij3M+wrsT2ZSEdAcCedcNH0OXu6T+tqG/BuLeM+keS/OOd2a86dbEITNEY6Pi2O+XR8SP99SbK1cifTkux0SbOXMztI+aXXPkbJgBRwUXT55OqIzrvR8K1XUbDlU4XPNNks8OilnCR13GrNebza5fFqD/EnHXRF/Lx0NI2beUl5s8P0FUmmLLeyKcOmy+PNHp8tDlk+69F/HMWZdVdh1o7WWcnv3n+FWinutSfMbc60bvHZ+oiTi10GH0n0tnGpt/K69Y2cq90ErQO7yYazcsBZ2efT2QH+gw75NI5xSxvPQbNyXN1s0d3f0E9K5k2LtU/5YHGL2Yf7DD5vMIt6e3wdZlpgKsEf3LuJSj1SwcplPCn3+L2TV0k+zGmNfizoCUpQHqRs2gnLvuKnd58xqjtU3nCyHjD7eC+KkPR2PFkrVNFw/k7KxHTpmZLaG66aHkuX8d3xC9jvd0nnHhEi7iGykSVPe21k25Mpx51swpNijx/Nb7B+b5f+E4eq40aOa8VNIHvvgNVlj7N+RppZKq/53kcvMXgg6DzdxGBbCmxbI7fXWpCKqwPD+bLHl26c897VLYqHPfY/sOjSXbOIZROilNwbfpTtknYaFjbnZLODEPBPf/izdB5JQhIZzrp0VPvx+vfKcLGToLQnM46PZ8f88ZMXURcJvSeRfSt9PM9lE3AtzZNen6IjeLg6YOkyKm949N5t+g8DycISpIyhYlcRpeqaS91iZ3eNELC0GX/88cv0303pPqvjBg1AANtPaJ/Dk26P4xtTzoo+l1WPJ6s93j27zc53NE1XYwq/lfxJgpbIWvLgsMO4adPPKp6We/xwdoeLP77F4PMmsmZtDBabnqHuSi4OE07Wffqdig8Xtzgr+rz/5Da9jzTJKlyvJ8EIkmlFQ5uRyVhow9y3eLLeYxNSfvDeS/Qesj2nIv+23NW0Lx2n7S6btmTucoqQEpB8+/HLyIuUzoW/3pgKRpBOLNlU8t3uMTOVkRrHwua8O7/Lk0eH7L0fNxzMeospCZDMG8xGcWJ3OLw75eFsj2nRxv6LfZJVIB9FbAwCyl2DruJ0tNU5zZ2azFhmVc7l+8d0HhjSZSCdNjR9fb2xuDlOyKae8R3FBsOalKfrXU4eHbD/LkgX//hky2uuY+O9sB2erHdZdxWFTfi9H71J/4cJ3VOHLuI64lqaYOJ3f1lpLkyLpNfgheRfXr7Oe6e3Ed/v03saWcEiIs9j+7sWFJsWl3sJj4p9Tqsd3h/d5P0H9zn4XpQCXm9OZpGL3jmB8QuCUdVmXHX4bHTAx49vMfhRZJfrMt4LnmOMdBmwIeUHxQ0mIafA8OHlDeTHHbqPA/nEYdsyTrSUES+kNpKRaHNlMk6KHdYh49ufvwLf78dguPRUfYVLBMkmtmJ7D+HDwQ5rl4AU/PDhHdyHfW78id2uJXHTSBcxrBcexChluO5xNt7hbLjLfNxh/9sJvafxO4xLBWYTN3Xq/Qwz19h1yrQdS6RXkx6bBwN6XwjSZUDVsc0ctLxmnAsP//jf//9BaPtP/9nvfKvyNdY1BB9QHvIsI8vbtFoJg14bxBabUDV4X1EXFWdPzshNHH8UiCjVarfoD1oIYZEyYH1DkqUMxyPmqxVBSoRM4xcmKVEodnZ65KlBCCidRyYp62LCaDxluSpQKgchEFJR2waTaW4e7SCtRQiJD44mOBrrOD27oHYOnESbhLJpMKmmN+jxwkv3WE4nqCBoao8l8OzknLpyKBnbRUanNLWn185ptTKUjNKipq6oneXi5IJV4SitQCmDcwFvA0gXW8QmQ+iwtbgbsB6lErxQlPWaEOKYqtHxQVoCOFBIfONQAQQa6xxZK8MrqJqKVjsjSQ2HB7sI4ZHeE0IDCOoQWJWRc2qShFVVgdIYbWKz0NXcu32Pbl/hbIlWhsvRhGVRIoUmiIDOMta1I0szWq2MJJOE7QPmcr5kPJ4wnC4RKLyLUhwkSKMQSrG/u0twLsrZjGQ2mzFdVJxeXLHX22dTrPAC6iBIEkMvT5F5lCM1rsS6wNVkwnA6YVU31LZGaU1ra+bu9dskRiNsQ6IFlfMsyjWnZ1dMhnOcU6BlbDIFgWlnKBnIkpwmgJCS+WrB5WjGxWiO8mnk+PqG0lu0UdS2JksNg86Auqopy4rFquDk5CKGdSYn+EBVF6TdNo0tuXvrFmWxQkuBsA3ee1ZFw+Mnp1ih8V5RNhYp4/nbbvfptgeE4HCuIoiGTWU5PR0xXWzwDoQSBGmQKombG1rQ7fSw3lHXNavNgtliwaNnZ3TavSg/Ch5bN0gCeZKQpgaERSod240+8PjkjEYopFQE75BBYLTChUDaTpHKkCUtVqsSGywXl0Om4xmldeRZm6IuEQikVDRlRbvdRmrLar3ChprhaMpkOsc1gabxyKCRRiKFp9/tYHGgBatyhUwkRV0xHI15ejlC65SqcQQTd+ibpqbd7ZKkCUYLhK9YbxrK0nN+MaRYN6AynHdok1DXDSF4msaSeIknYL1jNFpxNpyhRYvGNQgltpxohTGK2tWslkt2d3Ypq4bZaMz52QjbxOapkopiXRCEI9FxY0ZLRaZSVGpAwnA842I4YTEvCEIjlKSuLdY7TJZSVRXdThfbBNK8zXK9ZrFZcXZ6hnOOsm4IWwOL9Co2pgUokxKMQXoYT2cML4eUjcU1saWfJxllFSsKq+WMlkkRIm4QLNYVo+GU9cbGlqCA0takJkHaGuEVSmryPEUrTXA10/mSzbrh4cUZtQQpDN47MhORFt46ur0uQmjqqkHIlLPzIU/PJ+ikTW0tSifxnMJSrDb4ENjd2aNaL7EWKud49OyKyXROkLAoNrE5HCSdVpe6iUgKoWNTsy4bLk6HnA3HVDbgvCTTGUImlHWDDQGTpAwGA4xWLFYbZosFl1djnl1ckGRtpNc0VY02EiGhcZa81cMLg7WOygXmq4anp+c0jSeUDu8FlW9oqQSdaEIQbIqS+bygrh2j0Yz1pqSoGqyUBActDHVVERKDJm6WVeWa1XLDaLLg6nLKqiyxwZNmGU44XACPwDlQQlOullgvqGxgNJlyfnmJ0AllXYNRWC3wzuNKR6fXY7paMh6N8F5xfjHmajylso4gQesUJQNaSZQ0aCUpNhuKsgZpWBcN4/GYJyeXLDcWlAEtkQFC3ZAlGRrJalWA1DghWJc1w/GE8WTFpnIsbWx0KykjXznEe1cQjtmqpLGSyXzF+XjBpnIooaldHScsVELwjhCgbGo2RUnlBdPlmi8enrBaWopNjVZmi0dyaJEiBdSVxzZRHLaxDVeTFWeXQ1ZlgcVvhZAO7yKKRShDbR1FWVJVNaPhiMWsYFE2VE6iAninkMKgSQkuoEVsM0+nkygOLALnwxGrqojoDBHZ8yZIhFEE69is1mzWBTIoVrMFi2XB+GpCudpghYiS0BDIMrMdx4730N/69X/7b0LbvwY///3//J1vmXmNKhpECIRUU+0aqp5isxv42aMneCQvt4eclX1KZ7jYdBm8Jyl3dWyQqhjalvsZk5uRW5sqx6LJOEqXfPDoHq2z2DQJMo6EArhEsboT2B9skCLwsDgkkZaPZjdZPOzTuYicWsRWZray0XL/lYJERVFq4RMuqz6TukV4r0O6jA+eLpMks8iJHN5K+E/f/L/54+UrdHVszsybnNkP9zGbrXzLg21LzMqxvqkpdgM6CWjpGdZdToodPnlwm3SoyGYxEH7+EAYgG6j7Kj4XCIFtq+tAQrjYoBUusk5dS0ZmkdqGtkZet8HY8n/rnTg6mcwa6v2UuqvIXl4hJRjpqILBCcmzao9PJsdkH8SR/dZFBSHEZuFWtjb9eckr+0MWTYZJHO6TNu2zKjaFXBTb5JcF1U7CyW5Gq9VQYdiElO+P7/HZ0yO6n8dAWV4b6+NDrtMG+WJB5TXH2YKeLvnD4cusnvU4+POS2ctpFCLZ2LxsuhkPbnYRGjq6Ytx0uKp7/MHDl2l9ntC69JhVDNCLfQ1K8PBmi3a7IkjJzXTGw/KQH4zuUHywQ++xQ1bxXHguWfOtBJdqPh30GFYdEPCji2P8gy5775cx7MvjyHsyLfG5xixqVoMO5taGy6LLcNFhfNln/wc+hs5ZbKaaWYnrtzAlLN+xnK4GtFKLFIHSGR5O9un8aYouw3VzOgZNksV+hjkoKULC2qXUXnMy76Pe69J7EqVRyC0eQCsEmpHokB4WTOo2F0WPR5M9JqcDbnzHsj420dq+PWeCEpQ7hmlXUQmNUY7LosusasGfdTBFABXDgqAlvmWQtWfVb3OpMwa9DR9Pj1jUOZNP9ug+hWxmqXYN6agGGRuCqpRc7hsKnfDp5IB5nfP04SGdpwplRWwcbt8zWjK+nXHadFmLlA+nN5AKHi33ePjFEYff87hcks7tNabFbDzrQ8OipWjnMWR+MD/iatkl+Sgjm8bzT1VbDMMq/ltTQNFJmNuMeZWRP0w4fDd+vmpjEYLrcWTbUpi1xwwT1vc8F8se1UmH3Y8iokTW8XpMxgXCx+tJBmhSw6InSBKHD5KPvrhN5+MkjtRviy16VaPK2PxOljA5NqzqhHan5vFwD3feYu990FUgndkYCAsRG+ZaQNAMk4y1N6Dh46c3aH9hSBfb4MfGZn86tYgA7UtLsZ9wqTJGRZtnV7vkX6Tko4DZhBiITzbYfhrRBQJsohkONFlqsV7xxekBbpZx8/fXkUOuI6+6HmhkE0P76bGhkIrzsosXkk8/us3R98L1ZxA5sApVB7JxgwsZ9m7N2brPqk4Zr9rk320zeBjXg/azGKYjoDhKMesATrGQKVUG002Lzz+5xc4HknQRJxvqQcTKpPPIgG7ahuZ2TS+r+Hh4zMcXx+gHbY7/rKbZNmXNFpOAiOfmZjdhZjPOmw7nmx7PzvfY+b7BbGLgGtfdimo/iSgKD+lEMLrYYb3OCY9a5KNANnVRxhhiYJus4jngMkU28RSbNlfjPpuHPQYPoH3lUGWg6evIWN9iK1QTA1m5NKyrjIt1l+XDATsfxntpNouN/efSM9nA5sjQf1jTewQXYUDxox69TyXJKiA9uFzGTchUxrA3EeQTj5kpTmSPp5Mdisc9dv/U0L7w24mAOC1NiPcylwpaQ89Etpk0ORfTPu7zDp1HitbY0Xq2Jpj4fyRbDIMpAxufY09bTKoW6knO4CMZhYM6Tpa4XGKzGNxKD8kyYGaKepWxfNZj8IeGfBiPr0+3gbMP2CxeH8kmICvJwufMpx3OPz+g94GmdRkbtj6R6HqLFtURNZQuPXJhaFYZ5cMu6YWmfR5IljFEfi6Ti1MxAZdJVA29p57WuUCtFXsfBtoXcRMkcugDLo/XEwFaw4Z8HCh1RjPKMBeG3kPI5h5d+Gv0z3PBp9x6AP6yTVsRnltj/wr+/Opv/nYISiBCQCmxDRwVWWL4qZ96A6MsLSO3I7me5XrF5eWQi4sFVeNAaFxQJFmKbQpef/lFjg76KOUAz3q5YTgecX4+ReoWi3KD3zJXW60W/V6bn/7yqwjX4HxF3axZzWve/+gTpMpp8PggaJwnESk6lbz52j0GrTadtqGqN7hg+eSTx1yNJzgvSFROEDK2uZKMvcNdXrp3RHs76rkuK4bDIY8eP0PrFlUjCMiIRyChnQpeuHPA3iAjyxIcjpPzx0xna6YzqL0ieEndlOR5jvU1QQaEdFDFh2ThA01orr8Ei5+wRAe/td4KgUMgea653t5I04SqtDzXX0d6SOD+nRvs7++yu9uCUFPXlvlswen5BVfjOVIq6ibgpcGFyNKVInDz8IC7t4/QOuHp08ecXw5ptqIvZyNrDzw4uHvzgDuHA5IsQ6WaxycnnD67QDiNUoZNafFaYr0jTWPr60uvvEKv3SKVHicdl5dXnJ6NcEGiRIuyriit24ob4Pa9I46O99nJUtabGb6xPHz0lHVlcVU8LlUTcBZMajg63qHfbnHj1g5NXVKVluFszOXVlKq0eKdh+8BRVLFlJULNzZvHtLKE4BybYsX52RVp0mXVWBpbk7cjKzG458KHmsPBLjvtDovpnE1VUwbBahOZnFJHmV5tK1p5yv6gx9HREe1UU1cbRFXx6cNHNE5SBUPVBJSK50qWG8piwq0X7tHvtOi1UsrlktOzS5pGMFtux8WVxIX478DR6WYc7u6Qpyl5ajg7f8p6VbDaOKSIjFYh4+h/wGKM4vBol0GnhXcN1lom0yWLVUFZeLRJonxOK9xWzpX3Mnb6XY52dki0ZjqaMZnMWKxWBAFVE9BJgm8CWZKgpcIkkr39FmkaRX3n5xdY6+NnIeLiHSxIGchySaudcXx8RN5KWa0WUVpmHVUZWC9XZHkHu51vUzLiTW4e79Lp5gjhqGzDbLxgPllGLq3M8SiwLm6AyNjKTtOUolgSbEBogw+S2sZxcCljK9VZ8KEhzyRKxDaeFhKlBLWrCSHQ1AKhJOv1hnaek5potq/LDUeH+zgVJXxFZWks1F5gHXjlaZqKbppjQqBpotRJaIVJFK6sCc5SNRbnofECkyQ45yAEUm0IocFkKT5Y2llOU0cmr7WeUPvYWE4E1loq29DpdMiFBgXBNTEAD4IqBEDgvEcqgZJ6iziJrcadQYc0jaF3VVvq2lJbCELgao82AuEsaaeFswGpwHtLK01oKhvXg80GpQxV3SCEwtoKqTztVgLOo6Sg0+symUwQShO2jFfrXZSWOUmSJJSbin6eI0SgbkqcdCRKE7ymLisQBikVLt6K8MKSEe9VkoAyGpOllEXNpqwQacpqWWCMIUk0TV3R2p4fwcUNlu6gT7GpqOuaWngkCiwIGbAElBM/lltKiRUhTk8Et33PFpVmNGVNS8fpjdpZTKLwjUWnGhc8tonNmqZpaKwlhECWGiQxdPB1xIJoXHyteNZViZRy21qXVLVDaIX1DUYLtJAI7zCppi5jeG9EQtlUEQ2Ex7kGLdVWALo1lgdLYlosqhUmz3AejND4KiCCjTK3EMBE3q+1FqlUZDLbZrvu5zgfHaRaKiQCH2qCVyADSWYgBFwd37NOErwjcnk3SzrtNt5bRAKeOCWBUwQfHwhVllCVDu8DFnEtjJMhtoVzY6iciwIiAUkSG7t1ba8ZyYk2VFVFXccJkrKu2KwL+v0+BM96vUYIgRIKqRUCFd83cvv5KqRy1+ura+LkR1FXeBmPr1GKbt6mLEusjQ91CIVSirIsSZIsYke2kzhCScomri9patBCQgj8zj/57/4GbPvX4OeXf+6/CM8FNc/5gratqfqK8b+7oZXV3BnMAKis5vFoF/Ggze5HgWS5beFVnqarUGXg6b+pOHhtRG4iM/Rs0id80Wbvg8iM7Z5UiO2oY7FvWN1WvPbvPUCKwMYmjIsWF6M+N34nwaso9xEubCVNCptLTn/NcfPWhDd3Ljnd9CmsYfh7t9j9xCLr2KwRAZJF5NXOXtL4r8+5u5UafTo74PyLAw7/OI50JtugVxceAmwONMOfd9y8P+JWZ86qSfnk/bu0n0l6TxxmHRtcZtlQ7UZm5nMRG8QQOArXfiz0e/53wLXALCiBrP1f+DvYBkrLH8ufbDt+Tz/7uoYXNnz1zgkAw6LDk/M92u9n7H3cbDm2kSspKxfFXEpw9XZK9TNrktTi/7zPwfv2ukX3kzxigPOfy3BvL+m2KnLTcPm9Yw5+6AmC+OA/jo0sVTrqvkFVnke/rkmONgw6BVWjWT3YYe+9GDC5JIYOyay5lvVcvpOyfq3i/u0RZ5M+zkm632mRj3xsdMr42ck6WsDHbxo2Nz3Hb16xLFPWq4zk85ydj33ES/wEFzAZFzSDjKAEw68mVDux1Z1OBAfv19Q9Reu8Qq1r6r08BoU/8Sg8eSNldRfyC0E2CWQzR36+wad6ywqP463VQcb0VcPiNUu6W1AXBgTs/6sUXcRrI5k3MYgPUA80ycxy9TMZmxuecFzhl4ad9xS6gN6TiuePXM9FbADr44TZa5K67/GDhu6HKdko0L6Msj7ZbFEgksjHbEkmrymK2w6sQFWCzhNB59yRThtsHkV3No/oEbWx1LsJ8/uG+euO0HJkzxK6jwOds7jRYOYVLjcI66l3Iu6jyQWLFyRNJ6Bq2PvAo8twfW4/b5l7Laj6inJXMn/TIfo1DFN2P4jN83ThyS4L6t0MVf6YmRykYPhVQ7nvCRLMStA+ge6JRVV+24DdsoxN5BK7VFJ1Y0D0fE0TLqJYgoqbDMJHTq1oHNV+Flvzy3gtuDRu2ggXZXVIgTmf0dwYUPcNLot/P34rQdbx/aWzgCk8ZumidK9xqHVNcacXJVMbT9ORUYrUktftxGTpro+/a/1Y/tj0dGzEtuJGzmZfoYuAKaIo8FoouBUHmnlFcaNFsauuj3u6dAjH9RrynOPrE3nN6JU2ML9vaLoCvQ4ky0Cyju/j+TFyWVw/6l4MqPxWdljsxdcU1wOLTyJ3O0iBKmM7uTiI39VcIigOJf1HdrthF1BllEIGFT8f21Yks4bl3Sxe+2v/F9Zxs9ryt7fnu2w8wobIUm1JvI7HxOZRCpmPY0kgP1vj2glN16DXlvIgoerG8X9pA5tjSToNMbxf/3gdDgpUGd+vS+NEgdfi+v7w/DMwS3vN9y4PkohDWPvrz86mEtXEzymoeD9QG4twnno3Iyiuz1HbkthU0LQFuoDWMF7f3mzzlp84vratrwVjTS4jKoR4fqXz+N6DEtcMda/jd1KIjd26LemeRN64bOL//XwdsZm4RrpAvCf67cbjc4llvQ2dY9tUXp8Xzzdd6s6WgdtE5JDLxDVTOZlZNkdJDCwFP4ENYsutBWcE6SKew2rLx35+LFQZojRs4a5fV92JYjOzctfHq+4okqXDLC3zl7K4zgxrlnejkLE1jMfzOef5+foZX0fAmXjcVBNfp9oen3TSbHnEEtuKjPZ0EUPZ55gPlwrSqaXpqutw/vl6ZGZlFJYOsmtB5b/+5//ZX+q79l/p0Pbv/uY/DMpolHM0dRntzY2l1+2yu9vn8GCH/Z04QiyF4PTsguF4yqr0BOIIi1QpwQukbOj3OxwfHdBuSTarJeOrKcPxhDxvUxY1yiSsyoo0zSjKkn6vy62jffZ2B6QG6mrFxfmY8WRO4wSOGuslJsmwtadxDbduHzHoddnptvFNwXI559HpOU3tUElCqMAKgVAJ0iQgA/s7LXbaLfb7HZaLNafPzlisVmiTsrYeJQ1FU6GlJk01WgV6/Q79VhvXVKzWMy5GU0wyIHiJC46qqhBKIkQgyIBOFK70GJOCB+8dxkS5mFIK7z3GGOomPpDXjUPrbSTrot3de4+WEZfggo8P3NbT7XbQW5v8iy/com5WeO85O71italo6hDlYz7gid+Ngvdo+f+x92a9tmxpetYzvtFExJxzNbtvzjk782RbrgaVXa4SKhswkpGQkAD5wogSkkH8Bm64ykt+AxIyksFgybaM/A8Q1A+guACEsxpn5el2t5o5Z8RoufhGxDrpG+oOm8wlnTzKs/eaM5oxRsR4vvd7X8G0wvXVRMuNUhJLLhyXqIBrjgzDxBzP7KcDZTnx5pPneO+Z5xO3t/ecTzPNeEIYuTvPGKsPRnEasDQYy7Nnj9g7x1fvv1GPzAK5GOI5U0rfBIuQa8YOCnKupoDz8O7dO5ZZCwCtCKVVbX/3I8f5nv1u4DCNPHl8QSESjOfnX33J8bSQq3AxPebudIuxWgyQAnYQ8jJDbezGwDzPuDCCWNKSVaGRM24cqOh1cc7hjeDEYq3hvERSUYgeY0TEqLekMVgaeMM0jOynAQFqXIg5cX+3MDeDEVUrOudJy4KTTHGGw27CG4NvhnmJFAwYz/l8xqAw3QbH/XzP/vKCuiTENFpOBK+t0iXrIrRE9fg0xrCUGWsN1lqmMCAtMp8XrJ+gee7vT/ghkGkYaZSqY846sNbipeE7oMi1UUqhVZ1HFdOhXCQ4j1Cp7cg0TcTljLMjOWdq0/FunKU1nQO7vdoJiAjX15eclpllUe/ekoFSOZ/PhP20zYNhCDip6imZF1ItpFjwfuB4mtX71I0afCVCKUUhjHfYVgmmq5ytJfaE9pwzwxQoWQsD3ukYEBGFcf16mNqIi7aFI0KOCdM9aq1RMBqmkVIKpQpLKhQglooLttsvFPZ+YFkUHJaiytsSkwIoEVItxFIxxuKNA1NYnxXWWqxzet+bIcUCGGpeoOnxSlDQnFIiGMtSMxe7PTHqmJqXBSuiBYAOco0I4+SotWKtFl2sDBpMNR9xNlCNPlRTLQziaMH266uAjKZjg1IpNGiiQXTnM9CYBk/Ph2EadFxY70i1cXu8xRij4K8UjNF1MbgAteGcAsJ1/bEyklIip6qhjy0hxlFIWAqX+0uW80wqmWHak3NmnhdkDKSigNSJVS/e0kFxzUzDyPE8I17VobWCmKbnVBLGB1yVDdK64LeiSsqZjKF065smlpZ1/cglabFMhMkF9SanEXOi1szgA42iINcYvPfwsuyjAAAgAElEQVT92anpDYdpx839Hd57ljTTqsUYoVWLc8KpnAmDFhukNi23VQBReForMUZGZ2lN58Q0BpZlYRh3lFK0IGYNJji14akG27RYUqvOJ4zBhYElJYwRKFV9bU2j9rkWQtD3AhGgUgl6fVPGOLWgUF9yFFCK6d0lOvaabeTSSK0y+QBZQ1uaE2JVm4kqFlrDNn2qOaew27qwPUddcMynGYPt86UhYkh50S6BZUasA4TcKs57atGiQI4J7wdKSfqcdj0NXoSUIsMwqpqwVQWzxlClrxsNpmmi5YI0uqI34cLA6TgzjUGhtDEsJbOUjAkOk2Gw0tf0xj/5738FbX8Zfv7d3/gvWxu8+jAeI+nxDpMrx09Hbj+33P8w8b3PNSTM28LdHz3h+v+C3dd52+Q0dWiiWcPdp5aPv16RJwv1w8D+jy2P/8/M+bFlvCkUb9h9GUmXjvA+cv9m4u1vG/juiYvDmfvTqK2Rf6w+r7rpbT1QS1t2v/6dkeMnFf/pkVqE/PXEyz9UhV4ZVMlrZ/W8XDdxH37gOH5WefS997z/+pLL/03VcXkUdt9kqjcM7yNl1CChPBiOr4XTq4okw/jW8PSPooaWFYUH/qarDzuMKKOG5OS9wm9JCh01oGhtYxf8nYINf5e2jdsKDzWIym7gZFXG3b8OlMEQLwzz798TTx6ycPm/e3ZfV/xZW639fVWo0T+vjBrqfPtGlaI2QrivTF893IN0FQjvZ+YXE5IaX/6ep0wNf2vYf6Hgct0c77+Im7JohWDxyvHhR475cePR/0E/99Y30lUVkYPtMEQTzU/PHfefGOrQuPwpG3gwlV8IThq/OjO/mDi+cNx+DyQa8q7x5I8au2/Up/P4elDlcAdz1cvmsQkaeDPcKJgD9TBuwgYv18CpMtoNnqgyTdV8aj2QFRR2yL6Gt82PLOenQnNgMvhjY/+VQmoFJ/q59pSpfXycXg2kScgTDDeqngMY3idVQU6WGgzTz/Xc14KAqW3zbLZLF9PcZfJOA9v8bVc7DsL8SEPG1jkHsPuy+4HOat0gsWpAZw8GzDshTQqa1qT69bsl1Q0apQu/BTvNjyzhvpJHs0Fb9SJmG8/zs4BE/ZzbN47xfd2gi3SLjuHtTL4I27hNF448GuKFdGhZcScFYuNXCwBlp2rp9b5g0JBDb4iHDrCs2nmAhvSlq4BdNMRs9cyuzmyQeT1fd5dYnirgsUvdAM+6vsS9AlibGuEmI7Hi7jrcLpVmhfnFQLjJvwjH+nWo3uBOFX+XNtuGbR2A/l1CtWoL4E46ltcCxRr4pZ+jwZHhJnP/OmzQM3yI271df6dZhehSFI7ZpIBKiqpj1/nQxOCOeYPIq0owrUCuz62106CJYXh7BmB5OtGzqzg/dtrW3kMW9392pIkhXQ/bPTO5kS70/MuoSvM06f2rQa+TPRfShcOdy3a/axDuX3vCvY6NeKGhkOGjQvoVkJadJ+90TlWnEHa+ttu6b+e2gTg9Nx0fZZQN0q7hkOv6YeeyFca0zb2rL+dCDdr+Pj9yuL6WuaMWu+KV78+n0j2cbR/D+tnH55aLn2XyTgi35WHud/Xn8I3OE4W2po/Zh+ewwvpEvPYKXIzeg+GusFyoCnotGKzjfB0jKwRd712e9Bg2GNmvgST1i4170a6Tfuzrmr0CYL1WZbuv67gGHXtrodSdCvMThfzNsIF6Ux8KimUNq+8QunhDuFegnAdVXq/Pe1PY5tf8SAM413Oys/rXmqL+ve5ctZjSAfN6fOvvx0u7QVs790603LDnojYdT3y3QdE5EO4LeRLGt4nlsd+uVbjJuNtlE4+l62Fbd/5/AW3//T/4g1ahh8Ko2tLgsM1iLDjnePboMXN8y7IsVLPjeDwiznI+nxmmiRIrGYOVou0jzlHrkYCwt4EkumlUdU4G52mp4Jwl58zFxQW1qFpAGgRnubu/6TAm4vEY67hLCb8bSUY3l/tmmQTuTneE3QXVgDQhnRPVakt67lAn58zON1XUOYM0BSmmNfpaqBuyqtYJqWTqoO0KQTTQyTlHSoV5iRvwaLngutKnOXTDaBytCfM8MwVPq4VSF7ybSLHq9aFggqodfdU22dYMrqny1vQNdymFZhVYYAylVaxUjGRqSbQ2EZxnnqNuKkXItWGNxYonlcwoQggKi61xNOB+OWN9hz/isEa2DWYsM4PzWBpDtT0wrZJKY2kVcRbbN/KtFva7USFrgEoixTM1N7wP5FkT3RsCzmExumH2Qowz3lsM2hKeciEnVSc78cTlxDhqKNA0TRjrMS2Tsr5IpNIQ51jmhthMa5nSYPSOYB3pfGIIE6k2aKqcPOeF5t12v5dZQajQcAjGCpWiMCdrSNs8z5hscIPHeqEuCn8McLGbuDvdsbsYmM8ZEf09xLF0VZUxTUGstWi2WmEKA2VRaFRKorTaldaioKpErdjWCsYwDTvmkwJA/RFaM+Sa9B66hpVGygv7sEMwlLTgxHYwogF4MS8YC4WCqYbgBsgKyTRFuWKqwrQYIxXL4DyLVEg6vg820KSR48KwmxRwn4/UWvu8czQsSy5YGqM1fe4sNGkaFoiDZlnSLbUIg3gaGbGBanQctqzp1GK1LTTmQqaRc8FbCzVjBUpuOGs3WGREaBRYMtmCHQIxZkpuhP1EKDDPM34XyEtmDAOtNXJM4FT5me713gA6Riavz+VaOYyeJVZqy+AHUm3UIjQSrWWcUdBUa2Xcjd26QlWr0qHQkhVa08PUBjtiDJRSqDRCUAA2n84K/WNBvOnjyZBrZBRHqqmrMQEjiIFcE4VCEIPgqUti8mrdcHs64v2Ad4NayQDV6P84sVjTyDmBq5hi9c/E4K2nRH3pXTsAWqvElLUQ1dXq0zQR57OGzjhHy1pIyzkrrBsDcV4UJJdKEdR7MGb8MNBKYRgGamqkqspOK34raIWhzy8a3jrWR2tDyDVhnVAoOOMwzRDjwhgGSJVsWrcqaYi11KZjwhhLWiKHXSAnfU6VDkCdDRivL4utw2o/BEp/4c+t6nMj6fl7Z7DTRJwXJqtt8AAWOMcFRFRZfV7wPSjMDf3fznG+P/H42RPO57Na5gQdg60kXc9TYhhG9X+u+uwR9DkxGMPN+cg4ePw4kFOiFhibjumb5aQ+yGIxSC+yJKjqw+bFklvuz7BAM3RoDSF4tR2JC/vpQJoXjvNZ71WtDDsHqema7AdSWpA+73M6Y2WgZFiIuDBgYsWJ1+fZ4HC5Qc20VsH1OW30+VhrwQVLLjMNR64GZ0dMU5V9kEBpM60aaKp0PcZ7jDPY0hjCxJwjU9hzms8Y76D27iIj0CwNteZ4gL5CiRU/OmKLSINYG1O1eKv+4tUajG2UpIXW0urWsi3itCiVIiLCOWWqWCKNyXokFYx3uNb4h3/vv/4VtP0l+Pm3/53/qq1t5etPkw6ljG7cPvzQEm7RdPussKw6w/B23jbf9vSQBF4mi6RKOjhOz1SVNdyWX1R1dqBo58LxVdhgRHWqNjp8kTeok3dakN//fOmJ3hmTCvOLifNjy+GLRDrYbdPoTgoumzXb5krmwvJ0II8K5KpTe4MmaNs4PGzI5qbf0Ys16cKR9qLhKefK8CFTBtkUNKt6kdbBiDPQW5LTk53Cq6VQdm6DdyZ3GNb/uykKjMqksA3DtkE2tf0CLCmDbMe6wtThRn0MN7VbEMpgccdMvPbEgyqyVug0fTFvwKE5Qxnsds3cXSJdBU2av+x2F3dFIdNHDZdaQRANzs/8t+AP2/VfodgKNYDte9KFxd8pEF1hgzuVTW1ZRou7i6rAXHRT3/o18R1guVOH3zf6XmpqQ5ZMfDySDraDHNn+viql4tY9iBjcxzP5cqQ56UnzZgM4q8orvJ9pVqij3Y6/er0Hx5eO/ZeZ8xO3AQw76/eFG32OIYYyWsogOk7Q5PVVvbhC6vVnBXEr9ARYHodNrbz+mKrwYAWXq+rv/GKgWgUNeRDcUjfo5+6SFioXVZrlCxXobL7Mq/LN8AugKdykfnCN86txU0zGCw0hGj+o4nmFu6Y23O1CDW5T565Bf+tcBYXO34ZZ61hYlW+rws4UtUeRuWDPibLX417Vo2W0mz3G+v2goLYOFne3IHMmX47kC094P5MPAXtKxMfjdi1BgbE7lU156W8WvU4dgh1fhk0JCGxrhSl6fmXS/dCqTF5BYxOzwSN/q+Fj6zGmq0ATtvmS93ZTJpZR5/G6rmD0d+Kj8AvHqWNHNqCYL/wDpHzkaAKHn+kamneqGoaHMbYWC+z8i+uhjg8Fzmuhbh1/7i5Rdg53p1k0y9NRlc3OqJVNV6WukLd6o17Bff1YFcaS6qbEX9vH1/VgVavruuG3NULH7C/OhW089Lm8AUwU2K3zdgWhG7w8Zk6vhq1YIbFuBZdmHwpzq/p/PR8765prcoeLO1XB+rvM8sRv47wMokWZftx2+VZBb9RQsjxZhrczNz/cb/NphYg0NoAdL3VtW4tOq6o2XgiHPz0RH49bkVNyY7mypL1w8c8j6cL2e2u2P1////o767xbgfW356xdKqdnDn9uTF8txCuPZA0YhV4Q66pitRswm2p5LXTW0W5QfH1m56kXFvr9tEslTxrUtQLr9dg29W2/j/Gg0HWzXfKG3ddxe4+JF5bhpjA/skzv8gaUm+g1BTYl8QrCv/3u4e8rzep6uDz2pMloYaTD6VXRvBagVnhcndnW+FWha+9m0pPdth6U0fC//OP/4i/0rv0vtaft//iP/qefaNSMwSEK37pfqzEaNnV7d0+qkVKg9JaMUquqTEojx4yzjuAEiyHNi3oP4lhKw+VGNYIRAT8SW6K2jLXa0no8n1StJVZDxFoFaylG8MGScyGLwY87KgbEIkaQWjFUdocdc8pkgSZCrdpeLhiCdUjTpHWRfp4SiFUHVKlVK2Epa8upCLa3k47WQ9W2TmcdKWbmZaEVPf8QBkos2CaqIDKCE8dyjt3fVmioYnb0AdAW7pwbwzjoRl0EW1GvUerWjttq0jII6sEJhtQ9AM8pUZtgjKUk1O83FUpVJVIDaAUjhkqhdqggRj0LTVdSaX66oQK1n3PJmdFrqy+54qxV78am6qO6evkZ9YgUI8SUtdhrqnqsGhBjtY25ge1wpWRtARcRqIYUM2AQr59Nq6r0stJhriGlzDCO5KRet7lkghfd3FdDyplxPxHTDJLZTSMlJ5wVaoVcGmIdpaoqsLaqsCcXWqkd/lWkgfeBRtOk8popMSIGHBCmHaVUalXvXOcCJWuPcBi8QqOcSSkroMxF08XRoDcAqo630JVeTvT46e39tVWFSaUivXU2DIFSE6UWxAtGDEu/FsYKjUoV9V81xkAzWITzciaEkVpUqWmsVU9F08daazrOEYJzpN6urgF4XqGJC9rSnCJiBfHqBVtLplQFcKlkUk000T8vpVBbv+9uworRe58y1ei4E+eJKSIOBbCmh2hYy5IV3LTWqN1OI+ainrVd6WZF/SaNoStttb251Ebwg3rXVYM1TiFSEZz1GHHU1hAaYitLXPDOYcXo2BOzqdRNL8rQYDcM6tWJqvSkaZdBs657Jmswl8XgDAgFsYZUIoKO/1qK3lODKrZbptasCvyScdYyz2d2+4mcVYkYs770W2vV/0joY0R9VH2fY6UmDBqw5tAxQe2VVJyGqVBZciYMI3lRSF+aFhNatzJ0wXGOqqLJtVCtqkpKLlinkNxYoKka1VoAoZaCtw7bdJ1ppTIedsSctNVzSVgM3nmICZq2xJeqc6RBt3JZC36VVjLWWcZp7NcMjKDFhdYADRKsVYtgplZctwUwrapqNhe86FyLtSh4rFU9Us2q0K7EmAh+IC2JUitYwzhOfU3q7U4rfO3zKKcFmq7XXjyDDww+kM6RUipeAjkVjEDOiSLqr2762myDo7SC9RZyZQiBHBOHR1eknHDeE3NmsI6cEoMfKFlD4ES0Il1LxRg21XRs3R8tBPKcdA3ODROsWjugvKhEDWd0vetg8iOCjildDwZqa8QUVZE+eFLOVFNJKfWOgwpOQzTFWVLWfiu1OwDrLMY0lmXGOo/FItVgnKXmomOhJr0+pajqPGddo1Hgn1vCiloj5FL1OQLqF9+LYdZa9VGumZI1OAirPrn7/Q4nlrIkRh9YcsRaVSmXnNYphbGZ2vTZKh1Uq6ra0qylZkEkEGsvQIj6kRljtH4hQgaqGXohUf9tvSW3Rm4NnKUWtT+hVARDM4bcCn/wt37lafvL8PPf/v3/9ScPSp6uPAvS1WWqDj18UbU9N4GLbVNeLU+DwrZzoU6OfHAdHBXiI/VXH24VMq5p7KDt5ZI0Xb2Mwu6riEEzCWzWVl3T1Ce2TKqGcYt6REpq3f9R/QoxMHclpeubOkl1UyLlg25qa+ib26zti+Go32GTqmRsbBsMrUGLgPGRvgMguukOJwW2q5I377X9URVvsvkZho8L6TJoIaqCnTPp0aCbuUFBdbr0uHsFuDXYDTpWr+nrpi+Mpj3844/qbTi8i1u7pk0NrCHc5Q4r3EN7qyiwkaz+fs1rWn0TtFPh23+e26a+TNdevQSbXitVnPZ1PDdkTgqmug9nuFdwIZXN17M5vdY2VmpQdajEqu/kTiGMP+YtXOjbaq3mhLKzYAV3yj3NvRHusiqjDqoUhK4GvPZbwNbybFSwPa2hUHWDGjYqpCiT68FSjbIP2zGVUduxpcN6u1SaM9RRx/baEmwaGuS2aFha2um9d3PFHwt1EPwG0/Qa6f1UO4x8cN0XUQPgMA/tzM2pp2Ltv7N9b7/PiIY2ufsIRseMSYXWxzf9XIYPebtOknXuKaCSDf61oO9Sed8hpVNV6npuZdJxGd4vfb6JtvJnnS/NKUD3p9627/Qc1vFUg6MFeVBP57XNvYfZOQVHZecAVZ+G7m27foapqGVBBXev755tcBt0ViioY9lGLRQZuhI1qNe2Ane1SJNcNKTPCv7jmbIfqGG9703ha597dtGC5/Jk2GBqmTTIzvSx7o6q6vQfzrRgqaPX4CsnuGNUa7nBamijk1+41yv8l1Spo2V4NzM/V3BYwwNUrUHFOKaDSwVLvTiW2zYOTdVxaerDmoHRtT3cF8JdIR9Urbp6z5ahh8ZZhWjDu6Qw9qxFNoOuCc2vPez6uaqe1+NaA9TqYLFJx0J85PF3RVXhUdfjvLNb0FUTswHTNWRynYPrfW/esDzyOod6IGXeywYSy2i/5QWu596czpHq+vq1sxvwz3tdh9brg+j65O4i+TLg70sH0414rXYo61ja3CK94OaixZIGzQt570hXTtf0U+nPBqct81b9pm3U51ez0s/X9AAsva7xSv/++cVAOKpXqr9TKwK7NFWFLho42ES9YCW3bQ1v3S92PSZ31rVrfV662DB9U2XjqgwW/LEwP17HtA78tFcI6u+y7rUmqzC7aDGz+Q5T1/vWA7zMdkz63zFoV0kQvTcWEPXULrsHeKyhad1iJHb2NYrC3n5P7VypwWzP2HUs1iAPStm5bv7Q4WNifh4oQc99udJOHy1u9YJt78rQdaGPObt67hu99/2cmjW4+0wLgpvXh7N5sCpZdI32x0z1GmZXg4ZLurM+90yqlMthG0emamHi7/ydf+Mv9K79LzW0/Qf/8J/+xCCqwKwKuFaIV1shpsQ0hI50PTkW3UAZR06FDFiRbQNErgzW03KjioINaaIBNjGTcEDT9nDrmc8zfhrJVVuDY84deHlS1vCSZgTjNAyl0LAN0rww7AZyrZQOIq13xDliMNTWvflMr6TT2y1zxYgllUzOBWOtAhuls+TeIp2KQhhpuoDF3tZsRKGqD46YFoLTUJ/gAqmAeto5co54Y3tQiyNVsGKwVqgmAY1asra7l6ybvtpIgoJI8YjR3y3au0suBUQoDayMYAJLSpqI3T1zFQr0B7ZpWOuIVa9nrVAFUin987pvDBbTrIJrb8mpqEqoFt1wGgvWspSKdR6oBOcoOQIG6YppMZpunpas/pVAbU0Do6ylRYVFtIY4S229ra4VygqITzMhaFtpa2DE0FrWZPlgsVbIccG2Si4V71SVqgFv2u4q4ijFgAk0IxRTMV6vY+uANjgFad45nNWKeqVh8dhicE1wEihN/Y6XEvXcjcGYBjkTeg+4QkWF6LX7ISq47K3GNExTqNtqQzosq6ZRTKOgwLDQ/WmdI5aMOAVizjuk33/t0BYdRy1hTFF5elfEGWNIpfWWanRcGFEQ3QRnHRbBiOtLpY43Bc2atL7kpMBzSQrVxCBGYbFBYaG1loUKrWlLfalqpWC0oFJy0aJAUQiWmtpEqPepYMWQl5lg1XajNd3gxJgwVpPoV+UamC3wiFoRKsE7Si6Isfr7tVHbSjYNgwsUK7TSMIgC+e5NquUcLc6AkJek/pOlUHLZxu36nTEnUtGXWCMCVQF0ylkBsKl4p+NUvFHVeq0asgUYGsHqCxGiYAsxiASsWMRYpApOPOfjGe8mclFQ36Rhu1dtzQnrdD1sRu+eGwMuCHE5c5h20CreDYhY5phwbkdMCbxFJcsQXGBZIsM0Ms9nKgrha6sYF7rgWwt3DfXCjWnh0K0OpANS06CWhNSGbxaTFIgb7ziniBMFjM4IQ1cO0xSYNino0GzUrDYMQsUaQ0ozfuqgEJhjRIzomo4WzKyz3dKi4ZqhtYoXo9bWom3n1nlyUaAtXdHcKLRWGKepW07oHL44HFhy0oKgs5gmPXhESEU3vOozDVIbY/B6DKVSc2WezzhjFL7WSqmZcQikFPHBUGrW8Kla8N6xpH4vaYg1lFaQNTHbCZTC4HWD1Vqj9OKfsZYYCxYhuIBltX9wLDlh/AAN4ikRwkTOicEGjnHG+W7/YoxCcQPOevIKS2tVL+VqKFTGEHqGT4NacFXXb9uT1yU4LTBhoKr6xnsH6HpuxUETrNhu12Ogv7BVCl7UcoQSoepaPEyDKpdL2go0uc/JvHZpDI4xOOIyI01f5FpVCxkbLLUkgtVwsNatN1qtVDJidO03rTI6RyuZ1M79GdCVAN3/VwaHKYVJdONvM/qctlpgSMuCMWoNUjGYJngBUxKHacSUqOda1EN8cpZgRAseg8OJodXCf/wraPtL8fN3//4f/gTT08tLo3WIYLpqzt9FlifaUbWmPbtzoQbdyNm5aIBO9/sENGgl87AR6hvpcJe1ZbU1yl5bVocPaYNGUhTQqEpH1WUrUFkhygoQ3DGRL1StqO3lplsO6EZzVaGu+Q1S2gZmEFUsubk/y/umDWM28OtP5QECYDYwuXkF7pyCgIOjBkPaWVV0Gk0gX70HdcP6ENSz+QPWbynHYm+3TRV/H3HHRLoOHTZ35aZRBa1BVbsK3Az+46JQzrCp8FbZjun3099GmrNbCrk/K5halYUYs6l+awf3zerG1s1tu/7hw6LwC0M+OIXLK2ieO+AcFAQ0qy3Xq5pthcl5p2IPpKubYANPdRBVQF6ETWG8gjnJ9UFNeq7UoSd9d7WapErdOez5Abhi9BrYbmmwgjesIXVwmveO0hWBCoB0HJVJ/7EdBPiPUa85/VqjBQFQH0vp4EsK1GGFcm1TU65wYg0BU5WZjukVJq/wDTH42wTrWJucQsCzXrAWLDU4VUj28Qtmu0/uVDrEexBwSe5wfJANVKwDRdV+3ev24NSLd7C4o85tKfq7/uOse71JPyPcqDBm80YuOrdWr+YVKGEM7pioo8PfLGA1JMnN+n3p8ADT/F0EUYBnY+8SWeGQtxvkzwffrSpEwXZ5AOrrGrQq8xDTrRTo393hae8Ysmedx3ap2EVVswpSXYenGXeMCrVEr7UUNvVrs4a660rhPkYlt63zADGkC7sdmxS28yiD0PraWUfH8HZRRf5cNWCvqH2FtorrfFYFZKNZHUeqSs+cXw4dwmvIYfiwUHrI3rqvMmghINxm4pXbrB3WMUhXmoMWmxTuC+4uc3oZcEvrRSz7AAx7m79aSAhYw/DNTB3dNtbjhRYVNhD2L/h6pwvXbUce5rU/6jNheDdrUauPkdUaYV3P1VJBPV3zpN8vpVEHVTpizAb+1o6GeK2QdF3/jq8D4bZsx7SOY4wWlzA8AG4npGunxZgCNlXC+4U6qLWOmx9C0/y9dhPYWLc5Uia1sqiD3cKotI1er8+qOl0tIxR8t21NC91eJx1UGV0GBZfhtmxKUX8byQf1HE+XlumrSJnU/qUMOn5LMJTJ4s+1B9ypotpmDTuL1/pse4ClQt5btTYwq1qZrXiKYQPzkvT6g9mKHHrPTR9jXU0e+tpV9XfjlT5f1iJiDQ9A2J0rtndpLJdqy7L6AktXdJeg9z4fdL5LVRsHU3uB1muRDVGbEC1Q1M2W49vF0ur1OTVfW6rT9ced6zbm/VHX2SZmW/tWq5rjS31nQtgg9PLYk/YWm/SareP2P/1P/vq/+tD2v/tH//gn9/OJIXgaCVrEi6Y3WmN049gKpVTmJSFuUFWjM1QszVgsFtc3nhj9PRHbVXKJZE0HiILzA8v5hA2WZCq7MGJjwTeLbQZnnCZ902FwBhuCtiA3Vb1KaYxDYImpq5lUTVuWjG+GhkGsqn5Lh7diG+IDtSUaWf35KvhqYRCyd5SmlgqFSPAjmdzVfd0jpBakZVxXylnjFOKgrbzW6ubcBlW7ZdfIqaDCYY8TVRy2VhXGWYuzGkpknMf7EVuq+guG3oJpFBQtJWG9EKwhdPVao3S4VVQ5ZgWphtwDVmi66TUoGCwUBbE1q0VEDYxOsNUQxDMGh1RdeFXFl6kdni0psb/Ya9ANBRFtyafpIjC6gAA5aVW6MVCNttrRmoIpeQhpQTLWGsQacqoMw0RthtYCKWaGYVQABKqUbY0khSUtDOIwwZNyQ6zHOEHEUatF31Atxq0+iwpOl5xophEmT6HSWu3vXx0OektOCTHgRZhboxpLNlp4sEaQ1jA1MgaLoYJkCpXBqodxSXTgv48AACAASURBVEktOTAKKQwUU4m5YkUYsQxD6L64sBSFpVIhxgXbUD9UbzGi4NAFz+l4UtVr05cqa0z3K9ZrWkullKX7FxtEE4K0DVzUWqA6qyp1wDshLWdVWErD+6F/niO3grd6/M5rwrtxVn1wq2GwEyUpsMBogFJNCdO0vb42AdPwFkxTGwxTIVhVuY7DQM5nyI7DdIltliUmTnnGWB0fwfntge+tYRChZYVyRRq1NYyYPu+NAmOj51E6vKnN4KyhSSU4Hcu0RqDDnAa5OZwNeOupubDzMIYRg8EWLc4Yq0WFYQhYByVH7USoGdsMoygcby2SpGJawztPSA7pfsmLVEQqV+PAKJCWiGTDLgyIMUgHchXHOB1YsraxS64c7EC8vwUso/cYEUoqOq8ctJpoJTP6gbQUnFf/5vPpxKPLa1IsWqAwmVI69MsRnHSIbRBToIIznpQiwQpmiVz6ARFDTJHgV3VnYXSDqp2jwZk+Lr0+bKsT9RjLieAswRp8VV/YYhLFJao0jBO9VzJiuz1LKQq3vcB8umc/BAqWVisgWrhICW+thhqmiLcKTqVVpkmgNlr1lKbFqmbBDloodFgGP+imb84MDoJUxnGk1ayFPdMorVeua6W0wsE7Wi2MIWiRDigVWi09pKtijOCHgTmdkVoZvFBLxDshWIdrBW8EGfacS+XgD9Ss11PGgVoiwVZePX/CxRBwrZLzGQek3hVip0DKUdXlFAbb2I8DlxcDmMpVGGnHqPPQeZYcCdZqYSdVKAvOVp4/e8Tl5Y7nL56znO56YFrRsdUqqStSSQnXKs8f7fn0xRNePX9GcI7l7kwxlnlecHaAAi5XjG1Yb2hqgsP1YYdUgzeOjCGRGZyjtoxzRgMzk/rVDl54fn3FhfeYmkmp4IyHbCgI1gRSqfgp4Joq7w/Wc70fod+HMIyk86Ket1hMFVIGnCHmM2EwuFZ4/fQpQSDNJ6Ah7Mg0xKBqWme5Px2ZxolPXj7iu29e8vLRyNNnj7i5vaOmiPVeuwlEKAkGGi8OE1f7ke+9ec7r59e8ef6EXXBIK6SUyTVRU2K/21NiBGdwduBv/wf/3q+g7S/Bz9/7b/7nn4x/fku+GjfosSrsahDSpe/AtDHcZFWodVWjQtmuNB2kt4I+hHtIbvj7vClEy6At6OGjwj/fA1yqN5RdT7oe9N+rWqf5DmOPZQNuzQnpKuDvcm8PlgeYOknfSMqmGJYOytYgm9WjEPoGuZ/f6l1pU9uC1VYAuW4kVZH2oEheoaI7K/DT/75uBns4UYc5K9wyHTDVUc/V32jxtexdT/xeYVHZNvPhg0KBvLcKqzswbd4q3Kl6zq23R6/qvBosiAIwWVTJZE9JlZPWaBq7VwuIeO02SJ33ChUlKcDyH2eWFxPhw6LX+1uQHtFE7A2M8AClt9C1FW55IXyIG4hb27nThbZGSwF3H7s9g3TgovfRHZOqmK8CYDaoiBiwgvlWIBmi76Vry2v4uKi6rIOMFeaZpmo7A91KQ7ZigiQNTDKlKYDqMCde+w0maluy3cZQ6wUBUx8Ak7uL1MFpcNKV77YcGmRUJrvNqTr+Cyrp3p49fqVBaKrUNJtyuUxu85u1i0KgtbBg+mesCrpV8QhmA91mBQ17hZ6rfUPerUpgu4E8e1KLuHQ9qDrx2FvU9277O7X7NoO2iWMUEG5gNTfSdQ/ua3B6ofc43FXGb84Ka4Ptv6Mwuuws6VIBTOlBXgqrdQ+BfVDJ5b3bCg4Kz/U9fy0wgIKwFRC7U9IOgb1HSmN+6omPAlirQG0Fz6mSrgaat9v8tt3PdHmk90xVtwqc807naevH5m8WmrPcfepZrgR/0jF5fqrn2fzDeaUrT7hNCjG9cHo5ELriNl67ra3ddEXhqhqNjzzu1H1PP2SGdzO33z/gj6rql6TWIc2pl/YK+dfvBzYbhnzQNf/+s0nV7neZfOFV3VlgubaUrlYso3xrbaUrJRv+/YlyMZAPltStN1zsisS8tpXrvVJPVf28tFdv5eGmqO1KbF1Vz6Za1UKLbGNiBYfLtRZRTFXIuYYyqppVx2e80vEoBeKlJR0sy/UD4NuKJ32e2Ng4vQxIZbMZqOEhUG4Fks0p0Bw+qPJ6DXvLe0s8aEdJ3lmaF8Z3kfPLoduJtN6VoBYEb3/TcX65Kk2102FTsge1HVi/Nx6E81PL6ZXev/tPPFJNV2Ab/E1UWw9hK+yUwfDhx5bja+HmR+CO+lxyS++0SD3Qq0Px9e+/+ytw8xuV5eDw9wabYPiQFDzSPWuFTeGMMdx95qhOgbI/tz4WtaC7Kqol6XMzXjo+/Jrj9FzUm/3cyP2zV4sBd+z3vkPk0wvL/WcPit20M/i5Uftzpgn4k3ao+aN2aJTB8O63VE3sT/R1RIu11ZseZiaMX55Yngx889uW+3995vyXEh++L4xfeF1bQg9XC/pMTxeW93/Jc/+J5e1fK6TfnLG/c8fXjyeK97izFkttapyeedyyvmsJ//nf/v1/9aHtP/gn//QnYQiq4rIaoCJGJ2QIgZpzVzwq3Dq3xLAbqVk9Q7zzpJrBGmoTWq+QjT6oUqo2glhqTnixxCWxG72qQTs8sd5hxPTNqbZMq7KqkrK23taU8eJpRf1GCw26J6O2GDe89RrH4i3NdFVOE2o1OGdJyxnvXFdnCQZDMVmVoSLqRNDVUCK2A0+rlgLda1ZFrw4nouAnJYx4xHvOOSHOUUrurZO9pbOpx6apQk7gmmMMAVMSJVdEutqze8SKGGo8E5wHozYVuRZsV2AFCbSioWSa0C60UrcXJ2vtFsZiDDgrOBFEwDmvHoYJBe05ImKJeQaaqsFWv8gKjULwnlbhdDpx2O2w3f8zOKvWEK2rDMUyWM9ktD3XCcQ8q78oINjNu3Ga9pSUe/v5yPm84O1ALpEQPLlkKgaxGspWS2Xa7aGooskYrT7mmhRCW0ethWoEF8IWygP00BxHaVmtI4q2VNMac09H13Gk91ikaUr6GDgts17DaigpMnhV6FprSb0tfPDqhalt7+ii3V+ybYPRDdSiSnLnFLy1VrmYRkwpCI3BahtwsapsrKWiR6MJ7ReHg4ZY0QsXaAv7ppA0luCHbd5UU8lpoZmmSsv+Ql5NpUKvsBvEaHDWaD1pmbG9dTB4S6sLuTYQYc56XzAQl8hhNzLuBoIIF9OoNiPdXkOcQqDatNU5twZWGKwj0NiHkfl0ptGY80KVhndOx2VpmN6ePIURUqXESPCqUMMJgirdDNoirXYKFWMypmT24wQ145xe5yVnmgEXPHOLVLE063BGvYx3o2UcLHmZaVUV71UErCV1VXbOCTGCDwrwnj95wvOnT3CmUXtreGmGeI5QK7aqP6izhsvDns9evOA7n75iv/fq5d0g5UQ2ldO8UKF7pVacswzG8urxUx5dHBisw/uRknNfS6x6pRq1ffH9vl/tL3l8dcnV4ZKaMqVlaGrJkeleqCkRvEdoXEwjTx4/YgqBKWiLfLBOC1XOMpeFlKMqy1vjYjdxfXnJ08cXPHl0tanqa2lE0XtmnaVkVcaPzvL48oIffPdTHj2+xDpReI5QssU0xxACKaUtMGwMgWdPrvTaWmFZEq1o6FzNBWc9YgXT9M+8s7x88ZhXr1/w7MVTai0c5xljLDZ4lqoFx2CFVBbENJxpPH5yya/96HvsB4/BMMdFVfhWlcy0isCDOtUJrz99wfOnjwheSHHGB8t8PmEnR2uZXBL7YUIExjDwg+99xnc+fc2rZ0/x6Dp5PJ0xRkipz4+WcGK4GAc+e/mK68s9h4uJ73z+GYME3r57232shZgS3jqCd1iB68sLXj9/zLNn17x++QLnAsbAx5tbbNBUjdoytgqD83gRPv3kJS+eXfH4+pL97sDgHPe3R+ZZFcmlJUQKY7MEb3j+4jGfffKaJ0+u2Y97Bue5Ohx49/aDemSnQgiBUmesFeKy8N3PXvKd1y949fwpu8lzuj+p8l7AWKvWCzlhe9fFbgq8+eQT3nz6Gc+ePWEa9qQ5kxZ91le0IFlKxHvL+XjHYOGHb97w8vlznjy6htZ4/+4j4zSp13cpOO/VoqCo9/WbN2/47JNPePbkGRfTjmmYuHl3Q5VApFJaZeeCdsA4y2dvXvDJs6dMYcQPI1dX19QlcXvzEessTgw0GJ1HgGdPr/n001c8urrEOeGw3+H8SMqJ4+lMbQYRT86VwQ/EnKEm/qO/9R/+Ctr+Evz83f/hD39SDlqMq0F9FFcYl/ff8orrG7rh7Zl8OSiQazx4zPb3vLU9cblU5emqelF1am/5fDJsCtjV87CJbqxMAzcXam/Fd8eytQGvASIrxNMOLjZl26oAxKzQUDdTuslS8FsmVQKVUTY1qSpk19Tstd1U2yhXCLZu7jSoxWzH4s4rNIbwPj6oPDswq8FuCjFEPXbraLv1wtpmrmo503hQXmW9bnoyBnvOXelpSActhK+qpDrYTSGk6tyHFnuMwooy2u17ulfYpvbUtvjS76EeQx5lUyfnnUWKYfj6yPxqryrMk6okq5cNXKwBUMu1xc1NPXXvtd19VfO6c4ddj/3WYjs/9eoTvKk9vbZnf+taSKrEJwMG6TYBCqvsvPpeqtesxKIKSS9bOz7du1U6UAK6urYR3s9gZVMUqmJc7S/SlWP45qyKr9FtisC1JXtVBKaDtsy27pO8SZ1RWB8fDw8qu7HbwNXG/eug98wZLY50BVod7FYgAP3u46uwKbQUjqgK2EY959WftnqjLepZgbNUcHeZtU1+sx3oc2+1Y1hVgTWosCkf7JYWjzEdwo0YwN9Gzq8nlmv1ej6+tNjU1YxL9wNeymaVoD6+Ol7TXpifOsaPGakwfMybZYVp/MJ8mx9r551dKmm/giX98+bWdv0OLtcWdgPnZ2Gb46Yawsf4oHB8vzz4R+/8Bk+Xa4uf2wbiV5WvOybyZVCgbx8A97tfH/j4Y9sDBg2mqarT3cza5RKkhx5Z7j8d+fqvWuLvHrl/LMTRI1EIJ23pHr9ZsN3T1SaF5ekgvP3XPPdvIF468mSxkW510jCbTYeqJ6sz3H9iuf2ucP+pwzS/BdzZRQs16XrA3UXKQc/7+Cpw83099/mx3c5vXQ+Hjxl3zKx5EcdXnvs3ltvvwd3nqrZ1J10v/F3ZOgNWG5H4aODmu553/9bC3WcqQjNVtnsKkC61Y0GhpLBcCTc/NHz8kSWPQrhXzrHanWihgl6IyZSd5d1vOD78Bhx/mGnZMb3rKk0vhI+LKv33divw5dFw8z3H3d88cvvUUY0l3DbkW+3xmxJ6VOiYR+Hdbxnufqxj0c6q6h0/5i1k0s6V8zO1x1iuLF/8DTj93sLy65H7aaB6Yfe1HoO/Lw/QchCOLz3f/BXIny60Z4nPf+fP+dOLAxc/1S4OA4QeOpguVAF795nl9jcT7vtHdr95y/shUMRx+Wdr0c50X1whXuo4+eYvC/z4yPTmnunJifeHgeGtMN48FCzXv5/2wscfC/Yv3/D9N1/x7NEdNwfLzWvD/o/VKtLfZfLBdrCqdi9f/L7jw+9l/G/c8eGxZ3gv2Iju73tx1d/lzRri/NjxzV+FR7/zDY+//4EvrwfaHHAnvR82PaytZVQwHi8t3/yNxMWvfSR+njkycPknlXjZhY898LL6DmSD4avfFY6/s/D6+285P698vHbsfq7vOOFOLSzmJzpv8sHz1e9aht/6yLPLI9MY+cHLr/nj+oT9lwqnV2C9XKkP/e0PGofffsevvf6SYSj8+uOvOI6W2/OO4b3B9nvhFvVaDz1s7T/7g7/2F3rXdv/vf+X/ux/1oDOYpgNDFUcGYzQE57AbudxNNCt8vLvhMHiOR90UOwOlzoi3zDmx8xPWCa5WrKmMTnC1UMuCsZYlRUIYOM8ndjsNS2qor2fwHusc1UAu6GJjPBlVlUn3XBUsy6IhLt44muuei94SU9KXC3FIa7rZt4bRT7SmwGvnPeeqXoTqEuDYT4HYEikv+OCgTLRcaS1yGEdAQ7hKaerVZz1pOYOxmmYtCpR2o+divyOnxOnuFmcM0TSSNk9SWiWMA1IKJUX1yowJqHgpYD3nnnAtYsk5YoeR2D16SYUgQgDEKZSypTDHrtg0joXE0FuBxyFAyeymHc4a5vlEjDPWCE2c+sLakWIKxRgwhYvdjothIHjLNE38+Rc/U3DTKvthIJ7OCqWsp6bCGLS9/epiz/XFgfP9kY/3d8RgOM6JwWkyd2tCNY0wOEwt7L3n06efkpcTx9MtNWasq8QCYhs5Z7ybKDkyWMNhGvn0+XOExjmf+ZN/9qeEacf9MmPdqIBC99DUODMNge98/oYxDHgnfPz4ni+/ect9WqjNKag3hmlQpehuGLk87LnYT3hvWeLMaV74+stbXrx4qQF1BZaovpKlNC72lzx/8ZjL/Y5xcHz88IEvv37Hx9sTGIcglArLHBmGARcCL55e8eLJNbvBcX9/z89+/nOOxxNxqRS110UaDKYH5VX40Q8/x3vLNAa+/vprPny85/4040WrwaUUTIZyWpimiVefvCJMgYvgOJ7u+XBzy4ePt1gfSLH2ICdtNx9C4MnVJc+vr7k8jGDhfD7x0z/5E+7OM0PYk1tBasUaMCVzfbnj2dNH/Pg7n7EsZ2pLvPtwy5///Guqs5xPGdlZLZqIkE4zj57s+dH3vsNo1Uj+GE989e6GLz/cUKikUjEiHEYF3KUkPn/zmsM48PT6ktvbW77+8J4/e/sVt8uCc0F9ea3Fi6q3ry4v+OTFc55eXxJL5HROfP3Ne04fTpyWGW8sQUbmeaamBbezPH3yiM/fvGA3DqRz5M+++Ia3H+445cZxXnDjhJEORjG8/fot/+Zf/z2mMfDkek+8P3Hz8cRP/9nPKKcFCSOxFqbdQKgLh8OOH/3wcy53I85kXj17yZNHl/z0T3/OF1/dczwmxmlPyo1WCiVnvvf6OZ88f8b15Z48zyDP+L//+Ve8fR9ZTgs0GIaRczwSrGW0ltcvn/Py2VN1QcDw6bML/vzt1/z8yw/MS2K6GLn9eMNutyMtM9//3nf55NkjhMblfqAmwxc/+5I/+eILii0UI8gQIOn9ctbwg+98ysVhYppUafzk0TV//NM/4+ZjVtApjvl8Igz/D3vv8WRZlt/3fc4591z3fHpXWa6rqqt7uqd7HDAYuCGGTqLEIIPcUNKCC/0D2mjLv0ELcimRDDkoJIUoiU4SCEDAYAAMZrpn2ldXd7nMSvMyXz537TFanFfZ4A4rihHAjcioTVbGu+7cd3+/7+/zUSRS8bX7dxj0O3RShZSS/e11Ts7P+PiTx+S6R900lMUCvUoQDztdbuzusL7eQdgGheBy0TAej3n24gItI1pT44lojGCwtsb25pAbe+t0Ug3OstHvsjNd8pOffoBXEXGahAZLUyGVRqcx9w4P2NrcoBM5toY5Fs2jx0/55NFjkv4QLRSFDU1MlKfX67Czu8nW5jq2arizu8XL80u+ePIUG+Usa4P3ijTReG/pJDE3D/bY3OiRxgmxiujHMctFSfX4MUZIFk0QfmWJRnjH2vqQ7Z0NulqR5xnWWjY3hjTuBs+evSAWKdZbEqFom5Ysjbh75waJkmR5FNACiWLUTRHWcLWsqCOBQWDKFuNa3nnzHuvrHbJEkCQZwmb0Uk0cx3z08ec0rSGJJca36EjSH2Sh0ZAlaC2Jo5RRL8cZz/PjI+pFgTGeVgu8NCgp2Nra4GBjQK+fE8cR68OctazDH/3s4yA5Mx6lRBCYWYPEcffOLUa9Pv1coyNJJxkRiYifvPchXsY4QrI2S2OcM2xvjnjj7i06SUyWpSgkm1sbJHHGyfkEg8dKsL4lJaJtKm7c3ufOrRtEvqEb56iNHtVixHw84Wg6I87i8L3DQKpAasGt7SF5osnSLsZDRMSN7U2qcs7p1QyhNEmSUBVhDdza36CTxnQThSSh9ZCrjH21w7MXx2iRUHuB95amtWRpgnHt/59f//5i+3e4vRIHhR9WBdrw0ptMDMsdzXI3pG56zyTFdvyV5CMLaTQfSfSkot7Kr6UiToeXmVfpUpuGcWrTUcSXDfXGV/bkqAgpmFfoAFlZ1EoiospVmjcS1+O3etpg8yiMIvqv5FzXhYOVBERIQZtJqsFXQpxyTZJeuWtzepBtRcRLR1QE8QisRiUtFFvhVSkq/XURxMuvbO5BVBMKPfVGwmJXoWrIxwaTBYmJKkLh40+PAavaUWxEKz4emCwURtIrGyzVilUBbCXuWY1at91V2isKqSmrgzn71efQs2B8V6WnHYXGf7kWRmLjmSdeumsOH6/GtVfiHYDFXkyxLTA5NEPB+vsrbqkS1DvdgJ9gxdm0ryQ3guWeoNpviaYR+ZFALyXZeRCaqforqZbphDRosSE5+4ZEVYL0gmubvV58lb4NycnAdVzuaMbvgleKeCrZ/WGQ6mTHzfWxEa1dnTdPsyY5/YHC9iwis0RHEaOPIjqnQQgTLcOkSLOeIVYpy8Wuotz22NSjFynxBHrvLVh8Yzc0LCobihQrkdBiV3P1ANrtlqRX0x51GH4c0X9mvrq/Gkc8bWkGGptKLh4qqgcVo9GCy3GP/nsJ2dhdi21eHaO2F61EN/D8BwqXOdSgRX+W033m6ZyalYhJhSlKKa6FbeO3NSb31FsWfanoPoPBE3MtOHslchKtpe2nTF7TzO9b0u0gUy6nKRu/L+mcuHAtVe5aPuelYH67w+S+Yvf7L7hc5rRWMX3SY/19gWwVyWUTmI3GYTNNtGiY3sk4+0sNWa9GSs/5RU73U83GB4GP+uoeKXZXCcTGc/pLnmij4js3n/Lh+Q7zR0O2/hi6L6rQ5HChMeDywLCc34i5+Lqnf+eKcZHQXiX0P9MklxH52NDmkrabk0xaxCpJPLmnWXy3YGO4YFIluD8ZMPxckI0dyfEMl8ehIN4JxcLOh6d89F/uoAYF3zh8znnZ5enLdYY/TJBtHDAW8xq/FtP2FMstxfz7Bbe3LhgkJTfvXfKHt25x/ge7rH8A6WlJs5aGxtcqmX30qyni7Rlv7x4za1IAHv/+TeTn8nqEvx3EJOMS39NUA8XlW57R6xfspRWJMjx5Zw3/sz6bP/PEE0O9kZI/ndFsdVCF4cVf6mDfXqCU497WKZXVfPzJAZs/Uqj2K1b3q/XOppLzX21Z25rxen/Gso25vJUz+9EanWNxLWWLr0JKuO11efYfezZ2x3xnNCZTLcuvxfzxp7fZ+5eKcj0imVmSy/Z6pHx2GHH1jYY7N8+IhKOja372Yh/1RcbOH9rrhLh3YU2cHyZMHgr674x5azQGYHIr59HDLQ7/h7Dmm14cEuQrMVabS15+33HntRc8HJzi7ghm76b84e895Oa/CMgIkyuS8yogIIByW7B4WPPu3Wcs24SH3z7hn3/2JvkPO6hGk581Af81DI2HYkNx8W3DnbunHHYn7CZTPtje48lkxGI6RNXQOW3DOrUe3pfntwSDBxfcWxvzsHvCwibYO5JHxQGbPw4j/qoOWABdOqqhQvzyhFudglu9SzbiBZ92tjm70eVUbtF94Unmq8ZOHX6+/LuCW7fOuNW75DC7ROJ5tL7FD/M7JP8iJl6sJGelw2SC5a6g+60xd0djNuIle8kVf2XrIwqb8Js/+Y1Q6CxbokKtEs2Cy4eawdtjvrbxkt1kymw/48eHN3D/dANV25AGT2RIz1vwCs5/2bB344J314/YjOc8HJzyfyUP6P+P2fVzWHpPvRYS4xdvpsi/Nubbowte65zTVTVnN3v8s+43GX4EqgbZeJLGUg/CNXPyCykPv/sFfV3xrcETerLi8e0t/tfTX2bzva8ma7wUKxyCYPvbJ9wdjLmXnzExOaOoYPJuzulsn7VPQg2w7inSq5Do7t2bc3d0wTv9F+Sq5qzpc2uQoN528MebtJkkmYVnYYyjGqnQiPozbv9eF22VElRNQzfNcK3DrcahlVJsr63R72WMshQjLMNBztHRS/o6pjCGxgaZkLOGRGqksPSyjL31TaRoQXpenp2yrA1l0yJ0ROMNUZqwrCtwnixO8cZSVDVpmoYkW+bxq7RtkmQs63ploa7pZjm2sCQqCmORUUQDgbWoVonTNiQhVaQxAor5nLVBn1t39nG0LMsF5/MrqD2iDQVXpxxCeJqqJtYxWkpu3zkg1hFN0zC+uOTiaoIzgkiFMUohBGkauJDdTsa9W7eIU00aKS4vLvjyyRNYpQajKAZT05oFsVYkkabX6bHWy+n1c6ytOTmfsTg5Rcea2gmQYaxUSoFxjixNubG1xfqwi5Aty3LJ0xdjltMrOsMh4JFxQlM1wUReLtneWOfm4SEKD95x8uIp46sFdQRt6/Fu1U0VEa6tkR3Hrf1t+lkHIT3DfofTiwseP3tJHMfU9Zx4lY5TWUTtDPduH9JLOnRTxSJX5LFh+XxCT+Y4GVE5EdKvUlAs59y6ccDmaEgnT5GmpWn7fHl0zNW8ZOXfIo5jTFPRSWIS4blz+4BhN8fbmkbGJPIW5+MJdSkRKiR4rfOksUQaw+Zgne3RAB0pIiwZa8RS8fnz59TOE2uNUBJna2IhiIxlZ32NfqdLkkpaU7IoGnyrmE7nGC1wElglMJuy4sHdPXqDnEG/Q90s2d4c0pia2XKO8JqiaSDWqCikfrfW1rmxN6SbxkTOEK/1sVhenpxj/CJ0WSuHkgIpJFmWMhwOGfS6jNYGeGsDsiTOODm9YLaoV40WQSqgag15ErG7sUWcSHKtaLspw16XYr6gnC8ASSJjDB5nPAjP/s4ueRSxudmnMRW9XGHtIZ9//gXOCwSSfprSmJaIiL2tLfYP98kiQZp0EL6laQzbWxt89vkz0nRA6QsyHeRK/bzDwdYOm6MhUhq8bciMQCo4u7hEKU3hamQU0+KYLa547fYdev0sFDu1Y2PUI4o1l8s5y6JBekfbWmKdhrS55TYGYwAAIABJREFUiDjcu8PO1pBE1Oi0x2JZoYTik+VTrJDUrsabBJVqrLDc3BuyvbHBsNMj76S0usTLmNo+pzgbk8eaZVXS6IgsTfHWMuj2SJOE/d1t2nrGqNsllikXpxdcLaZY6zBC0uAQ1pAPOmxvb1EurkKjyrXkec7O5oiryYSmFWGt84GVmyjJa7cP0N7iVUN3GFOULbsHm8wXU2RVonVM2VakscbUFYPeGpsbI/qDBNtWtHXLweEmy2bByeklnTyjsYZOpwNA3knp97sM+z2UhJgGi+PG/gaPnn1JHCXMywrfxMRKY41kNOqTpymdNEFnMcaEBOzNuwf84e//ETrrc7VckmYaU5d0kpRet0O/1yHTiqZpkJFgc22d0+EFi7Klog7IEeGp24qtrbt0uzlKCVSkiaRiKx9QlFNEBAhPJB061cxmFd1+ztraGlEUkUcyrBk1ZDplvLfL6fkFpfW0pgYZWN151md7exfhLHGa4K2hQbKztcGnnz2mLkpaIlQa4YRgWUx57dYB+aADzjPo97FVw9ZoA1PDj3/6AUkvyMPa0qEjy+buFptb63QyHcaFZMRoPQ0Nu6eCpq7ROqZpGqqqoTE17+69TZIndNIUV7dkcUy8kTK3BvfsOcp5vHV4YWmqgq2NHbq9PDQu4sCq7iUxg7TL2ctzplclEo/OYopoibeG3lpO3g/CNNsGeeFobY3GA8qQRyll26B1Rmk8dzY2GQ7XiKQl1iq8UQDdbkaSxCS1QSRB2BfpDCkc3V7KaDRCqlCclVqxd2OP+JPHVA6EkThj0LEOify64mBnGyksWnkiYeluDJgua2QUpKRhBDVgjow19Lvr4TmbpaECgSLWMcO1EV88eUnZlIg8CDnjOCZ2EXkvJ+9EtEWFtxUuUuT9nF6/Q7acYYTHYVEqpXUVG/0Bm6N1XFshfEOiY1xj6Xb7RDrD+jm2sTS2RMehYTBa65FoRTOfkcc6JNpFQiQj9GoaKE0y6qYg1ZpFWYbvBX+x/bnYbKKILyvKveyaI+p0KPJdvKkpdhz6YE7bRCz3E9bfF8wPErILc20JF87TDtOQ9tqNuHzLQxSKgMMPJNnYkV6GNGC0DEnT9LQEgm1cOEjPG5qhxiYC4fT1WHvbj0hPa1yqkJWl3E1R9Sv2XZCIAdeGdmE9fpWwsVoQ1Z7swnB1R3P+i4DxJBeK4aPw+bxkNZq8Gllf2FAYzSSnv+TxeYsoFL0vFMPPzbXJWs/NNV4gvjIU2zEvv+8QeU23XzJ+OmDnh6uDLMW1PCUqLKYTXr7LbcHypiPeLDBGEX+Uk5+FwnE8DYU/dc08dJTrMWfflHCrQEhPc5my+YdhfL7azsP+ROI6nZxctly8mTL9ToXUYX/jj3IGjx3JzF6PyIuVpVUZh1cx7Xfm3N8+JxKWD/d3EZ92OPg3oWCsr2pML0YVBpvEJDPLya9Db3vBw7Uxn19uMBt1ycaK5W6MNJ7E+evUU3recPzLGcVrDb31JVpZFkWK+1GX3nN7bWl/ZYkvNzVNTzL+Xsve/iXWSWZFyrNul/7ngniqQ8GyMMjG0o5SnJZM7isGD8b005peXPN8MOQ8HxL/jiKehr/v5VdMTJMIpm8YOttLtjsF8yrhatzlyB3QO7Ih2d3aFW0+8F7Hf0URbZU83DnnfNlFPSi5LDfpHYWUXzIORdBXydnzBwrenfH6+iX9uOI8q/ii2YaP9L/18m4TiVPQrikWBwK1u+Tr+8cYr/gs2mTS7QAR3eMwBYoPIqNk0lKuSYo3KtK84c5wxqxKudjvkl1o0stwTTWDkGRWbai0zd5u6IxKfnDzU06qPsVmzM/bQ6LfUdej7PZGL1xXiWT8dUn+1iV3ehccdidEwvFbxX2uXk/p/2tDM9TElw3NWoyqHMvDDuN3PW/dOSJVLcZJpv2ML6JN1j+WtN2IztkyMCMLTf50xou/to7eWnB3a8xQl/zgxqf8MLnN8ukO+Vko7EdLg+uu0sixZPxtx+0HL9nNZ2wkC34+2eOLaJu9fx0aR/lJ4NK6WCJrw/n3MpYPah7unnG/f8a47vKTdwXLRZ9sDM12j/jlDJ1q6rUY4Tz17Q1kr+Vvvf4+46bLm1uP+DBb8LMn9+k/A1kbRNkE0ZoU1K9H/OC1T3k82+Bh74REGO4Oxpw+7FG96KA3sus1FwK+YedXjhglBc4L3hm+4JP5NvqNGc3pABcJTB6RXLQ0aymqsiz3NPlrV7yzecTcJEyqnL//+h/wDy/+Eu4jSbWdoOeWeqe7WnMTqh3LO7svyaOW3XTK0ib03654/lv3aDqS7jQUIk0nJFwnNxWd0YK7ows2kiWXTY7zgvNvT8n+6w5tLul9uQwF6NJSbMUMt8Jnut854agekamWh3eOeX77FtlZaBpdc2Nnhul9xXBjQUc35FHDWlwwuPOY35m8ESRWcJ187bxoKbck7WFFN6l52D3hIL7k0na42x/zb77zDUYfO9JJkD1BwJ9UdzX37x2jpGMvuaJd/eH+wwv8v+oRTw1q2eKSwIxOT0pefL9Pb1QA8EsbX7CwCb9253P+n+YB6/8kJMijwhEVDpNLpq/BnbunPBycEknLVjzjr25OeC895E+iIdll+D09t8RXhmhec/p3Izbygrd7R9Q+4vXsJfvJhCfjNaTJsbFYsYyD3Ovy9Yj7a2OMV3RUwygq+ObwGfl6zT969JfpHgUGvc8l6TgUiIdbJa/1xxykExY2oatq/sb6+5yXXWYcUK6HAmSbS9Iry/n3PN/ffsp6vGA/nlyvT7+UP+K/7f7GCguTXTdlXSQoNz3f3/2CXDZ0VU0iDf/57d/jH3X+FslUwKox23YlbpWi/fW3PiJTLZvxnA0957udR/y4dwOnspB+v2abB3RFses5yCpu5pfXn2k/mZDvL0h/r0c6MV89RzqBX14PPQ96p4ybLq1XVF7zdv6c/37bUA3VSiRmsbFAl46rG5K/d/BTCpugpeFWOqZwMW8OTzhO9/6UeCycS1XDd/eecJBMOK6HvN19zn4yIZGGtbjgx/k20niqoSKZOaqBonNqrjFNf5bt3++irYeuTmiLiihOsELi25Y8Tzm4sc2op1GuQciEvtX0e4qzszEvzxY4b5EmRlqJUoqdwRa7W2uMRsmK1dqwtpZz9OKUp0enGOeJpMa7lkxEgENZj5Yxw7WcwaAHwuJdw4ujU2Sc0hofxpzFimlpW/qDDqPBkK1hh8VixmQyZb50NCYUnL0TtM7hRWCfbvZGvH7vFmnmQdYI1hi+THjx4oQCT914RBRhhCdKJMPhgNdu3mBtlCOVoSiXdPKYTp7w/PwyCKt0hPCKpjYkUcbdwztsb/VJtEN5Rz/us56/xu/+8fskaQ9vLca0KKVIow6LouLb7xwyTBVJrFm2NYONNVpTMbmaI0XAAtjGkMQZ0lnu3LjBjf1tEulIIsuizMmjmFQJrmYlZWPIOkNMBFezgm+++Qabazm7a+sU5RVppkn1PsnpmEdPjoORnAqpEurKsDla5/7dfdaHXbSWRHh0akmTdU5PTyjnF6RRgveCLO5RXC1464173NnfoTU1MYYkG9Dtp7RxzPsffkoSd9BxTLFcIqWil/W5d+sGeRYTSYc1Ei00PnJ8+NkTbO0wrcM1njiJKYuat979Grs7WyhanAnyt55KyXTEfDrDmQaiiGQF6d7aWefBm6+Ta0FHB1RENqjINxNezk4Ry4aqbmgaSxRr6sawf/eAW6/dwLUVkbKUbUJn2KFuGk7Pn5HJ7oqLHwz1GxtDdnbX6Oc5OEuuO3jv6PcHNKXlfDwljzWFsxgVUBqv396l31d4WrTs0hjD2tYdlJac/PQCL1O8jqm8AGu4sb7OnTu3Wc8jpDDUrmHUTxj2dzg/e4mzS9pWESWauTUgW7YPthit58SRAluTxBn9PGO2t8/nj5+GcTnbEEcxSM/OTp+D/QGytWSxI0s1kNB6SWsP+ZP3PiDpdgNqQkS0OHZu7DLspyhlA1PVCPYPt0knCZ88eYSVBb4xlH5VsFrrcXjnEIklS1KaekF3OER3avTjL2mbkn4cY3wodCZSsrU5Ymd/DVMviKSksS3DrMNhucnV5BLrNbFOKSsPztHJUg5vbCO9IRYaITyjfkaed2mamvcffYmK4pAIqhqkE+RJl52tXbR0q4izZDjKua8POD5+Aat7QErJfD5Ha01raw73dmirGTEeLy3rGwN0rGjaiixfJ2oNpqpxpmS7P6CbJkQ+JxGW1smAANjY5bNHz9DK45ChAWVhe3edTr+HtzVKBs5mt5/RRTEeZkyurmiqMtCaZUYkFbfv3GLU70BT0dEZXrcoIbmxu8OHnzylRVFJgRKQiFCgPNzbQbkaLT1KJWRZaMQcHh7y0aefknYHGONopaG2Sxqf0V8bIjBoIYOEL2ppa8/m3jYnF1OybkrdVqRaYWnZ2dmimE+ojQlFbxnTOMtofcjzjx+RJBky1tRVRZZqOpliOEzB1cRSEkUJWZqxGG3ws+VnpN0RzijasiXToeC3PhjSyUCYAu8MxkN30MFiuby6IB70aZqGOI7B1uSdXZIAFiZWChVFJDLm0jeMhj1miyAj00LR2hItIrppRj/uoITBuoK0k1K3Dk9D1sloXBC9We8RiabAkg47OFsHYVskgkyzkdRlmFRo6hoRR0irGPYH6CRCKYExNSqKsAK8BVFW9DpdLqdVwB9EICJBlKQkWUxbzFAiReACw9VXyNhjrQVFaJokfWbTC7wRVKUnVxE6Eti6pqmbwAYmxRhBEiVBbukFy+Uy0GmNxboalaQIoVgsp8RJ2BejJUknxzm4Wiy4ncaU7YJEJkQiIDfm0wusKRBtkE0KGaMA4w2NKTmfXLGzOaJq5iQ6pZnNWBRXiMThTI0wIKXG+pJO2uPF6Uv29/eIbEtH68Bt1o6ymGFNTZ7keOFpfU3TFjhnKGZX1MvNwF+OBHXZMDOWcbXAxSmCwH6vrQERYVrPy7NzurkmjTTKe5wTXC6ucM6QRoraB4yKoaW1hvFsRppoupHGCIktapx3LMuKxoCIA2e4BSyOKA9F97/Y/nxsTgvKvYyoCGlV2YRCXrkeU75T8Ob+CXnUEAnH0faA5zsj9Gc5upRIExIpUoeC3PgtTfFGxb39MwZJycmyz/JQc/7zdXb/QF2PTgvnMRshQeZigckl1aFmufcKmaDY+km7SgYG9is+cDJV7ZgfJMwPBdX9Cnke03mu6ZyoIAtLXlmZPSIN6dzLBzHiB5fc6y4AUNLx8f4+o/ci0sswGokIo/Y2VcxuKNpfmfFrB0/JVMun0y2erq9RrWds/9hes+y8Euh54LGe/Krn/r1j9jpTOqphvNnh01tbZP9Vh2YQJEWvWLPVUNE5aTH/yZxf237KzWzM+7MbnG93Oa4O6T+118fpVTrXacnL7wkevPOU/XzKQTrh/ek+Hwz3qAcD+s9Daq1e06jKkT+e8Ojvb6Jfm/Gf3fspH893uJlf8lu9e4y769z4Lf9VmnOV0L18mDL/fsH3Dp+wES8YRQXrScHPe7ssP94gnViatRThPM1aTHJe8cXf6fJ3vv0jxk2X3WTKXjbjeK3Pe9Ud7v5mQ9uPMLkkPQ/py+VewvDXTni3f8lQlyxtzEa84H9zb5OOc1QdrpNXidP00nDyNx1//cHHdFRNaWP2kit+tHmbnw9u0D2JVizlaMW6hcuHmt3vv+DB4Ix3us9oveJRd5svhhscfXgbLyOSiQmj4rlGX7WcvdvjB9/4OaXVDHXJF4t1bo8ueG9xl40PWqp1jYvCNeuVYHI/4eD1E76x8RyA+/0zjFO4v/yC315+g9FnFjYSknGNKlqEjdj5jRf8wvoTWq/YimccpSP+2vc+5B/Gv87mz1bJZ+dJJmHSYfoLKZ3vjvnL+5+QSMOLakS+3+D2BJ9/fj+wbAuDSxX5i1BUuno95t7+GZtZuNZ38xnZ5hG//fJtDn47SAbjaUvb0wCM39L84Gvvk0jDG/kxb+TH4e9UGUflNvf/8Zx2mF7LqaIC1Osl39p5Tieqwz4j+I8e/pzf6b2G+Oer5o91xJchET+7qXjw7hNS1fJa55yX9YCvD45YT5cc5a+hC0dxs38tWjPDjPk9w9987SOmbcZ6vOC4GvJLW1/yP39rSP95hGzC9ZtcNgCU2wnfeecRmWrZTaZoafmNrU95MDjjXxbv8NpvrkSAUqIKg9eKeuj5hftfshEvr9fDt3eP+ej7FvmT1bnOAnc3O15i8xhVNPynb73HedNjN5niEPztzZ/w0/Q+eh4SrVGukZVFtpblA/hm9wnDqOAwvuC0HXAruyC65fiT6G1M/ooFHlKHF29G/OrwlNYpOlEoen19cMTXB0f80y9+lcGTsOYEWVpoBsW/eMl3956Ez987ou0qBqrk17/+CV/8Hw+JCou+qnBxuEfGX4v41ruf0okaNuIFXVXzevaS03bAn3z7Abf+94pmGNjPUWGJZjXRUvNg6wQpPJvxnO14BsCsSrh8ELHxQUu9kYZU+TCMmP/d2z/lo8UuT8sNHnaOab3ipOyxvGHZ/qOQyDW5CsdtXSPWa761E+6nvfSKUbTkjeSIj29vE0/71KPQzNOFo1mLkQ187eYxX+sfo4XFIpm0Hb7ZfcJvt9+g//kc00uI5jWmlwBQrQn2OlMADuILhqrgKsn5vaPbTG/HdI9DY+NVqtd2YvxGwxubp/SimoVNuJed8kRs4J2gWovQy3BvyCaEm5IrwWF3Qu0iMtWghaX1Ci0t8Tyc61dTIQCL213SbEZH10xMTlfVzF1K6xVCeIodyeBLE5A+coXniWEvmzFuOvSjEi0NGsNZ04coiMQgyLsW+zHdowbrBc8WI97sHrEbXzG1Gc/bNYyXmCzgPeqeIpmHZ1x0EdGPSgobM7UZA1WicPywuIfphOdmVIZJCNl6ul8uePlLPb5YbHCQXzHSS3JR86PZ3fB5RGDIexEkaFEd+Lm//+Ud/sprn/CkXMchOKpHnI377LwS3V3LDT3FZsTWTxyXb+U8jjd4a3DMWdtjP5mwnKaMWk+5Hq1kog69DJMXyZXgeRkaB1pYnlQb/L/lPbqPA0vdy4BmSearAn8N//SL73BvbcydfEyrImof8fvHt5EmpHGvj1MZ7pPffvoaG70lX1t7ybjtsbAJhYv5+GqbeOnwAuJ5mPIIhe6vEup/lk3+2X/13/0mvcObFiFXL3qAEJ5ut8v6Wp9IQW/QQUWCThbGorM8QieOJApjyUII2rYlzzI2NtaJY4WOJd1OSpbqMO7qG9JX4GQfeFESSZZlKC3p9hIe3r/N4e46O5tdtGyRNEgVuI3BoC0QQpDomLW1NQb9jMPDHfq9HIlDOhtM4gJkpHBSUNc1GxtrdPOMNFFkSUwSS3rdmK21PlK0eBkkY1EUIYREItjYWKPXScC15EnM1saI/iAPo0tlgfchPemco5vnjEYj1oYD+t0UpTyxlqyNegy7OdpbosiSRAItPVeLkvXNLQaDAXk/wfnA6tORZzjqoiLI4gQtA9MniHYE+7vbZGlCmmlEJOimCeujAUkUeJy9JIfWo4yil/fIs4SD/R2Et6wNBygEWZ6wsblOsZgSKRDOoqQjjiGNJbs7m+hEoeMw8tpJE0b9nE4SIbwBb5FSUhQFvW6XYX+Abw2JFuhU46WnM+yT9bu0zmCloyhLdJJgakM373BwsE8UhxRWkmist6xvjGjbGkGLICzmZWXwMkjGRqMR3ocENcIxWuszGHSZLxeoWOGxRCu5UZrk9Ho9hIK6bWmMxWDJuwlRJJlOJigliDTEcUpdN2RZBxUrpIYoiugkKVpFpFlMoiPsikn76t84idjc3sQJQ6LAuoY4U8SZJs4UTVNR1yVSQosNoqxYImKJw9L4GqUjkJ40T5BSYmwTJEkrBrBONUmqED6kM/I0I09j0kSHsWphUSo0TJAeEQmSLCZJNM4HyZTWEUmeknYzFsUUR0iIe+9obcNsEUbmoyi6ZgA3tqHbTXGuJUvC+L11Du89VV2QphGWcO1774lWDOVqhS0JqUqFUAH14aWl18tQqwK6ikQQXsUx88k04CZag6tbbGvI8xzvBFJKtNZEUUQUSXQS4YSjMYGBaluDbQ2JVkjlsK6ldW0Yj7aWsqnx3uK8IUkStI6hESRKITB4B+PLCcY5ahcYsMYY6roM7OKmpBMppLF0k5TIQ2sMVVMjRRTwLt5zNb9aIRFyXBtkS0pCEikaazg/P8e2bWBKS0UaRSyXc5wLkrxXj4iyXTJbzqjqmrKuMXgK22JsRTFfYIxBKY3WGhmp639nV1NmizleEcathWCxLGgah3UgVpJIpSRSeKaTMdPZhLqpAotYaQyeWbHEOEttLK3zeKVprEHHKbUtqaoCcHjXEqnACW+s4WJyiYrDeVcqyADTNGU8HrMsS6SOKauGpmlZLkum0zk6imjbNojNdAzG4nxLWRfUpkWIgP1YLsuAAklSnHO0PowYIyzL5ZzpYsqimCOkpLUBOTCfz7m6CsnmWCi6ScagkzMaDFjMrpjNrkjTmLouaUzLcrlkuVzStm04H6bFN45Up+RpxqIoqJsGt5IHzoolZdtQNSVSOSK5YmFriXOGxXLO5fQKi6A14T6ZTqfUTYnWAXuiY0UkA9qjLUuuzs5o64qqqvDe0zQNTdNgjGE+nWOcpTWGxrRIpZhMJsymBcaBNR5jgij04vKKyXyBJeB8vA/PwDjJuJotcFZSFBVFsUBFsFjO8FiWxZzK1jgRGOCREhhjeHl2SuNbrIBFsaQ1jslsyqKsAEjTFNe6FTc85fJiStM6jPUsq5LpYsZ0Mac2LSpdnXMs1psgpFSS1lqKssQKSdkalnXLyekY27R0dEKqNLFUxELTVjVCCM6uJizrlqK2tFZwdHzO1XRJnGpUIgM7XUhEpFdTJzVl2zBbLplWFaWxnI7PaYwLzO+VUDSOJKZumVxcUZYtRdmwrBsWRc1kVjBflJxfjAO/WYKWilhFZHHC5eUVAkVZltTG4olpW8dstgzIKR1Ty9AEEkpRtg3uL2q2f262V0gCv+JpQngpLLcE3zh8Tl9X/OIwjBU+HJ7yi7ee0AwcbRa4d/GkWcmXLPXQ886t52xmCzaS8PKy35/hbpV4FXAJ/k+9eXgVXn6sFpSbgoc/eET6zUuaN4rAaVTha7lNwvilbMI623ahPGw52Jlw990XlDseF4V9MalYJbjC2H88NczvOO6tn3PYnXC3P2Y/n9LbXjC9H/5fYGKuUAcSfAS/fvNzvjt4DMCDwRm/cucxzbahzULKNtjcgxBoua0YHEz56zsf8MuDR/Sjkp10xvf2vmR2Mw7G7lSsihSS/pOKyb2Yb28/4+3ucwqbsJEsGKUFxb7DxoJqFBiEr2zgXsGtt465073gdjZGC8vXB0d84/A5bTcwWJe7oWjpYkF9MMCMDH//9T9A4vkbG+/TVTX7/RnqzoLkrMCkAYlhk5WIrC/4D+5/yEa8YCue0XrFO71n/MrOY8oNeX3uXBTs8MVBjj8oab3iMLtkK57hELw1OIZBiyoaZOtJz5uAOihaii3Jf3Hn/2aoSzLVcJhdsrQJ3zh8ji6/soFDYAQL51HK8R+O3qP1in4UEtp/dfNDNnan5C+KYHV34SVY1ZamB99Ye47E86Ta4KgeMW0zHvROsYmg+2QZpHi5xnQUalrSDB2dqCZTLevxgjcHL1lPCny/pRlEgb/rWEnMPKYj+NsHP6VxEbfSMUuTsJdcsR3PMJ2QwApJdIlahv1YT5doaSltzFE9YrDal6xXByRE0QZW62r9tSlsdhYBUwe82T3iYfeEm/klzVBcS5xejXF7JXEdy15nSuMUmWrZSBYcpBPMwJIez1cFilVqbdmSv/S82T1iPV5QuHD9HDUjvrb2EmGhWc9ohhGyNuCCYGqjt2RuEhoX4RAMowLjFLN5FhjX8yC6Q4qAYnDw7bWndKKGdpUMPG96bCULOsc1bb5iVK8Yn9VmHLwislk1D5YMdcFWPMObkFB8dS5kbWgGGqegMDEXdYcrkzM1GY+KLVqnwn4MdBjDJ6AF/EpC9+Onh1y1GcflgMJoZk3KcpGG8W3jqHY6YZ3ayQMOZlHzeLlJLA2JNLRO8a8mXyMqBPW6DkKpXoRLFO0gQRQRv3n8LWYmoyNrNvScB+lLPr3aQpqvJgVeXe/5S8+TxRpfzNeZthkfL3aYmJyPFzvIJiBRTK6uhXlewdVJj5+cH9A6xcSExP0fz25zWvauRYayWonqFHSOPR+c7HK8HLC0CbmqubQdfjS5jWwgmpbXKfzQ2IiJ556jxSAcP6fY0HPW4oJFkTL8wgaJ05/ilNZ9yf95/CafX22QqYaPl3sc1SO+nKyTHat/S4bXDEOG0FvBF/N1jooBEs/U5Py4uMPZuB+42q0PjN3V/ZGNHZ+ebPHe1QFaGl42Q1qv+NHsLt0Xnnozx3QU1U7O/EbC9E5Kdu752fkut7ILvqy3eN6u85PlLeYXHaLyK440HuqRptqMEeOY06JHaTXPyxG/O7nP58tNxExfs03dSrQoW0d25vnDFzeZm4Rx06VwMb97cY8XxXBVHA0sbJvI1XFwLI76TOqcp8XadeHyqB7RNhH5iUNVnqhwxAuHiwS9Z56fXuwza1JmJmPSdviy3OS3ju6RPw8CNdWGRlAyCw2wxVGfxik+XOzzs8UNBqrkT6Y3cV6Qn1vSK3tdJDVZ4ND+L4++zstqwMKm4ZpqB/zrszdIz8Nxqkf6ekrF9BK6TyUXZc5lk/P+7Aa/e3GPn5ztk8zdClPhkDZMtySTUFNpq4hHs02WJubTxTYfzPbIPk5X0q6IeqBoujLIw8pQYJ0/GvLlZJ2P5zuM6y7/+LNfJD6KaToypGUL99XzLZXEV57n8yE/O9/ldy/u8XixwR99fgu9DMigV1vTkajK039quZpfSqHzAAAgAElEQVR2eDJd44PZHu/NDvg3J/e5OusxeOTC39WCNpc0XUW1rqmfdVHS8Xi2wYt6ROsV47rLs9O1wA+XQQ7no/C5kov2WtT5Z9n+vRaR/Xf/0z/7B8aC8SvAdxJRlUtuHBxwY38T1y5D5AeBjiLKuiVNI54/P8ZaRZLm1KsX8rv3bzEc5Vi5JPIa5xxZnKKSiOOTS2oLkU5oWomIFRWG1hi2tra4sb/FsJ8SC4POY4qm5XwyRUodpDtIpArsw0G3w/bmOkpaEKHo8fLsBAe03hMQUgKpwhfQ+4c3GeUJWimkD1Zq6wzLWcFiWSG8REmFd5KMiEG/h84VwpREUSjodvKU+WLO8xenJFmOcQ1SyDAqGkUc7K0Ra4O3QbwmhaBpK56dvAQlqSqDtR7hFUaG4uMbr72GNS1Yj4wckYKyqjg5HYMD69oAX65aEq1DklB4vGhomwphHLVvWZY1s0UQnTTWB7xFBGvdDp0sIY4ly3KOkOCMZL5YcnJ2gZAa56A1IOOQsFhfG5FlKcYEtnHTGC6v5nzx4jkOECqhxRFlMZVv6AwyBmspGocxDY2zNE3LbDrjydOnZGk3CM0a0J0MY1t2dkeYtiBSYNsWnODk/JKzyxnOaloncUJS2ZpISTbWcqQIhVepJTKCy9klk+WC48spEkltGoR1WBypzokihachjiTeQdO0LBZLLs+mIDRF0RClMXXdooQiTWISGaQyGkU7m9OWDecnZ1RlSasktXMYJN0sozUl3V6OjAMDsRYtdVPx8uSYyWRK01h0EtM6S9vWOCx5rsmzDAdYJ3DAdDHh7OyM84vzIH3wjthrYimRvqETa0TcIqSl9RXLqmRelrw4PmFZFiidYKzDmpIIT6wkeR44wlmqcMazWM549PQJHknrw+h5UTtqLHGe0ssTPITRa28pFy2n5+ecnY9pmiY0cyJNpKEqS7qdPkK5kGqVAte0XM0XjC8uORtfEukU6z3eg8eRJhJrF3S7KdbXRBKKouJ8PGZ8FZACtbEQRSAFrff4SLE5GmBaS0SE8Z7x5ZTz8RXj8Yw0iZHCkqcQK8VyOgkF9lijtcIIkLHgy+fPOX15TO0UTeOQkUdKi4piisIQxwmsZG3GWF6ejDk+OmNa1chI04k7RISRdgRkSU6S5LTeEwnBZLGgqA0fPf4cGcd4GTErZsg4BmGpbE23lyGFx5oaL2B8ecEXX35JUVWUDavz0pAkEcY2ZKlGaYlzLdYYimLO+XjKl8+PaX2MlRopI4wVKCSt80RJTC1M6Ba3FeOrOc+Pz1iUoTCkvcDXNYN+l0VdYbynPxyGwmLbMl8uqOqSDz/8jLzXp3GC1guEViglaeuWPE3AGIwLwoxFbfj00y8oKovxCkGCkEFI0TaGOI5Jkoy2KfBeMp8XnJ6POT89p2pNkFu2wVirhKFtLcZakiSh8Q2LsuH5y1NevBhjvcL5iMpUCAk6FlRliZCe7iBjUcypTCiunby44GpaARFVa1csck1Tljgr6HRy5vMZXgiW1YKL+YRHj49Z1JbGS6RWGOfxMsjILsdnxHGE1jFta6gNPH/+jLPzC5rGgIqwjQmpbMIzp1qWdJMurrHQOs4nUy7Orzh+eYbSCc4pJJpc50EC6gQej9KCsm6omprx5QWffPY5Xic0ziO1RsVq1QCIMLaFVGO9oKpblouW49Mxl1cFRWsh0YHzrSU60ZR1iZOSRjS4yGIiz9PjMz56/CVOJzhjwrPTSyLhmM+n1HVFkmY0dUtjHPNZy/HJGSfjOT5StK1FIkiNI45S6qomkjFNYxFKMZlccfTygmnT0iAhT2iaOjyH44RIKYrFkqpqMA6Wy4anz8+5OJ8HcV7ZYFuLjiKUiIGI/nDE6dPnCOmZXM24mM559Pg507LAa0dZNwilAlIDATjapubicoZzgqpsOTmfcnp+SVlVtCupn46hNgadJmSdhKt5SWlaitZRW8npZMbTo3OKpsFLBULjbAteEOuY2WyOtxCrmOWioG7hyYtjvnh+hEp14DHbBi1jcC5ghlzF3/tbfyEi+/Ow/ZP/5vf+QVSGxEwwwCuSi4rzb8f84I0P+P/Ye7Ndy7LsPO+b3Wr23qeNExEZ2VbLKrFskqBpyx1lyTYEQ7AgWIDkG8MyDD2CH6BufONXMGDIhgGbggFKsAnJBgyREjuJKjbVsCorm8rMyGhPnG53q5nN8MVYe0UWfMNLSuQBCpkVeWLv1cw155r/+Mb/d6UCZea5CFs+3N5HjjLhOw1+EE07v4347cjlX7Z889ELtrHmOAzsc8VXl6+Q2nD3g3PqdVZrga3O1dXtSOiEm28G4i9u+aU3HnPWdjRN4vntOWfvK515SH7H6cZ4+5YjfHNLFRIYuKHi9Pt2DhWzUV4noztD/I93/KU3PuQibKld4sR33OYlVzdHNC/Vp7NURhPkzxz7R4Yny4be1RyFnj4H/s2jz/mX1+9y/i+ndthOqeH9g0A8spz/3BUP2i19URK0somnwykvv/MAgPou63lM6e+l8vzSX/khd1lFltZG7lU7vrt9g+M/VoDEjboZPniiml/aUblMFM+ruMIa4fP9GZdXJyyfqytLmJLmS2VZf8lS3Yu8Ua/5o+27WCN0peKzy3uc/NBrHkfSVG9tz7Y8/4rnK6srrtOKLJYnwxn/9MnXWP1WpaK+UYuH0niqm5G7hwvqRx3n1Z7LeMSL/ojn/THPXpzz4J/u1DN2CorKy4Af4NOfa7mLDUdhYJNaAH7jx1/n6H2Hjfr5NgnhekdpKl59veJZe8xdbKlcoXGRf/ziWzx5fI/z74quJXcjOPVYHk4D31+cs6Piot0RxfGyP+IH148YP1uC9bSvRnLrCHcRqTzDecNHxyseHa05C3tejsdcDQtefnxBdadiQXUXcV1i/6jG74XvPLrAOPjy4hWfdfd40p/yj3/8LZrHgbCFvHCEXcbtBkwqfP/BW5jzRFcCm9TQlYrfvfwyNx+ec/Z+nsP50soTjzzZW17e83S2IrjC8+GE79++yQ+u3yD8QUt7qSKZ32fsmCi1ZziquTwL7FLNu6tbulzxhzdvw6+f4AjqCRos1eUOd7tHjhb8s7cesZGWkcDT4YwPt/f57Q++wuqDQNirPyTGagDdTcenbz7kpWsoznJR7/Q8nr/H+NExZz+KagcxFkxSgXs8rfgdeZv7F2s2qeE49Lx/95B/8cmXWH5c6XO7jnN+m99lRCr6r2fuYstxNXCbFvzfn/8F8ocrTn8cGU/VG7q/XxFXluWLyPun98jLwlE9cBtbKpv5J7/3LU5/ZKnXRT832EnsttQ3hv0i8MwueLVfsRkbnv7xQ46/W3H02YgES3c/kGtLtdGgvnhvwfdOL/ikP8U3wu8++xKf3p7z8NfsRMfD4vEGpY8MfhN4fL9hDI6rckQvFf/g6S9w+TuPaK7Vw9qInnM8CYSu8OH5CWMw7HLFLtZ8fHfBBx894o3fVnrQRR3zOo9YzBC4amo+3Z/S2YoPN/f5o0/eZvP7FyxeTgF4Sw1n27xTsXyZ6OOC7g29vi/jMX9w/Q4vtyvu/5+B4X5D2CT8VgtypXZUu8zn9465pOFlOmIwFX988wby/56zeJXxXdFQvYmKDPvC06MjZJW5HFdEPN+/epO7H59x+gE0t2n29han/uUyVLwcV8gq83Q44fvXb/Kb3/8mp39QTSFsUN0MYFXsrG8Te7egu1/43s1b/GR3jx9dP+T9999h+dhggPomaodHrcFPNsHLi4Y/un6TW9vyvbu3+M7jd7n4JzXLF0ltLbzFd3kOxjv+RHi8PGa/sNz0Cy73Sz79g3c4+cBQ7abw5UHmcEtxhrRtuHsAXamIxvMvfvIlXn56j/t/mOfgMvGG/lyDMlPluCwL+mD5cP2Ax7szfv/Td7n//6h9ULVRYbzUus6JM1zHFS9My6u84LPdOR/d3mP/g3OOPlFPdUTtXzQw1BJuLS/zio/7M67Lgst8xHd+/+tU/+gYm/V3XdIiZq4tixdC2jV8ftTwZDjlZXfEd6/e4tn3H3LxAw11DLs8B6/FlaPaCC+aJU+6Iwbn+OjJA/ijY44+z1RTIGLYZnAa8pZri7v1vNye8Hg84unNKevfu8/JTzSsK3SCH4XUqhhrBLZvOu79sHDrFzx7fI8nn97n9Ncrjp7oWG+vldhnKsxgIOyF/pMj1mnB080JV+9fcPSjQHPzOog0tZbmLpOWGohXPfFs71Y8vznhyf6Y7scnPPjnhsUrDVE0mCl4VcXe5TPDi3LEfmn46PqCJ90JP/m9d7n/m262XbCZuXiUjhxhm/k7f+eX/0Tv2uZPcwvcf/Ff/13JkwJtjMVIIjhL8IZf/PlvsGottRcM6hs6FuHzp0/54fuPaZsTCg4xIGPhnXcf8fbbFxy1AeciMUZSStxutvzh93+E9TVddFigpMKyOSbnyNn5CV969xFtK1ASsYz8/h98n34U2vqMIUZyAYrDW6GtDMu25uE797i6ueHl5TUWxzgmMkrzWeOQUhiGga+++Q4/89X3OF5ZUumJMXN5fclHH3/KOIB1DWMqRFHKqPLw1ptv8N5bj3DBMsY9r24uuby8ot8U3aiLJ4oBZ1mtlrzz1n3eefM+wQsp7+n3Ay9fXfHhh59jfcsYBYpSmoMIR0cn/Pw3vsrxqqYJSvvEGPneDz9gGAubbcJ6Rx9H2tDS73t+9htf4e23HrBcWCT2IJlPnj7lw588RWxNHB3ZqMdpWLWY0vGNr77HV999izJsiDFye7fng48/YzdEFWuNMBSDiGXRei7un/L1L7+DLYkyDtN5vOL55RrEsiuCoP6GobIsa8+3vvE1GiesWk+fMuu7Gz745HM2m4HkAiIGKZZgHaVkvvG1tzm7t+B4taJIz/XVFZeXa16+WDNmDaTrUlTC2FvOj5d89Z13ODlfMg4d3sH6bsOnnz/j+m5PLIbFomEcR8RAwHLv/JQvvXcfZyzLumaz2XB9u+HZi2uGKAwlEcvk/WYqmirw1XcfsVq0HLceiZHb9YaPPnvGfhwptmZImWCcUlZeePe9N3l0/x4NBVNbdts1nz95xssXN6Tcko2er8cwjiPvvnOPs5NjHr5xn5IiKRWubq65vLnm+rrXyn8UbMk4o1XU09Nj3vvS2zS1juvtds1+SDx+8lxDzpJ6PTpxWI1k5svvvcPJyRLvtWJ/fXvHp4+vSEaw1jOMiSFnggPvhC+/8RaP3rwgxz1N0zDGxMc/ecxm19H1mWIsPjSYlFVUrCveeHTBW2+cYTHEYc/NXc+rq1tubzvEeLKFrh9p6oAzifsPzjg/PeX89JgQ1Mf3dr3n6tUaYytSATGFUqBZtHTdhq++/YgH949pfMO+79juOp4+ecG2zyQMFMGbQBw7muA5PVtxdLTkwcN75DJwfbvm1eUtfT8wjODqhr7b4Zyb/KgrvIHaWVYnx3S7Lf2o9yV1Ee8rclSa2Dg06HBRYx08PD/naNXSbfdc3tyy6wecC2AcqQwE63DoQrY6WvDw4QV147AU9tuOTz59zJgMxdQUSQgoOes0LPH8+IjlqqHyQcXO51d0cSRbTUTOxpKzUu/eFk5Pjrg4PaYCjINnL67Zd5mMKN1YEpXzOGPx1uK958GDC2zwBOvY7Ta8ePGCEgPZOKIrDLkQjArvyybgbaEKlouzU8Q6Xjx/xTDCmNQ6Y8iFygneQZjo02Vbc7RqKQib9ZbdMDIMkeKmpG7jKTkjSb1+S0mcnh2rD24q3N2tGWOmT4L1gSxgEaVUDdiSWZ0sWDQB4yqeP3+p1wdDjBljAzknrIUqOKV148Bi2bCoG7yreHV5SagWDH1ErKUUNb53zhDqhjwOeAPLVjf9SmMPpCJgPUMeaF0gGMiHtd4a9ee1ltyP+Krmen1HFIvF4o36TG26PcfHR0iOtFXASGS1WpHGyG470I8R4wJjSVgEk0fapiIbS+MDldMCF0AeM9sYSVmwdvKa9Q5nwTlHHgecc4hk2rbFANtdRzEAKqhQRIPhAJvzTMufn5+yXq8ZOw1wzFPqe85KRddVhbcJi6HbR1bHR4hkbm5u8M2SXAUNl8TiSqY1HucN3TDQtkvGmDUQsxSMFRBLGnV8O+coqadp9PobKaRUMA5s8PRdwnkt0g5jRxHBVTW5QEDTgIPzM4mt7zoGg2PsI03TID7NHR5SDKD3KZVE01SUpOGYOWeYOgBKBhEhVI6U9Lo4o17dw35HcA11HRhKYsxqv1IAm800t+i4/JW/9z/+yc22/vznX9mfv/Sf/w9ySPIWp3YCaWGJrWH427d8/d4lb7Z3BJN5Na540R3xo+++y3v/KNOfuXlTaAq8+CXLyc9d8W9cPFOLgHHJemz48bMHPPo/aoqD5jpRgrZp7x9oG+36Pcfw72y5ONGW7k1fs/yVE+o7/Y56XWYKODdK53YXhu03Iu0ngfP3M8UZqm2eWrNlthcItwOf/ydH3PvLz/jFi8fcxZarYcn3Pn6Li39WEXZK22oQ2YHktVz+guXil17Q+MRt13Dz8TnHH9o5zAteh5HtH3he/VuFX/j5j7lX77mLDU+2Jzz74D5v/gbzselFhuousnuz5vJv9nz5gfqCvuiPuO1b1v/XI6qNsHyeEKfBPsN5RXUb+eSvNzz6+ed84/QlacJef+M7P8ujX9fsOL8vr0nQhQZ+Pf6rhv/s3/0juhy4Gpb86OlDVr+xYPFqOl+nhJzJSjjefs3R/KVXVD4xJs/dpsV+sOD8B3r9D6E7oJvP/X3P3V/bcXa05y+cv+Cz7Rmfvjzn5NdbVs8mL9jJNzctlHr87K86wts7fubhJUPyfPD5A5oPGs7ez4SdJn3XV3H2+rz7Ss2rXx5589ENd/uWtoq8enHM2e8FTj+O+F2iv19NBKZ+z83XAru/uMf7zFvndzy+PEM+XXD+A6g3efauNEXIbWA8DTz5jyzl/sijh7fkYnnx5Iz7v6khViYzB+Adwu6e/0WP/dkNF0c7nC08vT7B/+GK8/fzHACkHsiOsIk8+/cW7N4pPPrGS8bk6cZA98kRJx8YTn6i9DYoyVwqnX7X73jW/3bP6rijFMvucoG/8Tz4jop31Z2KM6VS2g/g6X/oiY9GXK1jzn6qQU6HgLuwTvi7jrysKY3jyS83lG9tSdFxfNSx29csfnvJ8nmmuU7zOYhV7+j+XuDqZx3m59Z4n+m7Cnm84OhjOP4sTRRsJlztiecLSmW5/kbF9r2Cf3fHsh1Y/+AeyyeG04/iZE2i49YkYTwN1Ncjn/+VBf3P6Llvbhb4y4rz72mIk+v03HLr8Dv10V2/6+keGuLP7CnRUn1ac/wRNHd6vw/+3aX2sx91qi3jkZL+9a1QrQU/yBTGZCfC+uB3neYwxKtvBbr7QnVnOPsg074YZ8r0EKJXgs6nu4eO228KeVXACf7G88bvFsI2K62fpudj6dUX9J5n855huFcoi0x16Tn7odDc6PME2opvB7V5yI1j/V7F3dcgt0oinv3A0F4X3CAz0ZqWE91ZK7F7+42pk6Et1FeOsx+qEHf4O+FuIK0qtct5WJFqQ1wa1l/R+eb0R1BvylwoOvhdH3xnizd0F5buoWCToX0hGrq3KbPdxkHANFnoLgI2C5t3HOOJWnEsn2hIXzWJvBp8aUitm7oxDPsHluEMsHD+g6m9Pco8n7thCiFbTbTuPtNdBPpzQ24MZz+OxIWl2pS5g+LwTKWFxY4qyO4vpvs76Hm7fvIJv9M5Oi7sHFYJsHugtkE2Crk2nH6c5kC03Oift0927N9ZYYrQnXvEQnff4AYlies7FSD9Ls+CcH/mcVEYji1xaWZa042wfJFUzA8WN2RS6yiVmYM3c9D9X3+qnRPLl3leW1/P62phUCYi1A/C3Zccy2d6L/SaljkEVJxhPHakWgNA2+vM7qEGwZ38ZCC1annzxeeiO/eUAM1toT+1hJ2SwW4oc6eFG8o8F9qofrCgQrrv9feK18LHwQf8MLZybWeiWRzaIdOV2Y7g0NERthpWZgRkeuOdJAuKM/hB/Wdt0nnBTd7jcWGxWbsvYqtWDwfbh+IN9aYQW8N4ZKg2r98Z7BR+mmodK8XB7/zKf/cnetf+U03a/m+/+g+/7Z36p3qNNSLHyLJtWK4WNO2CGEf6cWRIHdebO54/f4VITZpIOjDs9jsWqwWlFJq6ophEyoXNbsfT55fEKAxjIqHUTPCONAyUnHDe0dQ1dVMx5szl5SXdICjc5ihSMFhMceQiWAr9vuPq+RWShDIUxn1P6xtANLhrVNuD0NTEpOnmq+Njdt2O/b7ns6dPiBGSAK4iSiGVgg2WXIS79VqDVFzg9m7L9e0t19e3eBNwxiEFkgjFQCyJvu84Oz9l6HeknHn56poXV7d0g1LKRizWalU/1J6u7/FOaJqGyju2+57rmw3Xt1uSWIpARqjrGiOGtmnZbbdYB4LQ9QO7/cAnj58T6iUxFaxxFKBIJsqIMZlMIZeMjIMStpe3rLc7Yp589YwH56YqiZlEEUgxsu86Lq+VnnS2IWdDMQ5wWOspJVGkaJJ93xPqilfXd1xd37DZ9FR1y5hFPQerGm8tw9iRs1ByQcRwdX3DdtdzfXVHiZ6EpxSQgobSIfRDD6VQsiDAerPjk48eM0bBGEcshXGMGDxgcd7Tdx0pR3IStvs9l69u2ewG+iRKrVmHCBjUZznFkTSOjP3IMEaub7fcrHdc3e5IxWBCrW0+QIoJC/S7HbGL2np8t+FuvVZaWCx9LErDpYS3lrZSenm33WOtY7/v2G62vHhxxX7b00VDKZAFKhOwxmgRxajQtOt67nY9t5stl1dK8g59wTrPZrNl2R5hxOBdYD90bLd7jMDly2fc3GxI0YPovbBOhYw6eLxzjEPmbn3Lgwf3ub7Z8PFnj9lsOrIIYwaMY4yRygVdPKwnDSPjMHJ7t2O9H3j68ophzJSigmgpBV97csoEV7HrB6TAMCZevnrF3d2efRcx1rPre3xVqcVE1cw2C8M40PUDd+sdt5sdz56/Yt9HHa/OaSu7cfgqKJ0A7Lqe2/Udl1c3XF3d0seMGEfMBRFwxuFdoJSMFR0HpcB+s0eKkBLkDKOFbA3RQA5A5YleIAvOO/qdziPrdU8xgVIMznoVnUQoRQjOqTAUI9fXN/QpcXl9zc3tljEKgseInRYugzFgjZLh2/3Afjfw4sVLum4k5UnQMkxWNgVn9T5aPOOQuLq+ZrcdeH75km7UVv6SM+RC7e00t49YE8i5cHV7w/puQ06Zq5s7xAbGYcRZw5AKzsgkbmmrvAiULKy3Ox0fRVSAFbDOA+rbbUuipOlFXzLjEFmvtwyxUMSQRIg5UUoha0GYqq6JGUqx7LcdY5e5W+8wxtMnTTseU8aFwDhGnLO6ubOBrh/Z7rV13dqGyNRuLyBiqJoKoyw5OWec89R1TT8KN3drvG8Z40SkuYo+DYgp+KDjRIoQqsCYYcyFlNXcZ8wF4xzZFCwGbwzBOlIpZCMU8ey6jn6IROdJWTDGkkXwxjLkhA2ecewIdYVYixTLer0jJ1F7CzHkXKjrCikqoiJgXaDkQt8rSTzGTEqaPI1RAts7R8lZveetxflAoVDVC8Y+Q3GkVKi8hldary/KB+sKU8BYQyrQ9wkRS8GQRYMxxYCxTgNAgVBZ+t2eulYv8D4mfN1gQkXJusm3Di30pMiYIkfHR+y7niIa+Fk3zSQsK3lsrOA9+CqQU8QYyzBkqAJdN+DRFOEihiJQ1Q0u1PT9QF3VWGOwRr3O8UGFduewzmOwlCSIFLo0YK3BOo8UMKKBj6VkStb7hYAYo/c8ZwSwziGYeWyp9YxeIxFLN0RSRp+tGLHGahCR8+SUECn87b/5N/6ctP0z8PM//y+/+e1S63yfa6sCyih09x03F47VauBVv+Lx/oxnu2M+eXyfkz/2uq4Pk2Ar0L4Y6C4q7nIDJ5m+BG6Hlk9fnRO+v8R3hnqtokNup/bYXqaNpGV34nHLxHrfMH5wTH1rqDYFprASJsceG1VcrtfC0cfTvBNViOvOlR4VZ5SUKhPd2RteyhFyP/Hx7T2e3Zyw/M4C3zEFQBl8p5tw8UoyrT4vvKiOuQ2B7skRq88spx+lOVDL5mkTNxZCJ4SN4+V7lue7Y9Zjy8sPLjj9oaW5KUz1JxWSRYWm+iaxaxfcHltOlx0fXl9w+fiMo0/MfF1s1FZrcdqG2lwZXvglT1nyZH3KZzfnHP/zmrQwhP3UBj0JIH6v5DTJ873ygEtZ8OTyjOqHC1ZPlS5yQyE3FpjmIsBGw3VoWQ8N+9uW+sOGs/dFPQhHwSYmuwaDiwUXYXPUsL1bsGk9T35yQfNxzfK5MK6UbvO7RDwKU5tqxOTAUCqey4KbT84IrzzHnygp7PvX35GWasXW3GaSa7hOC8bi6F4suf+7jmqr48LvM36r6zGo2NLcCinWxFhx8+qI5qOaxQto7gphW2YqEqcEqesLdvSYfeBus2T8bMnysePsg1EFCmv09yfv4xIc1dow9A03S8/t0xPss5rzHwkuCfW1Wh24XoP3hnsBNxiaK8OlW9LdtJTLhtMfqbjWXI3YWLBDJq084rWAjDHkHEhXDTxpWDx1nH4gVNtJyPKW+vNbxgcLMNoK3FxBeOUZTWDxYcXqM+Zn1e+zghjOEY8rSm3xncE9rShf6+k+O+b8tyoV3EchbLVoGNYDaRUAFS1dbxj3DeVJi3tecfG9MhVXmAPeShv0fJaexWVGxJE3NXy4YPUE2mt9vtvnHXnhZ//m+jaSlp6wBbuuKE9b2s8997+baV9NZp0GxB+eJw9GSczmCqrPA6sPPWcfJuq7jMmoj2QWSu001HDUZ128wUZorwqmGEKvwkzYZhUvd5NQajTszsjr8VXfGFbPMjZO51s5bCyzuJhbFYyqjXDyk4LfeI4/NBx/pn6gJst0j1HBzBo9HoHFpcvAaLcAACAASURBVFDdWM5+BPUNs2B0EO7coLS7OKPk9FY4/TBRXzvufT/T3GjI1aGoFo+8Zp9EJTxdFE4/yiyfg4mO04+VLA1bLZpU60Sp1OICq/OjzfqMLp8Jixe6TlQbFZ5LUHDD9Tq+bNYitBvB7w3LZ0K90WfDd+rFbGPR7gOD3u99wSahvSr4veH4sYrkzdRGHzYjpfH4zTDbb6TW0NwWFpfC8rlQvKHaFepXowrbWfR5mqhL9SM3DMeO+q5w8vFIrh1hV/Q6rdw0Rwm59diEfkZrqXb63PlB71u1SYjTThDxSo6m1uIHFcttUlG0viuEXue2w73TMEslTsN6JC89tuh3rZ5m3AjVVudoNz0XhxZ7Y5isGIR6Lfq7O12PTGF6Rg25tdPY1IDK4g02q+CoIqIhdEJc6vkfrCFSM/13q0WH0On32KJrrfq4qkWJuOlzkxAXjvYmMxxb6o1Q36pHPAfrpangIV5J7GpX2L3htLiQhGqdGY8dubLYIiqwGn0vSY2KnGIN9Vqfx/Yq6jvL9Pk2QVxaSmWpb+LkBa/PixElYw+iq0yAzuH+Hubr4g12mufHI4tNWgDIte5DDh08ftBuzBJ0XTBM7xJmWscGvV7tdcb3el/CNlO8rju5svOc/N/+l//Bv/qk7V/7r/4bqVyFjAlMeu1xZBsWy4qj4wUXF6fEtGeMW54//RTLkn7vqUJDJrHbdoRJ+F2uGto68MbDU25ubtTLr2SlUoyllZr10JFEyFZwxrLwFd4HfMj04wAmU/kjUjT0JdF3O/WbtQWHIUehDhXRGKwUfHCQCkImGqERS7fbI7XHW4c3DSmPOGdpfSQ4T5fVrTnULftdIhvRYJSJjGxcoO926lfXtrgsKs5td9jgKchEylgVj8pIZYWFb8AJV9d3LJYNQy5K+PUjKavYcrRcst3vOWpr6jogY4fzFV23IzQt+zFiXaDve7xz4DzOOGLsqSuLJPXFa9ua/XpDs1gxpoxF2A4dIThyiVR1TZZClQPeZTbdnqZqwBqGPuOqGhkcrvZExtcem6mwqBx1E9jte6Qkaqub6o5JbDIGweCm8bKsK2K3plosWW9uaRbHGOOgV1JRpOBbR4wDY3LUtiJUlspDzplktP2WFLAYikkU6xEjODF4BO91jPX9nlBXZLQtGMD7QE46WXmjwn5VKc3knHrn7ro9YxGlnLIunOSCMR5rYRT1WXXeUIZMFRr2Uc33k7EIGRMT3joVtFLBe48NFmcM/TiSqRFfSJII0ap3bMlUoVUzN1Pw3uIV0qYf1D8UUN9YAx5NnbeiFG0IhVIS3jVEIPiam7s7Wt8Qc6aqlSJjCs2iCN5CYy0G9cE00ioFaK1uapIu9CmN1G2DkOj6O9rVkjg4ShGcc9hSGMaIDR4rllIKlQ9kWxASwdeUUpRyFmiXC7rxDmeDpi73ESOW1je0bWCIPSQVA1eLI66ur7HWY7zDemEf9XsTkbaqEcmImJk8TiXreMHoYuMNkjKLpiVOCb3eW4roy0cRQ07a+tz3I2SPD3pfvLeMkpWKTZr8pGKRwVq9QeqNqcJ5cJ7B9IwxsmoXuGIpQ6GII1mPUPCVYcw9zmlbtneClELAUiRgbUFsYN9pm3Qa1Q6hCBMRahEi4jwmqmClbwdTtTyL+tQag0Ff7LPVZyMntbJwAYo4FckmqtYWyzAMSg37qRvBq2+wlJEaD0Xohl6vi61wxZAtlIMvmzV460gl4qylxFGtQJ0lT8IZRUnxxgewFuug6zr18bQWJxCzYGxNzAKuTOSzqA1PFGpbaSXWWcaxx4eGMUfEFA17qxtyVhLaZgHrcJUhZyEm8KaaPNCV/LQilByVnA1h8iLXaxgp7GPCV0FfNKfr7JxDrCAp0zQL9vu9jvtY1Ie2ZLIUihOsqUlpnK5zmsag1w4UEcaxxwoc1y1pVHF4yDo/qXA9UjmPNDVQKGPEFRit4ENgu91imwZnpufPBcbDtaprJYFRf9VeMqWoxyyAdZAx03Ov84nFEdPAsmnpukGvi9MCU56I0UW7wkkhS2HMIyIG7yqlSAcNOMy5kLOerykwxo5mucCLQ4BdHDFG723JicYqrWG8YSiJfZ84Wqx0g4vRQooRchKGOLI6PlKytej4Tt0I1pPF0jR6vKFypG7QPzdCngQRZ+3s0e2NJcdEVXv6YYCgxU0n0LiaZIRd7HDO4b0jD5HWNwxJPY4rp1YsuRSS6GcaYzDGkYaRuq6VtPVaSBr2A03TYMSSU8F6pwK9Qd+xkvrnO2MRkT8nbf+M/Pynv/zfSzwKM+V3oHbikaM7d+wfGvZfiZje4naWsx8qpVKvi/q/jcLiZVSKskwedGeG9Tcj7ePA8qko/ZJViOtPHaunSsrZQVu6+/vVTH/V68nDfmnxvZJG1XVPboNuECs7iztfpJb02FWUGs48zVXU8LLGqddpLxPh40gtNDd6nqk1tFdKG7k+4zcDpVHysrrVwKb+TNsb00JJwgM9ZaPMXoYA48rSn0/dSB9FuntKCefaUN8kXK/nvH97Qfu0Y//2guFIN3EH6ihOx3OgbHOjdJH6AeZp867n0p862qtMf+qodoXiYPF88o8dMnGlATFxpXTd6unIcKYBVEoeHr7bzDTe4Vy6i8B4ZFi8UtFrONaN/IGAPfiiHujC/YNKA4JWltXnA+NpmBPfzXTvU6uehmGTiMeeuFCfQhtlCqU5EN/6d2xU8i3Xeg/T5F1b3+WZgKtu9F34IMSB3nMl65SUzLVhXFkWlwm/UyHBDXneVx6Eh7BJutGfyK5x5WaRUL1Z9V0+N24ucKRW149cGfWEjEpNuk7FSlNkFq0PP4dzOfydQ7q9Hafzbdx8TKYwh1WlRsWOuLAcfbxlvNfOn+13U0fGRNum1s0e0j9Fx/H6nhzI2fHEYzLU1yPDeTWPhQO9W91FFVucmYnNA9Gu9LTMPs/DeUV9rdSpWIPfjIizjOcV3bmfg37EGnYPLGcfqAXKIWCpfjWoiL7XYCtTmMe73oeigU9dpEzCHUUYLur5OubGzkSdjfr7aemprtXzPi/CJMo5/CaSF/61KHP4vuk6+r2OFxvLHJrlN4N63NZ2vg6+y/N8EzZxfi5S637qHqrI8wWf5y6rF+4X5pPDMYg1/790eTvKTDe+JhLzTDPacbJMmNq2S7Bzoam6Sz/97AY7f994pMfbXGvgld/lea61caIaKxUmXV9mQvZwnQ5U+eF5Gs7Up7Y4FV3LFMh98DWVqdgCzGPl8DMeHaARFZFzY2aK3m9Ghot2PufDfJVrMxPCubEzBarjVsd7mTxIbZbZu9XvC/XlntIEtXbwh8/T9cxk0fXkVZzHfVpM3qzTtTn4UedFeH080zGYgo6HYNm+VU1FQqjWeSZ/D3O0KYcxq3PW4dzbpx0SnN7XyTM5bCY/2GM//b7SzfVdngsHoLTsTz330333u0T3sKa+jjNR7HqZ58XuIpArteiptoex/ZpmHY9URLXjdKMmGvhAr5sitJdxtsw4eBcf/G/DJlG92NC/czKP88PzYUe1otk/qmeRNTVazDxcn+HE4Qf11j9cUw2wnLoVwuuQr9zoZ8alXp/DvJBrN39O80LnncPa0N8LVOtM8UaF4KnQ4bsyz1/iwPXCeGQnQV6vd7XO09xrZpq22pR5PTxcyzzNBf9akLZ//x/82rdL0cqHMSoQGgGH5e72Rim/3Za7uw3XrzZQrBI9OHLRDaKzniIFawy5qC/h9e012/3AEDPYiZA1nmQMUgpVCDij5FuaqJUDiXX4jlwSYhKOoF6x1bRhkkAuhuANlkxJCTFZPQGN0oq+bjQszGqIlMFivcUaj7GeIkJOkAqkovi5CATvJoHCUrc11nptlDQQSyGhG/E8UTcpZ5w3+BBISXBNC8bhnFacsrGMMSPW4bzTQrsRLFbPPQnOBIqzlCIk55UaFPWnrIInjZGSR5qmVpLNecQ6YhRwQQlUKZQY8UGvjXc1wyAg6n+ZraFqGmIpWF/pMYqbWlIzuYjSa0bbm2M2dGPCLSqddIvB1DWpWPAeS8EYh59armNR31MBqrpFUla/WlMooq2hSQwYpb18cEgxxJSwzhGTMKasG/o8IgjBGHIqOOcZo2ANiCRss5hEF4t1DmsdxhlMsIQqUGLGW0+OZRYpUxyRIiqElqSin6swOKxz+o5YOTCWNEY4JA0mFY0NgsWA0RfIIgXnjfpyZoipTL9RIBeCm0QrY0EsYlQAwYBJeaZOi7GT2OwpWajwOHFgHf3YUVVKnWFrCpbNbod1DhcsuWQqPDlHnM0ULBQmgcxRrCM5Tx+FiLYzJylYV5FESetQe7IMiBEWyyOloIuZRFjBOLU/Cb4GEcSoAJRFcN6TcsJZT3BAHslpwBZHGhQDMBiq0FBymQQij/cVJRXWm1tWqyOkZIqF7CbJzCh1rH3bDmscKRcE9UAOVYOkon0BGf37OVIbgymZPGobm8mWygUNmEILH95oeFjwnhQzphgcQe1XQIPvnCOmSM6Z1aJRagBLxmGsxZuAMTom8Z5iswpTJWkV0E3t0tZgnaEUHT3eekrWtmuy6NgAMBYjOncCVKEhpgxeh6EYQJTMzGJxrlYxOmZUZjQqZjsLk4ezLRZjK3LucXpiiAjJgAs631hrKbEgxpEwjCLYqiEfjssaRBLOGbUO1xRJjAPtgfCoRqnPhTGi5LrTY7POsd3tWa2OGHOmiKcYD3aiFigz/SnTMVrnEWNUfLRWwyednWyTVPS13mvQVoGpVIE1FdY4xjiCyTrnIAgyheKpKGisJYnFes+QM1pPjdgiKOJv52MJtqJkvQdlGudFRI9xzLjKIqJ0bfBK0tbVkhRHvU4YgnM446ico8RCzjpmCI5hHPHWYRpPFBVAjXGTGGxw1iIyFVkoIErfqzCu19kZSFPQofFKYdvp9rnpHkspOAPeBSQxWT94LU4IeGso40DtAjJ1kWANseicVURF6DFGRGC/7/TYjJ6jNxbnoWoWSqVWFdmAczUpZWzwGobn0PcF4xBx1LamDjV5VIq7shljHRBZLFugkGIixnEu+qn1kZL8SpwnRIF7DOicGLTYN8YR7yx1CPRDj7EWG/xsy4GxuKLrucESgieOERsqFDc0ajMhhTytx9YUXbutRWLBGquksRhKzFgsfRxxVUAQEkLKBWOLFm+cfR36aAzGGv7W3/jrf07a/hn4+Z/+/u98241fEAF0UqMEw9FnAzY7qivH8qnh+BN9J/e9UiR+nGiTakp5dgaXwPWw+sSyuCwzFXPYQB9ooHTkKLVX0q8v2MLkOSjzd7hRf7dUKgyllUdRFoMbD62PB7p0omAO7aiNmzbnZSYl5SCwWiWO/PjTLY82a6v8gQgaT1R4PAhovpOZmvmiWHMQM0KnRCooSXMIXKkmwaEEffcQZ5XqmRK0ldwFlyB007EkmQk01+tmdDz12hbqzJyObQRcUsrIJqZNZ9FU+XXCiCBOr3s8OhBV6PvMtCl3owoQX/T79J3QXiVKPdGI5XVbKjCFqaiAWSo7+QKCFfVkNUXP3YgeF4Zpw6thPaXW6xOm8J+wL0p1GgjrqHTbFD5UKqXSJLw+jsP1UVpSKT+sIS08Nk2fH7XfAMN830pQ0eZABH6RFJOg86v6cyrRhjFIeH3PMNrZZpPMQqYflFw+kLiglBsTEQfmdXDdRFQa0cKHTfK65T0W0ipMgp+hvupJK6WNjSjx1T7ZIVVAKr3PaRWUuqzddE1ej3EXdbxWt9qWbbPgujITwK4vk8Cvn99fBNyoglbYjNP56FjLC6VZD3SoHYt63B7Gf6udkQdB0fZ5Pte0UlGyuYpIUGrOZmH1eWT/RjUTnCq6lGnMlOkdVO+x77V4cBBgbdZjowgmq1dpPPKvxenp3sUjN9HFRq0EFpoWnxd+ptFLZWea1k4WS2GbcGOhe1jp+E8ye1IfCNeDR6fSoq/HZG49vsuTsGOn4oMKNQfS1TCJ8jDTtodjSEv1QjZyeIZ0vIRNnn1sbZzsN0QtJXyfVfRn4imm+dwNk4g7CYiu17FyKNAcigy+U5sDCXYejwfRUoJ97Q86zb9g5uvCF0h9LWrYOQBt+Wxg/7BSIjfJT9npHAoDNpZ5jJfpGa9vk1KOh3Z5a/R5DW4umrm+TLYaOu/LVHjRZ2UaR1ko3s4EJE6fb3FGv0NFE8Qd9j0qMBp5Pf+oOHm47jIT3qWykyDv5vEUj4PaJphpfmwtEjTo0SV0/pzI6mqt8+thbnTj9GxFmT1vbZ46C6NOLoe5X9w099ZTwU2YXg/NPOcc1jtdQy2HjhozEef6OUqm2jQJxzJ9Jzonu1HX1lKrGGkKNC/3SOVfk7CNrmVx6XBxEtCjPrduKNO8yjy3HObgdNowHrt5nY9LO3c09Pe0yOUGIeyz7kVbXTPkC903h/cJnfO0qJNWbiJbtQAxrhzNbQJrpw4GvR5a1JjIWqtruO/UhmIqA+tnjuqrK266pl6LdLbw+hofKO5glNoNTvdDnUz33Mzewrl67YWLgb/7t/79P9G79p9q0fZ//4e/9u1YMtReW4i9bkAiCd9U5KIb/d2+BxwORymeKEI2AkYFCuuc+uaJirbOtQgG72tiilS+gmKQrEnmOv0URApt3ZJLxBqHEYdkh3cWRKhFGIcdy9WCtM84sQQMMiaCcwSnxFewTtu/UyKWgSQZXwVSzoBgjBI3krXNfpxCdlLOZAxDHAlVRbfdUtc1JQNZyc5C0nR5o5PaECPO6MbNWqutohhcVbHerEklkYtusrFubqO0Vn0osxGK0YlxK4liDIMUOgMjQsEQDi8xCK13mlGV1et0FEdEJmIskUrCB6vJ6uInYdpS+YARgWwo2TL0kSIWisGkSNdvqEKFEYs+FfrilUzBWBWv49jrPY4qSKVkcdbjjCHlOG2eDWPR63xYUGJOFIRiLK4KxFIIXgnSIoVkE2IMhazJnoJu2EWvVUYrdlUIxJwxBh1v1kxUJFMrt1KKzlqGrqcOgTDdZxFtZc8l4TmEzYkKmuiLYsnaouqDo+v2OAxWDGYitcaScVUgl3H63ay2BajIUwRSjlRVrddHCiNKCoiItsD6grHql59zQSbRs+SCt2EmySqrC7yrPGOJ4IyGjIkhl0LJGWct3uuklHMhOw3jszZAEr3n03gbc2IcR0LdUpyd74nI1F5uYMxRF/+Jws1FNPHeGYx3JIHG15gxYZyKclhw3qolhTEYo0KbWAcpY40nxgK+xnlPzGppYL2KlgTLmDPGGjCOWIQM5D7igoei990aQ8lpalHOev+M0WA7UX9eax11XevLQFaK2hpPVVWkGMmpUPCIGKXDpxKrw2rrtHMkSZhpkcCi/24AY9l2I7GICofWMKaEMVbFPqfiEMYSy4CvHGJUlCpSQArO6fiUicrVZ0ALDilFsFo0GEuiTNTqLg74uiLGSPAVuWghSqwnix5LQVS8dFa9dAFnDG27ZOgGvKsI3qoAHzNV01BEKDkTnJ/E5OkZKSDO4Zuazb7HhoqC4JyKYM4ZnBVyGjEUstHnz4eKktACnuhLrJvm4eI8Qxqp6poh7QjBTS+L+nzZSXDHJKXWp7+fTWEwmTzNlcE5rDX0w4B1FhdqxjQVKJyDIixdQ9eP5Jxw3k+UuIrU3ut1Lsai/P20mdeUPF1jqgrnAiKQJu9Rbw0mJ5ZtQzWpoGNMU4eBvoAZI1jRdaqkjDdG/WvdocCD3kPr8K5WId4KqSScC6SSkcpPL4COYdBiFQZ80LVHsGRTcNZipExrjoaCimRlrZ0SzSlGsqgAvjpZMMSeLOpljBSkqHDtRMVayVGLQpNXa7VYkJIGwaWcpjlsoi6C2glUdU0IAUTwtsKgVj7FaCgZVu2CMkKxCbGRkhPGqGczKauPsVFim6zdCyVH2sqSYsLXtVoyJO38Mc5q+F0epnXX47yd/W6dm0iTUrBTwTVPomg1+fOWUli0LVYCQWpK6ckxqudVGqmCJ44dCxtYGKdFIYNu1CRpQc56JIMRo+8QSUVmI4YqOIKrGYeeo9UCySNiHLauSRQq42l8RekjxtvJSsFhDH8u2v4Z+fl7/+tvfdvtVXzwO/WjPATjlFo3YX4QFi+n1uBgdfN58E8V5rbdg/jh+2kzXpSsCvuiGzLD7Jl42PCZAsOpx09iDxMFlhv9/XHlqLaZ7kE1C5xpqZvPtLCTj6D+uzF6XGGX8F2exZND63Fu7NTiqW2uWPCd0kXVrbZ/16864mk9iwimqOhYrwt+lNmTtlS62ZUwCUpFRdDV5yN+UEH4oGUY0ffqEszc7mqTbjbbZ3t80g1xc9kTthGbprCSaaM3nHmksvPmOWwzfjeJWEnFizKRuKYwiYsqjB/ss2xSkeCLIkv9atTW4XmTqtfJ9eV1W/RON8wq1ppZpCuVCnY2iQohm0OQlN6/sHst0hzCsnKjxb7DMR/Ebwl2/hwjwNReroKfJ+zza1ELvc6mTGM0WBVpK6f38ChM1Lfeu1IrZZUnslU36PoezyT+2qTjrX7VT6KQkp82QrWO5NrNFJfN8rq4gbbX+k6pZrVqKPhtfC2SjZOYaw25dfP/VwFD7SkOlhlp+oxS6T02mIksVWFXhWU3XUdts3bjBPcELSKnpZsFDL9T24i89OrJuIvYVKZCyyQWbdNcVDn80+0TZtoT+H1kPK85EHOHsLQSLGETZ2HyQIce5gi/i1NhwOE3o+4b/SQSMoURGbTAus9zq3ypppCe42ouyNgkk9DvZiHu4Fst3hJPqlmszM30ZystBh2EooOAbCca9PDsijfqjzvNO18U1Y1Mz+cuT6DNRA77abzCPE/4baQ0bhbIDmGIKoipKGmzijaHsfPFAkjYpPkz22d7Suvx2ziL8nbUc7aDik82qi5ih4xUk01SZRnOA9Wt2gikxeTFuUmkIz9RnYdil53HYpmo0tw62scbJHjM9EzZIVMqR5mKBExCsBEl4A/U6kEoL5VWqm1Sv+W0CtTrpFYRU6HnYPFwWGu+aMPghkJYR1yfsEMmL5VerW5HmALLwkatFXRuhe5BRXOd8H2ZixcqqAqpfT1PHe6Bkp/MYzkd+Wm9Etxeyfrc6rO4fxCIK6djdncYJ1NRbRKSVVwv5NbP9j8H0jXsMmmpfq9h8kB3w0S7ThYDh3k+3A6zQF8mWhV5PUceCgzira6BRa0MDmKkjnddk/cPgtoRja9FzMN8m2s7X/+4moqLUYgrp8W9Y7UuOqzPYpltCOLKkZdhftYOoqZNMhfuQjfNV9NzaicLjNy6uVhoM7hO57iDnUp/7vGDkBq1mGhu0nx9/PS7pZr+N9ke5ErXVQOzdVL+QsEurdxU5IPunnpGp8V0zpPFgd/rNfL7Qn8R6O75WQR/XXRjWrsma4osam8xjdu4sMSFo95ktm/W8/wvXue3tFKq107vPf5gdWL/NRFtf/VXf+3b1llGRfSUmCwFKRErKp6wTRgCBcdeMjiv7b4x04+JUAdySThrKNNG0xrLbr9VUquqyTFrq6Cob1wRwFcU4+iGrKJTEapgKSlPD5RhyEDr2caIDTURbTcUC95ZEhFfBUYgWkOyhqZZaaq18zjnscZObb2ZMWd83ZLHqKIBujGr/z/23uTHsjQ97/t905nuHPOQGTlU1sjqubqbNCnRpESasjzIAmwBBrywvfUfQe+9886GYUnwABiWDMuAbQiyTTYEkd2t5tjNZldXdU1ZOUVkRNyIO5zhG7x4z71Z3NEbiwB5V9WNzMgbZ/jO+Z73eX6PcduJY+Yy2rYVd23XiSCtEr7zZDbbeHB6vqcmtYHMSRS/cBZ8YFQN8CGIwGszlNasVitcLypa61BaE7xMoCwWjUU5S9BJwJoROW4olHVExKmmlbw0pBDJS4cx4l7WTsSylAxaQwyBqhBXYwie3GUi/ilQxQBjM9rQou0mgtrHikIg+N6VhyKEQJGVdJ2nzDNC8jiVcFmO7qdmYsSLpBiwKWGUjLjEVZlw/c8OIaCNxhkLXoYAKXi00QQvkeMUExkaa5ywf7UIbMbpvsBIizPSWozLWK1XGGUosgKntfA3+4iwsYaYAj6kvjjIoKO4DX3XkeeFFOuEIAxELUVNXddjF4qcLni8suTlgJCUtL0bTfQejLAUW9+SjBE3YJQwuzEG3zWYQov4AeRlQRtlVVLo/pp3rGJH07UihlgnMV9r+gFI72C0kFvBR9TtGqUtutCE0LsQg7BUU0woHyi1JSVP5jRayzUbu4TTBaH2It4HhbMVua1oVh6dHFZrEaaNFP/dLq4hi+Q2fyVaIWzQEAL4DmcS6+UcMmExK+ewWUUkobRGmUCMIsINkghkKnesQkcsBDdirZUHgtbEELDaiCPfKILvxJWZEnXbgjbEJIVUTd1ijaMJAe2Eix2Jcq6dRelElhm8l6I625+nGKNwP1XC5VmfMNgIb47QRpzL0VkmbOgUcTFgtcIq0CmSO03yLU5rcmvp6hqfhGcLCbqGwll0z0XVxhA8dF0tgjFhKxh3Ucq28j46rfq/0/mAT4lM58Qgaw1BsALOZqwbj7MWfGSUWfZnu4S2oW4brBlgnNxHWiuyzBHatnd9y++/bm6pypxmtaAcljSrBRpPriw6BBmCJEfuSgyWInfiMBTptee/ipCs+k2qQYqygm/JU0ZhK9AZPkURp7UVcdTZ3uWsKZ1Bp0SuLVZ5RqMSHxtISgTqGEjRY5wVR7QXEdxHKIuqH/hYoaxr1btTYy/6gc0ycdtr3cfnA7l15DYD1Q/ftBJGsu9wJLpmTWhE/B8XA7RSZDYjJClJ7PrBUNe1cp1bTdIBVxRSbGnl91x7j7YWnRm8hqRabJ8USV3EoKlchY4Ri0HH3kXbBVRfYOmMlLb54LFG4zKDygxKQ2oaCmUxCQiJeh1wOoegMVYJo1YpWeONxscOZSORKN8xs6wWK6pyQNd1GGtJMVI4h9WC9YRvwgAAIABJREFUB2najpQsKQqiSGsjTlpre8drINGRu0iKraz5AWJUgDybXCxRSdEmGQqvuxY7KCnHQ25XK2w1YrFqZRDQJLKswumcWAesHolbODSYpBhVA3LrGOQDUhdRIeKsDEy66MnyjHot3yNGWbX2JmP2pwNU46mynGVbo4sSnyAqhSVKGaQz5FlBnlsmkwllpjFEQt3iXAlKUh3i0lVYrfGhZjgsOdofMx0PuHNwQFouyYMkkrqwJupE8B2Zy7Emo20Df+/v/pVo+5fh81/9k9/9zWQ1bt6iUpKm+4SINk7jh32mr9/olhctig0vL5FdNoTK9YKL2tCWSE5RPm/RUZ4jpk1b99XWKYm4LovLPuqMMAW1l38PJRxcECEDK+If/YYz5MKgDLnCLcUpZtaBdpphFz2jLjckJy6jDasyFq/cPRuRphu7XrzU4nT7Qov4xl1j17K5S/ZVnDfZjYPGYJpEOxOX6eJYNr75PEiplIbihbA7fWVlA6uFARkziWVitIgptYf+WSsib+oFxn7TaF8537qRJfVcvWR7kTWKu1Z7tu3kphWn7Ub8Q0krvLv1r9igPd9245g09SvnXTtz2HUUNmuXCKUcp2R7Jm7PCBTxR/eu4t5d2IuiwPYaCNUXN/C9S7t3/IoTWxi4+WW7xR3ETG9djaYR0SZmmvxc4sPtTMqANq7a1HOEdc/R3DAHhQMqP0OYwf1/T8RVuRHGN7xUt/CYtaebZCKKbAStXgzUnbBfN07e1MfTv+hE3ZyTbvwq2rwR7/zQUDyvcTet7CMK/aroZy2urY2zrhvLf+dXvWBq++vF6K2ItmFfNlOLTiK0yPBfo3wkDFwvpMg5C5WlG5tt2VMsJN2HVsTSUjy+QaHpJjKI3IhWwgKWPUQoDflFLU7BJoiDr8cKYMShrH3CV5bVgUUHEV2K87oXdgKxZ9NuRNGQyzUW+2twI8i5y5W8k7cyWNoUxLnrhuTk+Op+Hx2tXJu+Em6xConY4yd0SNuYvxT36e3gaROTDgMnrrkmYGpPGLje4aiJVgtaIir8wNJOrIj2qyCOXmR9aaduK7TF3iXo+oGDqcN2WGFWLckaumku6BIlQr9dBeyqw48ziecP3fba32AfYinnZnnkuH4rx7TiJPWlIeaG/KWIzX5ot45mEGd+frGm2c/Jrj3tXom7aUhG40dWWK094qEby+/dTuxW8N46sUuzFTI3Ls92amXdGVspe0IcpVra1UXkzfVWaGxm8nO7sSMWluVpvhUPN+uQjl9wiVr5/W0dexyD2gr9G5THRvQz/RBPBXGwbhAo3dgKjqEX0VUU1ArImu5WUvhn2sTiVNaHbmgwrWBcNve3WwpTeyOG+krc0hsX5qaAMxklQ75+ULNx00anqPczuY8Lvf2Omzj+5mduMD4xlyI2cSGLi7iZmq37t7gSoVyGXHorOIdC1mXTJza250xBedFR7zncKhJzeZa3Y0HB6CBDP3FIv3LUb5Ifm+SJDol2IoWTvhLzgO5krZH7Qu491wv5+cWaWFjqmWX4pCWUhsHTRgZbTcQPrWAp+mdIyGXwFjLF8sjQDRXNrmgGSSt878Z1i0A3Ena8r/qyOwXz1ww3jxJJG+odI6xtq7HrV2uDSrJudENNO9Qs7hg5tk6SRL5HlvgvOH9DnxhaHRiu34blqeL6q4GIox0bVFBkK2F+m1bQOyGXwrL/+D/88zFt/0KLtv/jP/5Hvxk0qBjJNdA2ZCrilBIBMor4YZxhyx6QwTkpRKxSEk3umXg6anJjew6pImhNk/zWDdNF3292IqERF5BKkWFZElNDV7eARvUPgHXX4JTFWYdWUC9XZM5iraKNDdGY3rkoBSCxDTjrMElhEsSu5xoaQ5ZldAmcMSQSIQY0CpPkQRe1IiYR+hIJ42SyolB93Zq8DFmlRVRNihDAZpbkxQWZgiepRNN1tJ0nLyp8CPg24pyjyAtI4IO4kVKUCYo1moAn0xrlg8RYVB+N7U1hOiliEAxFYRxFZsW5mSCgCR5CMjIdRKFUR57ltE0QHqWRBVJrhQ8tQSW6TrisPnSQEqFtcSicE8ecsxoVgvBcNXS+66P+nratCbGTJj/EfZTpAlJCJzBA1wVS0MSoabqamKII6F3A9G5VZTVt6vAWQJq5TRK0hDLitIopkRl5cQtdooueqBLrtsFYg7MaX9dYKw5T7frfQQnaIfbOZZSiUwkfOpSJwiVNUUQXYzAJ2qYhWo1xUvBkTY7yIqTLO7xEu50xWDTOZcQkUde8KPAxoELEdx3aikDZhEiel6QofnUVtPR9aE3TtEQtuA9ItJ2XY48jz3JS6MgziwtarqXgxTG9EdW7QPQBkxU0IWCyDIyiaVeoFAipHwDEiAZya4CAdYLsUNaQuo5MJfLcYVRi3dVEIlEplMmZZBNpjEdc1NpYLJHcGJwTp5+zA2ICZwVn0rVLKeNKUhilk8agqLs1KgacFURG7GQktvGgpBBxSsR8UcQ7CmfwbUdZVgTvcVisTpA6XOYISQoNu6ZmPCzwTcPAOXFDhoTRQPRoqzCdAsSBHXQic4awqiFFMisDm0QiKnHuR6IMn1Qkzy2GRGY11hm6JvaReUFKKGukIEwpjHZkuoQAySfyXoDtOimvazqPcYYudMQkQrAhkSnIjcJGT+EEf+KMhaT6mL4UIW2d+LklKXFdtus1ne8wxuKMoWtrtFMkAnmWkUJHWZS9KBwZVhWTvBKWa4r44HEuI1eWxncoa7G9szdFQ6BlaByqTxNoJ/gO70Vooy9g8qGjyAtykxN9JAWo2wbnIkQpytDKyrWpZeCD1jhlUSEyLAbEJqCiIfkWbUCpJGVoKUrRm84wMbIzyDG0jIdD1ss1LmrWoWUwqGjaFkPEKCVO7RQhBQbDiqOdIYVTKA/Jd6TgRSAFskwKJ4NR2MyIu52O6WRECp4YO3H0KsPqdkmV5VilyFKk6d3M46xgkDlmkzE6RlSKLJdLRoMxoUvETu6VwmWQIloFJsOCsnAMKkfoAsaIoKySIoSOTCdyozEKYmgplWZvMmY2GffPlUD0ibyQ4jJSJHSBMh/SxYBxmswoZsMRw6IgdlEcslFTDkpBEZBIIVFYGTplVlHmjqJwpBhIOmGNxhoLJgKhdwPLEmuUJCY2WJDUv2s4Z1GhQxt5OAQfKPth6aYAj5gEQ4G4+aPvIEa0TlinMCni64bT011O93Y4PNhhPCopcw0p0NYrQpKXepPAKE3mDBB5680HHOwMGQ8tp0dHYDS317f4mIhJkWuFiRaNlJmOpyX7B7ucHR+wM54yyivWdSvPzyhrNNGjEJDboLSc3T1mZ1KxO5uQpcjI5aSo8F2gC5AsaCXue3kkKf79f+/f+ivR9i/B5x/8w3/+m6aJW8eaTho/zgiVo53KRlglcdps3GebyPJG1NJdFMFiLYkXKRNRW3E0m4tbEWQz5Qey6bZ9mzRasTpwmCDimoJ+/YX8vMYPnYgnVpNdrvGjTOKai9792G/wVBQhJ1SWlJk/E2dN/SbMrcQJpULqkQ1qWxC24ajGTOKbmw3wxpG5EUo37kiFbMC2bNi85yWGRH4byW6FF2r7GG8oLe3U9XHk0H/n1IvgPS9xKAM1iWL2CJ6Nw9PqnqGoaCeGdmK34vPGwWtqL+6lvhioHRuyvngr5H0s2+k+WhwwS3HlmVred3QXCZWRY9R/r427bNO2rftj5279K7d17x7eICyieyUW6CCCvbtuxelVWmwdttxPlFwXWwGwsluhfyOybsp4No5bs/Ion8herkjOiCOxjsTCSEGQ1YIogO2/vzlndh22buLkVO9C9dv/7RYB1bv13I04KP9M6ZURcdpXImJtRAWUnBe7iltBfvP33HVNN8621xVKbUWc7Nqju0Cosj4GLzwJP7D4sUMH4VhuuJN29QojItzRsHWiinApAlnW82O/6KJNVm9LqUKut/FsFcSZ3Y6F6Vtc1GgvAjVWszyrBCOSXjXZi4DnevFeEUon4kpleye08GKTFmF4I/Tlc3m/8pVGR9UjEZK4WTcx4o1bOm2Ez1finG3l54VSHHEbETplBrv0LO8UIqDs262QFrNeTNyIjby6Z7qh3aI8uqHZ8lOVj9hFi+5k75RyK8zjXIno0hc2bYYObtHjFnKzZcBuhl7CjTVkq7h19mVzLy70pe9Fa7f9O5uf30wMJoAfSFeE6c/zZmihIrQ7+faYZcuIXYmo5UsRHTfCYTeR81LviHMfpVgdWBZ3c6LV6KT6hILDjyzuxr9KKPjeOd4kVoeSYrRtj3qIbBMNKqYthqOdWNqx2a6JXxQSN9zgDYpBBmYiuqFgdWDQQW2d+V9MYaiQ+nVK1on5A+GvLk8MxZUwl/OXLfVBLqmIXITr6F4hHtZ7lpc/Z2nHeuso3SA3VBTEy4bp6nuxEAWLO3JdGs826l+8WNPs5YRSkh/ZbSCUhsWJZb1nuLlnSFauiep5w/I432JpQqVpxrLWRqtYnBrqmWa9p9Gd2iJ10Gr7vGwndovUaaaa+UPN7T1FUhpbg+nkWtvcO6YVF6tbx+21e3PPsT40aP9KGG52rBSQ9c/CZmJwtXDKm6kMgVRERNPi1fNkM+SLmdnea5vBjuqvCaB/1vOqtK6JtDu53DvLuH3HiJmW63yTwIB+fRFXra0T51/TdF9ekb+xoDn03M4MCYNdiWtYIe8sMdN0A0mPff5rkez1BeODJUdfesFjNaR8Lu5i7eX5EjK1ReasThQ3ryXGX3+Jv9cwP1ToeSZifc/XFQyGHOd6V3P1nmd8b87RvUuyqqM+CKxjjmkUtpZrbCPUb1zn/+mf02lr/9xvdf8KPkZJa7tKHVoVZMbSNS1VUWKTwRhFxPdxWS0R0E7iWzYzrNv2VaFKECHIOUPXeMo8p2vW5Eoirq0PqEyxDE3vtkQEX9f/uczRBk3KZD9Ur1tG2QiToG4abG4Yj4aE5AnGYFSG954scyQvL0Ixiiige4GvyB3ee0iRdb3A6Yx10xG7tRRVxYQ2hrqpcVnWiy0SheyabutCalbrXvRNdPRFMyFKcUnwOOfwTYtWAec0hbOsQmK1XuLyDI1BRQ9dgw8Sc3UKUuHomhpjNO16TQyJ0jpiz5WMyoAVgSDvi3DKImO1uGY0GlF3ccvky/r4UiLhY6TKS+rVmtFgAKlD4clNoAvipjappEbcvFEbAgqXF5joia1nNhzThgVkhqAyVstaim+COKO9bmRxHFQQRHDyTY1W4rZuQxS2YaFp/JLMOLQBowOZtjijWC9XuLIkdBprDZ1KRA0dAZsUBmEm2jxD60ihEq7MWdaeRSsxchukJMYNBqyblkFVUTcrrFYYYygGmqurOWhLUhqQSH4K0GQBmwxlWVFmOYM8Z1TlfP7sOW3bMsql4EorLc31vsNoKYRKJKy2jIYDjFHM57fEekUeFU3qKKuSRb3G2ZJBUdIsF4yqnMwYqmHJsr4Vp3oGRkk5kstLPDJsKDLD6uaW2WyIwjMaT3ly8QKttIihOvWN9Emch5klC4HSWSbjEYPSobXm+bNz5qsFOmiMk5IvpRQkD0ScjuztTxlXJd57blZLlJZ7flF7LIbFekVWVqiuo1QZlck4PtmTIqBY8/mLZ7QqEnQupT9WkyeFii1RgYk5x0f7zCZjQlgzv5xzdXtLGz1KRbTKCJ2IjSRBbuzv7TAdVwzLkpubG26XS168nDM0Oa1PpBBRuaJrFhhjGJUjDs6O2BuP0DGwmC/45NlT1inQrYOI8lETnKELrYgyO7sc7EzIckdoOp49O+ciegKGNrSYQgtiwkj53nhUcXZ0zLAqaNuWq/mC84trbms5XlbnhOTxXcewqlgvVpwc7HGwP2M2GvLyes5q3fD42VNGQ8eibgDIjKbQhvXqBlNk7O/scf/0Ll303FzNOT9/yeViIU7NKC+sRm3i5JZRXvDw/h1yLQ/jxWLBBx9/jNPQrFp8UnRKnng3N5fsH+xy5+SUyWQCMVA3K66vr/n885fUPrKOYLICSDI86Woe3r/DdFIxGhY0zZrbRc1nj59ytVhDH1dXJImbZ5Y8Nxzu7jCdVhRFwWq+5snn59wul3RR03QNUSmMs6jk0MmzuzNid2dK7jLKsuDl1QXPz19wdbuUAZl2dN5jXAbKUxQ5s/0pJwcHpJBQUXF+dcnT5y9Y1DXOKZrGUzhFatYUuebtN+4xHlU4W5A7x2K+5uPHn/Pk+XOyzNH4hrbzGF1SWUNVZuzv73L36LAvLkycn1/x0edPOV81lINKhL4opX+jasz+zoiz432MUdg8YzVf8snnTwhdRlOvCN4zqkru3jnh4w/eZ29nhztnd3Eb1nDy+A7+8I9+TJY56hSwWpNiom1biiLjnTceMRoW22dRWb7F+x98yPXVDTftkoC83GdZRkoRp2BYZrx274zcaZzVPPn4BVe3C17OF9T9C59GC3IkNOzsTrlzss94PCZ0nqurGz795AlNMrRdjbaGSKLzG4RCwteBPK9oYkAjGA2FMM6TDbJWEUnrlmI8o/Md2sHOZExd1/g2EbWlDR5vdM8iVnQ+YnLL62+9zt7IcTCdSsomtdjdIfPdCb/7ez/BqETrPU3yFMaRuppH9884PpigQsewyOlqxcO7x4S24emzG+atoFlCkArgSVXx4HiP4WCAUpFyOiAMM/IMPn38giYmfOj5wAoK53j02l2GVcmgspLEKC1hUkEGnz35jKyasmoglYI+8UHE77/6/OX4bKOEUTbGm1KjTTnYptBD+0RwCl+obYuzLxTFpacb9Q6gRkRKXwr/tZ5KYYuwYZW4V7WifN4Qnd6WGLVjg1tHmpGIDqpvjs6uO1Z3KoIToScUitXdoYiYrbiTTB1pZ5YsbcpwZAO6Kb5pZna7KS0uPb4y5Jcduot0Iydunkoaw/3Abl0zUlITXsVcrxv8KMN24qTbFAxtjp8IaiKQdJWmmWjKy7gtHNpEgoG+yMeQKiD1hWqZQt1KzLTeda/i110PHQj05S+vCsgWx5p8/orpF4pN7FWKsOr9jGIeWBzLdi9p+Rm25+bGzMr7eRtRXZDBz8Buf6f5gwy3lD9rukRxIY3c4paFrBYxq93P5FrqN9SbsiV3G7YsSDeX4xedbLy7oaMrNeWll3KgnkNslxJ9tsvYF35tSsgs0UIztvgSygtL9awlFo5QiWi3ObfrfSdlZbmwB9tTw+TjTTml5F4FBZJwNxIL3xRl1XuK9YFl7w+kgGd1WmB7Xm10cg0LLiH0HEZxfMXXLMPHsXeaWbK5p9l1lE/WhIGjPhqQzTuWJzndQFPvKoqLRDEXIWZTHuUrcecqL2U75UXH/EFO0rA6VBz8QdyKvWkjDBuNClGiv6OMeseyuGOod+U8Tf8Uxp82Inz0gt8Xi598pbl+ZFgfRnSjqJ5rkhqgA5RPBRlRPW3oRo6kodl1rHcMl+8mYhnRtWb3DxVORXQnx6YbWbqx3ZYQxUxx8a5jfbdDeUPxxDD6NOEWWtKw1m6LCeVEweXbjuVpJO63mKeW8tyw82MpKHKLuF2z7FLEz8VJxvWbiu5hjTERf2HY/55cY1J+pbY8VLv0LO7kXL+uad9YU1YtXWfQPx4yLottAVPICxk09a7GxbHh+r2Gnb1rFquC8FnF+APF8FnALtPWoavrwPq4IL/sOP9awc0bnp07Lzn/eEZ25Tj4vcDyJKd63pdKj7JtgWJwiuvXNcNvXbCsMy6eDxh9YJh85EmmZ6YiZVmmDvhCsTh2XP1iQzFo0TqxPK84/I64MbO5x6w6bD8kGT5uuHy74PJridHxNQBXNyX285y9P9B9+WNL7Adtuk0kDU/+msWfNhwfPOdqUbE+r9j9gWbyUV+C169xZi3nvx0prt9K6NOayWjNs+sB5Q9zqmfC+sxuXxXebc7N/KFmdb/DjBpm4xVPPpkx/lPH5GMRtjdrxOa71VPD4gxG775ER83iVyPXH+ww+1HF4LncW+66od0RY1ozMTz5jcDe4UtOiprj6oafXB4w/94e2R9FohMxXe5zx2rP0uwobh8F3vvKT+V4qMT3P7rH+HdKdv60odkrZUhlRXxcHmdcv64pvnFJ7jyzrOXp9Rj9L8eYLie7DZgmsDjJOP8GHP1OkvvvqyuKosOZQAiG68ay/48L2qGcE3nmJNxC1obHvx6YHszZyVtC1JyNr/iD77zB8DPD8GnYloRJyZk8o5YHhqtfaqiGDcOi4eKH+ww+c0w/7LYlXdEq2pEc3/kDy+1Xa44Pr6k7y5PHU/a+b8huY89Hl/cDs+zwpaAx7DrSjA35jRRqhkJS6KYRHBop9uVwUkzq1onoEvMHlmwu+CQ9EiySqeOfKY0LE8PjX9WMH17x79z/Y2Z2yefNjNlbK/75W69x8Q/uEZzCLdgWgqmYePLL8LW3P8bqyFfHj3nRjXj43kv+9/ZrzH5kGT3esHjlmC1ONfrnr/jqzgVWRx4NzrnxJf/34HXUD8Zkt6rn6SaC0zQTxeKvr7i/d83b0+cMTMM6ZCxDxne5R/mDAe1AU63FrWxXAdM/2/68n7/QTtv/4X/5x78ZNEQUMQmjTVuJbFhnyaxhPMzJCycbmhikDdtoQogYJ5xQrRTO5oIUaFqqKiMFT5YbgZcrA1FjVInRCWsFam6Ng2QonKVtayliiRkpQOZyQmpQKRJ1Yt01KKV6h2NiOCgoM02zXuCbjszpXnDVGCXusth2dKEjz3MyHMpHssyQ5Za27YhBnKemb352LpMNV9uhrZRfSRxeYbRCK3HFxgTee3zwgpBQEpfdm82YjgcE34mgbB2qL4LxIYKxWCW4CK01tB25NWTWUGU5oY9CBxRJKdoQcFKFRlXmTMcl+zsz9vbGdO2KZrXCKUXXrvFRWqmtdRJvwzEcVJweH3LneJfJqEB7aOqGoGC97igGA+arW7JBhUmKqii4c3DA6eEhO5MBSgeWyzVRWbKiog4t9M7mUV5QuQKH4s7RIScHU3ZmFZrA/OYG5wwxSmGR1Y5cWWLnsUpT5hlnd045PtonhhqVAm3dAEZatVXC93gDay2z6ZT7dw85OdllNCjxy6XEYQNSHmMc69WKmAyKyKDIuX/vjKODHQ72ZiilxBmc+vibWPcgitO7dI7Dwx1ODveYDDIOD3YZjQa8vLhAie0ZHztxdCpLjIEyL7l755C7d46YjAuGg4yuaejaWv4936GMYVG3GKMZOMvedMy9O6cczCbszUbEEAhtoGlrjLa0PgqeJCVIHm3h3skxj+6dkVnHaFSQWc31/BJrMlJSwuzNNMt6yd54wtnRLjuTAdPxkMo5dkZjlusFWEXtO3Gbu0JEZ2s5vrvPznTM7mzMZDpiMCq5mV/TNbW0utc1RVWQvIfUcXK0z9HRHgeHM2Y7QxSB4aiibj1dI9H7Lim86bmrdc3d00OODneYjSuGZc5wOCBEWLVrtDGCI+jEPap1YjYdc3p6yP7ulPEwo6oMw9GAzz79BK0LotK0oUNpjbMZWmtOjw/ZGQ+ZjSoGlWMymVB3nsVySZ4XpCiuw1YnYmq5e/eUw90ZR4czkvIMqwqFiLRXL1+SOUv0iS4YlErkhebe6Sl7szFVZqkyh3WGoix59uSZOCaBkCJ+3TDIcoocXntwh91JxaA0WAuT8YDrly9pViuER6yl1C8EiA33Hz3k9PSErMgpypwyz9BEbm9v6HwkJCQNECPJB452Z5zu7XB0sIsxkGWGqrCQYF2v0WiyakjoC5FyZzg5OWJ3NmIwyLFWMagKjFbMr+d0bUcy8vd1UjilyazmrdcfMB2VaB0ZjwuqoiTGyOXlnGQMPiS0SmgTias1bz56yP2zOwxLQww1ZVmSZTmL1YJluySpREhJBnhKM6wK7t87ZWc6Ym82xtiE1YmE4+LyBh8UUSVxzHaRKrNyn985xilFVjjKcUkxLLi5vuby9pYOQ9QWbSzENcNhycP7Z2QuYzaakWJgPJhStw1XVzdEJYVxnVcSz88dZ2d32dvdxajEeDiicA6jFXXbsVw2xCQJEq88jffs7O5zdnbMwaSiyA1Ow3g2Zjwc8vzZOTYqVuuaoizwyrNubrl39w4Hu1PyzDIY5AyHFQqo64aLy3NBK2iZ6OeZYzYZcef0iDzPGFYDBoMBsWnRKnF9fYlf1BTG4WNEG8NquWY4KLh754jxsGJU5TgdGQ4HPL84R1lxebVNjdbQtCtSbLh3/4TZdESROQZVQZkZRuWAz58/F8eCAoyhQ0oOAZxKWK2ZlhWjLGdVL4hRCkkxmqTEBWszKwV6CQ739zg8mHF2dpd2VbNarfEklFbCKSZijWK9vuHevWMOD6Y4A9YpstKS24IsK3n67JrL+S3aOsp8gIqB/f0pR0f7jMpMRNiUyI2lKkpWN7fc3tzik2AztNYsFy95cHaHO4c7FFphtTh9B8bitKZrW67nt8SgJGUTEkVmuXf3gJ2dCV1bk6LHuoyirLDKcr1YM79doTNH7VtyJ2zrROTv/Z2/wiP8Zfj8w78vTlvdxVecvt556gtNN1CsjhV+qNBCZ5LNWu+E3JSKJKu2xWBuFVnvSnRwI3YkI7mVDQt2w9TcuFbbkSbrmXjmC2VlpunfjbpE/rKV+GgpjejLQ4k45gsp+/BDQ8ydxG6/4Paxq0A7NnQDWU/8wNCNLNmt78tD9NYlFApxCW/YdZsYdXKmb2HXfcmZlPrYtTiHN47Q69ctyzvikrK1RGzRbJmzgiXoC1p6x183snSVpt5122K0TQGMW/gtI3G9a1jckXjn/DWFrTXZQuKarmcMh7JHLSgRcZcHhquvRNJXFyyOobNWNpxtIn/Z0U0cxcdXxHFJcvIdzr+uufpKYn0vEJSlfLnhcRrya7911i5PCup9QRKcf00z/1Lg5oEMoIePA34gDeMxM8RCHGJSliai9sV7ket3EykKWihbvGJlqiTHN2Vyrm/vGS6/1RHGTOhcAAAgAElEQVTeXbM8SqQmIxlDGDoR5Iwif7mWKK61rPcsL74Nq7db1OsrlrbErNUrRIIVAWuDx6h3LFdvK5p315QnSxZvBi6PM8YfsXV923X8MzH9ZmZ58Q0NX7+lO+m42bfotRFRXPVFVVqTPZlDnlEf5Fy/bph/s8E+WrJ6zdOEAt0JBkQK1/zWsbjhwL74lqL8pUtuB5qbI0c3sIw+7XruJRLbrxzFkwXXbw05fw/qsw57soZZx+IsYa5y0IKbsItWhO4gkeOLdy3rs47d+9eMT26Z7yrMswy3kriw9ql3iMtxOP+K4+adwPHr5zw6fc6lzrnZ09gbg6vFhW1XIrhsSsSev5fRvrPi3p2XTHcXNPuBlS8orugxJnKtbFz88wcZ11/2HDy65MHBS9KOZ7GfmPxgUz4ohXWb8rlkNRdfM7T3Gl4/Pef+ziUnx5d8stilOhfnoQ49yuK2Q7eBF98saN9c86uvv0+D5eHOSx7rAawck4+7PgaftqVi7Ujz8puBhw9e8GByyelkzrywXI8csz9BUAn9vWHWnvVBTjsRkez+vXMeTi5ZFYb8eE3zZEB+K30K9OtMyAU18uwXDNWXr5hWa45Gt2TjlnNTUjzv4/p9aeCGwXv5bsb8S55vvvExxiamRc1ktuKpGuHmPQN1JCVzSSu6seHllzSDBzfcn11ROs/JzpyXLsM8kfOu5bVJEDmVoRtosr95yc8dPyUzkS/vP6GcNnwapow+6V24dXgl3HaRz39d8a1v/JSHOy9ZhYz98YLzoUNfZAxe9EVrtfwuodSs9g3Lb695cPeCX737PrkLrHLDPBVMPpBrasurTeIYrncN5tvXjIuWaVFzOpoTx4HVsxHjj1rsbSuDvL5EcHVo2fuFc2blml87/DFNzPjW7id8b31K9anF9DF8uwzYJtINDJff8Jw8uKCyHd+efcSj6pw2t3x6u0txKU59twhS5jZvuXmY07y34tce/IQ7w2t28xVf2fuc62PN6icTfKEpLzqaqcU0mvIqcvHNyFdf+5Tdasmj6Uve2nlOrR3niymzD7pXQz8lgurtPc3eV8/ZGyx5Z/qctybPWYSCp7ZEP8mxde+UXUsyo7zoWB46Lr8RmR3d8ObeOZO8xu411D8Zk7TgONxChkD5jUdHOP82HJxeczy44a3ZOW7c8fQwY/L7fQJjw3RedsKoVfIeEHLF7ZlldWQoL14lAzZMainvlO9n2sTlO47bNzqy9+asVhXFZdo++zfO1FBoypcdl9+O/MprP2XHrtixS46zax7k5xyUC373p28y+UiOVz2TodH8kWb49jU/t/Oc9yYfY1TkbnbJl6vP+K3Va/CiwDQiuiejqF50vPhFxW/83A+5X10ycTW59nx98An5IPLDdp/yscH4hO/TNe1Yc/SNF/zNw5/wvB3TRsuOW/JadY4tEh89O2L8maCWsmtPN3Zbh/efF4/wF9ppi1LYBCgjvMEYUUnThY67J4ccTMbsjkvWvqHpWm6XS54+fcZqA/9XGqKUM1kNO9MZd8/2+0il5mePP+PmumHZRJquRamc4CUmHZNikFcczUbMphVNt+bl5S3XNwvQhqbrcChhIMZEUWbELnL/zin7e1NyYyB1dF3gk599xPPzl0STU7qC5XrFcDwi0FFoS5FZpuMhZa+2f/TxZzg8tihRWHyK1E1DlkMXpFzm/vEx+3s7LG9vubg8Z1U3LNatNErFTQmKoe06To73Odrf4+Rwj0x5lvMrrtdrvvf932NQ7gjH1AfQNahIs6x58PoZs50BVSaN03XT8cHHn7KuPdGUwiBUYKzCasWbj85wzlJlhhQ7TveGPHvxks+fvMDZkuvlCq+MlKUoxf7BlNOjXWbjEmcjPjim4wHuqeX9n31EWe5Q10tGgwHeB5xynB0c8+BoD+sUqIbZtODoYJff/+H7RN/hjKKpW4qqYnG7ZGc85ucevUFVGQbDjLZdszMdgG+5mC/JtMbj0UqToqZdt7x17x32DyYMRyWxXXIwu8fzl1d8+OlT1tHT1B2ZK1Ep0K07Hj064vhoj8JF8kJDLJjklhfPX/KjDz+W4hpV41zCZZqhK3h0/w5HhztY44k+UGZnHOzP+N73/wCTVcIvVI6gA8YnBoOct995RLdeYWJHlmUYk7j/4JgPfvYRnhp0gY6WLgScMrz79utMR0OMaqkGI8LODGs1T59d8OLyGq3FmV1VBbHtePj2I3bGI6bDAq093muSOWQwbXn/Z5+y8h4wVLZgsbplZ3eHk/19jvZ2sTqxP7DMGHA1L7ld3jK/XhAyh7aGoDSlLphMRty9e0rnVwyrAgEYK9as+emHH6G0cDz9usF3NaO9Hd65/yYx1gxyEdWzXPPuW2/w0599yPn5LZkrJY5nHbeLW+48OGY6HpERSTox3ZkyCpHcFfzud78LOgdKtHX4qBmNp9w9PWMyqsicoEqadsVkOuXZd57jEiyaBmUsymoGZcXJ0TEHOzuUTmNVJHeOMgTefudN3v/gCTFlaJPRBCkt2d8Zc/fkmFEuUXajEspkvPboATEmPvjsc/K8BBQ5sKxb7u/tMxqNyJQmryZ0qeXkdBdlEh998lNyXdJ6aYDXOmN/dsDO7hG2VIJKMAZbWSZTw+X1JY8/e0qIDpeXFFWFNYHX79/n3p0jYuwwNqEzg7KOt999m+//4HsAdJ2idJbQRbJsxL27dzDak+mE1onKOKbFfRaLFT/77Dl5YVnWDc7JOOfu3VN2ZkOcjoyqSsr+fKKqhnz4yWe4YsDNeom1Dt8FTs+OuHPnlFHhSKET1qtOlOUBTz4/5/n5BVk5JOqcpmlwwyFvvP6I0XhApsVNHGPLdHfGetWwOmm4vK25uV2C0kRtmO7usbO3i+pF1sl4htaW6XjG7eqWz8/PGY2nrFYrrMlBt+ydHPDg4Rm311doDaMyJ89zinzEBx9+TGkMdfJ4H8iMxddL7r1xn9m4wlqZVqcUGM0GPBsVvLx23DYKkhL8jMrY2z1kd7ZDs17gTASncbllOBxKAVfj8SoRY6AaaF5cPOfr/9pX0NZQOEPQCZNg92BC03mePH4pyBWE1Z0Zy+nBiOODGWXwdLElyyu6kJhMJmR5yfV8iSsHNCmxePGCw9kee3t7pJQo8xxrwNgMO8o5Pj7ipx/9jGFW0IUWi6KrO4rDPfZ3d7i5nYNKaJPISzh2O3z2yafozLFqa8rRmHXbMBjmTIYFO8Mxg8xgnULpirKwjIdDXjx5AlpRVAXee8pswGpxye7uPoNcEje5NoxmM1bLVhI5MRG8lIPlNqNTihASHVIv/+iNtyizjOXv/R61F5650jnedwQ0pq86X61XJDPhYH9ECgmjI11oMS6jaQPRRgptQQW0VUzGQ5wzUsKmDMlbGgKrbk3QLbPJiDYmunhL5jSrxRWVu0sMntAEMpUTjSfqhqLMWLY1wRiCF0f9ZDri/PqC1966z83iip3hmNh56hiIBDofBfVipJDNpzVr7+lix3K5JLPigg+hoU1gypzGN5hcCUvdd6jQCmZJuf/fX/n+6vOv7hMKEfpMLTHrpBW2i8y/6ejeWPH1s8/49GbG9aKkPa+Y/tBSXPaxRQ0gJZm+kJim/vqcSbXm7uia73/3DapnmuIikd1GSCLqbFiSq33N1Tug7q7oFhnlx47hYxHU8tuIr9TWddrOMrRPPP15g3t0y2y4AmBe5/jvz9j9cUB3gXrHUp53rA4zTCdunnasWNwFP5GfdfAvZHPpSy1M+TqRXYuT1K3kzzz5RYt6bUl7WTD8UH7nwXO/PW6xL0FxC8+LrxfcvtXx7Xf+lKPihh9fH/HB033u/7ea1YGT4rMvfNy84clfG1PfbRnMFqxuc7hxnPy2pnjZofpIcChlA+xzxc3fWFGULW9O5oSo4V14//0TZn9o6KqM8SfNq+I1BVevZ3RfXvLV0yfs5ivmXcHLkwEfFyfc/WeJZtcJx/LOFF0HfGV48U348rc+YJLVAHx4Z48n70w4+Z8ydNdHsOcd7dRRPW+ZP8xZ/pu3nE1veHvynBfNkA/Pdjlnj+kHUtBlGnGBivu249N/o2D66CXf2rmgDYb6kePH75+i/rmlvAR34+lGFqvALj2Pf8UxeOeSN4YLTgZz+V5Hezz+4IB7/1vcXruhyvADy/LQcv7XO7755kdMXM06OD6fTPjsjRlnf7+PFndf4JbmEkf+5q/+CZdNxdA1HOQLvpM95NnPTzn9LSm2g03JlscPLE//7Y4HJxcMXMubo+csH+b8H9mXiH/i2PlxJ2VdywZ/MEb5yJNfUuRnN3zr+Aml6Zi3BT+MJ1zslpQvDfm5XM/NfkXx5JbVo5LLLyVmb10yzBsePHzJ/LTkp3sH3DzPGX/aCBagZ662+wOWJ4pHX/mMeVPw9s7z7fX22/N3OP1/BHHRTXJBY6w6FsdDvvTrP6EOjnfHT1jFjJ+Ve3z2t6fMv7PDzp92PVdW4szFZ3Pif1LwzZMnHOQLIgp7Elns57w/OmDnv36V0tgwW5fHGfrrc947fMpRcUOlWz4d7MDffMrPfvQWXaWpnrXbuPB633H1pcgbj57y2viCqV1xXVVcjAf84G+9zt1/FrZFXPamQReOy7dLBl97yXu7L5i6NWO7xunA/BdKPvVn3Pm/1nRjkT3aiaN43lF++4Kv777gpLjmTnHFeTvib735J/yf+m3cbwXacSU845WIQPPXHYdnF0yyNWNXcye/YurWLA8zvvvplzj8QYdp5D5qZgNCrrj4lYb/6CvfZRUyhqahNB1OB77z647BfzcEwKw87U6GaRLdQHP23ucMXMthcUuuPW+OnvNLBx/yjy5+kcF3BJVRPF9vS7qary355t3H7OVLvjn9GKcCq5Dz+Jdf8KPf/jJdpRk+reV6XwdefK3k4KvP+MruE7pomLoVXTI8fHTBP/3jb7H7x1Lop7soCYOq5NmvRv71vacc53Nmbskq5PzG9I/4z2/GPL/aY/RJYvRYXMMqJG7u5RyenWNVZGBafmHvI3Lt+eX9n/LfPP8VDv9ly/Ikp3jR9cgEuHkI/9lXfovfvX5IZVp+YfohZ+UlPxydcP3bZzQ7Gdl1h6mDJELqxPxbNX/j+FOmTu6dLhl+fvoz/svj36Dec1Sf91jAUhzmN6/B3z3+Ex43Mw7tHEo4tHN29m5pR7vYRtIbykfq/YLZH1/T/AeQmcDZ4IpFKJiYNX93//d49pUxzR8fAWxTGX6UcfWVwL/7xo94p3rC03bKneyS592EX9r/kH8yuMP4U1k/8pvI4FnHxbs5dx88xSfDw+EFM7tiYlfM9lf89z83w/3TyPqkFKdpX+zZjhV/584f8juXD+VZqT1n5SVnr1/yv/7+LxEtVBee9Y6lvPSs9x3LE8Xs7IrXZi+ZuBqnA48G5/zPxyfs/omcu3ZqMbWkPYqLjt17cx5NL7bX+8+PPuC/mP8a7bDEreP2O3WTHLPy6Fb+P4DFX7vlcHzLzcUp+U3sC0vB1KkflorulZ+vIE5449FTfNJ8ns2Eq5spsrmkc4RZLGvKyfEl+9kth26OU54uWT5u9/nR8hTdwfy+wy0Tbp1oB5r8Cu5Or2miZe4r7mQvqVPGKubsjZbUFyPhFjeRrtLc3s0ZfaDw3zJ89/I+f/vwj1nFjJ/Ux7TRomojDuL++9s6kV9HbuqCP7o95ai4YWLXzH3J582MgWlxt8LeD05hBpZk+hK5/w+fv9Cird80R2cZ+EBmnYh+1rC3M2FUOWJqGI9y6haqKsOoxE9/9hkkYY26zFEvVwyGUw729hmWFXkm5VePXrvP58+u+NH7HxNSQBl5mbTq/2XvTXolzfI8redM72Rm1+7s4UN4RGRkZHRm1txd3aruFhIlFsCGblCDEBsQEp+AD1DfgS0sYAGCRi1aoG56pLqoqqyszKrIjMjIGDzCh+t38Httnt7pTCyOxc1iRa2gUJdtrkuu6/6a2Tv+zu//PBofLWdHx7z9+IxBIdi1KnHxZGC+3CCjSoxOoyEqgo/kWcHDB2+RZzAsC3q7w/aOh2fHLBYzVJbtzfSGtvEQFOPDIQ8fjjkalZSVxjtH22yYTNdsNj1eWIROzNsuOPy25/sfvM+7j48ocsXZQU5hAvPlkr5tcNbhY4KmuxgSNkJrHj95iN0uyMuMotScZENOjsc0TRI8CSGQqHvm69nJMWcPhpQqstrsEIeS9XrNxfUdWnpkBC0V/a7j6PyU0+NjtASj0zi97Vskis1mx5vbGcaUWA/aGJwLHB8fc3BwQFmC0YLgoNWax08fc3l3x2K2IhuUxBgYZhmxcbz/9G0K5SmKDOsE0kh0KTg6HjCZrYjWYDJN8D2qVFSHOW89PYPegugpqxyC48HJMbdvpsS8pHMtRVayn77l5PyIk9MhPnRkeYmzkidPnrDatlxdT8iEwnV9kskIzfHhEafjI6LfYhRYGTg4HNFYi//SkauKiMIGR1NvOHt4wOHxCC3TdyNEoBSa8eFbfFIWLLdrdFXR256IxoXIaHxAbhQiaKqspG87Tk9P2fYOHyHLCqI0OCLRpRDx/OQYER1KZvRth8kzHj58yGS6TiI7aVKb1UjW9ZazszPGgxwpHZKIzgrOByPUcof9wqKRhBDx3jMcDmmahqdPn1Iqj/COsA+/j4+PGZTX7LYtRhnq3jKqjpmv7pI0Z5CTh0iuFa5PoqvHj8757LNP0SanbbcU8pg+1BRacHo2ZreNyNgnuV2uGQwqbmcls+kaaTR135GXhqoq0TqjKAqkb+m9pVAQpMGcnCJRZKagbgJZXtH3jqfvPWU8qNBa4kUkCIepMvKs5GR8wGQ+I5MSpTWN9XRhy6AsKcuSGJIwQjpLWZYURYENDciMiCQTOTG0KCU4GAwplSdEDwSccygio2qA2uNGhBA4m8KZECVlWdE1W4ieKAIHR2MQjmxQ0oQekRVk0dA0DYODESrPyHVA+SQl9AiyPCPKjrrbUpQnICPWe0IfOT4/o7cWKRJLVecZPoDJ02p0FIK8zLCNpdCG0eGYqsgJ1qKVQBPxIuJ1kqX5EBDeUxQFMUYCHdUgp+t35FVBxOIdGJXR9B1CCZquQ8qCGARGF6nNLSWR1Jg3QqCNpm1bvPdU5QCZFdS9RRuIWLKySHzY0qCEQGhF3bbkWYbvLe12l0YIZcKcTHdrDo6PyfH4ZkOudMKMSEXsA4Osotu2RDTNtsZKj4qBerdJDUopEDESXGpAZtrgg8TExDqVEfKioK07nEsLM7nR5JlGOoeKAd81WOs5GJ8QXMDokq4PLBYbhlWGDanVsd1uca7Hui61kbOSUVEk6VwISOcg9EiZg008ViFLNvUOpwK2c4hMkKiIguAC28UKSk1RZAkblBVs65a673AiGZK7psVExXQ+Z73ecn52RPR7zA+eKA19nwJSgiDXOSrCul0TQmC93WGtZTAY4JxFZ4rFcoWXgcYnYWjrPFKkcf266dg1DUYVCetjLcv1mr63ZFER5H5ET2iMMZR5xWa5YXh+RPSWrkthq+1bPB4pNbkwCWey5wY7PB7PyfEJV/MJx0dH7NoOhErff0xL3lprwKCUZDiE7XLLdL6gb1s22xaJwDuPkh4lFEo4gpB477mbzKiK8311y6NNpG1bNrsG12+QokBEgSEDBZPFjE2947QYAZHedQgXcDhmqyXCZOAcA5MnNr0xSJWx3m4oy5zG9uRIsixjuV4kjrpSWOfoBZiyoO9b2s5jTGLOS2PAt7RtQ73b0EeLyXPq1iMyld6bCPjQ/b9/0/eXr/9PXqr1yM7jRimot0Odxo6loPtWyztnC1pv+I2zS16WxzRHa17qc9763f1x6SK2TOObzamk/U7LL53MeFItWduCf+Nvfsrvfvohh1+lgOybsWxfSOgCy+/Ak1+/5ruHt3y2fMCr7AREwehVJCjINuH+4U66hFI4+P6M06rmV46uuGnHzPOKL7+f01+UeGPI9mb6b8Y9N48Nq+87Tp8seftgwc7mvFi/zfiZZvDG3UtV7EiTrSyyczz/90c8/s1rHg9WlO9a/uXoO3QXBWan0F1Abf1eFLVvDWXwt77/jHlX8Xa54OlwweBpx8sPvk2xSA+o34RSAMEo+m83/I33XvHh8Jbfn7yPfhJ4/eYpD//IEUpJ0Kkta7aB9TuGv/7OKw5Nw1m2Yaxr7voD8u85nt1+i+NPU1ika38fYtbvOL5zPuP94ZTzbE3tc17qE8IvC+qPHjF+3mBHibHbHaWff+23vuRb1ZT3iztu7ZhSWYZZx9U773L8hU1jtgON8Cm0bo8Ff+f9j+/3pwPdAPCjD0ecfLof592PJ8v96OngvRW//fgZO5/zIFuzcBXHv1zzJzffI9+k70E3Po3cjjLCOw3/1ttfYKPi3Gy4syN+4/Q1d6shqjN7GVQKU/Npw/zDAx49Tg2pge4w0mNGnt86fcHvHv1NRi92+MpgNjZJd1tP80DztJxjpOd7w2uuuiN+++1n/MPJr6eH+f1xodrUBu9Hkr/+/tdoETgwLTftmNN8y29+9zk///pDzCaFtiJEXKHILxYcvN/xaw+uKJUlk46xaTj5Vs0fmPfQO0MoDMKm0en20Yhy7nn/1695/2CKJNIFTSY96u3A5em7VBMFlSKftNRPKkZfLGkeGd4/mNJ4w9NyzqwfoqXnr/zSa+p//BhfKrJ5S39coDcp/PmPzn/En+zeJZcOGxVPqiW/dHDN33/wtwhfpwbaN2iM/sGIMq95p5pTqZ7r9pB3qjmNzzh+t+bafBs7VHssiELvPHe/Cb/54IbjrGbncnqpOc23vFtM+ehb3+PoWRqnD5kgn1myTUCeWN4/mGJDOs9k0vHtwYQfHbx3HwqJEHGjPI3YG/jVsxs+qO7oYoo3Zv2QgeloH7h7fnKUib/qqwzrAx8M7rhsjwBwUfK3x8/43/kubpRRzJLszY0ysllDd6IpjeVRueZANzzJZmx9zqN8yR+HFD7ZwwJIRnppIx88ueO6PSSXabHn0NTsfM6w6IAhIkTsOEPXnu7IsHtL8avjOxpvODQ1R7pm63PWrsQXMYX0UtAfF4kfawNnRxve7A54VK6wQbONBedmzb+afEBUSUp1vy8ONDrlm9ig2Lics2zDka550ZymqdfTnGAExcTjK5PG3weW692YgeoZqg4jPB/V73Ja1VxaKFaJA+xzRSzg6LMt759d8iRfsPU57xZTbvpDKtWlabsH+f0iXjZr0v6fFXy6fcxptuNI71L4HDIKZekHMsksB3rPIRd0IwlrwcoW9/v7u8WUQlhitp9Y2HU0b4/uxVh6K/gXdx/yy0fX3LoxNmj+ePM+TZcxar7hP0P9uMCWklAYtEr3Q5Xs789zRniuJoe81e5DSrFntQLCCz5ZPALgg/KWTSg4NRt+tH6PYh7uOb5mk3A9R88sF7dHPHp/hdvv77XPCQhsbXCj/aLlgcIbwfCyQzrND+bfYtFV/PL4moWrONI1v3v3AdJCsUjXvmLpCSZhYvJFZDEfMik6xqZlbQt++OYpeifuEUeQBGu2lIhjw3Qy4sPjO2xQXHZHbH1O22Qc2MQk7se/wJn4PEM3CYmzfd/gPx7z/J2CpxO/Pyb8/Wf0DRbA55L2rYpyEvny5VuIWjG+FfeLRH7PIvZ79rjsAzfPzvj6YH7/XRzpHV/VD/jp7BGqTcFwlAJbpr8//qLnq8kpT95JKJDL/gQjHVM74vXLU872hIL6LE1yBgNBwUezx7w9WvLZ7hHvlFOeZHN+b/Jt9Cpd73UD2TYFvdku8GZTMimGlMre7yuv6mNeLE/QdfpMy5kjyoQc8eYXOIY/z+svdGib6Zw+9EQPUimsj8TO8ujBI04PDhkUguA7hIwMi4x8YBgMDJ9//iVZXtD0keVmS1UNKIYjTt96C6UahmWJbVqECnzwrcf85KefMBoe0fUdznnqPTf35ME5Jw8OCa5DFCCLjKOTMT/68Sf0PiCkx7mYZFxeMRgNeev8HN/XKNkjReLvKQ4ZTUfcbTq8KDAmtWDruubs/G2+8+136eoV2kTwml/51e/zgz/6E0Ls8ZRJXqVTaEepePr0CYcHCqMF3nY8euucqqp4+fqWoiixbeKtKCXoO8fJcMjD8ZhV7Il4pCmIJjA6OWLy/JqsMCA1ffQ456iORpw9PMH7BqE1VVGglcfI9DBHSHb7DodQhnI0JK80UkZU8EjnyJVAHlcczIa8ur5BKk1eFCitqZsF1cBweFQifI0mXYCijPR4ovOMBjlCC5o+sWkPDka46DAFdLEhqKRfy6uStq4xUhC8IgSNDYEQPKPhmBgDWnvCXnY1HFZMJmkEIEqZxDJBE6KnHJboHPLc0PUOJcH2kRhSEGV9RBuDzgS7zRaZlegiR1UZolVIFehcQA9zYleQD3Ji9EQkCs3wYEjbd0mupTRCBLRXSB2Zzu7woWN8cMLOdUgZkULjVODg6JDZbMJwUNK5SKAhRIm1DcNRyW7n9/I1y2B4QGc7Vrsl44MKGyzFICdYh1CCoirRJscFQaELCJ6yLFkul5wdvUPbbYEka5IqslptEg7BOjKVY3uLD3AwKljN5gzGI2JwqFGB65vEj3Y9UgSausEog+9rMi3prWe+XDE+KPB7a6lzjsVsTq5K1k1PlQ9wwqGiJmjJ7c0dmQlkMp0Ptn1LXa+p65q6q8EGhJJ0XY33kflsSZEJKiOQKhC9xyO5ub1BmBwbEwDc4VjUC+bbisNtxvjggFwny2X0geVqSd1avFdIndPbgPUusYCbLbudQel0wQpGsFvOqbcbfNOTlQ5vLdkgjZpvtoHVZkWrA9WopOssRMG2bVju1gQVaZ2ndR3jwYDNuqZzO3Zthoo9mYbeQdtYnFXEoJNgyAdcbBgdFLyZXHA40hTDAVaCax1OQNduWG8sMsvQpaDvm9Q4FYqvJ9d899vvEVxP7gPB98QAs47LBHUAACAASURBVNmMtvGo8oAYIDMRrTSd7Zmt1mQqQuMYlBmttUynC3ZdiynyffDskcqjpePVq1c8fOuYGkEmwZic9bZhvpyhjMbagFIegSLg2XVbLm8veXx2giQiZE5sa5qmoW9aogfbp+DXBUtvPYvFjGpwzq5z9BHyQmF7y+WbG9bNjizLcCEF5bGzjKqK1XSOxDOsFL7vyKTh+cUr5usVKq9w2qNtj9QGJQKXV2/Is5LxoGI8TNKVPipeXl2x61u0SXcGLniMUXTR82Y+5eB4yPH4kMZ2bJs1tgncvlmQ5RXDPNK2deKBR1gsZqxOBxBKilJDlHSN5+bmhqZp8GjiZpO2z3ccDI+4enXNO08f04SIiJHGetrNjMlqQdc15FVJ9Omcrcucm8kdxkhMeUhXb8llhmwjs+kMbzuKzNCT+NW2sQwGB7y8uKSsDFoFcpkRpCVYxZs3txwcHLLqu9SCV5pBNWI6mbF665TBoGRX14TYo3TJ7XRN0wXCftpC7n8KFLPFHJXdoL71iHrdkpkhn714yXS5QkWSqZu0ELu1HSbLePb6Ei8Ew0GGiYnbPVts0aaiaxuESFI+GfbWWWHwMuPmYsLmcMzPn71mOBjT9z1OOozSSCmxMqLT5QVlJNve8dFnV0kwZwxeyLQ4GCTeeRqfRJ9FOeDNzQQbO9596wwdBbFzbLdbFsua7S7i/RaZ5QitEK5E5Uc8v3qDJTCsSnKVYbs00fHqYkp0oI2m63uCiogeIo7b6xWDYcbJwSFNdGzqFdd3cxa7bRKgRkkmBPiAiorpfIX3gcNhhfeREAR17ZjM12ybdH8hTYYjYpQghoDgz38j+Zev/3+/3EChSe2oqBLHVYTI3W8U/Nq7L/jl8TW1zzDC82uHNe8WU35y+JSP/9mv0g8SL3J0m0Qs/Vjwa+++ptKWvzp8ya0dY6Tje3/jmn/093+b7aNs37hJrNf2LCO82/DvPvwZtc+Rh5FMetxjyfq/f4zccc/CkzYJUupTw3/83p+w9QVD1VKpnqelYmg6PnvwISefuT1jNuEMiknP+tuK//S3fp/L9ui+kfVX/+0L/tfZ30Z4vedX7gVfwgCGs9+45d84/4pzs2blKv6dDx0/PHyH7A/GtIcKXadQNWQCs/HU71r+y4f/hH+0+RVsVFSqx0VJcy44/rxLmAQS7kC2nu405zff+4raZUgiH47vONQ1L8u392PSKaRWK0dUgn4ceVrOyaVjrGsq2fNuMSWXjk/eekr2R+lh1Y50slJParKj1Gwz0nGsdoxVQ0Bw24xoQmT3KCdKQbFIfNftQ8W6Lzg/XHPRn1D7hFZ6XK2YrhIT8RtLuK5T27E9N9ioONI1XdSsXMmvHVzyY/VOQmIECHmS1WGhO86oshTYvWxPGeuaST/CRUl/8IvmlisV2ZsN0SiyzN0HRZXquF4/YWxaYkyhgfDcszHrhwPyVUSJiIsy/Y7ueZA5/vHV9zA2sn2nopzYezasaj39A8c/u/orfHh8x6vmlC5oGm8QrWT3lqKa7Ftkm472QUW2Cfzs9iG/9OCGaTe4Dy6liPQH8T44749TiOdORyyvcx4+/TnP61MA1n1BoSztzQCxXyhzo+y+sbZ9UnB1e8p3D9/ggmKgO1a2oPMa2SccRz7v8YM9m/mgQO0kv3/1Hr9yfsPKpXuTlS354uoBD0aK4WVLe17urfca6SL/zdXf5kG54WG+4nG+4Merd/lidU62EBSTFD5EKTAbh+w8q4sxHw8f88HB5D7o3fmMP/jifd7dsye/CbjzL64ZvvoWHz1+wodv3fGkWiKJuKD4l9O/Qr6I+wZeOo5U3dOdGMKk4LOzB5Ta0nhDHxRfL05QC43sW+xozzEepNB6cBv4ye1jLgZHfDi+47I+BOD57ITyUiO8I1ta9HxH/d4h5eWG3bbk081DBrrnYb7iphvzdXuO79T9whWkha3m8YDDn0smj4csByXTfsDSVcz7itt6xOAmEpW8H62HxKN++YO3OfvtLY03HOiGOmTYoLh9dcz7iyRoFCFihxqXC7JN5Pdev8+4aviSM75zOOFie8TzFw+oZglVgEhYgWDScTL98QPM99Z8mZ2zKktOsy3/5/YDnl2e89AINNxzY6UNlNPA3U8e4H9FkimPFgEXJa83h+hdCsN1nQJe1XrM1iEuSlaHBZ+tHnBXDHlUrph2Q776+AknN6nRqNq0zwsfqZ9U/Oj2KT/Vj/je0S1rV/IoX/JfffRvMnwl8SZJYkNh9ngayelHgn8++B7Hj1Z05xojPW+aET/98fu8M3H358W0yCbJN4HDn0s+fvCIv/bkNV/tzvjJ8gmrruDgc0U/DMhHI7KlpR8bpI2MLiIv35yk42KYjo/r3Zjw8xHl3N2f33wu0W1g97Ri9cmQ4V+75qvdGVJEvuAB19sx2Vcl2cbSj/bCSlIYefgzyfPinJNix3Uz5lG5opI9f/D6PY5t4tGaXcBVGl0nYd74h4Yvxue8znseVBtab+i8ZvhZxuZJ5OCiQ7jUsm4eZBx8Hfn5997i8fGKT1aPaL1haDpeffkWx8t4z4H1RRJlRSk4etYTTMHr3zriZnHAqOrw//SUB8/s/aJiev8wuLW4SnL2uxl/0H/A4KxmWHTc3hxSvshweSBfJzZtP5AEla6HrpKoLnL6k5rmYcHwf2loHg2StLKxuGG2567vcSA6BbeDW8fwfwRpLa4Kv+DE7yWSxSKFz/2h5vDnkh8232X+mxUHWUuIgmezM1aXYx5NUqM3vR+IIv1Z/+CAf9J+l7dOVzyoNiy6itmu4uRHKah134hN95+bCJKbL855czLmV59e8nx7wvPJCeJnIw4v0mckAvTDfQhtBLyoeGEVPIKdy2i94fXykPXXhzy9dSlMVmLP0U+Ijz/LFv9/ev2FZtr+z//wf/udCOnhPkqEydDasGsaHp4dcTAuQHX4KFKAKCOz+Zyr2yltDyAZjEd0PpndR4cV0OJchzKKruuYLmbs6pbddovSqTWkpaQqC87PDjA6YkOLlElW4n3DYrYjBlBK4LxDSUGhMzIpORyVDAY53jb03uGjo95tubudo6ShafrEY0woL0xeMCgrqiIjNh1Nb1ksVtzcTEBo2rajdTZxQHrHoKqohiXHJ2P6focPnj703M7mTCYLsryg7ZKFvestWVGSZwV5qZKEyzXovUTnxcsr2s6hdL63nweM1jjfMaoKhsMS6zxSwXIx4/J2wq53oItkxwyRKBWm0ByOqhTeRUnnezrvWGy2XN3cJhaogHpXk5kMLQV5kaHwVGWO80kOtmlbNqsdV2+mIHLqrsfkBS6IJCLLNFlucNHSuxbbwnw25e52hpQFvVUEoYgisbzGVUVlNEYqQt/TOstyueH2Zk7de2yQSCWTPEhLpJaUZUYIEIkE22Ftz2K348XlFc4lAU1qUCmUVGRCIEVEaMmu7fHWMb+ZMn0zw/aCrvcoKqIAFFjXojNDVhRYl1Scm/WC5WZD3USa3qcxXGEQ0iBDwPU1w8GAIiuomy2da1muNiwWW7a7JoXoPib2I5GIp6xyhNIoFXG9p253XN1MuLi8oY+JYaz0vokWwv3+iEg8aKkEd7dTbq5uaepdsqsLSRCeGD3beosuMopRgSX9/na1Yz5fc3V9R+cgCokxOX4v79rstpw8OMX1LcIodl3Derfj4vqW7abFCI1EE6VDCGi7ltHhiCAlIXq6vmHT1Vy+ueV2MqHtIctzYpAgAzE4Ah7ve0yu6bqOpm1ZrLfc3M3ZNj3NtkaYDGs9w7Jkt2tQSlJkhhADbV/T1T1vpgvuliusUPQBemExOgfnWC6WDIcjBJIYPPVuR71tub65wyFx1qOkIYTEulTKoDODLjSt64lCstnsuLi45O5ujhSJT1vkBd45skzjvMX5xAYSOqNuep6/es2byZR13eKDICsLiAG8pTAFy+WKcpARgsV5x2yx4uZuymK6QghD33uETl90jED04D2ZUvRdy662TCdr3rxZ0rQtSmWEGJEEXHDYvqdpd2QmhbO7dsd623Jxdc1kvkKbHGsDQUscAZln1G1PZ9Px1dqebd0xX2744qsLPMn0GX0SrCgV2TRbbG/RSiOUotk27HY9r2/uWK4bOq+IyuCCQ6nECWy2NSYziWcdHE3rmdytuLq6oa47Og+98wkjoA2GyGI5QxUGH2DXNmy2O15evGG23tD5SNd1KCkhWrJM03aWtveITBID7FrH11+/TNgbmZr0PkQCEGV6+HDB46Jns93hA6zXOybTFXezJT3QkULsTBqkDLR1TfCeXVNjvWC23HJ5c8titUmtVJ1hCWglEUKmxnT0WGuJwGZbs1iuePH8grv5CisNrXWECFIIQoxEFHfT22RVDZHpcsnzy1u+enmBFymIjDJJ5Eyhsbaj6y1t29M1ls4GNpsdzy9ec7feEEWGVAajNDpKlJK44Gm6nt22oWk7VssN19e3vL55Q+cCMssJRKRJJt2u6yjzDETk5YuXXL+Z8urqlsl8k/jWSoNJ4/5KpcUKo3NsH1gud1xe33EznTNfrFg1HVvrINeYmIy2EYGPqWXa9ZYiz4kEMq2pN1tylZFrk84d+weaDEW0nt5HpJCJb48hCJ9a0ErhnEVpjRIaqRSZyqjbFts2bDYds/WGu9sFr67uWK1bUBKhFV5LhJIIqdAKnO+p65bVpma5rZlM11xPZ8w2S0RRIAGMJAoPiYbPtmlYrLbM1lvezJbcTVdsNi1977F7SWZq0Kc2eNta6uYXi9KvL2+5upownS7SpIaPKAkxBKSXiJDEfX/v7/6dv2Ta/mvw+q//wQ9+R+5bk2aXbOGuTBy6u18W/MbxayKCpR3wujlCSvg/Lj8g+7igWKZGa3Oeka8c7bFm/pbEobh1Ywa653V7wj95/V3axZDBnU/G6ZAYr+2xov9OT1F5Jv2IQjl2Pqe2GfXlCEJaGNVdCuT6scZWgqunBd87uGHrC67bQ9au5OvlKf7LAcGkNgskFh5S4irNzXnOt0cTbFR8tT3nj67fJf80RwDFMpAt7Z4ZmBo3t0c5v/7oNVf9EStXcdcN+erZIw6/hH6oKBaO9tiQbTxuqOgONF+eHpMrRx81R2bHv7j6DsUPSvJV4pWmh1XwVWqSfvLomMcnC2Y2YYT+8M23UB8NqKZpskHEvYHbR2xlmD5VNDFjoHteNmdctCf83s23ER8PgfTQXMwsdqCIuWYzKnhRHPDeaM7SV/TB8OPlO3x1d8rwY4MAyqlNDbQ2oKzgxWDMbmRY2orb9oBJM+Qnl08ovzQEnZpFqg97Czts3zYsjjUHRcvOFbzcnfDDu3fwX44oZklIF3WSRcV9SHp9NORLcUqHoQ45877iJ1dPOPxBhmkSH1IECFVGyCSrUcXX+RivFZ8sH7NzOT+5fIJ4UaEaSbYJxL0wTwCmDlwXB6wHimk/xKL5eP6Y6+tj9EJTzvf/fp74qNFIsBnLM8HJaMdX61Pe1Ad8cf2A/HVGNUk8RmUDCHnPZZwdDbhVJcpEFrbi69UpX/7sbR78cXoPqrGETN1zc32W8ZE+o8WwcxlGBj796m0Of5a2yVX6ngdKiJTXO5rqgJeHFZNuiNaBL2fn3FyccPaTSLZ2e3mZRvUBpGBwK5i+r5jZCicVXy7OeXZ7TvWnFYM3yWofTdoPkYJ8FXn28IBZLNiEktftMV/Mzpj/9JzjzxIyxA3N/fctfISQceMOWA4M1/WYV5tjPrt8yPDnBdVtIJ82CbGy67GPjhjcOXZ5xfxAsQ05r7eHXNdjXnz+kKPPk5TKbBx60+GrDIFgeAUX5wM6JemC4WJ5xHox4PATje7EPnROiBVfJZHbPK/Y5IrL7SE2Kl5NjjE/HnHyubsXJtnjMjX4hhnOFbzsj5ipnKAkL1Yn/OiPP2T8c8Pwqk+h8FDv37ugGyu4Knh9WLDzOYu+4rOLh9Q/P+LgIoBSmG06h8J+6AbNF+oYBoGbdsxXyzN+9uoxBz/P7hv43zDBdRswdaDbVEyrDCcFF4tjprdjjv5Ec/ylS837XQpGdeOJWlIsBN2u5CYvuNoc8uXinIuLM9765wZlU7gkAomnnQmq6w7daSZFxVZqrldjLudH2D89YvQ6kK39PnT393zzfCW5qwashGFlCy42xzx7/pCjjxXVzCcx1cYStaA/MomH/rpikpfcuCFfL075ZP6I8odDjp5ZsrUnW3aEIvUHXaXIVx69MSxlySVDni9Omf/hQ44/iygbMTufFvBcYmyLALqHvi94NTnlyg6Z3I5pnx9w/EUSZWXLHl9q3DCNtBdLjwsF88WI5+0hry4e0PzkiNFFmsxIMjF3f27QdSo+TNoRN1RcTo+4vDyFH4w5/sJhdp586VLYp/ehZxCMv5B8qU64sUM+f/mEr3/4Lkf/SqO7iHR7RnadGOSmDuSbSL0dsNoMuN4eMJ2M8X94yMlnFrnnPLtBWoj0+2uiXxfM5yOu14dMbw6Z/eyM8x9H8m0qw32zTSJAvg60x+mzPv1DwfCnmuxHBUefblKb+s+s08e9xNMViTNfXSkGH2XETwcMXkpGV57qTQ9CJLyQTP+HsmmRNVt77NjsufSG4q7BHuYJueHiLyZbhknmmTi6aaoE0vGQRJFpweQbDFEwSfJZzgOqkUwmR9xcnHD37JTiRwWDKwFxv9Db73m4MoXCqgW5MNTXQ25vjqm/OiD7pODoWQcyXTtFBLnndisLxURSXGgmr07YfH5I/ixj8CaS7WLCN5g9qz8meVu2FuiF4a4bcrU8ZPPRKebjgsOv9jK4mBrPMuxb2WkIkv/87/3NP9e99l/o0PZ/+J/+we9orfE+jVNJKZAxolTg9HBInis6XyOMwDnLYrZivtqw2VliVJTVgBADArC+p8g1Z2eH2OBAwbbe0faOy6tbhDIEGzEqPfxbl1o22UghoqdzNX3fstn2XFzdovOC2qW9U0WINoAPHI/HEBxRRVzwWO+YTlasNjvm6y2jqqL3PQGPMQXESMCR6UgIjrrvuL2b0HaOvrP0PiR7vA9IrdjVHUWRU+ZJPtU7y25Vc319Q4iSru2QJk9hi9H01qEzTVHmVEVFb3dMllM2qx3LxRajMoRTZFJjhKKxPUU5oN5tiMEilaBpa6bLHfP1DhtBREMMqXUagqBrek6OD5OuWgT6vsNaz/Ruynw6x1oP3tAHT9SBECUmz5ExkucFTV/T9Tu2TcPV9Rt8FAQ0lgBGEUJqfrW25+z8AdumYVBkbFYr5vMlu9pi22QCjyGNeie5mkVrhVASmUu29ZL5apfEQTGFHjFEpEoBtJICZy1FmZMZxabe0fWOy5trVpsdFolWGmzAh5C4f2E/HpdldF1H9JHlbMn1mzvqOsG8mz6dEKSEruvw3nM4HuJtj3eeu9mS1XbHZL7Guf3IhM4QIZBrjXcWoSXRB6SSbHYNy+WWy+u7hNpwIclqlErbpRWL1YoyK8hNhreOtut4+eKK6KC3aYxD7wMQJRKLsu46iixHKslkMuF2smCzbdhsm7SvIrEuYEy2l6f1iBjp+9TyvbudcnF5hUcmRLJIAHDrPdpofLDUzZpIT9t31HXHalvz5s0UIQ14UCI1LjNt6NueSCD6ZKX3MTV/Ly6uiVEhdYH3Ea0V1ifrcd/bvTHegVQs1w23kyXXb6YIociy9D50lChAIROjUmuc87RNx3S+5PJ2QmOhbRx2v58ms5HAhUjrOqy37JqO9W7Hi1eXdN5jbSTLKhACIUQSU0XY9R1N1yKUZr1es9nVLBdrsHuuqdSEEOh9Or63TUPdWaTS1J1juljz/PKSpk9BkQyRQmf43uOsp207uiBomgbrI23bcHM7Z77YEUjCJ6U0bt+mV1KDCzhrUWiavmOx3LLa1NxN1sQgiTIgcPjg0nlUCmwM1E1N5xyud9xOZtRti3cBkCip6GxHZnKCDzgRqOsaFWGyWTPfbJks17QBXAj01hMieCJSGVSe0XeOvnPUbcdmveVuvuBmNsP2aSEgzzO8TQsFIXiEFrRdg+0sXdcznSxYb2raztG5QJAS+Y0wSu6tpTEync3onGdb11xe31DvOmrnUDpDSQUatClwXpDnFdZaVusNk8WSxWbHzd0cFzyIhMdRygAxYRZixFuwnWC33lE3lundjPl2nfYhkc5x8E2rEcq8oG52LNcblqua1XrLrrE0LtI5QBmkJMk4pSDGiFaK1XrLbDplOpnR1B1t53BBYD3ILENqDUJgdMKDrFcb2r7jdrpgtW5prWXd7FBZjlCCzjYoBdI7PAElE/5htd1yN1twczdl13UIkyORxN6RKYO1/R4dIfDO0bWO2XLFYrllvt0gs5wodMI8CIG16fxstCF6T4yRPK+AnL6Hs/NTttstxhicd2mxARBSEEPC8zjn0r4nBVJpfHREFfAuXS+jhK63CJWCAikjMXqc7xASBmVOsD2ZljTWIVXah/ERISJCSRSGuq3JjMJ9sw1KkkZsImr/O873aK0JQrDcpO+xcwIXJUIbehsJURIRRCH36JQkO/VeMJ+vWS53BAnbXY0wBikkQkYCAaU0UhqEUMioEFGyWm4ICJz39/JSLdK1IhIQIrE6Q4w0TRLa3U1nOCFwAXRWErs+HRPOgQSlEgTDGMN/8O/9pYjsX4fXf/vf/f7v+CK1Y4SPRC2J+wbp5H1FNbRc1EdIGVl0FX988w7bizHlrUBGaA91GvETKaCbjwu+/+4Vs3aAQ/H54gHLTcXhR2mhTbqIrdLiumkCdZZzN8gIQnJTj5nVFXeTMac/EvhcpIBtPyUjPSAEk3ckO1lgo2bRl8zbARcvzqmuBYfPe3YPM7KNR3rwpYQomPoh86Fh53Mulod0n4/J1pBtY2o9DRTKpkW3cmJpDnNuj3OWfcVdO+Ll/IT8ZxXCS4q1v5chRZ2EU67QvDyseDjecNuN+Kcvv8vmbsjgMrXhkOmB05WKYm7pjwx6qXkZD1kbzev1EZvnh4xegmmSrV2E9MApbSTfRC7ez2kxOKm4bUdM2yFXL04ZvkqfvYiQbT0ypAdfO9AsTIYfwdebM17Xx7ycHSN+eoDs9zKnXZKjqX1rUW8k9oOel9MT3jua82x2hr0YUE4Fpon3obsIKeiXveRNUXGnSpSKfDU7Y/PqkIPnScRmmpCCPpUCjZBJhFXMRxm6cnw5PeduO0L+dMToOqCb1LQVpAd2X2oikrUomGcZi12FC4rucsjJJ4Jqmh7u81m7F3dJsrVFeMPibUHdZ2xczt2zM8orzfEXiU8rIomlKfaSKgtWGa7iAHRkfneAvs558KeeKEnBTJvu62Uf8KXm4FVkdVjQjSK1zVhuKo5+aO6/B4TAl/p+dFq3IBcZ8wNN0HD7/JSDzzWDN57qqr4PT/Q2tWeRknwnWKsB67rkqhkRng05+1Eaq02ir33IsbWEIvGD1SJnF3PuNgc0ixJ1k3H6qb8XqoUsSQXdUGHWDi9ydiFn2gxY2oL2Zsj5H+9laDpJ83yhUvCuJVkdwStW7YB1zGivB4y+MJz/aZv281FaEAh5ClNTaCLYmpxlV7Bbl/QvRpx+BOXck89aVN3jDstUUiI1SfUmo2lL1vMh8U3O+R9K8k0K79xwfw+lfiEqKuaCuMrZyYzmtkLfGoavYS+6IeyPJb3p6Y8yqjtPtlRsdMH1aox9PuTx73mKZSBkCpSgHyuUBdV4iqUn30TcpmTTlqw2FaNPMw5eRpRLY+XRyLTt9T6wFxLRae5MyWI1oH89JL/RnHzm/m9h6jdhZFQC04Jeauy2wHWa8eeacr6XE8a0L2XLDneQIW06ZopFpMtyiq8yspcZB88kxdKnIHjjkDYttgSjiJkk23pUoxHLDHWbMfhCc/6TLgXCAew4TSBEI5NsSgjMRuHbjG5TEF9UlLeKfJUCwW8Yw9JGQp6OK6Jg/AxsXcIkZ/BRRjGPFFObFrGMSiWCQqEbTz/WmDowuIbsqwxzkXH20y7JA9mHkPvx+mjkfQCoW0E+F6il4eAryfhFepbW7V6uuZ/uAmiPNOU8MHodKC811ZWknKYmp1nZtNn7fQqRSiU+lwyvI8VLw+BrTbaQ5Kt0ntZ1Cs7ZY298qeiHqdGebTSjzxXljSLbwOh1txdwCrJ1QnZEnY7lkAtkD8NrOPxScPJxSIt3IgWD0kf6oSLbpfMjIh3D2QZGL2D0CsYv3f2CjOrT+zY7jy8krpQpkAwJswDpHLX47oDBTY+v0neASOElIgWYPhf3ckwh0vGm96JQ1YV0Xg97FJEA6blfnNRtCmeb8zxd9weSfG6JKjWr08JR+l5Cllr9bqjR+7Y2UtxvTzASnwlMu5d/OTh47dMiwyZtU1SCbBf2CxUR6VO4H8w+kLVw+Nwxeh0QXjJ844hSEhX32/6NjAyR/oyA0aVHONBt4uzLvaxOxPR5Rpn2L+mhWARGr+HwS5CdQLd/5pq5F7H92X/blZL/4u/+1p/rXlvE+Bd3BO4//E/+s2i9R+WGJjiEkuRS49qOJ2enHB9WHB1XFKUBBC9eveb6ZkrvDVpLegtS7mvL3nJwcMCDB2MGVYEWgje318xnK6IwtJ1Fm4LW9smGFyNVUTAelpweHRJo2NYti9WazSqNioT94LtEpvFQnWMyyduPzzBFMmqv12vmk2l6uBRyH8ol/h0iR/hANJ7j8RCpE+NvtVgSI7h9K8ntGlRV0fqAjIrCaIZFxsHI4L1nudgQAth9S6ptm7Si6z25NCilODs/pCgKjLSstzXL1ZbgItF7lNCprRUjsTApVBXpoTXPNEZBUztCCCA1Poj0fiQgFJlRlIXhYFBxdjxAYFmvd8xna3Z1Yjemh1nwAYISxKAYZpq3zs8xhaLvO6bTKeiMZl0jgqLzDlEYousIQmOUZDwYMhxl2L5ht+rprEVqQ4yR1lmMkMS4v3HNMkyuMUYRVaRdrZGqxLeBKATWxyS3fx2dXgAAIABJREFUU5IgAkLsJeJKIURkPBzgrGVbb4hB4EjsQ20yOuepCoO1DZkuyRWMBxXOtvR9T2M9u06CjAg8kFqwmTZYaxkNyj2jOfG02r5ByZzG9YSQghIhBD5YvE/czkIrCq3YNDURiYsS79J7UEanfYYUSMWY5EIqeNbLW4Z5hYoGGSUOgZeBPlrqric3BZnQaK1xtkVEjykMEc227ZEIYhD7dnUSKmklyLKMXbPDaMnp0SG77RqEomkaTFHSd44oRPqMBQjpIUSGRu1FVSmQ7/qQ7KkxYIWnJyRbuoJSZzjnECbSNE1iyaLZbGu0SNJAay0ySyw+HyzDaoTvO4oiw7kkMPTeI7QhxIjddeSDAdYmqZuPNuFEEGk/bR1CanauR8kMISPOWVxIbE4pJVoapBD8X+y9y68tWZ7f9VnviL3PPq977itv5q3KotXVXXYLq9vIcoMQkgcwM4M2Q0+A/wAY9xTJcySEkMAjbEAGJARIDIxMY5iAaOM2dnd1dXdWZd3K+zqPvSNiPRn8VsS5KQbUzG26dimVlVf7xo5Yz1jf3/dxun/gsN8Rxh3ffPMN1ogaIOeMDZ40J4zpz2sKx+ORMQzEKWKtQ6MQeAVKaQy7kVNcGILMbaUkJMkPAzFHao1McWG3E5lzq4olRUIIsiEha56ln/aA2AOZxE5B2rZWCH6kVIkCD8Ggm5YCSxVhdM4yb1PNaK1xzlHjAlRcsNwdBVCzSgOytmmtacr0MYgUHWrC9zCuaYko4/DGseQkcvQWKRScdrTs8NbgtRKLjlZZgGo1aY5YY6TIUDVKFaouGGPYB4/KlZhOaOXRKjDHjLIwpYhzFrTCV8XOO2KcOTs74/TwwDB45pyYYqEqsU4pqUvbtO59kDic78kJtFWsvsQ5V6z18rwVUDL3UkpYbTB9jBqtaU1sTEyDphRTq7Qm66/GCKtay983vQBTWqaUhrdB7B1KJYRAaw2nDSWLR5oxCucC00mY/LmJnUdDLAhKzljn8L5SszAolqVwcbjk4XRLCIHjPGGcpWT5/Yrc2xpqmdMiL2ytURW0JqEywY+klEALW1PGl0gjY6kM48iyLL0tDa2DtrL2CruTKOv1kiLDWaDWTExt279jLVinKbUSjKUVsE0RS8Y4mdfaKMgVW6BqmCjiOVwrhb7Wa2EZWKtJKaF0E0/eEGix0lCc0iK+zKXIgaIciakx7s5Y5tyfvfdRKYRBfA4BaEWKCU3WSmMMrSk0Gl2kkJNb7mu2IReNwuKcJeUF5wy1Zhk/QTyHqxZv55IS2gZKblgv+4juxcBaq+xd1WJSFuWMFW9oZSxxTmhjevajwjjLMokUVykF3RZB00hJLHOWZeJv/Sf/0c+v2/rF55/Zz2/+G3+j2R76Ed6lzdvVLJU3v+E5fUe8YF8e7gD4vf/1S65/F/yxbpLC1XPWLJX7V5aPv9JoTyLWF8zv7Tn8kRzyho+FPKjNa7ZpxfTEcHylefheRhVF+MZw9seN/U9lfdO5bQcpVeTgl3aat7/eaEMB23BvHIcfgVnksCqHfDbvQGECNT5+z9Gs+NWd/bjKwbKHZK1gYfggsvnlwnB6Zji9bOioOPtKJJHuJCzT8KHfX6ykc0sJmg+/bLo0vrH7qebsTx5Ts4tTHcCUA6V4LGqaFollHiF0WSvQQbmGWaSt0pllujacXiqO/5ysuetz7952W4FBgFH5p9tJPPe8/3OKMoA9Kc7/QK4/vs+b5F2QCtCpkveW+88d0zOFmWB8KzLY6qQN/UfxYZTDKlt/5FFeO8Jto1o2gMmeHiWnOskhfrWVaFpx/8pgliZp8kX6zB6zJIvfLiLlPxXy3rCcGx5eacwC7tgYPlbGN8vWx+snHSz2JAFYaa826XK4qxSnGD6ItYOAH0h7ddB6Da3bv1kl/tIHak2trw09S7iRqo2H7+43n8nTC0/x8nx27uzAh0L4mXid5r0E39lJmHBpJ9fb/TQKSNS3Et2ZtnUwpLPH0Kn33w/s3srDDO86Q7qzLlUvYK7tcHrhSTsBJFQRsKF1gMSeBMhrVlGdZr6y2EXuKbxPLNfib7z76kQZHdVrzEkAWwC9ZKbPRnQUqbdd2jbuWi/M2GMmXrit73T3wKxebYnrTSE2FV26v4ajlZ3bmHfirSqhVfGgufq9E3Uw5FFApjyKj3P1mrwTxtz4RiwW3H3ZrrHKvs0i3rHDN5F46bZ7NkslHQzuXtrTf3MkPtujo/SF/XgiX+7kLNQZ4+WTICF7LJtvrpkkJV7lRjq3m1doPDdUo3CfePKauYqlQvf7XC1IANJOs/9qpuzs9lurLYKsIU3etS4sOsraCDC8zxuT0d/mLTxPL1mKAU1sceKZ2dY/fydzwhyl/auXv9O0EouqYJieOppW+AeRqlerCB/kt/y7ibLz2/ybnlj8sXJ8phnfVZYLTbitDO+FHY6StRMeWYc6Vo6fBez8GFJl5rr1s4Dc0jaqNMwpUwfz6HvawbE16FLVRngnPtzC1tTdjrEXnfq4MIv4sKaD2+wd0nmXzXe5PwiAmUaxBFrnsT09Bj/qKMWN1R92XV/vvwjs3yTiuWF8E3uxq8p6UtrGJJXiRn1cj7vKohlFPDebl3Pta8y6rrqHwnJhRHFgHve7ptXW1uvakvaiTFguDLo03IP8nXUMr+GMq+1MHhTuWHuh57Ff1jk8vJP9Mp1J0JsqbOuMjOvHdScexApENRjeJuKl3fZDeY7M/MRtY5HG1lbpYLf7BLbn16ltfV2tEoxJs61JJahvrbfuJB60urS+Z+rtvqWvZa/TsT3ev3kcK6u/bun7l6pt2wv9sX6rX4pT+PteLejbU+prlJsq86XBP1R+52/9Oz/Xu/afatD2r/31f7vF2mjGUGgolXDV0JQEXjkDn332gjlFTqcT892JVBs1Fw5hZCmFmBMhBElsThLScbkfKSVxur/DmECucghzzjHFiBv1BjQ6bSi6EvzIcnvCWseyzOSccaOEalkT0NVSa+b8bGCZJzm4UrDakEoGo5mWE55ArQIkgcF7i+Q+C1jnjMUpxDJAG6rOlFMCG4hUWuthLAox7/ZSlc1Z2L1NN1IW3yhrLYFAygvWasQNL2OcxhpPWSI1J7w2LClitOOkmrTXvBDjzDh4RuspyhHjgjeWRhH2s3PM84xzDqO0BO4YS3CNXBZs2PHmzTccDgeUt0zTtD1j6CBENY3zsz05zuSEVDKGHaUU5hQpqrIsE+PgMUqjW6VWYaW1LADHkouATDphtSNNCeM9Vimp9PawokpDFwVNk3MRgKqDgylFAWuNxiIs5Zwzw35gWRI5V+KSxNOwFLSzUAstJ7yxaORAfjzec3V9wTEllqrQzlLmxGB6v5e0jataK+MQKCUxxwk7ykE85dqBZ90Bv0BWhTjNBG+xXXqfk2gAvNUsOQpwpKTftdbkeWIYPMoqatTUUtiFgePDTHLC3tLDjjotjErCmA6HPdrC8XRLawrjBpbTSe7HGnSTRaqUjPNGwBKt5R8lhYKmqgRcYagoVBVgDWTPcN5Qy4Kpo+j0Y2aqHeQsIuf2zhKCI3awxxpPKRUXDPNpARRNiZdkTRljFEr11bxpahOgMTjPtJwErDRBbAGqALT9y2QiwYrEO+dMRaqJ82kijAOpyn9PZWHoBYLg96Ql4r2ntpnWBFQ+Hy8opUFpkgvlO1hvBJQxSpN7xfp4PDHsR3SqKGMERB6CBMVZjzagjYBABcP9cSaMTgK9SsGbPWk6Yb3B+L4W4VBNdx/ThDVNrAGCIsXGYA3GOOKSqRgB+xXQCjlGguljU0FKitYUmEqqCykt7F0gWMdMoWkB4FpreB/ISQ4MqYFrDV0bplUyjaQaqIJShiHsyXMm1ozWYJ0UgZRS6FZRzWOUQqtMbVpCqJxhmoQ1fBgHcmpSnS8Z1RRWN7ExmE8YH2jVkNYNsoiVgkaha2NEy3hzGl3XYAollgMpoRDQuWrQqqJVo1JQWrPkIoXDKoFypRkGYCkZoxzayroPMvZakWDHqiDnxM4Y5nnGGMNDB5x105Qia0tKieAtOQnTUX6nH5KUQSvxXLZ9LiqlmFMUYLasIIisLdZqUlwIYcAYwxwfsMZgtcO0bvWiO4AZI2TwPnCaI8ZIyKfWvUihNDFNWCvgczWPb05aid+x8Y5cxbM7IIWDGDPWDJ1NKs87zUfG/Y7SioSZeS+H01wIgyPXhFLSaLUCzZApKCdrs9GVmmVsauVQRjPnQlEwao3PmaibFH+UZUgNYyxLSsIQ8p5lmSk1EYYd0/IARuONqBKCsSQ0tIZD81CPYvOSVxBZCZtVOVCZUiPowpkSsNxUTWuKpqApQ0qZYIMoJTTkIoUP5X1fCzQFK2AqBt3B/DA47paEGxylJPJSOQxOrGdaEwuYvAjLGIPKEAosrWAGiy6KUk+yZzXIMWN0wPse3DfI4ertxw9Yazk/7DE5S/FHW5Yl8Z//zf/wF6Dtn4HPv/hbf6O5Bzmo6iig2grK5Z0mjYp3v6Zw94rhfcPfSSqzTuKB6u/FD3Y5lzAO22WDd68tOsH4vnY5pxyM0k4zfMjknX5Mnt5pTGosB/EpzEER7gpmruSd2Q7h68Hp/pX87npAkoOZnGeGD4V0prcDHYidwZoOvR7O0vjIGFoBJ2ALpxI5qhR60pmw1u0sIAlNQlVAWGLxIIfBPMp6vQLAOQiLSueeYn1XKeEx1Mk9ZOx9ZLkZma9MB/mqSClXJlMD/yEKcBP0FsYWzwRMKEFx/bt3TJ/taQYBKXKl7CzxwmI62+zhM4dZBNBuWjz8TGyEWwEs3MeZ+GSkBL0B5PPVCpSAvy+bXHVlXVYvHp4rOJD2WthKKwgwt62dVuBotYlY+9PMAiCs/W3vhWWqY/0k2Kf1Q7uwzoY3C3ffG9l9k3EfF5oz6CX3sLOKyo0ymg0Am67t9qxlEMmzPWa6f1AH/Mz2++kyCBDQJegyBizuPm9AWRkM1Wnxrb20n/hBNqYnlt03WSS+WRiIKlfitQROPXzmqVax+yZv4NF6bXlGAQMESBY/4Or0VrwQCXbD3qft2utzAB1sEj/3FWQB8O8jdTCYY0KVSj6EbRxWpzdWd95J8WK9VvW6g9xmA2DgsaASD2IXsoKVK0C03Q8CZKZzS9ppmUcdTFr9Ru1JmHru3Yl0vduAM3sqEgJXZK6GtxPH1/utIGNPhRLMNk/XApSZZZwPbwQwF8CODUS2R2HLCwDLBtSMX92TL0Zp/yWLx/AnQKaZusVEB2xAmJhrG9pjJl66/mdla7f1u5+2gy5s/r8A9pgxx8j8Yk88SLjjOt5WoNXM8tzmlMUWwukttMkepZDUtCJeis/xOkbKaKR/+viSZ1ffArRB2lflyvRqL0xuowRcVZBHI2zJWwFtP10H7UMUZnUHtOdry/A+b6C5zGNhIwuQp7YgtXVsbyDkbSRdeMzcCRRLIV55/G3awFfds1FWcPERgK1MT2y3pFGMP5mowQobu8+nraAwlW3er591PNAEdKwdBA23ZQP3oRfe+jqzXq96hf8oBacyCGjnjrXvE3oDXdOZ3tjGnz6/hE6WDYRc+wSk3Vxn55pZFOjp3JJHCapcg710bFsBdnni0FGsR9LBbnM2npsNyFzXV9m3SvfBfVwLVH0M0QwfEjpWlmtPPNP4h7qN++XCSNjZXSdtnBn8vdhZxAtLeC/ryVrA+RSEzjvN7utF1rr+DrK22bqWrO1+fOHEGsKtio/H4uAKClertsKtBA/WbX1u+hH0VhWWg+bwVZT1MoklyPGlJ9yVDbT3d30P6+zY5Vz2qxJkfJgkYawmtW3vj3tNuC/idQtc/MOP1NFxfC1FvqbleuG+8Pf+y3/3n33Q9q/+9X+zFRpViWenNwZVxE/IWA1kxsFRYyJPC0MI1O61WuaID467+YQyGm+GfiCv7IeBFAVs9MZzd7xDW0NRwm6qtaJ7MElrjaQUWIMtnV6thY11PC0C2lQksbqBs8LMqv0grgDVga1KQVvH6XRit9vRSiaVzopRwqxURgvQVQpWFXIsODRFaUqX8TvnyFF+WzWIURg3SmsBnZTGKrl3VQpKi4Sz0nowlpRBFMJ40lp8CGsR+TG14b2ntSIet8tMcF7AJK0pRcZWahJ6YIyRF9xS8T7QWiWXmVrpYDFdCms761IO49s9l4j3llYNOldKBYumasit4kZhUc7TkaEzQilQlQBGRcOyyPVLlRcXr30HxkUCX0RoT+6yz1IK83LibNxRa4YsgI0LvoMuCLindZchy/VyLYQQeIiLLBJa2JmuZEli19CUIzZh7dVa0agNEAQ6EOtJKVFTEsArZ5TVxDpjg2dZktgLtLaNC9U6owr5t0LuzVm5dimF0ViUEh/SOc04u0dpw1IqpomkuLQs99cX62AsLBnvB3JNMlZaZo6Z3U4YqSvompo8Q+oAkbcWq8Ufeu1frVpnnjtqUyQlfZ1SwhoBHLRqeD1gO1hpjCGmBEbTOiWi1cwKWBljiDESvMjUDQJSGe8oiExZKWEE57z6nSphWbdGTAtNKby15FQYtAXEdsEEoYB77yVgcAXAjN7GqlKqg4PCJD4LI7lmGknAWa03r81Tb4u1HWutnWjdHj3BlGFeFvb7PSqJNDw1sN5RWiW3iLWeFGU9CsbigzAac654KqqULjXvc9BUmrUorVHVUXOk1YIygFPkJDL8khvWCpN6ZQw2krB+U1+ztCZXAQWbsgIct4oxDaVlPisjLwBNQUmZtoGLIpm1io1JUY2AjUY76HN76m29844lTugGhioMxphw1kKTseuCZ45TLxxUltTYdZuI/qNkVVA6UyoMw47pJOBo61YCqoqdAAgjXSlFS7LOb4DnJ+t7iQmlwAd5zmVZ2B3OmFMkN2Hajj5QS+oM40zFUrKSACltME0C9CoJpTNxnbtKoZqsD2sfUEVno5use3YFcccd8zxTi+wjCsQiohQqTdieu0BKidEH5tPCOAaWOAm7q/QiG1oKOhh0XwOn+UQIrt+DE29shMnbTMIphVKNkmfsYFnmhlIG0wskK6tW5quiNik0DhYqirgkdrsdeYl93PegA62ZlwnfGa3CSG3klvFaCXiqLTEXlJG9kSoH7KQURhmGYYBSSbWQSqIpMErL3y8Lc0lY6zF4sTzq64zWmhilGBTGgViigKI1YZTHKd+/E6UwGgJRSfHDWoWtYJoSW4yaMRZoRYLHirDNU1lBd4XTssY5PLVmmm7UVlDOkrVCacsSJ4awE4a+lgBLANv3LRkzDaObWMJ4wzQteLen6SYs9QJpicK0LgXvRKmhlKJqg1YW+n4kwLvYKmgfpJCgLDUVnOtztFb+zn/6H/wCtP0z8PlX/rV/v+ko8laVCvngt0PTetieriWwyC6N5VyjSg/JSgJGHr6KG7NrZdacntqN1ZJ3cPa1+AWa5ZEJujLLJPimH25DB1Gs2ph4eW82ltz6d9ZD2HoQEwn6IxtmDTpTRYCFFYhsZg1cqRt4uAaD6LgCuxJkZicBjJoBd18egZ+pbAf2T69ZndrApRWIWZldawDJyiACOcCuB0M7VZZzszGXPwWlVuAMVgBzbQ+5Xh7N9t0ymO3Z1jZeWYzr763A38rMXcFx6CDapSOvIEBnQunY8LdJrt8ZculgN2bw2vY6VfRcKDs5gLv3J+ZXhw1kMcdE2TtUfjx/rgzWPArQuAJbw09P3WLAPYKwnQGlattYkY9jyWwgawlmKwzoJCzKFchz91m8JDvgu4K3TatvtZtOj+BS3lvMVNBLZrkZtvG2Mo9Rj6BKGVYZfkJ35U66CL3N5D7WseI/RJab0MFH/djnCBAGEk6WR7O1/zrmVGnb3BDrArUBjKr7pKYzK2y5XiBxD2UbR/A4Ttc2c/ePIOqnjMC1z1fWnjklan8/ynu7gcgCBvsOZsv57VMWajoz4mGp1cYi/5QRuIJG/n1kejlg5s6I74A/dP/Tt8vGstSpbs+z3WO/rvs4Mz/fbfe9zhGzFPRcvg1inwfSmQDFZi6kg9uAuhXklAuz3es63zcWcwe+xELDPIL84RFQWsHJdS1Zx1h1j+0tnsB5W4tVERB57a/qzAbGrgCUTnX7LZD/9u8mmlEsT3e420iz+tGyYnmc9+5exrH/EDc2sfs4szzbCVjF//v54qUjvBfmKI1tXJZBrmnmsrX7Wnxbx9y2Li0dgDz09ec2M904wm3BTOILvdyM25xcgd6VAb2OgbVQtzJP17m7zudP11CgqysemdrpYPEf01ZkWRUn25rWFRX2VJifOMIH8XD3ncFuFvnOpwWdZqQg6N9N5PPwrQLIBvr3tlkZ/yXoTf2w9uPKll/bbi1MxUuZ5+4+Mz/1j+Bzb/NmFP6jFAjX4tDK7k4Hg46NeDD4Ph8/ZbKuxdf5so+PqW0FhurUVggNHxJ5NFSvvvX3pBAle2Y8aNxRAkhXj9q8M9t3gI3lbu8jZed6Gz6O/fX6+RNWrZsqadTYpZFGse9Jo9j+iD3Ct0Fbd5+JlxYzy963tmkeur1BElyrOCm25iAFZvHeFWBWVVHb5EHUFPFMwNq22iuYx/GlqpyD7VS3vWLt/3XeqQJ/97/9936ud239//2Vf3ofaxROK7yVRb9WhW7ClmqlkmOhFHoQkSOmzBwjqdsKTLEQ/A6FsMmUUuzPznlYZpbSiKky50Q1CoymkjG2YqxGYchF0arFVA2pe+Hl2H1JK8oacqtUVXHOYIwwEGOMNIRd2wBtrYDCqVJSZgwDVDlsq/VQnwqtwTwvHRysxKLQNojfp9YS/NXaxkqbpommKsaIt2OrBa1UPzxm8Re0oG0HwrSAAwphYjUlFg+lVcQbQPUDnRwSS6pM04SyjrkkqjFU7ajK0rRD21XKuYhk3gnYQVGY5gh+JHfWqHOBLt6WZ6jpMZtaSVunUilKU3Ul1cxpmak07o+TMInR2z3LQb1StCYXhTYOpa0wyZQm5UUmkBVGmbYG5z2mA/FrsUJkzXLQ9t5vEt7gBjSGnd+hisJbAeUG48gxsbMerw00g8IS0cTWxKdSGcigYhGfF6PJccZbtjYqTVjgOAnMqrXQSsIqS5kzTgTueOMwaGwxmNZlH9oK664DyqVWGWc+gBKgK6eK9iOxlu4NrMApjvEkDGxt0Q1G41C5igy+RGot5CY2GLudgEVKSVjQtMwdtC8YY7HWodDUBtY6Sm2UUjHad5AbYWXXCKpgbAc/lYRzlVKIKZFa5ZgWikb8h7Wm5kbJCu9GrAkoDGf7c+I04a1C6YrSFesUzoqdCR0wHsMgfruq8XC8R2kBc1RZpeGP48uPg7QhjSUnmQdKoaxhWpKE2jXx0FQNpocjThumRYKFlgLGeFmDmoGKMFWTsGFTFhMVqwOqSrGhVQPG4ndnpCxy5LVdVhCsVgQ0gv7nipoWGQNF2gZtaMaibUBbB8rRqiLGLEWYWsXnMzdSLORcyQWUMX3eS8FI5q/MCTcMFNXte02jGbFJkeKSYn6YITfGaggZXG7oWPDGQisC+CqFtVIkKrp7/SDrSyGDyRhdoUZ80NwuDyQN2cg9NCT8sNZKqRFnGmU54bzGGAWtsR8dSje0Fe/xWjMODUXmbo4JZ43YITUpWKw7Yq2VnMXbF63I1P5nEoBHB0KVFXBzKVVk5dYwHx8wNHSCnRtRRTGMB0puKBwtt84SrrRuC9FaglaJc8Knxply2KWQTjMOg7WO2sTjtzZFM1YUGDlinWaZZgEojbzg1taIORHjjFJNChNVChat9HF6OnWvpcbgPIMbsNrhbRBQL2e0Upzt9nhjxRpEC//cGoXSBaUrMU84L32XE4zjSAiBUjLDEMg5UUqWQgwGpw1OC9ubWvDOkuLj+FJd/XA6HgnGkY4Tpily7GGbGax2BCvFCzNYiird4sMCelvD53kmRwlocAqcFoA5KVFfaCPzIfU92YfAkhaWGAnDiDZWwtxqxfsBrw1WQVFRpHPWMB7ORFmRCjonPFoKVdbQcsIAFNnX55pRfmVhI3LKtfBmDcpVlFckLZ5jmYxqGacLozOUOOGslkZQMh6bWtcBKdw2qzfW+M4PlDjhSyU+PEgB0Rsgo9dY22ahie+waRXXMqZGRqvxpRBo6BhlHygVo1W3S6hSzPzF58/EpwQ5QKWDvGur0mWGnTFn5ipS934edMfK8LHgpko14jkbL+wnkkwBbPdvMv6+4o8Vf/fIZFWlbVLQpuX6K1MKVslww9+V7pUr31kPOisjTsKqVuCJTa4rUvfGci1sct19atNZB1BKw7+fRV47STJ6tY+y8vU7KzgR3sfN11BAke6v1wGZlWnaDJ8AKo/AsKrtW6xfYAPcVuB1eJc2Npfqadk9e3ADD+0xf+v31t+JFxJqJszox1O3KquM//F3xWuzbECEPVXCN9IW40+OAljXth143Um+o6O0xyYLH40wzE5lGy/2WHp6uKF2gOpTKfP6LPngt+ukc2HATTciu17l3fHSCQP36Y58kH6UQ3fp/pzfBuiq02KJ0EH2PJoNSHH3wvDyt3nrvzIK8Fz2rt+TsCjLaDbAbJNTWwWd9apqE5B3a8/uQXqfN6AVEDuDoEkXnuoMy824yelXEHkFsJabsIXg+dtEeDsL0LQUarCUUdpGfBTlvvUiAK8AuGysUBAfWRS9ICHsMXeUfh/fLMJk66Dcyq7/tM1OL4MAeHspegt4pDdgaR2/y82AvV9oRjH85F6uGQzNyHWbVthTIXwzd2BZfsvfPu4tTSvcu9MnFkMdaHovzPKVnedu4wbsb+vWzqKySNvNaZ0bj/e4FUYOAoivTF5pG1C5oZOARDKOHttE1qZHUHmTXm9rlADRjz603dbjJECwmcsGFq7tvc7fVcFglrIVPFZ5uD0KWOffzzI2njjSwZJ3hmbVt+YCWubhOpZWIBTY7nstAuRDYPzDD5jjgpnSJunexkBqj4qGHuo9MGhnAAAgAElEQVQGML/Yb8Bn9TIG1zUmnYuFTt7bzd5A/FqlOXWqmFNCd7asnWStWFmi632uY1HmZ9vWwerEV3Z+NtI0LFd2+/4KfjYrYPGqTAAJtUpnhuMLL/PtKGD0WswyU9nWgXRmN79mAfTc1h86CYvU3cvczHuxS6D11yujxIaiFwyXKysM626PUAZREVSrmF/sSGeP/s5rYWKdX+4+k3Zr31WWaycqiUVsD8xcxaKlj8O8M+T9o12A2MHUTRWxXje8XTageS0Erv2ZgyYeBHhdWbXuE2boyqQePhYJAjUybteCqTvVzXpD1dVyRdre3WX8bZb3AiP7iM5SbEwHJ8W5vsetrGwdO5DvDHkvIHweejHDP7KIhfH8yKQFUY2shZO1GOI6wPypNcJyLczjvPtEAbCI3dFazNLx0c6oWpieyBiJe2EW50FtBc1PVQzruClefqt4xXyliHvNci6FleVa1nJRLKhtnvy8nz/VQWR/+7/4O7+toLMQFd5pWhIGnLciu879ZURbS1UC+JQWUQaKhtwatTPLtNLEdEJh0cYSa6JUYaP4wdFyRClLyhmtgxzqaGgrsle0QTuPAnJKeAMFmSADFt1U95kEpYQ9p7WiZAn62A+DBKpUoBpc06hmqK0yekuLBVsbXhWoFaMlGMxq+Y4ysmG1KoFUznpKUVjnaIAxWrw5DdS60FQUAFEhCdolckoJZz1YhdGrd2WlKfGyNVqjjUGhMF5j+zuI9ZrcIKNIraGsAI8S2qOxVaObwlRhdikUqUacM4Cwx1JTGN1wWqG1wVjHEkuXtWu0tzSlSbVJKre3ZFUlY944as7ib7zMGGdwemCZFgF4WkX35wGwxndWYcY4ha2K1v9XckTRsMYJ2JgbGKhUWq1452i1EKzndLztAFLBWEVtGVqBmjE9lXhOkaQ0zg8SBKahFgnnsdZScsEa8fZEC8hplDDmWq1gQRuFM5pSG74qUI2cCs4qaBWrGkYJw6tSqbXgnJVnrgKyOy2goPOOpgxzKxLkpqDkgjOaYZBnUygpJpRCaxl0o1AotaGMBO6UWtCAagtGgzOG1GactdTSKDlh6YcYo6mp4cNAQ0CxqgrGaVQRX16tQeFwIZBPYmFRAaUdyjmM1tJWfRx65zAld5l0IbdJ5Of5kSHekqS56yoG6+duYKongg8YEkFbrFZoKl4rsbPwUjnXTl6CSk49oExCUmqD2L2nnQvEnNHGomplDIFpmggHzzzPjMOOh7sTYRjF4iAXjJOAlVIVqiqsFnDbACkmgrNUb5hzQldJkl9iZgxWAvRyQiPBgKo2DoOn9rmmjALdqEoKHK0KEynTWOLC4IKMkRwZdzthtHuxWxEpvkFbeRatwRpNjZGxj4tcxK+21EJqUj3OpWCsYUkT/mIUKw6rCVaTYiKqJqqEWoFC6+FwayCIWH00rCrUmLDOo2pG54otFdtg0AaTMt4JIGWDRTtwrasqWt0Yvc5ZyjKhnQariK0CUszSWg4GuQjwm3NCK0MpncHVCjUVWftK4vxwhooL+yDVau8sFWiqUZpCB09qmTgnRmcJTTE6y+gcLSZ0tXy4vxUf4yLezFZpMA1lGlZL5TUoAdCzaiQqRcu1VxYlGowVT1vjGipFBqNpSewDppgI1kuhRylZP5pYWdCqzEVtO8tciQ8tIuFvaHluKiCFrVYrlIRTTcI9QQzHS6GohtKNkgvDsCOnRo6Vvd9z7kfUEnENWokYBV5VxMBVUbX4x6qW8IPhaudwIPfqHJSK7pxs6w1BG1oFrxt7B0YXVMuMw55WK2nJKFVEjVIaXlmyNlRtGZyltYnRap5cXrAc7yREy2omNKb7uRurZP4ZzYW30CLBKmqMaK1wylGWglIOcHgt7ZZiZTQaayqfvzjn5jBiY6TOhZREdaGdlYA/JGCs9HeV55cXPLs+58nZSG1LH3sVaxy5VkorvXjVbXKM4/pwxtVuZLCWWirOSrFFn43kmFFo4jQx7kcGpXn92XO+eH7D8ydPeLofuD8+YLVGKw1aMxXQaJRqXA6B81Hz/e+84vObJzx/dsVgHBR5P3FaCmyqKbSOKKUJBn7rX/9FENmfhc9//Ld/57dBWEdoLWnpvVCZ9xq0pJorOlu0J1KbRdiJOjXsLCE3ZRAwzj/IuiYhXUWCfJZKOjM9qEQOSSt4J2EuamOVNatkXVzkoGeigGPLhRW2bmrb4VR3dpfpVgenpw431y3xXQ5oAjjM107CVXZWgCQEPA5vZ2owG4iyMZU6e9OkNXhEbWBCDWpLMF9/SzWwcyG8FQ9S1VYWqTD8JAxF/CGb06xp7iujqqw+iXMRAEf3g7YTAHGVGlcr1gZN96CZHqi1tmUNhrzXNCsMKH8nwMPah6p2P8hgaM70lHjzLRn88C5SgqGMGn+Xe/DaJ8FErQd5aYXt7ClhwLJZOzStqKPrzEYZQzq1PraE9RsvLONbkXBLaEy/PtKuayiZ/zBjO0t3HWu60AESjYmNGuTfIMFq1cv41f2sKMxE8Y3MZxLSuUqkUd2yISgJOeq2FnlvH585SwCZQgByVRrhvQSgCYAonqHx0gvAqNlYewIqSX+aLjeXYkSDVa7uNWXnsKdMGYXFbOZMHex2HZ2qpLCX1gF4GU909mOzApytrOPq9RZwtQJrZqnUIIzLdLBUv7LCenhQf34JZOrssofcQwo1D68847tEugzCmjsP0m4d3NKfMICl7cTvM5/ZDVM1seLuovTx3uHuI81If8VrR3i3kC48/kMkXnuGrx/IF0HG2yRjXLXWpfsrYCxtaE/C1Na54T/MW9Hb3UbiddjaoBlN2Tua1Zw+G9BpXXs6gaO0jemrO0PR3UbilYD9ujTmKyuBhEZAutbtP6S91QbW69xErl4k1CnvDCZW7EPq64MEl7nbhXwe8B8XVFWkc2EmuzuxDVmDy1Su1EGYzluBpIOBEgbWAVQrgGUbPelq6KCY3e6rWQGhqldbUJKOAt6apWw+xu5eQEMz1e35VvsEscXQW1+sCoA6iK3F8dUASjE9taJ8OpPgP2kLGW/mlHF30j/x3DBfGZYri+4A6eGf3FJHsTRY2byqSVGvDIZqxDO1Gdmv3Klh+nzQuaF70Pc6TiRoTPYUXSWUL7xPpAuHuxOlVfNmA1hR3St3NNi5s0m9lnC78onNTq6A2oKqoHuXd//ozm7Z2NsrY9ksMr6mG8fxpelFLLGnqUHL/0fW8FV1sha67r4rNgmovt91FUCzsianM4NqYk10emo2xchyKfPeH/t+3UPF0l4sTHRuLBcW1WC51Nx9qXH3dD9XCO+itGd/Nil2KR4+F7JhPJM1ulklHsBL2+ii67uAnQrLlVj/fPPrho+/7MiDQWdh0pqlbde1U+0KH7nG+x9Ybn+58fC9BkXSxEwSkNfOEtyH7mteFv/tuy8N968Vy4VBFbkvd1rfaWTP8neZ+dqRDop3f7Fx++uZ6VcSH38ZwhsB5Nd3leFDf3fRcP+FZXquqH/lI/6fv8d8/8TPzgeqMpioyGMPedOP7zjx3PJv/dZv/lzv2vbn+dI/rU9VIvlGK1zVoCrVFprKnKKEszgEIIjzQth57h8e0Eb1g1FPSXcC/JpmCGZkUYlMoWgwXuPLAKkR2kCsDeu9nF9bxThNWXqQVk4UJwtEtVpCAFCgKhOFXCJh8LSYqTyy5tCGQubjckfQA7kI6Ne8o8WGzhnvA3q05AY0jamFVASgWOKC9gKqOGcoKXPwI/M8Q5HFyypNTrnLdMUqQFuRZZZc0Mb0YJMKreKLAJEUYfk0LUf62uQgWVKmaQcYlpyxVWOcJi5HCUFKktpdlUh2i6YzPgsYUFpTowTCpJQl8KmKjLu6gWk64a1GaQEsW2uozji0gpf1MBZLNZlpXhjcgFGaIeywxpDno9hjIJJRo5H7XRZhNhexrPDNsRyPjBcHqFCaxmgrm32qOMSzsXYW2OoNNecTZ+cXzCkKY0tVcmviyzo4cm74CoMWebHVldlkYlEop8gqQTLdaqJh1F4KELWKB4pzUD6pOCuDIZKVBKV57yWRXWloCec9zTRqrFgjwGktlZIz3jtKnTFaApyUbpx7Q86I7YUVpviyFJw2tCYAWDUKtBUQqCacF9uRMiXsENBOk4uj1ISxlaYHYsoE5TgLAVsMKsApL/hgUUasQTAWrS3KWIyz1JSwysmLUC0czndYCynBMmc8ltz9jkvOOG3EiqCcMNpxOZ7TWuNumUhqwliDqoqqNTVFUujgoKuwQBgVwYxAJdfGkiomDCy1khGrA9UgHieCNQQDu7MRreH27gGjQAfPPJ9w1tBqolrDVBb21weImV/6zpdQKte7gXlJnGoipgwlABbTJAgqGM0SZ87Odnz5+jnL6cjdPGOWTDKNsiS8MuRiSKdJLBNKIQTHzdWleC7XwtcfP7DEzJQW9n5HjhnvH72UVYPri0vO9jvCYPjqx2+oKVOLp5VKQaF1odaCbRWbKofDnsunLznsRqa4cHt7y/2yCGBfmxSMVKaVyH5wmFJ5dXPD5dkedOPj+1ve3d8CFe0EjiulSbHEGFSL7HaBL1+9ZDfumY8nfvLmLQ/RUknk0tBOii3VCMvUNsWz8zOur66Ieebh4YFpmfnwbsaHgdMcGXbn3V6k0nThfAxcHM4523mMsny8u+X29pZqLTE3jDLCVi4FRsdgDS+fvuByH9j5z1mmyFd/8hPu54lYCw2FNQN5WnBeM+wGLsaR771+hXUNrQI/e3vHmzdvqGHPdOr2IBoWVXHGU0rm/PySndVcX58xxcS7Dx85LpH7h5maCkO36vHWU+LC5dnIi+fPub65pObCPE388IdfEfMC2jJYh+oM+5wi+3DGGAzPnz1ht9vx7v0tb9/fcTzN3N8/YMdAcGIvYGzDqEQrC7/y5Wuur84pMfH+7Ud++vYdURlaM5isNpaoWiL7wfPy88+5vjrncHZGXiLTKfL7P/oj3rx7z3h+BgiTtrRCGHZc7AOfP33BzcUZOWdiKvz063f80Y9/RjFSGExR0I7aFM8uDhx2jtfPnxNCIMXG8SHyh3/yFR8+3lN0Qw+BYxHLBmcUui382g/+PLtgwWS+88UT3t8e+aOv3nE9GG4fjuhxIHVlSssL188uef3kMy7CgFeOb755zz/+k69RShGrWBO1prC2sBss15c7XtwcuLk8Y3SeXA0/+clb/sE//n3MMPIQZwbvGZ2n5kwIgZcvbnh5dcn5YaTEhRf5im/e3vHDH3/DFCM4UVGQCpXExeGcL19/wW4MOKXYj3u+/voNf/DDH5FbYZknrPfoXGmq4lrhy++9Zj8OXB7OaLngzBPUbuT3v/qa0ywKgZYibtzhvObicuTzz55zuduRY2EwEILjcLPj9373RM2KwexZmKlNoUyjfOKl9ovP/78/n/q5rh6uK0N0ldTnnfiz+mNlORjOfhK7n2fd/GZBb7Lv1fNtlYyWoDf56+p9m8dHuezK9Fl9RNc/FxaK3thdq29mOhcwQPMYSLI+y/mPZmGBzasUV28MvrRXVGs2lk51AnKWvcPd5c2Pc2WdPbx0DLernJzOnmqdVcvmy2pP9dG6YTToKIytNZAGhDWnSmfAdq/RlVmmasPdiWS/OoW9Lyw3gTUgS+UmBf7eV6oz6EQ22zZ5fDpYYWh6jU7iT5kPfmNXqfpo5aBWCXMP81FZZLzpcqB68cRcw3aWawls8rGHzjklVgBOb8+9hh7N130MrTLv7kOb93azY/hUihs+ZI4vvLCMq4ARZpbg2rKz2CRM0OVqLyzFIEw8d186cCTtu7LtSmADk1ZW96fC0pU1vdpgpIPbJPOASGCDeISXoQOOSdit+eA2z8U1MOj4er/5BqtiN7ny6gst1iP0cS7g+jrPbMybvN1OZWszatu8dePLQA7SZ+O77k2pYKU0roBBCdKH63MAHF/6ze/Z3xfSmetWGHZjnOUgwWzVK+5eSsHm7CcZGystyM+oImcjd4qdRbsHIB5E+gxSYFk9m9192/qgGYW9T6QLTzwY5mspOhx+LF60zYglRz7zG8s9vEvMz0cAvvor8lv+e9f4u8bubcbeP25QqzQ/Xlj8beb4wvGTf8ni7hX7rxu7ICCu9J/HLJXwPjM9D6whgLe/JHPJTIabf1Dwtxn/bmJ+sd/WwKYVw5uJZjS3X1qmp42819z87wpdHiXZKj7aHJTRUILm4aXh+LlleVawHy27rxX7nwpg35yAqrVIEWB+sac6xc9+Y+D4Staw/Y8Dl38g/Vqt2+bJp3NsemJ585sNdRWp946r/9Ow+0ZUDCvz99Pwsjxq3n/fML1O6JMhvNWEj4aLHyXSmd+C2lY2vSqN+9eB4yvNfNOorrH7sePsxxWdDe5Yv+WHDbBcGN79miK/Wri4PHF/HBj/jx3jG/HuFt9Y8dotO8vpauD+lWH+zQdCSDhT+PqfXHP5jxQ6nRPex80exN3GzYLl4aVhuYLpOwlzazj8oWN8W9n/RDyl45X/ViDe3WvHhx80wusHStHEu8DT/9ngHqSN4/WwrctmKpyeWpYrxd33M/5qpv7hnvMfSgDk7qsTZSeM6HUe1u63+sf/quHsO7fEaMl/vOfqHyrcseFK3fbD1Q5ourZ8+HMW/Z0j3715z0PyvL09Y/c/nfHk/5pZrmVPobEFhd2/snz8C4nvffk1x+j5eBoo/+jAy/9F1BTVqs12SKfK3Q8C0/PG1a+/5dn+gY/zyE/fnzP+/T0Xf5Q721Wx+5mEHMadplr42V9duLl84ByY/wXL7R9f8PR/09z+0o7DnyxUa6Soo+W5b79rePcXG5cvP7DfTfzoD57z2f+oWMPGVG6oKvvBfOO4+65m+dWJ3/juH/NL+2/4mHf8d7/3A17+N45yYdj9VMbifCUFoXimef/nGzc/+Bn/8tOvuEsDX3//nB/+wXOe/o5l+Fi2/XF9L7j/3PDxLy9cXT1wMyz86uUb/vv/+1e5+HsDdpEgvnU/VxXyCB//8sKT6wf+0vM/oqJ46u/5m/ov8eTvBsZ3ZSuWpFEA2dPLxrO/8IbffPaHfEw7vhzf8vf3X/LDZ0/I/8M5Ovf3n/varRsEZP55P3+qmbb/2X/1X/+2D0Eqs61XvToYarpvoqOxGwK1ZEotGCPMR2V0D+0ywuTMCqc0RktYl0axG0ZqEwBsiYmmLFhNo+GsZ1kmDrsRg2aZjgzW0lrFec8SE9bIC1htjVQbfvC0lvFOpPSmFgZnKS2DMhjre2q7pbWEEqNIrHc4b8l5QjdLTV3GrRStVvZ+EGagEeadQ0KmahaZsfcGraoEKGkJ59Haijy9FmEsGmE01ZIYlZEgG63Fd7UpbA+5AU0wDlrDaY1pDeMd1mkaBWsUcZ5x2mCwAnhqCcWpCMu4aWGMij+k6UnojaqF8TXFxBAC03TCKPGELaVQ+jPLu4gwY02Xhjvl8MaSj3c8vbpkPj6w21lSnIWp2Rmo0NCdvRWsYwgBp+HZ02tO0x1aV4x15JywVlNyAtWIRQJYRhcYvOf64pzzYeD4cCcBRroRiwTalIb4MCqNzoXgHc+eXnJ1eeDiMIpUI0rSfalCwahN7C+kPwy7ELi5uuT8LKBbo+UCVeTlWfUwMaVp/b7O93suzg+8fv0ZV+cHlGrUInYdzjjx6qyKWuPmd+mt4urswLOrC5yqxGnCGQ+tkZHfaEvGN02oEFA8Oex5/fwZZy5QYuL4cI+zwhBvKLEoQaMbKBQX5zue3lzx5OaCNB+hVnLNGKM3BnCpleA9qMqzqxu+8/I5X7x6hnHCuo7zRFoiDZhzEiZg93I02vLi2RO+88VnXF1cMA6e6Xgix5mqNHJXip0LBK2pp4mrJ1d89/NnfPHyOeM4cDqecE4S1JWyNKUwjR7MFri+PueXv/ddbq7OuT4M3adXgs1yitRWcdZSShZf093Al59/wc31gRfPrrk6G9kPntujFIziEiUsLhVZC2rhO68/4+WzGw77gS9fPWdvA8fjkbgkkTU7Jx6gOaJaYz8OvP7iFU+vLzg/23F9eUHLGd2Z+jVVrDYSDFUyTmu++OwlL1/ccH2x42wXuL66xijNh3dvCcGTlTDwUQ1dM2eD5/Xrz7l5esV+dBwOOw5nI+/ff5BiQlNYY1CtoJuwMJ/dXPP61Qsu9zturs+5POwoeWGeFnLKwtxtlVYbWmkuLw+8/uwFT8/PcFpzNo6M40jJmdPxiHGOkhOtVkbnKcvM86eXfP7qGRcXOy7OBi4vDygK799/pFDIVpPXQAdtsFrx+WfP+M7nL7g6BM73I2Pw3N3f83A84vxIakWsX2pFt8LnL5/y5PqCq/M93hq8s7ghUGplWqKMcay0lYabswPf++Izzg6BXbA477C64Zzm9u6BmjOrAtUaw/Rw5PL8wHdf3vBLX37BPmiG4DgfRz6+/4jRloL4L3tnccHitOLp9RUvbq4ZdoHdMDA4R1kScYmUqkSloZXY4KB5fnPB85trri5GznaW8/MLVIP7h3uaMVjvaU2Y+drA6Aa+/O4XfP7yObvB4p1DV4XSircf7kAr0iLFUucc3mkuz3d897ufcRY0uxCwRhGCZ5pn7h8eZD0tBWWUrIu68oNf+ZJDGDiMjvNxYDc6TAFlHR+miUbFWSilohV89vSaL1+95PJsh7eyN/8/7L3Hr2VZfma3tjn2evO8iRc2I31UVlZWJYtVNNUQjQgKlAQIGkiABg0BmgiCJtJI+hukkQABPeuWoAYHDbWqJZom2c1ik1VFk6xKHxGZYZ6/7/p77N5bg33fS3IkTqQmRB4gRvFw7bnHfPv7raUUWGO5Gk3978+VoC2urmkmIbd2N9kcdGinEUrDoDdACsnF2QV1WeGEwNYOZyAOAqDm/t1DWklIGir63TZKOPLKsFpmINfsPemQSoCtuXu4w7DfIg58c1lLSbvV5vj0xAvkbM1yuSANFVEgGHSabGwN6XWbCGFJGhFxFNOK2xw/P6UyfkFQWosUglaSMuz3ONjfIY01aeqnJ0IlWYznVGWBkAprHWBxruLwYIfDw12SRKEVhIFCSEeoIupFzmo6pxH4xUytBXm54PbtHRqNmCjUNJspYRCiI4WONBeXI5Z5QVEbald6ZrF0SAX/4a//+t83bf8ObP/on/zhf181r6cjvmpj2nW70ynfRil6ElnjG42hJLoq/XX5X+ETCudbRTbwi6NOC/JBgKq9eTua+ckrWIdnqW9xLndCz3ybGcqO8s2nVBHOvNTFKdZGenPD56taat1k9SGxv/nx7Se5lhiJdQkAfIu1TgQ690Gvqlk3EP3fZ5uhf83KN5VMKm8M1Db0jaabprFcN13XgbFwvqnlOaX+JrHoa+p1iOF5edw024TzMhMhoL5uz0Zr+ct6zPu6QXs9tm8SedMsvm7EqXJtF19zKnXuWbuytISXK+puTDBa4UKNSfRXLc/rJqHzbbcb7EEjoG565ur4YUA0c2QD5W33EpBy3VoVN++9amuKtqJKJeOHmvjKfy5uHepfB6TXzFekIO8rio5kdkux3NWkl/Ym3A0Wxjc9C99Mvn5tRVsxfkWyOIDlnsQJRbByvjlbrNuQtbtpvdUNRd5XTO4plru+XSbc+h4D3w42sZ+wuh53XuxqlnuS8ddrZocKqzXC+FFcHzr71rYqvVxHVl6+Nj/STF6RVA1NuPT7jrDcPIc0jqqpKVuKsqWY3tOcf0Ow2gwRtSQ9L2+YuT7oNTjtmaVIwWJPMbsHszsClfnfqi7czb4ua7/QUbV9oDt+GHDxHhRfy8mCgDqVBCuxHhmGaLxmE9fupt08ek2zei9jdWBZ9kKiqQ/sr+U8SEE5iKjbIdI4prdDLt53uK8tmHU14diPTocLe7Of+sUDRzEMmR5prn4xx9wtqA9L5o1ojWSQ6IIbSZQqLHqaUWzEnHxbYe5mbD28JDuoGfc1yYkC7dm7do1/qJu+DXj6rZDp6wY2St5+7wlPhiniKiJc4tmn8doaPy8QQpJtaC6/YUnvTUm2l6SHC0Y0MYFCimAtOfyqQW5Szdl7MYvXCxqHc1rDFbNbjlUY0f7C3iwWyeqryYTlVsDoG4b4zoJOb0W8mbHYdIRfhuv7Xf/4SHFzbLh6LWD19RX93Rl3js7Idg2Tukk4w3/vkbhprDslmB0GXH7DsXl3RBzVDIZzzrsBtg5JLn2wq9Z4j6IfoErH1Wua+tGC3e0Jg+0Zci/nSsW0vvQBvM6Nn74Q4uYYdPGOInp3zGB/Sm9rzrirUKchjXODSSTB8joc9sfdi3cU5lbO/b0L+umKNCk5T0JsFRBP+Wvv2QaSyX1N+f6c/cGUo86YXpwxizSzOKTxUqAqv58I57/PcJQxv93g8puGr3/7Uzb7M8oWzLcd+mWIjfwC3XWzvk4ldSyZPhAkD6dstJbsdmakrYLzqoVeKXTpy3nXn62NJVevKpYPC3YPrrjdHxFvZZzIJsmZn6O0kbw5/lyP+5/+DDx86xlHnTHdNGOkIjIZ0vvc3IizVOkb11UqWexLBt8843Z/xL32Jc2wpN9c8Ww6oHEmMOF6umW9iGIjyehXcjY3Z7zdP+bd/jMOOhO+iJrMVIP2F/W68S7X5yrJ6C3JwXsv+c7mYzajOfdbF7hQ8KXr0PpSUMfrNiz+O1xtKi7fc7x+7yXvDJ4Tasuv7v6E06jJ6vOWPz+u37ewa8audVz+YkV3Y8FBd8KvbP2UcZRwNu8RT9aPzV8/F45/tuTRrRfspxOaqqCnVzzYOOfjj275c24FyVlGPgypmpL5bZB3l3xj5zlKOO6mFxykE3Y2pzz7yQ4690ta13ibxY5ifge+8fZj7ncuud+8IJYVcaPmxeUAlfnzoaxh3cXk4l3B+29/xp32iI7O2IkmvrjetDyuBoQTSTaUSOM/t3hqmb+fs9edspdM+XrzCw7DEY2wJIwsL55skVw6orlF5+sG8BpD9UsOZuwAACAASURBVP+Lpi1OU5XONyOVQgpDIBJs6ei0Q4aDJoNhG4SlLkoeH79kNl8Q2AClAyyQL3OSMEJpRaAcrSQhTCUKwfn4AlEbKqcIgoC8qAilIhCCarGgqVOKScHu3gadWwOqIuf8aszx+Slxu0tWlIRhTBw0qBcZoQtpxhEHO9t0OynL6YyqyPjgs89pdzaYLFZ+NLsqkM6inG//NYKQ27cPmK9GRFHCk8+fQFlTO0EYJpS2AmsoqppWmhBrxeHuDt1OC1vVnE4vGY3GFMsapEar2K8u17UPF4KQVqdJf9BikDYYz8ZcXU04O79Ehw2K2pFVftw+kDXOlSQ6od1t0W42iCPN9GrC87MLjIBIpkgkbj3qGyiFqc26VSxgLV4TQlCsG3dSBkhjkEKRBgF5tqQTx779aQukhLy2VOtxYK01VI66NjRFjJaSJAp44+vfQghBp9ng+ckXzOdzPnt8DCr1LNuq8ggLBabOefXeq6SBpNVKWdZDptM5p2cjRuuCq1ECLcO15EXR7jU52jsgCQWYFXfVFp9//jlX8wJZ+VE3UYIMNRhFs5Ny/+4BrUZIGnucRa8zYT5r8cnTE8+J1H4EtshLWknMZr/H/uEOSgqUq7Gu4mI85YNPnqIIMM6zkYWUpEmDwFreeOMtohDCwBAozegq5suTE8rzOc5KTCURLiSNQuazCfu7m+ztbjPoNHB1gSm7XEwmfPr0OdZFnmlsAaWpdEQcKW7fOmT/sMdsPmKHPlmW8cXTF5xfTIiRWBFgnaWRpjhTsLs7ZH97iyiQhM7w8GCL49MzPn3yJVlVIImpnEVIjastvV7Knf0hzVaMdNBv71LmOZ9juDgdsRQSGYas6oJExxTlivffeYc4CmiEkiTS9JeOdnybl6cTnrz8gs7GBlVZY5zB1RXbO9vcO9qjn4RYVzHoNOk0Y05PRmTLc5yTZGVBKwlAOHaHQw7v7JPGCiUsuoad4SZfvDjhw48/Q0tvXq/KHBUmdJqa+7dusdFt02smOEpko0Wjl3Ayu+T4/IJQhmhXESYRi2JJtxWye7BF0gwJkURCsbUX0mgmfPL5U84nE2phPRoj1gSh4P6DW/R6PeJII7AIV/Nm5z5XVzP+6E9+jBYRFsdyURCFATvbG9y5vU8aCZppCoAQgkgH5NWSl2cTjA6QkcIUJaHU3Lv3gF4/JU4jQh1gjKPlHMJJnjx9wXi+oC5WuDAGHO1mwsOHD9BYWnGEEZY0dLSSfZ6Gp3z05CWV5ab1HmnB4e4m+1tDwrqGSKJkSLvbIo0Djp9/gQ6bCCyhjiizObcOdzg42KORhgRBQCA99qBz+5DxeMqLixEuTqnyikgmGBb0ehsc7GyQhIJUJzgnaMQRq9WCuq6YzBcEYYMKSxQEGLPiwauvoITBlTlSgkLRb3cxYcTJZIkQkqquiWJNVRUc3Nqn3WqgQkUUQF2XbO4MSDotPn78JZGOqa3EmhKtHS523DnY4nB7g1CUuCgkiVPCXsBiMucvP/scFzXQMoAaMlnSacTcv3eIMoZwHaiSJkQyZrWyZJcjUAHKSmoKUhVwsL9Dq52g1wI3JSSdB/vMZxOq8diLxnSEQlJlBRubfe4cHiJF5ff3RNPt75Cch1xeXlHmFavAy/1EBMvljLe/do+4LekQop1ERA1QAaus4vz8gsl8hkwSqL3gr9Vu0G23aGvfli+z0i8CbfSZFiV6ZHFYqmxFJRSNNOTw1g7tdhO15t4qIdnZapNjEV9+iVDgaksSxtgIpDbcOtolEBAlgcf4ODjY3+WTTx5TjBdEMsFpjcVQLJbsbPXYag9RwhDHflFpa3uT+Srj5YtnxGmAoUYhWRVTpLVs7W6hXYF0EIUJSsAiL9jd3uLTzx8ThxFxr4MpC/Ki4M23HrC1s0GR5SRhisMQxiFCQn9jSH15QYXFKYWoLdIa9neGYDKCMEZJqISjN+z5SyBjCIVHzljpF36jNCFKYrL5hE4nIV8sSZtdyiIn7USoK4lRFktBaQ1JI2Z7OCCOA7IsAyNRTiKcRLoI5zwnOYwjSmMQwlJbENfJ2t9vfyc23xT9qil1LeaY7ynmdyyN21MAroqA5F83aZ6YNcvQh5TXI8wmFlSJZLUlqFOwoaP7CTdcu2srfbW+gdYry2onIppZLt7WZHsOmQk6n2o2fzSn7MdEVyVVK6DorMVgDclqUzF5p2R374qzqzYm09z6TcF8X9N+Vt2IQ5zyTSYTCLKhZPZ+BucRtmHY+IEmwrcyq6a+sXwH05LVXkzRllx+w9DdnVMZRflxm/YTCOdrdt26ISUMN43F1bZkcWhp3F6weB7S+FIy+LDyYqVpTXTlR/HrdaiXdxXLHUG2bXHtmuAkZOtHa5RES9+0fvT6s7tm0V5v1+O2wbT8K2O//ka1GLYJJxWrW+2bJq/TAj0uUEvPuTTJV8Kg1brRWXQl0b97TlNa3t14xj/7yVuos4j93/UXzmbdXLwO6mXpOP1lS9jLORpe8fzrXfKTBt0PFd3H65bXunV4LXNa7kiq9+ZsdhYALL8dUP6rIe2n16OmPjy/ZnQutzSjny/Y2Zxwu33Fs3mPFwc9pmcRh/9necOH9FzGiuVuxPgVBY9mREFNFNRURnLyeZ+7/7S4EWuFa95ovhFSJYLy1yZsNlZsJAuG0ZI/eH6X2Q87hIu1AKn0zOZsGNA4Ljh7L2X2VsGDwzNqJ8lrzfPHG+z9zroBXTvUtVhyZci7ivOfMXzjzc/48HybjdcWnE1brP6kTf+jmrIXImtHUBnyrQhh4OJtDY9mbDZXDJIVhz835vufvEb/d2PiicWpr0R7OJjd0hTvz7k/GBPpmvig4iJrchrs0f0M0vMKG0ji84ximBDNKp7859BuZTzsjbjbvOSnBzt8tL1D+4OI3d+5YnWrfcNCFsZx+VaE+c6U7+19QWYCkoOKP945JP+oSzzxxwQ9Lyk7/nr08i1F+vVL3u6MSXVFN8hI7pf80z/7Onvf921UWdob2djqVpuXvwCbdy/4zvZjKqdIZclPert8+eEdmi8K6lbgf4MtRXLswzvxxoyjzpyNZMFuMqVzN+fD/haXv7tN/6P6pvlftyLqVHH5CwW39y45bI7RwhJIg/yl5/zh8W34n9s3zf/krKDsBFy+FRC8f8Wb3Qlf6z73+zaO/73xBqPZkM0fF8iixgUKWdRUrYCL71Yc7I/YbU7Zjmcs64h6KPnBr9wm/kGTzpee4X3N613sBqTfO2cjLHmn/5yliXildcblr57w+z98jcPvs+ZM+zC2bCtG7xq++ebnbMczADo6YzpM+P3uPeSfN284qlXLoxZOvhUSvDPmld6YQbRkGC6YVCnRxnN+//nX2fph7mXcywLXSZClZXoU0X404qA95mHrjMopXu2e8f3JW+hc0/6y8EiOlW+BytLx+nc/J9UVtZMkqiLVJdFBzUfVLvIDecM9Ljueybr8WsbrWxekuuQgGTOpUn7t1k/5ceuQ4l/seM7rutVuIomwMac/6/j5Rx+xE02pnOJu85K9aMz/cPlL3PnNyrd518e5eFQxO4y4850vaQYFm9GC3WgCwAffmfOTyUOSq6+mMFRhyQaa4J0xbw7P6QQ5DV3Q6RzzsxuP+d/Ofo4gcx6r0NLUoW+2Tu/Dz33zpySqorEOIb83/Jj/tfMu8yfbBEtHMrpGHEB6UTP7jZy95pSf63/KQC0I1pasp28MWH06oP1l+RXWoKdZbike7Lzgm/0vGAZzXhY9vtV8zHIv4vsnLb8QsA6H9dI3y/e/fsw3B1/QVAWBrOmojJ/b+5j/ZvHv48TA+xysI+/5BR2r4We+9gnDaMFRfElPrwD4b2//H/xXvX9IOHd+8dH6xb9wZrh4K+S9e49p6YLbySWVU/xn+3/If/dmH/1nkWf0Vv6cnIw8T/5n7j2hG2SksuR2dE5Xrfg3i3uM3zLs/Y5HGRSdphefTQ3Fry/4jdsf8Czr8yA5pbKa/fCKQBjmt0AVEp19JQh0SrDz9gkAG+Gcpso5L9v80uCnfMADvwjckAjn9y0bCkzTcphc8flyg+91PuSTfIe30y85LrrYXoUNQlTpA+twacm7kn/n/sfcTi74dLkN7N58zomqYB1ulw1JyJpznXt3zN90k//Pf/Jvb6vqgtqUOFtja4NcM/tkADv72ww2uzSSgCSQxJHi1t4m3XZCoA11laOlJJDK8y+VpdtrsX+wycH2kN3tAQc7WzRiHwYXVY5OAqqqRGIJ4hilFK++ep/tzT7Dfo/9vR3u3d2n32ug8WxKTE1dFjhlKMoFB4c7dPstoljR32jRG3bY2xkym5x7DAEGKzwrU+iARqPB0dER7VaDu3eO6Pda7O8N0bomTQLKarUO2DRhGBMEAZubm+ztHdBoNGi1U/Z2Ntna6BMFEmv8Z1ZVFVIpsqqm02vzysM7bPY7tFsJG4Mum/0OuApTZUicD0nXzMOiqLl774A7t/Y4Othm2Guxf7DF1mYPsFTuq39qbWN3zj+G1vKm2QXc/H9dr2U1UlLXFXEcU9e1f53S74ZppGilIcpZsBXKOQJCQJCXNVubO2gtaTQSyipnY2OD7Y1N2u0mUaS81V14fEG5Khn2huxubtDtdmgkEYN+j82NPv1eA0mFE2veFpYqy6EyDLsdGo2ApJnQ3thARYr9/X3SMPDtKGuJtUIJQZEtuX/nNv2eb3vFWtBKYg52tjnY2SRWFkeFqwucrXEY0kbI7t4Gg16Tbiui20pppw02un1aSeyxqlqgAt+ErqqK3rBHqxnTTBOaaQOsodtpsbe9g3IWIQ2OGkflLeVBwP7+PsPhkDRN6fV6NDtN2u0m/X4f62rPHJXO81SLjF6vx+7OgGw1o9tpEccR/UGHnb1tz4zVCuscaRpRVt44v7O7RZqmKKVodds4LDv7e77BuxbmaSF9KBcGDLo9BoMBYEkbMYHS9DodBoPBuhHowHopWo1DBprOsEO72yRJNYKaZjPl6PYeOrDEcUhRFNRVhRKSuq7Z3t5kc3MTFQYEQYCQlk6nyf7hLlWdgagII8WyzCmdIWpE7O/sEOmAKAjRcUBla27dOkBKTVUZTOWQIkRYgzSOTqPJZr+DrQtC5REoSZKws7lFJ20irW/yV3lBHPiG+NH+HlJ43IqQEqU1jU6TRjPGugot/W/ES8gsG5t9Njb7SGFQwpLGCQhLt9sGIAxD//fCUVGSdhI6nQ5RkviGshDUpmRzY0Cr4V+TtpAtajCKVrNHf2PIYGOIMBaNIJS++be5uYkx1VreqMBJ6tqi163nZiPBuRqlQWtJe/28ta0RClQYogLNKs/Z2dgkjmNK7I3ASmtNu932zfy1BM6s5YuNJGVzY4NIRwRCIoVnoSZJQrvRxpWGcpX5xRDhecHtZkyv30JridQBKtC0e13iMKFcZQRKoTRI6UDUdLtdoii6kexprUEK4tgfX40xaz6ub8IWdYWOFDKQKLVuOAmBkgFhGCOlpbRLlK5x0k8LyFqxu7mFUv7YmDZi4khDoIiSBo20hXW+TS5C7aWSzre747SBDjyb3VpDkGoIHE5bz542/jlKU7KxtUkYhqhwLevE84V7rTb1+thaFAVSSqIoIgxDrPWLADiDQhAi6bVaWFdR4eVXVkBRlgRxiA4UIRLjfLssL/0xpqo8hqeVtAiQKBR1XRMoTRx4GaAxDh2FuCCg0Wz6760AUTrCqOEfpzQIHbDKCqqiRCJQQlCtSo8Px5E7h9WazFQUVUnablFaS21KL5QDlHDMZlOGw75/revrCKkFUsIiW7Eq5lhZUdsCoQ3GFJSVwVk/FWGF/25jEdNvd1jNZ9S2wkrHslzghKWsKypboXSMUH6xt3YgpWQ8nbGcL5DWYk21FpdmGFNQ5QWmqr1Ydb0QLbTiajJDBcrL5eoK4RxFlUOsEGFEhfXH39qgJOTLFVXhr2/yPPcM3zynshW19Yzvqi5wBrLlivl0xjzLOb24RKIoVwVaCpxdy8mUJlABtqjw0laNkAHGBP/vX+T9/fa3YtMrsxZZfTW2r3MvHpm+XtO4PeWoN2anNafXWjH7Rs78wAePKreYWGBSLyKzWrDYF6weZQTvjInemHD5jmM19C3UcFrf2Lqt8vw9Ewie/SqUr63YvX3J0VvHzL6bMb3vw4ays27BF36cNpzbm8B2K53z1v5LDvZHnH8toHlck/f0WiTjqFK5Dtkk02/mHG5d8f63Pmb31ojRI0cd+bHiYFGv20JQtwKqRDJ5AF97/Sm3eyMeDM9pvTli8tDbtq8FYnrlG6LRVcViT6J/8ZL+/Ste2zhj8+6IxX0/F68Ku2bSfjW6Hs4NF9+piH/mkvfe/ZRXj07ovDVi9Kr66ntZi8D+qqStThR14nmw1wGujb4KX6+bz6q4ZiXaG2YwQLGZku01bmRDNpQ34/rxxDB9aGiEJQ+6F5zmbR7dfk771RGLXU3ZkoST6uZ5gnnF+H7A1157yus7J7zaPeXbB0/ZuX/B/NZXBu1rsY9eeOnT4m7NXn/KYWvMO8PnxEFN9mhFNpA3De+yF2JDSTAtGP18wcODU742eMndxgVv9Y/59Tc+YP9rxxQ9vZYDrYMv68gGEvPmgm8fPOXd7ed8a+sL3to4oXvviuVu5N93INcyIx+0zA8lD4fnvN495VH7BQCPtl9SvbXErPEE17zeYOVbofNHOa8enXC/fcH3Nj/htd4Z8daS6W0vPboOi3ywXjA/FLz52jOezXo82n7JXmPKN/e/ZPl6fhO8yspSbMQEc0PRltSvL9npzoh1zde7z7AIfumVjzARXJvg6zXTuGr4Ftr7h18A8ErrjGG05Oc3PyO/XWAicRMOmiTwY/NacLQ94vWNU+42LwmE4VH3Bf/xox9iIqj6KcG89tb3QKByw+xhxa8cfUhDFwyjBYE0vLV5Qv/ti5tx7LoVkpwVXrLUtPwX9/6A3WTGYXLFIFxwWTb59x79OXbNYZbFV1xrGwjinSX/weGfkZmQvWjMyobcaV4ye61ifhjdiMH0ylL2Y6qG4L9+/bcJpaGhSwJhaOiCB90L8r67EYypVeWbsMbx6PZz/qO9H/lwTRe83ngJwLvbPpAtm9IfGysvMiu6jkdbL9lPJ6xMSFPljOuU3zj8C7LN9W88DdCX68WIbc3R4QW/vPMhWlh6esVuPOH9zmO+d+fTdVPeB0sAKjPUMfwnR3/Mm71jKqduQr+f7XyGa9Y3gsRrqVV8WfHmq8+4k15ymrc5ii+Z1gk9veL1jdOvZIilFwqqzFAMLL9266fspxOG4YJUlWyEc15tHLPa9sdZPcuxcXDDj11tC76984ROmBPJmqYq+G77E2SrIlh6SaQfL/dvZnao2U1mvFx20MLSCTIAjppXqMRLx66Pi9es4CCsiVVFSxcEwhBIQ1MVbCQLbCA8Bz30+AWn/G/4/msvaaiSyineTJ9zPzkDwDVrVpuhR5tU66b/aOUXwaTlVur3w4WJKJxmGC79l7C+eLzGuIQLyy8dfsxmtKChvaNgZUI2gxnZQXWDAQnmPoAs24o6dWQmQK4frKNXHAQjfmH7U2QNQeaPVaL2QWedSpKoZDNaMK1TSqf4JN9hW09Y5iEmgsVu6KdY1mzXOvGf5VnZZlqn9IIllVM8aj6D0N7IyKqmZxjrlZd1fzzf4rxqkcqStsw4rTsY69mzXoTpOa3h3FL0BBdZk2mVcFm1AGjJnN+bv8rqTvXXBKVW+fN/cuH4YtonMwHjOiUQhplNKFeBRzusWeFOenzG9HbA01mfSZWwsiF/ujyicpqTooPM/PWBk/616TVPefG8zV9Od0lUxcpEjOuUZ+WAhYnQuUBWUDX8+e6av/v82ZCWLjjOu7wsegTC8Kwc+K9cgs4cVvmFPxMI9ExxUbboBDmf5DsEsubDfI/LsgGFQmfOB8PGLwY0Tmv+YrTL//L060Sy5qToMAzmVFbTUAUm9kxhWfOVtFV58dnfdPtb3bRNdIhbE8urqsCUax5mt8/+7hDpcrQzqCjG1JpGLJBmyPnxxzTbXYqyQgqHFI40iLh3dEAaKyLlqE1Jd7BHHEk++OgJcdgkzy1R3KCsS6yQNKKAg/1NkoYlz2beah23Odrd4qPPHpOmMYWrsNbQiEIW8wW72xu0mjFFPscYgQwcr9y/w8vjU6wrQQSEUUJRFChC9vf3uX20S5lNULqmPezSjhyBrvns6SUBEicERVWhRUQjbnBrb5dWMwSrqCqBBuJol2cvTkhDTVkKbOgB+0mUcLC9zXa/iytzbFmg0ohmtMHF6IKXp2Ok9k0g4yz5PGdva4vtrSGNyDdEbRQTW82RHlC4JdOlYDqZE4YJ0kGW5URrQVMYRwQ6xlpLVXvOo1KKvCgIwxApBFVVgbA4Z1CBxjlQWmOtt3xLEWLWEgKloTQVSRyytTuk3wNTWWQYUVYVnd0tPv78MePFmCBoe76ucVgraDY7tHstTL1AW0uFoZ0m7A8HnJ2csrQOaoGjopum5Muc1157jSqf4JwjUJJmGNPoSqadNstsvg5zJEbUyFDQ6ES0u020zcHUaBXhqoI0DmglAVezFU5FPrhJU6qqYDDokUQaWwsvZ0o0SatJ/4sms5dnJM0WeVESRQmhdKSNkGY7JsvnWAQihEaYsFisGLa7jBYZTto1MzhkMp3TbDZRSlKbgkgHVK5muDnk2ck5uApnLDVeEFSZirSREEeaOGyAcRTGi2/QAQQhtZSAwhm8FM5aNgabuHLlW5JVjtSCKPG4gdPLC5rNIU4K6qLCSoFSgiCWIDVlsSAJE5QSxHGKlZ6DHOiAPF9hbE0YecQJQGVKAuGwRjBfjAgii5LgnCXREctVhnCGOFLkxZLQOawQqNBL/LQLyF1BI0g92kFqjK0x0nJ+eUGgIVWawhWszJJ2GjPY6DB9fkIYt3FVTRAo6iJHCMdsuSANQ3JXI6MAi6GRpGAtcZqwmOe4MKAqCzY22pyfn6PXZzahfUhZYb0Az1ovkAsiHD4gnS0XlGVOKw49v7isMFXNclUSxzHzxRKDIw4jjCjJqwInIM9zmmlCYXMcjqwuKMoakEQiQEURwhmqvKAsS5ZLg8C37MuyYjqdMp4siNKY7GpCEDZxViOtRtTw4tlzmklAEnvbPC5kMs8xMiavoN1KGU9mtNOYuNXg8mqEpU0QavIy85K3bMFktqDValFZia2hxFELQV5VnJxf0E1jnPJoFaU0V6MpZWGIwwbGgXCCQHkmElYwm65II0VZG4SSzC+vKIxDhwllVrDMl5SmZtDqsMoKzs/PEa5Cx7656PKCs7Mz5vM5QRBQFBVSCAwWpQQXkyuMLehEIYQRtXCUWclylZGVFY1mkzwv/MKUMCxXc64mY8pGQBon5PMVSjuKyjBfeExJaHOccRjhG/hFvmKR5VgcDR1gSkcYSs4vxtTWL7BIIalrQxoqxuMZL168oNmMiaPAM3vriul8wWQ2IY1CCmNRoX8/Qeg4v7xkMOjQ7WiUcFBLyiLnbDRhvFgQpi3AEUqJthatFMfPTtD7OzSTGIPnpZ6OLpjNZpRZDirEOUkQBFgrqBc558cndDtNkDVCKPIqZzpbMZ5NwZZIpaidxTkvh/z88VN2d3cJWhGzxXyNBtKMRlc0kiazsvQ4IUBrxdnpJe20wdHmBrPpilarwTRfkBU1JxcjolaD2bz0o4eqQkaK2hguJ1N0MsBUhmVWUeU1x2czjNB+2kJ4QV0aJizmU7588ZLdvQ16vQ5SCaZ5zvOzU86uZtQqoTYVOk4xZUUYpjx9dkIQBAx6bYzxl1fOGfK84uzq3J/7jcMChXEsJlPiNKLVbdJME79sqgXHp+e8vJzgrPDHRQWB9rz5yWzBaDqj1UhwTlJlKxqNBufjK56dnCOVt0t3Wi2yLKc2GeeXU5ppRFU7BCGrvCBbVMxXBYtlhhAClMQaR2UsVnq00d9vfze2fBDc3HhdN27BBw7ffPNjElXRCTLf3qoTLrtN/o0+ov9RwHI7IJrZddAmyPqS1nfOebNzddOUqrclv9d+heZvCoqeJhp7q3S48DeI1Ybmm299zivNMx4vN+gGGTvpjB986yG3vu/H86OZH9PONgLS05Jvv/qU97uP+SIfsjQRoTIkv1BR/NnOjXSqbCrCub/Jnzyq+IeP/pDjoktbZ3y39ykfDA7458nb7PzLNTPXQTj1Y+P5UHD47gve7T4D4Lxq0QlzzpoLJj88JBsowqX9ilfbD8jeWfGf3v4xldUUTtPSBa/2z/jR4zfZ/HFx05JShSUcl5y/2+Abr3zC660TCqtZxSFHzSv+6D3JeNGn9dLQ/GJJ3QqxWhGNKqq2JrwqqVsBdarWyAiPkDCRJBx77qpnGV6zeJ3nnVp304zW6zFmuRZi+UDEkvcU+w/O+OXtn7IwMZGoOS66fHfwGf+o9at0nlY31nJZeRdFvuF4v/+EhYnp6SXnZZuvDWr+7BGsPt4ivViPtjmY324QTQ3/5Xd/i/OyTeUUm+GMZS/iQfeCf3X8OumlQmf2pulmA+W5gutGGUBP+4Bi2Y740dYenac+aBLGUQwjdAbfOHjGg8YpKxMxDOasopD76Tn/5Pb3aLzIKfsh4aSi7ARUDUnZczxqv+Cz1SaF00gcb7ef80lrk8m9Bp3HhmC9GFCnivTJFYOBoBkUTKqEW8kloyLl5299zu//+J2bz16tKo/gWBaUPcthOuYwHQNwnLXRf+VYe/3bYy1gkzV8+/YTKqvYiaeclW1CWXMUX/Jbt2D4QclqJ/JBVGbWqAPHMPSh4Une4X7jnI5ekbQKhAlvxErhpEIWtd+/1jfbl2WTYbggMyEfXO1i4rVIzziyfkRyVnguZbvky1WfzWhBYTUNXRBIH/arcUm+Fd1wXWVRI6uYf/zyPQbxkkG44FnW5/F0yDvD50zvSJrPauqWF8JeLwZJ6fijqzs8bJ3xNNsgE+RbqQAAIABJREFUlDWl1SSDDGhSdBSN09IHjIuSq1fa/OOX79EJfTgYCMPSRIwKz8SVtT/3moYPj3Rm+Ph8i8usyVv9Y3p6xcqGLE3Ip5MN6q5nd8vSh8KqMARzQWUVZ1mLR90XfJkNsQg+W20SLCRg1mK3rm/SZY7JKuGD+R5aWHbCCS/LHr979ZA/f7lH0BJEVx6N4TEnPmj7nz79We71L29CdIDfunoNSkkwXlIdNEk/HVHc6lN2NU++2KXY1/TiFZ+vtiis5jRv88HJLu3dkCBzhLX14XtWEcxjfvOzt3lr95gqUFRO0dEZ//zsTfRKUHY1sohBirWkz++Wf3p5wN3OJWdlG4njx5ND7CKgjiXB0pKcFah5TvFKh2Rk+e2nD5DS0QoLDpMrrBP89uev4M5i6tQRTn3Q6cV4Cvs45U/rA+5uXUITplXMadbi6XjAcFSRbYVE49ofy7QgOl3wyYtNrgYpt7sjvlz1aeiS42UHfRZSJ1C1gptFvGy/RTQ3PBkNKGrNq91TlnXEbjzht58+ICq4OR6q3J972k8yfvPDR2wPp+w1p4TSUFrFH18cEb8MyAaOaG4wgSCc+4mIzqeCD452eWvnmJbWfMGQl0WPf/b0TbafFlRNf31YtTxypY4E858M+CCssH3BwkRUTvE/nvwD8rMGTV9wXfNTJTJwqAJ+58kDXt/xDVKJ42m2wQ/PDomfh0C9fh/2hkl8/K/3OXljxuBgxb8a30cLS+0koy979IaS1kt7w4GtE8ngo5rPtvfpvbvij6+O+Eb/S/7l+CHP5j3aHwaULUfzuL5ZdPASOMfp0wGNV0pWdcBPpztMi5jWTyJUUXj00BonkQ0k6bnh8kdbzN+I+ObOMyJZ8zvT1/i9v3hI/2OBLux6cXi9ENmUbP0A/iw+4ujonFkj9q/XSY4XHXofWaqGIJx7VE40dzTOLGU74EebB9wfXJCZgG6Q8f1PXmP3z/2ihpOCKhFUqcSEgvSl4A+f3+bexqX/jIoGb7SP+ZPHRwx+pLDrBHW+r4gmHitx8tkGephzWTbQwvLB4oAniwFPLgakl+7mdyQrd8OKvhaa/U22v9WhrbX+ps6uzeBaKpypScOIWEdoB1o57NoIH0tNtqqoS2/7ljJErHmpgQpoNzsoKqSqwCoc0Oq0yVcFvbCL0BGrsgCpWK4yhv0+niNXk6Yxq0VGo9FCIolVhLNemFQ7i6ghJkBUhliElEKRJCGTScGyKKitQQtBWZaUlUEHMQ6JDDRCCJL1COVsfkUzjNFKMp8taHV3qJ1BBSClpshyhHM4Ss+QlF5QhrXEQUxdW4ypEEpjjCWNNabIsXntRy1VSF4UWCUJ0xQjZkRSsCpL/xzr4Kfd6oLLMKamrg06CNeNwBoqSALtZWSBQqqAqraEUYKzAoNACI2UlQ9s8xylNVJKyqJAKrAYpJJorSnKkkacsCocCEVZWWpnCQJJFILJK4IgYjw5Z6u/SV4VJGECSKbjCVEckEQhVWGp6hKLoEah44TZakaiLVVdUdoKRUSZF0jrCCzU1jMBryZjer0ek+mUKHC4ukDUFlHXLBdLP56rfbC4WC4JGhEoGM0mhIkkwqEQ2NpQ5TnZcsWqyAmDlGVRo3REnlXsbGwxmcyQvZYPTXBkxZLCeJt6iCSwUBlHkeXIyCuMx4sJYSSocNSuZrWYsVguWS6XWOsFIJU1aGtpt9u8fPmSne0NWqlmNpuhGwmnJ6derqd8EJCthW1pmjKdjlnMWmhliHTk0STGMRpNEEpSliVJo02RV7SbKbia05NzBp0YrA9QjTGsVj4cu2mN1p7rhLGUlRddBdoQKnC2Zpktmc1mrPIcpG/nhVJhHRSLJZOzCd1eEykNJgCT1YzHU+rKYqsSrQK/78cxprCcvjwmEDu+sR0EGLxY7HI0I9ARZVljhQTnFwVMXjJZTmklIUIGOGVotpucnZ+yzGcoDdgSYyoKG5KkIcdnJ9y7c8R0uSJMQ0y5BCTzxZRllmFqiViPMVeZZZbNGc+X7PS6aCN8A8/UXI0nTGcrwiChttq306UXBI6vpuxsbWFrqKoSJR1lWbJarTz/2RhQEu0CqjLj4vSc41aHYa/JarVChFBUJatlyeXVCLn+nWkdUFclTgdcnJ6RxptUNscsBEVeEkURi8WC6Wy8bp06lFY3v+NAhVRljTOWOAgpq5rJYsV8UaCClLywxEkDoRxFVXJ+NaLZSXHGgrbgNKuiZLlcUtcVZS2IGk2KVU6cJEyLFe2qJK4DtLMkQpBlBVVlGM/m1MaH8YGUFEVOp9nm9OUptw/2mc8KwgjCIGa6WHJxNaY0NTIMoPLN7GVeoAVcXo5op7HHiczn1Mafb+bzOUVRUJY1Wjmk8NMCo9EVrUaCqcEoQ1aWzJcLposlUdwkzyxSRjjhm8SNVsKL0xOOjo6IVUwQOvJsweXokouLEVVloKyJogCzPscppZgt/GSFlgJjLNlqyfRyytXlGIujEhVJGFGWGWkzYTweI2WHMGgBgjzPObscscgzVlWBViFVVSGE8J93lXN6egqyRxxH1HnJIiuZjKegE5ZZRbMZrdnpXqpyeTFlMNjAmCVpEFHWK4qiZDwe+31RWowQ1FVJEoSMzi84PNxECMHVeEqr1SKf54ynM66mE+rA+qZ4npMkDaytOR+NiBsNQtWg0WigpeL5iwvOL0YURUFghGfDqzVtOFCMz6/oxg3SNOXyakppK64mS67mKxAVOkpAKKwwVGVNEERcXs5AKAadJqaumYwWlIUhbTTIjJ9AUNZR1gUyCHl5OkJqRZYXnjuflZydT1ksC0oXoLW/2bXWUluJVpLTiyt0GFIGklhHzJczzs8miEBTOoMRgspBM4nRteD47BSlFINBn1arxdX0nNOTEXHYoqwyz+WvDWiJ0LCsCo7PLuh02mwM+4Qq4exiwvnFFHQMxrf8ta68iFOFXJ2NoN/x/HmhiUPNaDzj/GpO7ViLRA1izfMNQk1Zlv9Wrvv+fvv/fvMCpXXAtGZwCusoenAnvaQXLOmrJbkL2AvHVIni2aCHynuES992c8oHTnWi+ObGCxqqIFUlkyqlsor+5oxwkpL1E5zQJJe+gRqezhnf3wCgsorXmid8uNjh7fZzfqBf8YZn6xsqem12LrsBgTTcD095WfR4kJ7yu6uHPB932cmtt0HPvajqWiSFtgSy5tXGMYEw/MH4Ae92voDI0Dj24/S68OPGNpAEc4cWlsopxnVKT6+o7Vqk1hGoYt2QEf4GLBsobCVZmQiLYC8c83i5QWkVderxAGVHE1+U63anJJw5fqb3GICXRY9pldAJMqq14BWgGCZezFXYdSPU3IRban3DZwO5Dmw9IsFq3/py2rc2fcNXEE4Nq42QZLQe15+XyLzCNCJsxwu/6kRw+mJA5zDjabbBg/QUgD8Y3adq+oDft3/X7Ny8wobwR1d3uNu8pLKKk6JDQ5VMVwmx9t+dyhVVS9H+bM78XovfvXzIfjqhsuqmUffJ1eZ6PNc3kZLjzAdswOjLHj9I7rDbmNJQJYXVjIqU57Me8ZWlakiSiwob+jDy4i3Nn5/u3TQoAT5bbnKyamPCdZs6leiVJJxWuPVi1++cv8JGsuCibHFVpnw222A0atK/9OxX8MI3E0myW12mf6n57DXJK8Nz/ujqDp0w5//67CFJ5YMJBIQjg9OOcqtFeiL56WSbVliwn/qx7FHeIHoa41SJnleU/ZBgVrPY9zvB7396nzduHQPQDnKWdcRPF3volUDmFbIOb1qBACoT/PHFEd04Yyv2ZY+/mB+QnzXYXLPhPBs6QFaKYFrw6fEAdiGUhmUdsqginr8YkBa+zV2vG8Nl1wvp1EcJHyebMISGLlnWEX9+tsfsSZejtCKYmxsZlVNeivf0eEi+qZHCYZ3gjf4J/+LxqzRG7qYpLkv7f7P3Zj+2pGt61+8bI2INmbnHql2nus7UA9Bug4SFsIyBS27AFkJikCwZS/wFwH1zibhGSEZClrAE2LIYLCyQsARu4zbq6fQ5dYaqU1W79pB7yDlzTRHfyMUbEVktLjgSF7TVvS527r3XyhXxTe8X3/M+7/Pgbyv9I0f69IjdX7jlty++y5NuyzZKfxzOF3x4nuaEQ1xb7DawepN5fvqYf+XXfs6xO7Avnquw4MevnrF6q4grIwDWaFqoSmV4tYJ/ZsdQLOdhzZG1vD+sOb9e8zgIOxXEgNDuKg8+z/yf3/tlfvU77/jZ5gOsLrzbHfH+Zs3Jl8KccxtJopg+o9aauy9P2J/ccNN3/C/p13m/X/PBYsNw2XFyKoztSeogNxIXunbgeljwWf2ATxbXvB2O+eHbj/DnluIN/iaSH66ECXqbaD9vuXq0IE3oGQjb8bLj4VBx20w89rjbQHjYsjyF6w9aTo+OuXUd3z+64A9ufonboWX1Ekxf0TGTVh5/G9l80vLoJ5nwLxo+vXjGw04kRD5795TVFxY/JoFAmNnduSS/8lcrwrOB3Ynn964+YRs9OWvaM42/HbCbgMrC5i1es34OV08th+TYpIaXdw84Oz/Cvmk4Pko012J+qIpUSQwfrlj/wHP7L2T6teNRs+Pl9gFfPf+AR18IexIgHBnMMLKtnWL3fsmLpDluDmxjw1ebR4S3S54+TyL3s0/0j5wYbz5uMK8M56bwqBP09HR7zNvPnnJyXukuE7nVuH2RpFxfcDvF9mdrfshHPF7v2AXH1cUaf+qBgfb9gcNH3QwUVq04+hLefeeIQ3R89+SS88OKy92CxSuDCUUYqqHS9pn+geHky8jdn7NYXfiHp9/ln3p8xun2mIvXJzz5etS4XhnsQdqiU+Xks8LZ0YofLZ7xyycXnLgD//Onf4ajzw1+UyRWj4nIYhSpUSxfaX74rY94dnLH33v162z2Denlko+ep3ltwD0QGRea1VeG5zzj0bevGZJhe3rEya5yeOxorxOpEwas20k1xpMfFM7MCX//tsO1iThY1p87lmdp3sdFmkU0nVOjWP/M8aJ5xM1xx9PVlq8vHsLPlzzUFbermFhFcuMDS2oUT38Qee8e8rvfXfD0yR3vXz7k6KeW4ajidoUyyvKAxNLuohA+PeJHzzq+9/33POm2/E/Pf4P2c5EMBHAHkZQqVhGsYvlCsaPl0+YZzmSerrZ8/vIDFp832F4SBybes8TLQozZftHXH2sjsv/67/zt3ww54q1FWYcay3Vba3nwYCmu6iFirGG335PQvH53ysXNDYvFEYcQsdZRU2Z51OG8oV04hmGLdgprHedXF1zdbalaxNuLyiRd8U1HHCIPj9eU0oOScvIcM2/Pznh7cQ3O0avK3a5n0a0ZhsSHT56w223oWs0+7LHO8+NPf0zTtAIYoWmallwSIRw4frDAUPDWMOQBbTy3d1tevH5HLYZD6IVtbCwpDFilaJ2h61pyzXIgHRKbzYEvv3opIJa2xJwwTkCAo9Ux3ilab9jHHUUr7m5ueffmHK8NwyBlzQDGOfoUePbsCbRK2p4rNzeX3N5uubzcsN1EtHFUrTFGk7OYeSlVySVRU0YrMZlRSozRvLVUwGqD1xZfNUpBTAXnPMMQMEr+T1sNumCMJcUMyrAfMsZ7lIXqLENMbA8DZxcXXFzckKMDZYlUigLfeEIYWC0XeNsSSByGxGbY8ebNO25vtyjVQAaqwliDso6Hx2uGeACtCTEScuL6ds+rl2+ptBy2B9q2Q1SRKypFOm3x1lBLIuXE5fUl1ze3XF1uUMahrCHXQtM6ri/P6bqGo+M1OQRSSWz3Pfv9wOnpW4aSSaVgrMc6T1WK7f7A8cmKnBUhJmpR7A97nn/9kpQUfcpiwaY1CkWMAygBOawF4xpu7u44fXfJ2ftLMo4QEt4JU1qXQk6RIRxIZHRrudjc8vzVG97f3NFncE3DdnOgaSz90FN1IqvEojMYr+mHyDD0vHl/xstXb2hXa2IpYBRVG0IZyLnQtR2lZEqCzb7nZrvn5au3pCz9SclkLYC+MhpnDGWUlhhCYLPtef3mjKurG6zrMEgyYOh7jBPW8BAzi25JHwdCGDg/u+bq6pbdpsf7Tth9qrDwDdc3N5SpFD5I3755fcb5+w0314lcPLFWtHFQM43z7PYHYYpryClSCrw5PePr56/RxWC0w2rLIezpvKcm6Pc9MfcULaD79e2e16fvubjekXIllYTVDRiFKYoSCiEkck0or7m4vOLsYsP7i0sur27plkuM9cSSMcqhFQy7gcY3HPoDKWXOb+54c3HN+fWGahwRRdKgnKFQGULPkDPWNYQhstsN3Nzu+PHPPiMXhfYtxRRS7nFWkUoSUDNnlsfH3O0OXG/3vD+/4PXrtzjXAAVTs0hiaM1+cxjlBirDAMpZXr++4IuvX+FsyxAKWYlpkrKw22+5ur5isepQxnC3ueb67obXb085v7kF32CcpiglhoMh4VzLdn8gq4o2msubK169fsf17ZaCIxeFbTpiiDjn0dUw9IE+BLQ27HaR29s9L1++4fp2A1p0Co21lArWO0Ifub68I8bC3a6nD5HT0ze8OzsjVYnPzntCjCzaFUoZ7u62pJgY4sDV9ZZ3Z9e8fnPDUCL7PFAmV2XZWvDKcnl9RdWay82W692Bt+8vefH6DVkpsvfUCvsUaJoWg2a3P7Dd7knZcHWz4c2bMy4v7tgNAaNaShFt8RACrmlJMXC33bLbHdhuDpyfX/Pu/JLzy0tc21FHM8dcMxFk39olri8v2e8Tt9sdu0Pky69P2Q6J6DyFglOGxjSEEGmWKy7vNry5uAU8795c8P7shtN3l8SkMHbFMCSMduQUSEFkIS4vLthvD5y+fsfLr0+5udoRk0bpQkqFZrEgAbEWcjXc7QO77YHLy2tev3nD2dkVt7db9ocBZzuU1oQQxUjQe4wRyY6rq1vOz6948+6Ci5tLhlooVYmRWinkkjBO5FpqqRz2gdP351xcbvj58xfEbHHLJYmMcYpaIwvviWFAF8Vhn7i4uGazGzh9d8bLr8/Yh0o0llAUuYiUUA4Dzhpc03J9t+P12zN+/tVzUijEPpEolAqVLFIltVCzg6I49JGby1tOX73myy9fcLM5cLPZE8JoFmQqSYuZJ1bRD5HLmw3bfeTqds/Z1Yarqzs2mw2lStIv59G5XSnKELHK8m//5X/9T43I/gS8/uZf/we/6bZJSuS1lBJWrQjHhv0nBW8LZ/GII9vzg80nXKYVv/+j73P8hThet9dpNtDqHxs+7454fLTj5f4B3mSO7MAPv/qE1QsxC1IghmZ9Jh21uAN8+d2Om7qgaE2pmru04NVPP+TxD4NoSt8kuhe3DE86/F3m+Z9p+d3dd/ne6pIfb77FiT/w9n/9NmFt8Ns6G2SZUHGbxN3HDa+6FQ/bA1/3j2lM4h+ef5/4Ow9Q1dBdyWFdVbmv3BleHC1pTiLb1DAUx1VY8Pn5Ux7+H0bKzFG4nTB5l6977j7qOF+3fLy44YebjwnF8JOzD/E/aUlLS3MnZk5inqZprxM//eeOyMrSFzEj/Z33n3D39oj1V4rl2zhr2uZGz6xeGM14ch3NvsQsyPTCmtS5kjtDXFviyoBRoyauobmbStAFOEZpSjOCWaXSXSRC0/LbzUcccLw8POJnV095/uUzjr5UmCDjZ3cZlQp53WD3hq+eLlksA2/6E17cPeRnFx9QfnTM6rTOJtJiHmQBxdffaXm9OyEqw/v+iPeHNS9fPebR74n0RHsViSeeMhpxZeN4f+JYLaQM+iYs+OGLj+lfrTh6MZrzKDWacTmOn0euHi7oPtyziQ3XaclnV084v1mz/kNhh9thMpkSQ7vuHF5/3HCbWt4fhAX85uaYxW8vsb2U50qCQ8zr7C5RtWWbOs6bBucKP377DP/DJY9+mjBR5l5eOYoXPV8TFBfbY07Dmo1zfPH8GfH/esCjnyT8VSAvLO3bLemoEUZhVeTccHZica7wanvCu/0Rn/7kO3zwO5XwwGP3Y/Igy/V0srw9ablNDbvq+fTyIz578yEnv+ewI5OQsa+q01SjOSxbLvKCTXWcbde8Oz/h+A8aTp5PZnhTibKsdXtQ9NsFVw8Nb7dHnB1W3H7xgNULYc7FtSRbVIXhsWf9opBKw6VpOA9LDsXzo598B/dFy9HrIsZXu0QdS+vD2tJewmt1xE1tuIkdfbac/vhDPvhtRXWjPECraS8Dw6NGDJHuHJ8NT3hV11zGJZ+9/pDVDzoefh6xvTD2SmvuTYqq5eqw4qt0TLCG337xPS6+eMTis4aTnw+Eh04M/vYCqlan0VvHm3XDu9tjroYF118+ZPGjhoc/7cXkbBuw+yR9HMHuLKe7B9y1hsv9kpurFe/ePuTbf7diB0m86FTnuGP7yubimMvtGv048NPzD/ni9APsZ0s++N1MXkgcrV4M6dLS0l1VbuKK87DgzfaIaDRXf/iEb/3vwsxsbkc935hBa7qLzPprw+mjJVtl+fzsA96fPqT84REPPxMQqTph8lerxRiy1ewuV9zFljtr+frFByz/oOX4RR61tiXu2G0SuRyjcFuNuXO8Mx2XV2t2b9ec/J7j+IXELJ2hOj1LVzSbwuKV5bI/4vXZQ7Z3Hcd/6Hn8qZTGA6Slxt0JEIlWrN5kYmo53R7z4tVTDj895tEPFM2m0l3EUfZGzcZXYaU5+kox1Jb3Xz/i6vUJ/U+OefaPEzqKziwV2rOeeOQoXtFegD5veD0c8+7VI8qna46ew+KyULyYUqJkP8itwR0KqzeVfN1xd7GmfrHg+CeGh58l0tKgiwJkDbp9IRwZ/LbgX3r210vevHvEzdWK9W91LM5EM9VEYaGHtfRTODIsvzJc/vgxW1oufvqY+NmKx39Qcb2YAPrbRPEGE6r0n1acfFHY3B5x9ukTXv/+R3SvDW4ve77rK2FlcIeK2xdsqKJff92xfXGM/YOO9nPP8hS6iziysMVAVCepQChO0V0W1i+gfLHE/6jh+AukWiZPCROwQSo/9Ljv+hvFo99X+K88z/63gDtoUicM1gmoPzy2uL0ArO11YfmFJZwvOPzwiJPflZjhDgW3k4qh4jU2VFIn33X0MvPw96H7+xYVPe4gpns6I/Ej19FEUfZZfwuL15rwwyO2v/UQTluOXhX8tszPMravc7ua28rRc0gXHfVlx+aLY5ZfG5bv6txOMybYilOjNrPhr/27f+EXetZWtdb/90/9//T6S3/lr1aUGnXmjABMGMiF7337Yz784BinI85qSoq8uzjj9mbH5c2BkMBrKYkcSqAq+PYnH/Pk0UOcAWMrl5eXvDu7IhfRLoxB9AKLUWjdEofEJ8+e8PjJEcfrJSn2bDYbvvziOWnIaO/IsZCVpfoOozRLr/nWs0c8frzi5u6a6+tb3p2es1gsKdVwu93Rdh2liB5sIfO9jz/mWx99yKG/QVfN6elbzi/v0FqzD5GKpqDIxmBVYb3o+OjRA54+PmZ/uGW7ibw/v2Q79CgMqWRAtDY1hsY6vv+dZywWLc4rUiy8/PoVm+2B/RDAOqo2xFxpnCMeAt969pCnHz/l6YMlu+tb9ruBF69eUqsjJNFJzLUABe08KZZRL7HSdA37/Z5apXy9bT0xDRAUOIvWUlJVa6U2lhSECaYQXUCt5WcqmZIRPb8iWiwfPv2A1cKjtWK337C72zP0BbQhxsghJpTSeO+pufDw0ZqHxyf4toK2vH/zlpuLS7RtpDQ4F6oqGG9AOUwNfPj0GcdHLY1VbPYH3r8/JwyRmKVvc85o26CMwtbAyWrJ08ePaTvLpo9sbm+5uroiR0tWmkwVx3AN1sjPX/rWM9ZNS9u2XJy/5/Zuy2Z74BATWlucbYhxcnG3tJ3n6dPHWFOJfeRuc80+BErW7Ice0BjbQJnWc8XYyqMHK0IQrduCZQiJXJS0AXEJLzXRWIOzmlqkryOJtltyGAJDTAJRJzlwAOQcaDvPkwdrjk/WhCFx2GzZbnakBNZ4+hjQ1sjhoIgcRussx8cPsEYx7AUAEpZaZkhRHs5zxJqWUqA1nlIyzz56xG57hzYNwzAwDJE4GgHK/eTR/E762lLp1gtKKVxd3dAsOnJSo7YyI4s00YzawdSIKplckrxXNSlqlG3Y9lvsCCKrWqBWGmvw3tK1Lfu9sCNDjKiqqUlhlEHOV6JHaa2h7WTdHB2v6A+J/pBJEVI8YJ3cm2k9qlSsdjJfvBHNVaM5bA/0m51oBmtFiJlmsSCHjDaZmmGxaFE104eAXzYiddC1oh1thUkSQsBbzWp1xHZ7h1aVYy8SHocwiOGRcfSD9EUfehor8VcpkbmIcWCxWLHd7rFWqgVSGEsxlZhJFUTDumqFsV7AgBLEPK1pUFG0WWkd+/2etvWUkjhar+n3B9brJcNuy263Y71ec3l1R9euSSRKFUC/GpHMcdZCLlAquQaaxZJ9v6OkCtWitZljLlRUzSyXS/bbDaXA8eoYozT7fkcsmVozxYiOszbgbYPT47pxlmEXySngtKPo0fglix6xa4TNv2xbchmoNaOdtD/ESIkCQlhjpJJkFJGtqqCdpXWefYgcUqB1LaqIdmAkYZQVY8OY6LqOmjLGGBrnub6+5uThAzZ3O5RSokWtHLkEXCNFNUYLo9gqjbKGWCohFTEFL2BQxBpRWuPbhlISaehZ6m7UYlbEUKFmhjHRl0e9tLZpqDULS7VxoKRdShlZDzHSNLJ+rbXsDxtWXUvjl/R9z1SfbbXMtdBHTOsZQqBXhULG1ELrO5SyUDUxHHBG0XWOkgxD7MlkqnajzrpGI0xYpRQlZ7Qx5CrVABiLYtS9LxWnGGOuSMCUUZ6oqkoqBecNpkDMiarMKKNjyDFhlB772FNqopSMdRpbFQlFQtonBqaezjlCP8AoAzIEkRkqMaCLRjfyvnEWlB6rqhq0YUw4aHKJeC9a/yUmrJnimZ61if3ImrXWija1tdSioCSZD1mNwUsTAAAgAElEQVQAW4lWGWUN2hhU0vytv/Ff/OJ1W3/6+if29a/+a/9pBWHUyWFCzyWSp3/Rsvqnr1m3AyftgSFZPv/8I7pTy9Hzgt8VwlIOVv5O2HVv/3xD/ys9rk04lzl8vebBT0RnzvZyGJwMtYoT8Ovdn+/Yfi9x8uyOw+AI5ws+/AdSGinlswIKTCX++yeWy3+2cvS9G27eHLF4aXn8aeLwSEC/9atBSnxjnQHPN3/R8PTPvufidoUxhfrDI05+Lt/fXSYxU4llNs3aPnNc/UZl/Ss33N4s0Oeek58plu+zJPZ6ed6a2hHWmrf/MqgHgaaNhGBZ/N6Cxbsys1tVEXAqHDvsPnP2z7dsfiXxvV9+x+nVMcN1y6PfsaKbuqv4TZ5L5ssI0KaltDGstTCWR1mAcOJmSYTp8xNzVRXRsqxGjeW/wg6qRo1gjrRh+nnxG57+sTCCmitFe1Hxm5FdtC80V2E0NpJ7vf2uZfNtyEu51+OfGY5eplmL123zDMwAFK+4+HVL/1GGVcS8bzj5mbTZjRIdOoh+YTWK3GjuPrHc/mqhHkXse093rjh+nsf7v2dFiganyHW8/3OG8CThjgI8X7A8lfFrL8WMazLD00FA7eHYcPNrmtxUzEGxeFfprsScpj0P473/UTuY3Bhuvu9wO9E4nAx5RBogU62eJQbi2hIXo6lZFjmS4djQXifczUBp7Kw5C6CHRHjYcvVrnv1HFbtTAiScFexQSI2muZX5KLq+I4P02LD92JAbcBvoLgQwsYeCu0vzd+eFQ0fRPlYZLv6so70QAyK/qTR3ouU6lXPrkSU2sXpTq+gfiIzD8VcDcS1rzvQy3mlpMYMYTZVRU7kqAS5iJ6w+e5C4073dkzs3z1GAcGwJS81womnHcXA7YdhSZR5NGrrFyrgPRxq/LWyfGfxdpb3NmP6+isD0WQBYrcit6HCmRpEbuWh7U2iuI6kT4NltREJj0l9VpXJ4ZKlaNKDDynD05ZZ43GD2SZIhgN0MpHXD/kPP4l2gWsXdJyJlMSWJilP4G5Ec8Vc98US0eqcScrsvHB4Zlu/ibLY3xYM6JtjEMEqSO9/Uvm7fH4jHjayhcbya8z3xRMzKt9/ytDeZ7TOL31QWZ4HtR56Tz7bC8O+zsKxH6QZAQLppbsY6x6CJvVytQqU6r5FqJFZ3l/Jdm1/yZA/dlWh1TzF3KhOPazPPi2LUHP/SwkiJ/zf0sdPCYHeZwxOHGVmY34w3c5VBI2M2qkzM0grDsaG7TPjLA/GknftpMsOb9KUn+aDsFGGtOH4euPu2Z/kugWI2ZZxiyBTfdBSwsCqJmZMkzSQlNGlYx5WZ9X33T0ZteHfPEm7u8v3a02qsPpF18M3S+mrkHt2+MBxJgi51onm8/8AT1orm9t7EMrWyz7tdIXtFc1fwNxGVRKoinNh53kxGmP2JVK40m1FGaYw50zoSrVpp+yRvY/r7uCwGk8yM3thpdBoBzXGMZLz13JbpGmmhZ7NUkLbqLAna1GhSK9IMdlwfzaWYw/UPRMpo+h2/KYS1aPgWI3HDb8u8/idZ1mLVGFulPXGlsYeCyvftlfk0roWVxJ3pPlIricr5XlOdWcz6G8Bt1fCP/tZ/9As9a/+xBm3/rb/2H9QhBIx3xJixxkPKVKN5fHLMwwcdJ0uHs3B1dUGJhbfvL9Fmwe4w0CqDsoqhZrLSeGt5+vQpTWMoNbDdbrm5up0PLKqKlIKyhhQrpWhOTla41uOo2Bi4uL3AGE/nO2IcSH2iWx1zersRDdIGjFM0OqNSoKZKVor+EHBODqy1KrTWFFNFr04bPvzwKUYHdps9Q0gc9gFtwFpLGBKpQk2VxllK6lHesl4uxXRMdPfpU5ZSWKOhiHZsZxrIhfXKY4zD2MLd1UbEYpUc2lMKVO0YcsZbS1M03bpFlcqi0+QhSJl2Hjj0A9YsMN6x73c4Z4gxo0wjgIXzbPsdzjmM0nLoT0mkUYsjKE3RGZeqaBpqRRwCXddRcgYKhoqqnpwHtFUCoquKxVIKLBctJcMu7ClDFiCgBJRyxFRpmwVhv8MYg7MV4ywh7lGmE6f4AqEoSk1islQHMbLKipOVaMV2TnNyvOB2t+dwEKa1a5cCftZKLpJMcFXh0OjaY6zCtB1Dv8daj6Lhrj/QLlr6ECilok2l6zpcypgqpdclBhmLBFkXkZjIGa06Sg2kNNB1C5SC/e6WZbsklB7lLH2QEkOqxY1gj1WIXECKUCPeLbAOdv1AzpWsIIVI51ti6kVvt2tIhwGr12KQ48oMzBctvGKdiugeFjEWU1rKiI0Ch5Rhd92SoQ+EwxbTCFCVlZgxKcA2lsYtSWnAa0WKiph6nK/s+0TTLoixyPoowtSuueCcgDAg8h3OOQySbU4psWhadv0BazWUTNd6tHf0RJmfaixhTqKx6U1LygFjNDVXnIGSB9puScoHigJiYTcUlPF0bcP+0KOoOKsxRRILThthEqeBIUl/eVpU1aIdZhJKVayTdVaqRVmNUhqrNDEGGMElVTNBKcgFpSpN05GrRmnLIezExClmvNGydhQkZUgh07Ri/pZHMM96x+buBovlEA+s1mtiTqIHPfat8w2KShwC68YSs+jBDrmQc8bYBqUM+yByIl3Tok2lMaKjqZ1oc5dSMSIwjNOGmAsxJ5Q1Yo6IJgPYhpJ7wmHAOYdHoxXs4462bSklzYCiAPCi4TmV9yvb0G97tAGtHE4pNulA19oxNnqGIaJMEcCzsZSYUcVSqxJjOqXYxwPr9ZoSg8gUlIo3Ho2AcMZpQg6EKkZpIQ20vhPZmZEl2boj4mGHU4ZgxAZQ6SpAYs5YramIDIhShlgi1RRSjLjqaG1DGEQfO6Gg5hnoVDlRrKZPGW8bchykr1A4bTikQEkZ23bjXFFolVHGMqR7k7CYEo1rR0M5RUpB1lFJ5AI4Q9GGPBQUBYKsozymmHNKmKYSwsBCt1QszhlKNqjDgd5LkrNiaduWfn/ANXnu50MfSLnSrdbUIIB56Ae81QJ2OiP7LY6UMsZCDLLWrTYc+j3KWaz3XPd3NJ2lhgFbDVq1tM2S7eaW5XJBST1hUDSNpdjMfhexI8iciyRpKCKzpK2A7KVWUpEEhSZhcsGWwhADIQQePH7EdndANx2KIrq1zmGrxOlSECC+ZKkSqZKsIFXatgFVsLWSQqAqRSka6xxDlTia+gGNwjZepHVGYFlrRYmJrEGrKvIFFdTI4i86oRjjoVYYr8hDQNeCKgU7Gp3lnCUetQ0ZkTmSpKgWGaI+0/mG2+0tAK33FK0IOdF2HYTE3/kbf/1PQds/Aa9/6d/8z6q/TeTGSOn3WAqtcuX6V70YSn3c07SR4eUKnRQPfiyHxMV5pj8xwsIbD3JhZbj+NU1ajSYdF4r1yzwfWCbgYfqpY+Hu2w2pg7RQZA8nX2SyVwwn4gJthspwonn6OxviSUNcmfkQPQGTOkJ7m4VZdidsqWrEtAYlB8WrXzdkX2kvFW5Taa8FtM2Nwu0KdnRxH47MfADdfaiFCXgQgKS9ESBrLkffJ8JDSYpun1mKk9LP1Zt7ANXty8xmtXcC5sS15fBQ9CL7Rwo9OlHbg4BBqRFtve4yzeAiClSWA2z3ticv7Kh9KqBs7sQgbipPTwuDPeRZ93N43M3mZVP5vo51PnBPwJeOlf0TAYcX52LSFo4M/i7PwEY4cbitzJvpkO23eT6MTv+ewJIJLDOHzN13WpbvI/0Dy+aXNKvTQnstH5yB1BFAV6mQF3ZmcWWnBGTYSB9VDavXA+HEzaBDtZrhkRjKZS8giB6rUO1BDs9izlTnOWIPmf6Royphke2f+rk9/ibOY5mWluZ8T2ndLBcAo+FPo1icxXst1z4SHy0wO3l+6J922EMmzexVKW+f2lkaMwMxAnoaYcB9o5/tXrSHm7uM3eUZIJvZ16NxVFhrbC9ggh3qDLy054F4ZO/BpjFZo0qdtZ0B/HWY5xdKyr4Pjx2LszDLJfQPRI6jvRXTPAFsihicjaC+PeRZUzktZG71JwY7COAC0L4fQCuGh57mQv6elpbiZC7FTjQw/Tbj7tKsxTsBQZMmZW7VWNpf5vdyew/s5mYEqjYy16pRc+l8NQL05E76fAJpJv1b3YvEwGTqNTx0Uj7+NpBbQ3Nx4PBsIaZqocwAZloKwGv3md0zjxtB6mncJ4CwOdvLvH3cUQ0MR8LYLHZkMsZCHmUkpnlgd2kEOwVYnQBdlSt2M5AXnrgW1mtzMRAeeAHJRskLSYrcg30TqOZv0gwwpoVm8XrP8LhFFUkW+Y1UGzYXPeFhO/f3FHOqgfY8sPuomZMTskY0xch4Za/wuyKxqlTsRqQbJtmL1EkSxR0KsRNAbNo3gHmvkmSIjJkAwRIL09IyHBv8djLQm+Q6mPchlSv+WjTCdSikpZnnW3s99nmr589PINtkHjaNQTh2o6yLzJVpHX1zPPRwT3zqn/jZgGoCIt0m0T/x97q14/7T3OaZ/RtOLP4mkRYSC6qC9kpMAoeH8ru50SKHsTLzd5lYqUqSprlRcyxIjZK4q0TDdv3FZgb1c2dmcHv5LoipW6647T3o2Z4H+ZwbQerFSF7o65xImvamSXqoOEVqBTx1m8jt9zqW7+OcrBCAVGLpZFoGsmanWDTNgf7EjOsazDAC+kmSSc2mjPciczO1sm+IERjznjclCOxQZsmjKdFbDfP+lRtJHk9zII0grwnyHWGphdHcy2dGNSXcTuL1+qXI9EiCTKoiwonI//zW//gf/5MP2v6lv/JXJfuvhW1Zc8Eqi5TyicmYcobcH2isY6iVtm0Z+iCHb5SwOWulKjFyAXFe32xuWXQN1gjrte97mrZlc7jBuQaLIyNag422YhakBFwJ/YBSSgyYQhRWy8ia0lpc7J0zUAa8sYSSQZXZ9ZsqGqHGmBmMylpcri1VmC8xzaZaAjQpFk6AUa01SlfKEClKHk5SStjGi4ZjqSNgOjHLhIRmtQNVhJlTMqXIE0yMmVygKoO3FpMquURctxTDNGPwDnKuo4mYAcSkxlppS8rSzykl3KgjbBsB8jTCustK46rGKIUrheINUWVxr6+KUhAWWWYObDULnT+lRNe0AmaakaVcCrUqAVFzphRwxmJQUCCVhHaWkCLGCLuocX7WaA1pACVmP8P+wGKxQFdNTpLWUbXgFgt228MIrpQ5BVP1yLJSck0dhZUYVCXEHucFQBLQaSwFrgVrxJjGNVZYc8rQD9t5jDCgx3JVPZWsikeMgFgoalGUUgk1YRxCp0JDlbJ3azUxBEwRnUxrDNthR7dac7fd0flGZnYe51iV/nRGGFs5JWHVKWEvurZliAIU1VrRVsADrTX7FOicpcSItoY4BKzSYCGO6y/nTOMcJRUWi47d0GOqsI5lPD01aVI+CKtMK/IIxpqRhZbDCDjUQmNlPe7DnvViSd/vUUa0rrTWlJrQWoDiUgraNKRc7gGuCkWbGbw0TmOLMBCLNqAKhQIIyOl9x3a7pe3WlCLsZ+8ttYwyAK6lpIDVhjQEcIY0shZTHTOMI8BiraWf4od3hOGAtRZvLLEKy88Yi9GOvheGpDaZkjKMMc1oJ8w6BcZ7DqEXkyqxjpMxQmG1MDJVVSMYadkPBxarJTlPADbS50lYonV0qLfekatiiGGOIbVWMcwYTQSlAqBSCjRWUWIhVwVjAidX+YRzRky9ssSxKU66JNkmNTIFtTGEnAS0VTI3TNOIOeE8VmMsRto4DBJjlReZjLjvsdbhveeQhjEWCzN/Ss45Y7FOczgcsOPmb4wj9UkSOW7MhGLkT2OEoY0k0VLJksQocp8JWRO1VmLK1FKwys0gnNISG43Rwuq3lcNhwOmOXMsoNSOAX86j3pzWqCrzuQ8HnDPUrCk5orQdwfWMtw6DQpUiCU2nqIikjDBbuxEY1cQh4ZoxNpt75rHoBAtDOqVE6x05BpzSKKswVGrW0MAuK7zREBIqCyNf4VBKk3WeNd6pskc55yAXYoxY1c7SJKVm3Cg5NJXmV9TIgM8Yq0XrNww0SuKaUjLmWUPJBqpoouciQDVACIPEbswc26wVuaBSpJxptVrT972AmkXeB6iIZq4xhlqzsFKVnue9VJTKXqQYndqdJcx7dRaN9mGg6zoowt6dmOex5BlMtd5hrCIMEuO0YdzXNTEM1ApWOWKtKKvme+2ch1RQdowVWqp9/KIlp55aIsSKNha0zFVt5Z6tMeQgzyG+7cZKmYR1hhikDTFWstU0VpKu/+1/+Z//KWj7J+A1MW0n5qUqwhAC/sghxQyV4UjT3BX6E0OzEUBOHOnvmTLToTgsNas3gcNjMarRqdLcZsLasHwzkJaW3Cp0qDMoOB3QUqNnwGxisIBcYwJjJ0bOdK2JTTMdvIAZ1EoLYQhNB7E0MXxG0G5iB6Lg8NjNLBwplayz7u98wN6m+QD6TYaXjnXW+JsYyFObJkbnxLgsXtqeFhp/J8BCWJlZi2965VbN/TEBv7qXMumJFWx3aQbg9JBIKz+3e9IanAyoJrf66b6AmUk3Hf6nfpoOr/csqDwDUVObzciGdhthqKlSCcd2BBszdiOAZ1o73M3A8KSVQ/7kmj0C6t15lHHt78FuSkXlQl66GbSdABy3FfDC3cl1zT5RGoMeMqUV0CQt7sHR5jqO8+OeuQzMhmyTruYEzkxtdpv4/2DXqlTv+91rcmPIrejwDo9bFi/uCE+WIztQynv1kCmNmYF0HUZm+/jdaWFE3iBK1dDE0C1O05zvCY+6P8JOz+24D+1lP9YhE08EQOufeNr3A6Uxwi4dBKCddKu/CfSKLrGd+67asYz8WFizzXnP/uMFzXWcwZJvsrIn5mNxAshPfTqBeNPPSbNV5fs1MSUQdCjEtaN9syE+XNzP47UXEFlBXAkwmVs9s/cmQHiaM9M8zp3BX/Vihrz22E0gLxxpaWZQdRo3f9WTl25uEwh7emIKTmBpc3EQUNOZuS+rVaTuPsEzVQI05z3Dk1biyshwn/s8lpElrOf15W7DrOs7AWbT31WpqFRRqRCP/ZzMkHlY0CFRjb5vQ7hnIlejZvbnVB1QjcTF3JrZULA4NTNBp7Ga+qI4jbsZZpB8MjnMrSGuNO1lnONRcVoAxnE+p07en1jL3+zTeYmP63A2zSqV1Jl5bEX/U/0RIHAyccqdmZMO071PJlfF3Zu8Tet7AvdnreIRoBegOs6s4inRV7XC3Urbc2Nmw61pvwNm0NYMcg27S/M1c2vG+aOxB4md1SjMPs7mdmkh9zDtowDd+4G4lufaCfCf7kfAZXsfJ9U9A9ru89zX32QkTy/RhRVgfRr3qeplWu91ZJ9OGsDAbAIWx/3WjXvtZNqmg6zLKdlWjWL3gaO9ySOLOM+JnqmNU4ydTMaojGxyZoB5epbITs3M4mmP9bejRAaivyuyTuAO92B46qZkojBpi1EUO47rQZjeqVFzImWq2ugf2LnvTRQd5G8Cs3MbRmcwO9wDv6kRkHxKYE/J6uk6sn7GBPexJTvFP/5v/sNf6Fn7j7Wm7d/+u3/vN2sFlBGlvVKxKHJKeOdRStPngLeemEB5T0oFowylVJTSY7W4otRMGcGpOIjbJNqQM6RUqVUOVca2pNHkp5aCd54aKyhNKBU1AkoKLSBL1aTxIKqNpuYAFJx3hBBFIoAi96Mhp4q1HucaSoEi1db0dXygSpVSRK9RaIYjWGKtmL3USq6VGBPeidlY1RpQxJAkmClD7ANOSwlszpmqKsygcmYIPaVUamVkoo0l4zFBqcRSKAlyqeAsOfWAQmlxmQeIMdEfdlICjQAe3jU4bdAiMCVj5oV5BQqrNY22pJKoCmqJeGWpuVC1lVJUEFC5CmuojuCfsZY0At+ligSEUoqqtJS8pkQt8rmckgQ3rVGqQSvR0WI0gYJKUoVaCs4ISzUN0i5jNBhNyMJcds4LmKnAeU8uorcrmodSbqsphJgpVWOtYwhRgKaUaZtWTKbGw3utIsxfckVrR8rCvlbGCSBbFeQqwINRwsqsBooip3EzrAWltZQJA0oJZKdKpeSE0WJqprUhpoR2wuLTaCoTO0+PJnPCdGNcH1optLaYqvDa0KdILQLATPpiQwpoq8k5YcdkiIyRQRkotVKVwiiFV4aSMkYbFIpcMk5buQ8stVpyqrhGS1m60jI+KeO9J+fMolsA0BhHCQcUmdZ7hjBgm5Y0gqKlFBSaiqx/pSUW2FF/2VorZcNUKoWUo7BPkX4eYpTPj/2Q0lj2XBkfHNW4HpuxdN4SUqCMbHetIMmKp44gLKO2pFJKEkcVlNYyHqYBpUEbSpbPWWuhKgH7rfSFVoaSK2iIKVN0odQCTpFSRmlNTRmrDUZL0ocsBnGOCaBWaC86rSEnjJGEh7KKakcZhgqrZkFOlT4ErG3wXjbFnLPEvyJguDICaGWEEV0qGC3yJ85qlE6zA31KCWMNznsqkihSogoNI1gVsiQ+prgB0pfWOwFKjaYUSWKUlOeYop2VOWo0nV+QSpJET86ie1sElK1VyZobx6BW6KzBKE1kHCelQCt8047M/4nxIhropVYBRasi5Ig2VhJStZBTYbFYkFLEO4vSiqFPaGXRamQ0WUdMSWKpMVQqseQRiGZO5HnnZB+qdTSHgpQLtYjqrLYWpbXEg1JxzpJLAQPGOqakTymgxvnXLVvZE4u8a60jDhHjRGoCqyhVkoLOWGrOKO3QWBRSGdGXgLYWXTVKK1SVPUFbTUoFNSYQtbE0xkCSezZKk6ZDmjdyENearBQhJawRiY0+BnzjSTFKApNKsoqYCrZpR4kNAUJrLVil0FZka1JOWCdtr6WSxzmOUVAKWkkyT5eKNwZypm0s2ohJm7PiMEyppJjwY7UItcozh7EyLknuSyu5jtKKmAZ808j4KJG+sMZhquxVKUWMs7LmSsZo2dut0WPyB/rDAecNOSdyHp8ntDCFZR0pSGM7VEVrRYgDnWtQYwIzmUIohaoUuVYhTNUxeaAUbeNpWg9ocoqjln0hpIwxLRrRnC8VYo78O3/53/hP/j8+xv3p65+A13/13/2j35RDiBr1/wSsM4Ow9Ca2T1xp/G402zlU0fKL96CnGsv0dRRAQkBU0TM1sc6HJHcoo46qxFidhVkz6cK5TQYjcgoocPs6l1mqMpbgjs/2aQTwdBY9OjWGbTnYaTmcJmFZqgL+qpf7GXXsqhm/SwmImboRNO0LJsq9xoUZD2rywUmCQMqXhd1aDfefGcES21ea2zT2jTwT67lkvICS79JpBBKVuv8ODW6bULVid1kAMyu6wSpV0tqRlqMu7agXm1sjmrkocmeEQdjLeE7Xnf6uCjO7VMdC7qQsXxUZO9tnqv6Gjq6EHaoVUEZVqG6838n13kgVgxqflewIrJlDQudCWjrSUpiXoGQcEQDJjZq79pAxvWhi6j5RvUHladxlLrqdlMnnVrQtxXCtEk/cCAbKPq5yRY+u8RPIL2DM+F6Q5xBGiSO0okys0ijfY/p7SQkYGV5KjYxnAbfSctTv3GXRZFWK6qzMZyXfbfaR0lnMPqJQM7tykn5IK4O/TQI6LayswwrudqBagw5ZpBMqMwg8aaGqXKneiJ5xEp3jqhVmBHdUuQdOdRDW8gwEVtCHSF436CEzPG5ACWBrggBc4YHH3wpIbEdQVid54FXjg+9USp47K0BzMyaexySF3UcBhN3IHL6N4xiaWfajei3PFNOUy/J9k66qANp1/LeaQTBVhMU4xR+0mtfByHihuhH9GcdelTprGdudgNZTv6pcx/8XnU6dpFJQD2luc2mlnTqI2XLxmtwJ2K0yYpqaKu5OwFgdR5b8CEiqAvsPnMjKXAdKa0hLM8YA+awOZQYkda5y/fF7p/mTFyKfNrXP7EUXOC3kGmYvv6OqrF+0tCstzPx/ouNdZumI6uQaKElOMM75ahT+ukdnGB45KRUHzEG0kSet7qlcfIoFqkL/QJI89lDnOQwCxE9xH+QaZawcUGVcA2OCzISCCSOo9thh+zKyehX+ZpB+GOOhlP3nEetR8zjLOKgxoWXEqKsvc8yoVuaizhUVi8Q0cz/OuZX9QdUpUaekv+L9GhseeRnHKKQ6MSkTADp349kvVjASp6d7rCPT1B6KJBysGXXHmfu/Wo05jISbfaQ60S4ffc0ldh/yHKvVNxKcbptGoF4Ji35psIcyV1bYg1SQlEZICnoEruV5QBJZ7iDVKGUCmOuU2NXf2CPUPAZhJd81nFhZs8M9GxekrXFtZqBdx0pcaOKU8IzjmhznrtuJXrbs6ZXcCVs4+1E/ehCAtTj59zRGuVHowvwskxaidTsB7bowsu3H2I4A16reyycMJ2Zuox2qPHdUxqoNZlC9akU40oQjmYuiYSu/57eS3KpWEZd6lqz59/+9X0zT9o81aPs3//v/4TeNs6RU0cmilaNYi7WefdhTLBighIQ2ihwDtRQGFUg6Y2Bm9ZSccV6Yn61rUMrSNB3DIEY5TeMhQc0yOaySCWRShSyU5qoZtf+MgE1DwAKttVJyPxzQrYj+p3AYzZQUWpmR6VUQVlGBailF2C3yMsLWSoWaelQRxlslUmoUUFRXUpTyUuMaSiojuKuJIdH4FqulzHmxFCOf/X5D07SknNDjTphSxpgGhadkjbUGrfQsqkwFbT3WKqzRmFygJAEBYoURFFAKunZJfxho2o4wJGwjB/ghDqgqhja5ZAoG6zQqZwGCS6FRBlc9GY1plhQUQxhGl2wBrlOBBoOqijhEvBHtVYqwcEstkEcdRKNHUAV8KwfoWiGXQoyBSkJ7MQWrqpJLwRuLBdKQabsGZzTaWIYQqGicNaQoupG6CntSV1Cl4I2wvxUV5xfkGFgerTjsBxlzpei6hYAYWmFGIF4ANnGI3+9uscbgx/G0psVWRasdHplXDoPSjpIFUDY14kzGGEUaMt51pJgFINcaVQpOG3L42ssAACAASURBVIYUqEbRlwl4jtRaqEWjsCMbzUkJc7cihiDjisbqdmTgGbSWbC5Wsy9pBCUkQLtxHExVo+FaJtcoxdwFPJoco+iPjmxO5wWUiUOl8StS6VE6onSikHHay0Oe0rMOZa2IXilJGGwYaoFSNcO4McaYaJt2xFeVlCLHiK5SbgyZkAJKj+ANSvq+NdQUgExVGWMnFve4VrTBGI22ArRXFMpKoqbqQlVi5lErJO2puWKVrBHrGwHTjSfGQtOtSEE0iIu2xMwMFDqjgAwqUtjivKUUTYwZZ5akXMi54hpLoaBtocQk7agiuUCpWGNx2pJiYNE0ZB1INREoUBt2/QDOYI0mZimZd8ax3e3QjRiWZUApSebElEaGqKFk0a42xpIq0icIy72MyZq2dMJCCZlFWygpgKqkaglRGJXKGNGDRUDoXKRcHoRxmFUl5oQ1aoyTMhYFOQTZqjDWUaqiAE5ZVKygCkonQgzCWjdj4gMxSVytFsRUSFHYpikpnG9IQyLlAeeszLdJA3WUQ1BZjaxZjbf/N3tv0mtZml7nPV+3u9Pcvo02+0xWZZLFYpGlEk3JEC3BsGQBlAEDGnimiX+CPeEfMWC5AQwBsmF4IBuyCVMdLRWbIqtjZVZmZRORGRE3bnua3XydB+8+J0oDA/TEoiWeSQAXce85Z/ffetd6liQxrHKg5Dj0faBqKkLbg9N0IRBJFLXDKnCFQekE1hBShystq05KJhUKE6EwFmIk9ANaQ0xy7azKgu5uwdTWo+CsMFpjVEAn4bwOo2N0SIG+G3DGorKi7WW4F+JATGtJeDhHVCIeG62YlSXRC6PXGo3KWri+xmCMJaLEOe+hdBUFFWAISeNUiYotWg1oBSZrGuPGyX1k4kpsBucyKWuqpiR0A85aceglhVGCyXDaUGvDxBRUyhKioqAkryP1dEpKkXbVomtHyIGsM0VlSdlToHFRYbVjNfSoykoxndWoPlBZR2x7mlo417O6IvYtzilxL5MgiTtd7m1GrhtJnN9yn1DkQfi/zlpiGAcXJOqmGgcTFpMtziiM6nE6oHQmxEB04LOX2WEOOAwxSlnm0HfUk1r4x5uBg5VhV2FL0hCxycgC1Bp0jsToOTk+IKpAVRi62NNbwyoJU68uKtquo6lKCutkIDOrmU4bysIBlhgiIRnQhtYPuKbGZkUkU1nL3/mP/6O/EG3/HXj9t//VP/ntjRNo46SSUhlNc+ElTmjHxYcVV43KUNwGKfYZ3ZU6MApFGrdK9DsW9Ks4rRkSw86rgiI2LkMtDMCN+KiyLGDz1vkLqdQMO0bE4EXYLuRsJ86VzaI3j2LvRogA+UzJbcTZV25icV1pWYBGtoK1GePdYWpJpQgmG6HYrqO4ZEtZiHeHUoZTvxjwO68i5yrJgnRTQLWJ2W4W5psIfDZ6K3KlUm3FABXHtfq4nfqDguJqwM8d7m4g1rIILm6Eq7gVNEYhDC3CNCiGmQgUZshb1qK76UfxUYQA20b83EnBVivc3DB5JbpsRMRUiDCy2VZhKs/nehQE7SqM20tvBTvTR8KsII3HWL/nCCMTsPw5odK2IhhlJ/sZI0LqplwNLa7BDfuxugqjK1RQCKZ/5U7dCPFkcdNVFx2pslICFLI4UBvLsCtlbakUV/JGCE1Okwp5PxGqJTpt+vSvCeSxHt2xMeNuOmLtRDSJ4ozcCJrZGcza0x9W2KWUVDE6n20btwOEjcBUvFiOuAQrInzjtoL6RoQxYwQ/G02YWswoyG45j4248exC4t+mT9sUo/Z5WzyYarcV6PToINsMFTbnn/aZ4qaXZOTK43fL7TkUK4279a+Oi1Fo24hNIk5Z/NyxHQ6NJXoqCgPYtFKYlN3PHV8ysdwOE1QU8VQlsCMzdyMwxdqO55XGrgb8TiGu3wx6iCLYBVFWYmM3GrycL42I/XblCRO3dUHHySvBWPtEtoZUW4n/IkOb2EiJX7cvAqztEm4h+7d8tpDnyMpi1gOxdsTaUH+5IlvhbJohkY1sB7sSrnZ2GhU2bnwRyrcu+iGiQyI7w7Dntte37tAJ/kOJi9MtvWzrzTkzJOxa+LDbIjMjv2tXo0N9xIqYTkoGGYc/qdDbYyrVG86pHBtuOTJHxgEEIx6jPSlwK0kGxFJvxTrbies/TF4NJjYDhlTKvSBrJQaVqRU29biPVRYR2u/Iua4yFFe9fKepI1Zm6ypVWcTkMLGUz5fbAUoqrYicbGLzehSCE8Oeo7waaE/LreAZy821SJFKOdc357y7Ffc2SlFctYRpgekiZkhboVn7hBtTEMtzYe9uyvDQ4m7WSUT4jYPTrSN+txyvrT+HW9ByH95cH/vDQu4fTtEeivAZao0JIpLaPhMmo3A48piTU8Ra0+871keGftdgB/ATGZj0+xYdoLocSM5gRwHbTwwmSElZqDWxVNQvvAxXx+sQCErBDNAeyP5dn2r0IGkRHeQ4AeHpKrkUjIK8MGD9VK67KolY6hvzSvRM0O1bXCuIhzRybJNThFqOfdtLud92oDCWkBmfWR9p3CrT75lRjB3dvFaeGfzoio31OAhldBz7zNV7sl2GHUV5I6zv6qslOmn6PUd15WkPHKGRe2p7pOgPBd8EUuDpWhHZqsuB2JjtedHvGv7eb33n//+i7f/wP/3D39YZTMqUOWNI+K4lpkBVFGPcVmNtQU5aRCwN/aZkyRgMehQaSpS1oCwMEaek6IoUZYiEFDJZC2Rxc2WjyHlscFaMzrIStJEfmEjOoxs3x1HkUGQ0JEPO4i5SMIpOUDhHTImUxR1lYkab0UUTg/zcCncuJ4VSDinSAZWzFJWQpdhJ5a0Lta5rEaiMuNn6IZC0xrlyRBcotHIS8S0MIQZKraSJfePyzUlKdUaunlYa33U4paXcxQTQmaqSxWXO4qh0hSOkxHQ2Y90N+EFwABKbrtFFjVLSPhmzuK6sNZRVQR97kobBD+QQaOoaUkA6yRMWcYAWhURIvW9HJ6wIMVpphujxIwIBRBzPORPHkh6nNcYarC0Q9gKQwRm33W/aGHEQRnF0R6W2SIKYI2VV4RgnhIwPuiPDsCgKUhgwlSGEgco5QmhRJCKQojjpTM6kEDDGisMxa8pSGuHjoDBWo1XGWM2QvGxvIBLBScN5SgltSxEqjQabyVrcgzmJ6zopRVIKjDiDy6IkeI9i4zBM4khTGVeUI0vUozfTSCvfI+k0OreEdaoVDL4TH6tsKKJK2NKRjWEYWlKKFEVNzmHk2GoYjxWtDdZYyqIgkEV0w4vQiMH7hLOVxLR9FDbjaInUVslxGyLaFiQjSJFMxllDzJnSOoqkxBlHpht6KaRTkLQE6o2R8ykhTuWkxqizMUStsM6SyQQi2upRsBUXo7i2wRhFjlFEHKUJYUBpQGmsVoSYEEpoEvdjhsJI6ZsOHvTG6QzOaIbYy34fnYAETQ4FRpeyP7Qh54C2YJ0lZUXO4sCNIIuzwpJUEEFPOXLKYzw8kzJyHGYwDvFap0hhFS5BbQu6foXTIqSGPmGNI8SOui7HgY+I78ZYUog4pzEkSMgwxTlM1qTkSSqCBUiEsLnuaKySGL9BxL2kokToC0vMgZyktC2mQNQyhbYJcSGnTMxBWMo5k4Ykaw8lx741AU0SBz8GZRxai3PYx0CKcr0NYSBF4acqldEZfBCHfVk15KTQRhzqKkFpK1RWuNIQo6dwWpyL4yCwrisW7RJTOVQKMA5kCmPo+8BOMyEPAcd4r0KTApA0lSvRWRyTMcvg0I3DnmldohhwSjjegv/IUhKnNUolchQHqnEWrMbHhNzxLFpbcpIhj9WK6CNFMaEuailhqywaT107bpcLqrqhHwJqRBUYbbBK4YdWhERl2J81zK2h0B7vu20KREoc5T2NMSIUlqWkLopRsE5ynhitGXwQx3sIaKewWuFipHYWTaRwhr5vGWchpODRQMiRuqrG7S/3KGsUk6JkVjWsl2uKwpKUxobRPR8zRVmRkwj4k0nN0cGck5MDDvb3SSGy7gZcY2m9J2thXOeUMCpT6cRkWvLo9Jjjw12aqqTrO/qux2pFtpEcNUM3giIs9Knj8dkh946PmdQNOSpSUOQgrHljHFbJNUhwOYGmdjy8f8rrD88piwKDLKBd0hAzOXZonaCAnHr2dmc8vn/OyazhZHeXui7p1x7fDVSpwEZFqcFkKwQKozg7O+Le6RHTumGncUzrkhC98PtHHnVMWdz+2qAVfyHa/jvy+q//u3/+29mqsUxL4uSulUb3fk8cd2hBCkjsVIs4cdMJ576QhZYO4GfjcHoUSEOlcSMndiuqBnHqbF1QiVeOttEpq4QqxmbxpJLwEHWSv/vz8dSNiCOMQlmwbwQ249MoPKjtwnLjktwshDcuGXjluNwwIlWUz7Jxp/a7VriuY+y4uH3Vmr0p1smjs20jyPn5iELYiMKDfK5NhDc7cWmGiRnjq7Jfhh0RRMUdy9al2p5VVBe9xJkLcUn5udu6sjaCaXKaWCmGmaG6FZTFphCsPypRKBH7vLjsUNLMno3GjViJjWicrQg0WyfW6KLUkVeFP7XZxvG3AjwQpm4rkG8E0E1bt4g1o8OwT/iZJTYbhqR+5cpTbIWe5DSulaHAphHd9HkrWqdCy3cqR9yGUoSZEyFnbBTfIADcMqKSGqPEo/N0FPCzVhR3YfsZ1M813esxQm02cfrG4OfFlp+4dcTWkoYLo/igfR7duGydqyoIssAuB3mGMAq76MGNx2DOo9PWbNmf2sdxu8qxzDiI0DGP+10zzMVFmMbtsHEK2rU4mVHiJI613YrzG2eg6dNWPDWDRNbjxMkwYy6C0sYxWFxLfHxbKjUKj8Arp2jIW8HG9CJaqZS3bsFUmK0jdbN95PuO/8aM6eQevXU8tmEUxzcldHI8Z2e2Lu48ulBTbTFLcS3nn0NdqMx4/EGqRley1aQx1r4ZMJkuQBZH8wbtsHETZyOikB7xKSqkUXy26CBojzhx9HuW8iaQajvG28VJbNvIsFdgu3Gb9+N5PSI4Nu5Vu/KEeUkq5LMZn7fX2u3vRnEBozV5RGuY0V26EW8339EM6ZWDdHMMjI7eOHXb6+TmerZhJWfzyo26caeqDHYp7ukwsdju1bm4QRfYVgYefm5GpIwehV4Y5uJWD7XGjrHyjahnfKbdt0yetuTCbAcbOmbCrKC46Vg+bNBRMCtZj0mRIPvO75ay30d0hN+xUhBmNKuTcfgyDrU29yMVx8JBJe9jV0Gc4IzFY4WWIYOVoUJ2MvgxfSJMLd2+G8U7S3aabt8y+2Kg37EUI095cy2KlcGt43jvUNy8XrA+M/iJxrVs93Eq1HY/pFKwE92BxU80sRJhNGuww1hwt5AUyCadEmtNqDTdnh7vX0rKJcfh3WYoa7tMf+Bevdd4PVkfadpTTX0pWCITRpzB6E71k/G9DLSHmtu3FN07PcvXEjlYqisRUKtrGXLpmEd3sIi93YHm8luB9m3Pes/g7oRLG6rxHpgYufPy/sUi8uzbhtUHPe2BImeDDuP+U+NQpVBy7ywVKim6A83VNwP1X7rm5bwiFJLwDqMDWmVBLWyeOe4eGS5/LTJ55xb9zpq7HY1aOVwrx1UqhR2+KfYEuP4FUB8syIcD6rxncahhsJge2Wa1HGs6Cns3G/VnFm3tn+U//Zt61bqk7ceHQmtRJmO0kgih95iksZjRdSbe4xJDgUSGk9FoH6lMyRAyQwiUZYm20lAfYhInZfKE7EEb/Mj5HIikDDZnGleRFAwp0IU74dBqhRpEMOp8hwWsEu6rVRZKKYMxWliTIDexVbumbiYoZUhJuKulsqgkN2uUCB1KG+HM+oCzNaQBbCYO4hE3TtiEMUZKW5B8RCtLirBOPcoK97fQGlcYKl3gh27Lgi0LEQ5TGH6Oy2el31oltFbUhUZlh1aWUqstx7Xruq1g6UOiLEeHqsgFaKsojcXkRDdIi3aKkJXB2obs1wSraaPwLF2MTEqH7zy+X5NSJBoj0WPv0UXBkCLaGaZul+Bl2ht9wBYFhZZI8ZAiw9ABYNWEysnCszQa7z05eVJW9ClgC0d2mdBnyA5nBISulZLp7shV1LmTeHgeWIWeuqiFOZtGwcLB4u6G6XxCOToFV6sFZekIGUKUuL1zBSpnCi3iqgoR6xKlNbRtZjavWa1WGGvxXY+xEp3OVv5GkRvqxqFyRKdIYSwhG9q2JdmM1sIs9V7Yqk5prHI0RUlOiarWdH1gSImYIn3bsbs3Y3G3whWy/YwxFDpTGijKhmU70PnA1XJF2TSkFJgWCq3F4epjoLRTQufphyWlm1KUliF0EDLWlYJfqGq890QyShvW3SAu6jjIIMSADx1xxDGkBD52ZCIWh9Oak719+r7FNjUXd9ekvBHXHMlnrJOYccqZpq5pygLrZPjx4uUV7eBHhypgMjltMBGCf7BWzgc/iHsujWzjHCNZpZFFPbrNU0IFBXqMTEWFQhNyxI1sVe1EhDajwE/ypGHAOENjDb2P8hClgrBnR6YrSMlYGiPzUQeapqFdLiCJcJiywxg3Il+yuCMN5CCFfbN5Q+g9KUUpCkPcmVpZtE4YC1aJw7BSGhUHHhwesew814slxaSmbVsmdU0cS8AAyrIiBmGzxhGRYqyhNCVFdvgskfJVO4D3Ui7oPUorSl0yrJfMJxNh1JaJpDR9L9xZO3KKu36gqiqIslAaYg8hUdc1cR3RaRyuuEw2maFvqVXN0HmscZSTCckLeztbAykycQ1JCY+ZpGkKjTOa89NjZjMp3muHnosXlzx78RxTVvgYqIyVa41SaGPY2204Oz+mcSWVLbl6ecmzy1sKFLURtrArFBiDi5rpzi5vPLjPZOrQKbDqB1arNV88+ZKoLAThpYecKE2BDYGz4wP29/exJRSF5fpqxdMXN9zdijMjhkge3aBVaVi2rVzLQmRST2gXK6wpJVVRKEgdIViMLsldRLlMQeDh8Sl1XaKUYrWOPHnyhGkhschA2Aq3rnBoFA/vn7I3bTiYzcgKLleXfO8HP6XzhtTLsCyRKRyUZUlZOu6dHbGz22CN4ebqlpu7FVc3a3RKpGGQRdLQgoZ6WrF7MOHh8Rm1tjhleHF5zRfPr3jeD7SxpWwmrFZrZpMpgY66KHj3rdeZ1A2VdbTrjidfPSNe3hH6AWsMzhUMwTN0LbP5hHffPGEybZjWFX0fODrap/zZ53z40Z9S7u0TlZQ4hn5ADR2PH5+zv7vD3myOto5+WHN4sMsXnz/j+uYOTcGQPWXlGGKiKjS/+PZbTEtDVTU444j3DM+ev+QnH36O14x8fHmwjH7gweOHvPn6PVIKFEVBXWrO9/f42cef8fLyloihmQqbOGjP7u6Mdx89pioMTWVJIRNDplCGr55fc3m9pA8B7QzGaV7eXPBr33ifk5M9nEo4U1C7XVbLnmld8JOPPqMLkKKmk4kCuTAMMf1/9aj3F69/w69uX1yL8Iopl5zaNjxvikN0ZLswGeYGP51ixsV5Nmpb+mJXoxvVsHUWhUZKfbYsyvXIQV2GLR90485xq7QtQdowCENjpJyrHt1EfSaMBTFunUjOvGpOj8IE7PeLLbPRdNJ0vmFygjjkNk3Xm5Ikeb3irW64s9qPzN1RbFAR6ot2jM4GwqzclsPYddo6WjecQInSK9zilWsOZFEsRW7C2xtmZhsLLm/jlgm64SaGxgiXb4yVD3MpZCtvBXOgQkZnEWGFFwh1jFtm4vrISsR1/Sp6veWTWoNbJrKB1WmB7fPIMhY2oJ9ZKTlbJuxikOOlLvEzKawZplIaJ+IhFLdpGwXX3SvO5cbxlTW4Ja/KpqZS5lY/H+gPHDgR5zbc5OZpR3tW0c9kPzUXQWL8XdqKn2Eijks/tVuxN5aafi7FQsuzkuYikArhCm4KsrKWuL2fStv4pqhmU+JVXqWtoywVr5iyoTGEStEejMLarhynbhmwfcDe9Sxfm9I87bZlarFUDBONnyrCxFG/yJR3idlPbgjzUkT18/l2v4i7UFx87qbD71bCZl2NmI7abEvo7Cps+bD1S79FD2xcx3YlJV555sa/PWxRAWFiufyawy0ysXTs/2TYCvKbzxIbSW1on2kPLN1bjlRWJAP7P4mUV35Eksj5Q2Z7DBdX4kRWIWGXwzhkSYI2iK9K1OAVM/eVI10cxWiF7oQ9umHL2uVAKl/JGRsOZ3voKLZlRJFUObSPJFvAiFfYvIcehANcXo3Ptusg/7e0KC/O01TL2p8kwvvq1ApmJUCxiK/OaSvbi4yI0z9XlPX8VyvqF5n5pz1+ZilfdnQn9bZNHpB93UucfoMniaV+td/XUfi/z1Y4YDioRbw2cr1xi8jyfjlGsUdn/SJQ3PrxfMoUV1Igttm2buRO9/uFsJdHNq32IizaxUB/WG2dppuh1oYtC9CdjJ9jfHToDqUI8Op9iCc92iXibcHsp5bD7w+CS1iL896OTNFk4Pax4+brAbfbszNrefrFLjs/Esf95l4VRqeibzSrkx0ufj0wOVjjTOTmaoK5dBz9gd3yRIeZwfYJdiyh1Fy+r/APe1zZkZuOqye77P2xYfZUhi2b4i+Q8q/qabflZLcnJeWV3zKB5RjV29QJo3t4mCoufgXSbkTpwMVlweEfKdYncp3bFF+lQtE1jmQUL74F5sGKbz34nJA1f/j5A47/YUV5HdBh45JP+Kk4hoeZ4voXE83ZkrrwvHy6w/qpY/bZyNEev7+KYtjpdg2r+4rmmy95OL/joFzxTz95g/oPGw5+FKkuPcOOpX4uJXK2F3791V9vOdm/483pLZ/f7XH7x0doL+J8KDWhFmyNWyVWpxb1m1d8fe+SX9x5ypNuj+evz/jx9DUe/uNBrtnj37W9FKRdfDty+OCGv332CY0e+Lzd58evH3P5RwdMP5drsbsIDDNDoaDf0Vz8Vsf9wwvuTW45LJesvlHyOx+9zcH/XpHbvMVuZCWO24tvwm985we00XFa3fH5fI/uFxwf/ctHzD8B7aHd15hBSs2WDzR7f+0rvja95b3pM/pkuTut+Z3mLS72Zsw/EdxBNlI8uvfjBT/5ezXvvf2U/XLNYbnkF5ov+d7yIb93+Jj17+wLtzhK0SjIUGHD4v+zvP5ci7bZByxSuHKrDSZKlNXHhNWG+U7DvXv36JYL1qsF7V0kek/Ihqg1hIguLEOMUBh0DFQGymlFYSoWqztiGFABjKnFXYmIcS4kYohMi4qh70ApXGFxWlx8PgqaQEpOJBKbszSYkzWTeQGpYBgGkg+suhaUoqoqfBjQVhxjplDoGGjmM2IMtL6F0FFN5rTdgClKgg+CvcyGyhmiDxzti2tqvV6T0KzXHb5bo4xmgpMCMaAoNPiIc5nTkxOqquLF85es/JI0OsbCKlAVFcPIsNRZM6sq6mrOwYEmppbVsuXmrhf9iYh1lhgzs1Icz6XR7DUNb94/oywUJic++uRjFJF1Eo5v9BGN2P1MiEzLgvsPH+EKhY89q9s1zy5esuwS2Tmi0lBqlLHoHMhdz9lrJxRFQVNoFncdV1c3XC/upFQrZqZFgVKK3ncoZ2iait35hJ35lJQC64XnybMvUS6x6j2lhpxachinxsbw2v1TZgczcvIM6xUvrq65vVvRTGtWizVOOwpVoMiQIq89usfDe+doxEV8fXvDzd2CL5+/IGmFUsJsVTpjjGLWTDm/f8D+/pxhWJJj4stnXzG0A0ZZAjKNzDFhC0UYBr72wTtMpxbygNIlt7cLnjx5Sd9lshLBL4ReGKRodpopJydzDnd3cdpw06/57Mlznr24QOlMYUpCGzA5EbvIwc6Mo7NTDqcNjVPkYY0pa37wgw9JTWble4rKkUn0XaBWBbWZErNmMpnCpGG5vKOwjpQcrUokBVELDkSNPNJspewqDD2zesK6b+W+6GpMAQFPVhlbGHTS3Ds54PT0mN15jVWa5eqas2XFzz75gnYQV3FWjhx6yImmqnn04IijvR20Eu7xvdNDPvr4YxbLTmLzo+t6GKLgK7oVSmXqoiQlL6V2KjMEKVCzWsqdUvDIJVPjaolD4wx1XRHbnjJb/F3HdGdC5wdMyBTWYLTBZ1CFJqnMaj1QVDVDSFiXhUGLwsdMSJGYoZo1QKJIGpsCZWmE261kCBRjjyahs0LFgIkwmza8+fghs7rCFYrb2wWffPIJL25bXFkJy7rLCDwiUpWG+8dn7O7sMJlaYlAQM3/yw4+JfSCZIGkEBd4nrHY4m9FZM5kdoFOk8wMxdQSTSTowRHBNJQ71lCmqKcu7W44PKg4Ozwkh0F52+A7K0hK6SNlU+BBwZUG2gZQ986aiqWqsTvhh4OryhmlVkwhkkyi1ww8dU+dohw5cQzGdcH52jMrw2c8+xujMkBOejhzAaktKim694pd+5Zs0pWV/PiNnGegdNBP8esViCPiUGYYeazVF4Tg52Of+/WNmtcOVBXE9sH//lOl0xnLRYbIhhygIHadRLvLo8Tknh3Oa2mEJ9DER+hlD1/L5F88x4/Evg7RANHByfsDB3i46D6AVx/sHFNrweT9w3a9RwaNdQUyKoDTKOoYQOTk6xGqFiZHBK1Ly+NBhjaAsYkhkY6lmJUd7R9w/2RfUirHEPc20MXz86Wc8v3jJZO+Avu+ZNROWd2senp/x2usPidFjy4JA4N7ua9zc9Pzsk88Y6EkKXFngB08m8uYHbzGfz2lKeSg5mc24ulrzp+2n+LYj5UQyiohDKXh87yFnx8eUdaJyBVopqr2KqCPPb19SVAW3iwW70zl935NDy1vvvMXOdMK0brBGcXiww/7BHk/+t9+BwoJVrH2PzlL+9s5br3Mwa3CVJCxmkwm6qLi7vsXfu88nLy/QVUWvAy5DUToeP7xHqROlcyhtmTU77M80frXg9uqWvo+4UlI9qe95eH6PR2d7WAR74pWm1CX3ynN++rOnRBS9liGF85nZgr2c9wAAIABJREFUtOHe2T670wYf5Hg72Dmjj/Dl1S3r6yWVndB3PcZY+r7j6OEhzaSkrksyA1llmknNeVkRg+Hl5R1lXbHMA0ZpytIwnxfs7TQMqzts9nifmMxLitkBP/npx3TeoyhwWsoTrdZEo/6fHs3+4vVv2WvjCnV3gclnS1JhJa4+lposzy1Xv5iwC015JfFA7SVaaIQeJs6e5asSom7X4KfgJ4rmud7yMTdFNMmJ03ZTiLI6cdvFS2g0oakwfd7iBkKlcOtXztp+LsJde6gBg1uJeNJciPg87Lrt725E6GQV7YFEFasbea9u11CPIoDp0hjXHGOePnP9lmHYyZQ3ChWguspM19Kc3p3UUr5VjjxKwNeay18w+Hli50NF8zJteZamE+eyWwoqITnF8tQw7CruXtfoQVFdZuafx1FMFtHT9InVeYnxmX5mWDxUfPVXLWoSsM4z/d2GrA11glwzcvrY8lJXx5rrX4qo2pNbi7sy7P9QMenHUrOxlGfz3VXKXL6viDXk/QHzvGT6mWLn0/TKSXVeixP1Vvoj2n3N6j70ZxGiwl0bjv5QotTNC4mrb3i/Kklpy7PvKNTJyGpfWaYfWaZPRTyrn0tRnZ++Kv/58q9MCL+8wNpE6TyfPdmlfmI4/l56VfAzFt4kp1idGK4/UBw+uGGxLslZYb7vKFZSTmO6V7HbjSv687+ZmRwtANA6sfhqxv4fGYpb9SrK244lTDPD4p7l9t3IwWuXzKuOn315yPy7FYffl+0SJ4VgMkqDHhKLtwru3sxUr99xb+eWkDUTN/DJP3od7Xeonreja1XKqYb9gm6/GuO9JTwomTz34kSMUL5sSaVF96MYO5qV1MiyNV1kdVZQX4atKJqKUiLwMZMqKa968c2S5Xs9D86/Ylr0fHq5z8++1nD0e476Kr4qxBqFkPWh5eWvJs7euKAwct4//WCX6e82TJ9FQQQggrJdeHEhr8VV5ufFv1ZqZxc9frfaDhp+vqRqwzkG6A+rsbxHzu31WUl5HcbIu9uKgChxStcXfitux8psGbR2HTFrL8VteyJc+pkllAo9GxEnlSHMnJwbI7JDBh+a5XnDi98I7B5fU1c9T57tsffPSw5+2IqDto2CH0FcuMNewYtfNvT3PLODFXeD5Q7Y/Z8LTF9sy+EA7HIgVvW2dG99aEgWytuf43qHTLloiZNiW0zlZ5b6yxWr93e4ecNiOqiuhVE67AhL2e+W25IsFS3aJxb3C/p9RSwLTAu7nwTas2ocZGmGHYtdJ9rzmvJy7LOZGl5+YMg6c/p/sR1G2XXcMnu1z5TXgRd/J3C8t+A3z36CT4av+h1+/OiEm/URk+eyL9wyEkvNMNVcv6eof/GKXzt8wWGxYhULDs8/4n85+DrtiwmxeIW22Qx+Xv5q4v23v+Cd2XP27Jon53tcDQ3fW7/D6b+Uv11feyl9G8sn1dsdf+Xhp9RGvtPJwx/w95tvk36/YufTsHUH6yGNBWgKuxi4/GBGLCG6gmIp6QW7EiE7jk5blaDfUdy9CW9+8AUT19NYz/o1x/cOH7D3z0oOvr+mOykpbj2r85Lm2cCzv1Tzwa9+xMqX7LqWG1/zn7z7R/yDb3+Hs39hccu4LRbbbOub3/A8OrnizflLnI689viC//XZ13j2f9zHrfWYhHhVBHbx7cj7733O/eaG1+oLGj3w+GuX/H3/bQ5+ZPBTy+TTJcvXZxQLcf++/Pd73jp5yQd7T9mza/7T4+/yB2eP+d0//Evb87S+lCLNVCiuf7Plb97/Kcdugc+Gb88/ZrLb89uvH/Hlvzfj3u92Uq54l0Z+reI3f/lPuF9d81r5gkJF/vLsQ7rDgv+y/dukL6cUCxmqxkJEz8tfifzn7/8z9s2KiOarYZfH1Usm7/X803/yLbSHYiH3WV8r1qeah+8/5W/s/YBnYYd9s+Tv7v8enwzH/BezB9RX8kxRLDKxUJR3kWdvRH5l5yVvNS/w2eCz4YPpFxy9teC/99+CjxuGqWbyPMgAYadk93TBXz/+EU/7Pfbsmq+GXX5p+jknj+/4H/mrlHfy7OAbPZaWKf7fPGr/uRZtvVJo5yiQRZbXCd0Kn+7s6JS9nQm7U8vk6B7L1R3r2xVPnn7JbRcEOKykcCfnjFOWeTnlwfkR+3sThqHndllyfX3N5d2aSCSiSX2gKStCipTKURaO/f09pqXm5u4W7+F6vYYkBSzRB4kV9gPWFuyUU/b3Zhwf7dL1d9zddnz1/Eti35JdIeIrGhUGtCuY6JLDoznHx/tkBpZ3K65urrm8usaYCshSJBYjVeVo1y33753z2qM9coTp9AFPPn3KZfAMayWLZh2ES5c1OQWKyvHeG69TOI1zhpODms+ePOGLi2u0Fd6uz4msLGVR062XHB3sMZvNODioWSxuuXYWFQM3yxY3lqhoDWvfkiOcHZ9ysD/neH8Pck+MkfPTQz7/8iuKaOlSIinFkKTYaTKvefDwhLPDXbQeIJbcFobCBD6/uGMxDKhksNoKz7FqOD4+4Xx/j7PzA7puxVV9R1XB3U+u6NYt1aQRR282kCUG//j+A5ppxWxS0K9bJm6gbxd8dX0nLeBlgVZZCtFsyYOTe+wc1tR1gcKQnaKsLOt2Sde1uNKSk2Y9lsmcHuxyfn5KUxmq0o2O64aygNt2RX99S1QOqzUJKe85PN1nf3+XQiWm8xl+HXCH56zuPuP65hZFCWZcSHl4dP6Q0/05NmfKZs6y66kOdui6jr5vuVq1GEpSErzCar3gjYdnHB3sM52URN/RWMO9exPW3SVdG0mpl5Z7I2VdJydyXkxKh1UJioacM48en/Plv/pj9iYNXYj4aKlsiVaaPgR2JxNms4bd3YLlquLudsnV7R1lNcWnhFIZo7KUHyU5lpUK2LJgPXSklAiFFS5ODhBAZ8uQE1VteO3NR1TWUTjBehi7SzPd4+p6zerZNYMKBKVpqkIYrvsz7j94gNUZ0oBSUM0qVu0JP/34U2zKYAwhBEKItG2LtgajFJWx1LMJL2+uUQrciKMYQsQ5h9IVfd9TlpaYBsrKMpvWJB+4vF2jVYmyiqETPEthrKAlNm7aHNnfP2BnVtMu7nh2fUvyUoqmtZQtlqUlhECBpu86Ht4/5WBvBgx8+OGn3LS9uPSjYFjqpkQDy5sr3vult9if71I4TYgtO/MJ5/cecHH9EzIWi0a5gMlWyhoHz4NH99EqUTgZSvkhcXw8pftiKQmEFNDOop0i4lkvW957+x2OjuaklLi4uCA86wlxAOTmE704BnvvadcLysJyeHxKM63RSQqmXl7cEH2kaRpJNgyeHBMxC67m5HifaVVRFpZnzy9QRtOFiDIGcsb7CMqRtKKoC4wxvP3mIybWUmlYPmu46lq0FoY1GmISR/dsUnN0uAdpQJuIhOoS01nNfD5l8eyCUsuiUuVMt1yx8+brHB2eMPR32KxxtaMLidPzY/IPfkzbebQTpuzQdTw8Oube/WNMiiQCQ/bU9ZRQ1LjKgQko7Ul5oCymdF3PzmzO/fN79O1KXLBZADEnJ0d8/uWXqHXAmYLWR0xZ0IaOqnKUzjCdFewUU26B57cLQh8pixo/rOV+EzPNRNHdXvP2r/wii/UN07KksIYwJE4Oj/jRTz7EWkvftwD0nccYw+H+AVYrrDJYDTppZpVjp26oyhLtHR0DSimMywxDYG++I5if0cXuiewczjm4mnO9uKHvWopiQqWVlGEZRdWUWBuE5R6S3PuLgr2y4nK5YlYVZN9iVKKaNgzRM9+bk/oWbUti9LjKYAuFx7Ncd2hnMYXFqYpVGFDVLiAoFQyEoWO2N+WjT1psURKDoqkLOt+incXVM1T2DGiqsiLEgaQM9d4+rf8UVVYMQVzcd7eXNPP3WUeNNQmtDAoZEAUTKGpDuxioCkcOHtC0Xc/+0TFrv4bUgS5ZLO4IaJwCGyNR9wR6MoZZXWENaJUJ7YKiKFC6oB86fIDAgC0MPoLuM6bW1GUDPrNertEKiolj1UpZ3vpWvmdTFgwhETOgEv2gtw77v3j92/+ya3Gb+dko9KwGstUMu46LDyztg8D+vRve2Lvko8sjrm4aZt+rcE9eia+b2H1oDIv7BS9/OTN9dEu7Lmm/rJh8odn7aRRn2thW3R46bJcZ5ho/hcUjQ3eUmDzVmBZ2PhXhZ9PgvHH+hlKzuqdZPYrcf/MrXi4mLJ5P2P2B3rpaN7HOrEW8XB8Zbt8E/eaSnBWXL2smnzl2PpFFPYzR5pSFUXcdef5NR/3tlxRJ89bBBd/9/hugLW5lcOu0dShuXFl+onn5NzrqZmCn6vHvaF5+95CT3984BvOIUJB26vI2cvdmJh33fP3Rl/z04pDbL6Zk7Zh/HrZ8xuQ09UsPGV5+3dK+NvD+m08AWPmCn71fcfhdjZ9qyusw8jElwrs+0lx/I/CN9z7dims/eH7GRT2D33dMv5TUQ6gNxY1nfVZy9Y5l8rUr/u4b3+Vn7RH/bOc1buYzpl9pymtPv+e223nzuvo1z2Sv5RcOX/B8PeNyf8L1csbRnwT0iK7YsH6HmebFNzX2fMXhzhKjMvFQ8Xw2p7qqpShmbHGvLgZSoXn5QUn79ZaHe3fcm9yy61r+pPA8meyxflqycxfEIViJyzNouHsjc/jghv16zTv7L7jqG7761sD6co/5Z2ErBm9aw7/8TsX7737C3HU8rK/43s19+umKz2/vUSwcOx8tt87LbB3V856nv6E5fuOS9/afswoFN3sVVx8YqquC6kaEVUYGbtZw+06mfrTg4d4180KSgSkr0q/cob9Xc/fGhPJ2FNv2ijHmnLh7aGmPFe39QPHS0XylmP90QZiXmJWX7VtacYQ6MxbbyfvWl0F4qKMwKGVNI6d64Rl2a/K3b3k8W3JUL9lxHVPXc7k74dln92kuxKlpWk93XAuXeF/xS+9/wk6xSTcm7k1u+Rfvv0N9ORYsAaaNmNZTXLFlcXa7hn5Xs/eRmFnipJDP54VBKkVlPX6n3DasL0+lfGj3p/0WZ+KWr1zAbiHbINbiJL153bE+h+JWcfh9ud5srgsqZvxuiRmd1W4RePENR/tYBLzT33HMPm0FHeBFHOsPa7KB6tmaz//DKQ8evuSwXnI71Nw/vebJ+8fs/1j+thQTiiN7w6eefOOSXZPYr9fsl2uu+oZP3plS3YhDc4uJcHI9sXc9n/2tHYY3W3JUVB9V2N5ukwqpKLdibXHrKV+KA/vuDUW/lySZWzn2PgyYPtEfVSJqL/IWgZCt4+Zd8IceNx0wP56QDVJ6OLp+7VreT/sNY1fx9K+B3V+xM2u5uThk9xO/deZurqNmHVjfq/jLr30IQKmClKCpxHv7z/m9syMmz9kmEbKRZEF/Br/18Ed81e9wUCw5IvNpe8DfevMH/Iv0q9RXMoizq4hd9Dz7zg5f//onNHagjQVtLHi7ecZ1MeEPqne29w8Vhb1srz2L+yX/2bv/iif9Hm83z3Aqsk4Fv/rmp/zp779LNjIsKK/k2lO+bPH7DX4m5+D6LNF8pTn4ofCA/XwU+q3s9/bAUN5mfv03fsgPXp5xVC85q2658Q3fefMTPv5H75IqKR4EERdToVm+HtgpOnaKjoNiSW0G/vLsQ/7B+S/Tz2uSgfKW7fDALQLv3HvOfrmm1IGDYsmLYc6vH33Mf/P4hNnnhuJSCkSTc+P1LvOwueagWLJjWnw2NKanqD13j0p2P/a09yYiiheKbseQ14pvH/6MdSxoTM9NbDgu7gR91CaaL3vBL5SSNEmXJW5k/LxWvgDgMk55/eiSry5m20Fke2Ao7xK9VRwVC5yKPBkOeKf6isswpcsFxztLwvVEUhm3IojXn91Q7NX8aHnOjmtp9IDTkX2z5NpOCBNFdZ3oR34+QHmd+Q9O/pQ/WT+QHxTwu6t3ufYTchUJpZVBdC9O8eWZRVU9tfFch4Z75TVORT7tDrkYZoTBEEuwrQw6o1N0+yKp/nB5j1IH3m++4A9Xj7lfXPHj5SnZipPXrfLWAV7eyYD1z/r6cy3amkIJ9y5nCu+pC4k6zauCs+M9dmeOqsrAwMGkYlJoBr/L7adfyGIJJyVBCBLg8HSfRw9PMSnApGR/1jCdFKx+8gkhDRALXClFPUopKqO59+CUo4NDirTg7NAwRMv3vv9j6rJg2QuEWSlNDhkfBt7+xpsc7M0I6Ya93Tmzica5XWLyrAfDYGTRa1EMXeLBu+c8OD/A6YA2BWG3ZtIYyJHr2yUpRqyRWHdMnt29Ga+/cZ+mDkzLgjwEHj2ccXRo+P0/vMCWFQnLEIWhmqPi0dkZjx8+pF/fUVWOFAYmpWW96nh2fYsylXBFrWXRrrAm8dqbjzB46kZjdMO8duztTvnu73+PaGsAQszEmGnKCQ/uP+LsbB/Vd+Qo5T9FabBW8Qff+zE7uwfchowqCobccXx8nwfnZ1RlQqGwyjJpKkoLT5+/pAhyER9SxFZTZntzjk+PeO/d17l4+YzGadTejEnteP685vq2x3ce5SxKBbKy2LLk9dfeZGhvKLTnaHefZb3CqVNuFnesV4lskZI0o2mHljfeOcMpYRNCJFUFMz0nZs33/vhHVE3FkkQOEq0/OTzi/tm5OOPi6BLcq9k/3OHpxQU3OmBKiceHZJjVU05PjznYrVBeuKhxF6qq4KztWK5a+iD8Yq8yKfZMdhvqSSWOaWWZTwtC7tnbb1iuLJ03BKXJQaONpmmmvPcLX8cYj+9XVHWN60v25zP6VRBxpqhIOoFyrO9WPDg7ZTaxWBI5erQu8MkTQs3pyRHPn19gbIXWIiqiBZNx+t5bzCY1Rnvmk5KqKHl+dYfLmaAgpiAlZBG0sRgi2mT2d2oKM+Hy9o7FaolVVgqqtBSPFVXJ9fKON157zOLyEpMDIUZcWYKPnN875Ysvn2FsSVU5fOhZL1fs7z6mKB0qRcCJAJQTD89O+eH3f0xRTWgTI4vYbZ2ru7M5B4c7rFcLDmYzXl5d4pyItJt2+jxy5qy1+GHNtC7Z36nYncw4nFc8/ewF65TIyuOqin61Eh5tEkF2vV7z7q9/i/7ujvJwlz/98P/k4PBYOL0pk3OCoFAx4ttITp6TY+FVxnXi3ukp8elXhGTxOQjbSyliHGiahsm8pqocBE+hDMpaHj14yB9894+YV3PWucepkpgia+959PA+QWXmTU1hBS1irKOaVnSxxZZTCqtJUcr8jLHs7s3x2ZNVJKaet95+zIcf/WNcOSNnhTJajl3vMc4IFzlFDvZ2KLU4n+ePX2Pd/ZjL6xvqyZyu74RPisB3fd/x4PweKQZiWnN4fMCLqyvWa09ZlnSrgdJaUIGuazFFSVFU+H5BcjN8gtPjE64++5zBD5SNQWUpmiorh449635BVQpX18eELWuWqw7lNF32kBLOSZlhSpF+veTm6gWzaU1MgdJJhP/67hp0pk+eKpZYDBWKUhlW17cc7DY4I5zkmBNKj4NEW9ADuhL0jlaKsiy5Wy2JyWNDLUwlHehDS105VgvDmkxRanFaaz0WVUaIgfm04uYq4oeOnCLteqCua8gGZyB2giP56vkzDvcaUJHWR1LUvLi4lESDcYBBqYhKEa3hennLfr+DJVLYBpUzd4trVqs1w6BRJuOMw4eOoiy5vr3h+u6W0/1DnE6UruBu3TGEjqvFki5qbDnj/2bvTZosydLzvOdM7n6nmDKzMiuzKmvIRjd6BhqNQYBAgIRklImmCSIXMtFkMg07LfUDesX/oA1FmlFGCUZSC1AwmEkkCDXRQAPd6OqhuoauyjnmiBt39OlMWnwekdBK0IakBNxdVsUd/Pjx437e7/2eN5LBB0IfWK/XzOdzpjsOCofVjnbb0HSebReEWz+gSUzhWDctbRe4WlyyO6rwvqfTiu22ppqMuLpcYKoJxiicCvi+Y7NacLUqGVWWcVFiUgZlOJlfsCVQuDEhJrabDRZPCobVaoFOib29PZrtlkRkU0dW2xZfJFypsCGRFbx27w4nJyeM9BtUI0NRgNVQNx2Lqy2h9+Q+YJwE8AValEmczk8otGY6GeF7KXKplGg3G3FbKY02jm3fkpJl1dRM6pqdypE7T9AeoyEEeDk/p1eJpCLltKLPkegTdc7ERc3epKDOgRg7vPcUhWXTRKwr6futrHE6oa100vzl6y/GKw3uFRFuHd2BOP3WDwz9F2s+//oZb0/nANx/Y8nz/X2+V7/D9KUkJcdKQhtBNverR/D1n/sJEyst9Ms3Kt678yajC2m5tyTC2DI694N70XH11cjbj065M9pw/oUpi3pE80/24cAwOb0Wb0UUcXVi8usX/OaDD7jsp9y6t+F7d97kh9UbaO8YzRNuE2XzPZJ2/sufS3z9q59wu9xS6MDyrRF/cPAuSybMniX0sKlXA5dy9dAy+bcueGfvkp/ZeYnPhsnP9nz67m3af3iPMIiDxTaRnIjdp7+o+K+/8i1edvu8MzqnjiXv77/Ox1ef4/YPW67Tj8LIMDnuiZXhK7/wKWPr+anJGWPb079m+PDNu+R/MEaLhiT8yC7S3S7RX1vyXz76Hk7Fmw33d0cbvl084tH/FFm/WTJ70YECu/YsPuf49a9+yNujS5yK3HZrvjI75HcnX6D9/r0bjMC1G3F719B+tuUff/Xv8nfnv8IXJ4foB5nn+/s8++hddhhEfslqEWddqfgvvv6HHLV73K8W/Of3XvBHm0f889FP0by8RXnBjYMyVppqHvjyLz/nVllzpxBXa50K+luW3/VfYPJbhm7XMD6R1v1sFKvPBf7mF95jbGRO1bHga7df8Bv3PuIf/eDX2XmmSIVsaU0T6V937P/0Jf/ugw9xOrJvtyzDmNPpDr/7pV3G51rYxFrQEyokuluGd6cXALxWrPjFg6dchTHPHh6wuRhTLkWw1Elaj/3U8pu//m2mpuO8n/H26FLm1/3Ab2++ztv/NN3MKYDqrOFzPzPn5w+esW+3cv50z5nfYdmNOPrSDrd+7EmFwhtpIQYo5i2Lvz5B3W0obaS4F1jOdrn1viA5dBvRfRCOa1JDiJowFpfvOMIEdh9HxofxpsX7OngszArGn1zxd778O3xz/TmmpuMqjG/Oy7Mv15g/FrG13xtj60hxtmXzsODeaC3dk8COFQHoF37mJ5z+9iP81FDOvbAzZ+WAN1CsHjpW70I5h8Ujx/7HPWFicYtOwtqaeMOqjpXG1pHta4bNWxAedKweldx+LzO6SDdIk2IZ/m8u3eJ0Tfvf9fTbAj3pGf2vG7afvyPt605j0quOgOv2/vZzLXu7NZ23XHx1hvEVtknY+rrdXda27s4Yfavj/nQJwJ3RBqsS97+2ZPH377N5Y8roMtwE5pWLjuNfnRG6grdeO+XeaM1JM2PqOvwsUyykzT7MikFMFSxC+84U00JOitwb7v7aIeXvzgjT4kZ8vv79wnAW93D3DlQTuUbCXWguZ+z9pKE7KCgW4YZZDAWmDtz52VP6YOmCYfPIED4qqHpxMxdX4pAHKBaeVGiaiUM3ihg127ag+UJg7zG4taffdTdrXL9bkYzi0+Vt7k+XdNly3s/YczU/uHpA1iIOgwik10GQemP5l2eP+MrBEcsw4u3qgttFyTdPHmEAtwr0e06cuXsWP4WnV/v8lQePuVOsSSiWYYwmY2spKF4zyLXPA7dd8UdX77D1BXu2xmnpAj6tZ/S74C80o8uAnzlMF0nODHxzmZfuXo06nN7MnWIujng5llddJb//0U/xpbePAPh0c5s2Oj745AF3tRTiru9pefj36IXlg4d3mRQ9r5dLnIr8i9XnRQTdpgETJPgdPzWMf7Lko8O7/LWf+pg7xZp3yjO+ufwcH63vMnruqBaeWF0HUsp9rTxz/MHRO/zUrXOYwb7d8qS5Q7esuHUhoXHXfO9YaqbHnqul4/dOPssv3nnKWb/D03SbDxb36HcUu4874fKWr9BA5bnjmyePeLhzRTezTI0Udj746A1ev0j4iXBwx+eBMNLoAN++fJvSBP7qnY/4UfMGXbK8t3iDl6f7vFmLIzdW4rJefOUW+gPNx7t3eDBd8lq54Vax4Yf1m3zr7B1MK3ihOHDkBfOj+MfPvsreqOXL+0ec9TtMTSfnfiWFojASZ/z4PKCjxhyXfPvgIV+5c4zPhqUfMTE9S19J0Xqb0AG62RAq2CWWizF/4N/hK68f8cP6Tbah5NTvcq9a8eQsE0tBKlyvccm9QjH9eV76//lP/vW9TNLkLuK8YmrHVDhUtiQ0d+/dY7azgzYGoy3WOkal4d7rt1gtL3GFJiRPBjrf45Lh/u07zKqCotAYp3Gl5Y3XH7Az2UVHhU4QOgkm2tYtRTXi1q19piOLKwzj/V0O7uxSTZwE91iIMeJTZLJ/l6wrtM3iZtMJky2TaszBbB9nLHYI1slD0nf0LZUrmEymaGtRxkhQWGmYzCppyTBJpPVSxJOd2R5KKXanu1jt0FpTVCN0aSgnlm2zJiLhQ1hDipE7ewc45xjNxoTk6bPHjC27OxOmQyuzMprCVWzbLSjFdDaiqgp812OtZTIbU45KylEF8TqUSWOcQdkMNmBdRpeQtSfkhtnBhPHuDFdWdCEKEzS22JDYn01FaJpMUcrR9wntRrjRGG0MSin6voecCCpSdy3j2ZT5aikJ3VaE5p3pLsZq0hA6BpqkJNCoKsc4V1KMKlTp2MYWCo2uioGRrEkhi3POWZwTZm/WmS729KEjmYwqNOV4RJ8iXfDkECldQY6SuN62DX3y9DoRjCJmRdsKdiMnRUqQs8L3LePCYXKGXhhOfZZ2YuccffBklYBEHxLkTFGV9CpSZ0+wYEo38CYdhS1RSNBM3zVYnUg+MalGLBYrfBNQyULU6KgJTQKvIA3MZxQ+9kQVqesN682CkDxJJbpWkua1VlytF/jkSVrE58IqXKHJOhKztPoCNF3k4mqFKcfUQL4Ew136AAAgAElEQVQunKQ08EQzwYPF4ruey8tLts2GUlts32N8j/Idvu+IPqCV4uzklNB7yAmLQinFer1ktVmiSDijaeo1KSUm1ZjNeoH3HSkFjJGHuOg9yQdyGJhqUcY4xkhKkRBENEohMpmNiDkM7vaMtQ6lFFkZsgJthXudlKaoxlhXstnUrDc1aI0zCqUzTddAoYk6o50i43HO8umnTzDlmKb1VNMd6hDJpiCbgoBCmQJlLUlblHGcXS7E4Zw1p2eXnF+t8FnQE8YZvJeHs6ZpWC4X9F1D29US1hUCy8WCsizp+5asMz739KrD28h8uSCGTF3XbDctIUDwmfPTC0pdCQO1V1gt12PoPetNzWq5IfhMWUx5/uyY2fS2CIhKDfNRCX8zBIpJRYyeptmSkohE54sLUjbYssCHGm0VxipyDJTKcPfWbZ4+eUzXNWQMZxdXpCghkt57bAlKR/o+4OyEspiQfGK1WFNZizZwtphjtISfxejRg7vYYLDZslxtaFpP7AImafDQbHrqdYujwFLggyZiqEYz5ssljQ9s2p42R7Z9zdZ3zBdXhBxkfdAeXSrKUcF2s2G9XtN0DXkIIlxu1ixWa/ou0nlF3wfSEHCzN57SrbfU2y2jsqLpG2L0KN3TbFesNkvaFFBaE0KQMMAsoVYKw9VixcePn7FoGqyCSTViUo2IAYJPRDJ99nQ6crw4Z7FtWG0busazrVsWq6WEV6ohPCSDJtF1HYvFgsXqCjMqWXcNm67harFisVwSgkdrTcrCQlVKcefOHa7mS45OT1jVDcttTVFVLJZr1tsalR0kjRrCwnQG7z11XZNzZts0LJZLNpsNm7omYtj6jqBBF47eR4wreHl8RB0yi7ohaMX8asVi0zCfL5lVU0LTY2Km3fTEPjO/XBCzhmJM6xVt47m6XLKZ13TLjhAizloqV1C5ihgzR0cnJK1Z1TVBZZQ1LNYbzuZXgMaaAmVLQoauTyyutpycXRJjom1blsslm3rLydk5iUw5rcApIpmM5mq55SefPqOPgabuaLrAumm4WFyxXK8pRpUUbHOicBUxJQ6PjmiDp+mkUyH7RIqKk/mK7bYjKUO2JX32qCGM8OxiQTaOGCWV0aqK+eWKw5dn5AC+C5gs+CbnnKzXKvwrfNr7y9e/zlccWuKFD2hvGKs6wF999BN+/uAZhQ5MTMe+rXk0veCznz1i8rLFj5S07EYolkHa/r604uf3nvJ6uWRiem6VNf/+F95n+7oWHuvQmo6C4mRNP9Hcf/uCL+6dcLvc8pmdC37l/hP6HUW1jISxRvci1tZ35L5eGNlo79iGqWn50s4Rbz88J4wUoRQnElkCSEwXYeb54uyYQgcRPJOhKAPtwSvkwvVLRajvKYxO/NrBx9x2a8amk420ifQz2XCagXV7/Z7xZ5bcdUtxevkJp/0OE9tT38/Ud4thg67wE01xvALgq7uHPJqcc97POChqvrx7xP60ppuJ/SYPOcWpNDeOnOvfk1Bc+Qm/tPeY0X4j7rAh5CY5CVOKb7Z8YXrE1yePcTpw2O8zNh17VUOy8vnXbFTTRqp5Ynev5reWX2dqOmZaOi9+af8JyTEIH6/Ywclpul3Fa27F/WqBJvPd7dsAOCO8Y7QSbnElDMowNoRsCFnzvDnguNulTxZNppr02G2kWMvGtt91Iph4xbP6gKN2j6N2j4Ufsw0lT5tbspMd2KkSbtXTHShGTkQhTeaw2ychji6z1Tfn+5rlGWYOWys+Xr3G0o+Y6ZZNLNm1DeNxJ8ntQ4v0dUhVfcfwrbN3OO9nWC1jWOjA0o9u5pJpxUFqtwHVB14ud/mT+VtchQl1LHna3uao3aO0gZ1n4rC9nlN+KkKIiCiKopA1ebMcMXsswmx53qJiIlXCmmUQAwThIIzh3ceR0akIcGEItspaYepeHLnW8PeOf4WFHwOwPwhZH168Rj6pbkL3ynkvvN17U8oLzUU3oUuWHdvgdGQbS/pobgQJUw/j34nT17SR6XEQoeOWIFZ0Lw7YOB6yYpLsFZIbWsBDJkwUscyoi4LRmcy9MJY16rpl/zp4jwxpUuL+YAftEs2yIh5MZZyGtvpr5/eroC1F9XHFaj2iax37H8LODy9vvj8VIh6D4Cg4qjhvphxtdrEq0SfDj05fx+8KR/aa9WrXPWbbs/skErzh2XKfDxZ3WfYjlt2InU+0zO+hMBErCcUybWR82DI+zuTO4KY9L797n/qNsTiFB1ZzNgrdRXQbCbMC3UdYFMSo2Rm3+E9mwoOuZCyv3b9kcbhefnnM5R/fZbWtSElTfVzdcECvERFZyzUSJlZcsRFGJ5qdWYO1kZ2PrQQ+VeYGexNLwY+ESvHyZJ8X6z2WYcRkYOkcr2eUVzKnwsjczPlu1zB9rjlbTnl/cY/n232+v36T95evc/r8ANtm/I7FbYIgbGaG6jKzPp3yeHOLhKKOBd+6eJf/8/wz2BqKpceuvaBAnBQNinXmxWKPd2eXfLK9wzKMcCpyupwxPsmUS5kfpo0DvzzdhJLNniUmvzdh+kIKMs3dgvZuOXDThVHrtoliHZm8X/Hh0V3eP7/HaT3j+dU+1aGjXKUbBMh1GJtb9kxfZk4P95m4nu/OH/Le4g2+efKI8aHBtplkuVkbsob1l+9gXlT8sw8/x7fnb/N7i89zv1rw4dFdxqevGMnZvMIDmUaxXIwJSfPD5X3++cVP86P567gL4f9Wp81Q3JCQxuQUd76XOV3M+NbZOyzCmG8evcsnx3fYfSJoB93K35VXHtNFZs8ynRdd4NPtHZ40d/g/Tj9PcWEo1lHO43hw3O9otIcn79+ni5Y/nL/LVRjjdOTj49eoPhZ8SajUcE6yjO3LzMkHr7HxJc/rff7l+SN+cPWAkw9eQ0Xop0P3TC/O2d3HDf3v32bRVDze3OaT7R2+s3jI77z4IuNjjZ+ADjK+/dRguszt72eW6zFP1wf0yeKToUuWP/noHaoLwTGpJIXb66JO9XFFSoo6iIi/52r+99Of5rc/+LIUR5tMLAd81PhVQe/P+zLf+MY3/t+941/h63/57d/5RspI8IxSBARYXBUFB/v7OJtROaCUIuVMR8THzOWypus9KRuMLtAYppMJe7tTCifJ2ylKEJAPHT96/2NcUaGyPMFoZUhKMR2PeHj/FuSWbJKEE6XEi6NTep+xVOgcMWiiz7hCc/v2FOMUhTX0vscHz3yz5WpVs94Kp1BygeRB5uBgD2tB6YS1it4Hcla8fHlMs+lISdJtc0yEmMgZ7r9+jxRbtIoYoyRsKGmePD/BVWOakMjaoLTGOsdkMkaphE4JciCHjsV6xdnFFW3b4WPGYYSxazTZNzy49xq+b6hGFTEEyImLywUvjk8w2kLMKKMx2tFsaz7/mUf4viMTUTqSVeZifiWb+3mDjxmUJnUenTSjyZhRYejbhpR7Yg5s6iXLbcvL43OyEedvVhqFIUTP7myKshGlNDFm2j6zWF9ydnFO5xns/4rgM9t1w8HOPrt7wiQ2JpHR1L7h+Oyck6NznHPorMgqyzlPiaoqKaxFKRH92+CZX8w5O5/TtAEFQwiWoW47RqMSXYA1Cq0UhEBf16yuNjw7PkKXFSElUsxorQldx3RcMqocmAxZRLOm9zx+/BiNYdu3OGMxWtH5jsI4xuORIClUoouerqs5v1xwdDanDwplHV5B4Sw5R8alxZqMMgl0pqm3nC4vOT47pwdSMvQBnC2Hdnlw1gpOIUS8b+nbxNHxGS8OT7BlQczCxNh0DYGMKR3z9ZaQICXFs8dPCX2ky5qYNForEkmCKdGQM84ocevGRFCalDLaabAabS3OOGEElZa+7ZgWFVYpbGEIBELquJhf8fHjp0RdkpTFocmpQ+tIiIGsFFVhSTnStS2L5YpnL17Q9IEuZ9AWazRVUQ3CnibnRNt31K2n6df4lIFSxMgkD2HlSEKjmlaQAeumpd70zBc1deNFBEcLQ1srrLGEEEX0jRHtNL7ruDg74vjsgj5mtC3JOeKcBSwxeRIJVMJYQ900bLYtp6dnNH2kLEtCzIJrQOac9z2uKGmbBm0txhq6PjBfNZxfXrHuwyDgSKhgTglnHaQgLtchKK1ZNxwennG5WoNx9FnBcAwGJczuqqL1Hav5mrPzE05OLuRaIaGcJXoR8WzpyEoRQ48xBcurFV3dcnG14eTsnNr3pHgtPGpSnyncmEgi5kzdNcxXS84v1lyuVrShIytx/CplUIgICxB8c4NYeHn8kueHZyzqGpQhKpmzKSt5rzbgCnrfE2JGY+i8Z75c8uLojOWmpvGJlCwZg9GaROZyuSSrzGQ2BZVZrBvm8yuOjy9ZbXtyNvggYmIg00ZPlyLKOhSaer1lW3tOzy44Or7AKEflKrQ25BwwwwYixCBhb64gZM3p2ZznTw5ZbXqirkhBGMk6Q1KagEKrgr4LNG1PCnJMMQgfyBpk7bfCS7bGsV5vCT5S1x1XV0vOzi5YbWr6pMjaopWl0A5QFFVJ0zZs6pbNpqULcLXc8vLZKYt1jykqQo6kKI5fa0forLiazymcJWRN3XScHp7x4uUxfZ8EUxEDnkQxtuRsaJuO9XbDtg0sVy3rxvPkyUuWm5ZyNCakREwy78ejCrKi71vaZkNWhvPzOYfHZ7w4PiFmi9YFpTYiAljHaDrFd57Nest23ZI8LFZbjk7PmS9rUtaQNCYj6CPjyDi60LGpO0zhWKwbzi7mvDy84OJiAdbRhZ6okOs8C4e/71su5yu6PrLd1Dx++pzFYk0fhCYdciYpjTGaaTkitZHtqmO5adn0gZOTSw6Pz9h2PRFFiLIpTRkqW2BcwXK5oYmZTd1Sb1s+fnzE+dWGpsmkJCGNOUnRzVhF3fTUdYP3gbr3LBdrXh6dcblcEXNJ2/V0yeMHnq0DrNP8zf/wP/hzJdr+5ev/26+/9w//4BsqygbHhIwbwqS6Xc3VO7BbdugBdbRNJS+bfRbdmPr5nmxAo7h1s1HUrxm2byTGk4DRmSbK5mXej4n/dA8/HlKar0WHAM3dgt2fvRIOdDK0ybEJJVfv3aLYZGKhJQDKKUwvCdRXj8CUmXvliqNun9Nuh/df3qd64ZichGvDF1rSdVm+bVmNCozOHLia43aHLlnUe1NGV0na/fN1UnwEDOpLNas8pjCRiel42tymjgX9H+4SxhJspQbXYhxpLm+XvCx2MQYimpg13zl5iP54TLkU0TmMpYWWQtzMz75Wcdrt8NZ4zjpUJBTfffoWt78DqVSAukE9lBc91b+34rjfpaNAAV12/G8vvsT6xQ7TlxIGc80ezUaxvlVytVfwItyijiXbWPLdq4d8fPwau98zNzzX65Zz22VO3qg4s2OS1izTmKfbW3z77C3yT8YUm4wOshE2baQ8WrF+NOHlmyOO210mrsdnw4erezz74HVu/UgCsq4T5rMV99PH+3vEMSgNi27Mi80e3z98A34yoVoMxcMgyfXleU17a8wzN4US7LAfO2tnfHB+j+m3C7KToDvtE9kabK94cWfMaK8nK03Mho/Wd/l0fYf0rR2yUYzOPHFsSaWhuOrw04Kjg5JOWbCao3aXl80+Lz+9y60fQLGJZCPBV36nQGU4OhjTVgapdyo+Wd/hO5+8ze6P3M1Y2bWE6qXKsShmXI4cjw4uWIYxF/2Us3bGj99/yGt/Iq5L08vJGB1tRawpDDtPE22Y0IeCW39ssa20k+s+kp1B+SjMOi0J6GFiRZDvkHGJ+dU5MIo41qANcWRxq44fPbjPqnSMS8/cT7jop3zy+HXe+GfXwrOEvOkgn2M6xSfcot8Rl/Tz+oD3jh9w+t3XKReSXK+yBOb53ULceJXMt2KlKOeK0SLitrIXv8YWoBR+10no2sUWjGZyEqkWht0nmdFlwvhBFN14FCJKmlaeu1XMZGdwtWL02HHwQ3AbCaZVaXB1Iq7D67C07DSjOZiLgtkHhtE8EnbFVR3H4sLLVuM2gTi2uLXh0M1onOZ0O+PZs9cwn44Zn2Y5Hq3ITniicVyAUqRNxcKWbLHMl1M2Pzxg77G0e7uNiPG2Edesihm/66iuAtWJZfSx4/YPvQi6XboRazGKOLIS9NdFcmmZHirCdkR4MuHWjxOjCxEsVZbCjw6CVDBdwgRxPLtnBfaTkv1PAsUyiLDdBZlP10IvIuRmK+3d5fcqJn/smD33qCz8bcU1PzUhoE4FjeOqG3NWVLys9/nTlw/Rf7rD9DBRzYOcg4y4hUNm9yctjZ6yONAEZfjw+B7nLw7Ye98yPexlf7jxpMLg2kS5Spit5aXZ4cRM+HR5m+P5LquPDrj9g0R2gvlBK+mmGI5l24/4JO6RHcz7Cb/36ecovzNldpSGYlwaCmVKwjSbQC4kaHM0j+gg19G1KBgrufayUdg2kUrN+DTifUU/H9E8n+J+UjE+zoPTUjAlfipR9WFqqa4ixaXj5OKAs+2M+ctd1HtTZi9ERLWdrCfaZ+JI0D87zxPN1HHaT3m23OcHn7zD7Lslrma4ThJ2GwkT6QwpV+DOHIdXB5wcHXB2vof6wx0mR4KtcZ3MsTg2NAcWBbhtIm5GXOYxjx/fw/5gwv53jTiKncLvWLJVdLuW9sDi6kR3MeHl/BbP04yfHN1j8/4+O0/AhOF+Wcg6lYfz4taKq4tdjosRH57c4/uP32bnezInVRaMgOmEgYxRqKwwjebq2T5H212u5lP8H+4zfQFuK/PdthKIGZ2ivuvQAdrLCefHexyeH3D14W3098dUlxkzMPoBTJACQrYKd+SYr2Z8PL/L88U+Z7//gNknmtEiyppqro8DUqmp5pm0qDhsdvnx+jU+OHud9r0DRk/dDUu/XAYZ00pCVWOl+W//01/+cz1rq5z/zW2B+xv/2d/OpS2x2om7yIprylnN5z/30+xNx+yPIRJpQ0cbPSdn53zy+FAcSKakawNKGRyaB3fu8Oit16hGhq5r2DZbVnXLs+fHEgpkK3EmxYzKmqIoePez99kZjRiNE6lvuLjYcHh8Tt9Hug6Uy4SQMGqM956Hb97hzYd3MRbILXWz4vnzOVfLDTE7ku9FeAlBWje14dGjNznYmeH7hsI4PnnygqvFGuXGdF1DRMLPyBmjLLf2dvnZr3ye4DfklKi3HReXVxwenYKxZF2x3m6BLOnoOfHVz3+esoxYl6k3CxZXNU9fnKCLkroJpATWFOScianj3Xfe5PbBPqNKYVXPatPw+MlLFouWpEtJwLaGtu6YTmfsTkrefHiPg90JPtRE37Nat5xfrFistqQofE+dMrooQMNn332TO3tTrGvpuo66rjk6mjOf9+QyIwH11U31dWdW8fbDPe7euk3fdvSh5fT0lKfPT1GqxCtF1oacFCPjUDHw9sP73NrbYbpjWC7FkfnJp4/JydGERIyZkMEYQ9vWPHjwOruTKbcPdgdEArx8/owXp+dksyNM1oy4CN0I6zTVuOCt+3cYlQU59bR1w/HxKdumF5ekUmgMylhi53nt9g73bt9iNnOMKke9FlHkydPnZBxN51GuIpGJOhH7jkdvv8X+7pSdiSN0Hc1mzYujS+o+0QaPUgarLCEHdJZ23t3piPv37hJDT9O1PH35jBANdRuFMWsKCiXO3RAbHrz9Bm/dPyA2DTFGVlcr5osNV3VHyCJWm7LAexERNpsNs91dNIrSaZLv5bOUwypxBFqjyVkS051zUkFPCchElTEDX9YoGI1GrFcrrCtljGPPwWyfW3f2qMaKorD4pubkbM7FYoUyE2KAyllxvPueonKQI4/eeojOkHXm6dPnAESl8UmuV2c0ShmM0qA13ntC8EyKCixsmhZPyWwyQoUacsQHcdZPJiPaDuGrhkhRFIReAsxsKceaM1jliDGLa81EosqUFhyaLkQymoCWgCelMcYBkHOELO8rS2HWxpzJgoTCKfkdWmtxPw/tacWoom9rdnamtG1L03t8COjSEXtBIzhtBfmgFdH0aBSTUYlJYJRlva3JhaXvpEVaa+H6KiVFlLYXzqlLimzkN/o+MrIlnkRSGmcsTVfjnMP3HVZbDApTWPq+p6wcm3qJcyVKF9AFimKEwhBTSxc81WgKWpFCQ0ZCHiEN66bBJo3RlhwjIUcgYYwiu0SIihQNWlUYk0mqJ/SeoijIWaG1ATLOaiwRazUxJ7ZNhKzpQ8BoN7hHIYXIaDLCGoVSUjjUiJveGEXXJ/IgoFutSaHH5ERKidGoxKdIaRSNj8RgsFbjfQda4UlEBSNbYY1BqYxSmdgGmq5lMpkQUfRdpNAVWmu61BN1Qg0PLiklYtIyl1PADI5XrTWRfrjGepypMMaQszBYlcqYDFpV9N7jyeSk0FaRSWgNIAgOay0mK8LQeeFVT4qQEqSUMdaK+xuF0YLIyMi1kVIiRJnTKgSSMaSc8bGHaozJCtcnnMoEqzCD3FLqgmQyy3qFoUA5g0bOQUweZywpJawaMCFtIGlDNJocBpa9c+RC4bJCD9dM13mUK4gxUhQlvY+EFCkY5nhXMx3PpOsggrUO7z1lWaCcYr1dAQmtKqqqYrvd4goJ2/RdwJpCxsgHkg8UY+ngaDv5foZrShcG4xWFKYCIdpama3EYQhbkglISjGqMgQQ6Z/ocMc7ShkCpLcRIVY3EUZ0TMfmb86+UEvOVytJRM6wru9WErutpfKBpA2VZChMxJdyfSUX4rf/xf/hLsO1fgNdv/NrfyX7HEkYifF237IWx5vCvJ+4+uOJX733KNpY83Rxwupmy+uAWD/5FELH2OpDKSPvm+c9Y7v3KIQ8mS47rHQ7nu/SnY+78sabYDuEbPt8khvdTzem/nanubrm7u6aPhqMnt9n/vsFtoVxfO58SqVDYOnHyCwX6a0vKwU15dTVl9t2K3WcB3cvmtt8x2EZaPv1IcfqribsP52zakt1xw9U37zF7LmFU5Vo2hddOpuQUV59xvPWbj1n3JTFpThcz8uMJt9+7dudmJkfiHtveL4mFYvWfbNif1kxdz+Fyl/pwymt/JC6n8UUYnG+Co9A+c/hXLLxTc7C7ZVL0PDs7YPYHY3aeC5v2Wlgq5i3N62PWbxiuvhZ48+EF67ak85b2dMLsE8PsRUSHjFsPAWyDM/fFv2PY+8ycSeGZb8dsr0bs/Khg94n8nmvR6Lptfv1GyfkvJd767AmLesS2LjEfTXj9W/0rnMDgiOwPxEF8/MsG3q55cHvBs+Nb5Ki48/sFZETk8BK2lApNMW85+/kZ2wcQ322oKjmH8b1d7n7HiyDcvXK7+ZkjVsJXvPiFhNrtyUnB2rH3I83oIgkHdhvEbanEeXn5xRGLz2fU3Zb9nZr5YgLnJfe+JZv66tLfIDd0J4iBw9/YZftmpLhb43tLXhbc+q5mNI8Ui3DD/7WNjPHqoWN7X5G/tCYEQ1wW3PkjccZdC2aCHTHoPmObyOnPVfQ/vyF4Q/Iad1IwfSYMZ9NK2FAqNGYr42IPL/EP74jIs2NvQrx0OzB821fYg2suLHDj5tO9fJ5uI6k0dAeO8cuaOJZ2dt1Glp8Zs3pH4XcScZLQjWb/x4r9j9obkbDfL254pWEmz6svf11YmSrD69+Kw/dm7FYYuGFaiEhTymS8/u/tXXEjVyc1etPRvbE7cJ/lb3QXaO+OKBav+Kp+ZgVP0ScRgIeW9TgyN45dEGev3ytv0AlkboLH4siRht9y/ffXju7rFnqVMsnKWmY6cWherwsgLr5ik1g/EHZpdSVzL1sJrgoTe/PdyUn4mRS0HLFQJAfTY/ldbh2GgpSWgoNWxErf4AjC2AzhfTIu3S2HrdNNIFUxl4KAqYM460szuCQD/a5ldNQQJ+7PjKG5YThfowaux0IlXrmkBwE5VvqmZd60r9ZG+fcrTvB1yJdp4sBG52b98WNNqDSxBO1fhUW6dbgRqa/na3dQ3Lgqr89DuYykQt2cS3El68HpL9dvu29wTaab6Zv1Jlbqhsl7Pae62yPi8PlZg+ky5bynuSv85GuBMzn5Pt2nV6FkPqE7Cb4Dbhzr1zzfWErxIIzNDXJIpTwwaBXJSFinrePNcagBtQLcoIaiU9g2D+GbabhXyFinQubJ9XnOhhtnqQ75RnjMhoE7Lsd+/XvD2NyEYMWhY6KfyVjuPO+IpbnhwGfDzXOADsKSv+bL3wS6DXMmDK5RP9Yki5y3dboZ4+trxnSCdgilnNf6NSuc+5BvGLn9VL5ncjJgkYZg0/F5kJDSUuGaRCj1zRjpCP1E5mm1iDfdINedIdFJhwtAsiKe+pHGtkPwuxpyhIb3JasotjJG5TLSz+S93a7GdEOB28uzQzLqJtBSR9kjXs/fzRsGt85Ui0R16el3Zf0W/v8r2MG3fuu//3M9a/8b7bT9n//RP/mGtY4QEjFFUJmiMBIG5Cq0UiRaOt8Rvef09JzLs0uCVyhlIGpKV2CH1hwl/TOkrFCuYLFdsV63LFYb2UjlJG7QELCFhH7FEHBlSdu0nJ/OmS/WbDYNZI3FEEOPNRYfEsY51tsNIUdi79lsWi7OFmw3PTEpcXPGhFIapy1aKXLWeB8IMVFVY46OL9h0sKxruizAdaU1mYwzIuzEFGmblqQENH9xseZqsaTtEzlpOh9RWjEej4g+oHyCLlH3kabtuZgvODu/og5RWimVlSAyA2iNtoZtV+NzxDjHZr3l8uqCtglAiUFYoNZodvZ2WK/W9H1LypEYAkor2q7l6OiCetuTFfgQSQX40JCSpg09TVvT+A5Xjlkue84ur6i7SNMrQpBxUkZaSZ22NPUGZTXbTcu2a7m8WHN4ckkxmiKyiREhDoVCURUFOUKzbYg5sVxuODuXtNbWK3wvgq1WlpihrMZs244YFOvlhnbbcnm+YLlYk5Wly5acwOWM0YaMoe08Pkib3sXlJQDPnh3Sp4TSMi+0K8gpExE3a9M2pKxou56UNMenl1ytG3zSpKTwbUAVFqUNPiWq0XRwjkgAACAASURBVIjgPSkprq7WrLc9y2XNpu9ogjxEOiUusZAlcMnYgqvtltWmYbFYs1w3rOqGEJRUA7ORECwFaEufO/rOc3p2TtvBxWLB+fkCrwwBA0qTFQQ/VLZD4PatWxIgFaRVpw0eZS3WOIKXKnsOmZQ1Rov7TwiyGqXA+0BOCaOlzT+lRFGWtH3N2Fq0SsQQmK+WbFvP1fmKs4tLvFcoM6aPGTA4Y0XQsA5jC6LRXFwuWK1rji4uGM92SNrRey8uiSFIzxipIvoYUVrjXEFICa0LUp+oCk0OnpTSIDRrCmsB4blaqzBaEWKP0hltJPAqJwZBKaJUHoQa8DmilSLGJBgU68g+YJXGai24joG1hTISouUjfejRMQvPSStCymSGTrgoxZwUE33wKKOo2xoF9IOgDGLwN1EN51sEP60MxjhSlnnXtx5dFjR9j9JGEC4xkII0cycyaRDlYtdjXEEcUCmlsYQ+CrohJ5wrSFG6IJTWZG0IKWOdjAsKUs5gwFhFHwJJZ9oUEDk749tWWsNjQitHSvJZSgnWQ6HxUdizyhqU1XRdRmmL0gqlE8H34pbNgawyKctRaCMkNuNG4p7ug4SuWSXzO/Zoa7DGkrO4xkOMoAzed8QgyA+tDaGPIrhqaTvTOUlDhbVgDH1CBDRXQSHBaH3yaCtip04KlQ0M10uOglhwVSksZYbzZhQxe7yVB1mVMjpK8rY2GqMSRiuMtUOXZCYrTYgZaxxlMSIET86gtCPliMaSUqb1PdYN7F0yyQzHMoRR+T5gnCKmKGFx2uCG+WGHB3OlFH3sMEZRDIzHm22OkkJG1la6N7UGbUFnfO+xxuCcJntxgfd1K8GPk5I+BLTKJKKMfUxUI0cMmeAzQjtRJJVxzhIHd7txVubZME7KOTb9RjAFrsKgEdRxBJ2xTuN7QaskHQhZnCYyb0ckFWnaBm0sSls00PdSAFEoQg/gCNGjjZHN/SDep6xJGbQ2KDW0cCbBp3iV5Dmg9ZSmIOSIMpouBnwU91TTCcM4pAjWyPxXRu4xUdH6IM8WWcRd3/UYbYkhYaymb1uslrmcs6H3ErCYsqacOHxsUAq0kuvCh4Qzjr/1H/+Nv3Ta/gV4/f1/8C+/EUsRmkyfxO020ZSrSLtTsDSOszzlaLtL7QsWHx2w81hhO6SyAPQzQyyH5wulOWXCaRqDUmyOdqhODDsvpehtgqiDOohjaHQZIFg2I8diNaZ9vMP0mWFyJhvuWCpsL4w9W2eyVUxOEnUYs+kruvMR408do4uM7fON+AKKMNLDegm6NSz7EcwC2/cPGJ0rZi+DIAWUVElUFtdsLDWmh8PNPpdxRIcmP54yfQ7lKqMTFGu5l7W3C0wvz0G1rlhfTbhYTzGPx+x9pBhfRknxjiLaXbta0VBdQmxLrlzB1eWM8klJsZLfngp5doilZvNGxfSww9WKlByXfkKrNX5VsP8DQzWXDbSrMzpIO772meKqpVwX1MspF6OCdDRi9hNLdZkZXQYJNho2maYX1nA190RTsJjP6OcjyicFd77n8VNxa2GkMyQV4kTr9gwkDYuCizCmPHLsfGQwHYyGBHvTikNK9wm/VzI+D6hgsKcFflVhnlZMDodxXfgbV2QqBhzGvBfBvygYPbYEZbn7R5liK+Pu6nTjJNVdJI7EuZazhVXBKpbMflwwfQ6uyZhe3JoMhkBTe/xehekUKhrsyxJ75pgcKibnkfLS3zhYUyEFBBGhDTvPIuqkonjpmBxqZof+RgxQWRzDxouL1K57iloz/rCAVcHsE8PBRxHXipNRIRt6U3tQCt1HNl+8jVt5VJSCbHHVyl61FMH22mGqQiIVIhxlo27mtN1KiF0qDXbTo6MizAqKq5buVkV2Btsldp8Eqrlm+kxz8GHE1eJ0dqselTNxLM/M2QqawXSJvY8DkxO4/ac17e0CHUSMUykTR6/ELfkdUtiJIzu00ktwYNgfyfXZi/tVZQhTB0rmZCoF6XAzVwd3m4ri/jVD6Np1wJrdSqiX6dJNCJxKEMeDYKvVTTGE4XhMm7CbcOMsDWMpYF0LMXqoa+go133WitGlzGm3DoSpvXEMi7Am5/7afSlOUnEaCgdbU132oF6Nh/bD94U/cy4Hwd0Mwm+/Y7GtCOfaZ+Lgfh5u4LJX6ETU1kHWZ+1lTU+lwW0ips8UVx26D2AMdhtv3pcH8Rg9zJ0Bq+A24piXCToIqMO1g1JSxCgFG6LTKzd+tkrcjoXCtojAPRQ8MOrGlRsrc+PEt12WMOg63zhZs5VrLlZDp8KwfmarbsR+t01kK93MKouw5lZe5mspbn+ug9QHkbrbESHTtmkoeshYynzw6JDASKeHGtzqEjCnb9YmHeS9tpEx6mdyL73uwtBh+P0hUywCcWRvxlsN5+Z6TtttJBfCxTW93OvCEMApgZlynt3Kk0qDH0s3tk6vTsf1sV233qucb9aBOJLirE4isBabRLlMUmTYDAXbIPcp0yW6PSsc+lYcyOKmFlb89T0vOX1zPWYrgvj02EvhdGrEVZsY1kC5n7taXLDS2SMubdfkQfyX+0Y2cqzJidh9fa6vx9a26dW4tQkT5DOlQ2MQ/v/M+utqWZNdLSKyG4rGxTrhtrL2VvMAVuEa+a22GQbWKGwjY2V8vpljbiuFZOMZfufAMU95+P9QbOVc9jsGV+dXaJMo961Qaf6bv/X/A6ftf/S3/6uslMUgzjKlM0YnQi+ul7Is2Z2N2DQbUg74XtigTddLcI6y9D5hbUHTNBSjAkLAOE1OkuhOBLKRNHLrBkEro5VwCKuilPcWjpiDbASTOFIKZWl8PYiqJYGIsuK2Ioil2yiNNpAH5mq9bUlGnKBZiXPMlgUh9OJ8cpakxZnjY2AaLG30GFfSNh5tkN9CL/zVnBlVE9qmwSSZKGhD1Jk+iBtp5ApsSrQ+0OSWSWkhS9CIcZam78jaYLLC+4ArKkLsGE0KUuywWbieSmWabY8bTVBKsd1uME6L4y/JsbjCkPseZzWKUlLfm14WPZWIOVDYkjQIZYK7KOm6jqoY0fsNfYhoPRYXldWDYBkpCjuIZRbrRkSfGY0quu0GUiRjpSVZgc0KaxRGZRIiDGljCIPYkrOi8+J8akOLtsXA9pWxd0rhhkqJydKy3WX9f7H3Zr22ZWl61jPa2ay1dnv6cyIyKiL7rMblsmUjIwqLkoXAKltICOoGIfgjecsNfwDJSL5AqIASKplGAgSWsSWwLBvIyszKysiMzOhPs/u1ZjM6Lr4x5z7JVXGDC1cuKRQR5+y91lxjjjnmHO/3fO8LOaNzIpPQZkPCgI1oEkYDyUphIItnKtV2gVxQxqCtppSEolbAKRjjMMZxc3ODUgrnW0rWzLnQOkXJAV2EVFXKiKiQxdc0ZchFUuVNMQzzjHNWviNinyHhUKCViGwxR5TS5KTle6iCUhnvHKSIsoY0S+r7MMt4p6zJRVO0VKGdNShkTqUirdfOiDdTY8Vns6SERtWqk/yMMWKJoIoEek1hRtcx10ZVSjXhs9B9Y/XDbRpJh9daE2MWUaIRrzNdwGhNiAm/7bkdRry799UKccZ4QyYJSagUJcqxLfPQWo/WUhDqmlbWnDiTKeIvrDWNNiTptRShcRLv291uxzBMaJSIKlq8d3PMaGPEh7Wt80rLHDRFYarfsbUabTVjEo86Zz1kh3EaSg0vUlrWAWNQRte5HOtatRToiszBSlqi5Ri1tsQ5sOs33Ax7tNbSlp+ynI8SsbGgrGPO8rBmjCHHaj2TM8oZVsu/mFExr/58sWS2qiGUzIy0VecghKd1hhgSc1rWzoxRBYMiaiBFfFZV2DbMRa1BX6hMCpmSVfUVVmgU0xhoO08uipAiRlm0FtEw5Vm+PyKyG+3E8sYUCoEYMyizniNNQSkJmksKGiU0ZyYRpkjOBeeFejeuYR5usNZKoSDJmocxQgkTiVGIXrT4+iqrsEozhhmKRpuMpSHlSI5BLFqQDcVyGpU1oKIIZ41nGkY5HznLehoTxrvaDphx3jCHsq6X3og9yZwh5whJrm1vnXgga4U4S2hSiDgntP1caeRxmnCtWIPoAjEFmsYxzxHjRTC0xaCKlgJEfYaw1qKUeMj3viUlsUbRRgTkEETsjSnRti0pR5ZJFXMlAbTGaw1ZChzjNIF1eEX1zi3EXOialvEwYL1b/ZaVLnhjsUUxqQz1+KzxhBKwzmDrHt1USnyehXRe7vuoWL3NxXYizQVlC0ppxnHEOVe9pCcsllRyPf4JpQzedeIz3zhub69xzuB9KyR+Xqh1EeGVzhjtKAWo1GxWMiTOWOI0M2dF1/hKUwfwQuUWEjFIcSQuYUXGkKqeYpTMP1kfLN4mUinkIuvqNIVKUgNGxrWQUEVDAu9aYpj4L/7uf/JL0vbPwetf/df/o1Lq5kgv9IgX/7h5p5l3mv0ztfq42aEQO0V7lTGTeLS5u0xqxWMy7BaxRmij2N5PI3d4yzx2oY2StCO2l5J0vbwnyIZr3ogVAbWxxA5pJdKAXyCZVC4kL4KKDpnxzMrzxpBXknj5eR0riXRI7J842qtEajTNZahBU/fCj8ownpqV+lrItZXO6g3jqQjXzY1QNYdHUjQ1oZCcor0UelbEZ/kdOyTGc/cLJFPRIm7EXp6Ru1dhDZZaaLa4sSvhtFBjzaWsoyqXmizvhN5bxvjU4m8S07HB7fNK+akotKIZEipm4s6tNGNqhboeTyXpe3l/dxNXH86FDltaUbO9T+FWuazEob8YyU7uvwuJtFCJRct7mFDWVPWFPnw7mT47vVJUwEqdAqil3b2GNC2U29u/lxrF5ucHitEiQmaxL5jP2up7qevxsLYiL58h/qpW/BsvJEBr8dFdCDURMdRK1a4kb/0eKAhbW49TSLbUapqLao1QP2c59rixFA3+ckbPEX2YyRvpjAgnzS8Qict4m7fa7Bcy2F+H6s9Z1nOuclkpZn8lczMcWaHTrAhxZh/IjV3HdJ27G0P36X4NGQOwdzO5saiQKkFoUCmv9g3FiM9ydhp3NTE9aMlerWS8rf7C4div5zQ1Bncb0FPk8KKneRNECByrIqWVvLczmP1M6hwqZdLGrWTxQifGTgQ/fxVQMZN6Wz9D13ldhZdKuRaj1nFY1qrltfzO4sNi9yIymTGxf9aw+WSkWBH7lvb/5RhQ9z7Sy58v8wR1P4+XubzMMT0Gxif96pebOifibmNWiwh7O62BfKvYPchnhZ29FzjvhCKdj926ji1zbqE/3dXEfNaKH/EhrnYBpRZf3v7+C0ldzP0avX6HSqMWzUpKL2Tz4v+7EOILRepua0Bbb9bjW97LrD6+cuy6ivaxM/hLyfkoVq8E9vLe2aq1QLUeKxKsuAbaVeG5GCXrVWNXe4jYV4E7yNq40PPuVoIMQbyxw9ashG+p08RMQkbrUHC3gbBz+IuRtHGyNtV5Nh9b3G1ar7NUfYWX8wOsc8jdRMaHQr+/ff+2BxHFzZiYT+wvBF0t50XFst6nl2vi7TmiaoFxPHc0l5HU6pXQLloRtkK8LveEt8nmVAnYolUVbMX3dTmOxU988evOVsLSipFz01wGYm/qGOT1HrQcf9HSuZCtYt5IWFrsNfNGY6eCmcr9tZRYn2cWEXs6EmFYp7LStm6fGU+l0GHeouoXwjz2tWgE65qV/f09a6G+542I2TqVdXzevl8tJP8yB+etHPM//C//BSBtf/8P/953QRNTlE2yEV9bo6UdPOXMfpiknVx7CoYQC7lIgJDQUBByBl9pUoQ+QlmU9RQsWRlSTZLOKCFpihXqJAdQATmOjNaWFAu5+mSlEmUzWTfVqdRKqbrfoFGU0FIFoaqAnBJO1VAGBcloEWSKIqdAihmlPDFl8bXLQsxIOUN8QRUGZxtiFM9UZxtAYxTknKRd1mjIhaIKxVmKLjUEpUgbdi6UVFAaDCIyp1RIJGIY8LZlnq2EBuTIHAMxJPbDga7vASWt4NbRth0pChmGdoQkm8TNbsc4BCHAisYbh9dOvDKdeP/mlJnDJMSTaSjFonUhl4BCvHa9E7/hUsTrs2jDNEnifcwJpQ3aiICsVUEroZsKRtp/c6EoxxQiqWSsEs9VqxXOeBGxtCJTRIC2IlSNhwOpZHSlAnOaUVbEx6IyilRFroxCgshyUZWWVGtrtql4ZFlactCgDCkrQgw0TSOb5wLKaPrWMU8HrKoVSaVJKaLFHpaCE2K2iPintBbv0STkX0LIcVUKOguNGqvnqOx9MinFKvLCNAtJoLRhXlr8i0YXha2+rFNIWCN2AkqBClJ589qiKLRtIx6wOZOXwCSLCAIKSq6pGiVTSkZpEezkuAs5phq8A9o5klKV8CxY3zAeDijF+jmmio9t15FzZioyD0wpOC1Er1+pz+pPi8E7j9Hii6rrtbu0U8cQ0E5hnGYOcbUJoHpne98wxBlnLI2xzDkSU8JaKySx0pQshELMCdd4SkxYZdBKY/RCHgvd07SeOUVQpZKkkEKU5OtqOWFLQTcOjGIMgXGeKhEXaLqWUjIkSElISpQlzFI0UEpEXuMc0xzwvqOgcM5KW3spWG3rnFRoJ23nuYpuxhgRLeu8oSBhgLoRkdm1KA2JTMgyp7SVjcY07PHeooxijAPeNRKGpDW6aCiKHAvUcbNkyEnoR6XIATn+SrEqVXftRWOdhaIRwLlAzkLcaiGIdXE41zBNAecMSov4ptASrlbELx2UePyGxDTP3B1GtG9QFLR2so5aaaNXRtEqmXOxSJCWBCNqSk4469BKE0omIcTnHKOIrN5DUmjdEELGWiFJSomENBPzDLWgkxGq+Prmlr7bkjN0TUcMM8444jyzcQ3KKEpRlFxom4Y8TWAVIQSMUhQjm35VFw1VW+CzLoRKpGolFiZCDgvJbLQh1+C+goxbRvy/hdSV/9dafl9IdPDK1HZ8CfxLRYpVw2GP9ZY5JdrGCtEQC60y4pluJEBSqaXHSKyDxM5CU4pCabk/LpYBFBGDcz3vbSOdFblQ/YUtU45kU9DOSMEgy1gppUkZnDNo6yhFo4oQ8ClJcaekQikZU8AZ6eDIKkHJOGvrZ0gXjO8ajGlIRRGnCWctWtVMmGoJo5BrX2uxspDMySKWJ8aSsmxuwzRLQXPpUAgzWim8aUklYTDknOh8QwoipKMMw5yx1SpmjtIl4xpPRjoatHGMYSaXgvOOkBPIsCC2cppcDDqrOlcM//bf/pu/JG3/HLz+zn/+j76r3iKFFNLSKNSS+PZ1rwvdm1Q96ISK0VHE1UWgW/wp7Z1ACytRWmkYnSR8SOijstKAxUkQ8BKwsxA0svmS+9JCHS0kjZBnej0eFggpC80mnWOyoQq9xoTa1hkKuZENpQ6yiSpG1c1eljFozEpJyoZLaCc3CMkTOxHfRESFsgRzUVtiC6gsn62TjIOijodWQgw52QDLP4WwFSFVqCYRAuyY6b4cmM8aoHoyNprpxMrGsBJ2bi8k7+GRraKyrhSiWQWE1Gj8jQhz/lb2Kak1q9fpEhhWnJYW6nrO2pcjOrG+rx3k3BSnV3/TYoU0Ull8X5eWXn8TRazoRKzIrRWhtLD+vjkEUi+idXMl4W5Cscn3pRLAi3XD0oa9CAi6iiWLSBF3Tr4/rLRTDVbAzBl3SIRjXwV7ef/xUYO7iRIEhZxzO1YStPqB6mpVsfjsij+oCK56LvdUpb0nvxayT+wXImnjquAdqBV2EXxKFQG1iF8qg7ueSL2T1mat6j+aeNSCUsxnTb2+Ko26HOsiVrxVG1nGayE+1UJCBhHQipXihIq5+mWK+FWMJpx49NKWnYrMvQjuWjxWc1OFNaVqkaDU7yzXZ9oK3VqckXWgkrRp4zD1Os9eSRHA6XtaOhTi1tK8EWErHHvcbcIehBbEyPPNEi5mxkja+CrGunWO5sasgl/YWdzdgh/WOTEJ3WoPqVK9cgwosSRxl6NQkXcT4aS9tw+YF09chd3fe1sXI2Pir2fiztXCigjVOhZys/gKy+eYOYtIq1iv21LFfZScr2Wu5c5StK4WFbP4zhp5lvBXMk5ojbvYk7YNZozS8WhlndV1v7bOswKmkp66HsNCK4s3slgqpVbuCdnfn5/lZ7IXgjX2YumQOrPSxXKd6EpHyr0h9vK5/mqi+ewWnFuvm9QIke1uhDKfT13tTsjrOpMbKU6IZ7OMvRmlK8rdzhRvKJXsXoMDK/Gtq0AsxHZds5MIhZuf3DA97GSOnwpdGjcCRwyPG/n5+tw6H7uVyLWHVP2LhY5GaxlzLXPEhOrD+tbaIESxrA2pMWt3yyLmmUpcL2uKtPjrdZ1WWWwudBaK144yd9GK5o0EJbqbQDiWbIriFNORwU4ySZd1b7kvxY2Qt8XV59J6Ly1avhdK1mKd5PimY1PXRCnC5kbIaxPurR6yl3s6Cuws4mgx93+mSr320j0ZnJ3YGGWv74n7/r67RhW5hhfRdCnEFiv3clXXOnQNeKs++NQpXawSa4i6XttDXonZ7OQeVox0Gdm51EJkYTyVkLZcieH2zVwLbAV/JwJ56MWTWWd5LmivEiaUtVC8EL/rf9fi2YJH/4d/StL2z7Ro+5/9V3/w3VgAq5hSAqUpaLRqiFmRiyZoCfXYj4E5FeYkwSNluWFZJ3ROyTjvMUWLYXwRYU4bi8oRpQukjLMaTSZmafcvC6kTZkk4VxDChKo3ULSIXForfAE9J5xtKCHLZyURtkzRqFiw2uCQxSTkjCoi6lpt0UWsGXQSogyNiJYq461liuH+RmMNuYhthMpFPD1jEGIvTKA1RWeKUmA1h3lE2xqSFKFkEa2zgqIi2VZB0BqcdaiQMLrIe2oRm3IcUKXQak/jHAXFnIRwsjqR0wylkMjMOaNNwTWGcRyEDqyijzaafZzxzjGNI9ZZtNGkHDFGo3DMYSSXXIU+hEbDQCyUmLHOk2PCO894GFBoVIm0GnyxMjYlExCySHuF0omYZ6wyqKgoyeKNF+ElCbHotUbnjEWEM1MQ6tZaQowi9GlHyGUV+xSaOBtU9nhrxKNUa8I8o4rBW0MME9rKAppl+qGwoD3TOIl4lhPet9VOIlC0JiGtqt71GNcSpkBKULLclGIWUk7FhCqFUDLGW0KRG6SW/TfOylNciBPO6qrlS8EhpYzBoa3BYAiTzLPOtZSQpD1Zyw7MmpaSwTojhhRWRLRt3zEl2dCNgq9jvRPyBSXnUOnacZMoWsj5HBNeO5xyaMCgcRl0I8eTo1DKXdswHA5YY2haR8lJ7Afqg8VCPIacsIBHYXJBqUQuGY2IEd6Kxxosdg0GraTlX3Q/CdNrGs8wTyKS1JbmnBJog1LSfl6KXB8b12GVJoVUxRUrBRgQD86UaZyDmFdCM6SIchY0tQUeQAKEFi/gkALaSfFoea84zrW4IN6xXdNAzqQ5YpVDG7lWtQFtqwCnFKWK0lOexJZAicVHjBEwRKcYwozxFpU0FVSV9zOaFMX6JKdM3/fklCgxgC6UHDFIAUpnaJxHU5jnwNHxKfvDgDMSbpVCpPOeHGRjktGkxoGxjDlIccQJHWt0/T5ZodF4Xal1BcolSp7xRoEtaAtFBRqjcFqT51hp14jSNZgFSCHjN534MpeCLoFCWccbrWm9lza2YslaMRAJSqwQtlZjM0whoJxB5bpO54RKWZ4NsozNtmkxSm7iNhus0uhcSFF8dEPJYu/QNBhrUSjp/kAxh4B1DdZZwjzTGsfh7g7jLVMIdF3HHCbyLNT21jZMhwHfNYQabCezVD5XFUgKSsk4LySKkmQ3KXzkgsZCUShRQzHVlsdoQ5ylXd6p5UFRxk9EVhGuAZKKCGcPqkQ2bQspsXWNiMJWk/JEKbVaX6rfVw5452isljFMUly1tYAgVaYi4mOWeRlSpOu66u8amJFwzJjBWk/OEaMz3mqhe60TX1tVCCUypZnGtdVWRIo7WgnJb7WVZwIl97NQZpSV9VTlLNegUlglFgnOagkyo8E7sZ5Y6gvOicVTjknsNiirtQZQhfJCmmeg0DSSlqusRxk5BzEnQpkpNhNzou13XFxe4JyhxaBKwtmCLlKEtDpDCaQcRfwthpJh23YYCiUnKayliCqGHAumOCliOUtUsrb93r/1u78Ubf8cvP7uf/oPvmvqptpfTdJ1lAvZCoWigwiyOha6L0b8PuPuEtOJCHAKVqHFHAK5t6vfn2yOllZHZHNKTX+vhI2OCIHaamk3dGoVUhZhRm6C8uwct4tQIKLjIsIuQuoi7sVONtDuUDfFRQJjFmJV2i1ZN1RUgsdfL0Fmam0XNVO5F/72clztZVjfdxH32tdBWqbv4ltCLetmWwcRarPVpF6LsOE17q32T50ABfOpIx65+7CiShebha6fM+56pjihytrLWMUBvQpI7cuJuLX467C2Fi8t5yKQzGsA1BKsslBAOonQqiuV5S9rmLJSzEdmDbcxU6nt0XptT7aDUGKLOLCcsyWwLuzk/1Nv181+3BgRx/epilVqJRiX82NGESdipaBTa1abg7BzQmKZ+3bdRcQEEUIXASxua4vyPsgzxpxxdzNx68Q3echra/YSThc3dp2Ddi9+nItgt5C9ImAK2Z1aaaM2QQpySxo9bxGrqsBUSWu7lzmggNzaNTxK5bLSfofHXsT3DP7NgCpCOy9iqa608EJUri3aIRO3tl53MudSb0UoNWqdv/Opp7kQ4WfxJl3bjWtxA8SCILeOuBURBerP1feLO7/oEaun69KmvlxvC9nbvhaStnhTj7WqgrUgI2tBZHzUkFu7itRin1G9210Vjo99FXvkWWoVvWEVTOTiUSuJa+9mOTYn12MxEr4mFKsjbRzzeVvXuCzBelV0XcRhEV1NHUeFu55qOJcIm3ZIq1DlL0YRF2thBFjHx0wZXWnr6YFHh3tf67c9TYs1pKNGrC1uRoYXW9ovD6SNI20boVnPmyq6sfqxqiL2GmYqNSxM8Ku4wgAAIABJREFU1sq3v0/cCnGo6vOZrjYUdSMiouGRXb1Ol++1EPRLsSxuzNp2vojWqa0iFop40goNXP+ueTWs1hbDk5bkNf7mbasPatHvXojTqTA8bWXeOUPqhDqWe0smtxZ7FzBDEKq1kTmWm/uiSW41uXXYQ2I+dbRvhEp31zPzmcfdpTq3FfvHHn+bCTv5bqk3pE6eg3N77yWta/dCeSsccLER+EVhnOoZLP+2g3RgxM29+I1Sq2/0UpAyk9CfYqGjODwUCOTw2EueixUxeLFJcMP9eYobw3xspdhYr8XU6rd8m2u4lhEx0+0T45kl1y4Uf1vDtqtf/FKMCDsrY3gsFGwx4j3rrxPzkcXfiQ3nvJXzIxStXp8JzCwhqKvoWtel7MUT2d+m6gMrhZ7Qm7XYgFLEVqwTlmKyFE+F5AVFriSvnWVRnHeLwF6LuBnckKtHrtzXpiPDyY/2pM4yHcsaEzdm9VVOranPBaUG3cn97vDYkvwigqu1cKZDubfXcDKG7i7x7/97//L//0Xb3/+DP/xuQQnRY0W9M7mQyWijsE4zzvKA6b2BlIUqNABS7k4h4U0nG9IspsiNtajqI6iMIpWCVQYDYmWgMq3tRCiwkkzfO4tRSloPrRBqqcwoDKoU4hwoudD4Tio3qqCtkmfUnPHO4pxFG8sQJ5zzqPo0kEpC5pSknCssIYrfoc0aXQxxjnS9hIcYo4kl4p2EkOhqp+C9tL96YzG+Ek+FKuBKoI5TQqaJDYBs2FIylKhQWgJXlJbx1ragsJRYUGURFiNaN8SYJB08iQXDPIxy8VMwxqOdxWQRxOZ5RFtFDKmSwRKABNK2qwqUnEnVp4+ica5glUIXK23R1hBipN95tFGEw4G+6UkhYKwR/5vWM6dEVJmihS5umqYSZYFSiVSBpBUYQyoB77Kk1xtLSQXfWqYxSCdqrYhOJLLSlUr0QuhljXEGZcTx0zgh95w1kCPeGDQw5UgyhqwUSVfNsMDSxq2QoJ6UMtY55hiYqalTKAnziZkQAs55rFIYa5hTJOVM1pBKRjlNdIoq+aOzhHTFcSTMAbzGqkLJhpyKCMqNI8QRpRKNEvE8lgxeib+jAdu13MWAaVtyEg9ZXQsGhoxWmnEWklUDthShvZV4OaMUMSmsVuQy45WW9mAMphUBTVc6NaWI6jxxmPBtK8J4SiJKaoNzIj7HJDdRYyzDYaRxDdoaQfyUEvJUCfWpisL5pob0FJQ1VYjWYg1hNNYYWm9J4YDVEnoW01y9aeWEKWNJCPGcq3BaKv0SU6LrOg6HA4CIItqRUqRp2/vAsjRTjPi0mgJd3zMOA433ZJUk5ThntNPMU6RtGkIKdE7sO0oB593qDbtsmEkZ1VhCDFhjIWa8ki4F33jx3rS11dJ7KJn5MNI1DV7LPO2Mr+dB/HeVFqEHrcTb24m9R05REu2VxlmHNYY4B2yy+JIZwySiMZCyWNbEkvAq47IiFsRb2VbD+5RqUJ0Rf9oiQvN8GGiVpXMWoxVhnLCm2oOg2B8Gdscn5DDJRjEmpihFpJAzSsMUJow1xDRhjUZpoeOd1ZiSaW2DyZkSI33nIaZKgRe0LlgHcSpYrdgY6FNGOcg2SQcEgZwim64l5ZliCtYq9rd37I52zClTrKekQEozjYGiEoWINRarFSlnVFlsZuR+cLI9gVwYDgOnZ2cchgHfthSk6BVzRqeAM5pt7ylk+lbGrhhEVCxiraK0EKPKFkqKlJRFpLQGi/ydNRpnDCVHtAWlpPsixQnfeAqFu/0tjdVsvKWkiElFhM1a1JhiwGkrnsFFipb78ZbOeVKYMNliiqZ1bp1zOSvxs1USQGaNiI4FU+8JoK0nxUDTaJROOG/JCZy33N7eoAmYBC2Wrm0ZoyTd5zJijIXiaG1DGgLeOLFRaXuMaWqBU8hkp4xYwCSNtUIfa2XAGJxvMdaDMqQiPrcU8UI3xkOljkuZiSnhXeZo07DrtxyGEWs0jfXyDIIQzykrUi7kGNlsGvrO0xmxMolJOgIUGePF9kM3npAKTauhZFoUx/0GSyDGRNEarywglLdTBkpDyJkw79HK0HmHIpKHEW8bHAZtDY1rUGWW+7eGnCLGt/w7v/tv/FK0/XPw+ju//4++q7O0cC/ih4gDQlJK+7SQdYsYU7QSyqu+hw5VmHN2FTHDUW2/XDbytRVc7AVkEzodidAXtkL7DGeW5BXJC6WVffWnrRuzhYybd1XgSULQLE08sdfEXqim7jIT+rpprse4WCroStW46i+ZKrFjxsJ0ZqWl34uwJuKVBJqoIqKG26fV31TE2LLSUrq20Re32ADIsdnx3s92EaSL4d5/r/5dbtT6vcxYCFsRz1MnnpSLaJG9pnhpIy1OfCeXFu3FekEE3LrRrJ+hp7QKBnHryFXkWDwb3T4ynjuKEwFVWnCLEGwgx1GpYBEFCuHYipi9fEZcfC0XMlFoYh0gbES4m7ea5joRt2Yls5vruApVqRURaxEvF5FroScXKnxp6fXXIv7pKMFiKlWycKUgNWaIEhrUOdzNjLkdpYAJpK0QuIuYIKKrUKBmjKhYKtWnMEOs4I4Co5hPRDC2g5zE3MjDvlDgqrZWx1oYkLlhqu/tUhBJraH79JbiRShPvV1bnhfSrqmBf8WISLQIXqa2q5tDEKG9toSb2nqfG3MvCud7kc0eEnEhqxOroJ86w0LfLwJD83okHLkqJFUitnYrLaRf7Gt7cJBiq72dKc7c+1N6zXQsQn3qjFBtYyFu3b3PrFaYMVKcWYVg8TXVa8t2++WIYrlmJCgtHLtK4wkFp3JZPWGnc4e/DIQjVy0vCmaKIqhej4STFrsPTOd+FSZTL9fG6hlbCzRoEYOXtSRu5PoMO7MWX1SG+VxCld3NzHzWVIJdM581VeQXMXr16C1ybaZeSGIdpBiyEKq51dhR/JrTxuAvpiq21uth4zCHQNx5Ur3+zSGgUNLVk4RMT70nVVI1NRp3G5lPLOOJBGi5QyZ1eu1qaL64ZXjWixd5Abi/HywUqbuSgsh9sYYq9oqwPh2JwKgzDOcCKVCpz+xUpfgTuXOMjxrG01pgipVYHcV3d3jo1mJK6jTty4H9sxa3rwJllp8PRwJVqFyvo0Z8g1GsVLqZMjfvSZG8eTNz835H9zoQt1KsWDx3l6LY4ZH8+Xh23+GxEKLiyHXfHbAcy1KgSp0I+qkT64BfKJApuR7DzqKKovt8Tzj2HB7Wwo2Xn1kKMO46VGuWeu/ziv5lYKyE8NKpMB3L9VVqkTI3ag1Ji40itmKBtnTAlPp9l/t27KRAGDeG/rOxFl0086nMlfYy3r93LVxMJxYTIGw0bizMNdStuRbx3h5q50UdO/F/ZSVp40aTnDxX2FG6UBZ/4kWwXq4dN2TCRnN4ajg81rQXItbOR/edMtKRIR02JhaGB4bxzDCdaOwIbrx/tshO1fETQTkcVYjm1HP3XKyPTL3nzVu59k0Qingp1Im3umI6lj/TUcYi9CJCz7sqxluxXjFVxP0Pfu+v/amete2f5of+eb2cc5AS5CTtx0XkKJU1RgmK3ptGNphZQspSSsR5xjlHTkKzhDhQqreosYpxHCXdOQdMaMBJYFTOka5sUDFzF+4kJd2A8h3jMII1aISO0lYR0WjlUarQdfL+Icwr8Wi1pEDbxjGUJBtmIsor5nRAe2nxjlNAWUfjHFMJ6GnGeUdMiagdKQSsbQkx4p0TKhBLmpKId/OEc07EUaMpaabRDVMIoFuctZSYiDlLO78Wn1YJHXM4b5iGEWcNg/JoY1AU5mmibVvmJEKtQm66qYi3otKa1m6IYaZ327eEAs0cE8V4xnlGmR5vHTHP5HxAFYN1QhRilZBkbYtSLTFGlE4QEsW0jMXIJl5nNtuWw37AGMPRyTEpRPrOsR8OaGvWZPuSMhvXM4SZgCaXxHSYaZzHKENOQjelEoHCPgRJhjeKXBI2amk9RSovCaptgjyjhRIpzBjTiCpeIjkNeHeEVYaQpD23qEKRlRCLZ44RrMEb2YzPWewhbscDZycnpGGkzAc6Z9EBGtMQ4ow1ilgCXduS40yqLfRWG6xylCxhXzoZWlXEczcm+dxiODs65e7uBpsNQ4gYk6sQLteI0Q5iwnoNZI47S1GGOWb6o2Ne3d5RlGacA1tvKSER5gnftShdSLGIP2JtvTbG4VQhJCHQw1zbceeB3aYnx8DWWQ7DVNPKxb81hERRipwD2ivG4Q7rWlrvifOBptHs9we8b9HFEW2kUOj7HnQmpohWGmzGKovOmjlHXNMxzjMlG5xzTNOIVQXR1jOUXAXKyOPzB4zjiFKWZMRXNMwSLNT2LWWc8QA5s+03tI0UOoZpFIqxbcQTOkYJbkuydhUl1G9S4JXGNpo4J+bDAW8s5CiCTo444/C6QTkDU+a02zDGA9Mc0Mqhc8EojVFmJYGLVjgDx0cbuqahM4pxnLkeMofhTqj2ELAIjeeB7dkxw90t/WbLg+NjDrd3xKy5yJOQ4gUymaYKiiEEnBXS3HrHPAfG/YHGebyXwtGcIgXpU7FOLF9CDqQ4k4zDGEtOCWuzCKIx4l313wyarIRSf3h6xPG7z9ltLBevLhjCzJsY0I0hDgmtHLvtlml/gyoaqxS+27Ld9sQ0c3c7MoWRTdMSMzjbk1PGWkucR7Iq7NqeJw/P0KlwmGe+vLwUL+is0c4R0kRbLFsHxhacM+yOt5zsPGOcePX6mngI6K5jnGaKaZhSJCuN2R0x5sy27Xh0csSm8RymkZubay6vbplSprgiPrCI93FJid2mYdOdcn7cChk9bfjs48/onOEw7XFNtwaqTSrxbLdj1zQcnWw5Pz7h8u6K15e3vHz5ijmKn7VWEsqmkojfnW94/uCMvmlJFK7HAxcXF4SQUE5hct2IhMiuO2aKgadPHrHrG3pn0Sg++eRjDsPMIUrAWwyTeKd7TQyJza7ndLfh0aMzwhiY9oHXX15yvT8wTbVgYg2t1UKp58R22/Hs4SnHxzsOdxMvr2+4uLojohmLIw0J51pyChIIWALf/pV3OD7e0vqOV1+85M3VNcfOchgn+dmsIEZmItpZAppkHNNwoOs6VIkojPilC6tLVgmtO5yzDMMARRHSWJ8pqv2PUiRT0EVJoVRbtBEh+aRvefrkjPPjEyiWl68vefnqkjcXN2iliKqsG4JcEicnHe++OGfTeBrT0DrP1c3A93/4E3Ix7K/3sq4osVxQIfD40RlP3j+n71uhj3XH977/J1xd3hGVhJftm4RKct9r2x1HR0d85cUjVBb7nDdv7njz5pLb/UhQkbHMWO1QuaJlefrn8tz3y9f/968lxdgCekwUK6QMTjb1RcP0oFk95xbfWfHpq+KukQ3ZQjdmL1RO7ISEXEjBJfl8PLM1IToIaVRqxsF19XqsJE4xahWOiobxVAhJd8j1M5OIe5Wiba/SL6R2L+EqKgktWbQIvn5KqFRFplG+y+Ljag/iB5vr9zeTEEXuTkgfd5vqRl1InCZVH7/q0Wdq2FgxrBTikkbvb+Q9mjdhHU93F5lOnYT9VO9AAD2X6nGrmE6lnXx4IMJUqjSyvxO/TX8VV4HAjPcWAqmtXnxOBJb5xFK0F0/0+pKgr1E8MnNheNzQvQ6kxnD7osHMheFc0ruzu0+2V7lweORpL+N67tuXA+G4WYnA7PTqf9l9OVdKUUTu2BjGs4W0VXWcDTbK59iD2CIscweEYhsfuJogXrCHgi5ltQNIvcPeytoVd80qHObG4n7+muFbT9CzdN9MD1q8M8zHQtnFTkSn6UyEPV2DdlJXRcxlbjtNOmtZktsBYqMYPmjYfJmIraK9iJWgq36zqQjlx73H8+07zer3OJwZTn9wQKWCuzgwPtuhstC3YWdXAnUlaBcCrdfrdWIOYsFg95H9swadYP/E072Jq4i7+ESqpUhjxc82tYb5xK4+1t2rIIKbE09UlTTjow5gtTVYaDMRlqkt8nENCHPX4ruaOvEJBbkmVC68+Y7HX8v8nk4t9pDFE9cZ5lMvhYCdjNf+iWM+UtgB2iuZ92njiL0R4TsXzCDZEdU9CzMlwtaSGvH8dHd59UBd5nzceiEa1QaAmw82NNdp9f9dRM3sZQ4vcz72mtsXLdOJYj4p+BvF5lPx0Q47odpzpdrD1rJ/4mkvE8O54fovQHOpMCMc/7T6GB8iOiTCcSO07SGSejlXqRWa21/OhGNP2Np1XlDzBHJr5LrZR/ScpCjRGPGq7t3qd7t4VS8ey7EzXH7DcXhqSY8n/M8a/LUENGUDZhABenznGH8r0FtqxTt0+I7DjNC/1PjbzPi4qxSnXbsmzCh2G3dPDVffEqs8f2k4/YGIv8u1tKxLw9Oe1ChCpzk8VgwvEvbGcPwnIsJnq2ivU7UPmdHBkr2lvUocHlmuvgHhLGIvHf2XiqOfgb/O6/EsHsUqw91Tx/hAcfdegqyw+54H/0dhPLMi3PaGkqUA5q5GXv2lYw6PFeOTxOl7l7z85ITth5bzHwQR6rX6hQJcacQD/c1vKOJphKjoPnXsflbWAKzFt90OmbtnnuYm8/rXHcPvHJFOI8pGdv+spbko9K8ji1fwfObXtvvDA83du8DXImEE7hzbn2i2n+TVTzUbmI5FdNShMDzS3PzqzOOnV3x+tcX9qGf3Ue0moQrIvYj2izj50e/2xGcT/W5i+vERu5/CzbsN/eu4PgPA/f3WDvJ+/kbEZBSrF62pvq46ZLLRhK561nPvL6zXAgGrx7a7i4StFfF5q7h7YYjf2fMX3/0YgP/9w/dof9Ry9NMszw9TWT2FdSzcvOu4+K1Idz5wuj3wfHvN//nZM3b//RaVoH8l3yXU8NKi4PJbmvytOx6f3JKKwpvExX/7nN3HS4EO+tsgguyRYTrS7J8q7F++xKhC08x88dEDNh9Z+i8kD+Btj+D/t68/06JtThKoIW2GFZ8uCgvSIm81jx+ekPPMMN4xjRKso4pjHiJtv5HQJy0p9I0TzztjFTlEWmeZxomUFY3zJNswRxEFvfLV5zKTx0k2VJ1nCCN5DmRmjDboLKRhTLWNk4Tzjp1RWKs5hImYYY5RgpicI0wTRkGXpXW2bzeycU8BbxKj12SVMNoypxmjCxbISoRScqFzCqyiqMwwiGej1uC9JQTFHDUZ+Q45JmzSECJHR1vCODDPAaMdOUlrr7caUsISsSVjKSIeqoRthPrdj1P1Q9SUUkhhqgFUllACm23PxjUUlTnME+N8oGjx84vzEsJUpKW+gMoJHRQNBl/Ea9JZzzQPHB2fcXfY45MEvNhoURG2vmfbtTROcX13zRgG2upXwxjwWhFSYJgkSGscbjnbHfPw7AxTNF9eXDAGEfumIp4t06QpzpOStAgr3/PktOV027O/HLg83HE97KE45mGm9Z6iPFY5yApnO77x3vtsWsf++pKLuxtu7wpTAqN7IQwxQkinJCF11tA5T5wjO98z7weMLiQUw36g8R15CHitiXd7Hp6ecHS8pXGau5tb9sOBy3lEG0dWFqWRuTJD4wy73YZt43n84JySEyEc8dlnX1Cc4hBEcFZIAJPzHV2r8I3m3WdP2DQemyOff/mSzy+vsUXEOoVGV9EfYzje7jjZ9JyebWgbzcXFa65ubnn56g2m7SnFYiSdjZRGTo9bTo53PHp8zrFX5Llwe33HR599xpQLc5jwviEUSZ+2BTbZ8Pj8jGfPvobSicPdNV++vOLll5dgPFkrgoYyIw/nVnO2OeG9F48pOWBU4uXFDW+uCocgFhneFgkGLArnFI/Pz3jn+SOcyrRtw34YuL7a88cf/hRvPXrbMowj0zTRaIXJkd/8zgd0jWW3bRmmkZAKH/74ZxAn4hzFE7QYdn1PCEGo9qQ43+3QQMmKkYGYioQQakOrFElbrDGEOPHg5JRn56e0XoKsvnx9yc31gYhYJWgl1WKL0P8PH5zy+PSE422LUoVhnLkZRz786c/ZTzPWOqx2TPMMueD9zF/8jW/Td47OWebDDYc50316w+vrGyYjBHXOBd/KnB/HEUpAW4NqbJ3TGacdhzmhfAOxYFxLSok8Z7R29NaTcmAiiwWJgThPtKZhPExShLCRHCe+/o2v8/zJOZrCrul48fAhr68umYaJw1iDvKKjEPHGotTEV995yvb0iO22J+XAcAh8/tlrPv/iJc56spZ29ZQnPJp3Xzzl4fkZfSce27GA/7jny5evubgdGIeZvt8wh0hRI0f9jve+8pyzXsbWWcXTJ4949eoNn3zymk274XaeaL1jnGe2naNzil//1nucNC1OG+aUuR3O+Wff/yFhP1KMFMSUMpQ00TaOd5895uz0lL6Xe1SOiU3n+PFPfk5rGnIWgqGowm7X8/DZI7at58X5A4iBk9PnnJ3ecnrU84Mf/UwsPIK0/zNnTo47fuX95xyf9BgUrfMc7ja88o6PP/uSeU7sU/W6NZrLqzd859tf5913n1PigdPdEXEIPH54wg9/9BM+f3PB1X7Ppu9xKXF7ecv52RHfeu89NhtHpzXtWc/dOHLc7PiTjz7ictjXcEaxd9mHiQ/ef4/nz5+zs5nGaB70PeenOz79/DU/+vBjetcyx0QYRnzXsPWex48e8/DRMZvOY7Tm+aOv8erNFd/73o/IGsYxU5xDWU1JmRQLMR344Ku/wsm2YRoHfvLTPfs4UTLi0ZyThL2NI9ZWf8OYOD89pvUNh8MdhxiZdGFKCVPEBsZaR84T25Md3/zaV9n2DoelkPD+IU3T8PrzC2zT0DiDsoaRiCLzzlee8vThMa01bF3PMAwcH59ydXnH9//4x7THRxJQWr3pjFJ88M5TnFb4VuxaVNG88+iEw93N6pVPLjhtsSmxO+p4/uQYpyc2ux2mZDQ7NJHr2ztSrr7lSkQS8RjP/89Hsl++/gV96Sh+bVEZeb7WqtI4VaRoNRffFOGzuSq4O0iu4Ay428R0aitpJERLPJbfS06ImunY0NwkdBBByAS1pi6HnanhHELtHR4Ywk7RXJY1CCo1Zm3VtFNe2zhDb7h7IpvR9kISrt1YVl9adyPi4nhmyUYxnDfSdWYhbBzNTa6UkFCq2QtVvIjQAMOZ/IfK0FwE6HUNKRPxy++rCBKXtkzZJd49s9ihSJp0DT3TQWhTECEwdhLmMpyJIB4bCW7pX4nIpgNyPU55bc93h8zhoWV4UD1n30jYyWq98FZoSu4XqlgoorCzzBuN02UN3rl7Ztl8GYk7v4q8RYvYd3ikCVvYfgrNTZb2UADs+v3bSxEn/VXg+oOWq683FAsnP2Q9Ln8tm3R7O6GyR8+1rVxbLr6jGd+ZsW8cm48Nu08TYHG3gfnEoZJaxzT0mpd/w5LOA+aNY/szw+YL8Le5fj+3tgwv4UtFK6YHHWZMhHcfYG8ldV3PGf9mIh41lYwWAfDimw2HpxC2hfaVpn1TOP4oiKUG1O9Rk9t3lsNDw/BIcfeNGbLiYtSc/l8aVaTtP3shrsyY17C6sFFVPNjTNTNv/uicsz8S8W3qt1UQZfWHvH1h2b+A6Z2Zbjcx/XzL5lPN2Q+CiPeNlgA8xIrg+v2OuxeKw9cnnj295DZaXr/acfq/Wfxtwd0VwlZamSnibXx4aLn8Fmy+c4nWmc8vt7R/7Dj744RH5pY9CL28zN+r9x2Xf0W+t3KF9keWo5/q6n0toUwLSRl7y5tvWdJv3rLpJp5v7/j55SmHz7e8+B+KEIFPe5qLGX8dmI8dySk+/nc1zfGe9x++4fObI65HT/e/bgEZ06U9e3i+uT8+BZdfk2uKAs11DSgahbRcwplSK3P44hsNV7+WYTfDrePohz3bz0WUyU6tYl9qNclZrr6mmb458N6TN1iV+fx2x5df7Hj8D6SwIeF8QrACzFvPz34vcXxyywdHN1wMPa+vtkwnPSc/zvhbjb+cq1Ata6m/GLG3CKVcSWkh6B3tq7CK94ugbKNQj9OjXqw+qqfuGnB1YvFXUboRauDXx79jePfXPuWJTvzW2c+5+XbH//LxV9nfHtFd5DXISyVZt4qGz34b7IMDzx9cMQTHq+sN9o82PPjevXi3+E2HjeblX9Y0X73hq8fXPOzuuAkt3+/f4+SHlpMPJ/zFyPSwxd2K1eN40vDqr2ZO372kV3DUjlx8u+fiwxMe/FM4PDBsP4/Mpx5/OTM8bRlPNPFvXfLbTz7mob/l8+mY771+yvgH59iDvhe5FaikmE4tr/9S5vjda3715IopWuZs+OjsMU/+vmY6rZ611SpseL7h+qsQH878zq/+AAD35Of843fe5efvnPGV/06OfQnpAkQg/Jdmnj29pLWRd7eXfPj+Az5+/pDzf2Jw+0L/Mqxz6+gnAz/9Wz0vfutTUtb89Sc/4s285fVXN/zT//kb2NGw++me8XFH6DWbzyauvtYy/I1b3ju94oOj1/xK94p/cv0Vvvf0Kfu/f8zxz/IqptoxY18mPvnrnv7XLvgrD17yTnfJ9vnEH737lH/8vQ949j8phnMr9keHzLw1HB5pbr4Zeef9V3zj5CUbO/H865f815/8Bvv/5gk6GtqrtAqsMg4FM8Kn/4olvwjkO82jf2jpX0VUJVlVEoG7uU7r+q5j4eoDx3ysaC4K3UXG36RKcVu5Z1ZKeXikee9f+4i/cPIJj/wNoRiefOeG/3H7DdSHO1ID007W3iZndFC8+a3Eb37rI97dXPJXtx/yg/EZv/2rP+I//vTf5L2/F5hOrfjYViuj1Cie/LVPOW0OPOlu0YgV6B/+5jnNRUPoqq1HvTfFVooN6dfvOG0nvn7yio2Z8SbxkXnE5tPaYdDqqoGVtbD3p339mRZtS84orUlFPCeHcca6hqwVm6bj9HjLixfnTNOIto949eaK168vICoJ7SEzxYlUMkdtD8XSdJaHZxtUCYQpcnVzzTBDKZqcZYFMSLuoSYpnD045Oz5ht+u5uLng6jYRLwdKVJ9+AAAgAElEQVQyBrTh9vYSaz3WbHC2wZnE2cmOR+c911ev6WfD559f0rcdw2yZc6LbbBhu9ySraYrC5czxdsPJoyOu7vZc395wE2emqeCcRSXBPWMKbLoN07jHGMdm15BSQKMYp4Cyhqg1ykViHCiqYHRPOASeP37Io/P3KXHgkA68eXnF3TAAmoxQmzknIJEzbBrPg5NTNsctw3Tg5jAwTZc0fccwGAoJyHSNpE6f9ztONh0Pj3e43nOYA198+SkvLy9JqiGmQEgKrSSoizyz6VveffGU1jtyzrx89QXDOBJnxeF2hFxovSPlgDENm03H87OHbDaWTWe5nk958+YNn3z6ufiUGM+8l1CwxEzKkV/72lfZ+pauUYTpjpPTF3z/T37M3RjBOdJcW6SV4XA48LWvvMP5yY7tkcN7S7f9v9l7k+fK0vw87/mGM94JF7gAEkBmVg7VVd3Vc5PNVotNmeJkKxS2bEXIjpBDjvDKO28d3tH/hu2FV7akcFhWWJZEmaIYTarZJJs9d2VVZeWITAAJXNz5zN/gxXcTzZ24kuQwTwQic3EDwz3nnuH93t/zRKgLw2xxjs9Ggc3lDWmasSkq9gYj3jk54takh2sL7h0+4HJ2wfNX51ytGqquxbmIXhxRtg3uLWxZR9g2jBhnSYxFs6wK4jQh1Rm+88goCL7yOOL4aI/hIGPQS1mvcqq2oXr8HBuyD5QCHSvauguytzjlzvEhO7sZEkFTbYhSwaNPn6MShZc+jOp7jWgVw4MhD24fk6SSOJFICWNpsVFE+eIVrrPIOKGtOtIsx5iWcT9hfzIiSyRJrFC7I7JYcX0xpWsaKtvClu/adht02uPOyTH9LCZWDhcZrDAcmAnPXpyh0oyyrtFxRCLCKt/7775Drx/TyxSRShkkMdJrhLdcbToqK+i2IpBEKHpCcTw5YJTEpP0+XbMhiiVRpDk9vcKaIMDKkphFXXA4GXPv+C5J3JHGmihNiZIEHaekr88pyhorHFpGqETRrpfcufMOg2FOnsdESpL1MtbrDScnt7n4wffJ+rsY6/GuRcQ9RNPSj3L6/YyH90+o6wrv4fnz55iuJYljrAMvY7zrqG2HB3qjhPFunyxK8KJDa4XiDbPlhqaxRDIsyug4YTjo8c7RESKCfr+PNw2plmRpymX2hqZptlgCi7UdVb3m3Yefp9fLyGJFEkn6/SPUckm5Z7haXoHvoZIc4Ruc24Z/OkwHZDpDYKnaCqElq7IgiiJ865FeY4xDSoGIRAjIlSJJ4i1D19EZh0dSNqHVrLMIgWW80+Pk6JjedlVcKYfrHJO9MQd7E16eXdE0FeP9EZvVGqcc++Mx9x/cDS3lRNN1jkGSYeqC65mjsW7bdnZb7qfm7t07CN/RSxVKKaSOuXO0j2trFrMl2kGkGhyOurP08x7vP3zA6voNvbwPdUWvL0kixesXZyG0k2GxaJAkKN9xdOs2UaTQMkKngq6zjLMht24NWT9dY51DSU1tO2y7YbJ7zMntY0zbkCUSmUgMirptSeKMpmiQ0qBETGxq2rbkM5/5FTarBV45sjShNg23bh3QtQZJh/A5jRBoJYiU42BvwvHkAKdLIqGJdAoI3u0PmF0tqIoaH2lUnJB2DqcV/VSTuYqonyBtQ3/Qo+kcUkqKYo2OU7xwBLSUoZ+l7O8d0tklOg4LPZOdYWhEv/bY2XasTmuSJMWYlmzY59bxEa5e4pqOOIrpJymbqkQoaNoSKxVZntN0BucMB5Nddgc5nQuMXycVg/GQ/mhANZ2TpJLatgiRoWOFFgLTFAwyTa/XY2dnh+fPTsnjnKIpiJKUzjZgDXnex7kgwlyXNZPJLtI4eqnk4s01bdeFK6dW5CpGC0XrOnpxzsHOhLKcIRONiiKyKEGrjD/TH1GblihKqcoSqUF5y9HeAdp3SCswvmQ0GNI6h5CW/riH1GFcc1OXNK7laP+AfNBHYoiEonI1uU7J+gmosKBmrCUXmrYxxP2EbBDTG+ZkkcCYmiTNGEUZnXEk0RTbhPHPt3ztznt6Ov53ct/3l9u//S08QHETkCbTCptmSO1vmjyjL01ZFSleOdafDhg+DYDDwIuDeBls5JvbCQDtQLJ6NwS5qhb0XmnS+duGzTak3QYnNhFMv6So32mY7C9Yne6QvdKoRiKj8EDTO2tuwpAuD1/rdwTtexXyLKUdC/Z+GkZvs1mwszd7Ecl1h+o8zUBhMihOoD1piV/H9F5L+ueWaG1uWKQQwqDyICJZWVwE9URsx0cTklXAb+nKbcM4f4NO0KXj8msxxWdacAY91wyeKfIr9/P3ecu3BbZhk2L9wCH2GvwyJj2XJGtJOwjjp29DsmY3RjjP/N2I8sSTvLuklzZMF33iD3PGH4PqAk5Bb1vIb43b5b7m6hfBDQ0YT/9xRLLwqFqSzUK42Yyjm0ZxOZHMv2yJd0uOd5e8ON8jfp5w8P3QYrapIHrbYN6G6E//dgL7NTujgqJKmN9W7P2zjN6bAOCVraMbp+H4uio5/a0R5b2O3n7JcV4xH+Wsoz47T39usVe1oxlr8vOGxWcyrr5hee+9M1qn+OZXnvFPXnyexR+PGT+G9LpDGE+5n5JetQjjECagFHQbUBfVboJqHPnzFWaY3oilnBa0fYWZaOZfsvQOCz6YXPLJ9IDZMiWdh4ftEMorTKaCQMx5up6g+WrBF4/fkKqOi2LIaTrh5HfkjVhKWX/DZS6OJP6vLrnTK5lkG2Jl+d7dnIXJyK63wikdWL7tjkZVls3dCP+w5HBUcNDbcBoZFqM+O08kUemIZy2w/VvXDTZJsV9d897enP1sQ+sUjVEs34s4+o7fog6aG26tiwRXv9Ew3t3wcHzNJClY7aV8V99jJnJ2HovQpi4MOH8zur34ouP4aM5Jf8l13eN1OmKW9zn8ntxymwX1vmb4vGb6xQz1jTkHvZKT3pJJsmE3Kfk4PqAe7ZFfmRtG8duR/Te/mLFzsOTOzoK9pOBB/5ofz445/VLC5CehEa3qECr6JDTXqlsR5YGi+FZBt4rBC/a/GyRM7Z9r+QkHybwDD/VexOj2kqPhCuskn8RHuDhi9Cyc18yWndkkguJYknx9RiYdXxyfYZzibn/Oy8GY88d3SVZbUVgd2LR6uubVrx0y3t1wMlxxO1/wlZ1X/FF0nxfHCeITbvZdGKf26MIELrN1NLsxXrFt5gt6LwtMPzSRvZI/5yQLQsYYCdphhK7CAsvbYD69avFSBGyLhOX9mLtfPONzO2/onKKvGkoX86t3PuWfv/dVkj8NyIPVwx69ixbZeWbva/6Dr/+EzikmyYZp0yfbO+dftu/TPgtN0bfNZ5OHaY27X3uNFo7P7Vww0hWJMBRfiXlpThi+lJBHdHnw/8Szmuog5e996w/57vQ+n9u5AMCMFI96h6x/dBJYpRaSaUV5O8dGgtkXPCdpw05UMo4KrtoBv3z0lH/ywS69i7Do5WJJPKsRrWH+3pjPfuEFmzbhQX9KLlsWJufqoE87GG7P+2GhywwidGH5hV95wuPZBOMln8kveVZN+C/v/wn/Y/0tvMpuJgreTlws3/P86uc+obIRk7hgP14D8I1vPOefvfgm6cLdhHbNSCFNhNnvuDeYMYk3JMLwS4MnPGsO+L56n/yyxeZhEkI3oeleHQh+7Z3HTNseO7okly1/9+C7/EPxS/woGd2c/10qaAaKfuPoRo6/c/8HNF5T2pjb8Yxv3nrM4+t9vNwhWVtk66nHaruoKbn37ht+8/AjLrsBH+RnlC7my3tn/N7+IckiSM6SpQ0Lj2kIL3XtMIcth7trRkc18+/euZnW6HK5nZQRlBO9XfAM1/jVQ41XnmZXoD6URJstsolwbbdJWPCtdz3/1fEf8ePyDrlsGcqKD9LXjB+W/AvzKySrgC1IFz/fl5/77CtGcU0uW65tn98a/ITTbm+7SBnd3Mv03hiiZcv0KzlfGE5JpGE/XvO4OOCD/jmjUQmE+xzdeMpJCLq7HrQjz+3dJfcGM5Ztyt3RjG9OnrGqE7r+HqoN902qDgUJvV2E/Ytu/16HtkKZYC43JnBrI/B0dF5xePeI3WGf0TDGk+G9J0kiev2Ujz9+EeDsNnBK67oGlSFNy3v3j+nngiTVlOuCSdHn0bOXVMbghcRvpSxVUbMzGvDuu+8Sa0uaxvTHEbubIUK8ZjZfgfHkOg2NYOdQtuWzn7nP3m4fHXccTUKoMsgjnp2fo9IUWYNrw0OmEhKtFScnhxzs7qGyjn2xw/Q658mLF0jnMdYQnFKSVCSUi4qjWwd88Nm7OF8RaUWx2nBxOeXF6QW9YUxhNDiIowjTNBwe7nD/3V3SOEO7lNb32RsN+NHPfoaUCus0rXA4KUDmtDhu33+P3X6Cii1aT9is1ihvWawqWhqk1HiVYI2m7ToOP3OXuycTIjpE1zKWivEwI3n2gourFSVQeYuVntgLelnMO/fucjzZI4k11nYMBxnT6ZSqOEMKj1Ea5yXWBobv7XGfe+8f0JUbdnoJQ6/JY4NrOt5czXFWMch6eOGoCxiNBjx48ABsGOF23S5t1/Dw7h0+fPQUJaIQAHQWGYdw/HB/xGR/RKwtcRSx3Aji24pWGZ4+n4a2m/doHPVmxd333+fhvdtU1RXjgzFt3TEejOm9l/Od7/+QVjpq51nbOPAJhSCKFbY1SA87wyGrsqBzlkiFhkPZlMQiJhYZ09k1o37Egzv3cLJDakiynKIqOCk7nj57RaQ0QgrazpLHCV3Z8v7DB4yGGVJUxHEKkeOk30OrhO//9ENcrOgAh0FKODkYc+twTFWtSZII7x337t0lTa44Pb8A63BtRy/t0xlLnqTce+cOWR6jhEUKR5ZHjHcnTGc1Z2+usNaSRBECyU5vxN2jE8ajIZGWaBwiFag4Io77nF1eU5aWJB1RdeF3cr5k//gAHXlibYlUTLlac/v2bZCCs48+xgoJQqJlEFm9/9nP8c7tI3y5JNGQ6RSdaPr9Ac+ePEXrIa2DxdpArJlMJvR3Apok0hrnPYkKTfL379/jn//uv+Tg3gPKpkNbz2C8w8n9dzg63AtogSymbhuGwwHWRRweHnJ5vSROM1qjMaUl1QnXi0u+9LVfIc80g50May1R/IBHj55QVBXZYMimrJEKrO24e3KbL3z2PXy3oa0KHLB/MKYxJS/OX5P2B2HBRglqs+H23i12JmOk6FA4vFQkScBj3Do64PxqSmMtSZIEFIiHJEnI85w0ChI/5xx37tzFGcdlP+NysUGrBLO1kolQ7scJkMqTRjF5HDGfz+mlCmuDoFHZMA2AC3RNqUBIUN5w+/YJz549Q/s4NIajBOkdbdeglSVLBug4ojY1iYwwCJJehlYZ+eia+uUZST9nXUyDDA7PdeGQvRRvOrw1gQFtFQdHt3j68jVl0eGFwliHFhE6ihBKEkkVcAk+sNDHkwHnbxRpT1E3lqZ1CC0QQjDZGbO+njPOBjTekWYp1gtka6m6DetmCXpAJAKKZlNv6KU98rSHEx4rQMUJsdCM+vvY+iUqaXHGkcaajUyROsWakmEeoz1IIYlVgj464ZMnr3DrisjndHU4ptZth21qsjTII4PIMEZ56OkYLSNkFNF2Fmc8rWhJhikucsQ+JpVh5D4bDijWaywNTtR4kyCUDpLJWAeOlfLgBEkUYTqDkJrD/X0+/vQpo17KpiyJ8h6x3kFmGZ2siWTCcNijXhcUVYOOErI0J8vq7QVe0JgSJy3WtmzWC7wp2B2MEMBsdR2wK8Ig8gRrPIWp6WcRjSlI8oiqC2gdYwzeC+I4p6otVVODjkiyHNs2xHGM7TqcjPjk6Qs++OADNrMlIu4HsaOIabdYpXJTonxDXTekWUSUaGrbMMhTypnBC0UiJd53KCeRMdiqQUUCjeTi+pwsE0RIvFe0reV6tiRL0oBewaOEQ0pNtakpioJ8PADhcHhW1RJHkJwW8yWDnZjOdiTCk6Y5tqrYFDVaOXppRuQ0XgrmZR2YgsaEKRPvUL2ItWkYVJquDpLTLE1pW0NVdbStoW5bOgd2G1bXdUsSJTT+L5u2/3/ZvArj9nLLYDWD+OaBYvm+IHtnzdcPX9I5hfGST/r7nO3tcfS7Ci+2XLeBIl5sHxK1YP3XS25PFhzma54vd5k+6KN/JyObuZuWiXCgZx2rdxKOv/WK/WzDg3zKy91dPr5zwFJMGLwI37Pej7ehssTGguvfrPny3fAQlr3fUdmI3z/8HAffCUKXt6O5zV5o7NkE5l+2PHj3gl7UwgP46fNj5B8k2zaa245WhyZWdm24+lLM5D98TWIVo6Tm5XzM4qMhR3/kKfcV+ZW9aaGp2nH9+Rj5V+Y86BeM4opp1efyXp/kH/WCqLO1RBt3MyoerS2rr1ge3LmiF7UMH9R8/M4BV/EevVcQbwRuG2jIzhOtO1afFfzylz9hkmyIhIVb8PHJIY/69xk/grwzxLMW2RrMIKHaj7j8Jbj/+TOO8hWliXh2a4/50x3SRWjla8J7JbvAbL38WsxvfO1nXDc5vz75iOd7E35v/BmuzITdR3Y7dh9G0UeLjvWdmG9+IzTQ7mYzShdzUQ/542895M4/k9hIEK+3AiUFCEH12Zov3jvjMF1znC74w6uHFHnNWbXPybfNtmXrMYlAzwqmX0/4W7/0fZ5uJvzG3iNeN2O+cfSSN//RnIuz+0QbRTov6J9tW7xKhfH5bRC4uqsZvDYBp9GLEdYRXxXYQYrva0aPFpTvDPmbv/gjGqdJpOH4zopHw0NOv3rCybdDkzDgLALnV5eO7teXfO3wnEx1HCXL8B58fs53kgfc/fuB3a4Lg+hCuFZ8qebvPfwhH28OuZPN6bziv/nSH/D3B7+A+94OsA3z9yOi0lHvxxx+/YK7gznDqCaWhgf9Ke2x5veffo2DH3ZhXw8TvBRsHgy4/kXLN05eM4kL9uINEk9PtzzJauof3yKddnSjmGRaBeHSxvGV+6eM4pqjZMlBvOJnmxP+8899n3/I14h+FN8gLlyqiGc1L3+rx1/96scYL7mTzXm3d8VH8SHFXkLzh0ehabqxDJ+H6/36AXxxd8pxtmI/XtM4zd1shhlL/uTX+3zuvzuj+Ood0ssKmUUUxwniSyv+7sM/5ZPiFp/vv+bT8pCvTU6puojrz0/Y+7Cm62tk60hqS7sTMfx4yZv/LOVwvCbdN9RGc57tcPjP48D93NXkV11gcbaO82/mfOFvfIxxkmnVp7WK9x6c8+nmNkd/UNLupjfj2cnSMv2q4FdvnZJIw0hXdE4xjgp2opJPPnvE/g8d0bIJiJBUgZR0fc87ozl3e3MiYSldzH9//5/yP9j/mM0nt9h7ZG8QBrCddEjVTUu5GUqqXcXoWUN9mN20WeWfw4aIP8dItZHg8mua4z80N8iPt7iHaB2QDM2O4DhueL7Z5SRfMjc599Ipu2rD/z3+IslS0O0k9M5DA1g4z85TS6Y6tHAYpzhKl1Q25hcfvuBJ7z2y6+2iXG0hCxMQWjjGaclYl8xNzliX/PrBx/zPvaMwgbHkBr3glWTzwPKns3f4jcNHvKgmvJu/YWlynqo9zMzSPw/HYQiuIX/T4vfg4WhKZWM2Ng1t0GQOxzWqCegI2Tna3ZT09RqnBZWJ+NrklLEuyVV4/c79kn+afCssTOVhEa4ZKXpnhtLEnAxXTOINjdccpwsGsubOeEGRhfN7tAmf3WjjsH1BZSNupSs+k73hll7yhewVf1bcQ3b8fPxfEARdUoCDygaEx4PkkmvbJxKW+P0V/E7C+k5C700XWp09hVMwbXtM4oJfHTzicXuLPyvvsxNVNDsheH27Jetw3RGd5oer2xgn+Zv7PyEShv9z/gto5W6OPdVY8itPcRgRbTyH+ZrH5QH3smum3SBkcPGKeCnIZmFR7u2CSNcLbWtp4eD3YqZ/o8/l1YQ9Gbi0wnqiIiAQ0mlwVSTzjmYchUmSlaQ5tOi5BB8mc4QLC0tOhykYGwtc4vmfTn+FB4Prm6btabfLvzj/LPVYkmzRKzYROCVIZh0v52PeG14CULqY311/gcaFGDS/MmyOwjm3HUiaUYbs4MP5If2oRe9YJsmGziuWr0ZMCMxdkwRkh40E/QtLdaBZ1QlncsTD4ZSzeoen6z02Zcp4FbBOsgu4oXRhw2dg/XNU0b9pk//ml/y727z3WGPCw79xKBXYs8Nhn92dMXkWoSREkUIIx8HBDr0sQkmHVj4wIo0JfNK24+johNF4j8GgRxQpesM+w90eSQTC1UjhMHWNbwNKYW8n8PXiOMivrLWMBgP2d8eILUsmUjFaR1gBQit29sIYe6+Xo7XGe8/JSRg5F6YjVpKuaamKkqZrQUqO79wlH8SkWUQcx0z2xtw9OsKWJRqBlqF1nCiBcC0nt/ZJNUzGfVItGO/02N8bohV422JNTRQp2qpGeBgPRxwfHxPHoWE0Ho4Y7/TZGQ1DW8h3OOdoOoMU4QA+OJywM9mj18tD63InY7I/xNqGPFEI32GbCmktpqkZDfpEShJpgU4EMoJ+P2U47NNUNd6E8YFYaYR3JEpzfHDAZGeExJKlkv29Xe6cHBNHMgRSUtIYg9RxkFYphXSeUb9HZ1vauuTg4IDhqI8zLd5bFpvFli/qybMBeZrROotxXWijZRHDYQ+P3YZMFh1rNtWaNE/oj4bs7AwD79AJ+oMeuwdD0ixCS1DK46RjUy4RkWc47qFiQZbHtLYlymOyfsxoPKQsN6RpTKQFvqsCh01AU6zQEtIsJh1k5L0UpQWxVkTOkGpFVa5puprhzg7GwaqpqE34YNdtTZylRFFE19ShOdnUWGtZFxUy0ty9d0KcSPJBjtKeOI4Z5BlaCaztQqhmQeFpTcNw2GPUS9kZ5ChviTQ0VcF4NGSQZMRSkWQ9KhcMr7v7u+zs7mBtQxRpkiSiaRq01qRphpSaSER4D6YJoZFwYitF8hi3bZ5IiYyCPT1gLRpwFue7IJwSDoelcx1FWxJlKUIIqrZBqMCwFkpi2m2A0nnapiZNU6wHRISQ+gZPYkT4kpEMYrs8R6qEpnN0xoOSGNOSJDEoy854iPaCfhxTLtd0dUNTVhSbCq0UdVluObkNWZ5gtoIzIQKH2WFDkz3LqJuOqm1Cq9YJlos1VVUhIs2mrWm8Cw0+CalWFMsFQgh0nNDrD+gPB7x69Yos72M9LNYbGuvottf++XIV0C0uoGOMdzjhqeuaQa/HqN8DHMaYLUM6BJIBA+MRQjCdTomynKquw4KZNwHFIMOIucCjtygWqcLYltYaY8zNl1chqJU6NOsl4TU6SqjqlsFwN0gSLQgvw6KX1HinEEKw2KxRUfieQgjKsqZtWzabDVoHnmFdVuH64AX9wYC6rsPrlaJzlsFgwGq1Cot9eitdFH7LH25YLBZYa8P+sCHkazqDiGLaziKUwiEx1pNnGZtyzaaoWJdVwD44R2vCey2lJsuHOBGkJE4qZJywKks2VUnrLFZC3bSgFfN5gda9gCCQEiUku8MRZbGm6zpq06HiBO89VVVxevo67CsU1nuEFqAg0pLF9QzbGoxpsdZiraUsajZVjRXQeouUIaB2FqSX4H4uweq6jtV6gfcWGWmM82jhsE1Nmkek/ZSL6RVFbSgbE8RqSlFVDfPZgmF/QFFVpGlK3bUoCcV6GVq4m5LFfAUEcdrldEZrHVZYOhn4zg6JjlLqusU7QZb2WKw31E2HVBGbogqtbB+YwnmWhHA1TTg9f41xPpwLUFT1htlsFljGw3Dd7rouTNtI2HQ1Tmta4EePHvH01Ss2bU3ZVRhvkLGmNh1pL0cpxe7uLlrFaBVzfn3Nk5evOZvPWLUNVkiUFyRS09VNOE8JmC7mLFZLOuepOkfbCprGUFUNWoa2Oa6ll6U358uLs3PKpqY1hrLtqFuHsbBcruj1fn6cSCnBW6qqouks1kNRNzSdYbnYYK3fNtfFFtXkt4xxybpqWdUNXsVs6o6yszTecbVcorMEZBCn1m2FjhUOj3Pm3+4N319u/842YUPwGv4f2oS6tGyONdk7a05GS3qqYT9eo4Xj79z5Pr29EhsHmcnb79HsRqjOM/s8vHvrii/vhuDo3Z0p7xzM6Po/5/4FTmYYB17fF3xr/wm30hWJNJQm4uuHL6k/U98w6bpMBjHKdgT13aMrvrn7lC8PTtnRJZ1TfPXzz6gnQXLWDcLfkFx3xOtwv/HVD57x/uiSu/mcw3TNF+6dcf2LNpi4M7mVonjafgjnii/VnPSW/M3jn3GvP+Prxy8RDwtM+nP5mMnlVg4GmzuOv/fun7Cfbfji6Iy/fusTvnLyms0dyfbZENmFUOft6PtXH77kr+1/yheGZ2Sq45ePntI9qFEtlBN9Y5z2CmTdMThaM0k2HETrbeDQ8tWdU+xRQ7IOwXMQxgWeazuQ3P/8GX/7+AdkquNhf8rfuPsh73/lJW1PBp6sFsGirt+OgkMiDX9t7zGX3ZDzesR/cven1EdmK5DxDF80ZLPtSPOe4IP+OVdVn4XJMU7xfv8NO7fWN8fWWx5mehGCsL29Db82+Yj3ehdIPF8cn/FrR59gBg6bihukRO+iAylJDwtGuuJBf8rrZsxBtOa93gW/sveYfGoCs7EX2sheBeFOvOywaWiytcPQ6rKJCqKmXkS3m6OnG6K1obozQDWOH1yf8HIzBuBlOWY/22BTv+WFepJZh2wd+asSr+C//dy/4la64oP+GX3VcJBs+ObOE6IkNEffbi7R6HXH7cM5vzX4Cb+5+2F4fbTmRTXhm7desLkVxEHtUJHOAsNyeV/xrYMnlCZiP15zL51yVo3CMT8IoZ7NwyKLqu1WiBeu4w7BdRtQAlo4IhWwBXrTkkyrm+NRGE9tIyobcdUO+NH6Dvvxmkha7DI0PWUTuJ2ytrhE4xVcVX0e5FMKu22dScesyoMAq3aobVioihYzNsTKclYNucVh+nsAACAASURBVGoHACxMzp1sDkD7bgh6q6Oc6HIdAusi5kerO+xEJZ+Wh+zHa86qEXeG88A4jeQNI1d0oQXu0gi7ipku+9RGY51EnyWk8yDpyy9a9LIKnONI0g09T+Z7aOnoxw3v7kz50vg1ez8UNJMU1ViyJ9fEyy60Vh388fldGheaiuOoYN71qGyMXCs2xzGbe4GRK2uL2e0FdJt0tE7TeUUkLP/b9K8wSmqShd8ygd2fY676LS84BHNOBzyLydTN+6pqu234y5tA1kVBcmUTQbIQFLcCakGEMagbDrJwHiR8dHZIP2pYdimRsPxsc8Krdg99FWHT8LtEs/JmOqI4kDzf7LLsUrS0TNs+3xp+woeXt8I5ty9vzj+6tKQLy6fn+5Qm5nFxQGESHhcHvGrGICEq3Pb85gJLepKQXCmeX+/yp4t7FDamtAlv2iGNDcFdPdY3i15vg2p1nvBodshl02dpMl4W4fMrXmY3uJe3Mr31+zukM8eqTnhZjBnpks5pHhXH/IOf/cKWIf72nBWal6an+ejskKKLWXQ5S5OxNBk/KW/z4noc2K2bcKwHRIQLU09AYRKWJufCjPhXy8/R+YD6UXUQD8rOU+8ompFi8FHEk/mEj9eHnHa7dF7xuDygPB2wOYnJr8w26A5IgfTak6mOR8tD/unyywBct31+99l76CKIP6PKb8WAHpMp4pXEOMnD/pT/Z/YBT5pDYmm4vu4TlT9vSb/FJJgM/vjH77JsUx4XB0TS8Lg44F9evE+09pT7mravQuN/K5HsXXTILrC6b/0fCUd/6Om9MWTTLnDllSCZB/mdjQSLh0lAA6WCyU8tt74t2P+xYfCqveGdN0NFVDmkCXKvwVPJs7MJ103OJ+UtXjdjXlQTpst+kJENwr4rJ5J4HRZF2o+GfLI64LwZ8eHmmOfVHguTk5/L0HYVYcH5beM2XTjmm5zKRDxa3OKsGvGd6QNEK8inJhy7b6dmbFj4zi49s4sRxkt+Ojvi09WEWZHjn/RuGMZeQbqwmExuJXB/cTyC+u3f/u2/8Iv/bW//4B//X7/dGZBSI4gwFiI0SijevXeb8TDDmw6pHHLLdzHGcjmdU3UNWToIIG48bec52D8kH+ggDdnyt+Ik4c3VBcWmRklJkuQ4oTCt5e7tO+zt9kG0KAtaaqIkpihKLi4uiclwNjwA11qiYs2Dd/bxokFGHqESJCltUVIWHc4I6sbR4RGxDO0f75gc7qFVi+lavJMkecKnT5/SWYG1INBIFQKgKIq4f/8uWa6wrgsPaNJS1TWz2RohNK03bDY1KtJ03jEcD9kZ7yK9x2KxzlCblrOLc8q6QSAxXRBCxXFMsym4c+cASYeOPdZ0WOtpreN6saGuPUpJdByQBEpb7t89Iok13lsQks5Yus4yna0pqhapFJ0xRFGM6TqiRLK3P0ZFECmP71o2RUnXGZ6fPkcoTdm0yCjBSUXbNRwf7rGbZmgZYfDEcUpR1kznCypTU5sOnQQxXC/PcNazuz8J8joVeMKmaynKljeXU6xUOBRW2iBXE5bjgwlVWSLweOOoXcNsMWc+X1FtgiWz84HxGyvN/t4YpSSdrUiyhNZYGu95dX4VRFArGxim0qNVGszicUZjLTrSpFqz2KwAj1IQGYOzniRLkWic74jSiJ2DHax1xHgaZ6iqmsfPXhDnCdY7OueRMXjv6OcZSZLRdmW4gDpP29a0XnJ6fsWqq2nawEQVEoRrGY76tM7gZRCOoTRt1fLy1RtenJ7jhMYBtrMoJN4bLIbBIMPTYTtL6xzrouHlq9dUjUHFCcZapEjQEmKlGO/2MNZgvcVhaTrD+fSaxXyDE1C2JVkUEWmJtx6VaXQU42Ww3BvreHVxwZvLObNViTGSWGVEPkY7v23ca5wKwrjSNHTOc/rqDbNViVQRnXFIOhAeiSTPEvK8R5Qo6qrFWM9mveL0xWuKTUFdNYyGQ7wMo27aS8bjIda06EjRtA0CzdnrM87eXCJ0Qms8WoN3Lc4benFOsVkzHA7JewM2ZcXF1ZTVugAvSbKMsqiI0wgdScqiRChFnuY0TYcQiu/94GdUjcUY8E4GTIX16O3PO9w/JkkiEJ6i3rCuCqrG8PriDVfTOQqF9AYVSXppgukMu+PdwG31Fu88tbGcnr1kvt5Q1B06ijGmozYGpwRKR9jOgBeYrmaxWKJ1AjLBSYlTGqk0znU4G0YUtVTgPEVbUNQNq7IOaJLOksYpxrchvFThcx5pzajfR2sdRAvWM7u+5nI6Z76cg96aaQX00j6b5ZJ+lgX5nvQIobi8fMPpq3M26xbbWdBpkAB6hRYaZQxCCtIkoW4ajBO8PrvizeWMxbohyno0XUeSZOAl6/USuQ2oAVARV5dv2FSGN5czPDGe0Ny13qMiRdc1DAZ9vHN4PHXVcnF+xdV0TWs8TioQYSSubTZBjpdkdJ6wWKkSZtMlF9MrrqazcEy7YA2XUVj8MtKjkSRJivWOuqiZzipenl1StIbagpaSWEZoIZjPNnjvSdMMKxx117BZr3l9MeXsfIrHE6kYpaFpGhZ1AUlKrHKiOKLtPIui4OpqyZMXz2msQcTRVvgXIaWiahqMdcSJwqOoO8ts0fD0xRnT2YoNlsZ1Qc5HSp72Ka7XSK9o2xrvNfNVwemrNyxXFU3d0HmPsAFJoESENZquAxlLGtOyaWqW8wWz5YrpfImxHms9UZQiovCZbztLnCWY1uLa8PCFlBgsKo5DOOpAKY0xFikjyqbBdQGp4TxY54PAEdBKUbU1ed6nqjZIpaiMZbFao3VOfzSmqSyfPHnJ9WxDWZdUtkVrhWsb4ihFa03TGpwUeK9YFw1F2fDs5WsurlegQ3gPAq8cAoWUivW6oKpapEooyoY3l9ecvnwVrvm2Q8canEIJ0JGmLEvatqV1HUYJqsrw+mLG1fWcrvVIrTEqhMMiDiOlno7/4j/9W38ho+1fbv/f3v6X//Vf/7beWo8RoGuLyRU2ltz51hm/dfAhFkUkHIk0GK+4MgOqTwZkc0u9szV9d6FltHhfEu027GcFkbSBN5etef74iPwyMF67gUKaIFa5+gZ8+c4pzkt6qmUY1UyiDT9dHpE9ijC5QBpQXRhpdolk/FenGDRaOLRwJMpwXo24nI8QRpAsPboMD4imr1AtvH4YkcSGeZdjveQ4W3H+r2+jatBtQIT4rbXdZJLkryx5MLxmZTMkntLGnK1GRC8CGzcqHPlZGQz0paU4iZG3WxJlbh7sXxc7tD8Zkl9bIIz820yHZvKy4/zrGqlhFFWsTMbGpFxVfdTzhHTlbliaJpUQacQ3C27l660I2XPVDrioR5w+PyC5lltxm7uRNJlcMX0gkIlgNy7ovOLH8xMWdUb83QwvIZ122/3n0OuW669EPLz7hqGuuOoGHKVLfrw8Yfp0QrQUJEuHS2RozO5HSANnD2IiZZHC048aZm2fZ/M9ko9jdBPQAqoJI+MIyZsvaz4qDrFKY1C8KHb5zqv7yNOUeBGmgaJyO248TJh+RlFFEdO6z3G25KIZ8bza4x9/+BXEOmZw2uG13FrlQ7uw6wcplM0U7VAyfGlubOwmD4GJHab4KCz420zx6k7CuksY5A1vygGvVyOGv59hegrdbo+prcyoPIz5vfEdLuoBjYyxKC6bPqf1Luc/PKJ/5omWHV6F/YIQPL894LvuHoVIeVHu4oTkvBry7R99lqM/Ci1m2XlUGyRueMmf+NtMDlZ0XrOyOefliJ9cHxF/Lyedh9FvXYbQyCWSpheR3ilYtBlrk1LYhPNyxJNHx/Reh2mo6HJDN8nD+2A9T9M9ZmlMg6ZxmkWX8Ts//QKDjyNGnzaousP2oiDISiUezfkg4Zqcad3n09U+s6rH6od7jJ7ZrWDKbPeJp0syzndj7o4WHCRrnpYTlm3Kz2ZH2B+MyKeQzFo2t2MUUWi8Jwn6dsXGJgyihpfVLkLA9z58wOTH2+LUJgQy0ob2Yn2YkEwV6yPB3njD+XSHwYcR/VctAuiGEfFlgR2m2EyRzgQz3aMdO67LHCckP/mHnyebuXDcWo/ZyVCVwaUaVSvaz7ZMegVSwE9XJ/xoesLpZgf1kx67HzW4WIYJpURRHQSGc33P8KYa0PiIzmtON2Oe/eA2g5eQXbbYTKNqS7yoESaMhKvGgQhCpcHTEpfqG4RCkMjJG5Y2Wx41QpBfdmRTT+9VHVxAtaHbiYNATktcrIgKKPKY7FbJMG6CBNpLfv/8M7jHPUbPOgQgvMArSXUQ03tj+fRgyNyltEIjpecfvfwy9s92yK7COVRs/UOmpzCppIkjLlTG3qDgdDNm2Wb82c8eMHqkGT5vsHlgj3fDIFjsXXg2ccqbJKFF0wrNHzx7yPV0yM6HAune8kZ/LojUleTqUFO4mMrFnG+GfPvT9+l/qomL8H6Jt63WdWBwX6c5Z/WAUzfGCM13z+4hH/UZP7bbtqxFeMI1EWi78DslvY7CxLwsdvnTl++QfK9PfmWJF6HB3PUCwic/lTz2e9iRY+1SnhT7fDi7xfc+fMjej0C6IO5zUQgV+09XSBlz2cspM8HrZswfv7nPR09OOPq2JFlZkIQAMlO4KIjvPrS32GSSgoRPiwO+//oO6XcG7D425OcN8bJFutDArvY06bXnSTTm02aXxmu+d/YOH//oHZKziKgI51xkCC5dJFAdqFrxQg+4KAf8eH7Cs4sJ6xcjdh57dOtR2+slShCVjmhjMH2FrsKxKQTh2rfFVHgZ/Awu2krvdAhBZedDA94GCaTXAUtkM0k6M1QTHRZftCBdOJJXEU+yEXuTNa+rHb777Q+InyekM0+6eMuSh64nt0gfwSs34sz3eHY14cVsl9M/vsvkp6GgIG64vGyb3wJ/lbJY9pnqhLPZDsuPd9n/flgEiEqHSSWCEPbaRJLPLLKMuKoGLNqUxaKH+nGfnU9CgO61QDXhX2S4zknr+a//7i//he61/70Obf/+//6PflvgkVKBFNRtjVKSWCsO9/eIVGih1m2JkopNWVNUNW+mc4TUrNYlMo7wApwT9Ho5Bwe7uKYg1oKqKSnLisViSdsY+v0h1nu6tqWuWnZ3d0lTies6YiFw3lOVG66upqzWG4yzWDxIFYK2KGZvZ0CkIqQStFVoAE1nc87fXFM3FhHHGATWOTpr0UIyzHvkqSJLY2zbsVyuuLh4Ay7CiNCIMs6AhKZu6PV69Ht9VARNU1Os16w3BS9PXwdZlIoRKgoHBJDnKTujYTgBK0tdb6jrKpijNyVKJKgoxnlo2440ShkOMnrpgLYqkM6yrgquF0uePXtJFA/wSJz1dN6T5CkeE0avhaNuCqyH9WrDq7M3lFWLtRKhYqzzaCUxXcug12Nn1KOuyyCK0xFnZxdsNgbnJSpKcF7RNDXeW4Z5zqiXgBZ0tqHtWpbLFa/PtixcK3BO4p3FmI6uaRhkCXkvwZsGiaBrHS9enbFYboLIygo8DiUU/SxDC88gS1DKg/c0rWW92jC7nlE3nqZtsYDpWiKt6ecZvV5OmsZ0bQteYZqWzaZkerWg2a7CWA/WgowVxjsSndJ0HUXbkqDAObQXOBkBIty0iBBUSiXYFCt6gz54x3IxZzlfslyu2Kw2CJmidIz3DqsEjenoj3bIswypARkC6Ffnr7m6uqYylq5xRFGCsSaI+WxHnEQoKbHWYLoWU7dcz+csNhXWhpsHvECIMLaQ5QlJHCGEINIRm6JkvS54/foc6xVWCrxWNFjQDistWdJDIMBbjHUU64rTs3PmmxrnIBIRiVb4LsjPkijGWUusFALB7HrGfL7iejrHE+RoQZAmEArapsZJ2BkOwDtc0/Hq7IzL6yVGKoyxRF4FdEqWUjcbdpIEYxuMM7RdS910LJcFl1czdJKiswFVZ2ldFwKQqmJn1A/hpVZUdcO6KHl5+hpjPF1jSJMU01qyJKdrAubFWIPzhsvrGW+ugrQtS3Ok8zjrieIIKUIYFIJgGVqqcczLF6eYztC2FuvCIkPd1KRRiiZIq5piQy+LMU2D9Z6iqCmKllfnlyiVU9cm7F8HkYyoq4ooilBSIPA0Vct8vuTyckXdGBwCYwO7FAjN7q5DSYnpOkxnSeKMrnUY64Mk0YeQMNIxznmstyRZEG5pmYNQOB+st1KAkiJgDnz4OUpH/y9779Zzy7adZz2tn6pqjPEd5nmu7eV9sA3emCjkAhAKkeCGKySQQhIBEVIA8Q/4Ab4H7pFARLmJFN8g/gKRQUDsxIqxve29t/c6zcOa8zuPQ1X1ExetV31zcxNLCDB4D2lprjXXOFT13nrr1d/2tvflOI6E0FGKcDyeOJxm7vcjP/3iC0K3oVRDJVOL4KxF8Izj1O6tkLLw/sMV7z/eErNhSsoOjSkSvKfkQkwz+9Oe7W5LqYW7+wMfPux59+EjEiw5R5wLK9t3HhPzSc3CcoHT6cSHqxt++qdfEYYL9ocTfuiYpgmrLQ+cDke8taQS2R/33N8+cHW3534/M0bN57kknLM4KyCG/cM9IobgA8fDxO3dA1cfb5ljoRSL7zpKLcQ4k0tBrBBjArGMcebmds+Hjzc8NFawNUIwhhInqlhyLeSa8V1gnidO08jt/chXb94xZcMM5AR+6HGhAwfjdKLMM7e31+RUGOfIV2/fgvccTifEmpUJOk6TmoDc3eK6npwKt1f33N/uebjfE0vBWo9d1ixqhGkE7vf33N/fkUvhsD/w7ttrrm7uqNaBGDUSrSAYcqrMceY0Tkxz5MPHG25PI+/ff8CanlrRuBVDzllZ4bVijf5ZSlEt6TxDVWZ6niKUghWjxbLOE+PEEHqmaWboO2KO+iCfM9Z4LJZcMkWMAvolUuaJ/cOet2++5d2bt3y8vgNjGJNqyhUKLgRyKWAtMUf2DyMfr245HSa+fvc1D8cTYgNVMtZrkSLnQi6FWoVahf3hyPXNHdcfr9nvRx2TUnBN5sWiOacCxTZweMoc7w98/HjDw8NIrUaljoq+X4xnnDM1aYHib/27//YvQNu/AK+/+/d/+zdpBympEK7V+CPuLMdfT+z6mQs78tPTC87cxO/dfc5X10/o/7CjinD2zazAV9IDyOm54/WvfWTMjs/6e36yf8FP754xfrPFH+D0wmIi+FMl3Izc/8rAzYXnVDwvuj1j8fzJ4SVf/vQ1w9tmIjQrW8zGynxuef+rll0/M9jIm+kSAf6XL77P7n8PdHe1gc9NV/KQKZ3l43cCm8uJXzm74j72/ONvfwn/Bz1SBTeqgYo7FQShu0u8eblj9/zAzs98dXzCT2+fsv92x6v/NSvA6wWMXQGB03PH/HnCmoKVylf7J3x42GH/tGf3pjnSN6MT/5CIF553nw1cPj3w5nRBZzN/ePWSw88u+M4/VEDDFLCptrZNyxVbbs4d2xD56eE597HnR1cv6P7JhuGq4OZHI6DcW9xYuXrV80vfuebLw1MOKfDZ5oHf/4Pv031U0KcG9RDwdxNSKodfGsjfiSRxfJx2vBvP+YN3r9n+XsdwU3Rcmy6xm/TafvZ6x9PLAxlDMJmracP73/mMs68LoAdxKZUSHKdXgWMf8C8m+pBI1fDucMbN+3O2X1i6u0Jo7bzumMkby+GFxzyb+Wx3z8dpR8ZwP/d8uDrn7CdqdAdg56JsbKsAeTz3hPvE5kMmnutc5d6oyRtquCdF47Y6IXzwPDy33NFx/eaC8mbD7pvK9t1MNQrEmVwxUybcZ25ebeFJQixUEVIx/KPf/1Uu/tjQ3RbcIZI3CsjVYCB7Pmx75k44xI6becP93DP+7Izd1wpsCzS2pQIC4xPD/c6SxfC8P/Cjq5fcvD/n+T/RlmM3Kmvd3Z30uTFZfvZky2QMGcPD3PPN9SVnv9Nz+dNZdXM3QYGyogDbfGYZUyBtC8bAH3/9iu6rwMVPKqYI2NZJ5LWw44+QS4BfGgEhZsPD7z3j8kfgJi3GLPedzgP9XeHjLwfez1s+pB0fjzveP5xz8+aCyx8pY7/0lv464/YKtPc38MUv99zHniiWr+8v+OLDM85+p8dO6sQ+XzptY38WFFD3BjdWSvGMf3zO7o8c518mpqeu6cRWyrZroKoh3CeKdVzZDaWrHH7nOXYCf9BcY6LmqHjZk3uDTZXxsOHqueV6Vhbeh+szjldbXvxjZbKG20T1ugbTRrVB3w07xiAUEd4fdrz98hnnf+KabqiypKvTM1beeNx+pvRqFmfHQrwIuENSCYV2tl80vpeCSDx3aqjYmRXcVeBR9ZUXwFP/W+iu4cvdjo954Gd3T/nZzTMe3u/4zv+YSTu75nMpWtAAsA+Ogw9cpZ7recP8owue/JECUuFOdZ7dPpLOfLsvg/voefssMGXHxzcX7H7sefFPJzCCmauarE2FalXL2B8N9xeBu9Txzf0l7icDr39biFvD5t1Mbiz60llAGK4ip23Pceq4ujojvt2y+Zlj90aNEoVHXdvcWRBh+FZIxnO/NXx9e0n8esvZn6pUgQKFWtRxp9I6HSwyOt7Ilvf7M26/vmD4ccfmfWX7RoH6tGu5oWjsSLK86wfen3a8vz/n+PUZL/6RobtXYNNO2qmRg2CwhNuI1ED9euBD3DE+dDz7Xb3HzfsZ3aO1ANTdJhDh/GeFfb/hY9xw880F5l1g81731hIsNViqa2aIWbsqNm8N5l3HbRqQq8CTPxAuf6KSjVJU6sBk3UfcXPEHcHeO+hDwP+nov3Y8+0PVNDcFSpPcc4vJWIHSaQHNpMq8s7hWgCgNbBeaiV5SwNcfC+MTR3jQIrA/6h5uGju8BAWQTVJ5BGl7Sv+t5cMfPuf2j55y+ZNKFejuClJ138ydMo6l6pj0HwzdTwLmzvPkdy2b9wpSm4J+b2UFok0BKcLwoTJ84Rm+cAwftHgc9rnlAwVs3VQwBUzUGJJkCDeWsx8btu/UkLO2cSpBAdvuJqqU62D4T//WX/0zPWtLrX92Wu7/069//2//nVoFYq6INUhwTMcT277j177/fZ5d7Bh6izBSSiJW4c3bD/z0izdkLNZ1TDlirWU6jrx++RmvX77iyU7U8CceuL665+s377RFM1WKc3TVktEWw7/yV37ImbP0Vsgyc9gf+fGffslxToxZQRVrDOOUGMKGpxc7njw54/mLCzpvubq64c2bNxwPJ05zJBvPXCAET6yVeEw8vdzxwx/+Cq6eGA9Hvnrzlv3xgLFbTnOGKiAZZ4U4JS4vz3n98inPnp6Rxomb6wfef/gWjDKKUlVWkfWOWhLTNPGD732X508v6TtDKYkvvviKh4cDVE+KaDtVLWAGpsOJzz97zpPzS54/33A83DLOlW+v33N7t8e7LVNSHRPEEvPMtvd899VnXJ4PdJ1lnCPffvstVx/vqMWQi2OiEHNCrKELht1m4LMXz3jx7AJq5tt377m+veNhP1JcYIqJlAVbBes9m+B4/eycZ8+eYp0+oL19+56PV3cYsTycNAYchTAEapzZ9edcPjnn5YtzaoYPVx+5vrnnFBNjyki1SNEWjU0f1ATIO777g9eIqZRo+eqbLykYjnMhxqiH3JQxBqwVXjx/zueff4bUTJoz+/sHPlxdMcfMmDIlG7Da+p0LxBhxxlJNIVuIJROsw0vT/KqVlBJFWFvurTeUpGZZtv1/ITCeZny3Y5oTxhZ6DynCMGx4dnnOZy+eUtLE1fGOD1ffcjglknHkpDIJw7DjdDqyGTyFzA9++XM6H4jTyO31Fcc5cneIjfVoIBdSqgRvSGnk5WfPePHsGfNp4jiduLs/8HA8gQ0Yscw5cWYV/Oh9QIJjt+vpOiHFyulhzyll5lyIYyS4jlwUwCulYJ1gxfD86TnzPDKOI7EaYhY1xhJlIjgDMc4MfaCUhJhM553KBCDc3B0J3QCx4grgHMc8gal0FV6+fkG1hd569vsDp9PEdJxIBYrRdmEXLFAgF55fXtD7QNhsGU8HYow8PDxQi4qM7/d7NrutzmMp7Ha79d+rNUzTRNcNTJOC9hX9e4Apjmx2W0rKqk05jmxCR8kwzzM5q8lXjJEQAsPGIxRMNXgvnO225JyZS+b+7kQullQM2Iy1RkG2lNQ4zFQOhwdePHtCnaIWP2q3gpXK5Nd84pwj5aVVfQH485oHclLGrAFiTsQY6YZACIFaK/Oo15tzpFYBCqUmBV+dQ0QFc503WCsMocMGz3E8cX19rZIOxqlhZE44a6mxEsJArYnQOYxEDscRcR25CFOCKgVxll6kxW9kOBuwViAn5jhSoyAEXYvB45zj6uaG7XZL13XEsWAlI6ZgrZCkEuOE8wOH/chmc848HzEiqi3bWLnzPGIMzGXCuw0ueHIyzPOk41/rqqVaa8Y7g/ee6+uPPH/ylOPxiHdbDocj/eacmGecFUyFrgvEeWQ433F7d4fzyn7x1jPPMzFG+tABajrovGeaTwzDgBFHiieC0y6LKaqTN1Yd3Oc0Y63Vzglg6APzPLLrBg7jhHGWigGr6zSlpBIMKeOCJ9VCsJonS5zZDjuMUcZnsJ6cZ4oUbB9UbqLqfjv4wDRqwSaXQhEhloyUQtd12ukwzQQ/AJBN0gIbTufDWDXviKrvW0rhNI1473XsY8YYzX0iKtdiG6CrurhV5zsljHFahJsz/RAYxxHn9NDvW5eG2nhCEV0T43EPFiwW7zpMWyNzmbHOUXLWPbMqqOOtmn2Vor87jiPeGwqVKarEgc5jUDC5qIxJRWVcQgjaCmscJRtKFeao8khSIRuoGEYpBLF0MePFIN4xzhNVwKDa16VUZopemzF4W/itv/vfyP/Nj3m/eP05eP1r/8F/VaUo+FaNShiEu8TxZeDtv1l49b1rvnt+g5HKnC0304Yvf/8zPvttBTpzJ4Rm3uLvEx//8sDdb2QuPr8jV2F/P+C/7Hj5u6oB190pm2k+t9hJde6++JuF5y8e+OzsnkMMfHN9Qf8Pz+ivy2omkr1+rIojHgAAIABJREFUdnziuP+B4fjLmZc/uOLlds/vf/Edtr/f0zWWjZ0qfp+IuwZ8HBN3v7bhw7814bvEfN/x5Hcdmw+FYtVoaXnlTpmL998L3PxGJXx/zzw75MuBJ3+k73EnNYTxD7k5QIO/m3nzb2w5/CDiz5WssPvfBrbvNE+41i5vx8YSvJt5/69uOfxyof+VBw43A+becfHHwvmXatiySDuAaqOeXgXe/8uG8vnIdjeyvx/o/7jn/E/1YGhjJdxnNTMyQjx3HF45rv9S5fxXbwF4+JNLdl8Ytu+U2RnuFAxaQI3TM8v1vyiU759w7Vlb/uCM85+oTMP2nZpS5cESd1aB3peWw+dQ/rkjpQruxwNnP9MW0+4mtrgSJFdOzz1uKoyXlo//ihq7SRKe/47q9w5XaWVJP7aAC9c/9Oz/pVGfzSeL++C5+DH4Q11jCh51Ge0hqnlPrpgpaZv+eU/eOIpXBqg9ZSQV5qfKRMydAgnThSUHZTkqEJBJG0O4SxSvkgturJyeWh5+INTfeNBn/Z9tuPgT2HzIyiyetXAwPw2E65npeYfkypu/5kjbij0J22+E/rpw9uW0gmMA9pSJZw53ylz9Rs/9rxXsSeiuhd03he1bNSCrVnAPM8fPN6sOZzVwfGmIO7ATdLe6Lvy+4A6JeOYUyPJ67sidAiM3v+bwh0q4VyDOHUszxtKtIPcWd0hMz/yqrTqdq5mgnSrnPz2Rzvy6jgC6jxMA8SJw/S94KBDPoP9Y6W8q4SHjThlJFUlFtWDb6/rXO+K5kDbgH8AdK9v3ZXVcH96cmF70qyHZ8VVQAKXphoW7yHzh1z+lsH7W305ML/rVXC/cJsbnvhVVMmbW7gN7iKSzwPhU5UqKgzQIx1cKJLkD7N7m1YBsvXdRY7A4aH7YfDtz/cNOjcFuNH/YUaVDVL6AVXPWjhlKVWZzA7qW13JdxRsFcg8z8bInbVVn2j88zu9yr5Ia+Nrbx7nsdN7GS0Px0F9Xzn/8QLzsW8eByq3kXtf4stbnrRpQbj4kBdZixd9Oq4Hg/LRveU7N4rJvZpX7vOq4uqMy4VMvnP/ojun1jvnc4o6FEh61ed1UcI1x37/XuXaHvOaStLUK+u1VL9sdWpdIr2O2/LcUHdu0dfq5jSVuDOc/fuD2h2cMV7q+h/cT86XHHTNp0LGazg3+VBkvDedfzMporjRpHF0f86XG/ML+9Q+J6amnWHCjSu646RHYBmVrugcFn1fDyWce/5A5vPZsPqQml9HmPRYtgmxVIiJ90lEgpSJZTSerheEqM+/Mo4Gil7UIIZlmNJaJG7P+vTuqBMp8oR2F7lhU8gLWfX75+0V2Itxn4k4B7u56Jm01XldJn5YjqlGwUrKOkeTKfOFW00yNFzWe7G5S+34hblRCoLSYNfnRYExjWNfX8lvhUFSfvx1N3VHB5bgx6+fnnQLncVAmbmiasu6YV8kG00xDpeh1xa1+PvWP7PZFm3fR9ZcM4TYSzxzzmSUNet/dve7NxT5+PuxbQbIZ1/1Pv/Wf/5metf9cg7Z/8z/8OzWEQMwZjOc0Hem2Pemw5/LsnJevnnO+2zL0QE588/6e+/t7TtOsDNhcyFQFgZwAjmmceXJ+gTHCw/EWb3pqOwRNuVDjiHeWrF2oGGN4crmjK5WUJ26PiVwAhFQ9Jc1QI94bDrPqyT2/eALiyCmS0oyzlfEw4n2nB7is2rFVBGs81XuQhCuZXd9xfzpgrVVdv6DJ7+Hhge0wYCiUqAfVznU44xlTASfsj0f6oNqqNme8sWRUz3SeZ0JQzcLge46ne3a7c+7v9jinMg21Vnw4p4wjgwhVMtVBthVTWQ1OTtMR0MOgt4HgPXVOBOsoUvQgaExzm7fErLqYCrLpWI9z5Gx3TokRY2m6wYnj8cjN7S0XTy6JU2IYBtJJNSS7TpNiKYWzsy37+wO73Y6HvWoojqU0TV5DrkU1cMUynk68evaE8XjQQ39Wdpo4T54nOqNaOfOsD0CbbeDwcM92N+BwFIFpijQZ1saazNRS2HS96u+GgXmccM7T2aoSA9WQDarNWgqd70mTAlvVaCsquSiLtSig1AVHFjiVSN93xJyouahGa85Y0eyWc6ZQmlGfymbknMllAgwVw2azWYFCI1l1Sy3MU2nzk+m8gibaVpsIjSVoWlqoVRijxm48JbbbM/1OoLLoZXo64zjkE6fpyHa75X7/QO96LIKRyilm+mHLlO/YDjvmMdJ7jZ/jcWSalQVqasGEjlM54VyvMgBBGYwhBKY4UqRpNSpVEeXgViqs7euVjEhlLgdEVD+LJp9QUeBERNe8MTCEjpoSPizAnmrTOreANY7cAJQQAq7rmWcFtlJKyuYzYBtgZgw4Y0kx0jnPGEeVeTEe0wpConfACWXcS2PQGqtz7L0nNW1nisoylFIwWPbHA8MwEOu4MuuccwRviTHijTCOieB7TlNR0J8MXr+DmvHGk3PEBo/ktK6tWMEW8LZjijM+BIwIZFEtaCqx5KZ7qfIHtmac8ZAr4sEar8DTHBEDJWX6wVEygKUWlQ8YepVfKG1x9UNoGru65ksVdv2g2qQxN41OhwuGcZzxrifFEd8p+GUkk+eMFUc2jinOCobXSrAGakG8I80zNRe2w4bjeMJ3AWuFaYr0dst8mrFdJWcFg1PU363Fktyk8+BU51yixzn9DUrBK1lCr8cYBT6tUVDQeLx1LXYEakUSmL5jP57oB0+JiTxHvFEd7zlVjO204GPA5lnZ6CkTQq9zVmZSKTgbqKLs/CUH16ota5k9zg4qkDYpaE9pZmNimVIhVyFYtXhbQE8FEgMxjgx9T4xR91TRjhHfBZyxPNzfc7bZkqmUrPErRjWVL3ZnpCicpgmxaS3K1CLMc2S7O6cgHI97Xj59To1JOzYE5pLo7aDgcMmqi40ezCsjnevwVlnRtRQslTHVNRfEGOl8pwUVY3FGSPFAEmWKH6aINZ3K9NRKqhmr/VzEErHOUUthazxFdB9s2gqkCt56bM3EXPFdp8W5nHWPMDB0HWmOLUeYVXNWc4Tqflu0QwIjhBAYp8iwveB43OOtxaAGY0tiTlSKrQxYsoxYPHkWjAyUGqmSkGoIzhOlUqjMRc01yxSxUhGpTStZAWvvPfdx5Hy7YxxHggv81n/7C9D2L8LrX/8b/2Wdt9qqCNBfReYLhx0r99913P3zlfp64vLiAMDDP33G5q3Q3TYGLHpoURCoHdDvEnc/6ChOAY24UZBMqh70loPzcrgrXrj7nltBprOvyqqza8eyAnJpY+jfK0vr5te1eGNiMwXp9TC3HET9Pq+HXmUAte8YDMcXht3bvALBi47d8PUD0+udmp6kRz293AndvY7P8HZkfhJUBzaY9T7iTg/xcWdwp8K8swwfI4fXnt03s4IxU24HfnWkH5+4dVxsYyrZUQ/FXTN5UXDCEXcK/sxbPfhJ0XTuTwr8uObIvQKeueJvR07f2a76j/NOAZfhKrH5kyvGHzxVEO5pUJAxlvXQbmJl/5lj+z5zeGXZvU0Ur+DWAvyYWRlsxas7+9Vf3tHdNaO1Y1mBADMrE3ABpkDN5fr3E+OrjtTp4Tzc51WzMncKXkmujC/CGl/+QVlOi2s5PAIBev1+NV9SM6cFeHg0fJkvnGrUfhyZn/ZqFlYq86WCkUscq0FbWc2Mlrlev6vC+NwrQzurXuEKTDVwyR4T8SJgp6x60Q00BdZxlFIJtwoqKTjfrXG/3EPaGKYzw+ZDovs4Mr4a2Hy1Jz7pyZ1qZYbrmflJINxFTq86/F7NbuLGsPmY8Pdpna/l/aVXsG++dIQ7LXT4h9SMi1TDFpQZt8RVblrWn94rn+4WlRWIqlbwtyPVGabnQ9PNbHMy6vpPg1nBGhNrGyPVVl0KI+6kQGrxyhx1DyqJkQerAPe5I9wqo10B00aEMQpwdh9P5MErIDllZSCimqWL1iYoE3uZy/7dkfnZoEWKZg6We6tr/KigTXeTiGeW7iqqZmy77yWO09at62TRSTWxYI+J0tlVfzlt3ZqHpLAWHiS3eeg9pXMrsF8b81lyJdxpYURSYX4S1nwpuWKPaS1KLNc2X7oV2F7kFg6vA/6k2qSSlOGYe7PGpT0pYKhzyrpOPgVtATWybNe3gM/jq47+g4J6S3FvvvT4fdJ1Pin4uO4hlRXIX0zjqpGfK2qkZtIIP5/f7VjXGFtiRwdD3ze8OTWDOS2qrNe014KMmYsynTu3Av1xpznXHVVTOA92XZfVKuC3rAUza5fD43Xadcw0/yXMlNfixgJ6frr+p2ced9Trq1bW68y9sPn6yPHzDWZW8HAxSOxuIvff7XBjpb9JawFWmg65e4iMrzoFWD+MXP+lHVIUVFwA7iUu7FjWtS5F4ySeOeLG4CbNdbmTdc1CAz0v3DonqV86WArzuWV4P6/7pZS6FsmWzy5r5/hSCydufMQnlbWuppb+WIjtnm27VikKRLvp8Tlk0YMvXkidPgfkIKvx17zV54XTUzUeTZtmzPlJgcSOujbGpw4bH8Hv5flgyRP6/KTg91IsVg1pWUHrKgrgx41h9/XI/vOe7i4Td/8/AW3/xt/+j2sppZnSKBiIExyqfbfbDvS9x9qkRld4jsdRK3/TBBTENq3EqTRDD0sFrFM21zxGjHWMcaZWwVKxRihVjcWKBVMmXDEIHmstYi0pFlKppDTTd55YM8fxxNnZGTkmUgaHsnhcO8QhhZiSAnSAE23T9sOGyIyvqKGWU6OiQuU0xRV8EzGYCqFTxpBa3WbVR7QCtrUg1YwTqM24KjUAEwoudAgWMZmcK3HOWBuIjWWEUVfVHnVzDL3nlCdKVoBPWVWzsuKaUY+zlhIT22FDXjZpIKYJ36tmpHfKOKqtbTNXadqBkWHTAWX9/4fTEec9JSn7qGQVXNcWXGUbFoG+79WMqLGpU6kIVll8jZG0HJJz1QOvqZCyAIIYbT2qWfVuHw2ZIATHNB5Xrc1pihjxynq1FufUBKuSsQgYp+ChGAWhRPRzeSY4yzTpGFr0UJ6pOKvjKVY1P6W06/aOMc0gGfGOzjrqqID3KUVsa8UdxxMuKAh0amZA4go5FiQ/sshEhDRHQq/t7amqS7m1FlndMYRihBwnvLe4UvTaxDKlrIZOpmOalHUry4OF6EZpxRAplJJJJdN1njgpkJ9zQda2/wec91SjrtOd7xlPMyVmrNExy6VQg5BzxUloQKX+3mE64LwlhECaE1YMFtGW5wZCFG0IbrIPCjynNK9szpRnujAoa7WC7z22GErS66+1KojqhTTNeNfpdaFgedd1THPCd4HjPBG8gp9OjIKs2vjR2rEVWMbqdYjRa6hVv8uJZ0wjtRSCc5rjjDTjRY29HBvIFRMgCv6XqCCOa/IbjYkoKPu7pow4z/EwErqdXo/onFYKvoH/Fb3fzW7geDySC+TqcFSkqjmRgn6ae4zRNvxqWY0Wc2ys3Kqx4J1V/c+sQO6cJmyvmqHKVDcE3zNHdRWm1MbAzev6qrUom9FWBeIaq1bNtrQFPOekjBurhbllfq04cqyEDGINMWeiSAOqtZASnCfPUZmiaCdHKUUN0aK2nzsixgqlJC2WGYt47d4oVQtYphaIHkQZDbUWiilr7u3CoAWVBvgvYG79hLGc50TAgdN9bfCeWRrrqxrFgjFgVLYBUaY1sMoALOsjFdVtyzmvDOdljRZJmOoQnBoRAoiapJUMqVbEeUo64a3DOUdM02oCWQqrKR1Z5z30HbEkjS30t1LV4pkzhnlWlmuOiVqc5mBbV2Czim1zq3IXXTdAyZhSsVa4Px0w3hFMaO9Hx9oYrXNNJ4ZBWenWScM0C850TG2fEGtWI7PTNOJE16R4R8yzsl2LIZWszwahFQdT1PYoq3tXbz1eDJEEFjLSWKkFyQXrAjlXUqz4XrVoC2Vdv0vVz1q95zRH3d9S0RThXTPf1CKGN7qX1Vzw3jOniLVCbgUq0GeMUITQeY4PewhqMDgn1cEWEbIAXg36Ssqcb7akVJBW/HLNZFVEGFNk2w+cjkd63/MP/rv/+heg7V+A11/76/9FNbEqo6sdgIAVMDm89sznou2DTe+tv9bD3AJkVqtGHP5Q1sPxcrDLXgh7PUh1NxEqDTjS91SjwMGi31qNflc12nLpTmVl4bhjof/2tB5a3UkZUSUIqTMr0OuOjwev3Gsbbto8Ai3LgWthKHUfT4wvB9wpryBP3Bns9HhG8nsFM0C1DuuiI9ne4k4Kyi2/uRzW7FRXVo075p8Ds+ZLPeTOO0N/k1fQ1DQQfAF57Ni0O2Ph+NI/GscVPgGP4goqqqHcp7qPmfHZwgbTuevfK4glSduvPzVDqlbW+50v/WpGtIDKy4F+uY/S9JAXU5XiBHfSffBTlu3CZF1A5LizK3tVAdnUpA30EJ8GuzIGl7EtXlYzn4UlGu4i8czj72ZMAxrjmcdOGh8mPRoLLeZ2iDKjlmuM524FXsL1TOksubeE65G88drq3kBenVcdhwU8W65pAfQW0GkBO5fXp2BR7hrIJ+Dv9d7T1uLvlcn8c9csCwu8rKy7+aKxArd2ZbXlXgh3iTTYlVk6n1u6m7SaUS1gm+SqINOgsbUA3N27PaX3pLPwcyxsjQONy2X+SzArK9Meld2MqBFXvAj4h4iZEmkX1vf+n5mfCyC4XJedFAAKd0nH/cNIOgvrtdpTXvVGl3yyvJbYqkaffRYQLVyPCuadhXXNmvjYNm+nvAK9wFq4suMj2G8mvadPmYPVQv9hJjYQbhmjpTizrFMpldMzBYgWBnbp7ZpvFqbqUsQKtzpfC0C4AHiIxmva6N+rFIpRGZgGDLtDojpRAH7/2EUQdwso9wiSLd/zaXHLxMdxs82ETposyHLNS36JZ82UuLGyoQHhDYhdcsKSX5YiiOZeLQAs8778WY2shYPc2zUul7WgRpCPTOL5whEHzf/u+LiHLb/xaWFNc3lhfGIJ+8d9YtkLNEfmlmsWo81HgBZYc6CuY7vmjWWcqpX1vWvsNJDVnvI6DksOWApt657YYmD5XNyqhMACsi+AuBrWmZVR/Cm7unizdh0sa2QBYZfCXHG6P+++ntZ4Uembx66FJV8t7PrcGUq7t9Qbuoe8zpkUBSX760cwtzZjumUtLAW2BbBd9v5l/1iKTL7t4cucLkDswupfgFzJrOvg0zgpXt/rToXpXPeABXgtTuju1bwwLQXljHa37PMju73lidwb5q0hbmG4Kut9hX1e16yZl2cP3cf3n/e4aTHbq6ReVoO77iZyfBnob9Rc7n/+B3820PbPtabt3/8f/vvfNMFSSlbtQgNiHSmBdZ5qDLEmHg6F4wzH4wlEmTveBbJUMo5cBMFTiiirtLEkKSjzrhZqzoTgENEWQ2udgrMpKouqCBFLJRNTpJaMM1BR526sYK22vYqABWWnVci1kqUy1qIGJs4qa06gs8pc7Y1jnEYqglRLThnnPNu+I44jVkB7tSvzOBFcRy2qoZSpelBGcCLQNED1cOixYgl9T+MOKxhnKjmxHuwWsMSIAl7eeSowJU3A1qgRS60NNC1V20cB7xyIMM4TVgQpBSsoQA1IrfrTNIBBUADTOYxzlJKZ5iNVPGCwxiPG4qzHGIdBjc2Cd9SmU2hsYEoR33XE3ACDarDWNzalglfBGObxRMUQc0EoOOPXxFUWxmBl/VwplRgTxngqlVKEFPMKhCxtrM6pKHwVQYxlTpFaoTpPKgnxytDKcSYYy9D32i6LGpmlmtQIp6rzeAsaKkWZo6VQmsZyZ31j96mJnkHwweOsMiuHQRkntVZqyjhjsMaiGKAC1yyAMhUjCkwhVSEAA2KFWgrGWGLMON8xzjPGevpuIBWNC5GKaWBwzBFqwhgFvBZt3JxmXSdS6fqeKY0YgTCckYswl0IpEFPBuw4RQ6aAMQjCXCLW6vWCsqznecQZi/eOkptmjTWkeUaC17VglAOHMeRWHDEm411QAHpSdqaItkQHH1SHshSMGIJ1hL7TMXKOXLW4EicV5a9WSCWDaOxQMn3wahRatAW51KrsUgRbBFcNiboWBGLUoodzlmIKvsWpOEOuWQ3XasV7Zet7b/HW4J1FciYWZewVk1dQPoSgLRriMMWAdYwpglNNVRFdvM6ouzK5Yk1j99TKnCYwyrgEoXNBizNGc6Ag5Kr5x3aGcdKCRkkJYzRfViriBUPVNvUWj2KgUrHeE9NMLhkxYKxq4wYfWk4p+L7DeYdtYGSpEFNGjKWi+rclZ5ztMRb6viNnBVCd0xbxWgWRwGSE7LSVzRbVarY04LFCMFalJlRBTpncc2rznpGaG/u7UgvgLXOTAtBiAaQ54Y2DohpNvlNJmloLfT9wOo6UomB9rVrkMQJIxYuOUaRQOseJhPHafheq6rZa58AbYolko0ZcwSuobY20eS0qmi1gbSXnuTFZKw6nxoFZMNngxLVijCDWgFO2bEUwjYnsfdDnlFooZZEPUKkM01iiaY5suo7D6aTzmAullnW/KRVinvCdSq04p3p41QrOa2HGGoM1ul95Z3FO6KylFjU4PE1aEIlRWclitGBZq7J8XWPC11pw3iNorMWa6UNHapIHRgzWibL0LZhqVL7Aq9yCJAVIsUbzQhGMtZhaW3HD4sWRRZhiQhzENGs8inYSZBFSKnjfa74FclQNZe8cYgzzOON9wBjd3yuQpSLO4GyjZ9WCEUswjhQTTgQxosWAqkaRzjkdu1xJUfX4Uq2MZGzvSBRiKQTrIejeLBTNxcEzHg7UmkmtsBBjad1DBuMsaYwMrsMaw7/3C03bvxCvv/f3fvs3a2OOrAw6aczPYDAF3KiM2eG6MFxpvgt7PTSpOzWr7qxJdW2RNKkiSAMt1OwjbVSnWXI7mBrBNVBSW2eraqwey6p5JxXcSfVRq7f4fQarwMGiFeemgp21TXO5l9LpgWk+s/TXifnCqs5hfTwklmCUldjacFuKxj+otuMCuJi54o6R6i150Oe/uFNTmOKFEgxxa5vxio7HYmyGkbUtuHo1KhJYW3fDQ256d2bVovQPSQ/ozVFeWWaqZ7iAM8UJ/qTPQ/p8rJOnIGIDQLxQnM5HuEuYXJsmr453HpyCDE708N80DXWsDeE+kjbapm+SfrYE86h32RniVg11TGP2qTGSXU247Kwt6yYriCJVGWmfyiAsYJIdE+k8MF84uhttF5e66Lw2MKcB7nZUfeGFmZh2nulZ0NbjsTFbT0r8WK5fd3yNR21Lr5g5445p1eLE6r1XKyqn0My+5qedLo8WE6XTGMMoAKJnHcEtrEYr63sXIGS53uoVTFcdVGWNzpdOW5E3DbDVY5sy+aQZ+CxakFvbGHL6jKbmdmr+Fc+cusc/xKYNrCxhRIEmRAEQt4/UYHQcK8QLp2zcXk3HFgBquX9MG+u5IEnjzky5JQwhbxW49HeTgrcNxErnYQXQFmA6njlM1nGzswKPfv2NukoNLDlkfuKpXtHrtLVrLJqkuqgl6JwvgJ07avv+UuxIO0/aeapVQMbkihkjefArizptrIJwIitQtOQIgLRz2q7tzWrcGG7bOfmU1rWvuUwjbXlf8QZ/0DFb1sAiY4BR4BrRsQVpcTFSnV3zQAkKMC2miWo+RgMNZWX3uYOyOTGaa+yoAKo/5MbOtC0/6n2o+VZS3d+FfbvkUG+YL5TFWD7R0TVtLsNt1D0gVdU2n1S/ubT1E7e6RygIqcaQ4S5RvVnvXXOo6qJLVYM5e4yU3qnm7ZgfWbYCaWtaToD50tN/jJjWIi2Z9do0VgwY2r1D93FuJDeIW9VbLaHtMUeVuajOqJRCbQW6tscsbPJFs7jaJf5Mmxv9f7lT6QxMA3JbJ8US/5r7XWNECyaVVWZnMcFamL3jM8fm20hxmi8eCy9GzSMPWvDw+/RJrmwFnpYDNX51LHJvmM5UL/3wytLfKvDujqXptOu+JlmvPw+PWt5paPEmuj/P50Y1rMeyxmd4yK1LRSVX0qDn+oVRr3tVk31o95o2+r1pUBmJ7r7onB3r+uyg+0hdc9lSODVRQe00tHneP5q1uVGBVzfrHpzbPIPunXGrGti5e5zjJbbTYNa93Z0KNlfcCP2N7r/+pPtXPNN9WSqqj5sr8dzT30QtqrbnFn/Ia2dSadrb06UWqv6zv/5n07Q1/+y3/L/3ygKpZHzfkRdmGUBKSM3UkhjHiZxgGkvrMK+tpTRSGzs2F4f40FotM8F5TDEE8dgCdU50IrTe3ZUFZAv4osZScZ7pPDwc7xDJVCJzPGobtqkEU3G24h0M3uBE22SDgcrMFEcF64KHpt+n31KwXlk3ve/WQ7K1jpyztjp73QR7J2yCY9t11HSi5hlnLCUWeqfVctsYcdM06cKoChbNJ9UDrbWSS2SatLqszNmIDw5EzYFCCBRB27nFKHOtltZ+Xwh9h/UqG9CFoCzSpkPqPJQ6YyRjpULJDEOnjB7nWouwNDApI0YZTqloO3uuCdMkBXKOpDRRq7Js55yYGojonGfwA2Vu7fENoFO2XibWQsqZqcz0PtA7z3kY6IwjVMEh+CpI1vbunCu1CjGqbqULTg/KCCF0WOvoB4+YQsoTLO2qGKoYYi0ar7WQasJ1npQnTqeTMgCrmheJa/qJRZlN1VRimqAkbd8vEzmdGLyw9R0OofOBMWsbs1TWtuUQtAUFKUzzSRloWaAaklSSVOaamWsmovEmwWFNB9XhjDLxFsakE+hDp6Cv7aBavN0AMMUZGoN1mY8YZ2rNZJRFntMIJOJ4oMS0AsmH8R7jA/uUORVhqmBNwIrFO0ecZo2tVMn1kTGpbc1hZXgHq0w079X8zFplO4uz6wOoWIsT/YdcML4gooC7yYbOdJhkiXOFaqm5IsVhxTUm/ImUR8Sp/rJpxkVd15HbWq0GpjJhpNBRKeOJGidI08pYW/5cDJq89w3kLwzDsLLciqjcSXCWlFW5V4S9AAAgAElEQVRWoUghbAdi1U6BCsQ4UWJaW86z5NUgbGFTnk4ndYhvAGjwHcY51fKs+oBYqGCbLpdAqkn1Sdt3tMxLTJpXlVIbqMaQsj68TCURtqo3a/ugethlItUTSKK6wlRGxBaiSeAKzsM439A1pkxK8ypHgmScF5wXsiQOpweO856UR1LKWOc5HE8kA1iDCz1zjUQqd4cTdQZXPGWseCydCZgGUnoCLltMcPgu6AOps00vVeM9BG2tT/GR7d/3PcVKMx6s1NAxzbkVq3Q+jXFUmhPqwlTPmWAs1kqTdOmVpVoj03zCtJhQ+Y0CogYmlIipiWB0zwgu0LciT6yFRKbYzCyJXLWzhBghRj0EmwwlUYtFrEOMA3FgPKkaSrVY40kFYi4r2xNYi281l5aDDDkVxHhEnILOuKYVXXHeYIKQSdjOMGXdSzrbIVOCKdFXQ+c8tiq4Y0sDFXIhjtpG58RQTzOdWFIzm0zpgLWJq+u3bAbPHI9sB491Ks2Rmm6yXrchSyVJ5jDvqaaSqcwl8lBGphwJfc9pPBBCT4qCtzt82GKM1T0Jh3EBRNm4JeWV1S6tI6RQiTmTpCK7HuO8zj9qHkHyOOkY/AYiqk9uLLtuIJ6mdkArDLvtWmB1XXiMA2cpeUZqopZIAHJMzHNZ9+hFvkhEdB9PlWAsu92OnRHqaWaoHcPJE/aVS+nXPTNnZS6bXDnfbPnedz5nCJbzTUfvLNvOEUzFlEgpqbHvMymP/xef4H7x+v/Ky056UI0bPUR/qp+ooBZ0Nwk75cZkLM3YpTFrK415qKCDmQt+n4lbPfTMW9VOXBh/y6saGnNHVpaQOxbmM8Pw5rCCEX7fjDyMEHd66Exbx/hEnezjxjDvlPHq72bMlFaQxTS9vgWQdWNleuIedTyX9t7GVgWYLiynZ47TC7+CbWlQBt78tF8ZUcWplm+VBuJl1W1UhhwrSKrXoczLpb1YmbxuZZctbM1l/JXB5ldm4nzx2LJerbKKlaH0yMgan9gGtMgKVIKywhBlAttj1MNvYxVKUkadvl/3eP+QFbhrjtjT07ACBSvzd1Kmpz1E3CkTHpQJPT3xHD7vlfnWXLuX9t08WMzUTLP2s4JOwyPjLp5ZSmcbk1VW0G5hOkqpDYxKDdzNq/6n/7hXkNgI3VVcY0dB3fbvh/kxrBtzcXzmOb3qyBtPvOwUPG1AQtzpeC56hqBA+hK7y9jaUTVQ/V7jd2FILzGYm/xAaveae6Ps5dZqD6xxscSL3q/O7cLONm1PWNbn8vfTc5XR698rW254c6C7jvi7uZn46Hpxx7wyPJVFqQUTe1C913iu8RjPHNVJA3lbIWFhebaxq0YagNe6thYApckXpPNOi7SNebl+Jjy2Q6uea5PzaDImKlvRmJhN3kNBQ7+28y/remE6LqzG4qWBkXpN09OwMgUX6Yy4Navpl8RMOuu0yNDWsGuForhpxYbpk/suWpQKVyf8PuH3yjBMWwWQSm8f29kXeYIW23YsK4t1WUPLOJhPZDukVMyoazfcRdJZt84hqM6xWZi/VeNlGUONNS0AxIuwrh3X5EiWWFT2bKF/P9F/mLFjWYsH/bvDut7yYPEPqi+9++K4FmiUnWiZL1TOQaU77MqIXcH+lt9N1py6jL0dVWNa8iJHopIMC0vU37c85xVE1xb/2nIxTRdV1+VSHIznjyztcBfXbgE7PkrfrKx1Z4hnlnlniVthvNSuatU+j0gquIf55xin0KRPlqmrjx0Ey2uRn8heZQHcUeV3Fk3fR+mLx9hQ2YfGaE117TqplhUwt1G7DMKdzmM8c+t1TE8884Vfi3TLn5IfNWpz39bWmVHpiKwga/bC5U8ip6cq63N64ZX92gqky70VJ6tkwvB+ZtFOd4fM9l1qkiqW7npm3hplzW+syg04BTFTLz93/8s9aqeNMmdNY9Rq/tTfTW0dLp/NnWF8qrly3hriIBxfukc9fiNMT/16vWlo63gsStrInzCCt4uMUtujk2r0L3Pb3SQWOaLjS8fhpRZYx6eO8dIw7yz33+soVtZcsBQfHn7J8uav9oxPHA+fB6YLy/GlV1Z6yw/Ls4X7pJvnn/X6cy2P8O/8R/9JLVbBjW1VdlQyyqbUg0ylN3porGIZ54kQnLbgioKVQ1jMPfSgNY4jXTcQYyb4njgeMF5BQmcFlxyzCVRJqhpMQpwyaWouiCmYLI1ppWzJWlW6oQpqJtN3lJiY57mxMrU1uyCq95oFimC9MnY6H6i5UoseJLNkQHUJpaqGZYmVvkkMFIFwFohzRqohfdLWn3OGmh+ZkBVynNhsNpSUFVRZeO1FWYPG2QYw6QFxjDOb4AmokcpxGtVcpoHaJWkbc98rKJeStu47Y6G1YIcQKFnBPmsVhJtnlUlQpq/Bd4Exzjij2obFeCgV037DOUMuKgeg7EarTMDWLr2Aeikp0OKtVbZlVu1LSms/LpBLREzCW0dGyFUrMiUVHIKx+tvUvGohizePYIWoeV3wXtvdszLHxPomfaHSByVnTAjk2vRJUeA0psSuHzSGRMhpaa8G63ttT84TxqKs4KgMRp1Xg1hRlmOuykC2ypCb57lV8TKvX7/k/vaBlPU3Ezom02nEOItbwRkFWY1TY7Sc1eTONkZuaHILNqghj7LbE4PvqIJqIjo1CHJWq+fWWlwXeHh4WFl+vgscjiOucxhnsUULIjUlSBO73U4Bf4RiFJCe4ox0Xlurx6MCQTVjqKQp4YaOVHStlZLUIMx2jSGs7MScZi7ONlAjvQ8cp1HN9qryLFUGREFtqU0HN2ZimQhDD9UQZ13TcR7pfVBNZgqm0nRVDbYVF7zvmFPEiBY5ppJwjVUqFQavBk9ODBfbHSVFDvMRpOP+eEBMR4wTNrhVqzeXhKEyeLe2VxujuS2VjO0C1gfmKVIp0BiYvTguz1TqoCbD9f0tm905p6g6xGIMYoCkOcORkao5VrwjW6v3mKXls4QJvR5aU8JZg3OGSsQWgxfDKY5QM6HbMp4SVixzEaCoOVbVMmkIDiiMJ2VzLrrBWMd0GnHeEGtm6DqCBSlZO8pL5Xg8YpzHGg8NrMcItagMyOA81gAp4p0BE7g/jqQUG1vFNFOo1KQ+hBxnnlyckXMkloqYwOk46do3C9N8ojdOdWWNQQSenG04jQfNE2KIc8UHu0pwGKnEVjyz4jilIyEErDekw6zjauxq4ua8V3kM64jTyNOnT3lyueF0OkG1vH1zRb/tOY3aRxxRaY6YRkR07W2a7m/KM1SnORHtEAlOc6avgvXCnGeqJOac6LpB5UxM4HCacH0gzZrPDXq/IkLnVcIlxwlvemKtFGPJFeaiMholR4xpJl1R1x4ycxhPeN8xRaMMF9OYIlUld2KMdM7hvBpUnm22SIWrqxuMcYwlEbP+BlJI+bHL43zbMwSVLTgcTozjCMZRbOUwnnBidH+plVOaeHp5wTyOvH7ynBQnDgfh/2DvTWIly/Lzvt+Z7hRzvIg3v5cv56y5qgd2N4dWsy2RBEVRBg3bguGFbcCwV956y40B7wxtDG8MQ7ZsS7BhSpBlkrJEiE2aFNnsZrOHqu6uKSsrxzfHfKdzjhfnRmQ1YNjcmaZ5N1lZmZEv7o1774n7/b/v9xXNw1UlA2fWFiU6SsKaIwWRkrRaKaPBkIvz51xeLTBxFv7cKqRySO/BSZJUkSQRR3tDYq2QJuL84ornL06pXWC5eykCsziJw/cLIbh9eEiv3abXyri4uOZ8MuViMqeuHUUZeNlegooV2Jp+N2N3e4csTtA6oliVfPLwCdPZEmREhaOIwzUkrGPYzej3euxs9YkiTaxjnjw7Y7HMeXZ2Qe08ToogYNcWo8K19Q//m//y5RPJX21/abef/xv/uV+LGqudeFOeYuPwcCHLEFmMp8GNF12V1B2DXtQbbmixZYiu683DWnxVb4pG1nHxz/Iw6zggE37iobhxzXw2JmxjuSmkWeMMaJxEdSY3DLmyp4mu642rbl1gBS+FrzVvbx2F/GwE9rP8v6KnNuUsZTs4atYi4toJtRbOwmtfii/roilg4/gDNlHW9QOorD3RZU6+nQZxu+HMfrZ0SFYOmVvKQRBg1uiGtRta2JesxWgS2JBeBmFxXVazdjGtj80mus1L5ucaQbDGKIQPI/ziolB2tObEriPfa2frGhexjkOv2ZOf/T2AqCw2Mz8hcgA/EfkWtd+cI2vm62YTn0EvfCZCvI5xr8u41KIkP+hsHG3riPf6XFpjJ9as43WM/LPxZmjOuyTEgFUVRNn1eXn+ZkzrqdsU0a3Lgcx1EVzYyToS/9JV9dk4/PpzqlpNjLiJR6+RAuUw2sSg1+dunTau6DhcO61n5eZY1ZkiebEMzNOG97oWJgAWO2YTYV7jSNYMVJsq9KzaRP7XQuX6PBSNM3odU19fV+v9mR+H58CiJ0gvXeMqW1/Q/F+et+sSqvV/C+eDcDxMNnzQlwVpasNtXRc/ecUmPr8uRvMqiDUAdSyYH0ikDWVnAJ3HRbiGG47sGouwFpmLodmwMJ0WG7zF+uebeb3Zn7qlyPuK+ZEkvgxCYv/DgtXIkFzWG9zF+lwKQx+1GYCt7x/rfVzf09b3hzUzNTgvGyd7KkiumnRMSwYOtwnOeHhZFgbBJQlscClBMG6wAJPAMFaLimKUULWajpXmnpWch6HOOrq+vhetz/18qLFR2K86Db+2n9abewPwE2L6GtsxOTGoMhQ4egnJld1gF9ZCc9k3Gw60V4LJiSG5+smIfJ0F4dEmLz+n8H4FyUVF1dabSPx6nVjf59e4kPV6NLlpmB95oknYj/F3K/K+2hzndaR/fW2uSxrN6qUL/LP7XbfCca8yiY0E0SKsWev1SeU+DCTPPlM2Jz6z/kgoO2qzv1UqNpxUWQXX/PqcWQvl69JEgPSs2vCRccF9vhZ312tx2VXUsaDsCFbbwVXb+TgIqPHUbY7n+rNfi6XzfU3RF9gEkotQVAgg61CwV6fBgS2cJ76omNxOMAvH5WsKWTSvmYXXmFX4d1XhN9xbCIig1Ugyu+VoP5T0P6o3DF0IIu66kKzoCcquYPW5JZ1WjlaOi48H9N+VoXixKf9aD6MhXI/Pv+rJ9ua8vvOMbz86go8zuh81ZY2zlwz8tdA+O5LMXylpDVakUcV0kRB9s03rmWv2Jay9axzL5KZmfgT63oxuloeS1O/fILqQbH3PofOXbuv1uSKc/0uCR/if//GvS6Vw1gdhrWGoei9QUiBxxBCiglGIXa4Zf8ZETQRRoExoqyzLIF4uixVSBHSAkDVKSbzwKOfxhFFKVZUkaYtaBC6j8MGFWpQr0jijrizCR2RpQlmXAYVAKE1BiJBoEcGxkiZt8AJfgxQeVzmUMCgjAgZBKSITRM4kWbsfg3CnlEArjXU2cD6lAN2U2/iG01uHyK/wFucrHCFGX/kQKwYRopHaIFyJdAKjDYE3IcCHiLxSktwWCC1DqrJ21K4iTaLgcrJlIwrLxv3rEco3UVUdxC0RYrtlnoNQaNW0vTtLJDWudsHprMKXJFeXlKsC70riKAplXDpGOo/1Fikkzku00siG5QoOrSDWwbVc2xDFclhcE5UOqAjZOFMTIhXE4CAluSBoaEVR5E1JTRDK66pCSIEwkgKHEYErKgBbl0E0EmrDHq1thXU1RoVCF6UkdVWHYwtB2GsWb49EK0lZlCRxjPaCThJzeXlFEsc4UVHUZVgcaFzmxiCEDPFdW9HttDEChK+DOOUtRscYrZlNZ5goFPZEcXA3u7qk32s3TvGYuqoC91E0hVTKIBODcoJYGYR1SG/pZDGKgJiwLjimpVCBX4xtzjdoZ21a7cCWzlerMHwQIUqvdMMxdeGcMEh67ZgkkuzvjSgW84an6BFSY72nqh3OSbQM56iSkiyNuH20x3jQw7kKW1sio8IgRShWZU4sTYhrKkk7jdnbHrC/O6LX7hApRVWG2FJertBpQl6XOBnOEV9bEqUZDfrUdY6zIToWBPTGqRKFeIcSAqMVxhh8XTPodpEyuAjLvAjOce9DqZBoJmoiiD77oxGHOyN2RgPS1JCXFdPZAkEoKCvLFa0kxdU1qRJkRjFopwy7bbIswdaWuioxcUJlHdZ6XO2JjEbgaUWGw50B40GXmwf7dLOETrvFZHKNx2OFQgrwtSPSEoGnk7bpdtoc7GwRx1GzvpuG/e2IUkm1qnB1TbfbospzWmnEwe4Oh+Mx7Sgiigy2KsnziihKqOoaqQVxlFCWFVoIjna36bYSdrdHuLLEVSWC4PKXIojU1oahSqQVO6Mhuztjbh4f0eskdNoJq8Wc2CSs8iq4Km1gu6ZpQmoku6MhxwdjtgddnKuZL2aYyGBrT6h6Cl+MjQhO2HY74XBvTK+TsTXo46sK4YPTFB/QH65xlRsd0Wul3L5xRL/XYnd3jDGKxWKO0gHV4nAoEUTqvCxItGFna0jWMoy3BsymE1IVXJ11EzsyJnCmtdZMJ1fcv3+HXjtjOO4wHvZJtUFYzXI1AZEgfI5ONGWdh2GOcIDH1RVxZJAi4CwCbyUw3PEi3N+cD4MgZxmPO+zv7ITURgHL2ZwkzbA+DFvqqiYyhijWpLEmSyN2Rn1GwwGDbpfzswuE0gGx0mAC6rrGqrD2JUpSVTlJrEnjFOk1eEMcJ3hEuLdrRSwUquFMZ502t2/sMux36WYJsTFURcV0saKswESS0ubE2mCLMEy5e7zPoBuzszVi0AkP6mVRUdWBVxwK9AxOKpQ2jNMON/eP2NveYmc8opWlXF9OqCuL8w7nodtqMZ9OQYJKFDvbWxzujOm3EjpxWCvyxSKUXrqw7igR0ERpErO93ePG4QihKjqtNu12m7p2zKaLgKTwjtjESGrKfMn2eMD+9japUWRGk3USBv0eTx4+RHjZ4BokWiu8L8kizcHuNuOtPlmiSeLwPcR5y+R6hogTKhUeDIW1tJKYUa/D4f4OndQQRxKJp9dtoaVjtaqYzedh7bA+4C5ciLv+W//6r/y5Ilt/tf1/e/tv/97v/frLmKfaPFCtMQdeCcqO3DTMu1Q3rky5aemWltCC7SGa1eRbhuQiRL5d1MSDo+ZnRCLwognOtmIdCW/ijV4KoklFPjQhdqkEq6EiWviNI8/M601EeP3/8qEJgyYvQiS4dJv3vOY+BndNEGalYxOfX5furGPT6xjjupR1HU0PnMKXPFCvZXgvTQRW2oYXKIL7cB0HFbD5cxdJossChMC2dIOH8OTD8J70qnE7ixDND2pPeLh3saToKVxzXKN5+P5hY7UpJ6vbIXZbt9XLaG7lMNMyRII74d8suwFfoCr/8ng1bjkXhecDG8tQIOb5jJi8jk4Hh6FtBKmqo6nbwaVJ47JbC9h6sqLuhQGw8MExiAxOT3OdY1vmpTu6sJtzaY1p0HnjDm2wAeEzfelYc5HasHKFA5esxUiDTSWLXUXvx4umpAmi68B/V3mNyuuGw9q4SxEs9kKzvZfNZyiCAFVnktYzS51KVO2p2gpVBYF1dpKGkpyuRn9GKFu7LsO1EFx+iCBuLXY1NpIBUVD4Jo79EusRxIDg9FqNJGVXNoJEgxAoHS4KeB+1DOVJNpHMjjRlV3H+lsLMwUYN71kErqKelyGSnWrqVvjMViPN85+D67sKXNivsH/huEZnC6p+cEHXbcVyJ+LiDcHkDctqLxTvyaIpRWqGBPF1hagcLgmCWdHTXN81qJyGyxyu1YBOabADUm6cvuvrZ3psNsKIWYTotModZpJvHJjeSFYjxeUbAvXWlNb9Kaf9GD1VtB+XDaJJomcF+XYaBK+tiHyomR0rZjcV+UCiynA91i2FmdcNCsJTtwNuaDk2XLwN9Z0Vuz91yvme4nIvofU03NfWn5twoUBw/TkvDhTnbwvKjsY3z5XeiGYApdANumB+GGNWjuW25uwdyfVbltWoceaXguQ6cFTNIqBWqk7AxdhY8uILEcs9ydWrHu8a1IcIznuvZRikFBbbMlQdzeUriutXHdVXFlzuaeb7EfEk3BvSy2qDwliL4kVfcvWKYPnFnPpuQWEjsjMaVIdv6CyNqJsF1vhyrLl827Hc98xueXAKL8IzVcBQWETtNuvJ7Mjw7KuC1aFj/lpNkRrSM3CxJJpYZN0I3hbi64qyr7l8oFiNNdf3BNlzT9EPQqZuxFWbBvHVxZLs8YJHv5Sy2vOY4wXt2zPmfchVEljtIty/bBKKzDYCvAjnbNkJa6JurlcBzeAp3GtFI8ALD1f3NRdvKfItBU6SXoSyL1UG3E3gsgYXcjFQ5EPJ9QOY3IbFkaf/fuNIbdBCwnrUqkKWFpSkGASXadFXFIPgfBYIql5Yo2UZjmvZ1QGbsXKsthTXP1MQHy+QOwWTjkEVkvYTSzStgsN6LTSXIR1z+rUae1wwun/B8tgxVwl6EUTlgD4Qm8GaiyRX9xTXXynZe+WM7XsXPB3FRE8Mugj3dFnB7EDROm0Gp7Hk8hVF/s6S4f6U+ban8AnxxL/8t2uPjYJAXw4E87s1/8Zb38YrwRvDZ3RGSz6qhmQvxGZgUnTDc3g0d1zdV3Rev2S3M+de55SjwTXdwxmX391qnPZhEFAn4dzKh4LpWxWv3HrKUe+ag/aEo/41P7ZDsidhHYoWL1NAy7Fmeht6b15wf3TKOF0Qq5q93SuexwlumtB6Vm/Edq8bjA7wH/zbP/Pn+q6t/5//yv97WxYn5DbEP32SUJQFKjIUywKTGIa9LqlRSKMpihXzvGJV56ANlbO0oiyIjLXHKx3i5MJx8+AQJTxXVxd4a8iLgixKmNkFQjVFPkJR2hrpoR1FTM6vafUTtDN461BRihUVs3KJl54aSWwMbR9ik/1eG01FWa1Y5BVOOJI4osgdaRxa472FRGraUUZsJOmgRV7Mmc1WFJUlSwxVabEixLJTEdylAO1OQpIkzKczROxxDqxI0C5iVZcIlaAEaOVBBsG0320TqTZVUTGZLqkat7D2NShFVVfELkYIj5+v6HR79Pt9rHPkxZLlKjh388axW5Y50kMry/BFRRRpxoMhWZZxfT1lMrliOpuRRgmlC8VbeImtLLGOqeqC4709Br0htV1RlTNePD3H1zlIg6vD4i1kEAS01ox7LW4cHxIpx2Ka8/z0lPPrFWVdUcogXtWuQIk04CiUYnfQYTTaoS5KLuYlVxdnFEWFKyyttI2VggKHdTXOQC1rtNAoL3DC48oSHCFeXAfHrzayaTNfu3dDPYwUEqdrrKvwUqG8QVlPYpIQnUYw7PY4PNoligzO1dy9u8ejx2c8PpsQm5jFMqfV6gRnYBXKvG6e7DDodYNYb2uqquKDDz5A64rFoiCJOyjRZrUqgtN1ZelmmsP9fbqdjNlkzvPTUyhLUBmVdzgfpqtSKxKp2B90GY8G7I37nF/PmK1yPnnyKWVZoLWiqAt8VQZurPBstbvcvXNImmg63YTnj8+Yr3I++uQRyhtsviJOYiIhWc2mbO/scuNwlzSNiIxHHR5yNVvy7PySx4+fI6OYbtaFVU2+XJH2W3THbXZGPbY7CdoLhlcZzy6uefbiBcopBAphgtjobYU0EfduH7E17JBEAX8x7Wp2xwP+9Ps/JIoli2KO0GlwB3tJpyu4tXdMqxVTsaBwkhfnE54+Owsxd18HtnMtSU1ElRec3L3N1rBPog3VakYUxXz/3R+yXFWb0qi6gqqu6GYRr90+odNOSGJP6SHujYiTNv1Omx9/8JhIB1ZyNV0QaUM7ltw4PmKrl5EmMZUtmS5LTs8u+OTJJRqBl4rIO7SFOIm5efeEYTe4XwftHpGRdLc6OCl4+OkTUmXIyxolNMIJkshwuH/EeNill8HKeq7nC6bTGY8+fYZzApuD9gKlPHW+4GB/m+3hgK3BgEiDrQZM85zFMsfaObmd4VNDQo2rHcN+l8O9MTvDLlkWYV1FL22xzHPe/dGPUNLjbU4UC+raMuy0ODw8YDTs0u5kVHnOjf27vHhxRlHUfPL4OTLJsM5t0gMdbXj1tTuYSGOkxwhPq9Mjjg1PXpxRlqF0MBTDCSSCnfEW2ztDBpkgS2OwMEwTnp1N+PTFJAyXolZwTStDrxdzMB4wGmRIGSEUtNsJW8Mu3/j9b5P1eg2DNziS0zTm5tEBO/0BnW6EiWPuHR/y4uqCH7z3PsJZfJKGREQlMFJwfHzI1lafrUEPWRXhfDBtklSQnsd8/OwTRJnhS0GLmNxOabViev0Oe1tDkiRjtcr5+NNHXF1PgwNdNBgBZ0FHXM2ueHDnhBv7Y4wxtNM+Tx4/5/nz57yYTBCycQ4rjRK+GQ56bt+8RZr64OhdOZSv+ODhY5Ioo7CWCk8cRVS2RkmPdTU6NRycHFEsSjSKDx8+oSoryrIgTVOsK3CixDrHwY09xuMxW+0YIyTGC3qtjHbW4XL6IUkWU1U5RsXEKqPVlty5c8RoNECpilimSO/YHnX5gz/8HqULwyLnPUZbtFGUswn33nkFbWQ4ppkmbXeZrQa8//4TRJwh8ORFhTKSIl/w+XdeY5ClJJFASdCtlCQ21OVD5qsCHcUsbY4TkBeO+6/d4GB3iPQFw/4A7z3b29s45/j48VNKbxFS43yF1hFpV3J86zbjQQdfVcRKQhwjrObWyW3e+/B9nEpCUR4SawVp1ubw8Bitoa6XpFogpWZrq8Vi3uPD8yt8rOnUjkU+o/aSvfEJnTQmUjXO1USJIY5SUmF58ek5/ThjkdeYSAQms6hxqP/b72d/tf3l2VYj0zi3gusjmvlNKVSVSSa3FGU/PDzquSQ98yRX4bVm6VjtRD8hoK0Lr57+bIKLPO1H4aEznjqWI037WbXBAQjnQ5mHEizHks5jy3KsqJO4ESODmNV+VjeohOCYXe5GOC2YH0lsBHoVXHXSCupe4OrlWwbXiCfFwLDYllRtQdkXRFOBfSGIZ458ZDaxTGcEq6HGNI7B5WEGo3MAACAASURBVFhS9gTp6Tq6yqZUJLmsQ4yzEXwhRG5nR5I6Cxzg1hOHadiYdYOG0EtH1Y/Dg6WHxY5kfgSqFJgZJBcSvfLEk/BAu3ZYrcYmcDBbgukdqAeW5Ikhe+rpPKnJB5pobqkTAUhkGVi+Zul49uWE5bFG5hKVC/o/XDuBm+Lahhm4Lna7fGDIv7CglRVcX7ZovxfT/yDEQPWsCoIrYd/XzsyLVxWr2wUUitZDTecTtSl7We0MUYUL0eeiDuWNq8ATdVFTqJSvH+DVy4h245yWucVmTUmOfelSlGVI1a25jWW/1RQOKa5vGa7etoiswlvB5GuK7FuKnT8uqXoRyfMF+W5r49it2opnPy1QR0u8CyiFKteMfzeijgXZaRWEkSi4Htevmx1GXLztUbsrTk9T+u8J2s21pZcBI7HmexY9xflbEn97yS/f+wG/8+ldZi/abH1TY+bhPDLTGuFC1F5WcHU3ZvK1FYPuktdHz/jdH99FnEfs/75EbcqgNHVLEV2WXHwhofzinFFvzn66JPv5ku88OWD2boftbwXsxeK4BQQ3Zj6IWOxLpvdrbt15Ti9a8cGNEafv9tn63hrfofGqtXHwOi158fWaWzdOOW5foYXjO2cHTB/EbP1PGVVbk7xY4VXgYdpYMt9TXP1MwWA453IVUywiWj+KGX+n2pQkmWmIKhdb4Zp89nWDvLlg0Fkyz2OibMXlP90luXQvS6NWFWqRszhMWPzyjFfG5xxnV5wWbbYfzHm3s8viIOX4fy+DQNbRGxblYlty9cWKWzdOudW54KLI+OHpDtfvd9j9I7txZ7q2oE7C8Or8KzVHx+f0kxW/OP4Bv5feZbqd8H59g91/FYZb8VUVHNUKyrbi4h3H9u0Lfm78KR/Nt/jxs23884Ttb4qNk3LD4Sw8p5+LWNwtuXXjlHE6Z1lH/PjFmPq8jSrsplStHCbByXor5uIdx+G9Z9zunbOoI76/t8fVVcrhb74skas7BrWyXN+OuXzHsX3zlC8NX3BRtPiV177L33/0JS5me+z9Yb7BC+TjCFl5FjsK+UvnHCYFg3jJVrzko+EWD1v7bP2ZwMxD6kA48HG4n1y8olneKzg5POdO9xyA7xwdcPXuFuNvSaIqlOiZpphqdqi4estydPMMoyxaOC63Mp6ftLj7X5QU42yDS9ArSzGMePbTguG9c14fPWMUzfnkq0O++f4JB/80oOBCiVgQ/qpMcv3TPdzJki8cf0okLcfpJWfjDt/uHHDe22LvD9zmPF+Nw9q4HGsWB5LFazlZZ8likjD8w4jew2pTRrnhlwPdD+d88itd2l84ZzvJeWPwlH/y3hss3kvZes9u0hl1pgI6qBkOrn52znZvzs3uJZdFxnvmkN3fldieIJ4Fh3DV1U2xZRDKbSK5eMtj5qEs9OAb4b6jZxXlMNqwxFXhOX3HkN/Nef3GU7biBaNozqc7A749OqL+OKHoJQ0qSFF0JMuR5PqrOV88eURLlxynl8Si5uok45/9d1/BLgRmyaZYrI5DkdfO157QiQqOsyteaT3lVnuf33zxDoe/0zjlFcSTl98zPv13au4fvOCkfUlLFQz0kt/ovMWiHJGeemopyM5rpAnr4vm/Zvmbr/yATJX8zfH3sEj+o61v8J/aX+P8D042BWHSCuo4DH3956f8yvEPqJziJAnnomo7/rOfucHOb0VBa3RsCsuKvuavv/4efbPkusq413pOJksengx5fLHPwTcCPmG+Z8jOanTuSF6d8vroGXvxhKWLOIivuBWdcb/9gn/w468yPTZk5zY4bD0bjNKfd/sLLdrmxRLb8CQLV+EkZNKTJhHHu9vs74wRyqFUhXRdLiZzXjw/YzpbYZBNN0vAG5SFpd/d4uRkm1Y7RTjPYpQyn+d8+uQpi+WEKEoRIpTTdOKU1WLJndt7DLsZ5pV9LJbz0xc8e3pJVVsQ4SFKeEUnSVBeoai4e+8mrUxhjGe1zHn85AWPnz5BRglREpNXKxQChGE86LDV63JysourS6wd8Pz8lGfPz1gUFSpLmS+mgQcrJEJpdre3ODwY0Okm1HXJZDLj/MUZz06vEZFBOkFezhtWqWHQafPa3RtoAbER5PmK6+mCdz96n1pollWI9YcYfvhSeefkATujHnEmSNOY+XTBk2ePubqasSpriqKgPxywWi1YTAvu3bzN8eEuibZ4Sg52B3z6LOb5+SXPX1wQJS1WdYXzoIzAGMedWzfY294izTRlmWGXLbazFt/76BFVvkSmbYqqwiiJdpobO9vcPTkkNRrnC2IBvc4e4uOaF6eXQZzwAoPBVhWtJObNN+5jpGXQjbGV4xDPoxY8fPSYVR0YplJEZF7gbIyVoSBLa4X0FUWxoJ22KIqCqsEt1LYGEdrBE5WECLxOKERNXuUIEUplhBA4b7HCU1Q1sdLktuD1uw/Y6rQx2oILmALVYCNeXEzRKkRu81WO9prhoMvJzj5JaqirZZgCuhrNLT7+5BPKVYmra0pKZJLivOVgK+Xk4IBBp41REtfr0jGCdz9YsfQWi2iKwyz1fMXtV++zvzMijgI3sj9o0x4mmEzwJ9/5HnnhkDpDpQZX1HSSFg8ePKDbVrRTjS0thztjamFZzqY8enKO0DGK4CpMkzZ37tyh30mIpEVogYljdEuR9BIuLp6RlysqW+KFxBhFDPzcW29zPTknbh7u+uMBnfGYoiq5mObMVgUBKCGxVrN3sMPe3h6tLPCHpRd0RiOePzsL5Uc6QYsC5woEEUZF3Ll5h37XkGiJUTG2qtlud1hcXHB5tSBO2hTFCqEgz+fcvHGDt9+5w3x2jXIgu128EBzfvsEP3nsXjw4uAClItOPoZMho3MU5ixKOTHqkyejud0gTzfs//iRgRITBxAnLxZwHr7zBwfE+ul4RRwbrI1ptxai/zfsf/jZZe0juLaUSzIoVn3v1Pnu7Y5StSKPAaG512og4ZV8IPvzkY5wnxNgrwaJc8OrN+xzeHiHLnEhH6Ax00mbU7zCfXXN2vgwoCgVlVRGrmNu3T+i3MqgrtPRYaYlaHW4c7TJ5732cF2itSTAU9YLDWwfcONjFzq/QicJYS7Q7wCH49vfeI4pbVM6GYqs0Y9jLeO3BLeazKRGi4X1WjEdDLi8vOTu9QApNjqVcLMHBwSu3GA66LObXJDpG42kNuzg8Z1cTRLGkEiW116RaUtuKt95+g2IxYZgllNUKFBycHEEU8cGnn1BZgaJCWIuVitHRgN29IUqHIU1V1GRpi247pZUIRLVCJBmVE6RZl51eh0G/x/7hDl7VpArKVGNdibQ1iTGsKkuelyilyPMluztb7Iz62KrERBJPjckku919qlLz8YfPcd7hTYWMNdorJtWCd+6/Q6YlqZYo38fZkuVsjkXhhKD2oUAglh6nBK8/uE9dL5sUR81oq89sNoHpFa4sggOi4agvlzn7d+7QSjPSRJIqQR17bt444bs/+jFpHCOUwNUeJ0qED19Esiyjt92l22rjE0c5q5CuboZaISmipcIWBTJW3Dg8YmvYx5dztJAoD1E7QxjN4FHE2XyBTAMqZFkt6WWG0WhAZAxaKmIdHOpaaVSmsNMKW4Y4n5QGbaHdGmCFIosMzlV4HyGUJMvaJLHicrGg3eoSig9jqnpFq50wHg/J53Oks5jUMJZbPMlOmUxXSOeIdVgDrJ3QzVLaWQtXWIS1RFGHcr6km7UwKkI5SVlVmFhTVgWjfo9ut01V56RpGKZ4VyK9w+V5GBIZzbKyDftYUguBUBrpXeDyRzFFlRNnLZzStOKEZZmTK4g7rYANMTJc+16QtjuUqyUVllIIojTBzpZI5ZnXlm6WQlE2qZa/2v7/sMWT4GKsOi+5qfk4ohgaTr8o2Xr9lK50JLomVjU/fLRL+sOE1rPAflSlb7AvoU15dqRY/dSC3eEUJR1P7/eozlO2viXJzixVWzURYc98PyK5sjz5axJ9PEekBUVluPqow/D7Ar1q0i5FsDTN96PQCh3B5GsrdoZTenHOs2mX6Z8N2P52uM99NkIMcPnAMD+x/PTnf0TpFPMq5r0fHdL7gSY9d9ieovU8xDx1HvBIl68pzNtXvDo6ZVKkfPhihPooZet7sG58j65C3BgP0xsa8csXDKKK3daUJ/MeT58MOfhNhV65UJZTuZ9gIT7+eoy/v6DXyjnqXvHB5Yjpd/u0HwmSK4+5LpjdbJFc1aRnFY9+IWb8uRfcbk+onSR7reL/+O496tQw/m4exLImou0iSZ0Knn0NXnnwkFvtC06LNvMq5uHdIe1/0tnEYfWybgQEzbOvKF7/2fe53T5nZSPea+2Q72mu5C5bP/A4HW0cYrLyLMea67+9oJVO+PruJyxsxOoNwx9/5y7jf6VJL+vm59AIQKYRqtymOT6a1Cz3YqKpRS8C3kGtXrJsy0EEAlZDTTwL3OANX1K+bDhPpgV1Nya+cjz+W4J7xy8YJEsAUlXxh/qE07rD6PsFNjV4BdF1iCtPbipe/eJHHGdXXFcpRoaf/3vxbdp/mBHNg0PKzJoBh4XTdwzqS1d8fe8R42jG8l7E/7b9Gvq3siAylE3JUR3299lXNXdf/ZT91oRJlfL53cdMhgnfSY/o/n2JmQXkSOBzhvN98dfnvLpzxjv9T5nUKb/62neZVCl/dPkGu39sN/FwvfKUwwj9c5d8efdT9pNrJJ696JphtOTPuvusPt4hmVj0MsTy60xRdgS/9u/9S75xdoe9bIqRls/vPmYxOuUHs/v0PoLW0wJg4/q7eF3xpQfv82bnCUbWZLLky90P+XuPvgJVio0FNgu80o1Y9tWcu3tnHLevGEczpnXK09tdHp3fof9hsbn/IEK519OfTfj3f/lf8J3pIS1dkqqKyin+xRf6tP5X8xI/EWvKYcLp5yV/48YHrKyhq1dsRXNiUfPlwcf8Rvct/O90w/GNQrQ8OSv59Bcjvvbaj+iZFSfJOZMk443eU85udPjRP39tM9Ay85r0acH7/26HL77yES1dcjc7ZVJnm2Nw+XqG+t1BU7AYhgpmUvP8VzNef/MhiarYj6/ZiuZ0oxxuwPfOHjB43weGqAy4Ay8F8c+c89XdRwD09ZJpnbJzY8Y//+KrdB4rRB1E3qKniGaOi3ccP/W591nWET2zYhTN2b4xpzjSfPw/3Kdq680AIR9HzE7gP/nab/MnkxP24gmpqli6iF87/FP+7q0R+fth4BVPGrcpcP1mxd/e/4j3rnc5bl0x0Eve7jziH1rF7IO9jRCplgG9oArHO3/rXZ4tu/zS7g94UgwA+I9vf4P/iq8S/ctecL9GciN4Tm97Xrn/mLYpGMULzosWd7tnFGPN+zuvblyvZh7SHJcPFOZwxn94+/epvOK1+AmPsiHzKuZaHVP0FMmVJ2rKG+OJ5fI1xc/ffp9ZHTOK51ReMY5m/J2b3+K/Xv00/EG6wfSsud6dRwXLfzPnQW/CrfYF3Zsr/sf6S6SXasNjlXWI/RddibApX/6l7zEpEzJdMdBLPn/yiG+d3g23rIZpur6HJecV52/G7A8nnHQuuZudcpVk3Pr8Be/+L2+QD3TDCHZQscEprLYU8wOB2F1SOYFbBGe3VQJZhXVpPfh0RhB/8ZJfOPyA2im2ojltlXMQh6HL9268yuBH1eazyM5rpkeaz588YjuesxXNOY4uyL1hZGb8lqEpIf1MwWBXsNjRqMpwp3tO4TSVV2hp8cOSvB/T+2DJai9wuIuuRC8F4+GUv7P3x3xY7BCLmh0z4VeOvs9/P/prtB8HdnvRDd8bkkvH4c4VP9X5kNxHGFFzpCd8Wvf48uBj/lF6ExtpooWjSiXRwnF9R/Jg+wVnZYd72XPWmeGWLPC1pOgL9EqQXtgmTQOqBCMtmSrJVElPrXiYj9jJZjxxsBxrsvOaZGIpO2qDDVlfS69mT3lSDnhe93ia96kzj6yCmzd7XjI/DKWWdfznrxf7C11EpiJFlJimEEgilKS2lkQZdkYj0jQm7hjanZg40Yz6bXZ3+lQ2p6ZhB2KpqoJBv8f2Vo9BN6MVKyIDvU7G9nabrGWwPoe6aiLlLxmp24MtuoMWvX6bbjtlb3uLVhYhXJhySe9RwkFtqauCVx7cYW97SLebEGloZzFHRzv0ex28tVT1qmlU11Tek7US9ve20dLSbkX0WjGDQcZ43GN2dUFZrIiiiLyuWDmP1YL2oMN4u0sUSdpZxNYgY39vhNEO7ypEoIQihcAWJf1Ol+3tEf1Om3a7RZbGdHspnXZKkS8CgxdP4YOrd5WX7O7usjXs0soilPS0WzEH+ztYLMJXDLodptcTlIhQKPbGI1pJQpomRJFGacFoq8do2Gt4j437NAy76HUHDAY90lgifUmsIIkV7V6KiUI0Pa9y6rqmdGEq1e12yRKNFBaDpNWOSdOYYbeDsj40znuHthXCVbRbKVujAcNBB2kkaSsh0oqtYYtBL0NLAXWFd466tgih0F7RUhnHO3uMtwYMt/qhyEVqjDAoH0Skugqs3arIA28UQb/Vod9phyC2dSin8BaUUMQqCBWj0YgojjGxRhpFq9Uikophr0u3k5HEglgbyjLHaLB1QSuLSBODEY5OKyVShixJyZKIJInRkaKyJbYusWXBcnpJr5PQ62Z0Oi3a7TZpFtEbdmh1U/J83kTFQwlZXZR020GAUkqho4QkNaSxYXs0ItIGJUK5zmKxII5jpIR+v00aa/I8J0kSjFG0kpjhoIstV+BqVstFKMUTgl6/Qys1mIahXFQ5QgtqFwR2RODKImyIy7sisI2lQCjwOrStW2vJsozL8zO8taH0TkuMEg3L2iG8RdFwnssqxKcIje6K4JqzdR2a7LFEkcZEKjCHcUTa0+t0qPMyFLNpjdaaxXJG0grDEukdouFZO2eRUiC1JM+XWFugfU2Rr/C2RmtFEhuUkZg4wTqorUcpQ5qmaK2xeSgNKm2NjFKkSfCRYuUKpA5foEJxocRSbQqKpAylT5EK+BCPxBEclvliSVEUSEnguJYFvimOy9oZSRJR+woZSaoq/L04jsniBFuXgd/sAoNb+HD8pBY45ah8jdBBWK3rGiODOOhrS7nKA06lzimLJWkaFmgjFVpKlssZSZIAge8Zm4RiucLaivl0trmGtI6CQz3SSAlllVOWeSiJ1A2X2tXhPqkVRokGYiWJ4xgP2IbV7Hwo8XJVCbbG+ZrSVkijkSZ8th6LdxVZqvBVTiQFrmHVGh3jnaGqCuI0QkpYzRf0+32UlgHtAZvzvd/vU7kKoWQosrKeMi/JWh280gjhAYeRoXSvLEvyPN8UNOqmcE/qmLx6jtQrhLRYL6mtoLBuw4Wel1NW5YpV2fzs7gBbWfLKgw883ypfkemI06fPUFJjywptJLPZjKIuca7CyVDEtXaYmzgiz3N0pMjLnLwqkUYzWyyonaSWkto37b8+uJyTKIh9Mq/omYReHDNzOT5VVCp85aiq4NRSSjGdzLmaTphOp1R14Ik7AdeTcy4m52EQphXWVkgESRyQEmdnl1jnqJ1jVSwpqpr5Mme6XDAvw2uMMSQmwjooKo9TERYFQrLMK/KipCgrhNK0O51wPZlQzIiXzK7nXJxdhtI8JVlWK6aLOTqKGqQLAb/hFSZJOT1/wadPHlMWNd6H8sm6rlksFqyKJcgwzPPeIpTk/PyC8/PLzedf2VA2eD2dYrWgsgqjY6I4lLpJrZjN5zglyKsaLyTz+RyBorKeVZEjgdgELMiyKFmsluRVSYGj8JZlUaCUIV8VFEVFaUPqAilQkaOoSpyXm1TPX21/+TdnQvRbL+0mgqxyT9GVtO9fcdy9Yieb8cbgKQfZhNdPnrJ6JUevfGC0NdFNs3RMTgzTezWvHzzlfj+4xO7vnnJ455R8+NKJ9Nkoe51JOnevubdzxue2n/Dq9nNGr56zGommpCW8x5ecRc/qF2b80t33eGfrCTvpjLtbZ7TeuWB6pEOZzdJtyo/00lIMPHdffULfrHi7+5if3fqQ0eE10zuO1rOSaO6o2proqiS5rIPTZmz5xeMfsh3PeXPwhM8df0r8+jV1ui4Uomk+D27H+RH86vH3+dntD3m7+5ib3UtGO1MWOwozDaKoqCx6skIWluhsAQ/mfPHoEbcH52S64s7wnOjNa2TzYD693aL9OA+szUiSvHrNvf4Zd1pn7KYzuibn6OYZs1tuU+Ymy5f7Pj8QDA+vudW+IFUl++mEk/YlJ1uX1Ckv29+XFWZWh4KWnYoHnRdkqmQczXil94KjzjWLGzU2DmgCZ2SDD4DVtuQrxw/5uf2PSFUQs3aTKVs3rpgdB7wA0BSDuaZkR7DcNjz/kubqnub6dhyEBR14uM6IUK7ctJ8HzmnAU0yPNNMbMTYOrPXQ2t6I1N0YPFzej4myikGyJFUVb3aesBdP+MLhp6x2Qxld1Ys2zeuhlMxzq33OOJrxlf6H7MUTXm0/ZdSfU3YJ/MVlOL6qsMRnS1b7lje3n/Hl7od8uf0Br2ZPubN7Rj4Kx3UtythUIfOKaGfJl0cf01JlEArTC263z/mp2w+p2uqloPhitSl6enP/Kbfa53y4GHM3fcG2mfFW51NWN6oNMzi5qDDTUEr19s4T3u48YttMMcLyMB8h8SyLiHwU3tC6ICyI51A4TS9aYaSlcopFHTGvYsqBp/vBLJzvjYAVCoY8zguMrOmpFUZYZi5BSbcpFloXaMnShSKiSjJO54yj2UbI2U1nLPbFpnzLJoo6VUTPplRdz1WdEcnguKycYmUNQoXzJLrMkUXAMJjrUPrZ10tO0gvaqmDbTJnbmKs6IzVVU1y4djB71CwH6UlVRe0UP17ukqmCpY04L1pB7GqGC4HRKnFty3Y8p2dWVF5ReE2mCj5ejbmepzgTyhJl+ZLXXQ0sx9kVkyIlUwVnZYdUVRylV5QDv0kprDevoKgMkaxZWcNZ2UFLSyxrRBmOv9fBQWoW4RoRleBs1eZWOzgIt6MpW9GcD6YjikEYwsvSUfYM0aRGloI/mZzQNTmVV/T1kkmdcWQuQTvM3BHN7abcLSwUgud5l3E6Z9vMNu93tzUNqI7CNdzekFpYs7Mrq3hSDMhkSSZL/nR+A9fwZvNRKOQL7FuHiz3DeEntJOdFi/10QqpKPpptMT3WG/6zqH0ogZTw5v5TnpV9jLA8r3tc2jaXq4x8+JPFb3UW+Nx6CY/mA6ZlghGWgV4GcU6WFLO4KTEL14ZeOcysZrkbsZgnPLwY8tF8iw8WY5Ce2VEYXEWXefPZBWTAakvzex/dJtMVyzqInD8838bMxWZg5UxTpFaH4YmZCjpRwaP5gI9XIzJV8t3LfdTKoouXvOg1x3rNsvUKbu+cc7J7QXQW1r91Md2moC4WtB4tmDzu8e3zI87LFpM6ZWljfuf8Ad/89Jho4l8iDnQoaNMr+JOPbrCwERdlmx+u9niYj/jm9CbZM0/2PPTq1FngPKvSE80clZXM6iBK/mB+wKPFAOYmrBd7yU9wxQFOz7r8o9N3eJr3Afje8pA/ujzBxmGdVaVvcCoBu/H4g23+7vtf50k5oPKad4sDLm2b784OSK7DPgfXd3h9/wPLdx4ecV60eFIMeFH12NXX/PbV61BJ9ArqNHB1rVkLq46VNXy4GFM5xTenNzHScpVnxNfhHCm6Cll60vOK7KxmPk15vOzzdNXju/Mj2irn/dUOT5Y99PJlQaGLJPE04FDM6s/vtP0LzbT9B//4N369rkPJmKwszjqMEETG8Pabr2ESgRGBUuqFJEtTlEn44PEzRJRQCIdAgtRokXB045g0i1FYYqUxytBupzx/cQooaiGovKD2kDvH3s4Or947QUcCV1uEhyRLqSrH+fkVRBGVq5siM4NzlgcPTuj2MxQFJhIYpej3Ozx/cdrwdVtMlwVGpxSLnFdeucn29oCkFQdGrYQkjphM5symKxITs1otSbKM0ge+zCv37tNrJySmhUTRahm0kHz08CnWOoQ2SGMolkvSKOPe7dskkaQWJQ6LjjVplvDi7IKLswlSRHgEzgPGIoXh/vEu+1t9pCuJwiEE6VmucoqqpCoKtIhRQkMND+7dZthvI6UHUWNtRavdI89rnjw5RUcZHoPUEcJKtke77Iz7mCiIdjqOsBqidszz58+ZXs2R0gSWqxMkSnN0uMto2MY1ZWw+csRpTLGqePjwEVG3i/OOarXCCUer2+Xo7gkrH7i8VW4DxzRVXE+XzGcLqCweTUVNKRxGSwQWoRW9fmCPvnh+hqsDY7WuK2ItqV2FcDWxkThbM+gPGPQ79LMUXzkooV7VxFojrUd7kEIg6pJbJ0cICZGR2LrECIX1NdPZgsn1Bc7FmFTj6wJblOxuj+j1U1QkqeqavKgAx7xY8PDpYwonsUYhpKDKK5I45e6Nm/S2+pQuh6jCKc90MedqPmeVF3ghGjEu8A0PdkeBp+osJvI4V5EXOeWq4Oz0OhReNYxWISSxhP29HbysAr5XaeblFCs8V5MpL87PyWuHiAxaC6So2N5qI6THUlNLhXQK5zxVIXj05CnOCbSMya1DKk1eV+xub5MkCb6qcTW4usbXjk8+/pi8yP9P9t7kx7I0Pe/7fdMZ7hA3IjIih5q7uqqrySYt0rYg24RB0qRsUISkhSx7Y9gL2Uuv7D+AOxvwH6CNLAEGbEugB9iwvbIJ0hyaarLVza7u6qquqs7KyinmiDuf841evOfe7N5xZRAiL5BAZuS9cc/wne+c73mf9/fQTseEbouzjQTU6czDR8ekvsNpTe+33NzecnV5x2bT4X0EoylkXNWgyByenNI4gysKkBAn7So+++ILNl0PVUsxmhA9k0mNNZrDgymqFEauwW/XlFx48fyc+d0GV40oBQiRtm7QWaGVYVzVZKXoYmTT96y7wPXVFY8fP6MdTyAFsKArcHXi9OE9Nts12lpSEkHwdrHg8vqS5WqB1Q1Grpn+XwAAIABJREFUC9Ny7DRN4zAMgphSRB8IMfPy6QXn59cUXWF0hSqGmDJ1VVE3DZqC1RbjKlbrLbeLDY+/fA7UFFWhS8IazXaz5fBwhg8BdMYaCUO8uF7w4vKMkGAbPNpYCVKrFZbI4cEYXQTWr0qm7yPnVzc8/uI5ZWCVO2uxWpGy5eTkPplA1hFbDClF7m5uePryitVyQ8mG4jQ+B7rUM7KacVthNPSlB1W4Xa55+vKMi6trjKpRPkAqVESssygVqStD8B5tpJ3/+mrFxfU1y+UG33ucsyQKzWhMv/XEkGnqMbay3N3MWS633N6teH5+SR+gVJaUC9Nxg99sqJwl5V6EiehZzJdc3K74/MdfgNZkAtqALonxaEQInoPZSBjZWosDOScuLxc8+fITVouOYiO9zyQiVitKiqRuy8HBhMXtnFWfePbskvV2SxcjlTVkMtiCqxShJJbbDW07ISvFzWLO4m7Bi/MLsqqJ2WHNDgOjqZuWq+srRpVhetCSQuTqdsHziwtW/RalDettRz0eU0qkMpqiC9vgB6UFnp+95PL6EpUVWglKRhthKhclQS8bv+X0/n1GByNW2w3bbssmJF5cXPHyek4uBaWhbSr6vsMMfGbrjHCuU2S72fDi/IKLuy125IhRMA85FhHc0fjSE1NCUUilcHUx5/GXz4kp47MnqYxWGaeFgWxQFGPRTpGA+XzJ5dmcFxd3xKywtiKXjDGgnWG5WNJMDtDKkLVl4zt8CHz+xRNWwLb3WGextWWz7qirim7dMTmcEoFUNPPlivk28OTZDZ2t2PRrjBqY6raijz3tpMUqDamQU2a99fz4xy+5ma8wuqHfBgxSyLJaYSrFolsxGo0hQ+g9m03HfLHmyxeXSImrMDIV2+0G20rr9n/wd/+KafuX4fVP/oc//C27TXsupI7Clw1jzd/7W9/kXrXmfrMiF01C8/74Qviwv3cAGqqlMCZVEWFL/+KKR9MFE+t5rZlzVG34+dkLvvvpuxSlsX3BbqS9tbkNXP21mv/wl/+Iw2rLJtVYnXlrcsdH/SkHn+/Yt4Jn0En+vP1rz/ilo8+Y2o571Zojt+VXTn/EH3z2dSiKVGkmTzekscXdea5/I/G33vkB77fnoMRB8+b0jj87e4Pm0uEPNM1dwh853CqB1th/945fOvmc95pzRibw8+Nn2Frx4o8fDXxQcdS5ecAfO7pfXfNguuImjNnmiqnreGt6y2efvc7hj7YUO3BaYx54lob0Nz3/+dv/D85mHtYLapPocZzfHuFWwszLtSzuVYEHv37Ob558j9oI03ceRvzMwRkfbU6ZfdcMAuarUKO7DzRfe/8FR/WG2zDm2G1YxobTes3jH73G5EWkOE0aSXZHP7P4X+z4jdd/wDo19NnSZ8eb7S0fb045/KYmjPSw2Mx7p+27v/Ccz5cnvDu+5tzP2KaKce15/uIe7YVgLlUSEUGC0gauq9ZsHxX8IRx/FKRNvNGS/D21EvijIMwEjbB427B+K7N5HSgWpcXF7GeDw6wWzm7RiuN/8wqt4LRes0wN99yaVWr4/Pw+4+cKE0Us2oXS3fyc5eDRigPbcR5mPNkcE4vlRzf3mfzzmnqRRJjSCrMOhOOG7le2/KunT3na3yMqwyq1fPvqLeLTEc0d+4AnhvN984uK1w8X9NlyWq1Yp5onm2MutlM2nx1QrYciSEaeO6ca9Y0NoRiMKiQMfzZ/g0Vq+eLFKccfZdxdJzkVtTyjP/l6TW8qFmnEdZhQ6YQvlstugvnTETpCbjTVjadYTXMX+eRfmfDGZE7IBp8NXXKEZOj/9JD2DvpjNwjcMoaTM0y+vuAujjmtl3zRnfB/n/0MX/74Pu5W+NMMNfQ4Fsbz8k2FOwgcNxsymnloqXXi7A8f0V7JGlvlgltHukdjYmtYvKEwKvO1yQXPu0Ni0Zx//wGT54U0kc5SVcAf1yRn+G59wlvHt6xzzePtKT9aPOBH8/u8eHLCo/+3wx87VFHD+bD4Sc34qws+X5xgTWaVWrrs+PaLN3GfNYyfd+RGrqswq1ifWDaHipELXPopBc1NmHDZT3j2w4fc+35ElSFcymjsJtDPWr44GONspq0iU9Px/bvX+PaLNzn8o2rPQi1GQrnq68DFWy2fb4/plGVSea76MX/w468y/X6F3UJ97cG84h7H1tG9HtFGuMVWZb7YnPDhp2/x4Pd79I5BPhIEhw6K5XuZdazpimPqeuZxxO9cf53Vd04YXfxEMW6dqG47tqcjbu9pmiryrDsio/md86/x6Xff4viTQq4loBuliRNLajUf2VPyJHHlJ2gD89jyR8++wvbTQ0bnUC0ldM34IqzXteZTf4KaRQ6qnj85e4sfXj7k6pl0UVQLaUk3XWTzqMFt4NPxAc/SjGwNKMU3b9/ls89e4/XfDZJF4jNlCPvangpP+eyRYxVqJo3n0k+5DWP+ry+/QfMnE8bnci908yDol1ZY4zHVhAeR87MjXlwdcfidivYyU99F4rTCeOGQx0bhthlzXvHZ0YRNqviDF+/RnU84/p5CJ2HtpkYLc7dIANnRpz2fnB5zerpgFWt+/8l7rD88ZnQpY6M57wgzN4SpyTY1N4miNM/VAf2HMw4/LcJudsJx3xVhdSzC8r513LwH7xze8NHtIx6vTnh2e4j6/pTTP+ulqKoV3ZGlmSdSpQjW8qJuWeaGeWx5vDzhux+9w9HHkFqN3Q5dF6kIgzrDajPmi37GwlSsUs3Hj1/j9I8stitUqyznZGC+YxS+qnjOmGANd6nluxdv8Pzz+xx/qLH94BaOhVQNoZ3nmutZzY1rWdHy0fI1nvQnfOubH9BcCr5G+Lea9jripwa9clzdN1z7CXep5fduPuCjs4dMv9VSLwrtdSZVChMk9KxeJP7s+ITUQFSGPlt+OH/I0z9+g+kXkB3UK2H0x5EW1m7tuDg/Ih9FilJc+AM+X5zw45enHP2JxXi5prYn0hGTWsGW/HmZtn+hRdv/7n/5X39La4fJeu/0UsrQzkaMJxOcBh1WOOfwKZJKIavC9z/+WJxdWiMB0IrKVJwcHjCuJR62aWu6PtB1a56fXbFad6BqUlSUoqmrinFb8fD0CJLHGkNJ4sI6Oz9ns92SiiTQUzQ5g7GaR49OoGQ0kZwSPgTm8yUxK548P6MZT1DKUDL4EDk9PqapjTg3S4QEXR+4vLjmbr4iq5qCQmkHAcauYTppmU4b+rBGK1jMlyzXHS9evsQHjzKWfrVBawVJMT0YMWo0bdNQOc1msyLHzOPHT8gZcikUZXGqUGJm2rS88eA+IXUQM4lMVpr5csvZ+SVd7ynZsPWSOJ/RvP7aA2IKxNyTYkeMieA9Z2cXLJZLVDGSrpVA5yS8wnGDMkjKdjb0G0+/7Xny+AUli9tJGU1KBe0KD46PoAQqJwFVm9ix7bZcXd2wnEs7cEwJpQsxgjGORw/u40oibDccjCeEztOtt5xfXrNabVBmSlIKqx22QFaKTfCkkmnGFTH2bPqA0VZaxOta0n+TBIvFosFU9KFjdjDiYFRzcX3F1geqdkTSgLaCI7AObQp1UzGbNpSSscYQinBpz6+umS82ZDI+FCrdoJEK6dFsgnUOYpQApwzPLq+4vl2S1YgYFR6oxyNi7Dk8nqGBcVNBKoQucHe75PZ6QbcOVMaRM4L4UIrJqKKuK4wWNo/ThpINl1c3XN0syDmxjRnrDLoIO3g6GVPXEvyhtSIkz3y15ounL+lCQbkaikIXqGyLq4wE11mLKZlCoeu2PH9+zrLr8SGJQ7RypORJJTJqG5QxuNoQUySUxNn1NS8vb4hJblZZOXIprDcbqrohpYBzjj5mtusti1Xk2csz5ot+YCQbFJocIzlnZtMRymhsbYjAJm64XSx48uQl2jmUdsSQ0A5iiXTec3x8SsGQiyeg2PrMk6fP8CEQSPIgmcEahTGaw6MZanCklgzBe+6u56zmC7ZbcbqFCFFnbGUpSsLfBM3RoYuj2/ScnV1yc7vGViNihpwKKa4pOVJXLU1bU1SWh/uiWK83PHn6jJAgJKhcjXEWZQzrznP/5EiC1LS0wodQeHF1K+K+j/ggnGCn9cB1Lhij0LqQsjg5X55fcHe7ImRFTOBMRdXWpCyhdM46RlVNSoltH1mvNlxe39J1gUzBOUPJCVUyOUqY32hSS/hhhrv5Cp8yz16cs9zK9oQSca6SoLzQM2pqlLWUotn0ns3Gc/7yisV8g8KCEdZvUIaQs5xXo1CukLIiZsOLy1uu7uasfca1IzAVyjiUMfgciWTa6Yj5ekFVV1xd3HBze0fnM7FASNJ5oQrc3V7jnGF2NMWHgI9wcX3Hs+fnFG1ISg9caUCbYRsy1lUULQXIGDPbdcfLs3POz+/I1FhbU5QEjO3CGTuf6Lstt/M5d8sN89UaHxLWGpJWwihPGWMttmroQ6YLmdXKc30z5/Lmhhigi6BcQwj9EMQn17rFkXKm7wKLdc/dYsnF1UI4ucrgisYpRcoZqxWbrkNVBl8Sm/WGxWpJ9AVjhxDHBBot22MNtatRGObzFb3vaKqW7Tbw5edfcne9ROGIaIrV4hLvoakqYsyAJgRFFzMXl3POrub4BCUbNJrQB5SV6EmUpus8m63HVWNu5ivOzu7Y9l5wR8ZQYoCccNqSEvh+Q58CzjZsN57VuuPl9Q1dH/DZkErCGgl3U1m+Z7XY0IynKOPYes/zs2uevrzER0UKEiCaYqCqanJRrDYdRRfAstr03FytmN9ueXZ2jrKGEiX4cec6N0qzWC0xymCV3B+Xyy3XN3d0Ww+lYJQRlAYerQ2bbceonmB0RR8LfRe4WXRcLhbcLXuSUiRVyNljqkrWXQr+/t/5238l2v4leP33//B3f6sYtQ+CYmjf7440P3xwwGHbUanESbXiZX/IKjX0yZL/9xH90fAMEMSJF0aa7iuBrxzdAIqvtFc83p7yeH2P+Yf3GF2IYGm8cPbC1LI9Mdz7+i0JzaHdkjCEbPjy40fUtwrrB+eQehVkNv9GptMVtUmsU81ZP+MPrt7j0o949M2EPzBgJIzFLT1XH4zIs8J1nNCaSC6ax9sTvvzxA6ZPyyAGDwFCSCjT5UPLmyd3POlOqXTiD+7e58Or19AftlSrPLhURfQExfWDBnMUeH9yyaP6jk9WD1mnmvkf38MkgwnigE1jh4qZ7esj/C92fJlOiMUyjyN8sXzv/DWqHzbUCwl4E4FGo1Ph8hfgLB1yEyfMY8utb7nyE84/vs/47BV2YReOs35dU06luP5aM6cxkWfbQ15sZoRvHeyfVXYOK4zm7mfgzo552CwY6cAnq/t8urzP9Y/v0V6ofZI7Wg0hbobNz0YO6y1XfsIvzT7jPMz4cnXE9uMZ4wsRM4wXYSc1IpS2Vx4TFf2BwXSK9kaCqNwyEqZDkE4agss6+R31srB+pOFhz+QTS3OTiBNxO4EEBe34sc8Oxrz98IpQDEfVhusw4Ye3D+k/OWD6Qli7bimfz05TsKzeykxqDyiUgrvQ8uzPXmP2OAvrsMvYlSfOauw2cfHWiAs34mcPzyhozv2Mj84eUT2tqOeFMDXoKAt14zPzBy23U0vjRHQ/dht8cXzn8ZuMf2wxAerrjtxaitWg4MnDlvHY40yi1onrfswPzh8x/WMJ6mEIXS5WQqluRi0XpgELIxvw2fLF8h7PP3xIey6hPDoWyuAQ1z7z/HTGmR4xbjxz33LXtTz9+CEn3xeHnxpYjzoWmostcVLzNBwxby1Punt8sTrmxbkIWQfP4n5M7QIOdYL1a5Y747BN5taP+PHdCd9/+Rr3/rnc39HCUC1W3JT1HL54p2VbHPPccte3vFjMqP+0pdpI4Wf3udRqUqXYvp5Z64o7L9fSvG84e3aMu7Y0c0G52G3CbkUsJGs+U8ccnKy5WE9xNvF0dcT843vMvsgDXznv3blgOZs2tAc9oRgRO0PLZ7enuG+NcRtwm0SYilscrWmvMldf0yhbyFrzo/l9FtuG+IMZB08y7XXYYzFiIwGAsbL0laG3mkWsuVgdoH8wYfo0U60ztsvEkSVORPCkaG7aBnsUWMWaL1b3eHx7D/PZiHqlhcM9CGtyTuC8mmCOPT5bbsOI77x8g7tty/g7FeOzQDESsphGluIMOmiu7lWstbS8fzE/5uZmyvQTx+TFgDIp7EPS7DaTKsdK1ayNYR4aFqFh/YMjZp/B6DISB7YnenD9rwvaG64OLc9f3sONIuHHUyZPDPU876+/Yg1ozfTxmn4yIrwWuOzERfnxp69z9F2LzsP53gjyR65BcF1m3dZsi2VytOXOtzxbzNh8dMy9j/IQsqj3QneuBVdSLwppUzP+UjN6pjl4GqlWwvO2XXrFe640qdLUy0xZ1PirlvpxxexHEjxV38VXAWwMAr9VpMZANFysDri6mGGf1xx+UvZCXxrZPY4o1ZrmSjrW3LpQ3RkmLzO2EwyCQqGKGjo0JFwrTCVIj5c1P9qcog4i1/MJ1bcnTJ7K3G87cYjrwQHbzwymV2yV427bcrWcsP10xtEPBDug0uAuXkuwpJbcNqoFuFvDTVuzejJj9pGhuRO+O2qY1xFsigkFu1aoleOqrThfHNBfjTj8vqaeC/Pe9rLPOg33qgT1leZsVrPSFeeLKU8+fI3TbyOicJCANOsF4aQTjM8TyzLmWjWcXR8yfzrDvKh5+Cey5lF5KIA0co9LlaI5M1y4MevasI41Ly6OGH3hhnlXjq+JcryyVdS30B8r5tZxtZ5wvppy8/SI0WcVB1+KY1qHgolyL9yJ6v/g7/9bf65nbVXKn9+W+//36+/8R/9ZUUjuty5BQk7qBlM03/jgPWYzx7RKYA3r7YYUCi/PLnjy9AVV3QKWmCGEiLNj7j845CvvnGJ0FIZoUmw3ge98+EN8SCIyhQJZYYzDusz7X3+Tw3GNKRliZLlZ8+XTF9zeLKE9JIVEpqCBXCJf+8pXuH//PuM2kXOibRo++ewzvnj2kvUmUo9m+D7gTEUIgaPpmLffvs+9kwNK3GKU5ssnLzm/uCIXy+2mkzZZYzEKyIrDg4avf/Aeo1ZRYmC17ri+uePF2RV971E4vPdorfGhcHr/Ht/42js4lRkdtMzn1yxXHU+ePGG5Cijd0EVonCPFiNOWRw/v8fa7j6itoZhMzoUPv/dD1ssNPmlpCbcVPhju7u74mfff5Z2vvM5RoyhpiSpwdn7Di7MrYoIQM13wKKVIWo7nV999i3vTCdOJJNOuVis23ZYffPQYbVoiio33UBmsg3vTKV9/9x0MhelBxWI1Z7Va8PzZJctFR1QtMYPSiRITtWv42rtvc3x0hDUd03ZEjJ5PPvmEZxc3BDMjeRHuaqNQICgGZyTNfNTQdR1ZG1RWuKIk2M5o0tDzlFPB2orS94wnDXUFy1WH1pY+BnAthkKKPbWx+H7N0dGMD95/l8pJin2Mga7r+fDDH9DUUzbbLcVY+j7R1GNi8Pzs19+jbSxNrSkqs1iuefLskuWmJw383D5kUt9zOJuiU+L1Rw94cP8eTe3w245PH/+Y6+s7tKokhMxZAhmlLcWv+eD9d7l3dAgl4JRlte55/vKMl1e35Jxp2zGLbaFtHTl3HE4PeP/d12lHBqMdi/kNq82GTz97Sqla8iAwpL6Ig7kEvvreOxwfzqAkus2Kg6MZ3/zmt6knh6xWG0pR+AxNbcnZczhqqKuGk+NTtFIstpdcXi0xumHZdWxyj241la8lpTgVJqMx00lFW1esVitWmy05QVbS8uuqmhgjOQv2oBpZQYdMW6YHY+bzay4ubtgsPTnLQqGUgs/SQmytZdI2nB4dcDiasNpuuLu7Y7vt2fYBbA0pY3VFIQKZo3sHzA6nzA7G6AIh9Lx8ds18uSJlEaByziSbgEIunuPDGSeHB4xGI4iZi4sLNj7Q+8y2j7iqQauCJpF9z717Rzx8cMJkMkIZuL1dspwveXm7JAVJhK/rFt/1OCet467STKYVpzMJvru+uuHxixfU7YiUNSVDKQqtMkZpGuso9IzGjqYds9hs2Gw6tGpEIMsFnEYZyFkekkbjhqNpy6iRYMjLs3NyLGxTIGKo65qu69BK4Zyjrh2Hk4a6cVRVxWK+YrPZsug8OQForFF0XYfKiUlTU1VWnMN1RTOquTi/ZbXxQ4CToQvSwlNVFToJHqetDaZyGGNYrhdstkFwBFqEsOwHLI9SA+LFo3RiPDmg34pTcTobMV9vyEXRmAZKpnIGUkQpRdtUTEdjus6z2ni6HAU9kDM5BbQWkUQZ+R5NoWkaDkaCKlneLQmp0PeJXDSxBIrKaAMmGpQa2u2tJhVPLpJoalCE3uNcjTGGFCJFCfoihEBSSvbLaLbrFdpKYcLaiqhEJDQFtC6y715Y1845QLNcL2gai3MtCkcpgi1xKuOTfBeupZRC9J6pM2SlQRnBHSHhkuNaU8ogSMaCLwEz4D40hT5HupwpMVGUwaCxSpNDwBiDrjQpCe81pkKiEJLHFcWoGdNvtlSuFYdF33E4ntDHQBc9k8mEbttTcsZaOZYhBKraorwiAlVjKb0UF4wx+DwcQ5+IxWOGxbLKch6SSjjlyApiTgSbOKjHpE0gKUPOmVKKPDM4LYgeBMmkgOzzwKIHO2qIKZBSoihF7Rx93+OcI2SFNYIWqVJH0JqAYeRGdF03IGw0m+2cpmkED5IzShmapiH2mXWKFGfo1x5XNSQCZhBIrLUoA7/9j/6h4q9e/9K/fvVv/telmGGB56QFfIcjePabiQeP7vj5ey8B+OTuPtvguPvoHg++lfFjmcNsJ0iCMNLcfqCZ/RsXtC7Q2kCfLOeLKdPfng5i56vvTk4WlBe/Enjt9RsaK23bTy+PqL895uCJiAFmCOsR1mfh6a8bjt+/4atHV/hk+Orkiv/59/8GD/4YRhcBP7O4VZKW2AHbcPPXI++8c0HKmtYGPv/OGxx+LALGwRc9qTFDsJgM+8WbFvXrN7x5eEfMmi+uj/FfTDj+UALMigY37I9dJ26+XlN+45Zp0/PV2RXfPX+du4spx39imbxMAz814g8t2hfiSHP9c4bJv37F6XhFyhqfDTf/x+u0V3m/KFap4JaR6rOXPP/332X5N7Z88Po5KWuMzvzwkzc4/NBi+oLdFpq7IYRlOF7Pf1lTvbPivdMrrEp8dnPC8nbE6/+nJRuF7TPNpQRvpcZw+35F9+tLahf54OSCjy4fsLycMPueY3yWsJ20Qu9ECn9geP5rhZM375jUPX/t+Dnz0PIHv/tznH5HxsWuJdjP7P64qVwwW2Fs1rcBFYdF84B5APbt/yrmPTt4e+rwU834LIpgsJQxs2vFD1OLW0XmX6m5/fUto1FPbRNb79gsa17/3xz9gSS5qyzhV/7QYfrMF79p4dAzmvbkrOjOx5x8SzN5OaAIFNhFj+4i63dnZKe4/AVN+UDCshabhur3Djj8XNiQO9bjDj2iUuHLf6/CvLMCYNz23FxNOfhezel3O1Qs9Pcco6dr/L0WHTKLt2uufrVnMttS28TV+QH20vHm7wTp5Bn4xdqL4w0Fz3/Zot9eA+AXNccP54z/20P6qWF8Lp+zSz8EWWUWb1f4qWL5rnTmNC8Nh5+K82zywlNdrmVtNK33yIf1w4rNQ42fQXMJo4skxY9Q9iFLuzb52BrCWLN8y9CdFML9QPXScfgJtNdx3za82y6ANHJsHlTcva/ZvhGprgzjZ9DcFprb+GoOqfUeRTB/x7F+vRAeeZQplK3h4CPH9HnC9GW/PftxFRLz98fMv6rxR/Kz2Y8UzU2hWibqq440dsIZtiJA333VcfezGXvSSffg0xHthebkewHTJ3SXCLMKtwzEsSWONKHVbO5rlu8lii1MHlte+90l4bDe40xUloJDrjT9TBzN22ONP1CMX+Y9b9Nu0r7lX/ijci1uTxzLtzX9kSAvZp+K0N7cJkwnQnJ1F8ThODL0B4bVm5owKcRRoT3TNDeFyYu4RxukRstntGLzsCKMNX6qCBMIB4XDT2B0EQcsAvuAtDityE4TJobuUMIis1OMzxLt1S6IUmN6QR3s3PG7MVO0YnMq/Orm0rN+rWbyZINKmXAouLUwHfAAWtEdajYPFNVCxuGOZy5BT4IikPcydEgouplh80iRLbQXRRzMyySi2iBe7vjSwiF/FRylg/x/qs2ewb27N+3mxF24pcxp4qAuVlAguTHooUCRKy2C/Ug6DOw27ZEU7fM14aghjsx+vhUBXljDu3FPAbMJbN4Y7RFEgpTRmC7tQyxjLcUzt87kStFPJSyvWmXqW8HjUIZwzVoPc4vgAkwv3S6mK5g+YdaBNHZsT5wgESbisq1vAou3a+plpr4NrB9W1HNxL6dGDcgieX9R8uzgh8/qVEhOCkv9gcZt5VzkSu05vkXJ80BopePDdjLfLN+o9hgFwS/xirXbyPWiE0OgaCEO80YcHM8myGf6A029kGcZt8l7EdePZXvsJrO9Z2juEv2BEefvdaSfmX1YadHQzxS2k+IEBap52KN4ciXO793x+MP/6b/8cz1r/4UWbf/ef/yflpwzmkK24kK1yYCB49kxs4Mxb79xhLaG+XzO5WLBs2fPUNpQikIVTciK4CNWO4yrOD4+4N6DA3KJrFc98+sLVquOdjShW6+wtmK92Yjz0lruP3xAoy3NtOXi+oLcb8TNgoUkYl3VNtzE7RDsEnj48D6T8YgYtvRd4OLigt4npgcnvHx5zuHBoQiq3tNUNc5kTEqcns7ou8B8uaT3kWIqPCJ+FKUwRaGLwVUGpzUPHx5QUs/55S0+BoqqCCHhY4/VwkNUSdiEbz68R1OPyCrSxy3LbaDrOiiWnDO9L9TakFLCoskmcjCZ8uD0Hl4nnp+d4zcRncUlFtMWrRQZhzMV1km7/+HhhFnTsLhbc7e+oe89ISSKbtAmo3LC2oZaW1bLWw5P7/Ho4QED8xYKAAAgAElEQVQpBBaXa1abOdo29L6w3G7QVYvPksauQuDkwQk6FxpnUSrw8uVLugDWNGhVS8uu0zS1CCW2Vjw6PaEdBQqRGApnL6/wxaGygSwu3Kpt0AW6FAgeJpMJMUaInmKhrRu69QbnHF2IaCPjI6VMyYo8uA9zidgibFWlCpEoAXXZghIeri7C51W60MceQxHuKIYQe4y1bEIPRlGypW4rHBqjNQYz4BQyvZeHlk0QES4nQz1pmd/e0VSWyjqqWkOKXM1vmR0fkrLCd2ngKhZKEOZnYzVKF5raYCmD+9rR+SACZ0zivi0F66DbRqxpqSc1daWJMeM3S5TR+GCIKsu+VwKjNaL+Cfulbui2aw4PRvSxowuFxWbJZHJAFyLGGGLIVKYm5kztRMDJOeNspo89lRux6DbYyhBLxlFJyF3dsFrOGbeWutLopPCdsBmVrshxeFAbRDIoqBRpRzUxejKamDxGt8SU6fuepnUihLQTEoVSCm3jyH5LrWqUEzE450yfMyFmmqoRh5yRcaBcRU4BVEQ7cSqnYCWYqC9UTr5Dj4SZao2mrixaZ3QBq0Ww8VFEYe8D2tSU2GOVYlxXwmMtwvvNKXC73WLrhrXvMNTkGNDWUWLCaIVDU40qNt1aghuTsLwba1DG0WcZY9bUUAYBUxdKVlRVTciFkMX9lHJHowxx22MrR8lyI6ptBRSSCiQGzIxSxFyoXKHfeiotolLKATOpib2ncRV+6xlPHMv1Flc3kHdCbWF8MBVuru9xo4asRDjT2hJCTy4y/ouCmCNkyFZjlIwJ8DSNpV+vqJsR275gnJO5NmVyCNSjkcwBQCmZqraUEsjFkHNDLp5KW/qU6EOgMoWmqgldRzGFylgq1xB9Bp3JOpLoaUwFQRGKcIdDfvUAbrVBa03JmdQnmqah6zdkFcVlahS6NOQs5y8hTOVSEtpkmWOiEiyA70UkHYoTCXDOkVIiZQlAIxcEvK0pMTFrGjY+UIwUdCqjySWiVUMfAlrLg6TWlhA3GMTtIPeyCq0M3nuUdZiSxKEQIq3W+JBwdUUqClUKKSRcJcGPOWmsthSV9gKwcZXMN1ZD1oSSSH2HcRY9rBq1lTkpkbBKo5Qh+IyuLdFHrLKEkIgkslFUyEOaKlDZmqI0JXmayrLZhTxqWSxVphkwKw6vCtoa+pKgJDQFZSpyiDjlUCWJo1lDHNoVtbOEEtFZybaV4RpDigXZdzJ39j0xK5x2A09YONveJDofaXUlc35KJGPwiPsu5ETT1CS/wWg5rwCT6YjlckllNEqLUFtSphRQyH0rRrmekwIfA8bVZBUY6xpyQpFJJfHP/sk//ivR9i/B69/+u/9N2S1u1NDOnRpZ5M6/Ytg8ynztX/uSxgQ+OntI/nzC6b94JdoVrXCbLInWjSz25l8xrN5JqKiorzXjF4XxeWR7bGnuErFRtJcSAJUaxc3XLf6gEKeZgx9J4FO1eLU+URnCWHH4eU+q5TNX37D0JxndK9xKcfRJolpm1g8t9/7FLet3D8hW4VaZfmaIrQSYLd4Du1KMXxSqZRlasvOes5sGB0wYSZDX7c8CGY4+BreWYqTdCOswtWa/mNSxcPbXK8K0DA5fxei8UC8GATIWqnnEz0S0lTblwvJ1w/yDgtkqTr+Tceu8D4Mx3SuxL44MqdH4sWb1hmL7WqI5M0yeFepl3odLZSvhEbHV+IlmdBFZvGW5+0ZGBcXoTNNeSLhLtSyMX/QUq7BLT3/SUjTcvu8oFsIUii7c+zBTzxNpcJ21LzvQCn/o9kFel79Q42dFUDg9nHw/4lZxf38zm0icOuGR3nrMuqd7bYrpBpdeEsGyuhP+b3Ur7fupNa+ErZgpVu9F3Dy0PZsuDeLH4DROwnpcvi4icbUUAbtaZbKR8ZpqJW614bvDgTjZUiXFi9iCDvJZgPYqCJc4FfzUMPlyiz8SISsMxYvDTzas3moxoeBWr9x3KhVhih5K+Fk3M6RaFvgqQ70QF6HwLSXkLDZqf8z7AxHLbCdjcBf6Z7q833dAOL/Ivu+utcVbFrcqNPPE6Oma7uGI6i6I0Nsl4tRhtolw8Ep4CiO9F2LGX25II4fZhMElLiJx/WKBfzjds3fdSsb57lqQv8u1IQdZtsl2cg/biTGmK7i5xx9VVLeeMKswvSC1+mMJresP5Dy7bd5fR2Yd8MfNT435ol/NYYIIAe1FeHLLRJgacccbhdlEcmP2258HcadeZkGkAHYZZLwNvN3NfTfgKwrbexodYfbYkxpNc74l1xYVxPxDLuTaEEciWO/Gzy64qT+yFCV4GTcfAg1hP6ZVLoSJiH97BnEf8ceNsJ4bsx9bfiZZAqZ/VSDZXRdh6nBzT5y6gTcsnGjTJfyhxS0Tm/uO8Uu/FwurWw9asX1Q41YJsx3Yz0HmpmylEKFDJkzdvgADQ4FlQHUA9EeWaiHdD811INVmL3qqKNed6Ya1WS6EiR32SwTXHfPWLSJ2viVNG/yhk+6LwdnrJwa7K/QMBb4d89UNRUg7cM5397dXoq68t56n/XbtzoMO+afO2U7Ile/Jw3wgw9uu436u+km++L6DZX8vKyzeqhldiZhnN2mPaSkGYVNbtd8Gu47737ErtGQrxapdh4wOeV/wccu032YJ/Mz7eUvH8qrIUeT/5N8iqAJ7bnceUAS7a2knGMv9Ud6XrXRbxNbsQ+hUyvviTtGKMJV9U7nQHRopPvzEnOAnZhBhBxyQVVQLOai761olYRLvRFtVEMbvgBXZCdQ7EdT0eRCf1TB/GGwvAnAcabJRgkLw4uSt7gL9cbV3/po+SyjgME/4o2p/rHYFxfVDx+SFJ0wsxfATc72cx1hr7MDuNX0R1/ow9jf33X6uNL7wzX/2L4Fo+7f/k39QFBadMyVtZZGBPBDY2jIdjzicHKBVR/CJq6s7cUEWcTn13kOpyRm0BkqibZuhvVeElqI0OQZZEOdCv+kZtRMChRwKjXXiRLQQdMImCQtSOaGoiGWotsRAVdfSnpIiRRcqO7TEKE237miqmlwMfYzEnDDWYgBdYDoRF1qMXqrtKaKcIWQFJWFKwWik/V8pjFWMnaPrNkQrQp6PSQJuBtcegMOKg9RLmE6xmaIyKRqMkfy8HH+CuZQKVeXQZBKOGBfUdY3fRnE6OUeIUZis1hBC3gdDicBRsIMj1TYQQhJ0RFUTY0cfe6a2JQ6BUsZaKqekHTMqSpdQxhKiOJJUU+HDRtrXiyIlCc+qKovREsS08RLyo5TBKQspoypLLoVcJMiq91uUStT1GHIhavCbRC4JY8SZ5sMAry7ipspFDQt0afFXRZMRgUCCcQqhMLjzLJSAEN80RimsFjYyWcJ9aiMCjbEVIfa0bStuOC3BOkUrYvTi3C2v4OkpSdK6NQZdkOOiFJvNZr/I11rLthg55q5uyCHjNBhV0BpCFnEYZVBGo9GUUqT1FjBoshamqi6aLvjBeVZTct67B60VF7G4ECUoR2u5CYQQ6TpP1grniqTEm4ZURDTQWsKyjDFQPDHmIaRuJygFyHpwhSls5SDLg7pSikopcdOVTJcidV2JYKUUfUzCfy0FO4TxpVjQSliapRQRxGIa3JQiZildsKYip0RMIu7ISNYQE63R9KnH2FpEPYASaV2FxtAP11YMAWstISYRdxU0jTgblZJxG0LAolDG4WMYjjwSHqgtMfRAoaiMG4KoSikYrSW4TRVycSg0IRTq2pF8x7it0SjCIEr7ktCVw1pHt1qjtCZphOmMwhXQORALFG3QJYtY5CVgT2tNyjL2TFOTB8dwDAGFwSgnMZZqOFcaEYZSxlYVORRx6GoNZBEJUehahNQ+JeK2p7aWqrbEbY8ymg0ifFbaUHIkeXEWkgsZJaFXoVBVw3nXWsaP01gtLFZtJfgr5bAXs+wgVltjSD6DKRgDRg0ClkEYy+j93GmGh5XOd7RtSykJHzqMbSipxtgorkklBQVTFEprun4ryBc9uEKTQlkDWhFCh84FcqFqxPGdlCabshfTUkqMXDuEIyqMUuTiSblHq0oQA0pat2JO4nCtpaiTo4QmyjyRBnyPiOhKl+G8ZvJw7NAacsQM588YNYSEyf1BF0fMmaZx9METs8wTlAJJnMt97ER0TxE9hAzu5xWrycXjsmA7stoVm7TMEaZQckZli0FjLMO1sxOgE2QJuwslS3KqNpAyWrEPqSvIHOm9x3uPa8bYQWAOIWCMJeWMMYMrWAjzOGPIoZN7hqv2hQvjRGx2WpOdiNRxcNOqlEgpYNua5JMgYJRCa0XOcb+9Rcm9wfeBnMHV7dD9ssZaS44eYxzOWFIElCOrjLG7sDLhWOc+YZSl0opV8hQjc23TjNj2gdrIXNbWjhgjXSc85UwhD2FEhp1FZEh0BLIPVNYRhvGrrcKkofBoDUpp/uk//kd/Jdr+JXj9O7/2X5Wd82kngOwcQqnWrB4Z1m9AtmA3itnnwxwZyl50gJ92NHVHkqZcDNhtedXqPrRdVvPI9sTtA8N2LhWVwW0zcQgB2i3SbFf2AkwY671wKnw/ceykStHcJbpDaUmvlkMbdG32LuL1A40OYHr2C7ZdWBmIa28nJO1ct5sTac213SBmrUQs2C0m4dWC0q0yqVH7RerO7boTIHavnTiRB/ag6fLeHbwTjgUJwZD8niQs67TaCxypkQVkbMVNrKN8l+kybt6zeWO0d0Wl2hAmsljdHeei5VjvxYJl2p9zYcsq/Fjvhbz2Whx7KPbizk4k24kBbimuu72I1BXcMqBCIteWXOm9k1LlQpjVPyWyiWA1tOIPzlbtM2btBxFucMw5s3clplrcgADFKvzM7t3idpPo7jnsJu9d18LE/GnRC+R7Umv2wvSuINFc+iGEy/yU09FspHV8J6jvHGl2k6nmYX98ilV7cWFX1NiNi2ygnifsOu7Fr93nUqOo5nEv4onYwL5IUt0FVMyyDbkQWxGKdsJSqoWzCCJw7Nr849iKKAp7d3NqdueTnxJS7CZT3XSEQzlPwnAWMV33kTRy+3O0w1Lsvn/3ftOLqLMrJAhmQoRF08naG8AfV7iFCFQ7BzFAf8/thZydaLK7PnaBXzsRT46b/N/uO+067d3BZUC/7AXGXIhj+6q13SkRzX7CwWo2nnggjtjtg5psRAgE9sLOTzpSdR9RKZMbJw7MQdjaj4Gf2Id9cWa4RndzhBna7X9S8FOpDNwT+Xtqh/cPv3c3hnfOTZUL7q5Hd4E4awlTi93K760u14TjkbClyythb/c9djg+OwFV5pWyP5d2m/aCou4jubYyHlq3F7h3bubdNbr7zM6xvHfzDoWG6qajP2mGgpcUFXauz9140OGVS9HddcSDej/W98eHVy5T2QdBXJh+wNM0IljrPtGf1Htnr8zTItjtPru7H9pt2l87u/m8GPZ/3wl5u+Ln7h66+/futRNId3PT7mfFyPv7maFa/nQhZrcP1XwYlz7vmeW7ayw1r4otwk2XwsbuGo9ju5/PpItGhMTdfLO7j+3mt93+A/t92YmvsVG4dR7Y5PanxN7dvSI7tR9XO6fxzgG86wLZjaudILv73I5jvntfatQef7PrCNr9rp+cp90q7YX07KRIkxopgGWn9oIsMIjPai8Ay3iUe5gf6303QjGKfmZobqTYavq8LzzV8yRs4b2gz75o+JMvleSZRZ515Bjv7gFxEJn/+H/8L/5cz9p/sZm2//S3f8tohTVOLlw0RjtCzmRjCT6x3vTM77bkIAucznuU1viU0MWhtSw+SsloZ8gavFekAj5mgu/RSlOyAmWZNCPCdiMsQKMoiJtM6UL0QVohjRl4toGoBI8wMjW9l/TK2jiydhQMfcjEPkngSO/JqqC0Rlstji4ga4hJ43Ni62XSL0DoE3rgJBqtCWmLGcRnrYawgazQxrLtAlkLlN+HKAJASuQCsRR8ziRbkwGUpWRNoRBzIVLISgaOsoWQI8ZatqnDVjXBe2ztsLUlIuFAJWVyjsIHVWq/2IWEdoaQozjrSsKHXtKylSInCGhiKhQ0Sjvhw2Yl7qKSMWh0VaFRdMFLW62RttyqGHJB2mZTJmSNj8J0MlqRc0E7EWyVEuRBShlr5GehF15tjglnJL08ZTBGPu+UxQ1hW4pCbRwKwcCk4slJBEI1OA8LjpzlLtq09SBOaXFdqYxOBVNEFC5ABlKGuh6R+4zqsxjdjEOh8CFjXMX/x96b/OqWpWdev9Xt5mtOf8+5/Y02+0xnOjPtdG9MSXYhlTFSUUwAIQqJ/4ApnjJCgkFNkFAJJoVAUFZRUlmWATdl7JLTdmU6nJGRkdHcG3G703/t3qtj8K69z02YeIZVzk8KKXTPd76zv73XWnuv533e3+MRoHqIGW1kM6+UEsHcaPogkOusFMZZiErC2RBXBCqTiTitycFLwrmPWAzGVWANMURiuS4qi8svJnGMLTcrqrqRz8uZDGx6EWeU1iLCAimBDz0ZObZEJqSAVkZC5ILCBrmRKq3ZhB6fI0llNingY0Qpw8Q4fOyJRHz0KKPJShb1mCNJJ5LORCWCau8Tzhp871FoccyRCcmjdQIsGYPWYIwhA86KUIrRZGMIOZf/EjkLlmDqRMQNKaCNuJRTztI+7D3kjMqF64qBNJxzhXEicGWjSGics6zXaypraW1FDokcElXV0oeAqyqyCsTkySoTUo914tKemlqE2iTFmxijdBAo4TmHGKQNHykQhRzoUwKkgBS0hCCmkGizzIPxYUZrQg7UzongqBLRKhIK7SoJTjPCgaVcb6WLwK8qtBbRtmQTyoDODq2Eb610JiFJ92gRP5216Kyoq1q4hIjgh0LW6qrCVNXNQZLxQZyd1jZsN7IOWFeRQqRyEvyYszggDYpQRFlyJAVPVQmL1yVNjD2NNYKYMXIuQROTx1UVKI22UmVTJLRRxADGlYdYrRH8rCMXDrhSGWdNcYZ3wkRFigRt09AHzyYFXGVJJHzo0Vr+VgRiliAu4zQ5BpxxqKwxSh6itTXE5AmUMZw0uYiuOUv3hTVSALFWzqm03ie8l/ug0ZYUA0olFIqUpUChbRHUdUbngDHy/XyM1E0FPtFoW9Y2hDGdFNoqku+xWYkAHjwqSyCjJhOiMFut0fjNBg1kr8o9GGIfOZjO6FZrYgrsTqfkIIUSZWX9U+V+rMoaHkJEWS3IgRBoMRirJACs4EpSlO+dsojhGhGRjVbYDMSIBRoSOpcQt+yF+444y5PyZBLojFOmbJgs217uGfOduTjJrYGc6PuelDLKCl84IdgKl2XtRgtTzBiDrR1dt4YcIAZqZ+lTj60MMfpyzzYYk1iHTgoMWrPebsXdnMHHjlpX6ABGQQ4Bp8CHJbPplNVqDaTxvqeVPHRXribkRAiKlBXGGvpSrMFotFLUjcNkIFVorTAGyJa//xs/DiL72/D6H//R//GbqRYXJ4rCjdXY1ZBuDO0pzJ8kVBCcQH0lQsjAdh2YhAMz0QT5me0leMRt0shSRMH6xOE2slFCgU5FCNEiDlAcetLSnEd33ubQinP1FWFJJaiuI7aDfkccU2NQmBUWrI4SxGJ7qFZJWrLllolbS5uq/Cd8vgGRkI08Z+nI2AapgxyfWwYRiDZR2InbhF16TJeL01A21aq4qcSFJMeStcKthIdbnYvLzq2KU7mIsBJSwhg2RHEQDTzAXATCAR3hrnsU8j7txXlr1xJgrJS4OnXM2O6mPXYQGJqLIJtPAKXwMxG36+tIfRlxm4y79mPIki4tzcP1zLpwAEsaul3JOdFFMEi1wfTFgecMYeaIEzd+Lz+XwibFGaaLmEjB/4gwLMKIP2jQXZRwIVNCnKwi1SWIrbjnTC9CuPaMvMVUy7roFp5cieiEKixVp4sgokeHcLUIYzBYqm7mh0qM11YXAW24ViCYiVQZVBbxWXdBxpcRIdX0GTRMnnaE2Sv83pSpzjagpKhqujSKCm4ZUbm4TWMe+a+CRFBl/oqwXZ9ucMuADor6tMOu5e93R5X8exHCUWWMKsaxpUMu7FcZz2liMetQzqF8d7PxZCddKYNwlCqZa2Fiingv79dbEfvNJhYmpTCjhYksIVHyOcLtHIsbWsk1ttJbI4WQMu43hb1Zxlf9ckOYVXT7VriaWYQut4zEiRmFtUGwT404QTfHFanWVNcicJle1iAVC/N3E4jzChjEzFSug7zXrD1h5tAx0x24kYkNqvBwxcGsS8u27uI4trOVwCiVkUmk1Ch6y1gRgXdgX6LKmlTmH0XUosyBVBliK2tYvyPXJjtDnFUowC5DaceXXA+gBI3Jv8dGU194Gc+NdDuEmRlbzdHF+VmclSDia9ipCBNDmDpMn/C7rswVmdvDeRVRWOY4ZZ0aRP1UaTlvhVsu7uZS6FIQW5nbpgR3qZjBaLoDh7sOVOdb4kSug13JvFBFNDS9XPdhbYqtrDW5zOdsJKzMbEWsHebh4PLVQQoiqZJjyFaNjGdbWK7Z3oTCZS2fJ27e8n4Y7y8qyf3A7wpjttsV1jBZCqG2dFcMhTApzuVxncxGj/iG2BhZq8r5ohTkdJ+5fLOiWkrI4uJhjU6M6/VNF4D8DprC5S1FlXWgO6xkTodMLhgjHYb7iDic5dwVjm95foi14D2E0ysGk1Tm+LDeDAGNrzqA60uP6TOr2xVuI+dPZagWocyZIUQvjyKsjCR5VkhOujJkLotDt58bqkUgtsICdstyj60U7alHJxGw25c3jnq3TvRzOf7klBhkpga3SqxPLJPTMExFee6xMv/8VN88qxS2uluW+0cWQbefS7FxuH+nMv//s7//12Pa/o0Wbf+nf/pPf9OYsrnNsmHT1mCNBFRVxgg/R1v6uEVpDUqz9SLkpCzBScZp+tALJzQWRxhZnFjO4lMCpcm6ou+2whnNEoiTlWwOlVJFEEB+t7h1AprKNfTei4BmrQhrKRFDwGpNPQiOTSMuSNew3fbousYohdGGnLYiNsQO7TQpxpJybiSJ3cimVytTHGySDp+VJvSBAGyjiLmUja6xjjxsHp1BK01KgdhtsVqjcoYcJEglStI4WZK1U0iY2oBXOJEi8J0vASfSppuNJeZUHrQUffBMpxN8COIItoYU5Tw6U+G7hLMtRmussVhjC/sTku8xSokolzObWD7DGRKKmBMqF7i2UiSl6Lst1mhUThJilCnigLh4yVmCbrSFmLFaxoS2Bo1CJUWIfRFqxC1FAo0wDn3f4XMg6YCpFIEMFlzdELJCKYvVBmc0xEjttDhFlbjettsN7aSVtlRli6dfxBGjNMH3KAORSNQJjMIWkUjelSFnqtJO77u+BIWJY06rIjqjSstzlHEDVFl+XyE3NumEDsTsqTCgMil7KiP8xGgNfRTRVRuotcX3nbjVUsZqg0oJ68SpZqwp7MWEtQ7IxCSiRTtp0VbEclPer40mR4/JwqskySqWUyIjbjRjNeSEqyxGaSpXkaO4ebNRKGMJIaMsOKeAJCgBZ6hMcTxnsErGugZBK6RMDlIsCTGgnAVr2PYdVVMX5zISiBU7QgxkEiolxEsrRQqNIYSE0RZbxpkpx5FIhOhR2pCyOCtjSDRNLZcxZfre41xFjJkYM8oofEooLccrOIyMSgalxEWZc8YqTeiFvm5UcdlZg8lyjEWbIRm5WVfWYrMuwycTSOJQzRmbLN1qK+cWWS99SKgcscqQUyYGMT4kJZveXOaVD6FIquKgjoX3a7QmpyACoBXhUGlxy4hQqbDaYLRh2W3QxhALdiDmKO5lIw5XE3NpJYcUhdvbb4MEdCGb58Y5ulC6JFSSvxViCfKS/28rR1x3BO+JgLWOmLKcay0PSlprXO3ovTg/Q5BrllOmdg5ipnIVRHGzVqZGZ3GoO6uoXUW33QKFc2c0OitC53FagryUNRBFiBUiK6Qo/FJnpVATgx+fbWOQAl0MjEW1RCRnwQcknQtiIqNtRUqyzpFyceZqtJaQQW0qYohYJy7elJB7DeJYz0RxrGpLDJmUNWhHjGLZT9rSh4SyFamIgdoKT9gHD2bYZBtiljU6mUwzbdFWY4wmJgVljULL+PXrDa6qqKc1i+UabTXltoUKEQ1UVgTP6OXaOwXaaCxQK43PIrBqpei6LaDQ2qEQdu6mCyjjyFoctsqKqLqRhEpyAF2QID4FQZz0wux1ztH3npgS286zu7Mjm9IQ0CTBHHiNcy1aG0IGjaByhB0rCJUcE1qJyN71HbqpQRuqqhE+bkwYrVHGoI0lp0TKPbOmLfscCZwzxkpRwWRikiCTrDO58JGtcWw2a+q2IsSAsQYJI9PAKwWAAFXVFpSGkqIHWbjYGTSK3ncYo1BaguX+wW/8+o9F278Fr//+n/zRb8bibBVBRgSs2IjIFKalLdFI+2LZW1NdFqHDJ3Gz1lrcLuFG4FOJ0bnkVoLTUYlRNLSbVzbKRRiItR7FMBFDS3vuzOJWsuFLTo2hJkPAVb9T3PVTjd1Im3l97smFS5hqNYZCDcFpQ+hM1ko2rfYmyX0UiLZFeN2KA82dr8nOln8rgkPBrmQj93IVRDSMrdxvVM6kxpbwNVfCd4orqRKBJbYyX91Swr3MwN1M+YbBCbjrns1JjV2LcDeKTJW0YrtVIE6dXMOJJbZyrKkurqNaFXZioj7rsV0exUi9FXFOFmQR3dx1T2psEdLlnA0uY2knZjz/iuIAHgQMJ4u7XceyaWcUVLKRa2JXcSwQpOqm/TQObFut5HtMHSor/NyK6KlE9KnOt/QHFUP41XCeBsenfIaME/m9cq6R4xO3mSLMLBgR44eN9SC6pNoIr9QX4a2PpNaK4JohaxEspB1XxkWYvSJszd3o4qsWoYw/jS+u4Ni8IrwaI8zLcj5uxKabFvhsFN1hRS4uuEGsHsdtZfC7wzlRqCjXyS0DqSp/ayJjZBCN5ZpmhkAwtCJMZazalYwBPyvjwBoR4Euxw+8Wt2ouAv4mjmPIna2Is9qYngQAACAASURBVFrOsxZX79DerJNcD3Gd65uizzqUNciMhQKVchGr041gqUQg6/cbmdeJUZgZhPtstIjWg+u5FFOkuqmx2zwKlUPg3TBu+30Rrl4VTbVP5dwYcTkXUd1uBFehMlKEueoIswqVRUCyKwmOSsXhPbTx65BHNr60eodS9L1ZP+FG4IEi3JZrRQa7lLDCWAoX06cd2erRea37hO5jEQZlvMTWjuKgCO0iZg+iWr9nqS9FdDI+SZBzRroXGhEau0MnhbWtiJ6xuL0HJ+gQ1hRLUWoUUksxwu/I+uinItqaTtysg+s+toZ+x94Icci5zU4LrmMivGHKsaWqiNxWYYbrNDGjk3UQxk1xJA/HYTdROjrLOmx8GlEQ2chnDeK63cjnDi7pbPUo0sYibKZKroufimgpY21Yg9I4j1ESZOWWUcTYPo3rWhq6Lbi57qbPY1Gr33fjGiufdRPYmCqFW4sr1s8ts0/7m+KqHhi9ct7RNyGRYSr3/NQY+nkJGyv3QUEWDNdVxNv6dHtjUChrsg6Z9syXezhljcpSHC0og9hIsckto6yJFx3Lh610F4aCoCj39kEctqVINRTGdMyjEC336URVXPKypki3T7WMpQuAm2JAkMLxwNeXYoYq9wSF7QYmLqNTN9bSydPPDXabxucUQTiomwJAcSXrmAXL54vD1orLPFkpaMfC6bV95h/+g38Dgsj+3f/4H2ZSxuREKE4vozQaaf9vakeMlj72BOWp9BDAhaSva0P0HocmK0dWGlvVuOxJ0RN02ehiShtqhVGRlAK+LdzFbDB9cXQVh1iXI0pbhj6vgZOYc8aHjqqStua1X9E0DdYjSe9ZiziHHJ9Gk1IU4S1LO7dS4jo1Q1u0UfR9QJdgFhA3lDOKnIURWRq2sUY2nL64jZVS9DFgnLixcs6E7RZrhEs5hJOY4iKLOaCyRrmK7CNd1zGfziR2HhGRTCUM3EQWt6XOGORzrLXk4FHKSLt5aTPWZYj1fS8sPzIxerTKOG2olCNpQ9dvwEh7rNYV0fdYY8jWsfYdOkWmTVtE90hUCYsienG+UZirKfW07ZQQkrToaksooTwxSphQSnLdcIYuRAgiOOoorurURUxbg9GkBH0naIDaCvdyaN/XRZRUZaNOcQ1st1tSCjR1XYKHgiAWzND2DChh/GKkbd7pm3SOpBI5yxhwzmBUJHjIypFCL+3hpZ2hT8Jz1EqE5SFMqTKOrttKm3eSFlhlRBBKSsTZkCls10RWlsYobBQBzEdxbKfSAq+1iP4gvxOLWK6tEzxFHjixAGnQZbG1uLVTStSuwuRSbAjCvowonBaMADliTSVcWWCrMo02GJXZ+n50zeasgaGNWISsrKDRgrCwVgQZjSSsh9L+33kp3kSl6Pueuq5FMCvzOEWPScL5JYogHbMiE6C4KpxzwklViRj9TbCUaWV+eo9Cikzz6YzYR5yxgjYgYSqHD53MPZRUi0Ms7enSVk/fS5hT4bKaIIWISMZpC2hS7rCVhOW5usJaS99taJxgW1IXZW5qEX9CkDGcjAg0KfQyvuoWPJjRLSUt9ptNJKFwlldazSXgUGtpR82x4GqsHteanAXDIUFXVj4zR6qqEs60tsVB6bGVKfgINc5flREGMqBywk0qvM/EsbNUxpgyii501FWLSpYcA4pEayu2/XbE6USVUcaUzoRE5Sb05edaW3FZJ3G9xm2PsxDKDiYrqLVsvGJB01SVw2hGPAk6k4yiTyKCNs4ShvWibskRYuwwxo0s0axkvOWcUQYJ78qZtp1Kq76KZf2UTZoEenUEAzFptE5jkcJpBylidSq4FiecXGVRWYj4KmVcUws+ImuqyqBiwIeOaK3MlfJdYpCiQlVVRbwXoVTpSMaPTHfTTIh9JNvixs+y3lZWowiYBCl7lNLEXmPamtVqw6Qt58FVdKuuiOdWQuhyZrvd0tYV296jrRWWbNdJ22OMtNqCtsTcE/xW0Ah2gjG18LNCIkThFlLcwfLAm3FG7pm2uFkwlkSk73uwUnBxSopGMWXati3CvBTN2kkNWRcUkMIVDnyXAnPTktUWrMPowLSaSNheSGx8ILoKlcUdSyr3fCtdGJO6YmfSUlvDcrlk0fesYyb6TNKyhkmooYz9EDwuK9q2JW3B6wwhoCvZQEcfxCXXCprGRwmEaxt5Fui6Dp0llKzrhMeNVthKsek9Vd3i+y3/6z/+MdP2b8Prl3/tv8rA2Jo4BKwMwSvdrhkZnWYjgTZuEca2bwDdxbFNmyxOw6Hl0XRJhOCymRpwBSrm0YEa5tXYOjq0nVZXNzxUuGkjVUl+b2DJNS97+j0nmIDS7j+0fw+tw6+GNQ3O0OF7Dm2aQ6vz0Bo6BMyMrquCEoutGVvtB2anW4Sb8Jb/V/t9Ls7X8WddHDfNKuYRZTC8zJAAXhxIA891aBN+9XOHttjh+oFgDgahb2ilDRNDP5N/G3AWppPNrO7T+Jn1izXZarYnrTjayvGOrffFITXwZfuDanSoxUZc0mGib4LKSpANMLbrDi3Sg/B7w6HNI6fRz+34/YcQoAFJwSvb1uqiR/sonMdX2pYHruEY2FSK20NA1vAaOMYqFWZwPbjYKLzOG2alWwZCeyMADcfVzwzV4sadLSxJNbJFhzCgVxqJ6PfsyDy06zS2d+uQx/ZxOW41jpdXP+fVeTEIkANXUgLfHKm6ad0ePidMzNiWPbS5x9oU4VvO4cC7Heay/BE5bu0TpEy/X41OQHEYMs6/WGuqqzCKhWaxJey2owA2jM1X25yHz3kVDzEEM8lYTTdjekAIrLwIXRvP5t50DGga26pr9SPYEBBeqEp5RAiM5w/G9cB0N63wcj0lSK266KV40EgXQr9nR8zD+J1KK/0w9oc5LlxW+yPzdFh36rMbzMXAEh9a1FNxZr4634f5rRK4q74IyHb8LgO3dwhWfJU5+uoYHNbiYX4Ma9pQNBle2SjcZYffq3+EY77dl66GVxEPr65rfl6E4CKYDWNpXFMm5kdYtgNSRZfCSl9Et/YsjIiBYY0AxvDArBX9fHDWluu+SeN6EGt1g4MpAYjbfTt+72GuAWMxYUCJDAFpQ1AYSJiX2ySGQK3hfPzIeSx/v5/esJhNl8e5MqwTMs7MWAAbghpVgvrCl7lhRsbzq1zdMJWOsFircZ0c5kn7vCsoHfl7Ayc3TAx+ounnEra13dPUV6mIiYp6EcdOhe2+HKjxeeTHxkYTC5LAreU8/ShDujyjlnt/aGW/AyLgDqzoAWMwoEI2BxI6J2M/jddn4BT7VgqB1XVkeccN8hvJwfZIUV1mTAfteRxZvsP1GgLUolNsDjWb2wo/SzSnmskzwf4Ma3Vs9I/cX4ZQuM2BLV1H8oeHeSkFaQl0M305J50cf6zkfCar6HaEEa4HnFKtaC4ifiZz7vf+2X/x13rWtn+dN/3/9eoNtFVFXgvPVsJDLN5vIAf6PmCrmspqdKykfbZpiVsRFpTROGMwCfqAuEuCxydPozU2JpbFnau0RmcIKaG0wvWgc6bWimyM8AJTLG3hGm0k5Etax/PICFRK0fceYzTTakII4vSKKVFXLT5KcIzWRlycgEacdUZZQNi11pbW6qoFxIrtQ8QYi9YOHz2VVdjGiZDcdRit6bsOraxU9jLUqihzXU9UGZ0TtXaFUalIqrQkaIvfBFIKVMaigWnViOiTE9EastaEnLGVJfuAi4pYhDytMil6ZtWERdehbBamT0rilNYK3SiU7lFJS/O6MTAMYN9htDiDQwpsNgum0zkqOXLqqYxGO8t6vSyt0cLgS6XooK0V1qxSEj6TIiEF2qomBo2rp9Iqn4XjGb20F4fg8SnTVkO7tZL2AJ0xURGSWA2TL6EvMQoTtIRvkaRlvs8UzmbZ3NcNmTimhWttJazHZdqqwvuARiPDz4oTNiUqrehTFlexdeQo4pFVFcrCstvQNDW5CC+LyyvqdoLKmRzE6TbgKirjCL7De087mYl4QhZkSM4iquos19yVAoe1xNjhkqWqGzY+0GUvop2PSO6Q8B6NKizfGNEhYOsJClnk+7hBKSvFAC981u12QzOp6K4WTOqGWGs23YaqnrBaXotIpDKpF5xGMJqqMHnbuqJtHYpE7xUpFr6pViLmOWFxBgWVchjlaRtN3e6yul6QfMD7Dj24G1NH7RQaX4J3DLVtqKdTsk9sfM8qy1ONTooUhAtsrJZiQmUIvWdaHni6GFAWUlRoo8BAbWcoHGRF9j04qR770JXiQkDpWgQmXZh3WougWlnIBbGhZb5YIw7hZLQUEYwE11WNOJ232466bgg5YkLAFDd21CV1FENWkRh7JrbGo6XABGhnWPmNBJXJVUOrJM5eLNuNFAqUHoSvntY1ItxqiNmikhc3YgaHtO6HnHDG4H2kdRWzesriaiGJuznKWkUa3bcxKerKYWJiNpGAss1mg8uK2oEPFHZxJqkKa2piH9BkmtahYk/TGExVl+C2iEcx0Q7ve7wPZGxx+yuM8uWcI4gdJ0UX5wybGOm9lxb/2OGsuMR39yfkEKksLBYLUhTMi47i+o0xUzfCY92dOSato6r26LvMxfWa7bYnJth2Pa6piSlgqdAWwmbDwd4eO7sOq2B9teHicsU2lXuFNoi5NlOBONdVxcGtKc7AYrNhtd4SfaRPCWcU1opLfbPZUltLt11weHjMpJqgteHl+TWbbkvIIsAbrYRNrA0hbuWeUTXcvXcXozO1rbm8WPHB0+dkbUamKsaRiMItbxt2mob93RlGaV6+POd6sWKnqQg+YXCQNZO6lXtoSGQCB3cOaesKlRPXl0uuFyv6rkfsd3KzWEePy+KM358fcbA3Z+6mPD9/yenVgmSqcd1EQ+o8TV2Rc8LlRN1W3NrfQwNXiwWLpXC3g1EEoSPIWuwcm/WCibWc3DqkqioW2yXnZ9fkKIWGBIScaBtFDD0p9OztTznY2WM+mYOyLK82fPj4CR2eFHuSsqXwHDFJ4SrH3dtHTKqK+aQFdY+z83O+9/57TNtdLtZrcIKIskaKQpWT9dj3W4yZ4bcLXCNcb6UsIXj22x2ulwtxJGnNpJUJNGsn7FYNvuvxfU/sOmxTE42E1mkjhaqh6Prj17/5L7vw9AcV5CEEh5HBBlAtI6GVJPBBUBoCV4aN2sD0HLio0p4vgm9oFJPCSByErmGzPbig+l0RR6OTNvYhSGRoN1Vp4OHetKAOwR6bk7owC2VT3O/c8HEHRt/gmnpVqEhOgofaU4+fuVG0HYJfslWFNWqJtR2F5IGXN5wrKI6x8hr+Vj/X4yZNezVu1u0qo4MEQCUnAumr31ulXMQHTU6MoTaDCKR9GkNQsjaj+GlLO+/gUsrF1fgqZ88OgnAR5KqzDduTyfj+MK/JVlG/3BLm1c3fDDcb8/G62yISbiPxoEL3eeSuxhIMJKKGiI12KUFn2ss+Sz6PUWQBUCHh59XIMhwEkPH4V3HkcNp1HMetDrmMF0ZBbbtvxxCigQU7BNP0MzPyjIfxBbKZzlozedrR7zlUhH6mmT7t8XM7FjIG3qnq5LPsJsJagrOyZhRnYmk3RiGhS3PhIg4ihG+FodhcRKorCXRjCCEa5lopoAznf3CGgwjJg6A6cEmriyjBSteR9ZHF9LoIBJbm+Wa8rsM5M9tImFckp9gcGDYHMqbc6iYwDIpAgcYue+xGPi/W8jt+pmhPpWBSX/iRb6v7JILtK4FG/dzQz0X0aC4T7fMOEPbr0FY8sJ5jI8LtENxTX0eyFpZrLp2Jfq8p56O49cmlsJNH8T1rQRWokMa2/FfF8ZED3ChiM4i1eeTh2rXMV4Dq0tPvurG4klyZY8OaVcaT7iLrew12nfBFvEtW0uQH0Ucjjv9Uyf6guvT4Ehg2CF+bQzMiOgYH+yAQD0KvBCzJfNzuGVbHhtnTKAWM7sYdORRpdBfxu1XhfMt62VwI97mfG1wJWhzWmzhx47wfAtS6XUWsbAluk2C47lYjhbiVRyU3hkZGp0ZhUIeMDoU3O9E05wG76EcOcpgYuh3N6p6GBH5qmT2LI0pnEFKHtV6HzPK+odvPhEnGrhWzj0WMNGTqizAGc8VGkawUS65et6xPMqmG6kIz/1hCI6WQVBi5mSIuKvqZZvGaIjSZ9qWlOc0jj3wIicwamjNPv2uprgKXbzZ0+9Lav/uejPeB2SqisropdhhNN9ecfzmTHOSZpv7EcfcPvLh25/bGZeoT7jqwuluzuqtZPkhkl5n/wDH7NLK830gIVln7t7cqhpAslWHxGpztKtCJ5plh+jTjVj9q4mzPAmGi8a3m+pFj+TBhjzeYd6fsvJ/l3pZ/lOc7FLl8q+mPFddvQbKZyaeayfNMqFUJBLth0/YzQ3seWB9ZLj8nYmvzQrPzYUKtZU0XJ7uIoQN2Z/HQlGNa0wHhtOH4/9blGt4UwIZ7mJ9oLr+c0PsdD25dAPDhh8fc/W1Nf8uw+0E3fnfhYifpLEDY77GE2YWJprouXPNt5Pq1mtmnflyzN4fyO5tjxeqexWyE6298Hln9goviFZTSX+/1N1q0rRBxSNAAkeglpCUlhXMNqEhb3LWV1uQUsXXNIolY1vcBa4Vnm3XCKIcqreukjK0rZjkTMoXFF0rrtyS5N/OWkCLWQ4xBHKoh0biKHKMIAEU0bCppLW6qenSihdjJwm1bTJL3VlpJK6lPNK4l6YRzhhAifrNkZzKV49luqZwwKWtXEZKntonoe2pdYV0jrrrGsO03TOtGAm/QYLxgJrOCBCZB6yomk5Z24liuF2w3PUZb+hCISZNVZNrMAPDJo7PHKRGLpgcNXYxcLjfSxpDcyFJNXpxUlRPnZMJztNugteIqrIt7TxG8JvtMLmE7zgoyomkqJlYS37fRE2ImBY8xNaBZ+zUhd8xmM9K2pwqRWdOyuz+j957F1VLapHPGJlcewAIhemqlMdEzaRv2D6ZEvyV3NVcrjzOKzmdxAibIWe6IzomgiXYEMn32JCLUGe8yftsxqxq6lOh9oGokIbFxEhRmFfjiCBuYmOLSBWMbQteTdM/xwSF7O3Naq1l2K16+OGO1Dvgo7q+YNTGJQzP0mf3dKbN5zc60IYfIYr3i2fOXTCeWVSwuXQNKaWEjWsXOruOzb7+NNbDddjx+8owQEuuNx7hq5EA7Y1HBczSf8tbrr6Oy8EBfnJ1zenbJ2eVSzoky1FbEysbUVM6xv7/LbFrTTiq22zUXF1ecn11iMaSsCUnhFeTeYzTs7zScPPwM850Zm82C3mc+/OhT3E7D9WqNsRXZgUsWg6Jxiv3jI+7evU0uTtznL15wenpBv/H0QDaOEMXhl/vArdsHPLp/gjWZFDWLi0vOri45v7xk23VUbYMm4b1HK2hdzWv373GwM4Pc03WB8+sFP3z2jBQz2AplpVWqC4GqrdjZm3Gy94Dd+YScI8uraz54/FSwKc6x6nqUzXR+S9O0ECPOWQ72ZrR1xWKx4OXLM1Slpa2aEq4VxcXeJ4+zmsP5HKM1s6rh4nzB5WKJbZriDC1caCMFgZwDD+/cZtY07LQzrq+vWSxWfPL0pTyAO0ttW877ni5HaueYTBrmswnH+4estxu2vufF5TnrzQZTGXFuh46EOH+ndc3dO7e4dTBHI87EZ8+ec3G5JK4kWLAfWuBUghSYT2bcvfMa87amdjXX10uePX3BxXUW5EEyGCq0U8SNFDweHJ9wsDOjnTjCpiP6xLff+R4WQ04GFS0pKmLOqLzh7dfeYDJ17M2mWGO4WK54/vwlj58+p6rnhG4rrlcF2ngmjePO4R6Hh/tYbUg5cHZ6xcXZBZebDZ1PpKyotKLSmWY2Z38+4/7JCTv7MzabjuQDF1cL/vVfvkttalIWXTHrTLfx3Dk54rUHd9mbTcn0KBzdessPPnzMi9MrTDakTU81a8kloOwrX3qTvZ0ZQuKJqAeG05dXfP+HHxE7L9gbU4HKGF0xrS23jvY5PDxgb9rgQ+L8/JJPnz3nxcUaRSZ6AVAZY6grzaMHD7hze59JW2Mw3Lt7wtX1iu+884NSQDIoHYl9YNI6Wme5//p9pq1jdybu2oN5g+q3PD+/ksBPMmCprCUpw5t373Gwu8t8YjBK8dqdQ54+ecn7H32CczU+BnwQfnldWbSBB/fucbg/p22cuIZPTvj02Snff+9DlK2onMHHnu3WM53Pmc8mvP7wFrNZRaM1t+/ucHa54c+/+z7GaDpF4c9LYKUzhpPjOQ/u3mF/NiH1HSt/zDvvfMjVtTB2E1KgkXPQ01bw2msn3Ll1hNWGSp/w5JNT/urdH+DJKG3RqiZH2CrPrZMDHj68xazS7E53QFu2uw07ezV/+O2/YDLZIXvQJlNZS0wdb7z+OncOdrEK9mYz+i5zNN3BbBPvfPiEZjKRwn9WxE7QJk1TMTloeOPBXbplz/JyyftPnmALq7y1Eqo2391h03dI7Sjzk1/8EqHfUtfien7nu3+Jm7Zsui3GNORe0UwnLFYLYSv/+PW34iWt24zCkLRb3wSEAGz3NG6V6YvzNNaK6TNhzdmFJzWGmEz5mQYnjjqA0GjWtyvsNv/IBm9w9fV7TlprS1t9qDUqKfo9K2LkOo6iWrcvQTfdviUZEUGG9n4RI0UM8VP5LN1nun03hrQMQuLyrrT12k4CwbR/RVArDMp+1xJqRbVMbPcMzaWkrA+uJuBH3IEielm2B4p+F9qXmeq6oBxWwp5VETYnwsi06zRuJkOj2B4p3EKz83EoP483buDipvRzI+6+AOefrclGMf8kkEvg1OASHcW44ird7hk5rrmivsrYLpVQH9kG1i+36K1ne3cuLqh5xeq2Y3VPY1cwfR4ZmK2quJEGR7HfFZf05kCzfAi6F9PI7BOFbzX19XAc1Y04OrkRvO1GkroHB6Vdi8C7uTOhWkfswo+C2eBQDY3CrW+u8eCUFkSBLg7IyNnnHet7ibTnsS8rdn5gmJymsSDxqqvSbBPb1xzbW/DJ3xExuHluOfjLyOaWo33pfySAbQgVW93VPP07BlVHWMDhnxpMJw6u5BSEPIYBoeDiMxXLX1yjdGY22XL53gE77zv23r9xBPc7BruRYCI/1SweKrpbEXYjLDSTx4adDxKhcaOwatcSvJdqw+KR5tlnPUe3znh5PSGuHId/bPCTKbMn29G1PRRbtruG5QNF/PJSMl5gFGemS7kGKib01hPnDWQ4/6yl/+aSuvaEYLh8OWH2gWPnAz2G9EgwmxQ4tvuW59+C9tECgPWyxn1cc+/3Bo6tHp16EnJkWN4xXH0OmjtLUlJ0Zy3H/1JcbcZbEXyVvL/fk7HsW8315zX9bqZ9qTj43uBYBuGCupGHaxeeOLFcvF2TnGJ7C2YfZ3Y+6kfX6+BwzFrhFh5S5vlPObpbkeZkxfb5lPq54fjPbhzb3Y5m970V1XWk3zHi8DtW9J/ZkC8q7EKz9y5MTqXwZLYy15WPqGRZHzvOvqLQby6xNhGChu/NmH1846a8cS3KnFreMVx8WeGOV+xMt3z8fIfZX1XsfCTdRm4ha1SyhqqLbPcNL78O9uGS490lq97RecfOP5kTGoVKZnQMD8iDx7/iCPuBo3tX7NYdHz45Yvpuze0/lm6JIVCxKgXAzaHh/EsKXltT1YEQNPHDGdPHmp3HQRzZIRPmFX4m523xUGG+esUXjp/x8fU+PmoeP9njjf9ZPnvopFBJnJcvv1Lhf2rBV+5+SkgaqxPPVjuc/v4d9t+N6OJi97MSAmYUT3+j5607nzJ1HSEbrIr8+YcPOPrdehRihyCwWCvWR5rrtzLVawu+fudTLrYT3ntyzPQ7DYd/dePsl/cburnh2U8bpl8457N7l0xsz/tfPuLJ010e/W+MDln5vcz6yNLtKa6/2rF3sOKrJ5+wChUvH874RN9l/69EUB5efm7QXvPsFzP7D0/5mcMX7Lgt119v+MN33+To96vipBf3Z3RSREwWLr4WePDaCx7OL9hzG077KX/8zpvc/l253v1U4TaZ+sKzPnKsTxTtt075qYNT3piccv1Wy+998gb8L3sYp6hAPBJlLQ4TzcXnFdOvnfGrtz+iS5Z3L4+5/O07zJ7c4B0GB3zWsDmwvPxW5POfe8JOteV2c81v/eVXOP4XFW5zE6CmUqa+Spx/1rH++pr7h1f89K0PAfjowQH/+uQu9/+RY3MkWJdUEBDGZ17+bOBrn/+Q3WrLr+5/l8f+AHc38t9Uv8Ltf16NzxbDM5CfabodzfZAkX/2ivWyRp1W3Pu9NHYGdAfybLG842jPh3BDuP4PFvSdZT7bcHE+4/Y/r/CtFM+G77HdM8w+7cci8l/n9TdatHXpRkT1KeKcI6dMVVm0yuzt73G4O+NwPkflwOOnz1islnRpS+cVE+cgZwmisg6D4XhnzuHBjK5bsdp2XF+tibmXYJoIVWUxxpJUxnYds3bC/q1dmtawXq+5urrm5dkFtqqxToJarDbCLEFhE+xMG/b3Zrgms+q2PH92xjYn+hBxtiXljFZWHLZdz86s4v6br0PusNrx4sUp55dnbLwE1mjtqGxNxgsHMcOD42OOT3bx3YZ1t+b09JTLq4ijYt1vyCpjDaA1s2nD/dsn7O3PmE8M675jsbjmwyef4oMnFmZejyduA5PKce/OLY4O99idTnAqc71dcnl+zScvLrjyPSlmgnVoneVckHjj4QOOjg+JfUcicrWYcn52yXK5xSiFj5l+0+GsxfjE0eGchw/ucnQ4JfSBF5dnnF9e8uz5FT5bfLelsgaXJ+hgaSvL5z//RSbThrrRoAwvXjzjo48ek5NlvQ0EL23JjVJU2vDGW4/Ym+3iGhEdU7fi6dMXvHh2RdRJHEVBgmGyNqgYuXv3ATsTxWx3wuXlNS+urji/uiLEiCtt94lMO6mJfcfezi6v3bvNZFqjM1xfX/L48SeskVA4KadAiBD6jkf37/GZ1x8x6H88hQAAIABJREFUaRyNySy2LXeOd/nOO++x7SLLTYCqotLCHZzUlrdef8DOtAIS1JqjzZrZxPHO999H2RqfEyYldHGuvX73DgeHu+xMMlhF9HP29vZ4+uyUj588lRAeozBGGJYH+we8+egh85mhshNCCLx295ijvV2+8xc/5HK1hsqQu4hCse3W3H/wBvduHzNpKrRJ+G7C8dE+392+y/nlNUFFsqlwzrFYrXn70SPeun+fppXAu3nbiHBKxXs//IDKaDahozatCL0p8cbrbzCfz5jOapRqyH1gOm+YzRt++MFjVCeOtUq10AXefOM17j88pNYJh6GtK3Zmmr39CZd/cU5diYPdtiLmuAQPbz/k9skRs0lVXO+J+WJGDplPnj9nmyN9ytgsTt+9ScPbrz9k4hSTWtN7z/7ubZq24t333id2miZrUvBkwHdLTo4PeHj/LtaIQ/zBvX0eNxWPnzwl1Q6jDV23ZWYm5BjptOLhw4fcP9rHWXHjHxwsuVpc88HjTzDa4KoJvY9s11u2XceDByd85uEDZk5QCAfzmk08Zt17zs4v0cqwDonGNtRGs+7WfOlLbzNrGnanFSHP8CHBx4n3f3gBtoWsMI3FVQYVEm+9+YiDnRm7B1NyJ0Fy08k9Pnr8hMvzM3RqUShyVNhGsbu3y+uP7rEzr5k64RXtTCuOj3b53d//Q3wnzuJuvaGdTXEuszOreHD/BE2kaRqoW1SGe3du8d4Hj3FuRsod2Voa5Zg1e3z2rUf4fk3VyL3BtLtUtWXVdZxedcSUsbVwRre+48uf+zLHe7s4a2kqTQwdh82Ms/kef/TtP6eqLR6FVRpjHIdH+zw4uc3+fILSAWcNmZa9wz2ePH3O9dUaYw06K4zK6CrxpS++jcmZqgGTGnGx1xW3N7d4eXZNXVcIVjzSr1e8+dbrHB8fkWOgroSb7HPi/u0J62XHDz56TIgRPbEoFOtuyzd/8us4m2iqlsrBjm3Z35vRVDXPzr5L7Qxd0FhlMQrquubRg4e0NmMrjXOO6dSws7PDxx894fxiQayneJ+Z1obNesutBzvcubWPKgFrqs7UtWO77rg8O0dbxboDbSHHRL9Z89bbr5O6NUYp2rqmW3c8eP0tlp3ig48/IBlLtpaqqdis13z+c2+xu7vDwd4UpQPExP7uDlkZ3v3wI5R1LH2PM5pmskPWirv3jtk72sHhqArQ/7hu2X38hGen5+jaEX0v7G+l6f2W+/c/y53bx4T1hqqtOWymnD2/ZHm9wPsNrm7E5RQ93ifeeP11Hr52n8ZkjLLM6imYCR98/Amx6ws2JpBVoE6RNx98jv39GRMt3OmoNLfuH+Cen9K4BosFI479hGd3Z87BfIf5pEXlQEqJdlIRc2KyO6WZ1qy6Hq1lXCcDWicur17wxS/9LE1O2FZzvHeXDx9/TMKQlHB2wRM7wT6sVwteu3+P/aMd1kspxro8oa1nbLx0gtiEhCl2PZU16B9rtn9rXmGixzZScZIUt8xMk4xi8UCzej1y+OgCoxPnf36L9rmiWkjLX3dUA5R2SBGBzj9nWb0WMEtFfaaYPtUYH0ex08+NtFlGwRqsb4ngF3YS7kIx/cRw8L2O0JoxATo24pjESCjK+o5i9SCSm4Q9s+x/D+pracEcXGrZ3LgsV3c1i6/2ANhqjX5vwvxDJe6n4kryMxGefWmzfvl1mL99zXpb0V80zH6omT1O+Ik4dFXKJZQHVieWs68lJveWfP7oBR9d7fP8xQ4Hf+yYvEyjuOKW0nq7vVXx8quW7o0tj+6ccb9e870XJ1x8MuPwLxTzJxJcZrY3CfWh1nz689C8scB7Q4yaxacNs480kxeJ6IpLbhlGQe7iLUv39RU/9egjrn3Dd35wn+ajisN3VAkrE6elmgrjb73veP5rPbt7lxxP1wD88Ae3OfoTcfy1ZxFb+IT9nqWfap7/fGLv7gXHjbiV+mg4feeI3e8rjNfFBZ0Ee5CkffX0K5ruJDC9tWb9yYyd7xv2fujFuTarxpbV/kD+//qh4+ynA+3+EmsjL5/NOfi2oT1PuOWNoGE2EbPxPP2FHZpfPuXze2c8mpzz7fMHXH+hYfXPDqmvMtNnfRmLDu0TmyPH+heXvHl8On7W1esNT2/d4uH/nkbH5dhan+HpzyrswwVfPnkpY8BXvLw3ZfvOHrf/RAofySpUJYFL55+ruf6FDY9uXXCrXXLdNzz6xgU/eOOI5WaPnY/kmIZ27voq8vInNbtfPuXLe2dUOnLeTVi+VXO1ucPeD8Powg4TQ/tkwZNfO6D9+VPemF9jVeRLR08566Z8x97j1v9Z4eeO+nSD362FL5kML/9uYH6y5K39C6yKhGy4PGh5cusY/QeWpiA+KKFcT/6titlPnHJ3sma/WfPZ2XO+c3yXD+4ekh7v0e8YmlNxDuvSCv/im3D78y/4wv5zah2wOvKvjh/yaTrh1p8n6guPXfTEVlid1w8tm19a8Gj/mrd2Tnm+nTO51/Ov5o84/J0G4/PohhZnbeLsixXLr29wVaC2ib2vrfnk1m2O/zShokHVBnfVs7nTSIv3ZeLpt2rSTyyYth27JvH80S5Xb1Xc/QNx3IeJxi0i1fkWc7Hi9OdOeP2XPuSre0+4DBM29x0fLg64uLjH3vuebGSO9AcN/VzTnno+/XtweLjkmycfc95PuNhO+IAHzJ7euOxiLUzqrBXPfilx8vCcb9x6TEJx2k05O5jycXWPnY8zyeVRUI21ZnHfcPkzHfdPLvjs3gucjvhbhudvzbn8rx+OBSJ3HegOHXFiWd3VPPrqE2oTeG12Lms4md/+6k/y4Hf8GBKlEnT7lvUtzc//8nc566acNAtqHWit5/3JEZenU/bf3aK7QGwaVMxU51s++PcmvPbWc/brNQ+nF6xCTXio+aOPX+Pgv6vxMzsyTqNTXL8B06+d8jO3P8LqyFG9wieDu/0xf/xnX2P+iXQ6ZCOt9cnA3b/7MVPXcVwv2bEbjqtrruYT/ocv7JLfa+jnZmSOu2Xk+S87fuaNDwhZ86C9YN+ueeHnPPziBb918Q3u/V/iXB4QLs2p5/o/3HDSdNybXXG7ueYX9t9jfVLxj+ffQn13gp8oqoU4eXOt8DO4941PeW1+zq7bcOwWfGX+CR/cOuJP//Qr7P3Qo710cmxOKtrzyNXbhp/77Ps4HXl78gKnA28cv+S/XPw9+ic7JGuYnIYR+2BXkV//1p/RJ8vUdHyufcrH/SF7X9rwL17+JHf+ZXEnZ/BTuc998quJoztX/Nu338UV7suv73+blBVPfudtsobpM7n2ImJC+NKKn779MbeqBfeqC9apYufRht+69/McfWdwY4urNVUKt0y0P3HFf/TGn3ARptyrLvj3D/+E//wz/wntC/v/QdS4TeLTn9f83E98nzenL5mZLT87eQ++CL/3R98UZ20srtkM/VzT/dyCb9z7hM/PnvHZ5inXqeU/PfhD/tv6V/jO/k8QSsElOVBRsTxQ3H30jG/sfYzTgVWq+ULzCYvYsru3ptutmZyW72CUdHMaxc5HPat/p+NkvmRZed5885THf/S2dEkPfG30iGOqX245/8U537z7Me9fHTGrOnaajqv9uzQXBSPTKGKl/x/23qvX2jS/07ru8KSV185vrFwdqpPd7mYc5PEMI5AFAyOQB8EZCIlDvgFqvgEfAMTAAQJpAA0aMCMzgO1pt93u6K6u6qp68857rxyedCcO7vXusiUkzMlgDX6Oa7/1rLWe+L9/v+u6WzD+802Y/6ftr/TQNhBN0sabHY8zQciUsqm4f2+feyeHHPZzhDUIlfDW42OuJ5Ky3JAUOdYFrDVI77Heo3TGg3sn5KlHjgpsY1l2Nzx59YIsy2idwxuLc1BkBU1Zce/xAccnIxCG7VYx6CZs12ukSqhMrA/b1qJ0RlU2vPnwAQcHA/b2Unxo6DQJeMP5ZIqXgm1dgo218lGe4zPN0ckJe3t9RGhJpEIJS9FTfPzZGTLtEEIcTGVpSlM2vPPGGzx8MCbLQfZ7bCtP0TmhfXbLtvRokeJoSBKFN55US/b2Bwx6PSQl/bRLkSvqaou0ntW6RmiJ1AmtDgTTcP/ogH4vQwtFJ8spcknqBev1mkVT0ikKytrikVRVxd7hPv3hgKPRiMY2NM5QFDkCxXL5AhUCIpF4CUVSoJXg3skR436PVMZa+F6/hyZwdT5FI5BCYd0O7uw93SJnPO6SJxqlHKF1nAx7LPsdJvMVXkeuohISHzRJlvLg5JhExtSUEAqnFIeHlrKuKa/XyKBQAVyI9exMpxwf7DPuKWxoyXbDitAYFk2FdxLjPdJrsrRgsVlz//59hnsFg36HcltxkI2xruXZq2ucFYjXVnrvORjv8eD+Cf1Bh1SDCo6clE6vYH9/n4vLW/I8jyzE2pBlBaNel6P9Hlp6SOIgp0i6gONkvcf1tMShSFRCG1rWyxUPHn4DLSTd1OMFiETTKSSz6QrXGoLSGB+FZk3VcPjuW9y7d0xbL+nmcZiKNXT7A36afIyVFUoVyBAQUqBTzdHBIVmexIUOHYczm3LL4eEhs8WK6DBzKClp6g0H+0MGvQypAkVW0JoKGeDw8JDJdE7VTEiEIEiHC9C0Ffv7+/E4kg4piXgKmbI/HPGJfxY5o6SkiWC+XHF8fMh4MMSZDalQCDxpmqJGGVmWsVrVpEkH7yxZkjPqdHhwcsyg04k8awHOW0aDAcf7Nefn5wipyNMUKVJub6748vvvMO720MKR4si78TcZH+4jnr8g1CHa63fVe+cco/0+3WFOngoaa5BNy/HhPrPZjLKp8VqDFzTeAp5caw5HexSppt/NKMst/UFB1W6YL64Yjg5oWouxijzPkFJwOBjT6XQw1RatINGafmdAZbd4FXAhwuKDU7HWiOJ47wAhGlQmSaQm9bDfH3ImFB5J8J6qbSjShLZteHR8j343Z9usEES+73A4pDGWTz5+ESvVr/0TVtErOjy8/wDXbmLqHkHwkiQE9vfGXE1mBL9jcRtD3azY23+bg8Mx68U84mCUoEgLRoMx496S9dZAFtELrSkZHOxT9DLEqkJKjZKQ6yjSMnicCCRa0dYVSgrSJGdvNCbLMjQOJSRapshOQt14iiTD+pgSDjvubJ710FqCMCBTpFJYE1nHaabwwqN9EmWVGPrdPkmSxO8hGGSi8T5QdDt0uv2IMPGv2eKKOrTITKJzRbMTYtmmjaI8AuPxGHl6Sa6gthYpA0mS4IFOt08qwo49HNBJTioFygmMdKBSGmsYdDpROCcVQZiY5JWaFA064is6icbtFqaEBOkk/d4IpQukiIuoAlABFFFG2Ji4QOJtIEkDLY5yu0QDSZHF5kamGXZ6tK5Cax3FZEFQVQ2tMVhrGA77rLdL9vcGeDyNqREyAJ5AFI0KmYKziBDo9QqUUrElIwp6iSS0AiVTct1h29ZY78jTbMdQtmzKLY1xKCWp6hpCQuMNrTIE/zkyMUkESivqcsOg6GOaDd56vGqptmsIARkk4DFthSokUsBisWJvGHE1QkdpaFVVECSpyAjG4bB4PM57Eh05+Ijijq1srMVY2FRbNuWaNM1xrsFJhdYKayJr/Obmhrfu36cIkQveKiKzfIfdaWxLojTBOvZ7A7bLFevlCi0FAcGs3EInp16tSYqcpm3RzuOFQmj4cxDpv97+Bd9ep2zv+I+7qnw+Mdz+Us7mg4avvn2OFpGd3f12y7PPTshnkY+n65jcDJ5dQklRf61kr1+SKE/ZJtxe9rn3f0qafpSZSRNQRG5etnIsvgJvvH8FwMVsyPw4o5gkeC3u0imqjviEfGa5+PWM5p2a9x7e0HrF9bjPnD4HPxVII8mv4/AwaEn9Zo60gdX7lvcf3qCkZy8r+VHykOmwxxu/6/9C7b0ZxH06/5uaR1+/4LizplCGT4eH3A776G2XYuLvZEe2EyvwpisYPl7yweEVhTJ09g2rwYqfr94kKB3rylLgVTRRCwfiKyu+cjhhkNR8uXfJO70Jf9h5h9XNEYOXgfogIZ+Y3RCkZfF2F39U89tvfsRpNea67LMZZMzkHv3z+P0EFav49TjWejcfNPzSgwvu5UsKZVg/vuVVtgcfpTvZ3OcpMQJUB5K3Hkx43JvTVS0eQfYFy/OLNxk+9XdcxddpI1sIfumD5+xnJYl0SAJbl/LDdxOq+Zhitkv4pa8Zx45moAhf2PKtB+csm4KqW3ImjxA+YfDK3vGOgxI0A0X/RcXsq5qDe0u+cXjOk9UBo7dqzppj8n+2Yxjv3n2DEsy/1Gf1tYbfOrjkXrakpxoe9+YUQ8PvvblP+rOY2s5mJqaF+5r1I8m//PanjHQcVN+2fd7uTQGYfuWE/Z9z93sna0d2tuT+f+zopQ1fGVxQ+pREOLbDjP/15TeQbRx46W3kSepVw/K9lN9+72Numh5f659zY/oAfHV4we+630TtxHavubwml+j31hx2N3dDqfe6N/x0+YCzdx2jZzuMAjsJ2nzD5u0Bf+PwnExavtS94LwZUygDb8OTp29TzEQUZO0q/Mmi5tGbFY/7cxLpKJRhZXIGSU31RkLye6M7XqnpK3qfLRHvp/zqyUu2LuVetkQSOCnWdLTh4/6Y/rm7Y3WaYcrqseb+V6/45YNThrqidClbl/FrR8/5n77Qg58UMeW3l+O1oPvTc1b/+mN+5cE5e2nJcbrirWLCjenzhQfX3Mo3PmdRmzhgU41n+8BzuLfmfm/JddknUY7iiwuql6O4yLKrIr/mK7d7OdW7DY+GG762d8Fnq0PcoWS22KPz2ZT6rT3SZaz+t3s5SaJYvA+/Nrjhtu0zSkoO0zW/OnzKf7G4j7Sfp1LFzvVhC8W3332KFp6uajjsrVnmBU/fPKAdZJGV6QLZpKI5KEgXLb/+tRf86ugpP1k/JtkNL//m3qf851/qIP9JB2k+x00EBdWh4N/44M/Y2oyujtc+SWA/2/LiHc3Bz9q4wNSN4rtkUbN5nPAf3f8x31u8Q1c1dFTLl/IL/pcHH7B8K6N3uRNN1o5kbdjc6/Be5wYtDsikZaAr3uzNaJxmW3XiwLYbMTM+lZh+wcGjBcedNXtpyVBXjHVJRzW82htTje/fMUtjcj9g+tBNzd29KZWWyiVYJ7Hd3SJIwt1QbnuieJSXaOHR0pFJS+kyvlKccTD6MkHmd4LNICGd1QSZMEoqLqoBR+mKuenyIJtjvKbzaI1XPZqxJpvbHctWYa3ircGMe/mSo3TFvtrwXrph2Pk6Pul+jhJaWtYPI/u3nzYYr7BekUjLoyQOxr83ihV6ZQKqie4MnwjaQ8sgqUmlxQRFgqUvK7LExMbH2mM6EbljCkGygU9XRwzSmsPemmszJBOWf2X0M/5p+c1Yxd9JhfKFJ9k4RKv41vErfrh4zL959BPqkPDCHCLFa0zI58cUxO/swcGCrmqYtrGN/e3uU0xQeB0bHJ1bi6oc7SjZIQMsi0mPFw8PGOqKj8r70AG5VVFkGrh72DYdiUsFyUrym+NPuWxHlC5j6zOebQ6AneiLzxEFXgvMeRf90JMIRyIcJig+ak/iOd2NPG7VBrwVsb1TSNZ1XFw2PoYnrsyIazNkMenx6MbRduUuwf85xsHlEvtZn/TbCx4N5pQ2JdnJWF8LVtNlXCAVPrD4Qo9iEvhwco9e1uC85MWrQ8bEBTjbiQvdr1n0f55N/pfZ1He+853/V3/wz3P77/6Hf/yduvUElZAkOXVrcSKgheK9Rw95eDRGBUe/yKPNPoVur8PNbEm5rZE6j6bvJAUtOTk54oMvvI1QDf1+hzzR9Mc5k9vbWHeVmqAkVdtSlQ337z3mqx+8RZFpBJY8TRj3ejhjWc7n5HmKdy1SBAQSnWjee/cNHj06ije+VFNkBdmwy2qxpi1b8rRzZ5d23jLa3+OrX/6APJH0coUGVKrROuXqeoaQCmOiYKfx8fX17bcecbzXo0gFUsSa897BgNV2xXRxE1/snEGGWI19/PCEL33xHVTw9AuF94Y8LdBpwny1pGkClbPIJEElKQHPt7/9S+ztjRBK0LoKlab0RyO2Tc3t7TXeW4IXeBlfTO+dHPGtX/ll6vWKjkrodwq0lhweH/P06dP4/qcyEAnltmbvYMx7779Ft5ejkoBOFWmqGI76fPrkWeTzOo+XmsbHKdDjh/d55+2H+OBItcLbhl6vS90YFrMNwVqCUpgAi7rk6P4x7739CHxNnkAiPEpJUOC9YDpZEoTCEBlHKpHcOz7i4cMDslQQhIPEMe4OWC+XzBZbrHFgFVKlGBtlar/8y19HEOViUsU6cLfo8vTpS0SIyVSBjZKp4Hj/nTcp8gx8fLHXSYb3UFWeqqwoywqZCMp6g9CaB49OOD4eIXb77zzU1ZaikzGdzVmttzFhZQMmxAH4N776AUkiUcGRqBQhBZ1OF5BcXd3gERgpwBuCcbz1+CGjYZdUgbMW6QOJFrTOcHF9zbrcoKRGSokiMBp1OTzap9frIgGCx5gWpRXGeC6uL1EyiYgSEfmmj+7dp9vPiW6qEM31ItA2lrpqmEzn8dyQuwGQVLz76AFFnqKlAO9hl4523vOLjz4lK3rUPkCqSKRiPMjJlCZLIASHlvEzeBLOTi+xIdDagJOSoBSZznj8ziOEiOgSsKRakeWdmPq+vcEpjfWAA0XgaG/IcNgjUZBnCbU1KKnYLCuWiy2rssZrhXUBQiDPM/pFh25ekElP5hWpStg0lkVTs6oMAY12ktp7lNbkiWI07JGmgdY1JChUkrOtGrzL2WwMWvfxISIjkiTKh+4/PI6yw0RRVjXojI8/+gUIgXVRHFiVjjT1aBk4OB6jEoE1FusCxhhmsyWT2YyAiiIqLXDeIYVgfzgg1QpkIOzYn5tyy2q55fLiFq0ySARpDpuqIi00WarJtEJoj/OBujE0xvLy/ILKOERQeA9pluB8w3AwoJcXSBGBuW3rqJqW589PmS1XCKGQgMGSKEEmMhKp6eUdnIvyNeMCZVkzuZ1iTUxDJUogQ0DLyIbt9nv40OJ3g/35ds26bji9ukUqHUWHSmGspWo2jAcjMp3eiSKtddS15fnzMwIJgeROUFk2FUkKw353h3vROO9pjOfqZsJivsIS5XlCshPcaYbDLs5aFDEdWrcNtrGcnV8xW2xw1uJFxLCkSUxD5kWB9TayhV1gs21Yrta8PH+JSjOCSqib2H5omgqZQK/TxTqHcIK6bVivt7x6dUZrPY03SCFIVcD5Fh8svWGXNNW0dUMQsNmsmS43vDi9wAcdESXExVGpFUmWMCwGEATGe4zXnF9fs1iuKNuWdvfZrXX0ul3Kakl/2CHPNXVdE0TCZt1yfn7NZLomSXOs0HgXBWsOTwiWLFVoHZDO42xgu2k5PZ/SlC2kCqkCmigVFSogJLjgcMbQ7/eZTm558uwcL1OcFFg8o/6IalvjnUP4uOiFFiRZxmS65vJmyXSxxXqNUimByMFtm4pEWvKiQKvd/8fBZr1lvS6Zb0paa3cMPAE6Y1Nt6Qz6kU0vIhs5GMNitebsYkLTRpFe6yxCRfyRCAKdFExna9o6ULWel68ucEHjhMPu2HedJCMgIjJHRg56t9/HC03rLU+fnUZUjk4wtiXXKjLJhUCogAqK3/l7f/c//f/q+e+vt39+2z/4b777nXQR7/u2o8jmJr6Q5Irr3/L8na98TE+3fGv0gl7S0k0Mo/0tt58d0JnGAYXXseYvhGDygebf+1vfxSvJL++dctLbcHS44uzlEfncR+uzC2QzQz5puP1mwd/5V3/Mu71bgpAc9jd8+f4lH7pjeufQjBRqV1pCClwh2f5Wyd//8o8YZRVvd6fcH6xYDyXb637EOAw1yu7SMAbWjzR/+7d/wqPOnF8anjJKKkgFZS6QH3ciy3Ynl8lWDmkDs9+w/PZbH/GF7hWZcpx01vzm/c/4o+nbDJ97bB7TMpHdGbj9FcF/8qv/iCIx/Fr/M9ah4FExZ1LkbE/7pOtAPjOEJCY1hYd/6+//EX9v/0egBGf1HsOk4hvjM763fJPhZ+KuRhpFaJ7Z1zP+s3/tv+az+oSv9M751vAlWe74ylunvPruA3S7e/lGUNy2rN5J+dpvPOWDwSU91TBOSo6LNV85uuTV7z/CJdGuLY0nWTaIILj9lubf/+YfsPUZJ+mKjcv5xvCU75WP6JzqndU+MoWLJ7csPxjym7/2IR7BO8UNx+mKrmqpRcKpG9B/Ju4EP9Hwrph+RfP+18+4X6xwSFyQvP3ghvPbfYbP3R1HNSQy8v+05OR3zqNvQnoyZekkLcV+jfjDLkHKO6P7a/HR/t+64Y1dglALzyipqFzKJ5sD9FLTmcTafna9BaWYfkPxtXdfokSgpxq2PuP5Zp83+nNevTymdxEXG6SLg1i04u1/+5z9rKSvaw6SDQL4aueUJ8k+7S/6UeKzjQ4RaT2T35B8+d4lx9mahevQkS1H6YrzZsyrD+/RuTT4VO04p5LVI0Xy7pYvjm/IVRxm3bQDOtpw2gwY/HTHkl5ZfKYI3YL5NwOH4zUdZUilIwhBKhwX1ZCrzYDRE3/Hs5Wtw3VS+K2St/tT9pKSCOwSlC6ltCmd39WYviadNwgkvpMweTPH9gL3ixWNTxjpirN6TOsV85/uo1tI1/aO0Wx6mtE3p2jpOUrXSBE4Tlc8Smf8s8t36X0U0SS6cvH4SlPmX8zo3duyl5a8W9zwpDqmpxo+mp9QX/bondu4CFA5BLs06LFE7rccdrfc66w5KdY8mR4iLzJ65/FZ0O/Eaz7TtCPN5qFEdCw3dZ9hXlMkhslsgKBP56rBFRGXIgDb1Ugj0V/dsrUpHsnHyxOs0Mz+yVFMY+8+Qzotcb0MV0jO3k1ItGduuixMh9umz/WrfYZPwp34SZqd/EpJPnxvRNGN6b3WayqX8uP5Yy5uRoz+LF4DRQDTUxSXDaaf8NFwxKg98daJAAAgAElEQVRbIQWsTMHpdsxlOUB+v0s+i+xjacOOkR3YPErZHEkS6ZECzqoxvyjvMf2TE4Yv3B2vXG+jZLLtS/60f8S7exOWpqCjW66bAWfLETwrSHbsXJdHHrjLJVPdJexbSpeycTn3syW/d/0lTm/HjH4Qr4Oq8fhMxir/SjF9LBh2a1yQ9HXDtO1yvh0RvtdH7risQQkEUEw9H/f2uXcyp3YJPd2wcB2e1Uf8+JM36Zwpktojbbx3oBS2o1k+EJQ2JU8cPd3wpDxiZro8++lDhi/CHWvU5ZHlvkkyNgPJzHRpSVj6Dt9fv80vLk/Y+96OgRsgnddoE1O3z5J91EHLou0glOBJdcyP5o+wP+iTbiPawSfyrr4fQsL1YcJ+UXJejzAh4U/Wb/Ppy3ucfDfypaWPfFRdB4KWPOuOOby3wKGYmB7XzYD/6tO/gTrLKKZ+tzgY0LVne5yQzgWnJzn3eiueVYe0JPxw8Zgf/Pwdhk9jClTv8Duv752XDJgNEgZZQyIdS9flJ6tHzH56QLoOCBEXR92OzYsQWJXyM3fIuRvy9fE5//jyq4g/GCBN5DKr2rN6I6WYO6SLC1T/m3yLlcrZz7f8w8tv8unHD+k/i8NTn8R7SFBR7mW6mqthSp5bZq7Hwnb409Vb/NnkPua0S7oJqEiGQwToXltu9zvcFB2UDnEfkfwfN19g+9M9kg1RJLb10Uz+2teSSUZPHJf1HmerMds/3UO1Oy5vE2dH9X6C9OFOsigC3O6nbG3CsioY/mFOZxqRU8k2Im+C3K0xyviO/R/+zq/9pZ61/0onbV+/APogEa1HhWhCN2VDp9NHJQotU2rXghIIqdBSkGQpQTWY4CBolIHaWPqdITLNSG2OEAFHQzfvEbwgvB58CIV20Tjd2goSRTAmWppNtMUX3Q4i0ZgAqGhsT9IUb+MwtEh7NLKhaddRctbW5AIINjJ6keQdTV0bsiShtYYgWqQUuBAHCJmPFmjjAiqR2NbghCBJU6QWZEWKsgEtCoJUbLYlxhiMLfE6pbY1Lkko0i5J3qPelpi2wtiIijA2vuCX64YkK1Bty2KzpV90GB0csGlbmnJFmibkvS7VpqIyFS5IurqPTlPmVROHNECe58yXC6RW2OAJ1mKNwVmH2wnbbFOjkwKRKgKG7aYkTwYEYsJIhEC5NXTyLqtFg5cSj8PLJMrmpGQx35AmiqatSZVGEf8uySWr2hFETiIDhY5ss+18Tb+T4H2DDZ5Uakwb8N5jMSCi2dD5FklGayvqzYa0X0REARmrasW6aVBpgW2a+KDjHc4FbGtYzaZ0+xnex+WjbV3RtgaZCrxpSHWK9RLfOsQwY1XVJJkgS6PEC2uIwT7LcrkiyXrMt2sGw32aqmYyW7Cu75NLSeZtTLOohMV8jalbECk6aBwSLR2kgsntkk43YVCkyOAxzrFdLNhu1zRVBUqhA2iZkRUBYxoW8ylFlpAkklSnrKo1xgus2yUCtcAaF2tt8zUPa0dS1THhpwUCh7eesixxxqN1itIeESyZSJheTxkPeogiRSXgpcc7RYtl3VSY4LEohA1kKHzbMF9VIBOS0FBkKVZaytqyXFQMh2NK4xAqogikNFhnWFdrkqwLvsU5aJ2krNaoRNOsK1RS4EJD4wOrZsPFzZTjYY8s1wRhcG1gNVthjKFtLCFPY2K2MvS7PebLLUfbiqRXsK5qlJYsl2vaICltS+Mt3bRL1TSoNMUG2NYVdRuPnTzJcThuZlNsY0lt/AxtcKRKkKSKRbnl6dkZX/nyO6ReEKRnOZ2x3W5ZbZYkRYIQUYqXppoQAuVmxfOXp4zGA4R2KBJuT19FXEiS0tQNMgiGvSQ2ELRkvS1Js8EuFWBxxrJZlmiR0vgoc0pFvFYEZ1lvNmgtyHJJlqZ4F1jPV6xmU9I0RScpm3oOMiPv5mzqhpvJnF6mEFqjhKVxlsVsS1k5rBPkSUbAYYwhz4dMZktOxku6RYpKHaa24DXz1Toehx6klgQ0QmUYC2VZMcsEiUpxMsETuLi6YbmqkWmB8y3Gu8hkJrBYLhmt+gz6CqlzFpsN88WG6XSBCoAJaC1xzqAzSV3XbJuGpErJs0Awgdq33KyWOEGc2klBZUrSTGGtYb6o2NtrSGQUV7a1J4ia2XyFCwqhbOSoY0k7A6ras95YsjxhVVZ0uwXGeJbrLbfLBZ2iR2kadIjcXKEV17czOv0+jx8eU2/X1FZQb1smqzUqSxGyQ2Ur0m5cLEmCYr3asuyt6RQFiYDaeibzFaWBoHMCLXK3Op+mKattzeXtnLKu6OUZbWPYbivOLycYErI0ozKWpBMFltY4tlXL9WTJ3n4frQR1XbFelZxfT+gOuiwXLYNeFxKDkIG2tjx/esGbbz1CSgn1hu26ZrbcknS6LOstQQiEkmSppN6U3NwKDvf3UQosJVSayWTFarXCJY7gNc4J0iQhTyM+Zz5Zo7wiObnH1aTk/HJCZWxcCNCK4GC93RCUJkszgvPcTmY4D9utp60bzs8ucT7glaT2ljRPCU1FmubMlluy+YRG7NHrdeg6y2RZcTNdsthsEUoSkAQrothPZVzdzOh3uuh+l6ZaEQJMZ1umyw3WxVZOksRjUe3ER03jaAVc3d7GBa4spaob/M5k74jnrFSRbVu3DikFT56+YjgcYnzLfLtG6wRvIE1SLPHFrXEtWmZ4b/7vHsv+evsXcFN1tGQLG+t7PpUIG4dNxdjS1Q1jXfKkPAZAS8cwqbEFO4t0vF64TJAuLPWRpKMaTvIViXRsXcrXB6d86L8cBw2FROmAqhW+n6B3AhQTFO91b2i8ZmUL3DBKh16nmJK1u6vsjvslXywueN4ccVaP4+cQgTYGF5E7mczrxJTpwVXVx4YRHdWytAUn+Yp5p0Ppx+idsOq1Ndx2NVIb3swnDGTFntpgguaHmzdR9c5KnwjSWY1sU5q9BF94Pqnvcdv2uWkHd7XbyiTks4hcSDNF99mK6mGf1WPNT1YPOWvGHKcr3u1c82l5wiftMcIIykOFy2D43EdBVa5oB57/ef4NUmmZ2y5GKZamYGvTO2bua+ZlkAl4eDrf543ODLPjVCfS8fH6hGpP0ruwu4Fw5GmavsbrwD+dfJGTYn23b0NVIWTAFtC7cLGO39WogwFBws+W9/mV0StKlzG3HR5kc+ZNB1wcAsDOfL4TpekKLtcDjvPINz0sNvz89oTiOi5qqWrHCd4lKFXteHpzwP29JbWLfNsX8zFlmXE/ESgXML1oo3/NHH51vcc4K7lfrO6O9cZrsIL+ucN0JP1nG6pHfZKVpf8MfjR7xOPenMfFDOMVvaThh1cPUXX8+9fpap8rfK74w5dvc2+8orvfkAnLbdvn5+t7XM0G3F/FoZvPFbZQmIFG1JI/vn6Tx4M5B9mWB9mc783eZt50UA0IFxdBVB0TaePPPC+/0eWT/IhRXjFIahLpqFyCWWSRP1zIu3Su6Wvy55rrh3208Ax0RelTWq+ZVR3yG4kuW2TtIt91FP+NF2cx1TbOSt7pTVianOeLfSaXQ04eK4qpJSh5J/URjeD0dsx+vgVg61Juqx7nyyHJTtjnMkXSGvSyopdKfvHRI6r3r+6+2w0535u9jal1TAHuZGCy9ZQPO/ROBa/eH7OXlXxv+Q5d1fK9yVusypzeNJAsGurjgnRa4dMcXXmyqWK1LviMQ456Gyqb4J/0UE1kViN2Mrt+hu0oes83nKg+13+3oOi0OC+ZXA7JppL+WYPr6JiOzxWmF9Eexczxw++/h35QxsBHavn02T0eKaLgbmkIWlC+Mdil8RTr6x6bosbtEFqVSchuFTbzpOt4nNh+imzjkDtc53w3fYtxr+S4s6Z2CS8me6jLDNN32EzSWUSURjtO6d54tp91me53kSLQ1w2TssPsfMQbE4sqY1I4YiQc7Thl8BQ++uIJD/aWrJKcRV0A0DsNd+2LKEyL55OuoJ3n/LPzt+jnDaVNqF3C9udjHpzZu3NVm8ieVbWne6Y4ezDm4fGcXBl+//Y9np8fkD3PcZn7C+eUywX5wtHOcj7JjzjqbTBecbEZcvbkiJP2c5REPjWYXhz0d05TPnt8wCBvKJThuu5jvaR4laBa/7mkzsdrRDYPnN+O6PZqfjx9wPujW6ZNl0+ujhg8iWK9yCeVdyiX8cdwMx7xha9/yqvNmFeMud104XkHl9nYTLkusf0Ml8mdTEvw4mqfvdGWcd7hYjPk9PkhD2aROy52lX+I6dDuhWfy0T7/+/0uJwdLbnWP89mQwZ+l6LKmHWqyqcF2oxBR1Z5smvDjz97gzce39NOGy/WA9rbDGz82lIea3suS7eMOEIeQyRY2f7zHR/+SIFGOzzhgPuvReakxRaB/2nwu7uprsrlh9EnG5J0eubYs0xyAn7x4xMmF/wv3HNOVNMN4rRi8dPgkZV50+O+ff532z0bsz/1OxhcFbL1LCwGafhRLZk9ynpkjzmdD2iph/FN5Jx3LK0+7Qxe1vcjznvYG/P7mPY4OVrw7mvD9l28gP+tw/MreNWde//daCsYfCc6PRvTShvNyiPOST18dc/Q8su29ivI/1QRcN75rZiuH3jr2P5I7Lj7k89090wVkG7+DeOwH8kVsBR39gWb7IEFVMHzRxmHujqusm9gMSJcWW6g7bNJf6lntr3bS9n/8jhIBGSytt6RaIoynk/XpdTrkRY4QO/mNNaRpRtNYnr04Z7kqSZIChML5yF482Nuj282o2xLnDFXdstzMma3WWOcjc9Q4Uq1RPiADjPs9EJIkVay3FXXb8vzFKWXVgMjwLtZ9AxnGBtKuIsmJzFBrUB5mN0tWsy3GBFzQIAQ+OELwdDpRjrPfz3G+wgrPdL1iMV8yv52RpzllU2GkJ9UJzhm6nYxEgYolS5qmoaxrri9XeKtwJqCFRqPAK9q6pN/JUUoTEBjj2NYl1zc3rLdb6rLCWUORZ3gE69WGUb9HkHEQrXxDkILryQ3XNzdURrEpW1yQJErjnSVVCnAo4WLq0jZsyjWnL17iq2g+9z6yGiUBby1aSfqDHlJYmnLLatswn6+YzFYRb5CmeOvRSuKNIxhJr8jAOTras96UVGXJsxev2FYtNsREpFYSgsdWhv3BGHxMZdk2sKy3LLclp5cXNCbQeoEDUIqgJMvlDFmkJElKXbe0Tcvp2TnX1xOcSvAONJJGtJEvqGG2njMejUiTBLsbbL06v6C2BiELWgvWBlSWUluLc5Zet4cxDYmKKcLNds3LswuqugGhY7oraGwbhWbjYR+tEoxpML6mNQ1Xt3POzm+ByE7cugatJda0iBDoZGnEGXhoTcNsOuPq8gJrBQKFVgk4hwiBuqzQvQ5aamQIVE1NWZdc3U64ni4ROo37ZSOy5DWqQGlJlqWY1mCMZbnacHp6RWkETTAE5TFILAJDTMb54KmbimAD5bbkxatzriczQJFmORAr6WmaoJQnBEeaaipTU5uYIjw9O2e5tqgg6eoC6zxOKlZ1Q/COLM2wxlHVFlNbbi9vWc1XEZkiBEKJWFfPUiaTC46O9imrLSZ4WtOyLms++uQ5YifrS9IMlaYgJFVbxzSHCxRJh3W1oXXw5NUZ8/UKqTXtTqDoBHgpWZUlm7okaE1jHZfX10xupmwWW6TKIrogFUglMK6mV3RoTENdV2zXWyazJVdXt8zmC/wu8eycR6WRLxzwWCkxxtJai6kd5+dXXF7fIGVG6wJZ1sERsMaglIKgmM4WbLYNAcV0vubs4oab2RwTPFVtyLMCGwRKJoQQMMbifCBVKZtNSV23XNxMeHU1g1RT2govI4sbEfdntd6SZAltbagrw83tkqevXhFIEVphvcN4g9ZRCqiEpNpuCUHRmJblesHZ5RnbssYFD5mgNhahNdY6ZKpYzheY4BHes95uePriKReXE4JIsM7tFsA8QQAE6rqibCpC47EG1sstzz59xmy2wAhBS8A60ElG01ryNKXclnigshVVcLw8v2ByPqEJ0AZwTYkUcfCldUpV1ay3JVnRYb0pWa4rLi7n3E5mmNDSuJg8FkEQkLS2pawrtlWFdZ6qNVxdz3h5fsFiXVHtrMetteR5irUNVb2lrmva2lDVDYvZmsubCWdXVyidsTUVMpP44CBEREBrLavVmroxTOcLLl5ccXU9waUZrXc4IWLyKb7TRX7utmI9WbGtLctNzdMnL9hUDpmm8eFIagKe4AJplrNarbi9mbCtI0N8Mp1wdXuNSiR1GRny3nt0ImnrhiwtWG1KpusFt/M5N9M5p6cXmBAo6xaJQASHDGEnKMxQQjOZr1gsNswXa06vJ9wuV1gh8UFgHSQ6Q7aO4AVa5jgv2JQVr25ueHFxzWpVEyR4Aa2xJDolWIf0FqlzjNCsyoqrmykX17fc3t6QpBkWwMfrX9M0CCVBSnSasl6sqcuG2+spry5uuJzM2NQmMkN2W5zzC1KtaMqayXLBzWTKdLFmejXj+maGVQkIKJsalIqLmM7HxUI8EkGiFclO0CaUQKNIdYIOkX0bYQmOoCSmbRBKMF8ucKZFiYTgAj7RcTHcGaTS4HcpCyn5d/46afv/i+2//G//6Ds+lTHVuIm1ZqSgPkiYH6aIgSNRno5quWoGPMgXnFUjNn+0T/9Vg9sxXVUb0BvL8r0Uf+h4sdlnaQtercf86c0b8LJA11EcpiuP7akdn1Bw9kaKE5KjfMOfTt+Myao/GVLMfESy7KrmkUtouRoMed4f0aLpaEOhDD8+e4S6TuNQtQ13siERoD5QvAwDvvn4FUvTYdZ2+aPTt7g+H9N9JWlGimJi0JsovVKNZ3WQ87I3IE08a1/wstnn0+URm09GCB9Ttq6j45A7gKwUz+91SROHCZrrZsAvFsfcfHJIcSPIV5Gda8axEt29MHz4xphFKFiHnK5uab3m95+8R/eTlHwe6F7HtKLtKJTxtP2Ej+WYNlEYNNfNkI+nJzz/yUNwsY6ubKDtx7SmtIJZVtA7LsmU5bQa88Pbx3x2dkz3qUbtknfSBnyxk6wJzcVhSi0S3uvd8sP5Y36xOaH6owOKSUx5qTpyKBGSpIEXb+VUKsWgmZsOfzx5i9PLPcY/TMhWgWRj40CK+Bv2X7VMsiHn3YLrdZ/bqkv7oz32P4opb2l8TEQuXg8RJMmrnIvHCZ2iZd1mTK+HDH6QR0GPECSbz0Vc2dJiXcHkSDNpuwyzhqebAz6dHpH+oEc+9wgEysT9UaVBG8nLNwpkFrhuBkzqHpOyx+rjfY5/ENOT0gSyqw2+SFCloUz7LMYSu+OAX1YDfnL6kOSjLrKNcSpXRBYwQqDXikleUGUCrTxPNkecLsfc/OKQ/Z97kPFYiuK5eG4Em3Krc7KeYdZ0mNZdPrs6YvjDjGLmSBcRJ5Fs4+fXjeSs3+PadXix3afyKU8XB6z/5JD9XzhAYHsxMS1dTMG2acrEF8iu48V6n8v1gMXFkPGPNYNTg08l1VGK3i1qdK8kTci56mWs2pzT1Zh1k9M8GdC52p3jdUQqSOtpxxnDJ3D1vuKz9SE37YDzasSz2wMOfjcHGX8/19H4TCEC5HPPJnR4FgaMh1s+nJ6w2HZI/nDA8KUlaEmytgStkG1MQffOLWqaswoFt2UP8+GQ4RPoXUf2ptoN/oISJFtLda+IQ9N1hpvmuGcdRh9JRs/cTpQUeZUuU6Tzdiff86ha47YZtkrJflqw97M4jMrWjnaYxJp17XeJ+kD/hWQ5GzDzBeWLPuonXfY/tiSlJ7+tMKM0YhV2CXzZJDQmZ9WRXE5HTFY9sg87nHzfInzkrEoTryevU+y9C3jVGXJaDThdj2g/HXL/D+NvjJKo2qG3UbwGkT/qVznXdLjddlldDHA/GdC59bvvSZAt7K6F4EBLOueSqu4wFxlXtyPa7+5z9BO724d4juhNeyeEzJYBNU+5octV22N6M+DguynD545kGyv7ryWU6cLSjDTpTLFxBZNln1frEe4HIwZPBNnGk6zdXS096Jg4zueB5qbHTT/l+eSA68sxm5/vMf40CgfTVbyGBimQLqYp5TzFTgvmTYcX6zHzjw8Yfj9l+NJQTGMyPtlEuV1SBfKZRa8TnlSHzFY9lhcDkp93OP5hRJ/k19XuOIztWl150hKSq5TNusvVy33sh33GPwfdxpStbD2q9SgDQQsQgmwuSG4S1tMey8s+e3+Q0L12cVHVA1Lc3dNsT9G7cAyeSGbzEauPx7jnHfY+jKiUbOkQQiBtTIymG0/bl/QuPeJZQXjagU87HPwwDuQ7k/iuhIgNB58JmmFkrmbPUpbnQ25O91n9eJ/eExUTubsUryvUbsDPnVC0e+3Z+z70vqfpXsbjUFpItg7XiYPa1y2dpILelWf8IXR/ltD/TN2lfsXuWE3Xn6dgbS7pXAfkJsE+6TL90QG9DzXFLbsI6w6t+boF1JNk60DniWb52YjFyzGrF0OGH6u7RcXOrQFEFDva3fNMFc9JW0hMJ7Y+IPKnTV/jM7nj5u+wRIGY1lWCwQuLMuCziLSQLibdk018Jo/17Jgy/g/+3V//Sz1r/5Ue2v6Df/iPvpNoTUwy7yoTQNPUFJ2M4FrSRNEaQ5pqlosli+WSy5tpTCm2ntZ7VBIPDq1VrOPvUrlBBG4mMxbLEmdllJEhYi1SxgFu3smwFtI0oaoqyqrh+naGD4IkLbDWIlDkWYHfDSuFijw87zyrsub5ywvK2mCDxKNQQkZxSZay3W7IspxECqxrUSphtap48vQVPkha4xE6pXEQLFjjwXkOxgckica4wKasWW4qnr86IwQFKn6OECTOBRrTUhQZed5htdkQECxXay4ur/A+vpx7YnLNBzA+0EkzkkQhCdi6YrVtmExnLDcljY3njQwK5x2p1Gy3a6QU5FlK27Q457idzphM59SGmJZWisY4jBMgJY316DzB2RbTelabmrOLa9brLWq3mqOkwLjIqgoEsqJApQnOtTTGczOZMlvXrKoWnXYRUuKdxXuLSjR5nmBDwAWPdZ6ybHj2/CWrdYn3kkSnGNOSphkhgG0qjDVgPOv1huVyw2y+IHiJStJYDQK8FKQ6xTkfk2ZlfLFuGsfp+SXr0tAa8CJh92hI5S1ZntHULanOKNKMsqxYrTdMFwvOL29IkhSHxDiHUppE690xBlpGwVRjHNPpkqvbOULnNK0hiECS7AbnWuFbS/1/sfcuv7ZlWX7WN8Z8rLX3Pq97IyOiMl1VvOQW0KOFRIMGHYQoJGOBBB0LWfwF0M6mJZo0oIFArkbRQMg83KVjG1mWDCphl6mi3pWZEXFfcc9jP9aaTxpjrn0y5U7RAZuqI6UiFXHO3mutOeeYa/7Gb3wjL+z3B46XCy9PRz58/5n3nz7xfC6I84aUEaHTrbFcrQQfWHNmSYl3Hz/y3btPpBWcBtZzQtWaFy1pJa0r9w+3pDVBVz5+/Mz3T098/9lE3torMQQmIgc30deFliyzRlOezwvPx4U//uY71tIIPtKM72DCojpO5zNNYH+4oVR4fHrh23cfWEujNkEUWreXN1FBnSelSk4Z6cLxnHh8euHpeOS8LrgQyQ0ajTlMlJJwzsNgZj8dzzw/HvmTn33HuShlYBRSztTaab3TUb5//EwX4bwkLinx02++4/PxYk3qBqKitTqaclWonVYaL08mMD2+PLKWRmqKnyZybqgoKspunsnLSi72WcfThWXJrEuBrjSVAY21dSvq6Cg3d2/opXF8NFzB8+lCLp1a4JIStTW8i5RWUO+R3nA+sJTEt4+fef/8xOflgguB2ju73YGUCuonSmkEF0m1cD6deXk+sqbMp8dnvn96pOnMsiRErN6/1oYTQ4UggZfzkU/PR7755js+PR9JtSLOOLZesYZnQ+RVrHP40+OR5+OJd+8/kBKkBhkTwXNtdHXs4sSyrAQX+NnPvuX4cuT5eObp5YVpf0ttgqoao5aOd4rzDucDrQsvn4+cTgvvP3yitob6gLpoa2GK1GbrqeZErZ3n5xMfHz/zs+8+8vR4pCVow73k1d4mvA9UwIXIy/nI909PHM8r339+5uOnJ5z3Vl3gDIvixZNbQp3jcrEE3PuPn/j4+ZHTJZPXhHMR8QEf/GDA2l4Rwkyt8Pxy5nQ88fh45pgSDcVJQKRRGqOBpcW7Xo2b+vj8wvPzkVIbTZS1ZZoOhwXgxNNyQtUE/rxmjs8nXs4nigi5VXKrtGaNrEQVcUKv3cYzeI4vL8gQ+OlKKXbAQCyBWFsyfEtTauustXI8rqRUwXmWUnBiAlLwDlVL6KRa6Oqo1ZzIl1x4fDqRqiVdOhURKyTSIdY3UUov1FpRH/DOWxnodg8orQG14NQh6lhzRrF16VQpJZEvmVYavTVSsnHr0ukIrXdEPU8vJ46nRGkC6HhGQmsNp8ZPtz26I6J0cVyWTG7C6biiGljWjB/NzPCe2hXv43hpFXYh0moh52yoElEbq2YH/9wqpRVqsaS1utGYoY1EaBUEj6gx1VQFFSi5s99Zguff+7V/689F2z8DP7/xn/8vPzaxxLpZM5i28alweRP5th3QfePdcsdX85G/9+Gf4Xe/+Zq7f+g4/oXA9GKH7xYUAfI+8N0XkTkYf7s0x6ff/YKbn4BL2HlK7FCEgl867+73vMsHpkPhp8/3fHy85fDbHs2Mw5L97vrg0Cq4RflWb3mePMc68Q/f/4jw926ZP/VxmLSDqzRId479h8p65/nZfsfntGcfEj99/5Yv/1ZEC8STHerj42rCzCXTdeLlX6jc7pI5KD//En/83Rd89Xft8I/IQAVYyWd86Xzz5ga5K/zO+69Y8fzs27c8/B/eDoKpo5tImBr+lDl9eeBlp1QnfMw3/NaHH9L/4MDNz2A6DmfSKCEuB2/3ESae7pQPlxuOZeLj733B3e8r8dkOxd0J03MjnKxs3J+U393d867c8HnZ8fHdPXe/OXH7TbVn1O1Z+bvYgGIAACAASURBVJOhyLQpLzcTL8Hxod7w8XTgm9/7its/suZoxsFVXBqu1qA8Pcx8lw58KHse1z0fHm+5/19nbn+2uXKdORxvgpXdXjJh9ZzYwfuJ9s2O2z/uaLNr6U7RYuJtvQnoWsl3Hp4nvtM9L8cdb/63wOGdNSEzQcAqXqaPF8pdZHrpPB52tPvKnzy94dv3D9Q/vOGr/71QZnNx+bOJvHUf8JdKcjPvJhMin047jn90z5t/ZOtieqrWEGdvTt8ene1dLxOf3jh+/9OX/OzDG6bf2/H2tys3P11BZXAih2hRQVbH4xx5d7rl0/FA/b9uefOPhHhs9CCE52zlxrMSHzPxBC+/7Hg671jF8fwHD0x/HHn4wzFeuVNuPPnOc/4qoBVojoXABcend/cs7/b88O9WE7MPJtILXBm6u09Wnv34RlnWwPrTGx5+S4kvViJtpfVchSMTPYW0zrxooH6ckZ/OHH4m7L5vxqk82/ilN7OJ6VG5yI6TBo7f3vL47pbbvz+x/9hwqyVZwnO6CrDaOve/n6js+NnjWy5PO/a/OfPwh6/N17oXJFtCW+q4pyIc3nVu/kS4+0lleq6El2Il+Kd6RWisXwT82RIKriiHd5XpBeKLrRst/cpJ1tKuTuPL1xGtcHjfOHzbufmmEE4Vl2H6tJhTb3b4S6UHa2hoIlrni39Qeftbids/PNOjOUXTm3i9LreYcB2PjfkzuPcRPQX2f+C5/yMTkuPnZPcL6GpYjC1xtH8nHP7QcfsPPPd/UIkvha6KP9vvIUKbnHGWg9JF2H+jTN953vyfHX+G6cWaE2rp+FO2GPomEh+N3fnV3z8yfT8xf+e4+0lhvXO4ZIkVf6nQO2325vb3JoLt3gv+U+D+dyxR0p0Y5/nSqHuPX2wdumyi5uFbePjdztvfhNs/ybgi1zlbZ3vXrrPFxRqFm28r+z/2hA+Bmz9S3vxuuZbUG5qk06Laeg/K7vvK7mPjzW83bv/AMX8UwmIi/YbgCceCWzs9COXgTIj8pjN9Um5+AocPgwE/WdLeXYzpizAYpxAundufVG6+abhVcKUTnywmMkRkY31DHw2p/KWzf9/YvzMHaDjV63h0J7b+RlwBqyg5fFfpqhzeW2IsXNpVvDTMQaccbC761JmeK4d3Flukw/RoewECdTTh3ARu6ZbsuP1pZXoy52g4G/7EWMGDrxwEbRbn3NKv4rqJlpZ01eHi3XAjbbLvnJ6NK9+CMW790gjnfo058aVeG7Bp6bixNm9/Uji8K4SFsfbNUazV+O5+7WNPG+8dHXbfV/wZazzmzN1dx34AYkmagR8BYX0w1FI8bQ5pm79bbPBnGyfDK9QrKkOr4TVc6df3dul2jz3Y3L/8wOPX/qcWbaX3P70t9//tn3/7r/zHvSwXbkOkSiWnRHQTqULwys3NDT/8lXt2Gmi9kNLKu3fvOZ4TpTR6D5TSaHScE+Y5ogJf/uAB7z2n5xeW9UjNARHPWs/U1hDn7UAjgvfKPO9xHtbLmWUx4QcY7D1PbYzS28DsTUi6CTPEzvlyMbXBKa0VpCg6CcfzC07NLbSLE14q929vOZ6PHM8rXdSYmLVRROkdqMbQbDVxuz8QnKIOYph5//49jVGipuBdJKVEmGbOy0KcPDdhj7ZKcMJlXSmtczmv+NmTSwF1dPE4HFNozC5Yt3Qq7x8/M88zy7Kw1kbvgsNxyRXozHPEx0DLiYfdRFoXUmu0BsslEUbn+NYKlyXj5oi6wJI+89X9W2oppJSYh2B1rh3XQAosoTDPe0SEnDPRB3Yu0HultMrSCho8+ZwQCq0XZr8H7GC9v3vD+eWRh/tb1lRYTwt4h3Ql10LtQqrGVkwpEUUpNRGjCaZLTky7GZLgYuCSV6IPlFIIzhoG7ecdKFQ6S6vWkAlrDNPWjHeO1Dq7OCE0XIPdPNHrynpZ8DGw1oRMOy6nM747CoU5xFH67wnBMc2BpRgTuXXhvFygdXNhS6eL8TZ7yYR5YvKOXuq16VjvnVKK8Syd0mo2N3oXbqcdKS/0bk2/fvDV13z67iPSrVTS+UiqiUY3ZuOa2N8EzucF7yO5VqbdzFoyTQuOPbVASZm4j5RuGf7JBwRY0oITExKyVCIKqaASEKesJTPfTCznC1EFrw6pjaJQfSdXwQv41sjaoauVUkrE94oTQB3n85Hb2wOXslBzQ4rSfKDmwhxmUl3xh0i6LGjwzF3JOVNipLVKawbjDwR6s0Z36kycqb2hqnivnF6OOC9oFevYLplbdsQqpFYRCeAS87Qn5TNLXaFHRJRWFW2JGAIpLXSvtNYIMVJKMdZ0tXFPruOxDGdBUHXkZWW32+FUaaXio2ddV5xzCJ5lPTOFgHrP5XIhBE+jMhUT7puz3acJiA9IF+o540W5tEaYbH2vywuzC6ae+m5VDinRxSPazX3tlNrb+P+WpNJWccGTWiVj5eheLKnk1ZHXRAjGi5Xe6akw7yJ9ElI2y2fNBdFAzhV8ZhcnSimQHaDEOLPmM9McuKwnE7R7xzlHwxEGw7mOtTlNE209412kKpTLag3AWge16g3nHDlnpjAh6skUokKm0dZMjBFKptKJEqjdxP/ajfPk1ZEwd2hvFd/FmlipsgwsTES51BXngjmggTK4uyKN0CCdExImGhcTxrvSu/EjAaL3pJTx2iF6pCt1yRQywU+Y5bvj54lSE12wueFGyVherfS+W+MMEUfJDemVaXdgWY2rPntL9rxIsvWiyo3fsSyGq6muWEJL3cAOCT5ESqpEH1jXFd8qvZuIKTvlfD5ziHuCBtqkrOkyEgyOjjn6VYTenCW1sjXyErG5vyyLVcc4R5xNxK9LIsyRXIvxq7rScbafNuOPi8hIjO04Hs/s9zOXy4nDfmaty/i7iOSME48fc13FoerHuvQc04J35t7vWikK0j1eHJVGFA+l4lpHREi1EEZipOIJYaLmC9CQ7qla2DlPpHKq0NTRupCA2CuT66wLJrT6iBdlbYnqKxOBteThKLf5Fxgop2Z7ulOl5zNdrJHcNs9VPeflZGgZhNor//1//V/8P+uS8Oc//1T+/Ov/xl/rbqmcfjQZg/PcyDfOXLd75fSV4/O/2OEh0YsiZ8f9bzv276s5YZq5ymQcQNd7c908/kWlzp35kxCfTLBEIB5NpOmOazOOMgvrvdIChCN2kJps+k2fi7lmU8OdEvlhJj1Y2fLpK3uH2H16bZxnjDxoTti/S7SotCBc3nrqBKcfCbsPnf2HdnXOAUNwsM9ID8axPP7QU3ZC95D38PZ37PAMoMkOZdtzmj8k8m3g9LWVr5adMD02/NqZP2XKzuHPVhK8Pat071nvlJdfNdfNw+801nsTeKZnu54WhelTHtcVKLMJIc+/4ginTjiZYLk1Oulqh+Lp++3elfj9wvf/0i2aO/HUWO4NO7H7mKmTMfbi50R6E62E/FzJN57LWxMf/NKYnirNi92DgJROejABU1rn/APP/mPh+Vc94QjTKPvuajgJt1b8S+Lywz3hWK7lzRvWIH5O5Hs7X7UgTN/b83SLYTH8pXL5gX2fy534aGWq23dIMQ6zf0msP9jZHIjC8jDKb18aZTJmcVf7/Do73GJNdLaS8LxX0q0yPdv80NyZPlpzp3IbzDE8xk8GB3q9GyLNwGzYmjBH8vaswES28y/vDQcwUBGf/2Lg4ffzNVlSJ+vArvm12d3lC8/uU6FOJjyle098Ktfr0NRw50y5jfadQLo3ASk+mlMWwJ3sd+wZG24nPmXSQyA+ZvJtoM4yGgqZk9OdyxXx8PPztxw8dRbqEJt2HzPHH0Xmz/XajGvjtKYHa1BVZ0d4ybSgpAdriuUuFSkNzQMTcBOv97Vd4yYA1VmY363G7x3vrfHDicsv35IPem0SBNYkKZ4a8dHe4bdx6F7IN55wNBOJZkuIuCEsSzPer39ZaXOwpMk504PDnRLpi5014SvWjC+8FMNTqOFS8sNkrtuPC/UQkNLJdxavpI55MBAe273VWYnfJ+reW0x5Lte1BVznxIYq2Ny/bjXHZotq69iZqzIcqwmzKtSdOZHrbGJtOfjr50jrrPduVDDYWtqazrnz1qwtXmMkQL51xKdyLdeXUQW2YXXKwV8/x+ahHw0LrfTcKgcUNxyO4cXmpztl8oM18fKnQr71JlbmRr4N18/MN37sG1znmd2zITm2JonXOPLZYme+8deYWKdRHbK8snu3BoPbvN3EyO1ZbfceXkaVwYg7mvs1Rl3Ha1bbExujmuQ1Dm7xoasMzIHdS7p1TE+GIFzvLZ4cvlnxLyvdK5cf7sca9dekRTm40cQN6mzNOvONEp+rleSPZn1dhflD4vJ1JO9s7k7P1bAU4/d0lPlLY2ACrFlmVxMpt+9uUUgHZX6s18aM233ZQ7F/+Esd92jIm7xX9t8lli8j0/eZy5eB+PL6d91BDUKZlXhqtIHn8KsJsPt3Fq+7jkTpiK1l52wfubXnX8f9xqMhcFzuaOpX9MH2XZqsWWc+CPuPliiS2gkvNvfSrbMEzki6lZ1eHdL51hNeynU91527Pgew8asjaSzNWMHx9NpIbfepUPYjJq+dv/0//id/qnftf6JF27/0H/7VLtLp0vCtURCKdGrNhBDGQUOJ6si9srZiAkQVUsnMLtCaZR2oFrycU3x1Q/QWGiam1paZ/ESuZie/5HWIXJV5ns2R2MzSnFIilYwQCcGQAq01onf0nqF3JvUIjVIyYTIBVRys5wshzqgE+qj1qtUEptatO7fXYE6nYB3Sn88Xbvc3kCu9VRTBdfD7SOoVsgki67pal+ze8KLj3htBZ8gKXrjkC34XiXAVFhuVtVRjCbpIzZk5RmoqzCEMb6n9fskN1y2YJe2UsTp779RSeNjvIVcTcBrQOs4FOtaRvBTL+gGEycanje8WtZf/3ju9Z0qHMEVai6RkB11UzZnXVrSasyn1jWtamaeRAU+j66V0vJvJrlHaSsdbR21R8mrOwNoLZTjy3EjFOCfUmk0cbUMgSJ3SKk0au/kWVZsL3nsa3a6hWVaqdhOsUlqMXdih9oJiQWVyfriuArUsxpZqHXEM4QTArqdvh/xqYgdYJnBJhd1uR6+Zy2nBTZHGyHbGACkhtXGzP1BTZi3Z5lRjzL1G6Y7aMqrgajcnHI7D5LmUQhRHryaUr7XhxVOckHtmCpE2BORpmrmsF0qpiFrHSsUjOKRXnIrdiwprSjB5fIXSGxKcPcfBD3Jqc2WeZ5Z1RbxDgnBeF279hMfWzDI2US3FWLhOzQmtxsbyTqDJmE8VLwoodUlIVHBKpdPbTJU2MuuZg4+IdM5rwnnPIqNRXROcCq1UnBcQa9RVerVEQYi088rsA6lVFg9T9/jaqd6Z6J0X1CtRPXVNlvUe86v0NtY/HOJMxRqDWZyqFFVO68I8TfSSx/MMNnai1Ga/G5wnl0YpDe89nUzuULrHYyKziLlZpQ/Xj3Nse0Er5pQ/TDN0JbtmAnvw9JYITqGqoUi8xyE0bUPUWhCCzbOSaM0ctK4p0hM4pbRMFltvrfSBRTDHYU0Wf6dgnN5SM613VB3dKyqeJWWi2lpzIlR5vYdcGyKdVDL7/Q05Z2gdpFh8w4Sz9bJw2E045zhezube7+Oamj2jQIARj8wMHGhaWMtKdeARdi5AblQ66gK5rYi3uNQrTH5vDV68UlNGgwl/jQ7BhNQggnTjkTbvSb2y11vO5yEclkTwE+c8ROU6ri960jr2mykS3UwuC6VlajWXe2ueVozN5cVDr6w1Md9OLMtCKYmbh3trcLYkWi6EeaJ1YVkW7t88kIrxhoMIvmEIkVK42R8Aq3yR1hHt4J2JiWL33wfqREIYz9VzWYzDFqPHtYr3nvWysIvTQEso5+WCaiPMk7Ham7FdnbPy33XE9zB5St3WCLYvVoFJ0PH+3CrkXInTTC8WS9d1ZQqOtTZaxtjy3ZytpRScgnNyfQ4xzjjnqNn+PqWMeEfFkmdsTSJrxeg8tq4DE951lGpzPzdj84rQ8SB2mJiCZ00XenPgPdoatnItZi05EfzOEjz2JkQX6CKEZmtlCp7LciLESG6WPOkFFKFJsXUotqekYvx8ANFiDl2sMqT2Ztwf7fyN/+q//HPR9s/Az7/2a/9Z3xhwW0miWwfbdW+Njpq3g1o4N+JLMzdL6fiXTHoTryIEcD0olp1a2eD4XR3su/XOWXMpFabvszEyW2d9E64HMmmd8GIiX3dK3XtjyI0DvDTbr9KdxRy/DGbs0Q6K4cVcYdtng4lvm1tnO9hv/3RLY//Hz1x+5e71Hrw55MpknwujzPRplFpfqgki44D/84f2TRDMt6/MOqkQnpKVoGICXno749bK+ibQxp9rNSG5RbvWcCzGIAWkNjRVjv+8wXtNuHhlCktjMGDb9b6NA2zi1CaCYZjH6+F3QyT4l618mqsw0YIJE+bE/cfF2u25lEkIl4YbQn7ZK1JNALZmbe0qfm0i4iY8beJZV3Pg6VLRXE18dVwFP83tF0SVTbDyL4lyG6/fswl6m2BUJ7k+p+16Nnbjdo90rgf77b7ARM/1bURaJ36faJNdB83YtjYOwumXrCx+EyI2YQXsGetaaMGZo/Cc6U5Zvp6YPpkYIo1fEFzc2vCnQro3bIVUa7A3fZ+vf7+Jl93JcGXLdR7Ep3IdR7fYM/EvmTa7IQ7bd2zc5+0zpg9nLj86UGa5iuPA1Qm6CTHbGGx4FKlj3Ddcyvo6DpvQrauxcbtXLl9HunBNHMTvDRzcpoEcGcKtrd12nRPp7YzmxvomEI7VhNL76ZU3HOTqXM23Dk2/KJS6xQTzunOcv/TmkDu3q6jqL5X43Qvl7cHGeHJXV+82X7f17s91oEJMLHSnjC6ZNgd6cNdn9SrI69UhuQnal6/jmJeWuNi+7yp+js+32PWaKNrGe8OJ1MldBUL7O+OSb7zybZw20berkO79NcGwxYct9sXPyZIU47O3v9kSBwj4l8Ty1e4q5m/zZPvnxh2uk7D/dr0mGFrQ6/dtcXMTx+ijjPxxtUZ5u0B6G8cz6/9Y4khaJ90HG+8h0jVnZepbgs3i4GtCaxPnli8j84d0nWdl75g+GsvYLfV6feHF1kC+86QbS3hd98jDa3z9+bi5Ce/xMeMumcsP99eGc1JfsTTx+4XjP3sgHNtVVK+zCbz+JXP5oVUeb/vOL+yzXq775RbPtucaP13snm6nIVRbgmF9a3tNmXWwqu25hXO7OpG3ebTFkLJ31xiyzYc6j9jpt7P4a8JXWifvlOm5st45pueKVEi3lljZYlKLlvTRCtNn4xRvsfrn9/PtuW17zcZtt6SavaeUWa7X4kcM3xIT289665he7Fq2uL/tWxbr7b0B4SpYb89328/WO2X+XF/F6rFPb/tkneyeajSh9ueF4p9/VvpzjOa/9Tf/0z/Vu/Y/0XiE3/jv/ocfN+mUZiWSzntEBLFaeaR3tFlJrTillGJl+LL9d+itQDVRsEqjaSNl4+C23q9NyESs27M6bwex1gjBuHtxuN1yyrRWcaMMNoQJRem1MjlziCmVGByX1Vy5yDA5YQdAEUHEMATaQEaJQwNCnHEarGN7q3S6CX3e0dYMzbi2gL1cNAPV55St5HZYuacwD26slXi0BvhA78Ua+EhjOZkjLa0LwXm8enPgNLP0OlHUO5wquWRKszIcUYd2oatyyQsEtXJm4DDvWC/WbKnUjp8mUsq0VOhBaB0YzkpVQaTh1MqpRR25VGrH3Fkx0FqnV6XXQgwOWkWsRTrdqksp3UrWvRsvmaWZS6x1SmvMux25GO/KeSXnRtztOR/P+BCoG3rABWSIVk6VVju9NVQcrTSij0xuxrtACB4Uck5Wnk9BnAlnvYH00eWw19HQp6POnIJ+iLU+TKRcyD1bBqfZfVPFhN2t3KCOrLFspbeOJo41Zeb9js9PT6jv3BwOlN4HhFKs/HYtzFOkrInSGzoFzstiZcwC6jqtVbQLXr1xIjEkQ0dZS8FP1mgOZw3DcquUbqXtvXd6NbGttUZtts6cc1ZOgaO3hh+CvIo1yEm9oiFQhiDZayOM8WvYPXQxR2IZrsQGhDCRSrJS5NoG4qHjxdH75pxTel3sGtTiRS6FaZqpXai1oTGi3l2FdZqtx5tpRgV6Loi3OSneUCO1NpwqIlZmL2pCSauNGK1zZBg8SIchSvp2b97TtVHrgqgzxmqHoDOCI9VKoTLNk3Wid8rpeLJnI1BbtTnlPM4Hemk48USdaE0Iw/U9ciFW0q9+IDQFkSGEtzbm5Cgzb52qQqWZ27VYKbVTczQe1wsSnDkdndK6/Z44pYunjYSTiFh5drcyc+8Ca7KmJfROyQXtkIe45vzAJ6gjZ3shmKeZPJAYIh1RsfuYgjV96lYKU5uJT3lcZx/366Ojlow66N3GpJSC9E7OidmZU7L2jsaAOsNpXNZkzeIaIO56kK21oTRaLWg3d3WjIdrwk3GZo9v2WJuvnUarjS6GIYjO3Nw9LcTggYY4u6/SOk7cRrkgRLvPNRWmOJEuKzeHG0pJqEBwDj+FsdY6forkmvBqmAdz0BtzWEWJYbJ5JtbAsdNxCpNa8ihJGbkfh/MOaTLWkENFEVV2ux3H44leGzEEVAR6pdEsDqqj5IyKCaTiO3RP7RWvth6nGIFOvTqeG9NuR2mJTjcXaE74EGg0c+vWRm+A9yxrBudpKGin9TawHg7nbExqH8+jN1QdPnibo7XRWqfWbZ4ae+p4OnJzs0ecoxRrTkez0qx1vRCiYRJyLoTJk0tFQ6T2Dk65JEskLXml1Yr3zhI+zQTSoI5WKiEYH1wEcl0NvyQ69kFnsddZUq61hgLRTxYfB2qhdSGEgHcOoVhjMlGcBxVPr4ZVmaaJ0/lie7To2IM6rXpccPSeBms+Me9nLsuKjx5146WxqyEtxjtKLhlR4d//tT9n2v5Z+Pn1v/53fqy5X0tUaxT4ucMhmJAWLpbA9Iu5Urb3DWQ4yGQIO6v99/BS6dHKA7VZeTVipZZ9NK3RISZotiYx4WJl/YY3UASxZkDeSizzrb1/dSfkG8f8KTM9GlPR5a0Efczrwf+7HnR1CNN7a9xkQpiJTW5t4EfZeO/k28Ehxe4XhtjnX0WXfO8Hs3MclsczkM71euP3K/nBxKW6s+ZVLap1QcdKlbu3Q7K/tOthExkdyZuVXNsFWLJ5/WomPheky+AJmoij41425uG1FHMIoWACurnX+rW0V8tr6Wq+tUooewf9ucTuso2VtwqWIYz6S8VdmrEoT+0XUAv5xrH7kKg7c4Yh0GZ/fb+tQ/w0scaeY7kJ5DtPmx314K+uXyvP76+OvvE3jEN5DxaHN5REnR1ajLUYjpVwHrzNpV4P4Zt4DlzH+pUna3MlPpsL9fC7n8EHLj+crEP6qJzXpSAI6U0YyQ4rhZ3fL6BmsGhBB0bCxj+9CbToqKOEOj6u1N0QwbuJ9P5ccecMTq9Ni7b57JZqgmA0/mSL7trpvjtzmLvUB+9Vr13OtXbKIVzvcXsOINdSfpcadRcILwVtJkZs6IErm3cbg01UHeXa/lyuLkCXjAW5NaeytWwCy/LD2dbHGE+2tSkmJm5zROAqrmvulNtgyc/9KPMPG57k566vG3qETZxtVu4Nxih1l0K+s3f2FpT9+wSq12THdj09GsajTe6Kk9jc3psoaff1el7b0AOam1WMeVvb14ZFS6HNHn8qdp+TzYHdt6M59GLxZeM5bwknTeZS31AbWuwe20ggoXJlynavhFN9TX7kgQU4GTIuvQlXR+r1PlJ/FX+HCKrZsCfuXGhxCNJro+4MZWDXBuUmmAtXjGWbHiJtiGrGnbX4M380YVSXetVnwBJXOMXlZnFwklGRIdSdN4fwTWB7Wd6ci9awDcrBU268NcTqlvSQPoTpn+MKb/tU3lsZfHwu5DtPfCmcf2nCrxYPyn4kQoZQ2GbDutSdG4lH45z6k60XmxONrYmetFFBcWuoh3AcCY8tUaWv9979MDJ9MbH/ZvmF/Q1svdadH4lUaxZqMQ8QrnEOMZeu9K0JpcOlRr6b8JeClGb4l/OW+Orkw2jyNfay+bP1XdCBMNv2dxNw9SpKb4J9DyYCI3admm0eWZy3uLH/buX8dbQ9Z+mkO7tGbRCfB6YlW8Kk7Ew03uaswEgkKfFzwmWbe9v9d2/OaC3dcA7e9rZw7tek5FbZY4kcRcuownFCPhjjtztjwFuTMUfd6Xi2tqe3aOtIqn1nulX2H4YANcaJkZRu0cRnwSou0hB3606v++91X/b2jhVOtq7/yn/w/wOm7W/8T3/zx7k2QgjmpOmQSoVm5Zu1gu8NvPByPlJ7J46XLtcgikN6MydutcCQpDE5O9C2Ng6u6mkNunZSTkAn+sjx5UQMEwEGq7JB74Qq3EwH2lroteJHk4/gHb3BslyY5oCOcmTGobn1ZuW92qAnehV66SiONRfrvF0KwXnDNKhAU4I4K7cdh0xwuGmi5jM7L0QNrOcFmjM2QhN6b3icNWjb7yjaUG04sKY/Juua6Fg7PVvpbhA/StYbvTZyWggq9LrQRcxp2nS4nO0lb46Kk04rfQg41gjGhzAEJKGqiaDTaK4SnAlatRdASbkQZ+vYrZuSATivqBtiaGsEBddNkBUxoVRcp/VEFaW2RKsmYqtzpFLNeZgXY4yqp6wL0zTRF/t7RWgVgnpcE7xTeqlMMdAFggS0C7V41JtT1TmPihLUo90xkJlWytYaPgZyTqiP1LriJIzyb2UX9pzVxiFGoHkT/3MlOBPKS+0456k0XDRGragg3pNzIc4Tl3Xh7u4WeuZ0yebe7oILESed2hr7aYfESOp9OIeFECJCQZs1FfPihggeKK0SvJCBEKwEmO6Nq9w7GoU1rSbCOiE2mOLE+XJmniO9G8EXp1TJdC24YNyYU06ctcMUKEsyZ7VAdI7cGrlVNAa664QQOV3OdsTO3wAAIABJREFU7OaZdV3ZzzvW8wWdA6deqM5TSoYKqgER6A0TrHaRVqs1x6vZUBCi5FxRP9N6pTb7u7QW5uH4a9nQGHhPc45TW0wQUodrzhrcFXPCl97p0tHgCc0cF14dqTaWVkEDVGUO1rwsKJRiiZigkdoaax7l69GaE67YmPfeccHEUZqVgDenqAS8RNa0MM8za05jHXcThnuwTVawF1Zna6RkYQ4z+y6ICxbf5sDl/EJQY7EqDudAxOJU1U6cImVJ9t9wTG5HbdUSL2piPSKod9AsjjidwNnGFWKkpYzQETrdBSQEllSIcRr8WsPWPD8bdsF5xTnFTcrxcmLe7Zmch9opvVksywmdos0fBR88PWUCnVpX5mD3qdiZSaQjkyEb2oixUxeiwhQj82RllFUa1viyMfkx3mLiau+Fsh552O8pOdFzZgozk8JSVsT5EbfMzSh5oEf0zCHeEKI1AaM1WjcXZFNw3vam3DrVDMFEPOoqvV7w4piiJ9cMLZNLYXfYk5cFr8ainsIogZRKr9VYzb2S6oXdFKl9xbtKKxc0Ct0VW7vO42tDi1h8A0tm1AI0Lpcz3lusX/MJkYZ6tcSG6+S6UprFSRkvJHil1ox5bCPUjHSbn6111nUdybeO90KXajzuONPFQSm43khOEGcssSDeXLdNkKbsNKCioA0fItIqNS/M3lixTUb2HWvM1aThvTPRV4Tdfqb0RsqNSWDpmeob1Eb0E4qJ8iFMSDOVxyl4J6gIMhJFk3fQE1EjvWakK04EqY05ekQK7fJi+13ck6slgGigIng1fqyKZ44TpUGtjDkoxiTuUEoDCqJKV0WbotXQGjpY5j4IpTla77hg4noXAe0DnVHpKFPccTolplnwYp+FeJoPtKFEeXWIKpNG/t1f+zf/XLT9M/Dz6//N3/6xOxfKwYQjLcarQ0aZdN7KTYXDT864pZLvwjjUKPnGhIt889qoIzwn8oMJXGoULWPeDUExvtjBJ9+aaJHvopXPplfRse6V09fBmkU1a+jhz/XKC4xPJhJtoqcMh9YmBvlR3rsd9LsT4uCF+mU09xoNaoThJroKPm2IMOb6Wd54ysEaXG0HryujbpTmlht/PVy3MPiJs3sVJYdQVmc77PdgB0QdTaTKwV2v1S9DuJ6E7p2dX+7DVWy2+3oVXl2yQ6Nbje+a76zZUN3ZgXsTNbbSz+25bdy+rRR9a5hVd3plQHYnxltUMY5tauhSzNEWLBkWRwM7f2lXZ7FfGvku2NiNz3fDFdWC2rU1c6+ZuOauh/Ye7LlvDYfKwV2FXYHrWNdRst/9cN1O7sojXN945k8ZP5AAm4itw+XWguKXV1dri9Ywhm07u1TroP5xYfmVW+MhvruYSDoEyx4duhSWH0ygJkboaJpX9yY+dmcH9Lpz14SCvwzkw7lRDyaoSDehID5ZObR/utj50elg1jrmdwvp7YRuLrIh1FxxDRV271emD2cEcEu5isf5xsrN/cncztJtzs/vzqS3E+Eps3w5EZ8MXzB984zLDXcZDPZgIrd0m89llKmXG3cV9rvT4e7Ua6Ol7nXEg0ibPVqgHIY4kjrzu4uhRnbexOydCfDXEvPO9fkhxjINL4Xp0zr6KAj53mJX2dvaNmect0qnp2zJjb29a8fPhlfQ+sowBXPKbddriIKV9MaexyZY11mv61+zJXhadFcXcnrwLD+YRo8cLGZ8vFBv7N5bEGOgis1lE6PjtUy/RUe+N/fvdg7ekmOvIqsJcwgmHB70NYm2JTGG2FVuTKTtwZEeAvufnkgPk8WoaP/bf3MmvY2kO29z5tJMCF1MeN/m8OYm3uJmehhmmyHsbXN1S9q5S6HcevJBybee9cGjzRq7tWhrPd/H6/NoQUZjq8rLr0ZD73Qh31up+jyaojU/zG7x9TkCXH5gKBu/Wqxzg2OrwyUdTvXqkN3WQ/eWQKuTkm70mnjy50p6a0iPNhz++c7E4zwE0vXe4dZOeMmkt+aytyQYr0JfUMrBWVn/2BcsaWmxBxGmT8mSeZMjPOXrWHcno7GV4UbSvb/+OwGLM4zExvhn2ZsQGh9XpNtZvO287T21X2PjluCKx/qazDxYLC8Hm1uXt0MkFXPvIzIYzOGaxENM1CyD3VrnYWIonfULG8N4bKQ7Qz9sjQDzrX9N1O0H8gBzcddJro3Z6k7J9x5EyTdjLEfSCIH1zmKCK5YENcfw6x5h7vhX0Xu9c9YkMY347YQ69jBresaVF7xxl7fPyTu1hORowHZ95xgxPe/NRSzNRPT9x0q+sftp/vV33Ri3GhWckG4df/Uv/av/9Iu2/+3f+J9/LBv7rrYheioSBocteFqwUmRRhxMMZdCFUi37VFu9cie7DreiOFTNzeXEmYhT7KC62+1GuXLncLNHFFK1LtHBedR3Wi60PhR6J5SWCNHKgUW6OQtFSWtiitMVHWAkARMwwETYEDylFbx36Fh4uWWc69CaVdC2At4wBiGa6NJ7xXtoZYjBwYTd1hoanAm+KoTgOS0rVTqhK36UkXsfKc0OcnJtzGYOWRM3oUvHe+N1VmdOLOc2UbdTu5V8iiodczTe3jzQemfezbycjszzjlarufm8M3fnsqDqySXj52Av89Ls2oXRaMdceXaNmEtqCNe1VsQrlS0rCp0OzuPHi4hEMTRF9KSlmvtKBSmV2XtabjTf6drpauKWiSnC+XxmihGhU3MjzDOpdgiOtWTEWfm1qJoDGdCxiXsdGxeGdpDOGFeB2mi9k1sBH+mt4jA3rXQrr72kRKUbyzRnm0/OETQQQ8SJvzIxnfOkNZFL47C/oa4r+/2O5bwQnOJdoC7p6hbvfTgw9ecO9ViJrbmAHSEGxAk5mwu1tYaLnmU1ZqVXc3GpmICpw014mHdcUiIET+0ZmvF+e+2kVCitoio4VbR3dj7SazFXs0B33tZizkTvqakyuYAXd2WeRu+tyZ+60eU9D9e7NSWSkZwx8VBpOQ83o2MX99Rq7ogQHHRzgboRI+bdjuAnnDcxrbaGk0DNFbqVty85jy7xJuyrdJw2c+n1hlUFVLwGorMyz9YtGbMsC8FHe3bah+gPu2BzhNropeIHt9nHSKltNJCC6COtFJxACMbWFcSeSceYo9JoLSM0ggj0guuO3jy9dtSpxTJVw1WM5koxBtpamGaFXpnjzpiYXYk+WuNGUVJa8X5GEPrgc/bB/urKNd6WYizc0ixRBso87e1AQmeezGXunL9WPMQp0npnzZUwR06nMw8P99Sy0kslems6VeqW1MqjhKCaQ1A69Mo0B4IKa6mEYBUSAFcalyjRRVzv0DaECvS0cggTvlucVSekNQ00guJ3kVYa8xxxfsyvkgHPutghfnMPqAiM6+y1o9KodaWPfai2DiNZUnMmOm+OchRq47Db4QPsp0jL1WKDOmu4NmJOHPHMq2M3RSKNw+SYp0Bt60gSOttbnLmszeFrTtkQPYLwwzdfUAebtogxuQ1VYw0KD/MttTVuDgeCN7dtL6NMF6tqyW28ZIpQijFxgw9o7zjf2M8Ty2Vlc4EiigueWgvqzCmdy4oPjr4uTNEx7fY8Pn0iemv+qOIxBLCi4ig0zpcLThpOO7f7CTdQDF4NQSEq1lAu+useXHJimpSW02gOmGg0Wq0EF/DOKjykGqakt8ocI9Q23OIrwUVyKoh0W584tBsOxqsao7ZUgpv41V/+ZS6XjNPAsi7QIaqgzfauWjLBO5yHkhP73Y79FEjLasm7VuzzeqGJMaftVNXZ7ycOMdj9NauOqd32hdo7ncZu3nG6nLm92ZNz4v72wMP9LTe7QFkrwc2saSHsdsbcFx0on4bQ+cv/zp87bf8s/Pz1X/87P95Kls3RyNU9F5/ztVFKeDEBqE3ueoD0g9vnUr82mNGBMUDl6rTqfrgEV0MRLW+tORcIl68t2RePjTYNgW6UGmodgm00Ea/snR3Argc3QyHke8MnuDSwH/DqFB4l3P5ibp2tY3M41V8oi90EQc3dcAzdDrPbwV+H29GNRkmb6CoY087EJ2skVHf2rlBnN9ydr47fjRG7CS06DrsmHvYrsmFraOJSuwpl0sy9dfrRhBYr/9//9DIQFcMNNQSCTTwIx43BKL/goto6t6PmtNT2ej0Mp5qN8aZkcuV9biX2m6Ox7I3xVye9/vv1wdk1lf4Lwvnm/ps/pnEgl8G5NFesYEgCZDjVZHM7y5ijJtwJDP6is5L7q6POnlk4jb2qQR/O0C3RMH1ccMmEPX8xe1SdjUuY93ptqted0Cbjn/pjYvl6h+bO8mUkPmbKIZiTOA+31q0fYru7iqqbM3tzc7ZNtFe5liNv7sr4/Uq9ieagU3cVV80x21m+mpg/pqsgCSYOS+v4YzHxzys9miC1fjFfRfNNuOlxiFDDFZvvI3VWcxVe2hDIlT4H6n6I7oMVaqI+uHO53qeWkVxwyvpgjXWu1zVwbz0apiA9eMrOmg1tzfl6cLjFnONl766NthgiTnd6depu89dfKm3njW86nLgmeBcT/3W4jqPFtfWtCW7bnKmzu7J6/bleHXb51l1dc9u9o0K5MRF2m5fb95W9G8mAUabe5brmtkSFibV6/ex068ydfe/QZuuq3LireBlfymB3yvX7N/TEVurt1mYJh527cjS3RMWWTMoP/vU6xviXm2hr4ylTd57du5WXf+6AX63xW7oxh6gbIrdb23Xdm4vexiHdefJOiS/tGpOBq1MTEcpNoE3j/CsmUmnrXL6MtFmvTROtSsCE1bKzf5/uHDUqfew9yCjVF/ts9FUI2xJT3VuTMx1xZhMIDZthz9hvzlvg/KU1S1vejmtRu8b5c7kmVMqN7Rd1clzeOvKNsnyhrA9q39WH83lp1+SP5mbJq5FU6g4+/ssezXJltPqBXtgafl2+irgMl6+jJZRGA7dt/9ri9Ta2W8wqI9FVg7C8dcyPw+qpQwiP+sqXTg1/tHHfhMV057j9w5NxruVVpNyep18bu/eJFs0pfPolb+PYX9nJmzO17PW6pt2lku4sNoAlDXXsz+VgY6tGbhvzwhjU2z4XT428V6tqEOPJIsOd7A09UHaKXztlJ7z/V4T4aAm+6dnmYB6JPttDO2W25G5YGpe3juWNEk/dGp+t/ReSxlsTQumwvHVcvnCU3ZhvUfFpYJVGgm59cOzfZ44/CoRz4/gXPMdfcZy/Utw6+PZHuwaXGe8ghnFAhf/oL//pRFv/p/ml/69+Jqdo8JSUTECqZTRqMW6jqnI8nYxNOP6mnBdQ8JP9noxy/D44trMqNSdzBkmjdzuoEky4XGuxg6s0ejEHXafBaAaW0sLNvLeSS6nG5HSCOAgu4rpS1sQcPH7naC3jMHNarZXg7VBIjKS0UMUjaly/UocA653xc+lUKuKEkhamuIPRtdo5YR78wFqsKzo0uuukltA+OLuTYxe8MUL3M/W0IL2gc2QO9p21g6iJKzkthOCHO854s6Ie7YEudvANIVrpaAiUDq1mgvOE6cB6PLOfA/TC/S4A1cSFMLMumdwqu3Cg98bN/tbEDCo7B60pvdgYK8IlJ+Z5MvGhFHzw1FYIASbXyN7KUEtphC4UUdbLidv9gVRWnILQ6FS0CcfTMz+4e6DkhcnPNG2DO1tNSu8N5z13twdr7DRF5l3gnMxZ16URgpJTpgo4FXq2pMLdYY/QmJzyfDoiLlD66N7dHV0KKg4vVr7dlyP3hwNexYSD1si1U3bBRKlcDcuRFnqp9F5BOje7G2SvLMvC8Zy43e25FGM0zlNkOZ0J0TMFRzpdeLjb06ThtPHp9Mx+d8tpXXE6URjRlIafPYGO9kJ0ni/e3PLx86N9ZkrczpHLmg0/MoTGsi4sy5H9vCe1hg+ziWpxT14Xw4GoZ5531tgrmyvdOKTmdnV+uMFDZDmfOUwRbTbeTQUNwhwDX335Fa01lvWFp5cjS6qUnDnc3hpjundKr+wOe7gkbveBL7/4msvpQi2ddx8+ouIIfm9IjNporRInpdbE3XzD4XDL7c3Eu3fvKbXz4dMTN/Pe2FuXC3HeUVImBE/oldubma+/fEvO+f9m702aJMvS87znTHfyOSI8xszIqbKyqjqrq6q7ge7G1A0KQ4MCIJEmbmRGymSmhX6AtNBKe+kXyEwmM9EgaSHBKFIwmQQjBAEEmmDPXd2Frimrco6MDI/w+c7nHC2ORySxAzcSReJuyiwrJr/ufq7f97zf81DVLU+fPcfI0Jxr2xorAs6kdQ1ZltG2NQe72+TreRj/tyrgQ1qHRGGERxIC4ratwVuO9schlFMJk8kF6/WaIg/ioLp1V+tKGmuqMue1WzeItWLYT5ku5uTriuk0NCLr1pLoGNvU1GVBlmiiSLM9GrH/hV1aW9A0DY8enWHLFi8NTVWjVID6tLYglQZjFEnSpZsl5FXJ5PwcJ4J0SqnQsLVlTaJTau3QDvqxodPtEscxpy9fIqwEoQK2QwY5mjI6cIiblvuv3aSTxSglUEJzcnLC85MJMYbGObQT6A0TG2B/d5uD8TZtU9HNIs4Xaybna57Nz4k7XaTUsGEGS+0ZZDFHu0eMx6MQ6DWO6XTBZDbldBpkk1VbIiSYLKKtC9548xaDXgctHP3+kMnpS87OV5yLJZX1VD5swsR4Wt1ipOfujWM6/RiloK4tRWGZTC6YLXK8sxitqduKbn9AXRWMen0O9w/IuqGJWReWxWLFhw8e0e1v0dpqgw4JSILEw/XdLXaHXZJYU3lHvi45PZ/x+Nlkg7cJyAofGZytGI8GHB7t0o07dExMc1QyX+d89uwF83UeRhQjg0oSitUqrPPlml6acHTtiDiSCAxPnj7j5fkEoTfXHieItMHZhlgLOt2U7WFGJ82YL3KePj8jkXFYf4RECRPY61qiNSjtuXv3Ht0kxsiE5XzMs5MJiyKE50tbE0UBIaKEY9DPuHP9gFG/QxZpiqri9GzKw4fPGSQx66LERDFVWdLppGjl2RmO2BkP6XcztIqYTxc8fzJhNlvQeh/wCFmKQgMt/a2MTCgOj3bRWuKRPPj8OZNyhkLTNuBFTXRptRWO/sBzuH9AJ01JYsPeOOP5yRntmUBKQVMHPIoSim4WZHrXD7c52j9AK0G+rpnOljx+9BwnPW1b0+KwraM76JEv5ox3ttnfGbHV7WC0Zrla8fjxBOqStrFIfLh+VCWRFBTFnOtH+1w/HCO9I4sTVrOCJ49PqZrAGS8dSOdINqK1y029vzn+zT/qgQ43k8Xl+OMrOUjbCf8vfb6m3O9c/bveNGovhS9+M2kexCvyitX6L/P3LgMALwXRMoz8Xo6UXn4NbILJpb1ihAZ5zStxS5vJYIYuPeVQXYm5XCRohbqSo1w1Ptf2KmS5lLVcslGvRsQ34/uqtEHgdPVYgshKuNCQaTePl01r9ZIX2fQ01U4SGKGDYBpHhO+vNs2ky1Zv+JsCu/BS8iJbHzjVG2HNJWtU568awaGVJGmzmHhhKbbDLdzqRgaAUmCTIKjSa0+1HeRa6/0oNG0dlKNLBER47pyC5KLdiLhCGNamr85h1VOYQm7G8v0V29DMqyDUWtqNhImrUDQ9yZm+2UOXbsP/CyGYWdqr0NfGgtW1BJM7yqGk6kvSc3sVTLWdMEYuqyB5woeR0tVhOGdNT9B57q5ufMOIMlfhuE3C9wgHy+sxNg7hirDBrK475qqV1nbUFQNYbEK0fFey3pfEc0/2MoTkyVRdcSrjaWD/XjIaF8ehQeoMDFfhucleNuH9VLzifdpUhVBYC5qOYHY7YfRpSzWIieeW/FpGfNG82vSQAlU0JBdr6r0eZiWu5GmXTFXZhNduPYqusAo2M1eBjazshvvb0PYM0UVJuZdukBwhoGhUaK3N7qkNFkLQeRZCIFUKirHBrF3AJlQt1U7gbK73NfO7EM3DWPv2B82GAyqRtb/ixF6OQq8OFNU2lHst/Q81qpJsfVhRHKYIG1jQbc8EKVgWmnPrfcXiDYssBGYl2f1+e7UuXXJFm15ohpY7Bl04Jl80xBcbWVW94Zs24TV0+b02C/IxgJfvGeqhw3Yc3QeGdOJJZhYfC8zKb8blHdVWhFm2PP13UpquQx4V+CcZ0VTQexxCJLMKLVRZe8yqpdgxNB3J4jbwRk5Ta1yh2fpuWCcC9/nVmitLS7ttaFJJ3ReUY4imMPqkuWIb++hVq7IaaczKYROxOb+KpusYfvgKcXPJI72SE25YzJ//nYx2u0EYhzYW/UGHaCWp+5sWeBo2ky7ZzOf3DesvlPgSOuOcs4c9+g8ku9/Ng4Dtso3tgmhyeaSZfbHh9p1TjBcUjeH0yYjO54btDwL/0yzCOtpkEl16Hn1Low5yorjlrd0XfP/hMdGnKXUnJV66EMzHAUGgK0eTSp7+KojtCm0sdW6QM0PvM0P/SXuFxDEry3o/Ilo55jc18/sN8agkjRvO8xj7IuX4/wxfEzA14T3rjKAcKCZf8uzenXCrO+c073E27aE+6rD3nbC+6dxdrTuy9Vzci5jfb+iMc26OpsyrhOcnI7b/PKL3NFwzXSRwxpBcbILiyrHaV0zfcYhOi5Cezg9Ttn7W4OUr9m/TDZuUVV9S7EhWNx1+u2L+PGb8wyCXu1x/bCw3wXVALdhIcPpNS288o5dUfPxySOd9Q+ckrKGdk3oz9REEYMvjhBe/4ti6NuXGYMqTxYhnH2+x/889y+sR2VmQakULu2FEC2ZfjshvN2ztz+lEDU9fjOj8JKH3xKErR7S0m+t4eL2sDhTVCPz9FWncsPYC/2cjRp/6q2u92GwqeBnex/mBoLhfMt5a8sXunEf3Rix/uo2qQuhtNtcEawT5rkZVnsmXPHfvP2c/Lvh8ts2zp0O2vyeRrURXrzjF+V5EOmmYfDFm8UbD4Y1zBnHJZ2fb6B/0gM1UjQoCVV0FpFJ6YTn9isF8aUqkLDcGUz692KH5wYhoGQLlaNaE1u/IXG32/HWPf61D26YJkg4Z9EobTmuNJkIbE0aT45ZYhUDVOUFRV0FcIzYnwjskG0amCz8nEiC9v2LUQbiJasSmPSYkGklbhyZfrMWmYefpxn1s3mxwCgKlDFXT4IVHEVAHSdylKedEnZi2teRVTafToaqCBRvn8UKQRBsmnVE0TU0UpxgTU6xLkAorLc3msaSRwdkAT9Ze0Y1TRoMEa1vydUXVtAGpIMRGiCWQHtymBSkiTxwpRlu7XEynVG1N21oiqWmdDs0h5YniOMyZ4xlkXXrdjCpfU1ewLPMrm3rbtsSRQTlorcM2Nd004ejWEZ2uYbVacnZ2RlmWoSncKmIZI3RMjSMS4cNZL8042N/BNiXFqmFdlIHTagwdE1PVLe2GD2htw6g/YGc4QMuGdWsp84rVfIFzjqqqSUwUdpNbcNaRxhmjbcWonzHs32M6nzO5mFBXNgDMvUI5iVcG5y1t4+l2Yna3tomVpPUtZ5MJygtq62jKil7WpcTj2oZYanqdDocHu6RxRDeJOZ885+XFlLPpFBnFYCXCCJwQKGvppimDbkqv1yOKEuq2oq5aHj15ShIHQVCsYlq3QYN4R6cT0e/1GG/vYKlYr9dMJgvOZ3P8RspXuRZhAptUa8HueMTh3g46VbRlTpLAi9MJcdSjaULTXEiJs6FhrIXmzq3r9KKI1lbsDPtcLBY8efoi7JBpjZNQtg1aKeIkpmO6Yey5rpFSBhlVZfEioaxzjNHU1QpNSpb2KKs8NOYMCKNpbItGEmMQcRyM6tZj8aTdDtK3HN04pJfFpHGEbwc8fX7CMm+YCUtVVXgXWupKBtnT3vaQN9+4Cb6irT0X0zkmUpycXIR2nAzs2SjWWFtxON5jvDOi14lREkb9Y4q8oiwXOF9T1eFDj60aEm2wVcP24TYH21ukURD7CGVo64blekVpFaUI7T1PaCwrA1vbW2yPBlw/2mG1Lnj06FFozqnA7XOblU4biWsq7r12m2Enpt/L8K2jE+8zn6/5/LNH1FVLmnaZLpb0BinWFXQ6HUb9HoNORjczjIZ9FvOC1ewBtWtw0gepwia0VkKyu73D9vaILNLIqEtdNYx3asqTnMa3SBVEXFoLlBLEiWfYT9kb75AkCfPlgigWnJ8vQ/CqFFEURGh1XYOQ9Doprx3vE8UaB3QyxWxa8OTpc5Ioo6wsvaTLqloTG8nudo/xsEuaaHSS0pQV14/2aMqW2WwRrPcypilrpNbEqeT64QGdNKKb7qAVxJ0Mh2E6n9E4h7MNMkwKYW3F8fVjBt1os7ZrdD/CSUUjHGfzOW1Vb2SXgiJf8dYbd+mkhmE/RQuJtw3bWwMGacrp5BxkSt3WRAichFgpdraH7O32UcYgZIvWCVUlWK1y0sZTlDUtnihNqJucONbs7W/R78V0eybIy1KH1Ir++YBF1QZOrTZ4BNp7rl+/xiBL6HW6SBUay520SxRlnE2m1GVDamKKssJkCYmKGXZ7HG1vh8cnNLWWmDhitspp64bGttRFRWUdo15GXdd0+zEH22P2dgfYtqaTDVHaU9U5F7MlJgpNaJlInGhJsw6jYZcbNw7BQpKlFHnN+WQZ5IZNQC34MrDjpXUc7O4yGHSIlSIWhnF6gG4kP/r0M6T2RIkiiUKII5Vgb3eHvZ1tjHCkkSBLesQm4fz0Am+h9gJbNZgkBDB1mXNt/y5JqulkMXGcEgP5yxyrG9bCsm6DIVxoTV7lDOMBh6MBe9sDPJaishzt7zG9WCLZCA60oBYNWnkcju2ta4yGY9LI0NY5u9sjNCnPnv8QrzW18Og0CR+4q4rRoM/eeESWGgSebjzAe88zLbBFg44jvA9ysjpfo7Fc39lm0O8Rb5rMRnnm3YK6qJmul+gkxrctETK09iXsH+wy6CVERkItkT3B63eu8fS7P0Z0osAoF5fiRpCXNpO/Of6NPy6bWkFqEm62hQ026kuZWPtaj7r76oY0mdrQSAtT+ABXEhl4dWO9DSmcAAAgAElEQVR5yUq8PITbyHE2rEqXiquW0qVUDLgKbC/RCs6Em3vhgmzH6RCmmsJRd8LNfjppyXcN8cJeCT+EhXqoN6IQuRGlSJpUk168widcNh6rreiKaWpjyXpPsToGWUviKZiVRzWXMq/wc4UJwVc+1nipabqwOhZ0H0mipQ83dJvR00vcwKUNHWB5zZDvCcwKzFrSedFehee6sCFUN+E8ydqT7yrO3xH47QoxiRh8IokW/qqZVW9Yu2YVuLJewnpPMbvfImpBNJfE54LBoxYXy6tzdjmyLxvP/KZhcRtc7InPFWYJ2csQHJtFEKi5DWNR2GDhXtzQrI6hPYrQT2HwqcSsw7n8lyVMl0209Z5mek/Tdj2yDi8gG4srIc/6MCae66tW6OpAMX3bwaBhd2fBi0db9D/WjD5qNqPjlyFheL3kY8X6UFDuWXxmEblCrSX734FqFILXeqCvwmivIN+R5PuC6rUS30j0uaFNNKNP6itepVm2V21Tk0gu3jQs71eo2GIrRT2I2flJ+Nl6I7h6JQgKo8Qvf6mlt7eirgxP7iR0Hkn2/0V91Q6VjUNfrHFZRNuPaffSvyKys2mQIwnnURclthOTLGtsaqjGSWiEwlX4q9ehVVr3FV6mG7FP4KWW2wanBefveMReyaif4zxMf7xNemroPwmbLIEDLGijCFU5Jvdjol+fMACK2rB+1KNNDOP3242oa4MdiILI+ezdiNWdht7eiq62uGswm3eI53EQMl22NWtHPTThdXhHkb9VMhjkxKZFScek2ic79cQLRzwNzE7hwvvVGsH8pmF1q6V6r6KdJIy/I1+1FkU4J7IJ1nlhPY9/M6Y9Krm2N8V6wWSrS/48Y//bArN2lCNN/8GK/FqGrD3F2FBer9k7mPHm1inzo4QPX+7RnvWI1u6q7X4pPrORYHYPuJlzNFoQSctFkTF7bZt4HlieTouwmZFsJhm6ktU1QfVGQb9XMDvp02aGwWeOZNrijKDe4GjMKpy31b6i/OUlvayitZLz7Q7Rs4i971mqQRhPXx/GZC9qmr7m4k1N/Nqc4/6SQVQwrTLOvtRyUQ7oPXl1Ti/Xxronqd5bc21rwf2tE4Y65886dzhp9xk8TALio30lbJONZ/aVisODKQfZAiMtA1Pwz6xkWm4x+lhsmt1hfUsuWh7+tsFv17xxeErflLRe8s7xU+b7KcXPDsP1alkDEXKzqbS4Izi494JEt2jhGMQFL9Z9JpMDyuHlxkMQZEWrsJG0eMMy3F9yd/uMnXjNokn4nrrO6rBL93n7V2R1NpZMvuSJDta8vX1CLFv20yXF0PCj7IjykyHR5nUSWrqSaqBYH3m+ev8BO9Gavi44qQYM4pIHz26gKn313Jl5zeJ2SrR2rHcVsy84bt99QdlqXh+e8W1zi+Wyw/BBvUEDbRjE1lMNJevrjne+/ACAT/s7zOcjBp+5gHGwly1wv9ksgov7gp2DOePOiqNszr9/7cf8t/Ev0v9f0s2mhqIcXl5nYfaG5/bdF2wna17rnPFG75Qfda7x/POb4Tq/DvK3NgsBrFk72ncL9no5rw0nHKcXfNoZ8/2Lu8hGkk4g26CLvIT0vGV5PcLfX/KtOz+jdYrPVtt8+MWU/iOzuc6EDd1oZTdtbM/6pufNo1Nud8+ZNSm/e/xT/ii6R/ujPYTzVxsZXoYpnsUNxcG9F9ztn6Gl5d3+U37fv4P78Xb4++PQ2HY6/B6bSJbvldw8OGcvW/Jm9wVbcc63T95AryXJQ0+bhKyx7khsDKoC++aaL++ecJDMAYiUZfqNJdX3D67EaN6H/4bf96/wWe2v/6X/7x9CKEKDx2NpwxhzJ6PJW3QrGcSSa6/dIs4ELZ5nJ8+Jc0ddOdq2pfURUoCzlqZu2B50Odgd0O93iRNYLZdczNe8nM2p6hapIlzTbkKKhCTtEkeKnWGP0TCjrVvW6xXPqwmNBFdveJgqCqw+GdFJNEcH++xt3yAyitl8yedPnrNarZFSBuu1VojKYX2EUZ5+3OHmjR26XU3dWOarJWcX51zMW2qvcNKAcDR1Q2ZSxqMhd+9dI45bmrLCWs90vuCzz5/gvcAiUdLjhUM6Q2wirh3uMOx3UHjuHO8zX6948PlDZss6cH+VwW3YQJXzHB+NuXX9iEh6Iu9YFys+e/yUyfkc4i5xltIULa1WCD1g0Oty6/gaW11PFHu22oj9cZ9nz57z+Pk5FosVUDcl3gmSLGU8HnH9aIfe5SLkHOfTOfnPzpFRRFG21JVHpxFKGsbbPd584zYaSLRgWRXYuuLBg4qL+YpYKZyUlL4m0jHe1dy8dUiaRAx7EbGMGA4SBl3Dp58/o6hbiqrGRBnWVlR5xZ3rhxzv79MfxGRpTJkvOdzrc/rygkdPTzGxYFHOUEaTyJTtbsbd28d0u2ngUHpPpEf0BzFlW5EXLZ6WuoZOFpivg60Bd+/cBN/QNRnWNcznU6Qd8+DJUwyaqik3XF6Dlp63v3gf6UIbzSQptgmvTU/Lom4oWxvYozoIqIaDDq+/9hpKWZT0xKMB47190vgJnz98hncSJx1pN6VyjjiNuHn9BqNRRhrFCJvQd45R0Wc2m1FWDR6B8IJUCKQLTGLXNljpA8/ZvmqBC+GRPqAvYp8gJbhqSZZoirzClY44DiK/1raU1RIRSaz0CNEijaaqKn7uvS/S6xm0r0gihbUxb/Ze4/HTE6bTc8AghAl8GddycH2X+3fvInxJEsX4RNBJNc7ucjGZYl1N2YKOY6xviIxh9/o+h+MR0rUYLXEWulnDreIaDz5/glYRbQtShPmZ3a0Rr9++SRIrEi2pN0b6t5LbfPeHP6Jc5eikE/iaLoyb37z5OnvbfbLI0zroD1J63YyHj59xerbE+c2NXFMRRxnj8R7XDw7oRRKJRySCbuzZGnRZzF8yWSxZlBeoJKZqG4zT3HntFgfjbbQBE2uoSnb3t8jLnB+8/z4i7eB8gUgitA8oh1t3bhApS6QNQjpckjLojmjrhofPTkEqGhxtK/DCcHx8k8PdHaqqIE0isjTm8GCPH/7gJzgXLPeLlzMipcmyjF4n4a3X7zBIDV44lLdsDwZsD0penrwkzyuQkqqBOI2RouHG3euMhz2wDkRNJ0vB9yjqitl6ji9qWixR3GG5uuDGtdcYbw+RLjQ8vXXsDIdoaajzFU9PJpRNBVECTc2oO+DwYIwkIBTcZnNkNBAMux1++tMP0GkGKIqmpSlL9sY79HsdlLe0tiYyETpLWJkFB/vbPPjsEXESBcayTojjiNdeex1DQ6wNJgqtemsE6s51/uJ7P0RKR2OhaghN+NmEr7/3RYyWxNoHNE4n5uZoyPl0QfP0nNIT5CCRZzGfsfXuFxj1O2jniCKJEJq00yWLE7rGMMtLnBKI2NB6R5Yo3n7nLer1KrCHjaSbdEmihPP5jOn8HF9HuM3URmE9SEm3t83B8TUiYdFRjJSeYWfA7tYeq3mJUhlCNGgvaRvP7eMbHB3u0NRrlBGMhtc5ez5jJtfUdRNQCtbRNTGtr+n2O9x6/RaJ3GCB8KRZxqjZQn3+gMq2SJVSFmuMAC0Fx69dw3hHtuHaZmlMJxtyeHDABx9+StrfoixztBQsqsC2vnbrBvnigkxCTxn6gz6LnSXns1Ok80RS0jYlMjEI5bh94yY9RVgXvOLoaItut+bsdMbJ+Wm41ioFBBRHL8nY2hqFIFp7Ip2QSMXucEQiNVaIzcZP+BygNSSpIk0jmrYkMRloz8HhNo8ePaapLa1tMCKIFZuyJIpjojRmMOpSVUucqEmyjG6/wydPHqNTE8SkrkUaSV6WbO/vYJIIJzxSKZxS9IcdzicXxJsNp8QE5IvzlgaH+lfZ/v+b4//Xx6s2bODk/ZWWZyxZXZOs3inJuhV1pTHvd6h7ArMOY/663CC/bGiQzu4mXNz3iP2SXqdkNumSPIoYfRQaNpdB3yUjshoo6p5gcRv8cYGtFOrcsPWTEGiYNjSHbByFEVkjyHck03cst+++4Fq64v2TQ5Z/0Sc9Cze00cJePTa1CmHk6kAz/2rD7s6UZZ5wdtKh96li+Fm7CXJeiVbKHcP0rmL4jRccZ0umVXbVEht/W29G3f2VXdoZQd0TTN+1bB3NGJuW7V/I+ejFLumfdek/afFqM369aQCbRcuLr6f4r80ZZgXbac7D6Yjy2yOGn4a/v+5tTOWbVs/ymubi6zW3r58xTlcsridcvJFx/v6Yve+FRnKQwNXgwnmb35Uk713w1Z2XaOGoneIHj6+TXKR4BcnMBtnOBtFw8Yai9+sv2DE1x90pPzk/oGo0qz/fov+5DQxaG0QxTU9hrOfl1zwMam4dTjjIFpwc9vnsYMz4TyKSaTCk2yyIhfS84Nmvb5F/OefG7gX3Bi95uNri6f0B0w+H7H8H2iyi87QMAdZQM7ujUb9ywVe2z4ikpfWSrTdyHu+NWM/7pBchZNHLhnIvDg3jQ8HeN54B8IXhC9Y24kenR5zoIUd/EhrMobWnrizi/M45A+VITcMozlnfjPm4d4hwEb1nLdEsNEntBn+wONaYb044NC2JbjnIFixuJ3w4uMXhn7VcWt3rgSaae6q+4uxrjr3rU477U6TwLHYTTq91WT3ZIpmFx+GloNnpBrbjZlxZVfYKhXDJsPVK4ExoWjeD6GrDpRxHxOcNorGhAakDAiPIfcKodGiCh4bco39gORrP6EYVN7sXLJoE/tZL/vzHrzN4+GqduEQ5nL2bsPW3nyGF57g7pXGKi1FGZTXrh4eo2hPPAy9Y1mHTYnW34atvPSBVDTvRisJGTLY6fOcX7nLwp2xa8PKK3XrxpmHwzRe81Z1zI7vgpBywE6/4+DdKTv+nG8TTNjQI6w1ewkhOvwYH917wTj88hmpX83B3i/g7XbY/2DSY/UaaNcg4u5cwfu8F72w/J5ItRlhmo4zltZifnr7B8FPoPi1xcWiwt5ni5Ffgq298xlaUM46WPGaL37j1If/4q+8x/B9f2e4vNyp05bn1808ZpysOkjlGWHIXUe895o/q9zj49mYiIA8bJ8J6Jj/neOOtJ5ytu1zvT2mHM/Rblgf/6C7SKuJpy/DJEttLKMcRq31F+60ZX9k9ofWSnq7oXKv42fE+s4+uk56HkCxauiD6M4LmK0u+deOjq+vA24PnsAv/cP41ek/MZjoiNAizpzkvvtbnF299RqoarsVTGq/4naP3+eOo4vP8JrvfF1ebBQDzm4ZvvvlTUtUwjpbkNmI3WvD1/Ue4/cd8/L++RZuGzeH4okata8b35nxp/JSOqpg1GYfJjJFe8735Tb779hHX/rihGQZBtlOCpguHv/SU7WTNYTpn1yzZMUvmw5R/+rcapv/wenjt5w61aegOP1rR+Y8Xf+V7qkjzK29/zH/98N9DF4p4vmGaKsierOnecHz14DFDk3MUTzHC8oX4GW91b/F7u7+JfuKveKuq9BRbgr/3m3/OSTUIj0+2fKX/kLs7L/hPr/992scxwglkKxDOkEzDel+MBbfePGGcrtiKciLZ8oWDE95//Q6dlyrcQyhoY4EqYfmLOX/njR8zqbt0VM237n3Af/Xp72KfCqJVmGowlWO9Z9Clo9hV3Pr5xxx3pwCMoyVvJc945+gZj6O7xIuwsZTMLE0maRPJnfeeMkpy3uy+wAjLO9kjvtx5yH92/4jjPxAUW4r4JLR6u89qVN7wH77xfX622uet7gnf6P6MdS/mL2/sw8NBmDLoBhSLU2Ed9b8w562dl+G58Jr/4Pi7/OHwPv/o019i56cWuWG2Qwj6VweaveMztuKcVNWkquZecsLO9SX/3eC3UTWvJl4I56vpwfXejEnd4XY2AeDv3/4O/834b6OLgJ9psrAhe7kZnGQFX9/5nMfFFpXT3Omc8Rfjm/T/OLpqLQfsD6QXjtltRZLW1E6RqZrcRvzy6BP+yck7VzzdehgatrLxm0mJv35B4l/r0FZLtxl/FQgpcEKyzFu0UBxsbXHrcJetnkaYhtJW7L99lwefPeTzhyd04g6zKvDlqrpge9RnfzzkcL9HJ9EoBR2tGAy6FOUaW5VURYs2gRnrWo8w8Oa9uwxjkMoipWS+Ngjd8MHPPqU72KWqiiAOIqItGm7dvc7ueESWNGgJcRyact//8Qc0dUOrgj0+VRHWC5R03L1znX6s6aaSBsfWTopUDetViW+gteEiG5kEheDG8QH7e1u0dQFpEtqfPcN0PuXjTx7T6Y8RkURIT74oOTrc5+aNY4St0SYEQ9tRn+V6m3X+jNaJjfk94CTauuL+F95gmETUdUG5njIeD+gOuvzoxx/w/HSG0jGR6WLzAHS5+/ohdw72WLcTlLAYEZENNHFyxNPJS2wbGLheabI0Ik0jvvjFtzDKgc1RKMqmZnd/zLxY8/nj08AqzlKsc2hvOb52RCdRxFLjfEtHSmSacnRtn8n0w9CWbhwmUpR5SZrFHF8/ZNBNWK+muKah24lI4l0u5mumT56gCdwy4y21bTnc22V/f0iSCqQEJVK6/SGt9Tx9cUpRlqg4IdYx5brk4I2bZIMEFYmNjM2iugk6VfSHhqLKUSJCCE1bBl7o8dEhkVYYIxGuBt+yvdOlagp605SiarFC4JSkdZ7+oM/e9piqWKOk3ci9YrLrHYq85dFHH5F0B5g4oi0bhPO8/fobjPq9IJBzgf8cSc/O3oif/uVfMhzuYfEURUVdWe7evcPx0T5K1BjlwUnqRqFMTG/Qw84XVLXHETi0EgtK07gQVLvakqYJeVngpUeqaCMZ0xRFTmwixKUMDUsSd3C+oXEOoxWYltbbIBj0Fu+grVquHezgbIFyDlqP0oYo7nHjWsIPfviXJJ2EtXRoHyRBu1sDRv0uthUoIaibEpVE6KhDWxfU3tPICNsInK1Jh13Gu1skscZVDdgWbSJaJ4k7XVrv8Ba01lgsy3JBh4zBaEhTLvG2wZjQZNk92GP0eMh8dYpCkEYpWksePPiEX/vmL5MahXAFkRCUFRwcHLBYrpktSmarNVmUEZuI5WLFW3fuYJRBOY/RMsiaYhl4t0bS2AYdxxSVJUozEt2ytzck0g6lQDuPNBEmTokiTTfLqNqAc5FKUdc5W8Meg2FKU6yRyiKERzlF2bR0s4zGtkilaAVoHK4tybKEdZ3TTQ1SelrrKIuCftrldHJK1ElpWkccG5xo8E7TH/RoizVZltGICusEZVuBAeUkNmzJIVtFt5fhhaIoS7TUDLKYumrROkjdyqrCxynKOawrSYxG4qiqCiNFEFZJhW0swluyLCFvKowytLYN8i5XM18uSNMIt5FX1W2LbRps4+h1+hR1g4gVmUyIkoSmrWjaDfO5scg4orYtMjbEaYSUgQeuEJtQN6XIV6TDzoa/JWmspSpK5rMZkdmgMbzFW8Wwn1Gvc6QWNL4h8govBFJKTs8vkFrR2hrrLVVrA7olySjqgk5r0JEJbOi2ZtE0lEUT3pfC0bQNyki0DBjfxWJFvGmhWVvROEldB3SNw1M2JTWSuNOjqXKUEBSrglVZ0Y8EEELuVV6yWuWoDTtYIAigGcXJ6SlxJBn0U2wL5bqkqlvWTYVDEUcGtGfZlmRpzHpZYKyirUuMEgijeXlxwaqqsTIMfwTGsyLWmuV6ztnZOfs7Q+qmIY40q3WB9TV501K0oDailtqF96+rPBcXL8E1SKXQpqGuPGvrWNQWRBQ2VKVjvs5xzvLy7AI/GARBpFJMzs6Zn+e0ZYFzDqc1zlliNCZOmM+nTOdzhOzTWlCRxtcV5y/n1M5ivcTpgC4om5q6rBh4R1E3DDpZ2DRGUixymrxmtViT9no03iKVJko6IBsmk3N6vR4eQdkG8eZ5XmB6PZqyxjdhMqJpHEJJJtMFu7s7pDKiaBukkDRNy3RZIlCkOqF2Da0LzW6DRMm/adr+23LYRFwFiZetwOxZiUsUky8a5NemfOvocySex/mI49em/O/feZfdbwuKHUn/cUuTBb7h7G7C9C3P0RdOuTd8SUdXPB6NeH4wYD3fRTU+BGVJuDmSjcenguI3lnz54DmpakhVw3dPjzmXW9z6Jw35XkS0tFfNL1U6Zl+v+Lk7j3iz94KRXnM9nfJgd4fP/ue7mFwEbntpqbZMCLtiSfGNJff3znhv+ISzukdxzfB/6zdJJxLhXo3It51wcyx+bs5/dOMvmDQ9AF42PdbbJ/z5k3c4/j8WlHtpOF9SkJzVnL2b8Vtffh+JZztacVb36F8v+fbr90gnIbyRm3FoL0EVDdd/65RfHX/Es2rE4/WI3zz+kAdbO3z6j++y+4MSGweOomxCi2n+zYL/4r0/5EerYzqqoogiXu+/5LOvr5j88CYKH0zzzlPtxJQjya/+uz9gHC0B6KqSz4sxv3z7Af/snS9w8G2PWV5yCS02EeRfzvny8Izj9ILcRrw2nJCqhv/riynDT6MQem3wDtGspdoyfOW9T/nG1sf8aHlMYQ1vj55z3J3yF4/epv/QBQauCqPpeilZ3q/4rbsfcis9I5M1HVXx7vApv1+9i/1hFtqEsaLphd+xfMvx9nBGR9d0VE3lND1d0Y9K3j8YkMwu+acGVYawWn9lyjhdsROv0dKChW9c+5Q/WN9ndZCSTN2mcepQJawODH/vxo95Wo3oqIrGKwa64G7/jD+cfYm9P5nSbnU2xvgQzkW/c8Yv7z+g8YrWKfq6oEkUH93eJfl9x/JWB9VI4osGvaw4/7WIn3/3Y3q6unpOTtSArqn4cH8HVYcms2gD4/Zy3FyvNwFwY6l2EpKXBfggnPJK0vYM8aSgGcSbUDa02uutJLR26xBSwysepqwDAlDlDX/3rZ9S2Ii+LgDoqCqEU+9Znv3eHeqhITktsKnBa8Hi9Za/u/sJKxvTVRWndZ++KRnqnD+tDq7a0peBajlKeP32CbezCbmLANiOVsyaFN9t0eVmHDtRqMqSPl+Tj/v83Pgxk6pL4xU78YrWKf7B4T/nvzy+Qe9pGP1224Y2lgz+t/e5+59vcac/QeI5SOY8yrf43bs/4fdffJXeY0nv84JyN6XpZWRPc578VspRXNJRFbvRgsZpdqMFL+s+P47YjLwrovOCptehHCquvX7KfrJgoAtGek2cteyZOX/Quc96PyFehGanM0FQuN6L+fWdzzire2SqRuLpqorPix3qHXvVvr/EWojWIUcVj6cjbm+fEylLWRtO1n3yPc/2B5amp1B5TD0IjWTh4K3xKYsm4V7vlEndZd3GvFx1iXjVdr4UnpXDIK7+bLVD35T86uhDntZb7Jglaa8ingckwmXoHpr1nufrAaMkhIm7ZsnKJmjpqEaOeFIF5ES5Qay08KPTIw76C4rUsG4jTsoBeWtYNzGrw4hkavEybDLUNxNW5ZJJ1WHcW7K2MbFoeVaNSFVD2wniOFVucB+Vo+loThc9bvfOr65nJ/WQp+WIB6c79DOI1lyF9et9jVkaYtVyXnbYjVc0XhGLlt97/FWcDpMRqnKb60dEM0xYzT2nox5Dk5PbmMYr/rh5kwfrcZDLtv4KcdFuGLv/4vwmB9kCFKxszFnd41k1whcBpXK5Wdb0giDQxpJ4Bk/Ph8S7IWTfidZ8erFDMpFY49FlmIS5xO9EH2T808493t17xtpGfFLsYVaS7CxsUNQDjQW6z2uKHUMy8YySnHmTsBOvMdLy37/4RT45H5PV/iqob7PQrO68aPn4swPeu/eQ07rPtXjKd9Z3mLcpeqqJLwpsHCNsQIHYjbTtj17cAwgbNGbJs3rE+jzj8PkrVIyXgu7jAtlYqo/7fOgE23FOR1f8D5Ov84OXR6g6fEa45MzWfUWTKnpPWx4+GaGkY9EkZLqm8Yo/efoa8dpfTenUHUkys+F9eJTwZDnk58aPyV1E4xXfmx2jS8hetpSjMLnjtKDuhHW3+azHT8eHtE7ybD0g1Q3yYUqT+oBf8AG/oIuwgTv6tOXJ3S7z3pKPxB6papjUXT55tsuuhHIoMevACnYmIE4uEVN/neOv/5X/XxxuM87rHFZoGidx2tAIQX9ri9393c3XBEabbXJ2tgdEWlFVBSi7uZnUKOHpZR2G/RGdTgZITNShPxyQdXphlMgYrA182aZpiKKI3Z1tdKxBOUyiQrsu1QyHXZzb8Fc9SGWIkoSs22HvYHdj6wxjwl44BqM+eb0xXQKhlVAjhWV3e0Q26tKqFqssSEl/OKCqKiIj0AokFu0s2gi2xgNsVQR5lNYIoYiiiCiKyNIeJjE4L2idZLlc0ev1MEYRmwQhQpAtIkm330FFisY7rAzNqhCSO7y31GURGLdJRN00eAGD0ZA406S9hLwpIFXkNsd0DaWvSONLcZJB4jFGo43AB4sMiQx4hSwLkjdXWQwRwkqMjpFSoyOD0p64E1HUa5SDssjxTU2WxEjhkYCWG4mQt0SRDiIzGXivcRRhq5JOFKQuRgm0EpS2QkbBJJ5EMXEco4QkkopuEgchmpYUTU1VNVghWVcVTkhq5/BK41pB1Thq79BJuhlfDWiNKErwyI0oTOFdg3UBJ9FUDoWhLFuEbYm1wgtHlMXUvmVRzDGxZr5aBLaytzQuhIK0FmU9vqnBa+qiDtIfFL1uF+89eVlevW+8dTRNQ11Vm0ZrEFx54ehvDSnqguKSOysE0gqiBrQPTdO6bVBK4FzLy8kZtXVobUAbpDIIFSGNCeASAXGS0NiWKIpQShF0R47GBTatTiKcCugDrTXONzjfblipNaggEXLOh/emiRl0+1xMzoN8zUq0irDWc35+wWKxQksThHbWBi41YOuGi8lLvA+jx1KC954nT56Q9rLwWJ3F122Qx1lHscjJV/lmTQgfar331E1DVTVXMisAbSRKC1Z5TtM0JElE0zQ41/Lw4UOkFMRxTJ7nVI2lqCv6oyHL+YzFcgbCkRcVJkkp6oaqqqjrmjiOA2aiqtndHjNdzIOAT4IOL3sAACAASURBVAWYvI4Mq2JF61uqusU1YEQcGu9NCFzPz86wrg3rkrWUqxwaT163oAx5XqJVEgLOCFq7ZjI5C6I4Yanbmta3WCyL9QKTRTTCb85tWBdOJyfk5RJnS8qqAjxn5xNeTs9J05QmL8kig3ANtsqp8zUXk3O8lqyqAqsUeV1SNx5lNHm9wvk2NBMV+FZyfnpBmddEKqEuLMJr1ouC1WqF956yqmhlkF+h4OzinLJpKW0TNnoizboqKZqW0/MpKkoBF4J3a5mXSxyWPM+p64LGVigtsQJWZcVqXYDUuKbGtTVlsWS5nLNcLpnNZlhrsdayWq0o8prJbI6LI4RWWAlSKVZlzrpcM5mfsywXlM2axrdXN1iT6QV5EdjSkYooq5Y4Tnj+/AVV3m6wMI6qaahqx/l8QRlegOgkYVWGMf7z83PKumCxXlHWBW3bUjWOZVGTVxapQ9hYliWucdhW8PzZKUVVhH/b8LSLomAymbBer0FZdKSYrxdIPEoLpqsZZ2dnQfTnPHVrmc2WzKarV7I2ISnaMLq5KHIu5muKumVdNSzXBacXE2RksEpRtjbclKaG8/US5xwff/gJdeOpSkteOarGMp+tqGqHMWGtDs9/kFRezGasy4LWh9C+qirmyxnPTp7TSVNcW+NpiaVGOs+w2+PJk2dUVUMtFOfrnElesFhWSBFRNh4rNJWTbO/skWYDzl7OyYuARWisoFqXTKdTFutFuJYbhfdhzWoaizGG6fSCfF3RtJq28Tw7PeN8ucYKjVWaFkHrJUrFbG3v8vzkjKIWTJcrVmXOYr7m7OWUyGQkcRzWLClxUlC2LY3zLJYVn33+mNWq5OIiZzYvuJhOA/pkg3gq8gptDEIIjDS8PHmJrS3eBjHii9MzLqZzbOOoiwrpPMIpsALh1cZ69jfHv02HsBvJz7pFNha1bij2HL9944Orr7nXO2VtI3ZvndN0wkg/EG4mJdgI3G7NL+0+4N3eYwC245wvjZ9Sj7iyj8vGY5PQam06gt+49SFbUQ6E9s9Bb4HrWRY34jBJE4XWU0ALKAbDnP9k/0+v/q5J3aX1ivzQk06aK8YubBqCCn7t1sfc651yXndDkxDo7K2JF466q8Jo8uam28aCrx89ZG5TVjZmxywxwnKYzGg7PgQ/WQg2VOUwjyeUey3jaMmNNIRG42iJkRa9UwbZSe42AYu/Mp5bJ3lWjZhUXbbjnJNyEB7HQbjhLkeKZBLaa/FZTpI0fFLs8XoWWk+H8YxM1mzH682oZvi5l3zTaksg8aHlZhYYYRlHS1LV4OLAC6wHGnOeY2NFNGvxTvBW9zmxaMlUzdAUdHSFd+FmVm5GoKOL8kpm9YXeCc+qEeNoydAUPF6HoAUCnuIS8dB0JcVhB5ygsIaP1/s8LHdY25ifLg6xNhjNxUZmZ5YtelkhY0vflDROoeXmefCSVDV484qZrAqLXrf/D3tv0mtbmp95/d5uNbs53b037o2IjEz3pqCMjKqEBExQjRAqCYSqkWAIUn0BPkCOYMIHQEKySmKAqgoKLCMYIeGqsp3u0s5MhyMjIiPitqc/e5/drrXelsF/7X0zYWImyMjeoxtxzz17rbdb633e5/97SJViu27RqvCteknMhg/rFdf9CeW6IbaK2as97nEQ9+8+EVsZLwfBFuCyPxNhUcHwrVNUyiKYjp+UNfd+xnV/wnfae2odufczSlbsvj2lWUTqh9HhqWQMv2jWIv7kiqvhlKnx9Mlx8VnA7YQjChyD67JVqDFjJZzVcq3zilxZtE/oIPesYh7ZlcJmPAgoBx7pka1bQB3G38Sy/c6E//Pyl4+C7bnbsUs1v3X1b/LDuw9JjTiR9W5ADyNSxGv+j+tfJRTD2/6cSkdCNvyTT/8W/ZkcyGQn8+MQuHS1mfPjzXMAPqjWAMSiUXsJsTP9++qO3DhSBT9afsRimPDL7Q33w4whW/6rz/4DihEXenPnqVaRZhlRP/8JL+8v+P7dtwD4fPOcp/WOr7ZPsTtBHYRTQTG4TWTxN+e0bxwpa5yStnnqNvzx6js8hgluxzHdPpw1gnXRijcvn7KLNftUMdc933RP5ZqX9ThPe1KjcSs/8qjhf3/3r3M7zHjwM278Ccs4YRNrmmsrP7OPaP9e0HI/nrBbtgzRcrkVt+bDVxecfQH9WAUxPK2PoWhuV/iTt98iZs0Plx+zixU/WT9lt6/JDpo7f7wX4YhD/mbK1eaED5sVb/0FTiU5dLmfSHn5Y/8zPObTL+F2O+Nye8p1f4LTkR+tPuLd+oSzHytyLeNWhPfM/G0gJMPb1Sk33ZyHfsqJ69mFmlcP50yvR4RHlMOs5jGxu53y4/sP+Be3v8Q6NGxTzQ+XH/P15gnzr/WR36y9iGvtXWH3MOFfvvoFfrD4mG+6p9z5ObFoSlacf+Fp7gOpNsSpoV6L0/jHf/4Ji92Ey0764u1wzmI3Yf6Npl6G4320V/JMqr9puNqc8EcP3+ab7inLOOF1d8FnDx/QLArZKqnweBTusPGFbz79iNebc17vz+lSxZAtP9k9Y/a1ZXKXjt8xebWWwLpKcfoyUL6ashoafDLcDjO2706YvcnHwLtYa5qHAAWmV4XV61Nebi744vEZ3198wvlnmWKkjeplOPLGTz9fo2Ph+//iV7nrZrzdn/HZ5gXXuxMer07EhT0RTJFKUK+lrdtXjm+WT1h4aafX3QV/ePdtnv6g0D2vx6DJMoaTwvrnGq7+5AWL3YSbbs7/cv3r/G9v/g2qG2FJ16skz8xN5PFXJgxPW05/At2y5evNE+6HGe/2pyzenjG5EpEaOK5jrsvEiaZ947hbznnsW9a+4bc++zVWL8/GUNSC6TO2z6Ra8fiLNc/+1HO/mvE7V7/Aj5Yf8UcP3+ZPv/mEOBFX7mGt1VHGousK7Y3is9/7eV4+XPDy9TO+uPqA+UuO6BOVoVkmYqMoCkKrmX3p6KLocK+35/z2F7+Me13LgdxOkFKH6qZjWOpf8GO++93v/r/6B/9ffv7p//yb3xXOmpSEmlJojKYicf5kjrGRegKuVgzRY9wUHwpvbxbsgySXBxnXWOeYn55Rt5bkdxgtSeRKJe7ubtltd6SQUdoRE3T7HU+ePuH0ZIKzCasKnQ+kotnvEq9fXzNr52As+37AF7Cm4flHH1BUxLmxzNsUcopcX74jJCUlqUXSFJVxoAyz+ZTKOeE5JWFdbLYb7u/vsbomJk/UCm8U0/mJJFO2GqpEKolYAtv9juvbB/ZDjy0icmVfmNZTJnXNbNbiU6CogVgGSoxc3VzyuFyTI2gMpCKp7Ci+9fw5ZAmwsa5mGBLGWb786huGTk6sMOKMKiVxdjKjqUUMJSu6zuPcKYtlz/3tI7tdpmDEDdoN+G7Lh8+fUVwijw6vTGG73bJarXhcbCjFoJJCKz3yJQtGJ7QzUAJx6Nnut9zeLRgGT9Ea7yPOVoSYcJXDWYfVhsrICfwwdCxXa5aLFZ1XxJgoUVjDyja08xl10xI95BSFlRs61tsNi8UjCukzxsT7EAessbSNwyRF8D0xRVbrHbc3j+z2CWVqlLUklUlx4PRkgjaKBBSj2e96eh+4vlmw23mycRgtIl7OYJSibjTWWbSGxEDK8ObymruHe3yvMMVCSRjjRl5KoG0qtLPs+z0xBh63K16/fM1u02O1oSTQ1mGtYT9sMadOrlGJBX+xWnJ1c8vjcoM1hgjEGHBNSx8iyilKFi5mjoXKGFQs1MqIG9HKgQa5EGIgKXnoKGVk+FRuTHjOmJJJR060oYRIGXpOmwkahbUK7zsyml038PLVWzb7AR+hqh0ZTczirlRa4bSWtPgU6TrParvl9voR7Wo6n8FqtBbXXgmF+eyEnCBE8CGwXG35+vU1pRgKjpw1SYPCYIqlqVusEo5nigXfRxbLR26vr4lRAq9QklrfTmaULhNCQleOytU8rldcvrtlue3YDQMpj6mgKZMGjx/2kMEoDVqz73bs/MDN/T2vXl/RTOekQ9hgZRlCBKU5vTgDa9hHmVFvr6549fKaGBMYwz70NLOaogzBJybTGdoYUlCUrNjvA69eX3J/v2K395AjVmuU1SijWO06clG4Zs5+39Nte25v19wtduhqQhe8iM1ZY41jvdmO7HBDbcDvPMuHJW/evmO968mqRuPIEUw2xKHIOl85/NCjjWa12XG3WvLy9RWpONA1KYigrZyDnNFakYtUDAw+kIbAzeUdj8s1JRvQFq0Urq1IPoFyxKIkDC0B2nJzfcc3b96RlWXbeZpJPTosG5aLFVVt0baAscSY2feF1bLj+vqeHMXha7Uhq0JImX4YePbkGZPJnGHwPCzWrDZ7Pv2zz2kncwYqtJZDt5ILVVWzWq1Qxowu8cBuO/Dq9RXbnYTEWSX3oZH27faCYGnbCT5CKHB398jV7S2Pu55ULLEY6kpYsb3v2HZbJtMTQNFtAqnXXF3dc3m9JCeLD4BypATn1RSLwcfCarVhnzKX90u2j3vu7h7p4yHNVipijFUkDVk77u4fWDxu2e09X718ScrQ9wGFom4cQ440sTC1NTEJ49enwN577hcLXr275NXVNU0zJ5VMCB6jK4ignWO93qKLJiVNCIXFw553VwvAEmIUnrRVuJJRRQ70/H5gvdpTiubudsG7N7fs9x2lSFhoSAOVc8S+w6pCzonH7SN3j2tW6y33DxvubpeELOuUKoKDKSoCGe0qhiGy3u7YdT0Pdyvu11teX99QmQnETIPB5gIpkELPrK7ZbHrubhekCJdv7rh/WLHoO5JVFAq5REoo2MqisiLnwnbdcXlzx92m4+rmXg5jbENIkHLGVJLcLWE8mc16w/1qxdvbe5a7PW+v73jY7TDW0Mce1zZEnRliYGorFJm/9x//3b9Qou1ff/7//fmNf/K735XkcynzS7XBn1fEuWPzHcVt0/LhdM3Tasvr7oKZ9dz2M8qfzpjci4jj9umYyr57oUgzWOcJczuwDBO0Krz6+jnN/ZgYrxW2z9T3Hatfall+rDmvO+au58vNB2xCw/JxxvPfl3ATULR3AbsVB+bibyiu7RlPqx1WZVoT2Kea6x9/gO0l4bwYJdYULanxf/LhBZOJpL9vQ00qmtd3F0y+khRx12XMIM7T7qnlU/WU7aSiKMUyzljFlh8sP2b47IT2XpKglVKSdv5iTn9h6T6A6+GUiGUZJoRseP3jD5m/EXdUsWp0tYqId/23DUFZTqqeuet5uz9jYj27335KtS0jLlgdy6ZvvjMjnkDRmoziy90HXFR7/tXlL8LXLZO7eOwH02fcXvHwaxAw7HLDJjXsYsOPFh+xvZwxe1vev5eNrNS+qvmsuZB3PDSPoeXT5Qv6L05xW9mPuY2U6tudJNj/9sm38LXhWbMlFsPb/Rk/uPwY+7aifpQQM5Ul/RsN3bllPTesQssu1+yj43p3wuZmxvyVHjnJ0m9xVpH7hp+0c56cbJlacS+tfMOPbj7CfdEwuRMHIUZhfMZ6WH3sWNqKRZqS0Hy+fs7V5gT14yntfcFk4YeKaJTJteN7Z89xTaK1kW2s2cSa7/3wlzn5iaHaFnJjUWUUqLRiWeYszg3OZD7fvOAxtvzg6mPcH89p70WAPSSi58ZSLxV/1LzgMs9ZxZap83zv8ue4/Ow587e8ZxnvA2nqqB960KPIVhm0l0R7cddaMJpcmXFcKew2iNNaSxvKvzuEQzE6FfORz6uy9Oflxw0r13BSD1wNZ3Sp4pvlBf4PnjC9KbhtIs3r0ZUcybbhVk/ZTQzr0LAKLa/WF+wu55x/LmFwzV0/JtJrwtywLS36A0+XK279Cdf9CT+8/Jiz36kF+1TUUXAuWtr4zemEXBcWecYmNNx2c25fX3D6ucb2Iy6qgE6F4WlLT8u6b7mvai6aPd+//oR3f/aC2WvN5F7EonFoYQLYveKtOuGyntKrik83H/H14gl//uUnvPheZjgzowgk7V6vE1lXrD6GrDSfbj8mFc0//exvc/4HDtuD0obmrsNfNFLJuc9cXsx4tBXr1OCL5av1Mz7/4+9w+hOY3AYR5BoLWvpx/jaRc83lpGG9nHK/njH/0vHks4FiFfVdjyqKYjS50sxe7xnynLdqhp0F7vdTrl4/YfYnDfN3wh8XrvIYmtUXUmVYnhhe9efgFH9w9x1+9PUnPP2exQ6g0OghyTVp0S2WbsIyNjzmhpfdE3ah5v7zp8zeCjYPRLxMExlnGzOlG2oeVcV2qOmV46svP+Tkew0mKSaXe/xZJex0p5i+0TxOG3bWyOG6n/Fudcrt5Tnnn8paG6dmPJyAapcxG0v6pYFPTh+53J/y/Tef8M31Mz78Xx39ufRZsRpGlE2YGibvFCvd8uAqrvoTvl4+pfzhKZPbgusKudYUqyhWvqt+hN3jjOWZ4aafce9n/OirT+DP5py8TRKW2WfCicPPxd3Z3GsuL1p2OF5vz1jHhi8++4QnP5RngdsmGbshs/6FlthIqOH8TWa1PmX56RPePjzh5EtFs5J1+nAYmWtzdJOef57ZvDll8zCHfzUXNMljBK0Ic0u9ivgzS/+sxg6F6lFx351w9XjGzddP4Q/nPP/9JOFzvmD371EExSpml5mlm/AmzbnsT/jy9QviD09lvekKelxXYjviWqzCdopy3fCuzHj86hz9oyn1AlSRsWQ7cf67Tg5vdYTJW8PuqxNe33zA4zdnnPxESV84WddzdQjtkkNYt1PYK8fqYc7jyzParx0v/iASZtJIw5mR+ygy1/fPDfq2Jn014SbO8D86ZfYTw+nLjPVyHxL0J9gmFKiohMH7w4qTLxUnn1rqlYjb1S5j/Hh9So39o5hdJbpvTlj+4Anrt6dcfF9z9lU6YidiKwz+ah3pnllQiv/i7/27f6F37b/Uou3/8M//p+/mIsp6DgnrrIhHaWA2nzOZ1mgFQ+hIZPp95O5+wavLd9TNhJAyxjq6vidrmM2mnF/M0TkQvEdZy939gvv7JTFlTHEUZVHaYI3BFsXFkzkl9xKCljT7nefd9S0pw+A9MWVCitT1KX4YmEwl3dqqQEgdi+WC1XLDartjte0IQPCRXAxYg4+R8/MznDHjd0R2O8/by3esNj3GtRSt6WOQaNgCtas5rStaq0j9ADHh9z3ffP2Gk+k5uyGQMkDBaIvSismkRhlFiDtiDKzXWy6vbhl8QFOjirA3K9sy+MDpyYyqrrCVI4ZIUZavvnnJrhvEfRhkk15ipKprVE6czmbknEijY3Lfb3nz7jWL5YJip5KMW0Brg7KOxmmMa8g5oKyUa+72ey4vbxiyIkXQujo6Hfuh5+T8jKpy9N2K3kf64Hnz+orgoYsFg8Uqiy8FW1XMZ2ecnAgCYz8M9P3A7d09j49bumhIOZFToDIVCdCVo21qjJIFK8TA7f09d/cLdp2nKC3sWK1JSQLSrFY4I8F1WmlW2x2Lxy0PDzuyqokpYpyFDJN2wma/5uT0BGsrYgj0g+dhueTVmxtiglAKJWd0zqMYWWjairqqGaJntd2x6zoWjyveXl9idIsxlol1GG2FsVgSuSQpU88iIF1f37JZ9yQadl2gqRv6EiklYdGEoWdat6TekwosH5esHjfsd+LqKyictjgMJit0TKDFvWi0QRdxAtiiKDmjnBUnbkzjn6Gy9ViaK4cDFOH0agwpF4zWqFJwBgY/0DQn5FJwlSYmKVu+vnlg8bgiZk1BkUtGIxtOUsQYS9VUmMrRdT2394+8fXdNRg5YtKswVsgw3a4n9Z52NiMoxW7bc//4yGK95d39EmUMJZVjiGFtKsnpG1m87XRK10U2246rywf63tB1CWjFfa01RlvSEKnqFts07DY7Us68fHMp4U9pREzkRFNXJO+p6ooY5FCmqir2w8B60/Hm7TUxKrR2dP2A0oacEjElfE4UXTDOEXxisXhksx+4uX6Q/5ehahuGmMaNoLAsVVZYXeF9YrFc8+76mhAVUUlAlSpQSibmgqtrQkhsVluWD4/sdz3bLuFTIoQEqmCNPOwSGZyWw52YIGS2mz33d0sGHxhiJqHR2oHS5Fxo2pr9viPlAWUyKRketztuFkt224GCbJRKKSijwBpcMWw2O3KGFCKrxw2PjxvuHlb4IG5VZQwxBnIptG5CCJk+Fi7Ozlivd9zfL7m+WbDtPJ2PVM2E/X43tm9gs/XYyoJR+CGx7zsu7x+4v3+k66QfSkHCuIxBaUffe1KCbtfRD571uuP+/oF970kI6oOSJQRSG4yyDH5gGLywnnPmfrHi9v6eEEexOEn1SFVX5BJRWtaHve/wPrHZdrx9e8V2PxBSQquGIYwn+mnAWoNSilXfMfhE6CPXt48sNzv6kMA4dFUxBE/b1DI+iiIXcE3Netez3+5ZPO5ISuEHTzJK1h0K6EzIWTAAKRFSZtftKRSCD+SU0OPcKznjtCWljNWWUmCz3bHZ77l7WJIoZCxDknYtBRFIjAVtiKXgu57bu3uWyzU3NyuGkNnve5SVA42cEw5FVpC0YgiJx+2OxWJF772gOrwwtCMSjjb4gdo6YhKszH7fEUKk7z2r5QprBaMwlEwuWYp9YqKqalIshJCJqfC42tD3kcEHlNZsvAcn4ZZRJaLVKA198gx9ZOgjD8tHrHL4kNhHj7YakZ8UurJElSmqUBIiyBrLxg/klDFo+hgpY5CoUnIQSimkIGMFbUAZ1tuOVAxFa0pOoI2MpVSwzlFCouTAP/hP/qO/Fm3/Cnz+8X//O9/VsYwbSNmwplo29t0HBn+WwcKb3QX7WPFme8bVV8948bueMLe4XSJXhmrRYYNi99xx+q0N1mTuhxlKwe9+8wu0X1WYHlKl5UAXyK0jNZrVdwpdqXAm06WKt49n1D+coIOi3mRcJ+XW4bTCbSOrpy1vywzVFG79Cf/y3S/y1dUHNJeW+duI20rpqThaJcRs8XMNXauwttDFipePF5g/mYtA4hQ6Q7Xwo4ip8XOL+6jj5+cPbKI4c2+7Geb3puw+NKMING6mraJozeVFTdKah2HK4zDhi9sPmPxZTbUpx+CuYoTpV60i15/MSNPErPYs/YRYDH/+u79Icw/1Kh9/v05F3JXGsvkos8s169DSxYov189Y/elTTr+Rg/FDXxYjm8nXp3N2lWGbalCK+2HG64dz2j9rqDbiCitWYXs5hKvWcPeiwkwT73anXG/nLNdTTv7Y4fYSQpdrETPsVgKuti8sJy+2fLN5wsv1BQ+bKXw5Y/4amkXC9tIXcWowQyFZw+O5RtlCQfE4tNx++ZSTL4240MY2zZXGDJnYGobasmoty9hidebPr18wvJ5x8jWYQ3lypVEFuqeO9lbx+EKj6szGN9yvZ+xfnvLh70UZT6MQjlGoWHB9YX3esJ5prvYnfHHznHcP57QvK57/cU+uNLlWEtxVKRF2UGzSBD8vDMlwuz0hfnZCewPGQ3vr8eeOaunFMd4Y1OBYP1HsQ8U61mzenDK9NLQPIw4wQ5o4UqvJlaVYeQroIVEqLU7JMbTqIIKoJJxb+XMmTR3FauzGy8/AkYFrhkwZ3a9pYqjWge684SG1DI1mHRo+v3lO+vEJJy/B9XkMkCsifikx06TK8DB1GJe5Wc3ZfXHO0z9RmFhobz3FGcooGDeLgI6Wy9OWh2HCu4dzrr95hntV8/yPDsL0eICQIZxYEYO0Y+Mb1FnkenXC/e0pJ5866jW0C3m3UbmIOOwUOmjiRLNtNFeLM2IwPPl9y+mrgOnSiJjI+LMK44ugR7xmHRt2U8XV8pT99Ywn3zdYL6FEzSIIpzYWbJeodnCnTriuGh67llevPsDcVjz5c1lHXJcJc0f16FFK9ibaW3aqZt8oFrsJy9dnPPmBxu0LtpcwLkbWt90H4szhukJ945i+0dhHy+RWmOB2J/edayPis5cDIIVGDZalahjuJsy/tlTrQrUZmdjjuNGx4E8N7SJTYsVOVVzHKdt3c04+d7T3so6AQo+hjABxoplcF0q29L5ifzljfznj7AsJeqrWcnBiu4QOhf7CYjtFtVb034mkqwm7b044/1Qzu07Ui0A8qWjuOrAWHQvTdx1h2uCLY7VveVjOqD6dMP9K0y4kVE+ngg7v17d6lenShLerc5abKfa6YvaVpV2IGHdEjOxFgM6Vol5n3FbT2YqdrzFfTzj/POFGU5rtEqbLxKm4TtESMsVjTb9v2N3MuPhTIyLvXnjjbiWmBBMY8T+K9tIw7Br6riZ+Nmf2WlGvx3BGo3DrgL9ocL0cVJggXNXZZaJdZE6/TpggoZJmvJdUjTu4nRxC6ChCc/sggqPb56NIf+jDOBGRN9UKnWB6Iy7Siy8Sxsv6Wa3lO1SWdaSMGDUzFOxOMf/cwNuG0x9r3A4mD1kwP0FcsLHVmAjWF+pV4eSVZ/5K4zYaM8jzrBh1vFe3iYSTkW3rFO1DxO3BbWD+Lo3YAOmrg5iqA4SJxvoyiqZw8jpRbeTnMIrZuwGFxo28fdvLuGkeM9WmUG0Lp18L+sPtJAitHO9XDh1sN655+b1QPr0Ut3p2Wpz9ZdQdNOgIKOk/YRiMhy9XCTuU4+9CAUberXIt67fO8J//g7+YaKvKKIr+Zfz8w3/0j8pms0FjjiWC1jliBFNXnMxrfuE7H1Hngej39F3kzbsrVvsdTTvFZ8W286ismDRTWmf56KMzZu2EtjU8rlds1ntu7xcYV6F8kaAwY0ixYGLm2YdnNPOWZ6ctfS88wnc3t0QUQ9bUupI+KJWkpvsdH370nKdnJ6AiKRZeXb+TctqxxFKNgp+yDmMMlsSzsznP5y2oxN39kofFmsCUWFmG3Y7WClagNVOmzvGt77xgfuYIxbPbr3h3eUkfNH4oGCPOJWUNRINShbOzCR/MT3jy9ISHhyWP2y2b9cjlS0G4sdYSkpzcgufiZMp3Pvk229Wa1XbHZrdi23VkV5NjwilNDBlja2prOZnUfOvF1lizlgAAIABJREFUc0wlG+a75YLbhwcpwdcW7z0hBFRlUXZKnQu/9OJjpjOHUoG03/Kw3fLm7pZqcs5u12FNTYoBbSzWGarW4oziyUnD4nGNKonQF+LI+ypoYsn40GOt5XQ2ZzJpmDcaUzf4buBucU83jgtlHH1MpBI5mZ1Rkgfg448+oNIQQmDx+EDvE3f3C2azMygaSyEUKZe3ppBS4m/8yi+zSwOvv/6KgibjKCURQsG6hhQHtHHUjUGpxNOzc5oM637PcrMmYokhkzQ0WmNipC81xlXUzqJz4tnTU/phy3a7JWlhNe62gZIVyohjOoTIbN6Sc8YqaJqKoe+JKZGTZesD0+mUFP1YGl4o1NR1g99vBRMRPNoUSgafCzFmstJkqyEqVNEonbGhkBExxVY1sYhAPQw9PidMQXibKLyPNHYCKlOyxzglQrd2mFjACm4h5oRWUuZrjGE6mbBe3vPxtz7k8f6emAvb7R5XTVCmQuKsBa8wqwwhKowFZ6HrOio3Yb/v8QjORGmH956SM9N2gtUBPYrPbdvSxx3KaHZbPwo/giFptIU4Om6VwlpoJzWqtIQQCUPEexl3pRSCV9hKyqeTCtTWknOHs1NS7kgKSjUhDZ4UPHWRtrSVozHC4p5NWvbdlkk7Y7vvUBj63mOMIaOom4qAR6mWmDxtI/O9cRXbxyXoiqI0GU0fEs7VUDv26xVtVVNbQ6U11mX2/ZaQM7kYUtaYqsWHnlYXUh+ZzmfsfRBh3gpKRpdCUBaNVAhUlWXXbXGVJUaPsxqyQmdN205k3VOGIQg2xFpDKtLXMQQgM2lP0GSsBWJhN+zBSck3RRNjFHZqLjTOUhVDzMLudAgmZrvdgpW2jzGicyErmS/GyP+3SoLTjDG0bUvX7aQSoORRSJdAPUpCIezart9Ru4bQrSmuJmWo65b9fs9kMiHGSB96VC5Mpy3DMKCUwVWK5JFwOguVVlgER2N0TUgaay2kQ/misJ+tksOO3ge0rYlZQu/IhSF4qspiK0PX7zg9eUa/k1rlA+YnR0PIipI9dWUpMVFXDk8WjElIFGdIKYlTXSn6fce0baicwg8Zqw0hRabTGdvBo1IGo3EGcgyQNQZQSLJ7sYIXwUggqDLiiIg50TQNWUdiyNiqJgwyb2snjLsheOrGUVQh+QGlHUo1JBIlBYwGN/Jgc84UIloLWocs1xpjlGoTkAM/I6ffylXEMBBjZNLU4kzNCtfU+BhIRZ6BsUR0gFQi2opLu6pqTCr4XUchY9qaUIS577SlNjUhRZSxeN/Lu4q1KKSCIGa5lmlbE/c7KmdIWRFVRBlx7CilMNqhgThIEGVSUl4qGYgF7awc0oSMM5aioM+Rqm3w3kt1Q07UriKlyDAMWOsoJdO4GlOkEmPo99imJmSP0YLPUbngk0dNrFRUYPhn//g3/pqR8Ffg8+/8w/+mTC8H2dyO4USpFrEsO8XmY8vy3xtoph7vLWldcfF94coNp5LI3V7tKVoxPGvoLiyLX4P4zDM97dm/m9HcGM4/T6RKSr7rjax1h4CR5a9Y/Emh/OKesK5orhzP/jRi+oJbeQlZ0sJm9FON6wr3f9Mw/KJgoYo3XPyhxXjh09n9e25n0SL+pUaz+Nccm1+R8tfZV47Tr5M4G7uEXQ/4Jy12FxkuKvbPDA//VsZ90BF6i3qoePIDRbNK4jSt9ZFtCrIJX3/bsfoVMD+3Jb2cMXmnmF5nERpiQfsDFuJ9Kfjq5y3Lf9tj7yomV4rJdWZ67VGxSMn7yInMlcafWrYfGpa/HqHKEDXTrxwXP47H8CO3jZhdoBgNWuFPHe/+fUt8EqQazmsmLx0v/mAgzA3NzUBuxMWZnSY1wtENrWb984r5KxEZbFewg1y3BGNl7CaQG8P62zXDhaL7oJBdwe4Vp19Cuxg3xAaqx4geErtvNaMoBHe/romTgukV85dQrwpnf7ak/1g4wodguNSIk8sMhdf/IVQPhhe/n44J3CoL1iON91GsYB+KVjz+ksGfFCZXUn5seuFV6iEKq3Msaz60b7aK5S8bqjVMbtMxWXxy66XKcJwjdhfZfyjlwbHWDKeKalOO7OL2ak//vD0iR3KloSAHHdvI7kUl7lcrQmW1FZepDvmILxBhipEROoZbNRJ2lJ064iNyZUUQjgW7GQjncshAEbHWdIliFWksYRfHbDrO+VQb9s8s87cDN3+rYf4mY4dMe9mJAPxTZbwqF3YvKlyXSU4RJpp2kfAzTfsQZUyMgqLdeCiF/vlE8CMjj7M/k1L1oqG98cJxDYnUOvyZG69xRK40+ohcsJ0IVG4r80/lMqItzBgOmPCnFj2KXybInDv8nfZZOLBRRJv+zFBvErsPRMTsLjTTW5nT9VLK6g9sULvP4zUk+idOkBmnmunNe/FYJSR0byr3UN3u8M+mhLnFz+QwrH0Qjq1KBT3IAYjdyNqjfaZ75mhHJEpszbgOy3g7BNPFqaG56YizSli44/dlp+nPrbj6NVRruecDK7iMrG8Qjmy2YwAfMLkb0RdjGfphjhSt8BcNYaaxnaynYaIJU8X0Jh7Hme3SMagutuJyVAliIyXx2Sm6C0u7EAa67QVpotJBYJR3NT/XNIt4bHOVC9pncY3eDwxPa8yQqRY9RSv65y1uE2WejOgd7WUehbk7MtsPLvbU/OxrzeEadYJ6KUGDpksipINgLuZOmLMLz+bbDfVK2lAwJPoYwimYAIfK4OfinD3whI9jsBfx2C52+BdzwkzWg9ga7D7RPXNMrv2xTeLEHHE62anjenpY96Sv8k+5XBPDubCOzfA+XPOAMACoVtK+ByRSEX10ZK5LW8WJpl7G44FQserIxY6tjIXYjoJ4l4/jAMVxje2fVccAvzCR9ji02YH7bIZ0FNbDTHjy1TaNa9cBKyTO3OFEuLPynEtkq458fDO8R+R0zytsJ9ijAyoBxZEnn6tDW+Xju85Pt+shKMwMgmJQCarHIGv3RuZhdoKSkOocefaoMfzzp99zYi2BqqlWZKNkXm4FYWF64Qf/zv/4X/6F3rX/Uou2f/c//c9KW9Xi2hutxySOad/GObTWkIWP6ePhJUiJaGksxDxuRiKmdiirKDHgKksI4iA0RgSISltizuRSsNoRo4g8rtKUlOn6nbhQSsEoSyqi3ocQSErYoORM0zQYNNvtgrqW8nGUbCa1deQofNdiCnUlYlGtLduhE1ehq3HOse89KntygkkzZfA76mom5b45SvgNgZmt2XZ7cOKWCSGRFGPbSGloTolJXTGtG7ahA23wIRBLJiNOR6s0JEmdroxFIexGZTJaW5pmwmq7obaOpGC366icCOpt2zLsd2gU0/mEzWaD1hatNVFrYpQ+zLYiDhGtCq0x6Cyuo6px4COhQCyQihaeojEwlpsbq1gtllxcXDBpHav1FmUNOY8PmlzIRpxERmlKlvFQGUvIA1U1IaVA1wtDVKUogqQqElRfFK2ryKUg8lmkMlocfClhXUsIgdrVDPs99US4iVYbioJYMilnptM5+/32yBYsGVIqVJXFOcfgeyprSIOntRWBjPceUzcMKWKyI8VecANqxCgQaaua6AesFhdeVOKOjjkRfEFpB0bYukpJEFd1HG8idIVcyCisLqhiUDFTlCErj2oMhITThsrYkQlrJXCpAEVjEiL2FOmT5EZw+TjefEwj91ZEeq012TlK8FTa4EkYpbFaM/RbKJq6qtBZC85kdJ1WlbBiU/JUtcUYRUqJgqatJwx9DylLv5iKaBDhrhRqN6IiUOR4wBRkcdupQhrEeSmp70UEvAzOOXwIYCGTGJSipMBE15hiiX2PHgOfsKPT2mgJLVRjaXgUQTcleSiLQJjJCIZjVrekVERAV0iJvgGVEi0KjPRnjJGmaYhF2MS5iMBX1VY43ogwVrQi5ILSVjASSqoEwuBFjPKeFAOTpiWPLLKi5KGVlLCPNRKihxEWs0+RUhRGjafyBoRzXcjKkoomRo9VWioffMBpMzqbO9mcVRVKSdumqMkKTAalCxlx7ba1Zej3MAqIShlykmvIUQQ0lQ/jGWHragleSinhKgl1tBgRPAGnFTkxirJmZBv3WOsk5C4litHocceox41IzIlgxOUtxpAMWR2/p+9ETIx+wGnZwAzDgKstJRusrfBZ2Kp1XY/uioIzGq0EwzAEjzIinOacsVraxzlHDiJcZzRt2/K4XYjrXBVUkTYppaCoGE0z2PEZVkrCmWZ8/ohNJaVEJqFNTfSRyjpSilRjuNQOT2UsGjNypBmZ4ErCFFXBakVUiVQgazl4U8pQUh7RLSIM6ywc3xAC1hiMLnSpw2RLVVXybMUSkh8dsJEYI65uZK0tZRTIZb0LKeHqCh3le0KRfnXWEMKAiZrKGorRJATpI4cYImYqZY+wfm0kIK6qG7qRKZ9LwOqMM9Uozo7vDdbgY8DWFbYYYg7yfClFEBG9pLFPJsJ7836gWH2sohi8p6qF5R6Sx9oGHwvGKGIMaANWaXl3CBGtDakElC5YV8t6kGTt67oOazUlATGJ8zrKJs5qMHWDNhWd7yhFnOdaCy+5UYY0BOrGsfbd2DZQUhKuPeIcSQrCWKVQRta+SiJUa2dRMfPPfuO//WvR9q/A5+/8nf+6DBcjWkmro7NEx0JoxdnznisnJamHz2ETD4JWOIiZwicQd9ZBoDxsXsNUNuwqlfebt0oRWj0y4uJRkDlsqECcUgcRFsCfOVKtmFwN+FM3lnTKRuiw+QJ5NzxsroYTzfQ6iDNoYogTLcxYBSqW4ybTn9rjhviwuds9t0eRomj1/nrse8FBpUL/1LF/qpneynW7bRKXX8xHBIG0pSSzF63el4Q7xXCimb/x+FPhxTY3HXFeiUBxYak2suHbP7NMryPFyCbT9BI0dLgX0wWyM/iL6vj3h9LRg1B9EGqPJfTjhn3yzSPbXz2nuzDM345Ow5DHfn7/51SbYzuFmcHuM2Em4mq98PhTJ+LdLmH2Ae3FjeeftMdwIODoaDNDOooXhw1ymNvjWFFZUAtmSOw/qIRfPDoU34s7jjjRVKtImBu0L2PIjIh92YnzOlf6KAb9tGDRX1hsX4iNOgpLBzHkKIaDuNHGA4EwN8cwv4NIq0IiTeT+hX9YULH8jBgTpvrYn3aXjn+Xav3/EHuKPoStiTAPo3i8CeLGjRmVMnFeYTeeXFtSa3CPstcNZzV5HKsH0fAQgqSHRJy/TzU/tEO1kX4+9kv3XnTyp+79vB7vndHJqX0+ltYfxhZFsB1xonHb8dAmZOx6QKXC8MFkHKf5KP6BHLzEqUWHUZgar1HmTfqZcXgYU/vn1VGs0bFgRjZrMYrhwo3rRD4KXKbPY5J7RgVhBh8+KgtmyG69rI/OkBpzZHLHiRFX/5AYntZH0VNljmPyIHodRPNDf6tUjg7oQ9+IqzmjfcZ0gdQ6UmuOQmJ2inoh7yNx7o5j6yBU5kof1yKzDwxPG6pVGPvq/d8d5k1qzFG0k3ku118terRPpGmF7oNwhhtzHLsy5yTIUiWOwpUIaCOCYxTjjuPup+ZQnLtjG+sxKK9aygGd6aVdVRbRNE1kPY6tkYOKVUc8bd8fakyEuwwiSP70fEutOa73anQOqwT9uWH+sjseMMj6q4/PwcO69tOiZ5yYnznAOAQZHgLS4lTWgTCTdmpvBsLc/czz6PDzOohgeHgGHgTs//t6oaIEOWanZR3apyNnul54WX9m40HFbHzejvd4aEdxqpb3z3ijjmGSB47r4b8Pz+zsFH4m8+pw+HGY6z8tEh/mlRkyYWaoF++fp6mV+Z6twgzv9caDSH2498P1pkbQOyDVEgeB8/AucOjfMDdjW4gAfFjXD2tHahTJifP18N3FQKw1OpWjoPvT7xqynspzEjj+jmwVzfJwQFiOY2Q4FUF4mBvmb4fjAc+R7zu+S5lBDpYO/w4Oga/vRfi/qGj7lxqP8M9/87e+W3IeN4lAhqau6aOEA6lSiEMkZfEva20pGcIIfM9K4bRBlYIyUlabCyhrCDHTTub4LgCaYQgoJZv9PJYqWyMCrY+FUsBoSVTOCfldSdg1aIOrRDRgFMGLMjRVQ4qyCYtRkthVyWg0SmlKSeJocaPzz1REZQBLKoUUE5U2NE1DiR7jtLD6FJjR4Rt9plYNztX0wY9J4Y48BmNppVAU6qoiBi+OGidYgD4EkWurCq2Ep2uNlOFYo6U8vyRKLuiqFhfyISM8ZVKCVArWVYJFyAVFIcaEcxUaRYqFbggoZcA4SkpUWtNoOYnSlSaWJEiAooXTiZTOFYowIn1kMhE3kDMVs+lE+ikdkvTk56y1xKyIowACB4FRSkZ9SFJiayy5CPevKIlY0mhq597/WwUpBXzIUrpsazb7PXYM9skxUVmLH7xsvlMhF2End12HcxX9ELDGUrKiriq6fhB+by6UnIgp4nPEWE1OGV2UOBhDptKGpAS9AaCsFoHKWGIUBIWtKkqBGDOuruXUN3jS2P6GQhgCKoO1jiFGJm2LM4p+v6WpJ1CkTLuoBCUKTkmLAGqMG5384hrTY0lyMiIuZqNkTI/7Nx8j9aQlpoxWGqwZheKMSYdym4JWihgSTV3LAYy1GF0IKWJ+SiTXWqOV/C7hd/YkLJGRwVyQsnqSjMsCRhuCH9DakLNcQ1ESi6bQmAwpJJwRTEfwAXBo50gxorE4ayVMr1gmuiF3HqcNKDkUKAqyUlhjyEn4WMYYUswoLc5INQrCKcdxPSkYIymewtxMoESUK6PAbpQhk48CXQwZlASwWefECT1EXK1lXo6HU7VrMEj5f11PSFGjjSYX4d4VBX7wWDTaKELwWGNASel1KUWYyymjjRW69FgRZcay/5JF6U0pU7LGuvFBrrSssSlLmXUpZBI+eBFdkxIus1HE7FGjkG2VJniZDxFhK8dccEbWSmtrVFGypmhpFzWKACJmChajlEJBCyPZWqIfUBq00uSYUEqEO6M0WIXPEWvN8dCj5EI3DOjKoozDaOFc+2HAGgcoWRddLf+tClUtY1xpIwIYUEIgU5hOJjKHyFKmriAGWfNNBqsdKYgIp7WM9RASOgmv1vtAjhlMwVgrczMxlryDUpYQgzh0rSIleRF3thYmWogYrRi8J4/Cs3OWlJM8v9QYkKgSRltKTnKwUWRe5pTRZCrr5FE2HgxQCgE5pEzIM9kZ8563aMcgyFJQMQFZ0DxKkUqm7z1VUx0RB6WANRVZCTM5je7X1EeatpGDojTyHI0aq40iqiQ5YNSGrDJZKUoR0R9V0EaEZW30SEkHW1XEGKico+TEpJVnoTMOo4w4WkdsQ2EMuVBSTZB8RI9tBoq6rjAjYiJHieg7UCdLKZSUKKmgxtrvqp6Ma1rCkNAK0IY+imNaUBoaqw1+GCBHfJB3kRASWmkUZnxuyaFi5SpCKXgfUORR+BYkQmUNOiusUpSkwFk5pHMaYywURUoRVcZDspwoKclhb4qoyoiDOSYclr//10zbvxKf/+43v/ddHTm6QVWWYI56JeWFaCRR278vu9eZoxirk4iz5cg+HDeoSgTL4dxSbcbS+1WQUuiDuBMh1+Omf5elJLqWEtYjskEe+oA4fQ67m8P3+DMnpc5Tg+3zke952NCr8VrjRFzBOom4qRjdOT4TZhZ/bqWEvRZHkEojD1TJz/n56D5aJXGVjZs0xs2+MASNuCDt6P4JRZALuYg4YJQ431rZkxxdQuE987VeS1vBKOB4ceSliUUnsH2iaGHwxqmwIM1QjvxT1FgSO3OEUydtMW72hT0sKAQVpdxdJdlc61Bko2wVaVazf2bl+xPHNs21JrUa22d0nw6XKW7OsbTWbZKUVY8l5dqXMVhM2JLxtP4Zh7IeEm7tQYt43L7ZkBsnomoUYbtaRzkk8yKSpMbQPATp08VwFHXC3FI/9Og4jq8kQlG1FcFcj2JVrjVm5MOaQQQ7BQinUYQA24tYkWoZr6bPYx8U7HIvfTKryLXB7aRPUiti8fC0Ik0c9aLHn1fSdpVGHzbtRubKYbyKGKhIrRasQS+8RIWUvx/GFxzKip0I/WOJuOkjdjtQKjPiFIQla/aRcF6/D6GqRofa2LbGS0lxsYJdSK2mfvDy+3oRGg+uaHMU7aWf3E76RES+8R3cv58PZkik1h6vQxUoTvowH1x7WhFnjuFZM/JKRxREKmMZvIzNAwLggMtAC7u46PF7xzJxHUUENYGjC/dQ7q2yfH+u1P/F3pv1SrKlaVrPGs3Mxz3viB3jmfJknpyrUjVldVdBg9SAgOYCNUjNDYJfwB+oS8Q1dyChBrW6BOoSFw3cNC0aGmV3DV2VlV2ZlXlyOOfEOTHuIfb27e5mtiYuPjOPk3d5g7qgyqWtCMV2D3c3W2uZrfd7vvcVSxAjr7VbuY8XAc0Q5w63EgHLDCKw2QTiohIv0W0kLIVkzE7G1EgjulUYvGll3IkgJOdbZeRzD+TsuNYK6Tz4C6ey+z4qF1Jjd8czNW98WVURUcze9uTG7uwbRqp6FMtzLb7Tuk+YNlKc2ZG5dit+1Ci1W1N0LLu1R3ybNbpPoqMMIm1xo9CmhgIOO/q8WFn/3a0IuGabZK0tUL3aUtygxXgJX7O3UlxQjNY8IuQphiLQ0NouJMRojZDpTpvBp3YUV2V9E1FfjpXpZM4Xq9FR5ngZfudWESNwrnRhDGLeaNExFkZGEdz0Mvbj1Azt8HKd8K8lZFDBEDonzx9JTlWkyKDyaGPBMGdkDMTp+L7DOc0Fs5agZd0PY2AivqeqIOtWyINNiVwFs39jk1G9DsSp3Y0rHUWUH69rupeiognQ7zvcrYAsYq/BznplFHZHOwUdGILQ9O74jM8ZbQJSrbHbQpyJLtAeOWwntgnJKdIw77KT4zJ66MOb+bgjbhdCq6LEuuHzc0Nlue6P1h2qQJjJdVDHQQgdvOOrK6F8xzU21nrX5ePWeVd8HTsOZI5CeyAFNdcW3Dq/KeoN4yPOzHB8ZN4q1G5MyHcYih/D57PrtBNs7TrK3nS45sdG85/9h/8/8LT9e3/vd3/HakPdNMRQULXlJvVEJ4JRoFBZTU4R8ig0GZTOFJVJOrHJLclBMAEQ2jH1Pd56uu0WdEVB7ewBUooYrdHG0Hat+Nv6ipJ7Smzld1aCQAqZVCJ96FBJoZK0Jcqgyhir6WKk9D1KaxSZWMRzsuvTEEjmoViKsuii8UYoytwHameEciISUosyQsaVnLEuokxBKyFlV+sV83rKxNWE/laE1RLQSoKmckooCqHr6FKghMRyuUfXdqiiMUWhUeQYMSiMho5AXxK+mZCSCD9KKXSpcBi01zjvhhZMEZdjyngrNOBIu2UUZiD16hzIqcfmjDZIiFMMVN6RsizsXotorZT4HkYifYrEElETi3GK1esLKq/RCqpqQQqKvgdj5bP7qiZlac81WmGciHgpg7OOPmYMHooazlkhxYgdgsvIGa0KpRgRa7JYAyitKDmirAhYWmnwlpR7tC70cct0OmHbtThv5ffICuNQhG6D1UouhFpjK0+73dBUFSpmutRDkg1+zgXtNUEFOpNJWvhfp82OsC2lEGImFiEoK+epBsLcOgNFCdmVpH08bFpKn6mrhhANyhS6rqdu3GCtkNCD8EgBkiIVxLOUPIxZ0FbCk4ZbOBF2m4bV6hbnJHk85EQCjPPYIjdgE+cxBUwZKptF5moJCuM9IYr4IOQlUES87XvxqrXWSLvvUCTIiK+kweCVoQ09jatJCLEXS0aZiFYWEC8r5zzeVpSiyMWgtEJ5JX6foaOkgtMVq9CjbAPesM1rEeBTGTw+pS3a5IIafHBiTNjhO6WhjT3FSMkFa70IIzmjTJEsuywMtbZWRMUs4m4pGZMK1lgqXVFCwtuK3AcaV9G1HbWbQjI4bSldxuLwRkGMWF3IMdC2G+rKoYsIyNlIgcLXU7bbIDcH1knRwlussWzbFmulqJSiiLBai10MxeC1Z2IqaHsJe1NC7Flj0JVGmSKfQytSypgsomcmQ+kxKJxSVFGjksaZCUpZVDZ40xBzizYiiGujUBqKyhLENFC6xgqJq5XGKIO3kFOPVpkyrHWUTFJCLyQimEJHgMahnEM5Q7dtqasa3VT0KeCSEMrTqsIMgQolKShKKG6tKBm2ndDbISl6MpW3qIF6TzGgkLFeO0+MPdSGUiLeO0zM8lwFxRQpkKTArK6FoHUejMZg0FhSm6mqudg6aEvKgVIyzhlCnzHaYVSFGdrxS8rkqHC+QTlHiT0xybqWjVznEgWnED/WnEQYZrA71aCtFYuZJC34Wit85aVwUTJGGaxT6AJWSWiazOmCK4XGaNlMKCNdMlqhZxMyiqSUWEH4hlIsCk+OhtpIuNxkUtO3Hd46rNIolSk2QWoxyqKLIaqMcpou9VIoMwanhe7Q2tANpLB0OuShACTzMyexf6j9hBhEmC1FY3VN7HtIItjqoc2xKpoEOCckVspRbDwURAs5ZmrtxC7CWmLfU1lH3UghE6IU0UxFHy0pa2KIOK0HkVu6AYzVDBk6TKwn9lFE5VhwykPWKONIStGhIBm0dtR1hc4JbwzeGPK2o6REUobgJewtl0TpeqzWODVsXEuPt4qqssS2l24HNRBFWQrNmcjf/lv/3l+Jtn8JHv/9f/dPfyc1mm5pcMNmfPakxW4kCduts5CEAylku0IaRCSdRPipX7WYAG4txf0wFw+9MDdUN2nnyzoKluPmFi1t88UIHTNuYJOXjZcJZScGuZt+KKorwtDmiRLSz9+IKCRK7iCMVELtjALZuOHLTu3EOh0LYSkCmE4M4pvME9nw6mGjLZuw2bOe2zNPd2BxGxG8RqJsFMJQ4iso31tx+6ASIXYQUorTu0Cg5BV+lYU8mr9ptUTJxjhORHRIE4u7DqTG0O1ZbJt3NJfKkCqFiSK0qFTo90RUSrUIKaaT9whzuxPARipsFFfNNuFX4tOLkg307FkqinYDAAAgAElEQVSgXwiN1c+FLPI34mFcBsLU9OL1mb28l45jW6nF3fQUL8JQWPidaJVqIwRcRha+Mnz2AjrK5xuFB9OVnUhhWxF+3SrSnlTUF/3Ov1XOvyJO7c5rF+Q4psZQvQ5CWWp2NNd4zoqREC970wrlGMXvdTx2KoNdB0wbhWZdNsRlJULH4L1YrIgPKBECdSqEPb/z0nTXgX7PS5j0Og7jYPiSiuEcSet8sW/oy3EsjuFhudZMPl2TG5kDdj0QzJUl1w6Moj3yIsQOx3TndZwHcnD95vvvQnSG41qciH2pNrtzKQRqJntDnFn8ZUd/KG3qZhOxmzD4YA5UrZLjNwo4OzFQi2COkvUgTDTTTzdg5HNW5/3QZjwUh2IhTYQyHCnkUWyUgoZQuGYUuGqzE9xGInoESyTMaDgeiV2RJDWafibrWxjaqbt9S3UT6fYdqsiY0gPtHeduJ1LpCNVFK+fV6V2Y2ujfXJ23u/MwjrNUaarzVsRdJd9hPP6jWBtnlu3RAGdNhzC0LpMqQxnovDizAqO0UkAazxODOB4nhn4xdGxOreTiaCUk9jDGUiVrcBk8hXV4Y+Uy0qDFaoozxIWX7zDMNd3nQWzNO1FSAe56SIAfinj2uiXs1xRrcDedCMa3Pe1pQ/F2ty6DkKEYKSRVl70Iyre9WH7s1SIWujfCfbZK1sOB4hQBz8j1Sb0RFRlsIbanHj101o7jf6RE+6UVC49BXFSp7MIWi5NzmL0WT/Y0XB+G4pt0AAw2FH0WyruX1vfPFxPGcc1AWppO6P84FYI3zA1palFIEF9qhKwexcBUyTUkNppuT4qAo7BahvmnU9kVolJtduuiTkKAmr6wPbT4W+lmSJX6uWJAdpripKCEUVSv487rPkz0zsPd30SKe1MYHYVXO3jqVleBft8JuToUesfATxk47OZ1P5cQtNjoXTHVtmUoSMnx65Yi7sehaNjPjdCucsshAvFED9ewjG3lXkPGstr9mQZf8PbADIVPswtSkzVqKLbeDPPSKbqFoThNmBrCVERwneU97Tbjb+JQJCykSqwg5LoK/UzTLyxuK2OsOOmKG8VfE+A//Y+//f990fYf/MN/+DshRawT+wKFtD8binhlFlBaETNoJ22nKEXKQneFlHDGUFtLDpmYomy89UBwoclErJGUZaOUCAVKEQfP2YwkShtlqQYyRyno+hacozgRCbz3ItAVEZxyDsSQ8KZCCXpFFwKlpMEns+C8JYUe7ywxB4weKBhVhjR00MrsiC6KwRSNQVGMJfQBlTUpZuqqJiSxiSgojPWkWMhDK72yVgRN7wghonTFetNizWBhEBPGKXJOA6Gk6fpIVTWElITSQcg7q6CLPcZbal9hjaWPkfVmi84KrSzO1aRc6ErAVWYgDYdW2CSbbuc9UWURHIAYe4w1knw9hK4ZYzAYlBEaqXYGbxyEzDZATIbbtpWFXInfkLaW9eYW6yTgCAwFKwEuRTxjrcpooyX5OwlNbPRIvEJT10KmYUghYkrBegO5oEgy9oyW9yzgaz94u8r4yDmh0KTcY0omdbJRr+saYzUpZ5Q14tesNShNykI6WWdR1lD0IP4Zgy8eO7TaoBQFsX3QBZw1CCtsdoFy2mgRrgo4XzGkSWG8JjFQnkWE/IlzhFyISQkhqhTOeXKSi6LWEtbmjMY4taOTc5FxSinUlYhOReUhfGsgW4vClChEp0o0dUWIAe0tWQ9WJMZRBksBawwlKgmZQqEGsbWyhsp4uk0nLRd9xHuP1RqNBl1QDmo3Yb3dUFficVlZt2v/1klJCJICKISSwGpp6dSaFDq0FsIwW8M6B7QRm5SSRCy0SuhiozSuGELpQAl9q81AGimhTo3ctUgFToso3G42KBTeOWAIFYoZYzRd7IQoLWArj3aGWALKaDaxRVnIKlO0BI51qSdrIQ21KiidwHm2XaRomFdSbMk5YJQQm9prQuiorcEYBbrID4mcI8aIH7UxVsLwjB3OkVDBsWSSzmAsoQRilrJk7gbC29fEImur0haTwVlNUonK16AUXeoJBGnpNh6sZ7NpqZuKXDq0Espdq0IfOxmrWvxsSx6sUwZBkALWeRHGjaZOmsoachFbgtgHoSI7CfsKKUmHRMp4K3SyNwYVI1or8eAlEnNH7SuMLpQcdjS4cULnqqKwbijO6QJFY7XMGVOUCN05oooaiE6xuTAkUk5oa2jbIbRAWwoRVKEPHXVV4U3GAH3sSHSEKDYFMfcYB8qAtRpdNKUElAbV9aQcqZ2QtCGJf6HSCm3kZlEsPSxEg9UKrTJGDyRrFqsMpQspZVJJJMRnNcah1SUmvFHYKOSC0kYIda3pYkBVni6LNZEbwjydNlijySFgVKakgrdi7aCzFC+UE//n1Xot61ro0FYRdaEoQx8VGItSGuc1fZ+pfENlHbQdtbO07UbWkNqTMxIYWGRd9bZCoWgqKWpNKk9OEvintKKQxD6iqUkpShGsGKy3eGtRKVIZS+Nkk26dY3OzYb6YEwfKVSPf1VmIfWBWT+SmP0Sauh6u5zI2lB4FpyIFICcWLpUxlDjeEwybCgM9Ce0dURVCTlgLVidIceiC0OIHXoQKL6rQp0JlDA6DN4rQdRilmEw8y8mM7e0tJWe8kSKYyrJ+O2fpU8Bozd/+9/9KtP3L8Phvf+87v+PWA104CHYY2ajLxlE2hxJm80ZMs63QqHYTSVMnG7QwtCKboZW9LwMFJhtaBqJwJGHG1khpzxx8W+dm1zZYXaedwKODtH6P5FKq9BAOlHfUnirSPjmGqo2ij7Ru2oG2GsQ13ghD2QnxOm6mxhAslUWARclruqVsvkaSMzuhsMaQIgZaKA8ttWjF5GVPrmTTZ0ZCN+adcORf94Sl2xGQqrBrm61ey8a4WxpyY/GrSPOiHUS9wec1FKrrtKNlc2V2rdlotfOCzd4IJbsV0dX0eSfqjSIsSlrY+4WQRmhFfRmxXWHyvEMPn1kPbaLN862IT7ATB8c2UtNncmPleAytoaPALe3ZmvbAinAy0GzZy/dk6HiLgygybohH8Uw2/WoICRKhTIQrEUD6PSdU2kCjys8gNgV5zWjjMZKNaWIHmtLuxH+VRTQrVpEmTu4n/DiO9Y7QEpH6jUCyo/MG4ahoRXfkcJu8ay0e2611EKJ2JPDSxO7G3mhVMIpP3Z7Q1mMA2fhZcm3l74MA3u1ZsS4xI+2cSY3d0bV5FHPGVu/heIe5oZ9LSN4oKIa5CLhjkFXRirDnqF+0hD0BlcLS73w3y3BfPY4Jt0k70ShO7UBAyuc0seCf34KzYp+Rstg6VHo3B8NEPCFB/o/i9BsycSAIFexE41Rp/FULSgsZqd4EtWWv8TdhV1xJzdByPliq1BdhN95kDkl7/the/oZwhOoyoApszuodsT/OJxGvEv2+39GJ448JmeI/N1eH//fza6RpRfgDJYFTbdpZT5hOhCbbDkrnUAzarXMDXe9vwi5QaSTlq+e3Ql4PY2qkuP1NGE7Y8EcSC5JRBBSRWr5bagz90kohLw5i82CZoQfS2q57dMxSjJt57FYEQsxAKE7dbv3v993ufdLE7s77GNyUGnldHqwlxkLLTnAdrjFpIrSl3ZSd8JqdeBNLcc8Mgq1YOfR7ljCVOeyHoC+7EcHPtGkQ16TgU+wYyjaIer20x6sihT4+N/5GEVgoZoi13hHeI3E9ng/TZbl+hCLXpSF4EqSolqo34ZU6yDmrXkfQGn+bhzVSxFz5U/+cQBzmQxeAfVNQTLVm9lm3o0WzEYFWZbAr2ZeghYR3t4mwsPRz6XDplpr69RCKVonX647EH8YlWtEeOMLc0h5IMaSfyvV1FHfDVIj78X1TLYIoSryA2wO9I2Ob88D6jtsJ89nKdxXxtrA9lueaKJ9PZ3bnYSTxR1I2DJYbYSbnYWfVNBSV/U3adTrYtYjeqRIyONaDzUMQQVnobDn/cS5WNGFucBuxe2r3Nbf3DfVVHt5Lxt8okKeJxq2E8v1FRdu/0J62f+s/+c9LGTzvtC3oXCgxkIl4X4uHnptIuJUxaAN9F/FDEJC2Ho2QNilKsIcxhk0/eODljHWKkops8qKIrwyv0UYNQVKBppoChZwjqYgwGEIkaxGIc+monUea+zMxw8LO0EqxiYE+9ljvwYivVezEExKjUUaTi8IqLf9eeUKMWF+x6Xvxi932pG5LU9VU1rEJW1TR1MaTrARm7c5lLuKVqe1AVInwl1QavA2TeOVpNfj2KvrYYYwISWRFipFkLY1xVEqE1FIK2+2WZjEB4OZ2xd7yUMJ2KEMAjGI6qek2W/zEEinctkKXlmSFhVdBhEVtcb5B50i/WVOMxntPnzKonhQVzk4k9V3LwjBxlbSOq4JG2sjbvkMbQxcD1nppWc3Sbtu3HXNjwWuuNzdMmgVt21MUOGNlDA1kcIgdzjm2KmIpVFkRcRgn7bORRMmZ2jhcSnQhkaqKVDK+ZLQGX2nabYbBn7EQBo/OMvgVO2Lq8W5C7jsqZ2lzHjyZO7TyWC2V3VSSBHUZEZm9rYYQpyHsKhu897TbrYhaxkCW90spofyEvl1RYsfecsmm7xh9VkMXqU3DupN2OmcsuSi8M4TYYko/fH4JkgJpEe6DeBOP3qfWWhaTGnLPuuuFbNWWUICY0cWQCFhtQSVpER/akYsS4Xq77ajqWuZRLruWfWM0VouAZwrErjCdVfRZfKSz82/8htHiF6kzXb/hcH/JdrsWH9NihKZHqG6UIkcRn9EaX1f0mxWT2tF4Q7/pQFs22w6lNMZVFJ1o2w3OWmazCd1tT6UKewdLNu0WgOvVGqNrlE0kFXBlAkgRQFlHjD3LvSkHixltDLx6fcmmC+TosNazWa+Y1DUqSTgRwHIxk7WqFDabDX3fo2wlJPFIoye5cZsZT4hb6sphlSXGSOyHcSsIJdtNZDGrialDV0KWVtqTVfdm3BgJ2GqTjCWC2I+UUmgqwzZkSXpFyMUYB19cK+dFDT4GikxlKnKUm0I1tLJnCqUESiwoDMZLYEaMPd5pjNbMqor5fI5RihQi19fXbEIm5ARak4LYHOSc8d4z8Q6vFVVt8N7Rdj3Xq462C7QpoJSWlv+YiCpjBy9S15mBzk9sQxQvYqcGn+ydMypVJa8lF4qCpqoJIdH2W7L2xFRwXpNjoLYGikMpmDSO/fmM+dLTtx2Xlxsub1YkDRQ3XLsU6ETlLS4EnILD0xMmTcVms+Hl+TV9hE0IGG9IKLyrKSmgs8aoxN5syrSusE7x2WevWO7v8eLiUroTBm9fryqs0hJeZhSTqWfSzLi+uWK9aemCouuDeJtZTTKGogqzyrM/bZj4irvH+5A119crnn/6kq4Ussq0fTcUEApVU5NVRkU429tnOqnZrm+4vL4lZU0wmZSkuBnCeC3ITCYTjvf3mDcTVNG8eP6K56/O0U3Dto8kBZX3lBioK0+7XfP4/in70xnTWcPF5TXXN7e8XnUkFZBpIdR4ThIYWdnA0dERB3tLZpMpr84vePr0JTFk+lzIRtHmHm8nODS11cxnNXdODqgrz3q9lte8eI42FdpXbIpYmpgY2ZvUKOC9t99Bq4JTivW259X5JZe3t5QsHUIlK+nYKR115bh755jD/SWqZHxOdG3mp5+84nW7ZlM0WWlcFsK6qML+dMr+3kzWXsRz+fJqxdXVDV3sxKfXOmprSCFgnWaxmHH/7BhUpvYVuVds1y0/+/QZMRa0soQcdut7KYXf/bv/jfp/6/7urx5/cR6/9W//V+Xzfm1Dc4ZQqINPaarVEMSjdn6McSrF9dFnUkivgWapNPUr8dsbfRM/7zkZp2MgUtnRhHYd6Q7fhBCNv/u816RKeUdcjT6jq/sVxUBzkXZeecBOFEj1YKSlRw9Hvfv38bvVr3q6A49bSVhWf+DpFmaXLD36143BOONj9MocPTdHEW70df18UEyqhGTceXzC4P0Z6Y7qHcmqcqG6CLtjMXmy5vatmWwoB2Is1RIcVa0S/VTE9smzbnecx8fogxgn0qE4BryEqVhAwBvBcST1ilZsj8Tj2A6BVyqXgYhWuOuO1Lgdmd8dVbjbyO2Z+IDOP+loj72IJUXahkfqC9h5J1bnW1LjCAu7Oz6jYEIu9PteyKkhTGnnu2oVYap3YUCj/+PozTieB9PmnddstzBU10nE9cEDNjWjn7D4tI7/FmZmF+I0+h32M3l9eXPq3ggpWkQglQs3j2uai4FidTJ2u31L86L/uXES5mbnG/l5/0wYaLTb9HNzLdWG2zMRxyev0s4D2a4Hj2WrdoFT4/+R6oFIHEQLfyl+oTq8CX8Sr0gjlPvgWWnawubY4td5FwK0C82qzS5kyl9Hrt6rqK/ks4xjV/cDLQ47n11g57U7hhWO3tj1KwldGtcK97olTTybOx6/yvRzzeqhob6QYzR/0u8Edx2ErgMRc4oC2xZW9w2rR+BWiv0fJZrzsPPYrF5u6E4mP+ffvbovRKNKheYi/1xLtF3HnztHm7sVdpMlcKgSoc12QlqOxGh1vmX9cLoT6O3mTUDauFaO/rH+shXbANjN3/bQiYA8zGEd8m6MZi92E6PgXrQiLMZQqfzG43n8GXxMx3XHtIkw+BevTw2bU0VxoALMPiu7oLRxfZJikBS9toeGMFWEOcRJwd0oZp9lquuEv+oRixhZG8xAROchrC55uU7UrxN6KJqNQv/46BdvLPdUlsA6u5Wi1BieN86hbt8N3x+2B5rbh9AfR/TGMPtEs/xZ3L3PGEwJiODcaGKjuHkbwn7GXWn2fih+7eN1ayxYCB0rc+rmgaE7LCQPR98t3N7THH4/7OxeVC6EmSXWskbFGrp9Rb8vQYj1eaFaJSmKDF9dd3Jd2541XD+y9PtQPljJZ302Ze8HEnCoI7vjMPqmjt7Or75u6Q4z/loz/0iunW77+SCyvPNQ3R4Yrt+DcCJgx/SHFUffCyJwXwR0Fwl74s3c7Vuqq8jTb1e0DwLTww3bj+ZMnmrmTwaiePCc//z1tZtrbt6B/lHH4eEtFz854OB7CtPJMdah7IK94kTTLTSbU8X2Sy2zxZbVxZTJjz3H3w27+4v6Vb/zhb55aCkabn9rgzaZadNxdTmj/rBm/vFg07MtP3ddaJeGqy9LSKjWhcWk5fVtQ/WdOfMnifpcrC5GX1+V4PqxZf2gkO50w0LhmH5smH+cd0QvQLdvMZ34oN+eGW6+Ic9v5h0pafrXFcffkedkqyRMTbG79/lnf/+/+IXute0v8qR/dQ8hWPzQgo8qGOPIqbBtRWyLMRJSoa4ceTBf6oLQrI0S/73N7ZrJbEoWKBBTkFAkUS7JSYlgaeMQ6GEwVlqMndXU3hKD/M7VFovehZApY8mxpzIeYqGqhouqGYK0ABUTtWko2dLFDaCIqlDNpuQgCc+QyangjMUwELIxYslYIJGppzNSSqz7TtqtSfiqsA0ZaxQxyEZY+MlMVgOBVzKJQKUsqd/ijKErmhwSxhq0KtixFVkbSRivKkwxeCuLb587fFOzmB3w8uVLnPXMFoesWwnCqa0FMr4ydNtrDg72ubi8JFo1WGwZnBf6V2kryeda7CiaSkNQhKzotj3WVfR9oao8MXaIEahUZWMaxMQ+oqwlhoA3EHPEay2Ecx9ZLub03Zap1zhTKKXj/tGS1U2L1RC1Zt1tMcYJZayU0NI6UyUlvpLKUltN17YYZwlaCMIYIylFijESdlWksjObeq5vLinKoowlpkxT10JqamnXD+2Wuvbk0nN4MGNSGy5eX9H2AeU8KWsZo8qTck9VGZzWeK3Y29tjOrWk1NP3kVfnV8RuLd6zShFDQBkRtmzlKKnj8d1jdClo4Pw6Sbts15FD4roLYn1REOEkRu4c7DNrjqinlpevLlit1vSd+AeXKAF9KSWU0lS24nAxYbmo2V9OuFlvuLy+4er6BsGZB3J5EHxVKUznlqP9A5aLOU3tOb+44umzl2xDxOB2gW52oCS11UwmjrPTAxazJS9evKDtE89fXQNI2voYYBQ6lM7sLWpOjxfMZieo7Hhxfs3V6pbbdUulLX1JVJUbbBd6nMo8fHSPiTdU3tHdbrm5ucWkhNKeNif6EHF+SimJ7bbj7vEeZ8dL9vb2eP36lte3t8TYk5RhE/qBpBaiM6bMnf0ly71jTu/sEVLAW4/7JPLi8pZ1n1FJMWvmxH6L0bBd3XLn5JizoyP2FnO0tWy3Lc+fP+fZy1fYWoKValez7TqqpmG12fDWvUP296c441m3W9ZX11xerMTpIhVmtYNcsKaixMismXB0uOTkaJ8QAn3f88nPnpM05CwUuKnFc7RrW5zyVAZS6MUywVSYgZTOg5VFLoM1QC+p3jlnDLWQp0M4YtYepZJ4epIHWxqxUpg2E+6fnbKcz1C2kELPZKL5+MlTtNUoZ+lClIuXydREjg/2qeqavfkc5xVdF3j56pJPn79AlUShEHPBOPFDLSnjgdOzJYv5lM1mw9X5DetuS1CZVAbq1GhACkNTX3Pn6JjpYkpTWc6fXXFxfcGqt/SpJySYVBNK7Mndlv29BQ/PTlgup1ib6dqGxWRK+jhxswnkVJi4iqQLVTOlW9+w3J/yztuPZD4WqI73WVYLPnn6FFMyfdZioaEyhULtLUeHhzw4PcGUjNKJyjd89uwZjbOsWymEjYFnKfbUjefuySHzxQTvNQebmtt1x49+8ilNXdEVoeWtseS+w+rI4XLO3ZMDnA5UquFgOqNdbXn68kLaGhHa01oFuXB8sORkb8bRfIrWCm0W/PTj5zx78Zp+E2mahs16i7OG2WRCjj1n+yfsH8yYNI4UOpbTR6Ayr16vsAaUNyQSvnKkvOXO3X3OTo6YNzW2skynFa+vp7z4oz/FNTUlFwoWrTNNNUPRcny44OzuEVoVvCs8fHhC6CKfffYMpSxuKFKpFDHO4SeWO6dLlguPs5qTgzO0htB3XK1W5NThlUUnuVdx3nByfMB0ZiU8zWh0VYg0nF++Es92ZSUIUfo4mDcz7h0d0NQe7zR0Ab2wXF6+5rYNVFmTsqwPbbdm1lTc3V9ysL+g8mAwhKbGUYhth+kVXcoSLJkSxgl9frS/x3xRYbR4ENtcc5sVtdFcbTcUB13pqKzHDhD+Xz3+cj3CQNilagh1iYXqMkjLZyviRb/ndoEq7iaiYqY/8OKp+qqlO66HAJS8C2gaW9EleGUQVHdir7Qqx4mmW1bYTgjUsQ1zDC8pWqFTJi4qQDb3ANtDKxtqLxvrbl/uWf21iEa6TfTLWjaxg5Ak/p7is6eH4JFUG0kQD4Z+z6L7wuRlGDxnFWGqMK8LqVaY9k26tMoFFRJm8MzTQbxvzZBe71dpl1S/o7aGzb1KhbCwpMrvSCR/m+hnhnC/Yv9Pr0hTT3tnQvNCfHH7PREqwky8+a7fsuz9OO58MsVSwezERH8dCHOH6QrtqXSeuU3Gr/Iu9KVfimj6+YcdRKVR8DGttHubVmhnFQu6DawfzXC3EgoWG9mAvviVhsnzTKw8tis0LzoRmto0jAlLMYqwfBPa1O3bnUg6ClJ2m94EGvnBB9IqNieW+ZP+zWfbJrpDacNVSTwhzTrQH8h5v3rX0e/D/GeK+jpRtMd0QwjUEF4UliIQ91PN7X1Fd5jRwWHXir0PM7YbBNYh8btoBiFaQ4Gnv9lQDBTLLjV8pGRnH62Jcy+hdQaS0Zx/2dIdZfIiMv3QMXkuNiR6SIRPld4JMmFuef2uZXO3oB5suHzeMPvEsvg4MW7jR4/MUczcHFuu34X+bqJZtoSfzjn4nqW5jMQxk6DNu3CxYgzbQ8vlVyHd6ah/6PA3hqPvfS7AaiD+7CYRleHmUcXrLxbKkQRqVn/eMHtSmL6IhIUdCjv+TZFnYnj2a3P6/UyaJcytpnmumbuKokWQcttEmnhULtSXkVffqFh9pePB2UuePD3Af+YxnRD99XlAd3I+i5bzcv4Vx+Ysc/CFC1zrWUxazptj9n/gmbySvdj2bDr4zYp9wfk3Zlx9NTO5d4sziWfXE5of1Bz/aRBq9yIQ9mr8xZawVzP5rOXpb03ZPEiUOmIvHNWlZvGzoU0+FLZ3BW4aw/PWdxzX72j6L2zJW4vaGI7/UA0ezf7nAtbcdYedCM2q+zei2EjljmSuipl+XyjWN+vrG7pcJQmxEgJ0oDX7waO1S2yOKy6/mZncvUXrTN9bLvennP5zRXb25woXqdK0e4bXX4A4zdT3bpnXPTfrmsu9KSf/YvBczmU3z8cAsH7fcvUlTXsn4q4Ms08MzXnGbfIwJ9TO+qK6imxOHJdfVvSHCbPcYn7asPiphC7aVY9dQ3vshyJg4fqx4/W3Os7uXjFzPeebCas7DbqfMnsm87A9crsgyuomc3tPo37ziplJOJM5+cot37t7n/0/dCRX4W/TzsNXZbETuH5H437pirlNGJ15MT9k/7sScjgZCpwqS3inCYVuqbj5IDA/veVu0/Li0ZzVecO9/13RHrpdES7VIorHSnP7OHPvgxfs1VvuT15zc7/mD1ZfovnueB1kF7QFcPlFx+q9yIO3nuFMojKRH1ePOPiX4iff7luac/H+Xp8KGX351UL14JYHeze00cJdOOcO+z8SkX0cJ3Eqdj3nX6soX1nxpeMLFr7lZv+KH9054fD7EvjIzt5CjkXRiuv3oPrKa6YmcdBsePDNK763fZej78raGSYakIC25OT6uv1Sy6O7F+xXG7746M/4XfstrtqaxcdyDsfrBkBsFKsvJO4f3LBXbzms1vzAnPKiNSx/IsVPIZPl/09OsT1VnHztOQ/mr7lT3wAwudvzP37yGzQvZZ3VobA+tdTXifWp4frLkdOHl5xMb5nYnqt2wo/qO+jOk7ys86M3e6qUFJjezhwdr2hcYOY7Fr7lw8kR3fKI5cdpF9oX5pZYvwnp+0Uef6FF20BG58K222B1RcqRkIUIbX+bNu8AACAASURBVKqKFFty7DCqJ3YdzlUYCyFHvDfk3EsiuRH+NabCtNJMJ1NKibTO0KUI1om4ZLykx2cRbn1l0WT2F3OWi4a+D7Trltevb0ghMa8m9KmIZYIWf7+w2XL3zhFHe0u0VvR9y5PPnpJCYBtB/J41lZ1y23ZMXMXJ4ZS3790lhQ1dH3l5/oqr10JAVt7Tt1ustpSUCG3EV4bTw332FxXT2nB5teLm+pqbNpKzEJqMiY254HThoJlxdLjP1FmUgaurK55fXBOzUKt9VnhlyEmJOOczZ8dHzGcNfbthkydcvr5is16zt5xSsqGEHq8zJSl0Djy+d8L9OycDaVa4vDngw4+esMmRFAq5RLyFGDSV1jTW8va7b3NwVBHWW3JUnL94yfPzC4yeSPq5g6wNtljqynByfMhi5qkri9aWq+tLnrx8TukyMWa8dmLXsG6Fiqwdj99+TOUylXZyfC8u+emTZ9TW0ZPJWVFVEoIzqT2np3vU04p6aujbxMvzF8SQ+fTyBusarK2FcDSWdhuoTcUHbz1isajo80Ourq54dXHBuutJIRN6ESli7FEoZpXlrfsP2d9b4k3iQTzj+nrF089e8vp2xaYTP5msIroIJffee484OzkBMiFuKEUovufPX7JpW7KSgLSCRxvFcj7lYG/GvYM5i7ohx8DlzZIPP/6Yq5uWpq6xOHIEXzSOwqO3HnN6vKTxCnTkoPasbrf89KOndCWwjYpQAt5VGODs7jH3zg7Z36uhJOYbx/0Hh/z5D3/Kyxct680aV1lMsWxvtxwuF3zw/jvMZh6LELvOzCm+8NEnT+m2UQLVQiJkjbaWvYMlDx6ecriooBRm8zNuVx2LWcPHzy7EM9MUSpR0+WZ/wVv3jjncm9NUltInzu4d8dmzV3z3e99HGYctGujZbDYYCof7D3h474Rl7ck5w16g7Q/5k+/+gOv1DbqZkYsmo1AKqqri6M4+9+6eovqEO9pjf3/OrLb87LNndG3C6ooYOipTYerCw3ceMJ/WVDrjtZCyX3j8FnuLG/7kT3+IwbLpErgExnB4eMjjx485PVqiTcYoxWxqWM4fE9uWT1+8om6mkAuumhC2a77w+F3eOTvENwpnDNuuIx0d8bH/jOcvLzBKkVUhqEiKkapo3nv/XWbLKY3XlBLJMWOT4cnTzwih0PUdUYlXcu1EmGuc4uT0gMnigOurGz559lL69Z0la48uiUhGOU9QhWyh1h2Nd1TGsuk72hCJKlOKULnWKGLsWUxn3LtzwKOzI5yC5AqFmuOTQ/r1lk9fvpLgSaRDQYWE9oX7d06ovMZ7h3VQSs288ZQcePLsEpwTejhGJsbTVPDe2w+ZTS3GwvHxhOV8zg8//JBQ7GBtIuFnWsvPvfsn3Dk6ZDpt6Ne3LL/wgMWrmu/+2Uc4DaWL5BxxppBV5OGjM44OF2gTMDiqekKtNTEpfvjjJxRtd3Y07XaN0Zq3Hr2NVbCYz1Fk8TO9V9Orwo9//BPxXk0ZtHjj3r2zz6MHd6msQZdIASazKUcHC37/D7+LI0McwvNKJJee45O7HJ8coE3Ce8tkXrPoIs9fvuLi6kYCPYvH6ETjK2ZzzzvvPsKWgM5Ccc8mc9p0j/PV+RCcZSgUQipYrXjrwX1msymV2lA5CZd7cHaXvktsX74kBj34oHtSH5hOHR98+T1SbLFO0W8Ny+aYnOBm/WfSYdOL3zamsF7d8q1vfI29WUOlpPOlXkyZzRY8f/Gaz56fY60iq4zThs36grqxfPHLv0JJLVM3hCzqmrMzuLq+Yb2KxJRxlaQxh9xyevcBd+8eodJG2qd05NG9+/RtZLvJ5KJpQ89sOqHr1sxmM+7dOWXSVGiVQUUOju9ztNzysw8/xTlDn5VYbGjpEDhczphNG6HaKZjJhKlvuHf/mIvra7o2UNUV27jBqsRsWnHn7iHOgiXTVBMKhkkzZbsJbJ69oDYStjp2Kt25d4c7d09obGY63DsZU1jcO+bjF8+5XHekoqj9kpg6Qo5Y81eQ7V+Wh11HCcC6SIPfbMKuRSjrDtyQaj2ETbV5SOoWq4E0F8KraClSjonV22PHzSMLBapGU92kXbpydnonoBarCDNLNrB6pNncBbNV+BvF7EnGdIX1XY+/zdhBOBrJofOvOdZvBbAFtTGYPxKxsr4avB815KWledbSHVVcfqni+tstORTUrWLxI83yIxFE+qVFLCLUYPkgJPHFlx3rBwl9uEV/3NA8V8yepiGc5U1lQ8VCmhqu37LcvFNIhxGlI+5JxcGfvSHx7CoQ5+4Ncdxozr+m6U4SeqO5vXTMPy40F5H120vG5PqR+MpO8fTbNfnrKw4Xa2bAJ1885M7/YWku4i5BPgyevf3S0e4bXvxm5ujBBa9bT0qa8tMp+9+XDa1Og89ilHT2bml4/b6ILGYRMCaTnkw4/iOoCrtW59RI+2ycaDYnmtWvbpnOWg6nGy7WE7Y/2uPs/8r0e07CkELebYw3J4arbxviMqHngby2zD70mA5O/3lLqp0Q03KLgL/q6fc9n/w7mvp0xfnGYz+t2PsRTF4JUTeS2qYV4WB9ann164mj++fcaTa8/OaMV88W7H3PsfhEUZ13qGwkod2JAP/ytwPf/tKPAbjsJqSs+fHBffa/b2gu35Dko83DzQPL7SNYfvWcbxx/xjY5/vDTBzT/ZM7yI/HQHUWZMHeEiebFryqW75/z7kKEg9XDiiev9pn/3xPcbaG5FNIxzA3JKc6/rpl89ZJ/8+wjAH54fIL9WubJP37IwQ+hedETp5ZSQXXe8vr9Gdf/1pr7h69Z+i1WZ35W91zmQ+7+MyFk40QEpjG86eah5eqXIo8fvwQkuf3l+YLN3Yq735EQnTJQx3FiafcMr3498uDxOQ/nV8SiOXp/zf/6gy9T/54dqDuZG9WrllyJ8Kx/+ZpvnT6jT1J0eXq7pP+9Y+afRmnEjFkC67SiP624fTvxb3zw5zid2Ku3rO5XfHR0yvF3DP5GDwWlNBB7iu6X1zw+vuLu5Ia79TXrVPH8r1/zx/ce8uh/khb1+lW/m7ev359x+auBb773Md4k5rbj+rDm1d0ZF/09Tn9fhFqANPXY255P/uaC/d98ztvNhsNqzUerA7bBcTk55uDPIDm5Z7SbjOmkKPLitxPz42sezlfEognJ8KQ65uBfGNzGSDDWVoo4/UFNdkpE0vch7Cfqp5Y7vx92n1uHjBo6DcYwJRWFjGz3Df1c0VxkqushmGxYqsbjdXvPc/H1wm9884ccVbesY0UsmuatwD/efJOTf5HeFMwMO+p7+bULjqe3nDYrlm5LzIYPj475OD/g9A/88NmEHu6Oa7anjpe/Hdg7umXpIs07gY/PDnH/qBpsZ/g54TY7zfnXFbMvXfHl/QvOtzOa03N+dHqX2T9AbBXK2OouhY3X3+r40uNn1CYwsYGz6TWPH17wd/tfw/+jSsTdiFjovR5E6F+7YeEDv3T8KZrCsV/Bu/AvN484+yeyLo+FrKIVl19R3PvWZ5xOVjQmELLh8Tcu+eitA9T/cLSjvdXgdatjYfVu4u13XlCZyGmz4p3lOS9O5jz7/mP2fhJ2lGV2inbfsTnR/PVf/x5Lt2Wie/bdmt84/JDPfnXJ+sdnO/obZGylStP/6oovHF5xNr3mYXPJRT/j+ps1V+s71FcK20lHSbtn0AHaI8Xf+df/T65jw77d8KJf8DeW3+e/jH+T7tmhFDyHazRA/WJL+3cUXz97yllzzUT3vHPwkp/snfA//9JvcvLHUtgcz0dzkeiWhm/+az8iF8WX589wKrG0G558ZZ/2yRHNuRTBwkQPRGzmxRcLf+P9H+J0QlOodOQ/+tof8vc3v0Z1ZVBJzt361OBXhfa48JWvfMz78xdMjMzn/+C9P+KP7z3mf/nObxEbhVuPlLkIqrdvRX5t/wWVlnuPpd3yy5Of8U+/+g6rn92luhGqv7lMxFqxPVZ86f1POag27LktX5o+JRTDe4tX/G/X36S+VLRLKYKOvsxXHxTuf/EFD+dXfH3xhNtUMzMtf23/Q/7rP/h3Bxq+0O9ZTFuox8LfL3qv9gs/81/BwzlHGshKpRMlJ6z1uKxQIXDv5IRpXVgeTIgZLm/WPHnyhKappM0VWaeaSqO0kIeP7j/goFHMpw2vbl5x/XrFZ69WFOtIsaMhYdBkHA7DV77+AdOporKJuNkQu8jPPo6sbluu1zdkbSRhPTusc9x/cML9+8fszR05deiyYLo0/PhHT+hDpmQR19q2ZWIc9w9PeHR2zN7MkaOjz4XZwvHpZ8+4Wm2JScKAtpsV9WRGZTT3D494/NYZs8agYsvhfk0O+3zn9/+YpBwliy9nURCSZTbZ453Hd1kuLDNriSVw73SP2ZPnfPb0OW1fiw9nVLhcsNrywTuP2D9s8EajckPWis+eJ54+vSAnxaYNhKIwE09Igb3Fkq9++QNMSjiVh5buLV94+x4/++gJ6wRdUfR9YFIMxjW8/eht7t9ZgumpmyXr9YqD5Rl9XvH0xTlGe5SqKX2kix1np4/44PFb1I0i5hV9bJnOFigCP/zRx5jKS2iRTTRmzur6hr/267/CdOaJ7QWVM9ypD7AqEmPLx0/P8cqIwNYGpk3D4ckeb7//Ft12xWRiCdsNhwcP+OTT57xeO2JIlNzjXM2ma/HW8+477/L40Zy+26CzYW95l9m85sMf/xStwLqK2EZ8ceiSeXz3IQ8f3sGUjM6Zmpp506BN4vxPXuD1FGMVPQb6xOHenA/efYfN7Q3WaYKryDnz1S8+IrWXfPj0BnQNSeOtJ4bI4/tvc+fuDF+2NMax2RpO6hoqx3e+88dgEKKuWKw2LPf3eP+LbxG6DbVXxHbL3uE+9WTKp68ueHF1gZ/M0EXRti1OK/b35xweLeU11lBXM6ra8ejefbbrH6NNw/VtT+Wl3fbevTMenJ0R+huc+n/Ye5Nfy9Y0vev3davZ7Tn7dNHfuPdm78rKprJcVZhSWi6DbIQBIQQIC4yZMfIMIUbJP8EQg4RkgQUWppXlsgtTdmZVVjY3b+bt4kYfp99n93s13/oaBt+Okx4wyBEYXGsSodA5sfdea73fXt/zPu/vASkchSkoRwO225qXr65wdUJcGKWpqoovv/8eDx+cUK2vyIzBOk2/P8QYw/OXb+iiphwOWGyWjEYj7p8c8oUvfIH14pp+ryAYS1CBx+/e5+nzz9lsW5ra0StHGJGTqcg7R3fYH44J0bJn+gRvKZ3n/S+9w49/+jOkcEjpyIymrWpO7h/y4O4DVBYJAvJOkGWaR48esVzXNPU1LngkCRdwdHzMZG+MCB1aRvKsR+cD/V5B2TN8+mmgbWs0ChcUVWt59OCQx+8+pGtWZDpD6ECG4vBwxKuXz+itNS44TDTETBPJKXNBVhiGmUEpRaEKOu/YO9znZrFmu7a0sUMWWUKCRMfJnUOILZkwZHmGc4H7v7aHtSvmzy4oyyFOeJrOIoWkWq45fv8hdx7eR0TYGxQE0TCbbql9iwdETCPcQghcsIDj/rtfpFCaXlbw4cefYOsGXeRYZ2l2o/J7wwH1Ys6j3/gWgyJPqAEJ1nkKo3nw6D7PXp+jyz4uVBAEsbM8evyQwf6QGFKgYfAtRhrEUDMZ7nN6UbOuG4TRiCDQUlGWGYeTCUHVCAKF1PTvldTNHT797AwtNEGpXwahOce94xMyAiJ4+v0ejQuM9yfE7jMGvR5NFrHBg9SEXDI4GBKFx5gckETnsSpQjkuqbr3jxLJzwgp6wz6j8Th9b0hFjAIXYbQ/olyuCEISRXK0RAQxdPQKTb/MCHhKZRBBIPoDfJwzLkrmdeK6SSURvgPvODk+xGiLEBEtMmRUSKkwQTMuxgQBdeswQOtr8q5LKIwuUuaG4CLbeoO1lr3xhNOrC6TUKKOJ0rJezpOLVxmENQgy+kVOXUd6gz7yPO5CtyRtVyEVHO/d4Wp6ztHhPrbtyE1JQ4vpGaazGfvjITamcy+D5nj/iHbT4vKSQaExymAB5yP1tkoOiZ2b1QqLNJper0e9WTMcpmDR3BSpTkUEF3C+TRMuaKKQBOcptMI2HblKgZVKClbbJnlkg6BqWvIyw7oWJyIX11e8/+heYlpLQVM3OGUBiVR5GhqJMQUpdh7b1QghaGxDv1cgYkA5QetaFrMaITOy3NC4FmE0TkqW9ZYgFY5AluV03hKCoPGR0X5B+9oiZYaUmraxFEVBVSXebzQCG9IzThcDnatpmw6FIkaouhatNV2MyBD+b57K/vT4/+OROJP+lgHHjlvoesn9efUtQ3sQ0PdaulZjXilO/sTTTPSOu5g2q/ag3Im3kcvfEuhHG947uuHjZ/fIzg1HPw2IkJxg7YG5FQy7UjD7V2vuTFZ8tb9i2ZZsuozZD+5QXgqGb5Lr6W1SsysE199WTL5xxV88eYENmkw6/ueDX2P0j0qybRKMfC7I5472MOfqNzSD70z5yycvaYPmshnyyeEJrt9n+DLc8vLymw67l3h8V7+hGf/ZK74zuUSLwPJRQeMNF3/z3Z3LViCCRviUhL490ax+t+b9kym/vn/Kouthvub53w5/nf0PFNlKAFn6DD2FKyWnf9nz8MEVh+UGLQONN/z85++w96FGOihn4XaMXrWe+Rdzfv1f+YShbjnK1gxUy99x3+DiL4w5/L6hd+0xa4fZOOpDg88El/9i4De//hSAd3ozfrG8y+jBG362/QpHH8QdNzFdt2zruP6G4Rv/0ie815uycD0u6iHzSY9X8i6P/h74HTNXdoHmwNA7beA/vuY3965YdgX3yiXffviC/yL781zYI+5+3+H6ClcadB2ojjSr9+Crv/Ocq+2Ax+MZ86ZH/dhw8eM71PfK5JoNyVFcTJPQffrnNd/9jZ9zWQ8pjjuW90qeHZxw5w8UQYtb7EU3TA6u69/2/PlvfkxfWUplWQx6rA9z/ki9z/6TmFALRtyGci3e1/znf+6/48PqIfu64iofYoPmu3/xCX/T/gVG/8Df1kw3SK70+W9ZfutLz7lTrPha74yfbR7yF9/9jL/Pl9h/kgMBs7S3o+ybB4Z/+bs/Ztr2eac3Y9H1+AuHn/An/cd88NOvcfCL9jYYJ1skcbV72PHdB58zbQe837+mHhgelTPOf3PIdrFPUDmDVzXdOCMaxc03Iv/RV/+Iq27IsVnTUy3v9GY8Hc15Mvsid/+xJWt2qIOeoriqaX6v5W989Q/5xeY+94oFZ80e9d6UPykeIv6wSJiHiWHwbEP1cMDNNyL/wW//E344e4dvjF5T+Zw2aP7aN37A3/3D79K/8rfCne8buoFm+5sVv3vvFbU3/N7hJ1Qh42ow4r//3RHDv7XjWGuJHRrMouXmq4pvfv0Jfd1ig+ZBb0FXJLHsB2dfp1jK5HreuX9nX8755sM3SBHZMzWPiylX3YhvDl7x/mDK//n3f4t8FW6Fy+xqy+a3C/6tb/2IqR1wN19ipOdeAf/O8Wv+06O/SnOU7RpVMtV6yLB7gYOy4jf2XjHWFY/LG67tkP/l/h7u2a4GV6kR9hbV8vUvvQbgpFhzr1hwYwf8jXd/n/+k+vcYvwipOdYEskVHMJL+VcXsL+1RfHVOX0Tk/cClP2T0IlDM/a3QKXwEBapKgu71Nwu6YYT9lt7fNeh1RygUem0R1hEzzfrdPsXc8/63zvnO+AWVzxmXl5y2+7xfXPH9bzzG/B/9VE87F6/wkcvfzPizkytclDwqZ1Q+4zhfo/c8T945JvzU0HtTEdUvedvtnuBLjy+wIU31PhrM+erXLvnfF9/k0d9LzGrp4q2rX3aBvV+7YVJWaBn48t4Vrzb7HN5dIsKI7Z2MfOlva1Z4mByu8UFy0KvS91mUXNoRg3FNeaNuncjdIDUR64nkK8eXNN6QSYcLirnr8dv7z/n5+B7S7RqR6p+aiNj3fHXvktobvti7wkiHEZ7KZTy/f8zo5Y63+vY7lMiDL11xUGyRIt4KsaXqeF0+Zv0gre/FwtPtmpp2KChVhw2ar/XOqELGD+v3WFQl7QPB8U92bv88va/em4r3jmaclOuU00PkW4OXPNsccDFJ98dbl2m+9gQtWHxZ8ndffJ2//OgjLu2IB/mcT5u7jIoG/fGG1ft9zDp9hmAEs18bYDcdy7bkm6M3nJglb+yEheuRLblFjPg8oXeigmYieL3e4xsHZwQED7IbZn5Aph2ONMUhu5jMWz7dWwwdl82QB70F75RTxqrmH82/BDo5Zssbjx1KzDai28DkF5L2dzRdVAxUw/P6iOtslK6B3nHL2TGkbUwoFgGvNvt8de8CIzwD1fDMHvP6bMJYpsCwtwx93UD/LNJ6zaoruFssuepGVD7jVbWPuNOQrzOCTkzgbJnE6mwuqTuDi5LTdp+xrrmyI6Z2gNmm8yRCpLhOgYuqcgST/crPar+6vPv/whHqFh0CGZKuqW8h9q3v0Lnm+PiQO3fuMB6Pk7NwNOT44DDxWqNEoNLfkTT1huO9Ee/du8vBeA+iZzwYcP/+EbGp0J2nIIKzRG9BODItGBrFZJATOouQmnxQMDmeJGOZSa6j4CJZrpjdXHFwMODRvWOC3DHrpOfuwYRRv0ehBVp5Ii0ml1TbhtFozN27d8nyHFOUqMxwfHjEo4f3cbbDtRYRPKPBMHEZs4zju3c42p8QQqAYJAGrNxwwOTqmi+B8C64lA1xTY/KMuw/vUA4HCCXJ8yT6nZycUNc1MiYxSeLJC40QgZM7EyaTCcNBj15WkInI4wcP2Bv0ca4DEXAi0ISAV4LJyQFZP0+jQVoijWZv/4CiSOzhIs9T8ShDNDlCSg4OBuTybbiLIs9LsqxHvz+mX/ax1tFZS16kpM7CaEajQcJGmJwiKxj3BxyMR+yPSrrVhmgtSkDbVuztDxkMekjR0e/3yXWP1lUMRwVFqbDNFuctTnicikyXc0ye0ev16RV9pBeU2YBeOWI0PMBuW3KZIYJCyeSiMjn0+wZZZKgypygKgrc8uHeCIDnDG1vTeo/OBEpH9vb7FPkuqEcKMpnCmkSEIjfkRmFUpBQCJcGHls5bIBBjYpQik6stisRp9t4hVUAIT1WvOD6ZoIVGoPAh4Q+UUvT7fZxzhBBwweNEh4+OrNyFppnEkDVlQe078n4vuc9lTKzgGBNaQMFoPAAgzxOTtV+WOwwCCY8QNVobZNyNzChBY2uijOhcIzKNiJLcwSQrUK1lqDRKOqp6zXjYQyJYzG+IwdFsN2hVMCiHhJBc6EIINtUWXWRUzRZn68RdzvPEvQ2BpmnorKPfG2Lbjn5vjLUdmTEYlWFbR1s1qCBZbNc4HNFEnAsYk9E0FiMM9bpCRknoAjqAb2ziVmaJ0WutJTeK4DskEL1DG0lVbQiuA8KOme1AeNbrBXXjiUiiDnSiw+iC/d4I7wX1tkXJHLG7jjEIlusNN+sk4GXFIAWTxYjtGnSpcTKN9gciNnp8TNiPq/k19ApkUYLOsC6SFwNcl1zBUkQ6a4k+MJttCFERlaZ2Hd7H9P8UJVmmkTKjqgIdmsWqQsgMkGhpyKJGe9AeZIBcaYwwaDSDso8IGpymMAOMypHCoBAYJM16y2g0Yj6fsa62KdgOjzGK7XZLVe0wJcGRZZqAxxOp2oYQHFICIuyY3YH1qqJrLNZa0Cq5LltL3TVgFEGCiRqDpnOB+arFdpEg5C4sMiQucNfRywsWsxs0EWdtYpdLUtDeztHpnEtOSgJRaRarLaDwPoUDFkWBEIZ622BkShMrizS1UOYFMUY2mw06M3gcPrSAZ7VZcX0zpQtp1F1KMEYl12kQbFcb6FLNWR9YzFZUlaVuUzfZE+mcR0uF6yzL5TJxoLVGqoB1Ldt6TW0rNtWS1XZD27U0viMEaBvH9GpK6wO1S+tl27ZUbcN0OsWFjkhiEConGGQ9rk4vWcwv0VpjrWWz2bBer1ku1iijcd4nBnneIwrD1fUNrfOs1xWSSFOtWS9nVKt1QkpYiYwZGoPIDC5Elustbd1SVQ22aamnS5Y3MxazOUVWondhdUYYbBvY1p5107Dc1DQu0rh0vmfXU3zXopRIAZACnEt1+fL0DXXnaL2j7iyN79jahk1To3OTGLOhpQsdPgY6F6h9x2q7YdtWZP2Si+mUs+mUurHUjU0NlaJAKE3MCq5XW4JXuC4iZUYUKVy1jZHOpiA2qQxGSJSQbFZbPOBCwnZEPEFE1tsVF5dTQowEHCF2mEym9UlEat/RRE+Nx4uAbT2bRU1VNWldip5CA9EhYqDjT522/7wcsktj0q6vEiNTpM1YtnS4XNB8qeHOV6/48p0r3rl7Q/eoZfYVfRtA8k/zHrO55ebPFHz7t5/wO49eAHByd0Hv6/MkVubJQQa/DDDp+oKHR3N+786nNN6gZOCkt6Z5p02u957cJbUnVuzoyRoeV/xnX/xfbwVbGzR/6YsfU99J7hdXiFtxNJ+2NHcc/+F73+devuAoW9PTlu++9znu2+tbFywCNjs+rh1Iui9V/GsPPqT2hnfLKYf5li8PL1k/FokX2KQP0A0Vqu5wJfylL37M+6MpRngeFHNqb/jSF8/IF/FWWIyKlEYt4FtffMnv3fmU7+y94qvDC06KNd/9jY/YPgC1w+ipxpMtOqQNbN4J3ClWya0XFT3V8lsnL8lGLbpODEjETljdhUadvDPjXrnkC/1rjPDc7y25U6xoDwLVkcJskjBsx3rHx4z87v4TFq7Hnq541J/zO4fPKR+vWT7W6K27ZaFmK8/6ccmvTc7JpePPDM95XEx53h7z9YNzumEKmFNNQNcphGv/4zW+gC8PL3k8nlGqjvdHU94fT+nu2IRsGKaGgd/hC1xP4g46StUxMC2P+nM6r/hzX3+SmLJ1IJ93ZMsOXyQ39t69FY/LG46yNfu64l6x4DDbgkwhQnaUUr7bSRJvpYVLN07XNCqWXQnAVTfcparviCenewAAIABJREFUxst1Eiqy6y3fev8Vk6zCCM/Sl5TKMtI19ydLVJ1GYVXdIZt0/boerLqCvrZsfc5Rtuaz6g6Pyhl6uyvIXZjb23HguycLAB6VM/b1lm8MXzNQDeptAQGhULfhX8HA8/oQFxSHZo0RHiM898oV23fS6Hp1v8DnSbDdPO6jtecfTL+Ci5Kn2yP2TMV3xi8IPgUDIqA8b/ClIZ+2yFbw6eaE9wY3XNkRbdA8qw65tkOag4TdaE5K9NbTDXSq4a3hbDtmqFt+f/oV5l2fLipikxpE2SKxjrObmmhSgNE4a9i6nEw6jrI1a5dzWQ9xg1+OP4uQOJrFLNL4hA+5ly9uz833l+/zdHN4O+quqvQ6my+OkR083RzSV/bW5dcFxe8vv8bwxc7R2Ze7YLKYgoPyyNrmbHxO5XMu7YitzzC9jsnPE4s07kaezdrRDhVrmzMyDbl0XNshAcHfmX4b6ZLwms+6xJRudw2OYZ6mD+Y9qiZjdrp367L0hbwNh3s7zt7t5fi+IWQgjxrEjm+UuNCKYBQx07hBEj039zRPTo/5weI9uqjoouI4W/GTzTssF73dNIPfOeMTxiCfC2xQlCopYluf00XFB7P7xFpjNm8xDR7f07dMXhsU+3nFQbHlpu3z/bPH6Er80pW6Y9Wq2lMfGqanY8Z5zbItMdKTa0dpEnP1beCj6yn0NjnAZ6d7AKxdTi4dj8sbJJHNsrx1wDYHCZvT7KeG0+ezQ0a7e2vrk2j2g/m75J8XOxer+CULfOc6/XhxcntPnbb7/IPpV3i92CNf7LjlXcRUAVcknMybi302Xc4kq+hJy3k75uPFCfki0r/0DM46sqUjX6Z1IltH/uDlF3hT7fFRdY+56/OiOWS56DF+mtbOt+51bwT13R4ff36fH108YM8k1+yPNo95dn1A7yyFk+kmkq0TekEE2PsU1tuCH80ekUnH8/qQP1k84nwxYvuoh+riLX4h8b4j2blhWvX4aHOXN3bCk+0xP7x8xOi1o9lTuHwX6tWT6K2nnAbOr/b44eUjnm6PeNqe8CfLx5y9PEB2Ef+W4+3Z4W9g9MOC09WIN9Uen23v8KQ+4fPFIWaqU8jaDpdk6pCY2XXgfD3kx9OHfLB6yEm24r9+9Tv8t0++TbEIlDOPzwTtSN6+1uBzjQ1pXe8pSxc0c9dHbDS6jjt28e55Ixf0Lx2N06xtzstqQhcUtc/4/PqQ7KPebdCp9Am35EqB6mC+6nG2GfNsc8jSlZw3Yz6enVDcRGw/3UtvMUf8U1zyX+VQ3/ve937lH/5/+vjbf+d/+l5iMTo8EqESY1DpjPFwj3ce3eNwMqRzDUJEyl6Jdx3zm1lyCkSBVBqpNRJJYQxHx/tE4cnLjN6wDz5yenpJ21jQmiA0UeU0ePZH+9yZ7JFlZsdlFSgl2WwrXr5+jVQaZHLP+CjI85I7JwcMx0O8t2QypZ03rmWzbZjN16AzGhfACYzJOTo+pMgUQnjCrhqtbdhWG07Pr8myXgrB8g7hBW1w3Lt3TC83FJnC+YBAIqVhttpycXGDzAvYBUtFYH9vxGSyhxIxuYJ9JCtyrm6ucdGz3tZIrXAy4mPAEXjv8UOssyihkEGgdHIWbRrL9WJBlNB2HoVCo7l7eMKgKNFY2G0miY7tZsPz568ozACQNDaAyshlx6N7J2gj0DJhHIRQ1I3lenrDbLHBmJxIJDgJRtMbDjneHxJJrF+8o9pWbOuOi8sZyIygMnwAoUu6EHj87h3qzQIlNb2yh7UdUURO31ywrlqUymlj4kI51zHuD+kVOZ21KAJCOlbriuvpjE3V4nfJ6Nt2TVkWeO8Z7+8xKAvatsEHhweur+dcXc+oqpaoNZnQCAnBWQa9AcOiT+gsuZR459lsNsxXSxbzJoVMuUjbOLwIDIZpfDZxUmUa3fZwM11xOV2yqSw+KVyIkMJ6yp5BhUCmJSJabOfofGA2mzG9XmBkck8r0kN6rgSjQUZucpSUtJ3DI9g2Ha/PrghR0rpICBKtJdqI9DqFodQGvCXEyGpb8eb8mkVdUzuBR+CCIDeazBjyMgXQBR+IAeq2ZbpY8/LlKQiRWNFoMLC2W+7eP0aqVHeQuKw38zU30znzxZwoJD5IjBIU2pBnfYyQGKUgJqZ1jDBfrHn69GW6B1uH1DkxBjb1BrRkMhkTg0UZiXWWrvO8vrjiZjYjL4Z0bZecdqTQQpOrJK5FR9s2dM5xM9/y5uKa2kakzFE6AxcQURFiYDAYoWTEe8C1NLZlPlvx8uqKqBUOk1iaItLUNWW/h9IS6xwhBpq2Y7ZYcn61wAVD69P6EpyjzHpkWjHslYk5LNO6ua3WfPb0c0KUVBac6zCqIOKo2y29fuKBiuCIEeqm5ma25mo6o7YdIQak0gTSWhKEJwjJpunonCMAp2fn2M7hg0BoTQyBIAQuuMT8JglGtvNc3cywnSOoXWhZBC0NEdDGYLKcunP0xn2c8jjf0TjLuk5O7OVmCyoQVUDsghwJktFwhOtiWieRrDctN9MNL96ckTLYJDiPzAylzuhih/eOUZnjbMRawfnVNaeXS7zUSBMRUrDZrsiyjKIcsFgsycsCH8A6mC/mnL4+p6prrEsoHqRECUVdVeACZVHiOo8PHdY6ZrM5L168oW46lDJ0InXmhQcpJKrQdCFghEajqKzl+ctTZqsNrRf4IAholMzIjeFmes3+/h6dbcGkGt1sGy6uZ1zNFnRCEwU0ztLLDa1PCIAi6xGBprVUdc3F1Yzr6y1RZUQRiUKijCEiUAKyIkcEhfUSa5Ow/+TpS4QwEALOeSSpYZjnOevViohEmQwfA5uV5eXrK5brhhgVSE1Uks4HpDY417HabDDGIBHYJjCdrXn55hprBdpoYgxooxHRI2VgW69SiKcMyKxgsVjw+uKaKkgaFxFa4UJqXJRFSQgR3wWMLvBOsKlblpsVL15dU7epz4QAIRUqQKEyZJA0VUV/MGa1aeis5+Xrc65mM2wIpGTFtKZpZSBKtssNWZGmXWoH1zcrbmZrVlVHkReAxPsUwpeb1MxscTgRiR1s6prXlzNOz25YNhukToz6lKwWkFLQti0hCBCark5TMq9eT3l5cY3KCgLQRYtWGt91rFYrOmsxOiPLCuq1Y7ZouZmtma4qWkjXKnQoAYVSoBT/7r/+V36lRNs/Pf6/ffyXf/uffC+lOqewp6gSn9Dnks19zZ3vXPJX7n/IlU3fYZPRljM3YPBK7lwpvwxUikZhRxL7hY4uKu73lrw3usGi2X64R7HcJcl3ID1kS8f6sWHylSQsZjJhaXq64+VswvH303guIvFtVRvpxhnzr8G7xzesXI872YomGl5sD7icjhm92LlmFw4Q+J5i+SWBH6fshtpnRATn9YjL2Zj9n4pfplm3KbUpW3sWvx65s7fmnXLGjRugRKCQjg9PH3D4sxRmJXajuiLA+t0M+46j1B1D3eKj5G6+4g9efAnX5gwuQmKhNvH2tcS/sOWyHdE3llw6Brpl6Uqe3Ryy/9lOKFp2xEziS83867B3sGXPNCDg8+0xQsCL2QGH/ycp0Zy3iekSO5QMvr1gP68Z65omJFH82eaQ2bN9xi8CrkzJ7dJFBNAcaAZf2GCDZs/UhCj5eH2X8/mYwUcGQUpSf8vaNNvA5rc9L9YTcuP59d4bzuw+XdS8+vldepcQjcCs0uZUVR3rRyXTQ8O86dEzHULAz67v4p4O6U1J10FA79LSHhikjazva4qjhstqyKorqbqMj5/eZ+9TQTHtED7iBibdXy5ydVJyeLymDhkPixkL1+On8wcsX48ZvE6voesUiKPaQH1seHp3wMqVGBUwMrDqCv7w9XtknxX0LkMSGSAFOY8yPpnsU+WKwyI5A8/bMdd2yEdv7tJ/mpiPqkt1IULEjjRX9zQHvSq57up9Vl3BJ8s7xB8PUF3iEMsuEHZhYufDPmLs+UL/OjUWfMkP5u9x9sFd+qck52WXGg/dyGAHmjfDEmkiVczpoubz7RH/5NW7jP44Tf68dZYjJOWbDWe/PsBpKIwjIli7gt9/82XaF0OGr5OrXLVJjOvGBjtSvClLytISkGx8gYuKPz57h71/lIw2+bwj6pTe3jur8LpH/U5HHTIGxnLW7HHdDNj+5ID9J5ZumJAr3Tjf8Vslv+jvEwpYdSWvtxPmTY+nL0+Y/ERRLPztSD0kweWV38cctRTas/A9QpQ83xzw0dldDv4ovRdddSCTGJevIp/tj2kyxWm9RxUzXm4n/OjNQ/q/yJLotfKIKJAOmomm6yvCsU2CjxQsu5LPF0eIf7iP8oriqkE1LoX1+Ugx63hxuM+mlEQpsUHxbHXIL17dY/CZoZin2otGJlTHzsUuvSa71rg6RzjB0QeBbBNQNtWG9DvBs/YpY8dFspUk1Dn9zzTZNv2/KYRLEEqdJhbK5PKXK8PlxHDthszcgOfVIT+/uYP64xGjV0lwFQEQbxu4khf3SuZdyZUdIWXkZ7P7vHp+xNEP9O36j5BEI2kmGlPBpR9hThrWtuCm6rH5xYTDnyb2b5rukBSnK/wwp91T9E4VT0ZD1l0S6F+9OaT7YI9intaa5FpPwWr5tCGogvP9nLntsfIFF82YH58+ZPCDHsVih+OpkrsTlQIZV1nOm3aILgLDrOXJ6phnf/SI0QvIVwnLI7tIyFPTYvhccPlIc932aYThrNrjxc2E9pMxBx+5NCLvIVtYmgOTnK0252yUs4oFr6p9Xiwm3Hz8dl1PTUvpE4bobVNoNciZqYyLZshpvc9P3jxk7w/KdH1FElGjSt8hdqTIrxSbUHBWlKxdwac3x4gfjhmchttrJ3wSk6MWac07z3jd67M2GdfNgCfP7tH7kx7ZJk1dyAhux8aOSlBOYSF6vIkDXKb48Pwu3U/2KWYpIC7xx1MoWbunkR6Cy1gUmpkv+Xh+h2cXR0z+KKOYp58TkdtgtHasCBrqdZ/mwPPZ5QkXdsT6h0fsfZaCy0Rk9zsCnwkEAjvrcd3PWMecz9dHXD49RD0vGZynkEDgtlbsUIIQ3GyGPO/GzOjz4fweP/jwy0x+KhOeZTf5gxC372++HXHteyxFzoUd0aG5/vEJd3/gCHliUOs6ISiUjQxOPb4tuC5y8l7Hy/WET17foXk+Yu9pIF95Qpaef0ImEoMfwV//q3/uV3rW/mdatP1b/+P/8L0oBULnNJ1DZ0Xa7ArJZG9Mr1/SuhV5oWmcxXeS6c2C68USFwJFr8TWLaFp8U6QScHD+0f0eiXRdixmU5brBTc3a3rFGKU0QRtsjPgAMcCDu3chdEgZiTFgG8fVbM5iU1N1Ci8UNkaM1szmM+4/uIvKNCp0+KZGac2rswum0zVtF7EBRJRkytC0DYP+gLI09KWgc8lVUK8rNpsti+kSLQq0kkipUmBCZihyzajMUcIRcYQu0jQtr96cp6AsJE7I3aa1o5cbBr2cPMvobIPOFNt6w7axvH5zSicUFsGmblAijb9O9iYUWYmUAhcanLOs1hVvzs7xncRZT5YVSKVxzjPs94hEcunJtMT7QGtrrm+uWa4qlO7jQiDLDZttjRCR46NjerkiRgvBsd5UBCn57MVLbBeQxtCFxCOOUuKsZdgrKHKDiAHXBrwLvLmccnYxxRRjWh8IUoLUWNux1xtglCTPM9qmYVMtWFYbrmdz6lYiZJZYpQEGZUEMjigiRWbwvqX2HdPliul8SVU3SJPE3S4GnPckekUkzwrkjn+5Wjecn90wnzf0hj2E0fjOYX1HWZZobchUmUag8Vg6Whe4vFxQVx6ExtqUEqqMou0a7k4OKXol7O7Lqm25vplzdX2D0AVZr6SqW6SQGGPStSgkiJC4mVJys1gync9o6kBEpzAmWVCojPVqxf7kCCEFIToikaraMl8sefX6AqUzXBDYrqUoc5quQirJfn+8c3YKtk1FbRuurqdcLFYEFLaz9AcFq80aHzoOJmOiD8gY8VJStZbTiymvLq/wSmEBGwXWB/p5gcDT2Y4yT8Kesw1tU3N69prWJs7sW7uO61JSsdiJTJ3raNqOTd1wdTVnvlhhXUj8VZ02pS4EpMnp9weJfSMErW1Zb2pO31zQdZ6WiCpzWtuCBB8DOi+IAmxo6TpLVbe8ennFatvgQxprF4gk5MWINDkhOCCJ552zXFzPuJ4uma9XCJGhhMB7ixYCS4cygqyXITOJ7zzbbcPVdMrl1Qyjc6SQRJ9C2whJDO4PSpy3BCmprWW52XB1OccHhSdtyLRKX3gCR79Xpg2YgKptsM7x/NUli9UaL3UK/YuwU2xBKbwPbKuKzaZiOktcbKMNVZtCBLvoCDIJ8F3wRCGwTcdiuSDEiAuO0EaMMmiRNphN05BlBb6pMdIw2BsijMJZx2K1Yb1uOD+7TA04LVFSQ4xkpiCGJGRprdEmo+labCc4Pb9gOp+B1mRR7oKpQAtwtsa1NZ2L2MYxX204u75mua3TaHxIDzhvX8+5jsZahM5pfWTTtNwsN1R1TRfSJl8IcN6htKTUKYAwSMmgKAjOUlUNi5slN7MVMi9wTYeU6XwKJCEm1/SmriFA1XRcTGcs5ytsG3A2IJQiOI/WiugCMTistSiT4Xygqhuml0uub25oSe5fqQRRRLx1KJ3hgyAGz6aq2FYNl1dTLi9mBJHEbKETD9B6j4wKpQx1le4NJyPX1zfczFuaxuLcTtgXAoTEEdJmKAQ2q4rtpqJtPVcXU9abhs55uhhQWhGwaFEQAoQo6Dpomy3LxYqr6Q2nlzesrKMVGq11Co7MDEICUhERbOuW5SY11WbzDZvK0bQeqSXWNSAS0zUSUVLiWktTW1zn2Cy3XFzPWW8atCloXUBIMEKgpErBeNZRdQ3z5ZrVesPldEnVOJbbKoX7hYhWhmA7tEz16KNiu9kyX65Zbras1zXL1RrfBLxLa6tzjihE4s4CrW2YTmdsKsvZ5ZTzyyu6kBAPAiCm94RIvP1t1bDeVEhZsNpUXE9vuJwuMNmIROiIac3uHFLIVO0usFwtadqO6XzFbNVyPVunTYwIxNghuw4hNF2QuNDxV//Nf+NPRdt/Do7/6r/5x98TIYIUmHlNKA3Z9RaMZvWuZrUveF4fclRuOduOqLqM7tmAyacupajvJR6sdEnI6waa8ptLvjS6xkfJH128w5uLCb3nmmYik2PKk9KXbUImxG/VOBSFSiaNy3rI/LODlGZ+3aHbgF5bfM/Q//iS6XfG3BQlPdOxciV9ZfmDD77G8Gli3r3d3HcDST7rqI5z1vvw1fEll+0IGzRvVntU0x6DN8lZ43NJ1MlhFXJJNTL07mwZGIsNhtpnPN8esvnxhKgUugm33ENddTSHGZfHhnG/ZmoHjLOGX6zucjbbY/JjianS+yrfrIm5RrrIk/cGjAc1ufbMuj5TO+DDq3uYD/rILjl63c5xqRvP9o7hDQPGw5qTfJ2cPNWYm6cH9M8FPpPpdwaK8rxCSMX5lzUPxkvaYEDAB7P7WK9R/3BItknhWG9FDREiqhX8/O6YyaBCIFi6knVXsPjZIUc/bfE9SbboEC5tpvW65ZN3DqHwHPQqnrdH/Hxxjw/P75E9zSnm8VZIRUB9r0QEwU3oE4aeTZfzerXH+tk+o2eCcroTGOqA3naoOqBsAAwvhn2CEhid3FzDX+SMn1maQ5PEg8ZjNo5mYnC54nRQJveUMJw1Y66rAf7TAcU8GRfN2iF9EsyyTeTmzwjuDFcEJG3QPF9NWH6+z/6nqT66oaK4bogmoQVC1MwGmo3M2IYcHyU/evUI87QkWwlkQtDjBhq7l8KULh7lbLVm4ws6r3m13Of6zT4nf+wJRqYAtnVDt1+QLxwuy2gfOpahxzYUfLS8y/lqhHjS4/BnDdJH9LqlPSrovV6jveHmPUkVNVLDyhU8XxzQ/WyPu9+v0W3EbBxmZdHLBnsyoNM527rE73ls0KzaksWmx+CDnHydBKwklSV3XBSKVhoWfc1N0+N8M+bVdIL/fMD4Rbh1XSJ2Y++tA2O4OiyohKKNmuttn7PrffY+UJgqkq06QiYxyxaEQFtoy4xpKLmp+qyagvm8z/4PM4ZnKXxP1bugrZ2o5Y3kKvS5EiXX7YDrdsiTz+5RPskZvejSzxqFskkUz9aOrsi56RuEDlQu43S2B0/6HHzU4fopY0QECLnYMVgFs0PNxuasQ87ZesTsbMz4sxRApWz63KFQSWCUgvrQsJYGqyWniz3WVcHw+z1Gr10SlkgCHiTBSJAEp3Lq6F1H9p645DDtJ1E4FBqzcYguIK1DbVIwX7ZyDF9alJfoJtW07ylCnvbR2azGDTNUl5Lu18fgpWDZFrw4P6K57HPwc26Dy0KWutlukJz7tc7Z5gqVBS42Q1ZVQflhyeRTi4gkV3JIte4LmRpUreS6HTKvS9yrPgc/iwxf1km03DkVo9ZEnZoJxdzR6YKwMdibksELTTGLmPotozSxfIORtAc5uok4n9EdehqnuV4M4U2P8bMdOqNJqABl072iXCRGhZlqzunzcrnP5pN9+qeJg6qbt+tVEuPe1rCvc9aZ4dr2uVkMEM96SVRch8SG3jFqtU3nT3YCFzO2Nz02N330Jz0mH0WUi5htajik2nWwO9fZQuK3OZu2oP18RP4iI1ulBpNy8dbBrauQGgkeymuoZ31utgPUZ70UalgnoTYJhPGWOyt8EkFlpanPhnSfDTj4CQzOPNnK4QtJ79IScnkrjosAxUxgzg3zpxP0q5zeVUyhjio1OAVJ4JchiZGqEaiVpm1z/DRn9AvN4DLxhPOVBwldT+GzhGFQFvI5uNd99EVG/LRHPo+MXttbxIHPxW2oZ/o3yKaa8KZHe96jvJQMTpMorG1EBtI9s3PoigBmLeg/1SxvhsgPSw4+TOcqFWCq76B3fypBeRMprhQbnbM9HzB/ts/ko5iYxy65kkMm0XW6x5BpQqY8Vcw3Q7pnfXovNPuf7fjNu2uATFMwpkr3zl/767/7Kz1rixj/2Y0I/iv/9r8fowBhJHmvZLvaUuY9mm1Nv1dycmefdx+eINuKcjjgR59/xny2pKstIkqKImOzqci1oQuGIss5Odrn+GCE62pW9YL5csF61SB1hg8SG4Ad721QDnj34T1OJiOyzCEJTBdrnjx9QWvTw4rzKS09nUfJ/mTA/qTPw5O7mBi4vrni/Pya+WqLynvJdVa39FSOJ23C3314l/tHR3hapNScnp5ydXWFjMnV1YUOJ/xu/BL2RkO+8v5jBnnEZJLFcsN6u+HsfEbTOoTpY5sW4QLGGJzvePe9++ztDdkb5mw3S2In+ezZS1rr6SJ0EYRK424SyWCYc/9wwv5kD2srrLWcXUyp64au8QilWFUteVkg0CgJ+6MRD+9NKDUIbXh99prpfEZdR5QoEntPhGQYip6TwyPu3zmgKCO9smSxWDBfrHn+4hxd9LA+YEMSpMqyh29b3nn4iIP9PplyOAfras2rl6fYNqB0iScmYc37hIDYP2A0GnH3uE+MNev1mpvFkuXKUltN8BJkTGO8rk0JpMYw6hUc7Pfx3nN9Ncd10HlHpEtCFknQgjSOfXx4xLDsEb1jvrimcxC8xOEQWmDbmhgFShq8czy4/4jxsKQoNdWypt6mUWOlBHXnQCiCAGEEMgYOBz329/cZD0dsNmtc8Lw6uyACnVc0zqJEcvkpBEoHemXO4eGEXBuCiNzMliw3NbbztLYjl0lw1lFT2Yb+3ogH90/o9TO6qmE2u2G92BCkoW06vBRIlaNzTecqtJbcPTpkfzAiE1C3Navtiun1nMqFNAovAkZ58DlaS7KhZlAUHO0NEUEzXUxZbxybqk4OUa0INhKNwneB/f0+hcm4f++YZlvhouPs7AwhBFWVuKmJ/xnI83wn4EkOD/fYnwxxHSw3M1bLirpuk1gSBFrq9PAmFKiAUoK8MJxMJtTNhtVqTdOJdB+R2LXRJfZq9AFBIFOaw8k+VVXR2JbOpvfT4pOoFAMS0FKgdYb0kcPxCJ0p8JbTqzmhc8hejusCMoI2Eucc1lsGZUFRFGiTxqJXqw2N65Amo60tmc7Tl6QAhSCEgNZpDL/s5SyXy4QSUSVdcLTBYqQA4fBOEZ2g1y9xzjEZj6htjXUdDo33DuccSinatsXI5M6QUu6aMnZ3/wMaOpuaDT5CUAlPEIJPKJbOo3ZpIkYJCKk2IdWpQiFzQ2MduTapmywDPrSMyiHOOQiKuqtBalAdSE3bdrvpB3P7ubXWIByL+Qp8QJoM5yPWujQCHiJt1zHIcrwMuCCQQhCiwAuLD6CURgVJJH3+GNPn0Tq5htvaMpnsJU6oTA/aKfsx1WzbWXKTp+ZJTEFYzjc4mzYxWme4LrFuG9chlEQiMMawrpYYY2ibDUXRo3WeQdHHug4hJaJ1ZDpdH1S6HoGEPhDSEJXDtgGpSOttTJuZrutQ0aFkjjGpFr2zxJgcNC6IJOrLQPTpHMQYIUqkAiUjwgtUocF5QifxMT1o+p3YHHeojizLECSuubU2hTx26Xo3bYsNEXykyEp6mU9CvhD/F3tv82pZlqb3/da7Pvbe55z7ETciMjKzMrO6VF3drW7UogUWtiUwSOCBbYE0EBgLDQT6E+yBZ+2RwHOPPJBbYBvaQnhgT9sGC2PQQGAJTCO11OqqrqrMyPi6956z916fHrxr7xtlD9QDg9vuupMMIm+csz/WWnuv5/29z9ODQo8UIiUbSss046jNMscVLxaojMbs9g7GGB3LJoNot0YpBet0bHlvKTn2MeUxouNrswIZwkQjs6ZIcJ6UEmBwo6ekTHCB2It0Lni9XqaSYiSMWuCUpmJtaw3vVTgQ0fMVLLVq6GJtWW1UatH7VqFVgw+GYAYAEmqBlEsEfcqwXBaCUzuN2jIiuuYhsOYV6wKuZVK11FTxNrDGM8YHtc6JK7dX1zw8PBDGwJqiUsEIrRkeY8KQ0S7HAAAgAElEQVR7j2+VYsANoVuDRP6Hv/df/dwj4U/Az1/6y39HK1pGA7nGr1fiXSC8T8yvBt78acv0b36Lt5Wvrt/xu//dL3Pzr1SwbaKkzOHrJ1JufeZ4+yuW5ZcW2moJXztOfwCnn5TuzVp3T0L3GJk/O/Djv2CZfuU9t9OCt4V/8S9f8ep/tgz3KiraRamyzYbhw/cGHr8yjL/xlqtx5Yf/4iW3/9Rx8/ua+C6pMbyNLC8D0mmbH/9Fx82fecOaLd4WHv7Jc25/V4kaSbqxc3P32ztn7n9h5Jt/N/L8+SPX48K//PEL5JvA3T8xumEVg3/U/+aD+qn++C868ndWvvv5G3789oZWDVf/00GF5Lnu16yJUkPnV5a3f7Zy+vKey2WgnD3X/9QzvO90lBhOP1qIN77bHgj337U8/MbCzc1Fuw7+8XOufw9tCR0M7qICuuiGhje/NvL+1xP+OvLy2QM//sM7xh8GPv9fVhVWzgX3oF4M8fmExMpP/q2Ryy8k5Jipq8V/43n1j1SM21LugT347Ns/MzF/ami/+kBrhvTNxOn3LacfVcZ3WYPXUA9L/6D2CjUID18M3H9PyaNn/6yoUHvpwUk9hGxrC5dYefNrE/Mn2ll29Qd6TSWrVUUTfuazJVa+/vMHLp818nXBv7OED4bbf14o3jC+29q5dcNdneHdDzznLyt8vlBfj9jV8Mk/6uL0pRLeRepgqUGLD2UQ5jvh/vuQTxWTDaffF67+sOAf1c823YRuJyFq1fFq4Js/Z4nPKnYWTv/KcPy6ILkRPmiYnLYoq11JmSyvfz1w/rLSDgX7wTL9VHj2zzPj67inl+ejxtSU0RCPwvyJ8PCLhSaN0+85Dl83jl+rTQOGTnWqoHr//RPrjeHdn624e8Guhhf/e6EJjN8mFTG8YEoj3ji9xs7w/hctly8LZjUcfiwcf6Keq66HX5WxW6H0eVu9Yb22vPvThvDBcPxxZfhQetihznFq66Stim75aHn3S57hrQpF2xzyHzSAyJSqe4bRUka9L+9+0VH0scon/zghWY93E/vKoN7a7iExfzayXosmuXvD6SeF4V1WQvNDJF/5PYhrW1vyaFhv1LP19IfqiZ0Hg5/rHvSmY0s9UecXHrdWPnzPMbzT4o1bWiek1bLCv181aAu91ulkVSCXp8ewu5R9Lphce/hjJV8PmNy6yMr+/ab01v1Vg6uaNYT3iXStZGwZlN58+I7FLfod4/sn72bQbojWOy/KKPu1Arj+gx4oac1+PfOVnoN/vzJ/PmlIYL/um9WCrJk6OD3epiGXpuh43Hxu/fuVDz84Mr0p2LXs4y8fdGyED4l447vfsHqjKz2/rTUqpqWDepVu9zAdhOOPV/LR4d+vxLsR/yEyfzbu63kTFaA3z/bqVDDMg9q2mAqhr4XuUvdgSfcY9/PafMvt+iQ227Xu8+/j897uefV9nHVbGOkWAh+PpS2cMh/tfg/9o4Zobccb3ud9TU+3I/MLrwJxt6i5fOK1wNCJ4s1P2L+5UI46ceKzoONmVIuB7feAfi4aLmpXtauQ1HYv3C28zl1q7zBQW4rwIZOuLO7S94B9PqWTqE1PX++2ue/OmdSDO6uX3WN4G9+1X6dq9Vo16YWA2PAP6iu9rXP56Egn2+dRJZ3kZ8bK8E6PbbvW1eszASDc673Ok+zP13yQn5kbdq3cfzdw/GkmHYXwoMGmOg9h+smFfFK7ku26bsGtv/M7/+kf6V37j7Vo+1f/o7/RNFEZHkrUQJ6Ysc3qJsN7Pn3xnGdXV7y7/5avzxculwuj103iupxxx6AtzJfCYRgJgyctM1fX1yxLZF1XhsEzL2ekBSpN2w97OM40TZwOA3eHCWzm93/4Q9YWsNbhYqRZYS2VskZOpytiXLl9dmLwgdM48P7NtzxEFZJCCCyPS9/YDpyXi/riOsfpdOL6MHF5vGdeE+f5HmcPgCHWlTWvanAdK4OfeHlz4tnNFcfTyNev3/Lh4T3F6EY1RfWlNdJYlorzluvrE8Ebrk4DAqR3M/NaSKVybpWYEyJCjitXpxO1RoJ1XF9fY5zl4XxhXVdaNaQ1K13ZBbJpmjifLxwOB25PB4I0tXhYdZNaSiPXPrFqxYRMS4ZDOHC8mTgdAo3M49sLMc2UDHk1JCcsYhhp3WMyM44jYlW4Zk28f/9e24xLwRBY00pqK+N0Tc4ZYwzH6cDkLUIm1cjDwyMle7CBlA3V6rloIFAjtsTkHaM1lCWCceQqVMk457jMZ8ROYCqOhmkNFzwiQksqTEsxVLEqPNTKOI60apjXldbFr2n0BGmULJiUKZLJuXahsCBhIudIGBxjdTgvKpRZiClRjbCmqPd4GLFZPXM3f9uaMlfHE49pwTQBKt4MzHVVMS6veBxidKwHMcCoIXJSuP/wyOl04v48E0JgXS44Z6kiRCo1F27HCZMr0+nIepmpkmmtsFZLWSMewdlGxSDGUY1hnDw1X3Bm0LClAuu6IqYyBsd9zAzDyOW8cDwM2t4dV66vT8QY93EX44KII6cGXTgaw0CuBSswz2eOhxulGXOjmtLJQ4sxFkNlEEtaHhmOBz6cL0zDARe8imlFPYRVqDEkYzBGx+JkB6ZhpKZMTAutdiGlP9DEqui0+ZBSDc4I18OJx3XGOs+aCqapt+c4jpS1kV1CpO6il2uG1pQgyDkSQuC8ZgbrsBXWXPDek9LaRcKmxGquXWxukPSz5njGDQGzi6a6tg7DQLkk/GHsJKxhWS9Uo3Dt6APrqgFF3gyYlqi1YBs0o2M58STaLunC5EatbhpISdcWFxxLinjvqW3ROTc4KJVcYZyueFwywQrp8shhHLDWIw2WFLV9vV8XkF0MSykRQs/UFEOMkQYaRtaF5VY1BHEYD+SYsHYg5wXnDTjhErWtH4z6iLaGbRVnDbVExHrWFJkm9QNf54XTOBBrw9pAnPX5EkvWgChxkBPDaWBOK82qJ9rN4cT9+w9QC9YHoikqlCFqAdKSvpQMA6UZ1lKwGbBCqRWPjkVqg+6ljBgG0Xs0Dp41Q20RIUFrGBEdFwhN1ILmcAyU3GhNaQARwAu1ZYJ15N5JYIyhVnDOU0vE4WilYaz0oiMstdHhV0wG54RGoVWdA8YY0qJ2HzlXqvG0fl62i7xNDJUCzeIGz3l+1PEvAQqc08o4BoYQKLN2frjgqU7Fz7g2vPEYl8km0mLFD4FawfTnWwiBVAs1ZY7TQYtvtUIq6uNtLWIcKzBLRYzlYAKzibRcGMNABS6XC3d3N7x7eEMwHltdD01VIXmJK63pn631eyhqFUPd1gcRPI4lLQxDoDXICapJlNZ2QbqV7hWMEs/WNtaodlBeLLFU3HAixQtOGqzqr2y9ZTWOZVm0uyNljDSc96SkorGYTKkJkQOlqEd6NBC80EgcTOC3/+5/+XPR9k/Az7/z7/3nbfPDnP7wTL4adsoqH4Q0CW9/zZC+XPE/HHj2f7Q9JdomFZo2kse/V+IvnjRV+fEzy/ChER4K8coyvsv7Brg6Q/igdN3y3HN5KTx8V4/js/+t6OcOljKqhYx/yMha9s31/VeedGWYXzZOP4Tj15VqdbM93PfN4lE4vM7qOTcIl0+Ey2eN4a1heN84fFu0FRTdhPv7vIsh6Xbg7S8Hzl828vPE6XcDxz/U9mSb2s8IsOFdpI6Why8G0hHmTwzNNeys3+NmOLzOuE5Y2aVw/nxACsSjcPlMP+fw00Z4VCpoS583nbBannumr1WIvv/Skg/gLjB9+3RMm8/uJkRugtH8QlheAhWm1w13YbdocJdKeLsQ70YVxoumW1dnePhSB8bVD1Xo9HPd/Y7tJROfhT3B/vKJZ701XRxrnH6Sd3HXnTMmFRWYrgZMp0Pj84n1mduT6u1ad4/l8ZuZ6vvmfOwCRRfltgCb7RzVG7Fpu2ltDG/0WVq9etbGbn/RRL0nN3F+EwPsrP6d643VYLae+OLPSgKGew3WKpOnhifRbxOiHj8PHL9OTwLqQQnvj8W4TZSJXTgoQefB6Ucrl08Dxx8t5B7CVQ4qVNiHBZMKlz91q52Yd5bh4YmK9vcZWTPl6Hexa5tf6436wm6ek25VQbF6Yb2xHH+0kG4Cw+sLy6uDiipvF85fHfCPdb82/kHDsbawK1Mb8W7Ezio2hjczy6fHXvzQayqLdgs2a/ZikLtU4rXl9Ptn1hcTZTSE93kXsHah5jF2H8pKfD6xPHNIafjHuovF20+zZi+ybIJlGYWH7ziO3+iYDQ9qSxI+aMigpIadlYz8WIzchbWlko7C9Dqpd+xgCPeFfFDBJh9UbIknFcnUl1XHbxn0vpfR7gLS9hOvrdq7HGQX0ML7tM+LdDviP6yUyZOPbv/31WuRYgtcAxVt/dsL8eURjIq07vIkSIf3iXx0+3zOx94uvlTWZ47DT1bSlcedM8vLQPGG6lSw3c5lE8RMF8Pcpeyfo3O69M92+3xRmlHFVDd3UW8pu3g/vL6Qr1UUtOc+Rwf1593uv/+wsr6YVET8sDJ/dsA/6rPAPySWlwH/ULBLIXWBOF4J49u8z/mHLzynH+f9PPyjFkPKaCnDdrDsYqT/oMUYAOk2IM0+jbOtAJauHP4hE28d4UPe12Z48nZPJy1qhLeR9cWwz9Xa6WW9puqVrCRy7cfxJORtz7xm2YuOW0hds/o5GsypAq5d6y5yrndhX0c3YXhbd0xtagGArguHb9I+/gGmr1firWe9tvs6mSfZx7g7q0CL0bFk6iaisxfNtuKtpMr8athF7m19TwehOsNwXwhvI80Jy8vA8Db1Z0+3Fflm5sMPTlz/3pl8FSiDvo/YVYsm6gWsFPG2Hm/jdSsSVq//ZniXtNhUnp4x23jYA/36/K3B/My6kQ8qKJfRdL9j2e9hDfos9O8W4vNpF7jzqBTtJvzatT0VrC8F9xDJVwEaLC88/+tv/8f/3xdt/8rf/JvNWktK6idZYsI0oRk6AdUYRSjrSqFQx4HWGtIMpukGtArM64L1mpgel5lrf1JhxwfKqpUIay01Z6qD3CrNgHO6KR6GgVEc68Mj9hhYswoDwQC14YyjGBDR3/fBIBhqSgRveVxnxPaHdINadFOW85P58OgO1LzgxFJIKg7haFU6gQjG6ibbNKMCYauUHLmarkgpKdVpDEhjmQtiHNYLuSSlEI2Q45nBj7RY2Wpplc0jBtb5wjSOpLQyHQ/U1jgvM8dwohQVFCeni+SyLCrGOMfaqTyaIF5JKGutFsmNISel4kz3uqxGhQHbKt6CtQYrQq2RmFoP1yrUUhhNL+lJF1OsgVZ60JWjVLoIZamIkoRtE6YMfvBQC0Yakh1im4ZUGXYf1VrBu0DOFScDrWSlAoG5E33GVQzqkziMjpw1iAogUpViY6DFRw1xMpUgVmlHsZhS8QimGYwECgUclPS4b8qLWZX4jvDZ85fM84Vl7f6+rYvXuSg1aJRkW0XpvZQSjoZI03Ck3HCtQQ8tKiUhJpBi7Stoo7RMbZnBBxyN2iw0SzN2F/a80fmwkpW+E8G0BrkxuRFKpRrINRFLZppGJX+bXr/RqN3BEEbWlIGC2EyzGmBVWsWsQpEK0rBVv8dgEa9EbGmVWCIHP9FK1ZbwEGgoEaY+lRlvVbzLNTNNA7k23r/7wDgecK4LjCkpkdm0ffzoPQ/LhWE8kAyUlLEYBlSk7X7+JIuS0qXgh0HXAOcpJe9UoTglU40RgnN4I5R1UQHKe01970Fhtao4m2LB+4CxjSWrSOycY/Cemmu3k6DPH1jLilhPpRGAkiLeWpo0SnGIeKptpJyVyo0RsSBimKxnnmecVY9aoN9TSxBHiQlnLSnr/DXOkpoWK1prGvIXZ6Vw3QjN4KylZcEFJRWFhtgGNVMRjBFMNcSy0rqIGsbAPM9KQhpUzEMtDgTwRltaxBhaa0rQ1yfR3EoPmmyNXCI+BHJWmrY0FXMflnv1OUb9gmvV627FaTt/vwfNoG3oNep6ZgMxRp23LpBTw+pir2Q3BhDisuJt64FgjYZVj10xlJbwYjQ4LRaqc5Q1MnqhJYMgVGrvDGi0GvF+IEUQ48h1JviR0irrJeEGq3SmDXt4z9AKuek8jmyFwYGUdVwZo4Fa5IITwZRMcwLWkYq+bE1BRWhTNVyvtowdRYtCtVJEMAQMHm/1WWSNwbRKRe0vxOkYyDkirWKMp6IhO0aElCPWa1FriZFDcKRV18Q2N8KkNGtrjcMUWNOiYrHJGvyXG+N44O39PeI0xGtNK85tOapV1zsEUyquWc5pxfpByXrJeATZhU+l5VvW4o5Fx1iKWlDIAnOuGGc7Nav/31sVZKfpQIwrLS0EN9AKGKfjz1uHQcVuTKVg9ude64F0y6zf4yzM60XPp+izClrvHOiFohRxTo/ZoqTx4/mB29M1zgixZOa6IsbjRYvV+m8bjbR/zjgciHnVgtN05LxmrKE/m6A10wuXjpRWnFdP8L//W3/356Ltn4Cfv/SX/04rg1J96drtxIxugFUw2ARau260DztNY7ofangzU4Pb08rPXx2xS2/R7C3AW7vmRphsHosSK+nKsdxahocnscU/pH1jXkb9nE2oyQfZCZt4FI5fp13Q2ci9LSl++4m3en4bAbUJTR9vfLdz38is7f89fh52GncT58J79e3cxIl0pRt2u9SdSNzO/WMBx79fWV+MSibfqZA0fj2zvJpUVFoLy3M97436K6PZhWxgP/btHm0b6e0c7FK6gJaowVEObt+UbhvwjUiVVJ/CUaBv9p+mfx66QNUFnY8Jvu3PG/2kVGYXbzuVBE9Ubj467Fx2omr7d8Pb2K95b0NfCukmYJeyix3uQUm2fHQ7dRXezOSrATsnyuSVzuzCwibK6LGqiLSRwnbVzfPrP3fF+E6Jz02Q28b0Rmb5x7KLqe4x6vf0+76JLbCFlbWfEdi2cze5km6CjuN+TLSncbF9t3/I+zXdznu903ujx61E40bDb2Ll+szjHwvxWgPiQAnBJ8FExfVNgKrhSajcfGG3a7J8Mu0C3EZD2kuiHD0S60712rmwPvfYpTH93hvKsyPloPfAXrJaBKD3cnk1cPjRhXQzYC8ZiZkaHOkm7OQ5qDi4iTD5YPGPmXRy+1j3j52Cfa8F8XyltKVEFVHy8CSKbgLOJmipwGgIbxelnydPulYxZxeE+7jxD3kXdPLR7muOfmYPQStakGjO4O71eJq3rHeB4a0mxG904vbSvX3WFvZVun+ouxTypERi9fr99pKoQyeo+3wpo+wC7bbeSW57AWmjzQHSlWf4diafArIW7KwicZ081dtdnNzGiV113Gzreg2yr4d2Ljp3l7KfQxPD9MN70t2BdO16WFYvhoSnEK9NQNyu7baebeLrtk5vY/njMekfVIDeznUbz9t4zQfbr+WTGBdv/f78kv/LuvWxwP3xuWzzexfx+vIXb9xeDNsKGPmon7GtV3Yu+z36OJjTnZXEXJ/7/XrlUW0NijeExx5WuD591sdjeVu/dkG+h5x9/Ow19UlUr0HHRHingrG7FF2bunWFP2tBcH7+1MWyzStJGmR588/O6k1/6/H3Sr5uP1vBrIkWR6avV32GLFp4+Lg4tYnKpqD2Q91mxF9UuHVrZXiT9nnfrNlF4PA+sd4F/GPeqeKPn3H5YPdroOOg7s9rU/Q4h3dpp53HN4n1mSd8yB+dy9P6txG+23GDBi8+/MKkJPO5Mr7Nuwhs16fxvOUJNdH3pPCoc2B5Zjm8zirWLnW/D9tzxV2eSNx/+A/+kz/Su/Yfa0/b3/pvf/s3W22IEdZSMMbRpHv/GdSWgMqaM24ctd25NUouvSplqc3g7IC1UHLmOE1AY40rIr1VoxSqQKYLUT0MhVKxGExJzDWTxdCaZV4LYhrH7W6JblxLyXjviWmF1miim1qMpdZGaxBs0A1uLErrWk/wA60lWimIFWptVKmU3Ehl0RCdqsE7YqSLybkLCEKqkVQSIiCtEXvrrRElMo3RgRJNBQvLWoi2IU4rDMEKrQeceKcPhxAGJGngVd0khloIwZEb5C4sZSqpZGQIZNOYS2b0A84O5LhSaqaWQu2Gr7kWUs44J3gDtIr3Dioa2EOhYKlVVLCpBWccGa2QGxEwQmmNWhPFqHgwTIH3H+7V949GM4lpnJjnBWuVNo4xUsgYCRirYyWnGcEQgpBT1vuG3s/UVFAJg0VsBZO4zA+MQ6fUupdnLQ0njtaUmJOsG/AmKiYLphcZMsYLa4nqG9mFlVYHmg/U2vCt441UqgSqaSx5AasPAsRTsGA9a0xULANKrPnRkck0o76rYgKmryYtF6pUUu2+mG27l/0YaDj0JbcWwFqsCOGjce0MkKK25xqDGNGEe9uQwSI0rLekmKGqWFBKoQDGO5aUyUAyWplMsdJaxdIDpYzON0R9N7GGxlMrhjGoiX/OSBfDWgXprftOPKXoPWkGUs7k2hjGUV8UaCzrwvF4ICdNe7fO4ToBjHPEdVUq2hrWVsimUtGQopqySj9iWXMil/JkjWIMpXZiz2sS6TROXJYF4z1ryySqrl8O4noBEmIN3llKLLRUGMYBbywtF1rMjC6oiCxCrEmfEd5pa3UDb0eoUGNF7ABOtICVaw98U/HzMIyscSWWhlgV7sRrASy3ihGL6d5bqSaMMypGNsFaDfjCGuK6gtNwx4ZgjaNkjSnLeUZsIZczWMslZXAAQky69ojV8Vu3gkfT6zo4h22GYL1SpK2LXM3QMLgQAENtDeeDkpq2F8l8YI2LErzddS3mgncj1g0a/AiEwZFboonFmLWLmv2lvFZohlraUzt7rVgr5LpgXPcWper8wCDiETto0cc0qIkXz5+R1llp92KoRf1bm9HjLlk7IGrNNAvBCBRtwy85M04Hco4YsbuoboNVT1ZncWKoOZLTihevAVWmUvPK1eFIignXwJaGVME2JZPFGYpz+iyoBW+d+rRLI6eVZho4RzGGIoYlZWwYkOZx1kFfY+f1QhOlg3M/pxgzIg5bdZxXI2TUFqSZgjFaGNTltFFSQkRdlcchUJq2b1ZpxFbIrWCcoTYhVX2Op6aFkeACgh6Pc7o+OKdjsCUh2IAYCBJwPZwxuIr1TsM5RTcgzlmlbqVQEHJt2NFTBLJtOOMZnSPXgrTS3zka0CgZQtDiSDOGZntRgfaU12EAo8EHthdscy+OqC0LxNwI46QewC3hbX8/6EWxVpSixxhyzkrgiuhzbVmwh4G4rljR95VSCrVl4rqoB74RpcBXJaKtFcQ7Ysp4MdTUEAxiBdPU7oXa9Lyqw0ngr//V/+A/+3/wle7nP39Mf/7r/+J3flMKNKftwIiKHtuG0KaKVNTzbpI9REV9Rs0eiKNtodr6t3wydjpIi0WSNVBLcid0WmO9851S6t6JYhjfZtyifn3j65nmhPmTsM8t9SZsnX4p6s/eN4KgvoVSGulKW6XdXLWdfdDEdcnsKdGS207e+HPeg8U2kkmDRtD3B6eUqX/shJ4zhIcuAlmlEZs1u08qxjC8WfXPVq9nPnUiyRrKoReYj3Y/J5uaXqsubLqlacjbILilKKXrVGgI355Jz0bKpN6VSg89eY+6uWAvKi6WgwZDbeKou9Q9dE46KfYkcKsVgAoPahshmZ0oVFLyUYO1UHug9c4zvl46Maf3xa4VjApgpqq4hzWkk9u/wzRo3cPVbNei34Phaz2/nRQ8WGyq1NHtwpWKuz3IMxWas7St3dga/Iek1z633T9yC1zLR/XqNM1gs4ZMhfdRoYqKjqVVx2F4r+316SYofX70uF5wsLE+EaV0yqsn3NNFldzPmS7mqMCr32maji1thddiQh0tGLP7im7jY/vdJoY62CdRuAuxmzfq8D5hLwl3TkgBd07dk9XuRd+2i7R6n6S/K+7hRRX8faR5q/8+qwUBTT/HrpVysCqOngt2zpTrqV+DRnhzYfn0gF1URKuD7TS+fr+7X8jPujB8v+IuGeli6BbKtVFsds593urY1nNVgcg0WJ97xtcJeuu/myum6jn5+7SPs60tWYrey3z0SBeV1luLTX2ePxb1l91EsV5YUk/KJ3HZrhXJtc9Z3QfEFwP+Q8Q/alAhxvR3TBW9daPexek+RjZSU70xi3apPXTbAdeFJS97W72d9Tu33x1ezz2nAtxjxqCClKy5z+9NOC3k64E6ePJ10PsMO50q5eOxqeuBZBUP7aprUngfKQcV0E0D95goR50X4Z16EeeTFmVMr0VvnQWbQGZgJze1AKfj4+Prs/nd6pgXPd+l7H/37pcCftYCRu3Pl81TtkxaYFTBU+dYPurYT1cWGxvxRj3JgY/mhP7O9ryQ1JClUCa3C8WSG5fPBtysIWU1dB/00P/8kXcu6JrbvD4zt/tl6EWj2AjvIq2L22VU8T6etCNFw0Ebrova4b6Ttk7nhdrCdDq39nUg9QCyfhzNyW5hZBP7Ghgeihb4RO+1O+v98rPaE+aTU4EWs3vI1qDdCpL1WVatenWX0SqQd/K7B7L0d4cybCFw27n08RT12VYmqxR+H9PbOwdGjz8fbRdX2cMvt2f09h6imzO1idB1e7Nx6OvYQ9nvvxZeLOmkHRX0zym9yOfmov65o3ZoTG8zJQjhse73d1uj/EPWThBRv2v/oMBGGXTOh0cVl7exqZ636rO/vVNIVkH7b//1f/uP9K4t//pf+X/v53QYOAwBY6HGRKuRFC+0lrFo+6qkihgVcMAQ5xXTpJOoDU/AFPX5ownny9oJNEe1hiVGsilU2yi1Uj3MpYsj4jBYShOo4G2gxMKzacDTNGHXF4orpKVhceodKEov5VLUh7Bv3IwRlriSa9GAIRFGZ5GSEApYIVGJLlOsxXiDdQaxef+MVgqtbBO/Uk0iWG2lVA9VS7UDlYo4mMaR0/FI8A6pDRbB5sShWcq84mMlXy5Iq8S4KDWEpUa1DHDNKDnnDNU2lrIyVxWg1q6yvaoAACAASURBVMuMMUY9+NbIII6T97SciOsZSRnfdPIGRIXDWpnCiDMWa7rQZ/QaVQqtFQIOr431iHHqnQjk2ijVIDZg3UgzIyJBfWDnmek4qkBJpuZCSoVxOJFXFSJrzYgJnKu21iIW548YPDTH5tnp3aDXoFaMt1yWM5hKjokX19eIaXjvWEshFwgmYIpQl0RaI1Y8wzDgi+HYLCwLthZcD44ypgdViZLUhsxYGrVEStW275vDAVMKJUakifpJNssyz2gET0U8FElkU8nmicikVJwRSs36kiWWag2CpZREbJFqG7FEsoe1io51o63hIJwf71njhbUs4BuX+EiKF2JLxLjsFg7VNvUpXqOSc7kgDawEjuMBGwJGPOeYKAJuEKw1tATO6wufNIOR2qlVbQf2QR8CS060VhjEYHJlzSsEr7SvKF1ommhBwBQqiZYy4KjN43GkZcUHoaI2Fksn+az3pJgppWKrYTCW0WrLezGCuKGvAwUrgnOCc+oFaYxSq76/8Hsc3sBgNeDNGsP95QEZPZmKH/T6eutJF/WjFeuJtWl4Vy0q5saFmJMWjKzVgoizuuAb9U214jC1IgaW9Yw/DFQnpG2NqJnBK3XopeGlUVNkso6Dt9QYqTlhSiavmUM4UmKlVfVt3kWmWkg1qiVFScTLmeA80oSSle5WKbXgTEVqgVQQM2GNZwoHpFqQkTFMGJMwFA7BQxCG0TNYeH4c8K0xWGFdzhiUhLfeY4Lr1hCZS0zMKdGspTghpgXjDalEpmmi1UzOEWtgsMJkDC1ljBUQy5I0eFBMoRmrorP1jHagZrA+IM6rzUM1uDBQSkGMIUjBSiWnxBCEnBYmb4nrhdF6XHuybABwzeKCZzwdOS8zUFnjgxZxrKVJwZnuO2sNSylgHef1gWii0sJW/bZzrVjvNSwqF6wRvHW7rQsZTjbga+ZgHC03cqskko4rij5zbMPUTuNXPS8rMAmYrJY3xSrZGtwAtZFSRCgMprA+XjiNA07N8IBKMVmry6UioiF3wRsGgdEXrnzApopbE7YWvBWMUcEz50JMqxaFxDK5gDOVcVDR0FrLdJqoRj3uNoq+lURwnlYtJjVqhckfmdqENEukkla1HJHeT9KqipqmgdRGjolDGDGia+noAyln7X6oWe+Ts0ze6Z+bzm2H4eBN9zLIKq62gpQVR8U6up/4FTSrx9k7VCzqT05NSFW7gpoivpO1NngqhbVo4U3nX8IZyxQm7RQqmblbjFzuHxn8gBXBNospphcIB8ZxxHqhLhduDyPl8oBQMHXl+dExhsIwgguWXCGjwlEmY3ygSCbyRCT8/Of/3z/zZweWl0EFtqIimT1rq2IZZKcTq5fdi24TKZVs0Y1QE4N7UJ/J8etZ7QN6q3Z4u2DnpO/sq7aVDm81PLTpPm+noTZx5f77p52oBG0Tdb3lcPvv1hq/UWZPvo0Zu6h42ywst7rJ3Fp+3UV9dTe6dqPCNI2+bwjLR8eWtZ1VKSz12DW5C0wW1rvA5VUgnexOiNXBsb4clUw82J2QUhLu6Zy30B79/i56vU8M3864c8bfK62VrpRyS9eO5fMrbY9/LFSnZGn1KkzTwKTS2zXtU8Ba6CJq0YJpvtI2bCWj5IlInUtv/d+uRevXSH1g4/Mncc5kvSdbu7wk/bvqhfHrmTz1lvVOoG0/rR/z1hbcxDC8VlNNuxQ+/Optp5os7sOMmwvppFSgyXUnFOOVUA6O5ZUeUxnsTiQiSsBtBFeTntZdlH6WVHn4athps018rV5JTKUM++Z6zdhFvVqVhNxa8ZUS/1jUqc4gnViVVPGPGTsn3Id5Jw03wXX86Rl/n3cydvo24S5KmLuHtNPJKl7rHMij2QsPNQiXT1Q0w8Dw00e1Szh4atBCXDn4PWBv84jdaNt8dGDAv1PyNF0pierfL0/3SjS8a6faloLJdSdHTa6Ug9MiwcEhneQM72IvfljcY+z0oI7RdHfQ67Nm3Uu3tp9PmexOoDVrWF+MOk67XUs+OuKN372Lj39woTmzk6BqFyBK1l7pubuHpAnv54TpgssmjG7CUrP8jGfmRsA2p2JwPmhBRYlGHVPxxvVrqiS7KbC+mJRUTmWnfO0l6VqwKkX9sZ2Dm4u2TJ/VCsU9RFI/blkzZbT72M+TfRJ5va7N8fm0j8f4TO2mMLC+1C7VeKM08ftfPqlgda1erhi1KCjDE5Vul8rwzYXw+ty7BHSd3MZsvAu65i46/9JNYH3u+xrRbWbeRdhI2cZO7Ko1RaUMdhe+txZ1iVoUUDsKq5R9X++WO4d/UFIyTz/r1ZsnLQasN5bx61m//0N6soepbW9rN0W7FgANt+zz6+NxXYPsVHAZbL+nbW+3v3wayJNhfm73Dgx30fu3iajpavOalf0ZWYPa22zkrAr+bff/dWclMuOVZbgvXF6FJxsHenGlqo9v9YYy6ZhI12rpcv7Udc9V220E9Bg2H+fwWHdrguWZFtfirdup7vXO/986JkyB1C0VmtDpUcdy56gW9WW+VNbrJ+Jbr8kTNezmyvzc7c/R9cZ2Ivqp+6RavTbbPYs3KgQvL546P7bvr17Ik1ULg0H0PqBWJUqKPz234ClUzBT1MlZv8f5O8qBjYpt/ZVSbpq3wOL5TW6XpTWa9tj2sVD+vdqFe7XT02Xn/XbXCqFbfRd5/37NeC7EXkO1ad9FZu3X0uePPH6Hj/5qfP9ak7X/z23//N2vV4Cr1eIMQArY0BnGYDOIctTXEQTENH4Ju08TQEFJJlFo4DBZaYhwcOVd895szueHpk885DK23ZmsYixhDILCuiUzFSGW+nDEiuOaoCLFajGizqzENTCa3gvMe6ww5ZWJMhO57KmJYlhl8o1LJLeKMpVLxztISWCAnDfqiZcIwsa4J5z2tNow3xJwYhgOXUtRb0gg0yyT6oKkNWm0sy4IxEJeIDQ4TPM4ZigilakK4OItz2p7fGhgZ9Xo0rVhbY0hrwrkBTEVMI3gNW4KG9wbX/XdsbYxORdHaGuI9uS3Q1PagtohzBpEGIizrinXqlVhLZQyDVgprIlih+gKiL2C1k9diPCkrLVcqiPhO7Q6UFDF+wBiHdV7pMz9BG7GDZ46FVoUUZ31RqJXa1NtzWVa8d7RWCV49SmtR+wQ7HcjFEExAlszBKyVWTbcjaI1xGlhSxPqBNWeMNQxugFjI1YKxWmVDW82dh1YywfbkeatiZbPQspJVzgrBO1otCI3jYWCdzzqW6MUFcTQMgsMar23rWfDBa3s8qJdh0JZ2bx2CxVUI1uGqYJpW98Hgx9C9MLVlWoliFX5tb9cVY6CqDYBrWkESY0k0jBPWJWGaIVIxg6fmghFPCEHH+VKwrVFqwjjX6TRD0kOgtIq3hpozo3dgGlPw5GUlOPWdHcaBVtUGIqDEsbPaH+GsbkDHYJFssEXJRingh0AzQrCOlCKprBynkSXN3BwCvmTcEvHV0MQqHe6VvEMaowm0WgmHkXW5YJ1QMcSWadYhXYjNaDjY0QQlqA2INIJXSjJlSKWBdQQnXTA1JCo+BHywzJczx6uJJc44LxwQTClKIVtLNo0aHL6Ktuu3ivTvssaALdRaMMZSY2YMgZYTy7pwdTqxPD5yczqooFazVvmtZV5XLTpZvbaHzXqgVQYDjsrRO2rNeK/dBM45TCdiG2pjYK3Q8pnDMFFzhpa5CiM5RmIpRGNIxpLFEEvCOEFcwzlDfFwYw6AEvYAVodVKyXp91jVyGA76EonhIHBzHGBdqA28E0oFUyvWCDknqmiruRVLLpl5WXTs1YYVizVKxDpxWOd0fRRLq4ZpOGJaY3SBvK6cxpG4rGp/Yy2P55lCw4aBlDI1KqFMVfpTE+2UAm+l4ZwnpoxxSm0WmgahGbVDKKVSswq4qRVNCha1x6kNvBG8E8YwENcZ7/S8vPVapLFaZBm8xVed+8Z6xumAiCGnws3hRMmVlBpOgtpkZA3VkqZ+tnjL87srTseJq6sjvhZqp+/ECdUYmmnU1Lq/s8E24YsvP+XmemIMHkpBMHq+KHUr3uGto9RELivUzMu7O8iVEis1N2pWqtnQqC1igsd66QVNx9U4cT0ECHBZH0CgBGFZZ6ZpgtgtHWhM4mglcvvsxJpnbBGkOVLStcaqOy+nMGFLYfLCNDm++uIznt0cuTsdWD88aFGsFXLWwtHQSWnrDJfzwjA4pilwO0zkqN0nGqjX+1Zaw7am1LyhFzArY9DxFHwghEk7DoxQmwqypkFwAe8tl8dHfvVXfsAnhxEx2vHSSlFvbdNoreKCI9fMMFg+//QTvvvFZ3z5+Ss+ffmc0/EIuZKiPhuGw4HLHPvbV4JW+Q//2l/7OWn7J+Dn7/3WP/xNSbpBr6NaEuUrFYLSSZQkcWanzDbqSj3kzE7bSazEF+oNHp8F3FJ2MRErlIOS4y3YnWLDmp0eSke7E0AYGN7o5r9MFkngz1twVNtb6N2sm60alDj1D3mncxAlfU0Dm8EtfcOWGmXSd4EapG+0tSiUTpbhQ6YOsgur7lxIV5bpddrJL4D1zu3irqkwfFDC1n+I+zlq+/xHRKYzSrSuT0Rr9dI9IVUYdefSW1INzQn5pJ6eBt1QK2WqYtN63d8pO03qeptuCypc1uGJSB3eqUCjlFsl3rr9eu7f0YW4jdBrXo8HUTpKxRZD7h6xupjrJtmdVWAy6HUN385IFSU1W7fRKCoMhnerCnyb36Z5onmbV6/HdKUC1fpi3ANzapBOvWqrawtCuM9KMF3rJt8u2nPTREmq3bO0GW3LXSrNq6gg/f6VUSiTdL9Rpe4uL50GGLmtG2wjkDcSVAm0MloNpbnPvQ2/KF2HeRLFR0c5BaXpBKUcGzonZKPVVSyU7u3ZvFCHJ/o4XXU6DBUS3UXvS3goOk4f1B/SpKIgQxflNXHd4nabCaNZHI96XyRWyinsY8JgiM8GbUO+8vh3M/lm2InTfLTUYKmdbKvdFzLdhi7SOhUdvewUYL7SVmf3GJlfDQzvE49fTZSTp45OqdfcsJdMmZS8wxi1M8kaVBQeephVF11N0TFq56T/7uCZX/gnittJJ+wM/jGqcNdUKFPqWItT5eB2D+L5pWd4r/N/fu7Adku79uSDnA+WPIren0531sHsdOBGW8ZnGkDs3y/MXxwI7zOPX00qikn30z0Iw7fLTuTmk2N9EXRuiSFfecokzC8sNvfW+WL6ONrIyrb7LJsKy3OHFF0fz58F3KLXKzyqMGyXintYadZqd14PB4y3TsVohBa6WNn9ksP7lfX5AEbXq+W55/E7DjDYvJHwesyt+x9LLH2cWBUO3yyY1jDtiUxVUtR2YrUXxRrEO78LeG5pXF55wmPdx+/xp0kFuiD4c1ESmydP2Y3yd+eiVgSj9A4IOsFaqUEp2M1r1CQlZN1jDxx3+v8k93F/EOK1UpdlFErQYKrmdC3095F040kHq1vp3i7frMEtlYcvHLY/x8ooT3T9QT+jDHruH77vmF8Yzp+K+smWLv56u5PGkp6I0+qFb38DHr+wrLdqU1mdrgvNPl3XPKnFzUYYv/1lTzP6dxtBW73sIidGxeYSDM1raOblc1Vfpzf6nLFrZXyfWe78TngjhuXOIhUevnSEB7VIak59XvWdQM/1/MqraHxjWJ45vvnzloev4OEXLP6hB3+tvePEi5LSq4738XUk3jjWW+HxCw1PXG8EF9mfrdszfnu+bNYN67XaGKSjaPdH0oLN7iHd3wXSJEyvIz/89y33P6hk77CLPjf2To/ayKPgl0Y8CW9+3fDh34jc/ca3lF9Z+eaTQBGHu2hHULzu7xL07owGf+tv/IU/0rv2H2vR9h/89//jb4LBiqPUQs5JRY0MVjQ0xDhhOgy0qhRNTgkrlpKyUidGlLYpugmnVZwfqFV/14nVjTlGSdtaGLooFWPq4q0wTK4TOZ5xHKAo6Vtw5KZN3NbZ3vYJTmQPI6lVycyclXiSTsQ516sZKIE5DgOu//tSK8N4ZFmyti4jrLHgrac1TWsfxwEr2oLrndX27Fp4zIuKZGIgV7z3DN5zPBywNhOXs3pCWsFZp63LTds7W6k467BNVCwWMHXBAYfDQX07Re0Mam2M40QpStnFZYWgRG5zkEpimAb1NHSCwWEQ9a4tFYNWV51YaI3D4QBVaDmCNKQ1rCj9RxOccUx2gFKUGHNZU81bo2WDC47LwwUfels4jZQWTsNAKQnnDU0KNA0186KC/ziOVBq5FIZhJOWMd5bL+QO1KLGVswqL6iFJD3JREtoPHsnacmpawnpNnHdi9wdEzg3jNfzKWYul0mrCWfBOqWlMpVYVZHJrZNPINWFEiThrDKWsHCfP+fLINA7dNkOvn+mCkBhBEEZnqGnu4XBQogqPRoSaK5ZOaNeEEctyngmDpzXdgTgrsFmRFGgZgh9ArW57AJTFmcYheKbJUdZEroWGtlk3DEa6D2hrHIJj8Jbr0fPy7gorDe8tc9IAHn0pVisTfRPKBLGYkhnGAVsqwXrismCdU39f52lZbRucE9a0cJoGvG1cHw/kdEYqWKMvGBj1GVZP2YZHj//Vi1turibK4yOHIJSYMdbRrKcaLQy0plMl0Li9PnA8ee5uDqzLo/rABk+phpSrBjO1xmQd6+XM4Ri4PQ58+skdNS20mKBTdk4c1gopaZBbGDzrPHMzjjy7OnIYN0/RRE0JOzqSAFa9Wk2DsmamwXJ7NXF9dcJIotXEOV5wzpOTIRwG5mUmjI7jOFFT4vntNZ+9fMlpGhmcI84qeBrnqKJ0rxFLU4cDnl9f8Uvf/x6fffKCQYSUV9YUKaXotQmekosKv8vMOAZevbji81c3vHh2w2EcWM4zpVacc6SqxQbTGkGEyTvujhPPrk987/MvGZyl5UjJEbKuycGq4Dp6j0W4uzny7ObA97/7BXc3J26ujlyWhWVdu/gqxBiZpgA0BmdwBj7/9CWfffqS48GzzHPff+rLRGtZCwCl4h0EH/DW89WXnzI5x/EQKGvCWUdJRa9xzvjurV6b+qJ656gFPBYnnpgKIQxYo88Y5wPOKalgmxCsp5REqRqGJX2tbrUgTVvwjYAYi7cWWuXzT1/y5ZevuLs5cHV1hFY5P152n2AQ0pJorXJ9OnI6en7l+3+KX/jyC6iZIXjimvv60XDOElNkPA6UvPLqxR2fv3rBi5tbXtxccz1MPN4/IE2fz611qwJrOE0DV1cDL18+57NXL7i+PvLs9pZcCo8PZ/Va773ONmxifOV4GvnB9z/n81e3fOeTZxynQEkrpqmg3IwWz8RWLBVTEt/78jt8+elLbm+P3N1dIQbuP7yjObuTT955Fc6pWIGvvvycV8/v+M6rl1yfjqxrprZKqpmGYKzFGch55ub6yKsXz/n0k+dcH3UuHv1EXCPzshLcQG0gCM4YLPDi9pbvffUJX/2f7L1JjG7pfd73e8czfHPVV/N0x+57eyabZHOSREmmYQqWbAXxADiwERgBss0m22gZIECALLLyInHiwNo4diQjkmUrkkhRpkRS4tRiN7tv952q6tb0zd+Zz3uyeL+qlrJSNo4R6Wz6NlAXt8583uf/PL9ne531fo92K2Q2mWDkqhW6dgRB7DPHq7BTJ7Yc7mxyZ2+XYb+HloqLszOUELja44RcXaw4vhXtTsDrbzygEypakWF7Z5ssy1gmS6QymNWCtLECqBkO19hYG7DR98/cThQQGYtq4GoyJS9LSpdjbEhZFASBx8b8nb/9S38l2v4l2P7Jv/rWr9zEHzOHTkqf0hH4COhKIM0GfiF2I5auYtLXbrrrOPA137OOPP9RryKc11HS67Kisuv5d2ZRcd06XXb8d0ne055L2PjCJlU0N7zE65giwruhfAxzxQmMfMRfp15sc/ZasAM8hYm8v3LRKR+lLLu+VOZmaD0rvVtzFWkt+praiJvFv1q5H6OTBNQnC+k6VhQdRboVrMSEa3FQ4gK/n9digqyv47UrkaLln4V1IEmHK4FixdyTlS9nuRZN7byClVArnXc8FR1f/ObdgeJmMe9j3v6cXZcBpUPjBY5Vur7Rwv9+y/pGhLwWpFmxC/3CV6ziw4LworgRBK+FynQnQOXNzfEWQiKL2nNQV0KrrLzLrOzZG3dTcJGhSr+P16VmKqtXwonAqU9K8a6doo3yoopcYR38t90qcr4SV+tIrbitfh+qaNX83oibx6/KffxYr6LCXqTwIky6oYguK/KB/3ZsVsLtdfTZrTAZRVehqobZofHiVcXqGpU3bM9rh12jBMGoXDnIWR3PFarBfdLk7pmxK1f40g8i6kCQrkvygccM6HyFe8jrFVPR/44CyIchRUex3FWMXzLeFdnShKPqBongnWneeSgQnh0t8c4w6wVEO69orC+0qtrmJupbR5JgUpFseZ7scteXyTXaX3fXsWW9cl9796si2wgYPVQsDgyy9OKOKvAx5UagSn/dX8e4y5ZifqDJ1iWz2xo796KXC5R3My+L1XnxgyI7q0mHmsWBYvSqTymKRqy6FXyhXb26F72YbDDTksW+ZXbLkK8JqlijqtU9usKiwCfDIpU5sjXN/FCx3JY45Xsb4vOCOloxX9uaYFRQtjXZZoAsYXLPMnrNlxQWHYVZgiq5QXhcu4Rl1eACweSO4eznK+avOrK2QuaSYO5uUBnlqgitihXBVUbZN4xeMYzeaJjek2TrGjvz10kdrhisKwGvblvydcvkrmV+pDj/kncE4gS6EDfnr+rYlcM2wAWS6R3N/EAy+6mU6qhgtiMxE0048RH+xgj0vCIfhiC8q7oOJZdvBVy9GZBuhgRT/HNQ+nvJ76+8cUiXLT88ufgMFH1FsuGv9zqQ6FV0Xade/KPx+J5GefFZVg11vCpjm1c+5m/9+a4jfTPEclZRtT0q4VpoZ8UCvxZDr4X1a8FTAFevC2ZvFyyPHMmmBBTRpfPiYOjvWbNiKS92NdmGYPnXFjRfWDLF35M6XT2jV89Wu3DkPY0uGkYPNcmrGeHdOe3bc6bbAnFpvdhoxQ2+pQ4lyy3DclsxvQ/dV0ZEWynqIGFexkQX3AjS16I+AmojSTYUL37W0X59jH59yckwgsrQSO84vxG1V6I1Al58UVC/tYS9nOywJtUhrVOPGvGYjU+OEcK/sy8+I0kf5BRv5kx2NHruE2Em8X/v2o2s84bFnmT+oGbvwTmdrSWb+2OONwJIDeHEp1yu3zV14B3Ek/uW0RcKWp+e0L4943zTEJxqqsi/x1XVUHS8Ee56kLPY0Vy8DeaLE87ua4rAMPhJtUIE+W8WVTQ3CZFkU/Hib1f0NhasrS/Yf3DO8WJAeLlKi4Qr7vDqWT65pyju5nzmzhNiXfL62indfspT2yL6WBPM6hvh2iwqyq4fwv7jvyAeQf+/+7T7D7vlFBRlgQ0DKldh44iqqAmVwtUgqNje2SGOvd1yOp5R5BWLRUagNBU+UuyUoqkLtDFUeUUnMlR1RpIskSrANcovkJ0XEPO0IAgilGyoXYGwisgaui4gKyqWyQwhDMWqaEsJgTABy9mcbitEa7BAv9ulqByzokFIQyNywtiQ5Inn6FSgXERgA0wo6A9aJOmMxWyBsTHLXICR5EVF4TKU1TflRXubQ+KogaZkPmtIkpSy0cgwROf+Q7RpGrRt4VyFDQ1bg3W6LU1dJVxcjBlPFzRSIUPrOavC+UhlXdG2EcPukN7QYgPBcjbnxeUFhUgpmxbOQSlAu+txjKDfbrG1OWCw3mc8njJeTBiNxgS2S5pUnvrX+IF1qDVGCnqdNsZKAhtzPr6iEjmVk1DWCOtfhFrF1E7QuIrNnS4b/TadVkBVZszmcz5+dkzpKoo6JuzE5MsFYRCjVUOvG7PWHzActHErpu7TJyc8efIM1R0Cisytoq1SU2RLuvGAfkcyuDVkNBuTXS2JQs2iceggIFksESomp6YpK0xVsb+xTr/fRcuG8XzG5eUljbOkhWclVkZAUSGlItaCbmfI3t46UvsCnPFoysnpOTpQLLOSMLTIxlFKz1ntDvrs7W7T1r6o7vatXZ49f8F0kqAVXnBRmsI5lLZoHP1Bh3tHLyNdSVnWHJ+csEwrJvMEJxVlU1JkOXEcU1Up3WFMf9BlY72PKxuS0YjLizFJ4UAbKtlgpSKvG1wjELLm8NYea52IdhhQ5QuyZckPfvwRjfQOVh+fEmgU2jn2N7tsrHdpR+EN8/bJs6fMT+dAi6qsaYUt5vWcQFuslOxsrbO9tUYcGeo848nzc07PLykEVBIcKTkFtTNoodjZ2uTWwTbWCKyWjCZ9ptMlT5+eI5Uv5BONF87zNKMXWV579SX/8xLk0RaLecJHj56RVY4kzdBBxDLPCQKN0TX3bh+x1u9RVwWBkhzubvHeoyeMZ3MC12GRzogiS2AVdZXTGsTcPtxn2O8Q0pAOe0xmU0bTjKfnFxQ48jT3LGrhjeU7wyH3Dw7oxhEVOZQlV6NLPnj6gvliQRGG6MwLv6GBoNPi7tEmnXbgOcz1NheTER8dn5CVkqZWiKImtgGiqanqjLt3bzPotehY65mwznF69oIPPjqmFgF2JX7VtWdIv/7gNoN2TBxbAi2oBiG9fptxmvLeex8QhyHUwjNam5ooUNw52GTQDei32yilyZKcYW+d7/zx9+j0uoimpKx8TD0KNINuxNHRFpGNMMZS5hF53ueH773PtKzQgccWhMYCFXWVsrd/yKDfxQKdKGCRLIlaIY+fnHF+NYIgQIqKsnDowLG1tseg3WJvZwcbSC5HF2x1Ojx/dsJkmfuBoNXUdUHVVORZyst37rE5HBIGiu6tbaqi5OJ0zJ9++BhrA/LSI0LKvMBYRWC9Y7VuoNVqkacZodI0gWCZJgjdELUiyrygyrIbBM58uWCw3qWuG5KsQCpFnZeEVlI5jyEQoibSEVEg2NzaYH9rnSBUaKnIZgXrgy7PTk/odNdIi4JGkD0aEQAAIABJREFUCHTbIyi2NrvcPzqgLFI0BZ3bm6SJYzn9gCRJSKnBCKRzVEVBrxdzdLhOrC2twKKEJAsEVbnNh4+fUpYZlZPowKJqReMK7hxuM1zb8Nx1KVFO0InuUKc5V1dTajTSGFQjyeqc4UaX/a1t1tdiOnGLpmjoxQNCFfKDd9+nFYRkhSYMNIIUU+bcuX+Lze11AKwxtLRmsxOSThc8v1oQtWLSrMB02jRNQVEVfPrtz7Kx3kHUJUZZsnSJc46fvP8BWrXImgoha+9I1o7d27v0I0unFa6Y3ZbWij9+fnVJLQUqst7tbw1KNdx/eESvZTDU6CZgZ6NPp2X59p/8ECkj76aWjrKocHVKqxXx8tFt+p2YQAkGYYed4RrpbMz5eIaOYuqqRBhNgx9mvf7wAZ1QEltLUmVIIbi1u01WZsyWJVmR+fLRskYpyd7WBjsbQ2qX0xhLWAqCWBLfiTkZzciqKcIq5vkSLTRUhvqv8Ah/aTY7rW7ajFVa+v9mtY+zrgpurl5R5GtesY1PDGapiS8cRce7IoVrEPIT14nOGhbbCp1BdFX/mfInv0AuQ+Pb3HvmRvwtOopsIFjuGuy0Ib7wAt51ecq1WBu8WJAedKgiSdGSLPYFJoHWiV8IBw3kA014uUI4SL/gzTuKKoLlPgRjgTgXlJEmvvDXupnkaOXdjddohPO3Q/KBt59GZ4pw5DCpL0CrQ3WzKL5u2C5bgskDqLdLmlISf2joPHU3MVQ7rWikF45EDemWYnoPyp0CG5cUo5Den2qK5XWM1EeY1Z8pg5kdBYwfQHOYIp5GtJ9peh+XFF1FeFX+uUhyPtCUkWS5J6hDKNuO3k8EOvcCPXAzLK+ilQPYNYxeVqT3coYbc9LCsDxrsfkHasX1dTclNUXfUIWCxauWxaGje2dOWSuKXBN8t8XON5c3xWl2Uq6OmXe9LfYDFgeS5YGk9URhZw16TdM68z8XXCQ0MvaCeuNFv/NPG9L9CoKa4Jml+8gL8dGo8uVVq31yxjslkx1B8kaKUo4y0wTPLMMf+MV2dFF6d6FWKyRBxWI3ZPSmQw8LqrJm9AVB748VnWPftm4XKzzIwvMMq0gyP5Don5qglWORWeY/6BJeQfdptSpjq9HznHwjRqc1i/2Q+YFkca9C1BIzlvQ+5M+514q2JJjVNwzkF+9oiqOc9fUFRWE4nwfs/0uNqCXhsqRZDQBdqHChP07Z/Yz19QVd6chLzeLba7RPVyzb9M8Ug/UDyrbi8jVF/XDJxmBOVmqSbw8Z/siXsHn0hcNMc2RhkJHi4q2Q9HNLWnFOP055/HQDe2rY/kN/v6vrMj8rMNOc/FaLk18ssVFCHOdYXTG67CG+GWJnDeGFd2oHLxZUvYiqpTn9CgwOL6lKw7CVMHo7Jvu3XbpPKsqeJTzJKdYjL5znDcmm4fyLDZu3z7nbnvLs9QGnxz2ip5adb+XoeYGe5dSxvSngunwrZvmVJXc3L1kUAQDPHg/Z/l1F67RAz3Nc6IeuRd9Hns9+ytHanDOIU6yqefxoC/fNiHBSI5x3wmcbHlWgU8fxz2jEQcLRxpj1cElWG37440N2fkeuiqD8QOG62O+jX7bEB1Ne6k3ZbU2Z3gr50c4us2cxR79Rka6i496t6ciHISdflqiDBW/tnBLrkuNljyd31rn1zySLHUOjPBamkYJsoJgfSdxnZmz15gyChHEec7WMSX6tT+e5WzGIm5uCQpU7Zm9U3Nq/ZCNa8Nn+Y747PeJP+vsUf9Rm7cceWNzoVcFaqLh6VZMc1Lz22kdshXO+8eQOj1+OGHxP3dwfXL8bsho7LXnytQjuLxl2Ej698ZxREfPtj4/Y+I2AoiUJZo4q8iJ0FXsmqsed+PMfzGqyvkQ4TXiWgxTka158FllzM+yLnyfM7rZRZUN4UfiB4qrETGXuJlqfrmnyvmD2Us1rb3zMfjyhrxM+SoZ8cGuI/aM2yx1LOK5X8XtJ3pVMHjT83Jd+wFUe07MZ5fo5T+cD5lc7hCNHOP6kLFDnjvmeRn92zIPelFd7p7RVzuO1dX6vuM/67wU3rOxrBIJwkHxxyeeOnmBkTaRKWirndP2c7xav0P3YO2id8q7gcFIzfskwfyvj4eELPrv2hNIpZhsRv9F5hfa/siQbXoDPex7lUEWCiy9VPLx3DMBWNOfNzjPevbPHt6dvsvWHc/Jh6AcUcXjDkH3x93K+fPsjtHBs2DkfJUP+qL7P3m97wTkYlzRSEAKNguq1lNd3z3i7/5RY5fRUyp/2d/m1+g3W3tMoxw2SpAr8/tQ/M+WdrVOGdsm6XXB4dMU/2fgy/G9DnwwqGqRpbgrqkk3D7OcTXt09Yz+ewC6kLxu+O32dtR+XvuRthVq6Nn10/+YpX1474Y32M36wOCBSBVefi5nNtmmdOn+sugpR+mK58o0lv/TSuyyrgKqR3I/OuBVe8rDzgn/xo58hnHhsRXSyvHmmqLz5C3+r/UfttP3n//u//JWmudZ9pOe5Nni3UKvD3vYGO+t92nGADTR6FdkfT8co3Xh0QZn7+Hjj45hbWxvsbK4xHA7odSMUDUmyAJxvXXfOCwtZjg0Mg16fva0Njna3GPRaSAXLdI5SmqJqqCo/mQ5DQ1NVxKFl0I159cFLdNohURh4p1CRoVRDmlfUlSOwEVUFnVaHOFa8dPuQ9UGL/d1tjJYkizlaB+R1QVPViJuSsIbYWrY3BmwMOwz6Hbq9rscXlBVZVVA7fJRWKSpXEwaG2/u79LsRg15EQ8X29ibLZMFyOce5BqEsNL6t2krD9sYGd1++RRxJOpEhCEOEXhWTuQLRNEhlqMoULSXddsje3hZbWwOUEsRxSFOVSNGQJRlC+AvVO6QblGhotyLu37/D2nqXMLQMum0Co8nTBNdA5Rw20ORpgTWWbhxz53CbjfUesdUoYwgji3OCZZJS0EAtV65FXzx2+/Y+m4N1eu2YOI6RriAylnZoSZI5SmpKxw0Ltttqs72xzsHeBt1WSBzGxDYgzUsyl1OXNUoYlBBI7V3RrSji5Qd3aLUi4lZAHIcIHMskXcWxBVVdImRFXTV04xa3bu3QiQytyCJpGAz6LJM5aVYg0DicL4ZCo5Xk6HCH4fqAfiegHYVEq5s9S1MQggrvsq6FZznv7+9y63AfJaHXa2O0pdvrUVYN89kMJxWVMl5vrz2LcbA24P6d2wRK0YoDrPbi2zzLKF1D2TTUdY62mroo2FjvcefokH47JDKaQCrCIMDVkiRPKOpqxV726I66qnh4/zb9bptOFGGMoQHiVkxZ1+RZRVU7aPwE1rmatX7MnduHtGKDEo4g0CglqBHMlgm1wztTdYCVilBb9vf32N0eooTDGEl/rYeSktF4AgKKqqLMMmQD1mj29nfZ290iMppWK0IpiZKaVmvAx0+eEnVisjylbhytdsBar8P923cwRhAGhsgYrNIoHXB5cY5RMUoKlPbOWKsVt+/cYntjiFWSOA4J4xiUxCnF8fNjtLRIJTEqwOUlg16b+/dv0++0sFYTRhpjJIN+n4uLM5Q2ZEVNIH0pWF2kHB7usrO1RhxqOkGAcI719TXSJGU8mmB1QN001E2J0pKmqPjiO++Ac7SiAK21ZzqrmLp0jGcThCgoqwYaQRxHvHb/Dm0b0G6Fnh1rDK1WhyKvOH9xjlGaBigrh1ANw+E6tw52Ca0mCs3q3tSEQch4NmY6m/lyLCnBCIo84e79ewzWemhjiMMQrTS9dpvZMmE8nVFWjsY10ChqVxCFIQ8f3EfiiCMLTYkNAsK4zXSWkOYleVEQRhGNa7DKcmf/kM21HkZKBDWtOELrFmcXF1S1o25q72JvQDWCjY019vf2iKOITjumLgu00gQ24MOPnpJXFUErJq9LjzIoKmgECoFSgsAajNHMlwuSMiVohbi69EWLWgKNRwooiTErJIOAMNQeSyMVTVmtHP7gqoqKhtuHO9y9tY9WErHCbnR6A5QyTCcLijKnLAvEymmqpeD20QHDtR4NNYG12MDS765zeTFiMp6AUb6JVkjKMmdjfZ17d24TWotZFRT2en2yrGSepCyTjEb7OFpVe/zFq6+8jBGSQBmU8MiBKIoZTWccn55RS1+oWVUVSNgarnF4uIeVDukqjFBEnZiiLPn48RMCEyKFT9CgGuqq5JVXHxJGmsAYtGywJiKOYi5GM8bTGaE1CAlG+Pdat93hy5//PFfnp7SjkCLJMEGAFJbzs0vyqsDYCBtEWCWp0oSX79ym2+0icSgp0EoSBRGzWcrZ1QVSGdAC0TiUgLBlefDyfc+PFb7ALwgDjLU8Oz5jskw8kxlHHIfYwFLUBZ//3NskyxmBUURBi6jV4fJqxGyR+MGq9Jgls3Jo371zRFMX5EWONgqjLJ24z2S6YHw1RWtNni18CWiR8erD+4DDaOkxENoQaI/rmE8XLBdLijyj24pW6A2Hco6/+5/8rb9y2v4l2P7p//r7v/L/LCHC+UKQxa7h8g2BemVOa5hQx44sUJSRoPvURxZV6dmqN84kLbh6RTN/tSK9VzLb9ZzCaOQdfo328WEXKOwop2prprcNV2+C/uyUar8kUQHByEcz7dJzAxFejJWNIh0a5ocK+QtXVBsVy57wEcTMi6PhuF7xXw06cyx3vIsu/9k5vaMZe2+c8VHcxY69EzeY1jdlLt7lCNmaYvKgwdxd0NlfsNxxpMaiMkE49Xy6a3SAThx5X3P+RYfZTXi4d8ayMWw+uGI86hFdOl90tIr/moV3T45eldz6wjPWe0vur1+gOhVnKkZUGlmt3F9Gr5zNksWu5urNhv7LI6ypEWslqQuojSKc+njyjcCtvVidbkjUz4xoHc5xnZr0oGbRtpiFRNWNdxvHHgtRtTSLPU32xSWfuf2Ue71LlGkIOgUXdZvwCuzSi/d1uHIQLx1nX2rYuX/BZ7aecdQbUyrFqKeYbMWEI1/uY1ZFaghY7AeMXhWot2YMt6fMBoLlmsRMfau5zmqc1Z800AtBsmFwf23CYG3JxmBBsVYzFyHhlUdfXLt4r2Pjy13N8p2UveGU/cGESgla+0sWow7hZOUQW/lOfMRdcvaOoHd7yus7pxytj+j3Up6KDmruMRS68ELIdaHV+act4nNTAlPz2a2ntKOcZrtklLWJzkGtOJefRG8ls1sGvjImaBd01hKSWJC0NNG5d6GZReXdkSvX1/gli/jShJd2LrjVHbEWJ3TaGU+bAeHI3x/ewb1CPhSOi69V3N694o31E/phStYYlgPIihi78FH/VQ0VsmqY3jE0X5qxuzYlVBUb8YJTG1FVAe3nlS+tqxuqtqFqKfKeZvQpx+cfPCK0FYMg5eH2C57bGPks9Nf40qGXHhFVdi0Xb2kOX3/BTnfOw8EZVtcIAxfrms3f94V2dlygsop8K2J6x3D4U8/pBDlb7Tl3uld0opwPbY/uo5VzOrA37v2yrTj7Ahw9eMFalHK/c8Fee8qViJm7gOF3C1ykabS6cYnPjizTr6Q83D1jI1xw0JrQD1Ie7p7y6OM96kARTGrKrvWx/8Jx9o5m++EFR70xbw5OEBLe2H/Ou/Mduk/cTexdlv5ZJOuGz//n3yfB8srgjLYuCFVFGQkuqg7dx7XHqiz88CgbGoa/cMr9wSVv9Z/TNRk74YyDwYT383U672vvanSsMBIwvm+IP3fFsLPkdmdEx2REumLQSXhxvEHn+Scuf9GAndec/3TD/f1zOibnQeeMji347PAp3766Rfexf7aJFQFFFd5dfPurzwhUzYPOGQ2S3WDKsJvwkxfb2KkgmNU3rNSqpbj6YslLd09ZCxKMdNxbuyQPJcX7bXTOJ4gV4Qct45cDsrdSNgdzXlt7wbSMWLMJ7VZO/vU+du6He8G0uuHsCsSNE7Vse5du+6QkvMgo+4FnQq8c+cJdF5sJqpZZFftB3tc+qRCuysiu8SmVT4qcfxH+xjvfp2syJA3bwZQvdz+EQPLuxRFm0Xgu8sqFXweS6gsLvrb3LjmW/XDCdjDjK+vv8dvHr9B9thp0rpILOnFMXlL8wjvf41ZrxKad0VEZX+2+y3eLI+YXHeLLZoXqYLXfcO9rT1i3CXvhhLbKaRDcb53x70/usvndElX6FIrJvHV6/EDw6bc+YiNcEsqSTTvjleiE98tt1Dcjio7CaeFxAfjzs/PXTzhoTdiJZqyZhG0z5bX4mN98/jrdp9dcZ38/msSx2DP8t3//n/Hrp2/yRv+Y42zAMFgyNgHVB22CmS8GK9uKoq0wacPg5y55Z/0xVtR0VMZAJTyMTvi9yUuY98IbAf06kVN0JT/9M+8SKo9bm5Qx+8GYfpTxk+8f0ntS+OKyoiFb0x69tHD8p3//m7w/32I3nvIgPuXl6JTffvYK7VNfSNhoj7KoIi8M733plLJRPMnWGdiETTvj0/1nfP3sPt0nXqAPZiuecdlw9LVnIARrdsnQLmipnFv2khrF10f3sFcKs3Qs98Ibh70LJP/47/7/wGlbY0H4WK4UNVJet19X7Oxusbneph9LkBWlU6x1AtI0Z7mccnL2AttaR9f+JhVSsb+/za2jPZQrMEZRVgGDXozWkvPLCaUTHnUgGuJujyJJOXp4wL2DPebLS2TT0OluEtiGx89fEAaGupK+PClvCIThzuEdNrfWaMscGWmK2DHsRzw7PuPRo2dY20UIRV0ItI1pGnj40su0VEEch2RZwv72Bu3Y8t77z7BIonboF8Z1g9WGg/1dDvfXCUyJkg1l1TBohzj3hMWLKVq3EVKigGyaMdxY56WjHcoiwTUldw4PGE8u2d3tkyQJi7Sicr7d2zQBaZJz6/Yhm/tDxucZnbBFLeYcHuygpGHy4/ewpkWelRgbkGcZBy/f4WBvE+UqpGio8oqNuwccn1muLt7D2q5n7OIb2lVguXXvPmFssbpBxYKqqOh1QmbTMWmW4qQjy3OE8AU/Oztb7O9vIl3pyzIIUaVhuFGT5yX55YysrnxHDtDp9jm6fY82QFOQ1zUbwx3W+wVWNUznMy4nCxoRUONdmZ1Oi/t3jwiDirpY0GkPqAZ9FnXK1eMxUdRlkVXUpkbUkjRZ8KV3PkevF+LKJVZCZBXd9h3G4+8zXyyocSADjwsQ0Fnrc+fOEdVyjMDRDUNkYJjNhywWj8mzGnRA4wRFXWCN5tbhoWcG17mHTJQV9+/eYjmf8+jJC6QNaKSESlC7iruHh2xu9JjPRggtkLUgVJajg10+/vhjkJKyLJHOYW2EFTWv33sZoxzWKrCalmy4d+sWH//av8HVgqJymJYkyzICaVnvDul320hy1AqDAHB0a4eryQWR1hRZQ+Eq0iJlf3+PvZ0d8myKbLyzIw4DWu2YLMl5/vhdonafJPdt9lI0vPLySwzX19B1Sp4mGKuIdnZQJuKDJ8eEnb4vpKprpLLErQ47u1s0riK0Ado05HVFt98jboccP3+BMh0CozHGF5/du3+HYCViBsZgA02r16doxnQGbbLSx5MDayiSlHDQWzF9IbAKWebEcUSFYL3X4fxqghAKKQKkUuRpxvbaOluDdaajMzpxi/PLEdsbmwh5SZNntIIuhWuQNDQO+r0ee9s7lHnihx9S4aTGhJ4JPJmOMUIDFY1rCIxgd2edfidC1CWBMoRtS1JkDNp9nslzZOOFcKkkWZaws71JVZW0wgBlvCNSa7N61kI7NGRVSu58BB5AKYU1gtrlnn9qJcpJ+u0WAs/GdFLQrFpph+tdBoMeyXzimdZa0gpbNLWjKT16ZJGnSOvIi5K1dhchNFXZEMXKR7alxmpDt9VGOYlRIbV0IBVJ5tjabvtBkFJI6fmg5YpVPuj2+NH7H2GthbKBGpSTrA16RAFYoSibGikUURQwHA65+PBjtLZUjSPQCpQgz3M6nQ5G4FEfyvOfW70uqBprNdNkShBEngkufPGisZKmdNSqJGy3MFFKXdY4UWKMonGOqioQQt24UhECa71busoTj/0pCsLQF1zWdYUxAbPlgsGgR5qmdFoxjXM0TcNsMScKOxRJ6t8Z0qNJ6qLBWkkQGJIyAVWjTIh0iixfELUs2ipq6ae+rqkwgSXJCsaThHakfRQYODk/R9qA0WyOCUJcI0HWGKvJ05SqbBD4wjCxihyPJjPSIkdFhmrFjpcKGukoi5QiWWICBapBKkeSLXDCoY2hqHJco9GBpWhAxyGTxZx11UEqQRy1mC0zjNbkVY1RFqUUabFEtSJE0ZDMFzx+/JhOO6aqPD9cOYGoGurS+bLMvEIJSVmnBEHI1XSGbYcEUniWcpKjJSR5jrUhaep56KrxGJ/pYspoNCIMQMiaKIyYLObUlecxd6KQUnhmaF7WNHVJEEQ8f/6cfjsCIcjKjCopKNICVxUIGyGkxBqJq0rKqqB0NWaFd1FO4lzNdDplMc8wxpBVBYE2fljaKB49fczh4T51DR1CEleydBWikaR5RlmWBEHAfDrH2BBlDapW/NX2l2O7LiUSrrlZ0F4vPkevNfTvj3hn+ykAl3kLud/wbN5nPNpm43uFb2IO1U3ZyPmnJVufPWVflwzChHEWc3zQ48L2GLzvVhFMv2jNN0L0smb82Yb/4rPf4DuTQ6yqSTYsP+wcsvEt5ZuvY4ks/LOp7GjOvtywf++U19ZOsbLiRdZlfCvmw+/vs/d1L5xcR9KvuZrRV895vX/JK+1TPk6HxK9/yA+Gu4T/rkPZ1tTrhuisWHERFRdvw6uffsx+PKGlci6LNss9y/frl2idsWLLroqrlhXTuwH/8Ivf4KLokNaGf/TaH/CvR2/y7PUB6Tiide4wKydd1fLx6fXPzfgHe9/i/7x6gy/0H/Hb1QP23vwJ39T36TzzTuL2tKCKFXZacvWLgnfe+JCOzglkxaSMGB69z6//+HUGH2qKtiKoV3y+0lEbxfydlHvtBRvRgq7JGBUxs+2Q47NbRCP/c8HUu5pF3TB+teEfPvgOscoxwncpxLrg/E6b2aJDMJPYSXlTJrTc1nzlUz/iM93HADzOhvy9ve+Q7Fj+afx5lidrDD4oYVVsVHY0yZbgtS9/wGFrzLIKMOsnjIqYP7l8mf4jyNYN8WmOyldN3Fcp5/9lylubpxROsRksWHYs6cYZ7z97QPvU3VzHvhisId0U/Fdv/TaXZQcjahhArHL+h9d+nujc3pR/eXdYSdkxvPmZR/RsRqRK+jrhQnX41Be+yT8f/TT7v1uuGKbNzb8z+PIL/s7BH/OtyR0kDVbW7LanRG+XpH+wC4BKfIS/7BjKtqT7N0/phylb4RyA+SDgq5/6U/6nP/hbyNKh5hlVq42dVJQdzfw2fHn7+Y2TzogaYli8HZB8tE2+tuLFzgv0ZcblFzb4xYff5iTtYVfr43vdSx72z/iN+ats/ZEjXzeEFwVc3/N/Y8zXDt5noBOe5wM27Jy77Ut+Xb+G/c2achB6Zu6KwZytCe4/eE7VSA7jMet2wdN0jQebZzxa69L7ji9yK3uWKvYlPNs/dcxOPKNqJPvBmC07443OMb+afprFbp9wUuNCRdrropc1RdfQMjlW1RxEY2JV8GrrmNmdkOd3b7H245JGeoHtmuG5/+CS/2z/D/md8QN+qvM+//Ppl/gHt/+IX5WfQdQR6UbosSVKIJxkuS/45Qff57JoUzpFZAtyp9mwc8oYOs98yRp493rR07ReHfPFzY8xouZWeMn96IwfLA6oDzKqyOJWZYWNgmBccvFWyKiIebl/TkvlxKpgoJcEsuK31DbLLeMdupXznFmgZQp2wimLOmCgE/btFT3d5t7OBZncxaz4ur6QDuZ3HL+w/YQPZhsA9HTKp1uPKRvN/9jcI+8p4jNfDGemOdP7bZAViyJgrZNwUXTYsHP27Yhyy5ddVbG5eeaGo4zktYBQlbR0QSArejrhvOiyYefUBxm9f+FTGtdbbQR3Di64271ky864KDpEquCwM+Zbd7dYe6/0jtm0puj5Y2wWcLAxZhAkBLK6Uale753wO9ymbEvaT1PKnr0pSBR1c1MyJcuGoutLDmXhy62qlufzXqNXroU/gKIlqWJQ+YrXnnnR2SN8PG+29XRJb7/i0WzI22tPKRtF6TS/M33Iw9YJ31j681C2/blTuaNsCQbthHcXe6vzkRDLgkf5FlXPUQUCla/ev6t/JxjDN17c5eHaGUQQiIr//uKvY1RN92NHGQlk6X/vKvZIn2keIUXDbjDBSC9+/MaL17BTj4jQaY1or9jLElQquEjbDIMlALEs+EFyQFErdOQHcLL+JFVQhYIPTzdoHfr3zRvtZ3x99jIbdo5e+ONcG0GY1iQbvsQwGtX8ynu/xGvDU66KNi1V0NMpSjpE4VEH19gjk/ois6dPNtmIFhxEY8pGMa1ihmbOchwRtQThxF/vdSAwaUPnaco3T26z2V6gpONO+4p/O3qFpLI4I1js2husSTCrofHYl//lvc/xxu4JAB+kW0zLCJ2s0DWra6NsK999k9RcZS3WwyVSNLRVTlIHfOPqPuGFpIwbwmlN0fHfPsLBD//gHvtvn7CMLJ/tP+bd5R7fc4cYWaOnEpP6oUPr1L/TnRXUq2vxL7L9Ry3a+oWQQ2pfgFSWOZ0wIE1qoihgfXOdqpyC0wQGEBobRGgjCXRAkWdoEQINVkWs97cYDNbJFxNC27BY5Nw6OGSxzPjo0TPaXYMOQ+bLJSBpxxEH2xsEtsI5TVW2CAMYrK9zOVlwejnD6IC6qqFscCiCIGB7MKBYXPoSH1FhTJfeWob78BGyqalrMNJio5Bet0u3v4ZlQSMq4k6X3GW0RMMiTXA2JsmXvsXe+Q+RzeEa7U6McCmuLmgZSyFy9vY3OT49oXE5RngRzzSCvd1tAt2gtKVBsciXICStbo+y/BgJFMUCq3w5TbvdxrYMLvfMvqxIiU0LHTi6nRZKBiRpg44bcBGqcbSDDk0jPBO2zAhaIfPZiHZ3DWNU1cCOAAAgAElEQVQMVeVQVpOVFVJIlI6RwlKVDS2hKGSFjST5skRpi7IZZQ6VsqhAMR3PCKKY0jlwgtBENGWB1pp+O+K4znwxTVODacgysHVNmhUERjHPF8TtgGmaIAXkWGodU7olsqnQLUslNPMiZZ6nBCokDNosyhJpJQ0KGgNOYLSjrlYRalHTNDV1lmJkjWoUVVWTVzVpmvqIqmxR1CWucdSuptPuscxr6trQt4plUVDME3xJmcEYSS4cQghMIzFNQ7ZY0u91EE4hlGenThdzkJogblHVDa4WBMbS1CXjyQWNy2jF1rebW8N8NkU6A6FnXIZIiqJGqBKomVycsbbRp5ACV/himo+Pj4njFnWS0zKRZ282JWmyJIpjKCpQUKmCuhEUZc1oMSWZJzgVgQppW0cTRhTZgkmywEhHaDWhVKRpSpYtKbPSs31lg1yVfBRFxnSxRApBx4CSglpAXuTkeU5oNC5PMNISaMjzDBBMpnP6LYO2kmWeIZQkywpmsxk6CL0oKBxlU2FDy8V0ys6gi9EKVzcsFkuEWjKdnVO6kqzSSBGihCDNl8wWGXlZgSspywYpKmRdM5pOqIQCbXCNoHGl76CSijTPuLq6oh0ETCYzosAwvpoyny4Joy5F4xeVUvsISF5mnJ5fsNZuY5VF1Bm4iovLBZNFSopB25ByWWNUhQk188mSyBqshkY4iiyjdILzqwVKhiR5iTGavM5RNuByMuVyNqEfBZQOWkHIcpGyWFwidcliscAJsNaLTFlec3x+waAbEYX+mbtMMurKcTWb4BrPw3JNDUZROMf5aIYJTlhrd0BoSpeQzksWyxmztEQFMSJwKKloCU2VlZyentIOjyDLUbGklA2TJOPsaswyKbCR9q7guiIylvHlFZMsI9aCOhdERlMXDZejc64mc8JOCyrv/tVBSOpSPn72nK31Hq1u6Euv6pwsr5lMpyAtNQZhYVkXBDjKWcJkNKLf7yJKgdEKXM2zkyc3rtwwDGmEwOUVUitwjko0ZHnGxlqH8eSKokhARFQ1ntWMQhkoiwKtLEXpqOuaqqoIXUie+4GaDi2l83xpqSwFDa24x6Pnx+ztDEnzjOF6n7JoUNJw8vyYtE4pFw0u8MVmURRS0DBNEub5lEG/6wstUsF0Mef4/AUyiMnzBXEUURc5i8rRJCXj6QyIKa0v9aiV4PjkkqIUSO2LOPPcn49sWXL8/Jz1QRsnNVIpqgouLqckS0clAoSEkgrd+KLMxTLj0UdPeXj3NrWQLOucQsHz43NwiiZU5EVNVs1p2Rgqx+nzY4w8pGUloTYsl0vS0nF6dkWNIascjVQs0gRtLE3T8Oj5E+7cuk2AZ07XacHx6QVKedxO7XI/9BMeNXN2NcZEMd3AEAUaXTacXlxyejmlbixOFgitCawlyTM63SEfPTnh8GCbdhxwNbmiqdpcXo6pao+Dkdozxa0OqAS4Cs5ejGBrg1Y7pEhzxqMFi7zGtvvM8xQbBtA4JD7h8vjJM3qtmO21AUlRsZxlTEcLksWSumiQUlFXUDWeCzkrHS+upuz12yTZkqxocEJTOcfx5QwV9plnC0TUYpmk6LKmZaL/T777/mr7D785K1FJdSPYqmVJsh8TjEpcr+Ln9j5gUsYA7EZTALbDGb9ltym6CrNwN23vRUtS3cr42e2f8DRd41Z0xffrPf7rV/4N/83FLxN/3TdjV6GhdZKjgWTL8rU3vsed4Jy8p7kq2nTjS473u6QbQ9o/qG+EH/DMR9qOf3T473lerGFEjRE1Q7vk4511ZGlxxrdYV7F38yz2BF/ZeM6WnVE2ilfbxzzOhowHMcl5C1E3xMc5onYrpIKif3/Ep/rPkDTkTvNm5xnH+YAPXh9Tf6+HcIIyZlUMo3FvzDm0V7RVRuk031rcBWBrbUaZRjgFel76RnsFyU7AepSQNZbtcMYH6RYPOmcMzJIfb29SmzWiq/qmMd1ZSbCe4hrBfjjmouhwGI347uiQ7eGUMt5Ar9qz7cRz+morUMqxKC0P+wteZF22wxlnSQdnVpzIpL5pTo8/nuDafS6KDtDhXnyGQ7BmE97YPeFHP3iZOhA3DfZ2XFC2JI/naxxGI749OuKl7jn/1+gBkSqZL0O6ZYNe+uurXok08ZnmJ5eb7EYzbkeXfG+2j5X1TSwV8G7I4hrhIHAOksqwHiR0dcqkjDhLOgQTX+4inMIsfIxfFA35Vs335ofMq4Cvrv0pf7I44jxvAyscRCxXuArvrq5iycmix+HW2F9Tsqalc37/wp/HoqtWZVSOsquhgZNn6/yq+wz3+pc4BJEq+eHVDlbVN/tR9q8FT///z07WMAcXnNEhrzS7rSn/3Q+/SmtNonJFs932zfNSEIwKqt4nLeqTMmZeBVykbZ49HbI/cjet7+lOjNiKMEvHt85u0QsyjuIRt6NLPkg2eTobQKaoWvJGfAXQ85zJaZffVfd5bXhKSxVMq4iTtEe+tJSDBlE1VG1L2dG+0AnNh6cb3Nu5YM0m/HC6i5aOk0WP1omjjtQNm1gvvfj8+NEWnYc568GSvNH8yeQALWrmpx26qfODAO0FteCyoHUacJZ0ABhnMYGuCFXJe8+26WXcPAuEA2H8n19M2/wf52/xqf4zfnPyBnfbl/zW+SucX3SJ1rzLzbs6BWbh0Av4tQ9e51P7z9kOZ14QB/71s9doP28Ir8o/F8UvI8HkvMO73R22ojllozjNeozyGPuTiNo4oqvq5tqqA8XgJyXfefJ/s/dmv7Jt2ZnXb3ari4gduzn7tLfPdGYapzOzTGUVuIBSQamQaAQWUtGoBALxNyDx5iceEH8DBQisopAwohqJB0y5ZOOuqmyn8+bt8jan3/2OHd1qZsfDWBEn8y2fkEt2vJytOBGx1ppzzLHW/MY3vu8dvvHogmvT8IvzV/zx6m0+unlANjA59wIuzmSey9vAD3/4Ls/fOuTRwZInzR0/WD1hGwo+ffaAd6OwAnWQeTddYv6p4x9U3+Hxu9cAXA4zPlk/4LObe8xexr3rfSw18UGN7TOzHxWs7pU844jaeq5dw29dfI3pJwXV+ZIwKwi16Ef392rmTwM/ePWYR0dLNqHg65NLlqHmHz3/OvariuFY8pQdtbWr28Dz33/C628c8PbRQmI0OK5WEyYvR5mOsR29Ou/x84LJ1vPVqxN4DFanPbj4v3z4fd4aAeThqJD9kk/7wpHtE8UicPOtkoOnYYxR0J2Y1+XRkNBuI7mSFn49JEyX6eejAWYrBpB2O2qnO41bR7oHNeG3HT/+Ts3r1Yx//e3PuPAzDmzLf//5v0x1OxYio8RVe2xxm8yr10e8vjjk555cUOrAJhb80fkTjv5Ykyy4ZaA7LTB9on7VomLNs+dHAFxPJlgV2fiCr/7kMW+v48j+FTmj7sThFh2f/vAt5u8tGKKhMJHlUPHpF484uGUPUttt2ksS1Jeas99+wulfX7P0FT9cPua6bbj541PuI92FxV2gTJntfQHgqw9rPq4esDkq+XpzzkeLB/yju69z+gPRxy7vgAyT80CspGCx/e17/OCX4V6zpbSBj+4ecPGDBxwVorFs+kRWEgPFJjH9pOCf1O9weX/KB7NrDt2Wv/PFLzH7UbEH0LOBYaqpryObxyX+d2qe/pLj3ZNb/vHLD0hJ030x47DfyUIIy32YGuzYGZE/nfIDHnN9PGHRVty8OOToUj4zedXvDUl3xdhXf/CELx/3/PLPfcFH64d8fnuPq9dzjm7HY1glRm6jeWpz7nj68UPqbz/nR+vHPFsf4aNhMzhO/zARnWLyegAF1fmWVDnaByU/6+tPtTzC3/n1v/erAJmENhrtDMkLK+r05IiDqSMgwF1OipQSbbvl4uKau9UWW8jmOMWI9/DgwUOs1agsBllZZa4Wdyy2d6z6lmwsXfKEHLFai3bso3sMQ8A6TcyBTbvhbrni6nZBCEZYLV2PDxGtFe+98xbb7ZJpU2CsI5Ho+y297zk7u0RZC9oSvMdlTV2WVHVBURh83hJTT9f1bNuep88vBdy0BYUpMcaSc+b03j2ODmpyaLEq40Mg5Mjr83Pu7lZoW7Jte3rvQSmmBzUH85rQezGsyonBR9btlrPzSwYPyjq0tsSU6P3Ak0cPyaEl50xpK2IK3CxvSDHx0efPKMsJKXuImkIbDiY109mE7XbFdNLQti3WWW4XKxaLFT46EkZMW6KncQWzpmA2r6h0RmXPpu/IOfPy1RlGOUDT9eJmXRYl9aTmaHZA220orSYBm82S7XbL06fPyakQ5pjW5Kzo2o7333ublBKTpib7SB7lMG5vbnn+6py6mWJdQdcPxJhwxjI/mFI7R993aK14/foVZ2fnJF3SDx7lCkIWsy2TNafzI6rKolUik4jZc7u44+JqRc5KNJNzQmMotEOjmdVinoeKDEOPD4Hz8xtevHqFLWp5iAxBjOdi5ORoTt00GB3pB5HYuLy+ZbVuuV6sAS2GdQr80DOdltRVhbMQ/EDfe2IMXFxecXF9iw9JHMmNQgNlUVBXDZNZjVLSNtVuVyyXa16fXeKTJmbou47CWHTOApgfTIUhCOKIrg2vXr7i6mYBToPSxBD3Lbf35jOq0pG8J8dI1/csbhecvb4ioFl2HdY6ckwUyuKcpXCGoinpU6AfBm6WK84vbri9vWPSzMghE3ykcFb0mwtDVViMkvdDSNze3HF5uSRGiCojbiqJmDxl6Zg0BcYo2n5J2dSsti1nZ5fcLjfEZKhcxWa75HA2I6fEtKqldUQptDEslyuWyy1X1wuRJ/HSHq+1lrZ3rbDWAWOLZxhYr1tub9e0/UBIiiH2JK2IJPq+l/kzEWMCIUXavuN2ueTl2RVqBFRUdlhnBcQuHXVVUThL27UopblZrnn96py+j2Sl6fyA0YZMJudIWVdMygqnM6Eb6IeOs6srXr66JChDNmKUlrOY25WFkVxMpu89mcTFxQ0Xl9d0fZTcphTaOmICqx3OFBTakJUm68TdasXtYsunXzyjaCb4qEQWQxmSAj94audoXEUkkWJmebfl7OIKjDxEWGsxShNJaGeo6grrCkjCfNy2gYurWy6uFgRGhr8GHwLGWMhqNKjSFLbEx8Dl1YKbxZoQNWmU6IjRY7Rm4krW6zX3Tu7R9j0pZl6fnbFabdluOvwQUMoQciamTFGWouFNQhnNMIgkhTEGYwrS6KBrjCElhPEJkDJOG9CRYYigLFFlFJLvRrEgkcpR4EOLNprT01P6viOExHq15frmlrtty6ANXRBpCO8HXFWx3Nzx/jvvsF135ABt33FxfcnFzRK0mKjlBCSFKUv6rmVSObqhZzapuVsuuVu0fPXynPUQwcr4+hxonGM6a7hbLiiqCc6W9J2n7wM3lzdcX1+TtMagsMYSkrQWo8QoVCvIKZOU4vWrS84ubhjSSJLOWQqNQ0/OgSF6stZY47i6WdAOic9+/JSQFFpJx4E1BmPFrMJai7XSKZBipOs9d7drzs/P8WEg5ogpLGqXE0vH4vaGwjmK0cjubrnk/PKaxWpNNoou9ESVcMoy5Eiwmn6zEfPSoiQMmavrJZvtwO16RVlWqKwl/cSELhwhRsLQs1ytGLzn5vKOl2dXtD6TiJAzKWcsUFiLTpmh7YghoqxhuVjSd4GXL89o256kDZmEtXLfcc4RByl2Gm3wAYaUubu94/rqluvFHcoavErEwVMUBVXliGHgP/oPfuXP5RH+DLz+h1/77V9FqZ9wrReDluw0i29oHj9YcOcr5q4jZEMXHZ8vT1n/eM70RSSVCtNldADXZm5/HobSYHTm3fqagOX/fPkdupdTmgtp7ywXAdMGMW8xitvvZp77E+a2Yxlqfrh4zKuzI6afG1wLYTQ+siO4vP2XPH+yfotfOHzN3G7ZxJLn7REXmyn1x8W+XVw0ADXDoeb5vOJk0nI1TFnGhqebY17czZn8nhvdrc3euEYluPyG5vsPnhEwzG3L2XDIwjd89PE7NC+F3VdfBYqltGjePKhpHncswoSIxqnIeX/AV1cn1B85ytVOHkLarItl4OVfcCxVQ58cT6oFq1jxG2ffZAiWo7+fCTOLjgIMh6nh5rHj5N6aT5f3+d78BR9vHjJzPR+/fEj5wuE2WVzfx7nsTgzr+4pH9+54t7nBqMyHi0eEZIg/mIomr9KU170U5A5K2uOS6u0NX65OeNis2MaSjxYPeH53SPP7lYATo/yCTjKXm78SWaWanz84Y0iOIVk+v7vH6tmcB/8k0h9bUqUpbgZ0H4kTy/Vjy3Te8qw9pjCR3/n4a8w/EpZTsRwBhzaOBlKWi58raA56rEmEbNjGgo9fPmT6qRWz2Z2pkNWEiSFYy+1Djc+GoC0X/ZTrbsL60yMe/H4n2sVZwI3QGHTMvPqg4GS+YWZ7nrXHLEPFD798QnluOfyxHw2U1L41e/OgwM8Tk2rgpm94vZnTBcvFZ6ccfSyGeGLepclOM8w03dzgjnsxnVKZp8tjVuczTv5E2t31kCgWIhuSnGGoHfEtj9aZUke66Gis5+rDUw4/E1M6lQUAS1ZjQub11x3Hsy1ddgQMz9bHwpj7qMT0iumzLakS6cAwKxgax3KiKerIWXvAq80hHz9/yOTjioMvBzHTimA7aW1WUdFOHH6ecDZyN9Qs+5pXL06Yfya+Krsii8pje/28ZHkCpYt8vjrlUbPk46sHqI8mHDyNmD4yzB3Vecv6vQkqw+vTkqAVhYtYnfj4/AHprGb+ecYOeWzhFwO0bBTrquKqKlhTkVC83B7y1fUx+VlDcaewfcatwl5Ko1zD8rBi1RgKF7nxE56ujjl7fszpHyay3pkYihxIsUp0c8fmKHNUt3y+OsWoxIcvHjP70FKuBEQub7wYso0t8OuDkngSmZcdN37Cq82c1x894OSP5ZxUEuOzXbdDd+BYG0tvNWebA4Zk+eyzx0w/KyiXMqZ2BCRNnwj1qF1da1a5pEuOj84esHk+5/E/vCQe1FKcaUXyQOYSro4qqsOOrS/oouPV+RHzD40YXO8kUEYjyGwVd/OSbakJSrNOFV8sT1h8dszhJ8KA3IGpditmcVlp1rVjpRzTumfVl2w/nzN7KsfXQYBC3cvnt6cWd2kJHwy8Xs5ZxYp/+tH7mPOS6kbt5SBsJ6BrmEoMqyhzVKyz5HIn5ok6iGTM7u/k9GjMKGZ6KJGK2ElAoIRRrca4jfUuRygG57j/wQ2vNocshoYPbx5x89URk5fCSi2vO4bjArdN+EZTnhke/OULXi3mbCl4tjxk+ekRx5+MTP2k9gWXXBiKpWd7v2K1qpmcbvnx2X2uXx9y/3cVzdmwl3OxGwFt2wcVk1eKm6aib+BqM+V6PUE9rzn8XO7HqZA5FFBRAZpYKb60M1a5pI+Wyw9POfqRXIPbiuxPe7+g2KRR5gRaSm4rx+88/xqLuwkHv9lgd+xkLzIB2UoeCpUiVJrtquYiNlyuptyeHTD/WOO2ef95lHx/mGkOnkU2s4J1rbnspvzw4jHh0wOmz2VdVLdBDMIaMW6zfaZYw7os2c4UfecIX0ypbjTzLyPDTI8mcvKMk40SCZ1Bo86l6JWeTjj5I6gWGRMyCmHcplIzTPVosKnIg+MrM+XF+QnbTcn8jwuq27w3Ag2lyOb4RqMHGePXRcV5N2O5rbh5PSc8nXD4eQQjILrcAx3D3KF95j//T/7Kz/SsrXL+2QVw//9+/crf+i9yJDKkSBrZw1Nd0m9avvHB+zx+dMRs6shErLW0mzVt3/GHP/yUIWhS1mhlyQpIirefvMW779ynKRMp9+SYqF3J7/3BH7De9CQlbZOgUc4Sh8AvfPtbHB/Omcw023ZFu97y6uKaL5+9pCjnKGQzN+RAbUvee/yEx+/cp7QRnSKlK1jdLfjyy6+4uevoU8YrhVIOFSxNVfDkrfu89+5DhuEaY2C7HrhdLHl1tmLTefocUQGmkzmb1ZpHD4/5+vuPmTaKyhrWm5Ztv+WTT7+g3QaSqmj7DlM4UjYcHc35ha+/xbSZkFJHjj1Dn3j26iVPX5+BachRgFsfIYfI195/zOOTGc5JOzY5sG07Pv3sc25XLSo3aNvje0NSikenRzy8f8zx8ZQYWozKXN+suV2suby6xWehzfsQyIW068+mBd/95rcwROrScX23oG23/MkPP6GoZlhTsO16tNNoJa3Bf/F73+FoXqNo6fqA1vDll0+5vrojRkeXMyH3FK4hRc+D0znvvvsuB1UJORKGluvFLefnt9yuAz7J5r0pHEOCymom85K3Tu4znx+wXm94ff6Stu3Z9oGQNTEpiqJgGHoOpxNMzDx5+yFNU+FcZLVe8OLFBX6wDH3C50w2gRg01inquuTdJ485PpyhTWAYBtarLednN9xsV6RsyDkSPNRlAylwMp/y5Mkjjg5nbFZb6qbkR598ys1iRcYSsyIhes9FYUmp5b23PmB+0DCphdH84uVTuiFyd7ciadFEFaBTtA5zUnzt/beZjIZGq82Sq6sb7pYd2BIfIznnUc9Y1uMv/vw3qFyBUpnB96y3W569eEHXDiinIFsyDhMtqMDRbMKTxw85PZ3R9z3bTcerszM224511xOVuHE6Y0kpknLg6GjO2+88oiwLhu2G87Mrbm82BBRhBISdFn3TlAKmcLz/ztvMDw6wZG7vbrk4v6TtMz4GgvIoXe3zjMqJt996yKQumR0UbDY9N9d3XJ3fEpQixIxRGlcYQhhw2lBVDQezmpOjA4wyXF9fifSCcYSYKZuJFECGltIVFKXm5OSE+XRCUVf40HHx+oKby7VoHquMj4OAeDljbYHK8M33HtPUhpwV1zc3rNqBs4s7JrND0eW1VrS+o2c+aWiaitPTE3KKdNuW2+Wa9XZg0w9o50ghUxaWHALGKkwl0gYP799H+chiccfF9RWb1qOrCp86oh8wSozjdIaj+QGz2Uzaqde3dN3Atu3FrCxCtoYYo1xL75lOGo7mE+qywjq4uDin7Xo2baZuJnRDICYPWmGcwSVF4yxFUXBwMGW53bDdDvTDQM4QgpgdlMaSdRZGulEcHM+ZTacUleP88pbr61t8yOScRl1V0Wntg6cqJ5SVo24KCmPZrBesu5aUIESNNo6UZCPuSkfqOsrS4ZPn/v37hJC4uroS8LEsGIaBmMQtOuYsbaBaiyRFjjhjCWEgxjwW3xLGZmJQaFWOTtsZFZMUFm2m96JLjk4o42jbToBHJWaRWmXqusZpw2a94Otff5/13YquG1gslkRXkqyR4k9WFEqTrCETmNYNxEDTNHTthnZkZYeYKbWVjgZAG0fbdzw8mdN1W3IamDUThiFx2/a4ZoofOuIISpdKik3VpGGz7Xj44JRusybkQOojMSdC0iJP4BxZZXyMlE4R+o7KOkpd0MeEUplkxCQ0ochK46OncKIVrJUUjqKHsi7Ybjq516OxVjN0PUVR4KNIHmgD3ntSSkynU7z3dNueqqqIMaIQDfiQIk4rvPc09XT/vRAG6kkjXTVakVD7OCdljLMMKWJRDJ0UPCdNg3Eao0vWq1aeOUqH76XYPCiPtpLv1ust9+6dslqspcAQM87I5mEYBgrrGD3SCcjxqrphu9mgkbUZfcDomkSUfJXS/pnGWisxphRlUzB4z3a7BaNGE0K5L4fkMcYQcuD/+J/+x5+9b+vPX//cvv61f+e/zaZP2NWAirL2u4cNbhl49m9WlN9e8K17F4SkmRcdX62OOb+bcfi/TSnWcd8KPNaUOP+Ljub7V7w7v6WLjpg0Xzu44rd/7ZeoL5LoBI6ATtZi/vH031U8eOeGrx9e8eXymKu7KeZPpjz+rY4wEdbergW8v1fz+pct9ttLjidbnIk8apZ8dH2f9nfvcfBVEl3UbRxZs4r2xHD9XcXPff8pN21D7Tyvbw/wLyac/EDRXAbcMpCNYvugoLkYuPxeRfxX7/jg5Jp3mls+XDzk9e0BzW9MZdMWhYkXS4P2ibv3C7p/a8kHJ9f0wRKyZtFWbP/pPR7+rty31GjOZDcBlTIv/lpD/MU1TTVw2LQAcoz/Z8rB07BnBe7MVC6/V7H8lufxu9d03lK5wKuv7lE/txx9Ks7U7DQPowAt68cFy19ZU7rAo4MlHz97CIuCD359EHCz1nvTtuQ02ie+/E8z7z+5wqrExXqKNYnN797j4Etxry/vBADyM9EGvf6Fgs33t7x7/waAzVBw/vSYg48t86cBu00UNx3d/Rq3EgOz7anh+i8kqkcb2quG+YeWcpGpr4OAnUPCzxxu5Vm+V5Gc4vq7mXw84KqAv6g5+UONbbNs5LdiBKQH0absji3nf1lh39lgbaLdFnBZMv9EcfiFsBvJYLZe3O6Bxdccd9/xPH77mvObA44Otvj/6x6HX4jB284oSsW8v/bXv1zQPfLUJy3DYKn/qMEtMwfPAypCcdMRZoXIYjQi2ffyr1r8cUBVEXNeMvsCDp4LO9BupZ13p/MJ8OWvaPTMo3QmbhzuyvLgD9Le0GcXWzvTt7v3LLffjbz9wSU3m4bNbc3BDwuqqyzMzpHBHBoj4INP3L1fcv0vJph68tbu5850YhKVzdh27kfpjVLz6l+x+EcDpoyoZzWHn0K1SNhtwnRCUti9slac/aWS/iSR7g+o64LpM83Rpx7TpT3bOzQG04nDendo2D5SrN8PYDP1V477f+gFSOwyw0w0tYu7wDC3DBPN8n1N+ySgmkBuLbPPLPMv494I0W6CFGZC3jOCn/0NQzrykBT1lwX1RebeH63pHgjYuQdgY2b1dkF3olh9PYhZ9lIzfQbNZaI+F0MrFfKejb1rgV69Y1h9y4POlC8dh59kmss3ZnU74HI373fvOdr7Cj/N1JeK4i5T36QxBtnHeipkzW4eFqze1QzzTCwz808V1W2muRjoThzlbdjP+26c2xODnyi2jzLNa0V1k/bt5LYV1vEwt/sc72vN5rGmPR2oBH4AACAASURBVM3ESWL+seHwc7/PZ6mQeA21yLmIcZuhP1T4iaK+yjQXI2jexv1Y7WMrZGG0bxM3P+8wHRx9NohcQ61x64Tp5fx0L+e36xBRQWRnTPdmLHeGjHpIe9mf3T0hVvJvcRf2jN2sFeVVS6wdsZa8npymO7KEWu4TL/6aobrSFHeZg2dhvx50F4mNxc9kjZBhe3/URz+R+dutjZ0khN2O82EV5VXH1femlHcC3m8eGtwmM/+yJzTC9t7N9zC3e5ZwczFw+Z2K8k70yXf6qrt1mwqNihmzFfO+nQREPzOUq7iXEdpdh0oZsxGpi1gKeBkqAWf7maG+DvuOiFgp3CriZzJnyamful9tHopMQbEIIjsxahjv1nuYWAGhT4txHcjYdCfCOpfclEezvjd6wW4dx3/FpLI9dcRCjt1cyPl1R2KcGUpNMTKVfa2ZnA3cfLNkeiY5Ycei3z1j+KmVbhIlWumxVPQHmvomkqwi7YDqUa/a1xrbJWwnD0BhjKusYZgp3CbvTSZDo/fXYrq0z2O/+Q//q5/pWftPNWj7N/+z/zJvhg5TO7a+x2jHhAKGAR0N3/n2tyjNwPxwyhC3LBYL1tsNT1/eEJJjCB5XVHRdh1Kygfrm197l+GhK8GtC8Gxj4Eef/Ji2g6xLrBYjpyoaunbg3sNjHtyf0kycaORtOr54+oquj8L+iYmyrPF4iqQpVOat959wPG8wOeCM5ctPPqfvPBtvGHJk4z0JhSsact/z6P4R7z4+ZTqzdG3L5fWSm7sbuj6y3va4uiR7afl3xnJ8NOHRowccHThp6fSRy+tLrq+WoCwhaYawu5FXFM7wrfeeUE5LtBqwJtGuPT/+4sdscybEEpUUffCYsiYPgaN5xePjY9566xFD3JICPH9xxvnllTAboyPaFpVrtHNolTg9nvLeW48xamC5uGG1Dpxd3qBMwXK7oSgKEhmbHTYbnE0czE9o5hOs1SyvL1ltlmLWpiTR9b3HOUXhZsSQOT2ec+/4gLqElDXr9Yqvnj5D64KIpU+BIfTC5CgKZhNLU5fcPzmmrBzDquPs+pLlXUvrhdkXVcYlRVDgUOgyMDEFQ0zU1YzebxliQsVAiIpMgQb60GOdwirN4cERTdOwWl+QwoD3ka6NlMWULniyCbRdoChBW8XBwSHOKo4mJUNSXF1dEfqAKgvCkInRo5UYxSidMWHg9PSUwpYczCY8e/GUddejcCQy3RBIWeMqhdUGayOhNZzeO6KpLF0bWG4XhCiAQtQaj4YUqUt5WI05UFrH4WRCzplNu2bbD+is6YIwtpSzbLcbCiuauEeHBzRVTVNWeB9Zbtb0w8AwBMge0GhdQTI4k7HaYIxhNpW53fYdCkeXB0hqlJzQck1hwClxhj9oSoxRdF1L8pngNXfJU1pH2/bUTUEIQZjh5WiqheagcixW15AFnO78IMaEWooASinIkaJwFIWlLDRt2xNjxKmSkBMxJ6w2LNYLJo0APM7WGJ0oK4MNBmMUQ5/Ybja4oiBkaL2nKgzee2azCZvNhqOjOUMEqxIpZDaLLaayqEKz2cgaAc1A4qCeEbZbJqVFl4a+7wkY2j6RlKYAsoqQ5WZgg2jNTqdTytKxWa8xRUOfFb0f6FKiyhZyRCWZz2RFv9b7yMyWbDcdZWGJSbPyW5QBpSKFKsmCiFGW5ahnbagqS0qadugF0FcK4pv+RmsKCmcY/BYTIylqrC1IqiPnUuLEJNFy1YohKAplUBqwhuSlsIKyaJVRGFLoSQqqLOZlftMza2qiUUSdSCqx7RNVMyF5yGmgUBo/iM7nym+pmxkhBFJKFMqQGdClom17inJCCAmVFRpF0FA7h48DIYtmtnMOH4Mw6VWS3BYhK0UeEroUIC/5JEZ5pSOFiDEWox3DsMa4RAhgdMXgs5i4xYAmSfdITlin2fR3aFMKKKyEpWq0gqFDZ1kvB/Oa5fIG50qMK+m6gVzsxldTaovxnj6DdgqiwhWatttQK4tPEVVotIahFY3dmBWuqEkJYt9RTyY4m8QY0kKbRH6AOIo5pYzTjpgzymicFZkPV1pW7Q2lrklZmMXOOQFiQyTqjFNQWiM6wBR0IaBcIkZPjJGqmdB2Eisxy7FAUxQWmws2viOGgaoqsNaSEJ3aqqoY2gGjQBtIWTpyRM86UzjF0EtchSCyFiHL+KuUR5Z5xpYFGYg+4RQMocdqKWj2fU8ahdSdsUSlSD5QOMcwDLgioU1J2ormctQJYwz90GKcIqUgRRosKTE610a8F5CnqWuilxbeoe+oipKkYeg9JEM9EWM83w/UZYnUNGRN+SSgso+i4euMxWnDEANbJeYnQ98ysZZCN3jfkhUYp+lj4u/9z38O2v5ZeP3y3/zvcn0+kApNcbkhF5bugXRzJKd48SuBajLw7YevuWynPH11gr4sePB7GdtlMdVqjLQ3a0X7oODlX888eOeG7eDoe8dwW/HOP4DyRnQ0UyUM+OHQUiwCt98oWXwrk4482iXyXcGD31KUd3EPaPh5sW+19hPN5S9p0rst1kYmdU/7u/dwa6ivEsUmUZ33aB8JUwHMLr9Xs/x+x/17S25XDfnzCbOvoLzLNOfDHlArbjrixLH4WsntL2TKt9cMgyV5TfVZxcGXco+zfcat017j1E8NL/5GRk89xiSKMrC5qzj9zYL6Ou5bgd3dsAcU7j6ouPnFzNvffc1iW+OjIf2zOccfC8PUdALS7Jzus1HcfNPi/9KKogiszmaUF4bjDzNZw/RFvwcTY6kJlcI3mu0jRX+YSWWmPtc0Z5lqEfealcWdJ0zs3o3+5luO9QcRNR/IUaEvCx7+bt5vWIs7AdCzEd3SzUMrQNbPBfTUkzaWg48dk1eJ6jYIqDGIvqRpZbOeDcKs2yS6I02xEvA1GzCdABfJKoo7T6wMsdIs37F0x4rmXJhkthUAeZiNZnIxU1y3hFlJtorVWyWhhvU7YDeKwx9HbJf3xzB93AOjO63Lm29aQgPdo8C9PzBMzsNe77e4HfYxJeckm+6bbzqGI3ArmLxKY2wIyGLX0jHY35M1JYxSy/JtMWyqrxL1lTAzd7GXtaK43BBnFWYzsPr6jPZY0x8rTAvTV5FilXArv1/Hu+sIjYxVdIrNQxnX+kbAmfLW7z+bR8M2t+iIjUgfrB9bkoNimffFlclXa/xhRXG5YTidYLZedJ/npTAxnWL9yHDwTO6DO6A6G3muUz7K30CYOfzU0M8EBNnpee7iPFaa5osFw8PZPl6SU/QHhlgqYsEeBNl9r7zqGI4r7CaweVzSXAzcvV/iNplQidFacxmkvVwrqvN2r71qVwObdxpsm9ieWpKFYpWxfaK89qggsgU7RiSIlrX2mc0Dh5/K/MVCUS4TbhVwN1v8scjJoGQ+dqCn3UbWjyUf7ADn5sV2BBP1Tx1rmItu6Q681iFT3cZ94ecnX7FSDBNNsUkjmCQMQB3yaHw4FslGwo3dxH0xTOZMwCv5zGjC5RMqZIZDAdB2524GAQW1z5RXPcNRIceJeQ/eDQeG5qUwT0230/GWnJ+NyH74mdsD+8lpTBvpT5wA/u2bIsGuALWbi12xZVeUAlBB8oUf/z+WwhB2q7jXdo2lwa38XuM2Ob1v5w+Vor70eyO/HQC9K4JkI/lh9cQwexnxjRhVVQuZD7uRsfMzAfyKpQB8sh411bWnG69tL8+xivvj7YsnQ6I/dvha4mmY6v2c/+Qr7DTeFfhGGOCh0TQvtnvztR2IvQNid+tJpGTA13L+O9DW9JH+yFHeSI7YgeK7dRtLTXXeogfRuA71m3MeDu1+rHc6wzrk/Xn6id5Lk5huNDDt4l4LX6Vd0emNNnFotPxmpRmmImGxkwTZ/a4Ooj/v1pIPkpWYT1bY/vu5cDLPO6Be9KDzvmMCoL3nMOP7bhXF9DTI3wDdscX4jN0muiO7LzzvwOZUqD1w7RsxxCs2ierKj7mmp3vQyFiMxdVUSNHgN/7v//qff9D23/6P/1YG0BmsNiNjBIqqhhQZ+pb7Jyc4C8Yquv6ObevpU0FOmkgEZWiHnrp0RB9oipLpxFKUis2mZb0ZsK4Ebem3A1YbUoigpK01IW2bpnSoGPDDgNFONo46MwwBrcS9OWU5vxAEpLFG2Do5iAbptu1JyGYzhoTKwt7LMaEtNIWjHVqICVuXhCBGIcMwkNEYU6C1bPJcYWQjmjIpBbKCkCDGSGY0OgkeY2px6Y49tSuYNSWvL19z7/iEtu1F2CkpujAwBKlO1E01OpqD0wafewEkcsZoTWwTSmdSShhXklIexZETKSU0gZwjxpQo4+h7T0JMb1L2o/GOtAZXrqL1AyaD1aKP2AePwpEzoDw6J9YxYV1FZUq6dsOsdvThDZMqhIA1E1KKhCAPSIUzEINs5usJ276jNJYh5ZFJpFFoYggUSqO1JkQ5Nz3KM+SR4u2Tx6aIcQ4fE3l0a885jCYuNaUrsEp+w6dIjiJn4WzCEwjZYNCgMlaBzgl5phE2eOsDPgYBbILHGEdSCaM1OoghG9lSVCVt26Jdgfcepe0YVwJQadEqQFGCSmgtQFscPMPQYXVJHxN6NOJKKqOMxI5OmcYWDIygXoIUxOAoJgEbAFBZwEstLC2doR8ZkHosfCQf0CiyEYBLKYUrEHA2g7ENKQSigpQSqIRDo5TC9wOZxGRSCyhiFNoqckzEBFpbVm1L5QqsMYQkoEQYAtoYbClj4wwYLfq4LlfCmkseaOiHDVVtiCFjdYEb57+PAVsIsxjYx6rKkRgGuaYx4p3VKIwwDUcGZZcDFhmDQotphU/SDaCMJiBrx9qCECImifaksLeFgSeAYKbQWVq6fcA4K9fYZ7wPGC0mT4mMtgqGgC0dqEQOEYW0DQ10aFMyJDAJrFEQZD5iDkwmE9b9hozGmYLce8lShbSSV3aCUpmYRHc1JQETwxAJVuIuRo/KFqdLVBYwVBeaEKOAtCkRUhTGsrFo0n6dKuuIQw85CstUW4akBCyzIjPQhwgxUFYyr2iN0wLEKzJ5CDR1jdKG1XazH+fKFaxWK+qmosiaYYxDYwzayL1FtKcdIUv+MsahlROtWsR8bQeMa60JfYcpSmIScC+kTN/32KKUuDaG6BPkiEZa0awryRlZdwZyjGiDxE6WtSRAbSATcVgxuEwZpYxosiaDTpGkNM45+rAaY0lRlWMGTomyLNlutyhtiUmKGFpBCpEce6Q3TIDTIQYUVqQgssxHGCLOjTIJUcC/lDIOzZAjrpTOCx+C6MImuebOD9S2lu8BWinpxoL9vdsYQ7Ka7IO45mYoioLNZkPWUJcl1mrC4FFZ5CSIiaTkN5SR+/PB4ZztdovW0qoqRa4xn42gLMg9q8xy3vK50YDMGFJKZDW25mlNTJ4UwShLyrJ+nXNkpRnigHIWVCT5Qd73QcxIuwGKApU1oU9gRpM1reW+SURpjR9zat/3aK0pnSN6YbXqJAVgYyRhee/JKaGcZfDSTZB1xo2McWOcMKi1oQseV5X0fU9pHcF7DIqkQGlNyIGYJS5ylhxaaDPG+jgOSVjDyliUVdhRW/nXf+1v/zlo+2fg9W/81f8mgzgn74xhAPx0Zw4TuP4XKkLNCOjwU4ysXRt0cd0ynNToIdGdFmzua8JEjey+sNe9Le7CfsMH7MGjWOlRy5P9JmgHKrqVaLTuABAA00U2jwpiqbCtbLZioaivRdcUxCxqt/nfmfZs71nqm5H5Uqk9iOdWcc9WzEbAp9AI+JUs6CAbS9vl/aYPwC16UmmJtdkDD+uHhtN/tub256f7YwEUC49d9dIWelJLmyiy8XajlqJKo+u8FyBW2nrfbDqBPVtHRfasoGKV9oDFnslmpDW4nxuqmyCalpUmGfn8bsMMMjbVmejsDYeOYuHZPC4pVgk/1XsW1G7uTPsGqACJg/7IUl150T5di6bnDpjZMWcFMH0DRMObc7CbQKzNyNZ6wzglg10P+HnJMLfSktrLBnrHgNwBPDt2GbDX5IylMKRUylQ3AbMN+8LBjqm4m/cdkLUDTHZg2w64hZ8GjHafT6P8gfF5Dza4pcStHtIevNRDIBu9N+H7SbbrjkGYyjdGkLoXBl9oZFyKu7A/3s5wLrk3bDpGjV4QQCEVP82kA/bMsh07eXu/2IMiYrbGHtCunt8RjifEyuzHVnfxjUbxJuzB1d31ZPMGDHF3PcNxtWdA78DWHRhvtqN79NjGnrXaMyN37P3QCAN1z7jLoqe8m8MdAGm3Mu47Br/2aZ/TdrEYJga7EcBoxxD1M0esRAs6FXrPhDPbsGe/qvBmznc60yqxN2C0G1lvdjXs88EuPkwf2d4vaM6HvRHhLm53YP1wXJE1e/B6t953jMldLOzmd5c3fxIA0/6nGctyHbs8+yZX78Zox1SMlRGwaiHuWP6wxG5GTGME/vK4n2nvSVFh8krymNl6huOK+vmS4XSyH1/R0JU1lZyivPb4A7sHcVOhR3BtPKcxF0oukvd3eTCWIgPhFj2xcXuN2t13pQjBaDopRaXdmtj9n+mlcBRrM+bZvGeO7+ZpB+LujiuAaBSZlp8AsFXMDDNDffUG6PUzu//eT4KsvnkDWO5yN7AvXv1kHGgvAO6uEArsmd47lm1x09Hfq3/qezu97N29e9f9sQMmd6B5dd5Czvt7z66QuGOx/qRhm4qZ9RMpMOzAfu3zfs52OXe3DobZeJ2MMTwC5zvW8Q6s3oGtu/uXzIPEu1uHnyok7O7b/dxg/Js4tu3upsUeJN6txR1ztriTQpif2X3eSE7tz03G9s0YuZXfx8w+f46fD42mWIT97w5zt88pO21l00Z0H/CH5T6+/HQsEPzEOOyKsLt400PmH//9n41p+6da0/bv/t3//VedtZAzrjCgspjcWMUQBoq6YrPtCClzcb0gKdh2wu7LWhGHiLJgrCHFQE4JUiLGxHrb0Q+alBTDkMhRNqgpJYwVpldOSnRdksbHKGBuSGhrSDGJQ3jKaBS+91ijUchmNWvFehhGw3LN3d0Kpx0h9Rhl0dnhnBJdU62IBnoFISYUBpU1ShnR7VMarEGlhI8erBitZa+IITP4SE4aYypCBK0NylpizgJE5ERiQAUPWWGrmsXdhpi16MyOyaUuSgxqD9AFFNsUCVFBVqQYSSmKw3gWUAgUCgPKEBFhaa0LBp8FXOw6tLYkOxp5oSnKCmMcCkhkCl3gY2bc1pOjx2DIeSAhxjoZTWEtPgWMGTUnUfhRs9TagqHrIWcKV4iWagZTNWw7L8e3jj5nEhCSXA8pC7MvK7RWWAMhibZwXZegPCFuKByo3OMcUER8jKIHa8BYTUxiMLMdxCQCJayqvu9Q1hC8xGVSmgwMXY8ra3xKJJ1RRhiz1o5AlFHj7wpTT6NhnNMheGxV0HU9dtRENCOY4ZwT3RttBLiKEbQmRE/MCm2cgGGuIOVERoD4HAMORWXlhlRFBT6ihgTJkmOirEt86FBKYkSh0RhiysQMIWWyUiityGlsEVeapNJeX46sCCGhXYWPAa+ysO18wGGIIWCdRRuNKQu6YSBpzWYYQDuUVvghkEJk0oiciUbhnB11LAXgiTHKOKmC5CMuW5IPqJxH7VAZX6Uj2jjIZgSvjbTgR09GNGljFNabIuNMKeZR2lC7kuiTtCQbWas+RzCiGWuMGbVaM7osxWUeDcZgjCWEKBqriNFizGOuyomspDCDsfiUCRiSMvg4Jn1nsSNzXRstkgFjO0ZIogEeiWhjCTERQqRxo16xEralMmp0sRfAOWrRcR2ixxYWjcFiGHyLcwaFRWsB1WNUJKUJRtrWVc5YU4gWdQ4CMqnMQAKlyTlRFRVaKwbfjWy+goQhxCzmjMaC0oSRDWmcJca813bWzhFiQFtgBJ+0FkkCMuSQ8FkkPMqqIoWw78zTBoyIEGO0RRsBayd1gdaavu8pbI3VBTlmFOKYbYwlk/HRk1NAJWHR+jCgjSbFLAAsIn0SVWIIPVppGWdtiUEeija+w00qAfCCR2tFTJkUAtPJhOA9ZVEK6zhFqqokBo8uFSENwkg2gUTA6wGdLIWpxg2DkrFLSWSTlSamjFGW7aZFKUPOCa0yKUV2T48xiQ53CF70lLNIMhjrMM6Sk2gGZ0CPGu1p/A1tpB/aGk1ROEgRYxw+eqw1hDhgtGSJFBPWGMhZzlGpsei4y6MObQyltbTbDWVRUBgzykCM2tGFQymoTcVq02FsQcqiZ0fOJB8pC+mC6LoNdV2SiKSQUNYwpAgmYwtLO3QklTHWEkOQ87FO7t0k9NjpoZQi5cSIepOTx6JQOY7jqRhCwtlKnhe0E+kLowWAV2/Gaei2FOOzxW7uQeYpZ5E7SEkKZCAAd1bQtZ6qnpCjwjhFiD06Z8gyFyHJGKUsBQxjRetZ6Uzfd5RFgRqkIOcQVrNWihQCpCQGjzkxhADa0PteCoVO8R/++//en2va/hl4/e3/9f/91diI+IaAI8JWQSncKhCmluYyYjs4/lGPTpr6ygM7cGdkD1V2NAYSfTjjFZPzuAcT3TqKPmvagS9anleS6A6CgAhigi1mV1oOIwQFp0ewZaeHp9ERmguP20aMh+lXa2IjG6pUGrLT40ZertUMAjabblzXGtBvAFsAtNprZQ4Hsqm2AxRrMTNJxWhoY+X8xdBGWs61j6A0CkWqLAdfbISVFjLGS+4bjkuyEw1VMwjDsLrsBMyA/WfTDpQYx2YHsGoveovZKopVwE8EkE1O7Y8BEKaWPLILTRAWnFuPAMSog5pHgE77sc06KsJ0HL9Cj3MBbiPfi7WlWPr97++0IJPTVBdbjJffdXeDAEitsGwVSgBbLZvsWMkYap/pD+0bbdmpgFGhESTMrkeDPKPIhbCAs5X4kxgVYLJY9KD1HtxSPqESuOVAnFjRig0ZjMR2rO3++7EU5qSOmWT1eE2ZYh33Bj6xMj8xJiPolhGNYjNq7yLttGaQWFV5ZGWOLMRUyQY+Ng5/KEzP/tDui5sg4IM/cNi13Jd0lHWYrcSCGQTAU2lcFyOInJxGB3lTjWFstoE8rpkdW1HYdg4zCDCYrWh/VrcynuXZWggiWmHXXubnwUTG1ShCY8dziWgvALld9aTajey4sRgzzrMCUmHk+dTIuWmR4R2Z5FEkWaxG9wGzlaJ8bOxerqM/Eoab6SVG3Uq0rWFs8ilkbBTSKq9jJpVqD8jbNmI2XvLBDgwsRFdaJYjNGH+biNnKNdu16BfnQgsYuoudTp6fdBLzoTwCWMmOhn5toD+t9rrH2Sh53FKKYhneHBdh+e6vs7K4lRe5BvPme9qzB2JVzKO2rpGW9zyCYDHj7kSTWsWMnzuSlXHSUdh8Osr5mpFtCWC7JIWhESw020BqrGiab6WwoPaMVr3PjyorKTBlRlb/OJ8ZspNzQwmoiobiZqA9LchOmJNhaknlqBWsJAdlJ3nIrQM65f0Y7AHLcQ1JPhegzN0N5PEesgPXVIL6rCVM3QjiyxoQAD+zfVhge5GuyFbOoZ9b0ew1YPq8PycdMraVWPZTuz9fheQqlfmpeKouWzBmn1f346Lkd3edE/8fe+/2K8uZ3uc937Gq+tzruM9780yNzJE08mgUKJFgwDDgy+QqAXIR5C/x/5C7IHcJkIsYDuLAQZBAgSIbjhxLI0uekYZDDme4yX1e517dXVXfKRdvdW8yCKC5CCLBUQMECXJx7e6qr77q+r3P+7y6z4M/dyhSDC7YYt565d8WWcqeoE+VJkwkH8pe7QsP5hvD1nQsEsAXhmKZknU/KCSKNxRrCFNDfd7vu1Z0lLWUvRDfRSu6hWXyrB/uCzu6W2H6TD8XONBf9nSHTu5Zw366GwKXvaa6CEMIPtzL1PBnaDnfwpDIQVLDXqfKQN3WZr/HFauEpm2GmRC7Br9hbe8KsiD3njS28jmM2ruKd9d7rsTRLHvrrvgF/rKVwHWgrO2g7wA1dHXIPV+H3ecwoupQcv8Ji0oKT04Tx2+LJhLiy/kz/du9xV91ZC/QzC/rtNV/9Y/89b3c4IpLKbHu2qHNXNPHHmUU267FVpZt6FFaU5IMlYl9pIRIZRzkQuxlIqxTGqsN3bqDZEhhoEeNwhihPE3tyUYRkQcZU2Tx185jlJA/OSShVrK0z2qtqb1Hl4EKVpoQOjDQ5Z5CYjJqaJzDDqSMMuI7jDGiVCH0PTFGvLEYZ4kxDh69KKHfQMbt/gqbQO47TInYYvC2glT2Aws04JSmsgqnC1YlTImEzYawaZmN53hTQ9QkCqpoobqytOk66yXgkiyAEhLeVpRYSCVK0aTy4njFsE2FHgUW+hJwI8+274hFyLIu9eAUbeoItx2mT5iQhdDsOpSVoCC0HTYqLIHaZUwGih5or0xJHTl1gAzSMtZLizKDRsBIG3nf92jl6HoF9ZxsKvEiV2CMorGKsbE0RmON3odzOWd0qWn8gnadyclg1QiipVcNF7cRVUY4XyhIEAuWuq653W7QTmjKHbnUNDWZoWVfKcwwAdx7Sxw8niFG2r5DDT5IoaoUXZuIBVCOoh1KOXFtDoGgcXagEbV4R42iFC3t6l0i5IR2Fm09MSmcq1GqIua35JtFNqBKKZw2ElLERPaGqCFYRe083lZsbtfD2pJ2G62UBBUYlLZo58koCRYBZc0wZMhgbEUphpQc1o6FHjRgVMaSmdQVpiR87elLT09HKB14SyiZysr1FUJg5CoaK3qDeig0dF1H27YyRMdUkKCx4mFVGSlWmOG4IQETKaNVTSpCqaSUiCnjKk9KiZGvhABH4ZSWQCpE0TeEuHdFam3Rzso59kIVZ6BLkaSK7CWmEEog6UxJ0t69U3gYpTFKU5WCThkztPn3JbHpA8VYCpqMFnexUWRtaGMiWVm34vi05CiEXFGZqlZkIihNU48HqlpLoWAI05NRYMV1652DlPFWKD7rPZsg2gDlIapMl6N8HqUp3qCVxaEhy/5hNPyLdwAAIABJREFUVJLjOKgqilYSzqdE6aMQlORB6RGkYwFxp+LGxCCUu3U1FCEnvPcD9T1oFABiwmmDLkAf0QWqpqaqLc5rYmhx2mOVw2onTlNhj+lDuydbN12Pdh5fi+NUKTNoM6QYl0McOgw82lRoben7iNEV3tVobfGuwaDFgauGm4BD9A7KoLwla6jnY7ZdR0gBbS1q2OudcwM1KooMhQE0KQllm4In5xrnpmyDp6NmEyuK8XhqTJa9sY8B62XtygA8UVFMp3NKEirYDS37QvUOT3alMGoavDIYDNY5Ypaih6xhoHKyToYAdUeOp5TIsZCjKAaUGUj5lNBOD9eC7H87wrl0QfZbDVoja3coBuRcmE+XOCsakp2LuDKWnAI6FWLuqUcVbQwyxM0YjGa/HgyKUeOIqZW/fCFbOSdKFULomExGVJWQ6t5LcC+eXUtVi+7H6II1is16hVMFrzV0hdxnCcxtNeg+KikYItdiKYUQxE9tHViXyaljOR5DipiSsRTRfThLRNEbWMWOaDWBRCxJBqMVcNpRaUdjK1QUSlZnpLMDRW0ctii8NsQ+CeWdFTlmJtUYgsLUnqQBb8laoShU3mG0IqsiD2fOy6AzK1/wQ3mrOfnb17/bLwnIhIRsXnX4yx4KuJVQRP6il9bIC3H/7QgSs01DyDSQlhv5+Ti2pErviUDTpW+0m+o9/bkLI3Sfh9Zl6JZuT8ToIM5KIU+FyuoXbv9An+q3NJ+96VCpsL03pl1aeeDjbcu7aZPQVpu4p+12FNrOv1m0hDLf9B66TUYnSE7+vH4iv3dHNxUjoU+cVaSxExdiJdSs3STWj8aEYSq87t/SVjv6d0cQ7lrJd9QghW+1cJpW9Ar+ot+3xLuVEEX1Rdw7R+2NkHL+ohXqcKCdspM2USHUzEAkiquwm5s9eSRB+VtvK7Cnd3dUZxmISH8dBkWCKAwAVMyoONBvlaU7qmlPG7oDL62p3wgkstN0B26vNdjRi24Vmf70WgLVkd3TlgD9wtE8X+8HT6kkYV53VAv9NncSalVGHp6n0p4NQmDtHLC740URNYTZ7JzDw5qx8rtTpfZOyFSbPe22o9z8+VYcqU7viUehWCU02ZNvtYRxYe73lKtKoBPDGkx0cyM03OuthH6Dg3NH1O7WZXYaFfOeUP1mq3z28l1Md4lcm4HmkiAtNYbtXXEDh6nBrZJ4oodryKwDcdHI59hI+NgfeHTI9Esv7eXX/eDodfQHolbrj8eDw3EoBld6v+7272tHGydpw94Pf+vlz5H350hjL4HJOu5J0p0rchdMhZkl1waz7lAh4a47dCeOZdMl7Dqi+/KWINeKsKj2+0o/t2Qr14HuInYVqF5v9nScDgndp2EtF/xFi+6Gfaoye3+q7uKw5uy+a6A7qvdEs12Lu3XXQr67lnb7QZx6zHYoDLzZ7JUVdptwqzgoI9J+OFka2W+cZ7U/jipJ4dpsesym3/tod9eHv3rrMN4FgHYrQd+OBlapEKduIE+lMLK/P4zt/v8TmtzQz2TvMp2EwKnWpJHbU8qpGkLjICqc5lzcvXFsBtc4Q/i1IyXZ3yN259muhWQME9n7w9TsyUbeLi05rnEXdmb6g/pb12XRcq7j2IqGxCqaV90+5DO7jobhvMWRhI3uJlK92YjGZyyfS0dRIcRGCN3d9ZgqzebBaNgnpD3ercKemN692iMnRO5wPZo2YdcJd9XtqWHZR6Xjw6yDFJX6vF9Lu3uTHKO4D7d3a3FHtKoh/DadHO9UDZ0NA7G+elQTRnoYDPa2u0ANwbFthSz2F+1Q1JPrJ44M2ch13h1V2K10YAhBvfsAst9u7lbSiaEgTIeiyeCJ7admf03GWlG/3hJHmjA1+2ulGEWYmD1tvSN5dV8Gd3seukfEeaxj4ea98bBWZR3u1oBpE24VGH29wbRyf7bbtKdw09jTT+WehBro/WFQWqrlu0eq9F7rYQZ9hEqF7d3RsPeZ/b/XoVAU9FOzLwzuCtTy/UPo9t3++8u8/kaTtv/1P/kf/lEeQsqRrcRhkjWVrYXM05q27/C+RhVD0Y6EeAX7HAStsgaU3tOUCeh1kbZSncFo8cilMgy1GQbdDCENRpyBXiF0lDHoUui7HudqUBJYZV2EQCJSVMEXjc+Kyjq0r+hDxBRNRtrfQwfojNYGrQxWW7wSglcbQ5czWmnRDyQGqlHjiiElacHEaLQ2FC00X9JCEuVSKDnhjIYIXjt0tuiiQVmstvR9CyaRyxZnIeYOGXgj7fs5Jpy2lL5QClTOiQbBuP37iX2gqWpS7tBEjMoYLHZXKSHj61oG8yhDSZHaGSpv6HIA44QIlhNL6Fpc5eRhv/TEZEha/ntOA4Gg7NBWPlRjo1Cm1lisUxI++QpjLTFFnNfoEqhVoS5An/YhQR97+thinFCWIUdiKbi6og0dpvIUlTDeYGtHGyQsSHRD67MihjIUDBLeakwenKFKzklEvgimnEEPFXytKSEM7aoJpS21b1BZiToggUqREgsOI+umCCFYcqG2jm7dorRUGENIaC1fAnJSWOUpIWGVolKa3LdUeqBiS0/O4h5VQ9AdYsJYL9oILzSddRY9+CnbbY82BmcHnyYaZxwGRchgjB3UAUIKloGwVQpyERI0xR7rha02FrLKokxAigJkCDlL2KFAKVEz6GJIKQp5pwwKcQhrK19eYhexplBQGFMRh7kHzmmUEvJBay10fFG0uSVpRZ+i+C67DqsMJDDKyaCigky2j51USHVhXHlyv6Vymqa26KGV3g43z5jTUPyA2lWYorA4nLVSrddyTdWVky+FMaEMaOXpKYQSKRhaKT+gs0YlRWM9pigaZXBAXVfEnAhdx8hX+KykZV4X8HIca+9RyL4GkLUjk1BFzmsMPUZLYanBoIbCkMpl0L94rHFoo+lCR60bUp/xxlJyxKiCKD6ElmxDj/eO2AfZl5SWa6IMLd++wetCG2XQl1ETVJBjbIwUCYwzbLsWv1t/RlEZ0DFikHWUUxyKBbIOlZIHmW4gOyvn6EPEKUvlKpSV9WS9RRVFLDKQylhPVgo/uJx1jNgccEbUElnJug5a0eWE9nIsNIrQBryvyX2icpYQEiFEtPWUAiUW6qHwUWKEEqEoJqMxxIgF+fc5oYsWXcVAZZEL3jppX3cybMy6BrTs50UN9ywyPolipdc9RYMzCq0yVmVGVpP6jkobUu5QZnCZEelDpPISoEKmdgarC+1mI9e2EqI0x47Gj2QdK4cqiZx6amexumBzQZeCLhrrHMY7QsyQEzklKmvwWVNCwmlNbSqhdgHtHTFnKu2otRSqjJIQ0wOUHmKHtfJzKRVa1RNjBqsoBaw1otrQkPotTkXycN9GDwWlAGPbiJ/YWPq2pzYOg8ZjMHGg9/qMcxW5DdikSH1h3nhCK8S0qxTkLU3lmY9G1FVNipkutSgdME7eUykJZxTOi4ZB8LWanA3Wj+mjJg0e8QqF7pHjqy1FFepRI6FzSNTOk7WWIcclknJHnwPWKrx3pBSZTBsOZlMOl3OqytJtNnIf1DUhRkaVRxWDIlM7R2UUo8pwMJ1irKEg7zvFgikaQoYMHoM1mpJ6/pP/6D/8W9L2/wev/+a//MN/pINQJ+2RH/YHCLOByBtZ/FVPnFh2E6x37ZF21Q03cQaaUkhJEwruppefk+rz0K7JWxJwoCJ3tBSKwfc2PJhamWidGr1vr9RRHtB1KqgkD4JxbAhzTzEKt07kQQ+QKj08EAmpiJbwLI6FDtuFjcUIjbSji4sdJm4PNNTOraoHutG2eXgvA0HTCJ0cJ2ZPhcrkcY2/easGSCMZkrOjqkAooTiSQswuAFcJ+QxB3o9pE/2Bw/RC+2UngVhq5M9Cy3E3fdkTtP3CE2aG6ioO5K0QR6qA3cSBmFS4TRHNQJvkv8cdaTa0vg8EqQ5D6F7r/XuLE0uu5TOlgdALc1ErqDJQZEYIKbcKZG9kav067kO76lIISL13TGqqi46wbPbvVx70057O2g3IgbdE4i6Y3f0dxT5c3R1TtKJb2j3dld1bwiuNrLy/nWqgDE7RIehW5S25qmAf4sq5MkIcDsPJshtIrt2x/GbAMpCiO+Iy1Xog/DLVVSQ7I+FXNQStI0MZtAO5Mvu2/zB3+2Ozo8l2NFseSLtcGUyfh2DnbQjnNhIU7c7PPvDqE2FRESdGnq9q/e2Qr5ICSvGD8kILlc2gKdpdYzqKu1oHIUDDwstn3r0vb/aBpqwfoclzZdgeO3RShLmlXcoaNaEM4VfBDrRo0Up0At6QGivkM3LbVTHTLwdFklV7yndPHGeoLlrQmuLk2u2OanKl6Q4r0sjRH3gJo257uuMRaQhZduGsKtAvPWih8FSRkEmHtzSsHTQFdh3pDt3eW7tr4c6VEbLWKvx1T7+sMEGI+B1BjJK9SKjFljhxe4JS3ojsT7pLZG9JY4e/7FAxU7wUhFJt9rRq9prqrCXMJIzPlaafyb6Vq6GQMISRu71GQkIhGHNt6acafyvhZj8ROMet036om3g99VCYG1rNh3A2VRKuKd4GoHaTcDcdxUpnRLYKt5HCmk6FfmJw24Jbpz3xqzKEhd9TjDsKeH1HunhT49DDvyt2CJLroTDGoHwYfreJO5cqEh7vCmYF0tiRh7BQ9nq97xTYHlpsL7/DDi3vu/ujXyXCzO33p24mnunqOu2PzS487Q4c2RtyNahBYqFfOglZa9lPZE2Jqsdt5EakYyFO7bBe5H7QzfSe9N3R+WFq6eYG2w0qBasJY7O/D8daVDOmGwZMDh0fu06B7OUeq/vBURvZr788ENKbEyeaIQ1u1RMWXgL+iRz7YhQmiCrCxEKsJSy+vWcHPZEiDQR4uzCsHjq6hcEEOZZo9sSv3DO1/I5+p6J4u0/427ckcz+V/VPul2a/boodKP6h4FKcXJumL7hVJNWGMJbzujkx3DwxXL8ngbJfMVy70sXQHXl2g+y6uRQ0toeam3fMnuJVWfzgu3sLSpHGZt8F9J/9p//+L/Vd2/7VP/LX93JanHsoQ0h5GN5lCEF8sBZDtkLPpNShi7SbaGVxWlMokGRzizGSQ8HWXog0JT63gizOHZINCEGUZRiSKkna8aOEfSBDOrLWdCmgGcjX2OKclTABtae1YivEjFKKuGt1V5bpaIQ2gbZtIRcmrmITWrwx9BG0chK+xoQu4L2lpCJcp1bk0lOKIRfoSqT2NWkgqFIUP2YqWX5PyFhl2fYd08lk/5BsjQTGubAns9KeIu5JXc9kPCFrS069tOoqR1EZZ6RVIMSWUuSzApQQKVpTVRXrTZJWWe+EKipFwq0iQWvJEmTJlHZpadZKkUsZBpFpoVZLwaihAm+cKBqMQSkZIqaKeFdTKfIwqiCGgHeOHCNOQwk91hqKMkKY5YJzRojXKF7B0cjTx0AfO3zlqL1jdXWDrSo2XcBaP7gTE8bI4CutFKPKC0FlFN02ikJhoMZ2MHuSeUCE0FO0phnXqJzxw8+V0FNiIiSF16BVoXL17lkIbYy03IYtJSvGjXhp+5IlZNNmvwZ1ysxq8Z+GfsN8+FxFdaheWs1BdAaojK8spiiU96iS5SHfKm6ub6nrhlIpGUwTACXKBj04cL11bG9XHCwnFHrIAasGapmEUgVvR6JEwOCGgVqpRGzlxb0YInGgG1KGUsAAZIgp0bhKHhq8wnWQQyTrREkFowYXo5ZwIseEqi3bzYpm5CBq6lo0CtEUrKtpk5zfLnWgC7mIP7jEjsYZ9NAqvQ2BUmQI1s16w6SqSCnRbntSDkL7hVY8mMaQUXjt2G7XaGWF3PRWdBI546xGFF09zhqC0qw3LXZcE2MWb2gl9KIJQzipRJlhs6hTutAK9Wsd3VZCKOUtOkU5d0am1xcSxle0bbv3YBa9m3Y/ENra4C34pqKg8L6mdY6+VWijuW230mLeZcxw3QhpWbBKikp9ioycHcjAiNcVRRfIiKrEiEKFXGgaQw4GVxSjZsxtdyv+0UquJe+lTTDFQFUZrNLMF2P6tmfTBVJJaOPlwa1Ii34OEWuhMkBoWYyaQTESudl2KJ1ISaEYKIMs+4Qf1ZATk2nNrK6pUVzebnhzcU41nqGzYRODkPxZYZ0mpZ7RrGY5mzKrR6Su5c31NW2wbLct2leUJNOdKy37VexbDo/vstnccjibcX15ibeelGWolKuqYe9g8KDK/We7uWIxX9J3Ckoh6QI6Y3JCoXFKAlKlNdpXLBvHZDLCGs3r5y9wo4r1pkdbTSmJlBPWVYzrGlJkNmqYzSY4rVndXrOpOtou07UB4xxFG3LpqYyT9+QNo9GUxXxKSZHb6w03t2sShVhEYZFIKGVlQJexjKzlzpOHaFXYbBNnZ2eiL4kJr4UU6gfiv2okwD85OKByHk3m9eWK168uqJsJ2WaCjsM9ARQJWwIjbxgvl0zGFV2b2baR8+sbXO0wwzHyVmOswdQVTmUq7zk9OBKy+faWm9uero1Y4+hSRBkn16W1VE2FM5Y7Dx6JEiYbXr08o1UJld3wfhzkHqsUJfU44/CjisViQdGGi8sVXRvRyaC0DFvTHils5DLoHRIxtxgFI6cl9C2FVBLV4LevihYXbRs5OZhzsFxycrAgUwi9ELxXV1u6EGi8Ju2+J2mF1ZrlfMzJ4YKmqmm3gev1hovViqvLFc44iipYZ8kxoSPUpvl/9fvc377+5r7iyOwHqeymPu/8oOzbx80+fNxP2raKNPLfGtKzc/+lxlCcQYVEruy3h+cMf9uRiyoVlClvg4XdlGin0IPDc0+hfcOfKbTWt4dK7QI8cf0p1ndlKnV9JZ9vc2xoLhKhkknkO4fdzqG5n/ju1N7dWrQU0/11pFu6PfWjuiyTwofWWj08NFYXgfVdacPdO2R3A1IGInPnQNypItZ3/BBGI5Oxa43dlG/4QfO3j2GRgKGbaUZvhJaMQwC0o/V27j4hDgdyVkE/d3vidUcF7XyVexfsyOyDyZ0PckfEmU4IOIYW3N3gszga/KKVQk3fuofjyMDYYFp5P+2xH4LcuA8Uxi8GmvsskEZufz52HtLsNO2R26+L6iYRJgMhHsv+YXZHwtq1uDq7A1Ey5Inah+xqCI7DVIjLPLP741qM/oYjWQalVTcZdytu050HdTdIbf2gJnmF2xY2xw633tHRvHVSDrRtnLqBcnX7EKubaSbPA91SCvrJKfzqrRdy59wMU0d1tuX6gwnNEEalWqiu3XkNE3EqZyetuW4V98TtjlC02zR4c+O3zrdpE91RLV/XrNpPOd/Rp/L/vw2hVUiUsaU62+4HrEkwlHGDX9VdtcRphbuR+/zOG6xyoT/wEqQ5RX0R96Hb+OuW7d1alCFXgz5jbqiu07d8zWFiqC7C4FSNex2ByhLAx0qhnCI2ci5Hr/s9fa9SIU783h1cBu9vHAb32bbgV+IbzV7jrnv6pd+rPd76KPX+nFXXaa8d2NHf3yS0+7EmnEjAFcaK6loG8GWrGL/sxQX+Dc+nFA/MQApK4aM9aYQM7CLl/zYcLU6r/T93R81+D98eGkav4/76tNtEnMpn0UObe6w1tw8Udl1oLjKm09+iu3fkfRo5+pkcw5tHVvyvESYv0l4TsLs37I7BjprcHlrWdxVhVpg8hYOfdHviuX69JftBHTIUhLZHjpvHhu2djA6K+WeW6kYGeu32z6KFmpb1W7h6z1FfZK7es0y+zoTxW790HCjlXcdAtnK+68vEzSOLWw/7x+BN3bmVd10oysh1cfPY0B4VUlWY/xRuG8PoLO/P285HvDmRvWp9T7M9LaQmU78yVJcef1OohiFkKZuho0TyrFQp2gPF5n6GAvUbw/hFQYfB4d2W/T041YZ+YmgPFFe/B8UW3CXMP9ei1YhloEXBD4RqN9VkB9cfQpwm0IXJZ476L4X017HgNmp/T9h5grcHmvZI0R5l3K3BX8Pi8ygD2SZDkXcy+N99Q2g0/VRx9bHcG6tzw+hlwa8KsRFPMAqaM0WsFP1UkRrLzccBiiTn05868vVbgn/n7Y2N3NPiRBHGltsHcl1Nv9wdV0h66NCxu3sbFCVNkLv70PZQCp1y35CuHtsW+qkjjIWQPv8Vx/px4u4Hb+ijZd16zt2MydeFapXpDt3eCxxrKUqsHin6D7cs5muuVyN4XjP5UmO7TGhk/e2o4Gyl0+WXff2NDm0rXfBK0/Y9+BpdEtu4lZZZqwm5x1U1sW2pvMN6S7vtSAW00ZAUlEwp4lqtmlqGSKWOpvbE0uNNTSjSTpqKUKpKKVKWh7YExAQOJZ5HFcmpIxYoWoLVpqrFV6mlJdug2Gw2WKvQRpGKIqSMN462ODSZkjtCjKQg5K0q8tAeS8ZVDkUhdD1aW6yC3EeMsWgtzj1URU496+0N4/mMzfU1s9mMvu/xvmbTbuV32UqIWwp145lMaq4vzzEaSlCU4nDekVNPRlOUUKQlRxbTEdXYszickreR6+sV2/WW0IrzLpqCzkJdSlhcCGGDqjx9UbhqRB9aaSPXUoHSzgrBNOgIrHUs5nOubs4lZAzi8C26EvdiiVDEZ6szaBJ1bRlPxlAsNze3tL2cP2Msbd+SSmHcNIRtRw6R6XzKweERCmkdf/nmNXU94nq9gSIuRaUc221PVdfMGsdyMUPlwqOjBW/Oz+g6xW1xhC6RlR3a7WsUkdL3jEeW+/fvQta07Zbr1Q1XNzeYrGRafOrxSlE5uTgrpVgeLDk9PGC7WdG2Pa/evBbSTilutqIh8M6Sc6JsVxweHXJ4ehdLIaXCz756RmU1KdaUUhg7R9v1uKqiDVuOZ0ccPDpmPPJ03ZbNZsOr5xdkpYahdRIw55SxWMZWce/OKYv5BGMV11crzi8vuLxqSQSiU8Q4DNjJkaIyBxPLrBnT1Jpq68lJs+3j8JkDxivW6/Xe1VvnwuHBgpOTI9rNllfnF/TWcXHTYZ1BGUOIkRJEfVBMYTka8e7DU1xtSCFxdn7OF18+RekabRwhFwnKvMfaTN043n18yvHRkn67YbXZcrve8tXXF+SisU1NHzqccyglFPfBouHkaEltJRS53Wz5+jwTQoQsg4su+4hznlIKhycHnB4tWEzHmBhZr7dcnF1wfXNL7YchQVYIopwzB1XDvTunzCcN45ElbCLnN7dcXJ7z5mZNXY3YtitKMvgh8FsuZ9y7e8B40mB6IXlfvH7Fy1fX9MoQtWJFptIG1SWmuubu3RHHh0vGtuL85oa2C7x6fcPNekOXFMo3MiCKgq4yi+WSw4Mxh9MZKWVKNjx/9opnr1/irKOLEVc7tu0GXTRNJcoJq2A+m7HdblmtVqJP0QZCotaW2xjRlZDL3jkq75mPZ6gIaRtYd5eiNkiWWHqK0qJKyIHxpOY7H72LJzP2I0rIrNYtn/78ZwQU25BRw8A6X1lKCdw7PWTWjFksp2gNysHF+SXPnp9xcd2SbGFUjWi3PWMLTWU4Pjji6PAASqKqHKch03zV8PTFGb5uxK9mNJX2aBLj+Ygnjx9wPG9wCVQIvFvu8K9/+GOqxrNuW6rKs4mRknumo4b3P/iY2mqWy3fJKVA2D/g3f/kpURmiGZxpVroSrDU0TcOdO3c4OfxICmJR8eLlBU9fvKEUgymKtvQUZ4hdz8PFEcdHByzncyajmtC3HB9MWG16fvzZ5/RdD0a0CMoWcmk5OprzzuMnWKWpnSZvl2zayJ/++aeookkJcSHHDTpvmUxnPH50B+sMB/MZIXao00M++/wp55dX0uUSE7ZIMbGpHPdPlywWM5aLCYVI5Txf/qLw4vkZoRVvfCmF6BzOKKw33L17hzvHc6w2mFKYLsYcLGf84ucvcMlBVINCIbEcN/hxxcMnd5lMZVCeVZazy1vCLzou2pZcEikXpromxYhTmuPFlIcP7zFptAwRTHOevbrhR3/xKake0zuAhK0MOXQ8PDzk6PCQg6M5IXXk0rM8GPHq5Rmff/qU8XhO222oTIOxQE6M6sxHH32AMppRVdFuj3j+7A1ffPEC5TXRQRsDRnm0kgGLVcrMD2cspxMqHJeX17y+vAHfsFUFX9X09MQcqEaOj99/D6c1lcrYxomDNwd8ueKr568Yz6asNhucgtoZHh4dcnC8oJlZilKMJmOWB3OO2gP+7Q9/JB7bqmJbAkorrFWoov6fv5j97evfuVc3k4e/6lrCJRmM06NSJk79frCHaSVojLWEFPthQcOrFGk3DzMrgW8udEc1dpvoJ27vJ/xm0KS7uA/pzDahxxIGZKuG1vE8qAIy7aFDBwkxtgeG7KC5yMRKka0EHnaTBkdfIRshXNw2D4Hq4IVNYIc22B31thvCthtCI+3xUn/UseBfdWxPK+o3PbcPKvytBBL1mx7dBtLIE8cW08kk7c2JZvoskb3ak2FxpPEh70MQOWgSgISpYn0/o6Ni9MJQXZZ966dp4z6Ii43GdGU/cMmv8zBlu+zDwd053LUHm04C3ZvHFbMvu/2UazU48nfDqYD9oLDsFP3C0B7J+R0/z1TX8kAcJlZarrtIe9rsW3Nv71asHivxDQbN0Y8j7dwwe9rt3xMKqgsZVHZ7z7F6JD//+vue+WcKd2sYvwoSukYwfHvC+OZIc/lrIjW0N5rRc8Xsq/i2JftWwstduNRPNKtHivDxhnTtsdea5U+GNuAMk6/bwUFo9i3aq/cNt08Uuc6oXnHvnyuKsfuW++2pp7qM9CNLdZ24+Mhz9jiTDnvUrcVfapZ/OZCE7dup7RI6WrYHhotPoDzYYm3H2Ysx019oZl8mcbe24lYtzuw1Fdfv1dzem9ItFf3USWHtKg+Bngx8a151e99nN3dcvV9z82HEXhuWPwG3zsw+W5EaJ1TZuofs6JcelQ03jy3Xv9NS1YEYDPx8xP0/jAMhyj6c6OfSDt8uDM9+d0p5b0PYONxrRX1mufNHvRRraodddaSRkGgqFlbvWQmMZgmVFNUbw8kNp7z3AAAgAElEQVSfvG0RzpVh8vk1aVyhUubqowmXvwLxfkK7TD63jL80TJ5l1MLKkK3RMMwpFq7eb7j4pMC9ltODG9q2Yv3zOZOnNctPA/3CUp0HCU4GTcH1E8flJxl92GJtIkaD//GIox9H3KpgtoFm3RHnDcUoNieO808U6r019w6u+eoXJ+iVZfETxfRZxA1qgt1f2SpuH2rWTyL3npxRgiVkzdWPlxz/6TB46kYKAf6ip1i9J3Njpbi9r6muNJMXUoiI08GHvLSMXnRyrd/2xKmnn1tW9+2+3X90Ngz8g28E9gmLrONn/zDhRi13Dm5IRfH89YKj369wmyw6HL1T0Ui3w9l3Nd1p5Oj+NY0VAOyrp0fM/9yz+CLsA9fqOrE5cXQLxfUHhfG7VxjgZLzhzUcTXizm3P2jljCxZGf2tHp2is2x4ewHkXffe8FhvUarws1v17z6bx/TTypGbyL9zFOfBVQqrO9VPPs9yLOehw/OGSVDyBr+ySGmK/tBbLsQOlaadqm4+pXCyQeX9G1FmxX5L6bc+Vd5X7TzVwGVRQP08gcNt+9Hjh6c8d2D11x0I168N+Pq9ZTJP1O41VCYGUlIqmPh6n2D/+0LTqqe+5Nr2uT46moB/3i5J+Z311V2SojO77e4KvL9B19z2Y4wOvPFP3/M7AtF0AqVZZDa7n549uuKeL/jg/uviUVzd3TDv5x/xOJHGreVooj4wrMErBVc/mrhwcevmPiOiev4yekJTx/POPkjIWGBoVBZuHlsiA3o713z7uE5ViXmvuVffvkOq+14OOfi6V3fG0jxRnP1gcZ975JfOzjjsNrgdOJ//ovvcO9/dJiu7L23sREv/ZvfhPG7V/zD+z/nrBvTJsfrhxNef3rE/f9dsTkyVNdZiOVBu9IeKqq/d8ZcZ57ML3h+O+fZj0+59y/KflhZfTUogobQXa4Pw/phIU4To6eWg09lfdQXUbQxa1GydDPN7O+/4mHVcn90zcJtGJme/47f4Go85fSPYXNkmLyQ8LqbKy6+lzh9dMGj2SUxa5hfEu8bvnj3kPafzqRzqJLiw67w8029zV/1+hsd2t7mIAGD1cR2jXOOsW2khXUYemIoPLpzSAiJNzdXeONIvQSfkOiCeD6buhkGVsFiMUeVTOoKfdxgi/htjdGEkHDe43xFzpHKWyajMfNpQyHSrVbcbBEUPWf6NpJSwFmLyonV+oqjw0M+fvwuKW1RGL46e8PtuiUZhMotmRDFYehHtZB+I837Jw+4vLzg5nZFaDtGbkQpiZJlAFjJgVQ1hBQYVZb7h3fQKpNIrCc116s1ShcRpCuLt4aSLTYXxrXi8GDJeOKZTxxdu+HqeoNWnpt2i1GWGAJWWUrQHB4cc7icc3i0ANVRZoXMlq4NOFtRklA8iYTWhs1mw/HJAYvxEc1IQqs3b95wcxmwuqYPCVXBdhNQTlOUHkLwzMF0ysl8zHbdcru+4aYP4sgsUIqm6EwK4um8f++Y+XRCU8uk+/V6zKvXl1xfrUgZGu9Yr9eQG7Q2TKaOBw/vcFRb6nrEbbfC2J7zi1sqNL2WoKovBU2FjZHjw2OOj2ZMRp5Nt6X2nmevn7JatdLCkzVOi3PSGs3BwYx3Hj5kOhnhVOby+oqRd3TrLW2WoWrOGDSGGHpm0zEPHz9g5C2jumGxGHFzc0UuLc/fXBMKuGE4TNsFamt48t5jDpczpl6+nCSVCTbx9cs3UGQYXzG1tMqnwPHBnDtHSxbLCc4ZUm5I4zm2OL7+6gVGOxpf0bUrjBtTWc3jk/scHzX4SvyotTVU3rK5/Tm324BxFalE6UA2hnbT4eoD7p3c43gxYbPZcH55wdNnZ7Rth9E1KWdqJ6qB1fU1jz/8kJODOdPlhDDyzOcVv3j2inYdAGn9V0r8mzF13Du+w/FixmI2xlQSFlqXQCV+/vVzQgxkY0QnkMGSee+de8zHNYvZmDxqaKcdF1c3XJ9f0MVEVo7KjihdBJOwtefh3XssJiMqr4ntBl/Bm+tLQuxIymO1pYoaqwoQeHxywOF8Kg5qH5kaS6UKlTE8e3kuXQIlo6PGOs1iMeNgOeNgUkl3zqhQTRt0pbncfsl6e4uvLSkjww9d5uR4yXzaoL1iPhkR+oQzJ7S3LW9u1uSsmdQNoW1BwWw54eT0FO8UTdNwOrLEoFjftrT9FltJ+GpshdWQ+sR777yDVolKW3QDIWVO7h3SlsCzs1vpGoigrJzLPiTunB5y9/hQaO/Vmq9fFNY3a7YxEwm0MVMUEAPjxjOqHfdPTqitwxo4e3nO6vU1qRhM5VApYo1FFxn+dff4kJPFVPQqypHaQFNZ3rwZ8/rqmhISKC90c79hMlswWcxYzqaMJx6VEyVkHhyfkoPm/PxLnHbcrjY0lSOpwnwy5mAxZdZYrDZorXHacf/0mM+/+BmoiFYWQ4K0wZjCwzuPORg5vHaMrCJaT+0Mo+mM128u6eJAG2hPoKCdZb6YMLKaykJRmvpwzOmdJZ998SXaaXIRb3XjHSlG7p4cc3p0yKgSelxrS9cFbtcbVttInwLOGprac729ZbGccnA4pa4tIbUsD+csmXJ7u+GzT3+GrxtiyYQc6beRo3nD4/t3mHkrwZxSNNMlTRtYHkx59vw1yjoyBpVrlJFAe9x45vM5uUSm1QjvR9xZHXJ1uyLEzGYrDtfFaMrm9oqHD+7grKJpLGDxeszhQeLmeku7FS2NqzwlS+FuebTg5HSBt5raWXKJHPoFo9GMn/70MyozEl+3XOTcbla8+/67NNMx1itqXxNbxf3TO/zii6eE2wBeipzaGkrKtN2Gd37zO0xnDYaIN56cFYcnU+6tE18/e8nYTwgqYXLh+vaC+dGM+4/u0m/X1K5CqzGl0YTbSLf+Cc414ojViT5ADD3vf/ABzlWMGk8uhfnUcTVqsA60Fi+2tQbtHDprKD13jpYcHi2oqopp1TCbzPnZ0z9iupjjUqHoTAmJqHucTlSLCnW7xo/GQKHtew6WR4ToefH6gs16TUmJoAul76mnlmZcYZQMKHNO0bgpzW1LVVVsVxtQHXboMOpzxGr/1/jt729f/1++xi/7b3nf4sjQngq5taNKk1Nc/MBitorlZ0k8c33e04TuRmi57sALIdgYNo+rITyw+LUQSPL7zJ50FKJTJk9vTgzr+2UYSKMYvSgUbeUh7zZiurJ/KJ68CFx+4Pn6HxTUMF7g8E8M41dlcBSqvVc2O7334mUHX/89zeQrzfh5xm7EM7hrsd1550wnYe72SB6Mi3GoCPV5w+RredjOVhQAYToaBqfIw+XNuxAOIpu7BnermHxdyBYmz+OeHJVWTaHDVu9kmic3NEDOim2a4lYS8n7TSxhHhvoscPFxxfqBIcwzulPMPldMk7S57qaU+4seBvIwDsONVo8VVx9VVFeK5nVh8jy8DY9zQaW3lN3r3zBs70fMvKeuAy/OR0w+dUyeZUwodIeO+tVbT+b2juH81wuzdy85mdzy5fmSN27K9OfiWXQrCXbtQInFWnH1Iaj3bnlweMWLqxmX04b5nzsmz4eQJWbi2O9D5+t3DP0PVnx8fM5BteGHzx9wvRzhV4bqJktAMZIHdNsWVvcNl78RqZYtD5c3NHcDP3t9xFUec/Rn4DZDmNpn/FWgXzie/a4jP9nw6PgSrQpttLzannL8pwVLxrRl71jWoXDxkef6O5HFvRumdcc2OLqHhjO/4PhPIVXQHfi9EqGbad78VubkvXMOmg21CbyaTXk+OaC6cIxfyc/pTr/VKqw6YlNz+d3Ew3ff8OpqSv75mNM/Bn8VhkFciX7hMG2m+vqaF//eCe2HLfdOrli1FWf3Gub/qqa6FveiGYLFPAxYe/29mtX7kQ/vvmHsOua+5V9Xj3jGjHv/QkhuFQtYId5io3n1H2QWd2/4u3e+Ypsczx7N+cXTYyZf1/hVksLJ3O3dsqlWnP9mYnHvhoPxhnXveTWa030mBR3dZ1FOzJu9a/XNb2WWTy5ZNC1j17M6rvjF5Ig4qTj+N0PB4nZo+24QYvTRLX/3wVc0RvR0n4+P+NLeYfalpnnViZOyS/uw5PrDQn13jbWJ75684KIb8bRecnMz5+DTjAqJ9v5UCgkIPanfXzEddTyZXjD9sGPVV7w6v091LYWV+jzsKXvTZarfOWNiMqejFd4kLtsRX77vuL6ZcvLDtPexohW6i7iV5uy7FTffCYwPV1zcVvR/VlOfWZoLoeirywgDodyeNmyOLOe/VsjTgKoS1Rc1k5c7f/RAB1datGROc/53DJ+8/yVWJe40K25CzcPpFX/2+ccsP1V7+nlHka5PLd1x4vThJe8vznA6EbLhvU/O+IP0Kyx+JnvW6FVPd+AwobC+r6ge3fKd41cc+A1eR55WS370XYv9XwfKfxiGB0LRn/9m5t7jc46bW35l8pLLOOLd0Rn/9OAJBz+JQm4XuyfqswV7b8PpYsWd8Q0xax6NL/lnHx/y4H/b+XOF/G2HoXZXv1p48OFr3pufYVWm0pH/af2rXL2pGb9MuLXsDe3SMlkFNvczp48uuDe55qIb8Q9O/gJO4I+Pn/D5H3xMP9ZCwm4y/ipy+VGN+v41v3r8koXbYnXiO6Pn/MXkHv/Lo9/i5E9ln1bpLdEaJnB4cMv3T5+yTY53D55yv7rkv/j4kPT1hOq6UL+RwWA3jyua88Txb7xiXrW8OzkH4F51xaePT1i/OMLfKPx6R3eKTuD2EZx8cMZRc8uj8SV9tvzu/S/4Yn7I7e8/oJuavR4CoHldeP07iXdnK2au5b3xG877Cf/xx3/Cf/9//t7+mtg551WG6jox/+0zPjl8wdh23K8u2aSKjx+95IvvPubkh4ntqZeipFNMn7aYu4X//IP/g2fdkncWbxjpnnBk+K/a38FfV4Qh3C1aqGG7zbz53cJx3fLR/DXb5PjB8S/4x8sDYuXIVgqTqX4b2AKcf8ewfafHT3o+PLrg2emc0e+Lj3j3XURl0FGe309HK6zO3KuvAPjZ+pgfPPiSP2zfJ/7biuZCQn63yaisMLOeR7NLxrbHqsyxX/Fx85wfTp7wB9Pfor6S47rr4tkNk/tlX3+jQ9uQEyUmmrrGGHmwbvsO4x1WG+6cnvLk3jG1DfQpcLc/4uXrN7x+c01MG2LK+MrTbju6rsMozZMnDzidj/HeEfoNV1c3fPbFzxmNxrRR2vfDtkNbw2Tc8OTJI548PEGVQLte0fVjXp2d8+zlJdpMUJVMgU8pk/rCRx+8w+HRjPm4wugx5IyrNa/PruhD5vVNT04B4yy6QM6Jh4/u8+jBKTZ1vHN3wdX1ipev33B5fcNqIyoBvRtGliNN7fn1Dx8xbxq819x0HSEkfvr5l6zXW667NdZVZFPIITCuaz755GO02dJUCqUM/arluX3N+dU1YwtdKBJa90Ilf/DuR0wmGlSUFs6sGD9+QuPHfPn0Jdu2Y+jxpOs6ltMpHzx5wtHMkEtH3wbmtea8qXl9dg2qkJJm7DxdacW56Ef82ief0NQa7xSrmzWbVc2Xv3hGrzMRQyiGnBI5RpZHSx48vsdsMsYlOY6zscWagFG3vDzbysRM37Bdb5iOxnz00fssFzUzp4UwwmFNxFvF02eXKK3YxoJLlpvVDd/5/q/znQ+e0HdXmJIZjypiU5gtDFd/8hOU1mQjftQslgU++e738PkWrwtox8HBnMViSp/X/PhHnzOZeCiaviQyheXRMQ/unKB0lFbypFgeH4E3XK5XxE1CUZP6LWiPrx0fvvc+liz/j4KQA+8fvM/1zS3XYSXu1CJKC9803L/3hKPjMdYojNE4CrnOLBZTnr18Rghbur5n5CtSThwsZnzvex9zvXotA82yoa5rRqOGzeaWH/34M3Sc4eqI1obcKcZ+zN/54AOa2lBVonUYN57tzYbYXWBMI57LErCVR0fPx9/5CB0jvlLkWvF/sfcmv7al93ne8zWr3d3pz7lN3bpVxapikZJYVKgmkm3akuFAboDEgREYCGwgyT+RGTMMMkkmGSTIQHAQIwgSCIqNQIECKbYoiaJEFovFutXXbU6/+71Xv74mg2/fQ2dkjWQH0RrdwTn37Ga17/f+nsf5BCE8XVWw2lZ4KVEEFqqWkjdev8/p0SG6r5DOUDnL/v4+SiZMF3M2RU0cRVgj6JqWN77yiLO9QwZpCFjNTgz2+uNHbMsNH/zkY9J8QhTtUfQ9iYo4GOfcO5mgvA1NWuXD34hzvvdnP0BohRYBO1JtN7z95mu8/uCMWEtiESFMg00to3FKqiNeXFxge0EUDxEqMI7fePyIg8kAuhq0xkvLMIITt8+HH37EZLzHtqkCh9bCvZNTTg6PmAyCxT1yMEgVk8mEi5trrhczBukA6xriWFDXFadnx5ycnGD6ljTSDNSAvnc8fu0Vtk8qNrXBSYNXiqopuXd2htaaUT5AOrsTeDnGk0PWxZbn1zOM0SRpWJBK0pg8T/nWL/w83XYF3pMAr6Wv8tGTz/G2w1mH00H4gpR0tuH+3h4Pj/ZRUmKl5eD4gP6J4uLyFtiha16OiQs4PT4himKc7dBakiVDHPDw0QOWZYWyYXy8aXsSLUizmNce3sO7CoVEaI/QCXk84ep6g8JjW5CJRmjBej7nN37t22BbJBYtwzkxyRN0EjMe7VHUHToKXGadwmSwx1ffeJu6Kkg1AU8ADAcTDveHlOUGVYdWddW1RFmElrA/yokJsithDT4S7B0OSW8iytZiu569wWgn4nIcHuyTxCpYikVgXL/+6BW6zlA/fYFXmsZ19JUDZzi9f4r0jihRsFtE014zGU0YpBnX8ylpuk+kY7xsSaKY0WDMII6A3c2WM2RZyum9Uy6ub1BKYHbHgmktw9EBg9ERTnqyKKPrGjQRo3xEpCJWZYGUkkQrqnrDaG+IFbA/GiNsj5JB9jgeDtnb2+N6OiMb5LR9j9dQ2QadxkGEFYWgPJIq8HV7yJSmto5aQhKFxUjlIMsysiRFKo9zoBONUhHj8Zh4W4TAQggsls52GGuouh5KxyCLEcohUGglEL5HOoN0PbieKEnpmzC1s9msSbQK+BKt6Y1BSXAq8OdlODPjEOgop+4NSZrjvCESYNsOZzpwit70qDTCaxd4swLquiMbpSSpYjjI8U6QpJLJeBTaUH3YTzIVsBVULavrKZNRTtPUZMMBkdcsV1usMZR1TZKliDjI4XofRsB9HDjQ1vYI0dOaGuv6MF0UpVgRuF6m71FS4b35N3Lf95fbX/ymyh7tPO1Rdse5jFfmThw2/5oi+6UZrw8LFnXO1ddHJE8yDj4ObZaXDdx42d2FU1e/ohFvFYzylm0T01wMefh7gupIk64tqhE7IY2gPIu4/WXPr/w7HxJJy/Nin0WZs/x4n8MfBfmmV/oOgSA7x7PfyDCPa14/mzOIOgB+nD6k/jRGl3D4YQt9EIO8fGi/+QXF6OfnPEpajn+x4P3L+/DRkOFzGF6Z3ShyGLXFe5oDzeI/LPmZs2vOsi0/mD6kaBKq35uQzWD0vLlrNgrr6Y419u8tmSjHK+MlWjqm9ZDnH9xj+FSGUfutu2sEowXdtze8fbgAwDqJkg67v+KTvfvI76k7rAOEYHz9ekrx7YpffPUZnVNM6yEv7u9TPM3Y+3j3ffae5jQhXpswAj2OuP77LfvjFQdZxdPZAcUyxf9JGOfXrbvDYgjj2b4Wwbsbvnlyw2FSUduI7kTxp8mruCjl6ANzx4FNFh3l/YSbv9nz+OGMbx0+p/eKN8dTvpu8xjLf5+T7Aq0E8bzG5jHxxYqnf/eE//jX/gWXzR4D3fLO5IbV/YwnZyf0L/buQqyXr0kaz72//ZzjrOBeugbglx5a6nsR36/e4rXf7qlOw0JTVAZpT/FI8as/++lduAQwfHDBj3hA83yAbneNsh0iohsq3v1rnzCJmrvfmbc5D/7Kmo+v3mZ04fH5DpPXWvphxPrnel5/7YZJXJPrnqOkoHOa+iziox99Hb11xOue5jhGdp7NY8l/9e/9j/xf668xUC29VzwaLJmNF/zx8qvsfRbGv22ud8GwoznNUX9nzjfHS+5nGy6zMcuDnNv1A3Std+zmcE9iUwla8fjXn5KqnrNsS20j6uOI78tXicqU8bN2h0bQd0Gp+9U1//C1H7MfleSy4yflA37h/nPej+5RfXLI8NJjhyGojgrHxbc177z9jK9PrtjXFUuTs2hz/tG3/oh/evvXeP1/rekO0hDiXbV0k4j1a5p3vvqUYdRylJT0TvFXzz7nf4veZfg/iB1/VaBaQXJb8ezv7PHrv/gj7qcrHsRLFI6tS3l6cMT/Ofoq8vsvMQA/bauNf/WWX7v3yd0x03vF1/eu6d5U6H9+yPZRyuCqu2uXT99NGb254N3TCzLVcxxv+ca4oT/U/JP3fx3ZOdqz4Q5nEhAV27c0f+e1T1j1GY+zOd8YveC2G/N//GJGd7PP8DIE3MJBOu2Yvpsxjnt+7uCSTHWcxBuW+YBfPvqSf3L1V8OUQdXT7yWhCbqXUB9qfvYffMi0HgJQDyJuf2lI9LvDOyGgMGGhBQLepng34vHPXTCIOso+JnnF8Lx5zMkPOiAsyL2ccACwb5ccJiW1jdjTFW/l11Q24YN372Gej++kSqoMr60bC37jW+/TO8VAt0TCEgnLz+Uv+L+zN3f700tRFgyflnzrP/8IgFx27EclkbA8TmdM4obPHn2N7Kq5E+aZVFA8lPzjv/L7fFqe8MZgyr04hGX/7uBT/unjXyadqYDNsRBNu530UvCNBxecJAX3kxWRNPRO4x/WtJOU/LZDdo7N4xSnw2vbf7zgbLABIJHhXuc/++Z3+e+rb5OsQnCZLM2OHwvHb8+IleUwqQBYm5yjaMvf2P+IHx99jeP3G9r9aLfQFiYn3jq65RvjF3f7YuVivpZf8ttvN/BDdSfrDNgKR3Ms+Nn9GbWNeDO/5bLd4+fyF7x+POciGzF+bvC7tmg2txT3FPSab5+eE0nLUDUcqJJ3jy/43Yd7HH4oaPYUcRHQOtncsIg1SjrOsi0TXZMIw0SH9/Qne6+SrizJvKcfa7phmGaJJi2vj+Yk0lDZmMO4YKga6lN/J9XzMlx3oiJIPWerIT/mXgj3d/vJOG4CqkaHazqAyQSq7DB9yh8u3+B+tqayCad6zY2ZkEQ9wgTEkdux7KXZ4QXWgnv5htpGHMUFpU3AhPA/qt3dlIvfSUPTpaXbg3jY8fBwhfGSg0FF8epJ4PcmAtV6mn11t9D0k6t7vH12y+flMW8ObjlKCr579Tqul+S3/Z34zasQ3DojMU6ihWNrEiJp+bI9oTTJHWpJtYGPrGp7x13/827yX/8j/+a2kU4YREkwyHmPc47gaoqQUcz9hw8wDlSSEqURk1HOO2++irMlpq/REVRVgY4USZIQaclrj15hf3/MYBgxGGWc3D9kOMnZlCuUsKSRDqId0xNJwenhAXmSkkUJ+/mQ8fCUh/fvkWqIlUUQeIQqEjRdy8H+GY8fvkae5wzThCxJuX9yxuMHrzC9vEY7xyBKUNajlGAyHPHG49eY5CPyXTj98OFD3nnnHQyeRGUIEtoelExpasvrr77JG29+hcF4gIg1+5MBh/sjjvbH9G3DKE2xXYuyFut6Do8OuP/gjPF4HIzTXpCPYs4eHXFwPMThgq3e2dDc7RvunR0wHKXkacLeaMxwkJHGmnunJzT1lkEemsh4wWAwINKS0+NDeu/I85QkUdx/cMzh2T5lu4EUKtPRug4tBV1dMRxknB3vE0ceHXlG45TT+0f4VGDosaKnMR0WT9d1PDw+5q1Xv4K0gijLUfGEKJ1wfPaQfLSP1mCsx6MZ5COWyzUnhycc7h8H5AKKHs3k+JjBeEBLh/W7lcrE08mWydmYZb0lGiToTCEiiUwke8d7tMUWTBuCAN8TZxEHRxOiTCFsSyINSgukjFAqIo41aR6xrSuM3o1X6QgrQEQxSEXkIEGhgSSO2ds7QKnA2k3TfGeC1+g8h0ihvAvCHikwZcnRZEzbtqH53Vl0PqToW9K9FJ1o4jQmTiTSO5JY0puaLBsExqJOEDpjuS0Z7e1zNZ/jVERP4OE6H9iXsVbsTQYM04REjNB9CsYxynJSqQNqw1tkohgfjjh5cEy1LcD1KGXxsabtaqLdTVWWpyHIISLWCVmUooUhTxR5osP7UwlNW+FNR1GvqGWLjYNcT3rI8yGu8wzzCcJI8Dvxm7O0fYNxlq4LTZM0TaiKkqKomAwm+MbSNSVJquj7Aqs0VgRbeu8tLhKsmy1aWPbHOco5bNshI02cRlRtRdvXlE1B2VW0QlJ7z9Z2RIMxrXGobEBhOzpvmW9WFHVLZwVOKBAJSiZoIrSNEF6xWmwBGVA+zu/YyYH57Psg4HK2p9isiHSGEBLtFTiBNZ4sGaJ1QI5Ya8H1mL6gbwuUhLZtQEmiKAn8Tp1SliUSsTPeuzuR0up2RioEI6WZRCnGhBZl39QkQvHis89Rpsf2HXGsGQ5zOiHopKBXCis0dedwXuIag3KSpnPUJrRHq6IOYj6g67qdUEwGIRywWq0ptwXGBJxJ03XUbUNVF3S784HxjjhNQHpibfF9S9v0SBxaSNquZmtLltUCrzt0XFNWCzrTMpiMWdclrbe0OIzwdM6xWa/Zbrf0rSGOUnQU0/YNbdsgRc1ydYPwIczFGpSA5eKGulpTF1W46RSOZBCRKElVFBSbLTiDbRuqcktTlIjeUi4LxlHKQMe0zrFpK6yWbOotMtVUZY0UGtM7prdLVrM10gq0ZIfNgclozM3NDR5L3Rogpqoc27rh+fUl27YmGeSgUiwRidTYtmd6M6NoSmrT0WNoupr5as719SVxrOlNS5xoegxWW55Nz1n1JW1f0/QVQlpWiyXLxRrbWaIoCotYzqFVTNc7ZquC2XwZxH6tpd5uKbcF5y8u6a2lqCo2dUmmh5QstPkAACAASURBVEgXs100LG9KhEtoSofrBB7NYlWA0CgPcZrgHRRVibWW29tb2ranrRu8sdR1zXI5Z1NsGKQJiBClGmdJ8wwiwYvLCxrbU7UNddNRliXr9Yqy2OC1o6dHak9dbBmNhsznc5qmoTMtrWkpzZZV2TBfN+wfPsKLFBB42WHpEVpyeztlOl/StD2tFcy3Nec3c/xwgEljLIJNUYTriHUMBgNurmdsioai6ug7OD+fouUAJSStt5AntEpT4WmV5Hq6Ybop2XQ92+2WzXZLUxvOX1wyGk0Cw0tJVKSwzrGaFyxv1nSVRdsIWzmq5ZbNdEXTG4TSeBnROwtKYnzglv/l9v+PrXqY05xmeMXdOPbLwMxFAvuzBXUbM4xaJknD47M5j//mU6TxgQ+XysAlTNWuPSu5/0uXvPvggjf2Z7yyv+LkzRnliWJ03mEjQbenf8pPzQQP37rla8Mr3sxv+fWTj/n68TX3vnFNNxb0g9ASCyPwCr0o6R+1/Kc/94e8Pbnlm3sveH0441ff+Qx+cc3Bx90dV/Dl2Pz2oeLBr1zwC6fP+cp4xkB3/KOv/gnv/vrHqDYgIrxkx88VxMuOq78i+M43/hk/O7lE4vmrZ5/z1175nO3rjqgO46Cy23E2W8vmNck//sof8+7xBfezDZOo4SvjGWfv3FK8GoJE1e74qDtp1D9484e8u3fOW+Nbvn38Ke/unfNouOSdr56TbBzVkboLmOrTGJPB3/zKxyzanHdG15zmW/7u2x/gHtfk0xA+pNMuhAGZQm9bqmPJ33rzIw6yiklS8/howVtvXt7JUWTngyG8c+hty/ybjv/63f+ZWFmO49CwOkpKvvX6M7p9H7ifdbDa16cJoy8KvvnGc/72vQ/ovcI4xawb8O7pBeawJ966O3s9AJsCfb/i967f4jjesq8rtLRkqueXzp4Tr8P78FLcsX9Xr0fsp2HM9nE6Y6haMtUz0i0uD0iKwWX70yasFqhOEMkQyB4lBY+yBZOoYTxoKB+GVl8/lHT7MbILjb1J1AAwUOGe/izbsm4ztq+7ICXb8X1NpsivWuJxyySuOcu23EvX5LLjYbJk3g5o94Lo7mWLPf90TnPi+G+f/3VqGzHrhkx0Hdpu+59gByFYqk7jHYIg3DOXZ5p7ow257oM7QVm+dfic6q2WZBper4sCmiTaGOwghNdvDGcMVMskqjlLN+xNSkwK9VFEcxwHIdEoIlo1OCd4b/WQT8oz1ibnON6SqZ5XJ8tdyzJgM15uqhVMyyGrPuey3QPg8XDBk+0Z6UxQPB4gbAhxusnLJi3UJmLeDJi1A2ob8b3pY6LYsH4tCdzcLrRmzSghXXguqgk/Xt/nveIRz7tDPqnO+NHiAYO0I9qEYzW7rIkKy+jJgtvZmKt2QuXCZzBUbQiZoiBtHT2twxt4yWvtQCtHbSN6p5B4Kpvw3uYhJvW7IHnH/W0t7VGGSC2ljalMCKyWZsC0G5FEhni7a9INNNG6w2aKbOaIVPid3it6pzmJN/yL26/gx4F/Xbya34U4ujR0I8F333+LQdRSm4ijrODVw2VYZClDkCpbg55t787VPoJpMeBiM0YLx/l6ggxlY6IioA4QoeUH4C4yPlkds+lSNibjs+qUT6sTymVGVLqwQNbYOxyDGUDvFBfVJISwuuJ5fcAfbN4iepEgbPiskuuCqHA0pxnfnz7isp7wvD5g2Q+4aPf5w+UbfDC7h64cZhRhMkW07oi3YYLh92/fJJKWfR1AsxNd8dvLn0evFOkqyAlV40KwOA6M8k/nxwAUNuHD4j5f1kdIEVjG24cJ9WlCsnGMzjtU71lcT4il5fP1EZnqWPUZv33+s2TngVVtksBKdpGgeJQx/fgI6wVfbA8BeFKc8UerN/hvPv4bDK7dT9m6bWCjyg7e+/IV/mz9Kp+UZ9x2Yz6rTvmt63fJnqT0g7DQ0o8CO172ntPvwftX97ksJ3xZH6Gl5XeWP8PHF6eonQRMmNCcDlMVnuUXB/zO+VeZdiMqm/BRfY+PVyfsv6fQlSO/NQyeVzR7YdoivRVcvjhE4vm8PKb1mt4rvnv5GrA7J44C1z2/6QIb99Oci2rCZT2m94qPi1N+9+YdshtBdbzrfoqwgPuSz5r9IGe5zflyc8DnxREfbO7zwc090nlo40ZVmLhIV476wYD4acJFMeG6GXPe7POD8jG/N32b5dN91q+nd8GmcIE/6wVMPhH88dPHPC/2eVHv8y9evMHee6HZr+twXctflOGc3Xmq44Aw6a9zzud7bJqUqx+dYXaCt3gTrhXJxpLN+jAt8GTIj794wE+mZ/zZ6hE/mL3Cepsxfi+huB/fITdsFBjx+iLhg4v73DQjDuKKTZ/ypDjjvekD4k3ANuC5ayirxt61gP8827/VTdu6rtFxRGuDab2zPVKB8w3WxmyKLY+ODtEqME3brqNrzU6QpJBKkGUDeguL2Zy33/gKaZygzM5CqoMAp+2DfMRISessREGQZIUmz4ZYPNJ2OCHRMQzjPSKVUa1KnIvwSuOcZzDKsdLR95Y4EiQ+wViPko5EChCeSCo6azDCBkvdaGcqFI4kz2gby7YqkUpjvaOVBkGEkJpWCvI8R0uoy4ZICiIRUfcdpm8xXYs1Bt/3ZDrGmJ4kSnHKsy2KEApbgcgimm3J8eGQ88trwOGkxhuBk5IkT9hsNgwyTxILvBE7Vq6mxSK83Ymmwuhl7T37+QnloiTOoO8tIKnKhq7ridKEpu2QMliJm7YlSgYoHbOpauLII9BICc7FKA9SxTRdEH5505PmOduu4/z2hlQLrOkwBnQkqOuaxWIZxq2Vou06rAhhctk1yDIELNJ3ZFFEUZa4rkc7jdApUnq8EwyTIdt1xejkAFM1QWxlFbVpMbXFIkiiBOsFykeYpkONRyxu5hztD2i8RbUtOopYrbcB0yAUUguCT89D7zBNEGC1jSFONViwfU9XV1SbNXXTBXOrdejYInTPZjslT2OEhLYrkTrGKM2yKHEo6t4FK6mtaLuSclMwTCOUJ7SmLBRlQ9MZqrJBRQlaB1HVZDik7S1136HjDEG4cAoU17MFdWvoDXTOUvehVacVFO0GI114/dbiPazmFefPrzg8PGa9romTAUYLEJpISGaLFe0gIdOAgr5sKTuHlTFOQNU2ROkAAOsNq6ogzmPiSFG2HUIlGKCoKzrrcKZBqIBrqNqGxXbNG+krAW0SBblO13Zsm4qyqkBLZBqFQLG3ZOmIcltRlUFC1gGRSrDGcDWdYazAe0/dtWipEVqxWpd4Irq+Q2ERArxw+NYxvbohVhrbdIzSAUY48iSlrUrKrWZvFFO1G7SOKOuedbXFiyi0OgkYFOfAtg3L1Qp5OCJRCmMdxhg6A5uiJM9zyrbDeUUcCYxpuJzdko9SEikp2w6lIrZVyfX1HGcCu9jjgt1XSjySdVngfEymJELESO+p24qb2YaqcUjRB7WiFKQ6ZzGdcTTOWWw9SZzS+ob5bENdt2FflwpMx1hpsAaZJky3KyZ7OYf5Ptvlgm3Z0tcNUoIxFrVrtDZtTRQpbm5nTCZjTg/GNFWBxuOcpyxqbG+QTpEnKd5EeBvRNB3nN3MmeyNKY9Boqq6nml3RVC1t40jznEGmsU7Q9i2X19eM85yzwzG98Vjj2DYhAFNaBCwGligW6CRi2XYs2pJ9nwUcjpAIb1kWDbNVRzzYC8GpcFgkGIsUiqJsiZIULT1ZlrGtKy6mN2SDFOcF3gls1yO1pmpapqsNVinuHe+xrmvKTct6XXC72oR931gSHdH3Fh/Bcr0iG6TkeU7TdORRBt5TlRZnNdZDYzfoOCKRkq63lFVD2Q5IXIxD0nUG08NqWaJlQmt6XNMQR4pOQNc71rc1fpTQJYY4jinalpt1xbLucZEmUoJIKax3lGXBcr1kfzRgVTQMkpiyqLma3dJLi84yvPfkScKmqcjjiG69oXCO9VARx4qu8aw3G+bzBVZIvI5xVYOUCoWgc4LVpmSxXDMcZLi+wfSeqmjpWo/tHd5GWCHQQtBULcNkwOJmyjjP0PsjiC1dB9eLNdPFBhcrvPAkMgapyZOM7arlKlpw/+iAKInwnWQ2X7MqaqqmCotbyuP7II/0tqdqPOezW8b1gAjJfLFm07aBGS0cRljywQTbGWIRYXqHUBHPzm94cOroO8lysaE0Lc57nDAoH6GUQpEgZcJ8XWClhH1FI4Jk9fZqSVm1mJ3+uO8tXnuUFlzPpiRZTNRoTBRa31XRsF6vsZ1B6hghDNKGm2ElwfDnv5H8y+3/21uy6IM4aB0WOvU2cLPFLmTsr3O+/s3PmUQNsbRcVWMWdU7aBdO504L2KEOXhsEnC178vRO+MVgzjhp6p9CZo+hjxDa0sHTzU6ZdMIzDG5MZhU1IpKF1mr2oZnzQ8Af5PdKFu8MWyN7RnY3wveS2H3Eab3gtuWWhQwD2fLAPZPT5ziLdhrFFkyu0cMTS8FZ+zdPmiD9dPSJW4V78pRjFS0G0NbRHCT5xPGnucxJtOIk2PCnvs+hyZCsC39QSRCSNp5/ECAt/vHqdd4bXTHRFJCyf1qf8+w9/xH/33t8iyMC448h2E80fz17jnb1rHqczeq+4qk64l6y5qCY7tAS71p4nnfdsXk15sjrlOCuYdiMAPtmcYMvQ8nppJ/cyBM82j7Gx4AfThxxkVZDcakNrNDYO4Wa8MrhEg/P0+ynpjeK/fPobPBou2ZiMRZdzEFc83+wzfLazdCdhf1GtojnNuVkeEivLSIew8+3hDT9cvQJG7FjCCq/Cw7d7eEx/m5EdLViZHImndZovtoeUXcx4J0iSXUBz6Nrioogfnj/kV179kk/rUya6Zk9X/MHtG0QLBQQu8b869i4DSpfLcsK9g9DO3ZqEbZWQLIMAC3bysjQwRN+bPuC1vTmRtJxXe6Sqp+hjBi9C6zWdu50EzqNXNd1iwvowI9f9XXP2vc1DrrZj0kUQfZlMkSx7mtcO0KXgZjNiEtdo6e6Cwt989stEK0VUO6LCEs/rIEXLAzJh2wVcSWUijFf84e1rjN9LWL0dM3rRYgbqjmlsBxGfPL3H9HjIK+MlAMs2Z7kYctiBtCHYfynOko2hudhnkTecZlve3z5AC8emT/lifsh+6e6Ef15BMm0YPdUMfrWlthEP0yUOwVUz4aPZCens5Vi9vGuhtwcR2cxxtRxzMimY1kP2k4q6jxAfjnbHqSeZNTvmrmD83GCdZNYNyXWPxNM7hXWS+ecHDEYh7Koe5qjG0Z2O8KXmk9Uxv3LyJR9tT5nEDZ+sjrm82uex467ViRS7xSnP7HyPWBtO8y3rPmXdZizqnPxGUB9FZNPA7exHEaqxDH6c8t7eAw4GFT/Z3mOgOz6Y3WP50QFnuybgS+zIy5Hxp9eH9MeKR6MlkbC0XnOzHjH4OCGbd6EBumPgdnsRe593bF6Pef/8AaNBw6LMqZ+OeGW5Qwrs/Cjtqwd3n/XkU8fNvSGvPJ7xdHZAf5uxVwXZkar9T6colh021+x9pLm5P+Zrb9zwvNpnHDV0TqHnEap/iZUJSD48xGvP737wDqf3Vnwg7zOOGr7cHDBdjkg24bzZ7Wnas+Fd4Hv+wQnXZxN+7c1PmHYjtibho9kJ25sheRRG2NWutW9TweiZ4+nzY+xDSWlijpISieePrl9l/AV0A0m2CJ+B7AOyxEWC1WzI++l9hnHL68M57y/uE70/oDm0OwHhS0xEkB0OPk/5rnmLd756zvemj7lejLFXGfc+tngR8Cr9JIzwi1QwONdc7u0z2q+YFgMeTnbnk2XO2Pqw8LVscamiHcdBSDiP+fzwiONBQZ1ETOshRR8zPPeYVIbFrD5cQ6OtJS4szdWAZ06wqHMOsopn832SjzLGz/qdWEzT50Gwll10lC9S2lcivnf9iDf253y+PGTzk0P269BaFxba45ThZU99qIlKTzTT/On0FQ6yimn9Gp/fHCGfZhz0Qez+UpDl4sBMH5xLPnpxxsnRhsZGFF3CzWrEoPSozt8x220Srj/VaczkqeX6LGebhFWDdZXRfTTm/sf93fHxMuiujjTZjeD6wxPU1z17aY1xE548vcfgQpHP+jtB3UtckPCQzR39ezlPz1K+yBzDLzXDq4AOwQeGc3uco3of8BWNxwjByZ/A/BsDts2AyXPIp+buPPVykfclB3f0pUf0CauvwGab4a0g/SQlmzl0G45xXVmEC6z9/Q9hOkx5mu2z7RKk8FytxjRXA+51PjSMRZC44sP9z7/Kov/Xbeo73/nOn/uH/6K3/+m3fus7KtLoWNN5i/eeWEWoziGdRFpPPkgwPtxortcVvfFsVhWShN7aMAAqg1RDK81kMiAZxmzrLZ13XN7MuZ2v8CJC+RjTehKdhval94z3J4GRSIfxFmPg/OqKm+kU40UQZhFCEGMtw+GQJNEYD853OGG5na+4urlhWzVIJ6j7Poi5lMB1hsPJGBmFMMELaLqGzbrk/HyGjlK2RYmQCoRHiRBY5HGE0p7OdDS1oTOOFxeXVFWByg5371fihGEwyEgiQRKltG1F7wEJs9WS65s5i6KGOCWOdJAx9I7JaIj3fRiVtxbTdHjveP78nLITGC9pDGTpGNtB37f4CMajAaZrsL1nXbVMb1cUmxrvJUrsDH7GMRlP2Gw2TAYT8nFK2WzBSq4ub6kKS2ctjghnBTKSWGNxvg9tTx2hlaLrG6qm5GY65Wa2wMkUfIR1IKWibRv290ZkSRTkaran7yoW8w3X12va1tMbgtHcOfI0jJsmKkELgxAW7y3FtuLm8pau7/FO0BtLa1ry4ZC+7+jamkhrkiynt5ayLNhut5xfXmJ8hJAK412Qy3UtUSxJsyB06m1P2xRUdcfz81sur29xKg6ru96SJhF91xDpiDhNMcZQ9xYdp7z/wUcYC43xkKRUwjBOc4zp0UqQqRThHZ2pMcbRNDXnV1esiharJN5YojTBYajqNQpHEmuc6TG+YbVdUZYNXz67YrXeMtrbo7IWpTRaa/quJ4pi2r5jNB5zc3vLtqi4vLqhcx6jBE54PBYigcfglQcFThj6zlDXFU9fPKeooTEC6yWd69GxorM9fWsQQuGdwBjo+pbZfMHV1Q3ToqZ3Miy+NBatIuq6xUSglcL2Pc5BWTa8uLjk8mZF5wWd1ljrcCKMyfi2BxuOd28826Lk9nbO5YsrrHWgI3rXB5N7mgaxn4wRMgqLC01LWdVcXEy5XYSQO44TCtPhARFF1F7gpEMKQ9v3GOeYThecX17RW0AEEaLUGqEj2rql7XryLMc5QdUGceHVzYzL6ynGSryM8BACaAR9bUm0AHo8ms22Zrnecv7sCi8UXqkg/9LBAi68oK2q0NDWCucki9WGi+sFlzdThAoPMUpIvLN4axFSUGwrUDFNbZgvttzczkIgZR3OWISUCCQICSqiNZ7FfM1ivaXYFlxcz6jqwHOSWgaerhfEOsHZwCqXzrNdh/PepmyYzTd88fQ5UiXgdiZRAhbA9BZrwklNec12XbFdV3z69AXrskHFCV1ncB60jsiTAZv1Fu8FcZSx3tS0fcfz5y+4ur7GuCCvFE7jCVMeTRcWWRI9wDlB03mWm4ovvnxO23m63tJgCXQLj1IaIRWr1Yq26dA64Xa25un5lHJj6HvonKN1DhXHaC+JlKYuKtaLFdfTG7Z1xe10yWyxwQiJFQLbhemGLMtwwtPWDVVZg/FgJU3TcXV9wxfPntN6j0UEnInzSOFp+56q7qico7WWqndcXy24vJ5SNj2d8Ugl8EKCD989vWe53bBtajrn2ZQtz55esVwWyCQKo0A6SDmFEiAUzsHzyxuUjDg/v+LFdMpi21I2HdZLeu8JK7AOB4hI0/Q9l7ObsN9uer58ds5qU+OUDtJKIRHek2iNd5666Siqivlyw81syfXNjOliTtE2WBeuUUJIrDHkSY7pHV5piqphua24vJ3z7Nkl26qld+CtQqOxzpLGMcaGxYLpcst8W7Le1lxdzZgt1jRNWEA2hP3dOkcSxzg81lqK5Zb1pmK1KdkUocUkVLjOCKeDAFNKet8jBYF/HSUsFlvqqqGxjlrt2N6uB6HChIY1xErTWUNRVNxM51xfXHMzndN5sFKwqSq8iomiFGuCsEd4xc3tgqKsWSzXbMqOi9sZ67LGSokRDoeARCJNTy4Vwlv+o7//H/wXf/F3fn+5/UVvv/mbf/AdF0tspog2XeCgDuO7h3wbaW4PIhbdAI/gi+sjqjohug5ji1G1k2CJHZIki3n2IA2hxvwe83rA/INj9j+2oQWZiDukAuEZho/ujahlTOMj5v2AVZfxL3/8NntPJNGuPSlNaBSqxlGcJszHMQszoCBja1P++Zc/w+onhwxuguU9Wb600guEFdy+JhERvKj36b3mvNjj85tjJj9Q2EySX1QgZZhsiYNdenWgySLDRbvPl8Uh83qA++GIbOFwiQQRjNHSeJpjxeUoYX/Y8Lw+5KabIAX8zvk7iCcD9r7sET7Ial62gT57ZcTc56SJpfeaeTfAeMX3f/gmyTw8aCbLftcG9UQ1XIkJ0WnDusso+oQvbo5IP0vJb3/KW2Unktk+ShhdGC4eZBzslXy5PMAiefbkHukifHfShBDZq2BlF15zfpqQDXomccNNPeKL9SHTj484eOLu2nuqC4b7eNVx9XhIPOlwCFqvmbYj3j9/wOBJSrzxRHUI0KXZyem2ihfjHJMIpPR0TvPl4pDtRwfoOoiHo9ISr1ra4wRde0yd8jQfsD+omXYjPlqfcn67z94PNboBpEDt7i100WMGMR9NxjgF027EebXP89U+/OmEk/e6EB4QQttuLyaqHdNsSDGQLLqc682YLO5Z/PZDdOtJ1uEhPbktqR4N0I2jH2RM9zS9lNw0Y5Z9zsV2j/X7h4zOQ6ghrd/JgzzpSrCJU8qRoHER827Ij64fsJwPOflDyejLkvJ+SjptcbHGJaGJ92Kwz1Ux5t7xih998irF7ZDjH3niwqIa9/8KCoUDI1JWpMxcxqLJWayHTL6XMbi1JEsTuKmbDpdo9KZBdzkzOeCFH7LsMmbVkOdfnhD/JOfgwyY0w6RENQ6XatKl4cYc8CLNmdoRhU34bHlE+8NDTn7QEG8N0bbb4Uws0oXnv0pkzH1K5TWXs336z0Yc/8ih+lBg0qVB9pZ+kmAGknO5x0aFZ9MXxR5fLI9Y//iQvY8F6TKwtbOrBhEOc6J1xNwOeRYPmZVDlm3O7eeH7P9QE5c+CLH7nUBRQLL1qFJzux+zdQmXqwnrOqV5ssfJeyaI5F5OH5iwaBTXktkoY0VM7TWf3Z5Qno85+mE45qQLoUx4TUG058uEeT+gyCRbm/FHF6/h3x9z8l5/Z7cPo9Lh+0MK8qmgI6HZpMinKQdPPLp9eS70d+Hzyy3ZWEZPFeWLMWKWcPxDTzZ/GfJKukloW5qhRlqwsaTVEZ9NT1iJiKe3R1x+ccL97+6CsTDAGORnuUS1gNEsRUIXCZ7ODlnfjjj8/SRIFyNJvDWBW55p2qOI/EpgfMzFMOPTmxNuyxHizybsfxDObS/fq7Th38myR5YJ18OUpUs5Lyf85OI+0R+NiYrAM01W4W+oxmITBQiyc8WiHDNNYz75/CH+T8ZkU09UOeLChQmKWGLTsICXLj35pWT7ZI/NdET+ccTeZyGsVd0ujPTQ7mmk9cRbj15qChIar5lvB6w+POTsXwqSTQgKvZY77rMk3jiSpaRaD7guxjxf77P9aB/34xH51BEXYZHp5fft4nDdGb4QcJtQVDmL6wnj78eMn4f9VddhqkX1L4PLcF0b/mnEIh6w/NER0Q8yxk8dg5se1YVWtvC7cqAJi5LxWsCPB8xu9lmdTzj6rgrnKgN6NzkDgm6skA7ShUXPYvovh2w+2aN/MiL5LGJ4ExY8dbPzeXSO+kgTNW6HBhH48wz//pDRn0QMrj1RYcP5MFWoLsi+dOMRVrD/qaN6Oub26oDiyR7DzxXJOix86MbvMEfQjVSQlHae/NYyvBQMXkhGFwZpdoJT89Nw16twnfMK4srhtGD8zJMsId04knmHSxWqClOtqgv/Rz+QxKVncOvY+1Ay/oli8qEkKkF3nsHzCunE3bk9/GHYf+LoZznF8zHr6zHZ+zGDCxGKmnW43gofjsnmMAIh+E/+4a/+ue61/60Obf+Xf/a/f6eqGpxzJEgiIZEotJT0vUNISZalxImmaXvquuD58xe0bU/X9SiZYYWgNYY8DoEX0lK3DTKOub5dMF+saFpLHKf4zuKsIZJBviOFQEUC6SXeGdLRkKvrKZtizWpd4IXEWYmQkr43JPkA5wwOR56ldF1LXXdMZzNubm8ROrBQpVBEURDPKBVqLbGOkEJirKMoSqazJdt1AUIhhAapUZGkq1uEd+yNcpS0dKbDiJ7r6YLZskTFoaEqNTSmQgrNfL7ijVdfpVoXDAcpRVNjsSyWK15cXDIZH9I0DtO1OBeawulwQJTExInG9CFEvV4sWDct55dzZJQQRSllUTLMEsqmQOc7o3ukqJuWy+sptzdTJDro3OnwDuJI4vE46Ul1SpxkVHXLYlmGYKeusKidbM4jMLjekcUJSaKRXoP1dH3HYrlktlzT9Z7eKpwVaJ0gpaBrO9q+pTEmSLdMT102nF9PWa5rjFf0CLzwIZg2Bt+HcHc8GOCcZFsb5os117fz0CD2IagTWlHUDQKJ855IJTgb8AyLVcHNdMlm2+KcCiG5CN+9jhPKuiZLh7hd3X9btKzLitlyg3dgZYS1PXGc4Sx4FDiPswKvFL213NwuKLcl8/kCGaX01qOQSC/BCZz1lHUZREROsi0rrm9mrLc13a5KJbzHuAbnDMILvFDoKKbre4RQFEXFdLZguW7IB0OatkV4EdplwhNnKaYzWGsoioKuh9lsjbXQ9waBRyiJNBYhLXhDUzraqkZ6VzBouAAAIABJREFUR10b1qsVy9UW5wRKaJAe4QWOHh1FKBmxWK7Js4ymMxRFyWy2YD5fIXSKaQ1aSrwLxvve9LRlR57kODyrTcWzFxcUVYexmjjJsL0ljjSRiunaljgOLUdjHHVTs1htmS6WKJnRWYFREq80wgm8Fwgn2VZ1CMNQVF1NYww3swWbpsYAVoATAoWgt4a+qWnqCqUVfeu4mS64vp7Rmg6kDnGfUPTGhnHuKKHtWpxxVFWN8J6L83M264LeGoz3eCnBSRKdYFqDEiI0RrctxbZiNluzXG3pO4eOYpquDziEHaxH65i+bqk2BdbCetswW4TfsV4ilcYJMM6ilcIDeIfSIVwqtg1F0dHbLjAHhcDhiKI4NIl2wbnAY/seqWI26xXoBKkijLVYZ+i6JrB8ldz9PUfddfTGsVysuZlOme+OcW9DOBv2rYBNkSqmM4ayalhtS+arDcttOJcgJM6CTpLABEfQ9RWRVixXC7blhuVmQ1l1rBZbjPHIKAYkTkr8y+aVF3jr6auW2XxOZyxfPnuO8TKgL7xDyxjvHZHWKCGRXhAJTVlV/D/svduOLVmWpvXNMeacZrbWcve9d+yIjMysbBroEoLmCZCAF0C0WkgFaiEh6FdAXOc1vAAX3YizEIUEQrwALUG3AEEL6DqpMisrMjMO++zuy9cys3niYkwzj7zLK2hU5VJox/bth2U2D7bmP77x/x8+PfDp4cLTdWZJK06U2kOynAqpFQrgvFKw8MzLZeFyrczras+YUiit4cdAboXWHKXAfFm5zIm37+65XBPfvfmA6EBzgVwq6gPQ15SPVth8eOTbb99yeVo4Xy7MayLlZF0lKrQGpWQCHnGOpVSW68qbb9/y9a/eEcaJXKC6Rq2Z5ipP1zOtOsZxsr2yOD7cPxr17ZS0NNQHnG+0HvJZasOJzZ2CY14yT5crT9fVzJsElrzSnDNK3NpSQAUfA58eziDKdU44IqXCXArqQWKk1UwcI0teERVqhdocqTZycUyHG5ZUcD6iAkKjuUZpFqY3lwJULinx+HThep3JJVFLolBtD2iN1hpOxOaYEwYdUQlc5kyIAw17LjkEVYVWaXSRm2ZzfkmIRMiYXYldPl4A08NIydSSNWdwME4HXBPCOLLm3J9njhAH0poRVbwGs2VRIeXMUirneeYpJ1oMzOvSi4RWyNNm70cajt/7m3/jL0XbvwAff/f3//5PxzdXJEO6i5RD2MNhwlOhqvLwMuAOhQ/nI/l+4MX/FomPrachmxdceFhYPj/g58qTs9bI5B3LH99x+3NLu89HRTJo2v7fDlfXGHkXBi4t8uPTPf/gj/5p4neBm1/V3ccOZ1RLvvG4InwsR9yrxJv5xC8ePuPxZy94+YdmpaAZmgr5aIfDGh0XGfl49KhvPKaRr9+/QP/0wPG7fuBSxWEJ9uEx0zTw7seCj5Xvrrc8rgPf/OEPuPvTTmx9zCYOf8o0Fe7+ZOb6Lyf+/NMr/plXb/iDj19ynya+/uozPv/f4fHHnuHeBG5XGv4pcXl14PEg+KnwMR15eznxD//on2D62vPF/3oBNTF9/O7K9cuR6duFPAW+1RPtUHj3cCL830de/bG17jvHLrbkox26JTfmF577o3L5cOD657ecvhKO3xktJMUoPMQEreVVYD55PoXA4jwfLgfe/fIlNz9XwpO1tupaKZO1EoeHFb0OvHu84/6F8PZy4utPd8R/eOL2F5VwtWuWYkEQei1odRQi1x8W5hr4+dvX5D+74eUfYCJ9P2wjwvTNBbzHFcdljPyqnviURt589YrjHw+cvql2IH4quyhXJ8/hTeL82cS5DFzwvP/ujvLNxN3PQKrZHMhayaeApH4fVDnnifsWSdfA/R+/YnwHL/90pXnBXzI1eloQHA4pDvkw8PYw8LAMvH3zgvqHNxy/xkRCMHp9NioUcdblNAw8XiZmUS5vjxx/Frn7hRHe40cTSGu09ZFuPLIKLilfv32FXoTbP3XoCv7aPUp7aBkOcI7xvhHOyuwD5X4g/jpw+1VBE7RgxRVUjKqbInUQ7v6scv9ZZL0fWd+N3PxMefkzm9v+kqijxxXziPSPK8NZuP8s8qkMfPvNK8L/cmJ62/AL5JMFPuVToBwD/nGlHD3jR2D1tE+R6c89r/7EwojCU7XCT79lu3D5BlYf+VBGnj4dyI8DL/7Qcfo6o3PFz+bt2oKgT4lwbcQH4T5OrHOg/fGJl3/gGB4rOLcTvfqU0DmT7iLxsVJbJH8YWUU5/J8jx2/Y5+32mtKtNwuDQXFJ8W8Cy9sDw88jN1/1YLux7wu+0/i1UUYhXBrDB+GhHPj4zR3h5yM3X5kI24JDspGBZZDdT7NG4e7PMoe3juObamFH3UJNstGpYEFeuliIma6V5pXTr3MXhtzuVx0eC9rFJV2rFYQ+OXRW/C8Hbv5IuftZM0/oXgDYhOH4WGzcn2B4J5RfHxh/Hjj+Urj7+QJY4aqMnRwUR3gq5INw+1WhvJkYfulxHwZOv6qEue2ipRGU7D7ZroFbPOHnA/N15PP/WdEZTt8mwqMFN0lp5IOJ+TU40kGY3jfGXwQOv3Yc3lWG+x7wljvZ6Ow16Wrr0C923pzeVw7vSt/HzW9cZ/s9m0iuS2P8ZELs7Z84/IfAZ39gre1SQa/V5gV2PXUQwqVy+nXhs/8r4e8j01sr/ISLFbA2YVx7oGfzjnifGR4br/7Rwuv/40I5Rnwvymgya4jxjYmF6wtPuNheefuLyuGtWfWEay8odjE83fr9mqWBJgsLO7ypTB/6ltHMGsg1C0eD58+XQbj9xYxzwvSx2vM1wfipUAe3X0c+KcODdTpINhE4zCaEzy+E4cG+dysGu2ripxR7PTSY3mWO31bGjxW/0q/birWS+r6gVrhFHOuNUqJjem/ewFLM7xYcdXC9K8L2EpzDX61jpXln+3e2Z0ENQh11f+74p2Iw0WMGHMud2SelgzA82lqUAvng8VuoY5S+vmzviI+N6W3j8K6Y+P3RfpYuZS+uppMgFf727/0Lv9V7bdfab2+A+//2x7/2b/7t5hRyXndPW9SRa6HlwDQdSOXK4TiSlgVXV4qhkxYugnnUIMqogYbRuq1lpsPA5XIGhLmn1dclIZg4E2TEB3CSqbXy+tUtdbTQnPs3nyi54aeJ+bpSgebs4DWcDqgqL31kOATO5zPX85WUFrw6qo7M1yun6cBaC7RGCIG70w2tFWpJPF2tBX2ZC6m4Tm15lvYECFEjpxh5+fKGUlfmeWaeFzKOp8tMEEul9aIUb16D4zjy5cvX/PAHn3Gdz+Sc+cUvf8G8FBO250YIdristXJ3e2vi8zhwOgUeHj7y+PgEOFw78PDwwDgOeBwpZ+JxYF4XjmEgiqPmxKVVjscb3r9/j7hACKEL5w5xFZHK5EaDxGplrSuHw4Hrmsgt7x6orQhjHFivM9N0oJTM8e5IKYl1XZmXM9M08fCw4Jyn1srpcCCV7hkSLCRn9Mr8+ASizOtKRlmpiAhaC+qjXX9eCOKs3bU6irc2ZtXQbQBa9yMsONfMgzaovb4QqQDimNOKW1fwFiBTcETxz+2nOXN7e2K5XFlLppRGKis0O/yHMDAMg/0+Ks45al0ZvL2OdU4Mw8AFa+H3WFXJe9+D9xphmGi9ELumM655mqgRX81CiBqFw/HI+Xrmxg+oCGleSCIE6WMGtGJ+oblVXIiUVhmGgVorIuCbJ+eMF2XNhTnNQCPGSGkZp4LD46VBXpAmRqn7QK5uv6+tNZZyIU4jrirqhLSY9+kYImvJth/owkCAJORWcFjRoNIYopLzSq4NjcECokolOkVKsgFogmhkWa/EGO0wgeJCNU/HKqRkoYGtmc+1qtKcpdI7NVHv6emecRgYx5E1JdYeeujDQOp2LeNhwIlZDxx0BMTSml2j4rimFfq4iAhRvI1nLUgQvPdc82qWJKWiGliXbHtDbb0IlAiilFJQdcwUnHP45lhzMk9ep2TXWFqhFsco9jknlVzs/q8lU2mklPAh4lXI2Z7ivjkKDR8DtZotTc2V1sW7EMyuwIIj1e4bjYDYgdhBKitSPGjDiVGqImL3s9l6mteE956onrSaj7Sqss7L/rXi7P6VVlE/suZOYwqYx72wrqvRitkCHUvKqNo8AUCslX4MAyUns42QQEqJhRWniuAY6W1DIibci5BqIddKLoVaK6qK994O/bmgKoja96R0JRdHHCc79KQVUfNDS9LI2V5naY4YB1yrpNRF9lQYppFlXfdno/ee7riDtG4rMkRKqTisC6MWT2sJHwNrWSktUVLlMEzolhjuCuNwIOfMnGdCUBPHHdh7I8d2DgziSLVYKGZxdi8qpJqtkJOzWaeIWmEkRq5pNrJ7ThxGs2IQscA27yNNGqWYR7FzikalpoogeNcDDlpDvPKwnPEiqHpbw2JBW9DT64On5kLNs9EEMXK5GBXuNdiaFjvkhM3O3yUen84Mw2DdA6lQg0KzDhOqY4oBlUYtiZSShYzRoFRKrfv3OTXi3btt/w4UKs7Z9a6pUEvhxfHGXpfYXAdwTimloRLwrjKnKyEekGbztZRCKis+DpArtdg+Nx4GasqUVqkFZLB9oVtGc3OcuF6fqGtmmEbE23NJpHuJ5oxqQHE2bjXZXPZmZfT7//Hf+UuPhL8AH//i3/gPWlO6H2jbPVRlLrhSjYy8FObXwQKP3HO6dBmEpuxBSMudHXyMIGrML3SnveKn1KmUurfw54OQB+t6kQyf/pqlROsCp1+ZyJB726UkE1xkLqS7aP7zf9WTTjC9tUP1lhgtqRE/JS5fRsKTvbZ8EB5/rDupNr0z6mZ4KPiLUVvma2vFkXQTePqB5/wTO9zF+0Z8MKFl+s7ay5uwJ8S7Ytf/4a875J975Ho/wip8/g+U8b5YC+19Ih/97h38+BMLKJlfCfPnMH3bOH3b6ThxHL96Yn019rCpQrpR4qfM9fNAOgquwPQ+m1/fn1xoXshHj879Z/Sk+PmlCRC6VPyTjeX4PiFz2cUfVxvLy0A4lz1l/fJa0NXCvYZ7+/zpV7bHuly5/nDcx74Gx3IrrLeO+GDzY/xkJOhmuWHhdrrPs3zQPZ1+a3+t3R/QFRP0ZbU5VwYlncT8Ml+aoAl24N8sEVxtJsQetbefOnRunH/kiY+1e3VW9NLPB9dEOUbWO/NCbHbexxVYb+wQHh4L651nfLt2Ec6IxTLIHryXD7KLHZt/qKvNBMUeqkZtLK8Hpm8uXH7nYGLlpVoh4qAmRPRryJPZHey+vrfekuYV8iT4ayVPdi/iB/O1zTcRWesu3m7epU3dfi9tnrb9c+F+Id9E82XscwzHPg+2eZpuNs/OuqfdS26st7qHEZbBRODhzYV8M5hQ1j9qEMKnZfdHLYNYnWS2vcA/5d8I5dnmSO1ELA3G7y6kFyPLS084V8Jj2u0n/KUgS2F9aZ2K8cPK8nrYf/8WRhg/2Pso6c/6fBP2Pax6Rx6M4N3I5e+PcVPbr6T7i+rSKIMR/ft86H7V6WTjGe8TLhXWVyPpJHvrfVMIj714c14pU6BMim4WF5PuIXHbfrntf663yccPM3Xwe6u17XHdeLgXuGqQfS8Jj+avvN3PGoX4wewo0o3fx74M+htfu+3VupT937Y1UIONsX8ykVNn8+WVtdo9+x4JrEtheRnQpe5r3l8K4X6xORWU9UXY/cs3v1B/sXArvSRcqUagd5sL1wtOm73EJrimm2BFrMruxewvZtlQR92D5ZqwB2Ju6yw8PIewlkn3OViD221Fvr8nbeOSJyv2yZKRtbB8cdjvD8D8wvbg4aMJjOGcd6ucbV9sYs8p3z1fdam7xca+Rvr63Z5z6aQMH9Lz73kdCOduc1iebRI2Ovn7r7sG2b2zXbFrPH71RJmC3dd9/uw/3tZA99alGZE6vbPfv+1b2/flw/M3Hn51Id0NJuxfLKxv2+8Alldx71rxl2qkb/8aWXtB4FL3vacM9tzd1uS2b4dHW3OP/+Rxf11l2N7zd1E9OPIk+1jUYL60mlp/D9EtdlLbn+tSGro8P5fCOe/X8PQjK7ZttkfV276w3YdtXWy/d/s5NVqR+X/6b/7d3+q99j/WpO1/+l/+1z91Qj/0FBNs1YTbVAtVKk484iOFxrIUmvPcjkdarugQWEuiUSmt2UELKK1SagNnpI+IUnIha8A1oVUhlWrCQD+gLsvK/cOZ85KoKCvmzVqakWVOGl4Fl1bS5YxrwuN5YZ4zPowUHNfKTrnVlI2O9JHWHMt5popwWVeqN4/SgqJecepY8oqqQ5qJuDjl4XzmfLlSClzXRForh+nIui6IU3BGTFUA77leVz7cP/DNm3c8XM48PC0M04mlVKS3jB8OE+fHR2iKirBcFx4+PtAK1Owo1eGd7xWYRlAjhFMpePGgar7CACKcz2duphtKNoHah8CSF8bBHqalNoo4nFf8MDCnTANSKUTvaUuGArULIyGYAJeXxLIWaNaOez5bYnZOVskg90CmBiuwXBekOhPP42DUofbUv1wYwmBtbiUToh22CxUvnqbWKpxzotZi4kBLrGkmDkqj4FwheAEqqVQKDScmYtohOOAc1LQi6kii5JRpa++N6GJba87Em1IIUagtU0qC5ri9vWVdVwuOa5XD4YbLZSVrNZLLWeV/E8c1xk4zVmttd+DEaM4hxE4eNoKP5JKIPoITcq2IjIgGWml4rWirOOg0obmHiGoXEoWUrZgiITCvidwSx9sjGjxLWkGVJj1oyzlrF3ae2oRSGs0FExqcI4qj1ELo5FqtNs6qakK8mGhWiglAgqd5I8kqzRLdvTev2X0NF6JGaiscpiO51u6AIjQ8qhHnPOI9a1lZSzJrB4WgRuAN6llrMkrSNWqrLCkxjSO12ELTamvUi0cw8dd5IaVGygWVSPAjKZe+bjKo0OomfBZub2/Ia4FaGWMgrZnqlXPKXHNBfAQqooU1JdSL+dQ6xQePek9ykGlG9YvZcczrguaKi565FfNldc46F3q4V3XgXMVrtzdoWIt8g1YbKt6En+VCca6/oTfPcVeqUc/dP6rWhoZgNGHDKP6ScSHYPPBCrSZC+xDIKROHgWVZiMNoDwHnWFICkc7XgnoPteIEoiquCWnNBB9sYvaKfasmKraCtfyXSoyRXAtOFZxZbvgYaK7gnYU20RqiHtX+sMUxqpJSsqC32kVTVVJWwhiRnkhbXX/j4NwzeSmeUithsKDJWhvDeMDV2slMszBwWMU+qodUaKkQfURkoBTzEG69eAPdo7g2xAnqlVoqQxzMjgSgtT55HblmYnDUlDlMB1o2ejqGgafLBfWO2hLQkBBY10RDOUwD18ts96Jla/OrxcZDHLksaIMhBDKlE6RQq+074zjZG8SaCJOSciaEkSUlpJldhzrBVSMABCE6JajScsOLdQRUB6ll/DTSpVxqsTUp4mk0rnNCxGh4xdGqPZ+8d9SWEXGU2lAgl9T3D8c4jARVlmoEu8bQ57sJya0X6gATjKXb9ThbE8FZ8anUinpPcY215L7vZGr3xB7jaMU1VaSTw00czVmXQMOej84V6/5oNm9tjXvWCk682bpgbZ6tWZEn54z6YOS2s7HR1qA0xhhhXcxz2Duya3iByXtczoxh4nJ+Qr2S1YoT6pSmwu/9jX/1L0nbvwAf/8V/+D/+FJHuodoLVF308+fFDk5i1TBdKuEh4Sqcf2cwEqe3xbpqVIwRMQ1J7L5vklqnzywpHTGLM3+1Pa2pwy+VcIbbX2YOb4wuCo+F4VMyGulo78fswA7hUmhixOj4qZBHQdfG+H418gzz7/RPhboJSo92CJs+WEv2+CFZ+FE0TDV+Ws3funv6OeD068rxu4IkmD4UEzU/j8RHO5RtIrWuFYczeu3PB17+I8f0rXL7y4X1Rhke8i4OzK8D09cXW9PBEc+N09cVVxyaW/dzdeC9PasOslNzZTAf+PhopKwDDt+uXH407K2dZRDix4X0wkQs31t+ESijMnzKnfZdTezrhTwpJiLkqY/PYqKoAwun+fpKejGgVxM23EYvNgiP2cK6vHmzbtShiT8mwmyWGHbY1i4kmNBuxyOjUo26s38PjyvpNu5WF/lg99yI3y7sXE1o260h+nwL52L/1jFk19jTu+ugSCqUQ9hD4hxw/nEwcTfbHL6+9kzvEnq1OV4Ofi84+GvtYmfd74MRwhZklHoIVxNHOXr8XOxaWhfvvHv2NxxMJNkEGdcckq3teng/m4fnOfXvE4aP9pqWL0bq4An3ZuLreqs5zqGpdtqwmbVg93F1WFiWJiMWdS77HNgIe7Br0bnQvOztxkAnOsVeu4Br9qfOlXIMSKrMr+NOwm5rpPXr22i28LjuYn05eAtHu/W757Um8/0M9yvr6wnJFXD9vgl1sJ+30Z3+kvHnRItKPirhqXQxz0hba8NvuFK5/M4Bf7Wft9wp4dLQ3Ji+vhDePhndjZGsW9GBvleVwQSY71OT+Sgsd57hPtO8zcP4caacInRRPFyMDpZO/5l43S1ZBsW1ts/N5qUHBJadFLT/bK443E7Hb6T8Rnr6i33P5gGqq4n1ZdIuanrCfdpFdFchPCx2pu4UZOt7m12bvZf012JeyF1i2qwdpBip3tTucT7qTkO6ZsJomaxYZUWu1u+tow7euj2D3T9/tbW47QtNuyh88Oa1Xto+pzZ7CKMdTdjOR9tTpRdejHS3MWre9gEL4bMCoxTzld2I5XTj+/phn4OusHefSGqkO793NGz/4WytlFOf/58PO/m5noTDW9sLt66RGoRwb4Wg5fPB2vSHXvTsY0JvufezEd7p1tszExMoJTf8bEGMRnFWSheE80GJfS624PZnnO2RkG6UPNkYl2if07VZN8GolMms9HStpFvdx3b4kMwK4ns2IPlgX7/twbp8b19cbb9aX0XyQYgPGZkLLT4/F7Z5tO0TNUj3KG+dLoc8un2f3qhW/5hoXvf9f3h3Jb0czP5j0mebj7oJ+Pb3MlpBYRu75h3xbMWE8GhFN+mWLcBuebLt2TXY2tvmhHMWZuroomyycamDML9SUMdyq0xvk3UF9OJXDVao+nf+9d/OHuEfa9L2b/6tf6vNa2KIE0vODN5DK6hUcIE1J/xgqe/NYQFcw0i+ZnI2WqRU82UtUqBCjCPr/IjD05oSgqKlMa8rYbLjYCml+xI+k29js17N4mB2GReUPGeCdpJtNh9RXGGaBp6enoy8ioHWGiXZ4bxhhz7EUZKFBdUunF0vT4xjpJaC98I8z/hgJGNpRu4sS8KLHc5qrRaKIybYlZSt5d17E0idsNa2i4A+Wpt9KUb8xHjLus6EwdNqJa8rpTri4BFn9JQdnI3srK1Z+6xX1nU16miKXUSorOvKcJj2Q6TLMEg0GwgML11TInmHVFP+oljrrQ4Br8FEydzwQah5S2ww+leDZykmOIZ+MF/ySmoJP0RyTfhmQmPrwTjmrmnBQlAJojT1Fqx2OHKZr0gRYuhYfM5UsdR6DdbKmkuhpEoUtx/efTQ6LRejlrzDSMB1xfnQ25mz0ZudaiqlEJ09+KozcWYYAuuajdpLlVxTn3cGg4IJRBuBmFIi+iPn6yNx9J2qVPKaOMpgwUHrahTv8QTViGCkUVymYf6T1+uVMQ5Ao6ZM8EYoZ280uVNlaDZXnG+46qDZA1di4KkWGuCdUV0pJW7u7rg+Pdl8Syvq/C5SVVMmQE1M107UxhgpqRitHaMRtK2YVw8FxQLgcKELYVYyva4Lw+ghJ8oKRXsvtXMcol1fDMHajIsdCjUYKVBqI5eF6EMXZUw8UxxNC+oORhGWZGS+2r+3ljgMIzXbZn2+zAzT9Ext1ooirMWo4pIb3YraiEY/QoWyrGhQ0MpSMMHM/Fis1Voc6IikQr5cefXFK86XBdVAzpVUE61lQlTSal6zwzCgze3en7lVxFtlMCWbU9aW3VhKJgvEXigwEjVbAaZBbUa2lqY004bME1UE6XuABivyqAKtGLFeXD/Y2cStQJa9y52aC4J52ILRsus64/wzIV1pRo/PC6pq5HCy9ejUiGOAvMxEVXwQcnUsqdO9YyCljFahtbKTjK2Twq7a69/GzKkjr4khqBG8KDkboYx41royeM9SkhVD1oSj7mvSN+U8XxkPE6UZAW4eYBYc5RrksjJGI3o1dNo0RBpGFpdWUfHgGtLsZ5MKtT9zWrPXk5vtgdt4tl49kQYlZXwMeB+5phWVkUSiiNHx6h0sl14wqQzTSMmNvFYO0y3X6xNIormKdwN5Wbk7nvg4X/AiDD4grpno7pV5LTTqTlcrzojtZkWFp9W8hEuu5E4DOzEKPxeIIVDzc2GmpGKkejYKpkolBJufYJ7pU5yoXvbi1nqduQ2RKoGZQulzNfpOyDthiJH16YoGb2siRNtD+14jrZN7OBKVtXShx5mwKk1Bm/n1eoVq3RU1F2gWGujEG1UOVCdGx/c9X70VFAoNvKKGmlOrFUi2PalW26Ns70zUlqEpgw+0ZmNmE7mQaXv3hBez4WitUZ156wbvkWZezHXzelY1el7svZIXhzYrHuecGeME1RG0UcSuaS2Z//Y/+Y/+krT9C/DxL/0r/36LH1fSXSTc25/ATgmFc95pNtdFj+VVtIP19TfJKlltrqYb3y0GrLi3HaTjp7QHIJnfpX2fy3YYWl6G/m+V+Mn8HvWayKdoYWmd8gKYX3mmt6lTSrKHzWyU45bKvB0gJRshN7xPrC/CLiAMHxO5Hzilt6rGe/s9G+EmXTQuo9upno2MLKM8k2ZL2QnHjWrMR8U/ld3LVmejAvNN6IEvRkLl0Ugf6f9VbwdIvazk22Gn1vzjSnoxmNdfpzHXO79fp41ZMTJrI56OSnjYfB3t9Uoykm/zQoVn6ss/LOTbwQ7jgxDvs6Wpj7rTalsgl4npNl826nYjT8M5k06e4d3ShRK/k37b/d4UeZDHAAAgAElEQVQIJJ1rJ+D83iJeRiNltfsmllE60Vh24tNf7DlRo13rTtpiwkY6mWgSnqytVee6k08ut/37yig7nesvdfdLzTdhf516LVx/EHfSU+bE5SfH/nvb87h38m54N7O+Gvd7mw92v/3VqD/Esd6FXfjbxriJCQzjd1d73zIFXK7o08L1d24ssOsQkMXEHBOT6k5+AchiAXMmlgd0KeiTCXXfp4A38k6XZ8K7WbsS8f2VfDvs91WWZwpx/mJieDfvP89CHOg+o+xhVPkm7Gty2y9ctTC7pjYXNjJfr8UI/c/jTvOP315+Y/4DOwHb1CHLs6gMdAGLXozp13Gfd7rQ5sWzB3DzDpkL93/twPG7tM9FvWRcMRFan5Ldx7thJ/zKKDspbN0GRqLu++ZjRi9pJ6A3knZbL67YOG/zZSNHNzp+I1u3P+1nfo+W7R8uN/SaqEG7r2rdxe9tPDf6e6O+Xba2+eGt0br56Pf9uIn5dALo1QTe7VrjfdoJzI383Kjd7b4Cv0HIb69B58J6F7pob50dG8EZHvNOue6dAp0WN5HNMX19ZX012jztc83mghXx/PWZ5N32iI1S3Qj87XVW77p363PXyDYGGwm8jedGglbv9nHPkzC+T5RBzYd5yZRD2H9+62L6ti/rXLm+Dr1Fnmfxeq48/iRy+4uFMirrjVHD4WrjMHxMO427PUPXk11v9a4LwW6naLeCyDZH08kKX+kg+KUia2O5U+L5eR1s1DjYHJ5f+d7tUqEX486/Yx0hw33ZxzWdfL9HwnqjxLP9v3aBfOsM2QjeJlDVCh3h3INIwzMlvc+f7xcEtmJi93De9ovf+HvfD6xwVvY5YWNbdxp7+3uTZ0p52w/WO+tk+P6z8PvWKEaW8xvPrXzYROG2d1kYQd7290pleu4k0bnse1Pqwm5z1sXy9/6Hf+///6Ttf/5f/f5Pp8NIbiZGOrFBzFW4rE+4UQm5EsSq4d5bG+/SLPgoqrV/QyP6YIE6JeOd7mIcVJI2XFSiU8qy9vbqgbwmoHKYJkot4AprWk18LUZWtpYp1aia2irjMLEsiYZRf07s81YWAS/28Eql4kVpreDFUctiFZBaCWGgVIf6gdoSDWeCczhQcveMdFhImJgIUWWjjx1FrUW1Ac4bwRN8oKaCAh5rba0uU50dQlvJSPczxFUSdigt1bw2nUSerheGIVJ7y2aIYgSw170C6Kp5yaooqo5UE5lMdbAWoyIylTAOkBuHaN6OqtLb8JuJrauJxngTuYdxxDkL6hnjSK2F1DKpZIYQUMEsBKpDXKSK/dvMCjkRvG28zmsnrJRlWWwuiKOkBC4Z9SQgXmjAnAteIyqBskKIB3LpVGQzElfV46iUYtR2db3tVILR112gDABSqa4SW6NROT+dqRWWuo2rPHtQNqMYci54lNrM7zNVC8FxrhlNWkwYblQkeKiCD4qj0Zwl2lcxqrs0o4ArDefFqEIcuTVSs1beEDxKo7pKdQWItE5lZoFUnVGzUpGaCNKILgLmFati/rTSsPABAuphnS8cxgil4DHqV5xwXWaC9yxpsUC+nIlBUQc3xwN5MVFZUWpK1sobPCkbWe+lEn1EG9yMR9LTzBQ9rWaCeASjSWsueBzFAQhOlFYT2v29xAvigt2dVnDO/lQfqaUR40TDQ3OUXDgdT/imdt/F1o5TyKV0crIRgj2ApNr9K7Ug0VHUSFP1ynJ5YugE6CbEaRFaqYwxmj2AKjmtOCo1GxGZloUQR1I2qptWEedMHHWO6K3NX72DWpFaKRLs2g3NBcyPcxxtj2viKGSjFcX3QlOGkvHO4QYTll1zqHc4xfywgzfCr3VxSpVhGCy8MVl4kqcZK+vMVzfnio/e7CdozDVTgTUnBi84KkohOGXwSsqV2iysznnbX1Mx6txLIDpPXrKFNXlPLUb3u27R4L0Zvju1zgDnHUPwlJoJauJvaSvV9TAxryZyugJYkW2aRhPZ58w0HnFSSLniEHPc6OKbc5XaMplCHK1wZ0Je33tLZhhHWqsmfot2wdvEthACrSVEap+H1TyCa8H7YKFaVPOfDWZ5EcaIw2wqXDWrB5dtvTcsBA/XSGtinCYO44H1ulrnRCfphR7aGcR+Z/AE78lpMRqcSmkFdd6EfN26LRqXyxO4iveCSEVxrGVhDMIQPVPTvSgDFSfKWkzR9TFSmpoVgYLLCRELY0yuEjHqqKVKrUJphXiYcOJATEQNKgwxsKYE1ULI1tW6SIwib6zO/HcVh+uFsFx7xbuaeC4qOHHUVnHeIQqDBsq6QjMaqebMdDj2ANJKHD2tFaI6nGt4dahASYVJAipi3QNOTUTfqvcYke1q5eAj67oQRBnEWnWpjTVn1GPrtTY8QnAOj6PlQimZaTpY50CtnKYRcQ5KJdcM3qx+iqs4dQxekSXjij3svA+kCkGU7AoVT27283/vb/4lafsX4eM/+zt/76fry7jTSmyedHNlfPPsq2q0lf2/nyvxk71vtWCbTu+d/B6oU8Z+uJ+N1NzaUPPJfGbd5rd3tYPT/Hkn85qJjhvZUw5WMNal7KTS+sIbAdroLeGyk4KbD98z4aV72MsePpUapR9W98/3a0g35gFYDkYateB6a2Tt7ZPFDmHd13UjviS336CNjBJzOxGFs69pQUxcaRCeshFa6zOlOX7XhcLcqINSjoH4aaHFjcIyoc2fMzWaqBqeLAxH10Z47IEq12SvD7h+FpDiul2A7ff5YCKA0T5G1qUbbwnzb59IL6ed2vNPiXwXTRTu1F/rY+HPK/HTgkN2ggk22lVM6BmUOmgnoHvwknc2btmS18uhB2/NNmb+WnaCuYy6t5JKeib6dK67aL/RWpsYswm2rsLx2xUpjvjQ7Slab8Fvvc3XdV/KUfoBu9oc7KL35g9ZB+0eiQCOOvpONPYv6yLwRhBLss4U10yg8NfyTN4e/P61WzFEioEqOlfCk5G9rra+Bn0XUN2+zowK3+jV7kP9sLB8PuJwlMleXw3OWuFPkfCw0FTx91fKKVKj8vSlR1cT92tPaq/BUaaAf8r9vqpZKQTl8qORcK2sryKumcjXvLN2/n4PdO244lZEiUbrG6ErbKnzbbu3we0evpsIootZcJSxr+Hg9hAxf7Uwp6aye6Fu4yXJxkgSdraONg/TbbD5/z2q2FVId4FwqTu1vXkju2be0+UQ0Yt1DRqZt90j86XciOttDW8dC82bPkHvTEtdoN+Ib7tXus8vYCccrcNho3FheD+jc95JUeiWIbeKXqsVt26iFVT6GtV1I1Zt/bgK4dOMpGJhfXeDUZ/R/L/XO094KjuZvdHJ4VwseHBQcifGm7jdl3xbI65t6++ZNm8qpDtve+6ou4gpue73kH7PtjFfXwXb6wvML+1+hIuRuzUakNL6NUq2n7c9h2roAVTyTNtK5fl3deLedQpWatuJ8Z0WTr0dfy7dsmXrDCnkg+/jRR8Ley8l3at0I5T9U2Z94ZlfGcVdxk6xYIWCTTx0BVCzWAnXilR2O4Pt2WTWHLbGxvfJYI3BfneJQjwX1peB9UbNOqY+d7zgHPFsAuQWZDa/1J0WrVuXy9NWUOhEehduN8ra9XVVJmW9VcJT3Z99W7CanzsZ/5j3e4FzlGhCtGvPe2T1svvfInZP1pPu3TnNG+26vFDrMpm7CJ3NEgKBPPaC39qvKdozPR/tWVSHfnboBbGmwvW1J57NVmG90X1/Due8i7tg+105yN4FoktleelNtM2Npy8tiNXhdmuR5t3ua5tu/O6lvO01/mL3M1xbp3EbdXC/NWn7j7Vo+/v/3X//U+xZhQxKc47UMlVNYBk6YSg9nKn2MBBLsRakZXKzYDDptEopxUSM5hniAciknJnGyTxIiwWcSW9vXVs1O4Jm5Fgt3yNicqXkgqpncOaXaGCoIEEJIVI76ZrLVp1U1AXSai35tWW8WisxDWjWgpySeXhmMBrQCb7Yzcg0KM0O+o3NwJH+aEC9UV7ahZRaK149WEyJCQENLC/aylXBGY3jpRC9hYCFOO7XerleuLm9YV4WQojkbIKuhoCIQjEhZ/TT7sO4+TnGGFnTShgiokpzkJaVGAeW1do6K73KVe17vLc20CWtDGNkXVYEQQc14apVhhBR1/DaTNioYkFk0lsAqhFTrkHwkdpAqcRxZF3NDmCdZ1ptOEkgleYDqa6UBqIDtTbymnEitEFIxdK+kcK6XnHSaM5sGxDAVbNLECGX1UJ2SsE3zE/ROdQptQqup4DHwRNjIJdMc0Jp9mDyzqiyklcTh/vGYiJcYxxG1mWlUUjrSogD87zshGmVPndqIYSRNa1mv9B9J6mVmquJdc4KG61loljQjYon+MHmfWvkYu3C4gIhDqiDVhPTdCIOE+RMXk1UrdVE69qg1mRkrRiZdxxHEzWzQqmMg+dpeeLmxQ3zfOXm7kTNlcPhyKQTy3W2lvloInjrZKGjEnyg5UQrqz0gaqG1QggOykIcjhbe0xw5Lz04yESyVix0Z70mCwJsjsmbwJapZIVK66K8WXZQTez1neJuxewbalkZIhRMtG3AFjjRGpRsVF30nqf1CkBpjWVeUKd48fvrEmcP8ej9HuDWSmEKnqhCxASdMZjoN8aIAM2ZAB+CtbKr2jhYQcuoGFExWjMXG9/gba21FYcVuUILuObMJsN5cEbtmo1Dt9lohXW+Mh1HW2vN42phjGEP2LOCguM0TQSEgGOKA7UV/DhaS7gWcllpVNayEHzs4yrQBciaE7TakV+zlWidalUvOO8ZvVqIJA0Ntie6XsxzuSFxuxcmuLsGx/FE1EQrmckf8V5oNeHUyhoGeTZKuuAVxhDJayKqcggDpTXmlvE+WnClFVARNT/lcRwQLLwMsc8jnsPhlvmyUGojhgGHo9Vq9gZ+wIunUBAH83xB+xvhVm1uqVixxeMgOAqVKSrn8wO0hGwt+CrUeUXHwJxWVKzt/3R7S14tHG8alXl+QqSyrJlhmEgp0Sp7F0irjdPxgJPK4TgwjQFJCzk1Docbas0chom0LORioXdNHNTEFAMvbm9Yzk/2rEYQNSI05cwgwehr740OqSsvX9wSgyNVx5wXYlQq2B7YHKXPZ++EZU1MYeDgbf04S47DdxLcIVYY8EYmuGDzc/CBWipUh9OBXJx5OKtS84pT80tWsWfH9XpmGgIOh0qwZ4a39xoiCmScvf/FqyeI8uLuhuOkCA1aIw6B+XohhkiplSGGHqJYmLxycxi5PY2MQfEqlJyIQSxUDluPvodFHtVzHAd++IPXxIOwzjOUxhQH5svV9v21EQePd5bOHqIHMtMgTBKgVrtPzhn5D+ASpWa6jw3/xl8Gkf2F+Pi7v//3f+ro1EsX8PyT+fI1FdKLYU+I9tfnQCVJ9l6lBTEqLbddzNj88oBOfDn8eWV9NRg9JFt7of0ZHldq7ITcLnLawWkj/OroLZSph+nU0AWb3spfBjtk24f97PBkBJdkO7Buoq21I2/E5rOX59amDtamv7WTNnX7921UkIlmPfAEumhr5NnztYFk2Np4y6TUwUSudBS0H4K3A/z0ZuXyO5ORgV000KVSR/+chi0WQtO8kYNNu1feUQnnfvD20q0hEuk2MvT09E0IcrX19nI7GJu3aehiudBGI543P9M6+OcW7O4JXIb+jC1tbx0vkyV71+hIR6Ov8snj+yF+uw/SaaStzd6VZpYL4ozuesr7fQ4Pqx2sq7Xh7i3JXSjdRbbSusWAicY12rWZ/2AhH5V8Uvyl7odwnNs9Lo2W87s4XHso1PLShKwtPK4cPPHjuotEe1J5hTLJ7lVaJjXRvBPgupR+P8Pekv19+lmS0a/a513zQjmGvSV8+SySjpYNoEvbCxjNP9sfmAhpf798EWldmHHA+jIyfnth/tGR+HFm/vEJyY3ra8/yUhgeGvEhk066B2JtAVf56HcBualZObnK3l69noRw7aLbXPfXVAftQXfaBXMTnuaXNk/8XLqA92xR4p+6HUdfE827fp+fW7UlbVRuX+21W/vMRkfmozC+WXDY98RPC3X01EHQZOJj61Ya+WBz18E+3umo5JNZ/qU7sw9JL0cT5LqIl49ba/km4Nn4b+JNHaRbs6gR4hvVLu45fEplJyRhE53dLh42sULD8ioYGNH33PVF3PdI+uuevxxtjo+ye1LvhaPGLkD6+4VysG6KfAp237oNxbbHAc9BYT18zn5v6B0LulvDNOmUs3O78E63TcE55i8i61HRAvNLo3al2Pftdh3VimJlUtaXAX9tpKO1lm+hV1YQqHvBqwa7d+vL0EMC+7rp75mvnynjx4yuXeTrYq0RsBZg5VfraBg/pGdBt7IXg6ATua6Ldq8Ch68vOKzwUMYefJkqNSrhw4UWba1cvhzwcyNcTFDcgtE2j+wtWK5GE/pchcsXZr0yvxKWFyZs+gXmz4KJrV0A3i1l+ljNLz2PPxHiuRHmZuHQncANF/t9zTuWG9nF7PNfEdJR8NfG8KnsXusldtJ7tf20jEJ8KMwvPPOrbeyeBfot5Kx0AdWsUjbSOOzPXLNaaSaO9s4X65awvTpNwvixML/S3TpAaqfR+9duP6d2mjhPwvmvCPPrjbR3pJMyfiqkgwnA643tZ1sB4PoD4emHynonlGiBjutJ+9pmtzXIo+P6Wrm+Vj7888J66wln69ScX3mmj2UvtqSTrfHqbT9xOK6vlOXF8xzyvUC8rTFdt85M+Lf/1m8n2vrf5ov+v/oITsgtk9ZkJGHbSKBGdEpLhcGN1DVzDIHSnHkbgqU7OxNxUaGmhh8iJScTntRRVvPqG9SzXK4m9nnFeWXtxNMYJ9psd9VahIMF79S206TihEYhDn4XbKKOzKsZINdsASjOmU0CNMZBSfli9gs9YCmvxdodwfzyKBbqVZuRM82CgHyDTEZbQygcdSSlxFpNYHYZlmpt+wUTXWuteCKqJuI6gYP3zPOMU7oVAYh3LGuhSaSshajW+n8zBKYYyElZczKCtyg1N1KtHMJoQqoqUldqWziFF2aZEAKZhTQvqARGbzRTSQX1jdOolLzyeH3geLrjaZ5x4dAF8UBuudOPjYMMRifHFT9k0lwpWWhZaKq4Vs2bsS0EBNcUUaBcGdToq+VytTb2ZOE94pWSK600BiIiI17hOAWqq1znmTmtJtiLUipUPHE4PrfGp8LdcaBVa1/NubDiKBmQyLVknEDo9KKTFaeOL199Zi2q84K4Qq1ht+QoNMY44IKQ8gXvAq9f3hotHQO//vobggpFYLw5mBCnaiFZAmm+8OrVS5TIumYgI8Gb+IpSqqPliozBBM0Kgzpev7rFi/Lp4YE1NROkRClScdGS32taEWkM8cQpHrg9eb75+gODx4L+9MBSHbk285xsjuNwSykJ14TbG88YIx/vHyjZMblIeyq4VWiPxSwbRsfjfMZF4csv7sh55ma85fH+zPmykjSQawK1ZHaKmv1Ia9wdD8RwIo4jHz81LstMC4GnZTbBBreLl2GMNG188fozbk83fPjwgcenM91K2JLm+5rJZUZRqJlpHPjiR58zDp7rPPPdd99BacRxZMmFNRVUJ0QKw2AC749fv0bCHSLKN9++wcUD5+uMaEBWoBct4iCIwBefv2YcI2McOH+65+P7D3xcV3zw1NaYojLPMyLC5AeOo+cwDbz87I55njmfz3z8+IlpOvB0TSzpguSIiOKDo5FQcfzVH/4Q7wNfffUVrgqlNXyD8+VCGLyFhKlVku9uD3z5+hXDMCBt5eH9A+8+nrkGx+XyRMZxnE4s88JxnPAOfvyjz7k5TMRp4MPDB/7sq29YroXhMFGThXDdyEheZsY48PndLeM4cro5kuYrb775lvOSKE7Bmd93apnmHOrpoqrnRz/4Ah8cl8vMx08XLpeZooGWM56+9nAcD57TyfHF5z/i5uaGdak8PF54+/4NHy8rwQfyUmi5cBpvcC0xjcqXf+VLbk4HWm4spfJ4fuKbt59IYyfFBZuT0qht5Sc/+IIXtzfcnE4sT2fev3/iV2/eADBME0+PZ6Iq0XvEK69ejNzcnnhxe0OIyvXpzNt3n7h/uPL4sKJDJLVCcyY0qGu8Gkd++Pnn3N39Lo+P97z57iPnx0fzsZ08SxfTPY75ckWOEz/8/AWn05Hb48Dlmnjz7j2fPj1Q8pUQIqklEglVW++vXr/gxe0X3EwWNDk/nPnZL3/FZV7IS8VpJUYL78x54eXpxA9+8GO+ePmCEDxzgl+/fcevfvkGSas9vyWTuSIu4oMyHZXf/af+KtMUWZcr17nw7v09v/r6G2Lw5JopAt4NaK1orbx6eceXP/ycw9GT18L7D5/45rt3nOeE6ECulVgbIg7fhPN65YsXL/ni9pbT4cD9xw/86ruP4JTqhdqgiHkme1GeHp948SLy1//Z3+XmNFGXwp/94s8ph4mHD2fzWHeui/QjaV25/eLIT374BafDiGKHqe/efuTnv/w1p8F8sREH65Xx/2HvTWJlyfL7vO9MMWbkcDPv/O5979031auqruquHtjqJtlNyqYsUqINUDIMa2EDWnljbeylYXjppVcEDNiGBBgwBHgSYUuWIAGkTIrNZs9DdY3v1RvufG/enCIypnO8OHmzaMALemWBZqzeIu/LISIjIn/n9/++KEK1loPdLUb9LjL0rd6qgY+fPWc2zVcyNzzn13mO9d7+iK3RgEA7jnpbuErw/MNjXp1dEuuAsmmRkW/FA0QmRmrJ4c423SRh1OlzdnbBq5NTqvkSlMatpK3C+gXXP+OQ+cvtL/h2y+Zcj4O2t4GeWgudqlVTqtg0n8s6UuMXxSr7eRhX27WQBbGyPle+/VT3Q8ysWY/eOrkKaFpHtRF93lZccThvR5h9OBqs+W+3vERhHXVHE9zU67/7s9Ic8GKbW3u6U14ooktHEwpv/r6VqWS+QXOLVGgjQRtp1NKzZYUV5Jv+h7ZZ2HWwF0waRCvXWITbhla7auc6KaiGkuimXY973gqlgkm7DvTqjhey2b2Q5UCiC49UQKwCqca3NpdbMbJxlJnCaP++FtuaYGGpOhK1dOux5apvsCZY8XEV+Zbfh53jinwrIDmvcNKPX9tQrxuIwjqWI7843ISCNvTWbVXf8kDd501A8KxJJdafXbUKBoKZH5e95VHejnwL69ayliYUFCOJbELCG88mFq0PZ9d4iZUo63ab3fHHpF768DKYq3VLL7iu1iHjLT5BKLh8x0tA9dI3fG9FOrfHZ9s3OGXWIrvxI4010Iaw9YN6/XqqfuwFT6Fa4xp03jK5b7AB6MWqwakFurDrUEK5dj2G7xQUI8PNQ4UNNekrh1ms2I3GISuxXkC4PaarnmaxJSl2BMOfegGYsFAnmmDWolatU2Gh2A5QpR89n28pqi5kLzzTuBrGq9Fy8TneIhakp/51XX4hQpWOYksQnzvSc9YiKP+9WC3wOf8eZ/uKJoEmdXReaOJrH4hHZx5pIGu7HiFvUl+Aun6iKXYsnc806ekqsFpJqdSqwalWASMoig3FzRNoMouZaAbvO0BSp5GXy80+H9kvNn0IefFFiftyglMw+BkgQpLzZt0sv91HdeqDlpsnkqprsVmLuZR0XkD3s9bjUGrHcmjWYqHl0JCPJOVAkB826IkkulR0P2tZDiTJpSWYNGsu5y26pQ0F519WtKFj67t+IVlVDrUa/W9Tsx63tlowu6O5eeqwHUA3mNOA7ieG6MYSXdaovGa5HWNmXkzYRJKrdx12qyJKKi5fdNj9I0cwaVbn7xVe5KC7GtUOGT/WVF1HNWqRhaT3oSC5sOtxeVn788ktAqEJBWVXc/WOw4UOM1Z0XqwY4eWKTbwSuzmpyDc1xaZg/qgm256T5yHuNKL3kab7WbMW8AnryHdDnIDlQHLzVCC2lzgrsHNDeG4Y/sRiFJhp4xuNq2NS1o6zrxiW+5Btz1nMI/TziK3vr0bfu4r4rKLONHVHYpViel9SbFn0fkESVRxPEsKPNemxo3PSrFqR7eo74vfhzYOQ8VsOvSepzzXdDxXJuUWXlrrjcUD1RkIbKS+J2wm4eEdRjixqp6C+Cel+qMleelbrrXBM53aNyJndg3K/YWNryiBecjFPyf/PAdG154s7CXVH0YQaXTqmdxXTJw1HD19zN1rwet7j7MfbjH7gJdd1KtchtMcmQNmXtN+asN2dcVNEXExSbj6N2Pq+58LrpVuhc7ycqw0Ek3cC5k8r0kHBtNTwPGHjp5L0tF6H566jsAqaUJKctVy9FTN57LCbLfo4ZHO1P2Tti0kepeSnJKKzkvpezGd/UxJsLrCtJP7jFFUJOictdbya6lh6uakpLIs9yeJLBTujCZFu0MLy4Ud7bP2hYrFtCFYCUoCyJ2gDxeV7ju7RNYGybKZzrouEm3+5RXLm0Ne3aB8/mW0Wjst3JTya002W7HenNFby0XfvMvqBo9jQhDOP4JCrteKyK7Eazr8qcTtLvnzvBd/96B7dH4Z/BknB+v7nViz5593+tQ5t/age6CikKHOMibC1II5D2soSGIkWliSMmEzGgCUyAU5pWqcoXeslJ42XuCxrPyjatDVGQ5rFuFowy2tc42jUiknYOoQwXkpCSxwHni9pFWmUMi1m6Cikbitq61i2tf8gFTTOjzdic7qJv9FoFhYhYypbERiHczVNs8ChMSrCSEFoAjY3QspiyXg8Jgk7LJvSc3tliFIGqQ2LvELQ0Ot3SGNFEkjm04VvzMqAFkFdLpFGeXu5VavRbol2mjhUtG1NXRXYpiaQXgbU4NBRSFkvMSbBYTGtZX97i0D7lYWPP/0AJQOkkMRhQNkIbFOjTOBHg+uSjX6P7c096rrm5OwVRrUsFjPCwCBr6X/UVzUIQxIbjJb0hymJHrJcZkznU2gFlV1gdEDlJG3V4IT2LD4tGQxiRsMIW1uqsubl8QWzMsfqDCslovUjxGet5NcAACAASURBVEoZlNAMejH7O0OausC2kqurKy5u5oShobQShfZjzCLCCEcsJffubyOl53te38DNzDItFjS2waqIumk8pL5tSULBndEWw0EH1UDRLJjMcl6fX9PpRFxNl2il/BhqVQEtw1FGf9CjkxpCBNPJgrOzKxazyh8zYoUwQKCVoZNkbI02yJIARUVZV7TNgHm+YFGCrRVNbYnMSoolHcONDQ72dsmSgLaqOR1fcXk9Y1bVnjcqQCUhMjBUkynD0S77uxukWmOigHyecnp9w+vjc5RKWTbQ5DV1WxFGBuUEWZpxuD9EuoqtzUfMFnNevjhlWSxQUZ9AelGQrUpoKrpJxN7+kDgxOKXp7Qy4OpnQCId0ksgpWlGhooBpsUC2LVubPXZHPTaHI+bznOswYnw95bpckNeOCk1r/YpcREs2HHKwOyAINUK0jAYJl+MFHz97RSgVTe2/D16eVWOF4P7OLptbI7SGw8MjLi/GfPiLT5iXNc7EhEFAXhRopQjDmE4acm93m14WIdqaqg6oi5zxtMRZiapbQmlAen5lJ8vo91J2t3rEGlorGPW7/PyTTynrnNrFSBWuWnkNVVOwf2eXne0hcRJiq4ZBHLK10eN7P/2Y0taI0FC0liAJqMsKWsvu7jaRkQxGGWVZMusYikXO+PoSEaToJKGtrTewWsdWN2VzNGCjl2LCkM1+wunpJc9evCRKuzhnCJKMm5sbjIbdzSG9LGK03SU1BlqIpMSpgOOrCVYaRCOwZUO/kzGZ3fDkwT26/YBBL1u1Y3ukUcgvfvqRx3OE6QonohHKksYho1GXJAoxxhAOOsRG8urskvH1HOskQRQgaottHa7KGd3ZZtDrsrk1oK4KBklE1Epe50umtkILaFyDNiFaKVQouP/ggCgOsMuKOFN04h5SWK4/fbliAntsQyskqq7pRz12NgeEUtNKh5WajSShnOS8Ht8QxoaycQRGI1EMspTdrQHdJCXUhjZQbI52aZzl9OIS0bRI23jPjKuRTrC9uUccGkajHk3V0I0ijDQ4d8X1zRyJxgJGGjphTKuWbI4GDAcJQSCIgg6xVsyLDX7+8YckJqYVgqZxWAeDLGHUTekPe2x0U7QQhEFMFIRMJzmlrTyeQEhQLVIJkjTh8PAONDUbnQyaml6vx6SsePXyCiFrZssKjEIITVmW7Gw9YDTaQBk/aTKIA6plD7klePHsBTqSLFofDjpXoJOAvTtbmFCQpJokSYkKR6BDzi6uPcpIGrT03PBOpKjyOXfu3ScLQ7JughaaQafLzXjGfJ6Djj12wbYI2xKHisBk7O5sstWJ6aiIYSdkejOnrjXTuqUVEq1g0B+wmE8JI8FbTx+w0Qs9tiRV1OUeH3/0HCkli2VJFCdeTCclWZKytTEgjUMCJUjDyE/u6Iiz02umswVSR4BflJsXc4wUPH7jIfPZmKwTsywbet2E3tWI6ew1gfKyssbWaKOphKW/0ScJNbHxrfg46bC9tUFRLLieeqRS0zo8hsJL3LYHfXY2+76Nr2BruIERIdMPPsQ5Q14tCYxY883FrdDuL7e/8JsufOPnlsHpw1hL3Q3WduomFCz3JdlrPzVW9hTCKVTpPrerS89WDcee/6iWdv3DXVjoHLerRmW75gc65Zm1NvDtMKtBl5J8JMletzSRZ+DpvPXj3cmKeZvXtKn/9/xO6BsyK7mHmbfUHb1+DcL6ZlidSKpMcPNIYhY+lMlHmmjSYmbtmpdqFcQXNQiYHoYsR4Jy4IguBIgVr3oVXPt2svClgdUobhNJyr4Pe3Thw82q4xuHwdSueZy3jNsmkVx8UdIkvqG2//ufswGXG5pg5sPZKgzWI86zu4LyjQpbaLo/9/KT5NL6wFRpmliuJEtQDDVNDIs7UA0c17OQ5EQAgW/CJj4glpVdh7JNKJgdSvKjGlqBzCWb3xN0jmsvAWrs2m7fhr5xPLmrmb5TQSURDXSeGzZ+UX/ONw5ZM07r2H9GN18vEcrhWkHwWUj6SpO9btYBsp57zrmTgnIz4vw9SXNviTYt1U1IdGLY/KFlsaHpf5hjI7XmLwPcPAxZ3BGUmy10atRJyOAXXgqE81xNH1D4/ba4Z3zQsOWfo54HXNWG+NyRXHh5mw+8zHrEfvwo5Oa9io2tKWVtmP2iS+9jVosF7nNzuBZEs4rxQ8P0nYpsOKUflVyMM9T7KVs/8MdGOPZ/oxYVdT/CGsF8RzH+cgPacvq4xZ1FjL4viG5a31JbYS1uX9NsX3HzBYvoFTgrWO4Z0mcKXSqsFis2qG8Tdo4bnIKrp4b5WyVfOHrNp1dDbj7LqDNN71lLdNGiZyUy9q3Gqm+Y7ypm7y0J4holHZMjzfzTmIN/XvvAds2zvUUGWI5/OaR5mBMEDdtfuuHD5zts/wtNelqvwnxDdF7QxmYVVCuuf6lme/cGJS2TPGZa9MherIS3xjOBgVW7WLG440jfHLPbnVI0hrOjjOSfZB7Tkcs1MxY8T/L8Swb7dM5Ob07VaIKDhvGjhOR/SL1YT/qR9FuGJ0hu3rLQrTnav+R6kXBznhHeaHrP6/8bM/N2oeb6qWH2sGF0cEU3WnL9IGH+/oC9P7Qs+wqxHVN1JNnzgjbRXL4XkO9aBkfXPBhcAfDTbJepywh+JNYNe9E6pndDui9KPvtrIW6n5I39M1orOTcNx8OErX+6YpSvFhI8X1SRjxTzo4ZgsKQfVWykOZ/2N2l/GNL7rFkvJjmp1/iYyQNJcVBz9OCM6TKiuSMZZ33kj/xxdMugbtVt8xLar085yBZUrWI3m1EMDa/UNr1nrJE3vmnrg+3FHdh5es52MmNRh1RWURwapvMttr73Ofv29pif3NPYt+Y83bpkP5kwa0K4Dz9evsHw575841YMWyfAalg8KUl7S7598DE3dQyb8KfRAbnN6H+yek2N53Uvtj0uZ/IQ0vsT+vGSKss573WZXQbc/72GYmSQtZe8ydoyfZD6z+pOw/bhNWlQcdOJuMlS0uOQoGxX+0Gs902xJcneuyIDvr33EQDJZsU/OPkmwdS3T9PTZnU+9Jzx6RuWe0fnpKaiZ5Y83L7gHz2MOdcZW9/1+ID4ol4f820omL5dsx2VPOmdQw+edzZ4kQyo38/WaII2WjWCd3z4efHFkoPNG97ZOGYYzPnJ/h7PXj0iPWX1eL+o5WLJsieoUx9wbz265OnGGWeHGa9P76ELR7ZitreRYnJPk1xa6l7A+W+VvH1w4t+3rvjO8j6bv+8RBMl5TTnQNJEPOhfbitmjhu2NGcM45+3uMUa2hLrhsw+OSI8tVgH4RaH03FLHkr/+yz/gZzc7vLvxmheLAV/ZfMn/vjckPZFUHR/W6qXHZpi5pdqvOOjPOMzGGNnyKDnn5EmXyWSD7nN/36RXXFyPVnGMn0oGb1wR6IZRuOBLD17w02iPznGEcBBd+omDJlmdI/5fFCT+tQ5tawwOB43FyNSHr8biypooiEhjxf2jPSLZYO0ek9mU2Tzn4uqGMIxoS4cTmrqtPWtQSe7eOWRn2KETKq5PL7ECnn02w0UCS4Bt/XhlveK/Zp2EYS9j1O2AqsjzCnGZM5tPiXWKK/1YsVB+3N1Iwd07+2z1YsRKHPPy5JTr8QzXNJS18GOUOiLQIcJJdrY2eHB0iJEtWMd0fMPZ5RWX4xbVGKyQNLVD65YoCsg6fR7f3aLfj7DNEtu0zBcFH3/6GWXdsHACpSLa1rKslkRCc/fOHlk3opMFGC2YTXPG4xmvX52glKFcWi9AExraljQNePP+EVGoCQOHQNHJQk7Pznl5cg5Vha0EVjqMEhgkjx89YXuYEAV+JXaw0eHy8pJXL8/IlzW21VTOIaxBSkk3znjy9C5GViRxyHKR09Ly8w8+ZToraVqBdILSNSAlUaB468EOm5sZwhZonTCf5wRacXJ+xsV1Se0UCr/C17aOR0/v0e+E9LKEKBDkeU4cK6QWXFwvwDoaZ9GtD8wODofsjDZIE4gjQ1nXbI76vHp9Qv1qSYtiUbdICVhLL0l4cLjPzjDFKAikoa27DDo5VVXx6vU5vd4O+bxA1Q5lIpxueXT/iEHaQdkSi2WQ9elmGa9PJpxdX3pShvIt66Iq+eK7b2Hris1+B9tW1G1Dt9/n0+fPuH55vmoTSPJygVGax/cPubu/h5YtsRJUTYvJQvobBX/60/cRUiOcohYOVVaM+gMeHBzQ64UkQYBUmiQKMHHMdLHk6maBlYZOljDLHdZasv4GX3r7DRQVfqpJsDnoE4mQ5y/PuFhMkSKkLlqiIEAFiqdvHhEnChNIXGWRJqWrDYaG58fHiDBgMisJI2+N/+LjR9y/t4doCrRURLFhMOxxfTPjxR9+B2ECz8/FYzqybsrj/SE7232Kxjc4m6Jkc3uX8dWYRb6EIGLZlthKEEjHvft7PLl3F2j88S8atje6iMf3+NlHn7C0fvFISYeSlmY542tf/yq0Fb00Iq9mpCKh09vg/V98ysn5GIznZ7dVjdCC/TtHbI4GaG1JlAClMUXJm2884E/+9EcoI1lWuT83KNgcDnh4eEAn1gRGIRMfSret5Xwy48WrM6pSYgKDa8BZxdb+iO2dkW+Ua0cURWS9LkJqfviTn9LKCldLqqoiNgFZEPL2o8cEocRREScpNokZDrfRKuCT5y9AKq6urgiCAGMkuzs77G8PcasFLoXgoL+B1S95/+PnZP1NCrGkVo7xYkIah7zx1huIZomkpa0bOv0+qgdnz064npU0KwwIUrCYz3h4dMj+7h5gEa5BOcuD+3dQSnF1dolUgqaoQEkCY2iF5sGDB8SBP650GlOXDaPhDjIwfPT8NbmtPQPX1ihq3nn0Bbo9H6Y67TErJs0IkpjLswvG4wltG/pGs7X0RgPuPLxPksUeraAFohawEzGrtnl9dUpT+KBXNAJo2NkaMRqNcHZJkoTUpUUiefTgkNliynSRo7QD5XEfm1tb3L1zQFOXxCYAHdK2LcP7A4wJOT4+pliMibMuTVGgI83+aMTmRp9e1vHt/Lbm4PABn3z8kixIKdqVyM96/ElRFAwG9xlmXYywpHGMTBXzoGQj63BWXOJsQ2trtAqY3kzo7++TRQnatShnSZMOlbC89fQpL1/8S2RgsHWDawQIRyMcOwf7SNuSdQy2abAt7O/uUpYNppsyn8+JVIhWvqXaTVKe3DtiuVwQhQZnG7JOTK/b8t0f/QAl/c2Oay2J1uT5gp3dLXaHm8i6RluPB9nd2WH06pSzyysk0FRLRkmKbRrKouDR7gFbWUoY+pB1M9ti52LGT37yMWEcYaRn+c6ubwiMwiEZ9GPadkEUhKRBRHx0yMnpOdVsSRpGq6kYxXye00ljjg7uU1c5yrUI0RJGIc5JTBpTz+dILamtQ0uJQKMiz7k3xssW4yikqSuyJCaNY6aLitp64SN1w3wyYTQaIZqCII6oKyibljuHe1xMrrmYna/RSlIKmrbFBI6jBwcoW3kmuLQMtoYenSK9BHDleMS2HqPS2L+s2v7/ZbttFAG+7SWFl5E5H84WI8nNXynpdAsuG8XyJCU6U/Q/slRDuR5v1YuGcBXGnnwzYf60ZGN0w/jFABqB+iOz4naybkb61qJmvhcwvS8oHywBENcBCEV6Zik2NLFjHfK61Ujz6dc19lGOkA5jWuz3e2Sf+dcSTJr1iGXd8T/Wr99S7HzzNXfDHIBfnG/TfJjR+1itmrEruVgkqfqa+Y5i8WsL3to9Ydkaylbz6qpP8gcdzNyRVI0fwa8cwbiiGgSc/ZLAbpVsjab0wiXPLzdoXyYMfyRoEQTT1ej8qrGYb2qu/npBL8sZJTlKWj7Y3CZ8P2bnu7dNMrsWFNWx5NVfddx5fMJeZwLAy4M+x89GiB8o33RswcztWlaT7wq6v37KdrjkcfecF4sBizrk9T87JDnxRmxrfGvYISgHmsvfWvL1e88xsqVnCn4y3uPTZIflzyI2PqjQC7cWSqnS8urXDXJ/wdOdC+51rvlgssWz3ginIgYf+qBBF15gZrXg4ssweOOKrw0uedI540WxweR+xA9/coQufEiSnFfYFbplfhhz+quO+09eM4wWbIVzFm3A+wfbjPMttr+7ZH4YE1/W2EBQBZ6LOv72kif7Z3RMybI1LPYDnu2O6Pw4Yvizej3yL1qIrhsu/1YNreQb955TWcXVMuVmJ2LxRyN6nzTrxYbwwi9uvPiNkP2vHLMflBx1LjlddnkelZzvd+n+A7EWfd22n8dPM8pvzvjSzilvZGeEsuGj7hY/jneZn/YZfFQiGt86TF77QHm+G9L7nWN2dM12PEMLS3HP8Ifdh2x8x7Dx/tI3Dxc11UZEawTVb0y5350xjBY0VhKolh9v73EZddn7w6UPIp9d0Q5SEIKP/v0OX/7aB0jh2AhyDpMxi72A77y6S/xHas0S9Txa4QPbX8v5zYe/4NP5kG6wZFpFbNzLef/FU5JLCxjCcY2sPSv3+Jua9771AQAbgf8Opo9KfiAP2f/fNOG4WSEVPIpDLR37f+cZ+8CvDD/i5/M96MPVMOXjf3bE6Ke3glkf/jkpuHnHB4q9oOCN7Mx/z8NtfvFvQXqW0IaCcNysg+TrJ5rBr5zy5uCMveiGUDQkqmTSJPz3X/4W29/1fO3b9p2wjusnijuPT9hJp0jhOOyMKUaG78gjsldiHUTqWU21ETDf1Wz+9Ve8Gc+xTvAwvSAfBHAHfi/8Cnv/0oc+vQ9nNJmX/+VvLvnNpz/zbUmrSHXJN55+wu+2v0rn9wT5fkR0VaPzlt6nDcV2wLd/7ccY2RLIhkUT8s7gNZ17Jf/wF9+m+9yiC7s67zrC0zmvvz3gq29/4s8xwpHpkr/65Q/4b8w3GHzsede3Cz91x4dyg18+5Z3shlGwYLg556LKSA9K/kf9NcKpJLqskbVFLzxH+PW/4/ji1hl78RSLIFUlW8GUfxUf8cmnj+h9tmq6h340P99UxO+Oud+9xsiWL/SOfZkF+PtvdeF7Yo3fuEVDTN5s+PbdZ9RW8VbnNedVl1A2nH0rY3q+R/a6+VyOVzvGjxV/693vc1MnPE2P/f/RJPy7X/oT/ov4b1D/qE94Xa1lm20IV+9C98GYL26/JlY10zriP334T/nPfvzbzPc6RON2LcGqM0U0bjk9aDm4d0k/Kni7e0woG3402efD+48w+e25009opC9yZgcZ741O6JmCjir5Svopr+sN/pNv/WP+u+/9DawC03wu0FPLli+88ZpI1Xy1/5y8DSmt5j94/Mf87vxbNHFIeua5uk3oRWT5ruPvfeOf8XG+zcPkjNopvp59zKvhkH9cfpt2JQwT1k8DxFctl28b/tqTHxPIhoHOsQj+4/1/zt998IDhzxwY/1rGj0NkA8HMcfrtlscPT9hLJ8Sq5t/b/RP+86d3uPe/OKpsxRx30DltqVKJsJIn+2cs6oDDzph3s5c8ee+M/+ln38IsIN9aYRYcJOcN+Zbm3/7q97ksO3TNEiNbDoMr6q7ik+gIk9/K5OwaYdEGAotgEOYY0fKgc8lNnWC2CopRh/TEYQpW/HyIjmc8vTdFS8tuNOGy6vCqHPAfPf4D/stPfhv1ofNCvdI343XeUieSzb9yQqxrUlPS1zm/tfdD/r78Jp9Fj9coHX9d99ddvXR/7nu1P38n9/+DTQmJxFuOaVecFStAOpQS3L17SH+Q0et32Bz22doa0usmhGFA3ZSeOemcZ2s23vR9uLNHN0mIAs3Do0O2tzbY3hyglYW6RjmHqytCo4hCw/bmFocHu/T7CcNhxs7ugNFGRjf1gamSeLGIEtA6hr1NNgd9NvoDNrpdemnK5rBLrxeipVtxMCXCaSaTOZubWwwGA+IkQEmHxhFqwXDQoa5yjLC4psbSYF2Do2Uw6DMcDhGipdtJSaKAfi+j20upqwVhJGmaystatCFJEvr9LsONLkkcEBropIZuJ2RjkFBXBUribeetoC6WHGxtsTHo0u8mJIEmiWNGgz5ZNyHLUopi4QVhYUg+X7C1MeTOzi7dTkIcBSRRTL+Tsj0aspiNCbTASIWzgkW5xEnHaHuA0pZ+J0ZZMMY3ipMkoa0b6rqkbVsiFRLpAFvWHBzsE4YhcdLBOUGSJAw2MrrdhEBLotUPX+k0AkU/6zAaDfGmb0snThj2e37E2TZgW2+wF4LWOqIkYmN7k6SX4mRDGMQopXj48CGh0TR1jXJetBRpg7awNxjRTRIP6ZaSMDT0uimjzR6la5i3FS4JWIqGvFnSH/XZ2dkh6qSEaUIaRn5kdWOA0ZZQQVUWuKrBti1tVbK9ucHW5gZGC7JuShyH9PpdNjdHOOcII+MthMbQNDUbgwHDjQ1Co9AalFFIadkcDbDVElstEbYlQNAscrY3RnR6GVEUooxGCUknScnSlE6nQ9M0CCGompK6LtFao5QiTWPiOCYMQ4LYM5D7g4wkjRCuRriKINYUxYIsywgiQ5Z1wAo/9q8V2zsjNkYDANrWkUaeqxkaQxAE9Ls9oiBEIZASTBTS7/dRDpLAkBqPxWibmjBUbO4MkQqCIEAIQZIGJJEhiRStrUA62sZhW0VbQSg12kmkFYRKY4QkixOSKGLQ7QGSuvKyK+qW2ET0exmdToIVljA0hJEhSWO6vQRLS9nUnlccBLimJUpjkk5KGER+EsCCMJrdO/tUbUPrLFXbULmWWbEkzbqkWZcgCFcW+JrWeZGQkYrFfIqioVrmCNeSF3PCRBHHIXEcYgKNCTRRFBKaCIVCtoKiKNgYDCiKBVo5+v2uR6BoSdtUSOEIAk0nCmmr2jdglcJay2KxoJdlCOlD+8hESBShDti7c4c40oTaESoJyp9Pjo7uMVvmVMUSoyVZGmMEBFoxHA2YL6aed+z84kkvS0nTlCiK0FIQhSFxFFEul0RRQhAEKL1CW2ApmoqiWLA12qQoCuplCSvGdGuXRLHBGEOgtA946+bz4yJKcE2NEo5wJZBM08wLHQWwElm6tibJOug4RCIItCYyGutqgiDAOocSmijQaCGpygKNIIliGtt4WVXT+O+M/vyS65zzo1/Oc80aLJXzeBWnfBOzxTGezaltS1lVBEFAXddEScp4OiHSIVmSAV6mF4Yh5XyJbWvq2iFMBMoH9UopbNuuv1dKKYwx1M2S1pZYWh+e29Zf06QkVKEPJtvVZwIUVU69LJlMJiitcS0EKiANkpUIK6FeFrRtzXw+p679tbiuSxSOMi8ItfGSOOlRJcvlkuk8ByTOCWwLs9mCq6sxOM+OLaqSqm28ONM2Xiy6ErwFQUAax1ydnhNoQxjEq/br6r5BWCyOZVX7ewHprxOLxcIvuApB424FhP54tDjatqUpG5QIKIuGsqw5O79acWbxWKJ6Sd1A3EkJwpCr67H/oKQgjBPKomKZl7jGX49ZLXY45/z3sddnMplQ1hXWWmxdkcYxylmaqqVtHW7FlhdC0UlT5kVO3TbYlbROCs3J2SWVtYRR4kNYKfz7dv4YnE6nSCcIdIQSgvl0SlNWuBWnPwzDNe5HoNaM/L/c/uJv7WrUe22+Vp/v+zYQjL9WcX/vkne2Tvj1ux+x++iCYr+hynyzxJvQVyFe4/EA1ZfnbG9P2O7M+e2vf4/dJ+eMn/o2o39OsR6Tr/qG8VOIvnzN3b0r3jg8ZefpOdMHjvnurdjoc2YewM0DQ/Ow4IsHr/jSnVe8vX3C8tGSxb6gCeW6veUkdJ4vGD9WLA8q7mXXJLqmFywZdHLsvcLzExP/+NuxedHC/BC+evACLS2/tPGcJ71z3to9Id91mNxSZQq19KFlkxmKoUbuF7x995h7vWv20gl3hje4nZLZXS9isUas0Ao+sL58z/HOndf80s4LHnUveNw958v3XrDcssx3DdG1H7OuM014WXLzRPL2O5/x3uglTzpnPO2c8o2tZxw9OvVN29QLbYR1RGc5wsL8qCHSDb+2+QE9XTAMc1JTUvb9yLzJV1bxgaEc+B/Hf+et73IYX/MoOaexiie9c0Z3bsh3ffO66pt1KO6UQO4XfO3uZwAUreELg2PevntMsWXX46B6NUmmSkubtXx77yOedk6preIw9vvlt7/+PeqOb023K3lZNQhoIsEbb77kl4bPkcIRq4rD+JqvbL5kftSgpyXJWeUD+5uG6KJieqj59Ucfcq9zzV484e3uMb80fM7b945pIt+K1rPP7eyysvybRx/wK0efMArnfLX/nKPsive2XpM/rNZNUGEdbWqQyxZ7t+A3d3/KneSGgc7JdEmgWr54/yWidWtbeZ35x4+fwt3hmMN0TEeV9HTOV3rPeWN0TrF5i08QmLlFLbxR3hp42j/jqHPFYXzNXnSDkS17+9eUG39mISMLCC8L8h1JLyl4b+MlAIfpmKPkkt86+hnF3RVKpLLUu33kvKTuhdjE8m8M3+couSRVJbGqGAVz3tk9xoaK5WbAcjv2+7qy1Jngm/c/JVbVOkh+3D3nzc4J5caKZ7p6HrH6TjWpD4RjVXMnHLMZzPhK/wXZIGd614dqetGsFzSWA8Wvj37Bl/ovyduQw/iaw/iaJ9kZy912LTwys3rdrDfdiie9c/biKbVTTJsYLS1/+9EPMPMGVTr0rMLMGuLjBeXQ8bh/wV50Q20VpdNMmgQjWtrIEZ+VPlhZ+FA4OF/QdByH2ZiH6QWjYMEomHOUXBJ0qjV+IbgqmDxMCK4r2lDwq5sfsxNN6Zkl0yYmkRX74Rg2Sy/mKi020MjaEp0V3N29QsuWojXcjy9JZMVRcM7vPP4h1UZA1fHIDPDYgpNvCn5ytctpkbFlZny9+wlbwZQ7wTX5vZr0tPKYhtWkQ363SzOsuZtc0zNLjpJLHqenvCoHdLOCquMXstrQYwLC65rguuJvH3yfl7M+l1UKwGYwY9GGkNXUsZeZ1ZlBlg11phDS8UZ2tg5s70WXfFaM+ELvGF249eKdx7LAr3bKRgAAIABJREFUckOQhBWxqtkNJzwIzyit5k5wjWv8JEQ50L5Nv1hNYvRLJlVErGrGTcrALOjp/PMLnGONofFyLThZ9pjUEbVTTJqEeRvye9dfYlF4hE+dGXTeUvYUvec1Nmt5MvJoM4njML7m02qTtl2xnMXtOa70C4C1RWU1o3hOoiu2gikXVcaiDhHN54uWTewFcXU/ZDl0FK2hsn6//ny5z0nV5/evH2ONv5etO4p8y6Nr8t2Qm2XMvA753uQuF1VGKBvOqy7atAQzS9m9RRH55wymgj8aPwCgdoq8Dfnj2UP+ycmb/r21juiqXrHeWbXZobaKRFbcCa75cvKMf3j9NWxiqTPtQ1Hlr2ngG+9qpqisomcKJI7vL+6B8NdjvVxJ+sRKuNZ4XvC8CklNxfGix/PliP/52Ts47d+3KTyWSOeW5UBRp4Lff/VwvYsPgyteVEM+zUdrxjZ4PJNoIbyumd0VfPfskLM8o2gDijbgi9kLjGlR5Yoxa2850LDcy3h+tcHNMqZoA0bBnEA2/LfPvoHOhRfc1Z8jlUTrsBpenQ0IdcNOPKOjSv5k8YBJGa/vR6qeXjesnWR9T/Pn2f61btq6eomS8s+0QCSNk9A47t3bYvtgRFON1z/Aut0eQRDw4tVL2rKGQOJaR93UGBnSySKGox7V4orAGOq6pRt12N4ZMcnn5GXl5TBJgpaK6dUNe/tfpBcLtGupW4swFXcO9ri6GnszuwmpmxZbO6qyYnivz90791nmY0yoqZYVhwcHGGN48dmf0ulsUTVgrSSOUuI05PBgh6bNMUagtCJJB2wJxen5Ba+Pz5FhRoNGIimmOUkQkmYRdV55CU6oQFcMhwNevPoMZS00IKWXKukw4P6DI4pijBG+LahXj1/WFePZnLbW1I31PN7I0O122BptkI/PUaHBCkXW36BsSn7x/kdk2Yi8ljR5Thb7sMoE+GBCCOraN/HiJKS/2eXybIoKN0BIkkEHISUq1MRpRNlYjDToQOFaSSdLsfIEqw1tA8JZKCu2R0MqHdA2LYEROBdR1XOE8YFH29a0zrf2pCuRKzGT0gKHJVUKhEbEMZE2KNeSSMO88o+ztWNjsI1DsaxAiRDnQIUBZV2SpDFlBfNJiRQKFQT00gihYVkVJHEHIyOWxRQdKJSDvg6gqclrz11elgt6StFBs8inBKGgkpK2bairOXVd0LYNWvjgy9Y+zL66nrOxYYgELPISpTRFsUQ5H05W1RJtEupmiQkExXTO9fUFaRIgtaascoIoplouyaKEurbUJTSiIgi0/8yMo0Fg2wYloa4tYeoN6bVt0UZTtkuSJOHq/IIHh/eYzwvCQKBDSdk2CBMgTE1e5oSRRqqYvKipXUVjW6JOl8l0QRpIlAqY5XOWi4rJosSkHaqixTiF1p7hGsYRr05PiTWkcUjjLG1ZUixybAtGhizKirq2mDigtJUPQW2DkYq6bKlDyWx2hVMaEwbcTG/QQYKUEuFCFCm11WjhKNsGiaG2ltYJqmWNaL1hXTgLqiaINZfTMZKGRElMJFlWDXmRr0aSobWWWEdUTYvWEfmsoEhKAiPxvH4PVF/mDaGOmU0XbAw3KaqS6cxzYW+mMzIjMVoQhJp6WWIbx2KW+31ovYSvaixZL6EVgpvphM2NAa5yWNXQNrDMK9rKYZUkyzKux2OiKKFsGy6nN8SBIAhCqHOs1VxPzijrJY2z4ARaeqlhsSyZFAsaJ+l1UxbFktQYrsdXHi8SOhaLCUJ4OZyMQ07Oz0gGKWkak+c5ofbn3cY13FQFNg2p2powCrBSspxPmc6X3Mxyv4ikJG1dQ6tYzEtaJ6lqi0yM9/6h0AY+ffacQT9DKcdssSCKOsyWOefXVyyXK2anABNHzPI5k0VBd5ETugaUYLFcULYBN5NrKmGwYUq1CgajIGS+KBhfTwi7GSaAxdKH2S9PjqmLyoe+rQ9iw7DLfDYlX9T0N0KWdYmQhsa1lPmS65sZxbLBtoYwjmiaCh11mOU1l1dj+r2Msm3BCaqyJi8rrsZTgiQDJamrhrK1BCbhs+NTwjShP+jQuhptFePJBWVe0gqwrsI1DSZQuLahahWvzq8JjSYMQy7GE8LQcDOds8iXOKW9nnIlTIx1h8uLCVebNwxGPayrEa2/wXj/05eU0lE5R7WsiHRAEvn38/zVCTu7W3RD6XngaG6mBRfnE0Qraayj8ihJrNTMipIXxyfc293HNhVtU9JYRd40CKVw1iFNAE4gXEsnTphc33B6cU0niQldg3NLqtZyPZ6tmPIVxsGkuPLhshac3dxQC8EBvsVcN0uurxckvYyichRthUUQGEktHFFvk+/97EPeOLpPoCVlXXJ8fMVkPPPoGm0QeB5x0zZM8pzPzo65o7bpRIZimuOWguk0Z3y98K1n/I8TQU3SyXh5cspwdxstHLq1iNJS5HNen0yYFw1N26AChUR7oacO+eDDT9jZHiGUoWmW6FZxdn7N5CKnsJVf4GlbpJREykAJV5dTtDSkdU6FQ9iA87NLauHxCWXr76AjZRCuRTTN/+N92V9uf/G2Ww6hbNx6bPiWFzj+huarj54zKWP6pkDLli8NXzOMc06+e39tShbWoRYeWbDY1nz14CMuig7b8YzLssOXhq/5p08ymuOE+MqHFHXmsQvpi4L0rYYvbr+mZwouyw5aWoJ3T7h5to8pLO2qNSNri55VzH5D8Xff/lc8K0ZsBjNOyh6/84Uf8I+iL5D8sSbf9qP/ooVyFFP3HL/1zk+4qWNG4ZyeLnizc4w5aPmvf/6bbP2g9hbqwjPqwssl9SDm3e5LzqsuIzOjtBpi+NG9Jfo7hjb0n99tQNyG8Pfe/Re8v9hjM5jR0zk9U/A3d3/MfzX7DaqXHieh8xWrNVWonYL/cPcP+UF+j57OyduQX8k+5PSNLvL/GDHfC4ivGlRpyfcjljsNw3DBQOf0dM551aV2iqPsij85uMPgI980BSg3E/9D2liOsis+W31WfZPTOEmzWSMbtWoW2XXwcPWW4XjZZ9aE7ES+IXdaZChpUUsfyMnas4ydXgljlKVrljRO8m7mw0IjWn6SHdCGkjY0JCs+q6wte3evqJ3ieTEkVf682zVLjoseyw2BmSv6x3OcUdhAs9gT7EjLp/mIp51TdoMbPiq2GQZzMJb5/Q7gR0+LTUM4aVnsO77afcYP54fsh2NelwMWjQ869NK37uwqIPT7Q/MHr4/45f1nPIrP+OHskFSXfDofIYylzoxnMkcKlXtpWTtR/JPTt3jaOyNRJa/zHqN4zmXRod4NMIVdM2GbzKAqQagbJnVMZTWpKrmsOuzFE36iQC1KbJRgJhXVZkr089csfu0+P73eZSedMgzmvCg20ML6BefrlSjOCKKLCrkofUO1M+E7F/d40j+npwu+e32Xs3mH8ETTJPjAK9W0UY/weIrsdPjdD3+V3e6Ut3on3NQJsybk5axPWvvR4uS8Rs8rmk6AmTnOioxFEzAKFyyagMuyw4+u91esXUn2bEEbG5xemdsDy2WZ0jNLXpUDAtlQWU1VaZKFWzGC1XrRqE4F/+vxuwSy5VH3gs1gxvNiyCeTEaJctdXKlmojwMwbmlhR32g+mY7YjOd4FalDCscni02qniZ5lTN55JuRwfMLVNnje6d3WIz8+9hehWul1YRXksVeiMk9BsTMG4qDLrKGH57uM7rrn6NoAy7bgGoariWMxV5K7+OcahBg5o5//PpNDrtjtsI5ALkN+M7pPezcrM+1NlK0oSK8bPjsZPh/sfdmvdZkeXrXb00RsaczvVPmm1ljd+O2ZVpujBEyWALJlpCv4AbLAgHyd4DrEpI/AdwxI4EEGGMJCVkILCy36XZ3VXXNlVU5vfOZz55jWCMX/9j7zbIv6CvLVveWUpWVOufsiDVFrN96/s/Dbqj5C89e8aPtR/zW4i3/2/2fZ+0b/NwwvY1H64XUGM5/qri5OOHXz+742e5DdpOaW79gHRqatw4VI26Xjt7k1Srgrhv+35tv0diIO01s4gSA1cOMeSi4nayFxahj3/xnf/hv8qc+umZhB/7g4Rt8c/7AD++f0/yyoVnF98+TmWP6pkW/W/C7z77Fs+kWKvjB9uu8bU/5u5d/msejj/PBk3g4d0xuC+9eP+LZdMuTasv399/k1Hb857/8N1C9wS/EK1tl8SGfXnnCqiY+N7zaneN04p4577oTXrx6wvOlhNz5E4PtstjIXBd+/+U3+M3n17zoHwOIQn59QXoxJ87kPvQ+U20zYW64+APN9+df4zefX7M3FUO2/P3rX8fvqtF7GyiQppWEvm09kx8s+HL2iItZy9/e/TYfzDZ8dvmER3dl9Gse/YsrRX9uOf0Mvv9rX+PPfvSOLjnsaPL+4//rT3GyK7id+Df7M0f3SLzZr//gA+5+c8tvPr3mVXvOkC3fvfoa6vMpRRfsUMZgRAGk06vCd//w1/mLf/4XDPkDtrFm4xuuVwuejArbwztA0bB/5pi9K/y9T/8FPni85pPpM2Ix3HdTpi8tfp6p16JEP3kdj/7NZ59o3vXP2f25miEa+q5i8srRPSpMHiLNvQSXhoX4xPdnhvQ/fcDVX2mZTQfu2m8z/OSMs7diHxBrjdGFbMAOhept5O4PLvj+n7N889EDn28eE4vm5c0FT17m43uJeCsr+seOZ98NvPzWAlMnPnUeozNf7B6RfnTKyU3Gjv712SiqXSZONc3/veDNn5lhfjOzG2rm9cDtLx5z+haaVT5aoBQD/kyQavWi5hf1U7YXNVf1Aqszv/zsQz7u5Nnv1mL5488rdORX/Nr//z7mO9/5zh/5h/9pf/7H//l//Y5SagwzMIRSwBi8UZw/uqBxGlMitZX0dKU0Pnge1ltiUgw+Y4yTNEosJ7M580nDtLaSMj9u/l9eveXtzQ1F1wL9csZHoGi+9uxDJs5ikoQqRB/ZbVu8D7TdqNJBwMnp/JT5fIoxilRajMkUlUm+Z7V6oNv3RF/QyhB6z6SqmE9n2EpR2UJVKXLOdJ3Hp8LL128ZEiSlUVrT9hJC8s1vfQzJM6kMWouKqvOewWfevLlEVxNKsiitcVhq51jMppTQYU2hpIFhSPS95/b6jvtNR9aOnCXhfPA9H338EX3q0SaincbnwLbbcLdccrta0YdMCQVna1Ca07NzUBpjIsZkFNANLd0wsN5sGbJiSBBLgVQIw8DTJxdU1mJdAh1IOdENHbt2z3br2fUeiqEUTY6a7XbPo8dnKCVp3LYEhn7Ddtvy+uU7hqRJGKazGRg5JZrPJjiVmMwm9GkghcjDcsl+37FaD3g/qoq0om5qmllN5cA5yCmSUqZrOzbrHe+u7+h8Iisryidt0cUymy0kDMnCrtuAjizXOy5v1rSDZ8gK5Sw+RQKRmBPKFFxjUVbhh44QIld3K+7vlnRDgKqhJA3ZotA8f3xBKeLRkkqg9wPb3Z67h3u223Yc+wHlKrAaZy3zZkZlMtHvoCh6X7hdbbm9X5GVZkgFY41AispBMsSUqBpDHjwhRJabFS9evKWqpgyDpzKiwlVDoqTCk0dn6JKJIaBipB08lzc3XF/vgYquDxhrqSpHipFaK04XM1IMRO/JKXBzc8P1uyta70m64JFAwdpZap2onWVSaXJO+M5z97Dlfr1h3Xb4Uggpo63DGEvuBipT46xB46Akur1nt/a8vnzHrh2oJhOySiSlqGtLDJGzs1OGEMnJg9Ksliuubu7Ytz1dF2TMxYypDDEnJvOa6XxKCJ4cYfCZ5cOGt9e3xKggKemTHPGdxzjDbDolhED2kX4ItLuOl69f8+bNJRcXz+h78MEzm8/wPsi8ms1p2w6nNNvNjn3r+eTzLzD1FJ81YKiripIyOSRO5vNRcWzpfMdqvWa53rHvBoytRclXMmpSk0KhbhowjpQGFIW2H1jtBq5vH2iHKIcrtpJE1gxGwWQ6Q6tCLokcE+tuz7vLey5vl1STE6IpAtmA/XYtNhsKnDN4n+iHgc265YsvX1OKQjcN+74DBdNJw3K1xBgJK/SDeN3eL9d8/voVPgaiApQevcNhVtVMJw1+8Li6od23bNYtD1f33K82bEMkpiyhTFrTTCd0u5ZaGZS2JB9JQ2a12XF/f896uZaXr1KoqooUI8oHSoroqqKNHcpZVvsdeR/58vU7tsPAPnrqWcOQPGdnZ1xeXWErS1M17HYtbdezWvXcLTds9r2op3JEW43OnkJEFbEm8N1ASpH1dsvV9Q0PyyXeBxKiLi0FkvfkUmj7HpSmawfaznN7veTm6paQIikPGAWV1qQCzWzKfr8jZ+TAMSRevXrLcr3n7mFDVU9QxpJSRuJyM0WJV2+778kZYlK8eP2Gu+WatvOAhLspA7qu2feemBTeZ0rI9EPhfrnj6nbF3XJNUlrShlVCWzUqQQv3ywfIihgzXTtw+faON6+vGPpAVo5UIlpn+hAIhVGBHhl8IGR4WG74/MuXbHYdQUsIV1EgA9dgjAQPPqy2XN/csd7tub7dsNy2JMk1kSRYylg1IUromOGzL16wHxLv3l6zXu1QxmFUIeUEOROSRyuFMZbVZksfAl1MZKV59eodd8sN2zBQtCZSyKpgtEZrh3MNV1cPlKJZLneslxt+9vPP6X3AKIPSGj/0lBipXYMq8ky6u7qj3/es1x3Xb2/Z7XrW+z2hZJS1UnVSMsZohtSzaTuKrig0bLaBl5dXXD48EBWUknBWUxeNLmC1Jin4a//Ov/1HSrT9k88/35///r/5B98pRh+TtU0bx43nwOZbE+5OHU0VeT5dM9VSkXHdn7B8fY4ZoNqKF6dCNva754brp5avn66IxRCLZmIDX3zv6zz7npfEcJ8xvlDtxJrh7l82fHCyZWo9BcXaT3h1d0FqK5p72RihwW0ju69P6J5qvmxO2KWaxiaGbNnFhhfvnmA3lSQ1O4XbJ/oLS/dUcTVpuJi2fNSsaFPFJ7sPeNOfs/zeY6pdQfsCRlHfdphtz91fbLgyC741v2diAm+Hcz7fPGbZTjn7QykTP4RyxakAhU+fnzAUy4kTO62X7SO+2D/m7rPHnH2ZvxKApak2gbvfNvzQf4yxhdokrvwpv7v6Nr948SGnv9TU63T0vQXF7rnmSk0xdWFmPUYVPt0+5cX2gvbdnGoH9TphOzkism1m+acNeVY4rXoSmm2c8MX2Eau7Bc2NYXolm0iVRSU1u0z85NdPyFpTlGJuPW/aM97dnnH2ffHYNUNmeFwd09YfnjZsppZvn9zz6f4p6zjld959m+FhwuyNwu3zMV0+nBguT2f4meJR3TJkyzY2vNhe8NnNE+Y/ckxWCZ3UqHwS1d+7RzXaFWYu8Ifrr5HR/IPLb8NPFjT3hXoj8MvtEnbrMcHxd+tvo5uCMorrfsH9MOOLXzxn8YWiWWZU4RiAh1YsfwvaUrEtE9pU8WL3iC9vH2G+nDC7lCoYuxvDJJUizCp2Twpn046HMEcpeLM94/KLJ5x9Kuottw6STJ4KYWZ5pRa88wtOpr0Av37O9959jZPfq0iLmmoTiXMnFgGmAlWz+w0JM74fv+Pz9WMuf/aMRz+Te6gfBHylRY2Jik8W53zjw3uB1GFKFyvefvqUs0+UHAL4jNt67D6QTifsT6e0C3h8sqfLFTfdgh+/+Ij2xQmzS0W1z9h9HCG6VKZ9uThlaBTGZHJRvNxccHl3xvl3DdPrQJyLalv7TFxY1GBZfQyX+xP2qaYoxfeuPib+/ITJHUzuo4ynMfTKDoVXF3MW5y33w4y2VFy2p7x8+5izH1nsIAKIohV2F3FbT2wm3D023A9T9rnm7f6U16tzXn3vY57+oz3r35hTrxPVJhCeLbCtZqsmuA96vlw/4nTS84P7j/hi9YiL/9ORncbtpY39qZTwq6jZfAg3ccaz6Y4vt4/48eVz9JuGyb1cu46i2sco7FC4PJlzy4RlnIKGzzdPeHX5iPknFc2yjPYT4xqcYGgmbE4VwRq6WLHLDT+6ec7rn37Ikx8GwsKIYjFLaOT0JrB7XHM7qTlpeq6GU97uzvjs+gmP/qFFZwGvk6sOhaJ/UnHyInNVLVhZx2W/wLnEP3r9TU5+Z0K1L+J1DhIcqaB7bNmfG267KTSFd+tTfnHzjN2LU06+hNlVwPSyxqEU/qyiWiveLmbsjWUdJmxCw+eXT7BfTli8FcWlGTJxLpYoRStysbxxM365eULQlj+4/johWua/1zC/jDRXLeGkotpENt9sePzDzGeTCziJfHr/hC/Xj3j97jGTLyoWbzM6HQC0Pga9DbrmXZjzqj9jnSZ8evWU7pMzFi+g2kkFgvSF/LvOCpa1zN3NKS9WF7SfnbH4pZUDIC+VJnEhB3P+omJyl9nkGXeuYjfU3H7vA5rXjvNPA2E+2jV4WRcVAlXzvubt6oLXec5DmPCL3/8Wp5/B9F4YFOo9RJ9ee3Qw+LbhNQtut3O+ePUB6uczzj6T4EidCqrIz0vQpubkS/i5fsKrcMLl+pT7nz5h/oOKeiPPZDPI4VqziriuoDPEXLPeTXnXL7h9+Qj7O3Pml4Vqn9FpXEPFIY1cKVxbOPkywScz8rsJ008cpy/SCFNl7Twe5jiZ83YoPPl7iX61wPx4yuLNaPHjNHbsD9vlY+jlyeuE3025Xp9yVxr6710w+dwxv4xjaJlAVfHbl2fo2c80vtSs35xyf3UKf/eUasPoSy+wVifwC/EQblaZ08/gbneGfzel/8UJzZ1i8Tbh9lJFMZyOoZROU28Ss8tCR8NdnnB1c87qu085/4nYU6gMaWoIJ+LHLLYVhb/x1/+1P9K79j/T0PZv/Z3//TvKCPGPJeN9EFCmNZbC2emC2gIp4YeewQe6vuPl63eECINW9KoQDSQKQ46cXJyRbKFozZBhdXfHy7dvcdWMfkjoIoPHaA1Fc3J6glGaGAcJoVKaq5s77h/uQVv27YDCYUzFcrlmPptinWUyrVEqg9I8rJbcPTxwfbekmSxIKWGtoaDIvWcxn1E5TUwRlGW5WrPdddzcLskYMqCVJgyBuqqwxvDo4hRDJsbAbDZjvdnxcL9ltd5TMMRYcMailcXHwPMPP5BSUQtdvwccL9684er2npgNIYk9QE4JYyzKaqZNQ1NZgh8wyvLwsOTufs122ZIzlGxJgLUT9m1HPZ1wcT6n71uCD9hJw/X1Pa9fX0oJcBLITJEHjas088UcpSGmSN97Bp948/aSu9sdyjlKLiitKQVCSpyentDUFUolfIi0fc/l5S37diAkTdEGH3rQGqU0rmqoqwpKxmjDft+z3uz44svXDH3AmhpbOwbf4eqaEAeM1lKCbRzd4Nm1Ha0feHcjG2tlFLl4lFLsNmITcXIyZ993KGXpQ2C773j55hqfCq0PaGsJKVJZK6Fs2lDZmhQTcRjYdQOXdzestnuyMhJKVQAMJcuGen5ygnJKIEVK3N48cH11zzBkrK1RxRBjwlqL9x6rxMLAWMvQJ3at5/5uyXqzQylD0YZCQRvNZr9j4homk4pSFCkn1m3Hw2rL1cOKPiViQRTJMTNpJuSYpTxciSl6O0TazvP26obOK7pePGVTzlTO0u1b5tMZ/eCZzhaElNl1Ldc3D/R9YEiZnEThV+mK2aRhuRJV/Nn5I9p+AG25eVjx7lLaNueMNpYYM1pb4hBJRTOfz9Gmou0G1ps9682G+4c1yjoymqKMeDYqRU6FyXRKURrXTGh7z2bf8fmX7+h8oprNSEWJF6SC7cOS87Mzdts9s2YBWDbrPZeXt9wvN/ghYpUZk+UL2hh6P7DZbFjM53RdxxAT9w8blg8btHYMPqDNCL6MxsdI13ZMqoqTxRmbdse2H7hbbbh+eMDVUxIWg4MS0VGhlSIMA1Vd03U9/eC5ubnn8vaeGKEgqlXrDFZpSohiQxLFFmC/39P2gVev37FvO/b9gHYO7yOIUzT9IC98VT0hxsB223F1u5RxGC15vFdUIWUp6y4FrK3pYiKkwrube27vlmy7cATuKaURBBfQUHIhxIyxlndvr2U+vXuHqRoZt0XU8dHH8YCoJ8SM9x4wLB/WXF7f0Q0BY2opjyqCF2tTEYaB4AO6qoghkZPi5n7Nzc2SIRVCkWvRWkrzNYqu7XCTCYVCiIrNZi8AtvVQ13TBo7TGGsO+bZktFvjB0+625JzZbDtul0ve3VzjJmKdIGeSGV0SWmm2uz3dviMVzRALN/cPXN480PsE1lK0pWQIYWBRT8iq0A4DQz+waz3L1Zq71Y6260Z4J2A7xkQsiqIjIURCLLT7juvrW9arDdt9SzaiJLXOUEompQiIx/vge8IQ8aHn6uaOV1dXtO2AthZKxjpNLAEfItZKsrf4jTvW2z2Xt1fshx5PIpLRDjk80AIidFXT+8Bu27LbtCyXG9quY9d3qNHTMOZEUzU4V8n8MpbBRx7WG9a7lreXN6AtyjgG7zEZrHOi1h6D94x1gAZtGEKk7QKmMvgigYQUAR2KBCXL+oLGBznA09bQDQOppIMFKChFKgGlNLkocoHNesvN/ZLtak+77dn2PcpaYk7k8R+tNBQJClXacndzT0HR7geUcvRhkCspmUTG6NE6omSMsTSTGX3M7DtP9JHVvqWqpgQUaXyHUdrK/SZNjJnNpmN1v+JutcGnjC+ManoLMWOVJsbEEMUC59/9E2j7x+LzX/2t3/2OmKUpbJew657cSOBQahzbjwun846M5l1/xrv2jHebE6a/X+M68Y+tVj2mi5g+Ue8VN88n3MQpQ7Hc7Bb87N2HXPy+JU4FRDJ6fWYxxOfhecOycizDjJWfMETH9ucXnHwh1zi9EVCWasPiszXt8ymryrE47cQyBcX333wN9/mERz8LDCcGE5BAoAwqKtZPNdOpZxmmZDTff/c1Xt1dMP+llRLN0W/O7iPxbIKf1JhvdjRV5D4s+K35G3739tt0LxcsXslG1faZNNXh1p4mAAAgAElEQVTjhq/Q/pbHaIEWn++eoCj88Lu/xsVPBd64MfBLh0xxGu8aNmeKR4s9d37O1AZ+/803sF9OmF0fSnoFHKRGU99rNouKX//alcDgYc5p1fPjX3ydJ9/luIEu7lCrqvCzivaJAJGln/Jmd8bDfkbzgynnv/Syhx7hs8pg94G7r89QJxG04qZf8G55ivvpjMmd2CmoVKg2AveLUYSpZTU3bKiZOs8vl095uDzh2T+UjWxqNKnRAiSnBt0ZLqsJfa1pbDwCv7QUCKOTAFLxAJSU8raaEp5Fvlg9pijFu90p9zcnPP1HEihV33YUZ7A7T5pX+BNDMo7lzLCKE9bDhLf3Z0x/OgYtpTJ6tOojoNnNpuQnAaXhpl2w62uGT084//l4ONEo0BrTBnJjca1iUzdcVw3OZa72J9zcndC8qFi8Scc2VUkA3vytp7uo8aeZfanYp4oXl48pL6c8/kkQD9g+UKxBh4I/rzCxsJxOecg1bbG8XZ2xXs04+6GlecjUD57cGIGjM0Nz72mf1VxuT5ic96zG+578oqZaQ72OEuRaW+KiontacfIy0c0qmo9aXq/PyGiGT0949CMEZIRMsXLfxYlqkeh4eCIexW9XZ6wuT3DvKs4+TxSjMT6PyfUBhcYkxc35hD5bqibyZn3G7mrOR/9PoVkmwsLIOK9lrMxet2y+1bC+m+MuBgqKL28eMf3ZhPNPI9U2SoDcVItqujLUm4K+alg+06xWM7b7Bl5Mmb+CUjnqTZZ3TCP9Xu0S9Vrx6mzOB09X/Oz2Ax5uTuCLGY9+7ElTgXB5BEvFKbLVqM6xnhlebs552M6wv5zx+EcZ1433PIg6V3yfQUVD6mvio8jbhzNWqxlnf9AwuctMrz3FSTihAihQb2CwNcuZZT9UvLp9hPr5grNPoN4kdILqoUehxnYugGPTVALihintT8+ZfFYxv4zYTvpE1PEaPb7eUTS5q2it5eFnj9E3NR/+w5YyAuSDl7btkuRIrDWqt9zt53ilsW8aLn6qmCzFx1eCOeSfcGJwbUENlrWpWfuaNjnMp1POPylU24xr01jhYY5jrF4V+pnDF8NDP8FfzbBfNMwuC0pBfduBMaSJYXrjaZ9W2L2iXc0YooPrmsWnlqd/OEjIYpeP11WMrFfTm4JdG/pYs9pNaT6tefzjTLOSE5wjsN0H9h81mFiY3CfszlLdWZrXjtPPC5OHhG3L0RrAtnKgpZOo2W2vMUvH/BPL/E1mdiVrW7UWxf5hrICMr2pXMJ0itxXqkykf/p5ncheOa1RqNLbPAqEbgwJm15lh5qgvLRc/gcldodqJcrRY9b6vEUBaLyPNnUbfVrjXFfUSZjf5eGBihiw+uFMJmQwTjdvB6YtC887y9PsREBBabZOEvE00aIHXBzsjNRZv1NvM5FZsfqptHKH+qDBV7w8Ls5MQURsEyjbLJH9v3L+B/H0QSwUdYf42cPHzgLutmdwXCWpTHA8PDtUgCnm+o5Q884qhuZdraNZS2Wh9GeG7Qmf5+eQEylZrmNyL1+3Jq0iYGXmWh0yu9bFKRSHvBPN3idlbw+nnCttCvZNxlcafjbX0YzU+G//GX/ujQVtVyh/dAPef9uev/vX/oGgNxirC4HGupojUEOsU5xdzfu35Ixotnoz3uyUPqw2Xtw/kXFC6outHL0EcGsOsmfDB186ZVo7QD+x3G16+uyZrA9pgMeJ3WQLKGIxt+PD8gvOLObbOrNdr7pZL9ruOqCqs1rS7PU7XzGYzvPc8ffyYJx+ekfIAFK6uruiGnv3QE3weQxMshAarNKfzBR88u+BkKovK1d09D/crhiESIqQk3oldyOLfZx0ff3TBo/NzamOJqef25p6H5Ya27ekozKopu92OpCvm53NmdcWj+QXn5xPCsKPvPTfLWza7HSnVI/TSlBypm4ZcNI8fXfCNp2ej8bHi5uaGzW7LdtOhtKUvBZ8kHTz6wMXFBd/4+BGPTxd43/PwsGK377m6vMM0FdtWfE1DCBSVUQYePXrEs/MzppWj3fXs9nuu7m+JQZMVhBjR2opPrdY0VjGfTZhPa3zyDMNA27bE6InBYK0mqoLCMQyB+eyE2cTR1IZJbYk+sVyt6XzAmoau6/AxkBTY8SX3fH7Gx88/xBC5Wz2QtOLy5hZlFlTOkGOgC714QpqKHCPPH5/x+OwRbe8JYeDq5lo8ErV4F4KmxERdTQQeAKcncya1IcdE8J7L9QOqnlB8xhaNMZqcBRjpqcL7ng+ePUFh6Iaetm3pt3uaakE3SDl5Xde0fhDAUTvms5paG6y1XF5fixepmZABHxO+KJTV4BR1ylycXpBjoq4dQ+jpg2e1GcBpjAZCwXtPM5UymmkzQWuYz+e0+y1OG2KMbLo91jT4IY3WJoW6rok+0jQ1loIy4H2kDx5XVbTjAYhSSvyDk/gyO2cwVqGtwihL3/cCU0LGGEPfe1xtCUnhmikhDKiiWczmqByIObDdrlHWUNUTfACMlr+hNbWSIB5RnWqiHyAnttuW6XQqCuecqbXFZ89kMiEOHq0s1jp0zkwqR7vdEVQhFAVoks7kmKiqCq0VXb9nOm3ouoFSRKVuRi/SnPPRN7ie1Qw+CiD3A4/PLygmsd/vMdbSdxlVWXxOVGiSDxiraLTGGMd+6HHOYWxhGAYwE/EqRUnQUs5kP6ArAVoY6Lo903pKVTUsN2vxhB0STdPQ9wMag3M1A714dRqH0qJqiYPHUBNSplcZFTNNbUHLQVlVWdLQM21mDMOAsRU+Bowx+JgxSiwYhmGgboz4i4aMtRZbDDpmyNA7SCmhtEaFiLUClKvphJgGWSOsER/SGNHKYI3GeAkONMbhvUdZQ1FZKiJIVKaiVo69j+QsD9acM0kX8c4dvVgPfrJWgbUV+32HrmqslTHpnEGlTCmF2XTBerfFOE2VNPVU+sArL77sKR8DoHLOGCVATzk53IEs8yVm2TyFQFNN8F2Ls4bKGVLfk60lqkKtDAVIBSb1hL7vx/YVL1ufPCBWHN5H6qph8D2L2ZShl7Uj5oQ1FYPvaGqDLgVVpuy2a05O5rQ+4Zzj5OSEh/2KHCImZKgOLktymBr9IOVFjSOkKOPaODkoyJmgEk2ZYLIcjPV+oF5M2Ox3nEynaC2e3koVYoGkQXmFM44SE9ZIwKdPkRILrqnRxhF9EG/k5OUwJkv1DUUO4aCgVCGlgtJG1oCiQAViiZii5aCraEI/oLWico6i5AXVVI5+31K7iqIg5IRPmco0pNxTK0XJlmwzJFn7a1OTNLR+wBjxUk4+UUqhqqQ9tB7XIuPoY4AiB285Z2pnGbqWjPgmYy3JByb1lFwiJEVIYtVhjEMpQ8qZpJFEsXEsq6yoG0dKQTxsK83gI7Zu8HGQTQRaxmZliSphYubv/A//3Z8Y2/4x+Pzlv/Q3S3Za1G37SJzZY9hJajTrbzr2//qe00XHvB548eIpk5eOxz+Jx/LA+sGLIrIWgNk+MSz/TCGdR1RvqO41H/zeuGFDNi+HUmJJtlbc/paj/XqEKlNdOlEY3UZMnwVYjuFP7VOxPrj/047dr0cJhQTOfiYb/ulNwLQCHOPMHr9v+7Fh9Wcy1bPR7/CTuSirtll8XYP4tdYPkrYdFoabf8ngvzkwnQ/4waI+n7J4AZOHTP0gpfjTK48eIt3zCe1jw+bbED8eKJ3BbA2nv1Qs3kpbmU42uRQp71WpsPwNx+4vdBgz+oB+OmV6KZvxoqFeRuzWk6YO0wbWvzHj5l+B828/sGsb8ssZzZ3i0U/EGmH6pqX9eHq0YShGsfo1x+o3C5x7WFY0t5pHP0vHza3dxxG6iLJpODW0Tw39E7AtuG2heSjYIUs6/JgSXzRU60D7YU37WBNOlHjl9orFq0KzSsRa0ywjdhvQPpIbB8D62xPufrtQXGHxhUF7ePKDdvSAlcT66r4jV5Y0lba6/e2G7bcT1VJjOsXjn8SxPPlQcsyvtG2uFNuPLMOZAG3TwZMf9iOwYvSMVcd2OoCDu39R1vl6VaSvl4Hh3B0Vrf7E0NwFTCvhX/tnljBXpAae/GBApYI/k1J0u0/Y7SDgCAinNatfk9JYKfcu1JvM/Espnc/jM9VuesLFFID+SUU20D41NA+ZOFGYoTB/K/PObcOxrDksrCihTw2xGeHKPlMvBZjXd4MALK0IJ1bGgM/EmR09WRXJKer1CCWceElXq0Cc2eMaYfeRYhT7DyvxqhwK07fyjihWEuKfq0KiOIM/dcdSdeAYcjV5u2d4OpU+TGPJ9C7SP3LY0Qc61ppsYTjTNA/5aDsBHA9BwsJRDFQPnuFxTf3gIRexsRhhiUqj9YsS5Wy1jkfLjoc/VaMyTO/y8f7LqO4/tGmqZW6kSjG9jcSplp/dHEIDR6/SqR5L0suojJShWd8H+icVfqY5/aIjzizaCySrVtKecWpw23jsy8PvmjEw0LYJt+pFzXrRAOBWA3FRoX2mf1IJFKwFBOdKY7eeXFvSxOBWA+GsFn/pPsm63aijN2e9jAK7xoO1ODHHNrBdOnpZmyGJB7PVpIn5lbEjynqZY4ewxjDT+IVmepuOz5cDWDs8e3KlCXOLbQVmxYlmcu0pVhEnhmrpSVNRKapUaJ86Fq/64+8NJ1rU7fuDfH48uBsPv3L1ft2ybTq2sVi+ZOxmIJw1st7OHGFuj88p06fR01qC7/pHjnr5T7YzQDirsdtAOK1wa0/7UUO1SWLBMz7P3Cbiz5yoPDVMLwd2HzdM7gJxath+ZDj7QoLdDkFq7+9J7kPGvRFY3KWjjcUhGNSfOTkEGcezX2hmb3r2HzdkK8rSYhi92dMx5FPlQpzI71U7ORQIUy2BW4OstWYM0Do8w+G9P2vRanw34NjPgNgPVXIoI37J8v1+bo7rd6oUzSoxnAjUdm3G7eKxL/xiPGSLBz9kjv0+uQvi0a8FPqtcjn7X2alj+Fc1WnmkRg4LDmvBwZv/sK4MF+5XQlNTrUdP+vc2QYfPwb/cn8p4YhQVu00kTQx2K/A9LN4709oukWrD3/8//pM/0rv2P9PQ9t/69/7DUkg4o3FKlCI5gVMWWxkgSpjWbEZKgfv9mr4f6IcEyhyhgNaaEhIaxWKxQLKeFbv9hqQ4hpnYLInLMWRMVZhMZgw+0GjDdNaw2q5ophP6zhNjliJKVWD0JgHeQxqTuHh8zm63Yb3eU9difnVQHypViFjcWO66mE+4mDlS8Ky7nlQUnQ9SIs94D0qTQkQpjbWG+WyER1pCgg4bvagUTTWh6zoSinpaE4eeylRcnJ5gNLRtS8qZzg/kLJtXay1DGJN7jcP7xNOTCXVlWT6sjwE+YRgoykiJqhuDlpqGvu85nVb4Yc/JbM6+GySAx2n2refk/IzlconSWspic8ZZzbRyGKWIvRdrihRx03O6rpPy1lxQYxCLqxS+H7BWU5BwMWMk6MdkI0onpSno44a9toUYOpwzAknQYwhNx2y2IOXMEEQ5q53GuZoKMNHjnGPXD+i6ZreTxbhoUEb6rdICmdTQ01QVtmroh4AviUxh0lj8IOCCMRTPOYEPIQ40VmGMKAf7IpDCakcKCWM0JIFFXiUpz9aanBLayAKeYoSiUVoUndL/RVRgsTBralQeIZa2JApOj/PCGgm/AdCF2lhKyuhR7WqcIRIZApQxfKrS4MdAJGU0YXzwojIleDm46AeKylAsISScq+SQU2vCIBCpqazA+7GMGSDGKOXyxlKQcByr7Tg/C6UUUh5wRv4bQY3BOZrWj+Fozo6AQ1PZir7vkCr6REngqoZ924GxR0VybfRRURljpKkrnBOIWIqMPRn3kaZpGIYBWzlAYbRDZ1lbnNL0ORCylIoVkoQpas0w9LjKoEvGjOFiKRYJJ6oqgZFKoUboWEwhhchiMmW/3ZCNhO7tdjucnaBHtWlMAwBaQ21ryEX8TquanJOEhqUR3ChNZRUpeKLvsbMZShl2+47ZRLyLS4y0I/TV2hyBklXykPFlVE+HCFruzRhD13VUypBGZ3mdC0lJn9Z1TcwBa9QIlhQ+BGKWECVrLW0rqkytEnmE8TlntLIwzo+UEkUhKkVrGHrPbLYYQ8wCOWcKArwE7lpUylhEqX+4F1XEC0xrjiFhlXKgDJ0fJATEOQkhy3EMsjLHcWCNIgTxCy3j+u29xxmH8uLBWtWOlDNZZxLSt845kk8CbE1FyuEI7JORv5+j/N1MGNcFBVHhh0jVTNAmM6RAKhFiwdlK+t+U8SRaY3RFirJWaavxMWCtJoSBnAU4V1VF9EGuKci60vtB5qpRstaoglMNOScoiaIydlJze3fH/NETSJnUDRgnFi45JSqnqbVl53tcXVFKEdDdTIheLFGa2lAhBzh2rEDwKTCdz+i7PShFM63pez/2GRSlxAKpKKqmohsGtDWUHLHOiQJ1PPiwVmMzorz3gcq40UZoEA84pSh6DEOiUIikLGOuJKiNGw85ymhFUTAZmqoBDX2JDCFQWUtSoIpBpUhFIo/vAVNXE9UYeGjUeDA4vsxmgcE5xPfrg+9EFWwdxiiGtpPnnXJiAREH0KCchOCI6jZilMMHGV/GSDvElMDApHZ0XSfjdPTMMsgap6uaIQrkttZSW0cJkegDtnKEkqmrir/1X/8XfwJt/xh8/sq/+p8WlQtxXo3KpISKZfS8lCGw+9Cw/YaoW06+gHojEAg4bloPMLIYxe65rE3Zwew6vt80O33ccJpeNnD9uaXaSuhLf6E5eRXpzwzNKh2B3D/+OXjCZqdYf8syucksXg2Ek9FXblQfHsDEAUTvnldsvyHqo9lbUdY0y3j8joM/rfb5CCzap/aYED29jaRGNn+mzwznluY+CHg5ddguEeaW1bct2UHzUNABJg9RQlfC+w07SFvZ7cDDnz0hzBWnLwJ+LptO14lXom2TbGgLR7iz/VpNtc/sPjRM7jJ2yMRaM73xbD+uOf2iE9GAVce+7B5Je5lBSmfd6NtXP4Tj/R82/XGqcbt09ChUifF38zHcRYc8hqvIGPGnAgDjVB9tIIpWNNcd/bOJBEetvZSCO02cGsJMkypFnChm1wI1JpfjPiRlitGolImL6hi4JTBSFFLi11gYLhxulzDdeFhV6SN8sG1iOHckJ6XR9Vp+Lk3MERiBhPy4XTqO0wOwBI7J8AdgdYA4jKChf3aAYAIwdCjHNsxWUa0DKoyQ6Kw+fkd2ilQrbJdxmyhtHRJxUWG3/njfdv8Vj/Fc6J/Vcu/j9x/8pA/XdwBgw7kVeJ+lv4DjdRerj/2XJuZ4TYfxL+3HeN/yv81NR5qIdcMBcIS5oV6GY7tQxC6kuZbDkdw4zLYnnk6O49F0ArvjVFOt4xFwFK0wbcSfV1TrcAwlOxwGZacIE4E6tpV1SnxBJXzQrQbSzB3blvKr8OYIkhTHNUn7zP55zfTGo2Khf1IxuexJU3tsj8O4KlYdA8CqtYCko0o9vQdYYW4kYGwEdEUrJpct/bPJ+3Cku+H4HRLmyHG9ddt0hI0A2UqAYX0fjpDxsF4d4Gk4rY4QVsq95e8c1r9Ua+q7gbhwZKuO6kaVOILhr7aXiqLUtJuB/tl0VE0LyCeXo7pbPD3fw8LD3y5aHf9/MbJ2HGwBDmtOnNl/Yh05zPPUiI95tqNqMxfsNhyhbdGKMNeYoRx9uQV6a7G6QYCtGfLR//fwM4e5fQCih3lkt+HYPtU6oAeZd3Fevb+2rwDKr/aP245wbh9RIZGmTnzLe5lLOkho5+Hw46vtc1gzZd2D2ChOf/LA8PwE4LhOHa79AKhnV+FX1rk4kee37hP+vCLMNLYvEtjWZuw+0T92x2f3cG6pl+8PLgRu6mPbNvcyr8U7WUtoYyzH9TPV+giiw0wfQfChPw/t+tW5JupzCSE99NOhf7NT9GcyRppVwm3eH5h8tc8PbdVdiBLe+IJOYo2j/aiWHg+ADuM8O0W1Fu9rlKwponjWhKkW9fr6/XNZ1lJ19PyXcLb3/8308j39hT0Ghh7u99Am2Wnc2qNSJk2dHCaOv5saCfnzp5bf+dv/8T//0Pav/vv/UVGqoIqkWpdSqFyDSwh8M+MmOmeg4LOnFHXc8ANyypbSCEqldDFGSXxPKeGdAh9xxlBTEXOSE8iSMFpCrSRhO2JtRecHnDboPKqxcgCdcZV856QWqBNjZjpbiJJzVNPoAtodrk1RVDlep9UVldFoAilDUZq+SAkEgB1hjlUjzNAWyLjK0nV7Sef2nsVCvjMXURTZStrIjGWWOUqIi/cera0AHT2eVloBaDkjJeRF01gBlLGIekgjJaQ5g9ESFJZSwo3QATIYTVHlCJFBSlujD0dAqVRBkXEjfAIBg4d+VrqRDXOSUtBD+npRAtGME9BjjBHbB6WwGbkeDc45URkCVdHgDJk8AgxLCp66suSiGAZRf/oYaJqKduiZTxr2ux1Gaayt6IcB46oRqCiUtuQUyDFgFNTKMKlqokoU5fAxkCmoUe1UsvSDGuFj0zSAQIaUEiEnNOYIKeIApoKmEuWbZYSX46Y9jqFZlTakIi9gevQhzDnTNA1xCJKkayxZgUKgddfvcHWFTwJQK1MxDAPOVkyahna3x2SNqrSU1vkB5ay8vKXDWBkB8QgbjdXosWA4p0QuarxH6NseUbmpMdFdIFopmaKgKAk+67qByWRCQVTWPiRqVwmQL4d5oPHDgDEGi8DPGGTMaQ1KF5IP2LoiBI9zFUUVUgrjmJD+L0WAYlVVAteyQOIyHgJoDT4GqqqBogS6Z4V1AndRAlUr60gpi9evcfi+w1ZuDF8SixKQh5FSkJOUlsUo6j2NwKaqqkglooyla3vMCI6a2lFVAr/6tsOgmM/neC+ApVhJ4ExFyjtqZVBjmxUE/Cvk/lIsJKTPjXaEIAp+SkElRcgZrSrQotqMRcIEtYESA3Vd44eBWovtRu/jeAhSMBr5riigUkUpc3Kj0lxgoczjouT+jXMChCt7VIaSBPLmQeBmqRyJQqLQFE0Icv3ZCZTru4HK1mgna4D3/XHNBlHXlgylKFFZ5kjlBIKiBaSGIOpDM4I8NcLdPI5nq92xj+RgoYgyNXqUMlSVzJ/KVlTj+uxHaOqzZz5pBIwCWEccKyZSShhjxLpHFaIPzBtRyWYys9lEqiyCggRJa7KR0jyfEySNVonaOgbfj8F6iqgqjAJdAuR8VAtXVSN9bh0+DlhbQS7EIWAqhxnvM2tZl3LOzJqJtE9MVJWjVIY0gkxfElNXi09zLgKjrRUPYGcIKeGMxTlHjAEzll6FboeuRHmc8sDJyQndvkWpItU0RVGyPK+sNpAjFRqjHTFGuhCoJw1DDGitsEof19UYI87WdNHjrKjQK2UA6dOitByCxUAsCWU05jBHlZKNSM5SqdDUI8h2MEScMhSj2Rl5DtZF4XPCKi1+3aaAdWQULqvxMKFQNRVD8PSj5UDJmZKVWDHB+JyUwy2Q6hJrDCEEdHEQE0llis5gjfQtmpwDlW3wIwhgPIzUmuM4tZUjlkzrB6bTKSQIITCpG1SK5JjAiv0UWon3m6lEpWzgf/lv/8s/gbZ/DD5/+S/9zXLYeOshHSFZnArIOWzAvwoUZCNjjiAIZGOZJmZU2bxXJpkhHxVIaeZGuHfYrL1XwMSpKG9SrakfvCj/RgBw2IjFqUHHcgS9tkt0jx3Ng6hRizMjBFFHECk+lCPkaAxhbo8AkgLV0lPseJAzgryDAucA7Q5wsxiBSu1HDc19QMWCHiJp6saNoMEMApPaDyrxvRuv5bDRPN5TyEfo5M/cUb1z+Bl4XzZ6CP6KU3MsxQRGteh7uGOGfFQzHTbOB5ChxqXifZJ6OV7boT8P0Ofwu9kp6ruBXJv3gUSjStoMEkBzANAHBZGOAlcOUOegsHSrAX/RHMN06tue7vmE5lr+fmoM1UNPru0R7h0ghQqJXFvCiaW7sNhefEbdoXR/BClHZeMIDIbTUdXZl1H1Gt+Hp4WM7hO5NgLDxw29yu8DbMyQf0XFCALutJdr8hfNUbn5jwO8+q4jzivsdiBXlrhwR6jYP3I0d2H0WzzMq3Dsr2P3jv1j2kAx+tgPh3vVIePP5H2nWomy9QBhvwq7vtrH9V3H8HgyjgOZm+G0Ph6iwHt15UEVRxFVGHCEOmYYIVGbjoD3q2PiAIJNnwgLd5wXh3s6gtZtICzGd7YxlOsw/w7rygHchIU5KjlTo6mX4XgoIffOsf3/cdh6CD46AKCDilv7SDhrCAuDStKOudLsP3BUe1GWH9rwMAbCiT2OD5XKryp5h3SEU6kWiHdU1yk5hDmsK4f2+SpE96cSeBdORtA63qOKmVyb45g5jHuVyhHcHcDv4ZqPa8EYWHaYX4d7+JU1wWcJdjuv5Bqr9wcX1UNPXFTHvndbAZoHmHn4jsO6ceizg2L4oMw+APbDOD2M4UOfHwMa+/wra9JhfrltJM4Mfm5Gr9p8nDvth/XRykLG0vsDlMMa6tYe0wW65zOqpf//2HuvX0229Lzvt2JVfWHn0Lu7T598zkTOMJlDWsmEYJmQIMCwLRuwLmT4X/CtAfqf8JV9YRgOMBQAAybhRIGiKUiiRpSHw8lzcse9e8cvVNVKvnirqnt8I96ZFvkBg8FM772/CmutqvW8z/t70CFNBaWJMzsEi41zA5ic4v4mTB0J49oxzkdxjibiXOZEWNgpIA9euSpHZ/8ofqqYaU+rqTNiHIc6lEmo7o4qKVJMHTBmcsaOoYhxZqaiWtFqGmfjeFw9rKmv4uCmfVWM06FMz/Z+rqfCXn0lzlx/J27Y2GjK4FW020yYaerLOAnSYaGn9V8l6Bcav8qTaD65cIcCweuFz7ELACTEUOXC7Fk/FUleL2KMTn0dC6ER/IjtMv1cU91m/HWY1p7XReix2BpmcpxuI8F04vZV0zG9LryO46ZfGOHvj+vycD4yTl/Nuep8Q3cs3RF2HekO3eQ6Hs+DMTgAACAASURBVMcW8DNCfdGK3/+7/9mf6F37TzfT9u//z79ZojhLvdE4r1h3Gyqr0SmjtKKP0u6alUJFMEoEVwkfEYHIWENKYJ0HBoeVEQFGZXDWibCgCin21MYQcyTGJO3aBXE+aiBFlCpYBSUFdDUjKkVW0PUR75y4lZK4yXBmcGNCqSwhB7IBTYKs0DpjlJM2x9xTTMFpi00Fk0BpRdCFLvSD+JFJfY+ML2mrNwhDsHZOWrutJ2mFqhQhgLYe5URodFrLhjqKJFFUQSRwCX9KKgMRpRLK5MGpl4khUjsPEUrOsqlMAVTCV5au71FaTZzDEBNdQkK1tBUmqLUUpbFao9GUlFBF2JexlKFV1QgjtYh7y9sKpcoQgiVt5mXYWButqKuazaalFIU1jpgToQ8orTBGU7LFFxHjhI8s7M+SClkFAfhbK3xFNFrbAdytMcriXM227ahnM/qcBrERdNYoNEpZlHbYmIGedbshhhZtFCmLmBFTQWmFUhmtwRtDVJHNNqJMRpsaVaTKo5RBZ43VXoTwIoKYUoEUA4lMVkXwElqjjEYXJQ7ZDDoLD7TERC4FV7vBeSuuTKULtbXEGKiqhpITXddS+5qsIQeFKnpAEhgJGLOKnCTAqvKKlCIlasjgnUUBta8gFgwGPQI1kqJEhTWGrMUpj5bCRUg9SmcKASiYqLBNQ86G1ItI5bQmhy3kjFUKUwpEQy4ao2tiyhQKaEnRRVly0uQCyljkqVrEmT20S7fbLd55ssrCXVYOlLQGOzQmCF8UlSkMTsfiyVmjlcHkSKJDa8RNHJMwdXMiRXGoohRt36F8BcO8slqjtUJRiFbGoNYF2wtDOmaZB1rpIRQuU3lHRgSYsN4wq8T9mUImygUS3jUivKeUCTmKM5KM1gWQQogUTzTGeSiGPslYGfyLmMqTKSJUV144mjlgrSFnCSTLUV4sK1NhsqJtW5rakXKPNRIUlxkci6aI4J8iqnIQxE1ZUFhrUBpMQoLwYqakIvPDKrqux1YV2WiS1ihtxPWLCNRaJ1KE2jhKzFIICwmrDSYrnAGVM6YkVEyQCjlmnLOUEikh4bzDGIPSanBhSuEPFSkpYo2j8jNUseSiUSqTc5T1zntyTlSNJ6aekgSZYCrN9d0tWEMsGe0tWWmiNlKI04Y+9HhnMUpEu5wR7mzqqbyjKGgWc4zWQiyOir60ItplCWXLKeGNEUyDNvR9R105YaiTyKqQBzdxKcJI887RtXcs6iUlZfptx6xqMNoMDtNE329QJGKO4oBWehDkDcZ6ohIfaQ49zilq5UlhK/dYlcFJbYTtiqbkhFEFq4ucn4Z+u4Uh/FBZS7No2G42GKtkLmeHQct8HOBuxRS2pZBtJtmCmTlCiOAsxhp6Ml0ORJNRVpOUCOK6aOpK5nrSYHxF1hDygEwyRrofhmJCHpj2Xe6xpsj410UEaSOOtUzGhwFHkTIOcdQnk8RRmxIliNM/pILWw7EW6boggykGi6wHzlliFJd26HtxMQyu2ZwKRSeyLigL1hjpXkiJpAraWHSB2A7hkblgSkIsuYZUFVKS9Xo+W1JywmtNST0Lq9ApY4tsFLWVDShFSfdRzGhl+Fv/7t/8E3G2/vzz/+/Pf/X3/8lv6ixiU1g60sxSP1sRd8Tlhwa3GroroghJ2evhXUn+xohXMN3IeFOUQTxNjRYG4cxJCFkYxSY7MQABGESQMZAHrUi1iI3FanEQhYK7FREtNvI9Y1BQ2HETl8+uw8CNNQOLUk+cV7tJqMENmCpF8WYImOpx1+10jKaN5Ep+363TdFxh10kIUAHdidHDrkQwHsNi4syAkSA0Ec7kFFUquHWU3+NVqMzIbzXbRL8v+46JYbiR84sLi7+VgmN1vqVYg1tH/HWPf7kVbmgvrFzZSA4CwfDdpi/YNk0byLEdfcJIKHA3PXExtB8PrqpcGfpdS/1iK+nilca2CbMOgpkZrkuci9hQtBSkRoeWHoSGsCv3HwXZajB6CGASPmV10Yqoe9vJ43gQsmQM6snNB9CcBwmlsYP42g/OV/3KBRcWBrfNNOf9wLNUYNTE3ixWk2v5bobAm3EMmn5gIqYirUxKxqD6f3Fwx/C+OJPW8YlRqSWIyW4TccfLXvO2IxzUg2Aj9yQ1huzFaYsWMV/FTNhxIggMcywu3fA37TC35DxUkUwJVYaCQxAXK8j8s2sZL5MQ4TWpcVJU6YRjmZrXcCiNfdUenOTe23YYw0WKEqpIgJIagrZULjIPbwOpETeru9yQFl7G9KYnNyIUxrkhzo3MRyvt9yozrSVjeFf2r43lwpRqb7cSljQKPP5aWNeqiCCfGjO19Zs2D89uLXOhkjVkcmQuPQrBVegorli3jnRHXlAavaS9iytXxloc3Lp2YLGOY5SBLavH4tBw3O52KAgZBShyNYiHtz1x4eR3ukhuxHGKeiXwhB0rbe1XQdjG/eBuDXmal2MhyN1FxjDJ0bGdKj1dy+wGxnCBMQTKXffD/crTmJX5Jmu7CH6Zft+h86tCR65EjI5zWW8nlq2SNSXNRFCVAEUzMT+F2yv4Kor8bK6NCNrD2J8c0EVEWh1FxDStsGDH+bL4ZCXrXSu4GTmHItdzcFzGufxsGYpkaBnDYa9Cp0J36AUZMWAY3DoPLmOm3wk7jlwPWIm7SNh1vB7aqfv0KsxwQFv4m8jmXoXOBXcnmI/slTxLknDNUQqzFQd0ro08V63cK9sOe+Ukhcx+zwny4S7I9a7NEOKl0TEPa7IhNXJc3Y6Z8BRmcEJ3h57mZZCC2zDXUqUJcz2EXMqesL6MmMjEdnWbYd2oFG6Vh0JppmjBEci8UPQ7Rji+rRRdTSi4jbhzxwKivB+U6Vzdnbh1TVeG57yEAKsCJkgRsN+xw7Nanmnj/fV3Uc7bS0hidho/vAd0+xaFjMvU6EmkNu3Q/bDJYqJy4gw2oQxrkBR9piLVRtaKbGWtMp0I0ya8KjyMDODxWrSntYz7ubD0N8dSeI61nvjvKg2/15dprP2nf+vX/kTv2n+qRdv/7r//e79pjAgYfRG4sMcSC4ImGFwnsY+UnHHaE2PGVdXAyougDUoPL21Di62sHiL+kaS1WZVXLZiALGoFrJEQq5QDpWSUM+RSUCictSRtaLstGNlsFRRtu5W2Va2kKtFHQggSUqMtWoHNashhKBglx+O8EeddSlAKqUAM4rrxxhJjYlbXEs5ljPAcfU0uBeOMiGpNQwwJ67y0UCsDylJSoqAwSoQe5y25QBd6tBGPpHIWq5QIOVlcuQqD0RrnLJv1FrQIBWlgMUq7PoODNrPYXbCNHUpDKoG6qVhv7vDWEGPAW2mjzTkgSVsa7SwUjTay2c4lE60WJ9vgZLJDqFMu0vovbcVlYBcLWzTGgHEW6xwiW8l1KkBVebq2xTpBU2ij8Mi4yFqTcqZyFUR56RtbqUvWaCPXKpehJbpIe6oxGqK4jL0F6yymbhhlcGM9ZBGDvfOkIXjIaM2262iqGXFYLJy1lFywI99QG0Lup+CokjLOVYNbvOC0vGzIcYrYVs8aeVnWSAKy98i+RxFSIqTM0BOOVkY2/KHHGkso4t7NSY5Pk0glDS3HAe88MSUq5LpbY/HOg5KnYGg7jJeW8FgyWmm0glIiOYNvaijSzh1jwGhDTmk4b0iq0HY9dVVDSOgBYZJCwmpL6jNGW4zTpJIxThNyEOf1IPik4Zi1tSJaGUGLAIOIP7DtUqLSIi4pxXTdY5Sgv6gyCTBaSfiQzmidhkJLFNE3l0E8tihEhLODOzbkJC8HQIphWJ01IUeKKvQpUvuKEjPeedZdh29q1kmc0SUmcaArKEOSfTOf0fYt1hiUkfVv/LeSIt4Ka9k4K8WYYQyBtKdjNK6qyFG6FirnMNYQYo/RRgoxWpOGtnijhupEyYKjSOLWdZUnpkgqBeO9uDJLAaQQYrTFAn3bI4Kbp+0C1jjygL7oenEtKxSplGHjJg8zZyuUNtgRgSHDlRgDlEKIAesrEZEpGGsoueD0UCooCWMK1micrYaOgIrX8xGUNYIbcDV9H0hR2vqNsmhtyBm0ctJNUbKUIeywjtQ1624rDuOSiSEJq9QI7sNaizMOhrZzckEljVGKma/IqSeljLGGrrQUncDmwb2r6botkJm7Cleg3a7QxqOUw1Y16xJoyfQlS1GjZLyT8DdvPSolrHF4YwYMgx0xeuI21pZCYd5U5BzRCtrtBl8JtkUphTJOQt5iFAyLtvRF1jlj5FmQUyRsA1opnLGYoRiWYpBzpsjzshTWd3dY7zHIozfrgnIGbRWb9R1KG5yucK5BIXzjmKIU9pSiJDDOQpG/mbe9IJJQFKWonEMr6QSxypFjwRg7BPr1YNTAsQVrxc0TQwSVqRtHSuLSdUPwpVVSPDTeCePVMIn2KkVmVYW2UiQxQ4EXDEZbwStUlYSgWc02bCeerlaGnKTLxhhPSXEYJxqlwVuD0RKu54ymFIPR0Icea4x0yhTpYqlchQVUEu5tSj3GWPqsCCSUV5SUcNbhjaXkDqcLmsjZ8SHLxRJvhm4UI+tZKAo3BL86LdiK/+jf+/Mgsj8Ln//2v/5HvzmKMe4uyLNr4YdAFRFkspcN8rhxtm0eOIYaf9m9WmB5tZFRAzps3KSM4qTKZRIWUeLwHsU4PSRejw5LcQ2KeOAuN8PfkSJ3dRWmZHM9iI92I1zIXFuK01MYkISYyYMgzcyUpq2UbJ5Mm8i1Je5UmG2kO65Rg6XV3Uqbuukz2ZtJSLBtHjbQ+hWjcRArciUibJqJO8hdd2D04OKUDfzomNUhM6bHp5mhvujl3bQaNsZD58oooulY2J7VwuxUCh0Scb/BP7sjLeT449KLkDE6oYfU8VFk0WHIBdgGTCvmmFQZEXEGtIHKck11kI1yMcKuNK0I67keWJCxDMKgpG5Xt+Iuk2RwTVwY+ffE4JaUYv8oHukoIoIEJEmnDlY6d4ofwpwQASgsBm6y16g8tDlXYnAoRotLrxesQvaK6lIEL9um4fj1cH/UUIAQQd50mdTINY5zM4lIIugO1yxm7Kon7PlB/JJLGwZhoViN3UTMpkcNodZlEA3NNpIri13L9dZ9nlx6OoCO4vqNCzfcPzd8v5XjGYoBdp0mMc1uhSeanZoKDfKzmjSzImgNwvnkhO0z/qqdzqGMxZZhPo/J8dm/KqAInoOpG0P3IpIXJ8FWY+CQTMTBiV00uktSSBm7mAbnpG0z/dJgWykiZK8nhup4vUARF2ZymI+BhdmroeVdxK/xGusBX6EQsUWngr3r6AeBLi4MzbMtcempnt2BEWExzaRdWQ1Fq+7A4a/jK+fsIC6O61RYisA2OUtTmYRlQVhIAcQMhYSwV5G9xq2GAlB+Na/l3g0vpzBxm1XMpLkVgTowzXlxnKrpO1MtWBqUCOfuckOpZP4Wq6Qtu0ihwnSDYDsUQ+JMhNJcDaI4ch1NK2GsdtWT61diqmBSZA6WwcGdvdzrWI+oBT2FMo7/cXeRuDSCaegycceLU9gNLsNBvB9F4nGd63ctzbOt3P+IBP9VMhfdOk183nGdHMdergztgX0lPDppgZfi4vA9XuNuBE+2OXbERlArxUqBMtWa+nmLu9pKqOHcT3PLXwcpag1u9jQXg1CqzXQfdX41XjYnflg7FfVVJC6GIpGSZ2ExyNpkZby5dZp4r7HRmCCOUCl+mtcQCeU1EVDW3/r5RtAlQ2je+BwtVlM/34KW50ucDwUwr3Dt4HwfTF/TeBhd240mVcPavjAUp4bzVdM8kBDONAjZZfh3PbwHyNxq9wx2EClTI8XfXMm6niv1agyO4r1WbA9lrJkggWC2zcPzS36+3zEDXkYcv2PxcPydUZjWadiPV+IUDgtB8rhtITYKVWRdkWBDCUMcCxxhaUX4VdAvNbYfhOK7hFsLQ1gP62tcyNoQhzlw8XXL9hTCXOHv5FlhoqxbaXBfx5kUC/6T//hfgyCyv/q3/07x3pP6DoOiMpDaOPD/ighmpVBVXlqB7Yzb1Z1U1YZ23LHFHpUH7puZHKvW6qndG0QEklZLsFoRY8ZoT4jt4M4dWqptRcpDKI6X4JQU+yFpekAeGPBVRe4DtfL0fU+wCqsMsSSIr3iXKXeoIW0ehKmrvaUkWWEthW2/xVfSquqco21bvK9Jg+t0Pp/Td624drwCNG0XcNWMEsuAe5C/FbW0aZuqlmAepVDKkEOUdtQi3FNVwHpDnyI5R2KU9tcuStt6zpkU8hTQ47w4zNTI7hvaop2TACCNHniTUVi1QEgD+6Qo0W60onKepNKAcNDoPF6ngPO1tHsH2djGKH2cpQifEKPphrb2tm2pas3M1MR2CzkSSdjKkwrkLlGUQVtLVxKlj8zrmhJBxL6Cs5o+F1LJ4uzUInx4tCAfyhCYRMRrxaaNKAN1XRNCwhZBIoQgKeXjuWqtgTjwlgVVUbIhq0zbB6pmQcq9MFQ3a+yAAjEls1wuaO823G171KyhT5mSGYLkpOW6qMz6bsV8vsQZR8oQUk9WMtZz6KmsxRtLlyO9EILRSVMZTaXNxDx0RpFFrUd1rbRbJ5ljlTeYkkmhIyk5b6GVaGHfOkNBswot3jpsUWiDiLJWDbzXLZucMFVN6TO+OBFnShwwBwGF8Hfrup74pH0Q3EbKr1hfqYi7TWUR2K1SlJjQYtvBWMu27wU3UrJwOpWmsoZl4+mDYDAuL6+ljbrvJbys79HOkqLCYejpaZqKufWonGT8a8flZoUymqx+ttVeGQgDG0lVTkKPkpqusa8rIgWdCzplKiOIiJil/cV7T7+RNWY2mxFyoosRKRNlDIWiZS2fLRqWVcNms2G93pK1QRtHSBmvByB7CmhrsFqRUqJx0pVgref69gZtLTCEvmnBkcQYpd26D9jh75Th3/ouviZSRtDSch1KwjU1KuWhwCZttW6ccwWwA1fWWuyw7pgScUZhvKbbbgVt0WuUq2h7CGElztkcmblK1vIcOTzaY7u9I2d4cXHDrFnQdQk9hAKiFdlaKmtwwOH+kqauads1N+stq3VHSIqiBEFjNBNveLGY4SrLwf4OqWtp79bcriObELCuouu2QyEFdNHMm5ngLJoZi2VNzoGrm2uu71q0qWijBl0IJVLZgtUGrwuzpuHtNx5yfX1FIfD86S190mz6hHUStFVV4rpRMWK05sG9MzabDc5qPv/iGU3ToEzFerMhWIVTUGtNCIl5U9HUhv39XZpZRbuN3Nze8fT8GjUgWtToVtUaN6to+y33j45YzhwldvRd5vn5C4z2FCVBaZlC0zQyJ2PPwcEeZ2f36ENL10Y+/+gz+lyIuRBROK+m4ow1BqcNx0f7eG9xVnNxseLl5S0Zw7rrsU5TSsbETLWoKaFntpyxnNfMZ5b1KrDdtjx5foVr5kCRcDWvRBzVigrN3t4exycHhK4lxsjzFy9ZrTasIjhb47UICWZk+5nIg9NjvNHQRy5vbjm/ainGCtJEFygi+Ftr6dZ3vPngHkfH+8ScWa833F6teHFxjbEW5R1dH2m8iN0lR3Z2Z+zs7LC/s6SqPS/PL/j80wvatsM1NUUVQgkY05BKZKduWC5qDndmNPMZN3fXfPH4JW2wRJPpUs/cCAaGnKk9nN475uGDexgKZEO37bi+vuaTJy/Z9JE2gzaR0Cd8Vrim4e/+N3/OtP2z8Plr3/zPS1rW4viq7IQ0mMJokM16v5R3mOQVi8e9OAYHNMDrQVBlECBHLuK4QRk/di2bo7HVfmzpHNs5s1UTG3Zsw389/GZkLarBZTZy67pdcRhJiI2ZWq2zHxw1rzEPQcJXitQ2AdmsV5dham+NM9mkv97avT31uJW4Oie33xCOM3JnpS1aT+E2qTI/0/ouXNtx02ak9bSSJPsxKCnVBnfTE3b95CQNSzsd1xgIBEzXOc7tFEg1ts+O52ZX/SuGYiUczbAcXJHD7xTz6hrHmZnal+NMT6FRI/MSwN2I49ldtYS9mvbITT83MvtGV/XIOnY3HZQyMTLHz8h/1X3GbML0fhGXwunUbSDXjlQbwtJQn8v59HtuCi0aW4JHwW38TmDiEI7IAB2K3NsduQbdrsHfZVI98DSdYn1PU91IIvkYSDW2247t/SoX/LM7+nvLqU14ZFqCFNbiTjW1dtu7Dt1GijNDS778/6/zPYGJ32o3aUJMjIFOppOW4lcuyzS1VDdPN4ML3Ux/IzlFtgOHekBdjLzHkXU78hWLlnvUD47gEV8wCqvjR3eR7F4J2q/zTGXOCX91xI2oodjT7xjWpwa3LiQPez/tCYOoN/7362PQrhPdgWNzJIFqdguxgb2PwoQIGK/XWOQxm2FPMKxDsTH4Gykox6WbwgCzk0A5EURl/Rq5xjpktkdOQpBu4+TefZ1/uj20bO5pqsvC7FxCFrNVU4v8OH+KUcRahKR2z5AtpEqx81mcWsZHBMJYHBvXr3GdHBEMIzd1XGdlriXsqhdUwXCMI1JgRFVMbNE2EJeV/F0t9yk0ImJVd5nQKKrbPLBX4/R3VRLWb6zkO2/e0VTXImgf/nE7hZ+9zvUcA55Co7l5RxN2C+5WMX9SmD+PEgQ2zNFxvSxGsT51xAbu3gLTK9wdzJ9kmpevQr94zSm5ORZ++PZY0R5ndFTMP1fsfB7JVk38VrOJhF1PqhX9XNMeaFa/ukF90UCG/R+AW2cJAhvYsCO/eFwTLr6pqK4UsYbTfx5o9+U+z87jFNDX7RrcJrM5NnT7ivXDTN4LqFvH7Knm6DthOP5XgVbCatZUN4nzbzjakzy4PRX73y8Tn7m6k/Cr7cGAiQtw96Yif3VFv3GoleXo2yIE2lbWijgEiKWBnx0buHm/kGcZ1USqn9bsfCy4htmLMDlNs1d0wzO/PdBsTwv9YcK/NPhrxckfdtMzcAzpA+Hx9kvN6qGie6+lbA2q1+z+wDB7kZg9E/RRWAwdIUMQW7aKl98opLnM6fmnlv0fpQHdUaYi2RhM6O8Sz75lCe+05M5gXzqa54qD74cpLMzfRNoDS/Ky7q/vy3moNzYsF1uuPtnn6NuCVQiN/IzbShChCYXVmWF7qujeblnsbrl7uuTgDw3VjYRy+pvI5sRPY77b1Vx/qfDGN57SWMHVnK8XXDze5eifWprLNAUOmo0U5+JM/4mZtvZf/SP/333qAib02CQJ2tu+Z9EsREAxDustrhTquiLGnlAC1gnzr2RQJVOGTW0aQDdGO1CRUmCzXTGb70ggUCkD869DoydHrohrmaaZE+KWeVNjgmabInkQVkIMVEk2n6Pw2qnIut2yP1uwvdvi64pEpo+JohR15VFYSlE4bymxI3Y9lW8wlWObAs5YYkm0my22dlAS1igoiXllcc6w6XqcdfTbdmgJBqUjKRW8MxAlrbx2BkrAaUmR7jslG2AlDiCNJipJnyfLBteg6LqA0mU4rzCJtiGJwKutHTjAhRAjzg1hSF2HryuMEQeeVR7pOhfHVAgBZQ3KaHxVibvQisAVQqBWBoWjV5AMgzCl8DrJBr+qcNoQU6HrgiSuZwNa0/iKtg3UrsaZSL+6YW9vwWy2R58CV6s7rDJ0SpNDFgxBBDuzbFNkrhtiaqXbQwe8FhHOZGE+JqXR2ZBSJFlDULBrHKHb4iu5/zopSh8pVs7XGHG2auPIMWG1Y29/QTNzOKXZrFsur27pggSPxSyc1/X6Du89RiV2FnMenp2QY091dsqziys+fv6U2WyPrg3EWDBFXMQpibin0RJ+BBht0SqjtaeLiTgIm1prnFKYnMSZR5KQJC2YCGl7F/dkpRKlaHoErbEwmvms4nD3hEzh+uqG8/OXmKpGwolEAPNeqvrea/Z2lzw4PQENFxcvuL5OrG9uiUaLK9xoUsqkVIhxIwUFldGVIpeBM60tXhtyitgxJGoIhxvF3JIEPWCMiHsRBpYsRKvo2o5FU6NyYXdnwcHekvnMAprd+ZzVesPNTaYkTQ4aW1doK8ny82XNycE+93b2IG5RRfPkyRNuNwVnHZu2JSqF0uIM9misUThtWFMoIUHWNFbS23MXcCPvGaaQKnKm5II2Dp0LlfNsNgNjWElBxSiw3lPVjsXMcXZ8IGNNH7NarXjy9Iou9DhjSFEE2pgLpe/xsxnHJ8cc7e8J4ztFtIabmxv6EKhdRZ8TSushtKrHKIPKmWUz4/L6RkRxWxGjzN1Z42j7lqJgVnn60KNQaCVitHWGPgZxDGewWriiBilgOGs4Pdxld2/JYjEjh0joe773o8e06w7jZ/hmRuw6KYylzLxpODq+x2LWUNX3uLu7Y7m7z+PHT2iamm3XCkahKEoG7y0nBzucHS/Q2mLVHk8uL2lutqzaxNXNBYnBmVkKC2shdrz9ziNM7tg/3KNbb3m5CXz/px9B1IIBGdz01mb69pa3H73J/mzB4ckBN7cX7O4t+eGPP2HdRkFKUMhpQ4oNxilOju6xv7vk6OSQvYMF235LyZaf/ORzqmpBh7imU4nMdAXa8OjBfQ53l6h7h6y2G3zVsFpteHlzi6ychVQUViWMgcZr3nx0H+8tVe1pznZ4cf6Su3VHGBjlxgwOBjJpu+bdN++zqGtODxaQOtqkaPsb7m56shrWRQ2bbkMphQdnpxzuL2hqxXJ3QXfbw9k9Xrxcc7deAwqbFCVtqWoPueXk6Iiz4z2MBWMUTb3EGMPT82sqW9H3LTlHmr0dbjbXLJqaeyf77O7MmNeGsA93647tpqPrIeaCtcJJVrmgjOLBwxOO9g+YzR11tcN206Mj/PjymsVyl00oBOS5GEKhMYZ7B4e8cXpKScIDXixnJHXLxe0t1lgyaQogjTGxt9tw/+yI+U6FxqIPj3nhzlmv16SiSEZQSDuuEV5typweHbOzs2A2r8ip5a237rG6WnGZ5Fnpm4rYdigyWsHpyQGzSrO3FJd33RxRsuXjj56gAsx8Td9vMMpQUs/ZO++ws5gJvwvndgAAIABJREFU9sYoVMrs7e5hdeKzpy9QOmMAZS21MdTJ0L2uqPz551/rTziYiQA1MAWrC2GNujum4LDQaLpdhW0Vdlsm5qTKrxyjrws74wZOZSbO2ygihqWb2KKj8DCKF+2+lQ3viSd5RXMZJ/HDbUTEpGISXv1NpH6+4e6dBfVlJCwNpRPmIrnQ7/tJwBu5j6aVBOrk1MTkM13GXr0KPho5hNvTitAompdQZsJQFbFDT628ceBxAtMmPzRinvBr4SOOLcoiSPMzG9zkB6FEy6YahJHnbnjV8j0I2cAk3Mr5DzzQanAyTjxaOZ5RrB0FUHHZmSngrNu3FO0m4VAEFGFGxlo2+KlRmFbjVrKhHT/5qMFftoT9WsShVeb2kaU7UNg17H4SSZXwLXXI9DOH1YpcOaqLlva0mXiHRUsQmA56YmnqfgxyEaFTrzrC2/vYTZ4YqKMYOzoIXw+s0kECeO4eGfrdQqrBXyl2PslUg+PXDO28zbmI9ckpVvcNt9/sIGrcoufu+3Me/N4rYWpkv47iYn+6HNzLZZoHDCK4HHvCjKiQmSd7O4Xk6SBixNjirQaHaNypUEkJU7MU4lnF5khz95bMqcXncPCDTpLSB+cdBQku04ow19w9Mqx+TkLdqp/WLL5Q1M8zZjN0oM3txLs0bRpcbonszHT9i1GEpZsKDRMveG6FW+wHgWdwvElYVZ6Y1WabsDdb2vtLioa7B4bVm5lyKKzQzf2a+hyWjxFHfiqUWuaq3SS2x46rDw39VyTUTKuC/e6c9FjCg5qL1469E4d5rs0QhLaFbLBAd+Bxqygoi/lrpoOuTIxNlQrZiHjU7ziai1eFlolL3EjxanNiuP5alJ5qW7DnjsM/KlS3r0Kn2n2DHfioYa45/wB4e0MMhrKxZGtZPk64Vabbd1PQUTHqZ4KnVmeOvZ9sBxSDtNWbTU93PMNftiKqnTRTGGQxUsiKtXrF1B7WhpGzO64DL79saO8n3NGGlDRpa3nw20bCqbyWOdoKykaHwvaB4e6dDMdb7KLl7vmC7UnDyR/GnwlkGgs1/Vxz/aFCf+WWxiZmPvDyu8d0+47mhWX3oy10hVQLG3d9KvP68ld7tMu89+A55+sFzz/d59FvjWujedWFUcsa+/TXFO7hir/06BP+5fMH3N5v8Hf1EBwpiB0XEqZLZGe5/lDRvdHxK29+xu1ZzdPbHa7LPg//oQi1YyCV7gvbQ1nTz3+psPvWFVpnVhdLHs889YVi76OhQBOzFBtqS/Ii2La/tGZn1nEw3/DO8iW/+9F7bJ7NsNsyOUgFcyD/+/FfssTjnp9//1MAnqx2Wd+e0DwvUweEDoXZhYT3vfh5R/t2x/3dFYujnovNjAv22fmRYf5McFlxCJPsl4Pg/kHhwVee09jA3HX8ZPeIa7/H4R/J82ssWqz2PcvPOzannpuvRJZnd3y4f8V12/D4fI+b6xp/J+Ll+DwbWeAvv1HYf+clX9694tH8io9WR/yRfcTsdxSbe36YW0wIhu2B4erL8OY3n5CL4rhZ8Z3D+9yEJfs/ilMBUKVCP9fYrnD7yGK/dss7+1fUJuC/lPhnP32L+qISt2woItQ/lLFl+sLdB4G9e3e8sXdNFy1f/sUXfPv8K/CpYBPCXGO7IYTMwvWXCux3vH3/Aqsyx4s1H+czTv6xogTF9sjhXwvsvPpqwdzbsl9tmNmA04l/6/iH/Fb1NW6/fX8qaBetSPuebtdQ3b5i5f6rPn+qRVulk7ThWoVVhcp7um6NshpDYW+xy/3TA3wlDembruXudsvl1S3KFTbrSKfk5WXuKkrK7C08J6cnKAt9H3nx4pzbixXKe5ytp/AqADMwbh+dvcFsJizGu9UNz55dY2uxvLcp47JwUUspGAqzqubho4cysVcb7maOu3Yrad0FCQ3rIoVAILM7bzi7f8LxwRxVNJcXV7Sp4unz5xgcunH0BciyybdZcXpwxOnhDGMU29iy3rScX12z6SIhSPCUzuLmddawmDWcHj5gtrQ0zZyLixe8fHnDzXVPnyPOV2QVyP0abytKSuwfHXB0dEhTW1LuefL0GderLbqSEJkYCs5I+Ekbh9bpPmKMoWlmtKEn5IRydvi5QqIjlUxde0KOGFUo/Qqva9KAJshKcadEaDNJ+MGZhFWFD95+k/3lDKsyqRS6TcvT82s+f/oU6xr6LMK4dYbN5pb7O4e89+777DUG4ySg5ubmJZ999gSnHMEUct9JRFeymFSYLSzL5TFVrem6jqvbS2G6hiztWCVhTM9i5ghRBLmHJ/ucHL9L2wdWq1suXrwc2qA13eCE9dYTQ6ZuKt55+wHHhweU2OGMInQ9H9vHXF7dsOkKOUcyCm0qus2Gb/zyN5lVhp26wihFzAWzqOhVy+XVBm+lPbwkEQZVhjb1YALKKJzSxJBR2qJKYVY3rLqt8F+LBN05Le3NTe3pt6206GuIaJRWuGxQytH3kd2mpt1u+fqHH1JVit2lZ9tvOTvbZfGZ59nzl0QMfddjrMb5inZ7w5vvvcf9/QNmjfClT5czru9W7J9f8tGTp+RSCClIsURZSmkwFGor/N6Ut1Kg6XsJNUOR+iQt70GqWtpoum3L3nJJDIFsNG0vzMmUFXqmSd2K2mtSu+LRGw9469F96spijUd1HQ+PDvn+j37K488+Y7bcx3lHv40DOsPypXfeY9FodmYN1uyQE7jK4s8v+eTxU3zlMVmTtcUpSCkIX1QVdp0jEDB9L60hZnBJa02IURAYWoTorBSu8pQ+kEvLJkSytmjEPV9ZQ4iBEgrHJ4d88O5DyIV65tEl0PZLKuv4/g9/QnENBk0fA8Y4/Mzy7jtvsr+3xGiYzWZsNhvmy4Ynz57z5OlLUFnGiPBBcE5EsOOjIyDT7Cx5/uISX2k2XcTYTCqGqqoHAT3hfUXjNPvLBb5xvHx5iYmGzaqlaRpyHykUurBmXnmWOw0PHt1jPm9oakPfbiip4ctovvP9HyI7BEVlHCkHdCl8/StfQhOpvEGbzN68Ye5XhHXL4xc3+GpGCIEUxdX4lXffZndnTuU1FjAFDvePuHh5xe/94z/geG+fm1WLCXqaL/cfnPLgdE9ezIyhrmuWZxUvLi/45LPHzBd7sl6Tuby84L0P3+fw9IDlosaYwsHeDjkpeOsNvveDHxLbRFKaxoh46XzDu2+dUSlFXTJRW5p6ztFXT+hS5LMnzxlDRbyuWF1e881vfI2Dwz3m84qUI8u55f7xARfn1zw/P0fZGq8LpEgucLW54YNvvMfpybFwvpWiMpazewc8ffqM8/NbcoGQu8FtXehDx/sfvEPqNjRAszsnJkVlf55v/4v/m9ttj/GeUjK6ZIzOvP3GGbXzzBspWh0eLtlv9tiuf0DYKqIqQ4CZok2as7NTvvL1L9Nv1jgliIH9pWPZzPjop/8ni/0TOgWNn5NJLKuKD95/h5PjQ2zOVA6agwWz1YrHXzxju71FD4VC4x1tuEUry6O330KriCPRWM/O8QKnK7arNd/97HMWe/sYLS1b682Go4cnfPjhu2gSOktR8+jkXYz+gut/+ZSsKtLQkdK1G0pMfP3nf4nFvCLnQOMsGMUbb9/jR598hDaatuvZqeeklOm7FSkF3v/Se9xdX+BtQSvPrJlz7/SI5+c/RvmamBK2nrFq1zRNw/03TtExMJsJT35nZweFY3W94up6S0EDjq7diIt3MWNnsWDmrbBtK0cqid3DI958o+cPv/M9/M4+KQs7OSrBEv3558/IRzGJBKMD09/EKWzj9g3LzdcCej4kK1956ueO5afyfj27iBJGVUTkVLlw+4bl9gNBBZn1Drs/gsUT+a4w07ibkcgu4Sy6Lzz9Vk2/L8Jb/UJz9MeR2Ej7t7+WsJV+OfA7naI90Fz/RgZbY8819YuK2fMyOGjV5HqTIKee9ZsLLr5m6d5rUTpiPqvxtxVH3wkSOmLNFJyjoqAPXn7VsfqwR7kMN47qpWH3x5nm5av0+mJF6IwzzfpEc/NhoRz0LHa3nH+0y/JjzfKxEs7iED6io7j6VIG7R5rn7ySq3ZYYDdUfzVh8Uci2Fk5jlyfn65g8PobadAcefx0wG0EljG670SndH9TYdZxcoaN7GUSkmj1uUSlP7cY6ZGJjePrridM3rtj3PW20XK1mbL635PQPhD/oVmlIcnf4F2tuf/mA87/c88b9ZxzZwNO7JZ89XXL0BwY3cgSTuHxHt+/20LC5ZwmLglsplp9mtEHyELSgN7JTbM9mEuyzNLz4BUN+f0O49biXlt2fgOnMlAQ+nqNpM92u4flfKJy9+4yUNbtVy01Xc+1OWH6qmF2IUG030ubqbiOf/vua5f4tv3L8nKXtuAk133VnPN/ssPuJHMPoJowzcX/6q36aR3FmMNsytYe3Jw3NkzU6iCCXZm4Iv8r0hxKwE2eSS2K34o5MMxFe7V3P6q051VXkyd8MzBYdv3j6lM/v9rj7SsXj430OvyeMSTswl0d3+PNfcZx+7Rk/t3MJQHxP852n91k92uHB77ao+CqUDZAgLK/p9yqKEbHKt2lyxY8CtbS8D4FnVmNvO1ZvL7DbjI7iztQhT05c3UfCgRRsnn6rxvzKFW8t1uz6LVZnTr6y4n/9h7/AyR9s6Y7ryV0L4sB88m9nlse3/Nzxcw78hm1yfLt5yNP5Hvd/X4o0aUCRxEGEpohwtHprjt3kyXU3OfTzcO6jgDK4dOPMTFzd+iJMIVwqFcGmrAMqw9UHhtO/8pg94MH8hsYEPlvt8+PlAx791oBHcWoSc/q54vwvBt54+JLGBt7fOefHt8d8erBP/90lx9955Uwdi1hjUebqQxExn//SjKPvdpPLPHs517BXSUHrTsLO2n3D3SNNnBd2PoLkFXsvJHhuFFLdTU97WrE+1aSfW/HmwQ1vLS95vl2SsuZHf+2MN//Bq+d/v2snN+jO33jKoUmczW5pTCDe0/zTwze5bHc4+bYcn93kIfiq4vw3Ot6695Kz2S17bsuO3eIefJ/f+vyrNP/lLpdfblh+IQ7aohXtgeL2a4G/+pUf4HRibjrWi4rt0VP+xY++zv3fvaa9N5+KM8sfXvPFv3NI89YNHxy9YG56/s37H9Nly+/wAfq3K2xXMF2ZwvfCTPPg1x5zNrvl3fk5L/sF7yxe0rzxQ/6X1a9y9k+6STwPc838eeDj/0DxxqMLPtx7wV2ssIcvcF9O/ONP38Z8r5K98lBI0rGw89GWF79u+Y33fkjIBqcTD6sr5h90/G9//G+w9+MhGHGTp6KBu0v84l/8mE30vLu44EvNUy4P5vzeX3+fz/+nd1g8zQOqoEwO+uZbF7y1WPHV3acAvHV2wffv3ed/v/kF7FYTaym06Viobgovv67427/+ezxp9zj2d7xVX8Ax/B9HX+biH73N+tSJO/XQoSOszzzPfj3x5fces+Nb3miu+NLJU/6v3ff559/5OvV1lu4FLc/k6iaRbeGbP/8R3iSO/JqvzJ7w6zvf4x9Uv8jvX3+Nd/7eHduzmfDkFTTXgasPLH/hr/wRjQl4Hdm1W/7Db/4B/4X/68TPdtCvBaZVt9Khbf7GFd88eEGfDe/NzwF442tX/O7vfEsc1jeJ9cMa0xf8ShzKf+dbv8+3rx9xWt9R6cjPLT7nn335Tdz366mIEyvF/FlPe+h4+6tPqEzkg50XPNnu8mv7P+W3Veaz80csPitTt4i/y6zuW8y9DW+fvORLy+eEYnhQXXETZ/zq0cf8D798wt7/KGGKekBF2K4Im/9P+PlTLdqCsEsFcSBuP2UNqQ/Udc290yP2dj31UBHfY4cLe0G7ueVmtaH2S0rJhJTJXaBynkf3z9jddQOjrrDfVPwkf8Hd3Zo4JE0bawkxoLXnwekJJwe7LHc8fdfR1LBa3RJTposFmyHHjCYTSsZXFYdH+xwsa5qZZbvTcPHymu3nd7gUQQtzz1IolUdTmM8bHt07paoSVmtqrdi0Lavba9o+EoA+K2nJD4XFouHBvWP25hJ+06Waq5tr1q1hvenA1RitybGgFVxf3vKlD95nf+aZ70oSvT05JHaRzWqNVoY2BGHoUVDKMGscDx6esLtcoHOmzw0PH8zYfvwpbbvC+xrrDbe3N1RVRT2r6UJP5QbkAqC8gYElWLI8LEYXVwhhcIQWvDGUkgZEggOlRewxmtR1uLomx8hyd8nx0R61zswqy6pv2V0eso0927DPs4trAgZf1QDMFzPeeuMhuzsLdmrhj7reYN0em+0dF+cdYduTNYObURODhPAc7u9ydDRjtdpgTeTqeitto65ivV6jEHGxrmt2ZnPefvRIXLmNYWdeQ8q0T5+RkwgjOUa60mEwzOodTu8d0ThLyQoVM8umYd21rNd3pKLZrlucFtfzYrHD4f4u1hR0SVhXQ4wczQ7YbjdcPP8hXVIoVw8txMJurq2RkAiSjFGtKCphiibEQtYFowtGWVIvfFhjhNm8s7NDu9mShvmji8YoCbuZNzO2mw0nh0ecnB6RwwpFYt40kph+esL58wtCChilMU7Ttx2hDTx8eJ8aqIwS3rL37KZMt8z8tP2UarYkZmG5Gq0pJWCspm4E5bDeQDcgKfpui3cztHVAFtFoYGctlg3GKu6f3uPxyyeEPoCpJdwqZiq1gJRxunCwuMfu4hijE+SOajEjpcLZ2Rkff/pYgvUSYAr9kML+3jtvsl1f0gzfV6zh6OyEUODjzx8Tu4gyjtBHqpnDKIvSiuVyyd7uEpUiL548oYs9tnJ0KVFhqLWlhCDOYq1Z1J6D3X2sg9vbW67uVqxSoZAZ8Es451jf3nL/9B7LphYWdREcQTWfkU+P+eknn9KnISW+QBe2HM532dvbo64sTiVUCixqTzNb8PLqUjoS0CgljnaVhBG9szvj5OxA2thLpusC5+c3uPmczBBUN9h8lFJsVmvefP8dTo/2gETjPJ9/8ZSmaui7RIr95AD0RnN6fMTe3g5GK7RJVJXHGM/lzYamruiihLb1uVByYGfW4GuHVQqvFc4aUio8un/G9fWKl1dr+iChgFVVs1mtmc9mLOZzctzijcUoSCmyM6toasft1SWmmg2IAENMHacnB5isME7Wbd9U1Dt7NE2Dc27A3QjbO6XE0dEBy705TimImcZV6FlDjFLAadcbTGXQxlJ0ZNuumc0E5WKMwWlxsBYtzm+dA97UxBIpSbouTs9OZM6rgnUOpw1W1YSldBBscyQVcEpYqd57wWuEiFGaWSOc48o5vPekGNFOCmqpJAw93XaFMVJAKAirK6Q0rA0SVpBVAgpGKzbrNZX3cr66QJFnirWKvb09vnj2nKyFg15VFXebFfX/w96b9FiSpel5z5lsuKPP4TFkRA6VWVWZNWSzq0eCQwsayCZEglpIFAGhxY0W+hW1FiBopZ0WogSJlBYUSBASQVHgIDa7urqrO7umnCMjI8I9fLrztfFMWpzrXq2VakU1pTYggHDHvX6vmZ1zzOz93u95B6fUjUvvcY6+azjc36feduRR4nuL0Rrb9eRG4UJPVVWo0xOi90iZ4XrPdDxO58JInBcJVeQDRT7A+Y7BdEi7XVJojYiBfldkHE7GjMdT3I4nL6TEZAWT6QHOBQZG7jj2OVVboTJBVzuyskBLgc5MQsLEhs5ahuRkxqAiZJlh2zqKYkDrPEqAImFd0nyMzJcLjBD4ENjb22cxX4FIvPcgSHie3jIYDChHJS56RlmGEJHRaJTcuFnG3t4el9drBAInPMIIqnpFpGc8KdEEiBKNpO07pEoMPYlMXTUiddW4EIj+56/+/+n2/4FNcMdolJa7BHC/r1m96zh6uOL+eA1Ad1/zyeAUs8kYn3naPXUX3CFtpJtI1t9peXS6QMlA7xXXr40J/2LA8DK1bbqxuWv/jmrnGHqn5f7JklVdUu/lVNc5qkstobfuqqAFpklJ0Zs3Aq89vuHxeMHzB/u8+OKYfKl+Fqxi/5jzVCenYvGLcx4Ma6ZZwxfjQ1bLAdVVTr4O6Dokl+M4Twnj9wvqbzW8c/+aoem4rMecF4fkM8PgOom7t2zLqGD4dM2Lf2/CwaMlb+3PcEFynlnm3THFXBCUIl/5u1bnKKE5kNhvVXz99JpCWW6aEZfvB+xySLGM9COJLATDly12mtHvmTuBxmwdUf4svfyPt2PfukF1tXO+9cmdesvvvHU263WLmyQ0hhtqhItUp5o33zrj8WjBu6NzfrB6wi8dP+fvr95nsco4+lGfWoF3IkV3OuL6Vz0P7i/4M0cvmOqGj4t7PMss88Ux088EpuYuuCkYiaotroTqdcdrb1xzPpviswHj5xFTB/pxcr9GKdCVo98zbO8rTn7lAiUDy0EJp7Bin3y942Pu2sjNZucSPJA8eeeM18fzO+HkFw+e8z+9M8bOSrpekc0T2gKgPs145/EZ07yhVJY9k9ydf/HJZ/xvq2+w/5m8K2b4Igm3KagpObluU9OjFnfuU117hPWETBFyjex2HNosOcc2jzKKlU/Yg8oSjbobU+29kmJmmb1b8MtvfUTvFVoE3tm7xgbF771n8J8O0PWtkzsxb1XV89q3FzwcrnhczrE7VSicCn6/yuCfJQ5vSj6Pd25ZnyvaPYUdwuAmkK0Sq1NvEtM57IL8uAvfgvZ0kNrGv6U5/iAxf2HnOuwdbloCSYSsnzj+7MkrJia5fx/lC7Y+Z+/dGc2DvV2RJakgetPRHQ/4T3/5t/m0OuHt4VX6aATT1xr+1+Y9+JdZKtDI5EjvpyY5jyVs7yuqRxClZO9jyHehR2bZEfZy+j0DO4E2aEF1L2fzROCLyOCVYvrMUlx0qCriS0PUCj80FBcV7huGbx+c0YdkoJjohkdHC/p3FN2/uE+29SkEjRQK1r5d8ODhnAejFUdZhZaeb+2f8dbkhv/92S+k43Xr/G1/5rbdPjCs33GIgcMUjvVmyP6H9R37VvbhDn8SlaC4arj41THxq1sAZtOSox9IuqMSs7Fpjo+yFCw4TG3c799/xUBbSmV5OFgx1B2fZie0+xn5Ogld+dwifGT7KOd+uWWatdzPV5xka1ZuwHfefcZ/efZX6J5qzDa56u1EU84cxwcbvrl/Th80D/IlU12zcgPePz7jR/sHjM4TIuJWFFZd5JvvvGCoO6a6IReOh/mCt/MLvjf9Jn6YpQLbrcO+t2zf9Lx/dMVhnubr14fnFMKyer3g8+E7FMsknt++p1g4vjq9Yt4PGKmWk+GaOmTYoGkfJDyHHSUkzC0X9ZvvPGdiWlyUHGQ1x9mGfV0xOx0yHzyhmFlkn1zcukvXt/F+xU03ZKh73hucUfuc14sb3DC5QO/Why6xns2qY6w7eq/u5uxVP6GyGbqJd05TSAJpeWMZDWreHM0YqDTvjPAcZlv6hz3lH6QCnc8T5mB03mPHhg83p3xleE0uHWf9Pn9j+nv8MH+N84m6C/nT9Y473UX0TDP6ekeIgoHqmfshv7H3Id8rvplYs128KwR20+Qu/9r4kqf1EV8ZXKIIvLCHDFWPPXDUjwZ3Tvyo0trQ3Ius+oL74xVGek7Mmh9Ur5NrT7YJiUNbJCwCQLb1rKsCty85ybeMVMejbMb3N2/RTQX5Kj2b+Ewk1+0O6fN/XHyVvaJh43L+0vGP+Ifzb6d1ZcdWVzZ1CjRHhnZf4vqMxwcLjPC8P3nJVT/huNzyyUPL/odprdG7NWhw7VlEwS8ePGffVAAcqC2f1fcoVQ/yZ4zlFI6mdmvw//Pt2e32J9pKIaxGOYNwCu8kMWiIGbkc8fjhI9568wGT6YBykJNlOZkYsD/d47VHj6hXFUKBclBi8Dbw4OQej05O2C9KpnnOKMs5OTnk6EFql2wXV4xMxLgGrQRt3fDk8SmPHp4wHuecHO9xfDThva89YVgIIi05PUMjcB6skDipeeuddyknU2SRUYwKjh8c8867b7FcXGIEKcwkM9BFhrLkjfuv85XHrzPJSyaDkuODCW/cv59cNyogvGModGISVg1vPH7Ea28dMtjLKAaGyaDkyYNHnB6f4nrPQEtc2yROpdAcHBzw6LUH3D8+otCCcanZ35/y1ffeRpeppTdGQEqs0FSt5/5rb/LozTfRRcZgNCQreg6PM9775hOqagaiw7r2Lmxt09RY76ljDSU0NNiuR9yGCpAEHK2yu5AvIRJOosXSZeCNpOp6hJNo25DLQJ4LfGjpbM3e0YS9wyMGk2kK/MkkUnnefvtNQuyQMpLnOdZ7RPAo73lyesrhwYRBaRAyMBzAsDQcHR3R6RYbOkTMyLXCW4eRmtPTUw72JwxyyclowNuPX+P+/pRMJJawyxUdiqhzXr664uj0PjoX5FowVoJxmfPo9Ji+r/G2wtkKgUXi6doNB3sDJpkmFzDUEqMdSjpOjseUumA122BMAUGgSGzMg/0pg0FJxJMJRS41OZJJlpHvUsSVNHTRU/seLyOuC2gMtocGj8kUynuMCuhoGcrIMCgyH5A7lmWeF5TDEYcHxwyLId4lIdd7n4KhELRti4yBw70pUvQoJRnkIwo5IDpJYRJvUqs0fr1PLcCPHz1BdRJDRr9jMbvgGBUl0+kUFwO975PD3TqiD2QiIUQmkz2ePHzE1978FgO9h60Eg+IQKXIC3DkfTZZEs6LIOD7ex0xypgclUngyJEXU5EGlPqzYMxpohsOIFg3BrXYuu9Tm30VPPh7ShMQf01qTF0UKo+q3uNDR+zYF84UktMYYkQgymRFdChls247QObrWkucluhhyuHeCyQucA28hdNCHSC88NoNoFN2OwVxOCk6Ojji9d4RtKmSEXBuE9cltLCVlWdLYnk29RchAISWlybFtQ9/WKCHxLpKJNG4yYchbjwkxhSYJlQK3oqCvHEM9IrQO4SCIDqk8KhOYXLJcJ/SAkgXKZQzNlOAUShZoVSJlYtRqrQmxo8wF3jYIJVAZyFwl9IsNxCgoyzyprlDoAAAgAElEQVStDyESvMf1lrhjXds+okxBZxMLOstyFAkj4WRyZG6rjtVqQwwSLxR9EAQvWc1rvHW07RItHcTEpZuMRqwWS6rlOoUThJhwKAqavqWzLTJLgp/fBfh57bmcXVD3HmtTmJtzjuvr63TOJYTg8N5SDnIm5ZDrVxdUyyWyT2O6sZZt23G9WqOLEgZD4qDAZhIrAjGDy+U1HZaNrehCQ+drZvNXVJsKQ4H3iY0dRKA1kavFLAXNhYiJAnrHYjHHW0tTtSnIxTqED2g1QMmCbW3vOkqaqsZ3gpvLFdW6pTADhAV6iWsCwQkO9o+Zz9YImaFVie0hhoyb+ZzOVRiTGLau77BCIIuSy/ma+Xa7G8ORTd9ytVqw2K5hMMQqQyfSecwLxfnFFeuqp6kDggwjB1xeX3Ezn2EyhVYCZzsQAS/Y/c2K+eoKpaGpN9SbmqvLG6x3bFyDLDN6UhBd2wtczPj8o6d4G6k7T+Mi67ZlUW+5Xi3ovaOzPdb3u+Ki4eZmztXVDc45BFC3DXVl2WwtWbkrGBlF1TZ0fY/UgpeXF9R1TbCOunNcXy1xdaCeb1BBEqXBkoIGR6MBOs+Yza+p2oqgIsuqxkm4mM8ROnHinXM7xnabioYiEvDUTUPTVnRdR99bFs2aXgcakVKGE189Z1XNWVdL6rqmt5aqb4nWsb6Zs1luGGQlZVZghEYEgVAaJYt//Td9f7r9v7Kltnx115Z7y2ezE83ldyR/7Zf/gPePz3hzdMO9YsNxueW1RzM2f6al2LUQh0zQHRikTW6e33z3J/zG6Sf82tEXvLt/yV948zPWb0WaA0m2tFT3DHaw400uLfkvzfmr7/2Qbx+e85df/ynvv/EC+5tL2iOxa+2U1CcmPUTWSfj9zq99wuPxglJZHoxWvP7WJct/q2H0dIMtU5twc5gefuuTjPmv9fxX3/if+ZXDZ/zawVP+8uOf8lvvfw87FIQds7I9HdLvGUTV8urXBf/Rez/g7ck1j4cLfuHwjL/y/g9ZvetQjac5Mqg2MTeDEWzemfJL3/yc/+TN7/NkMOc7e8/5c6ef88t/4cOECdi1f0MSuMvLnsV7kf/43d9nkrV8bXzJUbnll157jvjNWWrvJgXRQLpGlucVetuT36Q1PL9pk1C2C7ZJL+TOHSnsjm051GTzFrNOIVjFZZO4iqXBTjPsXo7qUiv79pHgzx9/xrujcw5UxVFWMZA9//mv/NN0T6AF/dTcuRV9Lvm3f+En/Lv3P+Kbg5dIIt8an/GVvRt4oybbhDteaj81yM7jh4blu4F7j+e8Nb3hz735OQ/+/Evm34j0oyRuq9qmMKUQGf7zj1i/FRllHfcGG756dMVXj66YfmuWhKI+onZO1agEZtmxfRL5zuFzHpdzvj15wVGWHu5/7c0v6PZg/Ly9E2x9LmmOJH/59Md8fXyBDckl9ahY8ChfcHRvTbdz2fpcYtaO4rpH9ikQzBdJIM9vGtwgCa+347s7HdHeK7FjTdwlkfdTTXOg2bwhqI92AWC5RnbuTojLlok1vHkjUCrLQFveHb3i9XKGi5L9cZ2YkEXCbkgbkZ3n4s/uUWrLUb7lVTel8RlLO+D9yctUSKktukpC1q3w54apuFHfFyx+yXL27wTa4wxVp1C+25b3YAT9fnaHGGj3FMu3Jb5I6INgFG6cJWTGTrAFqO5p8oOGPdPQeMNQdRjpuO7HrKuCbqwoZna3Pwo3yenHkp9s7rO2BZf9hCOzYeVK+qAT6kqJOy5myCTZokfasEOGCHwG7vUWVyZ+dWLrBvQm7f+tCz9b2RSOthfg7YrVu35XTJLYvQKkwO1CF/vDErvK+aP5QySRrwwu+Vp5znU/ZlmX+CydBztWiec61vRjwTRvGeuOw2zLVDcMVM9FM8YdWqSNdwWXsAuGcwPJ5MuO8rhGmoAxnvYohQ76XP7M/Vzbu4JWd1Qie7Ervnqijrt99wlbeFTerQ/SgeoELqZiRhIXO86bKTEI7FDcuTpVndjPw4uen16cYoNi7UrOun26oPnn83eQFvKFwxc753aE7QPD5as9PlyeIneD+saOMdLxdHO4Y+eKXaEwreuqhx998hqfrE+47sdsfc7CDfnvL39952BOBQ3VBdo9Rfd4n/Fnii8Wh+TSMdENH1YP+MH2dX7/6ZM73IwIu+vGzvH/Tz77Kpf1mN9bvs6z9ogvmyP+0at3yWbp2Ksu3jm4s6XlR589Yt4NADjepUr946t3qWxGMUvXQG453HkSlTfXIy6qCQCf1Kdsfc7fP/82+UzQTeQdwka2CUewenvE75y9jpaBfV1zaafYqHj6xb0kXA5+FkBo6uQC/+TTB/z2+Rs8bw6Y9SN+Z/UW/+DZN8lfZHfXmWyduLD9VHPwI8FPr075rDrGiCSO/tfXv8E/e/EVbJlC5W4FTmUjqg2U14IPXjxiqHs+3t7jp9sH/A9nv4puYfiqJygwW3fntFVt5O98/1eoXcZPtg/5qLnP/7l4m9+5eMLoc4PZeMyq3/G6k7g6fgp/+Pljlm6AJPKT6iEfbe5xcz3GjmS6PgODq55s45Pg/cGIZ6sDuqD56fY+f/fVL7O0JcU88bKFB9OkQLLqnsHnkhfPjni22Kf3in+0+CarvkB9OLwTX1WbEAmmCgxuUudg4w0fb+7xaX3C8+aA62ZE8SJhEYqFv5tT7Z6C84Lfnb3OT7cPOOv2+YPqdQKC3589xlwZmkNFe2juWPa3bOOfd1Pf/e53f+4X/+ve/u7f+3vf7foGrSRaSKSKSBHwAR6c3GNYZER6YkyCoIiO6XjA02dfIFRkW1miNnTRIZXm4PCA4bhAaY9UKRQsIHDOcnUzQ2QZnUvOJu9BCsV7X3+P4JIDJcaId1DVDfPZlqYVRJXR9DYtPFIyHY053B9TCCilQgUJAZaLJctVS9CaYBR973DeMyiHHJ4c4l1LxGGkoveB1nmuZ0u2lQUSO9Z7S6EVo1HO/mSAwaT2UhdQQrJYzrm+vkGbDCEzuiYFFykpeeut17CuxYiAEz2u76mbDRcXl7R9TxQapSS9gt5bnpzeY5RJZOwwIqC0pusc6+WWy9kN3kqcl0ghSLXPiDEaCYgoETGliWulkuvXe8q8wLoevEAKUEKkQDSpwQuiixitiMGitCbGAFHQtDU6aI72j5iMCjyeIAJg6IJnVW25upwTvaJ3ghhCEgarhiePH9J1DUGlsLXWO+rGslpuuDm/RokcLUfEmFhuQkJhNOPxgECgC56+bdm0HbPVhrarMSbH5IbgPePhiBg9++MJIab28aZr2LQtl1fXCJUTZU4UASdg3bac3DtOoXBSggj0tqd1jqubBat1RdXUCJJzL7gkQJZ5lnARCiSSvq/ofc/NbMl8tQGh8Q6QCWVgg0dnGZ2zCClRIsPZSJQp5V4IRW89Ru9QGkpBANt3u+CajPn6Chsd1vcUWYm3jkwJPAFhBEVZcHhwQLQOG5Lzru06ZrM5y22TQoeiwAuFEobG1kwmQ3Sm0TIQXUgp5SHyww8/RQgFSGrXI2SGA5AQvMcYg9aa1WbNptkQhCPYPgVGKUkvJb3zKKuQUtFaR1YMaNqK7aYlBo+IBh8dEZHGnFZUdc14NERqjZCSGD22s1SN5fxizmpT0dqAUAobPEoKttsV0+kUJQu6XTp919VUdcXF5Q2z5RqZ60RhzuSdYy5Tms61GJNxs1qy3lborERGwSAviSlMPmFWtCHPc1yI1F1HkJpnX54RgkAaQ2+7hGywPd47AgEZI4f7R4QIeVGwaXu2dcvLsxucE+A1LoDHg4q0MjIaluhMYUkf7rxnNl9wcXXJtvOgDNaGxLGVEus80Qe61hGdY73eMru5JBsOWW03KK0QUZHFtL6KqIjasG1a2h6cd1xeXaRz6DzBJ/d6DDGFxoVA09QcH5/Q9F1yd3vJdlNxcbHg1cUMoTKCjxRKJQd/EBgtkFphfY91LZ1rubxZ8PLihsZHrJCozCCkIjiL7VvG0xFGaexuvV1uO56/uKCzAeuTm1RKhR5I1ust4+GUssgRMRKwbJYV6/WWFy9fIUVGby26zNhUNWU5JAQYDMdUbYsNEIViW3e8fHHO9c2SIAzWRkCReYMmxzc9hSnp8UQRqRrLxeWC69mS3kEnIj4ErLeUqtw5UDN0pgkxUPU9N7M5X55f0UeBI6QCjkuFOZNptlVFUQ7BKHz0zFcr5qsVz8/Ok3uWgNcgC0WUEesCtu5puh5pYLOpuVmteHl+wbbtaKxDSYWSGQRw3u946C45Pn1gsdxydn7Js/Nz2ghBgggenWuE1GyqCpHtig9dy6quuF5seXk5Y+sDlXMonbjgKk0SfOtQIkOrgoBmvtwyv1pxs6xwaJxLIm/vavI83wWJ9jiX1rsgIpuq5YsXZ8wWC2SWpWuYEAipkNJgnWdbtUhTENGsljU3szWXl1d0ncdHv3O2C8qiIMRI27a0faDrPc4KVlXH02cvaaJjWa0xmaHrapQwxKgJEbbblqZ3tD2sNh0X1yteXV3TOYHJctxufgrA2Y5qvSIEgULhesdm1XLxasX1TYX34Ptwx5Cu2h4vBjgnMUWGj5HoI+tNy8VywdVsjRMZfQy0rgcFUkZiCPyNv/5Xf65E2z/d/s3e/tv/8be/my06QpFa5MMu0Vt1gfn7gvFhjY0KHyVSRIwM/Prh5/zR979KlJrhlUN4yNaOqCXr1yXxyFFqRy4de6ahDYbP7R6DTwxCphZHuXv4jlry6DfPqXye0FdIti7nfL5H9iyjWCTHUzmzKQ1dCLaPNP0bltdGS74yuGJiOhyKL8+OGZ5nd+2Upkrt7+2hoXrD84k4pY+aQ1Nz1u3zRXXIzeeHDK9TjsUtH9QdDGhONPnjmsOsog0ZX2wPGWjLF2f32PskYQKikmRrh7SRkEumvz7joptyL98wd0MWdsjnqyPchyOKZUrYTunlPXrdcv3rOftHFaVyHJiakek5b/Z4fnHI9EON6v9YujikBPChgR1bNOr0HUKpCdlOvDlIgWAixDvnprSBmKm79Hk/NDuxSyF3QnJ2UxMKw+qdHP/AsvEldcxBwItmn+/fvI79ZIQIYsfqjfgyBQPd/KrgeX1ARYEUkS/qI75YH7C6mDD9LIn6vkgiSAqMELR7BnviqHzGq+2UZV0Srgr2PvNk8w4/NLihSa3vj4+IQrN5HGm8YZq3vNjscXU9Yfrhji/sE9dVtR797ILNNw75NJ+i80gUkvN2jy/qI37w9DH5uWZwk0SjkMm7ILsPjo6oyRmZjlx5ztp9XnVTPn52n+GzhK1SfUTEuHMB94Rcp7EvBaHYOVjjTmwXArNObl7pInHnVtW1R0ZBtyfZe5pCmVTdY/cK1C50TvUBpKDbN+gnDTYqbuyIlRvwcrvH+ZdHDF8Ksm0ScmQfiJmkWAY+fTjG5pJp1mKj4iCrmNkRH//Dd4jKICMUV01ydzY23af2ATvUuFwxOFcMX/k7lIe8CyVzmHVPNIqoU3CSHWmylWB4mfbpjxd/bvd3cGWZPRhwmZUEIenRXPcTPl0dU/94n9FZIF8mBJmukzu8fFXzw8cnuAxeVVMakfOy3uPTxTH2wwnTp7v0dgfINKaikbihItuAKyWDzzTj8ySqIwXtcZ6KJ5G7MDk71qguks8lnc+5/zvJYRqNRG86/MCgt+6OzezyAv+kpwkZD8sV31u9xYfLeyx/eIRuASS6DbtxAsUy8NnJBJsLLtsJQUhu+hE/eP6YwYcFg6vdeNlhTKJKjmnhI30zwkVNvMiZfh5xE8PwizUx06kYM07BWlEl5/fgKmCrkq7L2ftQMrr0d2GAfqjuRHHpwawlq68EXm72MCqJU5/MjpEfjjj+oz7h4HzETrPkBhSCzaDkzAy46kZcdRPOmj1++skjDj9QFAuXMB35jtfsgdawOBEMy56rfszz+oDfuXyD6z+8h9kmbMotnsTnktHLju39nG4v4oWkDhkfzB7x+eUxkz8wRJPWW58rhq96ugODcILZfsZnyyNedHt00fDx7AT5wZjDj2wS76q09rqRwpcK63KWE8VNM2QbCz5bHnH90TF7nwiyTUihgW3CjnSHhugN52WBzCNVyPlwdcpnT+/T/2AfUyfeuOw8qnFIFG4oya8VF5OCtTRsXMH3X77O/Mt9Tr8fUC6Fnkmbrn8iQlZ5mn7E8/UBL7IxP54/4I+evcb0g4zhpaeY2buQS0iOUJdnNNuCl3LITT/iw5f3ET8c8+C3+7siBrvCWpQwemXZ6CHzPcWnmxN+9/p1fvz0NfIPBgyvAsPL/g494TOBECB7aLKM6zKjDYYfP3/I/Pk+089Bt6B3rvJs67ETncRIa3jZTZnrnG0o+PT6mPC7++x97olG4Et9F+4XjMQ0kexa88PsiCsx5CcX9zl/dsL4Y0O2jug+7oobkm5PoXa4h/V2zMf1EZd2yOWrfebfO6VYRCZf9viBJF+mOSVDEqGzmaZflbxc7/P08oTZx4cc/SiQbVJgm+7SnGWHSlgtpjwNe/RGsOgHfH59xPzDI6af7kTtPiFX+rFicO0QVnHVT1gOUmHDI/mjq4dc7caWbtN5z5d2V2hN4Wd/62/+2Z/rXvtPtGj7d/7eP/iuyVMAlyAQQyA4h8kLBlnOaFAwnhRAckWJGFis56zWW+bzFflwSlW3ZGbHHjUZx0cHaBVwrqfvHX3wvDi/Zr5c09v0YOh9QAiFURnj4TCFd2Ua7wJSwmK14ex6QR9u0+oF+rZtxAVGecZoWCKDx/aWxXrJcrvh/GaOLodUTYcUhlJlhOApBhl74xHaCKy1tM5TNy0vzy8IUdL7sGs3Tk7H0bBkOppQSIVRSRDarJdcXc2YL7boLMPuROcoEmuzLA1FkSNIbsYYBBcXV8xnS5yXBAQheqQWiBjZLwvyzJBliVUSQqDtPcvllufnM0w+SHUzkdqhtdYEH+4EsRgSVU9JhRQypcW2bWqXFyngRkqZmMUqu8VUIoWgsx1Sqp3grMikRqKZjEaMJiVSRpx3CKVp+46b2ZLnz88QMocod2xhgxCC6XSCKpLL1voIMbBar1ksl9S1RQiD60FKhdg9rLrgKQYFQkiss6yWKy6ubuitJ0qJ2O2fQGBti9x9Tl4UOOfZ1hWvLq7YVh0IRZA6MZaISKUxBg4P9hExpCAiJHk54mI25/LiEqk0ru8QKrUcKiUZjwdIJVK4nncoJZktVywWG5arCiE0UiiCkjjn0cakIJ4sIwqBDCl5nRCROoUTaa2x3iG1pvU9oU+/y/OMzbah6SryMkMqQXBJQM9NRu96nLfUTcPJwT4aBTGN3W3d8PzsnJvlCi8USINz6fu2XcNoUpJl6XchCHRW8tnTL+n7yGK5xvmIVyYhHYJHSod1luBgfrPCWkdnO5RKBQHXOwI7flhURJf2zxHZrNd4b2hri5IlUkTarkVrReccUUR8bxHAaDylKEZIYajqGhsEX7x8xbqqkTLtn9xVUos8Q0nFcDBODwpC0LY9L85fMV+s6T0EqWhai5IKAcgQkoO371hu12w3FVJr+qpDK413FrgV8oEQCN7jg6NpO6q6wfmw44yKhIdwDoSgdz15WVLkOVIKjMnw3rPZVvQ2cP7qgtVqiw8RqTMinugdNjp0nqONRkZBcJFt3fPq6pKqrun6mB6QY8QL0jorICsKttuK+WLOcr0mH5SsqprJcELfOXJhiCKtP0FIYspTo3eBzqX51/UWqRSFSV0QhIhWBhEFMUqCD2RmQNtZIpL1umK+WCOFounaXYAiBOvQuiQCwQe0yTDGsFgtubmpubxaorMCLyNSKZy1ROfonE2CbJYThaJpHatVxaurK+q2x0dAG4IQ6eZeSLrWUuQlznus83Rd4OzymvWmSetYUVA7eyf+OR+IPpCbHGMKqqrlZrZiva5SoN7uOIUYcYp0YyWgcY6xyWmbDls5nr16xXK5JQAoQQgeY1KwX/Ae2ztCCGyrlqrquJwvuZ6vaa1FSk2UIHb4BCEFdd0gpMb1PX3vWa43XF7PqLsOoRODNZCKlFqndGhrHW1V0zYtbe+5WCw5u7mmGA5TwVQofEiOUKk0wQbWqzUIw3pTMV+uWK02eAFCJuebEQlDJAQgFXXd0jUttves65aLyzmzzRonYuIqS4lGpfBOrXDW0/aW2WzBYrWh6R2zxYqq6whElAbrPSGA1jlKG5q2Zb2pqKqW2XzN1fWMxXKTwg6jwDuP9+lYeW8JSDZ1Q2d7ttua7XrDxdU1fe+xIV3DtNYIEdO4NBl97+g6y6aqWK62XF7NaboOG1MohBDpGjUox7Rtg8mS67xuLK313FzcsFquEzqHSBSOzBic7TFSYaSmbTpcH3BeMLM1y23Pp0+f46IgM9kuELBMvPK8oO0lXZ/CDbu+x/YwX264ni2wfcDt5rnOkrO3d0mA+Jt//a/9qWj7/4Ptb//tf/ldNzKIHQtSBFB9Yjw2hwp/5PiF/ZdEJF3QSBH5Zxdv074aMn0W6EeSwWV/17Lfjw3qrZpR1rGwQy67Ca/qKds/PGLvqSfbta/fpoX7UvHR/Qm68Nwv16xsiZGBZ2cnHH0A2cbvHo6SCw0AITg/znl0uKRUlut+zO+ePyG+LDn6oCXkuyAiJekOM6SPtBPN9MGGvbzhspvwotrn5WqP8g+KxOPc+MQN37H+6xOFf2B5NFxyYGpy5fnR/D72kwmTLxLr01SeaCTSR9xQ8eH+HqNJixCCi2aCj5KPP37E+JlMbEEbUTYQTRJQ168XrMeKo0GFloHGZ7ys9lidTTn5Xo0fmjtRNbkx1R3G4raNnl14WDDpepXNO2KmEit4hw2Q/e69IYlwUUvMuiPq9JqQK/woAyXZPtRUxxGtIytbomXkxXafF1+ccPq7KXtCBO6EPKTg2cMRMUtC5nU3JgKfvzohf5GTzyHKxFYMRqQOPS2QvWQ1MbRS0XQZ9mzIwU9SeJL03IlGSIHeWjCa64eGwahjY3MubqYMfloyuEqiwR9n9UplcOOS+g2Pk5LK53Rec79c8+zjBxz8dCfKNUnUEDGJB7N7GqfTZ7chY6R7/tXLNxAvSsYvUghd1EmQUrUj5BrVOEKpk7C1ewgXcfd/HwmFTsKUkWTzNjlWC00/VgwvAvmsx04NqF3YTkwud7Nx6NpSzuDLrxXkWTIdzLsBZzd7TH8/5+CjJjnjIqjGEnKFWbZUp0NWQ8nCljQhY5q3/KN/9T66Fky+dDuR1yN8SG5srdDbHhU00y88uoF87dK4yCWqDTv8Q9whNtL+qdoyPO/RdieQ7ALXsmWLLw1m2SJdYtxGXTA/UYzHLZn0fLY4YtMUTH4nZ/yiS+IVySktIvR7OS43NAeRKARKRS43Y6oPDph8Adk2Ih3kNw0hU7txJUEmkW78pWV4YdNY36T5qvpd2G+ePkvENG5UHyhmPYOZQDc7lm2EUGjMOrWe63WDmxb0U8Vcl/RlZOZGfDo7ZlsXjH6YMX7p0btW/9s5oitPP8i5KTK8ElQ+5/PZEfInI4pZCi9jd68p+5CeGWzAjQzDC8vki8D4ZaCfKMZfNjSvDe8QKSLuxqPjTmDPtqAryeSlJVum/bdTcycih1wmbnGAtR/RDATzzZAmGtYXE0bPJEFLyplNAp4RSLsL2IuS3mdUuaIoLWcvjhg+NRz9uMOVCtUHotmNFw/5JrLNCi7znDYaXq0mbC7GHPxYMrhJxS4Rdm3/Mq0NZiuZTXNuugGzbsB6W5L/cMjoVUC36dwUVy1RJ4Fed4EYNf1QEPPA9WKCfzpicLFzFPep6CBtTPNdCKQVxHVOe8+z3Axompyj72mmz7o0zgGx4ySnQpeAznDlB5zf7LOcjxh/Yjj4yKV2992YRUl8mcI5y5nH6YxtV7CoB8izgr2PBOXcp8LVbs1Ka0S6L9atIFtJqvWQdl0w/ani5Pe22D0DUoIQ6HaH31AC4SXDV5FWFHQXJeWXhtHLgLLcrWtuqFOApEqFjWIBbTVg0xT4i5K9n0r2Pk8itfCpqObLNP5dITFtxGwE4ouC5uUYudJMniYcgHQ/wzzcXkdCJigXgcErQbcYsH42Jfs4Z/ql34V3/mzO+TK5mqWNDM97ZJWzuRkjrjP2PhR34ZdBC1whQaXw0JBLsm0gq6C4UuiXGXsfCYpFTAXF3b2N8NAcGYqlx+0CzMpZxKwl089gfBZ319d0rOxQodsk4LpSUqwCotNUbYH9YoQ+y3j8jxt0t2Pz1yEF1Ym0phTLgK4kS1Uy3w65bEf0X46YfpoCSE37szmo2oDeOkSE3/qtP/dz3WuL1PL8J3P7D/7WfxYhsF7NKcsBMkqcC3hveXBwzMPTQ/b2cxCBGD3KaObLJR9/+hQXBM4LYswJITAqJviu5b2vvcF0XxN8S2EyLuZzXp6vWFYVzoskNAQwStO3lkenx9x7cEiWBfb29jh79ZzzsxvW2w6hFbZz5DLDNz26HLFttjw4Pebxk1PyPFK3DfP5DRdXc6QaYF1y0knn8DZQZjkxtHz9W98gz1LyeLeqeHVxw3KxoZWCYjih7noCAh0NgyLn/sMDnpzs4/sNQkQuLy85v1zio2bbC2yQiCiRErSWHB4OOTw85Hhc4EPLZpMcsxeXC3Q+obEeZBKKCl0ykYaHD04YTyV5oTEm5/nZGTfLFds2JZoLcqz1O7xCRAqBR0BMqblGC/wtFy9EhEiO6aDAeocyhrrv0SK1eAsgRo+WqRXa9Q4tDINhQd8FmmrFt957h6LQHB5NaNuWzXbFp59/CULTOEEMkhB9EnxNxnhScO/ghPFYpRb87ZqX52fMZjdUnUeJAtsKZG5QKl30cS1KKUbDEqKmWs1BGxyJ6apVRt/3SAVSQ4iO4cBglGBgDPW2p216nBeITLKsOoaD/C48K1dw/8EDDsYjlEl836ppeHb2Eq0y6rqmMAVCCFwfUUowGmTcPz3m8GDyuV0AACAASURBVGBEU21RmeH8YsZ8WdE1KaDO2ZBcMHnGcr1mOJ5QVRVSSoaDjL7vkAS8kEgSmsJaiyNAltFUW4osxxhDcD0QEMLgnUxuRkV6v4w4kVzfg6JkqBTj8ZjWeXwIvLy8TOdcqMRD7S1aKkyWPnMyHDEaDCmGhvl8Ttd65jcLotL0wZFnA4RoiMEhGWCkwncWpRQyQJA7po8MhBCQQtM5h8qTCwihqLqWyXjMdtMlNrL3BBXQStF3jkyQHGUxfc9BWXLv4IjhaMrZyy9xIrKtKqy1aGl2BYZApg1d3zAcTZAITu7tYYSk2VbMNwuchc6C0llyN9oeJSR5keG6FpkLWleR5yWus0g1TvviPRFHZ1uyLEOrxJ4mpPYkmRuiD8gIzqagtiY6gkzuOWs7siiYDNPxGhQlru+xncM6h/M9Ho8TKVCJoMiARiaG873pBNc7aufonMS6Dh+TGGh0SU+ks5Yiy3AhoI0i88m5WBQFXdsykiXrukFnEitjSokOARuhyDK874ghQ4qAcJFynNH1jtslIq0BCiEjtuuJMTCeDFmtVgQPeVmkOUrA2Y7oPJnO8aFLbGOpkyjVNWiV0/c9She0tsdk3I3p2++ckBoDhBAQBF3d4GKgth1aa6LQBCHR1iEyTW4M0VsUhuh7Mj3A0iW2qtHkeU4dHduqYjQc4tueQZZTSE3TtgwGA9reI3c83nAr9vUVo7KgaRoKpZAk10WRG/o60OnEcg8hdTzYvscYs+Pt7rpQfHLTBgHeQZYN6F2HMBlBRYSIiNAQAmRZBkHgXE+MkSwrCMGlAo6UiW3r/Y7R6tEmR/nEyc5LjXPQtx1OQqEVoe3xqSSFl4G8GECbQrhC1GgtCd6RSZGc7kFgI/S7YixC4GPC5YwGY7ptDUajoyDGQNfWID2dlmldDiV9XWMGGX3s0DrD+4Rxcd4jtQIn8HQIKXFWpHC8wtD3LcOipNpuGQ9HhJAY6845xM4163RCMZSDAZ2VZJkiuB4TBdJoeuux1qEzjRIprHKQGaKSRKWp1lvG4zHeWXzoEg7IeUwxTNgFmZAvdV1TlAZwdBbGgzHOtmRmSAiOruuIwaJNwMdUCDJIvHPkIqd3ligUITNkIsO3a2xRUGaGutqg9IDYNQyKIa3bJYhnu32VCYfjrIW449ybFC4apEJnBm8d/8t/99/8/H1bf7r9G7v9+X//v4gA5YsN9nCQ2m4bj+wc1784YvFtz8HDJQAhSHLjuHx+wMN/kgSOWzamcJH6NEPZyIu/Ejm6v8J5ycloyyefPODgDxWT5y5xG3eClhuk8K/LXy7ZvJPCzr5y/5pPf/yIgx8JRucORHIm9lON7JPDtXzVcv1nhiy/05EPe7pFQfnccPQTd8fGFCEx81KSe2rnPv9LnmzUI0SkqzJGP8kZvwjkK083VZQ3id/oiyRYz78hePidcxprUDLw6uMTDn6URIzhpUuIgF3olC8ki68Ytq8H9r4yp7OG6mrI5GPN0Q+7lHQ+TwKQtJ7+oGD7wDD7dkTcaxkPW8ZFx8UfnDL5HIaX/o51edvKLXzcCZ4/A+G5of6//YwAVyYx9jaELLuqCKUhmN19btgJBZHUYj0wtEcGsw3kNw2f/4dj3MTz4PUbZush/fWAB/80/fliZhMP1CbBL+Sa6kHO4uuS9l5KGe86g/5gxPRpYHDV43N1F3zlc3X3VYMRVPfSz4ObdDOg2p0zsJDoKo2V2/b8+sTgyhRYVMyT4KWbFOA0fLqiOx0lMbVI7ek339A0Tywi88RGY+aKB7+dAu6KG0u/p3cs0RRYVZ0oFu9Fpq8vWa0GaOPRPx4yeRZSAI6P6CZxL91AMny6pjsdkV9siUbR3htgNu7uHKXXqZ2TNgV46ZsN/mCEG2XJCceOId0lTrIfmjsOsaot/UFBc6hojiTNvUi2TuPv3vebu+MYdwKqH6TWWzeQbO8r2uOIHUdGzyVmE9n7vEv86U2Hm+S7Ik2825/bduVbxiOkULJb9mu2sneBhQD5xZbmtTHFRY0b58jOpxb+0tzxoYG7wLPmyDD/uqI7DBz+UXKzDS/s3Tm73W83VJi1o7mXEZRg8VWJLyPZSjD5IqC7QLZKjkvV+rsiRj9NTuckHNs7du/t970NKjPLFjfO8aW6Q5bchhferhu378tvGoQL+GGGqnrcOGf7KL8bh6oD3cT0bxd6pbokNAHYoaSYO9xAMXtPJ073PJJvUru+6vydQGg2Fr1sUnhbY/GlwY5T8GC3n0KiqlPD6KxPvOHK3QUIqiqNlbswttswvH2dCl+7tvdgdq35u3WVmAKnxs9bhE3hZrct27dr+22A4W2repSQrz2ulJht2IVFppDE2zGtWk+/l9rS/zgOINskvE1+092tQWm+J0RPP9k5gncMWjuU6CberSP9WFLeWLLzNf2DCbJPKI92qijnnuZQka9DatuvUrEhSkF+09DcH5DPe/q9hLSJMqEKTJXcllH+X+y9y48tXZre9XvfdYnYl8w8ea7frapc3a7ClhFuyRIDLFlITJggCw9sCVlGgPgXQMx6CkNGjIy4CCRbIBAIMTEyiMu8wRjcdBfVXfXdzj0z994RsW4M3hWR54NJz2irO3vQp853cu+4rLUi1u993ufhByAy7511UXhb36uzNvrVoiFdOcJ92cLsVj9iTZV09H3M2bqRrly3gVh9c912T/ypdMsL6/RYrhQ/NcJDRqfCchsNyi/2WTpn0hObW9Ottz3AYPA+HWx9kNoIl0Z4KFvAnZvtz6fPghULgDz2Yt+drb/hw0wdPOfPIuFkADM8VMoo2/pgYZ/0Z9mjH7POhXQTCfeJy6uB3Xczpy9M3W7+t3aNNNn1cxdTMsePmXT0/Z3Axlh8MJuSvDMQHD9mLs+tuCyV7bNdaujSPnmnWO0tbC6Pr+1+A4T7zOnziJ8by8E6LIY7mwNl1H6v6J9Rma8d8aG/3xRIR/tsfy5MTz27N4nSi6GXZ57ho11D8wO2c13HDNg9ybvul1zYrun/8F//G3+kd+0/1tD2r/71v9laa+x2Ox4u98QwgjpaS1Adz26f8JMffcntlSeVC7/67js+fnjg3ccL3g+kNAG6eXG2DC+ePePqeuRwGDhdPnCaEq9fv2cc9kz3ExkxhVXLeLFKw/XNgSfHHQ+XB+Z84XyaCf7A5XKhBUdJhaBmA+CbUEvmendARkeSQp4nchFKgzxXRE2FWkVoOTP4wP4Qef7iCaf7e6ZpZlkyl5RBbHNeG/ggtGJ+jS9urwge9oNjnhLTMnOaLgiOpRVqUWptvQtJGXaBYdhxc9hzf/qekiHnzGWZqdVUqYbkGq4pO/V4L3z1oy/IbeF0euB0uXB/PoFG5nnGuWBgRHxXWwmOYP4/ZTYo2BoueM7ThEcYvCmGtEOIikCzQBVxSqvZfH/78DVFZuugoREH4fb6hsPgqVqYThPffP094/HAXbaQrVotXEZEGAcLiDlc7YmDZ/pwDyVzP51BI6DMF1O8qhoUUNeoJRGjx1VTCQLk0sit4oK3ja80TDRWEQZqzQTvWZYJ7yM1ZZo4QnTkmmgt4ZsVBRQhxkiMEfXCabqYd2Y1Rbk0xeMp1cLOSspcXR0oNXF92HN/PpkKsTVSHZCmOMks89xfwDzaGlkaTpTgzDKhtWbBaLWiEkhpIQyRS1oI4nBO7XycKZ9Tsu+vKaNNabl0v9KuMPMOCuZf7JSCQe05WWsNWEK0Inh1ZhGwXCg09sOIqpKWQtVKbV3hV2qHc0BzaG12TN7ZcajN6Vrt4ZPLgm9CGCJLLah68pJwKF4DVYVUMj4ozkHNbQvEy7kQhkitFm4m7dHDd8kJ54R5uZhC3DlyUwtHFI/Uwi5GSk3Ms4VHWRCa/Rutj17AItDEk6rNi5wtCEolkIv5AqPNILH3pGS/F73NsTEM+A6fBbXrVhKpFjR6xDmCaywP5rG7i3vO08S4syTv3CwRHqBkQb1BMSmF0D05/XCkSiGqebnWCrUAwVNqtXvbE+Wd7+2WrXFOM08OVwahDnubs0CrQkVQGip2zZsUxHnzf+3+F2HY2XmVivQwPKHinI231tvhEd3uUy5WNEMEsT09jkbdakaeVCvSPFIyUEAqueXelj8y54KPO1xpuNYQdUzTBF0BpWJFJ2kgg2dOC1Edzjl8btTWlV/VIGueE26MiCq0Squ2RnhRgouUDLX7eqkqVex+o40QHfNlwosSfbAuBbEAv+wdhYYGU1Usi7UQisg2r0K054AVqwJ5SQSUJkqqiVQyx/2e83RhHCMNWx+lK2prLqir5Fp6KFvD+2gWKiEimG+zjVPzTEupQ2N1lNYXyWpdGYC1/lAZhsD5fMb3Z+Tqvz2VRJOKV7etSd4bQHTB272w2YSqrUXOOQaxdUK8Y8kzoPgwkKvgBFqxa1+6rUruwNl7jxNH6ErfUgqFCn09oSlCwXnpFjK9sJUrRxcRUR6mMzk4Wi4UsTUwNLP5qV5pS2Xc77gsFzwQfaCmjGgzSxextaGmTKmgagXL1NflsSnVext3IiBWgIw+cL5MhDDQCn1NcYQYWYoVA3VZIHoUMc/iVlCxTUMT8N6DNpxX0lIJzhsYFsgIWgTxJov33pNr5b/8j//2n0LbPwE//+w//283KY3pqWf/rSlmgS0g48NvBj7+0xM//fINd9PIx995xuHXws0vkoG1TzxXwZSO7/7cwOVVY3lWiW+V4a3w9P9MzDfdr3Sy59Gnm/WHLwIPXwnjm0a8b6ZU2usWhKNzphwsbKiMtvG7/9K6CfzFNkZ+Nn9Tf7H3gxX2rZ5+l6eOj38WxrdC/Gj2Casv3QpPVpCznkfZCfNtIzwI4a5ZW708AoFPN+vLtWM5Kg9fCvvvLNTNTxZ+82k6/AqZpltPHoW3v9XQWRjf2fkfvjWAt0Iyf29t+Ov3rIpjdykWfrPC2dcn6hjMo/ZSaF6Q3LZsCVQ2UJcP/nEz330VUw8XS0fl7ieO6ZmpGcOd8OJ3FuYbx+FXpmTWVK2dW4X5tod5PVfywa4twPGbvI2zcJfNZ70rptd7k64cJfR2agwU+UvZgNzaQgsdbiZTgYeHvMHptQCwApIyqkUXOFiOznyLvQVsraCsNy+aQnCuzLfWJv/wucMtcPrc7qGllWNJ7wo1yg9AzVrkMC9SA7QrbN+C4c6ZsvfEtxcLU4qP5yOl4R8Wlqfj9nlSGm4ykFNG3e7T2kqui82b8HH5wVxu3pR+6eiIH/MGtKozuwYpHbZ/otZefx6v8WNAl1S2+aNToY7OzrHD9DXw7tMiQRnd5geqi42RdZyuMN68kA3ahofcg5iWrtSzf79CmObML9vNlXCXfwCNm7JZmmzXvNpx5IPf7ksNuh0rsB2nO5vnf76K+Hu7BytIWf0m/blugX4I2/mX0THdOnZvM/ONWQL4uW4hS24umwdyc5B2yv77tI3NFQBJaZ8A8orOZVNemtc40O0s7n96YPcmMd+GH6qf+3WqQX8wtiW3HxQDdKk/WENsjeyhXp/gIM3m6ermxzVrBb1l0O16b2Oyf+b6s96/dYzW0W3dAM1B/PC4LqzjeW2X93cG+1crAFN32zhsTrZ7un7feu5lMB9plx6DsVYI6s+2uKxFwjK6DUCXaHYvbqo9EM0Ac7jP2/GtRbm0V+K92RqUQfEXA7trEKd/WAwMv5lZbs1WYr1unwbfbevbbFAy3Oe+HhvkXS0QVrC+jqP1vIANjgPb8218vWzzz66JMLzPHRa6HjhqsFAK239f539z9n0rHNfSuyQe7LvKaMD20zHj5rqtveu6VIOQeiCoS49BYNsY6+NoXVPz3v58fmnX4PBd3goG62fmvdvmipTGfOsZ3ybyzpEOj8dqz+TH9Xe7p+5xrM63vq9PNs9tHNmcHN8k+676GBBo4Pox0A6gOitA2BhhO78y2nmXIIRz7WPGunjWgvU6dtc59vf/23/zH39o+9f+5X+91WpBXI1MqbbBxCmCwQgNim8JcdAo1CYsuVJKRVVwTTbIgzhadai0DikrNZtCt9ZqKjTvmOcZCR7tIT078ZSaabtIyxPzZOo7EWGqGRHFOVPj1ZRN7eg84gRz5zI4lFPF+d5mjYFCaXbczjm8DJRSSW3ZwJFZBnhKqziFlgDUErK10Wrm4CLneTILiBhJmI1D7aqmhgUveQ0owvXhirv5xHyZbeOvSm2NQoNSUSeU1tiPI9M0MQRPo6DOkWoj999zLnA6nWwz7Pv5V8Apy3JBvcOrbUBLH47ee6RviMFaqJ0z4Fmr3Y8QHRTZgGEjm9eeKlehK2LJLGmiVSG4SM2ZBY8nMs0PqI/4wVp2qxZTVNrFxKtjKjMk89WlGEgX76itMVTz9815oXk1Jee8MHpvfroUg2XqEexFNXTf3hBMydScZ6qZ/Rg5TaeudBOCt0214xG6rMBjqULOmXG/M2BRwatDVShiY8QppKkaaMQ29jU3aoJWFvOo7QrH1uxBNuCowvZ3FTagoaV1gGQgIOfMOI6kZH7Ic1q4urqitcYyzYTVtqJD1aVY0aE2a6dtVRh2dvwiUHPBR8d5uuDCwDTNHHd7M4WoBsWCH1CfmGfzeHF+tx2ro6HNjj3sB06nE8PQK/fNoFf0kZbNV1e9hbRJP0cv9lJinp6FJhkXB1KuqHqbd/2cRB6V4TlnxFmomV8X12khjgO59etYwDmD46XOiDdIKSJ27KK0aursYtnzSGiUlnClMXSwuIHIbOP9/v6eGAdbt8T8uls/F6dKytmOlUZwds9yq4QwILlu87D2YlXB1p1SrF3enmqKXxOFa+0BJguq0NJia2UzOwTRQK0FMI/MRCXT2C8eHwOnbAFM9GdJWgz0paUw7AziqmpfZ2ZagyJ0MGXr3BgieUnsgt2LFf4Nw8CSZrtP3iBTjJEpJwOMHWTSbI47Z8WnVs3SozbzFU55InqD8zUlYhw6yLNrIFSCBpaSqeKtGDU4UjVA6hQu02LWB6IMYv7HoTUQ+z3vduRqELY16WC5sSwT0XuiH2CRbZyVbJutYdix5AthHCg0lmVh5wZ7BqRMXsdUn8OhQ1lpBiKrwJJSB3OR6K2oFkLoxTVn8P+cu1+pggfFkVLB0whOySURYiTlGe+tQLCUitPQQ7AWXNCt8FNWdUoH3qqe1KyDaxjEPLRbY54nnG/UauMgiAVk5lq2Yp9ic6aUgvpgf07ZvBqdmIexOFotuEC3XHHkZEpbJ54sgV3wpPOEa42lmH1IixZ41kQYnK3XuZbH4s9i42+pzaxD5HHdkQbNmQ+4F4cGgxxL6Sp/56zVuRRcCJzmE16EuB+hNvberAqCOibJLK1QciOI4tXeO1JKxN1IWhb2+z3LnKnOLF4cbgshO5/PBGeQt1F68aIXKlrhgIdqhRykos6CKIIfrIgGuBi2EL0QDM74Pq5wSk1QxGyLRDz/1X/47/0ptP0T8PNX/oV/p0nf8Eh9VKMBFuzU4WcZH4GfJmv71bn8AMJ9Cm43RReP6iUpjxuX8HG294buuzo9CwYx+mYwfkwb0I3vJjuOwW8gsnlLYP4BHOvqnU/BzrrZl9J6iE8PdDk/bpzXz1sh03r+Fiplx31+YUouf66koyVst55rurX40oOhgvDwueP4TdnSoldIuapi1++bXkTih97uXNsGilbwUaMyfPtAHcMPzh9MMWmgLqBzRpdenN2FDdCu31U6aLXgH0s6BwNYy5Ng1/y9rRXnL0dK35yuSqO0t43xunm11mvZoOd6D9afMtq/AX4AKVfYudx4U19NbTvv+DEbkOqKp1Wdu57HunHP+25t0I95fj4wfnexdxkV8sFTvXnprmon14FHuC+bCnA9vi3YaS4bXFqVdtYyzCPwrK3//eP/pmH37xOoYIWGQtn7DRqsY8Vd7PtXBXX4MHH58mAq0A5wVjBkx5xJ1/4HY2258Qzv0zb+y+gYXp+p0ePfnVi+uN4Ul2uyfXMGz9d78im8Xcdn3pu1yKrkk9Jw50y+Ctufm1fzr+yquRUCreNAavv/qKvXQsv6O8AG5Q122PuYm80nd/us9fi6z/anqsb1e2xNqj+Akjqbv2q68tt1LqPr996z/9WZfBU3SL6tEaP+ACh9CqVW+LzC4v83cK1RHwHkJ/PAruNjYWsF4sB27Ot9BLZjdlNhehHJg7L/ftnWtHXergWb5SZsCvBVJS2ldQhs71K6ZPNMngrz07h9l+bGcu02xWwezdc1HZXhfd5g9zoHa3gEV6sCcV1j/SmTrsK2zuSDqUvXcbzC6/hg/z2sVjnnPiYHR3x9MkHcIbLcmPd27gBwXXtWCLgWN5rKpuxei47Wit+29Xa171nVs/4+sTyN2+etCu11zOW924p/K2gP92nrUkhHszDJe7cV19LRvmO9Tuu99ufSwaO1xa+QcnsWnUr/fUd4sKCtFbx/avuyqsHX4s2qLJfKVvRYCz8WdiUbeN/GbH9+r2Nwfd6vnRPL03Fbj930CZzuilc3NaZbR7hUA5OntWj7w7XJ5nfdvlMKzDeO+GDfZ8Fy0hXPbIWxMj4q3sND2c5nvSY1CuN383YPwAJH/aWRd+aXHx7M4zvvrei1gvt0Zc/tyzO/jcFPC4T54BheT1u3w7oeuOlxLbq8WkPeVmDbFfUdztraKlvxOO9tXcs7fVwremFBlwoC/93f+7f+SO/af6w9bf/9v/Of/3bOCaGhTkx51VWYPu5IuVHqhNeG00DN0IqpspxRS7JCpXvh5UKUShjMYzQt1iZ7vly6B16g5Ix3jkDfjDq13xZHxTawihK0wx5pxGEE8dCkg1rBOfPrW7pSyzkLwPHOU6oFmAi+AztT7JSSzVfWhR7A06tXNMR7LuczIgYHa1eN1ZppDopUUxRKpZVmKlsctTlaU4MKqqR04e7+wTbKzdNypTVn7fWtkAVbJNReUpw0WiuUZOm0yzwTOqDN2cLFhjhQoW8WDQD74IhqD1KP4NcNc7Zz2kZnVWiCc0ApiDpEzNbBfCETpVXGYSSIY7m0fg1MkYdzzNOEFAu1UmmmtqqVTKUq4O3+U0BVaKWSS0KHiPrQFZKeXBu1e8acl4nSCqXMHbIrrTnUBbp7Fq3fVxE1JWKrOLVgmSbQ+h+0mY8huP531UKTloXgAzllareU2A0HTueTtZ8EpVLMN6qZhysNggyUYr6+NGhVoXmDNZLIWm18COYJ5jtI7rC1FPPwdV430JlTJvgB7wK1WMhCDJHoAktK1FIZx8Egc6nUaseLCqUWXDD4pi5QciPlBa8OL4JWaNXj/GDQuo8LwSFiHq2tVVz/7ug9tWTLFagNRcALuVXUgSioE6Z5IYYBxcZvE4Ozw36069a6khsoteKcp1Bp6nDe0ah2r2qmlAzSx9am5iz23SbypNLMw9qZR62KoNLIJSGiBB1ZCvhgC3pr4IN9j3OKd47SEkP3cE6loB3wGqS2NWdViQNoV++qyKYgbA6WvBBCNHWyqI0fMb8fFzytFjQE8+asgjqHqDPA3Rq1VcQJi6u9ljHgXCa4ZnNLxWw0unK45IUYPXPKlFyIOkAx9a0MgVKzeVcDHmf+qShQqM3gGU2ssFYELwGHEJwAXQlLWUtZeOfxIZByojklYwFcqBU5zAva7rl0VWzrlhcoqA+mLBeHejuPUgs1QwwRr56oAS+eKuY1vZwXvAvmLb7Mm5q1tUabF477I9Up5MKowvXNkUMMPJzOCI7TfCLlmf1uYGlnpjQzjAPjcKClRhB7XpRW8NETfcDHYM+KJjTMLyyEEdQCvVqtOB9tUW4QhqErd7sHXzNlvg/2eRTIrW+CBPwQaaVCtrnvnTN1qxdbP6QxjAdTFddCovb/M9WlIFbEq+ZLjQilpr6eNbwPNm6l0dpMk0JrVtVfi4pD9HjxOPEoDhQazQqZFUzv2xmJ2BqNOlrJ7IbB1OACgqnrazObk7QsHK4OTJeZ0Q+IU9IysYuBJZ8R12ihYS4jjUajZCufaK64Bi0V9rs9KSW6rRVNe86xKLWZhUrzijorAvpsRTeKFZCLNGoTgghXe/POzq0RvPkaO1VytfW4Vqi1bV7XrQoiyi6EHoSpoN6OsUKjUkomiPmJt7ogzTo6aBVpDaHh1cGyUFHUm0e9DxZNa+8PSkN6d4Y9W0WE2qyzpuXGUgu1VDRGcl8j/uZf+9Mgsj8JP//Jv/v3fttdrBi4AlgLjDIVYjhl858bHSUKLq0bNLagITcVXFfNarZW0LxTRGzjNd96xjfLFsakS6XuvAVidT/D5pQyGHCosYPifVcYFSFfRWQVmqiByxqVcC74h2yFh2C/X3bOvP9m2xStG/cadfM4XDeCj16dgAjxzdm6LVLdErQ1N7TZRmzdAK4+iXT1p1QI94t5eZ4Kh1+Z2GCF2euG1p8TbupFJacI0v+NeQkjYmqwvd+UZcuzHenaQrncyUCjpkIdA/kYTBU4euroSU8GewTU9ti21j0zy2Dfw6q28+Yp7KZqAVg3gbIzcIAKrt8LBGv97ErDGuwdVRqPir8VvAlbuI4Beu3jym0gQJOd5/hmwS2PgXTI2p4um7ckzWDOBuU7HDMIZr6hiIDTXoBma6Ft3uBu3rvu2WiPx/lpYPftBc0VKY/HU+Ojb/IGdy4WUrduuPPBEz9mCx1KFXfp7+LBII8U83Z0s/l71g1ealfPKnVQNNt55qOjHIIVQVIj3YTtXNfgNoHemqzbtXJL22wFymBerlKFFpVyHAwmrApItXsolQ3s5IPb/I1X9ZfQg++CgYbmlfh+Il9Few5mG+fukkjXBv6kmXcodN/l2CF+DyKTamDQAJFd56Z0T0v36M/ZHu+76+Czed2Oz1/MC9TGZ6bsfL/PFgIoxc5t/czUgd+qCpaudJTSTJUXe8GnrSpD81qWSv837RGgFzvebUyKUAfpam7Z4Nvq8ZyPzgK5e7h5eOhK+77m5L32ay92rut86SF08S6jdzNR6AAAIABJREFUc7G53cyXtblHH3DohYaovauxz5c+5zfYPToDoHt7r1h/1vtRB7UwwZPdLz8VW98b5jXaVfrbmuFX8NSLQa5bt7RHRaomu0756E2denC2rpdusfFgXqYlmr2OgK2D/Zinz/c2nhost4GHzx2XZ8r+e+MHu+8uuHNifj4QHgrh7YlyPbA8HdC6gmK7lnWwoLk6KDU+2uZIg7I3L1LfoafNe5snZbTiXj6YnYxmG2dldJSDvVv53i0iFcrOb+tQjUoZ3RY66Sc7x3Ttt+JReLDnpalDDWxVL1uw1TqX7F7bZ346Llffdfs+G7Ppxq73pwXLdX1e58763F7v3bomzM/M4mu9Jprs3lsnTeHyIjB8zMw3lnsRzpXLU89w/+gJrr0YKLWHk/W1Z7UpuDzzxHM164Y+b9f/v86Fx+Kr2RvUYZUp2zxxvah0+sLWIz/1Al5X//q54ae6HX/t66L2z52e+M3bV8D2I31tcLOtR+u60VTIB/v+9R6VnSlwXepe8aUXZaTfD7XizPAu9WLDo7p5vU/hvmzrrr8UJFX+1r/2V/5I79p/rKHtf/qf/Z3fDt5ac0XFwmvUE+NATjNBGyEIuQlZmqXH9we2U6WK9hCrBlRqK9bCXTEwYfsenGS0CSnPxP2Oulibrqj2QBaYqSw0UKC3Jl9qZuytrDXN1pavtjGe0gXVxmE/2Ma7GRR1ThCU4tSCpngcPLXYZkxyMSDY2ziVZps2VRwDPgQ7lw7Ncm8bFVFarajzdsatbYrbXQws6cLYW9Kd88xlhujJWkitoF6J0dS4UiysqBXMH7IZdKEqXgZqLfj1O0U2aCS12Eayey0CBtEbUCuN1q+RgXMJgdIKqRRaq9SaiYOHbKFzaEV9s8CahgWlUCjSmKuFeKWSIQYWzL6gtGYqX+eoxVS90QU8ilpCkjGknCm93b60SlaQ6FlaRr2BPT+MJPMzILUO21VRbytfbRlxDaeYf2LNBu5bwjreC6JY261Kvzdi9zdELrmA+h4Elw1eU9FmwMSLoK3iRPr1KTQglYk4mFWINEHVChWi0AqUvsioFFK60HIlxkBwSiqWqi7SGMcd5/PZzqlWghqgC6o0GsGFrtKyjQnamFOjOUfxCQ2RVDMFa/WtTQy6eTtPUc9cK8ErnoY28+hFKkXMG7Vkg2O5TOz2I3lacCLb+CrVHjbWJq3kYmo174VGRp2j9bnponm/0jISAuq9wVagOAsTBFOvt1KJTTqwdeRSqf0hZi3+Fe8tSC9nCxHSCoMfKEIHhx2k+YHLMgOQitr9EqHV7tvplEolSAQqrRmMQTvoDJ5UCiEGA2vVrmNtUKuwLBPjYceUzCNT1ELyai1oK1ALXpXgHLl0yqymsi0tU6oBYkrGBVMQo0pZSr+uzdStq+dmNYQqThCpG1FT54gx9uKK4KK1vgcfUdco2X4HadYC3iwADBqqhVYa0SnUbG3YYmtRKZXj8cCUFpoIeCW1rk8WsXNrDfoccSI4IODQVu0cMPDonflEOx8QB3NeOmCX7thR8AJzWTjnidIhoIuBJJVLWvAx2lwtglfPILY58Kq2lteFmhbmywR9bT+MB1Qd0zzjnGd0EaX1wlhjCMGKFGrdFVNeqLUR1CPNEVyg9S4Q35ytvT6Ss5hyt1rhbFVSt9pw6vE+EH3kcrow7nbkXIghkksmqJDTAqIM3lFrsZfHlBGpBv5r5uF8wY9Db58XaqvQ17pWm41HadAKo7drUwt4Fyi1MEQLogx9XJiSPxJqYVRnhb8FK4xgBRt1jiEGW4uLFYlqs3ub+7OKUvFYld4gaMK7aMWQZtf2sLshlYuB93mBZpsb9VYkjNFTuo+rx16kvRPUrcU0e26EwfdiakNRHAE/elJZGGNEUVoy0B2C72GawtIKtELwzoqj0YoyORecOhowzTPizLc9+N7rSEOc4HyAaup1FW9OICgiC06VJhVchZYIta+t/WUXKiE4dj4wPZwZ93uWbF7X6jypTJS8IOpwwBg8UT01LziUVuxls2ijtAxqnQNBhcELf/2v/im0/ZPw8x/8R//Tb5e9KdtQIX6YacFZyMulkPeesnf4i/mHmjrKNiM1moolH8MG8VYPQWtTZYPAZXAW/nTO5GPYIBiCtfBXiHeWTm8rugGX8fXE8iTa5iuZd+4KZ+J764i6vBq2jdHqRWmQo2xgZkuF7gqcdcPawiMwyntHejLQoqOOfoN5yOPnrgFZG4ztG0LNjeXZYGFst4HmHc0L8a53EU3WPtu8kq+GHpok20a8DgZ3pFexzKvSQEFTHqFT3zfgept5BwSA3RfYjs/+WxdhzBX/YO/Skiq5q5RW4NqCeciitll2swHreJdxqdnvihAeCr5D3rUteQ0My8e1NfcRKqyKU1zfeJ8TODWVsFODjDtTqAnWwrqOxeZ129Sj3RZCVqWZbhYN1cumsq2D235vVXMN73MH8Y9BbIL0cSzUnmZeYx/DhQ7YTX31KbSqUUDtnN2ct2dOeFiQbK32pqQr/RrA8sQzfj+Bag9QMuhq8M5UWMPHTj7F3kPDx8WAZ4d6/mEx70b/iUJ2cB1IKPEuUfYd3AQLkEJkgxErBPJn85E0MKIb2HBz7cFWj5DUwKrf/l5L2wC1VDaA2RybHYKbixUlGlsYW+7KWcBC0Lp60J8tEK2OfrMwAJuL6dpvkF6zKR/LThnf9mCwU9rC9VZ4CrZGrOo27WNvnf91dPhTIh+Dgd4VNvWW6vBxYbkNDO/SBrvXkKW1qFBGOw7XQ4Vsbj2ee+tQqPnHdWcFsyuEWwtTWh4BNat1Sl1hfrcvka4u/L4rALvieAXxptqrG3xa16V89CuD3IoRmkwpOHzIW5Eq9Dmf9x1U7rTPGxtDNTrK3pkFVAfbKxD1Z1Mzo0L4MNPCDwsAZTQ7gd3rZVvLVng6vkuPPsalUXaedB1sPo6OGs1qRKsQH9oGA6dXI3hHfLfQopJud9tnSG0sVyYOWCHl8G7Zxg8i23xuascnlc3mR+pjkaY5e4ZZ8cfCudLe1MfLjbfAqoP7wfyyEEFnqn61QLwV9mlu7L+5UMfH4CpNVgRo/ZmyrsUgzE+chcDltgV2pWu/wdTmFD+ZkjPvlPlGkWrgd32uInYf07VnDZBMR4dmqIPdh9XapkQBJ+a5OtvzyMaCHdv5ZSBcbJyFh94p19dXad3uw+qJW/HOfIDFYGajXzNb+9Z1owxWrPWXajYjfRwLpvp2yeZDuLc1JR8cw30hj1YUdlOfSxXG93mbS2Wnj/NJVwBsdkWfPp9QtnV/fSfIe7fBZvTx76Zbx/ihMN944kPZ5nU4PT6zahTmJ97GRrcZWYvCbulgG1tPy96Rrj3/6t/4y//4Q9u/+3f/i98O6tBmai+vDq9Cmmei94iYPqu1ZmAUU+Dl1r35dGDw5s3KMiNeKa6hPb1ZtNBYDI5QGIYRh1JqwzfzQnV4mo9cSmEMO3alUioU56jN0rIzjVkS4hVPppUFdR6vat+VFqgdUvlgKhz1ZgWgDtc97kLweO+JIXCZLwzDaG3aGIAttZhHI2zqVSmg3nWFoAWZUcX8dTukUqdcppknt7csy0xc1XcaLFHeQRQY1NqAXIczNdPhg1X6SqnEMNKaWQRYuSTYhlQUJ8puHAgItMww7Li7uyPGHXNeiMPA6AIODCIFhw+OaT6xHyPBj9RqpYq5VZx3CAZ8U82oN5A3Xc7sx5E4BFP9jTuWVPAhkGlIcIQq7OKA5gIFmlQKZoGRc+mKw2wgC1NODSGQ88JQLfDKTloYokcVSpvAmT9pLoVpmnBxYMmZcYi0mtBa0BBBFKeCx3w5RcCJgzLhxZRsrcEQR0SztTnXikrp8FWhOPNSbWqKrlZQUaRkgjObj1UJmpdEzmZt4fBQHF6sDXc/jERVgiqGC/oLvzimnPExmLLWe1Qbl8sDQ1R8sLmTSKgXUl4QF0itkFtiGAekFrxTrg4jLTfGGEmzhbTZOQuuv4iUVthpo5WEx5kaLEaSVIRCq4n54cJuf+hekjNIYhiCKedb5XS5MI4HnPNdZeZZloR3gz2gVHCarcDRHEWy+RV3X+Fcq4Uz1UpLhZ0P5kg8eOoyEaQiJRGdsIvdFzhnqDCEyNIKU29FLz2QS0vicrpjdwzUZoYoDcFV23nVXrCgNTRYxdb3UD5xYgrJVTyE0LIVUJyzYLXdEPAm6qOkTGlQKrTg8eLwWgne1OU0NZW+1E2JV2oiON/hZ6CkgmJrUykLQ/S0VhiG0DsVTP1X8agGcm6EwZNrQml4YO89wUNKZ4Zoiqf9cMWSsilDe0uEKaVhcEJwhePgqeVCcEopmZSsYFNyIeWFcRgRKd0Ht3A1BKTMtDpRS2G/N1VkbfQ509BKH99CcI7lMnEYR5qzNvvcTLEogIg9Q3JbiFEJwfPZqxc9sK/SasF7A5F032QnjhgUtDIEZQyenRdaTbRmHrfq1ZSaCFFGe6Ykg6ojDm1mN+FUWbIpxH2zoqBJhBslLxzCiGsgHfLWnBmHkZLtuOhQt4kwL4XdfgetMV1OVvRzpm6vJbEfBuqSzJZhDYJ0jpIXroYRr2Yn0QQLG3MBECtstcYujOZl5ZXdwXNwjp/+6CuudgPLtBCDp9ZkAaHVnnniPFRQcRx3R57sAzdXe0rJqFqnQJNmonC1UDXvHOPgyXnhOAzcHHdc7QeoidIsKDHGyNxBMOrIS2EYBrNZkcKLp8/M3mKaLIG7ialGVbnMCecC0SvBQxwcz5/dELwVGi85oT5Q8mzHFnqHiFPS+SNRPU+OR653A7tRmc8XvHOmuhMrjg3DAGLKYioENWtkbVCL+Qw75yl5ocmMaiEyYO83dpw3xz2vXt5y2AWWyx1X+yvODw+ICipK9JHRO7OdoSBSeHJ75PnTJwzBs+SFqkJTJeVKCJ5BBS0VHz2H3cjnt1f86LOnfP7sqZ2jCh8e7olxZ5YP2fy1tVvd/I1/8U+h7Z+En7/9d/+X386HrgjFFJulb8hNadTBQN94mJLHIKY/lx4A5DYV57qZXDfSTR+T1FclWR06TBo7NOvwJb4+kW/H7mFpra+SC/6ccZdC+DDZxmj0m5JytVBYU6BXJaZtaPsmd++7yvaxZXI5WkDQugFfvTHdGurVgagpaWwT3zr0XZWrNToDnH3zNryduP/pnvhg7cWa2qawbE7JV7ErKbX7MmoHOGoqpg4W89F3hWLtG1iI72aDfkGZnwaDDwjpyrH/w3vy9UB8P5OPnvnGb6rMGgzMhQ8z88vdBoOlsQF688OUTRFadhaaNj+PpCvXgUHAP5iy09rahdI3sgbsZduY+ov5Q67nsSopa1TyVehKyGFTGEqz9mWzjHiEDm4qhLdnWvT4+5l0Ez8Bi2xwzNqXbaytSrU66DZm01X3CN31e+JkA/uCeYfW+Khwq6EX8A8GlZq3d21/seT2VZEuzcZi2XumFwP56MldRSjtUdsYPyTqzm9guzphfLuQrn2HFko8dTh76rZMl2TBVzcWGFZHz/nzAanYmD3XR0i2BbzZ+JxvfVc+GxReQ93WIkH8sLA8jeTRQp4Qg00ryBy/PZNuh0fPWxX8QzKAC9ucTVd23dZ2X1OGZwPzKqbSLJXlaUSLHbdL5qe8ArT5eaSFR9VlurK28+HdbHNgytTRxsbwLjE/tVAmN5e+HnVA25WmnypnTdXs+nVfIaZs424F9TUo8xNTqtcom/pbJ+scqqPra4HblJq1Bz+t8MfNZp1Qdh3q9nAks6do5Cu/jcVNrSryqLo7m5XGah9TB+Xy3PeUelNgA1tr93pv1jG2wrk8CueXpg7Oox2HP1uok86ZcK5mh6Km7qxBOX0WaL0z0i0WqhRPj2FJKxRPB0faW+t9OJkHqTZTErpL3oobnxZ+0lHJB8/bvxDZf19tjncV5Ao6NVkBIh1srV2u7Jky35oNid3TDoxnu3bl0C1KPlk/m7dzKMGCrNKVs8LGqpDGzu/8wm/WKcuVw2Vr3XeLFctWFeZq0zPf2tgZPpYtKG8FsdPTYGP66LZ1a73uD1/ErYiixRS5zdvasgL8+YlHK+SDMj31XF44vv1nhMsrxZ/E/FqzgePVbuBTq4rzS8fDV8r5C9BFKP05vkL5OpiSuozKfO3wU+P80vPwpeP8mXWJu6Xhp8ZyVAuY23eBw9QevW2d8P7nBojjg51n8zb3axDGd4kyKPlo/2Y5Oj7+ppL3JvrbvTfbBzf3ImEX0Zahe78elPsfOc6fC/OtEu/sv5kPsq2n6dptXRSIkEcLUKzeVK+rototbVO+LkdH3vXnt4OHLx0f/pxweS74k3D6zLF7WzfVbTo4lmsriLps33P/I8/HPyuko+DP9q4hpfv/7hzLlRVo86hcnjve/QXh7i8tfPwnG0kjeXRc/XK2gMX7ZHZM3ZdZKvwr/9IfDdr6P8o/+v/rR3xDfLMW9WaLVV6EpoFMD2ep3fC9VmsBbKYUUhw1VS7TBC2z8yNL34g7J0jJeCoSBuZU8d2fJS/JwGr3zKs54+bMcQzEWij5YirSqiSBEK1d3jcIuiqBHNoaiicvBYcpAVUEcfTyTsUhOLE/ex8MUDrH5XTm6uqKZc64YD6qqVXcECF7U7CmE845mlYkCLUUvNt1/1hTdk3TRAiR1mDc77l7d4enUF3G+z2pZjyQzgvj8cCSLek+10IGdjEgVFqdCc5Aokoj9aCoIlYRczF06wElpzODg5xmyqWxjx6PvVjMy5lZBBFrZ2cpuLRwjB7JC1Un9mNkTpPBSBHq6qep0j1eGy9vbjjdPzDsPNIql/uZwzh0mO9opbCIsJxPHIaIW85E8QRvKqbqlDlP5tvYDIjPacGLtd4iBS/CcdxRSoKuVHRxoBaYC6iDYb+zAByshXqMHnI2f2WBy+VM1UhVzJeWRm7KIIGjtwdkiJCrcLk8MIbAnMy/UZ09UEstNAFXKjs3sB9t43+ZFlpq3d/QPI6bKtJBtA/CUhNXuz1Pbo4s08T9xzsAhjAwp0yqlZIycbQ2ntoyuyHy+dULjvsdH+7ueXg442p34VVPnjP77qdbSkFqYYiR5XTiyc0Np9OFcRDS6stUE9F5lp5MX6KnXDIvnl7RVLi7u+vKbEfxjnY0X+dSCrlVhurwDnyoHK4O3D7Zcb4stCbMyUHzEADU1KM0Rhk5hsY4jrjoeLhMnC4XtBoIW5K1dWffSF6R1JBW+fzVM467HSkllpx49+69qR7V0URw2tg5tSJHjByPe54+fcJ8viCt8vr9G1IxsKkqOJRcW1fmOSiVWCq3T67IOfPw8AASmVKmqMHbPC+mdM8VZ0IabsbI7vaK7959zzgIRRzT6kNbGrV5tAnRNUQaqU4c44j3nsvlQkQtIC+bD2erFXGO6XRm3EWCNq4GR/COc2ksWWnNOgJyAlHzdm0qOBGuxpHjbuT5kyeUlvn6u19xqoWHh1O3/TCLEgQSlcOwMxA3esb9wMPpA5dzoXCBDLvDNafTCWuaL1Ab19c7rg5HPnt2S11mzpcLv/jDbyhpIS+LJdz7HrLkFB8dV4c9L188w3vHw+mO79985MM8MbpIzqZidFVNGT94DvuRr168QNTz489f8Lu/+CV39/e0HkDpuid3LYA2Bq88uTrw9NkNuzHy7t1rHj4u3L27AIpTrMXcL7jo0SyIVk5z5ng4kM6TFcuqcj5NpphUR+0VX3GQ2gM310f2w8g4jnz48IH37+9NgSkG05a8UIHd9Y7LdM/1bsfPf/5zvCqtVF6/+cg0TZynhYZZ44h3FDVrhHjY8fL5E26fXjP4wJu3HzidMm9ef6RUs6PQKuRWUKkcDwNPnlzx9ObI9X7PbrfjyxfP+Ue/9ysu1XNfH3DaSKWw341MOXFzfc2Tqx1fvbymlYWXnz3l2+8+8O33r23MLqY2juKQOZPnxPXxyJdfveD6Zo8TYX6Y+fr7t3zz+jWpe6MvuaBBub7acblc+OkXP+J4EMbooDle3h74/d//miIO763Y5WoixoZq5dnVyNXVFbc3Nwwx8v7jxK+/fcv7u4/EYbDANAQfByiNq93I86dPefHslhg9Io4/CN9wOSceTrOp3ZuBfQmmYB0dXB1Hrq/2HA/XvPvwgdN54fsP9/YOoDtyM/V9bhkUfvT5c25ujuxHD1J4ejvwv/+DX9BqZT/ecFlMlT0tMz46rq+PPHt2y/Fmj6uQFvPo/vrX3xL9gIaILgUJ0LRxPOw47Ea++vwFnoaoxw+Rdx8euMwTuYEnMIzmkZx758uf/vzJ+GlqSpQ1OAfY2rzd1H0iu6/o5v3Y4e3qATi+NYnN9CxsoSVrW33embVZvK8WhoKpY/LObX5yq3/n9OVVT9Vum4rUEtsjkit1FyiHgLvYZmmFM26qm/fsp5taO45HD8YymnqtBGF8Xzh9Hq1dd/U1PGeDPLD5MK6KmzXkpIymAFzDeNYWaimN5enI8VfztqEvoyLnuoUe5SdhC17yl2LJ4E/jYzjYXrs6ylRQq+cuQB3smWeKRNtMO7VN8PLiYND8KhLuMuGOrVV5vQbTZ/vtHk4vonnt9jZ4Tda2uv7kQXn3F/bs3xTmK0cTz/guM70a8Oe6+Zv6U+F4svbZGoTlqOTd2mIOw33ZPAU1VcKHmbIPSCqAqUJPr/z23W5pmwIt3Fv4U36yM9uNKSG1Md+4bdxKV1etFgerytBNhXTlOb0Kdl13gpth9y4z33ri3aNXIjyGRtUgBk6eKW6B8YOp76q3sbUG1Kz+kPOzQLgvPHxuIXrhDPtv7ZovV47IqiY1T9jVz3V64nj9W3umF3D4VePwnc0D+1wb3/OL/aMHsjOYGR8Kdz/27N9Ulid+81X0Z/OB9Re7Zmui+vufe6iOwzeVprF7tDakerP6WB6vVR6slXl67vB/5gnjO2t1Xn0aN2TQmfp84zi/VJZrU96Or2H3xorb8mTA35uSUqfS/WHNFuH1b41cXjT8WfAXuPnFo28ozebb/DRSw8ByUC4vhYcfV/xJQDw3vwvX50INpqhePTeBTWG7HB33P1F0hv33do2Gj6aGywf3qJrviscShIevlOWmcfsP2+arHD8A3YdWc6M4IR1tPQvnyumVpwwwvq+k42B+rT2g0ApYBuyXJ4G0NzVk3lng4ApF10C31RPWfHrVbAFeCJffWCApT/5Xz+5N5fBdogbdCmYA7pxJrwbuv3RcXjXSVWN87YgfH72cT59H9t+nH9zDhy8851fC/OcvtCLIu8hn/7PNw9XHs4xrMGUjj8LllfDws4QMBXnjuPlHjus/yCxPx+08yqDb/T4/V97/VgFJzH9p4fjf77n69WNw3Ooz3rOSma+Uh6+Ey48T8clM/eWB8Y2y/8a6plcfVQvS6nYvAvvvF06fRcLFbG1qUAvm2n2ixjYXMsKlcv+lY7mF5bpy+LXj+peFNfSteul2JZX5aWR8l7n/KvLrf65BBGrl8HuB+FEZ3/XnwIONcV3su9JB+fBPQP5qYX+cmX7/iuGt8uT/6hYUPSjR9yLR+YXy8CMoP574yat3/Jmrd/yjf+oFH//+Z5QPcPy2birlyzML9Lz/ynP6CnZ/8R2uCdPPHOX/uOLpP5BtzEptpKOtLX5u3H/peP8XC4dXJ4aQ+Piww/9vB178TiZcVr96U5rffxXZvSt8/Zcd+UViuJopwB/+fM+r/9GUyHnsz9YO3asXPv6GMr2s7H90x5PDmT/81TOW34nc/DKzHB27N2lTdQM8fO65+03Q37znaj/jtPL2+IL4Qdi9tvulqbJcdR9t35ivlfMr4fJZRV9M6C93DG+FZ/8w0dz6TtPXx9kG/Pd/SclfTNw8OQPw5id7PvtvIlIb5+ee3TuzPRruGnkwX/r7n1bqiwkNlXIxIefz3ymkg9Jc7J689lw9f+mYnsHL3/qWY1hwWvm94TkPvzwwfBhMwTw6lhtvBeIetvZH/fnjDW1DYFkSAYfZtAgEUx7muXDYRfb7I7dHCwV7f/eeDx8/QghmNzfuub+/cBwHaq5oLcTjjs9ubyAvtKVwXk7mgdKUyzyjIVDKAs7apg/DyDLN7FsjqMn3VZXLVIl+z1Qz2ppVVJuw70EfTw4HPBYIdbmc0OCZl0pOXSEblGUyj1t0YQye4+AYxx13kvvmGaau3PGtUdNMiIo2x+3tU57fHBmGyNt3rzmdJ07z0hVilua92w08nC/sjjsGdVy9esaXL5+S04U5F7757h0NJTvP/emOYRfJKSNhwJWF0TtePb/m2fUVl2Xi/fsTD6eJubTukxmY6kIIA0ri6IQf//QnDEPDiefjhzvevb/ndLogTSgtUrSCOnwz39Dnt9f8+PMX5JpoNXF3d8fX37zhvmIBbh32DWoKxC++esnN8cDLZ0fev3/HnBJff/eW0+lCajOujRb61MBF+76f/exn3Fx5XAOtlW++/p5TitydJs5LYRcHPEppDskTL1694KvPnxNDY4iBj2/eczqd+MPv3yAE1AcWtadxSYUB5S/+/GfQzlwPgdN04c3bj3yXKxmhNkctjaYQnZKTcnz6hD/7G1/R6gmAu/t7fv31W9J0AUzF57wntUwIkcMw8JMff8HgCzc3t9y9e8vdxzO/93//muSFpp5YTf05zw88/eIVv/HyC57dHIheiE75ePeeD+8f+INv3lJyokokeKtaKcJhN/Dnf/YbPL29ppWZeUqczxO//we/4uPdA4VowTsU0rywCzt+/JPP2e+U589e8vHDB2oTfvGLXzJPmTmbD/Qlz2iMqDRevXrG7dWRl9dXTOlMqc/58P6eX/36e5bqNl/JuSZTAHv47IvP+Oz2CVfjnvvzB87TwuvXr/n4MXF/vuDHwFIWkMbQGl+8es7nzw1INYWlwv3Dmd/93d+3trpmgLfURq2JMA78/Gd/hs9f3jI/fCSEwLIsfL0/8KuvvwYfKU24pMTgwKnw1ecv+fzVS5wTgjd3yUKYAAAgAElEQVQ7hRfPr/jDb77j27cfOS+FFg//D3tv1itZlp7nPWvaU8wRZz6ZJ7Mys+au6qye5SZbNEVakg3IN4QBA4b/gfUbeGH42teGANuyBAk2JAsaYIlWS6RIUWyyu9lzVXVl5XAyzxwn4sS4x7WWL1bkKd2ZV6IAcV9VJaIqI3bEXhH7Xe/3PCgbxjfaScyw12V71CfLMooqZz7LefXyFOmh9g5X+y+YbEKSmogH9w/Y2hqyWN6wfzjk8vKcV2c3CGUom9Cg9W7Dp7Qle7tDWlnCsNdFRTHLxZqTszOmyxXeySBqM0Eq1ev1SIxnNOxwZ3+bvFqjdcSTpy+Zz1b4OgTW66pERtAUOYf7uxzu7zHqdQPbV3q2dlosZxU/+Omn5BU4L0iSLDRHI8Gok/Hmo3soCZ1YUNYj8qLm558+4epmTlmbgB8payJt6Pa7vHnvkEE7JTKgaWMriHTKi+evqCJwIoiuhABjDFs7W4y6LbaHHaJI45oOW1tDPn3yjKvJgkhEFPkKk2QI6dkdDnnvnTcRriaSEqEkRr/Byek5L04vN+xSGdYQWyGl4OBgn/2dIdkGITPKWlR78JOf/Jy88eRFg5MBvSOLCiMF/U6L3mGbXruDRjOZzHjy4hhtdJjoEBpfW4wGtORwf4vtXo9eN0EZw3zeZTJe8ez5CU5HWG8DQgZw5Zp+FnP/6IDRsItWjkgqtrpd1nnFn/zgx2RZSo6nKAviOJzPe3f36XdiOq0WsZLsD7pMbxbU5YrpPEfIeBP0atb5mu4w4e7RPmkkiY0hNppOq0XZlHz8yRMSqUBFVGXJapVjjOLe4R6DTkorjohMQOh020MibXj67BWRTlkXJXES2jadVov79w7Y2RshRZD/2Zal22szPj1Ha8/aFnRaXRpnsfmK7U7G/nZGq50RK4k2CUUtmK8qjo9fkaYpTVEQq4Sm9th6yeE7D+h120RakSUJ2/0RmY74xS8XLG2FMprYhvZG3VTs3Dtkf3tEK9No6fFe8v6Hb/Ps81Mms2OcEJg4BC51kePrmm4n5Y1Hh8SRQgnNcLvHdLri4vonmCSlbMKmaC3qgFUwijfeuocSFmNBSMeg02d5k/Py+Ix6XeKERVmFR2Kt5+H9exhjacUGbxXJKKYdZTTrgnleUxU5KLvZzJEMuy2O7txBupxWkmC9ptXp00piLk4vycuGODLQVJsxNkkcRX9+P/7+4vgPe4jAnf33pUs2VhCHpmO+G5EPJcujcMPbPobucQN8Ie5KXs4oD7qhNaZCeHv93kb06CCebm6ES387evyaM2uqmnw38AnzkaZJBbION7TJjaXutDe2cblpkwvK7RCqzI90aII1EN+EvzvajFq+tmW/lqkAFL0QhNRdT3YWwkLhZOC1EsanReNDG9gI5vcUiwcW325InsUk1yGYUoGGhKzCTX16tqbcSqk6ktXbEbMPa0QlUAvJ8OchSNSlJjspqHvRrYDJxYqir5i+K2juF/hZRPbSkF56IiM27DxFdFPfyrXWO5rx1x20a6RxcJrQfhECHa9AVl+IeurEYBPB9JGm/OqKpgr4JXOuGf3E0Drzt2PpXonQZHWe8ZcF9X7F0cNjfnZyQL2M6P4sJh079DqEDBJ4LYJStefVr0v0wRpjLEZZVr8YEF9rWmeO9NpSjAz6dkxfMnk7Yva4wmRrOu2cyWkPM9Hs/CC8j14J9CKMwkspaLoxL37LAY7d3RlXkw7macLgY40uPbIWt2Oor8eW8x0Nv3qDc+H9v7xoMfix3ISdG9HMhjnrMsF6W3P1TYtoldzbv+b4Yog4jzn8vdAqDwGI3nCNG2YPYk5/RRLdX9DNCobpmifn27iTlJ0/YRPuO2wWbQRZsN7W3PzVNY/vvqJxkot1h7Nph+wPW3ROLJQOp/WGgRiC4fNvKOqB5eCNa6ZnA6aNZOvfaSITpFGi8cST6lZEdf2uIr/refDoFVfLFjeVpj5pMfqRICJ8foXzmHmDTRVeweR9QfTOnLe2LvnleIebaUr2JKJ1Kmif1V+0dQkB8vjLguS9KbutNdZJbvKEs4s2e78brvsoDp/7qHK3grOTv+J5953nXK3adJOCmzzhYmvE9g8dbDZOkuv6VlI3/siz994F27phlKyYlSkvHg5Y/2mHrZ8p4nFo474OvFe7msV9aB7lbPWXTBcZi/OMrT8VtxtMr8NB4V5zLiXjb9e8df+c5+Mhs/cczWcdtn8IXka3sik8txiI8fuaciSIjxbEpuF03Ka7CaWEDZsuVSeEi4ujhLInWNyHnccXLGZtXFyz/v0BrVNH7Dw21sTjIBWURc30axnzDyse3LskVg2Jqhm/0eb0usedv2uIJhWytqzvZMjKU+zG3DzUqO9MGJiGD0ZnnKx7nM27rP6fAcNPauLQ2wkBbydmeaCZ/lrB24cX7KYLeiZn1cT8y+w9Rn+ib78PVBHuX+tMMn8I1f2Crz94wU68pHSa7z044ua7A4afVjStzVqVhc/U9buae//Fc+5Ix26ywEjLH6RvcPGTPnt/1CArdyt+M0uL04LJB56t9674sDchVTX1XcVV3ub8nx4RTyXJjd3gLF4LtyTzu5r1gaY5KNGRxb/IuPO74bMtNzz21wJBgKvHEv9wxaO9K9qm5BeXe5zd6bD3h6FV+vq6rTebU/N7EeNvNty9P6YdleymC47fGHB206X9d7LbwC+6qam7mqotufwGmMMl72xfc5jNuPNwyu9evsmkOAwBseQ26E2uSpb7GcPHlxx1pxykM+4nY/5S73P+56/9OuJ3OhSbzarOwpJdhnM8+XrDwd1r3upfsR/PqL3i850tfpw95OAPoGpJ0mtL1QnB6mpfMftmwTcfPidV9e17frwzYPHkDk0iSK4qVgcxunSoyjN5W9N9f8zDwTUH6YyBXvP8aMT3zj5g748rioFGl46qE9iysnZMPlzzpb1LdpMF77dP4A78rcG3Wf3zLq3LIJMru2oToHtmb0H/vWseDsYMozW1U/CbZ3z3+1+i9/wL5Inw4bcKHpb7iuhbE/Zaa1qmQt+x/OzkAH4R8DTR3IbpnM13f51J3vjGS1qmZBSvMdLCAfzO1WO2f6Awa7eR830xMbL6lRVZWnF/MKVxkvvtCf9u6z6TckjrzGNWTUCkKqjakuU9z9FXTohVw/u9M1Y25v3eGd8fHrH85IBkam8lbRC+4/79f///O/6jDm1tVeOdwwkJQgVuqtRI5/BNQy/tcedwQCsKJu92JzSCjs9OA6vINrTSAOT3gNIx28MB24OUdjKgWKxZ5gmnFxPyqqGdpDSwkUiJIBiqa3ZGXfZ2tlDSss7nXI3HaOM3AWkQ8DgPtrJ4NFujPvcOd9GiRgg4PT1jfD1DiYSaGucEDk/czijLkljBncMdRr0wVtvvZVxcjqkXS7SQOAG1FUgZrOZZFHO4N2LUbSF8Q7+7z2y+5OnzE4yQVF5T1sEQHRmPLdfce/QW3VbEoN9G+Q6rskIIz+npOXGcUlYBbyB0MEknSQgJtwYZqRaUpUELSRIbrq8KijLIaGwdOLbdWHGwv8Wo3yONBLbxxDrgD549W2FkQl2DUibQ/GpLkka8ceeQzAgcGoshMQJXNdjzKdbVOKcJMiNPEsf0+106WYbWitFOn6qqaHC8Ol2jbGgVKRHkccU6596j+3QHHboJtHXMcrnk7p0Djs/Pubq+Jo46gTspA2mxkyY8vHdEGktaJvzAirdgkWkubm7wwlDmDUIahBUYrzjc22bQ7yBcRBIb2r0urXaXxp1ycTHDNhapA0oA62mnMQd7O0RGo3ULmhpNBjuOoqopLRS1I3KOODas5nMev/81ep0ELQPHdHdnBM5zdLTHL1+eEmcJvnE064p2P2Nr0KXf7dHJTGA/K4/WPdI44fj0FCFDM0vICOcdWafF2289pNdN8b4mihRaKzrdjKurC9bzwFmrfMA4eAFKa0aDAWkiMQoOd4YsF2uSR0d88uQpNpfYymMSjfWeYrVifzSgnUZkkSSJO3gaYmlYrXJenV7gpMF7gdaCIl/wxvtvs7uzTSwdlpJ+v0falESxYL14jpE1rnToSOGwtLKMfr9Dr98m1oaqccimod3tcHV1zfnFJU7GaGlIVExTVvTbCYc72xT5nG63g7XBzH73KGW+XDCersNY/UaQB46j+3ewTUW/06KxJdoKss4IpwTLvKC2K4QvkTqiKnMinbKzPSRNNFpAHBt2DnqgJT/68c9IWm08r1mX4GVNd9in041wPmfQaeOB6OAuL48v8d4gRUDDlGW4qWknhn6/z6DfJjU6WOBJ2Wq2mSzn5HZNZoYgA+qgXC2599Y9+r02OEc3SynLmrcfvcHPfv4py+WMKMmIlMb5hnaa0e/2ONjZRrrAp9Na0+v0SaMVSaxZzRZEWScw3rzHKMFoq00WCxIREWlQwtFt9RiPbphcz/B1+JEbKYPw0E9b7A23wNe02hFNWZGlKVtlzcuXoInDeigkHkfRFOzf2WHYbWOrHGWConk06HFnf4+L8zFoRRzHuI1GsN8fbmRvEiEEiTbc2RnSTmIuLi7DczIR5XqNlIper8/h4SFGhRaxUZoo1dRe0mq3mVxcUavwPRU7RZEXJJlm1G/R62Z0W6ERGekRk9kN17Ml3onAkFWKPF8ijeRwbx8tArZASxj2+pSrhuGgx826pqpCa1ZpRSxCu397a4jGkURB7jbaH/D85StamSavKtZNSZy10Ergm4atfo9hr43HEStJrGP6yiDkMVXTIJRBGB2QLNSgQKdREKCZwH31GgaDAe045Wq6gMhi0sCTLoo1d+8eUuc5QjhiHVE2lmGvx3VrTqSvKKsaE4fvO6M1VbXi4Zv3ubmehPa3kkTtiJWWaB3kgJGOcXVDYxusrTjY26U/HIVGuggs83YrI40NnaxLnq+JohiJp2kKcJ7d3V1cY0m0oswLeumAQbtLL22xnOdIJcBLPJYyX9PvtOl2Wghf02plOOdpHAxHffTzVwihAgPZKFITUTYNkdKkkSbSfsOxVexsDRBa4bwnSzI8YJsSKRV1WQEBVWJQRCrwc4etDmd6So0kyxKaPDweEa5VIWpiJWm1O6xWK7b6PSadLjc35xilEZHG1RUGSS9thfa9eM2XFhgt2RpsYauCBI10FU4pKmfDRnaR/4f8ufcXx5/jIesvbPfAbagRbPehmTN/XNIfrrBeMB21Wd3R7H3P4mVolFZ7nU0oEtpP0zcV5aOCna0500XGbJwifioRU1jvRujSIazatCKDpf363Yj5uw0Yh5pquk8ETSxvm2JeC2hCUCqcZ/qWYfX1NVHUIKWn+FmX7udhNFavub2xfz1uXXc01489rTdu0E4yPUxpfxqhc49NAhNVry1eC8yyId8yzD8seevoAiUdy4OYV+cD5L+NaVKNWTmiRWjzNJ0IVVoufkPh93LePbygbUo+n46YyBHDnwqqtiDqmE0zJ5y3sqe4/AYMHlxz2J1zOWxzEQ2oupruU0k827S3ygYlBfmdmOsPPaN7U+52p6zqmMtWm1ncIx0L6lSSrpvbBppwnqKnsd+ac7c/p7aKvDasOhET20HVAUMh600zqw6j9c1hye72nF5U8JWjl1zlbZ5V+5jVv2fxNgKnA1f4/Fst1N6KhztjHvdf8aObO8w+qrj64S7DT8MNvdMCsWFIlocJ+a8sOegteat/Re0UV9ma5+Mh1Set0PCcNbec2qalufzIcP/OCVJ4jtpT3h1e8PFwl1m5w+hjuxnPD59bgNWuYfGlkrtpQTsKKftVVDOmTzxTxHNLtGpux8qTi5zj/ybi7t6UdlRSWs13Hj3h3/CI828mHP2LEMi8bvKudyNmj0Ae5jzaHjOKV2xFS7pRwfPeEP5k6xbDENijjtXIcP0bBW/vXdE4yV66oBcVfHl0ync//4hkIjbiKnd7XVojcPdztrprenHBe+99wi+mu4y7bTr/byvI8kpBnYUwOZpWVO/X3B3NeLt3yVHbUDvFk/YW8+sddn7oboVcNlVEk4LTX+mg31owbK1Z1TGP904463Z5mQ1opllg726YpLJ2rLcVzWHJhztnHKUTzsoex2rAo+GYzz5/k9HP6g3HWlL1Aw5jfqT56P3POF32eLx9wqKJ6UU5Fx81LM/3GHxWb5r5CrdBXLz90TF5Y/jO9hOmTUbPFLzZveJ37DusrjL0Wt+2ns2yoW4Z3JsrtnorWlGFbUnUm2su2eLR3y+pBtGtiCksKrDaFwx35lRO8eXDExonOc0KxA9Gt8Hu6zF5gPV+TH6nYXjnhsPunFUdUdaaxQNN/2lgqBbbUfiNnIfNmctvKMxhKOk83BlztWrDfz6m/GcjWuehJd10DLJ2FHsZ60PPt999QrqBhPbNmu+MPuP7nfs86b9DcmFpOmFjVTioU8nyvuVL/RuOsikH8Q0tXfLN4XP+9tvfof9UbkJncdv4Xt0R/PqbvwTgceeYcd3hg+wVTx5sMfvTA5pEYNZh9F04H9AA7634y3desGoiujpn3qR85/Ap/+zxB2z9LGyEVD1zG47ndyxabjbnpOWN9Ir33zrhu8N3OPv44S1vNJqF63B5KDn44Ix+ktPRJdvRgsN4ymWry98f3aX3zN62y6ueJrppsL3QhlT7OW/uXNMyJc+yEbMXA/qf17ffa86I2w3D7IMVWVzRNiU9U/CXDp/z3eXbzN9IaL90mGXIBZwRlF1J1RM8eHhBL8o5ak3p6Zzf2voTfvuTv8F6q0Ny44jHAd/ymgncfXDDtw+e4RDciac8iC9xO4L/Wx5ilhvR/GYiRJaBZ92KKnqmYMcsmDUZtVJ85eAVP9x+j8Evv2iTC+eJbmp+48Nfcl1maOG4n4x5XmzxX23/lB/v3KFqJYH9moXpwTqVmJXnb371X/Gvxu/wIBuzE825ayb8ILrPv0zubiSiQaqmCo+sLNcfCn5175iWKmmrkqWN+Vr3Ob/ff5/lviGdWKp2WN9fy9l+88GnlE7zRhqe02/2fsbXD475o4MPaF1+wZ++XefvWB5vn2Ck5XH7mLWLmDYtBkdTmniIF5BMGppEbwRolqYFR905u+kCLRxGWkYPnvCZeA/ZBMSHcAHH4lRoxgOs6phRvGY3mpOpEn20wv6sfStL04W7ncx5Y+caJR13shset4/50fKI37j7Kf/g+Bu0X4UNAy8CA73OBO6g4IPBKQMdmryZqtgxcw4Ppvy9+oCyF9Y22Xgi63A6bCr/WY//qENbIwxWOqwPzMsGB9Yi0fT7fe7dP6DXkiQm8KWSuqHXjlkvb5jNZjgXUeRVGJMUEUnW5a1794iiBoGl2+2SLhY4pbmazphMVxsOogzMRyk5Ojpib6tFp5vgKfA2pteP+OTJC2zukXgikyGcASnZ39ni/v09WqnFaEFTFpiDHdLE8OzZGa4SRO02lYNiuSCOY959+IA7hwMiWSOUourFjLbafPLJE66ub1AqofEbzqlv2Nkacf/BXXxRkGhJ5Uva3RZ52fDi+BX5YolKW0GMVVi2RgMe3b2HNhajoKwWjOIOsZLYuuD87Ipet8P0ZkkjNTqK2du9w9HhAe0EhCuwrRaD4RYX42ueHf8YZRIaATQVcdxia2vE22+/jRBFCCedIPWSrK2YXE+5nsyJo16QPTUCow33D++xtTXAYJHK421JU0Wo2nF9NaZG4pQGHM7WDHpdHr35BlW+RnmL9QqpI+4/6DK5uWaxtghhUDpmvV7QSlK+9P67+DonUg6NItUeVVXc6x5xMr6kcCVNBXVlSVotHrz1Fv1RD2krlK9BGqK2odPvcHx2yfVkgSZGlw3eC6R03Lm7w9agw2rlybI2ZTFnNOpwdgqogrysiOM+yimcUKAE9472yddz0ijC6QhtDEmrT5FXPDs5QzhJ7UpcI9jZ2WF3axd8FR5va4S33DvaQZ8JTl4JmuUaGSf4NGPlYfvggDcP91ndXBIJR1M7YulpDVoc7u7x9MUZUidUVU1ZFuztbvPg4X2uL1/SbifQ2MA3kpJ7997g5mZJvihQUYTzlrKpefvhG+zs7IBboXxDpCJEyzMc9VktC56+uKSoc4RpUeUFvVabreEIrTyRBInDS402MUfCcXx+Gs4PMVpqVospo06fncE2tpwTG4GrwwbNqD/gaXLMajwny7bxVU3d1Bw8fIderwfeEUUJUKE2QrLhoMPZxTlONNRYJBFZErO7M6TXa6N1HbioWlKVDdkgw2iJ2jCDhZLMFyveevsR7U6fKs8DHzlJqaoKpQQ727v80R//KcbElHVNK02x0tDvDUiShNgIjNLEVuBqS4omVhHeB5yFEIKmbohSzWBniNQCaQDvsQ50FJAkAo/0jqbxREnYjIiSNvfvH7FeTIPFWEBLakRjiG2NVzFVU+KqHBMZVqsFOwf7tLOYYp2TtAwNS7pRl/3RLievxmjlyJuGtBNTFEFWVbuaxEgiocMoNhHSl2gv6W7WHSUVQgUm89buFomIyCJNoxqMNFgBUZIAkBrNqixBGjAhoANCcLgJD4oqJ0kiOp2MxXh5K/PzOKQtUdLiXUlsJMo3pJGmspAaSbeVkZceoQVWOuqmQRiNFAapJUZ4jHU4FL6xxCaiLGrK5TqE6d4TC4WynsRoEG4jfgyjp6NOj1evzhFG0GDxXpBmCetiyftf+pCb60u0VqRGE6WCTpYwna3I4oSyLPEC0jihcTXOObq9Ls7nSARJZNjZHnF+dkWRr4hNjEkVdV1ivCJKWswXOYNuB68UHsGiWCOjmGVREJmELEkpqpyygVQHXMKyWG/EaJ58uaDdHaDTGGU0xkQsljOUk8ha0KwdrpZYLFYJtA5cVy9cWBcvr+h2RqxWK5S2SCyz6ZhWqjFG09iSWBnWqxkSy2J5Tbc3YroKLPO8LGm1NKen5yQmbAppHTFbLlnlFXkTKCil81R1jTExjW+YrFcUZY1WnjgSaGPI8xW+qSldjowF62rFsDMgr9YIo5gvV2gpyaIOWmhWqxVlWQYGrPIsyzVKGmxT00oNV5dntDNDr9OiLgOWwgvB/OYGTxCjamGCyLJpQEuW5ZrSe5qqIdIG5S1lXtFxBq0M1/MlKk6IY43cNP7Pzs7odxM6vT6L1YpO1mK1WiKlw9aWKq9RDpQOmyHnVxfs7faxXm7WHsV0OqW2FYUt0VHMarkmzRKWdcmyrFg21YY1b8CVVDXMZyHQX1YNlbA0vkFphWteCwT/4vhP4WgyGcakN6IitRk19lowv5+w/HrOg71rjtpTAI7bA24OEqbTLdqvXBgnnPrbNmvZlYx+7YytdImWjq9sv+LHvQPOmh26TyT9pzVsuHRehXH/i69J2u9O+ObWJc4LGif5ycEh+nfSW1Nz3VG3N7njLyna3xjz7mDMVrTipk55kpRc9rbY+8MQ2NZthVly2346/i/h6x98xk68REvL+W6Xq3ttzr97h8FnIRx7zQwEmL6t+G8f/ztqr3gzveCkGnA17PDPbx6z88fhJvL1GLDKPdO3E771n31M3+RsRwteFQN+/fAzfpKtedbcZfhzz+KOofesRBYWlygm70c8/ugzvtw7wQjLshtT7yn+2dP3af9BuhGrbXBTXc3soeSv/+oP0NJiNun6qhvzqt/n2YsH9J9aqm6Qxr3GLVx9y/LX7z1hO1oQi4baK6ZNxu+ZR5RPB+j8i8BeNp75PcXf/Nq/4KQc3N6kp6rm6KtTvv/iA9pn3I6VJ1cVxW7Mt/7aT6mdYj+ZMTArHrSvuTRt1Fc9zZ/uhNH03KLWFXU/4ezbii/vnxEpS0tVoGA/mfHV4TH/5Ae/Qu+pDU3pja3bK2geL/mvD37Mjxd3ea99yot8i796+DF/d7AFQDzOqXtxwCtsJHe/9eUf8vlyi4ftMbmN6EYF+505H8/e4ODfhtBErxpE45h8qcM3Hn0KwNvtC9Y2wgjLf//B9/h70VfJf5Sh83BDL6wnu6wQDxv+p4/+ET9Y3ecwnjKuO9xNp3zYOeF/e/zrHPx+CLbNosHM1ow/HPI/fPSv+SdnH/K4/wqAllJkqmLraxfkr3ZJNuxHYT1mVnL8VyN+9cHnm8dWHMQ3GGnZ3l/wtyffRv+BJLoJ4ZdeVCyPUr5275cMoy+Cidpp+ibn33zNIP+4dYvNsIlEnU2o7kf8jTc+YdXE3EmmlE7TeMn20ZJPzDskFyXlVoysA0Jk8oHhzl5YDw7j6e1zS1XFj+49YOsnIaQWGyFevmWYvWP5zugzfhzdZTtasB0tOCt7vNO54B+ne7efdS+hdbzm+K91+LX+K87KHtMm4zCecsKAns75yw+e8PRvvRPYqouGfNsga8Xq0NPv5Oy0loziFXWmqJzist/dtMjBLOrNNVvTdCLyu4KuckRycz3VMa2oomQjMDIBv1B3DMnpgupRym985Sc8mW/ROEkvytEDx4n0lN0OwgVkRmRDOzC5yBm+WfH28JKTVY/77QnLKuYrWy/5x18asPMnFU2qiK5zqlFKdFNjY8WkzLjfnrAbzbkTTWjJklTV2FiwOmoF0qIReOmxkWD0xoSjbMqH7ZeM6w47ZsHSxrgsvK5ioMiumtt1X2z+0Uh7+2efFzsctmac7xzSPg/SudftdacFsWmoneIgndFWJe+kp7yqRqS9gsWdDumkuUWA6FWDl4ZE1UTSsh0t2NUzCh/ReEXVEcRzMIuGqq8DfqQNqa45ysJnqq1KjLDEogmTCD8MjXVVWIz1lENDOq757/7K9/g3l4/oxTmPWlfspQv+9e6Q7gtB/RoF40RgpReO2io+Gp1QOk0sGx5lFywexvz043eIZ2ENlVlo5MtGUmfw4nLIe4fnNE5RO8W/XbxFpBv0RQgty62Y6KbGzALDebyO+fnNHrvZAonns/UOX+s+x0YhQK4zSeu03OB7Ntxwq1g0MUsbMzArXuRbND6gBpI/CoiG9Cr8N14Lfnh5yKqqofIAACAASURBVMPBNdvRgnHd4X4y5qfrOyjlaJ9WLO5E9J4WFNsR6aRhva353598i532krWLGKol3529x8c3u6STMEkQzWrMImyU6dzSfqF5vhzSjQruplMGes3HqwOEE8SLsB6m1w03DwzpJFy/f3p9SNtUvN8+YeAV31894HTVQxUhPG6dV9g4bBiudwz6ScSnox2+uf2cs6pP6TS1V0zOe+xtPns2CRu4r4Ph9NJztWqzrGK20iWZrjlbd1nua2ws6H9e34buTgtUbfnsk0Pah3O+OjzmBzdHfLV/TDlJQ3hae6JlaAFbGTY8f/nLA95665R5nXBSDWipkt8/f4jKQ+O7SQTpVU05NLQuGq6vYn66dwDAfhaq7aeqz4+uDrFtyK480dLdctNl4283qP4sh/wzP/LP4fAbA7rQgsbXCAVOCgpX0x22GW31SJVFysD0jKKIdi8jzSLKpqYuC9qtFkIIKhxb231aSYrWOoxOekeUJkSJYTIbB36ukIjNjZD3gqyV8uCth1RNQZJkyMiwf3hAFkcU5ZpUKVxR4L0nL5b0einDfps4UsHAHGmiVuBf1k1J1IrJ8zzc4ApJR8doFEmUIkWEw6ONoZ220SaiXuVIkaB8RFPVGBGTRBFGRWgjcCo8Z4BWK8ULiLspMkmQQpPGbaqlRccRymikAR1lxEmCl55ur0cpa8qyxluH8Z75xSU7gxHdTpuqaJA6wklD6SqG20NMIijqJdYFvl7TuMBcVIHZa4xBSokWCuk1WgqiKKUucmy1QsqGxlmyLEEojRXBru2cw8QRFkHa6bJal+AtTZ1TrUq2B1vEXoN1FFUIecqypCpLRqMdHIbVqma9XBHLGKMMxaKklWW0VAy+wQtoEBgdk2/4hUoJ0jSjXOR0kwyqhkiLwDTGgxbkZYERElvVwT6vNQ5P1k7RAoq8QUqNt5udxjIwVSvboLMMqRWFAOsVnXaXyKRID1gQBDlcu92mqBzegZEG00oRiiCq8Q2ycSgfQq2mqqlySxxlgbusBEVRhp3tdc1qseZ8fEXjHI31YexXZ6wWoeEXJym1tbTiDq2sj6od8+spWhoUiljE+MaR5wWIhsV6RiMdBTWNlGgTU9Vr4kgircCYmLpyCKE4v55Q15brxZTCe9ZNgYo0wkQUjaNuBKCwtaPOHfW6Rtrw//He45oSVzpSlbJerri+uqIpK6oiGNgbt1kT7Ibv2tQ4bRBRwjxfYRsAhWvsBv0AZb6mtg1OWVAa6ySN91RVxbLMuby8xDlHK2nR1CGUW89XoV0rbGAYmwjpQeGZz+c4LDoKTTkpPM65cF2rIDeTKBbFGmsFk9mcxnnKOjCKdRRhvUMlBoeksYERLNkYMLWirCpM2iaOO1TOUzeOsnAsFoFnjQrA/kWZk3Q7WASX4+sgKqkbXN1gG7ierKlshCc0ZqVWeCExJmY2u6Gu1qSJpi4qjNSMb2aMb6a0uy2UFrQTg2xCCD69WTAvKpwVNNZRuoaLmwvKJsdJRWkdRWNZVWvKBm5Wa6pKsqpLnNRY66mcpamhrgP6pCwskUmJpMCuKnCCsqxZz3Ls5kayqgqKomK2ypFGYpWksAUOi5eeYr4OjGwVUTtLVTUs5gXWehpb4qXH2wbVeKSH6c01q3JJUxYICWVdkRdr8rwkLxoq51GdGCsdXjjGkyvm8wVVFTZAnK+pqoqiWFKUC6xb0/gKHQmE9OR1RYPm+fPjIO5znqrx3MxypEoo1xWNA6EMxhjWlUWahMW8YDxdsVo7pEq5mk4Y30wprSNLO+S2ZDabghQUtmY1X1FXjuW6pGwaSttQNDWrIidKOyyWNdXSYmRCv90Brzi/GKNVRlVDYyXImPH1lPVijnRhmsREEVZI2p0B1sH0Zs66KKkaKKrwHo+nC66mN6gkYrqc47XBeoXShunyhlWxIq9DoFiUDfPFirwsydI2RVXR2epRSImPFDfLFdfTZVifm4rJfMay9FzPwzXQNI5YCNLI0NLQihKKRc54csM6r1mXlvWqZLFYkS9XFEVBaR0+Tsmdx/uUupLMF+E15HWF1ZKVh8v5InwHuSAEq1DgJY3wzIuCZb5mVawpqhxv4dXJmGVes6qhqCvqqqJxDUiFN4ZVBYt8jXWAV9zM10wXS+JeRqUcVgaRmHOOqgEvYqbTGxazNevFishpVjc5i6rEGk0tg5FWK0HtJdZrJvM160ZS1A3TqmGWW6ZlwXSxxktwNGgTwn0hBMuqYrpaBUa+tQgiqsqxWi0oXUVDA4TJC9GA9c2mif4Xx38KxxfN2hDYehW46mZasN4XfPnoFQ8616SqZitasp0ueXM4pup5okVoPq32DF6BWTbMHsHb/Ut6UcGj1hWTKmOUrvGZpf+0vmXNeQl6FW4Um0HD//j+P+Iqb3MvmxApy19782OKkSCe1hRDfcuQjSc1xUHDrx18xt10yna04CidMEzXyO0iSE3ainhSE58vcZFkua8hctzLJqSqonKaYbTmg8EpLiaM/OvAV1R5aCfVXc9ONGeg16xdxKxJw/kalbcyqNeioXJkMCtP3+R0dU6mSvpmzf1kTOMl9W5NPHdECxe4nZkmfjGhfmvNXxo+5aLqkqmStYt4mQ/45p0XYcTzpkGVFpuajQALHAIjLDvRnExV9PWaRNXYOIzD6nVoaTodQjDTL5F4Zk3Ks3yL0ml2zIKy1pQDccu51LlFL2oWjxoeRJcAPM9HAJyuelzkHZZvhMmY5CInuQoirTqVHC8HfLn7kq+2ngPhOc7KlH6S34bbLpJUoxQzK1F7ocm/l8zp6pzShQ7Rk9U2TcJGNrQRadWO9ZYmjmuerHfDyPTm8adFH9uxmGU4R85IommFrBzrXcFhPCVS4QOupWUvmfNR/yVmsWEIt3SQYkmB05CqmlTVtFVBbiNumoyLqku/nd82R6ObZiNMc1SThP/l5Xe4aTLGdYeeDu/5TxaH4APXVq8DYqTca+MM/OH0Ib0op61K7idjaq/4dLkLQPvcovMwZq3z8Jr0WnCUhlHx7WjBZR0mBP/B54+RuaT/6RIzWROPS9wGE3C67DEuWwAsbcJl3WFctTAqjKDjQTShDem2+zCN+L1Xj5jVCRdVFyMt8ypBCxea2FVDdFOHsFcK0gtJXgfXzNpFGGlJVcXLfIDMv2Apy8qhCku0tMRXiv/r5VfIreGrrefcNBktVfHJYjcEWTpMN1YdhTcKF3m+d32f3BoGes1l1SWTFWsb8enNzq2V3StB67RElZbOC8grw+WqTe0UfZOzrGOkCePuqggyLq8FzqjArl1KenHBQWvGVd5mViZMVhnZSYGNv/geTF/OqYcZqvL83tNH4fPpJIU15I0hf94hWrnboPM1+7tpR4zPeiGE7UwYly36Sc6/evkm7aeKfD/DRYJiL8NpQd0xtF5KPr/cIreGaZNxXI34X199m5N1L0ymzRriaUN2WRPNGrovShbrmI9nu6xdxNLGnJZ9bpoMUYR2XzwP62Jghof352Td49PZDrMmA+Cs7HGy6tE63awfpQvXUx34xvNFWAN7Ouey7vCqGvH9myOaWqGLzWt3/lYalh1rPp+OmNcJEs+zcodfrA84mXdJpg69ClxeWYW1vfu559nliMuyzU2dUnrNSTXgWb6FWwQkjirsrXwyntTIwvJ//OibzIuYWZlyVvb43vkRTocA8vVGXJOFTSavBKvLFr/78hGf3uyQqop/+Oojvv/iCLMMbcvkuqb1fAkEFnJy7bG55un1iNO8y3E+5Ol6i4tJl6otaZ1VmGVgmy/uh3OkPm7RiUqu8vBaAP7h6UfE03B+0nFA3sjKke8nyBpevhrxfDbkrOzxLN/meD3g+y+O6D4JCIne0wJhfUA+xIrJqz6fT0c8WW3T02s+y3f54fgu9Swm3zIkM8vqICaeho3S7ouS2fM+jZfM6pR/Ov4yP5vs8/z5DnUmUYWj6hnKkWG9rVnvRMQzz8fP93k+G/LpYpdfLPf5yeQAMwvfzdGsCVz7aQj59dpydtFnkmf8cr3H0sb8fLHPL5/sI+twPp2Rt+x7XXqyMzi97vHz2T7P8xG1V/zDXzzGjDXZZU08CyK91xuYwnmyK8v4qnO7Dv3o/JBnJ1vUHYFZbUSF1iMbbvnR2UvF4qLNJ4tddpMFP1/sE18qZM1tg7vZiNFU7Wm90JzMwjXxr8/f4ntX97l4OaD1ciPatmATtWF2QzyWPDvZomUq1o3BSMtF0eF60sYsQefh73jdspaV+8Ik+Gc41G//9m//2R/9H/j4O//nP/htKYN93lmHkQrnHKlJ6bYysjgiMiAjg9KGpg5j9JPplMUsJ4pSirJBKUNRB9v01rCPNjXWOXAa50umsxnj8RhnBYIInMM2wXY9HPRQWqKUCw24KKVuLFdXE8rS4jb6u2q9RgvB9qBDbIJy0BFawrX13KxyTsY3NA1IEezWOtIkccKw36ObJSjviIxGOsP19YzT6xtWTtIIGUYtvSdf5exu79DthB1g4Sx13YBtGF+NmU6nyChhndeUTQg6+oMB3W4HiUOIBolnsVhivef07IL53BJHLawPjL1ERwz7HbJIkmqBkJamrlDSc3F6wvnJhMQkuIbQfnaCQa9LkkTEwqEoEKJBa8livuTycsa6yImjFKkURVlT+oa9w93wY8pbIiOxFlZFztV0wcuzVygTU1uHJ+yQpUlEnBgcNUo5qqbGe8tiueLF8Uvy0qK0QgiPdT4IcHY71FUdJGFaYGtYrwuWy5zZbImz0DQWqQXeNSjlSZMEYyRqIy6rqoqbxZLT6wmlhQaFdxaHorHh8VkkiBQ0ZYlVcD2b8fz4hLJwOGloKkccBaZuXVekiQDZoDV4KyjLhtPzc06urmmkxitDVTaUTYMU0O5kCOFJk4i6yBHeUxc1lxdXXMxWWKnBhJH5qi7o97sMRx3qKkdHkrr2rIqSVWl5fnxGXXmaxgcWrBSBN9pJEdKCsAjpqGzNZD7j6vqa8WQKkcE3wU4LnjhOiIUhTTJq58E7qrphMp3x6tU5jZekcRaYSM4hvaPXSlA4sCVOOjyC6fyG6XTFdD4HIfFKhxFdJWklKWkcoZRECIkvK6q8YD1f8+z5KVLHyCTGOY8nnN84ksSJoaoKnAjX+my55PjVCfNlQWMNiAjvFJYa4QXdVgetgom5qgLL9/jkgvOrMWXtsIATHmk0q/WK0bAHviEyiqJc44GyyplO15yfXyFkhPUhnBV4rK2x1pLGKd4riqrk6vqGk/NTytrRWEuqA64hiTRuWRCLcK6l0jS1R8qIV8evKBvH9XRCFKdhxBKBqixFVdBpZygh8cJQNTVlaXlxekItFGVdY0wwhTrvUbVEb9qyTZmzLkuquuH49JzJdEqz4VbX1oJSCCFp6oCs0doEydt6TVGWfP7qnKvpAisF3guUNljfIFEs5yu63Q5llVO7iqLMmcymPH12jLWG3DV4GQzltV3T1AXdXpuklVHWlsp6JrM5L8/HXE0XVE7ghEUojzDgAKk17f6QsioCR7komcxuODkbsy4AB4331BIsNTWW+XJJq5uxznPyqmKROz5/ecqsqLEC6ibIFZ2D0uVUdU6USLQA6S1lXnM5mfHydAw6o3QyIA8IEixlJMvlEqUMjfWUleXyasrT41ek7Q6VFUihqeqaZMNKH4+vkFphooRVUVNVls+evGK2LGkcVJUnMpqmKpC1wzehOdzpdFBCUpYli3nJLz95hrOKyCThvWgsdl0Tq5g6r4mjDNt46rLh+v9j7016rNmy87xntxFxmmy+zK+/bVWxSiWSIm2Bli3bAkEBhga2BRsWBAHWwPQ/MAyPORP8HwjQNqSJAAkEDBgQYNEGYTZmXy2r6t663ddnn3nOiRMRu/NgRcR3a1YjgwIrR7fJPCdi7x1rx37Wu951ecPN1Zabqx1kjVeeEBLO1+y7FmcUZQjkUihZ0e727NuBzz99xu3tFtesQGmssaQU0FoT4oDRFjDEDG3IfPHiJR9/9rk0b4yZuO9ZmZoy2pGcXV2K0jwldn3i4uycs9dn3G5uMb4ixEQsiQx0XY91HkIipUzfR+7uOs7Ob3hztcEYh3NirYHVWKPJZIahY7NrGUJm3wcuznY8e3XO7b4j6TJ3PS5Oix0TiuubW3TdsEuFy8uWV2+uudvuJdkSM8435JhBadCWROb29pZ229PtIm8urnj15g137Y6IQimp4snDgDOOFCPb7UAfIt57zi5veP7qjLObayIFdEGjyEXUxEXBvgu8vrphrwwpdNxd3PLqkzdse3mWwuizD2N3+b4ndD2UQhgSXR+4vNxwfnHFdj+Ix3JKlCR+wvJX8I//q3/4U3W0/dnPv9s///w3f/c3pAmTKBTjYizdP63ZPTbcnGgOm47GBJZm4DosGJLh7It7LN8UwsLMBzF/ExgOPekrA2vfs001oOiz5fzZPQ4/LnMnalXEO7A4zd0Hli9WRyzswPWw4GG95WJY8vrHp/iddGsGhd9EUm24/TlNXEFWmn323MWGq37J1ctDTv8sYvdjl21nSbV0g96/n/jw/iWHbs9Df0elE//2xTdQ31mwOE/YvhCXIhapLgauf96yfLznQXWHUYWz4QCAjz57wupz6bxeXwb8TY+/Gdi+V/P5kxplkeo/Hfnjmw/YR0/33WOWrzL9gZEmNEC8t+DqQ4s/jnx1cY7TiYthzcIEfufHX2f9PctwYNFx7Mxe4O4Dx9mh48Fyx8oMWJU5ci1/dv0e3UcH1Fdl7mRd3Ub8Tc/ZL9SoZSaheVht2KSGb98+5fUnpzz8I/GFdG2aYVN3XPO9e6e0ybOyA2/6NbEYPjk/Yf3HDfV1lgZmCmlmleHVNwSuvLu6JqO5iws+vTvh2cUx5nWNTgKFpy7qu3rJ1bFlUQdWtiehedUd8u2XT1h811NtCrYTUKRjxnZwedRQ7iVOqx23qeE2NPzhsw9Y/1FDtZGSf7tPhANPsRq7h989fUpUmrUfCNnyvD3m//zBNzn6lnRRn5SX9mYP3vPtR8cMxvDe8obzYY3TmYt+yY8+esrx98vclbxYjd30bN5vOHr3jvNuxUHVcTYc8J27p3xyc0L9hwvcvmD3GbdJKMR79dnDiqwVUVmsybzYH/Onz99l8/ERhx9L5agae9OoAv2h43uHxzxZ3/GmP6CgOOtWfPH5fU7+3OA6xXBSj53iZe5fPPUUD4OytKkiFcOfvHiP7fMD1l+A+DYW3N1AMZr9g5rdIWhX0LoQiuVsv+bZ7THLP6jAGorTo+qykLzl4qDCrCPPu2O2qeYuNnz3zWMWf1KzejFg2iD3kkYVv7WcP7LYKpOt4VV3yI/vTvn4z9/l3vcLfiMKNJ1kfJtLxauvWKLS3GtaPtudELA8a4/5+PkDDr8nthNm9CwtRmGCog0Nu0PY43i2PeaLTx6w/lZFdSdWLMNJhdlnwpETa5e15bVasHeGXV9hTWbzrRN8azj4yxvSWnyei7Nkr6muIzePGjbOiGq9XXC7bTj6I49ri5TsL+x8bdlrhsZzsXRcDQte3BxxvV+Q//SIw0+SNF000mBKIbHD9Io+VzyrFmgP37t+zPl2xfXvPeLeDwOmH1XMfvSvNopyueDskeXH3X3OhjXPd0d869k7nP6eG73EByZRrW0Tptd8/rjmaLXnWXuPl90h33r+Du2fn3DvhxG3TaPHuTSU1LHQ+4bb+4rn+2P22fPj7X2+/+wJzbcX1NfyPNm9wFg9ZFxv6N+seH3i+NHmPp9s7/ODi4eU3ztm/Vwaw9ldACN7T3UbifsFny5X6CrjTOb5/ojf/+QrHP5ZNe4BadwPkGaUlcFdeK59w2Woebk7pPvRIU9/N9KfOPxdHCFhIhyKPc3BjxUbW7NtNB/d3GfT1hz83wsOnidsV6RZ5MqJ17fTqKxQnWP/MGNc4fXugM/OTzj8nYWUxq8MudLSwG2QGGw7+OxgyS56LvsFP/r8MTcvDlk9V1AUYWlwu0RcWurLgbiw5GK5zTXnNHx6c8LZ7ZrFH4vXday1WH+OjR2L1ajecuNrrlTF63jEjzennP3ZI975nUKqlDRl6wu7RyI6So3h+IeZF/aIT8MhL6+O2P34iPVHhvXzQPZ6hKMZE6G6ieRak3Dcxpo3uxXP3pyy//iAe38p6y8utMRqFKnR6Ai0nu58yV/uT/loe8rzzx5w8qeG9Ys0J+SU9OlGB1lj1TPHM3fAZ9f3+P6zd1h9r2LxCkwSoJoaI/M+Qk4TCvUrR3uz5PnmGPejhsPvynfYYbS9NGOztEbmZvkmoVvLZ6sln378hKtv3ef4hwXbT3FdYaKMnc7ik++/X3F5d8hVWLB5veLoe4bFWR73NInXckNg9wr2judqxWW/4MXmkJdfnLL82LM8y8RaizVUkYRksSK++vV/8h//VO/aqpSfvmvZ/98///k//qelcpacAhSNdZpSCikqlnXDN77xPuuV+KzGOEAu7Pd7Pv/iNW/OLpHz8wJfrbm4ueHk6Ihvfu09DteGReMIQZpffec7H7HZ7skK+mhQKRGTeH4enRzyc1/9kLoCVRJNteD5y2e8fn3Gbj+gMAxDZsiZxjccr1e8885jFguNq6HozPM3l1xcXHH+ZovXhqLMCHMjy2rB00ePeOfxCSfrilIifcncXt/xlz/6Ids2Yeo1XdeCVgxdZLVY8Ct/+xc5qDUlDOAt1+fnPH/9mttdR7cPRBxtHDhYH7Pf7viVv/XzHK5qDpaK1HdEFC9ev+HVq0v2XaCLiqpyJBQ6G1bLmg/fe8TDk2OyiQzDwK695fLymudfXEpH+aIJOmFoqE3F++895tHpkrpRhJDYbrfsu55PP3tJzIrNroC1ZBWpvWPtNT/3lQ85OmjwVtOHxMXVOZ8+e8l2t6frDcYIbM29NHn7ygdPefT4Ht4mUeO1W7549op+KFzebjGuxmlNNpocE+8/fci9oyOO1guaZcVme8v1zR1vzi9p77YMoTBEUR+SLE3leffDxzx+eAQpsl4uePn6nOu7DT/65DNpRKY8xjtyEaCzbjxfe/8x63XF0tdcX19zs235/IuXxJwIxjAMEeMANCpX3H9wzL3jBU8e3afbb+j7ns+++JzdZksqoIwnZY1ynnbX8/TxAx7du8fx0lF0wZbM1ZtrLq/vuNsFQi6iFks9zgu4fufJU9595wGWJPBsO/DZFy+5vN2+9aU1iq6PrCrHyfERjx6ccny0ppQ97X5gs235+NPnhBBQrsZE8W9JZIyzfOMr73LvYIUymdx3hC7z7JU8f/gajSFEgfJx6MQT9ekDTu6tUVmhneXF81e8fHXGbt+irSjCwwClZCpvOTg44PHDE5wzVE3h9nrD+dkNl/uOdteJV2nRaKWwOWGt56sfPuXocEXJHdv9wPXthvOLa7qQSSSBQEMmhMDhas3hwZrH9+9xtGroY+Dm5oYXL17Rd4FsDClDzL34b/aZDz94j6P1goNljTealALXtzd88fkrhlBIWIY8iBo2RTKF2jiWzYL1conSkaubDd3QMvSKuq7Z92Geu5CTVA8YS13XLP2Cm5s7SuoZcpHSfGUJvXSR1YiFwLLyLJsFy1VD227JxXB3t0VhUVa8OkPM5FzE91LB8eEaVWQ9tH2H1rDb7bF4+R7txJN46Dleiwfz8mjBwXpNZR2f/vgTUfsrz37oKFqTcgQyq6aGkFgsHU8fPSFnmbPtds9219P2CeMduhggY51YcmiNeMhaqXrY7/e8efmGVAwUhzFjUkwlQoyslkvWiyUPH96nqQ373Y5XL89ou4EhK5KSsvKUEo2rybqgtcbozNI5bJFk0rbtMdWCVCJB9ZAiRkNtKqw2LKua5vAQD9RKlLCvzt+QK0tVLwjdHu0NVhsCA05Lk0NtkBgOGFvT94OU4eeAKoVCQqkyJiYjx8cntP3AMPpcpTQ2yYkDlfPEKNUK3nuU8zhnQEU0CqUMbdsCmiEkmqah6zqU0ZQUqesaU6DrOk5OjiklcXu7QeMopYDShBzFBsKJClwbSUA4ZSEkjJaGc0PK5KIJfU9VVfTDDqUU1iusMZAyx4dHEAptJx7P4qeuQGV0lhL8tu84ODig63fokhnQGKNQStHHQO1qQpYEjFIKYsJqg7aGOASWS1mXQ8qEBFkbYuhZ1g2qRHQfsXWFXVWUotjve/q+xyiDVQ5joetbfC3Pes5Zkr1DxHpRmju0+NIOY/flJF6TMWW0MxStCQWy1disqYzF50LMgagy6ILVhjgkdAFrLSFErLUkXVDO0t5tWC6XVM6zbXfjnE2nd0VOiaihqjxJIdePIu86+ZyiGPJAsZqh6/DWkmOQ5qLG0DQ1cejEYmL0Cc0xkWNiUBFrPaFk6rGi41//i//lZ3LbvwY/v/Zr/6yEtZ1L5FMtDYT0UGjvWy7+fs/jBzc8Xd1y24ti6NVmTfzj49lLFKQRx9Ff3nH7jQNe/YPAk8fXfHhwxVW/IGXNq//jPRavsyhfNmluEpad5u49y+2v7jlct2gF7x5c8xff+QqH3zfSYEuD32bcNtKdOO7eN2x/qeP4eMtB3cvnf+sR60/g3g964lIaB5leyrm7hxXnv2xxv3zN33v6CRnF8/aIH75+wMG/WbJ8I2rQ6npUnW4G9o8XvPmne77+UPxHAX744iGLv2hYvcii/tkn/NmO9v0DqouOH/16w9HjO37+/mva6LgdGj77zhOOv6fE9/A6MhzauTxy98Bw9R8N/MrPfUaXHJf7BW+uDtCfNTz4szx3qbdtIlWG/tDw5u/A/b9xwZPVLdf9ghcXR4Tbivt/ICBy+VL8W1XMDMee3SPL5a/2fPjkgvvNljftms8+ecD9P7AsziL+eiBXEot1yHT3PS//E83x3xD18kW74Pp6xfpPa/ymcPRRNx7WzahkKrz6Dz3d+wNPnl7x7vqGH1484Pb5IQc/MNRXGb8TcJnd2BDur8+h+gAAIABJREFU2HD+t+H0b14A8Isnr/i3P/gG7lnFe/+mmxXMk9WBDpntE8+bv5dYP9zywfE133/xCPW84eEfZQFFbcJuBnIlKtyiFVffrNl8AMufv+Zu05C3jvt/KGXiOpRR9S3+kv58x/l/cMz13yy4d3aUoqiqwPbzQ9afaZYvM25UMfurjrQUb9A3v1IR/r0tVRVwJnF1seb4//UcfzTMXtHZa9xNR/dowfXXHHffjJy+c0MucH29Qp953vm/RE1WtJo9iU0v8/7Ff2ao3tuidSFGzdA51n9ec/I96WmQrcK2SRqwdZm79z1Xv1hYfniLUQVrMrffOeHk24XFmbw3Zqfm5mrDoWX72HDzC5lSJ/xqIL5ciJ3JjwPVeUs8qFCxUKwiNoZUa17+pwr3VMYqXDQsnhmOf5SorsPcpNDsAmY30H5wwN27lpufzyyebmm3FfZFxelfiGJfR1EQmjaQlg67GXjxqwe0TzPm0Z7lohd1+LMV9/9E1HN6KLjNl0v+Zc239y37h4qi4eBTeVbdJjAcSROvtBCVvN2JOjKsDP2BZn+qWD8TaxG3ExWcjFOeG1/5q47uYSPfcaqor0SxvH4uKs6iBYjbXUSFNK+T2w89RYstTHOVyQaa8yDAefTlndbW3VdFJb0/1bSPC3FZePT7zP7e1ZV4tU5eqPsnEpf39wyXvyTXU11p6ovC8ixTnw9jGb4eY7wknbJRXPyyItUFs1dUN4qT74U5potNh6xDswvsnzRsHxluv1HIRwF94zj+nqK5yritgNrpuvrTei7/zl7RnhpSpfB3heUbKV2fbBQAstf0x45YKfanmuEQ4rIQDzLrjw33/6KTJpEHFttKLEmVQMa4MMTxnuamkl68clNl5mdcZXnmp9h1976lvpKyeIl/ZY6Dw6HFdOJfHZdizRMWenzeJL40l3LtbjMC4ps46ZoYjqSZXnWbuPmqE6XrsziPKQg8B7GamZJaqdLz9yQ/+p9uZVxtm2aP9sn/OdXyu3fvm3FtiLf34jzN/t5TCX59Edg+9dQ3YsHiNmIHgAJ3FxmOHLaVeZxsgiQhInt1+0BUrX47NmZMBdMGuoeNWB1ZRawUqRKVeX2T5jlItZrHY1J7T037/E0QAG/VT/jEzs91Hhvija1fbJukwWNtGA6tVJh0GTsqcWOtsJ0omuMIryd//KJlHPb3HcNas3wT57Uxf1+SJqxhLRU+2SlirdBJlLgmFNxW4oTdhNmPeord3bHB9oXQyHWB3I8OkhCIjVRgDEfy+b/7v/9PP9W79l9taPtP/rui1Hi4TomSEuQiHqDW8uD+IY9Ojzg6MpSU2G537Pc9X7w6527bolWNsp4uDKScWTYrHt075Z2nhxiTCPvI7c0NFze3dH1gyIWh32NNTRoilfM4V3F6esqjxycMaUfuE1988QX7XvwnQ8pgDe0QWNQ1FZb1+pB3Hp6wXDlubi55eXFJHxRdm+jCHuccJSUBRodHlFL48P0nPDhckXLgbt/x8vUrNm3LdjNgrSXGiPUGZxcMfebB4yOePHzAsvJcXr1m33e8fHXJMEQKAUVFGBJFa9arFXVd8/TBAeumwpvC9c2eTz//nFwUfRiVpqWQUAJFLDx5cEJdex48PGHYD5yfv+Lm9op9B10fSMrKtSVIMfLw5JD3nz6iWXgSidubHRcXl+zu9vRREVEj3Al4NAvvODg55MHpEVZrNre3bNuW128uWBwecnuzwfsGipYGa13P0UHD/dNT6tqilOJmt+HN2RmqaCmXT2KXsY8dZMWT4/ss1yuOj4+gJIa459Pnn9GFAW0M/T7ibEPsB4z3AmT1wPvvvsPBakFJmevNlrOLS/bDnhIFCioMXQpoa9jebfjGVz+g8pYSE5A5P78khEDWcvAvRuGMJmeBbTmLjcRi1aBNouRIt73FlELoB5r1mq4PhFCofEMYOlarFfXCYRtH2Hdsru5Q1rBpI8ZZhhhYL1fs9i3eaKrKsVzXPFgdMITMzX7Pdren70X1aYwhZ1FxGQzGKI4OV9Te0qwabm5uuL66lfvImaQ1Va7EP8kqMpmjxrE+XFFVntjt2bYdm+2ekEUZllJCW4NWBhsVkDm6d4yrPcYWtrd3DEOk7wJGj5tUSqSc0c5Se83d3Zajw3ssl0tyv6VtW7EjQQl8BpyrGLoeYwzWWoxVHBysSN3AdttSiiLljPGiQFVKEaOoCZMSH1RF4WC9hJSJQ89ut5NmScay7ztiLtTeQS4oMqfHR6yWRxwsK/b7DS/PXhD2ELNGG0ufOsnAaUWM0kwrx4ROinrRsOs7fCUN85w2DKFF2wUpZ+qlY7u9I0fF4cEpDkvft2gDoRMf3Vx6hgwqRFnLOVLXAvSstWg1jk8oaG2YYr2yhi4MLKpafDgpWCVAqhv2khwbMiTZOGPWYk1DweiCc4YhdAIxzYISAJtoQy+txY0WGwYta3yymvHGUnlN127wbknf7ylaUakFMSaUtgyqUEIkENG1gxxZLZZi94Amh4TVTpITdcXdTiBX2LcyvsMebQy7fkdjVlhr6fqAawx9GEYFi6haQVHoqH2FL4oudTjj2PWZgEKXHmMUlBqtCmWIaF/Rh47DZs2wExCqak8/7jFKAzmBksZZJRSUFRWjcZY8wlatNV4ZQghitVJkjJqqJoSAlgyPzI/RkKVcvZREVqC1pQ8ddvRDttZRSsTYMltKUDT9sKeqa/ZDL+pTW1FKQRdFypGcB7ytBApracymSySEgHcLrIZY8ghzBZJqjTT4I4uljZXqhWWzIsZIpS1RpXHdx1GtnKmVJ2UIOaMwZJ1JscdVHlXAIuoipS1KabJOdF1HLgVtIKYyrmvHPg5YX1NSwChFCj3eSFm/No3sMxlKTIhLtiaqRGQE98uaUiT5kaJ4zw77Fu/FL21IoK0n5szS1+S2l8Qe0JUerRJGaWmMFjPOeIoCpRQZTZcjlfVSJdQHtHP0KpNjgpxYLZZ0XSfvAqXImkDhnCP0A84bbu6uWfsl2jg6VYh2PAmkiM8W70URba0lkQhpPPhkSYT025a6bsRiaoSwShXxbfaeoc9yT9rijEKlRBrlXdZabE7889/6zZ9B278GP7/2a/+sCJx422QKmMvBr75p2X6z590nV6SieH1xSLn1nP6xZvUyyIFQiXes7iPdw4aLX3QMv7TDukTfOfTLmvUnUN2WGb7GRg5uw6ElNIrbr2rSN3eE1kFU3PtTK6pOreYmItWFeGv2B5r2kWb7Sx0n97ZcvDjk8LsOvylUd5nqOswd322b2LxboXLh7O8W3vnaGfvguHhzwMF3PYvXmeVr8ZW0e/FSjQsp37z6puPuFwbWJzt2nx7ibzWn306zCm0CP+RC+7SmP9Bc/lLBPhDIdPv8kAe/r2ZQLSXT5SegyNkvO8K6UH39jn3r8R83LJ+XGfLqQVRGZi+l3Zd/a8Xlr0T8UU8MZh7bxUWeO2ZPMCeuPftTx+6J5u7rEXzGnTnqC8Xpdwb2J5b1Fx1hPe45C1Gk3b3nuP0axEOB681zy70fjPZOXZmbfvkrgYbn//6S7gS6dwV6q9bw6PelFLYYAQJxaTGdlENPIOf13zGER2KLVD/zHP0wU18LSEyVlu+4kXfo6uUdz/6L+4RVQWUJTYcfZdxersffhNnyYAJsOmTiwtCeWmmsl6G6yyQna2p/z1DfisXHcCBlru0DS3+oyBWYPSxfC/CZ/CftZqB9Z0Hzpies5eC/P9Xcfl3A1+oZLM5lLiZwOV1PdnKov3vXENYwHGWWLzQHn6cZDOohEw7snHQQuFSxf6gIq4LuFc15YfUqzeBE94li9QxUAe7es6RGkZ34P0rjvLdNdnSQEuLsNP2hYfm8Y/NBw/5UQHl9Jara+irO8DkuLW4jirzYGLJXbB9LefHyTZrXdnYKu5O1o7tEWth5LIpRbJ/IejO9QOS5udLVgNkHwrH0PChacf11z/6+onsccTeGk28XURCOEMfdvYW2Zh/oHjQzLOwPDYuzQFgb/E2cPXBne4sjR/26RYVE+/4BYaHn51QAmRnBYmKKke6mZ7hXf6lMXyCPyvKdU6OzosFfD/Sn1WxH8OU1nWozgyKVBA4WLdY0uTLztabG0N638+dOoFDmUNZVXGhRc+dCWFn6A/HcHFYGv0li57LW2GnMxt81uyCqeaTBmspvG1OmWs9xtHm2oX+0wu4i3X0/z3H9pme4J823quu34ys3xQyJVSr0xwLX6ttEaDSLs4BpI8WKdYrEQz2Po78NbN6r8dssDQbHhmVfBorAnKCTZn/lLWju0uyFbts822moLEkjt88zzJzK1qc1p7KA72l9pcbMY0IRIKyHt/zM3waGQ0d10ZErsVyZ7mN6huPSzHEffjLREhst1zC9vhtpgua2aW5cNdk77B9WohhfG+y4rqZnQSXoD414v44l+1MyJC7MCHrlbCcl+owK5zSvJbNPM0CtLjrSws2JJz1IY09JOmpMP3krCXSf/NRNJ3GsP3HzOOqhEJYat8uEpTRHc+PcTkD4yx6vsncIANZRoG1c6Bm8TzE/HFhJ7o1zP9k1gDQ9rW7T/HfTOIWFFhulhWb96Y72HbEmqa4jpntLi8PaEVZiOZRqUbVPoF3FMRl3K/FgWjs65LEpqyTDpjmc9tei3iZfYqWJteJPfut/+Hcf2v7D//bXiyjDIm6SQ2tN7BPWNFRNDT5zuK5xVrO/veX2bkvGUkZgEcfSy54M2mAzVDqzrCuGfk8XC0VZ+hiwWvxNFZaSIJfxMBd2LBYrFIbN9hbr3Ai8MjFL45mQRV3ltMNogUCr2tLtd+SiCCbT5YjJdgRGEeeMZHKbmlo57u5usY2j71rquiakAVMt6LoOrSzkTIwDdWVwekFMAyonXF2NsFaTckE7T+r38vmJ8buc+M0q0KqwawfpQm4Muog3n9Ya4w0hFLSSzF9TOVwRv06tNX0UhRJojPXopAhZgv+yEmWe0oUQAkNOYCsi0vDMaitSeFOII8QoRVRbzmpyknJTpUVdXZAytRQGtFI4LZDjaLmm61sykDSjd2DBYmG6VqvEB7JekLoBYzW186Q4MISA8Y5dEDWCMQ7rkPvKisY1xDQI3KAQUpbDvJLsfUqJEgLaO0JJVMaBypSYcMYS0oD3nr7vUdYJeFGGUhJaW0D8cs3oRZxLADIpBXKIolCzjqKyrMMMTkvjIwxo5UlDICqIGjQGUzK1cbShRRk9l0ovFzWNr9hu71BWAJq2hhQLCk8pBaOhMKBzRVU5Sk6gAymJ4hNlUEaTikL3BcjoXKSTvDUoJWOiDDCuwZQH0JJsMEYaSRhlRb3WdSxWDSkJPE1JGtFtNhsBdEURkpQKd7HnYL0m94EUIxZJFNzdbVgdr2jbDuMsMSS0ksSDNTLPWmusTmNjIyvPSFZkpUaVW8A4eR6VUpSU8MaKP22IFAvbbUu9WJEp5BE+GYWodVHUfiFjClgtDe1KKRgjZdIZOeRkBbqAN5YwjklI0mleKSlH3g+SMIp59Lc1GW8ahj7RLCqGQfwprbV0Q09KCes8Hk2XBupxHSqlyFqhlaIfFZA5ZyiabujxdSXwKiRUStTGMSRR9mYKSsn8KgyBMjfdUkrKrLWB2nkBjtpRYiarREGaXGlrMUZTVJbvd358rh1aFby1hH07K71VsRhj2Hed+Ix7WZfKaEwlKtFKW5QKlGQhGZLqGGLA156cM946wtDNjawSiZQUSmu8chAD2llRvheJZblIXEwhQkgcLA/Yblu0NqA1ylhiCWASJovXucqFQRUqY0lDwHhZq8oalPa0bct6vabfd4DYK5Qkc5BCRGtPTmX0yi4oVcb4LHOUExIDKo/SRXyNvUIrOTyZrOlDoaoaUs5z8qXrelarJcYWUZCOz5kq4OuKruuwvqbrJPkTkfIca2SfzFlsFnKG7DJOgRrE+zsW+f/GafFhNo5t27JeNgKYlcDPHAvGVjhjBfYPgSFFcNKoRyfezoW2hBzQBBKFykoyBG0w3kHRtEMrCYCcAfHLLqVgi6E2AnpDCuIhHDvZ79Ao69i3PZX1aKUwSqNyppgygktZF6Uoco7ze4VC1nnO8txqbQkpElKmUgZbeUKOFCWNMb02GO3ImTFRoqEUhthjXQWAUgad1fi9oK2hT5GQIs58qQ9szlRO9s84BNCiDFdFvOEHMqhCKBmLoqSC9x6tNV3fj3u5niF5KYUUglhUGMswyJ4kVQEJoxUhSBwqEbK1BKVpimLodlS1BWX5l//rz6DtX4efX/0H/3MRxcxY1stb5U9sDMNaDvXbdzSxhuoG1i9EqUiRQ9900HM38l6VFo6wtrT3DW4nh0KVC+6mJy2cwBs9AcxMWFncNrI/dTMgS435iQO4XONArh1xaWcAtnugqW5F8enajL8J8+eLSsrOB6j+QEBzqvTcAMe2eVb7TJDBtIHhXk0cff5Q40Fvl2cFZHZ6PgyrLNc4HYzDQsDk4izMB3D5jjgrSG37FuJ2J47Q6PnQWm1EoSRzIWBoUmG2D5wo87QoiCZvQPHrlAZnUwOlCZKqJBA0LvR8AJ4O7HMTsiEL9FuICuju/YrqTpSlE+ADZig4Xau/DQJyhiLenIcGMwicT5WieTOqQb0m1XqGbP2Jw3RlhhsTwJjAhenz6I+psbvIcCigT2U5/No2E1Yaf5fmuYbRT3Q8JKfKzJByus8JoqTajJ9X3kLJcf5AgLoexHJBd2mGS/2xpb4IYzM2g7uVZmzdkWFxJgo4u5O5nQDTBD2m+xkOzAzhvgwCJp/D6fonIKeHMkOpqRmf6QUKA+gukP3ozes1qTL4q47+fv0TiuLsNPWzW/onBzO0i2uPu+lo313Oc51GmLB83rF/XM9KTdPJZ6hUpFx5vL9Ua1QSBecEBFWU37G7OD+P01iE9dsu6ipB86pluFeLd2osM7jUfSJXhuHIzkrtVI9WAuOanuZTJfm7YjVhPaoxvaydsJZnJntFdRl+IglSnCGsHW4T6O953FaUd6nWcyIqNRLP/PXAcOxl4hQ/AfbCgZ0VlP6qI679Ww9drRju1bhNmJMK0zNaNPO1TiBtig39sZ2B0ZzsKczgOVVmjqvhqJo9yb8MK6dxmpoHVlcB08q8TwAsVeIFHQ7fqoGnNWY3g/xuLPO4TgpBHcrsyxlWdn5uTJffxrfxuZrA9eYdL+rGUQU6eZxPfyvrRMDyVJUg8VIgJAqq8472ncUcIyfYOxxIQ69JxTs981NVx5erA3TIEsvU26TSBIdFEZzmz5uB4E2gfeRJXjyCUyWgrxglPurXQZrKXQ/sH9fz3hGbqWJA1LLTGp8U37LPyDVNazpVmubljv3T5awsnqDgFMtl38kzKJ/iS1yaeTwnv/YpQTr9pDEuNhfhJ+LjpBROtVR2FA1uL+u1ugrzficerh3hqJqTcG/39DLPo0pvP3v6nRkmTwmeNs8xPtUy119+N/gyuE21XLfbpFllLM+SmpOJ2cr8TUr6L/8MB2ZOPk5jVrQoad1u9Nlt05xUCStDNiNEHr/H9HlO0EzVEHJNYQa9phebBdtmslfzONg20d9zuK2sL4Df/5f/40/1rv1X2tP2X/2r3/4NSqHyAmqM1pALJheMq+hCT18CbRu4vLoiRmn0g5bDWxeH0aeyoLUil0xKkcov2O8FgKSiKIBxXnzjlCKmMkIqgTO+8qRYSDGhjCGlQkG62GslwMibBqstJUdSimgD7V4sF0AUfiVnShG1lFKiCkoK+tAz5ETdLNiHgLYOpQxKG/p9h9GWkgraOzQCzUw26PGA2u57UXMxvesoKmMoIWOVKC77sEcpzRAClVuKMksJFEBB5RvQhpwE6irUeBCP9CGQgbbr6UJEOU8ZbUWMMmABW+i6HrSly5EEaGMpRYl/r9UYgxw8hwFfO0AaQXlr2W1bjPFYLCVmVMhoJRmtmKIcsgFljBxMrSMpNXqNGlBjsNCI36wCpTQpyj8bbchAH2V9xCiKQ20sSSeGHOTzs3j0KmPRlSclWCxXDCHSdi3aaPEkhllZpZWsFdBkwFWeXbvHeUfOCaU0IQxoPSn8CopEiAN5EIVnSQmljYBhbYhFEROijEOhrcE4K2OsNDGJ91HJhRwjlXM0laPtdnPARSt0sez6ID6SQcDH9CylsYGZNmCtQWlNiIGiRb0qa9eMIM8Qh4h1Hm3kmQlRvD9TzqQRsqVU8M4RYsQaBRopY86IX20RUJdTIqeCUtDFgSRZEoy1+KoixkgphYIA1FxEEZpHkC8YB3IuMr45YbQkISigjMJZS1KJmANo0EXjrJeMIIXaeWkcFyPOGPF5rRy7do/1lpQiznlSLhit2XX7UW2nKAUqa0kh4aylqEJGwajgVFqNYLBQyBjjiSECCmccORWMEuCds3hfW1ePXtuZw8MVMQRyjlglgNd6OcwKFLK4uharlRDwdU0pGWVEUWyteHiq8QUnlzxCvoxWAnQVAqswmmZREeKA1RZtpg153FiVwK0UBypnIWaIWdS9Qdaj0oaQE9pachHYH3PE2oqiDFpprHU460lJYooaAV3IkT4OKJ2pKocyGm0UXllCDNSVJ6VAKRBzJJWCsWpUqGaccXLvSuOMZUgBZQzKGIaSsNqSYqHvA85XpCTNHinyJCqlUcbQlUyymj4FtAGn5PktJLIyhBhpXEUucu8J8ZtOWRpz7DupipDoIJ9bFLImpjgx+oMZVxFzFHXxMMiLmNbkgkB/Z8k54SrLuGAlruWCto6cFcoa4qiQNUqjR2VrArqhxzqH044wBLyr5JkrolYtJTEMPaYIXDVK4itFYbzBAOSCNpqiCsZoQuxxXvyjm7pmGHpSiFjrCCHirAVVRmuaSC6FxaIhp0RlrIBtowT8WkdBXmaVNrLmUMSSJcmlIcU0WlhonDGkIaILNM4xDIEYgyQaSkIVeZ4odv5nrbQkY3IhloRG48fnXGMx2mCdp+u68To0So8WBCFhjcVquS6jDSklge1KiSpYO4YQiEWjnJHnzhliKVSmomRIKKySmB9zoJQsCdQMMSXZR5XCO1FV5ZxRuoyJqwRjYk/JQoMx/milSTmTS8L7SryEp9gaEjlmrJFkSEkF6x2kLGvKOfp+EJsMpYlRKnIwipLlvlVRKDT/6L/+L3/mafvX4Oc3//Uf/AZKEQ6tgFIvh5HstECOm4jfJJrLzNFHPa6TAxNKfs9fdeRGDp8YURWaPhGOHPW1+C3OHoyVeGPqkLH7UYk3iJdlXNnZE5AReKk8HqpGAJmWTg5TqWAGATTNlTQNEc9QLes3i6KqGIF5esj42wG/E3hbXQVZ82NDEH8bZriJURRnUKOiaoIgzXn4iXtRQFiLGCFXimFtqO4iRWv8JjIcGFItPnzuLoq/9FreV3UUlS4jyJIGLREdobkM+KtekocZ2cO97CMo+T2FlJlOfph6VP2mRnw3FdIULqxFKGC3gbiyNG96sjfkSg6RRUvDIMWkTJu+U2F7gZ967NitYvnStY/wd4RPphdP2ew0JiIN1xgVbNM9Dhl3N6CS/De5n9F3sM90pw6/SdRnrXio2rc+y0wQupd9Q0cBt4vXvQChYWzYuAuj4lSUXGiF3Se5TqPFE1ELSFJF7tlMStBU5jXvb2SMJ+iiMuhBPDG7YyNroYAqIvErVtOchxFsyrWkWs//PpWm50rAi9uK+nECSDB5O4p/ca6t3EMqs3LN9Hm+Tx2yWCG0AjUpSmD9qEDUqVC8eQuutMLddDPYlsSHGT1k5Xd0GCF+lmua/CYnwIVWmJ3YTxT3dnxl/EcYNY5FasZ1HqSx0eRfmRq5plxpmjeDPB+hkBbSZCp7TfV6w6gmQaVMOPQCQBozehwzfx5fgjU6FbI32DbIOWkxfZdcixkyZhBYb0Zfye17DbaTtZtGpf0Ea9xW1LBp6ca1kqUMOpV5zad69PBUzPc7xb/ixF+4GE2uJJ70J158TBv5f/PaGteeDjLH03OrChQnMVgW/lhSbxQqFux2wLaRXMs1YhSpNsSlgH53G+Znx7bS4E5ivZe4ZjVhLd6q4dDN68F0aYb8uRZAG5d29pKNS4PbThVa4O7EcsKEIuXw4zM5z1l6G+/q6ygNzm56ijWjh/oI8aKomvtjN3obK8wgIM4M8qxVl6L+nGLIlPgCGctiFaaX+Jy9QLNiFG4T0OO+Jk0tmeNL+lKyZlr3xUisQ6lxr5KESLFqVvdX12JvkZpRQboW1bkJeZ5zt4kUJ2XyeXxu5NqMwHTGGJxlrm2bSKOv/HCvEtuJPpEaKxByMVnZlHnMulMZr7C2Y2+Gn1Qdq8K4j8k6NaOVgFIyrhL3tDwbYyKmu2fxW/H4jbWe1332o+e2QuKUebuObSf7QViK2rdYRfISC+c1jDy3ZhCP6TRaPBQjsdf0srerERyjxcfYjnF1+hu7TwwHdlaHp2raxyQWxIUIcGybZo/wuBrfoVORMRoh6uwjruUzpn/P4/rTUeCtrEVFrmTPniwapvlLjcz5pIyu7hL9oSV5NXu0qyJjlb0e1zD89//o7/5U79p/paHtv/jt3/6NpKANg/gkpiiDqRVFRZTJlCg+kNZbKam2o89dHDB1g7GaLFJACJF7ywVDHvDeCowqatwYmBuHaaspRMIwUBnpqq68G4GTqFiMUiOMEV86o8UTNvQ91mgUWkCbdXShp2QNaIwyFBRVVaGUR4+KVVMyKSacMXjnySlhcsIbIyWeWtSOGIvTDbkPeGNBKQYlE2+VlE5aDcOuo7aOIUWGEtHOEdIeqw0lZ2JJpLyXw2guxCgAQBUlh7YY0K6IYiplChrrHCllVIlY47Dak0tmkhJqq0X9CRQlY6iVQqeMKoHMMKrf5IAaYxI4UzlKFhhdcsJqUf4aIyqjFHuMq9HWUZAX7GHoRS1a5JCdYhJFmBa4XRLjgd/QOEdKAzmJYjdmjakEUFutxG8QRYqJZbVR9MmdAAAgAElEQVRmhWU/dCRnsFrRUwgl4XyFQpFSwZiKohQp9RivMCphKNgMJQvsCjkDBlM0yjsUhRyzQPghY7TBeUUsCWUsJWtRZ2ZoqiUOxZASRZUpzInSkp6MBI7Kgc5gdOFu2GKMldLjDM4otPEMfcSahtAPLJua0Af6GKmd+JymkHGlIoywCjLaiMrXFskc6yLjibKkLHCqYPEVlBzRKs/gaBh6jDUCdFC4IubpqfQoCk4rXEmYHIhE6srJhmobWRdDj7cWYqQxBpVGiFwKHkfKhZwFYhgrMMWUgjOytq1ShLE7u/hAB1GLIutOKSPJFbKoShVEBcYatv0O6wT+FqwAcwMhDixsjVWaWDJOe0LM1FmsApQuSMOxPKu9RS0rz82QIrrymAxKpXmDU2OCKeVC1gajoLGO1LUoFE1dE2KPdw4QONeHnrpeEGIkhIh2hpykyYVVCq9H0/wy+riWjLECAVdHh6QY0FpRKT3C3IIphaquSaUQUyaWgh4BeMyJURs6l7r7upIkhzUYZzBOgGHJiRQS9XIhkD4XURFaR0gDfWg5PFrNanyNkU64vkZrS8pJkglWEfKAN57ddovSihDHSgiTUIhiUKEoJZKzpKyUseSiyKEQkcSRSplYenxTyThPKuNiqTDkQfxRfdH4olhohyuakMA6izJlhu8YIAmQNsYQAYwkJbQDN/qUlpIF0FlHMVEAHQpTWzJKtqOSKVnWn9GTolQJEM49lTekLo0evw6lKlLRYCsC4qftG1GMmloSkDFJc0bnPKYYKf2vKnbdHuUhGUXRZX6eY85Y5SgZSipU3pOjwMNIwPsaQiATaVxNShKjS4FKC7zUxbBermn7nqZ25BzQGklShp5V40nDQExxbnQDhRiV+DVaTwyD/L5W1E2D1pqhyIuUyWCSwWqLdQZyIOtCKEHWSUo4Ne6nRqOVQGCApqpFkT6qWvsYWTQrQid+txRpLpiLksSRkdiwXNeoFNmFLSpHmqbBZIU2GhWzXIuWl8msxHLBVI6+b2nckr4fZC69QOQQe5qVp0+RQiaGnkXVCHhCDjJD6NHKyrtJLqISN81o4yDg1ygtSvUwYLzDK0PMEe8qUgatK0qOourVAm60NuJDrDQK2UucsfS9JGO1QVTOfRozsVoavyrNf/MzaPvX4ud/+63/5zdMn6nO9+gsMKJoPSvJ0Go+BOWxDLEY8Q8UIGXmEnAAheL2awvcLhOX0gBEclkjoBukdHJS7NntQDiqpATZTMB4PARVeoQxWoCcFw+7SQmDkkP61CBI9j5mL9TpEFusgJNcCbBJC0NYWQGNXo3wVc0HKZVHSJQgjAd6P6qZUm1QpRAbgR1SApnx26krdhxhrRxiJxilhzwr0FBjI5JJfTgCRJ0FKukRzkmjnREM8fbwbUfYqFJBj+qq6fA8lcNO3eunZjpxJXuVYjzzVKKcmubPDALHJ9WaTuIXOoPCKF6VposCM4ycn4pRZG/oj+18wEVL47FUacx4sLXdGNP7SP+gZvfQ0VyJf2ZqBHjbbSAtvHzfWE6vchnHUBIKorbUM5h3u8jUhGtqJjnBuilRMB3yJ+WfGQTUDceetBCPSJWZ74lRDW2ClCyHA0vxhuw0qxf923USBWahwY7z7naR/QOPazPuNhCO3DxncSGwa4LK0xzlSs9wrdhR7d3nGfrHlZ3BV/YCtN02UsZ1NZXTFz2C7RGmxoVcs+0Sw1GFyoq0FKXoBCEV0J9UM9SWezIzFALmhlfZW9JCSr9FEZfm37GbXkrtRxAhqrux2eAICO0+kb2hedWSGkdxoy1Sfms10j9YkmuL2Ufxth2VnX4TZ1BnehGuiPouz7DS3fZkJ4mhYvR8HWUENHoQqFqcpr8nDbqKUfRHFtdK4ggEnrnbgXDksbuE3Q1zsyy0IleasDIztJzuM1cC2/aP6hnShUM7K1KnEnITRMFtureq6vl5TtJkSlSGXxJRjHHO7t/6xg736jlJBRAOZLzcbWDzXiVihl2c11RcCXA3fZrjktuKGrF+s5c52gVJro2QdYolE9yfoNYEDCW+mBluxpWbkzhmUuEe2FnBHdaGuDJ0J5U8D20mV2qOMfIdMiZ2HJ+pedWUGExjNYBObwGdKkhyaYaxAiTtLs7NA2WfGj9rTAINh3beo6Z7lvL1twmfsLLzviRJLFGCxoWZwXJcGprXvbx/dRFVBNgXK8++xCxZK8OhnZWmpkvE1ViJHJnj6AT4woGdEzLbJ57mItAfWcyY+FNF1vbuocMO5W1zrP+PvTdpsiRLz/OeM/l0x4h7Y8ypsubsrp5QaDTYoBEASRlo1EIkNO1kMhl/g7biT9BOG60kbWQmLmQ0kSIlE0UAtCbBqdFT9VBTV2VGDjHe0YczaXH83mysiJVIifBVWEZGhF/34+f4eb/3e95eeNRNuk6+VK+Fw5AwCkELsrXfFwdCJvci6u750ltPzJL7NXUipLGYChLpd9WHGt1GfCn7tdJTz9MztT3qf8bsCn2pIESE+ijpWOVlKiY00zRfRSP3a+QugE26uB+z2cLRzrK9EBxVundmE2inmmzp92tfO8/6vy8AsZ9/7UCibPo5N5B9QGp6gwl54hebOo0/O0hf26Hci9y7YtivvtsI0rqazkngCkm+TEXlYHZi92tTrXRJEP9b/8n/D0Tb/+Hv/J2/7WNfpSXig+8Zeukm6yyJhsqkNmypFMvVCqU1LqTNpegvTgjJLRQDvQNN0DYtEdFvqPuXTSWRSmFth0CSmwydG4JSOGtTW6mUSYyJiaskpUTo5KbURhFFJJlkZB/CZFK7Iw6ERGsNUeJdQEiFQCYBWCtCDERiclXFsHf/xgieiA8B19okQMRI7TqETNVIhSTLTM8RNZBp6q6lyDOUkBid45F4SOKsTo7ZaCPGZGjVh6oogSUlxHcuIpUm1xld26XKszK9QyhgvUdIkUKOeuyDkhKtBNYmsajzHh8ixISeyIzp5xNBtKmtOhLpnMcFT0CQFWkCTsK4IUTVuz+TY7ooisTY1AatDUEKlMoAQfARrQUhsm8hDyLQ2BaVZYhokouXJIq3vctWI5Ko3osHSkImFG3bkJkMZQOJgR1BSYRIbFMlkrCeVyVRSZquQQhBkecoIDiHVBLbdZRlBQhc8Ojc4EIgxN7lq9KzD8nd2FqL0IKmaSjyEqPVvp1eSNO396bNvQggEdBzMqVMnMuus5g8p902DMqCEDyeVOaTSpALRdfa/UIlZCB4hxQK52JyQ5LTeQFRoUxMxZPefee8QwiJUhrZj11BEv6ETG5x5wVSZ6noIEnuUZPazZNj1xN8cqQJAnmhaLsWpSSB3hkYAprkggwx/W2CQEmNb20SBrOcIi8IIdCRxMoYIlle4LrkMkwO+tRiI7REqeTks9EToqc0JVooZO90DsEjRd+uF8H0wT/KpGfV45FZEnfTO5NAywwpNTpTaTyHkJiyPpApyUAl13OUyZmNEMQoMHlGU9d74SkEEDI9+zFGok+iTSAkHqnvqPIMW7dJkBECI0FEj1aKLEvuyijSzwsUvm2ASIhJmA0hkBmNUCm0yvf/7oJPOACRFm6tJFkfgOWDRSgBIRC9p+0S71MIiVQG3SNXlItkOkPEdL0IntxkNOs11oWeP6yIwqONTrgTGzAmx3YOo3OsT/NbIGCkYVfYSZctYUagFy5VQsjIAEqkIChrLVmWOKiRxPRUMeFDtJL7uTyNNY+NDqsiToND0tkO5zpE3+2gkIg8w5HaZJyWhH7uEz656kX/tyUxCXa1pSgLhIr4mF4wfHBIEVFapYKcS3PyoKxoNjUmU0TnMCpL4Y0BOudRukDEiFYRKQLWNiiRcDYqCHKt03MqVC/2p2AprwQ6T7zWGCJZj5opddEzUcFpiRWR6BNP2sZIQeq2KIshUimC82l+1xm2ddCzikOMKdAqeZeTo1tKlJa0TU1ucqQ0EMB3NnUpqBS+Zm1HLjNiAKOST7lr2tTR0K81QYDzFq0U1kdChMzkFDpDC71HJfgefeHdDgHQ42CCQ2mFyZODflAWrDcLTO8WV0LiZcAFl84tpLlJKYVSYJuGrm2o8iphbfqx0HU2CfMx0rQdo3xMs26pspxca5QQyZVuFM6FvpMnFY607MMTtUYrQWUU0Xp8ZyFAaQq6LnULOeuQmSHINNZMmeMIDIYlBRqFwOgsvbeIiNYm3VfvMVqRlzlSCQaZ4WBUcTAeYjKJiI5AEs9Fnvf4ItW/dwT+s9//j/5ctP334Pif/rv/+2/LNhX+hEsuseR+61ss814EM0kwiFpSfn5HLDLU1iaxaydeuCT8SU8v5oi9IKYavxcXdu5Vve5ACuzY7AXhnZixE7F2glY0qt+YvXbD7QQhs/F7wSW19iZhRUR6d6bYO6dCJntHIXvnXlS9gy/0wk6b2LBuqNFtSM7cnlEfstR+rbq4F9/y6wY7zZKgWCa3Ujq3XmDsN9auSu7bJNYkwSTkvQCnxZ6Bt3N1JXdTavFOu+hedOzFr1CoxIXMEu5h12aMSG64JHKIXgzqnUWrDlW7hDcbpZT75BZOIqX0fduzDdiJ2V8zX6j+a53Gys5N5tN98GX6fnbbETK1d9hCah02S4fwgVAYfN4LyL1QbgcJc+BGJonrmUyYpl7YpHcbJzZn+tn8Nol43YHZO+b2KIXDDEQvHBfJ5SZ3YkEv4u/Eh2yZ2puz6+0eiWErtd+MpzHyeoMeehe2XidXry/StfcDjVlYmuMcZdm7xaKW2OFrtAawd8sGnZy4O3HHrPt27z48KY23JHjH/uuo2DvdVO8AjL0DLer+Xb53C/oivXeLkJANO+wAMt37bGH3zsPdOPWlIlvYvbi5P5/+89iJTqKRTUK1bJPo44ZZQg707sTd/0f0LNcegSFtoDvMe/exQNfhtfMc9uewd4179gzN5ExOFzE53NL1V71bNpq+pXlkqOdJSNqNZ2DP5c1uG6LRvRhJukZR/GnXa/93hQu08wK9tvtrt8No+EzQDZP4I31yKdO7cEVfpN6d266AZDZpbpCNRzXJFbx7tn2p05xT+/38sZunsoV9PW57B2xy/cm+tT85FUHgRprizvciudzPhTtnsGoDbqD37eNm60H27OUekbNvl+/nvN29SV/vkC9JlNbbNFeG/vonYVvt//YuoG3X5q63iUusm7Bv79/N+fsihkiFBRHScyxt2K81af2QqZiiEyfY9A7d5HhO567asBdrg5H7dWB7rMl7xrH0ETeQ+4KX2exQBPT8bYnZ4Sv6z9yNUhdWyNOa6AYJRaF6R6zsGce7e9keGnwfYKWaFNqWhNIkkndTg7KR5jDN2cqmddcXct+yb4e9I3m3tvYu2VTIEWSrQDdMz4QIr92q+7W2Toxm4cGX6Z5ka5/2ML1TdIdv2P3d3ZrVTtS+yGi2Ye9+3iFxss1rcT0YiStT6Fc9Vwxf+MQ8XvrkpO0Lmbs5dS+o5+l3m02gniV3qoh9UNk67IXV7LahPi/Jby3NLDHFfT8+o0ni6q6YsCuw7ARUV0iaw7RnUDbNTc1Uk/eoBdVE4t65Tyo6t+lztNM0X7lezIV0f3zei/ZlGhdRCbZHktUDyfqh2BfT0vsF/bPe3xeTiiH/1X/+W3+md239b/4v//YOISJqJ+75xEQMEUIPQnGtI9M5tm4p84yu6yiVJnpPcI6iKBFCJ+aqNkgJddMmVELnMHlGjDtRAzKT0XmLbTsKbRBR4q0jonAq8Q9j25ErgavbxH4SHiElQUQcyVnlvcfIVH5QQmCjB9VzP0USBwmvnaC7ABEXXd/ebYmExODMdXKPETFCYYyisVt0iLSbmirPqAWYIqPb1ngvk4icG9ZNQzWoEiKg7ZC5StxPGZAoghd4FxNvU3ucT55t6yKmKHCdJRMSFSPCeXTaxSaxRCSroNJ9S24fvoRU2N4xVBgNgYSoiAqpDJ2t6YJLYpdS6Myk9vKY+IneJ0ZwwKO0xHuLFiBiSKKYABFT25JWnhAsQuYoQRIbo0QiyXPwPtLYjugc3tZILGVGGg9EYnAEa5EhMSljz1cdDIbEekMuNHW9QSuBiX0rXUxOTqkC1loEKiEyVM+2FJE8V0ipaTY1uUkBNXlIIWzb7RatU1tH21mUlkhVQkiIBOmT867tNkhVgJQMyoJMJifv7HCMCyXtZk3XeUJUtB5MnlMva2SpGFQVnbXEGCkLhW02DEoJoqVtu72AKqVMAWwSovBIERPpwigGZUnrPdvO0voV1XBI00asjSiZIUUSNHNt0FIQXIcU6UWtCRYtsiRKCo3Sya1JTBgHJyzIlqLIKYLGuoAj4rp0z12dWNLWtol1SUDJhHBoNynwKMsMXZfcjEELhNGgI3frW8osRzZbykGFMQrbOjJj8BKsCyAknuTM9bbrx2hyEx+UhpubG3SWgUgOVmU0SmV9cFPivcauZWQUm6ahNAPuVhuGwzFtY2ldh1IGXF88AWRIDniCxRhNkUFA0jmL1BpnW6SHQZkcEK31DIYF1tZUVUbb1cTOMSiGuFVA5yU2KLAJ8yAApSUqOPLMJHemkkiT0zhLax1IRaZyWteR55q2sYwHQ1zXUmgYzia8fHVFXmS0td8ZVlBC07UteaVxLjAeDXFdzWgywDlHY6FtPSGkglcIlixVWKB1xGgZlAKTG6qqotkaNk1H3Vpc2xCExNnUCprl6VkoTE7wgcIYWtdiZJqkrU1v11p5fFR9O7km9GzsftlO80jjGBiFMAaLxfUusBgCZZX3PHDDoMhRERbbJbbxfciewEeHyQ1FPy9pnbjl4yyjE57OW7omEI1BekFDcsvGxDUhNwKtYD6coHODygWrTctytcVkOXXTEWJy0hZVge8sWsF0UjEoDVIEgoss6/RSH1VaCwkOnE2fQxuE9IxKQ2ZSAWKxaGm6NqEsiHhvUVIgfEBGz6gqCJuG+/fPUTJyeXWLUhG7TQ7uxneoTJFrg9EGLSXGKAalYfbgCBkDzy9vaMSAdb0myjSXEj25TqzqrNBUg4KD4YhCK5ptx+puRSckS+8RMhJETIKvFITQkheG2eEkBXUNptgm8uLlJU5KvJCYwuB6jnFXbxmXBZmU5MOMKi+wwbNer1mtt/tCZ5EXWFIRr7MNpwdzssxwOCkx6oy67vjs0y/xvoMosZI0vgwJ7dMFsszw8OEZWgli0Lx8dUPTWhrrKEzGet1QjUqqvMDXlsNhRlFqykozKCu265rVtmHd2NRFkBdo2W+gQkeIkZPZAZNBznx2QLO11Nt0XoNCUzctZZ73AkZao6t8wMl8xnRSoU1BYx1Pn79AxoZtJ2mahkzlFEVO5zqijowGBWcHFfdOTyAEGmfpuo6PfvGUu3WXCmcyFX+kTE72Pz/+PTmkwOcmbSZ271k9axJSu+GOj9gc6uSgnQ97jmTAjbK9S27Hacxuur1bbMfAEyGm7o2BQm88apucYrtQkV0Yz68yaFXvaErCVt9+3DsWE9s07Qd2jFjgVzibO65fLw71308O0Nc8U72yiGD2vEBXKagSl9AVgmwVaY6yPc8viaNyv7Evn29T+JGNSNu3FPdu1cT0SzxY0QtTO2F1J5ioJrVD73h/u/PeMTjTZ+jda78SwqPXHXaS7xmbvndjBZ2Ecr3x+4Aq34erCf+6bXzXmr9zj/m+dX8n1uzQEamFvWdwZjIFsPTO3h1zchcCthNJdkE/UQqIvaDWu513Im8KV4q0I0V54/bnCMkJ7Mr0eXYs3yS6B8w2hdfYUeIa57eWbtKPmbFCtYr8qiX013GHvtg5Gnfcx/33+q/b0yF2IPGZYPlYojowK4neJpEkX6bgmuLWEYykOSkx61SQaGcmPR8niWduVm4vlkSV+Ik7/MaOy+pyTT2XmLWmunKYZQp5ym/tngO7a5nvJhq3c5339z9fgCiSqUDa5EBTdRrjdpzGFUAzUTBRmK1CN71brecy2pFJzNfxax6pKyXZAuqTnG4gyTY7vuRr/MLo85ruMEv3+iTHlr0TbaQSB3j3LLYJPbFjdO4CjpaPNJPPHKFMv293/jtH+i7kT4TI5iyjemVpDjSjzzbU5yXZwmEWXRJR4U8xgH3eh+qVyU2YhLj03ORNCsZrTsr9faqPTAqlmynyZRIGt0fqV1iq6bOkuSo9k94IupEg6J0opsiXiaMJSWDLFi4FoC0cq4c5uo20Y0l9rJn92BEnmmyRnIU7JqhZebqRRrWS1f0c3QQ2p4mrmi9SqNivhuzZodlP4yJEtnOFKwXtAWQrKK4i5U0SRIWP5LvxMUr3u5uaPiTMkC0coed/6s3rroJUxAv7+TGFyfVLR48ZqE/ynsUb9uNO+Eg7S/OqLQX1XBIMDJ8F9NbtXc+ydbhhlji5XSoeuFywOZfojcZs2I/BXVjUaxa3pB0n4ez2bY3PIRooX6agPlcZ8hu7v2a7kC6fC5aPcpr5a+7q4CJgCPiQOK6vrys0M0MUsDlVuAEEDYOLxLXdcUlVz1/erYGbswzpIq8+LPBZZPrzJPIPt55uanqGusBO0/PjcoErBc1c8PKNACoy/okmW0gGL13qLOh19G6UPkc3FDSHgvVDcOOAXgrKVxK9kYyeuf18bgcS6dO8340UizfTB2yOQDWCwx9HdBtRbcRV/Zo0TGGcq3sKNxDYgaA7CKhGU1wJqhcBmadx10ySY9rlkmztuf6WwQ0i4VFNU1g2i4LD72WohDdH98GiLu853yq5Wa++HYhZgOAY/dyQLSL5IqRAwZcdzdxQz0coG7l7K6ebCLpJpJsFzK2kfCmoXgnKfk1NQnMKPJQSbp5ImnPHg8eXXK0G1IuC2fdS0F9x52kO1D5YFaCZCVbvBub3rhnnLZerIf77E4JWlDcp8NQONc1UpTEaA5sTxd0Hjm999TMALtYTltuC8h+OGH/heqSC3LN3dy7yP8vx77RoW8S+rSkKNm2LUimNXGqJDAJbLykyw3g8TIy+bc1sMuZ2uWBSDWh8TO7Y6MhUQbSCXGmMFnR2TaWHiV8XJTZEXJsEXa00Td0wKCvwHjOSuChZbLYUWtEKRzYsexZgChKrmxW5FrTNkjzPkaGlkDmb2lJkAht6/qH1BNX2ATWKiKfMNK0NRNuR54MUWBMC5dDQOocIGUIKfIDQtJhMctNsqMYV0RhMsLgITiuU8GgkvnN46+hEjtIGnzdksmBaGVxbY8qMm+WSvMhAZjS1x5jkLFYIsC1SwHxeQUjhSsuV7wW2hDhourT5FlEykJJuu+Ho/IQsF0Q87XqbWjC9omnWdF6TySKxYmOkcxYhBcPCMBlOCU1H3Vk2nSWagtC1SDRdlEgRsV3NZFAwGIwYVjnSdzSd43K5RipB3QWUziEIbJPahDMyqqzg7TeOMDptxK9vr9jWltYqtnWN7DnDqg/0Wt/dMp9NKErDhJzWB26XS4SC7aZF6YLQWYzOiDgau2KQVRxPxgyHA5aLW5yPLF1H224oyoqkFxlyoYgu4oNNuIE2FQO6zlIWBRjPum4oqwFNZ6kyTddsuX92j8l4yGSYk8XAZjugbhsuXl5weydxPuIqg2xbBnpA3S4YzCbcOznlcDymq9dc3lxzfV3jhUtiWxMoyZEEjJEUWcn98xOq0lBoRb1tWS6XvLhesbEgVEVgndrtpUouQNkxOzrkoMoI0bO4W3LxcouJ4MWAYAMeyKRAEBkXiuPjM8ajioHKaeuOi1eXLDZb2lz048UhO0EmDUZohJG89cY9JuWAut5Q15aLFy+IoQGtiTEFX23rmqwwqAhf+8o75FmaL2xdc71Y8eL6js22oRwOkMbQ1Q1VqfAry6SsOJofMDsY0Z1PaZqWi+sNy4VNLNAQ8MLTCYlQgvPZhGEm+cb8MVZ4mtby4tUNN+0WYwqCNgSpWW4XTIoK7zrevP+Ak/OjhNxo1mw2NZcvLrlbbymVod3U6Ez13GNBjJaHD44YDYYUWrLd1my3W2zT0nYdoRcJtTbJKS7hzUePGBcZw+kIF2Fxt+Lpiwtu77YEqfBOoWN6MaqU4nBYMTs8YzrKCUQen8/58uIpF9slaEUQjrpNz/m6bjk7njMZDTk/OyIEh5KR9WLDxdNr7jYdDrB4rFToPLnRC6F59/EjqrFhaEratuXyZsHHXzzFBsW6CRRlgbUWk2fU6xWjXDEdj/HOIeSAy7sbYhQUekDr2sQ78xbI0EZgXYfvWk7uneK9Y31Xs1otUNmIer0mINDKIGTA5BIfOx7dP+d0PiXPFBmKprU8f/6SL1+8wkXPxhu8c7TGo6VhPBtyf36AKQQHkymLuzvqxvL9H/6U4WiCrH1yhOfJlQuCB4/OOZqOU0hWcARX8sXTL/niy5cMZE6UEisj23pNlkmKYc7R7Jj5eNw7/+HF5Q2f/PIp3kqEcnTeobRGZ4bSCE6Pj5lPh5RVjpTQ1J7Pnz7n4tUCjEL3eJOua5mUA7IIb37wJkWRM6o09+4dILzj84+fcXmzAuUxJmfVBpbrBQfTEffvzZgdDBmUCT1z/ugBn3/yJR9/tiC0FqU1RZERpcf6jpODI07mM2bTCUVm6DYd/gw++ewztrWDELC+Qx6WCB/oNltOzqacn80YDQqUBGzk5HjED3/wc1qRnPXeecAR3YboCx7cO2d6kJ4PieL25o4ff/YFm9ZiXYPtHDpT5DInU5LpsODe+SmFEhgpWK4bindyfvGLT+hcxIWANILOeowUTGdD3nrrLZRrmZYlUShmxyMuXt7y+bOndF3D4ewQ5ztssyYvckZTzcP7ZwyGOQOVCsOLu5offvQLhIUYPc61+GjIcs1kMuDeo1OGxpBrzSgvEQcG31k+v7ggyzWds3gRMZkC0fGND76CJDIsNUoWDAigTimvNJ9evEIYTYeAxmMyRXAtbz5+k8NJhYzJka37LoB75w797Jq7rcOFjuCTG0782d8j//z4//jRTfuNf4Ti5XbfAh3V6yCTZthRAIoAACAASURBVKJxZ0kgMpvA3Tsl489bNo+G5Lc2tfY3FjfK0yZxkiVHytpTz1IrqG7lflMfVeIqZjdNL6CkdkTdKgafr7EHxV70UE0SFLuRorh1KfypD/4JJgVfVZduH9qzS7XebdSFj6AFzcyktkkf6UbJCRSVoDkoyVYJWeBK9TqoqtKMf7GiPa72rbC69qjaErIcX6SgKtE5zMq9TtceapYPddoAl4Lpp3YfALNLsA994jVAUJK7t3sROUL1PCVb6yYxBPNbuxct6yOD2QRu3h/gBkAAs45kq4h0inzhMavEso1KIAuFWaWf3xwrtucC4SBbQHkV9s5CaeI+6MlsO9YPS+q5pDmCoCPZUnDwc0dUKgnyPYJmFxhjR4b6UPHstyWhDBADw880+U0kWwnKK5uEtd6p5/MkRNy+Y+jGsLlnMCuYfJ7ONb9qU6u5DXsRP7uz1CcZN+8p2nmgfClRLQxMYh7u7j+AnaSQJd25vSjrqlQsaA/TeC+fN3SHGdldClIzK8/lr0ncecv901vm5ZrPbmdcLSoGPyqQDnQbUtiTFmyPcvKbju1xxtU3BeFRh98YBp8YJp/sXHXpvNvDjKAFbqxox5LrbwXEQct4VHO1qFAvcg5+oqkuk9AinUe2Ad9jDwAWb0q2Dx3CQXmhOPoTvxfgpQ19snsK3Frd0yzejcTTlqPDG+7WJeJHIwYXkWwl/5T4Z0caWyXB/sVfilRna54tCuSd4PCH/AovND2HxWWHGxl8Lvny93PC0CPLllBrys8N8x879MuWblYm0X3V7QPRVvcNi3eAN7bc/rpBLgWHP8wYXiQxI7X9e4pVC0Lw6tdHNHPB7d/s6JrIi1XF5CeKSRehSq5BaQP5F7c0jw+RXeDZXzKId9eUueVyUxCuc0YfG8Zf+hQ0tnJ9gYP9M/fiO5r22KPGHeE2x9yCrjV5P1+8DiZKQtOLvxjJTjY8mt2ythkXzw8Yfz9nsmt573m3QQvquWH5hqR+w3Jy75rcS25/U2H/1QFHP0jnIG0kv05dj9WLjstvlmzPI6MPbgleYbTnxfWQwQ8LRl8qdBvQW/qQslRcaaaKu79cM5+u+dbhSy42E37+6RnHf6AxdaB63tJNEz7BVZLi2rI+z1g/kEgLUSgOf5bEwW5q9uFWu8MXEtWLrddfLZAdVK88g4uWYDLymw5hfeLNiiQMyy7y4jcVvL9mOqw5LLe8XA+5+vGM43+Z5rjiZSouZYs0/67PFHfftMiy5b17L/n4xRF2kfPW/+zYnGb7gtgOkQKC6w8DZ29dMskbfJB0QfH0X55z8i8C9VHi/eptoLxKIls3Eaze9hw/vmaQdQxMxw9/+oDjP9KU3eswwd1c1Uwlt1+JDN685d5oRa4dz1djrv94ztEPeuNdX2Qya8fqQY4dwN13O4bjJV+bv+KTr82IAp798ZzpLwJgsJVkcNEyehpYPM65+VZg9uiWDw6uCFEgvxL543/xLsU/Ti5TbwTtJLmZ9TZw/aGhea/hg0cXPKxuedUOWducj7/3iPImzXv5rX8dKLb2XH9V035ty4OjW0qdQsg+f/eQ4d8dkYc0f+4KizsEzeZbNW+fXfJweEsuHd978Yju780or1IRzWxDcpoOBHagqe87vv3BJzyqbjjOlnzv5k1+Ojoh+8djzEagZBLLs7XHVpLVQ0X4rQUHxvKd0y+wQfGzN4559uMTTr+X/t/izRzVRkwdaUeS7YlAfrjgzYNbvjp5jo2Kf375kO3fPSXKlB2h24BqI91Qsb4vEV9f8mR2w71qwdcPHVp6/lf7axz9U7UvUMku7ouH9m8umKvAe4evGJuGJ9OX/AFvcTsdUf5RHwa6dkCaQ6WPrH5ry7fuPyNTngflLZOsoZ4Y/vgb7+Bzw/hLh2oiykekjHuB+M9y/Dst2tZtk1qj+/R6qRV125B5xSAvEEJwenrAeDxEyACcc3VzjQ9N4uqhQKrk4OxS23aRZTw4nxN9h/ee2+UtsXVk0tC6iPORECJVOSKGSF6UFDpnNCp5fDJnuVyyXK7BW7RW+BiwbYfyKVCmLCsKlTOdZsxnE9abmuVyyd16i8kq2timl68oiGLHvQyMyoKzh+e46Fktbmm2bQq38h5VpsTpzjuMlOSqQEhPJSW5lsSouV2vKVApjVCnkICiKtBR0NQNo+GQ+XTC8WxMYSSL9YooBJu6TqE1MnF0TR+uVBYZx/MpZ2dTyiJjsVhQ3C5Zrxqu1jWddwQ8ZZGjyGg3a2bHh9w/mVGUkuAd6yrn+uaW9aojyw1CGFSQ+NoSFQyynPGo5CvvvkWzXuKdw1rL0y8v2NQLgtBElRPaNomqAU5mB5yfnaFlcjHXbQvPXvLq6pL5dM7L6ztykxOlQIuM2cGc2cGIyUQyrkrWyw25mfPi1TX1ZsUgL2hbi0LhY8RkGYfHY05PZkynI6J0PL14gURwu15RVANiNNiuI/rU2n94cMCje6ccTce0dst8NuLmbsHd8posy1hvamQfgCSNIsqeURgjQkayXBIR1O0apRRVVdF2HVmWU9c1D05Pmc/nVHnGKJMI58inBXfLO85PzlltLgjOYjwIJbC2ZToZ8cbpPY7ncyKecTmhrDIyI7l4dskgL5B0KfxLGZrac3YyYTIaolVkUBiKIicvNNebV2zubpH5CCErtBApTKqzfP1rbzMalkwLQ/SR48kBUgkuLxdsbQNCI0W/Wcky7p/f42BWURapFaQqytS+/PIlL798SlYMMbrsXV6R3GjO759xMJ4wyDNGw4rNuqYoMj765BOatkuOWBepdEa9WHL/7beYTsfkmWRU5azubimKgk1TE0Jk3azIGGJU3zafwxvvPObk6BDbbRkzoGs6FmvLRsTk8vaBLNd4krP84HDC2cGYUZ7ETusjZV6xWiyQSrB1DU1rGQ8qcJbT+QGz2YhxqYk+cHLvmOurBbnOufrRTyiHY1rr+qDCSCbh3skR88mA2WyGrTdMhwPquqVrPBeX170NJxBcCjcaT4YczpKzMc8UlTEpLT60NNuG2+Uakw2QIhKCw2SK2fyAg+mIKgclDTozKGOQsuCzpy/wRMpySF1vkBGm0ymnxzNGo5IYLBJHrjQxKK4/+vhPhTJ1raVra37t61+lHFcMS4OJiqgDZ2dnXN4uubi8RgjDdrvFGE2wLUWmODwYMR2PmR6MqeuarJQ8u1jS2QapxR57EIOkrRuqMud0PuN0fpg6Kg4tXzyVNK2nKCpCCNgupBeF1nJ+dsJbjx4hYmBQZYSuI8tLlDJ88sVTbIgMqgpnASnRuWA4KhlPKmaHI5rtlpPZlLYVPJ9dcn2zQMSExOjqluFwyGgw4GA6pSgSesaYEpzh9HTO1c0t2wZ830EQBOgs597xOYOyYFwViN49fDo/wlrPJ5+/pAshBehJQbANg8mU2eyAw0GVWKjRYbTg9PSYq5sF67ohKwvW2xXj8Zi665hMDzi9fw/hPdiG+XSIbzq8/ZJhPuCmXtNuthglKYdDQgjM54cUuSYzERE1ZVFxejDj+tUNy66jc4lFnEVNs7XMZycczaf7gl81qsAKRqMRermicRZdlsjaQ5SMTMXJ+JjZZIqSgqpICKHhcMwnn14Qty1ORIzsX1bzAYfHR4yPj9FSUGYphG5QDfn44iWLTZ0co0qhAnS2ZToqefjgAa5rMSYjBsdoNMLoiufVC26vr/FCEWNkWBas7m558tYDBkYxHE2xXYPMFAfjA1Q54ONPP6XMh9R1DSK5UJzrmJ8+YnJ4gIqu7/oo6VrQJicKR2NTEKe3luWy5vhoyvFsTr1apvm/KNK4LXNKZWhcRMlEMrdNi5CWg8mUYDuUSO5pISJv3Dtju9mghSTIxEPXuiBGy3AwYFhVVHlBs034nkwbRpMxt1cbLkXEqIgQfTZAAJ1n/xbf/v78+H/zyG665ErseWtRS7LrGjfKqU/SfHP7BPxZCwLuvgv5pwZpM6TjtSg2KPdO0HaiuPxQQEhtgcMvUvtld5RaEXdJ2e1RkdyWY0U7EdTHmuffnVC+FAwudm4cUDYm9qERSXibGZqJYn1fUL/ZoS8N1UvB6EuwpSRf9QEsIRJ3nF4lWJ8Zbr8KsoPypSBb9sFnJLdutnDoVYcfGLqJJqqKeqbpRun8J5+F1+7gnjtoDyt8ochuUqL53duK9TuWbNziLipEMJRXgXzh94gBVyWeYn2ouXsPzFcWnIxXfP5iRjcuKC8VBz+3mHVAuEg303iTWmDv3jLcfTP9fu8lm8uC0eeS8lVyR0WZXLvp/CL1Sc76TCH/2hV2VRGspK418vuK6ir0rrXeERySW+z2iUR8sKTKOwrjuFoMuWXI4UeOu/cqDn+4xI3zvZB397Zm9UYgO9vw7skln17P2AxywkcFxV1ge2zIl36fjG4ryfKxYv2k5cH5DT4KXn3/hKANk186uoNsH9QlfERtHYu3Ky5/I3D81ivu1iXmTcvii0m65wNJ9aJLwWm/wkuOPbYjSkE3TGM8cXwl7TzvBW5Dfm158Z0S8eaa+9MVXz+8QEvPebnkn6lHXH8A5csc6UVqRe8T1ZdvFLz6zcjZO6/wQTI47ng2mXBdDZn/INAeKcpeHAxZcglef5BTnG0o8463Dq5ZDEueDSe4z8YUL2tCz4X1hUG1HrPwfPKflnBc8+7ZJS5Kmnc0r9Qp01+Effr77jm0A8nNtzyH9++4N15S6Q4m8CN9Rm0nzH6wxo2yveAL0I0kN1+LTO4veHxwDXOSYD0ecv6/p2IAJMdxc5SRX1tefphh7q2YDmu+NnvOD6/PuB1XrK8HSFtRvNjiRjl+YAgmCfZX33E8efcZi7ZgeNxxU1c0z+dUlyJhQLYJkSE7hdpa1g+h+Mot789f0XjDXVPybDJl8DIjqFQMMHcN7cMDAK6+VuDeqPnqySUuSP7GGz/gHz5/n4vhAQe/6B3gq35ekAI3UFx+U+LfrPn2wy+5bgbkZ47nyzHLzSFHf/K642Tnwt+cSyYPbzgerjkfLBiojlm55YfdA/KFZvx5Csfb4VhsmQTb8wfXPB7fMDYNU73l/9Dv80rPOP8ji+wC7SyNw6gFm3uR4ZNbnsxe4aKkVJaXgxEfhTOmn+rUxi1FGh+rgFo1/OK/mHIw3vLk8CXzbI0NismTT/nJJ+9x9P30jpJfNvhBQp60B72YfN8xPV+yWFS4quD4X7uEa+m7J+wo8VzN0tHOc64+0NRPGqQO3C0y5v+8TI7TqUH45NzeBZBdftNw77tP0SLwZPqCLmhOyhU//6bD/MEMvfU0J+VeJPRFKhQdni74y/d+wS+3h/zVt3/G0+2ULz54k+knlijpkSSeZmrYnkom92+4N1wwMQ1H2QqAv/+kpP70IAVVtn24nou4QrD8esv0cMOvHT1FEhnrGt6HHzVvcP6HyWHaTfV+XmzmgvKNJX/x3mecZEvaoJnlG/7Jk5Luk5LBC4sdKcqLms3DivLGsbln+O13f4HumSi/d/+nLF3JP7JztseSbB3IVqmzpDlIBYjjx9ecDxecFkuM8LxfPufi/QmLz88YXCRuurQpFCu7aajfjHz7zV+m5x/Bk9ELAD46uofLFcWd3xcPANZnhs17Ld9++CUD3fGV4QUAt9MB/9vhX0SEhJJJ4zdd59Ub8PDkhkle8+7gBSd6wbeGv+S/Hf0+o6d9OJlJ7nOziaweSH7nmx+x6AqOsyU2aH59+gWzfMsfHn2dyedxH3K3OTEMLzou/prnycEt35w+5bIbkUvH757+nH+qLc0/Oqc+1BR3r4Vk3UTqNy3fOXnOSLdUquNhds2L0ZifFKeIAPnCY4cpkG/4tOH2vYrfe/xTPl3PGeiWr1VP+aQ5Ro072oOKbAGNUWTrkIp/Av7C+efU3jBQHaXqMMLzX3/lH/Df3P4NXK6RlUpM6CJ1BWyPJOezBe+PXvLpdo4RnrN8wW+Nfs73j+4RPx5hS0mUrzseXP4rtu5/w/HvtGgLqZVdihRAIyUpXdwHCJ5vfP2rHM8zYkztrpt6SVUeEnzDq5eX6FDgQkQJDcFhpOIbT54wGWli3/e1WI15+vyC27sFISYBNASwdETneXT/MfPDMeOhQfma7mjMy+trnl28Ytl1aF3QNC1FVmAi5DLj/tl97t+boKi5f2/G7WLDs2eXPL+8BsA5gTQZEJFRIITgm199n2Ep8dKzvCu5uV7w/GqBznOcS6xQpQShs2idc3I85/Gjc7SKCBVZLhZ89tkzGqfoItRdixIaIVOY0+N79zg9mVDkEu8aDg7PqAY5z56/wN91NNuassyJ0eKD49GDdzk7nZFljkxHquyQw+mY28WKxU8+g+iRWU7bthgVUUrx+PFjTg6H5MoRg8XPxkwGGT/+ycdElSfOL5LYWbJMMlSabz15Fyksk5MJ0SX+q4mWXz5/zqaDVWupBhXb5YLZdMpX3n0HpRRGC5yw5HWWgojsmsV6ycGoYtN2CAz1tuHRrz3k7HiAbdfICLP5hKbLGB9Mubr+V2glaAGipjCKzju++tX30AqE6ggx8OTthzx/+pLFR9coVdB6T2lU4m4GwcOzB7z96JSuWTEYVeBhMjqjWS/57MtLsnKARSOFxcYULDYYDmg3W+6dnrFdr/DWIcokGNRNQxRQZRm2W3MwmfDg/n3aekseHSLL0/cJHBwd8eWrO+qbDd5FbARfCJ48eoM37j1EiQDRkklJleUM85LLi0tE8MgowWiapuN4fsR7b79DkSloa0ym8TFSDkuOt4+4+HKJEhpVOIQQtF3N/GjOG2fHaCJSRUpVYl3Hm9LT1S1u0SFiRIpI29RUecV7779DUy9RIhKERwe4d36CyARXVzfUDjpSK3rTtrz78JxH9+9TaEFjG7LcUI0HlOuKzy8u6FqPRFOYjGaz5P75Pd55+yFVaRAiImNkejBGioyruzueXzxlMDwiOoF1jqZznN6fc+/sFBEsVVkRsQzyjHffecirV88JIrl0tFA426KE4tH9+4xygeo8PjqqImN2MGGz2fDRRz9HaIOOEd8EQt3x6IMnPHh4TrO8ojAltq2ZHx0yGHp++NHPaNsWJU1qNZeSUhreuP+QYS7IpIbhhBg9o7Ji21i+eHaRECZB4FyDdRs+fOOrDEZFYlgqie0884Mxw8GAm8sbuq5j02z2IYwEx8nJnEGhMULgXSBYOD+6x2ZT8+nTpyA11raomASjh+enFKUmeEdhDAJBNRowyMf84Ec/QwtDXVtMVaCE4mhyzPHBjIPJEC09wkaU0egsYz474OLVFUWR5pEYPW1XY23Lb/+l72DbGoVnOp4xHBZ88cvn5Fliq67qBi0SykN4y8PTE45nE6ZVRcBxMBlye3vLzfULTDHBeocSGhPBuZbz4yNGVYkMlu1my3A4wHmYTDIODw+5WSyxTYPIDLrIaNySew/OyQkQAsO8RCEY5BWTwZBm22CDoOs6iqLi6voVjx58yGR4iAgdVW4AhywkeS6YjCo22yXKpEKAUZrbV1ccfftDciUhWlRmCK5hOBkw2JQIFZBBE1ykMIZV0/Lg/n1O50dE2xICaF2gjeB0nvGR/ATdc73zoqLZ1ri6Zfr4EaFLGKDpcIgQkqzIODyY8+ziEkHOIM9pQ8fLq2sePzpPLl4REgNaJD7xeDRI86SNmAAqCEyeI+WG8XCA6yzDKk/sb6OQCuZnc37w2c/ZBkuej7Da4L1jOB5gDgfkMkPIkAIzo0DlGdb7FApalRBTCF5d12htGI/HuHaDFwGlJVVRMR6PeXl9S4wR6QN1iJRa07nEJ3bOYWOGyUqiDxilmAxH/PLFJYPJhO2m7YsIJgUYCkVtHXmegUjIH9pAZUo2my15McSJxKFvg02BclEkJrXOcD5SVBXeJSSK0BB0qsjnMiPXGXeXt4xHJUol3nmMnqKqgITECCExg6t8gHVbNusaET2TcUVwnrIwLG6vKLI8MUmRFFKCJrGC0eioaNY1SqS5rG1b1jeL5N7wEZmV4NqEdxI6YT7+/Pj35lAbSzQqJcNrgZ0WewfI5/8xPHn7l/ggeWt8xUd3JywPcpZuzsHP/GvHqEmwNldJrn9/y5PTV7iQvvfxW0e0fzJg9MueLRri3sFGhOd/QePfrHl0fMPAdFzXFc9/dszsTwTDC4cvBNldYv+5StGOFNffjLz99S8ZmJbZB1v+2fOHXP1gyuyHr91C4VfapqMC+/u3fGt2ydrmfPJqzuaXAw5+LPZcTBEiIddpc58Lrp9klL95xSCzKBn4/OUh4++VZMtItgl7ATBKaOcFz78rOHj/isejJRubMbv3jH9x+Aj/gwLVp403M9PzAwOXvxF4/OQ5R+Wa02LJe5NXPH0w5SfPThk+zZNQrTXZonfoGsHyL9T8ztsf87C8wQbF8p2S//PsXfK/N8QHUD0/UoS0ud8cS45//wty7fi142e4KKm94Y/LN4h/XFBeBQYXLc1RRnHZcfdOwTf+g58y0i1H2Yo7V/GiGvGjb51z6wcMnwZWbw4pL3tX7E3H5tsdf/2dj7jqBpTKcv/BHZ+vD1mf5PiPj/H5azvRrpX0/Pe+YJLXSBHpvOIbf+WCf/CzJ4yeZXuBeocd8JXm8tcjf/03v8+rdsjb0ysAzMkz/snqA87/KIlLeuv3XFtCpDnJyRaOVx/m5DcRZeUeA5LfJONCMzfoDWweBP7Wk3/G0/aAR+UVRnhsVLyaDvmts0/5R//6NyhvkuCgNhY1ynj+Hxq+++HPGJsGgJNsycvJmE9mc+5+8SDV1k367Nmd4+ZJzju/+ykPq1sAjrIV6yrnrfEV/+Ddb3L0L1+zQKNMYVh371V849c/ZpI1lMrybvWCV92Y/+u7knV9jG5fYzGyhaWeFfyX3/1DPq9nDFRHGzRTs+XDd3/J/yh/g/XHY4qbhM/wRcKAfPlXFd/48BMeDm75YnPAabni/dFLvj++z8uDR5hNIOjEtswXnpe/UTL97Rc8Ht9gpCeXjt88+ZzqrON/+fS3OPpXDc1J1bN0Hdmd4+prBd/54KdoEXh3/AoXFHrq+ae/E+Cjw32gmc8T5iHkmtMPX/D1wwvGumbpSt4bveTgbMt/f/27PPr7KVguFCYhFf4f9t6k17LsPNN7Vre7094uuuyYSYoS1ZKUaBVcslUwZMOAAQsouAzZNSgUDI/8Jzj0wDNPPSjABlSAXSoYsCG4GguSZZlKUiKTmWQ20Ufc/p6+2d3qPFgng6iZamJXQdrABQIIRNxz9l5r7bXe7/2e1wU23wn83i/9Bc/qUz6oZtz1I/6TRz/h8fQeT7NvkG/CG0yFkILmRHH/O9f88vEV97MNjJL4xTH8j6vfRH+Pnzns24R0af7Ljl8+mnOS17xdLLnrR/xn93/At6av+Z9f/TZmbyjvLG6QhDhD4Fe+/ppfHl9yL9uwdhVd0PzXX/1T/tvFf4z44wPupD4gLmrH279+yVuDNX1QvFctMMLzi8NLfmlyxYd/8B18IcjnFjfQ+Fyx+EbJ6OeW/O2HzzjSNUZ4hoOOU7Pl++9/wNmPOLhnk3ierR1qb6n+myXrrqA0lt948JqPTx8iPzyin2iCFlQX7RtcQS7h+jcV/NyObz68ovUG/Vbg6asPmDyzByd76jhwVeKDNr/c8O3j11Sq59PtA745PmfpKiZnDX/8/gMmz0n4k0PwX760qF/dcVrVAHxzfM5E13xz+Ir/7tH7ZBv9hnvaHWmmn225/XdG/Pb9C7QI/NLwAoBK9hwPai4fHVPNSCxdmwouo2c7fuWDOW9XKwDGuqEOGd+avuYnJw+JIku8Vhtpp4rhZY/9VsfvffVH2KDogubI7DkyewZf6/nQfDuxgvuInebkS4dqPfVbaZ1dthW/ffYYIx1v5Uv+9/d+jaOPkijaHGvybeDoR3Ou/oMzfm68JJOeiW4YqrSmfPv0Nf8if5gCzqvkuLVlYryf3t+w7kp+9egCIzySSKU6Pnj/Bv9P71PcNthJjmmScL2/rzCF41G5xgjPsdrjkUxUg2oSZ7gfSiBgB5Ji5QlZ5DdOXrGyFTtfMFEN3yxeUj8InPwkIY8wgmKZ8AJmH1n3Beu+5LYfvxFuz7It3UOH8FDfS4Fq5Ty914RxaBl4sj/jUbn+V/Ymzcmhk2as0F0qMGZbnxLEgcYbJJGFH/BBNeOzngMLnjes7H5icMPI926+wrfPzpnohoUfYKNiMqpxVCnr5sALbk6SO/3T1X2kiPzW2VNqn/EL5SV/tPoG+agjSo3PBcGkzg0RwVWCYdbxrD5lpDuOzJ6XzSkf7r+K1gHZJyQCXXLk2+pLt/hf7fqry7v/P1zD4ZCiKJJIJySFNpQmsWvv3bvHaDRAKcVgMEDKFMJVVUVyzxiTnHQRvHXp78qC+2f3iMKjMrA47j844eh4hJCONPIOoT0y4KNlcjTm9GxCNczRg5y8kpw+OIEcVG5obYvKM/pg0WWGKQwP337IaDymKEdEYZgeHfHuV9+htXWqSAjoQwpfCggG1YTTszOkUXjfcfbwhOPTY1rfsdkkkch4iXQCgmC/X/P+++8xPoSJZEZwOh0mHiCRaB1GSHIkXddTlQXvPDrj+GhEkRuGo8RMHI8Gh3AqR56bA3M3IhEMh0Mm42O0Tm4bEQRlXjEoSoSzlEbj2o5MG4J1jKuSs+MTtM4IIVDlBbjAvdPTxNiNB1ex1ASRROXRdMLRdExeaIpMogIUJmc0mRBF4vIpEWhsjzAJsj6aHqHzgt45+uCQuWJyPOH47BhNpGtr5IH92HvPYFCm/VmMKahM51TjI1xM4Ss2ekyeQ56Yp9Fbzs7OMJkghJ4skygRODs95mhYYOsNmYwoGShyiZaRd99+BB6MSvcwwXBkCmESgkgS0Lz3OOeIIrDdbpFaobXk3be+gjEl3iXRJ89zpJTU9Y4QkqO467rEVDaJl2NjoBiOCEqQ5wVeCMxgQD4asreOfDDGmWvJxwAAIABJREFUF5qg4huhJVMZZVZQVUO63tP2gX1jyfOcMjc0TYMikmeGGFKgUggHtrQUKOGRMaRkeDzTozHDqqDQGq0ldVenYKt8QGYKvE1MzogHn4QYKZNT1egcYwwQk6PPaDggPXy0WN9jXYepCvKywAlPVhW46GhCz9H9k9RObwzWWrz3BKnoosd2SZgJIeAAZIZDcHZ2n+n0mNCDFoZM58zmt5xOj1ISvPNkSHJtkEbjo6cYGGrb4GKg9z0+BvIyo232ONcDgbzK6V1HZy25KchMkQQ0oQ+inSIg2Pc9ZTVM+e1S03YWbSRKKdq2TTzUQzhYORiDMEQ0betwQYA0+Cjpe8dwMML3QEiYCiU06/UmzT8fCR60UPSdZ1+3WB+RylDmiq5vCNHhQgoVs7YnuoiWOUaXOBdQCMoqsVzxSbAdDkr29ZZmX1PlWWJ+Sg1I2rpDRkkIkTzLCJ0l9o7Q9vi+p29auuBSqIJSBNejDoFlbVujVCpeCW1QOuNqdsfOWgIC2zmiDTjfIWPAux6jc9qmYbteUmU56+WS6B1CKKz1WOcxxpBlGYqEnJBRgg8gJV3XstmscN2G0aCibTucc3Rdh7XJtdJ6Rx88HQ15NmC73SOEpHdJHI1BsdlvqNuWzvY0XYdQqfsirXWS6D25znHW46wlBGjqJAoKa8GlDoJCZuSmYLvbs97viEoThMYKjcUhjCJIkZjkztP3PYNqQrvr2K62ybFLJArwvWe/a7B9TOO47ZEuUJmcXGm6usHVLVWW40Qq9LTesm8a+hDJipy290SRcXZ2RgiBum1xLnV79M6y2e2o2z29s+xl+vGlou1qciOYz28Irk/PVKSuks72bPYbQFBVAwwB2fXkwTNQkn63YdeludZ2lhgEt7NZChk1KVTTx0DrLUVWYuue3eyWTEKUad6s12ucc1R5ke5VjOg8S6KqEMwWc0yZU/c9/rCubjYbog9U5ZiuaclyhTQanWdYAou6JQpBbyPeRup1Q7fr6A+s2bbtiDEeuMeSbl/Tty0hRmKM7Pd7lDRpLVcGlRla2+MsKJmzXtTYrk8/tqNtG0IIrFYrZpsVXgk2XYPKDHXX4hFs2wYbwXpH52IqMrjAZrlCqxSW6Puevm+JMbJarbi8vCT6FDDY9z3WWvq2o+49rQ3s2p4+eHSepaLO31x/ba7mYUF/XKTD9kDTHZmUrL7tWfy8Ynpvy9B0fGN6TSYdZ+WO9yZL2vuptdFWycnzpQOoOVb8hx98jguSoelYdwW/87XPqd/x/yor8OCElJ2nf2j5jfde8fOT1IZ4v9py9MECOASRrNybpHNfSFwJb/3iDb9+/Ip3B0tclPz6g3NG356Tr/2btmmzTW3X0gbqU8XvvPM5mfQ0zvDvf+Up8r09xdozeLUnZOJNGjhAdWsp/9aMXz27Ylo0nJU7fv0rr9i+/zPHj6sU/VgdxDLNe9+85O88esy71ZJfObrkl0ZXPLy/oj1Jh7TEgE0HyqAF+rTlt86ecpwlkUIS+WA44/7xhihFSuVufEov97B9S/Gd918yMQ21z/jV6jUAv/P+F4mvKZPYENShbXxhae4Jfuf+p7xdrXi/nDFQPR9UM+6dbRAhkq9TG3C+sAeOKPyt6TOmpuaqm3DdjMiU52sP7ti9Gw6c33AIFwK1bbl/vEFLT4iCwQFc+PXxLfv+0JrcpAMqpPAc4eE/f/QDTvM9vVc8KLcMVMd33n/J5r3ER7RD+aYd2BeSb37rKQCn+Z7a/Yzl6cvUYipC4vzKLqE6hA9U53XinGYw/w2PLdM4NVtHPzFELSlmFuESS/G8O+Is22KDToJtP+a9akEfNHaQ7q2rVBJnrneEgad25uDG6pjomvvZhq+OZ7THgnwbyFaO4qbDjjTdVHCxGTPWDe+VMwICIzyNT1iDkOtD+NGXrM3A7m3Br00u+Eo5Z6prfrJ7izpkHJc1rkxhQG9CmkgCVaU61n3BWbZ94zy8r9c8HKU/fxm2p/cOte8IZeBRuWFtSx6UWxZ9xZP9Gf/RvZ+iG3BVcsEqmxyLZhvZtjkbW9B4w8omRuzKVciv7ti+P0C6iC8OgWcfPWf7QXgzzt/Kl7xXzqhkT2cTEzK/rVG1xewcskv4k9l28Ob/fq+ccdVOOO+OiEOXXHQ24AvF9t084RS94JPNI35+eIM98A+eN6dMTJMKASt7uFcHp//04E4OihfNCUuXiqVrVxJrzf5B9sYF6gaakCv8VZlanr1Jjstsx3l/wsfrR+g2zevu2GA27hCIFNnbjLt+hI2KU7PlXrZhZkdo42mPNbJLRVJVO5qHBZeLCa+2R3xjdI2NilymFvyX9fEbBrUd6599Fwed1Vy3Y87bI2xUGOGpQwZZYkBnq1RgQiTnecg1jz97i9vZmN4r5l2FDxLV+jesTV9p8qVlcNXTnGgGFxCDQMvAuitYtSW+jLiBSkxuLd44vgHCzvCD+bvM+yHfmb7geXPKylZcNhN0k/YZZtWit/YwJzX72wFKBq7aCadmi42KP1l+nXyZ5lOxSPgC1UVk60BHbFC8XSxZugFLN6AOGTebEdmSN++ccFhL3Djnyd0p3795FxsUdciYdUPu+hFCpgCqL53h2T5Q38voNznfX7zHxpXUIcMGzc4XfLq+j27iG0b0lziMfmLQO8mmL/i58R1LV3Hbj3nc3McsFLqGfiAp1imcc/3Lxwgfeb46YWMLLtspF90Rf7r+Ob7Y3EPvD++jO4vqAsXK008M85dH9CE9ayNTkPvL5pSXt8cAtPfKVOxTgm4saU8FtjZ8vHzEylY8be9ho+L3z7+Txq4SFKs0z/NNoJ0oVCv48fItHhWrN/frDze/huoE9al+w572hUB3ScT90Yt3OCn2fL69z84XnHdH/Pn8KxCgOdFk2xSKlgLCBLFR/OT1Q1yQXLdj9i7n0+0DZnWVgsmOdXqvh/gmNFHuFXfNkP5wxv5o8w5D1aV1xxzC2VYpnDNkgupKsGtzZt2Au37E8+aMxmcsLqYcfWGRFkbnPd4IyrlDdZHXd0ds2oK7fsTr5oj/bfZrzLsB/U2FzwUuT8/9y5DJ0SvPZz99hxAFRnouuiNm/YDLdsp+mYq9gxuL2aVit/zX9Eao7373u/96/+L/w+sf/ePf/27fd5jMoKTA2RYRU0DO2TS19Y4GBcFajDRoTHLotS2L+YLeerTKU1BbEJwenXFyNKXKM7JDyjdBAIr9pma/TeEdKmqMzJDR8M79+xyNpwTryJRGqxIhJF3TMb9boCggKoQgJT7nGV955136bsV4WCDx7HdbQoAXz58hdIEPpATSGNFSo5XinUdnxGAxmaZtOqLWnF9dJvHRtimVXCli0PQ2JGF4WJLnGTEIbBDczlfM7pbIzKBVgSUd6rQx3HvwEJNrTLRoqek7h1SGzW7HcrnFWggHNoxA8fbDRwyGOUYrCD3aaHrbsFmveHVxidQZnQcfBAHJ0dEpeTkgLzRBRoIE5yV107KpO5rOYp2g9haVa1RmGE+HjAcFWvbE4NFSYPue3X7Jzc1NEqysI1fJTUmA09NjhAyoGMh0YsE2Tcvd3YzFqkbpChsCITiqasC7Dx9A9BRGI0VP9I7teovvexaLBc22JUiTQoGc59GjRxQZON+jlcQHgbcWqRSfPn9FQNO6SBs6vFAcn57gg+doPIJgUUbhXE/T7tlsGrb7GhsV4YBEMIeU+UIJKpNTmpzr66sU+ATEPom+1kdQikJnTMdHTAajlOouPdZHvBBYn8LjPv3pU5rWojAIwAQ4PRoxNlnaxUZH73tCcOy7mpvZHXvX45GU5YC2bplMp4yGJUIEoogICZ6Oumm4u16y3m7xKiMohY/Q9xatNW8/ekTd10jvyTONt47lfMFq07Df10hSgrwQBusDg0FOlhmk8EglAc9qv2Wx2vLy4opqOIIIGkmGZFQVKAKVMQTn0QRkDFxfXnNzu2C+WlNok8ZcDDgfOD06psgNMThyKQmuYd/seXF+znpviUSCjrhoqYYVRZ4zqAoO8c64PhCj4PpywWpdY4NCeQXBIKWm6z33T44oTIbE44MHEdnvGu5mc27ma9AK60MKPYyeItfIGDCZJBIIQrHb1yw2e16cX4HJ6YJE5wVt31Nkiq6vKTQUpcHbBusC6/WW2XLDbLZC6SytIz6iVXIbaykYVgUxBFrbg5Dsm55PP/+csqwYTScED70NhABn985QWiGkwopAHxzz1YrFZsvl7QLnFCovGE0mrNY7BsMBUikyYxAh4GykrVvWmx2X1zdU1YC669CFPoRB9SgNg0FFJhQIxa611J3j6vKW5WqLyku89WilsdZSVSWubdFCYnRG3XRcXt+y2VmEyvBCgA84AjofEqSi7x3bumd4dELrI6vVlrubO1zw+NAfUAMBIwX5obiSZzlZXtG5jt7VhBC5urnhbrnBOYU2hswYpDCo3hF6S5EVqAgInxjDTcfl7YLaRqxQBJmEUFMNaG3DYFhhY5/YkFKxWK15eX7Lqu5wUmE99CFS2wadGUK0FGVJmRn2+x1KwHK25/Jqxnq1JQpFUBKhCqJwLLcLJidTQkzs6NZatm3L7WzGcr9n3TZkVQFKEqNK4YkSpscnBASRgLOe2/mSq7s7mq5l13coI/GuRwLNgbGthEbEhM/pusDz5+fMtzs6H1O4IsmhIvIK1/WILOFHut7S14H9ruPJF8/RaCQijVtTEoOgbTs0OfeOj/HWopRksVhzN19yN1uh8oymB5NnKJmKm01bMz0+pm17lDTgFPPFhlevLnAIXAgEqbAhFY5611NVFdYGMp2Ctpp9y818xecvX2OlRGqN/TKZFNhua6x15EWJVJLooa07Xpxf4VwkxlTsl8oQvUCpnMVsRVUNyYuKpmmw1vH546fU+0gMka6zGHEohgqBQabAQ52RZSX13rLa1VzPVmxbR4fE5FUKnlMaZx191yGiAg9dbbF94HZe89mz1zgp6UWkJa2xxhiciwiRoYsCFxxEaDvHYr3n1asb9o2DqHBSEr2lkDlBwN/7u7/7V0q0/Zvr3+7r9//7P/qu2fa4YWpj1m1ImIRMsfq6ZPrOhm8fndP4jBOzZ2JaJlnL5/UpxQtDVqdgFw7BS+uvSsbvb/jG6Jr7xTaFkiKYyxy7qCjvAnaoiCaFYkUj2f264zcfvqD2OY+KNZOsJUjJ6/0xk2cHdxwp9EdE6I4kX/mNC27aEb81fUKuPJ+sHqUD7PcKkEnMEjE5Z3yZGLLZt7a4KJlmLa/3R7goyX+Ug0j83S+TyUUEs+25+LWSr53OeLtc4aJi1gzZPZkyfeLeuMN0HVKyfKmZ/bxgWPTcy7cc6ZqrfopWgYubY0avE4cwpcwDAvbfsnxwNOfY1Ic23ZabbsznN/eZfpjCs7K1S9xSF9m+ZzgfFRwNGrpgqGPOwg54vDljczMmXyWMRDHrU8J8rtg/VLSPImPT4ZAcmZq5HfJ4cYb5PEcGyFf2wAg+OKO/PkRIGOqe+8WW63bCxXqCeFwxfeYIuUA3yTXdnxbY73QEIXi7WpFLh42aj+Zv0VlDeFVRLpIQUMx7lIW7b2Z8cTxl2VeMs469y9i6glJZtv/sDOkgX7kUytVHVh9kvHBHvPvWjBAllbZsbMGr7RH7mwHlrcDsE6cVKRJXU0r645zmJImlRz9Nz9ZnEhG/dDc6RIR+mrF7WzF5uMVGTaV7rrope59z1w9Z9wX2nx1RzO0bF6wfZqy/ltxuEQEIrropXTR8urpP+9mY6s6j2vCGnbt7x9CdeTplsBiUCGx9yZPNGc2TMaPzQ7K6T+KE3nb4qiD75o7Pt/cptONRvmbnCv7i4h3URU51G94kwCMFpo788YO3GJQ9mfYU0hKQ/NHsF/ji9X3u/VmgPcsQCHyp8MOc/QPNRV7y1cmcxhumpqXUln/y5Jvkn+dMP9tgpznywGzVHcy+ppgMG2xQPCw3ND7j0+UDmg9PGJ37w9w7BP89OKI7MtyeGCICJzRzO8RGzSefv8vgtcQ0/CxYzCjMuufmF0vKUY9RgY0vCQg+X92neTLh5JNUHNA7CyZxs5tJwbUpMJXHRUXtMz5b3udHF29z+mdpvTOrllBqskVLd5yz3I2YTTRvDTdsbMldP+KHt28Rng44euwPYYDijUDcjTUvywFfO55ho+bZ7pQgJF+s7jH6w4xuqqjvS6RPLfzSRV58vSRmoBTc2SSs/p/nX6d9NeLsRxbVOEKmqB/mjJ7vWN0bsdKGbJA0j4Ud8KI+4ZPrh1QfZ7THinJmDyGBERlhmxW4U89ZmdyTP1y9w+fbB+w/PWL88uDi7dJ30LWjO80QnaYvJF0OV6sJ/uMJw8tw+L6pGKJajy8VykG2j4i7nPOznM22YnMxZvxEoPskokYl34TZ9WOFcIrZ0DAYdJw3R1w3Y2qf8dGnX2HyhUgBfaXGDfWbAMcYNLdjg8wiTmp+snnIs9Up4uOScu4x++TW1I3HDzP01vD4bMilndDEjJ0v+KOrr9N//5jRecA0aU00O0d52+JLTRMHrEeSk8mez1b3GZieDy/eQ308TGM38Mb5LSIMXilevZWzCiVbV/CyPuHH87e4fHzG8JVg+HKPG2VpzAcwtcdrzeod2IUci2JtS/742c8x+thQrALVTQpBVH0kakG58FwfD9gdzFlbV3C1n/Dqw7eZPItk2+SMRxwcpFIgesWNGjDPCs6bI66aCU9WZ6g/mbzBIqgDEiIhAyI2V7zz1Vu2rqBQjn9+/Qu8fnaP6RdJrC4WLonwWqD6SLYWXL+dxrkyEYfiX178POYHFapLIYvSRczOI2IS1nfHGefdiLx0rFzF880xrz+/z9v/QqQgyQMrHwFCQH6nia3hdpQhNNiouNxN2H14hurSZzftz7A3IRNMnkdeHg9RA8/T7SmrruTP/uRXyBfJaJhvAr5Uh46hQxiey7k1OaOq4/H6jE+uH1F8VlDdhITvKRUiQjvVaWy2OSuVcydKbvdDlk3F6yf3efcPA7pPAYfZMr1rXSnRbcQrzatiwF0/pEdztRvzyeu3mP4gp5qlziLVpjW+myikh//q7/27f6W99r/Rou3/8k//1+9KkV6u3vXJzRJSknZeFuRZhpARpZNzpes8+7pmsd7x8uUVqixxUdAHBxisdYyPhhiTWmQD0HUdl69vuL1bEkiJ9DLTOJ/cPQ/OjhExooyidQ1d33F1c83V3RwfQeokOBh9SIr1EJEMBzmdbWmaHb0T3NzOma/3KJGR6OcOozNc68mkQsRIVhXYtme/77m+umO12IDQKClxB/dXZnJ61zKeDCmygrqpCUhmyyU3d0siOolqPiQftXS0Xc3x8b10v4RnV9d01rNYbjh/dUnwAiEM8rBRdS5QDCskASkEzqXAkuVyw9XNgu3eEskQOiMKkDKJAePRCCEVUgS6ztI0Ddu64W6zoTkM5Cg1PnhKk6FEZJBXZEbhXcC2FhcsF9dzFuuG3hmCUAQ83keKIqPMc0TU5Jmmrte43nF9s+D2domXGS4KtIAsV7S2xqgcnek3oo1tOtrWsdvW3Nwt2HYWjybXBVIqdrs9SJhMpgcOpme33nIzW3Gz3NI5EEJS5DneRha3t5yeTDEKtNLsmz1RGXZNy/OX53gfaQXg+4PbNFkhQhT0zlHbnqbv6XxPDA6T50QRabvUxt53Ld52DMoRQkkCHkKgbhusjVxeXLHaNSmILEZMXtB1DtsfmLDaEGKk73u6PnC72HBxM8NGSRSa1qXfuVpv0EZT5KkI0fcdnfVstjWvXl/S9B4rNSEYrPMoJSiKChEDuSmx1hNtZLOpeTmbcXF7h1IZSpnkKifDxchkMqCpGwKevm2p657Fpubyeo5D0NgkPlfVgH3dYFRGHzT5QdgLAW5nK66u7ri+XnN88gjXt+nlJQTee7reUZYleV7R9pbeBdb7msfPLth1AbSh6/vEb1UC31tybdA6o7eW4COL5YoXF3fsWgshOZU9AnFo19Za07ctVV6xb2pshPOrOXd3C9oAvfdEmYo5QkGwjt5asqyg6XrazrHc1NzcLqgbiwgCJTTyoBcFm5x806MpbdcThKSzntl8xcvXV8gsJ+n7ihBBasN2s6OqKpq2x8dIYwOL1ZbPHj8nonFesNmsOTk5AQGd8+RZgfMebz1977Dec3l5xcXFLc5FJAHrHN6lddZZi3MBKQ197+l7z3yx5fzmhs46Qvwy/DHVwwKS9a5FaElrPU1r2e4arq4WvH59ecBzdGnt8y4FQ/YOvGA+W9M0PZdXdyw3e6Q0RNKmQIqIzg3OR6x3uJjW3YuLa25vl8xuV7R9R1QKGzxCK3pn6bwDJNv9ns4F8nJAax1KGs4vb3jx8gIfk2vcyFQo896BAxxIJzD5gH3dMlsmYXu+3hGQqdhBCkUTCnrraFuL9QHvJct1zdNnF1zfLBKvXEE4MOiVUoSQ3ND7pqXre6QwLJY7nr54TdMnx7QwybEZkVjXI41mtlwhlGHX9qy3NZfXM15f3OKEAqVRUlHv6hQWVla0TUtd72malqbteXF+ycXNDZvOYgWY3GBtj5aSzlmiEOw2e7bbPd7BbLbh/PyG2WZDkJLeOpQ8CCoItNQ0XcdytaVpLZtty83NglcXN+ybngjYEFE6w7pUENNGM5/P2feO+WZL03iev7xhsd7T+QbvAgidmN2+J4gUzLlcrlhtG9rGcTObcX51Q9N1dL1LDHVjiAEQkhgEm13Ner1n0zTczFfczddc3i6wQtLbHqUk0TsUEoFARknXWObzORcXt8wWay4ubtk0O2xweClASFpnkQcXuZKG9bbhxasLVuuG15e3zDcdVmj6EJBGJeHUaIgk13LTc34z43a55uJ2xtXNgl3b4YTAxsTjJ3qElHgBbW9ZbXesNjXzxZrnL+/YNj1B6LQeyEheGpy3WGcRWrPY7OhdYLWv8T5yeTXj5m7NvnMIo9PmWQoUnqgNVkV+73f/078Rbf8aXP/DH3zvu1EnjqbqknNL9cld1k80i6Fmq3IGpudlfcx5c8Tnq/vsX4558L2eqGUKCNl6QKAbwfOHJUXpmPdDWq+5rscsPj7j+LOAOhzwokoCcVSC1S9CrTJKbXm+P+G8PuLTz97h+OOfBYap9stDmEAEwWNOKE9bXnXHPN7eY9FUzD8/YfIsEvIktIj4ZRq2xw0Un+kTbAWLtuJmMcZ9Nmb8MonUIUtuQgG4gULvLOu3B2wnkqfrU5qQ8fT5A8afJ4eM7g4HVUBEQbbsuPnqgDhMwuGPV29xXY/57PUDjn6oU7ecTC43RBI910clL7MhwgiWtqILhj+/fA9+MmZwHZFpQqd2TyUwjWD5SLNVBqHgop7ycnPM1d2UwWNFuQpvDoSyD/THBp9Jrk4yxoOWjSuZHUTIyx8/ZPwiHTy/FIVVG+immvNJxV5r7lc7vtjcY9fnzD89Zfp5+t66Dfhc4gaKbOW4Hk2YZRlWaoZZz6wbcLUds5oNmX4qqS6alHY+MUQtqG4C12pM+bCmPbQQP5+d8OyLRxz/NJCtHUiBnRhkHxl/sWX98yWzKmOQ9zxeneGi4nI+5fjPDLqP5CubFAB+Jv5JG8l2nnIeKRaOfOORPjlHpYtkixY/NOidQ/iMZ2cVvdTsfI5H8myTxIAnn7zD8DwJFLrx+EqRbSyyy3ghprhhZO9zZs2Qq3rM+ZP7nP0wpPERIFt2+IFh9NqxLQesx5JeJGfkdT3m9fUxx9/XFHOLqnukDahdR8w0/TTjJ+YMOXTMuwEdho/uHhG+f8T9v+yT2JWnooN0CTkyf6dkthly5YdctFOerk95/eqM8cc5uhNJfDaS9lhR3fbYKqNtCuajjK0t2PuM7z19H/2TAaefdGy+WqXW5JAKJ6oLUOdcTgtGw5bz/ZS7dsjF1TFv/ctIeV0jEJh1Syj0IaRPcDcq6Au4bYbsfc4PX7/D0fcyBjcOcXhuqkvzR+8t/XDAdTMiHnmerk5YdANuP7rP9DEUK4/e9gm1IuTBba2gNlxUJeebKVfbMZvXE6rPcoplfFOMSmFWoJxA7wWztxXnuyk7n3O3H7J+esSDDyNRcXiGiYcatWBwE1gfFzzZnHDjB1yvx3z2+hHFPx8dwo8iw0vP+qs66RVrx35cctdXXNgRN+2QRTdg88kJpx+lMRW1RO8s2kLINKpTxN7wUg+5asbMmiHPXj6g+Kgi2yQ3qG4CukmFCukiw0u40WMeb094sj3l9m5C/dMpDz5Mdr583uJGGXrvkL1Ht+m+TZ9E/KZk/GPF5EUK2RMhprVKCXyhUG3A7F1aW6Jg+CPD8AvN9HEkX6f1Rtep6GV2jnztkFEwuA7Efc6rccXtdoQxntc/fsj9/0eg+0hx0xwKLD8Lv4pC0mrDrpQ8W55y+eIe4pMhR4/dgb8dD+OjB5nYsHFbsG4HXNghry/OyP90wOknFmVTOeVLATqFG0Z8ptFzw4v6mL1SnL86Y/J/FeQrEvZFSVQfDuJwEhnLJ4aZHDCfj1lcjxEfD7j/F+FQ5JSETFLc1sRM0x1pimWgWw9YrEe83kxZf/8e4x9phpcuOS0rhdl7Qi7JNg4RoLyThNuSKzti8fwI/9GI6ZOI9IegyOzLcZ54s8UyMHwlWK/HbK7H7L6YYH5QUs3SZ8/qkBzm+0jQAp8Lpk895+sz5i+O+OnuHuJ7Y0bPk8ipu2TeQ6bvrVxEdxF1nuMvKl5sj3ny+C3Ujwbkq0ixDofA0p8VwkSEwXWkuDRsb0asvjiifzrk9KP0rjFbRzzMv2BkMh9pQbaF7HmG/fGI+fNjxI8rhpcR00b0oQCbrVPHJjKFspVXCvnnFfXFGP5ywPSpxzQpuCzqwzp14DwNz3uKlWT8F4qbp2fEj4YMPzZUd+kZ6n165xBJ47GP5JvI4LXAXVSI5yWDP8sobiTqULxKgaoG3QTMPu1ppk86QlcSLiqpMz/VAAAgAElEQVQu7Yj4yYjRp4pqHg7YIkAmJ7AMoOvIP/z7f/vfftH2H/+TP/iuMSa1aYuYWva0ROkkTJnMMCwzfEzNfD7Cpt7x8uISGyJB5XQ2YrKCKCSd7ZkcDQn0RBGwIrJf7ZjfzolC0XQ9yuQpKT6mQ9h+t6bMDcpAjI51ved6dsu27vBRJJFXaaQWoCQueIKAB/dO2G5WCATL9Z6bmyURQ+dBZxlCCqztyVRJsA4tE2ey3u4PLrEFwUekUsRUjEAqCDGkFkzbkYkMpWC3r7m7vWO93uGjSsJJjAiV0AnRezJZEJwgU+kg3tmW29sZu7bFupjakEMKAjNFSWs7VPSEENFSMluuuVuu2NQ9+AwfBU3fkGUKJTW2aZHCczQaI0VAC0nTt9zNZyy2NV0f6HxAmsT6xHkyCUZrMm0gKNq+Y73b8OrqFk9GlDkWkcK7giQQ0UR8ABEc7X5N2/dc3cxpW0sTHUKpQ4tpR1mV9G3AmEimFF3TEZHczZZcX9+xawPV9AhhQQgDUWCKHBs9gXRPCJ71Zs18sWbf9fQ+MV11TKyiYVHiuhYhxRvxcL5ac35xwWq1pyeAzshCwAiJUDo9m0wjtEotzfLgeIsep2TCCcSIEVCYhJ8YDAuqsiB4T9fWWBdou47NZsfdfInJcjiMYXRGFxytdUyKir63WO9ZzNZc3s7Y71qCUEhhcAR8CGQ6x/ceqXQKvek67uZzFvMNu90+cSWFQukCo9ML1npLJgSu74ne07SO29mSV4sFjbMgVWrVVwqPBw3L1ZpqNEIrRegc3sHrizu229T2a31CZ/gQMHmePnsQOGfxwbLe7rm5nXM3W2HUkO2uQ5JemFIopEiu0breQ4xsup6bqxvubhc0TY/UBc4HsqwgehAuImUKKjJK0XaW1XLN5fUd265j3zdEITFa44JDZDIJ6cHRtS3OO7recXF7x+1iw3q/x+iStm2Q2iBFODjl0/xq2o6mbbA2cDef02zbFLYRBaXJCN5CcGhlsNazWS+RUrFcbpjNFszmM6LQWBvwUWDxqEMgotKJU9nanr5zNJ1nvtjQWctqtUFEhZAAgrZrkUia/T6JsAT2TcNut+P84oKu9SilQASMyjBSk5uMSGS/3dHUNZvdlm3dcn5xSd0kMbS3LmEvVBIAhEximYuW3vVsNzuWixXbfUsMER8iOjNvQicLkxFCRASJEZqu7xOX3GTU7QYhLFJqbOzxoYcoiCKihATSZq5vLZnOUVlO03QYk+NsAKHQOiOKiAvJmXp9t6TvPecXd1ye31BV0yQeix6jcpy3KaAwz1OC6m7HcrPjZj5js2tZrxtq2xCEQxHT2qACJr0w6BvL7OaG2WzJky+ecnZ8j6brMNrgvcWoDEJim2cquVjbpqFpepbLDZvVjmW7SmtfkcaHEIoQPcYUSdyVhru7Oav1jv2upa4t+32DMiW9s2Q6sVm1VljbEkKfKsltx36/5269prdJgAwCXNenFnuZgjy1NgQf2LUN6/WOuulo+p7+sEYGQdroCJHEjt6hc4lGsd237NqO3qX73XmHI6bijBBoLE23p7MOXZYsNjvqzjJbbOj6VCAQJkJIXR297VBaonUKzApB0NQN2+2OdbOnaVs669HKIIRg19RkyiCRSCGxNhUgV+sN1nq2uz06K3E+kOdZciITEEIiIyiZEEPyEFAWhIAo6XwPWuK8w8cAMtBbS5FnbLd7EILM5NR9wEUBusQR8URanxxpyihiiBitKQ6BjL31ib+sDTFC73q0lglMqyUhWLwICAHaJKSNxVGUI5q6Js9L2q4BAjJGnHMUucHFwGA4ZLFesW8a5osVbevYtw5pJEHIxO0OAuc7egVBCv7+7/6N0/avw/WP/qf/+7tukByW0iZxApHYmvk20laGeNazcwU2KlpnuLqdcvKhxjTJ9WPWFl/pg5vHMX+74DaUdFGzaitulmPK5xnExL4LWRLNvgyNUouMq2mOzwQuKF5dnzD6NGN4Hd4IwtHIN+gC3QYImvIbG57dnSIU3L0+YvKZQgRBdjjgfpnm7YYa5cAVmpUr2C8r9EXG9AtQBzFGBECl9l7pE9vW7CW3wxKnBYvZiMGTjNFFOtxKl1xoHAKNBIJ+mDEPFW4Qme8GrHcl5ouK8i4doKWNmL0/JLgrzFawMSWXcYDJPX/54l14MqC6AhCpTXXeY0eGaJKb1GUZ/u0epSLDrOdmM0I8rZg8T2FnZt0RdXLZIgR2INmXBjuIRASX2wnPr844/oFGWZIAVPs390B6QV8atrbgThXMt0PW24rRTzKKVSBfplZts3dky47+JEc1kl1l6AaRq3pMHwx3T06Z/FQzuHG0ZxlI+YaTnNrtBTM/ZBFyHJLucsjouUqJ5NskyIRcEpWkeVgiO8FaFVx2I2yQLF4eMf5hzuSlPRQMwI0Mvkr7D92kQzTiMK5DEhRUmw7pCScgcAODnWqkh+2xwUx7Wm+43Y/YtgXrbYW+MRx94XGDVNDL521a53cBs1XcPtJsmoJ9m7N5NeXoY0l155PApASyc6jO44YGaSV7nbPNFKt9xfbplPKlYXCbDv562xFyjR/kIASmifTDjHVXsgkZV9sx9vGYBx868lnaX8oDi1F2yck1fi1ojw1NJmm9xlrN8C8LBldp7Jq9I5vXaJeQKNkuolrJpq9Y2oL51ZTh5xlHj5P7bHiRxpSISciLOnGT1dJwIwbsr4fYz0aMHiuKZXpfqcbhR9mX5MFU3KklmzKj2RZsLsaMPs4Y3gTK6wYRwR/4sVEJ9LpB+wyzU9z0Y+pdCR8POfosMjrv0/xZtqAVIVPobYeQCt0I4iYnLjNckzF5LMjXHCrkgvZIo1wSgUIuMXUku85odIZ7PYBnJeOniYtp6hQYpPeWUKTiT8gVeidRW0XbFIRVxuClxuxg/LwBKQ8t+YkJGrUkX4PoNbU2dPOSelZx9kMo1onBjEi8VTdQ9KPkvqtmEbnO4CbH3pac/EhQLpKwme0CvkpjkTSMD+5sjaoV+sZQvVZUN8n5qbrUbZDPGkKpsdMM4Um/t5Tk2yTOhUxQ3vVEldZOXXvkIahRhEg0Mv0yAdnWYUeJs50v0vqv6zQPQ54ci7r2KCcY/1QR6hz5w4qzjzztcUKgSJ/c3roJyD6kuSsExQzUeU75WUZ1KRide/JZnxymRh4EWI2rdOqKqCNHn1tGTzWP/o81q29UFMuQijM24qv0uaNOhSbdp/b/bCXJXxmqK8nkeY9yaUx8yUJNoZGpMIWSHH3uGJ4L8kV6FtVNj8+TK9wPdJq3uTx8t+Q8zteQzyVHX7gUctWmMDHdJOesL9Jn80UK7axuHcNLyBeCYhl+VlTzacwKUuFPNwFfSnwmGV55ylnE7ED1kG0Duk3vjpgpXJFa/vNNCiabPO0pljB5DNkuBXt9uVaqPmI2jpDL9JlsRLlIsQxUNzC8jBSrQLYN6bMLQXnV4gcJLRRVek8pGxm/sphWMLj2+CwVae1EJ5etTOJwclgLinXCS4gAKvkUyXdJdFVtSAJ8iJhNT3+UMbhokQj6oaSa+4PgnFA8qg9kiy4Jq1kSiH2VmNxRJRE726cgMBlSYcZ/mRhJeu5fFo98LikX6X52Y0m58PQjSb5KReqo0n6kOzLoNtCcGaZPW6o7z/iZIN9AdZc6c+The/kiBb6ZTcI3/YN/+O/9lfbaIsZ/c9llf/e/+AcR+f+y9269tmRZftdvzDHnjFiXfTvnZJ7MrK7Kcrdx26ZpJEsWNwsJCR5t3JLxGy/gj4B4rkfEh8BcJJANog0WrwghMDcJGtstcNuoq+uSWZmV55x9W2tFxLzxMGbEzmok6Dda7t5SqlSZe68VMS8jYv7H/yKUlo1p4oRaK14HqEr0ws3tkc8+OpJr5f7xkYeHB87TgvfR0uLFUZtDmiM4O4jevTqy3wUolcfzI/cfHgFozdg7QzDWl4hjQHGlsL/aU2Pj4flEyWZw77zSqpqScvXuaxbKNQ6R0TdcLZxOF5p6LqlSqvncoQ5xtXcIheDUkt7zgtOBKS2I89S24JwDZwn1XjwOT80GsB6v9tTlgveelCtVeqiIeESVnCZaFUKIeKqFGV3vmJ8XUrPiPy2zAc45sxuvWEpBNZCmC6+urxm1MqeJ07xQxIEYs7bSEC+03NgNe0SE42HHca9cphP5kkgpMdHIJRgzWQe0VdQ1hIqqcrUf7L6LUlpmzv3vckHjjmVZ8BpwzjEvE2McCIIlk1NAOngeA6qBWjPS/S2DRua68Pr1HaFWLnMhpdmCj6T2kDs1ZpYPTCnjvWM3jKRlImpEvHI6PeE0UAoE9ZSScc4ZwFMbx9GsATQ4coMxRC6Xi/kjR2/zN2eiBJooS86dXVdRcdRa7HeWyXxPWzOP3WrrSlUJKnz34zuuxoHTeeLn33xgWjLn3KhOoIB4k4631hiGgVoWs5IISqaRu+G1U6UsiX048PD4RBgONC20NnM4HEgne0BNKTHEyPPphAsKCLUa01RVGZtjUM85XXDY/RiiANPzxDiO5FbxMVBa5Rh2PD09Ep0g3oD6m7vXlAyXeWLJBah9jE1eFaKCJIIT5pwY/ND/m1mJOK+ktqDB0dSYzNGpjesyUxwEP5BSNu/bAochUkohzRPOOUI0gGPY70gp8Xx+4ni4Zc4JJNDRTmueOKGVirgGJZPIDLuRGCOP908McUdr61hXqlTzLlVlN+w379TgBzR4GzOElBIxWrCiVqC1HkJk+6S2RqoVXyrOKUWglUrWRvSBVM1LdRz3tGZz9Pj4SNCIdwEK1LagUUnNalj0ASkZldLXZcIFT24VCvhmwLvzzu6p+0WTG1f7A9SGtMZ0sc8FmKodZnCCa+YFI7VRBd68ecP7dw8g2gFIcLWw5MQwBKqa57E6k9t7hJoLtWbCEDmnmeoERSyUyyutZGjd77WK1cvaKGTGceR8ubDfGZDeWiMOZh+hQ+Q0T+Rl4fXVEVcb59OEeI+4isvWHJMxWF1vQtRAzokwDlwuE8PewDznYFkW8wNWRZzHoWjLhG77EMcd0zQxDAPLsnA+Tez3ewrYnPc97pz5sFt9DSwpgbO1oM5Rc6NkUO3+gN68eZacGIc9rdkYgqkgvPc8Pz8ThoC0SvCeOierZ062UE5p5lWd80IcB9qUaE5J2ZocpVodqrkgrm61q6jH5UxpxdaXGKjaBATtz9WG6/cm1fy9m8CSGiHYy71qwHvzbG4N8yF2QikG1JfWv9NZ47JVpTZvz1McjkauCRXzZF4W8z4uNFx/FxO3NsUcqpG6TGg1pnNukEu1g2noIG1RQlRqyhaoBrRS+r0XO7T2sLLmGnUBSjP/8WZr/jJPjPsR14y13skIlCaM3i6sVru3UgohmKd5Sdk8eaP5dhu7wEByRZAeYpqaQ1XwCHm6WLMFOJ8WDocdqSxIiNSWrU6kRBCh5koups4QERxCSQvVQVOHk8Z//u//9Zf0oD/++cf251/8i/9uAwO2mrMpNymw1ZjlqDz/kuPp12aYld1PPIcvGrv3hTw49l8vSG5Irh3AtIPrw/c9s4W6s/9Z4/pHVpekGli7Bkc1FZZr892bXglS4OoneWNeGSuuM6zoFgmlUQbHfKPMN3ZQ3H1j79Tj+2zBRP19p3XLA8RChZajot33dXiwfexS3awB1vTz6u3w5k+Z86cmDc/9QC5lTX82gMZfDCzLezuES208f6LEp0bo3o3DBzuxuamwvIr4kwHL/pS5/5M7lmshPjX2Pzc7hPVaJBXzgy+V+Y0FxJ3eei4fC/G+4Sc7qA4PFpbkZgOKanQWEicW7nR663AJqn852Idz3bwu/dNC2RkT1j/OpNuRvNdtzux6XkKDVolucwbCxsfC/a8E8l4YPjS7pkcbF/sbIT7aeMf3E2XfvZO7f22TleUmuKWSD35LGV/H4fLZDrcYuKSTyUvHD9n8kVW2MUtXwVLGn1Jn13WLjS551vPC8nqHdEay1J6CHs2y46s/r8wfF8J75fZ3YHisjD9fbI2DAQ4AtZFuou2XwZEOJpHd1m5ne13eBI4/uvTGhvk+Xj4KhHMH4e4Ty01g98WJ5vv7Zq6UXaAOynLrma+U3bu8rf/1J34wEMNfCnlnTNXTJ5bKno6++5lmnr47oEtj/JDxT7YWy97jFmO7r3O9zuVyrV3ybn/fVNBzNkCuf3+68sYYS637/ZpdiFvMR/rydjDQswfErQFVy5Xto/FnZ6ZP9oTH/As1qXWrlXVebM4y6SaSjo7Dj86k28H24dTfYdIKGHrm1wGdmoFpe7+t19+/PtaQuOZkk127ZKzRvNMOtBhYpJey1Sw3F5a72BmPwv7HJ8o+bsCPrQcL5lrHad2HUjtrMrjt2s3moG61TteGA3D6NPZrNHbxGvy41pO1Rq734VLlw5+K3Pxu3q5lDaoLT+YVLMUaTjXa35XBZOcGUjpb66lswZSS21YDVon62kBzS2W5DQw/n5g/Gon3CWojX4WtTo9fnXHnhac/84qqsPsmbXV5DdBbwxNrdKSjR2cDXYd3Zt2y1pHwnHEdPEdeQL+0tzW1HC0sb7lyhFNl/PrC9PFuU5GUQcl7R1XMo7k/98Jz3sZwXS9uqdsYrWvFnwrLbR/Db+3zMjoDL/e+7ycDPGsQs+rppaM5Y8qu6g8aW2BfurL7/nZ9XWvL+rzRuSDZxqmMzpoiJnV7eSbpuhbaVgfX61rtK8JzD97sQKsxR+l72dZvGe15LOVb16/W+CqDo0YhPFkDcv37bT/Vl2BBl8xCqQTzvdXeNFvHwqVqgWSlbZ7L9sy3cbd5c9/6/V5/epCXKzC8Tyy3FqDnzy8e3zoX5rvQ90Lbnt1rsKfOFVoHUs8v7w3rGLTep/Dnso2JTi+1Y/hmZno74M91m690pfjzS9NAJ/u+pmx+0euYNoX/9m//23+gd+0/3Ezb/+w3f9BqRZzJLmnYP5XuRQvn6cTj4zPffHPP/dMZDSPfvP+A+kCVQowjy2w+iVOaca3xdLkwLwvv70+cp4QBUQA9DMcZMFWBEEZolalVzrmQcKQl472nOkcTR6Xn23iLalZVo30nC1SpYlJ0J2K/66AG6ybRjH8TXKDRKK2RcveWdf3wW6EI1KaUUvHqqRlu7245nc5QDQyuTlH1kIy900rBR0/JxszZDQPzPJGWQiqVCqgPlP6/3geWVAjeU0rt8vKJeV5ozlOd4tSbRF8w35G0GCNDoDlHKQtfv39nkucwMi0FNJocONee2GkTuSwzwzDwfDqRa+EyFeZq1xa8Ik5M6dS0A5wmA3deETWv1CaKqEe9p7RMyjPqPDUn1EVSLojzpFTI08L5MvdgnA4GqPL0/ID0ULBGxWtgWRacU+alUHIhjrsO1CpelVKTebWqXWep4F0g4wh4lnlm8EoLjdwyLig1C2kpaDBPTacGvKkqpTOjvXoDxXAs88JuHElzIgw7am2cnp95eHrk/vGZ05JoHUQXddSUNnajimMi44PNmXhlThlEWeaJpWScCNOcuTocqSURx4i4wrIkmnjEOQMOm4WZtVZw3oAUaVCLsQhEzDeygsn4vKcuGe1ArY/K+Xyh1IKrjlIyqoE5ZUIYWObM6XxGnY17bRZ85pxDA4hr5HJhyTPgmZdMRSiSKRiAKSqIOEpJqNj4lmw+qdmIeqS0oM4Yh9QG1QDiIQZE+hrxQsqF27vXPJ8v7PcHcrb7FrEgqyDa11vDxdjZoiPTnMA5A1pLg+71ab1wb6BU8AZKarADXjE2u+vgWQNozRj7raGuv4iVRhZHc85S3VszEKsYcNacAT/ifAddm8l1sCaDEfU8qVggU2swDoPZrnT2yyUtFLEmQ+k1Njgld9DYXrAcpdqYnS8X8/idE6LeOpK10V+fcaL4ptAqDgNUn09nKs3YwTUjUnENYvD2/70HDJAsNeOap+TCbneglMaSzd5CnCDNIThaLVtjo7WCU6GWTBwi8zwzjCOXabK1q2IM89asrkgjhkCpZ3IGUU8pBu55GRARcmdcqvedgb/gg0nP6QzflAuCSc1UHK72l7DgWGpBY2DJJv9bw7mkezlJZ7OmlIwFnDJeHeIMqHdqDHLVFYx1DOOOyzIZ6I8FLR72R2rLzJOB/6VVW0s5o+pwakywUjIUa9xUMc9lMHsJFU+Intobes4b8760RimN4/HIMs8UirHasbUk3dHPe1v/8zzTmmxhgzhBpBlgqGr1VRrjbqDWgnNK8NZIEmehjlUKcYhcpgnfa2YIwfZts4Zb6bLIVoqxrVvFqTKGEcEakRJDr4tK4tybdQO1Kq6v5VosQKc6SKVQpNGajXVZZrw6asm9F2FvjU4tRHDJCSeOy5Qs/FQd0n1OrMnjECotJ6iFYRhtvJs1HEst4B0iyhBHqGa1U0qxut73XxGrY2bb0A+f4vC1ElFImSaOVmGaL7y6e2VrCqhScAg52yeM0YIEDQSPLEvutgq2LoLai/Bf/Y2//MdM2z8CP3/9P/7vf2Aeq/2g1KyJYwXKzoO7byrxq8DNPxKuf5Qpg+P2t+9pQ7SD/tETnhbmNwPx/UwLyuHLRDwJN79b2H3Tw2OWF9CrOUGTvReugNH4UNm9M/9B/7xQR4/La3BQ2wBJgBZ6czAZqOYKGxPQZQupcdkaydLo3+NxtRl799KvRaQDrXbAWxmLZWcH0cfvj+y/Num9LnZANMCiH8yLAQG62HdNd95YXCfzUnS5g4xzo0aljmqBans7oKdrC1kZHuvmwbseoq3oOJMC88IY9Qvc/faZ8aGRDgZSSD+0Cy9zh7wANfuvC+FiLKnh0Vid+aB949v91GiHTNTRvOuBRd2ewQktCLp0EC6ahLhGZxJiJ4SpoRPs3hXQlTXVZeU/OZvn5WBvRWVUk8p6kwf72daRztY8K6N9vgHP0m04uifnZCyzcLID8sqgtXEzZmQLik6ZFhQ3Z1pU3GKNhTr6bf37p5n59Yg/25y43Nh9A/ufOq5/3Nh/nTpL15hzbimsD/AalfjBLACaF1AhdgAyPC6Ep4UWlOE+cf5sNGuGo71PxWebs9bfqZs6WjAQpwW1de6kN0McqJgFQpd8r+t8tRTIO2X8+oLOBbzilmbs7A6qxFNl9/XcmaILbjFQDpXNp9dfCuHJLD/iQ94YZ24u3bPa3hfdUml9rRjIpC8MwpPJn8to89I3Gcu13Xc4FZP2nwvPn+/Z/Wxm/ijaGuq+vGDMOPP8tPXglkod9QWsXEGpZqBnC87Y2dEZe/NcOjPUmiorS9PAkhWIrf3fd2l3eWnAuK440NnOOq0z8F22d5q2ga42D+XgDfgdHP7UPX1L6/Jp+z1pBi65xc7lbi4bw9gCk9zGCHdzIV0HAziDIz6Z7aG0FVh7aWLVoTNJ1f77/qvc647bam7zQr5SXHoBy1af21VKPt95sz95ehnj5mxOVgC9baCtzX8+BuJjsnC+d5Ot3dCZuhe7vxo95cqaBv5idXLbi4OtcwOR1WpQaVvj0F/qxjD1F8Mfmto8ryzltRbUQQinakCbGpMTsc9fx8lfymZvUwaT2rvcXjx1g/T5gnTjGX5u9+Q6O/vyJuBKIz5m8t7qvl1bNR/xfk26GJbTvD2jXOoerrXPxcEUuzbOL3Phlsr5k0B8MuuLFmxvrhY5OGMn44T4aLUpH5T1Gd5cBxj3brMhWu6CBax5e2bp3LYmpssWIDq+T9QODue9/sK12u93UH1lWzvzfjVbpfYSMioQThYG2nrjswVhvlI00a0FDKTW3jAT7N2gjIrLdDC+10VvIHp8snsZv0nkzupdieY23zaX0s9fy3W3KFwVFp0xjth/QyxIzPexcF1xo0vte59NXVB2VlPSvte/DrgO94mHX9kTexDZWqtWxvl863HV1Cx1sFrsSm9KdfAcJ/ybf/UfA0/b//S//Ns/IChLTTjn7AArQqbgwmASfucoScgZinOUJoj3dnjBpLmtKqdc0f0BEuwGO/zWIrSm5Dkz+ohzEcUxl0IbA5eSOC3P1NHSp4VKrSeqwFQaQxhoQC7ZQATXoGVKnvAiSKkUhCIGdNgB1w7h0qDlihaPVgMiS3HkKh08FGMKZu0hDn7z7XTOoQ4enx5R7xjDQENo/RCOmE3DabrYIVAqcQiWjB2HzgxWmjgDLzDriUajtkT0niaNJnbwRhRpnlZMWiVi8jNqwwuMQySVTMqZhBDjAedHLucLqgNLtWTt0QdGhEwlxsi0LFymswHOCJlkhatVllQZhp0BvQ7WVlQMgdJZeUvLVDzPTydUHbEJx91IuszouOM0TySEoI3LfMIriCQzwU8N70dqFQMXi0lto98BrQMCEKNaYZpPFs5VGrRGayaHzQ2G/Y68ZEoFDaO92EglpcnmTjzzYp/vxCHOMQxDt6IYmJ2Bc1LM/sE1k+YGTCITxJNOJ4IIS10oGplywblArpVWbc3tnWOvxt48l4wXo/230limGRHrVAY/0lDzavXQyAyhEQrUkqm5IqIoFVcz4rV7rTYu+YxXRZtCa+ziaMxzHyhiPrrzMuOHgef5gh89y3wx+4YKTQs6ROa04H0w8C4nfFRqy+zGgVIWkIK4TK0TtRWUHU7G/gKhOFUoQvCDhQS6HVojKhDJeBpeGlkaiQZR0e51VuaEBCW3ShyUUgtNAjqO5DRzGA/U1AgYy5RqLEvzOA1MOZPBgDSx0MJlXmilEpyjTpnBB2gOpybLri1T6kIhkSkENWDS5YqXStBmfrrVXp5al52rc3jXA1tKQhHwDrxjoaJhYOeUVhpaM84rToVCMUbjsiBeqDXhg4NaiN7hWqVJQoOx+GsH+30YoDSaQNzvuaTEYRiZc6ZWA8ocICUTBodgIGPzMKcJ8YLTaLWiQZWGqFKpDDrQakXUIdo68JwJO8+cKugeaR4R7Y0mZREDn8UrqTR2cYcrgjrB14S6hZYTcQiIaxsoOY4DyzIDQqkVLd7OnW8AACAASURBVILr9coj4BoBSKcz1/sdOTXOeaFJIPqdhUSq43J+ZtiPRDy1FvusEM3nFxAXERdQH8mpq0Caw7tIKhDGgdocpQBFjQ3cGaMxekDM31QcDTjsd8as9Z4pL4gzYLrWQi1WC0UVcKS6oBoQp5TmuJxngjemdsUUHevzyUK0qjUBG3ht1Jppy4yPDWolXyZUGk0i09IPHL2GeBT1heX8zDgorVoDsnlvuEC2F+mgto5yLfY93lNbNrAyBJZpwUdP8A7vYPSOMhcigZomvKh9nndEEWrqabGi+GZz6CTYOmqeIgtOwHthUMG3xkJhqoWpmFer84GUzULJdwsH1wSSWbfUWmgixChIFKoKQYTo7GXLOwtMFBpNCrkz4ksWllwRFK8OJNBqDzKqVje8Co5CaxUXor2boJQiSFNK8ywF9vtrylIZhz3T84noI+KUab6g3pN7k883oaVMzgnvlCFGLtOZ/dWBOWcEJefEuBvIeUKkUql4b3YRg4/kVJgBtwtcXV2RLs9c5hPxYME0XoW8JFQG/rXf+It/DNr+Efj59/7G3/kBQHxMGxDUgttAj+FDZ2J2L1Odqx2AVDcGrOvsweHLJ9rgkSYsr4ztth7MdLLE69ZBotXHL/78meGrM76YdyJiB1zJhfD+TL4eDag8vzDOBXBLM7DGmY2Azs2uSwx8MeCgKz06GLBKFP2ldNbberBkuxepbQMJa3Qcf3Q2tuONbg259QDvL5XxqzPpJiJAOirxyWT0rnTctJp80gDZugE/+ehfJK9lBWjt981XT7Z7rTvPcjvgT8n+OZv0vKkFx5gvYkKnzHI3sNx6k6JeKfHDzPDNbCzcAn4yUM6lRnjOpBt7N6TnWoAxA7WzoVegcPzimTYE8sFzfmss0ZVF558TZW9su7qzZ5vrTLSVxVcHk5dLMZBhBV907uCxiIW6rH6SnYEWHhZ0yqRrY5PqXDf5tTQDCNYxDY8zkquBn05INxH/nKl7j3+0ZgIi1NFA0Rpk89Q0mbbJh8NzQRMM77tn81Q6Oy4zfzQyvQ60oIxfn6lj2ADq8GS+iNKgHIKx1BodjBOWKwus0dTZYs7sOAyshNpBgvDNM3UM1Gj3Ob8KW0CgS7afwr01NcafPlH2sUu048bcq9ERHoxpvILttTM6l7uhA/wGyrnF1mWNbgM6V7sQgHwItmYPBqTWqOSjscpXqa/2ULfWmYkrKLeyzHRpL0DiVJk+jrgC5aAb+FqjvgBS94sxTr0xxsvemh1STervUjMGJusYOvN3nU2arVOh7I0JvDYB8voZ2UAWY1oX890dpDNQDSjb1tfjQo3K/DpslgqtsxnXOVzv1aW2Aaj56DfQx9iaBrwhUHbWNHClka4Cw7uJ6e1IfDSVAGrX3NRRdisAansiPloDaQOgV4C5f3+69i+p9H0trtYD8dHmaAUx/VReWKadUODPheXVQAu6sfVXD+B8NJBuBe6X20B4yh0Uq9YM6T64Na6AlwHoz98ZTLL/9ck+6yr0poMzv92baCBieWG4+lOxxktv1NWh79HOaF59yNcGm3bv06Y21tKbJFIbvgPsUhqXN4H4ZPc8vO9e2O7bgF1XZzQIz4kWtQP4lfGbmbIPW1jgSwPAGhuuA/frHnS5bf8OZGt4STGWPE42W4Ey2toKp8Z8q7gq/flJV4v07KHe5PQXe0fewE4gHQ3grINS9jZ/87Xi50Y6mB9rGaSfp42tqmsjQg0AX2vSChTrZFYCZXQsV7aX4nNlvM/Ee1NN1v7OYOxz3e5LsOfxakmS9taA0mTN2nzQrsRYJXH2/PNne35p945frQoMPGbbn4KQx5cA0ZWlv9qCNGfz5p8T05uALja2430hHQwQH+6zXX8PO6uD29jo6z3v3mWm135T5/ipstwFU9KIjVUZ7fOWG3v+xkcDmk+fBMKpMXwzkY+h34sF8ZUo/LU/YBDZH2p7hL/0r/8brdaMODuA1lpNkt49Hx0Q8Eg1HzinJmlMtVgSdy4gxtJcFrMGCEEp84JDGKInZXsm1VoZgjEL52IPXecc03LBqwGoirCUhWUccE0JNTIvF4YhkMqMDw7fTOY8MJJzxvlIcgXX6IwwO5w3p5RlNlZRsAdBadksCnY7Y1Q5O2C3ZgylQXuRd3awupRkh+K5EH2gOng4PbMLkZ0LUKFKxjullEKuCelgUq4VkUoqFfEW6pBpiBiAGMWAAd/EPFj7GJlk1SSmrtVtXnJnGBYMKFBVynQxabB0HxKnaIWqgpOBaZq6jNSS4JsCrTG4SF6SyVKrSdqdM09bmoOgOFllsV2u2iXU6g2Q92FHE090QmsZr41WEs6PJotPpQMYL+uqVmP95c7Wcl6NQo3duyWwN2NOumbhTBrI2aRrKVe8Kq5Vxqi0NNE0Ms0zGrxJp5tDsfsKIZAr1GYMLvWCwfxqn7skG8dSieoRgbnM9i7vHCK+M0ovxOgpRbDoKGNTelGccyxpZh/HTZqNlh5URl9Pjlat2l9K4rjb47JdSa1dv+CES54QrcYYLQ5w1FxxToj70SwkEEIYSLkwxMA8nRlDZFryBt5Jw0CPLmXaxcg8nQlxZEpLlw/b2jJWMz1kwBiy6tkk06wBBNg+aa1CzYgGRC0gqPW1exyNOZmmmdxMUB3UQ+pMWmd2HylZs6KViA9CLgvqYmdTK6UuiDgjyrdGyxXxCuqobZVOO2q3Qhn8SHPF7AVyQdWsWFSVnDOhFeLgQYRTaqgooQpZARpR1A7A02TyZmnWsFKlibEwxjggrjKVRKHhvdk/5GUmhICvjlyA5nEBxFXzavXFfDx7I2YI3dfTefNvVrMaCM5qq4jYOq0VFKYmeB/I5wnfDGgWEUqrG5vUR0VSojWxALdLhgitB9qBIy3FbAmK1WsdDNy8TCcGHYxNLfJiS5JnBucpwdjVgDU7nNtsJpZlIQSlMdJKMl/0lg3op1mAVvSoCEqjlYg5rma8E2ozoLq1hlQPTRmCw6uAK+TmmBd7VmgISGvUXNBWKV2GF4L5fyOFKsle8oJnWTJezT9dMWWHnZBqZ6daWBfNsYsD83SmqtUq9dE8yGern86b7Y/ZSjhSzqiriARSmhFRnIauOoAxho2BO/jAJZ9pTQx89JGcFzvQi0cRGoUYIyLC+XJPEwU8iHbbA2vKqSqC2W4MfuA0L9ZEcI4qFc2mCEhlQaPZcqTZLADMFsSCE3OrBlTmTK6Jw3HH+TRBVWOhegPQa60U9QQXUOzQXjSxrOxzwWwVUFwtOBGiWAOY7KjVWFGl1lW4QwFSb36MPkDJkO3F1inQHKUmvO91FtdVOhaiN4RoTaQYqdmeW6Ums2Xxntp607MHaeRs1ipLXgNJe02pQmmgKjQypTZyq+xCtPcKZ3vJOWfrHMcilZSS0XGB6AN+gFalN8m8BXN6z2W+dPAfWOy9Ixz25GXeQBsV5W/+h39sj/BH4edf+lf+nbbKEmlsMl3//CKXz3sDR1eZs1Rj1JXxRSptEvtkhIVD2PxqV0nxKvlcrm3txccuZ/YG1tVRKYNJFsNzxj8t1KCUvSc8zKSbweTZo5I7ELDKSU0SWX5BUr3K0N28+s76foArm0WBTrZh1t/1zwvpZrDrCo4yCsO71EGBSjoam27/u/ekN0fm1y+yy7wzBpPO9UXSPRlyq6cXwNlNBhLUwZOu7O9XyWVbWV/+RR5ew4tE1V/KBrKtUuLVSsCfMq3fp/2NHbjjfSIf/AYsrPLPpTMATZbKZkXgUt3WxiqFXsdotcGog6JnA2qlNNKV36Tiq6R1lcXnnW4S2PXzV2l46x7C68/63Suja52vMmgHfBt6XgzQDG6TKUttm83DKp021nAHDKe6SW9Lt0tYpcKr1FdnAwmaE+JD/n9cg1l6BGOy9n1CbdTRgEz/lIxpft/l3LAxrn7/WMZ3Fy6fHTZG9bfHPL679MaGp47dnqKzwNLRM351oQXd5ny5CcSHxHIbiPddZt6tSrbrBabXgfhYzP7gPv/CvJZRN5uE7Xr72lr/3fq/W2ODF+uR+H4ydUkqTJ9dbWw/PaftWte9jrDVknXfWdhV2b6zjIrOZfv89TvXfSWdiblaXki1kDtj2dk6N6C8bWsn75XUvbt3P0/dCsPsGoDNoiOcKungfsE6ZJWgz3eB5mD4YOBq2dm46TlRDsHqUmdpfnvt5H1n/vb7Wa5NPo2Y3H5lDOa9bt+1ysrB7DPKTtFL2WrRWgc265edbjV8bTysY7VaKvhz2cZ/lbdLsebE2lBrag2x1dN2ubEau17LWpus/urG5N7qbW7fkrtDeEod/NVN6m7y+Ebe6fb7m8wfC8DM6zXPPZgQNrm6MWNlu6a8d5v37rfrZ3jO23esdXT9yft1TGzvTXfK8GhNpdWSYLXHyQe/WSDoVKnRJPg1yLaWV6uHdYxXSb5OleXqF21c1npj6+HFNmPpIOLuXd7k/fCt2tzB7HUfLFdus5RZa+tqibPK+de9+O06vVyZjUsZrBmgU+X8cWD37sWm5NuWGW7pdgJRNnl/eM6UQTeLhW9bMyxHpXYxwwom67fY4Rsj38s2Tmstqt12Q+dGHq35typtbD3b9bsCy8Ft861L277j2zYL9lmV5VoJzy9WPGtTcGss1oZb7Bk7vbZwsRqEPDiqh3iqpJ2B1d+u6+lKyf3vzedXSDtHGcQ8rd1LTbUG1osyxcZZ+Dt/89/6A71r/6Fm2v4nv/lf/CAtM4MqQVwHTCow4mh2sC6FoCOlus1nz6lSW8UcTzMVOxg7J8YKHBR1YnLMsqwKIgMygaaNWu3wNO52XLIlwRfnGPZ7FE+eFqQVnDScVIIzn7l5yWgY8RJo0ky+7TH6vXc4MUCzYZLhkjMxBMadJ82JIQ4bKFdqo2o1VYwKTipOobUe8tStE1rztNqopbAbBygTwWsHME1mjRcqnbLvzSvXhw4OloZTTwzBLAeqHf4kNfYxoCqkmjbGa+tgbTHjQZoI6iysxjtHE2OEGVNQGZrDke1+a0VwuOr6HDoOweOoDGNkOk1dGdMB2q7h8N5Da/gw4powBM98ORO9dvuMCpj0lg4O1bygLeEAr5FlbhZ2lRupmQy0iVBa6ynuJidHBXUK1G4ToB3kBpFGaYuxVsWYqbqy2AZjZGmFGAeqCDnVzn6zjl2rlaBKjAFx1UiTTTY2Rp4r6qDmhdYC4Ci5dluKhjpl7D5ztnY8VQykzfTwIATvlCXNjMPI7dUNpS6od0zzZHPcOvDbbE81Z6FTGszreW0yaAhUqjG7ugevE8E5pdHYe0fNFnQWgwGFFYxh5gSqWMjPaF7D+xBRaTgKUitDsDC2KS8WKOW8hQh1kA7vtxAgk/QWkyc3MSsB56iIScfLYnPSGwtQ8c7Yzc55vFoTpALahNXA3zmPOrq1SQZnTaAQd8wpIcGz1MXCo1ohRqUUY3eqh1oWcI2cZoYxbGxP7wM+euY02TXXxi4EylTZD5E0ndiPnqpwyRkFfPPkebbAAxFCK5Q0s5QLQ1ScL8xL7bLphg/GFM6tS6zVsywJ6R6v3hnYL94Y3iITuWVTILjSNSWOWIToAyVVnOt2Bli4Wa2NY4xE7VYJzmw86M0GV4uBxPNiey2afQG9CdQoeBp7NW/QMUZybgzDjjkVQhysKdYEDcb+di0RA3hR0nRmFwdjKrfcX1A9GfPcJVVGHxmj5/R0z/XxQKsTUhX1A6rCnLOBhYOwLAYW+9AVhw68KunyhLhKWha8OFJOnX0caEVQ9agT8vxMcBUtFaUxDpGcElrE9k6toLYero87ApkhgGuZ0IPZVM1+xAB+Y5C2nLnej1ztdngqh+iJ2sgp4Z2xLTegTYx5O4wBaYVlmUiXM6oVxZPmBdFGlNhrYWIcvVkIVGHUSNTAfJp4dfUKUuPp6Z4x+m81hRzavaGl1F4XErfHIy3NqIqRcLp6oOJwzTylx7gjKFAS+4P5/bFA8IEWQmdSO0aNHHYHSk7UXDgMkSCNND1x3F8Z6x+lOHu+Oe+oFNRHvB/Y+8o+wiAZF8B5z7jbkdKMd57gI/bYqninBA00Csd95LAb8EGYlxlphVRAfcSpI9CbXq0ZQJsKUQO1mt0A4qnV7mdJM8E39vuRJU1QhVpmYhBKK+Rc2B+uqBVkVQOJMLrAbghQCtdj5GpUrnaO4GydeSfkVNBwoOD6+05ijAPLMnHYD+yjcjwEXFoYpFGLM5auBvDgNFBL5mocuNrveXVz4M2rK7778RuOuwHygiCI88wFNAw4rCnqvPBX/tW/9MdM2z8CP//Bf/Tf/cCfEukmGkNqBQsam8zZlUY+OLRL9nWpG+CkU37x2Ru0y9HL9vf58JIIjxhDhS4VXllE6cbYZis7M10H6uiNXdsZZ02lM+cc8SFtnn7S2aMbuOTdxpI1yaPDTZl8DHZwO5nsmMaLHDKZyqUOuv39BkKUzsDtkkhNleX1HmnGFKreGDMu/2JS+SoNzTulRUUvJtUvh0CLBnb7k9laXV7bfYXui7ceKDdZp7ywJzVVys53CwuQZCBquvJbGrcuL3YGFkomnN948I507Y3hpa43aV4YtisbrIyO6h3LlRIfsrGhnNvsM/LBGs2bF2eXGefRZMq6NPypmE2BV1Yf41UivPqL1uFbnrkiG8i9sqPoh+uVoSkNY/P1tZWOxjDzl9qBaTZZet7bmKws2jp0sEOMoVaHzurrLOfVlmGT8954BPv3+eA3xtyLn6atl/C4sNxGnj4f0GRzNHxIHdDo67H79K4y7zr6jT1nTMwefjMZ69N1H+PmDTCc3xgr1V8K+Spua7aMJhOWBnqxfeNKY34TDdzp4NVyY9cf3y8m1e0WAnTAz2TINr7LrV3bGgq0WaaU9uLD2hm/G5N09MZi9saUXdnoLeqLV2ln770wTw1MygdPeDCrvfC0dBZttbqxGJBevwVg6lRI175bVRhwWkYl3i8bW3x+FdC5Mr0OhFMxhl1ujO/SFrDkz8Ygr1Gsts2Wbr9c2Z4eHqzZI9mAebfUlzR6J4TH+RfqUlPZbMRwsjVIVjbsum/S0VQLLayhh2wA4emtefauDLw8GgD3bXn/Co6ttgIrmO1Ko+wcl1cWtDZfK35qLDe2h81CQjpTeg35s6DCslPCc2Hq9gjamc+2V8yKhAbzjWe+UfZfJ55/acBVNtZgicLwmK2xFo3RXAdbD+vc51HMx9mZP3LZqYHWTrbnTOkNl3CppL2jRFNmzDfeArNW4L+vXZ0qz58F8s6Rjla38s5trOiV4Vy7ZYNUeP7Mc37ryIMwvTYw389Wv9ZQrnXOzKPUGMbhuRBOebNmCN3je2Wdu9RYbvxWq+Zr++zhsfLwy3am3//0tAXBrd6zJcrmj76yRZ++65EqBpR2ZcGqQliZx/NN32/YM2QNk7T770xqtebW5aOA72z3y0fefG3PldNbjxoJ2ewLerN1tYwoO8f0yjO9Upaj25pd840nnOvW/Fj3dt657jULl48d0xub9/hsZIXVBqV5W3d5dJvnuSsvzF/Xg/LWhmg4GXB8eaUMT/Zs0Iyxh1MjTJXpTvvfYaFfYmM031oz5PSpcv7E2dzvHKWzh/3cgd9kDSxNjelWCafK5Y0yv3JcPnZULyxXgqtCOuqmRKhe0Aynt8r5rfL0OTz/SuXpzyWe3niaGN6ECPHJbDyat3GsKvy1v/IHY9r6/+9f+f/vJ6WEa+bb1rJtDOecJayLI+fG1f7I5fnJQMLWiEOgArkWYzWKkvOC+kwtmKdkMSbPXBecVrxG5nmx8BvXaFRq3xDP52c+urpjmiZ8jKCKL5ldiCx5QdW8+loH7oIEfAbVREoX0MboDp1lt7CUlbEphF2XCcfE8/lsgFJxBMwrj5wtWVFgblbkpFiYiHeZVsSYRFLYh0BdEpRCcSNzLogY8y6MgVwz11cHal7wrhqgnG0c1Q1M+cJYnUmNY8Tv9sxLZikZHR1DZ/iUUrpHouI1ULIxHVNKDMNoYTdpJgblzMySU5eWj1yWiguBIpVcCj4Ymw4H4xiYLs+8uhmptfJ0MvZhzgUkklOjZSFgEp5BKxdnLNUG7HYDaVba0ojiaM0xBE+9nBmPEZ9nIjNLgrAbaep5nKYeNlOoHSSM3pOrQ9RxddxzeXw29oU2KjM5VZyMZDHgOnrB0TgeRkrKXA0Ds07UNtFoGAdQKEsxRqIsJhl3jbIk/tSvfM50vvB0emZeMs5Vcq44FzA708JudJQ0cX19zavbI1OZeX46M7UFoTG6SGoVJVCbgSHShINGjprZuQvBw1wKLsKSC+YFWsltMd/U2vDicFXYxxGpBa+R0+VMqwWHg+KIco1KI9cnfDDbgONB0TGyDzuWPHL/+ECujpINSndemeeZ426klco+Bm5uj4xxYF4SX79/YHcYKbmRlpnglKpdou+EnBM7AaaFT1+95fHhRC5CEgOVp5QpGINuDWWKZF7f3SINTueZacmUVJmXpYPPBoC31sglszvuDMiJlanMXKbKuw89iE4cTj2XZeKw27HkCa+O1hJ3d3ccP/uYL774gqJKmWcqFvxnvthWG0Qqu9Hz+ubA2zdvyLnwk598wXTJnADxI081oQX84QBRaPPC9WHg1atPOY4jp9MDj0/3TOVME2UMA6U0klijShuUVLjbX3OeJ/MFTcbyK1FwUgAHZJx2q4Vqkm7xjqVauGKpdrCZlkprlXEMPKWzhbctjxx3N7x6dc3dcYcfB97//Bse7s/ca+ZpvnB1eEOeF5wIYYiEELgdIq/u7tjtjzw8nfjpFz/m6fmZcXdgqs2Q05YILTH4kdd3d9zeXHM47JguF6bpzA9/9CWpFFKujONoDaBcuDoEDocdh93IP/1rf4ZpuhCi8NXX7/nJj7+iaOYYB04ztOw4xgA1c3t9ze3djqurK5Yl8/TwzIenJx4fzgQcLQSaQKqF42Hk8fGZzz//HreHzxl9oV4yX777wMPzhVAgaWGaz1xfX1OXM6/f3EFOfP+XPmJ/OBBC4DxlfveHP+bpYkGQqkpOxrp3rjHuB17f3XJ7d83t7TUfHu758Q+/5MP7J7ya5QLaUI0IEyEX7m6vefP6mv0YEamcT4Wvvn7k/f2Dge7JpGt5quwPI2k58+nba8ZxZDd8ilTh/f1A++lMLpXgA3NOhOhpzWrSGDyvbu94++Z7DCGQFvj7v/MPydXCrOZU8HFAiwWdRcm8efOa129egVZyLfzk975mSpnny4kGjHHk4APTfGaIcHd7xZ/4pe9wvBo47JSffX3m3Yd7vvz6HSOOS0vUlAhxj9aJGJTvfvaaw0G5O15zuQhP58Lf+wf/By5GoJIW83AfRrPP8EH4/Lvf47OPbig5sxT46c++4vd+78fs/Z6crVmQUiOGwQ4SdeaX/8RboFJT5oufvQMq2ZnSwSFICzw/LWgYuLnZ89nbV9wcB3ZD5P39Ez//5p77d4VLKYy7kZQSWRrTNHF7e80vf+8zDnvleBhJc+ZyXvjh7/2UL6cHSg1c8oW9Brz3zMuJ73/vLVfHPa9uj6R5ZowjP/ziZ0w//Bm1YY1I52hLZoyBu2Pk088+xofG9fFgMt284+2bA7/z2/8XaZ5QH2kpmxpmSVwCf/zzR+RHzx1M7AFamxTzW2zR06eR8cNLaNfKOPWXQo6R0r0CVxZjuokmLRwMMFhZq/EhbzLIjblJ4/DFIw+/9orh4SVop4zC/GYkPOXN2mC9vjXsrATZmFLzne8S9moy9f4d+WBe0wD7r5YOwBrTqqxBRGpy6/Cw4KaysRRXr9tVCn157TfAUfv9rEzDfIzoOXH63n4LLVvDYcACksL7M+nVHqmtf34kPmTiqVJCZ+BdjNW2sgVXMGBldKUrT9o72p2SDpZiHp4LZRCqOsYPeWM++YuBxivDbL7uAMI/ccBlOHwxU45uu8aNAbf3/YDqGN+7jc26BofZuNpBeA02mu6UMhgIFE6V9GlA6sjVT2aT7OdqYKQTAx47+/H0iWe8r33Owxbc0nwHcXuoUfXC6a35Yp4+ccSn1u0HKv5s97eCeNo9TLMzxtaP/2VHeHLsvhLCc2OnsjG0aw+EzK8Dbmk8f+Z5+j7ER2H3dWMIprZo2lPPO4C5AkfT2x2nt8p8K+SdEp4bNQybn+QalrUClWvA1eW1seDKYJ7RtJdAqTXgS6dGGS3AZ3kbqAGmV0I4ea5/lC1Vfa4bEzc+JM6fDkiF8xvl9JmQrhv+WXj1D4xdvrLc8sFvDMd8MDby/MoK/89/PXL4suJnC+epUYj31pwxj0kDStPR8fDLdh/jN43hwVLeV5DwF8K1psJ0Fzl/7JhfN8KjMtx7bv/RTL4KBnh6Ib67MH1ywJ9KZ/LDw/c900fKm79b0Vk3dvJ6Dy5VpFgjabrzPPyy4/KnE22q3P1WJD42dj+3+dj/5EwdvEm4ARo8f6o8f16pH2XknWf/M8fw0C0B7mx/+JONcY0WLPX4K0d236QtQEnnipbGt5nj3w54W+tIOPWG12IM4ZWlnG5Hjl8snD+2PTC/VZ6+D+m7Cz4W2u/t2X8pXP/Ysf/JmfMv7dEpbwFvaed4/q7j9HlBbhbcFyOv/3fl+MXCcuuJ9y8syrxX0rXj8U84Lr+UGV5dmJ8G3CO8/R8N0PanzHJrqubmhNMnyvRGmF41pt+48PwIcUy03zny8f9q8376JHYP6Mb5U1MsPH1XOX2nUj9eaGfP8HXg+CM4fmH+qlL9ptw4fxI5/GTiZ//sgek7lXh9phRH/O09x5++BN3F9xOn7+3x58b9n7Tv+ebPF8LtzLhbePqw59X/EDl+WTaVxxps1iosN8LT9yvjd5759U+/4Ld++h30f7vi6kfGlFyfKTXYcyDtHU+fO55/GeJdQmRi/jBy9TsjN79bKOHF+sJfKuc3Bmh++CchXxfCzYy4ieffOyD1iJ/sXuKD+eJKg+pguRaePofdn37g9eHM4zTQJJQNnAAAIABJREFU/qvX+Al0FoZHYz6XHryW9/DuVyB+3wrgsnh2v7UnPDb2P1usZt8Fzm8cw0NjvjYA9vmfO/PZ6wd+9fZr/ut/+Kd4+uHI67+3Bju+MKXLICwHxzd/riGvJ371O1/x5eM1919f8fnfYgseC+e6sbKbWE37+V8o/DN/9nc458i7y56v/u5b3v7PlctrawyUYAB+OnQCVYIv/oJtSJcdr/6efbafbG1Vb3X48FWmBuHpO577fypz++kH3lw/8n/++BOGf+ThJ47xvjLfGCjt54a/Lzx+z/PwL0x89OqJP/PqK97NB376eM3z//SG8e83tMLwjYXp5Z09J7/85z3LR5nPPn/H0zTw6njix//Ld4iPZqnkOgguBaZb5fl74P7sI8dh4dc/+hIvla++e8XXv3rk9N98jL+8ML5LAFdg/H0hjP9vP3+ombZ/4zf/1g/Mn7GZNyRCRrrMcUBrJWrl80/e8NHdEch4IJdkIUnOd7l6QSWg6g10S8ZmvD5eoSlTKGh1eB9ZyoJXhxfILXOIkdQyoc3sglDTBe/gcr4wDtH8OVLuQJkSxNiOwxi5vjkYc6kkk/nXRhwjtNo9S4U0VaL3HMYDt4eR1zdHlssjS7qw24/mOdoqvlnnMo4REWMCv94P3B33RK1431jmid2wo+RKqZVxGECUfRyIzjMqfP/Tz7jbj7S00Dp4OgwDrqW++0zmryXjysQnH7/i7rjjEP5v9t7kV7Isv+/7nOmOMb0Y3vxezjVlVc/NarGbTVEiKcuGBEn2whME2JAWXtl/gBdeeaedd5IBQwIEGxBgwLAswDIESBRFUc0m2U32UN1dWVmVlS/zjfFiuuM553pxIl9ZK8sbS7Z4N4lEZrwXEffeE3F+v+/v89lu/hHIWON9aMF1eHwniCKDNh2z0YB7u2PG/YyeDot9bcPIthOCTlhsa4mlIVKa3Z0xJ8djdkd9ZjtDFB3rskQLgXcy8IIJY8ZeWPJBwmgnZzYZsDfZYaeXUa3WSGfRWtF4h5MglKBpSkxq2N0Zc7A75MG9EyazEdbXwTXeWISTITXqBW3t0FIwzAwHsxGznZzjgylZZig3G6yVGJVg2w5JOB9Ga7Q2DHs9DmdjJns5w50+OoKiqBHag+uga0KaUygiCYd7U44P9xjmMeNRn0E/oa5LlLAU65o0yemcAxMkW71Yszvb4fR4l8koZToe0I9ifFNTljXWCzCeqikxsUK0VSgeHEx4992HKN3R62Vs1iu8g8Z6VKRCClJoEhPRS1IOdscc7E/Z259hEjBGsamKbaKzQRDS5VpFiM6Q9/tMdyY8PD0mjxRpZEJaGoHvQtHWdy2a0CTo9XP2dqcc7M3IexFJooi0oC7WtHVoj3VbrIOWGlsVjId97p8c8OD0kCzT0DmktLTWBYGblHg8aZpQVSV5mnN6esRg2Gc2GRFLsI3bFt8JI8pIjNYoqYiUYGfQZzoeMpsN6fV7JKmiKCuU1pRNR9W0JEmCcJZEKHppxu50wsnBHr04Ym80JE5iNss1zoXkdpJkISnoO3KjOZzNONydksaCJE8QukPGhpubGyQC6QRJnND5FuEtO8MBj06O2Z+M6Q0i0tSgpaYsKuq6DnI/BFJovOuQBHGhdTVaW9o6iP4aZ/FduM7DVLbAt5ZOQtRJ8jRivJOTpTFGCzZlCZ0nSkJKVXYhZd64isl0xOHBLoeHM/IsIY4k/V6K0Zrb9RqjYzabNUkvRXUdti6ZDfocnx6S5TFJokkVHOxPqVcrhHN01mFtjVYeIRxKwunJIcNhTpoajJHESeBtLhfL7ch7h28tbrvWnx4dMBrmDNKIyWSII+AGWlvR1C22tiA0KIMWgl5mmIwHHO2N6WUJeRSRpTmutRTrDYggZuvoSOMI1VpmOyNOj47o55phlqB1Ry/rcXuzYl2t8cLAFgESJRnetTy6d8x41Gc0HGBMSDNLLbm8vAiM2s7Ry3PoIIkUh4d7jEYDelkQHmZJTG8woCwLqs0K2YX5EYclNpo8jZmMh4x3BsymE+LIEEcRmo6LmxukEghhkEJiIkWxmjMaZhwcThjtDBhkKXkvJ0o088WCpraBdx2GMmi6mtqW3D85YP9oyiBPSbKI1jeYKOLq+obGdbRCIrduTaMlvSzhweOH9FJFL49J4gTTKTbrDeuqJtaG9XpO6xwmTvDCce/hMeNBj16UoNqO4XCAUpL5zRrXeGwn0XEWBHxYpjsj7t3bp9cb0Esz8uGANNMsbhY0RYUUKqTqowipOoSw3H90xGQ0IJWSXpaS9/ooFdO2ntViHaY5ZGB1t3WNlA37e1N2ZyMm4yH9JEESGkHOgbeOOIlAhimQTnqm0yn70yH9LCJNDL28T5r0uLpZYNsmSPsEYeLFO/b3pkxHOWmqyaMErQxpljOfL1gtC4QwIFTgZasW72oePblHP88Z9wcowEQxaZSy3GxwtaBzAWzRCUnXWcbTAdPZlGHWx5gILTWJSUlNRlW0bEqL8HHwBWxFix7Bf/hX/kRE9m/D8T/83d/+bzoVxgtDyjSkYTsVZCk+krS54vyXJLdvCToRCqam7O6kWW6blvHxG9nLVqCiBesjg4tCiiUUb8Poro/lnRim2s8xZdhUVzsKZcFFkuQ6jH13UtwlspBhs+/iUFTcHIT0l/AheaOa7o6RGhh9YTTZ9jTlTLM+MSweKcwGorWnGuvQ2Oy4S1AGdmNICy4eGlanirancDFEm456JFFNGPmshyowqMca2zc0fcn5h4L1KXRoRBeSPO1Qgw4JVQgoBh+HdOz1U8P6VFCNJcKHBfiN6AexFavZkD50iWT+luL2mw3Vfct6pnFGE6+6LcszhBtUtS3w5pKbdzSLr7ZUb7UsHndYo0kvCQlCGx4T3tfw92LXsD6RLB91zN8LBQuzfJOIkkQrG8aRt6Izlynmb2sW7zuyb825eihxdbRNrIVEYGdCCkoVDT41rE5irt/XrJ541l9tWO9ozEJuCxrq7hrslLxjp66PJTfvdzQPaopDsLEkuWabOOQLfiyBZXn1Jc311zqSgw29kzWr/Q6/iXCRIL1y1Dv6jj3cKUEx0yyewOxr5+h7BZtHjus9A16TzL8YizU3BT4L4+SrY8PNBx1f/bMf8XHcp5wK4uvArTVru+VXBg5rM9IUM8X1lySr91rad2qKVGNTRXrdbeVrXxSE3zASy6lk+Qi6X15STjrKHYFeBWaqdPxfxE0hJVbsam7fAd5bI8YN9bCj7Bn0Jty3Ybpvez9u78HFw5iLb3XU3yqoxh0Wg9cSU/KFiMiFwn1801DuRlx8U1LtO+TDDeueRtbbCcZYod6sDWlIdtpcsTpVrB864scrqpmnGCii21CgTq4bzHVBO8lgy4UsZor524rmaxuYNaze8WwGEel1eE7SBq6qdICUlFPD1ZcFPF0zGpZkg5q5SvFKMvy4vkvtt0Nzx3Zd3DesvlPy1ltnHI/niJHlOorQC0O8/CJdeyfg2p4XU76R4TnabYL/jczoDZdWtoH33fY15USxvK+oxgqbCrKLIGds+4GP/CYJGq0ct48N8w8806dX7O6sGOYV8W7JVRKTvpS4niF7XdPsmC3io+P2LU35lYJ0p2KnX8CopX1aU5T9sIa4INjyWw64N5L5N1rGBwt2BxtU4qBvWemE3qsOb9Qd41VXHmUFl7/kMUcFb88u+PbxM25dxlxEUAdmpylDbeCNvKmYSVYPOo7eP+d0Ome0s6EZe9Y2JbkO911yHRpp9SQgT+Zvx9ivr9jbXfB09zUq8lQzh3+Zkp+7bVp/y0TdJu0vvuPYO53zwf4r+nFNkre8Njmjn30hjit2wzmvh4rbDzzZ0Zp74zmjqORkeMtyV7Ba9IgXW2HddnS+GSrKqWR9v2N4uuBPn/6Cca/A9C3nSczgZ/IuZe11kEtlV47ViaZ+v+T48Ia3Zhc83LlmmSuaz3OiottiA0Ja2Kwd0dLx+pcVw3dueH92zml+y1Xd4zJJGXwsiNb+LtkbksmCcibY+/A1jydXvDs5Z6df8HmWIq4j8vOw/mdnBabaokDajqtf8tw7ueLpzmtS1fL+7iuexz26Fymq3Qrato1CaTsWjxXTb5xzbzLnS8OXPBleko9qXpzvYYovJkruRG4SLr/VcXj/igf9G748eME7g3NepD1uqgH5eZg4cLGgHklMEe6p+XuQPVlw7/SKZLfkXOckV2/knx1NT909TnSwfCQZvXPDu9NzHvauuDeZczUyuGcZutquV4QUrLRw81QyfTDndDDnae8Vo6jkXn/Oj18ek73efoYIecffFq7j9lcaJrsr/szBz9BRxzTZUO10bC76yIaQNN8K2qSH1ang8ME1X5u+ZBqtGZmS++kN9/s3fG9zTHQT1nOzTRv77Xv3n/3H3/7/ftJWCIEgAhSlLVFKkWqFazyxkkwnUx482kd7i/Mto52Moij47OyctW1oW4HvDE3tiWOJdYLJZMpbpwforqWzJfMy5uzVOTXQtA4hFB6Bt54IzU5/xPH+Lrt7WRifVRGfn73iCocXgf+ohcQLQlFTwv7uLqenu2SRoqoLVuWGy8sbbuYrcIoWhZUN2ifsZDnONbzzXtjc+7ahOd1jsVzx8bPnuE6jpcELTdx1rKuSQd7j3vGEw+kA4R2N7yjLmk9fvGa9Kui0xjYdm7YNpsOy5N23njDKY4Y9Q6QTDqYZN7crXp/fMC82CO9wvgGvSOKUUc/w5MFDBlmEosYKx/VizfX1isXaUTRVKBQSCjq2rHl8/zEHkz69CLI4Yr5cMS77/OL5c25WFaqLcF1HFsXQdgzzjCdv3SOOLQqB8y2DnQFJlvDs+ceAJY0Dq7OsNwjR8fjRCTu9Hlo6EmEoy5JRHvPq1WtuFiUaCUpTVTVRFHP//n0Od0cMtlbPXluT91Kur265uV2g45Su80StJ01zJJbHh8ccHszQssVJGPRTennCD37wUUAjyAiHR8iOolhzfHzI2w8OybQkijS1s0zGY4x6yfnlJYuywHuNjgVN2TKd7fL4yQO0FKSRxNmaXqfxomW53tBYwbpYo3VEWW3Ik5R333+fyXiExtJ1NTjPII5Jo47bmzmJiLANKKPQSPJsh/3ZmJOTKdp07O3N2GxKdkZ9fv/3P0IpQ9WGlLTvAtMxz3PuPzjGu4ZEK0b9PcpxQ+ssr84vAy7AOlzn8FVNmqZ87StfxrcVqVF0mab1juEw5UfPXlCuNuAlEk1lK1xr+VPffEKexYiuJU5ihEjZ6aWMejk//NEvwmZm24KyjWNnNObhvROG/YQkNcgOesMeZd3wz3/3+5g4QaoIOsFiscJax9fevcfB3gBchTCSYXrA7t6Ef/H7f0jVOkoHWqUgAtfTNhu+/fTraNkR6yCbGgwGSFKePf8U5R39OMbXFt2FFP9bb58ymgxRusO4BucVw0GOcZKfP/+MDqiqirZtybKMk/t7PDg5xHQtAoX1jnv3TlgvNxTLJVfXt+g4om2CRGo0THny4JTdnRy5ZS5rJZiMJhRFSds0tM5RtwKLJc5SXOMZ5jGT4YzJ7pDFYsGzZ8+I45TGii0SQyGECtxpabCdY+94xsF0eJegf/bx57x69RrZCbSJaLaj6QjJ0f1j9icThG9RXhJpw3DUJ46u+NHPniFVTJL0aNoO0UnG4yknJyfs7U1ompI8TkgmY5qmYT0b8vzlOa4BrSWODlu1vPPoCQ/vndK5Bi88XRbS99pHNJuGV2eXCGNwskPGMd7WzPYn5GlMagyNtUxGO4yHI5SQfPr8X5Dnk8BAtTVOGXZ3D3jn7ce4ekMWxVjfcv9klyxJOb+4pmk7Yhk217askQruPzhiOEqQUhIpxXC4Q5RlvLi4oKsWCOmCXFKl2KomziLu3TvB24CNGPQS1ouK49keLz75nMKG8xr45YLr22t+9bsfolWH8OFzpfOW3cM9mtWG5c08pKFFuAZrPHuTKYcPTgNGgzCi3+tpBnmPZ2dnbNYNXgg60UHTUhQFH+y/zf7uAa5pEXh6ccp4tMPlxYLVzXNEFLAP4FFKsCkK7j95hHcNqYlwvmU8npH1Jpy9uqK8LdDxFqzftRTOcm//EJMmYAviOCbuFPlJjm0s1/MFEkuW5DS+oqxhOhlwenSIbBxZHGNUkEXujif8nGdYLZA2cNOREmMixuMxw2RE5y1ZnOBdS97LePvhPb7/g5/SOo8XAt+uiNKEm6sLhl/+gPFgjGzqgFBx0E8jBlnCpTJBGio8ta2JdETnOw729hmPe2BrfGR4q/8A5Of84vk5UkuKssYYhegcti44eneHUZ4Qb3nQsUnIZkN+5H7Mcn7DaHcfLSSL1QIjO6ajAf1BjpIB3xMlMc4JxqMdXr+8omxa+r0+VVMjZETb1exOdvF1jaQjzjPa1jKeTNgfr/njy4+JshSxbZxtypK0P8AJSeUtuQrXsHMCJxxx3qOx53RSksYJm7LA0pHq7F/fl78/Of5fPd5wFiGwDX0kqSYGVYdkzuKRZPitC06jhspqro56NPOEye8p8nPuxoz15gvG7PxJxObXNsRxi/eS4kWf8R8polVHdJdAfDN6alje08y/Ynnr8RlNneCl5/IP96iHyXb8/YuNpHAhdXrzVJF9/YqHwzln6yEXlwOSnyf0Pw1JN1OGUUybCOqHKarpuPjNhifHFzRecVuknL8asPdbQWBjY4nUYaw4f92wOo65/CXPk/c+Q0nPTZlxs8ixv5eTXmxRCaUjbcKoZnLT8fzfM+ijDU/3L9hLVvzivSnPP5vR+0nE4NOQMg3GeWjGCctTzfpXCx7sviI3NYsm5dmnu6SfRORnHekNd8zNbit1+vzXFNP3LvhwfM57vTP+j/N3eXE6YvVbA0bP7F2qrJpFAKyPFaNffc3b2YpMtxTWcLY35Lw/5eC3wzVQjwOrM7kOTNTL77acHF9zEtXc793w8XLKJ0cT9I9y+p+G9C4EkY7tG86+rcmf3vDV2WuMdLRjxSfjMWfPp4yeEcait+niehzhjeD8u45vPv05Q1OxsjHne30+nU04+Xv6jvf4htkZX1acf9gn/rUr3hldsxuvOSsHzO9nvEgOGP1U0f/c3iVg9aph/laPw+98zjAqedS7YuNiluOE33EPqF7HmE1Eft6GIvRNQ7UXs/xzGz48+YyRKfFbhNbVbs7vD0/ofxZT75gt07KHi2UoKr7X8f5XnjOOCr755DkfzydUJwr1Pw1xcUQ8t1uUiEdaQTUVHH3zJY1THPUWVIeGl8sB82rK7IfNXQKvq78oks7+gxcMveJ+/4bWK27qjM8PhnT/24hkvkVNGIm5rZCV5eyvRZyOF2jhOczDn8+OJnxyNOX4f9aAurv/delYPIi5/nbD4cGco96CSDo+muwyX2ZM/6a8w4CAJP90jSwaXv2nEe++83n4GdKj9x3nT/q4v7NLvHDEVyUuNYBGl4EjLf/6kpOo5ihbsLIxl7s9nul99v5pKOyWJ4M7LqeqPDd/puHxwSW5qZnEBSsbM9/L+EVyzPE/CtiNeN6iNi31LOHy64J3P/yESbxBC8+iTXjw9Wt+fj3jar7D+KM6sICLgHNZ3k9Y/2rBrz38BbG0pKphGFX85sFP+Tvz76JLhSlDcviNSFG2HcsTyeq+RD7YYM8yDn6720r7vuCWdtvQQ2Dqeua/JBk8vaKsI5SxnA/HTH/Y3uErgvAvJDznX3a8+/bnaOnJdMN+suQ3hn/Mf5/9Cld//0FIwY8MZhXW0tuHhvqrG37z8Ue8Lvt8MDzjUXzO582Ev3n5XSa/GyYJ7hi8Zcdnf07xV7/xO7ReUfgI6xW11/xhdMTFZsrsh5ZOyrtJB+E6Th5e8fbogrfy17ysd/h3Dn5Mu6/42+ZDDn6rpdpLw3XeeFTdsXir46/86u+ycTFP85dctX3+8qPf578e/SXOf/KAaONphm/Yrx1ed2w+LPjS/msi5ThIFvzazk85Mdf8l7//1wMKYjtx8eYaqfuaP/+1P6J0hlS1fHfnZ3xv+YCdDwpefP8x6Y3bImzCezv6WcHT/+IZQ1NihGNHF7Sd4q+984/568VfpX3Vx2x8CD34jmjpuHlHcfr+GTtxwcZFvNd7xTeGz7mYDPjff/vbZFfuDikEEF8W3P6lnD//5Kcs2wQjHd8YPOcvT77Pf/XxX6X3OuTk3qwxLpZkny758jdeUDnDk/yCtYv59YOP+Hhww4/+6D2Subtbd1XtSa87Lj+ER0kJwHE85zie86WnL/lbzbepnxm8AnZTzMqSzAXzJ4Zvf/nHpKrlaf6Sfb3g2vUw9xx/P/8Oqt4iGxTQhmmUzT3Lr88+w3rFl7IXbHzM1/Pn/Ld/Zor9H8ehCbT9LG96isEvViz+I/j1g4+IhSWTDQBfnpzxDw6muJ+H4rFqOuKlp+mFiZTB02t+5fBZ+Gz0mi9954x/wFc5+schZZte25CUlqFJob604MO9T7mXXgGQyYanD1/yt+RfIP+8YnU/xcaC/mdVwPk8qPlg8opc16xdzFE8Z2Ez5L0N7Y9zklvH+sCQLFxIxK8cf+rhJzReEUvLSTrnqunx6wcf8bffmbDzv4iAi0hDUTi7aLAjTdkaPit2eH9QEgvL2sUsbYrstZjNFhsx1mSXYU15M93wr3LI//v/8q/vUAqcb4EOoyJQkqKpqJqaOIuZ7c9QnSCPE/p5j35m2Bnl7AwzjLRkqQJcYLu2QRjy8NERea7IBjHDcZ/xTo/9yZDONWjVIYUOgh4jadcrjvdmzCZ9PJbhYID3IUnT6/VoywJvLQYZxn4Q9LKEvf0JgywmjQSpEUxGPY6P9rFtuLFcJ7AEdMH1/Jq9/QmTnSGRNmRxQpZo9nYnjIY96rKgaUPBq21r6qYhylIOjk7I85wkjZiOJox6fcajFGeXGOnQqoOuBdnhXMt4tMN0d4aOJSaKiNMBs8mIXipp1gWZyYl0jJEC29ZMZhNO7u+hYkme5xidMugNOdif0ZRLOl+F9JLs7qRuwzyj3++j4wgnHPmgT2/Qp98fIjqH6AJbuPWOTsLB0T79YY5zLSZVpGlKnqcMBxn9QRpkLh6qskGLINqa7IzJ0pQk0oAlSjX9QUqvH1NWK5RzdEVNqiSu3HA4mzEe9LdYCY3Wmn6WMpuMibUKkidnEXi6toHaMxnvEkcpUZSE9KNJGPT6HB3u03WO2tYoI5FakCQRs9mM0SAnzzQaSapzEpnRy3NUJ4J9XMQgAh82iiVxbLaitYAhMMZwcHBAkkS0tkJIR1GsyfOc9XpNv5+TpglGS3wnsQjSXs7+0SHWNkSxpm5KhAgogtFkyONH94njgCDQOuFw75D92T69XFPXSxId2LddF87JZDaj18vIU0MaGSKTsru7j8BTblbUdQ1S3JnptdZkWUJswnuYJgl5ktPPcnQniIRBusBvjKIIR0cyGZCNBqSJIVMJRkgGoz5xP6HrQjNGOYd2GmlhNpmwO5uRZDFCBHN8mvfo9ftbfi84F8b+0zQljiOUMTjX0u+nZNtkXBxp0jQCKZFRjIkNRVOijGI4HpKkMcYInAs4gcloh53RgM7W5LGkq0J6WGqNlIreTh+lBapzRElKZBJ2dsb0+zlRYoJQTnRBfOhapFRkWY8oipAKsixB0LF7MAZh6Q8yBBApg207XNORJ32ED0gYrTVpFCRCg+EO601J43yQ+kkJjaUpNgz7OXuHMxSC3cmUR/cfIFtHLBTKCpQNzCCJwroKrzqG0wleSJTRREnEZDqkqAqMMTgXoPzChsaMQpDHManRREZgoo6mWpOm6Vaa5/AOrIdOSMqyRGtDVZSo1uFaS9U0xFFC3g+ohMAEk1uOarhebFMFfmcnSYQiVpokjUiyhM5sUSYq2k5UGISM0CogXLTWuNbhrUeLBClipIno9XpoAU3TkGUZSZIA0NYNRmmuygW6lyKlpqlrRNehOoi2DFatNWIrw4uloa1aFvM53lYYtf03oKOlaQpsXbEpGoQKyXHbtuSDnNY5pnv7eB8Y7K7zrMs1WZ6zWi+oqiqwbiVEWrFarsP7iaZxntp5LAJ8x/ziikQF83W41hxV2SCEoipbZBKFUSXnkEYTZ2kQnbkgPMvzlE1VUlQlddsQJfH2m6RCOIUmIpYZTenoakHXdAgb8D2L21UQrklB07ZBDNl2aKExOsjfEq1wVRO65E2DTCKyQY/ahfOkOsMg7RN5jSsDk7ysKzZViVcCZEdtG/AO54LML4tiNjcLIqkomxKhBEW7QSrDpqqp6haQWBveX62DXbrfG1KvC1wdpFyd89jOUdWOsrahidE6GuexNgh9rtdrbouSxXKFExJjEpK4R6xThAzSSm2icO6dR3VQlBuqtqL1LdZ3VG1DWRegOnqDnKZtab2j3++j6CjWyyDT9OGclGW5le4FDIj0HU1RonGhcGwrrhe31N7iZLh+dBQetylrfKxpFDSiweiAWxJdkI2mJqKz7VbWGRK8ZdNSNc1WXOgwwpDIiH/JgvEnx/+vDxcHNiRwJwxKrlqi25amL6gfV6Sm5d3hOV+enHF/esP0+Jb1aeDPldOw6fCR3AprPLe/XHM4XnA8XPD+3it69xbM3+u28hm5Hd/fCno2lvlXLA8fndN4xTdmL2isRj1cU84EepvobVOx3VAKNrsS9/aGL81e8ah3xelgzjunr+Ery7vNuapDgjReOEYfrbl5V/HO6Wtm6ZrHgytORre89eSM9YkkWnmitQtcy9KjlzVtDg/efcW7o9fc793w549+zDuH56xPA7O1TcWdxRzCaH58uuaX733CwFTMohV72Yp3H55RTbstH8/Q9g0216jKsboP/8l73yM3NR8Mz5gkG6Z7S+zTDWbTfcE93IpmmqGGw4oPJq8YmpK1S8Lzm9xQ7nVhs72VGOnCIxws32v58uSMTRszjdc86l3xeHSFPizY7CmaXhg3j+dbaU9hYvVxAAAgAElEQVTjeXjvgkfDK+73bgCYpWtOZnOqmSe9sUFg5Anj9o0nfzrn24efYKTjIF4wMmW4Vh6dUw/knaU7aCpCZePk/hWH6YLD5JZUtRzlCw72brn4amDWmkV9h8VoxgnLx54P9z7laf8Vkyi8jqN8gZ81eBNwGm8O4cLY7UG2ZBIXlC5ipAum8Zo/9/ZPcH0X0k5SEF/VVHsx6VnJ/ekNT/ILZtGK0hlumozH+SW/+eSngQ+aB55yJ0PCeH0s+O4v/4jj7JbPixFDU/GfP/od/v0HP6DYlUTLkOZ1ydZgX3g29y1PR695a3TJ4/ySvWTFXzz9Y5BBQvZGuvSGo2xjwZPBJXvZCi08T/ILng5f8c70IuAoeuoLGVU/hqZlMtjw9vCCR4Mr3uudMYtWfDh5TpI3dyKfYIMP19XiCXz10Wcc9RZI0ZGqlpPBnMPJ4l8qFAE0kxQ7ykB3lNbwzfGnvN0/J1KOvWxFPQqlBb8VkCUXJS5WrE9TDnsLxtvia2Ej/sLBDxkf3wacw250l/71JhQ996cLclOT6ZbD5Jb9ZMmv7/0EsVfR9AK2o5MiiA/bUPR7u3/OQbwglpZ3+6+RouMv3g+z1uvDiE5C29OoskU1HfvjsG9OVcOOLniSXTA1K7r9mvx1i1m7O54vgF61FIeQvnOLMY7xkxvOfiXwKZvhFtsSyzsZlqpCAtcd1rROERvLKCvZ3LckF2VgeL6RUkkR8CzG83T4ioe9K/aTJbtmxct2zKPeFc4EEdQbu71wHcm8I4otz9djMt2ysCkf13s8jC/IJ0VoaGz5um8KysiO66ZH4SOepOc8zs45TuYc9Je0/a1sTgRUx5tmiJKekSmYtzk7umDtYmqvMZHFmyAa2+ybLSPX0o0bnqTnAFy0Aw6iW/7OzZ9iYKpQGFuHmLuPBG0vcIWjyKJluNbuJ1ect0P+4fL9O6TNnYzOg1lZdNXxk9s9tPBE0nLV9vlK/zPmdcbiMXeJUdUGZEA9jfnjqwM+2+xQuoihLjiObvjH63dpmu3aswmIHbltsPVedBzlCwCm0Zq1i/lFsYeRjngRMCtvUDidEthBjEjDfTyN13y5/4I/XJ3yB8V99CYgB94cnQriODtKuSp7OB9cLUY4eqrihxcHdzxns7J3hWGbCEgc1kseZlesXUymas7qEUJ1lGO5FWOFhsj6UGNzKKxhFq34yeaQ72/uU/ho+7PDDw6NUSgnivS8QWSWZ+spWjp+WJyQy5rvb+6zWIXGvqre8LXDayoPc4p1zMKmTM2KtlNctX2er8eYW4WLQjPVbLZoFx8K6TcvR3zv8pSNjZlFKx5n5/heaHTqMjRy3px7bwSbecpnxQ4XzYB5m/Pzco+fbA7DczhIQjK59GwO44C5uI2xnaTxmt1oyU82hxhpsa2iHobUa7TxdwI82Xq+9+KU15sBaxezcTFDU/LReg99He6JaO3DY9ItBkV0KOl52Lui7RRzm7FjNuE8LyKSW4dqQ3LdZoE1/P/k+Dc6aVs0fguP9nS0KCvwrSOOMkaDAcNhn/FI01Q1dB25HDLJEq7mSxrr0Xi8F3TK4buW/qjP7nQP0SwRLmzsxrNdnHO8vplTN+DaJkionMZHOSrPifs5qpP0I42iY1U7Znu7vFossI3HiIi2A+8h6+8w2dtHiE2AQHcJg1EPuVjRAWVb4WWMVx1Kd7TeM9vdpd/vIZoK7yExoQAyTAfc6A2djvEteCmIkxydpPT7A4QvAI+znjSNOdjf5ezsNS3QtYrWWsY7O1zeviI2BtUJ0mRAU9YkSUTTFeRZnyQraFyFdaBNgi829JMcZxW51oAnTYNQZ9If8r31nDgZ0loLosO1NYPegH4eiiJaG9oWEII0je/M4EKB6Qxla/HSE6cpwjsGWR54TJ0C26JFSBfbZkPTaKIoYVWXTKY7SGFwrQ2yKiWRnUPmGTrJ0SbFucDXzeIBwlsGWU5VFOSpwTXB3iuFIsk7kI6qXdP5iK71RHHK3mxGf7iDY4OQAh2l2K5iMpnw0bPnRFFE3XU0LnwAZnHEeDjGC7BdhzEe11VICbFWSKkDJ1k6mq5j1O9xe7vEtR1JbDBmW3T0HWVZoEVCW3V4ZRDG0JaW2CTISNDUG1Ip0bHBOWitZbMqGYx2WJcWEyU0dTCxr8s5VbtC1Q0mztFCcnNzg1LhOXXO0VgX4NjSsdlsUEZSrxtiqfBBlc5qfouWgsZ60tjQ+I5Ia6TSJEkoIpe2Jsp6OGexnWe1qUjTnOtlhVAG5x1ehuQstaOxJTEdXlps21FVBTQeZxsik+KkwAmPMjGXy1um6zF74x5VUdFJsM7Rtg1dB5GM6VxF5yQ6jhDC0LgS53qUK0t/NKSkJY562FZsC0cpbd1ilKGuCkY7U9brNZGCPM4wyrKpVpR1gafD+g4ZKzoPbecRWlCWJQkRQjqEiPFSsyoL2g7qpsJ5AzJIwhabDbZx3N4syAzIyIA02LZitSxwHoqqpmkFUWoxEXhhWZcLjNCkSYR1Htt62rakaWzgX8vAXPVdYALKVHO5vCVZpGRJyiA2Wwt9h2tbOqFQoqP2FqU1bWcwSvP805c8vn9MqoJI7uL6lv5gSGkbkEFkFBmDFAn1uqCqKqQIcrHGQoXDe0NR1sgoxhLSJJ236F7M7e2cXjpBa5B4lNBczpesipaibokjEEhsWxFHEUVdsVhtiLSENHw/cc6xWBbcrtcoo3FoGtuClqgoCWP6dUo/NfSkoqpK6tazWK3IhwM6DdfLm1BcFpbbxYoXzz9lMu7TdB7bBPHi1esrrPWYKMGrDqFCWhGlefXyNbE4IokVlSsDU2ld0HhD24TNshCKyjqSJCfKElrXUG7CWtc6ga9KNmXFcrlEixjnLd61KKMxSczL19cc7R/Qtp7OA95ys1iyqWuW1Rod9ZC6QxOkVFVVc3FxRT/PwFrwls7B4vaW0ja4Lkg3O+lpXUWL5ezikiSNGPT6+NrihaTatCwWS+quZavaonMdWkckvYzX15eM04w86SEJBdtqU7JcruniFCc6nHOkaJQXPPvkU1xzTDtOSOKIpm25mK94eX5FUTUB7WE9SZKxLguclZy9umS806eXpSihWC03vL6aU1qwIrCxm6ah64Lw7+X5a3Rq8DJ8GV8tb1Ay4fJiGcSQWuM6R9t2SNmBjnh1O0dqxXTY2zKnBZ+dnbNcbWi7DmUUtQufj863pEmf86tLBv171Nbjnef65ae8vrhCKg1G0DQFRkuUAnTM65tbsiyjrmqixICtKDYNrVWYKKOVCoTC2xYlJcvFhtW6JE4M6/UtUggWNyuurxZsijasF0IhdEesFVVb8fLskvGwj/QZSI2vG86v59ysG3AC2g66BBcrLJLLV3OUl0ifoJWgcyBruLpdcLWYo5KITkikkthiQ5SlAc/zJ8e/FUdyUeJN4E/ig0xMtiEBtTmGewfX/MbeTzmrRwC8O3rNk4Nz/sYn/y7R2gfpifuiyLQ5iPilRz9DC4+R4Tr6ztEn/KPmCfWwR7TqtsbmLevReog8B9mSgan4zuBnDHTJ929O+dnjiNkP1R2PUBdhg1jvaL52+iL8DuEYmopf3vmYf6KecNE9JL2xd8+pUwK5qakfV3xz/CmSjtprHqRXDHXB3zg4pv5kK8tyYdNqBzEuFnxt/AKzBcKuXczD3hWr92JWf3SILsPr1euGxZMeox/dstNryFXDu4MzXtY7PMyueKWG/GRiqYeKaO0D63fL+7R7DWsX88HwDIDH+SXTeMPoqOCfrT6k6asg7qFD1Y71oeZkNmdkCnZ0wUXbB+Bh75qP0tO7IpZNBMlNSP2ZXoOk41vTT5B0d+muOG5xSbCzm7Wl7WnSsw2Lt/rsKMvGRndptEhaUtXyLN7HpqG4ojeWahahC8fT2ettuuiMpU3xCCJpOcoX/IID4qW/4xE3fcnNu4o/Oz5jY+Pt+StZtCm/efBT/l61T5tLolijVw2dkthpTO/egtpr5jZj16xYdCmpaknyBq9DEzbI3RzrBz36LzyFNTzKL9k1SxY2FBmeraeIxKGLsDFHBoZlO4rpact5M+Ct7DVDk6GF57Lp89HtLusjRX4ROKpm1dL2Dellx2frHXLTMIk3jEzB3z//gJ6p6XR4Pmbt76R28es1mB5n5YDDdHlXMPn+7SkuArks8ZMUvWrvOL71UDCLVjxbT9hPlqxdzG2b8dOrXewQ1MdvGKkeYT2iqICE12U//A4Xc2sznq/HQLi+fS+Y5tU6cKeHP4cfPTzgNx5+xGfFDr4TLJqUVRUz2p4ztnI8lwg6GSEKRdkaPlrv8a3RMz5eTzlMl3xWd5hVEAjqjcX2IsyiYXma8fObKaO04t3hOUQbfrA64XaRs287dNXdyc904ekkXC16HPUWGOmQdDRe873b+7hSEy8dqg73pmw88SdXqF874p9dPODB4IZUBavIskn43s09VAPptbs7d7YXIV3H2fWQUVLysHfF3GbcthnXdYbfaIR3X0gIrUeLIGwb/hyu+kO6ccNwr7wbpX8zCeDiL2SJet1Q7aYMfzeh+K5lOlxTtob+zzXFcUYyt9vzEljh3kjEWvFpMSaSjpEpOatHfFJOQ5r0qg1CuNLd8YJtIihe9tCTa0amJJMNPVXxd19/yOYi5/hygx3Ed0n/ZmiI5pLfuzxhnAYeqvWKjYv46PUu+Utxl9h/wwd3seTzZ7vcFilvTy/4+vBTnpcTzjZDmlc51V5oEo0+WlNPU1ys0C81/91Hv8p3j55xVo24bsKk7L/49B6TOnBd3/C67+SEP+7zsldw1Fvw83KPxmt+cH2IKdg2osL/j24qmnFCmwvqKuaZmHCQLbmqezRecXYzJHsl7tYr4YLMsM0kiz+awAcwr7O79fCfvnyAv47pf97c4X7aYRDpJgvH7zx7wNHs9u5z03aKf/jsbY5vLLIJQq7QmHGoTUv+R33+Sf8R7+2+pvGa0hmelxPyl4FN+wa1EhqZimZk+Pyne/TuLViMUjLZ8FvXT1gvU05fhpqDrC0qkndIn8lvR/ykOWJxL+Got+BVPeS3nj0i+2FKevPF/VHvaPJzi2w1f/DjB5ydDnl3fM5Al/zz24f87s8ecHTr0WUoQJu1RXhFO9D0fmj4abRHvavJTc33b07JTY3+RRoSz8mW+Vw4/JYRn/044X/V7/P63oDGKSpn+OgPTum9FpgtYzhaWOodjWo7mnHE4CPNaj/mZTGkdIbvvfg6/Z8avHIQyy0j/A2HXtD7ScSznQkXm97dNfz5Ykhchobqm/zqG9Ff8krxxycHzPI1f3xzgFGO37s8QT9PSK9CCAqCn7vuK6JbifphxovTGKPC+9g6xec/22X06b/8HeoNh733ccxZtMMPlKMf1Vgv+VhN+cPnJ/SeBbyS8KHorDaONn8jA/1XO/6NLtpKZZBGYkWHrIPZXkcJ3vk7IdbNak1iIrQW+LrFOogThTSh8OGcQJkEqULRar0u6GmB0Ya2bfC2oygaVpuazmtcZzAmokNhETS1Y1NYUuMofEUnJVJ1lNVqa5uX1M2GLM6C6Ewpri4umYxSRCQQUczt7Q1t67YJMFBC4p0Cr4m1QSvFenHLJOtT+w7rLXXruLy6ouuCkMnh6Xz4ciF9R7UpSHTgkRJp1uWGxabAdWBdg5QJSZyxXG/oDcYUpSNLPXVrkduE0HrTsCqbUDDoIoT2aBK8aGlqKJYbEgMYRdtaOue4urgiG/SxTmC9oLUOJTWNs5R1hZGGQWoQFkrZ0rYtZVMjlcd1HVXjUHi00Kxv57hBRhwbpBRsNivSNGNul9AKIh3fSeV0ZPAO5qs1uVEYCVEqqVrL7XKFtU3YqyrQeYprHJ2VXL6es3uY02IRYYqe2rasqwqlTCiGeQdC0bqOlxeveVzcJ4osUktwlq7zvPr8JW3bUlUFSdTHqRjnHFpHXN8uyON+QDNoQEJTlqyKFWVd4TuJd5auhIUveXL/IWVZ0HlFPzPQCVynsE5wc7ugaFrSPEYrqKqGQZ5ze3vLuNeDzuFlh5COsqppbHj9yIR1UzAc7uDbBu8NV5c3HO1Osdbiu4bWO25XS1ZFjRchAVy7sAhGcczl5SXTPKPuOrS3FHVJa2sWqyWj8Q5laYnTjLquwDX0Bymvzs4Z5UGso5Wgrmra1lG3IS3cWouJImwtyOIBTWmx1MR9gSXCC4l3ks28xjtFpyJs29B4UKoDoVitCwZ5hFQRsgPXODaLNVEcsy4KhJII7bBNibWW69s5s50dWmW4urpB6YjL2wUNIFDI2hJFMda2KJNwc3ODt46ug1ZbbF3T+o7b+ZpN0WCSGC8kbReSb0Wx4nqxRPucQRZT1zVl21DWDdfrOcW2EWqkwgjII0ndNGzqGq0SRGvpOkFRVGyKik1Z07QOk6R0nYPOgRdcXl6S7E+QUoZ71npa27JcF6RJzqYLfG+5leg1zrHcFJxd3nL/MOPiesnlzZLWebQyNK7FG4DwZVh6TdU45rdrPvv8FbPBgKZpuL5dYZWhbR0ChxJimwiFxari6nrJsJciYoFtwHeKV69ekfZyWguxCClQ6/9P9t5sV5dsPdN6RhsRfzP71eTKdu/c296NjTtVFaZwlYSRSphClCVKMo0sDrgDuABzwoEvgUaUOKCREQgkEBIqC1EULnC5wd1uss9cK1cz27+PiNFx8EXEXGlOXCd2Ud7zJDOVc/7NiDFGxHi+93vfxH6/J4XCZn3AnDagNOv1mt0+8eWLS46PT+kUpBSIKTE/XvDq7o6zk2OW8xm2RwLzYuLV+o71oWcXCrokXCUq0njoWN2umVU1ZabYbKWqub6+5bDboXRh23ZgLEUbFHC72jCrPLYxEqqY4bDaEPtIQLEvCR3BoCil4LJ43W7XG1jOUBb2h5br9Y7bzRblPSkVCRRLgj13uwN3NyvqyjAvCqcNxld89sVTDm1PH8UnuU0dKhtSX7i5XnN68oCjuWfXtYTuwO16w5cvr/H1gi5lDJpUMkZruq7l6uYWZywpamauYr/d8OrlNb45Zt0eMNahlcM7Q+U8682e7SGibMRr6A4tu92etguoIuGYpRRKEVieUmK1WjPzjqu7OyrtCUnzyWfPaJZHrEJAA7mPYC1dF2njnpf+Fu8fSDG0X3F7t2e93eCNRdfSLRBDRmlDjInV3ZbFvOKgNG1JhBb2qz2NcdzGgtFyPXwxBAM32x0Pdh1OK5y1hC6T4p5921OGRxujxNNXlUIpPV0bub5dy4O1c6RU2GwSq21AGS2g2njatp86IHarA1eXKy5OT9DqwO16TQiBnJOsEaXoQkutarT1bDYHbm53HM0aChD2gS+eXxIK9ClTlCKEHqVhXje8uLplfnLEYtngbE2NYXV3y91qN607ZYqEZ+x7jKm4ub5DFQl17OMees2rV2vWOylqZAraQB8Ds8WMl+tbcAqrT7EKvPGE3HJzteFu14Hx7NueZuahaijaUfrDn/sz349+/mJ+sjMUqzGHIGoprwUqxoLuFduu4n99+S3enIuCss+Wq7CkLERdpaMAxVQZcHD8wZYPbi745tkVF27LVbcAoLutefxcbpC6l6ATlQvmEFD7mg/vLnjn6JY/2L9NKIbKRvSdAzLZSejX4cKhkyFX8I8/e4effecLnE44nfjNq29x10pLpOlE1amDHMTiyQxXRX7v7m3+9Ye/z+f9ObdxxtPulKMPtcCZMTRsOEQVC5/tz3h3dsPpkHT1/6ze5LPn51wM3n/ZafqzmsXTjvbJksvbnrujhk/bC2am57pf8L2bR7hLS/KiTC7GihVDyKi95QebR7wzuwXgqp+Ti+J/++wbLM+teEl2GTsklrtd4eVqyUfVBT9/9jFOJT7aXnDbzfArTXYCxKuVKJNSrYlXNbuved41LTPd8/ubd/jm/BW/G9/CAv1co4OA8Vw7dCz84NkjHpxtuKh2nPo9nx7O+Z0Xb6G6+wN6asw0Xv/o0/f4ha9/xGW/FHuEbLjq5nxw/QDnFGVQPQO4PTz8vcIf/8JjHjRbKjF3JGTDb3z0M9gDVKskXqXBylypFbtPjnl5uuTI3beTvmyXHC5nLO+KALOY0V1m8WnP53/riLA+JRbDz518DsA6Nmz6Cv9ZRf1qQ3fRAAzBRnO+/+Uj6rcD8JhDcmIrsDnn7lBz9rl8zvrZhv17R0NRAD7+5BF//bsfsOprDsmx6mp++PwhFy8GwFSD3SUBHMua2Yee3XsVX3LEsW/5aHXBpq2Yf1k4vH9OdXUgHFe4uw4A83jBb3z0M/z042dcdQtO3IFn+2Pag6fZIXYQQ7CYOUB6fMpqD/vO86RZcxtnHJKjS5bDtbSvq2wwbcJuOrS38MQSXjV8/PCc2gS8TrTRcnczZ77QzF70k5rXtGD2geWnS9L7mtt2xv/w5U/R2MBvfvZNjndlChULC7G6yF5z8lHPZTLkorjq56z7mlVXo5/WNJfd5Kc8BmBVly35kwXfrx7ynQcvedqe8vHmnJfrJc0nnupyI8Br4aVD4I1T7Fbx8uaIhesxWrzwn66O2VzPeedSgG2c20ntDWA/bHi2PJq+9zrU3LUN1UtLe65pXvZD2r0ZFNYty6eK7Cqu/prh+QcPOP5QY/e9BNEdBPTKJiL7q7/tOVYQ5wu+fL9C7w1vfhQxrXzfotVkQWC6xOy543c+fYf3Hl/jFolVX9Mmx/e+/xZvnkuRIR8NzzltZnYVUcHw4eUFszekHf3/un6PZzfHnPyhZffuAr8SuKjbQHpUcfKDwqt3j+ABvPBHAuq7Bj6Ys3ieaF52EnI2jJdpM/Vzx+HUkYvit+/eA+CTV+f4laiEmy9ln1RZ5sDic7g9XfKP/ds0LrD0Hc/WR8Te4A6Z+rInu3vbmzgzVLeKF1fHAGhV+HR1xsvPznhrsJfRvRROjBX7gtll4vNnJ6yOetozy3HVsnAd/rcXVHdSNOmODdVKrCRMKCw+U9y8PWfxqON7d49YtxXbL444/UNNWJopMHO8r/lVxH1cc1XPOQTH106u+ez2FD6YE5aJ2Rct2ddkpwkLQ39smT/PvLyc8X39kJOm5WY3Y3c94+G6TN0TKpVJoav7zOy5Y3va8JtffHMKIjv5rYrDGSy/6KTTcJhb1TpT3xX6HzpezI+53c6IwaA+b5g9l66AbBy2LeIxr8Uiov7Ssr6o+dSd8fn2lHVbo28d7Yni+DYOSnHpwHD7zPEnic03Kp43S5a1Z9NW9P0p1a2a1lGqtPhwa/lObgP9pw2/q97GV5EQDM0rzfJZoigm2FvdRlItIL2+Klx9fMzTr8NLsyB9IUFzOo1FHDWAXrF9mL0sXH22pH9sSVmzaz383hHzQ8YPhTK7lzmzf+g4/5PEl+dn3D6YUdeBB8stL5+fcPoUOe+FIp0gAdy13G+PPs2gHM9Oj+mHdN6jHxoWz6XbRA8+/X7oojn/4wBUfFGd4n3kaNaybSvc5xXLzwX81pc93bm8lir/ZE1tqpR/AsT75/zzd37l3y669rTtAascpRSqqsFbR20N73/9Tc6Pa5RKlBTp+x5lFd//+EM++fQL3OyMVOZQjISZUfiJb77H49MZXgMqsjt0fPjRJ6y2gWgdbdakvpN2Um15/50nPLg44eLYYmIglMjt3Y6Xr264utkQImjrCTnjTI33nidPHvPk4ZJFVYh9z647cHt7xyefvEQpQ8yakBXWWnQuPD5/xPvvv0ntMtoWdrtbVus13/vBp2jbsItRWoFzJmuLMZqf+8nvslxocupxXhFSzx/9yYesVwf6pDmkJG2TqqLSFe+8/YQHxzOOFzNq33O92nJzveKzp8/QZk7bapTLxNTjbMXy9Jg3zk95fDbDW0UohfXNFc9fXfLs1RXWzeiLpRSFKgqnChfnx/z4+19j7hUGxWp3x3q/44cffEqfFEkZTLbUs4btesOTNx/ytbffYLGwqJIoSQJenr244umzK5KSBPcYo7QYx8LX3n6Di+Mj3nx8Stu2lBL5/Msvuby6Y9e3HHpDQaOLxRTNO08ecX625Oi4Ies9Khvatuflqyuev7gk5DIAaDC2omo8j85OePetBxzVNdZqXl2/4vr2ji8+eyUH9ARtLmitKSXz6NEj3n3ygFnlcS6iimK33/LJp5/y8nqFUhVFGyrTcDgcoBT+uZ/4Mc6Ol8wqhS6aNnU8f3XHx08/p2TPrj3gnMH5OW3b8mNff4e3Hj7EloDzilQSt+sVz56+4Pp2D6Zhe9jT+AqtNSFsee+tr/Pg5JjFiaiMV7uWm9s7Pn36DD+fk4PCawlvq4wERbz/9pvUjcd7Qx8TL15dcnW5JmvDZrdmvmikfTsmFosF7771Jk8ePqDYjhwiORc+/+KSzz9/SjVfEAv0sVDnmhh7Hj4559GDY+ZNoXENOcDlqzsuLy8lAEtLK/kmFpTX4vtbed5/5w0qZ1FFsd20XF1dcb1uQVsOcUtVVQKOtUFbw6PHpxwfzamNo2xartZ3XK/W0rrfZeJg5K6cows9X3/8mMdnJ8yaipACKcIf//D7hF4U0kpLizUUfKXRqfDmo4ecnR8znzfcrja8eHnF+m5DTIo+tGQtazykiNOKk5Mj3n7jMY2vUMD6bsXzVy+52e1IGHKE2nvIEWcsVmneevsx83nDomnYbrdsNltevrwkJcs+FwqRVMQT1duaVAJag9Gi/E+xoLEoDNkEUVFlUEoz95V4+sZINDAzHq0UoUhbOSrT9z3Gi5cwxdB4z6ypeHxxzmxWU1Tm+u6Gu7sNXZ/ZbvYY74k5knNm3swwqvDG4wuWTYX3FaFPXF9f8+zVLUZDKdL2bpwlhQxkHjy6wHvPxcUFpsDV1RVPX7ykT3J3K1n2GG0gK6TV3hjeeusJOiRC13O7uuP6doV1NV0MVFUFCEz2yrKoHKfnRyyXolTaXK94fnlFMJbVdsf8aCmWNJ0o7XerNW89euveeVIAACAASURBVAOvFU3tudtuuNmsiAUSBYMh9h1eG1TMuMEmo5pXPLp4QOh6+v2B7e5ACAm059C2GGNRJLy3KC02Ak8ePUGZivVmT3do6UKLtYbQ99iipBU0JbTWVFVFzPDw/IL1zS2Nq7m+vsYtaiIQQo9BoZwi9QFnK8zwd8tZw363YR8ju92Oynm0hlQygUwx8nsmFULMnB3NaeyM6+trSlG0SuxxQgiiUg0ts0YKWiXB0cKThzpE6BELDQJoyz72hFKonENnua7zpqbyHq8Mt6s7qllD3/f0saNkI7YRGZxzKCuWBKies/MTdusWEnT7jmwhlYgxYv0CGucqQi7MjMc7Q8yBzWpLM59x6DucrVBKvksIgaaRw3wcrGAWsyUp9+x3K5yqiDGRjNh5FA1eyXuOxduw39P4Bq0SxXg6huLHYC+jlMxhZy2QaNuWo6NTKm3Y7XYk5Si5J1HA6OGzJSk8aYvVkpqulKK2jtV+TVXPiLGnlMKh72ga8YMOIUDOLBqPc2I3cjgouqwoJEB8zXMxaA30Eeccv/Ff/Cf3/cY/+vln9udv/tKvl2LA34Wpjbs/tvRzTX+kWP/CgZ995wu0KuSiuDws0Krw4u+/xZv/+14S3Ad7Bd0litV8/MsVD751NSlgPr89xfyDYxZPE7Yt+LuA7iK6l99/+i8fsXs/8PWvvWTueu7ahi+enrP4gefkwySHNiP+uXEmB8qrn1LMv3vLN88vWXUNX9ye0H2x4PH/KcDAdhm7F3iZneL6O4bTf/HF5Nn54d0FL5+f8OR/Eb/O2atAWJqpPTQ7xfN/q+PtB7eEZDipD9y1Dav/+Q0WXybcLlNdteg+kRpHOPa8+KuO7hst775xzZvzFb/9xTvkz+Y8+r9FyVOtxB7MHuR7bN+w3H638Pjbrzhv9ty1DU9fnjL7k5qHv9tL4Nt2qAQrSX2//TGH/ZeueP/0mrnt+f2Xb3L3Yskbv2nw2zQcJBXtiWH+IvDqZyrSP7/mvfMbrM7ErPn89pT0B8c8/J0o3zcXzCFNrdNf/kJF9/WWX/r2H/P5/pSYNT/4vXc4/kDRXGVR8b7Wyv7ir1n69zreenRLnwxGZ67Xc8oPF5z/YcHtBT6bQyJ7sXm4/rbB/pVbvvPgJWd+z9//+MfIn8x5+DvDAX2T8Lc9xYqC7eonG27/SmB+emBRdxidBdD91oyLP2oFGAxzt7oWleVH/4YodN86XlGbwJfbY67+5IIn/4eoAuuXB9LcEWcGfxf4/G81ND91y6zqOa5aDtHx6dMLjv6g4uRDgbbN8z3dRTPNxy//ek37zZbz8y1dNGyeL5l/ZnnyD/aEpRt8mw1uI+nqgLTTH0XcLBD2jsX3PKcfyrVonu1oH8/xNy0qZdrHM57/vKX6iTty1sQoYofqd+a88Vt7+mOHPSTspqc/qzGHxPV3a1Y/XijnPcfHe0IydB8ecfQhzK5krWYD8y87sRdoDO2p5cXfKLAIko905Tn6SHPykYzlCJPNPpJrMwToefZvFOKRhP0uPrKcfCxzcLzeIIDK3bU8+8UTNt8KNKcHutaRg+bN/8li96LoHL21AQlBdIrLn7a073acnO1YPT3m+E8Mi+cJ08rfqJjJtcHsAmnuWL9bcfVzGY4DShfUi4qzP1Icf9SiuyjWGSeizI4zQ6wVlz+rCWeR+YM9u8sZ/tJy+j3x3WyughSaBtViXLrJazlVeihu5WntjL60aghiOzwQcDaOR/vAkw24wb4EwG1ElTtaMLSnlvZMcfedjDrtKVnhP62YPwW/Kcxf9INXrvjgHh5VJKe4/klF/yCi55F8sDSfOh7+XhA7k8S9F+xgdXHzLUds4PB+h3YZ93HNw99NuM29CjRVRjrJkqisk5PxKkYA/uJzOPmwI9UGt+oJx6IeN20izg27R1ZCFh8NRY+XlvM/kj199skd/eMlZhcwqwPbb59Rv+x4+VfnxBn0x4X5M8XJx0Ha8AdFq4Qsig93nMk16JaG22+D3SvsAZpXYnUD4tWdKiPWGINVi98mXv20AwWzF0X81jeJVEtHR6qHILkgCvB+abBd5uZbltnzQneiOP0wEGYSnmgHBSlq6NaYiSq0X2r2jxTVbaG5zjSXgbC0orYcCmAUsaFIlQD51dcc3Qkcfyz2AdXgsyrvIePcXVTT3r194qbr6w5FLDQGS4X6ssfse+JRNc2v/QNLv1TEueLos0S/0GIJsZP3G32l40xTXnsKXH/NUF/J6/pNlmDJLk92PLKm5N8P54YwV5gOFs8D7amlvr33pbV7KUaOANO0hVQrdg/NpIoPM43t8hB6iYzTXDxxdRCrJbvPtKdmUulXmzRdt7HTJjtFmMl3r1aJ7RNHv5R7mQllUsoDw2eTe2G2YxjkIPo71iyfRvqlXHOVC/6mpz/zqCTFM4D9Y0+YacJCUd3lqdMEmPx/x2sRZ5p/+N/+B3+mZ+1/qqHtL//Krxbl1NBSU4iqsA9DgEm75eGDM37862/j5w3r6xtKENXoH/zghxhj6KMl5oLJmqI0FM3ydM7js1O8SZisuby74sX1HWgDxg8ejJY2tFTG0tia2dzx6PyE2ohia73Zc+gDXRcoWWTaFDvAMjnsPX54yslJRd9uWO33vLy+oURFKYY8bNIKi8qKuqo4O19yujjCVokXV8+5uV6RcyVJl10gqoQxiqwsbYw8PDsTyOQ0WkWub2+4vNkQekVQZWjrVhSVcFaLCq1L/MS3vsnd9SUhFi7vbtj3HTnZQc2nBQRYQ4iJs/mMBw/OmdWWWODqxZdsNwc6LanUsRhICmsUZjiAXpwuOV0ek2Nk2224Xd2yb1ti0jjbAIakk3jIlsS7T97iZNkQc0+779jud1zfbcgKUmqISiqGzhiIRZLfFwsePziliwf2h471riVlSLEFZehjwKMxGbSTw/rjNy5wWg3t3zesNzuiksNzTuLnqbXME5MyF2dHnB8v6GJgvd5y2B6IfRjGFfoM1juKKsScuTipqZ1n5iqMVlzdXLM7HAhRyaFbGbwztF1Ca0PTCPxa1JbGee5Wa/b7ni4kUf5aQymFPgacqyhdx9nJMSeLOctZRdsH7jZbLm9vUG7G4XDAVR4hJAVjDE4bjhZLShK/ya7riEaxPbRY7dHIBq+Uwhg7ABUJWKtrT9/3pAxtCmgtIE+lPHl3igdpwbkKsnzHqqrY7Xai/GwDThtSAmsH71WnsA581VBXFWF74PL6hso3AjIY3sdosWjIhaJkp3YqYpQlyp5IwZBKQRkNUUL3sAL5exVRRtPUDh0zuWj6IO3IJheShpAi2mlyzjRG0dgZKYrnqbRWZ2KRtkSlFDkpUuhwxqJNoXZe4ChFfKqz2E30UcbbGEUuUcZmALi77QHvDFZJiFdUCkOiy5mCJceE0eCUI4ZA4+vhtQCj6XtZD31IGFdjKkcbWrFJiJHKOVLo0UoUojoL7Cmp4H3Nvu/EIsNAF3qsNShVMMaRo0DA1Efx1ESAkHEyFiVlZq6m73sqV+O8JsaeLogHq3I1MSZKVmgl88iaikMJ2FhYNjVKKdq2RWlLiHJYsFhKKaQknQuiGpbPMK9n5D7InhzFbzjkhBnamYwxZFXQrhBjL163xaKLJSDBan0XiUQoWjobtCfGHqOgsopsLb3S0LU459jHnm134KiZAxpjrICvGGkWc/q+x6cCVqw6FKIUBoHHcaiCW2tBZXZdS+0883pO13Uyz5MalKwRU3u60OKto+SMxZIVhJQw1tN1HapAgxHApzOH0IORh6k+Bqz3FKWY65rStSQEfkdVyCS8l9BMTYGkaawAyTZ0+KqiKy3eGnInv5+VrIuCWD5obZgZL10P1rI97HGVpwtDoBdQlMKqBESxK7EWY8Siow8tWjliDPgK+phIWLSp6VOkso7U9TSNo+86nHP0IaA0ZJXRMaONQRlNUmAzmILA2ZRwzlGKeDbHGEkpYWbVsAbkYQut6NosoWFkcozMao8arB1iyeQsAX05Z9BarDko4sduRNF+2Ms8kaJdISH3Ww3Mas/hsGNWzyErSkxEeupmyW7bii/yoGDW1hBVgiyfP/ZBOgCCFExyzuLxGzqKMcScqf3g22xqSsqUWKSFGE0oAbIi6YwumVySrI+Y0MXKHqaZ3ktpLx0XrkDK9N0BY6VVRCnxO/+v//P/+EfQ9i/Bz9/4279eRpWMHMQL1eWe7sEMtwlcf7dh+4s7FrOWmxfHkAGfeee/M6RK4YaW/1EpBuIdefsdiLNC8Znlh5aLP+yn9/TXB3LtsDc74smM/syzv5DE+3iUqS4NzYvBq3GTvqJGGQ+v7bnj+juG9s2A3htmz7UoXQb1yghPxmTv7tiw/ppm/3aSz/R9x/EnaTr4UgSmpsEewt213PzEEbffhngawRaaTzwnH2TcXg7bKhXMPkyKv/bCoTI8/dci/pnHHBSnP0w0V0EgjhUP0RHymH1k9Y0Zq29Cf5rRneLoI8XsMuPXw+sPvoqpNgKgveL2G5bd2wN4vNQsP88010lAyezeg3RUcz3/+YrDWxHVadxGUV8KcFAJTJcm78Y0s8N7aXaPLLffKbitxt/C4kvx49P9vb9iWFqyl8NtaDS334FUF3RQHP8QFl9G7EGghA55AlNjC/ntNx3b9zJ2p5g9lwOu7QYPylba7MdCgm4Tdz8+oztW9KeQfOH4Q5hdSqvxWDgISzvB3u7ccTgzHB4qAUBPFfVNltb6QfmrcsFueuLCo1Jh9X7N9i1F+yhhN5rFF3D2fXle8dcH0txPczF7CenavGlRsnXjNxnbFprne1LjRKU5hP1ld992XrSAdT94O/q7MKgO81fCAUcYGGf3Ppjd0jC7jBQt1yHOrXhEN2b6u1grwlzTHYsv9Nn3WsKRnQoAYqXB1JY+rt04k3VtB+D1ulcnMPlKjkpdlJogtkr3AHJUKppdEIVgSHQXNe2ppLO7XZ5aq8drMQW2DWpKsYcQH0wdBaKOsMTu0tTyPilbh3VSv9gRlxWpMYSFGcLjRKn3+lyMM/l/7dlwbx7au/12UIVvRXGZncLfyTpXIRFOqmkNj8WLsWgQFkYUpMNcd6ueXItKd4S84ul9b2shPtQyXhRoLxxum+mXeoKCfiPeuKPFgUplmlupljC9XJvBt1dR342gTxTfqdbTtRzhot2Jp+z+oVhFxEr+ToIlRW0ITHtidgJMUQxWDoPq10lL/biPhGNPqiUwKjtFd6TREfx2+P8zTXMV8F+uad8V253s1AS+unMJWQvLYS9rRQU5grVU62nOjF6q9csD/VnN/oGdINl4TcZCllvLnjXafOhQvgJC0YruzE+Ab7SuUEn2iDRzqFQ4PPJfAX2mlTGOczPYBNzDdxBQGBbicR0WZgKKOha5Tw1rMjvN4dxiO7kW8xc9cWZw60ixalqPqTbT3iXKWAGWbjuErh0SYSHBeLqX62Y3HfFI5m135nHbOHjKy/V8/f5UtJr2k1FROoJolZj2h3FcKUxrAcCtZU8a95fDufz7tH5TmV73T+8x2cmcrm76aQ+QsU7TZzxcOOqbOPnpqyyv257IvigWLrLGslNTmGecybwJc1lT47WNM019Lc8OZh+k+DXYGI1e0GFpiJUe7IoEzI97nszd0S9a/t+4bsf/js0QkLrNk41EMRArzT/6r/79//9D27/zb/5qCSkyN5YQAvOTI1bbHVkNfqFGcbJc8ODxAw77Pd12y8uXLynaSNtsMZSY0WpQvAWBknWjMVpBBlWi+NFS6KPArpwzjW/oDnvqWmBPXTlm3pFL4Xq9pm7m7FLCIzcLVRgOjoO332xBEYMFQsoC4Pqe2jf0XZSwpCgqJbGclgNW0pk+RYqW+D5tGsphj1YdgYzBY62jD4nKG/lMcU/b9ihTEXoJJUkU8VnUkcobYmzxviZ3CV2gmlV0gxouZaZgqZgDJSZqU+GwBJ3o6OgOPUtrcK5imyK5FJKyGCOBLpWRg7BOCqcFwoQciTlgvAG0tMIqPRxwRXU0q2qO5w0hJ0LbkVXmkPYUVci5HuwhFCplnHE4VZFLxBrACZg6tNL4npLAKpUL5IQ24rfZ1HNqP6fbHnB1xaHbYaz8bckKpQypZPqYQCsa56icRedE3/c0TcP6bkUexmm+OKJPkX5Q7/Ux4CyQBeTtu1ZSzsmEkNDGSbhSFjsFtMggK2exptDuWjCWFAsxJpSRoB/jBECAtNovZw2b2xuOFkvatidrAYR5mO8ZaQdOQaBjDIF53UjrdNfSk0lG0ZaCVx6Pos/y+iW9HtoDh9DjtMUaQykZrQeYWayovowS6BAFjGk0egii6vseozIUKQTkXIbX0BQ1/lPGsm875ouGGDI5JXRSmEEFS9ECTbVGKVCpxxmPGuZXyZFAJuWMx6KLqESdc0QloDVkizeRGBOZOIVraeWIKVCKBIYZDa7UWCuKXWMEmocQMEoDekibzyhVyCVgrUYrRUQRYqSua243tzjvUUpUoDFGWfPDa2ptSblgjfhzmsphciC0gx9sTiQKFV7GLgE5Y71HmUjftzJfM6SUUVqLQo6M1wJo7KD+M0W+gykIVFWakIr4rFlDDD1V5SgkcizEIGOnixp+H1xdsTvsRcmrxUc65zwoYw2kiFVa1m/MoARkUTJGDerAknDKyBoe2xpTJA+3qFxkLWalCEnUwCcnJ7RtS3/oaCpRRIzWJsYYFALntdYYo4jsmc08KQz+iAl6XUg546p6CpirXC3+RjGgCyirKFYTZOlPKs6iFeu7GwmC0hpbN1KMMFq+QyftbErLfq+UQmsk1C/G4b81xipSiBjrUUbTd2m6xxitCUnmUdvLd9HDWMyqmkxEG9i2HQoj95oMelCJaiXFgBACtq6ksyIDoQx2GpFiBBwrpTDDmrPKfqXIaLyji3sJMhu8iI2R62SVph/sRJSSeTTC4jIosZU12KIgJpKWuTGrG0IIxJjRylJMwWgPqpCzKEE1jjSouuu6FtgaEnYY/5g6Gmfp2wMFKzYoSpGUhMwYpQeLGnk+cK6ipCz7FoptDmA0OgBW06tCiQVK4qQStfuu32PMcCiwlq4L5FzwVuZ6SAGjLQrQWg3wV8vYKnBGuhgqLwDbKOj6A3U1k+JWMeQSKAVUlrWjrIyjNmYoTiaM0fR9wNcVpZc5aLXYLmSlUVoLHM4jzBWbJWeMwNu+EJWlTz2zxZwUe1SUkDJjDEbZaa64qiIMFlMApuThOUSREZhcikI7+I3/7D/9EbT9S/DzN3/p14vdRQ6PPHaf2T+wHH3eoWIhHMnBfP22Zf2NjF8p3FZ9BY6+DhBiI4dW3SX6U0/296Bn9KO1m55cWVTM9Gcet5Z2aLeV1sfDmRzwTz440J846pcH4tJPIGs8OOpQaE8Nw+MG7iAQc3wdtxWPvVEpNR6oRhWS26TpkDmCxOz0BCtSrbG7RFhaDudGAOlrICRVeoIbovB002F5PMB1R1oAQipD6Ir4F44gsz+xhEbjDhm/ith1x+HJnDDTzF72ArC6SK4s5EI4liClYkQ5pPKgYm0HEIOA57FNdYS27YVj86bBHgr2IAfp+jYNQEOgwfj7cWYm9doYyqJyob4ZvDf7PMGicez8TUt3UUsb8loUUvWNtNoKzCwTCLAbgffdRSOBWFbht5nDmWH5tJ88RA+PPG6bcZtAf+wmOAnQHRuaqzCBRLuLAkoU6DaR6/uAuP5Y7Ciq1bDnjZB6aEkfFYsgwHL32DJ/Edk+sVQrUZO5bZrGaQR+us+kWv728KgiNGqCkaYT6B+XlYCc3aBcHLxRx8/nrvekeSX/PYCPEQ7JH3DvXzpAwBEC2E0gD77Q43Ue1+FXIEoBu+loH82wh3vwMXpKA9P3H4s3cXavrhzbjHWfRd34muLdtAJa7C4Slm5Sfo7q+zGIaxQvjKB0VMiNCtBJpTh8F/nnPezI5l453x0blh9vSTM/AW3Tip2G3Q3jNEDRcQ3L2KkJqJlDQneRcFKR7b2faqr0lBw/AlXdyVxRIaFSJi6rIXjJTJCJwgShRqXeNOcHD9mxkCQKVDu9r4y3pnkpdhijglTHe39cYFKAjmq+ae/yA2TbR9LM/n8A/wjGxmupQ8bsRGiw+dqcaiW2Ed2Zn17XreMUAmgOaZovpk20Fw7TDaGHyFhNlgWd/Ht/7AYAP1gBDPB0HP9R+agKzD/f05/VArCawY5i2Ifk8zAB21H9qLKs+fH6j4WQ12F09npQjErYZWoM/qad7IBULrQP/AQemy8P4p+9dK8VMO8VkeNrjOt+nKOmvVdRjmB+hMKAQNT9fdEgzu6h7evX1m0F6H61kMJUoEDxlVBAlQuHCzft/6O/8QgEx78d14JKhf7EYtoyAdnx/t2duSlwbyxymi5N32daS8O6pwgAFbAcpvuAzAc5xwFs3msoGmaXcfpco5/tCLnH9TJeuxHeT99zsM3QodAvJVwtOwmC7E/sdB8ai6/jvW8qTA372Gh1MXpaq1yG76C/WhAZ4LCKmTSzk+J4vI+NxcT2gZ8UwOMcmu7B+3sP/tFXeFSsT88kw/fMTv2zobT9V3/lV0tAwsiayg0qM6lAGuNwztH1kS5Fjk+W9PsdKcjDU1VVhBzF0y6JP5/CkFUiZ/Gvi6HgB4hQUp5aEFNKWKNxzrPdbqm9l8Nlgcp7DqEfoFPGKQ25MHMCGEWVqAdoB8ZoQt9iJaWGPiXyoLzMKmO1QpVMURmTCmUIA/K1eGUCWDTFFJzVxAAxpgF+FIGsOlGKIUWwTokRPQj0dXbyJTRGQI3WmqZp2B321LUo50YVUdEFqzSHQ0eFQVeiPHN1hTps0UlhlSWWTNQG5z0UCZHSRZRvIQlgMdpJmzgC3mQ8JGysFIW3npAivmhiloOrdpZD6NC2oqR+SqSXQ6XGaYcxhs1+zXLeiHVCEdVnHxLdoDpTUVqui1dklVk2Mw7rPT1ZwI3WWApdG3DOEzUCsTWQQVPw2hC6gFGKqna0/Q6NzL0+iqI6dAJbCkb8C3OicpYUW1G1KWkV9r4iBJm/lfd0ragxva/QptB3kaqp6buDeF9m6OKgriYRiwyh00ogDIZ91wuEzAJRjHOir4sRTSalglHSqtu2e6pZQ8mZHAtauSFkCQmxUtJCrJLCaE3pBUooYwlJVHvWWpLSaFXQWQz529CjtBVI3Ee0UZSS8MbTlkQc4N+kSksJhUVbhdRNEmaAB1lp8RYeAGcqAgRTHkCW8+QUiErC1toQxSc5BY6cp+SMdqKitNoTQ2bmK9q4w2dDyImsFbpuKCrTHVqcAbLGWAFbORbUsH77GLDaQQar7KA+TZMC2lqDqzw59CglSmbnHAyK7lISRleooolx8LKpZvQxiCK0rui6gKNH2cIhdBhlRcmbBNiPY4vKdFFujH5Qu4pKWovftUIC/1KmpCBQSFnysO61HdSrg4qw9p4u7gUeGoXVjtQnrPEYJbA3FVHeRpWIsWCVIyt5+NbDd1wsFqLILuK33Q4qZYvsi7qIml0bsFYg4ahCBk0sUEJBO0tRQxEhZ6w2WG0ENIcwFRNCCLL2KtlvAbENSBGlCihPLBnVJ7zJKG2JSvZJo7QEA+YiwFYpShAfVmfEBzT2AW0NaShSyL4IM+9EXeo9OSW89vQl4ZUhIpA2DrC2IB7ESgnUHWF4ShLGGLpe2tuto5SM0mkCv9Z4MoWSMlrJrlKKWLJoRL3a9x3GSmdDLBnrHJkiwWkxE7pBiq6V3CPbPZXzGAwhanIJaC/3BWc0MResyhRTCFnjFLIOAIqeILM2An2V1mQSKAG0RPBGVCpBFxQOShj2ewdFUxjWvVa03Wt2JtZTYsJWHoxmtVqxnB9BMRzaHd6Kb5nuE0kzdYOooWAke6jcM0tWFC0PrQpD0MN11pm+G4omGKyTotmh3TGb1WSlJXzOOXKS8S45CqQtUmiz2tIfWtn/3aCQjYkuJBaLJX2KtCpRlTLs0YZURFHsDBCR9QMULYW5cOiorCcN4YrKGkLMVLWTPTyLelygsvgYM6yjTCHHgtPDdyqaiGE/+N6bAj4rKegMB+DRTkOjyKVHk+S2nAXYWmUIIVGMJSnpbvmNv/cjpe1fhp9f/Jv/UTF7CXzqLuopeEblMkEWt47Y1YH9u0eTeqdoRTiyk1Kp6PsD9qggGqFFWN4rYoCvKHniTDN73g0epmUIQZMWdynCl0kBeriQw1a1EkXR0HQxtWeOB2W3ufeVfB2wjAflUbkTFvY+aGdQ1Ygap3xFtTpCVpUkDCbN3AS4YABrA7DIXqPbNCk965cd/anHbe5ByKh88zfSTlwMVJctaS7AIDs9KdX+tHp2hB4jGBoPoirchwfmyk7jHZZugGp2UhIXA/7m3npghJACBdWktpw9a9m/WU+eiKKoS7i7bhqDcVxUzOzfrKmvA3bTE46r6XuMsMK0GbfqpLiKhDrFpcfsArmyhCM7KU6z19hNoD/zE+xRabBy2IWvqB2BASraKeAnLOXaikpKYJbbRvpji9uk6Tv6dZoO0KZNXwFGxUh78QghX1d2jspD3SVyZUi1QKFwUt0D1Nda5sfrKvOfr7Ssj+BC5qH+SqEh1qOthprmRJGsYMJcYPTr1hYql2l9TsrucB/8NyrQzT5K6GAnkFv3EkTYXcjnHwsQo5WJud3RvXM6KUvNYfA1Hlr+q9tAWNhJgT5+FrvuSDMBguP8f11NbDdhgtixMRMEeV3dOPrdFq1wq37aD3Sfp/k77jXABHBHRajbxCHdXeZ9rswAptWkeh73Dr8aPJYHpeL4OcfrPyo1RwD6+vgWw1eUi/2JFBsmZe/gA51qPexDolwfP8c4puM1GlX5+0d+sj4B8WBOjSMNEG8EmSP4tPs8BR6N+8ZYlBJl+QDaajONud2nCQravaio+/Nm2kdHaPe60rpoRVhaxOv4vuNinOfTHj2gprEYZF5TIE7FByX7++zFsN5esxUJSzOpmcb9qQAAIABJREFUbaei1GsK59ctZyZwvotTIfFPg99UjXuJjNl4LfxKij+jLc2osh6B8KiOHV9//IlzO1lDTHvzGII5Au3XlJujB/W4DsZ72KjCfv0+Cvcq+LAcIadkk0z7vb8HtcXIuFe3steN90WVRH0PsPhsz/4tCWasbgJxPnRxGPF2FdBbps6bUUE+epiPn2+cN+N1eN3mYwSY1U0/wXHTDl72w9iPUHmE3LHS+KHYmpya1LBuE6c14O+kiDcWJkxXJtWw7CF6KHiMXTmZ/shgujKtdbuLhCM7/c7rRc/X1dyjlUGcyTyIlcJ2hfqyn/az1y2V5O+HwsRQlBn3cVnP8hpjgXlUL/9Zoa35tV/7tT/L7/2F/Pw3//3/+GsJUcFRCU0PReFNRUoRbSF1BwwZjQAC7xx5aGnuVZG096FdPMWMxmKstD0WrclJ2gBTSYNqR1K7U8ns9rshhEjAqdaKSB5airOAKz229moOrQCLnAq5RLQt9KHFKAW6kEpGa4PWTtKsKQKBtEIVAVZKKVCaGCQhXoAXlBKx2ZFzwShHKOLVAxmy+NwprdFWFD0hJqq64na3RnkLVqNLEX8frQgxUPtaHlBDwVtLRFLUe60wWVEqOxRHNSZmdNISYGLEY89aLbBbQaGQSsE3jsOupfIVfYxo7wdFmsZozWKA0ZV3qJwkVMdksIrsoRgBK7FtMdqgC9SVo8Q4JYL3fUs9rwgpEnMio+mzKLSU0fRxj1WaZjEjxkjjZ6hDorEOY43AoRgoI0hGWr9TimijsV7Mz3OIkBO1M+gYsNmJaswqslb0XU9dzen7MHiKWmwSUFB7R384oBDrgb7rSVnC9NCaUkT5qQyEUgBNu9/LQWJoHRfiINCrto0oEpV4S6I1OQNKk4dgqX4HRkFOATUo6kJMRKWo6oaUO0rSOOXJpRBQ9CpScoGccYiyLqdIbzWRQjaaqGX99EpUpiFFUWdqAcrKaGLKGD0ox8j0BErRGKPJ2pJiRBlFbQyKJC3LZVD4alGdhRREMayMAHwtsLZytbQaK1BorLd03QGlrUBfgwQN6aE9OAMorHMS/OOkLbiQME1N9JbcRXSGWjuMyphicdqJwDfJXCIrjDVDn6UAZ6fFQsI4I2tgUAPrQZXcdj3OWVLOWFXTl4x1FpRFG4VKkdpprNZsdhvM4ChZlEJriyqGFAvOOrqup6k9fX+QuWO87AMaSgxQWUxRAiNR7HNAZ/AYSbY3Q2XQlHGHhOSonKPrDnisrEtrCLkXRbuV4oO2oqJVKREyWGfJqhAIk4pUWc12t0cbA8aQ2g5jzQR9Y4o0MymeoTVJGRIK7TwhQtESTqi0JRtF0IU8FLWMtcQkBZFcMtZY2r4VxaMxzHxFCi1GZfrUCWg3mlQstarQJouKFo1SEa2ywK4+on2hkHFW9jdZ9/JMqbUa4GXAu0pU1jGTRuW4kbHVZOg7dDEoFNY4ikoYYwldGEKuBKD1IWGNBIGFvkUr2V9iyqQSUUrsFWrjKalMCm+FQiuD1oocMikrgeC5DG3wEpyltcJbw367Y2ErSk445cg6oYpFY1DK4IYQTVUUhoI3BlKPswVnBiVmv6OuxMbCWIHPVmlyCDgthRZjRZMpSmrxuDRF4Yqo43NIGOXEc1gV8ZatNMYKeFRBExMoa+lSIqoIKPa7wKyq0SVjYgRboZ2DUDB1xaFvxRNBGykuoYegMSlqlVywVtZnUhmvNaUESuNonEWnxMwaFIWgAlQKPfg7F6vo42AHUYaDpDWyHxCJUeFqR1cC6ERJgcpWeOsgJFIpmKJQGrEdKKKKnXlD7nq0NfQpEUiy1ouiULBaY5XcH0dvW6Ok0EgOzOc1KkvxBaWZ1w2h7XC54K2nbwO1behTL89HKuGVwlKwFJSGWCI6Jyww8waTI97IvJEuJEXKiVyK6G2V3HeNsvzdX/7b/+Gf60Pfj37+Qn7+3n/5D3/NdIni7RSMYfbSLi6QUNp2S2XFsstq4sIOB0pRBtpdkOcSP7R+W/l3PRzOBJzqKXAl1QM47bP4is4E2KZak+0IYM3QAqEkGC0VTID6uheFY5/lAG0VfgCi8l5yOCpGPqviHlqgkAdrpSR4bGhrRUvXhY5lgCYCguw+ye8z8GE9PJSPaqR9JC4tsw+vUUpDKRQnIg1RICbCicBNe0jEhZ3ghlsPiuM8gIAhBK5YTVho7H5Q0o1q1XIPiuLCUF21hGOPX4cBxsjnK96wf+zx60h/LO+dB7WUKoOCT1qzMIdIrgVo9qeDGjAVUqOlPfvUYfcF24oSyW1EbaaUwm46cmXpzhymy/SnXoq3Z1bmUga3DujhMJudKOt0JwG4uXZyfYKc0/pTUU7mSk/fR0eBOuFEoDdKCWAYFIXdicVt71WzbhPRXaR4uQZ6gCwoNcA0hb/pZB671ywAuPfD1VG6JwWiysFbIXNYpYJbSyCQ7pPM+dpgdwFziBNIVkVUuyZkzD5iN/0w/8Q/NjXSBeK2EXOQeWwOieIGD+MBhuhUpAOtl+cJux8UhFEAhNsNYWdu8FYdxrY/cdP8H6+3rBWN3cdhnSSKF9UhBYEYcuwYIKaRMdeDT2sl5xclx76hrV+RazOp9Mb3yl6LqjeIN344cTK33aDitYMasdIoFNkbee9hrcaZAKLiByjppa25WBlvfysFDjOoHt26J9dW1p2VAm5/Ykm1YfZsT/ZSMNBJxmAEw2mm8XdB2vG3g2LRm2kNjOtuUjk7Tf1qD1ZgjcCYoTtwmG+TzcTSSrFiIZ8re4Pbxmm/EWAo/0RJS3uuzb0SVsv7AcyeHyjWTGGJubZyzQf7lO7M4Yb5LS3oabjWModMJxdN1lOPjgndJ3JtB+gmUDx7jb/tZc8zWlS1w144zgWUgMhw7KaxHL9LcXoqOhQrn28E7qaT85iAslEtm4hLmRsohQnDec7qYQ9X014qa0ejsiJ7Wc+pHiwl1KhQt4SFwe2GIoaVeWO6LPtdl6d9ThVZf4zFDavQ8b6TQK6L3KP0oGAPc0191XN45NFZPo+O9/ePUeVchhb/4hRxIdctzkVhrhBo2J+66XfK4J2qI6RGD4pMGZNcyfzSsZArTaz1sB8hn7saFNFdmVTmthvAcitr1K+i2EkoRXPV0z2oyW5QLTOoW4u8Z30TZD9ErlP2eiiKCCDVQ+HKDL7VYWGne1N3JnPicCHMzu3z9DpFjxYOIkpgUMSOn1/mqUBMv0rTs0h/ZIjzoWjZS5ESJa9p+qEr48JhhvFw24zdpWmtqyJ2DrGW+3as1bAe5Xqj4PDADvuc7PWHBw63l88S5uL32x0bqo0UQ+w+ERduKL6o+73Zyb55uBDboLDQkzo81sOYDdB/7PzJVvHv/d1/4c/0rP1PtdL2X/l3/t2CyjRZY3Ikali3B2aLEywFUxJ9PIiCxnm2O2lLl4T3nbRGKoMdbsZuCPPSKFEPWvP/svdmPZKl+X3e867nnNgjI7eqzNqr92V6NnKGpClKliVKBk1SlgDDN/aFv4AvfOE7Xdjfw7AtyDZsWBJgQJBlwJZEgxIpLsNZODM9Pb1WdVXlnhkRZ3kXX/xPRFVfiXeSQQbQaKCQVRkR5z0n4vze5//8JPTRQExYLaObWssoZs6ZlDMhQNHTlVHJfWNhNF3TbkfTtZJRz6LofXhGAqXUN9KmHFAqYnQlrkJjaEPAWk9KYFOL7UMqr4Qqds5RR3EgamtoQicFLEpceSqDsQqHJga5wK6bFUU1gI2rtP/wMEaxjh2+sNuxyJz6kzIIhRY1tFkTjdyE26yE8smZ3HZbuinkvoRLK1J/876hrpzyMlqJlNJorcVJqKVUqm3lddITUuu6RVuFLcS5aJUlJ2iaBl/ICHShpEym8BV1u6YoChk974SO7GLvG0yG1K0wOpCdIURFWY5YLq8ZFKWMcmtRSxhjhIpGYZQ4Sa13BDJERYotlo7CWNqU6aKQkW3oUE7ThQaNgSiE43q5Zjgc0jWNEKmpk99nvdBpVnbXhGAU77Dqw3xNr/HQmizz8DL63DtFc+6Rf2tJSUZ1jTEkUj/e3NPIUTQgTdNI0B+RcducQVm61PWklXh4E2C1lEylNuASmKGnjYF1K07n0pfEWnaUlFFEK6PzdS2Utup/JzGhvahEZPTZkrIBFYgqozbFO1lRFCWxq8lJtAeBQMoK7wvaJqB03lJuuWspC6E5de9qtlbGjEMSF3EdOqyW15KDIqsWp414UNctSQe0tqQuiF6BntYzGpymazohBftCIYCQIiHWGCt0QIyRYTGEtYSWySqiisSUUD2Z1sQodGib8NqSckfod4ONBZ0ThRFNSFa9biHr3t8qpXabEXCrpJCsbWoZb9aWTEtSHV00FOWAZdOge/JXa40yjhKwZJahRluHzQaNbFIo47bEZNvWFJUnZSk3anRk6AfkLglhr+V9AdBG0Xb9+L7JJCWj5KYf9zbGkJN88UkpkRCNBID2erteBoOBHLcQKPtysBTBIOsqEnuiOZODrNXCOrpGFBxBg1KWdt31kweqd2P3N7YpQba4BMrKDjxJaGbrDSE2WJMp7IDYdlhj+mt02hKIOUSCykRjaEJD6cRnaxCNimxqgE7yuRE2Y+t9eC5j8wrbk7xaKaw35BypV2uskk0sYx0pBrooHt6Bd8Qu0cQA3uJtgcpyjc9t7pU3aateAKFejZbn3qzXRKJcg5IlNKINSKnDOi2KBpWxuidtemULWXQmy1VNMRyhXN6qfKx32y9iSmVCG5lMJttjmJXePo/YBZw24uS1ti+yU71KRbNu14xGE5q62ypVCFEC/yybTE6XW4VKUom2Pw9j22F8Ic7iXsVgs/yunMD4TFLy92IQ3zoJfK/qCCZICImQ9CkrTFGxblpK71A2E4iy2ViUGOPkhi1GCm8xyLRCSoGbZi0bd1k29YpCNnVTSqSo8OUQVEvsOgqnCG1HYTzaONYp0CYp/jNZgRVy2FqLj5aMKAusN1sNQodmFRTKGUZavlSTo1xvtax/pRQXy3OGox3qEGStZi3O9SzU9rBwoBJGJ3G7o6jXrTiWcSQsxnlIHcaIQibllv/t7/33f0Ha/jl4/NVf/m8yQLvjZRSyTgw+uaLdH26ddu469qPDiurpiqwV7U5J8UKKxrLRW/JSSJoN5dKPhfeBALwkWF4dm91Qjt34FQ9fX1i10SpsRr51m7aUDLwM07Zevv5Gc6M5sNctsXLbsfQNedWNJJgMA01x3m3/fXfZEEb+JSGmZLQxDF4W+GwIWXhJTYKoF8qzIPRN737dUEkb8k13Ml650R5sXLWv/qxdxy2tu/FovkrPhqHdknPusidmg4RasTJfoRe7kaU4a3vq9eWosIpZXuvYo+tIN/VbAmlDM71KIm7ezw0pGQv5XmvWcauxaPb6gqc+9N24QLfj1T1dbdcv10WsDO3E9DfbYUvIZqOwNy3J9QTV2OFP1zLmv3olsIGv0HSb42YaCUx5hYZ+VR8g61Zt//+qTmKznjYuz1ep1M177C4kvCVlwthtwzqzCv3mhkLXEd3FrdtWBTl2GweoO10RptWWMt+sp80mgztb0e0MeHU0ekMMbxUIG6K8H+PPfYDdTu2WjNuQkFvvZk+Dvkovb5UiPSG2IQs3dLY/q4kD9xXP46bIq7iMX6FOw0CIQN03skMfdPTj0Bvi/lU6EyQwWx8U2/dBSr7yV/yv7kIoU7TaFr1tqM9NuPpqUdCGNt0oD14dAX+VzN/4olUUstGu5HpUPltvj/MmmGtnotwYPG+3VOGGQs99yJ2c3qo9dBC60l6saQ5H2w2Czej8q8f1K5ReP3Ktu7z1lso172U4/Or69eftV0jpZu62o+gbmn5DVr9Kq7YTsx3h3xCt7jpsrzWvHrMNBbw5xpvn4S+7rS4klma7/jbk42YkfOso7cNUd9nQ7pQvaWX9Cm2+IS77MqrNe7JREmwoYSEY9XYKY0PObhU2fbHUemExXcZf9TR+ryPISkLcUPSKk9VLAnbz7yQjvmrTRFb7fluuuDlPNpuSsRBtAEggt1n/fpmonrV0k5fv5yZ43ly3Nh7WTbGYrWUDaPPZYJq01UqEog8dN2tAQ3keWR5YiuvNBsTL5wHyOruBhMIqCZnrl2l7LGKhtyRzLLWEzD0RG51CbxQbbd6uxW6kt8dTKGa1Pd6x0BRn3VZjYGop7+xmhawFp9Btph2LtzoWoAMMnoctqa8itGONji/XfTfsobtOCuhsnXrVDpTnspm1uW7J+SGfSe1ws1mcCWX/PIOUAhYnDWjF6lbxlfK1zTHMWjH5uGZ94CnOw1bn8Op7tNrtN5kchIFCt+Cvch/2vnxP4KWmSXeZf/4P/6v//5O2/9M/+od/N6WEThFvLG0XpGwpilKgrlfiv8uapg0YJe3n5ERZlDgUtBmdFCoroR5BED8NSiXsK+OUZEgp4GxFU3cYJZRSIuKU0CjWCD0YQsJ7i0ZT+oIQQx9MAWRSCmhAa9mRTkmcfl1ocMbRdQlrJKSzSp5TJpNiwroNmRtAiTsyIu7HFGR7yGmp4okhCiWbxWkaQod1ntV6jdHiAcoqEQg4Y2ibhhQDpr9ZvFndYIyh8J4QMykkDFAUJSknIVlTlBM9BIyVMhxthIBTmp7GsjhlCSHgtBTqqJ4gCilIUNg7KLsQJbgLglsqMilEoSh75C3nRA4RZ6yQeyiM0xLoGfEzhibgrUMrcXnW6zWT4Yiu7VDaopQhxUBVVsQoY89djKJa6SkjCZVl91spRew6KUPK4Hwlu8+9VzfEJGOpWdyXJitKV8gb3weQMSe0M8QYsNoKxZ0CuidQs1LknADdOxkldEpkYgLjvDzHJJRU6jcOjJGioY3ftQsdSSV8aWmaFqU1hYOURCXhbK9lcB6jDXShpwIzmYjuAw1vDMjUbT9mHECDrkQJ0mVpPKZ0XDcreb+1Fq9mjEwmAwqjybndqhisFs9xRtayFF0JFZjJkDTOZMhRNjMQErRtO7zxhK4V6iyGPpjT4nDUEaWlVMs6j0b3mxueEBIpiKva6oxTidiuKawhdw05aUIhRUp1XeOcpQmthEZKaEJylBA0BlLsKEoZhVdZlBhdK8EnSYNOFBpSbujqtZwD1uCVJdSy+WCcoe1dzIrUA0Nqe86QkXMgSnDfXwaISIlQ7guQrFVoFTA69/SwJipFDJvQEoyxWKW3jfJFpTk/v0RncM7LP0zG+YKMbB8rrckpYZRifzZjdXWJzQlFJoYWY/vwNgScKwghUhUOjWLoHZWTNdc1N3JdMRptNCEGYo6gJdCrvEMT8BpKZ3Aa6cvrJOgsCt8rUGSHXauMU0gQj4IYiF1DNR5zdbOkMI6YI94JPWq1EcqRxHw0oipkzaUERLBaCdHaH4O2rvFOQwpUXmF7vKOpG7mma0PXh2I5g3O2V85kvNNU1rCzmJBiK5S81nKNzEmuiV6Tk3iuCyvUY+kcKbVYrHhQUyLngPZCriTEHV0UBeWgINRrBoMCTUChSTmyrtdysymKZXKSoi5jFKNRIcfDygZhyv1dms39lw+5HntvMFrhvMKoxKAq0Bm8L9HaEEMkRdnQUVpJ+ZeWIsFB4RkOSlJf+JZCpOtatDISwhqN815K81TGeY13hqqweKNpm5oUA3pDdvYTAlllmbowmuGgxJrEdOBJJFRKhKbG+YKUEt5sQnSZ3LDO4lxCE+V62nU4NDnSU+jyPSh04uUtyoIYEkqLe53YyaYuGa8tXd1Alu8F8jke8QrGI4PVgXa1wiTN0FfElFEonLVY5zBGY3IGFVjMKnZmFXs7O6gYWN4siVrCWCGzLd5Z0Jm2W1N6w2BQcHRrl9lwgNeak5MTbFESUsY5cPKxJXoT55hOBrz28JjJsOJgf5dm3aCTfKblmDHOY3stjTIJ7xR37uxx63iP6WjMbDSlvllji4q6C6w7+Xy3VpPJGK34O7/9m39B2v45ePx3f+93/q5uhbrrhgZ/HUm9UzMbCTRjZdExC9XZ03kqZ9pZ0Ye1X6Wisu03Geidg5VQs/IzQi+FocFfSUiRrcJ08memzcTK9qSrUKUbqtKu++muflJqE45kq7c0Uu69tLGy2JuOVIpSYeP/FOowbcMn0wpZmfqATrdCoQnVKQSOafrn1pNGZi3/fvFiJdRlZHuTHSuDvw5bci1WmsET+bm2v8GUcWlDN3FCY/YUoI4vvY9xIJNduhN924Yy3GgG4qCnExXyHNcCLJgNZbfsaGd+2yK+Ie3Q/Xgwmxv3zfsttJMoEvTL8HstY8q5D5L8ec3yqBKyT6stodTs+i395VZJ6FD11RF9lWVNmSbJ8dCyNhTgllI4Z2t5/3WXyN7K+zR1CP0l5LWphUrVbZLn3octG4/mhhgGof90/ZIuNU0keYO9EWdwtn2YtXHVxpfvl13KaHsYO9xFDUjJkmmTwCGbArCR7YNHeX6b4HgTpodeD5K8JgyF5JaNTYNugvyenugtP78ijguhLI3Brjpu7lS0Y5mCiL6ndUvzkowuReW0DWF7qjcMN8Ryv+ERMvYmEEZuO06uYKsKCJUEl/TkWyxl3blr8aWaOmI2hXKFhDqmy7Qjw6aIzdRS8rahYf1FS3abEK0PUfrgTodMN3FbGjoV5uW66mOMdmTQodcOGEWqrPhzezI1eVEsbOhrWbcvi99AKERFH17pTagkRUqb6ZpUSKAotJzGLSOmTVu3p4ppq8GQRnlFOzaMP7wEZwlDs6Uz48aB2a93oSQNZ++OqE6DTBH0VOzmubplr5uoE81MSNr1rpHivbHGrTZlZHIsbS3n2OZ9r+cWrKEdadqpXMtNYEs+t2ODbfJ2SiA5vaW+o++7MJpMMzOMPlvRzYrtdToMN8G0XGcv73uauSZrjemnKTbTD0KP9o7WsVCYzdQIIZoR5/XQ9NcJORc3GwD04HI31NRzzdV92eBza3kvt5MbIW8J3zA0tCNZB+1UoaNc37qBxrZ5S8mChGQberOZGmwjLu1sZZpTB6hORbvIZgnKMCzJaVYHhmZu6UYKHV5S+MDL64aW55+tIgwkwGvmfVnjWIJ600pYuKV0+2MSSkW9sKx31VfOWbcS+jd5ge26Sm+LyUKl6EaKdq5pRxp/09OcRgJR3b1837JRRK+od2W9r24ppChCVARhIJ9lYWS2vlWhRFU/BSPnh20yYSjXyujVV9ZPN5RJEdO9nCxQGaKXtRNGdjv1EAtZE6aTv7c6kI0Uv5Tfvd412Fbeo1BKILzRA6msuHxkuL4HV69lstZUJ2yD0GxEzdMN5Vzz11GO+0Jx9rXE6ljKwmYfRlF/1OKwDf3r1BHaiWZ5pFn+2pKrQ83ZOwZ7Y7YaId2/R6GU7ztZKcJQcfH1wODtS9a3I9dHoK8dsVAUlxIOZ6+350Nyf3bS1v5Zfujf1sN3mcp4mlizipCsRfWBTFdnnC5klFJbTI4YB1oZcpdpVzWuLLCFgS7TqkCIDYPBkKapsSqyXi3ZW+yz6hpibtC5INOTqP24bSJTOEfb1niZcqZZC+GXVSTXHdp7bBY5u7Hiy6vDCmsMWlnaZs1oMGEdWgbFQAKK3GJNge8SXajRvmIdO2IKxE7IwEDE4ElJ0TQNlS/7G1kJVZRSlN6y7IS289ZTFmNSaChLC2RyDDitaOuO7CLDwhJiQ2kMWWf2ZjPWbcdViBgiZeFZ1zVGFaTYUo2HxHqJQxHbKI3hbaKLDYOyIETdux+hDQFvC1JoaLQEVDllDAYdJSTdhDRt79YlJlwhjdgxR7ogZKDKilIbspLin3XTCBEaGwrj2Z2M0PMpKQWWyzUpWnJIXK/XWFuQmpZhZUUXsF7ivWUxm3B1c826awkx06aALyp5z0MmtqrXaGSMsqQGnC7wURNVwNhICDIKLt8n5HcmFdBO1AAOLQ5irUnAqltivaPNQoaqnMi51wNYyCGBTgRjsBoCGWsG6NRhcyKlDNpKcZLWMsaeYDAU8qyr5c+9dqQukbOU/LSqxQ88Te6ooqYsLG1o6JLUxRoD5ASpgVCTs6UlClUWImUUH2jGcBPWODvCKw+qEJq71NLC3jUc3z7k8PARZy9OefHiBc9fXKKcRSlPBmJoUDiUjgzKId4mDvYOITbUTcOqqTm5uGE8moKyqJ4kUxliG9Gu5ej4FoNBSc4NL07OefH8HK0chfHElIihY1BaUteSQsN8f86onDIdTVHG8eWXzzm5uqTtvzSGWOOtQsWWsqyYDksO9xdUyqJs4Pr6mqdPXnDetmQ3oHKWm2RYpg5bGAYEdvd22JkeURQF19dLTp6dcLm8wVhLMpHUCXEbcsCkyHg0ZDL0HB7usmxbTp6cUHeBm+sakGIo0OTcCBhhFAe3p8znUwbW0nWRFy9OqZslGk1ICesLuiTrqbCW3VnJdDJkMCh5/3XFzWrJRz/9jJQtXU6EOqC8uE4NEaUzd44OmM8mvPP4mBgzH/38E+rVmnWzJpDRvkARKAqLUZHJ2PHw3i28tzIx0AaePD/hk8+f0bYK5wrwlty2FOWIbr3k8f17TMZe6Oeu4fJixcefPsW4IUE1KA25i/hBQeksA6fY31tgrWXkCtbLFR8+ecpi4GhDotJS/NTkhMqa6XTM4WKH/Z0JSmdS1/LZi3POT89lDaVWxtYR6tbowHBccXsxY7YzBWe4eHHBydkFXz4/pzAFTQbnPW0TmI8HmJx4cO+Y0cBjU6QOcy5Xke//4AcMRjssm5bch4nayEj+weGM3fGYqippQ0OKjj/53p8KKTkQxQsKGgLeK4yJ3Ds4ZPTQ4XuP+xdPT7harYWm1pqubrDKUg6rfjKj4+h4j93xAKvh9PySL19c8OR8RVd3uOGQFBPD4ZCcA8SO+w/uMx6OGJUV65slXcr8wR//EBUj3hqqomDVNX0gHVgc7HIwm7KYTXHasF7W/Pzjz7i8WNHkxDIEKRYjYxU4lxmW8Mbjx+K7zomL0wu1Dt1gAAAgAElEQVS+fH7CTVvTpsSyDRRGyi1D7pgfDDne36cqLKU1XC/lXP/405puvcQ5L1M1RlzxTbPmYG/BbDJnNhqgVcGLF6d88vETchIK2hpHV0esEzpWxZaq0sznI4rSoZLi2ZfPafsbm7KoSC7RpYjxiagT+4cLjndmFF7TXHecni354sUZmkxSoopREUzKlL5iOHbcu3tIWVqUcnAw5/x6yfc++lBo7qjRJkGXUSZSWs/j1+9ROMtkUFH6gq6LDMshnz55SmGArFnXNX7gsb7j9tEBs9GA+bREz0sKP6IwnqdPn3F5FVDKElYNeIe2UAwdjx8/YFzKZshgNKZddsyLkj/+8BNSgnIyROfEsllT9GWnf/H48/EIQ0OzI0RWddJtKcMwMD0dZ4VU6b1uG+pIxkuFVH2VWLPryHrX4a83pE3L2VsV1Vnatjmr9JJa3dBa7dT2N/pCcPnLvsgqIlBFP0K6Kf2BvlW8pC8mSawOPeVZoNkt5MbNmW2DvF2lrctVty9btc1KfKoqZtxF/dLjqNgGNu3MUj0Tl3w3dlsyrJsJWbppoHY9UbPa99gmUU8lyGneGVOdRYaf16TC0M6cuDW9IgVNu5BAuBvIzW5XKYorKV6q9/y2/GQzptyNhTR2l+LnlDFTCXmzNcTCiG+4HxMGtmGvbtOWPJPXJqF2NxDHru4D1nbiuHhsSEZu5MsTGd+dpMzwSSPKii6x2vdEL+FCO4SrB5rBl5nyImHXEkiHoUE14lrden/7/288w93Ibum6zdp41YkpAavaFl1tHZNdElKyNNg2YVYd2WhUlGA15w0lmNC1hPhm1W3p11CZrTty48i113Ks6/0Ku4596ZH4d18tbnPXnQS6V9J43wwM/uYlYS5BrtueayrmbUFeLDTN1JB1gUpZXJ4DgxnLmvKna8KkIIykfO7k65m9N0959ukOow8tix91WwLsKx5bo6h3He1Qc/Gm/F53pSjOMzs/jqyO+nJX7bbn3+Z9f/F1TTePqKQYf2iZ/1jK3rqJHBvdBpq9gQTwWnF1z1DvZuLtBqUzxY8qZh/a3r8qIe+m2KmZSxne5dsBM5XpufC8Yv4DxfRjoYDruWXwrBVlSuWo9zyXjzTrexE3SoTnFeOPNOMvIskXW5dkGPstsXtzq2B1S7F6q0adeiY/0/grzfCZTI9lAwmNSZuyMDh927G8k2DekteG0c8cxaU4as1KFBBmKWujHWsuHhvWtyNq3nL2WwXdiePgX0iQZ1dpSyuKN1nuCZ9/UxPv1NR/s6XrDOX/O6K4zJQXEVO/9LN2Y0v0iptdTf1LN4wGNaULXKwqwvcnHP5u+IrDkwzt2ODWic//skYd1ngfWF8XuKee27/TE5T1S89wNzbUU0Ozo7h6PYCPzHZvuLwcsPPPLBdvjPDLRD0vKa4i/kpez/Wx5fxtxey1U4yCNmqu/nCH0acSDha9FmBDFEanWO9qLt4L7BxdEIAnX8wYfaRZfL+jndqt79ouI1f3C6KHs++27Oxes+s7Tq6HnD8d8uAfyOfK4HnbbzQZklXUM1nn7uE1B9NrLlYVXTQM/tFEzkH3kmbeFEtGr3jxCwm72zAcNFytCtz3hgyfbo4DW5q/mcrmXjdUnH8tsf/glKFv+eiTfUY/8uz/YbMtgVMxs95zW+r//BdbdveueXvnOZ9ez7muC/T/MiMb6ApNPTdUp2F7bbh8ZFm+0fD63WfMyxWfXc84+91DBk80/jozeN71/nQhRbuBpt5VTP7Kl4xcy03nefLFDqMfeaoX+St0bzfQ2CZxc0fBB1fsTq85Gl7yo9N9nn+4w8HvWlxfoBX7sDY6RXEVOX3HsTxKlEc3WBs5+XDKwb/MkJGwt+SlO7d/7fXMcH1XEUaZrDOzP1W4pVxv6mkhYfRaCHLTZC7egJ2vPWd/eMPz5Yhnn+ww/2O21LlyQrxGr2gninoBg2+fcGd0g9VyfH/4xSEH/3vRbw7nl4Sxlo2DF3+ppRi2vHvwgruDc07aIf9q8Rq7v6dp+utudRZpR+KePXsv445u+PrRF1Sm4251xv8w/EUGPywZfiGvy24+q4yiG8HqV294ffeMabHmrfGXPKlnfHi8y8U/OJLP1bl4jwfPZf271UbR829+/Dsd2kbl6EIE5dAmbb2mEYf3juGgZGcx5XhvQbO+4ursOVEpnp3doLRhmRpSVDhlUUmI2EHpeHT/FkVhsRqurk/55NMbGeePNcaWNF3NYDiQkiDnGPrM4fEDvMmcn5+z8h0XV0uKakijI6FpUcnI+HgMDAvPW48fMRoWkBQvTk94cXJGCEnumFIUgrfNNGhidoR1DdZQ+UpCS6VxJJqcQWmycdQ54rwjhoCrRuI+1OA2NFHbAQnr6V2NGe00bcoUoxF3bu1yuL+gqW9ICT797AtubpYYZfEoItCoFldqaGsW0xH37x7hDbSpY3l5w7MXJ3Q96ds0QrRqZ1mnjC0LQi2jr1acEDLq7Sxt14HKKJ2E5u0LxigsdScqCNPTbYlEjhlVWWLTEoOicB6VE4/uP2ZvMcOZgLdSyvLTn3zCyYtLBsawjqBTpiyHxGbNzmKO2x2xsztnNhoRuobrmxuePjvhy/MaFRM2D7EmEnWWDbXKkrqOFAKuqDBWE5pI6PoW76xpU4cxUsYTshRwKWWYjCd061XfwJ3J2QFC1CaVcdaitCF3LYXX6NhQFgOWTUtROELsIGZIHRiPUWC0+JhLXxKwOKdouxXOWNZthy9KsoKbWDOyFV0XsJQ0zZJhWbC7mKFNwenlBe1yuXVmGq3JuQ/ZlCEqzWRccri7QNFwcXXNxeUlTitC12CNpmuWDErP9dUZe3t7vP3omNIrnO5YLEpuHb7OT37yOV+eXhN0yXK9wtoBqcu0TeTB8YLjW7t4o7FatBEvTk4Yukuevzij6zRZG6ySNTzdnfDowT2GA3GCWqXZ29vj8tYNP/3wY04urnDVCO+kXMd5w3y8w6NH9xmVHq8NTdOwM3/AH33/+5ydr6nXMJqOaNolXjsGxvPWo0cUTooGUR3z+YSiGrP64YdEbVnVHTlGJqWj6zree/8ttEpMhp6iNIyHnvGw4PxsyScff05ReW66SEqBnCLDyvH6o7uMBw5TWEYqc7CY8vzJKT84/wmmMCjl0EmT8RgduHd8m+Pbe2gVGVcVdd1QestgeMPPPvqMwngIGkPAW0th4Z033sASMaWiCw3znRFOaX728RNorXhUY+qvKZEP3nuH6WQgpKEXRcn7777G5598waefnZK6LORvYVA646uS999/h0HliJ04ZidjhbMlJ6eXXC/r7fpyztGFJR988DaToRdHqjEMU+Lg0PH09AXXNxckM0CjQRekpgNreOedd+nammFlIWdm8yFdhi+ePMMZx8XNJcZpvLE07Yr7j1/j1mIhY/rGYPUYW3gqp/n08y8xytA0EYzGV4qitDx8dJfJzhCrIIfI+O4h4/GIk9Nz6vYGW0y2RW51s+LdN9/k1v6CTIvLgYlx7JB5cbrDk2dXVH5KiJncJXE+K83jR4/oYoPThqkaC6X+7mv803/2OwzdQshfEoWxrJZX/MJ3vk3hPYVXFM7QdYbj4w/4gz/6AefnnxIweOuwxtJ1HaNxwftvvs7AOgTcyEzH+1j7jNPTnzIej1jFVkj9CMvVmvfefoPdvQlWG0qtqBZT0Jb9gwWffPo5Wo+p65aQEyk0qBx4cHyXymrmkzExBCpXYh55fvjDD7k8vZIbaa3pcoPKmf3RjA++/h51XTMeVLRty6w4hNDx4adLiEA/6RBjZDIa8/DhQ4beYhUYAtPFLqPxjBenlyyXNTEmmuslpiqo6yXOOd588Ahyw8AZytGQvd0drq+vOTtfE2OkbWuSRdQ+ObC8XvLtX/gm1mUODvaIqyUDX/LFk1POzy6xVqht7zQ3bcutB7d5/OAhPrQ4r5jOPMfHDvvjj/jTTz9GuNmBFHGaTDRr3nnvWz3VHRn4EWXhKKqSL56ecLq6pOk6UtR47yEb6vWKw/k+VWnJUfQr5aBkPp3w9MUJbco0bSdFkln0Dg8eHpPaBqMTo2pKzIq7d+8Sk+bk7EOq4ZDcrQkx0DQr3nzrbfZ3pujw0kU8Ppxh1xX+0y+wKVE0QVz8WtMgN+R/8fjz8ZDx0ZcN0LpN2z/vRpbVnuH6PlRfO+d6WZK/LNGtYv6nMm5Zvei2QVo2imZuWS80T38V8ihgCshfZvy/VCTz1fbnetfhryJhoFntGs7f1qRBovrMUJ5pJp8GmomWALP39AHoNrPas3z261DtiqKh+3DM9EOwtdm+tg3h6S8l1DJreZ3totoGgmFg8Jdd/xf68euB37ZHS+u5vD5gW3a0CS03DkR3HWmnlmff0vg3r1jdFOQYmf1+weB5FK/e2MmI91XYlildPHJcfKfBVx3t2qFOvdzgrlX/3MKWjPOna1LptqOfGwVCtoquelmqpnWCjm04CuAvOhnN70fIdScBsxQC9bTTVALCz3+tQD1aMihbZoM1Y9/w43/+gOmHUhZUngkh3UwNpstcPZDgvr7XcOf2GcvW8cXzCeMfenb/RI5bqAz0RUzye1VPO0pBkdy8JynA0VJUZ6/jlpA2q05CWK1YHjvcOpGcHJ/t6HYTeyLSbsPsTQlZM7fbAMPUX71R3oSoplHUM4OdW7pK45cSYpSnHbFvHC+/uKY+GgvNXYomoT6oOH9syRamP5OxZxXzVimxJQn78+r6yHLxlgSGwy80o882ZUWJOHCYlYSj1WdXXLw759nfaBlN1kyLGnP/hMVbK34yecDODzLWK8pnDak0qJCx1w1nv1LCB1fsDGqGvsWqxE8+ukW9KNn5cdwWVcVSArarO5bLX6nZ27lmp1ox8TWfPZrxyftz9v5vz/xHN8ShI4yLrf7g8r5F/co57+2ccFhd8/lqBnfgk9VD7M8T/qJjdavAX0W6kWa9ozF/7YTXBytuDy8BuDwq+cPRPaoz2bTYbBot7w6xq8QXv9mhTMv9gzMOBte82Bvx2f6c1WcDDv5Vohkbhs/6jaZ+s+TkrzTs712x5zpuFgW8Dic/3WH8j8TPLdeQvCX2nn7XMvraCUOTeG/xlE9v5vx8suCL+YCj/0c8xQCqsHRjQzvWPPr1j5i4momrOWmGcAS/Vz5g75+LpszUaqvuUCnz9G+3PDo4wejEQXXNaTNk+RsXfPy92+z/nkZ38WWZkYJuqNj9jc95Y/qcq67E6chw0fL75R1WP9tj9LQPzFZCS7tV4rO/Ezg+OKeyHYUNNHPL9LU1n/3JawyfttuphM0aBJj++lN0XfBgfgrA4vAz/un6bWbfc4RKMfm0IzlFN5Lr0OkvBr791kfcdAU7xYqZW/OHRcuX0332fl9Gvv21nFvdSNOOFde/vOLh/hlTvyZkw1uL5/zR/hHxwxHFeSdqkyjXquIq8flvBH75jZ8RsmbXL7G7kcvjij/48D32/2BNs3DbcqzNiP83f/nHnNcD5uWK7+7+nEIH/sFvv8/8v3XUB3Ktd9eifile1Dz9zxSLYc3e8IajwSWnzYD33n7C//wPf5XJxxGz6ghjTyxlamB5YMi/ecoH03PuDs9xKvIrez/jfyx+gfYjT3vbvwxfMwxeBD7+LcVb958y8TWPhi94bfgcgL//xl/i8HclUNyUCOquD7u/c87r42v+6sGPuIklb4ye8S9+JfD8nxwz/jxsr9ey6WY4v6P4td/8Az6+2eHbO5/wpJ7xS/s/53/lm1QvXm4WpX4U/+aWYfJLz3k8O2Hm1kzsmr/8+E/5P3fe5scfvgk9qbr5jC7OJVCf//tPmQOPpifcr065uVvwj59/l8nP5efdMvXXBYttEoPngY/+c7A+8h88/jFXXcnvzB8z+Z5n+kkgFEJ3t0PF8FnHi/c97/7yTzmsrtnz14wWNXcenfFf+/+Y8p/YvvxNtAbJyufV1/76j7EqUZmOd0Zf8FrxJf/H4AP+2aNvMP1ZxF/nfiNRJiDKy8gvvv5z3hg9o0sGpyOPB8/48fE+4ftz7AqK60QzeTmd8O1f+AltNIxtw7cnP+cyVvwn7/0+f7/7BWYfGuqZOJZtv6n69K9p/sbDn+B1YGrXaDLfmfyMmVvxj4dHxFLTTHrHs9Py2W7+7N+19b/5R/7tPWJKFFUprcmAfLsSMsd7z3Q24uH9W1gXmU0G3Lt/xGQ0ZDQoiKkmh46iKLC+wKuCFDJ3jg+ZTweMB56qNCx2Rtw+mEOsKQCbFalraZslhfEMrOX4cJ+d6ZDFdMjDB8eMhpbdnbGMKRYFpMxwOCaGzHq54tatW+wvdhmXnmnl2Nsbcnx3AbnDZFECWGtJOaIKkcWjhSjNOfdN6IKal1qjU6QwLx11ha/ouo4YI13XkXJHF2qsUxgrvsHNI3SJtm44vnWb4+MjqtIwGZfMZ0P2dqZU3jF0hbSoI0FvU3fYouTOnTvMJ1MmoyGT6YBbB/vszqboFPDa9I5gcejpCCYpXFGIIoCM66njFIJMPGQp/Eo59IEmhCCKA6s0ZVHQNDWQSETaGHqaT0mAU1Yc7s6YTgZMJhO8lff+4YM7HB/vYpwExqr3TGIsg8mYe/cfsljsMBpWDErLzmLMzmKM1+KEDO0alWW8uQktlzdXDAeeo8MZO9MK6xW6cDQ50uaWlDuc9eLe1NLI7Z1iNCzYnU2ZTEaiiXAGYxwKg3OOUlsIEdVFpHtNMx6PUCozH5TS4J5aMuLDanMHju3vTLnhYG/K8a1d9hYzMhFflYSYqOua0bCiXddUXtQIg8GId998g+PbB9y7e8jOZMhwUFKVJaPhkAw4JWsKozm7OOfOvWNG44K9xQ7HR4ccHO6SY4vTuXcoa0LbYBIMXMGD+3eZDEcMfcnufAfrDEdHe4xHBaFpJJgAynKA9567t4+ovGNcFUwqz8AodqcjFvMRXVdjbEQZ2bG6Wt5wcHDA0d07DKqKyhd45xgNPLs7Y3JqGVWllCr5ksIWLK9XHB0fsljMKSuPLwuq8YjxbMzh0S20NZTDknVosa5AKcXx0RE7O3Os06Ii0InCaxZ7U24d7hFDh/VC1oduTe5qDvf2OL61R1VqCucZVBVHR7cZjysigVWzEidvzjij2d/bY2c2ZzYaU2rLuJDXf/f2Pl23pKw0KbbE0OK1wZI52N1jUHimwxHOWEbDktu39ompQ6ne7asQ+jI0zOdjfGUphyVeG7wtsCgW8x0mQ0eOaxRyLsYusLq54dbhPtZoSq8xZAprmAyGHB/cIrdBlAtKU9crskrs7MwYT2c0jYT+hRVCfDGVENAYRQgtriihLyQ7OjpiNBoxKEosiumgYjHZYVoNJaSNQVzASsbZx+WAqizx3uNcwWQykamA/QXGO65uLtG+ou4SSVnQjt3926AMhbNYrXE6cbBYsDOfc3Nzg8LgfYm1JV0Ea0pu3bqFSwaXZbNsUA65d+8eVTWkLCpIufegRpqmYTisCKHFYChtiUmWneGYYVlQGkhBQjRFpPIONHhvKazBGyE9tXeYwjOZjVHWEGPG4Lbn02gwxhnLsBxhtaMqBqQuMqxGlG5IacRBrbVGZ1jMh4xGQ6qqwhUWXZbkwjCcDIHE8vqaHOU6WxhL18qEhjMeb/y2VM77Qq77VuheZQ2z2QxiYjIaU1rDZDiWEJv8ijt+RVFUxJTQ1siEQspMJhO0ERK7CRFXVgzHI+bzBV8+f4bWmkFVkXufcNuKhzqlQFE6yrKg7lpsYWm6loxMcrjC03QtKNGzlIPB1unc1V0fSCpevDiV8jVX4JXDYBiVE7xzDKuKSVlyfXZCDpmdyRSjRINTx0TGkNFYY2SSIcvUSGEdoevEJYNivepISbOsxc8dukRRjvCugmxwtiKETFd37OwsMPRlcaMR2hpCSqzXawaDETFGQggSGiuF6Ue/ndHEkKiqIXXbElJk3dTiFM4ZpRx1Gwgh0YaWnMUrvW6XKKtQfXaltGbV1GhjhPgm0dQrYqgpvGVUFhJ0hG67xv7i8efnodskBUztV4OsMJBxy+Vtxa3vPmFQtLx+6znvfPNj4q2G9X4/bhoz7dT1tKTckJx/PTC6c8Xu3jXzyYrq/jUnX5Obk3asxSfXJSm7mhjWO4azryeKuzfcvn/C8LsnrPcU5687dBBqB2C1J6PlxUnN+Tvw8PGXPN474d3Dp7jH15x9XV5D7J2CsQ+yNoQZQBj5rbMy92PY7aynLqfVVqfQTT1mJUTuZkx+E9RJMdJLGl3XEXvd8vwbmun7pxxMrjk+OOf+8QlXjxP1XLPa1a/4R4P4ZAvN+TcCD45OeP3gBbcOLjh8+zlXDyEZaEd627YtPlkphZGiJtEobMPA/nW+dAG+/M+sOrqxHKNmLk3nAKqL0s4+ErrKLgOrPYt/84o3Dp/zzt6X7FU3ABx/5wtOvt6PwxZ9MU0f/jSzzOhbJ7x+9xnv7jzl3vSc+3dfcHM30Y0MsZCiJRlpztirhvLnp6z2Pc++WXJ53xD68XR30eCuO/HdDqSZXsq2DN3YsTr0XD1S3NwScrirtLgwjZIiop5+RvU6glJxc9uTFVw+LHsHYd7+twm6pUxLbtbP3jScfD1z/vrGfyzUqD+rJbC97mh2PLpNNHsln/5HmdUHa9K3rrh6YFjvOZqFY3WrgNx7nvv1N/jTZ5x9I8Jew+LRGesPVpy/JcdsMyKeCtFcpNJS7yj+iw9+h7f3nvHW7Ev+w9s/YOJq/PsXLA81ps5b/UI7k2DVfuucg+k17y2e8ouLj3lt8oKHD56xvt9t3aVZS3hXfXbN5RuZ33jrT7g/PePh6JTD8oq358946/5TVIL17Uqm1IbiKi2frbl8v+NXjz7i7vCcA3/Fw9EJ35x9ytWbHckKwV2edrJWFJx9PfLv3f6IabFm6tZUpuN2dcXDe885fddsiwhVyltn6S+99hF/6+0/4v74jDvVOW9Mn/O33/xD4kEjIe9p2K7xOLCcv6F57fg53zn4mKPhJe/uPuWDvS945xsfY5Yd9cz0dHamG/XFUK/fcG96zrf2PmPPX/P+/At+843viepD03tRewJ7lbi5qzgor3k0fMGBv2K/uGHqah7ee87yVl+0pV8SzOWLll9/7UdMizX3R2cMTcvx4IJfXHzM/fefyIh/JfoBf95Chuv78I2dz/h8NWPiau5WZyz8DX/96EfEoqdHe70FyDn1N9/6Ae/uPOW1yQsmruaXdj/ib+39ATdHeuuhFS2DvKb1geLu+JzZYM2iWPHdnY8IWfPw0TOSh9GTQLLipN6EvA8fPAPgaHDJzK3Z89f8p3d/j+reNcMvOyGFR7KBY1eJWCh+/bUfsSiXLIoVD0cnvD16yn/51v9FPdM0814b4lS/uRaY7Sy56krGtuHx4BlTu+a3F/+aZpG37ufUE731XF7/YXnF7eElh+UVXTYMTEPhAsu7A6GlG6H6Y6Fp9kqOFxdMypp3pk+ZuRWPRiesoqddJNqZpd0pt27h5BRXD+G1nRMejU7Yd9cMTEuXDHs713JOvxD9il1H2pHoeVCZnWLFYXmFJjMwDcf+DHfT6zduRPVzdV+uV9fHnr3Rku/s/pxVLBiZGk3m7uic4ky8rzId8tJzXN+KeB0YuYZP1zvs+Wu+OfyYO8enLP7wnGQU9Z6XUNyAXcG0qGmT4V51wqPyOT+r99nxK9xNfz28if3UiFwXTZO4PbokZkVlOl60Y0amIWuYfv+sJ8qlmC86xXLf0I0s850bHuyf8sentwlZc//uC5ITIre4jNvrr3h0wZvI0DTc9aecd8PtZ2t5HtBdlvc4g20y3VhxWF4BsOevOQ9Dvuxm/Nb8X5MsVCcdy4PeGV1nqrPIesewCp7TdsTcLRmZmrGu++86oqyoZ5ryImKbTHkeuGpLQpaA96frA867IU+bKSShf8uLzXTD5otA5seX+wx0y5N6RpMsn7YLlrGgG2WWB1auu1HI380myp/18e90aJtJNOsarw05ZLzxQnV2gbIseXT3LpNSMx8N8d6TdcW9Bw8ph+KOLQvHzc01KTbUzQ07sxFH+7eZjksWO2MG5YDDgwMWe0NGY0dCow2MqymeAe0qcHzrPvfuPmR3NsUYh/eeb3zrm9iqYB1auizjN8u0xpSe+c6UB6/dYTYeMZ+MGY8GLGZz7hzdxXmhb6xKxFBjlCa1Lc5oqqJgMRswmZaovKYNNcFCQ4tJNZXVmFaE9ldn53ij2Z1NeOvxfeJ6iTVZvJrG4IxFt5GCQGU81hYs5jNu7e3jnGcwGDCsHK+9/hjrDderawaDAUZrfLbkaJhM5zx6/DqD4ZDCW3amExa7Y/b2drFEunpNVzdkI4VNpTbYLpFy5s7dW0L1dB2VgYExFEpTGEuFobIWFVu8gxwDt/Z2OT6+TcyBwmtUitsCriZCHSJxnRm6ir3FPlXhcQrKssSXBXboiC6RdSaZjmQyq9By1XWM57voyjIcDAAwrqIaTrh99zZGa+qbK1ToUF2kylAYy7wc8tr9+zx67Zg7d/ZIucU4TVUNsLbAogldS9NFDAUmWeIqszdccLjYY3cxpywcTV0TmpbYZXKIEDoKZRj6ATF5ZtMD7t6/x1uP71MWlnq9BjSqkKCzjitWNxdYIimsWCxGPHp8l73ZgEf372GUpuvW4kU2mdjUDEcFV+sbCWlGnv1bOxQ+Q77h4f19KhOpb85pVmtMhrZtaVtFahXT6ZS96UwK8YymGgy5tXdIXDXUN0uc1hjVoFXGGEdVVVxd1ihl0cazXgW8q9hZ7Am1GRpSbCG0pO6GUWlRqmE8rPDWEqNBmYKiKlksFjx58gRjHG2CmDy+GLEzP2B11btpU4v3lhBaDm4fMJ5OWK47QoZQR0KTmIznzBeHrNdr0JkudbShY73OFNZjbd9GnTVt/v/Ye7OfW7I0veu3pojY0zedc/JUTlWZVdXVXU2r2920bdndYHdytPYAACAASURBVDyAETKSgQsQRjaIv4H7vkPijmuQUCMhhKCR8I1tsI2wix7c7q6uKSszKzPPfL5hf3veO6Y1cfHG3ieLq+YGeegtpXL6zrcjVqy1IuJ5n/f3RNoQseOCLnpGo9FpQ5TOyIQtFaSebrcR3IlxQMI4EZoqVwk71RrqvseMHU30+EFUKZwmxqOLNRCDweiKsSuxpkJby1uPvsLhsCeojC2h6/cYo2jbIeAlZwptMEGR+0RZjAgq40lkE9HaUodAS2Tftxx8S0ARE4JdaTp6b0i6xBsrvFNncK5gvd/R9T0pgiJgbaL3iV3T0qnArt/R50hZjkghc3V+xWJ+T1FIcJsxTvi0WZG6hHOOkKENgT6BLQsWqxV93aCxWKOIIbNeLSgKEbtHtsDkLKF/TaQoxnRtEFExQ98FqnLG+lCTFEPLvoxD2/QUpqLbHAhdQMVAZSUwYrPb4v0QDJgTfeyFJZscsU8sb7dM9QjVaZJXbA8tN9dzuqYTCH8O9LFnbBWj0YhnL17Rx0Qg0wXhcK+XG6pckPuhe8D3ODSL7RqfM7t9TWwDhEiOic1mw3y1w2dDe+hR2nJoepwbcdh37HY1MWhCI2Khx9C1kRQVTRuxuhQWdJCwsNXmwH6/p0+JXZ/pYkJpTet7iumYpk90rUdrSxxcnLvNjq6PdD6hlKNPmfvFipiTBMxVBX3w1PsDs8kZfePpasV219P1AZ8ixahgsbolklEEptWIumtROGwx5Xa5Zbk6DOPY07Qdu5i42Ww5f/g2UWli70kpkX0G5bhf7wBNXe9pG09TB5brjtYjDuPJhH0v901jDMTAi9vn9CminLBb1/crsof33n6PlMB3UhDQUdEfeipV8eyzZ/gu4ZKldOfcr7fsdjvMRNNbz8Z42tiBUmzXG17cLkgqcTgc8N5zM1+z61tMVRGNIpcGUzn6nKnbyMtXK3zQtD7iY2TfJ1bLHU1QnF09YN+3eKVIRlHMZmy7jp5MmwJNqGn7hqY50DQdy+0aW5QcOnl4zkZTmBHz2z2HOoGy9H0gxMyhbrhfrKgmJX0M9H4If9SOpk74HvZNiysqfOdptjWhU/SHDu89aEc2FX3wssf/f3yY/JPPP78fFTPFJuCHMCU/kzb7cuHpzhWXv37Drz54zl94/BmPRztC0vxHv/hP6c/EgdpfOEavD6iYKVeezYeWP/sLn/OLb13zb777MR9eLPgPvvFHpA8a6rek1T5ZxeGd8hQGtvjlxJ//5U/4N772GZUNXI1q/tpf/x1iKS9trhERZzyX1vLtNyZ8+88+4V9/9Bl/9dGP+NMXT/m195/wq7/0GX4seQ6xUgO/VJ+cwN3DEetvluzek6Axt5dUbbcNxNLQPXAnJ+ro2Zowc6x+dsTTvzYa+KnSMh1LPeAiNP7MSojb2BE+aPlPv/47PKgOfOP8nm9f3PJXf/2PiKVieiPtvamQNnty5vCO5m/+md/mZ8/veG+85q+8/Qn/1tsf4765I1Qa1wjKQMUh8OuqOjnTbv5sxeLb0mrZPnA0Dyx+Jn+1D5wkeRtpt1Ypc/+Ljrt/1QqG4bwYBF4RyYptlDCpLtA+VPyVr33C16f3PCz3fG285HG147xo0P7IBBYXUzXvmTw/EB73FDbw7YsbAMbW88FsyYf/ymtpsV2JWzhrOdZwVlJ/6xHXfzGhfn1F8ys1Ogj2ontYCTd0JMKl3XanBHPTRVbfMqRv79l9fUj03kZMG9F9Ogmj/sxSvyWi6u6rhvmvZl7/25H+TI4FOLm23bKmnLcncW7zdc3lr99w9o015a/di5g9XIM4Eqd0+7hk/GKPaTzdmeHb33zFZCoCgPrzK9pLTbEOFFuZf24fTu7b7sOHfPCNW1wRSBkuz2r0z+/QvbhTw9icAt9SYekuFL+7+hCrEmPd85P6Ld6uNvz6u08I4zfc3eNarh8XGJX5patXvFOt2YYRVkfeHm/54IM7xt/5hDg43bXPxFnF6Gs7Pt2+xfujFQnFu+WKja/4z9/9DvVjcfIeWbWmTRy+Ouad9xd8sX8AwLyfcdOe8ZPDW5iZPwUxgayxchOhSLxuzvnmZM5YS9HgEAt5Jy7lurmtFEWO7rMz13LdnvPBaEET5d3o491jtEuUy16uS5I9S/eJMM5suopDKDlzLT8/fc2Fq6mMZ/kLU8bzMLjUxeUWnaLbiZP2eN5T0+GzwU8lzMvW8RT6Vt7VuB18tHrMDzbvsApjDrGg1IHb7QxbcyoIHQs0YWL5vduvcXM4o4kOqyMXtua6O+fF/JJiFxm/anH7gD8vJATuqy3/8NXP8LjaoclMTUsdC3wSvq8fa3QbKVY9bhcII80/evV1bpoZI9Nz4Rr2seTvrX6BMIb2UUF3JXvFEUMTxnBdn/FotMfpyKvukg9GC568fogavF86SEGuWHv8zLA8jNl0o5NLEeD/Wn6LtnUSxOUzbp9OQVXaZ/7v1x/yp85e4nSkT5bfXX3Ibz7/cxQDSsZ0CbcPNA8d7ZWl+/0rFs2Yja942j6kjgV/d/2L+NkbkU97wdGcf7rD1YnvLd/lEGR++Gz4x4uf4e7zB9g6UazFzVje7PEjRXXb8GJ+SRssaz9mbHru+ykvmksIct874n50EBFz+hx+snzIdXvOP11/lefNFU5HVrsx/URT3jeCqnF64Kkqirnluj7jdXOOUzK+v3XzK2gvjuTkFPYQGS0C+3cKikNieRjzu/cf8rK95K4/471iye88/+BUQKjfLqnm/SkA6+xjwz9+/XVC0hxCwWeHR3xn+y1efvyY+mtnGJ9PoYbHQLNPn34FgI/27/B5+5b8mVcfUm0itpVixui6PYncAL/3/W+yqUeCBUPxj+bfRCWY/7mHQ6dOIpQSBOYOmfbCUP/+QzZdxXnZ8v5oxdMv3mJ8m4cuG8/seUe1lsLn9Drye9//Jk0s+O7+a8z7Gf/Ni38Nd+dO4Z0qShia20eqReZv/+gXWXZjvqgfMu9n/MH+A/726lcoNrB7r2RyGyTENGb6qWb6OrBoxjyvL/n08BWeNI/4uHmbzWbM+RPphBnP37jj/dTw4y/e4enqEoDr9px1GPPp+hHjLxztuYRnFvs3IYh67di2FX+0fo9HxY55P+OT/WOW/RhbK2yTT7+/2EdBtbh/QZy2Vhs0idB73BBAknyPJTOtRlSuIvaB0HsKZahcQd+2XM4uGLsROmbGRQlDgInvgrintLSWWmtRGEbVeHALdTRNg/eBzmeqqiITGM8mKKWYTS8o3IjNZoMz8uKoCkuTPG309KFj7EbQSiJ51ooQRfBQShGip48tmYhzJTlHlBLOUggtFxczJpMRtqzQtpB09axJ2tH7dHIUFYU4pJwzzOdzHj16hPceHwcHa4iMyhIiOAP4iEqG3WbHuLBoJee+XN4zGpUoDYfDgRw0FsXIFoS65bBZk1OgtA6aQNeIAwtjKUeDEy5PsGpMVhB1RqtE9AnvI8Y50JZ9U+NjSySSdMAT6LO0KjvnaNuelMT9l7WiJaKMI2YJLaNymElFoxLzxR2ZRMJLq2hK9IeWcAjiYFZaWlO1cA9RgdRFfNtTmRKnDfWuZrdq6H1EG8t0OiXlfBLa+gR36y3rXc+q6Wk6j+8CXdPSt50stmwwUbCwxliMs9Rdy3y3ZrvdEoM4lo/XStysgI60fUNpFTF4RnbEbt8JczSI+BWjODSn5YTCOoxSRKtpQ2S32eOzwoeEsQU5K7RGOLkaat+hrQEDoYks7leSTj8asV5uJNjNOHzM9FqTqwJvNdEonB0zv9+gdUEMAY1iu92KQFY6UvSIdUuczMvlkoyi85E+JrI2+Bi4u5vj+xaTEzZLMaH3NU17oO8C+7rFD7iImAN9SCz3Oy4fP6aOIm7F3osotZgTvCdGIcH3fYtxJa9e3bLd7HHOURgNThNUwlSOxd1cRCqfhJmsAzkfODQd6/Uaay3GDtdEJfa7lrbpOLQ1SYlrOefIYrke3IcW58QN7jOYsuJ2taJLWRi5SZyMMUaa2uNchXMjUtQoDDlD2wQOdUsfGlIK9H1PFzraPlHXHUZXpAgxZDKakGCzP9B2kc5nfIiEmPAhsl5vqfcHwaHEhNWGsSvxfabedgSvaNuWpGC73XLoPV5lQk6CIcAQA7Qhc7850PiMypqUNKGNdE2PUeLKPJ/OZJ9Tit577hb3+BjE6a80de9pfGK53hJjpm37IQgpIuFXhsViKXM7yMN11zQ0+44cMr6PxJzo+p66bckjw3yzIBvY1gcR1qNncxCky3a7xVoLKuBDQ8o9kNgfdtTtAa81rYJDSByals12D1qTlYj1ISeMs/QhsTscWGy3JGNAa9omsdm1pGxQlAQUIUOTEgHFoe/wQEqKugskZaj7xGJ9oBzNhFXuLCFFzsop9IHNfEUbPV0MtH2DinIPyl5RWAdJMSpKfE5EY3j++ppdfSAk2G9q0sHz+uaO1WbLeDwWh2VSQ7hUyWHf8+LVHft9Tc7Qt57FfMF6ucE3LRcXFye3e4yRqqo4dC1142nankPT4LtI03Q0dY/KIgJmNEoZOt+jtWW1XtO2HSRN8HDz8pa67kg+0PWJGDIWR9d1pBRomoblfE3fBnJw+C5x8/qO29s5IQf2zR5TGKqqQju5H9/dzdnULa1XtAH2h47tdk8io01ByprCCSJAZY1VlsP1mrBrafYdq92OXdOyWG84NO0QB2zZ7wN16+mjCM63iyWb/YGkHS9ev2Sz35GNhJFarYeHIuFLq6RYrVas1zWNz3RtYL3csFltETtqIkVP3e1wpaXzLfebFYemoW89sevZbHa8ni+o6xq0wloJqEwpSGCphvn8jsViQdP29Anu1zv2hwadC6LvUbHFGCPiKpr1ckV7aNmsD/Q+stsfWC7XkMTZmzqPU8J0LI3lxYtXLO5X+D6y3e6F86wt9/cLdl1H3Xf0KRIIRDLGWaIP/+9Hsj/5/Av6iSMRH00rzi0dsrgcR4b2oeLd6YZDLFmHMW+XGz6YLrnuzvHvdzQPLMkpurekMO6nFjO8mJy5lnk/49yJY+jyrMbPxLlTrSKuThTbSHduICl+fnqNJvMrVy94NNrze/MPiAOvNisoF55i7XH7QHslK7VLFp8NKz/hvWqFVsKYKwbX5NHheBToTBs5vCdBLGE4b7fpUCGd2KhHgSbOqqGtX3H+E1h+u5IE+ProEuLk9PJjOR6lM7+z/gbfnt0wMp4LW/OdVx/Sn4tIeOIxVpr+wQjTZn6weQefDO+VKwBetpfowe7ezcS1FCZWAl0GkeOINLCNvMgCTG5ExNJeBFVbJ+xOsA9hWuC2GRWGBO6UKZathDINTmKAVFrKVebvP/tZQjL0w/g20XHfTNH94GAuhb+ZrSKVgnnog+W+m/LhaM7Dcs/nm4ciSu3FEXz4ihsELWHCup1n9hPL/tUZ6vmIaiUv2cXGY3f9wPd0pMKeQqxiaaiWmfz5hNHtIBJtA2FiTuzb46fcRGGx9sBFj7uTwCtTB2Hk9nIc7bszaYUuZSyKLVzfXVC3BYemlJCdlIfwLBFkqnkv4XeI8+vj51/BqMzbF1v2r84IoyHE7RDkGmSw+14C7caGp58/xtpE5x2FDbS3E2Eknwmr+PjRfeDsqWRiXNdnsu6S4b6f8g+++Ba2HdzEx7m8C1SrwKEu+cHqHeb9jC5ZDqFk3kx5+vwR8ee+JhiCgYNrdi31ixn7vuQQRcB81V0ysT3/9ZO/zOR6COiZyFy3TSQ6xeunD6mM5xDkzzwsDoSsSYuSs6ft4B41p2ti545X+3N+uH2HOhU83V/hk+HT54+xeymyhImVUKBaijN//7Of5bo+47Y/4xALlv2Ygy+Jm4IwsbK+T6FUGbdTzJczbtsZTXS86i550Vzyan/OaCm/Uw3J9iplbJcprh2v9+e8rC+482eswpj7bsrklaa4O3wJh6Lp3hpj68x8NWPTj3h+uKSJju8u3uWwGGMbEXqPorPuE8WyZfHkkrvNFKsSfbKsw5jXh3OsE1F4/1VxMmcNducpPh1xaEoWnYjCH+3f4fP9Q/7h9beEHbuRsEEVBy64gsOTc1bdGJ8NVkdeNJc83V2JA7SW0DW3C1Rz2YPOf5JIWfHR3Ve4aWY8P1zy2/dfJ0fF9FrCmUBa8VWQ71nfT3m1Oee+m/BZ/Zgf7d7m89UDeD2wvYeiiW1krN0hs76b8Xevf54miqv2enfGy5tLdJQ90bQR3UaqZcC2mWqR2bUlbXR8un2LJhZ8snmL6VNpLT/uf6ZN1O+LI/PZ9QNe7c953Zzz/HCJVUfxjdO87R9NcE1G9YHyDyZc312w8RXfXb9PEx2/99E3mLzQJ4a4dCsM+/Z94vDdB3yyfIuQDbfNjL/36tuEV2Nck9h9ODm53U2f6S8s1UJxvT7j+faS7+/e5b6bcr09o1xJq7/bhxM7vdxK6Pfm5Tl3+6mMtR/zm8//HN2qQocsxalOQgfdVoILy3Vm9fyS+2ZKSJpFO+F//+znuPhYUDOj6/bEzk5GUA+TnxT88OZtnu8v+aJ+yE8Wj9jdzE4OZu3FuX88tlhqioWh7yxf7B/wO68/4MX8kvMv4knkBBjf9owWkeKQcE3i6seR2+sLumj5rR/+MtW1O92LY2VOQWoggvT4uRQ3vtg/4FV9zpPXDxm/fvMzKoqzPow1xT5RflHxfHXJvJmy9RW/f/tV/s4nP0+1PLrKxdF8LDBmA9efP+KHz97hi90DbpoZf//Fz6LuC5JhENvlfI8M5PKVY78d8fu3X2XeTPne4h1eP5NClavlmcGPpeukPzM8/EPF4tklm67i9xYfsAslI+P5p8++yvhGjr84JEybT9zno4v9j/Mxv/Ebv/HH/uH/vz//4//8P/0GKVNYS/ABrTJaSRp4VlCWFaXTOGuJRHwM7JuazXrP/d0KYwtCTlAYyIayLLk4O6cYWbohZOWwb9g2La9ez1HKoZQh5Iw2iuA9VVmQknxvzJ4UOjabNTfXdzT7gAeMcxgFhXK4LC2io8qRCaA1u13Nze2cze5A2waycqAsMWeUgqZvKKxjvV6yXu1J2WGVI3Y9pSmJGnnRTwmt1CnduW1q6roh9PJw08U4uN8sve+llTQErCsYFQU4hSahgO2mZX/ouLm5R2tH7EGrjBrS7p1WWKcoy0p4rBm2dc39asXdckXr5SEiZ2GFxJDICfAB3/Yc2k4SxnOWYDANQUtufVSKrAwhKqy29E3Hvt4PIquiCx5ClnZrrbHeUyiHr1veOr9Ex4QNnrrv6TrPq+t7rm/XeF3QJyXtrUlRFBKKZlSBIZP6ni4GdjvPYrFntT5QVGPaEECrQWjXJA3b7Z7FYs9mcyCEjFJGcmuVwgcvrjqVBwEBQgxkMuv1iv2hoY8R6yrxocWM0hllFH1Iki6YRASvu5blZsP6UOPcCJIhmUAaEBcGLddyOmN72EPSHDrPq/k96+0BhQWSBPCljDGFtGFYjUmavu0pqinL1Z775ZbFckc2jhAUSWmS1hij0Srh244uioDulGOx2nC/XBO1wUeFKgpSVsSoqKoRyoIHqtGImMA4g4+ZV6/n3NwuKMvpaVw0Gues7MxKE3Mm5EBWluvbFTfLDYc2EDC4akhMLxwxxaHN2hGjIWbFdtuy27RcX8+pygltX1NWFSF4mlZYlhfnF6AMXdvTdi3bzYGPPn1KMZ6IE7bv0Aa6ukEpw/nZlEwkD0Js3QZublfczJcUo5G4/kMkZcVoPCZl0EqEnZADIUbu7+959fqO7bbGHa998JRVyaE5kFJiMp6Qs6INns224dPPXhKzoe89JKSQpBSHtqfvAqWr8B5CgqZpeXF7w918hS5LlLV0XYdRjj70hM5TFSUazWgyZn9o6fvIs1fX3M0XJCyhDxTFCB8yrirY7Q44VxFDxIdMU3fcLzZ8/vQZqihF4ExRgCUp0nYtfdcxGY1JfSBFxXJ94PXNHfP7LcZVaIaU6yQhkZvVlvFoTOgD2hbcr1tulxte3S1RxtEkecCNKpOtlUJA2zEan5Gzp2sji+WOL568oOmkQKa1tOFbKyGFbdsSYqaczNjsG3qlePLkBTeLFa2HNmawFuUsPkb66Nm3NUVVoW3JcrHl/n7Js5ev6ZMihYzPmWwMKIM2BX3r6ZuO6DNtH7lZrPj4J0/plCYkcXVjNQmNt4omw7ppZMdVju12x+3LJZ8+eYopx3gfSUZLkFxRYF1BH3o2+x3eyznezTfc3N+zXO9o+h5bOGKyKCsFgZCCoAPaDqML2n3H9d2Cu/mKXdOjXUGWjZkueiKwPdTEkIhoonK8fLngyctrDq0n24IQI64oSCGRYsQWBZvtlrrr8T6x3Xe8vrnj+mZJNgafEhlFzglXGHzoQSn2u5p9XdM2gZv5hmcvbgjGEbQGo1FkDvWWQ3PAWsvusMV3irrOHPYtr25WzBdr2l7Gte1bYZ5nKZaVpmDT1Cw2O5ZNw/18y/XtnGgMSWUJE7WaqCQ41DgJT4pacX23YLnasFxvuV3viE54zqH3lLYgxkj0Hms07aFhsdhzv97z4vqexWrDvvMoa+l7eREqjCXHjKVgt6/JWdH3isPOs1jteHm3ZLPbYpy8PAjeQBFTBgy7fU3detbbhvW+Zbnas1zs0drQdI24NsqCzns0mtV6Q1137OqG+82G9ebAi5c31F1HIqOUZjKe0rU9VTki9D2r5ZrxaELOht1+z818zbNXd/RkjHVgMilHnDIQMion/sN//9/7YyXa/snnn+/Pb/7md35DZQhTK22kVtK/JURJ8flsSjXxXLiGbRgxb6d8unxE92rKxWfy87Yb4rYR/MH9e4bzquW6PgcFH23e5vrugovvDeKaBtPlU/tid275Q/WYZBX7WNLEgs+vHzH9yFKtEu4Qh8R6Q5gawkjxrJgxuujpckFE893l+3z/468yujaU6yhJ7gyhVkqCneLEMXseOXvay0vTyKB7cZ5qL8/HKuZTq70OmXIjrE3Xighgt0NIY3FknhqKnbj3dhcl13qMtyJk/sH9+yzvZ5z/2Ayp4cKfPbJ0w0jxpDzDTgLbNKKOJX80f4f9FxdcfSxtpLaNp2TyL7umXKMY3wUwStxbI42OIrKcHFYpS5t9aSgOmfGdpNWTJcRM5UwcDZzbUhOmBtdkVj8L3hrOi5ZPNo+5Pcy4+8FjHvwgU9SJYhfkmDKSiJ4L1q6gcYZtGvG6vuD5/Ar1dMzseSJMNOVGAmZNL39XITF9FTj7QjF7md9wZrUCrbF7CVkTB638d9NGdNacPUtMbiNuL2FBKvNGLNNKBJEswk65A7MuOHuaOHvWnzjFUpxIoJSMzy4QJpbpa4/XFSwKJt8vOH8m3TdqEPNVEqE0O0P3YBB164LN2LJ5cc7sC8PlZx6VhMOrQxSeaGnJVgRJUxfUsaSroH56xuxz6aaSsDyF6SW0r7+sQCte9ZccZopdqKisZ9lOWP3gIQ9/FOnPLLbNJzxGHBlaVTFnxG2csOrHdMnxk4/f5ewjx3ieME0gTpzMjXGBio5bN2blSrahYutH/Hj+Fe7vzrj6IXQXlmoVTjzgatFDKrl/T9Njua7PebG/4Iu7h3zl74jbu1p4CXCzGrduya5k+Y6my4YuO5bNmOfrS+xHEx7+KNBPBPvkdhEdMt2lpY8lc1uyUyXbfsSur3jyxWMuv2eZvuxl/caMaRL+zDGeZ0IqWT3QtMnx8nDBT27eQv3DS7SHYi9rPA3hYKPrGtc6bqYT7toxr/wFz3aXfP5H73P5cQbnxJG/bsmFxW17rNfszkvWzvH4bMdny0ds64rp74948OMO7WV8w6wQnMrEMboz7EYlT9IZd92UZ9tLbp484uw7FWjhexYHeZ4wbaDcGXw74v6h5RALDqHk4xdvE354zuWnYQgcFPamColcGCav4cXVmJf+HKziu0/e5/DJJY++J3O8vG/F/dpJK79OmnlzQX0OTXbMDzNunj/g0Xcc1SpSLnvhTodMqgzlokPXI9q+YjF1fHz3mPlhCr97yYMfJsp1oFhKlxJK9qFinyjvHdfTCVtn+eGLd+k/n/Hg9y3lcJ3t3qOjJARkqykOmaad8DLMuN9O+fzTdzH/xxnFPuPajDtEULJGbJOo7jtGryvu44yXasL1zRX3nz7k/X8QhTV+5Evve7KzpMrhGkX52vGkfcDSFrz47DHnH1kungaqey+u4S4NgVZgmwTJsGvGzIuS+92U7qNzLj5RTF/3ZCeBmSpDsYuYLjF96WnylLUfcR0mvP7e24x/e8TkLqATA7/aomNGRwnkGr9WdOsxH3cP+eL2Ef4H51z+SA/oBdn39cDHdnvRfMq5YbuY8XJ7SfPjcy7+icV0EhCmAJQUC6u7hlxaxreJthuzXM14sbrCfm/C+afC11UZypWED6oohcl+Zpi+zpRPHMvnl/D5iMmPnNxj/ODgVSJ6HrtUTJ8xfebyh4rt8wtGzywPPgrYXq6faSL+TFAXpk/EkabcQHg2ZvPkkuYPz5l8YZnME2hx4GMUfiZdJrHUTG4ThzzivpnwfHmFfzrl4ruWs+e9cMRRglmJbzA+1Z3GrB3bV2fcXl+Rno05eyr35NFSnhn8RFNuZd86e5Ywq4LubsL2yTnNyymP/kAxWiT0wJ9uHhrKjXR9uCYxfa64vqo4ZMeL+RXPP3mby3/icIfh3ssQ1DfSJ/fzf/Yf/9of61lb5fzPLrzsr/+Nv5VzMpC93MSRoBWtFCkVXDy45GsfvM0Dm9Cqp1OKru749JMnNF5u2E0Wd41WI7QxvP3gMd/46lcgNigTCcnwxbPPePn6lkl5Lo4zrcXRYiwWy8NHF3zw/kPGVcnyfs7t/Ibleo8tL2m6Fl042kNNWRRE77m8vORr773FtLKk3LPftdzeLbhb7tDOklUipUBQxcClUxiNhEORMcpitEYFcFrRx4AqLL6Xioa1jhgyKfRUJJ3z8gAAIABJREFUVUkXDyijCVoLSxWLM5JGpo043KxWPHzrMZfnF5SlY7tcsNrt2ey24taMmWo2YbuvsVacT5NqxIdf/xrOKXz2zO/v2R32tHUn7dARjJIH05QyxERhR2QVSDrSdh1FNQJAK0NKkFJCqUzwInxWRUH2LSF5sBmdFTaBsRU+ZnwGVEK7gs4Hzs4nnE8nPDqbohTUdc3Lly/pfE/UBX3SKOOwHrRJFNYym4x499EjCp3pSawXDdfXdxSVpo+JPkVKI2JRyIlgFTl5Cj1CRUWbe6wdWsgyJN+TSgmpUEoTNaicUQb64CmKChKELpLIGKtEkM6RPnRY5zBaE2LEOk3nh1RbErPJGau7JWY0wlogKfrWU44qERSckkA7o8lI63yIPRpxcIEme7AmSYu/Fre4UgrjtIxTjNJiTIkKmqT8EG6bpBVeW7qmp1KGZDLRiFitrMEHcb7PRhOSzxRW8+DBFW89fshht6WuazabDcvlmsn0nBA7chLBNsSeoiiYnM24KCuMMWQFt/M5fejIEZIxLFZbyrG40ytXYI3mG1//AKcN2dcsFivW6zUhJ3wQh6/WSbiQKTKbTHHa8PDhQ0ZVxWq5Yb/Zc+hrsjakLPNPeMkGgubq8pxHDy4pneVwOND3Pa9vrglJE1Qk0jGeXXFYN1Sq4OB3fPjee0zHYxEW+5b75VJc0D4KJiCIO77rGipXoKzh0cUVI1eRiMxXS+pWwPIjJ27IQ1+jnSZlgzOOylVoJJQK1dF2B1ovwUp1LZgGsrwAjY3BluIivrp8yP39Pc4YOi9MVqUtbduiTIErK0Js0FpjjPBJx0VJ19S0OZCiZr8/YK3C2gKysJv1ECBYOMPV7ByVhePtfRTHccr0iFhYty0jK84gTUYZjTOWaAx9yvShw2WFRRGzIgEMP983LRezM5Tu0ThCF9HWErI4lH2SF6gj+9sozaQaoayhV4G23TPODh8DSYlI2HWdOGFDS1EUJJ+wusQOYXVGRUpdoJSly5CUnHPXdRRGONGjSoKzMBpjLRQF9XqLUZrKCgu1bTtM5cgpUDmLjgoqBzGQQpZ7kzEylimgVCYpmb8jW1HXLdOzCe2+pbIOVTratsV7z3g8lvNAk3QkaQMozkZO+MB9wFQTVGEJvceHbhCAwdoCnQZ+behp245xMUUb+e4uebRVkA156FbIWQEJlxVoR993GCOM7rrZyzhKSCsqKGzpSDmQvGAqcpZCXTJSSPMxkqImx4CxinIYzzjcGybjMXQdJityWbDZ7zADP7yNnmo0IniPjQaTI04lstKCZVCyJ3oSOkvbb8hJ7lOFdCTEnPApojDorDAo6hgxhaMwlhS9JI7noWhmDK4w7Hct4/FY0AG2FOSAD2QdKHOCLmCddN8kNF1IaFswUplDWzOeTkhtSxflJUBliEmhnNw/cwpoayh1QZ+FO5ybfmDER0JKxOSZjM/o+kTtD2gU02pEjoEcMwaLNpBSJBEJQXAl3vsTf3g0Gks4qD12Gxm62mO1RjlLG8U9XdiSrDS/9d//t3+SRvYvwecv/Dv/VT46pI6fI6dNRdh+1bL804EPPrij0JH7esxuP2L2f46pVtJVVq2ChC45aeue/6mS9GsSNqR1ousc1W9PefxPDrSPyxPv0dZvXGyrbzmaP3Pg0eWO188eMPvUcf4kEkpFtZJWwmLt6c8dpktsPixY/OnA5FFNCJpuMeLsY8uDH/ckq4b23CROOam0Sov9wUtQ1bH9U4lT1h3SSWwACMfQrCjOqWMgmDjDPHFcnMLJsgGynMf6m4bD+5E8DbibgulzmL0Ud25yivbcMHvZnxyF9VsFN38echVRreHsM83kRnAFR16nCOic2uD91J6cYMWyFdwCb4LXji4pPQi2/YU4OM0x6dqKCyk6cV8dz1mcxz2H9yr27xl234xknXErw1t/KM5o3SfsTsSZOBIWsJ9a9m8bVr+UyOOAqi2jl4ZH3/f0U0Oxj6fk9iOz9HhdwsyRtaJYtsSxiGR+KknsEkAj/NWjiJK1tNf6o7OtHUTRYnB2Ii7eWJmBlxgJozdhc8lpDm8XnH1+IJXiYAZxOPrzAtPKcR7HSsSLjG7F3Xj8+aNjWns5xqOL7BiypzsZ+1ToYb6/YUbbXY+/KHHrjv6qehNoN4hE5uDRrad5b4YO0qq7/rph/zMet7SUK8XkVeL8Jwfax6PTPBXWsXCE64eaw3uQnMzNyx9DcRAhX/vM5Mdz4uUElKK/qoil5tVf0KRRhgTTp4bZc2mZPrrLj0GFpvbU743xI8XmG5r+IjF9oRndJWFPfomlLC5mmb+bDx3bbybSNGIXFlsrHn0/YFopLugu0j0sqW4b+quK6ubA6794QfNIBCS3V1x8JkFqxxAu24g73q1b/KW445c/V9JdCDvy8tMozmij6K5kzoxuu9NaCRMrTkSr6Gdyyys3WdysGqq75uQmz1oYoaFUxEKx+5ri/PNEGCmKXabYCbe5WAvfVcLlBmPC4HhsrzRun6k24oqvbmpS5YQ3O8zPbI5BkIbNh/KuVK4zthExzLYZt5f9tryr6R+MBKcxFIT8eHCh74S1HSfup/YzmcAyD3cfTmTeWnGNJ6OkW2ETMAc/5O6oUwt4/ZYja5lLxVrCJG2d0GFAXKw7sjPo1p+C62JlhOO6lWC+/lzYnsU2ngLS3LojzAZO9AOH7uX3idAF1VzEuO5SHJvF+k3IX3dpSV8KdNIxUy38KcDRdNLCrmJGd5H+qqCctzTvjCjWgf7CkrWiXHnMwdM9qnBbEcfVsbgDHN6rRKxDEAdZy5jZOg0FCungyEbRTzXlNlKsPe2jQop2nbidj07s45o6oTQGRrHbC64njDXVvBf8x5f2wuO+o2KmOxfuth72SzUU7r587+jPZT/TPqN9on5r6MAbughmz7sTkqVYtvRXFaaJQxeOwo9FYMwKwsCddk0mGYaCmYijacA+mC6dmPHHwL/yvpOi68ScuM9q2BKP3zG+7WkfOtw+nVyv5VKCBv2ZHe7Lct/SQQLTsoHmyjKeB5oHgg8pdiLAfvm+cdyzk9P0szf3e2GOa0wnhU7dJ5pHjmKXKOc12Wiad0Yiyg94jmRkb9FDcTSMxZ3bnRtG9572gTuNlWsSoVSU20SoZB2VG5n3RxH6H/9v/8Uf61n7n2nR9q/9jb+ZSQqlMwzhXACRhFYlRTXi3ccPuSg1o8qy7Xbs1ntubuYkbUgpoY0Eqxg7kvCTYsyDyxnjwlL3Ww51y7be0vUBnUtJ09TIi6XR6KwZTw3TiSP6QHuo6WJHiAqtSqy1tF2HGsQGYzVWG5xFRIcMh92Otu8lUEUbMpBywLqSvpd2YgAt/R1YreTFSUuIVcgJtMJHcXY6V5CTwqhE1zeMz2Y0TUO2WjyNERiuq9YKcqS0jhwT47Ii5UBIidp34gJVBpPAFE4CwPrAbDaja1rBMBQKWxhC79mtN4xGgosIYVgRgNZmCFIpiESyikPLdaIcjUQsUgpnS9q+wVnBXVRFiW8askVaRbNsAMaJS7qP4kx1Q2uoLS2TakRqB5E8ijislMKnjC0rmk5eQgFpQfaJyVjC1lxpqduepu3JVkTPFAQ7oa0lg4x38hjlUEmjRwWha8kpMjJyw/cqkuIgzsUoV0/lEwohpYSzltD1lGUpKeZKRC9Uou8CrpAwHVtWhK4XVEaOnI1mbOo9o/EY3wXaumM0GWOUhiTtqwAhRrQVAaWtGwrnyH0ANE4rVDWirmuUjVhryUkRs7RJazKlnQwsQ4W2gmXwJMauhCgPaFEJ6kNrTcoZZaywmDtJGa+KkhA9bnAqoxJt34Ny5KxxTkS+GAPaQFmWNKEVl8gQBNZ7gaTpqAQnoDU+imhiB/FJkSiMtGzn7AkhDGtOkZK41UeTMcYYmqZhNpnS1Q0kRVFUZCmS0nWC+FA5SBhgSigrgtq4LCiKCu+9IFCcIyuDzx5llQQlBSmmpOQpxhLEd9ybtIE+iItOKYMdAgdDFOehG4/xhwBWrt1oUrHft0xGY0IjQpwaXkgSstZNknVWWMNobNkftlIkaltcMSX0AaclfCkmTzEqJIhQGRSCPRmXFVprYh72z5hQ2pKyFE60BkJkv99ydXVF4xNkczqn6ANKDeeVI7PZjBj9m/XbdcQsDN08hEoZZ0EpcpICgXUafKYwmjp1goCIwn89VhqzteQkgYspJTJgjPBbVZTjzzph3DDXEKHPGUHgHAMYk04iRGuN93EI/JI10AePGdZbzsIaJqnh3BRWaZLKgnvJYWhjT1gt4+oKjQ+BmBPKGnKOGBS+7wWNkzVt26KdpSgtsZfiQE9CkYRva2RsjtfYOlBZC6O6HYTZ4IkhS7dELy3x2lmICW2ytMwbEaODl+KU1Qbf1FTjKbvmQOkKYpD7y1GAVRgJ1BpQMElbYg5kEqQhIDMc17wBq0gpYBNoWwKyt2mGdHmt8TkJukiLEFoYS+w6EQxDAKPRWQpiWsvDf+hFMExZ8AsxicCokCLjyFnqrmY8mbHf74VLTEQZTZczWUnoXerle1KCUjlUVKQYScfOCYW4WXOisgafOkxRQlJU1hG8FKWiFiBCjpE8rGcJy8yEELi6eshutwcNCo1PWRzYGlTylMaBkjloyKALYs4YJQWrtu9wOQ94FXmW0MaSU8JaS+N7EpnKOPoogZo6yffnoaND1vBwHZ0meglec0rQMaR86uIwRnjTu92O2fSMGLMUWwoplu0OB6qRPLSbIMiScjImxJ6UM1kbXFL8L//Df/cnou2/BJ+/9Jf+ywG0Jv9+RAnoPp0EubtftrTveoqLDn8zprrTPPxROCWeZwP26Ia1iuaBZfuhor9MFCtNtRBuXbEZhF39JddolNbz5qET5q0Xd2uxi0Ob7RDSs/YnUSuWhlgp/Eizf19+X3WfKTfpxEo8BRRVBrfzp9CP4wtcrMQd5acSlGTbobV3EKjCWFq7U6EoNsJcrJZSaD0iFI6f4+/uZ3JPa640OjCEJQlz9XjcyYl44HaBwzvlSejxY02oNLZNTF53dFfF0Ab85j3t2Bb85Zf2bEQQ7M8txTqcXLzF2hMmFu0T3aUIKyLYpeEafOkFeycFqzhxmIMnjh3NIyeBKTNxTpWbeBK1w8hQ3g8iK29akOtHVl6CRzBapJ8ar9P3DkFpemg7T+UQvuU0phkwSlfFm2sxiJG2iaf5eXKCfen/9ecWt4unMQFOgpVuI6ky8p3D/N6/VzF93tBfFdg64lYt/YPRSWA8iramfRP4Vqz6N63DyDhnI4JS1uJyVVGwA7oLZGfoz91JFM9GDaJnT/doPJzPm7VwPK/jXDmOWXdusJ2Erh3FsXIztIAPYyACghxrf2ZOotXxGN3ujTB/FEyOWIw4MpjBqRwm5iRCm/aNUK59olg0pzEq71vq98YUG/kdRwFCGNn+dJ1iZU5BXoCwlyfCd5w97wgTEfLtIZyEzuPPa59OAv5xjxJRXNbUccxjqU/X6yigAJgm0j1wjF43J3H7KGySGUSufBL3/dTSXBkmt+FUJAoTi2kiYWJP18rPzGldZg3VbU33aHwSW4/7JwpUeONABKhe79h965xq4U/z+cvC4vG8Du+UGC8Ce3shLj7TfQntsPOydoY5kAp9Qhr0U/MGgdFG4tj+lHClUiaMzE+d/1EUNF0c9r03AWYq5FOB6oiLOBZKjnvLcWxipU/3guN6/nKBRXisb777yArXPhFG5hSwZmspEB3n4rHg1j0SFEOxlj09TMxJ4HYDX9R0b9r2VRhYuAMrOQ2M6uahCHOmk+9z24F1PbTtHwsUx/uibiNhCHO0daS7tIxftVLoGdbdsViYtThUbZdOa+LY+QBvzv+4vo/jeRR8j9fp+M9yzOlUkMuD61T3+afmezbqzRyHU8FKhzyIxum0H6gIzZVhfB+oH1omt4FYKRHLlbhbj+N+vP7H+0zWA9d8uP8fUSsA/YWVsMMB29NeGGyXT3P3WOA8jpNK8udNE9l8o2Jyc2SO81P7ZjaK/uyIaEk/tU/HUguveB2lAFun054g91sIlZL70VCEdUN3zCmk9HhMwxo+3Vv7NIR/viHKHgugx1C48cua+r3xSRj3Uwllm7zu6C9k7sdSUWwj/Zk5idzynKH5zv/6L4Bo++/+J38rK53IMZOzuBpTQgSh1GKMo7IGhcGHDu0CBMTxpTLJJ6qikhfKrIgKRq4ie2HNGacJamjedlpeKLuEjxJ01mdPjj0YUBSMRhVt2xKTOE3JhlFR0rXtqa25G4JvVJQWRRA3mG9bjFXYsqDrBcCuh7Q5reXF2wyuN2Wg6zpJpzODgHHk+FlLzpB8IJJwpaXpaoqiIAzOM0FhDQ+nw/V1Rp9eEHPwaGfpfcQYQx+E20mSF9SiKIgxy0tlzgNLL5KipxycO0aJJV4ZDVkYp4lMYHAbJY/OCq2UiIWDqzTGiDGaFMTF5rQhp0CX48DONGgEf+FjfxKbtNZE35FSorCOkStEzDNy3byXkB3fR2wpra4xJ5RSnJ2dcWjqQXyxxByJRGL05KyYjsbkrAg+Ds5nQ+Eg9JEcIsGA0ZZKAcrQ50gXJY1dm0QbPFZZrNIQE0nJ9Yo54Fwpxx7y6cW5dCWd7+T8nSMl+bkQAil6DIqok3xXP8zFtqcoHF1bM5lM8CHIeIeAcSXOSHuYThFlxe2p1AhTRPq+JWeFNeXJ4ay1IqX8U+KXUQpbiTiVQsQWDk3G9JGkwKuMUvb/Ye/Nfm5L8/uuzzOutfbwjmeoM9TcXd22O912TAfFEXIcQpCYLJCRIrhC4ob/gMu+QOK/aCEQRIBCRODCCBlQgmKjhLbjIXZ1u6urq07VGeqc80577zU8Exe/Z623mpv0FTS4902dOjrvu/de6xnW8/19f58vKaVFRG+sE0G3jlGMRhlxXIZBHJkpRSlGWF0FTYV1jXBtszBZSylQHarZVN5sLpUFPdGu5PpYbRiCjAtnLX3fk0LEteKEpAqlKUS6riP2lcNrxCWMVsQMxhZyThRk3HllqoNfAqWmacJW7nRIEaM9JSeKGMrFJVwSGIVTgqHofLNcT2M00zgKCziLg1m3nv4wohsRie38PkrTNS0pTlL0qIUIay3WCp4lVwFJKSWoluoGbLyXkLucSAWwBm0dGUm8zzlDTnjvGcYDMRURxwooZer3Vdjqpp6mKD9TDEY7chXInTZyn7UIwqnI/W6cp2RhB6MF+SLiaanXf0TpQkLYvyvvGFNkionOmLpGpYofEf5vVvIQG0vGeS0FjWLkOwz97bXXipATTrUYDeQMWgRXb6wURpwjpYzTmsM44JyjnyKbdSeOxCIudYpFFyOu6lLdOFmuryqIcN6sGMcD2hoR/pUSgdpIwU4VLWzpXJ3zTpzvKRawGo2sAaZe67EWm5xz9IeD8IOVhAOWolDeMqVIxmAw5Jjq/c9MYeDs+ISbw40UXbTGGs8wiEtzyol11zH2NcyOhFeOhIh/fd2zZD8pP7G/THV/cs6TqXO0rsGmFnfm+za/plwoxkIqOGuFnJwVo5W1pSul7queIY2yxpcCqXIRs1yTYhRmFlTiBLngXEMYRtrW14KTYsyRXMRl3DiHURLkJTufWYpF0yTiti7gtKEPgpgwxWCrwK6M7MtzyT+GjPV+EWxF9JZ9bRwHiqIKzJpxGDBGilPTNKF0WfZz7z1TyNjKbo9hxDYt4zhikc/kneNm7HHrjt1uR2s8XdsSY+QwjbLH6HrNa3uwrcXF+fM11pEphCr8WuNw1hLGUe5hlr1EKVVLB6Cto5Qkayqy1mYKRoPSWvjkJfP3/7Pv/ly0/Qvw+hv/8n9a5oMxtdVRVWFoPrSNp64eom7FF2nlFcfIdOJQicUxOJ7P/18WYXB2ucxJy3LwddLmWUWZYhTDqaW5Sot4ADCeOQkOanV1WtbAIiE4yM9qOYynRsSLWUy6FWur4Nloshch6sv/Rof8JeaeXkQdCTgSkTJu/SKqfJnJN39OYegKT292HrldWkJnZjedGdIiNswH/VlMmMPg5u+C+pKQXp1SZkjLYbpYtbi1FgGkHjRnUSCuRKB2N3Fxfs6HZ3tIy+G0WPmvCom49Qxn0npfjLhz7eHWjRy7KqyMsh/vHrfC7I1l+Qx6TOhJDsr9w7UgA4ZbYTWujbSwVnFpdgWDiIzuaiQcN2SnFyfufA3n+6UncUvOosQsmM5O1uLMIjjEVX2/KnLNQoO9GQknLfZmIm497mpkuN9VnmfB9OLOFuFOrt083hYhaD+HlYkAOgt1i/OZW6FrFqfma5WtIvtb93mprOG4tti9YAlm3uTC7VUyns0hErdu+S6pNT8hjgnnWNyOi2i0MotoMM8dMySmE4sZCqlVS4BTXBma1xN6jMSNl/sNIoJPmfHML4Ip3ArBeghkb9FBns2zt8StI9vacv+lOTGLttnLvBNHZCQ39vYzry32JjCd+YX7K/MrMh1buXZ7uR/+Vb8wh1PnsDcjqXNMZ/4nWJJfHlPzfZrH5jwnVJYwrtkVOAuC872dxXUQ4dpfRswhkNZuEa7nf5eq087t0vIz88/LmmCX6wMi/C4uwy/JNWbMi3g6X/9ZrNRTZrjr8VcRezMx3umWtXx2as5dCF8Wded1LWwt/kLQJPO4NYeJtPKL43/+fWFjcbu4FA1mZ2hcW/yrnuGN1bIv/N/X4Xm8qpglGFGzCOezG/XLzvrbdUste8n8++a1EVjWgXntdNdfGscvB3ZvrxYcxVxwnIs985o5C+nuJnD5lY7N07D87tRqQQhohb2ZGO53uBsZ8/PaLYKeBJSlzi3FnC/vL3aflrk4C7izaJsaceXO1/l2bv1kwN3sRvVXssaOZ25xfDZXaVnj5ffcjoG56+PL1+3LblFT96XZCQ0wHVsJlazFomzV4lB1u7hcs7gSd2xqpUAZW1Vd62oJeAOWdWYRQK1avru/vi3WFYPs/dV5PAvz8zgKWyMCbKsJK40dCrFV+JtMasV9Pq01m6eBuNJ0n4uLfzgVMbn9YrpdX6fba52qo3j+fNORWVjx872ar1lc3RbT5i6ipQhXi19zQWV+LpiFc3tI/K//83/8Uz1r/0wzbf+bv/c/fkebJAKf03KYKAodPd5mchyx1pCUJubI0A9oZUAbipKgEjn0akJKRFUYQ8QZsFozJWEsFuUYpgFUQRVxI5IDU+hpnBy0VRG+5tyObpUhAWFmt44BTRUXU6K1nkzEOsM0TmhrSCVVPIBBKc2UJawrpISz4sYpJUuLdSoo48FaEoAxeCufIaaE0wbrHSGNdCvHNA00Xlpyc4kUDTEHjPWCWkCTtBzOQ46UlAERVRWRrBIqxsWNF8IE1hJiRhlDiZqQMxOR7XpNRhHDtGwA+2mS9glUFV9BOUM/TFgjjtJSMplM4x0xBmEtFnGvTWnCOkvJhZgCWhtSjFir0EWjjCHkSOe8tOTGRMph+T1ZFUr24n5SBluscIYbz8uL56yP1kxpAuckWMp6dIw4KwzjHMVp5r0X4SgFJEu0gJE0eg+EHBhywrlWAsJiFodySZSYRbytXM6YIqFALpopJpSCru3QSmO0wVhLKZkY5fuqolBoiKW68QrWN6B0ZRlb4RmXKv624gq1rhEUhjYMcSSriHYdJWWKChjdMk5azAW5oiqKjGVdHEoVrCo4kWhwRuN8yyGMKAq6IK5Ao5imjKr3XO431ZVYUFYTyBSjhQ+bMsYgY8LKeyqtMAm8cYDGFiUg/QxGScVrH3pAeM8U0EYxhREAQyCmgjGOcZhQRknanhKcQOMaphBZtSumKaBKlgc3pQhxous60IVc5P7kisZQWaGzwjuN0QpVhO+Yy1jDiSwKTYxFhO2csc5Ja39IdL5DFYOxRuYOCmU8MUFRCoVjzBFswTcdzmiIqRZTEtZoUh6JUcadbxu0MoSUaNpGXKYoilLoYolBij5TmJjCWIXYUjnZuvKNbZ3nIsjknGQsl4IxSOBU5Wrf9NdYbel8S8riDE4pUQhYZ8lpApUJZCIF360IUVAbJRcsipxydfFC4zXTdMDW328MRCfhfNgWZaw8ABsjs0wZYUe7W+yHKgWFuBW7piWEAaMVVilSKRQj7u/GrihZ2tMBcRHHTC5a0By2IRZZd6eUaLxlmkZiGGk0eONJoa5/qqCMwWKIOWG0fE5lNOM44VtPGhNGGUKIiH6piakABWcNhUTjLMM04XxDGaUw2DhPjKWKsrDuVig0JYNvW2IK9P0B6xzeWVKsa71zUDIOQbM4Y3FYYgqCxtBgyKRpIlc3q3aaMI21EGho20bmvjFY59CdRWmFUwo9BVoj694UArZpxFFqZK1QSlypALXZRbohtIyzQsFqhXcOqw0pBzl0pMS+TCgKK6dRyD3qxwHvHTEFumZNjomm7cT9azS5gLKaghEmb5ZxnxIyb62VIEvdomxDyqC0oqndFVmVxTUcoxTXUojEkrHdmpQLVntSKDSVD++9ZeoDSttaBJR7b4wl16IxSJeCbxrGscc3DSbVQ1DJt0VY18nnLQVnHYUiTH6tCDEtbuZWGUJOZGcIJdGtVpSQUEWcspGEVfLErYpgkUqW9VdrjUIc+1Y3oBTGWUGFFiVdJVoKiSlEjBK3sbYKVRIxxYqNLKj6HJC1QhlxKqsMbdH81r/zmz8VZ+vnr/9vv777X//ud3JtNxQnq1rcr2EjbZmpM9ghY/uEvxAhTFVxJXVG2sGjcAPNEPGXE3HjyI3C34i7VuWCvxhASSebsGYV7joQtxYzzTzPsrBYUycClNuLMKjDrUhph8R4YjGhEDuNv0nL3+tEZaPK7y9eDsmzW1IncevYfRA2ZJmdcUrYvtPtQSu1BndITGcN7iYyHTt0LtXBBaaPFH/bBm+HVBmEaWmlFQFCLw5CMwrWyO4jiip0GY2u7EW3CxwetphYqqgp6277bA+ohWM5ty03L3tyazF9RKeRbVRXAAAgAElEQVQqhBy5L7VDywHb3aRbDuiQl/swB8MUo7C7iem8EydXqu3YvXxeXb8ziCMte032hrixHP3JK8b7K9z1hCqK3IpYitGktZPrEeT3zO2+ujIRZ2bszFZ2+4y/GBahyIzyfeefz40WJmDlxtp9kKCl65FiNNN5I2Jqa4WLG8XJVay+xe2VRe8nt1Y+hzPi3OykuG1GCQez+0jqbA0k0/hLGUOliuWqzGiJgWLNwr/VMVf3lozF1Nl6/cXdGteG9nkPujq+krQ5u+tJTBD1mujKl5RWX43bxeXvVCqLwJa9xlR3YGoMcV1zR1oZK6pA9mYZSwpxncp3l/Z1VUQQs1V49tciUKEq2qezxK3F3kTG80YEm9n5XMDuA+N5gyoKHRLmEFBTJB63y7gJGyOt2UrwfLOonJ1e2MW5s9hDuL1mU2Y69dKp5NXCNkXPxQ6Z8+5qFMF625C6ivDrbC2mCCJgnn/hyFGMBMaFugbpWVyzGnMIFGdwNxN2J67tObwPfcs3npnTOrGIkVDD6G7CwmTuPtuRWy/r1nQr5Mt1reKvUth9XNASdjcJO3R+r1iWtTMcOdz+triTvcYMWXjVyNqg6v3VsSzzXhp75yKdWpzu45n8vuIEA2KqaK1iJh55Ya+20slXjNy7mX067xEzdiEey9gwQyJuLWEjjn20cK3n+WAP4hgWIU7jr8JyL7KTe2OGKM85vXTFxZWsTWFr8FdxabnXsRCO5HO4et2HezJmVJa1xQ6F9kIKgGGtxQU65IomUPU+FOJaXLx2rJ0VWsadGSu2xUq3mz3I9VowNHXup05TnJW5WMXY8VjGobuJ5FqsKEYv+8N0ZCqHtq5NWkRZEVxr6ODGkBsZx/Oe3T47gFIMd2R8pkaQRWFrsUNmOJV9ctrKtVnQMzOj26paxAETQEdkv78J8mF0ZcPr+hmLjHVVxMFqh7IgGGyf61hIpE6u71T3EkHlyNgtRuZwdprcCJd93ufMJGKsv46EjeytMwJi7raZP/t871QWVEUx4uJPrbp1qR+EQet2so/P8zE5heuloCJNa3LfdJRxmm1dX3rh7s5zmrkYk0TsTo3ChBoKqm659XbuVLBqKfTqWO9vXa/GY8t/+Fu/9lM9a/9Mi7b/5d/9u98BgzYOk8QJpaLCGlnoppJRxjIeBhprpa0REWRJSXijKeK8JeeIN6aKSJC1HO6UVZQiB6oYMs5YGu/J1dFT0NLGGQNWK2wpMMnByDpL03i00SQt4lEsBeNuQz0CpYpzgtSy1rHvDxQtQmQKSdxqBby1GKWI40jjGpTSqFJEfCswTNJqf3l1ybvvvcur169pfccUEiVrtPJo7XA0WOR6kTJaFSgRXRS9EseyqgzUaXZeorHOM44TrrFgHMooOm3QJVOyiN2qJA7DgZQjzntiGNFVlHHG4mzBlILNghjoGs8UB7BgncfYhjzJiNVaQsNs00GxlFwFLm1IOdM2LeMwigiVZMKlmKEoUkl47eTaak0pYJzFe4tSkFKouIeCzRajLCXMm0umpCCiVt3NdBZxo6Qsn99orFHEGUehoC8Z5TxaG0rMIqZrBUWJuFMKcezRxpCjBLC5rFm3Hl3ZlVBE5LJWXK+1pbdxjhSl3TWXjEZ+Z5oCJciBO8WEVprGWow16GLIMQtXuFhyHFm5hpI1YRqISpOyYoyRxoLLk7TzloK2lqnMdAtZnFwr4U6gKQk67WiMI4SphhYpTBURtDZobcg6M0UpPvTjiAbiNMn4JVNKwgM5iVvTFsWQEhgHSjGGIC43ozHWMIVB2vkxOCVCtsagi4YEFEuMGY0mKkUughRptTh6Y5mwWtzgWlkSeQnns0WhkyaniFVgCmxdi7G1LdpAzkba/DEobYlRYUxDSlBUwXoRdckB73QtMIgIaLSI+qtWmMZTCrjOkxQiwBRYaYvLCULGFEOJYM0alCUm8H4F2ZCiQPmNE/ak0wZVMhbFYTzQrR0pSwDdutsQpgltDMZZYhA3u44ZU0AbxzgFsrEY5bE4ctKYQhVqtDBug4jvxmqKKkylzqFcwCiGXIM9tKHfH3C+QSst7ehKEA1WyWY2liRt6EmLiJoU077nZHvKNEqwoDISTJerwJyyuBILBUg0RpzrVss9yzFhbVPnHVgdMWi00rSNJacRrS02axHgjUNriCWhbCGGiaZxpDzSNo7WOYz2KCVhfKGIkKUUhCjjZ5oCTbci5og1Gq0cpihhSBddmac11EQpSkGwMOTKei0Yb4hxxDpBo5iKrlAlYbQilShivVI0rZROUpiwRtGsOy4uXrHqOukQUIVYEn3qOdpsKAm8bchR1ldlKtc01T0tBrRRlJTl5/OA0oocAo0x9MOOZrNhP/W49QaMIdY5k1MtasVALuBsdTkgyJqkC1mDMQ5VMRLjdMBZJ6xi47BVNEFbMIZhmvBtS0iJgiNFcL4VJ6wRN3vTaHIomELFQRimEHDeE1JCK+iahpxGLInOyz0Zx4R1nhAzKRcRZZSBmEBbjPdoK/tDKSNFB7LJ+M4zpoR2jlwK2jT1gbI+kObCet0RUsBocbBrK4XSogrWCm8OK4z0qDSNb2iMqw5y0FYz5YAzBlLAG03WIvSqJNgRpomTzRqjDSFFtFIMY0/Xrsgp02iLd04wOSlJB0LjcV7E85QURsu6qq0VcdoKkkcK3hnXSuGPFGitYxj3KGcZc6RRUqwyWsuzi9f87d/8uWj7F+H1X3z3H3wHVdlzvjpIZ7cpYHciKPprOUyrKnXlRgqiRUsoTFpZcQ5uPbm9FeV0KItIkL0RMWJtCdUdx8x5ri3/qa2CHKo6mwzhyFbnW5HD+5CXsBEdWYSLWURIjaZ9dlgOZ3pMhOqWS2sRh8wgDq9Z3J1FaH8xEo4c3Q9f8eyvn3D85yPTqcPfxCpmi4MydRLeVXztGqnib7HisJIHLn6iXbhYJUJ2FbXnA/p47hfhMq4daE33YkBPhbQS122xiuINce3EkbcwXBXTqQgkc6hW9iI8zOKKymW5pvM1ora8h2MnvNcv36MqCpmpENeG7G8P9dlJay4KdGDBPZTOizhfD/kgh+zsbx2eGPl8KlMd0UYO9oO4SnUuNK8k6K3MTPxRAnKowiIKTC8FeR3k+qSVo7/nKd5JcVHdCmoiPoqZZDoWsSx7januP2lNzz8hGBQr37E4uWc6VIG2il/jHb/gBUwvwpS7GonHrbjDKgaiWF1FbBbRc3Ylghzcx/NGwsT6sjglZ2F1DpzSoeBuxP3YvOwpTuZROPKiueWyOLcFFyDFCZmrMt5yaxZBwe0i492WXEXq2cE4j2EQgaxYLYWAINWH6ayRkLQhk1sjzlY7F2BEEM5tnauxyJ+9ZXi4prg6x7KMGzOV5brMAvHiyG9EmFKlFlGq+DE7xd1O2ttVEf5nXNv6uRJp5RjvtKSVrE9SMBKxeXYnx40UOUWIkjE5M3KF1WxpvhBWrJmyhM7dbW+d1Y0Uk+bxUZysm+4qoEOieLOIsNmbKgIp8kqKF/MY1KneV2+WopF/1cu1bCz+5Z60buoYyIvDWML3Cu5qkvWnrsM6FuzNyO7dNf4mETeOmaOvo1xXCX6yi9ActxZQ5IpIMFVAn93TqTMUJ2vmdGJri7tauh7ixiyFqKLlXoaTZunAmE4dyatF9HSHvIjzbhcXUTwceWyfbjEGdb1MrYyptDKS51LFYlm/uGW9WlULbZUN3Ji6DqllbJmhivWzM3uU/WbaWrY/uGK6036pmyGLM/eRPKOGtcbEUgXy2UFdxf06FkT0LMu40tUl3bwOhCNL92KS76EUpq+FyChCuO0TOooIqrMIp1LQkOdZEQIry/Va3M2pCoa5FpnmIkxzNfOeZT6ZCeJarl3sNDrDdGQwsTJ16z7hdlHW44pKGc/98vmGM0d2SkI3Oy2BYmNeBH6gFoW0zAd9u2foWAhrjb/JoOW7SceOrNPzGtDfddg+Uyp2oBgpxqoi++ZcMHQ3se7flunISnN5Xdv8TRRnc4GwNrfvNRekC1y/Lc5aN4ho7i8Dw5lbhO2w1iSva+BnZjyxhJVeipjZ6+X9zCQhcrkKszpD6ATppJRiPDa0F+JE9peRcDRjOmS+2b7wH/z7P10Q2c+2aPvf/r3vgEKXgsoGbTTjNMjzlZO2qFwK3hkMisZphnGgbRumOKGtI4ZE27TSvpsLKUSst3J4NtICr7WSlskqDpSSCalgrAGlUdbhrZdE5Yg4z3ImkwhxIhYRLUJKldNX0CWKc0hrUhKnzXq9Zbe75uT0lBhGvHVYI23HccykEPHGY7Q4omJMKJQ4a5QSdwKI2NXv5YDrxE3svYgP0zShixUhQltKSeSSsY0mZXBtQy4FSyFMYXGWlrpJa62hGLkG40gJE+vtGpUDOQXarqVQq6NKU3KQQ3BWKK3ROWJywWpNKpNwAitTN8dMmCLeK3LFIWjrpOW0qtoaVTmVipQiJWecF+HQOYvzjlwyRjuscajqdIxhQmkRdec2UV1dqeK0kgNz6wwli+Mz5Nrmi4GYscYsmAilZA3Q2ojTWimyBmtq+AMFo8AYaVePIeCspVt3lJQkRT1NshCWQq68QV2D2+IYxAUHrLqOME10XbuwHUu9KY3zOFtF7LYVx1ROSDe4HOy1NhREwAxxQpdC28h4IBW2qy06j3gPIUc5SGhNcY6cQnWQJ1Ie0UqEW4Okh+uiCLluPkpjihb2am2jTUXGvFaaxknhxFvhm6JSFcJqVSpnrPHiEjOW2U2rFExTwBoJQIox4pUmBxGyrDGUnGm8ZZwC625NSmC9gRQxRdEaX0WpgvMWow3WWdrWU0rGaCjWMpRMMeJsy5TbB1StZQ5pjTUGkmA2ShGXJApSFjd9yRlIUCJFZYw2KCU4DK2VYEFSlgAmhYyzogVX0A9YAzEm2lZcBykrCaJShZyqkznnihIx4j6fIpv1WsaFk/GAEg4zRUQ03zh0yVij6JynqVzMVHmYGQleiilhjcFYAXlaUygh0hhNYyz92Mu80Iap8oy1dsL3Xm0IQZAdKcg8kgzCJOuZ1oxxBCNirlYiJKZU6FphW1utJXhQFyAzhbEWcDIpRlpncDrjtMLWQ1LbdGSthE+qpJxGiRhlyQlyHDG6QJRwPKt1dd9aVMnkLIdXncFqiOOAtYrQRxpvSdNQA+oSRmuca9luV8QkPF+lIaeMs5qVdQyHPWLglnFEhhBkb7JeWMdKKVbe4S00zsianSLWGpy1pCSdAo21FFUwiCBeshS+GucJBJQy9MOBtmlxTXUMKETQNk7+6w2H/Z7Oe9bdir7fCzZAWyyKOI34VkTyxjhab9jvbjjarJimiLOe/jDgrBNXMpCDsKSVlrGitJyaUwrLXMkJcWaqGlaoBLEgOAKLdYKy0FrVsSzjwmhDP4w0TUsMEe8aADadp99fc7zeYCnYIg5u45w4xK3HFChBONqtM4RhYNVK0kqK8qDpjCWL3x9vLUOaKspEiZM1T2xWHmW0hCumCFo6PUIY6boGbzTO2+paV4zjXuZM1uLCVgajDNaIOGWLYWUdR82KaThQquM3homSxdWrEUzDjJPRGpw1rNqW85MTdI40zjGNE8Zq2X9CpvEdU8wYa4lhwrpC6y1aR1aNo7FOxk6RZ5iQRrQVrIMqhZQDzltQibb13Ds7ZhoOIoRHQcJo4wQxkgtGG5w2/NZv/ls/F23/Arz+8+/+w+8oqC5EEeGEF2jEdT8furaW7BXT1tBcBEEb3KTl0DqdOukYKqDHJM7EciuczXzL3FRBsYpec8vtLABQRbdZxNVR2p7NIG2Jdh8WZ2GxcthThSXcarjXsPr8wO69DbbP4vht5bvYIaGn+bvIIdbWoCCoAmc9SCqtaXY1HHNrcPtM2Ep7vL++vT6zKGSmTG7E6ZjWbnEP2n0Skag6hQTYrqvbRpy1Kiv6u3ZxE08nEmJZjK4p8VXkqyL0HGwUWwkcKq46ybZVjNgFSeeOIppmJyiIWaiRA2PFBVRDQ1yLOBzXVtxGqVRm3+y0qsFx85/3qTrO5L3DkQiiqTNMNRwHrZZ2eWG1qgVfMR/WZ2HShLIIITPntmglLrYqggpb1DGeybob10acgaYKHdUlmeuzgx3SwhHu71j8XlxndioLIgAgHBnSymCmQjgWhzioiiSoHNg53C6W5T2nUwdGxMXD4xVoRVjr6gLl1pEWBPcxizrF6ioGqsXR5XpRJIrVy9icxaFZaCtGEY6b6kh2i7BmdxPF3gon4vLTtw5mVe9FDWjTuWD6XB2KMiaE8SxORX8dGe414sJezaKeqW46EdLTyi4c1fHEoorcS1XAX04ytvcTOiQJZK1CftiY2/erxQRdHcYgawezMFfFd3FXiwg3O/Jl3KhFaLQ7QTuVKpLOzs/xWApNZsyYim+ZRdQZ15GbW/f54b5HZ0DrBUFQrAhbty3bspYNp5bpSPAWdiyLwF68/Gxqq0uwCKpC5olj2hra19Jyr6IE+xVrFvfmLBCHs3b5vipmzBAl2K0VgVgVERhFzBRhcjpraC7jgulQdUzMY0Tlgpky07EjrTRhZZY5Ph4bTLzlOX/Zna1DXsRDVYSbOwuMqdGLGIeWeSPrvjgU3SEzbU1l9N5yotPKsH/gceNtN4KpOJr+3NJc37aVzw5F6Uqo+0MdM8O5ZdpopiO7hFClVgRKCW6T9naVRTie3avZa6aNxvXSZde8EkZtWlUVUNdMhlYvrtLuZWQ4s/R3DN3rJK31dR7YQ5Y9DOGvjseG1YuJ3eMGv8ukihqJK3GiLgGFS9HkSwWvIEIndU2c3Z/UuZxaYejGToTb+d4kdyv6FqtoXg3ErcOMhbCRa3m4Z+guMjePLanVcp0iy1id0S4qS5FpPDb4Q6Y/16girlJVqis5ipAZVprmUgqHsZPPnb3icE+KQu5QqhNbioi2Fy5wWAvWQGcJ92uuK/oHcaou4n0j1ye2muHcsXtk8buydCPYXp4ppmNbA+FEZJ+dz7HT9Oeaq/dMZQIr7L6iCpyMk/FI0+wyyUshMTWa6UjefzyTcDkJb5M8CLfPS4cKStb20FWu9JHi4usKtxfx2B3k+WBhFheZN7HTP7XT1v40/+j/vZeu4lQm5gMqG9zGilOvJiwTE40reOUIQbHtjilFsV4dc93f0DQt0xRJCsiFtm2xqVSBL2KMw1hx2AwxYkD4dc4top5J8hlsKmjjSCpju4YYJa0+ZTnkOm1RERrrKWM9LI6JZtWSTGIMCeeEkahREHMNtLGsOo9RjjBObNcbQkiUUCqeQJh4IQVhVTqNbx1aFcb+QGNbbJFbaW3LyAA1nIgirsh+khZQFwut8YSwZ9VKEjlUO34GhcP7FWMYONp4jtqWLy5fstmcEFFUZIekT48TXjfSfpwV8kScSGFP6ywpOJxtKCFjJkPRE2alIIkD01pHiRGbI0qLK0gp4dka3TBNiSwJOrSNhLblONI00naTUsFah9GRbrMl1aRway0lRAgijBYVKWlksz6WBPa2YYoBkqoOx4y1jmkKOJ1FsC1wp4rr/TQypQkTxT0cCjg8WmlCTPhGHh7XrQT+bDcr4Zhe3ZBpMNZBUuQh0rVemJEqsFo5DAVjEt45+n5AxUABnNb0aURpJxxMaxgOVzx6eI8SImFKjGEikxnTgHWejEI3jYiCYaItHtcqGjviV0fsx2sCimi0IDuUQ1UBz2lLLg3eW7wR8SSMEykkrq5vODk5IUyJRGW+lkiKhbXzNI2IzMYWplLIMTDkQsILVkRb2qYhTsIQPlm1eJNZbU65ubnhcDjgjKAkijZk5ZiSOBa9CqiSONo0bDYbnD3h+voGpzM51vcrHfuYcWZFqzMegzIT3sN6vSJOmuHQM/UFjTh1V054vTFmUmPRVmG15uxoi0HR7/a8vtnRWMNQOdXaOOI44L3HFs/Ktzit0F6z2+0IeQ7MimQlbcYttn4ewR+YjWaz3pAyJBQ9AxbDYb/Dd54cRIQ22WCMhOhtuxXu2DCGiT71GOVpnOf6Zo/2UkXPWdYKD5yfbrlzdkwOkcNuz6urGwkcjAXlPNEW0JlcAlonNuuO9+49QKnMcJi4vOl4dXnFpCxWiwtZ5YzNhjAG4UIHCd/T1lKUxmcIKRKN4EBUkSJYKsIyQ2WyM1JkIaByAiUs6qbbCFvTWWxWNErhtObeg3s0nWcYA4dh4vqTSzabE3b7A37lGA47fNuSgdW64+So43h7JIWXUvjsyTNiKowZrPVMw4ixmsZ2ROXoWsfZo2OsVrTW8PzlDbtDz74fISYuX71ktd0wxYAOhs4a7hw7To62nJy8xXA4cL078PHT52jnZGwV2T+mlGhXDV3ruHd+zHq9ltDKPvLDj37MOE6kKMGMBPDOoLSIBUfbI+7fv4u1mr6M/Oijz/HWUVQmDiIG+xoI6GxktXWcnx2zWj3Eobm43OPCxBQtl3nEtS2qaFLQHK22DP0l5ycr3n3zIc4bdIbXFzc8e/6aMPVoDEYpEXedJqRCPw0cn56w3awwKG72I4fDIAgRZdFKuNTOO4jSYaGIvP3mmyidOBwGrm4OXKQD/QGiymy3x4QwgipMU8+q63j0xilvv3lM16xQKXPx+oZPn72g5EhMilgixjpW7Yqh37PqLB/8wiO6lWbl1ry62vOnP3hCQpGccLeNF5d+4y02BM5Oj7l39wjrDKlX/OjTz7g6TBgFQ8m02xVpGqVYVuBs2/LGg3O61WPGIXG46Xny+TNCDeszaJT3gCGmPcNwyQfvvcu262jbjqvLnufPLrnsAyEkoja1u2WSzp8UefPBG5yfnQqWRxlSLnz44Q8YnWZ3PXJzs8e1HUVBRnN+fsT53SNWncVhGCcYDoU//f6HGKXRtgUrxRStC7lEHj58xMnpEatVh8oDjx/e57OnL/j8sxekCGOawDpa5wnThPt/7kHv56+fgdfC3hzEFRvXFv96AKCYKqpuLdNaDrj7hyIc9Xcd6ydy0HU36TaM5NRXJ5aEcWXnbjmSNfW7mCroVlFvbv1NjSZb+bnUahgyphfGZTGauPGVvSiiW1xL6NZ0LCKWvxL2ZpnbFBEOZewM/R1xfbm+sHtgsL24FGeOaFwZzCHW8CBp6S0G/I0ItrEy+VLbSKgaenY9VO6qBFXFlWY8bnCHTH/e0FxLEv3Mpi1GWsT9VWH/1prdA8PZn43s33CYQdAN8j5mad2OK738vXynzHhkMUFCWFSSsBMK1YGL/LlyA2cG8Hxf4kpSuP0uoYN8runEiRgbRNQpWg7osZEE8N3jGo4UC6q5FZNTKwdxlQv7++Iaja2IpPNRcxax3E1cQp2KgcuvGPQIzWXB728DbGbnYzHS/h624vY73BWW4/6+CI5uN7N7q2gZC+Fc43ZyrfpzEW2Th7C2tBd5cYHN/MWixVmYvbiuXvwLHWQRAvxNWZLKZ2GzVLalGSS9PTxwhI0wetuXBXcQJ7e9mSjOUIwIVDO/Mmw009rS31XYXgSazSeBm3c6XJ/RUyFXnIWeMv39hnFbhSYHbi8iY3NRlrAmPYmzcQ48un7LEteK8bTQPXe0rzN+Di0qIhbOLMqwlbGxeyifKX1Ts3oKoXOSxl7DfLrnwitNp56wFlE4rBXDHYUeDc1VFTKMxwyZ8U4jTOshL5zP2Ciuv2bIDvyV4eQjYdI2F2FxWZohEbaO1Cr6M0PsFNnC6kXG9TO/9ZZBO51YwkbGhZ4K2Sv29zTmRKMDtBdy75ovhEs9u/Fl3ZHxuX+jIXbgdvLvs9eErWX16Q5ttSAzohgjwtZw+b5l916CCM2FZvsjaRNvtCDk/BzYVd2Mh3uWL/5aBJ3R15bVZx0nHyWc1+jJ3QbQteL6TZUZPK8XxWqmrau4AEFPFK3Ijf0SkqG2XSvBNMwCkdkH4lGzBPLFzjAeaWKnuPoA4jZjrwzNpWL7JLF/w7N+NhE2Fn8ZZHwYzeGOZfdYMTxMFC/f7/gPRRAsN5rYgN+JGDVuhS86nCp2b2tSU8jbzOZDR/eFZfWFrPtHHw8Md72wv61iOHdcfsVweJQ4PDpwuGkwTxse/F4iWxEqJaBSmMjTiWU8UVx9UCjnE66JTNcNd/93Q3OTl/AtuA2dLFqxe8dw/fVE8RFzZXjwj5yE3mUJzZJQPBG0Y6cYzhW7dxLqNNO0O8ZPN8Suxe0LR58E4anWbpDdQxGcrx5rXvyahTagTMF90nD2Jy2ury7/OsbmoLn2VeD6TUt/T0Tv7rmifZ3R4ZaRLuuW3NfhRNby199OoAvmwrB6qjn6MRJ0V+DweLUElfl9pj8zXHwrcfHtTHd8QOvC4dMtd74n64sdMnYQ92h/pmmuCsOZ4sXfSKyP99w5vuL7n9znwW+LM9WGyqOu6/a0lWLizduK4YMB10RiMBz9bsf2MxYu7lTRRmElgunle5brX4i440jYaeyF5c7v62W+z25tkM/YXhY+/Zsae6/neNvz8ukxmw8d208z7pCXsLJsWITWi1+JvPnOS6Yk42EMlvy/nOFvFOvnkc3nU0UtiLv5+l3F8O7I9vTAth252K24vml48NuO5BS5BqOZUMhG5vrrvwTlcc+dkx0rnZm+abn8o3PMJEULf11Z1Mci5IYvBZz9814/06Ktq4FEWQHGYq0Tlpw1GGPRFO68cYfTjUelCKVwfbPn+asLdHZsm45+GNBawlYUmkZpjo9bjrYtysDupufiZmAIE42xtMYxxog3deElkXOkcy3NSpyYh8NAiBGTodUGsJLDozS5BkyZDnxjKFc9rYLiFbv9yLrxTIcrvNYkIo3tKCWzaRu2HRyd3GW/39MPkRDGhZOXpgGvgGiwytEAD+6ecXqyYZombvY7nr54zXq7Ifde+H9JEsCV0hx3DqcyD+6eYXRhP3W8fPmSo5OWi5s96JUEaMWJKckhbxrh7P45X33/PpevL7i4itzshsqdjRhniAVxG1pDSZH3Hoes2BoAACAASURBVN7jeH0f2zgur664uNyjaShJE6Nj6CPaaZpmzTD0PLx3zuOH5zirCCEwjhOfP33OMAUK0npeTCKGTNt6dNG0reeN+2eQI4fDgZsbQVxcv97RNB1OCz80Z2hbTY6Zr7z/DqfbFTc3NyQUFxcXTNbQj4FYhN25Wm3ohz3nx6e8/egex1svLpMYePXykh8+eQbWME2BIfS0bkVOkTQVfvmXvs7RqiUR8FZxtbui5JFpLOx2wv71raPve1QJfPDVdznaNBytV+QozJjPnn7B8xevGEIhKEfbCZc1qYnjbcuj997izmbF0XbFOE68vLzh6dOn6CEwTJM4pNHs+8TGe07vnPLWg3PWK8FH7Po9N7s9P/jBpxjjmaYBEEf2OE2cHG9498373D07IqcJXRRXlzcUA69fXdFujglTT8xKUA8l8+DuQzabFWdvnLC/eo0GXj57wcurHVejtEGMYURPmdY6To477p2dcbzxrDrPoT/m+jDw4otXvLrYUWIkJ4NqrSTAN5575+e8+cYdmtZhSqYfRez9oz/7GOM35DzhjPCdnU40TcdbDx+z6hzdqkWpxOuLGz79/BWvLw5oo8lKwsJSGvDF8tXH77JqLCdHG2LqUeWUJ09f8unnzwFNSpBKxDiNVXC8abh754y7pycYm5mmiRcvr3j96orr3UQGcaPmIIgI5bh394xHD+5wtOkIOXHx+prnLzJfvLyiNYY8CNs4BinSrNYt777zBut1g9OGcewJ48gPPnrCod9xsrb0YwINbWM5HA78yre/RWs127VHKcVud41/vebJp08xRQERHzPWBUrKfO2d9znarjk9XuEqz/p66PnsScMnz19xSANKCQc6hAhxRmNo0lQgiMA8hAHfiBtj4zXECRUNSRcihUiC7MlKBEFwtMbg8MSS0RRCDhJcpyPf+MWvsvKetrOEpKAojtcrPvvsGcREHgzGrfHWsQ8DD+6/wd07J2xaSZxunOdku+GTJ8948vQ5u77HeUNGU0LgaNPy4OFd7p4ds2obVIqcnx1zfXPgjz/8If3Us9oesbs54NuGtjO0VvH40VtsNytar9hbw70H94kx8+T5F5SicM6S4oQ2mc4rvvrB2xytWxSBzkuQpbWP+OijJ5SbRNGFqxzptMGkyPtvPuD4eMvx8VauSdTc/9YHfPzpM77/5z9iu71DKQWrLFoVvFW889Y73D8/Ea4q8PDhQz777Bl/+OEPxEl1CKiUcN5zdfEJb775gDffvM9qtcJqQ+taHt29i0mKJ09f4krGOjkMGw0xBr767lucnx5xtu5wjaMPiX/8vT+iHyMJ2Set0pQwkeJEe7zivbffZ9VmWmfR+pwXlzvGH3xMrg7QGCYMCtc2qDTxtfff4uTEY2zGWwuqYX18wvOLV+x3E6ZpCdNISpGrceD4dMs3f/mXyGFi3VkUlvfPztjvBp49fyW4I6spY2DtGvrdFX/ll7/F2ekx1onLNfaJpmn48Sef8upmj7MaNU2knDmEyIM3znnr/l1OuhZlM6t7R1xc7Sgp8/GT58L3IqKMdFR0K8W799/h8YN7tNbIvD8542Sz5Xf/yR/gnWPSBuU8KWbG3TW/+pd/mft3jhkOe85PtxSladuW/W7H9/7wn6K7NV3XUXIiA7vxiq989Vvk1LNdOZxp0Ulz3QeG/jE//PgppnE1UCwQpj1vPLjL47fu0xojcywWWtdy1LYcrvY8f3WN9x0xB/rDQNM6pjmt++ev/9+/5vT2JeCkM/gr4SnO6coXH1j2j3NFKim6Z5rzP41kqzg8amlfhYVbW7S4cXePNf39QtGF9qVm++NMMwgjdDgxNNfCrZuOK2ImFPozS9iICNi8LrhDRnlVE5jdkj6tUg2F6UQ02hQ5BKlsWD2f6O86ccZuxHk6HVtUEodRf78w3k+41xp/pXC9vg1lmQpxO6c9O6aN5tU3DMObE+qgaZ8b7vxx5HDHoINZHFBzIv3urY7YKV5/o5AdNK8cxz/M3DyyHH8szjodRDRzO6romNi9rel/fSJ85lg/cayeZdrC0jZu+7SgCVQufPYvWdLDEdeMxM9WbD69TS63vTAti9WEI4u/jHzxKy03vzzg2kgYLOwcJ38kB3LKl9zCoxyms1WMR5qLX5Qx4S80q+fy+zdPRsKRtIv6mzlAR8wLT37Dkd4c4EWDnmDzqcHtNe1lwgwikA93Pc3rwOVXGl59O3L28CUg+Qif//iUN/5BTQjfJ/zFxHTqa8ha4eN/t3Dn/ivGaDjpBp48O0XlBrcvrJ8FCUfaWtpXwmH95G81hDcm7t+/IiU5HH/xR+ec/im0lwl7yEuSuJ4yN281vPqWw713zdfvPef5YcvnP7rDyR/LNZlDeCjQPu/pH6y4ftsw/uqeN86uSUXx7OUxr180vPk7kJoWdyPovuyF13n9TsuLXys8eP85JVq8jXz+9BSVV5z8cGQ8ddhDhP5W5H/5DcN4N3H81hWXr9coU2g+ajn6oWb7qWQ+2KteHGGnjus3DTcfJFYPdrxzfMVnV8c8f77m6PuWkz+PCx5DxGuZe6+/bgm/suNoPdC5wIurDZdfrHj3v4u14HIbjBbXlmmrefnthDsZOTveY3Tm8x+fc/r7EiQ4h+OVRgTWsDU8+Q0NpxPvPnzJfvLyM39wn3v/p4hKt3gITWoVN48s11/JnHzlFSsXuTp0jN8/Yvtj2DwtmCrY6klcdqHRvP66ZfjmgbfvveZ6aHnx5ISjDz2nHwams3bhStteigL9meHlX42sz6/ZtCOXu44vBsf5/9bQvU7s3tvSVFds2HraFz0f/e0Of7Tjq3deY3Tm41dnXKy23Pl9lrUkbl11dBY++VsG98aebz/6jDvNHoDvffGYZ3fucv+fQPsyVzegCLYwO08V1ECq7CWML27lObd/sKr/TtYt2ws/eF5j5g6G8dRhagBfsRqzD4TK7X79b/TcO73h0eaKV8MagI8ePOT8n1Y3YOUkT2vN6ovIxS9B85UrvnH+CoDHq0u+9+gxF9+7y73vFbqng2Byasjd7oHh8huZB+9/wbtHr3E68fE7Z3zy/Az7PzS0F5Hhrqd7Km7Q8dgwHiumb+159+4Fb20u+P7lXR69f8UfTh9w73tpcX/qSZz5w4lh/9f3fO3eS6zOvLd5ySf7U/6geZOTf9SgshT3Nh/vGe90ZKf47NcVPDzw1fsvaWzkcujYf83R/x93eOu3rzk8lgC1WIOswlox/pUd/9p7H5JReB05/eqBv/Phr9L992sRkocq9K402yeBZ9/28CvXvHdyzbHveWt9QfwFw/9k/zJ3f1+jV2YpoGUHZYBP/pWG/E7PNx9/xnlz4KObc67/q0fyPD5mVNZLEU72NUX8q/Ieb20u6Ezgdz76gP5mjY62CtxSCAsrKbJc/M2eX3jwgo0budfs5J6fnvPsz96me5mlqHnImBG2TyI3jy13/s0nHCfD105e4HTi13/1B3z31W9w+s8064q2UblwuOdpLyLP/+2Rbz7+jGM/0JnA62nFP9vc5+U/PuX4Iwnumgsvq5eRL77pKL96zb/44HOsyvzi5im/8+JrfJIf8eD3ZMn1+7yEqA0nhme/Vvj6Nz/hnc1r7vtr3FuJv//oL6G/e8600fidPK+YUPDPIz/+9xJ/7Ssf8dn+mH/1je8TsuH99gX/yet/nXf/jloKBqq6lLunA2/8R5dMyfCt88+5629oVOT3Lt7lj3/tHR7+QxGg52A/12de/pLlwbee8nBzxRvtNfvY8G73kh/dvcPvXXyL8z8JhI3Mxfa1FKvc/rbg8s97/UyLtqUUYhbwsamt/954aTtPhe3RmjfOt6xXGq0KaSr8X+y9yY9lWX7f9znDHd/8YsyInCora+xudhe7LVJUq5uULVIbS5BhQDBgELY22lj/Q++888Zbw9DCsGWYFGSLhmQYkqluiSbZU/VQXUNmVuUYc7z5zmfw4ryMhna9kiWbd5fIh0DcG/ed9+73fH+fz3Dco6wLpNSUjaOfDyiqilRLqqJmfLzLg9uHSNHSOEOahW9/xcbTdJbOd3TWILajAVkak/Vy9ndH9LOYtjWcnDbYqkGpCIcCLxDO4K0ljxNs13Hn7gGjYYpzcHJyQtHU9BKFs2HMfV0WJPEAazw6Urx57zaDLLQVl6lgsdiwWjiKsqHXG1BZB9YRaY0zHZPhmNt3DshShWs7hv0E6w3X8zmeHCMiWufIdeCeDnaG3N0fkWWKPNO0bU2sWk5OL8Povm9wwqGFIktSTOd4eOcWxwcZcerJozHjQc7J6QVXiwrjJUYpvBd0TUPddTx8821uH01JI4eUjmE/oB6ebU7xPsY6i+tsCHyCx5zbR3uMBjF5qllsCiY7Y5blks31EploXCdCICQimqZh0h/yxp0jhv2ISEkaM+TZ0+csVkv6eYxxYXxXa0nbeKRIuPfGHXZ3+kSyYzA8ZLFYUBWSstyghCKOUmq7QtjQ3j7cHbEzHZHFHq8hshphW65WOYvlGuc8edqjrmu8ExwcHzMajRgkMSr21M2GvWRC3RmePH5OkvRpuu04rII8yZhMhwz7EVo68njCZlPy1pt3WW7WrK5XWAGdDa3Gpq452LnDdGfMMI1IojhIyeJgAP/ok09prUSnA5o2GMF9pDncnzKZDtCiRclt8BTFzKYrFpuazoTxP2MMeZ4xGeTs74yIpSDNhjRNw+DOEUbCalNQNjVOCLwL4rwsy9g/mJAlMXjHZDTCuo62nlJ5z2ZWBjyGDJb0piy59dZ9JuMBEZY0SYjilP54RJJEzOefopQGLanamkgpRoMhO7sTxpM+zhpiqUliRaTgcHfKfFHQmBq1xTRUmxUP795mb39CHGliJWm7kt3xiE3dcnk5I+4NqaqGVnUkkSSLInbGAwa9FOUdMs3x1rG3t8d8scIUHdYH9quzluvra957+xvs7oxQ0hMpS5qlQTTVtKyKGuEVVVehlEBIT95LmUzH7EyGaOVJvSI7mBIpwWy2pHUGmSR0beD7WWuYTscMBhl5LyHygmEvpqsblpM5L8uCojahzSwEnWkYT4bs7+7iTU0WCSye3f0dEJrNZsOzl+fEOkHJGO0tbdcyHo6YjAZI1ZHIIG4aDQZ005rHz5+i4tD0Bof2oQUvvMd7iDR41+IFZFkWAmrnsJLQIHUOjcVjQiPWK1rTYfHgAoJGSomXZhtEKiwdO9MdJjsTlATtBXEauL53793i/PIMocF5u0XeGLTy3L17G4EhEhKlg6xsPBlS1A1fPD+hPxxRVRXOWsDTHw05PDwkkwR8RhwxylJ0HDMc5NjFmrooyfMc4xxlWTI93GN/bw+sIY4dvXwHqSLGgyFfPHuJ7PWxxiOloG0bhv0D9nZ28EbiXA1eM8gHeNvnLFqxZo5pO5JtK7XzHbeOD9kZDbG23YayKWUnma4rdidTitqidYz3EtvVHN27y+HeLmxfnyUpXmlu3zrkw49+QZbk1HVoh8da0grBeDxmPJpucQWGSEUIJEeH+5ycXiCko7UhhC2a8Hl46/gW/TQhMg3KO8b9Hgd7u3TdOV0VZCAylnjj0UnCcDRgNBkT0ZLo8KcaD4bsTqYs16GZbIwnjiPKsuRgf8p0d0IUWaRwJDphMBhweb0gjmNqZXGdCxM1zmJMCxqSJIFIgG1Iez1ipRmMhpycnGGNJdEp1rYIpVHeMx4NyPMU29WkUQRCoWTO2dk5VhSI7WRHwBYp4jjmYHcHW2xI4xTvPPeOb1MtO56fXGKsRUQRcayYX10Tobl3+x7CG/IoRcsI5yXD/oCyWNIfjIjjNHCinWUw7BFFGnAcHBzQ1lVgnamINMmRXuKdo6srYmLWzYb9w13iOLDLrWvRMrTbbu3scvLylDTWNKalayw6CpMtu5MpWiq0AmfDBnWkInykSJIEpRQgibRH4FBCIH71zf+/OP49P4QLkiYIwUC0NnTbRpdXgvWhZv1uR2+nJFKWptOYQ8lm3gsSlJmjOIzJLzracUQ8a1nfkfjfWtJXlnWRUgxC6NpdCeK1J6r8DR8SoJ5o6sNtW2q3xZea8c80qg0NGxlKWegmcB3rcWjnXH4dOKqYGUnv5xHplafai5A2hLi90zZIr9rQ2i2+WXBnb04vavn0bJ/qVU5+GgKJck+TzcLmyGsb9PquIPranLuDNa1TnB4MmZkBoyeW17I0vW5o9vLQMj2UrL9eMxqV3BkvuK5yLvIDdn7mb8La19iEeqLRjePVtyX3v/KKvWzD+WTA6fGQ7ucDeAL6dbvWh+ZpPOt48dcH7H/tjOP+klhaZvs5n0R3SK/kTfgtO4vdjmoL61l/reb24Zw3R1f88Ow2e8dXnJ7cpnexbeMZfxMIxUvD8o2Eq79smB4uGaYN10VO+f0J/Zee8jBG1z6IV9ItPkAJzv+SJHqwZpS2ZHsLXpxMaZcJ6SwIWl43ooPUCubve+7cv+LuYI7xkt244AfSsfl8n8Hz0PZtdhPiRQhjr99PObh1wVd2TunphufFhN03Nny4esCt74ax2HhptuG2pNrR2Ds19w9mDOKGdwbn/HR+zL1vzvni1VsMXhpk69BFGPPVy4r5eynyXsG7++ccZStGcU3/7ZZH7pjxPwlyI5sqomWDrMLvtXmv5YPjE0ZxTU+1XPSXPBrsMX82ZfjCoktxg3ao92LWdyW3H56ym22425tzUo346pdP+KfNr9E/UaSXLcI4hA2YjWYa0b5dMRmFsO/+7SuqLuK81MRLTe9c4lVM5HxoRi4Nq99zHNyZM0pq7vbnTNKScifm570jhs+im5ZpfF1hezGrOwnFfcMHt06pbcRBtuawt+JJtsPVr+0yfGaJl90WZxC4ui9/e8CdB5fsZhsOszVn1YD4geXV9RHTTwJ6IZ61RGt5s1Ezuj/nnd0LBrohkQaH4I8f5pQvhug63Luvg+T+xzNe/I0pd964JNMdk7TkuL/krFdy0R7SPw3PF8l1d4OTqKaS8o7lG3dfMIpqTF+yHM75MLvD6IsQzr+WW0EIvtb3Yby/5s54wU5SkO11XDU9fnzyNtGPJflFG5ANUqBLWL/R472HL7BO8ubwilWXcv/ejD/mIZurIbf+dU03CPxPkwqiwpMeb/j1o5dkquN2Mmducr6x94I/fjsh+uM8TBm0v2zXv978Ei4wm19Lw5rdDNXY7TriMduWXmi6E5qi1hOt/c3P06ULG13bJrzNw+bX5qHmy8cn5LpjHFXsJxuu2h6jD57w9JOHN4zc14dNJHd//RVaOHaSgp5q2Yk3/ObBU/7J/QH6X2mqWynJrENZCyiqPcEb75zyYHCN8ZKeavnLu18wiBueHT1AdVvUzm5AcWTXhtX9mG8/eAzAUbrgq4MX5LLl+8dvoP9V2KyTXdgo0+uW4lbCt+4/wXhJZcOG2zfGzzH3FE8+fkD/bCsUHCV4GdbR3fdn/N7xxyxNRi5bjvfmfFQc80/vjZi/NyC/NOGa+hD6Lb5i+FsPfwFAJCzvZqdYJL//7p/zR3/4O9QTRbqw2wZ9aPlXtw2/ceuEgW4wXrIfrYmkYfzla9zPpiCCGMsmkvzSoktL+n7Jw+kVO0lJTzd8a+8x/8O7x+hKEVXhs9Lp8P3MpILqwPPB4Sm7ScHtZM7GJnzp1ik/e/CQwctwjVQdNkyyq46rL6c3gXAiDfeyK77d+4R/wF/lRe8+8Sp81rZ9tW2rWkBzK1/d3AdvZJfciWa4/Rbxs/B3ey0h81u80tu3Lnh3cM7KZDzMzyGHgW74l/1xwMNsN6NMJnFKYHqev/ngIwqb8EZ2Sec0//Wbf8jfXf0XuB8EzJGXgrYnGH5e02UZX/36E7R0fKn3ih21oXAJv773ks+uhpQHMW1f3ty/xaEmTsPk0H9++8942U6JpGWsSrJBg9NZ2PAsHV1P0jtpmH0p50H2EuMkqy5lrEv6Uc1/dfzP+Xu/+C9phop4HVq9ZotEqB609OOGaVzSOs3tdM5BtKTziq7/y3a/ybfYxUjg5f9HmrZCBGFT63wwrMvXpntFP0556+5d9icDRGSIIkVRhA+0O8f7vHz5knLVkPX3cMaiiNnpj7h79+42TLQ4JbDekfY1J8/PMJcltctQOkNF4K3DiYg333mH6UjTNSWxcPT6Kb/4+DMsnq5pEcRBeKWCtGV/b4933nmHqpzjnWGY3WO2mvOjn3xE3tuhKAp0tL30wvHGg/sc3dlH2xbnPEncY3eyT101dN4FRqRUWOvwJhjD33n/PZR2JKmkKUqyQT+MU9cd13VHCQgtsdIhvOXo1j63Jj1G45SiXJDHA7KHb2CtwJ8s6YjpvAMnMMawO53wzjtvEfk5UoHSNVk2IM8GbH72KavNGiUTkixj3qzZH425f7jP0cEuplpibI2UMe+//RZNteLzZ2ek2fgGD1FWK9566y3u3D5CmBqNRQ0mVHXN0eE+69JSbzqcgaEYYpylsy0P37zF/XvHmLJCWEcWxfTefpsnX3zBi5Mr2q5GCEWke7RtyfHtQ95++wHQ4rsGIQSDfkaaSAynXMw3WOFwIscgmUwH3L9/RC+JkL5G6AxrK6bHB0yXFVVVYYouWMmTBGs84+mEWwcH1Ks5wnnyLDAt33v3IS9evKKuPUpGgQUa58Sx5sG9+5TljDgC6SVJqpAIbh+PmM+vkWjSOMM0Bt05Ht66w3hvTFHPSWVMawxewP7uhB/WJVE2QGqFtC1Yy/HtPfYPdkhii3BBRZBJzWBvlxevTriazdGihzENg36fpl5z5+BNxnkGpkMDKo2RsaYXR/TTlKtFA70Ib2G5WjE4PuberQOqzRKpI4TytFZydPsYrzwvn57THw2xjaXuNqRpStLPUWlMrLfwE+FJE4lU060gR2G7jlgKbNcyHvQ5unNMu5kxSmOEt1gBw/0pF6+uOH/6kuF0SNmFUfNWKnam+/SyIcJZtBbgLYlUjNM1e4M+p+slNpLEWQoN9HsjhqMJAoMWAqU9rjMcHU549uxZYOK1HcYJ2toyGu6yNz1g0E8wpkbQgvOMdoYgBZ89eU6Wj0lUGkRQWpDEGbvTXZIowovAAM2HKVGc8bOPn+IIbFtDQxRHxAKGo4y9vT2atiLyhkgIhuMBV/0Bz6pn6HwXoSSta+iqmqPDA2It8UTkKsJ4g+kcu+MJZ6czlIrwUUrTeKSI6Q0H9AYDhPbEUYzyAus6dJSSDfv0h0NOrxeMBhmqC/zwzgeupncWoSSN6QKX1FZBdigEnRG4qE+3hcVpqaDzSL0d8xISKVt81yG8CEEr4X7Qts803w3iLNeSxH28hK5rGA4mDAYjFssZlbWAobUC01YksaSqDD6NQQu8cMQqJU4SdBxTLNbEcQQoRALIsM41bUuSJ0ip0CohiSXFusK2Ad9i2w4nBdJb8l5G29ZkUuI6Q1Mbsl4fYxxZ1mNjwn0S64RE5VgjaTaWRLfksUdtMQBNsSLPQpva1hZRGbI8osIh05RFUdKL5Y0IUzhFEmV0jQPfBQSMDCOHRVNQtCXOtgx7fWrfYlceYwxp2uP8egZCkCWKsupwxpPGPUCgdUTXOpwMoj4nPV57nNBbFI5HImlwtFpTesNOGmG6ClFUDHWE6lo6K/DOQJKxsS3SWRocm7Zh1Ne01qJ8QlsXaBHTS2KquqXXH7PaLJGRojMNaB8ma4xBqJR6WRF7ha8lEoU1FoEkz2JyL4gaS71Zh2Z5GlNtNlgErm3BGvpJSlVXREqjBdTWcH19SdcN2RkM6MoWG+VsTEPZNlgJztnAdPXgcCyXSxbrknGcIUWE1ZrTixl1Y0lFhI00tbO0pWHQn2CouF4smQxyNnVFlggMQcpwsHdI21mKsgOp0NpTliXzxTWD/IB1sUZJwCnm8yVl2bIzPmTd1IGj7R3j3oDr03PWq4I0i0jyDGfD+2p2cU6iPHWxJMl7eC+RzqFUzHy2IdMZ6ShDSYVDs1g3OCewnUMLSWMtkQ+4jCBIFP+Wvun9xfH/9uHlNrRadyjnfzlGnymKg4jr3274jTefooVjN9nw8eIQgM8+SJh8qMmuDfVE34wcL97Oab5e8LX9M3q6pbIRpYn5ZHiA+3kP1QRRik3kjeBKWs/ymw1funPKos7YzTb8YnqI/md9ZAdRGWShsvOYrZF69o7id7/9I56sdjFe0h0oXjzf5c3/MUiK8ovuRkoDcPpbgr/9zk+Z6JLGa27nC6p7EX+6+gpRKUnWr9t+7kZ48sbvPKUfNRxlS06qEfcHM34Y32bJhNETSC7rsNHShFHl1Zc7/qN3PuU/HP+CHxb3YQAv/uqCH5t3EC4mqtzNqKpuHPOHmv/4W3/OfrQmVw0f6WPuD2Y8nUxZXh4Rn7qbUePR54bZl/skf2nG37v/XWa2z9z0yNSYb377Cf9g89c4+p6lGSlMnuOlIL1sefHXU/7ml79PJCwTXdI7bvl8s4P5ckF1npMsHcIKirs5unLo0nL97Ybf/9qfcd320dKyHGRc/+6cT/7kDfZ/yM1YvckVet2y/nrCN7/1MwA6p4ik5demJ/yLwVsU7ZDpx124rp1F1ZLV/YSvfuMxXxmdkAhDJA0bm/LXbn3GP7y7SzKX6GkcwpZhaEuXx46//8af8Kg6CMHJ4JzSxXz1W9/jf//htwP/Mgk293as6XqCv//B/8VHm2OO0gWJMBz1lmSq48df6xg9VchI0owUuva4NOLNv/KM39n7lJ+s7vAwP+dVM6FziurhBfGsj+1FW3mZQlYdFx9EfOXh5xxlK7S0RMLysHfJfzB+yn//499DPdpKnWpLcZQEic8Ha765/wSASVQw0hWJMCTTinK/z3ge2KzCeZIvrtgcHfH7X/kzHhX7ZKojkpazasDR+0t+7N/g4M8N5a0E2bqACRlHqEFLFnXsZRsSaUDDg/yK9EHHi+QtIPBPzSBBNobiNnzw/hdcVX1+Y+8pkbCsVMZvPXjCf/vo1rGXjwAAIABJREFUb7DzkWVzOyW76gKmY62QbxQ3zbuxLiGDo2zFs+MdZu8m7P1og2wtZhCY9dW+4Kt7Z+H1UUlfNTRO83fe+hH/yw9+G5MKkiZwqdW6pnpjwv03z/nq9BXLLkNv35TfvvMZ/zPfIP0/RjTTOLCUaxcakQPB/hvXHGVLjFP0pOX9/gk7Sckn8suBs9saVNVh+jFdX9EcGP6Tu7/geTVlN96wH6/41vBTfnj7LurPY0ymUDIEhFHRcvlBytvZms4pvtR7xVU3YG5yfvveY/75Jx9sTfeB+eyiiHJPc2eyIJKWo3TBSJdsbEIebzgYrlkfDNn9wYzy/vAGIaPL0I70UkAkiFbm5jwhtHB1YZCdRJVbvEyqAIV/veQ5f8PDdpEMY99J+H+TSTZ3HcYrSgN3sxmdV8zrPX5z9ws+PniL4fOAewGIi4APuZWveL4O7wlUeK8fJ3OyvMWmEdl5Q7edVPDbLKpoY16VI47zJZlqmUQFt/MF51ehPWoydSP/cpGknXjO6wEP+leUNmZpMt7PT6CV1BNJft4GmeUg3grPuPn57/XPGOmSV82Ei6JPOwmMUdl6iATVVJPNDLGyfPfiIe+Nz8llS+cVEk/Ub4mqLUN3i4WR1hPNFB8vDmmd4hs7z3lS77MyGYsuoxkJdn5WhpOdxqRXXeD/DsJ3qHFUctX2abwmwmCsRLswLaIqi6qCZFKvGsoy4Zma8ObxFYsuJxIWe9hg44So2qIvdGiBei3Rm4xH13v0DlpOmjFDXXFV9XGJv9mELPcj+i+bLf99O03jJThNJCyfNLc4iFeBZau3zGgpqHYU1U5Ak7wqRhz3lvzm8AmfVLfY2BRvw/kVhxHp3AbczhbP88nzQ5ZNyu/e+oSLdsjKZPxifkAyDzz011M9Lg4bA71Xij+5eIPf2v+CXLYsnea/Ofk9ynVCNpR0WZjWiAtHeZyiG89Pnt/mneNzfrC6z1v5BXOT0znF9ZcCsuI1fsNkkuzacv2yx2wv509Xb9LTYTrhj2ZfpZpnLN6M6J1thZfA6n7K+HHDJ1f77PULjrIVpYuZUPCP519H9AzJKjBuhQ+bljYSpC80T0dT7vdn9Lbr2/85e5/WKmQrMElg9na5vJmaMcmv/l373+kuhZSazjQIaYmlQDuHdJKu84zHU9I4ozdMSFWMQjPqj0mSLDRueiOyfo/GtSS9lMLUWC1Ikgwto9CwlILYWXYG+0g0qJiEKAQ0xmCcoN/PyVSGrSokgro1THd2GY4meOERkaZzwcRdVAWVs2SDEVVZbzm3EWhFfzAhTfoIGRNnebCZY+hMDRikclhaYhnaQ1EcxELC1cEKr7YLdxTRtQYlMyKp8F1LGidBxKZA0iGFJ8GRRBLtPLZs6euMJO5TrSrCI7AgFjG7o8k2YA3io9C69cRxQpIqVJSFUe84J081SjikaDFtYLs2VUUU54goRWpFUVd0rsYruZUzObyIg/W9BWEdwgnKskQIj+kc0rXEOiHTKVmacrx/F7G1Z+tI0ogKYy0Sge0EXetxhGvvhSDJUowxOOfIkzxIW7qOLE9Jkoy2abCmQggfGkVG4KzCmXANfN2hnUcaxygfYjqo6xIQ2KbFWCjKDh1ldK1HCY1ykrYxW2mW2rKBHUoFkVprOhbzObGOMG2J6QqEqyjKFbFWXF5eIo2mbYKshi0XTClF5RyNF6zqBoNnMBjgnGM9n6FMi7MNSmwlZVHMcDjGOwU4siQncgJTeUzjcJ0j1RnSGHAOazuapiPWEdJZ8ijDGIN1ktYYOhNCBoOjMR1VXVBUFXme008T+khiI+mnQ4SHy+USGceBzewlWEG5Kag2LSpWtJ1FpTEVlpYthyoKwgIhwNsG33RUqwrTBA+OTDzeQi/pkcSadr0OUibv8CSoKGO5LugwuERR1RalBF1VomQUrmEdkBTOhIDUOUNnBZ11gKSf9NAdVFWFFwovJHXbUZmWrrbYztEWDa4ztHWNN6FRn/Z7EMcsyw2NCeuEs2CFZNWWLJcrsjxBCo+wHUoouqbFW4tzjsZ1RLFCI6iLEmchz2IyCZF1xFHgh0njsE3NZr0Ab9BC462jqiqIEhqlcb7GbpuPg0GP9WbJfLOibVsqY7AeWm9ZVyV1W5AkWQiklMLhUBEYZ7DW4juDcRYZR1TdhqqtQ1gsLG1bBzmVkjjltxIxgW9N4LS2DVVncELTmcC07bom7Op7A87TdhVNVdPVTRBJGYWREY2I6LzESokRls52rIsC03W4zmCNwRtPnuS8Oj3BWoulDq1PNKuyIe2NubxaYIylaTpcB03tKap6u84Ei6hSCiE7lPHMizXXmw06z0mTDG88XW2pyw6PRugIQ5BCOO8xKNZFzaZqMZHEqIhWwXy9DmgSF+7ZaCvUaoylbiyL1ZLOeDqjaKzBYonShMv5gkp0wdIdaZxUeAvVYk3XtkipsV5Qdg1FXbEpKpxrAxZBQt3WKAl11TGbF0TRgKpxOCvpaFlUJU3dkUcJeTrCWEEcS3SUcHp5QVnWbNYrtJC0dU2xqVktKyKnUUahrED6wOiW2rOaXWGrhqoxmE5SN4bZYo3pQsNWpwllXZHrmEGvT7XaMLs4od5UOOPZ1BWts1zOZ+AD3qipCrxvmPQyqtWaq/OLwI7XMXVTYK1ldr1k3RSBpa4kKtF4C23nKWpHVYbNlE3V4pDUVrBYl+hsSNMFdANS0TqBzgZcrSusili1NZ0WYT1clSgU0ob1wkqQkcQJh0KyXK4pbUOHo6pbuqoO64YPa72UBHGEcNRVy/nZFXgNKIpqQ7Fac3ZyjunAdo4ouC1QNgKrmV9uWBUdZWVojaBtoCpbVvMNbefw1t003BEWpSLm8zVKxtR1S1l11EXHuuxYVZZ8MKTqDB0dTkWUxlK3jkVVsShLrIOiqGirjsV8xWpdBUmhUnjpIfY4rQkazb84/v9weLWVu0ix5UjKm1Bgc1vQH1b82uAVb/YuGemK39r9nKPekmjYBp7lJMhfuoEmO29QrWfQrxhFoXl4mK44SNf8+t0XN4FCOwgj/6oOD+7lnmQ8Kii6mEhZTjYjvnH7BcWt8GDjVWjBNENFdt6SXnd0I8+T1S5KOnbSUNoY7G1oJtG/welUjbsZOc5Vy8YmHMdzbidzduMNJg/NH6e2TFhCE0YXhn7UsJsUGKe4l89Cu6xX4lR4wO3GCd0oCcFD6xjsbbidzvm4PiJXLX3VcJiusA8qdBOaT7J1NyZ4k8P99IqRLiltGKV8v39CprttSyoEOMna3XBgtXL8qLjPRTu8CUg3Ntnya+VNk8lLiC7WeAnLLiNXLbfja97rnfCgf81/+u6PQzNqy2eNV0H25iKJbxWX7YBm+2DfOcXtfIFqwkN2tRuF0c6NodlN6fqe82pAYWIiaempEPZYKxEmmNdft/y8EmyOBcs240mxB8DGply3fX6+OsInDl1tjdxbbqWqLC63PK13AeirhtLFnNVDvnv5kK4XGmW6CgKw9Lyh6wn+0csPSKThpB7/G/e8iG1ogS4aei/rcA/ezmms5vuL+wyjmtIm9FXD3WzGOK0o7uZBIge00xjbi1EtrNuExmneys7Jt+e9sSl6E87bK6h3I3QT+JptFXHZDliYnHnX43k15VG5TzPLqHcE1a2U6lYaQqnjKV4K/ujFl8lUx9okbJXUPFtOiBZBdBVtwjh8dLUJgboTZDoEvFrasKnhFc9XE3QTGq2vcSb1QYbpea6qPoe9FVdtn84rhrriu9dvbfmpjnRutqG4wyWKKLJ8vt6hc4rSxUg8jdP4SqODz5bqqIfNQuMWF8Ki55sJTza7XLYDVibjvB0iDESFuwlzumkY/X92usNJNWLZplQ2Ym0Svjd/i5dnE5ppfBNsvg7rhYPOSk6qEXvxmoku+aLao7IR1VTSTCNMT2MGcfBBKBBG8uHiNgATXdI5zcf1EUCQ95nQWnWJpjrukZ85vn9yl8uqz8fFERubcFYP+cn1EfFK0I5CKcvFcnudYdPFFCamc4qrbkAiDZ9uDjhfDUjWDtuLb2SLAf/ibwJafNhAixcd8XWFMH7L3XYhsJW/lFdF645oZVBlG2SN2ymKaG3QZZDhqTpsHKUXknWbUNuIlcnovOKt4SX/8JOvI7oQWla3UmyuyV8UNCPFnz69T9FGnJZDGqd5Xk35yeoOxTzbrjli+zcJgdzguef82ZRhXPNGdkXnFRftkKeb6U2bOKBfgndDVZb0QvD59Q7LLmPR5ZzVQ/7Z5ZfQyy2z2npsLwTD0aIhXnk+O93nusm5bAc8rXcpbMIoqRk+hnRub1jp0oTG8sWyz6pOiKVhZTI+2hzz8fIAc5WG16bhPJKlDdzvleDRy30mScmTzS6li7lqe3x4dky89jS7SWgL16FhblNF+lHG0+WUn86PGUUVJ/WYn6zusDgbBEbzVtbmtdiKFiX+LKXpIp5sdll2Kc/KKdHzJGwq6dDcTq9CM7k4SoiXsHw+4vlmQuM0Hy5us6hSho+3DextSCicZ/lGQrpwfPj0DpWNGEclj8sD1i7jn5+9Q34WWLYQGLD48J7MrhznywGrLuWHm/tUNuZJsUf8KsYmkKzcjbguKrbs6Ccpdaf5s9l9LtsBhY2Zb3JsFJq40oTP/detaRsLTk4nfLo+4KIb8qjcZ17nsIhv5I+vsSMAybwj+SS7+fdP18d8tLzFv3j0dhAlblm5LgphKkD/heTJxS6PV7uc1UPO6iFP11Pic01UbIVm7pdIGhdLNi+G9KKW5+WEz1b7fFHt8ZPrI9RZQjWVN4gdaT3ZtSFaQ/2qz5+d3aWwCT9fHXFZ9fno9Bb5mb+5D1+Hw9Ky5b7/aof6zne+8yu/+N/28Qf/6z/+jncWGVrEeOtxOJI8RwK70zE6DqPanbWUTUVRrinLmouzaxoHAkVbt3gh0Fqzt79PngrwBiE66srRNI7FqmC+qujsVkOHwHtBUW44vntIJCRSBjv19WzBxcUlXeewaLyXNE1LL+ujffgQnOyMcVpQOY9tWs4vL5itCjoSmjagAVxn0GjyLA/nIsLOatcZ6qbi/OyMoqrZNAYho61qLtiqx4MEpS1aCUzd0nUds8Wai6sZddOilMJWFZEMQqnJeESSpAhp8KLDCsGqWHN+cUVZOlrrUVGKbVoksL+3ixZgbYGIHOuypu4a5rMZF2dXRHFG4wUiijAIWmvY391DSIuKFO2We7tYLlmtlmzKDiFTGmPwSRD1DCdTsjQhjhRGGKzpsLbh+dNXFJuatnbUTU3bNvSyAXVREaUJWZYDjta2CK04v7jg7PKSqu4QscYruUULCGxVMRoO0RFoKSmbhqIsmW8Krq5XFHVNHGcIIWiaFqkVWZySZQlKakzradoanOfx509Yb2qciFE6omobpFKMRiOUsEQaLBXOQ123XF2tWa7XrDYVVlqypE9rHTjY3RkEuZPQtM7iDRSLguvrBdeLJQq1bSRKamPYmQ7JYk3kBRaH8ZamNVxcX3JxsUTqBGND8ztLEkxryPKYJFE3v2vVtVzOVzx7cRJkTmmPqm5BSGxjwoZHniK1RkhoTLgvTs/OmV0v6Zyk7cA7hRMKYy370ynggiUST9sZrmYzmnVDVTVY50FKsiylripGvRxvDUqCbVq8txSritPLa66XJbU1OOtRXuOcx7Y10/EEhUV4gfOesm4p65affvKEzoGPEloHXWvRQhHpIINrqwodxbRdx2K14fnLMy4Xc3SchEZo1eCdIVaKWCvSNEUK8MbQtR1Pn59xcXGN84IkjuiEpxPB0qppSSNJGkU44/AeZtfXXM4KNnVL5VwYfXCeJE5oq5I8T8nShG4bLLad4/x6wauTc6wXGAHC2oCssBahFYP+CFDgHMY41mXDy7MZi6JCJBngcCZstlgrSaMEHSeoSFDVDWXd8Or0iqtZQVG1NLYBAb1hzvXlOVkSkyUJYIOMsa1ZrGpOzi6ZLQpklIJQtN5hVPgQd1JgpcQLQT/JSWSErVqiKMEjkFIGRIDtgiVUSAQSKS1xlGCMR8YpnXchrJQuSBNdGN2u24JBv4+Qeos7geVqw+XlgsuLK3CBf611Ak5hTVALexm44ipOKMqKs7MZp2cXbIqaOEnpnMULi/Ge5XJJomLiPKGqWzSS5XrDydkZq6IK47Fbe3jnPN55mq4jiTXeeTpjsdZydV3w5IsXGK/x1geRohKkcUxTtWS9FCUkFig3NZvS8uyLV6w2FSiNlAG54Y3Fmg5nA/6gsR2d91St4fJywfMXJzgPHRalI3QS0bUtbdfhvMcLQaRiuk3NalNydnZNVVZ4EdOZFhVJpFREccpyvSHr9ZBaUlQbnIm4nC14dXZK0TZYHdPZjjgSZChU52g2Bc46hIupKs+zk3POrxd4Iemkp7UGKSTWbrmL1jG7WqJ1zGZTM18tOD07Z7Fc0trweq0VUgSxYdM0tJ0jiVOsUxSF5fx6znpZcHW9xEqBcRYvBLFUWOHZNAXOe5rOkGYDVuuKy6slL1+dY72kLCsirTDGILaz/nVVU5YNWdajKg3z2Yovnr9gvlxjnUdJSde2KKURIoj3iqqiPxyx2XQURc3TZ+dcLjcUTeDsmtbStg3WOfrDIVVRYKwh7/dZbiourhacXFxSdg2dBBspGteGB0YtKdsWnENFCUIozq8uubie8ersIshQxTZQwqN04HFb7zDWBgloY1ltKp48fcViXbMuS3SS0HUuiCmUYtO0bJoGJwRlZxACzq8XvDw9ozWOGk8TSZQL4bNpLUIK/s7f/lu/ktH2L45/v4//7h/9398J5ukQ1gofHpK6gQ5j7Q9a0tQwiUou2iGfrg94PN+lmPUYfSpJVsF+HW0CK9OlitlbgtvjRfhMAJ4XEy7KPtWrPsPnlqj0r79qI2xoxVx/TdBPWyLpKLuYT58fMvxUEVWg2vD6ZGmo9mNspvBS0t7paKzm1XJM1UU0n4wYfWGDQGppkFtWr00V9VQzeLBmGpdo4bjohjwrp1x9ukt25emdtsGWzmtbveTTe32IYJJUXLd9Zm3Opy8O6X+mSFeB96gah80lUWE5u9/DDkFLT21jNjblR1e3qT4Zk8wh3oZrsg0yq+Xbkpf5gLnrIST8YHaPz4s9Pnt5wOBThckkydIEa3ptiUo4f1fRKE0/bjmpQ6D1/fN7mPOM7CKwSpOFQfgQ4hR3cl6NU8Z5zbUdsLYZhU34337yNaJZeGBNlha97qj3A2+27aWcTBIMivN6SKQs3330FoOPFeksfOe7aROmEmEk50eaNDYM4oZHqz2eLHYoXgwZPYH8osP0dLhPlg0uiXk1zhiPS3pRy8LkvCgnANTf26F/am4ecNPLGh8p1rdjXqQ9BmnLymRUNuZVMeaLzw/ITiT9ly2ydTQ7MdGqQ0jNxZuSVmoGccOreozxis8We5RPh4w+9/hYhQd1JYjnLU/eGjAaVkzjkrVNmXc9XpQTfvL4LqPPZBiRr20QIO0kqEbwcpSj+4Yksjwu9ni03ud7Tx+y86eS/KzF9BXJzAAh/CyHKVdjjVYOrTyvyjFXdZ/6ozGjL8K4bbw2v5SF1Z7T9yIqqXFe0jjNVdXn4otd9EaQLMM9KDy0uxnxvGFxu8elzyCGVZdhveSTxQGXH+0xfuxI5h3SeHy0DbKsZnPPEesgr6xszCerA14uxxz8YeDYCgfRukUXHS7VLHo92Ou4rPsMkiYEl6fH5D/M2PlFjUvVFkUQQiPTj3jUHzIaVkTK0XnFVdPnT77/LpNPPaqDdqCICocuWgSCOs+4GMYcDtY0VmO95CcvbxM9zsmuPMmsQ7Adfx+Ee/lymhGNWs6bIZWLOa2G/OjRPXY/DHLD18E+IvA+rYpYHAiskDgpmXU9fnR9h+bDCYMX9mZNkMYjLOjWMxvmrFKFThxPNzs8X0xY/XSX8SNPft4SrRoEgnI/Zvyo4tV0QjdyWCkpXcKzcsrPTo/wPx8y/MKGRiygN+ZGNig7d9P6L/cjuoFGt4FBLm3423kt0esmYMuUBCHwWmD6MaoM4kZVtjdoFq/kzbkka3h+mNNIyazLWXUZP7s4wj4eMP7Mh+JZEYz3iDDd0MqMlU8olULHjs/nOzz+7JjRzyN65xbTC0xeaT26dvRelDSjnJe9jEfFHmls+OHFHV59dEjvFJT5ZTitNy3CeeJCMu9nvPB9rpoemy7h8Re3uPU90HVgFyPC+tONY5K1Yz1OuI5ili7l8+UOj6/3WP5gn95JOI/XqAfpwj1Qu5xlEvG0nHDZ9nm5HnP2ZJfdH0ikBV2Gtd0mgrhw6FJgbcQLBsjMcdX0eXS+j/94QLIIn5lR6W4wHSZX9F9ZTiZ9NkrxeL5H4WN+8fkxo5/G5NcO1XqiwtD1NfVE42ONLsL9cSYzTpcjrr5/i8nHPrR9S0e8sfhIotqwBksDg6eCl+mAz692mb2YEH2UM3xhSdY24BcyuZ0OEMRrh68Sng5yrkyfx4td/uXn77C8GDB6HO5tXXukD7Iz1QZWeOl6nBQjuqHnF5eHPPn8Fjs/Doii7KrD5grVeLYDtMQrqBY9rseKl+sxj18d0PvTHsNnFtV6XLJl7W6vV1R69Exzuhfz0fUtns+mbP58j9Fj6F0YXBSEZNHGohpPeRCTLD2v2jHd1PHobJ/LR7sMfxqTzRxx4cP1XVukC+FoOnNUMuM6jqmF5tGjI8pHI/Y+dETV69A2XGsXBQEnRvOyGdFksGkSPj0/wP7rKf0X0D81mDT8Xq+RPPmFJb2SLF3Op8t9Cqm5+nSX/ocJURkmjOKVueFN68qDEPzd/+yv/Erftf+dDm3/pz/4g+9459BaBq6idzghaK0lTxPyLCaKQqvL47BY4jjm6nJOUVQIr2isI81zmqYjSROiOA4MR++pqoo4y3h+dsL1aknTgieEDMa6sGBFGgT005xIR5jWc3J6wWKxwjhPa0EqjdWatm7BGESkSfOYKNYY61gvFpxfXdG0FkSERKCkQwiJM55IR/SyjEhKsJ6iq1mXFY+evEDKFCsUxlZEscR1YLqO3cmUwSBHIjCmozGWk4sLFkWJl2HsFSRua3jXWhNHkjxJsYBpLYvZksX1iqLs6BwhrPMQ6cDP0doRaYFFEsmU6+tr1mXDvC4ojd0KqSzgyCJNnmjSWKNUCLzrpmWzKTk7vaRsLVbEeCnxuNAmQ5EmGXEUxv2rtqHuDOcXC87Pw4O30+AkeBRZllNWNUmS0u9lOO/YFCXXV0vWmxLjJEVtqJuOWMREUcKmqunlQ3QcYN9t03A9W3JxNWPdGqyQgVe6bdQVTU2apUSpAiFw1lN1JavNkuevXqFVStMRzkMIILSt9sdjhHP8P+y9ya9l25be9RtjFmutvU8RceMW+Yp0IWMLBLKVPQR0sESPwkY22DQMAv4D+ANeD/4EhJGQQTJyBxsh0aVjYQuZTEza6cyX+JW3iuJEnGrvvdaaFY0x14r7etlMkXmkq7hxYhdzzWLMOb/xje/LqZILPD3PPD4ufPX1a+J4CwIJaC5yWi7c3n6CNiWOkXW9UBq8fn/PN9++57IWRIxF7LwnVQPwHIKKkGulFuHdhwdev33H6bSSEiBCmAKXJbHMibIWXry8ZZ4zTYSn88LT08rXr98xTTc8nxbcYSTVhjZHDJ4QTSM1t8S8rDw9X/jqzWvmtZLFISEYu9kp6fLM55+96kxWR0qZ83nlw/tH3t0/UJuQ1hVpgmKb5WG6QZswDgOlCQ3Hw8OF9/f3PJ8WSjOdy7Rkrg5HcsuIKtNkEiTSgazXr++4fzwzTUdKE1QazjvWlnieFz599YrSGqUUlnXm6eGZL9+8IR6O5NzIcyXGkeAjl1K4ur7l+XwhDCPrvPJ4uvDth/c8nVdShUKy2NDAl0Y+nxmGiSrgw8Dzeeb9/SNv3n7gtKY+nyDogEhDfOB0nnnx4rYb/SnfvHvH/f0T7+8fybUgzhPVgPpEY54TglBSIRV4OM3c3T/zO7//E47HG9bFzPZimKAFyrIyOWNfX9aV+bLydJ75+pvXPD5YMsHCmaKtsmSTnKE1gvPMy0LKja++es3D4zOPlwVVj+KgNqLzZkTmPWvOeHWIKs9Pz4RxYJ7tkKreNDVDCLRmG5L3HqmKiKBeyGkFtRL06DzSTN92iAOtQmtKLuD8yHxKfPjwxOPDhceHC94NVFG8c5SSubqeeHo+U6vDucDpvLLMmfd3H3h4PDGnhAuxg35myFXmREmFIR6Q4rhcLrx+c8fbuwfWKtSmgDNKpAgqzsDXvJJWWFNhPq+8efOe0yUhaomq2qzUvJZGaZbRP6cz4jzvH594//TA3d0dj5cTKVccQm2V0iphCFSByzpze/uSpSSezmd+9uU3pCac1hUXBtY1k3OlodCU82WhNcf5vHL/fOL+4czT88q8JtalEEc1ffhSQLyB8KkYe7cEvv72Ax8eTtxfLqRm7GvUKhJAaTjWpjw+nHk+X7i7v+f9w4k1m/ZjzQ3EgfMgxhBGAs0pp/OFh9OZp8dn7t4/EMcjRcSaQcGrI6fMOB24zIm0Nu4fHkk18/rtB16/vSMHRyqF0ho+Rs5pYS4zwzhwmmceHp/55Zff8M2bdzycLqQud+yc4r0i3lFppGJrbF4XM6c7nXk6nXh/OtGcZymZXE3fODgzL21ZaK3y/sMHTufCN9+84/F5Zm4LVRspL1a1MwRCNOZrKXBZEnd39zw9P/HNm7ekZCA/3jPXRKuF6DzaEwIpJ968fce6FL789k2vCBhonVNVKzRp5Grs9pQzT49PzJeF5/OFh8vM3f0T81pR56mlUkVpZFQdNTVKrTw9nzifF968fseHxxOn80pzgdzdlJuatEipSqXw1//9v/zHoO0fgZ+/9T/8/R9pMc1Xt1rCUZM5y8+vAg8vHGWCt+s1uSmnNPByunD3k08Y39m8iU+F5aW3Mt5bz+kQefXr94ykaHN0AAAgAElEQVQu89OnV3w6nvjd3/qTXP8Uhse2A2X+bABKPjjOOrF8VrgeVk4pwG/fcPNLY/+E52wmOotd3AShOuXd7YBOmWUOLG8P3P6umMyBQOvu3IiVfKaj4+efToyjJYD+6cP3+PHbz/nkf/emOZrAzcYUkmIg6d2fDXz+2QODy9yvB7493ZB/+5abXxhI5s9bf5kpThoD31wNfP/FA4955GEd+X+/+oLhq8DhrQFyZjCkdpl2yts4olPlKY+8GC789s9/gL6JXP8cDu8y/lIMkAHmTzzPLz3cJMaQSdXx9fMtd++uuflnjsNdsVLjYv/55xXiwP3NwOVKeH254adPr/jq+Zb2u9e8+p2MW/vrc0OasHwSGN833l1PTJ9eWIrnZ+9eIT87cPjWDFemby/E+5VyDOSjaQd/+OzA4+C4tMD9ZeLxZy+5/bFy9U3GrdXmlhqz6vDtzOXlgW+ngZMMnPLAVw+3/PL1J3z6j4Tcmb/SL9zSgBqY/6yd2z+sBx7WiV+8/QT/7cDnv5XIVx7BwACpMH574u7Xb7j3gavDytvzFecc+fLHX/Dynyrj+7InDerg8KdEike+igfm4Hm/HjiXyD/5+Q84/t7A4a0xFKVBPniGu9mcwMvAh18Xfnl6wVoDX757SXs98tlvJZZXkcNXF1p0phHtlXTl+TBFTs7zIR1483TN27c3vPzHpovpLya1IQ3STSCcMnf/UmQR5VI8D/PEuw/XDL+I3P5zkxYJz2UH7Jp3XD71ZK/odeKSIql6vvrmE44/DxzeGBiYr0y3+vKFaRQ/u5HLi8Y5RxD49umay+++4PqXleUTYwq34KiDxz8tHN4p3/wLgUsKPOSRd89XnN8c+fz/quQrj59r19P0lIM92/Pnkfu7K86T8P5y5JevP+H6dwNX35im55acQZU6ONyiPFxHvi1H4lD46btX1F8cuf0xHF+v+/jlg9sZp8Od8u6Hyv154lwjP/9n32P60nP7k2SArZq+cvMWf4ZHeHIH3uUDXy/X/OT1Zzz/9JY//b88c/m1wYCfVEnXwaq2lsr8SeAyOd48XHP/dCC9OfDq/xGuvjKAdGOU44V4v5IPA+85cDkI3zzfcj8fKL/9gsPXrZeuGwCLCOnaG9DoFf+8UkcPTjh8u1D6vMMZQzPcz5RjREq1O2u/u6Ji8fJsoLbkSrm2ca1BWW89mqG0wDyPPAfH+6cj5ZcHDt8q118ZIKXF2Pt+rpy/iBxfF1r1XELg7Ydr5ueB4y88V19VhodMC7InEPLBZEFc8TxfRc7O8c3jLfn3brj9fSGeGprYk2SC9PmlaFLKJXI5Dzw/Ttz82HN4ayzk8Fx2XXBNrZfze9r9wHsZyD+/Qn8xcvuTyvUvF8LJzBy1A8RlclCFcO+Yf1A4Pw8sbw58/g+FcGlMr9dd291frCJCEMYPjdoCjx+OPH19jf9yYHoD42MlPBvDVkvbQXcAd3HwbmDNkfLb1xx+6bj9eTajrw5W2/NLB6wrx28b+jYy/SRw+7NKOFVc3uIzO0vTrUYUkQbDe+XwtTK9EV7880w6GKBqmvFC6ZJCy41jvK+Uy8Dy5ZF5Hrj6vcDL3wG/WFWHW6yPpneZ4T6Trz2Hd5XjN0L5zSvGfxKZvnGES8Ulm6dlVFB2aQ9UGB4a17/pqXcjw2vPi58ktEB4ylbREZU8mQGmVpAihJ9H2vuBq38auPq6Mt4XpNieU4OSrh35oAwPFb824r1w+dk1+jby6T+phLNprUszNq7UZhUqPRntL8LN7yqPlys++037O40O7BpDV6olB/KkhHNjei1cng6ktxP+68iLnxTG+9KBXXufVGhe0GLjcnzdiO8d4ceD7eNvS/9+qyaQTR5jMRb4f/I3/o0/0FlbWvvD6xD87/61v95UFec865Jxoswt43sJ8RevXvCD73/GzZXSfON0OVPmxC+/fMvzulASdnkUyKnRmvDJp5/zw1+7NYAxwP1l5qc/+wV3d3c4HUybU4S0ZrSLA7+4vkY0cRgOXFJlvpxYlouVsJZCA4IbyP3C7iqMUwAqOWeez0/EQ6QUT8PRXEXKSqsOLyPODVxdjfzg+weCVC5z5t37e+4fziRqZ64p65xwzuFdxCH84Nc+ZRo9KT8zL4lffv0BmmehEJxHcnfa66DJ1SHyxWef40JmeUo8vntv5ZtFaC6w5sQYIy2tTMFYYD/4wQ+4ffmC8/mZh8f3PD08cXdaEPW4aWQ+n1EqY4hcTQc+e/mC26uJEAJ3H97z1ddf8/D8xHR4QdFKa4EmM0HNpG3wE59975YpKK0U7u8feHq8UNbGonbRNW09xWnAd8bUzfXEEIxx9+HDB8Q7cq5dREfRwdFa47LMvLh+xTQIN9cHPrx7z5wL52VGgmPNFVftgg7COE3My4Vf//4XvLyeqOvC6XTicrlwd38xCQnnrFTXOVIxJtNN9Hz/czMpKgin04n379+b43ltzIttykOcmNeV62HiOB344tMDBzXDn6/e3JFS4SklqJWmjaBW2j16Mx37tU8/Y7weWOeFuzdvmS8Xag2oBmoBYrU/nWNwwjQNvHh5Q62V+4cnLpcFqnA6nxnGQFoLIURjeWllGgLHaeQ4DTydTzw+PXFeVnKuuDhQixDEQU644InB8/LmmlYzwxC4fz7x7v3dXl6gCjlnvDcw2LmA1MYPvvicIMaCu7u741xmcmmoekIwZrDzHqcQned4iMaEVTPuWpaFOTfGceT5dGKINl/FTnschpFPb1+hON6++RrRxpIai1TqWtEQOlvTkckcDyOtZI7TgK/wfLpwrlgMaI3WCrk0fPNQhcPR1vd4mIgx8vDhnuiVDw9PMHjGOLCcF7wacNmkWun34DkeTZJiPp070GX9oKrklAgh4F0kFxNNFwLqrGpARGllNVPGGI3ZiOtM/wOtrAzRU3NhOBw5r4l5nnExmpRDS6hvpFQY4pWZEnnHIZoh0el0oohnnReQQFHp7VsZfKCtmeqE1DIeK71aWsF7T2kOTwNt0Bp0CRNjCZsG9Hw+cX19zfl8Rr0jNYitkHMlxmjlk6UgXvAxIDi8H7iczgiOVgpK202SRIRcCs5bnCulWBwOgeA9OVWWlEACMQiNlZwq3jkolSGMZlyokHvd7tpMykLUMScDyQCCCtM44FplnS9m1He4Zc551+6qKeOdo2FGcOIdziveWxImpUSrF1sTbbQYMi+o2ndHb/Ie83JiSStXVwdys+ecl2TGWxWkCnldGA8TtVZSKmhQNHjSvBDFkWuh9tOdiJlUiFdKsz2llIJ6R6szVR3eTUgVSl7x/TtbFNY147F4h9ozhBBo1SRppEu7CI5UK0RPSqtpfqeFGCOhCa0VluViF5GeIKTYGquitCqgJs8j2lDxJluSngniCS2SWyNpJqKUNSFRqbVydbwhlxXJsJRMEzXJiprx0dZXcAq54A+jJZSWTAyBkhtNAEwiSMShvd+0msOtOOs3JwaA12osVu9MJqT1BMbaEpSK18A0jiynBeccIQTyvJo+M5lUVgKK0wHvA0vNhCGwLBfEBTvwOk9aEtr7HG24GNBcUQloMDY33tEoZAouQ3AeWqEWi71hiCCmQZ2KMZsjSspggkor6hypFmoTRDyhVYYx8Hf+5n/zxxoJfwR+/s1/679uGxMnPGfK4Ijv5x14ePevDDz++ZVPv3ikNvjw/oq2Ol7+pl2kXXc3N2Muiwn3f2bg/W9U9OXC7fWZ91+94LN/4Hjx4zN1cLszuj8Ziwbg8U8OIDC/EoYPjeGxEh+LyQQs9rm5u6CXUU3r9cbe65bG8duVdOX7xcsYts1JZ9ra60+fKx/+QoGxoPeB658oN7/MVvLtrU1bH5TRSivf/oaSbyt6EcKj8sU/Mof2+JC6ZIHspb5lFE6fO+7/xUYLDf+sTN8K07vK4a0xkf3TyvrJaKXpLz3Vw91fEOr3Z9qHyPS14/C68eL3Z2Pkd9f4Ghzry8j5c8/9n4P0vZUwZvjZgU//78bVLy6sn8TdfGzrLykmR/HhzznSbYMKx6+E47fFnLUfzJBr0/8tQ7+EA8/fc6RrcBe4/bnp2O5l2xjwLrWZ+dyfmlheCOfvwdUvYHioTO/MeMw/rdTB7/vF+iIQ7xPf/qsHTj80YGS4U4b7xot/nnYJCbAyVXdK1MHz/MOBuz/fz5cJhjvhxU8yZRD8uRI/rEippBcD8f3M+YcHzp/aeJSXGX8XePWPzQjv8OV5n7f5ygCt5aWdUd/9y5503fBn4eanrYMHH+cTsJvcrDeO+YVy+qEgGa6+tNdDl2m4jfhTJh+9lY47mF84Lp8Ky6vG9EY4fm195WYDmCQb2ABWTrxeKU9/UrvZVOP4Jbz88bK3pXlB50Id3S4XgMCb3wjkQ8PN9hzTnSUBTONUdxOvMijpqFw+VdZbkw24+Vm1Ndh1cqdvzqTbwUq5+5nn8srz+GeE6hu3vw+IscnjY9k1Sv0p77q+ly8Gc5n/1FEG4fCmML1LewwwWYCyz8XLp1YGv9wK+ShcfVlIR+XmZ0ufR2a8lidjTJsRn+mknj/z+NlY5NuPm8380M2FfPSUQffS7qY2NtufUky7eYspTW3Nb8+wXDs0N9ZrYXpfGTb5iEtBsjHp3Sl13VUza7p84uz17zam5Ucd7a196213sE8mf7CNaXhYKFNAl0w5hF8N4n2n1rmQbuM+98e3696OfB1xcyFdh102pjmbK81BHpTxQ97jWfWyf25Ti8GbHIWmj+OUD4qbK/HB4mK6DrssQ5ncvkbc0sij7CX44TlbPzkhvD9TR3umcgzMr4LJEjxXwlPm9L3I8FB2rXEDbRXax+eoQXYQ0J/rr7ShBgPot3mWrhylyx+Eh5XL90aLa4LN99uw96ubK8tLj+b2K/uEPxfStcPNbY9VzckuTWP9pbjFvlfKx6qP7d/LqHt/+5P15yYD4M/F4nc1hve25hB2uZ99fq0GGqeDdqZr2fe9vdw/yL43WmPpIL7pysd728+28fRnY05v5oCaKqdfC/jFzszx6Tvn/y3pO1dLoGBGacZarqQrNS3b/vrtMze5pK0NzYGubde63+JbGfVjPzgIT2X//eUTz/BkMhbpoJ2NbczkrQ/LoL+iJxuf6t6f67WZrtkcqXt7apBdBml4LHuf+UuhDG43AdTUtZn735vKrkm9XhkT18bT5o4/W2xsKuSjZ71W/sHf+S//QGftP9Sg7b/91//j5kSMqSYOj12cmlNahlYLL26uCbqgXnj/4Z1NLIlIFZZirJONceq8XeqDCuM0kBbTPNwYYXbpNx1W5xzzPDMNI1ARLZgEcLRy1lZxvpHWQgzmgO3iwHg40HIld0AIQKXgY+B8zrQmeCc4p6Rc9steTguuFabgKXm1ssdcKS3TqocWac2IUriMejMQUXUcwsDj4yOZhA+BXDtrzQWWZM7hrTVqy0QxQOhyPjMdR3NUV8GFyGVZca0xOMW1CsNgF/QY0VpoxZhyUjxrWZnLQvbgRIleDYjQQPSBoA5aI6VEyhl8ptSEMBjbrghRHEnAjQ6tjZxXRBy1a6+uQPQjKZlGlGlSug7kKjmvqAiTeFo1g6SUF2qttOit5FqFkoVhCLvmYAyBeT7jEBJ2wfZNTL/RVc6XhIsDGuJ+QW+tIZjLvRNlXWczflPTAlxKZfAO7z2uFLsAV1AXOM8LFZimiaeHB168eEFLFWnZmGDFNsk5F5Y2IawMzTKVlUZpwtV0ZE4r4kwUvbWGYkBVxcrzxQUup0eurq6MWUc2YGoYyKnh1ea0CwZo07pJmQjiHWsHqNaSGZxla1XVvleEKmIJh1xxrRoopR5XGlOIxMORp+cTpQmFDPR2+kZeDdgjNQOemtHhQgjUCqmawVgTIBXLIhte2DOwxsj0GjgnO6ylnHHOwCla2fVsa0kMfti1IKmNWjMhDJ1t6bhohVINbPYGgr+4vWaeZzzWB5ujo9QOcrWKogb6abO2l2Li9pcL14eJLI22ZhRBayNJo2CGiqU0vBtItTAM9mcqpkHdcjFpi3EkrYVx8uRcWefG4XBgrSu+NoI3WZWmtp6rBgqCuM6OF8H1Eg+caZIuxeQHmgjLMptUSBhY0mYUxp7YeX5+5vp4Q16zgVn9AGJiZMUYsd6hqrSa0apkaUgIPD3P3BwGSgegawfBVBWvjlIS4zBwPj8zBpMrsPlWGUKkLgmcgXAhOgPVCayo6VfXTFBhXWbUVdPTnWec2Fq3BF8H6EIgdUBwXTNj8NRaqFgsLmtidIG5a/puwKyqzbtaq1VndMPIDSSvzUwvtYgpg4uwrJkQHYWCqmc+F3DW5yFYZcVlmbm5ueG0zEhLFpv6GcAcfV0HDjHgtWSQ3s+tUFsil5nDcMs8Lwx+RLQh0gHd3CVxWsPVguSKeodg8bQKOITWGuMUOZ/PxBiZ04oLUNWs4MpacE0IfdyzVpY1E9VZnFTr30bB937a+tg5ZzqrLRm4WSrjYTB5AoxN7DSY/rUIaV4YgwEerbPiQ4/vITia99CUlCxDHbA+StLIxQDs1GOBiBk5jt5Zwse5vlcJ6mAtGeeENSeqOpyPZlC6mSH0wTAgOxCcVfeEEMi50FoGsRiyPXPJrct6mPb6vCyMhwEp1kc5Z4La3GytUftYNemyiM4Ooa01phg5zxfiOHBZL/gQiOqgFZa5AMJSMuNhopWKV6H1tg8aeSorzguhQc32LENwzGnFh0Artj5qrbQqaBBatfMQanvcOAymv60ObYU4jvzt/+6//WPQ9o/Az1/8i/9Vq1H3y2YZO5DRNd6kNh7/RCBPQvNw+9OMFHbtwg1YhY/ac5uz+XxrFyIzMHL7RVFT14nsgOTyqenT7RdTtnLktutV5qNjeDtTB8/6Iuwg8QYw1iDkQZnedYbN5KjRLlAb4OYuBorNn3hcMpMav7T9eWHTtbP/b046ixbOn3quv1x3sMLNpbN/POEpk4/2fJob6cqRB2H8UMxZ/ENnhw3K8M6eId343U3an+3SV/uZp3X2e3wuxPuEzokaPfk67EBEunKkqWtZXtp+4dxM0UyCRUhHJZztu6szFtFm6qap4Z8S+dpYl2AgaVPT3i2T241y5lcGJpUghPNHxtDWV5uLOBjLKV154r2Bghv4lCe3AyzjW2O0bdqqWwm30QytHf4pUQe3tyvcL6QXgwHLHajVbADT+OaC5Mr6amL48oHzn3m5t8/ACxvb4UPCPy7Garwd+hhXdK2cvzcwfMh73xkj3Mbiu887vJu5fH/qbCnr9/XGmXHtpDuAt4FMTe25WpcRyZMjPJm51DZ3473JYEhuexLApDTqDt7PL81g7fhtxi3WZpuzplHrzmkH9NL1R6/xdLC7w8ak24Eb2Pt8mzPb3J3ubD74k5Xz6mx7Sx1c12u18ZbCd8AbdtBHKgzvurhta+SbwUzr/vRxNyIMz3lfdxsLdHvej6Cg28Gb4Z2BkdvnN8cOtmyJB7cU8uQ6qOaNwXvOuymXO62srybcObO+jPhLwT+tzJ9PhKdMmQyQ0dT29bzNj62PNnBtB528EB/LDsCF+4VyCJTRER7sDrsD6qMyfnvm8v3jPoZbkmUfEpVd61RKH9vZmOHxy3uWP/HS2uMFyQ1dMi2YlqqulfVFYHi32POdrR/Dk4GRW7JBkwFs/lz2+bglJfLRE54MTC+jJfHq4Pdk2wZOb++vwdbq+jLu/ZUnMy9bXnqGD3mPJ1t/bcB0eFhIt+YH4Z9W8nXc2dNbrNj2mXzo0jJOiPdpj9Hba4f3K88/HDm8Wfex+pU+7THA3vcR/PwuqOrOieWz8SN4u68NiPd5X5P7nvmd+LBrrzZYbh3TXSZdOYYPiTK4Hdzd3r+Bsm62fsjXZny3AaBSoYzStZGF8PTRwHNri5TG8tLjL3UH1Lc9uCm7MeO2Tre9WVf7c+sPfzYgc/udP9cO8vu9n7a4uNzanratgU0TPpwqNVo7pbQ9TpQew7/7/M3buG3tcfPHNbCtvxrETOT6MG6xdXnpaWJ72QaYbmtvA4Sltl+JLTSYXzqmu0K6Usa7RJ4c6WgbWOxGpOFhZX0Z977vntosN8rV1+sO2Gpq++/H+7InDKrr1SE98SH14x4hxeZF6EmFGoT1qPyf/+N/8Qc6a/+hlkf423/37/5IFJxCa4VWMykvxgRykfEwmBbeejHtNx8gjJwvK+uaePnyE56ez6CeJa14p0gtCIXL+WTsp76T+uhJrSDqqAJrFTOwcR5tDhEoTag1gZhGSi6JOARaBe+tjLGtBdbCNA4IFaVyWaxUNVfTQ3QouVkZkndCqRlHwOOJQUhpJueM88K6FFwHQH1QxDVKLTuTrZRMAWPgqJULx2E0UK7aa4JaefMYI+vyzODtsJtcpZSMD5FaKsHZhVlUGGIk50RtFVXhdHnE60hKibVecEHsO1t3h28CraEqVu65rKBW9hliL8ldEkEGgnj73sEA8DklA2ubmP6Z86ypUgFawQdBtNEoOFWsfroStZcs0w83rVGlIc7KYVuzklDTo8w09eBAyIhUnHg0BtQrRcxFvVTFuYh2TcOcCypKa9DUI83ZuInDq6csFRGPNjE9xJSRuhorsikZoTQrKadBUG9gYVlQZ0ziUkz+wYUAORG8EFozfcEmIMKilYZpLraS8F6preCD47Q8E8eBXFdciFRgKYksFcTYb00hlWxmUs1KB6IqONclCACE3Mfb2KXdzR2I0bRhSy5MLjJ5mzNNQL2j1MrD44M9g5rubYyDtb+AxxF1wKlSWyWECNJooqxrNQauetaSyKOnWI08QRWPp2VTxmutUrSSSyb4iAuey+VipdAhUKqZ1NlcUFJdaZjGW06Z4DxrreSmOPHEEMkpcXW8Yj4vOBw1Y3rGKjg1dmfLdhhT9bYHeCX1OVxqJQ4jPkbWZYZSKWrmZKZF7cxBvpc4awPbSRrrmm2TAcapa02p2Dpxo2l1aaNII/qJy5yIIRLiwFotuUTF5iMCqqRSaKpUhbXPFaeeXKxUes2LOcs3OlgpqBrwPI4jlzTjVJAem3DVtDTL2g2XxCQWKlBBw0BZM8fDkTRfGKLNQVEDgoOzioSSEyE4xhBppXTZDMGLNyDaO0RNEoRWd7ZriJ7lMvc4U3t5kJLXwvXVLblkSq0YWdWqEqSa/rhKRaQRUFqtlFbI3kBkKzvveroAzeI6rdGqsXGHMdBKMsmXmlHnmPzEeU5IiCw547xVWSiuPzeEIeJUWZaFQmU6TFzWpRvXJVQ9FIUmDGGktYI6pWBlRa1rRDcHZV0JPhKcxfhaK+uaiDGwpJVGxTvrszFGY5eKyS60arILYQjk1vAxcJnPlgxxggsW487zBVXHcZrIabVYVpMlRLwaW1gBzHhLarWkDw0nDpW+VhrU1nBqh5OSE6KKerUEj/esS6E1IcaRdS2Ao/UMTYwjzkdyrX2fbeZQWxqDt7hQBXIzORQV0+qVbU42oZaVoB7tW6A0Ma3qLt2B85Q+9wcXqF03NoRArgXvAyqO0iqpWt+GMKBiCSZQagWngopnXldUoulQ42g0KoI4h4sD4j0Fkx9S53t71Vi6bcU5OD9fiEO084EP5NKYTzOuRVtPpRLV5IWkgg8TkkGdY8kL1TuGZsC6qgcaLVUzGAuRJa0GlmNjpc6SfdJjvdRGEEdrSkX6/tL4q3/pjzVt/yj8/K2/9fd/BGYcY2V7zYCODogut57hyUoP/aWXcwLTm5XwXHj8UwPHrxdLrnQ2WM8oMd3lzpIx1lYZTfuVDgqGJ7tcb+W8iF1w3HeYS5u5Cw3q6HfdOUSYP/H7xX54nxnuE+5i+pt1MBANFWpn1NXBym83INPPxuoJz4XWGU51sCd0yS6L0sCtVo5pxjHay6WNfUUvUc1Hq4xaXpgu53rjQLGSzc6q0mxmXG4xyYP12pmGYGfmHV6vlMkRzo34bABzjXYRKpOBcMJHkCGcLXm0gb5SG/4pUw6eMhm7aTMnGz6YiahmdnmK8JzR1D0Dhl5i3qzvpJN68tG0SXfZhdwZUPKxNHTT3oz3yUpdm30+HWxswcBZP5ePJjS9L5vSDZisH6W03VipjsaScnPfG4OxQsO59DnGbmaka9lLzOtxsHlaO+NxNY3DDUhArD/zwe3jLZjx1DZ3dAMdss3b6duLlf1fCi0a2BAe+nxrQjiVHRhFxeZwL8XfSmNbP0O52TQpYVtzBg7moyM8rmgqrJ8MLC89rvd3c/YcVz8/mxZv76t07feS6Tp68pWnBRvnfNC9/Dw+lc6SFfxzxl0yuhZokI9mQLh9js35zlQ8WHIh3p1p0XfZAgPLt/82HVPpTLR8dKY/eTaGdDkGdC5cvj/thmZaOjB/7uziHnt2RnMHk8LG1F0q6SaYzuhz+RgrOtNSqunV7uB671/E9FIRkyVZPxn3uVkOBpzhPs7xfHCMdyvp2ptMx7Mx6wQok+8arxC68ZmmtoNpzZvmcQsO/7Sg1XRk62jj1bwlbdKL0ZI30dacLqUnKhySaz+LYrGrs0M3eZj1iyv8OZNuOgCrYuN+9CZDMtv6X18EI/k4Y9KW0e2JMDY2NjbvNrmW8LDubbXzvo3n+YcTfq7WTmdzuIZeEr+V4W9zvScy/Fwok9+lVzbgbf/svj7r6A38/866a05YXnqmt6b/Hx+sIsNf6l6Gvsnq1KDEh2R98zIyvk8mC1GaAfVdUmC9dXucdYvJSWgxA7Qtxuej38vpNTfCw0q+DoQniwvWZ7KDoHXojNBmay2PxvSsgzDdJesXsRjoL4Xh9Qm8Y/58MMZ7tP3QXTooGg14R9jfa9rvQo1CC9bvmsH1JCBqcQE1xj0itkZ6m/OVN43s/lnSLIlTo5gea4/VW2JkvVL8anF+a6PJrnQST2+bZgNbm7N+3O4umj+2fUukrNe299ka6tUyk++xuZq+7WBrsXVzMrC4WKM98/Ahgfvu2PW42NeIsYxo6OoAACAASURBVJcNCN3ai7Inz+qgHN4m8pXbNWj9bPreLTjWa4frFQ6b3v7Wv5sesOZGuvHkoSf16NIK2czU4lPBWS6h9xf7fN5iUTrox/f0OPif/dV/7Q901v5DDdr+nb/7v/4op4KKUEqlaUU14FykSGPNibWs0JQlVS6lMS+5X3Yd8+lMdIpviamDDq0Zu07EGHNbeft8MT1GKmiz7zNgtNilzntKbagLxrBCEXGUCisFDSOlGKgmapqIpQrgCX6wxeIawQutJYI0VCrkRl0KDqAVSq4052kE1gw+RmN+GTJjIEzw1FLsAqvCkjO52SRuXXcxdDMgj0dRgnecLzNFPa1VksC6FkSDgRuYXqpqwTl7QhcaVEdtlThecZrPhGmk1sCai4F7CIZ5VFwwVhja0KA2ZgIueEqtgKNKY62ZRGUhk1sjDp0B2aycOLVN5yURXACE4AJOPet6JoSBIAGVSi4J8QYGLLkQXaRRqLnsgQ5RmjpcE5woea1IMWZZkGCO8xXs7i6o72zfmvDBsawrzjtqKogYK1Sdp6JmhoTSaMZ2lYbgDbwoGJXeOTOZaxAQNFec60w9hBIi4j3rmhCd0dpAA0OMFG1WcmzYGqUY06+0ApVeqjxRcgM8tRro1VozIMX1REPr4DOVMNihNpeM98YkQwqZbE7orlFIOO9o1QK6C551vTB5tf41DjDiBmpuppc6TgYIW5eTypmGlejGOHCZF0JnL4sISynUYn1RatlNy1pRFI9TZyZfYIBbKejgcarGkGuVnBPjNHSne2+AG5iEiggqoWukGpCLU9bVZEbw2kE7JecLPijraiXlEpTWVpRKa4XgHRhf0LLMOSMa7YK29jKt1crGqwiiWBIlN6QFggymWSRKaSYVUDNQcx8nKw83AJOuBbviXT/IUVEPLgi5a2KqyN7fSEODOUu3YkxApOK8kFJmSRdLLFVj5/sQDMyGnryqBD+ypEqIEwU75KVSaKURxQBGVUvSOGfM6yrGkI/Rk/OCD6a3WWtFMOBwLQVplaiCE08rQsPig4han2DJKHUGem2gaatW32D3yEqtZmbmKr0yAlDF+0DJBjQpSioLqgbU5VpQ1wxOE1snuRijp6aF42TSGIOLVvaOwzlvOa9q1QIi4FxgcIG8ZmK0daPek9JM9Fat4Z3H0gbdRKQJk/ayy/WCjx7nA6KmN+q0kWsiemNe55pQrwRV8rqg0ojO5jK5kXJGvCLRmdxCyQY6S2dfLqtJ6gTfE5lCyqXLbwjruhgDV00nNrgAohyHg1UQLAshxg74Fpxaoqk1wRehdhNB0yaPFKmUWikNY5QHxxADdSlMcaQ0Mw9NTZmXldroew04B60bVy4pEYfIZZltT6ORC4gGO+dXU3eNTqjVWLNUizPUih2jC2srqDpSbhY/OivW1nLDl9I1sxdGH6FXlnixyp2gPSq3hlNvAK4aSzslkyqIPlDXBJVucDrRpG0ycjgnhOAY4kClICnTmq2B0gHxmiuprNDsmHE4jMboRRjjRN3G2VnCmv75joYHaBkoPUHSuIoTeTWwmGZGcEqPCWBVI9X6xHlvcSYD6rp8i4HeqFCwvcmp8lf+0r/zx6DtH4Gf//5/+j9+ZCXTxlKTioFsgzPzq2dzH0fMFGt4txDvVwMXSmN4ML3HOijLq+GjPEG/DPrZ9HFrdAzv1/3C3pzY9/Wz2napMxaOgXzbwnJLxT9nu7StBr4iYlp/XYeuHLbXK+Xg+6XSLoHSDFzZma1L25nE8clAIal0TV8D4Da38Q2giveLgZPYZXG75KNQpm5AMynTXWdBFsFfWu+7j+BGc9KfT3aDmM38LR+9gYNHj+u6ws13KmvvN7vo2a2yua5tWLsp2tr7MNv3+ueV+GjtydfBQNMO5Phz2QGUcrCLapmMRRmerZy/HHS/3BrzU4iPxireAKsdgIEdeNmYeN/VVIwPeQf5NjBKk2ndGiNxMTC0V/n4k1XfaG4GWAu9MoUOwMk+ZtZ2TzmY2dk27i10tp2wa+qG57wDwAisVw63tp1NtgM4B7frI4ZToRzDR8ZUbjvQ2LzujDIa3aSNXiYsnelofYgY25vOANQ+lzeGmWlpJtZXE5tr+l4S3vuuHIN9Vwet/dmASnexEvjh3UzZgAc1IFo7M96tnVVOl/TwzoCy3DoAb//evFKH3kcYKJ5ejgbCB0Xad0q9vYE629hsbTPDOb/3F11ao0ye+GhGaHSwaAPs88Ht60uLMUjpBlvhYQFR4lPZGXqm42vMRwPxbPy3pFC6sddJM8DWxswSGdvvt79LMyCseaEMrpfYG7izxYUNsDKt5db7wl4TnnMHPX0HaK1vP5Z4u3384v1iZmGrfYc/rUhr5OvIpk1cJk+NBogZsFXIV8EYyJPDnywBsplshacVVMnXobPXbT362Ygc6K+C3JvudRk/AqVmzNfZmFfewDD3sbS9TN6kSzr46U9lNyXUudgzB9nXqL8YuiqlcfnMmPrpxu36rDXKzvzd1mWNjvXWklnp2rSRtwRTvvIfGZBN9jiImhY3AuEhUSff13eXeogOP1fSlbEc/cUMvTa5HZyQriyWAz0pBGyJ8lPa/790rd9wLrtMglT7u58/MmI3INtfiu1NCMsXo1WXPGWbw7nPo+jIVwayl0n3vWpjbNq6NUbulgDcQMb5E4dfTTognArxw4LLNictNvWKAS/Ex0Q+Osa7ri/7kCwuqe6genNCOlrs2wyzvvtvtj/2MTnXrkFMB9NbXz/G2I+nzvLFwOAyKFob5eB2E7IajX2+JU3CySo71itrw5bY2vboLfZvmrjrdZ+/YgxXNvA2CprMIG5LMF5ehd04bLl1uGxJlc1AbdtTa7BKji3htV4pLjdOXwTCbBrDtqe0zogXS9B0Zm0dlBIsMeKyfYaWtiditqRY8wb+/ud/5f8HoO3//Pf+tx+1Uo1N4ww0ALvkZDFjFe8i0pSC4HxEWiNosHJ5EXzweB9JZaG0gjiBpjgXdhap93ZBceo+ShoUxasSY8BFAzWkVbSzsFSVItIDoWNNiSZC7dIENRtoQzG2FGKOz7U1nBN8CObq7QM+BpxXQlBqSxAiGRAfKHm291YD1jAODz66DtZgh101zcANqM2p4JwHkkkGlIJzA5daaM5A1MEPDOpZ09rLt00Hr5SGYKCm4AkukpeFwQ2AAS+GJBuz1TtHzomUFmPV5czlfCbEwTQbUyLnBkW4Go92kBVFWjcmEtMNdUHNLb41mhTUK3TWpIqazIJrrEumIdRiJmXioNREyZVUhDCOtFIIwVPWxHQ8UiRTy0xsAuo6w7RLYTRjeKnznWnlWNK86zYKQvDOgqeolf72clpVMyurQO4luIZvNVzwOA+5LrRmZceFam1Wk7GQ/p05Z8ZhNIajDxSzkDPQs1SOsZfzeqVUCD6SiplpKV0oPVXG0TGvJwiCV8UpndnZ2WjiKGtFFIbgWVJFgie11nVnjTUrzVhlcYjGbnRCqQnnIy7Y2nEhGKjSlGkauCyLgYENcjO9zFwyU4xmNjQOKGps8BB6P2IWX7YPAM3Axc5uFHrZuyoxBGN/F0NrFLX6FrZ5YqCeE5Bif9acGKKj5ET03uQInCPTKK0wOY+oGCtehEYzA8Ga8EM0IzU1MNCSyg11Da8jXhyUtOun1lIYfaQ2qEXwLkBPBNRsTHZRQV0m55WcMjEYeOs1ktZqB2G1i1RaV3xwBLVkR63J9FdFqbVBM4mVEPuhWCo5V2MP5oR3Sq0GYg1holTBN9ucqjYa0hNTxhLNNXM8TCyXi20oOGoDB12yxAB2WsVLo9UZxKExQoUYRtZccNLHrUsW2LhjcRxHTnYYMoDbmMRDDBZfuya4lwYpEQfPJc/MZTFgtDRcww5PYgCV02hSG85xGAZ8zyY3t80pS6LVuq0t40KqVAb1BB+Mlaig4ne2su+mic4ptSnBh74mrdTdmJ5tB5RFLAlgwKTJEYzqaDVT8sLxaiKnZKz8zm52CjkbUNsoHMLBDl5eiN5Ryoo4Z5/vbL/J1YDS2mw8DseDXXBEkVpxgoHYteC8gXVW5r/ivbN5iOKdSfFEsXWZF6scKS33s6plg1suuNYY48icV+a8MA031FoQES7LhRAiPgbWOVuFClCB5hrD0M03nUfU5plXhTVRO1s3OKWWxGGcrCLBmZZxy5VajI1uSWpl8L4Dv5VQrMJhiBF6/wcXQRxzWozt2g8NglK9sOaVEE03OLihJzqNHV+z6cEjgqglEFRNu1vVYv/5fCE4b7EasTXqHB6ITtGmrJcLlIK0zOArqg2RincK1ePE97UkHI5XpCXj1BmxRa29JWWi8zj1QKVVmIZAiB5XKpWMC800n+dLP3s01BnY650zNq0AzdroXMGxktfMYZzskqiYuWUHq1WVECO5JP6Dv/zHTNs/Cj9/8+/9wx9toE4Nut3PDLC8FNwlGXu2AyHGyFQrw+7ssDrYBSqcameR9jjcWTObWcnGHC2TdnDC7ezAGjsYKQaEbaWmu1RD6w7jnR1FZ/u0zngxcEt2kHX7Ht9NTDYX7TwZU0nYAA3ZgT9d684k3kAeu2R1gGiTqPLGntsMVcAusC4bkDPczUhvS7rxrDfe2HjTd1iKHfC07zLA188Grgp0ht1H9unGOPUdUPVzId7NlGMwU6JTwc12frh8ETs7N3QGlO6ssxpN2kGXYsDd5ijfx8lfuiTN04pWYzRpMmk6t5qWpWkF+g5O2u+XT8LOZs1Ht7d9M5hqQTsQ5AzImBzxfjEQMtt3G9ja5S4G97GU2RtwJrl19qYxFDewukZjIe6mYvMGaHXwWK283F0y6UXYS5s3gFKLASKXLwYDKzpQV0bFn4qxV9125xPWl8GMbtpHkLp568eNOb6BxOnGM9ybDJo/5d0sameMJTPu2phYutoz1CD7GtjAkuWlZ3i3fmQwzsbYdhcDev25sL6MPSFiINXWjxso3Q9G1NHvbOINRK/RwCu3GNPOJoa9Z2PpabZxaEF7AsLWeLrx/Ts9/mRsUzebtEd6NfXPkn0eloOVXRsAaUxALR3Q2QCn0e/MwzL5na2+3nqbl7nLo3Qgx/US/OYMcPYXA+jSddjNq/w578DuBiyW8WNc0LIx410HFAV/zl1j2Nh7fi67mddmctWCAaZbEkJa2xNYANJsjrtLYf61kfCQrN1B0Vyp0RJNuuSdSV0m3ZmcWyVCPrieBNOd3dicsL6MHWSVzvg1cL5Mrs8j7ex/3RMaZdp0kh3D+0T8MJNvYo8THzU6QfY4VyY1zdnB+lDKx/mhqe7VB26tVu7kzFxtZxgmaOFjbNjAOOvHPn6dTbu125iTNm+a2mdo2pJ7wnLr0Wpg3eXzaEBvT6Zs7/d9rUiD+dMAPebmozNDLd3AZttL3KXglmzazINn+TT2vcnmgElH9GqGzubMBwOHy+Q6UGtgo1sb6UqpzjR985WzflD29mmP8cuNY7gvDPfJmPa93N+SMZYcDc+FzSjZ9cTOemP9ZjIWuieLtuRaczYXpMD8ykwby8GY41uiagMUmwrr9ce5ZMxcZb3WDkB+lFGIDxvYbvOkKbjciM/WD8N97jIQGIN3NQO6PPYkiG4yNCbl04JSohgr9mASR6iZU+bJ+ny9clRv+tm2uEy3vXY28JacLfEjaH155Ynn1pm79jpB+tg4Sj/7aDYZhXwwQHWLwXlUpvdlTyrWvu7y8JH5DTanNk1cP1cur5Smpm3rL6XH1S5Fc1DCXPlP/8N//Q901v5DrWn77/1Hf6OJnVZwOlKymDmPerKYNuaaEhFlzYlhdNRc0F5uLE53rTzprBucaa+a5l3XsHOmWei9Z5omK+VdFGmVwSlFMm21C1RKCYme3Cq55V2Ts6LGSCr0Ekor37cb/H6FtawxXdOPmVKEtBam4wGtihflXM4UhCKKL+k7PaKdqdaIrhsG5Ayhdf08A0h1MD2jWk0CoKyJ4CLNKakVojOwrNQEtTH4gdNspdHzPCMiDIMJrQsOWqJpJpWGjwNLyVYmXo2R7ATKujAMgZaNuXw+n9E47GZJrWYmP0KxcuU5rYShmyN1EDwEZ/qmhQ4SCkihSqVl050M2ihrI7iItO5u7z25Nk5Lwg0ja14MoFsuBA9UoaljChMtmbnTklZ0HGnNtAEzjegdlEy7rIRxQKTRokAyHduSlg5CF7Iz/WN1Zmg1BU+rK0Ewd3qMBXlZF/sO1Mq3a8OLmQDVWhnHkegD0vV/VTyFwlwSYYiUVPu4LkzTxJITXq10XMXjneN8STTxBD+xlktnE1bT8VwXggq+ayj7IXK+LDgXOJ9WwDFMRwvM5cwYPIfp/2PvTZ5lyfI6v88Z/PgQ83Dnd+8b7nsvMyuHykyyiqqCgjKqmUQLkwAThnZaayczybTQok1adf8PsrbGJKEF1iaDFk2DBqABAUVV15xFzvlevuHON2Z3P37O0eLEjaySyUzshDX4LtMi80aED+H+PZ/f55syn8+ji1RrxNpra53HB/FDDtA4fp2ZFESDXpcJxYBvHaqHmrSdsZgtKYo2obFxfJuwoRN9ELgAtQiUfr2aHRxKKUJVkRhFrg2pTlAulmhVromOWW1ofNx/0RcZS/GaKhYhGRnJvMbW1B4a5whJglsXCuUbcV2F1gZvG9q9LtZanJAsFgtUotfFUYJMxlXwVVODd6TGkycFdR3L2hoXkCa2vjeuRhJ9llrrSNdiCaGKlL9t0EkOIRLSQQjUmqiDFcErEiWQKpDpSJBbW1E6hZQ5dRMLm5xrYlGZEOQmQTYNWSYxacpsMaeqJY0TeFKscCRKxBF9IDOaRHhSo9FaUFZLahcXBsr6Rhci1v5UQSIC0V5gSZMErTXX19cEL0FmkWwWfl3ypgk0pEWCcJaqqinSjLqOLt4gIu0p1mVvzrkY4GpFajSJjAVoRStnMrmismDSnLL+lBJRQoNvyPNITWopaLVzFtMZK2dpfPwdqH2Iq7siurGVDmgC/SKn3W4jbMAGz9nFBc5LrAvYxiNNXPUwWYKRGqMlg16PzGhCcEwWFddXUxZlRfDRQVvWNUWWY5RCC4tJ4mJgkmrms4r5ssRkOY0V6zBao9b0feNKkkTRbrVIEs18PqX2Els7kArHp7/XSZJEejg4ilZKp9VGacHsckZZWRaNxyEIrkErQarWv3cBWlmGMYZ+t6BuYD5fMZ1bbO2oZEAQfdMIhyKQpYp2kdNrt0ikYLksefT0CV4agheYLBaiVWsFhpTg64rDg12yVLGalkwWS8qqISiNtQ4dBIlKo+LHOdIsodvvkGaQ5YLFrOJ6sqCqobYBt/Zzx2C8QqtAmmnarS79Vp/gLcv5nMvlgrIKNMHH8DKJBYoBT6vQGCm4e+eQsl4xv1xydbmgdp5FVZFl0aGeSEVi4udYzKd0soJBvwvAbF4ym81ITAFAHSxOqXidDQFJw3hrRL/fJVGG5eSMq+sZpxdztDabAlOhQKUZQggyldDrFmR5QqJS5rOSp89OEErRBIcPgbxI8Y1lVS4Yjge08pytYRstU2rrODu/4uziCmtDDJy1oGlqsjShLufs7u6wsz0kTRRF2uHy5JLr6ZKPzi+QaYGtPakOcfokODyBf/nP/8Fp+/dh+8ov/NPw/ywrUWt/pVrGopON93RmsT2zKRna+Px+yCV34627KVW68aMGHQupXJ5QjRLkmvoLSmyKaoQP1GsaKihQZXwvkUoKGw/jjWsSYuDw/7b59XtLlj66WxcN1SjBa7F2drrY3vxDJVw3281DuG3He4SbsrUbf95NqQiweai7cfMJx6akRjg2o551R5Kf2VietPaX1j29cS0CG5egyz4dCRbuU/9vpAf1Oixfe2ETuSmMES5QDuNCbpCxaOfGMXrjfm0KuSnVicHlmjBdu1RvPKI3nydI8ErgdSSUb9rVb/yTahnHseP7Dxv3LfDpZ1gXw90UId289oddhBB9ucqGjTdXlX5z7KiFpRpnm6DY5vGBWzpIL+3GC3ozTn7jNxVNoO4n1O34WZJVwCtiCds6UIj7Nfo5y6HGzPyPuCR/xJVcRMpxM07cT9YOzlh2k6zCxgnrE0F6Fp87bD/dvMdqkLAaSlonbv3/j8dAdh0D8RtP6Y2fVrj4GSCGHTdktqzD5ryoe5rspKIapxufo8skdWvtDG7iZ07mDnNZfnqepBp8wBWachiLcZwBvYJ05jcFVDfnYkymYwmYKgN1J+pGhCNS9YsYThCICwPOU42zT8+tNeW62FHoVaQI87O1+3gd5le9eD5ml9GrW/c0ZV+SLAK68qi1l5nApljtpgyp+iGSU6/8xtl6c67eLIBsVqcCa5+0oOrFgEevwqYh/oaavHHtBiU2BYJ1V2BbguIkkM7cppTq5py4oYirgcbmEttmfW4HkkXYaEt+1JEq1sdS3G91R+JS6D761P+70UGsP5vw8foSZBzRXo00ZuE3obFeuk0h1c3fqnuaqiNpctBlLIBsf+Ixs1j2dlPgFtbj4QCr4U1ZYXx9dhHIrvzGk52sFRyqikqJGHYJZkeKchzWHlJB7911Edu6LOqHC8LqlsS2YH4ETceDFxTPJO3Hn5Y5+mTtQt/KqNuKJhM0Rfxumwzys0DrxMVR/ypsHNsujeWSugrYXLDalpvXJ4uwKY2S9af32k0RqUnhoRwKVjsCZwL5iSCdBIqzZnPtvTn+lA14JViNJU0LFgcevRRk57F0MFl5zCQu5NwUd7lUUHUlq23Bcs8TMoe+0uz8tV8ft5H4lE3AzD51oEsHJ29Jmo4nmUhaT8BMY5CbrDxNKmny9e96HY+nxZ6gaQearsdcSFqfgJnFoFXa+Bq5/u1qshjgrrYFywMHXpBeSTofxbLQmwK3uOgR3+dyS2FbgvkXl7ipwZwr2o8gWcTv68braguJLeJ9RHHmmO8p5kfxe89PYlmmXYffZhFLT20habJIxE7uQ3NQkWQN9VVG/lgz/Bu30ReoOqyD8Xh9rXuC5V6g6TpE3iDPDMPvrCcxqngtLQdx8Sa79kzuKspRQBwvSFPLcpmi38vpvRu/2yDYXGfKvsIsPOcva8oXSop2xUFvwrufbKOfpOz/WbPxb9ddhc0l6cwhbeBPfve/+lvda/+dJm1/67f/l3+ilMKHhiYIghMoqaPbL8TCHy0cUkRSh9BgVIJvGpQOBFuTCIWwDR7QiSbPM3AOLSHPNEqp6C400UE4K1frhvI4Wg4BqQSJlpSrOc5VGNNGhlg+0tQu6g98IFEaX9ekiaKeTylyTZoqEu3RicJ5S6I11gW8dFjXIKWhdtAyBRoPjUcnkdqqHRilIrEbPFoognf44HBakbtAU60wOo5N+uBJsxzvm5gTh4DGUHuBTjMQ8YFSq5RUJqQhQTsdy8qUBBwJMahNU4VrBIKGLA0I6antHOK6BEpA09QEF0ejg4jD1i44up0Mk0qyNDbdS0IsWFqV2LpCJylSR7+vFNBrG4pUo5TDNzVKptHt29QoGX2xgkCiIxXcKTJaRmMS4v4RmtD4GJA5SWgiZYrXiCBRQtBvG7JWggoWnCNJ0+jRdQ0yxNWl0FiU96SJRGApMkU7SQnWonX02CqpCNqwqhuUTlA6ztcZCQc724x6PYa9FlpIStsgtQFjkAGkNiTSxLBBCRLpODgYkplI7FXVikRBnQTq0qKTAmcdJtHgJVLA3u6Icb+gyFKEd3hbEXwcyxYigAqRXhOCJJEM+h12t4e0jCFNNcvFlNo2a82IIjMarWMolGqFwrM9HnCw3aPdKQjBsVxVNLahqkpIItGOF6Tr4GrU77DVa9POE2w1x61pS6+g8TBdVpgkepRFE+h2DeNxl0E3o93KcdatXZieJBE0TpLq2LauU0M7zdnuD9jeGtPtZCgNWZpQVTVe3KwCB5SOIWtqDFu9FkUm2N/dIs00RZLgbLOmzyTWWVpJRiIbGuFpFTmj4YD7927TMtButSjnK7y1OIg0bpA43yCVROWKbrdge7zF9qhDp9smOno9jY8/1o33MTASjixJCK6h227T6xh2d0Z4FIkQVIAXCiHXY3cKtI96jEGvxeHeHvu7Q9pFhpaCclVTlTVC6kiTSklVW/KiRa48d4+2GHTbtLoGrTWzyQxtFLWrkApk45AqBmBZIinylPGoy+7uFkkiUcawXCxiUVgSCdDGVji3QkhJv9fmzuEh29tD9gdtsqRZN9MLVtaiZCBIBdIjtGCxuObwaI87947i8dsZoiWsquXGqQrrsrhU40Ogm6fsbI+5dbBLr50y7PVpyhI8rEpLolOcjMoDlWiCcPT7Le7ePaDfzxn0+wThqatVXGX2mtLVJDpek5V3DHst9rcHbA+HDLsdlJaMRwMqW1OW1absT4mGVbmi3SnYGfYY9rv0WoY0z8lNQm0tq3kdz3VAykBLJ3hX0eu3GA5HHO7FEK8oCspyRVVZcDU6RH2FlgKZSBIVGPf7HN7aots2bI8H0DiqqqSqLLnKsHVDqg11WZPnCd1Oiwd3btPvFAzbHQbdNloKqtUSGQSNEwQPwkiCd3TaLcb9DtvbA7bHI1qtglbR4vLiOgbnJsXhMLnBWYsUDbtbQ/pdw97ukH6voN8pCF5gFyW2tPFmyNakreipbRnJzu6Q8bjPzs6Ifrezdq0KrmdL0sQQvMc2NVIptAx0uzm7O0OODrZJtacouiQ64frymkREt3CWKlxdY3QgTzXDYZvdrSE7OwNyA91eh7NnJ4gQpz9c8KSmiPQ6DbvDMS/cv0thoFNk7A36VPMV5XJOq9Oiqkqklnhr14SvZTTocP/+bbbGPcbdLmlqaEK82Z5Uc4yJ5XsmUzS+olUUHN3apV0YtgZttNEEpZmt6rgQKTXiZrTLRWp6uLfN1t6IVjunlUi63YL5csF8OVtPboD1llo0BOHZ39tm0O8wHvfRBNpFivSKxdyikODjPUtYLzQfHY7Z2xmzPerRTfl3VwAAIABJREFUKlKUl7R6fZIkYTFZMptVhCRqOxocjbNIJL/+H//yP5C2fw+2f/Ev/vSf3Izh3ozPexOJlmDieK/PFF4LXBGpt0gKRXefdPFBirXPzmWxMEkQC3vKQfRgulxhewZvJMWTBdJ9Oloex5AjpZJO3IYSjORQDEldHgnFpiXXD5YKM3esxgl1R9DkcSxT1fFB06xDkRgKCJJZzWo7lliJdaCFin7Ppq03xK5Pb+gkj7KBsh9b6Ot2pHaUXbehr+klxNrVO3ebMeugIplTdRW2E0ct0+nazct6dF4Kqm4cbQ0yPrAHER9OCazp2DU57OK4snTxoVjVnvl+gu1oqr7ejGaWQ41ZBPRq3RyeiM3o8+xAUw0ixSQtP+L29UatfYhRkZDMHYs9E0OHYk1QqTVVp9bfj4v0owBuHJnT24a6IzZlPE2uNsfFRgOxPr7qTvwOyoFkuaUQPu6/Jv1UGZFe1gQjY5GNiM7Ms9cTpncF8zvQZIp0sib31u8xrAk4l0vqrsYVivPXNVUvPsibeaDJZWymX4+zR9o17puQCM5f1UyOBeVI4dceYOnFRoMBYk0fRy/q9Cjh6iVBuSWwbUl+4aOTWMdzyPZMpL7X5VjOCK5fFFy+HlhuxYmx/DKGEuY6lqTd0JZNJ+736/uG64eCckuilwJVsxkB1itH9miC62Sbcd/5vmZyLFkcQrklkPVaJ2EDTSsW2jXdFLVq8LlmuWu4ekExfcFR7gZ8oqh6kmQOysYFhRtSO3s2p+mmXL2YUA4k16/GgKwaSKSN+0s6SOY11XYeFzpWnuVWwuSe5OKna5ZjwWpLkMwkyq7JzLV+QK3Hrb0RzA8Srl8QzB46ljuCQDxWblzQN4VlsvFUazfqfF+x2JdcviwgRELXzPxGc3BDRMcxc8nstuLyTYd/eclsLGlSRTJbt8ZLsfHAJpOKamQoB5KzL3rKQ0c98thM0noeg7c43i03JLLtxiC8Gglm9wL+pQWLPNLe+UVAr+K1Tfg42SDrOK4+O9ScfcGzemBJX5lx1i/wSpOUkF3YTz3AAEKQP19x+vmM8y946pdLro+hygzp5Mbv7X5EwyKbwGJPMXkhYN9Y0uxZJncgWAPERYebBY3NuLiH+aFk9YUl7Fesjhw2pOhV/D5DIkkva5riJvyXzO4oFq+W7Ny/YOfwijNjWBx7xNKQrsNFvy5dSy8tyx3N9YsBebhivDdBdCyLroBlQn7pN9cplGS5naBsYLEvmd8JJG9cI2+VXA80cq5J52GjfdGr6IC9IRynxxL7xhy/W+MeVixlSjKTpFMfveSrSMeaefznxZ7EfWVKur9kdDTBHlqu0wy9UJFsrdYqk7V6YrGrmB57/IMlD4+fwcgyG0H6kYnnbio3lKdaq3MuPyMo9x0PX3rC/f1T+gczPmKIWkmSMiB9LPuqO3GqZTVWXL0ocPdWvPzgCfn+kmdJi4Ci+6ih6qp1eBs2v0nLPUH5Yslbb7yPaDuqgWeWJ7QfizUNHTa/b7YlqfqS+W2wL8a/oXo1YqckvF8QhCAp10Fv+1MX7+XLgu2fecpub8be9jUvv/CYt1fbmGm8pqazsKGLpYvHzuRYYb88Y+/4gu07lzxpZwQbC8G6jy22UOjKx0XdlWc1lvDmjN3RlF84eptlrjnzOdmpQtXrmxsRyVldxcB6dlsgjhf0RnN2BjN6u3Ouz7sUZ2FDWCerEBc8Grh4DbhV8uO3PyY3DbeHlzzVBeLCrBcyxFrVEd31J59XhM8s+PLx+9zvn5Nry85gxkVusFcF7ad24+NPVvF6H5TgP/tP/3akrf7/fsn/f5tNNEJJRG0wKiHIQHBNVAgEh0kVSWIwxtDrd1guJlSlpbEOKRNIJKVdoYyC2qMUpCiGW12KLCXRcHo1Y7WsqBoLpBQylnhZKRFKUNc1w07O3taIIjnA2Yaz82sWyxVepiCS6D6U0bEbxzA9r7/xynosu+bq6oqqcSxXU6wU+HUTvQ8GEzQdndDPM3bGhiI3LJYl59fXTFYVTQWuCbgQH6a0UhilGfQyDsdDuu2c63LJ5fU159cT5stZHOuvPb6ssZkiSQ1SBfaHAw72+igBs8mU55cXnE0mtJI2i8UKJTVCxQe6+bSkM8p4cHwfrTzSwXw55fp6xsX1MjZc64zGR1I40QnOOV48PqSTJ7RbOdPZgtPTM+azktVygdKaVqtHaRtCE52cR7dvMRoZ2q2UsKpY1hXvP3rO1XSJUZqyqfH+06b43Z0t9oZ9tgY5TWhYrSrOLy45v7zA2TiObm2FUGkcwVdwe3eP/d0eQklUorm4mvDk9DnnZxOcBx8Uej1ybQlII/nM/Yf0MoX10Rk5mU75m3c+xmQ51gVEkkRCQYH3DW/+2KuIYNEKUgxbLsAnn3B5cYb3OfgMqdYuQ2/ZP9xhNOjS7mZoJaiqipOTE05P5yggMYLGliAigbizN2Y07NIpEtrtgrqsmM1mTKdTnj07oaLGqlhkZpJYKHd0eES3MBRG0+5kTKdThjstnjw64/JyiUoLal8RbI1JC3qdhOO7RxSpJi8yVsuS7e0xP/jgA66nFulScNF3KGXc7y8fH9MuUlqpRoSG2/u7PPnkOY+enaJ1hspzpC7BNQidcHh7zLjfpd/uIomjyuOdkidPz/jg4+ckWTfqLtZ0cqE1dw722Rn1MLlBOEfjxywXFbPFO3gb2UPro8qjaPXYGQ24t7+D0oHEQN7aY3p5hfrwOVezOat5Saajd7PWgiyRPHxwh04ro20ypMujPiTXvPveR1RV1FlYbVnWNcI13N+6w+G4R88I0jQWQm1vdTi/WPC9dz7ABwXS4IUCZah8zd3DWxxujUmUIS8kh3sVz8/PefvDxzhvkTKNZVdBsqynPDy8x52jA7SWGBnH0EdbI1DP+ODjJzGwFIraNbRaBY2d89k336CdaXzlSTPJamRpF21+8Dcfksk2izIqYUQTg+Xx3hbDdQinEk1v0Mc1lm5mePeDx0jRsPLQarWwdsXtvS12t8YMuy0yqcFbhqMu5xdz/vLffZPgGirZwiStNcVuuXfniFvb27TSFJMEyB3tTh+J5e13P0R3BkAkYJva0ily7j84xmjFsJdRr2Yooem3W3zv7fcRvqZpJOg41m1dyWsvP2TU75AkCqU9IQj6oz5Pn53xja9/h36/S24SfLC42vPw+IgiT+i0c5RSpEqz3cpYrJZ05i0ur6YoZWLBi9IMijZ3bx0x6rZIhYvEPwLR7tIyBXb6DrNVHUfok4zazmm3Mh4eH5GkhnYqqINjNOiSacXHj5/x/PKKROfIAKumREnFSw+O2dsao0WNSaNOolX00eY5Hz96QiMsGIlNore32xnw8sNjjIQiMzjbUPmE7d0tTi8vqMsVOsmxHkQocK6hKAqO79+NBLiUZJliOEyoXc2jR4+YLWYkaJoy6jh6rYLbR7cwiSRNBMZonIXDI4MXCZNHT/E4TJExWy0BePDiC4yyAiUDmdTYVPPg7h1Oz654fnZFWS4RSiJ1IEiHp+Hhi/cpCo0WkjzdRQjJ9mCH2cWCy+kiaoccCAM2WJSC119/jdV8Rp5oenpAlrepV45vfuv7ZHmOlwqpGp49e8L9u3f5zINDlPAkqhv1JsC9h7cp36l58slzZBJLx5yJEyYHtw7Y3RkwHHRRIoDUdLYHeA3f/8H7JMawqC0mj9dcO5/zylufJTUKIwOZlBRbA4b9EZ88esJisaAoxihtqKqSuoo0/r3DXTINuQYR4sTJ9taI6XROQ3ReOyWomyVbwwF37t2jXE4xxiCbWBR5cKuHRvDOo08ovSMITb0sSRTsDLvsbY0icaMUtBVl7bh77xYfP3pOWsbphZq1bz35tNjmH7Z//ze1bAgq3tPYtt5QejetybYTqRDbEiz2BelVpHJU+Sltmk7chsZyqaDuCKZ3EupBwOWe7juK9DrSaADVVhGPx0VsLFezhslxxuUrATcIYBXt9xT5edi8jxtiNVmPRAYpeP/XNbJd4q0kfWxI5oL8MpD4SCduRuaNYHmrYH4omb9Q0xqsWFwUFB8ktNb+RGUDVP7TAra2ZnpbsXhjxdHOJSdPR5iPU7rvQ+uk2RR/qSoglpHU9Fpw/qbEvHZNqh3nZx3ab6cM3nEstzTFWRMLZdYFKa3nlvm+4fQrFpnWKOU5Pc8onih67zuyy2ZDAUclQQzLH/2sodmpGY5nXD7v0X43IT+NdKBLBcstRTr1m0Dk5C2JvLvg1ugaHwTPrrqkf9ah93GD7SjMdbMZHzfXlvPXcq5f8ewdn7GsDOeTgvSDjO77xICtArWIJXK2E92bz39c0XnlAiUgTyyPPxrT/3bC4N06ejCt/5HPEpTg0S9Bf++KsjRUIlA/a3H0B466rTaFXoQbAlzw/NcrhCzJU8uotWR+nHLNmO5H6+NjPZYqbSSJz7+QUN0tGY2uaKcVF4uCkx/06b8TA0/XSj6lyivPxWcSFncdZmvGvdEVl6uC87MOyXPD8Lsx0LnRDNhO1AE8/7zC7sRj6t7ogndOtnh6O2fwrZT+BzYuGMwjJdm0FLNbiumXV4z7c466VzyaDlg+TFj84YBuFcNSWIeWSQx6P/xlA1srtoYzlPTUb2lOvjNi52uRCPaJwSfd9XkBJ59LqO6V3N0/J1UNl6uCq3sF5bfbdP+oXJctRXJb+EA5Tjj5Auy98JxRvkQLx+RBzrPrLvZ5B+ni6+TaZVlvt7h4OUF86YpMO7azknudC755dsDEjGg/EmRXgXqYReXT3FP1FKc/UzPemvHG8JRcWWZNyl+2jkn+SG3cz3rRoE+nICVPfmGb2Zslt3av2G1Neb7osniQcPLBkLu/4ze08w3db2aepz+p6b5+znj9nh59dsA77+yT/148bm/I76AiqX768xk7n3vGoanYyWd8NBvCHXjGPsUfRyrXF5Jk5ih38thy/x9d8BPDU2qv2M8nfDAf853xEbf+IJZUZSfVhsqVNjC5D/Yo7o9WUsMuLKzho519bv0fel2k6Cl3UvTS8+wLBvfSgs8dfsLt4hKA1b7hj+7cR/3zFsIHzMRiO2sgxgee/EwHXp/ycHDNVj4H4O3xNhd6xJ3fmUVVCmxKseb7hvLLM0ZFxVf23+VZ2aOblHx3f4/J7+9t9CE354ZeBj74NcXO7XN6aUkrqWiCotmT/OD7h9z/H0pmd3KqoYnXXhv45KsgBitubV2Ta8t+a8LWvTnvXY+53E7pfXhDQUYid3Uv4+KLlnu3T9krpozTOYsmJTlwfG14xGQ1jnS6Erg1TbzYlmRfOWc7K3l18JTnZZfxwQf8YfcF7NfajL9rI5WqBdmFxaWKj39RcfszT+mYip1sRiob3h1s8YE5JPmrGMBC9KHqRcNiL+H4Fz5gJ5vxcvsJk6bgk3LA/p3v8Lvv/BTJIl7PI9Ecqc1qAF/8wg9ogmRoltxpX9LfW/Jby88z+OuE1sma7l9Fmn12SzN+64RRvuRWcc2OmTJ3KbM3Up64fTqfBKSNExytp7FL5eIrjlduP6XQNfeKc87qDj/+hY/4V3svE77bIbt2G1L6xpE9+spzjnvn9JIVL7RPkATmd1L+15Mv0vk4TmMIF2GBZBUnDX71l/6M9xZb3G+dMRgteCF9xn+5/DXGv51T9qOb12vB4K9PefoLu/z0V79FS1dsJzPGyQyA2Y+nfGv6IlvfbPBaYPMYWgNcfDYwePGCr+y/S1vFz/a54cf8pvsC+7+naQpJcVpHdcua0O79B6fstqZsp3OOs1NeO3zM490hv/XNn6M4cyzHGp/Ee5VkHid39r74nIPWhAetUxLhqILmNx+OaT0zUXNh4qJvMnNcvpjy2bfe5em8x142IVeW/eyaey+e8zvpq4j/s0c68Xgdr29NIREP5/zKg2+RSMdALwB4b7nDf/Py7/Hffe03qHsaM2nQi3XJ6E25299y+zsd2qZogvUIDK7xeB9b4oNTKB2dgK+8dLx2Dta4YcpyueSjRxXOKeog0EWOc7ExtWoatrodbh9t0c40djln0Gnz7PSSDx49JssNhVLMmwrhBY3zDHsDXn/jAcpVpKpB1oFuR/H8bMIHj84RxrCyNUolSB9oZSnHd25z62CEFjUEx3iUsywbri5PEQSquiLIQKYlri7J85w7928x7kpctaLf79IftPng0WOezK8QIsWTEEvRPLZp+MzDN8gTQWgq7nW73Nnd5/3Hj3ny7Cl1BQsaaqWR3tI4y8t373O0PaJbxJWNXiujO+ohP/yIp8/PUCrDe0/jK4zOGY0GvPjgNoNhG00sS2p8i6tewdXVD5DeUVcVaZLFkeDZgqODWxwf3aGlo8MxSzx7W3d5+3sfc1JNKeuSWMarqJZLdrd3uH20jzGWNFH41NAWBUZLvvfexyxrjxNJ1DOEACGwvzvm9t4uCZE4Kls127sDHj1KeP+jE4Lz5IVBeUhD4PjwFnfv3ULVM6SEJkBxa0yRC5rJgtmyogqSxlUYZejkBcd39jna3wFXorOUqqro9ltczRY8efacVS1I0x7BOexyyf3juzzYP+J6cobQDSJEgrv2I7QK/ODDE9Kky6KcY1RGkHDv3j26eYrzMeTITUqRGdrpJd9/5z0MikZqQHI5ueSzP/YZDve20U2F9YFeq0WeavqdjMePHxNEiggSSCiXFa+++ir7233auSQ0C4RQjLpthp0WbmXxjeBqXmKDREmBLStuv3SPrdEY4WuElvF7VIqj3X2q6ftIo1jYOUYbprM5g2GHu/eOCL7GSPDOomWgld0mzVPe+fg5q9UKLRMaB6VruHVwl34nQ3uHoCKniPs8Szk9u8AFS1nF9vc8U2z1u9y5tU+WeLyIZHrTNGyNxlxNZ7z3/keIYEgIqMZR1hX3Dm8zbCmUJupQfMXWqE9VWZ599zRK87VCeoVbNRzs3eHWziE+rMizjGY5QaM4OjpkMp0zf/I8FjNZTydJaJqGN+/fpzAS4ZYEL2hlOUEGjE752te+znj7gLry+MbiQ0231+G1V15iMTsnlRqhPHm7xYNOl0dPzgi2BhFL56y36MRx5/4hNJaiKNDek6TRx7q1Yzk5vcA2ELynaRpq3zAoDDtbu5TLCeQSdEPPGDKd8+TRM2bLFYVOsMqTCkG5WHD/8E0GnRauXhAE0bPcytje3ePf/sU3uHv3mLJaUlUVQXja3R7bO3uIpkIJQZZoLJbD/THf+CaotIVF0syj99O0DDv9Ebv9AYk2BBrqJjBu91iUjq2ra66rBucCBE2qFf1ej73tEVW9pFwt6JoUZx2DbpeDrRHnl2fYAD5opIp0/Xg4IteBlIQ0VdSNpSH++15/iJRgyxVFkTGdXDEaDGkVBhmiFkfiaRR00hZj7/jk5BlYSbMOCYb9NsN+HxVqCAqtoxN2UVnanYxWkbKsKxrf0HhorCVRXQbdQfRf+xWZMVgH7V6X5PSCqq5BxFIwjUJLxd3btxHeIXwSF18a6HQEw+6Cxz6WD5YuUAOudrRabdqdHpRLrAuYPCGVHZariiwrWC0bGuewboWQlqqek7dvAaC1Qqvo5A6+od/p8275AVJE5YjzjkQK8jSh3+2smxV9LK3LW7RVSn55hdaaRkb1Qqok0+sZiYOiiKGtJI4EdQdDzidzmnKFyVIaEfUmde0ocgM6WZfZGbI8o3GwsjXCaERwKCVYlCu8EizLJce7d7A1GJWTrOmGRV2i85S812G2KvFCIhYlvd4gli76BmUkQjc47xBCYZKMPGsTkKATFtbG+4Inp9y9d4ujoztcX50jpCBTBiEN3axNRydMbENmMppyhUgVRadNtWxomyy6taVhWZbkeY/haJu6uYhEQTnDpJK0naICZGnBan6FzjSFkbgQi9ec9ajMUDlPXdVoDfV8gVssSJFI15AmBiECq2oBiWSxWtJ4hdCClsmw1ZKq1FRWk0iH8B6JRkrNfDaj1Wphpit8s3b1Ny768W8aOf5h+/d+sz0TQwwZgy+5bqWOo+aRTjz7D0u0dngnmVoFV4adv4g0TjJzm5FnADN1XD1QiM9NeGl0wXWZc32YcfX9Pgd/Eqh6iiYXFKeRWFcrx+Q4w/3qBTtJw04xwyjH93d3uH67z/6/jWGOuazxafRVLrcTnv+U58dffY+hifdRH90Z8smkR/NxjyDBTGLQVPc1so5jj+7zU37x9ntMbQa78N7BmOmfb7P97+J7uQmIo+LA0/+5U17rXNEEyc++8QOq1zW/+Vdfov52sh71dOiZXbsqLR/8SoetN094Y/SEXNUsdlK+sXOLC7XN1rfsmowMqDJSXFcPDOU/mvET+5/QT1bkqub6XsFfPjvCf9yLBUczS91LYov6teX5F3Ne/dK7vNp7yqTJeae9Tfuliu/8by+g346ksl6XryTThvPXco7e+oStfM5hfsXKGV7uP+cvi9vM/vWY7NIjawWsy9hKx9Vrjq+88TZjM8cGxaPBAHPs+KutB+z+icC4QD3KcWkkZp//VOCrb32blq4oZM11U3DcO+f/GtxBVW3azxqS6ad6hOW+5vQnG/7R698HoJ8sebwasNwxfPj4HjtfL0kuljS9HOEiLf34q4b/9s1/yb+5egUtPIl0tFTF73425Tptc/tfldh+SnayioVOAba+eM6b48csmhSAOx3JSW/C21v7HPxrtfa3xmCqeOeCj39twC+98l1GZs5Z3eFL4ynf6h5wvt/Gf317E/YBmKuaD341Z+eVEz4zOKEJEi08o6MF9pbiz+sXkU1C78MatbD4NDpcrz7v+Yk7H5FIhxaeo+4VLV3zR28VJHODTzLykwrb1hSP58zud/jc59/BB8F2OqfyOn5fg3P+qv2AW/97JJJdFl3F5sqRvD7nC7uf0EtWJMKxahsWQ8O3iz2WfzOIRO+lwxaaalRw9UDx+bd+wL3inKU3m3346wd/zT99/Msc/HGkwZtWDBjVyiG+dMVPHnxIax2yXNuCf3z4Xf6nxVuk34n6oDjuH68rZ29IfvGV77FyCQ+KU66agn6ypPP69/nTZ5+l9TwqOvCB+lYfWTqOf/VdXuyckEiH9YqXO8/wCP40P8b89wWTl/qYuVtrSDzzfc0v/uLXePt6l/3WhEQ6jrvn/NgXHvGHX/sJWv5TzYlwAZcrdj73HICH3VMGesnYzFm4lMf3ByzfzmJxkYvnhZ7VTI9b/Cd3vs6Hq614HQG+PHqXox+74s+/9SatE081TqNfuKtIrxqOf+ITfm77+0yaAo/gom6jpWP6IKP9z6ac/dzduBg2ieF+teX46r33SKSjrSpeyJ7xvOnx0otP+R+LX8KlMdzVq7iQU7cVixcqfuP422ybKTYonlQDfv7WJf/z5Vtcvtyh+3GFrGJTk+1q5oeSX3nwLR6thny8HPKlwftMmoKt3Rm/+dqQwTtyo+oIOi6e7N8957BzzWF+xZ3snCfVgOum4PJuwfxojNexmGs1Tmh/UjK4s+R4cIGR7tPrmy14dfSMP7lX4L6Rx/1RxpLV6V3BnaOzGGwDA71koJd8b7bHC8NTvj4ak11GvcGNJsPlip/a+4DGrxfi1+HlrdE1z7I2yaQGDEFG8t9lki997geb828/u2bS5PzY8BEf3R7iv16AgnStzpFVQzVKeaP/GIDTustBesXd9JTvLA+pe4L0Oi4QqmWDUYLkuqLux/czNEv6esnSG2xQ7B9c0vzRTlS0lGGjZqn7gp/Z/nDzu+wRbJspL/ef81F/B5dFwbSsA3Uvofh4iskML3ROaKuKQlUsveErnbf5N+rFzbSKKj0kAjOPQSbApM7oJSuO0xMuXZt3F9uxyFpGRU1UPnjM2YLJzw45qztsp3O2zRSAry/vkqaWctCi/WytihFQ3h3iUpjYjJauSGTDxOUkwrGbTflaL55LaCjOGiZ3NFvfmPP8Sy3+64e/z+9fvUoiHMfZKYlo2NmZsNjdpvPEUQ0SVOkJEpZjzXJR8MrwGV29IhEN314e8mL+lNlt0MtI3Hc+abBtuaa+BfdbEx7NBvSTFa+1HzNxOUJFtZMzgnTmNxMzySKwsCnbrTmFqmmrikQ4HlVDAPJzj648wQqWW5pk5akWhkerIeN0jiRQqIpc1fzF/Jhq5KlbEmnVj7j6bxa7/zbb3+m7cukceIujIfga72LTOdZR6Bb9vMe4t0W/nTPu9xh0Ohzu7UVRvYsXgFjkFZ04ZV3x4MV7dLoFiZZ02n3STovBuMvu9hhBQ8CS+hAfOjHsj8bsdIZkWpPqFGUSxjvbdPudWEikNVmWoYh+11bRptvt0u910GmGTAo6nQ6DbofMRM9QkiQIH0t6vIB+t8PRwS2MSCjyHsZ0GQ2GjLb6NPWShgaFREiHkQZvPS1T0Gl16bY7eKGom4a9nR1SmVHXNcHHkp00zalXSw6297l7cIgSGmkEOtV0+x06gxapTsAYGmlo0LhEkrcyBoMBg04L5aMHMU0LksSQ5yaGccqA0tgmkLY69He3SAtN7SuMMbTbXbK0RautuZ6cg4JMK5aLGXmek2SSg4Nd+nlOcA3SpCAM2kTfokAhRfSdBgEhOLb7Q8ajEdIHNJGQs03NYNxjuZyjRHQlIjNmy5qt8R7WWrJ2SmYUJtVkAkaDIRILwaI1KG3wUoAT3LlzGyEE7XZBNVkhnKdXdClMSqgbMpMgNDRNxaJckBUZM1+TdAp6eYe8aBNkSqvdZTlfkXhPoqCQKo6bh0C/P8RSI0x03QYZMFmGzAwiOIRSWNfg167agpRc5iRCgosO47RokbV7636+QF0FnA2RfEbS6XQwAiQGHRJSmdIyOUblXF1dkOZxfF7ouPqaFi2EUGRpEVXM6xKzdqrodjp4L5Euw9aBVjun2+2jlMB7RwiCVKfgJWmSobVmsawRQkQFiUjQaLRJQCi8iloLKSV4j5IJy/mCcjEnlTqW4UlDv9/HBk8tIhkq1+V1F9Odfc97AAAgAElEQVRr8naLla0JGoTUSJFiVIaWSXR5hvg3lNcEL2lnHYq0jQyKsg54bQhekmdt6rpGSaCqUDLBu1gEFocfIkVn0hSCplk5FrMZPsSFgySTCOmxzpMkLTrdPs5ZLB7vBR6NFIbFYoXJW6BjIZYMnrppyHRCOVshrEB5T57nBJkzXa5AKxxRtdA0NcI7tMxotYdUrmFRlXgJSkhSrTg7OyEzGYmQ+CaWCi1thUfhEZSyRohAExqg4erqkul8Fou6ACQ0paXT6eC9ZzFfobWmbiqkFpikYD6dxyItIaicJwjBdNWQFl2cDwQRz18noKyWNI3HN46qsVgHWVZQVZYkSZkvSoLwmCzFKMlsOaNqFqyqBa6BImnhvEakKbNljS46JLJFgqFQBa28QCdxYWVVlUgjsc5T1zXOWlqpoXE187rGJAXWO4pBB2EkjY/y/MYFPNHRvVhULOaWTt6lthapBQhNanKuryZ4peNCQADfBLwUVDZgXYOtGpyA2jWYvMAB0+sZwjlM0kZ5jXAaW4PAoPIWTkhqt25TD4Fnj5/QVDUeTWMDWglm0wWVbZBpwrJyJEmCJu5bax3z+ZzKeVZNTdk4LqcTLD4qK4TANhWJjtSpTnNmq4raiehEFmLtSrZcXV1Ff7Z3VL4mNYrp7Jqmdlxcz6ibQCCe67PrKauyxEqHkzKec9aTyox+q8N8WVH5Ghci9YzzXE9m2Br64x3KJlA1gbIOOCGpXMPFdEIjJVY6posG3wSqecV8MscGDzohK3KEMhRFl+All5fXOBsLKpVSuFXFYrGgWR/MzjYI15DLhPNnz2Ppo3d4EV3HOFitakITCGiCTAjKcHJ2wXh7m8n1go8+fkymDJnUqNphl5bGCpZlQ5HkVKsSvCOE6Gu7nk5wQlJaTxU8tiZOwZQVy2pJbWfoBLz0VLamCoGL6SVJoSE3LBtYVg3L+Wzt2Y7BU561yUyLIAzLRYn30Fiom4ZVLbAi5XRW0gRFWuQIpajShippWJYNq2WJrUoau8JaC42laWrKao4LNZ6bElWHSVT8fv5h+3uxhbXOVVV+4x51Wdz/y7Fmdqh46/YjXtt/yudvf8yDg1Neef2jzQPeDwe2AOaypP/Tz3k4PmWULnht+JRRa0lzq+L8lST6WwPUHUXT0pRbhovXAr96+1sctCfs51M6uuJnDt/F7tabkq56YDZe19VY0tpZ8LPD79PXS4xs+NzwY3587xFVL5KsTaEiZbQuE5kfSv7zz/wxO2bKZ9rP2Msm/PzB26zu1ptW7pvSJ7t2B77QP+WL/ff58uBdqqB5Wvb53EsfYNvRzyd8wPYMtquRs5LOqxf8s4e/TVevKFRNXy95c+sTym0fdQAQ/1bpUFWg7gve3P+Efzz61ua/uZVd0ckqqr4gPV/9CJ1qu5rFgWc/n/K07HOQXvF6P9J41cjR+TDSPVVPkZ6VVKOEJof/4s4f8LPD72ODIl/PjrbTiqYVH9RviquEj6HZnfsn/Mb4LyhUzUAv2c+nXJUFvVsT8nOLS+XmGGk9rdg7PuOiKniQn3CcnbJjptzJL/ji0Uf4NR7k8tgYfkNF/tKb3yaRjs91P+TxaoAWni+P3qUaRtLbDov4vlaW9PmcpuP50+lDxmbOV/vf52HxHBsUB8MJ6VWkZptCUm7n0fPpAj+5/T7nVZtEOlLZYL1iJ58hdPQRBhXJTmk9oZXRGSx5qfWU7SSGE1dNwX4+Zb892Xh69Sw6SNWiwueBz46esp/93+y9Sa8tWXqe96wumt2f7raZNzOrshqWKFGkJQjSRIahgSEbkDyQBQgCDBj+Bx55xoEn9j8wDA/oiTygYQomPJMsm52KWSSrklVZdTPz9vf0ze6jWa0Ha5+dNDypIQ1yTy4SuU/siFhNxHrX+z3vglo5ZqbhWX3Hr03ekQaB6SuLnWrCwOzF3nJoMTLwcX1LGwyFDNTKcXi8pnl8z9HNgln3aMjmsWKse7pgiAg+qOYAfFjPSUXMrs4dniJUilgpBkXOQ7FRM1B23+aV8VQ3jmLhcNMyc1qloP0gMO8GnPdTbNSU0iNJ/M7F3ybOHOWt22/sRJPd1YPCsXIVLikGyjLUfT6/owXd4Q5FcdflIK2Q8NPIRTtmeN//VE8bCm77Qa6klQLZe/yOeaxax6v5URa7ouJBscKIwLUdc1RtaT8Y7/En0u0c9Rp+vni0d5pKEoX0+bqmedMj3ytFd5iZs+8vc7XX1pfM/YCFG+S/KzztsaRYeqrLFuECsdTYseB33v8an9TXHJebfI+l5avVyZ5hXSwcSQn0jpH6/PQhfzj/Nn3UNCGjtJau5lePz0nOMbj2+Fqh13m+S4PAF/OHDHdi3Mv+AS4pfm/+HexY7Pjf4EYa6VLmz/aKJha87o65shO+U1/ytj3EVJ7hZWbB+nFGqtSnW8wavt6e0HjDr4wvWPoBRgQ+u/sIVWaWqB8oupOC5kFBqBTzzYB36xkDZblyE877KStX8ensBt1HhpcON8680u6kQApY9lmYXbiaNhT4JHm+eIC9q9g+UjtmcB5bbpR4c3ZE7/V+/PVR0wXDeTNBd+zKyiNmHfI8beFPbz5Ey8DTcp7FNRlYddkkFcvMtjZrv39G/cHzb7O0FW0w3NoRB7rhT+6eYTcFUUN9k0OwQqWIRqE6wZ8tPuRte8jX2xO+ah/yuze/xo0d7Ryp2ZEbBhm1EgaG+kpy3Y64aMcYGfBRcWNHnL07wtfZxVssHXaaN2dkD//29Ducd1NcUrio+GzxMS83R8g2i7WhyvO0HSu235rS39a8aw/YhBK3m2j/j+WvsW1LVs9yWGKxdJmb7BLlOvLu9TFLW3PTj/i95fdoQsl5M8lc3ybunx9JCezJEBHhj04/Zrvrt2PZsfQ169shu/2azKaXmfV88LVn2dcsXU0TSsxuJ/Tl5jhX8MTcd0VMHP95y/xXRgzOJf/tz/8xn9Q3fLu6YqYaLv2Uxhqqu0h7mMch7DAtEZrTEStX8bI55lX/gGs75rcv/w7VjWBw4ym2Oy75jgFcriKfvXvGrGoB+HzzIV9snsC8IJS7UFMJ3YGmm2Vu/ulySu81t3bEpZ3wVfOA235I/+UE3Sf6sSJJKNeBYhWQc8Pb9QE3/Yi5H3BlJ/uxblZyH9QZVd4YdkO559n/Mp+/1Ezb//W3f/s371snAQFJTIakNdEHPnr8kMODITH0kDxaK2KKuBS4vbtFU6AR+N5nN01ZcTCbMR5ovGvQEvCedd/w6vSUJIZ0PhGkQqmIj4FHj06YDscoGZCAtYK222K95eJ6Q28hSU0fA4NyyEAVPDo5oVCOUiYUER9huVxydn4JuqD3EaU1Te/oPZw8eMB4VFGqHBhlfY/tLXeLntXGIQpDEom6rOkaS1GUPH5yRGEk3rW0rsMUsF5vuLq5pneJoDQ2RJJPlEpzOJrifObPeh8RHpqm5/ryhovLG9AVKQpsjFjneXjyMAdLJcuorkkhElyg2Xa8e/GGQpVEVeSEcqnxzjEYljx6+ACCRUhB10cSiau7azqXucRgKMsSqTSDYc3x4QHed0RyqJB3gejh6nZO21hCAKUNJIHrLN/97icslrcgHUprEhLrJLc3Wy5vFgg9onMOKQTCSMpBwaNHJ7T9hpAEMUHfNGyanhdfvqOoRjgv8EnS2Z6D2QCjNZPBAGFzubOUgsXdksubBT5EpKmIrcdoxfhgiixLjo4m9LbDxZ5oE+v1is1qzXLdEKWiD4Ft36G1waTEw5MDUgzUqgAdiSEREbx8+Zbl2iJ0RSKhVIVzjocPToCIdx2uaxiNhyzXK7qu5/zimnXTIpREm4KyKpmOJiQ6dHIUhSESCMHRW8vqZsVm29C6REISgmdU14xLw2RYY0RCOYeS+aX24mbB2dUtbcisZSklfdeRIhxMaqqyyGn2WHrX0bUdb95eEjAIqWn7HqkNrW15enyIJLNIfQSEoussy+WWq6tbhCxQJovJKUTq4S6oRytKVdKHjigE89WSi/Nrrm/ukEVJ29mMv1CJ0WhAURmigBAsISXarmPZtby9uMGrGqLBWk8vIuODMbXJHB+5K7n3UXBzu2I+X7PddJCy+Op94HB6SFkqpNI5SA1FConVpmO+2nC3WOFROy51Dj1UUlGUKgvVQgKK3vZ0W8uLF6/zd33IOBgM/TZwMJwyHhQomRgUJRJJs9myuJuzuLvDuTyeQ8pjczSqGdYDQgwk6ZBCsVw3rLc9l1d3BKmwLu7OOVcSzA5naG3QMebNrRjYtG0uxU+Svs8iUFEUJAQxdJSlZjwaYl0HSrNtE3eblrfvr+h9RjakEAjkF2ghYTie5EAyoO1aUtS8e3/BarMlCEPfBQQFk8GA5WLJdDTdudUDKEmfEqu24dW7d2y9pUdmzrMH71qEVuhyiJIhL8REYr7acnlxy9V8Re8jpigJ0RN6R12UROcZVtl94/Bs1h0JyZvX7zm7zHgPnxKegHUtSgsGdUkSHoSn6y0hSq4ub7m9m9Nbn3mwSYAwdN4ymw6JHvreY3TBZrvh3fk5Z9dXxKQIwaOURpLDIcvCUFYVyTu0ksiUuF0sOT2/ptlGZDKEEJFEKiMJ1gOBapjHTNP2xCS4urzj3btzQroXZhMy5KCsdrliMptmfmlMCK/o+8DZ2RXrdUNKoHTm+Cml8SFQ1GUOqyskTdPiguf96RXvz67wXiGEwvlA9A4hM2dZCkVKAR97TCqYL7a8P7/g9nYFSKQ2hJDxMyJk1lpVaVKM9L1ltd5yeXvDfL0iINGFISSB8BFtNKvlmulkQgwglWa+auhc5M27U9bLBqOKLECWA4Qu6Lue6STPJ9EHJIr5ouX12wvmqxVSFDgbSBGqwTgHC8YcvKeNJgpB7zxXywWnFxc0ybFarzieHaJQBBsIDtqQiCEyHo9ZrhZsNpb5suXtxZzeJQbjA1brHhMlhTQYWeD7BoUkekGzbbibb7mZb9n2HpTezQ2RGHe8WakZTabZ1e1389Xdmjfvz4iQ5wSfw+u00YTkkUowng6B/Czt2si2cbx88Y5BXUPKCbeJiE8JLRX/7J/+NdP2r8Lnf/ydP/rNnJqdFy7KZpariAnl4Prvwfc/OKePmoRgbHr6qHnXHzB7kVECYced9SNFf1hw8czwraNbFnbA1HQg4PTqkEd/kBmD1SJkJqzOAsTiB4JHjxYMdRaYLvsxL9fHLLc10y8k5dLnpOpFT39S0R1Kht9fcTLc8qy8Y6h67tyIz66fIT7P/bxYxxzWdWMxi47F9yrmjw1PqiWHZstpf8D79oCzt8cMLnMyuoiJ7kBTLQJupLj9VXgw2HDtxny9PeGo3PL5zVPSV4OMiLAJs/GZOTutOPu45sd8yMN6zcIPUCLyi+UjVi+nHP+53f+GWVvMxjH/QclZWXGnRvzG5C02Glah5sXyBP1nA/zYZCZfF4iFQneB9sjw0fcviEkiReJlc4xLmvdfPaTYanQXESI7qJMS9IeS9Klj7od0sWCoLHduSOML1l9NqRbZtRYLuStPd6R/1PHvbr+PRTPRHX00XHVjzt8ccfBF5nsWC0syOcH+dDrh1z99wy+2j1iFAU0oedse8vzuIcP/u9xzCFUXMPOW9Sc1Px0d8N3ja5RIFCpQKc/vX39K93yC7gUoiXIRPzI0HwwIRlF8suX99oBTd0gTS358+wFntzP0eYEIkmIdqC4aYm0IA8XLv1Hjo+JbwxsCkiYUdMEw/6NHjN+HXaBT5rTqjePsb4y40GNu/JjbfsivT97yBzff5mIzQfx0wOC0zUnppcLNKjZPFK+ZoMrESbmhDQXbUPK2PeLd5RHFraa+9Zkl2geaRyWLR5qHD5acFBtGumegLS5p/vz5Rxz9BKo7t0uplxTzHik0zz8a8mC0ZuNLulTwrjng7fYQ96MDVMhs4PKmIxmJuW149xtDvJJU2jN3Q6SAt5tD3lwcMX6eOcOxkMRKIkPmNl+pkvG453G15E17RBcNf376hOqLmtkvGpJWlLctQmRW8unDAQwjbSxY+ZqNL/l69YCXF8cc/UjmfpgEZtVjFh2bp0PuZhppEoUOrHzNrR3yp2+eUb0z1DeZ9ShtRLWe1adjbscVN6aipaCJJQ7Fn958wJfvH1G/MTkMSEr0xpGMJBaKN6MRW6PQOpEQvG9mnG2nmN8bZC7tJnNqUYJi5Zl/XDJ5sKXUnl8dnVLKwC/Wjzg9PWLwXmJa8GODsgnVedqHJddHhtM4o08GIwN/ePMt3l4fMnhu0DZh1n5fjq66wOJZxbLSVGXYET8EX8wf8cWPPqEKBxQrj+4CfpRxBzGW3IwNH53ccd7PCEh+tnrCT95/wMFnkmLlM9M2ZcFM+kRQBe9PSh4P1wgBX24f4ZPk/MePGZ1lN6FZdCAlzQc1k7eWn354QCrgrJvh0Jx2M35x+ZD6j0YMrnNYFkKQjEB3kY0asCg1ttB0scBGzZ+efsDrrx4ze54wS0eo9T706uJgyG2oeHywJCK46Ud8efsAHyXmj0c8/JOGUGt0F3YIBoUtNPYw4sgZJK82x7Te8OrrR4xfCsplrgRRfUSQA6PeflhxG4e8aY84rBo+nz/l+scPOfk8s0n3QW87jmo3NsxHGps0o6KnVo4/evcJ9U/qPTdXurxBYQ8Mwgneyily4hkZy4vVMV0w/NmLZxz9yQ6nIPOxM/9XUi0Sr5/W9FJxZScICefNlPZnBwwuEiJlBrD0iVRodC+4Hg3oRzAue75cPWTRD3j1+VOO/yyzY+83AmQg88eV4bUZsRA1d37EQdHwB+ffwv3kgNnL/KVk5I6TvGOWa8PdRNFiON9O+cntU66fnzB6J9A240C0TWReq2R05rl+WnEZBzij+b+uvsO7zQHiswmjyxwmJn0OkUsSylXgxQcjNrJgnSoKFfg/r77Hyz9+xuitJGq5d6/bA5OZuL1gXlZc1QM21LzsHvC7r38V/5MZ1V1i/N4x/25BKLLwqVzCLCVfxiMOTjb86OpDvr454ez9EQ9+BMXK0x1qhu87Qq1xwxx45vqS86JimSpeLY/4xbvHjF4a6utdldAm3yflwXSRlamJx56lr7nrhyxtzfNXjzn+TOUMAJvQXQ7HDrVEOMWVqXn0YEFEct2PeLs55MX8mMEfVvQHMs9xgV2GQCKUgv/qn/2DX+pd+y+1aPuv/rd//ZtlVdN1DVrlpHmjNL1vkD5yOJsyHFb4YKmqEu8jXd9zfnmDj3IHKyeHsaQIQfDo5IRBaRAktk1D8InLm1tWmxYfdBaHhURJSVUNEQnGswlCRJJISKm5nd+w3TRc3yxQusSHtAsvk9RlRT2oGdQKJaHrelyINM2W1XpL6yI2RlRp7lFNTIY10/EY4bvs/kLStz2nFzcEl5Da0LUdTdNQmhqB4Pj4iKosMVJkTEDXsdxsWC7XbPtAQCJMQfKBQTVgNppQDUqKncDmfWS52dB0lsW6zXxGoZApC0w6SaajCQJPochiZ29ZLjesNxtCUnQhIKTCWUdVViAiD46O8N6jjcLbyKrpOD27YdN4dDmg62wGQ0uNMYrhoM5Ci5SUpmC93nJ1dYftI/P5iogmuMB2veXo8IjJbIIUkqLKjt3W9qxWLcvFiuvFCpRBGk1MkRA8R9MppVEYI4khIVG0Tcu26bm5WxAjpCSQMgd5lcowHo1AZLZfUonetjgfefP+IgsWUaClzunuzZbhYMi4HqAQ2OgJwdP1PfPFmtvFiuU6p5NXVYUQ4G3DoK7RRqKVpLcB7xLzuy3bTUdjFV1ricGD1ChpGNQl4+mYspBUytB12S13dX3DxdWcJCuEViRgs90wLCvKsqTWCpLAe4/zkfW25eWrMzyKkApSDBRa4fuW6WRKPRgQUyDFQAAW6y0XF7csNhucT4Tod6XhBc55ptMxIkUEGhsCSlfcXC/ZrC1N6/Dek0JOuk8pMJ6MCT4yGc9wIdJ2Dolmvtzw/uIKVVQs+566HtC7HlJ20A3LmuQCUiratqfrHacXN3TWoo1BmwLnLSH2CKMYDgdZYBaCpnX0NvD27QW3dyuSKHLgklTY0KCUZDoeokQOfQhRsW4di03Lu/cXJFXgEwiRGdXEiCoUVVGiVUkSEts7olfMF0veXlzS+QgoisrgQ8hu1b7naHJEDIEYYb3ZsFhsuJsvSSJzbbSGrm2QSuGDYzIeAAHnPG3b4rzn/PyG1aYjkYjRERBMB2M62zE7mKJNDnxqW4f3ghcvX9PaxHKzpawqvA8UZZUdutogpWJYDfAhEGLk9nbJ9d2GxbJFK00SWWDTWtE2DcPBMLePd2w7x6brmC83nJ9fUJUVKUVIEWkU1nmSj4yGY4oyu6/71rJabHj95oyyGuKJaGlAyBxmVY+QUmGqAqk0UioWqxWXl3PWjWXTekxREILazQUlvc2uEm0UTWdxUXA7X3J+kTexfIzEmAh4EJIYE9PJDFMWbJoOKRRSVqxXW169e48PCWEUQsrMr46elDwHsxFKS3priVFhbeDs4pLFYos2JeuuxxQFbd+TlKbrLUIbBsMhi9WW5abh4nrBprMEH9FK47wjxuxcXm/WRBJVUeF8ZLVuuLi6YbNtCDHiQp4/lVT4GLE+4ULg4PCY1WqLFIrFYsn1zR3zZUOMioSgKHK7KCGRShLIY8mHxGbTM1+uuF1saHufgy8jJJmfgy5FYkokKXHWooSm3XrOz69pGpfxCgJ8CiglKYzGW09wntnskOVqS987Ls4uubm9xUWIIuG8wygNBKqyYH47RymN8+Cs52a+ZrXtuF1tECgub27Q2uQQLyLSGNq2Q6JoWs+6c1wvV9xdL0lIfIpIpbCuw6eEUYqu65BCkgJ0vef2dsPl9R2962mdwxQG7xw+5rASHx197xiMxmxby2rdcHp5y916gw8CIfLmkrUOIQ1JaTbWYYqSptkQk2K1ari4usMR8cGTYqAqStix2kPw9F3LYrUiBMnN3ZK7xYrWeqKMpLzlhlSRECJCKZq2Z71tKYqKpuvZtj3X13PWTYN1uwQGkR36zgXWm21m7yeBdQnv4fJ6yc3ditZaeh+wIS9kfUworSAl/vP/7J/8tWj7V+DzW//z7/9mP1GUC78LxMkuVbO0pEKx/EShDh1rV/FsuGBua07XM8QX2Wkj464aSueEeZFg+YPEw9kKLSNfLk9oXEn3fMrwIodkZRZsXkj3U0WUivVDgZApbxTKwE/efIC4LZh9GfccRhETySj6A8XdgeJktmWke160D7nsJ7xfzogXFdU8Uqw90UiiUYgA2ycF3ZNAUJK5HxGT5HQ7o/98hrKZ+1cuAtW1xU2yM/LqU8nhqOW42GCT4fXmiHcXh5RnmuFVyEw+uUsWPzZsPhQUE8tx3bByFQs34BcXD0mLgvG7fM1IQTKKWCh8pdg+TvRCczRo2MaSd+0BLy5OMJcaZaFcfMP6tLMih5Z9x7O0NSNjWdian10/ovxJzeAql3YWC59FgR078M1xjSkiLiqeVgv+9O5DXnz9GLWVTN74vVhfXWxYfm/C6ZMCoRKHdcPz1UPerg84vTygPDMcPM9VSEnLzK60keWnhvlYM6tamlBQKc/r9SGX8wn11wa1SxVPWuZN6qlh+1BwKytMkbBR8745YNnV6M8G1LcB3eSAqVBL6vOe9qTg+qGmKjy33ZCNr7haj7Hvh0xewfhtj1lb3EGVF95d5P3RhLYSmDJy1kxZ2Jqfv3+MujVUcyjn+dpFSsRK0x6VpAeWB4MNHw9vedWecNOOuPzqhKOfhuy23QVTladLukcjmpGkmlg8isXOwfb85gGjP6wz+9dl8c6NDbpPbJ5qwkFgE0scmqWr+eziQ9TPh0zfZN6h7Dyx0sRKY7aB6w8rFrIAKbjphgyM4/mrxxTXivouCwcygB9oRBSsHtfMXcWzB3fc9QPerA8oVODu/YyTz3piqahfLXCHNWblSTILcfHI06WCUnnerg9YrAbMPleUi5DDCEuNajyqjwRdcnuk8EKiVeTtcsbtZkj5oxGzL/ssKHaeVGjkqiXVA+ZPFNGQg9J8yfPbB7j3Qx5+lu+tbsNOuPUIofCVZjmRqDJgk+aqHWOjYvt2wqN/32EWPQKBHxt0m89RbRX2Y08XDH3UvLk6YnU2ZfyKvVgSyzxGkpaoXnP1RGGF4szOeLk5ZtHVhC8mWey8F1/7yOZZTbWMrD5Q+BI8ktPVjK0tML8/ob6LjN5s8eMC1bj8r4v4qmCtS6rDjnk/YOUqXr1+yPhrzfitJVbZGZ5UDm+s5pHtsaGdCS6bMS8Wx7y7PYS3A45/YnFTs+fORyMxS4tEc/mgIg4TXy9OuG6GvLo65uCHBjeS6B5ipXebRgE3MVhTsKwVKNA68sXZY8SXQ4YXMLhyhIFG9YGkJG4kMVtBSIbrquD9YsZdP8C+HnHws13QWxeQgb2gStK0jxOyiry6PcoVyipydTXl0e9nLnoWE0UWPSMkJNtnEa0jV5sxfdAs25rypwMm7wK+kgzOOsJAU952OR59XXKnatTM8fnFU27PZsx+Lhle+izYVio76n0iacnwIrGNA1ZjyXU/5KfXT5A/H1Nf5Y1K3cYcKLnbVCw2EdEpNs8ir6+OETpydnbI4Ksyz5/hm4C3+79LWhBdwVIXLPqK07sZi3czxq+hnsd9iKUIOfzQbANEzTwOeN3NECZxuxwx/FlJfZeF+Yyvyc9DN5KoXpCsgacdb8+PeLM9oP35jOmLXAkifdo7gEWCfqaZvnY0Rc2yq2naAvFywOBScvBVrvqb/viaNKhA7s5NCcxCsdUl75YHbC5H+BcjJq/yPTJt2p1/zCJkLVFrzdxUzFPJ6+0h16+PmP1CUGygngd8nYVu3eTxbrqI2SjOjwrezA95eXVC8fmQyau4F8RFFJQ7UVVEGF443NBwMZ+xDQXiomL6c4lp017YtjOTheqdEG02UJ4a5m5IejCRnCwAACAASURBVDVg+FpTzVMW9ncc5vvwz6QE1a2guxlyNSyYz0fMb0cMXxSMLiLFOuzHoG4z63x0Zkmp4IWY8a6dMu8HXH15Qno5oFxkvm6xyesL3cV9+Ol/+c9/uSAykdJf3rSJf/rP/4tUFAWbZo0uSqQu6P0mizi6pkDz6XeeMhlVSJXou4bVesvXb8+ISdH2HYWuCDbmhPEQGQ/GPHn2kGGdF+LNesXbs3M2fQ9J5++lRIoa53qm4wknh2MePTihriTb/o7Vsufs7IYQEovFirIe0opEqUtCFzk5PuaTZyfMZgUp9DTrntPzM27uFnRRoLTOXL5ly3Q4wXU9n3zwhI8/eorrV7QusV6vefvuHb3LolXf93ghkEJTmZLDoynfevYhBZGqkjko68Ub+s6xSbsUWCVJTQ8xUdUFHzx8ysODA3QhsK7l9ekbVps1rU0Qc+m5NJrgE1obZqOKDx+d8GA2wXvL2eKW04tL1qsGpQxRKkKUlGWJSNB1Hb/y/Y94+vAh7XZD5zbMFx2nZ7egIk3TYYyhDz2DokIl+PDDJzw4PmQ4rOk6S9d1vHn1lvenVwzH08y6FAIhcqH6eDzi8GjGo+MDpMgurauba66urlisWhw5GE4kT2UKjiYzjg4nzA5ySJHvHK2zXFxccnd3hw8Jl0DrHDokUi5Pf/bsGUfHB/jU0S8aLs8uuN42uJjonAciRmmqssS2Hd/99NsM6xKtJVH2bDYN528XbNZr0AapFDH5jGBIicFgwPHBjI+fPiIkR9s73pyes904bm/vGNQzhAjE5BFKUmjDydGMb330KJejr1ZsNluur29ZrjYIU4BRJOuJTqIwHE8HfPTJhzk5PYHtG+5WC96dXSKLGhx4sthVyAJVGE6Opjx98ojkekLM/fvt63cIJQkiYkUkBVAUSKmYDQpOTk6YTEZ4b7HW8ubNG4I39H2bUSBCYWMunSiqksPJiMcnM5TIY+3q+oLrdcumt/TOoUoDPlAqTSUl0Tt+8Ol3KbQmiMh2s+T68pJV6zD1gMa6nQstIogMTM10PGEymVBXktYGLt+dE1zERbAqlzJrBJ0P6FpQyoonj0+YzQ7xbY9tWt6dX2Ctpbeeuq5p+g5tcmBWrQuGwzHHB4eoSuO7nrO3Z9yt7ohFgRDZaWtURisEt6ULgU8++haTusY5h7WWm+WSlBJ93+dNAyRKKVahZTIaMxqNGA9HzOqavmtZLpcsFx3OJ2z0xNRhTElhBgTviTFSlobJcEIgM36bbUcIASETUYCUGdGRgBgjxpQ8PTyh71uiiGy3PdY5UoK+dyhp8CJgygIjMw5jdjBiYEoiiaZpsNZyt1xwMJrQtJvsfu47ymKIETBE45VnenDEarOl9w6Bomma7NrIqCZEVEgpKWpNjJ4nD4/RCNrNluu7XO6GUvTOIqTM5XQxoWRAJMdAjxmMM+4ihZ7OWfoQkSpie09RVHtkh4iJR0+fUJkCgWe76PAhcHlzncvEY8zl/UJhVEJKmEyHjEYjUhCIkEXA28Ucu7seaTSttciixMXAyJjs7nUWIyTVcIBzDmdD3lRICSkVrveIQhJ3YZuDaojvLaUpGU0r2mZF2/aE4BAyY2mEzOPH+p6hKamqghgcvrcoWdL7QBSaKLIYD0CIlNogdn1jPB7iHTjnMDt+txYSUkCliFUSJxJBZO527DuGRYFrfA7sImXurAKberQoMAG0lqQk2DYd0+mUEHskGQkUQg5X7F3HYDyiWW4xpiRKQdptBK1WC0ypM3O2HtC3ka3vCTEiFSgFSgtwCY2m1jVb12MKhbU9UuQxXpkC7yMo8N4zGx0xn98xGBakFNDJYFPGQwhT4ILPyBaAGBgNa5zLx+paS1WPCOEeerkL6EDjyfObSxGpDKNS47YNulAE73M5mFYopTJaSOu8KZgS3lt0mZEhAEpkHnCzafNLvZS5XWRE6yqzZ4WmLqvsaA+7DYuyIJDwCZzPgnDrLOzug0TsGM4SUo11HsQuTRpB13UIo6A0GQ0iBP/7b/1Pvzxs668//7/9/MN//N8nN5IMzvtdOWgWAWKpaE8Mbii5/oeWhw+XWfjZDtheDnn877JLr7p1+zLdHHiS2DzW3P3NhDjqSVHAvODkR4LhZe6v0YhdqWQWI9fPSpbfkcRf2XA83XB5NyFdVBx9nsvFJ6877NRQ3mRuqbSRu++XLP5+z0dPbglRcrkYoz4fMfs67oPRREhUVy2bj4foJocUPf27Z6y6ksVyCFclDz7L7qBQ5hJM1QWikdiZZvmJpv/7a8aDnsfjFc8vHjD6N0OKdWJ4YbPzAvaBSHZmuPoPNOF7W4rS0zYF4x/WjM4D1e39tecAEtVnl/LqmWb+G55f+fSUjS05/dlDjn4iGJ273TWA6gN2qklSUM4dr/+Tgid/85Kr5Yh+XVK9Kzj5cS4fra4tfqgxO1dyKGUOIvt0w9PDJdebIZtVzeSzioc/XNM9rPd4i/tSze0jzfpj4PsbtI5IGWm/nDH9EiZvHXrrES6LOfagYPFtw/qTiH7aMKgsbW/olxXjXximrwKqi3sRNmVTM+2h5vrvJKYfL/L3twXD5zm0TTeR4q4DIAwN/YHBbALv/pHBHzlkGUhBIK8LDn+a+ciQBQYRslgB0B5plt+W1H/vht4ZtsuKgz8uqG8ik+dL7MlwFxa1m8PHivl3NeIfzIlRsr0cUl4rpl/B+F2fU+sBUmaoxkJy972S+d91IBNCJlKjGb7RPPphRzT5hqr2m/CyWAjm39U0f6slRUFykvLU8OiHmassXcKsHcS0ZzivPipYfgfcIwu9QjaShz8E5RLF0mcUiAKzzhsbbqRZfEuz+oGDIkIUDL8qOPgyUJ936EVDKjOGwM2qzH12iTf/qUAMPMkq9K1m+jWMznLoXnWTHcDCZ3GxOzasnyraRwk3C5iFYvYLsvu8jftz8bWimGdh0k41139L0z11CCtRW8mDP4mYbS4V7w8LypueWKmMMDhQtMeS9bciqUiIXnD0uWD6okP43QbIjrWrdgxSfbfl/D86oXmcUK3ANHDwpYMExTLjEdxE5yDAt1u2z4ZsH0jaB4LuqUNuFYMzyeg0fnMdIeEHCjeUKJddq/1EsX2Sy/OLVWJw45Fuh3lwcR94JkLa//3VrxvMJifa1zeRYhP25xULiewCYaDxgyzirp5p+oPszCvvEmabmH61YfXtEfWNIxpJedPipiV+oGhOFKpPbJ9KBuc5+DHJHLh2Pzbux0lSub9Ll7j624ZYJoql4PAXbv9sMCu//7ukM1YiyTyuukOxQ8RAuYoUq0DUArP2uLHei2VJwu0PFG6SeaTlrUT1cPSF2x/7Hs3iB5n3uXmsaE+yOJckFGuYfe0xG78L1srXHY1CbXu6J+N9+/uhpp9mREMOBsvfT4r9f9+3SXdSoLqMG2geSMpFor77ph3v50QRwawd7cOSfiIz87yLmYO6ivs5R9q4v3durEkKioVn+7jYiZQ5sKpY+j1eJpQSswlZVO49/XGNdJHmQXaSuzr3ueo29129toSB2eEbch+rLns2z2qUy6X3UQuUyyGZZu3pjg3VDvkgfb729YcFo7Mc0pakwA4l5TpQXfZI60lKEkudKzCAUEnawxym6Qf5nKPObdRPFfo+tK7L/Xb62tEeZbasrwTFNu5Z67oJ+7kRYPswX6uIUM5d5tf239zL+/ukurh/Z0hasH1UYNqIL/M9SjJvIIdCUM3zBmbYvZPoLuXzWMf9O0h7pKhv8zx1399DrTLTf7eJ0h7uWO+70DRf5bCy+/YMlaDcPdvtzOQcAAV+177F9j44FaISlMuwf86adb6Of/tv/ptf6l37L7Vo+0/+xb9MPgFJE0kgFciIdz2DokQnxWRQMjsY41yPDx3bTUvTe1JKOASgsL1HakFdVWgBvs+Lu8lwQNf0tMHRR4/3lkoPsTZiVBY2tBIIqanqIpcmuhbvfQ5ScZGw44sKqQghOzVjhMlkRGESRsPdKifnWe8gadyO72iMptLFDijlOZgcEkMuMXchEWNEapEFiJRQOyFBoFBKMyhzyardrqjKAdd3S3RR7liagURASE0MAqUMlRYMdAFGsl7MkUbSO5v5lkpnETZknlWtDLooUClQSTBlwcpZfAik6LMTTShEkjjnkAiMMZSVQcVIWZa4vmVtLVGAFlkMhuy67LxFap0X3sEjFUzMkK3tcDFASsRAdvkh8D6glMrXn2BQGaqiZLPZ4EJmpwYEyESIkUIVxF0ZdFUajFGE6ECVrDaZ+RWSIKQs9pWFxvd5QWtEFqJj9Chl6NuOPkSiyuJxkjnlXmsNKYfDGSmo6xotFfPVDcPBmODAWovWO3zATgBREQqlkSlRG812u8XFQBASXShAEoNAisxM7myHKVJm8xY1MmT2oA+CGCOqFFjvCMkjooYowUsKk7EIdV3ivaOQAhcd1juS0hAFUXpiBBcSdbkTnX2gkFlsrKshXbvOHMiYnXopJWKEoqjoUhYEtdakqHLbdh0iQVFqvPfgQRXZKZ9MLp0HT61yfxMUdD6XuHsfkTHzJnVhcNZjdqgEmaCqKjb9Bh8Dk+GIZtVQmpLWebSRKECKQPRZfLE+MhyOWd4tUGWBKWsiCW8dUiWkivRRUFVDXNeijSC4HLxXmYLlcklZ1Aid+15MeaMAsQuiKIqd8JlZY9ZaeiJGKlQUBO8QIrNAy7JEW4mIgT4FVF1i+z4LOV1HURSwK2cPMouXbduihMAoTXKW2WzGcr0FoXd85EjoHMJLzECTUg4TSlIgtcibPTEgRHaTE3x2/VpLPRhgbb4eE3d8YZmIMYtY0misdVnAJO2QFSEjQxT4FPcYC+cCWkuc7dBaIoTKglQQ2U2YIoU2hBCoqoqma/N1puxiICZUyp5CIVLGPniPKQY4kbDBI4NEsJsDCHs+XARECkiRMkt35xjW0uCCJyVBlNltXpZV3k2VEmst4/EAH2wWtW3u40JkMY1dGwSfcrtrRV2XuU0KgypKrO2QPhKsYzSa0Hf5pcoHi9Ka4C0KkceLNvQxP5uMLknOIlUElwiUhOTRdUmMgRhEZqCngAgB51uULEkyEZMkCUXanXc9KIF8ncgyC8iC7DpH7bm1SpYoka+7rPJ4FEIQk0CkLGpmxrTPgmL09CFzm3Vd4reeejjIxwugokQmCC6RFHgD3jpS9JlJvhNOQ8iCpUiZs6yUIjqPVoLgoSoKgk8EEkkEtBI459Aiu7y9j6AVPjmklsgUs+guEtqUbJotRTUg2Hz+ptC0TbdD20igyxuywuAdpJQoyszGTmLX17XK8+Hu+VIoTYwZJWJFRMscPgc7gduY3K9iRMpc4eBiXpTHJJAqB71F2+/GnkSE/K/3GZkS4jcLI6nUfszEmB1GwM4NHfJY3L0/lKbAui5fvxQI8jHZ4VqUUruNZ8W27zJzv7cUlaGPdueY95T1AO87hDbEGNFCEkLI4i5QSM3/8lv/w1+Ltn8FPv/hf/zfpXtRSfhvFkqqdfQPBoRSsn6q2H4Aqs0J7PVN2i92dJP/Vq8t0SjsYZGdNG0undw8UZTLRLUIFEuP7AL2oMCsfXYmxVySmRR007yo1H12y9hRXgxKm9BtXnBLG3ETjQiweaJxQ0GoYfIqn4fZLZJ0EzCLjjAssuNm91l+bBAh5VTpNu3FZrPJ4kwodyWru9Tt9khhx1mgsFPBwXNHqDIOQu6Y0/dCwL1zuD3K88XoIi9si1XgL95jabPIeB/mFUpBP5H4WjA6D/vFqepCLpWXAtVkZ6Af6n2wih0LdAujc4d0EV+r/WLXD1Rma+7EFsgM1/VTzeBmt4CG/TVEI1FtTrW+D5Zpjw39VDK8zM7XpMRemJB9yPgGF+mOzP787wXg8anfHT+LGdJF7DQ7In2t8AOJHWUBJKd8R4p1/t5ezOnCnul7L3x1B4pQCqavepoHRS6V3WTR/T5ITzWeWCr8MItA3UwxuPaZf2rj/+uY2d0oKRYOP9Q7R7PZh/DpXSlv7iN+z4G9/7ixRvqM1dB9Dpsxbdr3p/vfkS6itja3eZWvO7vlEu2holoGVPcXgwBz27ixolh4ZJ+dpKRd286zM86NTe4nsG87EdN+Q6E/NHvm5D1rVTUuv49LQaizC9EPM+M1mtyHB5cW1Ti2zwZUNw431vvAszxexf7+mY2neVAwetvm+74Th1QbsuPQyBzOdFhl51+t8n1Ugv5AM3q9xU3LvYB+Lwzff/wwu+X9ILex2Xj0qicMvuFc35+Pm2h8nc0AxTru2y2UivJmF1IH+7kkaYG5bUiFxo+KLJZ+XGWRHghV3uSXNgtOfpDbIKM18txxP6+xEwjv/86ssxB9Lzbei5L34+JevDLrLMBKF/e/d7+5cz9e7sWq+/YKlfr/jBNpI26cw/X6A70X2qWN+/O650rfb1JIm/CDPJ/di0j381m21LOfJ5Lcufd3m3Sqz+emm9zXhY+o1uFmVR5XpcSsPdsn5X4e37tL5X7vex9Wd/8b3aGmuvOEUhILgVnn+6T6wPZRQbnKYrduA2HHrA2l+ma87ALd/CDft3tm7v1cFCq15yDbWd4MSzKHT8ZC7DnM9+xVaSP9YX6G7AW+9V+YD3dzdH6WqX2fs1O9c6uyF4JVH4l6t37Z/ZZZ+30wnmoD/ZH55ni7eSgji7LTVrf5+v1Q7b93f/wks+v3fsMvVAJpcwDovZCbN1PkTnTMwq/qdiF9Xb6uXKWSgwR9KRlcWvwwi/xRi7yRc+v2/fT+E4tdxU1Me2SCiPne3rfDffu7oUQG9u7vUEl8LXfP7yxw53nwmw3hv/jcukdR3PfVaHLfjYo9ekD13+ic+f+zP+b9+dzP89HkvgZgpxqzye8cocrHVV3c9QH250OC6rLBzfIGqRtm1/m9mOsmOgeo7ea2+2f0/UaCHSn+/b/6r3+pd+2/1HiE3/7Xv/ubSkpcsBgjMgtSJMgh1hgl2bY9y/WGprdYl2h7B0kghMwlpEGAzAt9rSTWtggiWhWZ+eoSm65DGo1UBSJJUoIYAqUpkCnR9xZTaJqmoSgHOJ9IUtFZt+NTCjpnmY7HSC0JyWNtdtJtmgYpFevNFhcCUimkkllpUAlkdiKSYLnc0LkOUxqc83Q2lznGmEhppxMJgQ8OVRiavqMPHu8dW+/wUmGFIKpEFAEUGK3RRqOkYNtscV1HSIlCK2zMwowQEsjbE0ppBlWZF5ZC5DL5GGmtpw+5TBuhkDKLAW3XI4Xc8WUTKUIg0nctnc0vBz5YtDRIKdDK4LoeJRUCiMGjRF4w297ifMgJ7Dbz+FIO10MphVQGIQ26MDRNj/cp38ZCE4gZLZF27e52pbe7RbW1Huc8LsTs0IuCzu2cTSqzJbUxtNstRVXTdA3KGBrXE3xAao11gYTAWYveCVDOO0IIJCFwPmBdBGHoW4/SEuczszJ4T+9ddryliIqglSK4gNY7d7DKTGbvHUrl0v5sRUmkmDnIQkoKqWnblqQ0Qu/6ocp9SIp8z6qyJO76S5SJ3jmizyE8KZFF2JRIwuWZU32zMSK0xiVPEtD1ub/4EEGCLE0WcgpNnzxayl2g2Dd9QkqZS/R3v+9iZiLlBs/3PyLpfCQJSRIC7/LLZ6E1Kom9u1qK7OwUMgtVvfMIncv1o8tl2yB3zkNJ73siiaKsUMYgZXbtqsIwnI5Zrm53x5dILYghUJgC3/uMBVGKlKD+f9h7k59btvTM6/euLiL23l97untumzfT2bkrm4QStvCMCaiQmFBSSQyQ+Ef8f5SQmCCVQFBISMCAAY1KpkBFFbZxk05n5s3bn+7rdhMRq2Pwrohzc+YBkk3Z++rodt+3d+yItVbEet7n/T2bLdNR+bnWK9vVeQvVaPu4tXpdKORSlUmcYJ4T/dAjqVCiCra9D+R5JibtIfU+kEpBnINCY+NCFiEboYrepKyxrUChEH/XhEbjA2A1TKhkUoZh2JCzFhJKKdRcmOK0jhl9iQYuVfA+EFNar1cuUGtDCOTUBNeMdcLQDy2UTVYXYoyJap2GcZVKsB2SM10ITEX5bRZLFUMuFYzT8mcRkipnVCoU1PlqrBalRK+1xZBToRZFG9RaMVUoJVFTehuSJOpclypYHIheo5SVg27EkVPGGWW8SC3UmCg50ztPmqf2vhWsYJylttb4pZ7pjCNnEOeYY8HYQE4FKQVrhBQjLnjmaWzhd1ATYPU+1A9BcRe1Yl3Q1tA4kaviESyC7Tpd44wh10rXB0pNxDxRcsR4QxXHlBLiHYXS5ovHB0eMyq4qooUD74PChJvAWXPW9SYr21QZ14VUIWUNZJOqa+E0RwwWEU/wjlIr3uk8r7FQUtFzKYYpqpCKkRU5Is7irCfXTMoZsI2HrPMkiKMWLTxVYK6FLJlaM8ZCjJnNZksuRe9VXUdBkS2b4HFAjDO5QkwT2+2G03GPt43jU5QpqwUzB85ixOo4dR3eW8Zxwlo9j8ZYysLrxjCEnmmatYhERZwjpUhFA/XWQrewvkeuFVNRnESaMbVixWBFFEeRMyUKOWvBaxFla0WZ8FmxFLVWrPdIi0CMMba1U6AapBSdhwJWhFKbAFwrJQtVtCAR44SVxqe0hj74VrQIzOOkgZClYhvP0bVz4L2l1KzXHfiH/+F/8Hd4hL8Fr//sv/qD3y/BrG2UWB1/AFKFtLVsXmR2n1eGNxV/gP4m637eKBtzSUI2MZMHT3hIVKNipD/oJmf75aQOL2eoVjeK2jau3WHdrYbxbF4l4sbiTlrU626UVykV/O3I4YNBWxjnqg69Q2X7lYpD289H3CFqp5k3bS4ByCoCnX06099pYrc/FrqbqPMqqlC6FGXdUQXM4VVcBd7hZcadMn6v4UcmllUcKMFQvGF4MeNPgpshbgxh31phzXI0ikeYH3W6mfM6D21EsQ73Ki4KtBZaIbw+qcgW1N0sVRPUu3t1FUtWgSwPei5LZ9bjr1awjekIaEL2IWsY2knv11J0e1W9fgeMCpnDyxk3Ket4uc72qOerBIs9JtJWW7WLE8Jez5M/qehmciW8mdrPq+CWe0v/8kTaeYaXieKF4bWiJqoFv1cBw+1n8uCwc8EdImbSNmR/1GsOEB4y1Sk6o3r9nv5uwsxaUCi9I/fKQUy9shcXvIEdM9Ubqms8Y1jFsmqFuLH0b/T8IEK4mfVnW/uzFHVWmaznzqRKuE3YJCoOt2uyiEM0DcxELQ4vfFWp0N8ocmDBA1TbWt5zVdd7r/NyObZqlFW5is9VWcRSv7H3F/2s7s2ElDbG2rVL5yp2LmOsWr3GNAEoPLRigbcrE3SZF8oznjRT4MxSgh6vADUYjk89u08OVG/bd9H3TmdhPeelidLjtaO7zyrSekO4nciDBipV+43Z2wRsOzc26/1MvOzbd9SxNl86dcAdM4hZW7D1TaSJrCqymSmv17V02hk3Pe5AVMQOh6IojwruqMUHe4zM1x121qDGpV093GmBYGEeSqXNUbQIcGzn0hnsXFbBaHGXulYoma69tsh/Q/h1B8VGmObkTDu3OoXDvWJTStC9gp216Lz8WULQpB1LXTjGvhVWRNcJOym6RPEzb7EnknU8fPO1vAeyfEb7HStviwrtmUVyxeRKvPDaCm6VE8vaWbdc2wptfbBtnPm9jpOl2KBMXRWIw6GsnymN7VqtMJ/bFdGjx6iuX9tEWhWATbv/qHi6FFz8QbEOy3GFuwRt3QRIO0fcmBbUpePZP8RWdNRzUYJd0Rt21nXEJEVFuLFgT21v0YTvcK9FOIyQdlrkyIN2ukjDLSw4lvCgxQVQx+fy/Za1zbWijXLodb2JZ24tqkhWx6ed6opv8MfC6ZHDzVWF+62OaZMq4yNP7gR3atz2Q+b0NNDdvHUIL8xmf6/rk7TtuGRIW0PqDd1torRimq4D+jPVyTr3MerOrVavsRRZv7vO0SYEz7XdI2QNatS1pK2LDWVhkmIO1utZ27oezCqe6r9LK74p2gFhvf8vzwrVCSW0cMig769zWK+vP+R2LQx5sM1xXJnPLGFf1nNVm5ice6sYm+3CATbYBP/pP/zXgGn7T/7rf/r7OWesQMyRYQhMpyPb0BO8IceIpeKMo6aCwyK1kqteZVubLioGazQN3uPouzPmmIkpNV7djnGaUXBlRkSdolS9MMFXchTEltY+aTWZHdZWyWG3USGt/VVKwhmLcY44q7NXWmuyMZVcRnxwGHGI9eAqNc0UUnP4gDMBEb3RG1ObINAWUBFU+c0UUxiMV0GWQo9gc8YWmFOmmEpJk7qrekueGt6ghYsVqVgMHtFNeS744Kl1Xjf7Mc04ERUTrUDRG+5utwEqXRcoFGIaSXXGdg4kqAOPQMxR3VPeUmPE6Bin5ELJmcEHxjwRvNdAKG/JOVJqYRi2TNOEtUZFm1ootSg3NyfEVhJV3bVUbBcU9+A9+EAtlYyQpZLwVBH9OS84axEq3giCOsKMVWehGN1sp1roQ4/xvjmZ3OquBHU2ed9RMVgn5JTZDAPzFJEmFlhn1cUoGRMMiUKRSqqZIplUZ1KBXDPOBrz1pFjJJZNiZnBeBc+SmSngmhBQKt4FaoEN+ncVUmGME5hKrYkQOnKdwWj7rjQxIRUVaAY7IDlDqUxxJDcnsT4WG4xzmCTImLFJsCWxdZZYla1ZDKSsifVTjFhjiS3wygRLlYpr4kluTl6yPmA47xArGLMEhwExq+hgDWIyttfW5WISplZCmwNitQoYa0UseKtCRkyRKoIrQpFClEKcE9tuoMxHrG3cztphAWUYK7Oz5AKpYDpl4QhVnXWgQjeom7w6Ym5rBirCW5FVAA5eb5jBWJwJGLEQPPt80gC/WslNbMulKDNqiq19X/Q6poIXDRvRvVtmTFkdtU4Qo+J7LNreHgukXLG9Qxp0Xtpa4Z06VUEo2ZBLoRuMMj6lkkpCBLw3OrfKjPeOaZ70+nlLrRpwRqE00wAAIABJREFUYp3BJ6HmQug6ci46L3PBBaFkCN1ALrqGDtYjOEIB4zyhIRoMELNuUpITqnEarlSV5Z1jQcRixWFb9wNGyCVSayF4ByXjrIFUcUUwGFLNVOPAGGquhNAhdSJUcIO2ps/ThLd6nqpxpFwBi7W+XU/lNmeBLgwg0FeDFZUhTU7EWR/Gu84jVdfonMB4j4gnIoyHSMwF27vm2q3NbRtwvqNWbatPRbsJcorMbV2wYjBSVKi3hlMT3krOiIVcI+N0xPsNRVQk9iZw2D8od9sF3WBWwXnBlETNEbGBWitdpwVNqOo8N/pwk2vbUBqBGDFGGK3BFOhNIFktNhjr2I97uj7QGU9Olb7rSDWr4FkLzgsSJ3of8AgTkVQTYvWeWsmKp2mO1dM0Evpe2c85UxxkEkYK1lSmnNgOGwztJlKrFmaqx4iOa9dbxMCUZ7xsyHEmp4kq2oHifCXlUdE1xpBypsRM7wOn0wkJW065UEOn88B5pGpxs9ZCbU+TxQoxFnynIWnVQPCeUPTc5ZIxZJzxiAWxFbFCLknvsUbIApAxvqOKcJoiFQGbdG1IieAUkWOrPveYOuPE4sSpgxfTHu4NtSZ8MFRJeCN4scSijl9bLb0LSNXKd3COzhnqNOJzC/e0nocpIs7wj/4uiOxvxes//y/+2e/bqegm+ZiYHgXC7cz4bGiurRauMpi11ZHWxi3wdiPsLdVb3DGRNtqiGvZFhdH7xPg4EG511y5VxZR45lZBIe4sNqKb7ebKMWnhCiojcb7q6F/Ht5zJqBsj3UAV8qZ1F7i3CIK0dbo5XjSn5koyWYtVeVC30rJZ01Zgo8e4yqwqnIxXDn9U0SdeenVVNTfZcjzLd1I3jazt1OqgVEaryfrfFieYFFTgPi7Bbm3zDmCE0/MeQZgvHCarmLO4zEBwzVnqjhm3b2FA7XssAXMmVqZLp+L4zulG16tQYubMfN3h76IKw7GswkEerDqMRLBjJp2HFQ2wCACIso2XjbUKQbqBrt6qENeEYAy6Vi1Coeh1smNW15szTfxQN1zaKqe/eqtsxubMtnNhfOxVYDT6XrnT+4KJuY3FiE0qYJmkLjL9k9YWYztqGr09Jebrjvbgp+9LC3qKhdxc4eOTgBT9LtUrrgJYRbVF6FkEmeoM9pSUxfyoa89krOJyHtzq3lsE1GqFGlRkGp92+GPGjFnH2FGfE/3dtM635TxL0eu1OADTzivb2KoYKYuIOhc9zooKjm1u5M7gDwlJpbm67Xo+TBPSMYa8cZio7edSNYTHRBV3/KEwPusxWVEg6qxU0UTHuQouy7WvVlYBNW99mxsqzJXOEs/sKkAuY6h0TsVOr8dYrSFudW4tc33zxakFUNkmkKo7XLI6nhFRUb/T30kbu7qCAcLNvAZLLQK4FrZ8K3qUXxIwF1E7b9xaoDGp6lrRGNkqAutzY9ro8dpRx3i4V5RSbnzbJfBuXXd3bnVbLsUPHXN2FTcXh2Y803mUdnZdXxdR352WIk/CNbHMNrdo6bT4tFwbPV+1jaN2bFVb5VUgLZgFY5iV24zRQl9pIqnfN7f84rg8tYAsJ2D0vfwhY+fCfOFAdJ0rTdyvVnBH3S/OF67dP1Sk1mujrtbuzaxuzlZcWhznaeuayN7Yo2NeBWK/T02QM+u8k0pDToRfWp9W5ntzdebB0n991DE02FX0y4Np7lf1RJnm/ratA6F4FaExRsVWEVIv61ravYlUZ5gu9N5bne5zN5+flEO8Uz7rdKGhk25c3KD6HaZLR94oo96dMvpgzzr3ln/uXo2tU4KVyWsnnfelM3T3mdNjt3JXEUGqUDppY6a29dzg76aGKClN1NSCXR4M7tTWNt9C1JIe+/BGBdvuTVwLAGnjVnTAUvhYCmL+oEVJG5eCiCXuWnGmibk5iBadjR6vbS7yZc7ouqzi8fIcsQjDKuIrAqV4/d3qhNSpAI0s102FXr3Wev/OvXaO+GMFo9d4umhjQtRNO59bTIE0aJeMybRgS8N/8h//O///F23/m3/63/2+blpB0LblzgdSzOoYEhXdQCHytfHoatF2RnUaGkrN4C1iLc7ZpZ9WxYYSV1egUNf2YlqbLlXdlS4EKgVj1SXVdUHDR5ojsNRWOS1F3a0YbEWdllXT4kVEw8O8wzur4UVVVFRMCWnip27uDFOOysMRwTqztmsaaRtwKXjXQiPGmc7tqDiQigu6cVZx0hHnCGIQ8VjbMcdIriqgGFHHX6VtDI0w55k5aciTtqIKU4rUKngxkFGGZtX2kdN0AmpzXQZqsYh3zPOJfvBMJROsU4ddyipiioBVvIL1DustBkFipgjkkulDT44zRiq1ZCjaKp5LVhYUmg4qGGoBQcUbalVHrHUUA1OcFWmRE1UypWZtKa36s3Z1HBZyMZTm9qxS9dpaRyn1rcCBNCd3wbQJvLgUu84T5wlnPaUmkNq+qjaeGiDOkc5rO3wqM8aCWMecFbUhTYCBSgieVJOKm0ZIoo7mkouOWdQlWXNGKKjOMmKAIkIWwzzNpJTxoVO3rxEqrR3EdZQ5q0PdCDVXNt2gjl/TWvANnIJhlkLxyu7EaVuusx5rPPM40Xd9O662yREBU9cH0pL1+5Scscv3Kcp3KajDXXDK3WxjTqQSpxFvPKbqQlcL6jo0lqk5Wp0x5FnDkGxzMNZclQHpPOSiInbJFNG5XEylNscsDX+BqCk05bEhRnSsWwzOiIp2xquQba2eTxF14pVE13ecxiPeOb0GOYF+Ark5pr3zeOM1aKg5LmpWh5tACzeacZ3jOJ20wFMyNeYmmnqizJxKIknFdB0lJ+I8EzrPHOfVnW+MXgfFESTFWXgH6LpTa4Wq46bzKkSnXDAhME6R0AVKK5SEEKgxYVLCAD6ogDjlGeOtXkuV6jX4TwwWgSLUGpFamONEqgVrHcb6JtZnyEWdj9bi3UYRALWCqThvmGPCOkeqmVwr1tg2m4QSs7rRg6GIUNC5W1Oi7ztyilQqcy3UlDAVvNe1KrZwJ0QLaylnUqnrGDTWEvOIsYKr2j6OccQMIXRQK7m5FWsVLaD4TlESKWO9YdgMxDmTakaoGKute4rcqCpsioqmxqpbUkTUCUygGkfFEZxjnltxSyB4R+fUuVopGPQ8bbY7Ylb3KhXl7NWCq1qsKbXifcBKaF0pAjVRagSCunNJGAzehub+Fl0vtbyFsVCrFixM1bV3nmfEGmq792z8BkmCD70WF3IiWINr60oxUIqu3znn1tpfmOIJN6jjP1t1w5rOMVIpoly0LIWaIjEmvN9grf63UhUHErpOnfqpMgw9Kc545/Rzug3VBErUFjpnLKlUsjXgPakeQRKGhBWPjjRDTpHNsIGi14vm7vVWqC3JPRu9JxgnGKP85VIEUzuc8aQccc7Q9wPH04nNMFAw7VlEFx9jdBxZY0g54XzQjo/2ADhstpS2Tlnj1iKjMXUJCFb2bxO1ndWxbankNK+u5CF0xFhJKK7lFCesFbzV+fuP/i6I7G/F6x//t3/w+8vmGKsiQrzwuFNpmzgVZYC3m1Gr9+LF6QPNGaQtDpTOtE1ec5NE/VlB4BvvsQhCy+YsB4Mpulny+0w81xZFpLUzp8U591b0yG0zuGwuqzHM57qJyhuLf8irg0gdZhDPPYvjK9w1R3vDIajLRwXPpQ08bfSZ1Y1FN1+1Pfd1smISStAUe/nGe4WHvIoLtSFWpELa2dWp4+/VUVqCvme4S5hSVTCrSzq8fufu9dyenRfnoJ5H/zArcuJuJu+CCi6LULY47w6RGuyaog76M2bKxKtO3dLNhQS0FvaKWbiclbeitn27kXcP6rQySc9lPHMtCKa5nsb8Sy7W4lUEMXN9Kw4tAq81K3MRaGgHFTeqa4zQFpqW2ueUhrPQQdicgEbPtztE4mXXQrFyO28GdzeydHTpwQnpLKgQ2IQNO6lQZGJZu1EkN6STqDDgjipyLDxLfz+rILz1K0ZiEY/K4NbjXObR9KRfxb+lDTu8OuLvpzU0CVQc0yAwi7+fma+71TW8OPdWAbRdr0VAXhzWiytSjzXr/qvNA38XwQhun1Rgb242KRBuJqoz+NtRz+/g1hDB4lWY0+vHKpgu7u1l7C/zbylQLA5ORSgsDnd19JbOKlu7jdPF+b0UiZZiQjzXIKq09doJmFj/v53a+2wcaaNr1OLolFzIZ52aQkpd51//YiRvFIthMmD1u7mHGf/qgDnNENwqnOetx+2jipZm6SDQ62CiFnZKZ9cCjvkGT3a+cGt4UXWG7s1I2vnVvZ92bp3by/pjUtWiSnOWL2KTiTpOSutGWBysfp+Uu9qZNefGTPpMaOZMdXYtGEgTXhecQenURW/m1Ipxpl2vty5Dk9RJK0X/zJfhrXv4IbUWctM46epmXItjRl2V9qSoG0QornFSvYpapRWD3FG7MVQ8rQ0top99eqxFLBUaLdOjoK7ZJoYurmHTCmelM9RWMFqxMVYU49HGu67PgXCvrPbqVASfz21bA9qau89Mj3sNDszLhmtBEpiGRFBXZW4i7VKIk+bOX+ZO9So+6v3z7Zq9CLk61xeXfSsotfXfRHXGYoQ0qHPTH1SAXAR/vfeV1WGs3R2iLOXe4k55daFWZ+huov5OkdWd6/cqWi+OUx33aKeM1/E5XTn8qai4Oi9IA/18LRDp88UivC64gWW9Ude+FjpPj9p+eH1OUCSDNAetnfW8LXMkPJS2Zgo5GNxYKJ0wnVuG14nxkcOmVvAY9FlkvYe2AkTuVRReOmOmC7uGlK3rglERfSk6qPtc74cLwzkHdfkr6kGYLgV/1A6U4oXhTaJ0Qt5okeqvKtq6v8oP/fW92sYuq3tlnmecscSWUo0IUSpSCrttz+mkinUVgwmeOE3M8USignV460hxphoDpZBnVfnH6aSbRSt42ykLcx4pAlE09KOamZIhj1FbP+NM1ysXJ+cWbGQtJemmXkrBOKebbmdVNEHIaaYWwzwrsy/nhEgleM+E8v9C0hZIbxypa+4yCl3fk5vgYArUqhO7xMRmu2UasyaglkKsGXGW3gYeTiObblB7vDiyVOYyK0PToA4iEUwVFS1SxniLb87VWDI1zex25zwcTxSBOSeCc2DUrWytZ54Sm83AaVSOXowzm82GeR7ZBBX1BMH2ToVQBItl6HtSVHZm8IFEpjPqWi0pQ8oE74kx0g2BeR7pOv1e22EgT5FgHTFru3cR6IYemxJzjLjglKuZErYmFa1LhGob3kJ5hDUnnPMgVsVI50gxE4aB8TTjg8W2812ztsV7pxtmax3OCdYV6jwTgtOqZMmINJ5r0cnrfYfrHbYWrKjbKdWM863FjMo0jzjr8d6taIRpGrFUfC3qqqytRd5Zqmjiei3qPFdRTtudSxW68zNKyozjqEJ0BqiIUXduIWOcJc4z59stvmorrzWFZFVI31VLNIaaimIYElhxeAyUytYHbC70xq5ifEoJ1ztKa1cPriNOM31Q8cR1QV1uRYPsnOvwJpDnCTHqTB86T7x/4PH5hsPpiB86FfqzoheCMRiJeOObCzpiKPjOgSjbNCGYja4hszF4p8FqpWgLvhWDUFUcqUKq+hApKIvT+UCaE7ZWgjUkI8SaoGSKsQTnKFmt/TGdQBIpqUBqnRCsir2b4Lh/OIC1iPNsO+XLqp5cESq5FrCWTeeJ85Gh99iaEPRpd9v1jKngasFGbdG2uYCzFFupeWLrVfQbxxHpLIJVjEMVXHBUMsZUnOmRUgheReN4OuE6TzZV8Qrec5oinXXqAp1meqcu4lIK1EzMlc4KYrWd3KDFGWOaYGugc4J1nlc3r7g+3zGNEVMMc6lapTUWLyrQVqOWnTEm5jiyGXrGk4ba5RzpvKM4DZjKxZKNujkNMOaJruvI48h22DGeTtQ8YzLEAtZ7LCr4UjLOVboQlLFbVJQ+pUSKyiautWKMo0piThNgGIwnBH1AKknX0RgjqSjqw/eWXAtSC2e9A1coZaaSydOB4eycWoXj6aSOXCuUqO+T4ogTQxi0nT1WwOr8wio/PTRRlKqok0KBlCkx4oYO0xAcGdOY4xPBeWJKdOJw1jHHUUPl8gnXkAoZvXdkA14qw6ajzorJcTYwAMdpj/ew3e548fK14nWKkErCeEcYOlKeMAY23tJLpDsLPOz39F3PPI96r5hnLfoA0O6VLaANo+38ZoxcdQP744HOO+YmHnjfI6kiJVOcRZwh5qyO1VapEOB0OFJrZRi2SK3s+h1IIaVETZlpjmyCa+ex6tpdk55qs9H7eK1YVPwupeAt5DipIO8daY54YwnzjOCxg+GQC9VVQu/pxSMJnHWQhHEa6Td6Pzge92yHnjieuLw4ow/C46stx8Os2Kf7iYIhbEMLYFNMBcA4T1gRtr0+y8RcFTuCstorYGpQkTomvCmc7TYMnWO3Gej7ntvbWx4eDtzd34PvOMSszzqpYiv48Df88fDvXv+fvpaNe9xZ/L6lOx8XJydrgMjhmaO/LbAIUdbgsrIQ7UnHYNppuMvCQrQN39zdKKNQjFA2snJkQfD3yl6VnVuDaRb2WzzTsWhibU60hYOqG9iF+7iw9aph3RT6fduITXllSvoHdVfFnRbP4qbDjW+Ly/OFW12yGrTSOHhZN5JLuI+6AaW1ZnuGzw9MTzfqdGusUvcwf4OhqedhaW9fNsl565trUQWX4zuB7RdTC6RK6/cPd1HdtA8T05MN3avTyggdnw64Q2Z8Oigf0ShbcWHzKje3CYTekHZG+avndmUfAsSdtnMvPL947hg+PzC+s8VOef3/C59Pg7WUyZl727ia9ZdZlC1MS8eRikZLW78dlUW7fPbClV24m4tonbZudSrX4S0LMu50k7zwHFVA0u+iQT1bvY7eUs8sdqzkXkC2SKq4+4nS+2+wlS3+PlGCOi+V9WlXbrMUTWOnVEpvSYN+j4WDPJ/3KyZhYXzqNTes4WVehcDDhxviYNg2XqM96XcY3zt7+xlNSFqOB2B8Z0sJsnKaXXPhlt4iTRSOZ+06Xjq9Hjt1FJtUCW9G8uBXZ9wi1EzXHne03HzXM7wqpEHbo4tX3EM669R5trFIc0EXb0gbvX6pU2RHsULYZ9xDJG9aQFgsvxSstvCHF37mUpQpw1uucmzXa3Uyx0JsDOV1LKFitGnidNxp8eP0KHD22bTOu+NTT9gX/czGbF1E9fHpgDuqAJc7YSFMHJ84+tusQm4LZFrGqknKxR6fdWs44MITXZzZpbFpqxMdJ81xJ6Wqo75TR6aGPjrC6xPpvGtsVmWQxq1Z56Y3IBdBP2M5b7AWQgCmc/3nyz8/cvcrG7qHFg621++6hJwtYxkg3Ebsw8j8ZEu4mdVFPRd1mkaHHfOKlCgtqDLcKqvTTJnxWafjvYm07pBWx64WonRsTGeezav0jfUxY0+RcG9XljJV+a7gGK/0++v5rsSt4gkW3nfaLAIbHB7pGikZpDj6uTA98kiG7tVE3rgVExB3yltPg2HeGtxY8e26Li+97/hfQji0vgc9ln5BqBhMdI2Hm0lbXSvmM3WA7x5mxiuvjuhehevFBWwmDZ0br10rWKrjdrw0DG8ycTCcHhse/cm0YlH0vmTXuVHtW+54PBM2XxWm8+Zu9kJo133hqS9FnWXuzFeB4oX7b3VsXqTW1p/Xea74I11fF3axiW/PVTWiAZtV2dnVaDgYwDBpt0a4i4xPwnp9Vu52Kyou4rxyvJcOFA0WW+7zdlS+7bw1a9DY5qU60+PWMF/o3MudXqPuvjBdqKlxeJ3XULf7Dx3zORy/HXE3ju6NsP1cC0rzLrQgN72uy/tkL5we67+X08LIfcsYLlYxSHaupEE4PjPMF5X0JNGdTcTPt/QvhPNPdcezeTGvCIzcCXHzV3/W/hv9VB5r1jb44BjHCe8Dc8z0oSOWmZgSm4szDg8PxId7zs/PwQjjnNlPM50xDENPrIWpJLw1eAxiNWxJw1nag4MYTtMRERWESkbZmdbinGOKe8ies805lczpdETILf1ZHcAltXTQaWa73XJ/f4/tPEUKWGVJlpSY59JaoDUoxToHGGqqGGspvce6oJvLElvYCaszVKqQZm0pNWKoJhEpRDkRhgDZEotWTVPS1m6qsOs2pPnAyMjlxVYFvBYq5oyn1Ipk01p3Mt57ZXzmsork1mpVKtYZaxy+GrzvsNIjTvmu5xcDpSZC1YCTTXeGTCdSrmSLboStZZomroZBQ4YMnA9bbu7v6DcDx/0D0zSx2Z1pCzEgxmlbbTDMNdJtHHE+cbHdctyf6KwlWiglK+s3J4zT0LDgPCYXHI55TDjjiKIJ7rUJTEBrfY/0TgOjeuvVvdt1TLEFbDl10FapWOc4TRPGVGVJ1sLZsOH25oZhGBBRJ3Op+t2tUXF5sJYYJ+I8sht6+tAxx8ImeMaoLB1cY1Ai2AKbrqezBjNOOOMZe8F4x7HxF421IIoamHMmOI/kxFkfGOcTlEpnDbEagrUqclflMPmtp5BUvOxARrjY7kgSkayOuIMpENUlWDoVHvxcqSXhmmml7ywxFnUt2tpcDFp8saLO3K73GISzQZ3FhkxMls54HJ6YM04EF7yKU7nw5OoaiZmnj865e3iglgmSPtR6p0zYEmcqgjWFISjrs+8DZS7c7ffEyXKcR1zXA+oqLqUQXFVGZ6oI6ng3tWDdQErKee1CB95T5omYm7BoDUMXmGvUQsyUEVu5PD/nNt4iouF0cxzBGubxxKOLxzx5ck0Igf1+z83Xr6hzwnvlWtqlraPxTc9Cz+XZTqt5CIfxxN3rAzZ07Lod9+kB16kAHtD1xHcdTx9fYQwcDgdePewZTzNVDM51bV2BvnMECYjNPLraasDRPHNz/5o5FbKtlBrZhB4rhvFw5Opsx2l84OnTdwhO8K7n9Ysbxlx4cXeH7/vGkm0Pk3lis9lwth04353x3ofvkMcDcT/y5YsbgukZC9ScFcFghTiP1DBjamYzBHobGPqe43RP3+n6PMeC7wey6JqQy4ytFS9Bg+SIHA8POOewVIoBj1eRQRRP0Hcdj6/O9DyfvcerVy8Zx0mDLncD46Rs6ZhmLi8vmIuu8zvruLq84O7uhtv9kcPxgHOBKZcV31AaykG8MJ8m3nn3KVUKcbpgfzhxHGfOho06U0XXNms9thh224Gzsy3dEEjTzOvXrxkb49zbrvFf29xC/351dcn19SVi4eF+5NXNLSVGZT2Linh9v9GNRM48uX7Ebrfh8swxpcjt3YGbu5E4Z05FyGRKSRjfXNjWcnm249ff/zalRMYpsT+cwAbyqOz1lBImGEJwilWQzPvPH4NEnj4949Wre+LoVPz2hsN4YHt+zeHhiJlnnZMOet9TqjBJYZ4i+I48eOaTFhFG4yhpxBjBF2GaTvi+IxW9V7kWomiNaTiTSExgq8X1jo4OS6bUGW06yOp0zlogrNUSOg3kK1R8w1ks9/CUlBk856zjPCeCVXZyrbrGvvPeU55enOEznB5fMR4SX3z5kuArKSZs8DjnOB2PbDYbPnr/CZvgOe876qPMm5s74uEFN/uRrh8AC7YQ44T3lrOh5/xs4PH1maKarOOzzz7j4TApFsUFcjHMkxatvM10zvPeu0/URSuJzdlj9vsN+/HEcQJvBmLRQD8jBloXzd+9/vV/uWNZW8u7G02Y9vvCdOVxLYF7fOwZvp65OGYe3u8A6GNl8/VMPHNMj1Tw8bfTKnZVYQ0ZKV7Aq1jTvYlUYwktvAZhFTZ0ww6H5wEpyvqsDdtiT3kNIFuO+/jEcfbZtIbQ4N66j5agmOJ1A7oILuoUba3/XlbBdgmMWdw/1ci6uS/NkeNPGuAVdxoau4Si2LGQt7oxPTxzuLHSHRMPH2+VOVvq6uSBuuDXARUfUnNE5Sa+LO5Ie5zfCohbp+dROg3k+XDbxGcVecbHnirgTrIGMxUv+PvE3bd75XRa4eE9y8XPE9OFZXgV8XcT49NhDaSptgnKRnCHTLxUYfjhw47htQq67qhhUf4hraFWdszEM20DXwQKDWzLq0N3aT1fxsQSApT6hqjA6xgQFXGXMKDiRcUgb9YAu/1zz/kn0yoWaKiMYCcV/dwxNyFS29qP7wTGC2Vinh57ursMCtv6pfM1PglMZ+qUyx2Ee6GzQvdq4pu80WrkragusH/u6e/aPrCJpvnsLdMUWMXn0jmq6Bh4eN9rG3Rj7fZfH/X97VvhWjEa6txaAvv88RvjdHGulub4PmUV1J2wf8fiD+Cs8oDjRbdelzTY1bkI8Ob76lq9/b5h88VbAXw59mWeLIiK8cqSBmG+ECTB2edZcSgv1bW6HJ/E/EsBYNVCbY7F3Jm1ZX8RFLWIUdbjGh95/H5xcc5Ua3j41sBZaw1fwr5AA6Juv+O4+25H2lT6V4azT/R308Zgx19Orq9WODwP7D9o/61o0OLZpzO5MxyfBXafjdpi3+ZbbELN7ff1GvZvei5/klVgbkUJOypybr4KxK0KtHffNsSzijtYzn5RG2dUxfbpyUbd+beRu2/3DG8yN98X0raS+8ru55buznD9xw8amFa+MZ9aQen4TDi9U3j99wZMBDtaHv1RIe4M3U1ZXbFalKur0JqfbJmuHPXaMbyKzJc6x93DvLrG7Sli5kQJruEx1OHbv5xXN61kxREsYViSYby03H9HKK7y8ndh+zNPuKu4k+X0WNfHBZlx/1FHOBTmneH0VDh8kBm+sOw+q2xeJnIva8gY2HV9qAb8ofLm1wQpDjs6hheV4U3m+F6P32uHFqJrbPaW41PD6VklbcFOlrOfQbjXwMy0MZj57XvruIX7jxz7D/W9+hcdlz8paxFBbaEwXXmK046NNz/oGJ/A+F7E7C2bzz3nn+gcCVMTw7MKfzoWYf++cPoHB1IyTA8dmxc6jxa8wlLsiztDbB0wb35bj+H+e5XdXzrcyRH2uhb1Xx8Zn23ovz7uEL2zAAAgAElEQVTiGxe3GiFeNoTfXVrDt2xvcI3TGm4j0jAzaevwtxN5q6GHS2iaiXW992oxQsO/UqeM4ewFyX4NEku9djromIV5tzi2IQ5aIGXQ4qWbSmPCZ5bAsLQBOzee+Lnl9a9a5u+d6PrIV3c95t5x/UfSxPiyCuH9Teb0yHL390cuLo/89pOvSNXwLz97n/G04+KTzHRm1zXBH/WzD88Mx+cQvz1SsyC2svlXA5uv6zou7KzzOHVCGoR4Vnn0my+56EbmYuHqns9fXbJ/uWF4U1Y2cvFCaYWXv+rrb7RoSzWIaKq0BMOYJrpuYI6JOCVCJ1wM8N6T5zhnGOcJaub21YndpuNwOGCtp3MOGwWTDKTE9dUFYiJBPDf3NySEOSXEBwiBOI6E4CgxKe8iV7ZmA5LxMjKfRrbWUG0llgoOpngkuIFg+yZCTXz84VNKgeNR0+6P00g1AR/etsV6MbhYMJI5v+gZfEc+TewPt2y7LbFCNmgLuPeAukatjfR9YLfpETbM84l9dVgfOMQRaWFhRgxxPuEcWH/i/Q+ulYtXEq9eF04nZaR65xnnxhoUtaR7W/GmcH15RrCOu4cjTgbG2ETfKRKx2h5cR/pNwBF578k5Llhev3zDPCVyjmQn2OogNwdehAEhp5EPnj8Dqm74t5n9w8hm22v7MKYFvRRCHzBkUpp5fHFGGkcmK8w1MlwOjMcJh6VIZc4zKSd632sL9Xwg7AbGgzrdJDjSw0zKM2KcIl+qw0shWMuHH7zL+XmgIoz7PS9f33J39MxTYa6ZsO2ZpkhNmZ0NXF3uuLzaMvQOqYVXZ4HXrw9q+a9CJCPFYhzM44wNPU+fPOFXPn6P4+mBkjIvvnjN65sHfFYerMmKcPDOUKTy7jvv8Pj8jM3QUUl88eWX3N0ftCI7actMLZGcVag3ZeL999/h2eNLKMLxuOfli1seDpHDPIIYXJ1IJZOjxXnDk6fXnJ1tudj0SMwcj3s+f/GaKUWG6rVdMCbMSQX6y8eXnG0HHj/e4Z0wniZ+8emXHI5HUqwaPBczIaijdNh0XF9e8uG7T+m8peTI/uaOL756xXHKHMcJZ3q8N4zTxDB0bIeOD99/h03f4byiLm7u7vjxzz6jM57TnLUd2QaMdcDMd37lYzabQO9dm4NHfvbTTynxtLqD9tOorsxYON90nO02XF1dUuNMihMvXt9yLBPZdsxpomawRsW+XCrOGt55dqUPwkb45KuXpGS4vb1XHqsxUJU5a63lW9/7Do8vd4QmpJWrHYcnV/z0xz/n5v4ejGXG4Kwhzkd2F+d8/OFHdM5yfrHjtL8n58xP+AX3h8g0C8FvyVHwnbr6f+vXvs9u29H1DlPVvXz28pYvvnzBISkrthZDTCPDxhHczMcffcjFWaBzGg64f7jmxcvXfPLFC3Udl0RG6Hc91Rl++MPvc37eM/SeDsPzZztu7/akn808HBO5GlJJWEl4B5cXAx+9/5ztELBWiGkDUZgqfPX6DrqBPGkA26lqMSpYi/cGWxO+K9zf3+BtR01C58+hHqBmrMlIbM520fb8gMOVDgmGmApjycouddDNmU3n6XvH4ydXvPfOM6wDJ/DRe1d8+tmXxJ9HTsdIRpE6gnBzc8PV9QUff/ici6HDSOH5sy33DxM///QrXry6oQsbxnECHMTC+Xbg6aMr3nn+XZwzVBMR0/EXP/6E0+GI4KlzJWw6ao0cD3e8+94THj264tHloEWXYnl0ueX/+ZMfM4gQ86xcYWu1u6CMvPveM957/oxN59WNnAyvXt/xf/3LPyRWaSGclnjaYyxcX53z4ceP6UNgcEIIPafLkb/85Au+fPmSIqLOZzrGw4FM4ld/8D5X52cMm4AYT64WK4G/+PFPmaqhGAFfMQ7i6cQHH73Lu88e0weL9xUh8O7VY774+g0///IV98cDF2eXbEwAq8GUVVSALjmTU0aM4IOnlESaZqyAd5YQEz4MpGmmIGy7C2KdKS5QTCFVxSmkeSRYQ46z8u29Z9wfuLw851sffsDj6yvu7078yZ/9OdM4IdKRc6bve3KqiEG529bw5PEFoVNH+JtXNxxGxTQAVCz3ZcZ1PWU68P1f+y7vPnlCkIo3FnFXjHPk5uGWwzRRkqWOmeoqNljeff85T55d44BgDb0PbHfnTCPkz75ULn3rTEEspzjx0fsf8Pz5FZ6CpELoOy43O/7iZz/n69e3lBLpNwPTNJPKkY+//T2uL88JVp+pQtCi8MX2kttXkV98/oqUEr0TYoVTnPEm/DU9+P3d66/jVVv78ZIwP194/D7jDol45nl4z/Lyt3pKAH/fNn7Wcnhm2bxSV8/0zDN4WV1f9x9rcnTaVHa/ADtpGMoiYNljaky9hU0Ipye6QU2D4A+V4xO3iqUA/l5FsrhVh44/FT7/vR7J0L+p2AmGN7klzbdNfdbU+dSpc3b/fGC6VAfw8KJyegzupEJvd/9WpUqdbu6nC+H0RAvQ7gTDC0PxsHmpaKfaWu7tsaWde/j6t6AGh8xw9lNPd6sbuzgI/W1Wl1T73bgxxI3h4SMhbSqbL4XUd/R3mf3HO7qbtDqjDCoKpE549feE0lV2Pzf4Q1WRZtZNtEUddrkzlEceO1de/KiJjRbS1rfvrsXMpX1W0+d9480Wbr7XYScNLwuHwnhlVWTpBRPVMWrGSNz41fk37wzdXWa8UiF2NxZtP17CXaysrqmX/ybYd06UKuT7wO4njt3nGt7m96k5exNSDMfnHfcfWQ7vOcpFAjOz/8ue858VoDFV7zUAuTjBPWSyD7z5bc/Z793w6nZHSYb+x/o7aWNWIXhpd5cMr39oOX17Znt1ohTh9MfnbL4Qiu8Jd+ru0hbzxku2wosfOfL3DwDE246znzg2Xxk2L1Mbz3Z1hObecvcDx+mZMD6Pilm4cTz6vw3dfWF+NKgTb0ELzOoQOz0Vpu+M+D4x33dc/QvH5pU69xYRNp55/ENkug7cftvx8KORzW7imAzT64GrP3T0byrDq0gatLjS3STGa8fpseHhRyNXV3suQuTNYcPNp2d88D9pO3/3Oq64giUt/uvfK4SrkauzIzEbvrjZcfkHHZI1JEwy9F88kC4GzFzYv99xfMew/6AxZmfH5Z/D5lUrtOxVkCvBrGn3qTO8+TVtec5D5en/4XBT5eznJ0VTzAUHSFLB6MvfDci39+w2I0YgZcNX39/y6J8FLn6mRebFGW6PicM7nhe/W2E38vzZLa/udhyjJf+vPWefJ8JDbg7fRfQtfPYfFa4fP/Dx5kjnEl8+nPPl02uu/6hj87KxREvFHia4VtH25e8krt99w/ubI9YUfv76mvQXZ7zzz9UN7U4ZO2m4nRT4xb8P22d3fHD+wPPNPb/41Ss+/eIaO+3Yfjmr4Deqkz1tLPv3Bfm37vje1Q07P/F63ALwYv8+j/64uZXngqsQ3syU3hJ3jrTxZK+C0+6L2NZNFcVAC3S5sysrF1GmtrK3PdVqAU155VqAmy8cp0eO+Vy4/07hw9/4gsFFzsPI9b9x5H/4w1/HnTz9rc6JatSJfPHTkbtv9+1c3bG1he0PZz55cc34zzc8+tOZ+cIRbtO6Tu/fddx+D3Y/vOVRN5OLwZrC7f/yDv2tOqelqNiuzsvEy98K7D9OPPvoDdfDEWcKP/7WE9x/v+N0rS3+6gqFeWvwp8qr3zSE37zh+xd3XHdHjsnzr77/AR/+k8WBWmCwuGOhWMP9x5b5R3uuzo78xvkNT7s9P90/4id/8BGP/kiQHEgb8/aeECuf/nuVZx/c8OH5DUYqxxT4Y/8uj/7nDnlo4Vq0Itax8OJHhrNff80PdnuuuyPeZF7/6pY/+pMPefK/O84+mzl8tON0bYDN2qGg6Ia6spCXe+WCakkbQ9x1xEHd8yZVjk+2+JM6YSXXtbvFtgKRmTWsK3v9PvcfOm7/7Ylf/9YX/PjrJ5z9jzsNVDPqGB4vFaFQjUAzQ919V++BeShsP3EMryomL4U+2H2ZSZ0iJT7/dyu//at/wTvDA0/CA4bKz0+P+Bef/Qb9Kz1XMr0t+r3+DeG3Pv6UizDyvLvjO/0LfvPsc/7x/vews8cdWTtTQJ8jPv37wnd/8DlX/REnhef9HX/4/D1+8b99yNWfKiJiOjeEh4KbKi9/58SvffAlz/oHvMk8C/fcpA2/8/hn/Jef/y7h/xRc++7+VOlv0tqp8Fd5/Y0WbbOdKanii6WmqgzIWLS9HMO3PnrOo0cd2yHgrDpuLi4u+JM//Qk//dkXDJsLjmMiZmXdiTG8895j3n96ya5XUPjhsOWrV7fc3j1Q9xNlMjDPFCOcbc85Pux5/u5jnj97hDVCsZWbV6+5fXPDOBUiRROqQduRpyNPrq/56KMnPNoFrIHb/QOv39zyxRcPJHHEaihVhbhOlN/5ne+8y6PzQK6VY3pgjo/58x//JacUqbZjco6QRas4aeJXPn6XZ0+v6AeLqRNv7m55/frAyxd3eOuIuRBPI5uzHVOFy8trvvedd7nowRbIZeRyN/DVV7d8+uULsjgEi+kM8RTxwNPHZ/zKd7+FNa19/RS5u3vgz/7iNVI7nDiKM9SkbsgheH7jh99l5yphCDw5HxjnyJ/96Y9xfkseM7laDMoCHLqBH/7q99h2jr5zHOPEe3LJJ598yps3I9U5qlVEgQmWGE9sveO7P/wB212HLTPWBX76i8949foG//+y9yaxtmWHed63mt2e/vb3vrb6KpEskqJIUyQlU41lRzGSiYE4yTjTBJ5lqEnmGWUSBIiNIHEGkuxYQCy3SpRAlihRpIrFKrKqXr16zb3vNqc/Z3ery2Cdd8sCMtAoCmxt4OFN7sU9++y1197rX////VLROY8KkIqEQb/Per3m9HDMvZOHJKVGYZjO1kznS3qJxCporI9t8MFTllFgunu2j6LFB0U6LpmM+7z38Wc0XYsLis40EY/hBalIePut1+gXEmxDCIJBPyXPZ1zfrFkuKnKdI9A44+nalq996xv0+ymClnH/GO9aBmlKUaZ89nxOaGLRWqlzjDH8zDtvsL83ZpgKNLF4ajx8haurG3743icolSGSlLZt0TpG19964yF3T/eRwqJFyv5ezuH+mO99/z2crZBZH5nkNKsNaQqDccnrr98lU4IyS6E1eFPS6+f86Q/eI0kGGK9JlcY2NXme8PDBKcNBRqI0eSLxE0ev1Fy8WPDTjz8jJAVSJITOg4F33nqT/XGPXAsyCcInHPRSJuMB3/vT90hULJGqvUfIwHhS8torDxmVsYhNJ5LUSrJSs6lqzi9nBGsReRGL2QK88uAuR4cTslzGnfwQKMqUVEt+8Gc/prMmimp5iW0sPlhef/gmg34RhRkhCKZj0Mv59LNnrBq7Q1sACNq25ejoiMko4/h4xKifsV4uGBZnXNws2G5nkc9rLSqJRWp37h5xdnZInkS3Nw5EyCiKgvrsmPl6SVqUeCEJ1jDMU9557RWG/YQsEQilGRzsxaizzvmT7/8I5xokJVolrOZTXn/jIacnBwTXIVJJpiRlkVAUJdYYHj8+B52Q6RREdOO/8spb3DmaoDQRO5IkHJ7cIS0Lnpy/IE8zqjrgVcB6w/GdU07uHpOJyALWwtErSyaHB3z67JK1bZFosizDWs942OPN11+jzCRqVxI3nuxRVxV37h1wfvUCITMQ0S0oXWR8+9YwGA148PAh682cu3cOeO/PfopW4EwUGAgB03aUMiGYWKz02oM7KCGotxVXV9doEV80relQaRqj+U3LwwdnvPrafbANWsWSNWfh9dce8uiTJ0hvKPN+3DRUEuslJ5M97h0f09YVeZHgQ8frZ6dUTc1iNqXxXXR8NpY8kWzWS7765bcZDHqI4AFPmuYkb73JcrqgM5Zsh30hcSy2S/76Wz9Plmi0a8mSFE1GkfWwTvC9P/oB+bAfMdwEWucZDQe8/cabKBnn9FRLhqM9EpnQK3M6EyILtmkoigzX1vR7Ga/eOWG1WtHLC0JQFOOC1gSePn+OlposycBAIjPSVPPqq/fBOoT00Q0eNIO7e1xfTplON9RtE53xLiJHjs9OGfQLEhHd61nWo7dX0jnJhx+do1RKU3d4EZAiQYhdwZaWkbmqJV7GMsqXmCSQdE2LyhJa0yHSmBJoRcBaQWZtZJiFgMfHVIiMoqqWsVTRKsfhyT6jYYZtVxwcHnB0c8hnT57F8kRrqKuKNI8Ji66uuPvKPe7fOyRLFUXeZ3uw4V///h+g+2PqqqXMChwZro2FZPf3D9EIlIJkV6g57A84Oznlj3/4HsVgjyDioiJ4w2RQIlFkiUIFjzWGVGf0yx7GdQhdRGxOMNTdmtq2vPrmA0yzRoWELNNYa8h7BaNxybqpMEZi2hYVLO0OpaB2KKAsiakaay37+/ukaRpLJwM44ajbFpWlGDx/dfz7cbyMa966+XqR22hLictSLr6l4OGWk70V/bTletvn68dP+Bf/4quc/b6lmSiKaXQPvRR+p+8q1FeWvLo/BeDxfELzwZjeM3UbgYToqNucpRRTx/W7Cc0XanTicE4inhT0ngvSVSCpuXXKIiBbOmZvpay+3vDFB59R6o4/uzije9xHvKdi5HXnIJbG0w0k3UAw/07LFx+cszUpF4shN1XKwb/MotDrw60oDJCtHM9+STF6a8rD/oZcGf7s2R2axwXjn3LrmNGVoz5ISFaS1X1N+0srvnZyQU93bG3Kn905o/mwz/H3Akrtnl/EiKotFfO3FAe/cMFEGzqvuF73ubnoc++fQki4xUG8jDM3Y0n7Hy340t4NJ8WaP3z9Pot1yd4/K3BZdJrKziMTiSM63RZ/s+Jkb8VZf8nj5R7qXc/NHx8zeCwQPtkVwQjkLt68ephz9V3PYH+B1A6RGK7/4JThJ3FcJBu/ux4p3d2S8lnF9c/2mX/VIooOnTh4XDL4NEbTdRtj7i8drWZfc/UNePvdJ/STlsYlHL+25gd379D+zj7ZcseI3Xx+PbqepPyVK762dwmA8YqfHB4xHe4z/FgweGbpxlFEkl1Arxo2/2nHW3s3aOn55vFjOq/54dEZl+Mjjv9QwK74qTmIjtwn/6Fk/8EN3z58zjipuOn61KcJf/TJQ3q/pW+LfJKV3TnNPE9+LeHhu8/IlGWYNnAfzl8d0fzPJ7fnG5QgmTfYQcb2RLP5xYpBv+ad0TxeU5fwwf4Zr/yv7JAMfscGjoiD+V/rOD5eMsoa7vfn1C7hB/t32P54yP3f7RBux8vcjZEnvw7HD654rb/krFihpWP7IOP7d+/A/7QXo+WtJ5/GWP/6vsJ/e8k3ji8A2EsrXhQDbnoV19enHP4witVeRpFUGnj+Czmvvv6M03KFDbELpBmu+Lg8IPvNPukmziv1vUGMtLvAi7/u6R2tOSlrRlmD85KfHp5y8Aea/vlLB2U80rVn9k7C5p5n780pXz9+wg+nZ9yc9JHv9ykuJcJ6pHH4NCMowfVXooh13N/EawFo4ZkNS36yuM/o01hgJFych7Z3c67+Zsfd4zknvRVSBL68f87K5Hw/v0vxD3s7jmp0D/aftTz7lYJvv/VjbJBo4blfzHhrcMknwwN+1L1OeRM/vxnFjU9dOa5/Hv7aFz9hoFsumwF3ywV/9/SP+O/K7xK+t087krdzlWo983cUb755zllvifGKcVJTjA2/dPJTfucPfpHeRXTIxqLIKOrvf/eC10Y39FRH6zW/uv8BP67O+Cfv7nP4A/XnNk1eFvoJF6hOFbOvW5Jrzc1XNa/+dnTOvhTngIhgOUxvUw7Pv5vj0kC2EOx9sMPeuIi7sJmO0fyt5/qb8Le+8UMAxrpikmzZuJz/4uu/z2//37+M1+I2IaBygWoC83fgu1/+kPPt6Has/2df/SP+m/l/zPCpuk2D6NrRThJ6lxb1d+Z8af+CZKdqHqcr/vDXGm7OH0Tn7lDdpiKKj2/I/8uUe704Ru4Vcya64tX+Df/kV7/Eg3+gdtH6KPhlK8/6ruLdX/7p7Zx+mi/57vAD/lH2NX54+C7pNqYxXm5WKRNoDgL/9bv/jP9r+QZfHjzFBMU7vXP+x3f76D+e4HJBO9ihYUqJ6Un+ztf/DQCJcPRVTDt9bfyEf/jxdxl+Kilm9jYRIW0gfWvFO/tXHGQbEuF4mN/w9eIRf6/+T1C/e3CbnpFG32J5IhZEfF6E6T5/Hr+cP5J1dD+nS387btKlRTbudlzf4l7Sz0VVm8XEiGo8yzcCd0/mrLuMX3jwiN97811O/ig6nXUF+dztHOiQbBxXPyuZ/OwVR70Nr/Zv+OBnTmj+27PolJ1Z6oMkIgFNwBSC737lx4ySmpGuOUpWLG3Jd0Yf8XtffJOjfxA5vy/TK8IHwv2aVDnuFzP6qmHmetxNp4wPNuh6cotokS6QTw3JtOJvfeMxN22PgW65m8+Z25L7/Tk/PT2ludToOiJkXCrIVh6dOEodN4buZnPaoFmagr9x8CP+l943sblC+Fhyls3MLhHyF3/X/ovLu38Jh9uV0AQfd1SMiTdj0zQMh0OyRHN6dMrecEKZZBz0hjSrDb3BiIPTE2zw6CxD5jmNbcl7BQfHBxxOhgx7GVkK41GP/b0RSkKegHSeTEc3Xdd1jIdjjk73KIcZvWHC+HBEOSqYHAxjxCME2BWfKBWbuPf397l/9yyKWcIz6mc8vHdKWaR4UyG9IViL9Y7GGbJBj+OzU7IyIyk1h8MRe/0+d4+OkV2Ldp4UiXM23hXCsb8/oezl5JkiSxMO98fcvXNCr0xpmxWm7ciSnGa7QSK4d+8eh5MjElJG2RCdFuwdTBgMS5Bhx671OO/J85xhv8+duw9I84w0T+j1CvplxunxAUqAMx2JVLSNBSRYyenxXcbDCXmWkSnF4WSPw/0DxuMxtq3oZZosiQ3hjTMkvYLj0yOGkyF5mTHKU/YHPU4OJmw3q1gM1hmSIGKBUd1wdnTE2dFuAZ0pUhl4cHRMT0hSKWKJUPDRIXB5TS/JODk+5P6Du+RFwnBQcufsmMPJiK6pInICSFNJkgjqakU+zFFZgpAvF7OByWRClkcGsfFuh6vo2Gw2KCUYDPp4EdC5QknoFTmHB2OM3TKe9FDCEFxHkiiKIiPPc7RUlHmKCpI8yUmLnPHhgM61iCTQmI7NckUqBWmmOTk8IEs1WgnyNCFNNEcH+xhn8QTqzhChH3HRdHxwRJakOyZljE+NRz3KIiFREqUUW2PRxQCLJEsTXrl7l1QGUiRaaPr9IYkU9IqcLhiccFg6dC7Z1Cvu3j2hyBP6ZYEUGiU0D+7d52BvBCGKrMJJElVgWs/BYECuBakE7S15lpDnCb1hjrUdpu2wtqWzHcFHRELeyxFakuZJLJDIE8b7Q7IsiRxiqeLcIAVaa/b2xowGQ7x1ZFpFp2qvJC1z9g736Jy9xXMQArYzHB4dIEUgzzRCK4JKODo9otcrEM6Syn8rwiAkZZby+muvxvImayjTjMPxIZPRHt4ZxO7FIQSH9Y6jowP29yeEEBnWSaLQUlBk2c79Jghd5FUL67FNy/5kzKCXkSWaMstxXoPKcS7Q7xU4V5MJCN4gUygHJVlZEJSkTMp4PRJNmfcY9UfkOkMhcM6RKM1yuWXYG5CmOUIo+v0hUqfUpiXJM5Iip3MWvCcrcoy1DHo5uUrARIYtQSJVgvOKVCcUWUKymxedc5jOI2Us8ZNSU+Q9msbgfURBJIkgmAYpHFJ4ZGtQpiO4lrKXIrVib3SAsJI0z5CZwkkXsRqppihLfJKgkozgYFQWZIlmsj9m0OvHeRmB9I7OGlo8JkRhsau2WBPxM8IHSASdN/RHQ+quIbgafBdFR60ilqftyJOcRAm0Vpyfv0AIRV23aB35sUIIdJ7RWIP1hto0WNuRCUVmwdc1Ukqsc3TB4aXAB8V4tMf0ZkmWFQifoIgxf3YpiKAkW9thRcBJ8DKyt03TorxEkCCdYrWY42xH17QUSmGrhhRotg3eg/CC+WyDVjmt84hEIlLNfLFGpSXInLp11G3Fer1ECs3V1SxiNULEwIQQdkmWHc+vM+RB0tMp89mM+XpFkmQ4B4oEYwPz9RqVpAzKPmVWIKXEK4nB03QWi2BTV6g0IevnWNfdjiN0Esu2EoV2oIUE60hcFPhTB9JbtAzkWu5EU0VnHF4JKtNi8KRlybbtIhM2TWi7LZ2pSBOF8w15JihKRfAd1tQIPHVdYbo4lleLJcvlmoP9o1h2lqZ4D6mLBZ29csBmvgUfkMJjuxbnAt4JXBPIVBpxMjIQOkuRaDaLBXXdYm18j5BSslrF558lYIWjdS0hOPIk4Wg8ZrFY0DYdSmbUnccCQcXfr9YbqqrCOY+zArxgud3gZCwuq9sWHxxZnnJ59QKdJiglUCIyjct+EYsw1V+Jtv++HLeFSbtLrre796KFZX1HYfueX3/zfX7l5Ce8Obzi1++9z+PNHmbPMn8jCnIu3xVvzRqasaJ9reFXH/yEb04+5W654Gsnz+BhhU9jtNsnAjNKaScJ6TaKDtWbLXvjLWd7S948vcJOLNt74eVrL8Atn1Uaz/oVz9/90h/zzcmnHKRbvnhywc98/THtnrgtDhEuIBtHtnSYgeBbrz/ibrlgP9/yyw8+4u17L5h+ZRfdLD4XQV4e6cMND0ZzHvZnnBUrvvnwMaN3p9QHsWTsJf81W0YO4OJnO375wUccZFt+afwBo6Th5+4+pT3aOWXdjkFqAu0kYX1H499dc1BsOCw2vDO+5NX9KW+8/TwKsCZEcXjZ3X6m2RcDXzt5xv3enLvZnL99/32++fAxm7uRt1cdpXSjyNzM5gZbwK++/hN+9uApr5Y3/NzhU37t9EPU22vKm+jIVW38O0EK9Lpl9iX49jsfs9+ruDecc1Bs6P/cDc1BjJ6qxt06rfofL2mOCxbvBH71Kz+mP6x55WjK6EtTVq9DuvG3yAkziEzNfC0xl8gAACAASURBVO4IQ0M/aZEiMEwabJB8+/QRph+dWGob2X+q9RQXFT6BL+1fULuEUVIzTBpem9yQvb5C17C6r3GZuHVWd4c9DvtbRmnDO4MXjHTNK8U1+0WFvFOTbGPBWDprKC/aKCCUlv/8lT/ibh6FnIfFlJN8xTdee3yLEUlnHbK1yC6+O+y9OeO0XNFPWvbSir204jtHn9DufY7lyC9rXJGgakM3EPxXX/5XjIuGk2LNKG34yvgZqrBUhzoW53U+tqkXiuK85j/44vvcH855d/KcTFqGScPfe+df4l6tgV3xkYwOZrXtOHvlhrP+kpNizX664Y3ikleKG16bTNFt3GhQtUOvu4g6SOFwsEELz0m+wnjFKG34ztEnmP5LlnR0pkJ0DjcPOr5z+AmdV9wr5pwVS745+ZRh2bB6KEk29hbPAaAqw8PXLkmU405/iZYeGyTvvP6c5iAyKW3v84gyQDsJ/OK33gegdgmvDGf84sNPaO6ZyAvevZsLH8vBeGPLL9/5COejoHqQbhknNa/2p/hy5/YO0WkYy7ACX3xwzrt755wVS97pv2Bl8niORlEdRgd3O5C3hUXtJAq1Pgh+pn9BJi191fL24BJz2tGMVXS775il2ac3jE/WnOQrEun46vgpqbR8WJ8xyhpM8Tl7tBvpmG44tNzvR0H/pRCZScvSFtieoN1LbjfZ4liBRDlql1Cojjd7L/ioPqbzGp24XYHTyyLA+M9l0Xna7gnQHne/ARkLEIMC1cbx4RNJu5fuovBRAGtODbYX2N5zbE7VDiUid/gSE52HlQcreFEPmHUllU9JdhP5eTumPoo81JfPnSBFZMGOHNO2x/3+nMN0zTip+e8//Q5BRqHrJVs1yCikpwtD1SU8Wu8z60ruZnNeya5onY4u4NqTbPxtGV79+gHTzybcKZdo4emrlo2LjmK/jvNmcW0iA3onXKsWpk2PnorF6KXq+L3VO6xtxDk048gIN7uUQXRsBn53+gVGSc3j5oBEOBLhuHk2vsXgFDNHMXP0zlu8hv/98c+wMCUmKM7bMSYo3l+f4tOXm5zR2VkdKIqfXlE9HXCQbdjajL5quTJD/tHia+TasjlR1Pt6x0+Nm4TZ3KBrt5tPBe1whybxAdXunlG7wjKXS1y+41WXEmEDdhA3xVwe0Ta2ULflddL4iNupYglaNpXcrHsU2vC8GqGr3bNjx2tuJrHYTbcvEz6CTZOxaAq+f3OPTy4PmL+pdw5vhXSBrhedyfWB5IPZcbwvhOXGDNi4jCYk0Em6kd6JyLvxOlb465yn6zHTrk8iHKXs+P3lW2jlUW1AtYF0Hcsa20nC/Etj/vDFfV5sh/R0y0fbI5amwAYJMiZ7iml0SL8sY7XnJZVNsUHyUXVE5VLuFzP+h/NfgNTjk8jjlY64WbErkvyLHv+/Fm2tEyBTTAAvRYx8C41WKalKGQ/GaOlQXUBZjQuKsjdgPOxRJIJMK+gsojEM9ICBzjkZTm5bwfM8J80zDo8PGO8Naa3BqQaZaqxztG3HeDLiZHLM4WjCIO2TecG9kyMO9sdsVlO00EiR453CWsdkMqbX65EqjQwalZUUeUmqFSiNTDOsCKgii260zjDuDxiPRihVkMgMqQtG/QFaJ7QusmW7tkVLUCHQVVt6vYTxeBCZnElGmqYIGWPoaZpCJtl2G4JKEULRS3O8DbEYTAZ0muCRZP0UazuaekuRaWznMMYSlKY3jKJHplJECPRSyajUDHspWuximUhMa6i7ll5eoFMFWiFVSesFxhJ5j52nNYEgNNZCi0f2SjwS7QTaS1KZg/Pkeclkf0hW5LS+oxFNFOVEwujggCRJKNOELC0QOjbFdlKy3KxJtEbJKO6N9vfwKjDZ36Nta8Z5SioVwzJjbziK7GKiG7OtPV1ncVZQZr1YTCcDSRoFGBcsyoM1NakUOCuoWrABkkHOfL2Ord8+Q8kEKVM6a9gbT7ieTmmsY2Natl2DTjKSvIhxMCsAi8UQNKzWdSyigrgZUGhmmxXjwYTr6ylNbRFCEVwA41kvl/R7Bd61yF0pDhYOhwe0tcW2ll7Rh04SfMJ8saKxHTJT+GDRwaB9Q+kEe9mQ6bMpRciRFjKV0FRbjA9UXRP5ubahcyDISFXOdtmQkMWFCIHEC5azOc55sixDKcHWb2lczXC/z3SxRKvoLBM6o3WWxgeWVYvSKUmSoNKEXKU45yh0SbtpYszBCjIvoIN2aVE6RyiNzDJSndLVln6aIX1gtpjHAiligVBTtXTbjs2iIk9yKtfFlxNl8Urw7MUFLpF4AtLEQr+b+YI2BIxIsSIDlWBCpN7O1kueXT7HeOi8ovOC69WC+XIRnYiOKNZ4iZIZ88Wa8/Mr1MtSul1hYNe0rDYNZdYnoGkaj8p6lIN95ssVm6rDWIntHCoEgonOQ48iqJK57ahsh7OKetOynG4QNiGsWnpCY1YdbVXTVDVN10GQBCzeezIh2Gw2bJYbQucIncfXNaqxLF5MqTcxsmi0oDUNKM31zSreJ96QdOBaizYQqppcK0zX0JiOJjSIVNM6y3Q2J3hB19ZYGxAhpzUp201HInKCT3Bo2hDwucQJsAiuFiuenV8zm665ulzSdoq69RgrEFpRtx3b1lC1UZDzUnGzaqgtNAbmVUUTQnTfqwThBMZ7ZJmx6VoWmy3Ox+fMujVUladpoao7iv6YqnM0NkDQ2ED8rjYVTVth65Z2awkOlquKrBxhrESqHCkVbd1QZjnrTcN6vcEGSxccs7bi+c0NOtV42ZJmHqUd1rRInbDZbJldLfEEqs5iUTx/doExJj5XpEB0DtEFtAfXec4vb6i7lmZT0ZgGoVKmswVJktBYh9AJVmhkWuBFyuVsxaq2bJoG6QOm7ri5vIkll9ZSt1Us88oLsl6f+XbNst6ybTu88SgUtutYLBZMp1M625L2U1ppqJqGe3dfYbvYcjOd43xD223w7ZrVZs3VbMratEyX87hRESzeG1AWoR1ZUWBdoNpaBAnI+DzprAEFnesw0gEBudsckEQmvRACnKer6shq70ycA6wkEzmiE9il58WTK2bTLZuN5fzxFZvpBrwgBEHnBC2ayglqL8nKMcutYbpoWawtm8pxfr1kta7pug4lAs43GGmxqWNd1byYb1hVDbPVisq0WA83lzOqbYcMCQGohcGnik1ruZgusSaw3bQsVltWq448n/DicoZwOZUJeBTOSwSabdWyWK3Ztp7aWJCBpg1cXc757NkclfYpihKIzlldZCy3W17cTNk0NRaPd4r12uJszmyxoTaWyoNximB3MePu//297K+Of/eOlwViemvjwqwXWZguV9ieoLyzYaIrRrpipGuWtuDN4RUHd5Z0oxiHh7joru6WVMeCb73xiIf5DYm0vFJcc5ot+fYrj9je8WRrj+zCLT82XVq2d+Gvvfkpf/vej/jy3nPulEu+++UPSV5d07s02J0oLE0UAJevZIT9jjvZnFK1DHXN671rjvM1AH7HZXWZxIxSVO3Y3vH82t6P6KmWg2xLoTq+s/8JPo+fwRRx8enyWKCjt457kwW/cvAhtUs4yxac5stY0FpyW5JTXLbRySMFg/0trdd8ofecZ90+46Si8wrZN6jWk80NzZ6OglkVI+J39qKAdb+YIQl8cXjOtw4esTlVsYxYCuwgQW8t6bQm7HeMkijWlarlSb3HrC1xeSBbuduFp2pcLNrSgtolTHZOt7NsAcDBYMvygaYbRDZrtnS3gr0/azjINrw+vNk5NT3WSfQ2MHzS7sTIKBpVD4YIFyjvrXlejfjayTMOiw1fOXyOfLCNIvWuPTxdmsjBbR2DSUVPd/ggOMg2jJKarc3wSfzsdpAhO092VSFrgy0Ef3hxP5637OiplkIZ6ipl/UCw92FLNrcUFxXFZXQL7udbapewtAUAc9PDeol7Ufw5cT5IwfCjNffOZvzWs6/yuN4nkRa/Y3n85OaI7XFEeQQtcb2I8Zi9nbHa5qxMzhcG0aVqvOL/uHyddBl2mIkQy6xyRXNUsHng+d8uvsyrgymptNwvZjyqDnC1pphGITy9XKPXHQiwg5QPFsccZFtK1bGfbjhM1/zr+dt4IzHD6ADOX2xJF5bqXp/zxweM0oZU2hgbbg74tD7gRy9OYxS+jEVR3V6O7DzdKDDdlhTKkAjH3TwKhk/qPXwax7ZPZByH65bqSCO0519cvEUqHSYoOq/5tD5gVeUU13FTIruJbsHIwfQ8/eEprdE0LiFXBi08Hzw+JVlHUemluK+3FtU4Bo/h9957m22TclkPuK77/J+PXyN/muB3JYmii/OWzzTuack//sm79JI2un8RtF5z1fbJLmNkX/hYvuUTQb2v+PHzE95fnHDT9pnb8nYDAbh1uI8eNRQ30SmeLgV/cPMKUgRMUPxc7xHX3YAn9R5iFSP/cVBFvKIf91nc9HlvfobxCuPjd9VXLR9fHJLPIyNUb23ktQK9TxIumwFbm3KQbti6lP10g/WKbrDDEcy7iCQQEV/y6LMjfBBcNCPmpkciHM+qMd0sxxQ7d2brSJbdztHrUI1j70PL8AcZ6knO8KexiC+ddahtNAtkNzX5ZU123ew4wy+5tAK9loweG9KFJVnHAr/IwrYEBfm15P2LUyqbsjQF/2bxKh+sT3hSTchm0Q2ZX3c7vjSRBX6teDTd58lmwsKWnNdDnJcUz/UtKzrypyXZytEcpqwv+zy9nrDqcs7bMb9z82Uev3eGy7gtH4tlf3GTKrtW/MHzh3Re8cHmhIUt+ScffAlZS6qjZCdcR0Etsozh0/fPeLTZ53wz4pPtIYlwfP/JPUwhyZb+loEeSxoD4w8FP7464b35GaXsuOqG/Ob5zyKMwGvIpjuchIJulDD6tKO66POTxRG1Sxnqmrkt+bPzM4aPIlrI9BXpxlPMPDe/cIdsKvlXT99gbTOeNRMql/KH04c8+viE/gvH+CebXUEjt67pIAVmlKEaTzF1f0401Gtz+/9LB6jXO6SCFrvyz/jzye45o+u4YSl8oBvp3XM9cPC+pX3S5/F0jw9/fI/i+nM3b7pxZCtPfmPIZoZuIOk/99SPBzw93+N8OiJ7r6R37kl3pWqqDZEp3wZ6LzzXHx7w/Zt7/PPLt2+v4W+dfxU917sC0UA+d0gH5ZVl8r6galM+WB7zr27e5vfnb/BKccP8wz2CjNgMaV8iI6C8tsyej3kxG/JocxDnw82E33/0Gnvf07hUUO/terFMLJorLyTvfXiPD2bHLLuc2qV8b/aArcnIP0vJF/E7f/nsD4p/d/AIidbgYqN3sHGhJvC3izXvBZv1lkGaRxEyBFrbYq2lbVuqqsKHAinAB0/bGmxr6XT8fdt0CC2oasOmqglKYrvIThJBQdAU5WDXwh6dpcI4RKJoasNg0KM1krqxqEQRPDTbOoqYmxqFQ2hBXXeRM6pzWrsgoOnaFhAYa3ECLq+uyBKiuOslAU9rHGVvRGM8aVGCbxFekihN01RsNhqdZnSmoe5avJBUrgWpYtN9moCQdG3NZrMhU6CKgFAS5xx13VJtDUWeY62m3m7QIonc4NZirUX5sCsOiyVH19MbqqpCCI1xHYlOAEXdtFHUWGeMyixyCK3HI+g8qCTFAa1tCEmCN4aubtisVreFUwGPFoLttma9qag7Qec9WmqQEUPRtobL6xn744IuBJrWMV8sWa5XpGlBawwBiXGWDBgPJtzMVgzvHhC8w/lAXVUsZitAIkNc7G+6hl5ekPdzlssNmZL0ixxjGrZ1dM+tVw2IKPgrrUmCR6QKY2MUtzU2xqOtwQRPXbfM5/PITRUaGTwChfeBm+mSvWGxczdbHIamM2xWDVlSsF2tyYqcECRJUXJ5ecXB3gSZZpiuwwXonGOx2dJ2liRL8UZAUPjgWa+WNO0pw2FEJggS6raisY62MTgTEKnAS4GtWlJVsliu2RsNEf2CNBN4Y2jbluVqS/AK01rKLAcfHeVJkrDeblCiBJ3E0qauY113NHWHCx5nDXmZYZ1n1a4oy5LFYsGwF8sOghY01tJUHXXd0SsnbJstWVqiZcrNbEGvlzHIEwgBnxBdbzawmK/ZVh1JrlBSUWQ5y8WM9XrC3vgkuq6txXvwxtE1BlzAuYCQEqkFxlhUorm5uWE0LGm8QweJ9Y71xlLV0RGfaI8UGhtaUpWx3dRMb1aUd/dYrSskgap2zGcrVKIJCLSDumrI0oTVYssgK7AtSOXx0mO6jq71NE2LMQ6pMoLzdMaBcCy3WxLdQ6AxbU2epJimo9o2zFdr2iBQRYKzDq0ybmYLHj68T2MNSZ5wtdngvGCz2rLZNug0ozYdUkmEgjRNmS7mFGlKmsRixMZYri6f03QWj45jSki8jzgE1zleXF5xdnzMutrQK1K26w2r1Yab+U2cG01LofvgJdumZbnZMshzshzatsLuYu4302UsJxQe4x2ZzggEZJYgJTRty3K5ZkuNcw6tE4IXBASBgNrFvIUwtF10T1/N5rEZOASCjLH4zjiMY8cCV7RNxdX0hiSNJZCFktjOgA+s1ls22xapI0pBSoVxHhHg+mZOrz8gkQNECFhgsVxxPV3Q+V2xXXAgBGma0bae6XzFnhwxGiRsq5Z11VE1hs22QcrI4JVpRqo0SZJwPV2SJTn9wSnr5YpemdF0HRcvrlCJRDYSIQNJEgsNnQvMZwv29yekKsF5wXS5YLndsOk6+oMRVVXF2I+DJM1Ybhs2TQMyQ4U4Br2HZ8+usV6jsxjzCt5haOn1Sp48PUdYyE8OqbqW9aLiaragMzFa6cPuqqiUbdvBzZIiH5DIEi0cwjpWW8PF+RVpljDUw5gK8NFlJQSE4JEizo8RageIgNZxg0mg49+QAhHiGLDWxa1nKTDWxwItrbAIklQjERhnUVIihSRXCU7C1fWa9aKm3cR4plQKFcCJgLGWNMnwLm5gZjLjyWfnlL08lvutq5hmCAbjbZzHJAivWFc116sV5Sjn+GBEt62oTEuz7VjM5hR5zsJ2NM4QUgloZvM1F+fX9Ac5k9EQ2zSsNi1V06FlQuZbhIwFkF1wiCTl2cWMo/0DpOjQOKqq4fLFjMW6Is0zBGCDR+mE1rSs1jVSLuP5u4Alurun0xmr5ZbOOpJeTl01JErEUr+/nNe+vzr+Eg7bj3FHM9CfFw+JyIETHqyV/HB5h58bP2Git1Qu5Wk9oe4Skm1sZ35Z7iVcINkEVibneTvBBMXSFGjh+WR5QH4jET4yH2Mh0Y6DOgpo4Xlc73OQbkikI8FRrzM2pwnZ2t/GCV9GVkOt+WB7xlDXJNLxk81xdLnk3AqXyW4RqrYdssv5+8++xWm54jRfxnMOCr1W1AeSfOFuo/gvOX/TTZ8/Xj1klNSct2MebfYxTpJFTSuWGu3ioXrdsb7u83HvgJ5uSYRj6zKerCaEeUoziTy7fBaj9d0gsmi3XUov6UikY6hrMmn5nadfJJ/FxWNS+93CTpPfbJDXA96bn/Gdw0+oXMayy2mdRm+jC1m1Pp63DwjjSLaBD2bHjI5rrrsBHsFYV7yYDTl44cnnFr1u8Xm8612R4DcJ//STd/j5+49Ztn2eb0Ysn424/yxyZpO1jQ60qsMOM1YPMppHA/rffIHxisomPFlPsM9LwOC1IEhFMqtoT/q0I8X6QvO4t8cXxi+4aft8vDhASU95uRMXVDRluF66E0QCQjsuqwEH6ZaFKZi1JWGZ0n8aIlfRBHyidsKJ508e3+cL9y64aftsbcbaZlxu+mQ30Y01eLHBjovoJO2lPPvpgP1X5hTKcNUN2bqMF/WAxfmQe2t3y/uE6EjrXXrmqwx7JHnWTEik49F6n+mqx/76c1yIND6yf/s9es8ln92foKTnuFjTec1lNSC9SBDeoLeW7ngQb8Mdm/n59YRCG+hDT7XcdH0+mB7BKkEahzQOM8mRjaNcNKQTzZ+8uMvPHF5CDtYrnmwm1OuM05mlOkrIL2vMOMPliuEjmO33WR5El2ntUqZtj0VT0HsuyS+3uF6CyxTdfsHgacf8MqN/t2NlomjdWk3nFU2VUhKxK0hB0AK5jQVWw08EqzPNxXpIP2vZtBn6Mo086h1XOCiBqBxukFBeO4rHCUxqPrk6QClPN885fBJuW+h9npDcbLDjkuKFZHukWHYFmbJ0TjFvSxZVQbISJPWu1MiH2+IyznOu8z5SBGZtycP+jHlTYhYZo8eGZNHiM33LIx5+At03FOebEQfplr+//jYbk/F4ukd+LbFFjFe/xGLYQUbxacrFaMhhsaGnWx5v9viXj97ErxOk/bzMSthYjCwNvPfhPb7+hUd8f3aPd0aXvLc848OrY0afxHlBz7bYgz4QN47Si4JPjvY5KCsuqiHbLmVV5bGYaodMUdsWOypuS8y8FqQLy0ALihuB6jyuULGUfMdu9ruiqSAF6TLOwXsfRnap2AnTLlck6zh2Xb5zX64sk59Irssej5J9tsOUbRefNZeXI+5dRQHVp/Fz6Cqe1+gjuN7vs8zj97TpMm6ejzj9KOJSIis5IPG0fU26cpSPU6oHAk7gg+UxT68npHMZueOKWESmXjr9JaOPPDf9Ac3+lI+vD7g3WeDXCQc/EriUW3HyJQJHdYHeU8nFwyHjsqayCf/8+du4i4LepWF7nFBM7e6ejQiZwTPL9LrkEvhRcsa0LumsZv9Poys3fs/xWuutpT5OOfie5Kk85Ky/5KPVIY9v9uCjHsk23GKFoggbKCsPaG7GI57mLf2k47kc8dnVHqP3NV3P4++XqHb3e7vx+NJxfVug9vI9oFAkL5muu7/x0qX7b29w6erzIj/VxDkxKBGLF5PIE/ejiAAY/VTQXQ0Zrdl9hogrkF28/yKqJBZ/tUPF0feg2cvwGoobH8vXAreli8JHR37vvGX0Uc7TyR5ff+Mx13Wfx8s95uuS3nNBM5YMnxjSWYPYi3Pa+JOOpz+esDgYcHZvytN2zI+vj0nnEpcFuqGKXUo6Ot+DFEx+oFi+WfBTdUiRGZaLkuLDnOETg+nLXfFn3BxO157yQuJVwmzYozWaZVdgnOLp4wP2XgSSTXyWZzOzw09F5/Bf9FC/8Ru/8Rf+4f+vj9/+zX/8G77rSKXGGYvBk2hBwGFbR55m9HoDnKtpXc22qTCu49nz5yyWG4SIjdLWOYSAdb2lPxlSZBpnDUEIjBVMF1vOL67prMR5jXeCtvMkOo/x/LwXM00qCpGr+Zyri2uWa4txkrLfx3SGLMkI3qOkJs0EMoWq7VBCcHF+ycXVnE3boZM88huDxFtPazsO9sdoHbED26ZlsVny8ZPHeK/pPNTO4YSnaSx53mOcjcizDNu1eLOlawLn5yuuL9dUTqB1FhnAnaeXF4gdXzNLBYvNmrbZcn295Pp6znzdIHWOlFlsPTdQaIU0MOz3cdbQOU9VN1zczHh2MUdnBZ3vsMZhvWE4GLCaL9nfH6FFwJqO1himixXPL69Yrrc4KbAikGgdRfjOUZY5xhqscHgJi9WGR0+fsNhsCDJD5SXGGFQqSZKM5bpDqQSdJlS1oWkdnz05R8mcqm2RMo3iiQCCxHtJu92SprFpvjOBi8trLl5ckSRRWOnajr3JBIKk7gx1vWUyHGKqFmsDTRc4v5ixXm8RKomiQgBtoJ/2aNsO6Tv6ZY5pDU3ouFws+PTpEyqnME4gZUZrOiTJTnAA71zkJAtN07Q8/vg55xfXBKcoixztJcFHAWi9nOO8Z2+8x7YzVJ3j+c2cz84v2BqwMkUIjZNEp66CxliKMsX4htZuqbc1L85fsFo3CKWou1jclucF6IRNW5PmBUhFa2GzrZivNjy/uMK5yIaUUoJQ+ERRhRa8I8i4m62B+WLN9WzJR588Qeic1gakUOR5LGlaVw1BKMq8ZF1v6YxjerFkOl2w2Fa4JKPDxHJAAgFoXUeeqtgoj2XbdsyWGz57ckmSphgfMARQIjp7VxvyNEVISbW1tLVlsdzw6PFjpqslIskRJLuNGfBe0TWG9XxBQLGoGhaLLRdPn9M1DTpRWNehvWGYD6jWG7J0SFW1rDZLqu2W1XLD+cU1ddeBkkitCaFFCiiLkvlshW07cqExLmCNYLWquZjNuLic4kT87EEJgvB459hs1gyHfZRKqbuGxjiWq4qPPv0UhMZ2nuAjqxutmG82CB2weJSzdJ1jerPis8trnr24QuiMyjTRfZdnoCRVXVG3NUqm1J1lXVU8v473R6p6CKdQyMgYdZZtt6K2LU54ZKa5Wc5ZV1t+8uQ52zqiBqxSBKXpTEe/7LOaLRDWEnzCpq5Zbda8uJ4zX6+wUuNbh1IxpkWITGrhPBKwpsFCRBq4Du0cWktUouiqijRAsA6lNT7El13vwBoTOakepFR4neKCx3Qtw6SHd3EjbrGpCF5jGsvTJ8/49NMnBJnRdpYs6eNCXCjrsqRqDZvGUlUdbWd4cn7N9LrGeoETENzL6+EIQSGUpG0M63WD84HZ9ZYXs2tm8zXOK2QY/j/svcmubMmSnveZuftaKyJ2c5rMm5dkVZEsUC8hzaSxQGpAiuBI3SPoAa5GegdBgkgOJIgC1UMziYBQFEmQrPYWq+rm7bI7/d5ndxGx1vLGNDCPOFmzGkklVgWQmTg794lY4W5u7v7bb//PuojHixjLutIa3H2857t3rzkcZl5/95737+9pErh/2JOGSLXixlTNpWAOx5n3Hz5Sq/D61Qe+efueu8cjS1Me5oXt9oL9fkZDYM2FmEY+fPzA+4+3HJvxzdff8fr2lse5YBppmmkshFQwxItvTbh9PHD/MHN7f+D24cjHxyNLAzQiGilm5HVlSBNLrdzt9zw+7rm5feS7Dwd++otviGHi6eiyNK06qCgSkG6lbtZbwDS4VIAIMSoxRtZckKCII5KEGFx1dRwoYoDQcHOktRVUHKne6EBdVkSEJR/JoTHTqLnSaqWocbRMF4nNGwAAIABJREFUqZk4RGgNqYUJYQjKscxYn5/cCo0VswxhABFUAmt2rfgwChl4d/PIN794x/1d4d27Pe9u3MyxmDFtLojixdlBIoNEDscj3716w/ubW16//8B3b9/zdFworXjORdBVSHGiaeJYVm5uP/Ld61e8vf3Id28/kAm0BrkJuRrVAmhizY1pGHh42DMf9jw+PHJ798Qvvn7N7f0TT8tMjAHaSgwBY6VJwVT4W//e3/jP/l8+9v356/+D13/xv/6zH4XVKBeuc5meCqVrHoZFeLgeGF8uPLaJb+YX/NHdF9wtG55+5yUX3wLibcbxWLGgXHy38oc/fMH4bGVfR3IL3Cw7fvbVFzz/PTeZ0uytqulhoW4SUiJf7q54YqSKcpe3/O67v4B8uePiu0Y8NuYXibSvrNe+X+Qh8s3lBk3wRw9fsEsrv/mv/grPf6zs3izUTXCD1g7gDYfE218PTGPhsUz84d0X/Is3v8az/2skZCPtG+OHGV2N8ePC+nzg3a8mNpcr93nD0iLvjxfc/t7nPP/SW0Hr5CZAWo35ByNVI+/TyPZi5Z+/+TVeH664+dlLrr4MXP9i8T0+OhPL2zADN2HLiy8eeCgbXs3P+MnDD3jzBz/gi3+x0ibXj3UWYuX4Kxfs3sCbvxoJo3Gbd7zeX/PVVz/gxe8Il788ELKhc6ZuE1oNJfDq5Y6fL8/5sF7wVEd+8+2vMvyTS66+zg64D8HlCIZI3UW2r5T9OPJ2nHh1d839w5bn/zzRkrL54IAU0qU1RNAqxKfAlxdX3NYNN4cdd7//khc/9pb98b4SnzKPf+0SMRg/FtJD4v2vKu+WC+7XDR8ftxx+/JztGwNVQgcV6iZy+CIxPhk3cYe+XLldd7x6uuLbn3zBF/9Y2H5wqQqLynA3Q1Av5smGb+olxyFwaANvnq44/LPP+MFvZzeR+Xxy/c5q5IvI5q1yX7dc/tojf3T3Bb+4f8FXP/5L/OCfybllGnHANqxuEBmeEt883/B2ueTV/poP9xfoH16we+P72fhhpm0i6zMvqG3fNR4uJ96x4fX+ip/dfM7Hb5/x8neEeHKsD4LgxjjTuwNLvOQ7LnhdL5jGwm+++lUOP7vmV/5PA4V072bE67OBdDejHy55sg3758IffviCN/Ml3/7ycza/HLj6uhAyxKeVcHTAXknoIfKLqx33beLN8Ypvb59x9/UzfvhPvM0/HipxnxFzoG26EX55fcleI2/vrrjbb7l9dc3nv5F49uXs0gVdJxIRNFeGoxDejdy1LY+vLsnfbXn5Y2N89M8IswOjT78ysvmQWa/dOI23E3wc4fXIi9/ztnLoa6mCxcD62cjVV5m2Try5HrhfHEx+9+YZ8vsXfP67mbB0wKoBBiE3Nh+Umx8mymC8f7jk26dnvHnznB/+o4CIsz+l2lmK4fKnD7wbf8DNsuN2GLk9bvnml5+z++dbvvgXM4gwvjuia6WNXgTa3Bj5YcvPhkt+8fiSt++v2fzulhe/5yxQtBcoNqEzAyvxIfGz8oKnGPjDX/wl3rx/zvP/Y2L7vhLW5gZ91YiPC/MPt1y8Mm7jjne24eOHS45vLhj/YOLqq0qcnSFrQzwXyk7mjxaUODfXAd87K75uAnUKWBLiU6FcuO51G7wTR/Bxj0cH5sPSnG2ZG2EupKfM/lc3aIXhXpBvR97YJU/zCL99xQ//sRckxvtCvoqExRBzmY/t+8z4IfJ0d8Gb/RX2L6+4+IUSZyNkzlq8iIN4iDA8wfQ28F1+xsM3V2z/YOD6l9Vb76OQnpxQYUEYHgpa4PJrePzd59T7icPvX3P9pbPiL79ZPadnc9BQhDg3th8a/GzLu3LF7R+8JP7WhotvhPGhsvtmz/LZyObNEYKS9pU2Klc/F8JPJ149vIB/uUP+1ZaL16XrFxuha8JqM+KxoVW4/KXwzdsfcPfNNcNXA5dfw3Tv8Y74WMd9pewCw2Pz7/H+ioevrtn/3jO++Ec+jrt3Pu9afQxOzOFTXvEYcID2LIew0fNYebyf1pj/flja+e+LQXxcPNeKeAdJH6/xwffs8b6hq6DF19HmYyXORhv9vVFYngXyziVz1Jw1ng5GOCmCmv/TkjA8+Li2QUlHuPqJcvebL7l7e035csf0k8Tw5JILy7NA23jc5otAvgiMt8Jnv9VYfn5F/P0N0++MbN87e7cFL0K06FJJIRvTXeP658bFb0fCH2548VtCnF22KB5bB/R9DxwePF9dvqrUp4n8dsPj11fIP71g97Vy8bZQNw52t1HRchpH4T/+W//mn+is/acatP1v/vt/+KNGJddMCJGoATMQDYQUmPPKZrtFonWmjbF/mvnw4ZH5uGBtoK0r4zCQQiRJwlp13cpirPPMWuCbV2+4uX8ixIGcV9d3C0JMSqmGRNjtEsf9A60Z724+8uH2gWMuSBhZlwUJmWZ+kJnzShwTx2VlmDZ8eHfDu/f3PNZKi5FqMGQjiiBmqCZKM9IwUFV5+/6G+8cDb998YEhbWgi0bCRJpOC6oRIgJmfNLEvj5u6J71695ng8EkJEo7LW2UXsmgOT++NMK42Li0vu7p54/+Ejdw+PlAbTNDhdqBnNCkszmuIsWJyN/O7mgfcf7jmUSkNoDGiIBB0oa2V7ccXT/sA4TdzdH7h93PPtN6+ZD5kQxg76BSiNjY5oU9ZaSGOi1cb+MPP6/Ufu7vcUGyjVCBpRE2p24MCksj8+kfPKcVl5f/uRu7sn1pwdCI/qbL3vtc82axyeDqyHlfv7B96//wimHJZCA3TcMM8LhmslllydvTpMHNfCze0db99+oFgjl8ZaMkkjpkZpjZwradjwdJhZWuXx4cC79+/YPz0RNXX2b9dsCQONglrlmGeExtPhwLubj9zc3VHaSAXWVkC8MjavmVyNec0O6tzecZxX/uCPfsb+mFFJbm4lgiCuHyoJxMe0WeX24wOvX99xc3ukEsi5sdtcsMxHmuGM6GWmrIV1WXh6eOL+8ZGbjx+ZlwIhkE1YrJHzClYIBlZg/7Sw3Vzy8eHIw2Hl9dsPZAONEZphEhxQU+Vpf6CZsZZCKZUPdw883D8wPx1YqrHWQoyB2iqqytIq635Fm5KbcDwYH24fef/hjsN8pOLgjdI1Gc0f6uHunrpUMOHm7p4PN7cc10qJASQitR80NCIWiKpUhMf9zP7pwN3DPcd1JaShM/iELMaxGyA1WaEVllxZ5sLTYWFdVkwia2nkNYMpooFaCiEGSl5Zj5l1Lay18v7jR+7vntjXgqaBdcms64KooEPk4eGJUoz9fsYqvH39nu9ev3N2aim0srLbbLFaEYztNHF//8h6WFiXwvsP99zvn3h8eiJMI/tlJQ2RqM5YFIRSKstSqLXy8NSLV7my5sBhMSRGJBZazSiCNsFKY344cvv+jtsPD9zcPLIsgrWEiRBiYgqJ0Is3qkppxvubOw7ryt3DPfvDkcPq0hStLgQFa40YxAFJEUSEYgIS3ChQAi0kKsJimRgDgjvDmojnizTQrGEGrfadXo1WFlSEzTSxNJcqWBrUKjzsD9x8uOFxvyAhcVhXwhA5rkfvNoiJWt3MyVrj8PTE7cMD8zKzYsx5QTCiBG/T10jDQBxsW5bGw80D+/2R/XGhNqEq5FDIkl1b1SqlS2Y0gZKFuoKosj8eqS0jSVy+w4SIuhYtgZBGcobHpyM5N9bq2tYSBSuFWjKDRlQUTZElV8bNlmUtHJ6OFIzjslI7m1qjYEURSyiJIbmOcgjK2w/vybWy1sZ+PhJiZK0ut2EmqGhnMDfm40JtLjcxl5nVCjoG1rpAgDiMzliVDsoHZ3trDNTWaGYOzufV37sFUlAUBc1Yc4ZuEqjL4vu6VaRVoiQGTVAFU2NpBUkBGXzPytk41i41I0IzCMk1hEUEidBO+ZRKQqAU4jBRKzQLLt1wAoqLISkBEUJAGxhK1cqhHCkKixU/GNsK0jCrqPmJ1PX3fa+4uHpGrs1zmwSqdNKxCkUagpGXI2OK1ArZKnEYWObVNcxzJWhA1Z9+M6h3LMXEw9MTqPL4tFBVWQ3WYugwspRGC2AGGgOigb/11//6n4O2fwZef//v/saPtPglqyUHDnodhDoo6VF483xg0UhDeVxG3t9csft5csOo6gDseh0pG6VuA2aRN1cjVZWbwwUfjxuG37zg+ZerAwGHiiV1kHATSLOxhoHlZeXt/RXHmjj85DnXP3XDEoSzBqlmZzOND3A3bPj24RlhW/jyF3+Bi58kLt7U86W4bIPr8oliSVjyhjebkYc8cf9HL5BXI5/9eCXvQtc8dJCw7pLr3IbE22nkqYy831/w8ZfPefm7wubWGbYWHBBwAxchHYR0E/lle8blywM33z3j6meBq2/8Ar1cR2+4FwcIxocGLfDNdMG9jby9u+L+y+dcfylsPlS/2GWflzYoca4cXyb4MPL15Zavvvuc/deXvPytwPTQaGPs2ps+h+vL0WUljoHDJnGokY+3l6Tf33L5dXOwpmvTWgqEuTigbs68m+cN3A3sfhq5/ro4AynqmYVtKfTWWAgF4sfIsp/g1cSzL30L3n6oaPG/N350sLROgTg3ymHD3ZTY329Jv5x4/gdGmv1CHB9XB4uaERdnZq27xPx+y8f9jvXtluufCLv3lbpRhjt/b2lGuUiddRsZPiofw4a7mwvalxdc/9wBKs1uPIc4q2z4uJAOjeEp8NP6GftXF8x3G/7q/7ayeTNjKdCmvjbU58+Cn1XCTeJYRuyXW3a/n3j2s+rtvIfK/MXkQNG5ULEScoKjz2N8PXD5c2F68K6eeKiku5lwWDtQFt2s8iZw/zzw6tvP4M3Eix8L6dB8LqzPQfb32Lw5oDbxeNyxrIn28x2bN8pwbwxPzkCvm4SuFWIg3a+Me2EZNjzOE/v7DcPPJq6/FLbvs4/loL5mO/NSEDZvAse8IUtg+MXI1c+EzUc30zpJhoCDfagzy0I2tm9huoHLb43p1tvqUwfd46Ew3mY/o2cHh4ZHY3g0tu/NDc46iz4+rWCGBWeYt0GJC9hhoB4S837k8qeRi+8a27err9fHQnxYHMSLyvbrR6TtyDcb1hZJf7ThxW8LsetcajbmL0afd2D9bOLiu0o4Bh6PW/jpjs2rwO5NxQZlejtTtwOtG56fQM3hybA6oK8Hnv+ukvbe8r75kDuwDVr4tL4MhnslfTVw8ZVy+QthfPTvrtWwIbK+mLDRwW7Xf1Yuf6oMHwMX3wrb9/7+mt3kzNKJnRwIa8OCG0GF2eNG11OONcLaSPcrbdPN1vuROh5c3ink9glA7S9dK5YCy8uR8aGQnlyuJRRh+1q4/Lmye+tdFtMH18sdbxbiofh7rkYb/Aw1fWxcfWWMD8aw946AE7joubezRcXBveGxsXttTB+kA9uuNRr3lfi00oZAesiEYzkDZunYCKuDodv3xUHe4MA0CmWjfQ5dszcdjO07CLMw7I24Ov6k1VnK5XJwlm7QM4g3PDamjxAWLzaExcfaovo8BsWietF0dX31l7/zRFoC6cnYvnN2c3rqYG81//2ta8MP94VQhOnWmO4b6akXrx671M2o5yJh7KB8G/wz49z/nPz3Paf3PTx6TIp1Lfmtr7HleXJQV4Q2RsrO40MrDD2fWuC8N0432ckpzcfcorNwza9IhG4u7frtwc8doxAynwzSzEF+PYH13wNytYGuwubW8/r4UNHm7+vP5WMKDubWUZjuK8fPlXh0aYOaPC9p9ZyVZs/taV/JV8FzeQfih8fKehkcPB59HFFheR4RE9YL5epb93vavfFCw7D/3rz0veBUnEPhP/r3/60/0VlbTsyWP42vf/fv/AeGCk0aI7iGnCoWEylGWi589vwZm82Ehsbh+MC6FO4fZkCouTBNEy0Xatdp1Zh4cbElSmMtC9kacy7kUjBz5H9IE7kWoJFiZAgjT4f3bLYjMQzM80zvyaTWSj2ZN8ToF9CcGdLIbrejtcbhcOt6ec2g6+lq9qBK6sE+DIlh9AtwzplaMyLCuhaCJiRG5lIxM1IaGYBxiIRudrIsC3EYaRXIKy0CQXlaKjG6zq1Yg3khRG9zJgZaEBRxw6QCQqA0lyJAAhe7DYf9A+OYaK15a2Vb3NBGYzebqUQNrKsDO1GN7bTh4eEOUmAuM2MYnUUlwQ2DLnc83j8wbd30JOeFJn75nUJirSsagwNNm52bsAFRXONXWiSJshyOiKjrN64rqPhlNyhmgCkxKXVd2WxHluPqrD5VHo6PpHGgWMMsE2WgFkXFL7hB3CwmDImGG9qs84JqJARjXQtYcmCdCuaMwWDuZC8i5LpS6cZMze/5YtrBxcrVdkNtrn8qIh3oc/BSrWEamHMhhcQ0Tdzd3nJ9cUmx5jq7IVBzIw0BEWOZKxq9xTiIEgdvwR6mDcEUK0Ypq49nUKxmmoCGxJgGyjpTSuF6t6WUwloKLQTMjJgSj/PBTYBojMNAzl61C0QIxrwsrpc6BCLuzJsNNx1qjRCiM+WkkVU9kVdjMiW35vEbGg3XfY3qB+QkUJYV22yIIVCLUclEFJtXmirFKnFwNnASRYxedAmspRDUyK0SiVj2jYUUKNVd3s2MISXKmt28qoP+a549ZqKxWHVArhlaAxudXGqlVdI0kMvSQenMNGzIORM1UEIjipKsv2ctaAgkIvuy+DM34VhWdEjeAq+BgKEGd097nj9/6bkn13P+S4NgHeCsBjalLv1yRCWiMWC1kWtB4+BrOC8Mw4CqYrV5Tm3ixo+ADqObYC0LdLAwNFCFlmGz2XA8HtGklOI59ng8EuIGVcitIlY66OoxmlJyUDUESquUFlA1P2kdV1QB3LTMzIgxuvGVBs8ZBsu6Mk4DVhsihpgzMJv63iAhkEguy1ErsYGpoVFBK3ld2Uw7cnFwNGdn7cYUaCWTkptPanSztnGI5LXSGoyDIq1irWFNaIhrklvrMjKQNFDMizrEhKp6PhwCrA0NARHl2LtGshXGcWTNC2M3BlyPM4ogQ2e5ibfftFIJqrTsAGnUADSM6GPVpMvYOFOSoC5d0H9Gn9tSjWEaMcvOCjYvVJmZ5wMTCJ6Paq1sY2SumRaVUSPjODIfj4hGcqtuEgYk7VrNtUEzdjGy1Eaxxna75ZiPfsgSofYxkxjczHLxeBQRWvMY0M7er9WLN6GayydooFrFyIi5rIGZeTdGCDSMpoLUwna7Zb/fs9lsyNlzc65GrgVJ/hlbApZXz4Hqefm0nrfThpob2iohRSqGtuQ5EWMaRqp5rsrVfJ8xN+5L4uz0Uorn9KDf07Luh1Dq+awTZIv1vvSqDhzTmhcevh8bKVKtMVjzWF7dgMzM9z2tRuj59iCZISY3N2y+JoTYzc78fVChoWe5GyND9SJOCIF/8F//V3/yvq0/f/3/9vVv/zv/uYG35uarSNzXc+vxyV3647+RWK/BojF+FOLeuPy2GwwtjeV59AvlmSkId7+eqCOkvRGPLmngWm6+BvLFJ0fvslXWnbJ7m5mfR3eMvv+0RnT91KJZJ/Vup4OzjQ6fR6S6Jt33JRGst2a3pGfduHWn5J2Q9kY6nvTrhPRUqGPAFIaPK1IbdZfIF5HlqushLn4hLBs9M5VOl8rpzYE2JfJlosth0gZvAwW/rLYTuDk7CK1zpU2uibf/4cDmprBehfM4xmNFOjBYts6CrpMS9/UMXhw+i1x+6wLU6W6mXI3Qr3VSjcdfG7l4tbI89+c6mWlJNZbn8dw2PL09Ui4GtJvRtM4gPrUID4/+zLmzZn1O2jlOTvOi2ZifR4bHShuc2bX7ek+5GAiH7DIEm4QuBUuBfJk6AGzdYM6fLT0WLAp1DOf5zJfp3NJbR9cdPs1zPLhGJwa61D/GsAZ4+pXJDWP6fIPHaHp0wFqaMdzOlIuB9Vlk9/MHDn/5irC08/fUtVE6qJAe/PmkAyCneSk7bw2XZoT5k7nP2Wk9fTK7C8fK46+NHZD2Fnlp5uZj3z6e5Srys9E1Qjsb8zTXkivlekPdeAzFfTm3PZ+0RYGzeZgzcRPxUM9zptn37DaFsyRIWFpveff5OMkCeHt2O8ftaV2ZypmBfModpzb501oHj/faGfz5wsegJemGgfzxNXifkW4+bimwvBj6mPocnL5DfFzIzybCodCmcH62svVOulMclW1genukpUDdRteGjv7sJ9kHC7D9+R37v/bctWKBsotobmfTL9MTS74bVM294BE8hsM+Y8mlFHV1XWaLbn52Wisn48dTfJ9i5GSyeGphn593w77goO9yHVxapf/OSdvVx9/HvWyVMFvPVx5jFj9t46e29lMuqpMQZjvPk1Qj3S/k6/H8fS34c53X5ffyka6f2vxP7x33heVl8lyTG2HvrP86hfNYukmdr6mTTq1UlzhxmZ0ToxbWy86y7fGSt/7nk7zFSebhZJrVusnc9LG4NvJ+JT+biI8L5Wo855Qz07iPuzRfD3XUXvRrlL62vj9Wpp7bTzIp6amc5/f0Csd6lhs6jWOdPv2d03if1tf8WWL8WM5jsFy5XI/ntnqOmbKLfwwoP/wgMT64FvTxs+TAtrqEQFgaYe6GbbtIul/dSKzLtXjc+liHpXaDupNR36c49xjgvOc627qd96b5s8Tm7Xqe89aLsidNZIvK/PlwlndwbfresXysHD9LhOz7Y00O3rYgDE8VqbBcBwdUs+ten/IS+Lkhb5S4dEkd5Tzmp9/5vmavG+n1OfreXnAazzbIeU8ISyVfRI4vAung9yVpnOPwJAWxuSnki3COu5A9Rk5SCyF/OgNoNxY9FYBCB5H/73/wn/6Jztp/qpm2/+3//L//qImQRdwdWaO3SNeKiaAhMh/3PDyuPO5n5qwc1oapsdYM00RpHsCLVeKYwCLHtXAohfu1s4v8uOpgQRgRSdTqQEarjWWeSVPgOBdWM4qBmdJKwaQQg7d4lpIRhRgDJRfWZWVdCiGOQGCUkaGBLA426ZBouTCkyLKuHNdM0NGZdM26gq9XCSQIsRUGlGiNZo2WM60WpFZyrRwozFKQ4JWHJIBGcq0YylIL1EaIIwtexVScvRViAAJpmtCgLKWQgtBacUMs9Qu6Fdf89Mt+oFUH1qxkal65GAaw6vITKLthQ51XxhiIEkgiDhC10pPAionrs4qMRBkoc6FKIJsRt5vextKclVQVFTeYqlbJVhFR1pLZpA0VSDFRS0ZFsert5qU5YzupkqkcliPX19esy4pYZQgjmDEOQpBIWzPaQMTBRGdIVWq/EJcCIQ7+HMyk7lIfJVCXGR0SWWHJihFcEiONFCsQB2ozNiEBSl0WgjiAv85HAoKt7ugqDcaU3IimuH5NNmhmTJsJA3LZoxLQFgi9clVqBTKihWlzxf7pwCCBtq7O2pJuuhWis9i7vqtoQKNSpJHFNYVFIzENrDkjwdmGqg4URlVvDwuJ2jLTdsKsIWELLRB16DFkxCjQFlQaQQW1gIqgvcVdVBz0wNuiU4ys6wopUlWREKCsLNYo0ZjCwPFwZBwnagiEMWLRmJcD0LBaKALVIXVqa8RhxNaFoA7ctyAkrZg1cl0pVGc4G+QizMvCOAxgQs3GJgxIri7ZQkXN9XGrVhYKSu8GwKvkm2liPc6EFBjD2GOrohppzRmiKIxpoAlcX14x3z8yqDJIILTmVcQhcKyZMSXqYM7+G93oCxVqKUTtF9niZlXbaYc2WHNhiANDVDJGjAmNStSAiusQT50VLaqUvNCaUUOgBiMyEHEDQR2EQ1mw4L1lorDmhWmzoRalmnluE1cgHbpebCkOUtO83X6IQIFyKCQdiDpSSmW33bHMq+e+Zmhwg8cY4xlc1WBQCuMwsObiz20QFZp5voopMoigahgrtRVCiLRSoGSGqAza8xhKzZ7fWm2MIUBtkP0ZN2lkbYVja9iUyMnZxsGURkMNBglkPE9I9BhQVYIqx2VPUKWqQDBarcRmhCrEZgwYlgvajFEjscEoYOvKoEqrDVFBNCJmpJTYXexYVpeAaOJl55giJubM+Wae/3rxAWlo8ANlXhfGTUIFyrySQsT6wWwtM7U2//spYotRijGEAbFCOc5Mw0CenXksGLm23sYfsQ5iShqIQ2JplblmhtG1tVOMrhmPF6lMhSF5+1LJmVEjATe4E9xYjAIalUrBQvAiSF1JYaQ2Lw6E4AfrzTgSqkGPm2GzgXklikB1Jtx244VCq5nSCoMKmFclSnE5gmhCsEArlaSRpoqpslohqjCFwLrO1FbQIIgYrayk4IVeqP6+1Q+Fa169y8SzpSfppt4lkSZn4yefR6sVigPzBr5H18ZmO1FbZRwGhPEs+6TF11uIgUNeERWyVIqBhAEjEoYNa23IIFgwqlSaVI/HthK0AQVrFZq5j4AZf/Nv/DnT9s/C6+/9/d/4kRTXJw1H14LV4pd3McGSMt01Ll43tu+c+bT5UNF+iUfcgEWat2m3yYGt6aO3lF58sxLK91gyjTPwEJZPrt7jQ6VuAtNtZnhyFqhfpP0s1E5gRZdhaKO6kdne/zmBBvkq9su/m6lZdFZk3gbGh8p013p7ZXcHtxMDyC/xdROou+hGW9WZMuoHJ+LRmN4vDB/dP8KigwhqStxntPLHxm24z50RpIS1OdAoDnpYCgx3C+UiEUqnNitn4LuNzuA5gQht9D0+zI3DFy7PcmqnP/wgEldhvY7UyVlYYhD6/4c+p4/9zDBpd8yuxH2lbny8xJxpBf7Mp/dwgMjZWN4G6+MU1nYGPcUcrEgHB/nSvjK9O/L0V3akxwoqlIsORF4PzqjrTuUnEx8xnP21OjM3Hj8VEMLaKLuIpU/P72yoxvBx6WMWXCrr6HuILoXlpcsShMVog7e+Dk/1zFA17eY8VwMhtzMTLu6dEZWvk7PUHr0gal2HE3Gw7MS8Xa8jmzdHyoVr6zqLTrres9DGrvV5AtuSnlvS41Pu8xIYHvJZU9JiOP+25KHzAAAgAElEQVT8FAthbazPR7R27UkcMD8BQicQ19nZziY9gdjS/FnD3IHCqC598Lh2CYNPIOuJlZifDYw3M+UyuYFg8jgcbo4I6uqBc5c86dqpbXQJkxMbXapRt9ELi/uTzIR0I6PGeDuzPhvO87+8SL6PX6Teeu+O7WFtpIcM/T1Pbdzr88GBwCGQL6MDS8WfVYuvAcHnWEvj6S9vGT+u1IuBfBFpgxd3JEbGm5n8bCQszkIs28DwUDy+l+brkA4SqreFW3QGb7kaKLvgzNEp+fhOLtOSngrr8+RnG3VTrJAbOrsxX5ui57TF18TUjZFOrMbhsbA+S84g7QUcC0LdaP9Mz5kWT3qjjXIRz3FatvHM1j+BfWJyNtdzQCzQpnhmkZ/yaXqq3iGg4hIO5SRnEM6mXQ5o1c7E7oW5y3AGCtso52eXYuTr6Dmv/3vpxZ7xxvNnPPjastNcBmG9CAz7k4mTdhalr8mT5nlYrXcy+Fqw3s3hrH1nZ+Yrz5PrZZfa2fVC3PckPcoucHzpTFnzBi9fV6ODr1KtS3N8eo5Te70AaV/I17Hntq6H3QHFuK9nwJYgSOtzdOHs7HRszM8cLLQuYRD3vh4Jet6jUS8YDQ+F8WOmXESGh+zfO0iPHd97c5+HeKzk63geK2e8ngpZ0lnynGPF8/wnM0JwVulpHcQeZ2JQNx2QFGH+fAD1vSEe3UStT/c5d9cxUAcfv9wlCbTB8NSoG2W5+iSndBoHqf45ZeOffzIEFfukXXz6LqcPbEkok57B4dM5BKBMjplZB5PnF5GweucQKqS5Exy6hGEdlOnGiZXpYOfiifbYGp6a7+PNmb0uGeHnmDrq+bPF3MRVGvwnf/NfC3mEf/AjqifqVnHdOw0Ybh4l6u20uc6YNqo01jJj4qysYAZUmlVUlDFErPoFJaiwSYm2rl07T2mdJbW2AlqR4C2ThrdRDnGgdf0JFd+0hM5yq4UYE7U5G0i7GU1rFaxQWqZR3AApRP8+5pdNFekAqLfCmJgblQVnbxIDRteu7OChJiiWaSIUEQiu7xc1OpjQ/IBUBYIGhhChGHFI3gaaCxojpTU3amtGEOG4zIioM7NqIwQlimGloBKo4FqBwb8H2silgLouYTEjjIlildwBz2gBjYkm5gxK563SMNbqrLWYBmrJfhlPCYIDd1LNdYLFjWTMrLdOuzmSt7UmApFiGSFT6oEQvDvZQqO1QhqSG99QSWlENbIsMyklaneXx5rPoyoaFIt+WdcQyKV4TPX5Goedg5MYEdDu6WfNAQNTyHVFKP4cVHIpiEZsLWwkemu3NZBEbZDGkVwrEiKECGoO7EvAzA3LNAphchmIVlZaqw4CaGAphaqdAd6ccWtiLHNBQkLEGDqgG0LwFmoVanNQ04KDCdZcU6k1NwGqUkACzSD22A9EZ3AFpVZvpRMVrDrYUPMBjc60LApQ0eBu8Q5qgoXB53StRE2dtYYzBmloN6GruWDNUO0AXvLNQebClCZWEaxWFHWR8gLb6YK8VEIciBqJwVsS25qZ0ggmZ8mG2FxeYQje3r5NG46HI7vttkv1OANQYnBGqCgt+M/jNLCWgnZDPhMhiLMScy2YQIheGAGjVWfV+ti47IKYSwN4W/zCdrvBanHgDO15xTxG+wYooiw5M6UtVqrnpmakoJjVM3N9qSvSQbnW+sXTDGpF1WilMEgEC6RhohSI4+QAkxjrklFaB8eMpi4+omECi9AgxhFrsJJZi4NGXTWJXAstBtIwsMy+ycUQyOWkmQRpSJTmZnrruhBT8nWm3k5TzWNHNYL5O4sKJa/+KeIaS1GDS0UkxVrxQ2DJ5CaEYUNeXec06Uiw4uZZ6iyIMCVMmq+r5rupSfR5HoIDXqKIeYwFCX7hSDgY2PzCX1ujWkO1gTgbfoiJXDIavM09hMicV88LAqjr96oKpbpkS9JESNFjBAgiqBjO83bDqyFtOWaX05DgOT+mRCmGFCUOgdayA9+tIcENIAQlanVN1TS68UlQrClWlThNFIC5MA0jcUi+toufcHL2n+faEHG5DUWZhoEo6lKCKmjJxMHZOx6jySUw1GhiWAio4Qx8dZavic+thtj3eGMYnSFP80NRxNd4iIEx+iFQO9tcq1DWyhAmnmoGE7YE5pwJm+QyDNa8CChCUqcPqAFBQJSY/LtpCMTg0i5ryzRrjDFSW2Xtkk0ibogm0nouP+krC8XgWMVzb0g0pEs6VGLE9zJ1djChUaubHQ7iZqPrMYMooe9RFWOIARWjiRJVIDubtqwrSQKpn0tUYJq8nTNg5GUhavBnq0arxhgGWlMQo2qkZF+TAizqckR/+89B2z8Tr7/3937jR84iMZcOAo8F88uvCM5O2jugG1YjPWQHaT8esSF63HWQJF85ONMGB5mW55FQ/D1PF6P0VD4ZAXUG4ak1sez8DGLBmSx+y/H9wsGKQJwrLfplPV+E84U6Hv0yjHAGi1A5A76ni9sJZEhPxb8nfonSk3alcP491xrkzOYJublZ1+haqIJ0MCeQL7xFsm5CZ+s4IBLmfrHrzMzxdu1dR/1SObieoBa/PIbVPl06FcAv29L8R/HoAHA8dtf5vbPCTgypE1AVFr/wpscVrR1oXVwSoW77mS/IGSRsY/g0br2w5sxfB7q9/dwv+nFu50voSSKjbuP5M73F1gGvsosO8I4nxqb11lJndp3mOz0V0v2CjQ4i50tnUZ+AyxOQdNJYlOrsTrpuqrcAZ06SB+vLjYdPBzTDYpSNa06e5hk+xcppzCwFH4uoZ2Zybwki3a/O+hYhzM4YFsPZmSqgemaRt0E/6eCuXQfX/9hBWUVLo26iA8CA5EbbRGepDeqgVGfHWdfqleYyHmHpTMxjIx6ys+SinxN9Pem5/dnn12NSsgMRlhzQPb3X6ZnC7OAb2rWBrwcH7nprtoMnxvL5eGbX1tGlVU7t9fk6ncGKE7DcolJ3kTYE5peJ6TZz/GKgDfEcRycQ3+UCfC238RPD/MR4a4NSOkvzBJqdQE717mQsnoo8HuNaDelt8fMPxk9SA9pzQ2/P54T3qJDuV9bnA1rNmbfFGX4OOFdC8bl3k9RTHgznnOLt4M1zQ3NjuXj8VChBxLVBYzibt51A0ZN29EnvVavnzvi4QC8mn0CxM8B8t57B1Xjo0i0dUItHZ8amvXWZAfkeK93+GLh/GrN4bJ9ycQdfpa9XaT7e6dFZrW0IzgqPHgttcDmb09i3Qc+FoVPB7MRwRXz9E/RsmtUGP9Oc1qnrZ1vPMe08Pl7Q8T2q9TVrURjv8nm9CHwC/5dP8jktuSa7JemyAXIudu1eLZSL+Clf99iuG9cBRpxBe2KQnosRdspZ/n3z5antX8/fuY4OimKwPotd71TOLNx4NJbrQDr4GNdNPLPl26h97TqYWDcuKSEGZecs8zqdChYn4zH/jg4YfgJpT/IP+SKcC2debNCuX+1guZx+XzwW4myUnbJ5dUBQ5peR8b7SRunayJxj2CWKOO/nYi49kQ6uhVw7MJ/2Hsf5wtfi8NQLnch5nZ4Azzr4z+JsbG7y+YzggLrvmWWSM4gcendMXHyu8k57Aa/1PdjnMa5G3vWicjHK6Hm/bH3vy9teFMUZyOt1dGxuEIa9g83OsHVgf70Mfb/q79/HGHVJDy3Gf/h3/mTyCH+qQdt/+A//lx+FUCl5ZTdOhORMLqmNIThfZW0FITigkSuRAKVyMW1BHTAcQnL3cAte1YhCqStGY61OHXfWoaDBAZw4Kq1lXHtOSRoZiWitxBAoy4omB4/NKipg5jIB1hr0BHE8HJjGzflia9QzaKkxuAM4UEtlGJODUzQ240BZizMazfx2b0YTaCrk4nIMoopqggYvwpZdi7RWHZA1iK0xCgy1MQaQUtmlgUpAzAE3QVELgDKpV4KCBrI4EBKsf2btLf8UqlVKrahEcs6YNUIQpiFR1koKA0sIDiRIo2p3fRej4aCriGJW0KBUGrl2ncrgWo0n5Q6NwXUuVWk48LHmzGbaYM2orWKhOQgVMkSj4fIOqm5GFCRQs7fGh+CHgxQSy7yiErqBkDOb52Vht7vieDwy9JZ3lcZuGKnriomwtKVr0vsTqSii3ma/2sqyzIxjQvGfuVZioLXGlBLR1AFeQAJAZpig1AW31costUGXcnCgK5N2A6VraFZzMBsRwhAdGMuLA7Kt4SbsynY7+doojVaaHwyG5O3T0pwp1gEnlzAwLtPkIIgJ0QRWc3kFcwZfjJFlzc7kC3rWlm21urSACSlEFAckHZhrNIQ0TTRCbxX2ljsT62w9vwxoCB7rdJfUcXRgyhqhKJoFHRKLrVg0tCiCsi4rY3TX+DENzGXt+qJCEaWJkj2oQAJSvFprraEIUxgdlNpM1JxprWKxIaEhNfNQjzy1hU0az23nQxqppRFDdOatWdfhdLYirbnBT3ZWY4yBIsKe7GROMUJKmOEt3K0hQZm2G47znmHjLNQkQluzg4kNTJzhq9IZDSa05sDTMERKcYDVmX+g1r92b9+2ZgRJVBFGUeZlIQSIAi2vWC3OUGxK6GM/hOBszyCIKsOwYc6ZiktiCBWJDryFIbh+q4FVYzNGrGVicFao66Aa3s/tcy0ExIQUE5YrQY3NNEHtZR4D0UaptYPMRgxCEgesW1nYRD+wBBkIGnveCQRTYogEVZTq7M0o5LJSysIQBz8cmnY2pDOJW1sZYnImc2uMgxIEphB4bBX64eDYKjEG3w9CcOAZQXMmAkP0QkelEMYBElT6ehHrWsNKJKIpMa974uDGbPS2esGLZSUXNAXimCitspaZ7dYlHNoKITTyuqIpOKZcYRcGkikJJbSINkO00mygmqIaSDFS20orK+OYGFJwWYlxRDG248CgAQkBidHZ+QGiKrZmggbGIdHKzBgVoZIPT0hzRrLvgc4+rWZcbncu4zKvhLyw226JqmymwHo8EoNSciGk4FrzUXpOHVCgog5ENqOUhTENGL6O0uCgapFGGBK1FoYQ2E4jNs9sYmQSwbLvv6Wzf5udDlTqRc0QiVGhZF+/ySU8ZByBXtAxYbPxtmQzl7IYQqK1hRSUIOIsfoyWF8aYSGNyrfO+h0hMVPHvKSqsdXX5oebg9/nS0RwATwHG1Hj5bMc0BKxl76qJggVnPUcRVGAMie1mgLqwmwZ2KZKaeOwmB/UxZ8eH4Kxwscrf/ht/bkT2Z+H1X/4P//RHbeh6dZ9PtCmeW3brRXItwBNDs7NrXNvTOP7Fbb9QBspl6KxN/cTSnL0oMjw6U00708WSXzpd47KdL+NlF1guncFapw4Q+vG0A1JyZqGeLm8ojDcr6/N0Zhid/t+JtZb2lZAdcM47PbNiluexf0YHLk+E1w58xkMHV7Q/g8D9r48cP4tnoDHOznpbryJ56/8lCIfPAzF39tLqC9hZcEJ+Fnu7ezi3krehG5wcWmdWVcLira+WlPi4nlm7y/NImB08SA8Z6eZBYemswg6EnMCvk1ZhmLu+Y9fbPDmRQ2eMdSBE19aZuSvriwGt/n4nRnLo7bqnubP4SVM0LPUMWoMzAYeHfHYi9/FtpLuF4w9GptvsgEvXNJ2/mM4yCcPd8gkAa0brbOi8dbbscLeQrwcs+UU6rM1JJaWRn09nkMjfwP+VL1z39ATSp/t8Zj+6zEGh7qLLCoiPhSU/a7dBqdvEcOcat5ormt0kaPlsRJBuMtNB7+hGTq4zrJ3lWnych8D+Lwxn9m6dvBhsJ8mJIO7ofu9avYRPxYHQQfRP2pZeUK676OB+bpRdOksuaHHgVCq9ZV7/GGh8AndOwPin9mmf2+E+n38fFeLjSn42doZyYLzNZ3A2zC4NEPfOdhY4A5onMHB55ozJ9ZmbUGnlDK4gwvarB4ZXd5SXu55zHIwKq53Z86iDhaeiuTP9nCVaduog7GJMb56ITys6F6xLT7XkACY4YDbdeAzGxboZWJdAKI45hMUZkae1fAKTneFaz0zi03uf8smJXVk7WL5eBcY7BzfPpl7VzoZJJzDT2atK6SzCfBEY7p35XS5jB2w7sDk6WHgqCKzPh97Or+dWcql2XgdnFq1A3ga0eYFtft7lk7pu6okNXLbBNXMHf568UUKB+bkDeW04Fd0+5Zk6ueanaW8R72z9sHySGDkV407AnmajXMTzOsiXDqguV4Htq+UcH+OH2ce6epGwpZMMjq/f3PVYTwD8Sau3dQmME0Ozbnyeprt6BtocAP5U9It9rbTJgfb4uLB8Np4LAy353tJS14fNjePnibL5tC6tN1eF4hq79j2jK80uSZR3DmDOLxzIP74MDhx2QDV9TxLHCxY+LqG4fIQppKOdJYEsyRk0DUtj/xeHc2Gljcrxs0CZlOWZMjw5gH/StD8z9IM/56nF/wREnuJYG9Sk1F1n8HaTv5Op6fGF33nmZ4HlOjjhJRvx6Hc+LXbOC3F2RnEdvEhcNkodeyfuaQ/rxbv5uceHNo+tvPNxyFuPhbh86p5Zr4J3wlRn8MKJTWzULiGT9u0MyJ5iwjpTdt0F6iSsl8LDr8Ny5Xk2Hr9XcGiu/W9BWC8Dy3MHmeeXfZw3pwoQ52erk7oURI+1fy1A2//uf/off1TNDaYCjbpm1BxIPBWogxhRIJghzYEiq0YMihSlGWgcWXIhpYG1NTRFSm2YCMM4+JXJPIjcVQQ3VJGIrZUhCtTCcT6QeguqBWfqmfTWVYkYwateGrw93jK7qwvaWl2LsFSsGDQHpVT8shgJ6BipeXbdQqozZIcEGmmIt9b3C6QYpGAMIWDrQpKAYkBzZp34ZcyaA7vNYLXmzxYSx3VFqKSozqBTwfURHbQj9Gp2gxgCa1sxjCENrjdIIGhCJfnFsINCIm4wZfiFPKo4aKvqAJkKZh1s7ow76S28qKJhQCSSq2u5Vry1ndY+sR1piDUGEYYYiGbdmMZBuHUFk9EZUs3ZVEhlSIHNmBhi4LjuaZYhBFCIQ8SaS0iIBmIaulRGQq0SrDENkbquDGNirf8Pe2/y41uan3l93umMvznmiDvnzbkqy5U1ucpdLts0qMGNaSPDhgULJIQQf0RLbIAleyzYtNggemPaCDfdNnZXuct2DemqnDNv3rxDxI3xN57pHVi8JyJdu9q5JfvsQorpd857puf7PJ/Ho00aHdOeXjR3kfsbLHmSMCxLtIfWi8hpJb51BAHBdRgpCEQmqTYKrROaxiK0AaGi0JyY6IryUQRUSuDbhjxJ4rG3NjoPe5ZkcDY6V62L+0bFh9xMe4wOcV8piUdivaNqa4zpHcI3/6Ej1RodHEYJuqZGiOiAvWZ0IgSe+KBOv3Kc6N1qIWBUvKA2tsMFTxC9864XCgkCSWTwimvZQvSxcSMJwROUwnoQyhBQWOeJRh0H0hOEwxuFDwJpJVpEV7pWgi50PSIliqTOO7x3GBPPE2+7eO1QAhuiAzLRElyH8RYfagQdzrVoo3GIeEyCoMwLcpNCV6MJDJIE7yxFkuJsg+t6PImSJCbBBY+Wsi/6CyRKkKUZSggGeQk2Psx7Ir6h62JpmQsBax1ZkeKFx2hIVEALjxIaKaJzV4tYzHR9PfQ++smlTPDBIrVEyui2C3iUtWijabyLWJLg6ULAycg+NUrRVRVKKxKtovlQGZzv2YUynnfBtigpqJsNWkq6ECOgwfp4LH1EZtjQIYwkNZpUG5QKeNdikOADSkq6rsH0YrBUHiUDRgbKPKUcZpRZglbg6iZem1VkhLadx5gEJQKJlmghGA5yBllKnhe0XXTuRz6y7/eDhyBRMrqHi0IxyVOmRUm9XiJcfHkz/fVPBkfaRyjxjixJSFPDIM+RIVAYjfCxVEqHPlpLh9YmngtCUiaGNItM8U1VETqPBryXeBvL0wIKoRWJUZhMUWaaPNPIEIeSVdVE7IuPAxwPxNqrmIQYDDMMgdDWFJlGhBZlcoQzCO+jY77rBUQp0FITqBmPc9pmg/MWrzRojetcZJAThxlag3c1aWbYmowZDjLSxLBZLnC2JdEprrMkaYp3HoJDCct0MmI8KtnbmpAag5bRaZ8l2Q33fbVcogUMy5wsTyjyhNEwI8813nm0MbGAMkTObhDxXiV8QCGRzjIpC3ZmI3JjML1z1TlPZ5t4f8rSOEQQEHzDsMjYmQ3JDORaxZKzHmlB72iWQpPleRyEhJZEe3KjwFuqeoVOJEIYRAgkQuKdw2gFwZPoBC08g0xy+2CPVAmk9/E+5x1JlrKqNtFdTMA6G9nKBEaZwVuLUQkqGJrOkqTxfM9yg8DiQ0uZaaajkluHe2zPhkwm47jeZKCpa3SaRByMjCJ3V60QwbK3M+bocJvd2YDCCJLEMT87J0tyfOUp0oSujdcwHTT/2T/5nb8Xbf8ObP/rP/uzf3rtsroWb1ECn33hBPOJ7Fub49fRaXbNEbxucBckVy2uMDelG7qOQw07UDdlODdOJLjhIwoHdtBjTs4tto9ZShe5htdi6rXrLJajEJ/hNp56N0G14aZM7NrZEkthrsWu3j3TRpFC2lhw5dPe7dn1Ys7mC2eeLTW2j852RSwzI0C6iiWKAnEjkKi2dwr1Yu51gVp8IfR/Q7iKItWNq6rfv2YV48B2oG6iqD5RN+VBrojPa0hx0/Ku2sgjlTaKWddCyM3Xvcv4F/a5ic9oeh3d0rJxqNrG+5z8wp2MFNhhQlf2ZW59SziAXn1RKHYdrxYhvqzXM0NXSrKL7ot4NzFKLV10/YV+fYkg8Jm8YQ83Y33z0m9W9saNeC0WC+idS1E02hym2EySrP0vRPuv9/F1qZ4toojnMkm6cDcCfBSaIwYjitRddFC3nnaW0A31jYMtit+9g9VEx7AdfFE41Y5VLzZEoUPagK49yekKV6Y369lnuuf5amwmsXlkLAdFdFqrWGQkQi+K9y/80gZUHZ2I8IWjLlnYXrALNyJrdIBz45yM+4Seqxi5rzfO4sqCjGtMb2IE+2atup5j2xdMXRexuUJHfIQLmGVk1V6LmbbUUTjvPMhYXHbtdo3neI8UcdyIZNfR+WuRqdnN6XYG8RxOBZud6HCttjS6jagMetHyGkfhsl4cIrromnEUoKqDHCk1QkhkG0V23bNnZePQlaedmOigLSVd2R/D3nUpepfjNQMVKW4E0msn9jWCwifyJoIfy/HisMrMW1TVoZsvXPVm7XCpxA7jOR6UvGGXXguk14Jvemmjc3Z1XZx3fSzjQOF6OHXNQ/UmCmw2jwg7n8Trmi1Uv9/i77W5jOLSjqSeXbuN+cLl2wvjttT4VNCWcbi03lfU24JmEgvVblAzvRM79EVqLpHoNlBtaRZ3Dau7mmQZuH5puXYsByVoR+pmcNZMFN1AstlReA31ton7p3eYQ7ymX6NAbCHZbCvaYbxG5Wf2xo19fd/x6XWJYMQhdIWk2pY0UxUHiVKQX9h4ve/dtNdrwmXRObzZz+I+9YF6pr5wA/eIFZdF/qotJC6J90sRYHUrlopd36OEiG7fbqy/EP5SibKBdihZ3odqB+xQkF6Carlxn3YD1d8/4jpb3pWsj+DyNUk71LhUQBC0oygE24GiOG7j/7+naEeSZiao9qEdA74XSB39cO6LwR8yOlqDEixvK65eETQTjcsFwkU8j96E/l7QJ+OTuN/W+/H7u6GkHYseHSFvrqfRYCAis9YFZAftQNFM4prNLxw2k/19XtyIr13Pp+9KicsE1Y7g7OtxsOH64ZUgirjlcXczuFN1xBPYVLI+iAkOl8UhaLpwdGVcB81Y3txzqm3J6i7UX6nYuneFPKqYk2EziVmCzeL6tln8LMkm3uevXoP6rQr58oblVNIWisGz6JxWFupZLF+F6ID/r35JPIL+Zb7pb2sTTT/l1NCGCORPUk0XPJKADAHtIUl0jO4qS9M2lMMC23mUCMgkwXtHGsDbDqWiAGsC6ODpqiWjwSgW2aDwwuNwCJdgbYxIOhvIEoWWKQFLYgyJSOh8hwuSuukIQpAlCUpIvHX4rsX4jrDyGCPJyoKm6fDeU603pDrF2i4y+FR0biEURR5LZTbNhhByUq2pVIK1tncXRaeuQTJMMnRSUOaBqrOcLpYEozAyJXQe27uHZB9BliiMEbgmfv5EC4T3dMGRpiVCKFbLDcrIWHbkHS0dysiIkvAepRTrRYtMDRZH8JEjGbxHJwOcdFgHQQZSHEoorFB0bRW5nS7EJu4eAXHt+PQCBCp+Rq1QUtC2DYkGbTLAY7uWQRqLz0ZFifeWFoc2Gev1GqkztJCIoKCNzEXnOnCe2SRnUBYs5pcU+Sg2sgeLuxakhEAEj8IQQnQjj4clWktWl5d0XYfQAa0lwSvqNoqHhdHx55RiPMqZTAb4qqKrOy5dQ+EDizqAFgRpSWUUlotMkiQDJrOcQEvXOj57fEWmSqz1CEwsTZMJ2kiECKRCMChSkkKiTMFyGQV17ySu7SIv1YNKY4lTYhJ290aMhilpori4OKOuLfNlCyIlELmWqn/pqOuaW7f3GU5KxkVB21rml1e8OD7DSUfbl+CJ1sbYtdG980sgfUDqKO6u1mtQEpMm0T0KECzBx9K0IitjyRTRlZZIhQ0Wr8AiQKW9YOYRXiCdx+jIbVVS4JSg9Q5t4xRfJzFKHGPcEDpPkiRUmw3W2344IAidgxAodRqZrRayVFM3DYNBznRvi53pCICmqbl8ccliscIGjUhTOmexXXxRGZRDRoOcvZ1t0kRjref87JJnJ+fUIRYDdaEmOGhDR6IkxSjj9sEeW8Mxbb2irTuOzwUvXqxwQSDSjKZxuNaiFWglmBQFiZKMJilVtWG1bKjrFt95pFBxaNCXNbVYTB5d3cG3pErS+sjHRgoSldDaCu0CZdaXpIn+IYmIOXHOQyoRilgY5Zo4AEORZCm+rklSzWg6ZDAqMDjOLs9ZNApb68geBxwC13UYnRC8RUrLIE/Z3tpFArbzPD+9oGk7HAHbbkBqgvQT9RwAACAASURBVFCYJGU2m7C7vc2kNMges/Dk+SnHZ+e0VRfRMkGjJbStZX9nxmRcsjMZYZIEW3ecnl9xcb7geLnGBo/GIHAE22JGGdPhiDsHO5RZ3n/+W3z+9Dmn5yushxAaUqNwbRuHgypwuDdhOh1T5DlKSKrlgk8+fsJ5XWFERtt5ciNpqhWDckSWZdw5nDCdTMAFms7x6aMnVLVjVVcYk/b8aVBSkgrL/VuHlIOMNE0JzjOfL3jy9Dnn8wVCJrSdI8kiKzyEjjQz7E3GbG8NybSkrmsurtZ88ukpaTJAJyltsKAMnWzJU832bMRksM0gNxAki03No8cXzNd1ROkAwnrSQtJUG27f2WNnZ5vxYIh2sfhrXBieH1+wqBoyldC1HRiF85b9/W3uHu2TpfEYeSeZzxc8fvKMsxdrtJK0mw1FoiA45ps5u3vbPLh9RJknEQFwT1LVjp+88x6dh1oliBDwwaG0oyhTjvb3mIyG5MaQJQknp2d8+ulzrtYVeZ6zrjYoCSEo1vWa1185YjLOmRQFRmkMCc+en/Hs5ALveoyE7nDE+48PnoP9KYfbI4ZFghCK9WLDyfEpJxcrPBqhFWlmqOsNgzIlNZK7d++TZpoizfDeY7vAD3/ybhykWCi0RotY2pdqxXA04Nb2lFGqsVKxXFR8/vg4MqN9hxCKuq7JsozWdjy4f0hpUoqiIM00XeuZDEecn19ycvZOdOIGQSI01jfs74yZTMbsbM8YlQUIT1MUdHWHqx1nFxuyHv8ktekHa3+//V3ZYhlHFCzNKgo23SDGjn3/YmUz0Tt/ouMlXXo2O7qPyIubcpFukkaB8SZSqLBZbLlfHRrMOr7UxIhrFKpU3QtWbXypsX2RU1vGcjKz8aguRAHEBdqJiS+Z9ouIq/DxJa0ZC5JlfDHOLiztODar11sG1zt5AJpZLJXJLqKjrhkq8gsbOZBlLMOhF/vWewqXKeqtQHolmH5gES7+PdFF1ICu3E0xk9eCLpfkjacroggUhMFsPM0o4rPK4+5GULaFjsJXKqmnKrIYk9hmHtT1S3TvluvdyLILqMohvacdmyjKVLH85foYRPHvi4KnZFlH/IUQ2EESRbhEkiwb7DC9EUBU5djsJ6gusDqIgrpZxxfU6//purgK+IUypfl9TbUTKJ4L6llGfub7ArC/wUsV8WeED2x2NOtDgU8hO42R4CBlXxiTkFzFgXG9kxAkOCNYHUk2Rz66RmsYPI5i3uBJE3+9j+tQL7vesSZY34pisd4IDr7fRYd11ZfQzRt8qnuep6Ida9a7ClsKvIH8pBeF+4K46xit65nKXSm5fFVRHzgYdCSfpZiFZPBUAArhhzclP0EKkquOk2/mVDsBd9gQNprsmWb6fhRHr0WyGx6u/sIR7U1yUzaWP6vi/i8NhC+KkIQPyMZS7xQ3JVPCx3Wjao+wEtkzbQVRxI/CrqQr9C8Ur+llg8ujYOZyhV5GZnU09sRCsfQisrCvXbrXpT71Xh5ZxE0URZOrjvW+YXlHsnkYy/PESlN+biif+5uSJ732qDqK+Os9zeZAsH61IS1bulajH2Vs/0SQzmP0/9rpapYd3UBSbSnOvuaZ3rvgapPRbgyDd1Nm7/YRcRmLzmRjbwqyFnc0toTNvie5kuSngWQBshNflLv1Dlq97nEfPU6iHUfm6/U50A00ZmWxuaLaS9A94zlea2OZU9KLs9KGm8Ko6/1ny3jOtaVkcyCotyMuY/CJonwW3cCq532rxv3CNdAbQbUrWT5wBA2yhunPVLwmdrHcEKJQ1RWwvCuoX6m5f3hGqizzJuPFj/eYvhvXvJIBb6Jz1Kw8yzcF1S3L7QenTLKKZZvy6OM9is8M23/dC6npFwVXNhcs72rar604nM2RIrCoMxY/3mL8Ieg6npftMIqdzghsKjh/S+Bu18wma3LT8WI+oPv+kMlHFlvE/6XZMiRzy3o/oRkLLr9mObx9DsDxqkD9YER6FShPuClOg+gObYeCi2+3TGZrZuUGHwSfPd+i+knG5GMLIvJRu6GKjHEXXZaXrwvcg4rxcMN8WSA/yzn8/xztUEEKyaov5+2ZtPOH0B51zLaX1MCz4zFbf64ZPonlVddrohkq0qXj5OuK8NKGh/unjJKay7rgg4MDRj83DJ65iEuorlMOcP6GIv3GBbcHK8ZpRe0M7x/v0v3lgMlH7ua41TsRjzZ8arl4TZN954yXpuck0tF6xePFlOqf76ArYgFaf99xRlLPJPO3OvaOznh9dMnd4oI/+ORN/A9GDD+PztfiRYfrhfr8pOXRb2f4WxUPD06ZZhu2kzX/4v03yP86R7g4zL0u7boeuJ2+ZWi+vOH2ziVSBJ5ejAkfDJi+Gwex1/spmzvWu4puKKi/vmY4qHh5fEntDKs2pf5n+0gbEQ71tokCa+Vph4r1vmTxKw17e3M2rWF5MmD6I02QmmTtCR7SeaAZR6F38501+7MFL43PuJNfcNoOYf8xf/L5S+j3BrRlvNd3pUA18XjURx0PXjrhW1uPAHi8P+PiQcEn3V1GH0fnsXCxMA8i3uKX3f6dFm2VSWJsWYnIDU1EdEe6EJmPrmOyvcNknINocEFQryuW8xWuaWhNirBtLDzqRRClFEWiGeYJmRI0oeP8Yk0qM1rvSBJN29Y0vkNoQ8CTpJqd2YCd4ZjLes5iscBaS7O2OBFLSISMRVEheFJtSJXn8OA2MkgW6wVV2zG3K1QwZEWKI2CUiQKL82ztjNnfvxVFhdYxX845PpkTuphtVQJ8F/A2Rv23dkpu7U+ZDnO8a1muKpQKzFcVCEndxkKVzkZBUXYS162YzMbcvn1I2y15/uyMohiyWm1YLq4gaJIkw7koWmqp2RkZJrMU5yzHz09pG0ueKtre5iCIrYkSgQgWEyTBRW5pkDKWsxGLTbCeQhnaLrJScZEfmZqkb6s21E1NkuUI59A9RmHTNkjh0BJMotgeT9neHrA7m+Js4Gfvf4yzMsZjdYySCyPBWXJjmIzHHOxMyVLFndt7fP70CXmmOT45RSkRsQMyJXiLkYG0SLh7dECaSHKj8LsDLi/PeXZyhQ+e4DyFDrStpetie/udO3tMRgWzrTHLxRneWvyF5cXpJRJN10lMqvE24K0jH5Rsz4bcub3FulrSdQ1ponlxcsFF3SDyEVobglc0rmMyTJgNSnZGGZPtLdbrDctiw/Pnz6lsh9YJtgOlA13XUJY55SDn6GgPaMm0IEt2aFvH/L2PCSGQyAAu4LqOPM2Y7Wzz8M5dfGjJtESXBVtFSioUz0/PcFVHG9rIrPUB4Vzk+2pD0zRIJ8HDYFjQ9K2v1kemp7MGIWLcOgRItadIy5uSKuc6VATG0nYdqU4IQqC9j3xep8hMQu0agg2kJsHaEIuKIkQTISRd1+GDom47EIrsxnEZhx1KKTauRSP7sihDmkgevnKXRHpGg6RvrM9Jg0B6R7es6ZqqX58VRZqxu7/DZDSizA25jq5uLaLQ+dGjx6TlAEfEVKRZRlFkjGdjJpMRuRQM05ImacmyjHr5Cava0Tob2aYqFlQZJZmNhpRlRlEm2OEQtwWffPyIqtnQtS0qzRBa46zDQSytEhIXOoamxFUbkiyl6WIBnXNx8OBsiOdfcCglESa6lJHgRYp3jqZtEUJFp3mZUVUb0iIgUsn24ZhMarIAu7MJT88WPHt+ER9gZQAbcM5h25rRMGd7MmJ/e8pkkkVMSBMBhI+eHTPOS5brhip0JDLBSMXtw12KPKHUOjp2reXO4QHz+ZJm0xI9ig6BJ1Wa3a1tZqMSNYiT9IFWjNNDjk3GZ6fvIJO8Z5sHtIEyT3j5pbu941qThEBZGtp2xtV8zfLyCpmX1K2NWBTXsb27zb2X7yK9jXFfpRmYLapFTWs75q3Hh8jMlZnB2ZaXH7xKmkCep3hbU6YF4dYBH3/6Obbt0EYjhcC6GoXk5Vfuk0jYmg2ia6ntMDtjhAqc/eSM1geSrKBzNZ23jPKEMjUc7E4ZDjRGCEZFzqAYsphXzK/WCKFo6oqkTGnbijIv2ZoOKDJNpjVWCHYGM9oq0G2eUDmHs4G0yGnbijTPuXV0hyzVpEqSJIpyVJKWGVfzJevlCi9jdEpLxXq1oSxvs7M7o60bBvmA9WrBSy+9hPXw+PO/YjgaYVCkWYa1cWi2u7/HYDBACU+aaBKpUNSMMs3xi3NaaUiLFCMU1reMpxN2d7fxzpKkEknHzqxkeTVgtWypO8d0skW3WWJx5IVmf2dCYiRlkpHqAqRgPN1iUXuevviUrb19aruCVvRJGMvWZMJ4WFKkAmFhsLWFbxwXVxWbqkV4Q207puMhVbXkYO8O5SAnzwzKS3QqyaYlu+Mpp2dneCdo2kCWxUjZer3mzS+/gXE1o3GOl4Ii0czPz3h2vkYkGuE8OkuwTUCJhL3ZDE1AJiryxY3HZCm2dUxGY+brljTLqOs6MvmV4f6D24jgqas1ZTlgPB7TZB3j7TXPL9dAjy8KARkk9m/lqe/vt7+NLQpk1/FS3XPnIApOUShc3tGsj3qhoRWYpaI4Cag2ti0jomBw3eLtjKCeCao9QTvymJVm9EmMcpp1FGQTPMkiIsoI0Tlz9bKkeVijn6QUz2OMMV1GQTfiBb5otW5GinZXcPllT9CB7LkimcPwiY3x0WmMXttC3mAD5g8kzesVQgaCFyyfZUx/Hv9+3Bd9tL136c4fKKpf2fDG0TGN1Tw6m3FmBgyexh9I5707dx2FUa/j/7y8LTh7W6EqwdY7gWorOgvL51GsuhZeNzvRGXX5Sty/soPpz6Pzsp6qG97ejWjXRzptcc1gjZ9L1u7GGQvRmWZWHpf1Ls3O003SG0Euuaixw6QvMjIEJUhfbHoWpaErJFeHgs2DjtcfPqWyhvM/PEK1Ct34XgzvS2yAesuwvK1YvNGRjBrKt1ec/miPZiLZ/llsvNebLjr9ejG8HUhOv+UQpWU83tA5xfzzIbN3+miv++JzROFLcPJ1ib9V8ebt53x0uo1zko0dkLwfxU217nA9FkB2jmYqWD5wfP2rH/Hx5RbrKuXxYMDkA8jOuigg5wbhY/He8n7J4r5i9VLH9tGc84sB1W7C7K9jm7vLZB8VF+hNHFzU2wL/1pIE2BqtmQ9zNpuEwdPITO0GPaKqCzQjydWDHL57ibGKg+GaQdIwv5fxYrDH9GeaYeMxyy46k5t4XL2RdEONWdr+/JS0hznJvE9BbTrsMIkoBxXXgXDxfKu2JLqOzr68i9xiAL1ssKO051+qmwb5Zqpjk70PdJMMtel+4XoRpOhL2lzk+ALtxGBWNhac9Wzk9KyJpYQhXhvaseHk1x2ybLi3d0HVGRLl+HywRdAJpgo3HGBzVdPsFFy9CvaoZjZbcTRa0FjNZ8mUi3bI7X/Z0I5jUZlwgXZsqGaSzaGgOIrfP9p6wfPNiOfDEcvFkPKFx6zi56SP8dtCsrobsFsd5bSibTRXDxWTf5OSXYae1XrNFY6CuLHR6Zu0nvVhPK9sqTCLOFiSrYceV3J9/l3zn68F3LhGPXrR3Ljt24kmPe/oRhHjt77fYUYt02FF/rDj2Tt7zN6RJDKKivF67FFVYHUrZf6SpH1jw2sHL9DS01jNR9xi7wewvGUojy3pWU0YJtG5+Pacl8dzXp8cM9YV5+2An33NcfH8iHQZj5uI9CTaoaR+peaVWy8oTYOWnjuTS779rU/559tvYb6f/A3RPg76qm1B9utnlNKT6469fEk5bvmjV1LskwGD53E9p3PHZie67OcvSW5/4wmpsmSqYz9fwgz+xfzL6HUUPGXnqacGr00cLn2vYjaouD28onWKVyanfP+b95B/NrwRkn3PN3aJZv4bFcOi4Xu3PsIIx1VXME03/ETeYvqBQa/dF+Vua8vqVka1LTFvzHlz+wVb6Qa7I/l4a5vL4wOGT2N60Sxsf546NtsKe69me7LmYLhg2aZsP3jOh5tb6EqRXUZcRDuOgmEzVAy+cs7eYMVRMecwuyIdWg7LOT94+mXcWbwuyi5yufOTlmYr5Z/c+TnPmzFfHz3ix8s7/N5X/oJ/2vwO+98PrA9TbCFphrH0EATrl1u+NDtjqBtuZZfcSi74U/0yP9jfZevnLhbmjSPqRzeBzWHg9YdPaZxmN11RqJbfuvshf3D6FfLTKKLO7yeRr94Emi3D4PVLJkXF65Nj7mVnANzb3+Oz40N2/8py9ZIhWfT3WQ2qgc29jlf2znh5dIqWji9Pn/F/uTexjwryiyjyZleW+T1DeuW5fDNwa7bgzvCSkamZ6A0vZS/4H1/9XSYfxGLGdOFoRhLr4xD0+Lc9adbx9s4TKmc4GQ/59Pk9tn/qbgopbRYFWGcE//Dh+0z0hlRaxnpDKix7Zs7FXsG7t19j8MTRDiXpPN6LkYr/5Os/wgjHJ5tt3ho+5Ten7/G43eLdgyPMO/1grcc3eC3Q/TDhl9n+nRZtOxcLukIXC2wECmcdnhhhH89mPLi3Q5ooBrmh7hqs8zx9dswHH39Ekqd4H2P1BEdT1bz80kPu39mjbhaMRyVXywX5qOD09JzN1QZELNgJwYHtGJYZL90/YHdngPQd+2rAYj5lfnHFJ/UZCoEykqqpyLIM2XlGo5KvvPEamfQE76majOWmZfPuOV4ZltUGoQ3ae7wN3Do85OWH9ykSidIxyjidZCgl+OzzF1gvyAYFXdPgXYYyhjt37rG3m6Jw+CYhyTIG0yGfP3vKJ5+eoJLo+hIh0FmYzLY42t9ndzrESIuXY/K04MXpBUIpstzQAUIp6rpBCcN4mPDw/h12d0rq1ZrDrS0ur6746NNnscjMDCKyQaZIJbBYOt/FUh9p+5f3IcGFyK/UioW3KAmNbUmLGKtV3pMqiRISk6a0rotFXUrihCJXCqMN9WrOw9de5eBgD6k6gg0E0fLqwyPOzs5478PHdN6TJAV1XaNQ7O3s8OrLd8mzQJEJFus1L907Yn55xfnFFTYopE6oKxfjsDjuHByxt7PNsIjCS+1biskAhOLDjx8hZImzCmUMre0YT0a89trrKOHxriHZ2sF3nnw8o9q8T9U0se3RiRhX1Yq7d++wMxugrUOnGS7pKM2USTnjx++8y3p1hckLhFCkSYqUiof3XyIVLcPhkMJIdkYp++OCP//Ru3Qe2qChi5Hz8XiL1169jxaWxEhcV1EUBd5J7hyuefT4BOEFVd8e773n4YO7TEcFWhQRF+E8eaEIdw+wvsOdL7lYtbEkI0kjc1RLjFFsT7bBeeqmYbWpUD07Vqgo0nYBpG4RrmE6HHN7+w5G++jmnS9xV/6mEE2YGDt21vbcSE8glt0N8zHL9QrnHKlSeCFomxXKaBAdWjk0YGQsdstzzXxVIYRDYNCJYV1bBHHMNV+sONiZsT2ekiaCTMdm47re8OD+FmlS8Owv3kFmGd47xnnJ4uKSV+8/IEsNtt2QpZqurihMyWQ64uOPP8S3CtCk0hDajmIn42tfe5vV1SmZ1GgVncrb2ZDjF884/eQpSbEVnSBK43xHnqfcuXdIZxum5QDnXORH3z/iw48fo5Sntu1NLEsicfFZHp1o0qwgBMGmc3Stp0hSZJHgvae1/TBMK9p2w+39Q7TWOOd49vSErouFbcE6ghBcXS4ZjUZ0zZwvffXtKGAJyLwghMBrd6Y8+fRZXOtpivU9bsEYPvvsE/7Bt36XIktBtOTZEBJNl2QsKsezJycYU5IIjTGOQZkxGZSkSiGEI6QepWHoU9588DJ/9KffRxdFLCJsOoxUDEcjsiJBImJBlRGIVLEtFLlJ8FJF1raJaI5Mp0wGE/ANWilwllYoXn79dX7+8RO8VAgBIVFshKStO24lBd3Gx3WSpPFvDTTbB1t8fvY8om+EogoCNh1JErh3tEPVVuBAp2NCgJ2djKfHJ7gXHThJkmekScF6uabIBwzKlM46BoMBSRpwraMdaIaDLcKyhk5gtEIbsLbh5TfeZDYuSIwg0xpnJflQMDtd8OL8giwdkOdlxIR0gaODO0wnE1Sw5CYhCIXtYH93l5Nnx9iqRglBimTlWuwmkA+GqK4hQZKkkhbFcDhhNJr1wyyJD7EET2nB9tYum3VHliZU1ZqiyBABtiZbWCHpQuTSVm0s5hsOBxSDASaRpDoF22GdYDabMRgUmFWNSgqC65De4UJAJYY0KZHCoehIsgEDnTA+s+h0g/KOuutiSVqWc3Z2TNcZxqMRSgaEjLmV2fYOHz56xmAwZrWq6GRA94ViRZpytLdLIhtUcKR5iiNhtruD/vwE6yLXMM0TGtdig0NrzWgwRNGhVYLSAWdbMhOdrN5okjxl3tQgYjR3tjWEykTmslJsbZecPbni+ZNLglHUwTJGsdqsGY+HtCHmxDMveo4yBNdS5imZTqllHAq3OAiS3d39my4Pk2YRd6MVoW7JdUZmEjqvqPGxNwCBD38v2/5d2aIg1jMuZSwEVH3U1WWS5ZHCfnvB/mDDG9MTHq+mrLqEFz/d4/Yf2b4ILNw4KZO55fN/mHD/m5/zYjXgV/ee8pfHtzgbjxl9JMkvHPRe7mtX3nrfcPrdji8//IxMdZSvtPz09ICLZ2PUv9FoEwtR0vMOO41loOtDyb3f+YRvF5d4BB8vtnl0NqO6KJE2RIFUgHARH3D2pYTtX3/OS+MzStVy1pYsjjLeN3fY/bexbb2ZavQmuipdKmm/tuIfPXifka7Y+ISdfMXzrRGf/fiQwz/1kWm4cjdC1uJOxuVXPLcePuduVtFYzYfjI4YfRJdyO4m/P0hBclHhTc7itib57hm/d+eveVRt8cGXdnj2+Ra7f6JvkA3XRWbX5WbXQhIBsuM19X7ZF3zFmHz5OLpHzdzTjZPImU0l7Sg6jm1RYJaOeqpJpUA1jnYr74uMOk5/veNbr31Crjo6rzj1A9LfPOP40yl3/jC6G7uBJrmK8dPzNwzlb73g1dElrw+P+cHZfba+82l0fX1S3PAOk6uuj6IHzr7hefDyMW/PPmftUh6tZrjxnA+5xa1/FVvAVV8aY1aW5a2U3/jNn1LqhtZr9m4vqZzh0/GM1Xyf7PLauRYHB95krL+x4Tv3HjExFePdmqVNOZ0NeHRri/zCkJ00uCK6Wv3Y4LUg+e4ZrxQbvjZ7zEeTHXgAn762hfvfppiNjxxVorN1fSTY+feeMkwa9rIlS5tyf3RB6xV/+Y2X2f9+dJGn511fYhXovrvg1w4eMzEbuqCwXsEA/uJLkst2B9Uaxh82N+Kn6AucukJy+UrWn2OB8sTh0r74ScXzV23a2NOgJJv7Kedf84SsQ2wU+TOF6hSqkX1ZX2TSqrqPzcMN33h1mDB41iLbXuzvfHTZ9oKvy1QveEZmZD0VjD4PqB5Z4FJJtukIMnJey0crzt4ec+/BM/aKJQ+KKOR8stnmN7/+Af/74GvoH6cEFY/f+u6A8vGat7/3jAfFGVe24H5+yot2xMujU14cDrj8V3f6AQtR5AzRQf+f/+4f86Or29wpLtlKVuSq4z89/BH/83v/mNl7kV0LEYEgu0A9kdx5+ymLOuPXDj5hbVPOm4Ifh9voOsUnIrr8+wFWxCrENeByQzuUiGDIzjrUpqXeSWhUEt2/K4s3smclO06+meGSiMHY+XGHXlu6SXZTFlZ+sqC6PURvHKt/XPFgskALz53BJTZI3vreM/7iR18lO49iuewL42ypmf6/nzD9rzWvjF4A8EbxDIDfbzIWJ7vs/LiLmJBhgi0U1Y7gV/afcie/wEhH5xUjXfGV2VN++B8q0v9hcoM/SVx08+7uLLgzuCSVlpGuuJedofD8xt2PeGf2lZgAaDztdeJgFPje3mcAbCUrAJ7VE/6nr/4f/Pd/+F/23O04HMzPLWZpOf1qxqLO2ClX3CkvKWTLQDU8uH/C5V8dxdg+RPwMERvw3771x3yw2QdgouN59Vv3P+Rf//TtG+REN1C0Q0VxatmerHh9dsJZM+Dr40ccpZfMs4KznQGrg32G/XlgC4lLE3Tjcd9b8u2jRxxmV7yUnrD0OS8XL/hf7u6x9W50wTZbpnfmBi6+4nnt6ISBaXhYntIFxVVXUL1mWL23j24iPqEZSsqTmPIYjRYUuuUwu+IoueRpO+Xb44/51wevs/PTPnXRs4N9Ihk9uOK95R53ykveXR/y5uApiXB86e4z2mobs4kDuPzC4Y1gvScppxXbyZq7eTwHL1zJ7279JX+88wZdLnH7kYnsNai1x5vAUTG/OeZHySXfLD/mTw4e4NIJzgjSuY9ogZFk+n7FSZ3wK3tPGesKIxxzW/CP9n/G7y9usd4zFKf+5rnDJZLNtuZbX3qf/WzBWFfcSc5Z+oxfuf2ED9NXe2d55PiaVRSHQxp4e/tzxrpiqtcMZc3SZ9ihR7g4fG0HiuGT/lzpPK/dPr5ZI7fSht/b/iH/3d3/gvDXEVORrhztUFOeOJa3FMfVEHK4n58yUyuGssYheWv4lJ8MX6MdfoG3Aehe2WC9wijHdrKmCZpSNnRekY5rmvEAXQWyK9e7/iM//Jd+Vvulv/NvYRMKvAhY1zemSo8SKcEHuiA4PDxkOilJVEOwhsw4dK7JHtxlsbzkfNERQiz4cRaUzHhw6x5l4phmU7wUzAZTpuMtEi05O34Pk2uMSJAqYIHDvV22ZjNGw5zQdajgKfdKRqMRP/30Q7JshnAKHTKquadINQf7R4wnI5rNJUWSo4xgONnm08+fU1WBRCVY6bEuMlqHwyHj4RCjLdpDQ8A7zdb2lPMXG7zzWKuwIcV7x9Z4yt7hLsJXCGwsXAkWJQNb29s8enKKlAoZPJicxWrJaDrjpfsPUWEJnQDRkuwnCAVXH3xK8CWua7HKk0jNarHgK2++ynR3iCCATihKzdbeNu9++DFGaBrbYa1H69ik3TYdQUPT1SgtMMbgOktQBiE9rbd0eAZKRVelD+RJincdWZGR5gmXk7l5SAAAIABJREFU6zV21VADjXMYZfA4us6T5SVbO/vkSQIuIE2KUR1l6cmyhA8+eIzWGus7TJpQLdZMp1P29/aoVucI6xinI4LzpHsDzIfPWFU1iDSWQ3mHsZbpeMbWZID0ntoHhmiQgZOkZDLd4dnJgiTNCDa6MwbliDTNcU1FKjVBG5yJzsuiGKCvGpTXVF1LFzyDYcF0NMHaFqMDChHRG1pSeoVJM2SwSJHgnWS9qTjau0Oa5qRC4KUkzQYRgRASbIC6qTFmgA8druvYnY3RQK4ThOvI0yFdVxGUYDLeQelzmtZj8gInoPYBZ2LreRcsiRYoYxAyUG48u3vbPD9bkZUFdVPRNh1FUdBUNQe7exzujCnylPWm5t/+8K9AG4JRKFQs1wsdGkNTrdga3WdnZ4e2uqLMM8qsxLrnnF1u8NajUokPAZ0keB/IdUrXbEiShL3dbbqnHXVd4/pYuTEZ1seCOG0hydJYsCcUB3u3cO4pF/MLEuVpNmtyY6KrSHgyZSiynNRkuG6DkgkdLpbzSNGzqT2KgDI5hcmo9QLXtDS2QsgW2w0wJsfJjq6zpHlGUIq6ERgjqTaOIitZnl2QaolyDi0NDhtLxEjQ2tCJli4IvDVkxhCERGclbeUi4F9qdKoZDccooWnqNSpNo7gtYrxVSoG3ga3dXSZpwnGzQipHnsWyQu/7m4PwdD4QHJHpGWB7POX09BRjonNaKIcjELylKDPariZLc05Pzzjc3yEzEpVrNqua4C3D6YSr58fIEAVb6zzaSMbTKS9eXHCwM2MyLWlbS5pofN3ibIvVGi86fLBsNhVXUkY2do8dkVb0uILAxlnS4ZDaeqRRWCTO1lxezinklGCiiEya0FUt801NmpRsGo9Tno0NOF+zB1SrilRHLnlQAaU0VxdzRqMJJy+uSJUgCIFJJMPZjJOTcw629lCpRkqFtxY6y7ppabuAsBKdwKZeMUxSlJacXKzQUpAbTY1FKc3l1RKpDMFKslGG7RwGwzAfcDm/QpkZSkLXtUgEl8sl0hhWVYU2GbX1ON+QlxlNXXF6foExitwYZCqolhW1DbSNpUxLKusQPUM6TzPWiyXtdEiapqxaTyEgOMmm3tDhMDqlsY5N3WCSHCU9x8fP2J9MQAiqpsFLWFw1LFctXdCxiFNAsI4sSTk7fk6+v43QBikV601L8JbNpmZ7vEPtOlZtG93dCM5fnLE5OKJLopssSRK6xrK4uGTdeVyAru1QKkYAUZr1ek1VrxkMCrTM6VpPtalxQrJaXZFtjblazBkVKVWzQKaKs/UVFJKRMSSiRSUlF6fnVFVD1TTIPCE0kBYpdbPBCcfTsxfc299hvV7hgqNtGy7nG6yXsWhQSbxUuODQRU4dHIvFinGeQQjUVQPSYK2nHI642GxobY0pDEopVquK58enjIuMNNE0bUu1sbS2IytSrFHYpqatoSgyLi8v2bQdVktwIEUc5nhr2Sxb2rYlKM2yaUB5rLUsG4e5WjEuc5SMbN3NZk3TdNTVOg7ZFLiqjgw7KRCxIfPvt78Lm+DmZfs68n7tsNIbz/xLnl87fMJBNqcLijuDSyZmw2ffmfPeyatMPrY3osO1q+nhr37Gm+Pn7O4t2LiU/KijO1D8UfEG0w+hy6FLJF4ZdOM5fyvw4O4LfnX6KV1QjPWGrww/5yc7t3n0f75GM45xfJfH0qN627B8o+W7Wx/yWbXNm+VTStXwtdlj/mD7H5BechMH11UUVetdz7d2HrGbLJipNY/bLTYu4cn9MZtHU6TV6DpGiIUNbF4y/PsP3kdLh5GOXbXECMfI1Dy/N8L/oMSnAm/isLd8vGazn/MfffPHjHR1s3t3vrriz+QrjD6PrevRGeeww4TseMNnv5fx2/ufYWLlNfdHF/wH33yP/+f//i62kCRzi9p0uMIQUm7Kl/SiwScaV0ZR9noyo5ctct1ghyPoI/P1VhRKm1GMBo8/i5HsdC4x8/am2EdvHO3E8ODuMa8OTgBIheVFOoQJfDLc5upf3sHmMW5sS016VrG+bfhv7v05764PkQS+s/0JHsGt4oof5l+lPI4OY5fFYxKkYnxrzn988FMK2fJhtcc3ZlHc+SA9ZHFbs/tXG7qhuRESqx3BreySlUvZTZd0QbFyKRNT8UfTPVwm8KaP628s1UHGG0dPWHQZ2+kKLR0TE6PQ61lCl2+TankTf8/OK158dcztYsNhOWfjEw7zOfMuZ5ZvqOsJyVUvvnYe2XrWL7c8SBoeDM4wwvFKecyLdkQXFD852uCSgmTpb+L0qQ/YTlE5gw0DDtI5A9NgpOVqkvPDlwvEOwXtLCO5qNGLmna3xFw1nL6VsP5yzXiy4fyiZPr78XlO9OJuZAXHojRzVbN4AAcvnTLf5Mgtz2YrZdEWTD+wvfMzCnKuZyLXWwa98bSl5Oo1MFV03F6LXq7QcXjg4nnYDVR/zYCrNz2q1Yw/aXCZxCzt/8/em/RYkqVnes+ZzOxO7uEekZFjVbGqSKLZTQqS0IAASeiVdoLGBbWgVtJv0L7W+gm9akANSIAmSIDGjcgmu8lSsdmsyqrMrBwiY/Lw8OnONp1Ri8/co2pXK4Et8W4y4eH3ul07x47Zeb/3e17GJw0PAUJnDcO54ofzA12sWJqR67AC4M4vMeZdISLVmv5cU28quljxsj/H6cQmLHha7Tmmhlu/eBBqq0Mgzmvq25HxkeUn6+/x8XzHySTm7OyMRgXBazQSHGbagF44xjOHynBa9Wz7hrn2OCfz5Or9FUP1PtUuPuAiJrwweQqh2/5Ow/ETRf3ZxGV9PJMxSe/4yvdBgyoVsoH+hyOzr8UB6XZZcjDGhEoZ/94C2yb8I0v4umb/+54PV3sWduTL/VNS1nQfaE6eT6FRtZG1oTHEH3zIV19XLH9v5N95/DkX/ozvVneEpNFhCogL4kqub0dWc81NvyQWzcqO1DqyDTNi0dxsVny4kqJMsQrTBfSYePHqjN86XRONJqN4Pjzhxq/4q+uPMSvN/FbmS3Ob0WNi83tznh0f8/F8R8iGUAwLO/K/b/8V2g8Vj3/mKboWfMfc0n5Uc/YFpL8HSzcy154uS/fAxfqUk45pHDX1zUD3yZzkFP/Ni7/PovL8YHXHs+4JKzvy48vvUqyMxXhqsEMhzBTHDx03r86YucDMBq79CbWO/PH17+BMYnEtWIF6E3H7dxiG8esTPm0+ZHfSUKvIV91TXh7PsJ0wl+8Z5igYzx31nWHdz7E689fbT/jdk2ti0VxtV5z2EGaa+pCY3wryITnFp198hz/4O68A+Kz7CJ8tf3H7fZori+082aoH3Ml4Ztk/e8Twr+5xKjG3np8evsNf5t/i1fYR1e+taLaJ+ZV/cHc/+ibw7d+fcfv+AqsTH9cbrv0Jf77+AfWdBBy6TpBH988C9Z3m2eExf3D2hjPXcukfybF5y6PrxHBqOHkxcvy4otlOGJFnM/4Z3+fq6Urc1cbzT998n2oHs3V8eL4YV5pml9C+8OOf/5B/91//GZ/uPmK3mPF2OOGnFx+zug8KZcKJhEKsFXav+avb7/BvvPecM9vyzfBUfmdUDE8Up98m3EEKUqlWzC8jn3/xCasPD3w83/EynfNV9xTVCQfajAUzgGsL/WPDyYvIi90ZOy/X9HzhuQ4ndKnil4f3sZ2I54sruW/bPlOuGv64+W1+//1LVlZwPT/rvsPL/pzxUDN3QM9DgTvONUX/5jCyv+HYMgnGUkphqwrnHEVlKu2wKJwu5DISyoi2ijR6KIWSCo2t0TmKCKRnuGZGPVvg+wG0oaCIMaKUBCzFkJnPZmgUMXpyzowxoOc1VT2jG3ppD1CRTdfRBo8uGrKSdmpEpLTGYTDEBMbWZDRFVWQMVT2j9yNFG4KXsKDKNTjnCHHEjy0pj+QoIo9VNZmI9wMKgXVrFLW1HPdrWRuypRTZYOWQWC1O8L0n+ghoTDYs6yXjYWS3PTD6DNZAqdBKYZHNPUo4oI110kJsLUPfUqLmeDwSvaeUwvX1LTFGco7ENKA1wo0lY61G+cK8mlHrCosVrm70EjSmtaRw2vLgCBhSIFeOXmn2sZPwqMrgyeQpzAruO9c0MWb6fsTaCu0U2hSyTxzXR+bNghyErztOvFylFH4YIUiidymFGCNt28rxk8gpQI7YAnbiwe52O0E2FLAZ8jA9cCYJPcpTqSsnSAlu7+4IyRPySB9l4zweO4bJSZZVwTpFVVt6P0KJaCY7PRkfBsiJIfbsuxbr5sQQsNpQV5YQRhmHBMUfCL5jTIlt2zMOHmdrtLNoLa3vg4+EMaBSprG1jJnSjCEzhkKKGkuDz4WQMqZuOB4G+r5H6SzXRE70w8AwDEQf8EPPsD/S2Jqqqji0R5RSHPYtow/sDy3HrqWaVZJAOoXN6RxorKLEkaaes90duL65FeErZ4ZhIIyBOLFKY5xS1nMilURIgWoxQzsrROfoJTgsA0wiXTFoLMo1+FGKOl47rnZbfIpYZfE+QoIcsrSdG0ucxI720FKiIuRIViJ0hOhJOaAMOCes2FgyKWvQhq4fUbohR08Y5Rytb7dYXWFVja4snoyZ1fTDgB88jIkcoERFjoph8IQxUtka4zTKanCF0XucM2zu1jhTMUZPUYXBD2y2R9qup65nqKSgCAalpIzJUKHRObG5uWa1nFN0JOWRSMDHSCIRc6TkJHiWUohBsd11+KzJWZOSIieDMZVwwAHnHMOh57jrCGPCx8Sx76gXc/bHjt1hL2uOkXMlXGpP5eZsd0fudlv2+yO586zfbklBcVgfSXgwCVdrjG3wsXAcM32EUAo+CXag63vGccR7j9aK0Q+YUqhszaY9ss+FLmViVgyHgW70DD4w+BGlwSjBP7hKikOXN7cc+z31yYysNPtdS3/sGQ49p/MlTltyLgSfGIaOWDzH8UjXecLoCX1Hezhw2LYIAEpay412JCxZO7ohEMZC9IlKKw67I8d9y3q9Yb6ckXMkBM8w7hnGPSEE2u5AKZl9e6QbesY0cH1zg3ZSoClaYZ2jJMghcdi3xD6RIxx3HaBJITN0gTEF2nEgkLHaMbQDQzeSY2boBmpbMQ6evhvZ3m3p21HCA4sk/aiUIUEaZE0doidm4aRvdlsGP9K4CqsdBkXWmTGMHNqO/f5AHCPJB0oq7PdH+m4ghpEcA5UzYA0xeozRHA4H2m5gCJG+7wmxMI6Jw3ZPTjCOI2EYSAUsisN6S9/39O2AbwdKHDnsb9keNmChG1qqyjIETzYF4zS7bUd7DMRsGMbCZrNhu1ujVMFNwktREEsGJSzs27stm21LSJqhTxz2HZv1gePY4+YNKEXXe4IvjH1me7MlhswwdvTdgI+Z7e5A53t8HDF2SscdB0gZN4VmppQYxoD3mfVmT3vsKcaSsmY2P6GYSkIwZzXtbiAMWTAmIRBSYtP2bA8Du6PgOpKW68fUDeubLcdDxziOxCzFpegjQ+857EdyliDJyrp3LvL4btP5t6//779UKpND8T4cphCWEvShZsK9u/VL5tpziDUAMWvCShKcVZJE6zgXN9O6nwMQsmUT52gKtY4oL8zJbBV2yOhUqHbCSX1vduSz44dkFBfjGf/n9d/ly+17wpdlCo2y8jdirVAuswmLBxdsKEb+OxcuHwVMF6fWckOeZW79kovxjENu6FLFH8xfMa8COiK83cnpV6wirBS/2H6AUwlNIRSDU4k+OX77vVtp23/gJCr6D+e4Fv7yRjZp96+ZCVBJWzaImOwfORFdGofaOzKKv95/ws43xKL5n1/+PnYK9zFDeghNAh4Cxsb35oTTSkS3IT20sWdnQOuJLSlc23ojyfPNNrN8M21mFxZ3COiQcLt3PFiA1lf88vg+Z7ZlbkQ88dny7O4x/bmIfCoV3M6TazE2XIxnAJzajozizi/5fPv+5JbVqGlNSY2W1unR8U/ufoddmrE0I2e2pUsi0OhQCCv34EJSUYJq/reLv8utX3IdVnzVPuVyOOVNf4Lbq2keSiBVXDnqtZyPxoQHQfxyOAVgc5izfD2SZmbiiWriSY0d4MXdmYxDNtyOS960p3x9+d67QKYpNE2PEb23bIcZTiV+Z3bFJizoU8Wb/hR/lHC81CjcIWBa4RjH24aXhzOsytQ6solzvmw/4KI9JYyW6pio1gP+vCGeNFSvtxSjmV8VODq2b1fYGwlJu3df3/NQ/XmDSoVw1rB4DZdfvYcziRgNaV9hu/IQWmXuubZTO7/thM8rPEsRSMLqXfHufm7cM3bdMQmf9Zg5/cLgWjn3tpXPleA0SWbXg5gPvtk8IWbNdVjhs2XvRYCNwTyEEpoxY0YexOJv9+dYlelyxbU/4c4v+RevP5HQt0Y6zdwxkmtDtVMPa8/9muCz5ZvhKbZTsqbVhjKFuVW7SJzBzy8+4qQZedmfE4rhRXfOm1ePmd9K+/q9gGXa8OCAjquKbGH1stC+L25R7TNmEDe2GbNgS6afq1wwHqqLCtfJHNc+PoRk5Vp8dOJ2jzS3inaouOvnfLr5iI/nO769fszy9buwMj0mcdQfAnHlaC4cf/XyO/yTze/SpYr/5eYPGINj/jY/rB9xbuRcHRMv7s54dXjE3Tjnelzis+H57pxwrIRvbpUUh2pLXDlmrxyf3bzPN7snrP2cZ90Tvt4/YXeY0+ykOHbPVk5zy/IlfP7FJ3y2eZ/fam7pU8Vf3X6Hb/ZPqPZw/K2F8HijoDHqXcKMhfXtim+3j7nxK27HJT/ff4S/mf/K/QpybeUcx8Ltdsl+aNiFhg+aPZ/efcjhZsnqeWF4r0InYQk320SzS6jB8GZ9SsqaH9/9Fp8fP+CunfPs6w8mjIKMV5oJf1TFQnOt6LxjSI6/3n8CwBAt1V5R7zPNVSdhbzNNvQm4A4SkuTyc8PF8x5v+lOeHc+LFnGabJHhvChS7x8yoqLlul3zTvscuzKh05KuLp7gDjKdm6iAQTEe1TzTXmm+un7ANc/ZxRsiGX9x8wG43lxDQiYH+cP90Cnft+GbzhJftGV+2H3Drl1x1K2ZXZQoWjNiJIx9rWXeu9iueHZ9wMZ6RUfx0/THhYgEgovPCPLhHc6WZv1H4dcPR17w8nvHnb7/H9s2JhMDN37G37fCOc7741vHnb7/HZpzzsj3jl7dPSa/mLK7Sw3tmt0HmyT4zv1C03vGiO+fL7gMux1P+7PqHVBuNbYWPfB9CKvddi4oK7y0vj2fc9Et+/Pp7VHeirxkvSCKV5VkgNZrNWuZVn9xDgeJP3/6QL968z/Ii49qMGWXuplqzfKlpb+ZcdSt2oeHGr9jGORftKe7ayby1irCU0MRs5NnhN32ZH/3oR7/xL/+//fqv/4f/8UdKiwMqRqlwJbIwN2cNThtOV48wBgnkMdB2A5v9kRevLinKUqzFZw/JgIKzx6eyYYqjsBxN5vLtHVfXa3btCFVFVIUxZ7AWXRSnJyeUErAWjn1LCJn9oWV77Nl2vTjyrCNkyAXmqwWVsRQStnIMPnC7WfPLr76iFAPKoYzFKkdJhcVszqOTFTWKnMAjG/ab9Z7N5kAyhhgT2nh01uhcePzoDEtClUBRjmHoCT5y8fKSwSdiMpA1Y4oYZ+n7jtVyRT1ryCRSCVxd3XG72YFy+JAkX34UTqlWGlsyq8VSkttTT06Zm+s7NrsBjMO6htEH6romhkgpBW0k1RFT4VOAyqJRpBSxSuGUFjegKuAMoyqSXDgk0hipiyMUhdFGgrViRlVglCWEwmp5KuIqiRxaxmGg7QK7fcfLN2/BWooCayuMNuRJnKrnFT6MxJAYh5719sjN9QajHbVuQGV0jvjUExQ8Ol+RSmbII0MY6QfPV68veHtzg50vyCRinrigsbBYLKhnDuUk0Ms4y8uL1+x2LcMYSLmIAGg1KE0zq/GjjI1OSBjQ/sibN7cc971UskOSgDejObZbmtmMk5MTciwErxjHzMWrS7bHFlfVpJIxRWOrSsKBGosGcgoM3cAYImOC589fcDi2KC3zT1nD0A2smhknixXeB3TRdEOiKMebu1u+eXFB5wvGNsSUqV2NKgWtDUprQgwc+8B2c2QcPMYacinEMVC5GVEpMHbiwiaquiIlwxgKF29vObQd2tZkI+3t4gjN5JzIJRFywpNZ395hKZQoIUzCYBRofO0sx6HHa9DTNdP7gX4YyVqTEf5tLrI5jQW0s/RjoG4aqrpB6UzbjZSsWK+3vHhxgQ+QjWXsBkrOLFdnbHdrqrpGYUEZ2n6g7Ufu9h2XtxuKscKFzHJdlCLhTbaqiSkzhMAYAjebPc8vLhkTDG3A2YYYA5XVtO2R0+WC5AOubtjtD2QFl2/vuL7dk6JGG4WPAVM5pHSkocg6EmNkdxjkpqUsRlkwhiEONMsKXRBh3ToOx5b1dkc/jsToKQVm9Qw/ejJRQhoTGOe4ubmjms2pzJx2HLm8vuP2+g4fojjjs8JWhpBGCRc0llwsQ+cpxbA7DNxsjrx49oZujOK2TJCDwukKVQxxiKQkXF8VM2PXsb478PLVK7wSYIYBWQu0Y7/f03c9i+Ujhj4wjom79Zq31zd0wyjOXz8SnSJlWB/2KK04XZxxtd4zeE/Ihjc3d7x68xZlHGMR3EQq4KwjhsAYBrrYkSL0Q+Ju2/L89Su6EIgaQslUVUPQiZgCby7fsJxV5BTZti27Xcfrq7ds9i2mqtAajMk0dY1Whv3hQN8FjK0p2bDbH7i52/H1t6+omzl9jIDBOss49NTOsd3sQGusqzh2A3e7IxdXt6x3LdF7XDOj5ELRhaZp6PqeYxvJynI4dNweDlxvdtxsW7rpWlHW4CorIn8p9N6z3xzANRzbwHYz8PzFa9a7g3SGKCiqkLLCVXOOxwEfJPys7wJ9LLy+vOb55TVBWypdYbMmh8JstiQpzXq3pxhLwdD3kZvrNRev3tDve446Q+3IOqKyx0RFilJsyNkw5MzbzY6b7ZFXL68oVcM4RlQxWCoMFbkoYiy8vriieOiPA5tNx/r2yO3tGjdriDmhENZzKoms4HjoGY4jJSk2h4HX1xvW+yPRanwUVEM1q1FAjpkxRrq+59B7QnZc3m149XbDdt8RYiEVg7GOnAoxZLSqubq6pj1E9vuR9a7ndt1yGCNjzHhliBkG76VwnCLHQ0cuMLQDMRo2O8/l9YG3d7L2JKVJSQJbnbN0+5YYCilbhgH6ztOOmZt1y9XuQOsjkUw0MMZAvVqQkucP/8N//zdKtP3b17/cr3/0j//pj+55sXbIwlsLIpKNp4ZxZpl90NHYSJcrZiby2e4DfnnxAec/lk2OTkVESSVFicvvOjpbsYlzFjZgdeJ//fLvMf9lzcmrKKiOPlPtpc04Vo79R4gTywU+3X7Efmx4+/YRs9eWk2etiE1O2oxNFLfkzapmKI6zqufb9gl/9uKHfPB/qIcWWrQi17Ip696zLD5q+bjZ0qaai+GMv1j/gJfPnrJ8IUnYZpB2/Ow02WqO30/UVcLqTCqab7sn3PZLvvj5d3AHcW+pAtUhkWtFs8m8edrgVpGhOLZxzp999dvMvqkpSgujtkhKdzGKYjVhZrl737BwnjbUDNFx8c1Tli/VAwOz2o6ER7W0iU7J22py1rq9R5WpbXuI5Jm4b4vRGJ8BRbUdQUkAjAnSTu5aYXuWyqBiBi3OPdsnXn+wpKs1szpxSA2v+jO+3L7H+s0pT38yzZFUSDMrIk2y/ER9SLMKXI6nrMOCi/YR37x6ysmXkjgfVsKcz05RHTN+nNN+XFinJc+Pj/m2e8K3hyeYPznhyU87cmPQURzBqoDOmrdPGvQyYXThoj1l7gL//K9/m8UbEU7MKKKkBKzCLx895k2/IlaGyiRy0fzi5gPKz0+Z3YpTygzSzRlnhvl1Znfa0Lzf0+eK9Tjntl3Az05YvYzCtB2Fu5oby/xKcXG64M41bMqCF+05b7pTrtsV5icrlpeCtVAZitVUm5H2wxnlw5Gr7gRl4av9U3yxfPWLT3jvTx2z20CuDKaLhNMKZSzFajAK3RvqW8PytfAas5M2YNsn4tJih/TAR613Bb+0tL4mHivOf6ZZXqWHIEB578RFDXkKqspUbeH0G/muKsk4y5owtZmvLLPLFnsYwcp8ma0T9Z3HhIyK+eH3JSwrU6ym2RauThb0C3EE/3L9lFg0v3z1AYufzKmPEjjkdqPsQT6ouHlzzt3c0WpHVoovtu/zbPuE4dsVT34qRo9i1TSGBVUMu3HJq2bOOs551r7Hm/aUv/j2Bzz5C011yFTrgTy36EHO2fw6sv6kZhdq7Czx6fVH9MmRPjvh/BcjxhcRhvd+EgqFw08BE2XdWL3yEzpBU6ZAJrcdCI+ki1LY0Zblm8jpsyis3MnR7x/X2KMwgovVD8LX2WdHdqtTdnPD+m7Fy88/onlW4VpBFeiEhMN1cjxxblBJo/YV35RTPrv+kOtnT1j83w3z2yx8YaUwPpPm0rngw4y1nzPOoSjN1fGEm5dnPP6JFWE5FnJtyY0RUeoyMewXbB9r3h5OuGmX3D0/Z/F5zexWHJq2k2vWDJHVlzuKW7L5UPMnz/4OL9oz2qFm8/MnfPBjL509h4gZhIccFxY7FnTr6LYLvsqPuDiccvX8MU//XDG7k9BH20biymH7hOszJ58aLp/OuckzPr/8kPZyyfm/MDx6NpKn4lWutXRsGMX8rSLvai7rhqFYXlw9pny95OP/q+BXhtmEn0iNdDqEE8vJy8DOrHjb1FzcnvHixQfkn51w+kyE5jR3cq1khX9kmd1lhtslu9zwze17XH37hP6rUx59CbObKHt/rYgzuS+YUHA7Q3e15Bt9wov1OV+8+ZCzH1c8ehapd/Ke+2sqzmW9CkPDF+qM5/tz3hxOyf/iVOZ6K0WYVGtMLAxncm5Pvs2sWfG6LHmxP+P5sw/JPzuh3r5jrgs+RZEqjQnQ+xkXacm37Tl/9eUa0oJbAAAgAElEQVQPaL855ek/FzZrtY9TUKEmVUoE8hGe/mXkrTrjbrckvp6zfG44+0oQHa5/F0Smg6wx86tMe1xxXRZcvT7H/XTO6gW4vlAd00NIKFqKc/Wx0G+XvL59zJfjOa8/+4j81ytWrzKuE0fuvcBtO1mDzj9L5O2Mu+tH7F6e4p7VzN9KsGp1zNixUG8jOsu8tHc12zzn2f6cV+mEzy4+Inx2wvxzQUiYUGR9MEowFHcZvOXGL7k4nvLt/pxvN4/ZfnPG2ReK+W3CjgXbF6qJf2x94T//w3/zN3rW/hst2v5X/91/+yPtKsYsfDZlJNiqzYUSM7WuOFnMUErjnCblgg+Z7f5IN4ijdUieggQO+BQxzQJXOZqmoaRMOw6s73bs9ntQFT6EyRmpOfqBmgajNE09o6SIVuBT5qtvntPHQjEOYytiUSgKWmlKUaxO5lTO0vcjow+s1zs2hz1gyLoQi4KUsMYREpycLEkkcokk4NCNvL2+Y7vbk6IlF1BktBIn5Xz5iEpbVNF0OdD7SDsMvLx4TQyGNAUBWZtRphCzI2bPyeqU3brFp8zl7TWHg2e7b+U7xMKsnjOOkWa2IHgRFRMQved207Letgw+U6YE8aIkVEwpLUzMkNAZYtdTKiO8SKUoJMgR5xRWV/jg8cXjIlTaEOMo58RaTGVJMQIiWCbf4lwlLmaTwWoRjJMmZMPtbs/l7R3ZF1IqjDFQOUdKGTuls588OhUyqk/cbg5stwfGkKFoun4EWyiAxZHGgVmzolmd0HYdfvRs7lpevrpkNjvH+0SFpqSCVo6YMo9O54y+RytN6AN3mx1v7u447gdCUmht0abQl4Rxjq4dBZ1ganyBzeHA7XrN9dsNYEE5rDEElTBOM4wjKWZm8xX9MBBL4fLtW7a7LbkYUowYlQFLVppUDCknVvMlFks2jn07cHe34fZ2jcJgjIFccMpQ2Zqh76maBegGpTLGNFze7Hhze0XrPUk5lKkwWqNzhCS8yEhkiCPdOBKyR2lLBkrMVNZRSiQjLUAlJ6xVHNsD3Zi5vtuRkmYIRQoyOUn3UUEsb2b6j7WoAglN9gljGqIq4pgH0IVuHDHWURuHU1r4ylOgV4ojMBWAUgFjyZN/e7/fM1+sGMdIzvJvx35gc3tgvTniI5hi0LUmpiIFkvCOxXloO3zMvL2+5Wp9YIgZZRwpJpTSGGsgQz8GshZHChl2x47b9Z5d21KsOPF9GNGVI5WCrRtCioQCdVPjx8Bh2/L28paYIWmNcg5tNFZpChnrLLlkfEr4DNpocgxTEUSwEEUXKBmTFEZblDaTe1ITvEejMVqTw4hWBeMswUes1QyhZ3myZLffcGw7DuNAf5TiiR+8rLfKkFKkrmrqyhJTZn9sUSWw37Xc3m7Y9z37MJKtRhUJHFRK4UsmpijrfNdT1YrtoeX6dsvbmzu6diT3Hl0Ziio0zjHEAasqciisN3tSKOz2By5vNuwOB5JShCzIC5SirhoWizmHrmezH8hJU3zk8vKW/faAeN8zKUWck/OjfECRGbsej6bbeY7bgbvNgTFFfPCoEiRpN2dq44gxUVVzxj6wPXYcu4F+yByOPdo5EhJcoJ0TNrhzDCnRjZ7bzY5DP7Ldt2y2B0qRoL1xDLjaCTKIhMVIAagPXL29oe9H9ruRfojiSHVQUsJkGJQCZQgFumHg7fUtYyrcrDe048gQvIT6RcH2KCXcyYwhKk1KhheXl4QCl2+uCTGQc6KoDCpTckIbGc+qaA5xoB0iN7d7tscju64Xt38XQBt88PR+xFqLjoESPZvtjtvrNd3gWW8OtD6R6pqSkQKOQjpbqpqsLNuuZ3toOex6+f1jj0ETcxYmcy5YbemHntl8QUwSjJjHyO3tmiEnhjiitGIcW+pFzZgHcQ2XgjIGaxz7Y0fvE+t9z77rSGRCyWijSSmQU2ToB548eUJ77OiHyOgLt5sdu32HHwJKaWH4GgUlY00jHQJK0VhFJnPojnLPSR5KJllBnsQYebQ4pR87FqdzQozs2wPJB/bblk3XcXM44EmMPqBQqJxQGuHdRula6mNg33XgFc9evOFw7Ek6ElWmGE0sBTCE0WOU5g//o78Vbf//8PrH//BPflScptpNQVFK+K7N5RGTDeOp4/hBJivN47rjEGvWw5zDxQnVRgTRepveibeHyNjMuJlVrJYjCc0X2/c5Pj9FMIsi2hWj0LFQX+yJj2bc1TM4CQy5wpnEtp8x++Mls21GZ0WuRCBCiVBTMNy9b1jOPM+OT7huV7QvTli9kr+hEw+CX5ppTK+5/NBxl5Z0ueIQGr69eczss4aTF+J80kEEP/SUzn1eE+agTeHr/Xu82Z/ydnPC6V9WmBGsL2gv7N+iFbYvEBzbD+HF5RNu+iXVZ3PmV4XT555ciXA6nFvqXRSGbg831ZyruODga26eP2bx3EzhJuBaObdpJhvWexZncRrTS1t1mkungEoZhSIu7OTwjNjDSJpXxKXFDJMIquRzzORmzI1FjyKa5EoTa8te1bSN4U13ymaYc/XsCY9+btFRzk21HQkrCYKKjUWNlu6DRJ8q9r7h+bdPaV5XOEFZMrsVcUllEUN0gJuzmnrluVg/YtvN2Lx+xPs/TvhzNzlbpShQjMIOhf13LevjgtYa9v2Mb5+/z9mnZmIkyua5OC284MpSbwztuaGtNbf9kq/fPCU/W3L2hbC71RTCZdtErgzVekDlitdnM272S45jTfrshOUrEeiMLw9OQuNFaCFaNu9rXC1c0JdX5wzPV5x+LQaB7DQKRZpr4sJRHeBuPqNThlEb5jbw+RefcPapYXGTZEwU5MpIGzEi+NohU+8Ks7uE60RU0UEE0rASJqOOEtgnyATN4ipTbQ3nv8wYD80mYrw48Jjco/fP3PdBd2gljtFJPDSTY8/0AQrUNx2ltsTTRkS2NlAqQ1w4OfYix276IMfoEwpF82LN+PSE8djwJi+IaLY3K5qXNSfPxbVWrJK9Y5eotxHXKbyrCE8jr67PObYNfL7i7HOod+nhWIsRoVoVRbWDPjVsqOmLYXt5yuxZxer1JM4vLfYQKVbmvyA7DPSOzcow9o72eimBgIM43dEyr9JMjDb3rFe397hDlIJJkKLSQ2jZ5KLMtRQt0Dy4nG0XZa5WwgMvVoon9ujJtcFtB4YP5iwvMua2or421FuoDlB1hWYT0algfCacOMKJsLIXr3uKcTSXhuU3hvlbxepNREeZiyhEKD0KQ9UETX2nWc8b9hcn5K8WnH0m4pKO5SFYzJ9aqt095gGqFxUh1Kg3Nec/h9WrKKLjUdZbUITTCv90zuJtwL1uKNnis2X+Wc3iDShk/dehkBYOtJa1XUsBxvYKs3GYG8fJt4r6WKgOUTohpoJDWNkHAdMeDfaixt1Zqq1m+TZPhStxkJYp9KkYJdiA28zqG0O5bmheW05eFHSW66raiave9mlacw3jI4vtYPm5xWwds0tFvZF7gIiV99xuj0Jh28TsLvH404DpKs6+yszuitwr76+8aTxULlJ8GQo6Kt7/Z5kyNpx9qh4ESJV4KJZlp8W1ujAs30TcnWP2tcNeVCwv5PtW+4hC4dpMfSuFiuw0KhVOv42svjaobcXyuWJ2K+KjKlPYXsgS/mk0JhRWrxPz1xp1XbN6rmlupfhmx4I/saL7NJrZ3XRPG8Slr5Li0VcFd1DUO1lzqkPGnxjqnXTB6F/p8lm+idi95eQ58v+jIHukmJExIVNtRra/2zC/STSbjDsoFt8YlheCNigaZmspTt3f+wHQMD6S63dxlZndFlwr42C9sMdtlzl8r2Z2F+jfc7g+s7gEoiW/ndG8tJx/KaLw/fojCeKgM9M4QrVTNG8NAcf5n0lxz/iC66WQZacuB9clcq35z/6Tf+s3etZWpfzNbYH7j//TPyqlCJsyIyKkskqAvrqCmPj+J9/l/GxFLiPWJDabDc+ev6CuZ7SjR1kHWlytII7cs7NTlosZlYb2sGez20PR+CQcOWstBUWpLCEkTrVluWh4fLZgs16zPbbUzZzjEMgFkihMAJgCVVVjMjx5/Ejaxw872r4jK1mTxhBBiWiWgvAfK+v44fe+g7GFw27NGCJX1wKJztTiKEwj2ji0dZA0Hz9+zHI5Qxs4tC1v3l6Qc0ZrK62zOaOwaGsIWVy/Y99zfvqIIQ6YkMkhk4rccHwukAtVU+O9xzhNiYlVs2Q3HplVNbpkco6olIkUEuKKLKVgTcUQZbPXTMnVSim01pQchU9ZMmMUIVIZyD5jzNSWijjoQhhRSlFXlViXUyRnjdGO+XxBCpFFXVOvpKX2OPYorVHFksc0HTuUBM4YbDND68zqZEHfBUJIBJ+gTK3hqWA00haspd3cKi3BTXokpEDMipQEbdA0DWMKqJJZNjVh9DSVpalqrK0E4RA8Y/AoDCUVrK0YS0s2M1Qy5DAwq2uW8xnkQDu0NPOa3o8c25HZbMnQ9bjakSnM9P38zZjKMQyDnNdSMEXGMKWAczOKMmhr6fqe8+WKs8WKFAfG4IkxklLCh0TOkJRCOytuTaWppiCcs5MZ0Qciin3fMvQeZQxGzyAnTIkYpcgaIhFtDJSCygmyQilFtpakQHtPLPeuYhEHtRY3mFKKVDI5CebAWo3SmRASOWeaqiHnPKFMJN1cZ7BKT85lQ0EErRACpRQaOwVRlAhZTWxgRabI30uJxjFdKxKKZLW0QlfG4ZylDz26ZPnMpOUYbKFyM1IqGCUMP2MUlX03HlGL0zukhCoFU9QkqMomSVlDiJm5q4kl0x331PMZuRR8GLDOkZDNqDMWXcQ1bnOiBKmA9tGDrYipoKyiFBHNKmMZx8BsNiPHTElJ/naOZG3ox5GQNYvlDLzHBOT8KUg6y+cgHNky+XZLmR7si4SUNcbJVa802Sh8HFnWNd5Hoi8kpaiMJSW53pWT822MIYSeqpo/FJS0lXW9GwYqaylF5rlSipI91kqrWNKalKFkQbToAmNJk8CYaKoZRIgx4ZxwQo3RE3NbVildwBiDT7L2liLz0FhpBWcSIK21xJKx1tKNB+pqhjKavhe3rpr+rdIGmyEmcYGji3QGZCh5QkkMA66uCJPb2hhDzFIUdHXFftfSNA11VZGSfLdUsnwHCiEkalMTk5d5GwvGKmIRZFBBY82E59Hy4GMpjFnW3DEGccJjMErh00hVVVilaWMr67HWWF0z9gM14oJKNqOdxvsBP51fnQ2BjNYKpWROxBhxzqF9eBhPpRRZiZtd6YLGUFtHKYUxTCI1gs1oKksKcbrm5f5pUyUueArDMKK1xWhH1oFUMklnmlowOKCxBsLosUpzslpx2O5wzuGNIke55mx6l3BurTjQo5rG4x43obQ8Z8Qk6JRa7v0xZZraUtmalKQAmktEa00XWpx2lOwwSqOKOCAKcUJkgJnmc0oJVzIZUEYzBI+zM0pRMHUPaSsHWYqszUbJvVVbaW3LpZBQaGWxRhy+TmlssYwhYZuK7OU+oMnStWAMo49gwGkDEVmXJmZ2zllQRGgpYhVxK2ltMQr++3/0D3/zvq2/ff1L+/oH/95/We55qCqBGWXDc+/0A3jzb1vCd8VxbuqEejnj4z+NjCeG+XV4YM6l+l3A2P57lv49RZwXmrVi9UIEwuqYsG0SAWAK9zFd5PjdGd1TzeH7meUrzepVwi8182sRHe7bpUFag8PSkJxi+9uSyj67Lsxv8xTeJRvv+2O6Z+2GhebyH0Bxhfra4I6Kx78I0zHzgO4Sp618p+t/zTE+yRRbaK4M7/1UEuLFQSmb6aInYbGTVnu3j+x+2FDvM8nJht6MgsS6bx+Pi3uxUDbSx48qVq9GxnNHNpMbV/OASdBBBLHUiLiYa4t/5IQ7adTD8Uj7tySZ50pcZvcMU3j3HU0bKEY/jDG8S7nvH0uSe/9EEBjzt4X5tXQ1lcnp5A7y+cKjNMSZPBe0H2jqbZHQrm4as4Ikt9fmHX8X+S73uAzXZREtQqG+7fHnDW47Uoym/7DBdlmc36eKVCvMWGg2meogXFURg2Xc1SQ46ZAZzyva9+W7z2+FwThbJ2aXHeOTGdV6IC0cKkryuZwHCaKrN+FB6LifR9qL40tlSf9urnt2v7Pk8D0Rot2hYEZpt62Ok+A5ypxyhzDNXUuqFfvvGcwggWvLN5FqI+7DhxbzehJukzgzs5vuE5PgWjQP18Y98iBX+mH+yjmQe6AZ5VhUkrbv+/A8FTP+rBKcQZ8ehLpi1QO7+OFnRt5DLvizyW02td3fs4Efzv2YHj432/trw1A0hIX+tYAk2+WHeamHRFw5tM8PAmh2Cr/U1Lv00PJ8v27cn684mwR+LWuZ7RL9E4cdhOEaTqspUM2LK3LiyKaFoEpSLdgONc3NXw37QivZjwJx5XDbkfFJ84AJSY2EAupYqO569HHEf3jysFaVe2HuXjCffq5yeZhfD9egz/hHbrqWpUDjjpH2g0ocmQ9z0DysdRJoVKZzmQgrg/bv5p0OmeqmJa0aGbvz6uGY4iRqmVGuvfs1OTvhXd+//JmsE7a/54QKh7U6iPtVmMpSDLoP67v/frl6t57ehzzer8n3Y1M0Ii7Wgv1IjSEstfBLpxZ0OW/yXSnCBK03UUTbLj2sf/fBjXFumL86Es4a/KmVkLBpzt5/B9tl/EpjBxHutC/kSmGG8mvrip4cldkpYq0mp7usN9m9m6fuGAlLS2wUs5vw8N3vXbuC2/jVkMWEPQT8WfVwD5HP49fOd3bvjkEQJr9+7xlPpgLq/t1YuGNkPJsKazNNmZ7o4hQ8asdCvZHjv19n7ueXf+Qe5mVq9BQcpjh+aFhcZcJMUbX5IVDrV6+dhzHrZYwlSEzO4/1cFbFdPtf0wlL3SxGJi5L7XzaCRIhzMxWD5P0P4+LErZuc/MyEQpjpqUghmKQw1w/Hdb8Wydy8X+fVr/1c1lM5z7FRD5+Zaik2x1pNhcvycJ++vw5A5iSI4SxV6qGY5to8FSnywxp+f97/9H/6L36jZ+2/0aLtf/BHf1RUAWtqCazJk8OGxKxZMISIyRpnCqiESelhAxtCIGsl7ciTAEQpKAyVM4QUUVpjndzIh2GgJOHaKaWIY8JVRjbRiLA1+p65duz7A9WsETZtEJ6rsxOiQctGqLIzaUeMhWZuREg0hmHocLUsfCoCRZOVYlbVEjyjNcYqunGQJDZd0KomRBFAVSpYDTnBop6RSkFbTTcOhJKIJdI4aX12ztG3Ees0qJEYM03TEON0cywSuObDtLgWwJkpzMijkbZuNQWtxBgFPYCVG11KWCvf1xgnm2ENtdH0fU+9lJTTGGUTG6MIOZUS4SdG/yBkC0OxJkaPMprKGUrKlJgmccGKOJxF6JNNslTckhYBLk1Ch9UTgymLiFEUmEoQEMLbnAKOlCWXSD+21E7GxJkKsrSD38OhfRxAWdAVKntKSmhZP0hFxnxWN2y3Wxazxa8EZDkR28cgrbOVEfcyCpXV9L0gp4irKsYyiGgUZK5WWjbhGRGUZ7MZpnIcu+OvXSd64jG27YG6nhEnkYxQMFrjtJnGyk5CjhbnGoquRHHKGYXTFhWzvMc5ig+TIOsmYT6hk1T9dQ5YoxizohiLcY5haJnPRMADiFkEHGdA5UDOEVsMdT2j7zxVVeGjxzaWnOU93sepZdyRE2jlsNbivTw42MYSxk5EwG7AOXHhaS1O06qyRFXoi2fsB86bR6SUiTHjamH7ojK2KKluWuFLq4lvZStDioG6diK6F4QtrLQgAhCWXCGKCIxGK/Vunt/jP6bk9ZwAFI21xCROQW0NFZqYpHoYYwRdiEXccM7WpNBT2ZqSRKQxVpG88J5zLKSYH65NZUUUzvcBCffnI2UaayjKMLQds9mMpDNtGKknQZhJNFfFiGuyJCmm5IyzWji+PlG5OSkl6rqW9TFNLTROE7sOUy+F1asd2Ezyo7S/GkOII9bqSQy2ggqZCl055yl0ahSMAkmK7VYxjiOVqZAvpjGT+F1VVlipCgQerkmTUIkyRB/k96yTv0FBKVnLjNb0OVPZCqssVcmM3hOzQjXTPJ/EPq1EGASFntagcRxpmkrWnihzWiknc2NygVolD2/G3ovaM/q+xxhHKvKQPI4j8/mcEAIpRprZjDTND2MM/dCymK9QWZNzmIS/jCp6EsADZkJjOGcxU7GlG3sRLeOIrQz9mKQIrN89tBgNGPBehPFhGGRuBCliGK1JJZJ1wRRQOArCXrr/DHJGa/lc0oQAmb6/0W4650YC/CZ+vHE1YwgYPVXUpuPKFMr0wc4puXdnaLSV+2OS2ZInLIYqUFUVhUSKBVs5YXErRdXUhKlbBpiKWhptNSFFKqbrxk7Cvavog7DwMwlnRJAfek9TzeXv02N0jdbiIA9J1q6YvKxRGEgZowxWa2Ic0a4Ca/BBRPiMFHByzsTiCSlS1/cFT4MhYZzFVoZxCFKkVEZ0NA05R7kMUhberKvIygIWkwJKF5n3aloT0CgNYWLbzxpHzIkYCsbJM1RJTAU0ua5K+X/Ye5dfXbL0zOu3rhHx3fb93E9WZlVmXVx2Nm1bGCQGICaMmgFS/ymMPeUfYAJCaiEGtEDqAfIINSCktkB0G7ttXLbrklV5OyfPPvv23SJi3Ri8K2JniQFmgLBwfZNUnn37vhVrrYj1e5/3ecSTOaVE41rGGPhn/+Q//w20/Xvw+nf/g/+kTEoXPT7CIR0y/YWjuY0CNOphZDooZSOgbYIWps9zmnvR4h9pD6IanMBAcxtnyFe0mg9ueizEhUA/v00cLyzrLwbCSuCYPTz6MaokNuIqSXL4BDWHE4Pb5zntPS7qs2CdxSpBf2ZwR/HGTU7RvY/zOExgaQKTU3v44UoAZrZIe+4+Yo6B8bytHrua9n0gdvL37DHJQbmvikknfqtuNx3W5O99G4SpXGYYOgGQCXCZIc/vJTsZM3NMhI3F34wzdJ2AnTnKf0XpJD8/gejpEG56AfOTQlPgiprbSSeInxo9H5AFlAnwMPswq3uBGcBnp2YQPUG3bNUMs+Kmma/jNE8mr9QJZs/XrIKHCXZlpxnOHaufb+mfLWdYOB3oJzWhDlm8jBtTP4OZ58sU7DbNV0AAVwUgZkj0F47k1ByeNs8PJ2ugfSuQaRpv+Zq0x04+kH6b+DbUb94dHgHI0s/en3Eh42b6R7g2ga6iBc7ETuMf0gyO/e3IcNngdjJ3dZ/QIRFXXgovUaDsuDG07wNhZXG7SOzMvD7tNlCsngsGAsn0fA1EfSxQ3hyiBNV9CzCGtcH0Atf1/YHD9y8ExBxFrT355U4BSEUzry1ghsGTgrMYhTmmGSbPXsa5zBB4Kgx9G0x/2/MYYDx1vzbfw8rMwGh6T+YQqjrdo4dIXPtHuFjnf1zoWigp8/jM4PVbf7NYsUkYT60oUO8Cw4VDj4X27YG4aWbQPgWZqVge13vMpIWt4YSBuPYCzE7c3PEwzb3pc0/FG0B8arWqimdRwqZqB2OGNBe6VJDgw/lapscxdXcDcf1YuJl+/tt76/SaigPTdSAX4noqdOR5jFJjaK6PxLUndoawFGsK06f/yxgU+/iYMRUb/H1gPHXz75vg5LcLixOkn0Dp/pmnvU3zNSwa/H2gv/LYwwQiBdzqWAt7NyP902YGiRO0nfY+e8jz7/82qJ5A4rSumuue4oxcazWNo8x9t5WgOHfXM1508xhO4Fql8utgPzwGgk5jMvm6U5iv7RQkOIHkCY5mLyB5+vvweL+c9toJluqQGc4eC1XTWE+/N6wmr2a5xzc3ARSElZ3h8rROp/uHPeT556bPlK2aiyAqFuLSElYC3IczEejoKPA1W3m+mDy2bf+4BmVc1QyFc/03vxM4OwFUnQQY20MSWF/vadnLfT+2EgI3Feem9zqtdxkzNQNdoIJhUWtPzyATQJ4KCv2Z+TVYPY3p9Aww3WunvWXcGNwu8z/90X/8t3rW/jsdRCYqHIexiqIN1nWUrFDZklNC54RR0hIfRhiLI6qGY4gUY8lZEUOqIFSLSs/BmIMcpMZM7ANxjKgiXofHmDhQOOpMUIUcJcCq3x8xtmFUGuMWHPtawRcNHQFHUpYUFSUbYgXAhYR05i7I0aGsE9iXM6oqh6IuDLkmYwPj/ohDV69TR6kH3DhOISaiQg05chyO7OORrDPKWJR2GO1JQyL1I8aCMgW0ocSAQ+OVE+WoFZWNwyKxYQY9RAlHcYZsFIPVBGsIucc6AUEmZpTWJJvI1oriS8nB0imwWuGMJkeB5RrFmCGiydoTY2QcooxVUaBFcUcWtZrFVusFML5h3w+EksmmUFyimEjRCWxGm4yKkdZIEI7W0KcB1Sr6ckB3moCoS50Rj9zGObTKxDSgtcIYUedRCnHsISQUEVV6Yh4pRUnruRIA4rQR5V+R8DmtNX0/iEI5ByKBYiKRgVxGog5EFcTsvwisknAnMUHOWhOyBFw9HPakHChjhCEQo4yDcp5QYLvdYsQ5gpiytEzHxBgCi0XLGAcJHUsJYxM5Rwlz04Y+DWSTiPkIJoIVSOeVoVMOF21VVtbQn6rUJpeqzs0krchaMZIJVQGplKg9lbEcx4DSGm0cqIJVlgFRJ7ZmSVGapAzKW7SVAgUpkotBK4/XHmM9w+GI1wpXAjr2rLyFNKJIaKsoKtOuO6xX5DJQdEB5eOgPxBjRQXO+vsDUMCE1VdC0FqhhNNhCzAlMIauIbSCGkUyhHyIpQogFbRzKeRQIqnUOkOCVzjgZn+n7jEAQbyxeKZItRCuBQEVrlDGUMbEfBpKxhCjAOCugaLSyxJwwypMzOK3xpraEN54R6Akkr4hWkRvxqM1KoLGor5P4fKUs6saYsE4TxgMxHNFlIMUDxgEqk0LElIRTWdavsjjjMbqBYtHOgknVnqYqnjEYp7FOYxYN2ip2/Y7lScc49nStwztQZcA5QwgJna0UKVbwQBcAACAASURBVLSo1A1aOiCiKHdjHGm8KKinwoc1BlXqXqIzQxw49HvGXMix4GwDUeFQpOGIp7CyioVLhHwkk8gFjimjMVil0apQUqxrQ5EA6xQ6HWEc6LzF6oRKEVSkqEDJgRSPdK0hhL3s39YR8WA9gUKphbJSi15jCIwF4hhotCejwYKzGldBuzbgGo32k3I5ooxh0bSQIykOYsswDLjWE0qW+WGEoBotgX8hBPr+KE9zakSrRAwDxspeo7W0PCqlUFrTKkVnHLrAZnMqxS5fr2lReNuI5UTSooQ31cg/RkiFVmvC/iiK7VoENMbgnJmV2GiZ1yMZ37WMocfZQjSBaJIU0mKPIuHJeDI5RBplaLXYDoQyEOjxtgUUxjq88qgU0UkU/2G/ZdnY6qOcwHusttic0WNgTFG8aq2SwpHV6JKlaJELSzxmKDTFEo+BXJR026hEHw4CiFXm0O9QuojlRxggi9UPgLde1D5aYRsBtmhR78YoayzHQVS4Ts/ANmuBCVZZyIr+KPebpmmgFiNNkQBJXSTE03eGnEeZkyRi0hSstF5WVXMuQfY0A60zlDySx0jjl5QIORZyGcllQJdAHgd0FtVR4z0hDjjb/r/+jPeb19+Nlx5FQZNqonPszBxMJodbLcqjqnbyu4w9ZtoKYKfAoenwKAcscDsBsiqJWtQeqyXCmPE3I831gH9/lMNjPbA190kOnXtRhjY34/weJSwpCYyohyF7zKKkSQVdoZz48T2CxlnZ0yeahySHL4WoZhb615Q3IBBMYKViXIv/XnsbWbyL82F2UlVJaJJAiumwToHQKcKyguyxzGMh4W56PoDG+t7dQ8QeUg2yqUnzTtpnRZ0nh73YCtCKSytBTAtb/Uhr2/U2YI7yvGl6Uejp8RHyfFvlJ36YaT5Utm/lWtSc3bllHkTxVLSSMJzGUJzB3RxAgbs5yD62jxV4CUwIa1tDfZKEd3k7g4QJRk0H36kFfwoSE0Bq5vcel6K+a24Dcd1IgFpfw+ZqscAc06ywK/ZRqTrP85BneNN9vp3Doab3Mx3c7SGz/Hqcf1ZXwO3uBtw2CTDdBswhVDgoz4N+K5+puZfW8ikAZxr3uG4YL7pZpQvU0J/0mI4+ihJUj3JN7T7N73nyxkUrmptxVmoBpM7h7npiZ2aFnBnKPE/nzxJqmN/akb3G3Q/EpZmVfIenck8ruvomV0VtaiVIboIni8/3AhGd4fD9i3m9TJ+jGFWLH/L57bGGnsVC7MTmRKVCcxvkM+/jHHBW9OQz/AhPhnM3j8+sarSKsLKElUUPUkixhzTvXSoVurcDZpDfj3r05S1GY46B3AjAjAtDWFXlqlW4bcLfhRoslkSpetPPSuDsHkGPqmGKU3CVPWTsMVFMLVzUoCFdQVtcWrEv6QxpYefuhOweCxn2mL4FQCUQciqA+K8eOF457HZgPPeEtTx3xqXFTLYLpkLlWhyYCiy5qlDD2s57KjDvSSrJfu7uR9rrgN3LZ58KdtOaDGvD4XnDcNnUdS8Ka//+WIPxlHyeSX28zQIa69oGGM79DOmnfXNSeg7nHntIs4pzAu7TfiXFDrmebhex21Fa9dePham40LLWavhTXJi5gGS3QfazSykiiZq8BtMttNxn+sdrNynep7C06ZWdlv2tsXNXw3QNi66e8Oee7DSH18tfA+uplcLaZLUh97Vc9yP5G8OZwHh5D3ouAk7FnsfOFnC7RFhqCZlcmOornOZCWtEQVo/zfNyYWYVrDxVwrsz8t8JKioayd8tnP145Wb+HqSgjxd5s5W+IOrkWPhpNNsz318MTT2oMYSNFHR0FBKssxdxxKeKS7r0IBMe1mYPKUltFdKtaAImi5p1eRYt9jhly9RWWzzAB2+meFut4tXcJlWDYTPuiPH+4baxWK6LsFyW9gHzbl3lPSo2qcLvMn2M4MbOqdtg8dhFN35dr10Ou+9y4kb1iWlt/m9ffaU/bf/rP/rs/VFoUkqlIWrxCY0siDiNOGaCQk4QaGOUpWdRvJWZUHYecxQtWKY22lhAjRhusM+SSyVkUuAVw3kmcj86UXMQbNRe00RQKVhU0ilW3oB+jBPwUS4wDrW/IVcWZc2FUhWw1/dBjrLTGGhq08lA0xjpiChgdiXnEek1RstFPHoeoIp68pdAtpFVcaUWJmeIMQdf2GGOwCVauIQ4jyluSVowho7UjRvDtgu2hpxiHRh5qtdYc41AfchTKSBLnMWUCmaFIm3ROgZLBe1fhqyIqRdSgtQKjJORJBRbWQAxEUxhzQRWLcxqtNKYeLGNMGGvx1oDSlJyrClHgusqZFBNWKTrdolCUkEBTlX7i26W9J5Dp4wDZUAo01qOSwuJw1pFSqd66BW8NqULwlBM5JygZhYCxrm1F8ZYlVE1+1uKMZRxGjLHSXqohhZGm8YxDbWuPkdY7QhQFm4RpVdsHbcQ/S4M2ilQ0GUWZPLUqiMlpxFuH1oYRKM5QxZqiGSyZgQROoyiivEoC/aOVEDhnpZXd+yXjEPBNS8oJrRUazTiOolxG/GxtAV0USY0VPili7PHeynvNURTDRhO0tHCTC84asUfIUdSluoiKPYwUdIXxGps1RktoXuwLzrSoUpUupRAzGGfpxwHjHUplnBcvXN/Yah2QaLz4UBPBIK3uzjeYpiPljHcdWglkVE4TUiBT28mNJqpETBGrNNZZYooUY8jFknImhkBbfbO1EpXeouvY76WVHAOkTB4HKIVl51Gq4J2V5uKq5teIYrZrG/oQ0EAZk0hgFQRd116W9vESiwRFeYMpYBJYlCiyiyLFhHGiWI1xwGqLopBTxiaD02BKpOkMOcaqxFOknMmqkEuicZ5hHNEYsVfJBaUy49jTGIs1LQlFLgqpJ4jjr1IKU1XIxjhpjVdyI/KNJx4DBkNRmTHHOSCMkslkGu3IMbFqGkDTNp7DfoevgWvigKCqfYBDxYwuGmctVkmng3EWZRQpFLxribGgY6b1DSplNFR7EksiUHJEay92H01Df4w03orqviSgYLWhxAw50rYGhfgvawxDEB82ZTRaGdKkKlXUFStwXSVwRhGTWCvgxFohAbZtyTGwMIb90OOsJaqEUtLK7rpOlLlI4U+Au6qA1ci9xhhyihID5xwxFbRJaJXJKaCMIqZIt2jx3lKIOK3RxWC0JRcpIKaYMVisEcsNCiil615hSL144xosOksRSCFBVrkoGtvQYDE2Y0pGTV0sCqwSNXwpUHJhjBmjLVop8YG2Bu0MYxSrkxQLpBGrRLFvETsNXTKmWrUYCjkMFNPimxUpN2JHozOFkezFHmJq02hXK0KRvTyNgQ5LHwasqx7aeKxSeK0xBZxrCCrjOs8xRJJSKO+wxeGbhhQz1noJCV0sJQQ1RVrvSSGy8EvyWGi0JY4RY6BtF3JvLoC14s2tBRLnEkTpXa0lFJnGWFHWagUpYUyDUwo1BLyy2KxwRWNUIqURbZAdQVu0tkBBK4PVLUVBEa0sKadqDSNzsCgpFJnSYo2X4qPR2Hr/8tYRc8S1LTFDqKpplELbzD/+R7/xtP378Pov/us//kOUqknIefYQzY0WINlNByW5d2X/6EOnMrJfTkrHqt6a2zy9HE6nw8/0vWkh4UrFmRmaTIEeOknLYrECkdrbWH0hRW02nrjq6zfBMoGF/naQ+2ifyV4KIhLK9PheTC+BPJOdie0n5ZiaD1njmaSNU1s0dWKGTKW2UR+fNphBrGLMkLE7US5KWIph8dWALgIPVRE42b4fxcO9gjeVC81NwB4i7u4I1swhThIqVWHkIWCOEYx4o4KECR0vnOyjvXhrojVpaWcFZbEKcxSfzbiy8nmqh2Sxcg0nKJwbzXDRMHnOArXFXDx+Bb4Wmvs4j13cyPfnzlVQ+wgRwtrOCr1ZsViYx2u48OI1GZnb3VNnSZ3G7R5Vsrmqx8KJxe0SqbOYQVpp7S6QW4FuKpcZ4ojP4GQNIGM4zekpzE7HQtwITJl8OE0N8Zpa3t19Lz2uWoFSAqofhqoYVRJmNQqcdrtEXElLOrVd2W0juXqdpoWrwKqC/v0oRYVjIK68jG8NRNMxY3ZDBTiKtDC1MJLkvYdMWDvsQWw6FKDHRG4d2VdP6iGTFtWfNhZsXyFUY/A3A8VJIFNcOvHEXUrgkN+lWaGNemx1T11dfxXYFScQHV1hTSgVVApwNMdIbi250bNyXIcsgVPHRDj1UuixAtmGS0/75kCp2QMybwTY9098VfcZMHXeIlBZV/V8c59kL8nz1MXu5FmOCvEnlZv4/Vp5fxNE1NTAMVWBXZrnoAp1bi4EdIcKgma15Jjn/S2szaNdSYWwaCmmhBNPatXsnZ0aLau57gfFyV6RG/MIvY0mLnUNSJSvu23ApLp3FpkvYSNe08enIjAZTgzt9TiHdaWu7nnl0a5BPHpNXVOiihcP4kxcyc8Vq0SJWX3Eh41hPLHisZrlGhQndh/+diBumhnETX7R031jOJPiclgIZ3BbAd3yu2VvL1bPatWi9byHp1kZKvPdHCsE7qxAsouG9puBtJR9E6XwD7Hee8RDdArak/BFPRclYisKeSm+6fl70AIHUfLz/ZklLDW6iLVNsap6sspn1YOsz9Qa0kLPe45C1PJ2qAWpRvam5i5W2CiBdGHjGDdGrr+bbG0KudVibaCpQWRisTO9x1y/t1i5h4+nFVRmCaqbwjjDysyF1em/cn8rhJV4//qHWO9N9bNrWU2qyJzyeymO6pAZpi6cqQvESxhZWIhiPXbiCR1bRXsrnvFFC4CNS4MZxJpDp8LhidiYKASk2hH6c7l2w8bgqoJ43IgFTdEy5qYWJsykkK3FWimswrA2JF+7ZhC7gtjJeI0rKUbHVtax+NrquZCY7eQnrsTmoIDOVNuRyWYjy9rKVZ3t9axUT820PxSB6cdCWIhy2B4m72f5/f+/CCL7J//NP/1D7bSomlA15ARKHCTYqNoTNLXtWWtR/JXqp6h1DcEqEmKSajsuyH3YOGkJTvWQ6qwXgFcyGVH5hiRtg9pK+/UwHFHWcOh7nLMCBaP4SI5DmA/DSilKvZl6axjGQVRjyKEtZwExKQassdiiBcYUJQCzgLWOmAYArDUk5PeJD2smVisIowxEaQHOKQKFWKFnsgKrlVaolDHW4BtPf9wJzEsJ6y0hjCgtiqxxEK/QrAUgeAONk2qU1ppcIgXZ/YuSg3Cp3nsxRkosrFYrZP0oxDs2YrSilESMkdZZrAKjZSE1rqm+jRFrDLooGu8kWb6I4qwgyjutFJkKGutnKkBO1Q3UaCjSoj7W+VFyRCmwShZyiElALoiPq5b2a7ERaAQkR/DO4qyFLL6nYRhJKbHcbLh/2OJdQ1HS/tt4S4mJzXIpm6qCEEa874TXaU0utV1OQ0ziG+ytgpLRCFxLMaKsJWsl4ogiFgrGaFSKWGsI44D3rnpga5S1HMYRow0xRVG8lSKwq4jiqqQiMCdlfFP/jhEwmKsPZ85RlIC6IG6xGWPlfRttsVaLfYDzxJQxFU6nlDhZLyljxJdCHDPeLcSCpBTII9Y6tHaMMdB0Cm0k7EkrRRwiXdcRxlChgah1x/hYPFFKWucbo3E1UCmVwpCSFHSKpJaNYaCULOBPC2BvjKLkSNcs0MpBTqQSUEUKPMvG4Sy0RtE1LSXKjSvFIKBem+oRLSo8ZxQpBdrGkscjqhScBq0sKQYoiSGM+NYzHo8s245SMt47chH/YGNkTYKo0eXZUoobmVKLIIasDCkkmb/V51VBBVIWhWI4jFhjabyrXrZycBDrBmnvLwmaZglaE8OIc45luyCMI04rWutkjWYZO0DmiDw7YrQGRCGrAaszrno/98c9i05UttZachGLAUnYTfjG4zSkPAjsBlG+FwmTM6a2wpeCdQ1GKVQpMjZGLCe6dkF/OGKNw9bW8VSqz2sKAu69hShWHmSIobBZn5BTRJdCyhFvDVYbvHOi/Kxq1jCIzUtSRcZZIwdYpdBKFLKohNeGNI5i4wBSZNIwhFHWV5Yxaqxn2UoYm1WgTcHjyKYQU5I9Kie0NmgjtgTWKLQqeA26ZBoDRWVylAAQbcUuoG1acinSph8jhYG2adGlMA4Dxmis0RRtKXVcFIlcEiJyEJU9JUKQcMA+jGQUTdfWAgS0XYNFEcOIKgOths5bjM5Y59CmIaRCMQZtLMbWPamIbYshU8rASbckDD1aKazzaG3ELqRkbPUgbtqW++0D3hpUjBgjtikhiP8tSFHNjhlfNA7o2gYVAzoMNG1LY1zdPzVKyTNC61ZiMVEkAV7pjFOFZdeQxgFtNOMw4JuGOIZa4JO1o03BakXXaqzOdF1DCZmSarcJiqQShSwFSa3E81vJntdoTWNEveyMk3lodd2TLTpFdEy0XYMhc7JsBPSmgDOOIUZ000gREVMLJ44wRilEx0Qpg8zNIsWIXGSfH/vAslsyDCOqGLSBpgHnCmebDqM0Vsu+M/RjVQbXvaJ6Ev/jf/Qf/gba/j14/Vf/6f/wh8VJuzIKgalaDilmFKWq20mboRlERVvzB+cDIDB7xElAU57/bU64r96QopqpUC3KQdYekoBgo6BAUw+P3XUgLcwMdYdzh9tVj0keve1QENce/xBInZ1baSe4O1kMpNYQW9m8JeFeVDL2mGtoiZFwqQqX7THNnp65EfAxHRKhfn3M0qWmdIWFAgXCSpTCcSX2CqmVdlpUhdp7+Rx6TGA1ceWIazt/XdcWYlWBcvHVGsIKCNNZcbiy2BGmEJ+p1XZSrIUTR2rlcEpVL6EF+sZOgFU4EX9VFHNoT/aaUg/1xdYArubRn09AQIVa1d82bOx8LVKj0FmCVlKF5HElajSBf4VUPRBVFsgbWzkk58bMSrfjE8fysy1x3aCzgMHxxKEy7F418pzUSFt/WNv5vU3t8jpI23nxhrA2gMCKsPEyt80jpFUFVMwz+MyNxRwEqk4QBaVxb++hceghkhZuBoUyRnJgz15+b1w9FiNQMu6pM+gowXLip1ohipd1khtD7hx6rC3tFajpkNFDYve6k+CflcEeBbDJuqq+9FU16XaJcSNwdXoP9pgYL5oZGqgia9jvUm2JNzOgG9dG7CT2GTNmXA1YKvUsbrdB4P3SytpqjFyDBMOln9XOU7CaSoX+SUtaWPpTUfnpCocFKNv5+1BU+CXFgGEj61J8SmtBYpBr1txHwtrhHkb6Z22dTwadKpisEFSVurdZLddzsqOoBScd82NrfCfPwKhaRKpWJf5eWt3HjUHnqgBUFbjmyeoExhMR0ZijBFodn3jcUXxFh42Zr3VcmHmfpT4zi+VGDVusYDh2muOFpr3LjJctdhel+FTHatqvwkrPgPB45UHL9dGRCqvr803d96Z1P+05AmklJDF1RqBnKnMAnzuKD2pYytldALpci92r5rEIM5aqIpV9MHYCq9DgDtUzdpA9aNZL1nuPFAlrp8JYKE59q0AI7l6KDnoUsB7WhuOlKDhTI3MjLLU0lNYgSh0KGIGspfrLTt+X61xXZWqFV7OdwHAiazWsDe4omSrDRhS7zVa6QgTSyTyJy2lPfPQ2VVlYyLQ2m1spMoaNnYsj45kjtjK+xUhHw3CmKwQUgOj332q1/xao/TZU3L608++YPqMZqhq0qXvfSrH+fJzf6wRv3T4xnoqwoz+TsRxXopruT2WeZysK0XFjsWOZi2Mg79nEaf8XoBw7RX+l0UFRnID0sDLzWgDIZprnavYrH05rTkSp96UgxcnpmSO7Op5evm9ca8a1zP3YaUwUZasZBcQXM713RXawfyFAWCd5j34rClid5L/Zyt/1+/zY8ZIhdDWLp9omFKfw28jhyomXsKLOJ1Eg717r6pc9BeylWsRV856E5m8dRGb/ls90/5+8PJp4DLim4zj02CKHCauhWyw5DD3eOeIY8FpBHNFAsgrnjLTgIwrOUowcymLEmtr6GgqNlVZMhSGEDCT64UCzdAIFI9CKv+raeh7cSC4j3aJhGAO5H2dlo9Zi8pyVorPy0DDs92xOT9iGI0YXCYkqGp3Fj68AyjqyygzHgc62WNewcJooJn5AIaYgat8qzV40LWPY4asysBQFzqKtZ3+4ZYHDO2m1JgaKNgwMGG0YhpF1o8hpT2M1WWuygxgPeLcm5hFTNE5pnBHokvuRrj4I61yAhC5O/FedIURRfa78gmHcsh1GGm1xutDHxKJdMwwBjEMVRRwDXdeQCcRhII9Z2krLgDWKqBOin9ZEEspqrLLic5sjVhXWbYtCM5QCxtKYSEkwjj2uW5CHCn0wpLBns15ATgyHI13bMOx2ogozDoola03MI4kjpIzVhjQEoopYL8q51bJlzIp+HOiWS/oQ8AtHOWRsKazWLWXs6Xc7Ft0aZzqOYxYw0lkwmpgHrFK4RkHpUUGzajr2OXEcatDZMIASZVwi45qWFAYWjSdm2JysuTn2jCHTGVEznriOMSe89gKoUo9VmufPnvFweyPhQkPm8mrD3d0DzjmGULDWoCygMhfdgjQOWK9Q2nMYR8gRXxxh1Hir8SgSEibXGgVFsznpGO4eOF1vGI4jjdMcQqQUjW4LefSMQ8ZqTUo9zq2xakSFjDeeUQUoI0UX+iStiyFLiFTTGJpG8+TyCbZEtrfvSeNI6pFrrzQoTYwZ66BznnVnWa07ztuGvh/ZDSMPDxIa1adMOB6x1jC6SNc49odbLk6XtM5AzlhlOfRHjGnpA6KcjwprxdqkW3WsFx0ny5ZFK2rkb97ecn8rgClrad8/bI+sNyeM++O8r5moKCVgnCPUtnKtNYd4QFcIG7QmxyhnQK1ptCaXJKrmauXRNIaUCzEMJB1p2xPieODq7ITloiWVkXHI3Nzd0oeRgCaMPdZIkFsphtxHNt2ai4slq9WK0A/c3u/YH3pSUXjfcnsQH+5GabCa1nlONivOzxp8hvdvv8HaJbc3D6x9x5Eg+5X1AoWsYbNqeHJ1QeMscTzysDtyd3/kfj9SlBU/6hRYG4cuGe8Kq9WG07MFYRhR2fDVm2/oVCKFjHUtY06optCnTNdaWmtYbxZcXp6y3265vzty2Pak/kijAseksM7iVIEcII88e3nOyfolzmru7u64ud2xPXjGiATZeYv2CNwLhav1CeebNadnG0IIvLu758u376VNvu7PXafF35dAMpmr0xMuzlekIN7Kf/lXP6VtV6IsV0bUkVaury1wst7w4qJl1XaQC/fDyBe/+JJdDiQaYigMZUBbRYwJ7z0XFytWi4ZFu+Lh5j0PuwN393u8O0HXYlPjDNYqQhk53yw5Ozuh9Qai4ubunjfvHtDeE/sRqzWYSB5GcsqcnW44OTnnyUkjc3UM/OKLa8aQwUuoVckjOQUaazAqc3q64OWLKwm1Sg1vb77h7bs7seZRGmUyupXrropmGI98+lvfp3OWzbLh+t0N7+8fuN3uiMc9xjXE44BbriEHxnjEXxheXJyzUo7GL3j37pbPv75jKEmUuUURYsFaR6GwWrRcXW54crHBFsOQI2/e3PHl12/phz2Nc+SccNpgUezv77h6dsGrF084O5N7WNonrm8e+NnXN+Lza72MVTKUNGCV5epqyenJCu86Hu4P7HZHbt7f0TWOEYMynhIDnfP0KXN21nB2csGyWbBctdzdbvnssy9EZaw0SQeUMcSYCKXQLhyrxvPBkws677De8POfv2F/HBiSIqSEs5409CysoajE1eUpz56uabyBkPCqZbvd8xc/+RnrdsluHEEZUioopYkl8pvX349XXDtMLy2VzXUPCzeHFfUXjvY6SGjWMRNqe2Kuh8Gw0Phd+rWQo7CW1O0JdIDAwuFE/t8dBSj6u8B44qoCsr6ZAvtnltVX9dB87qT1uB6Cp2CdyfuvP7eMa4PfJnYvLKuvJ0/Geghz4sdHha0qFfw20Z8JvD2ei+Jnepm+zN5zAMdLh9t/y1uv/rvYCQwMlw3jSmMHETaoXGY/QX8f2b9oxGevMVXlOrUnG8xB2rPT0s0er+K7a+fD+TTW9ih/eGpLPTxv8A+J5ZvIuDaEhYQ0HS+sHB4r6DBDpj+TEFhTQ76mw3LsBFabPmNgbslONaRmajHdPa/AaivjlDeiaHS7JFAv5HlMbMjsXkgrtt9G+guHfxC7ienzmKFaGQyqvg8pGJgKNXTIHJ56/C7R3CfGqyX+VlSD1O/ZPxE42t4lDpeW0Gna2zT/PurnSa20o4Nc//1Tw+JdpLkZGU8d7qFmMCztHE6kQ5EAqyGzfdVw8tMD9qFnvFqiYqb/6ELCrJai1BV7EcP1p47F19UaZJ+5+UHD5lfihem20hY9FTjuPunmEBxJO5cxnLwi48LMQVMTxAHYvXD4XebhA4vbFYa1EV/mwtz67bYShqfHXAN0oGjLuBQAK4Nh8bejKBwPEi4dThuGtebuB5B9oftGVG1mdLP1g6wRGdfx3LN7LmGDh+9E7IOhvVYsvhZLkWYbxVu4NdjtyHDZ0b49cveDFeNGxiE1hu5GzdYoOmTc/UDqHHaM7F617J9pDs8L8Syjomb914b1l0mAWgIdJMzq8GqB236rdb0WL6ZwrgneNNeDKP21wuzF3xYl7fxTWJmpKlOA8URayE2f0EOkP+2wfebmh47+omBGgz3A5hcyX80+0FXP59zWlulU2L1wPHwXhquEPmpWnzva64JxinFl2PxsR1z5uRV82Gh2LxXHDwJ6GbGftaTGcfJZ5PCypX0vFh3FGehkje6fa3afBMw6kAaDe+NZfqFYf1mq6rtg97IvZasIS8XxSnF8ldBHmYsXfyZ2FFN4ldtJ0F9zG+gvHMNGc3iqOHw3YN9bFl8r2hsBu+Pa0FYP9NA9tn3f/FgxPs+YLqK+6Fj9UrGsPt+TRcNknaOt4vYHjt0HGfeyZ+wdzU9bnvxJqR7JLSoXjhfd3PquA9x/ohk+6imjQY2aD/5IWvKb+/RrJISAqQAAIABJREFU/rFKK3Kn2L40PPx2YH0lOTHvvl5z/ieW5TdJ1JR9wlfVvD1mxpXh/rua/iqTzwLujad7o9h8nggrTTF29lCNjcYdMnfftexfZ9Ja5mX3K8vlX5R5T0+tJvHYjXD/oeXwomA/2WJtYnu74PyPPW4/yTLlHqGSjHXyit0rRfh0jzYZ7yMPPznl7Cd1n63evqX6zxdt8NvCz/8jBycjr57e8vkvrlh+Zln/svrEegTMdzL/5f5neP97CXd5ZLPsuf7ZOZf/StNQVa/1vh5rYe/4RLH9OPLhx2957ge+2a94+5NLLv60ob1PDBu5H4aF7E+L68zt9w3jP9jz2y+/4u1hTR8s735+zov/ucw2GcBcgEiN4u77EF+O+C4wvl3QvDOsf1noTzVu/9gNNJwq/APsPoD4auDifMf3zq75s69fYP7XDUMQcKvHapET8xx8eXhiuPv9geVJT+cD9398Sfu+4LdqVs+asXC4lKLl/SfAD3ecro544Lw78PN3F2z+aMXuuWX5Ns2+tmIF8/j88X/3+jsNbSV4ZgrbMSglaiSVyhxq9fBwz/MnT0hhIOdC27Y83N2LKhOE+JdMxEhgkxV1iXUWWwo6F0YlbdpFF7qmk6TxJGnNzmpIkfWiYRwOKBLeN+QcsEajnBJ7BSUhI87Jw4oh473m5bMPePPma7zR5KLxriElUaiqHCUc6thzdr7GdV6Cg3Jkt98SR0spoJ1FoxkG8de1WqNzqb5+ipPlmkM/MsSE1oXOrtBJ0rb3OkrQTxjZuIbTkzWr1ZI87Hh/88AYMsMworNh1XTkNNI5i9IF7w3eaDbLFdbC+7v3Ei5VNDlRYXihZPEsVSmyahs+eHkpLdaMbB8ObLeBGjsDxTDkkUJhyJHGap4/uWK1XKB0IoWRr66vIWu0c/RHSTZVyqCNwhhw7YKzy1O8KTzcbdkfeoyzlKA5hog2nsNxoOkW0nJK4enTK043HcPhyHK94u3bt5xdXrE/9JQs7dFaFUostK7h/Mmak3XLojUch57d9sjb65GQe8Yg6dpWO7RWhOOBJ+dnbJYtq87TesvD7Z7tdk8fM/0gYUFTGJ13a2LqiWHkdLOiDIFCwCoo3jKOPVob9n2PW22wxjD0Pc4bdv1egrmOR0DhnQQQSQt+YOk9Qwwc48Cm8zy7uuTsdMOTyzOG/oHdtuebdzdoLZBT2UJWQZLpDTTLhvOnT1kvOoYhcBh6vn73QFaaGATGGaeJsa+tz46zxYLXT56gnj1n+3DHbdyz7xNZe3JC5q2y0uquMs+eXnF5vqHtpAjz7u179ocDD/sdKEPjnMxJ43BWc3V6ysurS9rGsWgd+ekpu92OP/vzz1AKtPHkLB6NyjhWreOj1y9YdI5V5xmHyN1+y+6wJRx7lHboRuO8I45HhhS5PD/h6nLD1dm52G+MgTdv33P/cMQWLSGBueCqkvJ01fD04oyz05UEtBlF41sWyx1fvPmGFDShiCVJGMUb2DjLOETapqHve3KunpVV2WaUJQNZaUhZwp5MIcWRUCQkjYTsNaVwHEa0UXRdR3GWMRx4/eIpF+enrFYLjv2OXDTGWd7f3jPuelRJeGcJKdM6Ty57njw94exyU8P4VizWLb/64ksOB9jvDxjtZd2GxMK3nGwWPL06pau2As+Xr2lvdhwejqQiRZKSBcZDpnGGF8+fsl5usDqDcpycnBHSF+yPBw7Vt9krjTaiPH769Cln5ye0jUYXTQiRkBPvrm/YHwYCgWI8qWTGYaCzhVcvXnB6tsTYwmbV0fgtt+aBN1+/B6fQTccQBkoxlNyz3rQ8vTyj6zpa6+gag/ct7/7yl+Ts0J0nx4hX4m287Fo+ePWC9abFW0fKgWbZcnv/wP12izYd3jWisC8jRhU+ePGCi7MTtIp42xBC4uP0AV9+9c3cMm+srUFpmaZ1PHlyyeVpQ6tkbTclYFH86V//nJQVXbfAGog5UBhpmsLF2QknmyWqaE6Wr3l/e8929xn7/oG2XUAUUGhMwSnNy5cvWXYNay/3q5P1ijAW3t/c4puTaq/iOYwD1jg252dcXLQsG4exLW4Y+FAv+eu/+Tk2a4Zc/dazFPUaZ3l2ccWyaVksFoxD4XlzhXWOX/ziLdlaFBpGyCkwMvLBq1dcXQhILmHko+9+h9ObLf/7v/pTsLZ6q2caWwjjiLKa1xcXnG861sZjXUOrDDe3DxxudjXUzuKdhCb2hwMXH1zx/MUVrVW03ovnc5Egs6/fvRfblJxJKTPmwOUHVzw7O+P8/JTGiVXE8myNtt/w17/8gnaxYIhiHaSM4v645/XTJzx9/oTVaolJ8OzqGe+v74ghcxx6QhzwTUefpiC7zAcfvaLVsHQdSikWTxbEY+JvPvuKcdRY16CLxraWEgeM9rx8+ZRnl2vCbiRRePn8GT//1ZcMY19V7GLHMIaei4sTnjw9Z72y5DBydnlJvxvQes3V5TnXN3tUTkQVq8e7RvxgfvP6+/ASm4HHdlxRmtV0+aOo/LrPt7z/3bPq4QbDiWL9ZQ0Vm+wTwmP6fFyIuio2qiqFwO3lYK9y4XhuxWuzqnwmaLl7buthS1q2darKwSkopvobTt+fvICH63/DcvZ/wLgy6FQYl6b+LUBVles28vBhQ+xgPJH9qvtGlGNTyFcx4i872TpMiptsFdtXmvZGoG+2ivHci4o3iHdmWDvsPrJ71bB7rekv5feufyGfvRmkxXk4qy29l62oX1eGsFTsnytyU9j8HMxQrRlCETuzyZKgE3XU4Urz9vc12cuBr/vGsHijfy00yG2lfd5XJdn1p47+Us46Olgu/kxkPtkIMJs8SafrEhaa3WtFXBa6Nwp7ncX3uEBzIy3g7btRQpNqsNz7H3v2LzNup+kvHRd/Ebj/0LG4fvSMzVZhc6E/89x/ZDg8z6SziL63tO80F39ZcAfxc51S5EWZHHn/447DM8VwlWATePi6oXun8PfQ3At8dVu5fmFl59C2hw/bRxDdakJ28vutwr/ZEpenxM7gHkQZuXg7klrDaiueynHTzp6VKhf6px1uG/HvjxxeL7n+seX4OrL7pKB3hubacP5XabZkEHVjrh6nlnGt2H2nkJ8MlL3F3Rou/nVBGzCDqsUJNcPDbC33Hzpufld6sf07zepXAjumRHJ/n+cAuNRorj/tePheEWBUFKufilJ49ZUEAoUTj7sXcBtOPDc/cDx8OtKsB16ePTAmw5vrE9afe/G2rMpU8Tg17J9a3v/bgeXZkd+5eM/744Kvvjyn+8ZhD3n2Fo4Lg+4N7n7k9kcr7j+B8r09ShfCYOn+smX5VcEOEtKcnYB2HTLb15rdDwIvXr8nZY03iS9WZ4xnLU/+ZZwhZFw//s3sxJN2OLMSfJgew4UmtalKBZO+7U1q0GPE7sUyAB6LPFPI1HDuUdnh91nW3ocHXlzc8839iiFpbt2Sk5+LFYKEg4liXOBy4e4HkF73rFc9AA9nC/T/1tDeFBbfSAEHxFf1eOnYvVT0P+g5PTmgdWb4rZGH9Zr2RiBRdhqbC2MNAhvXhv3v9Dy9emDpZd72zy03x2d015ruOqByIazd7DN698OCeb3nyfqAN4lt33DXn3H615rFOwFXKkuRzW5HuHC8/4eZxcsd5y7AM7hdn5E+N1z+6xrAbFW1PjBzEU59f8frkx0vVvf8bHPJ++UZ5z+Jj0GMNdzOHjKHK8vu9468vLrjstuxDw1frk44/mrD+vOxBszJvcUdxYv03e8XTj68ZWUy592B+6Hli3/vkos/fezGmDxhKeKNuv1+4rsfveWD1S1LM/IXy2d85q9Y/7fVwuXczYFXKsO4Vhw+DJy/uEcr8M8jX312yfKNZvHNOAd6Fa1qG77h4dOR9cWeH15+A8DPXlxw119y+rPIuBL1+LjUdO/FZuDwvMCHB16f3bF0A2/bNV/9wTlP/7l0IvhtIS4UOkK2EFZw/FHPq/MHvndyzdvjmrc/Trxvznn2L8psTQECe/0u8eYPLE++d82L1T2xGD79vT/lj19+h/TmQu4bR/GuH9a2gl7F3e+OfPjBO/7B+Zec2QP/cv0Bn/3qu7Kv11CucWVIjaJ5yOw+TPzoh1/w4eqGj7p3/HJxyf+SNdu7S5o/l2cCkO4Evy/cfWw4/Kjn919/wYvunhfdA/9w9Uv+M/PvYP77c/pTQ7OtKlVg/aueN//WAvv9Bz6+uGHlBr7z8Q3//MtPOIyX+LuCO2bGpaa9S+go9hJXv/sNL1f3/Hj9NU4lfrR6w385/pv4/7HD7wSiZyP2FyrJ88Xdp4E/+P4vuO0XHKPj4dMd6V+sYCtgW0cB136fuf+uIX+y5+Or9wzJ8u8/+St+cbykfRb48+9/zMlPZazMUObwuP8nr7/b0NaIt19JGZNru6UCtKeEgNfw0fc/5OriFMpASond7kAOhhCOjLrFGoPFwJjo9zsury746OVTVp0nDzusgTc3O95c3zAkCEluYGkElME7ePnsKU+enlDoccaz3e/55vqa+4eBwSgohhRGSeUeR842J3zyvVesloZSEpfnjt1h5Je//JqH22sWi434MtpE51qGXPjO82c8uVgLGE4ShvQ3P/2K/VFSo/uQIBsJ8gmRdr3ht3/nA1orKeBjynz+5TU3dw842yHPbYq1bknHwOlqwesPXnB+smDRGUJYcXp2wv3dA7/68itsI4fPnKOkqivD1XrJBy+esfQa5TPb/Zr7h5Evvr5mHAIxi2eu1Ybt9pZPPvwuH33wnNN1g9Wa3fGW3arly89vuL67xehOvIm1qvA68PrVd3j1/BydR6x2qBI4Wa/4/M011zf3gCcbg0YxhMiLl5e8evWSk5Unpx7z6hnv3t3wyy+/4BgLSkFMSVq0Y+K4P/Dpjz/h+eWGzhdRiR16nl+s+ZtfvsUaxagKJQVSgWXb8eT8hE8+eY1BkuL74wGeKHL8jJu7e8AyDAHfOkoKLJzl09/6Ho1RdE6jyBxOltzc3/HV19fsQiZiccaRxkyMkfVmxbPnlzy92jAORw67I1++ecuhF0V1KIq26URZLFlZtMbxvU9/G20K9/e3XF/fEEMhpoFUauGgjDStpQTL69cv+OjVU5yWrynVkkOGEvjyq2tss6YvPboowjhyfnnBx598xGbZ4htHvz+In3H+FQ8PPfv9yGLRchx6tG049gd+6+OPuTpfsrCOsQTOz1vOrxZ8/dUd33xzR+M9Qw9YRVaKq+eXvLg4Y+U0m5MFKSXO1wuub+7487+8xXpLSpHOixfx2nu+9/oVV6crFq2lDxGlOrqu4Uc/zPzJn/8EpQ1KOYwz9MOOj7/zQy7ONzhdcFpxslzQNgAf8LOffck+jIwUdmOPw6B14eNPvsvl6YKSxPsyDCMXZ6f8n+y9R4xlWZ6f9x1z3bPhIyN9lunq6mpb3T3T4zhNgG4kDSmAFEloIQraayctBGgxSy2100LSQqIACiBBaihQGgqkSGqGM93s7mpfLisrbfgX8fw1x2pxXkWTG2G4IShw7ioRCCBf3Hffve/8/r/zfZ8+PeXDj18yGAwoi0jjWrI84/VHd9ka9RGxw1IlZEKlGO8NePbqJUr2KJRgVTeUZYkqJet6Rb+scKFj72Cb6+sFKttsOYkR4dPCVElJluuEfBEeUUgKqemCQ0SJDR4hdeJWh4ANHusMBzsjXn/jEZkWRGfpD7doasvbn3uTZ89fUj9+hlKRaBxa5zTrmi9+5S3u3NkmeouWqfE76Pc4uHXI7/7uPyTLKoJMQ5kYYDzo89abr1GqNNByoSOvCg52dmkWNS9Pz7FB3aBGQrfi7Xfe5mh/D2wkyyVR5hA1D+7dpes6uqtVGsghsV3LaNzn4YM7KC1JO7kyglfs7hzy5PkLfvL+h3ifBjWQQut7d/Z5dP8OUnQYb5Eq5+joDqfH57x48ZKyHNFYh9YFrWupioyvf/NdcpGkWaVQjHoV+1t7XJxfMZ2tMEERTCAnoUC2xgOO7h7SmZpeVSBcxtZ2wXy+5MMPPkFnSaAoUDTrJXfvHfHGo9eo10sG/QF4kD3J1nBM6DzPjk9x1oLUZBksFnO+9sWvc3CwR+4dhUxBf5VrelmPD569ou0cQVrqNn0Wy6zk7u1Djg5vIaMhRyJ0xXi0zenJJYYVUni8t2RBcfbqmHe/9mVuHxwiYqASYKJn0B/R1IbLizO6WCOkZjZf0xv2yLXkjTcfEcOKnlSU1Zi2btjd6TG7vObycooXCggJr2MM9+884N7De+ANuVAUuWCs+wgl+fjxC5RUCCUpfUK7RNGyf7DL1qgC5ymKPnk1oFdWPP20z8WsxoUUJDf1Cpxld3eXw3t3MM0cUeTkKI4O9mgsXE1+gEATEcTMs1qtkMHx6NF9iJaiyBKGI4Ot8SGrRcPkckpwEUHGajVjOO6zszXi0b07CByZEkSRUWSag6NddrfGCemxrvEiMfe11ty5e8ito128dRQSilxTFvtcTSY07Rql0n0awEbLnVtHDIoCJTwxePplj65x3D24xflkyex6SowKhE47wruGe/fu8eDOHVw7ZTAYoApJrx+Yzdc0zTnOOkx0FEWGMS1Hdx5xuDsGZ8h0Bt5RlJosV2xtDbmYzNJ3rkhaNHsQ4k9C239XDl/ItBLa8BQ/s6hD+pkdKE7+yjbujbRjJFiJvMyRTqObmIRelYQKcpJBffpWydWvWLb2VlivqHLL5MMdtt9PFvGsSYs9tTFkmYFi8lWBfnNJCIJ+1XF9MaL/OKd/nEzPIP4Vbur8ocb85oKHu9fsBcmz13awk4q9fyEZvjI0e9mNIK0bSYoFXP6y5+Eb58zqCikDrVd0/2ybcpIWuMWmrRkyudkeK7j8bct4OGddl0zrjPF7BVufWmwvtakA1rcLlI0s7pdcv+s5vD/hnfE107bH4zsHqJOCw++LzcI7BV++lJiBYvamRH9jyhvbU7TwfPzFA+rTATs/TG0h3cVkIK8U1cslL39rF/+tBV8/OmEnr/nZ9REnt8ZAj61Pwk2rDBJ2QHWBy29rjr5ySqY847xBy8D3bz9g+F7J9uON1XsTbOVzx8W7JauvtDw4usJ4Rakdnz45ZPd7mnKWQsjPGpdyIyB68h+NGL0z4QujBaWyPJ3t8uKdAXv/ROMKQb6KN9iG5rDg+i3F1m+esa8d9wdTXqy2Abjwdxk99UAKYP0wBXnNXob8i1c86q94bXCFlp6P7x/w4ctb9H9W0j8XSBduWrOqC6yOMqZvZ1Sfn1HXBWFasP1jSXWdFs+69rid/k2gG/IklDn7bUlUkfKkYPwkuzGCS/iXQqbE0z3/huL2Lx+zXdRomQogxis+5E0OfmjT+9F40KBqw/z1kvY3lnzh8JLDcsmL1TaFdnzsHtE7FVSXlnYvo7yyiVN5VXPx2zmj16d8aWvGVdODB3B6f4vu/ZKdDyNmlJrXAMJFpm9lLL5o2Npb8c7+GbXLOLk/5uLpLoPf5cZsbnZypI2sb2m6X1vyW48e807/mMfNIZnwvBxu890//zqv/a//koAvl+RTw/G3Na89uGC3XLNXrPmN3cd8b/CQ7/GI/d/P6F069DJQntX4Xk7Ugss/ZfncwzNclOxXK6Ztj+KB44M/esSD33PUhzlirCivU0NZ/PqUb996hRaBq67HOG/ZKhtmtyrUH+5uUBea8qzGbpX4QlKeN7SHFboJTD+XM37qNsgBNtvRN/e6XOJ6ZRJJZem7qRmm+1NUAlW7G9yJ7By6Vqjacf1Ojzd+5Tk7RY2Lkre2Lvh0uUvxZy55/L0HVFcJeUFMQ5Zy6nj2lxRf/OJTXJCM8pZKWeY7JbwO6//iFr6fIawnlomPuborOfrTr7g/mLKXr5jZHrfLGfXDnP99/i0Ov5fkaqFI17pwkRe/JfnNtx4DsJevsDE9w3//m5rVeo98KW+2rmdNYHWk+MI3n9LThnvVlEx46pDDXfjd7Xd57e9shgybc9ftVVx8XfKb3/wZlbKcNUNy5fnWa/+Cv/npL6G/M8BsaYqrdK/KZ5ZuJ6f6a2fcq1b0tOX1/iW/sf2Yyb0hf/fjbzN+7siWqWFtNuze1V3Bn3/rA17VW3x+eE4mPcW+43/45reprhSu3LRGhaS4tpx/o+Qv/Op7fDQ/4EvbJwD0pKE+fMY/cN/k6Dvips1oe5L+q5YXf83zjdefc1CseFBNGKuGt/sn/Gh0n/d/74s37O9qlXYvdGPJ9Kuer739jFx5bpULMuGx+y/4pz//JfKVImzUJ7YS7L53zeO/scOvvf0JmfS82bvgpNviqw9e8T9+7VfZ+VCTm7SdfnRpaPdzTF/y+V9L18jnRhe8WZ3zuDlk++hD/tbxbzJ+/AssT8IlBC6/IfnLX/whAHeKKdOqT7Hr+HviK2T/aLwRBEK7pW7Ca/dGw7cOnwHwsJxwL7vmy4OX/Ld3/kN2PgqoNjFuq+t0P5m9ofjam8+ZNAMy4bmbX/Mrdx7zX317RHiylTa7bljRvYs0qPrNb34AwN1iyo5aMx40/Nb2j/nPr/5j7JM8Bb0S+meW1e2c9QPP3/jqd8iE5zCb45G8nl/wl+//kP/ljT8PgHTy5u/3pWL15Za//vpPWLiKB9WE1/JL3nzjnP/m2W+Tz2QaCG82jGVN4PKrml/fPqevOwKCr/Se88zs81fffo+/9+GvM3qxEY0VG5YycPl1+K13f8q16fHl7WMGquPZeJf/5/gdilmSn+Wr1BzOVoH1O5a/8MZHAOzny3QOyin7+ZIf3bmH+KjYfL8QvxhE/2t81f63OrQ1Pom+pLWUusC5gGsdQVsKlXNrb5fX7h1QZpoq32LVrhmOevgs49NnpwitsTGiokfqjEJo9vZ2eHjvNs4sUZQIHNs7e2Qy8uzFJZ3IEFmR5GNC8PBojwcP71CUkuDWFEJxsD0gk4HJ2UcoXdF6jVKS6MHamtv3Dzg63MV1K5QqGFSaVd0wnVynxSORflZgSHzUnZ0t3vr8m3TLOTujESEkS/qD1wTf++GPEAyQoofKAzFIvOt4++2H7O6WhNiR5Tus53O8tyAM61VHlCk0QfSQuWZ77zYP7j0AMafQGkKfQT9w+/YtTs4vaEyL8wqhJdYLhoMBrz16g9FAoqLHeMOtwwO2tgy2a3n24hgvkgTKOQM2sru7y62jHYxZIFRkqAdsjQc0bs312rFu1wi94doaqIqM+/fv0FcWqQR5qHCx49ahpnOC87MJWVUQgkDojCgDt+7f5XNvvcn07AVl1UfFyKC8Q68a8M+/811i1icrephoMV3Lw3v3uX90h51RQWjXuOAZ7fW5wOHNmkVtIesjRWpl2WDZ3t+j6pfEGHA+oyj7aCL7O0Nmk2mSz8nUrO4awxuvvc3WaJsiE3TtEoVAZ4KDgwPOLmdpQmo7SpETMWRlydGdA+7c2afMEtOn7GtOJhfM6xVl0UtMSSEJIaARdF3L6GCL7VGFwHN08JCPROSTT54BJTZEsrwkyySL5Zrbt+9y78EDtrZ7OLuijDlu1SGU5N7BDsvlmqvaElWBkop+VnFr94jD/SOcX7NYLxn0kp7o6Ggf589QU483niIrsTGgRM7hrdvkKpBlgcpqkIK94Ri7gldPj9F5oMpK5raFKClKwcM7h4S2JopIrkHrHrnWPHtRsGwtSuY0PnFFd3Z2GA6HFKXGupYYJZkuCVpS9hv6w22mK4NSESUVRM3hzh7DfgmupdQly2bNzu5tjL/gYjClmSa5XiTDeY8QnmF/h6KQRJfQJbIQaJ0x6OX0+ppVt07Bt1bUTYfKemR5gW87+oXC+kAv79GYhq3ekMWyI0hBoUBi8F6SSUGmNHmhObp1gAyR4/MJSkaCEEitkiDMRQwGZKTzjl7VZ293j+VyTd021E1DcD6J7EQkSkVVDsmynNFoRDAdIiuIwVL2Ksqi4urqEh0klcyYycTIa/0KEHRrx6DIE7ZFamxsklhLCcgiSiS2r4hQ5gWmtYy3eljXUaocWTtQPbbHY16dXZBlWQqcI/T7A6qqT/J0JWYsRLyUZFmGQBGDSJLCosS3ntFwm9TKF+goQSu898QgKbKCfllRtwacASkwwRLcFiAJ5JSIxP1cNjhjKQd9Wu8RMSAtFFIwLHtMTi959PAh1qyRStK1DWW/n5jKUiBkRpYLOtfRAXVMrLvgsyQikw5jPFVVoXNFZ1u8hOgURb/HsmlYti0DWREjkCm0kKxXLVXVp4glQgSaukVlkVwXrJYtu7uCiCJoTWc6ZIBlYyhlxqrpUL1AnucopZjMpjx843P4mHYjBDRVXrKYr6h0RSlbrHWE4IjBMNoeIbVIgWKmiCKiZYH3kOclo/6Iq1WLkFD0CjIhsXVLHjWeASKL1F1NvyiZzpb0+0POrxZ0bYOggOgp84LpqqVtAplSeJVEi52BXPUZVCXrtsFYhQoZWaFxUrCuW5q2R0agCII2rnA2IoMmekmvzFmta4Y6nf+4arHXdWKIu4jHUluou1XC8WSCtWmxpiPLKzIRWK5XlGWJa1qqGJAuJxqLNw50hVQ5xnmG1Tayc3STmuVWTVEB5OANk2WDdRCiYrlaEZTA+4ZMFYyKEfPaMJ/V7AwLCgGubXBOkiHpWksoMkyzpigK2qWhXjW4VuCFYjgcslg1FEXBrJ7ghU/c5WjIpYYQ0THDtEuuL84Z9Xt4OsxG0mdNixbQ+SSl884l6aRXLFctBWl73KiX47zDmwgoZN4jbMR8Ukp8TFKzPzn+3Tiylb/hxK3HObqLqI0Ey4wUV19QHP3KMXf6c97sX/Dj+R1O9sdc231u/35aeOoNpzBoQRhpFq/DX/naD5jZHn3dpQXukeLvV19n/zsphI0qST2iEpx/C77y9U+4XS3oguZuOYX78DerX2LrkyJtn679TRiia8/8i5H/5M0fcmUGjHTDl7eP+cneHY5f3qd/Lgka6n2dmJw2Mnst4z/91X+t2QxzAAAgAElEQVTKq3abR4cTuqh50ezw+98s2Pmfs5s2YSxSm1KagPlzNX/u3hMan7GXr/j5/IgPzB0go5ok9vdnAWnIBKv78Ge+9nMgLdYYwJe3j1m/XfDeT7+ahCztZ+fMszrSjH71gnf3X9FXHaftmL/8+o94cmuf7zSf59Z34g3TVDXpM7l+5Pnrr/+E026MJPLlnRN+/eAJf2v+LUZPJb0rc9MS1I2n3c24/+4xb4/PqZRJi3vXJ3/T84ert9j7WdhgAcRNk3T5ect//c3/g3949Q4PetcMVMeXtk/4ve232frvEqbA9TV6adGLltNv73L368f82cMP6aLmygz41UdP+Nv6Xbw9YPAq8S8+k/foOlDfj3xpOCNEwdyUHPUWbGUNn75+yOClwheguhQuZgvHqz9d8FuHz3lQTXje7DHWDaO85Zdef8ZPP/j8DbLC7JTp9fUk03fg4Mvn3BnMmXcVk2GP+NNdBs/WN78HqQXrKkW+2To+vnuNlIE7by94LF/j7j9OAwtVmxRA9jXVyYrLb4wZffWKP3XwCVdmwBu9c6aujyTy4dcOWZ0P2fqkS430QuMOe0y/AP/+6x8wtxU/urzDWzsXjLKWT76wpO1G6b3uImasU3s01+x87pqdqma3WPPaYALAwe2f8983f4rD76UBQLutGbxIr9P20+ev8TkBwVE5Z5y37FQ1V995wODE4AtJfm0IpWL5UPDa/hV3iykXdoQLioNiSZPn5KOO+qDH6Mk6Cbw2Id7grSnv7rwE4M3qnD+av85/sPdjLpsBl7fuUM5S0zZ6mbjCUrBz0HG7P8cGRaUsVd8yzhp+un+Xdi+jd25S418mjEGhPXv5irUv+NL4hEsz5K3hOU/EHi/u32Jw4jZSuYKQpWGFG+TYnsT2JbMvBoLOOPj+mlAkKVxIYP/E6l6mVCdfNHT7PWZvKHrnkupaUF4GhPUIH25kd2Ynx1WCX955Rhc0mfTYoHh0MOGt8pT/8sUtfKFpdhSj511isE5bYMjFesDbO+dUypJLx1XXp3O/iGFCkcR2UQnsEGZNya/vX3NphjyqJvRUx7Ze090zxPdSG1ltAuf2QMPYbO5TawYb3szKF4zKjonghgfqM4GyUB/94hmwrWsAFq4iIBB9R32QJ/TGZ8+JpUX49Hobn3G/n4LeCzOi6XL6W5p85n7BDx1nrI4U10/3+fLXT5iYPoVwPGv3eLM6v0HNJCGeplgEsrmhvNY0PqNzmkI6MuE56bYQY4Mrc/JlkpAR01Cqdx758dVtvrB9jgspAdsu1rxY7OC2PN1QkctAcW2TeGqYIS81q/sFe8Wa2hc8b/aolOGnV0e4saR3Zjb814Qg2PmoY/rLgrUt0LLBBM3D3oQ/nL6OGadQNGvSzgoRoH4wIuhI7TJuVw02KnbzFStfUPUMyzsl2487okxDB1sJsiZwWC5ZuoJMeH6+vsNXBy/4vct3MLsB+YEgn7sbTEC3rem/kjyvd9gr1sxdj7mrmNuKnarm+nCX3qUnWzl8IbCVRLeBcFXw6WqP3WINJXzUHjF1vRtWfbOrbsLUmPoYTLse7+69JBOeZSj5cXuf6XSA3FHky0B16W6wLrYn+dnkiKPhgrm5z+uDCT1lsFERzabFrkC38eb/UEvJD2b3udub3Vxv31s84qrrIRz0zzzSp2et7UmWd3P0CfzR7Uf82cMPeS2/5MoPmNghsQzkyw2nd+4wI0UxdeRTzdyWLF3Bw60J/3TxNg/LCX9w8TrSbvBLbRoeALhSoGvBHxw/4gv756x9wWk75qico1qBL6BYbvAIm/A1OsmPr25z2Ftig+I3xh9x7Qccd9s3f/dnu2E+Y+1L+//9/exfPv71ern/hg+HwBIQmWZWT3HC4oQlhAykZrg1YndnH5VndARUUdLv99nbHjCsAN8mQ7sX2KZBBiizHGcsRV7ehAuDwYCiKKibZWol2RaEo21XFJVGyohSMZnsVaTslQzHAxrXEAgE322ajIK8rMjzEplJil5GnmukzuiPtyj6PXqDIV7CwnWIrMS0Hf0ix9cWEQRt3aIi9PMeEsHWcMBoPCCEFmcs1nXEGNnaGiOEIJM5Smi0zjg6Sndg45LyLyIxXY23HVuDPjrXKFmgZInpPN57lst5kmYpRaEzcDmF6pFnCllkOCJ5VdKrxhvRWmJsSimJMRKiu5HBhRDouvRvpTS5znE2kmUFCpXs7D6ihcS2HVJKhlWPTGaUKkflqekcoqZpOpRSGzGYxHtLXa/IEVxfXJLL1IjUQiJFTNzdmIK+EBxKZhSqBB/Jy4rOOAKCsten7SzInGwjP9Na41wgywq88Qz6fQQKAvRLnYzpcUOLFgJjPSpLTcgYI+umRmvNfD6nKCoCkaLqsViuKfM8bRfWG5GVyrA+sl6vMcawXq/x3if+cpAU5YB1bdBZiXQORZLmoaAoshTWZJK2rREisYSzLEMphRCCrkmCqevra2IQLBdrrPW0tkUoCCokgZdPreNgLJIk1eralq5rE7+5P8AaT/CgVMZ6vcaFQG26JFQjUhQF1iZxWIyCsBH9dTZJ8YRSCAWOJC3yMdAvhqzqFhssSgmsN/hg6bqO4CVaFdiwEUJYi7Mt1his8cSQkauc6D22a3CdoV0t6eUZ2ieL5M5gRLNuEVGiZZG4oUoxX0wxncNt0CpCRDIl0FKgENimpV7U4DsESSTU1muIHtM1qOgoS00MgjyvEEqyXK7RWYW3jkyAqRvmyxVN1zIcDzbCtZKusxhjErw8Bqq8z2rZYoSiLHoInURMSklC8ARCEm15gY4Zwm0Yqc7iXUsMDiUDVZbR0znCeLrlkmE5YL5YsajXeO+AQGtbrmbThMJQimbDq5RSUeUDptdLhoMd8DqFNs6iVcZisaLXS58DoSRSK0xw1LajsYZ11xGiwLpAQNN0hnXTEYhpWBSS1LBtDfPZgnXbYbxD6IgxBqKlWa8Zj0YIH/Am8YYbPLUzrOuEj3Ai4oMFGVku5yyXiyRq2ggllUqhb2cdi+Ua19nEsDZpwBCjwHq3Qe2AzyQy0zRNgxCK2ew6XbPBEamYzwOrVaDIRnjnCHiMYhPWt8zXSbTWti3WWnzX0TQdkYygFF5IZJERvMfbxE1vXIsIHmkcpjWIKFkv15BLTLDoLP0dWkimk2sW0wXrztDZNLRsW7NhrntUrkA4rO0Sl1oXXE2mSFHStAldsljNabqaZT1Hik1jW+dYIfBKcXo9pVkvMcZgY8A4i3Ed8/n8BteR5QpjDJ1JX9gXqyXz2TXtuiZax3y5YrmuuZpNsc7hYyBoScx1eg9tx9V0QmcaXHTkWhKDY3Y9oesaPB6pwBXQKcdiNWe1WhGIOB+xQNu2rFYrlsslZV5hO0ehCtwGL7TuDNY7lnUaxpmomK5r2s6zqBd0piGXAhmSPK6rO64nM2xnkWTgZOISX82wXhFcpGs6NiwfgoR5W7NqWqwXdCZgXGBdG2aLFV27ItNgTU1VlhAixrQ013P6uqKZNTStJwbFdLpk3RmE0ptnThLI9ftDlquaVWewLtDWKyKwWC6p24bFbE0mCiRZ4nEHRxMd14sF07ohqBx0icoqzi9n+ACd82RZTlCSICRCZ1xcXNB1HS54goDGGUz0tM5Sr1uCi0TnU9MupHuj0oI/Of7dOFTj0XXaHj981qDakERXG1Zpd+j5945+xihredVu09OWN7YmiNfWrG9teKGbhbreBKtuFJjZHnfLKQfZkpnt8a3BExhYqiuX+Jtm0+ycWcLAkyvPSDfcLmd0QfNWecq9/Snllb0xVkeZcAV2qJB9RyEcD6oJd4opPWV4e+sMO4g0uwmPMHxliAKyVaDbTSFGJj1Pmz0GquUrw5colRihq9t6Y7j36HXauvvVW8dIInv5ioHqGOUtX3v7WXrdq89ka8nmLU3E3DFsZTV93THWNedmxNoX/Pjq9g0v1g7TOTNjje0LcuW5Nj3uFFO+MDjlpN0ikynUDhsxm/ARVRvcVgVO8KLZ4X51nc5XMWPmelD6hIsY6CQ/qRRqbQlK8O7OS/bzJW9W5wxly6UZYoJCz+VN6/IzDmp+sUb1Lf/g8kscFCvuFFP2siU9mXYIiI08TtpAKFR6TcB+teJVt03tc97onfNJfQgk0UtUIgm4Wr9hE3uK3Qa9SW6+ND5hlLVo+YthUbbaIALatEW7uJLs5it+/+pNHlQTap9zr5ry/uUhdhQJCuxWka5bAboOFNeCeV3xZLpL4zJWdYkyYMcFxdkq2dw3sqrPQiTbh61eQ5k5Tpcjotyc+74m5Enslc0T03brSUeI8L3rB0xMn0/qQySRLmi6NkumcyVSi1Kl0EGvBCfNGBsUX9w75brr0fiMInOUVwkxUky6TRMrYMcF6zbHRYkNChM0Jmheddsb6lxqbekuEMq0zb/bDfxkeoeJ6dNXHRMzYO1yLteDjfxPJj6y3DS+Oph3JRd2iA2K28UMGxUT08d2mmLu6fbLJKcqJPM3esznPTLh2dY1j5tD+srwT2ZvM6srVJdYjVEJfD/DVxmh0KybgifzvfT+Sk9fGV6st3/xfueSbjtLIfcwQ8nAe9f32NI1c1exny85bce8f3aLchZY30qDAzPWZHOTtvALUDbSbQuKS0W2jpjtPGFONiK8zzAPn5nfQ5mGNnITukmTnoWh0Jjdiu6gl0LehaM5jPzh5DW+e/WQuUvX/tNmj799+Q1iq4iK1FIUaQhkdit6LxVf2j0FUuAJMMpanl7s0u2XN2iSqAV62ZFPYTbv89P5bRqfcWGH1L7g4/oW6ipL2JQNCzbohDdRJwVPpntcdKkReW5GAJzPh9S3E5dW2oQeKa4txVTwbLqN8Yqp67HyqQX446vbyIv8BrsBm90YIZKtBD+5PGJuSnrSsPZFCnqDQNfhX5EgRiUopwFE5LvnDzBe8arbxkbF/339eXoXgW6oEsfWhiRplImP+6PzO4yLhmfNLhd2yNrnxHm+kYqFm3BbbCRpAJ8ud9HSUynDcbdNJj3ZtdpcW2lXgN9sSe+/lDy52OOD2SGv2m1GuuF5vZMku028GUzoOjB63uF6ivJpQaEdLxbb9FXHH05f58Vim+oiMWrTro7U6lRdYOdngpeLbS66AZdmyEm7xWk3pj4dJBFhkQSbeu0pFuk8/+jyDj87P+J5vYMN6Tx9PDmg/zTJu6T5xQChvLKUV5H3XtzjVb3F1KVniA2KT073Nzs6NhiNJlAsPf1XLcWlonOauSm5MCOeNnv8ZHqH6iLSjZIMzYw1uk1DyWoSWLQFP76+w8z1OO62+Ufnb8MyY3DiyJcB10/3UTNIfPnJyy1mbUWu0v380gz5O8fvItokAPvs2Qmp/d47FXx6tcuk6/Oq2+ak2+Kj2QE/Pz0i3wSjug40OxuEyDqQzwVvb53zuD7gZ81dbFT844u30FeJVx1lum51E6gPMvpngcdX+0zbHp/Uh9io+P3pm5zPhvRPE+ogqhSeF4tAee0YPoPVyxHGq5vP7D/49B2USagKW6VwWLqE/uk/zpmuesxNReMzPmxuc9xt87zeQVynhvFnfH1Ize+bXU1/jEP9zu/8zh/7l/9NH3/r7/7931FC4aKlN+gRRERoSXAC2zl2tob0Sk1ZlXSdIUNgjWW1WtGsVnRtapEEHyjKiq7uONzbpSqSSVshsbZlNlvQtB2XV1epCSYkrTUIJTnc22Mw6OFsR65ARM/V9BprA7NVm8IQqQgoYgQtNKPhFmWliaTFrDVpO+vTp8esW0vnIQqFCBJNpMxyDvdvIURAZpJgAqt1zWpdc3Z2ge0CeZEnhq7KEFKwvz1CiogWAreZOM0WS14eHxPIMU6QF73Eao2KwXCIt4YsKwnepN+fz1mual4dnyPUpnUoK4QU6EJycOs2+A4ZLcYFEJ627Xjx8gznoXNgfSDPMiSC3d1diiJDBY9E4Zyjqx2nZ1NmsxUBReuSOVdojdSS/e0tVHBEoHOGuutYNZZnz1/ihcKGhPdEa3CR/fEWWiiUABUlXduxWNWcXlwwW7ZEIfEhIDdG8mg9g1EfZAqfV+sVjQ1MZ0tevDwjBkEMiiwvMU1HmVUUmWbQ61HqHFuvcTayrlvOL6a8Or8iH27RmJpMK3yAqjcgzyR5nlqEEYG1nnUTOD27oDOeGFNYKJVGak2QSUjXy/sslmsm5xMWq46O1OY01tEvk9HdBk9WZNR1zbA/xDmYLzuOjy8xViFVhhQKJSWlyhFR0B9Um6Zjj0IXGOfwzjNvao7Pp6wbg3UeIVI4U/V7IANFL8dHTyZFCqWC4Px8wmK1prEC0dO0pkVJgbWGYb9Pnim0FJtAG+aLhpPLM3xQ1DZSN5a8LCmkpq07dFGgpSAicNbSGIuxlo8/OUPkFVIpQuwQPtLrVeR5QVbktNbQmY62c1gveP7yjM4m06d3KYQWEpzzFGWBjxGpNDYElnXN6fkV0/kc5wMhRqRWqOAI1rK3u70JvEmMOeeoG8Px2QXrpk0/lAKdlayWK/q9HkVZYV0atpjOYRvHcm24nM2Z1WuqQY+mNRRZgc5zIL2W1WqFBabzBXmVpHORgM5l4pTKiJAbw6vamDybDus9xvvU2owSiHjr0VowGA6YTmfs7WwTvUdnmsYYnJfMpgtevDxnZQNUWWIrC4UIgWZdU+QZajOQ6JxntW6ZThcsFw3RgQ+RGCN5kdPUDUpK8rKCKHBR0nSB2armxckJi6ZFyowsy9JgJmjWbcfO/h7rpkbIwGrVYl3g5Pyal8cXCJ3jQwAlaIwhxsSV1jrHxUCIsFzVTKcrzs4nmM4TvEDpDG8DVZHTdgaUplck5EZjYdmsefHqlNW6wxmP1BobDd45XLC0Tcug36Moh0hdsWpmPD9+wWy9oG5WaFkkhrFwRB82n5cMpdPgxfrIfLni+ctz1o3Fi8QRjTEilUKKhHXxG652hmaxrDm9vOLsckrtHK33kMVkKFYKYy0ISX88pmstdd1yPV3y9OmLhP7JFDFEpBRkWU7XpQFd2esjtaQzjrZzXE0XXE1n1K0hKkWQito4VFGxqhsqlZEVPRrjiF4xna54+fwMbwJBRJQIECU+gCXSOkt/OEQphbGwagwvT85ZLBuCkMgsJ0afcEbOIYVgVa8oy4q2bWk7w+X1nOPjc3yQRCmTyXcjZdG6wHY2fUZcpO4C09ma04sZnROsrSHLc4wxyQbtBT4K2s5RVj26LjBbGi6vFpycTMh0Tt016Z5kGjQFSmUs12uW64Y8r1ivW1a15enLUybTOU6ke0LwliACNjha7+iMS6I8J2hM4PzymrPzCW1n8IAqCnwUdMEiMs21aVkHj8syOiuYLTouLmdczubUPiBUTvABoQRCapTWXC9mKKnQImPZWCbTFZ8+PaU1gS6m54aVERc9QilkVFxeXqOyksXaMl+0nJ1PuVouCVLggiciyXSO8yIxwmVGjAoXBevWsGwMy9pxfjWntiFtAY0RKzxuE97/1b/0F/9YRts/Of7/ffxPf/MPfifkaXHT7aXFelTJbq7rwOJhhtmNPOpf8arZYjevuTY9Tq62UfOMfMmG2Ql2pMjnntnbEj2yCAkD3XHejfgnk7eYzfuMPpH4LNnHi2sLQnD9jmJnd8W16XFYLlEi8r+dfpV5W2JPhugu7YCQjhsL9/JIEcYJPzX3PS66EY8XB/jvjOhfeIq5S+ZuIfCVpBtL9j93jRKRTATWoeC92X3OJmPG76emlB1ofCUT21cJHn+uR9SCYWaY2j4hSn58cofhe6mEkC09rq/S70tY72d87LfpV4YmphDku2f3ubgYs/eDZE/XG2QDpEWl+1yHR6IUnJkRAcGL1Tb2vTGqhWLpk/hqmBOVYP66oh1CngVK5ZjaPi/rbabv7zF6mVrF+cywaVmAkjx+q0+ee9qY87zb46P5AZ9O9hj9UcI6ZJvQGdL5nXy+oiuhyDyDzPCs3eP7k/ss399h+CqhJFQXbpqzIgg+Phiylhl7Zc2P5vd4vtzh+OUeB99LrW0RIq6fka2SpXwxLFF7hvv9GWftmKmp+Hh+gPl4xOF317iBJr82+J5GmUC7k/HT4Q6jqgUpsVFz3o14Nd2m9/OCYpmabrpJcs+oJcJLVqqAbcfkbEz2aUX/JHGJkZJs2dEelhRTi15bfD+juobnh31WXUFzMmDvRyQRlxLpuZVLzHZGVJJmP2Oqe+i9jlu9JWftiLmr+P7FffxPxlRXkayJICV65TA7GVEIPh0MmLqScdly3fZZuYLzj/bpnwrK6xRe5dOOmCv0ynJ1NKTtQ6+wuCg5b4b85Pw2+c96SCMoryzFZZNapqOcbKl4WQ3ocklHxqzrcbwcs6xLDv4vT1SJzyttAClo9zSTfoasIs/XO7ysd3i63mXeVdjvb5OvElVAtek9R0K0OZ+OB0xDn1wFrk2P9y9vsX5/m+HzdJ1/JlILebqfXL5ZsFhXqJ7HR8lZM+LFfJvsJwN6lwEQG8mQojpdc3y4gxkGLswIIeG0GTNpB0wuxow+kYyetZjtnOLaYkc5vtKICLoJ9M8dqpOMP20T63hhN0zjJAOUPt6gEqJOYrtinpie2ToNMT+LU1Tr8ZWmPswYvQicvKFxQVHmjk+Xe8xNxQcntxj+oKR3kYIqvXKEQt0EkT8c7dPkioDkpB7z5GoP86pP7zzxtdNwAnw/o5xG2rzgop9j0MxMjyfrfX5yepvBDwoGrwxsGqq6Ttd7eQWXDxUn11ssVclHVwdcmiGLn+9y8ANA/oKvXFw2SDRXo4pVqbgyA+qQ87OrW1y83GHrfUleJ375Z41As5ORzyNT2afbisxcj2eLHT6cHKJ+MKJ3HjavRaBXFmUiuo3oVc5kX9MbGHLl+fn0Fp98/wHDV5HeZUIpfCbdE4Ek8NJDXuU9Jl2fiRnw4ektRv8ip3eRhonCBWTY7IzQggs95MqV1Cpnu2x4b3KXn37wgO33Jb0rTz4ziHR1ETNBvo60lHR7nrP1iE/m+3z68hC+O6Z/7m/C+8+Y2qnhqnk67oOG43qL89WQ6bNtRk+huvI3wrnyvCHmit6Z4WxnzLyneL7cpo0ZPz2+zei9AmVS0Sjk6VUpG8lWnuV6xHoXYg6X7YCnk1344Yj+WUT6X1ynIqRBXswTqujFqMfz9Taf1vt88OQOw58USCOSOD6mUJGYWPW6EbySI866AR/ND3j86hbLD7bJl9CbeFwpydbp/GqTZGbr5YCrsea4HvOzs9tcP9lh56cSV0mqq/QM10tL2AzBynPF8nLI8aDk4+kBT672ab+zy/iTxAiOSqBcOl/5OoWk7brHM7fFy27Ei9U204926H1QUMzjRpAm0GZz70Yw/tTw/vweH5sdPmr2+e75I64f7zJ+LOhN/Ob7QrwJcEMmCC8qjvMBban44OqQT5/eYvzPS8p5SFiUTKDrmK5jJQhasv1x5HG+y+OLAz45PST/oGL0ND1nszZ9RtymxZwvYaVLJrFkYvp8PD/gw8khLy93GH6UUV19dt2mIYduA6EQ/Gd/7df+WN+1/60Obf/u7/6fv0MMiWMbIs56pEgSLlxgf2uX7e0epmmptCIYQ9e1XM2XLGtH00VsABdcQiVYx87OVprE5BkxBiIeaz2vjk9wXmA9GOPTYkxl9IociWQ0GCCDR4SAd4HpbMXp6RUIjUOhlE7VnBDpD/pkCiI+CZ+ajuvJlKvLGa0HfOI3KhHRMrXUtkdjogbrLW1jaEzL8ekp61WLsSCUwm+M7KYzjPsjtkYFUkDbGurVmsn0mqvZjLb1RJkWqXVTk+UVxnq2R1soFRGyw3Qt63XDYtkyWy+xAZCaQEitOteho2LU00iXtloZ23I9W/DJJ08BiUeBTKG4EkmQs7c3xrQ1wQVM29K1lifPjlmbLhnDdUYEhM5AQaZg2OthfGpbFr2SV6enG56tJApFJMM6T6Y0IiqqskRriTGOEGBR1zw7PiMGCEjyosTYFiEieI8nsDUe0XUtzjrWdct6XdM2NslapKbzjioviTaJkAa9EiUFmZKsWsO6c1ycX0Jesm4NUpAYizpHAHs7Y7RWmK4lhEhjDK9enfPs5XOMT009AhgfkCqjaSxd21Hqgulsxnq+ZLKeE6TEB4fSgs53qCzDWU+mS3r9Pt2qo15FLi6vma1WKXQWAqkkAoGOgrqp08JAKDIlIEZiELRtx7Jec3Z5TWc9QipsiORVwdrUBBy9QQ+VK0LXEWNgtaq5vLzi4uIKVAFaYn3a+i6EgBDJlSQGixSCpnNcT+dczWY4r+i6QFnk2M7QL0pC6EClbe7BB7x1zFZrLidXLNYBkeV4b5AZZHlO3RikUox3B4RoN8GUYLFsePzkGXnZp/MRS8QLcHhW64b+sAdEmlWLjYG263h1csb1bI4QaXEVA4hoKcqCum7Z2d1BakFrLBHNp8+Pmc4WuCAROsd2LvEo8ZjOkGWa8XgIZDgbWK4aXp2ecDWbJ8yI8ykcjZIQASkIEXQhqZuaIssx1iGkSNeTT3JFoRWtaxE6NXN9jPgYiEIRhCSKpOoUQuCExxKYreYorZDe088LWutYr2simsefPMa00HhP42vKXBNjIFcCZxqMMeRlhlCa1liuJhMuLydpK/dnchogUxrXtVjTokQkAmXZw3rP8xcvqJuO1luEUETnkUIjSYvGrEhDIaLHdZH52nA+mbOsW6LOsN6SSZXY5VEgtSD4mIK25YroBa+Oz1guG4KXFHme3kfEZtgkWK1WFHlB23Y0xvDy9JR10+Fbj8ozlBZkMn1WiBvkyGiby8sF67rh9OSSura0raBuPIXSFFKhRETJjExntJ2h8YEQBNPlghenZyyXK6yxSFmQ5xkiBlyMWG+RQmFN2qGwri2zxYLzyRXr1iLRoCQmBJQWBJHEdKYzWGMZDAcsmpqLswmdsTgXcVGQ/b/svUmvLVmapvV8qzOz3Zxz7ncjMXIAACAASURBVPXr7hEekRHZVFWKEQgkJgUSAwYIBohSIUiJRqrfAPNkwADBP6gBJAOkFAkCxBSVEIkKRAGpUoWyiSY9PcKb2512723N6hh8y+xchwE5QCJVEWcS4e7n7G3NWstsPd/7va8NCniXSN/tuFxGTqd7xAjLkvn6q294+/aWUsGaQAZSLVC1UOLFcbmceDxfkCw8PJ55/fU7linjbUeRTC4LKVec68AIsWTevX+PMY5lyrx//44vv36DWFUgWzF01uKidiUI8PR4ptTCPE48PJ54eDzzNC6kWPVZYyyS4HIa2e+PPD6diXPmdJp4Oo08PV54fBq5vX2iOw48jWeMd+RcSEkDSqmG169fM40zt7cP3N8+slwidQGC1zW6s1QcpcJSMjFlHh7vmZfE11+94eHpBE6INWG91bK/czpOcmWaZqZZAxxPl5nb23tiKixJrUkSUKUSyVQvdE47aB4fHjlPkfunM+O4sOTMkqsC7pIx3kLzLM9l4fw0cv944e7+xO3tCQgkE8FbzkmfBU7A5kpJFWc993eP3N0/8e7dPZfzghsC0zQDFS+BGJN6qZdKTBXbAsp81/P23QPfvHnH/fnMJUaqs1hRxXSxFYf5FbT9Jfn5u//t//K7VKgtZMpNpYVyGaTC/d8wfPq9O76abvitwzuW6vjycsPD5zfsvlFVmbsU7KQp7O6SefwNz2Nn+d71A7E6Mob30574RzeYWQNkwmNWxVQwjC8D3/ie3/74jSqkTOYhDfz5l5/w4kfPx1VbMJgUuHxsue09Ix5nKvfLjj/7+afsf6attrJ69HZCDobuvvLnv94z1sC7+cCX5xu+erjG/cMDw7uCPyvAWqGqPyXe/tqO73/2jmAzX5xe8MXjDePPrrn5WSHcqxq3BEP/5kLae9zFcPpBpbYC8Ovxiq9vr7Hf9Bx/3mBQ20QXbwinwtvhwO7TC9YWUrW8HY/85Oef8Nnf07XULhWpsv3N9NJz/M1H3k17LqXjzXjk7fnA8D/t2L2O2EWLMFvID/B2d8B9MvN6uuKr8zXf2T/xi3/4GTc/1eu0QgB3WjTB3gbury10la+mG6bi+dnrV7z4Xz2mqCIx7S3uSQGjIOTcsXwv8tXliqe54+3tFea9xz9abIJqDP5hYXkRMLlSxfL+E4PxlZfdhT+5/YS37684/KlHjGP4ZgRnNHju4KlGePqtQtcl3oxHLjnwi8cb4h/d8NkfnrXVvSmY/NMC1tDfJvzZcnfdsfu5o39fufnTEbNk7Jyo3hIeIiVY7BjJO8/4kcOeHO7ecfOncPX5pIHItalarWzeqSKW1FveDYFHCcRi+ebpitu3R67/2NA9aVHWnxPp4Om/uWCz5fQyMHvhsWjR75v31+x+GvjoR7MKWyrY8wzOgjVU43ncee5qh3GVrx+uGf/iyNWfa5tx95BYXvT4U2L8OGg4jlgeD4ZTDkzJc/flNeHHPYevFEiYRedT2lmG94XoAuWzhZgtzhZuzzvefHPD9/6HQtwbukdVoNul4KbK/pvM64/33BP4+eMNd/PA0+OOmz9y3Px0gma1sSmYjw5/b7n8eiZbePN0JFZD+vsfcfwL/czVNqQ2P0o3Oe53PR999xEReFgGvnj7kuMf9dz8WK+VOydKUDhql7KpMXNvGd5pUJ4/ZxC1a3BjpgQNt/O3FxBpHsgZGysm1w0MIipIsWPEXSL7n9xB13EKe5aPMq+fjry9u2Kqjv5/PBJOleF9JNxOxJtOg9MPVgH26Hm36xmt5e5xT/r8wNVPhO6kRRBB52DaWdyYcaNw7jruSsfxauRh6in/2wv6O+jv2rwrUH0LyBMYdwFmy50JzLPn8esrrv9EOHyjBSx3zuSdpfSuBWZZzrljDIav3r0gZcvhj3oOr5s9zsFua4idK1hheANvjjveP+45P+zY/Z8Dw7sVrBlVru8dxVsNLXzMPH03cP7pNZ/ffkT+0ZHuXugeK/3biXT0LEdVQJdOg+vCI8hj4JI6xl8cuP4HnuNXSQsyVkgHR7V6L92laLjt6HgvPT+9e8XpF1fc/LFl917HuCD4h1ktKGLFRHAXYZx2xFeJ85sDhz/zhKeKm1VhmwergPcpsbzwDO8S/l3gbDrOlx7/ox3XP26hYEFb5G1Tp0qBPDi6eyG/3nEyHY9fH9n/ScCfKrkHyQoh3aVQOt3P+AkOP7G8cUceLgP+84FP/w+1FhneRVWzXllKC2dE4PBlJNGTToHpqz3DV5bd64KfVNS0gsv+zcj4Scfx5zN29oQ3DvOmo39t2X8FNz+dWY6W/VcqjrCzQkVVhRoOf+yITwPdX3gOv0A9yguYqgWHdPD6z0lBrLuA+bojnzr2P/Icv9SFIJw1hGstiFSn4Lp7qLhHS/+ngfyu5+pzcCN0JwWddtK56c4KyOO1w59g+MaSb3vslx3dveDPqsStTvQ50+6/KRrw2b03nL46whcD+y9Me7fQTKT+9Uxt47B4g8mV1BkOv4DDF8LVT6G/VV/9/j63cFBDOJfWQVKxkyF2liV56DP1pweufuS4+nlmeBubd7qOG7uotc1fFtpKrfX//bf+f/r5V//Nf7uKCFWgVvVCdVY0L6EEDrsjv/bZC17eeLqW+H57/8CPP/+cVKFIB6LtiJrOrknq3/v0Y46HHiuVwpkvfvELTpeZVARjO3Kq5FzxPmBxvHh5zScvb7jae3JeePvuNW/ePzAvlcuUNETHeG0RN1CL8E/85g/4+Eo9Lu9Pd9zeP/B0mqjGUjGYFpNijXrovbi+4uVH1ww7z937N+yGIz/6Rz/mcH0FFG2vtgZrtc1+6HZ88slL+qDw5nw+8/OvvsT7jvO8UI0nV/DGkqoe0/X+wA8/e0VNF7wIb25veTpdGFv7g4gqFr0PpAK7fa/XaucoeWI8jbx+f8t5jlzGkTAckKzeuTln9vs9x+sXvHp5TT8E7m6/5ut3b3k8XziEQMkaomStJ+WqLcFkvv+d77bk+sLj+V5bzFv1N+VKnSPeOvo+UGvFGcOL6xtuDnuWnPj6zWtVlNVKSkUDiqppUMdgg+XF9YFXN9dcxgfu7u4Y5xkxCk+xPS50TJdRvU5r5uWrIx+/vKbfWe7vnri7fSAltX+wJlDqjDEOa3q86zBm4tXHLznsOrypPD6euL174DRNVBxjLNhawHnEWDKClEznHCkv1JoxFZIxGCy2WJJkcpoxBlwQrA+UDPO4YG1AsJSamr1DpFDVJsBq+0AcJ374g++x32tQ1un0yOVy4d39GeccuVS6LnCeRsQ5dsNAnGeOuz0vDh21FO4fHnj/9EA1llJVbdqHgWVZcMYSrIeU+OS7n1DjAihEERH1vXWBuSzUKog1SFU7isEFumDx3nK+3PPwcMKETpWKpSDOUo22Sns8Hx2OHPee4DzTMvN4Pqm1RKrkLPhhoJSCEUfnLcsyc/PiwOB67s8PlJLIuRLnBVMNi6i9hzeWENR24vb9e169uqHf7Xn//j2pFqzxFFCbjgpiKtYKpvlLWqncvHxJiYW721twnlwKU5qwwZPnjPd+80ktVJaSMaLtyGprYaliqJOWdkUE8fqdIoL3ntJagUopYCxSC1a0SFQMzHlh8AEnIKnQ9WptQgZPYJlmooDvA9N5bNYwEWpl1/UkV6lVA6tqVasXMU4tCpLaC6yt88aYbb3ovOV8PqvPs3Msi9pzjGlCgqMrQi36aBYvHHd78pQoRrh7OOH75uMrQsoLtkDouw1gehz7fcf5fKY6x+U845ylVLVsiCXjBGpRIOycwblAKYWcMxZd++ui19WKAqkqNJuFtFmLGK/p0impvcrD+YHQqcp7tXywJmCN+hLFqn6hU1zwGCRqAGKuBdNC3aRWJOmaMux3xGVmiRHjHfO86HpuMtboJpsMNWVu9tekkhnTBEVIsRD6nnlJBOepaUScxQnEpRKGAWsN4/mJLuzIWS0kim3hCzHigj47ioAzBYzDZAux0oVAmhec80w1UclgKjkZXBWCB2MLuI5UMmIMedEkYocQs47v3MZOrRWXM9lo94gLnVpplMIw7Inz0ooFHdM0EazRzZ2IAt1SccGRStTixbwgxpEKqoZN+rLkmso/l4g1HgGWccJ3AxUdz0bZK0Yc1htdX2obX75jniIhBFKetMiakhYjYtRnm7H0zmuKPfp8iTlRko4p0+aCdep/bIOHXDAVvPHkmAg2EGtRSyGBOS50O0NObey3Lp15Sux2B/WnB4K3jOOFEAIUsN6ypMglz3jjGdxAjRVToZaiVkKltVQbYZzVvmbXO2oLqzTGkUoh9Gpvc5517bACKZ4pWTAu4Kj8/n/2d3/lkfBL8PMv/Ev/Ua2mAZbWepkG2xQ9lfOnnnf/dGX44RMv9xfOi+fuixd89vdU0Sato30NOtFUZOHtP2VYPk4QCiTDi//dsX9TtnAwEyt2Vh+83Bkef2h5/BuJ/adnYrTUn+65+VPongr922VT7K1p8JIrn/8rPf6vPTE+9HRfeo6fw74BCinatpp7DSUJp8zDr3tOP6yk68Twc8/8svAb/93C5dMAaLp2NXr8JlWmG8v9b0O6KlRTCe8tn/6DTBwMuzdx875d/SWlVJ5+0PPmn0XDjobC1Z85dt8U+hZyVo22yaed1VCujxzv/kkhfbpAMtgHy82fqFqof7sQj377G7MUxk8D508tj79VKMdM/3PPq3+U2X9xZvzuTuHrJW8+uCUYcm94/c940kGfCcNr4fCLovAHtmNPe8d8rbAldcLTDwzj9zP2bHjxI9i9S+opPGZyZ7fzAd2IPvzQ8fSbhe7OcPii0t9rW3b/Xte0NFjC3dIUY5X7vzbw8NchHiv7L43Cu7kQHhL5Aw/S4kVbSR08/JZh/jird+Frp/f8tR5/uF1U3Qiq/l7K5hu52iwUr9elOiF3TXU5q1oxD1aBQgb/0D5LmiXEYPU6JbUhWM/fnRNf/82B+WUld5X+raG7gxc/Xp4Dv46G4W0EgfGVx18Kp+86Tj/Q8Mf9L6p63+aqsBBYXgQNYhss8aDfdfvbem8APvpRamFqWmTxjwkpdWu1z71lemFZjkIahOFd4fjFrBCjKZ/XeW+mSN4F7v96z+VTIe0r4VHYfV3ZvU3YOW/jfL0nSwvfefyBY7kRDj9X0GdjA1FOPWPXcRUPlmVvuP7Jhfvf3jFfCy9+ErGThueZpHMp9xqatHpkA2QvnH5NwwGv/kLnkZ2rWqsEsx2bFL04Jhbc06LgMBdK57Z2+vV31utkZlXlredGg77r7zyHmFX8/cTy0UDuNTh4vjLaBQDEBm/cRQFOdxd1jk/6WeMrj5t1vVRV6bMK0KS6zdW1ELSeOwLzlWX3RkPtShBV+O8s3e2yje/VnxmB03cdJurnXv1sJO+ebWzW74nHpm7N6ud5eWXYvdM50r9dyL3dCj/2kii93b4j95bcSwOgdfMab01aCqXis8rTxELu9L5q4UMhXPHC7i8eycder/9q+RDMt/zLqxX8/aR+0m1s21n9fEsr5K1jeXzp8GPBn5pdwWP61nVdAxelVB5/2GHnuq3NdtLOCf+YNGiwnY/aiRTiQZ8l/V1mOSqoXa/huhbkwTbor2sGsN3L5WhxcyF1hu4hP4/XNu7jwTx3oM26dtn2v2sAm47v57m7rjHVNsXnRefh9MLhL2o7EQ+OcB/Vmkee2/Mlq1WKFlyfP9dOeZsviK7buVeQu4ab+ZOOc30G1u2ZXq12kKzj20S1NwhPmbg3uHbf7VSYr1fbB+3cmF84ilXlcfZ6vusxrOFd2QvhKTeVsl7bOBjsUol7wY11Uz2Hx8xyZbFz3cZc7tUbeHzlt2CzZW/o7/T4AHLQoLf+rdriTC/c9j4hRf1u7aLFFoD+Vte58ZXX4p7ocbm5suw1+G94vTB/5MleA8zWuZV64e///r/3l3rX/isNbf/2v/Xv1nWTGaPCDyOVXCO59HRhx4tD4HgIWDGcz2fmuPD4cMI539SEddsclnauQ98TnGcIA0/nb5jTRC5CKep7GGPksLvm6enE9eGGKtD1nuAcmInH0yMxZUhOvU+NkJaEGEd12mY8SI+vC9ZCJDEnDdIxtaq6t2SCdVSrm3wvlmHYsSwLOZ6o2UD1iNFWbuuEKUW8c9SUcRIY9gNTmgCotWora4xAQazZvEZzzHRhQEnIwvGwx5TMtIzEEqFqgFStquAxYhUQ2ooBrGRsBYNwnhXGXC4XfOixRX1nrbV473GdJ+WFJUZSnPH7gdOycGhq05wrhYw3PSll9seBZYpYa+lFWHIi5oWlVFzwLDnha8aJISVduIYwUIuhxhlxlqfphO8CJus55FT1OhU0AdtoO3HOmd1eIaVtfquXs3roBjeQUiKEQJWCMQp1ljJy6PaUJZJioes6ci7Eqp6bFK+evWYhxsiLF9cslye6bmCeZy7LTNftmcZIrQq9Ui3gFNyrmCttwNEYwzwuDKGniI4Vi1BsJGbd9NsqgMPKDmuFcVED+UKhd0F9XJ2nE4szlkqhSmEaFw2ZQReU4HvKMqkdQ0lqqzDscCLE6URaIn7oOc2jAiixSBYN0Ilx8wMGqCnhV/XnohXgOamCM6FFEBHB+UBcFpwNGKtFGGst4+MF3wWWVBALS4qIsxgDwfbYErBSMQjjfFYVsq04F8hzxVuvnsFiVQ3rCqlE4gJdF1iWCWtaRVAcpSqEjA2cr3MoDD3jPOGcJ5UEpZJzUci5LBhL85JWKOet+i9fX18zXiamaaIKiDMKvhJqm2Is52Wi2w3Y9rBMacE5h8ESY8K6nuAsOc4sdWmAVqEtUyZ7wxwjXbejRG31CeLJtupnW73fwQWkFKaSsUaoU0JEWCTi8RhjsOJYqvpPz5cR06lPdcoLiNUCSAEQpCacGGyz7igZnY8CzhhdX21HrZUlJZw3TGlEnGCyVlNDsYh3hNBxuVyYS2Lo91gHyzwq6MM0aKzrtHMOj6e0Hcqcdd6mpCpprFFImjOUTM0J79wGulMs6tscM8E3oGwsc9KCCyJ0wXCZRrxXULx6ddeUyVYIIRBLZp5npAh96LEF5prJBqBSjFBLoksGa10DaJYiBeOFEitidPxLTZSsL0y51maTXaFGEIXHUtRKQSyclwlrPNb6ZouhrX9G1Ee9SEHwLCXhjNXrEfXtMcYF27UNXsp0nWUuiSlFdrsdqVkrWLsG4KFevO2F0XvPskSCWKzJ+KAFx8s8YbyjxERnHF3VZ1N0QmnPIB8C113HeZ4wnSeLgVwoBQWuoutx9ZY+dKTxomNMIBdDMQ5jK1UKIhVrYJp1TRBRDzqpa/FAvdUVSGrA3dPpgjF6P63Roq9gt4KEFR233dBv4H5Zlm0dqM0fHWBB8BisUUsSsS3ks3lvk6uq0Esi1YIJrhU5ErbovIpp1rluDHPMpJTohsBSKiEEpkUBvhGFq9ZaxnHE5rrB9mAdc4q4LpA6QxkjvhhstVgEI0JNkbkkcJZFElRDGHbkedqKLSWDdR05Z7qu4zRf8KL3xZuM4BDRtfdX0PaX4+ef+1v/cV03nu6cSHu3BWaYpRCPjodfd4yfVoqD/lbwT5XD15nU6WYMURVX2tttAzu9sMS9MN8Iu9eV7klb8E2qzaM1MX63Z/eLC6cf7pAKy8EQ9zrsDl+rigqewaAb87ZZBZg+8sTBUDy4sdI9ql3YGqjlLpl40A2XiZW0Mxoa08LJNuDczjd3z7CFZsMw3ZjNc1BK8yW9rAC2HUsFM2fiddiu6/k7juKgeyjbZlv/qCltnWxwYw0Iyg1U7d6m1sY8qSdop5BCfRANqdMNoT9l7JhJe0f/1RPTZ8cN8H6o2rt8EginTO6M+hae9ZjcWb0e/VMk7R25M9vfTi/dpsKtBnZfTuSda3YIDVbv7Pbf9Trq+YwfWXZv07bR7t7NmJhJx6C+tkevEMWbDbCNnw0K8qfCcu226yxtY5z2rrWVZh5+I6hX6VG9BYf3z4Eza3u9nfJ2TB+eV7Vq6eAfFpaXoYUH1Q3WurN+xgrrcqcgt7ttnr4xs7zsCbcTpXPEK0ca1rbluoUxmUUBWDx69WO22kKMEcZPO1Kn/ol20nE5fD2prUNT0C3XGuy0QpN1/KWdQhM36phaAZMb8wap82AVIK2evb2Or/42kXYWf9JAI/e0qFWCU6/lEvT3ilMwtYLrFZKlnd2S39exa6eCvSTidcA/xU1tuV5PE5//u55DJR4s3W3crApA50/eOeykMA6BtNNrHweFKk/fd/T3ZbvPH64FOl8t/Tdn4k2/3T87ZoXxq+d2MMSDFkzc+dm7O+31mW+Wgr+fiDf95qMdjzoeu29OpJtBAdvR6ti+i80TubZwM51L6/z2T1nv5UPSdn7LBnIVmLdFyCiMzJ2eu2TIvWygyT9l4lGvvz+lrQCCke16xYOu3cveMLzP2xjPncGfntfO1RsVtGU+7cwWkLRCTjvpew7QvJLbGlYqeacQXMObFIhJhngwG8xbzxfQosW7+Dx+LBvktbOu0e6S8Q/q5by87LUQ8ZQUqgMmZqgaOpc7u33GupZ+2Aq/Xr/1XPVif7AEN2Cvc0kYXs9a3Gr38fl32EId12fcBqybncw6plYQuhwN4akQbifmj/vtXufObMchWc8b2Hxt017vfRwUDG7e7y0QdDnoc8hdyjan8s7x9GthW2vXY1pBeTXP69By47VA0psP1sPne4GwPf82j+X1en5w3Ct4jzvD/qt5s8hZIftqObJ+nj8pODWpgdjz87N0hc7QxnQr4Or1b+pu2jGihZE1KFXXddk+tzhdT1Onxb3uUb2F1+JSPBi6u6TQ2T4Dbw2o02siuRL3ev/WuWdiacf1vDZKqds7gT9p4TMe3AatQa9V8bIV7XbfLFthKO3sNraK5R8PaPuv/87fqeIs0zKDybgWOlNLIhdH6FUFN8eEcQ5K0Y1WLNRcsKYiNUIt+MOep+miG17jtM0QSBmwanngvCWlST0lc1ElpbhNYVaqkOIIvcUZT53qBkenkjAIwXrmeWbfD5QqXFKib7DSIARTKDlyTiN2v8ch1JTRTLiiamLZNSVRRIwlVVVw1RzBGoxVv1hjmoo4q4LJOEtKGUxRJV9OiAlQK8EETcAUYVkiRTJIa7sR9CViHNn3viWRi3rGmDb/aoezIMEwp6rqKOepRQOWuq5jnmes61v4kyDzwu6wZ5omwKhFABo25k2PMRaSw4TCnM64bHB+x2WacF6oUoh5wQbPsiS86/DNlmAYOqaoai0v4J0C95JUKTfsDwqZz2eCeIyTFoSkm9W2ZOh1rYWypC2YbtcPjPNE1++YUmToe04PjzinwTUighkGbC10VcdaFkMIqkLLSb0Rc9YHpK2qpEslt3uswEFht8d6VXchdhv7pRRqyvReVX5WnIJkFP6UjCq8HcxZCxNd35PTTDGWVDLWQNd1SKnM88gw7DU0qSnhMJVQHBhY0oyRTB8G8hLZhx2lFMaasM4xTxrAFILOhyXrnBjLgnGWOmeCWIyoFy5i8aGjFKi5YMwCkrDVKuw2GqhGrZQ5Y6wGJomzjPMZaX9PHHVOF4uznihr4FkiI3gLeRm3IEDvOwoZUz0lVpIxkBKdt1gixVimoh6lYjWUyllVZtac8bUyxUWV87R7XVEVavU4r/fNe08coxYqvBaVQAOlLpcLrkF5cBgbKKLKT28UxvR9pw/DHJFqMGKhFDIFvH7nnCKh61hSVPVoGxu1KkDNMYFkBVBxond7rLhN+V5DR6kzwVVIsFjBN8WsFMPcVL2uC5j5RCkK6qZ5plYF7DlXkLlBe4XVNVUFt84T5wXrDAUtoIQQmKYF43sS4EuziJkX+r4nZlW51kVD63aD4zKNFIHqPDnnBsNbR4G1LCUrKEtanKhFFYIhBF0nrcLzEhPFLApa04wRx7IkBr+nVN1sWTGM40zf7VXhXDPOBZ7GheNNx93dHV1wGqQnlmme8V1T+K/gGockqBlMJyySmecRbwPeKISHouvgLiBS9Z53nlih73vmVqiqtZJjxItFnNO1dkmYoN9XlhlMIGUtHqi61iBmUV9XMhVHrFpYMMZC1kJezlGhPur3PC+FEILejziyDz1LyrDrSDFS5sw+9My1qZSNrnceQ/AQpwhesMErfC3aOmhzbgU2Vdqnkkk54/tObTKMoWYYul2bZ/ociylpgUPUGsJ7ixTBIBiplAVK1sDClNXTdk4Rq+ZCBOtUDUvGuBYoWfVZODYVszHgXGCZk66F1W5qWg0GzFinkHYtVoAWJUSElBLFgatWn9VG9DlnbVvLBcmGJSd2ux0xFYLzxLJgnSO39X5JM51Xn/GUoO92lGkhOM84TrB35BIxTjDYpnrPOs6c+rQX39rpEJZl2SCssZ6aC0PXMcUJS8VYBfr69qjXep3DtE6fsmgHi3OGJUOuar1S8oQVLaj9l7/3n/4K2v4S/PzNv/2f1Gqga6rL3Jv/x4ZactU28rYJz/0KkeoG5oDnjdzaRr5uENvG1c5lU/askOMZ8NRvgaAPj2XdCK4qzXh0hIfI+GmHiZXu3czyImBnVQHGgyr3+tfzBp02uMOzwmjdhCJsG9pVTVatqpxWJdeHgMCN+swv/tmGACAemppMUF/A9O3vBAjvR6bv7LFz3jbHH36HqvggPGbC7UQ6hu0arPAnd3aDhtUK0yu/3b/1WFYAv6q8qlUIskKHTU1XqgaWdQ73NJP3YVMOzx95wn3Sf78Lm5rRxIKZMsvLQPFC/3omHf2mrlwVjKa9G+VBw6UkZvLeY6bM/Kqju9X29XCncKn/5kLpPfa8tDFkNjBKbenhR4ud6qa0WwHUuvFe7816zcxSNiCzAsL1x8SiIZJt7Gwb+gZpPgSU4SHhnhbidaffmQp2VGuFeKPvdCskC7eL7rnamEtHBZar+nP9vssnXsfprcKE8BA3IGiitgKXYAjvR7W8KJV0DBoudNb790sorQAAIABJREFU/aFC9EOv5HXsrUE3K6BZgUd4r77v6RC+NadzLxsYklQxc9J71oCwwi77LVhm2zWPV25T/oX7+KwmdUIa7FawSTu7qWQ/XAdWyPYhJF5T6VcVIej9WMfvuiat4HD9u3CrVhwrHFohnN73dm2cEB4i8ejxT3FTl67zdVXx6nca3NOyAcW1U2BV5sajBoyt0HaFZiuIzDu/ja20U+AquZB3fgOTK7RfiyFq86ChcavyvFpR6H0XN9X4Wmizk6oV3aVs521iYXzlGd7FTWFt5kTxlrzTtX1+6Teou8JEKmqXcgxIqt+67yaWDbQWb/BPkfll2K5dCUL3PrLc+O2+pF7YfzVz/qzj+LMT6arbxlO4nUgH/ftV7b7erxV++VPG38/kvd9gPkC4Txs07d6NLC973Dmx3Phnle0HHREriFxfkdLwPFfWdWUNjVyVout9Xu1j1nkGbIXN9dnjH5ZtLfywYKafr2vV+LEnPJWtELeu1fHw7TH+oYq3+Ka2bdDTjRkzaaFj7SBYi22mAWtQJf76jNqAoRH1eLW0kM9K7gQ3lqaKVfV+8bIpRe2sz/q1YGhipbtdni0xOoM/JZZrt8FI14C+WfTZvp7Lti4OyqBW5WnutSAphW+pade1yz/lrVNhOehxZS9bAXb9fr32henGYhLEvdDfP7+DlA2+simZ18LuOm9zb3DnvN3rdX2aXmgw43p91rVEr7XOhw+LmDrf9bzCWVXF29zutAvmf/6Dfwyg7b/8O/9OdcYSY+S43/Pw9IT3voEHVaqkqsqYeZ7pXKcbwFLapkxBb0oKnmLJYMAWVfdZayk4lnhhfUszxlBSQmzQlk1aW2wLTRITSeJIRdV9JpcWWgbBe4i6GVflqmCNZ8pJq9V1IVeLdUIuhSSZ3qgyCzT8zBhwfkehEHNrAbVGlTtpoQj6kE1JoRdo23eM9P2uqbkUDNdam3JMN7Rx0VRpwTKnipGKSCVVhaoYVTEFJ5SoLaspFTKVgiflSAhOLRieHgkh4NoxzOPEamVhjIKtzugmGiPMzcu1zpFD6EnzBF436xWHsQ6hqg+oVUWUHp8qK60YgvekeSEE9YK8RPWoZImIUQiUq6rsqtX2fGeFkgXJCrcMhXGO+E4DIhS0O5ZF21rnuLTrZrU9XHJTZqmC1DlVVC0YgjO45u9aMhivEB1nWKZF2+eDZ5pHsCgIbFCqD9oW7L0GfxljmBb1bBUUmoSuY5lnBIujJVkaQ5qjwuamHsbwDBEyVCLedy3BrS3uzjEuM953pJqIpYIIPhZwlogq2gwFZyy2GMaYsD5srfFC0XbsoqhI1VimtYErMK8CYg1Ta5MHIS+RLgRijFwNe54eHgmhJ9u4zbk0RkKvKtAlL1QctYJp8NNbVXe71haf1SSFQqZY9fJd1zIrDlsLzkBpQMu0ZS7lhVISVfS7UilAbWtBZjd0xKVSc1WFcylYq+ef0RbwVLQNv+aCEdFQM6Nt+TlnYk6kUtj1jjxXYs7YTtvPg7UYWou6V89NJ4YSEx7Dw3xhNxzISyQMPTFGheK5YFo7M7lgrECzWUi5ItZRpflbZZrquVKk0gFLqcwlI8XQixYU5uCQoA/7ulxwRo/NW0dKRUPQMOox3O7zPCsUFaMqZGM7Uq3knEhNZSsiCsAEpOo1klRwviMEVbrHRVWoVSolZf3uKuAtsa2D609uUDBFXQ+qLdhiESnkMkNRgGetKNRr60cuOraWFOmwiKk6J51CZ2s9KTWLgmqRoJjdon5yl2ncCnauC6SUyDkzhI5aDM4poDddC1IDStX1wgmQSytkVGLVlvXcyHutFR90/qaUcFY/f1Xk1qJjzzmnx5czNUW9DxIQr4WafXeklKphhUaPP6aM946S2yYqJX1uGMFYLcRlcQqhY2EqqvTvQrOVKAXrOm27d5ZaFHBamjcuNMui2joSFJT2ziOoWrjUxFQWKKIbV2dxNlBFmFIr9DnwKVMEppJwwVOLxcq6Buq8oj1XStGiphFpnRkOadYbc1K4fp5mwtCzzLmpsrWwoUDbEOcEFJxVVXJMRYuWeveouZBiZPCDKl4NRJNZpkjvQwscVY/j83Ti6nDYIO72OdVSyYgoOBexhK5nirOqco3FtSITqK1B9Y60RIbQaUEWSxThkqamFtb5nPPag2exIrhq29ocgErKE86Z7T2olILxAVMUAq/KdOc94zJhvVpR9H3POEcKmU40JLPkhT/4z3/vV9D2l+DnX/zn/8Oae4u9JC7f6zl8fiYdwrZBX66sqvoqhIfIcu2f1UJT3pSAa8v8CpNWiLZuPt35GZh9CMZWdRiwtQ6urYx2yk2VIoT7qOBg3+BSU+RIUaVN95C3DfkK4OxcsFNmufHf2uSvQHlVVq4ttys83jbxrZUYdHNuLwvxpt9USCaWb22YV8CyboxXgLm2JX94/qsqOR40BG7dMK5K1LQzGwzd1EhPaYO7q5pzbf0H1LextYSPnw3Y6bldfD3OalYfUPOtf9e9GxWQHv3WJgzPal94Vp2tqsgV+Oad3za76ya+ezduQLBaIR4t/ilvcJJSN0j14d/aKW8KTPs0kY/9BufW1vx1U+yfnseav5/a/3dILlRvWW484T5urddrm7UGcRnMkkjHDvek/qh50PNcrRDyoBDvQ3C3jjmqquQ+/Mm9KrdWhbM963vuev3sOW7K1hVOrZYOJZhvA4PLs3J2VbN9OOaqeW4ZBzBzIt50uHPi9P2e/VfzBpNAocN6X1XFmLfzWNXHaWcUhjZl6vq30nxePyx+rArk1GuCuruUbR6vcEqap+SqJi0NPE4fhw0mrYB5Veau0NBMeWvJX9eLdd7qepIwU2T+eKd+tK3wYMe4gW0Ty7Y2rdA47Qz7z0/Mn+wayLMbbFnV3+saVt0zsLFj/tZxSK4NArKtk/5U8A+qfl1uPCZW/P2s9ldG1NKsjeUPVao6RxWCr8WVtLdbMWxtJ1/Xs7x6mrZ782HhJw2GZW/YvU2btYCUurXrr0Wc9b+tP2Yp27hfz68Es60zwKYG1cKR2Qp7xRvcUyReh2/ZpazdC3Yq2z+vMDY3MDu8i98quqw2MPNLLfSkzmx+ruvYWCHyWmhYbQXWdvpt3BYF5CbWbT6v57cV4tq4XsfhOgZWJaQ/JS6fqA/3an1RRQPT4s5g1teyVuhYx8uH3Ru5E1WH56q2C+0Y12v4f1e7Piuxn21+Vlg+v/BbYdBk6O7itjavXQWq+I+bdcZ6nN1d3KyP1LeezdpgvV5b50FTik/XdlMph1NRa6C3aVNVr0F1oF0Aqmh/LrquHQXy/PjT58ZUtm6O7PV3/FNmfuHafYDcCcPrhdP3O7rHvJ3b9jkN5ppF3yWWg6V7yNuxpO5ZKbwGuLlZYa6d66bO7d/F7dqtNhHrT/Gygf11Pq4F3nXur90yumZWtU+IOnb7u7StbfO1pb9LmFg2uGxS5Q//63//L/Wu/Vc6iOy/+G/++9/VTXJoKk5HkvaAMZVUCq7BPW8tNUVEKl0fWLIqD1NRDzqxqq65vrkmzVGdAqiIMexCYD6PeGcb/ALoyDWTaqSIqHK0JHY+EEWTdSWpT64Yg2AwRrC9J5Gg6KLSOUs2FVNVwVlrJqaIdS04ylrdkNlApYAYqoNKJaVIcAOlZDX5R5VxqWSsB9e8S611dF1HKQqvg9NgtFIyuann5jlivCXX1sbmoXABs2CMqN1A1vCfkiKOSloKYTeQi4b9dJ2G/8zjBFScteSUmcYJYx3WOpwTaq6UrJvraiuJ9rIIiLHYWPB9TzaoSq+gqrmSyCXhQ9AW2CrQ2j5dNZBVPWqNKpe96zG1gZbmqVlpLc4IzhiqLdRiVfPYApiurm6Ic8KIws4cI7az5KqtqlL1O41A57Vl11rXIIUqo6+PR8iZGNV6I8WJ0HvmNCMlU+LM1dCT00JFv6ciCtyz+pHWqiozAUpe7RGEnAt2VSWnhDVuu9eCttpDW99FcN4wL5cGb0MDT1ps6LsBI9qyYEWB78F1yFTY2UCsVcedNZSSVZVuHDVXhl3PEiNO9PoZowAdRAsExmIw1FSwVR9iRnR1DE4LK8o51WfRWMdy0eCvzhmct3TWY9VkhJgKMWZM15FL0XC1WvSa16JQqmRssBrAN57ohx3LNCvIcY5IIceMoC3TfbCUnCgGpArSFHS5qArZGY+uBJlKph+c+sAar2q0qk/RJUfEWz0mEUpKBK9+uA5VeddaACFlDf+hqvF5oSLe0Xfqh6oFBAWWYjTZPTgPpeJdpyBZHEtc2O32SC5qo5EzlNJ8jIVUFpxxgCWlSqmlPfMrkrICtVyIpjLGBdMFxFptsw8OTGsJL5UuOGrRsBNqmxdJi02WomFppqr3prUMAYwTanWUVJFaCT4gRgOyahvT3ntMrXTWUXPSuWYsu12vBRKg8/q2ElOiVPUb9d5qwB0VY9qa5HqFeDVBU5lGFsS261pVdStiyEWvRKmQVz9zMapElRbu4nR+UyvGVnIpSCk4vXBkY9RuQW8uwVmcNds98EMHNeGNp2Qhm0KuiSoKUIMLSDUMzmJtO6bWSu+tYKreT1O1AGhFqDnTOc/uuNfjtJYlzgjqPSwlY11PRrAWctQxF5yj5IgzasWgautCRTDGUoFgRe+jrUhxeOPwVgMLc0o4K2CK3gOj41iq6oaDc2TJ+ODIJRFcwHtDromc1YYi1oItotdTLKHf0VnBmkpnRJXyKYOjFR0KglpKWLF447DO48iYFDECoR+IUYNBjWgBslqDM2oFYNAiaAgKSHNeEG/UK74VjkLXNYCa6XuvHTlSEQpS9ZkHYI2Dqr6wnevorCqrQ/AEZzAGOiNqk1DX65y00Ga0U8Y4wAi7YaCUxOAdnffEaUEcCIaARUpCQzwjxgguOHrvsKWAVKwI4gTxBmpRj1+Rbc0Xa3DicFF0nRHLEhesrVvR1Hmrc9pY8pIJTgujWp7K5Krr5K7fEVNEjFCpkHU9LzXzb/ytf+0/+P/she5XP39lf37v9/7wd82sqsnwqInn9hIRdOPuxuZH6J9BI4JCinPW1uypaLiPATdlTr/W46amXGmWMNNLT3jUls4S9H0IaCBCoeUKCaYXVjfgBW3/DYJ6njxvTO2kzwvM6hWogGh8pWIIf8pbQFEe1ItQw3t0w76GptiLQp4VmNlZxQYf+sEiChTTlSojw/2s4VhNabNaBYSH2CCHPmdyp8UxeAZPClD0fHOvwFbbtRVSx6sAospOoaXdz4VwO1O93eDGGphUfFPLTrlBvazH2znyoBCkBN10xqMqj+y8qvPMdh+wRqFcVehQOsHE1ZZAIWcJzVe4QG3Ko9KvNgoqBljB3un7PaEplRTG6P21cyEd1KKoWqH6tTVfw4g0PEYB2vmHexCDuyTSwbXAG6u2AVY3vKfvaxq7KQ3YFn0nsVOiBreNIRpwrs5SncHOSX+/gj0tlM4pJGzXY70WZvVsHxz+YW7Xtr2Ht0LAfG23kLwSVC13+TSAtcyvngElsN0fVS3Dss6Lvgl+nKF7PyEo+Fvb301Lm1/vDwLpEJ79V53BLk3td86ko2M5qhflcmzqxXZsbizUplLG6lwsXoN78mChqYurM4TbmXgTcE+R6s1WmLFz1u7PBiNs1CR1Wvs0osFg8ej0WsIGcpcbDS1Ke71uJumaEh6fW7NpgDoeHelgKZ3mYqgICuwlkg9BPSRda+83wvKy25SX61jFCP4h6rhDSAe/gTF3Skwfd+0amA1AmVjIg9sgqVRdK0xUX+N1TFWna4C/ZLp3I9XrGFoD2BDBPU1aVL/R7sdq9V27OvXGXJXGNAWkGxUkji9bEFhlO790cFtBaIXpa2FrOVoNXGoQafzI0z3oXiwd9dz9KemYFi1OxKOO8+p1/ORBrR6lqgWUWQruvIDRe49p80nYgt8kt/nm9TmxHjOili/LVfuOdr4gpF4FPzayjTmp2q2wwli7qB+qSANnWddnM6tved5Z0l73n/ONIYfVY1hIew01q062MZIH09YtVYWPHzmEZ1COEcZXHqw+c+zS1qwGBePOaghjr8DZxoqd63PgVYF00Pu/Av80GNLQgHSs5MF8636qf6yhOiHu9TmWdvq7aW9IfQs5m5/ndgnKkIrVoudy1OfCcrCkvvm6tnNGROez03UrDbqOp16ftfq9Bn8pmwp1BZzrc0ZVpRB3ek3cstrMqAJafbd1/Nq5Mt9oX+IKTddrBQpi17G3HA3LQTbP8njQokI86L2UxklcU/kibf1r4WXjR/o8m1/oZ/mp0oImWA5mg80mKdzNnfpQF49ev4AKnox+tknPHTiItEKeIQdlNdVoB40W0fRcc6dFK5qdQ9zbbdy7pW7ze3qp/r3A9vmq1IW/8zt/uSCyv9LQ9r/6/T/4XWcKMS10wUHN2FowfYAqWONaa6RljjO2MwTvKHGhE8GKtlyXlPFWFTy1qRkRECvMccEX6IxjZxyStC3duIzUTI4FK7rRRTTF3XS6Qe6sxxgh1aovOxjOxVC7HcZ4ivNMFbwBbwyuelLRxFRjAyqaMcRYVEmWC6kkhs4rOMgw7DqohZxn8Oo9K2tYlXiceJamXEopc319zVISl8uFYbenVNQT1+pn1lLY7brmU9njikdEW3I7I3jRSqCEfVNVifry1cKuGyixQV9nVL1YK/1uYJrnTSnlqsOhqqvO9dz0AyYvpFKQapDOMy0LWQwFh7WVnEcG7whWfQNzzeS0kHKiHw7ND1bojG0qao81BSHjrcM6qy3RDl1plgSpqmIsR0Jo1hahsuREqhXrIJUZ+kKK+lCrVQg+qEdgrXSisJJayQJxWRhcoJ4vSM7s+4Hz6UwpC50xXHUDXirWVuZ0wZSKc55SYQgCVceThoXlpootuKZqHnwPtVCLBvw40cXD2oIpGakRHwwhGD1XEkuaESsYo1CyZsGJoziLDY7TOCJWCwRdP/CUIslA9AqqrbWYmrAi7JwnYDj0g75w5IrvBg02yhXjFLyXIgiGnEZ2vYbWOKmQZ4LRhHNX1cM5suB7r+D/sFOo1SvslVKfvXBLUaBYmn9kzHTOUJeFIPpZxlbEQBccMS70XYcVQ8wZ7xzBOpxUel/pnGDyTGc907wwz5H9fk8tQiwzoHO9OoP3wr7vsLVy7D1BMvMccS1NvkrBiYJVYubQ93ipvDjusTWxDw5LIaVFR36FTjwSde2pAkPo6KRy2AWMgRhnumpwVkPhRCwmV7WFMJX9fkecz4gkjsceZyq1LHg/kAvUOlCa5Yk1FS9O1dWl4jpt0TbOYFJh3+81zHDJuFw5DjtSHDFG75sxHmohLZGxRC1ElIjxCk6zRLIU+n5Q4FUXro8HdsEyBC1YZQqFghe/BayJQLCeIOB94aNX13gLNc/kZcRbYSlqV+J2HeJcswAp+C5w8HAcPPtgOD88KEQU3RwuUiAMVIRjH3C14KVwdegRCqRC5/VYlgoJNMQqg3cBZw19NyBieHXzguV8pg8BnBDJzLWSij5fehcQKvt9YGHixYueEjPGQo4w2I55mbBSoKgaWUNzAlkiGDj0A93gmOcLOc9IDki12rJPxlDpvKcPnhojL66vVc2cKzVHhi5QrSMtBWcqBUc1hd1gGa483/3sI1683HFztSOXxPly0oKiUZBfysJge/o60NmqIZa9cDgMeGsxSchjoRqnBTaEYDKVBRd0Ph16zw+++ymWkTzNSNYuhC6oB/RVr8/GkhO74Dn2jh985wV7K6RlwtRMqAaTsypiY1QfZmMhFa6GgY8Oe77z8obOe86PZz1fa6lLxmLwPlCXhasu0A2e49Hz2Xde8H+x92a/kq35mdbzTWuKOXbEHnNn5snpnFN1yjW5qmyX7bbbljHQBgwXiOGihfoWCfEXICGukUBC6gu4aRpkENCtVksg1G3L7Xa7XTZVp3xqPHPOe94xrfEbuPhi7+y+QPIFoq3GS0opldo7MmLFihVrvd/7e55hr6CXZ6wWKwIC7yxaSRKhSbTCNw2pFuxOJrz94AHTfo/ZeICtS5T3SCtiaC8ERnvKrkTlCpNK7uxOOd7bYT4qwLfxd6SO8i+l0SiMBLxDhJZepjmcj5nvTkm1JNUC33iEF3TOQhLPZ0IEtBEUA83ubMSdw13GowJjBJtqSVd3pDolQWEE20A8nl/oAlJIlLBx8Uw4QojtIIlCGRk5+V2L0jkOz2xnxHRnws5gyHjQo6lLRKtwjSNRKa7zsT0vJFJp/t1/87f+MrT9/8H23/6v/+Q/c1lsm0VRSby5e8N9i6Fh0DKGukbS9STKbm+wtzfKatta84lEeratlHjjk1y3uELTDhX1NDbIbm50g3wTLN7cYKtuy4NzkREYlED/M4iF7LSKYbCWSAvZdWymdUW8IdVNXKgNKgYLIDAbh8sluva3o/8ujwt9zTjevKsu3IZCiPj4kQkqt63Z2FZc3ytIVjGkaSfpm7ZNEoNPaaOUJIZ9ahv8xgCwG+jY4AyAjJxNwU2bUlHtKJTlNgANKv69nRiSyyaGpB58KrdBXwzQV3cTMApdxXsShCC5apFtxDT4VN22m7p+/H5WWwGMrhztyNwGK+0wGu9vwjuI/EeXxDbbP9vQg9igFAFsP7JVhQ8k6zi+G0xEFgj45xpk3SAGcTdCJ6+3JYw2tijbaRJvhgWUuwn5aRtDvKFhfWSwefz9dBkbnjf7vZ0k8YY809vnFW4Dntjg9TQ7JgaAPv6ezw1IeXvc3wT9XRFDHoisUrYt2fga4mdDbse3i9exYenSKOnqvWrRZeQyqzouhgQt8UkMcrtCUe4pghDIEEMmtf0cBROfh9w2ClXjqWfmtrUXw61tiJ7F56RLexsydkNzy78V289iHEV+I8ERPtyGz+1II12g7W9ZizqGUu1AYapAM9aEJLI3Y1AWA+5mFAOjoKAdRqO6WXRU22aiKt0tFzm+B5p6Hlvv5V5kXqer2Fq7kQe5bCt/84FyL8EWUVLoEkG9sz3f1OGWr9wNFSC2zW9oJppmqKjmEmfiCHU7UPhcIbvt51oJXBHf13pu4iSBFKwPFDZXKLdtW285yNLGgC0kCts3t+Gxy3XkA6sYcNd7WWRcbj+7m4MkhohK4fMtDzrE0fbkvMQOki2u4qa1uuVwTkwM1aRgcyRpJpJmovA6nnflTWs2uQnlYvDW9SQ2Fyzvx6BQOFBdfK1m7eJ5YBuI3obAmaTcU2z2FdVcU5y720WqoCRm2cTj3QU2hxkuFXSFYnOg4vMOgm6o8InGrLrYZDXbVnMvHq/dIH4PLB5IdCloRnHfJ2WUDqo6fr804xh4VTNJsgks72mkjZNquoV6rEhWjpDE99wOYmtUdfEPQLkraYeSdBm2r/HNIkLQ8U87UDSj+NxX96IMGi23x098TF3HgFXaWBaspppmLLl8T7A5hvUdgWwlxam9PWcgYyBXTzXtUMZQchQbmdVufE/ia4klGbmVrnfb1+FSQTuQVHuSs58VuEShWnG7AHGzOLk+iPtFWain8ecvvu6pZvE5eS3peiouiPUkydrHx+7Hc1d5oFi+BYsveNq+IruIrzcujMRFIduLge3mQGP7gnpXsHqvpdyXlDNFcRLvT6QDl8tbHr3cTo9cvy3wv7bg+p5g/cgT2iQyYbdTKjaT2AKKi4ggsrng6ise+9UN7aOWWiSoZtscDmDTGGDb/IZ5K6jnksv3YPPI0fbjcxAuPn5yw8NXMTh2qaCZCpaPA3xlzfIONLkiPxeRjz6MwXNcJOA2mIVt4L1djJA3C3nbn3GJoJ5IklX8/tdNYPFIsboPywewPlboTfyc6jaeY1WkyW0FaeLPHdrqP88P/Yva1Na6LmVs5Sgl6TqLD5HFlghJ1wkyQxxTtiZ++UkItsPZ2IrMsyyOgwNOSfAdwVt62QjhWozyWN+w7uKIu1QGR33bkOwPMqpNiZGCznYUSYbwHiE81scbrwRNkBIJdPWGNC8gWKqqwug0SsBEYNM1SKlQnUUStlZviXcdvUSgRYJtG7IkjRcHtqNraxBq2yRyJEiMjHxJ72MLL1GSPFFUm2u0lAyzDLkd6RbE8RIpQbiaXPXpLHhpCSiEM6RFgsdRdxaPh1BFEVaw+GA53NtjvV7jdUD4GA4SJNZ3YCGVCbIDrTxpqqitJVUSG2pWpScET5akVHWLtIIki2xU7RsQ0Msy8I6ubtE6Pl+tNE4K2q7CWUsvTXC2I8sSEiPp9XI2VUlZ10il4v/nQUqNzDxl65FohHR42zCb9NlcX2NDTaJia6/twNYtKklxrYjHWFmTGk2aGNIkEITg4vKSrOhR9BLa1uOUghCou5okiXiH0XDAaNxns1yh6HO5XtK1HmujPMy2NZkeUnUWVLyA7WcFOnFU65os6UV5kJDxPU9T5BaFEXEBiiLPaeuSNnSRzZwkscEnJN4GvGwQQlN1DuMhyRT3j+YIDavlhhA8adcRlMK6+N7KoEiUITVJZKzi2JkXNE1DXilWZQfeRq7qtvGpVKBrarJEILCkqWE46COE4PzikhZPs21N5qagaVuyJMPVS0ajAUWuGM/H1G3FetXgWknXWnRqKNuG3CTI7cnSZIbJeECeZggRPzN1U1KWmrKsY2teayrXkaPI85zBwJCmimEvjkoXyzXXV2u8bxEmoEISW2tKkJqEttmQ5Al7kzlFniCxvDy5YrOO7FAZwPoOZRKMVkgtGPeH7ExHjIZHlGXJ9WJDOL9gU1qUNjQ+nm+McORZigwVh4czeqkhyXKWq4rryyXnqxVtiJznzltC68i0QmLZmQ6YzafkIkCQXC43vHp9grUBqQDrUWlCVTuSNOI47FZ4h0y2bOPIGZZSxqak62hciZcWHeKYf6M6VPAUiaYICtFZnND4IAhCopW55Y/mqeHO3j5ppsiyyNG8OF/z4uQ6Yhe6iMPwoUPLBKks0+mY/sBQZAUSqJqOp89eUDcwrU1DAAAgAElEQVSWxsZmO60lMQYnPINcMRoYjuYHUaKkM5x9zqZuaYVCi4DxEQ2TacVqtWE6GnJ8uEORp7SN5/T0lNY6Tq+XsWHcSaRRYBQ2tMgQ2N2bU2iN0Zo8FzRNx+nJBYlQUZ6lJC60WCHxXcPRaM47u8cI7bEby9ViyYuXF7Rtg1FsFwolOii6psGYjt35kOl0ipKGpq45EYH1eoN1Hd5JugBGGLLUgG1o6pLjoyMGwx6tbWjalucvTynbBmmyLeu7IUsGIDoyA3f39zEaEgNt69FCsri4RAm/PT+AQ1A2JcN+n3EvYzIb0BvmuM5zeXHFSbPESE8sKSiUigJKJWEyyEkzzfHRHZxtKcyUUWH55LMXSJmwqSxJltI5iTQOrQLz2Yj5bExqFH7m6S3WPH3xkvXaRnyFjeKwKBu1FHnK7sGY6bhHqmBYdYxnE376yWc0bUueFbRtHf+uFC54jg+n9HoFvSIBJ2mto+0cF4sVwaQIJL5uSQtDV695cPctprMJ/YHC5JqeSQnScXq+4uzsDKkzqq7CyxyT5oTWsz+fsTPNyRJNniakeUGiz3l5tooLLT4yddu2A+GZ7/TZnU8ZDftoCWY04OL8kro6p2wakqIAFwNWESy9tMfOqM/e7phEGKxQ7EyHuM5xZq9oOk/Y4kaSVNPZJt4EJ5okjbzvtr1ZLJLcQHG62iG8JXQW1feM+wN2RjmjYYEWkrIsGQ/u88EPP8eF2IpItggL2zTov9BXh3+5/b+53YwcdtuJnih7eiOyii2ceMN8w3ATAbzaNt5uxFU75nYs8kaIInygnGuCTOjy2Ji5ucm9EafcSJ82e4b80tIVsfVTznREsUhxa9Lu+hLh1e0IczuIN3DZpafL3wSJ+UkT26LbUf1bq7eNz8elBtUE2uGbsdAbuZhsY5PLDmJg1OU3LcfIyq0nKdmVw2WS7rgXRxy7N6OTN+iEeiLQVQzeYnAmabZClnThYhhEZKOK7XTI2VcM+Wlsl92M5wJxHDmL7FSv44h4M5BkC0cziAFr/1UMS9uxiSP3RhK2EqwbkVV5sOVulnF0E0BsGaRmbZGtp9pLUV2gHsdQrd4RZBeB/DLeXN/gMm7GQbPzDiINC2kDV09SivPYng59fdvGumku37SSdRklMe1Y0g5igDr+uIsG8ZmJoe+2nZUu4oJCUILVkaI8DKQXMeQefvZG6HXT5G0mhvTyzdhwuWtwqSC7jqgMXcb2uNp0sB01l52/DTOrmYmytpW7FXmpZru/bjjFAtLLKJfpepKXv5hHYdtJ2IbS+nYMWFh5i4hoBm+kVau3QK8FzdjQe70d690ep7L1t63Wdmy2Iaqg3I8h0OhjT7KJoVeUNyWYpb3l5a6OE+odwebIkywl2Rm3WAGXKZKzDd1OccuE7QrJ6p6kGcdrBlUJkhXkF+oND7VzJJeBdppQTTXlnsD2od63yFLSTBP6z/TtKLFP1Q19kG4QA85uX3P1DtiJBR1wHyRk528YxaqOx4ktYutufSxZv2UZHKxYXRUkLxKE17fypWTpbo+pehrlf5dfCridjqToOH9d0HshGX/kMKv4fulNd8sy9lpw/dCweuDx4+g/yD5P2fnAxbH+GxRhqiLyY5DcYjy8iY3p2JaM582gZfy3xkX5YhexAy6NCz8+kdTzBObJbbP/BnlxM7oeJNQTwdVXHLLfMRxULFc59WcZ8+/Fc65ZNHSjNLbHsxgsre5DM3eYcYNXnuUiw/+pJl0EksUb5nE3UMhWstnXbA4F/mdWWKtQynPd9SjOPWYdZUy2nyAbRztJ6L1qWTxIufi6R04aFhtD/0OD3sDOD7tbBERQ8f/SlccrxeJJIExbTGY5GxeYtWD8E0/bk8h2OzXceHQTz0XnX1HUv75BykDZaMKzgp33IdnE99knURJlU3mLKbh6W1LfaxHGEzYam2uKU3/LX73B1tTjeC5MVp7Tr0vsXsO61KiVZP5dQX7htm3TgC4DzWi7sDEULH+2xmSWYVGzLjOu05zB8yipupFmqcaTX1jWh5rNHUF93DLY2dC1muppj/GPo1gvWbS3x5XbiihX9yRdPzD8yjl9q1hPcsqDhIM/CHgFxWlHN4gLc16DzSTLR5A+WnDcK2md4mRnxPhPUopTvw20t1xZF8/P9VSyfGKZHV8z7615vj/i5KjH3j/UpEtPPVHxvLJ01JMo17r+WsdgZ8PjyRUAF1XB1Waf0Sf+lvstbYjIhNLz6hcF/XsLHk3POckGPBmf8Xv+CcVPUyYfRqxhfmmRVtHlkSO/+IJj594VB4MlD/rn/FH/PmfFjJ3vbxFpOi7C3jB9r54o6ic1d/av6CcN07TkO8/u0i375JeOtnezuLb9PM0E67csDx6/Zp6vWbYZ+p7nx91bhA8VyWrbGq/CLcf35vrjRo5qNoEul7jRFldhiT8n5RbTJFkeq9vzVZF0nF/3OTuSHPw9g2wDWobtdcyb7/U/7/YX+rI8hDgW7RyYRNFZj9Epzsdmk2tqntw/ICkCSaKp247VasP5SQMqo3IOYSIrNk0MdV2zO56ztz+hpxWpSrGu5OXJJa9PG1SiIMT2oWssed4jAENdsLs74M7BLl215Px6QesSPjk5o9cbxFWGBlwTRz9To9mfjtkdZEgRWGzWXC03XK9KMh2Zq3VrkQqSIEmVYjYfcTifIr1FiMDZ2SVyU3O+WpOmKVIbyk1NmhYE60ik587hDqNhn0BLWdY8e/6cQSKwNo7iNk2Nk5I01Shp2JuPODq4hwgdwilenZ6xWjWsqo4OSds5oCEAdd1xtL/HIDfsz0cI5zm7cFRtx3KxoescpYvj3I3tyJMEIQTjgeHuvT1MIrleXrC4rji/XNE2gVQovFc4FRmWidLMBj0Oj/aQEnIpub6+5vMXzwlOxlKjF7imQ4vYyrCh4f69RwyHGZoOqfc4PVtweXnNWXNFECZyaIkjyQrHZNpjf2+fPBdk4g5VU3J9veb05QovFcL0kFgcns5WiETw+J2HTDJDngTWTcvyYMwPf/xjsmKIEJpG1SQqwXmFt4Gvv/cOw36KFB63MyDVio+ftlwvLefrCm8Ski6jbBpMUqBCx+5sxHRkEHi6QcP1pmOxsbQWhDSEzpLnOR0CZQVSKfYmE/qjQ5z3XFxcsF6vCcJQtxH5ERvlAtdUHD485s7+jDwRJFLhDgJVVfP0xRmXixUgcFs4gW0tk0HCwwePMUZhjMNaT9tZnr844dVJlIM57/E20FlJCCkHu3N2pgVFIUl0ipSS4Sjj4nLJpy9f0Ut7WNvircOJloP5jN3ZhFE/RSqBlJ7VuqasLP7z56yqmkQogrN0Hlo67s0POT7cYTiIzOamqdhsNrjuFdfLCpkYOh9FTdVmw8HsmIPdHSajAShB066YTlNepY6LizWLqwaV57gOpEhxbYNRgkcPHjAda4b5gGq1pp/mPHv5itPLChlSNt4SvMfjuH98h72dCUWmSIVg0E/YnU2Yjnp89/s/RilNFyJT1itIVeDhW/fZmfTRBLrgmR5OWJ9PWHz/h1BLXAYuQJ5mOFcjRODdRw/p5xmKBq0EuztDZpOC7/3Zj+msw6vIuc2zyCONTU9H1ktprYfO0XYdWZ7QtS3WhChO6lqMkqAVloDyNi6chMB1VRGCwIsEHyQmdHgbsSUyWB49esCo0ORJjpIg6XF3b4/MvODDj56SJBnCGOrWY13J8eExu9MBs/E4BuR1iUCTm4TnL06oF5soXRKCrmxJU3j84IjRsECFQD7doXWSrxQDPv78KU+fv6IVkoQM4QUax/58zPHdAwZ5jkkEIkjm0yEnZ1dUm4qLqqZXjGiail6RkKWKw/05u9MJhVEUiWK+M6ZuO6p1xbqsyJRhVdfITNG2NXcP97l774BcK4yx+L5nMu6jdcJ3v/cBg/4UFyxZogmdRYvAe28/YTBOI77GAaLHZDLhk49e8PLsmqA0AQtGUXYNuXI8uHePndmQNNGk6RjnFePxlH/yx98lxWGSHILCqECqU+4fHTKbpKRpjlIGITVVY3l9dsnlxQplDIlJ8Z0F2SCk5eE776KkI9tyfvfHQ0bZBX/yne9hRtM4KhQgeEdVrXnvi495++FbLBcXUag1n4LI+fTTTxGyIBWW4AW196Qm4e7DOQ/v72FbR57kWBs4ODhgOBzyp9/9gNWyRFqL2bbOCR1Hd4+5c7yHwKJlYDia0HSe58+f01UraikQmUA0EWfSH044ONhHQEQZKIU2UTS5/vFnbOouNlC0p2wXTHaHPHrygKatSBPJIB+ileJRb4Tzn7O8XlHV8ThvbfweH/QTHj64A6EhlZqgNHfu7yNMxsuzH0SuvFDU5YbhIMOHmsl8zMHhHt41pGlC4jW7s7fpWsnVxU8RWQbSoVFs1humd495/PguvikRFoa9CYIE+Tij+dGPOLus4gQCjq4RSGlobcN0krK3O0SnmlQVXL5e8PrsNSEEktQQXIcNNSFYktDy5cdfRNiSYVFg25pRMcVZ+MQ8J9QO6To0grpxW0a0+3+6NPvL7V+y7YZjJ7s4lhm5fZEDWE8Uqg08+3WFLyyyZ/ErQ3aiGH4cb1LzyxjCpldRApJedVx8IWPxjkPuNAx6a64bg/hgwM4P38hF5LblecO1Kw8E129r+l++4KJKcZ/00aXi6Pdrqrm55cXdBHntyHD5RUX3pEQpj31p6D2XDJ7GYK4rZBSGaIHfjopfP5LY9zZbvneg+2iA14rJh11kzCnIzzuaYQYhWtjPvwocNlGEfKWY/algs69jK7ILt6FRN9C4VHD5jsL/TIWUTfw/fjgkPxH0Xrtbbh/E5ppeNZx+Y0i1K3DvbhCyof24R3otKV694e1F9IKl3okLqKu7ivLrJVnWUb7ok79UMZTaBNqBQrgkIhByRdc3XD9SbN6rkbpjMix5+WLEznciF1xKbvmpLlO3ApnTX4Vkb0OedkjlefXhlP5TyeQnXQwD3BZflESR3OJ+yvW7AcYNzaBhs8hIXiSMf8I/x7KUrY8BuYCXv6oY3bnicLjks4spn3+hx92/7yNuYMtstYMEtR2r//g/UEz3z5kqT9Nppr2SF394RO+5YvLT5lYIlZ21W6yD4PLdlPVdT5ABVSl6LwSDFy6Kl7Zyr7jgsG2uasHlu4pmDqJTDD6VMQDp6yjXsh6fROFW0nrOvpJQfaVkMiyZ5iWVNZwu+lT/14Dhp54EkG0MbVUdqO8Jmm+v6eUNR1nDuklZlSnt+wPm39/ynG+CvG2IfPYzmup+Rz6pGBY1iXI8O5jT/1Rz+HsV9V4eF09sbGqefSVl9dgyPlgyVZ5EW16djuEqQdrIu3WDeIzrjUWvOl59u0fz1Q1v7V6gpOds0+fiso9sM4ZPt2P7ZQwjk8uWs58x1F+sePvoBICzTZ/N3YSr/oDhJ4L+52t8qpFNHJu/EUGd/ZWWB8dnfGvnMz7azPlwNmf53SnyJxJtBPnJzdi/4OW3NfrJkgfjJQfFknamsA8l37t7zN3fiWGZLrcsaBubjee/1PLk7gmjtOKi7jG/85wPH8zoXkzJebMo08zyrYRIoX7jnG9MzxnohrEp+fjhjO89OOb4f46yLeFi2N3M8luBEGybn9sQyayieEuVLaqKmIdi1d5ynW+mCcp5jF6Gn9VxmmqLW3C9BGMjJ9crwfKXK57snfNweE5PNRSqhS/B3xr8End+V9ENisiivupQtef6Gwb/eMO3733O3fySV80ILTz/aPSA+v0B6ULdSpduFkwufqXh3sEFue540L/gtOnz+reHnP3jA/b/OJ6rbE8RBhqbCS7eM/EYmV+xk22QInD1oOCnHx6SXieMf7Km3ssxS0u1l9AMJZdfCkwfX/LW+IJ3B695eX/M0/WE14u75Kfxs957FcWDZu14/c2E/a+94u7gilm6pvWa10cDvpc+5PHfLinvFHFhZhQXP2wmOPmthqP5Nfu9JbWLC1Mv7gxZ/uGM+fvdLRsWIL+w1BPFq18UjO5fMe2VHBRLSmt4/84dHvxNbjmwADaPiyWbb5V8895TjvMr+qrBCMeH5S7f++GXGH3a4RMRW6s3HjcJ+99+Qa477vcvAeg9avjfDr/Mnf/e3HLPb76Ds0vLyW95/otv/B1+f/k2d9IrChVbzv/TH/4mQcZGvPCB7NrRDBXnP+v5zZ97n8u24O3+CRdtn5956w/57/Z+ge535vRfdbHFWsSGL8DiZzp+5Us/RgvP2JR8a/oZzw8mfOd7X8ZU8bEjGz4G19Vc8q13PkELz938kr5qmO2u+Ju/8Iu0ZzsUpxazZrtvFYv7mt/8he/yvBxzmC/52fFTZmbFF77xkv+m/av0XmmyaxeFZ2tPM5SUe5J/7ZvfA+Befk7nNf/Jw3/Afy1/le77e7jtNUO69KwPIme8Orb8ytsfUjnDcX7FUXrF39j7ff6j+q8z+kzf7luXQnHmOP2FwL/3c39E6ROMcNyfnGOEI/uljg/Wb5OsuEWamMrjlSBZO64fGJbvOELiMf2W8HnB5EfbaZFexC3pJraxu55g/994ypH0fGv6GVe2wM4VG5fw/s5728XuyAHPFo5uEAP7P+/2Fzq0tXiElPEmyrWgNE3wSKc4vbrgq198m3G/IB9E3mKeWoZ5D2Hh5esTjCjI8iyOI9qAwTDuj9mfjPG2ilKlkHJ4uAvK8+rsks46pNCoxCC0IjMpWT/hC08e4Jolot8nG2ScnJyTBaDrcFvKsdKCzlt2xlPuH+/TSy22qzG9HiYTNPUSbBxxTo2JMjGlMUpz52iPgZGkOqF1ljTZhZMLyrIGAU3VIFHY1pEIxe7ujOM7+xjtcc4wGQ3QieLF81d01hGUQgtDIjVVWXF8PGM6GdLPE5TTUex1dMjLk0vWy5dRBhZA6JymLmPI0Fbs391l2JPkOiNNNWdXC8rVGi8DvSznelNHZl/wDPIexw+PKdKWfqrIzZxh2uBbx3l7hbc1vXGfxXpF8J66rjl4/IDpqI+lI1MSpcf4BD779AWX6w0dGZmJ1lgb4PDgiP39Q4SvSFUvylx2BEpaLq+v4ni1kzRdQ6ZS+jrh7myPySij6EvazpP2UoLWnJ5cYVA0bYn0CVIoOtcxygv257tIX0ZIeprgdGD/eJdnL04xyQxl+xAEXVMyGY7o5RlGC7LckI2GlJsVh3ePWH30jPXJgrw3xusE6QTYmv4oY393wrhnEFhs2xDUhnJzTec9WTqgK1vatsXaltV6w+PHj5nvT8jyiP0wqefkpGGxtri1I0n6uM5i8oRUKHZ2JuzujMg1VF1LUJJkkJFerli8eA6mQKkoz6pWG/Z3x9y7d8zrVy8oij7Ogqw23Lkz4/ziNTZYghWRV6xakjRhPp+zvzciTQPNuiYEmE13kFLy/OQV1rV0TpCYuGgyHgzZm8/IjUcLTQiOwvTo+pbPP/kU7RwqTWmdRylNr8g5vnPItNCoYDHakCaaabFDtWy4uv4c7wJBGlywTOdT+sM+u/t7SOFRCFIjaBrN3aMM656xKh22CyRCI0LAAlmWkWaGncmc0FoGvT65SVmvrrlc1LRNFBhGLiXsTXfYmfSwzWbbDJe41nIwn/OB/ilNVRKCQhmDsB7bdBwd3EELi8BjbAudZz4acGdvh8XVh6ADvSzFNR1tueLO/buMx0OapiJJM1xT0uvlVN6T9wpoBa2P0wTeexKlYxgzzBn2UhAFdddxUZ/TNhWJyaiDi/gEpSPqITi8twwmBfPpFNdZNqclzjoU8UJTBBE5yj6wfzBnPttDuhZ/g/rYNnn3doY8/8zR+IaqrFFZbIfP53NmkyFKBqzrSIsUKRL2s5zTyyvCxRItI5/J4WjKhr3ZEcF1ZDq2aZMkQRrJaDqi/uwTer0JwSuStOD6/JS79w6YTqd46yj6ObZpKfIELQ0/+uBHTPIebWvjWHnTMRhNuXt4HHl+viUISPOCwTgnyE+o24q2bqK40cP1+QmTtx8xH4+oqzUJkqA1qdLMJhEbkeU9NvUGKxzWBbT07B3s0jYlwiu0FEijsV1Hf1BQPT8nzROkNKzriswIBoMeR3cO4rSI71BCYLKCyUQgpcV2CpxFKcnGrujv7DDemZJletvIjwiBwWjIdDbjfFniQiDYjiTXuAZ0kpDqAiE7lBakMiAwFP2OtCiwhCjDUnHhZjKakiQZq02NTlICUcTlUdw5POb07BpCy0ZEVvflYsmxn6F1RnAtzjlMltE0DQJonSUf9CKCQQSUFNRVhTJq+7pBqIjryBLDbGfE9aLciskECoN3UbxpEgWdQ3hBblKMTMicoW9SNo3HEdnmzsptaN/S7w/BbduqNpCiKESK95CnOda3CBzCNvRMwXK5ZtRPUaSUrsPVFTpNIi5nK/MsBhNqu2G1vOTJF76IkJpEa4J3aBkbv5ooObWu3UoCQZgElKatbWxbJ1EU6rwnSRIQcfE17/dxfouokQolM5wT9Ho5uTJIZWB/xMn5CUpKOtvgWk9mErQSZFnGer1mPu7Tth1ZkiGTBBcU/czgHTjhWa/XZKMxtRf4G3vDX27/0m+qDgQZR6nN1nJtVrE5NvmzFR/9hxPM0Zrd0RqjHNejjGbPsJBDZt+PLSeXCEwVb4ptodjcCdx75zWtU/RNy8JkXL8HFwyYfeBupUXuZjx+IGiHgV/49Q/Y2AQ5DTwbjnn1k13a8U0TKz7+TRP4+u2E0TdPeTQ+Z9llPMvGXOdDzCo2V0TgthXq0jg27L+84tH8gqNiwUk94KJfcsoe2eVWurX027afpxsort8W3PvKC3ayDesuhSP4UXLE+PsmcgO33L+ur0gvOy7fzajudTzZuaJnGgrd8WlRcfLBLsWpeCO5kVEaZocp0oJ7N4ZlD4fnfKd3l4uPp6SXAmkF1dww+HRDN0rRpY/tuK9WzMdr3p2e8HQ44cXeiKUbMP4ojm6Wc83gWZQZp1cdmy92vHP8mnWbcthfkJuOZ37O/I8U448q1Kalm2RADGzPvqy5984LpAgcFEuMdHwAXCQTRp/eSNgCZmVpx5rNruLq65bD4wveGl5yVvWpeiXP/A78JAboxUl7y1BUm471vYK33n5FqizOS/ZGK150iov3euz9cR0bden2vS8tywc5vZ0Vw6zhqLfgYe+MH632OXlviT8Zkby4xs4GkU285Ulu9hTLdzpmRwuU9JStoQxjskuBLgXtJEGXDrOOgifzvOT5b05pHtX0hxVNY1jkGUFqBi/CbQP7ptnZjgzrtyy/8uBjHhenfFju0nnFLF/zg36f/tMqHrNpDC2Tsw3+vR3+4/d+j7/12bd4NDxnZVOe6TEXX4buox66ia1L0bkoZx0YqkcN33ryKcf5Fc+qCT4I3FuCl3IH9x0TW4obix0YksuazZHhyeOXPByeM9YlXVC8Go64bAou//jurQRNb/mY5W5C9bWSrx0/p6dbZsma66JgfFjyvyy+yeizgOxiuCRbz+JRQT33/PyDT8lVR083HBULXpQjzr7Wsm5m9F5nUXw2SG5FfM1IkfZqfuvg+3RB8Y3xhke9M/7H82/iPjMka3/Low1KoJ+s+Pbxpyy6jLGJ+3LjEn7t7Z/ww+K92I7bNvXDlqf561/4MWNTYoRjlm4A+Gt3f8Df/uovMXgaj4t6L4/ty1XL+b9q+I39pzwvx+xnSzYu5UujlzybTqimO+REKz2wXRRQqDqwOk7YHAhAkSwV449Bbxx2kGIuSwTgM3N7npWdZ32YsHgsER0UZ1GOd4OoQHDbiL/8ouDn7n9GrjoqZ+iphr6qOW2HHLx9Sv3dPZK1J7vocGkML9tHFT9/73OGpubaFtzNL0mFZfyo5O9+9HNbFMiNzA6SpeMXHn2CDZIHxTlLm7Obrkmk49nhnOSypt7L42P3JYPPKk6/UfDV4+fUzvDu4DUXbZ93+68ZpRWv//eHLB/2SdZv5I3lnuCLX/sU67dtbq+4k13xc8OP+a/8XbJrF/dZEZEYxSfXVP/OmH/7znf5qNzjKL2i85o76RWfP5jQTkc0Q0lx5tFNbNe7TPDLjz7ipBogRWBoanLVsWpTruee9LyOEwpGkr8qaacZ5b7k8XtPSbXFesnQ1MzSNfWRodX7txgXl0qKM0e1q3lycMqD4pxrW9AFxUhX/PzoY/7g3ntMPgzbiZOIBrhhne4VK7Tw9FTD/SyGhLPxmnYwu33+QYGuYfFWQl4s+YPlk9vP7EDW1MFw9lXB+KfxMClOowRu9OGas6/34896xcLmzJMVn9UzlPTojY9NWRcl4S6JkwYiceSqo/MKs7WDvZWf8w8feIbPtpgf/QZP1A0CsyR+jox0vJc/Y18v2Ot/hc/nM4pzjWwjC1Y1nnpH8Lwc89Xxs+3vWBSeB8kZ6aRGuP6tCCyouJjX7AS+e3HEN+ZPGamK71fHrF0arx26gKkCQQaqqSRZBvovWza/2qGFZz9b0gXFVK0xwqITh3Bx+iRIccuzBXjVjJglax7nJyg8K5+xm8bEOVnFlnFkc4coj95OoehpzXS0IVGO1wA/LiKztooLt/VIYVNBOxJ8fjHhr771ISftkLfyMwrZsrAF/3QmkFYiO+id2HguXLyZJvnzbH+hQ1vnIuuwcRaJIBDFIsI7hv2c2XTEdFrAdkQ9INk0ax68dchqfUHdSmpnabqaftonMRl3D48YjwSukRgReS2rckOS7LO4uqDxUXS1bCwCx97RkLfeOiLTio4UrwSjzJMlOS9fnLKxLdYk2C27MVUJs9mU2c4IuhVBQ1FkDLKC9XKFtQscgk1TY9IE33imwxH3D+8i2iUhuGglrzcIqbm8OqOsofMeoSVGZATbcbh/zKCXkWno6oAXFjHzeNfwySev8WjqroniJDQ7kymP7h+z2ZyQpgkhBHa24d+LZ5+zcQKIsq9i0MfXNQ/v3ePuzpyqukIhmAyG7B4csjg/Zb3ebMMc8LbDCDhpZkwAACAASURBVMV8OuTdhw+5ujwF4Ui8I52PuFwsWa43XK89wa4JIpBKQycs0/GUQa5onUeKjGTaR+Up5bpmU70G76JgxxRsqprpbJ+i10cKibDgfMfu7hRtAh9+8oK2DXg8Os3oWst0Z8x4PGQ2yMFHpmHrHcWsz2nvJVerDTIQTeJdx/JqyRd+9m3yLEMFh9IpnW8Z9DPKTcvrF5c05QaPwWtF5yz9QcHh4T6L63NSpRFAlhXU3rO3s8uzp+ekwuBCHLNHCHZmM/b2DgjtGh9q8jzHipyqtKxfXWNth9rawpOsT9cGDu8eMygEWgmkzJjsjDBGcv3Dj1Ha4ajiSlwbRVqj/gQpEqyL7d7WBowKzOZjJhdjrhdlrPRLQTrI6Y3GXC5XFP0eSkl0EOjBgKRLGA2GXF+tUd6gdUbjS6zv6BcZtnW4bsMgL2g7h5IDrlkTao9KIy/Oh4DWmryfg3WYLXtKiASPZd0uyIqUdePoug7rJEkm6WU9MhMXDBJJDKa8wiJI0hxtDOvOgfIEJCen5zx69ABXt8hc473FGIkQOUVmkOEE7wWIHKcCMni0gEQYXOMhGJIUTG549vxzqs6yKUt8UGhjiMqsQG4S6nVJYgAvUDrH9KHctOT9AeXVEoTZjslbHnzhCZtVRWY8/TTHuhqEoZVRGCYJOOtwvsPoBG+iRX69XpKnCYlRdNbQdR1FmpEkCWXTErA4IXBtixcdWgls59nbPcC6lpPXl+AgS1LaxtHXCYFAkD7y7Iif32k+oNApyITcCurW4UUMidI8tnnrsiRJDFVTg7P0+hkCiUBFKZO3jMdjXry+QKc5BIk2kiRJcZ1FGoVHxNDRGJabVeTxKhUla4BQkqIYxdZ/CORphpQWF2qE0AiVIvSAZevpZYrFpqQ3GuECLJYlg35G03QR69IJOuvJe0M2rSOEjl6egfA0TUPXOhLjkVojtcTawGa5ivgE4RDaoPMEFyz5YMi6rFisNigREFJHAZ+UaJ0xGowp65ogoN2K/ZKs4PTknMmkh1GKruto2yh301oxHPVomhbbRjlZ8FDWHVeXC6aTEalKY7PbOqpNjREJxmTUbQtS4z0UvT7Xiw1a9pBAsC2taFHC4GpHIRIq0VF3NTpITN3Sm6Ws6zWjYR9vOzAJm6qmto7eZMRyUWISDS5yt1erNW3jubi6Zj7NQUjKsmZVbrBAaWsqbzFKoXVKljnKVcXLZ6/ZmYyRWuPaBtfdiB4lXddReUtRFIQAKumzqSxl6Ul0DKb7eZ/1puL05BoVonwyiBguCSnZNC3LVUlmFEma0gTPcrVi3ba42iNqG5ETDvp5n/NX56zv3UFWLcZ7sjyjalrasqSpanzjkYlAojAKvLdcbi4ZbQYYo7C6RAjJZr2gqiMDPHhB7TwoR6pT9mZ3uDy7ppdmZJkhMRH3sq5rVl3NYLTDpnPIVCCCYNmsuajX7Pvdrcixg8YhVcJqtSLYyGT2ncWHKEq1XRQZLi+XXF32Mbu7pAG6siU3Q14vz0nSFKdEXNQUkmq1ZlXVoDyTQc6yK5HWsVlHHn7XdbTSUPT6bDoLUhNE9y/kuu8vt//vN9U4hA0ki3BrUL+xolfHA8yDFd+68zkAB+mCLih+sDig+vk11ydHJMvYzjErS7mX0PY0+197zS/vfsTC5uyaFV1Q/On1XV4VDdXLHRITWXCD5xGRdfWu4fBbL7ifX3DWDmi85hvzp7zsL/jkz55QnLvIe6zimHw70qweeH776IcAeARvD054Npvw3fO3UU2UxxSn3W1reHVf8J9+6R9sZXySncma71fH/K4IrD/fJ7sMt0Z1W0Thmn5vydemz3icn/CqHdN4jX7H82f+LukfRAlTct3dSqjWb1n++jf/MT9YHXCYL7Be8RsHP+bvNAnu/QnFqb3d7/VujrSBy19q+Btf/CM+LHfpqYZvH3zC7t33+R9e/Rq9ExfD7dzcWuIXj+G//Nbv8PcuvwrAPF9zcGfJHz19h+KVZPhZTRGFFlEStwk8vnPGN6af87Iek0rL/mxJrjs+vTqmODMkxEUsn0uyk4rqXsaj4TmptGjp2NiU3777Pv9n+g7d7+5j1v7Wuq5Lz+q+Yu/oin/98AcArPOUV82Ih6NzvvPhlxh8fmN7l+jSoT4/4ezfeshfGVzQ0w0jXXHWDvjy9AV/9/pr7HwQpW6icwgrUZuGarfHv//oT/iDi4fczS+RBO4Vl7woRrx6p8/eH+V0o+R2zB4iU/DdJy9wXtJ6xd3hFe8/MKwXfXqvu9tR/ChmU+i1ZvNezcHuNX3Tkk4s2VHHd7L7DJ/p2Bxttz6GtcUWisHBilx1XNmCWbLmVT1iJy1xDyqWD3KGn9W3750d5zQ7gb9/8iXujy7pbZud06TkeT7mszsDBk8jK9fmcR+o2jGdxXHen9g9vj5+ykk7pKdbXr6YQogBS7dFYbjc4EfxOJsnKySBvmioTMJPLndxQ0HvdUB3/lbCV80ko0HJYb7gKL3iquthg+S6KwhFbGwXr2LrDx+Yvn/F6S8PKK0hVx2t18yTFUY6ngxP+T/U7FaQJtsYors84jasjQnKsblkXy/4z1//NeQi4g6Ei8GlT6JtfW+04ul6wjyPwcpQV8yTFT9a71NPJPl5/J6yeWRyn/yiILne5bC/4Oujz7m2BQAv6/Etq1e2HtlJbCGRnUG2gg8uD7g7iG2903bIWTvgrfEFHxU7iPPYopONRTYgnNnKAQPVOw2hVqifxnC2HWvMylEfDrYTDG+ETbKD5X1JM4n7pZ7obWgTG9XtSKNLT3JZY4s+T1cTAN4en2KEu/1Td5rqjmD3u2+wJy6T5EV1+35U27ZpTzX8ycVdELxB27h4HVXtaM6qPgCm78hVy8alGOlgK1wvnq2p93sMntbUeynCwgevD3g0P+dpNaXzisZrLuoe9VhRnEU/TLm7xbCs4brOmeUxDE6l5coW/GB1EL97ah/xXdvAr5v1MRea3794TKE7tJxgvWKoK3pJx+qOJr/0ELidWKh2DP/ok4d84eg1s2TDedvjpI4BbkgDm7sFyTLiWWw/QbhAdhH48MUu7xy/5qhY0Pm4H18th4wKdTulATGIbcaBn77aZZjUt4Hn63pIf1QjbNyX2ZUjue5wucIWkmYHPryc8bXdF1QulmC+t77L1aqgmEn6rwK2eNOy7L+yXJUJv//iAf/K3R+TYvmz8g7vXx6hWsgvHen/zd6b9Mq2rWdaz6hmEcWKVe16n3Mr33tdp+20BVhCZIoEWUIopYQOjYTs8Bega9FA4hcgARIgpZAAZSoplULIYAwG2+Sty1Pus8tVx4pqVqOi8c2Ifd27DZSylY7OPnuftSJmMcaYMd7v/Z731hOmVsIFTyuKpeYffPQb/M4HLyl1YBtLdrHkrG64VeCaRHk30D0oUFm4tuq24Kerh4c59TtHn/Nn66+w+EgQDXoMVEOJoFrdKb59+4xnsxVPy3s+Gx7yB+tf5sXtKcYLPkAYxbB7aDn+JPHZXz+jMp7zcsfX5pf8pH3Ku35BvyuEx2yko2GPHpp9oXj39JiPyo6Vr7Eq8Se3X+ZyPWfiFCEJIqcChpnm5ldL0hcFn5ye82y64km14vvNB/zp5muEq5rdY8XkOpKcOJ4nr3ZMP1/w8psn+IkR1zpQqsD37p4KI97tkQeZ6ATJFJ10ZWxeTWjqgXrq8Z2lP1Gcf68fMSP5EHhWLjOb6wl/wNf5jWdvuGi/SmEiny7PJaS1zRS7RHs2zhXeI5B+ntdfaNG2j+I2EZ6+IgXhuA5Dz6OzU3H+pY5JIUnvMSdOJjOygco5eh/JQVp4NpsNj08fEgaPb8CVwsjwQ6QoLJvtHe2uIakJUWVMUZB6qFUBsSXGBCpQU9EFz25ouO86itmcmA3eDoJyaDJH9Qlkx5AMRguMvbQOg6Lve6JOqLIS8SkEsilY3W84KiGEzBBahhhohi1397fEPEW7GT5F+m7HUTXBJA3RsOtaYV/bLGEkIZNSIuvIpKyI2RKjggjtridlS9QO5ROrbsu6XQljVcuCQpZWHGcK+n5gGAKlrbGuoPcD128viErjignXdztmx8cYBX2zoyonXF/fMS0KchzwVrPZtWREXMHO6KM4YHVU2KDAWHKIVLYkKI8Pnt22Y/CRSVWQNi2pmEkIVA74XUscPMkEtLZkpbnfbdh2LSFmksn4GNGDpnIVN7f3PHvymNaLwKJNQmfFdtOgqNhulpSTBV2KJDLHx8dkm2iaDXObCaEj5UzrO5qdR+FwriTlTFaJajaha1revn7HfFaRozjFUsi0W8/6fsOsntHHjEqR7CzdENhuO26Xa07nNYW2DH3Lbtey2qwxDkKKDD5hdUk39FTTKVeXd5x+/QNCtyJby83VDffLFbtVw+ToRO4joxvNKNbbLVpJKE6hPNlH2p2n3zb4tseamihkV7z37HY9fd+TTSB7RYEm5cRuvaHU0iofUo+1FqsKunXPu4s1X/rgMeSWXR8k+C9lml2HUoqcM8pqyIaUI7tmoLSOqiqkLZzIrmvxAba7lvnREasmMK1n5GHg8vKSL3/4Ab7f8vB0QUTRJ43NBRd3d6ybFu0qDIqkLeiCq+slD46P6NZbpuWE4D0xZLa7Hd4bdrtAOQ8kIAwD83pCHCLr+w3Xk2vOzo+4W255c9vy2bsbei2p7X5oKIqKlBIvXr/m2eMzClse+JB+iOyGxK4JhCzpo1krqEs+/fRTDF/mw2cP2e12FLVj8JkuedouoVWJo2SIkZCFRffm9pbjB8co68gb4fzuui23N/cMQyBbjd8FXF3KmAmBpDRN67l+u6Tve9q+QasSPyQKV9L3LXofTmUlACNZy/J+S+czPga8NmRbYhCUS4wySo6Ojri4vGGxOGY+laJI1pohRbTW3G0b3tzcE4qSmA0pZo7KirvbNdWjU9QgvGrfJ7brNffrhvvVFnyUoEFngUDbN7x8c8PDhw9x207Y0zkStebN1SXeJCazGX0zAAllHW/fXFFZR2EsPYHZbELXtixXWwbvAeGd9n0jeY5e8eLlK775jS/TNh2qqtjtlmRl2DZrbG3Y9QGGFo0hKctyveNmtWVaOKZTC1Gx6Ta8u7xlu12jywk5ZrKXUMB1s+Pi5hatNVvdMp/P8UPi9u6ay3d3qCGhU8IpRVYFw9CxVpnrXU9dB1RV0zY9u909q3XL4B1JQVHVdM0O4zRvLq/QRclsXlE7S/aZnOFqecnQtKScJfwSjQ8aXVa8vrqmOpuTFMyspm9b+pB59faK5XoAowjBk0PEmQJbTrm9W/H06SnbZqAwBdum5eLunnfrO7wqUFpDHwihwSm4u1tRmwrnarRuKaxh12XeXdwSoiWpTFk6dk2H0pmTkxNeX94yXZywmJY4pbhbb9isO/pOQm9yVJhsiRmGGLlZb3iw3nF8NCWmjqoo2ewaLm7u8CYxmIzPA4SBvrP4MLC82ZBnieNJzf3Q4L3n6nrH5c0GXTiiCmSd8cimoN91vHxzg1YlZeGoneX2bsVu15CVw/cDWWUCA36I9CqR1ZKj2ZTi4RmbZkf2sN0NXFzfsOsbdFFKGxU91cRxcfGWh0cLnjwQQTfGgeA73ry5YHW/ITtLO/Zs2ZwwVtM3LdWs5OXbS+5uGk6Pjllutiy7LYWrMMoQUk8fxEXus+Lidk1RPWLTJELsMFGxue/pvcLYEqWgzYqoPSZ32PQX+uvhX73+f3zZdU+Yj4nrI9tz77q9/ZWCYZBN6i/P3jExPasw4V99+CN8NvwXR89wWw5ut+nbnptfrwmD43qY86AQwfZ6mPOo2vDJ9Tmn9xKMZbwSl1iEYSFjfBtLAL4xveDz9gGfLs9YvPT4mTgVTTMIO643mMey6bkcjg7twx/US/6JhXIjbeL7MDXTJUiWP7j7Rf7Fk4+58XPecMLb7pjL+zlPPw8Hsc/uAu4+svtwQulEFPrO9kMKHZjogcp4wQoEaZHsHhRjO7s4Zj7ePaSLjl0QAeSPb77K/cWcL92FP3/hlbib2Rre9sd8WN9xYne86M7571//GtoLZ/T4B0u6Z3Nhw24C4Ujx9y//BX5l/o6E4nqY893bp6ikmL8SYc3et/jTCcmJWKYRl9tX6huWYcLNMOPV8hi7U7RndgxSG9u+j0tUp9mEklaJCBSy5g+vv86bm2OetiLYmiagrWY4diw+Af9rehSeB2ozUBvPD+6ekBVMLwaGhaW4DzK+fkE2yp+sz/lrp2+4HWa00fHp+hy3NKACYWrRXniHw8mcYpX5rz75bb5xfoXPBkekjQU3qxnVhaF7PMGtBdWhYqa466ivj/jJy8d8/fkVD+ot1+2M4a7i+E2WNPcuYjcDsXYU957htKL6qGDxQcd2KAlZ84MXTyneFNSXLd2DgvJWxkRykgi/uZjzJ+ZDvnZyy8NyS8iazzZnxI3DtRKspYcxRG83UN5OuN7NiEmTsuJhuaWNjhfLE/xU2JOmi8RKgtPcfcf6h2cc/3ZHH+GHmyes+lr2mEsLahCc0dgcoULC3la8mJ3y4WxJGP/HR/cP2HUFp9fi+Jy/6g9OyrMfdnz2z834x+0v8be+8hG7WPB2t2BRtsx+XDB9uSE7Cb7aM31nHzt2Xy15u13wC8c33A0TmlDw2e0ZtoPyYks4rkV4bzPN8wnaQ76o+Idnv8nvPfkR/+jmN3nx/ac8/T8zbh1kru4GwrxEhcSLP3vK/NdueVBvOSu2/HjzmNtuypu7BQ9uEroT4c6OZrXH/4fmlT3nV3/jHd9df8CTasW7bsGb3YJiJXzrMJFAtbIVLMGDbydePzvB6ETImm/OLvnh6gk/efmY8wZBSGwFc5AKjWlFaK1vM9XHFW4nYk02YBtx4hYrCWzc860ZO3vmLzPFWmPbjGviGGY3rruDCKq7D6ec/Ejx9umCDx4sue0nhKx51y+kOPDyhA+/LUgEPQgSY/e8ov1iznK+xerEwnXc9FN+sHvCq7enHL9+34a/D1eqlpFPv/2c+dfv+Wn1iNp4Lts5bXAcf89hdhuGB1PcyF7NWnH+3czFfMrVpONqNxOn//aEz9+c8+SAiQgUKxEXk3Vc/tljHv+Nj3i5OcHpyEf3D4hJkCOxkiLRfj6pkJi9hO+8+ICz0y3hRMbu9+6e8uZHj3h+uefN7s9FM3szsP7RhJ+ah3zmzvjG+RX3Xc2rF+csfmLIShA5eSpoAbvxzN4qNp9UvFscYXXizfqI5d0M+7bkbNePjllDuRQn8/l3M9e24u3Zgg/nSzyGlBX/4OVvYncKHcSpr5JgNJJ1PPwngddHp/xJNPzC6Q1v2yMumznu2zMWL2QNMd3IMp4YQq2xLyr41Y7vLZ/xoN7y07uHLH9yyqPvp7E7RR1+L0w0Ry8St4spP6weY03itG54tTwm/WjOoy6OxQBHddmDVqy+WnP+bfj87Bz/2HBctfyP3a/zyfU58y2H0MC9wO+2ieNP4PXXzhieWba+5MuzO757+5T4kzllC24bsc0eXyConYtXc36QFL/+5C3/y92vctdP+PEnz5j91JGsuHlVEgyB/F4muYqfmke8ni14Mt/w2RcPsbeOSZafUwlsK9eh2Arb/ovHZzz46pZv3X1Ayoo3N8cc/3gv2Mox2SbhjyvOf+D57OuPWPxSy/dXT9n5kq0vuPreI07e5hFRMXLtK1nbi41gOB7+WaL54oTXXzpm8UqxeBEOaAPTZYqtHB9tZvEjw/1vF/z05iFt7yiKwOZyxnwn1zVrRbUUHJEEl/78XW3m93//93/uH/6n/fpv/qf/4fdD7FEqEnySpGQgRsWsnjGdVsyOZwLyV5khBCKBNxfvuLy6ZtMFYoBSOxyG0HlOT44pSnHMqZDxITD0nuube+7vW1A1Q1DkmJjVc5yGo/kJJkNRW9q2YRg6ok98/OkXmHKCwopjL2lMMhzNj5hMKrTOGGvpuoGm63h3c0nbJqyr8QPCqcsGYxQnJydoDTErBt8TYuTq5o6bmxXOLUiqFPErZCpXUBSOsnaYssQ5QSpcL1fcLdesNx1kR4yKGMFoTV0WWK2ZT2pCM5CyOKiu7+5ZbrZgS/KYGa6dZRgCVVUwn9RUlcWnwKbr6EPk81cvGfrEfPGQPgRQCENPK85OjkhdKwJ1aGn7juXqnuXtmpAyzjkqV9MHjy4KZosZhdNkEikFBh9Yrpbc3N6yaz3ZTQi+IyklSIZ5jbKKogC8l1CjzcDd9YrVqicrh9YFFQXETF1NcNYwm9RkEjllui7QbHtevXpL0/RoLYF3zlisMuQhcHw0xxlDSAEfA5u2Y7ncsLzfkLLFOieOzajRGB49eIBOkAYPOdL3HW3f8PrtFcu2xVQTSa4UXRV0YlJWOC2t9X3ruV1u2DY7QlIoo9G2kHRy60ghY5xhMimJIeFDZr3tWG9ahphovSeOyeLOGVJIVFXBZFpRTSp63xGDZxgSn718S+czwWqCl01EWZaEYaCuKoiwODkmhkDfZ5bLHV+8uaTDEG0hacLJUxYl89mMFALT6YQQA957bu/WvHr3lmboMWUtCasJwNCFntnJCaQEaEKEzXbL3e0dV7cr0uggHAZP7xskSzjx9MkjUBofAl0fuVttePfuAqMsZENZWvqQ8IOAxKeVw5UGtJdwogC3t/fcrzeElFAoyJlCaQHJ5sQwDGij2a5b1puWl29uWa/asVVbox0YU4i7eiI8V5UiMUcCisGPa8/NHcpaCf5TmZQCXddS1AWohHWC4fBJcXe35urqhpBA2QKlDEMYKIoJvo+cL07JPlPYgjj07HYNry7vxZGcIMZEykmuk5aAQKU0bdPTtJ7e9wQFGLHV+hRQRpN0QmlDyuJM9zGybhvaFEnGMqRE3v+/IeCcIxDJXaQqLfPpBEMiRSSEbbPi8uae1W5AKyuubieV9ug986MpOWUSUkRKMfLJp5+zawfCMEjYpFZ0PoIyLI4WxBQpSssQIz5mXr16ix8CqQ8MbY8zBmU0fd9TWofvBubTY/yQ6PrExeUlr9/I5i7GhDWGQjtSgN2uoSgn+BQ5PXvIcrmlHTzXdyvWqy05KsIwoJRmCIG6rEkktAanLcEnuqHnbrPm9cUFnfc0yZOVwpgKpR3WFPgYsdkymczZrLf4BFe3S5abHT6CNoZIlkBAoNQa33UU1tH3HdVkyvJ2zdvLWwbvydZAihTG4EpH5wPN0OPqCq0sfTuwazp225ZPX75EFyVJabS15KxAG7TRdJuGnBRaGXbDwM1yxXq5QcVEqxQ5aWamJqiIdY6+DxJEZ50EHdxsuLtt2Gx7WcOMlgCKse3flCWbwWNtiVUVsfHc3d5zc3tPIKKNFaa0qSiKkvX2DuzYzuwjCsvt/ZK3lze0/RjERRScggpkNDFkYs4kFMFnNqstt7cbtpuewUeiMvgQcBhKV1FP5txc36CUoetbIpnNrufq/p7t0JFMZus7ki7QWuNDBgxZGfq2F7e5j9zebblbbok5kVWkHxomswrve2xZ0HWeiCEk6PrE5r7l+uaOxku7VTKKPkWUthjlqOsZfjuwWm/RytJ1geXdltubNYP3JCMp1VmPheYUKQvDECRQsu0adl1k6zv6lDEjtw1jGDqP0gZrLLttS+g9DIGYM8vVls2uZbXt6GIgGIVWQE4USWOd49/82//6z5Vo+1evv9yv//y//r9/3zSebDWmkYJgKgUR0DyydKeZp+f3FCZiVebdcMzST/lvf/JbTH7imL0L4pI6soSpwfRw+yVNNQlMrKfUgZthxrv2iIvPzpm+FldqsZE0891jSUrn8UDpIs/qe368fcK79ojWF0z/V00ek8yxWrirhWb5pMCdDTidmNuOT3YP+Xj9kP7HR1TLTKg0bjem12cIleH+QwVWsw41b7sF98OEy48esPhU0tj3qdAKCYe6ndS0R5qpG3hUbnjTnfD/fPYVqheFBH9lwTbseZX9seNtWfHL55esfEUTCz65eYD9ZMLRy7GjZOTmJafFlbco2DxSPJ/c864/5ju3z1nuJiz+2FJsE82zmmIjie5hYiSY9is9u1QytQOvmhPero5RLyuOvgjYxhNnJX7hKEYR89NHR8QZ9DiaWHDZznn3+pT5Z5rpdTwk2u8DsNrzgpd2ynQykFEUOvLF+pTm5RHTd+Oa5DTDiROE27FmWZecnG3ps8Vny8vtKW+XC+bfKqivPcmJqypMLLGW7773z2FaD9wPNXfdlBc3p7iXJYvPPSqOgucY5JacZveLAWVgl0oymrftgqvVnMW3LfPPtmRn5H7HDEqhMDQnlnaiiGhut1P0i5rJVRYOoVLCW/2Zz4qF4fVZResdm64kXdZMLjTFDso7j+lkjoSJ/Hy/cAwnkUfzDW+bBVtfct9WlP/XnHKdsF3CtAGFIhwVmEFxdVKzwfHrD94dBNJ3r854+KdQ3gfMkFCMnOmjguaR5TrUnJzuWPc1q67i3Ytzzr+l5doW0l5uxu/Axcaweui4VhUBw2qoeX11Qn4x5fQnARPA+ITbBtxK8Gne1pz9yh0RxV035Wo747NXjzj+oSaXBpRiWFjc2mO2PaqoePFgiqsCUWnuugnLtmb7+THT11D00oqvUISZk/uhwO40V3bCt+6f8frqlLP/1zB71R8Eqewk9d40nuZxxcoVbLRjGaZsfMWymRC/f8zpD0SIyk5LCJ5PlEvhUf9YnaDLxE0/436oefHyAfOPNLZ/HxToNgNx5nC7yPIXHJvkmE161qHms+UZ7ltzpldJQvT6iB4i2UoKPON31nIF9U2i2CZsO8JMlTowm1XMMKJEtJfguNnrgepO5prdDIeAKNtECUnrEjopdkcF1cOGykpx4rP7c3747gmTn5bMX4ko7NY9cepQKFQw3D3StKmgSQU7X9IHi/2jheAEuveiX3HvURm2zy1NKEnTxFUzZ92X3Hz7EbZV2CjBcbG2oBXFvcfPLaY12Wpb+gAAIABJREFUrB/Brit5u1lw+9Nzjr9jce3YWl9p/FwCrCZXA35W8KI75cMPbvjOm+esVhP6T46YvhuDLNtENhq36unPK3QA7wo2lWbpay42R9y9OuH8W5pynaguW1kPSjOG3WlMp2iKAn0y8Pr6lG1fUn9Usfgi4kax0/RSLMxWj4gLxXJacrmbcTTvaF7NOfu+kvC4cf3wI8u3XCXKpebdZMraOa6bGa+XJ6zvpjz9wyy89cBYJBTjZTYKtzFsc8WVqXh7d0z76YLJO0YHa0AlaB4VglMoFG6r2OaKW11y3c5ofnLM/AvN9CISa83e0qky6ACp0EwuYVlWDCU0oaD7Ys7iE4UdxFluhixmjJll9lI6xrwpuWumLHXBxYtzyh/UVEsJGN2feyrkXMyQydGxShU3vuaT6wc0L46orzVulzEe3C4Sa8MwlzDA408yO13xxfaEqzDh4vqY6ccFk6uMjpnphR+xBWoML5XuM3YFbVtxvZsx+dwx/0KC1FSGYh0kILIV9ES1yqRQ8GI4ZrmesrqaU35cUi3BBFk75fknrPLu1KB7y8XFKe9szd1myubVgvkLOQ8dkX30KFy7XcQfyTNYx0y1TFS3iullkPBHJwqFyiJcoyCWIhjnoWCTS0KQEL3q0jB7k6juk2CetKwTfiZYhX/33/jdn+u7tsr552cp/NN+/d7f/XvZaAi+QycnXypQFK7G6MzD0wUfPD9nWpVoIjFGOt9xdX3L1fUdV3dLynLKdDKn6zx93/O1r32NJ09PKW0mhh06ZW7XDRdXt9ytd6Qo1RNrICZFPa04Oz7hG1/9kDjsMBqGduDi4oJ3NzcMMRFQGDJZG1IWTMNv/covspgZYKDb9qyblo9ffEbTJ0xRE5WmyA6SIofM8w8e8/TRERpPDp71bsunL16RjWFIipAtqIjSDvrExBb8tV/+RcpKEXPPMAxcXl9xcX1FSsKSBMZALoMJiefPHwv7Vht82nFxd8f17ZIQMmEUfWJ6f/1D3/Crv/hNzhdztNYs79fc3N1ys1xjjGMIMIQeYxxaa7TWPH14xsPTE6rS0XUt93dL3lxfMgwebRx9iGhTklJGKcX52TEfPn1E7aSyPvSB12/fcHO9xJQzotLEvsGWFSFmKpt5cH7Kg7NTppWjbVuWdxtWmw3rzQ5sSe+F46UxFEVF6eAr3/gys9LS9w339xt225ab+xUhSQBSUqBwFKbEGjhbzHj86AHoQAiJ5fKez1+8wLgaVdZ0XUciY7XBZsXZ6TFPHz5gVpX0Q0Pf93z++edgJrQZtsOAyyLA7oMfTOU4qiqmZUE/BC5ur3FVSfCZmGAAvO85mtQkHyitpbKOxdGEfghsu5526IkARuHjQImEKmgMuMyXnz/j4dkpqMh2s6TdDbx8fUlUmkE7wtBSFTUmGfTYrnxycsLiaMIQPEMIvLt8Szt4UrbjU0JBUiQfOJ2fonTi9OwIDOyahu22YdPssEqPrm+DUw6PCM9WKz54/pSJEdTJu6t3rNb3NENPUc3pPSgfwCmm0ynNbsfXnj/n5HiKz5lm03F1eUPf94Sc0NbQ+Q6lS1AO4sDieMbp6TGzqcF3gdXdPcvlEu0K4QQrKwK3cticxI1pNFVVYIyBCEOCPngRBoeBqAIpK6rJFOsHjmc1i+mE40VFVvD23TXbpqMLmpQ1WiV8yhhlKFAUZYkrFMezKdPplL7taNue+9WGLkS0NnReBMxshCM7L2ucggcnx/gc2DYb1o1n27VgHSElEa5BXM0pi5M2ZkyhSSmSVSJ4YXiiDOWYRJ9zHNcseVhqq/EjpxYSOgSUUuioSFrERWJiNp8wm1XMZxOcdrS7huX6ll0TSEmTVRLXs3JUhaNrdzw8O+NoXjObTNjtNmzXO9q2ZdtsQFtCkjTRpI0EnClNVZTMTEFWsNvtCDnQNA3WaqKWe2KMYXz6UbgKWxhsgqkp2LUNbd9hCsvgAzhNaTQxepyRwpQ2iUk9o65rrq5uUGbkohlD13uM0/gUMRjKQpOCJ/QD56cPACT8ctdiC8cQ2rF93VOXExG9c6bKEgxWFJZoxJVstGAmcpa/Z4Q1XNuK5APZWMqiYL1eU1YV2lmapgGtsIUhdQOuMqy3O+rJhEkxE2yGVrSDBPUlIgNgtRMeeu8py5KiKIjNDls4lNOE2JEShDhWpa0lRwg+CtYhBVnfrSBO7CgAB5/oQyCFlqKe0IcxDFEbvI+UZYkKmsJaog8onVDZ03Xiwo9ZUVYTvPe4WgpnTbfDaEdRVLJGjlgJH1qslbmb0WgtXHZnNSqMRYVeClBKGaKCPgVSiBzParbbLaqwRBTOWIx8hxXW/bRm0zZj6rHMu9b3GGPQVjjRzsh6UeoxRTpGdNb0SVACaEU3tFirqaoJKSWGzuOMQynBFPS+RylNzgU5g8JTlY6oEmqQ4AdXFlLkGjwk0EaBzvgUiSmh9Oi21j0xaVAOlxQ+CZIjB3mfpACk28T3AaU11oiYobNi095RTWpZn7GH5zdA23uqqmIInn/0X/4nP3/f1l+9/tK+/pV//j/IqbSY1pOcke+ClTk4SZbfNOjfWvHhyZLKeHa+5Go7Y/PxMYuPFGc/bBkWjuaBpVpFilXg9d8s0b+84WTW0HmLM4nLL05Z/Mhy/Jkfg8/EaSWBZ5bVVzVnf+MdrXfUznPfVnQ/Pub0B9JSaLp8SPVWUZxWn/9bmadPxkTr9ZThcsKz/w2q64FU6ENAGMhG7PKvu0MQWc4KfzHh6R+Nrp+ttNDC+5T17txx8Xd65tOO3lu6pqD6acX5D4M4a/dbKCX8w+gU17+pyB+2FEWg7xzlj2tOPorYTsRd4OC8BDCN57O/M8N8dYu1iebVnPlnmpOPJdzGbcQNmkpLtorkNNe/UbD9uqc46hnWJdUbx4PvBNxG3MJuNZCtONfQiuU3a25+J2IWY2L5znH8HcfJx8PBVbUPIjNdZDi2LL9h2X7NU5+1tKuK6lXB5G1m/kbW2r2rNRuFnxv6ueHqdyPlWUu/KyjeFFS3ipOPxBW4d3FnLT8fSs3qq5r2lzq55r2heuV49kcdsTRkDcVyQPtIrB2xNtx/zbH89fefobaWR3/83jlYvd0QpyWpGgUdJyFpm2eG7kzhtvDge8LL3TsV7aZH7Tr6D09QUZAS3bFh90zhNjC5ilR34b1bthkIR+Xh/QHe/a4lfb0BINxUuJXm8Z/Ekfvr0Z3HH1cHV2V7Zth8qOieBnSjsY3i7PuZahkOWIH9S4XM6msVWcPmy/Jv5a1icpWYvhtEbPbvHXu2iZJIXykuf9vgTySE7fgHlvmbQHU94OdOHJRja277qKS6Hnj9N2u6Dzyq0xRLzfFPM+VaXOvJKorl+98B2HxYsf6ypnuQMJ1i+kaxeOFHV3gU/MTobo3Ve2Z2vzCHcKFil0QE0Qq36lFDQPlIOJ0SK8P6SwW7p4r2ubTen3zPMLlOlMtwwA+YJpBKQ5gahpkh1Irtc0V/lrA7RXGvmL9KlCvBjRT3/oCCibVh+8QRalj/QsZ0iupaMXsbmVzIfDGtP/CVSSLEplEsjuX766+7eLg+w8IdHKFqDNITTIG4+ffcV2FcS0ifioKrAdg9LmgeatpHmVhlinvN/GU+YCRUyoex0p+4QwBk8yQznEfcnaG+VpTLfMCy2FZQOCokhtOCWGr6I037UEGC+iZj+kx9Gw48T7ca3nN3gTAVV2goFe25pr5JVMt4WKvk3K0U8iYat0uHMd+fKE5/7AVzNTo6i3svDubxHvq5leC3NnL3Tem8KFf5MNbdRn5ed5HhtBB0Rcj0C0N1Fw4t69koEfduvTjRx64LgO7MSTEoQn8kbl0/l+DC6npcI2szdp0Y6lcb/NmE9tzRnmnCdLyu1+JmdZsgnGirsG1kWFiGqSB6QiUMc9uJYGlHd2motYiio2t276RNThFKOXay/N6+wOln4uJW8f1c93NLNhw+T0LEBCcgxYwsbPdGPkel989HP5djjk5CL20j3RZuGw7XLJaCEslKOj9sJ9cwa94HcfrE+kuljDP1PtQ0jhgkOV/F7N37bpNQ6wOTOZv9dRDWdj8fs6IGEcTLzftg3OI+ECtDv5Bim9vKeWUNWQmbdj++VBqZuQth1e7/3ddSuDHDyMkfp7Zr8yGsT8WMn2rM2EHja31wBu/n34gEpl+YsTgAoZRjSVYxezvQn9jDWEtOMZI4qO4C/YnFbRN/+D//ez/Xd+2/0KLtv/bv/L2cIxCRltasSUMgZT2GzMCHTx9y/uCUpm+YlzXL5Q2v3l7Q9nHcNGuGYcAWFcMwMJ1Oef70MZO6oHSZpm95d3PN8n5N8JqcIwqwriRE0Bomdc1sWnF6NEV7uLi6ZLXeSvBYjCQUyipilM+rygkPT49ZLCqcgc1yzf1mLW3gyjD0AW0dZEk5zzkzm9bMKstiPqXdNYQUWW620g7e99RTES1zhDAEjqZTrDFYa8FFQhBBQ2tNCIk0OhmJgbIsYQiUdcF8JmJCGCLbtqP3kZggMFDWBSHmQ5J7ZScUVnM0KVmtNgfu6xACtnCEDP0gTDytLc6JeFsVJfNpTb+9o+s6ej+QkyZmRQiSlJ6zhKMYXbCY1lSuABXY7XZsdi1d36B1LSmXI8tU24I8iMjx4OwUTCLEgdh39H1HHxR9zgSVsdngipqU4KgoCSYyX8yg86zbnbjWUhLTq9b0foCUmUzmKCIEqMspbiot/hcXV1TlVK5dP2C0ImlFNEpYjilwNJ9isyJoYT7mpGn6Tly8tiQmT0pRxAAtLt5SW4LvKeuKdugpioLg0wGPkC1U1hK6gdIZqqKk6yWVtEc25zl6nDEYlcmjo1BrYf6WZSnM1tLihwaVNXfbe5S2ROMkwMqWqKxwUcaiNeI8MEba/rEiCMp8kPVCxi0UxqIUEgikhZEcQsCg6LoOWxaE5IkxjwIbY7iTFYc8kUSPTwofO7Q1lLkmEwlZOI4ajU6B0+MZ69GNprXFE4R7rSBk6VP0MTKrZ3ifmVQT+r6jsMJwVtGP40gxpEyKkpJus5Lwv7rCZ8hRCgpWadqhZ9CZkBJ1KYJuCIHYD9RlgVaZImdsaem7TNTImuMjBZrgxAk61wXKaHzyOC33p2+70S1qiBlUDChn6VPA6Iw2Iq4ao8heugystXTRE2IS7TyNXxRHkTckWfdKJ3M1K1kHUkrE6CmLghJx6RnjxK2IhBtprVEK0JkYBuLQo5VC6wnJJHwcmEyOCD4yn5/Qx63gajqPDok4unOzVnTeg7FMqxo/dETv0SQWi4VwqkMgJFmnklFy3GhJ0sVgssxLYsaVpWAbkHNzZUHfyrza83CdcxhjCSmQfaIyDhXE/emTJ9pMM3RkP7CYlBATxkgxwThLHwPJiDMz5oS1luiTuEtVJgweay1KZ0KMFFa+SGol3FznHEPXo7RwkpUVoS6RccmRkyYpEfj6vmc2n5BDlPXBGQyCtSltSRw8tqjFba81vpd21yF4nHNs+h1VWUIM6KwxKGpbjXMhYeuSruuAREhermNSEBPee+bzOTrI9R1SpKoKmq7Fj0Lq3GiGpPAJZtYxdBI2qXTE54QuHYnMbttSFAV+GHBFRSKj8njeIaKNw2o5zkIlMgGtoQkDKSWm05oYZZ1wVUkMGZQIxHHwFEWFsoau71GhJ1lLVBJMVxcSHOZyRuUk1yqqUSA1bIikFJlqCS2LOaFKB7bA9wOVttTOEfqOzg+4iay/jGigwHgcyP0si4IcE4USjjlA9HLfU1Yy71Mgq8SQI7WtcMoRciZneaaSM2VZ0g8JZRyGHqMhhEH49jnT9/IMMMoyDAOFVpAtPgImkUa3sUIEJ7n74iTX1hBjwFhHHzzWTSFlckjksXdUVhFwJgOJGKOsA/t1FCVjyhiChv/uP/uP/0q0/Wfg9S//S/9h3m9mpaVVHdrl23NxwV7/dubkq3cslzOePLzn7aszTr5lKe9F8NxvnmIlG/fmoePmNxXhJOBmA/6+5PgHlsULf2gJzVoYp6aT8K/2zNA+1Gw/lDF7/CPN/HUcE+JHl5RWBwbvcOy4+yVL8ySR6kR5ZZi+hsmNbD7dxh9ENbE+iaupeahpnmaKlcL0cPSF8HLdOtCfOUwnBVDTRrbPS2KpCJLRhe2gvpXWS9PJMe1fw5HMr2GmaR6LS0n3MLlKlGsR8Ewr7p29YJiNYjiWDf7uiWb2RriDts0U2ygts236cwLpYWN7JJ/jNlCsM+UmjmJ4xoxt4yrKdYqlCJd+Lsda3Waml+JMPIhXP3MvVJLN8vKb0jFjOgmiKjYiIBQrESJT5UaMQWLzvMR2id1juQ6zdxLwtRet9mIyQH9eHsJhumNDqOX+nH+3YTgpRjFDeLPay7UKUys4gmeGWKpRXBJnaXXjDyzm/fhIhR5D5UTYMH1iODJUtx4/swdGst30kBL+dIJpPP64PITEqJhFSAwJUhJBuDQHsS+NLOP+SI7JT+XeZQ3Hn/YShDO2sIe5dCSKcJIJpfwZC0V9u3dhyxjZB6ntE9z9TIQZX8t4tr0gP1KhDiKG6SK6jweBbR/utG/J1V7cnHs3vT+SkOe9kLcvCNx/1TF/Ew8Cg2vSwSm6xxGYXU/3dI7dBboHBcVKxDIVGQWYERewi2N4lzusDXt26/s1QFHeehHP24FwPjuIcnoIDGe18C+nmlBpcUIOeXSqesLcyX1ctbTP56CkRXs/T4qVsIf31yCPhkW3DocxcsAGZEiFIpTCNDXdyJz1kWw02WpibTCttMIPC4dtZJyAoCn0EAhHpbiSN1EwHH06sH33Ii0wOnjTAaWgYsZuB7qHNbaNbJ8WlGsRP4ut3BM95IMgVdxJwaN7VGOb9++1+WD8jhrEUb5n2do2HgpNe0FuX6DyE41rkpzPWADYIyv2BbAwtYe1T6VMf+IOWA7biLhfXO9QMdN8+QgYgxprESOLbRpdxel9kagfj0mB7uKh4CLz1o3H+v6e2l08iN97gVIc/FrEtPFeFCtP86Q8iJT781Ah44/ECSznLwKbOD4F7xCmlvrVGn8qTORsBeXzs0FV+zEi62M8XEeQ4MTmWSVmFCuFiW5hmNyEw33aPS1xO7nezaOCYhvpj6RY5dpMMoJKqS97/NxJ0OC41u7d4trL/Y7VWCiYiiibrKK+DeggoZRmyJSrKBznUaiUIlrGz0SQLVfx8J56SOg+jM7nPIqicq2TkT+zhtnbARUy7aNi7NJ4z3e1TaQ/cXTHEsxW3Uf8RFONmKAwMYdikOnlPIa5lo67qYxFEGF4z741Ph+KpW4jz2tfa2w/mpbGcxvmZuxOeV9MtX06vE+xSfiZ/HuxTQwzGcR7UVbWifeFFFlDpasmOTneWGqKVXiPN8jvu2j2zzY/0YdziE4djn//nN2/1//+j//9v/yi7d/+u/92TkmqxDYrUshYD14ZqmkFShGTp65LrNW8ubliNqnRQ6IyNe24WdNa431/cJMUeqy6j8zNrCMpD4Qogq1RFoMiBumCUErjnPDrnIGgMn0WR1uM4vDVWgZaYQt5CEuqCIlMVRWsNyvQGmstOmVSLxtwcSIKjzfHxLSqMVaJaynLRt6NQS45KwplyNoQjKULIsIYepRSIhok2SRaaw8On6wV+NHJlsAVBU3TUE9neO8xAZIR16xWsoEry5KcPM5aQi8BcNHLplslRURA0mg1iisJhWEYRVznnLg1YsQhpY0QAsYY+hzJWa59OW4YS2fI48YzRWhDh1EOnUaWnx3T0bMINCIulvS+J2rQ1tAOLcQkriit8Rlpz+97jqqaTb/DuglET45+DHPq3rdxx0gOkbKoD8FIQeVRlNbEMFZcs5H7ZuX+F6PjUaMga8pJQdPsRGxTACKWOGvRWY5dKUUfAlVZjmMo4AqFURofRTBIXqELi+87UHJeUsXQgLj1vPdYZUgqk5ANeEoSvjeppoQg478oSoJvsLbAt+Iq9aHHOE0Sq+44XkCRCMpQaI3Rid53B+EnJXFvq7FgkFKUMeoMqRdhwlpLWZbcbzckMj4nbOGwiVFos5BHEcsash+kdTEOWFNhcAwqoLUidhFrC7SK4tTD0vlBXHw6EQbhAiktYyqNzlNrpMU5ZyTUbRRbUx4wKuCTo6oq/NAJD1eNQmshTjeQe5dSGv8uX54NkENEFU4EYOMIfctkMiEOItblnBmGQZ5oYsKj1uIYDUqQAzmKYDIZXXn78ZaSzKOuk3A62fXJPI5KFn8VEHEzRQnFUglyFOZ3URBSxKeespSiBYCPA1oqX6ghjmNsvE5IoUcVWpzn2gLCzkwpkBISdpYzpbEHkTSFnhREwDycgxa0R1aK3g8iuGuNzmD2a0WOIjUVjrbdkdKYymvkS6YxDrysxdoYghJXA9mQEnTDwKSqhDFsLTGHw5ojorUiRVmftVIiZCmDzwGrGOdWIEXQhRZRNoRRtJY1zYeAokBpJGpNcxCli2lNu21Hx+m4Ro8COCqilRxDTDL3bDSU1Yy22xBywjlLWTl292uMMeKcVQhDJL0PZstWRMOqkFC6RCaEhCmMtLinhLUyXrNSkJKsVQfn117YD7iyIAz+8N7T+YztpmFIGVfXaARjEWOk1PlQyNJaBLysEnmsKOecqcuCvpFrUGmLj+KSycj4sNoRYhL+d5KAvJwlwbaLipwjxkQKJcVOjBXx0bfUVUEaP7vzA0FJ0Uspc7jednSXW21IYSDsuXEIP9sj16JIYNCkwRNSAjUWQ/aO5RjpNjvmp8d0vhNHsvdjETainRUh3Qcp5ioYYsA4y5DBWUvq5RmXTR7ny/vnWFFOxudWxqjxuYSgJnZtT11anJIimFES0GaMEQFcacgBhYwzpS1hEBG+MAWd74g2owpxUTvnCL7D2BKyZogJp41c+6QOYzyhcDkKlihEecZpI10tiLO+MCIa/8O//5/+lWj7z8Drb/zef5T3jq1Qjy3ipR43XTLXTJ9pTw2xhId/uqJ9OiUbRXdsqO/CmDitDkFeyWn8uBHK6j0rT/v3Tpbk1EF8y0aesb5WuDbja4XtxcWz39TtRY+9U1ME3PfOl35hmL0WESNWRtrxR6eUiiJ07AWC9oEjFopyLSFA+43pXhDxs/cuqGI54BeFbNyMBODsBZZ9Gvt+cyfH8v79quuB/rQY3Y/6IDRlq9F9wB+XB9eU6dPhGESgBT28d+fuBeisZcO6F3Dl/qQ/99/JqYPgQsqHgK5hYQ+bUO0z5d1wEK6Ag8C0PyaVMqHW4qYaRebiVhiUcVqIa3A7uoC1Yvu8PDjigFFwNFTXA2G6H0tyD/Yt1CCix16I2Tta94KthEDFgzi3FzSHhT04xvZC1J49unfV7d2bw2k1ChGROLGH8b2/j3u3JkCc2IMjT66Jwe7CwXm3F4RVzLj7jv7h5HBcezF4//7AwUG4n2N57Kbai1Rh7g73a39uKuTDz+zn5PuxPB5XpejnRoLOQsLselLliBMJrds7OQ+C4uE8PXHiSIX+c2MxTtyhMJC1orjr8MflQUCTN0HuhxccSKjNn+NGx1IfROC9e3I4kfG/v592MxDmxUG4VCm/P9/xOFOhD/dv79Y1jad/UB0ElT2fdP/KVtOfudGtmv7c2OnPZZ69Xy9EXHLLjuGsfv8eRr13wY9Pv724cjjeKHNb+Joyp/bj2DbxZ+apwu7knu6v037t24/3/fjah5HpPh5E371Iuh9zfm4P+JKfvRfuvhOkjTPy58/M5/0aW936A4t4P8biKO5nzeHY9i/tE24pY9vuAn7uMH0ciw7msEa8D1qTML+fnSs/W/zbC6x7cXN/r+0uHObDXkQ2fToUKso7WUv2Y2RflNnfFxXz4bkQxvb8ahkkdGxqGeZ7fIgwSvdzQdaX9wK46UVg3BfiTBsP80cPiVibw5h7P4cPb3XoHDkIkeP62jywTC+8cLP/P/berde2LT3PetrXDr33cZjnudZcp732rtp1dsWxYxNCotwQxAU3BCkSCC5A4of4lwASF4GIC0IkkJAQyJGjoIhIDrEtu8rlqn1cex3naYzRe28nLr42xiyLmxIXYGGPUmltrTXnGP3Qemujvd/7Pt/SH+Y3mTLxOOh60p773NlDgWv/XIznnnCra8d4Yg9s171AuS+0pYUchGRTFCfQfUiNmWyJKxVOdYzrujKeWg3POuiv88Hpul/L9usbQOr0XrpW0Ny/3DYf1uPsTeNxl8OztF8/JFXCdWLzJDS3+y89C40lPx/Zw3eB1GmRQl2/TWyOlbiQhoZoKI52neeVxU3aNC53iiHzrTDUf7NjuhgObty9q3h/vPuiw/57SjXqbi7eMC+F/jofij1ulw8pjdw1obeJ/Lk3h7lnP/bTQg5CupvqQfiVqOc5ryzhPvO7/+RXc9r+xWba/qN//DtiLFaEnMue5EEWiDWRTSKZxG7aEWPkxOhCtc2RiXhoQGO0Pbu6TmrGGEGcIZWEsU3oscpliinqFwdKE13VSWWcIZkEITTGYsYaS63grAMqRoSUs0Z9q0Zqay7MNZFLJSUVCkHdWuIc2RqsCJ043H6zXjSuXCrkVKgYdcBIJVHIZHZli0E3jbVWSioqoFGpRkUTqY7ZFBAhUalVHa4Vi3GBXIoyhnJBKEhVV14wgq2RYJTr6J1XprATjHfapEUMKUYFt88TIlBqZHAdlEhwwpiUU1KMYkPHmDSObQQnFmsdRgy1JpwY7nb3+K4JwlHFuv0ozlJACuI9uYC4QIwqYFqrDr3gHbYJL9brBnRMmd45conUWTfPIgJGqEVFRgOkOOFEtIlXgVL1eor1FGvIedYmZlYojf5LG4/OdYgYUs5QM3PN9L2n1ExwgTQnHIIzlu0cMSKIeMQIpYK1jtB5dSS2pk5ODLUWxLiGn3CUqlXgHo3qznVGvJBKxYjBWkdJmUpFHFCrinipkGKk6xbMU0Swygvte2KeG9vSo5pgIZdIsgVsksiBAAAgAElEQVQjhjQnTC3qgjQGTKcb/FyIKeOskOKkzsXOYZwwzpE4R2zwlAo4cAjeOVwB6yyxRkA7sMdJGZed6ylzQbwjxoQVpw6+mkjzhPcOMIg3pCagH4AyuWKweNT5Jq1gYEpzzCBYqThRpqqxHoPX4u4h6lVxtOSTc+RSsM5Rql6LnKKKMzGRU6JbDIzjjsWwINcMAhYVUJ2B6rSjvMMiRvnB2Vd1RTuNK5esDgw1wlWdA+LEajFQUsKiHFZyxhiwVOacWvOjBDZDVVHGiqNaR3UBEyzbaQdO0ScxazOqmqAgGHGI9zgMKScIRcVNUSHLSqXmiIgn15FcVSiSpA7iUgq2JJz31KqReN8FStZuq1qICRgx6lqlUqo6yEGYYqJWo4UZEbwbMAglV2opiKnaLE0KOSa9hlWb+fWdp84zKan71TotCuWkCIg0ayw9NxGzYnAVrFidC1MmG8AKRYQ5qdvc26Bia854F8hitNtqnDCi/NwKpHHC+0CKCWcFqQYplWQyYgTf5jcBvPFIhTlOYCrOGQqFlDO9d7rOAHkvqFVlHpeaDuelw1yQKnQWKIVqKhjFBIj1OoaNCsXWOqoxGOdIuaggWCHPM96IuqN3E9kUXBfIcaLkRGc9JSW99o2bixFyhmyEAjjr8daTGnu4Gj1Eax211OYMF3JJhH5gilvdEPhCqZWHiTPjTbv/BuZ5R60TRgaMeP0yUwqIBVOJCDWDGI9UAbHUqMz4TFU11Ri0ZWCmpBlnKilO9N4Ty4TtLMEqONdZofOeHGctTsSI8wEpGV/VQV2rwdsOi8Vp+UG/IxRdByiVXAqxJAhWXc3GKH4EwblATppaEWvJs643Mc3kObJa9JqMcMqjc9YRxxlEqEVZ1RVHlkpOCWMCzjoVc4tyaY1zrXsv1JxxTeBVvHDFm0opESkzXgzOGqRWQCg4onKBFFeUC8mq+9oieCr/4O//FdP2L8Prv/hv/tnvFK+bfTuVxqdWPqa6p1SQ6d8n/A7uXg4ghuHNTHeT8bez8ujaJu3PcfHaBmnv6smdaQ1FWmGuaS4S0UimqFPRGEO4yy1GrJvZ3CviQzl4WdmPUR2cpkK4z61ZWSStw4HTWp3gmmtzPnbq7porUppIk2oTCnTDqT9f1J30dqRaIS8aXiSCpCZYNCG6WtOYdhqN3TuPtQP3A2e02taYqLHw0jqo0Le2uLGSB8GOD7w7v2nJv526J91WhUGJlenMY6ohLYXufWtqEwt2LvjrEVMMJhVKZylBBSNpm/DFlxvKoO4rmTJ5UMHV1IdjVRFGxZB9nHcfB0/roNxRMSrETRn3YUs6HbCznrtkZUfur5GhCUOtwZaKS9rMTWLRa5LVIVydOdyXvdBpoP1eE7ya02o+UYdXWupGugx6f7s3W0XGOFFm7ayfkVYOv0mHc9tzbJXjrMe1F03mE9fubTp8bm0M1YNg5VW5yQuH22XcTl29+jt6DVNzyZlU9Lo1wUqmjN1FNfTsdPzomNF7gRi9tptI6V0TTpU1iWhM2G9VPJWkexkVbbW7fPX6nOz5w+6+sWuP9ZpVaw7vnZf+8OzkRXM3Ojk8p3/uZSAtXHsPOTT8Q4w6SYM0B6WySnWfuU/p6fkdrnsTsfbngBjsmEgrjzSRdT72hPcj06NBn9PmhgVD6TUiHY89pW/O6saYVqetNFa1Hnfp1GZbOi2OjFc9krVAkFb28JyWILh7ZY7abdSCfrs/1anYo8++0L9W5JPdFdwm6njP9cERaRt3tYmie/71XpyWVA/Pm50Sad1pQWalYnAJipYwBfKgqAk7t3l2yqTjDkT0WkzqDHbbCMYQriekGJ0DnCEvLFX0M/fPqE7EBjsXSrdvnqbjZH/+eXB6zOgcofN0PojKNEGzdNLETv33/bMqqeJvJ9JKMSfKAi8HEbZa/XyMeSgC7P99bHNUmxf2c3Qe9LpUJ6SlPld+o+MjD1bXr11hPvVIS+Xvn/t9YVKaw1zvrQr51Rp1odPWsTZ3HZrK8SA07h3CbsxtvRPsXA4Iie5O5+TSu0MhYe/y3Rcg09oe3lNm/Zk8KPfbjfXAZKcJjg/NwnTMxKXQ3baURVGKoYG965A8CNmro9hvVNjc33NdN/dFgDZftDFe22dJghwMNrJ/48McJVHXZbfVhl1ho+7/uLA6Z3dGBeNZ5xPJzd3sjGIjnOpd+2PM3S8J+jsVNqkqdKrDWtejKoZ5bZGin+EaFkKdq7Wtm+r83j7t2+e2Ilgv+G093HOJ6mi2c3PxWj22/cRnm3t4z/hVnrI+j+qh0+Pf39M0CDm0daLqe/utjksb6+F7grtLVK/j5D//B78a0/ZXb1n2/8HLYqBoNNU2Z6ZdaExTKNSS8E3oBJh2IyWpC8x3HdYbhEScNpiaCJ1FTNEIqwg5V6KtTKayy4k0gamemi21uQpFRKPfgBQhzYmSVXhJSUWBXBIla/MWKypoGBEyCWzBiWHRd/QhUKNy9vq+BxOhIRXmqjxAY4REJKYJVyJBKo6MKSpAlxYp7oylMwaTovIIS0V8D9bhQ1CUQckYG4hTxuEBPe9aEiY1x16szDVp1N8IJmSq1ShzKkXdTQZK412mmqjoCHcOPAFXPZ6AZHUI+U6bNRkq3lnEgAsOGxyZomYogVwS8zgRJDBP5RARRTLWq9BenB5PKYkUIWYoxpBypYihWiFNEVcFKRmkUmylpEoQy8JbFWcBFyymRhUp0oyEjmo9uwTV9Rjn8aZQjDa2E6eVtDjtDk2lrDVYZ3BOGnMR5rjBmIKzhWHhsUVj+L5bMY8RWy3eddRicN4SgooDYirUDCYTp1nd0RUd81PC5UrcjsRp1oZnVZtupWJUmDRB3W9StbkUGhcffMCMCs721bBwHsmFHCPOWWbUeZdjpu/W2jTJqTPVWYtgWPTKlOwXgzoynWMalYm52+3URVxzY3I2PiRF/44MnbJNnRWc6u2UKarolRKCUZEsg3UOY9SZtr8G3ipuY86jOgWDxRmBMuOMajTUSIqTFjpMwjkodcZ3igkoVIw3Oh4olCpkY/X/pUWWC5SsorWVQK0ZZywmCjUaciw4Z6k10Q8OJFMlMyw807hhteyJaaQSlQ1qtMCDKM+0mMiYdmymmblmnHEYyZQc2e12VDLOG42gr3pijRgLqUwYV3G+UqoKftYUTFXHMSYj3pKsugpFoPSGKIkiGvE3VjsT21SxaEPCsF5jrGIHzNi4dJ1rDlBLZ1Uo3cfMjTF4WeCAHEflEBvw3pFa99RsKsXDdtzoPCnqGqQJQSYXHBywICIC1jHHTEyFgieWwlyaCGsdVloKoarztKLPiZFCqRGsunareRhPzigOxlKxRgUraqKWiK0FWxMmz5Q64xxUVxCxrBcr/aw5Q1Y8g3OBIBZvnaI9pCA1Ycn01mJrYtV3OPGUUpUrXVQk1CJa1EKSVbbxcgjYWlgsFlgMBphTxvcDVZQ7jhX8ciD5VnjC0FtP3V/DxoiWtsY4IxiBVCO5JN3E9YH73ZbdPLGdHxp7SK4MvsdUgQS2Cp0EmBPSRPd9NF+TAJacKs4IwVtMTg1BoY51UzLOObzvmEtV3IjoJn6aJhXqa8IFvXc1GkzR4sKuJlKFWg1drZgUOVqv6bqO3gekQp5M61VoMXbQBIVzmJwoVpvDSWs+1/keWwSpBWtAcmawynodXFDnr3N0zhN8prOF4Ky69M2A+IAJAdD+iBI8gqH3gUXwTNNEJRO8peZEEIsrgi8OXxySDMF7LVx4ZdgGKzhbcTZRcyZOiW4QrC94ZxkWPdKY6IKhxEqeI10X6JzT90K0iFXVEZ/STM4RU3Nz+TcXtRGW0hOq1+cFi1ToncWKofOBpXi8EaYcmW1hpiAWnDWUbKnFkYvVsR484zRR/u9b9b96/f/0VRpzbu8eUTHBHDaw+6isNG50d1uaUOY0LttYq3vXVFy5g7Oq2H0su+DvEv07xSNgaO7X2sQfDk7RPd9O4sOmXZ05+SCW5c6qO69FngFyL0wXHfG0P4gm84k/nKckRTjsXS52bAy/XogrdV/uh73EtsE+7ojHQV2RqR5cf8CB07l3G7lNIg16DnsXmEZK9U39XTw45faureL3m9AHt5bb5oPbTM/LKh9w0D/VQVz/HAdxL1ClwVKCO9wrqjrF/J0iAfxdJi/Cwa25j9LvnX574dRtUvvvcvg3O+ZDnPiXr1FaB+Kj9eF402APDqq9cGVKpXu7a9eEg3OqysO1cvfzYdzkJv7shdoSBH8XW5zeMp17FbJ3D5HpEuTgDs6DJ679IXa+f7ltE41lLwxk0qDuQLdJbfztnwflEFZn/hy7eC+2Tef+4BJMC2G8DA/3ZrAa3W+i1HwaVPxvjsu9cD09WlCdYT7xKqoNFn89agOcNxtFMfwSXmLv7tIo7oODNveWPPgm6v9yAcKQlq4Jau7AFd5fg7QKev3uIiYV8tIfota5l4Nj2m4j0p6/9EusT9s61CvH8mEM7RnJ0oSo/XHui0P7Y94/B3s3o8mV+aw/nON0Fgg3kfFqcYjt62c+OORMKs3JFxm+mfB3UY+7PbPd27EVfZTVOa/l0CjMjg+Ylv08Urw5iFqAiv7tmVAXomImtNij2IQDjqNzjRXqDuNo/2daiDpAF1ZRKubBdV1F0TR58MiU2V3sURoNe9E+S2I9MFeLl8PzexCHe6vFh18qDri7GbdJh7llf31yp/gIbfSnYtoBndDmo/35m6JoheLMwX2pjt4Htz+V9tw2ZEEbZ6aomLV9vjiwf/fHnHtDXPvD+pI70c/oLPOJohi2V4Hc67g9CN9jboJbK7As9fe2l/o7u3N3QD/snZqmcHh+0kIO61DulHlK1fuR9gLcIU3R0lm7fPj8uHL0r7Z0b0e6N+PhuhVvGM/cYWzmTguF+/uVlg5/mw7cXgzNMWsaAsU8ICgmXRf2TFl/Xw54CZNR7MegzNW8d9iWenDKdm9HxXhkmNe6Dt0/9cxrYTq2FNsEzv19bsecFjrHK487Hwqv01pxAnv3dvGG8dzr3HemRcA0CNPaEpfKpY3LB4fvL7vVJWkyp1hNyOzOFGdgCsTlHgkjpHbuaaECb1zqf8fmmp0bMmXvMHbbwnSkx5kWwniuxrs06PjYi7PTkejxDfpeOjc94Jpsc/O7hkMwWdE020tHXOlYKe1aTMfqMp6OLNsLSxyEcKcNDP1dptjGL861pRR0viudJVxHLSj9iq+/0E7bf/jf/w+/o+bCFo81yjB13jZxRfkQTnSTYrqOWArWqPruC4j1SGsiUnLRynRObDcj/bAg51EjA1ldq9ZqRNG5AKgLN7b4djCOeQTvLMaizc9QEbFI1yLWM77rdVNLwRuN1+Z5xotWS7o+KGvTB3JMLQYbqUV/1ldL5wK5GuUtpoQR26omQtf1lBwR0WZQzjSHqFSsFSqGGCNirYqJ3lJQ16BUFdAsic4J1jQkhPVobSKDEZLol51oKnPV5uAGENRdlFKm61YqHjrPOEdc6EBgjonQ94pX8BbnBGLEShPbiEhJmJrpPK1hWAc10/ueEjtsNcQpYp3Gm53zIBUrgpiKJ7Na9XgHOGWFlipY46ipIkawtSA5QXPudZ2n75T713c9N3fXKm6nhO8DKWdc6ImTMmZrraQ0cxx6LKJOxWKgWEppsSNjVIhqgruOmUwtWRu/RBXvrFWRwAUVYeocMQYVZJwWEqxpnCOxzLFQHBjrECMEJ5QccZ2lSsZYMDmpq9kGyBlbhTlNzCkRlgtiyipimMoQHLUkpmmHD+qOdCGwm3WcxHnCiTqVe2txpWCrwRl1m1vrCKEnNX5nTDvEGqyFzlo6Z+idPodBDLQmQMUUDIIPQavwxlByZggOZzLBCTZYtttb+s5DbfBzKjFHpjojwkG0CR34Ch5BjAM8saqDkqoVSWcKy26BMxZBq97BtS8ZKWLFkMkYM9NZr+480SZpaZ7w1uCaQO+tME4TYrU5k2AbPkDUZWmFvu/J2TD0S1Kp5FRJBQIeUiVYj3UVFwyDNfic8MYydAtMsUQAH5ijztwiGpc3YhAq3gouGEyJOKkMzqGP1Iz6OQuud+x2G7o+IMaQ0j1dsBATeHXVZmOJUyJTcT5QqmGWgoghGMPJ8ZpCwthKjKllaBImgs1WmxqhbpXOC2m7ZTF4KBN1nOj7BXOOOGuxRUVHX7UR1eAtErd0TjA1sQhOxVUMldSKFTTEykQXAsYIVtQZuXfI16rzT5Xcmlllhq6jc45l12FNpfMeAZwYcsxY48m1Kq+4icaLfgFTpjPQOWGwOiZNTRQKuXF3bbX0vjGxnMcWzT0s+o5p3CDWqDPVVpKAeIex2sDu+OQYnONuvMY6q67tGBvepYBzTLNiK+acWQxL6jwSjGIdOutI40TnHCUlKlByOhQFjWgKhZrorUFqJqWJ1XKBkUIYPHWO0Jot1lSxzlOsMPQLCkYTIQIpzg1BE7UAaXQcUgqdt3Te46vB5sJgHcYWcuNc4ywNPY/BYESopeKto7MGL1r4Wi0XpDhjqlafrXFEQHyPs4Hpfma19NQ60gVDZy1xnnHeE3yHLSr+YwpD1yFFI/4imUXvCMawvbujD0HXMwNzjJQKoYmvCy8E7zg/X3N+tqLvmuM9KmM5UzHeI84R04wRg7eVobd4V7h6fEwcJ0iZAoqrMVURGq2QYGLGB8vQOVZLDyZSy0TJM4bcmoQVagHfnDvLpaPkLU+vzshphzMZyoy1nlh0re96D0nXVLGCdbbZnDPeWDT56NteU7CltHRRpfQB4ywdynhfO4uUCSuFWAveC7mhPYITxUCVxH/49//9v3La/iV4/Vf/9e/9zqFhDvo91Y1FxZm2aS3BUPq920n5hcWbJgrZFpFtIkDm4CbpX48tml8Ozp48qMDottpxGtQF6jbaSCKuWjSyiX97N5/GhPVPfzdRBqfupk5Iy8aHm9V1iRjmI0e4TaSVPYige0dtdbr5m9eCm1Wk7lozpL1wG4+cxiftgzPrwKT0D47hQ7S3xdhLaG61JjbOaxVSkOYs2hub2nXes0b9bWv4Y8zBpWnHTFq7xgrd80vV8RbudPMv+UFA3ndVL707uFsxiiLQ+L6+/3xkDw6/veBpp0JpIl31Qm0uxc2VbswN5iBWlfDges6dHFxFxasDajrRFMB04lh+tqF0Th17S40g50EF53hkD47K+xfDwcG0d7gdmMGtgFBdY3829x5VRW3EIKk0hm15EDtLPfw8om7Q3AnShCN3N2sXc9H33rsbcxv7NGTSPq69d9P524i/jQcXbW2R9OlE70e4TeSlulFzb+nejuAaSqJ3lKAs4704nXrlsldnSOuA22XyMjQh21K9EI8d80oFv9Keu/11MFn3yak53PZ/N535xtpVIa9/vWU+7Q7YL2kCpPuwbRs9fd+4tMSFiuB6bw1uM7c5Qq9J6YTdZXP1Ov27uNp/vo5DO6k7Lq2diptN2HO7rO664WGchetZnbd7B2h7FnOv88t8bHVMnSlHVp2UmbTSwkxa2taAzTKdtGZZgzCdBbDKLD10emf/XJeDQzgNcmgalTsVovKgLsIa5OAQ7t7P7XzUXRuP/aHEuX9u3X1UN2bjH/u7SHXaKO3uI32eEXXTt+GtYysI8cThdnqs85FG4sdzbcZOhfnU4TdaQNnPRandr/HM6574qDVIvQjUYBtugoP7EjH420g89ofP3Ts698+La8LoHmswnXnmlWV3YRt/1D4gDeaGw5irJgTaHLu70Pefjx1TmxfGU93rSlIHpW1O6/lUdZfpRI+3CuzOLd29xs1raxTsWuGsWiHcxsbvNay+nCiduhwPuJuWdgi3qrWE26hNyArEhT5HcaVM1Hkl2NQY7anqOtZSbCrk67FVp2Ln7qoDa0krd5jTMeqs3K+D44lomiCpyG1HnWP3GAZphQxTlMMa161AFwzTaROLp4IbyyEJsW82hui6GReWeS2Nf65IBjdVdZVJm6/G2sRwRQJtHwlUPfd5LfidJj3ishX/ej3f8cxRHMSlCrnjmZAWyuAem6guueI3BZv0GvY3hfFMiEvD3Sdw9wnMx5bUCyYZ/FiUPdvWTb8tB9b3dGJIC8OHH4KMqqlI0lQMtDXVPeBlcm+YTw27S3VAmwK2zee6hhik0M4HxnNBkuH9rxkk6fUwVZ3EfqtFrj1XNw2Nd+wVUQhayHJTbc+JsoOLN4fvMIpb0PfYPHFsrtzhZ8JGMRZ21oRBWuh84HaV/+w//tu/0nftv9Ci7T/6b/+736HWxiqcMFbo+p6KxVbBYjHW4E1lcEEdgl2HVEPnvLLr9mZGPAYHJtF1jhAszsJgLU6KiramxV+LxtLFOQqWbggomGBHZwNTmnCutVFKM8FpPNZJxtSEGLX4p1zAe/I8Y4Mnl4x3tsVMA6mCcwYric4ZAo5xs6N3Ad91jPOEcYpcqEadcilVgvNQMrXM1JoYnMeapCeaC4VICJ6cE8EFtpuR0FltoFUywVW8GNKs7C8jKnJKbbHW0BNTJtWKiKG3hvViRcpbjX/bSLdvHFOVxysejC2UmsnjhsFZSkkYk9mOG4w12KiOyTLrcToBkwuhD2y2t1gzseiXjLtC1+mm0boOsQNUtPN762zvpZLnDUYStShXtqCYC2e0gjnFCets43DqPaMUFkMgpZFF51o37YBJGV+1WmrIdFagRGwRrFNBf47a5CWWQkLF1VwzIdjGAy4MncXVzHoRyGTEGLThnLI+S56xUvFAFxyxjKwWC8JedDRQMvjQk/NMLRUr2sU8x8jgLZ0ERUr0oqgI38ZISXShY7vbKZc5GIRIHyrBW+Z5y9HJkhhHxFbmFAnF4AEnFepM77XZ0mAttSREZ0rECps5kiQdig/FCiJFGY+9p3MOSEBsRYfCOM244Ii1kEsFMl2wDFadYJiZFCPH6xXjNBKz8kEFENHmWoMPuJzVQewSMUfud3csuoU+0whzSfSLgbjb4LzBUsjTlkUv9D4o05qMmEIsE75TR5wUGHc7us4x55nl8ghKwZlKrUWbBHntAJuK8rHWyzXzPLEIgd5b/UwRjIUYG6JELFOaCCLEHOm7BdlkpumW5VHPshNMLQSTSfNEKZm+WmwROmehJmwnnJwMrL1wdn7E0WpBLjNxjkxxJuakRY59fMQb1l1HL4ZPPnrCInh1eIowbSa8UwY4tWgsP1gsiaOF59nFKeenA30QypTw+y/vzXmq2ISIDY5iJgyJl1eXDINDxBMqzHNGhh5TRAVPZ9A2cm1uFOHJkzOGvtcCXJwoNZOmh42RLYXghGVvIY90Qag5slqsmMYb2ndWppjph0GLdwhlHhFT6IaOp08vsKYS40yt0IeekYIxDu+NYleKOm+vLtecHC04Pl4yTqPG0kuhFm3OVgtkSWjaKzEsLCdnx5yerzg5P8XVynbcUUvBZfBVHZjOabElx5nLx6ecnKy5ujzD1MI4bkkFpBXnjKDXFrC5sOwCT59dcXZyzOXFCZRCigkxVpsxtv+BObhge9exXASdp6Z7TC1M41YdoaFTodurM1dLFpU03rPqPVKyrpciWGOhKFLClIL1ulHovWXZL1kdBU7OBnKemHazisbA0HXknDAULAXnhN5C38HF2ZrnL55wf3Pb1uaAsYZEJYpytG2JUGaePHnE86ePePn8MavVQHDC7e0tlMIughid54NUvBROT464ulxz9eiMs5MFxycrpnnL7f2GWIVihdB75hgZup5H5ydcPTnn0dUxq9XA8fHAvEts7yaohZoNxuiflERwGecMz5+c8um3XtLZwvnZGT54bq4/4CSAs/Q+UFIhGMHbwNXjNS9eXLJaGs7PTui6jrvtFmOdYhc6R4kFbzQNtDoKXD065/T4jMeXxxyvVsy7SMwJ2/XM40QpkW7wpLgjmAHrhKlsGTrhbnuPOAGrmBip4IxQqbjOkueZwVsuz1ZcnK85Oz1GHOQ8s91FxApOhK4KNeu6lWrlP/or0fYvxeu//Ie/9zsYyIu9A03joSZD9aY5p1TUmE6E4oTx/GHDW2z7jtOcQxrvV+ZoWmsjs/HMNbGxOZka+676B1dPXLvDhjkeWRW+Woz60JSmuckwGosFmvupsXHdXriVJpgpcqA0gSMeOY2TXiemE0caDN11aa67X0IDjFk34y2KKqkynj5EhPdi2Z5hGNeW/puRtPLqSPNy6GLvW8RVxdgHQTYtnYpPO41JzqeBzROv7LsW2Y1HvnW41vjwPoYujeU4njlsqiDQv4kqbDjTGId6nHvX4byyLF4r33F3bulutBmLaw7rvfAa1w/x67SwuFnXaGl8WknNZdSi5uF6pga9trZxFk2F8dzipsp03um5exWM1ZmnY2peyUE0ysFow6VNwzJsssa8a4uBr4OKX14YT3U8ba+8ImIsOuhEI+uSCiVY0lLvg2/NfuLSHtj1EqvG8KcmTnlp46cwnqkjTZrIaOc2tlqKLR55ujdbSu+pXp+J6VgdZuG+cv8saLzXGWUeNjdhHhTFNx0rkmI8FUxRIcK08TG82mHHpD8rgsRM9aIc4yCHa2aqCj1uroQP0wGhIZMiEdLaMZ5apmMdS35XuXs50F8n3DaRF5bi9DkqQ2A+DeTesjvTYwvbyuLVzO7CQUNBubuJeNrjtqkJZIKNMJ7q3OC39VCw8PeJtNTzrU7HSTzy+E1mfKTu9dzEQjfq84BRgdAA90+DNnA6s8xHTVhqsWW/VRc3RRvF7YXu+dhhp0p3k9hdOMaLfdd7dc1J1nmpOhUdDaY17nLsHhvuX8L2iUFmg9tpqsDdz8SG4cAYahA2V57pWHj1dyzjqVPxurOE+weuKTT3MYonuH8WePvXhc0nmflIIAtxqf1n3O6hCKZFEaPoGDG8+jcd04nGx+NSCPflgYmc9u7l5rRtLtd3PxbGU0s1Bjfpz7ndg6WveCEvVYAFncdNhe1jq5zvVlAIFb4AACAASURBVDhxt5OK/EUFK5uqJm9Xhvc/1vvndiBVHZP9h3hwCOcmgJdgeP9Dy/Y5bJ9W/J3O/3vExF60tHNtx2EYTw33L4TNi8r9c3WY9x8as7S3fy59UJze2w/fD9w/F65/qHuwcFtxY2m4AwvSrlenvzOeC+9/DJuXldtPq2pLkzmM3z3ixlRlnCMqou8utOAXtlr50GZUOtdI5CE2b3Qt8rvC9rGKd3Ft25qqa6EWlYTSovvTsTBeCLtLw+YZSBK626r/XmE6dQdMQW2u5+lYmI8Md9+C979R6N7qfDSvrRYrRhXSMYbcOL0ffmTY/vWR499+x+tLz27tWX2l63f/PmnxpAnfaTBsXhhuv5+QH2yYXiY+PAu4e2H9ZcLvSlu/dfyPp46b78D0g5EX33nN+nyLf7rjdrOgfw9UcxDE96J6XCoa4f1vZF7+W1+Q1oXp48TdEFh+ZUi9zo/TkbQipYq71z+o9L/xgekiM32S2KwCi1fqGpYCcTDYtBdgVdy9+WHBP9/Qf++OzdNC2nW4nc7z4T7rHNGQRXGpgmt3WxhPLauvm9u+6j0uDaVgimIX3FSZTgw3n8L2W4n4cma2msgb3pWDS3k+sgc8kxsL/+l/8nd+pe/a7v/xt7z/F17iAoXKFGd1XSHEcaaK0U1iShz1Sy7OB4J3VAPb7Za3b941Z6NuQuueW4sh5sh6GOj7gAueabzhw/uZo/WpxiAzDKFjpjDniVQg7qxGIxMUn+iMwaIRUmMdZAjoRlckYDAMXphSpLfCNdosSPrAtjUoyjmz7lfc3t5p8yOTefH8jKEL3N+/5X6zofeVSmY99GzGCRsgpcJu2rLqHCen55yfnGKxfLh5yzhFNjut7uVJ6ExHmTJDN1BMZXm6Zu2F89WKJIU3b94x7ma6bsk8JabG9ZvGkRB6RITVynJ5tmDejayXS+7vdszzQM0VbCWjKmPOmRgTz56dc7S4pO8CkhIfru949U2iL4ZoHGOquOBJ84hznjhOnBwtefHsEb4a5nkmTa8paGw0TRnv1c081cSqc5yeXvD08SmSMinN3N7e8uWXr5DsKAblCXohlcLRsKTzho8+fomzGWscm83ENM589vk3SC34kJimiLMdZIeVjouLSx5fHGPrzM10x/V2y/btRMoT4gM2q7fMi8OZnrPTgdPjFash0Hnh+vo9n3/zmjlB5zpijNrEyPXKg3QJFww/+ORbgKI93l9/YEpCopDmjLM9qSS860nzhvWyYwjC2eWKXBd8eH+Pq47dtMUZR3WeOcfWpCtz3C94cvmEy7MjSDN3m5HtTiNQc8oIQrGKNChGXdSPHp+xHHpOlp5YMpvdPV9+9Q2pVFydMQzkGAmd4/b2lhcfPeP4ZMXl+Rk17UhT4fXr99zcXZN3kWXfkWprvlcNnet5dLriyeMlXfDMaceH63vuNiO31zPedpAysSiHrpaRrl/yvR9/F0Mh9NoAbJ5nfvaTz6lxwppAJ548JvqwpPeBp49PWPaO1WqlwsS05euvv+HN2xt8WJMnYU7qhl4sFgTvOFmsOL04wVshjhPvPtxzfX1NqspzHorOR2OOdM7jnfDo9JTVR1fUatiOI6/ev+P97Q4wJIuyqGLCmJHHp2u+8+0fIjURLEzJ8ObtDT/7xZfUVIgG5nnGV4fxBTFwfLzk4uiYx8fHxHlkF8/48tU7vvz6PdZ6djmRMRwvl8TNhicvLjleBRarVWskVnj1zWuGLvDN2xtMbXEcY5Ay8uLpJU/OTxh6xzAMOOO4u7jlw/Udf/LTV7rpC1Cqol6qm3j++JwX56dYq4mD7S4ik/CHf/RTpjni7MB1ntiR6J2jD4ZnL5+wWniG3mGqo0yFr16/46uvX9MvQ2M9a6GBnCg1cnV1ydmxdm4dd5FpvGGcEiKepRuYtrvWRLCwPFry4tlTjjrLydkRuzQzTRM//elnvH1/Q5AeEPI0EzpDrZHnTx/z7OoM7x3OCc+eXDGOE3/wRz9h3M3MxhFNxYuwqZH1YuDpJy9Ydp6ltyy7BTfnW07eXfOnP/s5xQYtYHQ9tRZud3d87/sfsxA4Wa/pneXJ2QnvP9zw5TdveXc7stvNWGvxwTLHLS+ePObq8SXr9ZK+C9SUOT855vp2y09++mdsxxHrtVBhBXb3d5yeHHF+vObx1RkxJmqx3N1t+OnPP6N3C3bzhIhjrgmsInxO1z2XHz/m7HjFOI/c3m74+Z+9JfieFFIThUEoBBE65/nO9540JILnR9//lD/+yWf8/LOvMMUy77bqSu0UMZJr5OmLx5ydHtF5hxPh4uiYL794xZ99/ZpdmqlBI5BZDNlknn/7CU+vzjlaL+m85WQ1cHV2wmZ7y91GHbelzlg/MKeJ89NjPn76hMW6QzpDaAx6EYjbr5kz2M4zpgw5kVPl45e/jg+G0GesVQzFcjjCUPjy6/f0nWdqxdC+64CJ733ykovTBZ21XDx5zpwMx0eRr79+xzRn5V/NER8Mc9rxrRef8Pz5KYbEsl9ipON8FbF5reOkc4y7gpfmJKqOjz9+zhAsCz8QnD80Cvzjn/6CcWprh3RaeDWOaHb03vHXvvNtxW+sjvnJn/6Cm5uJdGgS0RqYZWFYBh49vuBsuWDoAzElnjz5Lu/efWDzr39CrbDdqbPXBYea0ip/9frL8do38wjNaapNlDTqPJ45bKzcP7HcfrtQFo0n+sFy/FNDDtBNjbVX9txa/f3NY8t8BKUDdw9HvyjcPbOEOxUEp3OPv8vYu4yMEbtVkXcvks7Hjuz3Dit10+071Benm9zdmaO7y4zHlqNfTFTRRir9W+W8ypTZPutZfrY9ND/65rcs6djTvRH6Nyo2mALxSVBR0+vGvXsf2T4O3L1QoaW6zPC5x9/B8LbgJhWK942I9p22t48Cmyvh/uOCTIbjn6pAOp8JflMJdxrJ980pW088m8eWm0/BbQ27x57hdcVvmkOUh+i2ckcTr39ryfZJJa1V4Fx85rj41xya1XQf1JFrG8fWbQv3L4Q3fyNQhordgJ3cg9u2xbbBEG4Sm6vA3Uth92s7rC2k2WK/7rj4/X38tjYhx2BH22K3htvfirgw43xm925ANpZH/4JDjNrf7YGLKubcfFuI39/iXGb+esnyS8Pi7UO0v3S6TU0LjUjffDJw/xHEi4RfzZQvBy7/DxVK57Ucms/sG4KBbvh//u8pAsffC+ufV8K9fobdabd6GR8azNw/DUwnwv3LikmW1WeQOk//IR/i3m6TSScDGLj9qOPDj6D79BaA96+X+A9QxeE3DzFrbcSnbuAP3xOm80L/7I7r0VPfdZz/vkaJ88K32HQhrh2L91te/a012ycV/+kdpRji7Ah/MrD8SiPi4+Ph0JF8z8189wPP/Y8muuXMvPP4zzuGbwzrL+qhKZzb5gOmZDoKvPp7CWQmLCJxctSt59HvaRE3dxZzPCju4cQznlje/diQzjPry1vm2fHhQ8/RH3rO/jhq5D1WbcLU90wXHfNKuP2o5/5FJQ8VuzOsf245+qweih3TWcBk7TavDkLDzXchXSZt1vrBcfqHHcc/mwCwu6iojilTjefdjzz2797h7D1Xw5a32wXXPz1jeG8a2kBxIXbpWly7snlW4ZMtf+/TP+bttOSz21Pe/qtLLn7fUvxA935GpsR4vsRtC+9/rcLTkYuTewYfmbPl1R89YjoNnP9BVPcujansDK/+Zs/2hyPHx1ueHN1yFEb++O0j3n5xzLP/5YEX/dA0Dr75Gx5+45bBq4Hmw4cV4gr2fxro7jKpE9Z/tiHkQjzpmU4d734duJw4Od4gBlIWvvmDUy5+X2Pcw7t0YH/uP+f9jwzzS72W3HrCnaW70XsxPl4QbuJhvGwvLO/+Rmb5eMPfuvqSz+5Oeb9ZMP3TY07/RBEs0IpwnQrBr39TWP/4LUf9xDpMbH4ceHO/JP+PJ3Q3hnCv7Nu0sAxvIpsngesfF9zZyNOzW7538pp/+foZXzw74+k/VZyOv8vkTguLqy9nPvt3O+Jp4uzZNZ+s7pl+4PjZ55cc/auOk58qlid3Vueg+8yr3w5MP9jx7PKap6sbSjV8+fExX315xqP/zTO8S4drVLyhf5+4+SRw+y1w373lbvS8LwZ5HXj+vzrGE0v/IWsKZZMBS/Fw87Hj9lPD6uU197cD9lXH1T+vzMvGL99jFVr0P64N9u++Z93NHHcj3/13XvNPfve3ePzPwUol3OcDXkYbqFXe/7iyennNs+WWdZi4+1bHZ//nE578s0r/Lh4+Q2LFzpU3vymc/9obfvPyC553H/jwaMHb7674l+9+zOKV4oIkVW3YtSlsryzH/8Zrfv34PYONPOlu2H4a+Mf2N3E7neNyMPQ3rfHiXPnWb3/Bab/lqr/l2O0QKj85ecT/Lj/g/Pd1neruFN0wnmi64/bf3vK3X/6Cj4b3fPz0LV/Op/zR1RV/9q+/S7jb86CrFkI3lXe/Ufm1v/YLnMn8+PgrhMoffXTFv/Df1XESK/1UiIsHEqz9mx/4zvqO7xy94Wl3zeu45n+23ye/X9PdqjFrXooy5oPy9ecjw2f/QcbYBGf3jL97wfLLom7m9trz68dT4ea7leHlHR8d3XE79vz29/6I3/382+zeHmFKZXinyInU6/penOFXff2FFm1j1IlPbclVm4MUjcjXlHAUPv3kKeIiQxeIaeZk3eMl8+bNG7wJ7FLBWU8CSpx5cnnB82ePqCaxXA3c3QWGruPDu62y96rFGkcpkZoLQwgYY7Fl5uLshKnM7O6jNpEqGS+NqSuFlGrr+myIUY/7+voa2wdWqxW3my25QEozfdcxTTu6YOl7zycvXnB60nNyvOLmGrabyKtvrtncT+Sc8ECZC8EEYpw5vTrmWy+v8M5S58TR6gmfffU185yINdP3S+ZZu6qbCqEKT05OefH0nJJ2uODx1nD94ZZ3b28pRjAGUs6UDLYYFp3l+eUjzk46nAtcX7/lvt/x+efvyKUiog7kOE/KInQ9H11ecbTuFWsQZ5arIzbbmTTN1AhUR4oVZxekmPjWtz7l6uoEZwumGO7u7jjPC755+57OB8ZYKAmMq5haefroERcXZ3S+MKyPiGnHctFjjePz1+/Y5apNsuaJfujZjRu+990fMXRwejyQ5kxnt2ydofeVD7sdpuvx3uOssBs3PHr0iKvHZxwtHU48Q/Esdwvu73ZMc+Fut8P5jpqVKTjlLU8//TZHi54haKf5k9MV2Vo+/+wVqRaKWI0Fi5BrpAuejz99ydHSsVqsuf1wTd8Lf/bFV3irfNdSGju0zox5oivww0+/x2KBRn27nndvb9ne35AB2/cavy0RK5ajoxUvnj/HkRAC65Njbu/uGOOWr75+RxXtyA6WOUXOjhY8enTJojP0TsglcrTq8Th+9vPPWfUDYzTkasgxse57nl89Yr3qVThYL5n6RB8u+cUXIx/u7qnVI6LPRI6R88tLXr64YhESrnPk4hjcgvfdB15//ZpaLNUEus6zmyd6sVwcn3J8tCI4oYoyPa+v33NxdsSXX34DrkIRjcr7DmsMjx89YmhfGFw3sJw7Srbc3yfmLIhYrIdcK9ZA7x3PHl2yXHaEEJimic73jPf35KLNqoRKdobdNOIsXB5fcnp2RD+0JiiSmePAzfsbcJ0yR52BCJ3zfHT1lNXQ47Nn6C3blBmGJd+8fk283TGVHc71kAvbeeTq8SWfPHtJZ5XXuVj0+OyQZx3XNxumm1FjaiWzvb1hOfQcH685PVkpQ9ZpNPrTT57zk599xk9/9gsWx2cHxIB1lUePLvj+dz7lmzefseqO2G12PDq/IPiBn/3ia1JJ5FIorZP9wg28eHbFkCNh2QOVRb/E0fHFV1/z9dv3FAc+iHqu48STJ4958dEVadzQdw5bLDUYXPDcXN/xfrPVooGBmBLUyPdffpu+sxwvB0SElPQYfv6LL5izVfRLVXEplsywXHDx+IKeQvCKVQgl8fzxJfe3O8ZphlqpFOXbiuHlx89YeI20z/OWLnScHp/w6st3fH7zBXXoNAJvNIlwcXbO47MLPIXgwBbH5dk5KVaCdYxZkT3TvMN7CyXz/OkzeoCa6KxQU2Xx/DlfvHpD5wONg4GQKVV4/Pgxjx5fUlMkWIt4x/HRiq7r+JM/+YkmPKo2Hqwpk5My+B5dnhNEdDyGBR+u73nz6gO39xvEeUqt+L37N0eeP33B0SJgbaZfOtbrNV9/8Y5p3JKcVuStU4zDZrfl5cfP6RcdzlqGrscbuDw75+3rD9zvEmIVqZDjrHPoxSmffvptSpyRUumcJRwvmHYTf/gnf4pfLpiB3nrmacf/xd6bBFu25fdZ3+p2d9rbd3kz872Xr6tSVb1qKFUVomRLgO2QbcKMsBkAAwZEMGPKgGAAEQQDRgyYYONgADjCEciEJGPLWF2pSqqqV51ek+9l5svM29/Tn93vtRaDdfKWFQzQCITRnuQkM/Kc3Z79+//W92WjHq/df0CWRjjb0rYtSZzS62m2d7dY5TdorQOuqKvRwoHy7Ozv0Nk8sMKFIUozDvca5tcFk3lOnbfISKO0RglPbxj2ufKOSEm87dgZDmlOjnn58hrrBUYLus7hvWfQH/LgwQOwBb51eAv9NKPf19R1GDQI4bFdC61nWSwYbw1I0ghNAItJKdnd3eX8szXCBl536zsQilWxotfrsbOzB21JjCZSkiTNWBd1aORaS9LrUddVwIZIaLuae/ceMDAGlEM6y2un9/gwf07RWqquxBmDF4Hd7dclwzdfZ5DGSDyDNMG1lnGWsTXoM1/k9NM+ZWtRSgepmpf/l99kf7H9y7npIrwQveLNeSXwbrMs0AeTc/HtNbFyHIxWLKsYdyyZJmNGjwVdKkjmji6WaELr5vaLhvK9Au8Ee9srrm+GNOOY/nPuZB7WCCLrwXmanfQuTJi9pTErTzJzdxy6Lvu53fwVKsHLjeRMCoaf1bhIUuxr+uftneSp3UqIlpZ2HFOPFJffEsSnK75ydM5Pr46Yz1L6H0Skt37DiQ3LmbteaC2uHkjEt2YcJTXWSepDTfX9bUwemovVOMhXXl0udU8y+YJg+N4tQyvJ4oaLdJ/emWL0xG7aej9n9rpIU24rpl92bN2f0YtaXpxvU+1G7H1f3JnIhfPIPDB6nREsv1ZxdDAn0R1Vp7nd7rOe9FA1d0t1VeXuGH1n39ZE7yzYioJA+PpmyHKdsP2Boxkq4pm/44R6KZh8USBeX7PdqzgdzpjVGcvtmOtom73vh4BC1Rade5qxIZlbJv96y9Yo55uHnzFvU572t7mZDahHGcmzV2FDWLaeTFomn0uw7+a8sT9hFJdcjoac7YxJrxOitSM7q0Kr2ocwK1p0zP4K9A5yTocreqaBY/iwfY3974dGmbCvOKEh9HRDxc03Qsj0+s6Ej6/2mAx7HP1B+CzGBiu9zTSqChI3M9Lc/LWS7XHOMk+YbSX0n2rSSXcnzUKAaC1EkuJIcO/LZwyimkS1dLuSD68PKJZD9n4UZFavzg+9ali82aN5q2RnnHM0WJK3Ee5A8CzZZ+8PNMVBMKybpkXVjvJ0yPK9mt29FZHueGt8w3k+4nrQp3DbjJ562GA7hApyudkjQ/OvrDkdr+hHNWVnmIwyloMRO3+yacVrQTvQxNOGZmxYvCE5OJqznRZYJ4l1x4fnByzf6LH3/s/vF7JxdDvhPEzfmXM0WKGkYxhV3Gz3eeIOya5D685pcPFGqGWgHgnmX2xJd0p2sor5OmU+yIgX6o7b6YxAt55o0aATxeytGHGaszsoUdIxzXqsiozBy7BTbWru+K3NQFJ8teArOzfsxjlvZZf8bH3C1XDJy49eQxeSaNHesXrjm4LJl4acfv2Mo2xJJDveHVyyG+f89MuW5dkR8dLe3RuT25Zyz8BxxedOLhmaiqGpaJ3ic9+64rd//wvc+80lzX7vriHuIk3+ZsN/+Yv/kP/+5b/Ke+OXfJLv8Tcf/oTfyR5R/d4RqtqwLjfivXI3BLYHoxV76RqAbjRjFFX88dEXyG48srVBIFdZVNGxfC/m7feesW5ijvsLBrqm85KP44bi7JDR0/ZuaPDqnn/7VwUMWh4cTYikZd1GXHb7HPxhsNq/4pC+Crvrseb4tVtO+guOkgVDU3GZDnj/vZj0OiKZb3APLoR+NhIcfvWSN0a3pKrlshxwlC351f2P+Luv/Qrp9/2G8+kQLpTsFm9IHrx1xlZccJiu6Kmav37/Z/wv9ZdpegOShcUmkmgZWLUA21+64bi/AOA4XeIQvD265vff/wr1UPIq7gp8Ukf1dsW3Xn9K5yWHyZJMNvzy9sf80+wdzn/7jTu2u5fijgsOIB+tGWYVj45f8kbvhn92+RbLTw4ZnNm74UyXqTvx5uK9ht2DJalp2evnVPua+mcHQVBW2Z9/JgfZTcfNLwre6q8ZxSWPejfsR0vGj6YUT3bJrh1ugxhRtSeeNkw+n/JXv/VDSmtIVcu9eMa9aMJ/nf8bJL+e3vG+m3HgmVfbmte+/oI3hzfUTnPdDngzveKrvWf8zv3Pk12GVqrsPKZwdGnYv3/l5AMmTZ+daM1JNOPETPn47X2evXzI8KknXoVn9atj/u74EoCRLjmJZrRe8eW9Z9x8tc/yh/dQjaNNN6gB5ykOJP/hF34PgNZpWq/4eu9Tvt77lP9i9Sjc070PIi+g/7IiO2m5n83YicL1kamav7P/h3y3/0YQ6WVhiAfQv2gp9jTfPnkCwIP0llMz5e3kgp+Mj7nJhsjOU49C07wehpa4KT1X71l291YARLqj/MaC5n8fIqcQL+ymMe5RbXhOr/5yzVu710TK8pXtF5Q24r3DM35wf0T/JeQHmnRqQyhc+z/FXP+/2/5ch7Zo8D6s7zE6YhPd4q2lKRveePCArW1NpEIbS4jwYtO7f4KJJC+f3aBkYL0JoXFKcrh/xPb2CK07XNuR7uzTyxL6/RWfPrnECBXYPSJG+A7awHv8/OfeZJgFgcfl9YyXL67opMQT0fpw02k3zRQjBC0dBwe7COkpVyuuLy4waZ8kyrBqIwgQ4OuS0WHCg4f3iG2HURqVbbO/FeG6jhdlFdiqtqFrwnLYNE55843X2R7HiKZB9hyt7Rhsv82Pf/YxeVOwrkuclzgJyjveuPeIN+7tgm7xBP6oOdhhb9zn/MVzkmyPpmtpPWRxSl0seOu1X+D+8RFx5HBWEGvFdtuS5ytuJ0taH9E2jkhFVEXNu2+9zdH+Hr7LiSKNigZ0/Q7bVTx7esWqWqPiiLxuUNaTJobXHt4nSTzKOzrrSfspve0eOk55+eI6MPyiiM5LynzNwc4+p/fuUa4nKBHsfF2SsrO7z4fP/zGYHi12I5uyfP7NNzk5fQjVCiEbjJRsDUcc7O4xW1fkzTmdC2zCvGkQWrB/csBrp/do6wklhlEkiPSaN08cP/zJT3CtIMkGTJYTjDFsb49587UHiKag7XKM6GGxvH56jG/h2dkVdVdjoox1tUbh2d075u03XqdcXqNVRG9vn8EwpWxqnp/dBvs7DiWDEIi25au/9E3GqUZgGScD2FWMt1dcT6ZEKvBmW6lQOkGUDaOdXaI0AduQmj51W5P2exweHvPy7AqjDU6AcxbnHMOtMceH+1T5kthoEIrOO/b29pjOV3z85AIVZ0RRjKcjr1cc7h8xyDRtsQwBhmpJegZpHvDR4+cMt4ZYV+OdROPZGQ0YD/okokEmEeul53RvxChN+ezJGbeLNekgwflgu09GMaf37rEz3qVraoRt6LqWR0cP6JaWG32LEgKlJS2S1TrnYPeYve09bFegjAo3ehnz8GhAPi84u5wFTqQKyzqLouCrX/sSqZIktkMrSX8wYNQbcvbyJcuyRsSSvGyQMsIqUMD20QHCaJTxxFqEBv9owAefPENJidYJvg6c5jRK2d3dxaiOWFmc79AyYjjeY2/vgKubx4ioD3i8a0iiHsNsSBpnaAFGejw12I6032M4HHJzuyDSCVoYpPZoHXF4fELX5kEgteGOrduS0WgbhcKgab0lNgYRJTgM1zdTUjPGdY7MpAhh2d3dIcsyqnqJUgHt0daOWGjGvSFtvUQKjVGCtgKTKOJeRv7ZC8Y7Q7quJZaS1nliqVFW4EWCtDK0r7VH6hjhN5xqoyjbDjpLmhjeeecd1qsZRgiaqiHLDKfHCZPbGeuiYFXk6CRCaY1WnkGvTyQM2ebHJc4zHOzS2Zjryz/mcOeEdRNCL2tbvOuItEI4i5Qd/SyjtSKIyLyADsqmJI5jIutYFWu2ez2yyKA7i/UQDfs0VUcU99AmAdngWvBWYvMGgQPnWOcNO9vDIJQzmulsyXCwxc3sWeAjW4WUEUoKBBFtExisVnjarqPrPFVnqbuWWiqiOGa5zhlFKdoYkiwlSmLSJMZZWMxXtI1luc5J4h51W4fzygo8jny1DjI0aXDIgPnb8MZr12EQdM7TWEesFaWDCkVVCgaJwJjAfY9UQmL63E4/wxiD9ZKyqhiPRkREzFYlW1lMLCXeNayWa6TXKBmFAVbj6XRL32TQeqxraRpBHBkipfHCs2oavJN4IbGVw7kGqRUdLRhFXjQkcYKsO0xq6MoW5zzaJCjVYH2NcC1KCZz1rFcNWnn6WZCNeSdYTReUiyrwY7sWKw3WOyIc/STj/Oqa7UGfSIYwU1vL7HZKb5CxyCtAkhhNXi2RUrNc3NDPDhn2MqQCvGG9qoJQTxHYsyIOiBAvidKM5aok3bDBO+cRrcO6hl6WMatK6lYAQQLnZZCgTtc5e1unCGFJ0wEvz69oyjDsfsVNVkKitaZYl6yWBb1Yk6jNILyz9E2PCEmxzENjGUHXNXSiQ6r4/+EffH+x/b+5vWp4hRdOEHV4YTVrx9kva947OWM/Di9HWlrOyxHd1oQfpq+x/cMg4tC124jBHOt3a75874xRVFFa/uXOagAAIABJREFUw/3hjE+3d5jujjn4XUmbijup1B1Dti85+7WOg4MpyyJh/eGA/R+4gC1w3ImiVBFEPDaL0IVj8jkDjxTR3LPzs5Kup2m2IlSm/9T3W99TfOmrjzlOl+xFKwa6Zvyw4B90X8OsNdqHkFSVlnagqXYMybdv+UvHjzHCEsuOm2bA7e41P/lH75DdenpX7Ya1G5iOZ79s+No3PwLAecGDbMrP4oqbN/pE7w+pRyq0i4uOejcmWnS8/FXB1997zGk6o7QRh70lk9MeV9N7jD8JYYkuwmeK5i3Pfi3hl99+TOsUR8mCvqqZ7WX8evsFBt9N6V8E3mVyVaAiTbMdcfr1M+73Z/RUw7xN747HTbLD/g/c5tiHgDealERvV/xH7/wOH+THDHVJ21fM24zxyWO+/5tfDcchb5GNRdYdT/9Wn7/x7g8AMMKipeX+wZST+zP+mxd/nexWosvQZEqvmhDOvOX4T77021y3Qz5aH/CLe8/4NN3l/b98yv3/SSFbS3mUMPhgiuvFLN7s87e/8QehmNMM2ItWLLqU7huST/wDjn+/wywabKaJpg0u1swfKf79b/0fPM73GZuSX3jznPf37vF0/pDD77UhmHnF5dwMBW7+nYKvHp8jhWf3IIfX4fePX8N+MKTthzauzl0Qe0lBceTugrVHvRuel9scjFZ89m6E/L68k66JjZw1P5L8tbf/hOfFFveyOQDTJkO+4bl4fo/7v7nERZvWufUklzm/+NYl3xg/4boZYqRFC8f9/oz3v+Ewv2FoT3vIJrR5XaJYP3R86eiC+70ZmWx4f36P//jtf87v7L/FJ997h/GHK/xOCp0PDdJxgvzK4i6c0dKyaFP+0y//b/xnzd+keRyhjMeZwJPNziqmv9rj1+5/SG5jeqqm9YqBrnn9KxN+Z/JF9t4XxDN7J0eLryte/rsRu8OCo8GSoakw25bFYcJnP3oUgi8J6VUdGsN1F1Ae94OUOzUth70lX9i54P3BCdH/GgXxV7IRmG1F1FuCrz14zm6U8yC9pbAxY1Pwr40+4j9/9JDdn7R3AaywnnackB8J3htMGJuC/WhJ68J94/5gxveODtn+KKBbXi3F7xLJv/nmh0ybjLEpOY7DMXx/eQ91VOISHcKywtKOohDYd/A/nH+TN4a3FC7i3cElEs+/dfwj/t72yabZKvAySPGageSLR+fMqoxIWnbjNbd1n91oTdsP58T69QGqcrSjCJ13tD3omzBcSlXLe4Pn3LYDUtXyB/IQFwlaFUJ6CJK3f++XfpcfLU7YiQuuqgF76ZrRexVnzx7ir6F30WwY5AFrU+17XkuLu88P8M3tGxZNSvSTjMmXt+hddTRDhWrD+bibrtHCEcmOL43OuGkGwCukSRBmteOYti/JzhrKB5JHw1vGpiC3MSfxjLN6i2FW0caDzbELx082/u758cl0l2+fPKHeHL/vnD2kOfRsf2TvhiZs5FFCeqZ1xkG6Iu9ibm2feZdxvh4RL4M0s+sp0hdLiodDbKpoRoJBVnN/OKNxit+7eYNVFTM8s3+qORpal+EYCZOgpKOxCqMskbJUMbDaoGdKG+R4o3DP0ouM63WfrSSci9fNkHFacTGErcchpFa1I5o2rB6mdD3Bd84f8s3jZ9yLQzj6h6tHGGWxsbpjtKvKkx+G1SplZ7iu+xynC042/+b3lm9t5IZs5GThvqhLByh+sjjmfm9GLDoO9ZxJ16e2GpsEUZfbcHZf8YR/MjtmFJV8aVSQiAaF5ndXb3OxGpBEYdiq2nD8mp6m2vX8/U++zi+dPOUgWnLbDthRa/7R5EuUu5LByxBwV2NFdtPhjCS/6vF8Z4vjeE6mQlP8D9ePEJFFOInuXoGGCc/0nuGHkxNOB3MepLdMbB8jLFWnqXY87jNBvNyI62uPKIOIM31uGL5RoYXjjeEtv3HxebKCEGpn8m5A0SWSdNLRXafcbvc57i8wwlICXxs943u9d8luO4rdwALWtb/DovxZtz/Xoa0QmwmHF7RNuMlILbGdw5iIwWiId+GFX4lwRXod4bVgOB6xbD/DySHea2QEUhqG2zukicO2DmNinBekW4d4m1CXTzFRYHk6a/HWo1SENpp+f4j3a2gWRFpSuw7NAFpBpjKgpROhhdcfDPFtxe7OFnEc8yfzGTrRSAVVVZGYjK71eN8hVMbu9imxSjCqIE4c+CBR2d/f5enLC0oLQvcQ0uKFougK0jTGuQ6tBd4qlJFUtaOf9Ljyc/KqJol7iK7G2WDVdiJIo7QJjLwkSqnKjq3BkLK1ge0rw1J+HWWMxj2SVCOo0E6S9GKqyjNIhrxYXWHSlEgZnO/opxnOWmzjiWVEhMQqQVtbtE5oygITKwpXIUyQd3nhUDrCyA4lFVEkKKuKXtwjSxLqukQnGa1r6NDEmcFJz+X1Bf1EoCKNENAUNVVnsZXDiATRdMRG4mxHX/epVjmJ74gyRRtFWFdR+hrpHE1ZEKV9Oh+Wkipj0FIxWyzRShDFbNi/DisE1kmiOKGuc0ajAbaTSFJub1aMepKsn1EXJa0VSBFRN2Epv/OSpgmyH+lhkPSY3s5IkKBzQGErR1O0aLdpuTiJFJKu6RiODyjKDkPLzniPulrjfVgC3tQ5Wntar7EiyLP6g4xqtsTt7GCkpGlLhPT00pjz8ylxmtFYie08Fk8cx1R5w3KRY0xoiFZ1gzERja2YL2ekvQQvTBAhecfOaIf1Okf5GK0ifNeipaAtPbeTBePtPfKiRhqJRCI1tE3FcpXTZAJT1WQDw7qeUdkaHRmSeERTdyilSHoZnYeyqsirBSaSCOFQUnI7nWCFRMUJRVHhnMR7S5wmWBryKt80jC3WtjgRlv8KBa2rECINYifn6A97rMuCwfZWYLVJQdt1rPIabQzFbE6S9QNvzFroLNLAejJhvLePykL7fV1UVFVFLBMEEU3XoJWgKErqRDObzxkOE9CBXdy2FcVljbKCGINrwEoLJqZrW5rGsViuyVKDNw7noLGaal1g645YxTQdCBHRuYbWdMymc7JMg+xwXUecpVTzgrqyRKM+qyYnSTKEgPnNhOb+KWtd0UWexESkJmJd1OTFguV8SRonFN7RdoDSOAsX5zcM+zGJ1tjOoU3M9e2ctrOYOKV0LZ3rwvDLaIqiYrEqGKWKNIqpqoqudcxWJd57aD10HZmJsFFgbz/59DPGW6MQJpoYqTWLaRhmtE6ihcQ1HW7Dg2qKkvl8TpslJEZjdES+LKmLmr3xLjUOkcU03oP1JFrz8uyKg90BBoWynsZqcmpmZUlnNEZE0Al8lGB0x2y6ZjSoGA5irLXkq5yyrridzlmVBVZonAPrLTIxUFsW13PGqaOtJUJFxFGPrlnRNA7RakRsKH2HthVSeC4nV2SDDOdsCNwkLJuG6XKNTnpk2pCXFT1psAJklHIzW7I9mzJIYzKTUZY18+WaOI2Yr1egJEIpjO1QQjLMUq6vr7F2izhLUK1gcjvFCo2KPe0rEZzUFGWFMTGTyYxhlpLGCbNliZaKxXTFzdUVg2yLvKjwUUQ67FNZR7WoyM7PMSd71F5giJnMVnz62QuG2wesi0UYbChD2Tqcd1xezXlwekRZlnRSIFTEclWyWta42qOER0lDZ4NUM18UTKolQ5GSRIoqzxFOMr8oKZfVxmkoN4gSS1N1XN1cc7C3S1nWCKCqCuYLy6x0lHVF0h+wKnKkFuR1xWcXZ5g0JtKCfmJIRMTt5Ib5Mg9MX6HxCKwyxP0tpKv58MU5+8enXN8u2O4ndG1F23quJxco7TfDgATXCbK0R1FUAc2kOtI4wVtNWVWsFg2rdYHzHUoKuq5GbBqQxqRMbmfUewf4zlKsZly8uMEriRYmXFcCHFBVNXGvz9XtjMGoT4Ulax39uMeLiyumyxX98ZCqbRFRROssXRcQLn+x/f9jexViOB1EU7DhutaBz9jut3ROkqqG/ublqIsVDkHvIGdwlm1eVv2dJOTgYMEb/VtyG7MbrSltxKN7N/yG+xzxchiMzS60TV+ZmLtYsL27wrpXhm+/4QWG1ku1F5a4q0oT31YUhxHCw/qNDtHr6P9GYHI6LYhmDc1W4Fy+kumsXrd3ge1r8XX4ngjeenTB7DunpFN7Z3MXLrTq9ochSM2iNQ7BUJc8L7ao9hx8APHlmnY7u2tZOQONVRynS3aiNefVmIf9KVfrPouHhmTmNvb1wLPtUkVylPN6douRli1d8Ci74pPigGf7xxx+t70TlghL+M4O8i66e+ledBm3dR8TdZg8cAiTm4ZXQjM89EzDbrSmr2qO4zmPi33e2JrwR1tjoqW7QyioytKOE7qu5e8/+0Xe3b5iqEuMsFxVA56vt0LTMlN4JWnHJizH3+14nm9xvzfjJJ5x3QxZdinLLsVpj1lv+LllwBe4SOJjx2/dfJ6hqThMlszbUMDxTWCCdoOIaNFRvDYOjWMt+K2X7/CV/TM+3z/jWbXLbd0nUS1mFRi4srWIlcNmoX1Z7jv+6eXbdy08CKGFLgLaQTgVWtsqtKSK0x71zPNpusOvnDzmRbmF84JVnrCz7ujSV9zYjq4fkR8azEpQWcNOnHNRjzDS8u74iuc/PaIZhu+uahv22zAmWnp+ODlhFFdIPM+LLXbinHmZ0H8ZWufC+rs2+eLtIdfTLgwZTEFrFWNTcNv0mTzdIn5tE4Rv2raBySz48PqA20GfcVLy1vCaP1q+xlk+CmKycYJetbhY0Wwn6NqTLxJ+sjhmJy7Cta0a/sHV1/BNCO3Sid00Ij3tKEJ28LPFEcOoIko68i6mtIany228DA3+V/tVdJ7yuIe7UfT3ZiSqpXGKi2LIi5stslgQ31Q028lds/PVfchMNMOTBalumVQ9Hk/2WK5ShqPAqdSrFpcokusSc0/z/tkJbx3cAGHAdFv3+Wh1EK7NkQlio01zUhUtpoj5wfUJD0Yz2p6icZrLcsD5eoRqAn/SrMOx0KXFlJLvnD/kdDznFqid5l4y42w9ol1HdANFepbTbiV4CdmnM2Sxy7PJ9l0LdtQv+dHihKezHfoXlmpLkU7s3WBKWPjuh6+ze7Dkfn9G3sWMTclvPPscqhTYXkQ8bVFF4Fi7RBHP4KeXR/zC4QVvZtc8Lg9YtCl/dH4f8/P8imovQjZB/vd3f/BNTo+nRMoyNBWpavnkco/MgSldYIq3DiE34sVC8OH5AfMq5bi/YDfK+dHylGmekXx+TLwKqw7MOvyH9Ujx/k9e58Fbl2zFBbe6z03Z5wN/QO9FCAVtL4SJwoHNNMkLw/tHJ7y3d8aqi/mRPeXZapvrpzucXti7RrJsQmsY4OpizPHJlNumFwYa6ZSm1ug8MH4RhEFOEkRb8QcpN+MQPPZMw9BU/ODmHlfPtzlOBOVOTHbTUR/2Q0tcC7Y/bHl+f5vFfsre1orbRZ/uLCMewvBZcAR5teH5xpLitE/yseaqlUT9hrWJKZ8N2GnCKhNdbPjKUpDchGHT4BnMjzKWw4R/fP05hlHFs8sdDj+25Iea7CbIupvt0MaPF5bLrS1+mtS8TMbsxDnfef6Q+PcHrI89vatuI+cTJDOL7BQvPzzg8KtLnqx3WbQpWjjevzkhvZZIa7FxEGvpMrDOk1vPj57fY3aQcdJb8LTcxUjL0/NdenNxJwGXbWjCmrXlyeNDXn/zksf5PkZYnpa7/PHlKesXQ7amIeTunzc4I8luOqLvSc7SET9Nj2AbXktv+J+vvsbPLo4YrwnCsA33uBkEWe7pb8HqUcw/fP4l/tLxY+ZtxqTOSD5OsMaRzGxgx9pwzqdTx3SdYZ3k7f4VrdM8Lva5uB4zuPq5bFRVDh+ybrpMMnrimH49w3rB2WJE/2cxwvk7vEGQkLHBuSjGHwou94fcrnrcjPt8Yeuc/+6DX2LwLLRs46W7uz/q0t4hRf4s25/r0LbrHNJBpCStD3wjKxzK6PCgNRrnJAJF11kUgV2br0vW6zVp1seSUFWermuJZIJAU+Vr0iRC+GAJXy9zijwnSRIQitaFhopQ4DvLos5ZrguGvQgnEpbrJQKDE3IjagnsLylhOByyXCwZDoes1g1FaUEanG/xrQtLDp0Ny5qFoCgrirymWNf0jaPyFucVtrPkZTB5s2HUCe9JYk3bGdbLnGS3j3MWKRVtW5Ev1/gGpJVkUUTbOjyeYTqgqGqW6xW7WxldWWOUIS9rinWBVDGrZY6INJHRtNaipaRtPOtVRRJ7RCuxVU1eWVZ5RZxkdHga36EEdMKzKHPWZRP4gpFmvV7jhSAvairnqGpH5wRRFNFLB0jXUTUdytdh6bC34B1lXlKWVRAGqZjWt0HgYmGZVxzu7tDZhlhqmrZBmIT1fI0VAi09WkukFmAl6/WadrtHL9VUVY7WCXXdUnc1t/MlSW/IYlWgkx5SxXSdZTqdsjPsgQoDAudB6JSymeGkwguJ68AjqJuaHh7nFXVjaVYdRio8cHl9w3K5RipDpCI8Fi0iXNtxPZkyHAwwaUSsYryTrPIFzkoQCucFKlGsi4LDwyOub29YrwtG2Zjb+YxeoinzluWipJcMcV7incAFJzxxbFitcoqiYNALZk3bONq2om0tnRM4qZBC0dY1SsJykVNUHZkIxlfrFPW6ZVVUVJ3Hqxi8QyBxradalywXayKtkXRksaQsCppaUOQtRVERp6PA3FUgpWRd5PTbAePeAW2xoKw90mvmiyldu7nunQ1SKmkoqpLZas1w1MN6h7eOno5QMmGRX5FXTQhvhcb7zf+xKmgbF8QJEoSM6LqWVZ6zymuEjMCD9x6pJMtVzmy+xtdwuDVAWE9ZW1Z5w2pdk8RDqrJDGQMOlFDURUVZ1lRdh2gMAoWQgrPzFyTpgLJt0SamrWviOKWoOibTFWmaYju3GSpAkVfMpvMgv9IKLxRaGmrXsVjl1G1oBmqpaKzDOcFinTOZLRHCoFRAsiihqKuW9XqN7QyjcY/WStp1Q9XAZDLFWhvafl2L1oosiplOJqRGo5WmU551VWKtZL2uaJogVfSbp1cSZazXBetVST/pU5UtUsE6X1JUDedX14ETXjfEcYxtO1rXMp3NqLsjitLhfI2Wmtp6JvOcVdHSuIAXEF0g/kdRwmpZY0yD6oVGYl2uKcqaVVlhPXRaoIWk6zq0jLmZzbECTk/vkfQSZvMlXdNQFHVYHm4i6s3QRGlF4zyzVcnu/h5tXZNoRV0HhnPddpgoCXZQ4ai6isY1FE3NvMhpRMUwyWiqhnVRM5ktQQQRl9Kaqi5QKiGOUya3K9qhZOAislSzmFwxn89ZLeZoremcI+uluKpAScFqsWQ+mbK11SNKUpqqpq5aJtcTlNAUqwK0RElDawMqpG06bidL2BmTL5es85LFak1ZtyRJghMC5wWdbfFeYq3i8uIWJWOGaBaLFU3TULavQnCNMhF5VQberjEsl0sur2KUNCjhqYoVZxcX6CRlWWxwMQiqskTKMAy6vS3oRTm9OCHygpvpis5plnlJZyGKJXXZglBoHTGdrRkMclKjkEbRNJb1qibPLU4atAZrg/jSeck8z5kuCpK4hyvCoGcxWzBZ5azajg5Bh6DOG5IkQaeSzy5ucEKzPUixXlIUNbfLgoubGcJEdF2H0THSeJzXSCO5mE0YjPu0eYuioy5qrm9m9OMepXVMyzVpYu4EhC3wyWdnDOIIpSPqumWxWFI0La0VgUlsBXSeVjR01vLs+QUnB7tIOoR3rPKKm/mappO0TZBnSqk2olGwXYtUgg8//IS9vT1m0xWLvKS2YO2maSsErrMIIfHWUxQV19dTxr0euh9xdj2lrh1l5+jwOCkRwtJ0LcYkP7ch/8X2L/2mqrC0sx1tWq8+IAhsHCRhQjsqazDCMusyRrqkdpqP5vvkNxm97SCgihf27iVEScdnxTaPejcYacltzPvzeyzmGdGGYWfK8JIku7BabfC85pPLEduHC6yVZJdyE4ayaaGBFyCVIL+f0XtZkd9LSK40NgpGTmEDG/OVpOxVIB3d5ESzLR4v90i3Gj6tDyhsRG5jXszGDOy/sENECBuMEXwy3eX+6YzCRmSq4aYZ8Gy2FRqNWtDs9VBFFxqU9zPiWVgaf/hgxaTpsxet+OnymNmsz1hB77zGy7AsPUjfoK4M78/v8bA/ReLJbcTH8z3SaxnESlWwz7tIoo2gdyZ4PNkj2rOcxDN+tDih84p6mrK7dETzDlm1dIOYcs/gtOBiNSTTDadpaFYBPF9uYeYKG4fWW1R0d8e/ucpIt5YsmoT76ZTzaowWlo8ne9yvLSJRdwEIgLnVzB5mfGF0zifFQQisyyFXxYDhp5J6BP3n5d2/UZUl+yxm8jAEtUVnqKxBS4daKIQNrFXZWYRT6GWN3De0VnFWjLiqBuzEOZ2X/PCjh+xdhGZYNwi/716FTaOPBeeHI8ZJSbr57i9utuhtjrdsA6s1vSi4fW/A9s8KzCSDY/gnL97mta0JL5Zb+MuEYp87OZFsdZC7DQTpleDFfEy222y+S8S8SlFVCPleLUlXeQtRkPeczwawBdemT95GXOd9ptdDThf2LrB8FV7GC8vF5YgnaUWsRrw1vObJepdJmRHNJMltTTsMTT0IQWdyK1jtphxv2ppP1rskquWz8x32NyxGVYYf3S6SJLct8YuY84MReRvTecm7oysy3RBfhLBI1aG9F+RJkF57FnVg06eqRQvHsk3uQpBXDUh8kCZlny3pvdjhuTuiefcaJTzXiz7ddUrv0tJsJ0TT6o5j7GKNmdfE84jFKkVJh5IOKR3Zj1PqLUsyCYGtylvaUUw6dSyf9ZmP1uRtxFG2pHGKF/MxvRcS2XWB4Ws9NlWYuaX/0nKzTrmNWkZRxVU5oLaaq4sxe089XrxaVu+DbGxpObvtU7eaXzi8YFKHsGiy7JE+M8i6wUVqcx5K2v0B/eeS5TgmjxuWTcIP56fkbcT8ts/W2uJU2K8AzdjQu+qY3Ri6Xcnz9Rax7ngy2SGfpTz4UcA7mEVNO4oD5zpvGT9RPP9axNP5DpU1DE3FRTGkfDZgfGUxKxtEWBuOb9tX6CvJZTIk1eFcuC0y2nlM79KGYLcKHOfwp2D02LFse9x+2XLcX/CdyweUdUQ5SznZtMlf4SdeiZ2yF4byNcOy2uKgv+a2yJhN++yuPG0mw3nuPPGsRa8a4lnM7dmI3y1i3jq44absc3Y7pvdUAT8/D828wkXhHpk+1VxUe0z2ehxtLfnx9RHuLCW99neImHo3DoO1SJBdeW7PRqjT0JK/KfvM1ym9ZxobOdJJuEEEPrvF6TCsGTwxrEi5mCbEN4rxuSeZdTRjs7lf+c2gEFRt2fkTwU1kqA8E8sKQFZDMu7vmvTWS9Cbc27tMMTizNB8kfGz20dpSr2KGP47oEk//vL3DNkTzdtNGtww/1bzc2edyu6KX1bhP+6ja0z/vAiu4p4gWYRhgE0n/ueL9/Xtsj3IS1XJT9plM+mzfhs/fxTK0YNuwMmC4tiyfJ8wHKY1VJLrj2eUO0bOY7CrI3nTpMMuwH2ws2fuu4ml3xPR+RtEZPltssXwyZvtnAq9sEA9m6o6n7xRs/SyEnasq5gfmHvN1Co97lPsCN5OMntTU2ybw3X0IPS9+94T6wPJd85BpnlFc9BlPA9rhX2ywvhpIqu8Nufmi4Z+pt1DS8eJmi/iTBJOHFS3lTliVH54NYR9oBeU/2Wb9yNF7IemfOeKl3eCU5F3rGxEG18kUiscZ5UnLPG749csv4NaGrZnbIHzC3w1tbn3XOP+zbML7P7+yiX/7b/8HHutCiq9AaEGDRfuESCt2RkPefnSCFB6jJIIGKQXX8xnnt1MuzuZYL4kiw7IqkVLwC2+9w8nOCC06iDqEM6zmKyaTGZ9dXVJbiUWQeI1QEms93lv6vYzEhJe4y+sbkJpuw/bsxRFWNHTWUzcWHUdoE1OXHcYYIu2w1qK1pioLtPJEUqB0QtcKpE742hffoR+DlZ4sk6xXJR89fsbLFy+I0gHCJHRdh3MOrQ0PT/fZP9hiOMxwraQpKuZXN1yeXzAtWhovgnXadeA8aRLx5oMjxqM+aRKCw+lyxfXNLTeTFZ7QBDUm2thyBTs7PV578JDRSGCtoy4rFvma5y/PWK5KWqFokHgkidT4oubzX/wCu9tDlHRkUnB9O+WnTz6haCyxj4iFohOKuqropQnDvQGP3jihpyVlWeIFPH/5ksW8YrEuaYWhbkvSJMF3luH2Fkc7O+yPemDCubtYFlzd3PLZ2SVRMkAIRedbfOfYGu3y4GCb/f0hxlgSD4vVmnnR8OOPPkbJGOsVwiiqqiJKEhLgnddfJ+unSNFhnaAoKv7k409pXQjhpQ+NOqSnaRq+8OjzDHoZsVakmWSVL3n58pzz62tAIkWEUwK8xHvP1qCP15I379/HaOjKmrPpDVfnF8goxSpDVxc4IdE6wvpwztzb3WVr3MO6mtWyZD5bUVQ1dQdeGaQpkAikMAinuHe4x8nhAd47hPQUqzUfPH1O6RxVF5aRj4dD1vMliUk5Otxje5iS9hJiEwLQDz59QtNayi4I95wKFa5IKLZ2R+ztbLO7Pca6AtcJrs4nXEyvKDfLKsoyZzAesl6vGW9lJNpw/+iE0XAP19RcL66ZTG5YLouwRF2rwJrVEV1d0ktjPvfodXpZuAZs01KVJS9eXJM3Fd1GPuQI15jtJA9fO2XY73Gwt8V8douQnsdPzri+mRCZLNhNnaW1HXGUoIVkkPTZ3xnivcd5z/Vswny2RCdZCMfaPPBL04SqqtBGcbC7w3aaYUzM+e011aqkcS408UWM9xYhPdJ60jji9N4hWS8iiiKapmYxX3N1c03XOYROmC9rpFbEsaKpS147PeHk6ADnSyDcRz764FM6K6gahzAmcL+VwFpLFiXsjkdsjzMiHdNULdObKbP1kqJr8D5wbpUKoj8vHFmW8cbbD2nvCahDAAAgAElEQVSrhu3BFi9enDFbLMjLIjQLOoHtOoTRVL4hiiJOtvfY7vcRWrDKV0xul8xWa5TRGKNomiCMcFLivSNOFK/fP2XY69OUBYt5yc3kdiMg2xjAhaLzDV1jGSQZWdZjf2+bzno6Z7m8nmB9R9EGGVfXBWZ3U5VExpCmKYd7uwz7I1zreHH5HCEEVVUhTEKe5xhjaJxFCYFRmn4v5Wj/gMgJrq+vWRd52E9S4NoOFRm8t3hCQLy1tcXu7gBhBUpKJrcLyrZjXZYoDYJglnYCROdITYRSiuFoiyRJKIs1q3xN27bkVUeSJDSuoa1XpHFGFmcIL+mN+vR6PYQQTG9vWa8KUJpWWMqiIEkSvBN0TYUSoPs9JALXtCghcQ6c9yA6hFD4LiyLk0biu5aeDxKswnd4FdrNURwkf0Zp2rYG6UlVTFE1JEkawhIEdV2xNRqwWoUWb+UtiTboztO5FpBYIbHSYaRh1BvSNE04Ds5TyRohBB0efAzWoQkT68EgZm88xijBdDGnrBpAUjYlasN96nBgFUZFeAFJFNPPkhD6K8n5zRWYCOXDAEsLiVca54OEzXvH1qAPziPpKPJwvKtmI+KQmqrNkVKSZhkWgfYBV6N1ROoNi2KNEY5OShqpsHUQyjkXmkVJFNE1LVK0DAYjFosVQpowgPVBgKZEwF7oyOBbQZIkmDiidhVGeGy9pqwkaT8Jz2YEbVdvzmHQKgIK6k6QZL0g8bSb69p7tARvO4SH1GisD/elwXCIimIW8xXeh8/Ruc0AwDqUA60UBsn/+Pf+2z/7uq2/2P4/u337b/xXdy8Cr3igZhW4gU1fsXhdsv8rZ0TSspUUaOFIVcs/f/II/dMeez8OL6FtX9J7XuCN4snfSjj+whX9KFxXWjo+udmledbn4LueaBXah/WWwcvNkkznyQ80zUjgFOz+NAQJqrLI2lIdxMgmvBTrZY1LzF140fUjup66E6FF8xabKtr+z9ENTgvmf2fN6zsT8jbi7dE1P50eMf1nRxz8UX0XaKjK3bVuLr6l0G+ueGf/isr+n+y96+ttW5rf9RnPM8aYc631W7/fvpyzzzl1qk5VV3VXpLttkTYGNV7yQkXQEERsFCIE/AtEBN+V73zhH6CIkIsXGkwIRERBxUgktJKGJDTpTle6qrqqznWfvffvstaac46bL54x528f86bfiB26Fhx27V3rMue4zvF9Ps/3Cby67Pnsp085/nbg5geF0H0frfgMzM8CP/1TsP/wgfeu70lF+fHvv8PV7wae/v1svtYP2UjV3tZvvuNZ/sQ9v/j+p5zSwMvznlcf3/Dsb3mOP834c8Hfmy97ejLiauMHfzpy/e03DCHzrZtX/F//4Ft88NcCu5eJ5cYzHxU/VeJ95fyu5/yBI/7zL/nO0y/5/bunlCrc/+Zz9h/D9Y/7b9zNG+V5//XI61+C3XffbOPk/pMj17/t+eBv3JKPgxXLmgquVO6+c+Dzfxqe/cIrrsfJUvc/+5A3H1/z7f++bPYDTR3x1UQ+RuangZ/8K434dLJiZ0lJbwa++T+0rxDfKxXp72d+79+6Jr1IxKuFd24e+OTzJ1z97ZEXvzk9vr/7gtLg7lsDNPjiX1oIYyYvytXfGXn+W8l8b1NDLwWXqxUkWyrpGPjsVwOXb2RccoyfKVc/sQIy4d5Ev41q7QXxPvvVgPuVO2p1iDQTsf5n8+4Nr+3aHr59ZPfJxPI08vJXAqdvZvRm4XiYePPyiq/9T0o4VYaX1j6bZclBuf1W4O7nK9fffsOcPKUI7ncOPP+7lfGVCTLxywuXDw+Mn555+Lkr5mvh5R8vHD+4J2cl/+6R4w/YxhSwefTKZPPrx/9aY/f8wrJ4ahK4C7zzt4yGe7vQWRnN8uGTfzYyvZ/57i98zPc/eRcnjZv/fcfz37qQjsG8Hi8FPS/k64G8U07ve9581yqwuwJPfsdIxdV+wJ8Lek7kYzRhbgy8/Md3PHwTyth49netmnt4qITbZfM9xln/T089X/yqIz/N6CFRToHhE8/Tv9fMQ1Lg6gcPnRQf8PeJj//FA9OvnKm5B3uS8P7/4vGziYlv+33LUpneCbz5eeX0zYzbZ9rZc/iR5/ijyu7LvBV3K4PZzdDg8tzz+Z/MuEm5+uYt6Tefcvz9ttGTZexzRBzxTSIdPZ//k4HLRwkEwhee69+Dmx8slEE2D2BgI6CXG8/Hf1Jo78/Uk2f8JHDzuyZCrhYBNTp0MkH29EFkeibcfreis0MWePZbRkyOL01M1EvpRPNMPg7MzwJf/qIyfVAgO975TZsDK0G4+2whH/wWaMp75fSe59UvN+q+cvxdz+7zZu1U18CJdK9zGy+33x65+455QVcPxx/C+Lqx/2wxSr+vnasP9/TUBMDTB4503Yi3jv2nDX9p7D9fmJ8GwoORzPOzyOW52ufecUzPGghc/cix/9zmRTibBcHy1DIbdCrUKJRBNqGxDEZnrkUiV/HZn62ImyuN8wujiNcxoVMhHcNWeG2dh/OzwPiFeUu7apYD8TZz+3MDh0/7/O7ZI6sIDd3ffankg+f+61YUbLi1YGB8Y+N2Dc7Ao//z6YPIm18Qyq5x9SNrW4Dx9WMBtvU38t4CCtMT5fJCtnG0rrlmw2Dj19W27cGuNm6/aQGY5mH80sTWtWAZQHyTaeq2gqd5sO8vg2N+6rj6SSHtrTBYfCjdG9ttmT3zjeIn+7fTe2aj8LYI2sR8dodbCzqshc/yDuK9FTWTBLtXmcszTzxV0s786tPOEU/m1Q8Q7wvzjXl1r5kfazFOVyzwOT2xZ5DhrvLwgY3J409t3y+D2+wQfBeV805IO+H//gv/wR/oWfsPtWj7p/+dP9uoDe+9pex7D67isBT2oI6f//lvsxuVw94OmjnNvHz5kh/95FNKGWheuXu4Y3c40FrjvefP+OjFC66PO5Z8ponyxScv+fLL1zzcT2YTEEeCOnKrnaBrLMvMEBRVRymN0hxLKdCMgqsY6YMoc7IJFjrp1wpbgbK8JIKH0Sspg4aRWoT3nj/hl375uxSpfPbJT0kp8Q9++/smsqi3tPRkIoUXz5PrAx994wNCFEQ95/OZH/7wh5zPE0mE2qySttdALQuOzLvHd3j3nWcEL8To+cHv/4g39w+IGynVCq80CjGMSG3UXPjoo484HDz7YeR0/8Cbu1t+8tlPWFJGhz3OB3JphBCQ0rg+Hnlyved42FOXmc9efskpzaSUaNUhTUhUWrH2kkH55jc+ZO8Dx92e17e3fPHl59zePxhl3Kz4TBPz1fTeczXsePHuu8TBChf99OUrbh/uEQIZIade5SIVjsOOq/3I0yc7bq4PBKnc3t5ye5758aef48KIxJEymSDuohCoPNvt+fBr74Nz3N4/8DBdeHl3R80FdZ5Skx3SKcToaUV47+l7HPdHNCzc3b3h5es3zPNMLg0/jDiUOS0gjlKSFfQphXefPaW1xieff4YEjyOwLJmsmYTidUDUBJOdepSFcYycTwvz3K9D1AjpUCgpE+OeVoXDMPD8yQ1D9JzPD9ze3nJOM4tTSjMy3YswoCZQlcTTmyc8PV4jLvHFqy95mCxd9rwkvPaocwFxjl0cGIaBF+88h5pprfHZJ58z1UzJjjlnvBNEnBUSKiY6HA4HdvFALYkpzzws90yXjLjIEPdM6UKlEqSZP6sLXB+PDLvI6XLhMk/c358YhoE0ZxOoVHDSaC0wjIH9OPLsyRVpmTid7slz5XSZqc2xtG6WrwJUggsEUVwq7A8j87KQ8kymkZySq4nevimtQYiRJc0EcdyMI4Lj9emESrDvFodvYRsnIsJ8PvPk5sgwmMD46tUr86oujVorJTucjiCOpTaCOgbvCMHz7vOn+Fp59eoll3kiNQEfyEumdS/tSmHwFlC4OgxIE+bLBNWKRGWBh4cHYoxWVd6b369zjcEH9uOOWi1l3AIYQxcfQZoV/GrRCncNKIN6puVCCIHLnKjYmiliRfRqgRB34CoxBkIX/XZDYDrPzCWTcrViic6RUiLVSggBrUIQpbpKzvZvToU5z2jo8wgYR1uXog/bPgFwiDtyW6zd55k5J66vrzmfz7aPiAnzghK8h1RYloXdbsc5L2jwJpSpR1vlYZrZ7Xa2DwU4Hq5J00zqIlxrUPNMKQk/RCOkRZEGT66OzFPCeUXEk0thmiZ0sPlXqhgZWSpBTXCrOTGOe3KuICa055Yt+0TEaNYlEWMk54VLnVEn7IY9ZbExh3qWZYICQ9zZ2uga3oGbs3mdlgUfAk5gWRaiD9Rq1JjQmEphv7tiWRbw9rs1F3a7ndm+1MpcF/bDCEuGJmQyTqG0zBh30IT5snA4HDidTuZVL7aOtGoiozpn2S1UtFaGYH67SyqUde8sC+o9rTlqSSZ8qiKdwB93kYZwWWayLQcIjSCBKWVQb3t1reTpwm6IRHGAJ5dGIbG0goSIGQvAnJOtX87hWmVkIEggl8LFJXwI1Cbk5QLO1rjUvenFuf490n3pCyqWGVRrJTpv8zwEWm40L8wlI72YnisLFRurpRRULDvAe895XhjCDoG+n1RElfNSCOrt/f2aTZyuttbvBnIpoJ65NiqO2IOQ6xOjNGjFivX9+l/8z38m2v4ReP2pf/k/bUBPkbeUfQAc26Hi838hM9xMfPjslrtp5LIEpt878t5v2AHfFdj/+MFEv9p4+cvjVizr9ryzivffP3L8IRw+rwyvjMZafVRxdA9d8/crwW2ed+G+U0I73chIgHC7ett6OyTXx5RyI4y8EbNnEwUkNb78Jc9H/+oPueTAj/7++8jF8eFfN8Fj9fRb0+XLINx95HnzT82EMSNamV+PvPMbVkjEn+vmZ7teWxPHq39s4O7nK3VXcfvMk785cP37dhhefUddNdKtKkiBz/64kJ5lhqcT86sdu594XvxmItybr2dTh17KRubcf+g5fR3mFwVXHNe/rexeVsKlbmSyP9dNAKjB8emfUNLTwuHFifOPj1x/Xzj+tGyig+sU6XoAP72wSth1X5GL8PxvO44/XjZSVs+PYsD563tOL4SHjyB9YAJp+WTH/mPh/d+4WHG7Lni5XEEc+eB58+3A63/Cnsd2P/WMLxvPfmfePIJXUXEtDkeDL38xML1o1NDYfSY8+X4h3hX0Uig7E8PXsSFLZnm+I++F19/1UOHdv7NsYqs/2X3oaaHuAjWorcXPAyU45hth96qaTUenwv0pG8m7VNLR4yqc3/Xc/ZwjXzXia8fh48b+ZTbLhtlsG8oukK79VwSN0zcqTeH6+8L+84KfGsPLeRMiV0p3fhaYj8KbP9anZoWnf68xvrH+C2/mzRKijEZ/5b1wfkdYnpioEG8bh8+KiTnemY/mm4wkE33KICxXwulrQjrA+CUMbypXHy8sN34b7/AoUC03Vkjv/lugF8f4ZUNnK6okueHv+xjpomc+eHKnKy/PhHhvVJzOFX8yQhznKDsTr/LB4+8TZe+5/0ak+kfR8ivrQh8n1TvibeLuWyPL0bE8cVz/oOLnik6PafVr8R9/P1P2kXTtSXvh9XeVOjQTCd/UrWCXTo8EtMvmI9vU0p2rNwFo7Rc/V3YfX0x479Ys65/WXoJkEzmH14l05Tf6bpu3pVGDsBztv+HOCjftXpkAtGZHrDYa+WDiWLpS0s5RvWN+4hhuG/Gh30dfY00Uz5S938RfyQ2dWu8biHcWjAp31sbLk8D4md3Tuk8AnF/ELSAW7wrhzczpoz3jy8dib+sYXouGhYfC9NSze5m2fswH8/ncfXJmfmdnc2ynPHwQCOe6FZSisdlbWMDu0fLg7qNAvG9d6HX4qW5ewa4Y+Zr3Nq/Mi9SCNtMTC3CBBRL8pW4BijWws3pZDy+NBJ+fB2QxahcsyAn0zAhLe8873fbQtT2aGGGZjh7JbROf45vE9G7cvmcNjkzPPOFkQcTwZmZ+d9zWXJ3qtqYuT6z/423m/CKy/3zZ2msNINQgNkd6cbkyOJYrWyviqW4Bg7X45drWa4HQtSDmdKO4akKnTmtGjiPtxQRjTMiUZJ6w0zNPOnQ4Z14tFOpXxNVwl7egDW4VVh062++sxdF0atv3++4pW/3jY6oJ7JUaXffXbyxXQnyo3ZLJrBziXaF6RzrYWiR9buvcNiuGPDh2X5o/c+3rhXYqePfSbItWEXmzmOqCfh6tMKaNDVtj0zHQlK+8d/XD/Zu//h/+oy/a/pt/9s81MLHAOdcPE0BbUImEMIA4nj2/RsQRhsj5/g1ffv4ZTQIiI6lkJAjTNLHbXzGfzrzz5Bm5LBxurric7zg9zAhinpKUTgFlnFecW0dVRZtRt2AHnJoyPo6kUhBvBzMw39rr6yumy4mgQk2QaUbUlIo2Kw7WVFhqo4oyaKAFoBVaEpY08/T6wN39yQ7xNKQaoTMMI4qDnCl5oWox8SOMqA9clongQEVYUkJcYVAhiokA3gfmlMA1cs6IKtILvGWFoGbJ4KpjzjPPn73gnGZChfM00VxfaFylpUzQaJROzow+UCiUUjgeDzycT5ATw+7Aqze3xHEPNaPRiqqoBkRNAJJaOF0u+KgUV8hTZYw7ampU17jkM/thT/SBac6IAycmRIhX0py2A7GThi8w+gBOeTjdcXNzjXON2jLnuZCdeZo6zJvNgxWCcYXYGsVVvB4oLpNrIuWMYFXKa220VvFBSPPCbjewJGg1Unkw/lhMNJFxzzknmpg/8xAi0qBOM9fX16RlYcoWlLjME9FZe9znGWJAnCd3MUooDINSq6VFj7uB+3urQG4Ul43PnJoRmM2ErZpMlG6tot6xUMkVgio1F6Q0ilQ0RJoTfKqUckKHyLyAV0etmYAJN9n1Rao6XC9205Zs96gmppcGpRSCBmrOJhamzDCOlFKYU2XYR3KeKS0bYbi74uH2zgTFLoiX0pAu7hSxaFYFSk1Iw8aq9uuqQDGRywH7/Y46JxNNkj0MujFwXozObM0o2NYcIRhpuBKRUYVCQ4I3ETPuyZMJeogjVyMEtRRcK8huMHFLjKhWMTpTxX6n1sZ+GEnJiNc4eOb5AogpTNnSP0trNImUUhijiYfqhME5huB5KAuVRqYh2awwWq2ksuA1kmsjijcRWhplWnBemZYZWcXIEEAarlm7RSK1lS7iFhN5W7X77vN7KZk4DFTMI1CDxzWBUsEry9T7w2WzjvGRJWWGYdj6w3vPNF3w6liSUZlGSTcriuUV7wPL6YI4yxaoNVvhqy4ye+/p+yulFFwzGx3FEb2J6eM40lwlJRP3XDOP9NqJwuxMDBzFb/Sx84FlWYz8FQuwqQjSqhWpahWnnpwKwZuPenP10XaiVhM8VbbforbtYUB8YKmNIUSbi/mMSqB6G+PAJl7vfCRnI8ebVmqzdauVSlQTp6UTys4p2RVElWqwKUsXZXEV12wcDjqwuEotjRjC1haVnsEhSm39EDRlhhA558xuNzCnBeesEGbOlkGSloKIBdVqrQS17JQ5TWhUghNrA8Bh+2gtxe6l2trZWkPEquGmlDiMO0SEViAtldLF3eAAHzhdzlaUVGwNCKX0dqg0bwG7cdybb3JeUO9wOeP8YIUSu5A6qInPRtA30pIJQ6C4wpJM9LZigiayi3NQMsTIJZkoG723MVAqNWXOaeZwvOKyXIgaSbPZFFh2jJpVSx+zJbce9LM9iB64iDEizrFMU//thZSz0foiBD+QcyUDXiyAm2umFlun6N/natueXXLONj+C9ZVzzshjun9ndtvnVHzflxVH46/+pf/yZ6LtH4HXStr6c+kHNNlSdWuwwxjA3c+ZyFh2jeG148n38yYK+nN99JJ9Fgl3mdd/bERno5mG160XEHFb2uEqrq5iwvpaqSswAXGlXjeCqV9bfDVx+ujA8NoOUKuYUUbdrmsVyMK9iS4msvUNpNmB6vbbI8efLJv3ZvNuE1hKeDysSTKKqqkdYOOrZRM+wn3ehLDlYFWx82BEILCROavHp/b0UOjWBLeZ179g6d1pJxuFZsWr7NC9ppP6c2W+0U3UPr3vOXxma/d8rdz87oPZBGAU1EqJVe9Yjkr1JqqtQrZOVl1cuvg+vLwwvdiRDnYIXw/Sq5DhT7lXR7dDftkp843dy/7ThfuPBjsIZ6PpVpIVcbhUKPtAOnp0riaGzFalXOe2iXc1yObja8Si9f8qYqzi+iqs+JP1S/ziZCRsUNKTcRO2Hj6MhItRk3mnG8263Hj2PzlbPwQ10RA2ClOSpZ4vz0Z2P75jfv/qK3NnrQDfxH0lRdpS6W1c6CWRr+J2vTIX6qBbiv4qOK7C6yrCVW/2CqzHdAd5p9u4WKlUmQuyZMo+4nLdCpgtN9bG4S6Trk1MWgXLy7uB/afL1j5rf7wtVK02AjK/ZTfSxff1vql21pjejY9py5fHNolfnCjHEaoFJlchVi+F5UlgeLUYeTjb3F4pbJmsjcB8Z8s+fIWwXMl2YKNBa5RtDk/vxi1oka4e08PXl6vd17Y2ZMnd49eK16Vrz3ytHD5dcLmhl0QNSroxEXYV7VdqchU6dW5dgMudTF02v9Z1XU1X0scHGylqhaXsvmsQ/H0iH8Pj/BrkMarK41gHerq/fWa9Pivw6Ihv7DpDF85XIdClQgtKGRV/v9CCWtvlZkT0cfjK2ASQ2WCUVfhLV0p4MPLQ9VT1dR1fx08NnejtGRW2lrFlcqz3sPad+TG7bY1dg1SruLV+59ti4ro/AJt41/Qx+LeOyRof9521P/wpMz8P6NQ2WvLt+ZuOuu1r6/z7Cm3e53fpQc51P0tHT3jIuNw2cbb6lcqt5J1u17wGfoaX87ZvNnXkg257xLrvSTIxP+9NDA53eSOZ14DCOk5cYXv/th+ugapz4fzCAiCuexevwcRVWN99NluAsBcWXfeede7oZJTrcFu2/WXtj7f38/naAhTVmx3SKrTaOl82ETnv5B8qyjV8mcgHT7oSSjAhVYoR4Jd3A+OrvPXRWuwx76zAeBPbs1cR9v9NRC9XPcBzquShFwU7Gym+CtBroKeMjjzY37cxCNszzCo+62Tzf91X1/m9BkPX54418Px2u/31//E/+gM9a+v3vve9P8j7/n95/Xe//pe/VzrN6mqhdR9YcRkRtSrLrXL75g4vA69fveb2zS3NK1lkK+SkNBqBKRVUIrVkcMrt6USeE0EGajH6TjxIMEFmyQlVT6ZZBXVx1FpotaHeEUTMZ9MZHZWSpabuxoFluaBEgkRLmxXHkhfU2WGQ0kg1W2eLQxxWcKSTMSrCnGYTEJylPlfs4OVEKK2wpMlSWktltzswz10EwRPWhSeANCX4PSVf8BpYsvnmqvfkkoGGNPCiJBo1N1xPV5WQucwTs6ssacYFS32tKqDmLxYloL1ie6nWHjEG7k+3nKYzY/BMRXBN8E4RAuICDUFbrz9ZErkUwuCZcyIt4NSzlIaTQJFqFFODRsU7B97b+pQTZV4I3tJUvQOvlVYr4pRSTDA8TRcqQsoFCYHSjPwdCSTfaHkh1mKl1MVxkUJ1Sq0L8+XM1f4KVaHmRi2KqgnF3inUSisN7wPOJfIysYsDpTnmUilOcIMSnOJxMCVGDRSF1ApJ6CmqDg3BiMNOvQmFMUTUgaNRpVCqQ1SZlguHq2vm6UItCaWhQcEJzVkAotSCeI/35rXbSkNi2Ir7eVUU14luxyUt7I8HaptRL/j1wakpgxuIEqjVUbsooxKYl0QISq4F5+iiZrP5Go2SDDEwp8Uq15eKjnumkhmHyDLN0Bqn2wcO+9jtHIRarPBOqxUfPIhQykIrxbxW1RmJ6Ip5SlLwXQAxm4+COhPUdBhwwXNOM84bPddaQzE/3NocxduczrURx53Ro1O2wkzZxC4UqjREHDUVoiqiwlxmtDWCBlopiArOiVGMl5nDsEOaUW/UirpGTY0GqPM41wW01mg1c9iPtFYRB+MQOC/3VGksznZFV5q9vxS8F1otRr7XhvS+EYSGCTU4R3VG6427Ha0WypLRptRWgWoFsNTmZKURx4FSHIKgMVJ7n1IzSOiUqeBcg1wtTV4duVScU6or1FYYxpFcMrlkmnaRVIQQB5a0UB3EYeScTrb4O7PEyWI+zeIaTj21Nkptm9g3DAOO1ilVT50XdmMkLxOXZcJ7xTkoczYBFkcqDSeemhu+VesjHK1a1oSo4ICIiXbOQc5m41CbQySSloRXNSFPzDJkLplCJajSlgylIs7hvRUCKyVzuNrRss1TX9TWWQHHjBdHa4Jga6NTR3PFfqM2SrHASc2FGCNeHdM0Mx4OpGTt4rpYfj5PiLM1Cmd0tKOxlIL3oQeiMs6ZCN5KJRfz/K21EsZIBoIPlFY6je3xNNI8E7zj0PtUXKCWiniP+EpuDfUDec7s93umaSZExQmUbEUnW1/fSq44HN4HpmlmP46U0rqgX0EdGoXlcmHc7zgvFyQEag/Q1FxAFKe2L9YqRu86IcaIaoDagzWAiPVDSQlxwtIauUEYB3IBFWX0EcVEI18dLkSiBCuslyu7OBIR8pxo2QrG5WxF0qoIqTVaE2Lcd6sCiNECfSVXWjVCuHqhuZ5BMkQjenNlyct2rbWCqJroLUrOAJ7qhJwXYgzMJdv3iLOio28ZCTYRG+vFDqw2nh1OFd9Ac7dRcJVMoboGvh+KxfNrf+Zf/0/+P3zE+9nrD8nrz//X/+f3dKlWVMO5jUZpXmjeoXMDtVTFPArXP6wcf2zWG34y0m99r1SIr2bq6NFkY/H444yfzL9Qk5FWq/izpuXXwRs9Fuz5a033bcFtRIt08dKfMnVQ5ufRiJmo5IMY9eTswF4HpQ5uExTW4Fnrh+cabJ2vUaxidD9kylxMuA0mlOjSCKdKOhiNNT/1xLvMWuQr75UWHIjY33eCX6BEx3DfibmhF+Dqv19GIZzLJjyAo3nH+KYS7zLDfaGpoLMJzQ6gQTqakOdwaD9kp6CZZ9AAACAASURBVINw9fHC8KkVPhreZHtO2nnrE7WDuQlatn37qZEPwnCbrfCRc0YtOmv3OvRiW7WL3rBdp84mpOhk7yujCRDN28E3Xym7LxKa7ZDe1KFLpQVheRLwJxOj89EbBdZg+HIxATo34puZywejiRS5C7PR2quMtp+7AnVwltE0FaanAU322Y3UvBo20W95GvC9H/VsPo94oQ5KOBXKIfRDtgm1LRptK4uJnHghvp6YP7iy3+htVAbpKf79Wbd/x9bWFVqQTiQ7G5P9/5PSCF88sLy724ISdfS4hhUHu1bSXpCyeiuaWBTfzFv7r3NVL12QVDsv5r0S38zmq+qs7+PrhXQT8A/dMufTC9N7O5sXzoSv9brKoH3s1y1g4Oh2BtX6idqogxWja2pBjhq7/3QUmgjDF+dtnrhibbbS8Dpl6/OpC5S5p47fRBOtrry16WIBWUmVfOVp3tLGWxDK3gTs5m2urkLp9N5g1+Rt/tdohYJW8Z8eUHC54kplfm9na58Ky9PA8HJBi30XmIAvyda5srM+Qhx6yXZt2ca5lNaFSIdkCywsT8xyUOduQ7G2c+YrYyEd1zFo42Qlmtc1mX4mR+zvyxNP6UJX0y5K5Ua68fhLxU+PfWcioCfcLUiulKtI+Oweafadrd+LZIPJEMFlG/tmBSKk62Djrds4SG5MT7t9xKu8iU+SepFB7wgnG6OSbQ1cizxKpheXdCCQr3QTPnWu2/rfvODPmdrT08HaONwmI217/7tmgm0ZxRiYpXF5NyDV+r5EWyuauk5xSw/s9OBgX1PWJyft4pwmE27LIAyvEssTTzgXu8Y+r4bPT6C6rTXrWPT32byWF1tzEEcdjGr1PSNAUqPs1Qp+7T262Pemo9/2iHxQzu96wlS/sndZ4MTGlJ8Kl3cC45tuu6O2P6zjrezsOhC79/gmMT0P6GIUNtjzcR2Mlp5vlN3n0za2dSo2f1pvIbHxK7UXXrvSLWDSu8meG5aGX0wsHu5tTNqYbdTBMd94I2L1sY+Wg51Z8uiYnwbSQQiTgTk1mG1B61k4/mzXla90sypIh37NyeZVjW4LkuBsjKxe/cODjc/qbX0w8bUT+FPd5qY/F9JBifdlCybbOtd1hmbvQ3gMMm7j24jmGvteWkz4l1S3z9ZB+HP/7j/3B3rW/kNN2v7av/fvt9apnExGxFO6nKA981BwRhGNe07LZAcSAqrKdDrjVRmCpZA65yz18DAwz5MJTqr4EFkWK2hUSgEFqRURxVXHUhY7gKs9kKorJlxKRFSt0E1rqIBvBaVRK9DUKEExCqY0I0vLtBhF5r1RabVSHfgYuL+/JeoBlUipC6kUdp1UC4MRfyAMQ6DkTMuFnR8pJZlvbhDm1BBnB76sPeJp48koptaQtz0/WsNhleFV1dLca6K5agWFSrKHv1JpuUFVE2e9bqnNrf92LkZfIVYEpXb6TEXQZqScekfbKKACGHUUY+R0Odv3NTtgAtQ5EUNPK3aVYQzUtEBVvNhDgIpQupeoBk+QxmXK0BSvDvFCqTNSTKgLoXtVilJyIyyNTKFEh5SFYdwz50SpSklGrWkFXCMM1halGOE5xB2XdOl0k3ll5lpI1agyHweWnHG+IsURNJJnG3ulFBrF6DyU7JqlWHuPNA/V2tVRSNPMMAxW6T4lJHi895yWyfoNE1ZTSmgMVCzgUZOJMSJifaFKbpCLecGugmHOFsywRm/4QU3YQaB1YQwxytM1YoyUlDv9qIhWLpcL4pVSEsOws/uruqX4BjWvx12IXHIlDp7p7p6nNwfmVHqbVgqOhhB9JCcj+YYgNG0s3dfxfD7jNeK7gX+tleaM0FW1QlWp2nh0KMuSbFy2houeZT4zSESap3aBu7nGLgzkaTbBv5mPZc3WR1mL0fjVxk4Ddn7HMk/mZ9psPpl9hNH1pXV/VKf4Lhxe5gvDYc85XcybM1fzuS09It6JOCf9vlohOsW1RnP6aLeCCbyK62nkNqZGP1qwCyHlpYvn8kjaYcSfiCAo/q3gxjTbHKTTy6KR82kiROmBCUX7OK1OtofyshQrsjYMlJxNKMyFGPqYVBvfuZOp0DMWOj3bKKzrvRGQCQk7XGvUXC2NfqUJW2MpqRcQmxFva74XT0qL+buKrSElZfbOsyxGpzoN5r2qvX3nZBSk2v6ypDX1PlJrASrjMGweui3b9+RcORwOTGkh12Trp3QBu6+rQRRtkBo4NeI6lYJDGYsi3jHXmaqWWp9zIeqAukpqtganpRBCoLRKqqULoRNDdTTpa04FdUJyzsYbJuIu9cJhsKJqWs233HujsFtrJDK5WV81Z+u3H0aWlAgaIRf7rGl5pFrQ4LdiYyFEpukCnk7tFgImAouYkHpZZqo82jpYcSyH1dWycT9dLoxjpM6Fq6srXr++5Xi4YiozToU0TwzDrhfj6+vBMOCr0cklLZsna0WsSJ12W500gwuEMFjGQMrWVinhY+BhMo/dqCPSMsGZtUOrZvehuwH1DpLZxCARemaG2VtU4m7klC8EcThvdgeCQ2vfX52j1kZuPbjkwGFZLrZnmF+3FUC1fiopM+zMt7fWSmnCIBFtRj2tB+hUC7U2RAO4uq3rjYUlVzQEI2lyQUsjODH//VL7mIRxHEinB4IHmrAo4CJ/7c//Fz8jbf8IvP6ZX/vPmuv00kqLrFTKSpWs1NRytMIpK01TBhOE6qhb0ayVVlmuTBBd0/XzaAf51YcP2LxBm7ivEGGrPYHOXThYU+urHUbzTjd6CtgqfBsB2jaidyVf1vcYeSTsf3K2VN/+G3rOLM/iljo7vO5pwZ1WpJlvoi5t896L948pyitdSKOTuPJIN/XXaj1gB2+3vd9VWI6y2UysVg1gAsJKz62/tXoarkXAVqJqFTjXYlErlQaP9GMZlHQl7D57pCxXwURStSJynfxLN8NGcK2pvTWaxUO8ffSr3Apg9TTlleZc6eD1Htf05I34XFP0b7MJpHOm7IyohEcvxZVoWo7C+CpvIuFqreHPpfusdnJQu4B+8BvNtAlQRj8gk9GpdQxbO6736M9Gy5XBbWm8eWdttl7bRn7HRw/d1SajbuKKCSs6lY1Q3Ojyt14rtflWvM0CFdn8PVcK1tVG3pkoM7xaNmo33UR0fiRyZS4mAk2F+VncaNb4auLuO4etiI6lQtvnVrLvbRrdn6y/xs8vlF3YrCfWgIoRjPIVCn5N/7b2MKFI7yfyzc5E1C5wutI2ynFdb9YiSyvRuI2jLj7OzyLhIX8lG2AVs1e6bSUgyyhUhfGVEZfjF+azupK34aFsY34TE1Pd2mK1NFg9U1fieR1LOHpa+iNJ+xUf5vR47etYf/s605WtMSttuq5dw8uZsvdbX6/zbSUz1++TORsJ3tuoqbPCYt0rU+e6EazAdn9vk9pr261kO44t02C1oJFU8fcLy7ORcLvQvI3j1SJhvde8M2L+8k4gdp/v1R/cLpyNsl3F3XCfutgv2zqzPHncQ1y19+tUOb8IW6HL1Trlbdo479U8Qzvxa4WqeqHHp4GqEO/r5sesUzVBNjizXujvz3vd9oP5ncG8ha8twISDtBdKNPHQ9iZb1yxYsNtsB8JDpYyOtDNheLUAWcXFNSMk3KfN2mK1Icij2yx9qhoxmnaP1gPruFotHmpv0/F13vp0XYPeJp/TlTK8TsxPLUBwek+5+cHCw9ci4xvzgvXnynKthIe67Svp6Mlj789uDxDOdu9ltOeCdY+ydVvInW5d2yoPjsOnVj8iHW2NTDvpwceGn4yIXTNb/PyoTa7fpamxHMxfO79VwK56t5G46/66Fvxa/83mCKSdZcGUXtg0PphAvxztmdqsNxrLUSnx0dakid2zFGtvYPPSXTN51gyhdQ3Ke9kKtK19tjwJ2zgD87cH+N/+1//4H33S9r/9q3/le0tJOC84ERMsasZpBCI1FaQfkJ0oORVAiGPkdD5xuDogqaDV0ku9KgWrrlxyMZJxyZYSi1VnFzGV3+WGNEdOGRFnEpKD6LyloKviCF0pd7g6G/QnwlIStQGuIa5RxESSlO1w5VTNT1EFqkNQSzMHokaCBmgVr7Bf06ODMk9nYhyMHGtQSkXVk5ZMafZU5BRwsNRKdnUT47w3+lJUKDUbcbmKJc0xLzMumAdeaxlxZseAOpz3TOlCUCtsNISRUruIhRF5c05b9XQouE73iTacQKtWlXrYj5TWWNJC6fRkEEdU5eHywPXxitqqUaNiD3ujemrOVjXcN+Z5JgwD0gm43Wi0XcoLQwiUZTEyECXPE8EHciu4OFBbRYOgKuS6En+OFA1HGDN2QK8NxbHbjVAx30svNGlQK0WNKgZPBbyPtNLIJeE1bKRga+YVnHPBu/6ArQ5q7emtDi9CVKV1ypFmJFSaZ7x6WqvklMEJ4xBJWnGxm6jn2QTrUhHnGaNZDxitCVEDu2EPrTIOI6pWmEmcUFtmEKXWRG4QQ6SWxOAVHz20Zv6I82LPguJYWqY6E8nBCNgglh6TUyKOJhaqeMOwm5GyIs7E1WbUn4oQ1SwihoNnTpm5ZNwQzWuxNbv32sh5YQzCGAbm6cJuGCilMMRI6++VCqoRJ0LqQmtpFY2RlI08cWpt0rIgVEbvUaCkSlChtMT1fs/gxMZcqTTXKLXQaqUFK8hlAGBDWiOqoxWLONYpcRhHTpeZGCN4T6qN0v1jq4MQPF4hUcA3hp7+X1Km0GgN1jzqUgsqjqge55KtfWKkec4LcRcpDbwOlhaPYwyRQYXgoApkqVTXcF5ozSjkNM+IGuEZvCItIy5xfX1FKQs4NeLSAE8ul5n9YeyfB2erKI5G8DbWc1oYhh3gmOfE4bDnPJ1prhIGjzqjRmMw4lJVKTlRUyK4QCvN0sCdEoMVDtmFAR8coRX2Hsg2Juac2fuRcpkprTFE8xR2rlFKNTsQVykpGYUM5surjkylMoM2QlDqcmY/DEYdt8Zht6P2oA7NIyqIqJHftRAUdrvYvc2zjRGplFoIfiAOQ1+XxbIXvMfVBadQpRLiQEq2HrdqB5rG6tcdepE4wbkFamEfo80vbLyFKrQlo6URh0hajOAeVJBe9K6UBaEaJe8j54czOEEHRbww58wuDH19FlQ94oRSsf2Jim9Qa2YIHlcLMcRe/G2h1sWEiS5GEswKIoijLbVbR8DCgnhQZxYeXhw0C8yUYuPHi5KWhUHVrB9isLEUB0pdkGYPUuqDFf/r+0Fwgscx+oGcZgOMKERvVKqTRKHY2opy2B9YUi+IFJTgGrsYSGVCxRuRazdlmTM1c3NzDaVQ+/4VnQX/VNZMBii5mLWPKOMYWc63aHW4skA+c3P0ULPR4P05olmSCGMcEOeITokSaDVTWuJwdYWKZfdIVUrNlJIQlMGbPYlIwUvl+bMr3n92ZIyKVljmQnCe1jLRx+6hCzUVWjbPa5xDYwQnlLVmQFkAo/pVOx2O49f+zL/xM9L2j8DrL/6Fv/G9eGdiiCF1q8DoNgpvsy9QK6YBkK88u8+M2LODfid7epEiv/SiJN2PLj50v/eHZJRTJ9ZWj9TVlqCpHeyMgjJa5e3/yqAgRuyaaGu0jAlbdhCS3Dqx9SjUreTdSvLlg/1GGYXpeSSc7MA73OYtNVSX1klRh790/95OGK2/pVOnw95K6V6Fk5XWqf1AF9+YhVIT2ci55o0AwxlFmA/aK713wdiZ0CepEm5nO/x3GhLnOlVoBKGrEO6Wnl5tfsA61+4frKSDcPhk5vTh2CnOTkA2mJ/6nlJqvrP+bqJ2n1QTAA3ICA+FdB3QpVI7serPiXII6KVsJGMdTMQ1aslo1FXYSjcmzrouhM1PPU6UsteNIAWjdHWuuNaQ0v1aWy8M1Nu0qWzUkk6ZOqzFb9xGPZXRqOB8Zc+hLYr9KQ5/WqijR4rZVrgG8xNvYk6fC8PrtAl2TR3LjeLnx1TqfOXtHnDMTyyVeHhjRKikSrr2mxVFvrL/nY5+owhLp/JW4TXcLjZ2owXG5a20aH+pG4m69h2wje066kYW1iDkK0UypOtAvC+EWwMY4pu5F2Dz3cvTLAumJ8pwV2w8zM3GUrdKMNHOxq/5jtoYLYNYSjiP64dRnkJ6OlKHboNxsPc/fH0gXQnLtSDVBL11bpnA34M6PYCTr8w7uAZBU+XybmD3+Uw6BqD7tHYCUrL5H5fBBCPXeGz/uT6Ox95mKwW7FiJc6cvV6iEfdJtD2q0s5qee5dqbrUfqdGZfc9Yxt9kYODYCsnnHw9c8uhjfaGujUc3Dy4n53XGjWbfGFEc+yNYu+WgZM+F2ZnoxMH4x2Tq2fyS505WtBzU+Br5yJ1P9KbN6SuOsCFYZlXxQpuce12yexTeJ6Z2w3V+6CVt/aBesJfd7X4M/YvfoL7b2u8a2/k/P+hkwNS7veDTb/a/gh9ln0NtLmJ6acKYJNHeiMTXK3pOOFkCrwW3ZC83Z/iTZBE1/tj5zpdnyWtlo0zX7oPVmvjzzNr/U2Vlv57cAYToI4VI5vxtYjm7bp/zcyegolL1n/PxiAfpOGsfbzPTM9pHqjbZswfaUdaysY2q5sTVruRJKtN+T3LbMgiZ9K0xsHsSrgDzcmxhcB2HpwSa3Bj5S3eh/f7HfcbWRDoKfYbm2dax6t+3F46tEHYT5iflP51GYb+z9qxifDrYHr3vk2k6Xd5R4b/OwDI68c8xPVrpWbM1uNq5dtbP7w4dK60T2SsvWlb7F9k9Njfloc2i5tt9YfWsBTh92JVVWetzO5WUU5hvd/Gvz3rIB/NK4vPNoyVF9H2e9T9cgqgWP4f7n4PYXG/ONtWV4wIqYVVgO3TqiB4xdbaRru57Ss390qpSdWtCh7001yjZn/qCk7R9q0fa/+St/+Xs4R6kVlwu1FBTHlECIdsCqS6/2Ti8YAvN0IkSPq25LW0GlCyeNUhrjuDcf2Fas2qeKpdfXjLCmTFvKrYgaSYgd2EpJePWUnAkq+GZiXSkFUVDv8V0Ac/RFXB2oouKozdLgaVZ8RUWpBRxqojEmaqjae50Xsw3yQiqJuVg1c8Gi6JWGj9GoGWyBQMEFulDYqC3hXEUU5tloqxjNq1KricoxRnrA2Q6X1b43tS6m6A5XHDKYb25VE+cUR6uF3W4kL3bP2guvxBDw6qkpE0cjlC7zwjjsSCnTWsaLkNPMYT8wX87mUekdtRW8V0tjVjWhWJzRtVhxq+DNL7W2hjrzTAze4xGkOgYfcKI0rzYGiglh5vfZU4cVWk4g9mBuKa2OIUZrp8HjXKG0YunjzjMVh/ORECzln+qIfodKxAVnwgW2GeRSoTlitIJvtTYjvro1wOA96i2lt9VOXteCerPNKKUwDsNGgh7UM4g3f9naWEo2wacCFHxQUknUClf7facNZ8Q5Sk6Mu9ECEaJEsdTkQiN3gVkUBt8Yh4CjIGL9W6WRSsWHQFDzthyGYN7CThh3QydSHa1qp81t86g148TGeujFgIaoeBUWV9BhAO+Zaukp2g0vQquVMcIYPDllxmEETPSp2YQ255wFPbySm1l7xBBwToghME8mbFt6eyMjaDXv5qBCq40YrOJvDI5lOlGWCbywlAUZAqVlpFoqVRBhcBFtDu8cUkyMHMZAKUZlz2mmBk9qldM8ocGj3hPHSHONIUZOD/fs42CWA81RqqNKY26FpTniGBkk4vFUFg5XV0zzzDAOZCpznrFnfPMVDk5t3IijUalByWqk92U+bwW3fLCCTM6rCXTeoVqZ5gtgQrwTwTnMfxmh5WyiG1ZobDeOJkoG5fRgaVZeY/f/bNAK6hp+P/TiXcphv+d0OuFVLZW+rWS/dEsNJUTzAN3FAcGCSU+Pe3KaCT5wms40VVr3ffVhILdCyZnr3Z7DfgBXOVyN1GniuN8xzxfiOCLBcSlnwnWk1kzOC9dXO/ZD4PnzZwxRqTkhRNJcjYB3Fehkfi24mm39UM9+NzIOgdvTbbd1EVI1ajWKmC9ogzE2zpczMnhy0S52GxlbihFrzkEpC2PwqMD+ELkaBnxqnM4nQrc3KDFwXmZcUChWsMu8UIX94cD18yc4qbY3towrFqgL48BpueBUGcddp+od5EzoNDLNMQ4DVgrRWTGxOkMzIXaZF772/jvsdnELlOU0UcSCJ2nOXO12FrQMVjgsxkAQIaUZB8yXGR9HRASPPVSNQ2BQT82FIXgGjRwPB1uTUqeH+oPZ3ntUlOvjkdPDAyqR3RB5/vwJh2Gg5EpaErs4GCXdMtqgtIJ6IZWFcQwc95HWMlf7nfVDzrYHe90Krjnboi1bJwaCQPBipLUzj2cVR00J8Z4pJa7GKwbvePfZE77+9fd558m1ZUf4yHIxCyBH9/8Vb6JsrjRgvNrxwYfvUZMRu3mZcC5SyYSoqETSkvFBefpkx4v3nvGdb30NXOYb3/gGtTZOc8JRGRyk4iizBTi0NqJ6K7BXMlEt4BycksqCRHs+CmIBsdyMmP63fyba/pF4/aX/6v/4HnS61j2muA5fTvbc1NjEFMmWzleDEO8y5RD+IbEXscOl9gIp/tzpuZ7yWjptuRJdq8hRO8W1CcDJ0ijXA89KSOlc3yK+1NKSxcCH9c+VgFzTVWuQrbjZSqYBvcCKdPsEtxUACudCuF02gRZnQkUdVhGNTVhcD99ua0P7vXBrnrf5SjtpZ4e8tRjT23YUa2VpnPnpOWfiwPBy6r6VuhFp8zujkX89Jd1PZUsx1qVZKjMwvJxYng74UzJCaLAq3JcXkXjfvYS7qFi74L56yzYv1F3oYnfvs25h8HZhsDJYf23p0x10o1PYed/VBtb772R0p15dNeEgj2JCgKOLGiaODa9sDOa9t/7ABH0TqSHtjUZ0/WDsqomMuqxUnaUhzzee5diL8CibUCip0oJuJHe6CSY6Vysulq7M8qKFR/9KKW0T3PzJ/HrP70XyKFs6sCZYri2dHzEv0zKuPokmcjUvLMfuB9z721UjOPU008ZA3nsjf6+DiUXqmJ941NzwNrLLrUOx17MwIcn6dLmyfloJSNcgvJ6ou2Dt3AXr5SZsRZnmJybkrn0uPb16FYb9pYBzG5WaDrYemGVCJ+OmZN6pe7MvsbFvcyEdlHiuRtqBfTaYwNi0C5V7JV0rdRDKYIJX2osJTYsFBeKd0YX+tBB++gqGSItKOihSbGztPk9dJLSxKUsv7HQ74+9n8nEgHZU8mFBzfuEZbgvLUQmXSrhN+Pulk6Ky2QP8P+y9yY9l6X2m93zTme4cc0TOmZVVrIFVnCSRokRNFNSCGnYbDbgNeGV445XR/4S98M47wzYMy/Ake+G21Yu20YLV3RLZJCVS4lADWVNm5RTjjTue4Zu8+G5ESoYX9MYWunWA3EVG3HuG75zz/t73ecMVZ9MlTIwvFNlFveHYJiExbEqaEMmt7Y2kmG/EZc/1OtQNFDHTL1EmMa1NzURdpw6q4w5UWjPTfkrD1ZApfKkT6qNUrHcV1ZnHl1fnu0wIFzZrNFxzUJtJErTyeWB+R6Pa5CbtHae1T4Y0zAmFvC6iW97IqHeTqareVSgnWB0a8nmKkUclKC5sEmt9RNeB1ZGh2ZLM7yeutnTpu5t1vB7oANf/54ob7gtBs5XE/cHjhAYxsw6z4d7ajSMSQSoNPPcJkdDGa4E2nVfx+vurLvFYg0m/e72ffkd15rGl3Aiygfz0Kr2dPnOU4HqSdkuwPoSgkth4de1FnQZj5YsalKLd4CMgCc5J4E9CdjdK9zO3Ea2lTdeBKyX5MnD6jqGZKNqJvBaIVZv+r1l5VgeGfJmcn7pJbHBXyoQPkJDPfDr/jCTkaW1pR2rDVE7JDtsXrPcl0oq/9jkRgno7sYTXh5LqLGyK7STz+4lRrzrQdaQdS5Tj+p6n7AZjsOGu13tp/9Q7m/usTViupEaTCr42zw0hE3TDdMx9sUEWaEG2DngjrjEkxdyz2td0fcHsFcH0C55wr2HV10nwXiVRFimwZbp3B32FFEp/4+JNgQziuuwsSoFyEVfIawyDKySL24LFq4Evfu1nXJDzztuf8rEfk5+n49b10zmsu3g9ZLB9hc8k+cKnAjYp8FVC8Vw9S1wNgvSGW/zv/9u//HM9a+v/F891/59vanPzVyHF5ZXSybkZIkLUSbC5LvlJbrkoN04+pQi2QxiRfg6IbYtREmUEvlujA4Q8R3qLwyO9h6uSEJ1iitbb1BwfABHIRESWGYEUgw0CWueSy7HIaTeN2iqLoPJUliIVdV1TlSXSh4QqECl63uHJdeLeahGSY0hBXS/J85ymS3wmKRWegBYaJVPju3UtZVGiiSgVaTpHJInQtrPkRUEMHWrjqBNAu1oz7vUQQlCv15SbCKoWitBaQq6JSqWXtipj0SwxSlLmmow1Qnus81SZwYqI80mM0zKJq708g9gx6JV0LuCcx3qHzguCSDFPneXY6NFFRrQdUVmMFuAaepso52rdokSKMYuQTnJjUplLUWaApzcsaBvPeu0QAubLJEa74DCkJnFtBM61VFozmfTJVElbNywXbVqc/BV7VYE2BOtxKPqVYlRpyrKkbS11bdNEWko6mwRdTyR0AWUDZV+Q5Y5emRFCh7OWZh2RQiCFhkwnZm0AqRUQ6Fclu1tD8jyjbVsuZ/MU+4jJKRwCdAiyvKDbiA29XHHjYIvVaklbtyzmASE0XkAnBZ0L5IUi1wpySVEIhr2S8nBCZxcsFiuml5cgM7oOkBlBpKHHsCgpMuhVhjt3DpldTFkslrSNZ7ZoWdpAaQpcDKxdg9kUABkjuXm4S6YVp6ennM8vyYqC0EFTO2QW0LJILtfoKYc9+gPDeDSgbVuwnqfPXyCjJBOa6DWtSE5Gk0Wic/QHQ0aTCSJajDQ8fvSExqYYdOOSq5yYmHFKCMosCXc9Y5B7QxaLBSfHU4xILwWZNEQnkhuQSGMtN472ubG7y1Y/RZWfnR/zyeOn1A6ci2hlEhNWQFCW7SR7xAAAIABJREFUssjY391hUhmstcxWLcdn59TLlrLoU7uOQimKssJoSb807G71GI96CG8h7HE5XfLJs2OClHj8tdgvSW698aDHYDDg6OAeF5fnuJ0B5+eXNGtPv+qzbmJyoAiPD5adyZDxqMdk0EdnBYvVnIuLSy4aSfBgSSVPrbWUZXKr722PODo8RAqHQrGo17x4cczssiF6DyJiVEIcVL2CKjNsTYb0+n3KKhXhreuWR0+O05CKxP51LhLWLYrAzmTE3u4IfXsPJQRt4/ns+QsWdcuC5KpXm0GAVoIyi/TKgr3dQ6SK4Hd5fnyBx9JYh8kyovMIIZFNy8HOmGG/ZHt7RFEmp/B0Pub8bE47r5ERFm2HUQV2btFRMBkPuHG0zbjfo6xyVnXL5WzBs2fTNNhSqaVVqECwFk3k4OCAre0Bo2EPES2286gPHdPLJUFqQpRkWmO7VLaWK8VkZ583Pj/ANwmJ8vz4gidPThCiSEzUXGFDYkL7UDOZjLl/55B+liNdYFHXfPrkOat1Q91aCpNdu890LhgOezy4eUCvLLChRcodLmcrfvAXP0EqhUaDg2HWw3tLs5pze2+X/b2tzXrlaKzjgw8+wceWdQCEIotQ5kPWq0vGw5xeb4uHD++wmC2JMfLi+IzTU0EdwDaRflFRr2p6g4rGLjk82OXW0TaF1EipuZjOOT+bcTxbJ7xF9PSyjLbrknCQRe7fP6QoJduTHWaLJatlw8/e/xQXFT4IOhTaSC4uL+n3K3a2+4yGfcbDHlKk54CPfvaMs/NLYtSpMFEpjAtoJRgOh+xM+tzYGyd8SoR1Y3n8+BnT6Yqua8l7GTZA21pMFinKjJuHW4wGYySCpvU8ffack4spIQhMr8S7SJQdi6bm9o09bh5t06sKqjInNALnIn/xo/e5mLbIvMBFh+tqDOlQ7h3scbC/zd7WmOViQJZXPHt6wkePPsOIlBDp2o5eKbHdilcefJHoHTIGjnZ3iAFevXvE7kDz/gePma08McvQRU6zcegbowDLrYMdcq1YzZY06zoVfVpJJz3etxRS0kiPDY6/3f712Hz+MvoufHLIpJep4jrKGv+KQNRt4rt/1V0bpUBvosYyJqeTN0nou2qSvnbUboQK2YXN394UKjUv2bLCm+tY51W0ML+wqVF9bK4bma/iv8mBlKL67U6+EVTkpswpJI7uMLnvXC42jqHUEN9WgmK2cfZt3DKuVNf7Ra893ZbEFZpgUut72hegV5ZuZJLTLVfEig3qwbG410t/Y+ppJvL6RVr4iF7H65isqxS9Z0mQaCYa20sOIrMOtLvF9b4JhUpFVS5S76eisdW+wqySI9LUYfP7NxHNMjFkfZWKqa7QD5AESRFeFpJdNZFrXpbOtMP0s/WOwCwj5Xk6vv0nTUIR1C/xB76Q1/HoxR2Jq0AvoTyNRBmv48FXm9i4WlcHhtUNSTuOmIUgv7xq85aYZcCOUgT8yhF81URe76SXetVEsjkEfVWUkyL3V4gNgNWBYfZQYvsBs5T0nmyitf5l1FgvOtwgQ69SKdt6R3HxTiA/V+glcCyJMkO1cSMSJie0HabX6HYkqA8E5yOJqgXlsWT0KK2hCfuRiqWEiyxv5rQjQbMt0L8wZfF8QHGs0UtJ/2mg9yJgtytk48lP1/gqHWtbSs6+KPC9wOBDzfhDl0QYm1yRUadSuKtYcDNOosnqRkCv0r7Z/UGKz9t+P8V650msvkIH1HuC9WFCL4UysPM9TT4PqDz9jSsUBAJ8qai3FM2OoNmOnHy5pDwVbL1niZvIt92wi80yCT/51HL85YLV2w2fu/WCZZfz9Cf7HH7LUJzbVDpWFBsEQ3LNNmPF9HMQb9f4WpM9N0zeg+Lc0m4b8nOLHRfYcYGrFKt9xewVCDcbhIycAeKJ4PBb6bqQbRo2hay4RrPMb2nq/Uj+1pLloxHT1jD4BPKZpJ0YilN5PeyRNjJ9mLG6GfG3Gkzm6E4reo8Uw0/6SUhbp8+v1452K8Xzp68p/OeXKBXIjePyZMDgfU3/yUvn75VDtN5WNFuS5Z2A37b0Rg0nJz3MVHHwnc3AZYNEUE3YcJEVl68olg8tF9/waNNhlxmT72t6x4HqyZqQ62v3rc8l7VjSbMPp77Yo1bEG9E96qC4jn/nrCP4VkuLibcN6PyLvrZgM1oyzjk+e7pB/oskXqeSw/yyhKPTa40rF/IFh+o5n68Yld4ZzPp1OuHg6ZPwjST4H29/wYDdCtisl528omjsd+4eXKBlYNDnH2YTRI4e0+q8VhflC0g4ky9uw/js1ziq08YR3B+x931/fu65KztxmADW/o2i/tuDO9pRcOz483UF9d0hxFimmkW6ruF6vXCFZHwr01y94bXLOtK3IpOfD57vc/ANzXXoJsLpdodpIPnU8/2pO81rD9tYS5yXni5Ltf1ogbUwCM8kV3fUU1ZljeSuJwr/0K+/y3vkeIUgu35sw+lBSkM6r1YGhvPCsdxX5PHD6dkb9pTWjwRqrAsePt+h9mrP9E7dJW/iEl5iH6wKu8693lIOWr9/6hO+f3ODZRZ+d/zPflEFyXdA1+iSw2pMs7kM4qnnl8JRcO1qn+fTbtxh9mIaXqokIH7CVxBWC1ZFhcd/zubc+Y5g1TJuKp7MR7Z+PGDxOBWBdPzlws0VMjtyRYP6FlqPDKZnynC76+B+OUB+8dBarLiFL+s8dL35JU74z5Y3JOZ8bHLN+NWNmS777v34e9WkSanWThFG7KQu8fA3U5xb83r33+dH0iN1yyXfefcDun2jsdSIosN5N97fJrx7jgySTnn/w8PsEBK9/7QV//OAhp390g97TTUloLsnngSi4Lm49/gWD7ad7SDZ/iWe6Sr80Y0Vx6RPS4+fc/maLtojrwplE/ozETcFSYoEKZOSaq+qcQxqNygzWJ9i9zpOgUgiBzhTRdjgRMabEdtD5Lg14YhJgrziqzqadn2UFAosQ0NmGEAXBOa7qzboN5zDfMA+zLMPaVFBijMF7j90wHNu2Sa3gPjF5o1HYdk2ZK6SIHO5uE4ND5xmLxYLpdIYxEiEUyAxr203UPIl++we7FJnBbLiXZ9M5beMIRIqi2HAk034UISKF5mB/h0FZcHJyRi/P0FIQtMGH1N7tuoYYU1FK26zZ2Zqkz4WlXc5ZzNfUbcAFgQ3J3RpjcvdJEcmMZmu8RVEaCLBarZjOLnFR0jmSyB78hq8aqCrD3vaQ8bCH8ZGu63hxcUqrSIVrziNlxHvItSG6jq3hNuNJj6rKWa/XnJ6e0rYtzYbHl14aDEoJiJ5MCu7cOGQwzNEqsF6vGfYtnzw+BpEKYuquJVclRZaBDxzsTdidFCiTAzCfXvLpJ8fY4Ml0xjImlmsuJVJrJls9tid9hsOCGJPI++yzM5aL9UZI1HRtOn4mLyhzza2be4z6OTF6JH2qUnF8eoa1gugFWidhP3qLwLGzvcPBZMholLO91WO9WjHrrXhxsmCxbokiQypwbRIilYSbR/v0ijxFgV3OsqqQwrFYWJzQhOjQm3h0s57z2iuvY3Qgk5GD/W22hgMuLi5pmjWejDYmV6cUktZatiYD7t04YlSVGBXY3RpwfHHCbL7ixekMdMA5yExyVWoCk9GYrUnOcNDD21Tyhp/w7PkFQUi64HDWkecCW6+4ebDPjYN9BqM+LjSIzlHeP+TDz445v1hgTI943dJukD6wvTXgYH9EYXp455iMCty6Y75eAyLhJKQiREFeZPTLir3tLQ53t8l1wiI0bkVzsMMnTxcUm2tZiOTozTPF3s42k3GfYZk4snm/QqjA2cXHyDxHyFTCJaUkdJ7B9oBbBzsYEVA6x7YeSeTFhaJZNggt8B6MMejg0Upw9/YNiiKjNJrqYI8sy/hJ/T5d62hbi/YSFyWy0JhcsbW7xXhYMa56ZEYw6SsKFakyw+MnlyiVhhRFUdA1DTcPd3h4+xZRQKENSkryPJKpPX48fUSIjhjBkhz21rZU4z6Hu1vIPEW08mxI2wRevDhJUXWdsVqtEu9ZJP7t7Vs3kFhGgyHOd4gy4N0Wnzx5Ts8orBfYtkNqhVQwnFTsTIZsDfsIEYlRUPT6dD/9iPXpJV5qnBLJoV9mWN9ycHCLXs+gTcRIjTYjyqzgs0dnqNiRGUWwjkHRR4nI4f4ee1tD8iwNd0xVMdkespw3rBdLRBQ4G8h7hkW7ZmdryOHhHmWh6ZcZ0SliIbh9+ybL1c+QKGofcdFhSkOzXPLg1QeMhhNKLagm6d6ioyS0lpPzJVmpaZwn0yBw3Dja5+jogFJLCqMxBWgtye/d4jvf+z6SYhOtFSA0zbrmwZ07jAZVitrLHKUMo/6E89Mpjz47IZBhlCY6qIqM2nfcvLFPkSlGw4Kma9guJiwXKz76+DFRF4mJ7mG9XuOiZedgwngwwbmGXmkwOudwb58//ZM/AyfBgA0OZRI7fNCruLF/wNawSqUnWU6vqCAInp9MEVKkIj+fnKFKCIbDAZNxHyUdSgRGw4pRf8LZ6YInT58jVJ6GPMJgcs2qW/PG/n36vYLSJHes1hmrg0C97ricXmJ6JTrPkNYRbMfR3k0mkx6Z9FRlTswMW6KgXi2ZXiwhSqTKWc4XbG1tMVscc//BHXa2hvSqChEi43FiXD99+pyiGrNuOgQGjCTLBdvbEwbDHmVmiCEhdoQ0jEYjTk6n6LCJ3IqEjHHOcef2EUZE2maZBi6m4CLLybVh3SY+cb83xLkpkZat7Qmu7dBCYnRCvvT7A7ArCmO48F1i5CqFyTNkTIOHw8Nt7t+/Q8RyeVowPZ8yXZ6hdE7PlDhqnEjPP0L5/6fHsr/d/hXcrrhsQQuU3zh6NsSQq2jtFa/yqgAlbhxfV9xAV6qN4GVwxaYArA3YjftHr14yO/8qR1U1/mV7/SbSrRcbccgm947wEVP7DQ9Pb2LWCr3mmv+p2sQ2DbnCLJJQopqX30OvHM1EEzRcvLVJqWWR8kQxeBQ2Dqv0+Uwd/xqP9vwNgx1GfJHcmcOPJNkixX+vGHU+V9cuzqAFs7sF9X5k/EESSH0O0slNgUriTV7Fb7OZZfpaycVbcYNcEJTHpAh6G6+/1xVTM2hB15Ms7gjsML3w5eeSwePEAkwc1s0x3Thpm92Mi89p1jc8sfCItWL8nsAvNqzCDW9R2kg3TELN4q6guWEpJzXLaUn3kcEsIJ8lJ+8VN/eKQ2j7iuNfAvZqTOZYTgvqA8XhtzbuWhsw52vcqLxmgV68KRAPlmQmrTfT4x67/1Jh1on7WT23qYhzmJxf8zuK1T1Psb/CBkE7Kxj+2FCdJEHZm8RGvjqn6i3F2Zci2eECFQVCBS4mfcbvC8xaIMIGz7EW19zK6Sua5etJMLJ3JdNpn+ZZzuQ9SX9ur7nFV/ssZMntlW/V3BgtWTQ5l/t9os6oXgRUY65Zmgm/4XnxzYgqHTtlw+GrCy5uVZx9NiabK5Q1ZHO/QTFI9Kxm9qDg9GuOraMZw6Kle6D47O4e1XPJzo8SC1pu2LlXTNfFbUFzp2Nnf07rFKtlwSkFO3+ZSv3MMqJqix3l6JXj+BdLlm837O3OWXcGJSLTbxYM/3nJ5GcvmbBXDNOoBIt7gvD6kq3BmtZqFrcLVFPRf+6BdK1enYPdSLPe0ywfWr752gfcLKYEBP/zosf5fMjRv3B0WwWq9dccyXaguHwtCbZ39i5onOas3+dC9hi/316L81e8YuEj633B4RefMy5qKt0xbSo+VVs07/bILl0qFtsI1T5PItPil2qGg5rD4RzenrFfLvh28xZ6bcgXyemvar/BjEiWdyLxZs1rhyfslwvqG4bvDu7STnL2v/eSK9uNNWbpOf5yxv6vPcUHyVF/xkC3fNzb5lFvi96zYuPcT6JcVGkNmk1g540zMuVRMrDVW3OxV9G+O9wMJgTlcZvWxWH6Lt2Xl5Qq8NbBc2ZtiToIvOduYr6lkPtlwkisU4Fa0IL1YcTfr3nn5lO0DLgg+WwwYVXvUJ67jYiX1qt6P0PVkf7rU+5NzhllDTvZklFW8+FoB/W9Pt4obD+JqsujjGAEl28F7r/ygsNqztjUbOcr9GHgT07foTx/6WxtB4r+s47pqxnhzSVHoyVvTI5xUWKD4ltfLqlOMoIWSeBv4oY/bnn2qxn6aM3+aMErwzMAvp/f4NTtMPkgXDOErxAYp1+Q6Ldm3BguuN2fspstGJqGT35zi/D7e9dr/1XxVHHpOfmVyK/vP6VUloNyQU+1/PLOx/z3n/4ah992CM+1yF1vKwoF4Z0Fh8MVv7D7mMerCbdvTvnD069w9C9e3mOki1RnCbOzumfZuTFjbgseTM7ZyVfce/A9fv+//R1IbwCYOiUzpE1Du9XbDV/YHL9Xeqc8Hm/xp7zKzg/TsDWVOm747lkayty9eUapLT3d8qW9p7AHf/LRO+z/ecIkZTMHI40tBdVZwP2dOa9snfGgf0alOiZ6xX/+uTH15ZDho1SA5gpB0GnQMn/LcuvmOfvlgjf6z7ADTXXQ8p/OfovB4zSEChqq447Z/ZzBE8vp1wWv333Om6PnGOExe54/cF/C/EVJM0qi9hUOoesrurstX9g54bX+MTYo3qieoQj8s6M32PoArmwHUYErBLqJ7HzlmFHe8Kwe8Vt7H7BjFvxgeBPbNxQXAbVh/ao2HftvHn7AeddnO1tymF1io+Lr5YcA/HfDI4afXPF401AtqvSMcPEmvP61j3FB8tPne3SPS0afpmK0ej9LZYFtvH6W+nm3v9GirTQ6CYkipjKozNAFwCvAEekQm7ZrlSnaJpAJcE1iSnopaTsHStHFQC0coYAczbJdopRJL7MiuV8jHoXGW08hU3mSIlA7j9IaFZLNOdNJwJFCJGEQSV3XGJMEYiHEpqjG4b0nyw0heIK3eK1obZOEspgjc0WQjjtHh+xNBlSlIe/nTKeXvHiueHF6DjF9f6NSKZPvLIeHW9y/dxspAz2jWDUritLw6NFzhFC0XWI7dnVDbS2D3pDbt3c52B0SY8Ph3oim9jx5dkJbd0gl0AZ86ACBFBnD0Zh33nyDwoDSgfmFYVplPHp0npiJEVoXqaoKQqRernj19j2ObgwT9oBIZxs++8zx/MkZioyoPEoYZExlKNv9CW89fAi+RQpH29aUI80Pf/wJzqXoPt4hYior29nd5bWHD1HGEW3DsFJsDQ2rVc3yh48RUmNdKuBRCJr1Jd/4xq+wu9MnRgfCMRr06OrIol7x/MU5ShuqqsDajta3HG1v8/prnyOIjrZZQoiMbt8gSsH7H3yIFx5lNSrP6HyLVoK3Pv86hA4JKBkY9DtyqXl2eszx6ZrF2mJMRq4jbr3gzv03efXVe6zXJ2Q6PbwWeSQvNO++9yGBnOAsRmusdezvjnj44C6l9BRFRtut6RcFO5MtLuc/Zr6qiSIihIHgcTZwcOOQe3dug21QwmFdZHs0REvFD0/fxcgerY8bJILi5v2bHN05JLqaXlmxXiypeiNEBp2I/OW7j9FFP8W/hWG5WvLKl2+ztzOBaBmYNFgw2T6ToeOTT7+Dynv4aJFa0bWe8f6E1x4+oCdb8AGfZ7T9jBs3tjm9+Ba1c3g0xpS01lKVW9y6/Srb2xlBeLb1Nl1bUxzscdE0TJdzomqQLiM6CNahS8kX3/4ii9kZZVkm9EWvono741vf+wHO5aAEzkeUNtStZ2d/j2F/i96gT2YiLngG+YT9ScG7H/w5/f44ia8C2rblzsENXn3lAc6tKY0mBIfsPLdu3ua9n32M0g7fbWLWJGbzvaPbjKsCHyImk3Q48tGE3dmMdf0EH3NcJ2HDd93a2+Lezdt425JngqZL+IKHn3uF9ic/ZfHsjLwckfmSbj2DzPC5B58jzxTrZoGRHdqUfOWtN/mzv/wJPzj/CdV4J6FhgkcXffb2jyiLAQqL0JYgApU07N6+ywfvPmW9aAmlIfjEiF7bhq8+eIUyU2Ra4n3CARzs9jk/P+dnH32I6wxK5oQY8SoyGA7YP9pndjFFFwV0EVEF9vU2z1+8QDpP5zTOBnqjPifHz/l7v/fbrJeXaOGRWtF2ju1iwv5OzcnJnBgjS9tS5hWXzZKHr7/Bzo091qtLZGYgSHa3t4jhgq3tPicXp+iiQEUFHpSR7GxPGPV62G6NMZpIwFvPpD/kpDuhE4nLjY2I6DGZZu9wm1A7dIhoaTD9AqUGPPr0BYvlGhMlsnMIDB44uHebMouYYBCNRUjP66+/zmLecnw+ZbpaMRzu4psGpQXD3pBe0cOENHSURjDZ3qJsOnZ2dpguOhpvsdHjvWBS9djf3UMSKLUhKklLS39YMd6a8PGjJ2hp0FLhXMfldEGuFePBFiE2OOfo93rEILl5eJOT40uOL1eYMiNojZY5zXxNVU7Iiopcg0bgvcWIwK3DHb7/7k+RKksDLZkUkuGwYm+yjVKbUjehGfQqjvYV77/3EUoZOhcIQVDkFbPpGQ8fvEovG4GwZKYkRklAY4zBaInQJiFjFAQRyMoc0+uhijwdO9fS6xl6vZJ+VZCtMjrrib5DhAYfLcJkyfkuHVle0AaBFJJhf4hQU6wPnK8tajxiVq+ZXS7Sui48Xb2mKg0Exd54hBISZz1CanJj6KSj1+uTqYwqHyRBWkmsc2ghGI1GiXUtNE23JAiJEuBcy8XpBYd72xghEd7R2JpBr0dV9VnXK5TUNE1HDILM9FhOO4pcE5XH20ie5awWc5bBIMsKIdYJnwSgJOulRxuT0iRAJhU7WzusFjW9qiTKjKa12ADBGPKiQPn4f38k+9vtX9EtOZBSjPWqEOiqqAfYlHG8LNAyc5fE0ybxHqVNxVSQGK/5RUBYjx3nlM9qQq6vBZ+rUqGrorBunF5D/EbkCEZcc2HtQCG7eC1EAeQXL11cV/xbXSeBKzWwJ5FZtQEz667Lsa4QCMe/JBg/POfOaMpROefbL+5wurPN5N30XaWN2FKg2xSxPX1b0/vaGUPteHV8yk8vd3k+3GXnu5Io5Yb9aTBLT35hWR3lnPyCZPj6GVWQrN5QrBc5g78oyBbppdbn8rqILRjJ4mZO+LfOea2/pG9a3j054PKzAXvfTaUnPleYWXIQX+2Dz76puPHmC3wU9E3HRV0x7W2z/aMrER66sbkukLt8oLn/ux8DUCjLi9WQJ+MJ/f9FJ/xEsekp2AifsweSh7/xMZXuCFHg9iRnt/u8uBhSnRTXTmCz8MhNGd2jfxj5zbsf4Tbq9+yg4KzuMz05ZOeHNjmRd3uotcMsHKdfLPj6b/6I2huO1wMA3tp/znfDQ279k3iN6fB5Ggj4QnH0u4/TPtCOSnfMdws+7O9if9xj+z1/7VhOpXiBs18M/Du//G3eX+xzUC4A+PHgkM/621SnKiHfXEw83pXl/O0+2a+d8fnhnNvVlGf1kIdbZ3R3FR8/fYh4Eje4ivQdde05fqvg97785wAMdc1pN6B3s+UP888z/EcZrpJks7ARe+H4y4avv/4uLkreHjzl3eUhD4en/JkMnK122P0fV6mQzUj8wFA8s5z+WscXH6Tv/pXxY07sgJ/mDY9vTcj+mU7M2LUj6AyzcMxvF9z7jU95c/QcgJXPebIec//1M77101+guAypGK6fbdioJasv1nzh9hMab/idG4941oy5V57xXz/6TUafvizWu9pntpD83X/j23z/4hb3B+fXx/298T78l9vXhWumsRvUheXylYLxwYIvDB5zy5xz6oa8snvG+68psn9kqY961yJvNrO8+KrmS9/4gHlXcL9/Ths0HyrPb7/+Hf74D7+a1pGOaxes7UnKr57xK3sfMXclu9mC42zIUW/Gn776FuW5Rl4Vv5Fc9Jf3c/6DL/xzlr7gdnbOR80eAPd/9RGfLe/Sf+7phgrxVwrMfvs3fsDNfMq7y0N2siUTveY/+fX/jV//4/+QwX+zoNsfbNAwydG6frXl7uCCnurQ0tMFzWujE/7BjT/jv/in/yblubt2YodMkp82VP9ey+3hlL18ycpnlMry5duf8h9/+ve49UcvWbzCpWtleSj57fsf8GentxiZJq1nInL/4QsufnwzpY4KQa9JAuzwL0/5+//Rh7y/2GeUNZTK8mQ95rdvvM//8OovsfVeGvwUTxd0uz36nyx59ht9/v6tD/hgsU+p0pr/7x58h9+Pv8zj+xO2323TcGLDI/UZ7N0751d2P+K0G7CbLQjdkLktaG51hA802absDza88UrwW/d/Su0NY7NmotfsmxnD1xu+9b0v0Xvhr9MAUabExsHrJ9waXLKTrdDSY4Tnf3r7v+KbJ/8Q8Z5i8OGC9a1+up9k0O477k+mbOVrjPTk0vF3t/+SD3qH/MGDA/qfCYqZv3ZMz+5qXn/1EaWyvFq9wEbFo3qHr/Y/orvRIe0GB5Mn1MDwUUs30rx99IzGG2a25J3RUwKCV95+wulPb7F92hF7epPsSE72fNKxVa65WV0y1mvmrmRfz7BfXDL5g5KgNjiE3iYxcRj5xQefMtAtRnr6quVr449Yv2E4/T/u440gW3qkh2akGD5uOf11wTfGxwDcyKdYo2mjxpdx40gV12kb6aDrSQZFS6UtNipmruTL1Sfcmlzy8c4A29epUCuCWV8hWiJH/Rk72ZJb5oKP2z0q2bG/PyPIXfQ6ML5I68LgiaX45ALYplCW582IN/pp3frmvZ/yA/MFdJPSPO1Qkq0C9Y6kqDpe77+gDRojPc+7MTtmQXG4ouul+0k+8wmFYpJo+/jRNvuvf8pOvsJIx8/qfe7uXPDZpE9xkZ5D8lnCzXQ9yQ8ub3G7mmKDYuZL7menfLe5x1/ObqQhtnxZ1gpQnntckXTB1ml2yyVfufOY754/ZL2bUgfpvEoFrd1IXw89f55N/tw/+f/D5mx39dqB0AYbE98zSo8XEhczXFAQDcErZG4SZ06p1KBDAALONWgBmRdUVqO8wKgcgU42UfFqAAAgAElEQVQu2KAwXqE6ReiScJuE3EgIHukDMjh6eYrkOhew1hM9uM6n0iqtNxgDee0OTg3kid+phU4V4h6KrNw4dR0mKrp1x93bd9jZ2WHY6xOtY1RUbG9vEz10XSBETedCaojPNOO9bfrjiqqXg7AMegUHe1tsbRU06xlaKmIbkTpDZwXlsOLOnTtUOqNXlGyPhgwGhiInISdQBC8JPieEDLxi1B+wszVC0aKcYHs04sbNPSItIVq0VGTa0NYN+EBVlDx85S5FZhhWJVmuGfYHjIcThNAoWaXSpY1j0dqOw6P91GZuDNrk9AZDdkZbDKsCrQSds3SNpSgKVqsVk8mEqijIhGLQK+gVJVU5YGuyjRCSLnjWYY0pC0yVcXDjJr1BHyk0ZVGgZEmmevT6Jf2iTNBwEr4CpelCJBuMCJtyrUkxol/0yLKMftlHiYSC0FGgIuAVZTFkONgmNwVlJim0osxK9vf3kSIxb0Um8DLSCQE6ZzIa45qOXBhCZ/Ftx6DXZ1BWm2PuQBYIYQjOUyjDzmibXOYEKyjNEKQhr0oynQOSGEUqpNIFUpeMR3vUdUsgJIEgq/BC0uv16PeH1HV7XSK2Wtb4JqBDIAO62ZpeXiCDp8hysixDZQq1GZJE5ymKil5vQJZl5BFC6IhYRoM+SkOZK3AN/WwANqC8p2dyBA6HRRcKnWuMUtSdp/OR1qb2q3Y5R9tAFhW2sUgLPVKbfcDTBU+eZ3jv8Y2lExpvMpowp6qydM3l6QVH64zMFAgMmSoJUYNIrewArbfppTRG7LolBIlQOT46FssZvbzC2cRyFcFvGuI3eRsfCFGiZEXfGNp1y3Z/gmwd0osN07hLxXm5xseANBnWgykMmdCUVYHzkRCTm9UrSSsCZBkXixkej7UeoyuCzLBdQEtDcJHoLY1dYiV4E7i4vGA6PQdbp9IqH7k4nzHoj/FRgoblOpWNtbbDa0kbHEIapMjRlCjVY914ateRFYYQXGIaBxgMe9TdGiEzmiu3MoLpbJ72j4hoBEpplJGb9ddhrUdLCAR0niGEoHUWU+TMbaAWAd1LTsqqyJmfnVFpg44G1yVOriDgug6dFcQoKEVGtJ5+VtAsai7PLjHBEFuJwrBc1DR1hxcB5xVR5KDytEZ0bSrWEx5dVngniV2g0gOcC9SxJcslQjqi7IjR47qW+eUMh0VkClkagghMp1PKLEeJDBF9YrE6h5GK1XRNvZTYpqWflwQvOTm5YN22eAS9rCIDEA4hIvW6oV41iYOqBCIY6lWD9YFl07KoO5wFvESj6eqO1XSG7yzWO4JzKKeYHs+ol5bcDJFGs6rX+Ai9/pguRFbtmoAkRIN3YB04B/hIoUwqW4yBzrWoXNLYJTJCtKmIrOxVXCw75l3ARY2NkJV9bEz3KNd5vK/pOkemMkJ0LJdzmsWKotdP3wGNUwpHpD8YUTeWprVEFFFGmnWNbxztOg0sfWdZLhYEAU3T0DQtrrN0zRolPFVesJ6t8NFRuxYtA0ZLtJHoIicvDDoXqUDMe5qmTte8tYTgsU2NDBaDI7YrlHDsHRxyvmjonMJHiQ+JX7RqO0xWIXTEh4baLQldS93MWXVLLi6nNE1zLXzWrWW+WFE3HY3tCKSSx4AAqZnOFixWa+qmY+0FXmiWdZf2Y2jwvkMJB5mkCY7VcknXpsGXMBmrek2UER2gW7VkWbq3NSEwXSzxMhUiHp9ccHw6Y73yzC5XZCqjaz3BOpR3CBy5AOEdMf6t0/Zfl0014dpleo0q6MLLpvImtXbDphRFp/j/lXB1tcnG4kqFHRja3SK5MHupyCo1lItNy7dC+I246DdsUc91KUi9a64jl3qdHKlXf//qb161x4dNKcwVw/ZKfAToJtm1U9dVqRH7na98xO/cfJ+vbX1MQPClvaeou8v0wrkKSJeioamQBOqbjte2Trg3vADgne1n3HvtOYs7IsWHy5efwxeKZiK5/+XPuDNKbrRv3PqIvd053SA5i6JITuMk0CThdnVD8Ds336dvWnq64xu3PuLhW082JWapodsNEhICoNnN+M2v/4ij/owvbj9lt1zyhd2ndDc6okx4i4RI2GArVo7FGx1D07CdrxiZhtfGJ7x19xnLQ3VdmqRXnnakqE4s9S3La4NjDoo5vzD+lM+PnvFgdMYXbj1JTqylJz9rk8Oqkpy9U3G0NSOXjofVCT3VcVAueG18QrOzEf1tIDtPw2e9aLEV1N6gReDX937Gm+MXHBRzzE6dmsQLgSvV9UtxM1F8decTjnoz7vfPuF+dcb9/zjdf+YCoIJu5jQPWX4tro5szLl3FUTmn9oa5LXh76xlbe/Pkat7s04RLCHSjdCxuVpe0QfOgn1x7R+Uc10vn1ZXb9IqHvLoR+Hi5TRs065DR0y0rl7O7tWC9m8TKqxKx4rhGWhibmoNizrN2zDvDzyhVx1F/hu+F6wh7QgRE7FbFeGvFQbngqJzTRo0Lil/Z/ojcWLqt5FxrDqrra6TZFmgZuLQVE71mrNds5ys+Xu5gViE56QBz2eBLk8p/ak2m/LXQW3vDs3aM6yeBV3YhYUm6QHbRUG9Jap8xytIxHZmaw3yGkoGuJ1+iFDbRf3PZpPMzCk7skHXIMcKxshnupKQ56KFrf31NpzLAl+tLGzQ93fLm+AVP2gmXr1Ybd3v6p2qPrSRGBeaupFTdtVB4mM9wo4ToSOVMCQthZqmg9J+8eJOZKzm2I/ayOeuQMWsLfMY1qiGfWvQqRZv/5Ok9/ujkNQD2sjk2Kv5g/g6He5eINomZ5bO0X8zCQSd5thqhpaeSHWO9pqdafri8RXGZmKTCxevB1vpmxeUsYZ8eryeUylJ7w/9+/iZRbYYreRpoXK2JqoWzrkcv66i9YWQatAhcrCpcCf1nluLCJfbu3OP2hnzn/C6fGxwzMjUXXUWhLEZ6RJfc2kTodnuJf3tQoeeSb53cYztfYYOir1r+5fIBzxfD66K9qzK9bBUoL9I58KwZM9Zrlj5Prsj+c+gkxdTT9Te4ks13Vw18//Qms67ACE+lWppo+Pazu3Sj5BaNUiC7NGh0Pc3xxZDPFmPOuh5f7X9E7TP+s/NfRU1TuWJz0KPrvxxM6Znm+XzI7fKCnmoxwvNec8RpN6A4i/SfJycvpCGbWUUeTyfMbcFJN2Ttc4a65h9fvIO8NHTDtE5VJxbVBtZ7GWbh+Wi6Tes0tTcsfc5pN2Bp0/V6lTaQNqUjQiZpFzm5dtigWIeM13vP+Mdn72BfVKgmYNZJsDUbHrRqBGuXcdwM2M/S2vBn87s8W45oh4mhfXWP101kdZAhLw2fLrewQWGD5pN6h5kr0cvEjFY2Uj1vkT6SzzzFzLNuM07rPiNd8/nqCT9Y36V2hmwmcMXmvr5h9HZDhco9IQpWPueD5hAbFc+7Mdanzx7VS65yMIL5O7uUnxrO6j5rl/YVwHuz/Wu0gbSRYprKR/PLQH1e8kcvXuPj9Q59lYYU762OaI57lBeO4tJfl9LJzeNs+Znhp2d7fDTf4d3lESuf8+h8guoSv1vZeF2AVsw8n04nnLT9dB0Lz5+v7mKjYpQ16LXYoIwgnzpGH9fpGrCRyfvw058d8cl8ix8fHxIrv3m+iPhNmVs32BThhZ/fIPE32mmL0NfiZ3AB7zwg6Aj08jy1aEewJAqyjoIgZBIFtcRHR5ZJlBdoJXExplKzBFdFKMikJnhPjJuiBJWKQSQSIVM5k8g0HuhshzYGaxMGwQWP3hTnBN8hUKk8IHjcBjadGUPcOG77vTHLtkmFSCJF/wd5AVHQNJZeUdI2a4zO8ASCXaSSFKDzAqJBl4pVuyR4BV4RuiWDasRivSIA88Ul5bDPfNmByMiU2vAYNc4K+lmfYGtaXydXYZkT5zXrpsUHidF9hAK6hlxpFpczykyjREewHjvvWDYtmJIQO3zn0SohIXJT0KxbylzgmjVaJucxXuJDwIkWpCKiqLuWzjtWm4KaQhmsa/A+sJ7VlMqwcjUqBEK2EbukpOsU59M5/R5JdPBQrz3RBRrbIasCKTKCdQRgHTt8kMzXNRNdgnC4jQC1alu86yBKMiRaKGogeljPlwwKQYipBG05X9E1jmBlKq4rIo1rcV4QhWF6dkpuwAdLrlLEf7ZYsFwuqUyPuhM44WicZVj1mS0uyTNBbhxCCZx1dLVn1VjmiwVlfxvbNZiywnWeGAUnx6dUhSJXgra2RBFZ1TVn0yl5UdGuG3RWYH3AGEXbWLquS2iELrldrfXU0WNFpBr2aBqXypKERqLo6g5iYha7TXlZs3LMzudENI3vUFJivcPFJLLUdUvfGKJ0IAyXsxXrlWXdeMpqSPAeHwKRiA8ti9WCMkv0U6UM0Rm89QSr0CKxkvM8JyOjaWvaribGIc5FhArp+MxqVouaMitpOk+MgYAnlwXtquHyYo4xEpUpdCaZz2Y0XSTLK0xbJ4HZWYwqkCrj9PScna1ter2CsG5YXs44my45Pp2i88R+cyH9rrpeMV+sefL0mP3JCK01TdMgomR2fom1ASULdAAlUvGUC57Hz15w98Y+pltSFAV1Xf9f7L1Jry1Zep73rG+tFRG7Oc3tMiszq4pFUhRBypI98UiA/QcMWxxJhie2/oPnNfbQUwGWYMDSxIJAyIBkGLZgkxJFFllVJLO6zMqbtzv3nnu63Ue3Og++2PsWZzWyKIsbyAbn7LP3iojVvt/bMJpC2xdSrvBSE0JGvCGVyPawpQ8jSKERg4mBVCIpGVYPW6ytiTmTbABjWbd79t0eTw1GGEsklszt5sBu37O4eMxClvRSKEMg+sLNzQ1VfExZzJkvKkCIwfCw2rBcPuFu9YAICsalnsp43r65xf1aRUPGWkem0PcDN7cbxlThfYY8YFDpdwqZu5sHzs8qUsmkmIlD4bCP3N5sKeKwvtJAPaOBiLcPK9q25cnFOejUzPXdPe0QWbctTe0hQiOeSOZhvWN5foEUqGpHaDtijuzbkbG1VPWSMDFsSR3ONTysdhiT8d5TuYo8Vtyv1npPqhldPOic0/dkIxzagd1+JFLAWtLQY7Njvd2z2m7IGIoxJGvV2qUUVruD+rEva9btwDgG2v7A/nCgMxpA2GMIUpGBm4cNy8snsN1ytmgIJRDHyHY7kmONWLXwiSUjzhNL4vX9Dc3lrzLGhJVEGRIxwH7d00hFnwYWsznrtqUfE5VzvHpzxSeffMJyPoMBQoi8vnpLEYv4QppYx7521H7Bz5+/o/pOzfmsQpyw227Y70fW93uMsdS+psSIYGjbnsNywVevb3j29IIh9Hjr2G1H7u8PtF0iWAHRwqe1lm4/cnO746OnHxMOkW4I2BgJfc9+rQztQ9wzrytCt+dsOedw2HP3/o6LiwWhtyznFRUN11dr7jc9xVaUVEgpaOGhZF4+vyJ9Ex5fVhzGFul6+t7z/Os7Su0Ih56z2YwuJbDQxpHN/ZbVsuXy4owxRKzpePHqivl8zuvrt5w9uqQYIUedK99crbg8+4i+LxgCbT8QU+Hm5oHzCTSvjGNMCW8rsJb1/Q4TDd/4+Cm235GL8PbdDbvDlso63dOQ8WIYYuFhvyULjFGox0DOmfVW+8nDfg9+RhgFnGFod1BrYeZhNWB4z/18BjER+oGhGCQnxFo8mTiGyZIq/DvZ9v3V69/NS4EPsBNQC2DbQP/xDDMFTh291+LMnsKJitgTy9VWcgoLOwKGABhNbz96rQInpm12+nvbT8DeqB58cabWC3Ghno3H7zSxYESl0Md09yJGJblZGWftx5WG9hiUhTZExgsFtN7uL/ibF2952T3lWbVjE7WYlr3aGlT76Robw+w2YKIerMds+Y/PX/O99XcIyTK/1uCR5dWo71/oATTVhn2o+FuPruhSxdvunL92eccfzp9hMszuwkmafbw/YVn4k4dv8+vndyzsQJcUMFpe62cfryHN1YphvHC82j/i18/vWIcZl77jblRE0QYFMoCTD7DbDbj7C7wkvjO753Y845AqXq8vGS8Ns7sPwW128h6W1vJ/Xf2Gskyzgg4vdo9JWeX9xWpA2dGfd3YPXfD84P4zzj9WsOphnDMmS/1gTn628awmzSzVvapJXmwe8zefvGPIjifVnh+sv8XYqm/v0c+4WgdsFzC54l9d/3W+sdhyiBWfzjbcDEs+v/6E2W2he+ppVhHbJepVS/utJet35/xs8RGfzLd4SWxDw223ZPWw5NmrA8PTGe4QiQuHtBq+9L+9/Bt882LDZ/MNr7tH5GL4yeZjLn+u936xGUhNffLHdAfDQzfn15b33A1LZjZwP8x52Cy47KB/6qlXCiiPjxuK5QQE/NbZNauwYG5H3u4vmF05yAN+3ZMrhztEpA9sdzO+2j7lbz26ImSlJf7ft7/BarXk6W3P8LTBJLCDgmY2wM9vn7K7qIlFWNiRkC2HUE2+oTq+wmVDXFhlpO0duRjWYc4h6d99uX1GfW/pnygwcRxT4aKm2hX+8P2vcN70zF3g0rf84f13uN8umC80cE+Z75k4r8h+zqMvEm9+dckPHn2L9qziz1af8fzrj3n0UwXpi2jCepxZ6rue+buKP/zzv8Zf/423XPqWl+1jxBQ+f/8JF8eAokaZ+EWUTXf//Wf82X+auGw6vj1f8bP9x9x2S/xKkFEBdDcFWZkxsnybuN0v+HM+5dvLFc5kdrGmD47la/WKtX0+zYvz52te3l5y1ijgezU8YhNm/Ouvf414M2PxSU/3zFNvdHzYNrD8suHni2fEIvzmxc1pfPzgzTdZfCQ8/tlAcQY5RNwQiQvL7PMZP6w+48n5gbkbGZPl7f6Ciy8Evx+1EDDN3bZL2NHxxy+/za989EAbPW30bMYZ64cF3/5ZnOanD4F64czz4quPebc+529/62tAmbn/9Kv/hGolLJ9vCI/U13W89Nghs3wJb58+obKJZ7M998NnbMYZ9/dLnh0gnE1zVDVZTdaG968eYyVzUV+olUTy/Mt3v835F46wTDQrnU/8XkHb2V3m+vacdtS590/Ct1n4gfXbc569LkicfFSHD+C7eTXjOhsaF/nfV/8R77sz3mwuaO6E+n5Q9vJGmdUSCxdfCPfPFvwLfpvffHrDnV2yGRt+evMx88hUhNO1LHtlz+bvX3D1n++57ZYs/EAslt1YU98Jw3mh3mbajzxnLzqqtSGcedo/ecrL397BE10KV/2c2x98zLJ8WANlVB/x8cLx7P+x/HT+McNHjtpF/vThM67uLllcCZjEcG5P/ryzu0D/uOKn/+ZXkV/fc3NYclH3vLh7TP56waf3yuiEycqmMtT3geGy4fY3lmyGhue7Jyz8yLr/iNmtyvztrdrpqH+uY34b2T6/pP1U16XfevyeT+oNb376Md94PoUgnqyU9LsW35vzx+FX+I1v3vCT/DGVJG72S8IfPGZWtF37Tyuald6X5i6wXAqv3zwhfWa465bMXOD19z5j9tjw8b9taT9rJi/5QrNOPPqho/vM83Z/gTPf4uvtY9qh4uwLy+4zBdpNFqp91kCy2rB8U9jJBV//luO1vyQlwf75ktn7wrhQz9wyBZjVu8T+5QWfR8vtxZKP5zvGZPn9m1/nzftHPH2pYOtxX+TWU5i3eC6+6inSsHnxDQDOO6h2Qe2TpuK1myxObPjlQVv73e9+95d+8//Xr3/0v/yT7xpjUcdtUVmxr3CpkFLUyBsp5JSojCWVjBVl1YQY8b7BxIwpBjGZcQwYLJgpcX4CA6zVEJxSJqm4scSSwWgYWTIQUlRgYRywoknXTpwGBhUzFRLVf1bEqjQxaXsSBak9fUqkkjFWKKZQ1Q4vhjC2nC9nmgooNWEcKAlevHrDfncgF8+YDLGCEBJ1NSMaOFsuMUWIYSTFwtu3a/rBsFrvMF7IjDAGKpMpYWA5qzF5wDaGUjy3tw+8fPkWU4QUDU50gkx5IMce42c456kqT9cFhj7w8LBhtdox5gzGEsNI0zQIlhgS9ewcZwsihj707A8dr968ox16IjAmsOKxVv0wvRgeX17QdS1IYb3dc79a8eZ+zW6EIJ5REt3Q08znhP6AiGG+mCvjcohs1y1v37xns28ZYqGUjDXKcI4xIeJoaou1GkDXdgP7ruPrl+8mqwaHF0tMSU2kc2Y+81QWMoUhRbb7PT/+8meEkhiNkLLFeo+h0Pd75rXHkGh8xa6P7PvA3XbP9fstbRjJU3qucY4wBrqgclXrK3IxhCg8rA7c3L4nF2GMI74WuqHDO4tQ8LVnsWjYd3uwQkiJtzc3rNY9u3akrs9IuWDEcDgciCHy0dMLhm5PVRR8ImVeffWaYUw8tBtMbSmmIFIwzqmVBwUjQjdG+n7k4WbF7cOGoRhSDJicWTYzJGeSBVOy+jHGzDBkNtuWd9e3tF0kZ0saI8YKrnG0u5bKVcxmc4w4DrsDXbfn/c17HtYrMBZfhFwMxTtsZWmHjmbeYKyjbTvikDlsBp4/f8EYIsUYShmwJuFFLVVk8t1Vq5CRh9U9794/8PCwxjpDHAbmzYy+1xC+VAp36w2HUljdr9ivW97dbmjbSD9mUpqmISxWZgo8+4p5XbHrtjhrebjb8/bultWuZ4hgvWfXthgnHNoD3jkMlmq2YHfo6EJktdrz4tV7xAlWBsCQU9GgtCEixWCi4Kua/W5H2w68eHHFftfjXINJhlIyYmd41xBTIJtCKgYjFff7A3d3a25u7jHZMRb11w70zKyl7TvECpcXl2x2O2IybDd7bh/ueXt9hThDzgbrLGIt4xgZ+gFvC7W1ECP79sBuPfD1qzts/Yg0gjF6IKPogTpWjiJCG5KCwg9bXl/f0MeCqxpinhQMxWigojFkqcgZYjHcPmx59fINu0PHIOBFCCVj6ooQIv040g0jc79kGBPDMPLwsOXN1TU5qnWOoVAZQz+FjLX7Ae/PScmRUmTf7ri7v+Nm3TKmhpQcUFGsw1shZdgdDpQI45AxOK7XK168ea22BCYzhEBtK0IseOcY2k491eua/fbAph25urnnYdcyBiHnwtD3+KrCFKGExPZhTe1nhDESxsi7d/dc397Rdz2GzDh2VNZg8kiMhjDCZv1AjpmxbXnY73l380DXFbqhJ5RInwbwNc5afOVZbx7Ybnd0bc+263j+6jXXdyvGnCmAWIN1MrFPhdhntoeBMcCuD7y5e+Crq2u244jNlqHtIEVSgaqZsW1bdm1PjIWuDYx94tWbt9zcr0glkbwhGvWHpwiz6oyxTwyDWjaMw8jN/ZrX9ytuQiCWjF3UWCu4qubQ9dSzBffrPYc+ECJk0/D1i/fcPuwxxlFSxhtBRIPAsrFI9ly/uyfnGfu94WF74PnbVxzGkTAkXF3RxQ5xDoOyXlxxrO7X7HYdMcLr12/YrLcMAyCOmAFTyCkz8zPaIXJ3s6LvevousN0NfPHzV8RiuN8e4OjR36sXPAhjLuzblnc3t2w2A69fv+dwiIwUivEUcYwhk6xgrefd+zvGMVJXM4bDQCmed+8fuLnbg1R0IbMXMJX6t9niKLkwb9TCph86+n5kTJHiDYmM+ExImQQYZzFE/u7v/M4vlWj7V69/v1//+H/8P787SSU0MX5IpLkjNw476qHMZLBjJpxZPdhMzNIjcKXBOOrp5ncTrcV8SEcvVhOs88QC/Qsp9HzwVfWHePKlzZV68aVmCpWaLBrM8fOsMh0xkCePv2IN9ToiQ1LvylSI5xVxJrguc/XJjAc347LuuBnOCMVy9b3PmN1oCny1T7g+KaPpzCGD5d0nnmIMd2HJbmx4+fmn+J1w8fUIolJxUyAutcB7/VHFzn5gTH3v+Xd48keqxrMBUqPnGjukKcTN8aZZkheFr3dPuO7OuX75hOUbBVhMAdtHwqUGrLk+8/X5Jau6onKJN4dLnt89Zfb9BbN7ZQn7baB4UasJI4xLz/CdyFe7ZyQMf/ruM9oX53z0g8z8bY87BOx+oL7tCY9qbG9Zuxnu0cgh1tz1S169f0z/+SXLK5Wum1RUlj9mXJe5rc7pzsBUsIs1LzZPeH33mMf/RtmAxZrJizhhYsEPlneP54Q5tLniVfuYL98/4+m/aPCthn3ZMVO8gKh35ctnl9yZho/PdvzZ3We8un9Mer7k6Y8Ss9sRiWXqb4JrE35XcdUs2deWLlds+hmvXj1j8UWNZEe1U8C2ue1Ii5riHOt5Q/Ok58v7Z0QsD/2ctz/6mPOXMH/TEs81HA0x1NcHXKjZ/GbhbXvB43nL+/acjLD5k4/we7j44nACuIs1ZCv8fHHGJjUkK3y1f8aLwxOuXj7l/OdCs83IoGOo+3QGzpJiw1uZE+eG1+0j3rUXvHj3lPmPG2arCfwJ6peeGsvsLrNZzggXCYzh5+tnvN2dc/uzZ5y/UO/ko7WJmQCkam358uKMg3hebB6zGWZcr8558vtOWWJTwFnxosz1NnNXLXlwFdEKb9tLXr5+Rv3jOZcv0mTNkRme1VRrBQrtWDj/yvBu9YQfXX2T+7tzLn9kaR4K9TpiJ2BUYqFUlsW7QPe4Zv8Yfnr3MU0V+fz1p9g/X3L+KlFvI9kL86sDWGH2viMsa27snDQvfHH/Ee+259y9ecQ3/kD7Ra5FcYGYSctK5e9+zr3USFP44uEZV+tLyh89Yn6TSbUBe0yzF/KiglBxnxdcxSWDdfzpq29iv55x+YUBa/GHTHFmCjGsmD0UBlfjv9nyo+tPuB8XPL95inw156Mfhg++o5UlVxa3j9Q7w3bZ0C8gGMvLuydsb5Z88vtJwwMn0FLn7emZhob3zLleXfIQZty/fsSj7zuaTSbNtPBm24A7BKqHjiILulzzdTlnHRqeP/+E2R/PWVxDvSvEuRYOsII7RJp1xm0r3j6ueX844+6w5P71Iy6/75EI9e4YrmVpVhpy6NeWm9mMTaxZx4ZX94/ZPH/E5c809Mn1ugaYVEhz7SP+pmJfGoHWa90AACAASURBVK66c3qxvPnJN/jo36rtS71NJ4se16VJRSGU3rM6s7y4f8Ltu0dUf7bg4rmy7iUoqzrNPYjiNs07x+qs5t2w5G5Y8PrHn1D/pKHeAKLKi7jQ9cV1GdsbttcXXKcl192Sm1ePGX50Qb2GZqUs2GatJMJcW+JCOHuVMNcNN7sL3rx/QvyDS55+nqhaBZ+LNZQpIK1YLTrY65rr/oL3N5eEH17w7PeE5VUi14Z6m3Q9GDWI7ezNyPy9YQwz9ps5t+2S5Z80PP5iYuKOWX3jY8EUw/DYUx0Ku8OS1TBn1c14+PIJ+YdnPPnxgO81kDR5Ba3nt5HxzHLxvMB9zaZf8LKc8ZM//jUuvtRAZDOFx2n7p9BIZ7n8kXBVLtncLtm8vmDx+zNmtxpKmWrL7D6c1u9ihVQb5q8th9sz7scFww8uWbyFaqfh3HbQPblE6B9bZg+Z9HzBan/G1/tHHK6XLP/VDNfDoy97pCjYbscy2XVoANr8tnD2uYW3DRffd8zutB9KBsnah92gf/P0TyNDv+BmWfF+f0ZXHHc/fsbyC8XLZqvM/G0LIphSyJVDcqF/VuG7Qr0u1Buo9gXJnEBh1yYtZIqO37//X//tX2qv/ZcatP1n//xffjemSMkG5xzOGQoF5xwilqqZEXNCjJDJCIaYIomonnuVAkaCgBGs84w5Y6zFWCGXTEbT5ksp5JgwRai8xxiLwVAQKAlrBGsMRgwGg1hLHDNGIMZA4yq82BMzs5RCygnfONJxE5EC3hkEQw4BMSpnN+KxvuHTT77F3WpDLsom/eL5K7A1CciSqNzUFivEw0BtPVYs3dCz2w989eIN+12vwJJzlJJxxlPXc8YhYKSmni8IAdbbNdc397T9wL6bGLDFIGZKrreOYRiwUtNUDYdtx/XditfvbklSUxAMnuXsknFIBAquqVnvVtTeYoyl6zO7fc/dw44hQMoWrEWMxeAoWf3+vPEYPF0/sm8PvHl7xyGA2ApxHhEF2C2GMBZ8VVFVjsOup21HHrZbblcPjMWAdYj1lJgpKWO8BoDNmobK1Qx9VHD0/Yr9rmdMhWhVkufFUcQTh4QYh3Weduxpty23dxv2+0DJAjiMNIxdoPYzNTUpYKuKMRfGPrLbtXz99oo+5ImNPeIQGlurNLgAxVDZChcN+0PH+9WK97dr8BXiG7Z9z8XyETlCMYbdoWd5dkEKhRjhzbsb3ry5JiaLcw0lF8gZKxYRQZwlk2iWC/oEozje3q94e3dHFwPWNZCFFLNaRAwD3jeAw0nFdnugD4nnL6/YHXrEawqHtQo2IUIIBZMMtV/Q9z27fcvb6xveXV+DdeqdasE6LaqYIsSclF08JNo+cLfe87A+sO9Gci7kXE1expkQFWQc+46YCnNfs7nbsFmtWbcHgnEEV+NlRilW2bveT8xH4fLRY15evWbfDqw2W9phJBSLEUfJYJ0GWjmvE3C32bBeHWjHRDdm2r7HOr0O6x3jOFCJYCjsdgfEVQzJsF73rNc7rt89IMbiRAjR4J2lkLHeMo6BMWTGMVH5mrv7O25XG3bdgZRBTAMx4ysdu9Y5xiGTipCzY9u1rHd7HjYdCRjiAE4PZLkkLQqFTMoJiqHtBtabLetdS9sFQkoInhADxjkNRMTStyMpqEfqoe15f/fA7f0d2WgRK+Op64ZSRrwYZUWPljAIsctsNi3X13cUsbRjC14X8mKy2lBktY3ohp4qOzbbPQ/rLauHPUNMlAjERO0dIurZE4fMYdsSIrRtx/1qxTCMrPd7LaaVRKYQi8UAKcMwRnJI9P3A0Afu7jYMYybkSHGRbBKBjHGWRKIdDnRhpB0H2l3L6mbNzd2OsYga69hCLglfF1IYcVatBPquZRgiq/WW1cOOQzswRmW3OOOIY8AaixNHO/R0fUu7H4gRNtuWzaGlSyPRFhxQe0vKI7V3DFH9xbte2aYP2z2bQ0976CkFktHATEQoRufZEAJjKAztyN3Dhu3mQNsOHIYO39REEuItsUDBsN/vOTs7Y7s/EIpwv1qDWHZtd2ILx5SpnENKwVk18wlSaMfMarOn6xRcNSViHBgvDClr6CVgESQb4pg4HHoOfUfXjZhsSJJxUjDG4rwnpgwmk01hGDoObc9h37Labhliwohl7EdCF9TuIhass9rGuqbrB7b7A6+urmkPLSVnYhzBFiKBMQy4xiFWCznWWfZ9x+6g/4wpaRsw+LoiM4Wklax9OEExFSEG1u2BFIZpjQSx0MceZwUxUDsHuSDG0B169oeWh80O52sOIWC9xVXaFkwilUAxEcjYqqJq5qQiGOfAKuM+m4GC2rwQE3FM1JUjxsT9ak0onvc392z3Ox2bOSMxUXLGWqgwNJJwBopYMI6cyhSoio7xFLHOk3ImTAVZJPP3/s7f+SvQ9j+A1z/453/03WMCea6F1CibJ9VySih3XaY4mdiPKre0g4JLaQoCO3qp5krU424CbU+syen3kgoYMx2IzSSNRcHXSibJs2ZIFCe4Pp0A3vHSEecWifpek5XhcwwjA6NA7cJre2Oh+EkeaSDUnl/7G2/5w5ffYZ9rfvzyU775fygQbIO2M5wpqJorgx1gddbQeuH95pzb2ws+/j1hea1gaq4EyYXUWMZzS73LBFuxPrPc9QteXT1l+ZOa2X1h+W5UgCXzC4Cz0fTsyvNwbtk8LBl/es6zP9YDpyQ95PXfaPD7hNtH0syxvIKbszlr8ay2C+L7OedfQbXLp/twBLZNLlQtvJ6fs7eO99tzwt2Mpz8Q5jcBrCFXVvcTaEiy7xSwuZ1X3K7OWD8sqV/WXH5RTmzk4uQE5CEG1wnby4qwgNvDks3LS5ovaxY3mWqbTgzsNNOVwvaZ6CtufcP1+oK71Rn+yxnzWwUSQfcSfjsSLioF642j9Z6r9py2rSk3DZ/+60y9CqSJjZ1mlvGRx3UZgyGLYz3zUGXu785Y/qzi6efh1D/nL7ccfvUMiXpon783XH3UEKNlP9SEH17y0fcVmM6NO8lZcyVgLcUJbZnRnsP73Tmbbsb95894+qeZ2Srp3xQtekjINA+JftHQ4elq4d37S9bbOc9+37N8pyCkKWYCxPVeuN6QrOP+zLHaLdjcnHH+ecWzP1M2tpuKHbmyU5CXYAdhJw2345x2PcM8n3P20jC/UYmxJMi1ADq2SiWYVtmZ7vHA+u0F9l3N2ZuCP2iAVZo7feZFbVLOX2ZCrkmfjWy/9xHVreXsVWZ2FzR8cBrbuVYGrQKm0KwLZ68K1VqYrfT9uba4Q6R4we1GwnkFBhbvCzs/J+0qVm8vqK48T3+kzOXUKBCZFn7yGrZUe7CdZd/OGeYgX885/1JYXql0vTiVyceFxRQzgatCGT03eU66nmGvGs6/zrgRqp0CZRgFev2qx0WHCZaAZ/VwRnVVsXwNs4d8ChNzrV6/7VT6Xm3hPp4RR0/3MGPxs4pHX2Qk6ZwoYyaceyQV0nwqmGVL6GvW4wx5V3P5E91bNTeDFtOKzhHABGiCX1vGuWDf1py9MFy8iFTbcArVChee4nS8u8Ewu4fU1cSHGcuvNWTx/OVwCuaTkLGdArfSR+ptJpUGtp6895z/3OD3ULXKtLRBAcwiBjsW5rcRt/OwruhXM/zrmssv1D5BAifVxnEOTzPBjuB3Bre3mDcNZy9hfpemuWd67n3WPl8b5reBZlUo65oyembvhOVVplklbK9BjuG8RoIWHqtNUpufjSDrCl7OmL+D2b2OCxs4FTR07Zg8vFuwe2HxlWN+bWgetF1xJhNzUtcrk2F2taf9bMbyasT2lsvn2vbF2+FU6HN9IiwdiCHOJiyiK9QrOH9ZaNZqE6ReoVq4rDYfrImO66UMhuZeAymbla6vClbqGpMbHX8GMxUn9b3VvWP5GqptAWt0jTkkqnXADcrMdkMhLCzNOrN4B4//yNA86Lh2/XQdg64LqRaK03UnVYbZDcxuDPP3hepQ8N1RrSNI0us5snPN1Idtb1i+ATuqBzhGi0rNOqovujWEpUAxZGcUNL8Vzl6qNdDiRu9ParQtR5BbpmJxqgxhYbGTGuj0+6hrWWo0EcL2hXBucT0snltiX8PP5izeadskor6/aFEynnnGR47U2NMcaYPuK4ryRVVdVOuzcXvN/MAY/rv/5pcDbU0pf3nDJn7n7/23BUTBUFsmC4PIoe2V3ekdJao0ECDJBDIYSDFz3lSQMm3bslheEGIkGdH3l4QpmZI04Mo5XVRTVFDYWJXkq9WCIeeMyYlsCiZlvHU4YxmGXplrUxuQX2AVTH55i3p2khzmnMk5MV80xKBMUGOFxs9onCWO40Q1z3QpEE1QD76csQJ+iheJPcyWC3JWeW9KiTEE8pRK3XYjs9mCMRywRn12K1tT117ZxDky5EiMcQpL0mso2WBEPXm9eGKMLBYLQujpuh4RQ6Lo/TAGTKauawqoRUJdkxI0TUNpewUzcFiTGLMm/1lXCCFhxSNiicPIfD4np1F/F6GPQYFzsWSjYKQp4K3He0eMkRplBHVxJJdCTgGxnpgLkhQIoYaSHcva01iLADlnuiGQMhMY5wkyIliqqBYXOWe8U/A79Qf14Jx8iiIGP5vRtq0GywDOKVvbZKitYxxH2jQqUzGlyZ/SkMaC9zNiStSNRyjUztB2HYkCTU1OhS6OeGuxxtD3LWfzBTlEUohcnp0zpqjAeNdphSoH8hTWlrP6MVe1I8fCcjYnDT0hFTIaDhVjxhZLcpOHUhGcqYjjyHK+oJ5Cf9a7DUkipSjIIkktI6IpWggBnIAtmWq+YNcewFjGMrCs5qR+xBbIpWCc1xnYG7KBRpQF1o8DcYzYyiujrlcvausdSRIGmFWesR+YN9Xk+TuSMmSpiFgwIzknnBMFKzI4J8RcqOYLDocWa2Ua04FGnFqhUIiFKagp4TFEgy5yKeqGU08PVCV9sEDBE01RsLdMBYIMkgvtoWe5PGcMLckZcEKOCUFwRiVcTVMzjB0UoRQQ4yAWQlFw0RQdx42v6LoBV81BFMDORSVZOWf6PuB8jXOOMgxgDYiGZB0DEVOJjH1AEniXScWSi/ZjIwUnFmPVfzvGiLM1uQRS7ihEJC9IRU0HjbG6QOXCTDwmB8QVSra0YwBrwXgKPSl3pKyM+sppIWxZz8gGDt2eYjSsURCdQ4eR6sjA9zpPkQtdHKiaegp5VMarF8hlxEhFij1V1Wg4pICIMppzHMkGxpAwld6v2qmdjbMWUzKGmkim9jV5CJRiGFLWNUISy8WccezJY4f4Cucqctax570nDjpf55yJqSAWQh6QInjvyVlBMe9qnOi8Jc5y6AaauVF1iBHioAevajHDVxV9q2OumKLS/pS1X2N0fjqqjkuFtTUQ8QZijpRJ1l4S+LoilswYAsU60hipm+M1GEpJKjU2+uxLKTjj8E6IY6AyH1Qog8mAw5iCxZBDVAWA9+qLXNeEdtSCkQg2KYBYVRUh6c/Vk95iTMGIY98PiAjOqUbau4gthhK1wBBNIWQdD+LstH5myKqWqbyCDMfAOGXWCkOeamlGAeGSsoKeRU7PwJhCiLpRTUk9l7HT/iAkvPeAHjBTSlirIDYmK/M6JTKGLozYutGwTDWxpvYVFkM79FRNzRgzvU26rqL9wClkSs5xun5Rj+gERtTKSVLA2oJIRZ7sRuq6YhxH9e4HRKbQpjSQivZNJ2o5I6LfkZI+h4TukVIq6hMcIjiIcaSqPWNUtYlzDorwT/+nf/DLJyT81evf29d/9l/8DwU0+VpiwUxE2eZ9d5JzUzixGI9+tyZmZIy031JpfnM70n1cK5trUHYPcEp1Px2kRJmBKoc2k89kmX4+SV6DHvqPqdTVbpKptn/Ra9nkogWz/Uj/8WySs8opkKx/6ieWlH7ucGEZLgQ7cPLLnD2kSdqp4EGulEmo4LTaIEjUkBY76uHUJD1I15M0/cgMNrkQlo7hXAHl7FRmafty8qI8+vQWUeA2zlWG2z5zytZ6UODueA+PbOVwpoDh0SJBxsJwofvOavJwPYbIHaXQrk16sHcKzHVPvfrkegUhqnWY2Hr8hed19Kp1XWY4txRB/QTj0c9VsJ0CH0fZOLnQfdIwnMuUIq6p6zKWUzvU21iICzv5yKrnYJpkosUo8Axo0N3c0tx0xDPda8eZJU+elsOZhtI0d+Ev9DHtU/q3ts+MF45sYTwTZg/aHtD+XN13pHmlhYZ1T/fp4hRkt/2VCn8op2s3efJ4Dh8CoEwupwC8wze8sgSP4NWQkAkwkkEBvCJGGaddov1GxXgmpArOX6lX6i/2Cwn5FJiVKzld+7gQlm8nsHY30n0yVzDAm1O/PI2PxNRH1J/SdvocbJ9OwE+qj0CvoX+srNruifa16pCRCdCwR1/rmMmNhalocWR9hqVl9l6l6DJmtSV53Jxk4LZLp2uPC3e6tqNlysku5MxNAlszeVv/glf1BCRmb2huR9pPavxe5xttm6oA4kzHxXBhqXZHiTinwKsj2/vY94dHnvohTCx4PtzLYz95UO/fNLOnnwOn+SxPQYrukE7j58h+tn06PfdffG+q5dTXNJzxQ0ChXqP2gf6JpxidS44WK784/x3vdRFDPKso1nD4WC1G5jea2SF9UpZwJdg2Es/8NL/KqQhR3wdlr7ZJ8cHJxuU4F6sNjpsKdHKaz0zS/uF2Qc9MUwilBLW1OT5LO2TGc3taY/wunp798Tke55bUmNOaEObqzXpcN+yQdT5rP9xrMxWP4vxDGFi2MLsNjJduCiAzJ5/asBTCTGjW6bSGaLt0TKfansI3j33z6K19bM9xnTRZ1wbXl1MglwyJcFGd1rMjy149ezULKTf2NEeFM0vyCiBW2/QXvvP4t2Eu2peN9t/sdV7P1RTquVT2+7F/HcdKEcPsXUv2VsctTN7yH/zYXZdP4Z6/uH4e+2JYumlt0B+FmZzWtiPoehyfx7F6XHO1gDEx6Md8GmPHZ//B5oPTc8rOnL5HAddCcx9Oz+UYdDecWbLXNXxc6HpQreOpXxzHmc6F5dS2bBWML6KBo9kru/i4brkhM5xZqkM+BYXlyVDWd5M1h6ifrw3l1F47FMal+gIfC8pHn2C9J4WwFPw+T/1M2/N7v/vf/1J77b/UTNt//L/+7nedc1hxxDBSciKVSNPMSSmxbw8sZjM9yBmVhYsItgjWOMgGQWh8wz6NFGcZUppYu4ITYTGfk3NCvRIMISYFAGE6NBtSjgpSUhBrIWdqO8mwJyBWrOCcpaDhZfP5jKFTCXYpBcFikiDWYgykmChpwHqLESbbBZVp5zIZhExeXxQgF0o2hKwV1OyEjCGWQhwjRYRYgnoRpsxs3hDigDirgUtiSC5jyJRBmWBkDQmyVpPOU0pkYzBGoKgPUSrD5KkbyAjiLdZqeFlJQZnHRtlnxkBOZjp0F2IfWCzmjEOrh2ECyQTCOFJVjlIiMY74qiELpJIYwkA2Qp4q99bZk+ews45iAiEO2m5n6QeV/NtU8M6jdLSEFGU7IRbjLJU4TEy0QyJlSLnoNZcCRlkbxhgkjYgUCgVjhJAzlTPEFEhiEG/JkjUYxpnpXkBII1oyMmy7HqzB1B5xTkHOlHT+tNovrXHTY81046D9CkPoWqSYCRRJKgmYgARrRPtAyMSQSEkQ8YQ0YgTE5onBpf1HsBoYVnn6rsN5ZSoI+jmuquizLv7qvQreWmKKYIR9d5iAEU0cNyJ0OSN1hXcKSBmr3p3RJELR8RHJWKnUSsEqUBmnKnVEWbYYQx4HxjRijiBpiFQYXIg084rsYAwBK5BCxHnPoRvoQ8D5hgyIGIpkHVdiiGHE2QpywiIYmxnHTN00hFFZwtZA7Sxd35LIjLHDOYtIIRZln4lRi49SMiEM03iAnCEUlROHFCglUsgMYUCcIxIRb0k5kpyZgCODKdMCmjOCAscxJ4oppJiU7Z8T0WbGFKkrSy6JlPIEsgUKyiAU1A87xYx1uqnNMVL7ihQHTBbqqmYIo4I7YikFjDgSZpoPMtZZBbJRv/Aygf5x8gAVDHlU1p1xgC0UhFQA0QAzrKEvI7k4XOWBMgGBIM4pIGsykhUYHNpuArEzTiYQTgRbeyyWqghkyDkRUyQQmNc1cRyZ/CmoRRnVmEwllcpTs1ASmCJYY0llan8p+myNQ4zV92Bx2UIqWImUY2oCqtioRIslySSMicRQ1IoCpx7QTkOdQoo6rsWScsEaURsU48E5YtJCh3eZlCKpJEpOlAI5RRKaTRlKIqbAfD5nu9mSh0AuWfuf9nKsWLxxFDLiNQzQiBBzpB8Oaq/hLEMZMdbQDwNOlIE9hoh3DmsdpaBsiWKwYnBO+6qzQhgC1jtlbhhDIeGqSougwlTMAmMyRhS0FgNt1+lhNBUF+S0K7BY5edIf73ERo3NDDogBKxpEqDZDRcdnMZiixRHEUFc1KQeMk1PV3Yql8hWVCM5aUowUdCyIMwQjZKOMXDMdBMWIejCXOPWvkdms0jUxBpz1eNEQU2s9KWXyZDfjvCDGqjpGCrEU2tTh6opkDKXowc9WFiaWbi6Z5WLBGEZl2hfdpNkimKxg6hHgNkaIMRGTMrZTSRQytXhNLspTMcp9ANc1zDMgRgPxwhTwWYpRwNd6amsJKZGNWv1kU4glYV1FEbV+0raogihN1YCC2kT93f/qv/wrpu1/AK//+R/+3ndzLZP0/sgQKYSLCjtkmnd7ho9mSFS2IEZZlsULpdJBWZxhuHTMr5UB5tc9ae7JjR4Ou49UPgu6rXXdxPab2C3HcC6J5cSwNQXGMz2IFjcdXp1Rtl7WA3X/1FNtIsPTGib2n7ZP9xkqqeQE5lX7CbQaNTDETAdbZfp+kBn7Q5y+Q5lwbij4XiWU7sgyHgvj4xrfKiByPLjaSTovWVk9yqg6gsnTdYY8yXT1++2QcWNRVl8qJ4BFWcLlxAqtVqPaPkzM5WN72mcKtikzTA+Vro3EpYI37giURZWL1tt4AmBBmZAygZBpArz8PilLsLY063R6znFuT/ZyxclJoo0xhDM9c8zvkjKXR23LUfZ+fD7ZiTKvJwa0b/MEhmj7FLhXwC43bmKUKYPboPdt8aaftB2c5OW2jXq/ROWwudL7ZycfxCL6F0fAp3i97uK0LyszWz/ft0XlymMB4cQcLU6BDQOnPiepMFw46k06AQNlAtlSY6keWgyQm8nrc25xg/aXxY0CMXFpldUnhvquo3iroNk0Jo73Q8fDEXi1U1q7Ufl7r2w+1+tZEcBG7VdM1y6hEJeeXAvDhX6+2yvD1Y4KbMxvAvVa2buSJsZ81vuEGGwbTgzi7FUiVe0myfphKhTU6tPZ3I/IkHC7nlK7U9uZmOA6dsC2o5JIjF7rUULs9qrSkVTwm5FSffCktUM5AUxHBjtw8v20o/Z99cJUsJ0yBTPtBuJ5pb7M/WSHEieAus9qsdFrAFtulA35i8AlxjCea6jXL9q56LyTT2y77OUEZCtAq/OR6zM25JN1hskfAHeTVNpOQa1IjKjtSzqCYmZ6v5nmYkup7Omz6o2OEy2OHf3A1WqkeCEuNYRaYsG1yiIfnla4Pp/mMS0SMf3/pD62xznLnBiDTCFRubHTmJIPxYeJWX1URZisR1STCnFhp/tSJqa+gvoYqB8CxQp21P7uerW6tEM+3WclHHF6Fmkm2KHgJpBPJtavzoXqo2r7xPDUs3jVYpP2KVUjcGpnXNhTXzoqHWyfqB/6yb7CKFNXDPVagX4bCn6vhSJlynN69sVNbZuKD7ZNk22NzoOSdM7VebucwOljQSHO9TNmd4GjUuMI8h/B/eO8evxe7WMfLINy40hT29QiZAJGRc+EBl1r7Vim71Dma66EcGYJCzkBlGYCO7NTuwGJak90vI/FmVMRTqJaSwyPVD127PtxLqfPP+4DgJMVUp7sSNyQFaxtJtXBsV0TkGqj9r32I0u113vip3n6OC8dVQHHdcr2GXe0Wpj2AOPZBOxO62f2gu/L1I+VFZ4n5Y7r8mnPYKfnNJxZfKf7Fxsnb/lpzTeFqVB4HNzK7D1+X7H/P7FH+Cf/7He/Kzkyxp6m1sN5KRlKVvDV16SQcKiUJOUCxRBjwBmV+o9jJiWVx3hxOKNsVSmFFAL92CHGMp/PCYNKU40zyp4rCURZiwrIQimZkhLOCJXVo6jztabLW5X7V1VFiJEimZBGZVmaTBhbGldhooKK1oiCjKVQRKgbTzGZLrbEkid23iT3L4LDIs6Ti8GK14TNHDGSISUsCvqayYu3AKnofwsq+ykT8yvFSJwOyZrLrgwcowVOjBhCSYgTRJT5CJmcjkFugrV6HyvnMWQqp7LwMUfGNCBW6MOBUhJVValM1VisrylRr7upasgjTgopW0IGZx3WKfhjBUrKCiIU0NOnIaEbjJT1cIuBlOMETBSKVVapiMUCJepm4AiEUdRzEmMUAJ38iYsxDFl9/ZThHMi5MJY8Ac8aRFZng0OOxGqM6H0eU1QPWisMXTex+QwGtRSwzpKt9o1cIs4oQ0/xvDIxrqCUSNUoCBb7OIGqKgcwZgLhrJn65bQJRkNaSxYa25DHPLH9Jsm8ZEJQD9EwFkooODvZQqRC4+bIBOKJQDKRMQ8YDykFxiGyaGpCNyjzLxXtvgayEaxoUUFZ65ksEFHGWDYF8RBM0U5ojIKjzhJShFIwppBDwlWWJJmYexaV12r+tJEDQzEWcY4+KpN5TAd9Bkaw4kg5UVkFX8R67bcl4bwj56R+xwHqZjZZliiYbbC44nHiKDFTFZCScFbZhd7OiFOb7cTUdEXBXeuEQqKUiFhDLMqQLyXjrOCybpCsoMqBrMF6YoWUIsZZojNQCnXllB16PBWREScnRUEIA74SjElgLFj93DEO1K6hchWlJMQYrIg+JzHKapeMrzy5ZMzEIqzEqM9vHCk54qwWCtKYqOtmAnkmwLOAFEMKgco50jR34FwddQAAIABJREFUjckwpoCRjJFMyQUpTr2FMbiJuV/VNYe2VRZ1TDjrkZgpYwBJ5CqCz7iSqazFG69gXDEYl8lExBuQrFLuooGDhYJ4Ua/nnMhkkIJRZxxKUXm4iKgCtCR85QBF4qUI5KQWI17Z0fu2xUmFkUIYB5z3NE1NLupZxfRva3Ue+X/Ze5Ney7L0PO9Z3W5Of/u40WVkZGZlZZFVLLIoUhQp29LEhiwbtgCNPDFgjT3xH/DYP8EDwwZhGAI8cQeJNmDYVGdKNlkimVWsysqsrMiMyOhue5rdrM6Db+9zs+BJjURZ4gYCkRm495zdrL32Xu/3vs+nhwWSThmTFU5LIUBpwev4rqcsK3yIFIUj0qNyROVMVU4IITJxhbBwkx8QIp5JOcH7DltofN/Ic0Mj48pqrFEkerKS+RzkvGcjvHarxGWjkyLHSG0LWr9FK4OPGVdWtJ2nKCtACeM5RMqh0V2ISe65jOA+YkYr4QD7ILF6ayVOr3ImJxF1VUqkKGLnOEflJA21pCBXgNZoA6SAMQryIOgb2ecUZbylrIdGZ5lKmeH5qcRBa4yMMzMU1YzGR3nGkSJOiZNWKeEipiyJDa0s5IxGnNnKZDKJGLy48JViMqnJRIyWc4o1w5yXQVvBQGRwRqMUFNbhm0B2joAmBCn8Zi3F0IQWJ6u2oDQxihArDH4nY0gNcVXj0KqQz8kQh+JrUgIn8t6jjEE7AxqSSjhrSShQ8lwXpIfMQUY7EgqjLX0XJFoWpRhorUOhyUrhtMMqh86av/0f/M2/EG3/Ndj+m9/9h/95NopiHemXYiwYXUTFdUeqC4lalnqIXd85IWNtyUbhBg6cDpkwtaRSFokirCRpxOQ07aHBNnng36r9Ah6GRe4YLUyD2FrJglFliT7aLu9RCWEq2AYV8z4ePrqQ+qW4HvYc3TEMFzP9XISo6jJg2ywLW3snSEjEUxZo46JSpbvFfXL6Trwe/k33aS9KiOAsIrPt8+DkESFDx7x3TjEUrPcC4iA0i5giohGwFyz9VJ5xYWqkmdc6iFjkhB+pY8bPDG4dSZXZC7EA/WpoqDo4l+w2yM8Uei8uqJQHofHueuhehOrRDTguekdhU33teFKhZdzoEYkhnzFiMMxuYA0PQqm78UMEXIRuE0QoFm6hiHF+4fY8XHngynl1AyYiW4276USkVQyCdhIxM4yfI84528Z9RDXWUmyQc2blowdXbB4EXxR70SZWgxCTpBneWGzoVxJnDxMZU6Ow4LaRWBoR/zOk2hFrETn7A0e2cl9kJ/eTWwcY7i1309GeTbCbcHcvjH8Pwtt+vA/udLPzImAOTlOzCzK8RsHCKhGdh2KosKLNXtRszsq9oDtyFsffLS9aMBp32++Fu1yIiy3MRuFW7c/nKBKKuAZ+aQXFMBF3ZzbiOh0xLGEmYqMIo3rvPpR714jIVInglErzcwKtaeP+/KTKDKKf2gtDemisl62MW7T8rTL4VSnu0KHgoBAhygyFDGlQ58ahAEB2wpn2c4ufG1lXj2M/DeNlEKXD5E5wZ0gNpEKu8Xj+x//fO1gLEV3H4oDuE2Hu9kkAd9vhNh6JlanhntV73MqYEvAzQ/26F8RGl4j1KJrl/T0wCq9+ZogTs8csZD3MfU7eRbQXB7EOeS9oAbjh38YxNTqj4/5aDRiKWu9FruQGcTGyn5Or57fkyu3n7lgZua+GV+29SFwMxSHNMLfrIb1hZO7U7EW7fiHPmTAze1dkNgq/dJhOmKOhEudqNnK92kOLa8SZ6bbx53jpsdLEyu5FvjyIhmPBYX+8Wu0xQN2BpbzyMkdtRZAurj1h5lApE2rZt35pRUjcpb2INza2zEaEcbeRAo3gDmSeG4VxNHvO/HhOx/vjTjAfnl/I8YBE92Mpc/MoXo+ojmw1/Uqe7XE4nliKu3bE7oggmweXu9o/W5BhM7wLDMW5Yd4Acd5KEUWEZdNnmiOLHpIpY+FFJ3G9juLu6J5FKfxU43ZSGLFtxrXCjB3HivFyLscCx4iKsDvBb4hjWM7HKBTb7mtFuqFwlAcXsgjso1A8pG6GZ6uK8m5ivDhnx+ZiY5FRvn9wkNd6cKePbme5z/+Tv/1XfqF3bf2L/NCf16ZBKm4GaVSTvIikKTOpS6yTTuNDqhGnFSpFJmU1cGkVVeEwRmFiRHmPiRFrFDklWagkJ2xVPzRLsiLeRRKD6Ys0uIR0hkJbnJHFXh/S4F7LBI1gA7TCZ4lvOu2EWaqNNEizlohUj7wyZOOIypC0RWnD9c0G3yesKlEYQpKFXg6gksRiNUocXTkSu1ZcQQn5N6NIw8PWey8unBAo0CifhuZfemAjGqwr8FkmPnHkZrIS0RUNReHISRARuQ84NKWxFIU0J/Pe42MQwRdLiIqd92hj0NYQVEQVUr3ufEvWBp8yfd+jrAH00BBuiJr2HVYBwWNTgcslKg2OTiUusZDFIevKqTAVraGoSnloWS3Hbw0papR2aOWke7hKwqg0mYQXwSt4dEo4hUR5o0Q7rAWtk/yczkCSKGq2OFeilCLoii4qdNbSbTkqbEqU2qNDj4o9q1m9xxUopfZx5BwTKbconTFYbOkk1u40USc8gZBl1KWIuLnC4JZTkIgSN0ZYmzEnlHYoV+KMFYHI9wQU2RiSNhhXoF1BOalJXgoRyonz2pKpnePq9oomtPgY8EijP2sKckyQFNYYCuWobSkvpc6hdEaRGXzDlEZTxCG+PAjsaEMeYu0qZZyxdLu1CNop4qxGGYvS4lCMhWPX9ago4qpOitJYLIq6mEJMhK4T53YGY2eyt1oTEHE3ZTWI7CL+5+AheHIKxJzIBGLyA9IiolPExoiKgUIrKe4UhVw3Hyki9KpHOYUnEL28KJohjk0a3PRZy7nSTrADWZOGiX0UZfqUwTpsVROTuA+zCns0iu+GQo+R+yNmcdYBtG3L6uCIEBVdn4jJ0/SN9JDRWlyROaJSJBNFwFRSFIjJoxmKUTmSfYvKHqsVLkHlimEfLWFYMO56j7GDo09brNIyXoyhVBkTIzokTBY+rsrsxcs8OJa0EtxMItJ2OyZVAdmDSoTYUZUWVxgg0QeISQQmjSLYTIi9uHdxlK6g73d436EwGFWSlBQOjJHIO1owGUGL0JWzICByzhATI8kmpUQOmWlVo2IQdEUKFNbRNjvm04kgFQpLUWgKp/B9QwyBsrCQ4xBDD+QoKJwQe/QgoqfoScGjjBPXYz0Z0AwKa+QcLuuaWVEQuxaTEqn3mJyYVCUxBOrC4bs1xmQy4jrXGnLwhNCL0I7HKREOk+8hJDQGk0R4zzkTs2AFCufIKTCfzigrB0Zc30oNKRIlz0atNX3fUgzJAItBJ41TDpMNKctxameHtIk8sUOGaAzZWLIpSYOLBiVzqNaWlIIkLYjE2KFIKJWxjkFYlz+BnoSnSz2FEU63M8Uen2GtBZXE5YqVvFMy5JDRRlEUxTAmFFZpKUTRom0mpIj3AZsspEDKgRwMKlmMrYR9bACGdErqsTpisqdw8p5RZU0RQQ1FwMJI87NgxNEv5z1QlIK8SSkJhzlp+iSOWKNkvtCuGIpSUmBQSpEjdL6lz55kEfey1tJgrSwoqmpI5YCPorBtm26/QLTW4mNAW4fSBTkrbDaU2uKUFGucgoyM2y72Mj8QyTpj9g70v9j+Vd+SGyO+EvE0g8MkVprupCJWRlxRTuKaYSrV/e64xM9EKO1XVhYshSZrWeCFWu/jkiNb1TVSjIyVGuKQaS+67F2fTuNndh+bLDbyPm68REtHt+qISghTS5g7YiUL+1iZfdMSu5P45ujmyUax+LyVZihDtNm0cVhIDovgYYEmgu1d1/hUqH2keUQJ2J2wPrNV+PmwyC3lHIydroW7KS76NDiGVczYtUTcR+zBKA6PncT9TONneu8S04PQYdpM9aYXocDpYf8Hdu1t3IvYdu33C+Axej46E8eYbCwHFIRmH4MWJ6I4DOPE7j9/FBLkesnYGeP4Y7R7FErlnKc9giJZJc3tSnt3nQdx5+uR/jGyL/sk4ry7DTL2hthsrDT90g3jFtbvTomlkWsyFLf2QmcYRNdSIvPjJlH2gG7vXJixMgNbc+BEBjmPY+FB93fiT5jaQQQQ5MB4/cZ7pF9aOa7RPankPugOHbOf3FBeerlGuzTEds1eLI+1w081/WGx/648iGLjdfPL4i6yPLpHkDE8NkkLE4O76fa/O96347nWPlO+aaWoH/J+v0OtaQ/ks20TUVGOPZUW3Uo6Tw+R/3EcjGN3X4AZUCOyP2PcXOYYEYqgn8l/+0HUk32UJoTZKMzWy7EoBs713Xw1HkOsRPxF3+EMwkTmGzewl2Ol90UHFTNmJ/fFKHyPBYGx8ALgLnesn9SYNuJuOnSfKN5s93Ol4C/ycJ/LPKO9OGP1INDsix/DvBIqibiPXG4RneV3yrfdHh8irF5DqA39YUG/GBr5WUUq5R4a77nxXMu4HCLmXlAY7UlxN97bSHdwN6fadS8NJ53ax+NNm8U5rmVOcusgTcjUIPwF2dfk1P6+jpXgLsZrHSt9JxyOeIlh3tqdyDwThqREPxMRs320RIUsc95cxv6I2OkXZn8NxzE2OmazkTl5XzjSSjAoQ5EgVopQatojx/pRye6s2N+jOmRioWiPHaaLdIdujxYYY+sjo93sn4nssTHjuE9W7fEf4xygvRTPVIbt/XLv4hxxD6P4N4q9bhOlIDfMx8kpfK32xzYK8tncndNxfpaUys+L4NHd7VM/M/v9GnEWodSE4XuSFTer9plinfBTEWbDRAwGvtaEITkwfv7X55psBJ0gY0Lm5mTVHjkg2IVEGIRiuVZ36Ac7sI/HeUxlCJXwcPupHHO3NPv/Bpk3VJK5aUQ86D7TT+9cu2rAubh1HFzJw3cY9mN3vC9VytLcbsAKZT0K1Rlfi0A8Xm83YB7qN3eIklhpXCOIhDwUGUKt6RZjAUX+VklcusXt3fNaJfb4hV9k+5ebafsf/R1pLaQ8jBUgZdBZBErvPVU1gb4lE6DQMLhu0/hCMfBZYz9wRZWwV7WzZKXEbaPiEG+Xk60QVEBGk40hKmlWZpIs9EXAGziWxhBiJKJwA56BYYE8xtD1+KBJCecsffAkZGEWskS7o08UQ3TFaUUfB6lYG9mXOC6qB7av1hRJJOUuBRyWmBVJQ1mWhF4W4tYK/865QuKnyKJcpbh3+0UyKQuDlyEem4nifEwJp92ed/l1QSYE4SKGEMhJGI57F6UBCNhKxFzvhSFLNhTOEnwni29tyDEM7M1EnyNVUZLx5JzpvUcpN+ARCkIUlp9GFu59L9fNGEOMItjFkDFOEwchpS5rNu0W4xyxa1nM56SuAy0x2D5KBFy4pBmtpOlUzllcjBmSls8XATbv2YpOO3EwJfEzO2shBpKRaxsHHEffSS5QD3xkawxmqErXdU2fI30MRJNJPhF6D8g51YO4nXOiKKVBXGEs0QcqJ9c3Kk1QUFhLiBKLn5TV4HK2xKajqC0KTeMh+KE6SwsSYqdyGu892Yh71A5OsBSHBw6G3kNICVtJsUIji3urRBBKKbFrGoxx+KjQShp8aSNinnGK7Huyb1nMZ+yaVsTlYfyFkMjOCe8nSYSeFKgKJ25NU6BMYtNscMWEbePJGGKS4yiKgmbbMJ/PBoEuYU1GZU9KEJImGktUco+jFcY5cgyYDDbE/XgKYiuli9IoyOkSsiGEhHMGcqAqDckLL3O7acRl78MgZAkHM8ZIJGGtpS5lDGYlk7tzjt1uJ5xPzIBSSdRlNcTDMzFGSpvpO4nQv71eU5cVbdtKxVRLIxSTNFVRQwStPLaasvMdu64lDX5CpR0pxOH+HoROI3OOQ5ot9l0gkqWxlwKMR5uSEBJ1ZXFoKXDEgClKUpYGgbvWM5lKlD1FEYFzSmiriERcqYk+YXD0IZF8wDnhauacUYXdzyvGiBiei0zuPLkPWFeD0fR9T1Fa2q4b5gKDJVMVlpx62uApqwk73+3F7tJV5BCJMRORedAYg1Ea5wy75pbK1KAVjZdiUO89zpXEsMOqAudk3gkhSMHFGLq+H1ycCqUczhnaXUNZllgn961BjkmwMOIcTmlgCg8uUD284JeD2BeG/dvebJjO57R9h7KGlCV6mhPonKXpI9IQzBhD6Uq6rgOlhIVcTQk50bUeXViqQhAyKQWcVpSFpWl7Qggk7dDWsLm5ZTWfSWNOZTC6ICVwRSLj8b6nsJqQhF/c9B1KC4tYFVq6wiXhGJN3RB8kjp8VCjdgeCDvBde0P+6qFGc8ScRmHxN9NzxzR+GdtMcKWG2IyVNVjvXNDc5WKFNz228FvxEy87oaRGthvlklDO6qLLi9vqHLMJ8t5TmVIlkliqoU3mvbYqaGwmqiDxwuV+x2LaH35FSQc6ZBCsrGyr1eT0pU19M0O4rpBJ1E+E7a4HMUR6wCsh3eSSQxk3wvBazhHUdj2CUZr0FlnFKC+UkZM+CZWt/tOceuKonJEGIL5IHjLIU/g6YsCmpnsNqgrcwrm80OkjCus5LUgzwXoMLw3/3X/+Uv/jb5F9v/b7d/42/+F3lkxI2biuwXfnYnC374+YXb/4f5Z0RYjcMC7uuL4HGhOrLtRpHm62LPyJONpd6LSPLh8vOmTXvO4riAU5E9c3cfjR44qSMjMRv1c/xVP3ciHkzu2HLAntWb7de+F2lYBOBugyAGBtasX9g9t09YsmEvNsbB3Tueo3EhO7oEx/P39Z8Z2a7S0GZc2ItgkLUsUsl3DEC4O+ZQye/ZXdyLzGN39ZENPC5edZ9x60C/EscXmb1Qpoao7sg+HZm7bmDmjmNi5MaOPFSz9XTHFfWLrbhc+8ju0WwvMqRC3NjjMY0C3jg+QASLUfT4OttYje5t9/PCHDBE2cXtFWoR/GBAEwxNyUYhtV0Z3DbhtmnvXDQ7WXzHqbsT5JKILsVNEDflwA4uNmlAFIjLWyL2Pd1JBRn8VNiF/VQ+p74M4uoczvF4vboDK2JtkrEbhlj3eGwiOIqgnAYheXRRxlLTz+T+qN8K49Fuw37Mfp1zObIm1w+sNMhqRHQTISreCazhTsTvDhyhVIRahLnpq4ifaCavexEkB3ZsmDuKi4bm/nR/z0kR5OevychbBURsTJlUmp9jx47OfXfd7a8FiDAcJ3bYL3GthlL2xc/snuupfdpzdXWfiLWhPbADV1JEIj/R1G/9zwk1yYk4PYo4xguPUpi+msMfdbRHjvIq/Ny1iaWmWw77bxWxlAZk1Zt+fy5H1uvXXegjQ9bXMh7dNmF8prgJdzxpq9BtpD8s8JOhgSIi7OgoKIPqdUN7Wu/HcL+y+/n66wzNOHzHOLfoPu/nm3EMjLzrMXL/de6n3co4L679fi6IpaZdyVxSXUe6haG+uDs//dLuRbSRJ5oKEcH8RDN5G2hXcu6qq4HPuo13/NFK4ycDg3sQxaNTlLdxP++Mc3d5FaTIUMq/yfFLs6t+eofQsQNPXA/YlWyEhapSlmZbTjF57dmdOsqb+DUn8ODgdpp+rvffHQuFnyncRo65voz7+XecR8fnpQ4ZX2v6uaK6SoIfGsZE/dWO9bvTPVs1DmKyn8i+j8dhBw5vdRX2+8DXHtf9zOzH7yg2Zq32jTW/Pv/0w3F3yzvPpgigYyOxO0EZBi+EleeZ9pl+oZi+SvhanvHzLzvC0AByc+7uGnplCKWM826pmL2IFOvE9p7dXyMV2fNfxzHup4J0WL+jKK/Y441Ugupa2PSpkGdYe2D282w/M3vmrA4MXPG8f07EodAwjgk/GVzUVv7UF2koCsvvyBwOycm5qa7jvhg7cnbtMG/t32Oi8Hi7uaFbybyQrTiK69d5Px5UGvZvuC7d0vAH/+1/9gu9a9tf5If+vLaQE30KWDKFLZF4fsRrT8oFo1OznhfolNGTUprzNImgArUq6UMYXChpWDRmZpW4dHvfoEwAVeFsQdu2GGRR0/ZhaHgFRkUUFpUDhdP0/QZbFuCsuC+Lgt12IwvXlERMrgvwUQTKnESkdQW+aZmMDtbgJVqZxNErwmOkLBxxt6Moa2FSkoVDqCEkhbaOLnqUHSJeSniSu65lPpmya5s9kzVFTwiDYKg1ScXB6abIwaKVpe/jEI8O6AwoEc/UUEEMqcXaCvwW7YpBbDDkpCjKEoUwFn3fMK1LjMriUB26URudCc5SZXEedn2D0o6QAjH11NpSFAWuEKFBjPYSQzXa4n3EuYIQPIWTh7lTCZMkzu1TxOgClTW7vh0EZkdlCmqn8KGnVJnCaZIp6P2aqixJURFykHi/zxRFMTQg0uQccDqTQ8IWClMWbDZbSlvQ9xlnDB6IeUB26ERhNGWlCW3EuoIcpHiQQ8QZESdCypAzRsvxHB7MiU3DxGl0NHRRnJKYROhafPYUlZNjpCDGzMFcmi4lrQCPKyQKnGLCK0VIIgTHsONgPudwuaCqCq4u3tCGKEJyVXO967BlRfKB0g4IAKs5OlgwqUtyzqzXa3yb8SGx64cGWTrj2w6nDXVVUrpMWUFZFLRNj1MF3idqZ2l7Yf0Gbciqx/sepw1HJ0esZlP6PnBzc0OIEEKidCIimUlJ1zVkErYwlJVmsZyxqKf03ZbSBbbbSJEzKe+oXI0PiXa9oywNtRGn7GJeEVPDzSaya6OwLL04ewtjaPoGoxSFEeHk4GhB4TSbmzUhGYKBqDQ5RIgBW8g8lJF7JtrMwjlp4DYtiX0kDY19nHM0zZq6LikwOJOpHExXU8piStN6Qr8ltsIQboOHQdzquoa6LKm0ZbVccHg4A6W4uLig3d0QYyt4AgVZlYSww1lNyj2zacVsMcOZgtwbrm/huunJlLR9FGyBBkOmchbvG06OjqiKguwjTdNwc70jKWF0NsGjc8JpQSgUVc2ynOGsophU3OxuaZueECM6C8pCp0QeIu8pJ5TOuMJy/2yF7qDd7rje9nRfi1ptd1vmiwWTqqDf3TCpa2bLGYU29E1L5xM3tzspvgTBSAQPrjIUWsTuk/MDbGmxXvHyxSsijm2fMEmziS21Fba1cTKfLlc19+7d43A154tPfwq54NXrC3rfUOgSVKahxBQaTWZe1ayWM1YHM6zVrDe3vHjxls2mIcRI1yJsX51RKhE6z9HpKcfHBygCqMzl1TUvX16To6BuuqHBWFUW9KmlLkuePrxPXRVsbtdc3txSNNA0npgz0UYKWw9MZU9RGO6f32NSWxazJc1my3q94/XFDdtNR9cHykktAnHTUBrN8WrJdFYwP5jgyoL17ZaLt7e8eXPJwXxC8APrPEaKAoiexXLGvdMTEXOT5suv3nB501IVjpgNUUVhzlmDyQmlOk6OjpnUjpwiX716y9vrC0xZgs/YciIMV5WIuaUsHQ/unzApS0qnyMlweb3lq68upOgXgxTXssFIPY3lfEI9sRwd1Wj9gM225dXLa2Iu8TmhbEIrR9+v0VozqWoW84qHZ4dMqorsO642kR988lN5P0igrJKiWfBopVjNah6cnVCVjsVkymbbsmsDP/zhp4SosKXBVhWkTKkUC1tRTWfcO/8GziqaXc/V1Q0vL9qhWac4YI3JpBgorcMVmvNHj1hMKybTmvXVhjevLvE3t4RsSASyq+iUPGdU9pSF4tGDM6bTKX3nubq6YnPboXIS7EzKVFmRVSSpyOnhjMPDCaUzGBIpGjabDV98+QrvNT4pTE74GFDG0MfwL/J17y+2P8fNNhG1FoHLz+6ahNjdnfs1K2gPxO2SrCwui9uMBdqVxn2tM7Qa1ojbM4n/jc2cgH3zl1gqQqmoriP93Mhitrxz3HVTEU/2DYi4E16ylp/5upg8Lm51HDh6u0RzZFBJeIN2cA/pkPe8vX6mh8YhIsiNQmlyat84zN2GvSsorxyhUtRdZHu/vBOBBrFTtxHLgBqIiuZoFLpFtC2kOwC2ufuusTO4OH5FlAJpojOKDaOIOIoJdhdpThxxaMgFEo+NJZhO0a8c2UiX8dFJrPtEd+Top5pwrKiu9Z6fODbwMrsgztomSjMo2AvJKg3R0lKTdRZnYGkwbaRfWdKRw+4SflUNjlxwm0B34Aahc4z35z07dDw3fqLvFvkLzeS1iDEjI1LYpvLcAfAzs792oVSUMaG9zN9haofzGr92LRPrBwWmh+ZoaKBzO0TEcYIW2HryvBD3ZaExneLm3WLf3X4cf+VNHO4NERW74wrtMzdPHOsnEOaJ6Rcad5sxvUHXmtlPN6TK7Rs0ZSXn9eapoTuQm6V+pbBNxjZQX4Qh9ixu0zh1tEeOfibij59BcSv743aJ9qAUsWm8l6PC7sSxff2eY/sgs7tnmL6QBny2TTDXVJeBWGqKGz844g3dXLM7VzRnCdOIe7x6OyAkYiatRHAuLlvCoqQ5NIQamnsK5WH2PFNfyDnSrRgjwrzAXeygFEe8ipmbJ5YwVdSvM7YVwUtP3J0oXxtp65LyUBjIrB9IJ/ftvWJga2rBpEwMxWUvQufc4GtNt1S0R5owyxQ3CtOB8TI2yisR6lUU1m97YOgXis0jRXzcynn8vKK8ke8ZhftR6JRouHA02xMIVeY2a6Zf1iyeBXG8X/s7nmlp6JbC5r76hqFfyGcV14bZ84T2wv0uL/2+cGDaRD8zrO9pYg1hApMXmdIobFPsI+pyHBLP1uGucdfbDxjmL8Psy0y5jvtxXL3c0TyY0h473EaazjWnilCDbcCtM/Mvwx4N0y/dcJ713vF49ZGSuH6ZWf3Aob1wrKNTTF55ukPBHcRSkwxsHhpuv+m5/84Fl390BsDqzyzFNu1FxyrkfQR/d0+zfZDQDxqKMvDy9ZTVnzgmryO2lUTBnh2thTl++a6mea8fErJQ/Kzk8GNQSQTC4kbE5+7AUWwT7Urz6ncSZtmR3lTMnmn8VIk42Anexc/MHlvga8XFdzLpoOfs7IbLmynhdc3yx5b6bcLlndm/AAAgAElEQVSt/ZC2ELGxW2huvmXojhL2dIeve9avZkw+dxx8Erl9b4bt0t3zaHDb7s4Vu49actCgA4vvl6w+C3RLs+eEixtXEwtxiV9/A/yBFIJWf6o5+HG/b3IVKz00+BZht5trLn8loQ56prOWlDTNz+YcfCxIiRFxoJII1MkpNg8UfgnmyYbeJrZvJsx/7DB9IcVZJePN+EwyiuZA0dxTuO9e8XB5Q0yaT56fcvb3Bl7wID6PXO9kYPNIs/tOw2Le8L2Tl/zo8pTL6ynHv1dhW3HrhnIolswVu3NFv8isv9kwrXqubieoZzUHPwS3u+Phj/dGP9eEWnH9y4Hp2S0Pljd8/vaQ/OkU22jspcxbKkmDtX7A+vRzxZvvGfJRT95ZJs8U9RvBiCSrcE3aFzJ0gOsPwT9umcw6Sufx0fD6zYyDP7TYRub5WMocjZIk0S+6/Ust2iakk3FQmj57SuvoQ4fy0nxoUpaUFRyfLlnOp/Rtg/eRr168QutE020oTEXyiZQi2jgSiflqznLmmNWazsNXL9+ybRpKo/BtD7aksk6U1CyIAmMyB6sl2iW2LeyagE89OSly0hROmvoEn3Ba3Gt1YSmMlaYzStN7RawLfIzCwdQFIaahGQuUpaEqJUJLLPApMClKYsjkrMnJk7PwXQFCjEwKw2q24Pr6mtppQr8TXIQtCSEQVMJWImZbq6iMpqo0RVGxWXfEACl5YtYDLjYTfE9pHVrDdFJwuJoTU4c7mnJ1vaNLmrbthasaJdK7mM259R3OwsnJIUVp2W4a2t5zcXUlLqDBtWmVpk+JYlIyMYqZNcxmEyYTS9f2vLm8YtsEjJZmMFVV473HGEdtLYXVHB7MUDrTti03txtx5ynNrJyKWyhBynGIVUdOD5fMZ1OMUfR+x/XVhjYGClfQRUg20YaWuqzofcvZwZLHp0coIiF33KwbEcD9EB1Oiaw0QkFMzCaGxXzGalZTxBVX2y0XN7cok/F9pLQlPmm54RSkvuXe8SGr5QR3WmELx+efvWJ9s+bo+JSbdSMcGO1JuSMlDXiOqznHB0sysF6vWa/XhJwBjbMl0WiJt8eMBh4/fsi0cigNk8rgs+IHH/8YNYq02tBlj0Ec6A/un3G0mjGrJPa8njnevL7g6maH1jXR9BSuICeHCjCZFBysJpyeLOi7BoDb21tefnVF2wkvOhYG7yPGZlxZcHp0xON79zBDMWW5qNg2LS9fXtN2CWxF03VUdUVs18wmM959eEbhLIXRZCwHhws+++wlu8aT1YQ0ICxM4ehJJBN5/O59lpOKGHZ08YTPfvqM23VHJGBVDSlhs8G0mcnSMV/MeHjvBGcV+eEJr19f8Oriks1OHP0+BkJoSEkczgfLKfeOlyzLmr7teH2x5u3tjTRrKQwhJeG5omjbDYdnp5yfHDKZOupywmazpfM1nW/x3hC6hE8JYy2FLskxcO/BAdOJYzYpSCEyPT+hMoqXF1fc7Hqyr2l1whQVTd5xPJlyfn7MdFZQaUvuPMupJfzsJbd9T1kYml1HqRUk6LYb3n3ygNPzFYV2kBJNs6GqNV8+f4tRFtVp3MTShR3lrKLZ3vL0/accHh6SQ+SorXl7e8Wlbuh9JKtIyOCKCh+2ZCInRyuOjldMnGNazuibgP/8Od3tFqU1xhhmixlB9/i+53Q15ez4iKOTAxSJrtny8s0tu90WHzrQFTEllDMUBpzVHCznHB3OUSZRYLl/fMRPPn9Gvmm5bXbY0kok3UqjusPDFQ8fHFFVJSp53rl/yHrb8uZVj8NIU8qoyDriAxiTuP/wgOV0hnOaorQULrLbNnRtS4pGsDxJGrF1XcvJ0QHn9w8pC0dpa6qipigK2ibw5rKDAd1Q13b4Pc2Dh+ccrKbkFDh995zZZcXV5YZPP/0C7QpiTIReEBB913KwOOD47AhNoK4LeSasVmybju3OM1nM8d7jnKa0E3LfMV9MODpeMJuW9H3L4vSISju6mxvWQZql9aGjdBV933J0fMDZ6Zzl1FGZkrYN5DOF776i6aOwUUuFir00cPQtDx/e5/zsiOR76oljebDg+YtXfPbVG0Exhw6DpTCO9e0N90/OWCymTMqC+aQkxERRVzS7jqvrtXC0cyJiME4zm0w5Ol6xWk5xRaKelEwmE1KC7vkr+jagcDQ+4QpxTC/mFfdODylKg9aK2XKOKTse7ZY8e3GFMpasM5VW9DkRsufB2RNW85ppWZDInJ+f0nWe51+95upqjckQfRasw4AI+eC99wn9jrJyLKcrDhYHXF7/CUgtl9JUZJXxfkOfez54+j7LxZRJXaJJLB7eI4TA68trSltKaidmcozosiD5wMOHDzhczUF57GJKVRje5gveXN2iskNnQdcEH5jMCw4PJxwuJ2iEnW5MSVk6nj+/EEe9kkYrhkhVVnRt+y/8ne8vtj+fTQ1NXUwTsWuPXxa4td870toT6XJ/+xTi/Y68s+hGc/CxRD2ra3EE6SBMvDQ0cNmdadr7EXfc0G8K5j8oqN7KYtY1A/Jn9TXXqJZF6PqxuKGqC0N1mfauHJUkihlLET7DxOzF5DBV9EEcL24rIqZrRPyMThz2cVjo+hn0S4UKALJQbY7M4EpiiJyKsOqQKH0/s2zeN8y+kG7uduigvu8a34grUoVMqpVEOheKMIX69R2eQY9OpCgCkJ+Lg7RbaNZPRPiKtWL2DIq1xJxVzBK7jLC5b5m9EMHu5gMIk0x5oSluFatP/d5RZ7q8d576uaM5MjSnmuYk4w8idm1Y/AQmbxO5HDq7HxTiXiw07YHBTxXrJ4ACd2uZvhDXWnSKdF4NbtHBzTU0FLv8qKQ5U8QiY7eO2ZeZ6ibihxirCZHiSgQ2u41c/FLF9a93KJ3JraH6yqGilSYuA4f1juea2d0r2Nw37B5k4jQz+cJgPs1kI+JKv5C/RyenivD2OyXbB4lUZ3KROPoDy/SryPX7JfMvw975pmJG+YgGbj8sWT8FEtRvNJOXae+sClMrqAQromFymsvvBeqjhkJndgcFqbE8/Ht6cMUJmsBdRxlTu8TrX3U07/asjjdYk3j7Zk79k5L5s7wX4/zcoZKgEZpDzeYx8MGGfufYAe5lwcEPpAiyb9y29aTSEqaWy48cu+/tsFYaIV/cr3GXhsOP1dBYDsq3Lf1hheki2zPLm9+KqEmgmvSkpFg/dJh/XFJf5D3uIzmJ6dt1jw4FV9+LHJ/f0AXDxUcVy39UMfuKvesVIFXCJm6OLLt7mvV3W1wVaJNCfzLh4IeZ+que/rDCbgNFG9E+0h9W3DyxXH+UKc439I2j+nHF6iejw33k3wqOpbwK3Lxruf2Wpz5oeLBc8/ztCr8pcGsrolRr9i5dPzgyr7+V0EcdD45u6KOBwzWv5sesfqhZfCFNCYvLllRaysue3Yc1178cKA5bjuY7YtJc3J9hupLZi0C/dJRvW/yqBKBYR178jkW/v2Y5EUfx1e0Ev6g5/UNxojKI+cWNFC2qq8DL3zEsH9+gsuLq3Zry04pQlRRbEZdMzHvxXqXM5TcdmycRddBzcrjmdlfRtgvK9V0kvbk/HRiqkYtvVdx8M3Lw5IpSwe22wv9wRn2p96mIkQnbT0X02jyG9KRBq8x80nHyrQ0/+0ePUMkwexH2qI9RNLx919B8d8fhfIdRmQe//oIv3hyQP6nxtd43cFKDkzg6w+0v95ydX7MsW07qDT+dHPLm6gy30eiYSE5QC1lBeZu4/NASfmnL4azheLLj8eyKf1A/ZX075/BHI7rB0B7avWB48SuZw4fXpKT57tNP+Qcn79E8q5n9/pDO6ATDECuD2yRu3zFMn17jTOTx4oony0t29ws+uXhK/RbaY7cvbPUrQQN1Z5Gjd6748PA1b5oZ3zp5xR8uHrHZTJm+FHSP6xP93FBsEzdPDdsPOx7du+L+7IYXmyVfpCNs46iuxOmfhmJd1uLYfPNrmum3rvDRcLZY8+p8zouzJQ/+T0GfpEL+9FPN7EXP2+9UFGc7zg9u+ZXD59z4mh9PTri8ucfsGftnoBmSGc2ponniOXtwxbJseW/xlk+Xx/w43cetpQFY1vIe4OuBUXtPkz7asKpbKuP5lcPnTF3HH3/vPU7/2Z3bu1tIcda2md13Gt4/f8NHq5f0yfIfP/0nfN4e8/f/+LeY/0yQCaYfMAQD5uDDv/pT1n3J2WTN4dmOLx+s+PJn79Iggm23uHMtu23m4t/qeHJ+wfuLt5Q68JePf8rvdr8JPyrpFnfJDxVHlBFcfTdy9vgSgGnR8/n8iPhZxfKTweFtBgG6zTRHGv+445uPXlIZz7JomZqej6f3uPrjB/uiFEj6pF0ayvW/IqJthRI3I5rc9xKDjVA7g8+JeaX54P1zDle1uLpWNSEEjpc1z58/5/K64bZtqYqK5COGzON7jzh/sMDYhCsywcNsabm+vuXPPvkS4yp8AoU0sbHasjqY8/7799EmoVVis7vhdrPm409+hjOa4BVWB7puh/eZw9URDx8ccX44o9CKnW+4vt3w8uU1W+/JKRMS9D5STSfsti0fPH3Eo/sHEFswifWm5cvXb7i6bYkZlHIEK7FTUuJ4NuHpO/dYzitUioRwypvLC7588ZysZuyagFIWqxJKafoYePL4Po/vHVLmgKcnRMuXL17z7Ms1yhWEINxIVxSE3nN8tOSdR/eYTy3WWrJvOVpt+OGzlxQlJFOQcsQYw67dUU8r3nvvMScnU3xoKcwhm82Gadnz1ctrMDU5OXoyPnroPU/ee4/T5QRMpCwtScHhyYJPfvQZ621HXS/YNY24WZVivlzxzuOH1EWmtJmmadg2Hc+ePePytsenTE7SHCZqTVaGxazi3acPqUtN6Qxd17BZLPnJp8+k4VOWhl+FLUkhc7Ja8avf/haTIkPqiTHw+L7m408+5/OffUkuVuDFQeicZb1e85vf/h6zaYUhUOjEeVzy9m3BT3/yBX1ZsQnC/lUGlFKcHx3xzsNzDpYVxkaazYaz732DL756yR9//COUmaPshD4I3iESePLgEU+OlpROXNnd6ZLb21s+/tGnmFiQoiWYngQ4bTk6PmW1WnB6PCdHD3nJ9brl6XuP+LOffIY1C9pdR12VbNc3PHx0j2//0kf4fkftDJlIPXEcHM746ecv+PL5LVoV5KRQuafbbfnGr3/I6mACtCRfUBSWk6MFi+mMP/3BJxLTbjtp+hU6Ht97zIdP3qXInsIqUgrEZUm76+k3O1421+Sg0BqK6OgTPL7/gPOjAwqt0SZJx3Mc2+2Wtt/S9EHQFRqKumLdXPLo4WNOj5esqgLLlC5mcp/4oz/5IWWxICpPCJ6sPX2OvPPkXY7mM2qXcKVGWcOkcEysxTcviCbgcibGTGEKjhcrnjy+x8FygqZHx4rV4YrT2xV/9P0fYm1FFxO2cDS+Z3V4xL3755yfrHDOokjMJgXKKppmy49+8gXaVChd0ceeqBOLacX7Hz2l293ijBSAgvdM5wtudn/GzSZKE4ucsDHidMU33vuQxaxiqsXZraeZg9NDfDL8Pz/4hMl0TumEwaus5v69E779nW/Q7G4pTUHyiYPVnMODJS9fvma3vaGsD/EJsCVXV2t++ze/x8OjJdYqmnZLOZ1zdLriH/9f/5yu9WyayOHhIU2QopbO8EsffoMcWqazCrLDLAyucvzTf/Z9bBZ+ax+lGZki8e6TxxwuZmCgrAysJhwenGLU53z55WtyCFhTsOt39MlycLriV7/7HdrdFmUjftuwnM154B/w6uITUg7oXICVZhuz2vDuwwfMpwV1XaNThKrm7LxkvfM8++IVSRUkZdGmY7O94d47D3j86CFxt6OqpEFZXR5yfHCfP2i+z4vLt3RBsDRTXZBS5t75EadnB+Q+UdkSQ+Td+/cgJi4uPiYPTcJScETvee/dRzx98pAcemErk3nnwX3qYs3r19d47+k6zaSyxJiZTuc8uv+Io8mKTC8YCB1RpuDBvQc8/9kfIX4VYYlv2objk0PuP30PowJOJ2azGcknJvcWhG3gB59+hjbCwc45knLP+x+8w/xwBu2WUjlmS8vZg3s0fccXz14PiJ9Ar+R5oG3Nkw/eI7Q7lss5NvWcnawwyvLiywu81viscFqanM2XK85O77NayLVQKKppRV05+vPM9fWanHpBhqiepvXcO1/y6OGpNBlUUBUFx6t7FLrm6vUl610HxoBNNJ3HWXhwf8VqWUsyB4vVjrOjJZt1xxfPXqF0EqwOFmM0jx8/4vD4mEpBaUUsVsqyWkx5eHbCm1dvyYU0MuujZ7Pd8vidhyyPDuh2wo7VWlNXBe+cP+DjP/sh5WROilEaUVopjt07PUerSFUIAiJGODw5xfz0BZ0PqMoSOs+0dITkmR5OOLx/jA0dDouzjvnZOc028fp2R1CRrDRd11MUJaauwVZoXUEOlLYgJ8tqvuLo6IifvXhLyKDLktS3tDsvDfb+YvvXYutXbh+rVzGRDfsFrtslNvcMm39zx68++pKQNBPrufUVXzxdsf6TA+afa2YvxAmko7ACX/8KHH/rNWfOc1DuuOlrLo8nvHmx4NHviYDqmrxfeIdKs3momP/V1xybiDORLy9W3Lyc8O7/OMRn20ysFMVtxG4D1+/XXHw38+BbLzmuN/zs5oDL5ysWP7TUb0Vksm3GbTzdgaO+CHzx1x3nv/pyf+zP36yYfL9m8bMonaAHlxtIX42rDysufrvn/vlrDlRm2zuuf3LI0fc1an7nigxD13O7jXz1VywHv/aGR9MN123Ntnfc/PMj7v0Bgnlo4r6piukSt48Lbn6j5fTklmnRk7Li2ZNDjn+vwk+18ByH+HN9Kc7AN78T+MbTr7jpKg7rHZ9fHPLqaMHRn44IAkEemJ0gxF7/u5rzD14yA84ma9ro+PSdY/L/MWX6KtEeWnEODwLm7p6m/fUth8stp9MNz28XvL2csfi/K1af+T2jNxtpKqtiYv2w4Pq3OpbLHYfTHZfbCW+ezjj5fXG2ja5gcVvC5TdLTv/WM3578QaAbSyw3038/v/2Hc7/8R1fVAVh65Zf3vDV31lwdHTF0nmO6w3bb5b8+IN7HP0Th9tqpi974dgOnNI3363Y/qUd33rwkont+WK94ugbO/70+094779v945Fu+7oj2p0p3nxVyfwGzecT3fU1vN2N+HVyyWP/hc1IBrANNIULEwLrt9zHD94y3/4+J/jB27H7795n89/5z7v/P1ArB3FZUt3XFO9anj1mwt+7W/8gMtuwtPZBQnFy8Wcq4cTXvzBfU7/UN01htMKd9Oz/ms937z/CoAuWE7qDbdPK/7k3gMe/k+Dg/Syx69KVMy8/A3Hu3/tc87qNeflDbtUsH2n5NnmgC+2jyk+Fid9dsJcBHj764lvf/SMs2pNqYNcD5X43y++TXljqK7ueNJ+7qivG958Dz56/zl/7eRHHJotz/ojfjf9Jou/a/ALadw0Mn/tuuf1v18wffeKD+ZrDqodViV+enDIVwcnuGaC7vNeFA9zx9UHBTe/2fLBg9eUNlAZz5v7M372jSMe/11DrCQSH2tDdeG5fafk9puev/TRZxyXWzSZX1q9xOrI/3zz6zz+X4OgWhS4tcd0mvXDkr/xl/+IF82Co3LH/eqai37G9eot3//qW/DF4JYvLbEWpunlX295cnbJRyu5JoUOcA7/Q/NrHP6upz0p9oJt1vDmVxx/49/+p3y2Oebh5JomOpbnDc8eHvDy4/eo33r6pd0LpbNPbvjhf7rgt7/9I1au4bPNEc10R/3Yc/FfvUNxG6mer1l/uKIa8A3JaY7+neeskubD1WsApmcd/6h6SvjdQ0IpTshyMyBEtOL2txp++dFXADycXANw+XDCHxYfcvqHg9uy0lRveoptYv3Y8rf+vX/4/7L3Jr+yrfd53vN1q6tu1+5OfxuS94qdLimqdRRbMa00shBZAYwESAbp/orMmEmAICOPMguSQAkcB5FhJI4TyDASS7IokxTFS9723Hv63deuvlb3dRl8+2xmFmUiC7DW5JxB7b2raq1atdb7vb/n4Ul9iBaBeVfxrx58zt99b4T+aJJ42eanDdj6SBJ/ZcV7h5d8cTijUul89MbDa/6Li9/m6PvcygKDkRSnG2Zf3+c33/sxO5/xVnmNEZ53BpeY+x/wO7u/QfmjhCFAQH2cIXxg+5WeX//CZ7goeau8ZqJrfuVnP+e/4l8nPKluFhzSoqKqPSe/pvm1X/4JNijuFSumuuadr1/y98ffYPfpAbqJmA03zNtIfaTY/VzDbzx4QkDwMF9go8IIz2fvHWJ+WN2iBcw2UJ11LH6m5MtffcnQdBzmW35h8oza53zlq+f8d/2vUP3vGcFAzAWv5YvZX53xqwdp371Rzvn5yXMmbzT8l6vf4ugHgqBT29ZsU7gdtODRL56w6zO+eeeEiWn4Dx8+4++Yb9O9P0XXN0zZG+TC9n6G/5kd37x/SiY9RnjeKq95p7rkf3yvJL4a3yI0AIqlZ/FVxa+/9yGNN+yZhi9VF/wHB/+M/7r8Nj988VWGZz+d7C8WqZGt3lvx83dPKJUll45Ds+E/vf/7/GdfOiT+YEIM6VjUzc31wi9KvvboBW9UC94qZoxkixGO35z8iL//zW8w/TRDhBRWZ7tAedYy+8WCv3rwmOfNIUfZhkp1vFud83d+6QEP/qFO7FvPLVYlaPi1dx9TKsvb5RU2aLY+58uPzrk0b5Jv0vWIaiP1kU4h7KHkzhtzBlnPQbHjMN/xrf2X/C+rX0J8kiYERIR8k5rSrydoCmVv96ENir92/Bm/8/Y9qj9ODOGg0yJbsfL/v5i2f6FFZJBGtHIXqLQGZxE+rS557xlPhrxx9y65yMhiTiEMA6MZlDn7k31s58ijpKm3CKFomp7je8ccHk0YjgcIUVLlA4bDMcd3DxmODMhA5xt8dIkhORpy5/g++wdjilyzN5zy8P4jDqZ7TIuSgYmY2FKadAMoUeRK8vabj5hOJwwGA6pRxeHxEZP9IavVIg1ImYxsUCJcxEjP8f4B40HBdG/AsMo5OBry9ttvURUVIHHpN9NbT9u23Ds65uHRAaMyY1wWHIwr7t6ZcHA4prcbbLB4W+Md+D4yKCuOJlPGw4qsVBweHoLueePtY4JsbgQ24NqervdolbM/OuDh8V0OxwOGqmA6PuDu3bvcmU7YbNa0tscSoIA+dhTDAW9+4SFlnnM03qM0hqPplOO7DxNnM2QoLSBEskwyzDPuHt7haDhhMqjQ2oBPkrn794+J0bNdbtGxB2XpXcfeZMSDe3fZG0yYDvcZlEP2Dw/YO9hP/MioyUx1I5QD33sOD495+PAtymKIUIq9vX329qfkpsB2DokhRMBLhA8cH4zZK4pk0c4KjCzRomRvNE4mRtum1qiGPEvir4PpIdPRmElZYYqSwWDEdHJIuTdKB7JP3GERNS5IppN97t49BtEjpWRvOEZHw6QYUpoysXFDoNAFto5sLhccjkbs7Y0YDofkeU6e5zx6622MUTjfIaQjUwZDYjsHrzGmBBLGQ5sCIRSL5XViRKoeI33iYRaaQVGSaUOukmiIKDF6wGRwl3F1RPSgRGLxem8xuWR6sIfWiirPGZcjpBccTPbYG0/xTpBnQwQZrm5p1jXT4ZiyzMlLQ5SRrCzQ2nDnzhGjUYXzHdWgQKkkbTPScDDZZ1gOKYsKrQoylaGE52BvynazwYiACwIXPL3tELfN44I8G1AO9sjMEC1yNDnS6cT+JSKznK4PHBwfMZoMyYsKZRKIRuuc4d4UdER4y2sZ4Hq9ZrQ34cHDN1BKI5UBbRiPh0z2xvhoEVqQS40UEanA+o6qKtL7TmLcKJEhs5JqOMXagM4qNIEiU9hmx4M7x9TbGq2yhCPBpwDIFAzK4Y0wzCKsI7rEr75zcESuSmKQqMwgdEFtA3cePWJYFPR1B9qDcjjnmE6mxM6RR0GZlWQmJxOK6XiCUgajS6y/EddFibc942pApnOapseUBZNizEAX3Ds4wkTBsCjpmh3e9UihqaoxwUvyPAeXRFHee5qmo8hyur7B9xbhk9KubRyj4ZSiSs9BC00IkcF4RF5mhOjwIeCDu0HGRJq6ZXa9wCOQXjHMxuAUo2pEZhRFUUDvwHrarSXPSmShEXpIQOGlwyNYbzqMzOnrhhg9va8JNmBkgcJgXUSrkjwboihQKsc7zXhyBxcCyqQGo1CJSXzn4D62l5gioXp0XrDrerJyjDRjkAUmq+hbmaSXKhKFwsgMFTTBa9q6Q0rN4npJCCSBlzJkRUXXWYzOkwjvRowo0AihcDGgMkN0HuECRT4krwY0bUtV5LRtjzQZCEOQGh8kXe+JxhCkwN9IxGrb0fYdtu2SdE4pQhSs6568qOj7JIrrQ6Tv0zSFFIbLiwXDwYQYI4PBkK71lGaMVkNk1CitCblM8sPW0tYdQkhMYTAmp2/SuVGrhCaRKqfzhqBKVF4ihEL4JMErdYkIhs1mi4+KwWgfGQSZULg+sdNzmVFvG9omQMySZFFI+laQ6Sr9La2JaGyUBC/ZbRpsn3A2PkiMULi+Z1e3bNueYrrHrrW0eDoCqlIs7Y7Z7BIhQxIkiiTGbBvQqkIoQxSSIDTBCkaDtHgmiQQXCSgCGtsH9vcObjnPWWZoQ+I/d33AuohUGaXR+K7D9T0DafAxJMZ5iOTlgNZ5VFbR9kn0J1WB7xwyeOq6pcgG2NoRM8m2bxGFRpZpXPkvt395tmAkdqwTn5MUxr1mtTV3Bf/R177L/XLF3XLDFwdXfH18ytvTa+wbHbqJ9KMU6kQJ5dwxemfJbzz48CYwCfzM5JJ3Dq5460sX7I5vBGMrl/h2u0C7L2i+3PLte59yf7jirx19xr//5e8zeWvJ6u2M+iiFJe2eupG1SOxQ8Iu/9Cl//e6n/OzklDcnCx69fUV9LzJ60SVW4AGeauwAACAASURBVI0o5ZaD+O6abx2+5K/f/ZSv75/xjTde4X9lTXOQbodeM/PMxpEtLYuvRX7j6x/wjYNTvnX4km8/eMzdr1yyeTvdPL4e3Vc3MpfmyBC/WPOto1fcKTb82w/eB2D6ratbCQuAqi3ZyuJzyfbNyN/86gf8xoMP+dnpKf/a8WO+/c6nLL4Cg9OOfGFvwxyz8XQTwW9+832+OJ7xWw9/zBeG13z7zcfYd+u0L2/kOABulFPfzXjzy+f89sMf8YtHL5hkqUV/Z2/D4r0UqFXn/a2oS296dg8Cf/vLP+RX7jzj3zz6gC9Or3nn4SW7B6/FXklO91o8I/vA+ovwW197n3vjNVoEfuHuS+4/mGOHN8IZlQJYSKHA6t3Irx095ijbcJBtOcy27Jma/kGfxFD2hnFcafqJJuaaL79xzq/ee8IvHz3jIK/5yt45jx5e0+399Mb3tXBLtp7to8C/8c7HAJTK8q8cP2VsWrJ7O7oDc8sHtnup4WleXLF72/LNuyd8ff+M+4MVh1XNr7/3YWIZdkmMZUf6lrcqfGRc/HQy4dBs0CIwfPnTm3g3zFIrK9e0B3CvWPGgWtHdgBgP8pq/cviU/o67ZQ+nFnfiun79wSlj0/Izowt+5fAppbL8reM/5dH9OaoLdDfMTdV4suuG9pHlC8PrFBpExYN8wZ6p+Y8f/iHtcZJONYfmxiyfPhvTtxb83N5L3h2cc5BtuZevGOiOwdsrBud9CmMaj2x9auLfNIL385ovZFf8bPGSB9kCkztceSNUGipUYwlGIVc1g7dXfPnwkvuDFSOdGqdD06MPb96/G1QIAornS5o7kb/5lQ8YmO42CPmFgxc8vLv4aQN0om+OeYluIvm0ZaD7m33eM9YNlezx0yTW8oVMkq89g9pZrr8Rebw+YpK1GOmxQVGqnncGl3T7aV+8XmgRPgVAf+1Ln3Gn2mCDYqA6jPB8uj7m3XdOqe/lt/xmSMiK+g2HC4qH1ZJ3q3Pu5Sse5Av+raMP8Jm4wYCEWxESvWXv7oZ7xYon2wP2b46Pr03OmH8t7a/2wYhs5W6Zx82BoveKu4N1OuaiZOdyFuuKbiLINv72PSOCWbY8OFryRrWg0j17uqbxhvdGJ7hBChFVl/jPUSd8S76M/J+vvkztMkpl+ebeKwC+fHiJHfyU5QqQLxx2JDDKo2Wg8RkrV7JyJX+weodQhtQY9mmxS9qAH+QEA7N+wMQ0vJ1fMtE1E12z9TntvfQaEt9Z3fJdf+6d51x3FYfZlot+zL7a8sPtm4yqjnAzzPGazf1a3GWD4jDfMlQdXdQ8bQ7JtGPytLtt/iZ8jiTbBPIiHX+ZdFQ3+9xIR9/rW4Y54kacNTEUy8C740tOthMqmY7HLqagkK25nVJQXQr+ique1aZi3lWUyjJUHbXP+aeLdwkDf9OuTqGw2VikT63TJ5/d5Sv7F9ig+NbgGU+7Yx6M14n1q37KIS8WHtNE7DpDishhvuU4W3PRj6lUR5nZm+8PbhnCiQWbcqqxafnK4BQjPP/39ivUztAdpImUfpgQDK5KzO3douRkN0nntmyLjYoTu89e2SbRWZEWpUwdkA7yuWDZlox1w8pV3DVL5n7I95q3CVcF7URRXlnylSdbJYFg+Urxv568h5aehasAqH2OWJtbzrEIN8gkH6nvSl5sp2TSUfucLmrqkLHqCjZvcSsKswNJsUrfi2Yb2bY5uXJ8aXDF2hYsbYXeb1F9vAme0/m3WHpCBmwMs2ZIpjwrV5JLx0ebu4SRv2HtQnXW3Urp/t8iyf+v7S90aLuNHdvY0SpPHxxBS2SVs1MBleccH96haXtCCAyHA4gGZEYxyDEDQe9sCkYzza5uuPfgAVpr2sZTiIyRMJQ6I3aeQhoKKSm0QgWQuki2b9czrgTCWoYmRxPpti139u+TmYqmdig5oK0tWmToKDmcHDAuhkgXyKSmkjn7wzHTwQhnW6pBhu12KB/xXc8wH7A/KShNTmWGVNmAylSUKkPTkqlALiSN3ZJlGSMzYDKYUOqCShVoU2J7ONq/R5lN8L0gzwbobJCEOy7wzoM3GI9G5DKjzIb0tmNvNCY4z3Q4RHpLpQWl0pgIdldjdI5SBiVzlI4gHN5blIyMq5JMQ1c36BtWLSHiOkkMGqEKBtmYQg0odclockjTOdreMxgMEEKw2zaUgxKvI8ZohllFZUoG2RApJdZ2aAONU+TZiOAiZZZTb3eEaKnrGiUN0ZMEVTFCDNiuwTtBEAEneiaTEc71SCUYZEOIkrKsyEqB0B5Eh/IBCVjn6NH0WtCFHdEnPo4LFkckGkFQAo8gRIVtLIO8wPqe9W5NHy1aaKyPdAJaZ2l8T1ARlzxyaBswpqRtLJkZIkUSMEUtMfkQqXKUztA3YhuFYDSaUJYDEJqyHKBMjs5Kdtua3kZUnhO0ZL3bYIzB2o52vaWvO1zrIUbWyxXKBZqtBSswIQUZKoBB0wvBqmnY2B4p03NSSjNfrOh9CvkQSVKndYYQhnrdgAVDjjTJJr9cN0itWW+3NF2S4pRlyXA8wkiD6z3ORRQGvCQGw3btkaJE64puW9M3PV3XYaqM+XbFutlQdzVKANHSdw3RC5ptT7Nrb23pmTbkpmB2taBuPJ3QLHcdF6sti/UOh6ALPXVdo2WSc1VZwdmLU9q2p/OOpmtpbJN42C7QNZLQp2As9IFROWK3WnJ5ekK93SG9JpcFu02NrTs0htgHPOJGvqcRaHRW0tlkVImk8xONo991SC/AeXxvIURiEPRdwOgBtpdkwuB7sG1gtl6D1kilyJRB5xk+CrbrFdv1Btd2YFJb2fmeDMn84hqiIpQitRzJ8B76XrDaWkoxhsaRS6h7x3rnGAwniUdGh6XFC8vh4TEXVzNmiw1GZSinsH1gsw04G6nrLdL1SCJSBqJ0NH2Dx2N78FHiI3Q20FvPxWyByip6IZB5hnOBoqhYb7a0nUVHDb3EtbBb7ghdxHaOiCVE0KrEdQHvFMvrLXa1JXhL63t2tmWxXdJ0O2J0CO2wrkZnnsurc1arDbbf0dsGYp4WuGzE9oGqHEFoUTEQnSeTAt9bVos1vfDMN3Ma1+Kc4+rqnF2zTlLLmERcXZ1kfM9fvKJeNdjaJgzArmW1aTm/mOGQWCGxSDACoXLm52uWF0tcb+lsT4Nls25pt5YyH7PpIlkxonORum1AG7ZNw269I0ZPDC0htGyWCzaLLVoVKJHj2ki32RGsQwnNxdUV3kd2257dbkdd17y6PmfharZNDUictbimY6hTc7Vergk24FuH6y3r6w3tLqD1gIjG6IrcDNmsW4gZq/kWb6GvPfWuZberaXY1hDSev2lryqApdYFDcLlcst11tK2j6yNKGmZXK9ZLT9MJrJOAQpARRc6zl5es2p4+eJzbYuslbrvl4tkpV6fnFEVBbTt6t6B1NY7IetFhW4GzHc5uIXg22xXL9TIhiVxiWAeZboK2Xc/8cpHY3n2d5F9tzXa5oGkaumaDkoFcaTQC6QXL2Yw+elZ1jfWW1nnOZksa2yO1ouuShDOEQEBwcnrOpmtp+kjnBe12R1c3LGbXbOoOrZO8xceQpHLOYzct1+dzmp1lZaETmuvFmk3X0W4aMhQxBhyOgGd2dUHdbFOg6z1Cl7RO0DnBcrMlH1TI4BlrQ9i10Dva2P8LvPr7y+3PcyvPaorTDdnS3oyjp+CvuuxT8PJuzeP6mD5ofn747Pbn3hgsyApLtgvYQbpJKi96rt4zVHnPs+aAL1UX/OLeM96tznFB8mCwoh8Luom8NWX3I4V0cO/OEiM9Pz95zqHZ8Kqd8ttvvX/LGUxCmpDs0oVi/cXAX50+JheOB9mCL48u+Bt3PyG+0aAaS7P/msOa2ni7Y8VfefCMr1anvFe+5GuDE748uuDOZIPP0/hoP1Lk14lNuLufo+/XvFud87XBCVNdM+uH/PbDH9FPUphhRxpXqtvXcvrtyNvH1zzMF7w7OOekm/LLd19gvWT9ZuIdtkcZ3WGBGySZki8DY93chBMJM7VxOVHD9mGBHWrMqscVP+VYLm2JDQojHT83fM6Xqgum45rdndQ4zJaW5k4GIpmu3xrN2fqCY7PhF8bPeHd8mdp48mYkuVRpnHusExpi4vh4c4c+aD7YPWCge1yU+DLeSuB07W+xD6qxdPccK1tSKMtX9lKb+dFoiRtwK84JeWqH6dqja8Fpt8erdppei/AsbQWdSsKsGznd67Hp7u6QVVfwJ7NHnLUT9kzNrBsy31Vk6xRKCJdEYVEKQqEIZeDZdp87xYZSWWxUuCgZlN2tsMu/ln1lEvvGEdleChPfrc4ZqJ5J3vDJ8viWeyw8VM/XKWjbOfJVZNkUt8HB7y/eYWA68sUNn3WgEwYjEzf7HP746i1+fH2PXLoU/OmOf3L2LqJOjXIgSedKRVSC56spAMfZmlw4cun4vflXMcpTvtxQzlNI2B0YmvsDZOlY2pLrfshQpddjo+K72y+Cvmmsb5JILVs5bClYvJjy3dnbfLi9z1ClEPW8HROCxFzX5PObUKdQ2JHBTUrGn0uervd50h/xD9ff5Hde/DL+ZZUs6StHcdXjS0MoFO54TPvhHifbCae7CU82B7zYTJk3FbbVScj1WiLuIt3DCfm14B8/fZfnqymZ9NwvV3ywusemzfHFT83xqvvpYkCZW05vAiMbFWdd+r+o1W0L9PXjRYjoWjAtaq67AXeyNWtXMuuH/KOTr6bXqxPb9/VxUl30fDS/w7pPC1yV6lnaijvlhsevjtPv9RG9SyGx7ANqJ/n+1SPerdLn4q1ixofb+/zTxbu0hzdc6saj6vT47dcO2X4y5R+//Bn285qxabnqR/zJ/BGqTwHf66D6tYm+WHhaq7luB2xczs5lnNQT/M5w8OOakCXcSDASVVvsXsGriykv6in7WVrwWfUF728e3LCzb8R1Ph2HqvVIC/OTPZZtSeMNT+pD3t884EcnDyiW4balmRAlkv2PHMuzMdftgMtuCMDu5rnJRtLs3+AzsrQv3SjxcX9ycY/zdsz/NvsGH+3us3AD/tGLr5Jdy1upJaRw0eeSH/3gizxfTZn1Q+5ka767+RLfv3rE4uP9G0FjuF34ihKGz+FPzx+wsiWv2imfbO/w4+t7XK8HtAeGwWmHHWnMxlFep2ZyM6v4aHUHFxQ2Krqo+d7yLeJFgcsT4kK1geyGH+yN4P/4/Cu0VvOT9X1+tHnER5u7/O6H36R6qajO+9tjUTeB+l6OeFHyajWh8Yatz1m4ig+u7qI2CneDmHGVpJ8YqpP0GS1ONTuXcdUM+XH9iA829zhZj9O5p5SUJzuafU07TSiR4WPD4/khL3ZTnjeHDFTHPzj9BrOLMcUy3KKL4k1mcfe7ke9dvMF5M2JmR1zaMQtX8cPHbzL+LKF9ymuH2Tjyxc1C7IVh3easbMF1n/b7H62+yMvTfSDhCl7Lu6SPlJeRly8O+cH8Da76Eed2j8/qO/xg9SbZSpBv0vm5H6XJk6gEw1fpvPu9qzeYdUOeNkf83SffIlulMDTbpAUK6dJ31PEPWl5eTXmyPeRVO+Xz3RGnzYTTlweUF+KGJ8+tkCzbBEwdaJ6NOFuP+aPZ25zVY/7w5du4y5J8dcO9jdwK5qafOsxC0lhD7TKM8DxrDjjfjSlOzC2CYfuwuEUlvOaR/1k29Z3vfOfP/OA/7+1//t3f/Y5WiQlptMJ7h/eO4XCPru8ZDkpGVQEi0nU7lBRY3zHfrNjsek7OZgid44KnKCesVyumkxG5lEThMGXGtq4RElarFbPZlm0T8eSECKvVlv3JiL3pCKIHWaNLQde3XC+WLFdr2rZHqZzeCSyKwXCMNoqqKigykqDLeeqm43x2yXbdkuclUmb01qFlBlqyNz0gBodUiqgNddsxm6+5mm1oraezFhU1bdtS5RmHB0cYk1pX1nZoHbk4v2S5XLOtOxrnE23VBfK8wEbHaFwhlEBKiZIZTd2yXbW8eHVOiAoXBL11DMYjgnVMxhPKPEPiiQEiirq2XJ7NWK52OKspTUGzq6mykuFgQFnklKXC9g3WdvS249XlJRfn10hjQAW8i3S2R0vD/v4+2ki0ELTW4aPkar5mvtix3NTILCcEkG2gUgOqYpQazSJxI/u+Z7XYMLuY0duAtzfCNWLCSihFVQ4xUVBmOVjPrqmp644PP/2MiMSHSKEN1nlErhnmQ0xhGJoCnMD1gd5Gnj0/o9k5CCWlKZFA3TWYPCfPcqRINnOd5Wy3O5bzJS+fnROCQpLjYxIOBOcpiwGDckj0gXDDRZ6vWk7Pr7icLUEqJBLXW7Isw/aWw4N9BlXFbrtGSkPT9ZycnXF2OcdJjSoHSQTlPUqoJJbTEp3nuAi7XcuTJ6+Yr7e4qFAqJwA+RmyM4GFQlYgQCLZPbMjWst7VzK+XLBeL1NwjiepyXZIXFUU5wIaAViVta1mttlxeL7icLciKMtnPY6Dt08h3nmUIl1ieQija2tLWLR9//hmt7ZEmx8oIWYZ1ER8i+6M9JIo+BDof6Fzg2atLVnVDlBqkSsKrEDHGpPdzsaPKCpbXK5arho8+/RxhDJu2QeucGAUmKnQELSRGaqqsxHUOKTJm10tevHiFkprgHFIbZAQtJbGPGGkYVGNCFLTW0lnL9WLFbLElinSz5Fzirs4XK2QUHO0fsFmukURqW7Pd1Hz+7CVWKHqhiFrRe09RGTabJUpCUeZ0XUsfAq2NXM4WXM0XrDY7hFCEGNFFRpAKLW8CfyPxztN3nvV2zdV8zbIN9F7SW5dkiN7hfE9V5fS5YON71r5nvWm4Xuw4u1yxq2sKk6XxRwyIQFN3jKd7ON9BEKxby+V8zrOzK3Q1wWuNVJpoQXtBLgwaSZGXIDR11zFfrvnkkycENK6NCARKGaQUtE1LliVWoe0drQ00LvD0+Uuul2tskChdIKTG4ZCFobEdfXAonc7dvbXMVzUnZ1fUvae3EWNygpBIFMFHYtCMihwJuODZ9Z6mdzx59gpMzrbzyDxx1JXRLLdLquEQGwPDwYimbthsGl6+vOBytiAicT6176P3BB/xzpOVQ5SGXd2wqVsuLzecn8/wscf5Hm0EPng6GwjK0MZIUFkS6znP7HrDq4s5m9qjM5mwHsGDBO8927ZlOCzxSHqfsVg3rGvLJ09fYkYFu65LrWISD9UFT+Mdwig65+l9pLGBl69O2e5aQkyNcqOydE5UkrrvcV1kMt7nernhYr7i7HLG5WxGHyy6yNKijhRorRAyslrvsL0lBEHn4fxqw6dPT6iDJWaKKi/omiY1ccsK27aYooQQadqetrasVw1npxe0rsOGgMgU292GEDw6N2lkOHoQiqb3XG92XMyX7PqWuu3ITIYQBUIqemtpupZts8MHh4uC9dbz7PSC8/kSFxKhPIbU8O1bi4hp8SrLB7R9ZNtGluuOy9mG09NLsmKAFhnRWbSQyCjwraP1kRANbeu4vFoyWy65up7fSL5kWqQTYG0gy0s2dYPSkmgFdWOZLWourjc35xWPFGBiRDhQQaJ0xmZbs6tbNpuO+XrHi6sLNjZhQzoXkvTTg9YZ0aXFDqMynBM0Tc9623J2ueBsNidKiXAOIUhjo3mSpf67/85v/+f/Qi8C/3L7c9n+m7/33e+E0uCL1JxUfUD1gfpORrb2rI8L8uOGgOBFe8BId1z1I/7g5AvUlwOO/sSBkugm0E8Nw1PPyVs55cDSxoyDbMv3Vm+jZOT9i/uoJwWDq9eyl8jw+Zb1lwq6h5ZWZAShmJqaF80B/9fJl4gnJfkqjVFm28S4bQ41Ppe8OhzwqFqy9QXXdsgnmzvMPjqimin6kSRomazYZfoWe/YoZy0qjIp0wfDh9h4fPrvP6LGiWKcb7pAr8uuW9ihj+4WAqqCNGdf9kJFp+QfP3iOelFRXkM9twq24SD/WyFazuCPolSZXnpHu+HRzh1ezKfvfUzc284DZOJojg3SwfaDYThVKpZDJR8nj9THNp2PGL1x6X/cNxdzR7muaY8n5NOPReMlFP+bKjXnVTvn4R2+y/1EgqiRQUV3ErJIw7Cdf3EMXgWlW87i+w9qW/LNnX8C8yBm9SOOaykaCUXRHOfVUs8gNaJAiclGPeXJ+SPUkQ98wg4ORN02xAEpSHxSclCX3x2sE8MnymJerPQ7/oUZEUH06PswuCarqY83Tasg70xkeydJWXLQj6n9+QLFMC9zdVIOSFJcNbmA4GY+otSLLPeOs46PlHeYnexx/Lx2zUQlUn3Bq0gaa/ZztQaSNhp3LsVHxw1cPsT+ZsP9hD6Q2mm48dqjRO8vVuxWD/YafrO6TKc+LzZSL9+9y8H6fGLa5Ap32ZchVkiCZIS/LAZd2xPPllIs/esDkmU+yNy2S66BPQR5ScXpY4IRgG3Nql3FaTzi9mFI+M4yfJ2GSiOl59RPN1agiTjyXdsxe1vB0d8AHF/e4fnzA9GOPG74WviVJWD0ecjbKcVIxzDq0iDyrD3i5ncLv7ZEvA0jQO4cgcXajN+zeDAgF592EV82Uy3pI88eHjF6l8FwAwgaESGG0iIL2+ZAPjvb4k7M3WJxOePMfpd9dvFwTiyQXcwOT0AK5YlbkTPZqdn1Gpj2nzw7Z/54hZGn0O96EkSFP7JRdacj2O9pgeLXbY7YbsH26x+TznzbXdZOOqeEnC5bFIeWXNnx8fYzSkaerAz5e3KH6gwrTQLZJjFq9dfT7BYPzyEfFIYwcJ+0eV+2QV5s9rj89YPyZYPSiT5+nPuDLJNG7KMfMswyTe9au5LIZ8eHlXeRnFaMXqQmpVy3dUYWIIHvN5X7G4/6QP71+xPev3+Tx5THPnx+z/2OoLjrsxBC1BClACLKV4PqeYtYP6ITmw8u7XD8+4N53E7NZ2YQBkT4S8rTgNM9Lwsjjkbxc7XHy4pD7vydR7kZAJ1JAipKYZUszHvIqDjnpxjyvp5yvJ5z98/uMnwiyXeKnIgS6DUQjKeYe2Rgu8ooLXzFrhjx9cYfy/ZJyHm/lgdJB1KmZHDGsjkEoWHQDPl8ecLraY/KHCR9RXdgbRElPNIrRC8v1wYCX3ZgHB0uebff58fU9mj85YPpxTPdhtU/4DX/DCHeKxSCnyRSfrO7wydUxu0+n7H8g0mtvAiFPTXmz6tFO0u0qPvH7nHQjzjdjNk/2GP6wYHiWuLxJ6peOeWUhnylOjnNmvuJVt88fn77Fi4sD7v/jxFOurlzaf4IbJm7Er0qWsuBiO+S0GXO+mlD+sKK6jCjPDe86fXeCIF8I6r5C3O/441dv8eFnj5AfDJh+Esm2AVck4aYgIYyQMHoVeNIfcRkqnjb7vPzoHoN/UlHNkhy4OypSkByhOdCYGuZVwZzXuJQ9zs6njD7IyVdp8gUJ5VmDdGlR61oPOA0D1qrg880R33v5JuXHBeU1VNcuiTJv5InZxpOvBO31kKd+j1d2zA9mb/D8+4+YfJjOlyL524kqPT4qQUBzNTS82E750/VDPji7z6vP7nDwfmpJuxtMkM9lQgPaSLupmPuSl7sJnz1+gHhSsfc48ewRN3zeCGaXGPjyMudZNuaKkpeLKS+eHTP+2JCvIvk6NcptmT6DwSSObnkp6K4rVlcjts8nDL+fUV2IJA9cB5CCfOUIr7ntreS6KrjqK55sDnhyeUTz4R4HPw5p+ueGcy5IYlYZ4T/59371z3St/Rc6tP0f/qe/950YIlU5vBmpTe9+5zxSlmipGOYGrQWDqiA4h7WBehfYrTsur+YoVYJX9FEQBQyqimpYIaTAuoYYFatNS9dHnp+cY6NGmBxVSrSC2FmKLOEUTJ7RNS3Ww3qz4+J0jlA5defQg5x4c3Jr2o79oylCRKx39DbS+sjLkwv6ADvvsN7jRY/OCqyFfFBRVBk+9nSNp256zi4vWcw3BAGtCCiVUeYjvAgYIqNRBQSil4Bhtl5zdnmJJ130SWnQOqexPXmZuLF7owE6BJqmZrVuuFquWO8c2mT0rqealPjoQWrarmdvOrmRd0HnO5abLaeXc/reEVAIIZAq/RtiZDgckc5ZAmt7rPM8ffKSXd3hgkJqRQwgpUrhtVcMqgEuSDrb4REsFluuZnN8ABkVCpDKUJUjGuvwLlANSrre44JkvlhzfjFj21rINGiBjw4hBc5CJgru37mDtx1909OjOD+9Yl139CGiigIrPNZ5clWwWC84nkwpVWoj7jrHYrXh5PKatnVkeY6MybxtigzvHabIiTGQ5QWbTct21/BiltpsUUiiIDECZeqKhd4jVWpD+pCYi+eXl2w2jt4FQvQ0rkMYjRcRFwJIjRYGZUo667m+XrFe7WhdICITviD4JAJylmo0YbnaUPee5bZmcXHNZtOwsz5xi53FqYgVkClDUzcc7B9BUJisAGHYNR3Pnr5ktd4QkIQYQRtCEBAjLkaEFGSZYbdZI6ViuVxxeb1AGEPnPVJLdrstJpPJ+q4keVHiQ6Tfdax2O65ml1ytVrROQJ4n1m30xKhSM0/Km0BO0fSWxWrN2dkSqQxSarx1GK3RNxwwF6Dpey7OL9hsOmaLJTYEOu8ZV0ParkXFiIgCqTS73RYhJFmm6Vygc46ryxlN29H0HUEEhM7xrqe3PTF4bG/J8xwZA33TM1uuuZ6v2TWOEATaaPq+p8gKLOkLRErFeDSi7i195zibrzi5WBBFjtApRJeZJhNgbY9WghAiUmm6LrCrG548f8l6WyN1au344Oi8JSKom5rpaI9ApN61RC+4uJhxejaju7FqKxSd7cmriq7ukFFS6IxKKmztWC06lssly2ZDVAGFIEaJix6lDNZGbBfobM98veN6MWezs9Rdm1jVwhO8hejJMo0Ugu2mow+S3kUur65ZXO/oXEOMoJUky3OC8xA8mTZJNhbTgsZ227NZt5ydLmh7nwRvUuKFI4SETVEqYH1L8FDvarqm5+piwXbXoXrthAAAIABJREFU0dtIlIaIwvmAiJ4QAxEoqxypM9bbjs7BbL7kerGgDw7vPUZmhKAxJi3GNLbHeo+1gd02Hbtns0XCc4gABAieaEBIRd201HVHkRVsNluu52su52uE1myaFUho65YqK8lVRm8tbdcRYqTuLNdXC67nW+reUXuPURGpJDE40KldHiLYrkGRoWLJ5WzJYr4mWM+u7sBIIpZMSDpvsSFirWezbei6ns7B1dWCzXaH9QHbtzcMcUeQnhBjasxHyfnZNdtdx3rXM7uckZkc23coJQkxEITAh/TeRkihYptao1fX87SYZiSht+RREkNCCFgSLuDiYsZy17BrGuarFavlmhAjTkQi6RpAFTllltG2Hf12S8TQuoz5ZsfL00t6H9n0O1z0uBgYFQOC7YjeoUXAdg5Cxuxqy3LdsNw02D59noWHTOYEC5nKiMDaW7re0/eOpu45ny1YrLa4kJ5PlILgHSE4jBY4AbttzW5VE/rIarGm3XVYJZJoMJe0tkMZg5SJg7ltO1a7lovLa+aLNYvVGiEljXUoI3DekymFDxEpFT6AznLq3mKjZNNa2j7iuwgiolREGoBIRCCkpGt7rpdrFpstbdNytVyyqbdJDCQFTbB4A1EqNvM5VVHwt3/7b/1laPsvwfbf/7e//x3hI+1hWgh53ULL1o6oBLbUzI8kZW75+uSUrS+YdUNOFnswy5h+5Igm3axmm9SW2x7l+KlHKLjsx9ig+ODiHs025/i7JJ6mTDf2vjIQBYtxzuRgx35e87Q+ZN5VnF5OGX+siFJQzTwhT01HIQTFInL+tqbXmqtuyEUz5mI7RP9wgKmhunKYJjVifZHEJPNxiR1HFn7Aq3bK89WU+MGY8YuA6iPZPIWc/X4yxzdVBseWgMRGhQA+fvqAvQ9kGo/s05SUL5L93Y40DTnjBxtGpufz7SEfnd9Ffl4xOEvW6GzraQ+zZOkGsrXg4qFGZJHLZsRJs8ez00P23leYXUDZdJ6JSiartxcsjxQ2FygZUtu0G+C/O6G68ugmiZmku2GWjjSNLqgP4NoOOa/HbF3O4tUe48cC3aeb55Al231zoMhW0Nkcu++53I3Y9Rn9iyH7H8TEjU35TxqdVgLVWOwoI//aNrW+2iHLuqL7aMLwNDEoQyYxO4/eWuzEMHlqufyKZm9Ss3EFj5dHPHl1xPRHknzlcYPUyq3OWvzApGbzMMMGQz0QPL4+Ynk1Yv8HGt2/FuikIDUhBiQiKDa6YKVTG+10NUF+NKI650ZYF8ivk7BH9QHVOoIueZkP8VnkYjdi/nSf6lySr9PfSCFZasDqnaM9NIxfBJrlgNXpGF6WVOeR6sLhS4VuAqr16J3FjTOKuWX5hQwnJLpwiWk7nzD844rRqzQuLF0KraVL16nCKi7lAFsFHi+PkQquTvYYPdagNNk6OQ7y03UaMc8N2yzDjQIrV3La7PHx+R1mnx0wfRzJ5zaFiTYgbWooS6GYFxXLzNAEw+VqxOrVHtMPRLqO0Oo2uPTFDcO5jRSLgPi4QlzmjD9PUqxsbanfGJItOkKecBVRC6pZwCnDZZWx2+WsthWDzzLyJZTz1FBEpuPKbBwyCFSjmI8zOinZ1QX28xGjZzKFUjYFlnrrsBODqT1CGk70mOFxzclij21dED8Yc/yDNPb+un0XjcQNFWbncaVh4UtqpVhuK3azijt/KBmdulssguoCZt0hfKScS+ZvatYh43S5RxcV/ScTDn8UyNYelCDkBrNqsZOcfO1xJmM9lhztbdk0Be60ojpRjG5EZ69RFbrxhEyl4KnNcLuczYsx+llOeSEorz2mTmm1sgERbxAwSlDMBPWu4roeIh9XDF4q8k0av/eFwo5uhIskZIduUljWeUN3VRIvcw5+HCnWaVHtNSJHutTKDnl6XjFowjKn3+aMPlNUV4FsG24lVq9DdNWn6u1qamiU5PJyj2ZToJ6W7D3xZNuYjqmbQM5VilAksaJoDM+7KavrIfZ0wP5PIrpLHNTX+KbXSILy2mOWmstxQXtRYZ4V7H0KSBic9Qggm3e0xzl2kpFtUkM+BkNc5JjnOeOnUF0HissWN9BEle4nIQWFykJsM5ZZRm8E9tMx5QuDtILqygGp8WyHimzt0LXHNFBeSuROE7YZxWcZg/OAadNYvUiXbLcj/MXcEqWm/WiMuM6pTiXTxw47kGS7NAGmbLqX061P54YI5QzyC418mbP3mHSvdzMF0Y8UMiZWq6lTcLj/YUCeFrh5gX9eUZzq9DMd6Rqgj4RM0R0YirlFNQofDNebIfWLEXvfN5gdDM9skto1ge39DOXT+crnAl2nsp/+PEee5gxfgqkjWZ2Czn4oUS69XyLC8MQTdgW2zmiXFYOPMkYvQHfp+Uh/89xcvA2vqytPNlcEZ/h/2Hu3XmuyLD3rGWMeImKtffy+/PJUB6h2t8BqhBC+s5Al8CUHycLmigsj8B/gF9SdJX6BhWQ1CEsWlrD/AxIIIxCSDYJ2Q3dXVldnZeZ32oe1VkTMky/GjNhZvqo7Grq2lKrMXWuviJhzzDFjvuMd73v8hRAfG+FiBm01SJcssH1NGhy/SRx+6fD/50j42cDx5/beMn60z/vZ7sclMzF1CfIoTO8bbhYO3/S1kQAM2N0YyVssxqfK7R82hj8J5KeRwx94Dt+YTEU+CNOHglsNuD786cL8OvKf/fW//Gu9a0trf3aFy/79//g/aTkbLT0EAwZLKVAFL4FaMr/9L3/Jv/TjL3l+eodHuSwLD4/PfP3Nd1SE0gQRZcmFYRiYXOBHP/iS25sRykLxhbfvHvn27UfOFwMANHiW55mrwxEplVd3V9zd3XFzc2AcR96+fcvXX3/Nw+mMhpHaBJXAXBIOYyi9vr3h7jgwBI/zwjffvuXjwxMXirnb18oQr0jrhWkYcDryk8/ecDNF1px5//EDH54eeT5fKNLdeMUzxsiyLDhf+K0vf8RVHBnDyOly4Wc//4paM605Um5Ue7tDUFSVOA588eY1d9dXfHz+yMenRz58fCSlRIwjqRbyXAjOMwwTqWRub468eX1LrRXnhO++e8fbd08A+OiZSzJmY2nUWnlzc8vN9cDt3RVPHz5yPp85rZnLUijV0bTimxpY6hwhBF7fXXN9bVIOp/MT373/jmVZaKgxCcUYyw5hmI6keeH66gqPXfP5dKKpsHSmcq4NYYXaoEVGMd3Aw3hAnfC4XHg4P+O0MqfMZV240hEBRj+xLmfGw8CbT+9prZBz5bv370iryQKkruvYKKYdXAohDFxNI957gvMspzOJzGlZ8WGgicI6UwW0AVRiHBnCyBgirTWenk4sOaFeqII5vkdzLNc4UFLhOIwcQqSUwpqsPbukjHOOXBpSMvT5TiqUVlEVjkPEU5jnleY8OWcEh0RlqRlXlaDGOoze8dmd6f9+/c0vybkacNIq4pRCQ7xjXVfuphtyWnj16o7rwzVPT0+cz+dupqXMS8KJObWLCErGq+Pu5opjGFhL5sPpibUDSSV3IzlfSDkzTpGWlUM4MPiB4RBY04WPjw947y1OxOGbsdfWvIKHGEYqjdAauTQWbV3uweHWglM7lHo/cpkTwxBY14Xj8Wif88I8P0OzGAt+YEkrIThqrbQaGIbBHI+rEEJgyYnSMufzguBAym40VGpjHCI5LQR1pp85ePIy83xZiWGk5QqdlRz7HDmFw+FASQuH6cras8tCSr39rwOYiFDVTIyCKCEEpGGg3FwYgqPWjHOKOpBWSWUFF6lNmKYD8/NHgjP2vDh4Op1oUvHOkZp5IAoRit0fQGu2FgQDcXFKA1TMgKwmO1w7Fwj9/tRB0cS6dmOpFsCpxXIreHWUmri6OlDWhHcjqo7z+YyqUKSQ60KMkUqhJaFVh3qTdlEvrCV3mZBk+bkmWmtUgaIBaY3oHd41ypqYhoMxH2vZ7ytG62wARap1KBQKrTpe3dyyni4IlXMptOBp1dgntVYUe37FWP9OBe+VUhLqBkIIPDw/EceB5bIw9fWRaunxIgyd4VzVsZaF1BKlCc45SloYet5otRKdEv2EVofrDFwR4WO54FWQktHaWHreEZdNEzkpMQ6WD3Kh1opo2mMrFfvcOI60ar/z3rOkbOB0rXiv1GZyH61AzplhGGhOGX2AdUVdtf27VggBqhA1clkXmgCiOFVyXgje4tfReJpnELFGF29x2NbCwQd0CLj+nK0qFYvFnLMZcfW9v6REHDxSCyJdUxpnuaBC6VrViUrN9n1FzByp1kqSxnGI0BppWYnDRK2VpTVaqTixA0SjWFwNSikNrcrSKsMwkFKieWVZZqZp4rycCRpMHzgXW1dScbnhohgjV4SaE9M0UVsmJ8EZEktdCjjFBVuHlYYLgfl8plAQVaQXE5e0Ii5AsfFREWIYkWJx2tJKKYmkjYTDu4HJG7P3v/q7f+fXF9v6zc//Z3/+nb/6t5vOBgKUgx3+dbU9sowOTZWf/9XI53/pl/zpu1tizFweRuLXgdf/1A6bbrG2+c35fL5zvP1LlfGLEwA5OfijA3e/D4e3GX8ydk14WLh8caA5ePiJ5/SDSvt84eb6wuMfGmB0/SfZDkZdozZ+NCZiPjg+/Hbg/INGvi7gG1e/H7j+eWX8kPFPCU2FdDvgzpn1VaQMytd/WWifL9STZ/oqcP1V4/hNQte6G+msd9auXQbHL/6KJ73OhJuV/H7kk//ZANHNlVpTbyft7Jz1qLz/Xag/nNE/GTn+Qrj9YzvEp2vTrnSXQj561msDuR5/5Hn87YZkocbG7R8I9/9s7XOgxI/JmI3F2jDf/6sDl8+F+YtE/M4zfBDGt82Ar9R29/bwbM+QD8rH33LMnzZqaAzvlLs/MGbx1tJshkCVMrj9vp6/6HqNpXH81rqZ4kM28KDrfG4/653n+XPHcm8H2cMvG9d/kqlBiB8T4e0zy5c3NCcstwZIL7eOj79jYJVb4f6f5d2B3Z+t9VmqMUJ1yZRj4PR5pAxCniA82xxM36yUye0tp5uhGkC6dizXjvXGQJXjN3V/Bk0Vd86Ug9//RtfK5bPI5ZXiVnZ3czNLkhddUCweNxO/GpTLm0AeTd/QWFd114cODwZ65YMBvuuV8uEvmqzI63/a8F0XefsbXQ0Z908rTz854ufGx9/yXD5rTN8I4/tGPFlRYlsTNWgH+K01+ulHjvl1w5+E669q12Guu+xE84I7JdZXI1Ibl9ee9VpI14I/NW6+Mg3Y+GBau3ly3eQu9bENaKq7qV74uOymXZvuqMWwmOzIjcefCpc39o5aguztwZqt9Ts+ZPLBWXGmNtYb67ySZsy04amga2P89rLPQzmEfczWOwP488Fa6fMg+KVx+HohXYcdiHOXQrr2+EuhDI7La4dbzQHeX2B8KLvO7PfjafvvfPTkg9IU4mMhfFxId8O+hszh3qRAtr9bXgWGD4l8cFxemdb21ddmAFhGi/ndSA/Ix65F0sxtvqkQHwwX2QoT1csORtcgpIOBjRt7PjzX3VgKDFDVte5yD+fPrUC1mTVN7wrNsZvIpWu/a7xu19zGNjzbOPvzS+40rVaLsaabtISxXC+vvHVEXoyV21RIV8rwoZujOWttd4vlmo9/YSCcrMA3vbM1ta/Tfr3qZQfWa7Q29w10Swfl5qvFJGaeMsursMs32He03pFhYxi6TrkuxqjXpZBu437ddO3McMoLNZg/CQI3P1t2M8oabI4MSDd5DX+prFcO33OHprbvaSb/Yrl0uffdNK2Ru4TJNtdlUKRYAUxqQ+dEvhmgWe4Fu1YTA2z3vHxUk47pxo5bDssHJU1KicLV12mP0RrUGPi1Mb+JVC9d27YXrbJ1vRiAbhIapkFugOdmLrrdT4m/Olb+Yu8M231+fwwub+wdN5xtvLQ04qMxnstgAOt2vfV74Gp8qvueRYP4kFheRYa3C+VgXTnbGtnGwIBly7FSYL7fOmHabipnxTN2szAtLznLLaXnWtnz1p6PBt1zfFMbB9vjDDj350IZlcsrT57gf/s7//mv9a79Zxq0/Wt/82+1lBK1ZgMmMJUCaY2r8UCuBR8dr+5vOYSIUPnw8ZGHhwdEhDmt+yFTgmOZV1wLXF8fuT4MjK5SXOPb9x/ItYEE5vPJ3MSL2IG4NXKZiTEyjgdqreS8UnMxx9sGpYJUR5FGKs2AtRjI68JxGknrbGCzCKdqoO1aVg7xmloSxyniakRrwYmwphPiAo+XE2hAFFClpAtDjJRsmoCTH9FcuVxmfAwMQ+zABqzFDsmqmImQelqDaRiZz2fCGEEb83w2IFF9d3rv8gmiO+AnQXBByYs5kLdcCLGDV04pOVMydlDPhRgMnBi9tRZfsBjzMoIUq1apIGqO4z4YeFtrpZREpSEirOtKnEZas0HOOVOLgRbRB7Z3xdQqhWbmcbkQQmBdTowxmG5qtdZoQUkpoapkqazZdJ6qE4YORknDQA7vQMtuAiMiYEIRALsmoaoBpMuyMIwjy7Jwc31NejpTW6YGR8oVcR6fzVm9tIqLjpwrPphkg3eOtGaqZAoN9YJkjOHplPO6EOOIFrG5KYVG2YEpqY1hGCgpk/NmuStm/uSUNC80l5BqYAVNaQ1SW+2lQh1g8+4R6mLzEMaBtVZaq6zZAPrBKY8fH5iOB0oxqYd1Xe3ZSu0mSPYcrTVqyoTg+7NnWq1cDRNpXnBjtLWaE7XY3LjqyXVBVZnTanNXPIfpilITp/VMGGx9hRCYzwuH4UhuBkouyXR+vHMEddTSTVaWhKgSmhAirGsmjkcuczZArQOQOS2oNtRVqILkDjg1c5b3Q4SmuA6OX/JKA4I6XHCsKeNcYE0nvLciSwyDrUMawXnUWxyk1YzGcs7QQWVK7WtSe9zBGGzd12o6lcCen1prDD6QqgE5rTVqBaceULwG1nVGnTEDhYJvjeh9Zw9Ba+AGA7PWZdnzhnNmbrWWSnGCFMU7h5dAKhnETMpUe0EN8GpV3dYsXxhAHfcXZYAipev8BpZlodSKKAQfSXm1yrPas8Q4mL5xCJSyog7bE3CklJjiRBVYl4wLtja895ZHmkIFrStxHJjnGY0TwF6gCyhCN1sTwQUrBgzB8mkIwWI6ZxDhOF1xfnrmeDhQl8SFgkZlXc0YrYqxma2CLoTgERFyWYFKq94KMiGg3rHOM8chWr6LnlxWpvGW82XGe09uldwLAhoHpDWkVdsMMea+qqcI0No+biklmjZ8BcWe67TOHKcrynqhqUMIey6T2hAq6oWilXld8C5aTHZmfRUzTGsCg1NatuKK955aK3RgHiyXxhBoycZVtBlojRCGyGUp1KZkaajC4DylzhZ3ooQwkEsxALNB870gkPqLu7c9OoRgZnF9D0nJ5lUIpJSZjhPLsqDSuFxOHA5mOpaXlSFELhczenPDQE0GOKda+l5v8hvSmhmFlYKox948A7nnYWngBHJeOQyRUhpkutTJRG2tF+R6zE4T83whehv/hrO4LcW6UVol10RwsRdi+77T844Tb0WGUiilMR0PvH984OYwUWlUFbxzrOvcC0cWc845vCjLkpiGkdPTM8fjxPn5ET9FFvHUAiOVyQX+3n/9X/4GtP1z8PNX/r3/opk+6Yvz93YwPn8W8UslD8rjT5Tlzphwx58L178w47Ghu5eX0Q5s8cEApOcvI+fPlXQNkuDV75feBmrA6/IqmkZotYObMSkdy425PvvZWEEGfrX9kKRrxZ1X5s8OXF57/Fy5fGJAywagHr4z0NY/zqyfHpHcuHwWKUH2A/cGBlz9ib0LbkDJdlB3SyFPjvneGJ/T+0IehfVamd6XHbRoyq4r2dT+9/LKM34spION5/BQfgXU2ICGTW+39kNxCXYYNsOc1sErA4jdYmDLehuQCulKcUtjvnPEU901R8vYdSLDyz1pMh3TNNnh1SUDIZsK4dkAhh3MmmsHQJR05fYDazh3UHEp+KfVmHpPC+lutAM11sba5IXp5Oa2A3ySCunOWqKbCmV8MWHZ5ngDl3aN0n8BnIkPifU2EB8Szz8cu7GLMcr8yQ7NZXK7+U7tOpNl7Pc3dYApm15uc7Jfo6kQvztRrke7XtQd3MxHb9/vDIQycPVFizBdG2jiz2Uf821MNtBZatvnpgzGzt50Hc0kyfZh/5R287XjVyeWTybcUphfB0LX1bW5ewHgqA1NhXwVf+W+z59HQgfBv79WLfbUWJ1Bie9n8pXJel0+CbjUGL8xwMPNVmCI72fmT6cd5AwPVlSoo9tB1g0ER2UHNcOTFSuGd4kyvegHu0vZpRC2mGidZaur6SxvcVyDgb5SbS5q6PqsUfFPiTo6/ONCvh7259vAcU0WG2VyuIvNWY2/mue2tb/cun1tuPkFMNs0ltON30FY2otZnsWWIzxlk8PogGWZHOnK2K1bbGyA7la4+hd/p4uxVDe5mu9f71cKapPbwaDwlKhB9xy8/WxM+DKoMZf7d+/xrELzZkyWj47wmMlHj1vKXtTYQLzlVdxBwy3GyuT2tWNzpTsIu83rdm/54H4FdCuD7kW+8bvV2K09pgHOn0amt4nzpwHNjfG9fef2fdvYfx84ayrfmzdbd8aaZS8WhJOZTfm5cXnlOLw14zxjxHewuRtEfb8otcWhrpvmq3RN5AKdydycPdf0zcLls8HyWH/epgYsb/++gafxY7K5Hl4mbiug0Sy3bNfJk9uBxK1A1ZwZ/m15x9aQ5b40CeOH0nXHbb2ttx7tTM8N5PeLFXO2/WsDbzejN5rlds1t30O2HG+Ficp87xgey75W5zeR6oyNv9wYcLzteZoby7Wzomfuud+9jMsGau9xPLf9+bZxu7z2Ozgdnq0AuIG8m276ViTZAHTLUbYX7kBqN1CzwkRfas724hLlpTi7NuZ7x+0fnDj9+NDnqJEny93bd/pz3YsH8akw3zsO32XObzzTu2J64g8ZyY31LnB55fhffu/XA23/TMsj/IP/7h/9dANoL0+PHONEUI+oN5dpHPPTEw+PJx5PFx6fLpwvM0tJDNNIq421rvho5jA+Rvxx4HS5kNaFeTnx8LCaqZQKrilOHOt54XA4MJ9n3OhoYaA5z5oKSYQ5W6t6a4qKQ6tpwJVizLgQHCmvqDbOy8UOgDjWnCm1MYRIEE9NCWpBm5BL5ZxWEnTANFEzDN6zdt518FYOdy5SzqYDqUGZa2FOK6kVmmukVmktE6IYkNwauUHVRpFmOo1p5jLPqCgyGhPISiVCrrawck2kakCFjyPrulJbM3ZOhdqlKgTFB28HWt8ZTyrMS8GFkVqx9sycaCKoKAVjtQ3RsawX1AlVHKWD4LU1fIgsaYFWKK0hqgwxoArzeqG4SmqJta7E3qrjEEbvkaJQbT6lA4gr2UBQabSc8eIJLlJSZcUAaPFCrUquNpdrWXHq8BpomPaliCBs7dIVdECDY0kZCZF1Sb11VsljIDrTdBo0sCwJJZCysS+bGPDbtOJcM91G9eQMo/eUbPERgumqog7nhNoqQay1vIlpuq654J0a+BU9SCPnjQ3nqaUiXkilUpoxscfQqDWz5oXWKj46qlbm0sA7xCsribUmqirB26Yy4aiXBQ6epc5AYfCR3FuZ15SQKSBSCa3gaDQtNvYCTXXX/slpMb90FyjJjHbEKRod6gzo9M40rXFCq4WSTftorQUGx7qYyc+aEj5EWm1M02jt191QzTchp4qGAbzjPC+kXImDASPeKbUsuCCox+JWHLmA02hGUx3YTqUwBANbWzM2KtpYF3uOnCqlZZbWOByP5LXgmiLOEx2UWii50kIgp4yKw4Mx92JAo5ni1QbqHJXCvK5UaagLULHva8IxROaaIRWaU7IUnPes+YJ3jVxXnK/UcsF7Y62Kt7F+Pj9zc33FZXliHCeWZaV6A1xrVsBTELI4nBtxXpBWcVJwWiitUp0dHkQUFYVm4J8oqLiuMVxpFArQnLI2oYpQi4AbUWmEaDIVqkoMHunxTTOWf6kriMltNKy9xPtILSBOqJKpVBCPqCC1oAqiQmiQ14JoJGPrR51jkMi8mFN1rQZ+rutq0h1LIsYB7wPzYvnROU8PCAqZ4iu1rbguQ2Zv9krwDnVCacbwbTQ0KI1Gq7mb4AV7CccYm6pCa5Wymj4kXjjNZ8ZpIvhALQ2/UfWlEryByeo9Gk0SBQGnjtwqxEgoIN7WWsrZpGjyQmgBqRXn7CBJLZRawAWcKjUJQQywbanQMmjPLx7w3oz5WmlMYSQtM+ocSCJGY0pPTWlrwYfIZV0RVXIDL5BTwanHaUWoRGcxghZC8JxOKyGqaV83MSZvq3btClEdDaENjZwqw+Cpa6Zh66c2ocwX/OAo/RrLMhPjgdoazVWKVJoLVCx+1QlNGqnMOGnUsuIdBBOPp7YETXAqOCfkdYaa8dGTaza9eOcIwbMuM+MQkGZ6VdqwYljKDHFgXReiBkq1wmDwiZpXnLMCgneKNKWuGZwVg0S3fkAruCQplAAuOHJZGIMwYZIT0EgtE3ww4LkmghOo1QoeopSSORyPpHTBxcAwDcxz4nY8IrFBLvz1v/YbeYQ/Dz+/9/f/h582p5w/j4zvVuZPB2N4SX8n7MzB659njr+EwzcwvavEx8x67dGuD1lHK1aVyVFGx+GblTAL8QFufm45W3PbAbFwKsyvPMPD92QPirGdwqUyPGRr/6yAQvVKOZgsQR38LnnQ/NYybCBv6OzDdBMoRwMapAHO2h+nd4lwMeaaS3ZANtOZ1O/f76DCptfaHIwfMsOHRJjt8BdOHYTp9+QvxUC03HbJgfhcGN+tJh/RAVJgPyxKlf531ltZRiWcagccNwZqJTwsoEodHC5Ze6kme4atBdStza7xsPQDud2/PxXSjTMmUFBrxe3mTXaAtXbeTXcWEdKt31l9LjXCqRCeMvnKW/dDcKz3AZEOuAXZ2VbxeWtzl5d4mAwM809Ll7ewz5pGrAGkRIqXAAAgAElEQVR7Zgqmnbldd43EMirD2xnE2lzDg3X1hOeCm2uXNTBdV1RIN36X9thAsG08NLfdkAkxtuV6b8zM5pU6hR0UqYMBJOXgKaNYAQGMvTzZ+iijdTpZqzLUwYBYRAinbGPcD+eaG+HJpCVKB1+GjxZziMVGeFqRUinH3i31ejBgt9HnyJinfjaAyT8tNLXYKgdjt0rpY99ble0IaWz4zXht0/ZETR9yfx/v87S1ubsOCPtnA2jDo717h8eFcrCzzfI6GkA2W1t/Hc20yr5TGL89E54z+SYaC3U0mYU6mDHYpkXszxYLzZveY43aY9dbS3xtO5Abnl5AWLdk/MPM+snBrhvtWTZw3c+lrzeTkKijgTs1OppX/Dkb8OhMe3j4YFJJLZiucI2m13n5NDJ8tPVLA3dO1NHjn0wCws0GIppkgwHGJhHQmL4+cfrhZBIqd57wZEWcjbkoYGvybJr/JjdjkiW2tuv++eaEnbXUQbpNomVrtTemqrG0NVlrOdjn8tEzfv1MC458bWtmAwzroPhejKg9V1iBwdl3CLuO7HZ95MUQLR+6PquKGZdlY3Ona8/wsb9Hp2pa4095l8HI18HY2O+TvW8Nrksg2Pi51OzvosXqBqTnoze2+PIizdCc7ONQJkcdBDDt3+///25tu47/9M3Kch8MvCvs61o6WGnjIb1QVyw+e8ENoBxMX5Zm+8Ll04ifqxXtnBkebvnQrQbc1iidXe4t128t9mrX3/7Zfj+/CgaUervpdG3reLk3zfASleldsnx0sUJVuJh8SI36UnDKlsfy0cDlcvB7LrMclbsEipAOnYBS2NnCLje09vtP9Hi3YiMixEfbJ7R/TpPpEGvq89M1vsO5UvoeXEbTBRZeCq81CiXorsVcRoutHaSfTK9+vrOx34ovZTSm9Xrrib1gtMkRbOBs7XJOJdpa2nP+9+ZJsHgO80vu9HNjvY8st9aBaXm+s80vFut50r5O7Fn9akXceDYgfb1yDA+F5x8NxvJt8Lf+w19PHuHPNGj79/7BP/ypqmOZEyEO1FaoJLxT8mrMGRUFjN0nzsC+GAPzfCHlTIiBVDMxTuSutedcIOdCrUIVA7NcE9SB96YZ1UpjHEaWvODV99bdaoBBrUhnXDZaBzoNBMitmubhasYitaw0dagLpqOYC5SC82aCVFslDMZSKrUgAsG1zowT1CmlZhqNhCeninOKw1Zda9YW2VozbdXggdoZZyvSFBGPdwaC0BpIM5BSBR0C5bIwDSMBewkRwKmS1kQMAzThcrkwny9M40Qp0kWjxcwnYugsK/vb1mysKgW0UcjU2hCFGAOtmq5pqRUn4H2gtWptzGpMOe3tuQZQ2nN6H0mldtmLijgHNKI4tAppXhg1kJeV4AKuGSt1zgtCw6lDnUPpLKSUgYbzjoIYuIpphKpTckmoClQQDIh0qmYe1jCdwTAwrzMUGINHysrkB1qtaPAUKuu8QG3UDD500MyBD4EmvULeGuu64FXJZTWR/9I138TAhFYN8O5IFA1lXhcUcy+0LmNn44XYi1yPSZzixeHFM3iPw5gsFuumGZyKSQkgjsFHaJVlXRgHD6XRqpm7OXXG2h4PVOeIgzGAD8eRy/mMR/FjZF4WVIUgjpxW02BtihJQMYA1hoFaCt55cml4F+0w4qzy29TWVymgwUGyVn9Ho3UwxCGEMBijXqGWytUwUtaVRqP2KiciNG3G1nNQsq03OiMYwG3AWTfyqdUqnS4EnERwntpZmbkUSs6Mx4PpeNqZBZuMSsoJDYHaGlUVJ0JQK9A0sbhe18QYTRrBtisBUdYl4Z2HJrZxlIZ3dkDa2rYrDXWOtC6od1SVDgBDDN4KVT1fKUJrwpoK4zjurNHpOLGsK0GFUi03DDHiXOhgkuWtXO0lhZrxotSSbY02A5pFXGfHWnzULk9gY2ta5KoGMrduR+v7+q1pZZoiy3zhMAYzZGyN6C3HiBijspaMD4GUk+lpE8CZ/ESzRYqKGBtRxeJDTCsxI0g0wLap4Lt8wxCU6AIU05xWsfkRlNYqwXtKKQb6q7PYL6U/J10ax/Iy6gBBqlCzzZ2XiKPSeru7gdoeNaVgq/yKENTv3SRTl6ZxYszS1ux+5nm2NURF+mFgzXboktYB5QZzLuAc6gM5r6gT8pps3pcVLYU4jki/ntIQdbYuvHYzrswYHFRjrHuUGAOipmuda9fiEivy0Q0ukbrrm2uLRBfJKZnuuZoEQmlCaTANjrYWY8M3a/EXZ2tg8BM0pVTMBFNAvO1LokrKK34IFFEGP7GmxdaPU3K1It84mURKKivLunBzc4VQcN7Z3hYn2yt9wAe/5wikWdEwBlywnAXS5RtWXJcuCSFYTmm2h6h2Bl2ueB/sfcM7Uq00dax5tQJRyhzHiVoq4zDgxPK5NMH7iG+KogTvqdUKTpfLgjrPOEyUXCldC7e2RsXMRtc1WT50jtwK2teGF2VJK+M0sawLvhd9YrC5od/jmjLBO2Qt0CpZhP/oN6Dtn4uf/+bv/vc/bV4ZHjL5aIZG+0GsH0Ta5tjdgTTNjXTlTYbgXChHb8DMlTFfpdrB21+6RmcxAKhGpYUNJBC0wnLTGTrRDmgG4hhgYNUw2bX//MUMcdzZruufDeSQQmfk94NksY2/BiEfDYjKB4emzuIUY12GU2cLBTuAm+v7irtkWjD2kzT2Q6oxXruxmch+cN3MRTYwT/p7R3jKVuDyBqCu95Fy0J5rbFz9qexMzfFDJr6fWV8Ne4uwmeGIARnlpWNFatuBcGnGFt3A0nwVOjDVQbFgreobSGRGOV37NtnnthboMprOp+bWQVxedDODEE7FWtDnSjmYYdTGmtuApuZfDtDxyfQea1QDV68jSGcjepvz5jdKF4RniwXtbehuruSbSHx3MUD5fgAVllcGbLZgrD//vCJYS7ix3uxAXiYzLELsnsJz2Y2GEOngOXuMSZ8v6e/VWkzruH2PEboZnUnZYo3OvDVQtA5KuvE7WI/QAWOHf177te0zIISHRLoLthcUY/HW0SQbljszdsrX9kzzK8f0zsyS6uQJHy4GwB3D3ma9PUMNQnxMO1hTJmNn18E6ufbn7A16rhtObaBn64WD5p2th2OwooYa0/Hy+WjrZgMfe9xLtQJMCzbnTWUvwEgzZunOJhT2wooBTH18+/f5rkudboOBl+WF0Y4I7nkxY7hUkVKp0ZMPbs9DUhrhcSG9Gn9F5gCB8JjMWK7ft7+UbvBkIJ/lQhuTcDIgWkoHV7tetDSLMaltB3rD00q6iz22GuvrYc+vBiBZoWjTsbXc1HCzraEtjjbQc5Njac5iaSt8udXWIN+bQwOvdB+fOroOhlWW14H4kFneTHsHUz529mafN822fvwp99/3s72TfZ3s9xE3/WjLef5cdsBWSqOOBsyv127XLt0A3o0FLKVRDq4DY9qZ8tpjWKwQ0oG2jYUrjX0f2gDHDfhELZ9vzGTU1ujOxu2g59ahUGOPzQI4YfiQqOGFuSrNWLq2iOkGeeydDRbjdb+/9S7gZ9tX0tGYz9KsRb6rVnYDORvv5c7mp0RjoaajMYa1GAN1OxoaQGzfYRPagcGgrEcDF7dCyW4SmRrzvcXmBkhvMgECpBuTI9hB715AqtFypb/UXrw0aYhwqvv68ZeKS43l1vJrOBXCQ+L0gxHUviOcK/nY5z2qSe1sZ3IMvM2jMVq1s5BreOk+8bMxWUVs3Gqw94dt/MoghNnyRDhXI1GeqnWh5Mb5E48WY9DX2AdSTTKlDF0+ZbL7qEEZ3+ddvsf18QN2Fu568zIGNYjJXXyvayY+FZZbe58pg62R9eh24DmcKvHZpBE2drpf2v8/jMj+23/4j35aSsaPnjUXqtgBJacVF5xVLxBj01BM35BKq5XgPIIxtRxAC9RaqHmxhNNgrRCjM91ZgFpYe/u8bK216qhgbtVOkJqJTnGtEpzDeeUyX/AxkLMdTp3TXZrA+YCoHe6cF5SG99LNWzwu6i4LoN7ejFJJhDAyRAM9XBW0KuoHvAg5J0J3Vy9Nesu/MIyBdV2BZsxU58kVnPedAKaoVKIKsVR8Kowoh+nIOi8GrBpKQMmZMJh2aoyRKz/Qat+Yg8OHQKVS8orQqNX0/AzI6SZbnRXpVPFODLisXbvIefKakN5SXmpjLcnYdb6/NNWCc5G6AW593kQU531vExaCc7TacLjepmvdvLlVanSs2oy5VyvRCwYPFHMdd3TwWGit9Hb0hjPCICEMOBFojSkMBO1trDEaAJEzg4sE56FVa6lXQBt+EFrXJTWdWuU0P5t5lYNaMlEcTtVAylrxXlAxgANRM51pBsI45/Ct7TEwDgYgqhjQ2BqI6xWeHoOtFcYxItI6+tHsGXKCVhG6MZwY6BB9oKTM6G1NTYeJeZm5f3UHtVGytbyHOJAFUjYge/SBlM7E6ABj7iLKGEbqWoghUEXIqRFUic4T1Fib3olJUKga+7RUggssa4JimyPazJU+L4TuZgwOcIi4LgmQEKkoENRgMcRMzKIL1GJ6oWu6IDRiiMQQuCwXxmFAteFzQ1GiDiYVglLVmLFII7VElYr3jiFG4hBpLUOrxjQvCav72Nj6YJRd5wwMG8VYzyFaoSaIMs8nGwvBZAZaQXGoWv4aB0/wSllWgnPQxIDuziieqYQQyRSmODDGgJTMEJQggrau1dkCY4hc5plGIw4DOa0m/eJHYrTNm6akdaU1zFxKxZ5JKtGZMeAwDNScCeoNSCu2qlAlr6nLRCSaWHEi18LxMIJUlMwUg+m1tkpQkzjpx1K89zgxXdZxMG3TKEIpGa+ehpCLsfUpmF5qLTgEr5sWbMU17UZ/DY2e0pm32jLj4NCWiINyPj1wd30kU8hltdxWGyEYYKvO2ZmrVbwIraxMg6OmM77n1lYK2swkSgWanFFXEE1MLloBiIaqAYKlYnFFpbWCIoiYHIVTIThjftaSGcaxm98Z67PUYsCy9wTnjP1aG7dXVwbgS2PwjiBKKwXXGmOI0IqBcqpUR88ZxorulRtjYipE32MBMztTwZjCzVigJa/QGq2/fdm40nWLwaunCnhtjEOg1ERrGRXBxYiKcDk9EYKztSDV8qCvtLTgnRkc+n6/jd5yihVOHWbI5pojuEDLC7ksvXBgkjhLXq0wsCTur2+Y59n2CRWTDkkmO6C1UlM/CICNNc3ioDVqsdzqvBnXSakch5HcpUlojSCBVjKDqhUBy4pIxqvQamaIgSCNtCyMYeD09MTxcDBpG8UKJQpxgNFhhQsURZiXhWmaaA1KymizuInBMwi4miiLoUYq3godWKGRrgU/jiPL5cQn19cEhZvrAa+Q1wtLzsQQDVRvFS8NJwMtBv7Gf/Dv/ga0/XPw83t//3/86W7W9ZR2MMVf6u5ibwd57aY0bQdDysEKyRsjTLDPbuChloZ/zpSjY72xg5Pw4hi9Hf6FbvbTmXhN2UGNPNoBcHy3UiZrT25BOyCmOzi1HQS3A6e1Fptr+May9BsDEGNElcnuS6B/pwOvxpy7FMrBvWjRddAm3XpjW7XOROwtuTto0MHBdFTylacOjuXWW3v7qe7afmBGPdaOXMlXzlqBmxVlmhfK1Md83di1vaU/bEZj2hmCxu7b2Xb5RX/Sz9aFUwdjrcXHtDNYN3BgYwhuoKXsB+gXgLkc+l4fzcl70yP0G8P5cTW2q0A6dqZabfv3uqUaENmBFehs1i4DYeAJLPeePBlzuAy6u76nu8Hmo7GDRBsDSwvUaKw7yY3h60fy7WjgUB/jOsj+PN//d8AKj0t5ARd6a7pbKuudN8m8DbTamKwd9NpA0vU2vMg9wP4MPcANdOsxlq8Cbi4s9wG3GFt1+JB4/MmIio1teFoNiF2sNboGNS3gixVMRCB0hvT6ajRt2Q7AWex68tGYlUjXcLzUDgAZSFMOjvDY2b79x1iGdv/V23qV7aF6bCEGPOWj2+eiDtqlACx2w5MVlfNVIF8FhncX1ldjZ4UaYJyvjO3a/AZS2Q7me9GgdvOsfOU7KCn79fPkMHM+180MoXWwbb0Lxt4bFZehHAPx/dwBZ9mLKNv8ldGx3vmXtXhwOzC/aSWHjzPlEEwf+/XAehdAId1YvLbe1g2Q7iLx/dp1UAP+UkhXnuXWka50Z0D7s4GbBhBXyhT6uFgsrzcvBacadQcqN73kGpXwaJ4FG0v2/GmXXXLC/Cp0dr0Bw+G5a5t2yQQrcjUDtbq0gTEfDRRzl4yuZnjVert8C5aX1mtnoGrPt5spo1tecvt6Z8+RjsrhbebxxwG/shcONDeL8bnsYO62pqXC5ZUxO0u0M5IVEHT/R3sbPwrzvSfMBqLV/ly71EWP99bb+U0iR/YYd/kFkNuY8BuDf8uVZVJKVJ5+6Aiz3cd668mjotUKcMudYU/poHuhZge6t9zQi2BSGulosQCQx+/p81YDJH2XYmBbn9oNrUZFq3WDmP6tstz2zpHSWcF9XqZ3Jv+Qpw387uNTjZ2L2PegLwW9jRG7SSTU2KV1mhV2NmYrDYaHvDOTH38yMD7UXXqhbkXPbP+9FWJMlsdiccvfLm+SDcpyZxjX5RPFL+y5Pk/2feuV6yxdywUl9gLwtSNPQjxZTB+/zVw+6R05KqSrrvt8JaRbe87q7VnHh2oAd2PXyvWLzdF6bdfzm6xE14s2pnvfy4Kw3DnGx8LH3zZt8/MXfd+dTXc3H5WtgJnHDq5X+E//xq/HtPW/zof+3/oJaluFq4Da4W7tjKXUKtoBvZwTXh1VCkIleoVSSGWhNo/6SMlC9JFcF2iZq8Nk4JMvlLogztHW7mKOmSGtteBEic2AztoKLgZKmvEummFSbdwcjib5lxzOe0pZ8N6ApCWvXA2B3AoigeYKLTRaG7jIii9ub1/3QSkpE8IELSFZcerxg+kTlvkJN4z2HDnjwHQpa8N38FerMl1PnOcTNCUEbwdTrwa6pUwjgqvIeM2aE2W5IIMnLYV1MWmIlhu1ZYKDseurTiOUDdjAWspHjeSaKWqAbC2VIZoOrRc6ACUM6jmzWGtyrQZoD8Y+0tUAvJvxwIfTAyFGgnes2Fg6NUYyVLQolMw49koiGYex3JaWueSZ6pRSDFBvJCOm1kKUSlAxsLkb27RmeoJNlZQXPLWbSxWcwlAdZYC6WiUu58zoA3PKIMaAzqURnUfJ0FYOLuwM1yQmETEviTB5pnhFbQstF7wo0+S5nBdclT6X1Vje4UApjct8xqlpj5ZWuBq8aSW7hmNFnNBQltYQZ9XEKpDXhA/KYRpoJOqyEhzE6YqUCik78OC0IVLwCtIyXiPOVwKJw2Hi6XxiaMrD+w9U8bhSGUNkoFCK6UsdnUdd4ul84RAH0MI0KPks+Jq5KBSpeIHb4xFt1XQ7B8eSFnRwnMqCSsBPEVeMCXYcDwQ86hrVJZYyM95E0rLSmlBSQ0JkSQkNHtQTFZaUKXVmCsrVOFFKIa2F2Ayo9TlTqgLZAPsoaDC94aCKSCE4ZVkarWvcNi+k1TQhVR0iDWmFtq4oibubG56fHsBDygvOBfygXJaEagN67K0J9SuxeWIwTdynAhKMCVuLoiURojHmHCApEwfHze0AzXG6nAleWTsodfSO8+WB6XBFK5kQIrf3dzhdoFROC5zOmVw9SOY4HljXBVdNQ9YD0wA+Or64PvKzr77F1QKMOA2k0FApaC2U5YSPEzUngmvcXA+cnh+RoVHrgdNTRqcJ74y9TV5QrSgrTiPH64hKYFkzz+eEw5EQaq40aagPqAiewjgO+CAcpom8FpDCkk0fdQS8GynO1mEIDqRy8AERy0NlA2yD57Jedt1i1Ya0RhiEus58+dkdx8EzXWBeGu/PKz5ajvXO0XLBqTANDlri+v6e2mb8/S1SA2/fPRCiI6VGSVasymVANRCl0erKZ6/vUSmUUjjNFzLKWjFWpqppz6rgpOJG5WY8cHV14Hh95I//8CvyvFK6TAY+oPnMsly4Ga9wdWU4jsRB+J1PPiefzzw9XljXjL1TBZ5PZ47XI+d1IQweL44xeD65v6VJZU0z37x9ADw0z3lJjIfImhdiax20Lnz2+o4lzdy7ex6fT6xrJuWEl0hKZmJphbnKFCKH0Zj9fimc14Q4SOuFmituOJBDQaNJI9Q08/nrO75485pwiCyXzMe3D3z3zQfmJiwNBoSUM1MYoBVG53G+8Bf+lR8RVJmXhT/+o6/J4nF+ZJkXrsIN6zITBFz1DMPI1XUgqsnOfP2LbxA3slxWqg6MccI5R0oLQxDmfOHqbuD+cA1NuVBRKVyeTFu7+UhKK4ijloWDP/Dm9ZG72ytur27xTjifFr766lvOwCUnjtcHkhaKWIFW3MqXn95wezXw+vqWj88nvvvuxM9//jNUAzULx2ngclnwwXPrJ07zIzIK97c3KEJaPe+fP9JqBCKq0mNupaWFT97c8MWbe4bgOA6TmRkGx//+T37GPEMrJ0IYyWWljInuAfubnz8HP/lg3Qm1a5fmo0kFNBX8KZNuPFLMZKyMBtw17cAcdLMedv3IdO2MIQScPo/EZ2sTdV2Hc3PY1g76hbVrwI1KOupu9mHmOP1gpfD8w7G3uH5Ps3TaNB3zrr/b1Artm5HRpqUpxYyUNjBu05vdtGTzIISLabaWwZGuDMCpwXRs9aDkrsdXgzLfe6a3qY+h60w4G9MNqAT7XXwuu4asP2X808Ly6cFAzWygzHJjB+T5vksJXExLWFNjuR+NtZyM4aip7lqHeWNPBmG5Vg5vX3Rpm5MX3U8fyQfl+cuJ2z9ayAdPmpT4LC9s22R0vU2nczPG2cYIYHgsjB/ybjZkbMFmgLfYgTpPxlxza9u1Azft4s2EZWNLpivPcmMAp6pdyy926N5Mqmh0bVW3x+nltQ22Wxr+rLvhWT56+OSqt5PbnF9eOab3JilTe4xJbSz3Hrc0hvdmErY90+nziF8alzeBEoWmZmAVH/IOZmgyQ7kyOS6fhA7CGaNtPepuOAN0zdIOlKnp+dYQSJNyeaVcfW0yADdfLUi2uVjvJpYO2iFwfuOoHobHyuW1AgqvA262VmZ/etGCff7xRPU2jvOdEp+srXrqmqt5ehGMnD8bSNOLGdPwWJjvDBw2WQUrFISHhRrs7/J1IHxcOKyV5VXk9JmBteFsLLf1qEybVAQdsI8vmp9pMrAjTxCfDAjZWJT+lH/VVK3HSQ3C4489h2970eVsOpJlDAzv0l6MyUe/6xmv15ZTqoer8PJdW77KB0cZXoyW0lF4/sJ0l6e3tWsNW7yWzw6M315YPpmQCmkSTj9wlF7jHt8Jh7ey63bPnw277Md8CORBWO6UMsL7f034/B+XfZ3VIPgzu3azW6ppj5ZGOiinL5XxbZdyyqaRSWBnC27fUYOtg8dPlOYhPJmBoH2/mZx9X1M7j8YqLIOw3JsZoK1xm/P11WgAbgcPN6mAyyvt92Jt79s6H98lk9hYrIjVegFHc+O7fyMyv2qsd47wqNz9oX3WX8pucFejMWABzr9t2MPzDy2v3P4/lTTpri/anIFhzVnOkdp497uB6uyepreWF+Ozxb4Bwban1CCUCOfPheVNQV+vHP/xAb90gHfTi/VC+Lhw+vGBEoT1WsgH+NN/G2RxTN8q/sSufXr85WpavO8y+aDkwcDUx9+iM3aVV/9XsfP3AtPbxHIfCM/FwNau6/rhLyr+GZ5+rBy/7pJB57prcqdJd8mLyyvH8kpIR8sz43vbAzeTzDKYfm/1bS/ovf1dz/q7Z8YxcX4e8L8YuP0Di6P4WEyC5VyZ722+1xtjw377b1VkKvAUePM/WZdEjSajc/o8Ek4WryUK6Qoub4R8aJSpcf9/2BqLTw3NtreUYPe5XDvGj4XTZ47nH9tzxYdNlqiRJutiDH1taW7Mr5SH31GWHyQ+++IjIa68/XiD/K83rB+E8UPj/MakVVyy+2oK7/71hvviwr/545/zT77+kvR//3P23uRHky2x7vvdIeZvHnLOrKysqje/fq+73+uJVDdlSqIpgjAlSwAJCTAE8D+wAcEbWwsvtDBgbw174YUM2CZtGTZNkJJgkqJo0myy+/XI7n5zjZlZmfnNMd97vbhf5eOSK4mAGMtCZmVE3IgbEeee8ztddv7U3orK+UQSzyxtLFkfidv7bnXi90eV0P/kzxW7qc9EXGFhdl+zfr0iymoORwuWZUzjoP6/x0QLdzt3vLjWXvDb/yLbX2qn7f/8v//zf1LXNUEQ0pia1li0DPA4BIG1LdY0dNMQQUu2dcyGUiNsSxRE3s0qFI3xvDilJdL5j+M4FLccOdsKRKCwxjtDrfUf6hKHsC1OWNIkIoo0ZbHyIrGpt9F9xWaxIYlSH1VvLaH0LqNAQhB6h1VVVcRRTNtYtI6wGLTUt3FSXoikkUYJL9TiHM74uLsMNUIpTGsIlaYuC0ItGfT6NE2BQ9DrDVhtViRx5B1PWJxpaOuSQGtCrWiqEo3EtpZQQBTF5FWJ1l4ocsZtI6A1WnknXVMuENJzFJUOWC03vvEe70h1/h2ZSAfUZY4zLWmowTRo5VmRFuHFSx3StB7cnaYhgYQ4CPxYSIFWggZDY912THyZEU7dOpLiUDLtZUShotnyWK2TQAhEZOk2+iolrs6JtaDfy4hjhbV+H8rcF7u1jS+YScKQuqpJwoA4CpmOBgx7HZJIkkUBRV4Shl4E98K+ASfopBnW1fT7XfamU0bDDlGkqauCxhnqxjtTq8rSNN4ZizNIHHEckCWhj51HCrWNqRu7jfFL6QUpCUoITL1hOOwSxRqcQqBoW1/qhPDi9ovytE6nh8TS73U4nEzoZzHWWMqq8s5roahMjUN416LycexeN+HwaJ9AK2zbePE6isCCdQYhBbYFJQIchm4vZdDvcHSwR7/bpSg3xFFC1YIVkmZbwCUtBMI7z0aDPsNRnzSLkUoQ6YByU6DRfkX+JUIAACAASURBVGHBOrTU1G1N1dQoZRkP+0wHXfrdHsL6ApS2qQiiiLo1/rzaBmsMe9M+0+mAfjdhb29ElsUY21CUBVZonLBbbITAGS+cRYHmZH+fyaRHJ4tYLG9o25qy8edX4CPoQkgCLYhDwWSUcefOMWkS+NIurSjqBickrZMoEWHxMXuF5fBgxO7+mNPjfdLMC0+thbYBayVSBrdxfCkk/W7CZDrk+GDEZDxACS/qr/M1gfIcYyRo5Z1+rqk4PTni8GDK3q4/boxlvVpTV613Ym+xD351sGE06XBytMN4lBFKy6Q3opsmXM/n1G3Fqqi9i9VJojCmqnP29qZMJn0mkz79TorFsNn4srzaNVR1TmtaZCBobM103GN/OmF3Z0yvm9HPUtq6QTpBXldA61MIgUAKR5ppur2Uo/1dppMBSZKglCYvSkzTIK0DD33AOUOgBYGUjEY9ptMRuztThJBYB+u8IJb+OSAFCKko8g2dLOXkeJ/BoMO43yHLUqI4ZLXJt2kGj5Gw1juNrWkYjXqMRj0OD3bpdTPvMFWK1XINwhEGEcY1JFFEEIAKYDzMODzYoZfFjPoDjGsxdUtRVATaFwnGUUQgvat60O9ydLhPkiRopRiNBpi2Zblc024XMHEtcRihLQQCDnan7E1H9LKETichjiPW6w2LzQodhJ8xfIMAh2U86DEZ9ZlO+mTdhCxLCALNerWkqfEu7C3+xdqaXidmNOpysDtld3dKb5AQRDGr1ZrGuu3/H2CFwzQ1/SwlTgP2dicMBxlHx/tkcch6scI2LaH2qIm6qj0LXgh6WczhwR69rINQMEhj4jCkLjbk6wK0xgoH+gXX2zEe9zk83GGYJSSZJsu2i5pNxabKSeIOjXMoscWoRJL+sMud4306aUw3iwmDkLzMqRtLu43H+XlXI7AMB13OTk/YGY0ZjwYMuh0wlrLebJnWCide4EocSRhw52SP8bBPN4qwtOzsHbBc5Fxd3yCU56NXbYNW4KzhaH/C3u6YThIRBYokSck6Pcq8ZL5c0+sOqGuLcwKtAqqmYDDqcXK6z+50wnTSw9mG2hiaukUASiisa1FKoAPY3d1hbzQgCiRJFBKHCaFOqEtLVTRgLe0WJ+OEA+v45b/zS3/ltP33YPsf/9kf/JNg3dJmCp0bH82OPmPavnDObg5CnBQUEy+cvSh+qXtq+xEsbstJPENv6z7pb4XY0Lu33JbzaCL/oWND7/x5EcMvh5KmI4jnljbe/v0t/iC5qKnGwbZkywsCJhKYVNFsG82jpb3lfr6IGJv4M0eqcN4JVfV9FDsovI1HWI9BeMH3VLUXl8OVL+paHyt07vdlfajpPGuphp7p68Vsz2htt/vy55l9TaZ8gdmVF5DdNlZsIrUVMT2K4YUjqN22emfPah9tN59xRr2rUxOuWoSBYuLdQW38WWw0nNWYVN8KYdUk3Lqx5G3M1EReAAm2hUiq/qyIru5rbCioeor5A0nTlahyi7l44RQWgmIa+IzMln9Z9TWbQ0XT86JtmwrimaXqbUVA44XSYGMpR4qqr5g/UKyPoRpIirH0H+mZd1S9cF8J68h3Q2QLqyPFzeuS9Ymj7gmCjSDIHXqzLcyaVahNhQu2/F0lqfuSYuIRRib2Ip2uPV7ihWPWl5WJLTfYsbyjaTqfIRGCYhutF/5cCQs6b8j3Iqz2At71W4LNgUQaQbTw15VTkmDhWba3bmYnWB9qbt5ytKkA68ej7vp3zVuExQuHmoHNnmJzBLO3LfmhI1hI6q4kWtmtGN5uo+FbPqSF5V3F+giqkdyKmJpo4d3nL0QGE3lMSLQVGBf3NMt7sD4QWKUQ1oszbRYQrCrvbt26UK/ezli8pMgPHOvXGjYThawVycxHp8VWbBewddX5++D5O7A+s5QTyJ54ASOcN9wyUiPl74NUUvUk83uKm3cN1Qja0Dts45m9dXq/wHu8KCm7/Lxm/jLw5RXzocahkI1nfMrWC323uJbt2C3PoPnCBntWsgkDcJLswt/P4cpjC0yib52u51/ViDdX7L5yRTF25C4iuYRw/aLQ6QUywrtql6eK1RdLOC1oUsfiTLDeCxh8YAjWLeHDawhDX17WU0Tzlus3A5b3oDir2RyAMIrkyhc2hmsfR3/B6tUbw+ylkNnrjublgnqvJT+wUIdYLUmutiXGWt4edzlS5LuCzVsl3ftzVgNBFQXEN1vnrIIXkXI/x0iaVLI+hfU9Q/1KRS0DhPHnqh7oW9wMQhDNWoodzdU7jvqwYXA6Jx878o4iuZDoynNndWmRrd1yVWF5R5Lfael87ga725ArjdGK7NIC3rmraj+XvBAJl3clzZsb7G6DuFNSthGy2T5HUn+NV32/oFT3BZsjSN+aEfcrorClPanJm4TOM7t1fPutGfi508SC2RsO9cqK6d6CZFyw6Er0VUDvcetdr1uOad3xfUfLM8XqriN9sECOa5qRYZOGJJd+0aHtKHThvEO+cawPNOs7AvHGku5LC8x+zTyJSC7lbRGkn+M9Z3V9qKj7gtXLDeK4ZPzWNU/HMeFzv4jSZD7BEGysF9YDwXpfs/lcyeHOHAS8vv+MTQ82eUZ8za3TFD7jgK/uSsq3cu4cXTPq5fTHG55kKaLSpM8N5SjwqInte0CbQH4I3XeuCacFw50Vl2lMOFOEa0dQ+nG0WwasF0UV668VTO/fMLqzoDmqWYoIvZToklvHstoK9tVQIt5d8vadx3xu+JTchPzdk/f4g6sz+h9wm26IlsY7kw1cfx6mr1xxZzxjP15y0FtiJy3P1wO6jwybA0WQc7uAEK4cmyPJ8p2K7ktzxvdnXJKici8GO8mt49cGPs2weMXx6stPGGYF93tXHHYWjJOcnxRT9Nrn4ILSbd9r/DPiH/2Dvxge4S+109a0jjjqULctkY58g3vTokOFVoJqs+Ls7jH9bkqowDmDc4Jn51fkhaXY1LRbQDtAURT0uyl37+7T6YSksULrgKfn5+RFw6OLS6LAf6g726BUiLPQ6aTcOzsmSQOsaWnbXdbrko8/+gRkQNm2dNLUR5CFQFnHyZ1dhsOEJAyobcF6k3N1veZmVqJESFWVHvGw5ZGmQcjdO2MG/RTahsoUXJxfs5iXNAas8fzHum6JwpAIxd37dxn2ErQW5GXM1XzJerMikhoaga0tSrONm2uSKGZvZ8Cg38PZlsX1JdfXM8raxzu18uxTUxsfKY809+4ekaUhggZrWxaLDU+eXjDqx1S1pRYKGWiU8wJanAWcHe+SpTFaeuH54SefUhpH2zrvJt7GtYMgQNBysDuh38nodRLKvOD5/IonV9dI61m5WmiU3OIYbMHJ4R5nx7uItkIFkueLNbP5imfPZjRWQGsp8xwpYbFccnYy5ezuAUpDJw7I85zNuuH9/BOMbVFxBM5StY0vwqoLXn/lDUbDLq2pCGhp6paP9SXnl8+ROgVjEUojkGzqnJfvHzPudhl0E0xb0LYtu/2UTy7OeXa1xNkaZxxsSxvSTsTp0RHTQYZyBmsMs9WSm9mavFpQG4cKpBfPjcHUFVkWce/BPTqpQuuYalOzWCx49vyGoqyQIsLgkFrRSWLKuuS1B8cc7g5IpGS9umHXdXn/oycsVjXrxmBbkIGmMQ3dKOHB3UMG3YRO7EuIVos+z54/5+nTS1ytCQL/8G+24uHewS6nR3v0kgBMRdO2THde5/LqmsXqEWUJBAGt8yJbKxrOzu4w7qVESYhWknKzZrlYYDc5VeMFT6cElW0glARS8blXz+gmEVEsqKoGdzDmve/+EKyiaQ1JFGJa63ETyvLq/VOSQBDrAERD23OkseX5leb8qvDXldZUdU0ahhzsTJkO+/S6GUkMVVXQTU95Plvyk4/OsbUjSFJfhobnmR4eHbO/0ycKQlxrGA0z8rIm/8kj5mtfmpEkElpBsVpzemefl146A2cInaCTRfSzGKlnPHp6DVbjHMRRhqMmCgLunh2TRCGB9rzJXjdhMKpoP3SsNyU6CWjyklBHKKV4+bUHDAcpe7tDTFvhspSd4RiQfPjw3IsypkEohzMNJ3f2uH/3CGE95znoKxqzpKxbNuWGp0/P6cdDwjCkrQuE0Ozv73N274Q4VFDXRKM+O7sjvtt8xGy+QQcRDo3SGutaxqMhL58ekYYRQaAIpUBIR5ak/PDPPmCQaqqqQgpJmZfEoeLO4RF7e1OENcShQo4GTMcDhoMe3//hD0BEGAGtaNEiIAwl984OGfb7SCmRwnFwcMKz80vm3/oOtpXoQLJpau/yV/44DvenCFoiZxEKnBghCHn85JxNWRMpTb11+Y/GQ05Pj0iSmE4c4WzLzmTCzaygbWC1WjFfzMiyDOG8C31nZ8jB/oQ4jOh3u1RFSdaJuLicsXn/CbaufNFZmVMox2snJ5weHyKsQGiFQIJt6HQ6PL/8Y/LaO8MREtUKjG04OJpycrhDqAPi0C+2TSe7WCeY57lHjwiBMxLrLPlywb3Pv854MKDMl8Qd70aPQk1b1Tw9X9DWDVY6VAuuablztMdoNCDAIrVfhNsb7bC6vkbMSxpjqdoKKSVp7J2qr716hgTCyLO+R3dP6WU9vv2dH2FsS14t6XSH1MZiqg2nL51yvLOLRoAWSCEYZJLNcgVOcv58iQkVjfWohrLKuXO6TxppkmBbUikUvXQMBhonWK5yXBBgCZDCkA0Szu7fQbQ1g96YQMDhzh5CvM+P8wtfcio9q9hWFZNxjy+/8znqzQKl/IJx2B1wuH/E+o82FM9npJ2I2tS4KOT84iFHb77J3uEBVe6xJzruEscxhyfHXN7MKK0v4dRC0RQrsk7MF954g8X1c+JIESiJUwHD0YDVbMPV8xVNWWFEQGN9QWknU9w9O2LQiT1+oinoHO8z7I/55je/jxVQG0cQAs6wN5pw9+gEaUoC5ed2awVZktEej1lvllSt8kV+WhJYgfy3+7r3V9u/w002Pk4crAx138fbXzh02lgQrixPvq5p9mvCtMFZXw4Y/iAlvnFEc+dLSLZbdN2wOom4/BoEk5zdwYpOWPGjHx4TzCS7f2Koev6DHecdnMJBvqPY/PSGUX9DYyTPq5DiKmX6h955FS8M+V64FX692HT5rkQc54z6G9Z5THGV0vkooPexuXUAmegzV2Ex1lx8zZEergHI1xHxj2M6Txy6cLfOwBec2aorefa1EHWcE0UNF7OU5MOI+MoXQgG3jdMv2JTlQDF7DdxR5bsInsZ0P4Zw6UUlEwtAImsvJjedkKd/TSDGJUL6ElD1NGLyHVicRYQr63mFSmADUIWj7Cue/rTEjBtUVGAKzfBPAsLlNjYdK+S2vMi3sguu3pLU+w2T3SXPVwniw5Tpe5+5RttEbRm93gF0/mXN/lef0hWOLKj53keHRI9Cxt/zIingS+S0IPtowZO/Mab82pogMJyNr3k0HzC/ydC/G34m0lcQrgyytkRLy5Nfqfnc0RMWdcI43nBdZjzKDhn9wN06CF+4LdPLmk9/LkLe3fDG/jM2TcS6CZm9lFL9cY/Jd7duXvvZtVhOAs6/Ikjvz4m0oTGSxeM+2aeK9MqX7njWorxtji92/XjIaU4Ytiw3IfppxOj7vgHdaoHcFs0V+ynRwvDJL2gevPGQe70rvn+zz+rliOW/HtF5LEgvamRjMFqi1zX5Ucrjvy6JD1e8PL6hE1T86GqH1Q8HTL7jtvxJ7wzXuUFYydWbAXxpwds75wBclxnNmeLxBzt0HwuiG2+RVIV3futU8vSnJeHpkp1OTieouVx3uD7vIlvvfhfWx5vDhX+vbVPFxd+pOJ7O2E1XXORdjJWs//keoAlySz1OkLXHMNhQUv+tJQe9FSedGQCLOubbwxOqDyImP3CoF4zkRU01iXj+lqZ8ULK/O+e4O+ci7/J0p4/7MOPkX6jb61XWW8d3ILh+y3H42jl72ZLWSj49GTKbdYhvQrqfFADUoxBQRFcFT7/R4+QbDwGYJmuW45hH+wOWekhys70wHFRD756uO5LyK2t2+mv6Uckoysl3An50uMui7JFeeieozr3T23QUj/+mY3R0zS/f/VPWJqaZKJpTxf/BVzj8PXHrdPfCkeP8y5p7P/UJWlp6QckkWnNVdXiy6XN5c8j0vRp7d3Lr7ncCnr8dEXz1hsOOn6sOsgUP7w+5Ko7of7pNPVjl+cCNZX4/ovzZFaeDJeN4w060RkvD743uY35ryPI09sK89mJqm2iu321585VHaGE4yWakRzUf3Z3wx0en7P9fW95p49mqJhY0meDqr9WcHl0RSkOkW85evuI3f/I6vYeRF84jSXJRb9EJktnrjlfeeISWlqN0jsRh7wt+S77F6Nua9MpQb5EzAItThfryjJe6Kz43fELjFMGp4XePHrCoJyQXjt6nFcU08LgGKZi/DNnrN+x01nxt8hGPyyEPh0N+8v4B6ZXcLtz50kZm8PAXJN/44g/RwhJIb6ICeDgecv2TOyRX9va5YbYLHFdvCV7/wifsxivuJlfM2pThUc7/YH6K7qNwW8TFrVgfP6/R/0nBLx59wMPNkJNsxqaN+FbnkFk1YfJdP1fLxmK2WI/Fl0oeHF0yjHMS1ZCoht27S/6Xy5/BSX8PhiuLs/5ZZkLB4G89Y6oM02SNFpaffefH/MbuG5hfHyMbR3Ldstn1oqqqHIsvl/zHb3yboc4BSFXF3xj/Gf/05j9kRszoBw7VbBdDNMRzS+edGXcH19zPnnMaXxGLmu+Nj/l13kUXmu6T1qNTjMNaKMeS/a8+QQrH16cfsB/O6d0t+M/l3yP7V2o7p3+GUprfV/y9X/49npYDMl0BcLg3o3+/4L95/kvEM0c5EuhcIDLJ+Fs3PP/CiF+59x4PixE74ZLhIOdBdM4rbzzi6U9OCZfu1p0c5JZ8qvkvf/7X+J35q5wm1+yHcxqn+MXRt/nPXv/7VB8m6GKLQsot0gjyqUR+ZcY39h5zmlyzNhGvD875w6M7uH828rzzjXc+A8xeha+9+yMS1TANV3RUReU0X0w/ZvHFmB9dPSBc2dvisxcLzX/R7S+1aKu2zc446Z1uzhEGAmEMbdUw7nY52JmQJgHYygtWUtNKyeNHzzB5jdCK1jmCMMVUGw73DzjYn4KtSUNftnN0sMtsueLp+QUAtWnBOGrTMu6NGU9S+oMucSRp6xJrJVprer0B81WOlNoXJuFdhL2hd99BThJpjNWEYUTbKFbLS2zbIKTBWgi2BWXT6ZS93TFxYHFWY/Hx3KpqKOYrLBGxjGlbiylruuMph7u7pBEIWdPvaYJEcXW1YrNe4ITatnEHIC06DOl0UiaTCVkaIbQh0RVBKPng46co2aOtWsA/KAUtw1GHwTClk8VEUlOUS3qdlPnNJYvVgigekhe+YCDQEZGOONjd4XB/RBwGlE2OMYZxPuDJ+QwdSKqqRqqIIFTYpqab9rl7egJtTaBh2BnRioabxZK6aNAqASt9CZ1tGY27nJ6eEOsWkgwhHGMdoWTM1dWaKq9BSFrrcRhxljKY7nB855T1/Iok0CgUceB4nJ1TtrDMS5DOt5nHIccnu2S9LkpDGGeIpiCNYo4OBXm+5mpRIqMU28CqyJnujul2exwcTmlWczqdLk3TUErFpOhx9XxBXbfESYRxgiSQHB8fMxz2GA97UJaebRhomtbyyaNnOJ1iDTjpaBuDBMajEePJAC1Ai4B+2iGO4fHFky321eM5EIKLiwvu3T/m8GBMGvgytN4wQwSa65slT55+TNQZIIxAOIVxLdopHtw9pd4sCKQlCBVi0EWmEXleMl8Y8tL5IixnKIsNOzsvMxkPoc6JwwQnBRsa9g+nfPeHPyAJBqzrCqcDRBQgA8tkf4eQmlAHaCVIgh69bsb7H3xM3QJq6/xOIopNwbAX8+DeXfLFNbWrSbIIKSV3T+7w3vd+gpQpZVkSKE1VluzsTRkPR0TaYfIWHYQQSLrdLqb+lKfPrgiDhKpuSYOEqlhztLfP4e4UrSV5foNOUoadAUqHPHl2Q161OBocnos7ne5xeufMLxwIS4MjSzqksaGbXXH5fE533GU2uyJNFXEYsb+zj5YxEs8y1gICGdDYgB+9/zFJPALn3f1lteHO8RnTyS7OtiAasC0Iy3R/xPN5zrOrjyD0/F8tJHnZonTAYDQmjROsDbCNxUSCTq9PZR5jrEMQEQchMTDqDYlkjFM5UaIoyw0iDul3eyThOQGSpqmphEMow2q14P6DU+Kog6AijCKUccShptvtczXLMY3FtPhyJifQVrM7nCCEZ1LZtqY1JToM6A0HfPrwMc5BJ0tpS4sm4M7hHZq2RCqQUuGMJYoD+t2M1WJOt7+HlY7W+LLCOIvYPzn0471FwQhrmIzGTCYTPv3kGWlPEycJzraEOmAyGSGEJAkSQmcQShCECcOe4dHDcwIcVemTHmWZo9SAXq+7ZX9LAhVT24bj4zt88MFjmqal1xmyKdaEaUK1XPHOW6/R6/c9TrpVhFFCknZYrD0LWglBaRqMBKUEUSfZpjo6VFVFEAUop+kNOkipCUNBUVZEyqEiTVPXRElMlKRoKTHWEQUxOoqJ0gwpAr8IIpxfqJBQ1zVRECOdIE1ThNIEQUBnr8v1xRxjbrB2K5wqR6gDhJOYxnr2r2uJpMYZx6TXYTOrqOoWiWfxWmtJOplfLIwjnLPESUYYx6A0xsI6rwg6GXlVEoQxo8nIu32V9ggRFRCEnvk6muxyeTMjCkPyqkQGkuXNNVmSEEtNqCOMMAgT+lI9lYEJWc7WRFlKZSzGWkQgCeKIOOtgViukkygHSmjGwzH2o2e0tt0yfH3hWhyGxNoz8LRWRGmPsrTUjcM2mjhI2Gw2BFrilGcGqzBmVVTEQejfC6zAGeeF8Shis859IkJY1usVp3eOWFzNSZMOWRTQmBpUQJW3RFFElmWs1iVGW3QgSJKIUS9GWoM0NaaGKEzJK4d2hqqo6A9GlKFAtA1t3ZAFCaao0ekW1SQ1KpDk5RLrSlRgQQva2r+XZGLLBP+r7d+LzQbis4IrwZb95t0vunTM7wVkr99wPJgDsGlCItXyZ9U+wXsRqrH+ZwvvaJK15fotwSuveeHkpd4lrVXEbzZ8/8kB5jsxAMHav28GG8v8XsDqruXlveecdm64qVNqo/g0GpLvT+g88hHXF8gBYWG9rzj7ov8b93pXLJuYD9IJl9WE5EIiV9vC3cZSpz4yPX8gufPKEw6zBa2T1BPFt6tTgnVAPPd8yaqvUJWPdhe7goM3z7nXv0ILS7ET8MfxHdYfp6RX3DJgX2xtIil2BMHZkuPhHCUtF/0Os2TIwe9tS1bKz37eSVjeUcSHS07HNxymCz5ajWl2Fcsn+3SfGKqtQxe8sNb0NDdvOg7evOCgs+Dpuk/RBMzvjhl/x7t+w0WDDeQ2Km7Z7AYcfPkpAMMo56f2P+L/rD9H+UlEsLZblym3yIrFXc34SxccZgs/RtLw0skFHwRTmg9jwpUfO1UYbFfTjFLyQ8c/evX/473lEfez56S6ZtZJOR+cEC2g89h/jL/gH1+8q9gdLUl1w16yQuJ4tXvOb34uZnMzYfhB68+XcUSPFyzfHNPu1vzKS+/xvO7yVv8Jz+suD5Mh3ztLGXzgo+z1KEbWlmoUcPlFiTre8PXDjwC4qjPel5ZFMSL8/RphLKL14pfc8o4XZ4r+6Q1pVNMJavp7BT/s7qG+1blFYLyItXe+9ZiLn7/Dgzce8aD3nMuqwxcmjwiE4X87+TI7f+qd0i/uK1kJ2kjyt7/2bS6rDpNowzRcIYXj/TcNq5sJ3Uc+Ni2sQ7Sepbr5uwm/cPwBAPfTCxqn+M7ymOSVBvPrO1SjkORZ7tOZyjvY4rMV/bRgnOT0gtLfI9OYD3/3ldt7L3qeY+MAfbXG3B/yX3zhN/hefsyzss9p94ZENfzWV4ec/K/eTR/elLSdkGDZcv16zNePv89RNGNtIobBBoB3Bg/57+uvI9+DpiMJl4ZqEvlSvrdzfvWlbzIJVnx3fcwozPmZnff5teDzFN/u+OPeFtA5Lbh5WfPFd3+MFI6urriuUu4Nr2n7Mz7ce8DwuyXFcY/0gxnNTpd6FLN6reaVqCCUhv14wSAoGEcb/t+XQ+LfcFQTP/9YBdHaMv+y4hunH9K6P79UGfAf3Hmf3z7+AtP3/HXbZp5dG1/5As8v7z3krfghpQvIbcTSJrTTmmBpkVXrHd7D0HNsTyoOUz/nnCQ3fLiZEkjDVycf8+uDA98+n2+LCwVkT1ue/UzEg84aJS27yYpM1Zx0ZjzaPWT4vl9YkbUXF71zXvALZz8gEIZAGnITMm9S0qjmeh/2/qhFWCh3FbrwqJef/8L3uKoyQmlIZU3jFMfJjIv9LsHTIZuDaFuQZrCBpu4LfurlD8nbgH5Y+jMlDF+68ynffek19n9/RTWNqQcvBHFF72yOlpazzhWH0YxAGPb0gj88OkX86ZAmkd4NmUrCeYsNNG9OL6iNIhCGnXDJk2rIf/rgX/Jf/ZtfIcgt68OQ9HnrHceXDeJXFnxh+pjGKiyCg3jOS9k5H15MMEGMCQTRwvgSyUhCp6Gxitd6T/m0mDANV/R1zl/v/xn/tT6l6Wiim5q6H/gIf25pM8dROmegc9Ym4jS+YqzWxFmN1aF3gm6xPW0k0auKUZaTyppXuhcE0rATrHj3/sf8009+0Y/fVryzGuptwqNoA77RfcKsTTmNr2icojiria8DopWff5rUC9F1X2CcYC/eYJ3gJL3hKLy5ncuz84Y2Vbe4hvmZZjyasxMuyU1Eqir29ILzts+Do0se/eTEJ0JuvLjfeVJTTAP2u0t2ojXLNkFh+bjaYSdcAtB90t6WiarKawQmcRxmC/I2IFUVPVnQVQWnp5c0Zs+XWMZymyJwND3HvchrcEO94Sy85MfVPk/qIaKFqi+8wzz1iAuThdjA8c2bO5x2bmisaWiokgAAIABJREFUlzONkxRtQNOB5MrdYnDi85ynP9Xjd+av8lrnKe8mH3Pe9gH4qN6h2ykoJhnZufFYplhQDiWrO9BThkQ1t2P+g80hWdggV4bVkSZauFukTzs25G3AICiYtymBNHRUybfzU0oT4LYoFVV6hnWz4/E7f9HtL7WZwrgWKx1OOs/BMwWmrhFOEycdev0hu+MJoRbEUUSiE2IVcrQ75mhvgHAbRJ0jihzpSlRQs38wJs4Ckk6CiiRaKpI0YP9gQH+QUFYbtJZEUYIWIZ004/TojOlgRCgV3bhDGidMBn16nRTTtJimJa9KWmFZFzmD/ogsTemmXZxRJDqmH6fcPb3DajFHYHEGX3plHM5aTvf3mfQGZGFMFiZ0VMrJ/iF7kzGBTIhESlk5YhWhEJyeHbN/uEeWZYRhQhL3uHd6j53pEBG0tKYgDCVt67EHZVOyf3jA4cEuWvhSteF0wsn9M5pIsNYVa9FQSR8/LdYFB9MDDncP6ScJWgm6UUov6bI3PQSnKMvaCwGhxrYV5XLBGw9ephfEZEFEliT0uxln918iTVPqtvXFbsag0YRhyM5klyzp0Ek7dOIEJR1nR4ccTCZEyvNvhfQ4jKYx7I6nZElKlMQkUUoSdQhDzXSnRxhCFAgELZYCpyoGvYzJeExbNz6iLiHLukRJwu7uLsvlAt9NJmgM1K4h63XZ2ZnQiSMyrUniFKcUYZqQdnsUeYmtBThFrCXdSDLud7GtI057SBmiZcThwTFCKPJNiVQBog0QVrFZG7Kww7Az8E47qYhCzWQ4odsZYT1zA8WLwimJc4ad3V12d/cIohClAGEJwtgX5m3L10xTQFuhpKWXZKRhjDKSXqfjcRa1IEky0jSmLhsfnZUgRYt1JUVVo5KUIEmxMkQGIdoYOmlEVa8QkUHYFm1ACEcmFUErUE5RG40lIpUdgloRuggMKCvJgoh2XbA72KUXJcRBSBpoIqVxraFpDGGcYgTUokWG/ne1hYPJCZfnK5xLCHQKLqCpJTqMbouUtAowAkwgURLqtqKsKs8xQmClxQiJEZIwiGmtL/Wo2gaLj/HMV0ts4xEYUisKa2mtQKAJCNGlRJYCVUl0q2jLCiGcZztrRScMUQjKvKCTJRSbgl6/Q5xkGNd6frIzxFqjghZshY40TdMwGY6RzqNM7Db6bxAUbYlQEG6d8t1OTLVY0gk1VAWhFQRKU1uH1ZLCSlbrksV8jckdpoHZ1TVtXW9LmrTHxrSSm+sVYZRQ2hYdpDS1IAw6SCep8hbjFKuixEQCqxxNa5GRX+jSCIS1vrBPeHTJIO0SWggaL4Y3VUXdNgRxwrowGOkwrkUGEOiEbtwFg4+9hzG5cNTSUNqaxWIGpiWUikCmaBWjbUgoItIgw7aOpqmoWkMrwUrFZlnQtv44AyEwxnhUS9UQxyGVrX0ZlYC2bdnM12glKeoKFwRUTUNVtFRFRZFXVEAbQiMMOlRYYRHKC32t8cVgWgY8/vQhnSxBosjLFTZQVNazfeezNaZuwBrCOASlWRUlUivWRUlrBNZEiCZAVpp6UVOtG5oiR0mwtaEqCq4vLknTDmXtkyZWKdrGUpWGIq/J85q6rrGuojQVy/WKfFMRhClWgH1R1Gk1w/4E07TMl2uqxhGokKaCxU1BWxu6WQepHAGK2hpkGHGzWlM0BcvKu9Q3Vc2sKClQPFlc04TSlz4KR90alusFm6rF1hWBc7iyZXG18I5uHSLDiLwskIF3Y68WS9brNYvVHEtD6wrKekbT5hTrivlNzbqy1ATY1pHEGU3TkJcVpm4J0F7EB8qqxglJHEYo4VEgiAZjGurc8PzJlcftKKitYTMvkKWk3Vi6YYKoHWWxRklIotgv5gYprZWUm5K2qXwpZKioDDilWVtDUWyIg5TN9bVPABQtsnZoF1BuSpoqpypLf40KgxGSwWSPx48vWVQNRdNS1g11aXxLbTGnXOcUReU5j8IipKSqaz559pTVuqQ03jE7my8hEAgn6WcJrWupi5LWOT9++YJGOExjafIabR1tWVFULUUJrQ1xQuOUJohCKmfYuOrf5evfX23/FrfPysXcbTHOC7dXOVDk+45fOv0uu/GKk3TGWfea084NP//GD1i+XXk81laUslt31eA1/zNnnWv6umAcrrnXueJvv/QD1keeX2diz5w0saScOt5650N+ef+P2Q2XfGXwEa90L/i5ox9R7Ppop6oc8VWDKi3Js5LNkePVwTlfHD2ksYoH2SXvTB/xtXd/ROdZc8vPfdF2Layj86Ur/qOD7/BW7xGf6z7hrf4Tfv7t77F8taXZlutEC7Mt5hGYLy351Tu/z1vdRxzEcx5kl/zjt38beX/tizYLH1X2H6HewbZ5veQfvvRN+lFBLyz56t6nvPP5D9C5JXmWEz9dESy8MzK+bli9UfEPX/omPz3+kEGQ84XRI74wecTiNe+AjBeeRdkmyrOF5w3/4G/+Pl+Y+GN4eXDJu7sPefMrH1CO5K1jTTZeoGg6msVLjlcHF7w+OOet/hP6uuBXP/8H3LzpqLsvOMXilrk7f8Xx0uA5J8kND7JLHqSXTJM17979lDaFurtl0rYeE7A8jQnOVszalEm0oaMqPtd9wkG2YPlKS+epx0IAqLxFrxrqseXvH3+Ld/qf8HbnIa9mT2msYpTkVCPPIn7hfG5HGZtdxUsnF+Q25G5yRaoqxuGaf3z8myC8s83qbTGMgOSiohm1vHX4hMpqxuGa42TGzx39CLvj3a/gy9DEtvRKWMf6QcMvnX6XflSSBX4e7MTVZ0VChlsnKFFIvid40HtOTxd8bfghh9GMxikY1JSTgGDt3d4m9iVk0jg+Wo/JdM00XJGbkEQ1ZGFDOXGflfRJ4c+ZEHTHXtzt6YIn1ZDcRLzWecYwzmk6enscmnI3ReUNN68oXts557Cz4OXuBSfJDY1V3FQpTSZQlfHcarXFPyQhl28H/E9PvkLjFJPIuzuvqgwdepyd358AWRlk5QX186LLx8WE3IasTUxuItYmQrSSuqNu+a7Bqr3lJf/e8wfkNuQgmhPKlvc3O9SV3uIQtudJC3/OIlhUiV8wMQEn2Yy3e49JdUO4cJSHXaKrgvLOgHoQ+HGxgtZK7mXP6Sg/fv2goCk1s1c7t0VLL5igsoGPVmO0sOxHCx6kl7wzeMjTokc79YslTTfY8m29M1ItFN+5PuBfLt9gblKWNuG3nr8OldoWBSqafogNBf3318RpzcP1kH5Q8Lzush8vSFTDw2KEqgXp4/zWGa82fsEFC/2oQEtLpmpeBPbbw8qXHKaKchqiNy3BqqFN4LuzQ5ZtwqJNSJW/vt4aP/1zc70lvWrRG0M8N3zzwgNE9+OFdyvqnKHOmSZryqlPNejcEMxLz3pt4YP5hEXtx2QSrtmYiOeFX9CoRzHBsvWoCwe6ssxvMnpByUfrCY3VfFJO+KjaYbWJiefWi32lRedbnI0BLSypbpi3KY3VDHXOf/vhz1JOtmzma4NsLOGixQaSiydDlo0X4w9D7/r+5vwUpSzZeY2u3C1yxCmByzXP8h6XdY+eLqi2+/Vrz9+hHEnCeXOL1tGVJVi1hAvBjxc7PKv65Dbk/WKXf718mWKW0CYeYeIXvTx2ZHW/S9lq/s3lPe8GDZZcNl3+n5tXkJXHFFjtr/lw448reBhxuejwR7O7FCbkvdUJ/+LyNeRCs/PN5S0/PthYwrUlPXdc3PQoTcAk2tA4xe/MXkFJb9ixgSS6qm7RQNm55fq6w+9fP6CymlmT8eNyn8u6x7NVl85jR3bREqx8EWA1Cghyy6P5gI0JmYYrShcwa1M+LqYI6xnEJhS3iRPVOHQu+KNPTgmV/8dHzYg/2ZwRSoMuLJs9jZM+qWECQTMw/HeffB2AWZvx42qfWZPRUSUmdp577SC7aIlnfmEuupa3c1SqKibBih9X+yzLiGDtRVQnQdaO9d0OO9+yvHdxyE82e/yk3uNRMyIQhifVkMUqJZpv+evbZ2G4cuz/keHmsscn6xGFCfnt56/7Y5SW1ZH2yZG1QVcOqwTJpwGzKmVjQq6qjKHeMGsy3t/s8GzZQ1afIY7a1Avd6fO/eIHEX2rRVjh8ozQ+4i9QKKUprSWvDciAqrGeM2kFCkVd15SbnFBptPa8WCnBmIY0ThBWeFYs0LQWoR11XVHVG7RWvsTJGqrG0FpHEIUgFUVdYVovptR1jXOOpNuhthYRhrhAYUNNjSNKE7TQaAS9JAMEWiqasmLQ71MXJQKFtBLhJOPB0K/UN8aX3gi/klCXpeetWjDOIqTfF4PAWEnVGGprUTKibWGd1zg05aZEKElebjAIWiFI0g5t27JaeR5v0DooDfn1ktCALBsC6wsKW2sg8PxZ1zpcbUnCBACzxRw0jQF85Lgsc9blms6wS+4ENomopEM4L7ZeXVxRe8ApsYoIRYBtLVVRk4YdmrIh0oEvCFJQ5tXWrdeCNVRNibWWQMe0CDZ5RV15dmue52AtZVkipaRsPANVCEFbN7imxJQ1trFYI9BBwvVs4TmopqZta3CGpm2RgUZHIU0rub5aoCy0ZYExBof0RUNWEGUdWmcwTYWtDLSSqqpomgZrfVnZpir55PETUCFBklJvm8a9201RFBVlUfnyKQvGGGazGcvVnCztE4ahvwfEtriNgNVizdXFzDeWK0lVtyzzAqVjHNIzkhEoIYmVd8e51pdblXVLUVnKCozzfKcwS2idwQoIVej5xEJSlDVNDVVVYm3LJs+pmhYnFcY0OBpaaj8WWvFoMaeViiCKcU6wWefMbpYkcc8LLNqX/MRa4VrH7HqGa/wxm7pBqy2b1ziCIAQkUiq0isjSHut1jtCKyrSY1p+TqvLnrjVghKY1DY1pUUFCUbYsVwUtgtJUvjzPWWbL1fbermmtL4WSW2f59XxB3RjWRc26rGgtNK0AFVO3FnSAaSyRighEQFn4MqW2bLaxacjLwrsjVUggAyTCvzwWBmegKhs2/z97b9Ir2ZamaT2r2401p/Pj3e3iRij7zCrIEgxKtIMaIFQqQIBQScCYOUyY5RTEXwAKJCgqUcEAqSiJokmysgkyozKajLitX2/PcT+t9btbHYNvm/mNEcmIRJkmueQDO2bb9l5r7b3e7/2ed9sTUpJihDI0O0/XDYQh0gdPVoqsFFHBtpO2+922p/OBvu9ptlv8ENjtWlxR4bJjt25QSmO05vbmns26gahJ0TD4xK4dWG1aiqLGRAshkKPgSd5d39ANnu14jUPKdG2kHxRtF5kdn6Kt8EOLegJolvcr2rYjh4TCoW1J2yWGPsjYcxVx8OQgWJam6WjagWazJadEHCJdN7Beb9k2LUk7Bp+JSVNVM8piStNGukHhQ6LtPF3nub9fc3u/JI4PBoVyuCzi7Ga7o+8iRhUy10Ng6HvW6y2dH8BYtLZY7YhKo23BZtvSeY/Sjqb1kC139ys22w5bTohK47MEFkYVWS6XvHlzwTAIY7ttW5qmJSXYLDdoZVHGoawhhkzTdPQ+sW16YobVdnNYJ+4XK1zlhCOd1Bh+5bi5vqPtI+3QklIg58gQMov7DUPXUjojHSXGkbRwZBfrDbumo/eRmJPcGzpPs9nS9/3IXVXCK9eyvq/Wjaw9Q2a7aIhd5H65oul6QhiI0ZOCJ4TAYrGg7weaXUffBfo+00fF9c2SZhepZscobYkojHEMIdE2PbttK+tc7xlComl7bpdLdk2DtZZpPSP0HaHt6XYtq9WG4BXDEERwD4lN03J3u6YbeqwrOJrOsUpcv5Wr2e7GeT7Oj6IouL+/Z9e2+BgYgicrGIJHa83ids122xJ8phk8fUy0Q2axajDWEXyia9vxAcRweX0NpqALgT4FXCmForvbe7adx9iCkBPaGjCWyWzOuulYrrak7PBeses9u17GO+OzTBw8BoU2DltOwFlChm3XEYD75ZLlcsndcksYheGcFSEkhhDpveJ6vWPbRxoP9eyU5WrLzWpFlxJDjgQynR9QxrJrPevNjrbrJJivbRlSIqK4u2tom4E8BGTVSjirpWvjL19/IV55DCFLTnBd+zTpchWp72RD8aY7ZWp7rI5Mbc+7ds6L7RnFxBPGUKnkFKbPtA8sRidu+ykAN8McTeZtd8xle0wsRfgyXabYpFHwlGP5vP2AbZQgoLf9MT4b0onH7RLZSOK2Dhm77gizJCKD2/EvHH8BwINiy30/Yf2Jw41sSRHyYPELDqPlnu+T5dg2OBV5185RvTiX9qFPIBvw4A3P+0dc9KfMTMciTPjB9lNyFudW1orqZpBAkS7TnVlyZ/jj5Sccu47zQtyHz5cPJBDJGeK0RMUxmRxQa4fPBp8Nv1K/BWAXSnQnoTMgaebFYqC63NA8Lfls84QhWd70p4f23h+++Bi3E5FzOHH4uZPU+00gnw3c9lO+M4qdmszz9lxChEZh1G2ChLVNLaZXfLl8yNv+mITiZ9unAFxuj6W9ehVFeNZKmKVJzlUbC9roOHcb/uHbX+O82GJ2GjOydU0zCFOzMuhW8T+9/avMdcetn7MKE3w2rPoKMyj8kcV0CdPHg8B620y47Wf0WQSHn6w+4D/+6t8E9gF64eBQjZXBriwX22NC1jSxYBdLfufqFzDvSrrHNWEuz9rZKBFuncbdWP7ni18bA2vh3e6Iq6vjMWxIHFKxGkX9s5mkq6NoUsHXzWO+3D3h1e6UPEi4kp9ZOUdRxD+VICbNi80ZN8Oc1+0pbXS8uz/CbUdBcZAQNdNGdOvZ3E35b372z7IMEx4VaxKKH28+5IcXH9KeCUqhPysoVp7hrIIMP7r4kJA0u1iyDjUnrsHqhOnFsQ2QnSFWhu7JlPomY3Tisj1mF0qsSrzbHZGywrbiUjStx+x6Umkpl5nPrx+z8hWvdqesQs0iTPidq19A9YpiEw/IiX3hZHgx476d8MerT/nh+iNWvua+nwAifuzn3XBsxXm5hiEZ1r7iyImz82fbp7xtjgi1MG/34Wj7ACm9NTxfPOBumNEni9WRL9ePYO2wXX4/RlJGD4nqRrEbCr5YPuKb5pw3/Sk/2z5l0U+wd04cthNNddXLtS40R88Ub69PAOhywcVwyvPFA6pLe0BHgKxzYVYQP59zvZ3xk8UHvGlOWPoJL7ZnvG2OKFbQPp3IGPTxMCYnF4ZX61NC0lgdKXTgxeaM3EhbfHdi0MOI05haqvvMxeqYr9YPCcmwCjXfNOf8k5uPqG5EtLObYcThWPojw/1yysX2mNthxpfNE5635/wvV7/CD158cggf8zNLnBboITF9F1lua6ZuoI2ONhYi3F8/oFi9D++K+zAyq7BXBRe7Y6yKh3XkH779NfJVJcGOPmOaMAawRWavE7/3k1/kpp2JqN884rPtE3zU1Nf6sJ5nrQSzctOgt4Yv7h+xCSX/2/2v8Ko9466bkl5OD2N930URS8X8a8vVes6zrRQcfDa864744cWHEqZ1JIiOZNWBIzt7BRf3x1zuZH7sQsmb5gS7sBQbQR2QhYWqkgRJXr49JWbFkAx/vP6UXSj5kzcfUSw0pn8/Zk2XOf6mw20V/aLiejfjsj3iojnms68/pLzX+JOKrMH0gv5x20C1SKS7kou1CItvu2Oer8+4fv6AyY0URrvH5eH+VK4i9rJkM5S87Y9Zh5qr4Ygf3H/C7vkx1SoSas3mo0KwEH2iOzEsb2eshooX7QNedOd8WC74xxffpbgTTnSxk/XKbeU6zl8m0k3Feqj42fYDXrbnfLV7xFcXj8bPzZSL92Ll6Y/lGj3bPeRVe8Zju8Jnwz94+xtUN8LJtW06BH22DwzTt5kv3z6ii47r4Ygfbj/hR+uPuX8nDloJURyZ+UaCRgFWvuKP1t9l4af8j7d/jd+//i76RY3t0kFQtb2I4sUqMP2q4MXdGV+tH/K92S3ff/cJL549plzJ7zZdolwEsobqLvPq6owXmzMKHfmyeSIif3CsXpxQLvOIUdIHx3T6f+G0/XMdRPbf/vd//7f64FEoKuOIQUJ7ogWlFUfTmmnSlLUlEonGY5xlue5Y73pu7jf4qMmmxGBIPnFydEZlNI6EIYCK+JjphswXXz4jq4pugDyGApWFHUNwEkZlSKAMNM2OZ6+fs21blHX4IeNjxCrNaT3Dlpq6MvR9j9Ga5XLBar3jq69fMp3NSUnR+h7ratabFedPHjOZl+y6LUZrumHgbr3i7c0V67Ynl5oUemKOKGU4nh9LKJZKpNATcuR+ec/F2wu8h5gzgYBWM1AOHxJlZTmaVKjgGVRk17Rc3a9YLDZkVYEt0DHijLCDq6LiaDrBhsAQBoa+Z/CJly/f0fVeWmKdhKoYY/AxMC8nTCwQPCEO9F3Hze2Su/tbFBprDCEpkjKEHAh9w9lxTfANkMhKc/36lvu7Nf2QSdGQtWUYApNJJZzQSYXTiewlVKjvA4v7FS9fvSOrmqKcC4YhaQptcVYznUzou5Zd01OUNVc3t7x8dUHGEKLGjqFFRiumswmTqiTkgKsmNF0AVXB5dc3F5Tv6pMg5Y7KhLko2mx0fPn3M0LdYE+mHHm0st4sFz1+9ZjcksinJKRN8wKLp2p7CFEyKKT5l2q7j4vqGu7s7dk2m9z0G0Erj+4H59Ajfe6rCUmjDsPWs1y03tyvul2uGGKgnUxHeYqSqaxbLFWcPTrDWElOgbQe2255nL14xBNh1A9ooUsxM6opuvcZpTWELprqkb9f0jXzHNy/ega4pqxp0JmawtkahmFc1RR/JOtKHntVmwdu7Fdc3G7JR+ARYTSKx3TbUkwmzWY0PkBKsNy3XN7dc3dwSlaIfFF2fKFxBDB2kyHRi8bHBYNk1O1JKvL64YrluybpEqcykcBTe0IXMfHZMSGlEcgRSyNzc3XF7u2C9awHDdFqTUsSaEj/0VFUlLdnB41PifrXi5cVrlpst2mq8Bk9EW0MYxKWsDGQPOcFqsea+6Xjx8gZbztEKhlZYrc5Zok+cnh4TtYglm3XPsm15+fKCPmTCKOSiFFknmq7j5HhOXZTEkMBn2m3HzWLHq8sb+mQYomIyn9H3A5Oqotk1ZO/p2p5t23C9WPHlV69pu0gIGlNU9KGVkD+l0DmjY6AqROzfrnc0vef2dsWbd7fivsuBFCPJK6rC0q53zKcznFHsdi19l9hsOp6/eU0TIwHQaHIMuNLg+wZnA9PqmBQT7a6l6QYuLq64X2zpukBGYbPCaMNu29A1A/P5GX3n8YMnxszV3R3X93c0nRTaCldQuxJ6j7aW9WrNZFKTcmbXbVnvGl5cvh3ZzRJqGL20h4Uo69/seMbgI9ZqNruebTPwzeUN69GZ76wbHcUFYQjM58doq+mGnm3T0HQ937x8zbYb2A2gTEkcMikHyqLEtxJtq5XF+0DbtNzcLLh4u2AIAaUVxiraXm74Pg5sm4ZyUhIyrLcNdzdLrq7u2XVbCl0SUkRlzaASWWsiiRA9RAUNhC5xc7vi7e0CrBGHtLbklBl8g7WOoWtBaay2hMbT7DpevHzFervF64RXIvKi8rh2elbrltJNWa47us7z7mrBYrEmpIxCo5SibTrqeoI1iourhQTV+MRq0/LmZsHVYkXT9GjriH6QjgtdUtiC1XLDpJwwDJmUHJtVy/1yw4vLd3S+x5qSoetlzKKo65r1uiEqg8ZAylxe3HD1bsm72xsioJxFFVaq0xlS6Oj7jr6NpGTZNpHn7654efOOYBxJK5LOlMaQkqBQrm9vKeuSGAOuLnl18Zar6wWbrRSuso60wVPVFf0QaXctu+DZtD0bn7i923J3t+ab15d0AZQrhFesM9aWDEPg6t0lri5pYk/TeXzQXL5dcHN3Tx80WSmUNoK60Ia2z3RBs930NENmsWt5d73mfrfBj0XGrBXEiM7iOsspkfwASCGnayLv3t5ydbXGe7k3VdqiU8SFRFVX/Ot/62/+ZRDZX4DXf/13fve33FpCgIYTi/birjK9hHXunlq2D+FxvWUTKrahYmI9P3n7Af7dlNOv9gw4Sb3XEd597Diad5wWLRMjgac3w4yL7TH1P6qFBbcc0DGTnThcb04ctkxMrbwfBZ+vn9D93jnTqwRKEtDtdiBOCtafFryq53xnvuBF/5Aj0/E7N7/IN1fnPPlHifahQwcobztSZZldeC5+seTBScNPN0+pbeDr5hE/ePUJs58VzC7iQRQ2XtAFiw8dzdQyYOlzwWKY8AdvPkX/eI7pJKzHdKMopSRcpjsqiE89WmfetUd8vXzI/ZdnzF9zCFTLVhMnwq/sTwtWTzWzoueyP+Vdf8RVc0T/gxOqZaZYh4M7MBcW2yT+9OkD5ictCc1iqHnTnLB+dsLxNxmMIlQSHnVobfY1q0/g2h+B1vhs+F+/+WXcS3El7UOp3MbTnzsymrtjS1FF+uxwOnKxO+HlxTmP/0B+Q5wKqzQbwTDs6oJ8HnjbHPGn6w85r7f8H1//Emf/l0Mnhe0iqZQgp+Q07UNHc5a5jKeclc0o6Cj+9Gef8uBHCPsvSljbcFYwu/Tc/VOK225KUSRet6cUJvLZ8w84/wNHfeMlsC1mTOtJlcVtNO+mE4qTgdthxsvNKdc/fsz8OdS3kWLZkwpxR9rtQPNBhWkVN0cldhK4bydcXZ1Qf11y8k3AbQL9g4Ji6bFtZDgtOXoR+NH3TinLwCaUvNic8fL2Acf/uKLYJup3rThsQ6Z74KgWgeezU4YpfPf4novdCVe7Oc3XJ3z4u16C8uYSmmb6SKocwVV0jxLV1NPngjftCT959wH89IgHn3kJRWvfO6yn15nVaYU5H7jrp3TJ8ePbD3j11RPOPktoD27dY+53pHkl4pHSfPNwxm0/RVl4tnyAj4b8J8fMX8mzE1rTP6wJY9r77ZOKt8MMWyTeNse0qeDZsyfMv7HM38hv2c/DWBtMa7g9KmCSue8mLPoJz189Yv5HNfOXIui7bcBiEcZTAAAgAElEQVQ2gqqwPVyUcxa6oMkirv3k3QfcvD7l6e9J+FN2WgoTRlyLKhiajxOLMGEVa35295jXlw84/76l3IrIAkiYWsjUt5E3H0+YHbfsfElC8XJ9xuWzRzz8YyhXAbeN9A8LimWge1hQ30UCJT/cPeX7y+/wJ88+Zfq/T6kWgpRJhSBK7G4MMCssK1vBkac0ka/uH3K1OGLxzRmPfiBFD5XHrgef8MeOcgVvH1eYOvJsec7F7oS3Nyec/75jchdxbTqEUoWJwTWZ1aQmnwXaVPBic8br5Sm7H59x/DxTLjwqJlIlYWGTqwG2FbePLJfbI677GWtf8/qLx0y/KqjvMm4b8TNDd+bQWQRZrkrenFVsYsGzxUNeX50x+/6E6Y2sN4xiKlphu0S1ULx+VHPTzjBF4vO7x9zcHvHw9yyzi4FY6dFRLsFXtsvsHluWzvFye8aL2wdcLE8wf3BMfZOZXocxKHJkds8LdG9ZqJpbW7Lsa16vT7n5+pwHP5YQLjSkUlPdB1RSuBbCbc2r+YRrP+NPrz/g5cvHlJ/X1HcZPzWjU1MdilnWQx9q3jJhqeVe8OzZE05+KsF8rnl/PYpNJNYa1RTcqZq2Uqx8zcvVGer7x0zfZoom4ZooAr+C9pGj2GQml4brecXV5Rk36znzzx3zVwnbyfFkoynvBvoHIqw++kFgoY64Wh/x+uoB+UdHnHyuqG+9HI+XQoOfyr/ZReb16YSL9oh1Lvn85jFXr8949IdSjIulpthKqFi2iv5EU70zvAinLF2B15Z/8PlfgS9mnP105NmPAYJ5DBQ1IWO3mrfNCS/znK/vH3L1hx9w/n25vmYMF/VzI9kZRuG/nPH10ZyFr5lNPH//J7/J7otTJlcyp3SC8t4znDjKdaK+86hFxdXNGT+8/5Bnz59y+eIhj39PwkizVofjiqWi2CX0FzVfTk55vjrji9Uj7voJ65+cc/IllJuEHjtz1IhVKO46yo0h3dbcrI75/OID9GdTpi+F667HcFAUZDearrLjNtRsdMGz+3Pebo9589kTTj5X2FYcwP2RIRlFrCRo8T/4m3/9z/SsrXLO/8/v+v/o9W/87X83RwyDilgNDk0/dJiyRIfE1Jb88vc+5ei4whWQsoesuHx7z9t316y3A6auhHNqLSprHj9+yMdPzjmeOCyBGBrutw33qy3PX77B1TNiklxykxx1XXJ6esxHHz5E6Y66cux2Dev1lmcv3pLR9D7T+4GqqtARQkj80vc+5dHpDK0SIQS62PPs+Ss2TUvSBpRDW0PyoLJmPpny67/6HaxJqCjJ4i9eveL2bomdTNh2HmeEEVMay2w25bvf+5iy0JTOEULg3fUVb+9uWNyvUZTUZU0IAeOEWZiT58n5A+bTGVn3LLcN1/cLmk2HMg5tLSZlnLXEwaOy4nvf/ZSzkxnGJtZNw83tLXerLX3waG1JEWk1HVmAk0nF0yePqOuS0jour665vbmn7z0hZjAGs79f5owyhqePzzk7PWY6K9mu17x6/pK+9wwpS3CSE+6gUgYdNE+ePubx2ZTaSTDM9eKO65sb1s1AcjXKFNC27Me2q0oenh7x5NE5ufesduLwu729BqPFLeX7Mf1ceMUfP33Ex48fkaO4xLph4OLymsVqRVbyA0JIVNMZ6/WaTz/8DqfzGafzCZFI5we+ef2S+80SY0t8zDgsJGEFpqCYzSZUtaNyJaFvWW9XKFfRho6QAyqJQD/0idrN0AZm04rKKsqy5Pb+jpwUuxAYhoHKFUQyOYNWBYVyTGvH0w9PSEocjuvlhtVmTVaGbhBXnXMOUzhijNRFyaQoeXR2Thp6mn7L9Vaclc7WDL0ETSVdYmyJSpGjWc0nHz/FWsswdLx69ZLN1qOVJSlFDoE8Oo2ttjijefjwAbPZjKo03N8veXN5IanqCrbdIPzoPuB0iXMlVe04OppwdDxju93Sti2r5Q6UkcT0JA+rZVGgkaT4swcnHM+P0CnSNS13iyXN4BnIRCVAb5cVOUE/dDx8+JDJdMrRfErf7tis1mx2W7begzI454gxMQwDVhumdcHxtOLs+ASypt22LLZrtrtubL9WdH3AFhVYaWyS32yZlSVD17DdbiW8bIgSAKQMWmuySmiVcVrx6OEDZuWU4HtijLx4+RpTTwkoYkyQMiEEyrLAe4+xiomWNUFrC1aKR2GEAVotm4HCFKTgMRamZcHp6SkxZrqmZ7Pd0vhWkohjICpLzBmtNUZJkePsdIbLii50EBPLXYcuRFT0vkerTCJjjKF2GlvMOJsck6K4TVe7htYH+pxwzqFTIvgdzpVMiwlOO46OJ8IMVIqrm2sSkIySroRsCD6SUqIoHTlHTh+cUViDyXB7twCjGXwkpUxlpbhHGlCuwEdFXTqcc5RlydCLW3M3MqbnRxVd12GtJsbxtxtHURRYpemaVoRvaxhiIEVh9mpr6NOAzQodBCNSVQXzac16vQRdEKNi8F6CK60iJkuIA8pkjBXB1PcDdVHi+wHnSrqhxygpdKSUiDmhC0cMg2BzjMI5J10QypBSxmhF1/c4VxASYwBVIutEWVUUzpGHREqBwYu7N2kpZWut5fdrh9MGi8anQFVV+BBQIOefjHZGxp6xKKPJWR7YNaPD13dgNN6L41WOs5cuAq1lzOdMYQo0BoPBFjXr7RLlMm306KwBTeGUrMEhUZmSMAzUZcXQt4Kh0QU+7EhosgKfwRpDzhGrLBpFjJmcwBWWNiTSeJwARcoEFUg54EpLCpH5/JjtditYBCwpJULIKJUJwdOngclkQoiDhKWWFTpLyrorK4bgwToR3HUSl7/T+M5jlMYYQwiRqqoYup7pdIpVmr5rx/NqSFHar7JCnM0xSuFISbdG1orSOm5vbzk/P6fvPdYKfqWwhhgjk7pE50xUkbKo6bqebT+2YBcFoRsogZAD2Wr+3n/1X/wl2PYvwOtf/Jv/aTZ9wm4G4sRJu+bKC3vUaZqHlqu/4Xn8eMVx2TEkmSuv/vQppz9VzC7DIfQLZCze/kbJ5q91fPRkwazoaYPjxauHVC8LPvzdjjCRtGzBKYhrbPOJwvzTK7ROPJ1veL04obua8uT/FOdSsUm41cBwWpCNwvSJ13/DUf/SEmcibV/QbkrOf7dg+i58ixco4kzWit0Ty/pf3VKXHq0Ty9WU+fdrTr/yxEpRX7bEqSNrxXBs2T3SLP96TzUdOJ01NH3B9rNTzn4Kx9+0ZK3oHzhMJ+nRe+be/a8busciENRvDGdfRKqb4dCeG0tNmIjjKBu4/Oc19pMdRRHYvJsz+9py8k08tP9qn8QJDcRK0Z4Z7n8jk44DxWzA/GTG8bP3rcYgbmngwFO9+auW9hNP/aClvZ3w4I8NxSZTbOMhOCkVehQG4PavOHa/3DM9Fodj/+URJ1/C7FJa3Q/YiZGHHCaG+1+2bH91gKQo31qqG8XpV9LdKE7sdLgesRSu7fQ370hJs2tK/Lrg5MeO4xfSXg0iRvanjumrHZf/0hG7jxLlx1uCN/h1wcPft5x83ZIKjdl54tSNv18CmnZPHMORJMabHqaXwmCuFlHczuP5sZuB/rwiG8XusaSy+ykcPxMH1uTaYzcDw1mF6UdkgFH4uaE5Nyx+XXjA2ivqd4r5RZT08oVHd5E4se/da2eW9oFm9UsZHRTFQnH2RaS8Fwal2wRibUbEhXTJbD+w3P0zEcqIaiznf6SZvgujA30MYYuj87I2hIlm8YuG7mEizSP1S8ejf+LFSZ6hvtzhT6vDse1D9JqniuZJorrRlMvM/I08X7ttwOzGTtWT8jB3V9+1NE8zyWaKtWb+PB9a8HUfSZURt2OGYuW5/9Wa/ky+w20V9ZViehWZvH3PjtVDwm56UuVoH5dsPjRsvyPjulhqZq+ljXvvvNu3swPj9ZNr3j0E20B9nanvIuWtfEecWJKTuSot6pr7Xzb05wndy7Pb09+Xdc10Wa43oLuIPxYkQSo07bnDjmMaoFjHg6t8jwPxMzMGOyqac832EzCDwm1g8i5R342O+9H5q3sRrFMh6Jjl9xyxBreWCXH0KhzWP7v71vpQaLoHjlAq1p9K8cy0ML+IVHceu+6Jk4Js1eGaN48coVbsno7FXQ1nn8VDq//+uPbrQ5jKdVx/x+KnEox48vU4PnbCaO2PzcG9n7XCNonu1BBqxTBXuJ20u0+uB8zO03w0oVx4wt6d64R1OsyE0ysCnDz7F9s4tq/LdXabQKwM2ch3dSeG5rFi+nZkbg9ZRNuxe0Qcp2kMdZR1eH98rpVAqXIVD4GMKo7OVies9uHESjv/RMKtQApxe2ZumEig4f7vyTDMDX4qRTcVEfFwxCjsx2uxFHxMmGhCKZ/dncgxZSWipYoiKNpGhNX9PN5/RizlO0BCxPZOTjciFbLZ8+thmIljOTmIJczeStdIee8P67qfmwPyoDsx2C7RnWrcbgyJVEh454iDELyFfH6opMNlXwz0taJaxLFgN7ruZwbby7nwExmvu8eayU0iuvH3RLCdCLC2F+drey6Oee0lXC5ZRXSKUKtDp4nt5W9kPMkY3F9v0yX6Y0OxTTQPBcHituBa+V4d3rNw958RS30ITlMR+iPNyecblr8yp1wnQiWi/TCTsd+eaZIFM8AwV5SrzORano/6YykI9HMRsFXK/MHf+4/+TM/af65F23/t3/nbOStF0gblLKHrKesC7wdK7dBJ8/D0hPOzYybTCoaObdtwcXlN1w1EHNHJRhEDKWrqesajkznHs5pZZeiHhsVmx939Pd5nfMwoo0khkrKmLiuMMXz0wROMjtQVXN3c03UDy00vLe9EKmtk45SknX0+P2E+nWCdQYfAcrtk2/W0QyufnxUi31mKYiLOoVJRVw6XM23fsVptiKMDNCnZqDstbrF6UjGbzajrmhg9u/WGTbMjW02IkQIr743xsCE2hbSgW+vQJo6YCI0fIsF7qqoiDrKRds5BstR1SVEVGJvphoGm6+mziERO7dsnswhZVnh4IXmOj+f0jZyfYdwUojVd75logw/y/uwszhis0vjQo1Hk6Il+EP6tcvS5w5oKEpS6wKjMpC54cnZC07Usuh239zcYV+KTIivDZOSd5pxRSktIjtZUyhBywudEDJ201BeOlIIwVG0hGI6q4KyecDSr6IdA1w3c3S+lNT+04GTjX5Y1IUZm5REaxclsio89275lvdtSFBofE8GnUTgYW3KUJaVMYUu0cegUiWkYW1YT2iRi9IAVbICtyDFhraUqLH3fY62haRp06VAZkg+yqc9QlRNSn5nUJUoHlLPk6PH9QMbLuFBW2oadRWkLaAn5iInCOUxOgqvIXricaCpdkHLGawELGCyYfAjwcVo2V23Tk0a0A+RRpPEUStp+qqo68Iq7bc/0aErTtfgUcc5CGtAY0qBIWFShsaUihYDWmkQe3a6W4BO6KAhB2ta7ds3pyQmh7+i7yNF8TmEsm7YljQ/nXRyoXQF9IKVMJFNVFe3gJRzNFuSuBWXolAhkzhWkKPMwpYQiURcFfhgwaMqiJiZPgoPQooxm17XoqpCWC1tQOUvynhRFwEpZoaxc05AiRSEbjhQ906o+IDXC+NuH4IlZ4XMUUWzkdhbG0sWAdQ6X8kGEGlIkpEhWYLIiIXOCQYERIa8uHX0v888pjSsLNt2GGD1lWdN0cm6ztqgs1y+EhkopfE4QpB07a0PWMu9kjBYYjYiy2ZGTpiosXbslG0siM3j5XaiMIkAWES8lmTMmJcpSxLndbgdaClU+CiYghoDWckx9TOgMWimsMnTDIPcPnbAxowvH4DsZjyni0KBGwbGsUTof1swUB4ahoy4cGIsfMgoLKmGMkfOb5Br2wUsByyp6P5BMprYFRHBahNLJpBIGbz+QQsY6h9JjC7uVaxWyzPGUEqV1dF1HXZRyLk0xrh0Zq8aNdY7jmqKIZOGajoKeUgY1ooAGH7FlCXrPCbfoQviwBnF9xtShVMSP+If9K/hEXdcQItoWIvoqGLoeYyyllUC0ytkRSeIJWsZlaR3BD9gxwE9rTd+3OOdohx5Tup+7RzlVYk2JyXJ9lI6EFIhaWq+iDxibiWNQlssiWmoUVmt8zKSswQr+RCV5fnAKSJGcBUkQEa5xUbgRkAKhl+Kw1pqkMjF5jMlYZUHVeO8Fl6QS3g9oFDqDQnAEKb0fF0OUYLbSWLSydF2Pm9aEnMZrI9chx0DpCnS2B4yDK2tUzhJOSRD2PVJsyTkTc4Koxu8RJIRSipwURVGMhQYRa5USpELpCpSSzyzL0a1tRt6vtgzDwCCgfVxhMDlRJMV/93f+878Ubf8CvP7lf+U/yfvwH9TPCxP+SFxZi1+ybL8XKE5FwPO3Nac/0pQrQQqYPmGbUXwImf6B4/5XLM3ThH7YETeO+rXj6HnCtVk2lfv0aJ/oHkjgy+1vKpLLpBPP9IuSYpU5ejUmU4+CwH5DpX1m+9TSPFakMpMszF7D5CZR3Xlhrsb3HEM/MyQD3almOFGECoqViBqmzweXrfbpICp3p5bmkWY4Bh2gus3M3srx2DbiZxY/kXTxZEYHqlMSSlLKhtKNXE/bpIMAKW2x0iYJ0J1oEUEKKFaZ+l42qKaLhOl7VInd+IMIZrrE5uOCYiui4p5lC1Dcd3SPatngj6zWbwsCyY2t/mMLtA4Zt+yJE3n+8HNLLBXtmWb5q5lioZm/ypx80ZBKg90OkDP9w8lBLNwHYIWJuIdMn3Ht+7GxF1FBwtKylo356lND+yThthq3gZNnQZiCYxFAD+mAStg9cSSr2H6ksJ2IcfM3PX5mxR3evf+9guwQQcvPjYgURoQEt42Htv09n9a0njArUDETayMb+40IEdXNIKK5kvGtQkLFJKFnPtGe75PsxXnl2jQKuCLK2108jEPgwMf1M0100u5dLeNBKNuPc9ukA5sYZMztBdmsEOdgzIfzGqZW5u3cfetzJByoXEWah5b6LmJ34TCu9qLvXoiPpT5cTxUztpVrZzr5DRJKpXH3DdtfOMZ0iWLl2X5U4afqUDBRUULUhgeC19M+jcKXo7gfSJWhe+AOQlO58OghHcLz9ucZrejPioNYNcxFGNE+/5yAVV21gkrQilgZ+lM7zmcRirR/75w0jT9gCFTM4gaOEEo18ilFhDR9wjThIKLuhcu9SBwm0m4PEqwoGIx8eO+38zxjaehOhWOZnMLXmlDL+TJ9Yji2Utix6oCp6U/dQWTaryNmFHr2Ar1di7idCuHc7oW57kTc0PJecdHvCz/749rPj1AbUqHo5yLO1Td7lq/Mq+T0Qbzcz4u9CBhGlM63x09yCreNY8BaOqxze/FMAiLzYey5jac/K8b3xMM82QttIMFxxSYdClhuJVzq/lzGV5iM4ZEPRJCz4xyMlXxfufCHeXdYJ2LGzy3F0tM/cBSrQDi4fuW37V3QKjHiCfZiq/BS90UfGQPj349r6r4ApqJ83rf/Zn9e9y/TRvoH4/5vLGypyIGJPczfi7l2xAuYTpy6w7HFdO9FzGQETxImmurOy/wdr58andmhEuyAa/MhSHQ/R0wfD38Ty3G9GYXzvUC5Z0KbUSCVzx2xIEmul/GCfvAzfSiifvs4dZB7UCrkXIN0q/jJ+/8np0ijKL8X7dMo6rqtjMP+SNb3ahnxEy1i8Hie9wWtYXS3qiRC/n4NdU06XCfgcM33hYfoxK3tpyPHPWX8VItYW8r9PWuF6RLDXApsrk2HuZS1uK/3a4Tb+MNaLcUJwx/+3f/w//+i7b/97/97OeZACIVsZEMkqIg2JTkGrAZLQQ7SyqrHDc6QIkVRcNtuKAqHRbHdbpnNKlLSpKgojEJlT+EmNL4nxiSt0uNmBxJRF1gy09mEoetxGFJQuFLT+IakPCpriqJgu2oxzmKtw5qCtttS1TO01ig/YHRCJUMXPCmLyBCUJ8UCowuKosI6hMXoO2JKSJOxOggYBisi6DBQT6eoHNAGeh+pnGymm17acatR3N6LhIVzpJTosyc5sKoi+0BlHJuuYVqUpOhHcUreq50Vzp02KES4ikrTJXH3mZixo7tTGUmZL7E4pQkhUE9LvPfklGi2G6bzI6wr2fQe7QCdCb6XJHZtIGuccTS7O0zhMLpk8AmdSzIeq8dB75y4mQqHtZamb0Crg2tUHEhONrXDQGENORv8EDBVQWkM7XYnLV1xACfcV6vlIUlrS6cVlS6YKUcfGrKKpBTEXVtVsoF3jpAyfe9RhUYlRWFLSpdEqLMGVEkISTiMyZNHL33OEWMMOieIFV03YI6lupt9pnQ1PgaUVSQCIYloalwJwYnIXsjvI3lsXZKsZmhaEd+DtDWGPlKUChs1RicG30HWRK3pkgR+qQQmWQnVCpJwrnTAKWEECypEg4/YwhEGj0ZalClG19lebCKTUkZFT9ajmG8qERVyRlsFyhKzQfmIdUDQwvbMEuqFNqAk4K8oCmLIaKAsnYTyWIvWmqgyQ9dDypwczdg0W7RV7NqG05MjfD+IQ1JJ6F03DBhniAirWTtJsk9JKoXTckroekGOqIwtCkIITKqadrsTcSMIC1ZHESZj8ri6QhEZfCNnRSlikiJI0+3QTlPqgqHzTOojQpB1yjrIIR8Ev4mbMWiP0iKuOlfQjo5xbRxxPMfCHI1YJy3h+5cyGt976moKKY8CZBBOtjEMo8gbc6IoK3GHenFMGmvJKZJCpJ7JOVdazrcrNJBGh6Y6CGyhD8yKQsLjfIcKCZPBVjX96IC1o5g6xEBlDF1S4AOlK8V9aBQYQ9d14paN8oDehV4c4FqTOhEE+9CLmzxGQta4oiBqKVbkUeQ6KkpAnLHGGEKMUIhw6MSiSJ88WmUKo1EEYgJGVnpVTvBdkjXJgFGKvm2oy5Kc02HepzHMrBhDPFJKeCdCdVkXpBCx2mGzOJr74Bl8RE0qCLLuDn3Lrm0E0TB0woP1kaqagBYhU4phUrCBMTl9DNorjBShWt9RliWRjA89Sku3QIoBkxi57obWy7mVvUDC1RNhD48PXsMghRdjx2JXBhVk3S9Lh967kcfzrZSidCVd1zIpHLEb0LMClCBwNBFyHJE7xSgQCud5GAaq2Zz7nRTCpPgYReAOkaouGHxGK8MwiLCZfSAPAWsLQhHRKtLR46wl9APOTgkJQkqYsiAPCXKm0hqTM04lok4M3hONOOm0sqQQyDExsQU6wzA+nack92lXVtAZFB1kj9JyHw4JKYBYB1GgIAqD1rJp6tuGspBCjxoLK7ZwNE3DZDol+4wlE1OQjhKgKEu6TgKKXFViXXngIBsNTkFROIZehF90RiHYpsoVDEPAORH7q6LEOgUE4e8aI2tK38n920m3RWVkw7NJAw6LcZoiSyHrt//LvxRt/yK8/rl/6z/LewdSrPTIm41jS7649mIlGxlfv2cKFrvEMNWcfrYlzAtipaneNXRPJgcRyM+NuFanmvouHBh+sTLSOgzoPhAnjvahO2ymdJDvqu9GV6cWR2N92ZJKS6yNcHfvB4YTdxAC9gJYuRpdPVZ4nCqIkOLnhlBqdMwisowOuuT0YROZnIgm0gova8VeMNiLYNXtWBA7sWM4TD4Iw9rnQ/DSXkDsTx2TNw3d4/oQ9BYm5r04lsRtlLU6iODFcnSoFvrgQstGgsKGYwlGsm2iPbMiEiaobgbaxwWhVMwuh4MoYNp4aB8HETiq272bWh1Exf313r/H9JKkHSp94BvbNr53So+iyr6NWsLsZKPdHxmqRTywWUHuKakyh/NdLMQ53TyyB/FZj7+/PzaHDfyeRboXSPzc0M8Nk5v3bsv9+P22i00lwSt825G5d5CrKOzUbxcQ9se5Z5KaLjKcOOxudE9WIjz/nOMWKXSEuSOW+iAmgLj03KIj25H7XNqxZTkxHDtQ4oYrV6PY9y03tenyQRiD8bx8a47uRTIV88Elv3ff7UWvvTC9d6Huz8le5N2/x8/dQdQZjgzVnT9co70LH2Dz3SnTy55sFcW7DbtfOMU2owBuFX5mD+d4z+VNTkt4mY8kZ4SxOQqPe8HQtpH23FHdj2NsMxCn7nCd9mJu1hyYtFkrdB8klOimIRuNPymxG09/Xh7EpL37bz8nu4cFbgx727vjpW0+HxyVsRKReu8U/vYra4VpAsMoMu4FSPn793NJzuvIWR7F51QIN1z7RH9WjCKZIFz2DnEV8uFY9qiP9ulkXFdEeNy7cN06HOZUf+oE43AsYYoAw5E5jEWQYk6YiwEAwC064rQQkTlkhhOH2wTB5Az55+a69sJstduB3SfTcXyng3vx22sDgFuHw5g4iJVwmPO2EZe7CN+aYhXozsQYshd6dXgv+oHMUdsk3LLHn5SH8RMrzTDVlJt4uLYgRQu3S1RXcl+yOxE57cYznL7nWe/F1n0BIY9aqtsE/NzSH8taNsz1GLT1fmyJi/q9qFmNrNb92h4memQP7z9TBP9Y6oMQjBpZzqOwWGyjXNtxnPsjmVfdwwI9vA/XNN9yeWejRsfq+32h28o6evT1BpSiP68xfRzvPYyOUykiuE1kOLYHkT9USlAfTgTvfaEwTMzIt5ZzuR9L/ak7OF61F97r/nd/uxugfSABbMUuHdY1KWL8fEFrXwCznQjE+2sCEny6v18Xm8Qw14f36JAJpaa+9bTn0qEeKo0Z8sHZO8zezxEpZirKdTwUF8NExlOxS4fvzePTcH8sbuNQiwu8P9KMGP6DSzjUimo5FihHF3Z/LPNietl/S7CV8fT7v/1nc9r+uWba/t3f/h9+K2cNRHIaUEpaGkMccEaL28wHisqiLbT9DjS0oUcZRY5ZkqVdReks1jjapqUoC5LKmEJYrxqFVgqjNcFHNJbCVoQwYJ0iZE9lnQgAJuNjwBqHCrL91UoTYqYsK3HRaNBakVOkcJbgexEMrSER0NqgrEUpi3NWxBU/oIIHHyArtJEkzagDOVvh0Vef0eMAACAASURBVHU9Ve3QOuKyJ5PJKHxKIpZlsEpjtKKPA9opYs4kDVkbtEXEQWVxqjyIw9YqbI5oo6VyPvRobUQ4NpCyOJZyVihryGS0QhxWPmK1GR2aCldYBsaHhJQJMWCtwjlDItMNHVhDisI6ij6D0ihjxvThRM7iGtqLx8ZpMhnjLH0eiCmSTaJNgSEFtDUYpSFJqI81jkAkegm0MdqSsiAfswIfAikLRkBZhzZW2uWNwuUsffMpkVUWRIFhdIwqlM1kp2Rj7QwxKdi7BK303A8MBCIJJddIJZRzDNFjjCaGiDLiTtPGoZWGQkmLv1YSkuZ7khJOjE+JthsoyooUAlH1GJNQWqFUxo48R4dG7fmpYSClLOJSTHgSkSjuVzKFceSkMEmhUiKngMoBY0DrSOyl9VuRIWdSDGRjiCEQU8IoJeNCQyCSCRKaF6PMJWdIyDE6BcYWB9dczgFSQKkIMWNIKJVQOZLyAFoYoyEMTKuSFCMxDOIALh05Jxnj3YCxlqIsabuWkCKuNGQ7ClNKyTlCGNghRKIfqIuK6AMMAW0sVmtyElexNRq1d8Jp4UD6thNkCImYPNZphtjjCotRSlzjSot7UluUNiJMj3gLayfkAM6V0n7+f7P3Zr22Zed53jPaOefq9tr96athVbErkiKtDlZiComRXARGAiRBEuQq+SX6J4FtIDGCBIGSiwSCbDiQoZiQqFAiJXZFuqpO1Wn3ObtbzZxzdLn4xlqHvLIvAkuwuIDCIav22WutMccYc473e7/njQGrLTkWCpIIj4Kos8yXUiSAKgQoRdZiiVgvDnVVgFIw2mDUbo4h+4bWKOlbxygFOQqiQgkvNibIpT45VOe+0RpjoG082ihSKKQoDk5nDVopClLEIUVSGHFKM/GObXVyeteR0YyA8Z5SwHmPVcJSLTmhAG8MKBHcc8kVrVJovMcWxRgjzjoRqLwj96O0/bcNOYmgLwRcLV1BWT6rM4aukfCNNIxoYwUxUDLZKHKqLUbWQhEXrqLuZdqRirQzGCX7tjYFnROQcU3lb+uCN5GsM0UlTGOJRcv4WnFBemVxSUR6rTRjyRAS2lupFCsRzSmFojTWW4yx6LILmzRYa7DGklOhcZ5xO+KdExYZct21AmUUkYIxlpAEAaF1Q84SFOm8J9fASKU0qshea63c33LMjENi0rWMIWC9ZsxB9rOcsShSTrjW1ftLIpJonTj74zjilISPWW0pumDQ5CRscBUHcHJQHVIvxYeicF5XlMqWtvG0zqFLobEWHRJt09JvhRkdxh5PgRLJCmFja2l107oWNZM4bbNgy3BaCc7BFPrtGjt1UujIGaXANx3UcNCSIrYUSkpoJfcglUGhUMVglCNlue9rbYSTayxFaXFzG4NWWYq+SlXBWgo7Ug9XaGNkHJX8iY5oLWJtUrVgotjf77xrKDlTUuZ2fU3TiPu95CjIkSz7UyBh6gldoVitt6AEu2GsJaRMJkugqnMYFHEo5DFj2w6Mo0RQGkR+LjWZVpO1oyjFf/Wf/oNfMW3/Frz+4T/6F7+347LJn+IMMdtEnDpsL4nPYSbsz/ZS9vP2lewXOlFFSPtGvH01EmdWXDFeDmjFKRGvnPBNi9XEaW2JbSRpftw7yiR4ZOfIVLB3s4QDtxcwi5WDrwh7GbuVA6CpSeEi8mlSp2tLcakRzKq2RsvfN4Pw7HYO3mFp5fN1Wrh1RRxPOsqhMHtx3u0EAjNIa7Cq3MfmdU/24sSzqyhO3NbK9zHC2fM3keKkZXfH16Q6efYMW6sJC4tbiTOsaCnexamIMSqDHSVwJTWKNDGYsdBeRim81QO8HpI8O9Rrq6pzeZdivRMeVBY2n78OmFF+rn0d8KtMMboyC+UzZC+isRnkn+yryDK8STjXUYQ7tFwr00cZh052r52DjYp+2X02tLQdN5cBhYzR/lSr6vitxdm4c6SJwKFxN4FSxTe0iNzF1nFHYVeCf4sTg7+trtAMdh1xFyvSohUH2JAq71aLe7PRdew1GEPq6ueKBR0SdhOxm4gJ1UEexZWpsyJ7cYCK+1JRnK7uRbn26HptQg0gG8u+5V1alGU9iACe94Jt9hoTMmhhJAsfsxYqdlqd3l0veZ+i1d71FjuD2Sb6U4dOMo9BEeZW3HJe5mluZW23r0N1JIszVbHjRsrvLVrWth4z45GX8SnI921MFXA0qd0JMxozVqf4Jtf5K8WU1FnczUiciYvUriO5lTmWOrPnVZohUbQmd1YKQhOLru7j3Mj47eY+iLi/Wxs7h7qCXxJs7Tq+GTenyV6KNVTzrIjw8uyeqzNPh7L/edMncWPX+bq7NqkRV2SxIiLt9o2dQK1ToVi5hnpIpKmlP21oXgfpwJnJd3O3I7kRgTNNZF/ZtalTxTtFZW6P1VlfICyciL23kTixFGeIE3EkFydOXTOW/Rza7Tcg4n6aWMYjL0Hxg+xT4tbMVdgtew41Spiduz2nVJc7qjrgK9u6mLqnTcSZaIZCnOr9ut4hKna4ApUhzgTjMy6sFBBu5blImLLs9yGU7KO5ExxGqUWIYt7gJ3QsjAuLv4nEmamfV/bxnbve9oVileAAKoPVjOLmjdPdmFVh0UjRLzeaMNOCyVon+mOHX8m8tquw3/9yI3NBnMLiTrVbQTEUozHbWIuUIvTu7j+78WN3zs3CX1ZZ2LKxlfFvbhPjoWdceooRXEOxSoToK0FB2E0mzsyeq6xK5ZzX+1mxev/nTowWnIORkMwr6QCxfcZUt+440yggNTKeUviV37fnwFbMxQ6lAKqObdm7Wnfu7WIEObafn33FKhTZh7IRXmzs9Bs8hxXnvAmF5BU6I/f0JG5ZXefT5ElPWNg3hVRb3bHqjTtc1jJMX0Q5S6zkWcPVOWF7+d3FymfbOfdVAS0+TLl9leq+rgg1neC//29+59/oWftvtGj7P/4v/8fvyXFEAOninIpV8JA24iFFOZjEgDJyINZaS+tfyhijQReMcQzDKMKdhlD/XlGOUBJFKfowMptPKVoOL0XLQZIs1WFp+80oLQcdVeRQpJQw83bOHKMVIYwoBX2/xVsnScyqkKKIimNIlAxGW2HrqUKMGW0sOAmyKVlcRUaJMGG6lrbrGLY9RDm45lywVczTRGltVIVhG9A4jLZoDagBlRRd48ljQltLKSO+MaiYsAqGoUdpRzEa5S0xRLRyOO3kwafxFF2Iod9jB1SqB14U3hpCbTYdRxGynLPkmIXnVwoJTSIwaWaQNEZbrLZQNIVMygnrLMq6GrIjQnRUmYEMOWG0VLRoDCEGXIGSxCWdSiKmiHKaIQxYJ+LApG1lvTjLGEesFxHVNk6csI2h325RXjPEEWs0jVYiFNVr3LSCKshFY1tPLuLGslajTZHnSC1uPGn/L2SyiMRU/iDsCwS6FLqmlYeHAlobmXc5Y7RFJJIgor6Sh0WvDFbXIgMZo0Vk1dqREqSY0EWTRmndss5gjMbUg70qBesaxhgxyuC1regIAzrjtKWkQttO5PNn4TYqJeJUULIucgo47wlkbCPtvdoajDWsNyumTUscR7QxKCPjppRBK03OCe9g4r18B6ioABGyqO7FVLEAKQ7kOHJ0vGS1vqVtGsahl7F0Wm4i1mC1xiiFGZOIKVqclF7UPZyxIoIahTcGowzW+tqeDaiELpG28QxjD1pTcqJrG0IcycjPpRQxTmOMwrcGqzLYgqkQcooixUijPapYLEYCBAs4Ma3SNJ6cRpTWVTRUe7RFLoWSIlqBsZqco3yGYWDSiTDptYGUKUWKQaoUhiROaDnPKBLywGW9J+VUncZ6/6BgyDgNztYWK61r6JZFlfqwmrM8lGekvTwHJt7jjTjXu7ajAMVaCXGikFLGa4ultuIrRds24uh1hpTCGy50FhEyhEAKse63GWc0KgQRcIEUIhqLMgaNlaLYL9wrdp0EWhUW85kgCHQhxEQpUIgSulcCWsl+b6wjlShzRhmcMSJyl5EwbnGNE2clkYClnXaEPNC1nqINuWi0M8QcwSoKAwpNjgalDY2Xte1bS+Mb+s2AJ+ONoaiCbRu2w1Yc9/U7NM6hgbjZMp9OGTYbusZTKkpDo9FFYVRGRXlI9V1Lxsi9TymsafDOA5kxZTlcAs400nKXkvCoU2E27VAlo41hSGBdIxxkiUHEW10FcCuCRoEyjjjrwCi01TgFKQtuJeRIKTKvJr6tHFpF46ekkgklYJRlGKULxNa5XEKEmHC+RWkJLXTaCArCaVIxJCWHimIKuhSM1lg01miZnyZXJIgix0C/Hpi0E0iKxgm/N6VA1zpS6HEG2Q+NraifSMoRVEbpgneQc09jGxIZ6h6P1hSlCWOgIEgLrEFrJaNWpABorMI4iLtrp1J9lik0rZdCb+vRWpzQwzAIL9qKIKwUUsDUirGMGC0Hy8Y4HB5rNBZo2lbG0jQYNFoLusVqh6vrCaUouWBcZjLxlBRRIWKNlD9iLDgtPOJYChmFVvxKtP1b8vpH/8Mf/d5eWNPqDXu0MeiYq3tuwET24SxmqOICVcRszF7AaK5iddmxFyxVkRZxnQr+cmB7V/bHYuQgFRZWRJUa5LI/1FWhTScRW1Nr3hz2q4sWRNwL851oReXdaRHlqoNRZURoqw5G+b3VjdlK4rsOciAelhZ/K89+qshBMTXy3jtHU3aK5jruxbOya3XVMBw38rP2jci6O3z667QXZ9FVHKmCCUDy8ry3E9524TM74SPMrHAaEVxCaquAuGu77UVE1WNmOPEikjRmL5SKoCXfWYRE9gKhGYQbjFK/JFTZtTgfVa4u0LFg+4pUuR7IncWtIv2xk3FQShxlncXejuTWSitvZ/GvhXHuruTfjwd2fxBHiUPXbfOb8KrxjTt7L74qEdHDgdu34tvVKNevke8l18WAUQyH9o246A1lNx+qS1APck10L4WIOHekTsS17BS5UbVIoNBVbBM3pVzD3Fr5vV7WAUrmqluJMB9nluwMaSLXIFVX37A0tRBQBR+t0DGLqFNdx2liKuZCk63Ms+w1zfMVw5mknu/GZbceiq1t7zNHf+zIjSZ5EUZ2IpmsABHjcifCkx4z11/wTJ8Gxsp93K1DlWV+pU7WWW6E0VuszM8wF9FH5pmMV5zJWKdWruPuvYuVNvvmpjqwCwxHDrcWRERxRtiuXtZlmGlyK88TxVU+aJFiRFh4VB1vM4hQL8FWinFR+ar1O+wcoyoLekFXgSY18vmGIyfz+NQDqiIkVHVs6/18T53dz8NdcSO1IkD7Swk+FDFeVWyBFIBQ0n6N3gmCMnamFhh2L1VgOPFSjNpmhqP6XJ5rQWAUF3Cc+yrg1X3nQNzIyYtYparILJxVjasBk2Fu5bNMq7g60SIeDmUfJpWrULcT/xUQZzuHvWZ1z2DHGjq1ljPObo+SvU/viwUShiX3ljgx+6KRXYsguSvU2E1mWJqKAbBVRJRCkt2ICK6SrDkdChgRB9GyJ4eZtMjHmSFWgTt1EtyVG7NnUI9zcXvaPrO6a2muhTOtM/sAruxURRpIsSNMRQw01VWeG70XOgVNI/ti7PT+vpG8Bg2bM+nYKLYWQRqzHx+ZZ2bv/M61MKaTiNf7tTR5c33cWu6vcarpjyoSA8W4sIJ/qY5if5vrnq1F4JTlTOhERzG1o6VUFIQdpGCkpCm47qGVwd6+EYd391kzSsDdcCpC/rgwMk5jYTyQ6x5rwTS2+g0mo96rUDKuOlJdzLsCS51zGfwmozP7+Vwqw7YY2b+T1/v5vhu/XQjZONO4bWGc6yoOQ3OTSF4+TzHSmaPLGze+COEwzIXDvOueGOdqf89I/s11So2qRSi113OyU/RHen//lvcC18v82IX07faffydE2//p9//337PWkUNBKVudURqtq1OwiCiWgwi5BovXHpU1rW1xtkEbcYOCqlUxCUlRGKyR4B7vZMP31tJvtjhtCEnQAzoVdDFYoxnTiNYKaw2lZLyV4CTBF5Q9p85Udqm1Fm1kQ3RGS+ts48Tppgtei4NWq6q6V8enOG4KVkloV+s1qhiyUiJae0sMowivVtxITesgK3LMEgajlLSmOuHappRJKhFiwjcObURoTiljTSMOMGNw3YRCgZzQKciB2IqrWZnq6tFZ3IBIMI+qVeiiFDYjrZq+gVzwzleHIhRl0FbkwzgGuT8V8aNaq2mdx1mDMxZVIiVFyOJgKrlgUDRdgzMizjql8cZSUhYht2S0UcSSxL1nZMPvugnjEGldyxB6EUYUGKOl4pELnWvRGEoGjRFhxDqMzpRSHXqqkLK4ASwKcsHmDCnhjRzYS4pyDZDWYQkUGnBWfr7kRIyJxrr6/pDjllwCrfcURPArSpGDtFiXmJhohaVQcsRpW/mGQby8pRBTIOWIskbcckbhbEPrLK012JKxRTNtOmKKmKYhJmFbYoSZTCxobVHKYY2EPFkrzr9EZggRO2nkQaSIMJCUIhWFs46SEt5o5tOZsBuLsFQZE1Y3NXhOHoSc1TgK3llUymjt0EbCklAOaTN2xJIp2tD4jk2/ZT7vMEoEm5wzQxyFqVqkoDKMAWXBaIN3HpOTOPiAOGSct8SKATAUclbokjEUnIMUA4eLOdtx2PvOYk4UoLEGbcTJLgRgKUHk6pLMFOGxKiN7QckU9cY927UtRsN80pBiTykDjRXEhXGGFBO2OiKtqYgR7zCqyJpAHM85ibM0hBFTRWQpWvhdgzahH5hOOlSBfhyw3jOGEes7DAZdCg4RFhsv13u9WuGdQxsFEmkn70nCii2e2WRCHge6psFoeZgVV2DBFJnjGllTaClOqLrWYgpYq8GKk1hrT8610KBlLSQK1ktoorNWSnYxMJlMGcNWXN8ktHLisPUNIfZ4J0KuMlCGkaYRZ66wbS1aGXGiJ2iUpeRajDGpCtkZWxAObHFo0wimIycoBVUMyhiMViyaltCPUJK4FlEQExRxnQYKgVjD5DKp9KxW10y6jkKqyAIt7t7KAzZGHMci6meMtxLmpSATUFqLq1ZrQpR7E86SSxGmryocLOZ0zlByL+87ZpxvKbmglaZEuQ7GyPfNZUTnRBw2qBzxTUsKGXTGKxEEvbKonCgxEQA0eOvw1gueJNf2NjLFObIGpQrOGcY80CiFSpGQBnKIaONAyzpy1tI0DQVxmxpr0aZQ8shs1tQgSUXfB6xrSCnSWEOOI0YrnLY4NClL54cqgvvQStzh025KColJM6HfbmgaD3FAa5h0jvm8JfZhf98WSq5GaWENQ8EqRSqBo8WE5WzKpDGsrq+FYR9gPp2xXq3onMc7h7UG02hi3OKNwplEHgNOg1FeCl0po4qt8KNCHge8VUxbx7QxmBJxChqjsUY6cqyxZDQT38qBJQVSGaEMkDYQhS9pjGBBrLbkoir2CeEnU9Am0TSGg2nDtG3QOjOMPUklVEo4Aykl2tYR1gP/9X/xn/1KtP1b8PqH//hf/N6urXwnRKgsjrE4E+Fu514S4UwRpvVgc2DkUFYZhvDGLamrq3DnXAtzEc7izOKvJUBmlxJftIgxyWvcWsSd2FUX7VQOm8XKIXDnSNw501KryU4Oz7GTg2qY7dpERTjaOXx24q8O7Ntvc6OF57gQgU8HScTOTW1Rr8KFW9f20SIimxzIVRVe1F4o3TFc9wfx6qTaOY3MWA/ju1bc+jM7l9NevNFVpEnC/Nu1je6+MwXGQ+l8GGdvDtw7YVknaaffi8lKDu/DgX0jItUW1J0YpIqImmHh9g6yODHVWflmLIpRmMrYDAuPcEcFb7Frz45zEbZya/di3XDkKE5acIszewEy+SpuVz6oGescbFQVkKvgNZVrtXOyFifCXPEau4rkiXBaVRG0QZzbvZtPxkaEBV3d1SKMSoFCpcJwNiV1trpkqxNPGlb2oTi7tnu3kjboNLEMS8OwFBEzdYbNqcNVocz0ee/a1L/gAEOLAGD7TG5qSM5Y8FfCKN0V2NFq72ZOE5nLYabZ3OuEGTxKq7KCGsok83EnDsdO7XmM8t/eCCA7B7TtRcgMC0f3KrG+60WYcCKouOsBs5Wwb50K7noAJUJfqNdknFXhuBf3oe3zXuDboR+yF3e9DoXbtyzNVQ0ygvoZkHljNcXr6qilMmnZOzllDOX5cifaq4o6G47ls6/vGHGyZWFeunXZFyZ2BYvUCQZhOK5M2FbXPUAccHGi9wKYGXIVAe0+LMttEttTMTb5axEg7SqQW0tuBAOSOs0wN4SZ7G+TiyRjZoXJL85FEW5TK6L+5sxhYmFYVkGu7ApMO8FMi7Cdds5Vtf/TbcV1r+DNXlDZvjuX/+69dHojIukA2xO7F+p3f3eHcrGbRJyJ7lDqehjn4g7FyFwoVq5Z0eqX9mAUFSEgztxSneXFmV8S6IqRwl32is1djd1Si3Ds9zxV14/dJtwqYSrj1I6F6bMgnORCFfZkPu6c1rkRcS12sifuinkKMLVwYrd57z7dYwjGshcFbx86hqXoB7k6OePUVDFZ5s1OoN65dncC8U4oNxUVEWdmL6ju7rtuk+uevhNA34jC8l3UvgASW2nhH+eVz7rNIpC7Wviv99Ew03sxccfWVRm2pxUJUQQpJN0DgmnQQfbfHXvZBN6InKmGiDrF9tRhx0J/ZGiv8x5lka1iONJszyQAEv1mTu2u/879nhrZn2/e1qwewniombwojAuFjrKWu4tEv5QAvNTKHLFDqf9f7sk7p2vsZB1I8UHezwQIU8X2xLA9kT0rdjJPd1zsXIvC2yMpavyi47hK1lWM3wXZyfWV7oo33QzZQpgrNvegP5HP4FeVk6vFBW3GwnBgcZvMf/ff/puJtvZf/yN/fa8Ut2SlySrTuY6+H3G2QaEZ8wZjNa6A8RYDqDjQ+Y5gDEpl0jgSqhsxVbcLBZy14hSMhc5m+niNVw1WO/y0E8FzQJiVKtE4TyqBrmvE4VQKaCOszQLGeYiV84f8u0ZrSomMsWc+mUrC9awj9AMaI63AowSyKCWiErZAShSdCSowcYJoGMYsh8dUGGqKuWsm4tRLiZm3WFVIBrRzbIYN3reAph+3eN8wJlUP0wqnCk5rbm5e4/0Eowoha1rn2A6DOEKNIY26ugMVTecY+jUpZVzRWKUwxrIZB7TzIJ0kxJKYNw5NwjhF1oGSFarVlavnySVhXUNJA1opQZiqDCR0imhjyDlRVKIg+ICioJSEirDNI75xdBmZ/KKck5K4Qr1W5AofkZb0hFEFRabR0tKeksJmcZBao0l9JIaRQGDiOgoB7yWBvWhJ/BaISkAradHeh/VUJ6f3Fq8cs6YjxkjRmRRKZcRqcknorGmMPMB4b7EuExBBu8SALgpfxfsQIm3bUVKBIkInbJg3CdsohtGTi2bTjyLsNYYQEl7UcLQKWKWxGg4POtbrLVkHvC70w5YUC9NFw9CP4or0msZbwtDTmob50rFZr6l8DYpvGLdbtLNoq1BReJHeGlY3lxwfLiVoKIxYpQjWUJJCGc0QIt2kkUA2bZk4y3ym0V7RXw/EUBC9SBFrgFxRmSEGEY515OTkiLPjCbEPbFcjV1dX6AEC1aVsq2MzG1pn6azm4OSUrnVcX95yuxpY9QPGNoQ8gHcQIsoZchmxWnH/zhG5wNnJIVerFWHMlGJQWmGNZ91v6SaN8FuVBBfNlx25FDbrIGKvEUFOoQU1ESM6Z6wKLGYN81nLYnJKKoXLy1teXlzShwAJci0K5TIKg3sc6BrPcjnBakM/bHjx6lrE7TziXUsohpgLKY40foJi5O1HJ0waCY27uHjNZrslG4OJkTEWrMpYa1CM5JRZHs64d3oP4w2vrq5Zr0eG9YhtWhGElSGUQhgF+TAOPXfv3iWEgWGUhPvN6EBpYrSEsEFrRSbTeM1i6jhcnuKco/GOdb/l8ecvKCGjrZfKby4oi/CrrQYi9+6fMW8cw5B4eZHYjJGYFGOoKeVZHM1WQeMts3nH1Ld4NC8unhOzZrUNGOUEE5IV2miC0XhvUURmkxZTeb3DEEjrROc6eQ/lwUIKGVMiBwcNndN84Z1HBAzjGHjy+QWvr25xSpOKjJUyDqMLymTmkxln79xn5iyNn/D4yQWPn79A24m0czlNxqFsYVAjy8WM0+MDGmswznB9ec2mH3j1aksshaBAW0ssEcpIYwxn54ecHsxxXpPTghg0P/7JYyDhXMdm6BGjeUAbxfJgxuHilLaxdFYRIjx+8ooNmVFp0pgIYYtvp6SU6OYtTSmcHC/xRnP5+oJxndDO1zVtyGnAWEPjHJZCqzyLg6mgcVLi5eubui4Mq40EYg1jlj0nrlku5ty9e8ps0jBpWsbUE4Pi408+5/pmKyiREGiNHAgogZQTi+mE87MlTSv3gJfP1zx9dkFpC23jGcMt3heG/oazk2NmU8f9O8e0bUvf91y93nL5esWLixtc09KnQYI/rWIynTLrOu7fOWQ68aiU+fIH7/P06XM+/eQpYdzQNFNKNpQEIW1x3vPg7hEnR0fMW8MQRvpt4bNnl1y8XqGLwxknomtOZJVpppbTwwX3zs6EPb6NfPb4JZ9fXEnR2ElonFJyyHPTjvtnB5ydLlAlo7YbLtcDm23m+fMrMgi6RikUicjAwXLG2w8e4K3cv3U2pKT4+NPPuby6YZszBU8oPTpmmpn/t/Sk96vXX/dLVyfjLoDLX4Vf4EdK0M+OwZYaKVz3h3KglsOQMP526dw7Lueu1TBONP2hpblOhJnw4/TM7Flxu9bisRUnU39o3xzskQNzduKugZqErWvCeGXw+XVkfddjh7JPAs9WQSvfIU5M5YPKL90JdnaT6E8cdpv3KdDaCh4gV/frLpxlfcfLIS3IgXVyERln4o6V7yZu06LFTRYmctidPQmEmZFAk01mOLT7cJiiwaTKLZxoxrnC38rhLlRhNTlF9yru2YXZiZi8vmPJFrKjtg2Le2r6LO/DpHZM3WyrYFeFalEOfQAAIABJREFU32xrEExSNdhFnEAU9u6g5nUgTo0cYBFRDdi3vzK3b9rsawjQji08HkjKuqnMzqLAtGrPQjSbcR9iNu5aoUv5ZW6h4Rdc0W9e41R4g+XcSoBcls8Ulk3li8ocDHNhaY5TcUrZ7c5BVhmKMyvsxVdbxuUUW92n4gbTrM+ljd/ditjbvUrVKSgc3F3YVzEQW+Ea3j7StK+qOFy5iXqIbM89/ibtebQ7N/FwoFjfcbSv3/QOqdxib8OblmStGJeOcaaZPhu5fL8R19aqcic3CpUyKIO7HgVL0Avrc3ts2NypTOYbMAPYrTj1bHWcUsBd9aTOoTrD5fue6w8yZlC4a83sM0Ux7T44KVtFtx7RQ2R40DAsFev7EA4y3RPD5Lli+jzW1Pa0D5JCUdcuvPim4BVef9kx/zThNpld+FbsNN3zcc+TTk5Eju27MlfbV0UCtpJ8HxR7hm52sr+s72m2p4V0d6SMhvaxru3j1dlb14YOmc1dccX3S8PqgQhC/kZx9MOI3RUmptVZ2NcwtepoffmNluEoo6Nm/vOG7lVGH3d7tm1qhaEtrE1YPVC8+rqhuMLsY0N7UZhfx1p4kiKB2uR9uJFbF159TWG2CrfS2K2EFII48c06VFFc2t7X90T0yl1GTSPqlefkzxRd7Woohr0YKfxquTYXX9cMZwmzKsTO0r3KwpGuhQkR9C2xOg63p4rhOJMniflPLaaHyYuEagTjsQsTsxvZF4tWbE5rcSZT97hEP6tu7voyQ2Z0itUDzXhQCL+zYugdeW05+IFj+fOwZ8QKLqaKwQrWdw3X31bo0575tKf/f484/5Ow35NU7TAoGprbxM1Dy817mTKLKJcxTxqaK8XBzwSHsPsOggyRYs/rLxq27w24NnKdNHkw3PlD2R9DZ+hepT2LVQRezfphpixGpgc9fe/o/mxC+1q4qLt751hdvP1SM8411+9B6grTzxRurcjO/RLPehdMFlvNcACbO4o4AR0NBz+l4gVg+myUe+QqS2FvW7h9qFl9deDo5JZ3Di652M5Yj47r75wwfVIZtEoKsjterQ6wvqe5/SBg5oEcNO1PW05+INzdvrKDx5kUTS+/aOhPM+9/+JhHs0s+XR3y0dNTzOOW5Y9h7BTNddkXhFdHmv4U/Ncv+cLhJVYl7N/P/MkP3+Xwuxa7LYxz+Twqy14eOwkude/e8t7pBS/WM65vJ9jvzVh+lMiGytetzzkpE+aa2/cSX/rKY2Zu4OV2xuM/v8vxn9cQzVbR7pz5Wv7+q28Ujt5/jdEZozNPHh9jLy3LH+32M4UdBMFnBljd14TfvGU+GThpBrxObKPj+Z/cYfapob188+xRjGKc/8JN71/z+hst2roa+GGLJW8l9KWoKK3WumCUppt5lvMJ08bL4SRGXl9dE0IPRVK2UxQ2ozEG7TSHi5blosG5gnGa169u2GwLV5drKI2EHCUpaVkvbcjvvfuQ5dyQx4HVdsP19S0X12us8aQQJWTEWsiRlALHJwvOj5doVYh9zzgULq7XFMT5WgBtiwikKmOInB0f03aeqDPbMXDx5BVOGQk0WvfkErG2Ok2HNY8enHN8tKBRhX47cHF1zeXlJZ1pGYY1BfBaRD+XFYXI+fEhy/mEzrZc33guL68IZFQwbPoNxmS8bgkjzI8W3Ds54njWYHVmtZ1ye7PlxfNrUsn0QxTXcEbaMqPm5GDC2w/uYJ1ms71mtVpxezuw3kZcFFHaaoNRlqwKk0Zx//4h04kDlelXI5fXK1ZrWPfiqstqxwSE7c0lB4cLlvMpp0czUinc3NwwDIGXrzZYPyFVZMGOjWu05uzOIUeLGVlFbm5u6LeZ1RpQElYUVcK0Hmdb1Lji/XcfMps0+KYhxkjf9zz/7DklKvqQaJuGfhyhETf1Ytpx7+yExbTB2My4Drx+dc3FastqC5sxSdRMdb11k5bZtOHevUM6ZxlD4tXlDZernpvbLSkp2sWMMQVcZ0FFusby9ulbLBdTlCqMWfH66pZPHj8jKYsyDXG8xSLheIqRR4/OODqYoIwhbHvGXHjy+XPUdaS1nnGzIY0R7RpCHDg9XPDWB2/jdRFObYw8+ewZF1fXqKTxRvAGujis1cymLV949x7OSIhRKYXV5pbPPrsg3EZ825CiwjnLMPQ0RnPvzjH3zpfMZ42EmMXI7Wrg40+ektY9pmnoBymyTNuOFAe+8eF7zCaORQ15KsXws59pHj99QYwJlIVYaH1LLorzsxPunR8xnRhUidw9OeDmeuAnP/+EF1fXZN0Iw7qyJu/fOWM58yzmnQQdpcL2tuflq9dcXG4YQ6HvB7TxpGQoYeCt9x9y984hiiAFnqi4unzFD3/6GVq3kAI5SphV0zlWq2s+/PI3WC4OWLRSkDmae4btLWoTiZtAY1piDMwmc2Lc8N47dzk4aJlNF5S6vx0vOz55/JRtMSRlSYCfNPRpxdvvHnMwnbCcdPjq2n+xbLi53fKTnz4WpnIC5wzDMHKwXLCYd5ydH9AYi3GWo9MlMRR+/NGnXF2vGJVmO/Q0ztN6w9HBnLvnR8zmnon3pH7kpt/y848vePFii24yk5nwvXOJNN2Uh+/c4/T4QFoYc2KIHSeHR/zFD37KZggMfY9ynoh0LcznHXfOT3n34TlWWDUsDzt+/vFTPntxg3M1YTUrlC4oEg8f3GG5WIiLW8H9uwe8vrzle9//Cb5bksaAs4qchb1Nynzh3UccL5Y4MkkNpKj44Y8/5vr6gsYtWKcRjKXRIgA+un/GYmqZNp6iPdYojtqWzx4/5fNX1+QUxR2fAsYqjo8W3L9zyPF8zkT83nSTe0znE773o5/hfUtGE0uW9vqcuf/gnPtHcyk+kblzOCWM8KfXP+Jms6H1U/rrG1RrMLrwwRff4/7pESYnJlOH1prVumcImU8/e8k4Brz34hgvwsl96/45k2lL5zTeWUrWtG3Hj374E8JakQHXtWxLIOaRphjee/shR/MOo+DR6RFPLq746OePKaahlIS1UMYEWuMazZ3zY05PD3FGikH3btZcXF3z8cdPaJwjxsTMeCwK23Xce+s+95YztEpMmxaYkEth1lm+9+d/xbgNeL9gGAZoYdOv+dIX3+X0eEnjoTGGrus4nK3pGtkbxjBS9IQYNqQ48Najc87OzoT5rGHRTjhdwsvFc558/hmFDqMyxjr6sefo/jHvvvMIDxx0rQTOJcX07Udst1s+/vw5xc7wvhNGfHE8PD/l3bfOcQq6yu/tx4z1LWn8lO2goIyUEiglMpt0vP/OI6adZ9o2mAKlUcwmCxI/4rPPr9FY5n5OKYFtv+H9L9/n9OSQEkYaWvx0wvF5w6rfcLtacXkz0nRLwrgFlXCq8MG7b9NacFbT+YYUSsVJ3BdX7s2abYzC+s+BUv5GPx7+6vX/4ytO7R4RsEvTVpk90zB7tRc0hmMJ5jADzD6RwxPU1uDqyEqNIhvD7Vua9f1MaRP4TPtJg7+ExeMa9mTeOA93jMvPv61o769ISRMuOtonhuVHhdiofWJ87DRmkAP+5XuOmy8J71yNBbPWLH4uAqapDMTs9F5sTl5x9UVFmCv0oPDXLcuPJHF6ONB0r/PeUbwTbJ/9tmN4p6edjmxvG7qfNcweixunuRXxKMxEFIoTAwVefdWyuZ/gYOTyScPssYxV0RLolRpDmEmy+uqe5/XXwLy9omsCTy9muBeO5Y9lfNqrVIOj9L798+Z9y+1vbXE+Mly2uFeW7rmmu8hvMAvtmyT57bHl1TcK6VhOsOrGMX2s6F5qJhsJaCKwD3xqXo/cPmpZPdSsvhBQUdM+Nbg1HP44CCJhyG/cb0ncUq+/aNg+CpAV7VODv4XuRRWZ+rJv047TCUXDZ/+BI56O+EkgDBZuHYsfmb3A9ouhSsXA6q7h+sPA8s4tjYvcbFqGT2csfmaZvEh0FwEKaIRbuT2ybE8V66/1zA62bDYN+uOO2acw/zxhhkQ4bLHrNwFK/VJz8VuRk/uXeJ3373H+Hb0Xoc02kTojnGcNL3+9MHvrGq8Kq94zbhzzHzQUDcPBFL8Szmu24jrffNGx+o2BbrImZ8V6tDTfn7D8qLbTzt1efE6tYn1muPz3Bnwb6JoNqSguX85Yfs9JWNu8E1TDxOBuImFhefFrjvjhmoenl4RkWI+O1y8WHH3H1QApg3/dU5yhvzPFDIlP/vPC8uQ1f/f0GWM2xKz5/h+/h/vum7ApgHDYomLh8ssK++GNCC06s/6y55MXR5R/1nH0lxt0qCn0vYzvi286tg8S7fkNTTOyHTxPvtIy+ciz/Cjh1pnmMu4xFCoXnv2uZfHVV7TSzMUmOC4+n/Ho/3xTINrhPcYDy/TZyMt/kPng7gt+6+hjLuOEP3v0kKv13eqkFMe8FD4cdih8/m2NvbfmwfEVuShWQ8On7yw5/Y6lvUr7vSfMDf6q8Oy3Nflez3t3X3LarThpVvzBu1/i6vmU+/9UCjQ6SMHK3yZWDxzb80L3wSXn0w2dDTx9tOBmcMR/NmPxacTeBvyrLXHRMBx4bt7RbL7Sc3Zyw9sHr9lEz0+enxL/nzlHP6wC9Wkr7uNYGOeK22/2/Pq7n6BV4chv+Gyz5PG7S/LvH9K9TvjrWNng4h5e3bVcfqXwG7/1Y478BoA/vP9F8h9POf/O7ZtwtCq2Fw2vv5U4un/FeTtw2GywH2a+9/gB0/+1FfFxKL9QsDN1X9f4t2+ZtiNDNNyMlvwHM+afRca5pnsp+5KEfik2f2fDW2ev+fLBczKKpd3wf7/1Hp9/75yTPy80IREnwhJOTnH9riZ8fc1vPnrMl2fPAPjO4m1+dPyQ9/7JQKjryfQybhnF9dcDv/PVn3K3vWabPOsPPK+GKZ/9k3eYPU2MS4u/itUlbPj8Pyx886s/ZWIDX5k9ZWIG/vT6bf549SVO/1QY22GmK3taXMjjb6x4sLzl0fySu+01oRj+aP4u4x+eCB+3zpHutWArxpnm5bdH3n34kqkb4Vvw/b96xN1/LmHncj+jcq4hdnD15cLyvVcs2oHzyS0/+PAu/c/n3PmXZT/P+3NDbKXIs/5az9//0o9pdOSbs09kzyuOPzp6n7/8/S/h1oowVfhbWe/dq8Sn/7Fh+d4rPphsuDe95v3JC/7p/S/yyfIep9+VUNJ9aFgoNL9xzX/59vcBmJmef//gx4R7lv/57q9z8xcPBCPoFQnBWazeynzrt3/KWbPiP1p+n01ueBYP+OpvPuUfX/897vzLsu+62YndL/8OfOu3f8pJs+aDyTOOzlf8bDjnf2u+zk04pLmSZwsRemFzqinfvuTDw0s+XDxhZgbC3PDy28/4g/AtTv9Mipuru3L/aa8yr3438OvvfkLMmnvdDfeaK8Jdw59f3+enL95n8YmE07HdXQ9F+7sXvDO/4bhZ8/7kBavUMDMD/9dvwMt4FzPqyhLW2E1C5X9HRFulFGRBC6gkoUqhJLRTGBJaaRbTGWenhyymnph71rcbuonl008/xSiLcZ5iFZtxIMSBs7Mz7j044+hwTkkj47Bm4ids+sTl5UdMJ1OuLleVOTey2ax458EjTg/nzFpFHByL+ZT5tCOG5zx/fYOfHICSdPaiwXnL8njJ8fEcpyD3jhAyY94KB69QW9AbSbgfE2+/e8rdu6f4RtoVVv3AvHievnxB36/IJdNMJowB0hh499FD7t49ZtIqVAzM5x1+6tFe8eLFDcZPRDTRihQlrOb+nTPu3Tlk0iph5TYD7dTxyacvKHiUUox9wluFd5qjxQF375ww9wnKgG87Jq1nsxl4dvEK3c4kTV1ZjDJshi1ffet9FocdOUf8bMHscEL+/Dl9WtM5xziA1bDpN7jG8uD+Pe6eHTDprHzeWSSpQEyBmBzbJAKCBAgFzu8dc3h4wN3zI7yuTEdXePbsGfNZw/WqRxtJdje6IZfC0eEBd85POFpMKHnL0azl5nrL4/EpY9KkHQNpTGhnmU+nnB2fMGmEBZtVQ5w0qJD46Gcf0zRz+lJIWpMGCdQ6e+cRh8s5Ewu6MUQTJKDLws3qBm89Q4S29WzXaxo348H9M44OPFYLfmIyn6FfvODq5opxBE9HyZExj0wnnqPDBaenJ8waS1GZbRCG8qq/5ZPPXuInC9wOTB9G5osp56dneJfwtoF2gjJADKTxJbd9gQRN14J2EAfeenCfg3mLNxlrNX0/0miHMfDk5TWFCdtxQLnEMAx842vvc7jsUHmL9y3j2OObBX0/8urqGhUU/VAAg7WK5cGU0+Ml07Y68opjbAf8pOX6ZsXjz5+waE8lFM7XZPquYeI6lrMDZo0I330/cnx0wNOXF6gYSWFLMhqUIeXC0cGSo/kh1iW8h9APlBEOD6Zcb3oJqwuF1nWUCOfLU84Op6Q8MJ1OGcLIomlonOXjx3+BNg3FOcYcSFpxenbIe+++jc5ryEba/buOSWP45PEzXl1fM2mnwrS1mrEfmHVT7t55SCwD2opYdt6ecnM78t3v/hUYQ441bKAfaLzj0b0HWKsE86szfei5e/eMV5fXbEKgHyO+bYkoxu3I8cGCg24CBkwWvvLJyRHzBfzwJ5/AOOBsJxgXb5jP59y7f8y0bWitAaNpiRQsP/1Xn7Je3dAtT9BTT4mJnDPHx4ccHk3xzRTGkcYpTruOVxe3vHz5ilI82+0gjkCrWSwOeHD/LW6vLzjoppgYaX2Dt4r753f5+edP8K4lammPh0w7bZnPp7i2k/ClBLNuxsHBIc8v1xgnhbVhHHGmYTrvuHfnLqVkZtOOPG6Fp2paJrOne5yBUgqtPWhom4bz0zNcKcy8JuEpWJaLA/rNwFhCLappILCYdZwcH0BKzLo5KQvPWB933G4CH31+gW0bxrylcYbtesN73/qQ6cRh5VyLs44v3LvLOPwcPW5pG8dmHDGtp3WW08ND3rp3QokD3npyMTQzx+vrG+YHHUNJbHLi4HDJOPZM557lckFjHFZZTMX0HMyOOD7MPHl2Sd8H4hDBW9bjloPjGWcnZxirMSViVBQuboZ7d8/4i7/8VzTdkjzKwbcxmqab8s4XvkB/e0nrNKTCCUc8ffaCfojCDk6GWAwqK/zEcOfBGVZrWmtwJLqTIxazOZ89fsKQpACZ0BAzziQend+j76+Ytp4QoescqRiUSjR+zhjWBNY4P6GkgbDecO/snK514nq3lhQiR4s5V7Nr4hhwkwVJGYoyWKeZzVvhxRqHIoI1dM2MdtsznR1ysx7RjRf8zHbN+XzJoZXgwnGbaJqGrmsZxsCim0N8Ai4wxrW08Bm48/Aei+MF/c2VnDBzZta15EPF88lz1ptb6RxRmWEr7OLl8oiJd8TQo3XGoFks59w/v8Ozp5fEYYVSij5v6A4szioOFwcMt2s8mpgHGueJARrbYNJAGla1/cxwfHiIwXAwnxHHrTDRvfBuZ7MJs9mEy6sVKmVxRhdQOfw1Pv396vVv8yXuRmHm7dKXJXzjjUi4vqvoP+h5cH7JenRcvp5xdWA4+q44KpNXqKkIks1l4tVXGtYf9nzp4TNS1rzeThgON9xcTjj4xLA5MSw+DYwLCSBrX458/vcmHH/hgi8eveAmtMQzzc9Pj7kd5hz/VRC3G9QALjnsbu4Xzt96zUHTM2bDTd+w2Zxgt7BL3o6tOFx1KDz9ux79lRtOJz3eJJ68OuDZWcvhXyqa67xP8HYr+d+f/z1L+5VL3jm4xuoMp/DjyRm3dsbyx5IUbSiVMykYg5e/ZlEf3vDO8oYDv+UH7h5XBw2n3zF7F6u7DYRZQ5hqbt9SLL9ywYcnTwH4eHLEy8MZ28sDjn8gzFyzDjB1ZGdoLwKf/yeRt89fMybDOBnYnjqGuKC5VmyPDH4truHuQlK/L75ZuPOVF7yzeM0mOvrk+El4hBk0tje0UZzWqROR5eJrHatH0H7lkoddj9GZjycnmB82rO9Y5p+FGrj0xtV7e98yfHnL1x4+JWbNy4czLp4v0H/mceuyF+h3jsj1HUv3wRUPDq6ZuYE+OW7Hho/1OZN/rhnnItiaQQTPohU3H2jOH17y9sFrOhO4nrX8xWi5zS2zp4jb+Tbu3cxh5tj82pYPHz5l4Xo20fFk+f+x9ya/lq33ed7zdWutvXZ72jrV1+15O5OUKJqyJFiCGEmGASN2YgQexDCQaYD8CxoGyDCDIAiC2EYQJzGSeJBYcsPIdqSQEkVe8pLF21Zzq845dfrdr+7rMvh2nSuPzEkSG9ACarbrnLP3avZa7/f+nmfMpdxj9Cyilx1umCF8xCxTI3b5AF597ZT3t45xQfF4tcOh9szPt7j53QbXV9dNdNUmJubNt07Z6VXcKJbpGJWe3+NdZJfTP0kJix1++ci9+qWaBweX7PVWbGcVT1fbvOiPuDLb7H0YEX4jUNuM3R/+Dccbt89QMnCjt+S0HrLqNRwtbzB5lCYrsmkHpBHfxT1N/XrL3e0FN8sFE1Nz1g6Y9BoOX9zmxh+tqO6P8GUSOmXTjnY3p7+15hsHz/ml0RMAPqtv8PCVm7iH/Q3DNyDTr0G2ju5+y6/dfsrEVNzOp5x1I0IUvLjXY3iYpXC/iXSTJCWsv9Lwjde+YGVz3h2/4Hm9RbeveDi4SfmHCb1CBL1skZ3h6u0eX//VT8nkxvQeJT1lOR6Pmf3JXSafrGj3CqJUqRW98qwPMn7njQ+Y2R6lailVyzv3jvnPv/o77DxMx/fLxqWpI+1Q8u4vPGFkGkamSefgapu9r6x4/uQBvUvIZomZrZqIXraoB5b3D1I4ODINF+2AX7//OYd7Exb/+C4gv2yfZ4J6L7Lz3jmvTy54rX8OwPlgiA2KP1VfpTit6HZ6BJ1dI1HWDxxv3j5jr7cC4E45484rM/7Z06+z9cmG93nVpYnTvqY6EPy1dz/kx1e3+eXdJwxUyyu9cz4tDvjOVyfc/MPULv+z0xDdWMB+w262pqc6ap9xe3fG0a0S3zcpcG89Zt7hS029a/j6O49wUfH1yXMqn1GqDu7Co+03E84gfZ0kPADQjBXjt1Ko+O7khLXPsEHxgzvvkM8TDidk8noKoDoQfOP+MwC+OXzEMvTYUSvym46/dzah+Bdf7utupCimjvBrM16fXDAxNQHBvlnw3772P/PX679DMCPasaJ3ka5V7Vgxe0PyV7/2AwKC2/mUyue8mp/xjy6+Tn1DYFaK3qVjeTcnWweqXcnk1pRbvQU72YqxrjDC8xtbH/PwwQH+J1vIWbxu5RanNcvbI37z1U/pq5aBahnrirvmiu5A83vvjdn631I7OjHXxaY9K/jbv/A9Hq33ro+Ty1dKqp8ekC2S8E2E1PhWClxfcPD2KQPTcW8wZS9b8tZrp3y0f8CzD94gb+M1gzxhIeA/eP8Dvqi2eXf4glM75r3ec2a+RIuAHUSylcdUG5ldSNzhe++d8WB4RV917GQrVj7ndw4e8l/t76Jbg+2JaxauV4I3ds7JhcNIh9kkzO8Xz/ne4DW+v3uPwQtPUOm7WYTI+K0rfnvnIctQ8Nzu8CA75538BU/tNn//4C8CafLLrNN3SFCw9ZUr/tLWIz6vblxfV7/Zf0R1L+Mf7X6L3gXXkyP5wlMdGH7x4JB3BsdMbZ9StQxlw7eHP+WP37+P+9lO4oNLSTH3NBNFVlh+e+chz7odbmdTLuyQV/Izpv2SjwZpEVj+mVvlxatww1h+beez698x1mkx5L3tF/z+3j6DZ3IzjSOvp3h+3u3f6tDWeoGWMo1Im8SZdcEm7mI2IDOKN+7fp8glvQyCM0yKIW1wSCQ/+/gJLhpQ6QE/2MBr9++wtzXE+5rM5GwPS6qmI68q7twYczmvMJmgtRaT9Sil5J0336AcSHTXIkyOp6MoMhbrlvP5HN/VKHKkVAQce8MR777xKrGrUnA87FN3LW+Nc/7gD76Pzgf46PEetElcv7ffegetG0T0aFVg1muGwwwvGg6PTvBSYLs1wSl807GzPeHurZssV1eU4zEuWGRuGE/6HB3/SZK2SIO3HoWkLHo8uPOA3W2NiB2dD+xu7yD2BGcXV7RTS6YVWdGnrWtMJrh774D9/W26eolSPXTpMEXHfZEzXy/oEEgMMaR9dOfeTd54/QHV8gIRJHm/pG4bBv0xH/7kY16cLRG6pG0twhh6ueb+7dsMe5osg7pe47Xk7q3bZGbOp58+I1pNlkW8yFjNGu7fPuDdt98hujVaa3qZpMgMBM+HP30GlFiXjhtrPf1S8eYb99nfGuDbmkxvIyeC0XiFjQ0fffoFKttGEYhR0DrLaPuAvf1b+HZGTxq64JADjUMwns84PV2SmQEr55CZYTwe89prr9FVUzIsyhSoAPduHnBiTvn86RcIk2Rrq1VDWeTcPDjg7q27yLDemNQ7JiNDz8Dhsy+QGASSVsoULu1u8+Zrb9AXgVw6XAStM4Zln3XX8OjJITQtqBR+u3XHW2+9xfZ4GxkbnHNk/YK2bbm5f4MnT4/ouoCTOUpI3Kri9s19tid9toYDgqtxzpFnJfmuYdXu8PR4msRoQuC7ljzPGQ6HDPslREOmDesIk15OIQd89PAxronkWQk6owst5SA1LI3w9HWBj44YMrJc0y9LBr0SveGa1rZN562HvOgz2d6iXc3ItaTo5bhwQYwRI0ViV4dIjGkEJjiFiJJB3oNQo4sBZ2GOj4KmdvR6AqkiTd2htWZQjihMhlQZhiRva0XH1taYXi4JEhrXkpk+9bpi9/4reBKbptAKI3s0zrM13mZ7Z8K86Wg2tnoXEgu51DnLdcWgp/FBkGcF9WqdBGGZxhQ5wXqCkNhMogqNNZqqbRiVJbnS9OkRdWQ03OHFyVN0VhA7i3MNI2PYGm6RaZGC4rZl2B/SupYsS03KtrG0rSNXmgiUvSHDwQQRfWKNSlDBoSM82NljeXaJd56OSNc1FNtDhpMxhTG4ECiyAk1EGM3+7gGHzy4ecGtnAAAgAElEQVRoI0hhECS+d1mWNNWKsugThEQaQ4iWXr9MuARhqJuG3iinsQ1agOgCmTDoDXvc4xMD1Ed6vZxuI+EK0dFZx2i8iyBNLnS2wiiV+Kk+ooxitu6S2DBIjIkEG+gZTT8v0MGTqzTGLlXG9vYuV9MFwSpsB0Im9E1RlBhRYrTDdhIpJJ2AQb+PC5LWRmImkKagbrvESI0S7wXGZNjgyHzBYrakyEsKVRA6yLICGyTzacX93V1W04rRoA8ihYcugMr6XFUNIeRI7/Auhc8y6jQ2rwzogDZpoanzPrG5hcaLCp0bvJa4ZYfrGqqqYlD2EDoJAtu2Q+kMYQxZnhODBRTCQGM7hgyYXS4ojSS6gBY5RjnyvMdytaBXpOOMLhJES91k1LZjVPTStEzWI2AS/9mlFp+QkVokfu7+aIeLs0u2xn2MzAmyoeuSiDF66Pf7ZGWfylogEoNgsrVP2wWEsvSyjCgEWd5HeonJ0gJnaC2yUGAUzcrR1BFFQ6YCRaYJNjDt1lSdY7qqCVImhm+AXq/Pau2YLheUvV5qqUdYrtaEAF3nyLShDT4J32SkKDIup2dk0rM1HBJtR5QZdetpuhahFc5HlCnofI0yBiMk8/kSMSoZmhwRPZ2L1Isl0imkyBDSY7FIHZnP1tigOD45Z6fM8SpQqAzbRULt0GTpWDYGJQIx2uuJhnq9pt9LCCjXJaFo8JFCG1znUNIQrEDpiBE//+r/n2//bm9JcqQ2nNnU/pCNxw1NQhD0JZO/fMK7gzmv98+pQkZ3oDmsJvxE3+H27yl0mx6mVJtQC/WvrvjNB49YupyDYsGDgwserm7zeX+XF79wi9HjNIaeLVJjqtnLuP9bT3lzdMbW5oF45XMeDK74/dOvsv1Jatr6a1M7TN/U/Pvf/i42Ksa6ZqAaPl0fcPQbK1Yf3sFtcA1yw5NtR4L3v/0JY9NgpGeiK35a3GK5l3Nob7H7QRqt15VPo7StJzyw/Kdv/Uu+O3/t+mGvUJbmwDD70b1NG5Bku88V9bZi/M0z/srtn2GE57idUN63mFc8P/z0fbJVamPZQX793uUvzPnr937McTthpGvGpuZZvsXPviFpjvpk65DYjxHMwnH6zR7/2Tf/MQ9XtzHScyef8lm1z+VOnyeLV9n7sEvB8HwT6OwY3v3Fp/zi5Bl3sis+rm+y9jnt1zSP+wcMXqT7ppe4gOJ4ycm3tvn2v/dDbFBMTMVXei/4g/5X+CNeY/t/TcHXS4mQ3nA9429M+Y/uP2ThetzKZ5T7LQ/3bvOd5j3u/eOw4SkqTIiYecf6Fw1/9d7HrH3Om+UJR+0WpepYtTmrO7vsPLTYgcQsU0C8vJvzO9/6gJN6yEGxYKxrLtWAr757xP9SfhX1B4PrB+DyRUuzlzF7z/G33v0BuXTsmiVH7Ra/ufsx/3L0JocfvEFUJVFBNne0W4arryje/MuPeW90zIPiggs7ZCdb8Uvb8Pcvfxn1nS+f0GXrUbXl2W+N+I9vfkIuHFNXcjuf8rTZ5VfeesRH3307oQjqLxunZ9/o8e7dx/z23kOqkDG1fW6XitvlnH9+f4D5o3gdUssuYEeam/uX/OJ2CrJu51N+JO7x5t4JPxjc5/z/eAXVROx4E2wsLc224O0HL9jOK746es7KFynMylc83jmguT3EF2kcP7+yyM4hfMZ2v+J3tn7C426PV7Nz6IFtv369SCK1uD4/opJEJzHS863BIwC21Yr/ff0ushNkVw3tbpE+31liWO/srLhfXlGqjn2zYKBaDpstvnr7iJOd1xKuYmZxw5zsfM3itR4rmzMwLffLK/azBU/qPb594yP+u/v3GD43ZDO7aY+KJJTb0Xx4dYv3tl+w8gX3sks+rm/C0hBMxJVmIyTbYF2MYNb0eD6b8O7eCfd6V7w7brFR8cnBPXY/jNfiONV4qrt9vnb7cw6KBVu64rQb8cuTRzxtdrlRLDmbKPJFwCwTx1t4cFuO93deYDbhcy4cfZWaqt/5iw390wGqTTgCvexoxwP6+yvuDabMbcH98ipdF6Wj9+aM9oMx+cJfC+iCEbTbgWfrLV4fXVCFjFw6Wq95s3/CPy/eoRtqiquOZluTz7/ECvTKFMRt6TVzV5LJvYTA2TGY1eY4tB6xDNQHGVoGSmk5bibsZUtykRAKwQjKZzZdn7vU+JcuwkTx2tYlY9Nwv3fBF/Uuvayjud9in2aJ1VuFDVYgYkeB3WzNXrbk1I0pZcdzu80v9z/j77a/gpl3iGAIWlBcbhaPZKByht18xXk35HY25Z9VD7g1mHMx2Ub61JLWVWD4rOHsmwUPZwd8YyedU7tmyaUfMNQtw6dfcsFVFwkqtfytV+jN/ttRK4aq5shuo1XA6400ME9BnKwtqo18Mt/n/a1jjPAY4Xnc7dFXLciI7X+JH4gS8qljfUPxr85e5/XRBTYotsyacd4wH0L/NNBMJNkywibwy6eRi/mA8f4FLkq2zJrK5zTeJESCSouExTShiWZvSf7587f45VtPuZnNqELGc7vD3JW4KOl2At1ApYkF0v5YPCio1yVSRF4dXmKD4nY+pRCWrLCYlURaeR3SZzPH0WpMX3fczOe8VpzRxIx/NPtFPl/sMn7qEv9XpImYeltzdTHkn2y9y9vDEwaqYel7/F/dHlXICGtN15fky0C24eY3E8XlF1v8Xu89vn3jI2xU2Kj5WXObL6ptkKDaiM8E2SpsRGrwo9PbaBH41ugRRjgOux0+rm+SaYfdfFamSmil4fOOi8bw3zz5VX779kfMfY+3ihecuDFd0Pg8ki/CNbpJhJj4u53h0/UBXxs+wwjPhR1io6L2BoYW1Wapie8Td9mWP/+99r/Vd+Vap/an1ElekmUFWhpkVHSNJdcZozKnMAJrOwQZRmaMyh7b4zLJfHyFC0uG5YCyKMlVD6LHGEXE4psUYGRKo6Ug2AoV04Nh03RMRjdprCP6QJ7n5HmPZI+GspengEkko2GwjnbVcGNrD+01OiuwMdA6RfAC4dg0qBQ2pNF9v5E/ZVlOYQoynSzrZVnSL3oUJtntRRT4mLAKItP0xgOa2FH0Cyq7RkoY9kuMkORSk8kkMZNRoGWyk9to8dEiFRQmQ+sMApTlAOsdAVg3a4KwDAZ9vIt0NqRAQBoCCmUKgnCpKeRBRk3XWIJ1KBFp25Z+v0eUEVdbBnk/jXtaR7/fo3ENUmUgJchIVIo2OKLSxKBQMqdfTMhUWk8wuQa74VApwe72HlJqynJA2TNApN/voYzGW4uWEhk8QkREDIwGA4qiQMs0omuMoW1bekUfYQqCSH+HDQpTFEnoFjVCSHq6wHqXGshdh4wy7R8h8N5jlCD4miIXiBgxuqDoTdJ+FobGQ904Oi9SK1dJRFawthFtcprOJTxDl46J9XIJXhIc2KrDu0DbOlbrFqEMnfNpjFoUeBfx0hNU2ARKGQhHS0iGWgk6M9gINkKe5zjfIVUKYXqDMv1f5wmtxXUt0UlW8wrXtXR1k84TEWkbT111lOWAYnNsGZVh244YPMF1SVDhIkYabGMJrqNnMmQE3zZowDWOQhlCm3jNYcMFllLSVTXVao0UgqaJ2BDxIbFgfFvR1Q3ziyvMhiVdrWvWtSUEgfUihbUboZDJM9ZVQ920hNAQXWS9aGnbyGy+RmcGLyDEiM705vqS0YQkVWs6i/cpAK7rjrb1BJdCRGMDI51hBLRVTfRgZJIAepuYYm3bkmtDoQw9nRF9YiovZnOW0wXVsqKXF0QXqZqOxarCEeiCIJAEa0IImrqm3x9uBGMSJRIoKW4C++AiBkmIDikFrvPUq5qu6WiqlnE5INgO6QVtvalG6BykxoUAJE72ctEQY8D5DgiEFnwb6epApgbgYhr1VorgIstFTdtBthmf7kKk7QKrdU3WyxHekUtQCNarmvlsSXCRrm2Jvk2/I0AIHiEjVVOjc01VVQgXyKShrVu6zhOdom3TiHe3tqzXFcuqxvmI85Es7wGpqT5frIlB4p0gBHAWqnWLkllC40iZPteuRZIYcNPLK7SUEAKFKhBR0jnLqq1xOIL2oCTrpqVtOrrW0bgkL/OhQ2u4mF7io6NXGhSC2En6+YBq3RGiJpL+nlznRJkWHlfVGq0lJlMYDL71GDRdG7FWEUIgOEuMLXVd09UdpRnQdQ1SQtvVeDzWWtqqxTmHtY7OOzyCVVURY6Srlwx7Ba5toO7YHW6znFUUZYGLLi2KhEAAFusVUkoiHqHAk2RtSim8DdQri+3AWYmzgsW8pqkapC6o20CIik56fAz4xqZrmA/EKGk6T1N3LBcrtFRkxtA2NSpGgmuZz+copQkBmnWNEkmQCGwQO0uMNGgMkXRjVXlL6xPyQSlFoXN861gu11SNTQGuMljfIUwSuTVNCqxFiHibwnjXOtaLjn6/R54ZlI5477HOsW4a1q1n1dR0rkbEgOsivlMslivqpiPXOcOyn6Ro1uFd+lwXyxlCeDrb0HUtRE1T2yQKMyrJ9KLhYrrCe4uIsK5XmwVmh20sy+UKpdSXsjSZFIiLZYVQms4GYlBYB3VrWVQNTecRShPx+JgQN1VT42PEek/dNnhrSbYEge0il/MVsigIWmJDxAFt8P/v3+T9+fZvxfaSDfiyfWUHmlAoghaYpccOBF/fOeJ+ecV5N8QIz1625FtbT3hw7zyhC5o0erq6ldHsGkb91Fjbzda4oJi7kjvFlL3eCp9txun/zEPe4q7mdDXABcUr+Rmv5mfpbwkKsdXhCvElG9JGzMKxeqtjS1ds6YrzbsgX9S61N2iZuLe2vzGOb6RTQafQ6pXeBTeyBUZ63hqe8vbkFDfy16PvLxm/SMHWqOKz+gYPepf8bHWLXDp+afKUnXyd5EL9TRt5M8YeNKyanJXPKVXL6+UpE5PqZ+1WComki/ROO6QNrPcV1iqO28l1INAFTaktwW8kORvEg162CBcIBp42u3xt+AwbFFNX8o3RU0rdISJUezqNvesvH/FckBw2W3gkNir6quXt8SmiTIHLyzHmKAEpaV9tyKTj3cER75fPAfjq6DnShOtw4iVvFQGrW4q9wZots+Z+74IbZs7TZpc3+ydEGa/HeXWVxuSvA0bhebM84awbpZC7mZBph+tteMltxPU10oYkpSGyk1e80TtFJtI5T+sdVvMeeu1TYNImrEdx2kIWeFztAnBhhynwXN4DUptRVx7ZRfSipfeiIUq4rEtmrqQQHSufM3c9Kp9BEHTbRfq7Z00KslpLzAPn3ZAzO+RBccHU9Sllx9F6TDdJEihiGuFXlYUAjy93uLBDjtotAPayJUfVGDlPbcZuYrBDjevrtKgS5HUQZ6NiL1ty1o3ovKLZ1hv2pscXAtlY7DDSes07gxdUPicXjkw6ztoBZprO7Xxq0/HYetwwR7WBWV3wnfk73NBzZr7kw9VdxGWGdFyfSwgQLuBLjb40PFrssg4ZNip+uH7AelUwOIz40mwWcV4K0tIx8EW1zdz1+Ky+wcrnuCh5ttgim7t03g5T+7e6P8IXkfP1ABck+9kCgIXd7IN1ai93E0O7Y1JhI1cMDluOzid8Mt/n7eIYj+ST5Q2KFwrVBszKXzO8o0wYjrcmZ5R5R09ZBqrdnDOKmL9kfcv0epWaw8+XE06aEU/rHX5z/DOqkBqnR9X4X7u2vmxeCif5yeVNbFBcdgMAap+x8jmxSqHUy/Ow3e2lpu1Zn6NqzH6emrbH7YQv6l2W5wNsP517dqQJmaQ4rSmPJY03nDZDXEgfduUzKp+DhN5VYir3LlLI3Q0kZhWpq9R8fdqk8+SqLimuBOVRg2r8RuiXzldp4acnN3FRsnR5WnRwJU9mO4SM68kMRGp+v5RA/fjoNhOTGoevl6cY4RGVpjxziRNsv2yompXkrB3wtN5hrGrmrmRbrfkHF98CJ+i2s4R9CVDtZ+RnFZ3TuKjoguZOPsVGxZUf8PnVLi6X2F769xL/omrBosmpN5+PjYqP1rc4bYY0OynoC0aQLdP3gq6hWuXMbY/zbshzu83S9/j+4hVCTMfi+kBf7+/5OxMGL9wmdO+x8umzqnzOn17eQ+gkG4tSoDYLni8D39P5kMs2XYOmts+js12yeSQoQTFLMjyz8tcLD7bR1M5gg+JJvcfTeofPz3dxRWJ/Z/PEl5Yu0j8S5MZRe8Nn9Q2Gsrk+Vj+72iU/TeLCJM1M163epWO9SujBvm55rThj7kq+t3iNdlpgB2rDr04Bv6osl4s+j+a7GOlZhuRembmS46sx1Z6iG6rEwLcRUwfUpeHRdIcPZnc5areuw86VL8hPNb0rTztMOA9bJmGj6AS1M3z36lWM8Bx229fXU7PY4BfyDQO+DUw+97RWY6Tng9V9lqFHGzUX3YAXZxN8xkayBuGlgO4kTxzbZoINmhM35qP1LX54fgezeCl027CJm0g2hdYaLtuSj9a3mPve9ed72fbJnuYblny6F9BNJF9+iZ35N23qd3/3d3/uF/9/vf3df/APf1ebjLZrUUZDAB01CkWR9RiVA3SmknVaS4SWeAHNuiL6yNNnxwjTSyZwJ8l1wXgwpjAqGehVYglWbUfdtlxeTZmtamyE1nYURWLlDno540xB6Oh8xAdL01qOT664mq+JUuKEpmoajMoYlH12x9tE2yJDssg771lUDRezFbUFHzUEiXeB4WDEztY2Mno0yVwZvGe5WjCfL6kaiwsS6Q1GFBiZMZls0cvyzViWp+ssVedYNZYPH36MyQeAoAsBITUiyxiMBxSZwAdHsB5nHVfTOYdHZ4SocT6SK02uckpdMMj7lFlBTxts7fA20DQdZ5dzZtMlVdtijEYbg+8C26NttgYjQvAYU+AiVNWaq6s569pycXFJL+shiBiVEVrPwc4eo0GBa9NIsRCSq6spy1XN1WJBFzxdACEVygdG/T5lWSCiAyFo64Z14zg6vWRRNVRNB1KiFUgEWgn65RhDTIIvAtYG1nXL+YsrptMFIUoEgmg9sQlsFyNyo0EFhHCAoKlamtpzfnZJZzuc9wRAyYJSlSgEWaaIsQMiznZU9ZqzyxUXi5o2CpQCGRVSpNZekZkkB/KSdd3io+LsfMrhyTmdgzzPkEaijCISybRhUGRYWaOMJkbBelVxNas4Ob0ClaGURnSeSd6nzAsyrci1oe3SA/9ytWY6W/H500O8yVG9AmJA5jnrtmW8tUcUm5FwJajbhtXScXJywapuCEESAEQkSMloOEZKBUhihM6mc2PZtBwfX2HMkLwocTSkCmVgazxJwaOCzlvarmO+WHB0es6q7UBmdK6hLHN0nicuLDq19ITgYrpi0UQeP3qGc4LWBpKgVKIzhZCwXtUoZbA2teBmq9RGvprNUUVB4zqkUQiZDLKNqyn7BdF35CojRpgu1sxnK+bzJiEQvCDKjflSOHQmyXs5OI91gY7IbLHixYtT5vMVUWU4H9CZxmQaZ1Nwuli2WAfT6ZKrq4qT8zlrGxFZkY5DH/GtRQboZxmZMIgYsL6j7VqWi4rjw1OqqkV4jdEZlkjMclQm0D2N0YamqwkispituVqtefTiGJUllrQSDiWA6BmPR2iVIUUSOXmvmM8qnh1fcbno0FmftuuSNMt5TGYo8pKoFD4GWtuyWllenJ6yrioiEWSgi4Isz/GtZVL0GA3HtM2KGDTGDPj80TPazvH50yeMxmNiDEQ2AbzSLFcVKnq6xrFedxx+cchq3RKUJthIpjO6pqNX9lkt5mR5WvQiBoQXrFaW6WzJ5dWcxjmsj6hMIxF03rJYzdnbP8DoHKKks57ZYsHhi1O6piXatLjXWcdwNGY+m7I92cFFSfCBLDM0VctsXvH50+N0fHjQKHzsGA77dK6FKCmyHNe2tNZzOZvz4mTGvNk0Kn1Dlmta2xLw2BgZjPpYC9ZCu7bpu+nqEh8ESIGNHhccvmvp2oZ+v4fWCmc7Om/xQfDk2RFnV1OcF0iZg/dp+qA/xNkGLTMMGhckdeWYzmrOzma01mN9REiNlAYRDc3aE2JaCKo7R+08j59fsGpafPB03mPyxH8vsgwfA4vlmqwoaQl4IblarHhxckbVOWrbkvV7tDQJ9dI1VE3DcDwkiPSdWa1qFouay8uK84spLgSUTiFGY1u0TuI7RKRfJKFiVIbj8ysOjy6wUdKFiMwMq6ZFSs267ohohExhuq1bZvOa84s101mFD4IYQsoziVzOpsSoKIo+QmQE77laVpxcXXFycYUpCuqmIyDpvENqxWpakWcFZd6H4KnWLeuV5fR0ynS6SEI85wkSQgwJb1J1aGFAaqousq4t5+dzjs9nLNuAkxIfWqQ0GFNgAd85+kUPiaKzgecnl0znNadXsyT6FAolNTEqmsYiZbou+CBorcMFTevh+fEF08WaEBTWt0ih0CJZcv/mX/9rP5fR9s+3f7e3/+G//M7vhkJj5h0h+zM81M0o6fqm4ElvgDKRTCVL/drnPK52cUHh/ngISqTRZhLLb3pbsD2s6OuOka6Z2j4fLw94vtjCPe0zeu4xVSSbtthJRr2nWO7C+/vHIATHdouZ7fNsvcXs4S6Txz6ZnJtIcVbj+4bVjYxbr1zgoySTni5qpl2Pj09vkD3OrzmBIoLqAuubmsWrkdx4tnRFRLAOOT++vM3qxZB8KtFNstz7UuF7ihd3M4aThpFuKJTjuJ3w8fKATy/32fufOnx/Ez6uXCp0SMF0z6BGnoXvsfI9ZrbHnzy/T/9HGcqxaUVqupGm3ZHUu4G9yYo3+qecdWPmtsfResz8sy2GzyPFhcWXmtDTyC6weCVD3GtZhR572ZIrO+Dh8iY/PryDuMjY/qSj3U3lCL9pu52+K/n6ziHndsi94gotIn9w8gbrswHDp5AtPWbpNoZ1zeJmjt8OCCmIQvKk3ePh4hYXP9lncBzJL5IB6s8KlE52M0QPbhVzFr7krBvx4fw288+3GH2RwmpkClukiyxf0bwYFzQiQwjwSJ6udjhbDtGfF2TrFNqqDU6h2Vb8pNgmLx1WJNbmZTfg0XyX5rMxk08dZp4YrUniJaknOVdjhckiDsVPFrdZuoIPHt1j9wcRs+hwQwNKEY0EFKf9Hls7K07thL7qsFHxcHaL6bMJOx+mBbaQa1CS1atDmi3FZT/joL/kWbODkZ4/vbrPk8M9bv6LNDIdsiR2jkZSnnvO7xYcihFIiELy2XKfp5fb5B8V9M99wm24jbXcRw5vDZiaHCcVTcg4aiZ8sdrh8/M9Bj8xuFLSDfXGUC+JUjO/C2uRMzINJ+2Yw2qLn50c0P9xRv8kNenMoqPbzTdSrsg8H/BpnHAUt/mnh2/z6fk+23+UBH/ZzKFaDzEhGKISFJeCZ9mYD8ItHnf7fHByh94fDpg86gi5Ips2oGQyVSNYroecbGVUZNwpZ9io+OMXD7g8HlO+SHi3l3iAZLVXXPVy5MDTknHajjmvB3zv9BXUw4Lhs/Z6XDwUKk0K1J5Ol8xPR/yL8ID/+/Q1XjzbYetngvK0S5IhlUIp1QWilvzwxg55zyEUXNgBR9WEn10coH/S30jiklxQVx7pIyfFmPMiZ1i0fFLfpI0Zf3L+gEfP97n1f9okRrPpWhIyQRCa9pajzDsy5VmHFAD+ydl91A8GbH3a4QuJWfp0jnjoSsPVUFELw9IXHFdjPr3aRzwc0LuIyJDs9mblcKMM1QkO72S8u3fCcT2mi5rdbMXf+96vIFeKW79/SXWnj4gpNJIhfQ7mVPODYp+jZsyn8xusPtymOAflJcoG7ECjV45uJ6c8i8zLgmVfJbyVEDyc3uT0+RaDJ5J8kSYVQpbOP7O0DJ5XnL3dpyoVVmgOm22+d3of9aMB2TpdC1xfkS0d69sFg+PA4wc9pl2PWexzkC/4rD7g+yf3KP9ViYgiYQA34srqZsGy6nMkSobDhgs74Fmzw3cO36L+2Va6Xi09xZXD9RVmbhHBsGr61PuRczvkRTfhaD3h42cHFM81Kh0mmJVHNwFlIdY5h7sFeea4sgOedzssXc4Xf3KXnY9cksepFFibOlDtGT5ye8wLzbhoubQDPlvt89nhDYpPC8wqvQ65CQlVasRWoeS41+OLxRaPVrvo744pT1MwmC09rkiZRTtW6C6SnRiO+z2O6xFXruSj5zfRD/v0LiO9K584zAt3zWldzkacHyiWvscy9ni4vMmfXtzj8ottxp+JTUM1nbP5lSUUkk7mnMcek+2KZ80ONmr+6cdvM/5RjqkiZh2ICsrjiqgVcdrnjD7PzJA1BS/aCd8/uYv9fMTwWXovf1ZyNn4cmDFkPpY4qZjHPn9w/Cbf/ex1xh+pJCeceaQl/S4jKC4kL4Y9RpOaKNPCzCfTfT799DaDZxLdgKkiysVr7nzbFjzt9WmE4aPlTT6aHfDw8R2GP80ZnATMhjMc9QZf0Aqm3YCw48h04Ljd4p/85F2a5wO2P4r4PB2LQQtkiBTzwML0uOoZZr7H02qXK9vnqNnipz96wOCZRFkwGxmgz9Px8p/8zb/0c91rixjjv/lV/z9t/+Hf/jvREZBe0rYtW+NtFtMF/TzD4si04o3X77G7M8YITxCA62irhsOTc7548QIrchAKHVMY8pU33uHGzQFRVGitIERcCMwWCz765HPaDkJUhCjJsxGu87z+xivc2s8Y9BQqRubLFYHI97//ExwGh0ztnxAwStPLMt55/S1u7vWxvsG7lrpteH50xuHxJdH0aIMA4fCVZTKc8JVX73Bnf4JWnqatcZ3lfDnj8bND1muLMCX4DXBdasaTAa+/eocii4zKglVbM10sePLoMbWT1G0keIGUSYSSa8PO7oSD3R2GfY23NW1jmS1XPPr8KSEarAsURZKtuBA52N/nzTdeJZMerR2uDVzOFzw9OWU6nSJktuEIBlRQWGv5ha++yWSrl4RqIbCq1hwdveD4+IIgDUFIlMzwG+Ps/du3uLM/oInW4G4AACAASURBVJ8nxudiueLsasrhyQXrtiMYk36+0PjOsr+9xd7+Drdu7ILrsL7jcr7i+bMTKtdgQwQ2LFoXIRru3DrgzVduMiw03qcW3brq+Ojjp7TeUsckqMhkaoFujce8ev8uo75Ba4E0kVXVcHg4S6zkvKCzDlPkOBvouo43XnvAnTt7lIWgXSzItOL0bMbRxSWzVQs6QwaHj2LT+JM8uHOLWzcmqJjEUMenx1ycz5jOltgILlhUlhNigiXe2d/n/sE+ec9AiPiuoq1qfvTJ56i8oGodpTS0rcWYjLIsuXVzl73dMUIIrKtZzVecnE2ZLVesvU8P8kbhXWr9DgcFu9sjdgZb5HnOajnn8mLObDFn3XaAREiJFYk5vTMcce/uXYalxChF09V0tuHw+QlXVxUupIc4ER1BeYQw3Ni/yf2bu2Q9UEKyXs5ZLtY8+uKQ1keUTi1ArSVCZnjnKLN+2q/S0y9HzOdLoktNQwAvAyozRBE3PMbUQt0aj5nNpgyHfdZNQj7EDT6m86mZWGQ5ZdFnf3ebvdGAsm/wLnJ+teDqcsbVfAFREKLHy4A2BikEg0HJrdv7bJV9BJJqVTOdzzg8PE4hoIuowiAU1KFGolIDXilku2nv90fUdU2IIHXiaAsB2kjaqmZ3Z8z+/i69YkiRp8b30eEJ63VN5wIiCFxMbUjnA/3ScONgh63hEK0izsLyasnFYsHlYg0qNXOt7/ABdscl43LEzf0biZ2rFMvZiqoJHB9fEYh0sUYIIH7ZjL5164BeL2fYz/HOcnmx4vDkBSEKLA6lUlsUoREh8MrNPfZv7NLLC/ABIQyPnz1hOp+xDknk5jpPZhKLO0bIsow8pKBwtVqRlznrqkEIiZYGoRK/1fkutRe1ZHt7kpiuoxGz6YrlesWqrvAhNdKFULjggTQ5oWRgb2eX3f6Ebt3S2I6zywtal0KuECNdDGTakGtNmRmGkyG7kzG2azBojs7POVsuaKzDKA3WYzJB1znyPGfcL9ne3yHPNMFZTs9mTKdLpNIEkfjbL2tIje3IsoyDvW22yhHCCy4urmhtg4+OpusQSiKNxseQrg0+cOdgj53hgKLoUS0rpvMl0/kCH6ALPvGSPVj3sgXu2draYntrK4mGAjx5fojtPF5CjJEoAjJIMqVRUlLVNZOtEY1tUrtzE1KG4FBCErrEAA4BPJ4YPYPJKN3QOI/3kRACPgacTW1yrSLOBfBQFAXjcsBgWGC0oCh6HB1dELxiupohpbiW8UmjUVojVGq3vnb/Dsqn76DpdErTBCwOr6HzXVpU0znOtvSynGF/gNaa4DrqyuJ9oHMOJLShI+Lp5QUypmtUowQ3tnboa6hry3LdUlcNPgZQkjY6sixLUwcxUJoe4/6Avu7R2o7WWSprU4PXW4SMtD7gQ8QYQ3SOYdmn7OVEEfA+slpWeB9YtTX9wQDbtWSZTu9fZQyyHCkiu5Mx66phvloj0LTe4WMgKk1wad9k2hBcx/b2mOGoTyYjtg2s12sW646q8/ggCMqh5cvXe/7Hv/df//ywrT/f/p3dfvVv/BdRNWmU0iwc81cLhocd1b7B1MlsffrrnldfOWWnWNP4FJidrQdc/XiPgz/26Cq1RV7iC579juTOm2f4KBiYtGC5shnHR9vc+n1FtkztqpfiM9UGDn/DMHzvkje2Lxjqlh+e3cZ6xei/H6Hr1OR92f5xpaLZUpz/lZbffusjLto+K5vzYjGi+WCb/R8kq7VZpCapbB3LV/q8+C3Ht9/7iImpUuOv7fHp45vs/ZGmPHf/Gl8uGMHytqL5tSU7ozVf3zni4eyAp4e7bH83o5gGiiu3GRUXGwSDYv6qpH275vbejNoaFusCd1Ry+1+FzUi2p91KI9qqDVy8n2F+/YJJr2Gc1cy7Ho+/2Gf7jw3jp6kV5wtxzczVVeDJ34rcvXXFrcGcRVfw9HKb+OGIvQ8d8lrAJq45siffMpivTXmwNeVGseRH57e5+mybnR8JynO3YfKmcFF2gau3c+ZvRG6/ewrAssmZP52w+0NBeeHQld/sb3Xdkjv9pZzxr5/w1Z1jFrbgk6t9Lq8G7P+TjHwRyK+69My1kQLNX8m5+BXL7o0Fg41l+4vLLeSPh9z8XovPJWbpUjOu8ehly/PfGuO+tuLGZMnVumRc1rz4ZJ+dHwtGX2yYrkaiWk8wKUR/8Zc0+r0FubGMipZnP73J8LFk/DS9j5dSJrF5Ljn9Zo/mG2uGg01D2hqqVc7df6jxuaC4sDS75hpvUW8rrt6PTF6/QgpYNxn1RcnoI83ouad32iZpVKmum3irWxmL18SXgruLkv5TzeB5oDxPx2yUG66tEsxez7n8hqe/v2ZQtEyXJV1lGP0oZ/LYXf/cNMqeAoHLdzPWv1RRli259lxeDZBnGbf/ZRrdD5m8Fqqlxp+n3k3nts8E9a5keOQ3pvP0819iQBKiJGzYjIr5A83ouWN1oCgvAroOCeQPmKVDL1vsVkG9a5JZ/o2G8aSitRr7+ZDhFzB+sjlXbUB2ia8ctKDa11z+hYi5s0bKSD3tUTw33PhBuv/Xa3/NRM0vaoJR12iXlyK+diQppikMD1pcSxZ9ITELx/Stgvmb4MYOM+qwlWH0YUbvIiQ8iRQbiV5q2lZ7munb4O60mNxhW408yxg9Ekwe2ev3odYW2Xlmb49Y3Zas3m1RuUdrT3deYqaS3R8HpE8B4cvrT1Tp/D3/C5puEmC/JVhJ8STnxg8swic8ScgSNgwAAUe/VtC9WTMZr9PfIGD93V2GzyLlebomqtpfX3NFSFiUdpRkjf1TRztSlGd2EyrJ6xH7l+/f5ZLFK2niuL7lKY8U5UlMrdnNz3wpOwPoxub/Ye9dem3Z0vSs5/vGJWLOuW77cm55zsnMyqpyqaQySLaMaCAQEj1kFUJUByPRgH9g/4Bs8w8QDSwhZFEgWdDhF1BYgIRkuzAGVzkrb+e699p7rTVnXMaNxjci1sle9SjIXJ19zt5zzTkjYowRMZ7v/d6XGoX7v+Z5+lFGL0p8UO7+eWV4KPs65c+FfHIsN475pTJ91Eg/mqmTRw+Z4z8+8PKfZbPPOFmnXz7q7m37+Knj6QeVeipQhZv/03P7F9mAWm64+Zl3xbcz+Try7b8y8PR5BYHrPzfrgS38sansKs2tU+CrvxmYfpiINwvr/cjhZ94g5Nr21v3t2qQbz3znePy+cvlBNn/VLHz0J+ZNu1nrSLUxmU5KicLhTeHh+57hvYHNcKn7OdrUuGWQ3UddKpw/8qaAL+DWhpZmBa+l7veP7fiXW8fTZ8r8ulHGRr3J3PxjU9Pf/iTtc2brTqlR+vpQ+cW/5amh4Wbh6qcQHxphss/Z5mG6cri5Mr9wXD5WSjT1anzXcKtBV4D4aGNlfuGp3sb78FB58/uO5VUlvlMOXzfGe4O8YDY26dr1goj5t5+/ZwGp/iyEJ2F4a59jwZ/mZ7sFvkmxMMvlZS8eJgvKdGvj+OXK9FHEXyrptBVwhfOHZkHy8NuV4Y1y9Qt73/hY97BTKXZvXk+KZnj4LWX6sFKPFZ2U4Y1y/LJxeFu/033w/Pp/9A/+7l/qWfuvNLT99//Of9gasOaCV09KiUG9Kej6RvoHn37G65cGKLyvlJJ48/jIF998y/s3T0jztAp+UC7nmdcvX/Pbv/V9hsE2S7UsXOaJ+3cP/MUvvkDCyGVaKKK4GjjGkcEHfu93P2F0Ag6WNfP27Vu++OVbltyozSHeIMUQHOu68oPPP+P1iyOHMbIsM4/nJ37606+ZUiEjFPEETdQEN8drjqPj808/IEhjPB6YzhP/5J//MzKNhrLkhncDzjmDD7nyOz/6Abc3V+R0IR5HfvLzn/HmmzemMErWIqnJ0sKjN5Xp59/7lKtTIKfE+XLh7bv3XNZkG/qUCSFYuNhwQKPy4YtbPn55h2rm/HDm6TLx86/eUVsjN4uMdc0xuIDiuLs98r1PXwONXAuXeeKnP/8FPpx4d/9AwawZRMQ8aYcj3//0Q8bB4aTy/vGJb9++YZpXg4TeIGT0gXVduT7ecHU88MHLW168uObx8p6ff/E17949UV3Du8g8z5RSuL27Zp4Kh2Hgk1d3fPDyxlqG88IXX3zNV1894IZIElhbxqvDiylTj075/icfcj0ceFgemdLKL79+YlkWqoDrtlYFA13DaWQYPT/4/BOmh3ukwRdffm3HIIGigqyN4gT1jvmy8PrumpenkevTgSrw5tt7Hi8Ta8k4H1mn96wiaIjUDFfHE5998CFXt0culwtpuvD09MTD0+Nug0A1mwDnPFKFly+ueHF7QrwjpZnzw4Xz40xtjbk1Ko2cV8ZhIOeZEAZ8GMzWwjlqSby5f4tzjqVkavN4dQZiqoGWw+D49ONbvA6UUvj27Te8e/eWSkQ0IKXQaraHEAQfD1wfBj748AZpja9/+RV1VeY0s5RE1UYVj0q/KeeMiDKOI6UV5nlBVYliLe/OOR4vZ4bBvByHYUBLsxbsZWHohYi1JdK6EsUeOKtAKQWCY/QHYvBcX18To6e1xjdfvyUlg3epZEpJZKn4aHNdEQslvL6l5cLT44VlNv/g4COX5QkNFhpncLGBU5wGCw/KGfXewghLITpTwW4t/NdXRxyNMQ4MfqDmRmuNVDLzmpjnGfVKwdayVFbGcaS1xul45Dp6ahYeny5clhl1HimV1CqrNwsRj1UJP775iNNVpAl8+/aBx6ept6e3bpsAg5pCcV1XTqcTGgLDEKFU0pKYV4OelUoIwY4PG4sqnsELL2/vLEhunZimictyYVUhpcT16Yq0zqa0bZFhONByYc5nDkcLFZymBR8iQ7PGyLzMPUTLznG8urLQqHWlBvCi5GWl1Ip2n+thGOwahcCSZrw6rsYDvnlKazwtE2tODEM0yNjDvVTAOyGK+fTmZWXJPRxDhXme8N7TSsVrNfsY7xB/YMorEXhxc8v5fGZOK6sIThXJFdFKE1BvYz6qo+aKq+C7VUDKK6kZfM7ZVJZTD4s6es/g4Op0x/3bt8ThwMPTGXFCaw2cY2mN0Zsn+utXN8yXCykl4jiy5kLBAt+aQGsW7GXWG5AuM+Mh0sTRRM1CplkoZvXFXrOsiJjFTYhCSgs+HGjFfKtEKrVlqFurv1mBxDDQmhUkDqeRZT4TvbIWMaU9jZozQR1ZCqWUDun9vtbH4DjqwXzla2WtdjxrXqleaaUSveLF1ouS2245UGpCFUQclUKpG1B2BqOLsrSCV+U0BoTAsqxoM7h9SQt+jH1NqGgDbdg4idcsvTMj1dStWx6IMRLFvHbXksmlcHV1xeV8JjrPkhPH0zXrvJKb+dH6YIGHNtYdAx7vRlKe8HHEBc88ncmlIa63RXclQykFr263Urk+HbAFsHFZExIiKTdSvhB9QBqA8sf/1X/xG2j7a/Dzb/7t/6zRDK6Ug6Xbbxttf6mkK8eX/5rifvTEYUgch5U5ee5/8oK7P1VufmawU2ojH5Tx28T9Xxu4/zcWjlcLV+NCKsr9/RX+5wOf/M+F5mD8ZkWXTB088weR5drxzb+zcLy29uTL00D46cCrf2IKI81th1LrrSc8Fb741yPLb89c3U6cn0b4euDD/xXGe4NxuhTK0SOl8fRpZH6pvP8bC2HM3F5P3D8c+fC/H/F9IxUezXuzdo+6psLP/23FfTKRV0c8JML/cs3dnxuoDo8GVZsX1ltrc3Zr4+u/qeQPEqxKfOO4/gkc3j5D2021l4/2WW9/zzH9/oz6Sn0zMHzjdjizwY8alfXGUZ3w+Lly/uvW2tpmh7/3fPS/VfIg3PxktpCol+O+YZ/vHN/+rQrXCR8K5YsDN3+mDO+eIWF4t5CvI/5x5fLZkfOHysPvNNznF9LXB17978rNX6xIaeaB/HZG18L5h1fEd5nlZeDbP3AsvzNDg3bx3P1Tz6s/nS0p+5xxjzN1DJSjtf9Pr+x7xQ8urF8dCe8NYm1Qvwy2lrnF/nu9trCeN38r4997mjZe/lPZj2ELdtmgbXw7c//71zz+QJg/KkgWrn6inL40f8QyGPTxj4nm7X48fTLy9d9Q1teFcO9wk3D4pnH1hV1zf36GqjXahv7djwJP3wcE3CSMb+DwrbUyD/cG9t0lsb4crd38aKrQ84e6ty7f/ZkBk/CQzQZjNJgqxYK85jvHm3+1UceKrMr1nys3PzMgsYE1qc8QoYzK+SPl/e+af+Tt/2UwZnj/PJ80PYdSuanQnLDeeXRtxPuV5pV07XdV2PEXM+k2Et6vpNtIc7BeOeJj2UOVwpOdo3T9HZC02BheXgTWk3L5SMhHU9Xe/d91B1P+Ui14bi7U0SHZVG+Pnw1cPrLn1cPXppx0s4GY8Zt1DzBDQLf5MjjyyQBSDTb+dMl78BwC4X5m+vRkAVU3ynr9bGfhJxjfF+K7vIew5ZO39fEuINXg7eVjsz88ftU4vLHXNmeg2z8mC7ruyuRv/3pkfmWg7fZfwOnLvK/D2zy38w3hsXD50Lxbkzkq4CesAHKfkNz2gsYzYBWWG+Xhh3ZN4wMM7+x7hceMu6xM3zv11HqbK+utR2rb57CmxvB2pYxu/zc3N9KV7mvwts7Fp7IDKDe3fd5t9xA/2VgPD3ZvOX8UKIPBstNXmfCYWG/DXjSyADO1uX6lzC8VtzTzcQX8Und/ZKnPHuc1GFgc3ibyyfH+h4HDt5XhfTFP7A71m+u5J7sVkB1TCUIee9DlVJ9tdWbz6x7f2nWaXgfWa2H6QLj+aSWdhOtfPN//wAqf652dx/vfDQY2OwgMZwN9mtoeklVG2SGkv1SWW7crzm2dsY6DrTjoL2X3MU5HJVwq+aA95NKe4bvTzv6+mhrrlb0mPhWml57hsZh9wrmSBzX1dW6kg+I78C2DkgfpgZCVdG2+7dWDZgsGc2mza7BCTT6ZMrYE86Hfvo8F4Em/h7c9DLCM2j1eG/HB1uTLB66D0Ub1di4Ob/Ieerkdf3WQTsr8SgiPDbeYijUdleM3mXTS5wDTs9kRnD9yHN7W7pNbmF46wrnhl4qbLbhtW0MRWG4cZRD81MijUAMM7+29NsuX7Wfzz9/O+/mjZ4ui8e0Wflj356rW//lP/vjv/X8f2v4H/9F/3HKC4hvVm6ImOKGVChKQptwMt1wdI8eDh5w45wuP5wmKcE4dsDlHWgvDwYBGEDiNket44LKcef/wtKvVKubJJ14oWTkMkZIKURyHEKgl8fB4NrVcDNYSqWoAE9schTHSsrXy5/PEkg0yOX+glETpXp4lzah4WvfOCt6hrXIYI0/3j4xX10xdneO93zfrolBdwTeHNuUUBuaaKSKUZP++5gU/xC0WmJYLRz9QsrWoz+uFcBiRaBtflT4OSjX1bK84H44DzglpWVkvkymrJJi3arSNe14zrtqxXx2P5GxhVS4GU7ipMq8GR5xzLJeZYYjdG9bUvdM04YNBqdbarlLLqYET1jURQkC9IzjPerlwiCOpriAO6WFeUhutVFpotFYQjdSSOHhT+EUfaCRyrn3DXtHBkRvUYupRRwcdQK1CHGyRyaURnKellZy7mrDZa4dh7BAARjG/0RYL02wteGu286Udyh3DQKkra1lprRDCgZQbtRoAUVdZZztmVJBmNxxtcBALGxNxXC4XxtPIklb8eDBgsSYLb6qKSsFpI6in5IXWhCIGdk112vDO/l8VajLYNMSRVBK1p5yDQY2ccx/LBvdjjNSUubsZuL9/5HQ4Mp8vBokbxONIuSzQLKzqcrlwurkj55UYlFYrLgm5jx8XPDkvPTywGpCJkVwN1IQQzJNSBOdMRZ5SMiuMDplaa3ix8MGmQqoNFyJKQxXm6YyomsrYRS7zmaAHoje7CiHSWqGVtHt+indUGktJ5vspEJwniKOVyhgi5/MZP0RKyaiDdbExXGtDnJBKhmYBUi2VrrhuRO9Jy4wopJwtrIja4bgVBbw2XD+mWqHUuoNKU0oaLPKDnStqM9/VDpZKygSvuApoYy0rpVWOw535UVdFQqX2ULFlMS9newo2SOSq0MjUAt6bAl69o4lQpSBivrjax6dzbh83rYGK5zCMlJSoUncAXXqxyOFIpRAHT0qmnt3GRCmFVldEPSkVYhygVrwa1Hdq6pAclNbHDNU8tlWV1srzdykVVSt++Ras2DYE1nLZrzWA4Po8M7VpzuYH3lrCDyNzsrngsylICfaeiJDWmZMbWbVBiKSUcaUxYNdrXVckhK5oxSog2LVSVaKzf6u1kMXGbaNQ8LiGrbdaqRSkFpwLDN0rXauCU9a8mF9zbbTSCEWt6JhWxnEgN1O9DiFSaX1cZBxCzYXgPU+srC1x7TytChF7QI7+QMqVVCu1FnzQHiooeA1QMxoUUk+IVbVz05RYu09uWcyjeQPB2BocQzAYH45MOSF+xGlmvcy0wTM47Z6/hRCCjQ3B1vPSfsUDNvpALpUhBlo1OGq+vf1Jq1VCsHWtKNDsvZ1vZgUSzKNbxK6LKXsrgw/QHLlVUrF1NIRAK6a4zSmh4lFVKP1+X2xtsvjXigZPLcnW0d7qq+oRF1iybQJCGFjXmVIy4my9EBFCc4wu9MKpI+XFVLXOPrPkRtPa13WlVLu3iZjHva2ZjsEPTPkC2otykone7uvalD/+L38DbX8dfjalrVsKkhu6ZMop7Eo5BB4/G5g+FJYX9pfjG+H4pa1bx69TV5o8b9almgrq8lqZPhSGe7j+RcZ19YqUrgLqMGR5af6X65VjfiFohqtfZmst9mKBOM7aoIEdoEhtPH0v4Ja2q7a2zaKutrnboNZ2PPmoVCfML4TT19VUeO/KvuHaIEVTa90tg4G1y2tleG+b1E3BFJ4y+fC8c5MK0yvX07rNLzMfuwrwPu2wAJ49DAHmF44SLQTFWk3tHPnpWTG6KUJ1rVw+ivhNRdWPdwMvG3TegpCas83kcusY78vzprSaum1T3QH4x27FEHRXsc0vPKErLTeYtH2/TflsPqeN5ZWtZevJAIFf2vMGvV+/bTO8KTbBVInpagNEFmK0/XcZDKxIaay3fr8u861ZP0g1SC+17fB189udXocdKkhpOyx/3vhbC/em3NvgS/XC/MKuY1Ph8G1ieeF3qCCl7UUOXesOzfLRIJMpUZ8Vh1Larmhtjv38rzdu9zneFJPNiQFUb4nyGwDV3Hj4fuD2X5oKfnhfQMwneAuRs2OA8euJ+aMDbjbQIoUOHuoOFL47lzaYuY2xfHQM38w0p9TBUUb7Hs0L+eD277sBCoPSmTq4/bzGt/b7zSv55Bm+uVCOkXTtd/XeBpltjPT1IFX8w7JbGOSr2MeDqQSPX6/kg+sQSH8F/G3qZGAHuQZRhXTluu+mnbMN8G5wvAy6A6QNrH33e0l5Bmdl1P38lR54uAdwnfweyuTPVjhaXg+7Gm+DVlsA3nfV/RuMoj2vPd9dv7Zj3EDtpo7ffncDkvMLZ8fQW8PNu/n5u/lL2YO5Nqi3/bnDwXMmXXfldYey23FtgZPp2tY+803VXxnD23fZ4WpprNduB70bsEKeiy3pSvFT3dWeeTRgWL3sY83W2Q5xL4XLh3FXufqLKcHXa+3WEVvA2fPvAPt3Sx1kbl0P1cuvBEtu52QbAzUIy40jTPZaYL832YHb/QUgPhSzMJhtHK3XusPU8FSej/GoHL6Y8Q8z0+c3Xf1sQG89GZQNl2d15qauLaN9/vY9NjXxdpzpyu1jeTtnm2JYStvVpOuVqdCbQo3mYy/V1tqta8PWtbqPwW3cbUXUdO1wc9vP+3ev4faTjorvwaDb993Gej6qqfmFHey6pbLc9NDLpXWQ/qyYXa8MLlf3fA6qt/C4717rTWkdLvZ8YX9na/3waGMxHZXYfYKbmg8zYn+/3Crd5toU0h3yV7/dd20sVye7L34TyKPaOHHCem1+xNu42O53rX/3vyy0/SvtafsP/+H/+GOk4ZqwJlN+KYKoIK2iNErOLOvEtF549/TIeknUIiylUVvDiSN4R8sVoZJLprZGWjLrmnhaJ8R7cm44d6C0Zt5Ky8UgVi740kPDEogPqPMgwlyTtYrn1ewahtHgkm9oE8jmZXsYT5TWWNqZtSaQSlpXnLewD1VHJnM8jORlZV0Tbohc1okhDJRuVyAKlIILgK+UZv6SczLlpKiyrAsxOKraBq2JGuBpZkuQawFVXBjIslJywktBpUDLOFVys+NsvpFypuZCQ7CmYsF4bjPAnRO1FcIwMBxGlnUiHA48TRd8jORSWNcFQagpQ22M4wAtsVxWxkPkMs1o8KCQSgX1VCql2PWupRCG2DfWbt/AqsssJRM6YEzbouoa49ABZjoT/EAtzVRvTrsKq5BbIQyBLZCFZnDDDQdSzTSFq5sjKRu8W/KDgQY3ktvWxiJQIaUVFcGpKcyqL8xpYhwPlGILnbiG6wvJtMzgHLkUfPCUapDMqUFgryM5KzQYxJHIiEBOiTlVxhBQV1g1I9JYaia1aiBNLKwml4TvgVBBmoXP1MLgFIqpV1sHzU4cQTy1WJq4c6FrvAVEKLWRc2EIA7UUalvM71eLwcaq5gWbK2EcqVRWgSVnioo9fKnBy5wyAwOUTM22oLn+IJtKoolQMghKHEZElFoawUdKTgjCYRxI2WCt676062ViDAOSG6IOsX9lXRMxBNbSmEsxb2zve/t+Q9yIAEjtIUKJXJJ5jfVW/dofLhRwIgS14gEUolNKzYxjZFonU+p279HSPJfJ/HVrKdAqrgBaTXkoi4W0tUzVHnBFJrqRZbXk59oNgEurpqqNA2vNiPcUqR1WN1JN+KiIFCTAMq04zBrGN8XhWFplHI8WalYqigGr48nCyZZSEey9cAY4vRspxbxBa+otZWS8gzhYMSC3tftjmTec00CrGDymoTXjBVQbczZAbXI+ITRTHYtXmtgabY6i2UKxipiHlXrbG7pKkWQPNM38jKM41sVUuDRIzKs91wAAIABJREFU68o1I9RKdRYqZzvLxuDNaiWKEtyASKKScd6xltngFjY+HIK57mSzSmnF5gQVqQYLtVtHyKbspCtmndL6vLRiWMCpo6TCGAKJxHm5MARlcIpXwatB58E7A+SuP0hV6wRArJsEaZTQEK1E54heTYGOI+W5r8WZOS8YaYZAoEqlSqNIwQcHLVNaJuUFL4K2Ri0VLUJZZ4iRGCO+FFTsYUxFyHlBJLCuBbxHnaN0a49SK0EDDrNYKM0Afs0V5wMtJVOAirD6Ck5AFfVKTolaE0kzrTrrYgGC2oluwXHJCwEITqErZwXQ6KFWpNn60HIjr5WoQi0JFcH7SKugTpmXGU8gqqP0UEgnBjipvUNFhFGtQOQEghfEmQVLQYghIAjaA39iiEzTzDgerKDova15raBVIKj56eVMlICrhevTkZQrLg6kueDU4cVU32mpiGuE4BHtQLxZ8GWRhiuNIB5KgwIBRy25B2OaN7kfApXKskwMMVJyxWtgXhNo92EXG9dNoVXzLHUKf/SHf/gbT9tfg5///H/4Rz8WetjVk42fFpyZ+nXlk18b8RHiO7j5WSM8NbSY6ktz7aDHNsB0qOBWa530F2u7bU76Zs68CFHZgZfmDkwbuJVdFYVYqrb9bsGfE+km4OeyAyKwTd300uNS92d9yAjPrdPxwWCemyvTq0CYG/Fsm6/DG1MJ+rWZJyj9uKNtKF3qCuJ3ppJqTogPmXzl0LKBPvBzb78ebLNm53RToFXKqP37mjLXdw9EqT2MxZZ23NLbW78D1QyoG3RJ157hoZAPyvGXM3V0+MnAJGKbYQHWW1M6xveZ5c5z+DbvAT7hXAA7/5raDsvyKdi1W8oOnltQO96TM7XSOXfQUVlvAy5V/ONKOYX9fCAQn6zt1PcWfLvly64iM8hvXsWXjyLhUnFrJbxfOmjowLMDlw0GNGc+oWGytvLhvbVHu2RDlq6AlQbjW3tmdFOhjG4vFOwwqXugAqRrRziXHjZVGd7ZeWvOjgUV4kMyxXD3QpVqCtoddB96SE6D9bofc4eTG+jIJ+0qVxsr2iEZIj2sLJM29eF3fIBRIVwM9G5FEZfM0zS+W/ZzhbNnbgO2fvfF1WTQUAr4qexez4iYr28HMPno9+NbX0TCOdOCwVtpGCR9Ya9vKvas7ITwuJKvAuFhJdxPuz+y5Nqvi3mgCtbC7ZbaPXj7+SnfAdfeGSw+eEoPycsnWzeWW8/4xhSVYvzf1KFfPtEGC63b1jN7tpd9Xvqp4tKzDUi+DsR3tkfVZM8lLlm7ez4qsfs82/w1WO3Pdr3pa1R8t9KCUkbXCyYW/rTe+T4OZV+/pleeMBnwbyodHneP1g1E++cihVsqdVDWKysguNmui5bWvZXd/t9bOGAd7POG912s0OddOSh+g73lea3bxkdz2/WUfdxtMDo82pjPV+Y9W0a7luFcOH8c7BkuP4NRabb+uMUUheloOSlabF2Nj5nmdF+varBx5JZGOdg62byNFXpHYOsBZyh7EFcNuq8nm2VL7UU2v1ihKkyN8csz6TZaANZocF5LY7npVh+ZfT5sx+aW8it/5ivHeuUY3peueDVIOTwU4v1iQhcxxa7r89slnguBayOcLQyu+Q401eaCAOUqUrtaevO5DfPWim9re9MOYcXg5KZo1Wrnrg7fAfDFXgd2vuhzpXnphaIO82tfB1RMsdusqDR+M5NPgXLooooo+3cQ7M/5hesqWvMl1mKBaWWw96lBGN5l6mCKV9+Bs9mM2NxPV/Zv643uBdJ86IXK3PBr28fPdr9PJwPNy40Sz/3ApJ8vJ2w6ROlAuEbh/LHdJ2sQ4qX1oDLtQXMGjLdAznLQPtY2j1+hjM9jMfW1t/VzZkVI+77xwe5HfjEbC5uHz9/J7vnP87IG4T/9S3ra/pWGtv/Nf/ff/li9sq4LQQJeI1IsPbQuheA8IiY7ysWMBCoV7wRcw9fSA3caqSzWtqKCE8U5pdFoRQgu2Ea1VRytP6cOtOYMgIWuUCoZjUqRRnNKppJrQbz2zb2gKK3axmtdV1SU6htTTozHGxzCEIIlp4viQkCaEIInzQuhh7g0LCF+bYXabwy1bwyLNANu1RLUxXdvxJrxQ2DKM9F7ai3U3Axq0cA1ak2IVFQLx6PrUMomQWtQpKAqHcgarDNIbVAYla6cqzin1GL+qbUVlnVhHK11P8YB9Y5SDUriFD9EmhNyW8i1MIwDy1NiiJ5WZvwYERreOducgk0YVaj2+YFqQCMt1Jo5DJGaM94JFbvxOhw5JbxXxvHIupw5HqKBBlWWnCgAGvpeV0kVxHs0BFpJ0Jrtr1ui5mQhaa0Rwkgxek3wgVZNdUqrxkZq5njwNCnM0xkdHeIbVSsKRGfqFYme2io+OERA1JNL7mPTsS6JwTtcv3RlWhnCgHqPc/2G4gWvwrqsjHGkZlNdpLLinCJeqCWjXiz1K1hbXaJSVKjOzMxD8AZi+wOYeEdpBVFlLfYU3ASC95ALTiGo0popLelqUKQxxMglzQTvOU+TqaOrouKoVbr/s0E4p4IETxNhWi7glMu6cDwdaVI7wGyItu5vC857nHfEIRIEaskgSqx2s5TgSdI6WGzkChoCiLfwwQZgifObSi94wUmjloXr00AMitNKBWotxHG0lukGQ4ymDvaenBbzgAYQA3ZBHJd5oraCPfs4xkNEtNFqJnghBsFJxg+geiDlAqKkXAFFcahrHMYBFQjeEZuQUyJ682T13rEWS4dHFKeOg47UlAkazebDeVPoNkvcXqXZtZdGbQU7QiXEQK2ZVLs9SgFCoFZBnafklYCgxUJJQrB28yF4VMRAdDO1egZUDaCGrsSnGQi2cQ4+eM7nJ5za2M9tRaOylgWJA+JATPvJwZ/29VloqBdyyajYeRIMpjlgHAdqWizQUSu1WVtarb6DLocPgTUtxBhorfYQkIr3ypxTB+AO5wZSMyW55sohDqzFfKybF0orVN+gNLzzCIoXoQi0bpGwzAvHYeDgIw7QWnHa0NEx1YUYBnwIpsRGcN7TqgUmri2xFgDPEEakOVQG8z5WG89KQ7vqkg5ThyEgbWCeZsLBoS1QmpDmhIuKOMEFpdI4no6sacHVSvSeOEZev37F+nhmjANOi9kSoMQ2ok0QVaoIIQ5clpV4OCCYKrfUREBRFO88aa2EHq4HxbxrEZIfad3X28tAy0pJMIYjecmMOqDiQGx9MusIR86VguCbY3AHaEJZkoVq0ChpRqjQKmkpiJpiVnH44LoCd0FpeAloFYrYmuljRLUZ8B6EGDy0jIg9bF1dXZnFQTLf+mVOuMGTSgJte5Bfa2ZnoqIEFMkFCrSqXDyUvml1GnAq1NpLEyqsFU7HE+/f3TOOA2sxOwsVKx4NMfaiQSOOI8F5nILXwnR54DgOZOzBMwQLkVPnyKkCjXE0qwqvtr7H0SxHxNk9vbReNKim+FWUP/rDv/0baPtr8PP3/8Gf/Lg5S8ouR085GJCoQXeYuoE4P7ceJNX9a4UeRmWKz9AhXB0cNT5v3qRCPpjSqHWvSXgOBGq+v67B8N4UfdsGz8/VPCid0KIz9V7/bq0nfDevuAzD28R6a7DE2rXp8Mh1cOH2dlK/NLSDu/j0rHbcoKeb6w4w/GRrhXbVWjk4xjerbVSzBRqV4RkiSf/c5s13Tyt7u+sGB2qwRHq31GcI2HgWBPTW+O1Yy8HjUiU+GBAav0nk60Dr6qIy2PpiLcMGOP1sVhLD+2LKqdL6JrOHiA2OTeKzXSuEHQ5vKrTlhf1uGQxIbOPDzQaz0m0kPGbm1wE/NwMrj9ZSa4ChW0Oc8w67tAO6fHQdGDbWWwfqzP917UrG4/bZdn63Te/80uDlcG9AfgNAzRlgoUMUzY062v9v7cU1dvD7VEg3vqsV7Xuut/4Z/PZNdRlNIbje+R1OuctKjb4Dqw6QxOaJFFNludWKGts5dauBZu0dlpuSNzxasURKo5w8YJ9fxg5CwgZB7Nqt18rhram4D19OlFPcFcabFYOUCmpBcdaxB8O3KwLErx5ZPjyaSix0FZ0K5WBK5hbs89PJLAa0W8Kla9e7sGweWJHB/KZbsLHUoqNF35V9lXr0ZlNysPOsuXH+XrTU+fgMsLfzg0C6Cft481NhufM27rA253JQhm/m/Xo3J6QX4w64y9GTr9wekmfXvavJn+y5oXkxMP0y7uGD6WTrwqb4LIMzYDln+52gLK8jmgwGlUFIN55yeLa5CE8Ff15xK7tid4OyWqxgUrriG6z4sRWU8snv0M6gtJgligfBvq+fzD6ied2LJVsxqFNqUJvP49cLNZpqc1Pth8e8A/dNETu/9FYEsOxtW1OmaufIyT7fLZzS5kBzBoI34GnQG3vtYMA7XTu0v6dUyIMwvis7NK3Bjidde5o3G5f4VEhH3UHkBvLLYN8jDwbMtCv9w4N5TC8v/A5sa1dCHnr4WDlYEYStYNNMqTk8VSvIiFklbBDNwqi6gtl3kDjaGtG8sFw7UAv2yielBd99XVO/R/X3yfT2e/MDTleO9Up59zuOcLYwsRoEl218pJP5ONOBZR6Vw7d5VzTngx17Ptpcz71AmDrkFKQDSCsSbPe/cuj2B8lAtZ+t+IOzwsZWINV+39C1Ug/e7DH6HN/m2bZmSeuBWtvYCPZ9SuzihA5v8aaQ1gYlqnXOXGytN+sC2dXm8weCm9nv0UPvbAgXuyfnwX5fiwH55jbA+3xvPXybegGvj4OtUDXTLT0qlw8cNz9bWW4d8anu7yXN7BA2BWw+KvlolghlEA73lfmlrRFgaufm7Vj95hl859FiSlu3GmDWvD0YWIHZQL6ts839/wba/vGPa+tPEGqw1DlPa4L3gVZNOZVzobZKFTGIIEKppStNG6WaGo0mlNKs/b8UWq2WWN5MdVQSqEZKtZbC2noVSpU1rQxDpHR1XmmV4BwqgtCoNQNKTpUQA61W1NnnqzO1b621K4lMnec0sOSEc8qyrvghIk5NrdsVjbkWSrWNow2ohqIgDlFT35jNQDEwVMru5alq7xVipNFINXN9OCGlmrKxZHLuykwRNARystZhFY/iDNS1Z49D89QVvHfb8xDq1WCCc9Rqx6vOmeoJcE4oOaNOWPKKU0erDrCUcGvjdKY4Xk2NJbX1oJ2thV9RsRbYWi1lPvgDIYwgjiINbZWokbwuBoTU2XkVUxw57aoJZ76srR+XCLRm4IbWWOaFQ4ymqnSCx0B/zVAwwOqaJcU7H8m5EMfAPF0YD8EKCFU4HI6mZG0F5+wG5PGUYhYQ0mxzXVLu4whaFVoD0UbVQqnNfFCH8NwGr0ptBWqirokxDL2CpoyH0ZTlNdNyIkbH9PREK8UUbWLBe61WBIMMpZiyitYQNVBfVSg1YyIBJeVEo3J0BitFhMNwIM0W8uNFOA0jJa2UauA/5Uyt1v5fW792mz3JEAh2XzKf0MHm5DCMpLV2MOpsHtE4BIfWQnANLwUvlZRWWm04p+RSCTEypcUsJbAAIRcU7+3aC1DWlTAE87gMBtBzMiWuF9lVZiLNFGvRWxv4stq1KgV1kNPKGCL2nKS0NREEWhNicDjfx3NupLTYc4w6C9fyngAkqdRWSX2nqs6TUyL4gDYDZtF7ojpyLqiT7i28Grj0Yor+AsFFg6sh9GCuTkhFqdJYSyEOB9iU6qLWSVBNiBmcIOq6otD+bK3hmq2xTgTnHSWv1FI4DQcaQkq5zyOhquBRpAjjYEFGXmxT77zig+9WD46gDqkVh8E7EUWw7+tFcCJo7fOmNYJXGplUknkKF7F1yBlwdgji7Dpva31utVeZnSl2FRoN751Ba2kEp8QYKF1xuiyzrQ9+MPCuulvBVBo+WjHMl0yRjOCggvOBy3yh0lA1G49xGK3f3/ZCZn3ioLRGqYWKbc5VTIFLhTEERBvOCzEoYwwWYFh7lb1WOze63QsELw5tDRMtVESVXBMhCtI8XYjO4JXDONh6o2ZdcIiRmis5ZW5v77jMM+ucWIu177uuKA8xkPPK8RjwCsvlycapOsAKFKJYUF0//7W1bmniUbqaRoTatoKpBWaJWJDnmhJe1ZS8/b4vCkLBeUddV7wzi5iymvmuemf3QNf6Om5/R7VilEPxLlCK2dnMk6lNzRZBoRUajXW1+6V3nugH0tptUAAXBt6+ecMwBpy3Lp/RB0rO1FporVK7r26t5j0rteBa4SoYdDUlcmIIfhOK0EpGYyTVhgaHk8TV6Pjkgysujw8cjwO11F7kbbSaTL1cVsbguB48N7cnXr+64fUHr3j37t7mey2g1YrKydKwaY1luTBExzKduTpGqJm2ddfkQmvJqv9254fm+KN/79/9DbT9Nfj5+//1//TjTb0DW2uo7tBkg43+Ur/T6i5dhWRzVYspicpoz3Zbe6luLboVtJiCxS22WXertf1uG5qmBn3Tre+bog4jD8420B1imuKydHsEU/NsSqxycHurqxY6GDJlrClu865m8rNZOriusN0sAjZFzGZlsG1WDVqb6kpLI195woOpEN1sIEaaqXvPnwRT30QDvaG3i27P8ptirgYDQeXodrBRhw4Muxp1O9cbRG9e9muwKWfNX3V7XyG8T7SwXTtBs3UhoF39u7T9vd1q7b/Wcqu78nmDk+VgClKwTW/zYonyk8FPRIjvk137/j2287dBxw0GS2VX3Yb3C+sLE3OY6qkH/+S2Q8QN6tXozAv2yjPcJ9Y7b23HFeaX3dIl92PyXe2W2g5HdxVjEFP85bZf5w26I/3zSttVcAbw7TstN25Xbi4vzJd3U4ema8/w7dzbn208bjYMbHuP2eDQpr7V1eCIW3t4kxP82dbh+XWwOSB2fJvnaxmV6ZXBAL9uoVoNTYV8Hbqi1kBxOUWmDyP5IM/2DVfeVHF3o7Xul7YraFtQUxZ788osg0HJMNnYaF52JeTwNpGPfld41kGpo3YrDwuCKyfzR09Xdt9zk12/MloRg27vtanXw6Xgn0ytuxU1/GSgvPYule8Wf/JVMPjeX+efVlPzOiFdO5Zr1z0/W1cVmmq2BYe/JPJV6ApzU/Gmk5pSzwslKPGh7POpRtdVe24/B35q+1jfxlF4sjAtRHFzBtXn+ep1L5jYONVfmecCvZigfU6aZ+62fmzzUZMVcJoT0m3c12mEXijrhYahryvKDu6t+NaVvV0VvPuDegNg2pX4m7J4e63m7n/c15A82nf2U31WNva1yAp31sauxa7ZBsRdgvhgPrPlsN0HbGw1Z6DbikMb+Go7jy5BOX6T9u+Sj471ReiFp96yng24+dXuSZoN8FYvu/p6ubHzUYIFZ623uv/OVmTcgXUHkHm0ULYNSm4ALo92Tprv1mQ3juXOlP/NdxB4Z/A6TJWnzxzjW+v0iE/PYV/pqOSD9K4Rm3/DYyWfTHC1w9WubK8d1GvZlKB2DNsa5VLdOxR20FrN07Z2Vbafn2GijZ+tQGhjY7N6qaEDn6020NdHMCC8rd9+tdce7sveaZAPst9X41Ptti/mixwvvcuj2Ji6/ZeZdK27qtesCZ6voeZnCFoGe00NMH2gO2j1q3U5bPdtqfb38fJscTF9qLz9A8f4BqZXihazMNDUn1P6fXu9VaaPhOljmH6UePxtOP68F/h6IcsvNg62cT882LEPj5XpA3utVBs3vnfRbEWWGu16/Sd/SWjr/zIv+n/rR8VAVRis/bekzFrNY/IYAxvMdRJtMSrZ2myj4oNSu69cSclasL1DpDBPE4dhoJYVtBGD0FrCHWFZHhgOA8tqsDa4QMqFq8ORdZoYhopz0cKxUPADc5kQZ+DQ+do9BpWWK91QAIdJJgsZ781PsC0FFwzmHg6HvqFt1jZOM2iVHKMXlnNlLIo4U/ek3rLZmjWBR+3Kxa5QXbWQRHZALWKLSlpnDto4zxPj4cQhOFKuNFbKjClcC6SUcL4R/UhKhUMcOC/vGYYBcLTawaOoQZtiyrjgHF4rDhN35lZxMiC64hoIlnIfvCPnhD86Ap66JAZxnE7KtGRKXQgycO5p6rWnvyv28DOMkeF45HJ5YE0rYxipOMboqVk53hz55v4d6gMtexRr101pxo2e5jxRCtNUGKLBpDUXnDT8EKAljlcH5vnSPVkEPQTG7qkpKqhY6/0YRvLynpvrATAPX3WBUhfIFU3d+kA8JU3mr6MG7srliWN0HH1gSiCjefOW1i0pnJAQ0pq5u7lG1gsBs1cYjwNFHKOLvD8/mZI2WUugaRBBMlyf7kyF7CxoyIspx1urBjoEVIVlSQQJ0BrRR3KaiYeB6ZJwruFULJRKzSuVgqlCa0LyCjnjg+BbQ0rmZjySciOXQqA/qHQv2kOwNtwxmpo3lZWcG21tqLNFrq4FFUE9UC5cnyJ3L64otbJOK63AXJWSoQwD53lmVPNerSUxjpFcVkanrBWWvKIOUj6zVnA6EGNkjAdqXhm94/p0hTpYlpnTVeP9uzMuXNn174r11hrXxwPT+cLd8RbRalXwBtPimAVyqbSqNMn46AnOYJTUgqNw++KGcH5gpcJlpTSHuoCeTkznC9c3R/LlQlTrJHh55/Hec76sqPMspbDWSqgedYGWV7JUjiflpAdqLtxeDcxLYV0S4xCZ54RWj5ZEiCOl2NpaklktRKlIS3jvTP1bK2sx/9FUC6lUU32WRM5nxnBCQ6GJZ51WjmFkXhJOoKxWsCk+IRppvjHNC7fXB6QsDEfPdOmBYW4wC4yW0VJNRVsawQeur0e8a8zTyvq0MMbA47QyxIOtfWIPMIcQyGuiVgij4ruXUmuJVYCmpGXi6jBaYWytjAePy5ngKzdXI5e5UIaBh/NidjRirikhOsiJo1Z8qHx4c8fpKpLKxLv7M2/fVZbSGE43tFbIaaJWxRXw3rGsZ47Ho63nrbGeL9YKT6EWQSh4dyBET1knWsl88skrxtFzGDwP5wcuU+b+3YUZEAk0KYzBIxlKWVAnDKPn7m5knRMabvj2fmLVBUdkHMzzfX66EMbBfFEdvL45Mby44/Fy5ss3bwmHkXA6MU1ncjPV7zSdGQ8rr19fcXd1xLtG1FfcPyw8Pi3cP86oDkxLZThE1mXFeUFjJOpKK5nrqwOlKk+XCa0zFlgGDcd5zYgPeBq5rpS+M1PxOPGAp6QV9Z6x3/NTMPuM3AusebFCZBxGUsmM42D3sN5ZEYdAKoXTzQvycuH7n77GB2GZZv7sZ78kuiMAeS1oUbSZf3rTxHCIfPzRD7k+RIboSJfEuzePvH06k7KwpIIO3oCtB0/hdKvcXN3y6uUNy2yA/stfvOX9/QPVRfzxSE6Q1yfubk98+MmHXB8GXo4BVzOfvbrhX/zsC8qcmJeEDo4pFdR7ROH7n3zAYfS8ujtxjJ5WPTc+8LOvvuHd/UzJlSrgg2OaHzlcXVNb4Yc/eMHp8Dl3p0Apwjdv3vHlL7/l/uliRYbmyVVRV7rW/Tc/vw4/G/grR+mtjsWUf7myvB52v75N7bMlSzf91dANXVv3ChSaOuK7xPIq7D6EWxBKGXr78K03T0cnu6fd5SPzJ1xuzON1eLTAEGBP9HbLBgvZ/VnBfOVKEBiEMW2t72ZPtW1Gl1e2r9Cu2tuSuKXq7lm63JrHZbjU7pHYVTHOYGUZhN6ghj+b92MZ3e6HBxCmxnynHL8pzHeO6aU3+KXsrdbArvpcbg3MXT5wHL/JrNcK3UPcvAzNLmtXnZ0cZZQ9QMfPldL9MksQkLh7w/qpkA+OfLRzsF6Z1/CWyp2PyvhV2qHftskFevCXqTrDYzHFbTboocmCxF78H0+msuyb+xLs3Nl5tXEzvEmsd8Eg4NMGIA+AeQAPD3VXS1l7rH/2ODwq4dHgnZsbT5+NNg6l7b6CsMF1vuPxyz62dG1MrwPzS2F411uOz88J7m4pZntwWXn6rSuaWAvs4W3px9pYb4Trn3fLimyAydp5bSxePjvu/qMG1rb5shEtg2fh3WKe0VjolJ8K61VgeLtSB1Ojbqqtbe4YcHoed2Uwv8+mwvmz0awl+vUGh2bzLp1f2DVYb8TCdibBTzaW1AsNefbr7Nf8/JHj/FlDE/iLINWZ53My3+vxy5n0Ytw9K5dbawVebixoKT52C5RLxj/MPVHeU1/YWjDfOS4fmUItPML0Wrn+RWaNfj9Wurfo+ZPIeF94+Fy7l7HN8+FdY3xXzNqi9eLCMZCPbm/zrwHO3zMP3nCGI+xBWel2IL6ZmL53QvPzGvP+tzxlgPHbRvWB4aGab3V/X6kN120OLq9NSfn0qRAfLJRpvnWMvX2+BWfq4aXsf7Zbx3plVkTlzsCvrSO2DvhLwV9K924W/FxZbpXqtxCpyvTa/Ix3Rahuf9raPD5mHj+PNIXpShneGRzcfGR3K4QOsNJBuXyslGjBZfEMywu/h85ZQcE8qKeX3aO1NPJB9rVQGoxvzEvYnRPT9w7de9dUjCUK+QCXTxzDW8WtnqtfJoPiXe27ecFeXts1ePd7yvKq4CbP8RfC3Z9nC7z7TjiazS2xsKyHyvTKPksqhDd59++V3FBnYDMdFE02Zt7+gZBvKnK7Il8OxHvP9U9tbG3e0/Od3Q9cL5KkK3j6HPxZef/byu2f1V1Ju9zavB4eNvUr5AM8/gjydWP8KvDyT01JnA7COFf8YuNvvLd7z7vfcVw+LbRY0VPG/Wzk8LVw8xfm7Tu+tXvnVsipQUin3n3wsTAlz+Hr7T5s6+H/w96bNFt25dd9v92c/p7bvz47JJBoq0BUA1axESk2DpFS0LJpWjYtemaPHP4E9mewR7Y8dEgKO2R74IFImRbFkKmi5CoWqwooooBKVCIT2b583X23Pd1uPNj3PVAzKsIDRpBnmJGZcePeffY5e/3X+i3hID9pw/Nhy2OWf27f/POFj04HB3B4jrltGabHqy8Y492WtdsMQnLFRpJk4Wh7wfm72QnDoZP3wScetZQc/ZGnCqJ0AAAgAElEQVQJbma2zFsltjzaIJK3pWD2tsbuNaRFS1NHyKcp5ecSXYW177YDN5NKTAr1RFDvW0Z3ZixXGdZK1h9n9J5tn5sJqCYkhJY3FYvXDdnOhm/c+JxCtXz3p27S/KtdVOtJ5qH0LJ1ZnA7u9MuvNxSDml+6+YB3imd0XvGP9n+axfcnFE8Fqg3PApMI0pnd4iIkp9+0xOOadw5esO4S7n92QPlJRPR5WE/Alsf7ReLkL3L9pRZtcYFXWW0ajAiuHe8MWRTT2hZEKBxKlCASgTFXJDnGmOAmlMGRGhUJrmsDIxJPWeY06w29vCBRksXikjTt0XUO4ozGerwI4oWUksh1FKnn7s19Eq04O7ukUZLlqsJ2gQsqpAQfbOJCSqTrGA4LiiwF4disa05PT8mzIViJMx6tBcZ4pJBoB840xEoi43gbP5E42dF6MNojsoiqbYjzKPCrpMTajjxVeCRN3dJ2DSLTaBwYj4hSuqoFBDqRFHlMEWsmBwPqqqWqKpSExkQ4JUm0DsU+sca4DqUF01FJFCsGLdR1x3rdBUHRCqKt6MzWtZmIloPdKXkSI0SFMY7VyrCpI4xXgMa1a7SIcE7iKxhMIo5eO0QJR1NvODm/ZLH0dM5SpAUQyoYipTF2QxxHjPoJaaoZ9Upm8zlNU5FmORebNTpLqY0nFjGpStBZxMnL5xwd7KDTFGdCI2/beXpRDkLSuRaDJZYa01Uc7u/QLxOK3g71cs355Zx1I2iaCiUSGrt1dCqDk448L5lOSvq9jKSQrNcVl5eXzGd14Hx6Sd02uEjhpQouRC2YToYMipw4UnhhWc6XrKWmbjQmtEgFV50E02y4d/uA/VEf5w11XVNVaz57eoaREqV6NK6hbbalbarAugZrKmKd4dsuxKxdi44TNnWLx6K3ztQ4jgGHViH+XuiMrnHkcUFd18GZq3xwyXnPpqnpD3Kmw112JyVppuhcx/yy4uRiw3pT44VFdBLng8O9dS3Ilps3bzEZ9REYqqbmfL7i6ZMZhohmU1+X5ggZkBJv33udfi9DquA0jqOc+5894vnzM5raYgwkkUQhab1h2A+x7VE5pCwSnjx7gSKi3hisCO32jQlOdu0rRmXOoMw53B+RZQkAy/mcYzyzRYWzBifLcG9EmrZx3L61z9FhjzLPscZw+uKCi8Wck2VH04JWEcZ5FIqqbikyzdH+gOlkSL8scKbG156P7n/GfNOi0ohFvSHLY6rVmljBwdGIshdT9gqSSLFe1ZyezDk+XSBcgAg40yGjwHXtmpbd6ZQb+yMGeQxCsLhc8uj5C140GyIZAwLnDM54klRA13Lvxk0m4yFKCYRr+Pzxc56fzxHO0nmJVJrWhIFZr0gYDYZMJinjMofOc7Gc8eEPH6KjMrgenABnEAT8i/KGvaMJN/anlKmi3VQsVx3Pnp+y6TxVUxNpiROazhiGRczB3i77B2O8aUl0wuXsnM8+f4ryEZ4wOKibhjiJaDAYbbgxnZBnCaLrqOua1WqFrzo6FaHjBIXC1oYijpHWcfuVm0x3Bkhnkc6ymG/45LOnmM7TSU3Vhol+koQiqztHNxkOS+IoxPbbvY7v/eA+66bFOom1gZHstykJbwzDIuFgd8pgWFI1QRB++uKU9abBSI9HY23LptswyjS7O7vcuXWAsS1FljKqB3gR8eEHH9HMV+HgkgQhPfBsNTduHjAcFPQyjRUSgyA9Puf+Jw8DN0yH0iwVxXR1w91XbzPoZ/QLTSQdOt5l+uKSp89est7UZEqxDZXQzzP296ZMdocUeUKkJBrP/kHMclHxne/9AOk8aRL2ubLXo203FD3FK0f79IqMWHocmqfPjnn5cs666iBOAqYhSamaGieC+70zzXYvClgAazxJnFNZS6M8nhZMcMLiPdaG5IcX28EbYF2NxGFsKMQUIqRhJPDaa7fYGRdkqSZBksYpL49nbIwlSsK90RhLpiLSFA53SvanU5ztGJQpbeMZjYa0nzxivlwRa433Cu87bNdAlnLz6Aa9LCbvZYEHrRJGcc5D94jzRU2znCOSnMV6xdHtfQ7HA8osJcZT9npk6YrWKz65/xkpCVVlydM+XRPeWyaTCb0ioRfLMKT2EfutQFjB7OIhVkmE0NjtgMabhtdfvcPR7oBB2SfGYa2jX/SIXMxy8ZBYadbeYX0opQTBX19/dS4bhcPgVRGRcJ52HBOtwkHcZIouV5hMbIXO4DjTzVXph7gWBeN5+Dfrw4R4adnsatpS0HsRBDBfAVvB9oorehVFrKaS45/3+F5H9iChWSvyY3ddtHVdXrPFCDgtWL6iqCfh4JPMBONPOqpJOBjJTlwXMF3FN6X1tIUi0u5aBBM2FMqoyuJHmnRmQpFLKrf8Ok+1jePHq3BID6Jx4FhelxsRnKSbaWi0XtxVxAtBfBncxfHShSRSvnX6bmO1NhZcvqaxmWd9GBEvIDvbNpjj6XK1Pdzp7QFXcv5lSTtyIB2iEaSnkmTm0TWAItmKvVdO5+WRYvFui4gMfq3p39cUzx3RxlPvhfceVQfxRNWh7XxxW9H1Patbmt5jRbx01APF4EGNzRTJQmDKmGakMYlg9MNLTn96hNm69LwUxGvHZj/s6UFcDG5EVTtOvpJQ7TvsxCAWEeUjSXoWCmJcLIjn5pozKDtPNVHMX5XUBwZRGMR5TP5M0XvmEFvkRjK3W+ewR2zLnGb3IjYHHls4cIL0RJKeC5K5v466isjjVYKuHE9+RbHz5ikrK5kvc+w8Zu9b8tqtG88NetliesEpLLaFa11PbdepwOnw/aen7XadbyPOZfguroYBm93A8613YuLLL5rog2Dvyc4Nq8OI+WvAvTWjcsOqTrh40aO8L8M62ZYVX5eDbYuZZl8x3LxzRl86Xs5L2kc95PeDyBrPuy/Y0QQH2/HfbjnYu2SoLLG07GQrvv1HbzH5YRBlVA3dMA3YgJVhcTvBS1jellS7jp0/DSJSfNEhW4stYvSiuS78mr+SsD4U2C+vmA5WALw4GdJ+nFI+dshObUWiLYt65Tj+ZoR6d87r05NrAST7PELXgmjJ9ffqYklyVlPvZZy+J+nu1tzau6C1Cusks3+2S/ksMEB7T2vaSUZy0dKVEbN3wO417O4s2C1WPJqNuPzJgNGPFNlWhL8SO7syDGAu39BE78346u4LAD463af5zpjsgj+3Zl1Yg4Ngp3nxS47DO2ek2qCF47M/vcn4h5p0y7d1kUQvOxLj2ezHLG4r1q9YRrdmALx8POLu/9FhMnVdTAVclyfZSHD+jqb9qTU3d2ZcVinHJ336H8akFz7gAvItm3djWdyOufiSZ+fNE6yT7BQr7r/YpfzjHBtlYf/pPMmso+tpkmUYDpx8NaIZb3FMS0l6uuV6bxwm+6IAa7MTmNBnP225+copkbIkyvDgZIr75wWqCcJget7hVcAOCAcX3+g4PLpgkm3QwnJW9Vj/7/vkZwG5ciVWX2FghJMsbmkuv2QoD5as5hkntWL0A012JrdYn+Dgz2rH4rZm/jp87f37zJuMt4bH3D/axTrJ8T+9RbIMzwQXCaLK0xZBGD3/iiM/WvHm5Jx1F7NuY876U27+AdfDQ90EVnpUOV78nEAfbri3e8Y42TCMKn7v7jv0PkhJz0IBZRhECFaHmtnbnuzOnDdHs+u/39zT/PD8APOPpkFQHQXpbr2vidaOaiq5/JmG3emCW9mGxmoefXjI8BNJfrYdMtkwtEovTCje2pbQddtirsDcDkVx6cyQLMIwBLjeH+R2EIrnWqRW24GtbsKQ7qok0mk4/abjzqsvuVNecCu74B8Ofo7+J8Fl3GXbd4Wlw8aKZiCYf6njy28+oTGan51+xsu2z6OjMQ83dyie++tyyys+cFcI+MqCNycXvN4/YW0SEmn47GjCp9++TfkI4mVYJ72nNWfvZXz57cd8bfiYG/EFfzP/Cf9X8Rb/w+YXsX9QBkF85rbcacdmR/L6rZe8O3rGN3sPiIRholZ0ryj+N/E1/OPJ9bDkioEtHDz7FfjKOw/5hcmn3IwuuLQ5j6cT/nH20+THCV0hyM/cF8mPf4frL7Vom+iI1oYGdomjbVuKNGGz2aC0DKVKcUS/iBn3y2tO5cnJGWcXC3RaoCJFVddITxCytjiBg4MdDnYneNNwuYiYzdYYYbEWtErAbptGm4Zhr+DunRsMSonwlqKXcnpywXKzCYtXxESppOla2rpBOsnBwZTDnQFpGuM7w6apSbTn8bNzsrIPLnBuExmHUhNrGQ0K0kSz3tQhdoxHpxHLRUOWpaybNUoJXLdFMzhLL49ou4q2rYmiBClD87REoyONMY4sTUNMNLLcvnnIsJ9hbE21rrm8dDx/ek6UjWi6ho1otrF0j6sMO3cO2JvkwYlrE7CCZ88vOD+fUaQjlk2IyislwcJkXHL7xhFSOKTuME3LPN6wWG04Pl2AiIgjFeKnQpLqiLu3DjncGyOFodqs6GzLcr0hEjF1F5x+SgSBWilFWRTsTScM+z28NwwHOfPLNS+Oz9EqoBWqekMaReAszlrefusVdndGnJ+f0LYts4sVSguMdVir8MIjFHjpEShu3ThCypZ+L6XJFEme8OMfP6HebIhSidQxFkeURkgpuXW4Q5En9IsYY9cUo4LIWyInmS9q5oslUsd4pWiNJ5VBWL+zv8top0+iJMtqRS9NuP/gMd5HKBUjJbS+Q1vBraN9dqYDhG8p85w4lkjpmE76rJ+e4lwdGLjCoqMU21q0DCza6aigqjd0rWG9NggfkUYx3bYIz1oLeKSApqkY9EoyLWjq0BAfyxBdb6wJOIwuoDayWHPrcI8ijehcRao0eZSRpTkff/IZ7aZBipgtgAFjHdPxgJ3JmKIXEUuobYqWiuWsY7lpQbhrIblpKnbHJaNBSZFEJPG2dMsKxqM+s/PLED1pQmxeOof1HaPRmJ1Rn9fvvcazJw+JI8mPfvSIMuuxqgzSiRAjRmGFI4oidqZjDvf2QLht5N6ipeL45JPgjncGKRwKR14kjMd9hmVBmeXgPJnIsXg+P36MjIpQNNh2ZHmC9QbnDPt7U8p+Rq/IkC7BbFpef/0O3/3gI8xyhXSGpOzRWcdwOqTXyxkNe+RpRJHElGlOhGaxqmhdh+kcnQtFWUmeEUnPZDhkOh7TSwVt14E3TJohz85nxFLRGUmUxsRaYLuK6bDHeNRjdzpASZhdnPLKnUNm64pN3RAIAJ40z/Fdy3q95MvvvE6/B8Myw1QGqTwHu3u8PF2EYkcL+HDwl8oyGhbsTYaMy5xBHiOKjJdqRdU0XD4+JYo1m7bDiZBOGE13GO8MGY4KlE0IqM6SnWrKpw+eonRwGwsVOHVKC/Io5WBvyGQ0INaKarni+PiE+f0XxEVK13V02329sS17+2NGgyF5mtKLFd4YhJcMyyGPn74gLnphiGZhXa/Z2x+zvzcF6ennScByRA03D3f5/PEJVecD79uFfSRKUnJlOTgYszsZMRr2EBLm84ok0vzo4wd45+mQSK3QKiLPUm7s76GVopcO6LqGg719ZhdLbhzu07aPMSLGSGitoTOOvJewtzsm0oI8VjTOMC2nrOcbyjTBebUdcorgpLRrxsOCohfRyxN8a0izgt2xZHU5Z7E4x3mBFRorBXW1YTy9x6AsibRAy/ASGKucSGlu7R/w8MkzojymbQ3GO9JIkUWS0c6AWAjKKGZTd0yHI548ekmqU1atJUoUvrMkUuFkyKRd8fK6rkF4iY5irOtw3oXYrJR4uU2QSIkUMuAQ4jBwtMIHzIlUgMRah3MSYRVlWTIej9kblyjp0CgO9x3HLy6QXtJtBxNxHCKHu/0+gzylzFKUzDG2ot8fkOeetDjmdDYLkcctr1oKyc7uiMm4TyIEWgm80jgLKi/o9XqcXqxDqZ6x5HmOlJJBf0SKI0tjcJ7hYMzlomLSH/LitEJ6Qb1ck+UJdV2htSbWmihSmC1KRmpNHGuwgVbtRXDbKqlpbY2KEtI0JUsSNC4kFJwhL3uU/T6LTYURIjicpQ/FoX99/ZW4mlISrwPLU2/b2Ku9hPS0vW4mb0vF+lCyurV1rmaW/n3N6H53XYaSzoI7yeTq2uVz9m7E5u0aPGyeJfQeB+fLVTEXhAOrrhyLW5rlz224t3eGko4X4z6LB0Py4/A5uzwIqMlSXrdvn39JIb60YK9cY73gdFbysijY/05HPQoH0vS8u27CVtKzuK1oS0jPQzGJcCGG23ve0kwi8pP2Gu3Q5UFQ3uwqorUnWgcHldsygL0K0V9dOZpxtHW+Ci7eN+wdzdi0Eat5RvMsYfpB+P/ipaE4a0Is3XqEccx/PsK/scI7QdUpKsB8mDL8zLKZKnovzDUCAWD+qmL49RNiZYmUZd3GvBwM6V5GjD8Kp+2uF9zLV86p+mdX/PbrPyCRhk/Xu/yb6k3y4/C9Xjn3AjrBXRe3rN7o2DuaYZ3k7GhA8kIz+aELyAopAkN4GI6S0sCj/2BM+3qFfpSi15LyscNuD/VXrtaQ8w1iQPO1NVo53t475WTd42VvSP4HmvjSbNdRcLleranTr4Of1Nzcm7FuI+QYzrIhNtUUzz3l027bAO/Ry5Z2nGJSwfobG94+OuZGfskH54e83OmT/2EQpWwkcGVwidpEc/wNRf/VGUo6Xhue8SLr81QNmb/aIz9l6+BS6K1gqJovnMyXryniS4+uID8NhcbtMLoW5FSzLUuKJPG8Y3krpRkKbBwa6U2xdbgtAtdZGM/6MKEZCUZfPeG14RkXTc5hb856MOcngx3S30+JVmEtXiE+VGWZ3cu4++pT3hq85DC55GF/yveSI9YnU7KXnhyu3cDRZcP67YRXjl7w1uAlt7MzNjbhtC0Rtzc0T4pr9q5N1bX7fnlL0LxW89+8/3v8Tw9+gZf5kFu/G1zH2Ys6sFo3bVgfrcNkgvpezX/6+g8A2LiYH2rD02RI8mfp9b10HWOeKOrbDT+3/4yfGT4A4KP+Gf/MvMvBv7HXfy+aW5pxhGyDW5w3V7yxc857w6ck0jAzOX/wSwn+f+mhm+AEbvsKvTYsbmnMTsvdozPeGJzwXu8xL4ZD/u/0TRbH+0RricYFPEwVnJ5dJmleqflbNz/lm70HPOnGAPzxnRLx/SCA603YC32pkK1ndi9i79YJ//HN71HKmt8/f4e3vvGQx0/ukp1fOasd7ShGOMhOO47/bsvtvQt+YfcnnLc9vqscp+/tMvmo3TpZt25JGQTlxW3F+o2G928+5RfH94mE5R/H3+DZfI/+k7D/ZGcdYps0WN0SJLeWfHPvEQNdAaCl46PX7tD/wyDExXNznSCwkaAaa8w7a96/9YT9dMFnqyk//OQm5TN5vedcsU6TpeP8LcX0xiWvDs74ev8RnVf8oXyTT2/cZe87HV2xdWJHgvzUcP52xPtvPATgZ0cPKGXNhS34H7+2g/y2uk5cCOfBh/2rmkguv2S489pL/ubup0TC8q3zV7lf7hL9swzhHXobxbeJoB4LXnv3CYOo5r3+U066kv/q5h/yu7P3+PS9XfQ6uXZoxitLJGE5Vtx4/YRhWvFG+ZIn1YjfPPyU/+7iV1nvpwgb3PtXl64c2e0Nt0Yz3hs+pfOKe9lLzl4t+JOz1ymeb9EsnUd1Annp2fzahi/tvWAar5nEYahxFM/YiZf8k/enHPzr8PyJ1w7VeJqBpBkK7h2dsJOt+JnhAz5aH3F6tyD60wFNX5KfmCAoeq6fg1fPXQhCu5dhwBQYzA4XqWsW7JUweSXemlxuubf/9iBVth5pgst9syfYu3XB79z4NkO1YaJWfP/tmzz+0V28IiBdrKcrwvN1/rpnfDjna8PHDPSGmSn4hf6Peafo8d/3bxF9Gty4utk+fzUsXvP80s2H3EhnTKMlnVdsbEJfV3y8c4j8SXS9TuqdGK/gb0w+5Y3kBe/EJwD8Ru9j/sXum/zwTsn4o+DyTy8t1VgRLzyDpOIombGvLzm3PUrZksuWcbbhnMkXyBIb7o2Af/DcKma8nTxjV624VBlLlzIertnsZxTP3bXD3EZii9T5i11/qUXbzjQIqbHGYmxHFsW41lEmKdZ1yEgzLPvcunlIL02wrsN2LVqOiBPF6fkFtk6JvGCDR6OhaXj7tTfZnebI1CBcxXiaMptVfPzpAwQRkYpoqiBgYQ39QY9h2WM8SmisIes2IApOzi/ZrCyR0DQXC1SckamCybDkjddeIUs7GtcSGU3mY7JRxqJtWNUNTkKiE7wPkczDg0Nu3xiRpmDqhnpT8ennL5jN1+T9AZ3VpDql8x1KCJxree+9d0ni0KJdV2ueP3vJxWKNaXuINBzInBc4Y4kix91X7nD3zgFNPUf7ArKIQR7TrhsWG0uiJK4Li890LXduH/Hld97BNUuUNDRtjRaSVCs2i0uWqzlRXtLYwJmNleDtN98iiwVqC0ETuaccdHz+8CfkmWA2X6CSIa5r0Rom4yH7+4dkhUIJw3BSctk2bCrLk5dLnAis0bbt0BKKPOXN115nOipQrSHKU9JVSpGtODu5oGsFnXfEKjiFhfDcu32Hw70xsYRBopltVrzy6qt8619/hyjOaBuHx6FEQEWMBxPiOOPO0S1wDZs2R0cL9vZK5Jml6jxexFTrGh11ZHnEzVuHCFOjI0eqBnjvmZZDnifHXJ4/JI00HRHKxRRRTL2c88479zjamxIXIQ7cH5ZcFitenM9ZvZhhY7BdaF6/fecmd2/fQtOQqAyhFUlSUJYjirJH29VczhWbrgquXWuIIsmw6PPlt1+lsyvK0ZgXn59y/8cPQ8mcDfxN10lUInAmOHT7ecIbrx0RxxE4+M53P8J7QdcJZBLRWU8WJzRNxZtf+wr70xHKGloXI0Rw9GVJxIteSdsI0FtBTAhiqRj1xoxHO8hYEClL6gR2ZWk2C5bLDhVnGEFwsK03DHoDtFYMJ0MgsFhN07LepIzLAZ+dPUXHBc4qnGlpmoq9yZj96RDftRwe3CBNz1gcznj24oIoyjFO0lMple1ARAipKfISrRR5lGOcxThobRBcpHDoLU3fdy3FtE85HNEfTRgkgSebRBUXy4xICTbVBmV9cL9uWrSOULRkWcZgMCKLNIkS1Fwym6+YjIZcXLSkQtGuO+I0xxvJuDdi0h+gtSSSikgbNllHkkSsTs9RMsdqhYpi5stL3r33OkmqKMqcJBIUW8Zob1EFRqdQeNXipGPdtAiz5mBS0h8O0XFKligm/T6fPPiUsiy5uKxoO4uIFBiD2O7DqdTsDsYo1VEUGVVVESWaxtUkpKhE0TSh9FCYllG5z6Q3Ym88QQqDjiSNVCw7h3n4mMYkCBVjZYczliTOSZKMXtpH44mVJFYxm8pxv/scr7dtgAi8DS71gxv77E8nJElEkkf0koh+lLJetnx+MieJMrzTuMiS9HKsksR5zHS0g1mtSHs5tpMM+xXn6SXeycB/TRK6dUWe5HhgZ7SLaSvSqKCLK7K4jxYXqCsONy1xmQYO8rhH0S/Z2Z2QZTESSdkbEulThv0BZxcXxGmE8VC1NWm5S5oVqDjcR8VgzKZqmUymWAsvnp5xvFjQSUme9sGt2dmZkBUZSnTEUpNnKdYZbhwdcfz0nJPzM7zyuDhms2rYnUwYTSdIAm87SQuUihiVEbvDEc+OZzihsCgMHXGmGEx6RCIiiz1KJOA6tJIkUUGaxiilsDJEl+quJk0CviKPBmE41hqG0zGLyhLFPTrhiWXghEtviaXAec3atBglthH9NHD2hMZ7S+Y9GvBErFuLljHSi8Az9sHR7rCgIrzXeAcWT5IkISngHcvFhqY2NJ0j1gIVawSKJEkDX911tMLjO4nDkEVjBr0hkUrw3hHFGevNkuFgyijPedpZau/RRcrGGiJnKAbD4LDdrIlUKIJLohijYnQcgQ5/ZgnDqSwqWM7XJMM+TdUidIRxBkMo5FFphKlbdCToaGhNy/l8gRMZjh65iLFIZJqidYxUDq0UDQ7vwh4vncAJRd1Jms4Hxm9S4LXGsaDfH3JRbRBocAa35Yn+9fVX44o2wXGqOo+qLc04RnZBLFKdxylY3pQ0X1tza3pJ1UU0neYy7tHlMaP79ouY/Wl7LQS9/M2Wn371EZnq0MLx+MaIT2/ssvv7cShNzeW18AmCzQG8fnDC39n7IY/qKXd75/wxr9DcH5Gd+61gG0RAO9LM70ru/dJn3O2d8bwaME3WXJQ5jwZjFsc7FCdhSNcO9Na95zh5L0K/P+Owt2bdxsyWOcW/LOh/bqimUYh8jiL0Nt4vLZz91oZ+UVOmNaergubPhpSPBLrZMjmbEMXUG0c3UZz+rOG3v/5tHldjhlEFR/Dh/iFn6wOKF6FxXHZ6W+RlOf6Zgl/+9e9R2YhCtTyv+gzimj/t36C+GNJ7YbbflUNuHF2pOPr3HvNq/4xCNeE3FJbTSckfdO9QjzWDRyZ8/q3TdH5X8uuv/Yg3s+cM1Ya/2/8+v3PniPnFgL3vdsguIBuiZSgJ2kwV4lcv+K2bP6anGg7iS/5p+S6fTSZ0D/rX6IjgmPVI6Xn6y4q9Lx0zyTas92MePpsSf3PD8B+UmDwMA4Tz1zHw+d2EIl/x3771ewB8WN3kW/GrPHvrCKcTsguDsJ74vMJmEe045o33nqGlox/VvFqc0jlFdNPyT378VcTT3hdx3URh8oz4suPRbyj+ozc/4CgJbsVf6P+Y3x9+iW9/9GWKl1/E/m2ieP7zile+8YRBUnGYzYmE5VZ2wVfHiu+Ob3G6OaJ84sjOOkwZouMmVyxuaeLfOIU64c70hO9/fIeDP1TXRVRXpXNhGOAwuWR1mHH+qzVZ3tIC439YwJaF67VArTuaaUq8tHS/csF/fufblLLiaTshkoZZV/Bq/4z/56OvEq+3ysl26+5Kzeqm5z/b/4hctuzrOW+kL1iahPvLCb0XgSmqtiVQwnk2h/CPHQIAACAASURBVJ67ccWvjz4AoBAt57bHxzt7PL6dc/ivtpzTzkEHetHQven4tXufYJH8l69+i39avsunx3fZ+YHB5hpVW+r9nPgiCLdewmi8YhSt+an0MRe2x7zLuKxShE0QTlzzk/FQTQXTnSW/MLrPrxX3uXSaO/Ep3zp4BZOXJGdNYPWW23s2DWLT7mDFz08e8KXsCXeiCz5td/nj+C7zu4r+w4DaiFaOrowQDu7ePOU3Dj5krFbs6zn7+pKPy30+6O2TvwzDK9k6XCwpHq148J8MSIuWrxaPuBuf8H76hFy2fLhzgFfllou6dUo/34AQmHcjvrrzlLFa8W7yjN955Qn//if/IdWeZ/gZ6GWLVzK850hBM47ol3N++8af0JcV7w+f8F+v/h6fp0Fk74ZJEKwX5pq9ur4R3IH/xf4fUciGsax5tDvln5wOUbUm27qrZWMQ1tH1EvZ6G77Ze8BYrbip5xzFM57cHSL+oES4L94Drtzfs3fg11/7hNvZGXfjU77Ze8Bno4f8zxe/zP63tyzTrZDVlKE4a5xt+O3p/8uF7fFmfMx8mPPDo5tsdvTWzWtxCpKNpRkHTNPf3/8257bHm8lzjs0QNWgxaRZcvIQ99yqmv7qhiMc1v3X0Pd5JnpHLhl/sfcw/SH6Z7994m+FPtkJ67UnPO7pScqs3o9ANM5Pz1eIRH2xu8/fG32FxL+WDj95m8mcdemOpp8FEtHzN8uVyRj+qOUpmfDl/wtJl/ObbP+B3P/4Zxj+218zh9NRw/k7K+4ePyVTHxsXcSc/oy4q/PfmQT+9NsR9MAju89ejK0PU0X9oPgu1bxXNKGUT0iV5xJzrlfy2/gY3UNe84qR3NtijsXv+Uvq4460p+bfgh3zu9Efi4VRBGpQWnQkJDGk9+alCVRdXiC1TGFsVTTyJMcoVU6P6tolEvA3/8Ci8k7Bec5bavSGZhLeYvJZerjMftBBtLSlmhhaXrheEeXhCv/Rcs8oHhq7vPOIgv6bxiN1rwp+s7/J3BB6ibG/z3c5J5eDZl50GEthPLO71nzE3Ovp5zaXPuxiecmj4ysUCEarcIoERiBoY/ubzD3d1THnQjYmE5tz3mbcD0uCiI9CYNeJ9o7fje45vUNmIzSvip/HOWLiYVHYOk4lQHRE2y3HJ/twgbHDxej3iU77B0GaWs6LzCWEm15xk89Nev1/8ugi1sMcJ/Wa9GCCrvQvQ7yti0FuMlDoUXmqqx7O3t0ev1ApsyjSmylP39XfI8xZjg0gQQRqLQ9Hsj8kQRa0UvTdAqIc9zhqMegzyma9ZUVUVtQhupFZI06ZHlPbI0uOqKOCePM0yzIdJQmQqTRLRKsF6vydKCflbSS3PKpCBKE9IoZtArSZSirWtioWjbNjB4hWA47LGzOyGOFKPRgPFkQNnL6ExF222omyUrV0MSsbYGledkZY8kS4miiLIs2d3fQyqFMBtEY4hsiClL48hUwng4ItYRaZSSZjlZr2Q4mRBlKVVb4YVDaUEUh9KU6XRKlqTB1RPn9IoBeVYipaDsZwxGIS7edeFFbGc8Ic0isiwmihSxjlBKMeiV9IZDdJwQJwm4wByMIrVlxksmwwlxnGA7R54XxEmEkBZjG9q2RWsdyq/SgvF0ivUOHwuMa8gize5wQC9NUMKSKIGwhlgK6tWSnfEOEkUSJWgdMx4MyeMUJSO61m85wBKcRHhFkeYItqxfL8jznDiOieMUY4KbrjENeb8AKRiORhRJQZkWZDrFbVMFpnMMh2Na6zBbN0XsPbaq6edJOFwXMVmvIE5zhFJEkSLPU6QWxEIRK01XN8QqJo4iyn5KlEYBS4ElSjRKxEQqxttAxZYorDFY4xmNJlgPSZxRbWqGwyGRSkM5kpfBySY0tjF4K1jP1/SynCLLUVISRRF1tQZnIApcWGMt666BSCG2QndrOqIowTlHJBVSxzgCG7nrOqQKD72qbkiS4A5X3mGbFozFu1B+l0QBDXK1rpRSOAHICGMF1jkSHcqabNtRtxU+EhjTIRVftLejiON0W8Jn6ff7YR+QHq0lsZKAI1aSSIbiLAEondB0BqXCmuhaQ1mWWMcWV6HwKEzXQeexnaNqK5Cetm3J0wwtJEWeBrFTK5quw1pPlhUIYpzxKBnhPag4o3Oeum4D4mQrnFtrA04DSdMatNB4F5iy1ivipAjMbw1JltDamt29PZ6fvCTacuKuSvu082HtdoKqqZEqwhiDRNA1hqIo6ZqGJFJ0bYvxEMcpSZpT1zVFUYR91IX7IYoihJJoHSGdYjHfkCQFy0VFLy9Bhpg6MhSnJUlC3TaApDUe5yW2FbSbDmGgayGOUqJYgvN445FC0zaGrnVgw2Brs2rQMiLPC5TTxCJBuVBqlkcJeZygVERZDhBekqQ5Mk5IshyBw9iamhYrYLlZ07oA+GqahrxXYjooij5N02KMw+ERUuKcI0nC/VNVLdV6jbMdXVWhkoTOe2bLFY6AdJFJ+H7btkUIggNaJ3SdoW5rrAvrLM8L2sYQSUVnLU5AazoMAutDQ5zxgY1cN5bNuqUzliTO0DrG2RbX1mg83nQoH4ooq8UGjOfibBZezuOtSGiD0H312a7wN9bVVNWStjO4LcZFKIlQAeobRxnN2lDXFV3XYUyLlJK6q1lXFdYHRjfWYU0o6KvrFtt2bFYVW1gPddVgOgKf3vgwGLEt3nvw8roQUViH7Cw4j/ABxeK9x3kfCu+cC8Ve3iG9QGiNi2Nqsy136xym7VDSEytHVa/w2qMzTVt3rNfr7UAhoJesDWuuqmuMD7+XlB7jDZ1t2dQtVVNjraVta5QSzGYzWuOI0h5xVNBtLG1ltnuXY71qsE7QWYE1jqbpuJyvUFFCZx1xkmCtpess8/kC52C93tAZh8Vfo29aY2maBiEBFV7XhBAsqg1VZ0B7OlexaTZUTct604CM6YwP+2ToOER4SbWu8cbTVg1SCOrVgqZehX2sMQjjiYQkTxNwnkj9pZ7p//X1/+MVL11AIzRuGzFutkVe4TCSXhhWb3QcTS/ZyVYc9ua8Mjrn/Tce0o4cug5xUODa+bY80oxGK/bTBV/vP2IYbbjXP+WVwzPWR0G0S2eGeNZeu3m60nO3d847yTP+RvljvtL7nJvDS3T9BV9U1Y54ZclPWtqB55ujh3yz94D3h4/YiZfczc/4+s4TulIQLW041C/tdft1dcPwt259wk624m/sP+ArN55S7YVocryyxJcd2YsagOxli40E08GKnWLFJF3z5vQE7q1x+otIcpfL0CKtoBlKDm+fc5TMeLU45bX8JW8Vz/n53QfYzJNeuuvyJpNLZGNZ3TW8nh8ziCoOk0t+avCMt3vPiZRlsydY3AylW3odDsSz1zW3ejNez4/ZjRccJTN6quFXhj8im26wSeAHQ+CNdnkoaRnoir9fnnBLz7h0GUeDOabYxjtrey3YeilohoKfO3zI2iY4BGddyd3eGb9+50dUO4GBeMXd7HqSeNaSvzYnVpYb+SWDuOLejRNuDi+3XEl37dQCQsHWVCCl49gM6Lzi3ewJO9kKk4diOC8F0byh2Q3M8cUtzTuDF3xt+Jh7xQkbG9N5xXnb493D50TrsBavPpPsAv/Wa89Ib3g7ecbN6IJIWKbxinYU4qldLzj99NpgM89OtuL94SN2oyW78YKNi9mNF2S6w6ZscSDBLSvbENVe3YKm04yLDU8WI6ZH8+sCHq/4wsnWBGEnfdlQ7Qp2xkuSqKOXNsSXX6QbAlKgI325AUBJx0nX52k7YS+as7EJu/GCWBrkdmDw57/j5KzGlo5nzYhSVlza8B06H5AibX/L6ayCMOUSfR3vPjc9ljbjbrQgEobaaKLFludaB8ewcJ5umKKUYy9esK+D2PLNUXBIXrEzuzK8+5sywhSath962Qaq4lG3Q6mCMLWuEtb7ARWiq63pxwVXPsDcZvywnQLwrBtzYzDHpIJ6LyFamu1aaVFNYEgr6Wi8pi9rnBeUsmJdx0SLIBQF7Eb4PZK5o7OKR/WUiV7RobgTXbAxMSb318zVrtToZcfFlwdMPvQUaRCiNy5h7TUTtWKY1agmxP2vCtK8ksh1QzP2nDUFr8YnPLMDLmwT1nvhiS876r0Ml3wRV++ycH9M1IpcNvyLzesc5XPyl57qMLzn6/XWuR2H+zFaht//STdh7RKWPuJFMwhnn42l2/4OcMW2hfkm49gMALhwKR+ubpInbWDLRqHUzSby2tHqxy19XfGLxSdEwnA3PiFXDaYX7lndOJJ52HN7Lzr0OvyGj7odfip5xont8fXiM+Rqy8fdYlSkDcOG5FLwZDHiz6obbFzMsRlSygpbhWHU1Xq/KpPUa4uX0C9q9vWcYzPgk+aAS5fTj2ra0TYhkctrvq3eCOZdytoELMyF7dF5xf12n4eLMaoK6ZKrMjZpPD52GC8pVEPnFZ9UhwD8y+f3AgdcBa77lZNV1/CyKrlocwa6Ym5yPq4PedpO6OyWfSzFtYDX9iTH6z6PNyNOuj4XtkchWz5t9vnu5i4Y8YWrtfM0A0UyD+zpTxc7jKI1jdP8uDlg00ZIw7ZkM7Bor8rirkrJrgs2t/frtQB7VRxq/HXSwWmx/Y38Nd/6im1vk1Dmlp4HLcgmkmjtaS4yHldjFI4fNUesuyQUdV2Gz9T2QmJFNx5RK36ymPKjzSFzk/OsGdFTDf/n5VdpNxEmE1v3ukMvw+CBRvHB8iYrm3BsBixdymftLv/84m10ZLeCdUiORBtH+iKithF/ur7DsRmwcCk/rg94uSjRG0G8dNfu46tnlD9OebYI+sFxN+QH9W0+qQ54MJsAYeDdlHL7LhB+n+hSYbziWTsilS2ftvt8XoW9S2yZ3OFeCvd6l/3FDRJ/qd/KVapDmZc1OCu3zegWQRQ4sHH8RUmVCHw77z3z8wXOgPIJVVWD1ERS01Q18WgAWpGkEdV6RppnrKo11lqcF0RpOFSpJMU6ATJhuajpWknVOFzXYJuGZm1QHqquQ2rNummItKAcDInjGK01rgElQyWU8eDbwOMUHtjyueJYg5doGdFuDJHMaJuWSMTkcUqiI7qmQekYHaVUVUWiYlwLeV7iuwopDaaxDIdD8vyUWbuh857OmdCkjWJ3Z8ymaVlvwguySCRt27KxlpfzGVYJnOtwQoMxxGmCEoLVakWiJVJoGuPCgT2OsUKyWC1RcYnygmq5Js+ywBMmuH8FHu/h7OQcRYpSEZPJDt7DclHhbENbrxBCMLtYEGlBXVuqTceL45c0nQcRfmspFYoIKVIuZyvynsBi0U6iRUyzqfFIWmuQShCpGNO1DAYjlss1vf0JtWmQWlPXG5pVF5ppfeDlSinQJvD8LmcLlsuKiySiF8UYatpG8PLlirrSdNIhlQNbI7wgS3Lm8xWpNOS5REQRVVUhRczl5Yr5Yk1ZTjGe8B17h3MKYy2XqzVSCrz3WNexXld0naWxDuk7ZBwO3jSOernGdwaVSrSKcVKxWDfMVjWzeY2UIFpCk7qz6Djj9GxGUeZMxiXtes1itWZeLREixGk7G2KwOgKkIO8NOL5YMjlvSPOIpy+eIuIMq9U2Xm2DgI7abvoR66oj1wJvgkjYtTXzTSgOUpHE6Zj5ckUURQyHYx5+/ozpuMR2nl6esF5XrDaWRdVhVRAzpdZY5xFa8eL0jL3phCTtkSiYbVZs1iuqjePl6QyUpDMNruvI8wFNteRiuSad9+g70FKxWa2xRrO8rMkHBa1xWAGJ0oDk/GJBv+iTpAWDOKWpN5ydz1mtqoBB0RprA21fqojVquPscoHWmp1xL/Ch645nLxc0rcUJg/n/2HvTn9vS9Lzr90xr2MM7n7HmrnK1G9uxZWwFxYkEAoQAoSREAqEECZD4F/jcSHxD/AURUpAQUoLiWMRgkDCKHcftoZNuu6uqazx15ve84573Gp6JD/fa+5S/NV9MIveWSuec0vvuvdbzPOtZe133df8uJYIwFlJu2G57ui4Q2kyDuFCvrxdcXm8JwQ7BQSXtXrgNzJYbrCuZjg29D3Sh53bRcjvbUhYHzFcLqC2F1sxulpwdHDC7XXI0nVA7gzOJ69mWl9dzinJMihCDIDHGzpKyk0CwpFltWiwwn9+waQM3NzOcK+manjgkcBauYLHZ8vzympzgzukRm2VguexYr7zsU1nRdr2EXBhFFzzPz6+ZTsTNOyocOiqaVvP81TX1+JCoIavMqByBU5yfX2FtwWbcgTH02dMEzfnlgj5CVpJ4X9djbhaXFOaAbhsIXrGYbxmXjmQdnSrotaWsK7qYiLEnZQs5sZjPWS63EDS+7smm4HY+Z9H2RGdAJ4mRDB11NWa2aLi+XkO0nEwK2n7N7fqKly8uqUcHhCAoH58CRhkmkwlPnz7l/Q/eYbVpGY1GoCKbpqftoF11TMcHtG0HSVjS8/mC5XrDdFKBiuSuAVOxbQM3ixVRWbRRqOCJSTh5TdPRrFvMpCZrjdOO1XLLcrFh027oug6cI/vIeFKzbrbMlxLKprOmsJqcDYtVy3zjKYualDXERGEMy9sl89mGhw9P6NoeW4/wXSZkWG7WzJYb4sCZqpxFa816vaUsS65v5nQ+MB1PuLi94Wa+ZNN5inGN7zvhGIckwYZG3LGFEwZ2YQu6rsMVwoxNqR++4EYgUpaOFAdR0xWonIGMsfI+KUkYXokjK8N6s+H+0SlfPn5BWVf02bBdr3jx9ILlJqBcQZ8CXdMI2sR7Xl5eYUuHcRarA0Zluq4jeM3LmyXr4FHaUBeOkRuR45ZX51ecHU3FPYyCmLFGc3274vx6RguYvidpxXQ04eLVDaOq5t6dM7alI61atpuWF+e3zOay96icSTlBzqSsePLygowl9JHjqsb7zO1iy+xyTlKCTEjbhoiI494rnj+/ZFIUFPfusgw9221LxHJ9u2S+2KDtiExg02zFHZ3+P1oAfvr6V/YlQWQ7bl0Sh1wTcXoIdqo0ZuzpoyEkeWoM2fDJy/uoIO2m1UzWSxgZ3DIQPnRY4N3qmh9vHvLB6ILvL96lDSIOhZEZxCKzf8itLjXX/ZiPuzfYpoJ1rLjcTIgFVDMR2EYXDXHsWD8siOPEmVuxSSWHpsGpyCyMSchx7x9Ijdqz+ygi5+0hZ8Vm/2d3nESQGULBcm0orxv8YYmOmXcPbglZUxvPoq/4uQfnfHb8Mxw+TvvwqjwUVcJ3HFezKT88fhunI5rMRX/AZ4u7HP9YmIoiUoswFaYOXOL7i3d5u75lZDoet2c8bk5xJtF7mL6IkghfaIqbhu7YsfAVszDmrlviVMRnwz+4+BVKF+gdLL4lfL3Rxc4FDJrMb2yOuG/nfN7f53w15eQjYZvKOviGo8rCH168w3tHNyx9xZFrOC3WPG1OUBnBUwwcYtsk1m/XrC4KPvjONY/XJ1TGc7mesNpUvDGsK7P1Es5UGxF0niVeXR7wv09+gV86es48jLhqJhx9qigXAbsJJDfM2yAk/+HVuzycLPjO5BVORb7c3KHQkT99+ZB3nmzZvjnCtNIKrb0UHnSr+Z2rn2F0X8SWdaz4ePEA0yqKhYhr31wvLzaHhKyZ2o4jt6VPlu/dfovPXtzj9NkgYAxhVygR4A+/gOuTKdN3O1bbkv5ixHvP+n3Al93GvZsZoDsrOf3I8+S9E/TUM/phzUS1e7wBQKoc2QzhfUnzo8VDfnZ6gc8ndMny5fYe3796S8J8nAiLoydL4rhk9d6Yu78Pf/Deu2xOS3714Gv++eZdvri5w+Q8fiMV3qA7KbKf/ijzyYf3eVi/zzv1Nb8eDvl4/QbXiwl3PpXr2657tLfC310E0pMxvzX917i4c8CR3fLp6h7Kw+j5lu5OhVt5dBfwhyKOHT5KXNw/5jfGv8RfPf2Kx+0Zv//kPfx1zfjVEGQ1jEFymtFl4tXXx/wf5c8zekMErM+39/n0o7d4Z5VkXTX+tRAZMvVN5GYz4qaf8HH3Bk5F/uHLXyZ8McW2Q3BQbShnnjASN/Sz56dMio6/Ni145k943N/h42cPOHii6A8tk0croCZMHYePGmbfrrl+fsQf3Xuf1bjmvl3wDy5+hRcf3+PhJKO93jus/b2aOibcQnHVTPid9Xc4cyt+a/aLfDk/Y/JYE8YWtwr7ufBTx/Rpx9M/OeW/S/8B//7bn/DR8iGfX9zh/kUglloCHW9bkjOoKOGOd/8FfH73If9X+XN8Z/qKUgUer06ofziiOxEhlQzdHQnzO/5YcVON+ZOHb8na7455tD7l1Sd3ebAZmNptpj02HH6+wo8PYO2YhxG/tfxFfmX8iG0qedyeYTaabgrlKg3FDFnH0xeBry9P+Yf8MudnRyQU//f5txmda4yPw31H9sb22DJ9InP+m/w8/+bDL/hye49/9vI9Dj4qaI8TthNXp90KxmV7r+D+7294+WuOf7r6kF8YPQfg+5tv8Xh9glsqNvct5VLWVawNk+eZTy7v8+GZtMn7gffx4/V9Xj4+49jnoTAnbf8osAvLs9URU9sNbtAV35u/z/XFASc3mWIl56JiprlXMLoMfH5+l4enC2rjeVAu8Nnwe7fvs35xQD24XgH8xDJ53vHlyxPe/87nPG1OOJ5ueBUOmYUxf3D9HqOnFtOL0NsdGpJVjC88/dTx+eP7XG9HfOf0kv/t61+geTzl5CrQTwx2+5pjbPrBIZsyYSxcZD/RFMuIH712SKvX2xD9gfxusYx7YXL3vUH2WRH/dx0t9fmWzS9Nuf87mt+dfMDFG1OeLw7p//SIg/MhBHKdKFZ5zy0/+lHJ4+IutfW8Kg6Y2o4X20PWfcn0o5LxRSAbYQA3p4K1Ofix4av3zljUFU0UTvhpseb7T96m+HhENffCDY+wvWM4/nHio+N3ufn2iE9H92ij4/nikPinh0yf7pARr92vKsK9P4TLdML/3P0qv/zwObfdiE+f3cc+LzkIDKF8wqe1RooJd/4F/OjsLe79pRX/JH6Hz1b32PiC5aMjDp+JuK+SuJ1jqfdM9Z/kZb773e/+xD/85/36+//r3/9uDj0OjTWGFKXdPQ2O0Ha75d6dE6zVGKUw2uI7j1YFXz16TsrQxYxxVhLsRzW9bzg+OmQ8LjFOAZ71ZotSJY+eXZDQhJgx2ZIjGDRKae6cHuN9i3aZ29tbZss1F9e35OxI2VAVjpSgb3tODo+YTCSx28dA6iM5w7JreXL+kqbt0RQYZ0kpo5XhcHzA4XSMQhFioGs6zs8vuVmssUUB2kLI5Jg4qCY0sxUP790l5YjWCas15+cXLFcbmk1HjoocE3VZo9G0iyV3H5yhFMJijAEfA6ttw818zmbb0ScPGIqqJPrAdDymrgsOx7WgJ7Kn8z0XlzdcXt0SsWy3gZQzR5MpxMzbbzwgek9OieA9XSti6tWrK2azJb73ZAWlK/C+o287ppMxhSvou5btZsvNbM71zZqki30YjjEGlRVt03J4dEhZWgqlUGi61jOfL3k1W9D0kagsYHBliTIWDJycnRAI9KGn9Z7Fas3T509RRuOKSlylWVGWDq0MOSWK0uK3nq73rNcSkNb2GVtWOJsJrYSjGQVH0wOMymQdiDEQQqLrErfzNfP5CleM6A2gFcoZUpAH8NMjaR9GwWq1ZrtuefzkJX3URDLJSjGizPDGw4fonDF1QUyZqpzSdoHLmxuuZ3NCypAtekgN16bAe+Hutts1OUTOX12z9S2BAFajrUYbhbYJTyBGiFnj28Bis+Z2scUjYqLGYLVwgIuiRKXM2ekJ5CAoETKFszTbDSFqPvviKxIaHzOTgykhSKUvBE+hHWVRorXlxYtznl1es2gaopHigCmcuESNJvjIuB5DylhlWS9XdNueTz57RB8jbesp6oJIIHQZW1l8imA0k2rCZtWwXTdcnl/T9glbjGhzJhpNTqCVOIa3245qOP7COV5eXvLk6XOSEsHa50TKGaWRkEMNVVVSuZq+6Zndznl+eUPTg6vH+ChtyNpZtOpI0VM5h4qKnBKbzYr1puPxk2d0IbFtBfmSyMQQB6ezwVpNHzrarmfbtlxezDl/dUVWFqMtFofKicpWhK6nrgoKV0ry7mrDxWLD88tLfN8RO+Fh5xBpm56qrgi+pxyPKF1N33m2bcNsMefi6paUFRhLCBHnHMYZyEiBoW1JOdO0gcuXEqxlrCWHjDG7cKhEzpnJeErbNIzHE1KIdH3g+cUrXlxeoYeUToWha1tCiPswuqNqBGTaFHj24oL5esNsuZIHoQzaaHzyaBIH08ngEq9JMdL0nvl6y9X1LZfXczIGZxyCVZBwiMI6FIrCOa4WK15d3/Lq9po+JpISEVBbi1EGUKSQ6H0ipUizbpnP1tzezskZcsqDazSLyxhFTi31qKJ0JdpY2kaE+9Vyzcvn5xhtUYVcs1VV0azXFEozqWrwHp0Sbehp+47r2xmrZUMEXFnig4Rl9r2nqicYZ4gx0/U9fcycn1+zXi7IShGTQimJwk0xU9qKqqiFZ90nujZwc71gvd7SbBpyBqLsZ4XVFEaKf4VxpKggGZarLbeLJecXVzhXkVOgKEqabUNZlWy6jtb3+ODJIXI9m3Fzu0IpR9O3MkZKYZRGIw7VajqiT4HCFfh+KF6ETMwRncEaQ1SBEAOQUSS0yjhrSCFgC01QkaTBmILkFaVyhJioiop1uyHESB8CLy9esW42zGdrep+JKqGtE2e/0jhb0PeeojCUwxy1256m6bm+WjJbN/Q5Ym0mhw0hJbq2w1rLuC4Z1WO8D2w2wvO8ulow326IQ1dHlz1923E0nTLbrPA54jNcvbxmvWnZrDrBJ5GJBFAa44yw+41hcbuhyI5m3bFcN1zf3DKbzfEkbOnQzmILRxs8tqzle5BvScHT9T2LZcd6HbiZzaQgbRQ+9mAYXL2W/+Rv/Ef/7Z/z176fvv5/eP29/+WffVclYdXFSlop0dKllZymmHuucG38DAAAIABJREFUv+OgSpQ2MnKeWTfC2kT5T8foAOUySNt7I0npxQYu37HcO1kxMR0hG340f4jRGfV7E0wQN2Uq5ME1FYqsNe3PBC78AaWJ/D/nH3Lx7JijTxU6iUOoPy7QEYpVZPmeQd8LvF9f8sofsU0lfbJ8//pt+OMJ9U0gD0Kk6TPZKlYPDfcezLA6sfQVz7dHtJ8ccvRVT6zMHgqiEqzfLKlmkZd/ydJGR2kDU9fxB5++T/XKUN8ObuSQ6c4qMJpyCfNvQ2sshY3M/YirbsLXV6eUjwrqK49bCmM8TCWMa3W/wB8nfu30K2ZhwjrIcT354j5Hn0nYS3UlCIPVu2Mymvd/6QWrUNFmx22Y8KQ5JWbNo0cPGD/TuG1GJYWfKFwDbgOfPDjkaNLydX+Xj9Zv8PmTB0y/0pgwiMhakUoNWlEs4OpNw3jc82C0RAHPmmN++OpNik9K6puI6QVRFEbiNErK4t5p2PiCm+2YxXKEflFz9w/WoDVxZEmlIYwM/VS4qClbrkeOF90RLzeHnF8fMvnMUs2FnRrGFtMmwtgSC838DTAu02NZhJpFX/NqO2Xx9IjJS0OoBbmhEBFWe0jaEt/rKAo53j++eZfn8yOm3ysHNmTaiyf9oeOdnz/H6cTE9mxiyTvVLY82Z9x+ccrRV+ISQ7KiiaWwdt020Y8c89WYEA1HH1vq64htomB+jCJbLe7LrbjaTZ/QbUHx0nL0dcRuBT+QrSbVVsSWqQSdnX/g6JRBWwDF/XLBF5u7LLqa8W9XGC9OyvZBLU5VLS7EV5MJzYFCW8Vv/PgXUT84YPpccCZu0ZFKi/ZRPq9JzO5WPDcTijLxg9nbvNoewP95gttKwGCYOHQXsY0I0KY13KgR5qzns8Vdns+OKT4vqZYQa00x61B9QCXIToNRFDPDk6MRP17f43Dc8vUnb3D3jxTGy7DuBG60XPdJW64OHAcHHc/bY/7w/B3KH46oZsIAttuE6SLZarKV1u2bwzHP3ZglNX+yeIsnN8cc/kFBsU5UV57sZN/ZCatd7biqCj7z9/l6c4ePVw9Zfn7C2Y+iuCgrSyplnfdHDttmmmPD9aQgGMsPVm/zzz9/l5MfGFwj7xtrEdzLuac/rjC94sV0TJ5mnnanfLm4w8vnpxx+KtegAkwTiGMnPNEMKhj62xE/4C6Xiyn28zH1jfAzdcqkwpIqM6ALYHPP4ZaG87OCWRrzbHvC13/6Bnd+mEiG/d5eLAK2iZTLRDQFq7cyQVu+Xp/y2Q/fZvxCc/ioR2WF9olstYRSOsPmvsHc6fmlg2dsUsVHzZv8/uW36L6ccvpJL0WGWp6bVJbPW01LbkxJMY789tffZv3jE04/ThSrKO3yw/Uai50iaFhvar7IRzxdH7N+ecD0azGP6CT7866TKDmFTorboxHrA4Nx8HVzh2fNMZ9f3aX6qCDvOiOsojuy1LeR+bjGnPVENIWOPF6fcr4+oP96wugVmF545a6RuTRbxeW0oi00S1/zvD3mqpnQf3xEfQk6Dy5WI+etI2ymJe1BRhtYhornm2O+fHKP4spSzuX4Ua9dxu3E8lRNGI96tqkkovne9bf48tldjj4yhJGMgR8r6nmknxgJZ1s51u2Ip3lKfjTm4GtFMtKdgoJUSjEuWYVrM92BoVjL9eM2Sc6vz/v7ZCwVtpdrcbeXZj2EjQ6BZKZNGP/6Ht5PDLYVJvP43GO7TN5WbD46JFzXTJ4J89f4Yc78wJMfGYp1ph87FlONtpnn6yMullNuvz5h8hSKTSYOyAbXZsqldIVcnDnOjjbcdGM+n90laMOLL+8xeYYUDRQUy0CxSqzedkyfwqyd8KI9YP7VCeGqoroZAu3aLHtQFratThBqxfglLCcFTxfHXF8eMvqq4OCJcHlDpQZOsqZcJFKppBOht3waT/lqe8LLVyfMrqdMHhvKmWCo3HboJhhuJf/Vf/prP9F3bZXzT67w/nm//tbf/s8zKDIaZTTbrsU6R9/3VEWNwXA8HvP+u29SlZYUGyDzxaNHNI1n0wYwmhACWSlwBT7Dh2+/y93DCUeVJuXIollxfbvg6/OXWCxltvgobbFaawkKmY45OZ1SlwXn15estyuW6y3OlYSQsKYArcgxc3pwwnvvPORgZCgLTVCBvm+5eHXNk+cvaLqENjWRTGXFnapT5tsfvi/J3A5ur655evUKnyFnaUEvrLiKQVrb79095cHdOxSlxhXw+WdfcXuzxriKtW9AR5wpSF2kLseUZcm733qbcWHISGja9fyWp89fEFDYoqTre8qiwGaDypoPP3iXk+Op0APaFozmRx99RNN6vC5og0EPbeZWKX7mZ97m/ukhKQVUFqHn4tUNr86vWKw60BmjtAS9aC0trtZwdnZGHxJd13BzewWIiNMHP8yDxeAoTMHJ6REPH5wxLoRvulitmc8WzDYrFusNMYi4p7UeWoI1b775kNFohCsM8/mc8/NzlDF0PqBtSUw9dB3JlhjnMEpjjMYlyMYTYiQku2/Z71M//Iw4Yd998IDjw5rpyBG1uL+uLpas1w2LVUPSIiRmrYgaRq4kh8DDs2OODqZYawmx5/ryiqvrOcoVdClgrCUOgtzdsyPefnCHspC1eTufUdc1n33+iBig6SIxCgM3pUTSFqdKcdUmj60MIfR0XrAcRV0NgkjCOBE7Sl1ImJ5XuNIRVKbxgqfIg1u8qir6Ib39dDrh4b1TTiYTjPEYlWj7yJPnF8xXWzZNh9KCyfDeU1UjUgpyTcbE8XTKfLPAFWP6FPExgHXoFCFH3EBwqaqKUVWjU8JYcbv1PrLuGrIdkBBtT2EK0tCWH2NkfDjGhszNfIYmgbaYsmLjO6JO1K4g9wEFVKaErJiMarqmJStpVW6iF2Exih6nMrK2+sioqjk7OqRZrdj0a7Qp2bQNtpA5MEYQIFvfEUNmPB6TfeB0KnO+bTr6mFit1yglXzidscTeY4ylNIaydEwmE3zf03Udq/UWWxZkleiDsAhDiuhsqauCHCJlYZhOxyidubleAZCIRC+C5aA5E2Mk58x4OmU6rqnLiqYV5+TVzYyclTgQg8cVJTklYow4YymGQloT2qHjQYorXdft0QraSvHI2gKtBZlCTJRlSde1RBVpw5YcQX/jmtVaU1UVqYtMigpXGNbrNTlnwnBNJ61ICiI9hVKM6pqf/eB9rEkUpiAnzWy54tmLK5abNRlIOkEWPnCKkaIohiBFRdt5kgJlNH3fYrOhNI4+xb0b3rlCUBza0LdReLShQ+lIWVh86+liIhuLMpqkJNTqrQf3ORxNJIBytWa12nCzmKO1ps+ekGWt9F3HqKq5c3LKpHKMnGO5aFiv17QhstlsMOMxXQhyZ0yR0llKZzg6OkKlSEzgA9zMluScsU7Rh04KAgl8CNT1mJEruXNyACHR9h3z2RrvPRFBFihjSGSSki6X8WjKvTunaDJGZWbLBav1lk3jUUrt504ptceFGFdQaEuRFY3vSUaRiOSksdai4zDmWtN1nmBFlCyVolSKHMEqTT8UhQyKPnsSGe0saviyn1KiUJY+SlHQOE0IicI6shJsSlYiEgcvYWNVVdH3Pc7In4WV+40yYPZ7SEKjKK1FG6jLgvVii9UFm65FVYaYpZics8Jp2TuqwjKdTiBnjg+mzOdzQoZt3xMTkDLa5H2bLQj2YFqPaH1PynJOPmaUhTh8F8k5o40ixUzWBpccMSTGlSMRUdHTRXl4VcnS+Z4YPeWkFFFbK3IKjMspnU9s2466rokxE5OsFQzEHAg+8Y/+3v/4U7DtX4DXX/vr/30G9qn35XVHqsw+yCk5xeJdx+avbjicNoQo9+X4uyeUs8zoSqwp4iKVNW22gWf/3hj/4ZYPH1xKovWrU9yjioe/Jw6Y7kDvBY7kFMnA+k3D+u1EOvJMPi6przLT57044oZWzN2xzj5wNP/Ghvfu3vBwvGDRV1w3E55/fpc7f6Spb4M8HPbSSmo74fO+/HcSqo64UpyCd/9Y2id3PNGdAwmktf3m5xybn2spas+47vD/5IzDryPRKUYX0iK9czK1x5Z+qrj5y4Hx6ZaUFO22oHxUce+PvIjataFYePpDN+AI4MW/nTl7ay7vNYyv+cfHVHNpey9mPdlq/NQSK82Lfwve+dlX9NFgVOaoavjR528x/dQxeT44ygqFH+/ES3FDLd+X1mC3Vhw+kpPMStLAJfVbws76sWb5rib8/Ib7J0uciTx+dYp+XjF5ppi+COheHt6TkQdRgMtfNvTHiVwlqheO048iWQmCIxv2ruod3zU5EQh8LS33tsvYRni5sTS4lbBXYynF3fO/YvFvddy9s6TzltWmwn5ZU10ppi+kbdduB7Zkn2jvFKgIV79oad4MUEboDJNHluPPpa2+WIjjyXSJ7thy+22D/tcXnIy39NFw8fQEd9hx8NsjtIf6Nu7XrIrSTrwXro2E2tk2UawSppX1pH3eM1FNE/EHVtq6jdqv7eKmIVVO0EAx0Z+IUzU5zfyDkttfyEzeWVDaSO0816sx+vsHTJ6lIfRMDe63SHdsxVnXyn1m8a7j4KkgAdw6Yjdy80xO79cvCE6jPZGgn+TArTNuA+MLj4p5H6bmJ1b+Xcj1tb1jiSUcf9Ht3zM5RXXRoPqAPx3tebvdsSATNnc1xSoPTNO8RwqomPd70e7zmjPH/ANDOc/U1xKut+Ntqsg+RK+66tFdoL1X7/eIVEJ5m3GbzPi83wfhfXNP6aeGfqpo7ihsC26VmZwHwiC8urXwsXd4iO5EQq+7qWF7T4Fmfz3tOKWyf7wO9yPD9o6luafojjLlXIoKx595EbU1uFUgjO1+/YaR2TNJy1u/35uSk1A4P7UUc0+2SgwVQ0hcP3QW9FNFsRKuZznz+wDA3bgmp+kOjQiMdzWxVtSXaQh92l2rwuY1nfC0mxPD4t/dUpSBw1FDTJqLZ8cc/8AyfREHli/DuhKRr5/IPpecolym/X60Q7LsRETp+sj4Wu/3a7dJex5tNtBNJbSvWMYB8SDCsp9YLn7V0D0YHNsLQ32pOXwUJThyLXidWGrcKtDeKZh9aGjPEvE44C4c9ZWiWMg9LZbqtbvUQHcga2Tz5s6BK2vl8JEI+6FU+3PY7UPtiaU51Sw/kLF0S83kecY2Mie7ed8VEPxI05xp5j+bwWayTdTPHPVlZnQtgWHJSCFv53h1w2f2E40fK6p52r+frAm9F+yzhmIZMY3MU3/o9gziUOl9kJqEakVU3OFd5KugGsLD3ODC3q1tPzH7ed8hEkwrx9EdijAse0/CT+TPrCEVIiabAcHQT2Tv6Y4U9VUmluzP23Qy/yqy5+F3h4btfRmI9VuJ8QuNaaG6Tft7UjKv3cEgY7C9rykWWYLEIrjmNddfgizZj9luPRqf2Z5p9IA42O1dWUO5jHuurw7C0FUp05xp3Brqm0B7bP4MYxd2OQKJ3/3N/+Yn+q79L7Vo+5/9nf8ydylAYaVl2MiD+8nZXULXE32ichWF0UzHNTkGVuu58E6Vpe87opIgkm7TYMuSrByEzMlkxNg5nE1cLW5YNT1+cOXqCFEjTrGcidFjtZMgJWNJOdBHcZg6U9A3LWVZErwIGz7D8XHJ6dGYw/GIlCzzxYzbmxmrPgBaUt99oKodvvFMp1O6rmM6ndIsFxilWYcOn6W927liYKUO7ekpYYyl8xFrC5pmw3hS0XUdxip82EW0yksbcS6WCsaTmhB6coQ2RBEi3ZDGrTJaWzRS+XHOcTgdiWgXPOvtRh6A0cybhmg0Oov4U1UVk7pmWloOpwe0bUvXB65n18NDYUdZ1ITYozBYW8hDaOnovacajVkul1J5UBmtFHoQlQyGnBWFrYg5MD4YgRanVegCRjuUanBWk5NlvlihrYRmSdtyjVYO7z2oiDF5z9kMPmOMJuPJ2pARkcJZPXApIyEkdLLCeA2JqEVkKbQh9D2lLRjVBQcHE3zo6LpGGJTek5WmDwlbCAs0pIQGNIraWULvSQnhHqVMBnrf4qoSlKIPntJJKM+4Ljk7OKDpWkIIrFYryJakFV0I5JxQOqOkvEnwWQLvkqJPLWYoCsQEMWaS1nuxLeeM1RmrIIdhLLI4izebDWVZUhUlTdNI4FJKjG2JyXBQjXBWOKDLpme+WWBtIRiHlIkxDsJvJilphbfWokICZ+i6HmOMsH+Vkvk3ihyDOAQTGO32PRuSMJ/3TGittaSdq7R35+WMCFVKEBhpOAZ5ZRIZUkRlEUOMVYSBqdy2LXYQzLUxNH1HoQ1aS7BfzhmnrfBa1xuKUc02J/rthvF4REzdaxELQ9sFjDG4Soo0PgQKxSAYGlKSY+t8T/Q9tRERxhg38FATDAWklAPe9xiryEmRAJRBWYNvO7SGspBWkZyF1Vk4i9WGECIpRbSB5Ayt79HGURuHyWKwa5pmEFXFNei9x7qSEIKMn1bC7vRRkuoHZ3eOCaUUpStpe8F1tH1D6SqsLSFKy3VZV3gfSSmQU8AoNcwnw34bsU4TQo+xNdYU+GbLZDSm6dr9z2WtqOuabdtQuIocZe4lnNGSVaJpOnwfpTBmICr260BrjR5afT0Za0VwVkqRUqAyJSlGkhbGbMwBZRVOO2LQ1OWEtt2SEX5q5QqiD+JWivIlxw9FtrIs8aGnLity5yEm4dUqEeOiUbQ79EgfqMsKFQMuK3pAOUvbCPstZSm0lK4ghCAO6JzwPlKWJWk3z7uxNGCyOJ4jwrZ1thJmb4aRsRhjWA17O+71XOgM2hYialbSThd6T11L+JzWClsIwzelhDN24FsbjLPkVkTcrBV5uB2F5NHawoAHanbnlaAoK5rQU2hNMAO6RTt0EFY2KDofqSclTdeKG3/YV+S+oQQTM4yT1hZQaGPYbreMRqNhXQ/3Hm1JYRBxCyf3piTFmkQeOmEsZhCu8yBKp5TAgKtKttstKUFd1+SY9gxepaRgYZwlJk9OCqMhDcWpwhlSVvtjd9pSFBVt20rEnlLisAW0Hva4JF0ObdtK4KY2xJSoXIEfOpGUsQQv+5p0qECkG4pm1XD/NoJsQuO7nqIo5D5WOrn/a03b9/z6//R3fyra/gV4/eW//T/kcilP2cVcWo2rFytW3z4SN80gZvVjTXNPgjZGF+LKUUl4gaZL9IdWBLBKi0ADLN5ztKcQ68zhFzB+FfYYgmTlgW/387u/Zy0PbzrkvZgoQVaS3G5aeQC028TyHcvqPUgPW3JU2Bcl08cwOX8tJNtNEHfcNrJ+WFAuE+sHhmqWiIVi/MpjtoFUiQv0mw+e2QwPsKtErBTljae5V1As414g++ZrFzjVH1q2ZwbTDeiYedwLkaaLe0EqD63tvtas39DEkiE5Xh4IVYKDJ50Ifk4YuP1xwfauZXtfsX2QcCsRYY++lLZ37SXsbCdy7B4gkxVRoT0yTJ93e67hrt1/d84qZfqxOK+aM7lH7gLXkpO/h1oK2NOngklwTcK08rm7f4OMXbkY5qKRed+lvquYCWO7FxKzVpj2tZBZzKW1PpVW8BWNzH9zYtg8eC2u1bcibKsEbuUH4UphGgn0Sk7THltsl/ZCxu587SbuBUu3CvgD4fRu7xgWHyDCWgfjV4NhJuY/IxbtxAzTvg70sps4CM16H3KmguRapNKgYibWZj/WZJnnWCmqiw5/WNAdGqpZGNrME829kugUm4eaUImYMDrPHDxuX7/XIKztricdRGzbXVO7c9wJx7vwqp1QusNDJPd629+fX5f24tU3BbmdGLnjvtpGznUXRgivUSC7JPdYGgkoO3OUM9kvdtzM8rolTIv9dbQLeusODOVCGNX1tceuetp79f73dszq4rYllVYc2k3EbHvCtBwwFmZAVGjcMqC7QHdWDexOCUbcOeC+KbzFcmgb/8Z5mG2QIsrB8EyR2R/7zoW4G1MVM+52S3aG/rQeChWa8tbjp4JFiJUZmMoG08T9+O2uid04xsFVm7VgG8qZCN3FbYs/LGXukdDF/tBit2k/jrvCB+x4wVJAso2cY6yEPb2556hvw/68VJK29Pom7MU5X2v6qSLUanATZ1wjPOJdoWwXgrgryIEwQEOt92Ko9mlfYNBDaJ/uE6ihEOalEFYu4oDzUMIsHkTxnahmh3nbYRPaY7Nv8bc7FIiVPayYe2ItiL/2WMYk1Aq3ESG0mu8wBzJGcs+Re1AcXPXdgYh3uz1F+zwgK9QwtvL/d6J11orm1BALuTd9s2D/WkhXuHXco3xMK+dRzYY9sRL3vIp5L4Dujmm3JlTKe3btbi4A+rHeI4x0yPiJ2RcBdmt1J/j7kSySYvX683fXza7wsisQ7QTnZIe9wyjqa09z5igGQX439rs5bo+kgLoTSXd83GwENeOHNSXvPew1taK+kX13N7cq8WfOOxbSUaPy4EQP4mjtJ2bg8w882VrjxwgPeOApvxZ42R+rH2vKpdxb/ERjOjn2ncC74zdnvQtpY+h4SHSHZn98diic2a0Uu0yX6A+MmFisrLff/cc/mWj7LzUe4df/0W9+F6MIWVwxKSVGVU0bemI/tFDmBBravmXbtgQyPif6lNBqaJ+NETNU7kpl0DlACgTfsWzW4pzKhsqWbH2PLRzZRrrg0dZJC6uCmDtCPzxgRnF/6gw6ZSwGHTUpKgnbCRWxUVxfrLm5uqLterqg6ULEWgdKcTCdsN7ccDAZ08YeXThWzZbCFuSY0UqjnKIuCoiBPorLU4SaDEpakEXcsXgfUVrRtg1lWUE2pBwZrEOE5FHR08aGjKFtevoYMabAWHEGxphQCE9UK+HStk1Hs9nSrRqsNjShY73dYKsSZTM+hqEVPxO7yHq5pdm2LBZbNttIVlItC3QklWi7HmWEuxfIbAeRqHSGECSsx1UjcROFTGkKtBKkqgLZzPE0RLp+i9+2+D7w9lt3WW8WxD7hk4h50uUb0boALDEF+tAMgVRGhLkoDEqsfIYkWomjsNDiJjTaUOqCHCNZGYy1kBja3w0xKaKCTd8RuobedxIqow3KWRHcBsE2BBH8jVKknJjUE3ofhC/LkGjutITthEBhrYisKZEzbFdbQLNuWlCGhKKLCeMcKQViimijCF6ER41CWfDRY51l3UkrL8Na0sqSoqI0BcFHQgxYp8k5EUIktFtKa0g+sN5ucM7iCof3PePSEdoOQqYNmYRh7TPlqKDtvIxTFiZxHtAIGWTMQkBlaH1PVgmlNaiM0gmlQetM5xusNSil8NFjrKHrO5RWhBjRQ2gZKqFMRiswyiB0BEuIiSYGirLCh0zhSnwfSUGuX5UZMBGGmKK0bmpF7z1JZYzW+LajtFbQCAqUSlhnSAS2mzlV7WTfMdK+HUIgxExUwiGLWeFsgdWW0PY4WxGNFFZs4ejaBmMNKOhjT2EsOieyChitZVyMofUtSiMp90i7+24cnCv2ArMrhIertCKmiFJaFrYGhmKPVgU2ahyOHAJJK7SxsgfkLHsUCqU0WkEY3LHee3wIKLNrfdLYqiRHKazoZIgJUg7UI4v3gbIopTCQsjj2SXRR+KBGC9YmpUTsPaUpqKuSHHqs0oThnApryWiaTsK4slWgFW3XiSO/T1hbDqF/ER8Cne/YNg0qK1AiLqecMSYTQg8kDND3LWVdslpLYUKEP0VZOHzfoQoF2siazX4I8MuE2KHRWG1RSZMUNJuGwjnaboMCCiWFjxA8hZMWexPl3kSGHDOltWy9xwMRsMqSgsdkLWgblViHYY1IPhmKLC5Qa2h8LwgEFNoWNF0DRtOFXgoXCNLDakFbaGvIJBIiBpIjiUTMUQKvhn1RK4VRVn4+gzWaFCOQcXZE18i5Nn0rrHalME4KbiF42q6lHAoNfhCNw7Aek4dRPWa1XGKNpbIVelB1nTEYFC5AbSu5z8cOUxSyd2RHzgpjLMZBzkkKrASquqDvt9JWOaxdFRiEakubWox19GG4z/s4fF4eMC9JWMHaYp0U6eRalznLKeKTxziD0xLaVliHNpYYPTlHUpZrqKxKuedqxEmt5LuCIWIcdDkTgWQ1OSWssQS+gdnQikxCD67fTEcIQ9ik1minUCpijOjsIXo8mZTAWXGIK2vIRAFJZAFK+JwxWgpSRlspVCtFzIGqKAfGcqZ0jr/11//Dn+IR/gK8/u5vfO+7KHmg7Y8lwKQ/qygWYWhNN9gmCUpmkalvM3YIDCrWsh9nq3CbKE5YJSEu2SpAYXoYvZKH52xFbKivBUegY8YtpV05FfK7phfH5a7FdBeYkq0mVK9boN0qopNGecXokWX8yFIsoFxmym+Ix+s3SsbnHes3Sqp5JDvF5KUXt+C+bVvTHVsUA6/V6SGMRcTJWImImQoRGNGK4lYENpTaO4xVyug2gpZ2SZ2hXEQRy5wWZ+LAb0QrTD+ER2lFuchUM3HwxFIcU+PznjC0GtuNB6uxa4/1itFFolgYJi8So+ss/NYgwpkJUMx70OL6tF2mvu7xB+IEth0Us444cpheBM9+aobAGYYvwwrTQT1LlKuE22ZsC9e/kqmuNaYH2wBKWpZNl2QskbmWcdLEWrAbtkukwuxbpmHAn3WJMDHoIIFK/sDsuYq7YCbTSou2OJXEfWVaaZuV9ngZRwXyc03ENB4VEtnJ+23vWIqNuHiNT4NoLm1Hpkt7R6zpZa1XN+JCHl8K2ssEKOeD6OmF25kHXm2qhvWuFG4jIlz9coNpAuRBVLEiRPpDJ9zdJpJKPXBDxQXoD2Q+RucNYeyItYh5zV0nzsesKFayLscXAb8T7oa28jy4xGwzCMtO78emnEtQkCR+qr34kq0WVEIlgrUdxHW3DGQjyJPktIiH8iVYig1DinwqFLZNlNct/qDArnvCxGE3UUQRO3wH3c3NEHgI4hYV56i03/vDAh3k2mAIM1MR6qt+wBIk7CYSaynemFYwHXr4L46dYBsGTIvpInbjSbWlmHfkwgyf2xMnxf46l7UgY1K67Ao6AAAgAElEQVTMgzyzDboBQ8iQbSJhZPe4gDiy++vXdPL/hlUtLdaDkzwVmjgp0UmE1+TMfg/YI1mU7KHfdGHbjd9fJyj2++XOoW4GYbQ7tthNIkyFKZpKmSMdMsW8H/ZW+R0dwbbyGe2JRSfZ22wjx+8nA9JhFgbHulyj5UwMCW4VSKU4J22XcdtMucqMruRYVRaxcyeE70LlUqFwm0Q/NYxfdviplfVnlbhsN2nPiM5G78cTJUFVO7Z6NnIO1a3s39VwXH4ixTbbiOsz2UFUHFAFKkvQ1+jKYzce3Q1ryGdiIfcU2W87YrnjaAsKb4c8qG4EF7Hb56rbAEq42DsXOgpBPAzzKWMx8LIHDILt8h4dEWo9YCGkeCbzq/Yiox8ZylXET+Tzdms0FvJMZxu51/mpJVnZD2WO5XlLRWhOLJOXnlRquiPZp1QWhEUqBHnQHVv8xOAGTrDK7H8uFYLN0UMBSEdxzxZruW/k/T0DbC+s3HIRh2taChA6itC/c+FnIzgUlHRahFru7bv7vUrifo2FkmLpMolb2wi2YffdwPRSKHStjKlrhq6doVgnzu7Xxczd+NqWPWpjdx9KRsZVR3BNHvY5RXZiOomFBI7Kuhcnrx/JOLBbZ2lwTedBNB+E5FhqwatY+feuCKVQ9FPNf/0f/5V/9fEIf/Pv/BfZGHFh5Zzpgh/SmaV1OGdxxuYITguXUtpzRVjNSUTOEHrcgC+IPlLhICmC0wQSTilS6FFkgh7cWCmhdJQHIFUQoqdympwjVhVoDMFLpaAsSzb9Rlwtg4hUliWx7dFKJjwkcaUVhR3cQwaVM+vtlmpUE4JsWMYYVEj4kAgqo4wkqjvniD5ikPEwWhFjR0yeUT0hhEDUegjVGtqMtMHHQFZmSEI3g1sHIvLwrbXwgFWGlORh2zkHSsuDfYh7N+iorGh9oA2enDNFaQlB3KTWFjhbYnh9DCIF5KF1NIujMIBVimQNKu7E54RSkMJwfqTBqTQkhw/3LEmwF4dmPxyDc+KeLYqCO8cHaJXYbFfEoKVNOEMMPdODE0lNbzsoLORI6Fr6vqcsa/o+oG1ByJlsLKYqWC8XjIpycKRpigg+RupRQefbIcQKuuBxgytVKflZlTKmrNj6RlrMjYaQ9i23VomQpLWWRPacODg4YL3ZSBq7GwSvrCm1FdFIQ5e8uEuH9tlqMtq7Tb33IiBlcUiHFPat5ipLC70xBlymDZFAQmdLjuL8rYw45rSzZJVR2Yjj0Fq8lwjXjAg6ZBGf7S4NOGu8D4xGI7KCrmtBaQwKk9iH2ihrUEYEgpSCFFOs4BN6HweHV7cXCJXKWDdsqFEPYyzXmcqgBoewypC8xxUKn2Rd+2G8++CHtSMiVY4JqzSt7ynrgn5InS+stM37fuBn2hLvO4zRKC1r0UdIZAmHEC0PfMZqS7Ka2HsUmcoauiRt5jkpHEPQWCFBazElutBT1hUpJEpd0Gy2FFbSNNWw70Uvrl65Vs3e5VcUBV3rQSXKsmCxWFCNamm7HuZbKUXykaRer4ccZUzaVpAG2YhryhiDzQNuIKfd92txRueeFBEXqDJy84pxKLxE+nVDPRpRFyVt38nDTeHwQYoz8j6yptOwN5RKcBkpJYzSQ0s9r52KxMEJa3CupI89KSuUsWid8En2Zp3FVZtyGLAVis73lFVNDMI9tcNY+kHcN1r447s1lHOmtI4Y04BiUVLMCa3Mdc5E4r6bQGuLT9Ki3gXPuBiR8//b3rvsWpNsWVrfnGbm7mtf4nbi5KlMaIFoIPECdOiDqmhAlaqDRAN4Ax7gtBEPAI1CINGgOHS4vEJRL4GQKgV54cSJiP+y11rudpk0ppmvHcpGJa38ybQhhSJiX9Z2dzM3cx9zzDGM+3Xn6Wmj2IGG1hXjrpDc7+OaNmII3qmhgSAKPRiuqVCrEU3QVokaqGpYqF312jAT6pF5vrhiNKSFPddOmHq3RJDg6+I9I4tSSiZnb4M/du92iLn7xK7BFb9wzi2LihVXipbDbVDWNdEwypFPZSv4GnRJkdyMXAoNt/dIIaI09pK92LFEqL5OYR4mKhbY7/55IfR51R6k5JGreyMH7zDJefeOAxJ7PhARUgpU83VZtStLa2NtStdFQUwU23EpXyYflZAu3I8d1cAWfD6oNN9zcHW8mYBWmihqSi2NFJbzGJclUPe7FwMwkIBJQE04jsLT5unOzfy89+NgS94WOryeravtTQWVhBW3WTAzwhK4Hzcu0VXzAaF230VVf3apNSMxEEVZdKHUAyR4JwQRVTjylWXxfTItrvQ38zXphPl8o/k+6B0i8E//2/96Km3/DuDf+fv/hbmnrJNLy4dyKrza4sRnW7QrfMIv1KfAqYByBayv+SPdGlyNNJRKoy023B8KxNHeOtRxxzfeelwuI13aw7v2r5Sn35eu8JFTseIK0JFu7/dI7krdsnpb7vNfHOzfOlk22jqtB4ecqkN+2TI9QqaGaur2q0jcnWwJ9076aSc9ru1U4w0VrXZiD+g+geFUJg1SaVw/zY37rxLx1rh+H9k+VJafyyO1/lYJb5n6lChP/rIdupLWlV7tF0rGoYyS+mi/BU512zi/oZwaijp4KC3LJmcb7FA25xflp3/D58D2B08u18z5Evv57wVXwPY2W3ACIX0uPd3+QQB6e72y/tlHjt+8nucwQuH27xLLB/89wD1Vnx5Kw9G2W5Oru8bX4TFuQ3noqmgf68//6srzXxxOuqq4GvOr9fw7LcrZQjzm5vGNzx0L4orIruYtT3rOZZ8P7+4bYPnxjt48gI3mBHL+Zjvb7N+3dw81uA8UZ2hZuNfzvwHim7d2j3vVzzc82vD7OJs+LEvaoqc6Ln0uv1BnhmvpBGk4j+W9om4oisf11dweQYJJPTBuUeKng7bGc76Pc1h+vJO/WYmfMpor5XX5xe/XVbui9TEnT1/bTpoOsqdu4byvLAjHN67uf6/cfq8KDHsjfdjJX6/nvbT+eJBfk8+3MNaSenYHjCLRaLlPn/wa59fI07/4SP7u6Rc/N67JWB/jrZ5r5vKjq2/HZw8F9lBFj3VHqisoh5p2KHTHeYZrJny6k79/Yf9VcmK5q4fjW/nF54xjb0nPe+n9HBhrzrChaEnPUMH0uZ3X5X2AXkt6WsCMey99dGX6sDwZnQnxnVp5rD9jXI/XcKorWxLK6ipHV4VzKtP3b5NbI1x97U8fDu6/WZHq3SD3Xy/n/TjWgRa6DUtXE9fNScNhxeJj9lDQlks4FawhP1r7x3FrdtuP9VM997vR0q+HFyvyRV15qdIDEH3dSp8rx1fBz+9wpelQ/Pp8sVNlj3DuJ8erE5fh3k6V6thPb99FVzNfH90n+eJjt36qp6J3nOf7dWD5WP+KEniMUfrUv9f/Xrz6vnra2uhQ1dt5z479c38N594C/CJUK15d6TvsO/Zv0znuppzK17HvjO+FvZGf9FS/jq4PrQ97nTFO6ZNbp4B3QYxjHs8eQ3n8/m+1wPk1aXZ2g9y/9bVrKHZ9L6Dv+Y+ulfzsBW7gPK73Nh7p6sc/FLZDuTxQtkFcO+kuzfjn//Q//2s9a+u//Ef+5uBtw3SlUiMQWJaNl+3C87oShO4r6i+eOeeTpBrtr7X6S6cFV+AaSgmBLAY0Wt2p1sgilKF0GdJyBalOvGqDpIklbLQK+5Gp5i3LH94+e1iIFUwaSKHkGxYqmYPDqhPB0iil0Rr9xckJyNYaEvvDrShFlayGhUC2/u9c6EsOYNTmhOjleeN+3JAAZb8TMKJ4G2XDiVInRftD2DtSl9Y8lbrUTub6w8JxuEdXMA+JueUDXRMf9isahW2JxD5zVCNPTy9clhUrXYWVoh+neqvqUGGBt4NmvG23tuwkTXX/0jUmN6FHeEpK6ISKt7T7C3UQc2IFoYkrlwmRivDzxysfrwdvd+Pz/cb12KndTqJ2H1cn83ZXu4pS++e0Ku5TeRzsJXP9/Mmvnbryq5QDS0LY9PTZvd/vjLpqK5UlBj+uBhUh7xmRSErurymiqAW0RaSrW60JS7oQdCEfDQis6+oBbc8vbmweAu3YkVqpuTjZJhDWdLYHl07kHLdKLcb17c6RCyBEjQTEj61U1gKX0viVbjxl+CasPIeFihEWX/ywYZUhPqe1EwzRCY5SG+t2oVRAoivogrLnnc+fPyF48ryqUMSoSq+aeihQKQdLShCcXG/N25iPXHi+PHciWby4IU7uS1BiXM65nOsBVKpV0rYiS6Ka/3yMbgcQggd5hWCIFgz3rKxSCcnngoiTrErAspMjXoBprjauThAR3FvbgBgXAhHLONliRj0ymPW5SS+uPMZIxK9/zRmlEjEWDQTpNgLPKxLdizdnJ+edrB32AcZR7pi5KrKFdrbub08XV1SKERSWEEn9/EUjIS40UwgJU0UXJ/jU+7AxoKiT6u6dCWJ+/YMo1kondiJiHgBoeBHg17/6VVes93Thxc91Wy9IbxUHJwlN3Dpg2E9syzqEG6TuAyvBVebOikeOUjqp160Msq/pSZ3QFRrRIKqhFFSMbX3Mk33f2bYNEeE4DlST+wd3awTtxa6KW3cUc7Ix13KuXVaM0JR6L9jRqHumWXW7HCrVmgc4dm/aXA9yPSgtc72/+U6rRlo3D3fTSBbjXjO1GaFG2u5e3K014mVlp3pYngYqBhXu94OXlxdqyzRzj9IlPrbx1lw93HLBKq5G18DT0wt7LtDJ8uCVA0pz9XEp5TzeerjFBq2iCikFSmmA9tb5SGnW92Ynest+oARaNUqpXO937qUR00aIK6V7sJZSeqeBB2Eu2+qF1640qwIWIhkhrBtVFEkry3pBZcFMWOPCqpEtJEI1tNH3UcMrXq5OvlO5SmWvO4hwL9lV79vFbTrWzS1XAO39ZKNAPI4zxYjU4ipZ8bUgJe+8ud/vgPuahz5/owTCkkgpuDrn3L9gWXtx6jiAgJkiHlmOFR+PYasg0Yu/QXxuCrC/ZajQciNKJBKJ6+aFSbxo1yrQBKm9NbA1L0jkTEB5e3vDZWWK0EgqqNhZLHlYMCws3WJl4m8/hvpzkJ51DeTXwPWPV65/tLiVQW/pDbu/NIY81E9dIdQDfUYboPuTtrPdWHNPWL5WJ7nuD5Kkbt6q2rpiNz+ptzQWT+Qe/pCvf7oTbvXxwourWcaLZvpUCbu/pMebE61x76TjxdVtwzWsbE56xrfiv//58PbuQR51hE4YjxZdU05fxLo9Wj4H0fm+Xf5U7/EgS09SDG/lB/q1Dd3TU/jqT3f39/smnm3SLSn7r7fupdnP4d3LoL9IvyNl+rmEvZ7E3VADH1+7yq1clNuvYm/T97GE0aYqp+pJczvJwbAbL/+X8fQX7i36/JeVpx/K6dMXDv+ZFr2NeLTzepu/dVIJ4qed+OHG8vs3bPV12El79/ccimap7sU52quleuDNaF8PfY4NSwVpdpJp43pIpaekB//crv47vk7ETzu3P3l28rB/pgno0dzuoJNdUrva+la5/WYlfnLSd/v9cY7jaAU/SdivAvv3Fz7+m9+w/9ETb//aV9x/83R6256Q7i07+I5O5Ghup+3CaJEf9hHLh8Llz25Ocr9EL4TsTmaOcY63enrq+tjVx/c+5dP31aL+grC1QeD2uTT8b91jVd95JLsKbtgz1KfkxGCQBxm1V9oa/TNUuke2n+sgmi04eRJv9fyb3eOItoWzYDQsLc5CyDgOdTWrFLcwccLTr0dLSr0k8qt/zvKze0m7J6lbp4xrO1SM4/pgj4KI/2Pk754easdVyS/BW9Q74dwWQTqJ5erLcBKv42thb+eYS7V+vXunQXtPgI5zMdoa+fBvfeeqxb4elosXgY5vEgjn2OpeXcCRm1sBbMrxTTwJvOG/PO6RgfTOUxacOLcg3cIknOdct65ijW5V8N564/61/3/8lH9Bzo3rG/d2rkfh3lg/Vif34mMeuIWMq9vH9S+v6bQhyK+xq7WdVI5XL6JdfsjnuYwCIUB4yyw/3gl77ZYlzfeD4kFSy6eGdquf8TvLz5m330Qn5bL7veaXx7XSYmcLvnSS1YJw/z6da+UgLcGJP82+v47OgHEPAZ0Ud6JvXKvWi5vD0sW7KB57SLgblz8Utp8rZdVzfx77+PhdL7aFk7AFzs+M13b+nqkTpMPT9Xh1Enn/OniIWZJuZfIoyG0/V9YPlcsfCsNiaf0pe+dCt0/JX8WTND/3SRn3vI9dvshZDG7JbSDyxefpsKuow//8SSkXX69HkViaUdbODbwodXFPXv8e72xB7BdfM/W/1dKwOoD1U7eR6KrcsnZriv7cE2/2CM/Th1/uKEjUVbn8/jEXTf2YWxxq3HfevM9Cfv7rU7FftD3C7/7n//W3rlbylucQg6sxi7p/YSc8Y1pcBbVuPYjJFXnFFAkRk8C9KS2McDHDYkHlQKogKbHXRtGAtYr0oBUlsMZXUvK0dTEn9q7X29kiL0Hdq7Qu1AKrLJRbQXRBNJErKBEDlrhS6aRV83ZWYsI0crRGSgtHrUjzv9MQFoOLREJ2Dw3Vd/5SceHjxzvbuvH2eWfbnlCJ3G9XNGj/2ebtlr39c0kJa5ktrGhzCXc7KhyZp5SgNZJGynHQciGtCzXAjYw+Ldz2g5WAhsCeMy0lcqlOvETtL/qCWUMxIkJolVUTx34Q005ToVAQMksQrApBElhFQ+CQ2q0C/IW2VEPVlXCyKMUqaGSJz+OuA1OKGfe9eLo7kVy8zbtcCzkbx73SmpO0YqF7DG6orMSU3KNTjOe0sBV4Xldy2UnNoBlHcPIi6YoSCBYIYfFrnjMaF2oLNIXWQ5dCNmhK6mQMNbAuG/fbzrYumFWOwxCN7PeucjYn6kp25WUpOwTBMBKKHYWwJJaYXLFolVarK0iXQFwDhYpGEGu0VlCUJSVEldLEyYLqlg0Vg+AJ8doMjsIiiXIYAb9vNERqU6y5IjJFV4zu+4FqXzQNtpi82NEq9X4QVlfhLevag6OqW4tIpJaGVSFXwZpiIqzrhXrspOjXobWANagVQrq4UsEaIQhFlbcj09zTglIbXRqAWCLgthRmHrLjLcZC7Mq6lBbMlG2LSG600mgxElLwMKpaCUsipIVmgeMwVFcI3j4aGqwaESs0qVwluzWDOHcUWElpY79XJC0YTlCHGD1YSHxdKaVQ2uHrU3GrjkUiSWOvhheWELCWWUIgW3GlKY2zhVHBCsSQSJrIzZVSxIBK4LYfbOsz1/1OSPEkHJP6A9Ye4a7GkTPBAopSSybfD7bLE60KMa6U6orFvR2IKjEo5Z6RIOT9IC3elk3z4krUgA3v1tyIDZYWSSKsIVHvO0E3gir74UWvQVDnnFERJDRK272bwipSb0QMadWD6YgEVdRTxKAJtfjahxnbZeXzpyshKq+vX3G9vvnY06jqpGOFR6Bb9DX9072gISFxIS6b+38eTvarCYt5K6Y0t80YCmEJSm5CSBulOqlfm/i9Z5W2Z57TSjgqTyFhBQgCCzSppCVSys2JaBNKBtOEBbdauRcvRoUU3R87RKyBDIKZSqlGDBtZGoJ7wxpCUG/FM2noslCsEfu9rEFISyCqk/Ti/KcT9Aa1VZaUXGXdYAuR2PCCQAvUFgkh0hBEo6uC8cIYGpEmRF2IklwVm+BomWKZsCi3/eo2QzESNFG6bU0tRqmC1oVAoByZJSiLBu8IQIkmoA0NAgFUR1BbI4pL9RXfZ8q98XL5hnbAs0ZKzV4kUKHUTLOG6sayPtFqY9OVWhshRlLy7hJX3yeCPIEEoq1OYJuH7jSgFn/pS0v38jYvRC3LSq1+3CKKIIQRwJe8M8DhD8bbuvoaJhWJQhWjUYjmKvLQfIGUaKRugZJiQJsXZaIlQtoQlLAKVTKLCFDJdScmMDUQt2uIy+LWLhT+4T/4+3+tlq2J/3/jn/zun/+W/gIzAn3C4R5x2lwZEkZi9LtQr7p2X7+7tzNrMU+L7236LXnXxyjKmArpQyZenZhD5CSE89fxDP2hEwrbH3Jvv/Z2UyeW9PTPjZ2sQ5ygsk4al2d/2fb26RFI0lsuPxdPtf9Ue+tjf4l/SezfJW9x7scsXm/EovDyp28c3y5c/vLg+DY5cfZz6bYNnZCxx0th6dYLxzfRn9+6pQMI918lBCe74t1J6HoJhMNbzNsSWH9w6wWL/ne0OMFM0IdKqbfTOnnmpM/xGlhGS2oZVg1yHs9oebUopM/tDIKDhweiq4geaqLy8njGA0j3xvqhsXzqx3WttNWTw5e3xvK5ETqZgXDaHFj0NtthtbH/5kL+duP2m5V07epIUdLHg3gtlGcfj7a4rYRFYf0xd5sBe4T3vIRzrpXncL5Q5xdl/blwfONZJelzJzx/zpSn2K+DKyPLSzzPVxvUpx5wtbpa8XhR0tUJJDFom5PdrsbVc/5bEPKrF53Dbr8Yq+G3WJ4fVgrlZRBQXYUeHypkpBPR5lYWhME8OFHX1u7d2wsm8dZDz4qhpbmKvV8rqb0w0Xr6/NfJlciX0FvaO7m++32EOGnRFvefXf/8I5q9CyVcy/nsM1Tl/v9OhFofi7r5sdR+rY9vEmhXU/bUecTHpa5OqmqF5WPuZJb08Q/kr3yP1mwsf/kJW+JZCGqrE6fLzweIt2+Paxr23rrcOC0ztHRV36KUl+jj0e0YyktATKjPrgSOb9ntGszO9UqrUS7+N9PbgyRvSVl/OMjfJNbf32hrRI9K+Wo5W8TTh53044346cCWeNpFxGv1eVro64LPs/TRi85tc4saVNwe4imyfMxObBb3I9Vmp91LWwPlxYs+x2sPYVx8rJYP5bR9ACflTyuCm3s8W/ckbOs7gndxMsxtSPz73urvk/L4JvL8FwdtUd7+ZOHyQw9Oaw+P13EPOMnp1ilP//cbltyfsF70LDgNZbB7vHZ1R19PxNvD3D969Xv4LIiJdC9f4/brhC2R+/eLC07MjzUcPvfDbrR1FCS74tf872w/F1fJpu4PHh/3OPT2/N2ol4cn92jBH3N77CXp1k5rAYtCee6e15XzvFC/58Lhfqoh+5zbv+5Fkd66P+au1uHn2gOt+rU1FT+nbiXUkrB86uT4Imw/dE/f4D/3IG6tW1G4XUPc/TjKkyDNycm6KmL+mWMfGc8Odet7Z1+zw939kbXC7VePIFDErQ+kFwVzL9AeX4XzepeLkO5OjA+/XzFOCwV1usa7Vg5DcC/e8b0WPYwvHj4/zZcTH8tud5E+t1OJC3D/xvfIuFu3zHEVbdl8/6nLg3/zAqecRWu/P4Tau2+sr+f52a/Jcm0naS42LBP83+GA//Qf/i2wR/jH/9F/bK179Zm4X2daI5Krh0gFJcWVUo/TNsBffIay09FagywnIeAt3dbbj0v37HOC737z/y+tgbgSJYTaA6mkv6C6cu6476TUX6gKXiGTXuUIvuhV6+1jAm6CuiASULlhVn0TVmG/u2pN+3mIeAhWUv+a9ZfSJngbbG9PDxJc8WfG9faZl5cXrPRUUCu4QtWDnXKrrtDpZHc+PA19BAgNVdBOITQeITxBOMytC7CKFr9GIS7s1Vv8g7qKd1OF2tuce4tszh5opMnVfqUVVF3ReNw+k8JGFEWiK6v789ZJhoQQaUemmqHJlXBN8BfwI7N2VV2uhVt2lfDLywv7vqPAU1gIJlx3DzjyawLFrBOp7fTYStvC5+POtizs94M1bU7cpkgIrmYKEtHg4TxCV1EGb9W9Xq9d7R1RDWj1ZHpXyjXEIlhwQlWNfb+S0uaK8OTzUGOg1kwI3tIqBiF2iwlzr9Exr0WEvRXCCPlqjRDdxiOIuldhrZhVD1Zqjdg2Dild6beTQqSUxqWrzsSMoOam+cWvu4i49UXOCN4m3qqcIXQhBFfgdQV5A29Tvmwc9TiV3MfhoTchrhzFlXzad+FTWZZdtXi73bhcnt2n11qft4XSA3fQeAaYxbgQmqu0c95RhJR6S7eYhzK5PwIJpeZG6DZbSbR72AqmgZQ8kEqTk6ZjjUxhwbJ7XUVVjnqQRNxrWJ1MkepjsK5uFRDT5q3XyW0wlqAnKV+zh2Mhfp3jskD1teN2fTstC15fX/nx44+usDYPJzPox6XcM4TUA+XM16woQkQ4Wu1roitWPUDqCjSedSVbI142Ph93ZI20mv3+VsXasHtxa5fLsnoVvAtCMtULPwwltK8rhpJEaaXbhYjQrFBa4ZI2EtHbwNVc9cwwFXI/2tYa1grbtlGLdIVsZntaTjUv+PqUxJXDwyJEgtsRWDVazsSkiJh7isaFfd+7p0VXpYZALZC2hGkGU2JXjKqIk3PiqtKcswc09vuR6grKIm5pEEQ4aj4tB0RCV0m6NQb4mqdWURrJvFhzLSDdEzUs0oOsalc8W1ebB3KpVIwlRPJxsC2L+9qqkmu3gojaPbhXjETG90aqP+wfXR0dRvvbu3Ukiluf3K6fWFIiaWB/p8A0q8gWaMOvqSufy933MaMXCqXRunetdPuXYR1krRK0Uat5GJq4dcd1v4MKa+nrgQokt0zZS+5K7swq4by/aq2UdpxKVumq9tacqE4hIn0dvN0PNCXuZiwoMfk9H8LqJOySkF6wdYLVO3j2kv1cA+Tjzna5+H2WIvnwOddqIUZ1+5klceSGSvHAulxAvc8BusVPX6cTbgvRgFybd8tIdc/65uOK9WDI1nh5eeF6u/UgR6AU1pCgVPZa0CViIpi5Atra2E983w4hYCWDGGEormX4eQ/rnopED3IUEX733/xXjwepib+1+Lf/8X9p2pVpQ9Ez1FjrT15EKE/hVJi6CrCHj7ybISOgaJA5p3KzWxmMttOyeTtii04SjM+v6+Pl13/WzhbX0km0QTD653Zl27uvv0+J9pc0OY8LHkFM49hNvV28PIVf/KwrLL0NdgT1DBXa+vs7tz+5/OJnx98zdfVX3VwN5KRmO49/KGyGwmucpyfON+KnnUViQ1UAAB3gSURBVLaMFnM9bRXSh+Pdy7BbSPgPdVIwuDJutPyOUKjRyjoCtzyUqr/s3t9ds66iG2o2t7B4tNYPr81xfssf3Ibu9ifPLB88DOn+fXKrgj+UU61sp1rsoY708VYuf3bj+G4jfTjIXy/Et0J5jmfA21Abps/lJAWGaunyQz49ZUd79xivMRbAaaUxrBXGuWEPpeb4e4Mc8Nb2x8Qeas4xdwbBOf57HOdQgO9fu4fuSJOP10r6+U59Wgi3zP795a/M3/S5nHYNY4xNxa1CzpZ3exRVus3HUG8fXwVPZO/zK33K5Nf0CzuEERY05vNQ4S4/HhzfLb9oGx8WHmO80s9eTBhq2mFpMBSCQ/X83hKkPMdTVWw62u/rab9QnsI5d06rjr4WDEVg3bwFeozdqbQVkGLkr2JXk4bzOoVrobz6/TFCyoaidATVgc/39QcPLquXwOc/Tnzzf9zIr+lsv35vGzJCzkag3LifxjiPuRXfvEV+/dHXztuv0xnA9fTnHgAopZ3396ms7orf47vlFy308a2cqn0n8/3cR4FqqKMHaan3yvHdQn7SX7SGh3et62OsMC8saSe34lt9/P+Y/30/GOrloS4cymdXoctpEdAWOdWm4MfaFnWl9nM8C1vDJsKCKyi7I1UvxvUOhHehePHuyti6CMtb6/tHO/eX0c0wPnvcz/nix3b5Q3nsXe/GdqwHY20cStj8Gs5QqdD3u2H9MMLzBhH3XlULvTj4bs/wPar1c1XKJmw/FvJrIF/UVar2uKbeIWCnfcM4f+AxN9+tTcOWYqx3I1RyWA1ohdzDvIAzMHJg+dzOQLwRvqbZvVddXerzFzjHacx3V8T6eG0/+76+/uTnVlb1IuKq59wY88fXL7+Gy0f/vWE15Guok7bDfsIVz66Mrauc6917K4XTHmdcn9NLt6+LV/+sYbn0PoCxbE4MX3+tbD/a6SsMsL/6ua+f2l+xV3hvqzH+9iCCx/HX7rMbsp9TyI8gNlPhn/1Pfz17hC9aafs//O5//O22bNRipKjkfJCC0vBkgCUoQiWIh6QE8RZEjP7yXwkiBBpRiv+8GuWo0JQgkUolhUhojVqOruqFGJ2KCAYlZ8CDPGJUXAV2EDTQmrm/rsjpV0oACUaTRrZCUfc0LbURUMQqKuakQ4we7CPVqyxJqaLsxdWUFqO3FauTTNu6cOSD0gpL956LQWk0JDohelj3XRH3wYwaadU9/lQe1gwWjCoF6/6zxSpNXFUqCE0jrVWeYiLUivTWb1FXKJbiKp6yZ6xWzzmy7iUYE2BYJ8qCgZqgpREkuW9mK966H5RmUCS5ClaDp2xjqJrbGHTVb1aoZpgo27IguA9irYUUg4dkRXfGDQjWfDc0gCRUO6hWyTWTNGK1knrre1iS+6lenqkZ1vUCtZFSpJZCyI01rbztd0KEUg+en1dK2Sl5537fMVGWsCL4g1gmM4KNDOOwAwuVww40Adoox05cI55XXqjVybi9dgVz8Ctp4i3szfzaxB6OFQyorSe9B6wZrfo9QyfjQZCqRJL/nW57kLYFFSXSyXE8SEc6cVnMuKQI3TojiLKmyHErpLCRWzefD4qoB6tJcFYhRqVI9Qp8EJqZz8noSmAJgSPvrtbMrl4zIMREK5UYnBgJQy1bCkZjWZL/bD1IIWC1EHuIWbNKTN6GLOBhPOrEaqndD9cCMSVyaaDRrU0QtBPfOR+8vrzQausEq6sIYvP1QENXTDpNhY0QphApQWhd4fq0bpj3K7titHlokLXaBbIeNGWtkYIfc20Fs4OnlwshKk0bn2+f2VYni1QjFJdAtuZEHQi1daIVVxtKM4J4Anx8CogaYpVWMzEqx7GT0gbA9XpjiwspNyxnFIXag5wQbrUiyYlixQlRMSFUDzWrxYPQSi5Y62rHBhoiQu8aaLAs0VWf7aC2g7QutGZYrV4xFaimhNC9c/sLsXa/aAxa9RAlqSBpocXuyaoeWFnr3Z/9rRKX6CGUa/TWccMD9Krby6zbE6VBWALNCloDwZR6FNSls6go+SjkfCel6C7dIjQ6cRHd11fdlLsXpNzL2clfAbVOokOiP6D3NUnWxM1cCTW8Sa343xB5KDFrrlzWzYMJ8Qo7vXMEU0L3dVZT0rbB8ISNS3937Z6HwPOy0bwUSrEejIkX2mqtrvbHK/zaw+5ElKZKa0cnxwMSEibBZ0X3S3cWw9exIAZWSOLhfimGHg7q6vsWhcO82JAssOmKteJdGhGsHkirxE4uqyqXZSUUP4/mtzytGjG4NzeAa4p9vS01Q9n9ATFIv3/dhkFir5masSCIGqpuWyNJe4gfZygkJihKM0OJbrWnrl4ethFuHNII0jCrLMsztVRiGH7y/djNcH03HNXDhTR4sSBKexyXJARj0ch1d0sRq5UoStDgHTQh0IJyt8oq0Y+zwaJKF0Fgmt3OpHfqJHV1uoggXVGjql6E7pYPhvKP/v1/bypt/w7gv/vv/9lv79/4y1nZ3JOtPPeEbnH1maty9FT85JduRXRvp/KnLf7SlZ8CBHG/NnvYL5RNqYsrXYDz552oevy8q6BcdZvealchdoJrqBb3dvLFWlwtFm7egj0CnoayRov748bbQ102SLDlYz5Vmqff7erhOOnNw6LyS+xqUlcW08mJ9Cl7uFBXFddNu8rR+jEaI+V6fPap/u0BZ3QCVXPj/n1yT1DtLyLq+4WfTyDcep5BPyftSqzxAgl4aMpQ/iQ9g5LG+ITDuqKsN6nFboPQVWThcOVYvPZwp2quFlb/rKHOrM8L9Sn1sfMXbHpRExvKL7epqBcn4eolnGOy/pTJXy+EvZK/8vbu2v2E3dsxcPl/dj/ve+P260S8OxG9/uRK7fwaXeUqTkx2FcCpqNZqpE/lDIiK9z4v+vUaberLx3yqu8d4uZenK63GuDrxNwKOtLdG20kCngrA4Kosrd1P0YT6nJyAv8RTIa3Z5+Pywefu8av1fNlvayB/HVk+FcpzIL3VU43nalUfP8EJqni3k3zX7o1qSYmd/E0fD9oWz5AxLUZbOzFyeXxdmt9/Uo3yEs+5Vp/TGV4EXYW76hl6NcL59GiEW8bUBUl1C6TPpV//Ed7m91v8XLj+yfZoNe7Ealsfyupwd9XwUIgPtWu4N6S44Ob+/XJe+5Y8b2JYfpj2cLrg17w8Bbe66+F79z9aT9Xi05/vHN8sfs9EPYn/MEhiE3QvEPW0lUCcRFx/7sGHIqciuK1K/Fyoz640HmFq9RLQxi9U/RaV5QcPUZXWPbG7krJ1FXI42lk8G1604990exktRn71+2l4x5aLnsF+Fh6qby/AdfsQeyj3EXnMd/WHCKl9/HoxaAQpnlYmb9XDxj4/OgfiW0XvHi43fJ61dC/wfgyjo6Elt1NJb5XyrKelSSh2jl/YDUuucIx3JyjrIt4JUpx0bt2btYyxgG4Dh4eW9QyBenncn/T7Zqgr798GQvYQMOvFPc3+I35N/HjKxccvvbWHzUc/FzHh+mtX+Htg2PDX7srYwyi9Ld4/V85jkK4W9f/vZPxoJO0WQmOdGNf/fahZ7hYmTmpbtxkqXcWt7F895kPr6z86/KJ9Xt+/jZ1w5vy+5l7Y6/daeBeoNoK7pBcgWifNQ7eAaNHznY7ncBL+w34n3t+F0EV/f21RCMPvvhOrZ8BY7MRvH38xOJ5dVVsXPX/nVOL2V+X1s3d4jO/X1Z87WhSOFz+u/Kxcfmpd0esk8bgO4L+3/VQ4vhoeyf67de3331iHOiHuNg56rs3g82TMnaEg/k/+0V9PaRv/5T/yN4ehzqFkmhrfff3Kp7ePSFBSUGIMnVBthF5et1zR5IEey7IQKNCg9bbwI1dPScdfQi2v7ApNAjHW7oNbsNoJSoMli6vuUuRePABH8GAzMU+aXxYPhNIYsNrYwsLt2KFUtnUhV6gayVQnbWrl6Xnjpw8fWJaFlDZaMSwLJpWk3gYa10BQZQ0CrVLKQSkHGlJXYSnsTngtyQNuQn+BjTGSc6W0SiIi6t6wTiKtmBq1VYq5WtHDj0CbK0MThaMZTReyCsEaiyjYQbPxUm+sywKyumei3ZCQyEfGtPH6+kLOO7kUluikufZUc2PBYqBgtNjQHuMXxH0RYwzkfIAWli1QOWjF/ToBrter+7hmJxpaPqjiJNu9K5+fnp4o9yv7ceXyeiGmwPV6J2d/8W21oDGxXFY+X++ktLDUSm6FWFyNa8V9AT8cdxYrrJcFIRBC8tZ3DSgLYYFGI0txhXQSQk4kgVYKTYWYlCaNJSj77Y0lRGRx799mSjsCyxZdgSuVWoR1eSbnSq6VdEncyazJVXYNQ5ITlK108sgUFaWVSK6FGFe3T6BhlskmtFZpfRNsYhSBXTzpvAGlFZ6TW2i0FjECEiJHrk6qrI17/UwIqfsEZxLJA7L2HZFApXIrN1eGNyEtK22vHK3SxAMAl21h3w9iwMPaulJ1SQmFrm523+m121XUXFy93JzQEk2E6GQ6vZCTVKE4AZ0k+HXRStPqBO5xY3t6Zj8KjYhEoUlDzF8ExfpG2pWOzRrN+suPeOKjiJJLJS6u8LtdrzylFRBazrzJwZKi+7q2UajZGQFVrVr3LPXxJ2e2dWPfd+pu5OwEYNS1qxWTk5waqCWjS/furoHc3MvaWqMBR4BqlboE6r2xpAgoS4jUYjwtrx4S1T0vS6vEZh56Nyw3zKjVeF43V2iLcru6MjupssjG/chEWbACqZPeqivVdogZOwpIQrSBJaplNDaWFNxbem/E9AwhkJsrY92b09Wfl8vF1eYa+/OXcbvd0RSJrdG8W5QYE1WU2o3vnHONECO7RZ+XJdNsQWQ/OzG0uYVLa5UUAq1V1gStHB5CVouTcKF79oqwpoXjltmeI3vx/cfbbyJLSrQK1YRDMlUKkeSKRvMxeisH69MFEyOXynNa2fedZVm4Hnu323kobe+1kFJy+4ggPVDQiyCYekEnCEFd/Z+vHjJ3tMqlph7SZdzEIApJoR3ZrT16K90INwTcEicX8n4Qo3TiXQkqkHckBY58hbhhFbIlb8u3ypIu3PcrIomMF1Nb6y9d1Qhx4VbcJoFSiaEh0qhU9ppZ1MnIo9wIy9r314O4BESUfd+J4mGCm0TurbIufv3CZYXmbbkpRci+78Xkit5cCnXxm9vyzioBqFRVdjJmkVYPwqIc98y6rl4Jb4JaIMXQx2mjdnWyavDibglO0sbAIUoz746xApj62N56QKoK62XDqgdFmgIBTPweswpRE7VVMsUVuM3DTfd9Z4mJYy9EjEtITlKp79+lqvu+W6NpV201l/eZGRo8ZLTl7F0HMVLMbUje3t54ff0a1e5hPFigib/1qOtDMasVfv7XF17+vPrLRXIl2QgYGQqWM9CjGvk1vFO2QCztDJwaSeLSPCBkhAS1JN1HsBMJKuQXoQVX4awf6i9aUU0hfark10C4PwiL+zeB7adeAPl2cbJt95BUqW4hcv8+8fp/fqa8rmeYDsYZXBRu9fTlPF5HeNpDqSrtQcy1JOSX5P6PQ2m5uerwbC0OD9GMBcF62/gIahrhRSNErDwp6ZM9rAYW9RZ7hoLYFVu8C+Tya+KhWDTj+q9sxKurOofH6any6rdy6m334/jOsK6umgTIr+6ZOUKbALbfH25h8NlJL/fl9Guz/OTdbfv3K2FvLB8y+3cLeVO23x+E63GGR+WXQHlRnv/ioDxH8ou/zDqh7kTU8XXk+V/4WOWvFycAFn2EACU9g9bi9XG9j29SV5RZbxd3D0zrQVLeAv/wwpXm83b5UE5F6PFVOK9heQosP7kCWA97BDB1kn28lJ+q5ms9z1Oqz5kRojSC39zTsiLZLRU0V8I9cvvNSguLkyOd9IhvlVR9ni8/P9Th49jyi7J8fLRkrz/0dvy9UF4WJFfiJ0NzpbwslNeF9OGgvfOq1exqwtZbqOO1upfvV7H7IffjPoa60n9u+8vHXlafe4de8XtpWIVo7iTuvXJ8u7B8yOd5h9I6GejqyjMcb3PxhwdyPZSJQ909FMDrD3fuf+RqZS3G5S938ms6VYqDaB5EkKt//X7Xfnz7t+49OqwJWlK3ILiOIlE7Vb2nX3QU5NqoT+kMDfN/3Ld26T7I4NYcYTf271e3I6jG8XU6A6ZG6J238TuZvf+9l7MIsP5wpz576ODx4knzdfPix1CrDhUx+LnRyayxbrQoZzDSGa7Iw8d0BJQheAjiO7sZgPXHoxejwjmH6/YIZhxzYChnlw/lDG7zADI5LT2sqyilPvxmj68exSffB+Rcf4DT6uX+dWD99Hgekea+1lqNkI301s7Qwbr6HlI24en3hePVfa7TzQs/yycPU9x+yN23+dGNsfzsavz1Y+tt8q6Q1LOjgq6sd5Xn5a2QXyPpk695w5v38qOviUs/z/0rPa9Tuj/U6/nSVazdGmaEqY291HT4svrvjo4NcO/Z7efa7412+qsPP9q6CNtP9ex22bd0qq3XbBzD9/XNz9Ea3aLG59D6yX1ypfnxrx8b+6ueoWz06zZCtvLF1dKlyhlKOcZq7wRnyMb6qZ7XsS2uoj++9vk81Kj54mNwvOipRB3PBbUXbvMzpDc5ny3GHMzPwuXHeo7X/pWe9hHeGeM/P86tdMundHML0rj7tVk+N45nZXlrlFXYv9FzzzTxQFKfG3Yeg9Z3SujoxYXRjVMuQtihRePp94XPf5x8Pb//f3vO/qKVtr/7X/633+YSCDGBBt7Mlaq5eQswtQGBRqBooMnqyhMMMXUirIKXCPylL/RwpBCktwwqKpU1uYJl0eDqSvWKsIVIVqMEIfVQEO2fU3HW2xWGFVFX2DQrKNnVMjS0SW+xrDQppBSIzQi1scQnUnLPPPzIvbKeXK8fFMSjbsj1OBVHMfpxmhi1L4zVhNa8Jd4MSmmsaaFVsCZETZS8k8KGmRPDasq2rH7DEmnF1VFLXNAasDVwHHdicLVorgcWGkECMXjJ97BKwRW/pRViSK48TIn7fnclULc6KDX7udWGLkqx7CqpVtEoiB1YBW2uPIZGq5VgESGR4oaZj08Ubx+1ar6A1G4T0HrlDghkVyrJQqk+H4JeeHp+7unz3tZbgyLSvBXV3UJpMVIU6lEJMbEmJwVKaaf1RZBEzo24Lv5yLI1WDicczYgY1aAKYLDXQugeiUtaoRn16GXm0Kg0rLkCW1skSOitxw1rbgmhBlG9WJBScrsA6MQRYG6RIK2SdCOT3Q9aKkEjJG/lXQjEEAnWXKBgkM3J8kVcGS4hEFTIzdWL2I0UhSCu8h0ey9LTpEp1ZatodWV6SAQSKko9MiFGaLAF9749ile+NF6Q2oPTYiDXzFE9NKzRCBp7QJA/CAaNxOBEhGuq/fi37j9dBJoKa0xoJwCDeZNyFFw1XgwNkQAEXGFuVtDqoW3RFGu1E6EN1Pq9jlfrmrcWtQIpRXQoXfrDKPgLbH8Gd8Woy6ZhCeSj0Mzb3RWQbH79Q6BSUBU0KLlmYkoYQs6F2MPKPCQJWBRpjdBV1bU2RBNLShy3O+u6Qq4QXBVK8A2wYa5qHj1poqctgAloSAQTDxjUSjVY1JDY0AQHB6LDdgYgcFS8fTsqJd9RCqGfR22gthE00/A2e0nBF1Ft1FZAGqJASGe7e1LFSvHxE0FSV+JaIwZ1JSHeXSEiWIUUvMVbl0ArXkSJtSCKq+ljRGtjteFnFAiyeqAThsSAaCBng5CIcSGZf63ljC5GzuahlaZUC26vId5hIbHiIobo3sXalebmbftN4F4bez2I4nNKg6taDQMNtCasGgjFWEOkanUlOt1PLKoTy2IgXuIXdYuI1ipqrm62WkEiS7e3sFxIafUOBjP3A2ZYCqnbowRX7IcQu4WEueVBdS9GK4qNkD31PVANWqtUM0gLhlDN164QoEn0IpMWRN2b2pXvcorKYgjU0iiift1aI6HsxT2UBT/eEMMZFGdRqVFpbsBLihHMKEndM80M1K9VbOb3vgYkBA86k4AoTvI0f1FLQT3Uk4AUL3hpdNJdgrjnr4J4GltXeAkHBRMhkVDtRL/XN1nSBjRX3YormwVXLbu9RgTJiHi3gglYH5cYU3/eEN9jk6vxWoOmRhV/Llp74UQUL5RIt8Aw86DJEFFzFb729fPoat0l+ni677H7yP+H/+DfnUrbvwP4J7/733+brv7yhsDT72sPeRktka5WC4cR742QveDlvoacqjAZb/sip1JuqIA8oAiOr7q/5bMXXcvmv6WFHlzlL6bDW6+lh58uKqdP6unn2gkngpz+sm2Lp5dm68rg+uIEyPD9O5V56+P4kH6Ot4cn7y/UUbvLnTycpisGm/VW2tj9ELvy8dYoz12t8+xr0fHt0r0+9VSmludwqs3Sp3oGQqXP7h3+/hiWj9Wvz6VbVWw98DAp689upSXFsKAPZZf4C3a819Nj1KI+VHKxj5rIqYJD5fR0LU/6zvfUh1eLnUpGVLz9efV9rjy7R6sWv6b7r9dTvSXV3E9QPczI22PpATseOGdRKa/eIRKv9eFPuobuR+zev+M4LHWF9yrEriL2a1W6OtFOX1hXTPocDSOgrqvprM/9UWRoi552FOnN7S7i3UnH9/Nn2IUMNWxdQ1ex+tfjzYnRuqqrz6JCUOLbQdsS+TU5Yd4Vm+nWThKiXkKf/+1Ug/uz5UOdZurn1bbYfYPVfWmX3m327Upb1ZXIIqe/NPjcj13d6J68fp1dEe7HdJJ23QPV1I//+HZBEMI1O/n79dKLL4otAUvBVbnaA+GiYimcfrdePOj796an36jm9ov5OFRxY7zLU8AWN8YcXwd/5tRip+L0VD3jhQ09KuU1nfPbx9SV3RYfhHi99KLEtVAvEd3dP/ZUEar6OVTrAWNKeQmkT4X89bCY6z7frhXx67o8grH83LqqsHLOM2muKg27B2+NILL05vNWmp3zN33Mp9I73Ptz/aJnF8KwXtDCGXbWy23nOnb6dosTk6O7YbSwj3niRSs9lejDW1f62qbZCTwtRnkOfe4Ly087+ZvViypfxwep3hX/Q51t6jYHCN06wMco9DV4eev3BE5Gh77OagbUvUvHPNNq/R9OteT2U/H1YAvndfdz7t0BxQnitnrwVuwdEm7r4ftN6MTyeNf2e0lPVXZ+CQ+C7jme62t5UkL3fa+rdqVw/5Chjq5ORtZFfG+tdl6XsdZo3wNPG4xuTzBERuFwUrJ139tQ/NqY9rkWxl7tf/NBVtqpEC3d633sGbX7uKZbO48zHN6lIXDaJcQeGjf2Vq19XdShIJaHt6xK/17nDHpAV9mkz3HO4zPte7zyuP/6+S6ffTyGp2w4OD+/PClYt9zoc3acU1m922d4//rf4FQIu8+sz510cx9a+vNJyD7n0q31ogFnYWic9/BtHkW2lrwordV9bUG6X/FDXd2i8J/9B38LPG0nJiYmJiYmJiYmJiYmJiYmJiYmJv6uQf+mD2BiYmJiYmJiYmJiYmJiYmJiYmJiYuKBSdpOTExMTExMTExMTExMTExMTExMTHxBmKTtxMTExMTExMTExMTExMTExMTExMQXhEnaTkxMTExMTExMTExMTExMTExMTEx8QZik7cTExMTExMTExMTExMTExMTExMTEF4RJ2k5MTExMTExMTExMTExMTExMTExMfEGYpO3ExMTExMTExMTExMTExMTExMTExBeESdpOTExMTExMTExMTExMTExMTExMTHxBmKTtxMTExMTExMTExMTExMTExMTExMQXhEnaTkxMTExMTExMTExMTExMTExMTEx8QZik7cTExMTExMTExMTExMTExMTExMTEF4RJ2k5MTExMTExMTExMTExMTExMTExMfEGYpO3ExMTExMTExMTExMTExMTExMTExBeESdpOTExMTExMTExMTExMTExMTExMTHxBmKTtxMTExMTExMTExMTExMTExMTExMQXhEnaTkxMTExMTExMTExMTExMTExMTEx8QZik7cTExMTExMTExMTExMTExMTExMTEF4RJ2k5MTExMTExMTExMTExMTExMTExMfEGYpO3ExMTExMTExMTExMTExMTExMTExBeESdpOTExMTExMTExMTExMTExMTExMTHxBmKTtxMTExMTExMTExMTExMTExMTExMQXhEnaTkxMTExMTExMTExMTExMTExMTEx8QZik7cTExMTExMTExMTExMTExMTExMTEF4T/F5ScXkRTB55xAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af9855668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(24, 18))\n",
+    "\n",
+    "# Plot all channels of a real image\n",
+    "plt.subplot(121)\n",
+    "plt.imshow(colony[:,:,:])\n",
+    "plt.title('3-channel image')\n",
+    "plt.axis('off')\n",
+    "\n",
+    "# Plot one channel only\n",
+    "plt.subplot(122)\n",
+    "plt.imshow(colony[:,:,0])\n",
+    "plt.title('1-channel image')\n",
+    "plt.axis('off');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Image manipulation\n",
+    "\n",
+    "### Getting and setting pixel values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "153"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the pixel value at row 10, column 10 on the 10th row and 20th column\n",
+    "camera = data.camera()\n",
+    "camera[10, 20]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af87836a0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af987a278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set the pixel at row 3, column 10 to black\n",
+    "camera[3, 10] = 0\n",
+    "plt.imshow(camera, 'gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af87402b0>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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HVWJ6AYWyfIZ/mkcAwqGe7uZkn9RrapsUAM4UaDKUfdQYVPtucxCsW5VBFYF90FiZn0or9lnbsEaD+RftlyqK0pPXlOaaKCO9LF+swqvxs4qr8qHxO4tCdDV0iqaUZipf1nNr3Upf9kE/VU4U1SrtOXYac+Ujn1ODaROYGqbRUWoYxXoUyRI5WjqdrswJxBAWAqiHJnOB4OyDrnq0OwAtYQEEhFnnx/kbC5VeE4ys0y5rtuhA4zwr+OrdVNGUDsAUctC61VPp70QI7D+TkTp+RWDqKTWPoHG//q+KyTbZb/VumjxWY6DGNyycI120Xv2dIZkm1LTvek0VQ0MitmE9r7avCU+dflTUQeXVses4Wbd6fG1bDY72R8NBNXgqG4pQlC9W/uyZFUqD2ZQ5YRiA4L4Fm5GlIKiXVAPCpJ+10LqijfcCweSjts/f9AwIFhU+3qNe0m4XVo+jSAMIzhBonMn40SIh9lUhLuui12Bhn3X1oAqyzeCrIPOanbqkAvJ+VUDeo/XaMFCVQRVIhV3pqNd1FsYiSOUljZ3KjdIxEok4yE86UTZIV+UTQyTlm/XgFs2qYbOOTA2BGhSVVzUwijx4H2VGk9y8h3JjV11qnbMpcyKUsCGAnZGgwOi9LDoHD1TOUFAp1Njo5ip9TvtBL834jAKrv09MTCCXyzklVIFWBMO6FApqSEQG2qSrIhybiFJPyPt0N6MViDDh1ljYemkW/q5QW+G4GjvtlzUO1mtqf5VvNOjxeBzRaNTtd7H8sXmYeDweODFKwzYqeSqVCnhZhkk804HPhSE9AC5XoLTV/Tjsn8qYDU+ouEovz/MCOQ2dUdF7bDij+TH2RVG2TlnbGa3TlTmBGKiwahSqq6sxMTFRoTS65TkajbrTfRRKkagK5/g/15Hb+9Tr0ogoY7mQiEaKAsn/fd939YZBVoX67LsqG+EyM+gUEgoaQx/tmyZFeb8WC/d1vb/O5FCgudhHVxWyHo1XaTA1VNGj7tSIs4/Kaz6jRl5RkS48Iv8VblORLCzXfILOJmjczfGqzJE/li/qdYGp2QXSmTKqjov3at5FHRLHQX7rpjCGr4oGq6qq3IItRQ4sbF9Dbp1GzufzSCQSFY7wdGXOGAZm5ymwukkEQEDYFMYqIfi7GhgW/s6pIt5XLBYDx4ZZyEfPRAhXLpfd5hZrnVVp+bwaNQCB06QV2WiIQMFnf9RDam5EwwAVdi265p/0VaVk/aXS1InCSlv2QfMIdnkwx6+0o5DqGML+twaGSsZxcXrQ5oSUL6yLRlXzDaybs0KKynQWSQ0UaWTRFWXQGk5gyghrHzXfoXThc7o1moZDjRGPN9T8ioYrNkxhOKuyzTwcN/PNtMwJw6CFRFTmqGCHeSBVPLWc1vKroCq81zot9KWg8nedPrSnOynMVchqY2vduKTjUMVisaGTFp3e076r4bOLlVSYKMQ6d6/9t7S2+RMbX9t1DzYkVCMXdo+GaPxNcxu8V5/lWD3PCyxvtuNUnqusKG34u9LFGnpCff2uqEL5qXLB+pQ3lueK/jzPcztf9TrrVrpq3epc1HFMt59jujInDAMVWT01iwq9xlwUZptY1PX3GitSACjYqrgqXFRYZYbGlVqfFQZdGEOkoHFzGKTW5KOOV0MhNY56Ta+rUeDvNla1SVu9VwWQtFPvq+hJkQ4FkmPRQi9NRKLF3qsGUemqyUelv/JclUbpokpmjRzbVIdDHmpeRcMYpbFFB7zGZ9QIabhq+a78VxqpchMJcXxhoa7KGu/RcakTmkmZM4YhTClVcNXiE7oqkVSwbZJHYZhCYo3BLfRUhdMkF+NfhhXAVHxsvQehnFr16ZJ2mlHWcbIejb9VoAhN1RApjLcGg9/ZNn/j+H3fd0eTqyHSPti8gBoIVQJdkcjC8xW1WM9M+mu/7f1WabUf6lHV4JCH6jAs0lRF4xipnFYW1eDpNUUsGsKpAWdR6M9wQjfIUaZ09acdh4af1oCG7amYSZkThsEyyFpaAC45pkqhllyLKrqGAZo4VK+u34GpNxLxWQodl+wCwfhQLbd6LjKNhop9VsPE9nQM1rsRWtsX47AfNpbXZJjCZfZPN11Z5bGCzGQbhdW+qcnS3fLEhjn2WZ1VUt5q2KCzPhZ2c9xq8G2iUnMD6iQ01KNTUOhtjSzbZWFfdFrcGkk7XvKCfNaxavhj5UH7bHMM9lAWdYLThTqnK3PCMABTBOdZepx60ngPCFpmAAElsHCfCq5QkwktmwBSyO37UwdrULCsQCi0tmEAmaLrGpRZOmYLScO8O1GHKguTipYG9CR2/z77Ul1d7ZYHZ7NZJBIJNxvCvpBuamioiKokum5EwzEdn/JCQya7v0DXb6hxoVBriEWjSgSlNFcYzz4oalQ6s75SqeSMJWdXFIWqIdWMv05JMqy1U4NqrPRezf2oEbKhWTKZxMTEhJNHTQArirZhEetVRzubMmcMA9+1x330dqejhes6aPX0auXtbkEKlCqNXYmnyqAM1piO91MJ9Xcb79Pz2njfbtHlb4p2OF4Kgd23oTBUjwkvlyd38sXjcac4v/zlL/GrX/0K/f392LlzJw4fPuzoEY/H0drailWrVqGmpgYLFy5Ec3MzFi9ejA0bNqChocFtBGN7Nv+iYQkQ9Kxh8++koU7NhhWll0J3TveFnTrF/9WpkJ/Ka01o6qlIusBL61L66noR9k95TAXXkILP6pS27bPSiQ6K9CyXyxUniFG+o9GpHZSKpqyOzLTMGcOgHsEm5BRGWe+hkFlXtakQsk4VyLDYUpXcektep7KHPU9FszGd3qPeQpcn8z7NF4Ql7NiGegE1RtFo1BnFRCKBQ4cOYefOnXjqqadw/PhxZDIZ5HI5twuPY+7r60N3d7cbR01NDVpbW7FhwwZcccUV2LRpU8BY6viVXhai87vCZUU/rEPHGCbE1lDbla5aJ6c3dfGU9sFCfZvEs+NRHloEq7LE/vN+HXOYrJD+KgM29FW55fNhbYfRn87DLgKcSZlThoEekcqsDKuurna7KFWQ1FtpnEUFIeN1y6pafCaWaHk1aUnvYdEJPbQSm+FPLpdzDOf7Du1mK8aEdsUm26UHUONAmKrrEnQNhholCuWXv/xlPPzww4H3afI+hcqkmc6c+L6PkydP4rHHHsOPf/xjnH322bj99tsRj8dRVVXlDJzG5lZBiAA1N6QIiWtBgOBiIVs0tNBknOYQNCeSSqUCCIP5KYtcwhKVpK/dgk5esZ8cIxGANeqaB1NDxHYtytLfOFZ95eHpnJHO0mUymQAvyf9Z6eOs7n6NCwVelZbXGQdywGHJJ4XuQHDzkCZwNC5ThqqA5/P5gLcj1LSJMDK+WCwGoJzv+y6zn8vlHNQsl8vIZrOBabhSaXLlIA9q1TEDk16EZ//l83kUCgWnIIVCIZBQLRaLGB8fxxNPPIGHH37YKVs+n0c2mw2EWiqQSkeb7IpEInj55Zfxh3/4h9i1axe6u7uRz+dduxwDn2dYqIu4qJTst64C9DyvYtWlGk6dqeJ3Fs23sN8aFlCxmEuxyUdNBOtqSsqhGnMaev5GWauqqnKyqWGNOgjS0fd9l+fRttQ4s9CB6MyCDVUVFZXLZfeKOl5j32c7MzFnDAMFkkqiCICFSkChoTDY+V8+q1lZLq/WmQqbLON9QHAu3UJMtdwkPhnG+qgM7I/COxs3K6wlA+2qOgohQxAaQb2P6Cefz6Orq6viqHUuq1WjowbCrnPQQgP3T//0T3j22Wdx8uRJdHZ2oqOjI7Acmv2iQgLBLeKqJNwPoXRjiKAhI+VBDTcNLdtSdGl5StSifOL/mt1XhGDDCxo4e2yclSmOV40KeU4jrQaB9ascqeHTcydUF2zOgnUqH5T3lp+nK3PGMKiiqrUFgufhAZVvUFLB1vtZnzJJk0MKFxV6UtnUY2vCkfcp6mBfNE+hYYSO0eYhKCCZTMY9q4xUuEtBYNHlyTQaLS0tOP/8812ykEYxk8m4Y8b4py9Rtf0Po186ncbXvvY1PPXUU/A8D8PDw7jrrrscWlJB5h4WK9xW+C1fOQ4+w7X+eh+Tq+wr66SxofFUr6kISF8Vzz/rfVk3vTllS/tP+uhuTOWdolt9+YsquYY2Sns6QvJMZUCdps7MKM81iartzaTMmRyDJlxsvExF1MUxCv31Ob2mOQOg8l0BKjiauGR/PM9zU3pquHSGhHXwmj6rggYE0YL91N9sKKXJMzUE1utomzt37nR0U4RjBYRCrghM6avTfmq0f/SjH2FgYAAtLS04cuQI9u/fjzVr1iCVSgV4qtOQ+rzlrRok/dREsgq49lPlh21ZxQ4zfPxU7xu2rkU3vllIrlOsbFfll9fIPx0/+61Tl9pXfVavUeb0/SfqTJTHRDZqiGdS5oRhoACRSApvSRQm2XQXJmFTGLQHgq8CUwVWA6LxmibS7BSPEjsMbqvR0JjRhhLA1CGqwFRSUQUAqDw4xtLGZvXZbiwWw0MPPYQf/OAHgbHZbLntu+ZiGAYp7ayw9/b24vHHH0dTUxNisRi2bt2KWCyGc845J6BURGBquDXpqEURoI6dn2o8OQ6rVKxfacJrFrUob7RdHTfvId+0T/xf+WwVVB2AXVui4Yq2pzKq9+pCLcqO9k+T1mxvtrkFltOGEp7nfc3zvD7P834l15o9z3vM87yDpz6bTl33PM/7N8/zOjzP2+153oUz7YgySafcwmCa7tGnUABBFME5fIWLqnQaT+uqPgoMp7uSyaQjtM4YsC8q8DZzTEuuy2k5HtavCTUdl71OQ0MacWzsSywWQ3V1NZ5++ml85StfCSi/Igv2TZVP+0m609MwUcb+Ew7H43FMTEygr68Pg4ODePHFF7Fly5aAYba5IIYuOt2m/KdyqkdVo6nKrIhB62eOwiqaOgmlL2E3f7N7EtRBqOdVhdd8DZOQFkFyHKrcmltQ46D/83nKp+aJ+JsaA5uPoOxPt05kujKTHMN/AbjaXPtLAI/7vr8GwOOnvgPA7wJYc+rvgwD+z0w7olDJZq4pMCQAjQOz3zYrzzo4s6AzBzo7wGeYmNL8gmb+aXk5XWgXqPBZTstpErFcLiOXy1UkqjSrr0lXzX3wmrbP67lcDvl83t3PrP7zzz+PfD4fWFVoQ4iwGNfSgfRhPzVutc/ywJrBwUEMDAwEsuhW+Nm+hl9hHpr3at5Ivay+Uo5Gz8b+VFL2U6c0NTTkjANlSh2IJn1rampc2xwXke50L7lVZ1ddXe3yLhrSqJPi83QmYbNUGopx7BpOWtSp7c20nPZu3/efAjBoLr8TwN2n/r8bwLvk+j3+ZHkGQKPneUtm0hEOUosmyfhiUiqLhafA1OpJJm2Ytdd6bA6CeQgKODCVBKPg2lWFjniRYFaZwm7HYTP/uqGGz7MPWpfGzWG5E36ncMbjcTz//PMBo3SKhxV01f+tZ1RDTEW1QkmesY+ZTAadnZ148cUXA7kdu1yd4+M19ZKq4GyHvytqZL/s2gtFAhyHek/eowiQ91HGkslk4B6OORoNbiBjXTbcVGOlRou/05BbNKXOTflicyxWFrRODUGsoZltSPHrzkos8n2/59T/JwAsOvX/UgDH5b7OU9cqiud5H/Q873nP854fGxtzRNF4TgWF0Euzw2o1LcE0magCqZ5JLb7nec6ak8iaw7CQzcaTLHyG9+p3hXrsG/9O0WSSKZGpaUn1WEoLhhPsXywWw/DwMHp7ewMexSqGtmPhqP5OQ6lGwQqXJtVKpRIGBwdx4MABh5A0hGJ7GtOrobZ9tqGiGpuwNSQ2mWf7yH6oIVRUorzUpfSsU0MYlT2OQVGIhlJKfz3O38J+NYwcmz6rsk0nSWOmM2q5XC5wDmi5XHbIeTbl/3Py0fd93/O8WR9D6/v+nQDuBIAVK1b4VmkUQagiUYj1msIxTTRZKKVJP2WgLnzRdjVPwGtEIjZm0xwA21ShVKGxm2Z4P+8DphJJaphsnbqYJpFI4JVXXqlIYoYZAeulNcZVgdL7w0IRKob2o76+PqBUilq0HV6z74JUetpnNMegyjfd84oSdQUr69G61Oj5fjDXYPM1tm01FLyHhlvHYemsTk2TiPxN94GQ7iovtm/KZx3fr1N+XcQGRpQhAAAgAElEQVTQyxDh1GffqetdAJbJfWecunb6jpjkGBA0CCzWy1kva4lkCUVFUGGm0ChsVuSgdfGPTNEVfL7vI5lMAgietMyi3sZe4/9kPgXGemyFzpqAi8VizjMojZQeqtAs6pmsAOtzlsZhtPV9HytXrnSxdDweDxy0ql5XDaUqp0UDSnv18mHKZPluaaFwO4zOlAXNtRA1aX5HUVJYuKJ0472UYx2DyqTKPOlspxhJj7A1FQy3bHjEOv9vTVc+DOA2AJ899fmQXP+I53nfAnApgBF/KuR41UIhscLPwuSOzRDryb2auKE34jmGkUjEQVsaAV0TYD2tLRbmaXZajcT4+HjAo9tkHZ/RsagHs1NibIv91TyIChzHqx5RaatGwXp/9TxKCxXWaDSK+vp6rFixAqlUCtu2bavg0Zlnnonu7m7s3r0bxWIRdXV1WL58OTZu3OgWKWn+gglTdQiKVEgH3cuiEF77aOtWg0vlpczQKNBYWCNYKpUCKwj5nCIFKrnKJ+tSQxZGY46L13msvT6roaw+Z6fRLa+5jJryoih5NuW0hsHzvG8CeDOAVs/zOgH8HSYNwnc8z/tfAI4CuOHU7Y8CuAZAB4AMgPfPpBMK2VVwdc85z7/TkMFuuVVobBNp9kBRIgLNemt/6DV0Lz7rSqfTqK+vd8Ki8E7/17BCUQlnN6gYmkhVoaMxIhpQg0ThIXz/3ve+h7vuuisAiy3y4jVOgeoqSHsPP5PJJBobG9HS0oKVK1fivPPOQ11dHZ577jkn0PwbGBhAPB7HihUrsHfvXtTX1+P888/HyZMnMTExgVWrVrnx8LRupReFWRVc+69rWHS7vHUIOk4qP/9Xw8qwjnJEJeTOUw0p9MBgzWMRNejsCvvNPQq6NJyf+qdHx1sDQ+NkQwiiRBoNK4v6Wyw2der2TMtpDYPv+zdN89NbQu71AfzvWfUAwYUgwBRTuasMmDrd2VpAzUOoxyVBqHB26bAuK2b7qsgaz1pE0djYGFB0m71WBEBh1peNUqBUCYDg27EU+vFenSUgdIxEJjc4bd++Hel0OrD0NywsAIC3vvWtaGlpwa9+9Svs2bOnAnLzM5lM4uKLL8Yb3/hGtLW1oaqqyu1cPPfcc/HKK69gzZo16O3tRU9PDxYuXIjvfe97buqvt7cX27Ztw7nnnos1a9agVCrh7LPPdspgd5eGjd2GXUoTpbeF81RqS0elIZ2L5oz4m01e5/P5AGolXW1SW1GgdRLKb0VHpDd5R/lUpETjQPRikYLKsKLvXwctAHNk5SMwtaGIjNP4jwTg4g6g8g1FqgS8Pt2uMgqDhiV8XjPf7IsSXBNhdhMU61LhUiYBQUvO5/Qeel9dhgtMnQhtldzzvMD6gbBQQWPRD3/4w8jlcnjqqafQ09MToBvvTaVSuO6663DZZZdh+fLlziidPHkSx48fR11dHa666iocOHAAnZ2dOHLkCFKpFPr6+tyaAM/zMDY2Bs/zsHv3buzfvx/Nzc0YGxvDJZdc4vhGuqjB12SeQu8w463yYUMtPYnJ5jloFPipdFKaKGrQRCa/a/F9vwJp2nyVIhftF+/T8fFsUa5jISrI5XIVx+lrWMP+kz42XJpJmTOGQaEfEJzj1nsUzkWjUSxbtgwnT54MQCWF4arMNq/AYhfk6H1hiU/tj8aPuv7BegMbb2rIYy2/TbLZWRBLo0QigZqamoC3V9pR4ZYuXYqzzjoLdXV1OHnyJAYHB92btFQZ3vOe9+Cd73wnqqqqMD4+jnQ6jf7+fnR1dWH//v2IRCK46KKLUFtbi56eHqxfv96hl0gkgkQiEYDePAtgYmIC6XQakUgEtbW1bgMX+zsxMREabytM1/HbsEm3adt4Xj04aaxe1SYF9T59Vn/jJ2XNHgeouTLlhzo+9p19plPQJKfmSshPq+gW4eq4lU4zLXPGMLwa5KEXVTjPwZJYFmlYZdM2NIlkvb2iALW6GsuxDvUoVvBYmHzUbLEaPWW8TWhp/zXJyj+Oqbu7G319fYGEZJhxaGhoQDabxfj4OEqlEi6++GI0Nzdj+/btOHLkCEqlEpqbm/G7v/u7iMViGB8fx+HDh3Ho0CEcO3YM3d3d6OnpwdDQEFKpFHK5HJYsWYKzzjoLvj956tOSJUvQ1dUVEGz2p1Qqoba2Fvv370cikXDnTxSLkwfOLFy4MHAWpCKmsGSeIh3lidLUOgYbsqrBUY8d5gTCrrFYWaJy08lY2VOnZdGR8kzzI5QllU2bGCXdtNjQeSZlzhgGm5ixibNSaepgVzKwWCyip6fHWVmNvZiw00Nldc1DmPLa8/spTJrc0biWntbWp95ehccyzPaZdalx07UbrFuFORqN4kc/+hH6+vrcW5u0D2ogenp6kM1mcfLkSXR3d2Pz5s143eteh7a2Nvz7v/87xsbG0NTUFEh6NjY2orGxEUeOHEF3dzdqampwxRVX4I1vfCMee+wxtLS0YMuWLQCA1tZWrFixAocPH0Zvb69TFAtzaSRuuukmvPTSS3j++ecRi02+hu+ee+5Bf39/QPBJB/WydAJqOHQ60Bpw3V6u/FSZY5saruoKWKuMLDyUVsem75GkPGl+QZd08zuNJENaNVjWSJIGih58f3KzIcMZlb3Z5hrmjGFgsfGYFjLAKm8mk3FCwU96fn5qoolCogoZFjtqCKAeWhOcvF/zE2Q23xtImKkzIAyFVLjtAiw1ZGyXgkDjF4/H0d3dXQEtrXfkkWT5fB6ZTAb9/f04ePAglixZgsHBQWekUqmUo1M8Hkd7ezuam5sRjU6+J/RDH/oQ2traUCwW8corr+CZZ55BdXU1Fi9ejHw+j6effjrwLg0bCjDr39jYiPHxcYyNjTk+5nI5F2qoQvHkcD2pyXp3pYl6d1Ue9cA2TCCNdYZB8w6KVtX5MGTQqWlrZPSgGcqkIj8aLpUxzWFZlEnDo3kaDWFIE0W8/7fWMfxGixLTxvoqIDZjzGd0mor3UrEU3usUlU0cWkinSkmB0LULFARbN38HgJqaGgDBHXD8nUymMaGHoUFU48Q5f64F4Oag3bt345vf/CYGBgYqPLPNx1BAd+zYgQ984ANYvHgxXn75ZQwODuLkyZNoaWlBNBrF2rVr0dPTgyVLlrh9A6lUChs3bsT555+PtrY2JJNJxGIx3HTTTUilUti1axeam5sRiUSwYsUKxONxNDQ0YPPmzWhvb3ehQi6Xw/Hjx7FixQpks1kcP34cIyMjWLhwIZYtW4YjR47g+PHjSKVSaG5uxsGDB3HTTTfhm9/8JlpaWlBXVxdAbaQ9MBWiqRLyNxoD0kdRlYZ6umBJaa95H/X8NPac9iVPqdQ6C2FzE+S1RYr8nccC8lnL27A2NRehusNrsylzwjAowankNn5UCH26aSDNBCvhwupTJVV4bg0NUHkSjvbJ1mGTp8yS67JcoPLUHkUVavUpOAwHDh48iMceewx79uwJQEkaPfWseljNjh07cPPNN+Pcc891eYQFCxZg8+bN8DwPS5dObm3huZTsC7d1Dw8Po1AoIJVKoaamBm94wxtw9913O5TR2tqKtrY29PX14d3vfjfGxsaQTqdRVVWFmpoal5js6+vD9u3b8cQTT2BiYgIf/ehH0d7ejrVr12Lx4sWor6/HmjVrEI/HccMNNyCTyeCFF14IhBJKc46XNLA5Bg3PFAWoV7W8ZtGsvs6Y8LvKEu9X5Kv3Waem+Q8aKjoRlQ29VxVecyiUMx27OofZlDlhGIDgRh5VYJYwQxGW1bWKZhM66pVt/K/18T5VKk0YsY2wnIhCUApgMplEOp0OHOF+4sSJwAGt8XgciUQC8Xgc6XQa+Xw+IHi5XA49PT0uKTg6OhqAiKQJhaShocGdeqWQ9ZFHHsHrX/96TExMYHh4GNXV1Vi+fLmb2aCi0Agnk0kkEglks1n09vZi4cKFAOByENXV1S58WLp0KZLJJI4dO4alS5fiy1/+MvL5PN7ylrdgeHgYW7duxebNm3HeeefhS1/6kkNAXEI9Pj6OPXv2oKmpCc899xze+9734sEHH0QymURra2sgXOSnokzSXnmjhl5htU1m26S1JjFt/seGITZ8CJuZUvkO+9TToK08KXpRxdd7lPc65jB9Ol2ZE4ZBFY5EVevLorEfMMVk5gzIaBoHwnMb21tCaX0AAvdYJulUqE0OhaEMxp9f/epX8eMf/xjZbNYJjy62UTqECazCXvYpmUy6FZHsn+d5qKurw5IlS7B06VJEo1EMDg7i2LFjTqAeeeQR/OxnP8Pq1auxbt06NDQ0BDwwDVk8HndGJZfL4Ve/+hVGR0cDB9/U1NQglUohnU67fh87dsyte9i1axdisRg2bdqEhoYG/OAHP0B3dzfa2tpQX1/vBJ1rVL7+9a/D930sX74cv/zlL9He3o7vf//7aG9vx/XXX+8MqT0MRXmgsqTfFV0CU2/D0hWY9jQmrZsypWd5KkS3iqn8pewxock1FOxTJDL12nubF7BGTQ2EJkWpL5QvtsUczWzKnDAMygT17EBwXwFXg/EZEtguGtI5dFV6a6HZjs4hA1OWW2EoEHwTsuYE7JSUXvM8Dx0dHbj//vvdGHWsqvzsp97D+sKgpJ31KBaL2LhxI/r7+5FKpVAoFFBbW4vly5ejq6vLjYG0S6fTGBwcDBx/HovF3LO8ns1m0d/fj3g8jsbGRnz5y1/G0NAQ6urqcOaZZyKbzbr2C4UC6urq0NPTgy984QsoFotYu3Ytzj77bDz55JOIxWJ44YUXMDY2hqGhISSTSWSzWdc2t+APDAygubkZO3bsQGtrK84666yK8VojSkXwfd8pvEJr5oUoQ+QRcwUqQ2EIQmWD9LfhCfulMF7lnHTS8IXtaYJan49Go4Gt02xf67ArgnldD+mdTZkThgEIbkyx26aBKeSgMxJ6pp0mkSgkOiVkld4u6mEfWHeYJ9cElCoun1HBZZY7n8/jc5/7nLumQuP7fsUKNjUu/B5mFGxyiWOpq6vDRRddhGeeecbdU19fj4svvhjPP/88CoWCe+VcX18fWltbkUqlHL1ramqQyWTg+5NLv9PpNIaHh5HJZLBo0SLEYjFcd9112LlzJ0ZGRnDo0CF4noeamhrk83ksW7YMDQ0NGBkZQTQadftKzjzzTLS0tLhx//7v/z7WrFmDO+64w7WXSqVQVVWFwcFBPPvss0in03jPe97j9mcwtAKCm4VIG30vB3lvV1Tyk3+8j8ayoaEBAwMDjvZ2mpIKTD7qTBH5YNGkLqZSA6IJRw1VbL6ARpp9LxaLbmWvdSCUF8o8+WrfMH66MmcMAy09M9g6Zw1MKZUaBn6SqcoQG0aQ8NqeDTEU8lmltUketq1CR0bqHo1PfepTOHHihOuDTR5pJpnPqHBRCPV+9ZQ28bR//378wz/8A5qamrB9+3aX8Nu4cSNqampw7NgxlEolHD58GCMjIzhw4ACGh4fR1taGQqGAxsZG58XT6TRqa2vR3d2N48ePY3h4GAsWLMDIyAj++I//GE888QS2bt2KoaEh5HI5tLW1obW1FXv37sUll1yCiYkJZDIZ7Nq1C//1X//lVk0CwI9+9COMjIygoaEBzc3NSCaTyGQyqKqqwh/90R9hx44daGpqQl1dHRKJhFtmPTAw4PIxugakXA4eWmKTkpqMJj/tlGCpVMKJEycCp09pjkHDOp0mtLkI8oLfp0t8akij8q7JSMqhfd8J0asNlbQPNGSznZEA5ohhUCbqwg71pHZwShAqTphiaf38X+M97YNdU67TXGoEwhRbN1R5nodUKoV7773XKYMKT9g8ua2XY7SzG3pd6cLxZbNZdHV14Z3vfCcOHz7s2stms1i4cCFKpZLLAXieh5GREYfSGI8uWLDAKVE2m0VnZyeOHj2Ko0ePor6+HrW1tUgkEg768jmeIvXyyy9j3759AQ9+4MAB9PT0uH7v27cPV155pcuVMPlYLBZx+PBh/Pd//zfe9ra34bLLLsPIyAiSyaTznnV1dRULglSGdKETr6uikW86HUj6W4W3KE2V18qd8sHeo99ZP5+zPNTQ2i4O05yFDbupPzaEmW1+AZgjhgGY6rxN6NhMLolLa28VNozxJLRlpnpdhV6Wgdo+i8468H4qWyKRwPDwMB555JEASgkTAK3fCpfNu9j/7fOEmX19fVi1ahU2bNiAl156Cb7vY3R0FLlczh39RRqUSiWMjo6iWCwimUy6KeNEIuHCCE5L7t+/Hx0dHXjHO94RCG08b3I9RldXF4aGhhzfisXJN2R3dXW5cISeMhqNoq6uDosXL8YzzzyDyy+/HNFo1L1ibdOmTbjiiivQ29uLkZERnHfeedi3bx9GR0exfft2ZLNZFItFXHrppVi7dm3gfSM0nDYhZ52Lyhz7ZR2NnVlQmVEe6UwF5cPmQOysl9YznYHSsIPt6bPaDzVK9n4b+p6uzBnDAEytNLQQnb/pnxoIz5tabKIZWyq7ZYAmb6yX0bl7ZY6GKNzoo/2ktY5Go+jr68M999zjYmcWPerLGi1tU5FEmAAqvZjF5jP5fN69W/Kaa65BQ0MD9uzZg1KphKGhIQwNDVXAUt/3MT4+jldeecWNjTEt1xTwbdhvetObcPnll1fAao5taGgoYGBJl0wm4w5bvfLKK7F+/XqkUimcPHkSAwMDqKurw+joKK677jpcfPHFeN/73of6+noMDAygpqYG0WgU55xzDvr6+vDtb38bu3btwqJFi9Dc3OygNo2zKo+GaBoa2oSkDVu5wEgdk+YTCOO1beWpvtBYp1R1xoGGiHVxVkJDGe6u1JAlbBWjNWhaVI5mWuaMYdC5aR2krkAEgrvZSCz73gk1LlZ5VeEpSHxODUAikagwCmqA+J318KSoaDSKb37zm9i2bVugTzrXzb7wNws/1bvYGFKRT11dHYaGhiqg55NPPonNmzdj0aJFWLt2LYaGhjA4OBjwHOPj4xgdHQ20kU6nceTIEcRiMSxZsgRtbW1oampCOp3Gnj17sHHjRlx11VWufzZPwzdKE86WSiWXcKSyXXjhhdi9ezdeeeUVbN68GQsXLsTll1+OqqoqdyxcNBpFf38/IpGIO1iWXj2TySCTybil1KOjo1iyZElFTooyYhXbGlg1FKSxLpHW5LbyhPUyBLOI1EJ9/qZTzoo6mSCkjLHkcjkXYrGEhReaXNcZsTDUOZMyJwyD7/tuLYJaTU288ZNTUqqUTBJZQmvOgPfxWV0cYxe8KKzTsAKYgp30NJ7nueRRdXU1Hn/8cfzsZz+rgPwsNl61sbEmq2x4QYWkoKnn0Dh7586dbiHS4OAg4vE4mpqakEqlUF9fj66uLuRyOQwNDQXWgPj+5CKqQ4cOwfcns/yLFi1yKyPf+MY3OqFUPqg3ZX1cgblw4UL09fU5ZMNFWmvXrsXTTz+NSGTyoJm3vvWtSKfT8H0f//Iv/4KGhga8+93vxuOPP45IJILf+73fw9atWzEwMICXXnoJfX2Tx4wuXLgQZ511VsAAWNhM5VE6Ki2j0agz+Lr6VtEjP3VqXOvSRJ8N/ShvNsfE9ik/9uAe9k2VW49rUyeoMqUIMoweMylzwjAAU+9yoABxvlkZQGhHAtsz8dRgKPoAgqcuE7rxu1pdGw+y2A087EssFkMikUBnZyfuvfde/OIXvwiEOizWEOgsiA0XrEHQPIb2jbsQbSkUCvja176GtrY2NDY2YtOmTW6HpJ69ODQ0hJ6enkBMC0x6qX379uHEiRPo6upCKpXCkiVLnNLTCCpPgKn5dWBy/rxQKGBkZMTxIxKJYNeuXVi+fDlGR0cxMTHhVoLu3r0bP/3pT7F+/XpkMhlks1k88MADDjF86UtfQrFYRC6Xw+joqFM4NZTxeBy5XM7Rm7JA78/rLFQwe+ArjxHkM9qGwnp1GppQpLyRVuq02Ac1Knbnp/ZTZUSNlU5rWplW40Je/9bmGNSqK4GBqQNBrbVnCSOmwj6N4W3CxlpWm6DS47W0sI+JRAKZTAZ33303nn766UBW2CY4bYLI1mfDDO2fQmCFrbqRSw3HCy+8gA0bNqCnpwcvvvgizj//fPT39wOYzJMsWrTIoQcKr4WdfX19WLlyJWKxmNtRqbQulUqBHIcmYzn2gYEBNDY2OiM1MTGBqqoq9Pb2BqZxV69ejfHxcWSzWdxyyy0YHx9HsVh0x+gNDg66jVhMNPq+j3POOccpnt1ZqHS1sxSWXrxfnVMYj8K+64Yp8khRpg1H7W8WgSji1XuszNtktl3mH+agZlrmjGHgQG1iRT27bqFWRVIvoDkEu49AIbtCP4setN0wheG9tbW1GBoawqOPPort27e7+5WJWqdacqv800E+G0rp/3ZeHkBAOS+44AJEo1EMDw9j9+7d6O7udqEa43QNw1hUAK+++mq0tbWhoaGhQtB838fIyAjq6+srxgRM5mlaW1vR0tKCeDyOs846C8ePH3c7RTW049kax48fx6c//WmMj48HDL1CbS2c+fC8qQN8NU+lcmNnuCgvmr/y/ak1MZo0VLrY6UBdlq0GXA23PqdIR/MnKm+ksyI53m+nJG1YqTrCZ35rVz7aWQD1tLo6jDEgn1HPqcTQbLNaZAv11IKToOpNLPrg4iXP8/Dd734XDz30kNv2rM8rc/msNWh2/GF5CfVotk5VAF1dxzn/LVu24NJLL0VdXR16e3tRLBaxZ88e1NXVoba2FtlstiLe1rYB4OTJk3jzm9+MoaGhAIwFgKamJoyPj+OSSy5Bd3d3YIlvLpfDJZdc4jaMLViwwCUWORvBMVRXV2P37t145JFHAEwl0PRcRAq4yoCGKL4/NUOgPASmVjBqXgiYWiRkHZKGSFY+7L4UPWeSbfH3crnsQi7SlMaLz2uSU3mgbenGKo7Z86byccxRWAPHvs/WKABzyDDY0IHFWnoSUhNDXAU3nULzWT5vz+MDgvG9rpundWb8TKj5k5/8BPfdd19g7T3rsahHPa0iAJsQsyEOx6fP2Hs0rlfDkM/nsWfPHixevBitra145ZVXMDAwgFwuh8svvxw7d+7E4OBghZdXZfL9yelHVSbSrlgsor293Z252dTUhKGhocCMEZXx4MGD6OrqwgsvvOB2aabTaSf8kUjEHRxLgxA2S6DGnAqiSqe0VyRAHupSZhoETfDxOseqvysaUDrxCDeLOKnobFNP6wpbps262I6GRfyftOKJ1fxOXdA9Pko35e9My5wwDPS03N/AU3CB4HHeYfGZxrRKDM1Z2ClLJi35nVbVvumYbbHueDyObDaLO+64Ay+++KLr/+myv6rQ/D5d2KDt8RqLjknRCfvLpCD7NDw8jLGxMSSTSezfvx+ZTAZXXXUVvv3tb7s1FlbYFT14nufOeWSsr4nhxYsXY82aNbjvvvvw8Y9/HPv373fxfzabRSaTQSqVwvr16wMnfNsdgIlEAtu3b8eyZctQVVWFjo4OrFq1yu2kVANPJbA7TdlnKqDyVu+18kKaKgpRmM82iSB0fwbv1ZkD5Qe9OQ0rjZM96UvDVbubOCwcUUekoavOCln0O9tl0XPCMABT0J5vj9KpSiopizJBBUetNefYFYLbWQrNOhMOKsPp9SjUe/fuxWc+8xmMjY259tS7ax81k83fFM3o/TbfoM+F5Ul07FoPhYRwur+/H/l8HuvWrUN9fT0KhQLuu+++ihhcjYOF4b/4xS+wevVqt+WZv/G8iLe97W3YunUrPvWpT2HBggVu6i8Wi+Hss8/Gs88+6+bnfd9He3s7WlpaMDY2hoMHD8LzPLdzsKmpCb7v484778QNN9yACy64wBkH5kLUC6pn1NWNqkxUZg0bNAelCEGnbi2057PWOWnC0ToI9kNDXkVJinwUQVgDrf3gPWxT26bca+5J5Wo25dd9d+VvtKiQA1Mn5tAKakJGM+iKGDThovXo/RQwjVlZ1LhoHRT0bdu24e6773YQWJWadalCWwVW6Mrr/E2Nl5aw0AKYnFWgd+X4WW+hUHDboPP5PGpra7F48WLkcjmXC6HRjEajbvpSeUHBZPy8detWPPvss+45wlieaXnrrbfi8ssvRzweRyqVQkNDAyKRCEZGRgAAq1evxplnnolCoYBNmzZh/fr1iEQiSKfTLsno+5OJzKGhIXR0dODAgQNOEVVR6W1tIpFjoDNgXxlu6X2KINXwkiY6vclnrGOiDHC7uMofP5XOlu7aD7al+SuOjf3TvvE+fVZpwz5oOzZheroyZxCDMkqtuhb1kDYWtnGmKqlmiMOSbGqx1VpHIhFUV1fjwQcfxKOPPoqurq4A8W2/tB9h5zNoX3i/DTHs/xb2ApOGoa2tDWNjY8hms247ssJRGo3+/n7cf//9OHr0KABUrBJlP/P5fGBDEulSKBTw4osvolgs4nOf+xxGR0fx8ssv4+qrr3b7LzZv3oyamhq8/PLLznAUCgXnyS+88EJ4nofDhw/j9a9/PYaHh90hstyMxfUKwKRBGx8frwhzrNezCMwiR/6uWX+LuhQ92GeBSsNBfpJOrJN5EU0wK994n5U/7YMaZRsOKUoh3zTE0HrDnJzNe52uzBnDoF7TEgmAO8wDCJ59YDe+AMG3WmmS0oYJCv+YVAOmFlsVi0XcddddePDBB521BipPnFKmsX7mS/ReGggLhe1YSQ9+WsMwNjaGHTt2oKqqCq2trejs7HT3sh5C2O3bt+PDH/4wVqxYgWPHjrlxEV6T3k1NTW6mwPMml1uvXLkSF1xwAVpbWxGNTm5wOnDgAH74wx/iL/7iL/Dd734XTzzxBC688EJ85StfwZIlSwAAw8PDiEQiqK+vR0tLC+6//3686U1vQrlcdjs7zzvvPDQ2NroTqrl5KhKZPFD2hhtucOPWhWkcH9/SpIhR+WtzEwrj+Z2ywnZZN9daqIwpv9RLK/80aU26aphn+8X7FB0rDzl2hobsI/s1XajBPlrDOU1LHSoAACAASURBVJsyJwyDKg+/29idno3EVqYDCMSU9Jia9NHMsiIFhY36nUZh27ZtFVNZ2gdLfDVGmgjjs/y0DLa/8f8wplN4i8UiTpw44YTHGsdyuYx3vOMd2LhxI/7xH/+xAlozJlXvE41GceONN+Laa69FY2MjDh8+7E5DAqaUoVgsYmhoCI2NjVi7di0++clPumPp9SWuiUQCQ0NDGBkZQS6XQzQaRU1NDZYtW4atW7c6wS4UCqiurkZtbS3OPvtsN51Kvqgz8Dwv8PpB9fIcC8encmXXkagHpvLx1CobGmrCkMWurVCnoHJs+a+5B5UfDUnJG67C1PoUgep3GjMdK9v4rUUMQFAxp7N0Snj1vvR+Oq+t2WKWV4PrjJGPHz+O22+/PXBoh4X/inDCvAX7orBQF0CpxVcvpEbReihFD0qzsNWZvu8jmUxiYGAA//mf/xnwIqQXMGlAGIpcdtlleO9734u9e/fi05/+NLq7u3HixAmHKBoaGnDNNdegrq4O9957Lx544AHs378f3/nOd1x/b7vtNtx8881YtmxZIClGGuXzefzwhz/EFVdcgb//+793R9Tz91wuh1Qq5camSIuFSkwZoFzQ++vsAWmhPKfB5n0qTzzj0npinT5ke54XfJENZZM5Dq1Ln9Mcml1DoU6P9VCWrPyqEyJd6HDYP0VGsylzxjBYZQEQsMZ6ny029rRxIxAMVWw8ye+JRALd3d34/ve/7zwx61eoahEN27RrKWxyU5+xENiOIWxc6jnUQGgdqgSpVAr79+93L3HRMVPZLFLo6enBfffdh/HxceeBJyYmsHLlSpxzzjm46KKL4Ps+9u7di0OHDrkj1xoaGtDZ2ekUTc98UEUApvadcEs3+z4wMICTJ09i8eLFgWds5l7RonUIaoz0OxWGPFUHYvlIOllZ4702Z2R5xf4wz2M3qvF+yqKGSOxTuVyu2FVJmeYz0zk91qU0m22ZE4ZBYTQQfEMwiyUsr5FZuoCERWO8sHlcRSaxWAwnTpzA9773PfziF7+oWDhki7atkJSfFmGE1WORgf3N0iUsJ6HPWkEulSbfQ6FnC/C8QD2TIRKJ4LbbbkNvby++8Y1vuHhf+zgwMIBDhw5h5cqVaG1txejoKK688kqsXLkSxWIR9fX1+Od//mdce+21aG9vx/DwcOD4Nc+bXCvy4IMPYsOGDaitrUVrayvq6+tdErK6uhrZbBbLly93YYTNFYTR3tIfQGChj3pnTWyrfHCsWpc6KeWpTfSxj4oK9DfLZ/5vnQPHq+hDDVqY89D+UTa0L2G0mUmZM4YhzBgwhqPHUOIovOLgVfnDjriiFVbrTAhbKpXw6U9/Gp2dnQEYRsawzbCcgeYolMnKbFts/GuNRxgisDkHKhuFyS60yWQyqK6udnE3Zwo01q6rq8O//uu/4tChQ/j85z/vjIgKt+d56OrqQnd3N/7gD/4AK1asqNiwlM/ncfvtt+Pss89GNBrFSy+9hFQqhVWrVrnQIJVK4a/+6q/wN3/zN7j00ktx9OhRNDc3uzMd0+k0zjjjjIrpP7vyjwZBzwbVcStv1THo2YkWOZAfXGbMogk/LXw+EokElm5z1of0jsfjge3UNpykrCpP1NirXFD+NZ9gE6xarBOdTZkThgEIrgdncktjKJs80amnMEuuEFkNAL0C/yej/uM//gPd3d0V+QA1BtaLquKrcFohYJkOBeiYwuBmmAFJJBKOPnbbLp+nkBYKhYpTsWlYP/e5z+HEiRP4+te/7k5v0vu0r74/eaz7unXr3DFtVNzu7m7ceuutjl8bNmxw+QLWk06nkc1msW7dOuzYsQOZTCaQgNUj+RUN8NQsGkHCaeWPTnMrLfTFw0zoqVKStwxvdHbKhiXW+0aj0cCGMMqChqx2paT2WZ/hqlVOGYehGhseaN9YFEFpmDLbkOK0d3uet8zzvJ97nrfP87y9nud97NT1Zs/zHvM87+Cpz6ZT1z3P8/7N87wOz/N2e5534QzacAQhMlDmqPdWAnDwLDbu0nCAz+iCJ75D4a677sJzzz0HILggSdvV2I9taV38zqlAQnYKm96vwjMxMYGJiQmUSiX3qfXxPs1W+76PdDqNkZER96IXFoXO9OS5XC6QCOX4rrvuOgDAV77yFXR2drpMudalUBgAvvrVrwb6EYlE0NXVheuvvz5A53g87t5s5fuTy8l/8pOfYNWqVVi3bh1++MMfhoYCusKRbdhVjcyNKGLUxWyFQsEt9FLl4338tDszdTaDPLZ0V+Ous0/KJ+2TdQQqh/qbXYVJ2dA21QjYnIrynnoT1ueZlpkghiKAP/d9/5ee59UB2Ol53mMA/hDA477vf9bzvL8E8JcAPgngdwGsOfV3KYD/c+rzVYsm7sLiOh20ftdrnhdc+GHhpRItlUohm83iG9/4BrZu3RqAoJp7sFDRJvFsPyzq0TCAzFWYrDMFNuTQOq2XUEbr27vVO3DRECEys/nt7e249dZb0dbWhk9+8pNuRSQNkp7UrX3zPA+9vb144okn8IY3vAGFQgG9vb0AJsOEnTt3Ynh4GGvXrkUkEnHTj5lMBvfccw+efPJJfPe738Wdd96JJ598MrDYqlgsoq2tDR/4wAcCi53oWdUgKBrTWSrNI7BMN8OlfFP+2vCU/2t4QjQbhhg1RFRjpt5e0Y3KOethuMMQwxpF3dFpDRH7qBvGaIhmU06LGHzf7/F9/5en/h8DsB/AUgDvBHD3qdvuBvCuU/+/E8A9/mR5BkCj53lLZtKZsEQKLataP96jn8BUBlwLvTchKBW/VCrh6aefxuOPPx54XpVRvQrhOD91Dbz2keNQA2EhuTVwvG4z3Bb68x5rVNT46LitkDJDfuONN2LZsmV44IEHMDQ0hKqqqsA0sRoem7cAgC1btrjFRYsXL4bv+zh48CCWL1+Ow4cPo6Ojw4U6vu/j+PHj6OnpwfXXX4/nnnsOd911lxN0LhOur6/HLbfc4urT2F6Vi/RRWlv5sbDZGlxrJKwshU3tKX9tGBsml5ZfkUikYi2E8k6fI01s/kARgho2/rFf9k/leqZlVjkGz/PaAWwE8CyARb7v95z66QSARaf+XwrguDzWeepaD16lqAW1ysDBKWSiEL/a/KyFb7T0sVgM+/btw5YtW9Df3++mlfiMWldaaKscvO903l7/J/O0X1ap1TioMVSPovWEISwaBU1WaexPo7B169bAy3EABAyezeuwn4cOHUI0GsWCBQvQ0tICz/PwxBNPYNOmTWhqakI2m0VLS4t7VfvChQvx/ve/H+l0Gp/4xCfQ19fnaE5jfuONN+J1r3sd0ul0YCxc4GMTu2FFFUlzDWHTmAqxraG2hsWiSDUG/K7GS3lqHRfbsIZceWkdgzWCajSsLNnQj+OdbY5hxobB87xaAPcD+H983x81xPQ9z5tVEON53gcBfBAAWlpaAolBhcMkIK2oPYiD0Ixr1TVeA4LZY1rtqqoq3HPPPTh69Kiz4iQw27dwVNtkG2FQzVr5sBOEVWh5X5jAh82A6P2sV9dbaOJTBa1cnlzA9Gd/9mf40Ic+hP7+fkdTXaFpjYSFuwCQyWSwZcsWNDY2orOz082333777RgdHUVTUxNisRiuvPJKx5eHHnoI99xzj5uOTKfT7mzHyy67DG95y1uQTqcDaw2YfWfYF5ZLsfRR+SANlQZhdFe+6iyHNda+H3wrFOXV7qcgH8h7m5jUPnOMzCcwOcrr1ujYKdfpFvGpbFhazaTMyDB4nleFSaPwDd/3Hzh1udfzvCW+7/ecChX6Tl3vArBMHj/j1LVA8X3/TgB3AsCKFSt8VTbLEHoV9TI2e65WkckvDS30vuPHj+Pw4cOBcxlsGCD9DFhhIg47xaQxoD6rdVoDoG1bOMnf9R7tixqXMISlCGNiYgLXXHMNbrzxRnzmM59Bf39/IEbW6TpdPGOVRu9/6KGHkEqlkM/n3RHzkUgE69evR1dXF774xS/ipZdeQmNjI376059i27Ztrp2+vj53bsTKlStx5ZVXutkAm9MhalBDT1lhboVj5nh1RSOVza5fYB1qNNT7W/hNmnMq08J3Gljlhx4cSx7aXBH/t4uWtC07Q8G2FGXrOQ8siqp/40uivUnqfBXAft/3vyA/PQzgNgCfPfX5kFz/iOd538Jk0nFEQo7QYkMDKrBN0NjYEwgmp2hRecoSp6CUYNFoFPfee29AGGxsbb0zMLVght+Za7CwDwiu4qRhChM021ZYuyooYQkxjknRlsLPQqGApUuX4oMf/CA+//nPY/fu3RUJT9uHsD392heGE/zf8zysWLECa9aswb333uvovWvXrgCCIgTmOy3r6+vx9re/HRdccEHg5GcbDik9dCbAGnV+18QbZYoyYelK/uuCLzWynOXgGSHcg6MwX/mqMsXvlGVFj7o+h323sw76MhuVAR5WS6Tm+75bn6MzS2EGbqZlJojh9QDeB2CP53k8tuivMWkQvuN53v8CcBTADad+exTANQA6AGQAvP90DXBAVCb1FjZ2B4Ie1RITmD555HkeOjs7sXfvXndNvfV0sN+2y7athWZ91jPZWQ7bxqtBweliUd4Xdp2/lUolvOtd78K1116LP//zP8eRI0cCyUs1IPpdvZlt37bF8WYymcCJ0fxN4S6hOHk1NjbmXoSjx/NrOKQhnUUv9n/NH9jfNT+jykzZY7/U6ajz0EVWShMN2bR+lVFdwaiyapGfrnuJRCbfwaEL9agf2Ww29IVIKssaYobJ2OnKaQ2D7/vbAExnct4Scr8P4H/PphPWcyoBOXB6G417WcLgexgzy+XJbb861cPrNmcwXf1an7ZljZoKp/4B02/kmu4eClAYJLUhhNLzrW99K6699lo8++yzOHr0aGCMOn6lv82l6Ni1rde97nXwfR/PPPMMIpEIuru70d3dHdh0ZpXEIsDx8XHs3LkT559/PpYuXVqBioDgrIgaXXphpZMqidJUaWLHoffqYiBLbzoCnc5UNGqRnToA0p1v6rLrbRQ9an/ZHg0GFT2ZTMIWRdSWZ78OYpgTJzgBQUHid0sonZVQy6qEtQKuwqK/6ye9lAqxCrP94wwH/2exyh3WlhVIHZPtH//i8TgSiUSFMZqOLhz39ddfj7179+I73/mOy42owquiqMJON+2l5brrrsO6deucojCpy35EIpPrGKxX5xQwBb2jowN79+51aE0RjYYR6v34u+2TdSaWL5aubE9nnXRRmaKf2tpatLS0oLGxsSLPpclJG45avtqj3vQZDQEU1YQtelMZYNHfKNM6rtmUOWMY1FNpggmYgnQK1/iMJspUwFV49Pn169cjmUwGYLPGolyJyN+1ToWP0ejkSUXxeDywDoB/3NevJ1hzLBZp8LoVdN/3UV1d7aC60kmFwnr3xsZGPPDAA3jggQfwb//2b+5VdPl8PrDSkm1oP1ifvgVMi+d52LBhAy666CJ39iVpzbULWi8QDCm0cDbp0UcfDSSPyS/ds8DZBg1z1DuGTcdZNEGDZ//X7dr2EGLSbPHixVi2bBnWrFmDxsbGijGSxxrXq/FQVEr5Id0ZRqkzU+PIetgO69bwB0DFLIaiNxv2nq7MGcOghFP4xUFHo1GXyQYqma6CoRtMqAQkZn19PS688MKK2FU9BYlLoeMsBJWdjFUvpEZBjRQZyKlB9t0qnBYNTXK5XOCt2Woc1RtQkOvq6vDFL34RR44cweOPPx7I9tsQJ2z8upx7utAmmUxi+/bt2LZtmzvAha/qY990bYgqtPXmkUgEPT09bv2CGlC7TFidBvupxkTHE+ZN+ZsaFyb3KCc6s+H7vlsqrWNJJpMBtMApWfLHvvJOvTf5ZmcU1KDYPrJeYOrwH7bDPvEe6o5NqM+2zIlNVOrZdT2ACr/GbkDwrTwK5zRnwPt1t16pVMJNN92En//85wHkoUUJqQtrwnID+jyZoEZHBYCbgHQWJay+sN/4uz2jQhNOpVIJf/u3f4tSqYQvfOELgdyKCoqO16IqXUMwXS7l4MGD2LNnD8bGxtDa2oozzjjDHfvPVaG+P3m4K3cZsv54PB7oB/v/gx/8AO95z3sqaAdMKYOFwzom5Z01gJoA1THbcJT0HR8fx8TEBBobG+H7Pvr7+9Hd3Y1CoYDa2lqMj48H1juozKic6afKh1VUhoua9LRGWWXZ933n/ChPdFi6rwQIvkB3NmVOGAZgajGPzgyo0APBaShgan2DxoRal94DTK1+rK+vx/XXX49HH33UQfSwGJSCq0IWlumdLp7VMAWYEnRNWNnchvV8fF4VoKqqCrlcDu3t7airq0NHRwfGxsbwvve9D/39/fjsZz+LoaGhivBFwzPtnzWQmudRI0YElM1m0dPTg0984hM477zzsGPHDjzzzDMYGhrCNddcg3K5jE2bNiGTyaCzsxN33HGHexkNE3A2DNi2bRtuuOEG1zeF0Xb/gyqKHZ/yncaSdei6Bd6jz0UiEXfY7djYGBYsWADPm9wf0t3djcWLF6Otrc1tDlP0ZvnEtmjISXs9PUpDZ7vy1xpCG6Lx7dx8xhpUleOwRVCnK3PCMChzOFdMoqlnUeEkYrCeVZEEgAqvQOj35je/GcPDw/j5z38eUDqrmGF12L7zOQpHGPQNyyWodwlbVKR9oUBTUd/85je79zYUi0XU1NTg6NGj+PnPf47BwcHAc2EG006BqQedrr8cKz1UIpFAIpHAww8/jI6ODlRXV6OpqQmpVAonTpxw+ZVEIhEwojqnT7pqmMW+qOHUWQibdbf9tAld9dRq5Ni2pQH3xPA+z/PcNnMNIVU+6YDswjs7JnUKFiWqcbG/h+VS7D22UF583591jmFOGAYSTQloCcD7NB7W3IIaFxoHO/XF+zzPQ2trK97+9rcjn89j69atFd7DKqgyTPsdBvmtYqvVng7O08OEoRAWQsmVK1di+fLl6OjowO7duxGNTr4U5/nnnw8s/FElqq6uDhhT7aMmfskHRUo6Vr6oNhaLIZlMIplMuoNWyuUybrvtNtTV1eH+++93b6Wy4yEP2Xa5XMbVV18dQFhsm54+LNFo6awOhL/zGj2wrhVg0d2bqVQKq1evRiaTwcKFCxGLxdDa2opCoYAFCxagtrYWvu87w2GRJvuhBi1MycOUWmcnlDZhMhmGMpVuHKNd8j7TMicMAwun1DRRpASy3hYIwl+Fifo8f9fFIhMTE1iwYAE+8pGPYHR0FHv37g14Gm2D/1tmvprFViOh6981jrfeQddq6MpDTWo2NzfjxIkT+Na3vgXPmzo9m1uVU6lUQHE4Jgoyi3pp7Ysdrx17Pp9HX18fBgcHsWfPHqRSKaTTaaRSKVRVVeFP//RPMTg4iMHBQVxzzTV49NFHkU6n3RJ1OxsCAG9729vwO7/zO6EHznAVJsNB7ZfG1AACcbWdvbDhiO1DuVzG+Pg4li1bhqVLlwbocMYZZ7j7S6WSSy6q4dOQxfLSTv+yPX7a2QheV/lQudHDcFUOOe6w/SazPQx2zsxKaBKRBkKnlXRfg8268rsuW45EIu5Nw6yH9apilstlfOQjH8GqVasqwgKg8t0BOvug8aU1EApD+Swz9eqZw5RR30jE5xoaGhCPxzEwMIDR0dGKk4lYTyaTwfj4OLLZLLLZLHK5HHK5XACWc1zsp13+bI2CllKp5F4Ic/LkSRw7dsxNh2azWezcuRMvvPACXn75ZaTTaQwMDCCbzTrBtcpx9dVX4+abbw6EDoqqGEtbNMZ7WY91AjoejlvzV2E80gVIynu7kMrShPKl7dql8OSV8kBlQBWZbeheC5tQ1P5RRmwIxXo0hJlpmTOGwRJU4Z0yUglIoupUEwuz4Rq7clpJp+Ty+TxSqRT+7u/+Dhs2bKjwNHxWrTmvhY2BQl0uTx5FTk9dW1uLxsbGAGqxJ/woM7kGorm5Ge3t7ViwYIFLfjU2NrqzBLn2gnTShJQKvl03TxpoboNF67NKxno3b96MTZs2OTpyncTw8LCrd+vWrfjABz6AjRs34swzz4TnTb7Ihor28Y9/HLfeemtgOlUNPo2fTTyqceDYFDGqM6DD0FmqsDrI41Jp8iW7yid9qxj5Yrfiq8ePRCJu9kVXOmoS0oZMqgOsg33gegWL8BQh6wuZNSRnv2Zb5oxhABAQDFs006qrEC3csvDQ/vE626Oh8H0fN9xwAy6++OJAPiCsnM4o2M9CoYChoSH09PS4F7yqZ9A/G//aBUlVVVWora0NvAbOGk+t2wqQXlNBDMulaJ12/K9//evR3t4eoGmpVEImk3Ge7ujRozhy5IgzHrlcDqtXr3aLwjZt2hTgJ8evxi1sKlnzIEQivFeNoe27zZ0oLax8WcTEeyhHmufSMIeF9+qUb5gB0O80cupcdMwWvWgSns9beoXRYSZlzuQYrLewyRrdmachBX9Xb6DPkyEqwKp8Ci9Xr16NG2+8EbW1tfjZz35WkThSL6P9VgWyOQi2CQRzBfxun1MPaZWUY0ylUojFYshkMhgdHXWbl5Q+NlSxQmiz8VYY1fNaIxmJRFBbW4sTJ04E+q+ePRKZfB/mQw89hEwm47zk6OgoamtrkU6n0dfX56YEyQu+pEXRGuvVhKWOSROT6iHVGOp9ahR4ze6e1YSlIgIrC8zzKM01eai0tbMvlr7W43PsNi+hMm35qrLN9sP2F52uzBnDYAnOJCRjI8LmMCtMAuhUkCqhMlWtst2Ylcvl0NbWho9+9KNoamrCli1b3DoHVbTprHCYIFr0EolEXK5huoyzCno0GkUymQysrgOA5uZmLFq0CLFYDH19fcjn8y6/wFOPVHhZbJwfhhCsYun4GOYUi0X3ElvSfvny5RgdHcXY2Jjzoslk0ilZbW0tli5ditraWnR0dOCpp55y76gkv5lLIq8112SFOxqNOgSmawNIbzUEYUquy5VZn8JvDcN0ZkT7Yrd/69oLGxrY8xpsX/mcHnGvOQhFNxr6sH9MxqpsvhryfbUyJ0IJK7yq7Cw8JoyKRULwu8JgEki9DQlqE0uqbBTIbDaLm2++GR/72MdwxhlnTGsUpjMM9pomURk7W1hqVzOqgWQ9fIckpweZBMzlcm5MdvUciwqz0srS2SIU9X76+4EDBwIZ982bN+OOO+7Axz72MbS3t8PzJvdOMGxg/mHPnj0YHh52b8nq6Oio2LXIfAiViTQrl8sup1Iqldx7G7gsWxWOSmydA+lAXqiy6gExwNSGJxt+cYm7OgI1YuQF+TcdSlXaax9VXtgXnf5UQ6RGkTRQNEjez3a6ck4YBsJnhWJhuQEtGvPppieF7Roz2thQ69BpHr1+ySWX4KMf/agTHlUooDJ+s1ZaoR/7pJluTRjamRLfn5yd4DsVqOgc7/j4OCKRCAYHB931kZGRinyCheWKnE4HY+1+EF6vra1FdXU16uvrkUgksHTpUtxyyy1YsmQJ1q1bh3PPPRfJZBKFQsG9zJZ0HRwcxLFjx1AsFtHX14f+/n6XzGOb7K96ckVR2n/mcBSRqcG2YYidyiM9OFY1PLzPrg3RE6UsulIDxGXO7L9FNTpGG1ZqGEsZsYYnTMatAbBGYqZlzoQSOiBOvShxbMwMBBNDSlgKiVVUEliFiIwhAzVeLhaL6OjoCDAhzLtqsd/Vg/N3VT72V4UBmJoCI7RkUo9eMhqdfC098wycU9fNS2F7C7SPmiDVEEuLjZ0BYNWqVXjqqadw+PBhLFq0CG1tbVi0aBEWLVqEPXv2oLq6GnV1dejt7XVet/1UorKzs9O1OzExgT179mD9+vWB6TRVOqV3WGKNPOXvurTYPq9rDZQGNozjmgvrANRgE+XpFLTKGdtRvtvQUUMcoi/tHxGHvgvD5hvUSFqea4jyW7uOQWMnFuuJ1WuGGQDeo4ZCCafek21q/fo8AMTjcezbt6+C6WrJVcFfLZbTMdgkJH+346QnoGHQvfx8Rj0Sr6ky67hsWyqsYUJrwyf+ds4556Crq8vt3KTHp1BzyTSfb29vxy233IKrrrrK1TM8PIy6ujrs27cPBw8eDLSnvNLsP/vOvupYyc+wcE7hdRh01/Eq6qTyWxrRM+upUzbpx7rt7xqe8XvYsfIcG9EqEY1FDHQgtn4dg+XhTMqcMAwKcXVQyjAlVhiRgMoVi/qpMb4aB1psq9xED0ePHgVQedqR9fyq7Pq/wmP9s2MLYxyvcdciEQ4XGY2NjcHzJtcGLFmyxGX4OZ6wvljvpzTS3y3aUj6tX7/ehTLRaNS9sRqYhN2asI1EIrjppptw0UUXYdOmTa4/VIhMJuPyDHaFoPIpzGiF0UyNBp9T+bKQn9/ZL80d6POq6NYZWOVTh2Pll2Owxi0sN2DDDOYrbAKZSEhDI+V7WCh+ujInDIMWJpIsDLar1hhu8BBMLepB7TJrWmfGe5z54GlDmtT8f6l789g4z/Ne9PfNDGffyOE6pEhKIkVLlmxZtmXLTuzKThzHjeMEsXua1LcHbdItBRrkFshFWiC9SM9FelogG3pw0yBtloMaTpwidnKC2LK8xovsWJa1S5QoUqS4z8bZOEPOzHf/oH4Pn+/VSJZwby+UFyA4yzff8r7P+nuWN5/PI51OO3xtk9FM/ICakyYpzX69iYgmfP2Zdlk4B9xuLRQKweVaj2isrKzIfVerVdHS4XAY4XD4kuuYWl+bv9rvNYWqCVr6/X7Mzc05QDvujETtWqlUUK1WsXXrVnznO9/BjTfeiGKxiHg8jjvuuMNhWXV0dODFF190uBJkNt2LkZ+bFqK+VxOY4z2a+BHPoV0PvbkQf6tNcf0bLXT4HcugL+emmdiHba+3HtQt73mMpiPtnmgsSvMG14o0R4tNZwxfy7huBIM25YBLKxnJwNpkMn0nLUG1KaXxCrMCTufi03Qj8Zw/f/6yxTLNTHrTjGvmXpjn0YJFax++rlarUgDFhKZqtYp6vQ6v14tisYhSRVsJHwAAIABJREFUqYRcLodMJiN5ACaDc5imrekumPdrapv29nZMTk5e4pJRYLEkvKOjA3/913+NYDAojFapVPCJT3xCsgLn5+dRqVSwsrIiIKpOgdd+Ov9rAI7DDCXyMzKMdiG1sDSFohYUZELTotLX0wKJ9KN/o7/T861BZz6rpg+tJPTnzdwsLQhJg1QmWjheyc1tNq4bwQA4J0UTKhmb/rQ58doXvZyfqQlcn9MMC9FiIL5wLROqmU5rHS1M9HNqUxJwdmKiAGg0GiiXyygWiyLYyuUy4vE4lpeX5f6ZklypVLC8vOzQZs00rZ6jzZs3Y8+ePQ6hpI/T7zds2IDTp09LKJLMqIk9FArhwx/+sOwCrf3lvr4+bN68GW63W56ptbUVTz31lGhdzTzaFeMccZiAmvmcZHKNRZjMpK+llQmZi396LsyIElvamXPVzLfX2p/3YNZVmAJB9xPR52lGu1pYaVfGFKbvN64bwaBNPu7gbEpkvSgkEOblm4U2pjYH1hNY9ERTw/KcOorw1ltvXUI0ze7btCK05teCQoNZHCaxAU4BoXtQ1mo1lEolWJaFmZkZVCoVuN1udHd3o62tDbFYDG6325EqrTWeOX9kkscffxxf+MIX8NGPflQ6EDcTIpZl4VOf+hQmJycdx7DlHYmVWl1rLf7V63X80R/9kfRoWFhYgMfjwa9//WucOnXqElSeTKqFKgfdTg3oaXOf1oDeJ0Ob5qYW1u6ebm6rhYeJE9XrdUko00JJM6Lp1kWjUSQSCbS1tSGRSMias0K20VjPdWHkg2tFV0dbwFoA6sgF55JzdC3juhAMmvFs25ZwkWbKZqiyOS4HPJnX4qA20v61XtBUKnXF6+l714Sm70ebeAAcAkMLFK2tSWQmQKiLilZWVpDP55HNZrG4uCgFY21tbdIjwXRt9BzzfyKRwMDAAKrVKh555BF87GMfcwg7093x+Xyy2Q5DpKFQSI7z+/0iXHg9HYpuNBro7e3FTTfdhEZjrdSZKd2/+tWvHLF+U0Pq13xvzq+5TtS4ppmv5990WfmZLibTrqK+h2aChsxoKgjeQywWQ2trK2KxGOLx+CX40eVoSVtIeg35/eWwjN96jMGMNvBzDSSaJhHfa7PJ9Js1YfI6ejQjqnK5LIk5Ji7A1/yuGW6gF9e0KKgJTKGgr6+FQjMNwmYprGxcXl5GPp8HAPj9fnR1dSEcDjvuyxSstVoNd999twC5ra2teOihh/C7v/u7CIfDDs3UaDQQjUYxOjrqYBAAAgC7XGuNUtva2qS7tUmUZLYHH3xQNF0ul0MymcSxY8eQSqUcSoCWiJ4X7QI0m3dz/kwGJt3wOy2E+Z25JiZ4yevytd6rxBRWnO94PI7W1lb4fD6p7LUsC62trejs7ERrayva29vFmtJCTPOA/k7TnrZa+Ecr+bfWleCg6aetBE4waxr0ZNDE0sg/AElbJSqr/Te92PwdiZCdid59912H5NeCwPyvzctmlk0z7ARwEjnvlYDZpk2bMDw83NRFYcPVjo4OtLe3A4AkP2WzWelJyMiIqUU0sv3xj39chA5N4scffxzf+c538MADD8i9NxoN3Hfffdi3b5/jXJxHjcjrxqS2bYvAIh5RrVaxefNmbN68GY3GWsZmd3c3XC4Xvve970mkpVwuo16vIxAIIBQKSSs5VpaaFokWtDTztRAhTWm8gpYB3VXOr7YuaHFUq1VhTP1bs207j6FLQgaNx+Noa2uD1+vFxMQEjh8/jkKhIK4Fvw+FQg6GJt3oVGpdTMihi+l4ba0or2VcN5mPWrtyMc3MMboXWjKaC2VqBxKKFgycJJ0YQg1Kk+7w4cOO65ouSDNhob9v9tp8r7W4GUk4f/58UxPQtm3pYsxniMViyOfzYmrmcrmmiLR+hlqthsHBQQdq3misNy/1+Xz4/Oc/jzfffBOlUgm1Wg0f+tCH8PLLL8O2bdxzzz3IZrNIpVJy3maCtF6vi6vBeeO833rrrZiYmIDH48HJkycxODiIs2fPol6vIxKJAAAikYhDeHOt2YSGwsNcJ/1eWz3aSuDntJjoxuh9HrgeOm3dVAAayzGtM12zw7ny+/3w+XyIx+MoFAqS98HfaUzMtD5533rXbZ3OTR4BnHkQZvj//cZ1YzFoBgHW3QZKPh3XBZyZX/r3usCKi61NXf17bW7pvSMsy8LRo0cvMUXNnAMz4/BKUlkTjv5v1klQMzCjkHkQ+hhqqmw2i+XlZXg8HkQiEdl4tVgsolKpOIhEzxF/f+ONNwJYIyBeh89Dq4TdlyzLQiAQEKvi85//PLZs2eIQKiaWYLpS/Jzn3rRpEzo7O1Gv17G0tIT29na43W6MjY0hmUyKS0Kh4PF4EAqFEI1G0dHRgc7OTsRiMccOWOY1tcXJ+WgW4SBtkH60uW6ez2wsw2NIE3ytFRgAzMzMYHp6GrOzs1heXhagfWFhAfPz81haWhKhTmbW82i6wbQuNVitaUuvxW+tK6H9PNu2HdrOjEBoTW0i7zyW0hRw+odmGzN+z8+CwSBef/118XWbxZP19ci0/GtmTZg+LLDe2FVrHk1IWlDqECrdDWqPfD6PxcVFieRoAaJ7Emq/eHV1Fb29vdi2bZtYCTqPg8986tQpyZmIRCI4c+YMVlZWEI/H4ff7xeTWpdKamDVBm6G4er2OXbt24d577xUmOHToEPr7+/HCCy9gYWHBEUVivsPy8rK4EX6/H93d3ejv70d7e7vDotQ9EvTakZ50noMeTD3Xa6gjLfw911QnUWmNTpoioMiqWJ/Pd4mLo4UJsRlzLTnIGxSuJm3q/+bcX8u4LgSDlsbApQUiZHjNMBr40YOTrS0JXkMfY2ICwBqIlsvlcPDgQYcA0PdpRgr0e9OqMM1K/dcMSG3mmpiCyNRslwNfgUtb0/GYWq2GoaEhDA0NXTZq4fF4pE6k0WggmUzi8OHDsG0bPT09l9wT790kwitpqqWlJUQiEcFJstms5I8cOXLEES7UFqPpXra0tIifblkWCoUC3G43gsEgIpEIgsGgNKsNBALw+Xzyp10UE2zVf+ba63mm4tHrrmlDW6aaUZndSXeXWE8zt0jfk55TfW+m+6hdu2a8cqVxXWAMWpOQaXR4R0+WCSBp81ETDM1jbSHotGpt7vF8Pp8PzzzzDPbv3+9IM+U5m/3XDGJexzRrtQbiMAlN+8IcJpHo45tZNDoEa86zbdvo7OzEbbfdhs7OTkkyMi0U27bx5ptvwuPxYGVlBVu3bsWxY8dgWRY++MEPSvKVvjddZ6CZwhTWxWJRTOr29nbs3LkTL774IizLwsmTJxGLxbB//34kk0l0dnbKHGihoJ+b8+nz+dDV1SVWjMYKmpn2pDNiWaYFqoe53nT3+J0WHBSses3MXBpaDbr+wcQHTCFArIz9LUzLQltMJr9cq8VwXQgGYH3rNc2snED9ucYO9EJqid9orOeua/PeFDqmYDh69CgOHDjgkK6mJtRaw/Q99TEcmuE1s2rUudn3miA0UWri4XvODz/j/8tZYtyXwmwprzUM05Q5N21tbchmswCAwcFBxzVN4acZQtc7EDQ8fPgwLly4gHA4LBYD521lZQXRaBRnzpzB4cOHcccddwh9BINBOb/en1RHENxuNzo6OnDu3DmUy2XHvOj7I42EQqFL+hxo4aytLKamcy41dkEQV7/XAsflcjk2sdEuG4WDmYhldn7mPWqBot+beQ5aOFzruC4EAyfeNHn0IpkFNfV63dH+TafSut1uwRj0OblY/L027dxuN9577z2Mjo46zEtT45lmfjO3gt+ZQzNMM+GjrQ7TAuL59W+aCa1m35tjYGAAfX19DsFgWZbUMLhcLkxPT4tVQLeF4br29nbHRqy6UKelpUU2H9a+vl5PtrP3+XwIBAIYGBjAM888Iw1QisUi6vU6pqenkUwmHVgG709bknpPUDJaW1ubpJXrOdR0waHph/67acFpJtVZtqYg0c+pNTmvQ7CYz0DlR4C8GU3xWc211ha1VoqmUrlW4BG4TgQDF5CTTwLRQBIBOK2ldDt4PZnabAPWF0ZLaW0C03T+1a9+5fD/gHVAk0JF34OW2KZ7o60KfsZ7oWmp/X4tgJoJBZPJTb9Vf09iMn1cnvPWW2+V7EmzDoHzvW/fPrHWBgcHUalUUKvV0N/ff0njVD677mjNedOEWSgUMDU1hVqtJkAcaz/27t2L5557TqyMSCSC1157DZ/4xCcuIXYKI7p/DN253W7pMTkwMIANGzZIC39WfJpVkwTxOJjTQOFgumqcXz6/Xkeui1ZiOkXZtm2pL+E5WNSnE6IoaMw8C/N6WhHo0L7uWq3X/VrGdSMY0um0TBoXkIRP4KhYLCIWiznMZcC5AQ3f60kGLtW22o3wer34zW9+I+AXh+mz6YXRuIjpxnDoBdGEw3vV92G6RXro95c7j/ls5u81Iw8MDEi4TJu4ZHiv14uTJ0+KANu0aZNk4+3YscNx35xbnss0yfUcptNpHDlyRHpXFgoF1OtrLeeHhobw2muvoVAoIJPJoLu7Gx6PB4cPH8bIyIgDiKQprq+p93rQ7gZdDq/Xi3w+L2nuvH8mgGkQUONS5jNo+qLgNcOelxP8/Ny0Ok2wXQt0beVqi+JytMZnaRYhu5bxvoLBsiw/gFcB+C4e/1Pbtv/OsqyNAJ4EkABwEMD/Ztv2imVZPgA/AnArgDSA/2Lb9sSVrsE4tpac2vyn9C6Xy9KHgD6n3gBXDzMWrQEozSi0Kn75y186gEbTZzc1siauyy20JgoTWGomNEz3pZmAaIZhmMc3+y2vu3nzZgeew/BZrVZDMBiUfIZ0Oi3n6e3tRTAYBADccsstDsCLc8vzm2vAzxnyK5fLwrDxeFxa4be2tuLmm2/G66+/LoIqEong1VdflZCmZa3vuJTP58WF0UCddi31XLB+I5VKIZfLiXDiHh2a6Xmv1Ly0FHluzaDN1of3RCFAxUEMRTevbYYP8XOej9fWCUxm7oZJm+9ncb7fuBrnowrgPtu2bwawE8CDlmXdCeC/A/iGbdtDALIAPnvx+M8CyF78/BsXj7vi4ETQ5NOav16vy5Zrbrcb+Xwey8vLYlXoh9aLoocG1bRmdrlcCIVC+I//+A8cP37cIRhoRmuXhn6h6U5oMJQCyAxHcoHpA1OgmQKHx+rvTIbn+ZuNZj6vFh5/9Vd/hXK5DL/fj0QigUQigWQyicHBQdnp6uTJk1Kw5XK5JAvR7XZjaGjIcT+a+TVBkynS6TRse724islJyWQSyWQS3d3d6OjoQCgUwmOPPSbVhvPz8/B4PBgdHcWxY8cQjUYRDAaRTqdx/PhxTE9Po1gsYmVlxdFkRQsKpomTqTweDzZv3oze3t5LcAB9z8wV8fl8QoNMN+Y86AQ8zgVxDh2t0dalZVmOyleWc2tMjNfXVjCtOQKOtKw1FmY2IOIzaXq8lvG+gsFeG8WLb1su/tkA7gPw04uf/xDAJy6+fuTie1z8/n7rfZwc215L82U/Afp9GszjHoxmrBdwmq2cAC016Y/qPSq0G/GrX/3KIe3N26X0531pU7PZo5lWiWZQE7EG1jvvmMfp96YW0JaFJj7el+lOuN1ubN68Ga2trYIFVKtVFItFaSibSqWQz+fx8ssvy3VDoRAWFxdx/vx5tLW1OdwnHqPDs/oeU6mUdIk2XQ2v1yv4QKPRwPLysmN3Kt6j1+vFU089Jed79913cfLkSRQKBWSzWWmfz/UhrbCBDffwZNFSvV5HW1ubYCUU/pqedKatnj8NOpruK5lTg4MMl5NeKGxIk6z7oKtDgU43Sa+1Pofpvmjrxcz30MlT1zKuCmOwLMuNNXdhCMD/ADAGIGfbNlGbCwC4RXAvgKmLD1WzLGsJa+5GyjjnnwL4UwBSyUcwTOeAU7pyAS3Lkv0UqtWqFJ9ozc2JNMFKswirpaUFY2NjyOfzDs1tIr9a+/I4DQjpczYz501LRTOvy+WSPgoLCwuXMLe2HPQ19HXN5C8O7WI0Gg3ce++9sG0b2WxWLDNNPNwr89ixY/L7/v5+tLS0YHR0FP39/ajX60LUJFJdAEYtrNF2t3t9IxQ9B4wYkBEqlQo+8IEP4JlnnoFlWVhYWEBvby+mpqZw5swZJBIJDA8Pw+12I5FISJSE6eONRkNqJ3ToUJvd9fpaHUgikRCMQ0cceC/U5nquNQhNpUXhojEHbf7TVdO4CM+rlRijLixpJy3TleY9mgpQZ3jyGH0fmoavZVyVYLBtuw5gp2VZcQA/A3DDNV2l+Tm/C+C7ANDV1WXrRh8kdloIlKL0Ry3Lgs/nkx2gTTOJ5cY0J5ljT6vB4/Egm82iVqvhhRdecBARf2cKh2aWgSm1TVCSvzc1vDb1qQn5GYlYW0sX58txDi2sNGqvz0tiqNfX9sh45JFHYFmWCFJNnLSe/H4/FhYWRAjff//9aGtrw49//GN885vfRDKZlLUIBoMilHhtalafzwe/34/l5WV5zWQqAKhWq44IBq2Hjo4OjIyM4NSpUygWi+jo6EAmk8G3v/1t/PM//7MojkAgIIKVzANA3Aket7Kyglwuh1gsJpYpsx+7u7ulQ5PeU5TroNv6aRrQ0Re6GHpNtBVCV0dnxGp3j+nexDoo3Fwul9Cx3+9HpVK5xBqgUNOuJ7E3hpq1UL6WcU1RCdu2c5ZlvQRgD4C4ZVmei1ZDH4Dpi4dNA9gA4IJlWR4AMayBkFc1+DArKyviApChuTi6lJr583rSu7q64Pf74XK5MDY2hpaWFiQSCSFYt3ut/di7776LEydO6OdzLJzWFBpw0i5MM3SahMk/zeTaYiChUePwWJ1Rx2EKAz34nbaO9HH1eh2f/OQnhZh4jBZQ/L+wsICVlRW5z0QiAb/fj2q1iv7+flkjPj+wjtJrQUgt3traKm3gtPvA9aH2y2QyIsCHh4dx+vRpuFwuHD9+HMPDwzhx4gRSqZTQQbVadVhWfF5aMmaOhY5e6HmmwgAguQ+0FrRgtaz10CIHi9Y0begQI5mZ1hMVG9eMcxIMBqVGgn9MvuJ5SbdmeFpjPDwvz60FyLWOq4lKdABYvSgUAgA+jDVA8SUAj2ItMvFfATxz8Sc/v/j+zYvfv2hrCm8yOJHaDyqXywiFQgDWN1/RoSDLsrC0tATLsrC8vAy/349oNCrdnnnsysoKFhcXMT8/j0gkIkh0MpnE5OQkJiYmmhKYZiwtKEy/XwsHPguf53Ln1KAU4GwuqpN2tEWgr6XPa06tZnLNuLfddpsjwsNz8L/LtbZZ7tjYmONeV1dXRfCy+tHlWmvIQnSdwlMPno8Vkn6/H5s2bZK1ZHiRDDw/P4+TJ0+iXl8rrjpy5AgmJydRLBbFTRkdHcU999yDpaUlR9yf/rfGkjSzaheHeQ50N8hoFBAEMTnvmia1gADWhBCtCg26aiY1Ixw+n08EMueSxzD6wahNLpdDNBp1KBfTQtNFWXSVNXDMY69VOFyNxdAD4IcXcQYXgJ/Ytv2/LMs6AeBJy7L+G4BDAP714vH/CuB/WpZ1FkAGwO+/3wVs28bc3Bx8Ph9CoZC4EKurq2hra0NHRwfK5TKq1apIUWBNePA39XpdIhdHjx5FPp+XfoItLS1YXl5GIBAQCRuJRPDss8+iXC5fUmWnJ1VrX40ZaIIhY2k8gJgG4Myv1+ehH0ofXwsoouisVTAjFjwPTUXTDdKW16ZNmxCNRh1pvdr64V84HJadojimp6dx+vRpCVnyO73jFQUjiVYj6LoaMxAIoKOjQ+6ZfR44F9PT04hGoxgeHsa5c+fw7//+77BtG8ePH8eWLVvw/PPPo7OzE/F43KHV9fUpGDWDApAEOgDiBtRqNbEgKJQJWLJsPRKJOMBIlp5znbSvT4GnLTe9TR3njcqM5yRTa4srlUohlUo5sk5Ji9xUmM9CQUKwVh/L+30f3XzJeF/BYNv2EQC3NPn8HIDdTT6vAHjsmm7iYgpro9FAPB7H/Pw8bNtGPB7HnXfeia1bt+Lw4cOYmZmRyj4ivnNzc+jr6xNL4cKFCzh16hRmZmZQKpXEVPP7/QAgzDI5OYmxsTEHAHQ5c59/zfx683PT3dDSXYOf2vw3XQb63NTElPY6cqGBKH0+jS1Qw2zbts1hQWgNSoCQg/UR/N7n8+HcuXPo7+/H8vIyGo0GAoGAaEPeM6+nr9HV1QUAjlR2HcGYmZlBsVgU6295eRmLi4sCCnZ1dSGdTgsTHT58GL29vbjzzjuxtLQkfS0ZweK9ENSk9q1WqyKUyVCVSgWFQkFwKro2mUxGytij0SgqlUrTCIKJ+luWhWAwKO85t6RVPT+0UCiMeB7LWtsXdHV1FZFIBF1dXahUKkin02htbZXrejweiSrl83l4vV7pcKVzHugyM2v1mnjymo7+TxotLS34yEc+grm5OYyNjYl56ff7EQ6HUSgUsLi4iOHhYWzevFmy1+gmcFMYAkyRSAQbNmzA3NwcFhYWUCqVxLyknzY3N+fo2WBaBtqVaIby8nNt0msQyHQ1tADQLolmZC2Q2AJe++/aPeHgZzpqw3PRUurq6nJkNeo0XY2al0olzM3NOeYkHA4jk8nILlIkRFb3AeuhPO3/sqRZW2O0TCjo/H6/IwkoFApJW7eOjg50dXUhlUqhXq/j3LlzsO21NnFtbW1IJpNyrmZmOrU0tS2vwbVkzgw7ROl7Ywg0Ho+jo6ND3B6uBbU4NT7rQ7jeXF8t0Lk+eo6o0fWcaMygWq0iGo2ira0NuVwO+XwehUJBLLHl5WVxjefn59HX14fOzk5MT0+jXC5LExu60tcyrgvBUK1WMTExgUKhAK/Xi3g8Lsj33NyctEpfXFzEmTNnxFRi+GdhYQHAGmGEw2G43W5s2bIFu3btwoEDBzAzM4N8Pi8L4Ha7MTMz4/DZgUvzDwCnZtbMrwlN/16b8nzP70yrwvye1zILwHQjFAAO05SfUyCZfiWf9eDBg0J8PF6HgltaWjA3N4d3331X7iMSiQgYVy6XsW/fPpTLZckiPHXqlFgAWmvb9trWfgsLCwiFQnL/4XAYXV1dUjyVSCQEQKa/vrq6ilgsJu4hU7MXFxeRTCbxxhtvoLOzEw899BCOHz+OcrmMvr4+xONxvP322ygWi+jq6kJPTw/OnDmDubk5jIyMoLu7G+fOncOZM2cwNDSEHTt2yPrW63VkMhnMzMxgbm4OoVBIcj50H1CumU4mopWgcQQdWdIKgsIFgCOaoS1PjRNw7pklyh4Sc3NzUvuRzWZx5MgRoa1wOIwjR45gZmYG8XgcW7duxfT0tFTGXu24LgRDrVbD0aNHpfiEYJfL5RJfL5fLYWpqSppwAM4dqrUvR3MqGo2iUCgAcPZAKBQKl2RNaobS/r6JL+jPTHfB9OOu5NeZWIH+z5g8CVGb9jyOn9HP1QlEWlg1Gg3kcjmcOHHCkRymoynEYSYnJzEzMyP319PTIziN2+2WVu90JbSg4zV5Lprxtr22d0Q2m0W1WoXf70ckEsGmTZvQ3t4ugr+9vR2JRAKLi4uYmZlBKBRCf3+/I9xGUO3gwYO4++678dJLL2F6ehp79uzBTTfdhOeeew65XA79/f3Yu3cvDhw4gJMnTyKXy+GBBx7A5OQk3njjDdi2jW3btgm47Xa7hSbi8Tg6OzulyItds7h5DhORaJHR6qGg1YCujlIBkPnQwCNpTYe1+Zy0LojFMATZ1tYGv9+PpaUlSVCLxWIAIFmt7GhFofD/Ocbw/8eg1qe2pfbJ5XKXaGdOtg4jAs5kKJfLhZmZGcnN19lfHo8Hc3NzDrfAjBqYOEEzPKCZW8D7auaSmBEEjZqb59Kugv6tvo75WlsQFA71eh3d3d24++67cccddwgBcy7J8NSKP/vZz1AqlYQgt27diomJCSQSCezZswd79+5FLpdDV1cX6vW6WHDmeng8HvT09KC/vx+WZSGXy6FUKmFpaQmLi4uoVCrI5/Po6+uD2722I1UikUA4HIbH4xHhH4vF0N3djQsXLgid+P1+TE5OIhAIYGVlBXNzc0ilUnC719Lli8Ui5ubmxMWgjx4KhbBt2zYcOnRI1lJvxst8h0ajIXPA/R8ikYgjfMjn1diMxhsoIMz11IyvW7jpcDbdlZWVFZRKJdl+sFAoCCifz+cd98GaE86H273Wk8K2bWk0q+nlasZ1IRg4EdoUBSCproCzBp6Ty0mkMNFMy6QVYL30lWgw+zlqPMBkem1hmO+BS7MQTeFivtafaebV59GCQ+MX/J0WZBrI0oJFh6UIPG7YsAGzs7Oo1+vSlh24dLv2w4cPizazLAsf/vCH8eUvfxnhcBijo6MYHx93NJk9c+aMw2ymsKnX63j33Xdx4MABiUK4XC50d3fjlltuEVckl8shEAhgx44dct+Dg4NIJpPIZrOwLAsf+chH8NRTT8nmOj09PfB4PBgbG0Nvby+OHTsm0QNu28eCr2g0KqBiS0sLbrzxRmzevFlAS5fLhcnJSbz++ut49dVXJQGLrg9zMYLBIIaHhzE0NISenh4MDw8LAKjBSHODZa4NBTJdDlofeu0YZcnlclhcXMTi4iJmZ2clh4Q8sLy8jJMnT0qWcCQSkczZfD6ParWKpaUlh4to7s1xNeO6EAwc2iTV5jC1EUNgNJ+bof/6vymxfT4fZmdn5bwmZtDs91dzrzxe5yJcyVXQAkmbkPp+OAcmgKmtJG2C6uP5WTAYxJEjR2SbeQASpzexEZfLJeFeYL22olAowOVy4dChQ8jlcsJ8LS0tKJVK0q9RC7aWlrWNbU+dOiUFWLVaDTt27MDMzIwQNcPH1J7pdFrchlKpJGDknj17sG/fPjQaDQnJPfnkk5KbQaak9UOA1ev1St5CrVaTehBakmfOnMHTTz+NY8eOiWKiECFuwn4RuVwOR48eRXt7O+655x4MDQ2hvb3dsU2ediOYcUuBx6xG0geGEFgJAAAgAElEQVQFKdeyWCxifn4e4+PjEqnRIU7tXlAA0SIgJtXa2upQqnQTTVq9mnHdCAbtexFwM4kNuNSsppDQsWt+pgE7TmYul7vkutpHNxnaNO3Na2uMQn9vCpZmEruZW8L3+txmOFT/3jyv/p5zWCgUBH1n4xJ9TTIX0XtgTdvSkgsEAojH48jlco46AV0gRG2o0Xp+TyJvNBqYm5uTcOTmzZtRKpUkTO3z+cTVCAaDiEajWF5exu233y4aPZfLoaOjAxMTE7jtttuE6VdWVhCJRFCr1QS4JDhIq0HjStPT03jttddw9OhREbYao9FJZ5rZLly4gJ/85Cfo6enBjh07cOeddyIejzssJ7fbLdekYGA+Cj/Tc3ju3Dm8++67SKVSssOYqWRIY/xjHgmfqVgsSlq1ViBer1cqOq9lXBeCwev1YmhoSNDffD4vEk4TLReeBGjbtuSZ6+SWRqOBYDCIXC4nZmM8HseRI0cE2GxW59AsgmAyn2Y8rSXMMGYzi0O7ARREzTS+aXU0A/q0G6Cvp4UNfWjbtiU1eXZ2Fhs3bpSqyWJxrXDW7/dLDwaXy4Xt27fj7bffht/vx+233449e/bgBz/4AYrFoqzJHXfcIcyZzWaRTqdx6tQpZDIZtLS04Nvf/rZUa87NzQmKnkqlEAqFUKvVUCwWUSgUMD09jdbWVtRqNcn4o1/t8/nw6KOP4nvf+x4ASP3Fvn37HBWMgUBAsmDp8rDeYGFhQTpHVatVvPbaazh9+rQAvcvLy0Jv5tpqhmQU5ezZsxgdHcWrr76KjRs3YufOndi5c6fUjwDr0R/+Rkes5ufnMTExgdOnT+Ps2bNiCSSTSVl//lansWv6ajTWKziXl5eRyWQcbjP3N9Wg6NWO60IwuN1u9PT0SC49fTaaYz6fD/l8HpFIBJVKRfLhWRUZDodlAumrRSIRARsrlQq8Xi/Gx8cdC/5+zKtfm8PU7hyXAyW1q9AMnDS/u9z96Ws3wzNIeB6PB4888ohk6RHYO3PmDMLhMEKhELq7u8XcZJ8Dhh/vuusuHD9+HF1dXUgmk4hGo9KBuV6vo729HUNDQ9iwYQPGx8clFBgIBFAsFlGtVtHV1YUbbrgBb7/9toSLmSMArLWPz+fzsoUdiZngIzVruVzGbbfdhu9///uwrLX28ENDQxgfHxfhR4HJfIFyuYxGoyFuSrFYFKVAnEmDsTp0SBOegoBzSyuUTGzbtuTRTE1NoVKpYMeOHejs7HT0C2GUhhhPNpvF66+/jtHRUWQyGViWJXUXvB/9/Lw2sJ7fommS1gjxlUqlItmbdHV+Ky2G1dVVLC4uIhQKwev1YmlpCV6vVxbS5/NJZhxNvEZjrTckXQOG0DhxoVBIssFCoRBWV1clFGcSw+U0M4c2KbVm1uHDZiAhv+PnWgNpS0EPHQnhuJwA0+fXx7ndbuzatQsf+MAHhOlYcdfd3S3o+4YNG6RJyje/+U3HMzz66KP4+Mc/ju3bt+OGG27A8vIy2traxBzOZDLSeOXZZ59FsVhEb28vurq6xJyfmJjA1NQUbr75Zpw4cQJzc3PweDySqcprcV7Zbo7ETDQ+k8lgw4YNGBoawpkzZ8TFCYfDAsyxl0elUkGpVJImLm63G+VyWRJ82trakEqlJIGJKdAUArwvjXW5XC7JByiXy5cIZMuysLi4iCeeeAKJRAK7d+/GQw895AAeLctCJpPBqVOncOLECSwuLkotSTabFSCWlnAgEIDH48HS0pJ0LSsUChgbG0M2mxXXJR6PizD2er2YnZ2V6sx8Pi+JV6YL/X7juhAMXq8XmzZtknLYTZs2AVjTKLQg9P5+rNajVAyHw7AsS1DiRmNt5+ZKpYJQKIRAICCIuzl0dKIZTsBh4gHmZyaTX+795SwQ87paCL3fOc3fA8CePXsklOjxeEQYsMqvUqlgampKCHFyclLmgnhMqVQSX7lSqWBwcNBRwzEyMoJMJoPp6WnE43FkMhmsrq4iFAqht7cXyWQSzz77LO644w78yZ/8CX7+85+jpaUFyWQSfr8flmVhamoK6XRaOnMVi0UR/sRCGILs6enB2NiY/G7jxo1IpVLSj5I+N+tsuHsVmYSZtJlMBl6vFz09PZLpybkLBoMSLtdpxJVKRdxUPQjq0mKdnZ3Fs88+K2sQDodh2zaWlpbwxhtv4NSpUwDWBHUgEBCQlGnLxEboTmuLhQ11iKlo0JiVnOxuBqwpXOZA/FYKBuYxhEIhxGIxRKNRIQZW8XFhqClCoZAkpXBSmezBXHPbtiVG/9577wG4ctKR9sVMYdEMUASc5a7aH9Ta1xQ0Gh8wsQ5eu5nloo8zQU7+J+MPDw8L4zM8Rr+U5jXLfqllea2Ojg6cP39e+hlkMhnkcjlkMhnZIm5gYADxeBzPPvssYrEYhoaGkMlkhAFXVlbw9NNPo1wu44knnsDnPvc5PPjgg3j++eelgIp1EH6/H8FgEMvLy5Kht7q6ioWFBczOzorF19nZiUQiIW4ArYR8Pi/ChC5EJBLB8vKyhO6Y9HP06FGJtNDl8fv9QjdkVB0VsG0bExMTTWmAgpUCwuv1YnV1Ffv378fExAT27NmD9vZ2yQSl4AkEAvD7/SiVSmhpaUE4HJYkJhZJaSuzXl/ri1ooFIQ2aUVQeFAw6LA/+6T+VroS1GTUUsQJOKi1mAzD2nSaUzqX3ev1CqDm8XjQ3d2NyclJTE1NOTQxpT9RZOBS89wkBFPba5eB73lcM83fDE8wPzPzK0yzlfN0JYHxF3/xF7jrrrscFo2uI6DbRY30i1/8QtLMq9Uq/viP/xg//vGPsbq6it27d0sNy/T0tJilf/7nf47Z2Vm89dZbSCaTuOOOO7CwsIDh4WFMTk7i5MmTeOWVVwTsO336NP7sz/4MO3fuxI9+9CMBLFtbWxEOh7GysoINGzZg8+bN6Ovrw9zcHA4dOiTNY2u1GjZs2IAbb7wRr7/+OmzbxoULFxCJRDA+Po4jR45INKO/vx/9/f3o7e1FsVhEd3e35D9YloUzZ85gcnJSQEwqD65nMpnEyMgITp8+LSXeFLgE9EhvdAuj0ai4I21tbdI27/nnn8fGjRuRzWYxMDAgqecsf2cJAMO2jEi0tbU5djVnRigtKgoGs1iKrg7Xl23trnVcF4LB7XYLgWhpFwgExL2g76k7NnEx6UcBkL0KXS6XuBzpdBrz8/MO0+xKQKBmasAZljSFB/9zsczjzHPztZkDYbolVwI8zaGP93g8+NjHPoa+vj6HgCGYxXvVCPnx48cdkY577rlH+ix+4AMfEBNYZ512dHTgjTfeQCqVQk9Pj5TIM6zJDk3VahU+nw+pVAo/+MEP8E//9E/i2h0+fFjK42klJhIJFItF1Go19PX1oa2tTUJ4brcbIyMjmJqawsTEhIS1s9ksjh49KjTU3d2NcrmMSCSCcDiMcrmM0dFRABAN29raKhYiqyu5U3h3dzdisRhuu+02ibYUi0UpeEqlUigUCmhpaUEsFhMrlgqrtbVVQFICtSsrKwiFQtJzk2BoPB6XTEgKHpYGEJBcXV1FLpeTDXfpbtBtpsLgb2mJktbZAu9axnUhGAAIWFSv19HT0wOfz4dIJALLslAsFiV/niALpSUXpbW1FcAa81B6M3KRTqclzRW4VAg0EwhmHoEWAM3OwWEKEfNczVwTLRQuF7HgMIWZPkej0UBnZyeSyaQjTq6P1WFdt3utBP2tt95yXIOVqS6XyxF+C4VCmJ6exq233ioJPwQa3W43ent70dbWhqmpKZk3YhK9vb1wu934/ve/j8997nPo6upCoVBAqVTCzMyMA9col8uiEX0+H9LptDTlSSaTGB4elgY7tCTz+Tx2794tpjldn3K5LFhDLBaTPA7iUkTu2eOBWFU+n0dra6u4rOy+RGuLkbFYLCYZhlxfMjbD6C7XWh8HpucDzi3s+UeXuFarIZ1OS2ITLQcKJgpJCngmclnWekGdbh5L8HJxcfGq+fG6EAz0g7kYfX19Utm3urqK8fFxERDsKESiYN18JBKR/n3cH6Fer2P//v145513xMwDrhy6aQZAagBIo8yAk8EpAEyXo5nwMYe+jikQTGHTzOLgb/x+P77yla+ItiYQNTAwIIzK+n6Px4NUKiXalKDt+Pi4NMV57733MDs7K9rL7/fjsccew6FDh2T/yS1btuCmm27C0NCQ1LywwEgXU0UiEczOzuKzn/0svvrVr+Kuu+7C/v37EY/HYdtrae6zs7PYtm2bFDaRWZnRmM/n0dvbK1mF1OTnz5+HZVmS3ETF0draKiXWTHiiP06tS9DRtm3ZRBeAmO39/f0O4JFCTuMQ1NLaKiMt1Wo16TfCNaIA0OFz3jOFutvtlszSG264QRKbaDXoLftoOfh8PlGwmtZaWlqkye/VjOtCMNTrdRQKBZF44+PjYhXQZ9q6datIPAqA+fl5MbMSiQQajQYWFhbQ0dEhmmB2dhalUsmRwMRFNoFGzZza19d+vfkbnZnGYYY3ebxpDVwOCDWZ3kxfNt0YXhNY0/Z0uZjt2NLSIoIBWIv2hMNh+P1+HDhwQKyKWq2GkZERvPLKK6jX6yI8hoeHsWnTJnzrW9/CJz/5SQwPD2NsbAybN2/G2bNnHWFYrmFfXx+GhoZEw5Go6Rp+9atfxXe/+134fD4R3BR48Xhc5jUWizlwoJaWFvT39yMajSKbzYrLEAqFMDs7i127dgmIyPNRmHP+WLHJnA0z0qAzB4F17a5T1LU1xutQgGn8iH90hUkTjMrw+hQIusuXtk6166s7RmvlpdOsGfLl92aXsvcb141gKBaLUmZLAdDZ2Sl157ZtS4iHJiDRXVa9BQIBKUkF1tur6UnlhOvJBpxuhF4Us5jKBBj5vZntaLoqpgWhBVGznIbLWR2mq6P/arUadu3ahY6ODjExaXXxmVtaWtDZ2SnzdeHCBblOvV7Hxo0b8cYbb0ieA0G32dlZuN1u7N69W/x0XYwEQHzsRqMh/jRNa47V1VVxdX70ox/hD/7gD1Cr1XDs2DHHfeg14/l127Tdu3dj//79EpVqaWnBO++8gz179kjqt4n/UMBw3ZoJZ12Po6+vfXlt1XEdOMc8hjTE3xP85dD0wkInt9stSU762s1cRm058H4JRBJk1bR8OSV0uXHt2+D+Jwyv1ytagBNM0JA7H+fzeeRyOczOzuLChQuYn58Xc5UpprOzs9LuyrbXwMvx8XG5jp5wwNm2Tb/nsfzTGkXn0AOXboeur8NhEhqHdhHM3+t71dcyBY1+T6ZnyTHj27ZtSxMUouV0y44ePepYi8HBQQEjR0ZGsLS0hEwmgxdffBG/93u/h+3bt+PXv/61gHUa3NQaU1tfzGglZhAOh7Ft2zZMTEzgH/7hH5BIJLB9+3b4fD7BhwAIuBcMBgXHANYsxg9/+MOSuckQ58zMDF544QXxv3lfZDy9vwSjAASo2Q4/FApJfQXDqLrnI8+jPyO96poLfqctS73WpCvbXosEcWMcFrhxzXXBmxYO+ly0wvgsptVxraFK4DoRDJxY9vZj1x8uDJtm6DbkgUAAwWAQ8XgcbW1tYk5ygSORiOTha8BPM6bpAphmfzPBoRmSxG9qb22+6nPqv2afaWvAHKbFYBIHsMZI5XIZhUJB/hPgKxQKlxRQcdMeDmbbMcO0s7MTPp8P4+PjmJ2dxe23347FxUXRUiweGh0dxenTp1EsFnH8+HHMzs5e4kLovon0jW+66SY0Gg088cQTuPXWWzE8PCxClMxN8E7XDlDA7N27V4ieiW7vvPOOaGc+J011vY7UrET/tbVCYU+kn1EAk344dO0C79sUBtpV4nNoq5QKR+MSummttn50d20Ajh3SdE2GpptmyX1XGteFYDBBEgoKhpEoECgg2BqMwiEQCIgUp8R2uVx44YUXHNrdHCaDaSDR1PqmsNBCAVhfePN6zfzNZsKpmUXRDIsw74uj0WhIzQg1N+eJsXp2IwoEAmhtbZVkGJ6zo6NDzNNgMIjOzk54vV5MTk4iGAxi48aNOHbsmKRRb9q0CeFwGKlUSjp26xRsXk8zOhmAfQluuOEGFItF/OIXv8Du3buxYcMGR79L4gBkNMuyZJ137dolbmOpVEIikUA2m8WhQ4ckiYsMS3OeiVFMJqKrym5ITJKjhUXmZIIdGZYCQwsUFmTprt1cC00nHKQfWlO0bnjfuhuWVjiaJrR1oAUojyV28Vud4ARAml2yOGZlZQXFYtGRWUaftVqtSjitXq9LA5BqtYqpqSkcPHjwkmakAC5hYO0+mNaBeYz+vZb+pkvQ7HNeQ2uRK5mJpsAy70Nfb3V1Vdwx3ZxEYyHMKXC51hqbvPPOO45r7dixQ5hneHgYgUAA+/fvx+HDh/HYY49JpiLDyLQwurq6MDg4iI6ODmzcuFGa85LwuT56TulPB4NB3H333Xj77bfx8ssv4xvf+AYajQamp6cFpdfzyNoGnmP79u04ePAggHVQ9cknn8Tf//3fy3NxjnO5nFgOrL6k4CEdam3P7tJ8T/rhceZ+k1ors5GLViDaLVhZWXFsMsN7ZQ9J05XQFgP5QO+gReHr8Xgc7eN5rWZW6JXGdSEY3O61TjQUCMB66IZ980kgBL1s2xbkVYM8wFo46M0333QsRDOwDrgUdOSxZlYj4HQ1NBHoY6/E1BwaRDJdAjISj2uGVejzaGbbsGGDI3ef59Zt67XZy9Z5PHbLli0SOhwaGpLkp0QigV27duHo0aOOjscsVTZdo0ajIdqYSLt2rXjPTOypVqsYHh7GwYMH8Xd/93f427/9W7z22muYnp4WRcCcC4J7fKbe3l4cP34c1WoV6XQa/f39mJ6exujoKLq7u4XBTbdE4wGrq6sS/WL3J13ZSNeCjVkBOHpR8FkIXFKQAHAkF9E10Wvjcrlknnw+n+y9yfVlYxp9Xu1uUCAxyY/3SOuMroVuQ3c147oQDCRSAIjFYnC5XJLFBaxn6lFKc2JoIeiCEqaHjo+PO5hTCwBqGy2JL+dGmJqcv2smUHgeoDkQeTkBwe/MPAgtBEx3Qr9eWVnBjTfeKGChtqioRVixWq/XZR9JdtfmNaPRqGib/v5+5PN5TE5O4v7770dvby9GR0dl/l0ul2TiaUFJ7aldLxbAacGm/WPOKfcPeeedd3D33Xdj3759mJubk70hSAsa12FC1/j4uACufr8fU1NT6OzslGQpCgia9tSsZHjiCKxF4DOw2pLCja4F74H3wTR+MjVdAwK+XG8KEE0DFFr8Lf9TSHGzJApFVo3yWJ2RSmWq8ysoMK5lXBeCwbIsaQFGDaMFAZM5SLRacmq3gBN0/vx5yevXWkxbFbyuZsxmboSJsPN7HSG4HIahz6VzG3g98zg9tPnMe9GukBZKt912Gx5++GHZRJb59Zw7Nighwfj9fpw+fRrnz593XNvr9YoJ393djaeffhp9fX14+OGH5Rw6m5H3SY2q76nRWN9QmP0XqKV1Oi/xAvrxN910E374wx/iyJEjeOyxx/Dzn/8c6XQatm1f0mXZ4/HIJrhzc3OS2zIwMCBYyIYNG6RTuGVZAjRSSJDZqXFZ+k3LQu+fQTokk7IFGxmP7gOtXJfLJUJBWxHcdo+f8VlMmqSrorMjGQ3hHNKi4r1p8JPrQsvhWsZ1IRiA9dg1kze4IJwYbRJr1BiAWBgEzY4cOdI0Q1EzsZk0pIfJtKYmN90M8/fN3IdmAqjZb670nfkZn4FdgqkBzRRdfV5aVel0Gul02nFvXq9XukJnMhkcPXoUDz30EJLJJGZnZx1uGUOQ2kcm42kglW4Ah36tBZdm2oGBARw8eBCBQACPPPIInnvuOWQyGflet5OzLAuDg4M4d+4czp49i0ajIRmTCwsL4l6ZqeEUnqQ3muXabdGamYxFjUycQueIBAIBsSg4uCcrhQUFI/+73Wu9L7X7oC0HbdFqK4AWDxOl6DJw+wWdd6Hdj6sd141g0JlfumO0LoXVwoBaWGftcaKPHj16WcY3NbY24U1BAjjNX9Nt0C6Ftgz0cZc7vz6HXnzzOs1cDD2ILQDr5rmO0GjrhuFDZprqVvHAWi3E6Ogotm/fjtdeew0rKyu49dZbYdu2NNHlfehkGh07p6lPzamfmZERmtSmRca1TCQSAIADBw7gnnvuwf3334/nn3/eUVLM56pWq4jFYtKrwbbXeh+EQiGMjY2hvb1dCp343NrqII0B64yo3Qbu9cnmJ2Q+Hk9lpf15zfwcFJhcJ007tJ60dUyBZeIcpHUNblLQcW3ZsEbn3FzJqm02rgvBwMmhFNaAikZxOTlaGtIspD82PT2NxcVFB2ObAF4z7IHv+V+b75pJzaxJ7XKYuAI/o3DRvjiHKbTM8+jj9P3wnB6PB/39/Y49J5lJykxEIvDUduxGrM9Pk3RsbAy33347nnvuOQwPD+PWW2/F6OgoUqmUhD1J+Lp9mRbk2u81LTWuIxUAANkTkkAkAHR3dyOZTOKLX/wiHn/8cXzuc5/DN77xDXFLeB90CbZt24YDBw44LKbJyUl0d3dj586dghfQDCf2QeCbPSQoQHWTl1qtJntKaouW9BAOh6W8WQPlwHo2qKlQdG4FBYuOkugiKD1/PC9T/jX2QreEYWLem6nQrmZcV4JBS1kSGq0AlvFqE5IPSxOTPpfWPs1ARH3dK93T5TAITvSVwEEtNHQb8cud37xX85r6d/pzVu2R+fW+ira93l6ctSiNRgPpdFqyBSlM4vE4lpaWAABvvvkmfD4fbrrpJuRyOZw/f17WgUxNIaOJmGtIwaCrAynI2MhEPyfN6VKp5HhWj8eDj370o/jpT3+K1tZWfOhDH8L+/fulMxeBxVptbQepWCyGbDYrwoHbuRGfIt1Q41KZUMBQaAUCAaE30hIFgs4k1BaHdjNbWlpEyLCzkvb76TbpvAT2kqCwdLlcsqkOn5HH8noE6HW1JYU114PfX2tPhutCMADrvhUfGlh3I8jwrPBzu91iympA0rbX4sCPPfYYnn32WSwsLDi0b7MQpNbmpi9OwtXn0KCnNgcvF0ZshnVc7vkvJwi0S2Be484770QwGBQwS7cPp3lKoqZmPnv2LObn5x3Wz6c+9Sm89957AqoNDAzgwQcfxNTUlFQZ0ufmf91olAStGYRgGSsaeR/6GYD1cDOzXHWCEQDs3bsXP/vZz/CpT30Kn/70p/H2229jZmbGIQS9Xi/uv/9+PP3005L7Eg6HceHCBekwTgyGQKPJxHqT3WAw6ABUSXMulwuxWEwETKlUEiYn03q9XinHZnk350nndtAaIONy3sjcFA4aWNThTf07vUmOphVaMEwEu9pxXQgGEg0BR01c2vTnYNYaJTZLTYG1ye/t7cXv/M7v4NVXX5Utu7Q5phnZdB9MDa59Wv43mbyZC9LsWPPc5nOZCVGXsyj0dxs3bpT3zCYkA3CuaKpSUDAVmkQEAPfeey+ee+45YeTt27djcHAQR44ccQgb8361JuTc8p51/gSZBoCE8fh7AIJJMK2Xr5kNODAwgCeffBIul0tyKnhNn8+HWq2GZDKJWCyGdDotZeMtLS04ceIEdu7cKdfWe0CYeTFaQehsRpac04LQ1g6tIB1KJSOaWAOvqXMQqtWqI8+A57NtW/ak1LgbS8QpXHi/dPXoAmkc5bdSMGgAhqYaF4ZCQ7/W+eK6Mo3HNBoN3HjjjQgGg3jqqaccmZPAOsPrODTgzES8GrzBxCbMYboezQBI8zyXO6aZJeFyuSQaoVFvvY0cNRuJkftH6PthafXU1BS8Xi+i0Sj27NmDRmOtOE33t7BtW/xbMq9+Dq4jrQhtIegIBZmNTErGKpVKImhoBhM49Hg8eOqpp+D3+7Fz50688cYbDsAtGAxiZGQEb731ligPn8+HY8eOYffu3RI+1IxPWtINTqj1aa3SnCfIyuQtfsa10/0U+JtGoyF+vt7SjuupU5Z1EhXdm3w+79iJSvej1FYwXQmGiHUOEFPjr2VcF4IBWE8zpWYD1hOYtOtADdTS0uLwySgUWIpdrVaxceNGfOYzn8FLL70kHX/I4DoUpIcZVTDfm8PEBfRrE6PQr/nM5jm0cNRuhBYY/P2WLVtke3YSAABpVkJBoAG3SCQiPRRoNW3YsEFciNbWVtkEd//+/dKUhIRGAI8+PhlXEyyZjddmg1OuLbe447GmpaRN5VAoJBYEezj+27/9G77yla/gtttuw89+9jNxo7xeLz74wQ/izJkzmJ+fd9RvvPnmm9i7d680SSGzlctl2SGatMEaHDJzOBwWgUXhwDaEZPRQKOQQPJqO+SyM1rjdbtlng3kJFGQEFCnImfZMa0+7WBSkxNVYTsDej7VaDe3t7Y4s1asdVx3ctCzLbVnWIcuy/tfF9xsty3rLsqyzlmX92LIs78XPfRffn734/eD7nVuHYiiBOcnczJQSlR2cuKkGCSqRSDj6PbKtVm9vLz7xiU/g9ttvd2SLNdPymvn4PV2ZZtqfx13OYtDPpwnfTFTSr83P9J8OW1qWhbvvvlu6JLHghpl2OoxbLpelI3KtVhOi5HluueUWHD16VLTe7bffLnt2kHBZrMbKVjKD1+uVfoTUuC6XS7QUq125dvV6XXoxsn8nNTCFPBlJ0wJLsgcHB3HXXXfhS1/6ElZWVjAyMuLI9AuHw9i7d6/MOYXDu+++K1sREJPxeDxIJBKIRqMIBoNSwUsa0UqHFmo+n3eEYmlZsNM2hUWttta1fGlpCalUCrOzs8jn80K32sWgwOBashiO96r3TMlms8L46XQaqVRKunhrF5KVx0z1/s/MY/gCgJMAohff/3cA37Bt+0nLsr4D4LMA/u+L/7O2bQ9ZlvX7F4/7L1c6MSWnzubSSToAHEANARXGmW3bluw2wMm8lUoFgUAA9913HwqFgmxY0kwDa2yD701trf+0X20yuzblzUXh9c3wpoqy+y4AACAASURBVP69iV+Yw7IsDAwMOHxZam1utlMsFiW8R1+UwkHP7Y4dO/DrX/8abrcbbW1tuOWWW3Do0CEpkuIzaAyI901Ajeel2c/7ooDi9bnfAzsjs907NTHXk0AlXQSayQzD7tixA1/4whfw3e9+F5ZlYXJyUlyD7du3i/tZrVbR1taGdDqNAwcOYM+ePRKhYViSEYx8Pu/Y8k5nkJJR+Qxao5Nu6RoxmsACP26d53av76VZq9WEZhmV0a6Cdp0pcDmfvGaj4exOzbViaNXlcqGvr08si2sZVyUYLMvqA/C7AP4vAP+7tUYZ9wH4zMVDfgjg/8SaYHjk4msA+CmAf7Ysy7IvZ4tfHPQTE4kEXC6XFJOQYKhx+J6l2LqrLhmNE87SbUrxT3/606hWqzhy5AgOHDiAVCrlEA6mptZCAoCD4bUgaGY18FjTldBMr2P9PMYURvxcn7NWq2Hr1q2OKA39UxJjqVQS4Vqr1SSe/swzzziu5/P58OCDD+IP//APEY1Gce+996JcLuPcuXNIJBIOkIwmL01Tmqou19qGLqdPn5bKSp0rwKHbmaXTaQH9CEzqqID2+Yn865j+5s2bEQ6H8Y//+I94/PHH0dXVJcKt0Wjg5ptvxsGDB2HbaxvWhEIh7Nu3D3feeacIGQo8naPAzs2cazI8qyEpOHQeBlOjo9GoI4OXaxyNRkV4MKMSWN89TdMUsZtoNCrrw3viYBSkWq06LGmdY8Jn1Hkd1zKu1mL4JoAvAYhcfJ8AkLNtm8HRCwB6L77uBTB1kfBqlmUtXTw+pU9oWdafAvhTANLXjwJAhwRJmCQq/YCWZUlBEMEYToZG0W3bliYhgUAAd999N3p6evCb3/wGx48fd4RItfzSzG6+1hqfg59dzk1pdr5mLokWIvp6/K5eX2vBRsIA4Ng4hSEtbQ5zc9Pjx487wq2xWAw+nw+ZTAaxWAzbt2/HG2+8IVsCWtZ6vJxzGgqFAKyZq1u3bkWlUoFlWTh06JCUZVMoUlDx94ya8B7oJtB3poYksKn3WtBgJzMex8bG8MQTT4hwmJ+fh8vlwuDgoDSPKZfL6OzsxPLyMiYmJrBhw4ZLBCZpjM2B+J2ufKSmpwVBzR2LxSRZSa+nLgTkfGicQQsFnfvB+WGEodFoOPpcUAnQSgDWG7jUajUsLS0Jv+h082sZ7ysYLMv6GIAF27YPWpb1O9d09isM27a/C+C7ANDT02PzwTgRJECd520CgWQMRh2IZNu2LYvG1nBE1gli7tixA8ViEaOjo45qwIv3Jv+bMbPJ1MA6eMrvTe1/mTm4xFWh+9EMmNTH9vT0yK7S7FHA5q8UFvTbSfi2bSOVSjmuv3HjRkxNTWF5eRkjIyPYuHEjXnnlFclCZIdlAGKqR6NRyQFgqq/OWtSmNZlIh9W4sxSFGS0DWn4UamQcMhkZijRRr9dx44034vDhw/jJT36Cv/zLv8RLL72EpaUl9PX1YXBwECdOnBDt6vP5cO7cOfT09IhmpSKhcGKmqKYDPju3e+PzchBg1WFhrcBo/ZJJScs6WkS65/V1XojL5RLAVnfYAtZdNR3a573oQjcef7XjaiyGuwF83LKshwD4sYYxfAtA3LIsz0WroQ/A9MXjpwFsAHDBsiwPgBiA9JUuoNNK6WfSv6REJWHoYhEAwviMMROkoZTUOeqUyNRIBIO0yU3i5QKbWAD/mxpeuw2mhm/mZpivNcHwfTMrhX7l1q1b5brxeFzulYAek3xoYfT09OD48eOOph2rq6t45JFH8OKLL6Jer+Oee+6B1+vF4OCgtIJjg126EPRX6eZxV2sCYoxEaELVz7G0tCQWQCgUkmeiGc1kI+45SqC5UCiI2+Jyre2azTDgXXfdhdOnT+NrX/savva1r+GVV17B2NgYBgcHMT4+jnK5jFwuh+7ubpw+fRobN25Eb2+vhLpJAwCQyWQk4qAZiqAphSXnllgFj+EfAVriKARj+Ty6mQ3BS22N6X0o2OBYC0muH2mZiVA6KkSLR0egrna879G2bX/Ztu0+27YHAfw+gBdt2/4DAC8BePTiYf8VwDMXX//84ntc/P7F98MXgLUEDgJWlHZEtNkQlATFBeHkazOPufMUEMC6NmdHad0MhgTg8Xikhz99NsC5wQzfazOQf1q7UdtpLEG7GPo3zZi/mVDRgGdnZ6ccw3ulhUHByTAbNykpl8t45ZVXHJhJo9HAXXfdhffeew+NRgP9/f1Ip9PI5XKCvlerVczNzUn7NrbsZzu0UqmE2dlZwTfo5zYaDdkohUAY12RpackRjSCGkclkZMOZ+fl5XLhwQcBKmuD5fF4StHjs6uoqBgcHkc1m8eUvfxn33XcfotEo+vr6MDw8LPM6ODiIr3/96xgZGUGlUsHCwoK4XnRTGaEgNsV5J23p3AxaAfl8XhiWFg1bxZEeCoUCFhYWRGlxrgKBAPr6+pBMJtHR0YFwOIxwOAwAAroyksEepuznqeeCgioQCIgirVaryOVyqFQqsm3j1Y7/N3kM/weAJy3L+m8ADgH414uf/yuA/2lZ1lkAGawJkysOLhylLUEXhiZ1GivdAvqbDE9yUYgnsPBFx58Ze+c5GOvlYnZ0dMC2bUkXpulHC0Kb+GYkQlsXZrRBWx/aujCtBT3M2L4WMoODg0KkBOw8Ho/stcD54oa19Ftpuvf09GBwcBDbtm3DwMCAnL+zsxMnTpwQs1qbumTKUCiESCQiggJY7++4sLCARCKBcDiMSCQi68N2ceVyGYODg7IOPF+9Xpe9QXRXp0qlIu4SQcRsNiuRKeIGnNM77rgDr732Gr70pS/hi1/8It566y1cuHAB2WwWDz/8MD7zmc+gv7/fERFgOJfhyHA4LB20uQclrQXbdlZeBoNB0fzEPLhO3Jqe0YBarYZIJCL5G+VyWdybXC6HQCCAWCyGpaUlqeEA4HDjqMgYcUkkEtLgiOA9WyLS7SL+QYziasc1CQbbtl8G8PLF1+cA7G5yTAXAY9d4XnETaAbT5NW+Fr+ny8H9A+l2ABCrgim/JFq256Yvm8/nRcrSX2VarW3bsreF1szaSjDvXzO3djW0QNA4QrOIB4Wi+XstkFi6zC3UuZmOZVmy1T37BPBYr9eLCxcuSF7CLbfcgocffhh33XUXYrEYfvSjH+Fb3/oWZmZmUCqVEAwGZeckzg0rWBlpSCQSSCQSgkHYto2Ojg7xt2kl0JVj1MjlciGXy8n7crkMr9eLcDgsa6e1N6MGdBkZ429pWdsCjgAd3cAPfvCDOHXqFA4cOICPfexjeOCBB/A3f/M3yGazGBsbw/z8PPbt24elpSXs2rVL5pXgdC6Xk2Sxubk5YWg+OxO0SAuxWAwej0dyGLh+3BWcUQYm5BETI+1VKhVxUUqlkgDIBNTpIhBYZMgzl8tJJIeKj6XYdPe4ZnQtrmVcF5mP9NPon9KMB9ZBGsta311Hp5zqydG5/36/X6Q8/Ueeh2AdTVoN+nAfTF26zXvktUkAugZDuwamNaDdC30ufSx/b3pd+jPLstDe3o729naJyADryTicR/q4NB9dLheWlpYErZ6YmMC+ffvg8XjwwAMPiNaZnJyUvoO0ojg/OgkHWNdkTOElcJzJZBzAGHEIru/CwgJqtZrsCUlhwUzDpaUlwZWY7k08gdfU7hGjLayBWFlZkV3LUqkUBgcH0draikOHDmFubg7pdBrnz59HMBjEnj17BKim5UFaqNVqIgBIF8zgpMKihatpg64Gz8W5bWlpkWYzupktsA5ecltGCv5Go4FIJIJSqSQJX1SYjOQxasPP2amrpaVFqm5t23aEO69mXBeCodFwNsKgT8nJpQYlQMiF4/bpBGZ0UhEjFDSHCTJRcqdSKSmJJbNT64TDYXFLtL9vugHUisB6ApbOaNOWBO8NcLoO2mrQmEOz5CfbtgVtp+ZiaJL3RveJTFWtVqXKkH7/kSNH8Jvf/Abd3d3SODWdTmPv3r1IpVJilhIcI4jG4iQSN90yJlSl02kRcJyPTCYjQp0mLgDHvpq2bUtKMdecTMZoAF0/xv5t2xY3keAzU+GpAE6fPo1cLgefz4dTp07h8OHDsuENNzPStQ5ut1vuI5fLyf3QZSoWi4hEIiLkGB1YXl4W94umPjEeHeHg2uhkJILozHJcXFyE2+2WfVEY2SGQSGvO5VpLgdZhSgpt3l+lUkE2m3VkdF7tuC4EAwmHJhO1Afc5yOfzEpYJhUISugHWM8EajbVt0UhULS0t4sPqklWXa20LdKaK6mKUYrGIWCyGSCSCgYEBnD9//hIAEHAyOQAJtwHr3YCYSkxm1wCcdi14fT0XppXAa1jW2t4PyWRStJVuNqIzGv1+P0KhkFTcnTx5Us7Fe+zt7cXXv/51LC4uYvPmzejq6kJXV5cQue5zUa/XEY/HJSKhBTkxCJYYazeGxEsGo0CgRuT9cj10SI65/8FgEMViUeiCbiGT4UKhEPx+P8bGxtDV1YVyuYyenh4sLCxgdnYWiUQCIyMjOHXqlGj4YDCIX/7yl3j00UfFsuFcsuWdTqybmZmB2+3G+Pi4WKicQyoy3dOCdNLf3y99OBnqJa7B8xBjKBaLaG9vF+slEAjInHIemXdB/EBXpFKpEsOgAKIreC3juhAMOhGJBEAGIU5A34rJSAR82IRT++GpVMqRh8/vSKwul0sQXboBtABItOFwGKFQ6JJUUo01NLMAeAzDe5rYaf0A6xaBRr6bCQMNcgJrNSHEYdrb21EsFh39EXw+Hzo7OxEKhbCwsIBIJILW1lYsLS1JdMS2bUSjUSHGRqOB++67TwQJtRrnlnNIkzWVSjkAMgK+dFU0MEwmB9aEVSaTQWdnJyKRCObn55FIJODz+bC0tCRJbtT+nK+WlhYsLS1J4hRBVL/fj8nJSQnxDQwMSAl2JpORRKVyuYybbroJo6OjIqACgYB0puLxDJkTC0in0+js7MTq6iomJibEKmpvbxchQkt0cXERpVJJImXEyegGAGsWRzKZRC6Xkz6dbK/H3d4LhQI8Hg8CgQDa29tRKpWQSqWwurqK1tZWFItFpNNpWJaF1tZWZDIZxONx+P1+dHZ24tixYwLMMtWchV3XMq4LwaBNf6LUBILoT9q2jXw+j0AgIFEH+rz0pwqFgiNzja81zsAmINls1uGXA3CExizLQn9/P06fPi1Sl76sZmAyth6mG8DrNKsk1K6JDsXq3/MaZMCpqSkxdcvlMkKhEBYXF8XfJ7pdq9Vw8uRJhwvGudiyZYsAY9x1igAsXQX61gRjaQWxQI33Vq/XsbCwAI/HIyXdbvfa5sRtbW1ob28Xl6+/v1+qJtvb2xGNRhEKhdDV1YWVlRWxElZWVqQwi3U0nNdoNCoCq62tTYqhFhYWhI5YD8H6ir6+PunVsLKyglAohGq1ikOHDuHmm2+W3AQqIDJrtVpFNBrFyMiIWCft7e3IZDKOzXdYR1EqlZDL5YT2otEoFhYWxOTnHqyMvBEczufzQodUeLRikskkfD4f0uk08vm8nJN1Jgz30pLmHLKXQzab/e0su6aJz5g3/SqCSQwPAes5CWzUQg3AFFBqN4JWbW1tjlBfqVSSRBndTZfMWSqVUCwWBQ3esmULzp49K8zNYUYh+F8zNRF9AqgUTjqPgS6I/j0tEYJTOm7OTVSi0Sh8Pp+kyFqWJf5vo9HA0tISotEokskkjh8/LvgJs/1uueUWFAoF1Ot1bN26FdFoVBJxeL5YLCYdl8PhsMTymV/CZ6hWq4I5dHV1oVQqiYuQSqWk6q+7u1uehX0GarUa5ubm4Pf7RbjRXGa0IZlMylZyFBhaMDFmX6/XkUql0Nraio6ODrHU2F7uk5/8JP7lX/4FbrdbEqZef/112cGLfnwmkxEgkFqd65lIJKTOg1gXBR3vkfRBl4xWVC6XQ7FYFIsok8mgVqshGo1KJSQrUIkHZbNZ5PN5EdRkcCop5lAEg0GcO3cObrcbk5OTACAhZpfLha6urmviyetCMBBVZzybsXlqMJb3EiEnqKK3YuOmHLr/Hk1MavxGo4HW1lYHCq2Tk+irlctlSTLx+XxCDGb7MmBd42sBo48x8Qng8p2h9b1rV8W212oONm3aJB2paDKvrq5K9GVxcVHmJJfLSUycvSh4bRIK57ynpwe5XA4XLlxAPB4XgRWNRsUFYU5JPp+H3+9HW1ubCG66AbxutVpFPB4XYU80v1wui8BgbgMxCybsUJMTMKN7wWxHmu8UaszR4N6mZBxiRvX6WuUkN+Bta2uTreo4x9zgBoDc68rKCqLRqJRTE6SkkNThRV35SGvKjJAxCzcejwtg29HRgUql4uijod3kaDQqdBqJRCR7kuFd0qhlrSWO8TeRSATxeBwrK2v7bOhNmq52XBeCgYAhQSaauNRClJj0oxk7Z1y+tbXVgRMQTKLAIMNxAQuFgoOYtYldr9dl0xH61ASMdBmzCUA2wwh0GMsMRep8BR7DPAZzuFwu9PT04N577xV/PZVKidtFE5iZhj6fD4VCQaws3aSDlgXxCZrq586dw8TEhBAew2PUiDrvgnF6Cmmi5Uzw0R2KOjo60NXVJW4ImVxnatJFIti5urqKrq4uhEIhzM/PC9jJFGYCyqz7YLo8tXw0GpXoCkO3NNtHRkbw+uuvS41JIpHA+Pg4HnroIWHCcDiMfD5/Sa0NQ5hkZFqBtm0jk8lIchHnqFAooKurS9aHOQvlclmiRgQtmcXJvBt+T4uFFjIBZtteS8RjRGh+fh4ApL8EBWlra6tETq5lXBeCgdKMGuPUqVMIhUKiGRlmJMG2trYKgMbOwDQzdSkr88zpi8diMZRKJXR0dABYN8eA9VwKAGJ6sliIRAFAwDkN5JmZjCZz6wxJc5hug7YYeC6Xa22Pxra2NgHJEokE2traJNMRgCQFud1udHR0wOVyYXZ2VnIy+Ld161ZYloUTJ05I+Iv3yWgQk2boVzOESY3PPAq6ZeFwGH6/H6VSSYA3NhmZm5sTyy4ej4u7F41GJVuzUqkgmUxidXUV2WxWIhicW9bT0ILI5/PS2yAej/8/7X1pbJzXud5zhhxuw9n3hRwOKY4kyraWeEliIXGLOnCcwDc/nOIWBXobFL5A758U/dEmKFCg/276o+gtUPTeFC3QAl3SIjexkba5drwgsWwrtiPLkiiLy3D2nbOTs5Ccrz9mnlffSHIiwbZEATyAIK7DM9/3nfe87/M873MkAxkfHxzH5/F4JLvgaxmNRpw7dw5bW1vI5/OS1h8cHGBra0twBOIoBKApb56ZmYHb7RZxHMsU4gHsMgUgmdvu7i7q9bqwUjwqcGNjQ+jPWq0mWQC7Iw0GA3w+HzqdDqampsRcltkbn4/9/f0RQJSBlgGRjNwX4sfwRQ/WxAR9Tpw4AeDmYuEFHx8fl8NvWa/v7OygXq/D4XAI5cm0jzWf3sBETxnqhSiM/Pq6lQ83MQx6HZJjJjWlByDvtPhvxSB+X5lxp8/Hx8fl6HnuWl6vF5qmwel0SmsxFXjNZhMmk0nANn1HJQDMz8/j4OAAm5ubotmvVqtS5zqdTunw499pt9uo1Wo4c+YMlFLI5XJSegGDYJpIJHDs2DEp/1iesdTheyYrUCgUhB5m4N3e3hZUnoBptVqVepnlhdVqRS6Xw8mTJ0VJSAmyw+GQDk7eSy46p9OJaDSKQqGAfr8vAaZcLuOpp56SwLC/P7CkZ8agZ5WCwaCwNmzL5veazSZsNhssFgtarZboOMhIUOrvcrng8Xiwv7+PYrGIer2OTqcj6lE+MyzJqBA9ODhAtVqFyWSCw+FAuVwW2TODEVkLalxMJhNCodA9rclDERjGxsbg9XpFukzKkEgtkXEuALvdLml+JpOB1WoVIQtBISrwPB4PSqWSuAazJNADgdyF9AAjHyi9AMrlcqHRaEhay0NgWb7ogcM7BQi9gAm4Hay8lbZktmCxWNBsNrG2tib1LyW3uVxOHIr48FHURI68Xq+P/F1mDNevX8fx48cFgZ+bm4Pdbhdsp1gsolKpCNDndrvx0UcfCY3LjkoCwE6nE9euXZN0PBwOo98fNEdNT0/DYrGg1+uh0WhIQK7X65ienobdbkcmk0EwGEQymZSyhAvNbDaPqCqLxSIcDofYyBNr8Hg8MBgMKJVKsusT62Dm8cQTTyCVSiGdTgvz8sEHH8Bms2FlZQUOh0Owpp2dHZE9s6SlZoYZKJ8Tioj6/T7S6TSUUnA4HNjd3UUkEpFAx3KOgGI0GpW5cXE3Gg2k02ns7+/D5/NhdnZWMh8GPoKgfLbYw8JSizjUvWYLwCEJDIy+jPCs7Sg/Zd1Mf0E6PRWLxZGboae3WAc2Gg0Rf3Cn0Z/SrB964RF/npQouwBJlem17Axo3D31r3crtnBrwLjTHPh+KHHVMxx6v8FSqSTZTCgUkvIrnU6LZLbRaMg15d+2WCySaS0Mm5o8Hg+63a6o/Pg3pqenUavVZJES9KPaknoFqvqUUlJWTE1NwWKxiJK02+0KzdZqteDxeGRBT09PC0bArIIKQu7gBKCJQQCDbIpiHva5kDrVuzIRKyENHYlEkMlkBP03GAzY3NzEmTNnUCgUUK1WhQrle2Q5w5KXYCw3BL0nCHdwYibUX3D357PCrIp4D8Fxivqov8nn8yOOTcxoeY2AgU7C7XZje3tbAqse4L2XcSgCA9F1do1R4krFFh9SLhD9hWPdy2Yq6u+ZFm9vb0tdXCgUJP3T04S3MgAMDHrNAVNA6tcZDFhjsobWz/dO9KY+M+Dn/L7+5xl02ITk8XhkwTHYzc/Po9frwWw2S88+6TIKWwio8fUpekkmk+j1egiHwyPsgsFggMPhQLFYRLfbhc/nk/ZhvRcnFyyRcYK9BM6ovuNuSwARgOBEAARcKxQKknLrgyGbucrlsqgAzWazaFb013RyclKMg9ngVSwWMTExIaUf9QWLi4v49a9/LbS1xWJBNpuVZ4bBAhgEyUqlIoGXgC+xhoODA8ke+PPUILDNn3gZQXC9lJkZAjcilidkf5rNppR2eml5o9GQcz6ZtbDs5nPO0vqhFDhxARJcpEacaDcACRTb29tSZhSLRTE9ZZrKXYfWZEw9KfyhMlAvUSZQqQd1gJsmoLx5pENrtZpQQvoL3uv1UCqV5CHXA4l3KhVITenLF/YmEBAkks8alrs0X2t5eRnxeFx6P+x2O2ZmZpBMJoWC1AObLNteffVVWehUCfLhJZDLw3Ln5uak+WphYWHEno28v9/vF6EVsZ1msyk0MoPCxMSEKPWazaYclmuxWODxeKS0yOfz0hrN60ehE3+vWq0iHA5LCk3Z9tjYmLAZTqdT3JWDwaDQmKdPnxb8iH+DqkK32y3XX5+5NBoNZLNZuN1u6aug8evu7i5sNhtqtRq8Xi8MBgMSiQS2trZgs9kE42AXJjUhDocDhUIB+/sD/0zeK24uPHyYQHm9XheqnmyIfq4HBwMna7IqbNii2Olux6EIDJSkNhoNhEIhqUmJLBMQAiDAj17cwzZjgoVU4J08eRL5fF52Od58j8cj0Z03gqIj/eIjzUnEm0Am60OqLolP0Osgn8/fBizeSeOgBzuZ5bBU0TRNXttsNsPn80lm4vF4UC6XMT09jVwuJ9iKyWQSoI7iIH19ycXV7Xaxubkp5z1evXp1RPRlsVhkEczOzkp6CgDZbBaLi4sjMmmyOFRbMrVndsdSQk9jcrdjWTc7O4uNjQ3s7e0hGo3KLmq1WsUUhmcksN1+YWFBAghLrTfffBNzc3OiZ+h0OrBarQiFQgJcEmz+yle+gjfeeEPS9cnJSbz22mt44YUXBNuhDL/dbgtuw92brMve3h42NjakTCKlGg6HhUbUtEFbOp+jyclJeL1eyXwmJyelq5dnaQCQHhTSsf1+HxsbG/K3eU30LIXJZBpxoSJTcS/jUAQGo9EIv9+Pg4MDJJNJkcpSu04wkUIT1lb6TjW9vz/dd6hYY73L9l969wE3D3nRA356paH+bAPWdvqmJb1QZnl5Gevr6yPUpf53b8UbAAio53K5BCzSG7t0u11Eo1F52La3t0e6QtmLwGzAarVKafXqq6+O/B0AkpFlMhlEIhGUy2UEAgGk02lx3uYOw8+Z3gODwFCtVlGtVhEIBATrYQlDFJ2Zh8FgQDabFRMRdjdy3qTpSqUS9vb2hJPnwqdzEfUjpDK5kAOBgDAgU1NTeOyxx+ByueTAGWaSBPrYhLWwsIAnnngCb7/9NrrdrpQqjUZDMiDiXnSlymazIydm7+3tIZ/Pw+PxyCbkdrulhKMdPbGKTCYj5QiDKcs4bjIOh0M0NmRYLBaLuGiZTCYEAgEpCfP5vFjGUeUIQDpSWV48lHSlXunn8/lgtVplgdJvn5GftCMxBGBQpzKAMLry4tvtdgHK+HBXKhWJpsDNsxT18yHTQOyA8+PF19uYNRoNPPnkk4hGo7h48aK8xp0yAz1VyZ2CAivuwPxZWnMtLy9LO7HD4cDe3uD0agZJujAVi0Wp081mM6rVKp588kkcP34cP/nJT9Dr9RAKhZDJZFCv1+H3+zE/Py/9J7xO3NEIoFGUZDAMOlMLhYIAmKVSCR6PB3a7XWzgKAEmDcjAp5TC5uam2KHzMCHqBdjfEAwGJfOYnp7G0tISFhcX0Ww2R+aqr8dnZ2eRzWZHUnNSnswwpqamJADkcjkopfDcc8/h5Zdfhtlsxvnz5/HSSy/h+PHj+PnPf45kMin3iEHCbrdL9sSMjoC41WoVgR43Ma/XKydu6+83M5FqtQqPxyO9PtzhDw4ORtgTbla5XA7BYBB7e3vibcHnjfOhYhMAzGYzMpkMFhcX72lNHorAAECMKghAMuVlDUdUlU1VrPuo5wcgZcf+/j6CwSCcTqdo9XkzKpWKnJcAYCQ4sHbXlxN6H0ru5GRA+L39/X0xZ+UgGMfFBowyEnowiukl01/+25qWWwAAIABJREFUPCkwIv3j4+PSkEPh197eHur1upQT3ClY/7fbbXzwwQdCWX71q1+VUofpd71eR6PRgMfjkYVGfIf+FfV6XSS3xHUoGqLWod/vw+/3y87LUoA9EazJCYaxL4PYUjAYxM7OzkhrN4Fjvh9gkJUwK6QFG5mXZrMpdbd+0fB54nxmZmbgcrkwMzOD999/H9/5znfwzDPP4Omnn0axWMS5c+ck+HLXdTqdoj0gyMz3ymeM95v3fnJyUhqtmJnoQVMAAniyPCCjRKqXzwGzVb5PZtX9fh+lUgmTk5M4ffq0AK37+/tiFPNQZgwEY9jEs7u7K9w8eWDeaEZ/Pujc+YnUE7h0uVxif8VUm2k0ketbwUd9LaYvKfSqQeIJBCp7vR78fv/IwSpsFSdLcSvDoT/RiCCSHlxkiaAPAHplHVN9vaOPpmlSYk1OTsr5CkS/v/vd76JcLuPb3/42fvSjH0kGRDCM14aU1+TkpPSokO4lS1Aul4WBILWslBIvAKoxyS4xWDcaDfh8PmEP9M1yBDwJHAIQxWm325VdmteImZUeLGZXKHsR6FrldDqF/SBOQDXgwcEBvF4vzpw5I/4R7B8hcMkMze12o9lswu/3Y3t7W0RHtJnjdWSQIK5CYJ30L9vd9/f3pavV4XAItUg2iowKy5PJyUk4HA6RVuufpVuPAmRpQm3ErVT6HxqHIjAYjUY4nU5ZFDTboCEoAAHf8vm8WLJxJ+IDls/n4XQ6EQwG0Ww2RUvOEYvFpOuOi4yvwx2Ji4CNXQQnqYzkQuMOcHAwMGdtNps4efIk/H6/CHgImPLhnZycFCMOcutcPLyhrPG5WOiQzXqRqTt3mGazibm5OdF9KDWwruMuVa/X8b3vfQ8vvfSS8PHnz5/HpUuX8PLLL8uCP378OBqNBmZnZ1Gr1TA3Nyfy77GxMWn+Ybs3ADnHYnV1FcvLyxKAfT6fpLWUQwODnb5YLEpgZwCzWq1YXFxELpfD9PS0KPvYMm2xWFCpVMTWjte9UChgbm5OrufMzIxkHY1GAy6XS2p6bgI0PGEqbzabEY1G8c477+Ab3/gGUqkUwuHwbRoZXvOJiQnROPB66DsjvV4vbDYb4vE4XC4X1tfX4fF44PP5cOXKFZhMJhFLsQ8nn8+LTwMZBipAKfxiqby3tyfCMWbKjUYDNpsNe3t7eO+998SIyGazYWlpSfC5exmHIjC0221cuHBBOv6YmlJ1Rxqw2WwKX09ul3UWac18Po9MZnDExdjYGHK5nPDoBCD1NlxE15myM4Wj3JdSbe56rPVJJ1ksFkxNTSGbzQpAxfpSbxtG7TqjN3dsIvik9DgfCqb8fj/q9Tq2t7eFriVrk0wmhWkhv08r8fn5efFDLJfLuHTpkmQhAKSvn63AVPWxzMrn8+j3Bz3+BPd2d3fFt5BBm2g7DU8oMOJ7ZinARUTFaKfTEcaoUqlIWTQ2NibOTPR2oHlPNpuVMnNnZwfb29uCJfB98z3Qhm5mZgapVEpSdEqx6VlAlufChQvw+XxIp9MoFotYW1vD7373O3kWFhYWRHlpNBpRLBYlkPNQWU3TsLm5CbPZLKm93W5HPp/H6uqqMD4bGxvS59NoNKQXw2g0IpfLCR1O3IL6C2I3rVYL2WwWe3t7ohUhaM2+GPo9rq2tyfN7L+NQBAaj0YizZ8/KLsx60el0isiGdTYf0J2dHQQCAWEk2AHXbrdHWk2r1SqUGpxBEY1G0el0BCUm60ClJC881XdMB/XKPQASUBiVzWYzIpEIbDYb7HY7isWitNky2DCFHB8fl8Yu8v1MQSm+4cM4OzuLSCQi82UjD/ny5eVlCXgABPleWloSjMDn8+H8+fM4e/asvFelFK5cuYJ4PI5gMIgrV64IfQZANCCUF7vdbuTzeWiahmPHjkm5woyE2EEikUAoFJLGIIPBIIBhv99HtVoVQZDf78fS0pJcn3Q6LS3XDocDZrMZ8/PzyOfzAiDrA0A4HMb+/j7OnTuH8fFxOYe0WCwKVkIXI+7ofD4ASC8EAWGLxYL33nsP+/v7eOGFF/Diiy+KnD6ZTMLr9YpNGwMDG5z4Wmz7ZsPTzs6OgLFkb5hJMrOoVqtwu90j7BevJ1m0UCgkGBQ3xWg0KpQvM6jd3V18+ctflqwklUqNbBb3Mg5FYOj3+/Ig1et1aVRhak8bb5/PJ3QS/fUsFguMxsHBHax3GRSsVqscDkq1I+khAo3ATeUlOWnWnnpzFb1MmupMSqwdDodYnOtvMIMApcz0KmDNS4CTmQnNbSkD9/v9wsqw9ZZBgAuTLdbk4tlym0gkxP3HbrdLoOPcSqUSYrEY5ubmxEOADxezF3oktNttAfiazSbMZvMII9BqtQR03N7eRiwWQ6/Xg9PphN1ul7lT7ARAsABiM1T5MUNgNsEMjTZpXq8XS0tL2NzcFJ9H4KaPpcPhGDlhjN22FBi1223J6DRNE33EI488go8++gjtdhtra2vi70CKkr0onU4HkUhEjHysViu2trbEJWpmZgb5fB4A4HK5RJHLMpTKWWYJZBHIdlEfQb1NpVLB9va2lFMMHNxUNjY24Ha7Ybfb5TAa4mcE6Ylx3Mu4t/ziCxzcUR0OB5xOJyYmJmC32xGNRmVxEXAzm80Shfv9vqRK1DXQK08POhJh5q6tVyQCNxcyATX9or01OOh/d3Z2VupC1ox8PaWUZBF8XZ4KxEXNn2Pazt81m82wWq0iA7bb7SI2IsLf7/dFQ0DvCi481pTlchlvvvkmNjY2JG3nYTHUHOgFYGymstlssNlsCIfDIsxhYOH144LrdruIx+MyXz68rVYLtVptxOCGEnZqGkgLs/GHGY3e3JQ7LbUr8Xhc7OEJ9Pr9fukAZcnHE60oQ3a73RIUSHky47Lb7QiFQuj1eojH47h06RKi0aiI4eLxuARmvWkP03myKwymVKOS8tY3gNHxSR/wGo0GUqmUUJi9Xg/ZbFa0Ofl8XnpWWDrzGTQajVJqkvJkcHS5XHKN7mUcioyB0Y2yXKaUVqtVhDbUjvOhslgssNvtSCQSkkFQLKSUEryCyD7BmUKhMGK2oh8sKQhqapommcOtweRWuTOjNHd0liEc7H2gyIYiIGYwAKRBanx8HDabTXYG7qJkYlgeUVfhdrslMyLtyiDZ7/elxZiZx09/+lN4vV7Y7XbZQRcWFoS25PwYCC0Wi5i4UAoO3PQm5AEolEHraUKCvGazeUSwppSS9mBiFvv7+yiVSmLs0uv14Ha7xawnHA6LzoLW7MRppqen4XQ6BcjjoLin0+kglUqNlIUsJ4mXsFXaZDLhwoUL+P73v4+FhQXkcjlRhk5PTyMWi8Hn84l5rtfrlf4JYiAGgwFbW1uSybK0I+hqtVolQ6DGQe8nAtwEFQ0Gg4C2/B2qQWdnZyUYEbdQSoncu9lsIhQKyQFKdzsORWCYmJjA3NwcarWaCJQIpmQyGTgcDrjdbtH0U6qcTCYlm6BXIBeMw+HAzMyMgDRMtcbGxuTBIfXHjICqNnYP0paMJQWVj9xlnU6nmLMGAgFZOKxbma2Q86aLM2s+fQcenaOYQbAVeW9vcC7j5uYmZmdnUSgUBFADBg5JLL/29/eRTCYxNjYm7ciRSASnT5/G4uKiyK7z+byYm2jawNMhm81KtpXP56UpB8AI7Up9gs/nE4Cr2+3i3LlzSKfT6Ha7WF5eFuoQuGlZRiaGLdOkA5mNGI1GHD9+HJlMBnNzc9ITQLdmlngHBwO7toXh+RqdTgdbW1tyj5LJ5MiCTKVSCIVCoiHI5/OCQeRyOXG+oiCpXq/jww8/RK1Ww2OPPYZ0Oo1Lly6JtRpLs1qtJpsSAHFjttvtsosHAgHJzpRSSKVSsNlsknWwjCDF3mw2Rd9BzUq328X8/LyA0AwuvMZjY2N4/PHH0Wq14HQ6cfHiRaysrEj2NjMzIxTw3Y5DERj0TShE2fWde1arFe+++65IQXkTSd/pdQsE5/r9vuxy1D8Qp6CIChhkDXzomQGQe9fjCfpeCoKjPp9PbLm4a2ezWQCQRcXX5M7EVJk7n97LUt+KOz4+LvXpO++8I+IhHuqyvLyMbDYrDzOR852dHcRiMXEFmp6eFkGQwWCQRfHhhx+i1Wrh61//OgqFAux2O5LJ5Mi13NnZwcrKCprNpgCATE+ZHdAhiemvz+cTEJAMEBWJfI+zs7NIJBJ45JFH0O8PPBloLjM9PS32/7yPJ0+elOCh16MEg0G89dZbkjHoyz6yQgymuVxOFIncQMxmM5aWlgQfKhQKcLlcyOVy6HQ6uHHjBs6fPy8gd6VSQSgUwm9+8xs0Gg0EAgF4vV7JsjweD7LZrHRKAoMsMBgMotVqIR6PY3FxUZgsPgMAxJuz0WjAbrfDbDbjS1/60m0HAPf7fSwuLiIej0uGDUDmsLa2JveKQfP69etyYPPdjkMRGOjYwx4AnitYLBaRyWRQLpdHvPL4MFKiy8+pFGPNTBSfmv1oNCr8tF6mzB2b7AB3Bvom6v0ciEnwb7B1tlwu46233pJ0j++Lbbr6tJWBQd97QTqUNSvxhGazKbsOcRYyGhQAsQQpFotYXV2VB4JIObGZg4MDpNNpeL1eBAIBZLNZoR8JKjINJhZAYxKDwYC5uTkkk0l4PB7EYjFp7SYmwIVHDGh6eho3btyAy+US05GFhQVomiY9CwAET2EH7cLCgmAlNpsNsVhspEOT2YzJZEIkEpHrRRcr4KaqkJZxpH8JzjYaDZkbsQ+WBQQTr1+/jtOnT2NpaQnr6+tiDT8zMzOy+Fi+FQqFkdreYrEIO8HDeM+ePSv2/5qmiRJzd3cXwWBQmDHSmMSX2C6wu7uLbDaL+fn5ETETLf9IyZdKpZENbWtr697W5Gdf1p99EO3mAvL7/Xj77bdFX+B0OjE/Py+HeuhNKphtEPiiTZbdbofVah1pvkokEtJByJ2cuzkdf2/VsnNnJDugDw4ejwd+v194ZYKe3PEZkBgQgJunFvO1GCQIeFKN2Ol0UK/Xkcvl5HAYmpXwYQQGO6PJZBL/w4mJCfzyl79Et9sVSozAk1IK77//vrAQlNbu7u4K08DTjbxeL8rlMsrlsvDxlUoFwWBQUnPqDObn57G9vS2Ls1KpSAs8qTuK1tg2TwpxenpagFIGNGaCAKRuZ5McQWneV0qUS6USNE2TPpqtrS34fD4AEOdx0qe8D8eOHQMAcY6m4cz4+DheffVVvPHGG/D7/fjWt74lcyH9SdqbTUwEE2ltR80F29FJQV++fFkCAstKfWu3wWAQSrharSKbzaJYLGJ+fl5Mj8lw+f1+UabSB5XPVKfTked7bm7u4QQf+/2+iFV4yjCj+8HBwOOuXq9LFkDpNLOLVqslDk9sZGFNy1OF9vb2YLPZRLDDjIEgod5ghZoC1s9MoZnak+okvsHORwJ1AEbSRM6N2AizE323JVNgfo0BxuFwiOKNlByBWZ/PJ5Qavf0KhYIYrvC8Ar31OaXI9IGkjRrdhnm9ksmk9Aiwt4C6fRrV8Bh6NnuZzWY5LYpZGf0euZuzBCwWi1LWMVtklyEFSs1mUwIcHbnYaMWshMHFbreLgGp1dVWu9djYmHRbkt4mYNputwUvaDabIi2em5sTq/7V1VVpkGOA4kEznAsAAaz5fFLGns/npYwjw2Q0GoWFMBgGDuBut1s6c6vVKsxmM7a2thAKheQUc7IUxKOY5bA8jcViI+eMMMDzebmXcWjoSn06PTExgWAwKA9ppVKRBpReryfagampKZw4cQIej0fMM4j0GwwGLC0tid2b0+mU+pIWZMDNczPZa0A7LSrNuFMRh+CN4UPCXZNOwKyB2V5MWs7hcMgCZDDSN2zpW7UpHCL15nQ6RfDCjIO9HfF4XBZQOp2W04rosUidB7MyUnp0o2L2kEwmJUBTPUehFx2Wms0mqtWq9BnweDyWeLxfvI4ulwvValWs0UnP8XdoPEJQkIAz27GnpqbkCHoGoP39fQFbadZCRmR2dlZEQOwK5f0j7ccFa7PZUKlU5D3o3bimp6exsrIiysG1tTWcOHFC+lv0jMbU1JT0mOiNitknQfMXgsMshamx4fNKgxu2e2cyGSlR2OnKDmG/349oNIpAICAAKztW2YjITY59Htx47nYcisAwOTkpoAyzADoDTUxMYGFhAWfOnBHRB3cg1ljHjh0bAaYCgYA43ZDC6XQ6SCQSsqMwFSfLwBKDQiiCn0STuVvzgeUBsKdOnRKlGb0pOVwul2gYHA4HvF4v5ubmEA6H4ff7hTlhaaEvVfL5PLLZLLa3t5FKpcSjgJlMqVRCIpHA6uqqINUUijWbTTidTpjNZvl/bGwM6+vr0pCUz+extrYm3X/MomKxGFKplASaSqWCRCIhcmNe41KpJCpAm82G8fFxbG1tyQE9U1ODsyz1jlYA4PV6pSWbQOf8/LyoCFkSsAxrtVr44IMPkEqlpMQaHx/HjRs34HA4sLOzg2KxKIwFxUl+vx/ValXYip2dHYTDYQQCATH1PXPmjBil6EHJ9fV16V5MJBJ46623EAwG8dRTTyGXy2FrawtbW1sjWaLf75duRnY5WiwWnDlzRrofo9Go2LxToWk0GuW9MfBS9coshIcYs62degdmcDs7O8jlcuj1enKsI4NoLpcb6VK923EoSom9vT1cu3ZtpNbP5XJYWVkR4Q7BFT2wxoXEC0DQkDU4NeP83Gq1yoPHQaCQABqZAgYA7mJcEGzuOXbsmHRqUlPB3VevL+j3+xIsmJlQcMK5dzodaRfm3JgtkLbjXEnrkVtnmkp1IfsjjEaj7LbcydfW1tDtdkVVSpqt2WyiWCzK2RWrq6uIRCKwWq3iSUAANpvNikybJVUqlUKj0cDi4iISiYR0BxqNRgSDQZjNZgSDQRHgsDlocnJSjmkjf08alWn+9evXMTY2cPvWl2FsqiNgWq1W4XQ6ZdeMxWLSRMQShZkS+yNisRhWVlZw9epV+T43jFqtBpPJhN3dXayvr6Pf72NlZQXvv/++ZGB8VvXdtlNTg3Mums0mWq0WfD6fZJa//e1vcfz4caGJU6kUlFKyAbIhjHgADyO+dOkSAGBhYQE+nw+rq6uYnZ3FtWvXcOLECWF+6C/Je8sDZ/r9PhKJxD2tyUMRGCiIYScfhS3ULXBhnzp1CvF4XGjLcrksYJLD4ZDDVfjAAzcPUWGdTV8Btuzqd2u2bZMjZlAg+8AgQt6eLd48VITCo4mJCemH5+8DNzs0ifJTIWixWODz+VCpVJBOpyWtNZlM8Pv9iMfjwlCQaQmFQrLjsEuw3W7j448/FlrV4/FIOdHtdpFIJMRMlrvc5uYmLBYLnE4nxscH51ecO3dOjqczmUxwOp3Si0KNB8HYsbEx+P1+sSlj9yl3Kd7DVCol8ml2YFK/z5R4amoKiUQCOzs70s3JjId+ktvb2yMpe6VSEVs7LuyxsTGxZOffDIfDaLfbomspl8viTOVwOIS18Hq9cDqdSKVSsjNns1m88sorePHFF/G1r30Nr7/+umAnBKdJjVLGnk6nsby8LM1cVqtV8Aza5HPT43F/+nLSbDYjmUzCZrPh2LFjwkqVy2U4HA4JetQ8LC8vjzRhsQwjZc11crfjrgKDUioOoAngAMC+pmmPK6UcAH4CYAFAHMDf1TStqgY5418AeB7ALoB/qGna737f67OtVW8+woVLSS0fUgDyQDHyEiykLwLBKb0rNB17A4HAbdGTD7Nelw9AqEjuInwQeNw6hT52ux2xWExqWi4I1r3MRvTKRgqZiK0w/T04OMDGxgY0TRsxc+X1YFbEgEZRDfUct+44xElqtdrISc98nwQDZ2dnR7wX9B6LfGh5KA2FXNREUMtP/p4gHulFHgXPbI8ZFdPjUqkEo9Eo+ggAok5lqUAqjy7UwKDfgo1YnU4HXq8XRqMRa2trcs8sFgvC4bDcf5Y0ExMT8Pl8ElDJZFksFrHP41F+tVoNFy5cwLPPPit9Ekz9qcYkVlGpVOB2uwWgJJ5hMpmQy+XkwFzeTwLjpBrZMEixEtk30saTk5OSFZjNZvHjJDa3tLQkWhqejUmXrHsZ95Ix/C1N0/RHGv0AwOuapv25UuoHw8//OYBvAlge/nsKwH8Y/v+pg7UU6UngJlfNjAGAHO9FjTt7Dagb5wNOVVupVILb7Uar1RIBzdTUlCjK9E1U5L+5eNl4wt1dz16srKyIfoCoMJ2N3W73CGjKRcysh8FDv+Mx+LBe1L+3QqEAj8cjyL2+GapQKMDn8wlPzoXH9JrIPZumDg4OxKOAc2DGcnBwgFwuh0AgIF4Bek3+zMyM1PcMuEyBuSOySYnBhf4OPNmaDzmpSpZtZAvYsMVFRjCZcwmHw1Kuzc7OYn19XbwHeOoT/TAY6ChuY6ZC/QHLkp2dHTFfZTBm6cCMzmQyIZ/P45133sFzzz0Hk8mEdDoNg8GAdDotWRtfm3gU8QsCvey8pOKVGgSWrDw5rN1ui6KSxyQyE2U5XK/XEY1GBag2GgdnT1Cw5nQ6xXeEuNS9jM9SSvwRgGeGH/8XAG9hEBj+CMB/1QY50XtKKZtSyq9pWu7TXojyUqNxcLbg22+/DZvNJi7FjK79fh9Op1Me7t3dXWQyGcRiMYRCITlzMZPJiHCHOwEzEgCCiDOYAJCFS31/r9cTXIEGKL1eD88//zz8fj+CwSA2NjZQKBTkb1Evz7qfykRyzGRdUqmULAyyIMDo4TNerxeRSEREWxQzeTwe6aZstVpYWVlBLpeTnZJgGLsmuUtevnwZwWBQznOgepAS4kAggNnZWWQyGfh8PiQSCQFmieQDN0+DJvAIDHbfVColGAzrWtbl169flwWYy+XEDVyPAa2uruLpp5+WfgUe5OJwOMS8JRaLSbDKZDIC3M3MzAh7QnNb/n1mK8yGGo0G5ubm0Gw2hRbnPeP7XVpaGslQe70eUqkUXnnlFZw6dQpnz57F5uam4Aoso8hc8TAitsDTRm59fV3wCWbFxCS2t7eRy+VkU+EhQFSj5vP5kYDHQ4nZW7S/v4+nn34aFy9eRCQSkfXE9/xFHWqrAXhVKaUB+CtN034MwKtb7HkA3uHHQQAp3e+mh18bCQxKqT8F8KcAxKY7n89jfX0dwWBQTDaI2vMm8bg5OuzWajVYLBZMTEwgk8kIqjs+PnAh9vv9AhBRikt0HbhpH88AAUAAQWYDzGIAIJVK4fTp0yiXy3LGI70KaMqhf51isSilAksTSl45SKPRpFYpJa5GtNUnTcsOO03T4HA40Gq1RNXGB6Xf7yMSiUhvBuc9MzODarWKiYkJFItFkU3zYFla9vNsBe5sXJjMnNh3wtZwirCo+/j444/l9Gv+TL1el/fncrmEFqQV/bPPPotr167B7/fj8uXLkt0RMFRKiSsShUU8bIUNRaQImb1xJ2cDEudEsNPtdktfDk1ue73BQT7ErGw2G1wuFzY3N7Gzs4OLFy/iueeeQyQSQSwWGwm61H0YDAbMz8/DZrMhk8mIh4LNZsOjjz6KQqEg54AQjGUZZTKZUC6XkU6n4XK5YLPZ4PP5RtS0LOPY+s5S8Ve/+pXQklarVQ7upYbnXsbdBobzmqZllFIeAK8ppT7Rf1PTNG0YNO56DIPLjwHA4/FoxAZKpRIikQg++eQTuN1uFAoFuN1uxONxkfUCkNrQYDDgxIkTWF9fF+dfGnSwY44nD92qSGSdr/8a01by1QwOrJ2z2SzW1tZEKMRSJxgMIhKJjFitA4NFT/svtlNTgs2eDe68V69eFRlst9vFJ598gn6/j2PHjmFjY0MwDV4nUlocBJ2oRpyenhbfBwYoLoyJiQmcOHFCcAIu4lKpJC7EBBC521MkxCwkHA4LUAzcPBaexjKbm5tiS0bRFa3wyVSwce5nP/sZ2u02wuEwzp07J2IspZQAo1evXhU/DoKfe3t7coYjLdl4v3nOA6XBPEldf4o5/RvIRLHdmfT11NSUMCqNRgMXLlzAiy++iEcffVSYsE6ng42NDUSjUUxOTop4bGdnB4888gh6vcGJWPl8HpcuXZJNp91uY35+HtlsVnQw7J2hQI/H2cViMZhMJqFavV6vuIXFYjF885vfRK1Wk+xgbW0NU1ODIwK9Xi8qlcq9LM+7CwyapmWG/xeVUj8D8CSAAksEpZQfQHH44xkAc7pfDw2/9qmDLkkGgwHJZBKRSARLS0vodgeHlFJNFgqFRLJM7TlvAC9yMBiUxcVUXimFq1evwmQyScagVzESEddLpCkK4cLn1+lBwDp1b29PdqZMJiPt3iwLmKry7zHQADfNRWgeSjUmMwY+JO+++y5cLheeeuopEeVwEcbjcUQiESSTSZRKpRGnYWINPJWbxrqsa2mOo28W45mM3N3pys3rsbCwILsXX5dlV6fTETUjAAlex48fF3s2ajCo32dp9fzzz6PVaiEWiyGZTIorEgP29evXUSwWxf+CbdmlUkn6El555RWcPXtWTF648Pk5cQACwfPz87h+/bp4fPj9fnlvBKDJGszPzyORSCCbzeLy5ct49NFH8cknn0jPjslkwrVr16RDldnEu+++K7aBnANpX5PJhKtXr8Jms8m9YYlMOtbpdErDVqfTQTwex6lTp0QdSYaKTWykUJeXl+VoPF6Lexl/UOCklDIppcz8GMA3AFwF8AqAPxn+2J8AeHn48SsA/oEajC8DqP8+fIFjbGxMrMltNhucTqcsEIqJ2GTDxU5loT7lo6osEAjg1KlT0j1Jxx29nded/Bj0MmU2OdFsg6liKpWS13K73ZiamoLL5ZK/QUwCuHlGBf9RNccFyUyEmQt/jwKdfD4vaHuj0UAmk8H4+DiWl5cBDJiZVqslqlDahumvU7FYFOUgzUSIchMQpKdmPB4HMOj2S6fTyGQyQrkBg6wkmUyKzwGzJeo5qIkol8sClJKmJX0IQKTrAATboOKRKTVBZsrY2YAXvwsvAAAF8ElEQVTVbrexuLgoxj7sGp2bm5PeEX0tz3vD0pMYExvqCPJSNERKk5lkr9fD8vKy0LBXrlwBADky4LHHHoPT6cTJkyfFTLfZbMpBwVRj8lpQBGaz2TA/Py/lHoMdQV6bzSaWdWQseKKWxWKRLLjdbkvGR0CT14CUMjehux13o3z0AnhbKXUZwG8B/B9N034J4M8BPKuUWgfwd4afA8D/BRADsAHgPwL4s7uZSL8/cIfmwxwIBKTRhDLSnZ0dQdAJWnEx8shz7oj06KM3n9VqRblcHhE0MThwMeppRM5JP1i/6+3nWXa0223kcrmRE5v5d7ggKBcmA8EMRe9izH/ESEhbGQwGablm4GKdT7aE74dCMD5wPAOiWCzKQqMsnE1r1CdUq1U0m034fD4xiSE7RNaEsmZmS7SD56IoFovSHTgxMTFicsuHnmAYAy/1JfV6XTIQWvXRPIViIaWUAI5jY2OyEOkOTsyJPQbskeFzwz4Tto2bTCZpyiIIWKvVZCPodrvSsLS/PzhOcXd3V5ytotGoNPHxtDAAkp3Qy1PTNITDYQFoGXio9OUBPLwWvL4EGM1mMxYWFoRx2N3dFfEf7zGpUdrRARixMbzbofRI+IMaSqkmgBsPeh53OVwAyn/wpx78eFjmCTw8c31Y5gncea5hTdPcd/PLh0L5COCGpmmPP+hJ3M1QSn3wMMz1YZkn8PDM9WGZJ/DZ53oomqiOxtE4GodrHAWGo3E0jsZt47AEhh8/6Ancw3hY5vqwzBN4eOb6sMwT+IxzPRTg49E4GkfjcI3DkjEcjaNxNA7ReOCBQSn1nFLqhlJqY9il+SDn8p+VUkWl1FXd1xxKqdeUUuvD/+3Dryul1L8bzvtjpdS5+zzXOaXUm0qpVaXUNaXU9w/jfJVSU0qp3yqlLg/n+a+GX48opS4O5/MTpdTE8OuTw883ht9fuB/z1M13TCl1SSn1i0M+z7hS6opS6iOl1AfDr31+9/5Wz8H7+Q/AGIBNAIsAJgBcBrDyAOfzNQDnAFzVfe1fA/jB8OMfAPjR8OPnAfw/AArAlwFcvM9z9QM4N/zYDGANwMphm+/w780OPzYCuDj8+/8LwB8Pv/6XAP7x8OM/A/CXw4//GMBP7vN1/acA/juAXww/P6zzjANw3fK1z+3e37c38ilv7isA/kb3+Q8B/PABz2nhlsBwA4B/+LEfA80FAPwVgL93p597QPN+GcCzh3m+AGYA/A4Df44ygPFbnwMAfwPgK8OPx4c/p+7T/EIAXgfwtwH8YriQDt08h3/zToHhc7v3D7qU+LQW7cM07rW9/L6PYRp7FoPd+NDNd5ief4RBo91rGGSJNU3T9u8wF5nn8Pt1AM77MU8A/xbAPwNALbzzkM4TuGmF8KEaWBgAn+O9PyzKx4diaNq9t5d/0UMpNQvgpwD+iaZpDb0m/rDMV9O0AwBnlFI2AD8DcOIBT+m2oZT6NoCipmkfKqWeedDzuYvxuVsh6MeDzhjuuUX7AYyCGrSVQ33G9vLPeyiljBgEhf+madpfD798aOeraVoNwJsYpOQ2pRQ3Jv1cZJ7D71sBbN+H6T0N4AU18Df9nxiUE39xCOcJYNQKAYNgK1YIwzl9pnv/oAPD+wCWh8jvBAYgzisPeE63js+1vfzzGmqQGvwnANc1Tfs3h3W+Sin3MFOAUmoaAxzkOgYB4sVPmSfn/yKAN7RhYfxFDk3TfqhpWkjTtAUMnsM3NE37+4dtnsB9skK4X2DJ7wFRnscAUd8E8C8e8Fz+BwYWdHsY1GH/CIO68XUA6wB+BcAx/FkF4N8P530FwOP3ea7nMagzPwbw0fDf84dtvgAeA3BpOM+rAP7l8OuLGLTxbwD43wAmh1+fGn6+Mfz+4gN4Dp7BTVbi0M1zOKfLw3/XuG4+z3t/pHw8GkfjaNw2HnQpcTSOxtE4hOMoMByNo3E0bhtHgeFoHI2jcds4CgxH42gcjdvGUWA4GkfjaNw2jgLD0TgaR+O2cRQYjsbROBq3jaPAcDSOxtG4bfx/3Se6Pl5iR2cAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af9855cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set the first ten lines to black\n",
+    "camera = data.camera()\n",
+    "camera[:10] = 0\n",
+    "plt.imshow(camera, 'gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af86fd0f0>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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FAHnnHYCSlJkItwbuysbxIii5CyOMwqQwuNxPfEv7FLt4NaU/DxfOQx4vukHoXKk5gu3CFYdJLhDe9/hvlBSegKRgfR1Uc7DTC5MYA4TXQTPILZxUD21cMXus7ZgHVh2QFqJvKAf3EPyZnt5jjt3TYl4cx/F+oBAIFX08HVRmTBE894rwAnxMGPc4hIQIGRoBqfCY98V5kvHw+gpXzni//lmMTcT9nQpNG8XggoZSaISsw4CeEeB+t0COIMegHu34ugUXBhd+rLQj4nFNPtWQ8SIhxw782bSN1XTPwAuJaAPBixkDN5GCK/cqfC0D7bm7SttJkoRqP8baGSzOSPB5I0vvQuLXu8JnLPBWGHPGOGZu95jcevtYOH/UamPFSADACHZsCPA+fEEc10vKhGGAqPl8PlO/4fe5x+TgtStH9x5cAfgYA0zSB7xp53PnYfd0+TuuEeEZU6FpoRhIUcbZBqm+wATXzl1yjzNx0zxmc0sPxa6VKxhnDgbZwUMEEdcOJuA5lUolLMRC6GJl4QzhKdaJmMkVnrvvhC4OXDlQ6uv4JWXKyRkzQiCvRGSMY3TcQU/Gjvdn/L0dr9Pg2njFIwVi0pjr7mBkDGYyV4zpwMBAmFcwEZ5B+EdfvcLV2/G2XcA8neoeB31zIY3BVHjMwybna8aSZ7mXS+m0Kwg8IN6H3/EeG8yxywLzQ0XmVGhaFDihFXG70fZSdp8CBCaeVFy22KXzAh08B6luzUjXtba2Buvl7fJMCE8Fy+EotaSwhkJSpigJZvawBaDQ+1etVkNRkrvP9MEXivG/W85GltWZkDjbw6U4xPB3ZMw8r+73e9oPpQNjM2++ApI2PXTK5/MZJethn9doMK5e2+JjgdASojHP4E0oQcbVxysupAOz8THDWDB/nmatVquh0C6eW3ilVCqFZ5fL5YzggpfRb54HSO5YBcob3nSe4l4AT8a1qakpFOtNlqaFx+ATDLjnsSCr1LzIhEEklpayAs3ke3UjwJVrfAbWMQm3ujCDW3QvWHLsg98wAO7n8PBwqJSM13gAaDp5SpH3ghndLWVsHCfg/eK/GQupbhH9PbD4LviuXLyvpMAQWmdsd3M9hQiDokz43ENEAFUPI1k/4GnLWq0WipIIg7CyAK0YG4BPsCL3LjzcoG3nM8eQGAv244DnKpWxHaw8xMCLRJl1dXVlwHDqUnx+WltbxxkvPGXPfICj5HI5bdmyJXgCyAPrRuCNF3RWQsqul4jzs1C5XA4oNevO8/m8tm7dmslHQ6SiJAVBJb53FBhLGQNRvtClEcIf988X3kjKXFMsFkM5NR5OkoyVdEPuwroCdGZx9x4B8w1HEPBG6UzHPhhvxwpQCChmBIXnNTc3q7OzM4wBypY2Y8AVivEbGNUBPs84MNYo2EZLiin1dayCd/FsBQqytbU1I7woFA9JGRMEH2Xs2JWvo4A8fY0SdNCS9Q/uCZGy9GpHV7DxGgn+lur4EmtIPPSIsxLwUiwbO6JpoRgcrZXq6T5nhngZLEzrTM9vLJWj9B4bOkgEUu7uGNfHC2tI7XlcTJ+8tNW9kJihYtfdY1uP/325c5wl8HRWXL7NM8FHeC+PlWnP++f/8zcZFMcUXGnEHpSPnTM98xQvgoozDVK2HNw9O08DOijp4yYpCKLfTxv02cMbV57uicXYABgN1/icxOFerTaW4fHFZ1huwi5vD8H28XOMyt8R5QHo7ArYQwxkiOtjGdsRTRvF4JZQ0jhhIF4lds3n88G18tJe2nBLifZ2i+v5cWk8E+Gmci/MgGXwWJf+MaG+2lDKplrpJ9e7UPHuXmJN39wV90yKV8ZJdQXG+3t/YE4XBoTTazkcyPV+xH30FZoxwMtvB4MdTPO5iMM0D/cQDldm3neEyzNGHuK5V+LC6+QYSuztoOgJZ+mbP5P3BI9gzCH6zfduOKR69asrGJSw82Q8B3FaEiUQe4KxZzsZmhaKQaq/JIwr1Xclxtow6V7HkCRjq96IIX2wfQES10pqyMwe8/m6C1cYEErJ05WNlgt725Iy4Ykzg7vhXpyFMkJwCaVgZIAstzD015d0e4UoMXbs1TjD+jJ1xsBdWweLYUJcY/c6YEoY3hVArFx5TlwD4tkX76ffB9jHWKF8HCDk3d3DIrT0zVBQEBOVsju/eqjB/GK88Fb53vvtSs8VmmNLgJ3wAZ5GkiShPJv2eR8vsnOMaaI6i+3RtMhKxIUeaE2UAJPKtUwuisT3PkiSJKxbYILdGpBWigUere+Vfg6SOabB9UNDQxoYGGiImLsH4zURML9PHEqK0AWF4GAe7qmnNHEVuY6qR1cUUCPmdivm7+bWxS0yffOwgHY9c8RzXDk4hsSzKBXmehdKirPYuMbnxfEVhLFQKGR2jGpqalJbW5uam5sDDtLd3Z0RRsDgjo6O0DcUgvMhv0ulUlBgzFGxWMzwTnNz87jKRgdzEVxfsJfL5YLCx8g49sGcMM+uYNwzxQt2IyvVl8lPhaaFx4DAevxeLBbDFt0IDa4oE5/P58PWaAwYngOT4puPSNLmzZvH7fMXWyFfR4FFbG1t1eDgYLCOUj1GhXFgCNryOBihY5JgQL4HWS8UCiFG5nmknrBQHrKQXkQhOnkpcK1WC+si+E6qg475fD48l9g4XivCZ6zgo63R0dGQdQGYlOpWtVH6lz67VcYrYgcprvN3RYA9v+/eD587PuJz4viAu+rMhZeUu/KW6tkFxrlQKISKRAdpsdI8i34x93h6KBJCFX+2pJABGhgYCErOPSHnG5SBl7bn83kNDg6GrQanQtNGMVCBhpZjkYiDd1K9HoHB9IHw+DgOExiY9vb2MIi4zWyNJWU9FmfaNB3bw7BaHdvb0VN7CC+hThxju/eA4HvdPIyMYkQRuauOkiE3LWV3YeJ3vErP92/AmjnSjqCNjo6qvb09jK0DmwiIV1syrsyJbzjCZ35//DyfQz4n3MFigrwPDQ1lah28T144Fltq9wwxBg6eehbJawh8TN17xSDEipO59nDM+cYNDxmJ+Fleb0Gf4BVXCHiI8IPLA1k4xxtYTOXLxidD00IxODGIji/E6Lm7yJ49wMq7y9koQ+BxMBPjTB4Dlg7EcV+1Wl9Q4yAbfXRl5OCPA5AuxI4b8L+UVWoxshzXZcAQ/g5erAQzeepMquMxninw3xCZFWd4T2+61YyzGbxT3K5fg/eChXdhcO/L7/VsVHyoEEKDAfAwjvFi7lDOCCfvGyt6Vuv6cxw7oV0E3L0ir8zlGQ44+tzjDftY+jj6dfSV+ff54xmEU5OlaaMYPPZt9LcPMExIujDOXjhy7ZNGDMkEOaLrwoQH4DFsHJs3Aia5Byb0mgncbMiZ25WUM6F7JB6f870rTY9n+d5dSn9Pvndl5wzIM31vBPdaECJXNLyLpxUpTMIjcXJwljGNFSrMjVdI2259acuxDsbLU33x8+CbeCWvW27GgXdwnIV5dR4ql8tBUaJE3PjwTOdZ/+1j5MJNyMpcucJizh1L4xof56kCkNNCMbgWjXPnfB/H1WANXtnoKxM9V43LyOAT1zla766sVBdYLzZhw49yuRzCCqm+O1Mce7PJqmt1X5FI/6R6nhsBjTdbaWqq73ztri5jgDuMoDqG4IrTBTdeYclYsjW5MzThme/V4EzvKVoUKsLsQl0sFoNbi2C7i8xYx6lJJ1d4/O27Yzle5J6RVM8oOZ7FOMFncWjhllmqez6xV0QbnjGjHebNx585YI587QsKgWd49Sf3oRjog4eJMeYyVZoWisFfzsErZ0w0srv2WHOpjg344OP2uWXETeVaB9ZcKRCTSnVPgOW4Un0BFAIThwvOWB77oxxceXnKyy0n/5fLZRWLxXA+Raw0vLjJMx8QAsu4OEbgcTvX8s4oYhQfmZEY5IRcobnn48+KPQfGIragKM98vg72TrSrlFtFlJS35dgA7+Nj7GPq6cZGSiG28gi0p6nJiknZPUV8POh3HFb45/ztIGvsHYBFeNqS59EXb2+yNC0Ug1RXAoODg+FlpfpgMcCxRsciSfXjxZgwNlphYNGwvkuQn9jkKTgsYyxkfO8r2OibD77X3KM4UFYQguQeSSPr7rX1PMer6tx19PQuAgpjADCC+m/dulUdHR3K5/OBuQCt2MDEC8Vc4THePrZYUU/R4fp7Pl/KrkYEvPTsCs/EA3NFQwgAag9feP98nD07A8XpU8JSgE74DGXovEbf6D9KHpffjRnv5SCrY1O8O6GDp+6r1TGge3h4OOxf6gAw/WfenD8dP4m/nwxNC8UAourLQ91lham90EXKbgYi1a04Awg450CUC42UreCT6uBmo7gUpvf0pANCMLAX+jgT5fN5FYvFcalAJpEfT5PhUsJQTDpWDQEGjeZ929vb1d7eHgTl3/7t33TbbbfpySef3OF89PT0aP78+Vq4cKFOOukk9fb2amRkJFPE5ArB8ZhGQCkhC2PlIQ9gYAzg0Qb3MrcoQC9O4h3jEMJBRMd7Ym8OfqOPzI3Prcf8MXgt1Rf6wX88k4VczOXo6Og4JeV9dhyqVqtlDuupVqsho4Un7F5rnCbmPsdCJkvTQjF4HMkAxgPEdfEKPmdIxxWcCV3AHOyTsrEl//vzEHSpHlt6as5ddHcr/Xp++J4+4Np6TOqpxJh5YqZ0j4E+eJFNsVjUfffdpx/96EdatmzZpOejr68v8/9b3vIWnXHGGRnPxMcID83BS94/Bk3j6xwklrKH/sYhTpy/R9idFySFRVB4gFTKuqKAl/BOJppbf08HUWMvFPJ747GK59Wvg9/9b7xV3wgHRUCfPEPB/47z4AlPFP5NRNOi8lEaG7T29nZJCusUisWi2tra1NbWpq6uroz7BeEZuKVlgvE+8vl8aNuXHOMtOCDIQLa2toaiIiaUtgh14iwDpdlYQT8/EmEllIlPNoqVnX9Gf9O0fq4kTANg6ZgF73LWWWfpE5/4xJSUQiP6wQ9+oDPOOENPP/10WNfvHkrcV0khvnWm9MwL8+BL6v0dXNiwsAjmyMhI8LyoC3CF393dnXHH8RKluqFAID1LgPGIqzoJF9nkFwHlGrxd3+CWtvB2XMnHwLN7jFLdIMXH2kGuTMHZeB57goCXSPVVyVOhaeExQDAOmhohJ5ZnUmAamIVr4wHGnaOsFw3t2Q0YxFf+sWkHQuguJy48TOVFRi4kaZqGQ0dB+LE6LBNvamoKCH21WtVee+2lzZs3Z9x1vAgsm29FVqvVMrl7mLBcLuvGG29suEP170If/vCHddppp2nRokVasGBB+NzdWsaZPiGYhIMeazvDszSZPRPwjsgE4YZL2dWcuVwuLHaT6mEp37W0tGhwcFCdnZ1BiN1Dw7uMK1HpF/OA4sKVd2PhpeoOGmMUKDIidIrDScbD0++0G9eYuEJw8Jy+l0qljMcBkD7VzMS0UAy8qANGxJa+YGpgYCBTEYimROCwYC5U3rYrFRSQlF396KAjhNuJgnIF5pWWjvJ72jXOUqAkfDksgsP3LjRxVsEBON9zAhBt/fr1Wr58+W6Zq5/85Cc64IAD9OY3v1mlUkkDAwM6/PDD1d3dHcYYwSWtCwBJabuj6fFcxVkWqX5gMePGOEkKxoJNXrwexcekVCqNWxjlqWwPN5lPDzlQDjzfvT3nR89UYLCYM7Zui8Mf5y2vgJXqHq7XUvi9DuwmSZLBoXivGLuYDE2LUILJdGvvaSIEG+FqamoKbrlXkzl6zuDAqDEgWKlUwloMvAa3zlL2rEtf5ekurReauDCnaarBwcFg8SRl+hOvLMzlxnbk4X09XPI4kmwL5G3jou+77756/etfv4tnqU6rVq3St7/9beVyOa1fv14XX3xx8GQ4abpWq4U1LL6pCuPVaJk288p7IGidnZ0Z3gA/IVR0bKVQKIR2vG2sNONEP1taxrb2Y69O+uE4kYdwHm76uFOy7ViXVF9BiYJirmNcQqrzmwOilUolbBPItXhP3OcLsvCICGn5PK7n2BFNC4/BybWoW9fR0dFMcYy7/vyOC2J8Gao0/qwArAHW19ulza1bt6q9vT0TCrgH4IgzzOOZEdrhHby/TryrNB7F534sGs9AyGBGXHFpzLLvTtq4caMkafbs2ZKkO++8U0eIQlvYAAAgAElEQVQffbS6urrCNbjyvs4FQXMwzcFmDwnSNM0spccbi4FXnuVep4d+tBkXYdGmZwx4nhsmMAKv9oSw1rxjvJ6BPnga3T0Cb4P35jPu9UIw3jWfr+9cHddl0AaGDk9kKjRtPAY0Nm5YnBFA48P44AItLS0ZYfGMhO+45HldJh3X0t1LhMvTXXwHU3qMCtG+exC4dc7IkjJpJRiR6zyWlrKbiMAA9Mfb5f+WlhZ99rOfVX9//y6coYkJBXHjjTfq/vvvz4RaksIyYvqKECLE8bvinXl60N+fOXShjVPGLvRSdvMXVx48B3cc8iwLf3sakzlxBcd9nnb1v73uQcqWhLubz7u6dyvVcQjnP5QP74liA7yPFdBUaIeKIUmSbyRJsiFJkgfts5lJktyaJMnD2373bPs8SZLk75MkeSRJkv9LkuTlk+0Ig4H1c6GP10QUCoWMUHvcxU+hUAguLO68W2BCAqnuilG+jNvX0tKiGTNmBLfWsxjSeGXgSgkMAVAJRcekeSZFUthTwNOrWB6vVfC0mrvkLS0t6uzs1Pe+9z098MADkx32XUrf//73w3hIdaXgyq1cLgch9LkjRHIw2RF998xcaXtMXq1WQ/m4pExbzD9j6ik/P9kpXpPgXp57bC6wXnXIjuPuVXoGxKtpfV2HEylg+BNjRobO78G4eD2MGzBkKK443RFNxmP4pqSTo88ukXRbmqYLJd227X9JOkXSwm0/50j68mQ7Al4g1TU5a//dkjMAVM5hdXD/GPhKZWzjVRgOJmXHZhgMZJ9J435ST56OYs8BX9/vcb+XbLsbOTAwED4DmyiVSqF9Picu5HNwEFB6FM3o6Ni5CqVSKbRHduJHP/rRZId8t9CaNWsy9fkocLeeWH1347HoKAS3ilJ2r880rS+B9xR2jF0gpNyPMvaMFIYGoXMPBKOCsenu7g5paHArBJzUduwJ8C5NTWPHIoC7xMrG8TSpvp+ol1Q77uHK0kNXlArP9/GeCu3w6jRN/0vSs9HHb5L0rW1/f0vSH9vn307H6B5J3UmSzJtMRxwbkLJLVyuVsVOKsTYID4MAEyFQCBg5YHdHnXlQGLjzfpgqFoZJR0v7xDMp7r04VgCRWcHrgJnQ8P48tyRxLj7GTtxbiNN2zxf99Kc/zWA7boEdC5HqguNhHuPk7nqSJJk8vGcJyBTwDLeMGAwfEw8hPSyFbzo6OoLH6kAyGSCfW58TqR6C4EGCaXhogSJ3I+RK30ND0u1eaxGXiHubHoI47/jvydLOYgy9aZo+ve3vdZJ6t/29t6Q1dt2T2z4bR0mSnJMkya+SJPkV+xYysFL9YFkUBuGAI7COHTiYJdVBRDyM2HKR5cCKUKCUz9f3HWxrawuWzV02j/d84qV6TptrUSqxqyfVi23cPXVB4X35H/CTz9xCtLS0aN26dTs5nbuO7rvvvnD4CsLs1pDx9zShhwiuXFGaCJkXRTlGQXaJZ8XLl+O8vmeepOxp5lJ90ZyDph7CeNYLvnMvhEwX/WS+fV2Op7c9zQpAC4jpICvjiJFEmdHu6Oio+vv7M4v9qtVq8JynQr8z+JiO9XxqKzTG7vuHNE2Xpmm6lNgeJkAZ8OOunQuJDzDXOnDpnyHQ8cpCqe6uMglcC0M4o8VVaEwesSvkLp6UrVbz/vjEY1HdE4pdRcdVvJ2Ojg6tXr16qtOwy6mnpycIbFxZiFJwq0hKDvIMBJbUC9QcnJPq2/ARCsZoP98TPsZxuGcJ8Byw5h7+OcDpnqxjId6eVK9W9Xucp5hzrL4D3Lnc2KpQnsF39AU+wJDQt/h5O+tB7qxiWE+IsO33hm2fPyVpX7tun22f7bgjufE7FTFAnkL0+MkBRAbKYyl3I2EqJsKXp7JLMEyFCwgz0RYT5JYNHASG7+zslKSAQ7jWd2sDOarM+7o18vAFEC12E1F4fnjN80VLly5VR0dHqDPwjVZdCF0o+EGZuHss1ZUKBVSSMq6zNH4Blac2JQVBQ7Cw8hQyOUbFcXIxroPyYG5QPF5T4BSv0QAEdHwh9jpd6J3vpXoq1Eu2uY5wy8NkxyTitnZEO1vH8GNJ75L0yW2/f2SffzBJkn+W9CpJWyzk2C7FCLLntfP5fAB3YtDGd+5F6KhXGBoaCkd20YZUdxXdhd8Raot3AoMyAXwOI2/evDn0hRVvzjSOrnt4EFtCXFQH5VguLdVTcFIdmY9PYn4+aMWKFbr11lslje2vefjhh+sNb3hD2JDU92sAMHWD4Ig681IsFjOKQspuBSdld7zyfRGkukAxJ/QDZeHGhD7EW9255wrvuIInC8X9nor1OY9BQEq2nReRA8JSvy/GLbzdWq0WTjwDq/HVp1OhyaQrvyfpbkmLkiR5MkmS92hMIZyYJMnDkk7Y9r8k3SzpMUmPSPpHSedOphPE/MRmMciSz+fV398fXDxenHMCHMQiXnTNWavVxlkBvpPqmtvz1wCd7rpjbfr6+oIW9rQkVsjz3ZKCgqANUmqebZCyZwn40lxWbPoZl7xLpVJRT0+PPvaxj+kb3/jGZIZ7p6mpqUknn3yyzjzzzAmvaWtr05FHHilJmjNnjl7/+tdr9erV+tWvfhUEhH0LSDt7xSNFYmAp7Kch1bMMHhK61+jVi3zn52xiJGq1WsbKwi8YJC9oQ7F72IYngJFirQhtAwyT7YgzWO45jozUz15FcUnZQ5hRZM6PvBPfu8Lj3TGqjPeU5npHF6Rp+o4JvvrDBtemkv5ySj1QffMVR6vjdQDt7e2hSCRGhiVlBpw2vKzWqxJ9ACHX5LEH4tYoTdOwPwF4hr+Hg5NY+TRNg0tN++wv4aGK74TMfVgf3gWvgixEPp/Xz3/+8ymvoJw9e3YoTtoRnXzyyVq8eLE+9KEPqbu7e7tM9vDDD4e/V61aJWlMWRx99NGqVCo67rjjwrv6knPGWFKmhD0GEqE41nZvC+IoAYgMlK9LGBoaCitdmTMUh/eFbfU8ZYpyIBvkygIjwbvBG4RDXkvBNb75CgrDwxeMRbzgS8pucOyg5lTTlNC0KYmOF6z4GgYYBEGQ6uv2URQObrlFapSmoR7BwxKpvu+it++rAePwBi/AK/GYECbQwwSpHgJwnwNysYbnf6l+0rGDptKYQpvM5ivQkUceqYGBgR0qhTlz5uhtb3ublixZone84x1qaWnRo48+qmXLlmn27Nk68cQT9fOf/3xS25J7iHPCCSfo9NNPD1aWd3GFj8fo4K0LJONCOtpBZD+By3d3QpmCB+Tz9Q17WF/gwuV4D4qEPnia0SlN68cg8Gz4B0wEReanbiP8MRjNIrShoaGwuKxWq6m/vz9zuAzPoQ9xytQX602Wpo1iQCkwWL7KTRq/qQUCtGTJEq1evTrjZntNvAsewhoDMW4JEFwUhK+1cNeOPnu6lPx3zDAOQHnGxEFMiFSVCwQMDTngWqvVAuA5GTrmmGM0a9assLKwEfX29urSSy9Va2urNm/erE2bNumJJ57Q8uXL9etf/1qSdOKJJ2rWrFmTfi60adMm5fN5zZw5M+yqzJwPDw+HeNuVJbyA0LhF9DmJlzEzTr5aUtK4exGgGNSFZ9ytj+fDvV3WLkh14+Ogos+pg5BS3WBg7GIAmjDU6xQ8AwIfucfg3m4jA7k9mjaKgUHySffv4uWkTFrMDCiFWNj8GZ7qcmtPe86suP0e70t1r4EJcNcwjg0d6IxTUp6VcBDU03q8l6dl6XM+n9eKFSsmPc79/f3q6+sLQjJnzhxt2LAhc83555+vlpYWbd68Wffff7/uu+8+Pf7445lrfvazn4U25s2bp1qtppkzZ2rhwoVavnx5w7EYHR3V4sWLdeedd6qzs1Pt7e0BcykWizrwwAPV1taWOYXKKxc9CxMLP4QQcz1C64ICeY0K7Xpa0sm9G367FcbDcV6CbxxM93468OzeEZ/haYBpEO7QF34TLrvi8ee4JzVZmjaKwYXVU3wMRqVSCaEBLtfIyIgefvhhNTc3h3gSAQXt9U1l4zp3yNc0uKJxze4rHlnHwT6BvpJOqlt0t0L0wZkpXu3nbbliilFzmAAs5u677570OG/ZsiXE/scee6wuuugiHXfccbrrrrvCNQBvLS0tmjdvnubOnZtpo6mpSWeddVb4/+mnJ5V4GkeHHnqoHnroocxn3/3ud0M9hls7wgYvkfYNdphfLzSS6goc78RT4C7oHqLE4Sptu4fnXif4A22TSfH5dsPhGTKuKRQKQUkS0sbeAJWz7r14ZqtSqai9vT2A2o2wuMnStFEMEFoyLhgiUwCDcG2tVtPWrVuDtfXUIe6la83W1tYQU+JeonUbxY4OQHr/pOyBJ2maZuLKJElUKpXCLsxkUNxbiNOWHmd7QZTH065IQL6nQuVyOezsdM899+iQQw4Zh1EA+hYKBR155JHae++9deyxx+q///u/deWVV+qGG27QyMiIlixZ8jttGxfvLSmNhRpeNt3U1BROZGL7fHeRpfFHvbl1hycY7xjLgIcQOpQNytfbdmUNfwJCY5w8TEHJ+NZzzLN7f4CbzmOOYTn+gHLDiHnfnDc9re6b30yWpoVi8EIUz827K+hgnRcngeDHoQfxvseVvqybQQdXcEZDacQa2zf/cEUSew0otO7ubkn1VZhxrtoZ2K2j4xSAqeVyOSgZVn7ecsst+tKXvjSlsb711lt19dVX68QTT1RTU5OeeuqpTJhQLBZ1+umn65BDDtGMGTOUz+fV1dWlN7zhDbryyiu1ePFidXZ2qqWlRVdffXVQGN3d3crn89qyZYsKhYJ6e3v13ve+Vy972ctCqDAwMKBly5bpFa94hfr7+/We97wnzNHixYu1YsUKPfjgg+rq6tI+++yju+++W5/85Cd1ySWXaMGCBZo5c2YIIT3EkOreGcLpQB7CDi/4yemehfBKSeaNOaIYysufUfaUJ3umwjMgjVx82nRP0fdVYDtAfz6EkXRMwb0KxyigifCkiWhaKAYvZsGlAtRBQ6KRHQhCwEg3oTzQpLhlTHCjQhZ3sdzN9BjTY1r/3PEQtyh8B6NI9Zy6r82QsqvnyEfHnwOYVioVrVy5Ulu2bNG9996r+++/f6fGu7+/X8cff7yam5vV19enBQsWqLe3V7lcTq9+9auVJIm2bt2aqfNgc96nn35a5XJZXV1d6unp0Z/+6Z9KGjtFPKbrr79ezzzzjPr6+tTa2qqenp6ALaxatUr/8i//ksEpXvKSl+iSSy7RQQcdpDlz5ujVr361CoWCPv7xj2vLli26+eabx2WSIIyLGwBJGUF1vAhBcvSeufb2mIsYY/BCNK+jkeohoteiEDLQN8+UOB6CAmK+EX4H0XkvSRkZkeohC89zXGYqNC0Ug1S30g6mSPV43QWc693tRju7hxBfz+eeSoRcw3oxk2cl/HsG3SeYiWsULvT09GjTpk265557tGXLFg0MDOjhhx/W1q1bw7MpIS4Wi9q4caNKpVJG4QwMDOyS6sbPfe5zeuc736mhoSGtW7dOxWJRRxxxhHp6ejJKmgxJZ2enOjs71d/fr0cffVQHHnigJAUMYiJ6yUteorPOOkuDg4N6//vfr3Xr1uk73/mO3ve+9+mEE07QuefW698oBurr69Ntt92mefPm6aabbtI111yjq666SjNmzNCCBQsy1t+FR8oeOOSCzXUebkrKuOYoiDiMdADYMQywJ9rzNhplAuBR+MjBQn6Dk7lBce8HpeLeib+Hg6excnxBpitdKXjcLWWX7car4JgUgKU4xQNYGdeWu3Z38lAgzmBI2WWvzjSeKuJ+2if+/Iu/+IvnbFelHZErl3nz5mnOnDmh/7xLZ2enisWiBgcH1dTUpP7+ft12223asGFDZuObnp6eCZ+zevXqUPB0/vnnq7e3N/y/cOFCPfHEEwEtLxaLampq0vnnny9pTKksX75cl156qW644QZ1d3friiuuUH9/fwgl4k1z3LK6deZ7wk6MBxkQP+KN1LWHpvAn/WQvTw/5PK0qKWAWMZ+ASVBD4elH+uOhCG1LWbwExeJyI9XPOcX7aG1t1eDg4AvTY/AKN1+Rxv++qAk0l0kGKEMpAFIxYXGGwN14afxp05LCoiopewQeRUbVan1Lbn78NGt3aXO5nO69997nVSnERVFOTz/9tA444IBwQEtra6u6uroCUNvW1qatW7fqiSeeUKFQ0Lx58/T3f//3k3ru29/+dkljyue6667TN7/5zfCdV0hK9R2NoCeeeELS2JZxV111lY455phx61kceJMUBKFWG1snUywWM8LLPHtGAB6KcR2uj5+JYHO/11t4qtnde4j/AR9jHqUftOEp8sHBwYxMuHGkTXgbT09SOAcEYHyyNC32fJSyO+SyFNfrGchUsKaCyWfw2J2JWgSsotcZABhJY5NQKpXGrV33+NM9BE83eagDoaFjl3JwcFBf+cpXdv8AGi1atCjz/1577bXd6zdu3KinnnpKmzdv1vDwsLZs2RJAxFKppHXr1mnLli066KCDdPjhh+vUU0+dsK05c+ZoyZIlmc9mz56tI444QvvuW194e/bZZ+urX/1q+D9N04z3AUO/8Y1v1HnnnadXvepVGhwczOylwN+kLin0yuVyKhaLqtXqW6lJ2WMCWHfj+FFLS4vmz5+fsfAerpK+ZiMgTy/Gbn1cOwMvxSA533m6lbb5nvM0OeSoUqmE8fGQxb0X+JiMFtdPlqaNYkALsyKSlyKEYGAgf2m8B7bNcq/B1y54ZoA9EmfMmBEAR+5lQtiD0Ssc3aqx8Aa32tvGjf3rv/7r52oIA11zzTV63eteJ2nMUnR2duqkk06asEKyv79fjzzyiFasWKHHH39c69at05o1a3T33Xdr06ZNWr58ue68807dc889euKJJ7R+/Xp96Utf0lFHHTWurf3220/Lli3TAQccEPCHZcuW6a/+6q/0rW99K1x3/fXX60c/+pGKxaJmzJihGTNmhO/OO+88zZ8/X0uXLtXs2bPV29urYrGojo4ObdmyJaRbAWWxzkNDQxoaGgr7GPDDCkoW4DnQiOJgkdqjjz6aMUbU1aCA8Eo9fYhw+j4UEPd6kZWkoLTc44E/nedIg3u/PSSKwUj3tlFEnmmZLE2LUMIBIC8U8fjeB9QRf09luivlZavuSntu2ieQeNDDDEIK4jZiQZ8Ed/foW5Ik6unp0YUXXqgPfvCDu3fwGtDy5ct1ySWXaMaMGWptbVVTU5O2bt2q/fffP1NZGNPatWvV09OjBQsWhGv6+/u1YsWKzKlWra2t6ujoaNgOu0hRRAX94he/GGe1urq6lCSJurq6wnGEkvSrX/1Ka9eulSR9+tOf1vr168NJUuVyWbNmzcoUBHlmIM5SwQ+eNpSyxUEeQjTCquJ7pGyJs6dG45SkY1nwpQPTsbLgOk+1+vO5zhUKnyM/ccamEZ62I5o2HgMCFa81wDMAzEGjIsgujHGWwfPA/HCN/3aU2WsVpGxayMnBOhiKvnZ2dmrt2rU7XRH4u9KqVavCEmmWGj/zzDPasGHDDrMafX19evrpp/XMM8/oscce0y9/+Ut1d3fr6KOPDte8/OUvnxC3mGhBV6N05uzZs7Vw4UKtWbMms4YFr+xtb3ubHn30UT355JPaZ599tGrVKq1cuVJf/vKXdcEFF+hDH/qQ7r33XhWLxYzVde/Q24zLnD2VHBcKMZ9xkR3GI05/e/GTtxV7BM6TPMM9EPoVg/BsQ+ipd8BH+u8Ao3/mymeyNC08BqkuoB4bSdldijxdiPblc8qTPaXjaUmfREeyITAExzq8aASlUigUwkIf7ycxanNzsx577DFdccUVu3/QJqAVK1aoVCrpggsuUG9vr2699VZVKhWtX79+UvevX78+CMTChQs1e/bsUKZ88MEH67zzzhtnDadChx56qI4//nh1dXWFdmfOnKn169fr7LPP1kUXXaT77rtP11xzjdasWaPu7m41Nzfrta99rR577DFddtll+vWvf6329na9613vUltbW2ZVpVRPPwNcAzrCTw52x0v5m5ubNTg4mFH2pLB9fcPo6GjwwGKPFXyGUMfxKbIpvpyaGh6qeyWFFcLU73gxlpRdT+P9Rwm6RzPVrMS08RjouGtVj/0dAyC2b29vD5oUBNpDBd/UIy6V9dSil8xCHR0d4T73WsiCuNtZq9UCLtLc3Py8KgVp7FSoLVu2qKurS3/wB3+gl770pZo7d24G/NsRjY6Oau+999a8efPU39+vNWvWaMGCBfrgBz8YFPBUT1CWxqpBH3roIX3hC1/Q6tWrdcABB2jOnDlqa2vT0qVLdeyxx6q5uVlPPPGENm3apP7+fm3atEnPPPOMNm7cqM2bN4ej/AYHB7Vx48ZMHM0Pc4nXgCCi/N3rlMaf+OR4lKTM0mXaJ0vlRWtezxIXsdEOYKl7nQ4QgpXUarWQLnaLj4IidHKsgY1ivYjKQ5PJ0rRQDGmahh2TKOms1Woha+BbwPsmnZz3wGeATnGRCT/xkm2Q4jhW80IXj1/xQMg6+Br7SqWitrY2/eM//uPzNo5Ojz76qJ599lk99dRTam9v1/z58/XSl75UL3vZyyYt0E89NbZd5/77769jjz1Wl156aRAoX9A0FXKc4Z//+Z/1m9/8Rhs2bFBfX59Wrlyp+++/X6eeeqr+5E/+RB//+Md1yimn6DWveY2++c1v6sQTT9SZZ56ZwS/+/d//PSiqRhWukjKuNPPoG7N4+Opbp/kCPnggDjEB+BA+eBdQE2wKAyNlNzXm+V5MxztQKo1QYyj9b/dUPBz2sGdnaNqEEngBrGlnBxvHCBBoBJ3vEV7QXCYeD0LKHuyBhvUYLC4eid0xP/LLsyUtLS3q6OjQQw89pE996lP6sz/7s+dzGANdfvnlmjVrlubOnaszzjhDc+fO1QMPPJDxbHZUP++Wrru7OxSSjY6Oqr29faf65c/09m+55RbdfffdmjVrVlj/ceONN4bvv/a1r+lrX/vauPaYPwra2BAXofB5JzMhZVN6WGdS4v39/YGXeIanyt14eUmzZ7Sw6m7UPOzE2mMUYy8jBiTjrEackfCFhw60+qY/U6Fpoxj8pRsBfWx75nUCEC6cb57B95458PZ9oNC0aHwn4lIvXqGNfD4fUmif+tSndtlY7Craf//9tXLlSt188816/etfH4qGCoVCKGueLC1atCjjTvuYTJYckY8Z9VWvepUWL16s1tZWHXvsserr69PIyIjmzp2rkZERrV27VsViUf39/fJzSI4//vjgQYIzONDshiUuWycbhsWVxoPOkLfHu3hhEpWYfOe4gnugXgTHdQ5UOu/GWwDEOIiX/0vZilsf753xGqaNYgCEiZeHegWhr4j0GMvjLwcNfZAcqPFiJde8WB8pu2IyLqvFmsyePVtPPvmkPv/5z+u8887bfYOzk3TKKaeoqalJ69at0y233KLf/va3YauwRlmCRnTaaadp0aJFestb3pIRbElTtkLHHHOMenp6Gi4BHhkZ0cqVK1Wr1fStb31LfX19GavPHhuxl0MxGwA0/OLpbN98R8oKvfMJCsNLiuMMWXwP3qVU5w+u5dmxYWpU2ATIDb85uO73836eYiWUghw0RWlOFQ+aFhiDlI3lPWaTFIpUvOxUUqbyjCpIz2xwjgCVapzaQxGKD67nht2yxGlPr2f4yEc+og9/+MMh5z7d6LrrrtOSJUt06KGHasuWLRoZGdHTTz8ddrmeDK1evVrHHHNMOPDX1x/Mnz8/83t7NGPGjBDGdHV1jfv+lltuCe0yl+VyOcxZpVIJ54kODg6GuUexk3XwmNvThOwg7e/NZ2AIcfUioCX4gIetjRbZSdly5Wq1GuozWFkbe8aEOfwP4XFUq9VQLk479IGCv/j0szj1yfdToWnjMTjDuUZ2l821sS8JLhaLgTli5BhCi7J8e6LacQcupXrhku8BWS6X9cUvflGPPPLIrh6GXUqbN2/W0qVLtd9++2W2fzv44IMn3fe1a9eG2n6pvlZgZGQkbBM/kWL0kMELpBrRpk2bwt++A5HH515D4Gi8Z5p8wRx9pujH041eTu2L83zXrRiL8gIlUthxliNOd5NqZLMUT6FyHeQbsjgW4tWZuVwu7EzG/6yEJaTBU/ANWqZaxzAtPAZfdEKs5rEsSsALP6TsltvuOsVgEcAkgCLpJUeJfQs4PqNPMENra6sGBgZ0/vnnj1sENF1p06ZNmbBh1qxZU1JomzZt0sqVK4O7DPCWJIkWLlyoffbZR5L0ute9rmE2YLK1Dr6b07333hvCBt8YV6pv9eegnoOAfuRdXNrs+yo4aOl4iYenXs2IoPv1rhTgNzxdQE140vsXhyQertJXxpr7/Z3gVV+8hWzgVUnZ0HjKeNCUrt6NRKqSVXGu0SVlXEPHAXC3+JHqGQh2OnKQiUnxthlQ/vbP8/l8qNP/r//6L33oQx967gZlF9Dg4KBe85rXhP/dMk+Wrr/++rAoCatNbcFf/uXYMSJ33HFHQ8xhqjiEJN1www368Y9/nNnKzddBMOe0Pzo6qlKpFOYUwSWE9AOGUCi+3wTKxg8likuoK5WKSqVSCDlc4HxTHSm7XX1ct+AW3pWF87B7vzEG4s+MV4HizRECc93O7OExLRQDA4Hm97UJDI4Xczh6i1Z0UBLyQ0GZcA9BnGl9k1Fvg9Djn/7pn/Td7373ORqRXUezZs3SIYcc8ju1sWrVKv3gBz8IMSyZmsHBQXV0dOiNb3zjpNuaP3++Dj744B1e94tf/CJjabHY7rlI2a30+Q2A6AVxjh05n3j9gxdDOQ/QhpQ9nJf4HeND+/wGG4BP+d8/c2NFGOCZL0IB+kVa39+J9/atAhgr96SnQtNCMUh1LAGQx9NKcQGSA0Ue/7kb524dbQNgerGLVE8L0Z4j2u3t7bruuut0++23P5fDscvo8ccf18c+9rHfuZ2bbrpJ1WpVG5d5pKgAACAASURBVDZs0A9+8AMddthh2muvvTQ0NKT3ve99ete73qWDDz5Yixcv1pFHHqlFixbpgAMO0L777qsPfOADOvfcc/WqV71Kf/M3f6O3vvWtO3yeL6mXstutx+tXHMWPBRp+cI+ReXccygFmT03yuaQMUOh4lx8pIGWVlte8xOt54ipN53XfpMW9FP730v9Y2XnWjmc2ygRtj6YN+Ihb5xtiOnjlx9VxvYNFzhCAZTEYhPXxEmj3Uvw8RGLcc845Z8p78k8nKpVKestb3rLT98+fP18LFixQU1OTTjjhBN19992688471d/fr49+9KP65je/qT/6oz/Sd77znYb3t7a26itf+YpOPvlk1Wo1LVu2TE1NTZo1a9Z2w5pLL71UM2bMCNudUYnIPPtpYnEpsuf9PdUs1Qus+N/jc+5n7QS8Fde/IJTePsrA9+TwMMD748A6wuuKCeXFs3wDFwcvPX2MgvAx8JL9qdK0UAy8kO+d52mlXC4XUi6ODDuhUKj0YnttVwa0H6PY7m7hflUqFV144YW68MILn9Ox2NW0aNEiXXLJJVO6p6enRx/5yEc0Z84cvf3tb9fQ0JA6Ojok1QWAoqPe3l4dc8wx+tu//Vtt2bJFq1evziDqJ510kpYuXapqtaqBgQE1NTWpp6dHhx122ITPP/TQQ/We97wnrInw/RpRAqTtpHooivUGHI2zGF7+Dp9J9VOsPDQgfEGR+HF3EDtGeTWiu+0xnzp2BfYQp0gdUG1ublZ/f39GkXhYzPW0zXmg9MUVxgvWY5Cy22a5AMf5XQbDV8WBDHv6yVOgUNyWVD9luFAoqKurSw8++KCuvfba3feizyGtWbNG73nPe3T22WdP6vpzzz1Xvb29QSHGJ1uDD1x00UX6/ve/P+7+o48+Wp/61Kd02GGHBcsHkzY1NalUKunTn/60/vzP/3zCPrBpizO3hxVegen5ehQHtQl858Lsawqk7A5IaZqG/RGxuvALXqOHEL7fKAbL6xtKpVLwXtwL9kI9ivoa8ToZNLA1ngvB876ACs8Kj4Fxiit6d0TTRjEAIHpI4NoY8rx1o0o2/kdBxPd5rpvPUEYdHR1asWLFi0YpSJrUobPQe9/7Xq1evXrCI+96enp09tln64ADDpjQPSW1xnNJbxK6SQrl7RPRIYccEnZdQrDcffb5RjjixXPuoseFb1IdZMaLcDee94hTrV5GjUJpxINkywh1yGb4O9MfL7Dy58OXLtA8j01n4zF07IU+eHtToWmhGGLNzqR4bO+5YsgHk0HwwXFXrdGCIWK+NE3Dac6f+cxndtt7TmdasGCBDjzwQN1xxx0TXtPX16cTTzxR+++/vzZs2KDOzk4tXbpUQ0ND6u3t1U033aTLLrtMr3jFK7R27dqQb0fAW1tbddVVV+nkk0/WzJkzJ3zOEUccEcIIF2Yojq0dYIZ8QZwrGHgC79LDS69Z8XvclfcwVKqnt32HaAc03bBxv4OEDi7G74Mc4FU4cAp5Ot9DKi8W5Pup0LRRDKQoXUNSH0+u1xeJUOQSr1XnO985GqpWq2FDTQaQ9M/IyIiuueaa5+aFpxldccUVOvTQQ3XzzTfv8Np99tlHL3/5yzM4Ai74Bz7wAX3yk59UPp/XXXfdpa6uLh111FHhRK6uri595jOf0bvf/e7tAqJeVuwGwjGDOKZ244JXiMcg1XnJi38cpIRXWltbMwbJQT76A3/BQ1Q34vpLY657qVRSsVjMPJcCPgchAbobredwfvfVw2xf58uyY6/FvYRYue6Ipk26km2r3MJ72tFTQLhTvs+ilB1ML4IBlCoUCpmYF6YYHR2dMkD3YqGLLrpIDz/88KSUgjS2dRtH5fkipRUrVujyyy8PlXsnn3yyDjvsMHV3dwcBffbZZyVJr3nNa/TDH/5wwmegAIiti8WiJIV9BsCDvJ6FjXvb2trU3t4e8v4U+vhhRlId8EbwAR0xNhgMT3XGXgU1LgCz9B18AyXgXo/vHEX/ycZ5/UQc8vDMOLRwDxqPmsIunhdjNJOhHSqGJEn2TZLkjiRJfpMkyUNJkvz1ts9nJklya5IkD2/73bPt8yRJkr9PkuSRJEn+L0mSl0/iGZlFUuxjJ9VrFjyrwCDEACMDDMULXLwCrVarhTMULr744kkO14uLEJKpFG7deuutmSq9fD6vFStW6MorrwzX4JmhFLCk7LPw//7f/2u4twLkhWy1Wi3s0YFVrVarmdJh/qfSr1wuq1QqaWBgIHNQMdfhobIyM0nGDiCOz3uIq2rjkIPrhoeHAwjoWQLfB8EFEz50Xsbzxcix9gGPLK55iDEVFKkrFvocZ/kmQ5PxGEYlfShN05dKerWkv0yS5KWSLpF0W5qmCyXdtu1/STpF0sJtP+dI+vJkOuK71zh4hGJwsAUFwvUMJhaDa7yQxbVmPp8Ph6r8viqFk08+WR/84Ad3ClP5+te/HkKyRx99VNJYmPCTn/xEX/nKV/R///d/Wr58eVjJ+fjjj+vkk0/W5ZdfrmXLlul973vfhG2/973vDadNScoAbdXq2IpLSnxhfMp++Tsud/eiNnfNpTGjRJjqCgnFA9/EezbG/OgZLq/Edc8VZeBZDy/c4jrKvwHfPSvB/b49vo8BmRlfM7IzZdE7VAxpmj6dpun/bPu7X9JySXtLepMkDgr4lqQ/3vb3myR9Ox2jeyR1J0ky8QGHRr4ZClqOUMK1taSMSyiN3xzDC0uYnLhm4Xvf+96k9yV4MdFxxx2nww47bKezL3fffXdYhn3wwQerVqvpnnvu0eGHH67/+Z//0S9/+Ut1dHSEZc4PPvigVq5cqbPPPls33nij/vM//7NhuyeccEJoDxff1xh4nO8LnNzyStmNg/1zKWtUoLi8Gs/BjU7Mew4kxjURcV+ojfFqRq7lBz4Gv/AqS38nN5z+GWnauDSb0Gu3go9Jkuwv6WWS7pXUm6Yp+6Ovk9S77e+9Ja2x257c9tl291L3PQTxFhxU8R1r0KxMsiPEMUgEpWkaquhaWlr0s5/9TA899NBUXv9FQ0uWLPmdsy/Nzc3af//9te+++yqXy+kb3/iGzjjjDM2fP19bt27Vvvvuq3K5rNHRUR144IG67rrrtGnTJp1zzjkN22P3aXAIBKxUKqmpqSmzHDr+LWWttqRMXO5pS69AdDfdFYCn+MhO4Ln4Ck0vucdS+zoK/oa8Epf2IS9won1+POOQpmlYM+FtuNLwkMSXhE+FJn11kiQdkv5V0vlpmm6NENA0SZIpBTFJkpyjsVAj7MjsFV1Mjpd45vP1payO0rKFt+MOnuKR6ig0i4B+8pOfTKW7Lxq69tprQ6z/u9AXvvAFzZs3Tw899FDIDpx55pmhIOryyy/XBz7wgTCnV199tX71q19N2N4555yjZ599NrOLF5mH7u7ucCo45HUGnmVy/pCyZ6F6HYJUP2owDlEd9HbgME3TzIE9nvKE4EnARAwSFCuDarUaQE/PpoHL4DXxjoQPvGOjIj73Pnw3q6nQpBRDkiTNGlMK/5SmKXDy+iRJ5qVp+vS2UGHDts+fkuT7lO+z7bMMpWn6D5L+QZJ6e3tTBN0tvRe0cKAt7hJFMl7IgttWrVbDcXWeVkPTPvjgg1MapBcLXXbZZfrEJz6xS9q69957G37e3d2tzZs364orrtDxxx+vuXPn6oYbbtiuUjjppJN03HHHBUXOVm1Y7P7+/swO37VaTcViMXgT8A4GBMQfgBFhA8T2qkgvLXbr7zUxkoKgszwfrwBF0NTUFEBIlEmspOICK/d88SY8BIm3AvACJ5af0168lkiq1/7szI7ek8lKJJK+Lml5mqafs69+LOld2/5+l6Qf2ed/ti078WpJWyzkaEhMKNqVAXIAiLgSF5HPEX6+p0ya9fkMOgPY0tKyXUT8xUpf+MIXdplS2B45ZnP77bfrhhtu2O71S5Ys0SmnnBKOCogPYQEjcotJ3YRUB5Z9AZ6DcPBEkiRhExPHrMgIkOKLcYPR0dGw83S5XA6KiH4NDw+HfSKkOubh2Ql40+szCGnd63GPiOMI+E6qr5qkqpQCslptbPOWeLcm+H+q3oI0OY/hDySdJWlZkiS/3vbZpZI+KelfkiR5j6THJZ2x7bubJZ0q6RFJJUk7LNLHI8DqM6HxYDlz8L9rYT5vVBeOUvl99BYuvfRSXXXVVc93NxrS2rVrQ8iAIDqe4OAiHoQvpoLxnRc8xsYw8B1C77UvnCYt1Wtg+M5rDSRllJRUByK9fe6BPM3qHoODrN5PaUwJ9Pf3Z5YF0OdyuayOjo5wv2dE8J6Rn3gMJ0uTyUr8d5qmSZqmh6dpeuS2n5vTNN2Upukfpmm6ME3TE9I0fXbb9Wmapn+ZpulBaZouSdN0Yh+y/ozwAlJ2G6o4tUNMF6POns5B2/tuPWjxZcuWTWmAXuh06aWX6l//9V+f725MSJs2bQphRqPqPIyFo/u40wg4P+62S9mt5RwsbGRB4SG8C3e9ETjfJxRvxoXP38EF1ovxYt5t1Dfu46Qrb18aSw3H5dHuVTh5e1OhaVP5KGVry+OCDXfP4qpGBN/TRnHFGm1MVXO+kOnCCy/UHXfcod/+9re7vO0zzjhDs2fP3iVt3X777RnBQag8jeggG8phe7s4+TyDzEv1bde8YIjt0LjWQWxo1qxZ2nfffTV37tzwbMIKVz54C44ruAeBseI+7mnE33jOfiKW12jEvOzYCZ6X/z8VmjaKATcKhiD2k7Lbu3uOmkIP3CasBt+5NWGCXve61z1v7/hc0pe//GV97nOf0913371b2n/zm9+8U/tHNqL169dn5pX5cjQfIM9BOzxI/o7JrbMDhQg2/OIb++AVkKGgoOqggw7SYYcdpqOPPlrz5o2V5XhFIcoFQJC2AQSpo/H0J8qBMMq9Y/eMuJ60PTxN+IMiZc8SQjEvGoyByR3RtFEMLHIBqGHiUA4cYhsXhEjZLbcYMFI6ft5EPp/XnDlznpf3ey7p8ssv169//esdX/g70Pe+970pl9luj6hf8Ly8l0Ez776lGduju9WmDbfaUh1/cm8hl8uF5d0AlvAKxolSafgwn89rxowZGdzCt6GrVquhCnN4eDgoMi+Bpj+eUfA9FHg/Dx88PCbrQtoUmcEz8ZWYjZZnT4amhWIAMMECOFgSA0FeQkqu19OVWBUGjxQlMerIyIjOP//85/N1dyudeeaZqlQq+upXv7pbnzNR9eLOEgVXLkAIKVbY3WipHka624zgN1rbQLue2ZKUqQ5saWnR1q1bgxeTpqkef/xx/fa3vw0ncPf19WUWZtHXuIybPmPxUaSxoKZpmlkU5sbPa3ry+XzmkBm8CJSFHzwDwf8v2B2c0MhekAL6jHfAghd3D30Bi6eCpHqZtC+OSZKxo+Xi5bUvBpoxY4aOOOKI52Q361KppOOPP14nnniifvjDH+q+++6TNJZ+rNVqOuOMM7R582b95je/0X/8x3/ssL1KpaKrr746/O/KPlYICDXegltZCFecLAKC7PtGgkX4/88884x+/vOfa9OmTVqwYIGSJNGjjz6qlStX6qCDDtLixYvV09MTipgcE8FqA5DjBWO1eRfCAZ7NtV5xGVdJOkZCmOM7Zvt+IyhJL/KaqtcwLRSDx2ps8uklongBHn/FoI6UXYCFqxeDmRSHTHars+lMCxcuzBx8s6PTnnY1dXR0qKOjIygFSdp7773V1dWlhx9+OKynmAqhzN3iexmzpHFKwrMTriDiqkYvL46zB+6xDg0NqVQqharEXC6nzZs3Bw/UFYkrh+Hh4QCi+jO8n265PfzxMmqE2sNlB9vhc/eEGmEsuVwu1GZMdPLaRDQtFIOHCdL4XXvwBhBuH3i0oTMBVsG9CAjNvWDBAp199tm6/vrrn7sX3YU0Z84cbdiwYccX7kaaMWNG2J8R+ru/+zvNnj1bV1xxxZS2lZs/f77OPffcjHAgAMTMhBYIg2/L5rgTxgIecQvu7j8Ev4yOjmrGjBl65Stfqf7+fu2///5qbm7Wfvvtp3K5rP33318zZ85Umo4dXR+XS0MojJiPIa/YdcKoeT2CGz8v8vNw25Up4+anWflRdZOlZFcCSDtLvb296RlnnBFcLK9ak7IDEgt6XGLquWN2uEGzOmDEs1pbW39vN2nZ3bR06dLtlkI7feQjHwmCD8Mz5+AIUnYhEjuCuwsu1T1H2kNo3P2GvIZm8+bNWrJkiTo7O8P3MajJEua+vr6g+Ogjggye5eXIHso6IMj1XobtSsU9J/pOMaBjFl7H4WeNehXkTTfddH+apksnMx/TAnyU6tgCKR5cOD/p1zEEBsEtiu/wk8/n1d7eHgYcZcCSVqk+0S/0LeKnK002nXnZZZdlKgwbrTfAK8BL8KI2hAfBiUNMFAspxdijwJhQ8ux7N1I/4Na9kXFiPQYEfhVXNsZKwbNwfCbV8TOv1HSF6P3L5/OZDXYdX0DRvGDTlV4A4lrPd2XyGA33sFarhXSPhw4jIyMql8vhekpfSR2xwKZUKqm7u/s5WUfw+0arVq3SscceO+H3Rx11lK655pqGW6hhbVEAKAbmHI/SLTDXwQdYagyCly1zr6c6sdys9kVI6Z80Vo1YKBQy3yOInhnjrE9ceU8d4q3GWIIXcGHIqtVqwAfiakxXLPC2e9aOt02Vpo1ikBRysO4SQl5RhlvoC2JgIF8zz+DFQKWDU55jPv3005/7l36B0qmnnqpZs2bt8LoHHnhgwu/e+ta3BoH2OQPgQ9iwuHh4CLPXHEh19B0lIo0/UyIWHs8kOD94GCtlN/zhb6l+eAztQ/ChhxiOoXGv9xGvIK5jcAzN15E4CM/7NCqJjsdhMjRtFANaPt4yi9+eUXBghViLQWSyIXfbvGTVsxy4l69+9at11llnPZev/YKlHR0xB7EKshE99thjkurCh6vv1tdTz5QrNyqB9hWRfO+Vjn4dbXsbhKGOWXkxlVc0Stmt4dyjcGDQU5k8y3cRo+YAD8YNooOsbtBcYfo4OX9DfDdR1mJ7NG0Ug2s2BrtWG9u4AveNlI6vjcBVBIdgoGmDQz9aWlrCTsBMCngDg9rf369Fixbp2muvnTKK+/tGu6IG5Nvf/nYGH6pUKmF+3LL7XPq2ZxxDiND61mZxutNPbXIX271S37kZoXKsi/SrL+OnutEBUi+8wqslnYlHEnvAXpjklZB85kbQC7MIu73/kjLv/oKufHSNhlb32MjTQ62traGaC6bx3XhAq2EQqb4zbwwsxTnx4eFhbd26VZ/85Cd/b9ZV7Aw1WoPx5S9/Waeeeuqk2yiVSrr33nuDUpCUsc4YAWJocAe8xnK5rEKhkNmXwIuFPCvhmSmvWfANXykxRpD8fEkXsra2tmBwpLqX4J6Dl/J7WMBnHiZJ2V2oXMnhLfj+Ix7eICcOqHMNfWEr/anQtFAMDsz4JPik+QBK9S3GYRrfikuqx5u+azDfe3hBSpO//fPTTz9d73jHO56HEZn+xFkP0JVXXqnFixfrta997TjUfnv0+OOPq1AohG3M3Bp7LE7VoFRfEEXYiNFAiD2cwBDEFZC0754BC5BQPFzn6b9cLqeBgYFMOOvkwGdHR0dIV/IMPBcoxhs89egAKQAqSo1rPJsTF1DRbiPsYUc0LRSDlF0JR+WjV7v50lPHE6R62sfdJ5aoSvW99XC5sDz8TfuOT6BQ7rnnnudoBF5YFC/lPvDAA3XggQeqr69P7e3tk27n3nvv1bp16zLurltHZ2iPzz2Fh8VEeWAUYq/T93N0T9I9inw+H3ZDagRAEu7AXygB37PUvZy4zibexwHB9fM93Uh5xsa3hMfAucfgNSBcwzOmeqjttFEMXsnmn/nvuFw2RpCl+vJtJoR7GDiYQKoPbiPkWhpbqLJq1ard++IvQJo/f/64z3CtXQE7HX/88VqwYEHD9u65554wNx7fO6iGcLhL79mJGHh08noFQlaMgNdIeCZLqh9g1Ai4ZtGS9wnC2tP/GFB34QVH42+pvgEy63zI1nkY4f1utJiL/rgnPhWaForBl9U6KIMX4QLNBhnuHnl4wSB4yspXV8bMEJ82jJJoVDq7h8boD//wD8d9xlj7/ofQcccdpze96U1629ve1rA9cAYHD2P31110vyY2JLEgImAOSnOtZwx4nq+F4DpXWPzvWRCwiLja0JWc412Nqjs9zdoolJLq4bHLhVQPkxyD8MyHK63J0rRQDE4ASZ6vlurhBTEje+FxIpKTMxgZCwbH19Wj0X2vB4CafH5spd0eGk8rV67M/J/L5VQul8cdEShJF1xwgY4//ng9++yzmjt3bsP2SqVSpjKPkJEdnqF4VyKu9ZjbBd/rXHy3cIyPV1KCLZA5QNgbeS5x5SSAJPdI2fQ4YLqTh7OUONM/qR5CuKfkoZEbLXCF5uZmFYtFFQqFUCnsFcNToWmjGDxM4H8PIYjt3GVi3bu3IdVjVMckvObeF15RmUb5KbtUV6tV/e///u9zPAovDIr3zazVaiGVx65CknTxxReru7s7CFp/f/+EYO6TTz4ZFDMMDWaANSSW99oGSRkEn+8oq+c6rsXaI+AOBnpGA+/B73MPFM+F9kkv+s7PUj2+d772Oockye4l6V6DKxMPffjx0JtQvFqthrM7Gfc4XJ4MTRvFINUHH9RZqlsJmMRBSqwFg+Q56ziLEVsZrIGXp0r11E6hUNDPfvaz5/DtXzgUr5r0/D+C+bKXvUzt7e3hsGKU7qGHHtqwzc9//vPhaPd4paALoW/kwvkTEIKBl+DrKjA0LkwYDAcGaYfzI4eGhjJnSeC1AjA2NTWps7Mz9NEteaMVlG796QOegxdgQSgdrvW2eSb1GT5OHvYwhlOhaaMYGLBKpRJOJHKgxevNmUTQXFcY7hngKjKw4Akek5XL5QxI5MUjvy/nWv7xH/+xPve5z+34Qknvfve7x31G8ZGny/L5fLBaFPowVxNVl951112ZhUYILe49VhYqFApByXtmCqGgktJPenLX3K0w+z2CR5EeRKnEhU8I4ejoqPr7+zM1F7w/5Morl8tpr7320j777KN99tlH++47djbT6OjY+RVeUwE/usJkTQTvwZjyf7wRjSu7qdC0UAxeDupuqaPAMaoca2epvp7CU47uXfAs/sdlczR4qvneFwMdccQRGhgY0LnnnpsJBRpRo1QkexTUarVwmK0X5zjYW61WtWjRooZt//jHP86AxG4hmS+E2kNKTz962IBy8c1e4R+Uj/OLf16r1TIKKUb74yyIZyHwANwLQSm1tLSot7dXe++9t+bMmaO5c+eqp6cnFO55KjbOXrjQxxk7By8bYTBTpWmhGKRs0YoLpwurb/oKOWiD4oiLXOJY0dM+kjJMBW3ZsmU3vOX0o9bWVrW1talarWr+/Pm64IILtnv9L37xi3GfsddgU1OTZsyYofnz56tYLGbicoQNy33aaac1bH/NmjWZzANbo9EGguoCgJC60oi9An47noDwxZkMBxsRKhf2GGCU6iXi8XtCuVxOvb29mj9/vtrb28PK3lwup/nz5+uAAw7Q/PnztWDBgnCYjIc89D/esj4Oi3g+70qY94ItcIJwAT31wkSwKYdrTS9waW1tDa4s6yJAZT1L4dWVAFpJkoRS1xkzZvzeHHp78cUXh8KZwcFBJUmiT3ziE7r88svHXVssFnX//feP+5zQDUFgzQrCQXaHOR0cHNRRRx3VsD9f/epXw7mUW7ZsUaVSUVdXl2bOnKnOzk51dHSotbU1HEEoNd4PEdfeBQYBQ0gIT4eGhgIIjcV348FO0oODg0EwefbQ0FAADwl/HRz18zLmzZun+fPnq62tTQ888IBuv/12bdy4UXvttZcWLFiguXPnau+991ZPT09GoOmLl1KDr7g3UC6Xw/s5X8fex2RoWmztJtVDAzQdE+tKgMHyNe5uUbjXB4L41tFersEySPV1EsSaEx3a+mKjpqam4DJjkSqVitrb23XttddmdtR+//vf37ANxjo+DYzPZs6cGebS59HDDafR0dFwmM2sWbMy5zwilP39/RoYGNDmzZuDUvIMg6+cxdoSWrpBSdOxE6zJVuHuxxkAcAfIa2g85ncBBBsD63riiSdUq9XU2dmpYrGoefPm6dlnnw3eKZ6Ap9ZRdr4Ck+wPXgrGDqzBPRr64ZvFToamjWJoVIuABkZjeh7ZQwPux9vg/qGhoZBhYNC434tKyBkTh+5M3veFTNXq2OngXsLLgiIn39/xs5/9rD760Y9qcHAwuNcuOC74gHL8j0V+xSte0bA/v/zlL/XOd75TAwMDGQFkhaM0pjDK5bKefPJJbdy4UeVyOTzDlb1jUdSzAHCSmuQ7BA7eob8xsMmaB+7jfd3Y+OdY7RUrVqharaq7uzv0c+PGjRocHFSapgG/6e7uzpQx06Zn66T61nauEHxPCmTjBR1KwFxocy9O8cNqvYgG7Yo34Ntoe1mux2hx7Cdlj/bq6ura4QnNLxZaunRp2PaLOg53o++6667M9b5upKOjI1gsMAafHwfCvNAMQa1UKjrttNMaptFuvPFGrVq1KngW1ECUy2Vt2bIlLJoqFotauHChlixZov23bdzKXONGO4AJv/DOjbY727x5c+A9T2FjPDxdSd8cPEcIUYYsDmtra1NnZ6fa29tDbQ398bUdDrw3KmZi7OJDaqSsF0NfGtVETIamhWLw8EHK1oxjwRFeX1XXCDTkO89eSMooEx8sjyWLxaLWrVunO++887l58eeZXvnKV44D0yQFho7rOPx8CI9tvRqwUWFOTMzB+vXrJ9wF6pZbbgmxe5LUN0PxzALufqFQ0OzZs7X33nsrl8tp06ZNam5uVnd3t2bPnq2uri51dXWptbVVXV1dKhaL4cczKC6kbqj4cUDSLTLGKC5lJjyjrw6uw9N+8DKpSPeMuTdOy7qsuEGlzoP/ueYFma509NqLMXwJLu6RldUoigAAIABJREFU1yR4XOeCj7bmdB++oz0Gk2fTVqFQ0Be/+MXn9N2fL1qyZIkOOuggSdmtxRiLNE3V398/4b3UKEjZMl7m0dOBkjIx7saNG3XXXXfp+9//fsMFWdLYSVd33HGH+vr6tHnzZvX396uvr08bN27U+vXrtWHDBm3YsEFPP/20tm7dqmq1qvb2dh100EGaN29eAJK9YK1YLGZWIjpYTTbL62ec4rQmXgSVmFh/eMuVo/MnvI0Au8KL8QEHH70+hJRynHVzbIRrUSRTLXCaNhgDax/iteYMquehvSjF4z8GEkQWQInvvV28Ea8Mu/XWW7Vx48bneyieEzriiCNCXA658FLtB/lWbi9/+csz+xXEqUL3PIaHhwMjs0HvT3/6U61du1a5XE6zZ89WZ2dnQyV0yy23hK3cW1pa1N3dHZQNR8Qz3whJU1OT9ttvP91///3avHlzxr32VF61WlVbW5t6enoyG6y44fBVjpKCoZHGCztFVLThfAbflcvl4A04fkYWjjbgb2SCsXSPJN6tCUyOax2o9HBqsjQtFAPlsjFC7ZM0MjISCkDwHFgXT2UYigLwSKq7Ws4YUt3No1a9ublZP/3pT5/zd38+qLm5WVdeeaVKpVImd18oFAID/uY3v8nc4xZnv/32y+y9SRwtjeENHR0dkhTG1tcg1Go1DQwMSBorlurs7NQpp/x/9t48Ou6zOh9/ZtHMaPZNM9pGu7XEW2zHwW7sLMZZnGaBFJJSlpDQkgOF/IADBc4ppT3nV6AFCu2BNEBDyDc5TSAtv35ZEtqEBBLiLHZix3ZiWfs60mj2fTSSZn5/yM/VO1ocGxKQk95zciLJs30+8773vfe5z33ugVU/58DAAO69914BkdnmzNORG5EbiINV9Ho9GhoahE0ILKWSXBfLT3+VSUu8hVHA8o3Iw4YOQHWGKmmOGAY3tMFgEAfI6IwRLZsBVX4GPw9BRr6WytPhXlDXOL8Lfg4VhzhbWxeOYWFhAblcTog2VMihx+XiUlOBUqkkU4XVWX28UcuJUgCkZKZ+oWp9+FwmJ53Pdt1114k0mkoa4/0xGAwrAFhuMAAVub9a+i0WiwKKqSkdXz8ajeLEiROyyM1mM2ZnZ89IJnvllVcqcn9gKZzW6RZnh3AOKQlWOp0OW7duxaZNm5DNZpHP55HJZJDNZuVE5jpQxU+ApU5HUuVXA+7UNJQKTerpvbCwJPmubuhSqSRrnI+bnZ2twDX4GoxIyLNQD0R1D/Ba+He1a1MtB5+XTVTFYhFjY2OYmJjAxMQEgsEgJicnEQwGMTMzg2QyiWw2i4mJCeRyOeRyOflC1VotbyZ/BlZSofkzFzPzz0cfffT3f+F/ILvwwguRyWQEB8jlcshms3KvWBJUjVEACT7LiWaqM1a7F9VQdmJiAocOHZJTOxaLIRgMore3F5s3b171s/73f/838vm8iL6qZLdMJoN8Pi8nrsViqeDCmEwmeDwe+P1+1NTUwGKxYHp6GuFwGNFoFOFwuEIfcmFhaeYIUKmSpIJ/vObV+hB4D1SBFz6eFQ3VVM6N+lw1FaAxilE5Gmq5Xr3fKmN4Ob/ibOw1IwaNRmMC8BQA4+nH/0e5XP6iRqNpBfAQAA+AFwG8v1wuFzUajRHA/wGwA0AUwC3lcnnkTO8xPz+PcDgs5cblgCFryolEAoVCQUAfh8NREV6qptZzgUqRTBVZ5s174oknzvqmne9mMBgQj8eh1WqlfFYsFuF0OlFdXb0CqHK5XPD5fAAW50ks5yyo91g1tZavCpbSamtrYbfbYTAYUFdXt+pnPX78OL7+9a+Lw+GJSzaluqmZuxuNxhWnJjkYY2NjmJqaEuzD4/FU9IcwFVFTFYbzvG4VLFdt+XpTf6ejyOVyMJvNFaV4rkH1/qgldOIHqqCQ0WiUcuzyKsVyKvpvw3w8m4hhFsC+crm8FcCFAK7RaDS7APwDgG+Uy+UOAHEAHzr9+A8BiJ/++zdOP+6MxhuUz+eF7AFAAMR4PI50Og2DwYCZmRmkUilkMpkK3UaVnLQcbFEBHPVm6nQ6uN1u/N3f/d1Z3IY3h330ox9FIpGA1WpFU1MTAoEAurq6sG3bNnR0dKCrqwtPPfVUxXPUkuLFF19cccLR1K5Xfp8ajQbj4+Mol8uw2WwCJBqNRjQ3N6OrqwsbNmxAS0sLPB4P/uIv/mLVz/zLX/4SXq8XTqcT4+PjeOKJJ/Dqq68iGo0il8sJf4I0YZ7UbJlmtGE0GrFz50709PTIibt8M9L5GAwGKWfOz88L3RhYch7L0wxGHNyMahpFoJuCNgsLCwLwqpiYWi1RUwZ11AGdowqsqzLz/FmtapzriLrXjBjKi7uUCWbV6f/KAPYB+LPTf78PwN8C+FcAN57+GQD+A8C3NBqNpnyGJKdcLiMej8NgMFQw2NSUgCw4Ao7MMYGl9lKWpvgF84skhsAogmU1rVaL6urqt0wlAljUa4xEIiiXy8hkMhV579zcHLxeLx544IGK54yMjFREcKRQ85RiPq2mF+VyGWNjYwiFQuIU1E3E8qHRaBTiktPpXPUzP/LII3jggQcwOjqKn/3sZwgGg9iyZYsAyCr6X1VVhXQ6vQJPosKSRqNBY2MjAGBwcLBC30EFCQFUpKrEuIAlFqbKTgQqo9RSabFLWK1EVFVVwWKxiPPhITU3NydRBHGH5eLEpFazJEnwXU3b2CKg8oCIS5yrfPxZgY8ajUaHxXShA8C3AQwCSJTLZSZMEwAaTv/cAGAcAMrl8rxGo0liMd2ILHvNDwP4MLAUDhJ9JqOOaDZvHjd8LpdDIpFANptFIBBAfX095ufnBYBSsQZ+2SoVl4vXbDbj0KFDuO22287ppp2vtmHDBtx6660IBoPiFBhpkffR0NCAD3zgAxXPUxWGePLabLYKSTGeciTyMHogd0DNrRne88QkySeTyeCWW27BD3/4wxWf/eDBg2hqasKuXbtQVVWFQCCAbDYrfAKWElOplGg/ABDnD0A2lMViQSAQQDQalUNGxS4YvfI5wNLhw3SFkRHLpoxS1PC/WCzCbDZX4Biq6IrabMV7yAhHjWoLhYJ8Rh6a6r1nlMTHqNiIyrA8Fzsrx1AulxcAXKjRaJwA/j8A3ef0Lqu/5ncBfBcATCZTmRdFL8hSE9lgxWIRDodDboTZbIbf76+YDkRzu90AIOGk0WiE2+0Wj2swGBAMBjE7O4vvfve7uOuuu37Xyzkv7HOf+xy0Wi1qa2tl8ajEmerqagEZaR0dHbjoosXJ6bfffjsOHDiA+fl52Gw2OByOiseqWILFYoHVakUqlYLFYoHFYkEsFpPH5nI52QTcvEajES0tLat+9rvuugsPPvggqqqqkM/nRTVJ3TwAKhqLmEqEQiH4/X6USiXE43HY7XZYLBZ0dHQgn89LCquWJ00mU8Vpr+bxVHZiisF/5ybkIcaKAjEKPk5NEWZnZ5HJZOB2u0XsJhKJQKvVSgpntVrFCapsXTo1GqNhu90uBDS11Hkudk7lynK5nNBoNE8C2A3AqdFo9KejhkYAk6cfNgkgAGBCo9HoATiwCEKe6XXlZy6uXC4nvAV2PPKmEnjhgkgmkxXKuR0dHbBardDpdDh06BBMJhMaGxuh1+thsVig1+vhcrnws5/97Jypouer3XzzzThw4EBFB6QahgOLJ9dyufxAICDOYsuWLQCWiFAqQ5Wvw43FU7y+vh5Wq7ViYpTJZILFYqlg9QWDQRiNRrhcrjWvYWRkRNYE+yVUijEAmVRNUI8hO9NQzoxgSdHlcslzyX1gtMC1wWtjaZJmtVqRz+crWpsZSRQKBWQyGSEvzc/Pw+FwVLRCm0wmmEwmOBwOESPmfyaTCU6nU16XeMdyERw69uWUf7PZXOFAztXOpipRA2DutFOoBnAlFgHFJwG8C4uViVsB/N/TT/nJ6d+fPf3vT5wJXwCWGHIqeppMJmWRqFRdhmPk2ut0OiSTSVitVvh8PrnBzLvy+TxGRkYwODgIj8cDs9kMt9uNrq4uHDt27Jxv2Plq73znO1dw/JeXFx0OBw4fPlzxvEKhgFQqBWCR2MTvwW63y2m1PFUAFiMQl8uFxsZGmM1mWK1W7NixA+9617tw4MABGSXPMHtwcBBPPfUU5ufnceWVV+Kxxx5bcQ0HDx7EBz7wAYRCoYr3Y2MccaflOgQkQFVVVcFut0tPAVNLflbiJ4w62JpNYyrLE9hisQi/gxGYillw3aoVDpZU+XlUpi5xAzqt6elp+P1+iTCY5nA7kQbOPcFUmjqb3AP87OdiZxMx1AG47zTOoAXwo3K5/DONRvMqgIc0Gs3/C+AIgHtOP/4eAPdrNJoBADEAf/pab1AqlTA+Pg6tVguXy4VMJiPEj/r6erS0tAiXgSkBsOg8zGYzHA4H5ufnkUqlhNocDocxODgoCkXJZFJSEWARaV9NdOTNah6PB8ViURaSSi3nfx6PBydOnKh4Xm9vL+LxOIDFsiUXmMlkqhDsZaRAujJB4Fwuh9raWmE5trS0yEaJx+MVBKDe3l7U1NRg165dcDqdKzQ3f/rTn+LOO+9EXV2dkNH4vjyVGbWonAcAEoECS0Nji8WiRBAkZEWjUdF60Ov18Hg8kn7SIdIZqIAkqwZMbQFU9Ovwb4xg+Po87Mi8pGMZGxvD6Oio3GeyVJkO83tg2ZUkL5PJVPFYft7XncdQLpePAdi2yt+HAFy8yt8LAN59Lh/CaDTC4/HAbrejtrYWg4ODABbr3Lfccgv27t2LRx99FH19fejs7ASwGMYVCgUMDAxg48aNgva++uqrePrppzEwMCCvz5x3YmJCwtAzjWd/M5rK2VCrNETEaePj4xXPU8lOyWQSCwsLsNlsFdoAdNTqBjAajdKkpVaQCHTqdDqcPHkS8XgcGo1GytAjIyOIRCJrnnD33nsv3v3ud2N6eho2m03wJ0YQOp1OvlsCeurcCp646XQa0WgUNTU1sFqtEmkGg0GMjIwgkUjA4/EgnU4LzZu6HmqYzuvRaDRwOBzyu1odU8FP3gcyNFXVJ+IKhUIBXq8XbW1tyGazGB8fF7k8RkSFQgHZbBbhcFj4GBaLRd6XuIbT6UQ6ncbc3Bxeeumls14v64ISbTKZ8LGPfQz9/f04dOiQnGhdXV1wuVyIRCIYGRnBrl27sHPnToyNjUlNeGRkBOPj46iqqoLP50OpVILH40FPTw/6+/srlJ/fqtbc3Iz7779/RdkOgBCctFot4vG4OGWaymHQ6XSYmZlBdXU1crlcBTGHWAEjEYvFIurRNKYs3MTc2HyO0+mExWJBNptdE4RMp9P4whe+gO7ubtlYjDqsVmtF9MAQPZPJVDTalUoloUl7vd4KUI+8Gb1ej66uLjQ3N8NsNktEwNSEr817SpGV5WrlAMSh8DOp1QKWcZd3X5IMVSqV0NDQgOnpaczMzCAajQoOkk6nUV1dDbfbjcOHD6OnpwdtbW3o7e1FIpFATU0NjEYjRkZGMDQ0dE5rZl04hmw2iyNHjoj3q62txezsLBwOB/r7+9Hb24tsNovR0VE899xzQt/lSUHAzGw2ixTY7t27cf3116O5uRmjo6N/4Cv8w9ro6Cj+6q/+qoLJR6CMm8hoNKK/v39Fv4iay3/7299GMpkUFuHRo0cBYEVuXSqVcPToUQwNDcHpdIruptvtRnt7OywWC+x2OwKBgERwVqtVyEo+nw8ulwtdXV0rhucCwL/927/hk5/8JH75y18ilUph48aNqK2txcMPP4xEIoHW1lZ0dXXhueeeQ39/P/bs2YPOzk4cOnQIzz77LN72trfhyiuvBLBESiI1u7+/Hy6XCzt37qxo37bZbBIZqCphTE+ZHqn/rrISiRMwlVWrGWpVg7jH7Oys9J5wT5jNZlgsFvT19SGbzSKXyyEYDMr30NPTA4/HU4HPbN68GSdPnqyQSTwbWxeOoVgs4vHHHxfwhaKkOp1OvH0oFMKJEydEhIO8c/IWqqurK4Rh5+bm4PP53lLkpTPZE088IVO2VN4/S1wmk2nFhCkAFSdNPB5HLpeD3W6vWGRc1CTYMC82mUwol8sYHh5GMBhENpuFzWaDx+PBRRddhObmZgwMDODkyZNoaWlBIBDAyMgIent74XK51uyfmJqawqc+9Sk88sgjAACfz4errroKzz77rDzm6quvrvj9jjvuwLFjx6R8ffnllwuhymAwIBqNolAooLa2Fm1tbZJesIzKkfZms1nyd/5dZXsuJ92p/Q6k77PyRmfB6hB/puOmshbnrPBADAQCsNvtCIVC0goPLKYqxN3o4OkUztXWhWPYvn37CjT8TIUMNTV4q5QbfxfbvXs33vOe90h+rToE5uEGg2FFdKVWG/bt24dPfOITmJqaQltbG+bm5tDd3Y3e3l5x4kxJDAYDOjs7sWXLFmg0GkxPTyMWi2FmZgYjIyPIZrOYmZnBxo0bodfrkUgksLCwALfbDYPBgEgkIqnhWqZyKGZmZlaQ1Janj06nE5dddhmApb4Zn88n+Egul0MoFMLCwgLi8TiqqqpE7t3r9Yp2Ax9Px0cjTqCS65b346gbX5VwU5utKHScz+dFpGZqagrRaFR4PTMzMxWt1Xz/QCCA/v5+6PV6eL3eiiarc7V14Rj+195Y27RpE/r6+uTUYZuzurgWFhZWpFwf+chH5OeDBw/ipZdeqiACqercKjGoWCziZz/7GR5++GG0tLTI6djZ2Yk//uM/llRkamoKdrsd+/fvl9e68MIL0dXVhWAwCJ1Oh9bW1hXcCmBxQrbJZBJi01oScTSTyYR9+/YBWMIidDodjh8/jn//93/HzMzMGZ+v1+uxc+dOdHd3Y9euXQIAsrOTBLHlkZTKbWDKweiD2ArTYgCYnp7G0NAQRkdHcerUKeFKkFGaTqdXTBMHFtOacDiMbDYr5eXfxf7XMbwF7Hvf+95v9Tx19sNqqtE0jWZJ+h9YBNsymcwKVabW1lb09vYil8uhqqpKaNUkO42Pj8sGSSQScLlcK5iYtEceeUQk5vmeqi1n+xWLRenITKfTSCaTeO655/C9730P3/zmN1/rVlREpnv27MHFF1+M5uZmeV+1IsOOYLWsSGYvtRPoSOlIIpEIBgcH8dJLLyEWi1XQnplS0DmsZixVnqvuwlr2v47hf21NO1t+Pck6ROlVqTTVSqUSBgYGhBexefNmmQtBrsPo6CgymQzsdjt8Ph9SqRSuueaa11TXWi5Fx1Kh2utAZ1EqlXDy5MkKcdtzsd/85jcAFuni7373u1FbWytphUqkWr65CTjSMTCaOHz4MH7+858jHo/D6XTK/WNlg3qUy7s5l9vs7OyqWhq/ja0Lx3DkyBF4vV7heYfDYTmBeDOy2Sy8Xi+KxWJFCypJT7yBDNsIzpBpVltb+5YZIvN62Y9//GPcdttt0Gg0uPnmm1dtbrryyitRLpcxOTkJv9+Pp59+GpOTkzAajXjooYcQDofx/e9/HwMDAzAajeIUgMVTnK3TwGJViZUmjUaDcDiMTCYjw1mmpqYq3lsFlpdHCCQQ0YaGhiocw+thKlfmiiuuwIEDB+BwOCoapUqlxUE4FJRh2jYwMICjR4/imWeeqUgNmI6owKWq9nwme72iBWCdOIaqqips2LChglPPkg374sPhMLxeLzKZjPDhGRqSzqry3L1eb0UdWyXxvFVs8+bNwtLj/Xj++efhdrvhcrnEARPwU23fvn3iSLu7u1cFAjdu3Igrr7wSL774Il588UU8//zzsNlsstnb29uxZ88e/PjHP15V7DUUClWUR9Wfy+Uy9u/fj1KphHQ6jRtvvPGM17pcOCWVSlWE/+rEpzfCnnzySezcuRP79+9Ha2trxQg9HmRkggaDQTz44IMrHB2AChBSZW8yUlhLufv1tnXhGDZt2oTR0VG4XC5UV1cjFArBbDYjlUpBp1vU9QuFQkilUhU9E5lMRm4um6b45btcLslRXS7XqoDNm93e//73y4a32+3IZrPo7OzE9PQ0FhYWsGnTJrS0tKzauPS3f/u3uPTSSwEs5tS89yrab7fb4fV6KwbT8NR3Op04cuQITpw4gauvvroirAcgTMEz2dTUFOLxOLq7u7Fx48YzPnY5kzUajVa8H4lbainv9bZDhw4BgAwHZgWCGgsTExN4+umncfjwYXz5y19e9TVY2bDb7aiqqsLMzAzy+byUVF/PqOBMti4cw6uvvor9+/dLOyzbfJkKVFdXV8z3s9lsqK6uFiUn9k+QFLKwsDi5OZPJwOl0wuFwvGUUoGk7duxAU1MTAAjXnyxQh8OBTCaDEydOCCdkufEE1mg0uOOOO5BOp7F161YAS6fZ3r17EQwGK57HTbd582Z0d3fjX/7lX/Cud70L3/nOd9DR0QGj0Yiuri584AMfgFarFSYro5fVXisWi6Gvr++M17v8GpaDpeFwWDgUb7QFg0HU19fjlltugcfjQblcRigUwkMPPbRqlKAaRVXC4XCFAhPw+qYKr2XrwjEUi0UMDw/D6XTC7/fD5/MhGo1iYGBAiCbpdFqES9VQeH5+HtlsFoVCQcJFcs1JuDEajSt4Em92e9vb3oZXXnkFBoNBxq6zrEjOvclkkgrCcjt69Cj27NmDcrmM66+/HtPT05icXOysn5+fh8/nw2c/+9lVcQdgEQz80pe+hGQyic9+9rO4++678fGPfxx333035ubmEI/HMT4+jnQ6DZvNBqfTuQJHGBkZAQBRd16ti5O2HK1XKxbAYkTZ2NhYQXp6I40O84orrkBTUxNefvnl13QKwFKz13IW5e+br7MuHAPRWa1Wi0QisSLUY5qg0+kQj8dFU4HSWRxEWlVVBbPZjEgkIojwhg0bVmX0vZlt7969+PKXv1yhkK1Kh9lsNuEi6PV6/OM//mPF86+99lo8++yz+NjHPoZrr70WQ0ND6O3trXjMnXfeib6+PuE+XHvttRgeHsauXbtw7NixCmCO9sILL+Caa67BN77xjVU/t9frxc6dO3HBBRegr69PTkhOdt60adOa17y8wrB161ZcdtlliMVi6OjowHvf+14YDAbs2bMHzz333GtutLXo2OdqTz75pKQvzc3NMJlM6O/vF9HW5VEAKxKNjY2IxWLi0Jd3mr7Rti4cg16vR11dHdxud4Vwq9VqlVmDpKNS/g2A9K6bzWZotVo4HA4RDaUCssViWdEx+Ga3T3/609i0aZOg4uwCVBt1VGHc5QrZH/zgB4Vu/P73v3/VIb/t7e0V0UKhUEBDQwNqa2vXPBm/+MUv4qmnnsIXv/hF/OIXv1hRJYrFYggEAojFYpifn0d9fT2SySQ6OzthNBpRKBTWJDypf6urq0NjYyM8Hg/cbjeSySQOHjwIYBF7WK3kp94fp9OJzZs3Y/fu3Ziensb4+HiFEMpa1QGyHzl2jw1PTU1N0Gg02Lp1K+rr61EoFKT3wul0olAoSP8Iey6MRqPM3TgTh+SNsnXhGIDFRUEF3c7OTlgsFni9Xmg0GkSjUfT19UmnJbvdisWipBaUH9fpdBUViXA4/Ae5sX9I6+7uFvqzWqKj5iEdAmXSljMe1ZPZZrOt2p0aDAYlEtNqtbjyyivR09ODhoaGFZoOqv3lX/4lvvvd76K9vR0dHR2Ix+PC92fEyNPRaDSKYw+FQtBqtejuXltV8MILLxTptunpaUxMTCCZTEoEarFYkMvlKqY4sTRKR8KeiUgkgrq6OjidTolADQYD3G53BQjL+0MlJmJdlBJgidJmsyGZTFYcagCk65KsVOpVjo+PizP8Q6xfze8T0FjLzGZzefv27QiHw6ivr8fb3/521NTUiCrNiy++iEOHDongqzrfLx6PY/v27diyZQvy+TxyuZx09BkMhrNitb3ZjD393ATAohAsF+DExATq6upgMBgwNja2Ipx94IEH8L73vQ/Aotz8o48+WnEi//Vf/zUOHjwokcaePXtw00034eKLL8bQ0BAeeughiThUoyRfoVDAPffcg+HhYdx9992i88CTePfu3VJCZVWF06m12sVp1stBTwDYtm2bOL9cLiddo7lcDplMRvpClisrUb1q+YAXtSmPzU8ARNxFBQaZmi0fugxAekj4fAofk+ikSuTNz8/LwBlVCl6VqlPl8Pl8OiA6v+Wpkslkwq9+9asXy+XyRWdaO7R1ETHMz8+LVy4UCnjppZdEdCKRSECr1eKyyy7DyMiIqN8UCgWR/25ubkYgEMDCwgKGh4fR0tKCTCbzurHAzjczm80yGp6VgJ07d8rCvOKKK7B7927YbDY8/PDDK55/3333iWMwGo3YvXs3br75ZvzoRz9Cd3c3vva1r0lpDoBIiKkag42NjYINGQwG6TWw2+1yMj/33HOwWCz4r//6L2i1WuzZswcLCwuoq6sTzMnv94uGJLC44VgdWW59fX247rrr5PAAlkbGqXwAsiApxa5SkwHINbBlWp3ToI4loBNgekFZeL6P+tiFhYWKobhWq1UwBrZbq0xTtUWbsnWqViSvi++t8h5UZWz+++s+V+L3YXNzc4jFYtiyZQv0er2Etq2trchms9Juarfb4XA4xDkUi0UEAgF4PB7BFCg+Crx+DLfzyXbv3o29e/cK9sJBLCxLmkwmtLa2ikjIcqB3OVdhfn4eTqdTyoU33XTTijydm0CVine5XKivrxcKMKOSQqGAzs5OOJ1O9PT04Ktf/aq03fMxPDGXT3xSlaBWs2w2i9tvv11IbUylyJLlZuNmXw38Y3SgirOSN8M+B/6d1061Z4q08HpVgWLKA9DYpg4sOl/K4RUKhYpr52wJ/o2OhJULOgPeZzpiGq/5DZGPf6PNbDZj69at8Hq9mJ6elhBqfHxcwDOWJqempqSKoeroDw4OihZAOByG0Wh8TQLNm9E6OjpkYIk6FUmdgLS8FKaaehp3dXVh69atKBQKePzxx7Fz50585StfwVe+8pUKUpQqNsKDPZmZAAAgAElEQVSTbbkOAWm+DHcvv/xy/OIXv8CBAwfwzW9+E/v378fBgwdFBZrfr9PplB4DbqZcLodbb70V991334rP//3vfx+33XabODhGK5Rr52uobdPcaABkcjqvi23QfK6qSA1UDvbN5/MVE9mJ5ajYDp+rRipkSaqRF7+bQqEgDkYFjFUnwwlfxEtUTQfeh+XM0NeydeEYqqqqRLCVvAOGXgDki1Tbe4HFL8Vut8NoNFbcULa2Tk9P/yEv6w9iHMQDLAmlqmG4Or1I7VugqROLWltbYbFYpPZ/4403YmRkRE4ql8uFeDyOgwcPwu1248ILL8QTTzyB/v5+aQZSJ38BS1FcsVjEVVddhccffxyf/exn8T//8z8olUo4dOiQfF7VmahVAYKSPp9v1Xbp6upqpNPpCjVlVfGZEYP6uahSripRqaKyqkQdsHLkgar8vPx1gCVKs5qOcJPTcamblxubbdn8rKpcPO/L7OxsRX8FR+jxADhTJWUtWxeOgTcIgGxydRAKH8OL55fMG8neeFVGS6fTrQqAvdmN165ucJ62PD2rq6ths9mEQKSaClq1tbUBWBKI3bFjB+6//360tLSgqqoKO3bsALBIyrn66qulp4VlOObU/E4AVCx0g8GAvXv34rHHHsNXv/pVfO5zn0OhUMDRo0dluK7RaJRUkpOt6Diuu+66Ve/Bo48+iksvvVRG1fEeWCwWRCKRitwcWHQ2sVisoiuUG5z/vjwl4QZUAUZerzraXhVwYcTB91UjKr4P1zijHd4zHorLu0ZZsufBqEYHjA753HOxdeEY1CggGo2iVCqhpqYGwFIHHhe6xWKRvodcLifdbPPz8yIAksvlzlgyezNbTU2NEJhUwIoS5GQ9ejwe/OQnP1nxfLXZzGazyaSunp4efPKTn0Q6nZYyMkNYu92Oj3zkI2hpacH27dtFnJcCLfx+uKB5YhcKBTgcDrz3ve/Ff/7nf+LBBx8EsLgRTpw4AavVKrk7N0IymUQ6na4ABZfb8PAwbr/9dnktrq3p6WmJDNQIk86LpzdlA/P5fMWmVqMeCrOo4KF6Kqt4AZ01p6oBi8xQYjJ8z1KpJErTBEM5I4LP4zWT3EfhHB4GLEHTKZXLZWSz2TV1HNaydeEYqN+/sLAgDoEiF8zbOMpMr9fDarXKdB4AAiTxi7PZbL91r/35bBqNRlSXmK9yTBrl9QnslUolhEKhiud3d3cLxuDxeHDbbbdJunH99dfj8ccfF8Xjubk5oVJz06onWzabRTqdlvCa78tTk5vJbDYjk8lg165dABZLn4899hjy+Tx6e3ulPMeNxTCdf1vLnn32WXR0dAg/gLm2Xq9fMWyG08w0Go3oQKoDbRmlkmXLe83TmJOn2I8CLPEUcrmcbEqu5+UOJpPJYGFhcXhMMpmsmLVBARa+rqorSc4D9wKV06m5SSehpuVna+vCMSwsLAge4Pf7pX99+XBbjhgjCg5AwCh6TYPBsObU5De7XXLJJZiZmZGFRdDOYDBgenpaBqyyNXo5cammpkaqFFu2bJG2dqPRiLvuugtPP/00rFar3GsSkdQTTT09mf5RaownsjpceHZ2VhzFjh07kE6n8fnPfx5/9md/hm9/+9vo7++vGDqr8gdKpRIaGxsxMTGx4l6cPHkS1157LZLJpKQhnJhOjUvOdyQwW1VVJakGKws8fLjpk8mklFLVuRLqgF7eM0YNxHy0Wq04EJpOp6vgLwBLIjN2u11mYDAKnJ+fXzEnQ8VfqCytpuIERs/F1oVj0Gq10vSyHCzjYlYn7KigFAefsJQELMmav5Xs+uuvRyAQkBOHNXWejmpYbrFY8Mwzz6x4DbPZjJMnTwJYrG5QEu4zn/kMisUi7Ha76BOSAwAspYLq3AaCYgaDQTQEmE7w9OZ3zQWfyWSwbds2HDlyBDt27MDf/d3f4R/+4R8wNjYGoFJlmanKWvMnAOD48ePYuHEjotGoRCo8OXO5nKwjNaJKp9MVDVvZbLYCR2C1S6PRIBaLyenPDc8htsQruCFVjcxYLCbXos6/AJbwIF4vKxoqtkY9kmKxKN8xCU9MJ1T+BqXmzsXWhWMAliYEsSLBL4Q3hiUYMshUlhlDLrYUq/oAbxVzOp0yikydgqwCaAAkF13tlK2urpbmJ3ZS+v1+fP3rX8epU6cqgMTq6moZsqKy/6xWawUwTNCOpv5MyX9+r2z08vv9CIVCuOKKK/D5z38e3/rWtzA+Pi4pJEN4ppAdHR2rNm2Nj4/jmmuuWdGVydAfWNJiZFhOp0DmKA8dRlLFYlGinEwmU4FD2O12WYcAJI1TCVUEI1kupvYlP586qIZVBprKkGTEQ73MeDwu1RNGSUy71PTjbG3dOAaCLLxg3hzeaH6BqjKxquPHi18LkHozWyAQwOWXX16BcrMsyU3Bk5pg4GqlSpWbwIrODTfcgFKpJAQngnDqhueAWmBpEhMjFUZ4/FzV1dUSUvP7U0t15XIZgUBAnMO9996LD3/4w/jXf/1XpFIp2SjcQLlcTnCp1ezll1+G3+9HPp9HsVhEJBKpiDpSqVTFdGuuQTWSWFhYQCqVWsEipFPjuiO4qPI6+Fm5gYGlqIBgLEuixG9IeGL1A4D8XC6XkclkRCGbf+f9ZOTFtEnlVJyLrQvHwC+KjEaGRiz/MIdjpKCy0Ii6Op1OlMtlCYXfSrZ582ZMTEzIOHoySVna47BfnnbLx9DRiOmoKkc33HADDh48iLGxMVgsFolEOBiIxrSCHAeVJKQyBfk9kp0IQABG9X03bNiAyy67DL/+9a/xzne+E3fffTfe9a53yXMYqlM4dsOGDejv719xTVNTU9ixYwdKpZIMh+HJS1Uqj8dTMdK+qqpKaPeMHth/wgOKJdSqqqqKQcxGo1EwCAAiEbCc98AohU6EFRy1N6JUKgl/gk5JlXjjqEBgibHKfgpGI3T2pVIJr7766msvptO2LhwDsKRcA0C8KLBUl8/n8+Ihl9NZKfhaKpXekv0R8XhcxE+JSJM0k0gkUCgUEA6H5aRaqw2dZCEutsbGRvzN3/wNXn75ZXlNbmqCXMCSM+Dpyd9ZWYrFYhIVsJUYWFKhpgjw8ihGr9fjoosuwuHDh/HRj34Ud9xxB77zne+IahdDZk6QWsvoRNTqAJWcgcUTnWI/LHtzvalRqQqe8nV5/cASR4ONW5QaJH9EZTvSaXMzs8mL91Sv18vn4Huo4+tYTSGOQ2yBHZrUymQKs1ws97Vs3TgGhrgsSQFL+RyjB3p13kCmHWqY5XA4JAx9qxi1MonREIdhGYunGMteq6llb9y4Ea+88krF3+68886KsYDUHuSGIWLPsBuonHzNcJ36GATcWKXgY5nHOxwOWCwWCe+5mOkc7rzzTnz5y1/Gj3/8Y5w6dUqGyxKkttlsq4ql5vN5eL1eOeXZB8F7wkjBYrFI2uVwOGQwDT8jT3g6obm5OSQSCdnk3LR6vR5+v1/6TJZ3WapVNvV3Rs7c3DqdDna7XYBFu90uzE+1cYrpDStQKmGQ0ZjVapUq09nYunEMrHWrpShiCsvpnIVCQeS4AYiACwHKCy64QKYmv9lt8+bNMhxWp9PJcFg6Si4wdcbnavTYD37wgxW/ezwePPjgg3jsscekeUlthgKWQEe1XKnmxXxvphJ0HlVVVRI1qENeeSrytOZpqI7P++QnP4nrrrsOjz/+uLwnK1ddXV2r3qOjR49i9+7d8n486RnG0wHw/yRhceR9qVSSwbvLEX7S+YvFopz+fE3+u8r6VKsEPNnz+XxFpFtdXS3r2e/3A0AF7sZIh3gEezV4CORyOQF2ea1rDe5Zy9aFY+ANm5+fR1VVlXhmtVuM5S11YCpBSVWemyfBvn37UF9fv2rf/pvJHA6HhMGcKp1Op+U0Ue9joVBYc4Fw49BuueUWLCwszj9wOBwCjBH84uKlqf0FrIyQJ0DgS63hM6JQNRr0ej3i8XjFeiDIRsziG9/4Br7whS/gwIEDePDBB8VhEWtYy8h/UQ8LrjeG2sz7SSpiSkRchBFSPp8XUhL/xmoDgUSmvQsLizM5WaHgNallTqbHjLx4Tzjjk/egXC6LA1Ebw/g7I2mj0VjBAWI0dS62LhwDsNQeqraI8otmGEfvyxq9w+GoqBNz8dtsNmSzWezYsWNNKbA3i5FbwHAaWEwtCABaLBbJcUmFfi1rbm7G97//fXznO99BT09PRV7NsNnpdEpKUCgUcOeddwJYIuwQN2Dey6ijVCohmUxWiJ0sB+cymYxsIIfDgVwuh2KxiP379yMajeLIkSN46KGHcOONN+Lv//7v5dSsrq7Gvn37VkjVAYvTvm+44QaZG8nNlkwmkUwmK4hbNpsNDodDNjObxXj9JpNJZAgZppNXQSfCdczqCb8DpsLEfMg9oCOtrq6WUj2jJzpY0qALhYJUnOLxuDi9SCQi3AvyWZqamjA3N3fO8yjO2jFoNBodgMMAJsvl8nUajaYVwEMAPABeBPD+crlc1Gg0RgD/B8AOAFEAt5TL5ZEzvTZPB41GIyeRXq8XLQYAMmmYkQPDPYaqgUAA6XRaiCtutxuZTAY9PT3YvXv3700d+Pdp3d3dolzFHJ5qSDQuLt7b1U4Ok8mEz3/+89izZw8A4J3vfKfM7PB4PNJ4RdVtYhdqNyDl/Zk+WCwWWK1WifC4YTQaDWpqairow9lsVqoFxEFMJpNUQAh8WiwWNDU14YorrsCTTz6Jn/70p7jkkkvwzDPPyEnqdrvhdDpXFU9lyZCPLZUWR8qnUqkKiX2e7CSKMVLNZDKYmZmREiY/k5oWMFKjgCvLh8CSqDGvB4BscA5SUtdudXW1KEslEgnBXtjfkc/nKzAOs9ksqZnD4YBOp0MqlaoolZ6tnUvE8P8AOAmA8do/APhGuVx+SKPR3A3gQwD+9fT/4+VyuUOj0fzp6cfdcsYPcfqEoZdkfqSOGecF828M+ZgDqpOUiA6zlGm323HllVfiscceO4fLXf+2detW2egMGTUajQzbicViclIQsFoNd7nyyitx//3344tf/CKARcXnRx55BDabraJNm0g7qc4qVpHNZjE7Oyv0YqYsKpZAQZNMJoPa2lpUVVUJxZhcCLPZLBuxVCohk8nA4XBImMx8nGkiZ0QeP35cyEmcar3cHn74Ydx8882IRqNCMGJ53GKxIBwOo7q6WrAODpJlpyeZiARf8/m8RABqGsBqgtPplHuRyWRgMBgE1CSngkQt3i+e/kwDeR/UdZ/P56VCxFIrow9g0TmycrFx40aJLF544YWzXltn5Rg0Gk0jgD8G8PcAPqVZTBr3Afiz0w+5D8DfYtEx3Hj6ZwD4DwDf0mg0mvIZxCXJbkylUmhqahIePsMndVgKvaPNZpNwkPVjekWKi3LcHWv7N910E3K5nPT+n+/GTlMuEupdzs/PIx6PCwZDZ7uWBubHP/5xXHXVVQAWyVL/9E//hMOHDyMQCAjBjGVKVonUZh+NRoNLL70UzzzzjHRWsqSshrCsBpRKJYyPj8NmswkBivgHAWhVVEY9FUnWufjii7F582aMjIzga1/7Gjo6OvDAAw9Ijr+azc/P4z3veY8cQEwdVI5CKpVCXV2dRATc8Pl8XoYfsQTIvD8ajUKr1YpOKZWygEWA0OfzCY5BZwJAqgwsI5ODMDc3h5qaGunVSKfTojwNQJwnAVu2ehOb4P0n4Uplwp6tnW3E8E0AfwWAcagHQKJcLpNnOgGg4fTPDQDGAaBcLs9rNJrk6cdXDEfUaDQfBvBhAEKDJnmEXXD8AlWuPy+QSCw3B08k3gy1/gxA5mKyzbenp+e8J0Nls1kB5XK5HKxWKxKJhNTFCZwBi5OYVlN7Bipbrfft24cHH3wQoVAIyWRSTkamCHNzc0KacTgc2LNnD7Zu3QqtVouf//zn0pZNOjvpvtyIPD0ZShMboRqySlGenZ0VjISlQH6n2WwWfr8fx48fh8vlwle/+lW0tbVhcHBQTs/VxhIePXoUGzdulA3I8JtrjCMLisWiDDrieuLgHpW9ydKkyuFghEX5QQAVKYSqykTchY9jykInwRSM5Xen0yklTepVAEtRd7FYRCgUEsxEpZufi72mY9BoNNcBmCmXyy9qNJrLz+nVz2Dlcvm7AL4LABaLpczKAkttCwsLorWwXF+PwQdBqXg8LmCPWgIih3x+fh4Oh0MWlk6nw/79+9dUADofrKOjQ8avx+NxpFIpkc1n7k4uv16vP+PszldeeUXwhe3bt+MHP/iBlHsLhYLoJ1I8pKamRhw59R1UFh4/Ax05K0vEhTweD6anp6UEyLIqTza1js9SJ79rRkd0Urt370Y8HkddXR3uv/9+3HPPPQiFQrjgggtWvdaTJ0/ihhtuEP7A7OysVBfYtUhMQI0q1V4KOglVLCWbzUpaQPCRRqyBm5R0fkZ7dIoEKfm9AUsYRDKZFKq0WnVSyVfEHthtrLIm3wgx2EsA3KDRaK4FYMIixvDPAJwajUZ/OmpoBDB5+vGTAAIAJjQajR6AA4sg5JpG6mYqlZLyI0Ebhp7UxVMptQAkZLbZbEKU4fOcTmcF8KLWfckGPF/t0ksvlQVGVWWCggT7yDhcWFjAhg0bVkXrb7nlFgwODuKOO+7Avn378Kd/+qfYtm0bIpEIcrkcbDYbGhsbZeNWVVXJPaczaG9vRyaTQTKZhMVikQiQIJzK05+ZmZGUwOPxSMmNzoVAqsvlgsPhQDKZRCaTQSQSkTSTjEJWDq699lpks1kcOHAAhw8fxg9+8IMKFevldurUKfT09Ej0yegKWGwec7vdwtTk2ikWi6ipqZFSJQfMqoIx7IAknZnRDqswdrtd5q0y5WKUQayD0ZjdbkcikUBVVRUaGhpkHast1OwvKhQKQoRiVEKnzb3wums+lsvlzwP4PACcjhg+XS6X36vRaB4G8C4sViZuBfB/Tz/lJ6d/f/b0vz9xJnzh9HvI6aGSnHjjWM4h0sy6vNVqldo4w2iGcnQQwFIplOkEv+xz9aLrydSWZ24mdgESKfd4PJidnUUymUQikVgxZg4A3vOe9+Ad73gHgEUNhvHxcUxPTyMej8v3wB4ETiAnrZmhciQSkY3MFGF+fh6JREI2H/PfVCqFhYXFocMswzHHnpiYkIiBosC1tbVIp9NywrL0yUoGsHiqbtu2Db/+9a+h1+tx/PhxjIyMrDpm7p//+Z9xySWXYGxsTKourLSYTCbU1NQIw5Zri+3RuVxO2JPUkgAWKzJ+v18qCblcDjMzMzCbzQJAqq+n1+slJaiurkZtbS0SiYRsYt4/p9MpTkMl+tExMComb4STt6LRqERvvMa10si17NzcSKV9FotA5AAWMYR7Tv/9HgCe03//FIDPvdYLsSTE0IgMPZ1Oh3Q6jXA4XBEyORwOAVwcDoeEtgRdXC6XIM2kWet0OrhcLhQKBQE2zxRer3fLZrOIRCISggKAz+cT0I7j+Siuu9ro982bN1eoQre2tuLEiRNSq+emZfQ2OjoKjUYDr9eLhYUFEZ4lyDs8PCySZY2NjbBYLPB4PAgEAnISbtu2Dfv370dDQwMMBgPq6+vh8Xig0WgQCARgt9vh8/ng8Xig0+kwMTEhKWAsFsPQ0BAGBweRSCSEdEUps71792J+fh6bN2/Grbfeip07d1Zc6yOPPII/+ZM/QU9Pj5B+mJOn02nMzMwIgh+Px+F2uwFA0H/m/Tyo2OrO78Pv98PhcMBut6OlpUVeC1iMOLxeL7xeL3w+H6qrq6VnIhgMQq/Xw+fzSbOX0+mE0+kUbEBVrGJ6FggERLlMjbDpROfm5uD3+1Eqlc45Oj4nglO5XP4VgF+d/nkIwMWrPKYA4N3n+LoVyjNEbklY4unIf0+n0zAYDIhGo7KAiX4zTM3lckKQoWpwKpUSUOt8TiOAxVCT+AqjBUrrp9NpAfI4fEbtrDOZTPjMZz6DH/7wh/D7/RgbG8Mtt9yCbdu2IZFIwOFwoKGhoSKXz+VysNvtUjEKBAIIBALI5XIyiby5uVnybZbLiEGwaqTT6RAKhaSngxvc5XJVSK8bjUbU1NRI1YBzIN1ut7yex+OB0WgUcLRcLuOyyy5DqVTCj370I3z605/Gs88+i927dyMYDOLQoUMYHBzEXXfdhVAohOuuu074M0yBpqenYTAsTggfGBiQDa0OAs5kMpL7s+LAw4apUSqVEsISy5jEzGKxmKQimUwGXq9XUgmOsovFYtDr9fIcgqmsyIVCISHzESNhpYQOiV3HqhDM2dq6YD4SHKEyjUq1JbJKbw0shc/Mr9TTkaGY1WoVKirDKMpssxvxfDWfz4cbb7xRat8sg/E+EmyNxWIAFsFAtamMaYbRaMRHP/pRkdU7duyY6A4S3FoO9vEEoyOmAzYYDMjn85icnJRSZaFQECYfF/TIyIiwCcPhsOTEJPKEQiF5b6fTiVwuh2QyKa3NwKKmJx0Uqy3sWSgUCrjsssswPDyMsbExbNu2DV6vV0rYwGJ5FgBuu+02WUOpVKqiaapYLIoGQzqdhsvlkhH1zPOJJ6iNYwTPWTYNhUJSvpycnJR0jA4QgDQDer1ewW7sdrtEKvF4XGQNGf36fD75Xnjvqe5E0Jf4zMLCgsx2PVtbF45hYWFBclpVTkuVCmPYxVRjdnYWHo8H+XxegEfmqvTsjA5Y1slmszAajXC5XKsqGJ0vtm3bNhEPIf+frDueruyh4NDf5dWXaDSKt73tbXj11Vfx7LPPYmJiArfffjtGR0el94AS6qQv8xRkbZ1ou9vthlarxcTEhABddNL8G0t8zI9zuVwFnZ1diGy5Z6mVJyxPUp7cZBYyBbXb7bBarVJJ0el0+M1vfoOpqSlRmlpu0Wi0YpSbXq+Hy+WCTqfD1NSU9COQ1RmNRgWTsNlsonORTqelqsG269nZWXFmTJVramokwuP94UluMplgt9sxOjoqGhGxWAyFQkHmdrIfpKGhAXq9HpFIpKLXg9fJQ5bsVbPZvOY9WMvWhWPQaDSwWq3i+VlmbG1tRbFYrMAY1OoFsHRazs8vDkChtzYajQiHwzLgVhURra+vX1XB6Hyxrq4u2TycoOzxeFAsFqVERfYdQ+TV7IILLsC9995b8brt7e2wWCwSgahCpQTJCCwyhWHa4na7BZQzGo2orq5GJBKRDZ9MJsVZMQQmZZqcCUYfqVRKhFkdDodsKMrHkwzHU9JiseDw4cNoa2tDMplEZ2cnhoaG0NfXh0AgsOr1/+QnP8H73vc+OaW50alXQccwOzuLU6dOoaqqCkeOHJESKwD09PQIp6Curk4is/LpIUlbtmxBKpWSA6qmpgbFYlGcXKlUwtTUlOhRNDc3C0DLOZ8azeLEd1KwiaX5/X4h+BHITafTwigld4Jl0nOxdeEYyBYrl8uIx+Ny+tOjEu12Op3SQEKVHdae1R70kZERuYGs6XIRqg0n56N5PB7ceuutyOfzaGpqQiwWE/48T5b29nbY7XYMDAzA4/GgsbFx1ddS+yY2b94Mn88Hs9ksJCm+Lgk3xH9GR0crRE7K5TKsVqsQawgWOhwO2eR8v8nJSbS2tsLj8WBoaAiBQABmsxmhUEikz61WqzgOVgvC4bD0bZDMRRyAIOHmzZuFbjw+Pi4gdDKZlFLhchsdHUWxWMTExISAiczrx8fH0dbWJkNwnE4nIpEIfD6fpFvkFQwPDyMejwsjlzhZIpGQqCAWi6GrqwtTU1Ooq6sTh8eKWTgcRiQSkQa0pqYmJBIJjIyMYHZ2FvX19YjFYhgfH4dWq0V9fT0mJiZQW1sLm82GlpYWPPnkkzAajWhtbZU1z7T7XGxdOAZuXIZfbIednp6WfLJcLiMcDsNmswmaTLCGApiRSARWqxUul0tCK0q+EblPJpMoFArnbTu21WrFK6+8Ikh2IpGA2+3GyMiIzOCgUM3c3ByefvppGI1GfOpTn1rxWqoGQGtrq1SFmCowt9br9RgeHpZOQLfbXSFYqtVqMTQ0BIPBgPHxcTktR0dH0dDQgKamJqE8b9myBQaDoWKOiMvlQnt7O/L5vEQJ+XxeFJooyMJSKJuwstksGhsbYTKZ0NjYiKGhIWERMlKZnZ1FNBrFxo0bV72fTz/9NHbv3l1BzrLb7ZIuZLNZ+Hw+XHLJJdDpdNiwYQO2bt2K8fFxIZUxVWJ6w2t1OBzw+XwYGhqSkD8cDgumQ0fOCV50ILlcToDLRCIhVZTx8XGEw2HU1NRgeHhYUimm16lUCkajUe6h3++HRqNBMBg8P9uuGeIzLCJoRVCFCwVYktdi1YIhKgkg/IKz2aywHlVFqHg8jpaWlnPuNlsv1tnZibm5OdTW1qK6ulp6IyjBz7p7KBRCTU0Nurq6ViU2tba24u1vf7v87vP5kEgkRCZPq9XC5/MhFosJcs6eAYfDAYfDAa1Wi1QqJcNlEokE2tvbK07J0dFRZLNZIVmxw3JmZkYGCfX19QkwyWiA3/Hs7Cx6enokOlRbiokpkQMwNzeHsbEx1NbWoqWlRdISj8eDRCKxZgv+9PQ0ampqYLFYkMlkEAwGBQjkqc57EggEMDY2Jq3W7JtgtMH1R7o+MbBCoYDp6WnEYjFYrVbY7XYEg0HMzs6ipqYGtbW1SCaT0ovCfpdgMIhwOCzEKFWkhTgDB+UcPnxYqnar2bkIwq4Lx0DAcX5+HsFgUG4681eXyyW5KMtFfBzzUg7loDRcqVSSEJNCL6VSCQ0NDcjn8+fcn75erK+vD1VVVdKBWigU4PV6USqVMDIyIvdkamoKMzMzsgCXW0dHRwW3IRgM4uTJkxWyZT6fD9PT0/I+qVQK0WgUZrMZDQ0NEqaGQiFByKenp5HNZlFXVyf1daYkpPWyOYiLf35+HiMjI5iZmZGWY6YTPCCIOzHNTKVSQiFmxyV7HYDFAyQWi0klpa6ubk2sZXh4WCIKlUBF0pJK1CoUCvIZE4mEpC7A4uqIpbMAACAASURBVMHDaIq9EMBilMfIoLa2ViouLS0tSKfTAgwur1LU1NRIr4bH44HFYoHdbkd1dTXcbjc8Hg/8fj9qa2uRSqWk2gRAnNJv2yy4LhwDc0nmcGSeJZNJpFIp8ZjcEKSlsuZcX18v0QOjDjbHpNNpKVPyC2S76/lm3d3dUnqNRqPCqmNeGo/HpXwbi8XOKAAaCASknMn0QC1pUoOBJ+Jy+TZuzGw2i0wmA6fTCZ/PJ9UjYHGhk+jDUJqbnK/FxWu326X/oFAooL29HS6XC4ODg8J6ZcmOgPLo6CiAxdCbDpKkntHRUSkn2mw22Zhrmd/vRyqVQrlchtvtltCezqW2tlb6Rhht8nQvlUqYmJiAz+ereM1IJIL29nbRzGTklUwm4XQ6pfpGUJc0bzb7MTqLxWKCpfH7AIDBwUHRnxgcHITFYsGGDRvQ09ODlpYWxONxNDY2SkXpXGxdOAZ6yunpabjdbjz11FNwOp2oqqqSZh6j0Sgkjvr6epTLZdTU1MDv92NyclLKX2orq8FgEEIMaatMJc5HCwQCApIFAgE0NDQgGAxKvm8wGGTEH9MlzoNYblqtFr/61a8AoKKlFwC8Xq8sUDJGWcLs6upCc3OzdEryfdxuN6xWK+LxuABvTDn6+vokD6+trRUxHZ/PB5/Ph2AwKK89OzuLYDAoFQw6fFZeKAcXDodht9tht9tRX18vEYjBYEAoFEJra6twG5LJpPAtVqNJA8CRI0ewe/fuinXjdrvhcrkQCoUQiUTklJ+ZmRFuAQfTsJyo3kOuu5mZGYlkKdX2/PPPC7uS4wNJgQ6FQoJnkF/BSGNubk4G8wKLEczExASam5uRSCTE0VIYmfjMasI1Z7J14RjK5TJmZmZEkGPv3r0Vo9NZC1YltbgZ4vE4ZmZm0NDQIOE1QbVMJiOUXQqYqMSS88nsdjuuuuoqFAoFpNNptLW1yXCWWCwmYXo8HpfeBb/fvyZfQ+09MZlMCAaDkoIFAgHp8Gtvb0e5XEYqlcL09DQOHDgArVaL/v5+2RTA4gJ9+eWXsWvXLkn/CP4xvGWzFEt1Q0NDqKmpgdfrlehmYmJCpjNRzo+5eDgcFhKS3+/HqVOncPnllwuTMBaLIRaLIRAIiFgJO23dbjeKxeKapct4PI4///M/r2hfrqurE8YllbYXFhZwwQUXSNs1p38xopqdnUVdXR1qamoQi8UwOTkJrVaLWCyG7u5uaTFvamqScvzw8DBCoRC8Xi9aWloqOjOZkqXTaWkVZ6OXyWTC2NiYOBSmkaxaUBnK6XSiu7sbzz333FmvN816CKltNlt5165dIpRB1SVq8pNnH41GUSqVUFdXJ6IhIyMjqK2tlSoEgavJyUkYDAZs2LBBEPvW1lbMzc0hGo3KpKXzyerr62E0GoU3UCgUpAbOUJHkGrvdjubmZhw/fnzF6+zduxcdHR3CYdi4cSNisRhaWlqkU5MLFlji6TscDnE6ZI+y5l4qleByuZBMJiUc37p1KxYWFjA5OQmbzQafzweLxYJTp05Jv4XJZILNZoPT6cTQ0BB6enrw8ssvS9rE1NLr9WJyclIqE2NjY/D7/TAajYI/nTx5Eo2NjRVsRbfbXTEjIhaLIRwO48UXX1xxX8xmMy6//HK0tLRgZGQETqcT0WhUKNjhcFjo06RtJxIJCfOJ/LMhTKvVwu/3I5lMCjmPsnAej0f4GOTfsGyp0+kQDocxNDSEYrGIzs5OubfcG06nE+Pj4xUKUm63G2NjY8K05EFJXcgTJ068WC6XLzqbtbYuIgatVisnFJuhYrGYcOdJQDGbzZienkYmk0EgEMDw8LCQTciepMY+AR210YjlsXMNq9aLUeOAysLEBFKpFBobG+FyuXDBBRfAaDSiqalpTeCJDDxgMVq46KKL0NbWhmw2WyHwytx/ampK9DYjkYikFaSb22w2eL1epNNpaXe32WywWq3w+Xxwu92SYw8PDyOVSokjItBGMg+7awn4sT+AAinUL2hvbwcA2Zw6nQ7d3d0olUoYHh6WMJ+HDXNzrVaLLVu2rHpfcrkcbrrpJgwODiIYDMpa4nuzQS8cDsPlcknjlcqTIVDLE5yUa/bvMGJSqxfFYlF6J+rr65HP52VqOR1Gf3+/aFKwSqceEMAiT6K5uRnj4+NSquS+YIR9trYuHAMFMFg9mJ+fFyYkKa48lUirJRuSc/zoHVm/rqurQzKZxPj4uLT8Dg4OCnB5vllzczMaGhpgtVpFK2HDhg2CRG/ZsgU2m00WHCsGq5nZbMbLL78MYFE3Uq0usCQ3ODgo70GQ0mw2r2joaW5uhtvtlmYtNq6Rfef3++V7pdPR6/Vobm4GABFuHRwclD4WOnudTifNXCQs6XQ61NTUCKcFWCphWywWwVwCgQDy+TyGhoZQXV2NWCwmQq4OhwOBQGDViVypVArj4+NC0QcWQcaJiQm4XC45nRl5MNXw+/1y4LBXIZFIwOVyoaamRvAyrVYrPSLsa2GaxFSEbdZWqxWNjY2IRqNycDJNLhQKmJmZkQ5LVWSGToivs9YskTPZunAMpJ5Sj4GnPMMmAPJvY2NjsNvtmJmZwdDQENxuNyKRCPx+vzSisHmFlQxg8WRhKHuuwzfWgxFQImrPnoS3ve1tOHr0KMbHx0Vk1el04tixYxWNQ6q1t7fjgQceALAYhUxOTorUO5ueCoWCnKybNm2C1WrFkSNHcOGFF4oDYp/GxMQENmzYAJfLhZGRERmqyhIn00IAAkBarVZEo1H09/ejXC7D5/MJsGe329Hf3y/sVPbNEEgLh8Po7+9HMBjE1q1b4fV6pQpRW1sLvV6PwcFBwRTS6TSCwSB6enpkU1999dWr3pvx8XG0tLSgoWFRqZDYDTdvb28vWltbhYlrs9mkglZXV4fp6Wm0tbVBp9Ph5ZdfxpEjR+D3+wXAVUlJ2WwWDQ0N8lmbm5slVWJXcSQSEZ0RAqss1ZfLZUxOTqKhoUH4PPPz85iZmcHMzIxUI5jSnIutC8dQLBYxOTmJSCSCCy64QNR/iSwTKQYgcw4sFouwJLkIKYbKVOTSSy/FwMCApCJ6vR7RaBRtbW1/4Cs+dyPvQKfTob29XWjJp06dkhq+0+nE1NSUEJHWMvXf/H4/fvnLX0rfQTKZhM/nk03gdDrR2toqp2tfXx927NiBbDYrlPVCoYB4PI5QKCTireQb5PN5eDweqRAQ+A2HwwgEAsJEdbvdeOGFFzA7O4s/+qM/gl6vx8zMDPx+vwxeaW5uxvz8vLTbb9++HX19fXICz83N4Z577sHGjRulqkI+wsaNGwW4pK7oanbq1Cns3LlTCF6UBEylUtKtSyITHcPs7Cyef/55DA0NCfvQ5XJh69atFQOEW1paMDAwgGw2C4vFgo6ODol8zGYz6uvrBdvgd1QoFARcdLvdqK+vxwsvvCAVH86N0Ol0Fbwf4haBQKBCuuBsbV04BqPRKIy+Y8eOoampSXJTApCTk5OSd5JTz8YaziCYn58XHX0VcyDxJZPJCHL/RthasxN/V6urq8PWrVsFcGIbM9HydDotbbc1NTUS0q9lap/I6OgoOjs70dvbKyrI3BQk5pCtBwC9vb0IBoOYmppCZ2cnmpqaMDQ0JClMXV2dSNmz3Njb24tMJiPsQrfbLT0U1GJgPwBBSKPRiO3bt4s2Ae/r5OSknOLV1dXo7u6WzkybzYarrroKzc3NGBoaQjweh8/nQz6fF+YguTEXXnihzKdYbhMTE+js7KzI4zUaDU6dOiUlc6ow9/X1oaOjQw4hsm9TqZSoOJFLcPLkSUlHCoWC4GhULzMajWhoaBDCWCKRQH19PXw+HwYGBqRU3d3dLeMV+vr6RAKPaTRTMqfTKenFeVmuZBlrYWEBHR0dojrDzjrOF/D5fKIHSFIIsJinsquMZSrOR6yvr5eSEnPSycnJM32c38r8fj/279//ur8uAOzatQu5XA6zs7NobGxEoVCQUzOfz4sK0/DwsCDvXq9Xnr98VJ+qjr1lyxZpWSb2kslkRB6Oi5q09bq6OgwODsLn8yEej2N0dBRtbW2or69HNpvFwMCASO45nU6Mjo6ipaVF9BpfeOEFkWifmZnB1NQUnE4nXC4XIpEItFoturu7JfJwOBzYuXMnLrroIkQikYrPypOcFZHe3l40NDRgaGhIdBDZN2E0GmGxWDA6Ogq32y3cCpZoad/61rfw6KOP4ktf+hKOHTsmzVoUbK2vr5foiRWRYDCIVCoFv9+PWCwm+ILZbEZraysGBwelFZrNfYwKJicn0dHRgUQiIRUQjuR7xzvegRMnTuDEiRPyGfv6+tDT0yPft0piMxqNMBgM8Pl88l16vV709vZi+/btr/9cid+HZbNZeL1eOfnp6cgAY+jHpipVf5+5tCqu2d3dLbz2bDYLh8MhJ+5atezfxagc9EYYpxMZDAZpyCGhplAoIBQKCXYSj8dlChFNdQp0LLS5uTkh8LS1tclGY/2bi3hmZga5XA5+vx9ut1u6Ok0mE9rb2yVE37BhAzweD2ZmZhAOh+H3+4VB6XK5YLfbRXWLfRlsDe/p6UEikRCx2WQyKZGPVquVYTl8zYWFBdmsrCBEo1GMj4+jsbFRpPsoHpzP5+H3+wUgbWpqgsPhALBYsv3Qhz6ED37wgxgeHsZ1112HSCQCk8kEo9GIVCqFQCCA1tZWSdUYthMMJtOW5cJ0Oi2CQcRjxsbGYLFYKgb2JBIJ4SNQ2o73kzqYHFbMZrJoNIqamhoRrh0eHobFYsE111yDaDQqaR0bts7biIGipdQYaGhoEM8cj8clV+K0oOnpaYkyWPfN5XKIRqOYnZ1FIBDA9PS0EIKIU8TjcUxNTb3u10Bw7Y0wCrGoir9er1fo4QTnqDBksVgqcltgqT3605/+ND7xiU/I3wmGcVNFo1G0trZKl6rZbJYyslarRUNDA8bGxiR9q6qqkpOOsuaTk5MiUKrONwiHw+jo6JDqAR0LS5bcXPzsnO+QyWSEqMVIkqVH1QFSwo+9CKFQSJB7qi5zI1H+jAfOgQMHYDKZpBzb2dkp4+tmZ2elvBqJRNDd3Y3x8XFpOstkMkK8Y2u6VqsVMJefjRoLdrtd5kx2dHSgWCyKEjdbty0WC4aGhmAymdDa2irsSpZfSRrjJDan01mhhcquY3IjVtP8PJOtCwqg0WgUwVCdTic98GqzU3t7u4CG7N2fm5sTrUhKZ7Ebr1Ao4NixY7Jhy+UyXnzxRWnFfj2Np8Zaswx+V6PgbSAQgMViQU9Pj+gdZLNZ0VqkxqMqZAoAN910EyKRCKampvDhD38Y4XAY9913H3p6esSx7tmzB4FAAJ2dnQiHw9i0aRPa2tqkjl5bWyvOljRlipv+6le/Eg5EIpGQCgOFS5gyLCwsYGhoCP39/ULeOX78OAYHB2UaFABxGqFQCK+88grGx8eFMNTe3i6j4vr7++FwOJBKpURxqaenR7QNmpub0dXVBbvdLmBoJBKRtHN2dhZerxd2ux0PPfQQkskkTpw4gZqaGkmn+NzW1lZMTExgamoKJ06ckLRgamoKtbW10vXZ3t6O/fv3w263Y+fOnXjuuefgcDjQ3d0t1YWqqipEIhFMT0+jqqoKIyMjkvqxGsIW64aGBpw8eVLu04svvih4RCaTQTwel/tQKBTwox/9CAMDAzhy5AhGRkawceNG1NTUnHES+Gq2LpiPer2+PD8/D6/Xi3w+Lw6gv78ffr9fgLVoNIr6+noJmbPZrCj2kv3H042aACzzMHStrq4WgY/Xy0hL7unpweHDh1+316VddtllKBaLUq6tq6uDzWbDiRMnoNVqRaTV4/FI1LRlyxb84he/AADs3LkTH//4xwU0A4ChoSH85Cc/QTgcRiwWk07CxsZGQcrn5+eFQUj1JOoW5nI5NDY2ShcrOfwApNFIHY3Gz0+R14mJCUxPT8uJXV9fLypHgUBAqL0My1l9amtrk8hvZGQEfr8fdrtdNglDfj6WpVu73Q6///9v70tj2zyvNZ9XKxdREkVSIilxM7VRtmTLliJLbRInjt2s7R8juJNpewsMULT3z7TzowsGGKRAUUynwKB3gMHcZNCgvm2Tps2kaNCkvWnjBt4VSdZuUgslSqTEVaQkbhIl6psf5DmRms26Vm0Z4AEMU9R2RH7f+573nGepQzabhdvtZogzyeGNjIzgscceQ319PU6ePImrV6/i7bffZvh9e3s7vF4vk8uIuEWVwO6GNvF7ADBikaY39LFOp0NtbS1kMhnGx8dRWlrKNzuJHu++rgkHsbGxAYfDgampKQahEUGstLSUezbk4B2PxxkomE6n7xj5eCgWBqVSKXV2djJQg3YxgkdbLBZGjO0mprS2tjLjjuS819fX4Xa7mQC0vLzM6kN9fX1IJpMwGo24ePHigeV/4sQJmM1mGAwGvPTSSwf2c4GcKlF7ezvKy8vZL0Cv17PvAgFhgByJyul0wm63I5lMsonvT3/6U3zlK19hCrMQAn/5y19w8eJFqFQqvPvuu9Dr9Uy5JkVpm82GSCTCY7Z0Og2TycSNNNJVoLPx6OgoHA4HtFot/H4/iouLsbKywmpMfr8fkUgEGo0GlZWVqKqq4rP25OQkHykImyCEwOzsLAwGA/ebEokEotEo9Ho9PvjgAzz33HMoKyvD4uIin7VtNhsvDGTnRsxFYutmMhluchcVFe2ByH/5y1/Gd7/7XVy8eBEejwfj4+Noa2uDUqlkWcH5+fk9VH9CgKpUKtTV1TFmYWZmBtlsFiaTCXNzc8w76ezshMFggN/v30PrLi4uZlwJbXx6vZ7/fgK4qdVqBAIBBnJRT0av17Ny1vj4OPOF8ujHBwsSvb29zSVqKBRCNptlcY50Os3UW7vdjnA4jOXlZdTV1SEUCnHDkgRho9EoYxZqa2vZHJTMUegMeJBB3gifBCi6myAgC7EW6YYkG/qioiKsrKwwJFen06GoqAjDw8MAcuCxV199leXOgdzF5/F4MDQ0hKNHj0Kr1bI4yOrqKguvRiIRhMNhxONxRiBGIhGGVBNPIhKJoKWlBTU1NfB6vRgcHOSJhsFgYP+J9fV1bvbF4/E9RC5C+WWzWQY+Ea2bJgLj4+Ow2+3o7u7GwMAAKioq2HGKjiFU8ZA68m5r+Uwmw4IydCMGg0FYLBa0t7czr+TatWuQy+VQqVQ8ovT5fIhEIkgmkzh58iRUKhU3v4eHhxGNRtHW1oaqqio26DGbzdxopSNZNpuFVqtFOBxmvIdWq+WJDOEjqNeztLQEr9cLnU6HpaUlZrtSdfzBBx/AarXCaDRiZWWFjyuk90gu5/tF/B6KHoMQAiqViim0pANoNBrR19eH+vp6ZDIZ1iQkim9DQwNLpRO1tLi4GFarFdFolFFfZWVl3GGm1fQgc6cpx34bPJ8VlZWVvHMajUao1WpYrVaWwMtms5ifn+cdcXV1lc+SdMNtb2/jZz/7Gfr7+xnluLi4yLsLAYOo3FSpVGhvb4der2f8hNVqZaFeAHyBk81cOp3GyMgITywIFRiNRuH3+3khJk4MLWBkEkvWbOTNSDcIifJ4PB5IksQYlJGREXg8Hni9XtYEbW5uRjweRyAQYFRhLBbD7OwsNz9Jo4C6/NSoVKlUqK+vZ7Le/Pw83n77bfT29sJms2F1dRXDw8Ps8kXkJQC8U9PCTIvp6OgovF4v36TLy8t7Xj/SLyUDpFAohMnJSWxubmJpaQnpdBpTU1PcfJ2dnYXf72cQ025VbZlMBq/Xy1wWasYnEgnuP9GCfKdxKCoGmpHTDa5WqxmxVldXx3Bo6rRSJWA0GjE6OoqlpSXo9XoIIWC1WiGE4AYmjfYMBgM2NjYwNzd3oFwJmgjQefUgg+THSY0oGAyyhp8QguHjFouFXYyor7KbNON2u3nXisVi+MEPfgC73Q6j0Yi1tTXEYjF0dnZyo5CUkkhMhXbFyspKliEDctMS0l3w+/1IJpMIh8PcDKZJSnFxMbRaLTQazR6YOlGSCQyVyWQYF0HAH6vVykfHEydOYGdnB8lkklWUstksi72aTCbMzs6itbWV//bdoKaJiQke+yYSCeZwEIR7N/bj1VdfxW9+8xt0dnay0jTpQQ4NDaGxsRHV1dWQy+Ww2+1cbZHxb1FREYaGhrh5SUcBAj3RokyvFYHJzGbzHnSoXq9HcXEx5ufn95DICA1K1UUoFIJOp+OKkRaCSCSCo0ePwuPx7Au/cygWBrlcjvb2dvj9fhZypbL/9u3bqK+v57KKuq9FRUUYHR1FfX09M9poPkxnuqqqKrhcLmxsbMBgMDB0trm5+cBypyZga2srYyUOgr1ZUlKC7u5ubGxs4MSJExgYGGBl5aqqKi57rVYrI+W2t7cxNja2h8+v1Wrx/PPPo6uri9WC6NxOOAKTyQSXy4Xi4mKYzWbWWiApNJlMxp4JJGba1NTEtOFkMonnnnsOk5OTSKfT6O3t5fM4AG5eEsvR7XajoaGBx4FUjcjlcvT19eH27ds4duwYn7VJrZlYggRxJ3+NRCKBW7duMQFvdHR0zw05NjaGo0ePslbjzMwM9yCmp6cRi8WQyWT2oEX7+/tx8+ZNnDt3DpOTk3jnnXegUChQWVkJg8HAqti0KQHgDYdcyL1eL1paWphHAeSOLqWlpVx16HQ6ZlKazWZEIhF+XzY3N6FUKpFKpXDs2DGEQiHG5QBg0d6NjQ186UtfYi2KN954A2fOnGH8BCk87ev6+/dctAcdJLNtMBi4YUUqOiR68dvf/hYtLS08p1UoFCgvL8fCwgILXxAMVK/XI5vN8i5HM/StrS00NTV9Klx4v7FbFUqn0+1BFd5N0Phxe3sbv/71r5HNZtHc3IzFxUVYrVb09vbC5XJBoVDwYtra2oqmpqY9WgMqlQpGo5GbsbOzs0gkEnjrrbcQjUbxta99DW63G0ajEePj49jY2IDVamVj20cffRQrKyvcAEskEjCbzUilUgiHw6zQTSPGxsZGSJLERxzSXyAQUjKZhEajwejoKM6ePQulUolgMIiVlRWo1WpUVFTA5/NxlZfNZvHII49wRSaEQHl5OU+BXnnlFayvr+9pAgI5NirBrclfgmD0JBys1WrR3d3N/aG/vXmuX7+OF154gZvcS0tLOHr0KH7xi18gHA6zDwctlo2NjXC5XIxRII8Nh8OBaDSK4eFhWCwWnDhxginZpGZVWVmJkZERXgy0Wi1OnTrFlHXqEVD/zel0smQeAO5ZXL9+ndG+tGhevnwZZrN5X9ffoVgYSktLUVlZyZ6UgUAAX/ziFzE3Nwen0wmPx8NKv9XV1Xs4+AaDgd9wQorRmIwuNjIE6evrYw2Hg8x9a2sLCwsLeOWVVw5s0dFoNHsANVSmEiyc0Jx0Tt3Y2IDH48H777/PRwAArIlAtPTJyUk0NjaiqakJ09PT8Pl8UCqV7NtA6FHilZAvZUlJCTfojhw5gsHBQcjlclbXotHd6uoqNBoNi7leu3YNFosFTqcTm5ubOHHiBACwfdpuiT6lUont7W1mcAK53XdwcHAPQ5PEZaurq3Hq1Cl0dnbi9OnTiMVi6O3tBfChXF0ikUBrayuLpdLrF4lEcPXqVZapI9zCbiXp999/H+fPn2c8AvVMqqqq+OajBWt7exuzs7MIhUKs4kQalKurq1heXkZZWRmeeeYZjI+PIxAIYGdnB7W1tby4OhwOBi6Fw2GmwRuNRkaVrq2tweVyMcKUwEw+n48FY5PJJDweD6uHFxcXY3h4+MFTic5ms8xHLysrQ2trK375y1+yrqDJZEJ7ezs8Hg9isRjPkGUyGdOrySHZ7XYjGAzCaDRCr9czt540CGgMehChUqnwhS98AdFoFE6nkw1CDiISiQSCwSCmp6cZoEKeD8QtAcCArerqalRUVEAmk+HNN9/kn2M0GhnQJYTA7373O25cEoWZ/BEymQz0ej02NzeZUbm4uMiN4KWlJTgcDh7fETK1o6MDXq8XNTU1WF1dhc/nQzqdhlar5dEdycp7vV5UVVUx10WlUjEGQKvVYn19HWVlZXxzkt4BwarlcjnnQpLwW1tbmJ+fhyRJUKvVMBqNGBoaQlNTE4CcFfxuZSQg15Tt6ekBkAMVBQIBbvaSLuRbb72F73znO/j2t78NIIfAnZ6ehsFgQDweh9FohNvtZgWqTCaDhx56CD09PTxCbGpqYon/TCaDP/7xjwDAEgA0rSFlruLiYvT09DAHw+VyYW5uDh0dHSx6TFL0zc3NjH6lxYPo+dTbKS8vx7Fjx/i1uNM4FFOJnZ0deL1eLCwsYHl5GTdu3ODu9dbWFpaXlzE5OcmMM1J5JoYeCVlsbGxAr9ezyAeNx2i0RGc/6j7fbVB/QyaTwWAwQKPR7PsN+KQgoZD6+npmi5IyECkmNzc3o7GxESsrK/D5fAgGg5ibm+OfQTNwguUSaSccDmNkZASxWAyjo6M8AVCr1aiuruaxI72+5C1JC7dGo4FCocCRI0f46BAIBBiqTUhN4ENhWbLSI4Vnn8/H0yWLxQKNRoNEIgGVSsVEOmIq2u121NTUMA4hGAxCo9HAaDTyyI8EYWUyGd5//31EIhG43W4sLCwwDmJmZoanMPSzdounUPm925zmr3/9KzweD3p7e2G327kJqdVqEYvFIIRgPVIArPNIJC+3281QcRKY1ev1iEaj6O/vh9PpZAo2Qd+JlDUyMgKbzcZQZ6fTyUZC8XicqzXCpgwPD0OhUHBlQCNjwmzsJw7FwgCAZcVpV3A4HKiqquIRWyqVwsLCAhN5SM3o85//PGw2G4tokmN2UVERHnroIR4lmUwmZrwdFO1aLpfD6/ViZ2dnjxLwQYXf72exkcrKSqRSKSaYlZSUIJlMYnh4mOfrExMTe1SJtFotdDodysvLGW9ARqebm5usPUh0d1JK8ng8/F6Qn4Pb7cbKygqXxCS8Sv0hAixtb28zE9RsNmN5eRnRk7C4hgAAFjxJREFUaJQ9L9PpNO+SxcXFCAaDWFhYYMUu0oQgjQlaWOLxOJLJJDY3N7kMd7lc8Pv98Pv9SCQS0Gg0DAKi64C0FgnDkEgkWCxmaWmJK1DaWGjKQkeegYEBXL9+HQ8//DA3BOkoQ+CySCSCSCTCPAz6O0g5iq4RgrHL5XLU1dVx1SCTyeDxeBCPx9HU1IR0Og2n08mCs1QtAbmjYVNTE3p7e9HS0sKSefF4HHq9HhqNhiXxSFaO+B/7iUOxMCiVSnR1daG2tpZVaog4I5fLceLECTz55JOIRqOMZqMbfmNjAz09PcxDLy0thcPhYKUbGq8lEgmMjIzwjnIQYbfb8dhjj0GtVrM47X5dhT8tpqam4PP5GCe/28V4YWEBY2Nj6O/vh8/nQygUws7Ozh6QlVarhclkglarRWlpKW7evIlkMgmv14uZmRkMDg5CoVBAq9WysMfg4CAmJiaQTCbhcrmwtLSE0dFRrKysMPK0rKwMCwsLjAIkM5dbt25BoVDAZDKhoqICgUAAHo8Hc3NzvGDa7XZkMhk+GlksFnR0dHC1sLCwAEmSWMthZWUFv//97zExMcGOU2VlZbh27Rov9uQbmclkcP36dcjlcjQ1NWF5eRlDQ0NcZR0/fhytra2wWCwIh8N46qmnEIlEoFKpmBAlhMDNmzf3aFa88soraGtrw4ULFzA1NYWRkRHcunWLj2S1tbVsNEzu0k8++SR0Oh2eeuopZLNZeDwe9PX1wWKxYG1tDXNzc7DZbJDJZPy3ra6uMg6DVKC3traYhp5MJuF2uzE5OYmNjQ0WPF5dXWWIdCAQYNIUuXztZqneaRyKhSGdTuPSpUt7LqLp6Wl0dHRwGUqS2YSZJ4w6qfHMzMywoAeVrUIIrK6u8u5UW1vLYJm7DSEEotEorl27hnQ6jfr6erS3tx/owpDJZHD06FHmIBDllgQ6SMuyqKiIuQO7Q6vVMutua2sL169fRzKZhMPhgNlsRmNjI4DcrHthYQFyuRz19fVYXV2FyWRCT08PVlZW2AAom83y+Jfk+oUQmJycRCAQYIVn2iHLysrgcDjQ19eH8+fP49SpU3wcqa+vZz/GyclJ7OzsQAgBvV7PUwPSkSASk16vh9ls5huf1KHq6+uZlUjArNHRUej1epw/fx5AbvMJBAIIhULsOzE4OIgzZ87g8uXLTIgi0xmiKwPgY+yZM2fQ0tLC5X0sFoMkSUilUlzFkAdEJBJBNBpFcXEx5HI51tbW8Oabb7KTlyRJmJiYQCqVYmRoa2sri9XQ8WhtbQ2XLl3C/Pw8NBoNurq6mKw2NDSEyclJKBQKLC8vIxAIQAjBEn/0GtbV1WFsbGxf196haD5SmbPbZdlqtbIAKNFZH3/8cQwPDzMrcHFxkYVKGxoaMDU1BY/HA4PBwDc/waTJhSgYDPIc+G6C+PpkaR4MBnkkeFBRXV2N5uZmDA8Pw2q1sndCZWUl2tracOTIEVYJpsbd7rDZbEzWSaVSGB0dZeahTqdDc3MzBgYGoNPp0NvbC51OB4vFgp6eHtZfVKvVMJlMfC6nhbmyspKNYJqammC32xGJRNhxiujKPp8PVVVVmJiYYL3FoqIiNDY28nvX2trKx8CRkREmdW1tbcFkMkGj0UCj0WB+fh5er5d1Pan/RBRxYisSL4Yo2rFYDMePH0c8HufpiNfrZWWqhoYGVsGy2+2wWCwYGxvbIxj74x//GC+++CK++tWv4uWXX+YcyIi3vLwcGo0Gzc3NqKysxOTkJE6fPs1anHV1dTAajSgqKoLT6YTD4eBNjHo7pOlJzNWxsTHo9Xo88cQTWF1dRXl5ObxeL2pra2G32xk0ls1m0dPTg46ODkxPT3PVQNT8yspKhkvfadzRwiCE8ACIA8gC2JYkqUsIUQPgdQBWAB4Az0uSFBO5zsc/A3gaQArA1yRJuvVpPz+bzeLIkSPMbSer+5KSEtYwDAaDe/wlgA+56FRit7S0YHV1lZtgpHRM/gfV1dVoaWlhLPvdBP3uxsZGGI1GDA4OMqHroCIYDOLcuXMoLS3lsRPZl5FtHAGPPB7PHgEWAMxElcvl8Pv9PC0glavdeghqtZo73NQzIT1Bqs4CgQD0ej1372dmZpgFScrN2WyW4e00XiRVI1pUaAq1tbWFVCoFj8cDmUzG+AgAvHjTUYHo1AqFYg/foqurC0IIJJNJ2O12lJeX4/r16zzf1+l0OH78ONvC0cJNiEXyd6BJVm1tLRYWFrhnQfHaa6/hm9/8Jk6ePInKyko29jl+/Dh2dnbYRs7r9cJms2FtbY1vTL1eD7VajZmZGfh8PkYnEgOVGMUVFRWw2+1MDzeZTKz+TB6WSqWSaeFkUEPQa4VCge7ubnbaslgsSCaTWF5eRnV19SeaD31c7Oco8ZgkSSd2sbO+B+A9SZKaALyX/xgAngLQlP/3dQD/57N+sEwmQyaTgRCCR27pdJoFNwjIEQgE2BGJbMg3NjagUCj47KvRaLj0LC4uZtl1QoAdBIaB9BCop0BmI/Pz8wfKlygqKsLs7CxsNhv3MYgpCuSo042NjXyh7cYvALmjBI3pPB4Pn+m1Wi2Pt2pqapgxOT09DaVSySrD09PTrLhEaL+ysjK43W54vV4m+NCOSJoa6XSaAVCkVRGJRFhJipydqS9CVRyNTn0+H4/pgNxRk4xXOjs7odVqWSOUEJyZTAY+n4+JeLS5bG5uIhKJIBgMIhwOw2AwMFUbAB9BaEH0+/24du0aN1sJTxGLxfDaa6+hrq4O1dXVbGJ0+/ZtJnoR3JqOfoRiJHh4ZWUlmpqamJdRUlIClUrFjlQEliP8DvUv6Jgll8thNpsRj8fh8XgYg0EKXIODg6w0Tf0LAHwN7Cfu5ijxJQBn8o8vAngfwHfzz/+rlKvlbwohqoUQBkmSPlE2SQiBQCCA8vJyPPvss/jVr34Fg8GAqakpXuWJk2AymWCxWJhz7nQ6MTg4iLa2NlitVkiShNu3b2NpaYmBOwaDATab7VNNTe80Ghsb0dbWBofDgf7+frjdbtYoyGQyXB4fRJw8eZJBWzQnt9lsPNuPRqN47LHH4HK5IJPJcP36df7eoqIi/PznP4darYYQAn/605/Q1tbG0FyicWezWYyPj6OlpQUajQZOp5OPLyQlRqNjAKwSRbN3IFc9TUxMMFp190z90UcfxZUrV1g9aWpqCuFwmEeCNA69ceMGLly4AK1Wy6QiAveQY/fAwACT5pxOJ2QyGSYnJ5m+HYvFsLi4yEdN4oaQNiN16I8ePcoMTiIx7VbD6u7uRiAQ4AqVQGw/+clP8Pjjj+OZZ55hRWtS7V5ZWUEymWReC1Hgu7u7WUauv7+fKz2aYCmVShgMBni9XhZ23d7ehlqtxpEjRzA2NsZjz62tLV4gLBYLm+US8vWFF17AG2+8wbwX0oCIRCL7Vhi704pBAvCuEGJICPH1/HN1u272AAAalNYD2O3k4cs/tyeEEF8XQgwKIQZJuNPr9aK/vx8Oh4NFKqqrq6HVamGz2aDVauHxeHD79m288847GBoagtvthlarhUKhwO3bt3Hp0iU+jhD3gnYEmUz2sYam+4nZ2Vk0NDSwdmQikcD4+Dhu3ryJmpqaAzmmUHR1daGhoYHLzd229ORaFIlEMDw8jKmpqT3VysmTJ1mUBgDfQMT/v3HjBi5fvszTDr/fj52dHdhsNoZ4E3qRCGjU/DSbzbwzmkwmbG5ush18MpnEu+++i4GBAZ48NDU1wWKxsLCIxWJBKpVCRUUFzp49i8997nP44Q9/iLW1NUZ5qtVqBh4tLi4ik8lgdHQUiUQCFRUVWF9fZ4YmqT3RmV2hUDDwLZ1OY35+nm+i2tpaZDIZqFQqWK1WJu4ZjUYEg0HcvHmTDWvIjaqhoQFArhf2xhtvoKurC6dOneLm77vvvsvK3bQgdXR0oKenB1NTU3C73bh16xbq6upw/vx5NDU1cZOQbBPMZjOPl8keMJlMwmAwwOFwoKOjA6dOncLp06dx9uxZSJKEzc1NppBHo1G89NJL8Pv9GB4eZkGa8fFxxqTsJ+60Yvi8JElLQohaAH8WQrh2f1KSJEkIsa9WvyRJLwN4GQDKysqk9fV1CCEwPz+PkydP4sqVK7BarXC73bBYLBgeHmZBFiEENBoNj88eeeQR3Lx5E9PT08yszGaziMfj0Ol0qKqqwqlTp9j9527jxo0bWF5e5rEbGZ12dnYeaMVw5coVZLNZPPTQQxgYGGDNxKKiIng8Hh5pAfjIpIXUn6uqqiBJEubm5lBcXMxNPblcjocffpgJRTR5iMViLI1H6j90oxKqlEhRHR0d2NjYgM1mA/ChShN5bA4MDMDpdLLALJA7hpWXl0Or1cLhcMDv90Oj0eBHP/oRUqkUjh49imeffRbpdJpFdhoaGlBWVob33nsPoVAIPp8PKpWKcQVerxcVFRU4fvw4k8y6urowNzfHOqJ0lLxy5QqLrVZVVaGhoQHBYBAVFRXM9yBCGTlNkyozkGNdvvjiizh37hympqagVCoRj8fR39+Pvr4+KBQKLC4uAsj1Vp544gnWFJmdncU777yDeDzOJLOOjg64XC6Ew2F2sCKWLk0f6uvrMTQ0xC7n4XAYdrsdLpeLdTGefvppdHZ2IhwOI5lMYmRkBEVFRdxYViqV+9ILuaOFQZKkpfz/ISHE7wA8BCBIRwQhhAFAKP/lSwB2yzA35J/7xCBEXFFRESYnJ/H888+ju7sbyWQSNpuNtRVIRYcckKmUpRfn2LFjaGtrQygUghCCS2xSLFKr1R9r8rrf0Gq1e9yWqEx1uVw8AjyIIL/EN998E0ajERcuXGCIN92Ew8PD6Ozs/MjfVVdXB61Wy7gQIDe5oeZsRUUFqywRKIdK2Hg8Dp/Px2KlBJ3e2tpCZ2cnn1dDodxbnk6neQckTUYAzHHp6+tji/ny8nIGUA0ODmJ7exsTExP41re+hW984xtQKBQYGxtjt2nCM1y+fJmdxxoaGlBSUoJ0Og2PxwOz2QydToeLFy/i1KlTLPJCVnTT09PcEyJvyM3NTbS3t+PKlSucb3NzM/9tpLdAwsQU6+vruHjxIp544glcvXqVJ2kajQZXr14FkJP6GxoaghACr7/+Op/xSSKOxr5qtRqXLl2CXq+HTqdjg9u1tTXGgpjNZgSDQbS0tEChUGB8fByPPvoolpeXWa+yt7cX6XSaF/aKigq0t7fj8ccfZ/r8fjUfP/MoIYRQCiFU9BjAeQATAN4C8I/5L/tHAL/PP34LwFdFLk4DWPu0/gIF6eFXVlay8ChZa5EQLEmE0fxco9GgtrYW09PT0Ov1sFqtkMlkqK2tRWtrK86ePYuSkhKkUimGlRJF9m5ibGyM7dCo5DabzWzdfhBhMBiwvLyMmZkZWCwWlJeXIxQKwel0oqysjHH+SqUS0WgUCwsL/L1CCGxtbTE8loxkk8kk5HI5C6Jms1mkUilG3q2srGBkZARAblTqcrngcrn2UH0DgQDGxsZYY5HYfgQmW1lZwfz8PBYXF9mKncprwp0AH7JSid9AlQVRvGmBymQyPFHq7OxkK7bu7m4W9ikpKcHMzAxPAdRqNSsnpdNplokvLy/fY11IhDqytCPQEGE1CFWYTqcZCQmAJfMefvhhZLNZnD9/HiaTCb29vVCr1axCbrVa2bCXNrStrS0olUqYTCYYDAYcO3aMe18kcZ9IJFBSUgK9Xs/VByEeHQ4HTya6urpQUlLCnhYEmVepVNwIJXrAfq/LO+kx1AG4KoQYBfABgLclSfoTgP8O4JwQYgbAE/mPAeAdAHMAZgH8XwD/dCeJZLNZ2O12dkJqbW1lxh2N52KxGDPW1tbW2ESGhGTJuUqlUsFut6O9vZ2FWerq6vhivdsgvDq94Jubm4jH45ient4z4rqboBEVcexLSkpYjJTKcvL4LC0t3eNpQVLmdMGRVuTc3By7dZGLF7mA0dGLxnCNjY3Y2tricTCxWklijmDPMpmMwUE1NTWMdgyHwyxKQlbxxNoknAMAnj4FAgGkUimEQiFuApaWliIWiyEWi0GtVqOxsZEnUNRwpP7IysoKHA4H+y3IZDIEAgFks1k2OCamY21tLRobGxl/QL4ZRPIisJBMJkMymUQqldqj4UFuWFarFdlsFn19faitrWU8AonkEJdGpVLxxnT8+HEmetGmFQqFkEgkeHITDAaZKEh+nTU1NdBoNKwvSqY2xNyk5if5ZJLSFACe+O0nDoUYrBAiDuDuuoL3LrQAIp/5Vfc/HpQ8gQcn1wclT+Djc7VIkqS7k28+FMhHAFN3ql57v0MIMfgg5Pqg5Ak8OLk+KHkCd5/roeBKFKIQhThcUVgYClGIQnwkDsvC8PL9TmAf8aDk+qDkCTw4uT4oeQJ3meuhaD4WohCFOFxxWCqGQhSiEIco7vvCIIR4UggxJYSYFUJ877O/4++ayytCiJAQYmLXczVCiD8LIWby/6vzzwshxP/K5z0mhDh5j3M1CSH+KoS4LYSYFEL858OYrxBCJoT4QAgxms/zB/nnbUKI/nw+rwshyvLPl+c/ns1/3nov8tyVb7EQYlgI8YdDnqdHCDEuhBgRQgzmnzu49363OMS9/gegGIAbwBEAZQBGAbTdx3weAXASwMSu5/4HgO/lH38PwI/zj58G8EcAAsBpAP33OFcDgJP5xyoA0wDaDlu++d9XkX9cCqA///t/A+Af8s//C4Bv5h//E4B/yT/+BwCv3+PX9b8AeBXAH/IfH9Y8PQC0f/Pcgb339+wP+YQ/rhfAv+36+PsAvn+fc7L+zcIwBcCQf2xADnMBAC8B+A8f93X3Ke/fAzh3mPMFoABwC0APcuCbkr+9DgD8G4De/OOS/NeJe5RfA3LaIo8D+EP+Rjp0eeZ/58ctDAf23t/vo8QdUbTvc9wVvfxeRL6M7URuNz50+ebL8xHkiHZ/Rq5KXJUkiQw2d+fCeeY/vwbgYHDmnx0/BfAdAIQv1xzSPIG/gxTC7jgsyMcHIiRp//Tyv3cIISoA/D8A35IkaX03Jv6w5CtJUhbACSFENYDfAWj9jG+55yGEeBZASJKkISHEmfudzx3EgUsh7I77XTHsm6J9HyKYp5XjbunlBx1CiFLkFoVfSZJE9lOHNl9JklYB/BW5krxaCEEb0+5cOM/856sA3LmQwL8/PgfgiyKnb/pr5I4T/3wI8wSwVwoBucWWpRDyOd3Ve3+/F4YBAE35zm8Zck2ct+5zTn8bB0ovP6gQudLgZwCckiT9z8OarxBCl68UIISQI9cHcSK3QFz4hDwp/wsALkn5g/HfMyRJ+r4kSQ2SJFmRuw4vSZL0Hw9bnsA9kkK4V82ST2miPI1cR90N4L/e51xeA+AHsIXcOew/IXdufA/ADIC/AKjJf60A8L/zeY8D6LrHuX4euXPmGICR/L+nD1u+ADoADOfznADw3/LPH0GOxj8L4LcAyvPPy/Ifz+Y/f+Q+XAdn8OFU4tDlmc9pNP9vku6bg3zvC8jHQhSiEB+J+32UKEQhCnEIo7AwFKIQhfhIFBaGQhSiEB+JwsJQiEIU4iNRWBgKUYhCfCQKC0MhClGIj0RhYShEIQrxkSgsDIUoRCE+Ev8frRrZx0rLW90AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af872a358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set to \"white\" (255) pixels where mask is True\n",
+    "camera = data.camera()\n",
+    "mask = camera < 87\n",
+    "camera[mask] = 255\n",
+    "plt.imshow(camera, 'gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'>\n",
+      "(300, 451, 3)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af86b4b00>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af86ee6a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Change the color for real images\n",
+    "cat = data.chelsea()\n",
+    "print(type(cat))\n",
+    "print(cat.shape)\n",
+    "plt.imshow(cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af9825c18>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+zbZKgTXFcOIeOAwaQHNMguSQUUgo9Ddoll9PLBz2q1BoRPLqngaeLHQtfdtQa0hJI7iMfDFYU6CtQ5keTQQVkCDYospUQ0o4W+ZBQzyicCeQfEBpiwRP32vKqiIGIUqDGI0rClxpEVGstiv6vqfrOlxhsDaHWiYdklZgh3D3MHH6PjAa1ZSlwftACnnDotAwXIscaif6mPKA1ImitPTtnv2+RUtG6Bhhs12BSkynU5BE2zb45CiKhChF13qc6mn7wESGRU2gKh2LdUB0RVLgyoK+9fgYmJUzbpYLfNejlOLyyRPmpzMUBqOEgOCTYrnaUk5qXFEQYyR0GaXtNitOT+Zcrxa8vnrLo9kZy9WKqjaEZoVxBdevrvjxP3tBu90QuhafAmVVMR6PuTi/xKK4Xay4urnl69s7/puf/ZTFm1tmF5coLZyfnxH7Nzw+P2M+cayV4c7DbFpxt12z7RrOqzGFWKwp8N2Oyk7YLRvUTDEajbhbrSjLgrIsUUpzOpmy2+0pRagKTVFbOt+x7zy9F0KCs/MZojVvXt8wHo9x1uJMwbjWGCU0vWfTtFycjRHfUpUjRoUDZeh9xGuPcYk319c4rQh1RTW2GGMwpsgoX2t8HxGGhPGoODoEpTJ1KOicfASczY8h5iS4EInRgxj0IYEp5AT5YU6ZPC5jihiljo5IHz539hgRaK2REHGDIxMgdD1GaWIQrHVEoxHRKONzLsxYrFGkYe47KTHYYb70SIjElOP2A5ALQwSg5JCkzw5cFGgSxg45NiXIkDNo25b9fk/TNPeg8EA1aoVR5khzHKJkIWBVzkUkrVESBvonc/sqZ9ozqSIDo2DT4LDz/nPEM3w2UCtavxPNiOD0PSXzbey/WLLzm/ZOwlKZYxhvjHknSXMfxujhUXQalAh5cYxRhujw3rGblN8LoghKSAPy6n0gJQ0pIKFFsCSdb2BZFqgY6bdbIjl5onVAxBNjxPd7rC1I2oCOIAmJbU4SiUMC9NJRlTOKiUF0/h1RaaqypIqRfd/hY8DHnhgdRttM20SFURqtNEZplDE4Y4ZkU16QnMuovEtdTvZQoSTvS2uVcYBO7PsOqxQGg/eRvltRjieZVtGKzTbQ9TvKosZai+8F0Xv2XUKsovMt47pgMsrOdlRVGXEkRWFhXFUoUXQR+hDxMU/c/S6glOHk5ITCVcymNZvNirhrGZUFtqjY+5ZTe8LzR484OdXUTtP1wspHLk4m/J9/8MfEIGhT84E1kCKn8xmffvmGz2/e8qPYs91ucVXN7WJNNIoPHj+i3a9Y9D2//OItNzc3/PDjD3l7vSD4yHlRMjs5Z3H9hmcvnlIUlmW7RzuHdgVBhO12T9tEwjjR0bFc3WHocE8rvIAyBT5CijCuRoiJFHXJqKlZKEOyJZo9dem4vn1LnyybbcNuvIeioO0TPkLpFH3b8HhqiKZks23pUk8vHm0KMEITI7Yo0YNSSCnwIbFa7xmVW5re89hMGdWGEEJGwLUD3WXVy+DcMr+tafuARE9RVMjg7JIBPXDPIQ1aqehRKiGSSMEcF4CD4uUwv6zSRz783TmstSaRcK6659aLMjtLMiWXEvn8aDNl2mSFjjEGXEmXFNoWwyJlaLYNIu8At0GaIjHz18aYTBkpMAOSzo8JwSBWgzGISnQh0oXIrunZdS2d71A6YFJAm4HB1VCIGQBSQqHQAkk5RIEtXGYLVJkXKCVHqkBEsAwUjYBSFkMYkqE2A9LhICIJJVnFJlqy8yfv7+/Bp3+nfS8cudKKYlBgSPAY7YbVO6ODQ4b4KMGTlLn0KMfIRsJBNve3s8LaarTXFFrjY0akXdvQGIVVJSkElHYoNTjc5FFKSAjeewrr8N7TNj0pekgRHRW21oQUKYwmHs7N9/kcvKZRaybzKbPLc24XG5r9lkk9pxj4vq7d46xmQ6QsDHVR4pMniB4GsB5+q+AKQ1mN2W22+L6lKAqc1WhlhtcVdTlis1wRgs8JmphY7beMx2OUczRNi+oC3b7FVI7xpGS5XOKKRzinafYgyhL7yGwy48svXhHrwHQ6ZjybMqkn9G2DSGI8nTOaXdC2LSMEZyLz6YhXV7fs+o7J6YSqLhiPKjofWC63PH1ywbQa8emvPueHH7+gKiqsUzx+8pzruwWfvf6Mi/kJ128XfPz8kovTE87nE2IUHj95jz/50z/lbt1w+egFi8Ut2+2WxWLFk8sn9DFwfXXDZrPj66s7Ts8u+Mlv/w6PH53RNhtePnvBaFTS391yMpuy3we00VxcPGG1WlEWga+uFmyDZR8jX9/uORkV1GrFetlxtxSevnjM4m6L7zowibqu8W2H9wEjiRfPnzKd1BjyAjurKi5HNde3txSuYj6B2bjg5MNnXF0vuF1t+OjpGcY4Prtastv0aG+oK4cQs9qi3/J4VBEnFYttg1bZEfZvrzkdOx5NCnRV0PeR9WqLMwWFq0EComDTdZQiaJXYbndYLUR/T1U4pen7gG/7zHFrCKEHyfRd3wkRhTMFagAXaaBN9NEZ5bko5HyUwVOWNaOqZr/fAglrCwZSASEc+XYRQQ/KsCB+UON0JK04PTtHoqdtO0hZ3FBoM6Dv7Oy0GrhzW6DKgVZNPZp7R6iUOSZzm75n27esdlte316xa/bs2xajNNjqoKKElKPS7J8SKuksdiCQDpFKynmEnNTNstOkDrx9Qg85vnwOxeC75EhBScr+SqXhoEcHD+Y/xYvzvem1oo7hhjEOY8zxtVLmKD08JBgOq/67fHhK6RsO/++SI6YBScXo6bos6wq9x4eO6LOyIUZPCD0hRZQ1GRW44uioD+eRQ6OYkxs6Jzly2BYIvkNSQHxL22yRGKiqrPAIoYcUKQuL1QZN1jvfI5sc3obYZ+pG52SNxBw2Hjj+nLS8/90hRlCKPvbs2oamadBa0XXZ0cQYQTR902UVQ0wUxlIXBc4ZnNFAwCgoC4sWyYvLqKQYOFalFL1vc7JmkEfFkDnToiiYX5zTdB6fIilFRnWJUkLTeUxZcPnkElGa2fyMZrdnNyTAQtJ89vmXKBy75QaJ8NsvX3IxP+Hq+pbnLx5DbKmrimk1Qsec3BtVNaXRLJZLrq6ucKai2fXMphN++oMP+Z2P3mO72XA+nw+0XKIqapbLJcYYHj9+jDWOzXbLcrmE4BkZxbPZCbXSqAh98IyqMZV13Fxdsd5uEA0X8xnGZvpLSUTpxKguCH2DsZGqtDhn2PeeYjTl1dU1GIU1OR+hklDbnHzvfaD3wmRSMaocvuuQQW5XlxVPH815fJ658yQaHxW7ztN0LU3f0XSBtskRSgh9dmBa5fumbFarSDyOsRhlSIDmSC3GLKOz2mSqJGtViMnnpLfvQKUsa4S/Nb+01mhj0NZgnEU7iy0LRCuS0jkiFcnSPGVQ2qKNQxuHsUV+z+ToE51BUVmWdE3Lerlit9nSt90QGdssInCWe6na4BNQGE2uD7GK/EIPczORJND1PfumY79vs5hhiGS01lgNerg/xuY6AKNyhGy1HoQRFqvynx5UQ8ZqrM5z1WCxymGURWHQyqI1x7/jearD9T74tCwB1Vrjhrmm/hOK7r8fiFxlXWfWe3tizIMlSC6YOOg1D4763mFzHJz3iQEZeOtczJLDwZT5QgUSs7yu6zr6wuALg9YFyqQhhMzZ/Bg9YZAdtm0LSXCuyOFm9KAPly4OIaOBGACdFTDBY2tIKbBYLbh48oRTY2jbnqIqeVxXR1kiWmjbNitLHMSQF6MudlhTYBTsm5aUAnVdD8ngyH4/yLUShOQJMXOT1mnWizVGZUcu6Q7nHJPRmLev3jAejahGJV3rGdejTBPFHPo5o5jNStbrDR++/5zSWXxMhC6x9evseH3ClgWtb2n8DpUUJ5ePcLbEGEdVlJzOJlhrWW93hNDywXvPcMZSjifUvbBrf8WPf/oTnj15yl/+4hOkT8zOTmn6ntOxo57UXF/f8uzJU/7ir/6SZ0+eMh1bLp/NKYqKMiZev9lSlFPmjx8zKiGExD/53R+x3K05u3zEl19+yWRSURSWzWrD2fySXVrQNS3vf/ABCo8PDYUtkKT5yccfUo5n/OWvvuCz1w0BYeMTe7/DtjknMTo7RYWMxoMXXOkQJdRGYZWwXm5ZbHb8zZc39EEwxnG1WhN6z/svGoLvKazh0eUp07bClRWm0Fwmw81iSYcelCYCRNCak9MJt6s1fUigBdl3RKexKF7frFhusgQv0PP48TmzcUXfJUplMVohqQcixpKd34Btuq7DxwwInCkQlRCyIxQLMUbMoGRRkkhxGOsq67t1aXC2HBKYZUbcSmELhzGWGIWiHgMJY9wwT+PwqDCiQaUjXxxDXiDquqbdN2zX2yGpmROPWmti9EewV7os/1MCiB9yS4J1BjA5CSpCItL2HX3w3GxWLLc7NrsdMfRAorAaJRovYLUZlCrhqJ5LeFQm29HKDL5GI+Ze7XXvfQZeRlwu9oO87bDY5F2m4z6U0t+QWB/8oYjPnPq3tO+NIz/czGRdRhQimPSOAiXdJzYgDu9HYODO333+bk3nsNrJIHR9V4aVkrqXMOnM/R4Q7rFqc7Cu60jaYkxGUVqZHP7KoBvlPkKAhBkctDZgBlmRLgwumjyIzRAO2lzY5PsG77tMK5GON/6QI0iDHCsvSpYoIcuw+sBqs2Q6HuVrEBMGcM7iY2A8qvB9JAahKBNiEvWspqoK+r7HkPW5QQIhCYXWJKXYdy3jyRnWavp2h/ctBQVlYfED926Dol836KKkl4izhvF4zGq9wfeR1gTe3tzygx++Rz0qWa7XTManbLdbRnVF6Sx93/LZ1VueP35CaS1Pz06YjCqCTzhX4GNienLB+cVTTuqaLiWqquLLzz4nWcebr17xT372Y25urnjx+Bmvr64o6xrvPd2+Yx0aiHBzc8f7H/8Wt3errGnfr9GjEcvFApGefbNhXE54dfWGq+2GDigkkwFt76kKy9lkijZCTJ5d67k8nxNjg7UWawMGofWJX311xWbX8PLpU4wkEsLdYsPdtse3HdZoHl84xuMJxoC2jpASu32FpFykMx1VOZeRck2DM4ZxXbNtm6zcUhqJQt8GSA1JwIeW0WjEyXiMVTLw2bl+0PuQcw6FHCszY/T0wwKuYkSGilhjc6WtchZvFZFD1JtrIozLkbIxJm+r83g+zM+sFT84XZVR7LEyN4MNyMnBICHz4trgBp156CPKaIoy888pxswdx4RzgxJkqHY1Sg9VkHneKzUkNFWW7SKCj4E2eNres297mi47dRHBHBR/YrGSsuIlZQlhPFarmmOxUdaUD9GIZK5f6zz/85wd/JWOSDIc5L1JxXuqh8MxGc41L0hZ1HGopHWD9v3b2ffHkSuTS1aNQoYw8EC13BczDDpRlYX6afguolD6nRLdY3WVOuoyf52CgcxRpfi3z+XwXa1V5uFh0AlbrHUwHFtEkVA5w5xycZIkIQ0Vqcrm0Ms6l78P2HKocFP6GJIqa5Dk4Sh3OpwvxNgfwzMRofNtduR9pKpGgGa9XqNVZBQrok8k6bAmUzbVqERSn5UqpaGsK2zhcKUdFqQcAvoo7JqO6Ti3SvB9JCSVZZbBE7sWKW1ORitNnwQTEtt9R2UM682GqqpyQiglfErs7pYkMTyanxK6jtBFgov0TZbeaRLLuxsqVzI/mWEkEYuCTdeiiCwWC6p6zI9+9GNmkxO+vLthOpoyqWrK8Yjl1R0f/+B9Ep7ZxSMW7R5lDS8un7DqEnfbPe89v2SzXFHXJV3X0Mee09NTrNLc3t5ireX26pYnj5+yXG6pihLvIyhNEuFkNMGpSGUd80enYDONZa0lhp6ma2mbHVVVEfrAardnsfdcnk0gbHj29Am2LGh2W3a7wN1mS6EVFycjTqoxQXuquqSLPecnU5Z3S4LXBJ8wCjSCSXAymlAVJZ33WKsprcGYPM6cNmyjxwfB94cCtZQT5DqP3b7pCJIo3Qil1ZB7yoooay0xRdA5Ms4a70EqaHNbB2DQPnOs6vxGFXVMQxScnddB520sAyVzoGNyGwKARKJQ2QW5okTrzHf37fa4UKSYoxKVMnV5oBcZ5Mnp8HrY/wH1HqZ+SkKURBt6dl3HrtnfO3GJdyizAAAgAElEQVRjYBBUSBT0IcWmsiRa1H0RUObLNepd56r0gJwFpSxIHECjDIVfWWV3AHtHukQN1VOij3kFRU6MpsMiofhbSP0/Zt8LRw73Urt3qZKcqc8XPQ03MzuJiBHwSbD6QLnkpVUluS/WUSoXPojKSpK+pW/3aAnMKoMly6dCSmifNbEhJTAaYxTNdpcRsNLMZpMc/sU+9x1JggyhYEAw7hCGSb4ByuLKimI0pqwqPIm2bTk/OSNIwsccuokCUqAsS5IPhJhy5V8K7O62REkURYEtS7RVbNbrnLyMiq5rKMsSoxNvXl+hBZxVzGenVFXF7eIVwoSqrunbPT4VXFw+gRRICYrKMBqNj4qgGHOJvtOGR+fn3G4b6nJK7yNFWWPcIQzUbDYbNvsNb26vKLajjP7rGVdX1xAi1zd3/OCHP2K7WdE2WW52dnHO11+/RjuNXwfe3i65fnvFT3/2M7RKtM0Oxwjnhf/jT3/J6cmcf/HTj6jHBb/4iz/nvffeI4aOm+WC6fiU7lRR1hN6D6NRwe2bGz58/yP2bUOz73j54pKTScW0Ttzdrmm7hNElk5FDjOV21fP116/5yQ8/4Or6jl7gi1dfEdstv/XiJa9evSKqwMv3nhKbHf/s936H1gd+/vM/Y1RV3C6uMwXoO0a14+3NLbeLJVWpubpdcj4dYwrHfCL87Lff59PPX6GT5+LiMQI0MbdkmM7OeHRSouKa66srXt1ec9rXlK5Aacs+7OlSwClwCubjgspklP780RSrhMe6YrcvcFbwbUsXAp3vKZ2jaSObzQ7nMuKtXEXTNGyaPVoJk3pEUVconynCFNVR9usqKFWJ0hk0KAPe+8wi4oHsgHPeLw69cnrats1VjDpzwrnY0eCMJSED/5uPY8sSiZ79fkv0Ad8FIGU5XjpwxSY7SSISuoFuHWpNgKiG/JU1KOtQSggp0Pqepuu5W6/YtQ37bk9KuR2A0S4fZ4hCoh5K+012qA4NognkIioz8PfqXp6CNTmJedSCH/yZEg4ylm9UmysQMe8AxuLYeyYrboZIHIX+9szK98ORK9QQnoAaKikP6BwOFyIObWoSGslluYMwX2sIiRyK5PLLYUUb+LiUSCkMKCVXNDprMIOGMy/rKeuyFbl4aEDwEiJYhRwQTO8HTvwerVttcwYb0Dqj9JQSwSdKbbBlCSrhQ8CnoYeE0UPpf6Z+FIKyBj8kubTO/S3aGIjA9ND8iFxdmQeQIClyefmItVtxfXVL00Ss3VGNa0iR6APiBLSl95Gz8wmr1Ypd0+GMpus6isrjBumXD4m97/CSWG83nIzHhKRpdh0nU8t6vWW92uK9x1UlKE3T7CjdE6LvEMkl6iGvh1mn7Bx97zEpkbA0bUQXNetdy81iwWQ8IsWOdpuwrmA0zejztz76mGpc8fWb19zd3fHi6ROWyw1GadabBUYpnl2e0+x3fPbZZ5zNT9nuV1zf3mDqGbPxhOmk4upqy83tkucvoSgKRAlXVzc5ktGaXbOncJpXn79FYqK0ie1+hzGGj18+JzQrXj69xLmSTdfTh4R0Lb7fczI9ZeMD1pUUtsJqR6FrkklU5ZT9zjMqDCfTKVd3G8qi4/J0Rt+3rJYNpkjsdjtGVZk56Chsdi1aG8a1RSmfOV8xedFuG5KCLuZxX41rLBHrDkjODHK2DF7EQhfymLU6a8pXu5Zm39OGnnFpj/LClBJiwrGk/dBDRZTLTkUypZHC4MhFEbVDdECMQhnBWk1IgaTIjdBClysiJbd5I2ZNtR7aWhRVidOG1eKOEDwpJHQcHGUcaAiVF5jcciOgUq6RyKeUG8DpQXAgUQ4MI12I+ABdH2h6n+W16UCBZM8jQ5uNA12VhSQGkYAkwZCpUT04Za105svhqPkGcvXpO473wH3/eoGiIoO9+2ZZclTK5EgjI3r7DoXzbex74chR9/x3pjQOHLjKKxiHqs6hCkrl3iEMon2RLMxPSkEK96tdzOicJCSJJAm5XFcJRiv0oINNKeVKqiQESYSuRw6cdMirt6hc6ty33ZCNVsestdJ24Llyhz1rLDIkrQ5ce13X6L6n61u0UVhXfKMq9ZBs9D6HalryNYl9JHQ93mq0yY2NmqbBxYhD47tI6SomoxGbco2IYt82hBQzclf5Gha1IwbBGUtVVOw3HaNywt3dHcbVxN5nRYsXfCFgNJv1im5+Quh7Vssl8+mEV69eQcwNvOpRiUKoq4qu2RH6ERfnc5q25/z8HPEdkBUBi/Wa8fg5Shu2+x0xWeq6pipKED+oLQQfE1E0zy7Pef/FI0qtef3lW+bzOavVks1ugwisF7dcPDqj7/b8zV9/wfXdgpN/fs7tao1zJc5onl6eIanj+uaOlKA0DiWRsqy5vblmfjKmdIb56RnNfkNdFEyqmvl0wpfLFudKSqcJUVPWY0DRtC1FadEmUJVl3qaOoA3FqEY5x5evv+L8ZIqzNTFpyukUpRRl6ZjWVZazxkTTN/i+AaMYVY+p6hplLNttgxiFcSWlsVidKEqHiYFxWeJDIuTSGnzIHQ7raoyWNidKSRAh+YSuLeOqRvqQI7EIfchdQAyGuqgxKicmMRkL5ag4V+MdeoyklDDa5LFpQKcsBIrx0MLC4qylqiq2+5bCWJSQpbzpvuQ/0w6JGBOlKymKir717Lc7JIacC4vmKHAw5Pnu9JAsVbluw6g8BpUkNBAlDcV/Q9ROYjskOPehJ8RMsYjOyFkd/jtQIdnDHgUShns1CUqjD+eu7iklBfeOekD2HN97J1en3nHbIhz6s+R9xHuKSu5XAqX1N5D8P2TfD0cO32hTKmLvu4sdVCvxnUSC0YgVkvf3yBmNESElcy/nE0Mk5hanISDBIzHgjMJoM2hUW5Q39DrTDT7lKrS+7en7QOw9noD3nhAyzXJyckoKQlHnQhqlBczQmjUqwoHD00LX7pBF4txdMqpr+qalb1rarqMsimFgC1XpUErTB0+7b1ivl5ydn1AXJV3XsVgtGZVjbOFQKfHlmzecTqecTGfcLm8pK8vZ6ZRtlxsJJQmcnz8ihEA9rtjtdrm3S/KcnMzoQ482MKpL/viP/x+MZK7w5GRGchNG9ZR+G/jys9ecXZxSuIqgLa+uF4yqGpsSUa1QZJ79L371OUlZzs7PuPrrLzg/v+D2+poPf/gRf/7zX+KqCjHXlJWlLAyPnjwjtg3v/eynrG5uuFvv+O3f/h1i9HzyF5/y0YsX7JZLll3PaGQ5PRnziz//JbPZhOn8gj/77C2/Pzvl8y+/QrThxz/5IanbcXEy4/OvvuYnP3nJZrNhubhhNp3mIh+rePr0KXfXb7HWcnY65wcfzfG7O1Z3W378g0uq6gNiL/zhz/+Klx+8jwlbLt97zvT0jOVyyfL6LR+9fAoxMK9q1rsti9WCUT3mj/7qa/7szz/ldFzywxdzRAK/+Oprflx9zG6zoigKLuczVncLOu9pu8Cq8fjU8YOPJuz8gmcvH/PF//0JfWrRCM/PTnhydorTmup3P2LbRf6vP/2UrhNMZXl723IyrTG1JUnmp6uqQqmOxkRi6pmfnlBXlrvb5dCBMHfJLF2Ns4oogSAmtx8QQ2x9LqAZEpzaCuPpCIUmDBp0NSwWkcyLj8YVJ6dnuQUxeY4eOogqOag5Bv05QlkUOO1odx3BewpToWxG04SsGkshCxiMimhz0HTzTsdBndv/ap0VOTonPpsY2Pc9d+sb+r7Pkti+QxIUSsGQd/NRshTyoH6zAEORk87VryoplMkUi06C/TuUJIq8KGYGYJDlDq2kDzm9NOS/tJijX9NyQP9DHg4Gp5+R+X2T3W/hP/8/ed3/7PZOX2Hyj7tXqHjgIKTPpfo6ZZSu9T0FYoZHLZY4IPokQkxx6MMwyAZFsvif3Fkx+FxcELUidCFrPXU66kthSN75AXm4A78V7xcZjjlViro6NgxKKELMksrtbkelagrnkJjoxdN1zfG3KXJGvixLJOYCiv2+oRqqOLfbFkWHS5GiKDk9PWW7WmOMzYoayX0l6rrOnQtTAmVz/+9hYPX9oJkl5bYAWjGdjumblsZnnXoMWY5mnWYyGbHd7pmdT5jNZ/g+UBeOQivQhpubmyyPNLmycL3d8fLZY4yCoi6pxiUp9HQhMHKOosiKhMXdkhcvP2C9XdO0PSH2XFxckCFRwhqw2lEXNa/fXDOqK7ZDr46z+Smv3r7m8aNLms5TuYrZdMrN9YqPPrrkrz//K1KEznsMhvVqw9V6z1dfvub3/0XN7WbJm9sFL58+4fHFCSl47tod8/kYQXN3u2I0O+f8bMaLy3P2q8TJeILExO1yQQiBzWbD08untCFSYzgNuX/IcrlCieZ8OqYsS7a7Dqdype/mdseHHz6nqgr8qKZbtNhhnMUY8T5hlaEuS07GFfNJxaPzU4gZRLiy5Ox0wjgo5rOK9TpwNp/Sx8C+6xn3fsjuCdYnROus+vFhQMyOhMJ7IYQ29zsxudOnLSzpgEp1RpMhhKGdbE7wO+dIcehXgkKr3LjKGovRLrdxRuhDum/ap8gRc0zElPcnAkU9oior9usNPuR+Qs6WBEk5UtYDgvX2mBw8tH8dRCnZ9NCVP3dvIylNlERIgc5Hms7TdT0+HGpM1NB24D6hqFSmOrTWBOJ9mwGdJYmCQg00h9ZZZKGGUvz0TlOrA9L+9fqVo5In5c+iutfgp0Flo441Kipfc8CoeJSJfhv7njhyclGAyn0R4D7peUTh3CtPvI9AxB0ULRKPn4lSWAq0TjTDDUzR432mG7TWaJfbb3Zdi6icue98T/CJwoKXRPSefdtiVS6qCCkOVW2SUbtxhNihRZO6iDYZjbuiohrVWFfmbQWsc/i+p7vbcz4/y8oAW7LfbOnalk2zxyl97JHcdS0qRe6uVtSjXB1Z2oSKHUkpQoKL+Zx207C62zKdVUSyHFGJYVSNs5rBGMLQCrgwNbf7DSldMa6rXB3Y5wl0eXnJ12+vcmm1KHzydKGl8Vv6GPBt4OTshM+++BynhNIIs/kZt5sV40nBy2fnNL7j6u0NTy9O6XzHbDaha3a0+4ZKJUojTKqaX719gy5qUvKcXZxQFAW3t4HpySllOeKTTz7BasVidcfXr1uapmE+f8ZqueSDDz5g07R89eYtP/7hx8zPZtxc3XJ5fsbF5TP+w3/4A6aTCaenMz755BN++tOfYd2Yz7/4ZW4O1nc06z0Xp+d8/fprZtMRXbPh8vIxXev5+s2C+cVL+r7l4mTMfrPgernn089+TlnWjEeWH/zWhxlpWYdv9kxHuVhoNKr4rUcj3j/7EC25T3dZCBczzXzmSI/G7JotytQECZycjqkDLHbXPLo4Y7vvMQKp73hy/pj333/E6zdfMy4qei903ZrJ6TlnJ1N+/OF7LO5WnJ2f8unnX3C3WLPdtzy5mGO0MHKGUlskKRbbLZvtHqM0mV1pGNcFRaHZdh2jyoL4LLUUjdGW7X5PUILWlvNZjS0sekCqVttMyxSOKD2uHGNN7nfftnuMShinc1V050m9IaQ46NUVxlU459hsNrSb9YBkBS997ssPKFdmtZQ9IO/79q8CQ5+YhFYRrbOmPQx9fPogbPc79tuGdtfjY4LUk1SuIJU0VFYqsDqX8CutSUFwyjAUyOffqASM4MWgVAJtMKKP0kcYGkCKoI0hSDpy3EZbhAPNe9CiCEbuW9+iQu7BYs09RaMGGd2vtRr5h+x7UtlJVpyQs7q5yutQQWmPf4fevnpAmceOh9xXW77bBVFrcCZXT8aBWlFDYx1J+V+9MUP5cejCcTXuuo62bd/pxBgP/2gQKYL3PX1os7zQWVxZZgmhVkdUYwqHLYtBRwtF4ShdQZd6Oj+oZ9SghVeJtutomoboA812x2KxYNvsaduWGIS+g77PrWydy5Wuo0lFNXaIgDLm2G7Uh+5YGVpVFSlCPR4xqUf0bUcIif1mT7vv2G23nJ9N82JRWCbTrGjo+575/BxbOLzv2e5WNH2LqISrSmbjCaOqpigKzh9dklKiqgvW6zWF0XSdp3AVu6bFVTVK5T7mRWkpnWFSV4zqEq0Up/MzClez2e5p+pb33nuPZr+j2W+pxxU+Re4WS56cP6bbNzx79JjSFUiyXF3fUE8cfdfmLnl1xd+8veLyyVO22z19itwulvTB4wqNLhVXd7coV9J0LZCZYOs0p9OK2O+4vbtitdqy2XruFjtuNw3r/Z7Z6Qmz0znz2Rz6wKgu2W42IAqtHCezMRen0/+XujfpkWTL7vx+d7DRzacYMzMy31RVLBZZLJJNqkWiBUiCAG200EZQf4P+BA2oF9JeX0ECtNBGgFbaa0IvBAHqLknNIll8rHr1ppxiDp9sunYHLa65RyQHsAgJrdcOODIjwt3D3cL92Dn/8x8opyXbzYrj5SIGWZQFUVbgUL4nTwLBKkwfmU+ZTgjBYYJjtduCHujbln7XIURCZzy9lQQbcEGwOKo4PpsyqXRU/UrBaldHOwmVEmyIqskkpW461ruatreHTjp+3Mblm5ckUoOSJFLhB4exQ3T7lBKVRFMu/IDEIUNAjwrVNE3RWkemih+iwlnup9jIFbeuYxj6WMSVIs+jYtN0USG8F7R5H4VwPlqRj9RhOV5HTiBi5KCPeLR8qvsYYn6ANfTDQG/72DGPNtZyXDPG+8erlFH5GaRAaoFS8flLtWePJIA+0KDjPswRpMOLqBs4UB4Z73M4BuHD5zr+nngdCNIRpBqL+n4nMU4DQuGlwo97gV/n8h0p5I8ccMJjMX6aAATxQO3pOU+/v+eh75ePT69R9hox973SK4S4+HBC4pViCNCPfNUgo91nP0Q4Ro4SfFScw7yIsm3jDEmWUkxKytmUpExRuUZnOUkWjY6SNEXnCUKCTiBJ1UhhjJ7mdVvjgsMPFtO3GNOhJWR5hFi8h87E+CmZSLzw0TNZKWQimcym6CyhmE3IsoJMZwy9Zdd0dG3kjidSjYW9Y1JqtvWKptlgXFx8dV1HkgomeY4xhiwr6LqO6+tbiqJASeLjJElUlRKoZlPSRLCY5lRVikoyjDPMj+aUZcnpcoYPhmySEFTknOskIwTBZDKhqiqEULSN49tv3iEE1O0W6weKqqI1HiWjOjFNo3BJCLi8ek2SBF5cnPHxJx+xerjl049eYrqed9fXeKlYr9dM0pz/+2d/yd2253Y74NF8+vH3CSiu7jf8y3/1Z5yfXRBQSJ2z3dQ0taFpdtyt7uKeYrBstlsGa1AicownRUmiU3RaYEMAkVDXW+xgaLqW6XSKThRKJWitECHyzbsueuGkSqOEZLFY0PtxCsSx7Wo601NvtiRCogJs1xvSJMJf3WAw1o1maQmJ1gdr2jQTFKVGqEePeWMZiQKK3tiD13Y5yeOJ3T9COvsUnj2e25qeboRjDp8vH9+zYXQDHWnm4/10fK+G4dBIPO0k93sZpRTVZEaepAxtgzfmwPoIIWCtOezFgAMEKPEjVjzqQ4L74DYeh/UD3vbYocf0NWboIr13f7uxYMZrOBTbfWe9b/ri9yUxsCNy+CUi1oD9a5JhlNqHD+rQ/mspfbyqR5fDveju8ef7pjP8laY07huk0KRSkfybBq2EcdkgRtAqRrnth5HHpefeetPxCKU8boj3tECFEPvFgoydgg8xrs1bEgVSeIz1DAZ2fTNifYq27xEGmmHkhzpDqiRaSyZThXAK70fPBZUilEJnOrIRsgqVZiTJJL5xpB4h33H7LEAmKTiLHQY619M1LdvtliAFro/c27ptmE4qTo6XVIsK2/cMw8BmW5MlKcZu6E3Dws2YLuYURcZutyMv4p+yrmuU8HR9jelTZsucEri+eh/tZY1g9dBw8dGLCDe1nlwVPH92RN/tohWnF1jbMS0qlmWGV4LJtOTT/CV/utuxXJzS1A9cXDxHJimL6Yx/6/d/n9evX/Pyk4/5/PPP2W5qXjx7RvGq4puvvmaxmLFd33FycsYkT+l6w1dv3/H69WtmszmvX7/hk48+pVs3/J+/+pq6jxFgf/SP/pCf//zn/O5PfoL3Bp1pptMp7999i+sMb95fk+clizzhaF5y8fJjblcPZEqSS4Mm8KPPXrCYaq4fNnzxzRW/9ZPfR/qa3c7x7NkLfvXuNdPplKbdsd7seH29pe56EqXRdqAoJBfPltzc3JCUGX6IOgLnLXmaIaWi7wfOz8/Z7XZIpei7Bd9+/S3zSU4IjjxPyYqcQseItjd3D3S9pR8sm3WNEpL1zTtOlkuECOQTRZ4XXN9e0bpAVVW8lJGFZU3PfDqL06uP3Oy94njAY0yP3Tj6tuPk+BgtFcPQs1zO8d5zfXcbc0bNgPMD652hKgpqF3NVrdH4Mk5/vWkxzpIk5uCBNFgbKYVJwaIoydIE09VAjCB0wR9sBhKdkWjJYn5C13RsVhv6Lto6eHScSomsMSlcZMb4DqTHj5L/fRKXFD7Sg4PFWU9wMdDDWUNnanrn6R30xsT0L0aM+oktQazHscArHv3EYx3RB6gkzmmjcEeoA+Rr0QeMXuJHJg5RrITEiSgKIkTsOxpjSaSwEDdhcKhpHOpUfFbq0MUflq6/5uU7UchhTMRgL18dj1R4PMjx+iG5Po4lI8dcPHI2D/8feeJ78/Z4JgSCwHpHPxiaricER5o8BtMmSRJNcmSkZKkkfkj84HCWg6dEb1qUDmgNmigM0ImMcn8RIRvnYhKQCEQv4kQjCATbIUT0tm4HA0qNfsvdQdVazSvEJCeEwG63iS51TexkrDWkSoOzkZrlo8+LEBI1qvGGYWBXbyjzJJozGYNzHjf0NNv43Cejo+HRYkp9PMMGixcxZqwzAzqLPtf7/EetNVmesF4NVLMp3kNTb8kSzcnRMdYMmMGhE8nd3Q2z5YL5Yko1Lak3a5QQ1JsteTXj7fsbsqxgWmS8u9ziguXd5RVpmkKwzJczCIqjowVpqqMcejxm7y/fotKUo+UJvh9QZcl0OsUPhkxJ5tMZQ9eznJUs5x/x7t07pvNX/MGPf4QSjjeXbzk7esbnP/9LUJaqmvEv/sWf0bvAettjnKHKBbMiYzYvMP3AN5crHtqBj148p0gk5+en3NzcEeqaLMtojCXLC4o847ptSbOMqqrAh0ME2t26Ji8KpB2YZ5G+ud2u0VoyDIaymvDN3QblJXboSaTEtDXdoLFO0BuHSmLzgPAkSkQoWUYqmxIgRz1C03cczRekKkGNoSCouAdxHrJEIwToA4UuqjSEDCRSofesuLGweU90LvQgZTqGaKvRvzw2V867WIZkNK5SKk5jxhiapsH03UhHjOZQe7qgDHZ0UIwsmlg4o8VGGGsAQsXPDg4lAt477NDhhx7X9wze4fzeOvavt7N7nraU++n/yVIyxOVlpBSOXuGoESEgNmUyBlnIvTKTx8Xpnm0STzyjSn2/TWX/70jbFAofYtcOjAvOSMCQY+2yxBPAr3v5zhTyD9RPT9Sd4YCP+b0KHthLfvf3eYRYHv1O4v0dDo9DagWOgz+KFyGOts7FaE0pUFoiHSSZJhk5tBKBSlJSLenFMI5WCoUcR/5xNEtSvJBk3iOkG7FsgU5TcgqGfsAMLULEIIY85PRNi9CKQhcMKjrA9T6OvP1gWCiNTAqUkqhUUW9q7OCRWo1iA0WaaFb2Pm78laJp1wgR04q6rkNuNiRiih08zgW2m5rZbEZdN7hx9NdiwLkJz5+/YNc3mKGJvGFVxi5sXOjuTE01zcmyKONO0khZvLl5z/GzM5JU0TVNHHHbhvXDCmsNr15coKUa+buK3a6mD1HYdHrxnMvLS1Kd0O52eAaqasbd+p7n55+w3q5IdcLd1SXnFy8RMgWloylYllNMCqrlgqZt0SphtVpxcnJCklbc3Nzy6tPPcFbx9s0Vf/CHBZky1PUWyLi9X/Pm69f80R/9BIiw2nq1pRsGBmdQwfJifhaj6XYdX725iif5jzQ6lUwmE25vbxmsARFI04Iyz5kUOW/evqOaTSmL6JmSp9Ey2PoBqWOa08XpMb948wYvElrTorTCDAM761g/XFHmBadVgU5L+s7T9JakiOG/UvkRc87JspjRap1hGAYWVYFrDEFKejOgs0hLrHtDlicHf6Esy8gShfGjV0kQCB956WmqSbRkGAaQjlRFZosYO2UlVexA/cAwxFARb2MHKnW0YEaBlhlC5zw8rBncgMfFBlnFiEXvIx3RO4MU4RDwEgQEoQ7FEaEPUZYEiOZ1Pd72hCE6mFoPLkj2UqFDNGLYW6UEpFBjYwfC7/1P5IETfsABRBjDpOOqcr+LU8IdBEFBiLET30NZYYyU2xf/fdcdDoU8QjcWOfrNRAh5/xiP0cyJ8H9jeuXfdvlOFfIDnzI82QofaIgS790HZ1s/2uUE/yRP0Lnoqub3X4/hycQ32eAcwVlkEJR5QZEmxLir0QRIBHId7SV1IhBE2pSUCcplSJ2QZhOklJg2wjJd70iGQCo8za7GSUeSZFBNSbMMnSiyvMQFTb3rMX2PlJLpYk7b9Kx2G/xgKIsiPm5vMb1lvV5TznLyrORoecbpUnJzf03TNAzeYawlz0vOz17y9uqah/sH8jxn29SoVCFdS9sEVg83MTCh6Wj6jtzk9AaQgsvLS5q+h1/8ij/+4z/G9jVlVkbsPMnQleLu/obCZ/SD5XuffIpzjsXREhkSnIPNZkeSpbx8+ZL/7X//KcZ0XN3d09Q1qEChU+pdz8YE5kc186MFX37xC16eLfjk5XPeXl3jjOEXv/glR7Oj6LkRAr/44lf8tOv4wx9+xtnRkqG31F3HpJjgvKbbGTbrms8++ZRu6Gm6ltOzc05OT/nq9RuElhwtZ6xWht//yW+RBcNmV6N0whdffMGrZ+f8g9/9EaFv+PKbr/jBq2eEwdDf9tzsHFssf/DbJ0gRmFSa40WFcR2rzQPnn17w+vU3zKo5fW9YNy1znQsvW7AAACAASURBVBCcZTqdcnHxisvLS05OT7i+vqVpYqzf9d2W+6/ecvHsmJMXz+i85f5hx9V6y6zMeHhY8b2LF/zLP/slq13Ly/NT1g9rnHPkaYo38P5qxenxkqPjOaeLGcvlkqLKePf2OgrIBsHQGfIgARP3Ewiu7lccz+JS2w5x/1A7aLqe01lG8NC5CKFoJfDecb9akSU6NkLI6MOvdRRA5RkPY8D3YBxKytjFHiTnmqycsdvtCK4nTQQymxBGFakzPbbvIDikNEgsAhHpwkSrW4HCj/4tYSyIHo8fWmyzwpk2CveCICiNFxECZRQLiRADJ6LonSj7V270bIm+SbGe2Ai1jKSRZCy04NFEH5pYp0CEcWEseBTxyBimsjfeQ3icjqcIESJTZq8IhwQpo8YlMi9GSHl04RIofLBI9euHL38nlp2B8YwWxBOL2g9Nrv4uvOjpKOW8jbzV4CLk4D0iOAhDTAQKMdevLFLKsmBSFOSpjkIh9cgFVUrFjjWBNE9J8wydFKR5jkoFZVWQ5/nBr1tqhdZ7R7joSdH3Q/Q9twYVFHJccDji8rUoCo7nS7yB7aZm6CPbZB9BZdqOZreib7f4MLCcLZlNZiR6n77iKScZZ6dHzOcztE4psxSGgTwrUUrQN/3BF6ZIM4yxaJWRIKnyhHmeMCly6u0mfqgQlPmEm5s73t/dMV8umE6nMfEn1bRti1IZnem5vr1htphyt3qgGyxNa9huW2zXI13g/m6FC9FN0buO12/fkOYZeVlydHTM5eUtaVqikxyEQiWSVb3m5OSEMi+wvUfoFIugqxuKPEMqQVVVDM5QFBOEkEynU6SUnJ4eY5xBuoDtDVrA0WKGlJK271Gp4v7+lkmqeHGxJEk917f3FGVCO6yZzTPyQuBdj1aO27v3TKqcrm8oiwTpAn3dsNs23N6tR9dBz9W7q8i8cI6ub5ESiiKja3c425MkGqEjln5/v6KYlGw2O5TOaNuaRHj61pColNNFxdlywvm8RPqepl4hGMhSQW9qTpazMZ3JM1vOWC7n5GVkEKVFjrGGro0RfUIIuq5j1zRoqbAWUpVSZDGxZx8XKKUgOk6M4RGEuGRnrDXOg3eE4BisRwoFUtN1DX37IQMrEJ0UpYKu3zGMx0NrPeL5EWu31h642Hrv1e0d0juCMyDi+1uOoE/8PLvH2wwGP9jo3e5Bs7cCiPj3X2WxHeDVJwTt/W32DeNT3/DxFge4RIxLy0cmnYwqVymJQRyCoBwiGf3N1X7f97hQfrzPHqP/G+phCMiQHGwBfp3Ld6IjF0SJLQB+T66PX0rxIa98Lwx6KnkFRqeyiJc7otQ+DAbXt/i+YWhrfDB4Z1FSY4OlLPNommUtbujYm+erYA9CI53EP1qQgiSvEMSiopVEKB9DWT201mDsgDcDOpN4LbBeMLgBZxP6IOidpW2aeB+h2I6OgWWeMD854uFuRbNdoWVgVk4QRAe7rhvY7eISdLaYkxcpQseT3na7pdca4wxFkbG6u6ecFFTlBKXiG+jy7SVpmlMtJ3z9qzd4IXh7/Z7vf/oZdV1zcpyj04yHuxtOT4/p2i19P9C6wM3thh/94DfI0+gP0/U1AUOSVORpQr3dcX7+nNUvv+Jnf/IX/PjHP+btm28Yup5Nt6Z3lul0zmbTUDcdV5d3JErz8tkpb969xRnDRxcn/MnP39HUhuX8CNt5ssLzG5++QmuJFoFUS7q+oagm3FxdM/gWFSUuVNWEqZryz9/d8Ls/+QM2q1uKDJ4dn+L8QN953r654vmzV+R5Tltv+e3f/h6u71BFzrNXz/Gmo0wlkyyjyiZcXv6U/+Df/Q9Z392isgwUHM0qXpwvmJUFD7cPVFWBtZavv/6a1gwoAdNqwm5bc7RYspjN+eJXvwTiEvp73/+YWZHyg48u+Oqr96zTOxbHR3TGk6cVq3rDECS31/c8n5WcnSzwSmDdwKyacbSc8vbdLUprJpMJeZ5yJ+4ISqNGM6rJpOTu4R6lYVJNyFON9R4z9Oyadvy8hYMieZKVURDWW5CCIksjXm8cbduR6gQrPaZpoohIK7LJkrScstt11Lu4H1EyjAHDniByymxCmubc390wmBYtNXboR4HS2EylChEy8AO+s2AMfujAG0gkQkYYCjKkiP5DPkBwPd4ZwjCAN4gAKs2xWoNIwMko9fdxTyb2yEYYU41EXCrEPdpImvABpD8U1z2sK0b4xB1iJscOmggBwcjg0WPjKaNG5enS0qlHRWhcfgIElNj7Sz3hpMvHMOa/D4/8O1HIY0c+HtDDnnMPs8SvHxvyiOUF8ST+7Qks85T+FH/usNbQmn7sxh1pGh/DucgZFSKMRS+gREAIT/A2dhchELwizXJQASU9we632knkn4YY8WSHuDVvehuhk+mUYCVWDqigsQy0u5oQAmmRs21aOjNgbIzcynXApilKBpyAPE0pyxKA7XpH27YURQFIWtNT1+3oZpeyaVYUaUHbdqy2HefnilkWEKlGJbEQVaPTYaI1XReXnfkkZVJVBC/oRPRIcQQyB0nnR2reGmczutZQVDnb7Q4ZUvJCI5XHDYZnp2d8++1rtts1XdcxyQtWrEh1TKg5e3ZK8/qSotBIH/Hds6Mld3cPDJ0d/ccVQVjyLGE+n1FVVVwsljnbuqUqc/q+JU01eV6ylQ1lWTIMPTIklGUVOfK7muenywNme3N/g1KCSZlyff+AkjE8t8xLtIwZjJf3D8yqgnmVEnzN7/zGK84XOcFUJInmeDljKDsGs6M6X3JvO5QSbHY19w9rqmlJXiTsdjuyvCBL446iqiq6ph1FYJosKxAk2NDz6uSC1W5Lmad4PCfTKUWi6fuGRAmyMsNKy8cXz+g6Q5HP0ckapUUM8SaGLDgXjZf6tiNPC7rekiqBEoHBD5RZiRCKh+0OqWJYizHdKOWPFLum6ahmE9S+H9p/Ng+7q4hnWxuhMh/EwfYhGQ3gIs8b8jQnyTOsGZliScLQD5EH72JSj1JRfKNklO8HH9O1ghuiAhuBGPQoVZcgoyLai+hPjn1MPApK4mXMxmV0LIyga8SqR7uuA/6NFCP+LUeLgX3Hvg/sBCFiyj1EtOVQbIX8oNYEL0YFqB8LfJxKohX6I7a+Z9JFjHxfm/76Xm9/0epR5fnrXL4ThRyeYOThr38fnio9BXtCf/Q2iAfkbzaY8YeD5IwDMZrsxFX9iJ1HCW68v8AJhya+gfZLVyEDCI8fPCQhml6pFKU0UmaIANYHejngjKA3dnTRU5El4EGQEjQHDq8U8Xns6qheTIRn6HvysiAESJKUNCvQOnZI3jratqZta4RQbOuOruvp+56To4i1m9Zguo6bh3uapuN3fvR9Vnd3nD0/ZrNaMww901mJVimTQlNkKdnpKamWWDMgqOiMZzI/4v3791w8P8f5nvV2zZQZ7y9v+L3f+z3evrkaLYEHtAwMfUM1nSCl4Osvv2Fdb/nNzz6NSVtJXFJlacb52THNNi5S7zctegwAfthtybOMaTWnmk1ZLo/xbiBVCS9fXbDd7dhutxRFwWa15tmzZ8wXx9S7jmq6oOsMQjsWyyqKT9xA3zuKfIIxhrv1isliRt/3SCy7esXJyUfMpxXXl1cgI91UCs23b79htlzw2atnVJlgUWX024ZMZXT9CpklyGTC/cM1ULHernEuMBnNsNb1lrwsWG0eyFJNlkryPCUEx67eoASYvubjj55zfjxns32gKgvudxuKomJWTWhrx3xRMD+a0RnLkFnqrh2VgI5pPo92s8FT5gXBWfpgSdOcYdRASLEXwChQnumsoHrISZREaYG10ULZjyfVzow5r0LhfRgdQ6PJVBAx/MR5cEGQlxNM39N1O4T3WO9IPOP9JUVRRB+jvjm4e7rQR4BEutG5NCD2imwX7WP3OhLv4sTtBkfAouSAUNE+Ay9giIU8upaOcZA6eQJ9CKT30UQvFg3kiCIH9+j/HT1NYiOH0iOWHe8iR5JFCOHAsRci4t2HAh/i8jSC7zLCS/vEIikQfnRmDCCeYux72uOBnx6P22HhKgRWuGhJ8mtevhuFfOzExbjQeFq89wV8zzPfv/AgolAgjj77uKjoYbwvlvtcSyklkyrDe4UdDEM/RNvLMGCdGtPAo6eLVx6lU3SqyMucLI1mVtYq+r6lW+/QWcTOZ4s5SmdkOifJSgiSTV2jO4UbLM2uPWBvlg4P7JqOooieK0dpQiMDD9uay9stRZYidEtapIjMkZYSmUgQmtn8mLKs2O5uEUKg5EBbb1Ay4e2370h1ZNGU5ZQXWmON4X61psg1YWg5WlTc3N/x6uKEuq4ppiW9aciynLbZslgccXy0pLMduS756OIlWip+/MMf0bsYWtv2V3z79lteXJwxdJaHu5i5udlsod3yvR9cYD7/ls6ayLnWGbuupswnNE1Hmef84ou3lGXJ8+eSh7phuTzl4eqBs8UCIQLN6h2fvnrFpnVcXX/LZ59+xCRVuMmUYlLRDxLvU54/r/jzz/+co/NjlM5I84xUJxwfzej7Leu6RqQlF0dzvvifv+bly48wQ2A5O+PieUM1UaxW9/zpX/ycP/5Hf4RyDiUtL19eUJYlf/rFFxxLjUo0v/zyV/yD3/093nz7Nd9/dkoqLNv7S6riBZvNPXkhabuGd++umE4r1uuH6CVeVChVcHwcRUH/6k//nJOTIzarNT/8/qd40/LxR5/y+v0Nu/d3TAowtoeQcHryklwXNP2K7bahWp5yffNAu2l5efYCKSV969FZQTcYXN8hlOTq+prtrqcOHi1yLl5MccIiped7n76IlrGjz35ZTGjrmiTLCUKSqoyizNjstgwmRM2EHjnU3uN8YLI4xZueZrPGdXHJHVkwjoSMsphhe0NTbxmGPhpOWYVQMQdTBIlwFuEdQ9fGoIYhOhpGoy2N8BpweBs/kxIBOo36xxDjm13wWAFBakSS4pOCIGO4urMWoUYaYAiHUPbgPVJFOCU2agKtxkxTIcdIt0dyhRDqQIfehzyI8FjUtfT48WQpRw66kDYuK71HJNGXJriYNiRERBIijzouNyUe7yPUo4QbiRtxmvo3j34YX1c8QDIcirEnslT2fuF4GYmYco+RRx6nUBLlB3wAr0ANkcnpvcOZFuEHoiW9xTPafNqAcoFUebpg0EKPC1eFyuLSTaXRb1sQlZQ5CYwQih0DnPeThC6igjTPE4SMsvjaC7wdlWjOR3Mca5CkpDoGG2dZhpMjPuY9RREXp0WakajYBaEE3YjhN60hSTJM6xBejrmKgd72ICW397cEBso0QThD0xjaXUuZF+A8pjPU2xprHcen5/RdT71rKYqWLEvw3lNVGYu05Msvv0YuPcY7kjRhPs3Z3T2QB0k2KZkuF+w2WxLf4jpPqCo++ewjJu8l799fRY94KbhdP0TsUStOj6dkacHgFcFHeltnthiz42i+iIlGYeDk/IR3765JdMlqu+L42Rn1tkYnkr5vKcucgI4+2lhcyGN4QNvQtQOTyYTptOTm/obtrgNkTFdXBUWe4n3g6n5NmlWUOiedwLbeMj85pt7umJTT6JQpoUwEwRrSsuDs7BTTtyAV221NbxxJEmXk+7DrJIu+OUU1QXQ9/SAYkHzx/p6smGL6HiEcvbNMsgnPFlPeJpoES55pdFkgM01IFfV1jTEDaT7hYbVCqkCgxbq4s6jyAnaBh+0WYwyd6bEEvHX0Y9KWUKM1qwCVp7imo7WOIkQeuFIiZmqqgPUO6300YRuDi0MIGAJJWpLmJaZv6PstPhiEI6bf6EiLLcqS9eYhcvuRBJHg8GgV1ZLBQRAJYXBINyYMWE+wwwE/DUKNZm8jzXA0vCKMfikokAleRfZMkAlOSjz6caIPImbn+kigF6MoL/K9k1hzZBgLahrhHqnZV5b4uR4OE/2eWx7kUz2Lii9oXMgKudewROpi8BInolGXAoKP/jUhxBOflNFe4xBzSdQABB9jLf4+l+8EawUecW14hEmeehUEokT4IOMNHz71Q6K1iuKXfUSc9Q7r3GiPCcIFtI+LUDvsXQ0jw8S7AYRBKk+aSso0IU0UUguyImUymzA/mpMXkjQTaCXictbFjiXYSGPMs5KyLFksFkxmZfQCD/F5p2mKd5HNIlyU4y4WC6ppGVkwZYVM9BgtV1LmE4qswHtPVzcxNNp06FQx+A7jGnQmx4zDYgwVUFgvscbhnaRt2+gBPliyoiTLq/i6w4BOBOv1ms22pm0jVNM2kWmhE+haQ7Pb8e1XXyMR1LuW9+/fs364JUklk1nJtvNYq9itdsjQMF/mTLKUWZZFTxQb6JoWN3iOT+dMZgXNpkZ6iesMWsfuyNhAWUwpyoxJlpFpxcPqBqE9k0m0DM60igsuGfjsk09J8wwbAkPw3D3cc3f3gCeQpzldY7i5fuBoWnJUlSRpyRDg8199DUCSCqpphlCwbTuSJI3PN3imVWQFWet59fJjhBBUVTmKtnqm0ym7ruX6dsPgEx62HcWkpK63uFGJWYzBzJOyAmLClTM9mU7ouwYAlSoskJUFWVlhA5TTHGM69rJv5yyaQLCeYlKy3XWY3pLnKVIJehN9yJ0PWGuZJoJUBTSCbdMiQkDrFOMEaRIFZvssWIA0zcmLGFQSXT8FWuUHfxFULH6TckZZVjR1tEre89GFUHgnycsiipGGHi8tUnkClj2ne8/2eLrD8sOAswPWGfz4eEqN2HJ4IrnxEZJBBmSi0WmCTgqESkAoAoq9Ve4e794zRcS42Nwz0vbfV0rFyV7xwc+eMkw+YJs8YcE8fu+RwbLH55+yW/bPwyMjRMV+JxcLvpSgBWghSWS0EU5FcmDJ/LqX70RHLojp1Z7o27uHVwJ7RkrEmvxY4JUHL+yjkmrEvPYHOrho5JPmJXWzpjcGUzd410cWp9ovIjx2HL2st2gpSayOlCwfl0IhEEMpXEzRyZMUtTyJSffEcFmVaHprokDCCawbswulRybRI8aGARECk0mMjKs3W6R3VFUV/UfKmOV5v9kyqRYY03G3jbxwpRKOTo4Z+jlt2+O9Jc9zPvvsE9zgqTdbRBY34m1Xo2UUobjgcUPDyckS7xzlbIofLGkmyfNifDNrfvjj32K3rultYLGIv/v2+h2mM2TzjNxrci25urmnmC7w3tI0LVmu2G1r1qtmlER7zk8XLJYVH3/ygsv31wjvkNIzmZZstzWFzrm5fkdrowT/45cX7IYhLrKGhkyWcUF59RaBJ4jA+dlz3rx5gxISlcXuUQnJq4vnfPP1a8TihFIrXp69YP2wYbIomUwr8ILnSUL1hz+h63tk6vny26+4vLykoKUfDG7w/Pmf/F8URUE1LXjx4gWmazian0SDqllB28ZIvfOzE9q2Y7fZcX5+zps/+RwvNNumpjcbrq+vSXXC9dUdz8+fsWuaSENLBMobns1Lbh/uefnyBberbex0jaU1PTKRrNdbtIesKGlNjx0Cm21HCIHedCxmE5JS451ktdliA1xf39E2MWpuW7ejC+IxdW/4+s1bTrYV5vSIdGuZzGaU5QxrLxkGT2/qaJ1cFogwUO9avA9UxWxUmkar5QCcnJ5xtDyjMQPW9QgRKLOcMp8glObo6AUqTbi7v0KKuOwPjLm7QiKRI3/c4TqD7VpsW8esWh/QSh0YG26I7BBv7SNO7FOCkoiQIrMcdIJSGc4bjA2YoBlCwAeBlOlhkpAyZgDvIRKl5KGOIAJq3/mO8v+n0MmehBHR8hFm8R5GV8MQHj1qEDpODIw0SvkkS0EIEi8OJ70Y5ehHodFoBuijMMi5uJNTB4OvX+/yHenIGbuPp9en/M+n2+TxDxP48Laj+cyer6q1JtUJkywn1wnOWoyxB7ENPkTnw9GkKNeKVMfi6300seqHDu/jQtWOMW1eSLK0oChK0BlCZ0idI2R0m0uzgiwrSJKMEBTBKwYTqOtu/KBENVme5zR1hDXato5dttbMZzG5PssyhEyxFpqmGwU6iufPLlhMj2l6g0OQ5hlZHl3l0kxzfHwcXeEICKUoyjR2Tz5gjOH47ITlcoFUCjN0aK1Ik5zF0ZJiUpKXFVU1i92E9Lz+9i1111HN55w9O+Xu5opEptzfrzDdgBs8wivSrGC769jsWpwL3N5tOTp7gbcuMkMcUW3abzhaFiyqgpPlgqbeopXH+46ySEb2w+hyiadtW/quI1EpbTdgexuDP4whyzI2mw29HVjv1lRVxcPdPfVmgxgsszyKXwbrmUxn6CRBOMvJyRG//OY1phvIkxTnM27vmlG3YEdVrCFNC5RSZLlCKs8kL6LZWd2QZQnVpKDINM1uTberubu6HE3PUpquHZ+jQkvFbFIxn1UolXB/v4kCqXXL3cM6MnFktGqtu5a7JhZYZy2b9Y5t23N9twKdoKWiyCObKQRouih9t9Zyvd6x7hyb1nLbGK633dilMqp14+Rpenvo9qxzdH2PsWARDAHySU6al+g0x7g45pfFjKKYYPoWrQJZEqPndJqgkqguNW3H0EfrCQmHjlSOmLG1JoqATFT/OhsnayE1QmUfdLww7sVGGCIay47kg0QhdIpIc4TOEGmKl1HRGcSjiPBp/fgbu22edtz6sYN/gg485Zh/eNsPu/vD4ysZxUPyw58FKSLxQYyvYnyMQ+Lz+K9QcpwwJOHv0Wf/v+rIhRBfA1siQ8eGEP5QCHEE/PfAJ8DXwH8aQnj4Ox4nYnSR6BPHJwJ2xMhFvNH+7wijveMHo4+I+xJ4VF5lWcIwKQlhYPWg8cbS9z0CQ1nkBE10wCOOOZ54QH1QOK8ZDOgsIT0sPC3Ka4rpNLrYJceEIDDOYq0FL5E6IUkylNTUtDhtyMsJSRo7OzsM7J2Vs1KBCjRdS5lP0GiUDiQUiFRQ5kn0qNhtub36lul0SiLj+ErQfP7Fa85Pj6myFOEFysPxvOTlq1OMGfjiL78hT/PowyEERZaweVhRliWTefx9to+uc2lWcnJyFsMikkA+KaPc2Us667i9u2HoDbnQtLsV1STl6v0lZ2fP2AiDwHO0nKMlbNY7yjRhaDZ8/5OPeHh4iOOntxR5QlvveHU+ozcDu7YjVdl4/HqStOTq8g1BlORZxS9/8RXXN5GxEn2fK6RS3N1eMZnNuHjxjDKRrDY7Ll68ommaOHVMMlrfsd2uWMwrhmHg/bffUqSC5azg5r4gn+dstlukDlzfXPPi5REP91vK6ZzXby45OjpCKE05mTOdTvnp//FTbq7ecXyypEgDP/h4yf/60zvu7rcsFxWDiyOzloKht6zu13zv049RiaZ1lm/u7vjq3T3LasZPPjtHSs23ry/j31VrFs8Wkc43mTA7mrPZtqzrhtvViqPlgu97j9Ily/kCF2C92WC6Bmct27rh6uaBurfc1e8JLvDi7IQkSxn6Fi0FeSK4ur7Be9AyNgtNY3nz+ooXz89wI/urmsww3tJbR2cCukhHLcCKYGsSJUizEplmaF0xny/Zbe/Y1etRep7hQsy7JFgGa+g6gzWRgihcFBrJLE5WQgiEt3ihwQ6jb5BACT0qKaMPoghipIBrZKqipazSIxXRgIvqSj+qSmGc4kcK4qE+hyciKDdESmAIEB6tL4DDxB93d2PPK/wBMRBCHzpuhPuAmvhh4xkQUo1BErG/j2yh+P/oYT5a7e6pifLJY/0al/8voJV/P4Rw++Trfwb8LyGE/1II8c/Gr/+zv+tBpFTj8Q3jZjeGoR6Oy6g6izeOG+nIAd+T82XsyoPEqb0aNN5cKUWRaEIaTaZkyNBCoaVEC4lQgmDdeFIYH0/FvEHjGnCx89Z4CDZa2CqF680I5fh4FgkSa6IXhhMKqcI4xnmKMqeazXGDwQ6GfjD07ehpkqQjy2YgiIDpDJvNhsVizrSckKUTbu/vMGbg1cVLpJTMXMrpyYzbm2uGNO4DZvOKwQ5Y2ZGlkXrXGsE0S6OvuZNkWUG926CLyJsvsoym7qjdBr2WHJ2ecHt1Td+3VGNEnHaBSZozCEVTG7rNjh+ev+S2v2S3ukMIT98OSKmZHp2gpEWqiDErGYMGAEI/sJhOSLxkOivIhojXeh3DmgfvaOoeO0DdrZi9HOXn0xlvX7/h4vkptunY3j2gdLTVtR68lqzrHb/xg484erhH6zj6eg/GWJRK2e5q0jSn0BOahzccl8ck8ojbmxVJcs/JsmS33kSzqCzDuhbvDWmao6QmTaKCN01zlssl0+mEIivJijf0/o566Hl3c8f5s+d47zHda5QQZOlv0tsO5+NicKYLKp1R36/pZcDqlE3Tk8oIAxRFQZZlgGYYLE3fI2TE87NM4YJH5yliVOo2uxprIzFAqSSaVEmwbqDQ0Q9fjQZZbd+xWu8IpJGxIaAsKx4etiQyQeokLueJod2RrqmYVksA7DBEywqiX5BQmryckOUl9w83EX7UaoQpIkwqJRhvnjA/ovWt3DdsY/cVM3Xj75ZiHwk3drZPsGglJEoonJBjgxKb2b2x157LHcS4qPVjIph4ZL7t6coKDrs0RMwk3deMPR0Z9p393o1wrwv90EbE7z2+xtvHRWpkzMkgGYI9QLWxtumRVePG7h4OYfGHneH/vzzy/xj498b//7fAP+fvKOSRUejHMi1GbmjAyqeFXHM4eH/Fp1yMJ4C4VHj0+02SBNmlSJmSJAll6dEqMLRDVLaJmPYjLSQyBiorJcnzPNK7zIBUEuUlvoumPJnQiOCxNhCCRymN8w4nJNY6hsEwOIMIchSb+ENRSUIg0RKdZGQh5SHEGK/gAtkkQyiFaZqYJu49b95ekuWaxWyOlJpdbbi+WvHs9AytLSfzgaFZ0TZD5JFbmE8Lun7AFy2nRxOG4DHOx0TxreHldMkw1NR1iwqKxWKBlx7fe1Y3d2iVUmQp3g5stzvSpKTb3iCCJ8skZycpxsDq/j5+CJyj0gI7SObzI9rG4HGcXywjPOAGot3Z2gAAIABJREFUZhOH6w06E6TKkM0nuD6gpWNSFvzZX3xJWU4QPtBmjjzVXN7vqPuO0+fPqMoJt9c3VJMFIgnUfcMsWYAXaFRcEilIE8Xx8ZK7+6sxzBkCPd7n7LZbTs9zmqFlwDLJBbgGY7ZkWcUsz/ECnNAYl6CTKfmkYrNds9DL6PwnE6yUOKFRWU5aRi5+IqO2oR8sm20LYeDu5p6Xz1/gfM9u09K10aDs+emMzW7Hu0YwdD2qSOnblh9cvED4aCuQyAzTDzHGralROrAoClKp8N6RyhSVSkw+pfEeYw3ToEilQ2jP2dERl3drAgNH0wVJGuHGwXru1juqyXwsaorJJEepEN0eyxxro+VAP8r7p4uCWVXStWsCJmaeqwRkCkpRldPIhjINavTzD16QyASBoOtMTGv0gUSKaNQm4nIw2HHidpGRFuFSDUoTgoE9BKJjnFyiU4JOCFoA8uCWaPxIJ1RjODJj1uYozIlgd7TGliGmZ0opEeGpnawY2TD+0IGHMXBZCkfwTzjfCCCesPYggRgzSuV+kcsjgcbL0cZrr9aU0QpXBIGLdLUD6hBGtk1ktfzrK+QB+B9FROX/qxDCfw2chxDejz+/BM7/7ocRJGnE0dz4omQQPHU8iy+cDwr4X30MYNzIh2jqkxUU1uKGAZEWJFKglcKIPfa9H308QUtUmqKlwAsxhlAIjPV4oljCe49U8I//8/+B/+6/+E9Ii5JEa2Sa0lmLG2wcFXXEWHebOprej+ZdZrBMJxXL5ZLZtOLi/IK+j0yRIEArSS8F/WBid5YKmt2Wb+5uSXJFVZQszo6wWjJJCux9x7PzC6QIGO/IkoRffP6XdK0h0xnPzk+ZVBmJVBR5ThAe0/VoqXCiRwrFerVCqpSuMUipefvmW/pxSRekJBEZSpesVvfoREb5fzXj+u0tfd8QBkuWZUglMd2ai5dndL3l8levaZsOoQNHp/MYglBVrLaGtt5xfiZZb3c0TUemB46nnjTNcFnGxcVzvny34S8+f8Nn3/uIo8UJ//Af/tv8yc9+xk9+97fx3uKG6LkOkKcJz0/P8B7yfMKLFy/Z7Rqaestms+bsNy5Ybyu22y2zyRFtM5DMM3755S959eoFbhjwQTGbTFjMJ/zsz/+CH/7mDxi6nqOjExIV8dNJVbLsZ+SJJhhH3dcopXBCs955Pjqd45xjO3hevLxgPq3YND1t2+FMz+/85m/xs6/fcHu74xjFrJgwnZTo2YJJnrI8mpDnOY4egWa3alhtej57eUSRaso0wcqEbdPEzr1UvDqacHnd8ubNGwyavne8PCl5Nhc8Oz1F6pQklTw/e8amg6YeuHxY4ZyjGQR5knN88iy6gMqSJAl4J3C9R5Nwcvac8+cXPNy9w/QtbVdztDgGoUiTKc57bld3gESqBDkGOAQp8CFgXexig/AgBVqkUQ7vgWidgrDgbCQ5ECRKKuRoYieUROoUXcyQOgWtCSodyQ89QwhxFxX26s0Rc34aGhEkLkRoRY65sHJs/vxeqh/PKBEelfH+UdVNLM4jgSQ2lCNzTsZAZhj9WKV4QqOWhxg7EYgWuSOzJWpgZIRiRHjkrgs/epPHZvVfJ7Ty74QQ3gohzoD/SQjx+dMfhhCC+FtWr0KIfwL8E4Dz09PDQiEEiVCRV+3HP81+zBFB/i1F/JGy+NRFUSYpSqeoJI2Yk4+yV601XohR6BBQaGQSrVmlUvggRwwrISDxIXonx31Fwn/zT/8jemKajvCB4B040DplkmdYAkXhSFTNrqmjjWmWkBVxYdP0O5SGSkxGCpIcrUA9bnD0bU+wMRggyRLyPOfhfo3vt7x/e02e5ySpYzmbsl6vowS6tawfVvzgs8942GwwbcS+e9Oispy8KBi8I4xHNU1LlIgJLkhPmmpWqw0qS8nTaKgktKTZbVg/bKKftoXTk+cE50m1il1SlTCpCqz39MYhrOBoOaOvG7wUvL9dUVaOKtdkRcK3d9dIEVOHUqm4Wu+od5ah6Lg4PeVnX14zKSouPj7lV19+y2bzgBCfkGUpTb2j66Kgqtlt6AZL8J48S1nOF0il6fuOapLRDNEbZBgGvIeb61uKPKPMYvpN08U4vKrMadYxYEFKyWaziW59dojWDm2LVSl5PiFPc4p8wny+BNtjreVotsR1v6KXnr6PFLoiGPpBoYpjri6vCViSLGE2zZgnkhfLCts3fHR+TJqoeOJF0jdtDEnuHdebG+rdhuNpRqYkpu65vFoxOV4g+45qUhJkinGSXR+4XzeUkzkilZSpophWLOclmz4ynJJEkziYVjlvrlokAmMMWkI1ySB41BjaLZQkyRWmH6jmM9S4GO/79hBKrlVGmpds6h3dEJk1e59pkTy6kQLjonLsRkM0FgvOEwaLtw7hR0GQ8AgZ4+i8iL7ryFGUpBKQSfSU0dGWwGGiOjXEd3UYtTYRs+YJF3s8WxDZJ8HHZk2K/4e699iRbUvz+37LbRs+7XH3XFe2WdUtkmqJkGYC9AiaaqA30UjPwAfQQBNCGnEijQRKAgkJIJtdzTLXH5cu7PbLaLB2ROatKjarRUC4vYGDzLNPZJ6IHbG/9a3/9zfRFRGOnHV5ygM9YtWP9ebIC3mU/odTP87IT+cJGvKIkce6PAZcIEdfKEbxDE8epx7/Hkbl6Z94/EcV8hDCm/HrjRDinwF/CXwQQjwLIbwTQjwDbv49P/tPgX8K8LMf/Sg8TocVPoRRxvvEuD24J25gf+iEGI4pG+HRNRE3ZlzK7/MxU2NA6dgOMC4a8vuxS/EYL3wIKKXRQmG0HnM8Rzc3E5kWfhiiMYw+smYSfEHsQrSOH1ghQUT7zs1gx98bPb2DiKu4CwPKKKwIHDYtvhVMypzVco7z0FRb7m/f0FnH55++omkazi+fUSgTPUTcwOXVkqHtqPZt9KbwnjQzlPmMpqkRMkTHuNE8yfqAyiWlzThULVZKEh0x9Pks58PtPa7uyXLNw2ZNkaUU0wxvVaSIOSjKAmMG6uZA53tWVwULVxKEp9k1+N7R9gPT+ZTb21uqQ6RI4gRFmZAkKbe398ymKV998SWvf/wztHrDYbfDWkdV1Rid8XC/5bPPzrkfNmRZztvv3jCbTNHaULc1xij6vkWKQJ6nZIlGCsO7d7esllMSmZIawX6/xuiUrumRCpyN1NS7Dzd0TUORJzjbMTSS6fkE7x2Hfbx2RZEhfcruUFP1PVZF/+ymaUi04vmL53z99gYlU6rOYvuKxXJGlhkW04T724ZnqwkfvTijbSybUJMmCWmuEMHjXY9SMJ+VrIoEZwfSSUHVt8y0QEmJFIbODdzsarZ1zyAU00yT5hmr5SSqpWWCUIGsKEiykoyBvBh3nR7q+oAgoI1CjqVAKRWTqNoOIRTT6RzbtXjrxkXRE4izliwrWN/f0tUNSj4WPaOOLqYOqQTOH7MDYkNmrSUMjuBcVFUGMEKOhUzihYs5msKAlCiZxPvyCTskiDh/sj4Slr93nIy8j4C3HGdw4VGST/wajoyUIIkY0BEbFxGrH0nQSsRaFILlpPJEENSxmx/x8vDopRL/DzGiCmqkN4rHYh+OEM+xIwchHymL/7905EKIEpAhhP34/X8N/PfA/wL8t8D/MH79n/+kJ6JjrmZEv4idruDoCB9TtccjPMntezz3pBMfLTLboafvagZv0UHQ+ZErmmgkloAcO1KJ0TIGuAbLMQk7dh8ak6j4IRPgvGNwA3IMlgg+rulN3xCCIMkzQojnyzKjmGRY67n5cMtuv43KP2MQ0jOdAkSxTHARty/ThNlkihCC8/NzXB/YbDbc371ltZiDtSynM3SSsb6Lar797mtsiAOlTz/+iK5zOBfdGQ+HikFl1IeGJFNcXTwnSbO4DQwhFh8swgmyvKSsa9rGUR+a6D4nez55MSeRccG5f9jj64Y8kyRpgjeG9bqBOmKqy7mh3e/om4TzizN+8csf87vf/YpUS5yFan1gNZnxUA1sqxYjc2ZTw221ZjlfYDvPdtfQNzv+8i9+TnXo2W1uCUEwW0x5/+47rq8uOT+/Zrvf8PLlR3x4+443b97y+tPPGGzL27dvKHPF5598ym66RJvA55+9pG+2pGmgb1tSmWBSTyJj0EGRzRBScXt7z7PnZ7x984HPP/8xh1FQ09YHVhcT5tOPyJOUttkzKQr+7JNn9Ns77ncbfvRsys8/e8a/+c2XfP7Zp0jpePf2LecXS4QLnJ9fstk1tIeKn3x8QRN67po9KknY24qXV5/zxRdfMVucczbLCOdL/td/8X/xo4+fcXV9RpEmZKnCOs/N3S3OOW7Weza9o+o9bXfg9ScvSNOc++0erx15uaKcneF1RpolTCeWy9UZ3jrKPCU1On7+lI7a5yGQ5hNEkZMUmu1uj6JjMT/DO8G23jOfX1GUU968ecPm4TYaX2lNG2ryvCSMxVZrDd7jg6DuOtwQITHXxV1RcI8wxyjliYXNZDHsRce5kTAapEHrJHqOu4CzHusDLoAbC/eJ533qw0bYhHAaIAoCaizqAj/yzAMuOMSThi+GTsQhJEKcGHHImA98KtTwpBbJ0/8tECcoOIgxpQyFCHGOB6CUxEXcBckIy/ixuI8eUn/q8R/TkV8B/2xchTXwP4YQ/rkQ4l8C/5MQ4r8Dvgb+mz/llz3iWceYtjBSmcQp4WMM6MYd8asncMrT33H0Oz6es9Z+z3RLqZGUj41UxRHeEELgg6AdKXlxTzBe5Dxuqbz3SBEVb0PvYs5gkmASTd8N7PfbkdgfyJMJOomPTVJD3mcjr1ohpYpGVSJmZDa9JUEyP1ueZMJSG3wWu/aHzT3bqkYIRdp3ZPlA03d0dcP11QVJNgWlSUyBkZa2C7SVIE9nJCYycgbv2O522LAlCJjPZ3RDR7VrSJKE+ejpnSQDeabZbDY4q+h8hygTXBjYVZZh6DhbJZyXGUWa01nFoaohKH775XtCGFidTWlsz1k35dn5ax7u75EyMJtFGXQSohCqqhraYWB+9pK2OpAKyXIxpesia6SczWmanvl8iUlTQui4v7/lk09/xnq/YTadcPcevvzyS378s59z9JLWOsH7SEG1Q8f5asbXX3+gbnaYROCH+Nlq6pq8TEkzw+FQc7a6oB/60Viqj0yC0dBpOl0ym0XzrcH1kKaIEPjpjz7l17/+NaZIeLt+4N3dhvnqwKTIsd6ivGJoLd71fHI9J/cXCJOzu2uxvWM6T9luW9bblg+3G5aLC9A5SENalrx8/oJgB7bWk+aByTxnv99j+8i5320rrJf0wVC3DiE1fW1RwnJ2kYOIu9y8SGhahUgkQUbozOMQCgY3oIMkL1K0jJz/NCnjbiCVOBvZVWmakuQF+6rm4eEOGdwIB8TwiZPp1Dio9Nae6K+ne1ZGEyvvA1oeR4djKRoZJVLISDNUBqliuHQsAJ4QHn2YHpXeceAcvIhCPKli8RWcOutj4356HiNGHXjknkfnxHFHMDJhAiBOHuX6Sb05FvQwMuzia4tiH1ByxO0DJ0qjj0/ze/UqwirxOhwj4hzE5vFPPP4/F/IQwhfAn/+R8/fAf/V3+V1HfEjIUQE2eqmYIE5qLxnkqZALVJQ7j5J94QP9+L13LoZK4FHC0TsbQyWEQ2iBRo4irrhiaikQSkXPCeJwU6VROCG1RieaNCuQQuEFGBVDcb2IQxiLxPY2bi+VpmqakTGTUjUtNPWooBTkZXZKLo9xaTHP09KjE4ML8M03X+E8I14PdvC0bcvLVy9IU4OWhuZQcXP7Hokn0SYO8UKOMYbN+haTGryN3bjzPZOipO8tloAmHa1wwXUt0zwlMYq+7rm/v2G/r6NQAxkHkYlEmwnr+4rMwGcvFuwOFe8fWrp2i9YHFvOSzz5+RpZlvL+d09ie/f0DXQP/z1+/JxWePC34sD3g+p6r8xnPzgUygZfXF9xsWt6+v2fzcMc/+ct/iL9f89vffM2nn35Ctb+lmE0xRmGDZlIueP/+huvr57x+9Zz9/sDVi+fcPNxHkbY2rJZzptM5zWDROmFz94Hd/S2pCGg7cDkr+fLbbxmGQJGnpKonSTRf/M1XPH/+nPPZimxaEKTn/n7Ny5cfgVMUWcrd3Q2TyYT9vmF5cU4IXzApUlaLOTqd86/+71/x85/9kg/vb5l+/jl91/H+9h3/4Oc/ZWhqZJIyuXjJP//f/nfe3Nzzn//ic1azOZk2iL7hFz/7jI7oLLjZ1nx09Zym7mnSASkd6+0DQQqGwbLbH7i+XFHttixXcyZqQIme+fmCvbWUxZysKCMdsu+ZT+d0/Q1FmmKVopxM6fqAkIb5pMCkOcPg6AfDcnFOagw3H96ymJ2xbbeYNGeRTajbivXde4rEUdWBrrMs0hQpYmGWMkKIQQSyPKETA6aXoy+KRydRzTiy7QjO07vhURWpFcKkiDRBSIMwGpUkBBEXirp3dD20zcBgPc55NJGgEA3Lxw57LJAg/r3+U8KPdMIx90AS/Za8kgTvIhFCxCBlKUTkrITRewU7iorAifizlnEICnGhiFxHjtVbC4mX+hHmQaNGdoof8X0for3C30HY+cOQ6MNx2ACPwXzETLxwRK3E42qPQOBOqjVBiJaRcMLHnXPgIiNFjH4tWoIaFVl28Phw7Ow9eZ4ihMQIcAp0qjDjsNQkCiVinuGjmXz0rYgYujt5N+R5HqlIQVLvK6ztsTYWFHw4qU6FjOo4LSHRKSFEW91EmlPs3DA4rLCEPnD74T2TIuPjzz7ibHVOmsXkF+cHpIhuj6GzhHaP6OM1mk0LgkhIdE6eSZyERCmE9LjB0vRgg2c+W1KmJZPJBKlu2VcNQ+8pyxIpotMcUrGtKyazJTr3FPlA7y1KGz7cP7CuGoqi4Pr5FRMGcJ7ZZM7ZxTm/+e0bdruWZVHy1W5Hf+OZFM/Q2vL2wxd8/qOfobTA9RVD11GmJVInbPc115fn6Dxl8I6imMUOOi94eLgjn5TkWRpdKZSiqg6nuUV0i9c4BI1tKSYpDw+Bpt7HgdcwkCeKVAUIhhA8L5+tGPoKJTOUEjRN9EPpRy8TQc9uv0HIcOKU4wNNu6erdnhp+PTjT9huHpBC0HcdN7d3XC5L3NCTZPF1pami7nr6ITIdpI6c69lsyuJszlffvovBHk30dOltQ922XF2cIbRGBIGWBqk1qdGslnPOlguoPjDJcgYfUDohzaLnzxEWaLo+qmSNgfAYhSiUIc1LBmcJAhaLBXkxYbN9IEli0ak7R5blrBZLunaDVuMw0TcnCMG7ODxXKqZkaT36iYv4eQ7an6h+3j2Ryoeo43Aj7BFVxdFrRMrYmStpCELSe4fzYL2LYcvu6JDqIxkiuDgfGKHZ6P8tT7UjHNtr4n3/FNt2cszJFLE3Ps3Kjl30iG8fbcKj6+FxKBofFmelxwocz0v5fdz8WD8iz1yfdnyBJ116eAIR/QnHD6aQnzjhQuD9ccL8+EqO/Ex4nCYLji98XMkRBClPhfw4YT8OFQMxi9Oo2BnIMTvQS4X3kbOuE4Mi0gy1SlAm3uRCxkSPo0TXC7Cux+gUrQ1JksYtYYi8UKUE2sT3VPrISfVKkiQJ0hjA45VG6gQZUpz1OBxNE2PKskwTlMI5sH6Mh1Ket+8/nOwH5qs5h92OxXSJUdC2LVXbUjV7qqZmNV9EXnyqKdIpOs0YhoGmadjXO/oQgwm6dqDIEiSCSZGTT0raZmC/2aOVJVUJpdG0rWFb9YAin8ypNhX7mx3PzlMmhWQY9gztDGcD5xdzdJqRJjm/LOe8e/eBumpZLM65u19zu+6ZZo7MFHz51Tf87KefkyUp++2Brm5RacF6X/P8hWa/qygmCyZlwWEfE9n32y2H/ZbV6pwwmlRpLRlsQ2IEPvTjzkawWe9Yzubxde9aVqtVFCSZjDLPaYboMZLlKdZWBOvomxYXBpbLM+r6wPX1Nev7h+gOmReIbIKROs48tOb8bIHMS0w25csvv+RiuWRz94GmapFncwbnKfPiBAX43kZHPgJWONIiJcnjwFoEzWy+5Nu3t2Q6x4poUDWdTum9O33mB9tSZobFpIzXVucYqdmvDxiVkSUZSkd7hBACh8OOEEIMlMATQkcICXk2w1pP21fkWTGag7U09Z48N/TWInVKnpeYRFPte4wxGA2tUVgRO36n4qKvjEa4KJs/7kaFDJF9IhU+KBDhyX0ukWokHiiFVDo6ICqN1AZUZHp4AtaHyPAahhimfmzGjhCLP7JJwmPBluHYcBMAf+SEh0f1ZGQjPmnbx+9VvFPjCvCkgMc6FGGeYxEOgUcRUxiDpEd2TIRM4jD7cdgqOJIXY8F/EnH5dyzNP7hCDsSOlngRjy8srlJPHy8J4fhBOCZey1PRVkox2I6uaaLzngsEN5LRE0VmChD2tP1q6gMySOzgmJYpWqvRRTEacB0lX0ZqtFZYAUYmI/e9x2iBs9DZnrbu6NqB1eqcRTklyzKstXTOs91uES5Em9yT+CAgU0M5nTBfTqmqiqZpuL29wzuLcwNX1+dIKWnrjqHvae2ORkFZTPnw7h1V15IkCWerBcvsDD/3lNkUkyhu371jt74hzROaro0qxbRgPisxxlBtN2NArqetezwa76E6bElShV5MaGzLoWt5dn3B1eWKQ7VmuS2om4HDvsEbw9nFJc3B0fYd7dpjzIHlInp2vH79gsVqye2HG968ecdX373H+bhLmRQpf/Wvf0VRZEzOFvTO8ayYYK3lN7/9glfPrvji3/4V+ueB5Txnu1ZI6Xn39humk4JgA69fvx7hqBSjU6pdhU41wQ/kytDstqxmOWeLnPv7O148v6I+HEAMLMopy7zgN19+wWK5Issy7rZ7fvSTn3PYt5RlyeFw4OFhx2QxRycFdx9uKOYDVy9f0nUdVqZ88XbLzd3vcENPmQ94W5FNSg7tQFbMwAe+/PoLdrsdL19d4eU7UqVRXvLixUu22y11XXN+viIoQd/WVE18D6bZkjzPmaYJ623Fer3GjIvUw27Ph80Nf/76PIaRECjLlGmRMgxDZEZZy2azjjFrUlI3HYXR1M2BwXsm5YJqbynzjLqp+PDu29HhcIql5frikkxrtutbwGOMwTuPyVK0FwzB0Q+OfvCYxCEzTyrBDV3ciaYFIgG8iEW4H9BJFMY46ZDiMdcSbRCJRiQSVPQjqfsuLjZ2oK57urZn6FpCEIxxzOAjk+VRI2JHqmHM0z02e7FoCgiOo/EVI99bjPitCDKy5I5N4hiEfDTyG6vQCVphnOG5UQBIYIRgBMEfF4EY1HHqT8cdw5HGGARxEOsFToL4+5YQ9PvU8N/nij/K8ONxpF9G9dbxjZE45yNP+skgdHCOwUUFpQjuNLRQKr45fjTMV0qRakOSF2TmMVg1GtzE0IrgAn2wSKNRUp8CLHywOB8Hl2maIHVciOS4OxiGgWO4dJnPxoUm/r3rO7q+QkmDnkaDLIBUJ1xfnccbxoP3mupwABRCB5wV3N69Q5stn332mktzzjA4tEhIiwRjNIfdAQYXKYBdjUwk87MLjE7xwaKFAR/lwoNrCd6TpAFjIueekNO2PUPfo1VgXmj6asf2xqOzWDCzBLKzlNViibWexYspnWs5bHdxGi8UTd1TlDEA4OLqmtlsQbXb0zQNk9kMhSPLDN4PfPfVG4YgOV9MuT6/IM3iDZRkKd6DNJLrF8/58rdfkhY5Td2hRbT9bZqYDG/yDK0jz1vgCSoahk0nObcPFXYQiOAwRqGlIU0LAnCoalaX1xhjePXyNV3Xcf3snKquSZKM2XyJ9Z6AIC2itfB0tqC9vacfPL/74huCkFjbU/ctwQ4sphlXV1eEENjuqvG6RkMwIxVKBfLCoNNobmaMYZLEYOtFmbP3NmZa2gGT5nhrOez3HHYHyiLlze0dt+sdQnq0lITBxV2fVrR9z7QoUUrRDJb9/oBzNu4mraMXUSCE7FjMV2htMCbjcDhQVfvYGHU9KEluDENfowQMRBjR2uj0OQyOLC/pXLzmx5u6GywSiZICJdRR2hiTuWQESCN7JCB9HFBKGS2ZIxwRzd8CjNi9jdGI3RAbszGsHTx+NNILgej9T6QUCyEwo930WChGFsvxweIxtIYnQ1nBiXQQhD8Jhwijqfp4fx9rkBdHlokcm/dw6twjxPKE2fLkeKrgDEIS3JGlN1qV/InHD6KQH6GjY8EOT5ROJ9OZ4+T56TFm7/knrXpUdkYxR3yTI3YnRJyEay0xRqINgDoR9lU6ocgydCrJk3xUcfoxdDW+Oc6NOwBxzM3cxxvDH+0rY/yTcwNFkdMOLYmK3W0UMIwZi97iQwyNaPuWQ7VDmxxpElrrybIMnONsUWC7HucCWZYwK6Z0ztO0PdoIHtYZXWOxrSErcyaFpkxnWDr6ocWHgcTkpEmBzyDJCmaLBcakgIzS6rpi0+/JMhWx01GJ5z1k+YKHhwMqdJytFgQUSaFil6dT5pmi6xL++m++4927LVmW8Yu/uCI1M/oeDvstwvX4TvDmy69pmjVXz19TTif85Kc/4v37Nwx9z1BVLCYrYtL9HuUDDw8bEm349OPXvL+9wYbA27fveWWeg4zqwaEdaA4Ni0X0Atlut1xeXnL0znDOo5K4Oe6GlvLsDLlr6QZHkCnWWzyeXAjaYaAfdQfSxNd43IX1fY/3kJiCzES7h5h3KXjo9qQm5fPPPuNf/Mt/Q913DFg622KbAx9/9GM+evmMSZGx3h8QQrJanXG3rmkaT2oKXB8LlfMDh2rLcjLD9y3Pz1e8J9APNSYdPa+1xvYD3jk2mw3bpqfuLML3JMKQ6hRhEpTJUSZBSYlWKiqIh8DQVwijscFxqCqqxjK4wEevX5LlBmMkd5stVdONQ3FPkRU426KVoGkH0jTDDj0wYtXOkoZA3XX0fc/CzaM5nFBkWYFQInI/bCQyKBll9mHMBThBLEfm2pE2OBZq5wRN19IzpoaBAAAgAElEQVQNlsFC2w84O85uxhlXpLJEbxVn7Rh27GKep380v4pDyAiLRKvdCK+aROHdkUPOY9FmLOKE0cj2sVbF+dv3m84gxBgZFxlvkeWmou22GBeUo7AxRN1J8ONzjyTJWG/EkQ30px0/iEJ+mik8Kdrw+Dqe4uenoYAUiKBGJZZF+MdhaGS5gPcDIvQI6TBSYXT8ExcFhVYRLzQK+n4YuZuxEMcVWkVvhranrfcEBiSCIcvQOiB0XJGlFIShi9awbY9Xo2eLkhijEUJStxbho9LM+wiXRETN8+z6FWlqxg4+Gv+EIHB2YFtbNpsNd+tbZIgqPBU8aab56afn8fVqy3dvv405i02PSTOyLCMpDb7vODtboS4vCUJhhCKRms53BAKL1Yp8kuP6nrvbDzT1gcHGD12aJ0wWCjcseHNT03nP2dkSKTNw0Y2u7wUKhXU9QqTcP+yQCVw9e8Wr1z+iqir84Gjblmbo+et//Rtev3rF4AdQkiRTZMWKv/r1W+azCb/4sxdUXUfbK7abijfvvuHlJ5+gZMJ2V3N394B3kh//9M948+Y7bh/uMVnGdDJBm8DdwweemecYoxBCMtiaSWoIynDYHpAix/uEEATn5xdU1YGh36FVydk0pUgktx/ecXb5gjIrEZhoNNZ0ZIVmUmQI5zBJjvUDUgmKNGW/W3M2L/EPa54tV2y2NVoq7h4OlNMaqRu6tqfMCxarM/7qr/+Gf/SLT3G+IZ1d85uv35EisW1FWZZsqw0kmg/3GxazjNm0wPcNfXAszxbc3t9xe3cfB+LDwDw3TIuMLNfM5iXb7T0T5qTmjLquUVpyeT6nOUQPopv6Pa3z3O33ZOkE28LZ5ZLddku1X1NmJu5aveByGRlASimcjOI22w8MzlGmUya54s3Ne3zbo4WkOuwgTEmTuNgNLu6So0upxIyZA/WhJdgY7CKExmuNkAakBiVwNs45usGxObQMQ6DteobuSDkcRvjBR/JDEITBgutwKEKIEnonLIpRX3J0xRIiNulBoIWIrox4Qjg6H/pTI6mDOLkfyhP2TpTRjwVYEjt9zRFGCSPtMtrrSjEGT0hJzAj2kd1yUnvG4HdGjxc/2hz8qccPopCfhtfh+531H5vaHleyKBSLGy81kv2VkHgpUSLEYaWUMWU+SQnBok10SvPB4geLFDoqP41CjPzQgBun0gFrHThL8DYKFEJ84/umpfJRgi5EpC2iDDJJyKSm73tsNyCT5NRh5IkhSaMgoGsH2gABR17kNN2B27XDOxgCUXocAqlMkN4hvCUPnqpquK/bkfkSPbkXk5JVUTIU0cUwmIQheISz9JWn1Q5lclASKQT3D7e0bQt4tI7d5WK+oqkadtuKw65mW+1JkoSL6wsmRcHifMrl5SVeCNq+RUoV5wPBooWmbWuqekvAsbn7wPL8jLZt8Qh0ouhcpHHlWcrZ5QVvbz9wcV7i38Lh0CGSQF7G9J0P7x8oZxld4zhbnqETw3YXjZ6WiwvyvKRtW4oi5+zsjLu7O9I0Yeg9BINzgv0+JtMbNRqnyRgIXG82KJmQ55FJsd0doihEBbbrhyhmcY7ZbMF0VuKdRwvJMHQsZhNSE2/yKOfXkZ7mPLPplHfv37De7UmTjEUxQfUDWWKYJpq+rSizVwxdjXWWPFP8xT/4M/6P//Nf8ZOfvqBpKt6/+Yqf/+RzQl5ihygaS9ICIWLoQJkVaClxDrIkZ+iii6bxlkJJChOYFJpUCVIlMUqjpYoKXqmYTWZxPqM0vXXRNdJ7MgHTPCHNC7q+Z7fbROaP0hwONVcvLnBDR5Lm484wI4ze+TKRGB13E2ma0vXD6FZocD7QDT2qa2PHayIbhdF/5KjrQOkT9U9IOYo7o5Ooc4FucHSDpesG7BDibsSOjw9+TJqPGb5yHF7GUHWPEGacecqRGRL9XhhnacKHx/lmiNCGOp2IQ8swfhueTks5DjjD93DhIxP82Hj68Psd/O/Vsd/jF4oRnhFjlueJn/knHD+IQv4Hh/AjE0U+vuHjcTShCWMe4Xgy/tgo9lEqslMSbbBaIhINskQqHyOjZPSYyIoJaZKTGkOa2CiVlyKm0fc9XXvA2oFgHUMYkD6yTlQGRkp0JkmSDOscu90uhkbEaHKkEGRJinXRW7luKrpekpUF9dDx/vYDm20V/ad1SpbEgej52YppWeKB9XbD+9tbwmA5X82ZznL60DOdzuM221mMNlRVNFkq8ynJJAZCWBu9YPI8p+/2dFWHGO0PvB1iUIZyVFXFV998R3ADiTa8en3OK3nBbLrA5BNkAJMWpGPcHEZhB8/t3Zqmrum6lueffMbb92/o24G2Hbi73bA77EnTlLPrFUZIijwmtW+3D2w2Gx5u3lHmKYvVnF/9zVdMihSBA214uGtiBzcpGPqe66vP2G63NPUb0qwkSRLu7x+4vLyISUgu2p9macrZcsVuc0OezAliDLvFM9iO9nCg7hueP7viq6++wFrLJx9/xDB0KC05Wy6RJtoPl4Vh6AN1cyDTilmRMrhAVcV5hlQDzf6BJJUkeYIximq/5vPPfozJJfV9w+XFOdNlSRA9N7ffUZYls9k5za7h1199xdc37/hH//iX/Pbffc3Xv/uSq/Mz2qpmMcm5fvGM6tCynMxYzTSZUTgcaZohaZkkglrDsjSU0jHVDq0kVdtE75asxKTR0XG3j+HaUilmsxkIwdC1NDLEHM80dji3b79lu3lgcbaiaiNBwLYVX9685dNPP0XrhKaN12q2nCECVFVDYnqyh4FeSZKsJDhPXR2i8G4sbEW2RKsEOPoHehIjsd4hvB+x8Rga01uL9dB1A13v6QZPU1usHedf4yIkR5KDEAqsw/qYC+BdOIlrkIGg3Ji1GWc2/H7DSPRC8eMCc5rRhSfzuiMcImP9HVs+OGLnECFaYU51SYmjzH7MHD3VtlFo5EX0VCfCLwEZ48/G5xP+vkW9wSNsApyoP98zpeHxosdxU8SvHgn3jxibUkfGiYrGV6onhKjSC8HhB5CpJDOaxMTB5zAch6Tj73UWnEficcGRmCwmYgc4HCoSpZiYjKHtkEYznRQMg0N6CUkSubdVSzvUSKnY7xqaEHjxakYvDEm+4KpYIITA9UMMdNCa97e3vHkfhyPe9XgbcT2TKBaLOUkWKX1JEr3C67pGKU1va2zf8+a7d0ip0VqCeEDIwCyfUhQFOlExxch35KUgz0ratiPL5wxdE82/kJRlgXU9rqsp0gzkwGADzkoSUWK7HoFDCM96vea3v/kq2ht4z6TIyMqEpmrZP+xYr7e8fvU8WgMb+OUvfkTfOX73m7dstg/c3K85v1hijOKw3fHVNzd88ulHvPv6SwCmy3P6vo/e40MMm+77jovLj6jrmslkQl0fKMsp1lrS1JDnZezKgsL5Hq0TdocDHkdV7/l08ilFkfHtt28AcINnMp/iEBQj9t22PdPJgraPiT9934FMsbZFSMdu22BUQkhNHFArybxIWM1ieHTwnkkxZb9rWK3OydMJUhpMZkZHb88kSTAm4+Z+Sz/A/d0WbMckSznsGz58uCdPJXkqKDJN1zqywjN0DUYLprMSbnYUmWaWGbreEcb8SGlicARCY9KMwcXko7Io2O02ZEnCIKPvUF6W7LYPtE2FFgI/BqWcn5/Tdy1KSIYuhkNUtufV5TWJTGhtSyAqfZ0fKIsJ0+mcQ7XF2wElEtzQo5N0zAgd/baPBfLo/S+iK2kYB6HOOQbrabuBvnN0jpFBM1pVuzHGTTzxVxrFgEe1pyA2VE9pho8zuPH3eH96Dn/M1+Tk6/JEQS5kODkcyj9SPr/nsfLk+9OQczTveno+jumOQ9fx37z83oLzHzp+MIX8ePyxC3E8vjf49FFxRQg47PceJ4XG6BRjDKqL+GgILTE9Myq3lAooLVAybtH6vh/fqJ7gItOkHToCMWsz1VF4Yof42J5wEj8URcF0liPDQPASGzzOBjofB27BBfI0YZZmnOUF5AVhtoqcWu/Z1xVCaIZh4O2He7aHPbjAYlKQJgng6XrYHzqWwuAZaAaPNJLZcsF0tqBp92wetsxmnv1+z25Xc319TqLgo+fPmJQzgpboRGH7jkO1xjrHcjVDmDTerG3Hd19/Q9VY0qTk2UfnMaxXCnprGXxPU+/ioNEG0mLCZz+esTz7gBSOPC95890HzpZTjI6udTrJmKQltrM4CXfrA0IoLp5fojPNerdlNp3FToqOm/s1u/o3/ONf/JTtdksIjg83b1kuVyMNLLDbbbh69hGpMuOswSCVRwVBVVXkeYntG4QS2D7yrsuyfIz662OmZJbFwOHeOkxSnNKAnG2RImWwIKSnb120DXAeoQWOgcF2ZGkRAxG8p8xSrhYTkr4FCS+XZ6zf3ZIWGS9epORJpNT5EStO0KwWM6p2Rz7Jad4E2s5HhafwPDw8oLXmbrfjbL6iKAru9k3MIgW0MkwmEwgO5RVlHkVlBA0aBApl0hOLpKoq8jQnTTKCt4gQgyPQhslsxnr9QNfVLGYTnPOUWcZyMmO7uUdqSdX1EYP2YoR2WvaHPb6rWN8/kOZTinJO3w1RzAInLYfWMdgky2S0rNASGTzCudhwEIV6nmhtYQdPP/R0nWXoo59+9P+PjqUhHO06opFebNAi/TC4aJYXUJFKOPpfnzptH5BKHFeT79Wdp1oViD8qxRjTJvxj7Xny9fdr1xGKESN+zvj3Y3cfxgb0VM8gNq5CjkU+PgcX/BPWy3/4+MEU8u+pnuKJyBGVT86HOOE9QU/enfA2N/qp+OBO6d5SRcOrEBTOCaQIpCZD6yQmfIdA3dY423PY7satoMV56LsBEBg/DiGDwNpYxAfrCd0AWc98MUVLi3QDCZ5D37BvW+zg2e1b/BhE++L6BSaRPOzvUaN6E6nwLipJzy/m3N+tWa7mvHzxjLOzJV1z4LtvP9C2Ld/e3iFQzKcFRW5OuY0g6XrLpMw5O1vGlPnbQOknzKZT4MDD7gMPuw8xfd5Hqp5KIM+mNG3Lw2bPYV/jXGA2m5GmCXlm2G3uMWnG5fVLnHOkJFjrcdbz0cvXiGCpmwPffnGgGwa0rnn+8hV5mrDbPJCZDEfLu9sb3OAhKK7PP2I6mbGzFcpcUM4mESJKFNvNnl/9+gu++/odX3/3lk9fv6KqGq5fXxAITMpVfL114Hf/7t9y9ew55XTOw8MdPrQYUSBdVNrutw8Ul+foRNFs2sjRblqeXb5kfXPPpMwwzy7ZPazRWrPf7zm/mHH/4TtWF+fs9vc8mzxjs92zXF1F4yMXKLM5iSqYzhOQjqXMsbqjSSSvrhbU1QPz2YRv1wPrw55nZsnd3R2L2YRpltEMA3XdcnY+oSg/plrvYYi2qvfrOz568Yxv37wnT3Kurq5omz1Z+QJ0wFNhkgusB2Eynl1fk/3qS6R3dIOna1qU0bjOkRUGJQQmF3TWI6VmMZ3T9x3C9VT7DUJaprMlQ9NiqwqpAl3X8ezZC9Jywr46IFXB+TzDpDHFyRjDevtAtT+QZQm36w0eSIg+QkoK2q4mT3LSJGG92aHTlvPzC4xzyF7QO8/gHXlpTve2t4GutwyD5dC0VF1P0zicE6PqProE+jBgRtqwQBD8EKmG1hHcANYSE4JiwRchRBl+SEZ4Q8brJ6KXypGnIoOL1rbhcZdwrEtHZkrsmsWxQkWe+u/h4Kd65sUo84ywuhRjTUIQxgJ9EvmLgMSBkCfYPIyU5z/1+MEU8qdHVEn94Wr3x+g4J2XXWNB9GFWdw8Bg+2gxOyo9g/AMohuDEBTe96Mj23B6EyyKoRsIVkRBiXX0eISNtpvBWlzXEdy4svsOEfS49XMoLUiSmHBUlAmDlxid0DuP7yxZWqB1Su8dwYaToddgLdfX1/xsfsbgLIlO2a4fqKuBdze3TLIlfdthlIkRY2mU96/mSzKTgYr+0lmSslpM6bqOvq1RytG3PX1vyScZk1KADbjBUYuaw76l7xUqm1FmBVmWkGQpeTlFmTTGg20e4jXTUVAhtaI+7BiGnocxy1Pi0SjefvUVve3wHs5WF6hUkCdzvPQcmh1ffPsbFosVwiRxqFhO+e1vfo028PrlKw5VS1sd0FLx/v0Nz55f8N133/HR60/ZbrdcXFyxWC3jTqaqmM6XGJOiDWQ6JTWGpq9Rx0Gz8AQ3kJmMd9We81XJ3cMDKzXFO8VgB6bTHMKADzERqusGZtMlQztgdEaSaNIkoekqQohdprUuegPlUG/2LBYLQoCuc3ihuX34mh//+BdczqecXaxAK6wTKGnIEoEPCXZoybMMYwznkyVNdYihxtMyCrFU4GJxSapiaHSiNEaP9r/eQt+ySAVlNiUEz+AsQsW5UvTyiYplrRJMluC8Zxg6hAroQiGdJskz1ndrpLL44OK8Jkto6gqCYzopSLIkhoyPNhJN1zCZTVGJIlsXpGmKDZ4sSdhv1+R5ymQyiSHn4/OJ1NwBN/R0zpEmebS70PHedMFhg8WGGAZtrcP7wCNFODoXInXciUt5lGPG5u6JcNCH6NmEiFxsOdpyxIIhIuNNCBQCFyxH5++nmPT36s9x6Amnon4SKT6Bd56eB4EPj3i7PzojiieERR/jKUUYs0URpxqn/ohV9992/GAK+R8wVmDczown/ai8InK14z+Pk+fw6K8y2D6a4Lct2/WGrq/AduRaIqRHStDSg7fYvgXvUdJTljlSapwNHMIubr8DiDwjE2YsXA3BeaT19P3Aeh14eGhRekOWRUx+sVjEtB+huVwuEHL0gvCRJeqDpaq2Mdyg65lOp5SFxvbgui2/+/INZV6Q5yVKKz5+fcarlysm5YIQAkNXsdttOOw3CCHY7O/o2mj6n2UZWkBeZJhM0biBJE0hyZkbgzSR8NjbDist+ITZ2QqkoB250svLc1wfvafbtiVPCg7NQBAZBgEudkTOWe43a/Z1y/VHn5PkGfP5nCItaJsKOzQ8PNxTNTu++OobEpVgxcB2W/Gwbfkn/8V/SaKjAOQv/uF/wnb9wL7es1yW/Gf/6S+R3tHUFSYxzHXGN19+y5//4hfcr9fc363J84Kqrfn62294+fwlQThkgLbrCGGgLAsOhwNlAuv7DZOyRLh3vP1wx+FQ8+LqEhK4ef+Os/kVVVUzKUqyokTpWLycC5ydXRK8o6nj4lJMcwiSpqmQCN7dbvBDTyITBidIyxWVS8jzM9YPBz5/9YzpJMfo6I1jlCYTgrNZhiSwW9/x8598zHxWcPPmLbkSXF6sCCi++OIrVsspIvTMygmOFNc1vHvzDdPpjMvLSy5ffETf1hRZSlttoh/7UON8lLHbIWBMSmpS2m7PYBu8c1wurqiyltv3HxjaDq1guVwxmUzY7u5prRuHuoZkUmK7uBttbMtPfvTj6Hff9cyXF2M4xoGur5jPS4JPaNsOb3umZUqZ5YjhQF0PyMQwX12Q6BScZvBDjH7TmnaIHDQXNFKmKB072yAVQo33PNH+Nna3/ijewBiDlQ6hotpSIQgqQUiFNgrMUTzon3DVQYxOpoQxBGjExYWSpwIes0dHjHscZEb7299z4gqRWng8zKgG9d5H36jjw46c9j8ihDyecn9fJfrH46kq8/cVn99/nHvyfXi0tLQxOHYYBvq+ZehrpLdgZKSNOclgWwQxZzCM3YLWoESCTCAX0UnQ92CJgxatJASDDR1Sx2BcFYZIGfIQhgj79F0HchQieRt9iIViPlthu571w5bjpFubqI6UUlHXMX4L/wjh5ElOVkZMVCXR6OjdzT3r+wf2+z06CBKtcbZDGchMQjmf4kVk15TlFG/dSUnX+Z4knzAx0TZA6+i+uFkfKLOcNMk57FvSpOD84hIf4sJzOYvDw6Hr2awrKufIi5KL62uevUzQ0rDZ3GGbA/tqhyky2raJME2Wc3UeAzDaoSbR73m4vePbr77g6uoCrTXb/YZpOWOxWPD+/XveffeOTz95QdPUbPcHnr/8iOVyyft3N5xdX7PbHlitltTv4sKaakM9OFKjo7gkuNHHJNJTXbAE4Uk0qKCYX64wOiCFIUsTmrZCZ3F+EIJDmzgcq+ua+QLCaF9cliU+eLzv8e5IyRtQEqpujyXw4sVLvnn7Bq0lVR3Vq4IzAobtfsekSPCjTiHgscGijKGYzmjd1zxs1ry6fM3gYqh3lijmyzOEMpRTQ9NGamKRZwy2o64PzKdT6v2W2XTKdDpFyRRn432ktUJqSfB2vE8AodEmo97t6exAkmiMMiTGxM/i7kCWJwjvo6BtsOyqhq5uuLq+QAhFEJo0lUzShL6rqKqOzCSkJmG3r7HjQDJPFSaBofMoaWIBP1rDCnuyqh76+DmFGOxs3YiHexGLoPdRaOPFqahKxu5aBiwBhRkZIfH36GRUih5DKwClNEdARYroufToffLUyCucmC6M+QdP9SxP2SzxpCOC48eO/fv16o9h6pHBElk0J7OssZ5pFf7w8X/L8YMr5PBIH/8+FShyLI/yWeG/r/N8ugA45wg2YmdaiJEBMuD9AEIzdD1a+sjS8HH71XUeZwVWBAQaETTe1/TO0rcdiVBoZTCJxIme1mqCc2ijGJyj7S1gcUGg0zGuyvd4BtI05yBNFAolo+ReeMoiGm3lSUp2fU3fdigi/me9J8iAty19b2k3G7qhxeNYrJYs5ucYEYdY/VCRF4ZEaQ67hryMDJEgFU3XMFusICh8PzApplgfSLIJIUA+STg/N3gHbdvTu55D3VA1NW17oO0avvqmpet6JIJPPv2Ysiyxg4u+0lKz29aApGljV6+HPeuHLW3bkhQZ11fPWZQ5w1AgnOV8Mef+LmL2z58/R+uE7777Dq01P/rsNefLc9pmy+X1C25v76mrjucvrtltv+P92zecn61wznF9fc3NzQ1CiEg1tR1SQrWvKIsCQqAbWkwScfPZdMLQx+iz4Dw68xRlTHyaLBcIoZAjfUyN8XeHZstkMmFouvj58p62btAqIVgQLubK7usNi7MpaamRwoMbcNZh0uiSaPuoxLSuoEgT0CrytvcV+bQk7Qf21YFFnhKEwg89OM9yvmBSLknMFCECdb/l/PwcAQxVzbLQ6NCzbvZk5yusj/J7F8aBp47K5WGICmHvPUmikNLRVDvyxCOCRxuJyQLKDCgNWkYr2ME2dJsBax2z+YTlfIYdHEJpyjSlOtzT9B1aJTg/0HQ1dd9FuNA7pklGNIg7CnE8LoRoC0sUlYUgGHxkjAURQ1cIgTSNn0sXPMFF3oezsZM91oVjXKOG2EE7jzkWU/2YKhTUseDGhCV4tI09FlkXHnMJ4lON7oVHlkr8L/54ZvCRJ/5IRRQnjFtKeerIx7nn48/IuFM/ZS4Q4Rsr7B8q2f+W4wdVyONg4fGFStRp8htCXGd9iBmZwrvTzygeV+mj/wmAUgEtNIqAkpZEl0gp8N3AIDwSiRQw+NiptEM7GvRUUQbcu+iaOFiargY9JosIhTEl58+nKC2oq5btOCwdBgtS4P5f6t6kR9YtS9N6dve11rm5+2nvvXkjMoJIiEylUkIpYMSwQEgMkJghMaq/AH+DUUk1KAEzfgGjklCplKQKqpIsVWVWNhVxuzj3NN5Z93W7Y7A+cz8nEiojCoQCuzq6fsybY25m39p7r/W+zxtH6rrFp8ThJHK/PAUgz3mFmfvxhqZpGLo915dXlEXNMEnhr23B9x/ec9M/0DQL0IoQ4F/++V+xbBesmpbPXq5YPW9IoaKbekiReqm4vbvl/lZxcXnF4Du69yPr9Za6aolREYaJ0+EtxkqPXkh/CudKYaEsa1H+qIa2LGk/W7JcrsQePXQEP7Hf3RH9KKcVW5DnNPDt5SUxZaq6ZvIj0U/Ecc9wiJjC8fKLH3I87unCxM+++o77uwO/+/u/y2KGXu12B3a7HUklLi+ved2sSWHim2/foW3Ji2fXDMOArWru7t/z+uU1JOlV+mmk0IoUZYH2U6RZtTAoptTj40RRaxatJQcv0XKrFV/84AeklOj7E1dXZ/11xbIqaBdr0Ir1ZsXpdJhxuSeSgrquOJ2O9Keezz77jDwFdvd7UlJs6wUX2zXPtpdklajbgsuFpa4LppAZx8j93ZGf/OQnVBqqOPL7X75m1ZRslhVf332gdIAKkkpIpCxb2mB4/Zlmv7slpcDVy2vefPc9p6HndDqhreXbt28IxhEQrHLTWFxZkJSiXazIKnG4v6NUGVM4umEi2YLk4X53R7tccOo6dg9H6qWnbCu22ysWTcs4wXpZo63l3e0Hjvc3lMay7z2aSNtUlHbAkXCtYYpHQsg4W2Gtw+oCFTwpgK1rQoYUM1NKhCzKlaZpiEpjlHC98/z6nrlKaRYz5I9qRgrCUkrhTD1VhDR7TbIm8uT4zkY/tTbsk3xRjEkfJ5FJ7TEf9UA+3jBmpZ7MTI9F+Vy8noaiGY1OPMolIWOMIDJyPrfOz5VPWjom/38bvvz/2i09WlvT4/EC5KSS50HAxwOFhBTfnDPEiMoBkyMhJ7KCaVavpDhSGHGwaUCniFfSlxu9FDL/iAZVOFWyKB02T0y5R1kLhWUEwigtm/XS0dQF2llQCuccdV1jjKNLA11/IkYBCRVGo51FpUTnexSWrhPlS3fsaduRIQzcPtzz6tkVm1WDsgp05PrZmhwXVOWC7999QGX48RcvuZzZIofuxN1uz6JV1JXkHT48eJaLLZeXl/g4cVleYnSBtQVKZQ6HIymCM5mhnwgJFttXtIsVVVFC8hz7npCDmJ+yFO9x7PFhZH/qSFPg8P6WYepJSuPqhoeHB5TKbHc3NO2Stmxw2mANlGXFZDMhZ+7vPgDQLhf89o9/hB8nPrz7nuXFVopkN7BoliQ89zcfWC9Xwq3QmruHByBwcXHBqqk5nWq6IVBXkbHryEEWyuVqRU4CMxs8mMrS9wJ0ssqKP8CVlLZCZ+GBl65gig2omnZm2p9CiT0AACAASURBVKScySQUBms1ISRsUUlyTZD2iC4daRiwVct49AzHnuvrLVodaeoFaEs3yGPetyUxjiyaJbvDkcJCZRXT2GF0oKo12ogzcRz2qDxSWc3UPwgvX2+IuZN2QxCWSGUc68WSt/mG5WJNP00c9iOr5Yl1e4GqA4XOxJhwxpKQ04sxmqKqUblm/+4XfLG8wPcdUz+wWC0Zg6cPExe1ZrFosKYkZcMYPCEb3nz9Cx5OOy7qiilMFKZgs2kEb7FcyIwlRgrT4HOPJeKUBSaGIVGURlCt8+CrtBmlAkVUjJNijAEfRGWS5x28OEvETQtyPZ8NgmfRgAK0dvMprXhK/ko8atSfcgiEiCqLxDm96Bz5bh9318Jbktat1Rp1dqeq9KQyyaJ4ybPKTp89LojoOWpp86mkMDoL31oZnBK0gFIKq7Lk/p67Dv+m3vIv3X5jCvnHT8jZ6POJnOdjeWISeHxIT4PEmCGkTEjyYoXJk/0kUKucUc7Iip0jKmVC9PSnTgJljcJkIyAkm7A4mqoUCqDW+JBoSk0XO1KIaODYHwlKjELTMBKTJiThCAcMylqqxRYIHA4Hhoc9AKbQooJwkTRkwtBzPJ7Y3d3ih5H++pLNcsV6vUZrSR459nvIJxa1o67WDH6EHHHaU9cFz7fPcE3JOI6s2kQ39KQsJhqSYr97IPiILi1tXcqQp3BcLDfUZUM/TBwPD7BY8uHDe5xzWGuZhgOgUcahUhYs8HAiTh5PoNmuWS6X7B4OvH55TVVVvH93x373AZVHFm1N0VbYoxE9elac9ieKqiRHGIcj3nt290f6/sR2c0FblnTdkcsXL6iaFcdjx8Xlitvdnuvr53TdkXcf7klJs1mtuL29xWgoipK7/YFlW9I0jof79xSFZRw8fgykkAleEZTmcPKsNwUYZsRrxlmLszXOlaSkpBhgCT5jHBSuZNGu6PzIcrlG5YjWULoSd1ESpkH44G3Jcr0gRmT+kiNVWQk0bfJijFKR0/GBRSPP835/gKQZhp5mtZTdtg+kGCidRSkJJMk5M429KIScZpoy9aLl2lb8669+Qd2U+DDiVGCxaMhEtLLEIDtaY+QEWpgCm0VHP/iAz56kFTd34sadphE/9Fyul2zapchR2xYfoChLvv3FN9ze3rLdLChswf7YcflsI5RBP9MN5983Rk+OnlwbPIEULWAR/KtI+1L2hOAlSDlmfFBEkiBhYZblnVsp9qllcS6W6dMec5plh2F6GlKe4x/LsnwEZY3BPwK7oo8oo4mPmZn6scDmfHaan3fYwopRSn+y6ZQHpeZ6lCWMRinBZ89zMYmwlIKXZuep9OU1OinOHJekPvXG/G2335hCfp5dyu9xTvA4f1YBTyERaVbpzFp/SJkweEkLCZnD6UR3PHB4uKMwAe1gzAWFmyWGQQaYPsiUPMWMs4acIU2WXfLSslGJUlsKnRmVp2otZSPBEwUGnSD7iYyQ62LIFEVF1tIXvbm54dT3HI8dKYM1hlMnqTMheoY+ojB4EikMfPPtLa6q2a6WxGHgyy9f0NaOi4sNp9MdIXgWlQD+i6ah66HrRv7oj/8ZyoC1mi8+f4V1EMPEh3c3HA4HqroFNOvNht0oi09tHTYrhtORonKSyn46sG1WmELoft14xGrFECLTNGGrmmWlUUTqWhJgcvLUThC1+2OHtZ5VofG94u7tO0xR8Pz5S25ubhnTvMt3lhfPr7DqkmkYeffuA8Np4uuv3rJ9fs3V1QWFa4njCZUjH96/ldCMMLA/Hvj3fuenDF2PYmS7vqQ77CkXCy6fbQRXbOTC6PselSLT0KEylE3J99+/59WLF0zTxLPLKxbrFe1yQXfacflsS/CBcRwwZU1RSsHxOXDoDmjAOghhoqlaDrt7rKnYXG747puv2Fy0qJwo2wXVoZ8t5ZGqaui6AYWj6zr2U6QpK5yOBD+w2z3wox/8EOMfuGgbxqGjKTU5GFCJ5XJNVprjccdw3NM2jqapOPYdV5cv8N7zB3/477Nwif54y+fPLnlxtcG4hpgkFCX4iLWOwQecK2nXW6q24U/+5E9YtCt0irQF5DSQRnj5fEVZLWbFiJaFzjn2hwNlTvz0h59z7Afu9juamQm/Ox6FmT+MnLo95MiqKjHaEZOcqEPogIEUj0y+n4d9BqMlcjAaTUR2t0UWF2dSzPNLTVIS1A4ISybLxirG8ml3ngRpG/NTati5CE/pU8y11rLzNsaAyUzjU52JyO5YKz0zzhUhp8dIOkIUfbs6E1jPWF3mWDggZ6wxoLz05z/C4eosJ4VzAHRWSfTk8Alp8Ve5/WYU8o8WtJwUmTMY/v/6l5lzVp+ORykRyYQZiiNHqUiIEzpFglKEMEc+ZdGimqLAzIyHRx6xFda39dLX6ofToz58moIMw7TB2EIyRLV8n9GKqDJFbdFZMyFpPYfTJNS/YWR3Gph8YtsoSJEuZAiazVIYMN/eDMSUcWlgv5toq5qvvnsrR0TzlpeXLU1ZotoCZSwQUXnisL+jP55oNwsWiwWn08AwdBjj5HFahS0c24trTOFo6hVKKUpn0Chi1JyOJ46DXFQvX30xa38z49Hz0I+8fv4Mu7Z03Z7v3rwj+YmcAlHHx+i6aYK2qfGmxhWGUMjOtA8DD6e3IqnEsd/t+fb9L/jTP/2nvHr1Ga9evWJxseT1Z5eMU8/7N1/xzV9+z7Pnr1lvL1m2DVOICJrM4lzJ119/y4vnl/MCKvbw6CdQ4ookCwq4Ox0EnoSBrAhDIAbL8TCybC1VVdIPR6ZpiZ800ySO1qIoiVlAS4lE8NKCIiWqtuLUdSSf2e12bK6u6YaeSKSoakrrcE3Fcj0CGuNKco6cTh2n00CM0+MOLYWBvpfg62maaFdLtNUsXctxL9p9YyuKcoE2JToErFGoJAEoPmpp/aXEdnvBdPhAzpG6EUt8USmm4JlCIGWZy+ScMBpp1eBQSWG1na32ws132mC1RqdI4TSVNcSU6H0PaWK7vWacJsZp4qJpMMpy97DHWFitFhyPosyqypKJRJg8loQzikIrslXCrkoerNBBp5wIURFTJmX9qPbCiHrFzOjirNVjkTu3XEniyjxjanOSjM3zTvDjwm0wj/jb82465UyKSYLR3bxzTorwcY15NP7IUFMcnCJ/RuryYxk7/8ynto4khqlZ1fLxYNSgPlGMP7ZzlH5SxPwKt9+MQk5+HBqc/wqfvgDAo/5TB8gqPk7Cg5YLLmXJ8XPGUFlHrpek1OFjYJgiJslRq25KnLGURS2Sp1mDDDDGHu8VMWbWq0v8GOimDh0jSSXKUkvYQRROckITM/goxbssakIAZRzBTzIgHCO7U4dB8fOjvJBNkdFp4jSMJK3YOEVdWyafue9GpjFxXRvWm5ZE4l/+7BtStmzXFbXTVNpwcdHgnOPFyytcWdMsWjbrJa4oyNngigJypB8EP2C0I6eOyY+cBk+eLEpZCdW9eo61hQw/Tz396Z77u68J08A/+Vf/iKEb2O0GdkdPN0S8rWeHbWJ7seLNu1sOp54hJcY0oZVlu73kzft3OO1IMWKM4qIsUTHw7HJBt3/gm7/453z56oq3rgDt2F6/xLYV37/9hnfvv6OuKlbbLUXREELielNR15fsTiNFVUFKhCmS0o66XRL9SFSSYLNardg/fGB5sWQ8dlRj5PPPrri/faApL8h4jg+31I2hLFcolYipF+mfitze3RNCYrHesLvfU1cFMZzoDkcmn1guFxwOB9p2yeXVM97/4luM1jhlaJcLpjFStw1Za25vPzAcD09tAJOpasPXX3/F69efk2Lk4vI5lozTkdv9nqJZUrRrXNUSs0GpyGK1Fb74w57rq+fkELl9/4G6KXj79i2rzZYXn71G2xqtNfd3O9afX+Cjx5SOqT+RXUFdCG3y+bM1fjiQZ7744XiiaVdgA7k0FHXm5EfS8IH1+oLVs2u6UYN2bNYlh/6IdYZXzTXHY8fubkdR1NRljTaKyR8hB2EXmRJtLcaWorm2DrQjJM04RCKKmAEkFchoLWYezdyKmcUG6WOLvKQMJfLjjvy8q3Uf1ZCztFGMhWH+OWoeOGamHElZ46dza0A2eUZrMfMwExtj4ix8Vjqi5gouiWWQOZNY1WMv3mlHzFLIdUb4AuocNpFQaLQ28ntHICuMivBJif83334jCrl0R86C/0+PQo9f8/HqqvTsJJQ8PKU0FiWGmyTtlqIo0G1LiIocJkoDKUyP7rToxRQkdv5AOYfMJiSjUmkt32MkP7MLCZM1NkH2gTHKLtEYBTai6kzUimkOuA3phMJT2kiv4xzSmtgurKgEspVdZooUKtEl6Hu4rCp+cn1JmEbalWMYj7R1w49eXPN+3/H1mxuqUgIODqfAxbrl6nqJsYlp2jMFh48TZM04FlSzxNH7gNUD2JqqaAlh4JQiGcvF9oqhO9GdbkinW4b+wNh33Hz/jsPDwNvbex5OnruT4k2XmLJmCB1aK5aVYXX7gMR/GUqbKRE79HjasWkqUhbpIikxhR6M5a8/PMhOhcTBRy4WJckHvv3me662S7bXV/TdwNAPLNsFMWasKQnJE42nLiRRx2pYLBpO/ZHSlsQpMqWBwgrTom1r7g8HOZ1oyzTGmauTOJ4esFY9MqFjFE60c4aYZEfYnTpc6WcHn2aaPJeX13z99ffUzxrUFCBlkY4aQ1mWpBQoCitDUu1AZ9qmoDILchLsq7agCWw3Fzijebj/QFM8g7KksCVl1RD8CEZAYT4mnC3wIVLVNV0/CF87BPq+o26kRVDWDdpV1EUrWIlpIBNRWoqZdRqnEkZ5TA6UdUNdVdze3LPrOw595iJOmJDmGcOJCcPzZy+pCkNIkdMoTtkQPRfNQmz7+x3aWS4vLxnGI37qCdMEIVIYR9QWZQxRG3KSU3BhZNesUsYZI8EvWFAlmIzOAr1LyFAy5zQDx84DSnlN5qwhqR/5LJd4qhsfbwYjwqbJ8yBbDlpSb7TSH6nknlAfWX3KPTl3DHKSyAmAHM6CCeE/JSX698cFR4m3JGvzZATKWjguHwfOP4LEfr0a+htRyOFJOy5nlLk/rtInLwKcX5ggcsSzxBwwSnSYySgB8SSHKiuMlxQYrQPaSUYkPhNyZBqO0oJREZ8LTDYknSnlJM7QB1ltY6KqyscX3ydDHDPKBUKYRDs+97k6f+LkR/aHjkMXcBYIkdZaphhYFQULa+ljwPuMTlCVjqwMfd+jnaZdiLLClZayWLHf73n+vKXUBf2+ZvAZVypM1dAHxbff/oLNuqGwmjgEyrpisVqzPxy4v82sWhmeKhfQcSIEBUaK3DRNvPnmz1HJEyfP13/xlxx3R/p+ZAgTQx/5btezGxLHbDkGoeMZm1iWlkqN1LWhco6UA7WzpAhdPKuOMim5OTsx4rCQIq3ThGQZp8i/enskxYOgB9izvbnhBw/3rNdr6mbN3Ycbnr14Lak9OnM8Htk+e0mOiRQCrixwSpjxzmrGk0dZR2kdIZdoZ4kkKlcSho6LVto/RhmyicSsqHRiOHWst5eEODJMmUW74tQNoBJ1XTFNHq0N1hVcbGeUsEooHdnt76jKhna15fb2PcoUlHVJ3Tb4UXI/vRUM7mJZ0w8HDvsTV9eiiZ/GDE5T1CVWWRbtBafTgVLbOUlHBvZGFVhjKAtHTprMRFVo2qrm+mpL35047I7ktcS9mRKmOGERl2OYQJUGtGYKHm0L6tIxfvuG0J9onQUvbYHBT9gi09gMWjNiGXc9ZEXSSOunajieBpxzZD8xjQMfdkcm31EYzXa9wqLJPpCRwmVKgx8nJu+ZwkDCYMsl2pgZhCdAvEe5ntGEKC5O0bhL2YoxP56kP5Ugq8cetwLOGPKcMzYaovGz2g28Fr9JNhry2cCnOO+OU1ZkHWW4jbyHs9CwSUayD1DCrUcx89ERfIOK8xIj/XGUIC58jmhtpR2kBDGgsyapJMqXnH+tUAn4DSrk6dGo+XScEFTt05HoXNRTgjz30mYVKcpY6TclRFalFNNJ0ceMHz0aj1EyGLHKQ06ivvCekCNpCFht0M7h6ixqF50oy5qyWgrYPgxMk8epiCsMMQrLpQ8Ti1KzbCW78tD1nJYVN7c997sHfAxs6hKPpesD24s1n21qHm7vGEdPVJ7VYknz4pL7hz3f3+95dnWNcwqn4dVqyd1+x9D1/MHvfUkIidvbW9bbBUPX8cMf/JBIT9f1lMbSWA39nnh/j1aet9//jDcoXNuyffGK5WJFJPHmmz/HZs3uruPbN++5vT8SjQzRppQ4+cgYFPe+lDe4nni9KqlM5sVFSVNK4VRGEZWYg4ZJ5FtF1nMyjGccPdZaUp5glBZLIOFUJrtI7sUwEdPAXYL3J8U//tMdIe1R6it+Z+P4yWff8vs//aGA0JxjOt1hmpIpJ/JU4ShQWWNtQVO3dP0ByCijaZqG7niiXRiqpmIaDBeXz/jw7s0c9yWvy6vPn6O1oBZ8lAi+tm2piprj8cjxeOLi4pKbmzvK0vGXf/Xn/M6/+wekBM+ffUaYOrwf+Mu/+jN++rt/yOX2OSlP3H54S1VVPNxOlOUCZx03+1uapoIkARRp6qhdge8H6qZE4Vm0NdiSjKabBhbVirIVRVRRi5KlO8LF9hnG1WSz4OJ6y/39LWVdoL3lanONpcC5goiEL1jTCLY4R14827K7f+D+/paqcixXQlQ8HiTqLTQaW9RMXoMfiCgWiwWFq0A77u8OdN0RkmfZLtGF4dkmUlgZPO/2d5zGCZU9pStom4YyGpxTDEpeS6UcPkGK4sEwRs2KokzOUXbhNkPWJCXDV6UUrjSPtUF/tAtPSfjfZh5sSkD6XCDn1oU4wUWOLJpyxRzaQ0qRlMJj+HFK8ryFLDv6s+wxR+lva60JzHM25dFai6Q5S1KYyYaU5euSFm28mnkwWT0xWzKZfHaZ/hpmIPgNKuQf354KdvqEfvjx53L+yOn5iUifeTouTOUYMmRLCH52iAWcQvCkU5CVVckFn5GBp4+ZGAU5WeEgG5yDcTyhdKaoK1SCoctYVwCG4EdGAt0pMvqJFBK1GvFOMSoB/bTWkPNE1x0o3SChu9ZCLOdcyMT1dsW+O5HxtFWFq+RC3ugM6wXHrqcqG7788kt2+xPr9ZIOaKoVy7JGxZEP7+9QIdGfjtRlxWJZUTWCaQ3DiTf3N6xWCwiRYz9x340oU2Fd5DRFdt1EUZVMObMPgYGMM7BpDS8vCgoSLy8qrAWMZjifSDBUCsbQ05qKYRjQ2mFSIKdI0gWTSQSQAVwyQIFPAzKPVFTK4HXmoo6kbNkNmn96O/Hz7g0//OJzFnViPHXU1ZpluaBylhwlsSeECVLGOEs4BLSa5X+VzAqGFDDZMnaenAwhOtpqSVGs5P0240RzELlZSgGUI/oggzBriTlTNQ03778XMNQkAR5dN1AVlmHoub56OQ/GNQ/3DzMDaCQET1kWok2fpY9GRwFcqSU5WWk1KDUjlp0c1Y3gX5VSKJ3mFk6NtZq7mwFXlGhbsFpvKQs3h4dEQorUi7VksSKFBCMtp/MgzhqRRjZ1SVWVgnQIArhqmgXe9/hjomo3clKOECbP8dBhqwX9EKiqhraSIWlOgUmLAqo7HjAqUhlIM55XuYRxlhQyJon5BaWoS+kjZ+S0YIpy7okryGku3hK+8LFV/rEufFQjtJXv0+nsDjeC0UgiSoDIEwv8LIM89+BnlIFYiOai/fRzznVJBpgzFjvO2cKPOBZxZZ9Tx5J+ql0iafybQ8yco+ACzgEWv0aoBPymFPL81Bf/pCh/NHmWaXH65P48x2anuXifSYcC0Apz9uaEc5mURXsujOQMKpIyQlNDY2wFyJvAuYLaFtJeGLtZFyqFX+VEpwLTNBHnAYhKEZct3ltud3spKDGRo6bzinGKbJcFtrAc+pEMDGPgy2cbuq4j6YK744lp6nCq4sVmSVloVitHNwZJnU8Tha3IjPjoUQYut0uccxzHnsLVtO2Wh9u3rC83AvcqSwqjQA8cu1vIhs36GUVdc/+wR+kSP00YZTFMLBYLvn97RzSGAc1x8kSjKfFs2oLnm5LFQtOWBe2qlgvAWHTS+CzDX03GVBCTQSlHjkFIfzERIkxK4VPiYdAce09hFA5x8coLILyZsi5E+lZMjA+K0zHwP//xv+APf/IZL6+WfPvdz3nNFywXC1LwlHXDNHiKuXfulPBFQhSqX1E4BmPoD530ka2hbGtxOy43AHTjwMItiDEyjIPMXqxlt7sXwqI2El5RSWFcry5AG7IC40Rv3w07NpeXlHXFGAYOpyNFLYPHfhoJOfHq+Qt8nKjbDSl2uEJhdSWpVnWBjx1KZcpKFgN5H2e0ykzRY4KbiYGBbhzkWK8Vy/UlMUYWKy/4COvmeDUxzBSlxBpqA34MIr0zZ2a4nV3RQuV0jaOwCj8MVK3EClZlQV2XvH1/hysbqjJwsaqw2tAPe5QGS2R4uOHQDyIFLSxNu8DaAMFTaokwiykStdjzVYqgLNo4EhrrrKQFpfxoWydLVFwiP6o5niSEn7ogn6iEc+9bi6xEduoiGYwiGxHy4bwgfGIUmqUpGcRkGMGQRWY4tw/O4e4gGQh5XiDlwWlhkp9/nvrY3j/fx6cBGUqdOYy/pE3/FW6/EYU8z46tp2U1P96fE58W9vkzCuTJUqB1fnwjqvmJNnM/KkWwRhJTxnFiilECk01muVlQlQ04QwqJoZ8Yu4kujrJbKczjwGMK4IfANCbGcCSlQN0ULJqKRbvAxyMpT1TOMmnN7uHI6dRhtTi57v1IkyNVK9mIY7J89fYdn7+4oqysJL6vtvgc6buebbnmcByFa86Bl1/+Frv7A8+Xq/niVjSrNVVds44d/elEN/Zcv/4tRn/LeDry+tUXoCI5DXLhJ43PipwMtSoY+8D2qqYeI21d8/37W9q2xbQtb25uOSUBE/30tza83rZs6xZXjKxWC7LVZGPxUaF8ROdEqQ06yYUWlWYMgRgM0/2O6AOF0wxdxPeZX9ze0o0ACWvhcmlFn28tgQkTPXWhaV3Jj55JQO/Pbu756o/ueX294L/4D36P3e0bjg+Ky+ev8J2nKSv60wFtC4qmZugDKhf0xz2FE4aKtWI2KZqaZ+VrrKlQrsJq8MmTfOB43FM2LX3n6bo9PvQ471ldXNOHgW+++YbPX73m9uGe9eVz7u4e0M6QdEHZXEomLIZjN6B0piwajDFcXGwpi5qmaXl2dc3D7sDz7UrYP1ZhdYQ0MaaR5XrBOHqUtlhTMY0d+/u3aGeFy4EFFMvVluPxSK0N2IoQR7Rp6f2Jwsoi04dBhvvKUhSWOEWUMjhT8NU3f4V1cPVsO0PbMilMuMqR08D19jnVYkm5aMm65OgDrz5/JaqMEOnGjiElbt9+w9gf0Aaqeg4w1mC1kwVoFAnwoY/k1OPmgHJtHZlMt++IyqJNga0gK8nUBUhaY61BjDl5ZlMpaYcwnzTmoafUCchZEc701CTJ9lpBUVpCyrJfiJEUpaWSFWgDyefHgIs8s2nSOcAiRWkxPvbBPWrG7GozoXImZiunsSzDWa2fZMoST6nOqRjSaiGjtUIlJb6YJMU+/v+1tfKxUiX/0v3n/z99nES/qf/m8UNlLVCqlD75XqNF4qMwTGPAuowfIoXLFKogGXA6gxWwvY+KQjlAUVUVgY6so7QAfBSuRC0pRL1PDINhHLyYYoziMI489IHSRgoDl4WiaS1KR6yqqG3Fzc1EGDwLa2jLkmHyKAtGJ0wOVM6itOJ0OsLQ8eJ6xc9+/i111WDrkjy/em2xIIWMwtAUK6xWuNxy97Bjtb3AFQuMgjANWCbGIbBaL7lNe3LI5CEyxglTOWylifuTaIz1wKaF55uK7bKmURpbl7hKQGEpZzE4ZI3TkkjjCkNMSpx4IYG2hMMBU9RMSZEqzdB1DPHJKzCOijvjaRtHyBOlMjgNKQrAKauMNZZnVzV3p8jX7478r//85/zej7/gYlHT748sNxthchhHSB5XGHS2s4xMwplXqw03796y2lwQvKFuFrOSA/zQCw3Sj+SUCD6hjaU7HGkXNVplnC3ZdyfGwUOS1sfhcIKsOZ1OrBpHW5ccDqPowtuWbm9pmhU5eAyi37fasGiWKCMxcylIuyHGiLUlYUrkQs0KGCSAJCWmsYfoKOsV2miZCxk5z4AmRZjGgM4RlAPlmEJC5UBMGXIxX0fnODR5zZpK0x9viTGQI/STIKHbtqVZlqKlV5aUNMuyxeRE3/X048QwTRyOO6buxMVyQVtWZD2fYMcA2qFdRVUKA0kctvJxDrN8D6EXJpVBaYwVBZox8v6IShxBcj0bURppIRp+vLlDnc02yOuDexQozG+3uS7MmZrGkJDNWnrUPGtSTAgFYK4584n/nEqmED6MRsFs7T+3gAVjOy9kysiMM8enIGYlm8889+zV/81Q89ex58OvUMiVUv8A+M+A9znn353v2wL/E/Al8BXwX+ac75X86/8d8J8CHfBf55z/2a/0SJKsrlnJ2qozRLK8KWGG3py54x4xVMwtlhyJs00/zsES0zTRj5Mcl2KgMg6nM7pU6JQJKnPseqYpYcpB1AUpoG1J0RrUFInTJE6+KbFZLIlloO97TmqgKmoWrZPEoCmSeo/vBnb3R05zuHGpz31QjTINMTuWlcEqRYgTLz//TKzCOVJWkaapuN8fsCkRxx4/90e3lyvevfueqmtZr7bc3++Y9jt+vHnOcrkR7vbyQhaQ4YHF5pLF+hmFeUtInuZizTRNKJPo+0y9XNCPE+tFyzh69g8jQ4CuS5z6jtu+YyLxYt3ww+s1z5qagkxRRRbrBZOPuKLGWEsk05YTPigZ9k0SQRdUhc9Gsi9ffEHWLWMwPLy5RZU1X3xRMfiB/njidNwTAux2gWfbRnZGyQudMsCiLTFKUZrAujIMq4b/7eu3/PXb93z5e7k98wAAIABJREFU/Ir/8Kc/IClFUQgBUmtN78HYBpULQj5QOMvyYkPMBc6WZFugbUlSE37KKCpCnhjHkeVqzX53ZLlccnHR0g8j9WJFJGOUpa5bjqeRy8vnfPf2HUZl3r/9BRe/u+T29oaqqthebfn+zVtOPnLpFDe397x49Rl127Db7TClY1VcQTiwXl0wdiNVXTBNHSoGcmokWPt4izGZodvz5s232KKkWcvOXhCvBm0LhhBpKw96xE8SQdjWDfuuxxh5n7Z1RUiKaZBwa2sKNss143Cibi64v7/n8HCkPw08f7bC2oJTr3B1Az5hjOyI9/sOazVGZ9pCs3n+jGFYURSFiA2suC+NknDlnDOncSIlPSMzxOMg9ntFjCI50yqhyIxdD9ZQli1GW1EjzcHNPMoOzy2Up83cxyTD86ldxCkaq+w8D4uQhHwaYp6zRBV1WdGP/WOuQQxBZHAxkUwmhSA/W3lJI8p5NgXK4hGDxiotsCuCSC2BnDU56ycTk8qoJIoVnSFEjTZKcoi1Ij/+p3+t9sqvYh3674G/80v3/bfAP8w5/xj4h/PfAf4T4Mfzn78L/L1f+ZH80u3xl8gfO5z0vMoZmBnR5yc+hPDYIwee+u0xzZAdkadppzG2wFpHWZbSM5xh9UaJYkGIcQ3OWGLMTJPwUo7dQQh6VSU9UJ/EqGEcXsOkFcMUgYw1mqvVgu2y5OqixTqB7Pgp4ydQFCy3G0xhyDZRVY4YBvx4oqwKrFGsljVGCSlttdrw4d0NTVvy7NmWqqq4v70n+kTXdZJOZAynw4HhcCBMI4vNBdo6+uOBuihx5YJFuxbuijUo60TtoJW8qYEheHofGULgclWxWhY4F1kuHO26wJaZotEoaySkJUtwgTOaHCdckQlxYuqPDIcHQn/gcHfDzds3vPnuW/ww0lYtP/rxT/ntH/0OP/jxv8OLFy9YrxZzf1vhs/Q086O6IAEJo0Bnz7oybFrNPiT++t09//qb73m436NTJE2jQKd8ZvCJom5o2iX1YoktSurFBmyB0oXojZVhGEaUNcSgKMuaHCKuULLTHD0Jy6n38zAsst8/cHv3jjCcMMqDmjCMEqgRJsE/TAlrK8YhMIxRLPpKYbUhhgkf53bgeTet5oFXjhSFFC1jFUpJwIYCdNbUtXu8PoSXnYWJbgx+kjCIkDTOlY/XiDGi7NIAKZHCiMpBevAqkeLI0B35cHPP7nCiXpSYwjFOgWwKotIM0whaeu1az4x+bVivL2jm0GWlz9eUDGan4JlSlIOZFpqhMQrjBP07hsjkB1KaKKzCOUNhobCG0ljRh+fzKTvPf8597nORk8VFWhhPhVWso4AGpQVHK7vmJzS2Ukp+dsizc/tJwihPVSJkye8F2YVLjeERCXyuOSp9lIkwdw9UEnmkbNblweSsiDkJ9O+jx3TmS53rnNx+9V3537ojzzn/I6XUl790938O/Mfzx/8D8L8A/818//+Y5RH9sVJqo5R6mXP+/ld+RMxtlvnjc0H++KhxjlI6219DmIePMQrCdL5prQk+oBEVilLIcdLKi2GNhaRkSh/jvIuQXEKvIsFrCLLi991IUVqKohKJ0nwhWRRxCBAhejF7BBWFeW0KKqcorBH6XXcgx8TyYonWGWMDF1dLuqNhPE4YW6EwHA47qosVVV1g6xqlLdViyfrYcTw+4FzJcr1if7/n3fdv2GzXHPuexWJBXS/ouyNVWVLWa4rgSV4ShExRghOzVEAxqUjbFoz+HDytOZ46lDES/tyWFCZwcbXG2EzV1OjCYBJEZRm9OChziKASRV3gTIGyGlJEG+gjTJ3nZrfDq5K+V1QlNE1PU9asrxeYJKlDRilxZiYZOuskx9NEJGuNNpoQFFYHnq9KMJH9YeKv375nuahoWkfTLHAp4bKh7wfUoqFoFhKoHQHtsEVJVTXCGz8O0qNUgvEtCovKkTgEUoqMY8QVCxb1guPxyN2HO+pSFq5hGinLlhQ828sX7PZHktJ03UA7DECibiw5Jk6nE+M4UlQ12jiUmtifTrSVBWUoyppx7Al+wlqL91mImlaLiUobqraSnWlUqGRJmllZ4jkb5YwrmKYDZr1+BEA5o0jOCMAqK6L3VG1NxhPiQOks97c7utOBpqxYz/RCpWXDExO0iwU5Czht6Efadslq3Upo8qzgcGV6JI7mpIQaqKRP75yhcAatHcPkGfpAmnfUWmusc6Q5BCJnYaSnFGbHtsIYUfAoI207raWdce5Rf6wE+WVVy8ddike41vlOpVAzpiMkOck45/BBmOp5bm35MOvIecLoysZSCrCZ69F5ITkX8zP+I8X4NxYJpRC13LmACyB93u3/OhXz375H/vyj4vwWeD5//Br49qOv+26+728UcqXU30V27Ty/uiKdE6e1mlUs82ZcCUAnfuTXMkbkRSEJvSxGzzgNsy5cyG3RT8LfYB6MWCvuMa0f5Yk+ZHJI8vx5L/bgqKltJKrIYrXE2ZK+72kWQiPTWnPad3gfSPGI0Qo/BabjSDgNRDXIoCRbmqbhMJxorGWrMi+vLinqRvI0p4EUHUYVbLZrHtQd/akjoClNw+7oWfYRZTLtomYYRy6fPyPGyOFwoK5bnl+vuLt7Q9Vari5foEzJ69e/zTR2HI9HfDGKzZsl4yQ8bgBbVmTvqcuSoZ+42Fxyv3/D+9sbxiAogk1reHFR8urFgtVGdu1FWeOzWJFThpClCIYYyDbiioLKXFO2G3bdxGmI6GTp3/+cQ9fyH/2d/4o/+z/+lA/f/jkP3//vhJTQtpAAAJUwVhG9DMhUqUgzna7UcjE6lXClE72/jfygMRwWLX/xixN3x7+mMo4XlxvKRY0rSoqi4nA4SFamcUwT1IuLx1NECIK2vb7a8v79e1brC2KSATm5pG0qNCPNoqbvd6AV11db3r17R1tX+Kzx04Fx8Exe3geLxYLPv3zNh7sHUohcrrb040BZSxurDAl0QWYgTidMXTH6KLMGo7FZnJdZKZzVTN1I1oaqabm8eol16hHHOoXIYrmhaTPjcCIh4cnC41cMwwhahq5t23J7+0FcslmhbUV3PIBydP3I/cORZ9sNRSmBKWVVst6ssEpYLH6cKMuSRbtksdwQQmAYJ0pXoDRokwjThMoK7x2FNTR1i48TRmuyZi7yHlc4lA6YMRBnJG2MIvdUZKqmBJPABEC019JilhO5Ng6ttICu5vbGmS54LpxSRM99a5g94wCPTlc1m3kk7EIwuF3XiRSYhLEWP014H+T3ipGUovTIU0JpjdE81hRjpBVjMNITV6KISyrN1MZzl+Fc9JVw0M+VTedHQ1D4pc3r33b7fzzszDlndc5K+vW+7+8Dfx/gJ7/9o/z0S/IowTnfkprv+2Tw+dERJn/aYvFeetRnrWhKAZUFPSkMc4W17jHwNGQxB4SZe1I6eQTGVeLaspZunEQqmBL7+z3T5KlKsFqOWadxIinNxtVE49lUBc9XJXEtGNK2ren7nsVKAh6GsUPSvqHzI0YXtI3F++9QJlG0FYdDR7Oo2T080K43+AiTH6jairGbqJqWsqmpKtFsF0XFqe9oqprMCe9HKluQAKMdxlqmcZxDYTPej2QSVVPLjoRENpY0nWjamqYVzrotqzlpxWCNjPezDxS6mO3uGq0dZItyFdlWZKtBTTijmU4epRsWm9dUi5/TNAusWjNMA/04cX/c4ZoSPR/dpwArW4kcLQsf0qBQBtSsBFA6UxBBGUpn2IfM1+9uKJxhraB1DqdmrCmQpoAunGwMjMUax5SCtM58JmWDtXaOZptNS2EEFYnRczgcWK0vQCmGaeTV8iUPxyNxPMrieuxZrbYo5en7njiJ4ihVNcFnLraXOKcfW39D14tHYC4+dVUxDCc0kawtGZi84F2LRQVaoa0jpRFnZMgnpMHhkSMSJs/YD6QZ5TxNE7pwn7QdUxYaaIgDMYrs0NUlm8sVq0VJdzpQtQ2ucdIHN4YpeKparPjWGU7HjpwzdVMw+fGxcPpJrielxL04TYHBD1hrqQuJNDRKkAXWRLT1M+o2EkKcgVLSYlJI+1S0ZwalBXnwxCj5pJag9dO//ckAlI8/lpL5yzJnpWSnfV4gc87EJMP8sqpIY8fYT4CavSiz6iTHR1mjhFZI0QYeYXwAVuvHuEWthXqoQIa9Mxokz7XoXNQTmV+9jP/bF/J355aJUuol8H6+/xfA5x993WfzfX/r7ZfbJ+mXj0N8elxJSfraT+HLQXbnyTP5ET9Nc5yU0PByFDus1obKFficZUVPSkIE5if9kdCWE37siFmcpKfuwP5wIsbMsRs4nXoWlaOcL6q744GEonU1m3VFUzu0C1SFZdmuiMnQLvVM65t1sKqgqGqchW68JZO53F4wDJ3Q05QlTBHnnAyHrKLrRJlglSXmyOX1FcYW1AuB+msMx+6AddLPNcbMxRbcRyYDrUUjHaNntztw//AgO0OjKIqCsnSs1gumHFi42biRNSlLqKwLEaUV1hXsh0SKEescyhVYq9FJU5qSQOLUB/aHjj/6J/+Y25//C1ZqYN22lNZR15HdcCKFSNIKtH1kX1RVgcaQuiBH7vlkZY2lVRn0iMqeZZm56TI/e3dDVZiZ82wodIUuNXW9kIxVK5/TyjDFhNYWV1T4kGibtfSaLYy9pywrzmn0Po3sjw9srq4ZR09RrqjbC37+9Z+xXVe8f/dOwixiT10vuN89MJ0GXrx4gXKWcVLUVUXVFNzfPpCzFNl2saYsLOPQSQar9kAEI4EI4ziitLwmWouz0FrpP4ckqppxOD0W8r7v6WZMsvcTXXdChwKtYBzH+TpLFGVFToJUjRQkJq6fP+O021FXa2wpC7iyhqSgrJvHjZI/ngQM5goUkbHvRY+uHSEqQs5MoSNGAVillHAuE1OisCXW/p/UvVmvZGmWpvV84x5sOpN7eAwZmUVNnRSNUAOiESAhAT+CKy64A34I/4EfALcgwWVzCa2m6e7qqlJlZmRkRkZG+HRGM9vjN3GxttnxSKq6oiRoRZlkCnePM7jb2bb2+tZ63+dVVLrCqCKNgFnc0cPEnIOYmbJk7xptlgVgkoKu9bOi7aQWWVQi8uvvFmkp+AUyKJ5DIOBkpRc9eRSZ2/m6O3lS4jwzzzOmtqAVOeZFonmqS/p5nJ2gmLKc5gAyOYkDXS/+ntPcPHOiphrxeyxNatHLjaIU8VX8a+jI/xfgvwL+++W///MHf/7fKaX+J+A/AJ6+33z8r27oT1IisdgmylLA5zmc3wwhBOI8Mo890zQt8WOD8L7nEacjpCBFQBuMKmhvMaomJkUI8nWbxuFcSy5B0ryL5u5hzzQGVFZMeZFadR39nJjnwO1RpGdSHCKVynzcOjYoNksQrfM1x2HCVQ3e1bi2WsIFLCHIrL6kzHZ3wf7hjovLFpVrpr5j1WqKMjzuH+nmzCef/oiXLz/i+LTn2D1x+27gk08+QzWK29v33Ny8IM4zw9DJbDNBDI9sNhu0F+ejNR7jDWPo0M4Kv2X4FqWMvJYpU1u42DYSbGysqCiiIqRCMQiQH8WcRnKWYGhjGpxt8VVDURVBZ0rj6bvIl29ecxxGvv1Hv0ClTGU0/9G/+++xqhMXxvN4PHDsO4yxDCqQosVqmQc3lUE1nhJnTJhwTmFUxttCSoIIeHlZ0+WZXx0L73/+mr/31PMP/8igU6HdbairFmMtWVtSKJASulKkBN6t5JjuFdPYs9lusaZhHI9YK0vHNCaudheYAvf3t1xe3tB3I1dXV1SuUNUNfd/zhz/9PeY5E4Yjn37+GdMUmDsJS44xoqJnv39cur7M7vKCoeuxfi3ZmkMUpYax2FqYK1pVZCOabYUjFcccIpSRyrWUrDkejqy3K7r9O7z3TFPk/v2tqL5SYrdu0EvDE4Mi54GcBeQmBqBESWI/VyWyXl+ijHBGNIkxduxWV8vC0DHFwNPTE5FIKnoxi85YbbBGTjZY2TU5J4wibdwi5csM84DWGm9loWm1BzQqyglvjsjpKbMEeAjsVZPIixRTa01JUUxjSkw1pSROiNjzAb8UMlK4SxHFWyoJVFlO6mVRscjo5eQIz7GcT/h5AaIppcjLPk1csokcT7ha2cNZBRrJI4XxHFwCi7lLLTcWpYjaLX+2zPJDOffkheFvVcj/RtWKUup/BP4P4I+VUr9VSv3XSAH/L5RSvwD+8+X3AP8b8CXwBfA/AP/N9/6bPH+/7ywjnl1Wz514SmF5wefludjxgxR2SoKcKDkSQmKel01zLIzLYk9beWGNkRivkzolI0e7OUb2x5GH/YG3Dw/cvn/k8Dgw9wWVwWRNNxfux8B9H0hZ4a3gcUNOdMPEPI6EGKmaFU5XKOMZh5l5yhQtUVfWN7jKM8dM027Ybi6o2oZiFG/vHvCV5WK75fj0xC9+8TOa1lOvGirfQoyM3ZEYAtY63r9/vxzzNCEE1us12jpCSFAkkq7rD5IarzXOGdk3LOMmhVuWY2LJjzGSkyKHLIk1H2zkU8yElEHJ1zg73LQGRI+coiLNYErGq0y1zCVLVjjt8FgqY+XGdlIbFE1S0Icoox5tqH1F7TzWSJdXW4NRYvqqrKMylo0VJfBxhl+93XPoJuZpYBgGJK28UBZZ6On0FcuJY5+YUzgvIg/HoyhxnARlozRV3ZBy5uXLV2w2W3a7K/q+px8zX339ms3uSjgeSWFcRUqFcRyJITDPE7e37yk8n5B2ux1pGZ0opXBGY9wKbQQlXLIB7cnakbKmYBjGWTjzxkmBWApKXdco4PHxAY0AnsapZ7Vai9JmgUjJ0R7C1MOSjBPCjDWaHCIlliXXNRFyJMRJ8klD5NAN7I89D92Rp/2Brh8Yjz2NsdiSidPIHAbmMBDiLBiCxWKvcGi1YGkxVLaidvUyJzbnLlgjMXTei+hAmCQnF+QiD88fuLuXx++6wT88uZ9/T/orP+c7z/Q7zs4Pvs8pl+A0HlmqFd8toSIzPD2/I9DIYvopWZ2VeL/7dzzVN8HlGnl+z8f3Ua38l3/N//rP/oqPLcB/+72/+1/9/c6z/5wLOQVImTlFchAeyTCJ3C5OS0eeJqahl0I+j4R5FCUFULJhjomn44TRhdpbmlpexBQCaZbkeKfF/DPPhb4fBfQ0dWx0IdjCb489+26W8cmmJRtDVRtcMoQU2TjP1brl5sWK7cZBLoypYDTkEqg3O0kPKtAPA/vjgxDxQqBpWsY5kaaBODzS1BU3Nzccnixv3t6yXq/4/POXfPPNHT/7s7/AOcfLTz5jnmd+9eWX/FurFVkbLi4uub+75+rqilIkk7PdtBz3jyjTklNgt71cjo09xrayGDOKfd+TSsYbAywXtcokNZN1TV5uDnESLsWcMoG0zLUzxlicoEKIKYuePyf2xyPzAj6SWC3FlEf+2Rd/iUXx+5//hJuLS/b9wBSiHPdToa00m9rQWElpSjmjmxW1KUvQQIO3GZ0TrBMh1Xy7HwnK8C5l/vzrN3w+XLBdd2zWO5EVGouvWiiL2mFZVlWVIadA27Z0XUeMYF213FxkF9EdJ1rX4I0lxsiheyJT+OLXv+Hlxz/h6vpjYi588/pbPv3J5xyOHSesRD92bHZrQsmgDRcXF6xWDf0wIxgAR11XRO2gJHIIzOMRbzXTNFCt1sxzoF21aI0sbqcIIXP3dE/bNICmcgZyIoUZX68AUAWGWZodRUTVCsUpek/jXWQcjyidefnyivfv3tGsapypiDHT1muSMqTYyR5lnnF2Te0rUtYMT7dMU09OgcrVKOdxqxWmQImDdNLOLVGUhVQUZhmFeFfBYtpJWaPM4hkpsuxNC1CqKCOUwFIouiwdOaQMarHhn+bU8p4HyvOI5PTfnJY5eEmklJ8zPD8ooB8+JcRZmE5KmWW/9hwY8aHcUWuFVd+VQKplQfsh0uvDz19EKnwQ2bzM2qHofEbqfp/HD8bZCcso5UPpDgsQPi0v6vLChyBW6mHsl65xIke5Y5al09AKKusoOqKLxmlQrlBVDuccU54JKRJiZgqRKafFETozTUmyHoPoffsw081RLqxSMDGw9jWt9csJwfDx1Yar7YrrV1e03jL1A04ZyQZDM3QHUg5YPCUmGl8v225QWmN9LZK2cFiuUMkXlGVbIsfM1fWWEEec9zw9vuGjV68Yx5mH+471Rcs0jIzDkbu7yHa7o20uGMcRpWTGX1eGlOTv0zQbnrojzsmCT5uCtQKM0hpSinhn8F5jisB8KuPJeSTnQE6GmAtuic9LGcKcqRoIMZNCBCw5BDFioCT0Ywm1vd8/UltPKpnKOZw2jGkmqYWbbRSNE9u+wqFUwduAUlESyDXUVqOi5jhEtJnQVhMmhVWGN8cR73tCkiJmzEwKCWOXzFJjIQdxoKEXhyQyuz6xV/oeoxVd12FtTcwF8szd3R3X19ds1juOxy/5/PPPcc5z6Pc064amaXjz+i3WWtarlnE2Qqy8feSrX/2am5sbdpsVU0ysKjEwhZBQKqMVon4omZIDKUwYalRKTPGI81eUIvTNEDPulOU5B3aXN4R55uLiQsKgp0jXH6isJRi42K6IMVNVNVo7cagu77fd5SXd4YGmbUnFQARnW+IcCUjSlRTiTExaUpFS4O37O8ZxZJyEB980DbuLFW21wnvPpt1gao/2brG7K7Ky5CK0SoyMQ4wxZ2ZSjpmYk6R9aTHLSEceyVmjtLxXlVIiOlk+/1QtT6OV5+76WcXy/+rUP+yEkazNRdXN6Qs+KxXVWaFyXlxiENSXOp/w5ePs2Zr/4YThd5Uof5UyJS+c9b+NguQHU8jP2+YCeVliJiUFXAr5TA5LEESKhDgzDRKenPJEnEeh1JUAKS2bb0kN0obzHfIkPUxFeApKSfbk2AdZCKUoCpYYeZh7xjlx6GeSUqxqLx1ZmjnMHRvn2G1WNM6wagzOZKY546xBNy1W2fNRvjAzDxpTe+q6FoJdIy6/gmaz2TANHXZ9wfFwj6FQtwrnGqYxEHNiGmbpKn1NVVnu7295+fKG4/FIN2R8VfHq1UuOfUfXHanb9dnRdzg+ka3Bes04zsQFGTpNE5+8uuF233H39M0iM3RYJxdwW9fELDh/7fzyxrHkeJJdLdpaVaAkppAIWZY6McwcHp/IMcrlvrjrtDbkGCjKME8DvvYUEjEHtJPNkPceZYwcR7Uk3TtrJTBCFRrrqa0YgLw1VErhSsEtb6r9mHn32HPZbBmHQYBFzoshZRlLYPTCt5Ojsyw+oW0rjB6ZQy9v1GXxWpTi7v4ddePPHVvjPbv1hr7vGYaJ3e5SZJMpghEN9TRMkCVBKsaZyim6YaI/9ni1IhpxNNaVRiVBLDsnp4ScI2GccMYyzXL9hyLExQ8FAgWwbsXTY8dl1Z4NKzlO3B/uaZqGlzfXsJD6Tv9+oiJGwSHEmPG+llOCqQDL4bgn5YFxEiriEGfunzqmlAkp8vb+yNDPjLNAv602fLRbs2o9q8ZzddGybmsudlcYU2Gco1mtZd+V5kU7rqhsdQ6YjjGiEd1/zpBUPmd+Fgu4ZwDVSYUCcEpsKEWcoqdu+9lYJjei06+/I5w4de0fqN9OhV4UKM98lNNDo5bCXdAaMEtQhFKLWUstOndzHiH9q8femu+YgP4WYsAfTCE/RzFlSIuTKuZwHpP0U09JMgufxpmcZqbhQJgHQpwoQWzHirjELGXGMBKXcUxJstUOvhIuiYd205435NP+W0wQh98wz4IBnaCEzErDtmmkayiaN2OHU6J6yFOgqMz1xTVV7UhEhmmUhCKjsbUnzAlbNEobcglYZTDeih18s0EbeQOoFOknS7Pb4lQi9R3KKpQJzKPMICtdc7y/p/7oJcYWHp/ec3X1kmIMT4+PTN2RerXGVp67929o1ztM1XBRNWIEGntc5WWRtECNnPG82LR86+Ghj4RiiMoxjBP9WLParQEZb/h2S9GKaS+clhhHcmnQRaGNQRmh2/3s61/wiy+/4YtfvSFrJek7WaSLqQjyKYTAfr9n194AhZIjRnlYTCDdPNNocTNa5SlxIKGEud4aStGUCKFIsMGuhRBnRhTHrNB9z01nCWGgFIf1G4yXE8k0DYv5oyJnRYiRUhJ15TgeepQ6YoxhGhPr3ZYwBx7uX2Oc5ebqgq+/ec2bb77l7//036SkzO3DPc16Q8qKYQx88+1bfvrTnzKPM7/96iuyyvzJn/wJ3VNDbRw//8u/5HK35fJyw93DA03lqC4aYgpE63De4aoa1xe6sccaj3UVXf9Eu3lFmDPGO3IvubLGV/hmja07xpTwZpZ/o3YMx5mr3QvBNHgEIYtGm4p+nPGV5JC22w2lKKYxchzuCFPk27eP/OZxz8MkGaRFZUFPIAxvUsbaxDxMdMdACIpv3z9JkVOFTatYNxW//3LL5XbF5e6C3ccf43zNxcUNWlk0likEnBGHau0tU4KxnyklkZDAGIUhBy2OSR3QVubwIh80qLQESywdeMiL0ztBiguyOgp5Mcb0vNjMmZjm5caX5JlltKGKJmtJJKKIoeF0c7dmMSYphTIGY8TQpLWWpaeS06c2J624nP6+o9BLmZMpCjiH0KPS30p++P3TPf9/fnx4J4wxnkMfQgjM80ycZuZxIkwz09yLwWVZdKYUxImVghhhTzOqInfNkjJhnIjTTAwTRhWsllBXVSSkOeZIyBGl5Ig1xJl9P9BNMzGVhfegUCRJ71YO5wpNBc0yYyUXaufxTnChOcvFo5QW9YvR5wtHF1hXK0zW6FRQaZaFHNJhjCEyzDPdcRQinPf0Q8Q5i3GG12/vuNxdcXlxQcwKZyucr9HO0g8SaNE0a47dnpw1YBnHEWsddglmnqeBHAOxTLjWs95tZUmDk93CWBiHRGUrIGONorYakwuVXdLHrSfkhHYWpTLDeOTt/WsO4545isZYON/SmajFtae0JhE4Th1jyMs+BGKWxWTlNW73FR/cAAAgAElEQVQhR2p5m4pCoIDRColvyaASTWvxDqHRnSxxRbIWp1lCR5SxeCcQqrIQ5mzlmZZlo/c1Mc2MYy/h0SFjrWe72WCt/NzWqwsudlekWJjnkd31hikOWKcYx4Gbq+tlyRtpvGLqDvzm69dgDJ988iPaVc1mVZPLRN8fubzckTP0/Ujfj5glxESVFqVbEh5fVyJDTTOVMVilKfPMOAiB0zjL49OTnCbVaRFdMLoShK3K7C4aAbYphdENKC8AM8oi94Tar0jKEBP0+47j/Z63b9/zZ7/9ltcPd6LuKrLzqZ2ncjXWeJwX4NumcWwaT11rMeohnWylNCZFhrGj73uO/YH5sGfu9qSpl5NZjhRkbh2jZFVqrakq4fUrpZ5BW+VZTRKWxu7Dp1A+A6d8TdJ3597fRV2nc1eeYvmO3r6kZ7niOfLtdxahp4dSSnJFvzPT/qC0LovN312iPn/8M3ZABsqZqArx/0uL/r+ORykykz2/qHNYDCrxvNAcx5F86sjDsChUBnKM5Bwk9kspsioYLcd8rZRYu8tp+yxHLHJBRTnahXlmmgPjEJjnSDfOxADTGImhYLXGW49TsPKKlXd4a8VVVyl264qqcuQ4Mw0ZX28xVlCWvnYUFDEKH73ve7bb7TITjey2rcz7y8w8HfEWtCpQFNZUBDUSc2E+SvBCU6/p+4Grmyuwhffv7nj56mMShhASTdNwf/eei8tLpinRdQe22wse7m55+fIVISWSVVijMXjpOKdpSWNZiG1FCmYKmTRFVC504ySd2HKTCGEJt3XyxhfEqJhz7h/esZ96lDmZthKliKpFmSJUusWZm0vm7ukRbyzHcQB94kcnvC54rdBaMMTkk0LD01aaGAZAYr9U0YsKYhBBQMwkJQvWx37gOASa1TPqIcUkpqaimKdAXTVo5wmhQ2tFXTvm0RLmQr12dGPPZrtiHhJVXUtBMxVFWXxzwb6b6PYH7m/v8N7zdP/Iq1cv6caOrh9ZrbcUIM0Tq+2G9XrNq08/oWkrCkGO5doCnn7sKUWfiZVGj1SVLBZlFgzjdKQfAqaWIONpzrQ5i8JoMSvKz8eTK8d2c0XTyg1BilRCNNRKdgVoQpiIY2YcR77+9o7b45GHrue2n4nARSXLPGH/mHPgcaUMWil2xeM0aFM49uLS1MbTOkttEWZ6LtKIjT1aKcI0ys1GK6ySEGVyQVmLLidmUsFoAd2Vk5dESeiDSAbVWaasTkVSiWzxNF46jVZSKmeo3rm4Lx14WerCic2Uc14khB9irJ776ueRzofGopNRaPmA5TNKUR/EvpXlz0Av5fe5Q3/mrZj8nL/wfR4/kEKeRQOektxlwyTjjjwzTz1pDgxDRwxiP55DR4mBnEfM8m81J/F/OSUHJWKOpDgTSyQRmWPmOCUO4xGtwRvPGGbGKfB47CgFpjGw9Q6vNZ9et6hlwz5TCMaw22x40dTkWJiGI21T0a487XYHgLMNxlnmkLGqImtomprd1SumaRbUgJK08D5JuotVjqwKJc3sNlvC1EEp7DYtw9Bxf3dH6EdUThy6B0qeuLq6Yb/fk0vk4vIF1aolZ5Gtffnll7z86IYXH/1YjtbzQNc/oLSl7wecNnh/6nYKn338MSW/4fpyw1/+9h1jhMc+E3CMaaQKIwrHPM2iYEBTikgM52HCWEOJA1NUdMcRlMKpRRan5CIGOWpmLd3Xul5hjCxoX9+/X2aRhnVVcbPbUbmanGasUYsjNVDVK0k5ShmrIjEGyFGyOX3CKYsDZM3qISW6qfDm7S05JepmhbWyYxgXc0dd10xhojE1qRg222uKisKNHwMpazabSzSR/dM9603Nu3d3dIcHjuPA2B9wzvHj3/8RMQTAs2o3HOcjVzcvuLp+yTgcub+9ZRoqri5fEBJstzdY43l/+5brqxcYjKQATQpnIYaBfp4pUTjizjsihZgT2q9Rk2IaM84WrnZb7u9u8b5Fa4+vZEfkTKZar6iaWm6mxZBSRmVDXTfEkpljJseZd2/u+OrNG758fcs/++2eMUqu5Ec3O+pKTlVZgdMWsxAms4ZaeVqruKgNJVf0U0SFAw9HER9Y7dnUlleXAnozDpSO5DJy3D8wzD2VX1G3W6y1YngqGU3GLXJTCUheFpQfdKklFcyibGFZEC590HnJeVKrxJhkRh6e1SopRuIsktQySwjJh0VealMB+6ymk7l3QYqukSWnklN1UQWj8yJil2dBxAzlhLk9SX2VoqgzPFf+n1KccoqLkf3T9338QAq5wHhSSszheS4+xYkw9cQ5MA4H0hyIKRBityBsF6OPKmgtDimtjADptUGpWZyZC0kvpUKcBYikDWQXIGdUSKwW4b62ekHUa7xWNMty0lUW5yX/salq0hyoK0XbthgnFMGmarDWY5zGhFlcXqoQcqBSLbauMKWc0bZxnDGVQVtLGGfaxrPv7vDOk+LMqq4pJfHixQvef/uOlI+krIhBXHylJIahI8XCC/sJKUmgsq8/Zp5n+uEJo1rRj3cd1lU0lSQh9X1P21riIClKF5uWq21L46CPkWnKHIfMelKso8IYKSLFIOoNJdmGWhf5+8fMHBPKZDGFVJaqrTBOE2KR4yWA0uSU+PEffobJcHt7z13/xDxH6bi95+rqirppIU4UhMch/OkIJpFyxBmRaQlASW6MRcmoBaMwKgKZrDTv7+/YrhviHChtEbeeq6mdR6GZokCn6rYhhUiKGqMq2tajScQ4U5Lkqs7zzOPjPdok2saz27RSIOZELprD4cDNiyv6p5mL3TXd4ci7p4Fvv3nLH/zkU4yzHPsDJSdCyjS+Yh5HnLHLyEiKiPWafgzUrmKek4wHQ6QYS2UrUuhwNUxhxDhNTBNlMrRtS/5AD41+7vLSQpU0dUvWmqnrmfsDXdfxf33xFV98c8vb/cAxSRKPzoUpRFFPaYvS5izrkx+ldMvWeIxT5DKjleVyHZjmvIxj5HvqovDGYiuBUikt46rQJ3IoGFfJqQ5pyvJJlJAKORURPOQCqWC8WkIZWHATom4h51OJRcmHcsrmlI78rx+zyBg0nscxFOnCldKkLKYkALLsBhTLKEUBlCV7c9lPLhr489FI6jNKndKMpDtXS7d+qoGo8uxoz5ln6uvf/PhhFPKcOR6P5BCZ4sQ89GKkiCPTeCROs1iRTzPxPC10MJnDOatR3qKsOc+rjNZUzmMUZBs5hokcIOVCP00UVYhRi2OuJHER1hVbJdhOlRNX65raWQnDNe6sLe7HgRwzq9VaeCcrj61bdDGUWEjF4ayjWlUknWl8w3AcqWyhGyJGWZyviPHI4XjPNM2s24qnp47GN6Qki6VuTKxWV1A6/O5I2UMTxFL8+Dhy89EFSilev3nP+9sH2rZltVqRNLTNmnGY6Ls7Lq4/weia7eaG+4db6tpzsdsyzRFX1ayio6Sel9cdN9sLvrk/MsyK1w8HrCtcv9yQSsFbT9QTxkCMhkEZklbMaLpJMK1TyByOEyq3rOuKm4sLfjPcIRd7xhiHc55/+fMvwMiiWWNIGnKc8HWgn+94OO5RMeJ1wOSKtvLUbjkqL/P6XbsijBNv93sO/cwYFHOSztEYL+G7KjOHhLEyL56mQLNqsV5GI8Zaau+J80SMGu9XhONB1CXeLAEOjmM3s1m3vHv3huvra375qydefPQRF9s1+/2RX/7iZ/zb/+Df593te0qaUTnx9vUbLq63+Ebzo89fYX3N8TDz9NhR8sgwa64vVrSNsMV1DhAnqrZBF08YYXPh2Hd7UTsYSzYGdKFZV5QcqF2Dw/L5Zz/h/vGeqgYi5Djj7cKvyQ5rPEUJRqHEwH44cugO/KN/+s/5zdsnvvimI6mItZ6rzUoWhCkQc2QMBmyDcg7ImGKolEYZ8ItSw7YNKTm0j7y4jmjruL07UIoixMIUZ3xQaF+jqTBLoEMKcoOq1i2oClQmZ0fRZiGaKuaYl/DzSEiFydqz6stokVflUs51U5ad6qxQKVmRlvl3mKQZzEnGPDFNMt5MCUKCGFEpodUyglWS4GNAmsWlcZBDgPgiWGTJqmgZ75RCzgpz4qajyGfXaVp07884ADiNfp856yWx8GO+3+MHUchzyUyTOMhijPTDgRQiMc1MnTCep/FIyiItzEVeQGP9styB6AxkhGNgrMzPlEWpRFGFXBRRFZKGUiKpKB76TIyZTGDjNZdVjdea2onEbrWpKcoQlMYSyEVhlRMWS12TSiDmmSqLASjlRJgSREkgn4cenGFIGd8IuyOHgUPfM0w1lTc0TYU2mWkaUAVC1IDBOsswHqQ7c5qLzZbeWJRxHB6f2O5a7h4e8VXDzc0VIQSmacbaa1CBkgNeKYKxdPsDykxkU7jeirb88fGR1bomh4ipPR7NxfUNNxc17w97xiIA/HkM5w47e41jvRz9JjxQcsJpw0AiRGiMAVd4HBPWaepdjX7jSBGKK6gsVutSEiqAwooSIBWMU/jGYGyGqaf1jtoaKpswJpCRrs0bR6U1mojRhawSKQ8E8YIiKNQoVmwMXcg8HkfmeaRdbbAO8sL+Vspw7I6s2w3GWcZe1E9aKYyxZEDFyLpdMY9H1ssJ7aJt+ehmRzcW9seeq5sLnvbv5YaQQZuKkASD2h16bi5v6KZMdzgwDAP1quawH9g0W1olQSNzLtSrNUYtN6B5JOc1vhL9t7eOOcpJTDsJMsgxyFA2FcbhQON2pCzs9sKEUrUc/5eaYGzDw+GWh2PHr9685pe3B+4OE8Y7vHXyNbNICXOGEgqjS5KMsyg2tJGAZJELlDP72xtPCZq1D8yVoq8s+zhhnQRbRGfJNsAqEEvGGE9SEtCQQyRpi9IWbbTk4UZhtCilcXWFCu4sD04F+nGmWkIsCvZZraeNZOYuTsqSszBczstESDlS0iyNRIE5RVIREqIiYwok/Twb1zmJ2zQLqVNrTU4FYzWUhQWzhLvL6VEkkCdD0cnQJKOZRaH3QQ08uVdBunqlTmTE7/f4QRRyMTREcojM8ygEtzgTwsQ4SHeU40QuUZQoS9iCsWpRIHz36wnG87R8UAttLGPKYi3QstALURaRla/wStgcxSqK0VitF1zqYkZpKvzSCbhFPxqiHOtijKRpwhq/QJbCskhZdOxaDCdaw3rT0h0HpnkizxIjt1pvOEy3OGt4eLhj1TaoytHUK6YpSe6hrVmtM/UShkGOXF3seHo8Uu9e0LQzxhgOxyeMN5KOxCW+avn5z75ivdlxPB7Z/dEFvmllVDI/CVCpf6IoR+UKN5dX7B5H+rsHjlPhEsvQz2ycY+VrpjCTchGedAHrPEWB1ZJ9iDF4pWmXc+aqtiIrNAuFb3Gt5QKqSOhBLgGlNavVitrJrLzkdJZ4KW3IyaK9xjUWQ8E6h7YFveiFnatEDseER2HRGK2hCJrheDwixMeAmWcwGqMTxgjCIIQJV1mmMLJqG45PPWj52uqkqMgJ39Qoo7j5+CXW1Qy39zR+xYuf/B5ff/Ulm82GtqopBPb7R4be8Pb1N3z28ScM4wGlE0rLYnqeD/T9wM3FJSEUStZQLON4wFJQOspCf6EZal8TpojxoiQSo5Nm7DqU89TOojWMc8FW5swBss5SisIqT4oTj4dHvvz6a3725huOQTgin+0uoURSzjzM/RK7psBYiIocQFceZxWlBOlS7WmsCcYWzIJYsNZQ+Yw3mm4sDCUwtInaJapKckGVTuQwo62GoohhXEYRctoSoZ7IhmOJKFdTVRU6OYFRlUJW8lExZlhyNeUhIZw5p+90vAo+ED4840BOUkn1wXJRPl6CO0oGZc7ezMUZjKT+nJQn5vQ9Tw5TySCWycsHxMbyXMCfl5mn8UpcfqsWufTfsRl5Son901vCNJPmicPxcenIR+K4pN9o4VVbLVAeYzTOV+Qs/F/rDJV1KC0XltaaFAPaFLJVrFOLHg1xDjSnTv6U8kFg1cjx3WlNVYv7s91u0AvXwtiCtwK8ckY27SfJk14s5ApRyhiL3HS0oq1r6rrF6ArlLWaUgj/MEyzks/3jI1opUgrUdcU0zmhlOM49bVujbSGMmW6feNq/Y7tZ0dYN7+8e+eiTj5n6jjEmjLJs2jX7oaOuVjw9HYg58NOffs4wDBz7A/dPb1m1GxKKTbvFKSD31LXGXNT8+LNr1ivHn/6s8NX9nlWjeXw4Mhw71o8tVx9d0k/jOVovxkAKEYuiqhyzmSih4JKAra4uvOSjhiBg/uW4u0CsSQW2m5aPP9mwW1UYIi4Ftq2h9ZK/WldyE6yrQuMMpiRaGaGTs8RspayY5kLb7KiMGGtUTmidmWKh62f6IVE18jYaxh5fyU26rmuUkczM1WoFJfLq08/oug6VAq4yTMcjVVUxTz1PTz3Nqubd+9f4ZoOrK7756kvW20u8q3l/f49KilVV8+d/+udsNxdcX17z7uGWf/Gn/5Q/+IM/4Hg88vqbr/n8k3/Am7fvuLrcUohM0yA88KzwpqKqGhI9YRZHbVUbchqwBqYQGXvRu7uisDbT94/U9ZbteicRdk0jwctFM00dXb/nf//Tf8xDP5LngguWFxcv+Wjzinnhs6f3rznMPRS5PsvJmJcFxaAU6CUwRelFwVGQxXRt0EVTlKMbKu4eAwHIWIoSn+7cd0so9YopSiCGPSRy7UijJXQVEcUQNbk4svWUZNFKQFxtK3ueVAQdW0oBo8+/TqdTgjmp1BJpmakbY3AKLEpcpUtzl42luISKhkg5h5qoAlFlzCnuWomTU6W48MelhqllMG60XT5XFC2w2PDzs+nx+eZyGrNI8Q+pPBuOlGSqft/HD6KQl5IZhk5mVvPA0B8X4f5EjtNyJCnoItty5xxGCXHMGIPKhVMEnNEK6+QHFlHMS2PhnKOJhZAQx6BhKSyRkDSrSmiFVmmMr7HWUlUN1nnZpjsJ2zXGgCAtzjbdk8sPwFZWXIwFUhRCo1KGylt0ctiqAmNonOH923e0lXQah6f7RdplKUZJCEEtSfDTOBBCj/Wa7eaau9tbysWKq6sbYozUtae2jv3+SMiiw51muQHWq5oYC3W1wdVrSIqxn1ivt+Q5MJVIVXnmEHDG40xFZSsuNzt+8W7PQxfYD4Gd8hyPe4yPFOUWrXlhGgNNbQlTZBiPZGSZSK4W55vCekOMiqwWbrPWFKWJOWNU4tXHl1xuHTpHGqPQJVFbD6VggBgQNrZrUEaUCqUE5hTpu8icLOMUWNUbVutrbFHc3/0Wq5GOSRtiSIzjxDiOrLa7pRmQncocApvNCuec7GpSXJbJktJz6G7x1tIdH+XEnDNdNzAPkXnac3FxwaeffMS3bx+pL1qm4cg0RtpmTQJ+/OMf83R44u7unjkWLnbX/Plf/gX743G5llshBpbEPB6RDHWP1ixcEYvRFcMwYSyAFvb3SeFQConEOAkwbrcS04wxIg+laNHITx2/evM10ziIesJ4jvOephS8k+SrqI24O8Nwbh1LNsQk/EGlxYVLOcUuZiTtKqNswChNsRZrApWTf4O1mrrR+EqMM1mgJ0QCCWHzlBCxSRKJUJEhJbJpZP6MFemqWvhL56732f4uooeFDQ6ycDwvC8v5Z43JCwqAs2qraE0KUVQkUU7QMaXzQlOhcUrkgErZBeYlz0JaxiWL1BAZ46hlnCV8GJZR76mQL6OVIqlcp7Gs1ga1SLAVmsLftdFKzox9T5gmwjQKjzsFWSjkWfSzGIzKGK1wJ/mTOsW3CdWt6IIxHmvV+QenZkUA0BDdM6reWk9lDboUcpkl6WOJpdLO4qpaSGzOL+YiDdqBdjI3UxLEqjSUGAmLySdnsN5QCnhbEQpMU4AyU1mPUQZrPClDXTdM40RWiaqpIRecN5QykXOkaSpKhqHvqVpHmA+sNjXT3LI/jjhnCGHi8uIKrKOuW37z1WvalaGpDW/fvGV8B599/m+wWhnmFAkPd1xff8Tj04GryxUxBNarDW7R2X700QsoMx9/tML/OjIMiXd3R2q7o15LuK4y0FQOR4FSyYjJaCksKdI4CxGsNoRUuLrc8np8osSI0QJJyiVSWcVmU7FrFTlErFI0laGuPGKH0Fin0S5hvcFphbMZp6DvAnGKhDkxjIl9r3l18yOaZkMuE/e335ATVF66/JiCjCiyBI9sNtcoNI+PezabDdaKpt45xzCLp8B7T9d1VFWF14Vp0BIkYiSbVSnFqm7ZtmvGOLPdtqzWnp9/8cB2u2OaBz66ueHi+oqf/ezn9H3Hp59+ysXFBVdXF2xWFdvtlqHrSLmQkxi3jMoLiMwwRTHEpRyYY6KxniQEKoyumMIRX8TlKQS/ZWasl9FKlNc658i3717zmzffkrVCWc2xm+nnyOFwRN9I1J22Du89ZjTEnETqaeSEeWJlq2V3UJbxlVIZ44wg65UCc+Lda7QqWGto2gpfV1grxR8UYZl3zyEwTxM2ybI15plARjmLsg5nCiZC1lnCkycxcVlfo400U4myKJg0uZzCHdS54NvlBkzKFK0oKLJNKFVISnJE0YUc09IcprOCRZadpwKb0UgsYVncl1KnpGMUFbTcdIqS0Y4qemGYCz9dsYx3ShF+1HJjylFC5UEOrL9rPPpXPX4QhTzGmaf7bwnzTJwmQpzkqGY13mq0ElOOXxjfxixHOrNIm3QGvRxZDLiqkiin8PxCpN7h9MlKq1A64WtPVVUimdMVRluU0QuTw4HJjDlhlWFVOZRxpFTwvpKibSocoIpmjZgJ4hTRfoHmFIM2SkIKEpADuSRy0ljvaeqNdFrjga470lSeNM8oAutVtczmLcZtePfmFoXh7Zt3eKO5vrriN7/5VnId9Z5DP7DZbLi43nE49OyfOiq/pt4U3nz9JZvdNdY7Xn76e9jaMY4j7968oWkqvFNM84AGmgZefnRFs6r55Vfv+ItvbvnNmz2pZH70oiWpmvXaYlKPc56mNjhVM48d2jisNeQC1UHknmunefhRgyXy9l3HOEvy4e99uuPl1ZbKa0mLKT2NV2y8JDLVVngrGLi42lDVnsZnVLHEYSLGTNdN7I8z7x4Dj1PFP/yTv4/3FW/fvxaTidHkHNDWkJPieOx48fJjkV6uNuQ54ZcTkXMOtDhw+3Hi5uZK0pGsqGke7t5xfXlBd3hgnmeGPqK9pdnteH33QEkTn/zoD/j5L7/g488+JsfIm2/e8h//J/8p/+c/+b+p65pX1y+oG8/+/pbtuiW3LYfjDNlSOcVxGPDNGpUS4zxR12uO3Yh1lqp2KCM7ljdv34EO2HZNPw50KbJNa3TMokppWpSpiVOg5JmcZ+4fH/iXv/g5UQVKgRALMWRxLs+PfFF9zafX16zbFdt2Rb+oxTQSzB2iLBiTMVSVQatE0plVVaGREZbkLCiU9TRtoOlFcmi0xuiGpt7QtJZKR+HyzDNFQUtNUpopRGJK9FMhKMOqbrF+g3c1xogE0hhLXbdLd63RWWR8Wi2jHSPjug/dmCoXdBYzlNOGUOS9aq09n6adc5AjU92ejUIUYbXnHEmL8UfnxTmrTtLBpcbkIpirfOrQFSrJrD8tEYsfWvPPOnXFOWdYQuVPRT3+3SvkOSemcRDVShLGuFEaVYRIp7VGV1aGcUahrV7mcicpkAUVxQFGxGDOAa2oQCHIUi1zPq5ySq/OCeNqqtUKa2qsd0vQqyIv2naZW8mxx2kjIgEUU9+dxyvCiNaY1p3ZCUY7oakF2WTPpSwXRsJb4YEr5cjU7B/v6A57XlwJDKnvOlablrpxKDRv3otxY+oCh2ni7u6Bq4tLusNBxkB1K6jVlWO3apiM4zdfv+HickUJgfn4xEEXclJ8+umP2d5cMvUGUkQpx6q1ogrKmaqSMIDdxZrN/ZF933H/ZGmtZdXWpFhot7W4TZsVY4C6SsSgKGNhzIHWTUwpsJ/lqP/qhSNlz+EhsG5rXlwavAk45Sg5yFipGHSQLquutahL0DS1o60dtYaYxPY/Rcs0aPb7RD87nFvRrmpiyAxjJwqUeZDxmRad8NAHnh7vWa/XjMOBy8tLtAFK4nAMWKOYp4mr62ucs8zzjNOGwzxzff2CnCa6IXLsR/px4OXuE/aP96RJItVub2+5fXPLH/y932caDlzdXBOVoqjIeuOXGLuJ/bt7Xn38krfvHrl/emSzamAClTKutgt3W679aR4llFsZzKkIKYd1nnmcxICiNUOYmbKmrtdYXYFyWGeZ4545Bu6f7kkEUlFkbUgqyz6pQCqBftgTckvKVnThxgKKqMK5iOUsyV1Fm/Oy78QZwaoF2FrQXpGDwS7afyEBSiKVdzXGS15A6x3ZCJ8+x4KtrKibtMYqQ9tsJXO2qlHWCZfFytfVWhOQzlUpqCovy0eAmGVhfhpNLFgGka4qlq4K4yDljDVOrpOcocgoN7kgHfKSbmRSknSfnLFGCmwuEXXqppNsAtAnDFs5LzPPIxj540VHLsagHBPiONJyIlhuDCWr7wC6/qbHD6KQl5QZuwdZPCoxnGgglMDaAEVTaVk0GqVxC/g6huc7mfeVJKtY4VJorTFFCqzyhdkNsrwpmThGKBbqGmVqsB5fbZb0HIdV4ubMWqMXgE5ISQIljGzYjTG07frMf9BayG3WPVPcitw15KilNbUzmCLJQFMYmPt5CdGdWK9bpgG+/eY9JQd2mw3dcQCtaKqatSmEDM1HG6bJ83D7yMPjka7r2V5cMM2BEBL77hFfO169esWPiuLd/SOr9ZYwDzileP/ma54eH3Crlj/+4z+mqVccjzPaZLx35JJIIVHVLf/hv/MnXLRr/td//Ge8uZ04dhM3ly2GnovtWqLd0NjKEJMllJrBHqgs7MdAzJpKJz4xMxebzCfeMX7SEkOm2Ii2CW8jrXFQ5Nhd1QrrFdXaY7SibizORkiRQx+l8ymGX/76DbfdzH5y0Lzg0xefcf94xzj2vL97AylhMiidmQCPIsWRHCOrpsG6iuV6D8cAACAASURBVDdv3nF99YK2bQklolTN5eUlT/s9IU5460gUbm5ekNPMl1/8mjBPrLcrrq6usJXn2O2xXqOsYjw+8pMfvaQxkTEb2vWOf/4v/gmVtzzc3vKHf/R7HPb3VFXFNCbm/ok9Ay8v/ghtK+7f37KqDat2zRgNfT/S1g1GKWKYibmgQkMKk8x3S8IbjTZIBB4bNrsbtF4Kvykcj3u+/PbXvH77W7QNeGVElhq18GSysGFuD/dsHiqsFrVRKYVYMrEIrVLlhSMek2ipVRZVhjLoxciE0VizuBzbCucTakkMqtsKV1XYqqKuHcYqvFWksxNbsnNjzLTRUazHr15ijCMrLTokbWXcssyejXM4JTeJdMrKLMsEtHC+AakE+pTxGxOuLHTVKCqdU2ZqypnYhkUIESkLPymUBWuQT1zzcP5aEhgNZEktey7SCws9ZSF4nvAj6cMZuSKrSMkyUlHlhB0Q12xOv1sp//rHD6OQIzrvlCEiYvmiMqooSUDXetEHK7R9ThSRpYdgJJU6wdw/2Axr0MWQtaMYTy4zqSyqFiPyqXOaNhlUJGfFmJcUIfJZ2VJ7ia06QXy0siK/Wgq4XnjWSmlZZCvZqJx+oPv9XrIwTbW8yQy+sijl0NFwfJpwrvp/qHuTWFnX9b7r97ZfU+1qdnv2Pp3Pudd23JEEExACS5kiIWYwYQAiDEBMGMEEpCgzmgkSUhAIMQDEEAESggFCJMEkMSL2dWxf33ua3a61V1P9170Ng+er2vtcW/Z1iNDNJ22tWrVr1apV9X3P+7z/59+M3eKB3WZLVTu0rtCV4831ihB6HlzO6bqO6awiZk1OgZcv3/Lw8RnT6ZSZrrlZbbm6WuO9oZpU7HYHynLK/fqOxWyGs56u63n79g11XfP4wUNC2JGzF6Wm12hjKKvMJx+f8+CHE97crjn0hpvtgDKW29t7ptOSYuKYnM0ptKFrEzE4crbEUtHTEaIhNnuMClQeum2LVxarKzTgtR97kDDuUDLOCE7rC4N3amQHSHxW6Fv22z37XaBPnl0Tubw4x1rpUO9v72ib3Xu71pxEMa0Mhy6w3uw5O+sohxZrJbm+GSL77Z7LRzP6YeBwOKBNJvQ9jx89QutAc+hYLpekcce42x2ophO65kBZ1kxnSzZxJWlGZc3q6yuUMRx2e549e8pmtcJay2w2JQ4d682Gs+UDXBFIuWfiPYXR5JjoUk9MPZmBqpox9BFjNDEf0ERi7LG6wriC7VbOfYOiXJxJ1J42pw7zfrPibnVPfwwKzomgNIOCgCYqEVCFIbE+7ChKR+Wr99eXlgT7bBXJyLWi9NHwSaGzaAOMtThBzeVzTLKTNko8WuQjkNxNkeJbUUCP7oUxZnLqpQI7j3ZHAZLkxHrvsdaLdD2O1EHnTgNFrQRuNYyBDSmPl6B85VjIR8cTYpK6ciywQWAdo4/JPY4cZO5gciLH4b0aNMg1n+xAzl469Wg5egvJwDJADmSViaFHZ8vJHGtU75LzKZ2LPEr2j9zzKE6KP+3xM1HIQahEMSviyP3UY/EOKWFzEv/wHHDGjsVaOocYBynEycsbpEGpQM4GdMFoPyjmQNqSVcJZ6fq8NxiDFPQsSq6Qw4mBAshKnDXJK1GMHVdOoG/aMXFEoW0cY+OcZPeNXTkcKVM1MQg1SqtEyoasA/0gq/uknokb4XCgrs4Zuoa+P4zujx1PP37K1ZtrdocOrQvWqy1ny5LF1DHUNXd3G/b7jsePDdOZ493NNZeXDxhCpm0FH64nM0KCymnC0PPmxbc8efIRzXRJVU3pu0jhNXU1JaXEWh1Yzgs+ffKAw75h22u+uV5jS8sya9ruQMZhvWRMemeoJ54QBuGYY2maAa8KdOEoU8QgnuWuem+eP51PBBpTCmsjVemx3okK95DwsxKts4h8+sD93Y59sGzaTNvL4DqEnpA6ViOGnfXokoe81yHD3WqDynBxvqSeT1icnaOU4tA2lHWF1iKxXy6XeCcJ9UVR0DUt+/0e5yzaG9ZrySoFmM8uUMC0rhm6RjIzQ4fVibvbO37hl36Ztjvw7Nkz+r5lv9tROi+J9MZSeNjv1ixnk5NxWegbhijb/+OhxzSjlALWKJrmQFFpppMKox3JKAovnbjSo+dIjuwOe5q2JQwAYiGslUMz2jCPvOaYE7f7DRk4m0W6oRfmhjKSaXo0VTs2UWQZACpGfEOjrRRm5xx5vIaMdmOz5dDaYq2wwLTWaOUlclEZMlGgUa3QyoOSEG6MxPl579HaCjXQ6BNTRR3fG6NPUQwihZcwE9kZZ2z6iUKuEnmU9OecCUo8fVIUgdCQIowYulGZOFjp4mMkuTTmB4eTU2KOY3FPgZzSSH2V4n8ModYkQk7kLItj/EASJMPP9wKipKPs5H/K42eikOcMMQcSSf7AAULOaBvRDJRWBDA5l0jaB2ASNlvxYUDTh14EQmHsUAwYIxNnbQ1lKdzTRjeEENHeY4oZzlisLdC6RmsHRuGL8YIK4kuulKIPPWFoRrtQwbxzDKdFx44ylKRGmCUmyrIkjqZbdTVFGc3QhTEMI6JxeOdIZG7vX1MYWVB2mxu0UiwvzskhopRG25JPP/mMF99+S9+1uMKyWq24ODsnHnY8++iSpum4ebeiz5FPPv6YEALX+5Yu7GgPDdZOsRT87g9+j0nt+f7Pf0HfrHj1qiEAk8mCTz76hJu7W5wrePDgEX235Z/9C5/zaz//ET/4/df8H7/zFbe3DelXHvP4wZQlGbXa4l2UeLyJpqwc1XLCMBnY9wpsomsjNoMdFN2QYbRZdc5QzgqM0hTOMaky1kdQWrj3gE8deQj88MUtV1drbu4GXmwKlPZcnD3AZMXFZMpv//5vs99vybElhxarZTsOAiPMz2ZMSsP6/oaHTx6jtKfrBiaLmul8xtWbNyyXS9q2YzG/oCwr2v2GkFrO5jPC0LDfbdEYzpZT1qsbvC04HFqGdy1G13Rtpp5NAM33v/8lZSkX/9Wrr5nOL3HVlJ7Icjnlm29e8Onzx6gcePHyG6a1pxs2zOyShEKpREwHYu4x2mB0QT8kFvNz+jDQ9g3GWclPHSDngYmpIcn1c+gHXr27ogsdSsGQFUp7VFI4bZgUGp2Fc13VniFG3q7vud7tSLFHa0PpHFoLJ740Do1QObMWXyNchTKGYlrhncakhEbgiF074FyFdTXOShC596UUXS06EOW8QKM2Qx4QTx7hm6O8hDYbS4ARuvTieKnE//4YMBGVNPPvs34VYRTYHGEWlTLZJcLx9lHxmRIUhcwmxsHjMZlMXFgz6cPIuCTQzJB6GKGZHGXhPMIpMQ0iYkyJwfeQBEbVfXF6XBwCmWEUNMliSpZIOX3ycfnpjp+JQg55lLOORVq//xtkUOIxykv4wsgLV1iMS6RT+s8IdxgDSgq4RKBarALjDUZbjBaM2nqH81O8sZLsYQV/c/p9MKrYdWrZJsG41VFjh6GJqUeNMt5h6ISTmsUi1FpPSh6tZIsUxvxR5+QtD91AzpKDmcjUlWdoJKcxGOEBr65uKScFvnQYV6B1RTGbcDhsWc5qtqElhMBsMWW7W6O0J2ZLv2/43f/nd/nHfv1Xef7xE159+zUPzue8ubrmfF7z7MkD1k3Lu9trnjx+iLGeZuiJ3YHrqzc8fPhQ3nzV40vL00cXLHcNC6P4+s07rrYN377eYJTGMqGe1PR5oM8Nk3IGasCoGu09WRvi3FNPZKjV9Q3dEIm6xHsrrBslMwRjzDiMDigrJ7lDoWLg7mbNm9c7VvvEPjl6ZZlNZyjnuXz4iMvljMPfOzDkAaXFp1vwSCAlSl8S+0yjWkrvqCqDTgNFUVDVUzTmlNnpnJN80n5AE9BaVIISROHoOlH8OWvphwOTeYnS0lQ4pzkcDjz/9DkvXrzgs+Vz6mLCxu4JMbNf3zEpK1JR8ebtDU8/eojJETUyJbKSDtOZREqarKzgvlpzGCQQuXQ1x0AFY2TE1/fDqYgZpYiIVfKQJKRFbF0H8TJxJRkDVob+ThkupzM2bcsuHMghSoaCkcBu4wyu8OJlZDTaGZzReKfHxVhYXhaF1ZmcO0KEfkh4V8nuw1pRNxuR+But0VZ8Y7Qa49OyJieF0UbGpqNRVzaGmETHYa3FarmGjBVGDCALEpIclMfwhiOVT2dQxFPx1mOXzlG2P1IBE4kUxvfQaBjNx3ROp0IeoljeCvYuFtlHD3SVNX0AksZkCD0C4ShDjAYdI1p3kCGETBy94NECqagkWQg5RrQz/+ixVo4OCEqZ0QND3Ms4prMrTbKC51nzHgu3VjjeIcuUWmuNMh7nCoxxeGOlc9CanEpU7FDa44MYPGUi/Ui+V3F8Xq3Ro9oqhjx+QKIgzAqsUihToNGnyC9yRKkoyfTGkkeee+gHspVwXZVko6ycwDlWy8qrVCYODUNIDEl2B3iHzgOWjs1qiy+nPJ09AjITX5IW53SHHednDzjsN0zKBYWr2e4adKGpiwUvX75kv95Q1jM2256qXPLJk8/Yrt6xXM7R1tLutoQu0LZ3KApS1PQ+st83nC/P5OQ3BlUsIBWkbPjlzx4wf3PPH9zuSOmO/aHDO4VzFmskODn2CZzAEtZqppUmDdClQGEqUgHBWeEZ60w27uQFYg2AxhsnDnYx8vbFhtubLXf7yPqQOPSGSX1O5ac4LOfzCa/fXhNDj0G6I/ECAXQkakcLDKkjZEdZTwjBsjtEihL6roOk2B32nJ+fUbiC0A/Uk4q7m3umE4v2MLSZrh+Yzc+4u78d7UkdxtQsZgs2qy06wd2710wmZ1S+4u3VLcuLc1qV2F69xBeas1nNdn/A64zJkj9ZFQt8oQhDx77bYJ3D+powKLQvxKskZZwtJMw6B4ZBaG1GO0hGtBAm0SsZzrVjapYIXAJJaemyySgyg5XFMyXFfXPg0Hb0URTKRlkZG40S9iNdzmQonZV0HKtQ2o2LQkEQg2MYNIcuc+hGP3+tKEqL96OYz43QCo6UxUM9Ki2UPa1R4y4qZoVSmjEqW8Klx4VL1Nb2dK2a8fNWYyEHKW4nOX4UajLGnGZoogrndFsKvEAkCpkbSC0KMmfLGWP608/HaMaEJEtKnpSiGGllgXyN6sdIvR41aLSRmZ7AYwlMQEcx6spZOOw5BrIyxA8MtX6a42eikJNBY8gI71Q7fVJwLqY1k7JiPp9Sl5NxUOLRpw5OnzphwaGkSJrRLpKjsXzIxCgJQbvtRrZ5Vpgu2hqxn9VupDeJXNvYTApyW8xyhH6IlbU+Z/C+wBkjmKExeOtgXE3TEMhaYYz4T2StaA6y3TJWodG4YoLpLMMwUJSO++srttsNhdGUBWhbcmh6Xr56zWQyYTKv6PoD2pX4ssJUBW9v73hwfk479GwOmSeXS773xXOWU8Dc8ed/5VNu77bcrG548PSCRT0l3HQ8unzG1dU7iskEZxo2mxWPJgWbrSFimM5nVNajikRhHT2av/hrX/Ir32v47R98xY/e3vHVj65Z7QcenXseLApSChS1o28H9tsD1o0GSEloa64SbHU69Whf0w2RpunIqUNrhbNK+MJ95LA+cPVmw//9g1fsWsW7vcLoCVU5Z16fMZtMqaqK//F/+1/ouz1FDuiRx6tHC4E2ijDD2hK0pihrQZ7VgCtqtrs7yllJ026oioLCFQxDz/3qlvXOspjXWK8Ifcdh32KtZbNdM/QHnC+5WMzks449WUsk4ZMnT0BZptMpq+2Kt998RR4Gfv3X/jy2rPjm6xdkWn7jL/8Gq/Ut5WgP4H1JXVaEvicnizUVq9U188WCECBjGPqBbhiEmpYNm22D95k2NpxfXshAMSaGoWN9e4PXGesUQXtIcp722uAKix8Thg77ge52S1Rx7Oo1hIBWoMye6cSj1SCeK9Ggk8Yag9MWr+Wct9rgVabUidVmz83NPZtdx6JeUpRQV47Ciz+SdmOKfMojzm0otSVmw5AiISeSlmKoh4xynqKanSAHXxYSToIQIURXokevHU6F/MOgBgk34YPv33fix9vj5m28P5Lc+Djie3+XLLYgJ1bK+LMhSAhLHwcZfuZwSjKKQzgNSsMgYr+cxbYi53wK0smjp44smuk0h/lpjp+JQq61ZlbVkEc82xfjVNszm9QUzuELj9KWjB7fCMORYJ9zJIX21H1QyEkveqwxzcZ4nHYnaa0yduwYLdoLd/bIJtFHy55jjug43NHjh2eUhKs658acvvdDF0Z5vjpCKqNZvTcW5SSHESDGjqZpZHuswWoY2o7L8yXT2hO7nqFvKFxmUlb0aWB7fw9csFw8oO323L67YT5fsN1Gtu07zs/Puf7699lsdjy8vOD8HKyylJVlOinJ/RlED7ris8+/z+31Cy4enPPyjWC9wxD45sevySgePHrM937xF1hvdpzNKxSKaV3RtUJ/+7Vf/ISHj875e3/4it95veH2fsvj5YzUBiZTy+LBRAzEQjhtq1EKjRgubQ8NYd0SgyKHAetFoOWSY2gjL17csrrdcnvfcb1JNMECCybTOcZ4FosFF7MFd6tbdrsNpVOjqZqwVBQiBc9aaHMg4QiHQ0tbioNg1x5YLs8IIXJ58Yi+71ivV7RtRz2pJPkpJfpOEXqYTCv2u3uKwmDNTNKA1g3T6Zztdof1BmNqsnZ03UCOA+eLKV4r9vs9lw8e8O3rK7S1VHUtqU7llDevXvHg4pyYEVHY0DGE0f1zaEksGcaghxgHtpsbsUaYTuh7gRBCEtMmo90pq7IP3cgVFyzcjIXL6syQA1pFrMlI6JsW21V1pMsqVA5oZSi9wRpwJuNUFtWtVmQdZXdpFcZGnJHMm66NbHctIY6QpxYI6H3jJTCoc45s7Sj31zBSG48RjcpqnBWPonyqFe/rxocJ9eq4A5MrVhrD8Y4PO9s/rpCfrvGcxwZN8FuR8isywziY1KSkhfueM1q9j4pTanSYjGrkm1uCEiVz1CMjJqUx+FsKuNL2ZMFw9EW3MZwWivRnqOQ/E4XcaE01urX1OdIPHSEO6ByxVrx8BwY0LZAwdswDtO5Emj+KDsTvWgQ9zjlSdKMQQI9bMkh5R3840O5WUoy9hPBqLRjo0VMlawMjMd/ZAgjo8cS0So/4eCSTT7sE+C5m75zgdW3TQtuilcA+OctcIMRIVonp7AGd72gPG4r5XHiv8SDdWc7oGLE+MJlV3N7estlsmSwXoDTPP31G6HeEoeNXf+n75Dxwc7Pi7eqcqHpU/475dEJ1VvCjH73h629eUc8nfPnFxxilmTdwe/2OXdNQlAFrNHdXL/jNd1/x5S/8ImXxCWVZYbWwNCbVjNB3nD9K/ML3v+DZb/6A3//2DW+utry43jKfeD67PFD5jJ9YHl7WEltWGs4WMxgUzW7H4XAg9Jnh0JO0oShKXl+tuF+3vL5r2beZfdQkXVEVU2a+JqRIVQhn+7de/j77/Z4JCaJ4OEdlUDbRBNDWYfSAVRlPwnmxebAG7q+vWF48YbO+59mnn7HbbTkcDpydnfHgwQNefPtjpvVD+vZAGwJlZUmDOEzGEAldkLzSYWC1WuEKT13N+cM//ENSCHzxxResVh37VnDvYjLlRy9fs9vtefX6Fb/+F36R9f07ysJyeTbj/u4dVfmU4BSumNLtdgyHltlkym6zhQzb7Zq3b16zWJ5BYWjbDuOnrLd7Yh9wPzcGm+Re9Bj5SGcDjaZymaQShywxd6XXzBYTDn2k3ysgkBPilzI6Uz48XzCtHaXKqNCjnMHZEj3aYLiyoCxKJoXD0dM3A3/w4yveXu0o3JzSKmaTgroucc7hjMXpEmu9sMi0mENFpPxmlTFhZKTYo6AuklOLVlZgOC0FXdtj04UMzjhi5ABqnE+9j/iDkeo3Hh8W+KP8/sTdVmnswhUoc7qdkLAMGXCGU+EfotAO3dilkwJhjJXL4X2nLTz0cOriQ3z/f/k4OB0Zev/I8chjjHKyoolK8v2MMdicwTuU0Zh4+qzQyQse7cbimfWJ1lQUpQhbUmLoOzHeSkk8JxBSf3sQT+gUBqE2VR5dTsAY+ugpfIXOBTjB7YzWo4JUOnp9dHxjxPXG4YrWGqst2Yxp3iMvVGuDKypC7IhJDPKNyqM3SyYSiFmScDCa/WErWGPo8c5hjSG0ewrnWK9WTCZzptMF1zd3JJMonYLCU1UTVvc7rNM8f/6cb16+4eGzh6DPeXN1z+Ks5Pnzj/jhD79iu93z+u07PvnkYy6fPgFjSFd3pDBweT5Fq0TbBn70ez/E2Sm+KKhnU8iWru+FkqYsxsJnn34ERM5nBT94ecv16sCuSRROUXjF/M2aylmK0nBZN8Ir1pFEpI+J3aqj7RPDsOH1uqPLivtWkbIlDJlZ5chDz33boVLksDHE0J2GTlFnchooxHaShMYpsbA11mBGocXQJ/wYTrHfbtC+4NHjZ7T7A8VkyoOHF2hlubu7Z7lcEmLPtK6FLaFh129xNmO0ofaX3K5WZBJ1VeOqmtu7d8Sh5dNPPuL+5i3KWO5XOyBR1zV312+oqop5LSrP1y9f8fzjpxJjGHsKZ2gPDYXTtG1D0xyYL87pUmS72RFiR12LlSxZceg7qmLJdpsIeSAdB37aEIIk9GgzOq1mToEzJmVS1mgSVW2paku370Uvjoh7jFFUlWdSOCZeU2io7ShqMwrnDMaPeLezUvxTpusGbld7MZVzidIqysJindhVaM2o3ZDZlqits8AsMYyZAvJ6tTLkpAgxoE2BHrF14IOOVZqmpJNYGo9FW72v18AxbZOfuJ/Tc8nPWLT6IINzvMZzTiNsozFHhaiS+chpMRj556MxDTk7dIxELf90lk7bhkiMgiZoY7Apn0SF0pXHD27/I9aRx5w4hB5R8GiU9xwjkbQZt1M6c+THG6Nw3lAU7oMuWNLVleK0delic3JPFLhclIFxaMVUfugIaUAR6JUMWUyKI8wyYHCj2Ei2rNowXhWCYR0TZsa6zTEO6uiImLUUeWOFQaFMiUKMm3KSIU9SGqMtzW5PCIF2FKNYDX3s2XUHSu/ZbbdYbXBVydDvqaoJpVU0TUN3SMxmNZFMObUcDgc8JU8/esI3L674p/7Sr3O4OOfrr37Mz31+wXT6Jb/3o29Z33f8we5bZmdTtLZcXi65v7vj+vqGzz//lLYLDPuOH/zOb/HR82dM93MeP/2IuV+ggGFoiEGEPI8fLim9ZrdpmPc9394PHFoxV7rZDHgXcDrxwmzHIXQUZkmG3QEObaQPmj46nK0gNBilUGogDTtihIhBGUvXdXgVcdoRguCsWTNS4sR7Phnxpc+DQrsCawvadsvUlxJecuh58HTK0AsjqesbimrBar3m1cuXfPnF54SuRReaFHrCqFcoy5rtdivpRrFlOi0lfSiCjopHDx6xWe0pnGF1v8YYja9KrIPL5YxM4tGXn/LtyzdcXj7k/n6N95a6rtls7yUQIjk0gabtCJMpMUWadoMvhEse4zBqFsBamEwL+gCZgZwLUojCugnSMaLFqTASicKxw2RwOrGYFbRtxea2ZRQcYoyl8JpZ7am8wppIWbhxqK0oK4cpDMaBd2q0DY6EpmFzt2K13qGVeJYU3olPjFLvq+jRbjZJWrzNRhgiMUJSAjkoRd8FBM7IaCvXHOr94DXrJLL8jHDZkzp13uOdwHeHhh+m8nx434fe38fbR71KzvLc4lMr1/cRbj36kSujR9XxiMunEZ4yGhMNKWvpvI3BhLGQayO0yPH7o6mXdOWJn3iZf+LxM1HIQTiYFqEKmZgxGpxRWO8p64rCeQpXjqueeHAMSaa8akznttbiy4K6ruRk1J5gj9zQCApCjmKUlSOHoSWliB7J/ZpEHAb6BgZT4LPAM4DYTY4TfGlc7Mg3l0zBY6CrtRb6cVqtFVjH0brVOcjREIwmIYtDCGIgxPhBG6Xoh5bQd9iiwnhJrzc60XYDymim0zm7fUtd10ynUw77Hav1LUY7FssLhqDph4wm8v3PP+bv/ubf5vxiwWefPuZ2tUUZy5/7hZ9jUtdM6xnfvrzi9ct7qqqiqArmiwU//PEbfGH4/ONHhJB4+eOvefLxAw7Nlrqc8tGj55TlFK01D4aW2XzCs2fw8198TlSKb7/6lpdvbrjtBn7n2xXbXtMnhc9yykWNZHwq2bHoVEqHrSKHVjp4k0CThM6lFDYlrOpRFgplcSZhS8lodb6mGVqxrI0ZrRxt0+C1xqoWhRoDnTVN0zFblpINW1VsN3f4suDl7S2TyYxnz59STTydiewPa8pKrIm9r9lsNxwOPYNV9Fnz0cVj+j6wurslxoGmaXj80UNevXjNJ59/yt/9u3+bX/7lX8UYw939PdpWfPPiLY8fXpBTJBFGCl1iv76hLDyFWWKtZzqvsEaS6612kkdrDW0rwqOimGCtZ7ksCWk4qYydgUINxH6Ldk4WuVEMY0LCmIRTYs361BbMjGF1e2C/F6riw/OK87OKSVkwLRWVVZRVQVl6Cg9uUlGW4g5aOI0xA81mzZu3N/zBj16y7xILHylV4OF5xeX5meDvMRFNIhPIWcI/lAJSps+Sr5p1lpD1FAl5QJkCZSViLuVA14uBl1xPiaikWRLPFdmpaDPe98cU5+Pt99+PM66MZL6efu6orBRdCiO2fwzVzDmPIc8jXfEnsPecI27cMR4Fht/NCBWzLpWl4TguTscB6j/0jlwp9V8A/xxwnXP+pfG+fx/414B348P+3Zzz/zT+378D/KuI5fq/lXP+n//U38G4yil5M7VVOO+piopZOaf0pVB3dJQsz4O8n95bSSUhiRw2iSlUxgPi8ZBCJGfIJGJSRERWj9Hjm5XpjcGlBJiRMQNaRUkNSfKhDKojp5G76ywYCZZQowT5aDSVjs44WmH8SK9KsjVrmlZUXxj5fz2ac42y3GOqu7GKJiYJnw4d1hhM4fHasG870nZHWUx58/oljx5e9WNQqgAAIABJREFUMKkclb+gaRq8h7K85PWbd9ytdli35vnzp+zWd9zf3rA4mzNEcY38zb/997m4OOOzTx8ynz4jdIG//wcrrm7ecHZ2xrTydH3k8eNzvLfcXa94+HTC4bDjZvWWelpTVwWTqsCoSCbipgvJRnxyxnxWst43TFRkcwi8XnfshpakDW2rqbSEYrd9JDmD0gqnE5OiJMUOjcJ5i44ZZww2CYXOG0tlDN4ZrJXnSEMgWwPaiIgkaxQ9ZeEgik2xdZqiFBuAs8UlMTU0rWZee5rmwHa7o65rlvMFbXNAqURROGJIKK2I44WptaXvMlU5J4TE9fU1y4sF2hrOL8/Zbdc8e/qE9tDwve/9PDnB/XrFdDZlvd4xKSu++eYbvvflF7x+/ZpPnj9jCB1ZCV2w6zqyhaKoUEZ8vI0TuFFCGeKoaPYcudJVIT5EejRazVqL7cUI/RmjIQewCkUvFLmc6HLEuszZ1BJDg9OOs5llWohPTqkUlXM4L2pbW1oKb/HOUPgCRUAnRXM4cHW75vp2h9OGymkmtWUyqSgrL37yRnxZ7JEqjMNYLcPpPpCTqKrDMIzXhyKrjFaZEEQ4o7XGugoYRTvwvniPRT2jIKfvdNYf3j4GHMuAMp/+X9D1D9vgP9oSi6B8XIA+2GScVJrjAFWnMV3oOFRFn6iH6XQefRirNxpsRf3BYvAPl374XwL/CfBf/cT9/3HO+T/48A6l1C8C/yLw54CnwP+qlPpePrrE/EkvxAjLxLmC+WxJVdVUpRNZchykEEdZvYbAyBoxMIajKisFPaVEHCTgWI9BEylmQoAYFGFIkILIeJUh5kDfD2jd4FzAmwLvP8DZRu/klDIJdcLkpcORD+ND3G4I4eTCNgwDGfn/PIoY+i6gdRoHcWmUKh+lxbJNNEYxn8+5Xd2jlWW/3zOfVfjCU1RzUoKhj2gNV++uqYqSmBPDkDmva4yzfKQe8ublGygcQ9Pw8PIR2+09q02HsZJlOqkXfPPVDa9f3fPs6QWPH11gvaH0ltAd+OjLp/RdAzozX0xRyrDd3eOc4f62ox2mpHjGpJwwnc7lPYiJAw31fMFsvqS4v0d3W3LWfNHCy7s7tofA1ToRsmEYEqWzhJixduzSCOhCwkMKnam8+F7n0b+jLkrU0ImYJEmOZLCZ0I4YqcsQIqVz1N7TNRGVFNY5EiI7D11geT6jbUUAc3l5wcXZOVVV07c7kpKADKcE385xYOh7nDMjN7igaY/SfUMfAvPlOW9fv6YqPMMQORwOTOYzGbJbzfXbazAJ7z0fPX1CGCKPH11wf3uF8xqnoJxUsjvUDu8tKYjzdx755nKeiK1EUuKhXlUVVo3noVIMMTFkyMqKW6e20lFqRYz6NNTLQyT1LTprzhcar4XZtawtzkkiTuVGOrDTFIWjrBy1d3gjC2IKAwnFzbs73r6+Z7PumBYTysJSV4aydviiEI9z42ReZEQolrQRwXxOxChJUzH0aIQ6qpRQAodBWCPGiIW1NaI8GbKElEshG/HznCVGcOzOPyzgx69Zf9iRjwX05NnEdx4PCJySj933+2s1836Iao92AOMHlPX7YmzHRLL3r+F9kT6yeI6PfX87/sMt5Dnn/10p9elP+Xz/PPDf5pw74Cul1B8Cvw78rT/xRRjH8yefSbdrJEczxsDh0BGCBEu4wgheroSKpLJB5YDRo/taEjFKCpGu60gJ6sLijBPzraO/b4Tu0JGyxMChFHGI7Dd7lHG4piMlhdEDU+MpXYU2EuFkFeLuZiSVOxvp9Mlm/HCE9pbTMWRVIJR+iFgjJ4JzMoglRbp2R85ClNTaUtclecQ3yRGnJZV+t93JWDVHJospISQWy3Ocf05RFOx2G95dvyHnhLIzAgMDLZ9/+RhlLD/8va9wVlMXkcuzGb6ac7sLTEvN5c8/4fb2julU8+b6FdVsyfnFjIcXZ7x9dUXf9yyWF+wOOwYsu+3AxcWc1ebArh047BqW0wrnLNPpjGp6QWU1lS0ZuobzWcXHj58yDJEmHPj8bs1ut6M7dMQI97crjPWE3IM2ZEr2mz1PH15wf79mSJFp6chRKKazWUFZF0yWn7Pf7xn6zOp2Q4yRm82BLmnuDnuC8kznM4pCqIBRaVaHAy5nLhcT1utbMIHdYc/lgyes3t1y/uiCrAfSkIhZjNJKV9EPLbHryTHQ9XucK7i7u0cZxX6fR6Ozlrf3W+bTCUNoQWWK0nDYb+m6gbqa8PjJI0iR129eksJALMsTi2bia6azkqaVYfUQIkp72jax7w4i2U8ikjHKsrrf4MuBxUXJ0DYk76hjj1biZpj6Dm8VzdCRdUBpg7cW5SX/dOgDg3EsVSTFSLXw5KkEogwklIeydEymJVXpmCxqJlVFUWpqb+XzaBqafc/b9Z6/+Vs/5N19j9MFj8+nXC6nPL5Ycj5fUNVTfFWhlAHtyaPBXBoicWy+wtCTQitOj1E8SZpmT6Ina4NxpTDYyMR2D3BqopQyp93xh9358fjObfSpI9dav7fiHQu8VXZ8vNAClTKkGEarh/F3ZmQB0uqkKP3Jrv9D+uARUz9CrB/eL/TD9zCKMfJVWCw/vUb//wtG/m8qpf5l4O8A/3bO+R74CPg/P3jMy/G+P3Iopf4K8FcAZpVHqUyMiWaIDH0/BuyCsxKHFINjSJL0YbUbO/DjMCKPtEChV4Ugirdg/ftMv/E48Viz+s4Wpu8HyIE+CIXRWostPLpWFKqSpG+tZThpwOlCBrDqvTVAzpmQIkrL1qnwnjTGT+UkaeDKcsLHhmFgGAacNiQFfS8hxcoaQjegnceVBXM1R6WEs5puv0Nry+3NOw5NLyyI+Zzzh49p24bV3TVVVclzJjh0EvjQ9R2FHamTBVx6z5s3a7yxzKYGS+bjx4/58Ys3XK97bu93/BP/+K8wdIFv37zg8YOHPLh0TCaGMCS08+ybxP3tGvt0oCwsKia0qoBIzEH+jhwwpQGnaLaK6axmMS3ZrO9IOOazkjy0DJTiN21KdnNHWWom0wXNrqFyTnxrrKLwsvNZnC1wzrBdb1hMC/p2YGeNMFOUOXWjp8xbWWVxXpOieI9r4ONnz4mxY7G4wLuaEEZr5FFvkHLAqAgms98f8L6ArFkszlAqs90f6LqOMLTMp44QdqQ8gHIyzOsjzkrSkPOa/fbA1dtb/ul/5p/km2++4WI5o2sOaK1ZbVecLeb0fYux0PV7YrSEtIc8UFSXLEZs2Bcje0hL8AojzsvoI+KVwSmNRUnTkzQcRW1O0pZihEFFhl6h/egXkhSd1iTvKcuKuq6xpRndBy2F0zijBeLsO25vV7x6u+ZqMxADFD4zLR2LWcVsMRVmi5FdgVaWpEVtfYQSQ45/BA8OKdF2LUMqiApJ8Eqj8VRyaD0mBBn/gSAwvy/kI259KtiZP1LYZfhqRG19LLAK4nhNG8QlVak4dtfvnVWPz5ERmuR4gp1+z08OVyW96I+tgaff/eFx0sP8/+B++J8CfxXZZ/xV4D8E/pU/yxPknP868NcBLmZlfn31Fp0l5aMqx5zM8Y8UPwnxFU45EJxGDZIVmLPDGiUTdavei4DSQHtYk0Y8LmdQBlJIKGvIwUC06JSAcHIrDCGzWd2jtSbGgaFuqOopk8vL0RtCMHKjLPYo/z8KhpT4M5wGKEqhkvhoVGVJFwfI8ncZ1+PLihhkGKKVnNBN04xDEWhDRBmLcp77u3tIGW9huphhvWEyn1AUBe+uroFE4UqGZGi6yHa7xpc1i2oCveb8/Jx3129Rds4QPPPzKcVamDUfPXmCMYr7+zU6aZ5dLHHO8bf+xt9hNpugtOVVd0cMmUePz5kuCjbbVyynDrPwfHt9Teh7nj664BMLrvAYtwBlseUMVESFzHk1IzQdKQbmF3PSIKyEnKDt9mhtKSfnkBW73Q2hk6GxKyZMp1Pu7u4kkMNquqZBm0xReuglyqwZWpTxZKuZ+lrgBjKlNey7Fl1ZDn0gDz2JKSk3rDZXnD86J6eG+80Nm82OSVVyeXl5Yk4lPdCFPYXRhKGlbQZmy0f0Q6SqPLd375jWM168+ApjDNPpjOlkQlaJqnI4GyjPF7x49ZbLs3P+0q//RV6/fk3pK5ouUJQT9vuWttkwryfyOeZMe+gE0hssm9WayTSTdALl0CphjPi/9CHhEZgxpR6Ve7zK1FqxT4M4c+JQCpRTFNNKRO9J0e6N7DYqQ0iBIULUc1xZ4HzJZD7Becu0Kqm9w3kY1gd29ztuNyv+5m+/4u3NHobMzHseL6c8e3LJw4fnLOZz6skE640ETqgMKop4abxGcjr6dEdCEDjh0Ha0XUcXWxHLaIOJiqHtwWncGIieveDL8L5BOx4Kw/uG9rsF8VhsLe9ZZqfUsREKxehTd54tQrVFgTlmlUpTeRqOGunug4ronwxNjoljyEVK3xUnyYxMoF4RFoEMWIUA8tMe/0CFPOd89cGb8p8B/8P47Svg+QcPfTbe9yceKSWGYRAF4JHQr97nbn7wu04fwjEbT0Y7dvTqEByQbMS/JQVUFpdBpQzaWHCCpR4/vDB25NrKNsoi7BGlMn3oaHuN9lD0hSjUNKjoQWcxf0/vif4ySS+O7xFhHKamJCfnkZ8rJ2w8KbqMVfStdBlVVZ0m4Sm0dO2AMoa67ujaHXd396xWK5Q3PH7ylO7QsDw/Y7/dsN6uuHz4gLvVhvV6y4OLBc+efYRJLWUZ+eLzj7h6tyZ0He++WvH555/z4sUrXr/dc3ZW8PDJQ7b7b+mGFld4pvWE9eqOn/vkORnY9oa/8bd+iy8++5Tv/fxn3G/W7A8dTWdwSnF3d8ejsyXO1/jTmRUxWKrSopQmKIiDIitDHmEm7RN6LxS0qqxpDgeWi0txmowKX8/Y7XZMpwuxOogDXdOzWHi8gZv7NUoNGFXQ9xqnEloLA8NpRWcVqVO0zcDUiYhFouIaJlWNioF2F7jbv+Pps0+Zz+cjtSyRQovSMnchSW6rMpw6yvX6ltI7VE48ePCQEAaW8zlDL81EUShW97ds14bKV3jvafpG1LePzkl9S9c1DM2Bhw8eY3TB5rCjqDzOe7bbBm8s80nN0O+JUdGnyL7Zk3OmjD0qg7FOQgywxByJOUn+6CFgg8EX4FwGnIREGCVBzpUhGEPoPCoa2fnVhRjIWYcvHKWHaakoNKQYuFmvuLnd8upqxZvbLe2QuHQlc69YFJaqKoR9U/jvdJspJeJIuU1JjKiOO9MUenIcpKD33cjeUKMhVSZnoScbNQZNKHEnRb+31/3wa849WtnTtXj8eizCAEmFU00xRyLDCKFgJZNTGo2M1ZGEQWWJY9Nawh901qfZVsaMqk/xVDkW4mOwxPE1fHhbvv5RCEX95Nz1Tzn+gQq5UupJzvnN+O2/APzOePu/B/5rpdR/hAw7vwT+rz/t+Y5UnkQm5pF/+cG2Q4x7BJdKxwGk/IBsF3UmCoooKsuRhkXKI64OyggfVOVMAzC6r6nRTzwlMc2P9GgtbABnS5wrcKrAZDBZjHUSEYUMUGHEs47sFN536XF8QIqRIWbJBrVptK1MJ9UqMWG9kyHmIPQsUmZaz6hKaNuWZrcHJZhlCKLAe3f9RpR8xnF2cYb1lt22ZVJVmAvpGOIQuDxfcH9/T1E6Pv/8U3a7A2/evOLNy5cQMt+8foVSz4jRMVtcyEJmHBGNK+a0saPrOpSq+MUvn4MaePfmiuliycWTS8Juh/NTjMqsmp5Vf0Ph7pjWE+rSUxU1KTgRSyjITiiDKYqFwK7t6HpFVdRMJxOsEYw4ZY0znm0zYFwBceBw2NE3PXVRE1OPQdE2e4YhsJwXmC5RRsNhSJhCEZNGF45+t+UQgCARY7tDw9wXTBclXb9jyIqPnnx0CtAOKZBjYFo4+r6FJL7lQ46cLc7ZrNa0oef8/JztZsUQGjSR2WSCUYZN846Li3OGtqcuBGKrpgvubu9ICS4vzpiWJav2wNu3b3n66CExtfSDIvYDvVJYY8gJIv0Iy+wo6ppmP4w+QUa430oRknCqU0zEQeYsMcOh6fEBtCooaoXOo4NolqbHWo/INCKJjEuZyWxONhqlHKUrKH3GIhTftm359vqet283vH67pd0nnLEsasdZXbBc1NSVp/QWbzXHtKE4xp7FFBlGqm0OWYIuQpDinVrSEOiHMZQ5aBJavI10idZqDJI+Gl+BUnKNHWuh1I1RBaXD+/pyHC6q9zkBH8IaEu+YT0Z8OSnBZBihUZMJKokbJu89ngzjwpA1UUUZyI+iquMgVWdNGFk0H8K5KuXv3Hes3Mehaf4zVPKfhn743wC/AVwqpV4C/x7wG0qpXxt/89fAvz6+gB8opf474HeBAPwbPw1jJQPNELAxk7ylRpOVFpSCIzVRn2z29ej5bVCoICU85dF4RhnqopaOW8t2Myst3sla4Yyhns4IfYPKEWc11ik29+tRQGHYtBtChpdXAe8LJlXFk2cPmU3m1HXNpOsoS+HSHvE9Ne4gchwYRnZFyrLiG6tkYGq8bGkN5GwI/UCMAykPKEbT/XHYi4J915JyQBvL5ePnEmP2+mtiu0apzNl0Qtu2vH31Df1+yWQyoXCZXbOhrmsO+4F3t9fUdU1RnxGC4mZ1w2wy5ZPPn3F3u+H2ZsVnn33CbrdjdbemKB1Pnj2lrgqWy0+4ubqmDZmnT5/T9nsO27Vw3PWEzeqGF99+zawqqWvDfD5nvZV4stXdDbut7EKePLygLiUQuypqcXi0FckkjHUsyyl5OieGwBAHTFkzdPFE7zurFJv1iqFNLCY1el5z/fYtKbTs7vc0HShr0bFDdz11UTK1Hl857jZbdDY8vzjn6+sr4SIPMPFL3NCxu34L3Y6zh0+Jec9iKQtJ38sAeug3tE2D0Zr13RXT5RJvjQzK4zAyTDTb7Z7zeY1W0omXznP37hYA5wpWqx2z5SOePH3IZrMhjVQ7S+bLzz6lPTSEoSOnHqM9sR/QhWVxvuTm5obdfou3cFgdRivYEl/MWO92zGdneOXEf9tYhhDYbte8u7vl9e2OWSFEgNKNxFzrSSqSdJacWe3QhXiFOOdQphu704RJiW6beLfZsdo03N42/PYfvmO1buginLuSZeX5/OEZjy8nXJzPePxoQelLjJEMT2XkecW8LhG67tR5d+FA17QS3q0iMWS6Fpoh0SeHLQrxK0nDOEDUo0e/FOys/mg3C/K3hGNHrNR3xDUyYJRgk2PTePSBEXsOJ2EUI/MtWyVwDgqcORVywdDfwzNKiQBIjejAh4U86/fd94cYutxO31lcBJL96Qed8NOxVv6lP+bu//xPePxfA/7an+VF5AwhSpetU2aICTNuX2TbMdpt5nF7NHa9SieyHu1hcx79EBId3egdbdBY1MhNP22/jME4T+6bEcJRlIUnjbh46YRzniPoEMndQLvZ4WLC5UgqFCH3HEI4CQW09eNJYTCjB0zmfdLQ0PfyOtTROhRRgZEIQ2AYGkDsYLUecw4rT9sI/SrlINFn1ZLmEBjCju36wMOHlwzDG1LsaPYZ7SvCAJv1gYuLC0LsuX53x3w+pyjFi/1+tWO5mNH1QaTTKfBzn3zMu+srLi5rgmoIMTDgOH/8kG++veGbl+949GDB3f2O/X6PshWL5Yxn86e8fnnF/XbHroGYoBsUQwI1Wnze3q9Y60xROx4/kOCDwjgK9/9S9149tl1Zlt633HbHhb2eLn2ZVqMlCBD09wVIgASpUV3dZdInyWvDHbftcnqY+0RcZhdUqTfmviBIkGEOj5l7rjnH+IYoGfrhgDElMSq0KcE4ylLmv1VTcv/phtIWqCLTtXtZTg8d7b7HD57z6yX3uw5rLU2V0ZWldJacAmcry64b8SFSWQkuUESGMFGXFVVVUxQVy/WGoijo2gOaTrqxJF1TVdaQE9eXKwKa434rJ7UUufm0ZbNesVzKqOz25haVPZuLcyzCXO+6gdVqQQojt/dHtFI0y4boI5vza3YPdxhnsDNDKGUtcXH7IwsUy6pm6loaZ6ExRCXsH20sRQExeYxbzMf5jB8OhK4jDoG2z/RTYAgDdXmSJ3ZoIyOiWAq7xJp5XJACTJKyPMUgMsrJ8+5uy9225+7Bc3foJX7NWDaN5aKxnC1KNusFZ+slhZUwCtTJPGc47UJO3WeMER96xr6n648kL+q0EBL9CFPMUMiYkiRGnEQElYSnoyR443R9nrYjHXAm8tRDPrpBs3Dxn0Yxs/LFPIWoayU+k9KWSNqYnKpN1nKimefiMvo1P5gcEDMq/3AmrzOPC95/o14+jYs/V73wly864Ufi7MxkQoaUMypEAeWoJ56CjCyeFgh5XjJkpcgn9xWgtCfHzDQNAuVBAyc7cwKrHrksMYsVP0VPMhFbGAISw6S1kA8bB1onCjNRKI/Bo4n4qYcYUTFIh20dLic0DtwpiEI/LUJnlUpOCT0f91RU+DDi/SSGBy9Sy+DlSHtKDC+0pqwKDq2n8wP1omLyS7a3HeM08fDwQF2JNGuxqBm9Y7Vc0vctfd9TlQ5F5tOHj1xcbijLmq4d+e39B1zdsLy8IBz39KPn1evX7HYfsVXFarEhpIDCcn2x4MO79xw+Drx6+ZxjN/Gvv3tHXXziyy9e8fLVNSFmdg9brIkQW/w0Ym2FdY6223F1scTpiB93TEn0z3p+jbSx5Dn+a/KJMLYURUmMmak9EGJL1pHd4YG+HYg+MBxbFrUhVyWfPhwgRIzSLBYLjtOIKSNagSsND9uIHzwLK2npjbVoDClkctBcP3uOn1r8LmFtgTOarANlURO9JYSJsR9wZUlTrkAXjCHRKIVRkfZwx2azYWi3XJ6vyASMKykLy+3dB4wtqBY1Q/BUy1KQwL38rqSVSEr9wHF3R2EdzlpcWXJ+WdAdW8LU0RSO6COLRUM7RZSW6LRVUzGFNFMOIUaZ5/tpYOxaUoIxSbyeKQzrIbKsgsDhKkOTIs4ZbJkl/CRBTJpxgr6fOBwmDr3nu7sju6OnGyIqGhbWUOrM9bLkclGyWBaUZYkpRFooDddTNzx6mXn7OJt+wsTQtvT9kanvCHPIsfeJYbJkU1BZAdiJPHzOKdAWiD/osE83h1PNOI3HUOGxeJ7MQzl9rjyRhagofyzKaKwNj8tOlaOIJ0IGE+UGkqVZ01qjokC7ngq5CM5PHbn6zB9y6rA/v+H8Wxr303WSRf6l14+kkCN5mAlC0iSfQUM2Rra8Mc/a6jlQYl4onrIClQFFRiuLtbIg0WTCJKaRlDSFNVglMUtOG1RRsVSaME4EL9FsKUI0kSmKXteHgLOZFALH/T1hGvB9h3sw2MJiylpkVU6zXG2YjGPoV/IhUxZTlI9H6Jj84xtGa9mIG2XBiKmi0GKISdFDHEk5M6ZEWdSC340jcQhM00T0A1objseWnAKKAW1qujZTLRcs1wtWq2d89+074qri7/72Z1RVw939nru7G8oKzpxie3fHECbefbwj68Tl5YbLywV9jBzffaSwpYRO1wu+/sUvyb4nM1EvSp5f/Qfevv89Y3fH77c77rZb6rLip988E7xq6tntWr7//p6vvtjQj4IyuH34QNNUNMV2BuprNpdf4p0cdU2xYhwDQzeJMsMG1gvL8TjhyiXOwnTsaO8S90OYM00nuq7D1AsOxy3XVxes10tijHz6dIvJE6XVTFT4NNKFwIe7HX1d8+72gbgsub7c4KpEaSCMAT/s0fWS3dFzcfkcawsK16CtY7c/4JqKkALy0Qy07R3LWjgs1hisU5RlST8EjJ4IccIWFbWTGaxxAZ17wpAZxx4dB66WJWW9YEwWxQDR05Qab0qGQXTWbbel6zSH3FPVC4pmIb6JrcespWBt72/5dLvl/n7LF6sF2sC+n/jjzUj62OO0onIW4wLna0dhNDYrphzQ2tBPkf0UOPaBQ6tl1BdBk1kUjjfXDWd1xdW64mdfPmOzqlmfr2b3qSMgKo+sHFMCTWQKg4gahpGH9p6+7bj7+B6VeozSJFURsiLMIRXWWYGA5UT0YK2ZTysTBknWyegZUJVJygt0C/CZ2dE9x7KB5MimJ+XIo4lv7q61EUHEacSSrWaY5Y3ZiInJzCdvq05KFfvYnSudHscsTw5Taej4N5aZp697/PqTJFKrp5vDXx9rRe5oNivsDD8SF/mc1IPkVubHu9RTIpCMMGY9p85onThFvT3ia//MORVjAqUxzs4vQsaNA7kQlYlRE9YpeeFmhsPoFSkOTGOgrixFcNTGCM5zzuWUo2OE2ewb/QmgI3NBkFBmpRTDZ9mAiihu0zmFO+dRLNT9SOclzEC5gkVliF4RUmZIng/bPRex4fXViqQV+8Ot3HD8GV9++SWvX7/m5u6WT586moWiqBdUtTgZdcr85Mvn5KT4+s0L9seRh+2Rf/nn9yLdyoH/+Pc/BxPpupZpjEzTxPm6piwti8byd7/4FUM78PvvP7BYveJhu+fdpwMXHo6HFpU168UZb7+748XzC+pKTlFm/tD4rNEYXJHwORF8JszqgWN3wDnHebPmsD0QhpF+t5WbV4jUy5ox9NiipO9HqqYiTCNvri9ZL2v208Dt3b104OuScGhZ2IKQJVLv+XJFYaGqVhQpQIxoreingcPugUXl6Ebpwt/ffsCVDc9XC3btHZFI7gcWTcmntweyn4gZRh0prMKVFQlLQvP8+TUpZKy19ONAN7SMY8/5+RkhSEG12hC84djf0I0dz9/8rYw1kiGkAVUYFkWDOgb60FEUC+63LT4kyiAnTl1bQhwhaowrOQwDAcXFwuFMpjGJKQZ2QdEOinGUaLPtIJp66TohZ880ijM0RkVK4lNuLDTWcbGwfHG54HxZslwuuLi6ZLVazTJVSduKqhAaIIacDGNS5OgZhoGu3dEfd4IhSBNZIaY+W2CVAoyIEv7sM/u4EMziFclZEZBAY05SboUsOdMceIyMLlNMjwwPSoSeAAAgAElEQVQT4sz6Vor0+SgjCuvl9PkkzyogpcjGoqxwXQgBe3Jzz+hsqUl6HsM8zeWfZI/pUZnyOepa/n+exjKf//Xncsp/7/pRFHKjFMtKotmcs5zXFc4IIlVbSZghRRSzBdcJo8FYfgCrl8WhvKA+BnQ2ckPMkqkpKT4OawpiSvioyFGRtEMVDToZTIaiHtExc7ZshGsRMv3kCTHj+4nkJ/xocU5RrjTOOpwVbKpKYh8OeZR57CnN3csCKc6pINo8ze2s0tRVRfCD8Dxmx1fR1Kgsp4ZxHEkYcpIu6nL9nIta07Utnz4dCCpwdnbG1199iS5qtruJduiwTqF04N3779kfO67PrkjREwKz7bqkn3oSLecbULohxgyx4PbjW8pa7MV973G2YHsvnc3u2HJz98CLVy959fyMzVnFL3/1E7p95m57w2Ih46Ttds/rL18TY+TQjVycryiLmpA8uJpD2zN8+F7mijEx7I+s1iVTDJRnV+zuW4bRMw2esljiy8QYj4Q8ESc5fV1drDm0I+a8YLvdEvJA6QrWZYGxlu9u9ijgl1+ec397R6ga7oeBi02FQ7G/v8UYz/2H77i6uOby/Ix3n+5Rpme1ajgeJsZpT1ksOLu4xI8Dx8NHghWC47NnX3N/f0933LPZbICMNZ4wHTEKnDXEkFk1C0JQTGNE6RJlEkWhJB4Qx2bxhtFPHLbfYYuKNHjSVNL7jqapWFQbxm2gKBMvrhd0fmSxXDIOcTbMTMQUub97S7fbob3nol4IT7xe8/rYM4bMH+4m7tqBdoLtFOkiqGQkCEFpSlPQGFFVPWtKame43jjO1zVX5w2X6zPqqsLVNYvNGUVRUDdr+RyiGULEI7ud/tiKxr/bczjs8WMrTuYMZEEz6MJRlLWEtGRN9Fq0439W3FJKRKXR+SkF6iRFzihUOqUAeZEOxlNCWEJJOjIxPY1apFAqUhZFijL6UWmmp4k0y5+TNlLMlUIVlulkMFInw9nJun+SPs43HXViwQj0TdR5El8n3beot/IPVHo8deb6L5+T/ygKuVKwcAXOWgqjKQpN4Ywk2hfFnEotL0CaFyUmzccq0vzkfDZfMsIxjlFSfVCSqB0zGJMx1WwoSIlAnPGd87LDWdmSx5FIxOEwJuLMhNLi7tRGDA2Tz+jRo1xAjT1aFZCSpOFkC9pilATNFibjCSDvJ8FVzpmKKUMK8kJXhUHrcjYnSbZoSJk0BHJMOCxtNxD1yGEITF5z7COoyF61fPh4R1EI22J9tkQXlvbQUToBGH26vWHZLJjiwPf/+pZf/vKXopNOsNoswUbGbiT4zKEPPNzt+eLL55xtDPvjgbZX7A8Dh+PI2eaaOMGvf/t7FktHURR8/cVPsE1F0TR07cB10bDb76jLimPr2d5/y7IpqOtylk4a8iQnm5wjZ1cyEmnKhqHridljLOQwsN+PmMris6JQS84vNYfdnsMhsj10nGvN9fkZIUx8/PBAWVZkAn4KFLrEBNC2Zhp63py/5G63JahAeZYYty2r589Q9ZI/vr2jH4+s1kuMcWzOLGXZoIGufcCoxDh0HPZ7Xjx/w/F4xLqEWmhcIYoL46G0DcZmrKsZhpGha7m4uEKvNT5nymJBt99SWuiHPXjkOcHhdIGuF4TCYoMm5chuf4+1lnGcMM5h0I/v924YyUShcwbPGEai8jSLkuWioSgtRV0wTRN1UbNrK3ZD5N2uJWZ5L8fZ1LJwjkVhaazm2aqhqDTrdUlZFiyXDZsLuRnbwlFW1aPDEjWDumImpUgYJ6ahhzRxOG4ZuiM5BWLWxKAYpzijpw3GOIloSU8+EfXZ51pSwUR+LGHNommHUxCGzOT/HDilsqwNU9azu/pJvy1hFvJHkckpPvbQGSRURikwyA1H69N/mevWXLiTQmUPfA68UuQ8B18rg5qVKUrrR7t/VgZj7GPR/mEnrv8KCzkK6zSFsxQW6qqkMJaoE1bLljjMrJITLSznU7LGjJA0hhQzqCRLBiWwoXnjQsrpMRU7hBLlHDPEmjyTF0GO+qgklnkfSUa280ZZyJJCHqO8sKEN9FPP5BOrDNaOQqwjYk05z/HSDAsSHClKHprKEVdowjSS8mw4UZn9zsvYyDgwzLpWhzGZzk8M7QGtNb73lEWBypaDaVmWlsIY3r/7Hm0NdbVg062ICr764huWqw0+Bv7p+Gv++PZPJJ+4vFjy6eY95bLg+dUltih4Vr3k4A7c3m65ujpDoxj6kRB6lqslQ7/lYlNRFVBYS1U1GLPBWs1+v2V3/4mi1Dx/8Yp83hD9yMOd5JmerQpubzPDMBAZWNYihVPG0fYDRVFye9viCoXNkwRo13DYPaCQIIN3bz9RVCVnzxZ8/90NCsuYjrx5s8YkLW7InFk2BVXpCMlTKs1yuSIaBzahdKD3E4VlLiwVxz7RBM2f/vQdpSnpDy2bekGYPO0wERvLxSUURcH+Ycv5+pKcJt59+wcAXGXw40CYMnWzYL3ZMI0j3djTbz9RuJKqdKQUaNueqi4weoE1iu64x8eJHPJjQIoqSlaLDVrVHMcDcfJ0w0iYRynDMIKWsGaFYRyOECvi5BmHSB8mNGKIcoXBlpamWeBHjy07lmvDdVBcnTliUgydJ89O0aaqhSPuDGdNhS4t62UjmbRFQV1LzB3WCmvdVDCHLKSY8T7i/cg4DOQ4klNk7A7E4CFJgHVOshy0hcEaJ4vtFIlJoShmzPcTXiOr+bOUJcLv9O9khCKaciEUyE1AREciWRaDTv6s2ZMOWvTa4lBJ+WTqmYsziCpGKVCgtZrHoE8LzDwX59PIR4QNsmDN8TNJoeExBEVEDLIQ1loTtf3BiEb+rkELRPAvvX4UhVxrxaIsqAqHLSxlozEKKlsIRjw5IdUa9RjDpo3Mx3Kc8CHQzvFJOcuGWFyiAAptZkKiEeiVPNFhjmezogXOmWQi02ApS49WA9kPjwvWxaIm52Yei4jsyBOYQqA9jgytdAeLWiRnxojLCyuPd7XakHOmcKWcDlJgGI74UTI9s5+wZHJoJQIuSPd+Mhv55MnGUhYVYxclNDolHImfvn5OUolpaDFqEMxunrj98D1FUfCPnz7RdoFJGWGmzPPMZy8vAWiamuPDA511dL1nHAK7uy2L5sDrN1dcnK0wrsJVNTd3O843DVWtORwmdocH/vZXPyH5wKJcc+gHLq9e8u33Nxz2LSmOvH59yWa5og8TF1VFGAPEwP37G7a397y8clSlwwRPnyaKYkPSJSEJrTKlDVprnr1Yc75ZSLH2mZ++fsZud+BglnTHwPPna7LqeP/9R1YLR9GUTIPmq5caV1j+82/+wPl6RVWUaDVx7AecNmwPA02juf/wHu8HbF3y8sU5q4sVh8EzxcSmrui7Az7IuOo4tOyOO8pCMx47um5gvV6zrBzaKg79HuccValoTIFSmm07EDMU1qD8yDAeGAK4Zkk4KDZnkk/rjCGZmso0jEHRlEsoEw8PHzHWMbYBZ2sG32ILyzSKK/rh5hPBJ969/0CpC1TKHMaRKpcsigXr1Tlpo2imbg5EEARzzplhmMhaVCGFLeVkagzOiLGnqBqUspJ2rwuxsluDdWeQNePYE8KE9yPb3R1+7JmmibvtgzBhAnI6LQq0Ggk5UNcNttDosmCIkhYVk5BMtbacohCVjo+7rpjDoxrsdJ2KuZoBXCgp1CdLjnqsMzI6kbk7mKxIM7UwxQjGktTTyR9E/aKQwgzIuPbRGnMyAgbyrHOPfnwCXik5RZCfQF1WPZ2ipGBLYdczdljZk/M08Vc3I9cIFVBbI0dAW4r8JgfIIgGURD3RXhfOYqxDEUg+k4Ii+iDAoyDBsikYqqIgBjCFxZT6kb3h5uOMyIMiKUkxRYGyWVyWMZLihFGJHBNeWYwWvKRKCaUSYQyM3jP6TJzDJRa1Y9E4amcpigrUiC0LvClJJhN9IIdE0oqxD+TZVGKmHmMtUwKfAikGjBK2TMyKKUEOnvuHoxw/jaMpG5lD+5blylFUmTRasvcURcFivaYLLUOXIUQKl+kfbnnx/JpmvSIPAl3ajQ9cPr+Sx/tpR11K4ssXXz2jXizZ748c9ves9YY3L1/x8LBlubmiWYwoEp9u7lhsLqjOr7ncZDzw5evnTM8GlLbstkfutz1GCXEx6kxTN7y4/BrvPe/efc/aLbBlg5k8/ZRxdSESRRVZrRRVVdG2LeNMvkva8O2HPYfdnjcv1lRVxXff3TJNE6+enRO9RwPZTwQtaODaaRZVTWEsN9tbmqZi9IGP7cgywnEYWFYVdeNYXVyQ0Ry2B9ZnF3x8/46ffv2GbCz7wwGrEmcLS6YEW3AceuqzC2xR0LZ7nCvJOTEFPzcUiWfPX4hDVkM37Tk/v8bffGDaP+CcY+zFbu/WFq0CYziQdU22gj9WaoGfDsTYUpQlaEvwkZwN7aFD4yVpLEZKWzJ2LbUxmBzRc4CwVY66WkpHOXeJxhjqOhPmma6hAGcoXE1pS1Bx7iI1hrngzHK7cexJKXE4yAJzmiZ2+3tGPxFjYvcwkEPEFKKOUSGStCFZjSpLTo74nDQ5zTJADORA1DNwipkkmjJazdJC5FSulNBGE2E+NUPOQfThxPl0Lio4OczLiTtnUcjl+dSuTvLn0yhnZrkbpQgpSvykEuDaqWZpMlMS01+KCjGyiKU/PGZuZnSOoETBEjAkI/N9oww5RQpm65BWZOzj6OX/j0f/R1HIlVZURSl6VjsHOyD/G8EniWadnVh55kkbpbBaE5LY8+18pPIpUMzhsTkmEgEfEq5oZhG/RLZlnu52ckyapUoqS1iBc0x+JhqeQlYNcjsxohEtrEeTsCox+iwktwBTH8nBkbIBlykjROXBB4KuCCEKGS8ICz0R0SHgssdpqJ3BZ8U0BTKaHCfGfY+2JU3lwFoOx4nd8UBpDXWpiV2SeRswIWENdtQ0iyXX14mH7ZHttiOYDvvgebM6Z728mO3RmX/6x99zeXnFi1dXFG7B+cUV//v/8b9RLyq+fPOas7MzFosSX1fc7Y/81//2B169vqQuZQZ/c7ulrJfoXHDsen7182cYo1isasqylCCCxrC93xE8dEMHSuFcxc3dxO54z9Vl5Pp8g3XS4fnUUleO4BXvbnc4VbI5r8lJ8WnXYXTk1ctn9P2O5drhXODli5dMYeCuDYz7geVqRTv19IeB5XJNVolkROZWZEt/PLJcLdBhoigihSto6pKpOzCOI+uyxCjPxdmSw/2WP739QFEUXL+6wiXLer1ivYBXTgxkh91eMikJ3N7cs1xecHZ1Js7ZB5lxS8B3TQ4GrR1VJR2YKUuMLdG5kFlwTuQkVEGdEpvVkoebDxitGEfPFAOVDcRJTmO+8xjjWNY1t8eepGDwE00s8SlTI3jesihl/py1vPfmXY4zScaNMZOtxlp5PNnkR2kdMaBmhdboPTEbvI/c3u9o25au6/BjR8gejaKyiqAVEDB27kJVxFmFydIv55PRT9m5m5Y9Ug4icMg5yoSUJwSsXMI1ETv+Z/Pxx39/Uo2dtOP/vW77cdii4M/735NVPuX0WWCMjFEiUpLUbBbK0ROyBH6I70WqFYhA9bTPOKEFTjROENetLE8VOsmIJ6m/wo7cGMPZ2VqOG1by7mNKhBgEahUEHCUKFWY7vkYZhbMlzpaiFqkShXNoJXdileO8FPH4cZKZWQxENT3K4NCgtcUFN+u9o8ylS40mEMNInDw5B1AaazTKKYw2mGJBDIkqJvopEkJiGEb6fqRte6ztcFryN7fv3qGLgmq5eXRuaT/SVA6tJ3xuyZPi9jDhUyaME4tiZpIkUNYwjgP98SD4gqQpmiVjTChVUZaaPgWK9Tk2TFitmLqe4/FIjmAwXJ6tcM4xTYFf/+tHorrDR1iuNqw3C/bHPetuTe8Sx2PLmy++Zr/d8c+/fsvFRc/ZuuT5i3OeXa9wBqKPjL0n55qz5YIUJnw68LNvLnnYH7i528ooahp5dnXJ9YtzQX3myNAeOdssWa8M/8v/+j8wTR4/CWgsRM+7Dw8YXfP+3YFiIUu6//Sf/oaE5ePHj0y+4/JqQ1U6drcD79/esFmdz0wZS7nQLM7O+Hi/p932WKtJLs7WA8UwjlBZDlMkDS0vNg3rdc0pk3VojySl6Y4HmpgwMeFN5Cc/fzPL0yJh6pgGOY2hI6Up0ClijDiSy7JEWU/KE2ebBXFsSUaRkkdnjVY9PnRzjFyBKRpsUZBCjykLUphYrpf008jkZd785ouX3N3eM04D0zRhugPjEClsQVAj9/c3KAIWhVeO3eBR2wPWWs4uM66oMGUFSFdZFhYpkUbSs5QVxksWHfbkPQQwihn45jm2rfgZMpwiCuOwx+bAwia8cuQso5m6rEg6EGNmHDztMJCiFOQYBpTRJCxRW+FfaUSOm+ekn1kdIHPxLDfidCpy8x4sZfFTnFKB5mSvNO/HRGIsihjJDxAXa5xr6UniePpnkO97dIOiRPKo1IzFTvKYgiLhZwDYbPRLGp0VMatHnLXVmfgEg4HZMzPjFv+Ngi3CyR+4nv6d60dRyLVWlNVJfA85ZFRK5FwQ04gkS0dsgpw1Nmay1oQ0Rzcog3IlYRpAyXYaDc64x5i1mGW7HMnEMZCUSL+0NigF2hbi2oqQ9YA2YO0s5jey4rB2lhoWknQim/JAVBlHQhuNUhUhF6KsMcL5VjlRWIVOkYWRjj6S6EZPOytO4qRmmaQcb1NKdH1A65GqLLFJEVWmsIZ2HJm8HOeUUlROeB7OOawpUYWisAazbOh8z+6+B2TpkuRMg6Ki71rq5VLwtSqw2Wz4v//Pf2B9tuHsbE1VJa7OK+q6ZHfY8/125PmzK1bLhn534Nj3GDSb84rJdyirePvtgYd9x1dfPuebL5+jlKI9BHa7A3/6L79nYTRXlxueXZ5Tr5eM40j0HYoCn+H7t/eEENmszlDRc3ZRUFQVfqr4f/7zb/l4e4Mi8x9/9TVtu6VrE0TF+cWK7jBgUPTdgC4W7Hc9x+0OXZTUzYI0tuCDhDhnhR8n1k0tzkGgruXmdOwOvHr1goeHHav1ijGNROM4Pz8n+omUMz4M9McOUpDTnwWKMPseYFOfo3UlIcXGME0D/fGIayoUVpDHaKwrsdpIiPU8+/VRTqJV1ZCywrkSawJZT0xTYH2+4dvvHrAWUB6lo2AdjLwflVKUhUblxBSgHwL94GXCoPVsfhF+uTIKoww6O3ISrr9RhjgncqXoCTmRo8hnfeiZQiSkTMiJYRzpu5GYwpMMcF7aGesIyqBSJuZIUlBaRxcmvA/k+XMn7BjJFtWPHbmozaSDFWe3KLxmbjpILOT8tUpnYcdnRVJZuHoz4jarJ0W3UoqkNGBQKv1Q6fIDaJU8F5yK/Fxr48k8rxRZJfIjllY6/pPSJs8BF/JhDpL5m+dTkBZUh9LCgVKChCLrGfWblezL/toKuVKKorCP+smYk9zRMCgvnOAYPVNIxJiwhZW0dOMISUucmy5R1pKY8P6ATqKGUUZmezklYhrIOFRMc3Cqm2VCoLXBmEzQkZDE1JMQrSgmw4zVzFmhtRMNtp1zOucxSYoQs8ZHcWBOU8DHyDQFPj30hBxwh080RYFTAcJEzopukgSSxjmxlRtFWVcMIZKTZQhyUjGlYaUdm5XGh8DQHyVxKCTawxGlMuvzNbZeoRcrCgMulGwWDdZagsqPuaNlpfETxJi5enFNVpo//fFb/v4//JxpEBOSDx2390e+/Po5Sg0MR8VvfvMblsuGi/NzNpfPcLai6zoymr7v+errL9icX/L92z9SDJ5nV2uun11wcXlOO3WQSt69/cT/9Y//xM++fsFXr5+jVaRsAovliuKbrwhxoJ8OjH2mLtfkEOmHA8+uG7764qfU9YL/+o//ws9/+g3nmzV/+t3vIUWMTSyXS9px4vu3H2iahlevzvh0O9Adjvz8m2u+e3/Hh4/3XF1fcn9/z2pVsCkDGEPXe85XG9q257DdkoNnOLYolQl0qPWKw+FAmCZWVSNgsmNLf2yp1hXrV9cszje001G0ECmwKFdMYeT+7o7L1Yr2sMeQGS0Yey1jJysLzof9B1arDeXyHOtKpikyBoOPE836HO0KUkocd7c4BdY5/DSyWqzZtS19O1DYkph2FBqqhZWxJCXeZ6axl/e9MZRFLdLWYSIqhc4jUwyPMYJTGB9Td1KMWCfjGGMzi6oghMDYtxzbLb7ryNoSlWWYIj5onDMYlcnxQFk51k2FKR3Ryk1K2UzIos4xrnkCeSVhJul51Alyc4iEx4J7ckrOOfYAc8c+56pqfjDK0FnN3f1T501GsKjpqVD/oDtXwkqX7xKcrZhUoixRU5oLsdQZrZzICK08ooI5QWiO38unkIg0J4Nm+VlZaZQ2jzgDyVM4jbL+ygo5c8CqzMOYWbwJqwHrSFgUkJTY68kyrzNGEYnCBM9h/sYkfq+YcFb0qSlFMeDIoE06BnMKgzXzqEOeYO0lzCDHBOn0WARrGX3AZ9CTwWDAGrTR4DQYgwoZHQ1O2nqccoxR3KoqVQzRU+ZIkTVOO7CSMbkwFqVEvtVNGTNjOmMQ7C0hURWVHGOtIWTIhSOFjEdR1vOxFE2cFGYYiIDbnEO1Yj89sF40FEpx3LfEkJkQPWs3TNz/7j3XL57z1c/+hg8f/kTSmXpZY73mytYslxdoXbFzRzQTKXgWqwvQlpubO27uP/DFi5e0+yN/+vYdyX5kGCWubWc6Xv/qC9q2JasJqwuu//5nfPXVmjDsSLpl8+wVEsIc6PuO5aLC6CUmOWJM3Nze8+LZFVeXG1J2vP94y//0P/+PpDzy4fYdRalQuiBhGAI8f3FJnLzAogZYrkqhEe5G+lhz195Sr3rO1g2FdeQYaIoKrQK7w5FxClxXZ1xcXBC9p52O1I0h68z11ZqulaxOS0ApS73YgDbsbu/Jmwms4u5w4Gy15u7hE+fnl5SuEvaNrZkGT1GWJAykQJgGAhqnNEM7sFzX+CBhJ64oUVG69xShLkru+oGmKmn7AZRhGAamMTGFkbIo0DET5rmwM4qcJXhjnCa0VYzDAc3st4gjACGCR0Ygx2PHGEV9UbpqLjgyP1dYrFOEiABdTUHTGHxIDCGRQ8aPXkQFhczgCRlNjTKGKUUCCmMd1jUEJelB6rNO/GRRF6naXJD/jUT5H3TRfC4vlM94TCd1ifrB9ygtzdvnZqNT0f78+z/HyJ506o+/d569n36D1YJesHMXrsyTW9MoS5oBX6e9BMhsXWsjpft0A8mi2Ik6PYlj/oLrR1HIhTM+J1OnPBtrJX9TaYV2YDHkbB6phmaGLEmSTyaH+Lh1Vloi13wW6ZC2FpOtQNSShDtELctSYyTkIKWENZrCagyOnKNYnnOe1SOeFMDHRE4G66FwGlcUKCzeBtCZ7BOjMlijxXGQInkYKWImTRB8kBfSOiprMSkT8WiBy+BTnmeVcvMw81ywP+zJuqBcSXpP6ZRo1pXClRbChJ8GQRxkmdtujy2lq0i6wCdxz20uzuj7nt3+QLGoUYVmpTzd9hPdwy3VxSW2jvPizaK059B2VGXD2WXB7377a7588wXv337iOIzYwhHHloeHt3zx5jnOQOt7/vC94tu3t/xNXUFKWK3FaGQmFIHLi5e0xxWHw45f/+vvqOuaurT85M0LIobjqJimjqHd0ywKxhi43Y70wz0vnj1n++kTrkjkcUQ5y3J5wW//8A/88hd/w+3NA81qw+HjR1abNTFm2sORqihZVZHr84qUoKpLjsej6O4JdEMrTrsouxcfA/vjcY7Oq0lj5HbYUZiC475jcya45LJ2aKc47nekUFAVDQqDLSpcOFKXDXGRaLsHymXDYqEkm9NEBj/hilos/IcRVManWUJ6yolUZlZvOcrFGc3mguhHJp9pj3usKyUkWck+ybrM2aJh8pF9N6B1yTQzynWvKMyOYAMq1aKUiEnMO9GTUsb3AyllRh9QTpaNmIArCyqnmaLgFHyAurH4AFOf0CpRZomCE2Ob5uATU1D4IAEqLmfqUuS7Q1SYQuBfSWWBmOWMNZYYJymjKgn6Isn3iCZqttvn2U2tTvmZJy25IeX4GK8W9dPiUKMejT4nBG5KSRqhLKPblBImp8cYuJzFagWiSZ9fFJiVJyKiYI4qnAPVZ224VnPuJ7OTNGXCzEQ3Jj89JiBqBB86//gTBvcvuX4UhRxEa5kzxODFSJMl6CFHWSw4J3psYyxFKa5AZ08xbplplCVasomcpqeCfmJRzGhYrR1WObKRFybGSFQZc9J3upLN+YZx6Gh3R7ELJzlaGSsv5OQ7sqqIeUFUekbBWgl9tZpSz4aFkPHJs1wm+mNPNwxMw+xUzYmHfU/yMq/PcSABUxJY/TRNJJVwQVEay6KsMSpS5BabLKlXKOtAWXRZQrOhWV2QphGVE8ZZiUEzicJr2n3L1m8JqWJzfsbrL97QtUcOo+dmGzg7X3N+vmEaE+/f39P3A9cXC4bjXmL0jCIzcb45ox8iv/vdH7i4WJN7g+oCu3FLjooXr7/km6bm+vyWm5sbmhJ++6dvcWXD/dFTW0s3dNz9+lv2Xc9iUfPlq3POVrIw1apk9IFp0qjijCEGiqXj6vlzHj7coIG+3WMWC7KBdt9z3Wz44x//wN/97c+JU0+9qIgx8vNf/IKoNL//3VvaIYNRtGOgKGuWi5rkM9MQefX6guhbSuN49vwK4zRte8discCqCaUN3bjFqgXTFCkWjqigWq4BSYBarC8hO5TSPNx8xDQVSidSnOj2t+zvHji/uhSoVIy8+/iO5a7gxYsX+KlDqQX1xbWgkzFoVZIipNgxhogrK3AVKntWmw03799yeblEGyjKBd2xpd5sGLteouKCGGKiEsSFtZb9rqdeKMrkCYPHmg5XFoSc2O+OeB/mxycIWD2/5uLfMMQY2e+PGDNSFAXOKQ84JUEAACAASURBVHyq8ENLYw3laklGs2s7YgiS8AQz0uHw+PnVbknCfuZsFIa3m016MUYwBn0in0rtJc+GvEeTUBbjX55/R1ZKwjVykJHIZ87IU8ebtIxHn7pfaQZDzBhk1Cg1Rdg7ZDVrv0/KlxnJmzPGpCcInpOibWx+LOInzwvaEGeHKSkTP2v9n3arimSUiDZiIqbpzxQ6/9/Xj6SQyyVsYDsfo0Rm+HnC9OORRsssPSHC/tOTqeYXqyiKx5i4nJM8mRlUypAT2US5I590qDnLCU4rtDLopDHRznOqxCk8OWkt3k+jMVZJnJeSJBusQWmFUcyKGEPS4JJ0+7lR6MKQSnlz+ZBwTgwKKQXCGFDGYpxgVEmJdpxmR6jFZkVOgWTAY+SG0g+orMhTIhcK4xzVcoV1DmMKxsGTmeTNM3lSnLCNoh/vMceJi7MNVVVQ1zXb/Y4//nHH5eUlF+cNU23IU8fZouLT/Y77hy31suLFs4xvH3i2rqlMkuL+5ozCWNo+8HCzJZ5nTOE4v7rk3bvvOTvLhO0eHwMvvvgJwyjOwil6mnrJ3V3H3fs9dVlw/vqcu4cRkwfq0vLyckEXAt/+9jf8/a9+jlaZrhu4ud/z/ffv2SwXjNOey6sl/XAQo8zQ8erVa8Yp8ds/fs+iWkBMrK7WFMea3/32D6w3S+53WyYSyiSiz7x6+Yymkag5ayqMcRhX4qdM2x558/UlSUGzqLg6f0blHOPUs2qW+Kl9NIK1Q8/1+Yb9fktpCkKILJYNXb8jtTtWqxXPr54TfE8kcEpTN0mY+yHLIg9tcK6hNIFhGDHWsj/eUlj3CIRbrCq0Mvhc0hwVZVkQqgk/iXTvslqK8zFmqlWNVhFra1CGMSbao0TG9f1EN3mcTizqBXoGSMUo670QJ2kgYoSU6b2cQKMK9GNPZUumgywxgxKvRuEUcWzhs6ZJKY0uhE2ujEDUFAo7yxszmWQsKiWJUZNjLUnFuRv+jOutMvk0epEB92ORjLPq40liKHPVPCtZ5tXk4+hGqRPsalbC8PQ7ntQs8wB2lg4+dvlzbKRWcwaCEp+KGA4lFOc0bdCZx0zlk5HJzOwV6cghWxmvntC7f8n1oynkeZYYnWZXZkbYps/ikJ6+9rTNnoMnFLNDU6RL1j4dazLhMU9TUnokLejppyVOcHn52ZFsHAovi049ocy8eFWWzMxrUcxFuCeERFFXRCPuUZE4GZSaLeE5C9mxcARbSlCEHlkawW2GkMillzeeFhqetY5lEwXkk0EXJToH2REgDtd+aIVp4UdhSuTMsd3jioKyzEx9JuXI9m5LjJ7ziyUX86LqcHdH33aA5tXrN7x6I1GrHz+8Zb/fM4zCwJ7yxHplKMozYkr85vd3LJqab768RpeOsRs537zApxHtIvd3R95utzRn58SsiJQc7rfUlaW0iqgmphTx3vHNV7/g7GzNzd0HukPHw3HL7//hO2JQPL+8IC2WLJolJke++fpLIeZZTecD79595Juf/RRnNf3+llKXBDL/9E+/xhrD2fkVDzshKN4ed+ScUA89ddOgtWHsO7758gwdN7z9/oZlk1AG+rGjWpZMw0gGhmHi/mHP2eaK/f6AceI2TrHn2I5UxjB1ByKK5XpFToqry2ckn1gtVqQQGceB0jnKoiTHiTR1OKvQqqBrB1xVYeKIKRJFscC6BbYs8VMUnbfNWBdRqcPmnhhrlutL9ocblnXDMAw0pWEbE9pYut7jgyBpy7JGExlDz6KqUWScsxI2rgyjnwQRrRQoy+gT1gRsacnKopyFhAgAVJw5QxM5iUFnnCbCFOkdpDTip0izqKgLiST0dkQpzZQEJZDSDLyyRnZBMwoWpBGbP4SzjJBZufI0z/7v13/z7HlWmJzULvJHfpaeF48gM2r0rNjhzwiIny06pXk8SQbz/GvU4yhHKfUYRqG0RmmLNgYzRy9yKu6z5T4hPheDdN7y0OfQ9kd9uz5FkJKCIqinYJp/7/pRFHIJlhCErEqKFEHNaR2PT2hMgAEl+YRq1mLGnLA6kRFnpcpR3FFmXphkmYepeSYmxzlNMpocxNigrWQSCr2tJFUBZ5TA+UdNmkbCIBtxAb6LgiXFkaq0VI3DlRXaFeisiVpcXFbXIi1SCupAiHke+xhRtvR+htcn/CR42aHr8WFAEyhKRSCjU2JqbyFlvLPyhjCOgEI7gzXlzC42XC5WMl8FjPLEXNMAw+h56Ac+/rc/UlhNXTkuLuSG+e1v/oXdvsP7wMWz5xinUbbk4TDQ9yP9ccerF9dcXVY8vzhDKcU0DYxhxCfD+7uW1dmGxapkOULbtoThns1mw1hDWaxZLBYsNjVJZd68vMLka3aHI//6z7+msJrN+TnPn71kv70l58zdbs/N3S3v33/k2ctzVDZcX1ySdaKynp99+YzlMtB1HWVZ06zO6dsDv/z5r7h7uGcMI1+8fs737z8Q+8TF5TnvP37gsPvEFy9XnK8sMURi1riyQRvFNE2UVcE0Sijw9qEljAGdJpweOVte8enmHcPhDmeWDK1n9fISpTUP93v6fUddl5w/f84wjIz9ONMnI+M4UamKru0oC4crLWW1kOYjS6h3GPcslw2mPsMHyUnVpWSbVuWaw/1HmmrF7cM9pJFCy4x8sVgw9C0Pu47gRQuQs6HzmTF7FpWiqWqmcWS5rEkYxiApV0VRoLUh+kjOgVF5bOFJwTPFiWa9oTGW2/sOnxLM0sSyKFEGfA5kYxmmgdKV1E1JURVMITD5CVuJU/O0xFNYYnQk7YhZoaMUwqSyyAG1uB7RmhATaq4BKhq00mLRV3Zu5qRbBx6fx5wzymQUBj0vQLMy0pRxUoZIR50/kxearMlzdFvOp/HKqaDrR0NQ0qJJRymMKTBWcCBVVaG1JDdZK/LOU0eu1Gl6oB67cOBxhn96XEkruZmlTFbScP2l14+ikMOc4CHvwCcrbvy8b5aO889ZvXn+euaRS06GnE4kstMMSj/eXGURGGfw1ry8iBByeCQiGivuTkG4RqYIOQ9M3uMU840hUNZLAEKMkiqTxYhk7RySMHf+Rsk/a5PxuSRnhdWJwsjSJuaAKyrC2FEUjpCX6Cxhz3LD8HRI965V8Tj/V1nchCl5ktEolQjTQM7C+y5Ki0ryfA1mwpcVd8PENA6oaeJBPbBcL7g6O2dRl7SH/5e6N3nR7Vvv+z6r293bVH+63/m1V7qSLWORyJAgQ0jIyJ545lmIQ8CTZBDIICZ/gUcBjwKCDGIIJIEEkoEnIZCBwTLGshO1V7qd7v0159Q5VfW2u11NBs/ab9VRrqSfQAk3G4oq3raq3r2f9azv821a7m6/pqhKzs5WYCe88exj4idf3/J+6/jk45foqHHVOTpNlErx1Zdf8v72a6yrqVbnXF6u+PLrI0d/YL2+5Pj+HobA7mGHW9SEM+iOPX13oCgs67OG9dmSi4sLPvn0NT4afvzHP6A97Hn27BnVxSVD2/GwfU/b97SHA6+fn/PDH32NnyLf+c4v8v0f/wEXFy9JtsKaGmUX/Pb3/ph+jJw1Z2z2PdYteH15gdGa+7sdPnQ4rbm+aKibgt1my9nKsDirOGw7jtsdy6YS6qaPHPctF8tr7u/vSY2Ii/aHA+uLc65fPIfjRNu2QsfUCaMN7XGidIbVcsl+v6cfhDUx+Z6q0Uxjh1aWxfKMomxQyuH7Dlc4Wh8Y4ogqLEwTEU87TDhTUK0WhCExhq0UH1VyebUghMT0ZkTrSF0qjMvDOKXpuhZcpHKVGGDFRByFpdUfW6ZoMFj6AaYIow+8uX8HzlEWnrosSCmyqhsREiUt0YzTxBhFQT2MMv9RUXDlqm7y5E4Koo+GGFQW9RSPnfDcueY8zUSm9imVWWpP8jef1I3ZKVBunJ87Dzczlv4Ek1an58jO+RQMoUAM9+QepSCJ4ol46vqzmtNI9zzP7ay1Gf83ouWwcv3NyACI5H8eduonjX6uThkjTx8Ud830revnz0UhVyhMgpQ8IYq3AiESlOBxykOICZ3x6sJIQKrSErWESmgk9TyGkZgynhZlGEIUU5yoA0Y7sA7nQ3ZGE5UeMTFFj08RHQqcttT1AmcKfFGyiSNaW4auJXQTzkWs64Q+FhNtSlhbkZoFOjwmmoixTiAZg1aKIggnJ+g8eSd3BaagqC0KwcyjDwxjJx++LahNIzh9GjBGhFB+FAdIokPbHIs3dcRJnmdDA9ZglGXRVCilOGs+wvuR3XZDs7BonXj7/g3jJPzh84srrLW0x44YNYuyoitHpingjwPbN3fUtcPXEkVXLc4ZtaE7dCwWhu7+PT/+/pabdUW11Oxv3/PV27es1kueP7sU58gIh/aWxhZo5eh6i9cDXnc8K1c0TcNHH33E/f17Jt9THreoKWLcisY4lLb84Kd31K7h+sU5b+8eeP3qU477BwpToFzk//ytf8Wv/MqvUC4sv/t7f8TZ8hKrA9WqojvuZbhpHYW17B8eKPUF6/MzDvuW7XZPTCNnZ5fcvXlPc7YgeOiHltEbyqZk6raoFNkcNd2hwxaGZDRVVdG1B64v1wxDj9OeFBWH4yS2ulaGeefrM3YPD9w8e05QmsX5tfh3Dy2mqOnakeXZWuTuU4ePgaKqGXcjzeoagLIZuH+4xey2bA5HiqLieOi4eXbBm7sdwxCotWMYPbUuUdrw8DCwXBqKMolFhIe+E6dEa2QnGakYx45jPxBwVE7x4tlLCTT2I0TFOERxliw0Td3w/qGl3QvrRVmVC5p4Ihnj6KdeXjuJOE9rizbzHCpDmkjXnlIkJotRc8E32bMjoeMsX88MkzQzP2QxkIJipAM2+gSzzPVRaXt6zol2nAAt+LfO3uAhK0pVxrdNyOwSp05upoUrKbNbpS0LIWCUMp+z2qCzAdZcnE9Mmyy/T1GRdKZ1KpUp2LPwSein3/b4+SjkGQKZV9XTijjfL5HYMhTEZtVkwuRptxjkIDpilUiEPDDNyisFIcZsQBwwzgk2FrPJuwYxiU8n+tFEyDJqSbev6wUDo2DafcgS44giEdJEMhNERVGWIseNCl3UWRAwu7M9yn5nts0sutBGSxejBP9OJoEpmHP/wiSLmNGVvEb0lDbvSLJ4w6hE9CMEL/huPIpqrlpircvUrgoVLWcLeHh/izZweV2jG4efEu/uD4yT7HzKSlwfX7284OFhy2Y/sQ8WpSpeX1+yWK7p24FXF+ewXrJY1pjFgvvNgtpMLKoSlQqCUry927D70Rs+f3WNWzl+6YuPQTne328ZUOzbo5gpxYj3kRh6lqsGV5QME3gv/4f378X0a+yOrM9XbDY7losLfu8PfkijHQ8PX/Pys09YXVyzOrvi3d0tl5eXIhXvYL9pMcbiqoKy0DBFVqsVDw9HdNtTWkeKBh8V77ZbmkWFKQymsEJVdAWLesH11Qu27YHD3Yax32Od4+z6DB0UmsDufgNA0ZTUywXt/oAfdmIIV5WMfY9SibbbM3jF4uIlRtt8TidMYXIgsRSomCS6UBvw055pElO3swuxtrBti0oTRgXutgFMxbv7N3z0omIIXiyhraVSirb3KFOCdkRGUJauG6VaEVgtGppmiUIG1ovaYfNnE1RCmcg4ymDTGpH7D2OLMoGqLKiqApshzHH0jKHPHakBZQX61I6EJc25lpindG8+yLvksYtGPXqU/yzl44ePz/h6yq30k0Pn4p6UOrFJkrKc3kZczBFOoihAxUvcYDIrxVqLdlKwXVlgtXvs0pV75JInpKEEYechM7unhRzyTDjluqAU0Su+7fFzUcjnaXKImdOpkgh1QAYYJMHBkyJku8gZGzOuQCXp2FVKwrnVDjFynx5ZKcHI5Fup0z9r5pEmpfPUPHM7c8p2DFnFhciaXaFJleDwkKTgRA8qQzUuMo6VWFHi8sQ/b/MC8kmpLHpQKQ90hQ5ltAhOQpCtVyKhbXHC63Qu2kZl32MfSNGLsZBSoIMsRF7hkxQDHUZCStiY0CniEyinKYoS3ZQsDMRx4m57oDDSRRkV8MOeoZ+YGhn6VQtN4RSXi5Jh7Gk3E5vSkJKiqZecPbshBgmCPq8X3Fw94+H+lofdhC40n3/2Ma8/esXuYcMPf/hDbm/f8N1f+py3dzIc+xt/469iqoLj8UhJ4idvvubFR6+Ik0ebKFBPteSbL7/ixbMVlQt88fkrHnY9q/UZu82Wq4tzvvrJj3j26iOmoPn400+429ziXOLcVEzdyLvbt1zfXLK727HZDTSl5uJswagGbO2439yxWl7QjjkoOyVioVg4Rd1UhH4Q61ZT8Ob9HX3b0qzXxBg5Oz8nxZDZCwlbOY7HI5WuSdNIYy3t0DEGz9IpCmcYW4WzFeM4srl7z82rT4mTCOMmLzABSQQ1RhnGmFgs19y9f0P0QeTzUb6qpuawuQcDReGYOs/N+TndMGSlsWcaR+nyyoKkFM40dLHHldIZz/mnECmKEmu1QEsmQYzE5Om6jikqgodumJiCpSktzjSY5DHaoFIEHfOwuKcbJyneyqBcg3YiLEpJSep8Fv+cxO4nVohckypzsD8kPMzhDU9qiBDPT0VcPcFU9BO2y1N45ql0/6TgBOmas3OfVpxoiuZPwCgfQCv68Webw5xBIJTTbiFpTmVXS8i3ypRGMdnSuT5MfLj0/NnHz0UhT5nnqlQgBIEgrNGimgwRP05MQcnJFEaG4440VazOKzQKqy1kz5UYRegj3HIteLkCV4pZpGDSMggxJpvhk0+MKAMUnYx0z1om8wmDchVaR4nlsoKHSwZ8EqijkA5ERSnayiTEujJ3Blmaq5PGaE3KQbdVZeUUjvIY67JZfXZEBMH5Z+8WEEWYz2ybaCClEq16YpjQZaJwopYLY8mcTB/TRFGVaJe7CQyxPsc7z2K1wii5YFwBy+WC3W5PO06YyrJerymsYexGvr59zzhM7A8tY0w0i4lPPv0Owfc0ruH73/8+7x42/Mpf+2WWyyVlVXN3d8ebN2+4uXrGv/e3/jZfv/mGrh94/nzN9mHHP/1n/5rVoqCsHF988QU3rz7i/uGW4HsKa+mOR0hCK/3x/Xu++93vcuwTv/d7P+DTT14Tp57aRX791/9tfv97f0RTKopsuRBT4qc/fcN6sST5xJuvfsqqbvj4YkW5LKnrmt/9o++zPcL37hOBLT6M/Prn19SFZhyOpL0njIl+HLm5KdnvNqxWK86uzigK8Z+ffEfoLb6d6I4bmrMGbSzdcWB3PJBSYnl5wbg9cne4Y3215urmBf3xPd1+x2q14quffMn55TOJKdOaMEpUmUJTlA4TxJFzvX6Gn0ZC3HJeNwx+IpiB63rF7Zt3WH1kXSicKvjJZk9hCoY+MAzQTUABZfQ4tePmas04BbrjwItnDSEEvnm757BrWSwWVFNL8rDftuy2B0JKvHz+Qs7vpBl6j9GJalESJrnuqtKhMAzDQN8dGb3GuIqoHURHqcqTFe5M9FNJPYE8sh/4TB38oPG2AsOkBLPIZj5mh0alcueeMXIt17POpjohV0gxcJOfY6YxP00YUjwqP511MudyFmvdacDptBR0V0pIuikKmYmZR8WqDFzze6IzLCSwTcreKvMQdl6YYizwavzWNfTnopCDrL4mb0VkWJm3R0qdsOaQh38pRrwfRf85489a8vOUNMsAaCX0LelqpePldLLoXBi1YG5RiRum0iQvLJjEI3d9/kBOgc9ZsCTm8IL5SacfMDmsYprGx8SPpFHWZPVYwth5eyhMWWccyhp88qgQiVFjrZJpvFJo7YkxSHA0Wk4mnU9qPzF5we1kjuAFo0sw+glXFSJuUJHCZiVdSjSN+Jkb7SFOeO8pqgptPZflGatsiNR3E8cg/itffP4JYeipqoIeyxQi39y+5WxVY0rLZ7/4BfXtHb/9f/0OL1+94JOPn3N9fU3b9my6HeWmwWqHbUq8TzSLFb/2yTV1UYO27I4d1kZeP7/gsNsTpok+wevXH3F3/5Zf+IUvQGva7sCv/dqv8YPv/yE35yuGbs9u+0BVWJqi5Id/+BOs1lw8uyAmodKdXzqUOqcpHMF3NMuKtjswUXDX7jlOMow6rwsWTYVlZOwHpklsXkFSo1bLBh8DaVSoMHD/5g3VomKYJGFqfXFFu9/gXMRES1ksOR6PGFVQNRPTNOBDousPDEOgKhuGboe1Ad9vqVbP8cpTFzVjJ2pP70emMFI1Bf2uw2pF3Rg2mx1GO3QM+OCxDnbbg8xIyhqHxAyqFBhCYhwGnl+eUxUlKXrabqAsS7z33D9Ic6G1ZVFqUoi0u5GgDMFDVdW4QtOUJWMvghVbGIzTjGMnPkja5OizKHGGgZz01KBNQVQl2hSna3A2nUpJn6pqDFFEQnPBk1RomZ2pp3TkJ9awgHoi0xe+eMaodR6IzlTDU9VJj4PQqMTMCiFACI9fo4VAI19G4JSnX9o4tHMYZ0UQlCmHOnfkSolv+cwrfFrIyZa+Shkp5hnNkdoS+YuU55+TQq5yUZSCGCZyAfT4Of2aR1J+jAEVteDL2TPEOCfbJ6uJkzzWFg7l8+OTeCeoE5E/ZEMtsFrk9DLnDviopBOPjyvkPPk3xohaLk0SZCG/GC4CeGxU4CdZjd18YWiaaiEF3MzyXBlCppiHHNkmIMSATjH/Lxyz/7qKsv2cFxEx+cwG+k6w+DANwpufZPrtCkPRVIwx8+i1oTSWhCYY2SpbF6nKkmE0KBXwQWEKsDphTItxNfv9Vv7uGEm2pmqkuz1bLlDK8OMvv+LtN4HV6oymaXh2fcXFWUWKimZxxvnNFa4qub19y2EnWZq2dKyWFqjohi4v5IafvnvgeNjxS5/dUDtLtJbLesmPv/qGq8tzuiHC0PPy1TM2m3tevbpke/ceawUaef78BQ/3B5rlAudkcV82C/rjiFOGReNQKrE8v+bh3ZYQDIcxsk8lyR/5+OaSq1p2WpOPTF4W/3EYWK7rLJKZWC0aimZBGiaapkFbMEXJ5mGLOUbqUlwVdQWH7V68TIYjymqsriDCMHTCbjEOFQMqDajUg9E4vURrS1062n5/CpKY7SSGsSPEkWE4Mg0ebRK7bXcyRauakqTgxc25PH4QJle9tGgUdVOSkuP9vRivVbVjDJFpCjhXYuKYZyoKoyK+DzitsVYzTDvGaRBMvFkIfXYS0zhlNMfj8RSraG1BMg5nC5RrZHebnUOBx11x9I/4tLbCfDmxwf1j8U5ZNZOPU0evHrtnZfRp9yrNjTnR/+byKANF4a1AVtxn7xTRYMp7JZW1IUoiJ7WVRB/jZIg7QyvWPO3ERTiosyhRdgbzQNPALPM/2dg+jXRLmTf/7WPe4OekkAsdZ45vEhGMTwmmRAgyOCRmDmaEGBNF5pfGOMMOQsLXyqELc1qxde5aQ5gwKU/lY2IKiTRMGe/KHFcFLiisk9ecgvDLA4EwBmIunKpqUN7i2wMpTJA8MfW5c4iChxlLaddoY7DG4ooCMzuhpZTDRB7TRoIfMpY/ZlpkIqmMAyJsgZSEmyoiKAP5bw9pEkvPogRnMUXI7zFBEI92o60sbNYismPpKGL0EvMVNc5a6vOFsHDaFlU6VivLxUcvxYo0JOoyknzgzfu3hKFjsVhQJ41dOVIc0alinCQIuKgKYhr4o9/5ffb7I9Z61uu1WLiOAWMkPOP23Yai2HFxccGrs5r65Zq2PdInxc3Nc7H31HA8Dmw372mamh98/8fECPfv7/irf+Vz7u+2LBeXbLd7Jt/x2Xe+4Pbtew6Hjaht9cDVqiL4nkW14P37O9ohEoNiZQyf1BP/5utXTEPLYr2gPe45W66IY8N23xOUZvPuyKH3mDhy8W6PLS0vXj+jOT/n/v17Li9qVotCOmQV8dPAoqgpzwou6iXj0LF/2LJYLGhWFYFEU1R0xzaHnYy4cWDqekwl23FjIaaANZauPWJMjUYTlWPoewpVczjccegeqBoJzji/WjKME9PoWa5XtO1IUVSsz0q000TlJeXIWi6ub5hGT9f1rNY1pkEM4lKJnyL9EDl0R4F6phGbLCSL0jULazBW+PdD1xHGSJ8049RjCk1V1RTNFcqusM2lFEOjT+51IWZXupTQIRKSz26ECdQkcKSSgPUTldCI94pS2Z9JEJiTkltbQzImqyXNY3HP3buO0oXHTJggZSgz5sBneXEUVqiGSpE0OFtQmILCZZaKdbiywOniFIlnjHhAyXuax9/phNvL64Vs1GeSJiqLebpH0LOlrkGZ/59J9KV8BwlIJp1Wc3LAsuDYcyRbQkWxjzXoHNjxyAaZVaEnlqhxxKiEg54HHsZoYtR5IZi9GwRzNoi5flIKa1PmYcugMHqE0mgMKlrxUPA+n5gZ9049pnAostWln4jKShJPSk9WaSXuikqhM6dddgGzzaXI32e7BTGu1/hRmDYJmZ3OBj3KlBmW8iQt2LpG4samyecF5BEaQokEWGlDXTlRm8bITN1vmoa+b+mHgdSL102KI4f9SF01LNY3TEPHdifP29ztxXypH6gXFUVRkYDDsOPtl19hrWV5fUGCDBEsiMWCEHuckzit2/d3RB94/fKSRV3SdQM/+MPvoQpZbKui4Wy15vzsmi+//oYYWn7pFz7lyx/9lGbVkCaPSYlPP/mIf/4vvsft2zuur1ZcLAtWixKlNSFabt89sDpbslw4UtJ4/47nN8/QKjGWNWOa+OTTV2zu7jEqcn1e4H1k0yampJm8oVkUKOdR4YieFJcXK9598zXr5YqqTPhxwoeR4/sHjsPI4mXNolnjdCnYcdtSVBXRJ8IYmEpF4QpCSDj1eAHHIB4kfTfih55UTYTYMk09Y79nGEdcpbG9/L/KSiyLj7uB4BV373bsu5blcsmNXhOnSF05jLEEL5Jxo2QR6VovkYvKyhDVR4bgJSLROqI2uNLhXHFKtzJYpkmCHJSSJHqjNJUrKVyDrVZgVxhXn2BIKZygVK/06wAAIABJREFUdEEgK5oBkiLpRPTZkkPNGfchs9ESES0wRRS6oNIz5JltOqyRnFw1q7HFAzzkokyQtB+lxK+dFE8MN7TAOk+JEDY/3yox2DPG4LSweOYB5zz8fFRymhMMOxfymTyukaB3ncjGXcwa1A8ONev4v+Xx5xZypdTHwD8GniM19zdSSv9IKXUJ/A/AZ8CPgb+bUnpQsvT8I+BvAy3w91JKv/VnvUeKibHrMx4mzBP5sHMAcYjZSEsRY2Bsd2itWa2vHrdcOn+hT9PsomiEVRKFoJ+iRxExSpNMtr+NYlBDkGHHpMHaKEG0GT9HJ0xMaLSk3U8efMjDUSPbzKlDp0igwzoJrBjbI56ENSXJVGirKMo655MWuLJ8tBNAnf6WkDNErZ0hFYh+ADWhjVAQtTXoosJoh1IyfEkhMk0DOjNlQhzFTrTKXNvMXZ1XAadnD4zHwW9IHp8Daut6wWq1ABXFo3p0YpZkDb0/sFhKrqb18PHrFyyXS5QuSEZTuFr4/HHkr/31c1GCji1TTCwWZ2hjmbzMEM7W1+y3BxSBjz67JsZIHzyuWXLRLHn79VcUWmNcTTd4vvnmG9arEqcbUJoXn3/C7/3O7/LJ61fstkd+85/8Js+ub/ji09d0hz1xCNw9HLi4WlMsllSuQDGidY+fJl6+aIQz3XsuVwtcoWi7FgtMyhKSph8HXt5cEMIRP0Zu33VUjaM9HFjUA8tFxXJ1TVFY/OSZfGKxuOLy5pJycw8xcdxuqWrL2brCo0hjIGpQVgbk2i1xizOiH2mqgr4bcMWCGD3GiKXxuNtwuHvHoqoJXYdxFUkFbGOIXc/U73h/d6DtJsqipiwMrqo5tHtu7wvWTcPKCR03hMjt11uqumS1WDJMPf0wYEyi6zpIltVizWBaxsGjlcZ3nv44CtQWEsZPKKVoGhmUTpPHVUuq+gxMhWqu0bbC2BKyFH9WPhMVJpT5HAyknLaTdCAQxNE0RkiKmAJaRVBiqasTRCNw50wFVGTudlZWqnmWlRQ249IpQEgea5ywovLONxLw0YtRWErZAwXJ9zX65N9kbYGx4gxaGDHvM0beVxuHUZzg1BNGrswpfFnNLJinhVxIOhJsHQX+jdELDPMtj2/TkXvgP08p/ZZSagX8S6XU/wb8PeB/Tyn9Q6XUPwD+AfBfAH8L+MX89W8B/3X+/qce0n3PaFXM26DHYSPMkAonZ8RhGMSWUxUoJdayWXIlJlZkMxuEDiYZ29K5R+KJYqhUdkxTMw6eaUsJQgwnTmqKSpJSkNU6IlicTZqEIfgZzwvSWURPHKMkGfmBwIDSFdMIxowYO1AtEmVZYp2TZBAgBI+e2TVehEEpBMahBaLsMOyELkoKLeY8zpWEMBvxC66HUmhbonmcMRj1xIhHiLEykc/BHSB013lBEYmwxlrxfi/qgr49kBTUS2EerKqGceh4uLtlOO5pmoZpkosthIAtCurVmqIoKeySftuy3W5JKUgcnnGEODINnsJqDl3Pan3Oui4py5qhnTi/EIveullQLFaUZcn7d+9oh54X6zV/8Ic/Zr2+4N2bB25ubvh3/uYNh4c79rsNH71aMx4HSrNGTYGYjlycr4nJcLfdYYxl4SoYI/Ziwf5wwLqKwil20yCqW6tYrQyT7zGmxIeWuoLd/oEvPn6OM0oKXxxJvcJPPbiC5apmu7vHR0+cYLk84+HhjtXKUdY1uvQcjhvZgmsYe2G3rC+dBKmME2VVY2zC9wNhihzbPXHyUJH/Jyum/RFjK9bLkjff3HP3EBgnSTy6dCK/r4uGoU20KuB2G3Ea1IbVdQ0oVDSUWlKmiJpY5sU729CiIn4aQXmqxRKF4di1VKWoRMMU8dGQlKJoVtiiIekaW1RgXS5KYiVtZ1+jQB5EZsghgc5JP0lpof36kBN+NHgx3Uo50s0YfTKcMtZilIjUMBKNfGISJgWZB59UzFBtQhmVr4ckdFMlym6FkB50tr9N2oCxQqgwUqC1fVKszaNX0yxCmgMlHrH7J5ed4lRXTj//iZr4J+mWf97x5xbylNI3wDf5571S6veBj4C/A/y7+WH/LfB/IIX87wD/OMlv8ZtKqXOl1Mv8On/au0DuIrXOVPyYcgJHPJHpEwIJCF/aMw5tdgesUT/DoB6AmY+uIkYnCIHoJZ5LBgvpdFJonb1Y0MQU8TFmQ5sEcxeglJwoUYtbHRGbCig8aRpzh58H5CcFWoI0EmKUDj9ZUnJMk5VFKEn3Tf49FfPgVwx+kjK4FGQbmD/yFCLR5wgp5cHlYaiVhSiEACoSUTj9aHJ/2rCdgjzkvYwVfv18MaWUcE66pZhfTynFcrlGpUh73OF0xFm4OL9iGFtCiCRdovRE08BqtcKVC+4fdoRR5hFn6yXj4Hn79h1WR/rjnvXVmsI5rq5u2B02PPzxez77znc49h337+/Eb92ALmrqxZLtdkcgcfP8GfcPGz79zke8ffMNf/1XfxU/jHz99muGKJFsYRxJeNpxS2GhthVKJR7e7RinhPc99txga3Gc1BhCKgk+QXJovLgCOocOgf12T+kKun6iqs7ZD4nVosDaQEjQ1Cts3UiXbTT9eMRZEZ2NU4fSMQ8KNU1hKVyFKRz9sWXRGIa+J3gNLqGt4WF/K9hsVeOnjqJe0vYHdscd51fXbI89YRTG0T5EhhTZH45E7VisCkBgsZik8RinHj8u8SM0S5HIx5iYJsGVx9FzOBwhDwen1AMy9EwxYm0B3tC2A4PXVGWZRXGBaALWlpTlSgabuhSYU1mSKSBptH7KLBHIE8AjEMY80NSMedgos6CkDcqKIlrZJLiyEcjjJM6ZYcPZeeoEZwierqNkYSYtf4v4jUt6mDJSa1TWaSQlQ9ek5HeW17Un1ea8E5jhk6RNthl4godn6wCl1Smx6FQT1IlAd6qA5gM7gMfX+TbHXwgjV0p9BvwbwD8Hnj8pzm8Q6AWkyP/0ydO+zLf9qYVcqXnLodAKikKYHX4y9F58uo0C7YQLFJIBLcZN1hTZWEeGgTMrhFwM5wJkTUHUmqQCiomEwU9SGGOKGDXHOXkp1gqUEvpUStl+0oiCtC5kEp+cwfuR6Co65Qh2ZOx2gvXHIMERppAFRsu0O5kCpYxIurV0vVMILJrVSdYsq7uc1K6Qx8RkiGlEBcExlXGZuRIZY4+ashWmF3tRGcBIHqLJ/NiYFy6T6WzaxJwQrtHJ5UIfsfl/GJWIoaLv8V5ocP0otsCr9QVFVUtxvvJsHm7Zbe7ph5bSOvyo+PInb9k8bHnxyWuapmGxWPDD7/0+WsPl+QJXNEzjOcduYLGqWJ0taZqSwmre3z+wWq15/dELlmdX3G/2/PEPf8Rh9w6nDZeX1ySgWtZMxwc+er7mpz/6MV07sVqtOOzf8tnnH6OSZ3u7I8Ujq1VNU5cMY8fZ5YIFiXbfcfvmLYtqQTISabbb33NoNfvjCDmCLw49xirWTYVSht2hI0bwo2cqEtXZkukoYdcheQqt8McObILCYirNvt+TMJSqRGnLu809YYo09Yrzy5ds9wfqxZqRApsKtC1Ylhdi+xo8xkysVxX7d7eZ2aTo26OIywoDQ0k3BSbjqKzi8nyBTgPdJFDIclWw3W4obUJbxzR4Dv1R4ubKhq7zHNojKI0Pgyg3VUlKgaY2OFsSEjw87IQdowP7e9nxuboBV1CVNVE7lBIBUMzFTCjASlgfWme4QaAEoeNKQxNjJJqESwXezDOykuiyPQcepdIHRfRpIQdwWkIoonnsaufrOJiASaWot/WEyjGNAYVJkRDzgqXAOHltZ1QW+mgZalqV4RQnTJVc2FVU4uaoxWdm3uXLEU+L5qlTn9kxcGooT49P/i+3I38stmoJ/E/Af5ZS2j1dLVJKSc3s+2//en8f+PsAF8s67+kFB8/3Y53GeGGrBJ1l+FqTPCf8KYQghkIxnor2U673yWBLzwnYMqxMPOZXSohrft8EalaNqjzhnn1bCOi8amqlCIXFaAUmUMSJaBQ6Lk7QSOEKsEJPQhspvtpmnDoRh0AIIyEJbh9Dha7r098v/hPpdOLOrBgZ0k6YpGQAoyQy7tG6N5KiYgwjKMOycfn1ItoKLaosZKupUyKMQ1ZHADoJu0ApTJIdkoiOEj6M2eAnSeeYoKoqCqc5P79gvVzSHlqCgsPugeX5iuW6pFxUHLuOXbvl2etXWO3YbA/c3z0Qg7BbunZic6+4urnh4X7LxdUVMUZub+/4g+99n+Oxo1CO9WKJs4oXLy847FrefnPLzbMr/Kj56ic/5eb5S65uLtncfc1hs+Hi6pJx7FiuSryPdL0nBVgsLU1tOF83jP1A1wZWizXjOHHYJe67juOx43zV4ANURYVTI8dhpKwayrqgKgu0LmlbOBzvKQvNs+srxr7j0I8cD3s+/+I1Sg0oHYROqS0pilFV6QqGFKjqBcM0omxNOySYBkxR4kqXDbt6SAHvR6z1NE3D3ftbDv2emDRN07DbHWj3LWkacQpWizXv3t1Tlpa6KVmtGkoHi7rm7fsHVmdrLs8vOCuWeO85HgdGP1FVJd4HvJdMnOQDRalZrVaSYnRsidNBZk8GpiQYeblshAJsjeyckyImGegnk3BGrJmNk65WIcle8/BTSAfq0QYgiOupXMdPSAnqcZ4k1/kjwWG+7vWcrDSHL+emZFaDzrsN8XGZLbGTVHvk+hd1eX6PXD9mCGXOP5iv08cjZbuPxFOnrpRkF6Dmwn5CDQKzOblS6jHYGQhPHvdtjm9VyJVSDini/11K6X/ON7+dIROl1EvgNt/+FfDxk6e/zrd9cKSUfgP4DYCPn50lyCwPlYhxZnYYwa8fnyMUPi22t4XRp+2TDAelg5TNlMp3hdNzVZIPJRrQKWF0Ka+udJbnkr0P5GehrSl0iESmE6MkJiNODarEGIXSosBSURSqhPyBpYBxmXseZPvrJ3+CPWRQKfijShEVA3Ea88ciFp06+8gkK+/pY8DHmBk8AWNGCr3M/6NChjMnU+NE0NCPA5rsAW1s9mNXcsJqhSnEtzyECYwMi5U2xAiFtrTDhLYVlbWMU4tG4axFGcU09BSmJiYYfaJanDH6wPNX53TtAaMi2jWcXTYMXcubt1/SNA314oy2H7m8XKAK2b7WdU173LGuS3zb8+abW45dy83LV3z+nXNS8JRGk2Jgs99yv9lRrRY4Z9kdtnzy+WfcXD/nzTdvWCxXrNZL9lsJ4vZjQDVwtj6jO7b4/kChl0xxyoZiQTIwscTkRCRoBqyGbhzwXqH8QLNckBJcnJ+xubtnuTpnmiZSCCybghg6fOg5v7ymWa4IXrNarWWn5AQOKKqKMHmGaaRerHClxY+BppRtvo49Ki6ASJhGpqmnbY/CLup7bFmwXK04HnZoZcXPpOt5eH/HYnVBU0JdKNp2pKorjA5431KmGqM0k4a2bSkKy4vFDURoGoee+jyb6amLkjF5+qShLBljIkyStznFAAGsBqu0NDYarFbiyYJDY+VLG5S1Wc5upXvVhtmM6jTgz+HmpxqgEmIXnT4s5JiTLbXQhvUHhXwu8PCkliZNUOG0WPhcsE+wyFzI1ZPmLzdrRht03uHOzdQHnPUnxfb/Aet+WO/kMX/i51ltLs99fJ7+C7XF3461ooD/Bvj9lNJ/9eSu/xX4D4F/mL//L09u/0+VUv89MuTc/tn4uPz6fua95aRqMWwP4luSIn4YCJNH64grGspCKEBPOZpSrBM2r8wxJJLJW5ao5v+ixLol8yjJFQWRvD1BaEkgu4QkNgE6DyGVUngeHdvkB8lDJESGrqMfWsI4cbjfEcNAmCaJj7aQlBVlpRaxTFnUVE2NcQUR6H2Hi+50Qk5PT0wstpwVYoopc8+D72XIG6N4OLvZBzl3HEzEBMFD8ONJgSafr8SBpQTaWmyK+DAR/ERZVJLBXtZ5xyOFoSxLhu5IxIORgW6zvOLMlhz3W1wc6McBV9VoEkVR0R6ObDYbKutYLRqGyfP8xRUX6zMCiu1mz/e/98c4k1ivVqQkfuzWLVBq4O03P6apl/zOT7+ibXt+5btf8OzmnKQi43DEucD52SWTb1lfNPz+v/gR69WKv/Ir3+V33n7N5eUZn//CRwSt2W83pGlgGuHd12+xGpZLRzFa9vsjz25KVscJzs44dgcaqzC24NgqdtuWpqx46O7BGqb+jvOzNUOwYpSmCqq1hBovm5qH/Y7WS6NxcXaOLR2HbYefOlZnK8qmZrffUBSO/nCgLEv0ZEhpKUKeNlAtFiwXBYYBpRT7zTciFNOWpqjYbg4YY7g+WyFjQYOPgbKWyDutNNOYoAKVJq5WS5TWFIXh6AfQhmPbgpaQk91RVJkhJJbLGoXh9p2obMvS0JQVzlrQif2xAxTWVBRlgzY1qmwk7UqXlHWJtg5b2Gw2lTtypXFJnxxWYm5OUlKSwZssYZpnY/4UMDPL8gUufSymc2f+AUY+78Ahv6bAlD4KZKPItrmZ852CLEiQu3IjRd1p9TMohhJWkZ5MKueUIjHESo93MLugJtHHzEU6ZWuADD2d/G2B/zcEQX8T+A+A31ZK/et823+JFPD/USn1HwN/DPzdfN8/QaiH30foh//Rn/sO6ZGNQkykJJieMpmi84S54r2HJJmcthbcKmZ7W0kC8oRk88qKrKAoSflJs49CyCoCnf/xlpRhGaMFjwwhyNAyZVQld6gz53PGnMkpRmEI4oHSD+ADYRxQeHyYhAOuwWEkVMAVmLKiWi5zYKs5bfOEevQk9YRZHJQHN8rK/fMwJsqloK3kHc6QTIxRYrmsQ+ko6mctyeJRSbSZmrE+42QnEwGlTwukDxNBKapCeNRGWQ7DkRAnLs8uCdqjEgzdkfu7DVXV0DQNxIL9XrDbwlnabkfSifVZybu394T7wGF/xLpEYRNJrVFG8+nnnzD0R+7fv+H6+prlck304jFjQ2Lz7h3XlyvOPvuEyhoW52u6dqDdv6dyFduHO1KIbN498OrFmo8+ekk33PPx6xvCFLm/27Nrt7x89px939F3R57drPFjIAHDMKAS9O2Ac4boR84XBWEMeD9Q6IlqVXFsB5wtc8NRs9+1DGPH9Rev6YYWZ0sGH9A6YLTGFQ2OSNksxIbBRKahZRhGTNdRFQUpQVNX+CkAhpgkMGQ0PSjLFCfarqVaiLy9KBzdOACewoFPms5HDu0BP/aoynJ1eUFC4Mfu2LK3CVcU7A8tIQSaELksC4wRwdkQJNnqcBDo53K95mLVcOg7LIpFvcCWmuQyFELINNiaWFVEW6CcEwdHrcUzPw/ANI+uhCcWhxIzsFOHqtxpviXMFJMpyepJIX+STq95UsgfMXOBS/mgkM+FdO68FZrkOPmf66CJSnbD83sqPQcpf9iB/6xuPCowOezmSV+N1O3Z8IPT9SzHz4ZOHmd8f4nQSkrpn/6p7wj//s94fAL+k2/9G8hzGPvx9I+ecza1iZSldKfOyUAhhMDkA1PfYcqastQkI92p955+cjTIqli46oPt1ryNUjldJKYknUdMpBBIBIwW3FjwsiSW5illnxOPigLrqPiI7RHTif4XSUSjsGVFBZShIfpAGCdUoanrJc6VaA3TsTttq6aU6ZK2wDoZkEZUNhPT2MKJh4UyuGIOnc7UQiWYuHQ6Qr/SWktaUR4gz8lGpFmqHCT2ClG56Sy48CoRYsQ6KyZA0TMMksqCijgnYqthmjg7O8dpw2YaMTox9Vt+8uYHmUkkHd1UVizPLhmGgaJUvH71MWPXs1ot6MfAdtNS14PATz7hkuf6/AKFOOc1zZJmueTY9dy8fMHqvEElzb/8Z/8KVxmeP39OWRqsNQyDksT7Ty3FMDEOHV4JpOQKI7uIvmfz9hsSHk3CFhX3d+9JIeHKBld6SlcQk6FNQrELPqCxNGVNJNHFFqsCw+TxyVNYxc35OfuHI2MIHN5u+Pizj6jqiofdkasbsR4eOk9SnqEfaRYrQhqkAcGy73uaqqSoaqiuMeWaIUTKZUUMGlcuSCmy2+05u/hltg9vWCxKdvfvGX1g6kecs0QGhqSJ2pFQTH0LWnF5dQ4GnC25fZDIOtNrvvlqQ1lWdIMImKwpeHV5hnICdRmnadQcfZfoh1Z2n8hrXb/4CO0qKCqSKokxkw3SoyeKFHCTYQmxipACbx53m/malGuKkyuonN/hw8CFU8FOH17fPOnKydbV+bW19rm5M/gc/fgU1hnHkaTniEnByAk+IwMRq8Q0Sz35mg+VaSgnSFJuRcVZz+HBPjak6UkziFKZsaIgxDyf0uKH/hc4fj6UnSkR4ySqTaUEH0oTgUjyWdZupJCY3GEKgpzwKApj0LOVZN4ugXTvKlOHVHyi9FJC0ROfYAlMnmZIJQSCzini2iLT54xhKy3GWyHi00SIEzEgQ1QjBRUDOlih96mBqBTJaEwplDSjS+IwCbc4yOKlCw2U+AlCVIwxUhQVNsgilZJCRSue5XPHkcUNad7N5PBYo5MMVqMIq3wMMiTVCmGFB9n2xbnrEUaPMHPEW90J3YekJG7PBxniTDFRFSVET5g6dvvA2cUl1fqc9nhPqeHly5co62SolDTL5Yr2eEAl2OzuaA9b/DBytl5ytVrhvdA8+9DhrKGwBTFGxraj3R+5v32Ls5qyLKiWKw6HA0kZPv/lXyAqKaKx29P3E0wjnZfYOI9lvTojxIn9QWYPY9dydbaiXizY7o8Mbc/QdiwqxX7XEcYkkWtjL66PS0PowJsCncDqwO2uw5ULitpwUa0Z+h6lC3xQaGPwY0TZmq73KD2RYqQ7jFS1JYQjSjt8GDj0cH62YOw9yYvHyWHYceFKQopM40htS4ajDNEXixUGQwiRqX8gBE+YJrb3DyxX57RpgJQolEHFib4beet3YnRVWdqgmI5HjOmoygZna6q6ZrPfUS8XrGtLiCXTFNn3E3jNED2VTYxjC0G0BpOf8BFpRrIGQhkjPG8dBFMmRzkEj/hIRMDLLpKEyr7f+pG0IV4nefCniUzaYERIkllrjwZZj41Znntp/RipSL5dGZROp1oAM4MtYJ2RrXbONkgpZQ8l8WQyxmQ1ad5FWHPC28VMy2ZF5pMjiDJUh0foJ6mcQqb0aZBKEtPaNPuzB0/ShqgMOnvKCKPl23fj8HNSyIHs7Z0TOlDZa11nibrgSko5YVxYRdIa7cTxzypNkQt5irKVDD6hioCJKSeKqGyApk4DRqMEKolKJtFzB26igiSqSqUMkpWqIY1ZaWYlhi4nisuHLCnaRnM6CWxsSPQkAq6woAPtsEWFiCJg8/A2+glrND6CHyIEQ0iJyURRR3ox99LRnYp4IJ0oaHN3IteELFoxD3pFtTaRvOD/6OzGiOwyAJSaTpSwIKplUkpMYcAYjysEznFK0lGiMiQf6LqOlO64uDhjvT5Dp4SfOqYgLBxi4uHhniL7w6yahs3tl4yj5+rZDcdJoumG3UYKU1Hg44TRsD/IgNYaTVMvsbZg6KWodYcddeE4P2+YpoGgClbrK+KilTlBipRNzbbf4xgpS4cpK2wE7TT3mw1nqyVWefpu5H6zx7qam8sLgVeU4dCP7HeSMH88HjhfremmgWM7UpYKbQoO+3uMK2inwJluqEuBQBbrimfPnvH27S2riyVTGkjdhDGKsqhZLM85HvccDgdUlBmCV5GiKtCFSPh1EbFFwXFoWS/PQBmG2DOGCTUNEEeUTiwvrjjs+xO2K348ElDhp46rFxd4FWS21IglgHFin7xoCg5HWbhWiwvBcE1k6Hd4P9GNHXZZMY091sk5GEPElQVNU1Otamzp5Hyy0pXK7zFDg1ZwSf0IJ8j3KPfBI/ySs3VTkiCNQlnIO3QZ+M8d9Ifn/KMc/sNCntIj/PF0+JhSEpaYMR8wS2KMKG3xsxsjM59dINl5FvcU7tBPYPA/weLL30+3MN+d0nydPj5Z1JzCUJv/vvR0C/Itjp+LQq5IGBWE6kZCayfGSkYRkhFeKIrgIz54UiVeCqLcTFkF10sEEwpsmaEHTl4MKqu2UNI5CblJaIhaW3QxqyoVIQwkJRxtk5OsjRMzrBASY0Am72P16NeCnIhFYbApyWS9qkVqnBLdfk/XHWm3O+I4goqUWrzJrU6UxSTUKlMQk3RDyoC1iWgjAZHRu3xBSTCFFGRZ82Trqk0pQ5js9jhTGMXrWaOSlY5cJ5zLwbCZAROjR1kynKPwKcgQNF9EMQVCEny+KCRtJoaJ4dgStaIoLSCwSqENSSWqSjG2D6RxIsaJ7/6VX5XttXGM7ZFeG6axxaiCuizRJO7fb1iWNbvtBmUSwQVMqsRGYPDUpcGVJV1Q1PUlq5UhJIhBbBhKPK6yNJdrkg/c375h325YrdfoJNYND5sd3XYA4ObZCx5ut7SbDYlAWRaE7oDTCruo6IbIoRXorVoKTBOjElVuIWrC3gesjdTVim7Xsd3cc36xYPCGwlh2uw3Pnl8xTIH97oHSKc5Xl+z2R5r1GWuTSEqze9jw7MUNXXukK5YYV3E4HISXvt/Qbd+xbqRZ8H6k3285r5d0U+Jh2xFTz8cvr3NxW1GXikQhylOlWaxWRK0wTtP2HUXVcGh7ktqdKLvWaRSBypU4o4jOkZAUnGKxYHl2TlnU4mui5pBiCHMh0iMJQ0xKtA9p7kiV6BLCJD/ZgqS0CGYyxCDsaqHikQtozEPEmdmh1JzZK86gc3c+SzlPop4krzjPn6RwWnSURiOoQNCKoBWkSEhCtJhpijqazCTMokT1SFGWAh+fwD+cAppPM4BcyXUK5PVBIJQnRf1pgYeE1vn7XzZr5f+LQylFVRpigGgMOlpCiMSQGKcBrzTOygeSfMCn/5u6N9+xLLvO/H57PMMdYsqhkkVSTaolSgbcgB/Dj+0HMOyG3YbVVIsqilU5xnDHM+zJf6x9bkSWSjIJGEb1LQQyK2O6Efectdf61jfMWC9JPMXpNDSBAAAgAElEQVQUHF5GuZzIaEnw0UJvynXQsy8WFALBCLRSkKKbkU4glVCXLpIwdDk/i6T9oHI9/TWqqa9vpZkLO0ZM8c3in5JUXdAmGl0oxhKc+IpP1e7W23q6l4xx1TfCejmYlEErh9HNC/x8GR9fiAiqiEepQq6nOpXOWpR4kxctvu0CZUXBKI28kRW5REIYRTRVZ1+lNCEsmOJzsO00DxgtDKAQJrCO42HGkAjzkcIsTIEsIqPVaoMylv3TI+P5xHB+Yt139H1P323peif+7URu71bkONE0a1GcKkOMheE0ohuDt5YQJlSaSNOeMcwEFVh3GxGEecNqu2GaJnKeaVZXDFPm4cuOq22PQRGHI60HZR1zKKzXPSkcKSVzPB7JSTMPMzEHbm8atLZ8/Dyh0DSuZRpO9KsVYT6jTYvBU9CMIZGKZZwCCkOIkmTvXMfhcGC9vca1ljhPwjZxlnmciaYQZjk45zCQTEcskcasmFNBGRjDiPaKkCbmNBOnmdWmJabAZnvFw26kC4k0if2wMZYQAl3vyVlhG0dRQYy6cuF4CsIoAeG4U1BYLAXbelJKTGMglYJvOvEVabyoV60nq1qcE2SyFOMsbo06AzpBliQrsalV4j5Y0sXIDrVgxbVAU4v5AmvUxlVdagXyEbWoL3j7y0IuIehfUwEXOObH3fnyZowRV7wq5RcDv2f2Wik/9bnPz2s5NF6+fynGpb4tE8Ol6y6FrC9GuhcDQPm4P9/5EH4mhRwFTSN+ITGJaVSOWfygx4mAgt4h4fSFMMsGWhsLTqS9juUX9bVl5PIQnO3l+wRDU1AtLK3wvvOzfWSOtatXCrEFFEGORdKLknm+EC5BVVn6fI1C5UIuE4WMcS3GKEKYKLOGIK6KSimRB1dalfGgTME68M6Cqja7LDh9uRwky8VTCsRQOwkVyLpeBBEwMmaLIUG+ULOkay/PW3tr0VkcHFGZNIuPjTHi+55SwnvJZ9SqEGMgloBGYZwnDWfhCjvLFAPhfKjKUk/bXxHnmTk8cr7/REmZzWoNumGcoFs5pikwTTPX19ekIDTMeY7YqHGtxrUOa1oCnnHKvP/he25uGrrOcf3qFaY1fP74mb7pWfcrHr8c6boOVQqHxx/wJnP9eiP0Lxzruzvm057hcGA+T5SgMN7gGvkZj5/vq8OqYr9/Yr3eMk4R17WEkOjaDeM0M46Z2/Wa0xzYHZ7om5ZhnvCrO2zb4TrN/f0j13bNaXd4jgZzDTGPaK1JJZMiKGPwzlEIlKA5Pn5G3TT4VQ8UrG9I8ch43uOVxq9WnM+KOAzMeWB7s6JpOsbTwJQiJSW07ZnnkWE4s7aWpDP9as08ASozDCdub68pzMQor3HjG0KaiTHjvCfnhGuFbWUbsRRYIMdcTZ60EjlLUQlVTMWHE6UkwYFzRGbcyroiX3xN5At8TbdTSoo5VKuty738ApJQ9kfd+aXcCyyjnu1vL19Tvyz4dWItgtcXNPlFw/dvNcW5QiVF6Relm8vXfP778/f9+odbcg6+/veia3P5olP/cx8/i0JeMsyTQBjoTKqjV0lRuKIZShQFggJsUaQpcJx3WN/jfAumxTXgbYM1rmLeXLifdnFIUwrrvbibmeXikYuqVBbLwkKJaRaRTs6kHBBcObOIWG3OtePUhEr506jKy1ZCQXSWtt+wunmFInEd00Vhqr14GxdVaHwvSSreQzXG9627LHfEOzxTamZpiYEwz6QQmecZG0fxzJhmYeCUglOenObqbmgu1MVSxDBIGbEnSDmhEZGHc14uIuMZw5mYIo0T57phHjA6obRYq8YsrnCn/Z7z8ESOgRAGnNW8fftOMNc4MR7+hTiPpDTTrBzWNugSMWVmnmd0ucUwMg9PxA7avqPQ03iLLoXhdObjdz9w/zAzBHGFm3PmYdfyzbs3PNz/C0oVvM5071qG8UQ4z8TjnkLkzeu33H/5zOFhR7/d0PaOFGdoWzarNe+/+4TtHNM8sD+eGIfEcEx065bOaebdxHmMhCw2wsM4QTnTrXq6boPSmsZk7m7FudF4xc22Y5r2dFdveO2/EaqhuaUYGOeAzYb1ZkVuICbFbnekcw3ONdx/2dFf3ZC14rj7SNpbus015+ho+1seP/2AUzDMiTzCcEwoHTiHwnmMjOeAXzVMMbJRllXX0Ps1n5/OuLZBO9EMGGtxWM5D4K7boiuGb5uGeDpinaVvVtgcce2KxvfYxqO0lwV6CSjEm0d8TF4yRiQfNye5XvWsUbZAiZfmJapn+9lMEqJA9ReSolu7aiot+FLgXhpRVVxdJJW1oFAXlUvBp/65VMeXUIvUAHFf1M8fs6QV5Uw1xJbJXZBFmXSdquZeCpTGvDDi47m1u+y0lKowkUrVREx0Ly96dpZDJhH/ohr6syjklOftci6RrJKEBhtQlbYjL7y80MUoiJo4R0oexJZybGVE6h1ra/C+wTX9cpwL37t2z2ma5FQt0qUD5Jikg1aZlGvIQ0ykOJNjgizpO5QEdYudYj09iybWgu9tQyzC4Q7TKJSi6t1QiIQpklIglwhxElvNzMWNUVslNpwIFqusQduGZnWNbTzdanvpfPvummal2XiHqYR3XSCXQI5JfECyuOYtfpkl5DrSvewcQoVeCilFjLEoLcteYxxzSRgD1klHlZDfqVGaaZiZxzNxkii6t29+iXOO+4dP9CvPdr1G0zEMA87CcH4EEv32FRFFEzLDccc8numMo0yB8+7IeTpjrcG0Da/evOb69pbrTyfu7+953A18//HA+X7iv3z3yJublt/95g3/4W9/yzAMPH0503caaw3OOj59/p7Nek3QHTlG5uOR+XQih8zAxDe/fsU0ZqYPR7Z9j+HAdd+Qc2E/gzINqVi0tVBmbq7XfPq8w8WRm5tb4vnIHCKnbPDeVwx+ZrVqefz8ie3qhr5p2c0DtuuxrXh2q9Zx+LInpUJMBbPtOE4znW8wxpFiZs57XLPh4f339OtrxmEE61ltV4T9nmG8R1vHnAshBfb7E+MQaVOkaxzhPJGdxjnNeqVESxALiSjFUUfIMIdCyImQBnSYiXHCWi+sJ21pfSPGbmrBhhNZG0r1Ey81LpE6nYpSXTpeVQpJuUvTU4ylaIWPimRk0k1KcGplF0aIFNnnzlyIEOUCxSwfsxR3fZmOLwvOoi8HTS4RYVBo8RhKdTGqI2qhSJaMWYpqEVoyUMOTnw+BmkAhsZJKdme6/pnVgos/T73LY5l+TankBE29L/Xlay98eaPcX1RCfxaFPJfCOAsDxBiFTR6nS132yWjvvUQdK6VYb7eoVrybS9FM08SwHwlj4Hj8wMPuib5fc7W9wdeu12p9EdUs3iRGFbmAjMZYgShMMSjta+cKSjuwlpxEISkYfcIUUZblnEWyrEVROk2DCJMKtMqQqwFYToGSLVpnwEGCUI3tIVVDrEKIBVW7lqihVEl9GvcY5zl9qRxcbbFexCFGy4u+BL4u8WHWelnuVq9mDHS+kwWrNRffC62fJxdjwdRpJk4jOSUpeimhVBGXwzAwh6PcxGTWa03breR5qURRIjc3ceLx8wNWC73Ou5ZpkMlhPmZsJxF13hb81UqyUpNoB2huWK23ZAqHp0+Cxacjxs3cXlm8v+PD4xOP+4mH3cB//i/f0TQNb15dY63C+Z7jeWA67VFZ8eH7P/HtN3estivmeSTnhG4sOSsePn3GWsfd2294etozBUW73VDiTJlHpjmR84A1mXNp2B1H3nz7DmcS//Jlx/X1LZs3PeNw5PbuhjAGApHDlEjZkkxiawtb7whjkOvDaU7DyOf9wKs3v+Dd7S37pwec71FNR9EdrjFM6cQ8BVzfcj5/ZDjvIM087RMpTLj+luPwhcTAtrVs3r7l4/2jNCxBcm6Ppz2rTc+7X71mGmbOk0wB1lqmSTMOM2EKTDkSQmCzvaZdSaoUWLxrMLZBG4FMxNsbQqkk4MtyMUHRZIIU7EqRpQiUmInC00aRQ6TYFu0SMRq8LyJwK4lS/KUzfhb6SMetKosFnqmIy2OJi7xAGnoJUdaSxVtx64IS1WYS+1pVZO+Vs7qQFxaBobBMRGG+eLhc6pbisnvLqh5cUAkEL5xKXzxPXfdyy98zzzCKrpMEQM7zv4Zk/p3Hz6KQy2Z56VTBG4upCkOjNE23pru5Q7ctzjeUkBnPA/ff/QFKIhRAN5eFxHxOEBNN05ApNAq09hePhucNuJIDtp6iy8hExcWLscI3V3JKFxCv8yXB5MWyRCOuaXISS9KQWoy+5IOl811Gz2JkQqAuJ4ts6VWWzlZoroZShDPfJihGjHtKgcUSZuGrGlttf0t63qSnLOKgReqcCnM614PgWSSFSuil+9E1fcjUgFnvcFc3F3e5LgucQ5yY5pESRuLpiVwmzvsdf/rj74lx5u7uThwP+y1hGmgbResb2nZFyYqnw5GusUCm6zY4d4VSjt3+C84Z1t2KJbHJO0WcA1n1WG3YP+6xqfDN5oaVntkNJ66vOmyAxz99YbNt2R3O+MbSe4vVhu2qYX96YhifhG3Sd6QgIQN+tUErRcgjm22DKZsaTWdZtS1jbPj8eYeyhrubLZ8+3/Pw8Qv9eoVtV0wpkp6euN52jKdzzb4MrDZrlBcM/HA4cHtzxTQFrG0YxwGjDFMU351xOtL1Tnz2syWPB/rVFZ295X53ZLNeU+ajeOZoQ5yCRLi1FuUt4y6x6jrGaRL6YhH2UZhmjHbMo+wzSsoMpyMxK7p+Tdd5nHNMcySmCa9brBO/7ZKR4m2WbvdHFLuMXGNFkoGkgEpDJkCLEuJBgqLjpZFSOoiFrY6UuWBMJiiF0tWraCn4+fk+M+ZrmOTHj4X18fK+/Eo//7LaFC6hzxcq48Lz/tHXzDWOTgCWr5edy+f/5HMp5XKgfP2+9NXn/BQ7Rd7/32Fmp9D8avebZzn9mg7fd6xuv5ExczqTz2eOjw9M04AGLJZYtKR86FJhElO5oJnheGKR/b8MRrVaY5XQnBZeZ748l5ra/eJfn3EvI45oKqHIBBJFIx7WVdmptCQJyS7jeWGjrBhhleIgJQxJ9jtLGhJZRD2ljleFy3gKqeZvavwF1xceuRjgA0VX5aeubwZrHMUKoasUGeWKUQuEeLlRjJYbXP6LlCzGYvNiNKQlAEM45AK3WO1QqkH7BmtaUpzpV98Qi2fYv6/ZoprzJF/v+uqWlArH457T+cDV1Ybd4wPWepzS7B/+yHCeWF+tmbKwLURUZNDOyWLJBUzXsLWvWW8NQc1czzO/sa/w3uEUaN2QSFz1K8gJrWaaVqxnu02DsYKHnk8HfNNxc/eK+89fOB5GUko4bXF+RcgjJRue9geCcphVQ8qKcTrRd445KK5vOg67PTo39FcbDocRZwrKOpJqGJNiZS15Svj1miEqdofIu3evOexHtC5sV1vWXU/JspNpfYO2DSElpmnidD5h2paUB7wzDHXpbazAWvvjmcPpCCUx+4b94QljFCWJtcFq3dN6TdN1kKULb/qOcBqZU6wZlIZxmmiso/WNXHtFoAfrmmfXPy3GyaUsRk9ynf8UE0QXUCqRs9wNJWmhKmbxL9JWCAdaV+KC0ijzLKMv5VkzIfffouD80YLw0pT9a5bHM8yiqh5FCnv5CYtYqRPCTMuLxcXFqOvlz6deHBIvv0etI4pn88MfFfufEvqIOPFSfF58zf+PJfr/fzyMc7z97d9SiiKGieOXz8zjmXnaEcYdFI2zizWtIiBGVqrSpdBQskUZj2k969bXr6zI88Q4R3KcMU6M7hvnMM7TOi+4m64qUCVEfKWqB4uyQmtUtRMv0jUYJy+6yVSeemauiq6Y5xcviBVlZynS8RoDVabsNFI8c0ZZRxgzKovsXit18XBRKJxzQuWbhXopzzNfKI8eXY2yHNo6sarVhpALJdRuu0IrIFNOQdgEqMKcJrEd0ALrUDRpTDVQ1uIagQSyku4xpUjUkTENQMbb54Os7Rt09ytMceiSCfOR0+Mnvnz8J1CGp+OJ169f80//7Xu61rDZbBiTptiG7qolpsw4jMzjwNXmGpXhu+++cH29Jeczh/1A0zQ0Nx6TLCs6GrsBnSg54BQ0rWPbt9WVMHIeJ8I0QMikHBkPJ3SJ2GvHYdwRc6K72hJPkcfPXxjHM+fxJFNTMiRmblYNxyEznEeMawiM3B/3oC0hZMxQSNlxGmciM7d315yGmVQUylqe7g/cbMVu4B/+4R/Y3mz55u6O3emJ3W6HUoZ21aLxxJCZQiDlE9ZoTocjTl8xD0eOw5HWGlKKXL/dcDpMrLs1u/OeuUC7ueE4fCYFDUUgvnmemWPE9xuK8VyvWq76Kz58+czhHFBYXNtVvnTEekez6iV9q9JarRGiQEKoi0WJBD5Xu9pL3mwR2+clwqTkmYwR90kV6jSYITiiTmQjGo/Gt3Lt6knCNkyglIbFllbk9V97kMs9li5Qhvx/+apI/1ioc2GhvfjYlFLlwAs3PwRRbadUGzmdUFoaux934pKcWC7c95KkoEsITa1vKFIRfDxrUZKXvOD5/1ZVfLG8/TMeP4tCrrXl8PjIPJzJJZLGGeFVOtIsGK5yppLqFSqKj8ecZ7k4tL0oz1QErVqck65GI65qWSHmUUAqlhwC7hIVpbEyCBJlAyEXSz0qFFSfb1nq6MpRt3pJsS9V+JOqg5uE0WbE9lPUak68HOpmvyiLda1gdHkmJ1sXsk5Gz5hqKKxQMqdzwBhHcM0Fk3NtJxeU0TgtKS66FMHzs8EqSy6CbRsNGEk9VwpC7Tg0ErFF0pc8QaUM2lhKicQYsJ3H+QajSvVaAWehU1eUmPjy+aOkm8TM9dUK27QcD09ycWVwTrNat4Qwse1a3n/3PbvDSLGZ65vIb1evWXVXhGlgHk58/rTj/Q/fA5mr7Zq/+x9+x2G3J06RjV+jlWZ43ON6T7tesVn16Eazv98RY8Jh2e0lD7RoxZxhTpIL26zWXP/yjpvbDXNJnPZ77D7htOdUJkKSG3mYFHOINL4RG+EAvW/ASODE+ajQesX6qmEYBu6fztzc3DBOiZAS41jYbq44jyemmMkh0hCYjhON7ShZsT9PjFMkzOJKeec7Ys5Yq6VghhlTHBbLcBho+jVKH0hZMY0RoxKrtmEaZjbrlvF8YIojTju0E4FdiiPaO9quBRfxzYoYCjFm2m7DnAac6xinwGrT4r2nbzu87dHaMueCsvnrTrLoyvJ6hh9Szs+VSz6odqkC3dUhWbr4HIU1phXF1tQgZnQu1avcsHC7nzUT6qsi/cwJr514pe49qz5VLb71+SJ+FiWVqq2AxVwOnov6S4w8F2GO2AvV9+tD4OXjp/5/eT6ol/4wy4L2+e/Pn18uE8df0o3Dz6SQhzCzf9pXel3CWyPMDCylBFKGYZzqyQvnOZFjYkrSNWvr2G5ajBMJdtHCPumaphZYhzUeW2l9TsvhEGMkKYVKBaPs8y+5/qILCqUbyBljqkmVUpAs6EwJgmfnNMF8gpQxpVzoSDElUh3FTM4UY9BOxnxFFKgiZ3xuaFxHjIHZniixkG0gx5EUC+MgPiGkfImUMs7Sbde0vhOngM4yzxN9dyP+F9YQiESS4JwlwSzLxJSEyqmr0sI23eWia7oGreSCDmmWwyXBPAdKFhZMjoFN5zHOQCn84tUr5jhxOp3YPT6gmMjhRMli+KvigJojJY6E00BvFVOb+Zvf/S2b6ytZVp/26Fx4/6f3/PFPn/hvHwd2x8DN5oHVq7d4DcNuR7PyvH7zDY3fSmKLcyijWDXX+NcdJUfmeUK76nlh1mj9RNd5uFpjnZhxueaOHBKbq1tW2y0pJU7hjG4MalDc7wJTsozzjm/fbXE5cz4OPJ0HjF+TvSaamR/uI3Mo6MZwfDgxT4moEvtwwO8Dp9MAceLd69d8fjrSOI1V4HJEZcvD0wljFX3fkZKwPYpvcHjmOfP+yyPtesNms8ZiOcUGE2dK9qhT4hQGrDNY3ZDChHUW2834XPBOsd52GC+JRClkhuMjm6tred0dKKN53D0xDTMxrthu15hSiGlAYckYVHIU/ULEUqpgJksTUFMuKdU/XMGFbqey7HikjmYChZRHlHJMakQZB9rifYupFrelyUTjCLMcxkY7og81lUdVb/OXjBYu1/Kl0ALP7oflAq2UkqX218XmUrxLTExR7KhTkOtcNCHyM/qsa/IPl4ZnERUqELbOhX0CRdXdmlJfsVfUi4QgY1SdEKQzv3g5UZ0N9PPX+397/CwKeSmFWPmmOcliw5HRRhRVOUdR8iktKrIoil9ZRNRlSz1l5eRbvLYVEjm1GMQr4ZdXs/jLx5VnfE2J3PPy+cufpWgW0cHitDZVrxWg8scrHq/lECha6FY5y6iZoBqDVdxtcXhTWsQUKNnc6yJOWNpSiliKFgVJFSmMgCsFkxIlBlRbUElhtCKbQtDVOz0pGmXIY5ZmSReKkcPOOY8z/uuTPxdhlaQJVGaOIyWL86FVsvxc9ytaZyllEu5vzux2D2hrubm+w716yzSdGfafCeOJcZLiVULC6owznhQy3377LYWZL/fv6V0vv1sKdxvH5jff0Nh79sPEnAu///3vaa3lt7/6BSonpuMZ0yaaVUeMia6/IpTIhy+fMVrT9w0lj8zngXE88+7tL/DecxhGfN/QrDLjcc84HBjPE21/xXr7iusrWb59CB9YXwXyufB+H8kPR27WLVerhqve8uXxiPM37IcZFQN90zEWxef9TAwZSuTt6zXn84i2iqQchyBe27EI22SdVzg7sdqs+fTpC852xGi5//jAm190NE1LiGe09Rx2e6xRpDnhvCdEzfHwyKtXa46nJ/KYUbnQd57xeJDrJReykc7WOU3bN4wxQwkM406iD6PAbvNcLRpSIKdASDPW9RjTUJKYvimeu87n7rVcJtAfQxovHwIzCAVWU8hahHUyLU/oksQ8zMi9G1LAWy8RdN4LbZZKn60W1QuVL1dopRQlRfNF5/zSMOvl8+bF8xX77Jo0lvIF7sw5i7XAjzrj5076+WtCNfwq/zaufSnKRl8w8WfrAJ6ZLy/qkC7/Gvf/tx4/i0IujFBd8eiErXJ7FBemScyaknL1+xUOszdWwln1ckHl6tUiJljWOHQVGRgl/shaLSPOy3FHXzbyyzpCcOi6uEBO2KKqq6CWjkT4rNVB0BlUgkisrJeCMZaMvrgyag0pLlad8fKilSw0qOXC01pTrEFXf3GrpSPSgPaLMlOTFGiVySpUcZMmTWd0MmTtsaZjVglnFa13GOvA+9q9JFGZFrH1hdrVlAJoiLBpNyIu0lGELkaYPGMstE2HcUK5XF/foYxYLJyHEddd0RlHlyJ9mpj3j6zTxDQfCekHsi68//4DN6/v5HW8mri5u6WpRkzz8czNMTPHJ6bDyOloOKRI2+x49WZFOh9xY+I8TMQMn76/J41PtN7imgadVjjn2G63rNc9MQdU0qQUmGYHxYDZYNbXbLayfzlNA6fzgRgmbq6v+f0/PfJ4THycFJ8fIn/lOtIUGLXi6ZS4YiBj2B0m2uwYyoxzK3LONL7hcDjgnGMeE8rDaThTVEPXGOYZbN/w3Yd7+r5jnDNjgj/+6SPzPPPqrWK/OzJNE69e3/LP//wHjk+J1hfmMNKubnk67NE+E6NYKCgic5goKaOsx+ZM1/YSGm0tKXEpXNNYKE6DajgcH4gpoYwmRhFozfOM9tVHqIg3ypIXIJhyvgho5nmu/ybWtloJVWCBH5fwBZ0lo7YkSLOI1GIoxFIbL+vRXiizzjaXxKCl8dG20gRLFKIMNfpwof9Vy4kfF9IfUxSXf1vSgl7CKbFEUdku93Ypl+bp63JVF7dfLTrLV99PLd4CSBe+BNygxFMHBInS+pl88ONH/ukz4ScfP4tCrhSs+k5sJkuq8VaJeRa/EzHHkri0nGGKCassrmsBxZwyp+ORMEXsNJPRtM1aLDyrJNpa9+KXpVBiBlFT6PPFmxuMUKZYZMESEIuu9CpdxE8lFYzrSHlCRY1yBWMlLDnVjX/JQhcEof4ZMllnMRhKUOJSvBfLgYqfZ4WloeiC954QAvOoiNEyTRIrF3PARBl905jRFrR3+FWHNmIjm0KgZMkFzCEK3XGeZImTC841WGtJKj13IlUQYZRmLhrrHG1jWLUdzonEG4AsOKvQLgtxnIhpJsUBhUwHRSW0hvXNHSlGVvmGxjqG855v/yrTr15TcCjbcto/sHv4ge1KQ5zp3Zm/+rZlvfoFu/2J9faa22/eMJ6PfHr/gTFEhhAZp8R+nNhsVhg98atfrph3Axt/Ip0sSUHTbvBuzXbVkBP4pkF5Q1ZeiqBKtE1H2W7xRnMYR9693aDdxA9j4NN55r/86YG/vrO0naLfdJyi4nA+kZWV+LO5cN0ZYhzoS+DtpsWSMCtDClnENC6QEOjk6cs9oSjG4cS67/jhn7/nd7/7G/x6yz/+H/87b9684dOnT5wfbxmOR5qbO3J3w3D4yG7/RMiOf/jH7zEoXLNiCpHbjcObjC+OMYy4VBhToowJ7w3nMbJ7mtjFiQ9f/siq6+k218xkvnz6wLtX13gzM+weKXnG9xuSFg8ZtHj85Fwki3MxoavmD+arUIfyvOQvoFJhjtIMhJgZJuHSz2Eppgrje4imKosL1nqUBpssLnmKyqRUF/XV9bRYy7P7oZb6+qJjlon7mWVzKeAVUkkpXd5iCuQQSXEmxUgqEaOkOSpKiYWA0pciDPVAUAvdML9YwkoRr5E2fBVOcRExLQ6vXD5ugaVADsC/5PGzKORagTMSbEwu4kNeiuBvOaFSAhMvHWtKIkQgiAmUQkRBKRZ0SmjfUYqi73vBxBBeNDy/0LIFl26cH+Fr5OVkFfpfUQVdhK8t1KL64lgDsSFZuVhLKagkHNjnRc9y6qcfSY8Ni0FOziJZBrlYFvwukigpoou5PM8SqdCUjgEAACAASURBVHhcwTUd7XpDf31HVIWQMsOnz4Ihaotpesng1BXLTMJ4cY3DO0fjWsEarfD2tdY0XYup419RCO2RSCETQmQcp0qNFO/1UsQrWllTD89XMiFFoVeqMrN7esA3jXRNumO1dlWCHKFESpp49XbL3ZsNcRrZqQduvn3DPM7kMNNvG5TNPH7+BE5x8/Y1U3Dcv//Ah/0DKUQedp/5u998y3Q+4Vcev75itdrgfIvGcDjuaDuN0i3FGVy3IoRCaxz3H/7INJxQSOhDOgdU26Lbgi0DG28ZUPzhaeav3TWWiZurlpADT2dNnDPzrGE48ro1XLWKrTekEFEm1UW8vA7OiSGcazXDmDlME9YbXl1vOR4+462j97LTuLu7A+0w1nI8nvnhyxPWW77sHtHKcBxmNv2G02ngOOzRasXKadABawET0bphnM5Mk8BUc5w4HGbKHOjWlr4otHY8LrxtA3YlB11SkIuSJsiINz/FQEkykf4IZngp4pHIxnr/lMUUjouXyJJgJcQQDWFCF6Gs4kVgF2OVqVePcFUbqeX+kns51+JpUTUiUp6Xudz7cr+/pDYuXXh88fb1+8niaWQWsgPPeLyoSBWx5Ivdxo9/D7JslSb1Re3/CkpRWrOgJy9hK6XUc/zbn/n4WRRyAJ0FIy1KUXdoaGAcnrGqRc6uVBGXtZhQRqGd+EnkImNKjKJQCyGQKtyijLl05M44UBmrRDRzOcmpS4p6IVYjWDSQlarFvBoFFYFvpOOWDrcgHiYZLdd8BcNSFhwvZamORQwXMMbVCzFVipX4mOSYKmapxHBeaVFy2sz6zTW+6TBNS0iZwzjx9N0fMUbRtS03d9fYVjw7Vqs7TOvpehmxcQanG7zxFCudf87iwliiXNxP+zMxjpSUmKczWkPfdpXhkrAOnHV4qwWPD4EYoVhNmkaZCLQhhSKUxVxofEfJAUVivdlwPBzoV2tiEi9ucmGaBqyBxlpurq5omp7hHPj08ZHjbs8vfnHDP333z+LhjuHzw4mQCzfW8mm3J2bL+azYP35k0yu+/fYdjW3IwDSfuL6+5nQesV4R04SbDaSRYRjpekfT3TCOO9pNxzrD435gnw/8+qblwxx5HAu7k+G7Tzve3GyYvpzw/Ro1nMjzmVWJ/HJ1y6ttR4oBnRTOtGQDbSvTZiaS04TJipVOrLcKPUKKA0lp7PqGp8cn3rx5xf39I7ev3lC0xnc9OWQePj7iNj3HceZ8PpM17M97oZc6z/E04jrPuldYq+m6jvV6LTBFlGbDDZrdfk/XdHRdR2cDWmX6ztF1Hda0OOMw2uO0AeNJ1Gu/vLSQ/bopWh5KKaHlKrkHcpb9TNHqQpc1qgaqp1QBiUSuEEpWiZwcKUvIis4NxQgPvShJU0q5dv/p5fd/8by0ZXEYhGdGyMtC/bKgL115yRFSROVUFaS1IL+gLC4F9uXnv/zZSzXIftZzLJCtuLC+/N2RK6z5ovn+qd/pn/P4WRTynAv744jKBaMzTdezhAc31SwqhAhek0NCJUOx0jEqI1j5+uYarRzFaJrqR2J1PQ2LFKWF1jQhrnPR52eBUFUulnqhoVXlw9YlN+K1nBdzIGNROaKNQumOVPM/S5gRID+hiyLnWex1Y0ITGUeBRrQGa7uL7FkpMLohloR3DQC3b96gfYNrPKv19eU5KqVJpXA8D4zDzO7+iXk+E0NgN4yomOm7yHn4gZwznW0o2tQjSeie2skO4fnik5vOaKEsllLovCS/tF21AlCZxlX5/jwyjyMhBIxWhPNAzpnPx3vB9uROrDYBSfDinDCq49W7N7LazGLxu1p7mulESokxFnSO9O1AKE+8+1VD89u3GBRvfvE/kXNm93jP3zee3ekseO//mXg6H9GM/N3v/op3v3zH7unA5+kL1mpcaxmPB0osNOuWqBTnwxOxWFb9Gt06LIqbd7+mlMJp98CvfnPgr37zhv/tP/+Bt6fI/hR5/3TiFALTac9+1vzCaP5+o2mUYdtv6JsGQsKqUhPeofOaEs9i8VpkjxNTROcCIfKLxpNiwdrA+f4Dehi4n0aCKvzfHz7yq7/+DR/ff8b7lrdXljQ9cWUyc5OFjpoyU0wcIpyLRxvLqysJk+66hjlLhJ1ZN3itaTeJXCmkOUN3vcHFxN/f3FJsxtfJ1SqNxZKq90fWXKydktJkvSTuLLFt+bljrUt+4Jm5YsSlVGlJDVo44ctCz+iCou6N4iSdrFsSugqxQqBiulUu9ENdv38pCrQY0un8MnziJWHhBR6ekgQx17ecI3MMhBzJJaOLmN8pNJaFPCEWvbmAwaCKqsKnugeQVu7SUS9Qi9b6IiDKdWl7+VX9qF4vnuZW/2Wl+WdRyOvety47i9i1FohIQrs2EtagVBY/k5AutKeYBcOOoeB8VTRaL6yMRgri1wKC+j3zs7Ixw7OKy3BZ/oGW51PHqoyVC08lGbesl011KViq74rKzGWqnXxGIiESWiVSDjgr6FnWYHyDcZ6m7WlWW6z3uK7HaFcZPEKHitPM5/efGKeB0+6BOI2Mw8D+4YFpmurJPqENvP72l3SrNXMAqx3aGhKGlCNFGbIDZwquKApGhByqZqRqCUtYQm3ncGYKgd3DA33fk60ihVG6mDAJ/l5/pzJjyGInxxGrWppmi/aOlAL74w7fO/rWcTrv8W0vSkrveXzco7WM5JZMMZ4vD++hjDjAmh7nGox3hPnMet3x5fvvZcufFG/ueprWct07wunIP/3X/4u+X/P23RtR/PqGtu2ZxjNKOxRgrSGOM4nCqn8FRRPOJ8J0QlvH1ZtfMRzP/Kf/dMUf//DPfL7fgzLspondaSJNI9PpyPW379AxYJMhj2CshhRkrDeGWMDahpyiFMLqy22sIaYohmxFEcOIN4Zt72najvP5TOMM7nDklVXMwwnvIapISBHvlHiAl4z1hs63PM4KHROt69heb9lcr2hWW6ZhZJpmioIQIte31xyOJ0oIKG1w3qGKdLyUTAqB2QxSwK1GV2glxUqtvSieM6liyLoWKPl3zWJzkZW6bO2WLnYhG1hjLhYWWivQGmsqb1wnUS5Ti3cRN1SKoWjxLUoIXW/pzinmxT38nAe61IBlgfkVV/yCly//LlDQS/aL+neWqEsHLt4y8rPrwiXQ5vL961MpLxSby/JzacnLi8/J+b9D90OjoG+ew1BjFlpfCFM10jKAhcozbU0kpcJ5DsQ0MsaRYZ7E59k43lmLLVvwXfVueB6NCuViZ6suF9Wz70iqocwLRq4rzq20/L3UZYZWiqJFFEGp/M9SsMbhKpOl71e4akyljCGkwnQepAtIE2EWWXjSmdPugRQjcRpRypBjIqSZnAS/i6MYV6VKR1QxY/RM65JQD1dbGt9xftwxHPa0bUvXbcAaifeyDuULje7RSQl2LsxNcikXfPF0PKJrevh603G9aSgE8Wsu8fkiN5r1qqeUQphH8iwX3ul4IKcDmDV4T2tXnKY9GKoXd48zax53T2g9M5x25CQL7abpCKngneHt3Ru01ozngePpCacUqWhWqzua5opmdUXOkXE8c/NN4jwc6dqVZFsag9dFvOxz4cMf/pHGt7z77d8TckRpg9YNisjpNLC9FVqqXb2lmBF0RtFztXU8frnnTnnW3wz8asy8/3jP+w+f+WCemGPh837gdtXWCLYT+Ryw1tIqj7eFcD7RNa0kOhEoJUDOKG3wShHjiLNOVKhoOtcQnr5gcmYFqN1HHApbCvlcMCUjgTaKQKZ1W2IxlOzITUbryOsbh29AkRnPo8CR2mJ9gzWF3eHAN69f463mUE3MzscBbRXkzJgznTMUbbB2LVbM3UagOCXuic9CoFD3WZVDvuxPcqUIlkgyBR2nmicbMUyUGHHaCpeYZ9M2rTXWVx2Jzqgakl3STM4GdCIAWidMzqSqOC0ZlFlUng5Fvkyw8HyIxBhJL+CUlCMxBVIKzzYbQuK+1I2iEHdTSoVSX9IcuXycrgCKTPnmq4K9MFAqC1jeV+kqF57Fi+78Zbjzn/P4WRRypWTcV0oRU2KchXNLjsQ5XMz4sQ6lCiRd0esa1ZbFI7zERMhwOB1R2tOt1sQYn82hfvSw9mvpLyCDlH6hIls6+GUJUccksbasHPKicW2Hcw7vW7KRse10GjifThKqME5oJXCD1pKSrYvGall+LQeKbRyqFAIFi5HtdS7oHGQETAWjLVoVmmYiaS2ByMowHMVzQytLGiNBBYovwmjRCl1HRLHgjaTqy6yNImZZrHornujOG5wzzGlG6USak6hSEc7tqvEM5yNhODOdd4zjWQpy5c+XdOSf/vQD2jv+w1//HZvbd0zhzGk4k8OMyhPDUeCY9XZF22/R1jLsdmSt6NstD1/uiSnx+u2vOR52rFvHw+MHYoz03TUJS3t1xf3HT+TsOD2dOT2dcY2l23hKCkzDSJgj61XH/sMHVtsVTdPIgYNmu7mBmDjHmeHxk7y+2tBvXjEME9aveP12zTRNvP/4CbPq2Ny84ukQ0T7y8bDj4TzWGzlilCbmgas24xTcNg41jliVMDph6+RRkozopURKqmlSBmIYKEnTGmEr5TSKsVoWMRjaiASh5JrHOmNdR1Az3jhWfYtr5Tr0zQrXbhiGgXGcGYdAzgnbWlGw1m50GCbO5zPWC67umgajvYjtjCzmY5CmJddDRMRWGmtk6vXVAVErJZQsZMoNOUACopFcXpNAi6XDPAUoi+d+kbSqCnMuRd2YyvwQpyuBOFOkFEsqklAk+y2hLy+QrEJVGmFtxuo9nF504T/uyn/MM2eJnuS5Bgj3W1/gk2UWFSry1zXmJUa/iIkMX3/Mwh1XdQf3spj/JY+fRSEHJV1wPSnHORCjINMpJKxONHWM1NpCsRQtHUcFyiXXs2J503DmrB2r1erir/IcIiHYnqm5ncvvLb3ozpetOEApCY0iKoSjC2hncdrgu6vLixXCxBgju4d7QpjQOQlroSymQMiyRj2PjFopbJUqhyTwxhxlBMdaYbKkmgxEDblV8WJ/6b1HNY34iRwP5JSIJaCywtmIto5iMg2iXCulZikqQzYJY70spMhV9Wrp+zVhPnM6jaTkaqfciKWpko+zER4ePoilQhiJ04mYxvo9pNvv2jW//OU3uMajSuLpy0eyyRAD43Bi93DPer1lvb3FtmtO+zPjeWC93dA2LfcPH1iverr2jvP+M43SfHz/CcNMHAfO40RSlpzB5IjSivb2ThKJ1htaryslrPDUfkeYZpTqOJ9HcjFcv/0VJSWO+x1//K//K7fXN6yv34IWT/HD8YHt1St2ymJdg51Hwr+853w8SujxdsXucIJ2xf0wshsG8S3PMGaDfhjpjOZ3t4Z3W8OqKNZRkbQ4LpoMzoi9cJgnxEhKCeZbhF6KLuQgE5O14gsv/jgWZ2VRHkshl5liFNu14/XdFmU6jN+QTCtfV1ucyQzzgRASRRucscQou6fhHDgeZ7avthi/AeMZcqbEmbVSpBIJkwRbp/K8tFwELUqJGZeiwmPWoIuSTFkjCfe46scTM9oKQ8Zb2RcthXRpuNwSql4n58IzLKKyiItKCVK4awGNPMMpopwWbH6pLxe+dxFb2lyEYvhVMU/Sjf+U3H75WeXPKutXQnlcanNRWUzllKrpRM9d+QUL//dYhepZ0/HspPXnPX4WhTyVwjlIgEPOmRhkiTZn8QxWzlGKxRqNc5IxmYrCGs9wFpMq1zqUFpqfZiCMmfsPgabtsdbTbbZQPYVNKag44ZIU+ULljWsti07nsMZUxz85AEII5GU5MgfmMjFPg5y2WRJAAFROtavTtbMX/MJUMZNZeKd1+RXSTCxSnC/fv4oNck5krVFZREHKSTHNMYAHqzvmeWaYI8aJn0QZhBY1jyO5WEz0zyOra/BhxjlPqUst+Z6FOEDWmuM80fZrNusrjJU8xt3+VJe3hcZmcpg57h+Z8wmrFdvrV3T9htXVK07nifH8yOc//p7D7gHtgGnCO8dwGlHNGt+v6dfXTMOZ8fQn7P179vePTMNIt12xutrgtGJUiqIshYxzjrvba8bhxDiOHHdHjGtY39xgmi1zDJg4Y0qhDEdUf0NMljgHVPeKbiUiDaM1KEspCttv2fgNV9/8RqwbfCMmV8ay9nJD971kWebs+fbXf8Ovf/sf2T0+8cO//MDnTw98etixXU0094+ElIm5sCqW9zEyoPhfPh0xHzPXpvA///qOJgz4UuhKZq4illLEX8SsruTaGPaUMEljo0StOysgG7JSzKWQYiaogukbEgVvNH/3P/6Otm/xfU+cZs7DQN8Z5hSZUuDp8cj+8YDvDdvtlhgUp+PA/f092+2W1irSdOZpvwfr8E1md7ZkbVDY2uQonGvqZBovBSpacTVUSmGr3YVSci2KrP95J6WVCPXadS/7o6qdeGmI9XKCLmXB5uVQE4dOwaQlX9ZUuCJeaMbLhF9UBl3xf5Bmh0CM4cIOS2kxyQpiLaAVURV8geoVTUmVSmxlTyYTuXTnQmMuAr9qmdQXky2QFKTFssBQu3D1XNyFmllISLbw8jP/FIrwbz1+FoV8EaBQRFpslZD9SWJGlcrzKanrQsQqTSoJ3xjh5SqNNZbETMoIwb8MDGnG+45oFP16BYBRBmtkr14PTpTRGGtpnK8hFuLJ8XLk0nAZuVReFFmyuFG8gF8uxFHhm8rnK/GAAVSSpcriUqF0TbZHXt3lVL688Mj3pFDHT2SHsPDCtSHqglZFcMIilMgcZvnssyVbYTio1hJKwDUFF7VYxAbAWGLF+OcY5HvOmcZ7rjetCDFU5Hh6ZGbk5s0r2vav6dc3WNNxPD3y9PCJ45fvmGNgtW4Bj1KGoZwY54BqHHE4cr3ecH//BWvkws0o7t68Zr25IqTM6XTi6cP3WKXJRaHbDtuuSDriTINbfcMc9wzhwPD5M0bvWG03tFfXMjor+PR5h9UWpzVt2+O9p+m2eO/RpkG5dX1dJjAK3fQcp4k4i6dPSoG27TG2YRonnvY7fGMpKdP3a0lxyhNvtit2B0Xc9nzZnYVCSqFBMwexJJhi4Fgs/3gI/M22R8czQYuZWdN6chS7iDLJ9RZLFngqa3SS5ixniNowkohZpkLTrFDGsmodt29vsE2P9i2oBmUVq23LcNwJFJO5dN/aWSgOoxXaDKzWHW/e3tToup5NFic/5zuMl3tmCpEQZsGiY0Ibh7UaXVN3ClpSpZQi6Xhx76zVVsRppVzuZaUUQUuDIhCKlcvf1Izalxg1mryQE/Jzcs8SZFEKcgBraY+l+RL8XrBoe6ma0n2nF3BKXdjnF2wTXtha/z/UvVmsZWl25/X7xj2c6Q4xR2ZkZdbgcrnL2CpjYVkCJHhEavGCGonmAUTzAEJIPNFPSK1+g0Y8IRnxggRCSCCEwC0EggfaeMBud1XbrnJVVuUUETci7nzGPXwDD2vvc25UpctZyELVW0rdjHPPvefcs/de3/r+6z+MnTwHu1yVDxbYjDUBOIDdP1bdcr6TMPT5xygWulvA/wnkkSu8F75q7MOeH644+HKHLJQhGyNWG7STMIU81L8QOqH0KUOza2VQ1geMLwghikw5J+E0l3JxTMsa5zxF4XGF32+vVuvbw1Zv2B6NkXB7nqdW6DwMUnUcUrdhr93Kd33NB/xsYN5kI12hAsG/1ShOUhiZPu67lzz4tui7ytOBRhl7GfAymAjpgTmjkrBlUkromOh2Dc4ncoz7oVffZ5zxhL4HpYhJTLzQitlshtUOX2jpGFNP6Bq0UtTTY56++xXKekHTNGxXt5x98oe0zZJ2txbamkk4K9z1vmkoC9BVwbpRLOb38JMpMyU3elEUtH1LzJGXLz6jX27IqacJW4x3Mkdwhml5zO3ZC1KzxllwhWUyn+GLCZqaEAKXr1+hMpTOM58fUxQFOBlkuqIEO2XZd5iYaFfnkNLgatkR+jOcnzCdzGXuMJ3RdwGrS8rCcP+eZ7vdst1uWS7XeKN5/+tf5/mHn6J1w+npKZe7QL/doQmUJlMWjpuuwRtHUvB8ueHUZ+5Z8KMYTXu0GQdxYyKNE/x6TMJBhDOrnOh0oq+OyBhKV/DOe0+ZL6YUhaOYHVMU4ohpdEcMLUVRSexfnzl7eUnqI1VdkPqA8YaydDg3pZ6UVHVNUZTkPhGVpixqXDHdWxx7N17jAlWK9745dL7DkdIwi8miM4gx7v+tciSGw4CzI+4LLzjgQM1To1qUg6WG7GSTzMiSYNg5JYwST/+UI0EHwdbHzl4HGWDCQCGO8nXvOy4Rjm/z4Q9QbGLPHvyxspUg6X2RFr65kScL9vsXV7x9PfhJXFxqxz+B0AqZQUZviVH8DkSAknCiwCHHTNu2hBBobUtO0AfE8CeLHaZzbrBfVeSgKHwNMWO942RxwmRxRFnWCIW3oela2r5j10BhB5hFqcHqVaFSRqmIVlrUlyOFaRgSjgU7KyMNc87orIkEdFZoJVZQ5AwhSud+N6YKkTBHLTi4mPZE0kgHNG4/eIlR/q4YHaRASqAmiiJlOjuEIpQRU2zpQ0PqA33bkVJgt+toGoVylioFclFhywpmU6bzI548fQ9nRP6/3tzSdj29TmhfUU8mHB0d0XUiQtltbrm6vqX/5EMUidBsqX3PpPA0hRgtoRQaJy6Dk5rYiMvZ/GiC9SdMZ8dcXN6yXl9QlxZSJHeJvk1cXd/irAWlmPmSyXxOB1RVxZuXLwjsmNqS64sbqk1Lzpe0Tcfi+ART1Piixk5PaRTE7DDJ4VWmXzU02ytCEAuHpx98Fa1K8XnXco1cXC+53raSIGQShMiueQNa+MztdodRlkePn7A4fkDf91xfLTGFp9m2PJu3bIoJ2xB4k5cYpXDecdMGjPUEpfnB5Qp9/4gqJ5wWH/rYB1Lq6ELEGCf2akYsJLqY6aOis5Yr5ej1lPv1MafHC+r5hHuPTplOp5R1RVVOh2ukJQxY7/Lmlu1mhUFx73iC1prF8WzQMSisrQHFZHZCOT1CYajrgeViSqyv5H7cNkNRzuLNkhgG61KMrDs0PRiBHozWVOWEnMUjZhTptaHd485OiXeRZvxvYL1EYZjFkR44dORZicVFTkrgnkF8k+lRyYCC2CtGa12llNAch11s6hN97kWmHw4SfWGsxIEmKKrOu2IjuMtMYWC8DfCJAqUODJnx/s5DQpH+CYtd9oPhfPf58ipDSfzisAp8gUKulHoX+K+Ah1Jy+a2c83+mlPqPgH8LOB+e+rdzzr89/Mx/CPybyLz638s5/68//TVkFTYoMIdU63GNF8x54GACfdsRU2bTJro2kIwiNwPeaz2+qHCu4N6TJ5zev081mdJ3kbZp2Ky2WOvQJuO0k1UbJbuAjND10iEKbhyEGHMIQ92v3GoAu9Idf+EoXZ7U73iASfTB3RAgEQ+RU8PvERiGvZgiK6STyMJVVzkPqlE3XGhyM9hsMIORUcoBdCZqjUqRGCFEyQ+MWWGrBbPTe9x/+pgnT94hxsjzVy9p2x6VIkezKY8eP6CczkgYtqs1n378I4wSMYTXGXqBEKw31JMFzfqWGAO+qiS42Vna3ZrJ0ZTQdrg4wZYz5ieP0dpw+eo5cfuGicqETct0cUzQgZw7nBVzq8oXrG5u2a63vP/Nb/Li7CWb1ZaicFwvNwInKZlRpA5WV0sevHPE6nbJ7c0l88VCrgfvODk5xRdTisVTtDZ0XcfN9QZnAq7wVLOpuCh6w2y6AF2wXN4QYodVmqIoiUHhyoxzjs16R+E1tqg4PTrmJmnaNqKdxWe4Wq+prBpYQIZt12K0DDBDhC4Gkk2k3MvincUVMwdNjIlkhQKLhkZrWuuJriIgi54vC3xd4eqSsqrRxsoA1BXSAJlITo62bSjLkrJwtLstMQaqSUU1k92nLJhOYJsh+EAZS5Y4+yHVSgp0YUElTUeQjjqL5iPlgEIRwkjHM+JtP9wrwUjH7Yoa6zNFiujtdk8FVLkf68xb99eoSQCBY9KgPE7E/SIlvuaDgtJYjNlL3mQHO0IfMRHSoL6OQpVMMRJDIqbIHXHm/n28xQFPo83s53DJRw+VsegP869x15752ULb9nCN+Umx0E87vkhHHoD/IOf8D5VSM+CPlFL/2/C9/zTn/B/ffbJS6hvA3wB+CXgC/O9Kqa/lEYz6vEOB00LdMgk6LxPuXepQzmBQFEaLB7DSYCXM1zQNzmaUcTz90lc4vfcIX085e3nO1dUVn3z0I85evGRWT5jMF5Slx7kCZyaAIhtk+qjFYjYDdhQGjZPw/cnr33rLQrPbv33BCIeB5X6bmQVTywNNKQ9ubXnADUHEC6gkMExOWO1EJAJogvi8JLkcclZYPUzQM0QjuxVNT8odLhliliLfa4uyExbzGV/75i8zmx8Rc+KHH33GxdlrXv3xH/Fn//CPePalL/OL3/o1tLFcX9/y6uNPuLz4EG8N5cRjncZ5T5eSSM+rgtn8hN45+tyjnaEyNZqMs5pXLz+hcCUqFmxbOH73GyyU5ub8Na9ffJ/SRlLomJ+eYo1nfXOJcz0qR7JPHJ3W7DYtm3XH4vgex4+e8Ce/9wfo0DM9nvH0S+9jfEG33HB59YpmvcJhWF2fA4n56TH1bMquXTGpZ/R94Eff/x7ee+anj4nJUk0XBO3RpcfYktVyi7WeFAyXl1f0qWNxdJ/KzHG+2t+JyZWye1CKZtPgfMHs9D6mqHn0/lPsn37I9fWKaBUfvzijNIanJbxTzTAJpm6BoWPuDCYXRANd20CE0CuSkWR7jQcrXfgFjlUvWbDf+Mq7OFdwfDqnrmvK2YTJ9BhjLNo7dr2oJvtWdmimmEpzknqMLfngax5lFeVkCojPd9+uaNsWYxxt6Ci0x+pabCuyIoVu1FuitcJpK2nxSUQr4109+ppI19zvPVXadvw+e3aL92KfXJRqDyHkESMFiW4cZgXj9yJhz1XvCEWWowAAIABJREFU216sJbpERAnUqBTKOIwR6qW1FufF6Ctruw+IDn0gpECKUURaSUgM4+vnAVLRahhAaytKVQNmECplnQchUnG37snuYGiw7j6+//4XHF5KQ5h/JnDlLy3kOecz4Gz4/5VS6rvA05/yI38d+G9zzi3wkVLqQ+DXgd/9KW9dtmtOqEqFtwJFDOotYG/qpAc2iLKKdx8/Y7o4oppOeX15xQ+ff8rVxQV6EBRVRU3Siq7XzNVUoCvEt0S8yQd4JCm0zSidiAy4aRqmzwOMYsZszTyk7WS19yKXviGRciYOfNZxSDNulNIwpU4SkyIDlWH1tXrwLDeDx8sQdntQmEbMmAieJHw3K8i9vE5UEU1FyAHjZigHs0nNB1/7Or6e8ObsDS+/+32u3pyxuXqJr0qePXvKr/76v8C2iXz7j75Ds1xCTlQTCeNNKRKzRSvNervGWUtlPTlGbm6WOJuw1pH6gPeWrt2xPL+m0HLzH73zDeZH91mv17z69NvEsKWqKvHLDop6fky/2eCrGusSbe4oygmuMShuePrV95nP53z6o+9zfOzoOsVf+/VfJ2XFbrXk8vXHTCdH4CXYOKTMpC44PVmw3mywWXH+4gWha4mdoqomdG3knXe/zPnZJ0wmFUY9lp1OYehzh6kLnJowVZZd6IhJLFrDsCvruo7dthWxVVkSiBTzObqckEKDsQqrE3PvMClglWGiLNpkpmWBzxGnJ+LX0bdiY2ALUk6EvNunqEcHvTHssmMZLHXtmZQVZVmjvMBMtihld+dL0lB8vFVSoJIM0UPXsmvWEsBhNa505MH3RxsrnkOpQGuPKycoW4qzAhtIwi5R2Q5xZoM1RAwo5bBWAXaQtydyHOGEQciZR371wbY550zXKUmdslbYUzDc12bvbSIwQ4KhC9/z6LPkFcjvk0UjpCx/kzHkmNFaXqeP0gw650BHEcDlkYM+yPRDR0yjGV8aFqNBV8LIPAGtIiCpTQbZdWht5DkjyyYzaFrEXA+V96ljd4eXoxOiMHEOWDkwJBIwrHo/uQP4acfPhJErpb4E/Crw+8BvAv+uUupfB/4Q6dqvkSL/e3d+7Dk/vfBDhhgsOUgQsHYeVYuEN+1aMctyUJQiDHr45D3q6SlNB1cXl7x6/kM21+fkmJikSKfkdyQri0CMkS4ELIgHi7U4b/YpIzKxHj4QJVCL+I+PqMfYkYw627c3F3mkFB4+J/l6aDL2jx9wtwMGJ0r3EV+787vVMCy1hpzFwlehBz6tKEtjDBijSUBRljx5/DXmRwvIhucvzri8+ojrlz+CHPFFwT/7L/0rzI/us1qt+D/+/t9nt7qFuMMVBUU5pa9OUEEKn9WO3bpnMq2oq4Ku7wldizNK8NKmoywKri9vZNisDOXpU4rZKUY7Xr38hNw3OAXTqmKzvaZZX3F8fEoTMq6ace/JO7x69ZK69JS+5PWbCxbvfIXZYsabs5c451hlzemjp1JUmx2r9Q1P3vsSZy9eYJxiOrMoE7j38JjV7SV9F+maJZU2hEJRPzhhvWl49OgJbWh599l72Nk9YsjyN13foOvZkNe4IaZENZ0RNMQog7nddovXjuniiH4oXhpFs7rBGEdRFJzcO8Zay0effMy0rtBdh82KiauwebAFiBGHQAGKRNcFQhTfb2uMDDm1oVUFqyCcae00vrQ4r5gdH1FMpxR+EO4EUTCmFEidqJZFlCS4r1YWrcXeP6JwVgbao/92yAk9+BmFbicUQqOHgSniFaQljCUnPRTUfn8/aC1Dy6T0XhkJsv8cWSF3Y9NQ0PWBvh/j3KzMgZJwsPf88az2Ht4xpP11P2YOJMbh5GC8nIQCmLMR/58kZnspJaFPWgWDJXUmknO/f39jYR/vybuxbOM9+uMd9fjvPVXyc1gpd3/mLegoDZ9QHqIj8+H7IzaeAJ34wscXLuRKqSnw3wP/fs55qZT6z4G/gzSkfwf4T4B/42f4fX8L+FsA89qjhuixlAKxBxU11lQcvfuMyWxOHzOr22v6ZsubF59h3SuUk23VyUwxKR8gYIQajGsMKYq5lHEOV5SD0Y4ECOfBfnIcUOo8OgUPLBMZ3UAWYx4ptIcIOO6epDt/V84KNaL7w8osi8HIdT1Mxw8p3ewn+3BQmUpRHzsUUBGxCMhyAU2np8xmM9CG9XrNZrXik08+Ivz5FlLg0btP+eYvf532a1/ls48/4ubiNb/zv/yPEAN96umjqF6dKQndlmbVcuIs2TvWm1vqdkZhCtYpsF1vcN5wdDyRwWfTEzZrjo+OmBzdo54eMZsf8/LsBTeXF8TNcq9WTLngarXB2JrZ/Rl9hMlkwna94uZ2R1XfB6W49+Qpva3YXb1m+XoF/Q7jIk8/eIYGLs8+AudYLGb0UTM7uk+33aKU4p0Hj8l+itc1ut/x+P2v0fctMe7YrpYcHx+jjWPXrrjdKE5mR2Rt0cahTYlOgV23wxuLJtFvVmLdmjX1dIr3XrDk3YoQwn6hPzqeQ0w0XeDk5Cl13dJ1msuLW2LoUUUmqAayxnYaa8QaOMZA3we2uxalDNYXQ36loXE1L7cNHZoP3nvEw/v3SClx794TqumE5B0UFcpq+tRBjFhr6duOvu1ou35gwgiHuet25JiEPpkT169krOXLAms11nq6wX4BEKGYdWAVURlCuwUlBmspK0I8VJgD99uirRH1cDByH8cwJHkJvo4ai6UMCvumJRphmHlXDrauiabthoxdPRR2j8YRdaTVLYQ43Kk9AkvLjkBsoiFmGcrm4T4JWYMVvYfKEJTkHqh9Pu/4xwjTRVtxHN0X9Dv89rtWAsoI7LLX8Nwp7lq/bZt9KBBq37IlJbzlUdCp73jS7J/0BY8vVMiVUg4p4v91zvl/GF7s9Z3v/xfA/zz88wXw7p0ff2d47K0j5/xbwG8BPD6e5BA1WIt1NdYXTGdHlHXFzfWSV89fcnH+AjNQupK2aNtJYrtVGGcxWtJFqgxRy8qWtIMkajlrrUAmOdN13V7dCQJ5jCIHY0aF2fi3HWLj4G3V113O5+HrYdAjoa/jv4YTHv+CgcqwjVP7nMGR4aIG1orQnJwvxH50MkVrzXK55Pr8grbbkvuGwmkePX6P+4+esN1FPvrwU9588n1UDPRhR+kVIShIh9zDtu/kRnc12+VWugQrn1kuE9PCM53OqEvHan1L27bonDh9+BjnKk4fPmOzveVHH/2AtLlgu1tR2Qm2qIhkNu01VVlinaJd76jrmpvVDdZ4cBXHT96lMJbzszNKetxiwc1qCaam37QUxmKdoW875otjcjIYEpOpZ7k+58GjR9iqIOUO4w2+mJBToG229N0SV5ZYV9HkzIPHv4DSBc2uw5hECCKm0n5CkRtABn9Hxyf7ndK2GfJSg2zRi2pC6EFb6SpD12OiAGkXb96w3NzgnCHsIio7QpDgbKUloNqgaEMgkgkqU9iBVWEsSWlWMXCz2nL/4WNOp8dUvmA6neMnFcrmwXukJ2lHUU3JMRJIIh5TIp1vdltS7OiaNV27JfWBsvJ4a2h24jS5WcN0PkXrhpxFO2GtwzqxfEhJBvZGO2J+G8f+yQYk7e8VuY8cOYlnkHS94Q7MoPfXfBoIBTm0+yKpjCHGQB7tNRhShHTGYQnGojL0WmNyxGhDL0xvea1R8B3k3h+zRLXMZ4dCLuZ8WQ0Mm6EBNMoO+b1DF67vwCf7Ij3sWIbd+yF17O0u/XOhkdHon2GYe8dEa/yeMOb+innkSl7lvwS+m3P+e3cefzzg5wD/MvAnw///T8B/o5T6e8iw86vAH/y018hklPNM5yeU0xlae9bXt1y++oTV6pLUd5QalDYQNMFBbHpQFmt7bJ/BapLOBAVGOZw2cjEhqR5ZKTKREBO61xgKwZqz+CCMdKikEm7fFau3Th6MxVuEPm9vtWAUAB3kBLIQyAkZ8HQ9zrBFWKCUXMj7LnwQBsmqLIq3TKYoJ5STKdN5Rdu2rNdrbq8vJNWk66msoT5+wsP332e1WvLxp8+5+uQTyEl2CEb802PI4m2eAjoq+jZgFAQb6elQcYc1BpPFwvZkdsKDpw9pNhvOz8+FyWEtJ4/eoZzOmExmfPbD77Nrbgntmtytca6gzwhvHRF4bdZrmrbnvadPOH/zino+w7qKk/tPWDUbPv3sBwLBzE/pg3iD1EUJPrO9XZFz5sGz90l9wjrDcvmGtlvx+OlTtKuJGWLWlEXJZLZA5YyrJ2TuE9qOGBTz6QIxAHO028gu7MRRLwVM0TCfTtDacu90QTukrTebrfiFAPN5zbbrubq6YlrNCUFYLyNUYZxlfrQgqcjN1TXrqytCG5g4jwqZYKI4daZEn5DwcOPk2tSKpA29Mry6WWJKizaJ0/v3MN6hnMe6AjOED5M13SBeEs+gtGeCjNdrt+vod1tCtyN0Pe12I0VGZ7Q2tO2O9XKFc562W1PV0/01PraJShvx78z8xLU9HvuMy737oVj4ojLaQe4H0dAAZYhmwgw48Zi5qcXQasC11aAzEJ/wYSFQIvbxwxAzpLi/f1QenAjz21YcWQ9uhnrwRsk9USF+4+TBflZhhxSIQ8FWd/6WQzeuhiHoaJ+rBgciuX8He4Dxe3eKeRog2nEOmgfWz74G3oFv7kKvX/T4Ih35bwJ/E/jHSql/NDz2t4F/VSn1K0iF+hj4t4c39KdKqf8O+DOE8fLv/FTGClAUFSTN+uaW9e0V7W69zwLUWqO8IWtJnzdGhmkaQ7NuRAVmJLVba0s2nogUcesKOYHaopV0oGVZ4mzB6ImslAxOvfdygkzanyyrD1sjOYmjQ2MkKb0XGdw90oB/wYHnL6SVcdEYMLdsyGZkpxzyA1EZY8S6d3HvBKOFj728umR1c8vt+euhyBsePn7GbDbjxdlLlssbLm9uufzd/0e8N0KPKTQJhbMTVEigCuFRp0RQEgGmlaVQBo8k2ZjphHfe/YB3n73P7WrFzdU5f/A7vyufXTXhV37zN/Cu5MM//8eks49JXUPOEkSRQqDfrIg54VxBWZb0OTFdPGAyPWK32fL64pzje4/pc8n15Qva22s+/egjjOqZTCY06w03V9eUx0eYasrkuIT4BIDt9hZtDLs+cP/ZVyAm+q5lt7mlqCuq2Zw+ZHKuIQZysgTVUx2doLSlxWBdRVaOqSnwXUO/a9AaoVE2W4xT3F6d00UoihJyxFpN1zRst2BcwdHxKc22BZ3ZbbbDNlvTpQblMnVdUh4d07x+w2ddwwJFpWDGoE0wJRfdkhThaAjiwMMKxTpkboLmyw/u8dd+8euc3HtIUmCtx7gSVCAa6Z6L0tO3LaETd0qVJcZsuboiNFvazZL1zRXkyGa5opzOmE5nLFcbiqKgKCcQWlzhoFJ0qaOPQN9ikGFk4Rw5CfU1xsBbcTcc4MEYZVAo6k3Bw3PO2NGszQADnJFSBKSgo0WZme4sEEI9zoNzqcAtoc8i/lEMWZea6WJOihIk0zTdEMbCoB4VfUkIMk9KOshueOjIiUkYcIOkvnQVVrm9Y+KI/Rvt0EZ2qNoYrHcSKDPg4sJhHwegB38YGBhsOd/B9wUs1WOnPTaDQ58nG55hmPtXPezMOf+DfWV6+/jtn/Izfxf4u1/0TfR9R9YtiYMBD0k8RcTFJGPUoMhCSyZmjCQFXUykXqbX3iJ+4VqjXKTtA1mNTBeHrQpMrwh9T9aKoqgGLEuL25vWWK32kEsCjBJnNUxGI9AMRmGSAj3KfEeaYX4LDnlr2LlvyiNk2TnoFIXBwCAOUApfTyjqCq01tzcrsYhNidhLGIaZlCwWC+rpnMvz11y8eUXY7lDiOk5vsuD/2qODQZmEzgIjdbHFKkeOCR0jnd1hTCZrQ2FmTGbHfPNb3yKkxNnZGa9fv2R1eYnxjifvfZn3vvxV3rx6xfL6gtSuiLGlcIbYKfrQk0IroQm2pJzOJPw6JW7Oz0TMFRO//Bv/Iuevz3j1/d9FZbjoOiaLiqo64ur8gnB+jS4c07Igtg27tmHX9lg/mKApQ+0MqQ+smg1d03E8vydJQ73GGy9hHb7AovCDCVWMia7tUT5K96s0zlWUriZrgdtsVdG07V6ufX15xb2T+2STKOdzlCkknHi7w2lRP2rNsAAUAtMYw2y2QOlAUTlWNx19FzgqHF5rrNfs+p7LNjIvPL3RKO/oyVw0PW02vPPwPg8fPmQ6n5G1oppMBWvOGmsLDDJs7kLAuYImNKJuHK67tlvSrK/IoWe3XYsW2Bhi2xGKLdOJNEVeOVqjiNrhiwITw1BYJAQlJYNSDm0VTie0NvR9Jxd11mR9J8GecNjV5rD3IO8jGB1QQwi6wpLyYZeaY3yrC4UDCSBmCfwWs7geow7xaiplci/Ms8pP0MoPyWDi156zIrVhuOc0/Zgv2yVQQaAyrcmDHUC0WVTVo9jP6ME3JaKtwShhKosAKA24+V24RXxWkjpATGq/076Dw4+lYPh7DTL8vFu0hTXzsx0/F8pOrRVeOREIqCDUNmNISbb+MUDbtfQhkVJP1kIH2hv25MC272lQWOspfUEmMnFzcCIzN8aAyoTYs9ttcKmn98UeDxe/Y0WnLZ7hg9XCBtH5MEvWWhFyZlTdS8d+yO1L+znnnvLCyIyRIeuwUqeE9+LrYgpP6hPNbrf/j5iISM7nZDbh+Pgp6+WG64tLrl5fc3F2zigrzkaj0SgtTIikghRrC1H1AkkmUVvG0ElHlHuqVDOZL/iVb/0aq9sVZ2cv+Qf/1/+J04YYAk+evcu3/ul/hu/9+Xd5cfacN69fkAkU1lFXBcZZ+hRww+eYbMX9x1+lnM64Pfshu9tzun7Doy99k9nslE9/+AN+8Du/TbtbY5xFJUWhMrevL4izCbOq4nbX4qzis+99iLElpvS0jWD4777/Hk0vOwqlPdN6gncTblaB0J+jjdjtamXpI8wWR6iqoiwnkDVJWXZdj9aRdrci50TpSwIGZT2761tyiGjvOXl4wnw+Y3mzxFhP1JJM08QWZQ2ZiuXNNfW0op4fc/76DcdHD1BKcXVzxQfvfoX1OvD759+j0LDpRVHbdok3txLajPVkV9J0iatNRxt2PDo54Td+7Vep6inaeqyf0nfiyZ9iRzdeN04WqKuLV4AwUurSEruWAk32NX1qqKcF5DTk10Zi39JutxRFxW0befD4HaaTBfiZFC9jaUO/7zq33Y6cFUVR4r2nLEu2m2bgdCsRMKVB0DSwP2LqhZkx8sGHmYsxBqOydLdaS6OW79jH3jmEgpuIQVKrirIecO506Ig5wJ3OOZxzVJVYMoviW2YBqZcYtxgTKWbC4CCqo0Yrad5IowfMARqx1uONJZm8n6FZ69B5HIaaAyV6D7tIEE0e1JxjXfjxvy/nLGSKH+u8RxHRzyIGgp+TQg5grYakiH2kHROpRzc1By4YshEhgiagMXt8zUWNGYaEVmlZOZN0WSZbjNMYb9CuwFlPURRoO+JYMkGXgQwHz5RhZyD+DrLtyQyOa8PJGg85EXcw83GMrQff9JxlYp8PU35XldRFSd/3bFdb2kai7uJ4YrXi/ulDirKkbVuef/rZgAWGPU89JuGpWu3FES5DNlo6MyWULj1MwGPOMmEfbHKtt/ziN36Zo8Up3/3eh1xcviC0DYWR3c6Dd9/h9NkHfOc732F9fYm1GoMhakMfIWVF1/V4ayApfDHFuoLje09Y31zT9g3BKlz9iO2u5+OP/piJUzSxQ1lH1AXayzDspPZgS1brNdOjBSrDattTFvIZ5e2OsvJcvHyxDzKYLuZsQkOIicJPWJw+5PLyUsKDneRcOl9DNqROLA+ctuJy13UoU1AM5mj9ekPfXaONo6inNG3HZt2Q4wajLLuuxbmC9e2SeiaOfddXL5nPjwih40fPP6UuJ7iqYLlcoh1M5zVHR0cCY1lLQ2aZYNXBbauZlJqbTcsGzWrXsesjX358xNe//D6TesF0XpGUZrtdDy6csoPM9HsqoS8886NTcs7smjVdtxURWerxzmJNTYobvPdYbTDW0TY7tN6grcYVQlvcbjcsJido51CmwPpxwB5pmg1KiRpWaz147nuxwI0dSkVGW9e7lrB3Q5aVygeeuULgloHZEeNopiWWBHLbjAPOtznYAnWI9a/cd4akkLnP8DvGwmm9ZxoTfdPSxEC757ln1GBHbbRGJ9ldW2XZuxiqz88m3ePndzQtWh/iF+V5w60/4PIwMlJ+vOId8PC7Bl17aoT+Kx52/v9x5AxNH2QLnA1tF4bVmz0+XVSIRWnO5KCEOqQFJkhahiRDrRqEPDDiGSPMYcg4o/eFfPQuFsvLO5Q/lVDKDu9toP7dmdbLM6W7/rzV9rDCKkZsRWHAZCYT8btoQ8/NzY3ALCEMEAtDVyGhuTc3N1xcXgrdKkW0El901EBhGniywmEVHE7fcfdJBELSguUhF4crDbP5nIfvvkPTNHznz/6Um8s3qCA5kqePHvPe175O2wa+/Xt/IAMrOyQchYwzisl0Qmha6qqiMDLgtdMpRyennL9+Sbd+gzKOB/eeUtUzXnzyXe4tPDFG+q2jzT2ToymxDYJ3Og/ZMpsuWG+WQrV7+BRnLBdnn1AM3H9Q7HZybmNKNOulUMyS5fzsJaks8MWU6ck9iIld36NJLK9fk1Li8dP3UN7j6wrrpajdXl1zdDSn9Q5blOx2O7z3LK+X0ukZT09H113z9NG7RCLL5ZLZbEbOkWaz5cnjx/i64ubyhr7tmc6nbG7WnC7meGvpY2SL4eVmS9sGvC/F395pzncthMBx6fnNb32LwnlMUYqlqRElMDrT9h3a+CGcPpNiQEVNxpJVxPuSXbch9j0S0mDROVPNZkIDVYYYoSrnhFbmT94J/dAYxXa3pMhTnNdUk4V0riHQhF7ScUaud9eJctI7dFKk1O452OP9kWLexwDKgC/LfZrENC4Msnarh6g3pUjpACaMjUxGDV2vJRtLNpI7cJfal9BoNRjEIVh9F6Wp0VZhC0uhM30yhCiD/6zl/ShlxYp2tJ99iz44DGyNfD4jw2vcaQv0coeieOAwDh+Eeusz+Wlo948POH+WAj4ePxeFPKXI9c0SAKsNhbMySdaJnMX7xDlPoYbCWsjXppf0oBghNO2QeBKxSME5nhSYwmOLYj+kCXmcsHu5QIZU+zEvT2vp6mW6L/7gSikx7lIRkhpy9zLjJH7EwEZ61ngarLN4X2OMYbXdkFJgvV6ThwvfDAMO5z3Hx8eklLi5vmSzXrK+uYYcxXQLQBsJpx6dD4mD+5xM3cN4ASlFQpStKluyFmz/6OiEkwf3ubm54urigg//9M/IvXwWpyf3efzBexhj+c7v/yFv/u/fEWqVHjBTkgQCZI3B0jVbZkdTCT+oZtw/fczF+WtePv8MExvc/CFF6tntWjabHZiSDujTDn/vIfdnR7SbFaoWz/c3L19T1wW73Y5H73+N6fyEj/7s23Sp4+jR/eGmc5wsHtC2LbvdhtBtIWaazUbWXue49+Ab+HrKZnlLHkzDyukEbTJt1/Dq9XPKaoIrJ0znx4Chnh9h64p+s2G9XEPKbLu1XGcJdKmopzXOtLx+9ZyYxMY2JOhCiy4nWFdxdX6F9SWVcmyWW7JzzE6PuXd6xMvrJdcx0PWR0hrxTY9y3r5yf85Xn73D0WTGwwdPsdYSyIIjY7FWE0KDtV4k9KWh3e7IRPqQWN7eDIZzGo/DeEsqxVitbzsmkxOUk3PYbnd0fUt1tBigRolQlGLb0XdLcuro+x7lPN6VnJ7eo+t7bm8H/nwW2ELgh0xZVpRlyXqtZL4QMymJXkMhgirxUJJuNRFRXQNoonEwYOfGuX0BG5OCvC32zZZ3pbxPbweyAncWEEU/RCLGmIltS0iJNom/UMLgbYkzBZ3pIGp6E0naoK1FW5H2mzFizhiMszJgNgacUJx1NqhhZ/p5RXzc4Y+ak7GJzHnILh2OETYZyRx7gdCdIi64+RdHyn8uCrlCURQiODAavLHDai2T8L7vyTGJH0pKKJMHmOMQ5eS1QUWIg/lPVtCFiHWJ1ApXWOVMbHtimYeA3IiYkctwRD44s2eQHPjg6mBVOdhlkjTKfH43bozF+5KcFbthWEkcuvos2FrMCestdV1jnObm+novd1ZpiIbKagidVpI6b8aCmveLgNKamA6q05zldZRWsgDagg8++LIoLc/fcHZ2NqSgQFSJclbx3lc+4OyTz7h8c46KPTZnYRgoEXaMznYxaTCW6aRGhczjd95nOj/h4w9/AP2G2DbUxyfU0wndzSXdkAka4tCBuZLj4/v0bUeI4o92eXPN7N6CbrtjenTC9Oghl6+eUxQlu03i8vyGJ0+f4pxnfXtDJmJyZr0LuLLi4fE9unZDn0F1G7a7NW0X0EY+h+s3r+XzsYZpPaGeTmmawMX5uUjdteKjDz8hR3HmK/yE0lcElZnMpmhtSH3C4IldRzkt2TQbbAwsLy+4/+AJzz9+RV2XYC1X1zekFPDW8vzsBYuqoomJtNyQooIkfvgT56hs4vHpMY/u3xOYhkzX9TgvA/a+bynLmnJS0zQNRme26428vyBoXenssGvsiUmYFjFB6HsKX5JDpG3F4tl4KZxm6NBzTLQhSUKUDnJNmUhWiRwCTbemSgk77BKbpiGEvE/vGouUGUJYUAalWkLfS3zhcD/kJN382BSMjU8kCnMEsXEe50l6zycX9pbRDuulPqj9TlpeN402GWos7GlI+FKoJDtZlSX/VqNwWuwJymREs6E1xmiUlfvWmKGQG33QnijZUUsXbvfWvZ93SD2I3LXBHR//vJ/JA5vlZ2GofN7x81HItWY2rYXLaSUVyKDIQzhrToFm8DVOMYMzqDSY1zMUci9S5JxldScPw9CYyCnS06ONWHfGlDCD4kzcAzXGjidHLCyF592Tst5fMLKtktS9pDRmcFPJ4tnJGIuWQ9xbdo4nMJBki+13AAAgAElEQVQGLw3ZGpelp/QVu92O3c2KQ3fPwRciI4o4wJAIadgiqyRZn6mVXQOHCyUS0VEUZ0eP7nF0csKb1xcsb2/p2w1OQVKZZDTvf+VrzGYz/uQffZu2bfY3HpghzECGyignfjQKZrMZ2lne+/JXaDZbfvi970Pc0fQdWjuKcsZ2eU3fJ6IuRDzky30och8DbejBQAhi5bparTm6/4R79x/xve/+KSo0hN2WkCLH9+/TKk0fI672oC0xZp7MTzFOsNtsNZV2nJ+fY7SjnEzpd5sBV1VUvqSLgdvzM27fSMhxOTkhNEtR8/YJbQyF92jn2cWe+XwBGG6XW7RJXF1d8c6zL7HdbslZcX1+xeRoyscf/4h3330P60uurq7YrjeY0pI6QDuM9Zi8EXsIpTAqooicFp7HJ/c4nsyYTeYYV1DWE9q23Z8HpYxo63NJihqnNV3I9C4N165CuUJ41vQ0/Y4+Qk4BbSx9hhB2pNiiMxSuJKSEwqLJaGvJcSeQQ3aS4tMFnBFnRoVmu92itKUqSgonZlddEPfC2Ic9U8cXFYW3GC3upCLND6QkWa8kmc1kZSTkOSdJ0FIDCjHShLXoHRQKN1hLj9mdWlsp5G+l2guEY7KQE7QWb/KkBVpRAz89JMhZvme0Q5nBjnYIlJF5mEKZjBpFgUajjXDF90NNDhBLUiNaf7cQD2KnjEDAIyhr32769rREDri6Sodi/7MW9p+LQj5StsSuVi4QlTJJJUKTUdoS4k5ogzljxdwXvYdDZCsE7C1nnS0EP/XFMFycgHU4WwrVypjhwrGArMQ/PtQYj5TYCwTGQxuwzh/UkW0rw6HNdm+mpVGDU1yisI6qKgghsd1uaXY7iT4bWS1vNfajsnQUNOQBVtECERlNn+SCzDmTchyYFHDv/j2mRwu6puH5R59w9eoNcehWjLIsTo+4/+gh6/WaTz/6mBQjOSXZASnp5MPgBqe0oRzEVK501LM5X/nFX2K93PCHv/8HKDqM1fii5t6Dp6QYuXj5AlNptruW2Ae8zsyPZ+x2G4iK2DfE0NH3LSpriqLmg1/4ZX744Z+xvLyh1oqgFcFa2rZnkjVWFygsMWmM88wXM169eU3a7qh8gSuOwFsW5YIURK7uZ7Jw7DZbYrcVNoczNE0HaGzuCV0nykjjQSmury6YFhW3tytuXonPS1SWB0+ecP/0AWefPcdahVNwdfaSbneEL6astw0vPvxzjmZHw8Ih3ulHVc2sKgnqiJwzk+0Gay0PTo45KUtSG3n27Bnn5+c8fPyE29trJpMJVhtQidvbS9E3rAzGKi6WK1KITMJMbGi9ptlsCX1LjD2TwtF2HSn2eAtN6OgaoW4ao9isrsQGoxJjNJ0ANfjeY0lZyk7sIjErBKLXxNRxOxhdFc5z5CRUpKGnbVvatmG73qCcqIEXRxU5inDtdrMihEjUw5BzUE8qpcWUy4z+KhL2fFfwM0IbalAZG2P2RVUOhXFy43Rdt8/odK5AkeiMFzaacWjdkoJY8MYhnKae1OihiFe+QDtL4YoBqvL7Aq8we374XXFgUgxCw4FCnA/CH7IsJuMxan/28W7j33BnKDp6rnwei+cvO34uCnlKibaJaC0mMin2sv0finrfHTrbcaihlcK4YfCh1F4FJgVL6HVFMRk6APFqlkJeyM1hh6gqDaJbMshg8rAlkhM3Up2EHzpSnfSwcIxy/zF3L9+ZPINAPb4QmuFut6HrAmPSyVjkP28Ssle9fQ6jNMYoARrIAMl4h1KGo+NjtNO8fP6cftuhVSZbBSHjjMXXBYvFgs8+/oTNZrPf7qosb2FMF8IIPXK05TWl5/jolAdPnvDis5e8OXspvo6DcOrk5ITdds3N1TXHixm28mw3l3hdUU5qbq4vcUbTh4bcBbzzrHZbymnNk/e/yrf/+B+xmBi8MaybDjKkpLl37wEJuLm6YL3a8uTZB9RFTeojRjlC36K9xxVT+r4VkZa2rNaiLg39FkjUvmI2XXCzvKaaSnJR1BCaQN4F2tjSdg2zuqTZbXEmorTCWs3p6QO5BvuIswWh33F7u+TJ03fQztKGyHZ9w8TXOKe4XW9QBpRxtCGQCdisMDFTe0NlLFOtmU0mlMcF5WxC3TZcXFzw7rtPubk4p5zUWAXTSnYSKbb0XWQymbFr1mxWVxSupM1jsk4AFdl2LfSR3HcSZ5cSKXYSiGEVbSte/FmJd70rADMM+u8oFQfgZABq85733HUdsU/MZjNmswJttrIjQjzvU4go1aOyZOt675nmiqbrafpOmCjckblbdafjlk57lOkbY9BOLHOtFQsOo+8MHJEiOoprxiStcdY1DkzRh+ZMxEd6/3jMGTM2biM90rg9rdBot2fPjPVANAgHhonZE5Pfvnc/X3rzObVP7wUmMti+w377WYq5+v8yIf2rPh6fTPPf/Od+acC9NM7kwXZg8EFQSlgpUTpFazTWytYHIGdFH6JIfPtATFIsy2KC9Q7ra8rpHOtrnCup5wus8Xhf3rmQ3HCixog2UVyO2z0p+laYF327t6Xdq7cOYi2UEqVoUhDa7o451kHBKW7nw8+muH88JXkcBnFBevuCkARxGahoqzg6OiInxc3tlSTFR4nE08MiYbTmwbN3uL6+ZnO7pG8knUUrRRiofDkm0uDyN1rrxhjRaIpJxTf/qV/l5cuXvHr9gn63lo4KSzWZMp1PePP6JVpF5otjQlTstmvquialQNfuIHQQEyGBKS193/OlZ8/45KMPhyzHiNaW0jtWN5f0/Y7F7ISrqxtSzMxOZuLPctvQxwZjHMfzB3K+U6ZPgaZpmM5OML7AOcNuvcL7kmJas16uMMYwn8ylGQiJm+UVIQRub285fXCfyldst0ucKyiqKcv1krquWS6XxBCYTCas12u2zYbZdMFsfszl5SVV6Wmajmo2ZbXeMp1M+Pjjj7HKst42fPzmlcBsTc+0KjmaT1jM5pTec//+fSLiRVK6ksl8QuoDoW+x3gk0pxRuuEa3W4HgjIb1ekkm4K3HGi9BG+0ack/bNxTVjLKeEPoNfbul37XkXiLQXC3eM1iDLWuMLgTGdAPtzxR7TvVoQGfUyNUSy2lrLXU5Aa3YbDa8efNqsIYVDxljDJUvmE9lsNqFlph6UuhRXmxgvS+xin0XPuLT3onBnXZj4LPDGn/YvY4s3zsUxxDEyTGEwG7bkvrEppOFJnSRzW4tPv+pJynJua1LYd9Ya6nrCd4WwmjTYpzlXQmw370rpcAcCvtYxHPOewuANIiy7joaKqWGgs1PPn6HR37XNGt87J//G//aH+Wcf+0vq6E/Fx15zgg2qBTOWZwZTWuGRA9Gcx5pHZU2GOdQBsntSzJAARlyGj1sw1Sm7XsSDTbUKN3JCbkzNd5Pie92/PogCLJGflfb7sj7NJNxizTaX+q9X4v3Hl8J97vruiESSiFp3EaCIJQm/Rh18a7Xwp5jSr4zxLy74MoQ6PHjh2w2G64urkkpDBcS+2HPYjFjfnrMJx99Sg6RnDr0MEyKsT9s40ZKo9akEBBObGZ+dMKXv/ZVfvC9P2e7WZH6Hmc8MWaqSUVdF1xfXmK04/T0EYvFgg9/9EOM0YQ+0fUdeaSiKVDe4XzF40fP+OjDH6CIOKPZ7hQPHpywXS1JKVBNFixXG5TW1KWlqhecX1xgtIR0V4VnvbnB+Zqbm1tQEeU8bbfBpUDTZJw2pJRYLpeQMmVZcrO5pV1uRLKtFMubS6E1KsPtboN3Fdk4rm5uKUvPZrOR4aMVd8mbmyvmRzNC7Hj9+hzvLW3bMpnU3K5WxJh5/fI1tSvYDQu4VZ5tvyXrYSeHgT4zOZZsTa2AKNdd30esdcRuh1HChU4p0fb9fncYe3FeLEvPZr0D6/ceQcJxyhTOk+nFllYbrK9QWHrT4qwBbcFasavV8lVwFIbdUNozKgBiioMNLIyeLl3XEUJgsVgwndbc3Hiaphmu30hKiqbvcG0jdgBFQcYSoxdrXm3x2mOdNEPel/vO3Dk3dMlu+IwOsMaQqfg598zB+EprTTJJsnCHOmKEx8BeSn9HzKMHXyZt1X7A+hdh1WM4s3yT/XP2d6e622G//TxgLxS6qznJd+rR3W78Z8HJf04KeRZZdR4N6BVKC+Y7CgxiztgRZdAGpY3wbA2klLFWiXy606iBuWKtp3BWJNcpE2PGhETTNFgnRVwMhyxKSRc7Di1HTC6FNCSo3OWKHoaS4qIIISXKsgSlhGI44Nog1A2DElPauxdITHserVLsL9bRkMsOmPjdIm6NZ7FYUJQ1L19+uufPG5VJWRaJDPiqwE2nvHh+BjEMpEiH1bKt1loP+8FMVApdOHKIOGuJQyjyL3zjF3nz5oLV+hZixGSISWG0oZ7WrFa39KHl8ZN3mC2OeH12hsVQTqZsb69FSq40WUuXM5lMOD2+x9X5G6wWG4GQ4fHjI7p+y64R8UvWiv+3vXeLuWXL7rt+Y16qaq3vsu9nn6vd7W4jy0jEQASO4AFZimQiRF6QSECBB0t+4SFISCgWEhfBCy8EEBHCkhEBIUIIICK/RMGJxFucOBdjEjk+cXefPmfvs2/f9+3vttaqqjkHD2POqvp2nz69Lce9zzm9hrS11+1bq2rWrDHH/I//+A/tGnzyuKbh7OSU1geGPlnElCJtE7g838BoZdZdXJGTcn59zuHBARnPatWwu74gusjzJ88Zhh3HRytiDDx6/II7d++wWh1wfnmF957Tq1Nu376Dc3B5bp1zVusOkcRq1dIPx6xXx6xWBzx7fsL1rufW0Yrnz5+zS5mD5hBJSnvQ8eLiwrb6Q+JofcDQ96w7g/Uy5gi7ZqRbR5IkduNI7gdut4bRjtngMFLGR6u0deIK3XaD5lQ64XhEilaJE7xGc16a8cnkGrLviKuWsGoQNUjSNnXWiBs1uWcpjCeH7cwkW2tBoxGmKR/kxFqk7dLA1ZXptty5fZsXL8+4vr4mqp1T1pFBdzBCs2oIPtKoY9AdHqEJEV8ceYzRdE3KvedcmKJfgzfs39zN3gKqV/nXSxy7Lgx4GEIwMkXOBsm4WZK2argv82OvOtH62o3cmcxRufEkPhvdyDlPkM8flH0hHLlzwsFhV7AwivqZiVOlOEyVYmBJxlgihRpBqyrJKaFrie0K9baCN0VrWfC4GEvUqZAHhmHESSKNniF41ofHVu0WW2tE0fc4pWBmbmrgYMdrlXVOHOv1ihAC19fX5vCxiKEiZ3WiJbHnU/PlEnk7hVG0sE/M2QdqNyJHLhHIwXrN4eEhT5884fT0FDgFKBMws0smrHT//n2urq64vLxk8+njORMuUqIFK3awBcAqY02DW9DguXXvPh988OOcnZ3xt//Wr9ufpKFsNyMxBg4PDzl5foI0wte+/g0uXl7wrecfEkJglwaGsxcE5wuGGTi+c9+6zJ+d8NHH32HsB2K7QnAcrtecb87ZXm/Q0BlLyHkOD23BevnyJUNyaFa6YwEXGIGz05eoQLPuEHW8vLrk/p37iDr6YaC/vOT5sycc37pFXEUePHjA6flLLjc9bjfwtW98jb43aCWlgeurnnc/+DGur3Y8+uQxX//61xlOTxHfMCbl4mxDE1rOzs65uLiiaVeENnB9ZVCPGw3SWB+v6XvTGtnuNnQHAU2Zo65j3a14cPc+L1485+BgxenpC+61d/EiHK4bckq8fPGcg+MjyKNpuIgwDpaD6Xc7MongLTgxlcpoxUFOSmenDGJRpYIllemt0OzoHuLseGwOQggtzkUYktUfgGkZiScBOQ+Gn+cyZ+o0wnbDp6enqCp37j/g/XfeZxxHPvn0E3yhxFZcO+WB6K1a+qAziMs7k/at0AplJ32zYtIou1NV5lJ1tLY9rNh1SRw6Dy5XtllGoxJSILs89d+si0fw0ape/cyaqVpLMxFBC1VZWKqeihjODoXyK4LkPAXgN1kq9vfgqeBprcAWOxWyA0n2N1mY6k1ex74QjtwwZcsQBy80TTc56RQqjiyFDeJARvIwsNlemYZJhkEpUXQkeEpnlGANh0NJLhYdBVcqxpoQCL7Bdw1tMAaIRd8FN18kHixyt4szZOO9rlYr6wp+vUHHhC+4dMXN67nN2yR3Y9WueJ93xmmVrATfGOMMJYs52tVqhfeeZ0+fWhSvtjCUHhlkVY6Ojjg4OOD58+fFSc+/v/y9arUQwTkjUaJKiIH33v8xPn3yjJOnT4yk5hylYoE0Zo7vHHJ5eUloIu9+8B7r1QFPPnlaGi/kAiX5GWMV5dadezz69Cmbq3OcOoIExDU0MfLgwUM+/PAUCj83uEBGOb51l37X0+8S4h3Nyr5fS7k44oldQMSzudww5szV1caqFZ3n+uqKw8ND+pQ5jB2nLy9NfkGUbtVy8uIl4zjSdg39qDy4fY9nT57x4sUpD955l0fPTmhC5OrqijSONE3D0G9LoUjg6uqCg4Mjxj6xvd4Qus5UCDvPdntJFGicoDHgW4ihZd2u0LJbbJqOW3c8Y9/jmxWo42prO4M8jMTGM+y2jGp00SkaH0cr2CnQXhebQmnNxcl4xmydo5qmsfySZoMgk3Xd8u2qBEAjoyoB8NE6dFXMeUrYqfWgrXMnq1Ez5gjVtMPPz8/Jh7YrPTg4YNgaHORjTSKWRKTTWWFQZsfpqxN1lSfuJp1vLfvJrEUBsS4k3LyXTFLWjllEp++u0h3qhEyeHPmrEfiSrfa9Ebj7noA7p8ULTm+8v3TiFpGbD/BIaZ2HVWZ7cGJkDZWqkligoy+bIwclFnrPlAwsJ127hKhVsNhEG9VKtLfbybmnlMhlRc95hfdWlRiazrDKwh8Xo2IA0HSdsVmAzWYzZa9Va6LQcPa5Uotpe9l2HdvScCCX6MOoj37hRI1VMONeWOmxKlXHGApfu8BJOY/UYoLVwQGr1Ypx13N59nKS8XQIjBaFJTL37j8g55GTkxOjpRV82Na+WTtmGm217uc4Ry6Q1eroFu9/8OM8fvqM82dPGPqd6bgXvquqEprA1dUVwzDy41//Bl0T+Pijj2hXHUO/od9uCd5uvBAiKWfu3r3Hp588Mj55wXk1GpPorXfe5uzlqW3bYwu5cPiL5MDFhQk2xa5lGEe8W5HG3l6LbbnW0O8Sdx/eZ7fZMQyJPu9o2gN2u0x3eMyzZy/ougbNEEPDOCSGcUu3XnF9tWG9PsK5wPXlFW+9dZ/oA+fjFf12xyoGQrS8SrtqrOAsWCvBYRjY7Qa2256j1YrtpqdrRy4uLgrNztEFz7pbkZJBF7HxHN86RMSzWgVOTp6bVvqx6ZrbInFFmyJAwcSVseC6bRPYXu+smletXkGcMvYJfJodbpF3DQWqs6R2af8WLQeFOtK4Q5zJDie1np9augWpZgSZEuB2p9ZCvJLgc6aT1Pe9MaFUrfLVmwxArdwMjbekrb/pvCtmPUfitWKyMlEw5oxp11q5f3V2ixzT0j4LFlkGV4W9fcNZfw+cokuIZWaO3fi9aZdbE6+veLXyQu07Wm2pTV737tU35bKQ1r97XftCOHLbcCioMIyZ3fVLc+qFSeGcI8Sqf+AYB5PtbEXok0UnoyaGPDIma0ycvacLERcGQunkEoIl2+4+eIft7prNdqQfBB9KAUBpEBBCYYY6V9Q4bZBjbAw+2G3YbbcmmQt2AXK+MRFuruS169AsDJRFkFw0Y7xgQocmU7peH5JS4vzcMEfJhn/Xi18x7sM7R6Rh5Pz0hFTkQKsTX0ZWy0hjmhvO4VJifXDIww8+4OmnT/j4u9/h7OzMZE9DIOtolaTRKlCzCtvdFd/85jd5/OgJKQ+0seHs7ByhMmVAnHLnrbdomo6PP/6IKEIThCHXCSu8++47fPeTR2hKrFYHxtBASSrcufMWTx4/RiQjIRb6qSONVqEYXbTxSIFxt+Ptt99lM1gXnJ0ObPodbWiJMXJ2+oxVd8TmemDXX01OLYYWdMQRuL6+JI3K4eExaRi5ePnUFqUQ2PWODY7NZsPD9x6Qs6lUgiXettdXpDSy220Qpzx5+tjmaD/gnTW6aNu2yAUYP3q1WhnLYoT10TGbzYazszPadUTTwDhsyWryE0Pf46MjJ2XbJ0Ijha67QxloChTpG8ewsw5HIdoCnVLGuTgl8EYdcT0kPE23IoQVuNLjMjq6LjKOiXG8MrE3LNGZMdGr2v8ySIU6slVhh4DHGkFsNhsODlasViuapkGdQsrEVekRIM5EvAql8Mb94q1mQUKc/AIA6qiddCxfVhzj9yHcvRppB3HkYIwzJmnZ8tUCOCFjVEZr5CJF0cNNR7GEWVL9YZ3veclaoKibAmKqRVs8z5F2nvcO5MJ2EfuR6b5N1Bzb69kXwpEDRZ7UcLexN3aIpp7gCnleGlyJWMRHhIxrFQm+CMZntHTqCM2K4JzJoPoG5xu6gyNWa4uEnj97UibuemKoOAl4F4jOTd+z3GatVh1jSZQ6FaJz5Dw7cJEFjreIFJaZ9VdXbMP/7DPeecQF2nXDxcWFYfRiPNcxJ8Q5u+EcSBDWhwdcX15ZpFuStIiQinzoq7+zTBCJmL7Mw4fvcvvefb717Y/ogrDb7kr1adnhaCJrputWuOC5vrjma9/4xiQl0DSRzfU1wVkvRzDGEIUf/Pz5c8MdRafu7jmbpOrT5y8susKUFE0UC7r1inGwFlzOezRhUbwqJgmX6VNPiJE+D+CdlY7nhPfWhKPeqNvtFidwfXlu0Nho29+maSYu9dAnfOPoh60t7ldWuRkFojNa69XmgqPDW/Qb+53KMd5st/ggOBz9bkPqM5pGmmaFJqtqrM5HfNHRLzs0H81puyg4bRmGwRhD3lgUEyUteJy3FnGQ6ftE9IG2jfTDiJKsj6YKLkTS0NtC3Hqrbg4Rgjf9bOcwhnhPSnYOTiJOsMheZNJg0TyU62W1ELmoByKmBmp5l3l+xWC6RRVfrvO/iw1EijiXx0sgFM52VRGEWsMw63YbRTZ8T8S9dKivRqzLea5qOw7UznlpuXQbUg2Ts9XixKstGW0zTq43Hlea4fJvlt83FOGwZW9eSyDLDZhlojG62W/8XqJx+II4clWl3/SMo/FAq3CN85HQ2EnHaGXnznvLduMYGaZkyJgzHptM1qHcszq+zeHtOwxDsgTg9kXJjjdIssYVobGtUxYKeUvL4jH38dQxsdnsppUzlay9yewWbRYxKplotnJ85yYpy5StFdVyi1Z/E6+sugNyzlxdXVlkp5bEzKSpaGfUPFEbt1fXXJxf2U7Gly147UNYJ02dEDXPKeDUkSVzcHzE1999h2/9zoecnr7E+czVVVGQ88afD+IZkkejff8wDHzjp/4JvvWPPiRgcri7pOVvFJccCeX+ew/JQ+L5k+cARHGkoSepJXrfee9d2hh5/Mkjo7VNN19NisFmc0bXrKeoT8Vw1e12i6rQlCYOTjzrg5XBCr3R9GKMtOX9EILNJzwuO1xjHdtz7s3p9TuG3Y7D9T1Onj4jkWiqeJME6yq0OSf4huAc5y9Op7m5yxc06xWND4xJ6bfXtLFDBkXyji4K3nWErmVI49QtKSXLr2gy+YO+H22niKNPvTGsvC1+qrZzdxJMJ4WepKX1WAz41WFBoRwBaJrbNldyX5J3JqkWyzUUlywWjC0xtKS8w+Fpm8jVbsf1dkMMDetuxWZwU7Vyn0sxHCA5k0qQE3zEhZkyGEoi3xdutvexUAkDITLBJ1OVpISpV63XCGILRuoHJNg9NXXUKvdOYpzmTJbCrCnQhVvktKoE9nSvZStYyuPIKMbA8TpCqrBKj8YZPl1CpSyKgmo0npNOdS0TdVlst2zt6WDMaXq/OuYlHv9ZmPzyt/iyReQKU5RniaBgzBXNSBHynbZDWRG14XQh4sUxjokYpFQjetQF1odHrA46Xjz9hHFIxGZdtoBC2zU48XPD5RBAbYtTaYOh6J2Mu/57MDfgxkosoqa3UumjU0ReWSKupGtkgkVUjeLXFb3x64tLS5aq8dhVDeOuDIF1a6yey8vLqdCnTg6LXrCtmRO0JECdc2hKBO9tgony9ttvs1qt+PSTR1jRVMNmezEdt3eeopUKTui6NTln3n3vPa6vL3EukIfKhKkX0JMkEWNDvymyplJhnYaxJK5UMsfHx3z07W9PN4mIkIbBfmvVcX29sQRssvetObrlSqRAUN6b6Fjr47QjijFOEeGo48RYcGr0/SCOMTvLQ0jGO9iVz429BQRd203XbhgtV9G0gdg0xFjaAZb3G9/Yd2qanNN6vTbZWh0RtdcUW5RVlVCuWXC+VBUa7IDYNQ9lUTY9/RKReUvSmZ5QQ8auj0PA2TwFgwicNxU/RsEXB5vV+oKKCFmHwpOreSh7H7Uk+27Y0ueR1aolkotWUC1cSxN27ZwxzUSs+cLE/w7FwQdfajkq9u1xTqcE5wR9MOuMlDC/MKpmHfMKq8y4+HyP1bmvWoAgzbPDdIqBIGl6faIykwrHP5oAHiO14KfCPdX52tfNuYexwKk5mTTGMjoXEXLSiWywdOQTPr/M+TGfy81ds5SdxOvTFb8Qjpya3Cy8TmOw2NYr5d4+gkUTTiGPieQSLnnDetWqzQSPOOHBw/uMSXn26SMUK/d3sQPJ84C5kqkvW7CpYaqTqbprO+yILhq25ufBtwSFkIugvtObWspVaL8W61ty0S6mL041tuYUTk9Py5bSOPTeuamBLosL670xMSw6KRO4/h7gymQdRssriBe0wABQii5au+k+/vhjdn3PquumPodOLKlliS0TC2q9JRTfff89rq6uePbkKXnIkBXvpETUtviEJnL33j02my3X16Y3Y9ttnRbotm359NGjCc93mDJe7ZzedSs2m+0EReTFvJg4wtG27pX/OxQp4yGPUySlo9I01rQjeRNt8hIY1JhFQIEyLC+Tc6ZtIxQIJ8aAc9ZIoYvREpwC5BFfkufeWx3DoONUwHBVFBoAAB26SURBVBJdRIIleochlWIbS3pnMt4b9c85czQ4h1cpjnHu9t7Fhr5cR2+bFcAWo1yOwyGILzu3nCcM2S624LxphfgiLWE5J0te1msSQmCXMxnF+0BU4+qnlIi+oY2Z7aYv3IC5C473c8GdaXU7Gh+IpYBnyc8O0ZWq0Gz5IJmdcO1Qb3PckrLOWwIfLYlZkRK12z1n9+qrBXJMi/YUAb/yXs6JlIrWipjMc5LB5AiyFShBaUsntvD4WjiHm3o655wtUHolD7WMuCuOXx360pHPET6Tn6jzW7WI8KGojDew/B9kXwhHrgiro+OyRfM05WYVTYCxSvp+B1ibtb5X24blHu8Dzre882PfZBhGNtcXPProWwQU76M5W/Eg1vhVc4ZRcE2g9oROMhC8Z3VwAGlkc31dIk5hKDeX5NodxJrJ6pQOMqtQilVt+elxvcFMXxjW6zWbzYZ+swWsWW4IgVQjzUV5fdt0eGcY8Ob62hwcmF6zWnRQo8c8wRSWjLKbIDCKcu/ddwkh8OTRYz755BM8QheidZdPpS+2lIUTSkGIcHR4xIMHD/jd3/1dkyPICpJMJqBE2Tln3nrrAefX11xeXnF9eVmOMRSoxIq9Hj58SIiORx9/YsJNZGNFiJVgr1Yrzk5ODS6qTT1QcDY+fd/Tth0qtntbxQP6fmuso6KfkQe7HuvGFt8QArFtyIMxNpwoMTiD8HZwdHxgkdOQie1qWuj7TU/T2iLnXYdzgncNt28HNrudSbbmzDhk2gK15LYBdTSxI3ul67wtyK0nDeM8xs0swepRxiZYGX7T0hSGR/SBtkR7McZJX17HVHJCdpwJ092vVEIrmoEYjhjHTFKhadri6D2qIyknq5YuzkhCNNaUuEkg6vzigrYJdF3H4eEhm8G6BDmxXYgFOpZUbbtgu9uwIhRRKx+kOHlPDL5g/tYbN78SiS5xZ4OoLSpWZnXSXLDzOWE/R96TDynJXVUl5YFxyAy9MgwmQbErTarHfstY5l0eMlrkavOYIET6MU/R+SS/UQKbmRZovzkyd0Ja5sSqLbHx6iOW+TODExeLWSHUqerE1ntd+0I4chYRgmFxtuJrHk0qtpxU1VTIY4+OCXzAN4fcu/+Q5y+ekoYel3q82sSsOFyl0SmZlAZLdgxC9lUXwXNwvOLq6gLyOMEZ4h1adVfqVo4pUObV4gCm1bewUnROhjYx0LYtL1++LJ83+CIUCABuJjhijGQd2V2P03s3Ez1lKVGdbppxHK3fZkrTpFmvO/q+58mTJ1amr+ZYQ7Dk6XTs1AlkFLS2XXH/rQecvDixQqU0UpNGMcYp2nDOsdn0ZLVClRrZ1ygj68jh0QEHhys++vZ3pht3CVd16xXbzXZxLPWmqKwAw3qd84x5KFGNTNFNGm0cmq4ti8cw7X4QTz9uyNkwcwkBr0JsUlkYjeVkynqmjFcL0qx8H7Iv2+ohGxZcdgJNZUI4VyqLR2OCeEWT4EJTKt9nJlGFk7wXnApdW+AUZ+XhFukHklWQmZNBDO4SZ8dS8y9qnGiDk6yAzKAcPzmhyi030mrEkp2ZnDI5j3RFHXTorUGyFB73WPjqbdua2uZg7K8gjhCZuOAxWGl9Eyp9UEpQUDF6nc63+q/ZIVZ2CNO1r5+RArNktYBOU2mw6I25ZfWmN6PyZfS7hFJyGgpLyP7VJKQEIY8w9gPBhYKx1/Z1C165Ci7MRYhWrG2c9mXyc7qHym6zVsqqalF5rN+55LDP2L64efFY4uevY18IR66q9NsdeUwMcZg4wsFZ5ZMtTPNK5pxDWs/x3Qe0zYrLywuG65elI3fpq6l242rdVmoyapwo47hDG5DRJt+q7Th5/twGvLAacs5E30zHZ9llmzqZsl1nIidNkMfynNCEc7bFbELk4vpqcXG+v/awL/IA11cX00QJpWBpohSKlIayVvWXyoKXc54q/I7v3Obo1jEfffs7kBKoEqMnxMBua7ID41iLh2zsSEY3PDg+4unTp2yvN9YUm5nWVY85iemTX19cs1o3bK6u7QTczSz//bfe4smTJ6ZdXTPz3k03hIh1eFHDjG5GIq7wmEVuLHR1HOrQiQihsfPWxUKmGVv0YcqRZMAVZ5PGTGgaggMtySUJ5owAXBRzfTlbU4Zoapvee1yYFwEXHTLUallfPhPM8RRNHnOUOrGvauOUiUcdZi71Mlp1UuEEw8KlUNaMwlaT2zcjuLrFXxaoiRiTJVeRNgc62uKyyUNZAJQmRobUM6SRtltbwFQWTnFKE7xBny7cWPRcYZhpxXgLHj9dygLR3IAjnC10iIEKS2dez2Pmslf44WbThqXdYI6kAU3GBhpTzzD2DH1vO9ksMMokIRtCjysyHjXhWO9DEUeQGd5jcc8vzQoNZ3aYX1yPel1D2anWfFQdsyVObk4dvnQR+ZgSj58+n1ay4CE4k5jtum6CXNrG03Qd9x6+y3Z7zdXLF2wwzLbxxs+UUClHUFkiU5FOVqNypYGoLevjW1xfnnN+dgZY9V1yRrfyodCoisCPl3BjlRTRUqFVkk3Z9GCcMikWHh3dYjfuSmeVcRL10YJDT9tKyVNyUkTod7ty+KVaTYr2RTkezZkkMt2kpBlrw0Ncddy7d4/vfve7nJycINlEpFK5Ufq+t+RYxezzYMUheeTg9iEPH7zNyckJ28sLi5rqOU+Qkl2XuOo4OzsjBuHq4mqKXmJoysKZObp9h+9+5zumMW9gQGE/2PiFGDk/P58jGmGKdHBGk3SNJZc15alQI6WBPJr0sfMGIfS7gq9Peu4w6g48rA/WjGM2J+4c+LYkvo1H7ZzDx9W0S+n73gKBrAyapsU9tM0UMbnQEFtX5nDP6tD48E7BRZsvGbVq0CaSiypnCAEf/RT9TsUwutjZieJ9ea0wesbgjOuMGjxA4T2rae2gaqyPNOCi5UP64Zrg16X4xuZUqnrz2XGp5zRxTRtbdmkgjdDESGwaUzJMiTZGVodrm5KOQtu1Zg9TNXWYHZTKMglp19OJUSCDc+S6M3QwJMOKva+7wmSR98I/iHc4KbzsLIylvqQ2GDc4JZPV2GTjODKMAykNpKFn128Yrq4YemOfZQZyhiuZVRXH4YJctORDCHYfxdaStq0jJhtPY3WVgEbm/FOoyVI/S+1Ouy8swV5fs2YVtV/wzDyztFjl6acJBXgd+0I48pQzLy8v6IquSes9FK3wIfcMgDu6jRwccnh0j5NnTxmHHV1T+LE+4FurCs2qNKEtvFSoolMSAsqIauDg8DbHd+9w8vw5QYK1exIp/NJ4Y2WsZcSVymXBoW1Zlaq6yI2Lp2pqe+dXl9RSYRGjMdao2WCMOcqokc1ms8HXCMq5QltkohNW7L46k5wzuVZSpsSde7eJMfLpp59OmhrVovP05SaoVgXJKFHs22+/zemLMy4vLy0piZ+SUsbptt/suoaTl6e0MbDbbKcI0LbUMAyZ1WrFrVtHnJ+dFAdFwTot+spq1Mvd7qTQ7Gy7XOMcO9d5y2rOvi4seVr4XMkt1L9xIrOeiFr3eec8YAyktuum7XUoY1LPCygJyVoeboqWVTSqQljL7a+IdWGfICox2QXvLWKfoS5vQlA18n6lqW/d9cACT5UKI9y0Gqku4YU6PuXKArnsporSZa5RuT1GrEp6YKANjbFrKOJZvuqA2/Y/xqYc1wyrhGgURCk64tM1W+zIlsyqKaE33QNFMFoptEwAZ8dHnnjdJijoppoLnSfIPC/qbn2BWaeUGHMijdXRj4xjX+Sbjd5oBc4OspK9ievVZhZOHS5CGksie5pftQJ1bgJdI+22KjYuFmiR2ZHbfJ0dufmHugiW3YCzcxX3+u75C+HIQaatq5BwOuKyI7iG0B5y7949XPBcXp7zyfkpjbMikSQNoyqx6XBqiRrvZbrp+iExykjwgVVoaNb3uHP3PmcnZ7x4cQKqJEk0zqPem8CWGIyxxI0Nn571w+vrFkVY+4c6uVWVNCqb6521masl+uX7Mmol6KXfqOHnkd1ut+CtLrZbME1KOxYsi64ZmHG29cEBR0dHPHr0yIpEKqaelVAWiZRqKlPxnilpFnyg7VrefvttPn38tFSTCiqe4HzZDVjXGkQ4Ojri9PSE9cFqkh+uxxyDZ9TMnTv3bEH5+GODpMSS2nXrrHjWq47dbiidlGbsVLTEZMWh1O+et7NFCsHp4hoxOQnKeKVkzJE6frFtEIVhHKfH4zhyeHSLYdeXhdxmpBOjpe6uNzTeOtUEcQw1qewciRFfVDqd91YvEEorwBJRxdJxRkRIzuBCgyhmqGqaU27WFKmcbFVlZHZSiJDVJJ1VTbLBro8jlV6sjAM+Z3wQhqFnu93gfWC9OqY2XFYdLJdDZhh2pKKkCBY8OFGj6ZadceWDe680TVw4qpoYhKplBHnqcZuSVYbmokKNFiiqLIh1DPJouD04xM8Vn3UMXK2ydIYS5pytbZ1pMJvTT7Yg1Gufcs8wbun7vsgpmLR0UoPREjvgGhEHGkgus2use1LTNDRSrlf2RCc0sSF2kVhUGl00RUWDSeaIvAZ+80LvJ4ZbhXfr65AJUt3wXPldoabXtS+EIxdgFSNRBDBcWQn0wDd+4psMu56L06cEybhgug1e5kPPKZF6xblUumYrMgoxdERnq9/xrXusj445v7g0ji2Cby3KEK+EYOwH77VMSovmiuslG+9rPubikMz5RlaNsWu2261NuhKJuBo15DwJ4kNdCOyiVye9jGrmSKNg/gtS+bTIlO+5c+suofE8ffoUJU2iYCmloofhJk6spzqLPG3dYtvx8OHbnL28mBcUfHEmliSz5E6i7VaMqadtV4yDJfWkhtqURSZn1gcHJvIFoBXnHOc8R6GnbTYXJUqfxwmZsUZgSm5VyGUphyAijGPFyxdqdWJ4tvcFOhHBdGzqdcyID6RdonWO0ER2/TgttFmNAukbK3GfKG1lPlF2AiFYdaA4w7xFjJYaqLsmmPiDXudqRpmPdzkn6k6sMiNYUO1yoalKLjAKc5SbcyqVq7VAJiHOpImzZnIeyK2NrYqQK7TnCzsrJ+td6x2OeaHxfqYTijgqH3zpsIByver5zOflvQfNVppQju3GPCdZMY+TshgnCyKcJXcrTLrEo2tcpFlZcsfrddWCp1u+JEPOVlk8BUNqFELEwjCx8avNq6fkLFZkF4L1SGhCpGtW82eauYNQHYtJ6vdGsrIsQ1J580v9o5vXv/YgTc4Cmte1L4YjF8GhIDbwfWhYdWve++B9nj17TO4HVt4GNzhLIHkfplZkljMxDFwwnqcstt1Ne4BzjtMXT2hCRFxD9FasoWIJVbLxU169uWanOW/dbZtnEECtattsNtNOoBYQqCZS7d/5iq541SdOKbHb7aYS9VAaBUxb5lcgFRGrFK2vGTMj8+LEOt5I2V3XG0bV8FTTzLCOQ9XZJRz4wMOHDzk9PeX6ejsvKIWV6Jw5kJwT3rV4H7m+uKRrI1ebq0kIq5auj3nEecfLly9LxMV0nsuxjY1fsGbcfL6vWtZFU4M5ESpijp0SkWq54WphmXhzLnXIaxSYxhEXHM5HxlKIVJOZy2RqXRhML8RwZR88kgpDJrRWTesCFKpglUnwYoqbVaBqYqwsStKXi059Ps2PgoOLKlkWpeIFDpqcNzOEoGmY2DtIJmXjq6eUC9WztgisErEwjr1RRZ2bvi8WvFe9LWhLTnjd/tvfV0G5m8n6Wjh04xyzIzMv4jW345yzeZnzBGFOGiXcTHq/mlg07FkmZ501TXi5jUmRFcgmNTvXSEANzirLRzUzpLKAF5kQEW8SBsESum1rWvKxawklZ+EWeYGbO5ObBT6qs8OuznuGmvKUEbDipfo33KBq/iD7QjjyrImrYYdzgXu37/Jj773D1ctzzp89oY2e6Kz1Umhc2b631motVBjCsu0hBDTB2gfSqNx/6x1S9Dx/fsKLJx/hXSTHiAZnSnTdmloMpK0wisfhiXFWKKyR4TTZVfFOcS4S1x2bzYZxN4DkUk5c9TXKJC/YummRWPzRNLYT2JakZij4pPe+RJ0zP2ZCjJ1BDQZvGJ/83v0HnJyccPbyJZqTLWzm20olpZ+aKYsLkPuyrQNB6LoV9+/f55PHH8+6FqlUjapO45swJ9WuWi6vr+hWnTVTpjhKvRml3b57h8dPniGq1oxCLfrNeWb2NMEWvyCQbLUoyUFujHd9DDM9s46Ty/POZoaOimhUUaRMo+UrAIbRmEjDYMku71qa1ZoxzzTMGCPb6w3dqiuLp12nKvI0jNm0YZqIwwSgVJXdkAihMiCsAKY2kZiCjoUSZu0lubR6viafXCLOPFqCu+7Q8jjh/xllzExiUGkwxcHKeR7F4V3LONoua7PZ0HZCbNdoSdxVXZymaSbuctd1k3Rt0xj/O8YlpbFi3VLgxc8qNWc6z6lN3EQJnBdK7yLjOJYmEnZt+zGjORVMf4n7z3mA5QIIpvaYk1qNwABpFPpR6bONU0JQ58mM5TsVpw7VUgTmhOwKVBQiPrbEtqPr1vjY0K7WNNGE2JoCreDDZ573lMNZxGK1tN/g/AqzWOu5VOZfbeNh1+71nTi8hiMXkQ74f7DKnAD8JVX9j0Tk68BfAO4BvwH8KVXtRaQF/kfgnwVeAP+6qn77B/wKTXvM++++x93bxzx//F3IqWBztpWJXYs1dhBUGghYZOVMdzxIuXG9DUfTrdiNyssXj5FkfTw1eHIfcKNhnuOuREhFvxgf8OqnSN+hZC0ttsbRFAGz0QPbtuPy8swmt9SkBmR1yKI4x4qOjO6VUEuGlEbRU5l/YRPUyHBO7DFprWjK4Bw5WZb7zq27vHj+hIqjT1WQLlhhhTqcJgYdEARJNYo355zpefDgHs+fP7Ood8LMK0cbmKLBQmkcRloXcFmRZElFkxhVVDxI5Oj4Fpfnl7hSnapSS9RNa6VCBrt+tBtaHKrjzLcVY/Esj3Us47msgJsTYAZfOOfIOpb0g+BCZwU0kqbtuUhiSL3twtQSmVKSe2WDz5gMQsLZEjokQLUk/ebkJJiOTT3mEJkoahPumZn44gaZ2KZaRCA7VBaVtzX5ploSYx71Qk4WCJjjSQxqAm01yW3TxOFcRovKp7GTAkkcyIDXXLr67MjakHUkREcqyTSHx8rpQ9lU6JS88xJKkGjsFNVhcsSqDtXq4GsZe91dlWi6NIieEtOl7y2U3Z5YS7ecZlXC6IV+dEWCuUIxJVdVmh/jBNH62CPaT7uTrL1BTYyQhwJJyVSkN2Jqh+ZO7Fg1C8GZemgQE87DGS3SOTc1XbZo3GAmdWqw4RSBe1RGUAvGklZ2kO36p8cuk5M9TqKFjWVMpVTHdrETex17HaLiDvg5Vf1DwM8APy8iPwv858CfVdVvYu1qfqF8/heA0/L6ny2f+1zzzvOH/smf4vbhihfPnpbioGba0sUYiaHFxzjpRsAMLYRKNSv6GOv1Iffu3eHl+cmNCGLCD1UZcy582gLpqBoUkhLkEfI4RRC2JZ1pam3bzvrl+Em9DNyNcv1q9YJUPLVu/+s5LBOrSydeJ3GFJVJKrFYr7t69y+XlJSLeKHV5ibndtBppTzBMsfX6kGfPnhlLphQlLbeEy++q16BS8qriYoUDKs5ft8bL94EblMKKHy6hlqkA6zPm7XIym8O4yRSacEk3MyKcsxxIZV1k1HjqNeJ9Bd81iEJuRPyfeY0WUEM9tlcTXPXxDZN8oxzd/u57+7Eux32qF6gbsrLTmhLLtvoTxJX5NyfYgsyJtnq8N6mzlWEVbhzz8rPLf3V+vFqUtkz8T6f6fSLUV79zOg9ddKJfVEKGxRjeqOJcPLTPfvb9VrHwqlBqNE2DNZbntYSOlvcHN66JN22VSnVEbzRNXp7Lq35m+XzpTzSN31uoNAwMQ884DujQwzh8z7l9P/uBEbnaaF2Wp7H8U+DngH+jvP7ngf8Y+G+BP14eA/wl4L8REdHPWV7Wq47d5QlpyLRB2eYG33Y03uNL2fKQRkiuEPaN7pa8EII5jVy6ct+9+5BnTx9zcX6G6GArvnc0TQuFChSjlVP7opHhsI70IuBFZgH+oKABUFYra5Z7fW1aInZT1RvaMuaIbbuFWgxTuaZq2LdY55ZlYq7azerOeoPPzBZV5fDwkKurC1tE8Fb4ohVvr3S18p2L0XYS6q1Cu15x5/Y9Ts9esN1cE320pIqW4owaVWVlRBA11sLV1YZVu7L2Y95ErSxZakyHxgeOjg548eIUYa5GVW+JzuW2fLVasRl2E93QAntriGzYsJs64IjzVG8mOLzPZXsqN5JB45hYtSbwlX3CF53tGCNj0vn3S5u7NsxslirCZQqD4Fxn4kilUrVpAlVNUF3BwCXgSBPN0Eg9JRnrDL+137NUV31MWl57LTc/ZT9QGD05TYlx7+w8rTDOZH0zyZ7n6iQLvOFm9kP1fa5pICcURxKD/nypoPZuDoaapp2c2dTBx5WyfW8qjaZMalTXyjEvqZ3J4bG4L4zpNOXB7XhKmf7k7GTuBaopGzHF2T1bNU1SuZe0SNJqaVhs/89OWrPlr6Q0ZB9Gw777cS6MapqG7A0/D+LJUqU1HElstytlEen7nq2LZA9NaUDTN+Os8lghs5rIZUTJpd+pVc7mnCHLFJ2jyijZGtKMmcxIHouj137SIP+99vh8LYxc7Mr8BvBN4M8B/wg4U5NGA/gYeK88fg/4LoCqjiLyEoNfnr/ynb8I/GJ5evmf/sW/+eLVz+ztht1nPz4/yPZj9Pm2H5/Pty/i+Pz463zotRy5GtD7MyJyG/g/gZ/6fRxY/c5fBn65PheRv6Wqf/j3+71fVduPzw+2/Rh9vu3H5/Ptyzw+r1/MD6jqGfDXgT8C3BaZyNzvA5+Ux58AHwCU929hSc+97W1ve9vbH4D9QEcuIg9KJI6IrIA/CvwDzKH/a+Vj/zbwf5XHf7k8p7z/1z4PH9/b3va2t739/ux1oJV3gD8vVQsT/qKq/qqI/H3gL4jIfwb8HeBXyud/BfifRORD4AT4E695LL/8gz/yI2378fnBth+jz7f9+Hy+fWnHR/bB8t72tre9fbnt94SR721ve9vb3r549sYduYj8vIj8toh8KCJ/5k0fz5syEfnvReSpiPzW4rW7IvJXReR3yv93yusiIv91GbPfFJF/5s0d+Q/HROQDEfnrIvL3ReT/E5E/XV7fjxFWgS0ivy4if6+Mz39SXv+6iPyNMg7/q4g05fW2PP+wvP+1N3n8P0wTES8if0dEfrU8/9KP0Rt15AV3/3PAvwz8NPAnReSn3+QxvUH7H4Cff+W1PwP8mqr+JPBr5TnYeP1k+feLWCHWV91G4N9T1Z8Gfhb4d8pc2Y+R2R94BfZXyP40Rtio9uUfo1dL2H+Y/zAa419ZPP8l4Jfe5DG94fH4GvBbi+e/DbxTHr8D/HZ5/N8Bf/KzPvej8g9jSf3R/Rh95tisgb8N/PNYgUsor0/3G/BXgD9SHofyOXnTx/5DGJv3sQX/54BfxZQPvvRj9KahlakKtNiyQnRv8FBVH5fHnwIPy+Mf6XErW9x/Gvgb7MdosgIZ/F3gKfBX+T1UYAO1Avurbv8l8O8zq7bc4yswRm/ake/tNU0tLPiRpxiJyCHwvwP/rqqeL9/7UR8jVU2q+jNY1PnP8Y+hAvurZCLyrwBPVfU33vSx/OO2N+3IpyrQYssK0b3BExF5B6D8X1ru/GiOm4hEzIn/z6r6f5SX92P0ium+Avv72b8A/Ksi8m1MgvvngP+Kr8AYvWlH/jeBnyxZ4wYrHvrLb/iYvki2rJJ9tXr23yrMjJ8FXi7gha+kiUnB/QrwD1T1v1i8tR8j9hXYr2Oq+kuq+r6qfg3zNX9NVf9Nvgpj9KZBeuCPAf8Qw/P+gzd9PG9wHP4X4DEwYDjdL2B43K8BvwP838Dd8llhVqH8f4E//KaP/4cwPv8iBpv8JvB3y78/th+jaXz+KazC+jeB3wL+w/L6TwC/DnwI/G9AW17vyvMPy/s/8abP4Yc8Xv8S8KtflTHaV3bubW9729uX3N40tLK3ve1tb3v7fdreke9tb3vb25fc9o58b3vb296+5LZ35Hvb29729iW3vSPf2972trcvue0d+d72tre9fclt78j3tre97e1LbntHvre97W1vX3L7/wFvDk9wjfKmYgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af86c34a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set the pixel at row 50, column 61 to green\n",
+    "# [red, green, blue]\n",
+    "cat[50, 61] = [0, 255, 0]\n",
+    "plt.imshow(cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af87e52b0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af8706f28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set brighter pixels to red\n",
+    "cat = data.chelsea()\n",
+    "red_cat = cat.copy()\n",
+    "\n",
+    "reddish = cat[:, :, 0] > 160\n",
+    "red_cat[reddish] = [255, 0, 0]\n",
+    "plt.imshow(red_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af8674fd0>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af8704780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Set brighter pixels to green\n",
+    "cat = data.chelsea()\n",
+    "green_cat = cat.copy()\n",
+    "\n",
+    "greenish = cat[:, :, 0] > 160\n",
+    "green_cat[greenish] = [0, 255, 0]\n",
+    "plt.imshow(green_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af8632390>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af86e3a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Change RGB color to BGR for openCV\n",
+    "cat = data.chelsea()\n",
+    "BGR_cat = cat[:, :, ::-1]\n",
+    "plt.imshow(BGR_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af8668898>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af863da90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Invert (or complement) an image\n",
+    "from skimage import util\n",
+    "inverted_cat = util.invert(cat)\n",
+    "plt.imshow(inverted_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af8625a20>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af86a16d8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Convert to gray image\n",
+    "from skimage.color import rgb2gray, gray2rgb\n",
+    "gray_cat = rgb2gray(cat)\n",
+    "new_cat = gray2rgb(gray_cat)\n",
+    "plt.imshow(gray_cat)\n",
+    "plt.imshow(gray_cat, 'gray')\n",
+    "plt.imshow(new_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af85dfba8>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af86539b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Original image\n",
+    "plt.imshow(cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Converting image dtype\n",
+    "\n",
+    "* Function Name  Description\n",
+    "* img_as_float   Convert to 64-bit floating point.\n",
+    "* img_as_ubyte   Convert to 8-bit uint.\n",
+    "* img_as_uint    Convert to 16-bit uint.\n",
+    "* img_as_int     Convert to 16-bit int."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.5/dist-packages/skimage/util/dtype.py:122: UserWarning: Possible precision loss when converting from float64 to uint8\n",
+      "  .format(dtypeobj_in, dtypeobj_out))\n"
+     ]
+    }
+   ],
+   "source": [
+    "from skimage import img_as_float, img_as_ubyte\n",
+    "float_cat = img_as_float(cat)\n",
+    "uint_cat = img_as_ubyte(float_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af85a42e8>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af856e240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(uint_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Rescale intensity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 231\n",
+      "0 231\n"
+     ]
+    }
+   ],
+   "source": [
+    "from skimage import exposure\n",
+    "rescaled_cat = exposure.rescale_intensity(cat, in_range='uint8')\n",
+    "print(cat.min(), cat.max())\n",
+    "print(rescaled_cat.min(), rescaled_cat.max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af855f8d0>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af857cef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(rescaled_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 231\n",
+      "0 57\n"
+     ]
+    }
+   ],
+   "source": [
+    "rescaled_cat = exposure.rescale_intensity(cat, in_range='uint10')\n",
+    "print(cat.min(), cat.max())\n",
+    "print(rescaled_cat.min(), rescaled_cat.max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af851b0f0>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af8549908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(rescaled_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Display image histograms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af84f1048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img = data.camera()\n",
+    "plt.hist(img.ravel(), bins=256, histtype='step', color='black');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Image Segmentation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af84df748>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af84b9eb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Use colony image for segmentation\n",
+    "colony = io.imread('yeast_colony_array.jpg')\n",
+    "plt.imshow(colony)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GDockPwj8F+CXquqV0WXtF3+tctuWa8PuJPNJ5o8ePbrWf06SNq2xwiHJ97EYDL9TVb/fil9oXUK05xdb+RHg0pHVt7ayk5VvXaa8o6ruqqp+VfW3bNkyTtMlSadhnNFKAe4GnqqqXxtZtBcYjjjaBTwwUn5jG7W0HXi5dT/tA65Ocn47EH01sK8teyXJ9va3bhx5LU0xh7NK02ucq7L+BPBPgceTPNrK/g3wceC+JDcBA+ADbdmDwPuBBeBV4EMAVXUsye3AI63ebVV1rE1/GPgs8EbgofbQKun1egwGg3U/Ac6rs0rTK4uHC6ZPv9+v+fn5STdjKiRhUu/zJP+2pOMlOVhV/XHqeoa0JKnDcJAkdRgOkqQOw0GS1GE4SJI6DAdJUofhoDU1NzdHEk+Gk6bMOCfBSadteHMhT4aTpot7DpKkDsNBktRhOEiSOgwHSVKH4aB14eW7peniaKUZ1+v11v1S3cvx8t3SdDEcZtxgMPCS2ZJOmd1KkqQOw0GS1GE4SJI6DAetG0csSdPDA9JaN45YkqaHew4zbKMMY5U0fVYMhySfSfJikj8dKbsgyf4kT7fn81t5ktyRZCHJY0muGFlnV6v/dJJdI+VXJnm8rXNH/Gm5agaDwetXRZWkUzHOnsNngR1Lym4BHq6qbcDDbR7gWmBbe+wG7oTFMAFuBd4NXAXcOgyUVufmkfWW/i1J0jpbMRyq6o+AY0uKdwJ72vQe4PqR8ntq0QHgvCQXA9cA+6vqWFW9BOwHdrRl51bVgVo8U+uekdeSJE3I6R5zuKiqnmvTzwMXtelLgGdH6h1uZScrP7xMuWaUI5ak6XDGo5WqqpKsy/UZkuxmsbuKt73tbevxJ7XKHLEkTYfT3XN4oXUJ0Z5fbOVHgEtH6m1tZScr37pM+bKq6q6q6ldVf8uWLafZdEnSSk43HPYCwxFHu4AHRspvbKOWtgMvt+6nfcDVSc5vB6KvBva1Za8k2d5GKd048lqaUXYtSRvfit1KST4H/CRwYZLDLI46+jhwX5KbgAHwgVb9QeD9wALwKvAhgKo6luR24JFW77aqGh7k/jCLI6LeCDzUHpphdi1JG1+m9XLO/X6/5ufnJ92MDS3Jhr1c90ZumzSrkhysqv44dT1DekZ5drSkM+G1lWaUN/mRdCbcc9BEeFBa2tjcc9BEeFBa2tjcc9DEzM3NkcQ9CGkDcs9hBk3LwejhFWPdg5A2HsNhBnkwWtOo1+sxGAyO+2HjJecnx24lTZwHpze3Xq/3+t7j0h81o92OvV7vuIfWluGgiRv+OvQ//OayNBSGn4NDhw69/hiGxbDeYDB4/aG1ZbeSNgRHL8220eAffrHPzc2N1f052rU0fJ3BYHBcN5TdT6vPcJghy/XZSpO2dI/wTI6HjQ5iGB5b80fF2jAcZsi0H4geHnvwV+DsGAbDar+nS38ArdXf2cw85qANw2MP0214DGH0AWvzhT08JjHkcYjV557DjJiWcxtW4rGH6TLalTnte646nnsOM2IwGMzMLvXwzGnPnt4YltsjGN0zGAbCpH6czM3Nvf63Hea6etxz0IazdHRKEkekrKHlRhKNGmdU0STfm6UHqbU6DIcZMCtdSssZ/Y/v0MXxLP2yX+6zcejQoWVHt01zt9BwOxzmujoMhxmwGfp6h//xq+q4k6dGl630RTicniXDL8FRS7/sl+tmGe6NzdLnxmGuq8twmHKzvNcwavRLfekX/PDLb6UvwuEvyqFxwmTU6Mlbo+svN8pqvbo3NnqXzyQ4zHV1GA5TbjPsNazkZMExarmTsU4WJksN/52XhtHw1+msdM9Mu9HPwGiQ69RkWj/E/X6/5ufnJ92MifIXkTSe0cDfzP9fkhysqv44dTfMUNYkO5J8PclCklsm3Z6NzmCQxrd0D8IhryvbEOGQ5CzgU8C1wOXAB5NcPtlWbTyj483BYJDGNTyjengOzZDn0pzYhggH4CpgoaqeqarvAvcCOyfcpg1jtF+7qo67vLGk8Q0vA77cJcFHHwbGxgmHS4BnR+YPt7KZd7KzT5eehWogSKtvGBKjD+gGxskesxgmUzVaKcluYHeb/cskXz9B1QuBv1ifVq29wWCw0njtmdreMWy27YXNt81Ttb1j/B9dyXpt79jj3jdKOBwBLh2Z39rKjlNVdwF3rfRiSebHPSI/C9ze2bfZttntnbyN0q30CLAtyWVJzgFuAPZOuE2StGltiD2HqnotyUeBfcBZwGeq6okJN0uSNq0NEQ4AVfUg8OAqvdyKXU8zxu2dfZttm93eCZvaM6QlSWtnoxxzkCRtIFMdDitdciPJv0zyZJLHkjycZKovXzrG9v7zJI8neTTJ/5j2s8zHvaRKkp9JUkk21GiPUzXG+/tzSY629/fRJP9sEu1cLeO8v0k+0P4PP5Hkd9e7jattjPf4kyPv758n+fYk2gnQOfljWh4sHrj+BvA3gXOAPwEuX1Lnp4C/0ab/BfD5Sbd7jbf33JHp64AvTrrda7m9rd6bgT8CDgD9Sbd7jd/fnwP+86Tbuo7buw34GnB+m/+hSbd7rbd5Sf2fZ3FwzkTaO817DitecqOqvlxVr7bZAyyePzGtxtneV0Zm3wRM8wGlcS+pcjvwCeD/rmfj1sBmu4TMONt7M/CpqnoJoKpeXOc2rrZTfY8/CHxuXVq2jGkOh1O95MZNwENr2qK1Ndb2JvlIkm8A/wH4hXVq21pYcXuTXAFcWlX/bT0btkbG/Tz/TOsmvT/JpcssnxbjbO+PAD+S5H8mOZBkx7q1bm2M/Z3VusAvA760Du1a1jSHw9iS/BOgD/zHSbdlrVXVp6rqbwG/Avy7SbdnrSR5A/BrwC9Pui3r6L8Cvar6O8B+YM+E27PWzmaxa+knWfwV/ZtJzptoi9bPDcD9VfW9STVgmsNhrEtuJPlp4N8C11XVd9apbWthrO0dcS9w/Zq2aG2ttL1vBv428IdJDgHbgb1TfFB6xfe3qr418hn+NHDlOrVtLYzzeT4M7K2qv6qqbwJ/zmJYTKtT+T98AxPsUgKm+oD02cAzLO56DQ/uvGNJnXexeABo26Tbu07bu21k+h8C85Nu91pu75L6f8h0H5Ae5/29eGT6HwEHJt3uNd7eHcCeNn0hi10yb51029dym1u9twOHaOehTeqxYc6QPlV1gktuJLmNxS/FvSx2I/0g8Hvtion/q6qum1ijz8CY2/vRtqf0V8BLwK7JtfjMjLm9M2PM7f2FJNcBrwHHWBy9NJXG3N59wNVJngS+B/yrqvrW5Fp9Zk7hM30DcG+1pJgUz5CWJHVM8zEHSdIaMRwkSR2GgySpw3CQJHUYDpKkDsNBktRhOEiSOgwHSVLH/wdJc1iK+ldK9gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af858a668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot histogram\n",
+    "img = rgb2gray(colony)\n",
+    "plt.hist(img.ravel(), bins=256, histtype='step', color='black');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af8450be0>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af848c748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Use thresholding\n",
+    "plt.imshow(img > 0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Canny edge detector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.5/dist-packages/scipy/ndimage/measurements.py:431: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
+      "  safe = ((np.issubdtype(dt, int) and dt.itemsize <= int_size) or\n"
+     ]
+    }
+   ],
+   "source": [
+    "from skimage.feature import canny\n",
+    "from scipy import ndimage as ndi\n",
+    "img_edges = canny(img)\n",
+    "img_filled = ndi.binary_fill_holes(img_edges)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af8101630>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af81bbeb8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "plt.figure(figsize=(24, 18))\n",
+    "plt.subplot(121)\n",
+    "plt.imshow(img_edges, 'gray')\n",
+    "plt.subplot(122)\n",
+    "plt.imshow(img_filled, 'gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.5/dist-packages/scipy/ndimage/measurements.py:431: FutureWarning: Conversion of the second argument of issubdtype from `int` to `np.signedinteger` is deprecated. In future, it will be treated as `np.int64 == np.dtype(int).type`.\n",
+      "  safe = ((np.issubdtype(dt, int) and dt.itemsize <= int_size) or\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Canny edge detector on cat\n",
+    "img_edges_cat = canny(cat[:,:,0]/255.)\n",
+    "img_filled_cat = ndi.binary_fill_holes(img_edges_cat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af80f85f8>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af842b588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "plt.figure(figsize=(24, 18))\n",
+    "plt.subplot(121)\n",
+    "plt.imshow(img_edges_cat, 'gray')\n",
+    "plt.subplot(122)\n",
+    "plt.imshow(img_filled_cat, 'gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Watershed algorithm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af80644a8>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af81bbd30>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from skimage.filters import sobel\n",
+    "from skimage.morphology import watershed\n",
+    "# Elevation map\n",
+    "elevation_map = sobel(img)\n",
+    "plt.imshow(elevation_map, 'gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af0cac320>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af81bbc88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Separate pixels into 3 classes\n",
+    "markers = np.zeros_like(img)\n",
+    "markers[img < 0.35] = 1\n",
+    "markers[img > 0.55] = 2\n",
+    "plt.imshow(markers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af0c66d30>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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EdB4RfYGIPuXfX0ZE9xPRHhF9nIjO99cv8O/3vP3S0rpzWWtARpgII8J9gpE86WhkIkzL5WjQzIKIaeDEivloxwuVzekPjr6l6zHbvEwLfdJvbRpP6O8E8Ojk/XsAvNc590MAngNws79+M4Dn/PX3+nJZxDpw2wlaA5zy0Y4XapMkXmhCxPpI4jO1a2pYs025Plxbjs/WnoNEA0dfjk+teFoaYvFSdUl8tPVxKFrQiegogH8K4IP+PQF4HYA7fZE7ALzZvz7u38PbryWBekmDe5gwNSZZ6eCXsnb9hmGcS+kT+m8B+CUAf+3fvwLAd51zL/j3pwEc8a+PAHgSALz9eV/eMAzDUEC8oBPRGwE845x7UFEPiOgkET1ARA9oxjWMUnpJecs9MNU8ZC0hlZxQ26f3eLEEDi4laYuvAfAmIroBwIUA/haA9wE4SEQH/FP4UQBnfPkzAI4BOE1EBwBcBOA786DOuVMATgEAEbmZ7cXXS1/3Q+lDsTSg3LStNXxybBIN2vpa+XBtEg0hWyzdTNKm0Jxdss0zgUL6cuMt+aTutVw4Hyix9sbi5vZRr/FiflzET+jOuVudc0edc5cCuBHAZ51zbwdwH4C3+GInANzlX9/t38PbP+sKPoq4qXe9pddpPBGVxsjpo1oaasfW7vPe4N7su9j2GNN+0T7j6T0eUCcP/ZcBvIuI9rDZI7/dX78dwCv89XcBuKVC3V3S2+Gh5BA2Vk56UN1bPK4P93C7tJ8l8XL6IeSzvaZ58C7VF9LAsUl8tDVI40n7nXr+BJ9vuQDpbYPeFk/DMAwFHnTOXZ0qNNx//dd4ujQMw9hFhvqv/4ZhGEaY4Z7QR6bVlpD09DyWJSTxAfIybWKMkMGUEy9ly2WNzKLR+ry0j2I27n0jbROX4Z7QUzmcMZ+l7I5UvJy6Uj7Tn1oauNk+HA0hm9QnRm4/pOJqZ+7k1CWJl4ovmecpDRpw2yTpS40Mq16ymyRzlmuLMdSCLl0oamlYK8VPEq+Hw++YhprfXHo4W9HI3OkBSSaQJMtIWo6T1ZMTsyRLKKWhxnngUFsuRPG/0bj0FSfmU6ohZwLm6o7ZYhpCdXF9JG3S0MCpS9JH2v2aGy/WJk1b7vWWGrR9Wtt6GCsuQy3oQN+TvfdJ1kObdrGPUjbDaMVQWy6GYRhGGFvQDcPolt7Pi1KJGDk+Ggy35aJNaF80ZpP6APnpa5rxSnzWjMfxqR1vFzTE4nHrkvhwbaHrS/dASl+OD0dDrr5pGQ19XIZ6Quekc+XYYqlFIdv0ek68eZnQ+xwNOfEk6WYxWsaTUDMbiRu7lQZttPufW66HbLA5Nc9GaozhUAv6lB4mvpGmh8PCmhq0D0pzsow0mcbTaBP30DlW7/Z9jh5pPCLZn9WT6Jv+y42XYqgtF6J66XqxOrnxQj7ceCHtsUmb6yNJ1+O0o9TWQ7z9loHTe7zWdUmQ6JPE4zLUgg70ffO0nNAxtLUbhjEGw265GIZhGOdiC7qxL8k5uB6hnli8VApdLJFAqy6JhlrxQtTQl6M95sNluAW91QBrD0jpQHHrSk0KrYk5va7VLmmbJPVMf87rWbKV1hPKBJKMU6qulJ6l99z6Yn3E6bMcDbXiheZ5DX0xvTU+7Ida0KUTMGXL6TzugHD8pUhjlNyItelBQy+k+mKX+mqtbJ3e40nrGu5QNBdOJst+IZYBk9tH2v1amo1UW4N2vFR2UyxmrK5Qel1q3EM2DZ+SNuX6jB4v1H9chlrQJZN2awvFi9Wl5ZOy5VJST267ekgB065rF9skravlPWCkKe2/obZcAJtMhmEYIYZb0A3DMIxlhtpyGQHJHljMJ2SL7fW2ipfyWbJN941DNkk8bR+Jn1afSzRwfOY2ic/WFmtrjm6OT46tpE0pm3a8HO1chnpCj6WvcWza8XLToiQ+S7Y1UrOk6Vzcw0xJeliphpxsH45tKZ5mZlFphpUmsbmc8gu9126TRrweNcUYakGfUnpDtKx3rdg9LQA16P3MRDvlTZtSTaOlBUril57ZxTRo6Jsz7JaLxuCUpuu1zFLgagjZUmlyNdLXYm3STIeL6ZP4hDSE6D27ZFR9vbe3ZTwuQy3ovQ9wLwPf86Ttvb09PkkbBpdht1wMwzCMc7EF3TCMbpGe/WiesYWSI0p8chMxuAy15QL0ka6X0qdZT400sFx9MV1bNFPHekw3K/XRjpczjzj9UFtfSbyldnHizf04umM+IVvOuhOLF/PjMtQTujS9TpI6FvKZxszRwNHN1ceJx/Xn+kjhxuNq0I5XGlsbqYbYfM2NnVNvq3I12aVzk6EWdAkjDVZOlkuuj7YG7Xg1y+2Khlg8ouW/U5myleiLZQJxD5058bQOyyXxtPt8qkHSfymKFnQiOkhEdxLRV4joUSJ6NRFdTET3ENHj/uchX5aI6P1EtEdEDxHRVYL6Fl9P34c6N+QjuQligxW7rqV7HjPkk6svZxLVWFwk+ji2XdEQiyehhgZNn61N4tMqnoRYrNK6Sp/Q3wfgD51z/xDAjwB4FMAtAO51zl0O4F7/HgDeAOBy/+8kgNskFUoGK2VrRSvdrdqqPdENwyhDvKAT0UUAfgLA7QDgnPtL59x3ARwHcIcvdgeAN/vXxwF82G34HICDRHRYrNwwDMM4h5In9MsAfAvA7xDRF4jog0T0cgCXOOee8mWeBnCJf30EwJMT/9P+mtE5PRxclaZz5daVq6HVYXJJOpzERztejg/HphUv1d5YrBZ9zqVkQT8A4CoAtznnrgTwFzi7vbIV6ABkqSOik0T0ABE9sGTvaUKHMmpq+9TUkJM90bJNIWpoWKpz+l7SRzHtmhpiSHyk8biLsLRNtfqIWw/HJ1dfqFwOJQv6aQCnnXP3+/d3YrPAf3O7leJ/PuPtZwAcm/gf9dfOwTl3yjl3tXPu6gXb4uul97k+3A4smRS9luNSs7+0tbaKvSus1Uexw/UeqHmGVaO94gXdOfc0gCeJ6If9pWsBPALgbgAn/LUTAO7yr+8G8A7acA2A593ZrZlqcA8cJYPAPeRMZYSUaNAu1+qAVtpH3HpKFwpJPM2MkFT8kL5YPEmf52YCTe0cn1B5rk+sHomGlvpiPtLFnko+mYnoRwF8EMD5AL4K4CZsPiQ+AeDvAXgCwFudc8/SRuF/BnA9gO8BuMk5t7itMon/EnFbvT1+mhuGYVTiwaVdizlFC3ptlhZ0wzCMfQhrQd/5/ylqGIZRC8nBpuSgnMtwv5zLOItz+b8IrGdiuiVtDdmmN0zIpl1XbX0cn9rxtDWU6qs9htMMGK5tnljAtXEZ7gm9JE0tFktj60maKieta/pzfr0VkpS80PiFYnJSxzTSw6QZUaUZVlwkWUba8WqW057LOfEkY1gT6cPYUAu69gCPSg+pgKXxJZksNaiZZVQTbQ0abS/tS43sppiNU1Y7WyqmqcY8GmrLhYj3dyxj/tx4JfpCKUnSr1FLsYDlr2XabeLoyLGlfEJtkvrE+lx7MSiJvwvxWtbVg/aWbeIy1IIOtB18CT3U1cMTpBSJ9tw+bzlGhtGSobZcDMMwjDC2oBtGRVIpatp15WqQ+EnT7jR9SuJp6ZMmQdRMnBhuQdce/FRdEg1aN4FkUpTGW8oMaDVpe9KtfSOGMi60FnVOvFAfccouXY9lkXDbK/EpjbcEJx4X7hhor1fAYAu69uBvr+X6pDTEyJmAHGpPwFA5jUlbGpuLxqJZ2qbeGUmrEWaoBd1Yl5opfmuV4/prp+RpU3qYzM3q4fqEbBo+22vbf1rxNH048XJsXIb7XS5bvUsNdk4nLbA1o+o2DKMZrN/lsi/SFntnVN2GYfSFbbkYhmF0QumOyXBP6CMj2VqJ+YRsUh8gvJW1ZCvx0YoXo5WGUePNF4+QTUOfJJ62hlJ92m0K2Uq2YId7Qk+lWmmlqWmnr2ln2qTSrEJtivnk2kp8YuTqi7W1hQZOjB7jcWmV3aRdjpsBpt0mDaR1DbWgSwaLG09Dg4S1JoyE2JNGKWu1vfc+n9Iqw6elhh6ym1qeYdUew6G2XIjq/eKpnI4uWQSWUpU02ySJJ2lTzKdUg8bBd2m8nHEK9QXXJ2QLpcot2TjjoakhVE8IabxcnxJ9Wj416uIy1IIO9D1YuxjPNOx2XRofnlx6f3reBYbacjEMwzDC2IJuGEa39H7GkZugwYlX0ubhtlxSKUm5X9EkKU6xulr5pOLF6lmqSzM1axfixWLN7RINpT5LfrmU9l1Mn2a8pfkciydFOifm+uaJDtwx1PjwGuoJnZPlstQpodS2UOxQnFRdEh+OhlCbYvFyJockLU0jHhftdDhODG3dvbMrWUtr0Uv/DbWga9PDpOtBAxc7oMpjpP7SOLCcvteO1wKi5V+KFbue8zplC5XLYagtF24aWMy/VbyQds3UMa00sBINoXpCcCaqhr7t+5gtR18qXouUvJZZJz1k4EjjtaRVxhGXoRZ0oP/J1EO8GLUWYQ1aLjBSNG/GXhYlY3fY11suhmEYu4Qt6EZzej830E5Fa0VIX0y3xFYST0tDSbwQLfpomqCR27cchlvQSzpX2vEa8VK2XCT61vDJydyJobmQpnQv1RfLtIn1kaa+lPaQvtD1VJs4OnPjaWkoiRfqV019OWU4dXEZakHndi63M0p9tGyaSOrRaLuE2ILaov4aMVuN81pot6/lQW8P8Woz1ILOpeYg9HB4yKU3PXOWskVaZzVI0shqzwFO/Jx0OE6favuEbDl9J+mHGvE4fb7kszSfp9dy+o/LvvmbojFbCO14hmEYQlh/U7ToCZ2IfoGIvkxEDxPRR4noQiK6jIjuJ6I9Ivo4EZ3vy17g3+95+6XCOpul3o30NG4YhiFe0InoCIB/C+Bq59w/AnAegBsBvAfAe51zPwTgOQA3e5ebATznr7/XlzMMwzCUKN1DPwDg+4noAICXAXgKwOsA3OntdwB4s3993L+Ht19L9pj7IpqZMamDRW4WRAmSeqQZQpoZR7UyVrQ0tIonybSJ2dZok0Z7p9clbZJqaJ7l4pw7A+A3AHwDm4X8eQAPAviuc+4FX+w0gCP+9REAT3rfF3z5VwjqVRt87cEq8dm+ntuWXqfizeOGYnA1SCZmTHdMS8wnZZP4xMqVItHQSzyOT45NokF7bLTHV1JPzfkGlG25HMLmqfsyAD8I4OUAri8VREQniegBInogVq724JeirWGtLzOcenvobw16/8LYg76W51SlsddizbO3kt/l8pMAvuac+xYAENEnAbwGwEEiOuCfwo8COOPLnwFwDMBpv0VzEYDvzIM6504BOOVjslcKivwCJ66/JjkTNaR7auPWI+mHlE/uIbT2WHDi5fiU6uOS0gAsZ0tt/XJT8kI+qXHK8SmpKxZPy6d3DdK6uJTsoX8DwDVE9DK/F34tgEcA3AfgLb7MCQB3+dd3+/fw9s86waNdKL9zauP6cGK1jLdEDZ/ceBJ67gepPgmpWJIPS80FQdrOkZ6Y9xNFeehE9GsA/hmAFwB8AcDPYbNX/jEAF/tr/9w59/+I6EIAvwfgSgDPArjROffVRPzd+C5vGIZRBisPfbj/WGQYxpiktpF6jRfbnpP4CHWwFvThfh96qnNjNomPdjwtDdMP4pCtVTxJrBxbD/HmDz7ceBLWaJP2GIau58wXTj21482TLZZssQ+WmL6YbilD/S4XaYpTrVQ5bgqdhgYu3D7i1qPdplI00usk5bgsxdOqo2abtNMC1+r/kahx3wy1oGvD7bSaB0D7eULXJHUQubVrj23ooLVWbK1yGhol8WI+ksPkWJlSH61D51hSRUldwIB76LW2GrS2T2Jo+0j3EGNbP1qLj/b+Zi91GcZK7OYeuvRTW5LqVfqEUNtHO+VMc1FsucDaYm4YG/b1lothGMYuYQu6MTytDmWdW//vb6ZsuT7a+qRtCtFK3/Z6jk3ik6J0vg635SJJSdrac76a10rbyrGV+mjEm9p6blMsPSyWiibREIvHPZ/gxNNuk0RDjr7SNuWk8XEWcsk4cetZKsddX1LtjZ3dpRjqCV2aCjXvqFpI0rYksbVp9YQr1dAyhvFS1jqjqHlOtVY5bgxpvKEWdCm1UtQ4dbaO1/LgtdUhKneia/RR6YFkXAA9AAAL/0lEQVRxq5RBjRS/mhqWbEupelNbqI6c1D+OvlC8XBunTbntDWngMmzaonYqn2EYRsfsv7RFjt0wDGNX2RdbLoZhGL0gyX7hMtwTunGWWEYFUP9/zEoyY0bRkBNPO3uIU5d2m+a20jZpx6vd52tkbIUyfkp2GewJPYA0x7TEZyk7J+Yz/RlqQ+x9ro8ky0gST0ODdjyuj3YfcevR6Jeey2lnQfWchVYSyxZ0RXo+YG5Jy1QvCZLsjt7bVJMeUgFrZ+Fox4tlxqTqKJlDtuUSgKjs73mG4uWmWZV8SEjixXwkKX05kzPURzENsX5N2XI0cPTl+gDLX7FT/dnKJzdezK+krhHjSesqxRb0CD1PplE1cJ5QtOri1KcVrxd9mj4t4xk62JaLYRjGjmALumHsc7iH01ybpoYa8WLtjSUgaMRKUZrSONyWS09pYDk+u6iBE29uK0310o7XgwbteBKf+Z74NIsqFC/kk6orZIvF47aJGy+lfSkWV/fcX2M8uAz1hM79pGyVrpfj00oDN56GhtKnqh6e8nrQ0DtrtYmz4GrE3iWGWtDXgjv403I9HEL1rqF2PCK9v92oXS7ms32f29ZcTVwfrn/Mh2OL1cMd21g8TntzdedqiPlprB9DbbkQpdMCt6+5ftMySz6hAYl9Jcr1CdlSPrG2LLVXEk+6IEvjaabXxdBsr7aPdl3a8bQ1SOuK0XKscuNp+0wZakEH+pjQvcSLoR2vFb3rM4yesS0XwzCMHcEW9H2GdpqVtK5cH2k9UptWvB401Iqn1d7t9d7j1dYw9ZHei8Mt6KEGxzqD07nSjtf0qR1v+z7nxi/RHYq3dD3lw2lTiJBPjj6OT0yHhgbteNI2cdoo6aNYvBwbx6dmmzTaJ2W4Bb0UbseXDpDGYJVOWm2ftcoZ69JDtpTGXKl5PlMzwy2H4Q5FS5l3fGiixLJmYrFzfULaUvpKNWj0Qyo7J+aT0ieJF/MvjSdtU+xwWtKmkj7Xyh6qEU/LR7uuHjTkMNyC3moyxWw9TM4eNGjX1YOGGvE0NcRs2vpiWDZSnwy55WKTyTAM46UMuaAbhmHE0D6fiR2aatZVGi+5oBPRh4joGSJ6eHLtYiK6h4ge9z8P+etERO8noj0ieoiIrpr4nPDlHyeiE1LBqQyNXFtJvJhGTQ259cSQZqxo+fQQj1PPyPG0NWjoa92m6c8SfdNrS7al19x6cuJx4Tyh/y6A62fXbgFwr3PucgD3+vcA8AYAl/t/JwHcBmw+AAD8KoAfB/BjAH51+yEgJbdzQ7aSeFqDz6mHq2FaXmPCcH1C5bgaJLFTfhxK/aXxJPVqa9WmZtu1NewqyQXdOffHAJ6dXT4O4A7/+g4Ab55c/7Db8DkAB4noMIDXA7jHOfesc+45APfgpR8STZjuv681+DU1cOO1SuHilqtxYJurhxtnrTOc3uvVGM9STWsd/mqXkyLNcrnEOfeUf/00gEv86yMAnpyUO+2vha6/BCI6ic3TfRbc1Lv5dWCzCK6VXleSDperO+RTI960TOx9jj5uHdLrKVvIXqIt1OcSffsp40i7rh7apLHYF6ctOuccEak9ZjrnTgE4BQBLcXuYGD1MwN7jSSfnfstg2m/tNeoizXL5pt9Kgf/5jL9+BsCxSbmj/lroumEYhqGEdEG/G8A2U+UEgLsm19/hs12uAfC835r5DIDriOiQPwy9zl8zjHOQHhZqHjJyD2i5GkY9qJP0q6QfUj6t+jWlQSMLJ5XlIp3LW5JbLkT0UQCvBfBKIjqNTbbKrwP4BBHdDOAJAG/1xT8N4AYAewC+B+AmL/RZIvoPAD7vy/1759z8oJVNaJ81tNc77SCJLedrccynRF8rnxzbfOIt+Un6bsmPo2Hux+mHkE9MQ8hHGi/WJs2xyPHhfqhJ+mFJX8wnFU9j24oztiGfuZ/GB420XdTz08PSHnrpZF/rpuLaai7OpYtBjl/uB1tNDRo+LeNNbWtp4PpM7drzX9omKaUfeprjvmB70Dl3daIJ++N/ioo+6Sr6tDoIm9ezfd+ifqLlv7UYsqU0bX1iftz2TmPl6IjFy9WQqjPUfxINMTh9xPFP1SvRl4rH0ZdDbD5I5l5sDHPHl8uQv5xL00bU5u9spnxaaAjZcm7aHL9cpPGkfaEZT9snRm486fhKyrfqu5a01lcSc188oafofdIYhmFwsAXdMAxjR7AF3TAMY0ewBd0wDGNH6P1Q9P8CeGxtEQleCeDba4tgMIJO06iDadShJ41/n1Oo9wX9MU7u5ZoQ0QO9awTG0GkadTCNOoygcY5tuRiGYewItqAbhmHsCL0v6KfWFsBgBI3AGDpNow6mUYcRNJ5D17/LxTAMw+DT+xO6YRiGwcQWdMMwjB2h2wWdiK4noseIaI+IbllRxzEiuo+IHiGiLxPRO/31i4noHiJ63P885K8TEb3f636IiK5qqPU8IvoCEX3Kv7+MiO73Wj5OROf76xf493vefmkjfQeJ6E4i+goRPUpEr+6tH4noF/w4P0xEHyWiC9fuRyL6EBE9Q0QPT65l9xsRnfDlHyeiE0t1KWv8T36sHyKiPyCigxPbrV7jY0T0+sn1avf9ksaJ7ReJyBHRK/37VfqxmPlf0OjhH4DzAPwZgFcBOB/AFwFcsZKWwwCu8q//JoA/BXAFgP8I4BZ//RYA7/GvbwDwPwAQgGsA3N9Q67sA/FcAn/LvPwHgRv/6AwD+pX/9rwB8wL++EcDHG+m7A8DP+dfnAzjYUz9i84fLvwbg+yf997Nr9yOAnwBwFYCHJ9ey+g3AxQC+6n8e8q8PVdZ4HYAD/vV7Jhqv8Pf0BQAu8/f6ebXv+yWN/voxbP6C2hMAXrlmPxa3cW0BgY5/NYDPTN7fCuDWtXV5LXcB+Cls/gfrYX/tMDb/CQoAfhvA2yblXyxXWddRAPcCeB2AT/mJ+O3JDfVin/rJ+2r/+oAvR5X1XeQXS5pd76YfsVnQn/Q36wHfj6/voR8BXDpbLLP6DcDbAPz25Po55WponNl+GsBH/Otz7udtP7a475c0ArgTwI8A+DrOLuir9WPJv163XLY31pbT/tqq+K/UVwK4H8AlbvP3UgHgaQCX+Ndraf8tAL8E4K/9+1cA+K5z7oUFHS9q9PbnffmaXAbgWwB+x28LfZCIXo6O+tE5dwbAbwD4BoCnsOmXB9FXP27J7be176l/gc0TLyJammskouMAzjjnvjgzdaMxh14X9O4goh8A8PsAft459+dTm9t8VK+W/0lEbwTwjHPuwbU0MDiAzdfd25xzVwL4C2y2Cl6kg348BOA4Nh8+Pwjg5QCuX0sPl7X7LQURvRvACwA+sraWKUT0MgC/AuDfra1Fi14X9DPY7GttOeqvrQIRfR82i/lHnHOf9Je/SUSHvf0wgGf89TW0vwbAm4jo6wA+hs22y/sAHCSi7e/rmep4UaO3XwTgO5U1ngZw2jl3v39/JzYLfE/9+JMAvuac+5Zz7q8AfBKbvu2pH7fk9tsq9xQR/SyANwJ4u//g6UnjP8Dmw/uL/t45CuB/E9Hf7UhjFr0u6J8HcLnPLjgfmwOnu9cQQkQE4HYAjzrnfnNiuhvA9oT7BDZ769vr7/Cn5NcAeH7y1bgKzrlbnXNHnXOXYtNXn3XOvR3AfQDeEtC41f4WX77qE55z7mkATxLRD/tL1wJ4BB31IzZbLdcQ0cv8uG81dtOPE3L77TMAriOiQ/6byHX+WjWI6HpstgHf5Jz73kz7jT5L6DIAlwP4X2h83zvnvuSc+zvOuUv9vXMamwSIp9FRP2ax9iZ+5PDiBmwySv4MwLtX1PFPsPk6+xCAP/H/bsBmr/ReAI8D+CMAF/vyBOC/eN1fAnB1Y72vxdksl1dhc6PsAfhvAC7w1y/07/e8/VWNtP0ogAd8X/53bLIEuupHAL8G4CsAHgbwe9hkYqzajwA+is2e/l9hs+jcLOk3bPax9/y/mxpo3MNmv3l733xgUv7dXuNjAN4wuV7tvl/SOLN/HWcPRVfpx9J/9l//DcMwdoRet1wMwzCMTGxBNwzD2BFsQTcMw9gRbEE3DMPYEWxBNwzD2BFsQTcMw9gRbEE3DMPYEf4/32B9My/+lFwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af8043b38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Image segmentation using watershed transform\n",
+    "segmentation = watershed(elevation_map, markers)\n",
+    "# Set background pixels to 0\n",
+    "segmentation[segmentation == 1] = 0\n",
+    "plt.imshow(segmentation, 'gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1481\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af0c299b0>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af0c7bc18>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Label segmented objects\n",
+    "labeled_colony, number = ndi.label(segmentation)\n",
+    "print(number) # 36*48 = 1536\n",
+    "plt.imshow(labeled_colony)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# [Display image histograms and change contrast tutorial](http://scikit-image.org/docs/stable/auto_examples/color_exposure/plot_equalize.html#sphx-glr-auto-examples-color-exposure-plot-equalize-py)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af0be6c88>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af0c1a8d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load an example image\n",
+    "img = data.camera()\n",
+    "plt.imshow(img, 'gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af37dc9e8>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af8116828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Contrast stretching\n",
+    "p2, p98 = np.percentile(img, (2, 98))\n",
+    "img_rescale = exposure.rescale_intensity(img, in_range=(p2, p98))\n",
+    "plt.imshow(img_rescale, 'gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af842bd30>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af804af98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Equalization\n",
+    "img_eq = exposure.equalize_hist(img)\n",
+    "plt.imshow(img_eq, 'gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f4af3791908>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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naLVaMBqNKBQKgh7TeIfDIbrdLoLBIPL5vDiUyWSC6XSK8Xgs2QyjbLfbhcViQbfbxWg0wg9+8AM4HA6hzGazGcrlMpxOJ1577TUEg0EMh0PMZjPk83mJSr1eD61WC5VKBQ6HA71eD+12G41GA9PpFKVSCbPZDIVCAQ6HAycnJ/K60WiEXq+HcrmMfr+PRqMBl8uFmzdvwuVyScS8O/Lw9Y1GQ+jA4XCIw8ND9Pt9Kc94XoVCAb1eD4PBAMfHx8jlcpjNZjg+PsZwOITRaESz2YTL5ZIAUalU4Pf78fLLLyMSiaBUKmGxWKBSqQhlfPPmTXi9XnlffgeDwUBoX2JLt27dkpLFbDaLc3G5XKhWqzCbzej1eggGg2g2m+h0OjCbzeh0Otjb2xMns1gsxDn6/X5omga73Y5Go4F0Oi1lK51YIBBAsViE3+9Ho9GAwWBAp9MRA57NZkilUhgMBkilUpjNZnC73RgOh7h06RJMJpNgTf1+HysrK7BarUKpbm9vC9X97ne/W0BRh8MBTdOE9Xr11VeLn/rUp/78XmzygcgYIpGI/qu/+qvw+/2IRCJLtauKiquPE5klIJTNZgWINJlMcDgcUjYsFgvM53NxMna7HZ1OR6hDUpIECF0uFxaLBYLBIFqtlpQSKoah6zp2d3ellFB1Dy6XCxaLBYPBQOhFUqEsSQiokQv3eDwoFotSXjBlJJ06Ho8FyGy1WjCZTCgUCjCZTHKck8kE8Xhcoi2vzfHxMS5cuLBEV5ZKJXk/ptSkIgnw8pqn02m59kTe9/b2AJzy7So1ycXH+TfSldPpVNJkpsqMzqT8MpmM0I+sz/v9/pL2otfrSRSeTCaSaqvaDAJ9zCgIeBLs6/V6AmTGYrGlMiEajaLRaKDT6UgwqdfrS8BiNBoVoJSgKqlYPsfr9aJYLKLb7WI6nWJ7e1uAUwYNPka602q1YjabIRQKifPie3i9XjgcDsFw+J3RJvb29mCxWKSkq1arCIfD8Hq9+OQnP3nPGcMD4RiCwaD+wQ9+EB6PB7lcDqFQCH6/X+ppAlelUgmxWEzSV4vFgmw2i2QyicFgIEbV6XQEcOIXbTQapbZWIxMNu1QqiSDEarWi3+9jOBxifX0dg8FAXlsul9Fut5FIJOB0OkXHwCym1WphNBohEAhgMBhIPej3+9FqtUTbsFgsUCwWEQ6HJaKtrKzAbDYvOTmVEqXn5+vb7TaSySSKxaLcDKr+gswDyxKyENR/8Bq2221Eo1EBBsvlMmw2G1ZXV8Uxj8djHB8fIxaLYTQaIZvNyvdC4+f/iSlcu3YNW1tbUuLZbDZhB2j0nU4HtVoN586dE8N2u90iXDIYDBgOh7DZbBgMBmL4ZISMRqOk8fV6XbQMKpbg8/mk3ufn1mo10SlEIhHcvn0bdrsdiURCXs/sjvdPPB5Ht9tFPp+XTCGRSGBvbw/nzp0TsNdgMKDRaAAAwuEwKpWKMF8saQKBADqdDgKBgJzLysoKdnZ2lsRes9kMXq8X/X4fmqahVqthe3sb165dk+tMG+E1ZokHYCnT+8xnPvM/j5X4URcNwGKxiPGrLIXX60Wr1ZLI5fV6oeu6UIz8m9lsloiv67qIhQgiOhwOFItFeZziJta6jNbAKULNz+KxWK1WrKyswOVyodVqwe12w+l0SppP4BSAoP8ELElLEmj0+XxS0tzNz5PxSCaTqFQqCIfDqFarSCQSGA6Hct5WqxXpdFrETurf6AjsdrvQtOT8R6ORZA82m22JkSDrwEWHweMlYEvtgeqoVd1ANBoVh7O1tYXxeIxOp4NUKgVN09Dr9fDII4+g3+8jFAqh3++Lw9Y0DQaDARaLRQyFGY6maQLKEXSlSIgUZq1WW1KQslSkOMrr9WJlZQWVSgW1Wg3RaFTKUgKaBGAnkwlSqZSwQ7w/aHzUR/Bx4kJ8L34XdEQEoaPRqGhtvF4vGo0GfD6fsCa9Xg8AJOshIAtAHPDdtLLX65V7qtPpIBKJwG63I5vN3pc9PjAZw2/+5m/K7+S/yUDw/0TFKYqhmlBVIdJgg8EgDg4OsLm5KVoEl8sFk8kkEY4cN+tVKhQJYFK7sL6+vlQSMJpTJszMgw6IET0QCKDVakHXdUndm82mvDfLBBoVMxOPxyPvS7UeP1MFGnl8PDZmE4FAAJPJBMPhEIFAYEkxmcvl5DgjkYgIbMrlMsbjMaLRKDRNE3qL4h06i0qlguFwCJ/PJyj5eDwWQRAAAR5V3QOzCbIK3W5XSgqDwYBQKCS4AAHOer0ukZXAoaZp8hhxCR6v0WiUEoTlA0FfKi3pPDqdzpIGgaVNIBAQ8JCZAYFEOqhAIIBXX31VsiqHw4FOpyNpP7GIer2OZDIpIjKCwTyfeDwuOgfqIJj1TCYTDAYDeL1eodiJZRBTIvBIh02nze9SLScA4HOf+9ybK2NQ5ZzAmViDnpQ/+RyyEsPhEFtbW1IaDAYDEQgtFgucP39eJKler1f05W63G8FgELqui6wWAAqFgpQBNHA6HgqkzGYzfD4fLBYLCoUC2u02PB4PSqWSlAlc+/v7cLvdQm0xK6Cghg7DbDYLYAacRpxSqSTAJgDJGshJN5vNJWEUswcKtngcBCnpSCh6Ak7VfKlUSuhJm82GbDaLd77znTg5UdtdlhWisVgM7XYbnU5HIhf/TsPm4zx2/p0iqPX1dQEkp9OplCzlclnSfWaCjLKFQkGiMzEbAHC73RiPx0uUZzabFccQj8exsrKCfr+Pg4MD2O12ofji8Tja7bY4mXPnzgkDQuYml8vh8ccfRy6XQ7fbRbvdxtramoC33W5XojpZLqPRiGAwKM8nltHtdpFOpzGfzzEejzGdTpHP57G6uvo6uyBQ2ev1pNyYTqe4efMmNjY2UC6XJUOg1uTk5ER0DMyqeG/cz3ogMoaVlRX94x//OABILQsAjz32GEqlkqR+BFyA03TX7XZL7wPBOEbRlZUVDAYDzOfzJSbD6XQupWtkIgggsn+BctzFYiHRzGq1IplM4vbt2/B4POj3+5Ii0kB1Xcfh4SHW1tawvr6Ok5MT6LqOg4MDMU6qNFutlvxfVWYS3GTmQGPmP4vFgmQyiePjY8k8GJGGwyFarRY2NjZQLBalnDh37hxyuRyOj4+RTCZhNpthtVpx9epVEeaoGAFTVF7vSqUiWYAqiaaU1+v1SqQCzpw8Mw6WKZqmifExCvv9fgQCAZycnOD8+fNCB8bjceRyOantGemphXC73ahWq0sUqoolkCZmQHj3u9+Nf/iHfxBWIJFIyPVgpE4kElLLq5mJ0WgUfIQNUNFoVMqDdDqN733ve3KvqlkIswyWSLlcTjIXahSYlTYaDcliAoEAcrkcYrGY4C3dbhfxeBwvvviilLpqf0Qmk8HOzo703rBMjEaj+PVf//U3H/j44Q9/GAAkM2AqBGAJ9WZtzrqfOgKn04lWq4VOp4NYLIbhcChSYofDAZfLJaUEa03+nReWJUW73YbD4UCpVMKFCxfkcwaDgWAFTOfJWlSrVcRiMei6Luq9RqOBYDCIdru9xFAQAxmNRlKO6LqOc+fOiTaBjqHZbMLj8YiBEwfh8VB/QSYiFAoJZjEYDKT/hMyHy+WScoDOjqVCNpsVB8zaXFVAclUqlSU2QnUG5XJZcAU6DlX9mMvloGmaGD01CQQQyQxUq1X4/X5Eo1H0+32JwkzF1Q5HshZMtVVWghqDXq+HRx99FLu7u0s1fDwex2QyEfkzjZ5Rne9BTUMsFkO9XpdGLGYpBCOJK9zpZpTnUBlJkJyqS7WDdDKZIBqNiiOjSpNNU7dv3xaHc3x8LNeZNuPxeDAejwVPoK0Qr3pTgo80SDoq3lDq3wmQsXlqPp+j1WohFApJZCJGoHbV8bH5fC49EFTNUb3XbrelrZbGD5ympOS1ifAHg0Fks1mJ9FRY0ph7vZ4YpEo5EgMhrkAWgzQmZcVkNihbJp4yGo0QiUSkBGJGQfCUmMFiscDJyQnW1tYQCoUAQI6x2+3C7XZLWcJyhAChmg1Q6ARAyi1VbMTMglkDxTeM6AQQmUpTvETqkBQh8QGbzYZCoSARkp/JLKXX6wkAmMlkMJ1OUSwWEYlE0O120e/35f80TpYTHo9HSkWHwwGHw4HZbCYiKsqtnU4n9vb2pLxhyr+6uirP42LQiUQick6qlDoUCsn7UC/DxdLJYrFIGzWdDI89Go3i5s2bsNlsODg4kEyE9Koqc1bVpJTJ04aYvd3PeiAcA6PPZDKBx+NBpVKRiwacdSuyRrZYLIIl8HGKUVwuF3RdR71eF12BKh2mAVPDEAqFhKJkVKd8mI6gXC4LIk6BDR0TEea1tTXJNOgQXC6X1J38v67r8Pl8QlVRvEL6kfUg8QayMZRKM7ugM1EbpeiAyF1brVY4nU4RVhGUpJ6B9efq6qpkBLzWqmJRdRbAGX3M5xBzULsymYUBWMIfjEajlHLsZ6Dq0eVyLb0v+0SY1fG6Mfr2+31MJhM5xn6/j/F4jJdffhlbW1tLjoVlBfsaHA4H0uk0rl+/LngAcNYzUS6XEQgEJOPI5XLw+/3Y3d0Vh8Pz1DRNxFjEDZgN8J5mWzfxCAYkXier1QqHw4FqtSpZAjUX/X5fZl3we+Rns1xQW93ZJMjv6u4OzHtZD4RjMJlM0jrNm3ZzcxPAaWPVZDLB5uamUJKkHi0WC0wmE5rNJoLBICqVCubzOQKBgNStvOGIxBPV7fV6YuREnkkvDodDVCoVmM1m6WhUexoYIXmjud1uEZswlSf3Xa/X4XQ6pUzhawCIQIllBeXYNHRiHOwUJUvhcrlQKpWQSqVQqVSQSqXECXa7XYzHYySTSZnPoOu6KDHpbDudjtShBLB4TlQ7MtLQ+IjxEEDkDc2ZCdlsVp5XqVSkYYyGUK1Whabkjc/yYXt7G+VyGd1uVxqGaICMuNVqVRzAq6++Kg4sn88LNdnpdLCxsQEA8n7sZKWjYERl16WmaSiXy/KdpVIpGI1GYVGoW+G15awIznhgqn9ycrLUTFUsFtHv96WUMJlMUmpQ70C5OQVO7Nmo1WqYTqdIpVJyTchmZLNZ6W+hAwBOQWGyd8R7iO9cunTpvmzygZgSrfY5eDwevOUtbxEHoWYL/LvasKLrOiKRiKTmAOS1VqtVZhww+jFbIMUWi8WW+HZSgnwP8v2MYMwWCoUCKpWKpP26rmNtbU3YhIODA0GtzWaz1KVErTudDiqVCkKh0BLtSuCIaT4NWNd1uUZclUpliQXhDAB2EQJnaThw2sPQ6/VE5s300+fzCX3IDlKWBry5yELQwEiDdTodufmYMTCyR6NRxGIxwRQICNIRsbV6c3NTHBI/k+UIh56QrqN6kspBtdGO0nMVlEun0yJK4/W7G7tqNBooFos4OTmR8yRYSkZlNBpJo16n0xEnRxzlxRdflOvT6XSkn8XtdmNvbw9Xr14VHcLt27dRq9XE0fD6MfvodDro9XpYX19fUkMyy+L14WupDwEgXcjb29vY3t4W/OjKlSv3ZZMPBPgYCAT0D3zgA7BYLJhMJkuDV1QF19bWlnDl0WgU4XAYOzs7WF9fR7FYFGUg6/lQKIRSqSR4QywWk6yh0+lIrc9mJ9Z6nU4Hw+FQlGWqSpLlDLURdCLNZlOiMjUNfI3ZbEahUEAkEhE5dKfTQblcxvb2Nm7cuCHHoes6UqmUUIoqoLi3tyeUqOq8iBPw3JldEN+gk6lUKtB1HYlEQs7j6OhoKfvpdrvStsybjd+BzWbDq6++inPnzgkTwKjV6XQQDofFofBx9oIAp4ZaqVQwnU5RrVYl5V5dXYXdbsfBwYGAekT7GTVJ3dEBcL5BIBBAPp+HzWZDo9GQ8mQ6nUrvCJurYrGY0JXT6VSmI+VyORiNRpnPsLW1hX6/j+l0Ck3TBJvpdDq4fPkybt26hXg8LlqL8+fPY3d3V8bAsUQoFovCrlDGzuyBSk5qXmazmfTcMFPijAm73S7vzQYvq9WKL3/5y7h8+TKAMzGe3++XnqNarSZdr5VKBb/2a7/25mIlAoGA/r73vU/KA6LEAJbUdq1WSzouW60WIpEIPB4Prl+/jlAoJIZLQQ+joK7r0l3odDqlnqfoyel0Ls12VFF+dvqxYYWfQWd6LDVeAAAgAElEQVTBMoOlhspu8LOofuT4OEZMPo/HS20Fj499CS6XawnIJMbA65BMJpHL5WA2m4WWXFlZQTablVZxvp6vpbOgA+LNqsqjgTMGglOf6LDZ1s1yw+PxiANQGSWCf8xcGBlpvJx1YLPZRNTDck+VNXNWARkFSqFpvKoAiriAKl6y2+3SuGW322XMGmXYpAM5S4H9CxzkQnyG5Ua1WpXXkUKkhJrPq9frWF9fF7aH/R4cRkM1YywWEwk4adNerycAOf/G8ioej8u9yayHuotoNCrBU/0+W60WPv7xj9+zY3ggSgl1OCrTaqZNTNUZxVqtlsh+y+Wy1OD8R/kxbyYi0JzVQMMwm80yYcnv94vh8x8741heUDSk67pED6aKVA+6XC5pp1bLEAAihKL+gik/AHGILFfY7ux2u0WjoKbAavrISMGsgmCXxWKRv9Fg1W5RVbGolg1MVfn/dDotlOV4PEYwGBRunE6B3YqkVUl5ctwdlYks2djUw+Nlt6nBYJBBM5qmyXup/RoAxIlQ5KbW2el0WjKcu2k80twESGlYdFp0aBRo8fMASDakHoP6U+0I5dAgzpfkdxCPxxEMBgUD4nc5mUxQrValK5Illco68JparVbpU7l27ZqUMnwvvodaahD7uZ/1QICPFotFgCFSOmozCGk8AAKg+Xw+rK+vC+CXSCQEyTcajdJhyc5IMgjstuRwFsqgnU6ntNQy4rAvYTAYCHvAnghSq9QTuFwuySjoJPjZdA5qn4Q6/IXZBpugSEnSETDD4PyJcDi8hKGcnJxIrV2tVqUZiw6oWq1KlpBIJEQNSQUnywDWsgRt6WxVis5sNmM2m8nzGQk9Ho9MaWa2R+EZ9ROLxUIyMGIjzAjYUcgyyOVywWg0wmq1ynAZIv5U+TkcDtRqNamrOXyFzAMzBmI7zDRY5nBqM79P3g+UhfPaU8JdrValq5Nj+fgcip/a7Tb8fr9kHjRQZhHMhKbTqaT8LHn4OEFG4jOapgmmwcyO/2g3dFqk0em0VYd6P+uBcAyqpJf10Gh0OpaKFF44HJYIQb6e2cX29jay2axQd+ywXF9fRy6XQzAYlBmMbMUmBUbHoM5EXF1dlZuT9KYqcrJYLDh//rxw706nE+VyWZgRKiHV6dBkHjgnkEImsgwUL6VSKZRKJQCQa8KhNLdu3ZKhM4z+t27dktKLURs4HTbK7Eg14qOjIzEy4KwfhUCYpmkolUqSntO5tlotaJqG4XCI6XQqU41YTxcKBQHImAlwhkCv18NsNpOIzHS/Xq/Lzc7srV6vS8/EcDjE0dERVldXUSgUBNRk+QCcGiWNllgRtQxkDJxOJ7xeL15++WWZuxiLxZDP5xGNRnF4eCgtzpxlEY/Hpa4nkMvOTQCCU9XrdZHdM5VndmCz2eTvvFY8Zjq9wWAgZYLf7xdspV6vy4g5fj+cOn358mWcnJxIaQZA7IbS6vF4vDTt7H7XA1FKMKoQfGHjESOLx+PB7du3JUJHo1H5fyqVwsnJyVLUpWqOQzuINLNmL5VKYvAAxCGoOggaLZ1RqVRamuTE6UHXrl0T2TPBPQAS1QFIpxsNPZfLCbfsdrvR6XTg8XiEXWGkJoZC7IKgn0p7svHH7XYjFAoJcKuyN8BZOUHHwUjGKKfOolRvJJZuNHqL5XSeJGc2EKAMhULyngDgcrkwnU6lrGPUJr3p8XiQTCYRDodlmEi/35fR8SsrKwgEAjh//rxci7uzHKb7kUgEm5ubuHDhAoCzKdirq6vwer2Stj/77LMSaQFImfS2t71NcKxoNCrlFBkg/rx586aUXiw5Ll26JL97vV6Z3M0sjtkQ6/1EIgHgDCzkNTl37px8H/l8XsoyPsdutyMSiQg+A2Bpcrqmabh8+bJkT1arVZxENpsVB3Kv64EAH+PxuP67v/u7Ui9VKhW8853vFOrO5/MJQ8Dalkb14osv4sknnxQKMpvNSvRkbwFxAVJTmUxGaDeHwyGqR3ZEUk6sDlbRdR3Xr1/H+fPnpXzhrElqKzKZjIBAdCykR6m96HQ68t5s/FosFtKrv76+jp2dHVFlEvhaX19Hu92W0WVOp1MMjxmPunRdF8ENyxsCe+zXIKYTiUTg9Xpx8+bpLoKpVEocj6ZpMnmYRk8KjTcuGQzSjiqbQdoROOu6JEjHfgem1263G/v7+9jY2EChUJDI6fP5UK1WhU2w2+1IJpOSvaiAH/sgGLG73a7QtJFIBMViUTCnSCQiLAEnNiUSCdhsNpmFoAKOXq93KZJvb2/j4OAAfr9fwEjqIhqNBlZWVlAulzGbzeDxeFAul2G322XaE1vUqcgNBoMwGo3CTOzs7GBzcxNXrlzBhQsXlkoRXvcrV64IbsKyQZW2f/e738Xb3/52eL1e/NIv/dKbi5UIBoP6c889BwDSIw+cenR6cnptekGCcTR8dXgrB6L8ey3R4XBY6EzSc+fPnxcxEV8zn8+lNGFPAUuJzJ25fyobwQ1taIjqDAjiCmQebty4IXJoVXhEh9FqtcTI6ABpcCsrKwJ01et1uFyupbo3Ho+/LhNShWO8vuzEVAd5rKysiKGp4iQV6GS0IitBw1eNn2AeNSScSUAqj3qDyWQiICMZCeIAbF2Ox+NLSlMyFUzTaeT8HtW9O1gu1mo1RCIR1Ot16bEwGAzyeQwQNLpUKoVisSj7hfx7DVH8TPZJUJXIEowNUiyN2DPBDXa4kxaH+qbTaSm52Doei8Xw2muviWKU1y2RSAjgyD4IshHqpkD8bgHg9u3b9zXz8YHAGHjTApARZclkEqVSSbIDprnsbefryPMzO+BYNWYNo9FIqEeWDwQHGZGJY9CQ6ThYmjQajaUpSJPJRIA8GjVHnfM41PMaj8fy/jRugol0LKqoCThLX1UWgbgKFzMfgmBM99k5ORqNxHlYrVbZCEXtjNR1HdVqVQaRME1n34NaVlSrVanfGaWZ3o/HY+n752tIr7G04fP5vqoKcjqdwmq1ClhKQ3O73dLcxPdj5kC8gXMhdnZ2oGka/H6/qBbZB5LP56VdnQbOEWlsZGLLOvsx6GQ4Qs7j8aBWq0lLN0FGHgOX1WoVuTYVlpzq5HA45Ph5XhzYytcRYL1y5Yo4XV5ztVwj08Jjpeyb8nT2rRAzuZ/1QDgGTdMkqqr8Pw0RgDARAETKu7Gx8To9AVkDVe7Km4k6ffZVcEgLuw/VQSuBQEAMh7iC2lVpsVjgcDgEYNR1XYCnk5OTJSFTq9VCIpEQ50IHRGEUIzdvTg61pcOiw9HvNJoBEOyFFCJpTmIQLpcL+XxeKE/Kovl3OhuWHHRug8FgqbefaD+vG6nacDgsE5RpFBRHEawLBAKiiKzVavJdUxBF1J5Rlr+zJqcIyO/3L934ZB0YIDhxmkCdx+OR4b3UADBDUZeqCuXxJJNJGQTMFn2VNRiPx5JdcGI5cOoM9/f3ZV4kR/WT9lZVjgBkHiRFTJPJBLVaTUpCfvfdbldKILaWE5xX2+BJvVKuz6yB94s6x/Ne1gPhGBgNqTYkEk9jZ7RhTW+325FKpWC1WmVEPOk5qhb9fr80LNXr9SVlH3vjySLQSMkQMIKzKYl1H3swDg8PsbGxgW63C13XZWJUvV6XbsnFYiHjw+noyEiwCQw4AymHw6FsMMOBJXSKZFxu3bqFRCIhG+pQQ0E6j12mFotFQC7VYQAQMBCAaA3MZrNsqDIej5HJZHDt2jXBA4DTvo5wOIzDw0OZsEwGggwDNRc0IJYb/Dkej/H000/j5ZdflsEm6oBW7kfKDIJG6HK5hIWg2pCZClF/ZjJqExIdCF/H75SOgx2NLIcYyWloZAsmk4nMglSzGwqxgDP8hNkLn0MGIpFIiANmox6zDnZxsoRJJpOYTqdwuVyo1+vodru4cOGCYBukMkljM0MgO8drPR6P/92eintZD4Rj4HRmUnus1SloWiwW8Pl8snciaRgVxOv3+yIJJtjGtJ7ZgM1mE0dC7YHaVacOWU2lUigUCqJd6HQ6yOVycpycvEThEsuF8XgsmonBYCAZw8rKigwWociFfRLczPbu3gweF43x/PnzS+UBAKmF+Vqr1SrRCjjbH5PXhbUvAJEs87obDAbBAlKpFJxOJ5rNJqrVKpLJJMbj0926+N6kDVV+nQ6CpQIpPgqIyMfzXzgclqjOlm4aFhuSjo+PJRUnRqCmyhQ7cZs7Pk6nwM1jNzc3ZUQ+jZzPYTcly5BkMonRaCQNT1ykJpmF0JFyI2U6J553o9GA0WgU1SMFX8Q4eJ/QCfE62e12yR7YRMWShd2sdAic8D0ej5f25ySQr45IvNf1QDgGpo+6ruPq1aswmUx49NFHAZylQoywVD2yBdliscgkI0bjwWAgN+G5c+fQ6/WEFppMJgJoLRYLQb9pPCwLCoUCAMicxLsbqzi/kc5HnbxEloJAH2cfjEYj2W2Ixl8qlaRkUfUSwFnLLOckEO13uVyCUwAQR8A9EVwuFw4PD5eYDzo3gq/T6RS7u7vY3NyE0WiU8kjXdRldzn4KKkXJ0NCYotGopODMFniNWQ6QzWDNT2eyubmJfD4v5xmJRISn59g2ipt8Pp8Inpjl+P1+7O/vy2BZOgdiBirewW3nVKEPQUeL5XSO5/r6OgwGA47ubErE3gVmMpwyzq5TSqzpIKjq5Pg34DQTpvCu2WzKjEuWYbyu3NSHpQO7SYfDIZ566ilUq1UcHR2JoG0+ny9hbxR1qVOz2Bpvs9mWNhm+Z5t8UFiJ9773vZhOpwiFQoKS8+Yi4sqfFosFmUxmqeYniMamJcqc2RRFzIHeWKUQWQoQY1AfZ7ZBbQMfZ28GI7y6HwUfZyQ3m83I5XICjA6HQxl7z2EznBfBKT/UNnCasKp5YGZCfT6zFQKHnGnpdrtljP6f/dmfIZ/Py2d/5zvfAQBJZ7e3t6XFnMIut9uN559/HplMRug7gl8qVUjKUa3jmTEwhVXpTxVXYKrOjk1N00RJycjK55FpYJclNwjmXpcsISig6nQ6MjGardJkcAwGg1CVBoNB8AAqH9W/s2Tg3EWyC8QP+HpmRqQrSY+TkRgMBnIe6hZ77NNQN7HlBjKc48BjoHiKqmAavtojwVKc3wNt6bOf/eybi5VgnzqBPtbppPiY+lssFtHP87mck0hacLE43biWBssajtgCQSVGeXWsuzq7kQwBvTofo4cnpcnXU/kWj8dRrVZlfiRBTn4Gd8fyeDxIJBIyY4COh46P1wE4Yzco0qLWgkao9muwZ8JmO91E9fOf/7xs4c6+k+PjY6HiWJK8/PLLS0NcMpmMbGCyubmJj33sY3KzAmdlhIopMCMgjanq/mnQfJ7avq1mEhzlrgKSAGRGAX+nYyBox1ZzTdOwt7eHra0tKZkoAiIeQCAzEAjIDAROdiIVyePk+TIb4l6UZERYFnJRdKY+l8dF2TfLzel0KhoNllGFQkEyBr/fLxJ4grxUnQLLw3AIQpJp473CrOF+BU4PhPKRNfnFixdFcJNOp/H0009jc3MTGxsbeO6551AqlZb2i+SgVu7zMBqNJDuIRqPY2NgQhJ57VrLlmCl3u92WEoTgo9vtRiqVWmqXJShJGnRjY+N1rc9bW1vY39+Xeo9OgrtFJRIJTCYTrKysSK3O17OOpY5B1/WlJppwOCzqR+BUwZjP52VSE5kDANI+/ju/8zv44he/iK9+9auo1WoyKs7v9wsWQSOORqNIJpNYW1vD2tqabIP3wgsv4I/+6I/woQ99CF/72tdwcHAATdOkWYzcPWcJMMpTWMS9E9QuSc5J4PAV7j41mUxECUlqDoDMLlBHp6dSKSlZCNDxGr73ve8FcFaK2Ww22WCXZSiNhYZF7UYoFBJVIks3KhIPDg5w6dIlcRLAadq+u7uLdruN69evI5fLATjFBahCrVarUl4AWNqMRtM0Ac6JP1Bjcnh4iEQigUKhIBkU8RU6hdFoJJhDLBbDW9/61iWBE3Cq2Nza2rovm3wgSolQKKT/4i/+onxhpOoo7QTOZtqpTSGsxy0Wi4CULpdraZMZKhtdLpdMcyLASQfDqU+j0UiEMM1mU4QkLB9YBrBJiW3PfFzNcjguLhgM4uTkRMoIMhLhcBjBYFAGaFgsFjz33HP43ve+J8xEp9NZkjWroCL3lWTEZ/nAvSO///3v41//9V8xHo8FO+FIdtbPTNm5yzczBgDyk6PxJpMJMpkMnnvuOfh8PjzxxBMAIEbMso+1PIVLxBnUqU5Mw3O53FL/AWcnnJycSEROpVKw2U6nNnFCs8VikZKB+2TSkTBDoYPy+Xy4ceMGHnvsMRn4QoUkW6ApSFI3nmU6zzKGwGY+n0csFhNhFbfIczgc2NvbkzZulhL8nGaziXA4LCP75/O5zGtgJraxsYFmsykqWKfTiaOjI6E1+d7T6VT29mCWQJsgK+HxeJY0OgDwe7/3e2+uUoJMAG/GUqkk7EM4HJadlL7//e9LpKARqwIj1tiU6PKmVseKUYJMB8GBK8ApA1AsFkUgpGYD7LFQSwqWGuyhUAe0RCIRcR6kRnme7GPI5XJSRqjcO+WzasmkOkUqId1uN7LZrLQR93o9ZDIZ/PM//zNarZaksLz52TRGyTe1GKyf+R7ELGhs7GWoVqv4yle+gne84x3QdV0MamVlBe9973ulliUAeOvWLTz66KNSUsRiMcRiMQEsL168iMlkIhvCME3m7t6j0emg1tu3b4tsm8fORqu1tTUBAumg1NVutyWTI8XHjIYZHUsCRl1qC6is5Lb1mUxGuijVidZqOcOyiipSzmt0u904ujMUh7MW3G43ZrMZAoGATIQm5cmp1+vr60Ilq5Jr6komk4noKWy20w1wWq2WzDRV55ncz3ogHIOKigNn4g6mQtzDkqkipbbc4XltbU0MfT6fi/AmEonAZDLB6XSKKISbyXCWg9PplJHq7JJkM1WlUnld3U8mpFwuS8ZCaXaj0UAymRRntLOzg2eeeUZEUGyrDoVCS/tTsjz6l3/5FwEy6ZiAs0YbRgYqN4Gz7eSoGnS5XPj5n/95tFotlMtlpFIp2bSFe1wysrJc4A1KKS7/ZrVal2jOXq+Hw8NDVCoV/OzP/izW1tYwGo3wta99DbFYDKlUCmtraxgMBqhWq3jyySdlMjRpROBszwmWIdSpsKRjOj0cDtHv9/Hkk0/Kc1lucEw89Ql0rEyr2TPB8mNjY0M0D0zFOd+Rw1uIJ7DpDYA4J0rxKSBSZ1A6HA74/X7JGFTnRFyh2+0ik8ks7QzOrIfnwSlT1FLcvn1bAExSlZRic9EZsHw9OTlBOp2WGQyU1r8pdQwAloyEEVcdLFIul/H000/jypUrojKkSpBUIQ3V6XRiPp+jXq9LvUhAjYNWudUc6TyPxyP9ERSgmM1mvPTSS7h48aKUAcwCmEGwVudot8ViIYItGi8Nn9OmeZ7EEdjfwQyB9CZ7HJgxtNttoRHV7IFiHzpYANLdRxSd11it3YHTmtvlcgkqz0jsdDoF0SazwtdPp1O8+uqr6PV6cDqdqFareOmll1CpVPDud79bsgzOdOQwFaa9rOdVuS/Tf4KQBOkCgYBIhinDJrvAyAlAALtyuSyshApgjsenQ0wCgYD0HjDac+YCG62YVTFzOTk5kT0oOp2OMAUGg0GATOIozEC4HwazVGYBFFZ1u11hyjiQmFjReDxGo9GQ8yDAy6yXUu5sNitKSGIpBHh5vdXBLfezHgjw0WQyIZ1OI5VKSYnACE2H8dRTT8mMBgqMwuEwVlZWRD1oNpuXpi5xBBZpRxostfsE7lgGkMkgkEnul1Fc7WegcbLcUJuS+HNjY0OGuarTnLj9G3lpqtfYck7UGYBoANhZyWMi6szfyZKYTCZ85jOfwSuvvCKRnj+ZaqslAq8/nU+v11sCwlRDVcHQV155BT/4wQ9w/fp1TCYTvPbaa8hms5JKs5wYDofI5/NSTqgpcK1WW5JKT6dTaW9necGygcIh4hbMHql05LViGcnsk6UNS0wC1ACk3ZxGx+OgcZJJYpbGjVzYVMafzBD4PPV3grssxYinsB+DBk3j5TBZMkYcaEPnpWIoxOL42na7jUgkIi3naqfrfdvkf/QETdP+K4DnAFR0Xb9457EAgP8XQAbAEYD/rOt6Uzu90/4vAP8rgAGAX9N1/eX/6DMIzpBWYj3JxejIFHtzc3NprwemZzRwk8mEc+fOyXAWdeAmAKEXqU9gBx6nOrFvfrFY4Mknn5QsxuPx4LXXXsOFCxeW5ibQGdCh0Alls1l4PB6srq6i0WjA4/EgGAzKQA5+FpmYRx99VEoMDkYJh8M4d+4cbt++LWPQ6dQo6KHQxW6340/+5E/w7W9/W86Tk4LYjMQ0nTcsaVayJRTekCpNJBKYzWYy0YndnRaLRVqQo9Eojo+PMZ/P8ZGPfESESjQ2pr+USZNGo9Zf1Sqooiij0SjNTwTe+By2HlN1yojPUfaMruPxWCZOqdLrdrstasvd3V0BJLmRrdfrlSnYBP96vZ4Is1i6cB8Uzksg1VoqlWTDGYvFgmKxKJvixGIxrKysSP9Fo9GQFnm20zODVIHijY0NaJomGzOrDAUAmaepdriy1FSDzb2seyklPg/gTwF8QXnsEwD+Rdf1/6Jp2ifu/P5/APhfAGze+fdWAP/3nZ8/dHH2AG9wjmBnKcGbiGUBqcFbt24J30+jLJVKCAQCKJfLcqEIOLL2Zy3OqEKVYKPRkOxif39fGrRoFM1mExsbG2LwACRqU93m9XpRLBYlarRaLdnpWlU3qo1fnU5HsAuyJbypmRJzLwzy49wtaW1tTYyOext0Oh1xgrxeagMRHa2avk+nU7kOZrMZ/X4fZrNZwFiOwGck5WL5wT0t/u7v/g7PP/88jo+PoWmnsxwYBVk23D1jkupTMiQsQ0ijMvJRzt3tdrG5uYlbt25hNptJO7gKztlsNnH0HN0fiUTQ7/dFu8Aa/8KFC6JsfOmll8RJeL1eaak2Go146qmnZAd1ZhmcQJ3JZLC3t7dU3lgspxvmsK9iZWVFHDGZCB4XGRACs2tra+j3+6jX65ItcBtEZpG0Cy51ZzBeAzWzuJ/1HzoGXddf0DQtc9fDzwN4z53//yWAb+DUMTwP4Av6ac78PU3TfJqmxXVdL/6wz6A+XkVOrVarqLto8JSeqqovNXXjjARSjPyyiPxTYOR2uzGfz8UBccfjYrEok5CY1pNdaLVasjcmF6M1MwoaNpFgUmYcW6/uhMUSg5gAwbbV1VWRVdMpqBw9pb+xWEz+vrKyAuC0Qerk5EQYB7ZR3106qO3U3MRHbQOv1+vCuBwcHMDlcqHb7cp7cVCMOpWbx5LP59FsNpFMJlEoFJDL5ZYGx9LQVbaEgB3vAWJALGNsNpuMfqezGY/HIoemc2O9TU1FrVaTc+bzSOMxAyEN6nQ6pcmLVDNlyRzZRnqV78lpTiyHaPAsVSntZkZLEJKfRzaICkdOMqdAiZgSsYrJ5HSmJac9AWel5nA4XGIsWGKyT+J+S4ofFXyMKsZeAhC98/8VAOr+6bk7j73OMWia9lEAHwUgNTkXe+tV5RpPltJPzlkIh8OyjRyNjJ2QVD7SKBuNhhgAZwiqE5ofeeQRmU40n89lN6fhcCj6CnYAspQgYEhHsLm5Kftr8nPpAIhfsIlHHepKfp/SZwJ3d66VUJSMrDQilgvxeByHh4cyMYh8P1Puu5Fy4Ky+JT6izgUYj09nGEwmEzQaDSl9gDONA8FHYjqtVgvFYhGvvvoqnnjiCayurmJvb0+ivKqELBQK8Hq9CAaD0DRN6DhKwolRMHvqdrtIJpPo9XoCKlMJyWyI1GKtVpNoSVqR17BUKoksOZlMyl6mpPiIB7CnhU6EGQulzBRrMUipm9QyKzo6OpLOSYPhdOetdDoNn88nU6G63a6UnixZJpOJfD6dI+Xt7HHhblTcEIc05Y0bN5BMJkXW3W6373uzGeDHwErouq5rmnbfKild1/8cwJ8DQCwW06nUoiekIQIQ4I4GoXYzRiIR6VKjgozKL97s3A6MOASZAxqsCoiRouNzSEuqLdAc2Mp0nxGeo8KAM2fHTk+LxSLNS0xpmZ4zkum6LmAT8QMaKc+bkRuA7GFIoOorX/mK9I7QIfC5NB61jGCZRuyGE42Y0VDDwSyBiwAmsxI1m+G0bZ4XX6cCZyqzwGioRn7+/B+9hn9jiQVgKUPg5xK34N6TTLVpkKqUm7s/UTV6cnIie1fQkVBbwR6KSqUiTqJarcpzeO2TyaT0X7BsYEnKzID4GsFjlhgWiwX5fF76U1gmqUpTn8+HbDYr8zD4WWR91P6U+10/qmMos0TQNC0OgELsPICU8rzkncd+6GI9p5YGTG/ZG8GlTjaioyDdSDpRfS4jMG8Sdhlymg8AobHY4zAcDmWbdhoa0WkKnCjAYuZAh0Pgi/oAGpbD4cBgMJDUm18W6Sg6ELZmk3+nus/j8cgux+weHQwGMhHo7h2T1WirOoW7RUDqsBEaCrMYXmteUxUn6fV6kjmwFX06nSKRSGB9fR2bm5siBW82m8J40LgJ3vEcmTXQULnnIxWes9lMQM35fI5GoyHGz/NixOY/1TAY1TkHgS3i7FVgNnZyciIUJ2cxkh2hAjMajcrekqQt2WuhLrfbjX6/L7Me2UFqtVqluU8NVvwOOIqf1Cr/EVDNZDI4Pj6WLNNiOR1OvLW1JYGVuh2W4tRh3Ov6UR3D3wP4MID/cufnl5THP6Zp2n/DKejY/o/wBS7eiK1WC+l0+nUTcre2tiSKApD66+rVqwImUhZNkc3+/j4uXbokxp1KnfosRgHW1Gx0YZ0PQL4oLoKf3PA2EokIsMP9HvL5/NK05UAggGKxKBkPIzDn/NGoOSSWnYaUL6uyZaLZ1OIHg0ExKnX/SrP5dLt7qjl5bRl1VUk5/0ZBD0FG0n2kZckiRKNRJBIJWK1WfFoCUM8AACAASURBVOUrX1nCGHRdx2OPPYZ2u42//uu/FpFOJBLB6uoqzp8/v9RS7XK5sL+/LyyJKmriubPG507ijKyUddPAgdMMk9+hOnCWBkVmJB6Pi+7A6XQil8stORQ6Ae6ZSQdNR8ZSgbMX6Bg4jp4ipF6vJyUjtS4sSXleDocDbrcbt2/fRjgcFryBYrNMJiPKVI6VZxs6dwgjsMhy6fLly1JqU+vA/SnuZ90LXfn/4BRoDGmalgPwf+LUIfx3TdP+dwDHAP7znad/FadU5R5O6cr/7Z4OwmQS5RZv3PF4LMNgvV4vXnzxRXg8HkG0vV4vdnZ2pE7na+mlvV4v8vm8qBaPj49x7tw5dLtd6arkRixMB1ljA6dMxWAwwMrKytKQF6vViqtXr+Inf/InZdx7LpeTMoCNUVQTknI8ODhA5s5AD0pm9/f3BZ/guPVWqwW/349msylgJYeCDgYDOJ1OyQIGg4H0E3z2s5/FP/7jP2I6nYpIiZmAKmqivDsWi0nvAoFQ6jL402KxyMQot9stzW02mw0vv/wyCoWCzJM0Go0ySSqRSAjDkbkzl/K73/0u3vOe98jYvFwuJzuY0wlNp6eTojn8lW3gdNbsVyCoSsSfDpIyZOBM8EQJcywWk+5Fo9EoZV8qlcLR0RGi0ag4gccff1xmMRaLRdhsZ5vr1mo1YTSY/WSzWTz++OPSWZlOp+V7n06n2N/fl++BOAG7K2ezGc6dO4fZbIZarSabKFHURGdFDQSb0Kh4ZE8RW/NJS3Lnazr0q1ev3ospntn9g9BElUgk9N/6rd+S3ymj3dnZkdn/nN2v9gZwDBb7HbgHwmKxkAEZ6oa07GVgfUhwkMIgvpaj2HjTqm3hFFkdHx/jkUceEZaAJYbH4xFAjlp1pru8qRlVzGazjIcjh62KZHiT83wDgYCAU6RbrVYrvv71r+Ob3/wmvvSlL8nnqWUEIzJwajC/8Au/AJfLhWKxiG9961sisOL7USyWTCbxyCOP4Pz58yID5kyLv//7v8fh4SHW1tZQqVRwdGdjGLYaE5jljZ9MJmG1WvH8889LaUOtBiMfcQlN02THK2Y+rN2JPxCb4Ng3td0bOC3RKpUKYrGYSL2JFdCR+P1+5PN5pNNpmZvA/gim/SwDG40G0uk0Dg4OsLq6KhmRWrrl83kxWmZHzA44zIZ0pNFoxGAwgMFgEGyCpdpgMMDe3h42NjYk8+BemJqmiXCPi7Sv13u6bSPLB/7f6/Xi2rVr+NznPvfmaqIi2ENxE9V/Ho8HpVJJTpI99vqdcWiRSEQ2bmXdbbWebrGWzWbx6KOPol6v427nl8/nZTAqcDZMFIBsLMs+eFVERMCOU6S41ZvVapVJzYwaHAjCG5zlhM/nk0hHh6PKjdnyzXFkzBAIshJMpAGRF1cFV3QoAJbUjKFQCB/+8Icxm82ws7MjGAmvG2XXly9fxrve9S5sbW3hJ37iJwTDeOGFF2S46zPPPCNNSC+99BJSqZSMRafIjCKivb09HB4eSh/Gr/zKr8h2eARryXzwfqCjYqlAgJl1vdFohNPpFLoVOAV8OXnL5/Mhc2fXLpZRBO/IKOzv78Pv9+Pg4EAcBudUEPScTqfS1ckykcCkynho2qk69MaNG5hOp9je3hbchCPjKfW32WyyQ/dsNpMBNKSyWR6+9tprInrj/TidTvGd73wHGxsbki3wHj45OZF7gUGp0+nIXpf3sx4IxwBAnAJPgHoFdTK0qt0ndfjII49I/clW62KxKCi/zWaTsWSc1EQqhysSiaDX60n7dbfblWGqxCF4DADky1N7IggE0XGwfx44rfuJnvNGIIfvdDpFHsvIxxH5bLYhwMr3U2tyljk8T+BM8Ugnwp6NixcvIpFIyF4Uw+EQmUwGR0dHcjNZrVY8++yzePbZZxGNRnH79m0pNzg6XdM0bGxswOl04ubNm/jpn/5pmRTFzycOAGCp7ZsZTObO/AUaFfUnh4eH0jfAVPvuremIPzBa0zjZX0B2i+9N+pDg3Ww2k54Gfm8EBYGzidHMTIg78G+c5KRpmhgg267ZVJXL5ZBOp5fKOX5vFKsxewUg7FAkEkG5XF4ShPG1/D/pbFUS7/P5xNGrGhVS4Grj1b2sB8IxcJYfAEmD1FTJ6/Via2tLDEZlLOg4KC+lU6CkF4BEFFVz4HA4pHTghSTSbjabRahC7pt8NTs7GaEnkwkKhYJw4SwLdF0XJ0PKjJoIk8mE2Wwm5QtnQDICMjPg4NPZbCbDZx0OhzQ4ARDxFW9+3sA8Nt6YxCtogACwvr6Oxx9/HLu7u7hy5Qqq1Sre+ta34plnnkEgEMD+/j4KhQKKxSIqlQoqlQry+Txu3ryJD3/4wxiPxyLvXSwWsNvtMmmJcnP+BE4zAYfDgb/5m7+RJiYyKUajET/zMz+DRCKxtGEK9QyM2Dw30qJ3YynMDjgxilOiKAQi0q9eI2aL6vZvlGRzqT0mdDp0UGozFcfGFYtFmcO5uroKj+d0g1++LzMrMh3UpKggrNvtliEtLHPU3oq7N7RVzx2AgLOxWAyHh4f3ZZMPhGNQU20qHOkY1FLi0qVLS/w+9flMIbm9eqPRQCqVwo0bN7C1tYXFYoFmsyl6dMqnuYg3xGIxFAoFwRm4CzInQjHtZvPW/v6+CKaIFQBnAiJuVMPIRe0Cf2+320JxkZ7z+/0CrhmNRtlUlpGFmQSzI5PJhOvXr+Nb3/oWzp8/v+QYeJMRzyA91u120Wq1cPnyZZn1WKvVRNlHrMHpdAojUqlUUCgU4HQ68ZGPfAQXLlzAv/3bv8Hv9+Ob3/ymULXJZBLZbFaAVdbSAGSADQ39Qx/6EA4ODvDiiy/CZDLhT//0T/HCCy/g5ORExEW85tyjgyKk0WiEfr8vjAybvBgZ1YlSDocDN2/eFGyH1CGZDUrCWWKoE545lp5pPK8HWSmj0YgbN24I7kCw85FHHhHmCTibzMVskY6t1+vBYrFgc3MTnU4H5XIZ8/kc8XhcrhWpalWiDZxpXciqVCoVPPPMM9jZ2Vli8ICzrPFe1wPhGNS0nhQLJxoDkJLg6tWrWF1dlS43UkDXrl2Dy+WSIR/cGJQSYc5XILZweHgozVWDwUAGuaqTqDnPsd1uYz6fS1ru9XqFhWBq2O12MRqNcO7cOei6Luj80dERtre34Xa7kcvl5EulA2FKT5qKABgVkkzHCWg1m004HA6YTCaMRiORJbO/g4vpqkpjptNpQdrJaBSLRUSjUSkriLtQJxCNRrG1tYXr169LBvWBD3wAb3/72zGfz1Eul3H16lUZaT4ajfDCCy9IxjWfzwXYVbfzc7lciMfjosUwGAzQdV36GDi9GYCMj7dYLIjH41Je0bBo2MRSONOBZQydx8WLF2VGqLqvBCd7sY2f5eZsNoPNZpNSg9QlmRNmSwQvY7EYTk5ORN3JlN/j8SCXy4lTsdvtKJVKIs2nYe/u7kr263K5ZFiP1WoVvQUBz16vh62tLRwfH0sJwWYqlnpsM49Gozg5ORFbutf1QLAS4XBY/+Vf/uWlx8jPWq2nu/ayhFBn2amNSBQPcb4Ch7YWCgUpDzjFebFY4Pj4WDoomdp1Oh0xAG5Kowqc2MTFEoYTnThkhFkD50PSyElREVxS2RFSpFRF8qZnusp0k12MTEGNRiO++tWv4tvf/jb+6q/+SpicZrMpqbLagXj58mXMZjM89thjeN/73odsNouTkxNkMhkcHBxgb28PALC5uYnt7W0EAgHBcWazGQ4ODjCbzfDMM8/AZDJB0063dfvLv/xL7O7uSpv2cDiE0WiEy+XCU089hXe84x1yjWezGb75zW/iPe95D6bTKf7pn/4J3/jGN2SCUqFQwJNPPgmz2Yx3vetd+NKXvoRPfOIT+P3f/334fD48+eSTgiuwlZqLZVO73cZkMpHhL8RtOLlpOp2iXq8Lw8JsgWpUGiHZAr7+6M6INab0BCZjsRhu3LixZLhGoxH1eh3pdBrNZhNGo1HAVfZnmEwmDAYDyZT9fr9kggQniSWoClEAUkLwXMkAtdttcQT8DHbVAsDnP//5NxcrQYyhUqng4sWL0mNOYQ5rTXpQlgGUHSeTSekC5EAUp9OJbDaL0Wgkw1wBiFFyLgMVh9zcQ40IXHRGd2MfdFYUz6jiIUYtvq/T6ZRNRNQMiY5BnenA5/Nxk8mEWCyG+XyOF154AbPZDOVyGdeuXcPf/u3fLh17LBZDuVyWrMDpdEorsKZp2N3dxU/91E9hbW1Nzt3v9+Py5cvQ9dNt7KjraDabsskLjZ0OlTTu+9//fnzwgx8UR8Yhuo1GAx/96Eel8YfK1rW1NTGUGzdu4Mtf/jJmsxl++7d/GysrK/jgBz8o2dLa2hpMJhM++clPYjKZ4Otf/7pkEqPRSABWRmgaMZusAAgYyN3OOVae9CVnRNBZMOgQC6CoiN8tFbQEQgkS8jpSK+H3+1EoFGSjGXb40gFRtcmAwI2TmaEGAgHBm/hcYggOh0OyGbJJvP4UP/H9CGy/KUsJACJoYVbAn6zXVboQgHThzedzUdhx1yTgNIJwbDu9MEUlzWYTkUhEsgcAUtezbGDHWigUkmPk55ONoB6d0YoTk3jzsjmGQGmxWMQ3vvENAdw4XYkRlY6NTosRnyDYbDZDu93GdDpFpVLBlStXxJEwu3A6nRIh3ve+98lNazKZpDX6+9//Pi5fviyt6BaLBbFYbEkqzfekpp+S8XK5jG63i+3tbRHcEAzVNA3JZBI2mw3ZbBYulwt//Md/jNFohCeffBIGgwGvvPIK3vnOd+Jtb3sb8vm8nBvl7NVqFd/73vfwlre8BX/xF3+BT3ziE/jDP/xDmEwmvO1tbxNn1+/3ZQcyMj6Uz9NR0KC4VwNVkHSUqqaE3ZmUbXM0HFkPtkfX63VpD2cwi8ViMkOCe2eqvReUp/Ne4r1I0JWL4imWsdShcJYk28s5l5MANIMoQUhN02RgEa/tm5KuZCrEiKkCJ8fHxwAgCi6VjimXywiFQrh27ZrgAaQGJ5OJSGnZYUkm4W4DoKiHlCfVfGy5Zf0+Go1kihCdggoOqT0J/MLtdjvcbjf+4A/+ALdu3UKpVFrKHoBTUE7TNEnRmWlwuIw6pETtnLx8+TIODg6k52M2m0kaHgqFEAwGZXs06jmoIr158yYSiQTS6fTS5r9cKpbBa3t8fCwparFYRDweh8VikYjLuQW5XE5ee/v2bdlZLBAI4Itf/CJqtZooWemc/z/q3jw47upK+39aW7fUrbVb+2LZlizvGBjsQAgO2DhAKiwFJFMhTAYmk0rNmsykKsmkJv8kNcVMarLUZJIJGYZ6MwxJCBDCAIawmCXBwQaMLS+yZKm1L61uqbV3S2r174/mc3RbMBO76n1/5dwqlaRev8u9557znOc8Bw/kO9/5jpaXl/Wb3/xGp0+f1k9/+lO9/vrrqq6u1q233qre3l7rTep2xnJl57xer1VFwhxkt21qarKqzA0bNqi3t9dS3qQ6YZS6WQ8W+LZt23T8+HE1NDRY6TuGA5o0rEfk3VKplBkySubJNlATUVJSotzcXCtzB+tgPuD10JULUNYV6pFkFGgKANetW2cg8YWMi8Iw0I1JWq3Xx9rV1NSYanRfX5/p48NA7OzsVH19fZZaNHUV0mp1If0dsJ648IB41NCnUikNDg4aEEfxTTqdVlNTk6n6bN261eobEPqkdgB6NTHlo48+qqefftq+00WbWRgYO4wLrip6ha70OxkHEG3G6OiobrzxRpNFY8FB1MIbohI1mUxqdnbWCEmEOQCQXCvk0MiUPPbYY5aiRfwWrIWioKGhIf3DP/yDVlZW1NzcrAMHDujRRx9VIBDQK6+8ossuu8wo6egfYsQoMCovL9fx48dVUVFhvTMx6uzCxN4QkQDZOjs7tWnTJssicL0nJia0YcMGi/UbGxutaTDajGR+SL26dQaDg4NqaGiwkmZk5CFzYUhcN56BB4j6GKXmcDZ6enpUWVlpsvYwJktLS3Xy5Ekr9Kqtrc3KbElSe3t7lgAOhu6tt96SpP/fiqj+r46VlRWNjo4av/vs2bNWVsrAcyDH7fP5rNFHfX296QrSH2JiYsJINCsrK6YOPTAwkPX6xcVMvwRJpqREGS6LlhsNhx93lZsjZQwQABMINBJtL774ou1GhArEnMT0uK94DUwKxDskGVIuyUBRBjuc3+9XW1ubNVgBG2hpaVE4HLaYmsVH7A+bkIklZXCOiYkJLSwsGMCak5OjAwcO6PTp05qenlZvb69yc3NVWVmpubk5I1C53huyd5TLp1IpfehDH9L+/fv19a9/3XZZwrHx8XF1dXUpFovpa1/7mhobG9XW1qZIJKK5uTml02mrVnWxna1bt2apWXk8HgMKccsJIZCB27JliwGaVVVVqqmp0enTp419CDhJTA9XBi8EsNI17HgnUiZzBkgIiMi8DoVCmpycNE+HSlSyLeAR4XDYemtC9T537pzhcYx4PG7Zoenpaas1Kisr05EjRy5oTV4UhgF57mQyqbNnz2r37t3WV0FaFfT0+XxmGBBCCQQCSiaTqqurs+zBysqKNm7cqOnpaTMI8BcouqqvrzdlJxrSuGnAhYUFaziDcXAXjSTrDyllFibtzYLBoPLy8pRKpXTffffp5MmT5hISW2IIwBeo62Aiwl50y6LxEiDFcEzsRGRKvvKVr+ipp57SmTNnzKVuaGiQ3++3Iq9IJKLTp08rPz9f8/PzRgXGoIHEFxYWanJyUiMjIwqFQuZqf/azn9WvfvUr0yeYm5tTc3OzUYyvuOIKLS5mBF87Ojr00EMPqfdd4ZJ0Oq0333zTyuX5TnQrbrnlFp09e1Z+v19/8Ad/oGg0ar1Cn3nmGaXTaTU0NFhX7cXFRQuXaEAMtgO3oKysTMPDw4YZ8Bj1BNFoVBMTEzp+/HhWwZV7vclu4BWRwkRtidd4PB6raMSTAb/AMNB1G44M/UQgv1HmHY/H1dDQYPofcDsaGhoUiUTMENFHAv1OeA1LS6tCtxcyLgrDQN6VunG3jpyBUrL7HK4uoCG9JaARY7W5sVLGyNCRil1bygCFkUgkaxcmpKA1Hpbe1S8A+AKAKigoMCWib37zm8axgC8hycCwgoICq6zkGGAESjI9Ajd8IP4NBAJGwuG1ksy43nLLLSalDvIdDAYlZUAu6jSGh4ezgDA6KXNdpqamND4+rtHRUY2MjCgQCMjn8ykQCGT17iQtNz8/r2PHjpmMHlLrQ0NDVp0qSYcOHTJAElA3kUgolUopGo3qwQcf1K233iqPx6NTp04pFAppfn5eqVRKdXV1WZLvlFsDOFZVVWlkZMRYj7ABqU0gmwCF3fVYYFi6dRVgL5CMpNX0qLSq4ExamjnNosWzJBSijBo+xcTEhGWCeB8GhkIs+DJ8B6l8zoV7v27dOjsfftZiauczLgrDQJyVfrdfBFZcUpbXIGUjsL29vVYSTHaCxTc9PW2TmMXkDnYoPhvdg6KiIvsOKNiSslBdbgbuJhkQYvTi4mJ1d3ero6PDUljEg9x4uP48x07K41J241g3ZGGiYlykDA9idnbWKiU9Ho/a2tpMlHVhYcGwCkITAK35+XnTWSA1CfgK2/Dyyy9XT0+PwuGwPvzhD2dNPAqaBgYGjEfB+xcWFhQOhxWNRg2whRFYVFSkYDCot956S5s3bzaKeE5Ojvbv368dO3bolVde0dzcnG6++Wb953/+p+bn59XR0WFpzg0bNujTn/60kc7IaBGKYpTWVm+69zQYDGphYUHRaNSYiRh3GJFce1SqoUNzj9B+hC/B61yjxXOEPl6v10BexGkw2BRpwQ9hY8Kj5P2cL2Ak50dFLmGF6+mez7goDIMbxyOb7QqySKtiI+7uwoXxejMakcFgUKFQyCr7qHDEq5AyC4jGMABwTKRoNGpqRKTxiMO5sBs3btSZM2dUWVlp2QtJlj3Iy8vTc889pzfeeMMUnyA51dbW2m7gusFwFUDZ2a3ooSCtgpJcF8C3zs5OM4DT09Pq6uqy7Mr+/fv15ptvqru723LlgHvs1Oxk4XBYKysrllvHwIZCISs7jkQi+sAHPqCrr77ariUeTlFRkVKplMLhcBb/BNbq3NycZmZmVFNTo+uvv97k00+dOqVIJKLW1la9/fbbuu6663THHXdYURhZEY/Hoy996UuamZnRN77xDeNGcB2ZFxQNJRIJSzmyoyO1BvBIWs/V5cjJyfS7PH36tDV2cXUdCgsLrR/q7OysNm/ebMVTEKAWFxfV0tKikydPqr6+XlNTU5qfn1dubq5l3ei8jgc7MTGhtrY29fb2qrGxUbm5uRodHdWWLVvU29trmxfZDzYTtEbJOhDG4FmTuXDxuvMZF4VhgOBEmtLlK0BWcS0g1NKGhgZjCm7dujVLgp2iIYwCxU24fICSuM2wxDAMZDYqKystY0KpdUNDgyRlkaOo5szLy9PRo0fV3t5uWQWkt0DtiT0B+9h1wCGIZaVVlxUjwo/X67VWZOgG4Hq2t7fr+uuvV2FhoXbu3GmLEu8Er8RlSBYUFOjs2bPm+odCIas9mZubU2dnp7Zv3679+/dLkp0Xi5bMEp4AYdbOnTvt8+fn5/XRj35UZ86c0cDAgC655BIFg0Ht3btXxcXFuu666yzFODc3Z0AuLFEMF16HtCorh8aCJIvF2dVZRJRPswPzPZKMEEQogcdBqDgwMGCLj40kGAwqEokYPobyFF4hc3h2dtZSjmNjY3ZO8BwwcB0dHSosLFRvb68B0SdOnNDGjRs1Ojpq5+KSqshAVFdXy+vN9MiYnp42BW23KOxCxkVhGJaWlnTixAn5fD5t2rTJaiLOnj1rOyYXmdxsMpm0GgHy7G4be0RhYTqiMFRRUWHpoLm5OQshkEDHlcNToeYd41BZWWmgJj0gQqGQZQx++MMf6ujRo1nuvyvoSQaAOBCDsbiY6cm4uLhozWlIY5Enp9oTQVSXHMNETCaTOnTokG6//XZt2bLFwjJ29eLiYpuc5eXlikajJhizuLho1GiYmJWVlQqFQrrqqqu0d+9em2gYU0BUeku4zVfz8vLU0NBgkndgQb29vdq1a5fOnDkjSTp58qT27t2roaEhpVIpPfDAAyorK9PevXtN4fiKK65Qe3u7Jicndfz4ccViMUmyewfRy2XGuuGO2/vSTasCvFJPEI1GTVEJ1aSZmRkrc4a7wH2bmpqyv5PJpClUUziG7mcoFFIikTBV6lQqZZvhxo0bNTs7q7q6Outhkpubax6kG/Jyz6iNQOINYB6Dxpx1vYoLGReFYQBbaGpq0tmzZ7M4C24ZdklJiSG4ktTS0mKKNwUFBWpoaLC0YSQSMWn5lZUVo8TSTAXCidvqnGwCFZTEcel02roO0606Pz9fwWBQgUBARUVFeuSRR9Te3q7f/OY3tmPCaWdi4mqSIwc8w1i4Ooa4vC7zDe8C1evDhw9ngV2MM2fO6ODBg8aM3Llzp5XewlsAd6DgizqSdDqt3t5ehcNhbd261YRZaKXngmrsagC2AJJ4LhMTE5Z+g2X46quvGqEokUjo9ddfVzwe14kTJ/TKK69o8+bNGh4etjoUQp9HH33U0tCugCwFWDk5OaqoqFA4HDYpe58vo99RW1trIQHSbrm5uRoZGbHCo6WlJQ0PD2txcVFHjx41jQW8CzwJSEa8h1bzrpI0oDAFU3Nzcybug5eCZ5hKpdTd3W3cGmTl0d9w58ji4qJisZhyc3NNjk/KYCWItBQWFmZhdOXl5Za9uJBxURgGj8djoJm0mp5k0Mrc7R9JzEQMiyuLyjOFTKQvpdWGILyHElyIQslk0pSiWSQbN260SSytAlYAdGgoHD9+XM8//7waGhpsd0J01ufzZXkGLgDGRKKgR1LWbzdVJsmKg6DEoljk1g3QKKatrU3RaFRdXV3KyckxKTUo5GNjY9aZGuCSIq90Oq1Dhw6ppaVF+fn5lnUB+KT4jmwGcm5+v9/qU9LptE6dOqW2tjZdeeWVVvaem5urgYEB9fT0SMp4H7W1tdq9e7cWFhb0uc99ztxvjCFzg3QcICot9AD5mEsAcVx3dlkISclk0oBmDC+AYzgcNvyFe+SChRhv/j537pyam5uNDOVmO0hDYzBd/AXP1VWipvQ7NzfXvAG31wbXGmajtBomrKW1E25cKB1aukgMgyQ7Ubc7tSSrakR/AZCHVB8kIpB2LPfMzIz1XZCUtcBgwxGrcvNc1zyZTCoUCmUZBVx1blwwGNTJkyf13//933rllVesdh/g0N39JVkxjku2QVaO17lGg+OWVivqXNBwcHDQ3uNKvONWb968Wd3d3ZqZmdG5c+dMGJc8eSKR0MjIiIVhDIyqJF133XUKBoMmcAJ92+PxKJ1Oa2JiwtB/nmMRhEIhbdmyRdXV1SooKFBzc7Ppa6BPUFZWpsnJSfuscDis7373u6ZwxOdh+BC1YaAu5fV6FQ6HrQkRC7mwsFCDg4OWbeCacg6kJgFhKUSj3Dkej2eBkm5owTwB/8ILlGQ42NjYmIVcFFRB/15YWDDl6tnZWSOJuXgFhV/UuSwuLqquri7LyEOzducMIcXo6Kimp6eNMXy+46IwDC4Lz83lYu1whZLJpDG6QJwpvR4YGLAswfz8vMrLyzU6OmoeASECC21yctJy8OzWqOLgkvEYEy2ZTKq2ttbYiffdd5+6urp07NgxCxNIKbkgoRsSYMVdrUBJNkEZrofipqTcWhGk8Lxer6lPz8zM6IMf/KCWl5d1+PBhXXLJJQoEAorFYlpeXlY4HLbqPI6RXRPjQKZEkmKxmBUv4TGgKQGtd/fu3ero6LCCsPz8fA0PD+sTn/iEsQwJ09g1+ayioiKVlJSot7dX999/vyTZ51DCjdsN0EgRHHgMxLCGhgZriYdhwHOrrKxUcXGx8RtycjKS72QbXLk/uCVIupMR8Pl8phcBA6tT5QAAIABJREFUttD8bhtEKbOZgRMRnm7bts2OPzc3V319fRaeujUd6HIAhsIAzcnJUVNTk4UZMzMz1l9j3bp16uzsVFtbW1Z9kVsFDG73e1lElZubazUSkmwHwCJiDbmIVLURS27dujVr0TDh4TeQ2qMBSiQSUXl5ubm7qVQqC+CpqKiwY2hsbMzSYiCl+uCDD+rkyZOamZlRS0uLJGVV4THJXK+BtBrnhdqQy9Djf24ksbK0Wt0pyViC9MrgMdSGZmZmFA6HFQqFTK6dcuGNGzeqo6NDXV1dNoHc82fRg3izc7vfm0qlVF9fr02bNmliYkINDQ0aGBiwFKb7++DBg1q/fr0CgYAxBc+ePaucnBxzqeEvkHkAMKbpC4sGYw4xCCPM8VErMT09bSpL9fX1mp2dtY3CXeDs1NwfUpJSxsODQg6nAa+RTWxsbExjY2OqqalReXm59XiAg9DX16fCwkLrReIKBuXk5NimJUkjIyMKBoPGeJUyRpoqVJiWb731lvXwwGOA2Nbc3Gyegquo7kodns+4KAxDfn6+TZhNmzbptddeU1tbmyTZSbu7o0sugn9PYVA0GlV5ebkSiYQJvMIygxpdV1dnoBW7el1dnQYHBy2DAJqMl7G8vGyZjh/84AeWPnLJLCwu1xvACLjuKwg3AyPCTXabkrqWHrKKJKtq5HMwlpdeeqlhK/AWCgsLDZy64oor9Pzzz6u9vd28BcICJj2ZD3qCjo6OGmuSFC0NfDZs2KB/+qd/0n333aeenh5LA8/Pz1tR0u23326AJ2njLVu2GOOyoKBAhw4d0qWXXiqfz6cXXnhB+/btM2Up15NCV5M+JBgqmsQODQ1ZTQf3dmRkxDYLdB35TSEZnijeHKrjhBtLS0sKhUKGxywuLhoQSdUvNRGQ3Lq6urRhwwaNj4+b1zE4OGhNiCnCQnIPMhYhLRwNAFzm2M6dOyWtGnOMTnl5uWX3fD6fNVvGMF3IuCgMQyqVsi7FXV1d2rNnjzo7O7NITS4YxA0DlHKBRlDf6upqY7QRO8Iy47W4tn6/3xY6yjqk9vx+v1VJ/vCHP9Qbb7yhzs5OScqqa3AZigBQKCtLsnPAO8BgcD7SqqcQj8ctVsVT4TN4HJqvtJrKBd9g9+vs7NQll1yi5uZmA+H+/u//XpWVlVnX1U3vIX4DIezxxx9XY2OjbrnlFtNsBMgsKCjQ3r179dprr+njH/+4du3aZeeRk5Ojffv26eGHHzb3OJVKae/evQqFQpqYmNBLL70kSSbKGwwGrTx+ampKN998s2ZmZrSwsGAFUxg60qXoa4yMjFjjXQreaD04PT2tmpoajY+P2/vBisbHxy3DQm2JWzgF6BuJRDQ+Pm6hIpsTAGFZWZmFFxgXjBLMW7/fr7y8PI2PjxvxDoCSOQabdWVlxSTt4VcQ6rLIXWzDbW7b39+ftfG4Huj5jovCMCBAAnrf399vyK4kI3AsLi6aS+Tz+RQKhbI6OQHA4fqS8iILUVBQYDz12dnZLMCRxyCiuDzzRCKhH/3oR+rp6bFCIJeMBIWZ3c/NpbNTkXLi2Llx4BjSavdphqtW5HoHbW1tqqmp0cjIiOLxuAFwknTq1Cklk0m1trbqnXfe0R//8R+bPNvRo0dVWlpqO5aUmVynTp2y60psnkwmjYPw5ptvqra2Vjt27LDrQql1cXGx7rzzTgWDQR05csTk7GDz5eTk6Prrr1cymdTTTz+tK6+8Ujk5OTpy5Ig9jysNEBuNRrVjxw4zui5AnJ+fr8XFRVVUVNiigcdA346lpSXTlCR74MbzFEBBHSZTUl5ebkCkm94DJ3IbxbIJwKlZXFy0eYOHiUYllaBVVVVWnk1aEs9VkhVLEVqBf01OTqqysjKLfu4udvQY3PIB1+gDpF7IuCgMgyQjZbjilmvdaWoA3FibyjxSbaR7yEBQoktc6e4C3EyXS0B1m9/vt0X+z//8z+rt7dWRI0dUX19vx+AuEibT0lKmF0FDQ4MpFVPow47MsbOzS8ryIKTVmgiKkZLJpNGUi4qKrKgoNzfXNCIwHAMDAyZZPjExoeeee04vv/yyJGn9+vX2Hfn5+SotLdWll16qs2fPWh7fNcjRaFT/+q//qqWlJeM1hMNh7du3z5Sa9+7dq4KCAm3dutXKt8nR79mzR83vlrV/5CMf0d69e9Xd3a3f/va3htzj+XF+qIaTZgYjcrUnMNosSmlVDg/PEQ8LtJ85hscGIEyNA5kJN/ODoVhaWjIqsqugRCOfcDhsYSCgLGXbqVRKXq/XdEchP2FkqObFU0ulUlksS+Yh6lJQtQmnXCMAK5PrwJxylcjOZ1w0hiGZTJo7ND09bZOGyfLiiy+a2ITH4zGeeSQSsUkD0YW0XDAYtCYkWE5iaGTZ4SwAFlGIAx32a1/7mp588kmrw3Dptu4kwl2GOBWNRrV9+3ZDsHEb3VQXngM3160mdamsGAZAtt/+9rc6c+aMmpqaVF1dbdkEd7enaUlHR4duu+02XXvttTp58qQZ3lgsppqaGmsCs2fPHv3iF7+QlNlxrrzySm3cuFFtbW2WppuamlJvb69efvll3XHHHXr11Vf1zjvvqKWlRY888ohqa2vNW8AIIi+/a9curays6NixY5KkHTt2WBMYQDwm8fr163XrrbcqPz/f2gPAbgV4HBoaUn19vWFMiM1QAQkRDfEUvFEKqdgI4DfgjaRSKcNU2KCgk/PdZET4bgwM9TbcVzIZkkygGH2KvLw8M1KBQEAVFRXW7xTQdXFxUTU1Nert7bV+mbSqwxgwj1gPsHbxIMDoLnRcFIYB8NG90JCPiNU2bdpkhsLdrfmJRqPGPRgbG1NTU5OVUcM6I/4D9V5cXJTf71dRUZFRojEac3Nz+va3v62+vj6ztuwogI2w4/hMdjGk1pDZYrDYseTwGtwiLdfKr00j8nwymdS5c+c0MDCglpYWVVdX69y5c7bTw3GYnp7WBz7wAW3ZskX333+/iouLlZeXZ70joIPD7JMyqP6Xv/xl3XTTTQoEAurq6soStSVzkE6nra3azp07jRsyNTVl6UZptWiNAiCA3WAwqIMHD9ouCaU6GAyqqalJGzZsMHwGY0qPD1iuiLbgAULEctPUXq/XvK/JyUmbOwCP1KsgvlJfX6+amhrNzc0ZRuTxeCz7QxZFyqiC0eyWOUi8z3G6IQl/A5JDbEJmH5IZTYgqKyt19OhR+Xw+O06wBtrO4UVDAiSbgaHFYIBHnO+4KOTj161bl/7c5z6nqakpU51xrZzbpUpS1s5KbEkGYXp62v5np3XDCUk2sd06CnaNyspKPfTQQ3r11Vd17Ngx1dXVZbn8TG5cNnchYzBIc3F8cPB5DecCO849R77DNQ7cVNcjwFj4fD5t3LhR3d3d1oKNcemll+rmm2/W8vKyXnvtNVsgLFqO5+TJk4rH4/qLv/gL3XTTTers7FR7e7sGBgb085//XCsrKzpw4IDKy8u1b98+dXZ2at++fXr22Wd15MgRw3bS6bTuuece3XbbbVYuzPXGgC8vL+vJJ5/U7t271djYqKKiIjuu/Px8lZWV6ZOf/KTuuOMOdXd3Ky8vzwwAHIlYLGbsTIBLyrzxFFHKBhh0MSOEUyRl0avd+8rG5BLRAAdddiP9S8E9SAnT8AhNRrCGwsJCTUxMWJgRj8dVXV1tPAeMBNkEwjK8sLKyMutXikSBpKz50NfXZx6Mix396Ec/+v2Sj5dWi5VcYI423+7A9ZZW0Xifz5fFhsP9g63HhMRtplUdLiiufUVFhR599FGdPn1ax44dMzFOF8F3U5MuJgCJBlXlRGK1X2BOTo55BUwsl4TjuoW8nvNwh8vtIDySVlWA3WsnSfX19SahT3yOUWDHZGJVVlbqhhtuUCQS0ZNPPmnqTrW1tRoeHtbmzZsttFhZWVE4HNapU6cMBKyvr9fbb79tRhcPxK3k5LxgZra0tGR5VAMDAxoeHrZr47Zwi8fjdp8AHKVM7Ly4mJGop4AOfAnOC/eMqklplVBGpSLeJM+7EvFuNow5wE5NlaabUk0mk4rFYmpubs6iWCNEwz1DPIfQgOZCSOk1NjZatow1UlhYqNraWvsed6wFt10VNHfDOJ9xURgGSEikIam1p+OUJJssrqFwiUPU1HNxcE/xCngfDWml1QlGpdtTTz2lt99+WwMDAybvhdsJR93lJuAtYABAmZloLsfePT8GE83NYkir3Hw3W8Hx4k25348rzWRlLC8vm0AKVahwIbq7u602oLi4WF/4whcUjUb19NNPq7u721LA5OXHx8dVUFBgBUlzc3O64YYbDKcoLCxUOBzWLbfcYvJvboEToOQTTzyha665Rvn5+dq8ebPxT+AixONxHThwwLwfpPa4d/AtwIzm5ubsOgAUkm1g8RUWFioQCBjGQ/jnsm3dEBYAluwFWQkyHh7ParUmaUo6ekkyuT++kzkKsOjzZbQmCOEIO1wx3IKCAsViMQtNKysrzZNx7zHZKIwBGSzmk+tJXMi4KAwDaUhSldxcSqzj8bi6urpUXV2dxQ4cGBgwyW5JdmOgLrsdfkH4GxsbbYLhwhYXF2t6elrPPPOM2tvbTSyFRQuAmUwmjejj7hIYDCaRa7mZRO7ANQUscjMShCAu7sDrALzccKSlpcVa5AGKcQ1Onz6tjRs3GjYzNjam8fFxy85I0rZt2/TFL35RAwMDuv/++9XZ2WmNc0lHer1evfDCC8rPz9e+ffu0ZcsWM0aAt3Nzc/ryl79s9Sl0j+b9jK9//ev6whe+oBtuuEFvv/22qqqqzC0fGRnRpk2bjDFJ+g83GpYg+AgiKKhoIbePEA4l9FJmgdCYyA0vqHolM1VfX6/h4WGbgxw/Enrch4aGBgMOu7q6LLvS3Nxsmxt0ZQBiFLrJ5jB/GhsbFYvFjL+Qk5Njrf3YYABhyaKtW7dOY2Njqq6uNoDeFWOBv4PH8Hsp1CJJtbW1VqOQTCY1ODioyspKc3VxOZnQFImAPrOL8hvxkoqKCiM2MYHYRXD7FxYW9L3vfU89PT228OENkO4ClZZkikwuIAfIJa0uCkA3abWClAWF9+ASt2pqaixth+fhhhucP+rGpFaZ1C5lV8r0q5QybMHed1vds3slEglFIhH95V/+paLRqJ588kn19/crGAwqmUxmAY5rSVWg93hf1GBAgvJ4PBZWuboUlMhfd911euaZZzQyMqKFhQXrQwEQ56prkQUYGhqyPg3Ez1xPWukRMhCX5+XlWZ0Nr6fQzU13ezwea/QSi8VsDuCR0v8SPMLFFFzxYkkGsrKJTExMmApVMpk03UoyMngJMzMz2r59uzVYRjQI4BXafiwWs1QmHBZCM6T48BLAINx7cL7jdxoGj8fTKOnHkqolpSXdn06nv+vxeCok/UxSs6ReSR9Pp9OTnswRfFfSTZLmJf1xOp1++3/7jpycHI2OjqqoqEiRSERNTU22MN3f0ipqj6uNNfZ4PEbiCAQC8ng8qqurswXIwqTHJczGiooK/d3f/Z0GBgYkrRKO8vLyLLeNSCthB2kxd5IwGdxwh7w5mga4r+5AwdftxQiazN9MUBZ2IpHQc889Zzd7bb0EhiSVSmlgYMDOzefLlFufO3dOU1NT+ta3viVJeuKJJ/TCCy9YpR8GDhUmUrzJZFI///nP9cEPftAMQ15enjo7O3XjjTdmnRfXzOPxmAF55513tGfPHlVUVFjaEgYqtSx8LhoGeCNI2ZPz51oRHmEgpcyCYcdkjkCRB1OgXH2tPBvGnIbDXAM3FARbwOhTMQpRD2m+ubk5UyUHc5FkPT4Ik5jfk5OTJiCUTCbV29urqqqqrEIyPABEWWChsg6YO2SzCCncUPV8xvl4DMuS/jadTr/t8XiKJb3l8Xiel/THkl5Mp9P3eTyeL0v6sqQvSbpRUuu7P3sk/eDd3//roB8EIJG0uihIx/A3sZQLuCWTmYYxtHTHM5BknHzayft8PlVXVysSiej73/++Tpw4YeAk5BFuBD0w2WVdA+ViCG7pLgt5cHDQFinHSAs79BzxROAJSNlELr6L74Xn4eIVp06dks/nM/1LDNobb7xh12379u06d+6cSkpKdNddd+nGG29UKBTSv/zLv+iRRx6x0GViYkK1tbWW0nUzLgUFBXrllVf04osvmppTR0eHhWVjY2NWUAVLD6/m4Ycf1smTJ/W9731P//Ef/6Ff/OIXllHi81tbW7V//36rmsXlR9V6bm7O0odQnYnjEfIhxcixo8gNNuAyGwkVe99tWDs9PW0hg5utgJFJare7u1vSaik85eMYHhibCLaghzkxMaGKigor50dfwe2sjZpUTk6O6urqjB06OTlpm2Rtba3R8pkzkUhEAwMDhrOFw2HDbqjIvJCR87tekE6nR9jx0+n0jKQzkuol3SLp/7z7sv8j6dZ3/75F0o/TmfFbSWUej6f2d32Px+Mx1Rp3IUUiEdtpecyNETEWGAkILLhP8/PzmpyctB4Gribho48+qnPnzhk4RjNVJs7w8LDGxsY0MDCgrq4uDQwMqKOjQ+FwWCMjI3ZMcCi4STQ9wUC4FZHSewujXFzBfX4t0CjJKv1cQ8APjD/wGZf0Ql/HU6dO6brrrlNVVZUOHTqk5557Ths3brSeERgvQMdgMJhVCi9JR48eNY2BLVu2WJVrcXGxTpw4oZGRkSxti9nZWcXjce3evVvhcNjqJ0hPFhcXa926dbrhhht07bXXWuEbk9qtF5BW43xwB1f+DpDQBSJ5LYacOgd+MKQs9OHh4azryr1jw6KgDwDULXN2Wa1SBogMBALy+/2mFYpnw+djoKRMOFBdXW2EJzeTw+vj8bh5Gcx7r9ebVQODUfD5fEYQu5BxQRiDx+NplnSppDckVafT6ZF3nxpVJtSQMkZjwHnb4LuPjeh/GbFYzPoruCxFKWOZIa7wPNYTHQIpuwaBmJtdlVwwwOJPfvITdXR06NSpU6qtrbU+DWj98b7Z2VlTL5Ky421QY0lZIQKGieF6Oq4WAc+5x41ngEuIcYDRxmSgVJhqU97j9XqN9chEmZ6etpDl3nvvVXV1tV555RW9/vrrampqMkBMkqkfJxIJU9NmcE/OnDljoK3P59Pll1+uX/3qV7ruuutUVFSk0dFRtbW1mahKUVGR7r33Xi0uLupLX/qSjh8/rqqqKjMuCwsLuuWWW3THHXeYR4AcP6k+9DP4ITsBkIwBZbEDHoNbgRFA6gI/omzbBX/hYHB9S0tLVVlZaR4B6WjCR5/PZx3WKdZDVMZdkH6/37gK6INgQJPJpIGqLqbAcVJ9SR2IK47szjOYjoSDYBDgEOc7ztsweDyegKTHJH0+nU5Pu2BGOp1OezyeC2JKeTyez0r6rCTjm7u7FVacRQPY2NnZafJmoNmNjY3q7u62Cwjow04pyRSjQdzffPNNdXR0ZJXoAgRhld89ThUXFxtRxevNCLS61tldeG73aa/Xm1Vk4wKnTFpCG9eL4Nq6j7k5a17vgol8H3iGe2z8vuuuu/Txj39c3/zmN/XSSy+psbFRlZWVJoSKe4vnMTQ0ZKk3F1xtb2/Xs88+axRj3OFvfetbpl5VVlamnTt3mnH46U9/qscee0zT09P68Ic/rImJCcXjcfX39+uOO+7QXXfdpVgs9h4wraGhQfX19YpEIpqdnTW8grJwwhi8ht7eXisykmSZGjgsrrfhpiqZA7FYzNrKEYriySWTSate5H90HxiVlZVW+FRTU6NUKmUgI+XTGAe0MFtbW9Xf36/l5WU1Nzcbb2PTpk0qKiqyMAIchXR+IpEwqXgGoSNzo6qq6v9ddaXH48lXxij8Vzqdfvzdh8c8Hk9tOp0eeTdUoOB7SFKj8/aGdx/LGul0+n5J90tSXV1dGpBordtM/re/v1+bNm1STU2NCgoKrCoOxBdXGRVnVGtYsFjvvLw8/fznP9c777xjktuIb6Ij4BJbqIzE0ASDQTU3NxtzjV0PUVU3NckCI+RwF5ckwxi4mYQ/bvEYNxTvgZJswgl313K9JTyMkpISnTt3Tt/4xje0d+9ePfTQQ3rppZdMLk+SAVV8H+Ebi8h1mzGUhw8fztKYBIhrbm5WLBbTAw88oEsuuUQ+n09nzpzRSy+9JI8nI9PW399vqcjrrrtO27ZtM6Yhxi0/P99i9cnJSVNPpiy8pqZGXV1d1vmZRV5cXKyqqirF43ENDQ1paGhIjY2Nhu7DyWCzIUMBjgAzEtedeYCgS2dnpxlhr9drYUV1dbWGhoas2raoqEi9vb2musSmQzqXnR3Jt6qqKsViMbuHfr9fZ86cMXn5+fl5C6mqq6vV19eXRWDq6enJqpoFICX75DJrz2ecT1bCI+kBSWfS6fS3nKeelPRpSfe9+/uXzuN/4fF4fqoM6DjlhBzvO0jfJJNJDQwMmPvGCREzuoQgFjwt5KCvjo+Pq76+XufOndO6deu0uLhobhceCDHywsKClV8jHeaWUbtYRUNDQxYYOjQ0pO7u7qwYlJuKuGxJSYkGBwclKat5jCTLLriewFqvwH3MpbdKMi/F4/FY7w13t4jH4yZU+pnPfEYf/ehH9cgjj+jVV1/NmvRruxQlEgmTSEsmkwYOEgpx7L/85S/t+AoKCnTTTTepqalJX/3qV23yHzx40OpQ0um0LXyMXFVVla644grddtttBpYRG09MTFg2RJLRx6FDd3R0KC8vT4uLiyZIQ3q1p6dH+fn5ZtgwDmfOnLH7hQfBrnr8+HFJMt4CXsXU1JTOnj2rSy65RKdPn1ZLS4uJ3rjXXJI1OiLVC0+iuLjYQFk8hfr6ei0sLBgoCCGPazc5OanW1laTgkMMllYLpaWl2rlzpzFbt27dKp8vQ4cG64pGo1k4xYWM8/EYPijpbkntHo/nnXcf+ztlDMIjHo/nTyT1Sfr4u889o0yq8pwy6cp7ftcX5Obmanh42MAoYlkoq65rjZfA/7DXPB6PLRRILLDtiM3y8/P12GOPaWhoyPLv7IQYIyYM+XBSQu7CQNXZTRNKMho0E4JdCP4EO/3S0tJ7iqXc0Izz5zWcLyQtF3CVVheta3jwlr797W/rqquu0ve//309+eSTVnXH57piLxw/x+4+zvdwvG62Aobg4uKilQhLsnJzmv3CMMUgIYqTTqdVVVVl9wPvAA8MXAevj3YBcAO8Xq/1xsDIo70AyYfjxMBgGL1er3p6erRhw4as68a5k67t7+9XTk6OhWpcE7zOqakpBYNBK95C0CY3N9eMx/j4uC38WCxmwsR4Xoj70KH8zTffVH19fVZvTcKlXbt22bm5XqWLWbn3yDX+5zN+p2FIp9O/lvQ/sSP2vc/r05L+/EIOgsIRJp7r+kBMgfUYiUQsL+1OVLehiJQBGzEQsM5gWLp0W6ovCVnII7vybK5xklYr11yWodfrtdDHpdXi4bgkpfcDJlnwa5mQksygcByAkLx/rW4F8eVXv/pVXXXVVTp79qyee+45m8S8DvzB9VyIXV3DgdfgAsKf/OQntbKyou9+97tZHgRENAyEW3NAdSPy52fOnNGOHTv09NNP69prr7VdVlqtF8Gocp0xFsXFxVadyvet5RqwufA3XgALx81MUG8Biu/ySgAwGxsbTT2JUMAVYJVkRpISajAvn8+n7u5uC1/QZOD+AoICUM7Pz5t6NMdC1mTv3r1Z38m9AZRfWzbghq/nOy4a5iMoNCdVWFho+WGXOMTC4KaUlpZmZRHYtZhQiGv6fD4r8MFbkGQ3j7y7i05j5UkvMWCgSaspJyYXvQZ4HYtpLQbAwMqvDSPcFBjaBUePHs3qr+Auavc91EBcffXV6u/v1/PPP28pMGTjXWyCyQNNG9R+7XCN4549ezQ4OGhZi7q6OqtwTSaTqqioUHl5ufXNDAQCBu7x2fn5+Xrrrbe0fv16M9zQqKWMIA2aCH6/X73v9nBkl4V05fV6rd1gJBIxrUOOGW1GvBruFbs075NkBU3JZNKAWUnavXu3iouLDeh2iUMwDDGklGa7rRCZE3QBIyVLYRXGACIXBVXxeNxa642PjxvISmrZ9RrRfmRewdqVlGWIzmdcFIbB4/EoEAgYww0xV1cdGrecnZK/oby6HABcNFdODZWlyy67TIcPHzadASkD9CwvL9vkQPiTm0PnHyYhwGdpaakh6O6u7fV61draKinj8bicAne3csMJN5fNSCaTuuqqqyRJr732mnEX3LZk/HbTmnv37tVXvvIVHTx4UE888YQ6OzsNbONz3WORZB6JlDF2LS0t6u7utuNm+Hw+3Xnnnbr66qv14IMPSlpF7devX6/x8XENDQ1lqS/h6bjCObxvaGhIR48e1T333GNemtebkSKLxWJ2ffv6+qxOBJl2SpgxHG7BFv1EuZ9FRUWqrq4248e5FBcX6/Tp0wYicn045sHBQaVSKdMDcRe4azwpokK7FIo3YO7y8rKxGKnujcVi8vv9qq+vV09PjzXDCQQC1lciGAxaJoO2gszr6upqk0GUpE2bNimRSBghD896bdbrfMZFYRigwGLdksmkGQXy8uvWrVNjY6NZcNcVBGEndmxsbFQ4HFYwGNTAwICRgvx+v3bt2qXm5ma98847ttMvLS2ZpiDfyc5RVlamdevWZXEFpFVDQNzPzYFdicdBDtl1QX0+XxaQuHa4n3f48OGs2JGUoosrjI2N2WI7cOCA/uqv/kpjY2P6yU9+ovHxcSu4eb9sB98HUg+zEp6Az+czzUw3pDh06JDa29ttkdXU1JhxB62HEu1mPTBAuP6BQECHDh1Sd3e3GhsbrdYCTywajaqurs6OxefzWS/MiYkJS3PjWbregJvKRanZBYnLysoMQEboxs00uKIzbtjR0NCg6upqdXZ22m8wj+npaSu5hvVIOBUKhSzVPjk5qYqKCi0vL6u/v19TU1Nqbm42LQbo4C7PYWlpSbFYTJs3b9bY2JgikYgds9sc2HdmAAAgAElEQVQ0yNXv+L2urqQ7D5NckoGQWG6fb1ULQVrNAiD57fIeZmZmFAqFDPgKhUKGUqdSKV177bV64oknTNLc7/fbwnKZclIG2fX7/YaoM9wLjUGhgIZzYEJybPSoGBgYyMIGpPfiDmuJKzzPIme4qc7x8XHdc889WllZ0eOPP25VlK4kvatJwGe7n+/2ZWDisZAwRP39/dar4tZbb1V1dbXtaMFgUJs2bVI6nbaKRnozTk9Pa/369QqFQgYUkt49ePCgPvvZz9pEdrEhlJEo/WaBQlpaW7eSTCYNN0LE1cWl3Ovl8hmkTOHZCy+8oKWlJd14440m+3fmzBlt2rRJXq83K1XIfSJkQNYfJWgyAwCtkJfcsbi4qNbWVsu4UJ4N2EhYXVpaqubmZi0vL6utrc2MPal8PAi8SknGc7jQdOVFoeBUX1+f/pu/+RtJ2QAXKja47/39/VngmRtKANB5PB7TZsRlJn0YCATk8/lUUVGhf/u3f1N7e7uOHTsmv9+fhRNQSIV1B/nFPZNkbDspGwRKJFZFUcA7WJQsSBZhIpHQ2NiYUVx5zvVK3BoKuB7r1q3Ta6+9pj/7sz8z7OGpp57SAw88oGAwqF/+8pc6dOhQFobA+12jQPy+1jjwffF4XAMDA3Y+pI43btyorVu3qrW1VVdeeaVefvllhcNhxeNxbdu2TSsrK9q/f79OnDiheDyuF154Qb/61a/MhaZB7tTUlBmQvXv36jvf+Y6xW7l+fr/fcAmuJ9ySYDBoISb4hnsPCOPwWKqqqqwtAfMHHIuFd/jwYQ0PD2thYcF0IPAWi4uLVVZWpi1btpjUXGlpqc1TVzW8trbWKkEpmiovL9fIyIhqa2uVn59vIQKqVOXl5cbfAG8jswFfBjIZ8wlcyT130sFu5iSZTOrb3/7275eCk9sJqre3V+vXr1cikTDwiTQOE5uLDQjJDoIRAHSZnp7O4qWDPSQSCbW1tSmRSCgcDmdV0LkILrsI7rW7QzDWYgsublBQUGAW281IQJNFNTgWi1mFHt7CWncfz4D48R//8R9VU1Ojc+fOqb+/X9dcc43Gx8f1xhtv6Nlnn816n5TZgV0+hyvDj3fGQl17vC5Xg7oIJqvX69WxY8f08MMPa9euXbr88suVl5eno0ePGmbg8/kMz0kmM/UvNTU11nKOnZ1rQkdyJjdsSJiPSL3BeuQ3YCT4Bi455+TWNmCk6INK3QmdsACSPR6PCdbgWZIdgHxFqDo8PGzCNS4tGRLd9PS06uvrrVIXcRa8BDzaZDJpIkOAmAjf0DeVClLmC/eI8yUUHBkZUSKRMCHl8x0XhWHIy8szUAg1YHY2d+cnLw0zjzp8dhNCAPABdhIXTcca79+/33QEH3vsMeO58xl4Li5I51phKdu74f/3W9gg1252xXXtvF6vampqFI/Hs9DutcSU0tJSrVu3Tvv27VN5ebnGxsZ0//33q6amRvX19Xr88cfNcLAgMQyXXnqpcf05D46RDA7GAQyE83fPddOmTSZ5l5eXZ5O3paVFs7Oz+qM/+iN5vV795Cc/0eLiok1SMkJ4WhMTE5YeRl8CngL3geOprKyU3+83bMLV9+R/t59HIpEwPciSkhJTWBodHVV1dbV1s8Jb7u3tNRozpfrJZEZPobKyUl1dXVpaWlJTU5NuvPFGpVIp9fT02MKUlMWvQT4edWnSpgx4DmSwMGRTU1PWs5P7QpGYq1wVCASy5grzDk/B612l9JeXlysWi1mW5nzHRRFK1NXVpT/96U+rqanJcAY3zl6rj+gO4neKf5jIdDsiTVZQUGCxbXFxsTHCCgoK9I1vfENvvPGGVXfiKbjEKrIIazMHLijnLnbk76VMSTn8d7j179eollTd4OCgMdqIgTFW11xzjWZnZ3XmzJmskKqvr0/T09Pas2ePAVauDsC5c+fekxIF4AU0cye365K6RrKqqso0G6g/oSFMQUGBWlpaNDc3p7m5OW3ZskWnTp3S8PCwet+tYYjFYmYcKLDav3+/PvOZz1jFpVuoVlBQkJUydnGJUCiUVc/hLpSpqamsUIm039r+Cq4LPjg4qM9+9rMmhCLJ2JSSrGfI/Py84vG42tvbs74PchUZkOXlZVNZctPW3PPc3FzrNUooEQgENDExYY2Z5+fnLaVeXl5ubexc+TbmD6EvXeGl7LLsX/ziF79foURubq7WrVtnKUpupssFJ16UZEBSJBLR9PS0Nm7cKEnGcIPFWF1drY6ODlVUVMjr9VpTVW42smQf+9jHtLKyoueff15erzer9HqtgArH834GFbdTklGxk8nVluZkVdzsgpudINXa0NCQ9Tg1IrOzszp69KilP9va2tTQ0KBoNGqT5NVXXzWyFQsd7wGDC1YgZSbV6Ojoe7IOfP9aQ0jvD1xhn89nojZUQ46Njen48eNqbm429ShEdwmjYFnedttt+tSnPmUhoEvrDgaD6uzstF1cWtWFmJmZ0fj4uMnYQ0EnxHRdasI0pOA4L7zOZDJpHhJdoGBOIvhCERNhq+txgEHRd7KwsNAUp9zuWSUlJRobGzPPaGoq07sykUiosrLShF64tsPDw1bVWVRUZJ40FHiyXaWlpWpqarLiLjwm+Cnj4+NW8n2+46IwDG4t/cDAgCHQ/PZ6vSYFT6wEuj41NaVwOGwXiok8NJSp24KC6vV6NTo6arsLE62goEA7duzQjh075PP59NhjjxniTR6c3dz1VggvXAPhWvBkclWBeceOHaqtrbUYdmhoSD5fhtfOZ61lPra1tSmZTKq+vl4NDQ3W07GhoUGTk5M6d+6cKisrzbtguKrLLkDlGllJBvLhyrr4SVdXl52XG05Isnj97rvvVjKZ1I9//GNLK3JNotGoZmZm1N3drauuukrBYFBzc3MaGBjQ+vXrdfToUQUCAf3t3/6tfQ59HIqLi014haIkshGSrGmL21x3eHhYgUDAQod4PK6SkhIrpecx8IBEImHuust6lWSGWZKxacvLy63bGW3mKE13PYDS0lL7PhrgVlRUWGdtjA8pVsJFOBgLCwvKz8/PajwzNTWluro6TU5OmkAvOA+b2MLCgs6ePWv3irAK404jpQsZF4VhcIGppqamrNzrWrYjF3QtyQc2IpOYwhU3DZlMJm3igW4DMhUWFuryyy9Xfn6+XnjhBQMj3y//6+6u7B5rMxTu/+3t7Zar9nozpdg0pXGxCDemZnDOblhTUlKiuro6HTt2zFx+F99gJ+R/t9CHx8jxAz76fD5T034/gNXr9WZ5FC0tLRoZGbHwjfqKcDhsCHo4HDaXemlpSV1dXbrhhhu0bt06eTwe3X333SbdRvUqsv+xWMwqIjECgIBzc3MWO9fU1KiiosIWHKpOLOSFhQUzyF6vN+u6c725RniQ9HgAR5FWwwjmEfUuVDoSLoAHTExMZKk54X3S6EhSVt0MwjToUQCcwpNJJjOVmLOzs9bxq7S01DaX6upqe2xsbMwMiGu4LmRcFIYBj4HsA0AkCwJOP6W27Kqg7FhwPAbiUchLFNhIsqIcbg7MslQqpeuvv95c3h/96EeSspu/uDsEw73oLprP/xyfJGNKYrzKy8vfkwXAxZZWswDkvaHOgqQHg0FFo1F1dHRYtobrs5Zq/X6cfjcjwfeudbWhR7vnTAWim0alIpIBhTmRSOitt95SIBCQ15upEqRb2MGDB3XgwAHDREKhkOlq4NlB1S4oKNDo6Kjy8/OtYpOdlh0RQ4BkHKEF15vHXSOKQnhlZeV7qPV+v19zc3Nm2NyMEuFhQUGBmpqa5PV6jdw1PT1tmqI+X0Z7Ew5DRUVFluRdWVmZeYMYFd6TSqVM/5G0LiIx0L65rxg8SQb28v2U31/IuCgMg/TeYiKIPM3vNkStqqqyIpfx8XHbRdgZ3WpHFjPuO54Cegy4dBUVFfL5fOYGz8zM6Oqrr9b111+vQCCgzs5Ovfrqq5KUZazc9J206jW4u7KkLPec2Le0NNMqr7y8XJOTk2agXBASIVJJJqMOAw8Eu7y8XKFQSM3Nzdq4caOSyaTFosePH/8f054uhsM5uJ4Yxy29F18gddnY2Kjl5WUNDQ0pGAyaPsbevXs1NTWl/v5+DQ8P2+LcvHmzioqKDFxjFzx9+rRuuukmA4anpqZUW1urvLw8oxyzk+LOuy5xMBjU8ePHzUiGQiHrRkXohMtdWVmpSCRiO3FZWZkqKyuz2IFsJPR+yM3NtQVIH0wk6kk5wq3xeDLNjaurqy0rwevKy8vl9/sViURswRcVFZlHgE4Dafienh7l5uaaPsny8rLpf8zOzprkPaDq6OioampqzHvB0DCv3BTu+Y6LwjCQtmLQQch1obu6uiStysYnk0krGCF9xkKtrKzU8PCwmpubbUGzCxNLcqHdgUaDx+PRvffeq2eeeUbFxcV6/PHH7XiwvCyktbjDWpfNJRBhHDo7O81o8Dler9fk3in1ZUeSVuXnAbPwshCXgTNAWmrtogaw5TnXCLheD9fQDY8oWuJ5KldTqZSVVF911VX667/+az355JM6fPiwYrGYmpqarGYBZWpETHw+n0ZHR/Wzn/1Mt956qy1ON/sgrapo9/X1WRMWRF3m5ubU1tZmABs7KRgDvyXZc5x7QUGBpqamFIlEDKDFSOOhwSVALt/j8dg50yWLOTA2NqYNGzZkLWS8o2g0qtzcXBUVFUmSibKUlJRk8TcwhLQ1QOqNcm94DPA5OD/K1vGa3Cwe5eAX2rvyojAMFDCRTYBQRKWcJEOI+Q32UFpaqp6eHlvkXBhETEmduflvdj5X3JO4m0KW3Nxc3XzzzfL7/fqv//ovi0nXpvz4Ttdtd6216zVAolkb89E1iffwHDvG4uKilSQTAyMGQk/FVCqlaDT6HjxhreECaHO5Gq634BoUt7AKo7iysqJgMGgTvaGhQaWlpbrnnntUWlqqAwcOKBwOq6WlRdFo1BrdSBnKeEdHhwoLC00BmZRiWVmZdYdeWlrS0tKS9bAgnMEL4TGuV3d3t8m8QWBjwcBdoNFxX1+fWltbrf8C51ReXq6zZ89aaT4ANFqhbEJ5eXnq6+sztx+iFBgM1wmV556eHquJKC0ttS7WkiyjsrKyYjoOcB6Qp8c4hEIhFRUVGfWcOYxnNTo6ahkLshJcD3dDO99xURgGKRtN37Rpk2EOLGTCCHZ+F3gEX0DbD01/aiFYSDMzM2YwRkZGFAgEzCtB2psFh8s3Pj5uSDN5dcba2JvH3MGiWGtE3DjXjYNdYAraLEbL9S4Ax2ZmZjQyMqI333zTrh2hAHx5hluZCqCbSCRscUIcc911dh0MKbtTR0eHTpw4oZaWFkPhA4GAMR6Li4t18uRJSZmdd9++fUqn03r99dc1MzNjjVxR3g6FQva9YBWAiExyMlNcR3Z90tFkniorK7WwsGDvh1w1NjZmKUM+h1CD9CbduQGYPR6PzSNwAEhPY2Njam1ttVie52HYTk1NmcGCxg1ORFiLDP7Y2Jhdw3g8bp4VnbHm5+c1ODio8vJyC4fd9DnzB6wBjwJcC5DyfMeFaz79Pxig4eTZ1yomsXhdlxzmG7US5JextDSdoeoNeTiv12uLD1aZG1IQTvD32NiYue3uAsVD4Jj+J+SXUIjnuJl4DwzYm9JqaEU8u7i4aAi3mz8HmKQXBp/j862KwDLcv11iFbUGa+s1+FmbrvR6vaqrq1MkEjGZeFcpa3Z21mJr0Ph9+/bpyiuv1JYtW+xz+vv7VVhYqOHhYb300ktWKo12g9/vVyAQMOIV/SaTyaQpEmEcXDYnOzcxNe414ZBLoQY7cGnf3F8wg5mZGS0sLBiASCVufn6+Wltb7Xhc3gll036/X4uLi1mKWChkAWLOzs5aGIBupuvZDgwMKJnMaENwrHgCnA8q3xgDfjCG70fM+13jovAYWLiEECw6PAZpNd1IPwnIHi7i6i4s90LQBVnKLArYkDxHqsw1ECDlWGCAQzdMcCeUC0jytxtCuI+7fAI3pl87iD8RV3Hz2/Dx0Tk4cOCAEomEzp49awYWw4gStYsTMJFJ6+HFcCwcL7Rm8A7ESn72s59ZkRHfJckWqZSZ9H6/X3v27NGnPvUpvfnmm/rpT3+qdDptuMjs7KxGR0ftOFdWVpSXl6d0Op3F4CTt52Yp3FoJPhNMiWOGp+CGRW5Kl0WIZDwbCXOOBrMs9KmpKauZ4HPARyoqKiwEwihQD4GB55rPzs5ahiEvL0+JRMI0QGgwMzU1pdzcXMu6wOaFm+PeS7cCk7nJJsrjFzIuCsPgyqjt2LFD4+PjJuHGxWtublZnZ6eVYbe1tam/v1/btm3T0NCQuX0eT6ZvIjsGlpVUFLluDMX09LQaGxtNDAaAKDc31+TMqQbk86RssVaYhlJ2/QRxqeueuwbCDSfY1TgmSWaolpaWVFZWZpOdSUnxDcedn5+vDRs2KJVK2U6xFmfg2N0y7KqqKiM8gYFwjPRxJCW5fv16xWIxDQ8Pq7i42K4zWROKgBKJhD7ykY/o5ptv1o4dOzQxMaHLLrtMH/jAB/Tqq69mxcBnzpxRQUFB1vkCsCFrTzzOtYVf4F4HjNzKyoomJyft/KEwu/iVlEmPDg4OqrGxUf39/Ua0SiQSRkNGGg7yETU1IP+0NLj00kvV399vKmBIsVHyTSaG804kEorFYiotLdXg4KCFqolEhipdV1dnYQdCt+Xl5SbHzzxKp9PWnm4t1Z5Qp6+v7/eT4CTJCqTAEPAWmNiRSETr1q0zRJ8LjsQaYJokk3Lnb3LoxK7V1dX2Nzn16upq5efnKx6Pq6ioSF6vV7/97W/V2dmZlX1wefx8LyxHd6G73o471noT/GahspNzc0nhQYxhN2UHi0aj6uvrU2lpqcWtoP5rsyYcN9iCGwYxmdceM7UljPLyco2Pj9tjuO2Qmuj6XFxcrLvuuksbNmwwADg3N1d79uzRqVOnNDIyYuDy4uKiXnrpJe3Zs8fUvmk+S5zOcdB0lzBiYWHB6h/Ydel/mkhkS6e7ng9eqWv0uZfQ5vGCoDC7oq3U2QBA0r2Kegak1Px+v4aHh+0zwUVWVlYUCoWUm5urmpoa80zBj/DGMJAYvZKSEvMwSMnCmgQnQbEL9uNa3Oh8xkWBMXDQdA8qLCy0kAEcAe0FkHppNZ5GGIN4zpVHd7URuNEQR0pKSoysA/uSNnZer1cjIyNZKdP/afA8biXuG1Lea11Od0FyDpKsBgJDg8bj5OSkRkZGLCMxPT1tNGNKn0+cOKG3335bJ06cMHcewpiLJTBcg/HBD35QH/vYx7LYpQxX50DKGLDR0VFt2LBBjY2NlsYD4EOUZOvWrdqwYYPi8biRzCYmJnTjjTdq3bp1Ki4uVk9PjxVSPfXUU/L5fGZAAPIoTWcRkpZMp9NZ8v0QgLjeCwsLGh0dtSwFi2RkZMQAyXg8bgsVHCGdTmt2dlbRaFSRSESRSMQa4AIGo69A1mnbtm0KhUJZ3uLaXZ2BUcM7nJqaUjQazaptIFWLIaMmBM/JTUvi4UF2cpm/GE+v15vVFOd8xkXjMbCTjY+P68SJE9bxOpFIqLy83OoB3CKbdevWWeNYThykXpIRhVwtSfLiVBRGo1ErUMIyI/RCxSI38/3iNBdwdNOW7FSgxxw7sS2GixvLpCYFRWjS2dmpqakp1dTUKJnM1OkXFRWpu7vbqhu3bNmipqYmLS0tqbe3V6dPn7a4GjCX46JcFyNRVVWl3bt3a+fOncrLy9Px48f18ssvvyfkIZTYvXu3HnnkEUmroY5LHXaFYOipAbAKG+/aa6/VwMCAxsfHzVs7ffq0/v3f/1133nmnNbHNy8szajEkHSb8ysqKNm/ebIuae7qwsGAt5BsaGqyA6NSpU5bedoFgsBswjby8POPU5OXlKRKJGCDt9XoNX+CaxuNxRSIRrV+/3oqifD5fVhMeKnnRgdy1a5elU3NycnT8+HFFIhH19vaqpqZGS0tL1tl7eXnZwN7y8nIrKoP6TAiIYAzS+5KsyKqkpCRLAOd8xkVhGFKplOXj2elcGWxAR4go0mrowXANgiTL/+L+AV7hGZBSckFHr9ebRbSan5+3RQHAttYlY9K7hBcGC93lFhByrDUoazUZ13oXUkY3oLq6Wnl5eWpvbzd8YPPmzeYeb9261ToSnTlzxgyA27DGlWPftm2bNmzYoGQyqV27dqmgoEDPP/+8HZt7vkVFRebGxmIxDQ4Omh4jO1lZWZny8/Mt9AGwRMQ1kUjoox/9qA4dOqTOzk7T5MzJydHbb7+tu+66y0qrXReYBQwew/fhIfA90JbBCvLz87OaGLkhmmvEXHyEOUL5MwaHBRcIBMxjoEs73IJEIqOfiUAPHlAqlVJVVZVKSkrU0tKiqqqqLCLV0tKShbMoji0sLFjomEgkDKQldOA3846wwsWI0LV4v7D2fxsXhWHA+rol1gCJLlpeU1NjO6uUMQZlZWVWGutyDGhhn06nLS6VZEw8twmtKxKD4aHluvReAVg3jedmP9ydWJK5se7z0iq3wcUV3JBCWg19AMxcncctW7Zo3759euutt9TV1WXXz+v1auvWrcbTz8vLU1dX13tYmmR++vr6tH79ejuf5uZmlZSU6POf/7xOnTqlZ5991pSwlpaWzEMjFGNxwqnIycmx3qAuIMj5SLIK18svv9zK3GdnZxUKhTQ8PKwnn3xSBw4cUF5enlZWVhQIBNTT02PXD++Bwilc9pWVFeMMkP7mXqEARegG6EnWguvjFh5NTk5aKCNljAQkLDYIFv7KyorOnTunYDCoeDyuvLw8Aw1dtaetW7eqsLBQlZWVJuxaVVWl1tZW49AUFBQoEono2LFjRoUnCzMzM6Pa2tqsehCMcjwet9Z8GA1JdkwXyny8KDAGSWYIFhcXjcpM1R+ZiIGBAZWXl2c1ERkfH1dZWZmqq6vV1NRkIcX69evV0NCg5uZmWzRQrUnD8R01NTW2o1Bp+fTTT1ulmxt34zlgEFxeAhJf7oJwKdGcD+caj8fNm2hqatKmTZusrPyLX/yivvSlLxk2wTGMjo6aB7R7927dcccdkjIhR3t7u8mQwy1oamqynZLBNUqn0/rQhz5kC2NiYkK5ubm6/fbb9ZWvfEV//ud/biK6y8vL2rRpk06dOpVl/ABCXYUmJNyj0agWFxd16tQpSRljW1xcrL6+Pt15553atWuXlpeXFQ6H7XoePHhQgUBAp0+f1pEjRzQ9Pa2tW7dq586dpjVJhooqwmAwaLsntQOUY09OTpp+It3OCR2QPaPUP5FIaH5+XpOTk1naEaQNu7u7LSXJNTt79qxaW1utPsPn8ykUCpmHU1dXZ57F5Zdfru3btysvL08PPvigPve5z5lBaW1tVUtLi4nlomfJ8ebn55uYcGlpqc6ePWsbE8aQupGSkhJVVVWpsbHRMIYLVXC6KDwGilaY/OyesB1ZnHgMKBYlk6tl2oBr5KS5ccT73Gg3v0wsCs3WbX2GTD03wt352WFdD8Id7s7/vz2H14HRcI3N/fff/76klMXFRR08eFA7d+7Upk2blJubq8svv1w+n89wmHg8bm4xk5hzYfT19elP/uRPTE8wkUhYExRUiD7zmc+oo6NDv/71r5VKpXTZZZfp4Ycf1tLSkq655hotLCxoZmbGUpbub4C8yclJNTU1GRPV5afU1tbq+PHj1mEMuTpqL9LptBVVUR8CALt9+3ZFIhF1dHRYvYPf78/SeOS+uZ4Z84x5lExmNC/QsfR6V3s5kLouKiqyIjApgxnwWpdM5upUkv0YGhqS15tpR/DrX/9aUmYXX79+va6//npFIhGjYEuyMEySGZjl5WULF5CVp0O2JGufAJ4AuYnQEbbnhYyLwjCgeiPJftfU1Fgo4WoLuOlAaTUex4DU19cbfba3t9e0/MfHx43oxIJkwVRUVNgOT5pqdHTUDBTf5y5UjouJ8LtSQq4xcbGI98uyIImPAhRuMThEMplUZ2en+vv7dfXVV8vv92vr1q2qrq7WiRMnrAlJ87udp91jljKGY3Z21mjo7GjUJkirwOKJEyfM3W9ubjZCzhe/+EX94Ac/0OTkpFKpVFa3JbwImIxzc3OmtYm8fiAQUE1NjTZu3KhwOGw9Ijwejx544AHdd999VkAkydKD3KeKigo1NDTI7/fr7NmzOnXqlGEKrqYjAKGUWdDxeFyVlZWKx+NZ4jWIrBAKRaNR05/EqMFr8Pv9hrXgomOIioqKrG1BTk6Oicj4fD5997vf1fLysnbu3Gn8nJdfflnd3d1aXl7Wli1blJubq2uuuUZ+v1/d3d3KyclRWVmZgc6uDmhVVZVJAGIQqJVgzmEQfy8JTpBXpNUFRPbA4/EYhwEQiceJH6lEQ+iCDti47FNTU5Z2m5mZsRwxg/6AGKiHH35Yp0+fthy7i767PASQfxdbIDzgXNw6Dz5DWpVid28YYJqUHYLwXogs9N9MJpN6/PHHLYxyX4Oby+ejtp1IJNTT06N7773XEHAQdNSHa2tr5fF49Prrr2tkZER5eXm64oordPjwYevZ4YK2YAxQuHkcAI3FxmM0ZfnTP/1TU6BaWFjQyMiISktL9fbbb1sDGsqgaVSbSqUshi8qKtKuXbvU1tam7du368iRI+ZiQ4yiXyb3DqMRCoWs5wTXHqxg165dWSxYQtW1tSuEeAjMwqgl+1VWVqYNGzZofn5eDQ0NGh0d1ezsrCYnJ7WysmKbGtkDv9+vlpYW5eXlWV3PxMREFv6Bl7Z2/kmrehNuOtblbVzIuCgMA/ReJjEegIskU+Dj7qIurZXY1s0iuCk32t1ThwBQ5bpuPl9GmyEcDpuhceNp1010gTxJdvHdKkkyFi6AKmUMhuspMFwcw32tq1OBYYGj4GZuuH683y2wwShwXUKhkJVMMyDl4Eb39/cbLXjDhg3q6enJ6t/xfh6Sy/lgx+ZxXs/9evHFF0XSVMcAACAASURBVOXz+VRXV6fR0VFrzhKNRvXNb35TP/zhD60hbkFBgRkEmLLSKnDd2Nio/Px89fb2qr29XbfffruamppUUFBg5zU9Pa3W1lYD+dLptCKRiFWBorK0vLycVbfANUHzEVl54nvcdBSZ8B7JtCWTSaVSKSNT+Xw+43XEYjHrcdnb26uKigoFg0HTbwBcRTkbbwRtDmjgGBdXi8Elev1vnc/eb1wUhoFaCUlGcJqamlJ5ebnla3Hn0YKkL4HLICRvz2JtaWlRJBIxlzQUCmlxcVHz8/OG7BK/wkp76KGH9NRTT5nuH63W1sbpbmpRWq05kGQ3w6U7s8uk0+n36DJiOPhx05uSjHAlrYYbLhHM9Tp4zl3w7nOJREavsbGxUW1tbdZXkevApF1ZWVF/f7+5xsT/+fn5amlpMUzCLVhi0RJKUKCGuhMc/46ODvX29mp0dFSlpaWqqqqyKlYAvJMnT+q+++7TH/7hHxrzkM+XVpsgc+2lDCvz7rvv1qFDhxQIBGyRuw2FksmkHSNgNLqiUqY2hnDKHbyPzykqKlJjY2MWOD01NWULeMOGDVb0lkwmNTk5aenchYUFa1wLjkBnLkhMSP3TOzUvL88MZ2trq06cOGH3EzC5qakpS4uCOiBA8QsZF4Vh8Hg8an5XUdhF8BHSgCzkYg1lZWVW9wDq6tbX19bWmn4gzxcUZCTPycezA3k8mU7Xjz32mPr6+rL4EMlk0uTUoOa62AOegdfrNcl4V2bLZTm64B+iomAX4A9uqMG1Id+NopD72aWlpVm0XwwKxsH1NNg1UBki/y7JJmM6nTYAcmpqygRHAoGAwuGw0um05eDxAti91v6NHga5eDQi33nnHY2NjZmX5ff7lZOTY94BxLAjR45o+/btll6sra21nbukpMTAYkhcUgaj2rdvnz7/+c9rcnIyC6Pg2Ah5vN5MpWhNTY3Vovh8vixcAU6BlMl0sUEsLy8baIhxhiFZVlZmmqN4noFAQF1dXUqlUpaC5twbGho0Pz+vuro6jY2NGQi8fv16KzZLpVKmIgWWgBFZWFgwzQlJWZuRy1+5kHFRGIacnByLM90Baoy7xIn6fBnFHJhtjY2NZk2np6cVCoXU39+vdDptpbuEBeyk0WhUwWDQmG65ubnq7OzUM888kyUJl0wmTbobw8QgpGC3mZqass9/v5jO9TYIAaTVEMIFRF1eg/uY+x6kwVyZLwZGAZzFHSUlJdq6dasRa6TMDoxwa25uro4dO6aRkRFrIe+GM7t27TLtC1KTuPduaTP6jBCcuBfQjynN3rFjh06cOGFZJZ6PxWK69tprrYCJUnRUlShmAnuA6FRQUKBPfOIT+vGPf2yKUBgE7o+bXkWC3+PxaGxsTIODg1m1EXNzc8aFcUMIaiPwFrxer2lRJJOZhjU5OTmm7lxfX6/jx48bkFpRUaH5+XnFYjFt3LjRjp1rg0p0TU2NRkZGTMEJrAQPkzaHLlOVGg5A0d9LjCGRyDT7AH2dmZlRKpVSb2+vUUlpChoMBk14JRwOa25uTuPj4+a6FhYWWicq8tXsbFjwQCBgfQIk2Y7W2dlpOATVmIWFhVkNTNltJVmqDqPBrk2I42Yf+O32G+Qz3YzL2g5QLG7ezyInJiZV5XojMCJ5PyQeJgdy9NFo1OpTJNnunp+fr/b2dsViMSUSCe3fv9/o1OvXr7d28HgIGAbUiUivzc/PG3CYk5Ojvr4+nTt3zgBEFsHc3JzWr1+vjo4Oy3AUFGTEX5966ilNTU2ZniPGHoIS4SbFb1VVVVpaWtLdd9+tT37yk0aIC4fDGh0dNWNI+BiNRjU8PGy07v7+fgX+P+rePLrt8swCvj/ZlmVbsi1Z3uQ1XuM4drYSnI0AIWyFQNlpp3Bm6LQzlPbrKT3T6TbDtAxtSlsozJSdQsvahCVhgBBSQggkcRI7sZ3E8W7HtqzF8ibJlixZ+v5Q7uNXhrbh+86ck/mdkxMvkqzlfZ/3ee5z732MRjidTgGj6e3Bf3Nzc9L6Hh0dRWZmprBfp6enMTIyIq1kh8Mh3YRwOIzS0tK4liq7MTxU6CvJ95vr3GKxyNpmqcZslYN8WDqxE5WcnCzvNdf6uV7nRWAIhUJwOp1ic8Wp0VTtMd12OBxySgHzfgVM9QnSsPWm0+nEw5HRno5OtMniRnj55Zexb9++OOt2jgjjxmOmQpIUySc0OlmYtqlRWg0QfK6qbkLFFtRNrt534eMwo1IzCWItwDyvQMUxAGDx4sVwOByIRqNiPEJ7dZ6UFKXRyZh08pycHGE5MkiRt8DPQtVOJCcnS8rv8XjQ3t4un+HMzIyk7YWFhaJNoRBOp9Nh27ZtWLZsmZCoaOs2MzMjmgqSilJSUiRY8UTPycmBxWKB2WzG8ePH8dvf/jZuUGx5eTmysrIwNDQkhjzEA6LRmBEODyPVjzIajRmrMLPhuiKDkd0Pnv50BOPEdro4paamyutKSUkRa0F1+hSJZ2lpaYLtWCwWDA4OSreHNG2LxSIuVgQkF07XPpfrbwYGTdMMAD4CkHz29tuj0ei/a5q2CMArALIANAH4ajQandU0LRnAHwCsAuABcGs0Gu3/a38jHA7D7XYLOstTBoBM8SGTjkxGnvykjZIAw+4E+7lMPzUtNu6ennmUCXMBHzlyRIxBVISfYJOagrIFRl2AClapKT07GOrjAfMaCmDeYYh/S+18LAwQPPXVMkQNWmpJwvuqnYOJiQmsX78+Ll0lM29yMjbYhOi/Kl0nThONRrF48WJ5/7h5FqwX+Z+UdKb4/CwZWKht0Ol0sFgsyMnJgc/ni3NWGhgYwIMPPiifFz/vrq4uwX1UD0wGfZ6QzBYzMzOxcuVK5Ofno6enB9PT0+JAXVBQIJki8QJOoNbpdNJ6pJszgyLfQ148yNimHR8fR2pqKubm5kQWTe9J3p4Ygk6nQ15enmQMExMTiEQiIsvmeENqhOg8TYyNAYL4BLkWqgbk81znkjEEAVwajUZ9mqYlAfhY07R3AXwXwEPRaPQVTdMeB3AXgMfO/j8ejUYrNE27DcBWALf+tT/A1AlAHJGDbzBRY7Ih+aYYjUbxZ2RHgpgEASladLEHzY1LADItLQ1bt27FqVOnZM4DeRQE8Nju498AIJuCQCUzDbZBVX0Fv2c2kJGRgbS0NJw6dSqu9amCr0wT1TKDGz49PV3KEG5MPh/+nBkMyxIGm0svvRQulwtZWVnSFlOt2QwGA3bs2IEzZ87IJjYYYv6HVEaqIKn6GfKEVMG9gYEBmM1mVFdXywQump0WFBTEEdtuvfVW/O53v4Pb7RasyO1246GHHsLDDz+MUCiEw4cP44033gAQAwPr6upkrTAA0cyEwYEamJSUFDz44IN499138cwzzyApKQlWq1WwhISEBBiNRhQWFoozFiXvTqcTFRUV0DRNshaLxSLiPfJVaBFgNpvlM6AqlC7Z/Ny4xnJyctCvjPEjuDszMyOZUklJCfx+P5xOJ5YsWYLR0VHYbLbYBg0GYbPZBGeTjRuM+U2Qy/B5rr8ZGKKxlec7+23S2X9RAJcC+PLZnz8P4D7EAsN1Z78GgO0A/kvTNC36V2iBkUgEdrtdrLPIgKN3v6bFzFr5xvHF87TJyMiQIaDp6elxp10kEhHjUYMhNsiD5BZmDO3t7VJ3quXG2dcvdGvVtZo8BG469eXxZ8Q4VBYeoztxECAWVNR2JzscNF9VxVoMGMC8c7MKOvExqLRjOZGdnY2qqioUFxdjaGhINjowT8OlaOnYsWPy86VLlwruQRIY3xdeDIYLgc7BwUGho5OVyN8z46M7stfrRXFxMQoLC4XizCzgk08+EQzo0UcfFfMaIHZC1tXVCQ5FYC4QCMSNmGcaTmZhQkICtm3bJiUmAOG3ZGRkiGsTT3SDwSCBQNNiw444DYsnc1FRkRxKgUAAVVVV6O/vRyQSQWpqKmw2m3hdqtiQz+dDUVGRHEp+v1+GyzBTpYs6Z3rwPSBdmwGZHhnA/FBgj8cj9O5zvc4JY9A0LQGxcqECwH8D6AEwEY1GiWgMASg4+3UBgMGziyesadokYuXG6ILH/DqArwOQDcv6i7UcU0/2hhnVvV4vnE4n/H4/KioqJAuw2+1i0T0xMSGtSZ4ejOysg5OSkvDBBx/EGcoCkBJFnUmhEpcAiJMUW4bcMPxQKNTiffgcVRIUHXyzs7NFpEPCFgCx/laVoXw8Bg96FnyW2pPkKmYQZWVlACBzF4eHhyWtZxpsNpvhcDgEwSeIxtOP5rMMygTi6NkIQIA2prX0OGStrRrbaJomfog+nw9XXnklmpqahJXIk/epp54SU1m2JOnVyQE+s7OzMjdzenpalJ+kO0ciEWFubty4EQcOHEAgEJDBsQQPA4GYsIoYDM1iyA2Ym5uT099mswnFPBAIyPuYnp4Or9eLxYsXw+fzyRAajqRjdkfHLbPZLEOP6WJNLobdbkdJSYl0awiyJiQkICcnR95fsizJmTEajQgEAhIUP891ToEhGo3OAViuaVomgDcALP5cf+WzH/NJAE8CgNlsjqanp4vxZmJioqCylLWyd01vw8zMTJSXlwsAqDooV1dXy2nJWQU8HZky84Pdu3ev2KwzG/B6vXJKMDh8VrtHFaaQpclAwZ+RnsyTnl8zSExPTwvffXJyEpWVlQAg9+PFLgQDAJmfpFGTDksAMxgMCtHL5XLhzjvvxHXXXQdN09DQ0CCpv2p8SoLTww8/LB2c6upqZGRk4JlnnsGTTz4pXoQseXhSnf1MhRvCLg0t2fLz89HY2CiZxuzsrLwmANLWvOyyy/DCCy+gv78foVAINpsNPp8PTz31FP75n/8ZN998M3w+H3Jzc4WBSJo1SzuWDVNTU/D5fDh06BAaGhoQjUbR1dWFwsJCZGVl4atf/apQ2AcHB2G328UTxGazSXAgV4J409TUFPx+P3w+H4aGhiT4LRRT0XuTYCoPKmZzOp1OZnQaDAbR6bS0tIiaMiEhAcuXL0dvby+ysrJkvZMvk5+fL0GPZK6KigpMTExgenoa6enpcXqZc70+V1ciGo1OaJq2F8AaAJmapiWezRoKAQyfvdkwgCIAQ5qmJQLIQAyE/GuPC2CezEOuOHX9aveBLST2wOfm5tDZ2Rn3O6rfeDIR0WV5wk2wa9euOCNZbjr2oclFHx0dleBAjIB1PheWSmBi3ZibmyucfdUhmLdjZsCODMU9pWfH8qmBgUFKDVB83xgoGJCYUXDDer1eNDQ0iJ5BvS+BWb7/9JAkaGqxWJCWlobp6WlccMEFch9gvitEbj5/p2IqxcXFktVxihaVicwykpKSMDQ0JNOlrFYrBgYGhAmZlZUFp9OJ7u5uAdVoU0+mJsuZvLw8Aeio52DnIikpCTabTZyRTCYTcnJy0NfXJ+SxyclJjIyMAIjnj/h8PslC+drLy8ulbNXr9YI3RKNReDwekW9HIhGMjY2hoKBARvqR11BYWIikpCShbzOAFBQUoKSkRNYrh+by/YtGozLpC5jPVImrseumadrntnUDzq0rkQ0gdDYopADYjBiguBfATYh1Ju4EsOPsXXae/f7g2d9/8NfwBWAemedCY32nshL5pjC9I8+BNSFnEXKSMtHoqakpdHR0QKfTobKyUuzQTCYTenp6MDo6KngCUXIgXi6t8g1UaTSzA1U4xQDBkXpkQwKIK0dUIDM7O1uGltDTsLq6+lOgpGoww/urphxsObLrwUBhNBrR0NAgqb8aMNX+fGpqqngncPFyU2iaJoi8yi1h6rxwNmJiYiIKCwulljYYDLjyyivlfuQRsOPU1dWFAwcOIBQKYe3atRgeHobD4UAgEJAs5KWXXsLPfvYzuFwu4UYwoJPfwCDAdiSnTAUCAanFuX5GRkZkXdlsNlRXV8Pj8Yi2gua7fM0seVRiGfEQtf3L8orlHh2lkpKSUFJSIs/BbDYjNTVV1JcsvZjRHDt2DKtWrRIeAgld/PxYVszNzYneiNPJyOkhAKt2ss7lOpeMIR/A82dxBh2AP0Wj0f/RNO0UgFc0TbsfwDEAz5y9/TMA/qhpWjeAMQC3/a0/EIlExFsvNTVViB2kNC9fvhwOhwNTU1Ow2WxSz7NDQeBmenpaPCNdLpd4HxIv6OzsFADMarXi4MGDaG1t/ZTCkZ4B6nAZUk+BeK4AENukVLnxlJ+dnZXpP9zgHLrL25DGShsw1p70mygpKZH6UJ0DoWIX7FuzVCKNmSBue3s7brzxRqmNWW6xtcvSish+X1+fYDzRsyKj4eFhbNy4UVqWZEcyhWU5wQCl1+uRmpoqtu2lpaUSHL785S/LnAVOkuZ71NLSgsLCQmzZsgVtbW1wOp2IRCJwOBzIycnB+++/j/r6eixfvjzOYj8UColJC4lw1C3wOZFLwUOIG1zNSkOhEOx2u+AF7FKwxCI7lKVncnKynNgZGRmiY1AJYMwwGNz4OOpcTL53lHIDwO7du3HmzBk0NjaKjwUwLxrjYcauGdu2SUlJcZmm0WgUbOLzXOfSlWgFsOIzft4LYPVn/DwA4ObP8yRYd83NzaG4uBjd3d0AYqKYX/3qVyguLsaBAwfQ2NiI1atXQ9M0FBYWYmRkBG1tbdi0aZMEi0OHDmHnzp0ydFWtD91ut7Q3x8bGMDAwIKQYlY7MkoGiGWYHpFYDEEWbCiqq96Vmg5t4cjI2BZr3IR/hs0xgDAYDysrK5JRyOBzQ6XSoqamRrIqLmyWVimeopdHY2Jh4BnwWMYwnPi+XyyXyYYKbTqdTlJisuQla8vHULASIIfz19fUA8KlykP8fOnRITrKBgQFMTEygu7sbv//978VenR0hnU6HoaEhPPvss7jhhhsEaGZQ4HOjJoRBj6CfKkIjf4DCLmZjiYmJ0p4kB4ZTtrn52HpkSm8wGMSnoaSkRDAbsg5Z3jKDVTEYlgTMAhISEpCVlYWcnByUl5dLCfLBBx8gPz8fxcXFSEtLg8ViERVwT08PTCYTampqUFJSIut4fHxcRgey6/P73//+nPfkecF8NJlMeOWVV3Dw4EG8/PLLAGILh6Pew+EwOjs7cddddwlSz1O/t7cXLS0tSEhIwJIlS4QokpKSguHhYRlDx5SU/yYnJ0X0AsxHXiBepwDEUnm2ftTgQKRe5Rrw5FfFYFS8qYFH5TmoHgxMi5ubm0Vmzm5KT08PgHgnZvpWuN3uOJVgcnKyyHOJuXBhqqYqXOwkcY2NjUlWMTsbG/Lb2NiIW2+9FTqdDl6vV1JbZgwqZ4Q4BnkiKgFILfGAmBqwv78fAFBaWirtN5rIDA8Pi1U754AEAgE0NDTI5mS3iig/M4hgMCiZC+/LbJAdsP7+fixZskQ2eWJiIv74xz/KmsnNzYXNZpNUn5udZi3qzEy+XhK51JM7JydHaOkck8isiyQ5FZ8CIBlVVlYWysvL4Xa70djYiMHBQTlwvF6vzKvYtWsXli9fjiuuuALbt2+XduaWLVuwa9cuKW3P9TovAsPY2BjeffddtLe3w2g0yuBZOhLNzc3B5XKhtbUVx44dE5/9QCAgijydToe9e/eiuLgYycnJ+OIXv4j8/Hz893//t3D+1Ys6AZYHPAkZFIhAe71e0emTQKM6QC2kQDMLYBbB36utRJVwpJq6sP/ODzEQCIiHQHJystjNud1uIVQZDAYMDQ3FUawZgPLz82E0GjE+Po62tjZJ21lSsKXImrijowNnzpyRqcucakVB1ssvvwy32y1EMBrRqopKbnwO68nLy5PAYTQaUVdXJ1RgflZutxv5+fliSpKeno6rr74akUhEwOHx8XFkZ2ejp6cHP/3pT/HQQw/hpZdegsvlwsqVK7Fs2TI8+OCDGB0dxeLFi7F69Wrs3LkT/f39WL9+PTZs2IBt27ahra0NtbW1+Md//EfBD0KhEBobG9HS0oLu7m5kZGSgvr4ehYWF4gFaWFgoGYBaHmRmZkqQINeG07vI3WCJTEwAgNi8MyCRrcn7sPVJ9+n8/HxcccUV6O7uxv79+6Ut2dPTI10HvT5m7//2229jfHwcRqMRQ0NDOHLkiJSH53qdF4EhFArhueeeEwoote+nT58WM8/BwUEcOHBAgB8uQgJoxAHa29uFGltWVibAGbsW3Owul0t66ADiJl+r3AEGiIXpPoC4LoD6P6+/1jtWyUgL/6+trRU3ZHZJZmdnkZ2dLc+FAcBkMsFqtWJ0dFQG6KgYwfT0NKanp3H06NG4MXKqvgGIZWgul0vmD1D9RyMRUoNnZmaE6APM04BVrQQdtYh7HD9+HENDQ+KabLVacdlll6G2thatra34+OOPUVdXJ2Xk8ePHkZGRgUsuuQTvvPOOPDYZjb29vThz5gx27twJr9eLvr4+hMNh7N+/H4FAQL4/duwYhoeHMTMzg6KiInR3d4tP4tzcnPhikGxFXkJJSQksFot0niwWi3wGBAm55kpLS+H1ehEOhzE9PS1AM/k47IABEBbk7OysZA4qY5RlBbNGZnPUZDDzqaurw/DwsJjpMLPT6XTo7u6WTFXTtP9PQQE4TwJDJBKRPjQ3Kxe1OiiDbyo3OGm3wPxmZb03OTmJ5uZmUb2pwYEa++npaWHHEWfgB06REgB5PPU0VlmKagdDnUisqiQXiqNUhyj1NmQzqhd/ruorCKTx9xzTxlTU5/NhbGwM1113HSoqKnD55Zd/StvA04ob/8knn8Thw4eFZJWXlweHw4EvfOELuPDCC/GVr3wFPp9PFnh3dzfsdru8r/z8UlNTsWLFCtTX10PTNFxwwQUYHByEx+NBY2Oj2JtxczBrqqioQF9fn0yDzsvLg9lsFht/li0ulwt5eXmIRqMyMIiAHEFGXuSO1NTU4Oqrr8bw8LAAeIsWLZKSKhQKwe12C3kpMTEReXl5yMvLQ3FxMWpqaoQxG41GxQYemOeAmEwmzM3NwefzxbVRiVEwGNOFiQGVAVtdeww0drsdLS0t0i3x+XyCwQGxrIXdNL/fj7a2NlGdUnOh+kSe63VeBAbWswTqVDm0enIz9aP2nqc8NQ0A5PeU81I0xahNmivdovk7kqSAeY450XrKm9VLpSirbUw1SHwWKUoVgDFAsSxShU8ENxlkkpOThWzFMsZoNEqGxRNmdHRUFu/o6Cg2bNiAJUuWwG63IxwOi3sQgDjcYW5uDgMDA3JyJSYm4vbbb8e//Mu/wGAw4OTJk/jRj34kfyscDmNwcFD+Fv+x7n/ppZfwzDPPwGazCa6zfv163HPPPXKq0l3o7/7u7zA3NwedTofNmzdj1apVsNvtMBgMWLNmDfbs2SNeh9nZ2UhMTMSePXvEDDUhIQGVlZUwGo1CfU9OThYGa2JiIoxGI7761a9ix44dAjiTbv2b3/wGHR0dMlOSa4Vku8TERGRkZOCCCy7AqlWrcNttt4lq1O/3Iz8/X4BDlTJOcJckPU6pYnaiZr4ckTAxMYGjR4+ivb0dhw4dQk5OjnhEpqamwuPxyOdEoRrbtL29vRgbG4PP5xMTF+Ief4Mx8KnrvAkMlOpyc6iIPlWX7HtTb8ANw/YXCVK8LwMJUXH67PFSnYuJtPNvMm1c+IaqpzRPOm5snrQUx/C1/KUsg49BViQBM7bZyGIk8YZu18xsSJZiQONJRZepFStWoL+/H08//bR0HtiLV4lO/MfJXUAs86murpa2aV9fn4xgY5+fdNuF+gm6K3V2dmJgYEAkxHa7Xcxq9Xq9kHasViuSkpLQ09Mjr93hcCAzMxNGoxG1tbVoamqS09VgiNnrV1dXiwCMqbNqdsLgwPXkdDplutPExATeeecd/PKXv5RJ1yoXg5wEvlfT09N47733sHfvXhw/fhw33HADKisrJVNTlZZkTxJkJHeGt2HJwOwXiHWt2trasG/fPvT29sYR+chfoU8F6fZsoXPtq/4jzLrVw/TzXOdFYOCGJvCkquT4YZElx/SbHz7JMiwHaPRJ0IdvFO+/sO6nhn50dDRuGA1rPW5M1tR8bkT7GUiIN/C58XZqexGIFx8txBZUAhRnETId5O/UcoItS14MlLxYo9I9ifiEKvpSRU8k4QAxqXU0GlMAlpeXC5BKoJGLj2Qd1aTVaDTG0XTZBYlEInIyh8Nh1NXVwev1CvXZYrGgo6NDBtNUVlbC7/fjpptuEocnclvsdjtqamriPCcICjJTI9lJbTWyM9LY2CgqUm4stauivh7ej+5SO3fuxJ49e7BixQrcfffdKCkpEcCR5S1BRq5ZAuR8PL4v09PTaG5uxsMPPyygMqezJyYmSiDjayGxi2uL4PvMzIywLNnm5mGo7qNzvc6LwJCUlITy8nLRQFCDoGYSRNy58XgCcgy6aogBxIAej8cjmYXNZhNknptVJeSkp6cLWKRqI1Q1IxBfHjDdByAeDfw5Swz1Ig2YgCAFQgwMaqngcrnigiCzF7ZMaW3PkkTtlDBoqDb51JhEo1FRczKz4GI6ePCgtPgKCgrw5z//GYWFhaioqMCNN96IX//615ItaJqGyspKXH755ZiZmYHP50NKSgpOnTqFEydOQK/XC1j8wAMPoLu7W6ZIezweSfvHx8fh9XoxPDwMs9ksI9ko8qJu4fLLL8f27dsRCATEv/Po0aPyvtCHge1EtnhJoe7t7Y0jq7311ltwOp1yH2osiF9x7RE/UDf2zMwMhoeHhYCUmpqKq6++GnfeeSdKS0vl/VTJZGrXJhKJYHBwEG+//TZ27NgBp9Mp65yCOnIzqKdZyEFR6fCRSERUmvx7BOBpwvN5cYbzIjAkJiZi1apVyM/PR2FhId577z1RuhFY6+3tRWlpKRwOB/Ly8pCeni5U58LCQjmlvV4vEhISUFJSIoucIhb2gFXASTVZ4YnLjgFPm7/kuMxNrfIK2GZiSqv+XN3kAOIGpTL1VOXianBRMw3eRr0tT0QOhLXZbFi1apWknExLOR2cXQOehl6vV55PKBRCQ0MDuhXikAAAIABJREFUPv74Yxn7x4GsRNBTUlJQVlaG1atXY8+ePWhubkYgEIDVapX5iWzjFRUV4ciRI9DpdDJ8Znp6GqOjo5icnITP54vDc3hKskRyOp341re+hddee02cn0pLS3HmzBkhCvFU5kUkniev0+nE6OiolJ0E+AgILtTjcF0yGDAt5/vOgDIyMgJN07B9+3bMzc3htttuQ11dnXhbMsUngDw3Nwe3243/+I//wMmTJ+UwIr+EAYRT0pghsBwmXsFyhM+RJWJqaqoEFbIrAfzfLCVCoRBOnjwJh8OB7u5ujIyMCI1Zp9NhbGwMo6OjmJqaEs09B35MTEzITEEScFiaFBYWoqCgAFNTU6K0A+bBTsqHGVQWcg2A2KZTnYJU4xN1kpPKfFSZkCpxhQGELsEqnsKLGYiabfC5qcGBi1TNZoi/WCwWNDQ04Nprr5UhrLQM83g86OvrEwLPokWLYDKZ8G//9m9xG+Cuu+7C1VdfjYKCAlx//fWYmZmRrgzxD+pTXn/9dczMzIhz9+zsLNLS0gQRv+222/D2229jeHhY0H6+Pp6sDIQMcpqmoaOjA16vF21tbVi/fj3Ky8vR3d0tJQxlxQSYqSUgyEowdG5uDsePHwcAydIikQiysrLE+4ClQ1pamjyGesrSXYqtdLX00LSYiewTTzyBN998EytWrMAvf/lLyWSI30xOTuKNN97AG2+8IcAzh/2wm0KcoLi4GKmpqeK9aTKZMDIyIjyThToiAvYctcDMhhyS/w2txP/6ZTAY8PWvfx2lpaVCHwWAvr4+MUQhiYNe/LT2Hh4eRnFxsbQHSfzIzc2Vuis1NRUvvvhi3N/kJmNnIxQKyaZUWYwEt1QgkffliaB2FHj9pe95ciy81AyBj7cwY1h4P3VaEp8byUdVVVVob28HMA92EkzLy8vD6Ogojh07huTkZBQUFIgRSDAYm+UIzOsLaPVWUlIiGyMajeKmm26C2+0W0xmVe1FaWgqj0Yi9e/di3bp12L59O7773e8iOTkZ119/vYCFb7/9Njo7OzE5OSkdI7ZQWbZMTEygs7MTWVlZ6OvrE3p06dkRfGT/MWtaSOAi2m80GmG1WoW9SV9J4iXqJiM4qKb/DOBqZqJSn0OhEIaGhuDxePDDH/4QP/jBD6QD4/V68Z//+Z9oaWkBAAERiZMR1woEYgY/ra2tMpMjOTlZ8DGWBsRFeFiS3RsIBOJ4H9RJcIDOuV7nRWCYnZ3Fm2++iZKSEmzevFlsql5++WXk5eUhNzcXAwMDAsQtWrQIX/jCF6SGraiogMfjwdDQEKampsQUE4C4IPMDIbAExKfnbO8RaGT/W503oHIJeJHZSKUl8ZGF2gm1S0GAVb098OkWKO+/sDPCTc7WILEI+hSWlJRg7dq1aG5ulkyFCy8UCgnISi0BRUcsTVTXYroUnzp1SpSfDB6ZmZn47W9/K+5EbJWxu/Tqq6+ip6cHL774Ip588kn88pe/xI9+9CNMT09jdnYW+/fvlwnUZrMZXq9XfBYpaHK73YL/5OTkICsrC263W0C1mZkZ8dxg90qn08lUqOnpafHyqK+vx//8z/+ICpEZBslI9FEwGAyS3gMQ4x+uE1UbwhSfSld+fh988AFOnjyJu+66Cw0NDdizZw+6uroEuOaGJ1WfFvijo6PCYUhIiI3hI+jp8/nEgIaZXTAYlNfMDhWDGRA7LBggP891XgQGYL5ObmpqitssOp0OtbW1sngOHTqEsrIyWCwWWK1WeDweWaRMtzjVKCkpCZdffjneeust6ePzRGFNfebMGWHyqfW+SjYhwqyyFYH5UXpAbMEspD8TKCPoxduqbktqyaHX6yVd/yzMgTwFciEYMFhz8sTfsmULqqqqUF1dLc9VFUrREASInX7Hjh0TFyifz4e77roL999/PwKBAK688kocPnwY/f39Mnbe6/XiySefhNPpxNGjR2E2m3HHHXfgwIEDuOOOO7Bt2zY0Njbi/fffF57FqlWr8N577+Hee+/FjTfeCCB2itLbIhwOY9GiRdiwYQO2bNmCvXv34s0335SNGgwGsXz5cgErI5GImKQ4HA48/fTTQjnOz8/Htddei5mZGfT09GDTpk0ixzcYDHj33XfFcVylJTMjSE9PF4yFJy7reZXhyAOGn41aBjLjeOKJJ7Bjxw54vV6sXbsWWVlZ2LNnj5z4dIdi54F+DA0NDThx4oQIwhwOhwRmVXZNzIymNcxi2aIkV+fzXudFYKBLDUerA5AWFlVjfr8fRUVFGBgYgMViEVurYDCIkpISADFb9GAwiNbWViQkJKC0tFTUdTwNiFKTw8CNpwqnuKlJkVbbnAuxA/6fm5sbV1KomQU3ufq1WvOpnQ9+vbAUYUeCX/NiFsD7W61W3HDDDQIUqiecirQD8ynxjh07MDMzI3TqK6+8Es8//zwikQi++MUvwmAwoKurK45GbTQa8Zvf/CbOfq2+vh7Z2dmor6/Hvn37BOTTNA3j4+O49tprceTIEezevRtPPfUU3n33XaGncxZnMBgUdWNZWRlycnKwePFi8fWsq6vD6OioCMpSUlLgcrlw4sQJmM1mmEwm2Gw2eDweMTKx2+0yRWtoaEjYgiqNm9kXBVzl5eUoLi5GR0cHTp06JYAogDgZNNvqXEuJiYlYsWIF9Ho9Tpw4gWAwiEsuuQS7d+9GaWkpysrKMDU1hfb2dvj9fuTm5opYqra2FiMjI5icnERaWhrKysrQ3NwMv98vYCY/U5Y3xGf4Wlg6sY1JHwe1/DmX67wIDEBsniEJKN/+9rdhNBpRU1MDTdNw5swZvPrqqzKRh3P7KO5Zt26dtInYjuObNzExIUo6lYyiymO5aQlCUjTFD59mqADixp6r6T4v9X4MMKq0mrZeqpCKv2N7io+vBhNeak+e2AgXqs/nw9KlS2E0GuW94MLhpaaZ/L+7uzuO25CamioMSrXWJ3mrsrISgUAALS0tcXbla9asgclkiiMVEfEnk/Q73/kOHnnkEXzve9/D0NAQJiYmMDg4KCCZ0+nEm2++Ka+V1OszZ84gMTERl19+uciNeYrTCOXaa6+VLOL06dNoa2uT9Ju6EmZwBPm44YuKiiTjm52dhcPhwKJFi1BSUiJTzUikYncAgLSN6bOg0+lk4E9tba2sE5q60FnJaDRKSUVsob+/H1NTU5iensaHH36I3NxcsdMnsYuZMA8slhTEIRY6ZLMLo9Pp4uQFf+s6LwJDYmIiCgoK0N3djRUrVsgYdiC2kHNzc5GYmAiHwyF1I9+QmZkZvPPOO7jnnnswMzMDj8cjLtMA8MADD6Cjo0PosQBkoAhPUfUEZjpIVRt9Cbhh6dizEEMgokz2orqZ1TJC9X1Qn4Pq+LwwIJBIxZQ1OTk5br4mcQa+J7/4xS9klgAXitVqleyCYiZ2cTo7OwHECE5LliyR7Co1NRUjIyNoamqSrkxiYiIefvhhvP766wJYrl27FldddZVMEuPtVOHa3Fxs2nNHRwcqKirQ0tKC+++/H9/+9reFHBQKhdDf348vfelLMBqNaG9vl/o5LS0NmZmZOH36NBYtWiTAHTGK7u5upKSkoLy8XAIZeTE+n0+QfG5sfias5QsKCmQ+CdfE2NgYUlJScO2110rXg5+n+vr4vjNtZ8nC9D4YDKKqqko2aFZWFpYvXy6lrWpxR2CRa+Xiiy+W949YBDEDfubkSWhazPKeA3P5cz7nSy+99Nz35Dnf8n/xCofD6OnpkZSzq6sLeXl5SExMlDmCjzzyCF599VXo9Xr09/fD7XYLa4025dPT09i2bRuuueYadHd3w2azobe3F319fQKyARB8gYGCAA75CgaDQbQTwWAQLpdL6nq6NTFb4XRm9UpPTxdAUC051GzgL3UnAHyqzFAvNY1V789TmvM42dvnqb9hwwZJM0+ePInKykpkZWXhhRdeEJr47GxsSvPrr7+OcDiMrKwsJCUl4fbbb0cwGMQ3v/lNXHrppbBYLDAajSgoKBB/SGDeb4Ej55YsWSKtUk6WrqysxHPPPYdVq1aho6MDjz32GJ5++mkhYM3OzmLt2rUyr5K8Clq6Z2Zmori4GDt27MD4+Dimp6eFWt3c3Iz77rsPfr9fOg8cF8huEssGErJY7qjycXIlAIhZD0ljJA+pACFPar1eL6Uqn7ter5fnkJWVJZkcOTKqt8LCFil9Spn1keGrPm8GbAZ9ljXshvD3KtfmXK7zIjBQUnr11VfDbDbj8ccfBwDcc889aG1tRU9PD770pS9hzZo1sNls0Oli7ro///nP4ff7xX3HZrPhkksuQUFBQVwPmTRVfqBWqxUjIyOSFQDzdu/8x02cm5sbR4empRhPHN6OvISF5QVvs5A9STyCU70XBgmWGPzbKt+CjEcGH5YUbrcbVVVVqK2tRSQSEdPTmZkZeby0tDSsXbtWOPj0quApmZOTg927dyMSiYjRi8Viwd69e6FpGr73ve/FCZV44jG11el08Pl8SE9Px9q1awXx5ynodruxZs0azM3NYevWrfj+97+Pr33ta9i+fbtsGs68VDMOZj3sGHzhC1/A3r17xRMjNTUVx48fl/dJpQJzPkMgEBDQkKd5KBSS0os0d244bnqi/3xt/Dl5EuFwGE6nU0b5kS9Dv8e0tDQRbvF94tpkmRUKhWSylEqQY1eKf4vZpdlshs/nk9KDRD4qK7nWWRZ/XgDyvAgMBoMBW7ZswebNm7Ft2zZRoD399NMSAYlQA5B+rsFgkBf/1FNPiTcBx4QXFxfHadEZ1dVWEwVJJAfxhGKA4O/Y887JyYnzXFTdlziwlp6RfL6qgzNLCZ5+akbAqK46PfE+AETzACDOdZr3sdlsWLZsmbStCDzp9Xr4/f44WzOag7CM4GWz2dDa2opwOIySkhLY7XZMTk5i+/btuO6662C1WvHSSy+hurpaTmGVIcgMhTVxQkICrFYrEhMT5ZQuLi7GmjVrcP/99+Ojjz7C9u3bccstt2Dfvn1iHjs9PQ2LxYKKigpxzSIK7/P58Pjjj+PSSy9FV1cXXC4XSktL0d3djW9961t49NFHJc2mopEnLNmENGHlAcL3mGi+Wl4ybaeBq1qCMs1ni51qR2p1VE0ENzuDClvMHLbLIJCamirZwtjYmNjTM2sg+5EZWkVFhawBygKmpqYQDocF1GUGca7XeREYMjIyUFRUhLa2NmRnZ6OgoCBuQweDQXF1YhkAQKK1zWYTJqPVahWDjaamJrS3twsXgR+WOhRGvVS9AQOIehue0MC8upKbmQFCNXlRSww1I1jYgeCJz+8X+kDybxNwVCnaKu2a49xZGpCCq2YvtHkjTVglvpjNZqm7SVM3m8144YUXMD4+jm9+85sIBGLGtCSEJSQkYOfOnVi6dClWrVoFt9uNjz76SGZAcnxgYmKiZCBALMj98Ic/xM9//nN8+ctfxs6dO3HxxRejqalJygaVp0GDnuTkZDGCWbNmDU6fPo3ExNi4AbPZLDMZGAB4wvp8Phk+xFSbIB5duVRci9RvIvssD9TfAYhTMjLTZLnH1iHLKVK+CRYycJCazgG3BM0J3s7OzooXhF6vh9lsliyQt01KShJBmslkQmpqqmQ+CzGvc7nOi8DABQDEfACZKgHztTOj7fj4OHw+n/R+o9EoLBYLRkZGZMFzUz/55JNSay+8CAqp7Tt2OkggYtrKD5RZAzCvgGSAYEbwWWUDMO8Exf9VrgSdnVl2qCxLFdzU6XQStBbqNziJSDUGoYMSb8+RcLTbP3bsmGQSJpNJmI3hcBipqalYs2YNAMS1+E6fPo3LL78cSUlJ2LRpEzo6OjA2Noauri6sXr0aHo8Hc3NzKCkpgdVqFeUlu0BqC9VoNOJrX/saHnvsMZw+fRo1NTVYuXIlPvroIxQUFMiAFqoxg8GgnJ4A8Itf/AKvvfYa/H6/4BqdnZ2477778OMf/1hMUSjB5lQnYgXc0DqdDidOnIDJZJJMQ/XJZBeC5SYzSkrOiavk5+cLR4YdEOoqGEzIcdC02FAcBliuDQY1tjBVNSa7acQNDIbY2DwCvizbqOMIBoMy/eov4Vl/6TovAoPf7xdT03fffRdzc3PYtGkTDAYDOjs7ceDAATmZi4qK5LZ2ux1LliwR1tqSJUvkMQ8fPoyuri5hQALzmzMxMVE08vw5N//C7oF62jIoUIrNvrvaogQgJYJaKqhMNLV1qWYaDAKf5d/AQKCKvnilpqair68PVqsV+fn54g7EVJ6n4szMjJi7JCcn48CBA3Lykc1Id+vc3FykpKTg+9//PgYGBrBx40ah/FZUVECn06GiogIJCQmwWCzYvHkz9Ho9SktLUV9fL4GOpzG9B9hipBampqYGzz77LL7xjW/A7/fj0KFD2LhxI44dOybj7ollaJqG9vZ2CX4ZGRmoqalBY2MjAoGYgC4zMxMvvvgifvKTn8gpzi5WX18fpqamkJCQgIGBAXkMBi92YggsUn3JjcUSgCVpVlaWlCiqAxmDHslJDPhM7wHIqDx6RhDjqK6uFvxI02IDgAhQ8nF1Oh1sNhscDocciJR6p6enY3h4OE5tTHvEz3OdF4GBo8lmZ2dxxRVXAIBw1t1uN4qLizEyMiKsMJ6A/Wcdhi0Wi4A8ALBo0SK8/PLLmJycjJMJ0+iDuASjtIopEDij6QmAuCyB/6udA5UBubBjoGYAvJhVELBUT3/Ve5IAH+vdqakpyaCAeYch1sicckTADJg3HCHDjxsMgLQlSdEuLCzEmjVr8NhjjyEvLw+RSAR9fX1ITU3Fj3/8Y7S3tyMnJ0few/7+/rgRdbymp6cxNTUlsmnSnXk/koLcbjcWLVqE4eFh3HbbbXjiiSewefNm7N69G8uWLcOHH36IvLw8jI2NISMjQwbEBINBqZuzs7NlFN7w8LBgEr/5zW9www03YGpqCiMjI0hJSRG1IS3SmJqPjY3JNCqOqGcmpX49NjYmxDG2evmamPWxfGEWQEEdM1TK81kmGAwGtLa2CkbE94ylFG3g2Hkg0AjE2u4cmEQy1+DgoJgAp6amwuVyYWZmRgR/53qdF4HB7/fjlVdeAQDcdNNNMneQtTJ70WazGYWFhXLC19TUCCK8sP7s6+uLA+i4GNVo7/P5JECoZUQwGBSwhqcIT3o6EKmnu1qfMitRjWIXBg1eatAgekzQjhsWiHezAuLFU6FQCJ2dnbjmmmtQXl4uJDEAUntyToTFYkEgEEB+fr5Invm8kpNjTsQc8rJ8+XJMTEzA4/GgpqYGaWlp2L17NwoKCiQb6ejoiBMX8aLlOzGgtrY2+ZzVwbjkppD/sGHDBuzdu1eG5axevRovvvgicnJyMDQ0BJ1OJ/UzwUCr1YqioiKcOnVKOg+pqak4ffo0pqenRY3p8/nEn9JsNsNoNIoSka1uTuIKh2PDjsi9ACC+Fx6PR05vdV2p9vZcs5zePjQ0JOtwfHxchuwCEKBxZmZG7N0IttbU1GB2dhbd3d2orKwUDcrQ0JDgEuPj49LJIGXebrdLGcmBOv8ntRIpKSm45pprAMQ78aSnp8tCys7OFvffhIQEsSEjWYURPBKJ4MUXXxQUmNmBz+cT4w5eaoquCqRUwRS/5vwI/p5j4FS9g3qpuAXJSyr+oGYRTPVV2ioBSPXEZ5qvzlMIBoO48cYbsWXLFjFO5VBepuI+nw/5+flizWYymfDss8/G2Y4DsVF5e/bsQVJSEq655hp861vfQmZmJh577DFEo1GUlJTIJjCbzRKgmB4zlWYGxrmTp0+fRiQSEQ/OjIwM8WXgQJfZ2VmMjIxgw4YNuPjii3HZZZdh69atSE9Px4cffijrgScy8YeGhgbY7XYMDw8jGAxicHAQlZWV6O3txR/+8AfceuutOHHihDxHZjxMwemBmZubKyUGORQzMzMYGRmR7Gt6ehqZmZkoLCxEQkIC2tvbBddigCP9ngat4+Pj8h6R/EQ/S2YNDLTMEvi+8rl6vV4x1SEWlJaWBr/fj5ycHExMTAjbsr6+Pm5KeiAQ80aloe65XudFYADmh7rYbDaEQiF0d3eLaSZ/Z7fbpRXk9/sFyEpNTcXg4CDGx8exYsUK7N+/X8wqiDyzLmVrbXp6+lOnPAAB+D5rk6olgBoMVIIPv2cA4M/UFudnBRJVhbnwUsFZXnRg4n2ZybC7YTKZpFRS/Qj5HoyPj0tg4HPNy8vD0NAQMjIycPToUQwNDaGurk7MP0gfTklJQUFBgTgP8X0jfsPXQLUny59oNBp3WrLEGBsbk1Hws7OzKCgowO7du1FZWYm7774b5eXleOedd1BUVCTgXjQaFRZjcXGxjCQEYvU7MQLW+MQZ2MYdHR0V4DoQCAiozQNkYmJCanMOrYlEYm7mZHy6XC4JdmxxEigF5rktqlcpMQh+FpFIBEVFRXFsTVVuzmBCHIF0+vT0dIyMjEiQ6u/vh9frRX5+Pk6dOiVlEyUB/yfblZFIBHa7XSY+j4yMCJjT19eH0dFRLFq0CFNTU3GL2Wg0ymRiDtgwGAzo6ekRgxIAcYg42WUqR8FkMklm8VncAQJ4C8VNrB9VWXVmZmbc7VQQ8rM2vtfr/UyBlnp/lgJMn/la+Dw5b4JBkHoFEoSY+hJ1Jxegvb09zol60aJFGB0dRW5uLp5//nmEQiHcf//9ACBzODlseGxsTIIPyUAAhMfADIDtOSAWeEpLS6HX6zEyMiJBnrwCIMYZqK6uRjQaxRNPPIF/+Id/EPu4jo4OqbWZIQaDQdTX16Ozs1Na0ZRqj4yM4Pnnn8eiRYvg8XgQDAZFDMZ1YLfb4ff7MTw8HMccZIBlicmyKxKJSEBm1kYJuxoU2A0j2Eqpu1q28vlyqBEFVWazGfn5+dJy1TRN5ONzc3Po6upCfn4+zpw5I5nX8PCwWNp1dXVJUOMa+D9p7RYKhcTjsLm5GR6PB2NjY6irq5OoSzXa5OSkmJaOj49jbm4ODocDS5cuxezsLLZu3Sq/pwxVbVeytcP0neCjKrvmic8Wl5r+qwNo1aBAghNvx5+x88DWpjq5in9L7VgkJycL74LBQa1naXvPlpjJZEJ9fT1OnjwppRFT4JSUFJhMJjgcDiQnJwtu4/F44PF44kg8DF6HDx/Gddddh66uLuly+Hw+vPHGG7DZbMjJyRGJMUs7FdCMRqNxvgHkjjA49PX1CY25s7NTSGM87ex2O3Q6HTZs2IDU1FQsXrwY27dvx80334zvfve7cLlc0Ol04qrldDrFM7S1tVXowwaDAX19fTAajaioqBAMh/wF2tzp9XoUFxejpKQETqdTpM8DAwMyrWxiYgKFhYUykjAQiE23Zhu0trZWOh6cFcnSMDc3F+np6cKJUA1XeOiwy0WOQjgclse3Wq3yWJFIRNZrc3OzkLoACPORk7A4ZHfDhg0S8D7++ONz3pPnTWAYHR0VVF1t/1itVlitVrS2tmJoaAizs7NCPWXUz8/PF5KQ1WoVNx2eZvwb3Dhqek8eg/qPv0tJSZGyYqHWgZkALzWTUIMGT3RV9KTeRgUkVY4DiTlqV4LPi1wGIJZlOBwOyZBYKpCMMzIygpmZGZw+fVrqfzLtgBg4RVLMwYMHAcQ2b2JiIoqLixEOh/HnP/9ZBg9TY+LxeGRUIJ2OiNbTRctms8HtduP48eMIBoNi6+bxeNDb2ysnv9FoxNKlS3H8+HGhVfO1Xn/99bj99tvR2NiIn//85/jBD34Al8uFJUuWoKOjQ7obF1xwAfbt2yfgYDgclk2dmZkJu90u6TR5MDabTfQTdF6anp5G6VlvUYK3xAyI7rOUIc15dHRU3lufzycGPAaDAf39/YKHkKCWlJSE4uJimZWRmpoqqtCBgQHJ7qqqqmT8IMHi5ORkyQ7OnDkDp9MJs9ksVvsMUHNzc6IpMpvNgnWc63VeBAYgFvHcbjd6enoEmOnu7obD4cD4+DjKy8uxdOlSdHd3IzMzEw6HA1lZWRgaGpJoDADV1dVYsWIF2tvb0d3dLZstHA4LYMnIrWmatCfZqyYIpA6hJdmKJwWBSJYSDFa8VKISP7iFGoqFqR2DxMJSg0GAHHwGCZ7E5eXlyM/Pl8XAcoG0WS6a2dlZmM1m+P1+nDhxQsRPJA6tWbMG7777rmRZ6enp+N3vfif25ly8FosFDocDKSkpMg1qdnY2jglIJSInLi9evBhJSUkoLCxEKBTC8ePH48he5PYvWbIES5cuRVNTE0ZGRsQd+aqrrsKmTZuwd+9ePPjgg5iamsKf/vQnZGRkCDaRl5eH2tpafPLJJ/K3zWYzhoaGBCsZHBzEzMwMsrOzYTAYhEvgcDgkaBG4LS0tFblzOByWGaBJSUloaGiQGSWtra2w2WyCWU1NTcFgMGDdunUig+fr9Pl8IlH3+XzIysoS1SgZvWzDj46OIjk5GTU1NQgEAuJzodfr0d3dLcIyZtA2m02yBX7+ACQ46/V67Nq165z343kRGHQ6HQoKCtDa2irpMQkrJBGpbkV2ux12u11SsYKCAsEeJicnsW7dOrFw6+zsFFGNmj2o4Bwwzw5U2ZAkpQCfFkWpP1NPbwp1gFi3hV+T/sqLf08FOdVpWOrjqfdha5B1cFFREQAIjyMnJ0eAtbm5OUxPT0sbd3Z2VsxthoaGxCsxOTkZ9913H66++mpYLBbo9XoUFBQgJSUFf/rTn2A2m6WFzOdHL01V/ceLjEKXyyXtsqSkJBnKm56ejrGxMdGLRKNRnDlzRrIQp9MprWTSuPPz87Fx40bs378fJpMJe/bsEVCVPIb6+nq0t7fD7XbLyW0wGHD48GHx6CBbEJjvBhUXF2Nubk6CAT0upqamUFhYiHA4jKGhIZSVlUm2xO6IyWRCQUEBHA6H6DL0er2QwDIyMmSmB2enZGRkwO12Y3x8HOFwGL29vTLiXqfToaioCE6nE3Nzc9i8eTM0LWY2y7W5bNky4U6Q40DL/9zcXAwODqKp0RAFAAAgAElEQVSwsFACfDQajXPzOpfrvAgMxBj4Qmlx7na7RTXpdrsxOTmJ4uJiWK1W9PX1YXx8HKWlpVi0aBF6enok1fL7/fj617+OP/7xjwiHwzLBmBeJIwTpuNjT09Ml5V+42VWRFDBvB8/bAPEmocA8gEmaMRmACzUUDBDMIhaWEEw/1Sscjk044iZT7cYcDkcc/5/BYWxsDPn5+dLRYQtNJcNw2Mmjjz4KANi7dy8yMjKkxcjuEFtzCwVHfG7BYBD9/f0iTWbKrWoGqPxk/50GK8C8M1Vqaqp0pXQ6HS655BLs378fzzzzDG655RYJkBTO5ebmih8BhUkjIyPIzc2FXq+XcjQxMVEIY1lZWVJGeDwejI+PIycnB2NjY0LUCoVCYpLb19eHvr6+OJ5DSkqKnPzsiLCTs379eszNzYmXR35+vny+dKuiSa3D4UBOTg70er14RNpsNjidToTDYRQXF4vIkPRsljUmkwlOp1MGKHFNl5aWorKy8pz3I3CeBAYCbWazGVarVQgbDQ0NAGL93OHhYRFIJScnY9GiRaitrZXTkcNl7HY7Vq1aha6uLnzlK1+BwWCA2WwWy3Ry16empuIwBl70WuDv2GVY2C7kxU2u/p4EI7VtqYqrVNUlbwfENsPk5CSys7PhdDqlE8HH4YLmYqiurkZOTo4w6Gh4UlZWJo7LOTk5QgqikImemOR0cHH39fWhsrISZWVlsFqtaGlpwXXXXYeMjAx5n8vKytDY2IgLL7wQzc3N+Pjjj+NKILbWEhMTsWzZMpjNZpw6dQrFxcWwWCzw+Xw4cuSITG/KysqStjTv39raipmZGRQWFqKhoQH9/f2w2+3QtNgEqbVr1+Lo0aN455138Pjjj0uLu6ysDK2trdi6dSuCwSC8Xi8MhpjD+OOPP46HHnoIbrdbAuTIyAhOnDiBtrY2lJWVCQ08KysLdXV1spkbGhpw9OhRhEIhlJSUwGaziZ5hcnISlZWVyMzMFJs5DhMinZvdNh56mZmZ2L17twSl3NxcjI6OoqOjA2VlZejo6JC1TCLZzMwMysrK0N/fL4E/LS0N+/btQ1lZGYxGI9577z34fD50dHSIp2VDQwMmJyelq3Su1zkHBk3TEgAcBTAcjUav0TRtEYBXAGQBaALw1Wg0OqtpWjKAPwBYBcAD4NZoNNr/1x7b5/NJHbR48WKZE9HS0oKuri4AwMaNG5GUlASXyyXlhV6vFzHV9ddfD4/HI1F2w4YNOH36NK6//no0NTWhpaUFJ06cwMzMjGQWCy81AyCfYXJyUshCvI2qq1iICXxWW2gh74G1ocFgEN2HwWCIA0jZiaAUnMQmIs8AsGTJEgmk9FcYHBwUMBCItUOZeeXk5CAzMxMdHR1xCr/c3FypkRMSEvDoo48iEolg165duPDCC5Geng6bzSazHJYtW4bc3Fw0NDTgd7/7Hfx+v9TMAKQ9t2zZMuTk5MBisaC7u1twjS1btgjvQtM0nDp1CmNjY7BarSgpKYHP50NRUZGUSUajEdXV1SgtLcWXvvQlvPDCC6irq8OJEyfw93//93jmmWckVV6/fj22bduG7u5ukXpTS8LyiLTn2dlZ3H777aLqtVqtmJmZweDgIPx+P8bGxiS7tFqt6O7uxq5du1BeXi4ZGyndbrdblJK0eD9y5AgmJyfFUKiurg7JybG5HxTkJSUlCX5TU1MDg8GAiy66CKdOnUJJSQlWr14tHQVyS7KysjA9PQ232y3+kQaDATabTSaRU75++vRpFBQU/K+2K/8fAO0AOKN9K4CHotHoK5qmPQ7gLgCPnf1/PBqNVmiadtvZ29361x44IyMDNpsN2dnZcLvdqK6uFh49QS0KTgoLCwEAg4OD6OnpQX19PUKhkNBuAUiKRbowT0G6QgcCAZGzqp0Kbk4OreHGV81bWEPq9XrBH9QOBU93bn62PNVLFV8NDg5+SqzFbkY0Gj/uXr0SExNx0UUXyQIjjpCYmIisrCzpqLA0osJvenpahsPwuvPOO3HvvfcKOYkCsZqaGnGEYhpOWTE9F/j5uN1ulJSUyN+w2WwAYlwPTdNgsVgQDAYxMDCAqakp3H777TCZTDh69Cg8Hg+qqqqQn5+P4uJizM7OCumnra0Nq1evlpPXbrejqqoKBQUFqK+vR1tbG+666y6cOHFCtAy//vWvsW7dOgSDsTkjWVlZcLlc+PKXv4xXXnkFhw4dgl6vR05ODk6fPo2JiQlUVFRg9+7dKCkpwejoqMzLYJaQlpYGm80Gq9UKu90upVdfXx8sFgv8fj8GBgZQVFSEVatWYWBgABdeeCEmJyexatUqHDt2DBkZGWhoaEB5eTm8Xi8++OADWQcrVqwQp2yWTwzI/Hk0GpslOjw8HEdz57qk49aGDRtw5swZGI1G3HvvvUhLS0NTUxOuv/76v7YN49fXudxI07RCAF8E8J8AvqvFcu9LAXz57E2eB3AfYoHhurNfA8B2AP+laZoW/azVffaKRGKTiA8ePIj77rsPycnJ+OSTT+B2u1FTU4OioiKJvIsXL4bZbEZtbS2SkpIQCAQwNjYGm80mm2nv3r0AYhJutoWmpqbwH//xHxgaGsL777+Pvr4+dHR0yIZXywfSnElsYQeDH+LU1JQMTOGHsjADIcikSqhJfGIQcLlcAD49T4LBQcUz2L40Go1wOBxYvXq1ELPIbgwGg+J5wE5BWloagsGglBJ33313HG8iIyMDV155JR544AGUlpaioqICkUgEjzzyCG6++WZMTU1hdHQU09PTMm+C4jRa3kWjUezcuRP19fWCZ7A70tjYCCBWLvLEnJ2dxX/913/hggsuEHZqf38/6uvrhXfCUmpkZAT79+9HbW2tLHy9Xo/7778fDz30EDZu3IgPPvgAK1aswMsvvyy6C/IaaFpiMplw8uRJRCIRaV/q9XqkpKRgw4YNGBkZQV5eHlpaWnDjjTeKrNrr9WJkZAR9fX1YvHgxiouLMTY2JsEmFAph9+7d0Ov1uO666zAxMYFwOIyioiJomibOZFlZWZicnER/f7+0JFeuXCklASXlNpsNU1NTuOqqqzA3N4e8vDycOXMGN954o5S9ubm50uInvuR0OpGbm4ukpCSYTCakp6cLpuH1egUfOdfrXDOGhwH8CwDT2e+zAExEo1HC7EMA+JcLAAyeXQxhTdMmz94+jqytadrXAXwdiKWKk5OTsNls8Pv90ubiKXXmzBk0NDRAr9dLPUxXHW4yatCJSJeWlsppFQ6HUVFRgYyMDGn7vPDCCygoKEB7e7uQq2i5xUt19VGJSNy4CzMBbrTPojUvbEWyLariCCpeoeIaBBL5OG63G1arVdyPAcDj8SA3N1fES06nUzYv0XPK21lGTE1Nxc0jMBgM2Lp1K5qamtDV1YWHH34YwPyQXj43TsKuqqrCFVdcgd7eXiQlxcbYNzc346KLLhJQt6GhAWNjY0JnX758Odrb20VabDKZsHTpUnR1dWFoaAhFRUWSWTidTixZskRKAIfDIU7HHR0duPTSS/HUU0/hsssuw549e3D99dfjT3/6E9LS0mAymQRU5BxNn8+He++9Fz/72c+Ql5eH8fFxLFq0CCkpKfB4PFiyZAlqa2tRUlIimh22Eevr62VNFRQUQK+POWZnZmbilltuwejoKEwmk6yl9PR0nD59Gg6HA5qmic2eyWSSti/xLkqjJycnhdJsMplQWFiI8fFxzMzM4K233oKmaVi/fj1CoRDOnDkjxkSapsmUrdHRUWzfvl0s7EdGRmAymVBWVvZZ+/ovXn8zMGiadg0AVzQabdI07eLP9eh/5YpGo08CeBIAMjMzo2TVnTx5ElNTU5icnMSVV14Jl8slSjEOJuHm1TQNk5OTeO+995CdnQ2/3w+Xy4VIJIL+/n6YzWYcPHhQ3niy10wmE+666y489thjGBgYkFYig4MqrCLeoLY2VbUkf0YRCzDfimTJMTs7K2WHerFkYDaRnJwsgJk64YrZguptkJ2djba2NqGFT05OSg+b8wcmJibg9/vFFJXGJby4UMnhsFqtyM7OxtatW2EymaRH7vf7RbKuaRpsNpvItBcvXozdu3ejpqZG8CCLxQKPx4OBgQFUVVXJrAe2AdesWYPh4WG0tLRg9erVCIViU6c8Hg+KiorQ1NQkY+9MJhOSkpLQ29srw4H8fr+0DS+66CLs27cPV1xxBfbs2YMrrrgCb731FioqKmQyFteMwRCbj0HsgBvZZDKhtLQUdrtdbN7JNOQ6czgccDqdcDqdMBqNqK2tFfMbs9mMzs5OzM3NyTxMjiqIRqM4duwYxsfH4XQ6hTPDE91kMsHtdss08KysLDQ3N4s5cnJyMqxWKw4dOoSCggLYbDY0NTXJZ83hTJzUFQqF0NLSItPByD5VfUnO5TqXjGEdgC2apl0NwIAYxvBbAJmapiWezRoKAQyfvf0wgCIAQ5qmJQLIQAyE/ItXdnY2rrnmGuzbtw+FhYVYtmwZfD4fPvnkE2RkZGBiYgInTpzA4OAg2traBAnWNE2GpXDYSVJSEk6dOgW9Xo/KykqUlpZKf9jr9SI7O1tovMFgUBhuFOIQAMzOzpYhpqzj1VNbPeFVTwNgfj4FMG/trl4q21H1bSCxCpifd6FqImhRdskll0i9mJiYKHRwZgZUBnKxBgIBbNq0CQ8++CC8Xq90I4LBIH7729/i3//93xGJRHDzzTdDp9PhJz/5CVpaWtDd3Y36+nrU19cL0ScpKQnDw8OS4Q0MDOCee+6By+XCgQMHsGLFChEmcW6C+vqfe+45OXFvuukmUQ8ajUYsXrxYQMPNmzdj5cqVOHbsGJqamvDBBx/gO9/5DoxGI9LS0vDJJ59g3bp1CIVC2LBhA371q19h3bp1OHjwIO68805pvbIDNT4+juzsbExOTuJnP/sZfvrTn8JiscBisYjmAABeeOEFbNy4UUo0loxjY2O48sorMTw8jJMnT+Ldd98VjMpms0HTNFFWjoyMYN26daiqqoLVasW+ffuwfPlyrFmzBgcPHsTx48fx7LPPChdkcnJSWr9vvfUWdDod6uvrcfz4caSlpeGaa65BXV2d6C2oVqW+aGBgAGvXrpVMMiMjAyUlJaisrERzc7Osic9z/c3AEI1GfwDgBwBwNmP4XjQa/YqmadsA3IRYZ+JOADvO3mXn2e8Pnv39B38NXwBiZcGpU6ewZs0auN1uqbM3bdoksycDgQDef/99ALGJvnq9HitXrsSpU6fg9/uxcuVKmYVIKvSpU6dEUUfEm8QmAELfNRqNSE9PR0VFBfbt2wev14ve3l7pi6vWWMwKVKyAJQ03L2dgsp5mygnMlxu81JamOvGKmAbJXsxM7Ha7BA+SZmg6kpCQIMQen8+HJUuWiKhmYmICLS0t8vikMC9evBiHDx9GJBLBnXfeiXA4jI8++ghNTU2S6rLVVVVVhUOHDqG6uhqDg4NoaWmBw+HAjh07sGnTJqxbt05MTKenp7Fr1y6ZAUEMqLGxEaFQCN/+9rfR1tYmmo+pqSk89NBDKCwsRG9vL95//32kpqZiy5YtOH78OPR6PXp7e9He3o6SkhK0t7fD4/HIqXrVVVfhtddek8137bXXorm5WbIeukUfPXoUMzMz2LlzJ15++WVs3rxZ5M0WiwVXXXUVysvL4XK5MDIyIuQ40ri7urpkPqfdbgcAfPzxx7jjjjvQ1taGmpoauN1uPP/881i6dCk2bdoEt9uNbdu2weVywWq1wmKxYPHixZiZmUFlZSW+8Y1v4OOPP8aRI0ektPN4PFi+fDkcDgcaGxsxPj4uLlKcwqXO8PR4PLjhhhtwwQUX4PXXX5dZrU888QRuuOEGDA/z3D636/8Pj+H7AF7RNO1+AMcAPHP2588A+KOmad0AxgDc9jefxFkDCpfLBZfLJX6BtPqamprCqlWroNfr4XK5cNVVV6GrqwtutxubN2+GyWRCR0cHLBYLwuEwqqqqYLfbsXLlSjQ2NmJubg75+fnIysqSWQTZ2dmCSZjNZgQCMd//Sy65BK+99hoMBgOGhoZkVmReXt6nOhGq1Rvr8MzMTNm4TP+zs7OlE0FsQlVgLpRtA4jLQIB51iO9EHw+nxia0LItJycHg4ODMJlMsFgswuWnXT5FOeTukyCkZiWHDx9GRUWFMBFJVR8eHsaePXtw4YUXYuXKlWhvb0dLSwtmZ2exZs0alJWV4YEHHsDdd9+Nmpoa+ZurV68Wu7S+vj689tprSElJQSAQwPHjx7F06VKEw2G8+uqr+Nd//Vf09vairq4ObW1t6Orqwquvvirj5D788EOxdjMajbj00ktx5MgRSem3bNmCN998E2vWrMH+/fvx1FNPYcuWLbDZbKivr8crr7wiZKvq6mphFB44cAD5+fmYmJjA5ZdfjubmZnR3d+P2228XrOaqq65COBxGfn4+xsbGcPToUVx88cV49NFHkZqaimPHjuFrX/ualKPr16/Hrl278M4770g34eqrr0ZFRQWys7Nl6G19fT0eeeQRbNy4EXfccQf+8Ic/4OKLLxbNyOHDhxGNRuF0OlFUVASv14vCwkJ4PB780z/9E37/+98jISEBXq8XBw8exK233orExESxNbzjjjswPDwsh+q5Xp8rMESj0Q8BfHj2614Aqz/jNgEAN3+exw2FQujo6EBra6u0kIBYG7Ojo0M2WFtbG3JycvD++++juLgYzz33HKqqquDz+dDW1iZ8d4KWnZ2dWLlyJQoKCnD69GmhWNPYgi3N1NRUSeVKS0txyy23YMeOWAJEfIAceLIA1U28kM3I7xlIFnYkqBhVhVXB4P9L3XuGd1nt67p3em+k905CGqlgQg019CqgIE2KUkSYygQRBZkoTSYqiohKERAMJRSpoYSSAukkIZ303nv9Z39gjTFxrbPOdl5nn3253i8IcgGB/xjvrzzP/bzMRxDbCKFfECtP4ZOwt7eXFKL29nYJalVVVZU25+rqahoaGujXr58U0Jw6dUoiw0xNTfnll1/k2/3+/fts2bJFuiv9/f2ZM2eObAMECUl8v6CggPnz5zN//nzq6+t59uwZPT09/P3vf8fIyEhi1/T19aUcWLR2wp4sNhc5OTnY2toSFhYmyUrq6uo4OTlJlJ+9vT2FhYU0NDRIq7iTkxP9+/fH3t6erKwsucWYNWsWV65cobq6GgsLC6KjowkKCqK4uFi6DMeOHUtTUxOXL1+Wf9di23X58mWsra3x9PTks88+o7KykpkzZ+Lp6YmKigqOjo7k5+fLz8KsWbOwtbXlypUrtLW1yX/Dx48fk5iYiJeXFy9evMDU1JSxY8dSW1tLeHg4Dx8+RKFQsGfPHnkZXb16ldDQUG7cuEFMTAza2toMGTKEsrIyLCwsKCwsxMzMjHHjxnH48GFycnLo378/nZ2deHp6YmtrS1lZGf369SMqKoqMjAxGjx6Nvr6+rHT/7POXUD5qampia2tLSEgIhoaGco0HL99ionXw8vICXu7GU1NT6ejoICcnh5CQEHp6erCxsZG9uqOjo5Siiv5RiIMyMjJkWIcISxWac6GUFL/3q74JQK7rBObtVXu1uAwEYkx8bf8ZwvLqt+LCEMas/8yH1NLS+gMuXxB6lJWV5URaUIhErH17ezt2dnYSXCIcmLW1tTLkd9GiRZw4cUJmKwAcOHAABwcHEhISZFCtoaEhra2t6OrqUlxcjLOzM4Aso3Nzc7G3t5e9+smTJ6VVOCcnB0dHRzIzM7GyssLGxoYjR46Qk5PDzJkzOX78uKywJkyYQHd3N4cPH5beh7Fjx5KUlMTdu3eZMGECcXFxKCkpMWTIELS0tCgtLeXXX3+Vl6CdnR15eXnMnDmTvXv3smfPHuk0ra+vJzAwkM7OTjmQ1dfXlynqCQkJDBw4UF7WZWVlpKSkoKqqytOnTxkxYgTl5eVSOJSenk5NTQ01NTUkJiYCLwfQ6enpdHV1UVNTQ1NTE7/++is9PT2Ym5vz2Wef4efnx9OnT+VmSAxzMzIyWLBgATU1NXh4eMgVsXhJ2dvb4+npSV5eHgYGBixatIjGxkb8/Pyws7OTIi13d3dGjhyJk5OTvGA6OjpYvXo1Z8+e/dNn8i9xMTQ1NXHlyhX09fWxt7fn4cOHUgMg1jUaGhq4urpSVFSEqakpBQUFLFiwgPT0dAIDA2Wcupjqt7W1UV9fT3V1Nbq6ulhYWEjAhZubG48fP6azsxNTU1M6OjokZlyhUEgVnNA2wL8yIEQbIKAfYl4gNOympqZy2Pbq1sLQ0PAPmwbxvFo1qKmpyf8nNjGvip0EYm7QoEEoFArZYwr5q5qaGm5ubrKMFGIcLy8vGaEusjCePn3K7t27WbJkCVu2bKGiooILFy7w+++/09jYSHp6uuQW2tjYoKWlxdOnT1FTU8PDw4O4uDiio6Npampi5MiRjBw5kq+//lrOd8RMYffu3aipqeHl5cXIkSNJTU1FSUmJp0+fysl7b28v06dPl1kIgumQkZFBTU0NcXFxDBs2jLi4OHx9fWVffePGDWnRDwwMJCgoiIyMDMrLy6UORYBSKioqpDdCW1ubtrY2cnJy5ORerE5Hjx6Njo4OR48elSIxcSFdvHiRefPmYWpqSnBwMPfv38fU1JSHDx8ycOBAnj17RldXFw4ODpSUlDBnzhx27twpcyaWLFnCpUuX5KZBQ0NDrhFdXFwYNGgQH330EWZmZrz11lucPXuWnJwcAgIC8Pb2JiYmhlGjRjF58mSUlZVl9KKOjg4BAQFy8zBt2jQ0NTVZu3Ytly5dwtPTE0dHR959990/fSb/EheDiooKgwcPprq6GgcHB4yMjPj999/5+uuvqa2t5dChQxQXF6OkpCSrCnd3dwBKS0vJzc2ltbWVwMBA6Si0trbm+vXrDB06VO6MhdPPw8ODgoICCgoKpE6/ra2NxsZGmpqasLKyYtq0aVy6dEkebjHkEpN/8ZYVB1YM6LKyslBSUsLGxgZbW1tJbBbhq6IqEAYbExMTeRjEakkIt8T3xQZFYO8HDhwIIF2S2trauLm5SSyZkFMLN6CqqioNDQ0y30GE+q5atYqwsDBZOa1evZoZM2bg4uJCQUEB/fv3l7zMlpYW5s6dS0xMDPX19cTGxkrkmxDtjBo1Cg0NDfLy8nB1dcXLy4uTJ0/K9K5bt27J2VBZWRldXV3Sin3r1i309fVxdXXFxsaGhw8fcv36dbq7u/Hx8SEiIkIKpp49e0ZBQQFvvfUWenp6eHt74+3tzdq1awkLCyM5OZl9+/aRm5tLfX09w4cPl+ATAYvV1NRkzpw5xMfHS+VjfHw8xsbG7Nu3T6o1W1tbqampYcWKFRgZGTF16lS52err6+P8+fMkJydjYWHB0qVLOXv2rGRqGBgYcOfOHWJjYyktLcXc3JyVK1dSXFzMrVu3GD16NE1NTRw7dgxfX18iIyPZsWMHKSkpdHd3M3r0aCZPnoy6ujq3bt3CwsICHx8fOX9wcnKS+Hqh6E1MTMTU1BR9fX20tbWlD+P/+Fbi/8Yj4BpdXV38+uuv0syiqqqKiYkJkydPxszMDFdXV+mYMzExITk5WZarIuizr6+PgwcP4urqSr9+/cjLy5NRdAYGBnI4JIZKAibb3d0t5wEi2mvw4MHExcX9QWcuCL7ichAMvlefxsZGaTvW0NDAxcVF+hFEWyAqAtFvv7riVFdXlx9iMVgUuY56enpSiDVgwADpJhSZAhoaGvj5+aGiokJVVRXOzs5S7y9wbJ2dnTg4OEgrtkKhICgoCHt7e1xcXIiJiUFXV5eCggLMzMzIyclBTU0Nc3NzCVl1cnKSGYvCB/Ds2TMCAwPJz8+nrKyMESNGYGtrS3R0NH19ffj7+5OTk8PQoUNZsGABW7duZe7cufj6+nL48GHy8/OxsbEhKCiI4OBgyScwMzPjwoUL+Pn5oa+vT0pKCh4eHvj4+HDixAny8/NRUlJiy5YtDBgwgKqqKr799luGDRtGXV2d1HuIzY2oQNva2vjpp5+orKzkyJEjDBkyhMTERIYMGUJtbS0RERGsXLmSgoICfvvtN8k5EC8fJSUl1q5dS25uLgkJCfz+++8EBwfj4+NDRkYGmZmZsg1QUlLizJkzfP/991y9epUPPvhAwmkuXLjAkCFD+PHHHzl58iS2traMGTOGlStXkp2dzYYNG6isrGTDhg1cuHCBxMRENDQ0+Oijj/j000/58MMPpZFr37592Nra8v7779PT0yMNiP9udqXKtm3b/j8c6f8zz969e7d5eHhQXFzMpEmTUFJSYsCAAdy+fVtCTnp7e0lPT8fGxgYzMzP6+vqIjY3F0tIST09PEhISZOCIKBX9/Pzw8fGRl4yysrJEdD9+/JiKigpJahKBpaamppibm0sjSk5ODk5OTv8F6iKIyMJW/OojLgqhzqyoqKCmpgYVFRWpgIOX6yYBS1FVVZUVhhBUdXR0SDio0FEMGDAAFRUVoqOjyc7OJikpiaamJlJSUoiNjSUpKUkCZOrr67l69SppaWk8fvxYVh9NTU04OjqycOFCDh06hKqqKhs3bpRIcmNjY8rLy8nPz2fw4MEoFAqOHTtGcnIyvb290segoqIiV4Z3794F4MqVKxQWFpKXl8eRI0fw8/Nj5cqVWFlZMWXKFIyMjAgJCZFDsenTp2NpaUlYWBiTJ0+Wc6T8/HwiIiJITEykqKgIIyMjqXodPny4ZG40NTXh7+/P3LlzZb5pamoqz58/l9P53NxcRowYIUVEgu7U2dlJeno6gwYNIiUlhdraWlRVVVmwYAGvvfYavr6+FBcXM2PGDAYOHMj06dPJzMzk22+/5fnz57S1tbFhwwZJelIoFHLVqqKiQnBwMOvWrePo0aMoFAoZtvzll19y48YNEhMT5Qvh/v37EpRbVFREYmIiwcHB3Lhxg507d7Ju3TqKioo4ceIE8+bN46effuLcuTxaT0IAACAASURBVHMUFhYSFxfH8ePHGTBgAM3Nzfz666+Ul5fj7u5Od3c327dvR0tLi4iIiPJt27b98GfOpNL/RmLwf+VxcHDoW7p0KYmJiYSGhpKTk4OXl5c0IjU1NUmT1KhRozA1NSUjI0OSiYRhRbAYNDU1uX//Pm1tbcybN0/2/IJeNGXKFL7//ns5mBHT/56eHry8vHB1dcXCwoLW1laUlZW5fv06qampf6gMBAnqVUWjELwAUhglvhWXACCHXiLrAF7KwocMGfIHlaJQPQq3XlVVFV9++aVsK4yMjGRASVNTk4ynF2Rtf39/9PX12blzJ5cvX5akqbKyMubNm8f+/fsJDg7G0NCQmzdvUlJSIi/dvLw8LCwsePjwIdnZ2ZiYmFBZWUlJSQmenp44Ozujo6PDw4cPycvLo6SkhIaGBkJCQkhLS5OhPa2trTKBadq0aTQ3NxMSEiIrKTc3N54+fYqfnx+bNm2itLQUJycnjI2NefHiBQ0NDRw5coSkpCTKysrIysrCxMSEuro6uTERkufm5mZyc3MJDg6mX79+2Nvb09DQwK1bt+ju7qaxsZG4uDhJXNbR0aGjo4P+/fsTFBSEh4cHqamptLS0MHv2bGpra3FzcyMqKkqqFV1cXPjyyy/x9fWltLSU4OBgSkpKCAwMJCYmhmfPntHQ0ICJiQnvv/8+mpqapKSkkJGRQVRUlKzubGxsKCoqoqurC2dnZ1avXs2tW7eks7OtrY23336bH374AV1dXcrKyqTmxs3NjefPn0tCFSB5GQIw8yrYCBC4w4S+vr7AP3Mm/xKtRH19PRkZGbS3t3PixAlcXV1lPkJOTg7Tp0/H1dVV6gGio6M5f/48tra2EvYZGxtLSEiIdAAqFAqCg4NlryjCQsaNGydBHuJNLZDotbW1cpUpzFeGhoYEBgaiUChkMvSroiQ9PT0JZ33V0yDeBK8iv8UjWhb4F8G6s7OTFy9eyK2Grq6u/AcWbkMHBweSkpKk9l6sPIUDLzU1FR0dHVRVVSksLJRAV1EZAZL1GBYWJpOUNDU1iYmJ4eeff8bDwwMlpZcJSqNGjSIyMpLOzk7Zi7e0tJCamoqpqSl6enrU1dVJ2hBAfHw8ra2tUm0qJNzKysrEx8djbm4utQ81NTXcvXuX6upqzp49S0JCAq6urhQUFKCsrIyfnx/Nzc0ypPj06dNyXfzw4UPJ8BAhLi4uLlKK3NrayoMHD6ivr8fe3p5FixZhbm6Ov7+/FAvp6OigUCgoKSnB3NwcQArbMjMzmTVrFpmZmTx+/BhbW1vKy8vJzs5m0qRJpKamkp6eTkhICGVlZSQmJlJWVsbs2bNpbm7G3Nxczqb8/Pzw8vIiMDCQ0tJSamtrmTp1KoaGhjx58oSAgACUlJSYNm2aTNfKzMxk0qRJPHz4kK6uLrmuHDRoED4+Prz++utMmTKFcePGyeFqV1eXHMCLlvFVItm/8/wlLoaurpf5guPGjePixYsEBQWRlpZGWloaVVVVPHz4kKamJsn4F7DRxsZGicwWZXBNTY1EcZuampKeni5ZD6KNSEpKkvp5MezT1NSUykGhcdDU1KS1tRVzc3MJHUlKSqKvr08mE4k5w6vEpFdp0/DyInj1MoF/EaDEzxFDrld/jnj09PTw9fXF39+fhoYGlJWVJRC0t7dXJiw1NTXJiqmlpYX29vb/kmKlqqqKvb09o0aNIjc3V+LRvvvuOyoqKigsLJQfIiEeEgGwYoYjfBhC4yH+HsWhFATnvr4+Fi1aJD0Y1tbWkl4k/q6eP39Od3c3w4cPx8/PT5Kfdu3ahb6+PlevXqW9vZ27d+9ibW2No6Mj48aNw9HRkYMHDwLIz8OoUaNobm5mzpw5/POf/5Q/PnDgQGxtbdHW1pYeC2FgE+vgOXPm8OTJE3p6epg8eTLnzp3DzMyMa9eu0dDQwKJFiygrKyMmJgY/Pz8A6drt6uri3LlzDBkyRLIkOzs7OX36NLt37yYmJgZTU1Pi4uJYsGABt2/fJiAggOLiYgwMDAgICKCiooLIyEjGjRuHnZ0dHh4e1NXVMX78eK5evSrt3WVlZbi5udHZ2cny5cuZOHEiYWFhrF69GoD3339fEtBu3rwpK8vMzEyJ1vszz1+ildDT0+sbPXo0paWlDBs2jKNHj8qDqqurK/tZgcQeM2YMnZ2dLFy4EGtra7Kzs3n+/Lnc8QrMlYODA3Z2djJhyM7OjoKCAmxsbNi1axc3b97ExMRErrZepU4PGDAABwcHGa9eXFwsyznh/Yd/eSFeXW2Kb1/lSf53N7ZYV75aUYj2Q/y3kZERU6ZMYfz48WRkZMhe29fXl+TkZLnBqK6uxtXVFS0tLVlW3rp1i+TkZDIzMyVA1tramsuXL7N48WKys7Px9fWVOZUiyEWg0cWGpaWlhaqqKrS0tOQK0MbGhv79+6Ojo8O4ceMwNTUlNzeX7OxssrKyCA0Nxdvbm/feew8tLS1cXV1ZsmQJ+fn5aGho4OHhQV9fH48ePSIuLo4PPviAzs5OLl68iI2NDf369aOurg4nJycuXbpESEgILS0tuLu78/3339PS0kJAQAAAnp6e1NfXU15ezpMnT1ixYoXkh4q1qKurKzNmzODZs2dS0aqtrS3fyMePH+fKlSuMHTuWvLw8QkND6e3tJTw8nIyMDEJDQwkODubixYucPHkShUKBv7+/jMET1YGSkhKvv/46tbW1mJmZ8c9//pOKigp0dHSwtbXFxcWFkydPUldXJ1fjHh4eTJ06le7ubq5evYqBgQE7d+7k+vXrPH/+nICAAFpaWigtLaW0tBQNDQ2SkpJQV3+Zvblu3TouXbr0B3apgN2YmpqKNu1/VivR09NDSkoKVlZWmJubs2nTJjQ0NOThiImJkT3plClTJJsPXrrKTpw4wdq1a3F1daWhoQEHBwcACYIVWQiamppykAhICrAQJIkpu6hghHS6qalJ+umFFLeqqoqoqCi5cxdVwquGoVeZjfCvGYT4PV69OF69oF81TwlJs9BiNDc3S9yY2CaI1au2tjYFBQUUFRVhZmaGl5cXVVVVf9BKiM1Gd3e3tGBra2tTVFQk3+QeHh4y9EewCdLT08nNzeX3339HSUmJ8PBwCgsLefHiBQqFgsLCQs6cOcPWrVt58eIF48ePl3b6N998Uwq51NTUiIyMZPjw4Rw/fpzp06czePBgOWv57bffMDExkZkiRkZGHDx4kOrqaq5evYqbmxvjx4/nrbfeYv369ezZs4euri5KS0u5ceMGsbGxLFu2DHt7e2ksy8/PZ9iwYXR1dbF48WI++uijP8BZxbbB09NTUpurq6sxNzeXcxIrKyuZZ7lmzRoMDAzIzMyU2gVRgT58+JDZs2cTFBREZmYmBw4ckECaLVu2yFX52rVrMTAwoLS0lEOHDpGVlUVSUhK7du3C1tZW/tknTJjAlClTqKqqkjO1n376iUmTJrFu3Tr27NlDWFgYxcXFhISEyJmSaC+ePn3KmDFjOHXqFCNHjvzTZ/IvUTFYWFj0bdmyBUdHRx48eEBaWhohISE8ePAAExMTZs+eTWdnJ+fPn6e7u5s1a9ZgaWlJQUEBe/fu5c0336SqqgovLy88PDzQ0dEhPDwcc3NzJkyYQGJiIsXFxUycOJGenh4yMzMJDw8nIiJCEqGE1kCQlp2dnTE1NcXGxkZ+IHR1dSksLKS+vl6SnoSPQPjwha351Qvi1WpB/Ph/ZkiKi0IcTtGaCJSah4eHxNiJ+DHBVxBejba2NhlIo6amhqOjI/Hx8dy9e1dqGFRVVdm6dSszZ85k2LBh6OnpERQURElJCYMHD2bq1Kk0NzdTW1vLL7/8Ig8PIM1L9vb2Er4r1rvd3d2yLBfBKBs2bJDqPwcHB+bPn09gYCCLFi1i4sSJNDU14eLiwpAhQ3B2dmb//v1s2rSJDz/8kJ07d9LV1cXdu3fx9fXFzs6OmJgYqUrdtWsX8+fPl29ELS0t/va3v7Fq1Sq0tbVJSEhg2LBhWFhYSFequro6T58+JSIigkuXLkm2qJg1GBkZcfLkSfz8/MjIyMDR0ZHU1FQMDQ0xMjLixo0baGho4OXlJdfDSUlJZGVlYW1tja+vL/DyRfDll1+iqanJsmXLiI2NZcCAAVRUVODt7U1raysDBw6ksrKStLQ0goKCUCgUMldUWVmZ8vJy/v73v1NZWcnRo0exsrKSRi5PT0/09fVJTk6WCeRNTU0MHTqUffv20dnZibOzM35+ftLA5enpyaeffvo/q2LQ1dUlKSmJjIwMmpub6devHzdv3sTS0hIDAwPKyspwd3fH09OTS5cu8fTpU1auXMnGjRtxdnaWQR+PHz+mt7cXc3NzXF1dUVZWlum/oiLQ1taW5iKx2xWTW0FjFh/0vr4+mT0gNPDCLVhRUYGKigrm5uZyRfrixYs/fF2izXh1biAuBrHuFP9PYNjFIRRWZCcnJ3p7e1FWVqa3t1fizQUZWXwNpqamknCUkpIivfliwyEArQAhISG8ePGCsrIyAgMDcXV1Ze7cubx48UKmOQnTlY2NDampqQBkZmbKwZ24LMUAV+gC4KWMW0dHh6CgIPz8/Jg+fTqGhoZkZGSwfv16ysvLOXz4MLNmzcLZ2Zm0tDQcHR15/fXX5TYmPz8fc3NzCgsL5WxCYM+qq6vZtWsX8HLDI4aue/fupbe3l927d2NhYUFZWRlWVlZ0dXXJnAl1dXX27dvHtWvXJHNBkLtaWlqwsrIiOjqaI0eOsHbtWrkBsbOzw9jYmODgYO7cucOQIUPIzs7m5MmT8oXg5OREXV0dFRUVVFVVERAQQFdXF5mZmWhpaZGamoqBgQGRkZHk5uaipKQksQK///473d3dLF26lMrKSk6ePElVVRU9PT3o6+tz5MgRkpOTqampYfXq1dja2lJQUICLiwv5+fkAPH78mE2bNnHgwAHmzZsnzYnnzp37L4FH/7vnL3ExVFRUUF5ejrW1NY2NjTQ3N+Pt7U1dXZ0k+7S0tNDd3Y2qqiqZmZncuXOHpqYmyYh0c3OjsbGR27dvo6+vz+zZs0lJSeHq1atSuy/swSKkBpDqQnERCB6kSEkSSUVC0tzR0YGdnZ0EbbS1tclgU5HILZKXX20RXpVJC4CL+D4gkWXiMTAwkAdBSUlJ7vENDQ3lGs/GxkYmHgcEBGBqaippziINqbPzZSaH0HEI/NtXX31Fb28vo0ePxtzcnNLSUqKjo1FTU2PKlCmcO3eOxsZGli9fLtWcr4b7CoSbgYGBBJSIIBp3d3cMDAy4f/8+AQEBElgbGRkph7Nz585FSUmJgIAAYmJi2LFjh/ScDB48WA40lyxZQnd3N+fPn2fkyJGoqanh5OREfn6+xJWJMB0dHR2ePHkiPQZFRUUkJSVhY2NDZGQkt27dwtHREW9vb8zNzamurpapZRoaGhJycvDgQYqLizl69ChKSkpSej527FjU1dWpra3l/v37hIeHk52dLduy+/+Ryt3U1IS5uTnR0dFMmDCBBQsWYGRkhLe3t/StlJaWYmtrS01NDQYGBpw/f14GGqWkpNDU1ISXlxebNm0iKiqKuLg4cnNz6d+/PxoaGhQXF/PDDz+wfft2NDQ0SE5OxsbGhtzcXDQ1NSktLSU+Pp7q6mq8vLz+H4fa/2/PX6aV8PHxYebMmdTU1HDz5k2MjY0pLi6W9N9ly5ZRW1vL5s2bGTZsGPX19Tx+/BhjY2OcnJyYPn06aWlphIaG0tHRIaewwqRjbm7OyJEjpfFo/fr1REZGYm1tLWXEAvIpfk0fHx+ZgyBgq2KFamtrK6sLEe4iiL3nz5+XB11UDQJd/2rMuVhViguhra0NbW1t7Ozs5PpMrJr8/PzkxZCZmSl/3alTp3LmzBl0dHQkI1NXV5fr16/T0NBAVlYWL168kH9+ePnmHz9+PMnJyaxZs4aamhpsbW2pra3F0tISPT09zp07xzvvvCPTnpycnFizZg1r1qyhf//+FBYWoqWlRU5ODr/88guffvopHh4eHD58mJSUFBobGykrK8PBwYFBgwbx4MEDFAoFzs7OaGhoMGLECC5evAi8nDEtWLCAsLAwCgsLsbCwYO/evXJzIfQg69atQ0VFhdLSUtavX092djYffvghEyZMYO/evcydO5eAgADy8vLYv38/TU1NrFmzhu7ubnbu3MmXX35Je3s7DQ0NhIaGYmlpKXt5IaIbMmQIGzdulNufhw8fypatvb2dnJwcFi9ejMN/xNi1tLSQnZ1NWloaCxYs4OzZs2zbtg19fX0WL15MX18fwcHBTJ06lcrKSiIjI6VgraSkhCVLlhAeHk5TUxPvvfceZmZmPH78mJycHPT09PDw8JD6B2NjYxnLZ2FhQU9PD66urnz00UcUFBRw69YtnJycpFnKwMCAvLw8VFRUhHbnf1YrUV9fLyfnn332GRkZGejr67N69WpMTEzQ09PDwsICJSUl/vnPf0rtwo0bN6ivr+f58+cUFxdLM5GRkRFZWVl8//33XLp0SXrYxU3r7OwsNQKqqqrS+COk0SLpuKWlRSogxaRXT08Pe3t7qqqqZMqQCC8xMTHB39+fM2fOyHWdOPzCSCN+7NV1qUCVDRo0CCMjI7keFMG8+vr6cv+urq5OUFAQT58+xcLCgtu3b2NoaIiJiQm2trYkJiZKcpJCoZBDWviXErO3t5eGhgZMTU0ZMWIE//jHP2htbUVDQ4OioiJ8fHxoaGjgxx9/xNbWluXLl3Pq1CkAdu/ezU8//URWVhYaGhqYmZnJN+iZM2eorKyUeHrx5jIzM8PMzAwHBwfpfj158iQff/wx9+/fp6SkhJCQEBYuXEhFRQXXr1/HwMCAiIgI5s6dy7Nnz3jw4AFr164F4PLly9ja2rJgwQL27NnD9evXpXXew8MDf39/2e8nJycza9Ysvv76a2xtbenu7pagXBE7J0J5VVVViYuL4969e6irqzNjxgx8fX2pra0lMTGRkSNH0tvbS1ZWFrm5uQwbNgw7OztaWlqYNGmS3JR8/fXXTJw4kePHj7N582aUlF7G9i1atIja2loyMjLw8fFhzpw5nDt3jgEDBuDr68u8efM4cOAAY8aMkSpYMcgtKCjgjTfekJ4VXV1dRowYwfvvvy/dr8KnMmbMGGbOnCnDlJ8/f/7f5qL8d89f4mKwtLRkxowZ1NbWsmnTJgYNGsQbb7xBaGgoKSkp9OvXj/3795OcnIypqanMrSwsLJSORmdnZzo6OkhLS8PV1ZWenh6qqqpkrLq44cVkV7QOvb29Mk1JmJU6OztlOyEixIUnoKWlBS0tLRwcHKT9WoBIBAtRoOlFNSbMRmpqav/lH0hVVZWhQ4fK/b6ImRcXVHt7u3yLW1pakpSURG1trZT0imASUaJ7eHhgaGiIoaGh7MMBKdsWXgHxoYmIiJBWYOEpEa2TqakpVlZWktLc19fH2bNn+e233/j999/55JNP5CQ8KyuLvr4+Fi9eTEREBC4uLqSlpaGjo8OOHTuk6MfBwYEpU6ZgaWkph3KTJ09m3759VFRUMGLECMLDw3FycuLy5ctkZmZy8+ZN2SOfPXuWmJgYrK2tcXFxYevWrQQHB0v/yjfffMPYsWOJiIjg3r17zJo1i5KSEslJjI+PJzU1lRMnTvD555+zevVqFAoFHR0dqKqqolAoiIiIYOPGjRQXF1NYWIiDgwPq6uq8//77uLm5SYx8cXExGzdu5IsvviAqKoq9e/eir69PTk4ODx8+5Ny5c7i4uLB+/Xr09PTYsmULYWFh2NrakpOTw82bN3FyciI3N5fAwEC0tLR45513uHDhAs3NzTx69Ij33nuPkSNH8sUXX7Bjxw5GjRrFunXrqK6uxtnZmffee4/hw4fz4MEDoqKiUFdXR0VFhXv37hESEoKNjQ1PnjwhOTn53zqTf4mLobW1FSMjI9ra2li6dCkLFy6UODVfX196enrIyspi6tSpcnI/b948GY46cOBAsrKysLCwYPjw4TIzsaamhnHjxlFaWopCoZABJomJibIUF29wEdsuLofm5mbpVhTOyt7eXlmamZubS3ZiUVER/v7+eHp6SnS9mIeIqkBVVVVqC8ScQRhfxOpMSHUBud6rr69n2rRp1NfX09XVha+vr5T+6unpUVNTw7x5LyFZ9+7dQ01NjezsbIl18/b2xsLCggcPHtDW1oaenh6nTp2S67gPPvgAQ0NDjh49KtO6SktL5Uxl4MCBsp0QTsazZ8/i6enJgwcP+Oqrr9i4cSNDhw6lubmZzz77DG9vb168eMGoUaM4cOAAb731Fhs2bEBNTY2QkBAeP37M6tWr+eWXXzhx4gTDhg1j4sSJREZGoqWlxRtvvEFvby81NTW4u7tz9uxZFAoFWVlZDBo0CD8/P1JSUigpKZFitZCQEE6ePMmCBQu4dOmSVAlWVVVx7NgxrKys8PX1ZceOHdjb2/PJJ5+goaGBkZGRXNvq6upK8E1ZWRkrV67E29sbT09PiouLqaurY/jw4Xz77bfAyxeapaUlhw4dIigoiPnz5xMVFYWjoyOPHj3C1dWVTz75hM2bN2NnZ0dQUBCurq4YGxszZMgQioqK+O6779i3bx9PnjyRlvDCwkLKy8u5c+cOaWlprFixQsrz9+3bx+eff46SkhI7duyQbUl3dzdGRkYYGxszf/58YmNjUVZWZubMmezatYszZ84wadKkP30m/xIXg5KSEunp6cyePRtbW1tZ5grPRHFxsfwLCAkJkWiyyspKxo8fz4MHD1BRUZHhG/X19XzwwQcy07KkpAQ9PT1JhAoKCpJlvVglismyIEaLXEVxMYi3qShFRfKQsGz7+vr+QYsgNBl5eXmySngVo6arqytJUOL3EjzKV/Xt4g0u0owF/7Bfv36Ul5fT0NAgRU8WFhZkZ2fLzYlQLebk5FBeXg7AyJEj5eUFL7UctbW1JCQk8MYbb5CVlUVbW5tsuUxMTOjfvz9RUVEUFhYSFhbG4MGDefDggdwchIaGAi8ro23btsnMhd9//52pU6cyYMAAmpqasLa2ZtCgQTg6OtLV1YWfnx/d3d14enpibm7O0aNH5VC5vb2dhIQEbGxscHJyQllZmaysLADGjx+PjY2NtBtPnDiRvr4+rKysePToET/88ANLliyRlZSo5rKysvDw8MDR0ZHBgwcTFhbGsGHDWLFiBWPHjuXMmTPyBWBmZsbbb7+Nvb09hoaG9OvXj2XLlkkNS0ZGBiUlJUyYMIGLFy/y7NkzTExMePLkCd3d3Tg4OJCRkYGBgQEzZ85EQ0ODjIwMvvrqK7y9vdHS0pKszri4OPbv309gYCCjRo1i3rx5kvR94MABnJycGDRoEM+ePUNPTw8TExMiIiIICwtjxowZdHZ2snnzZlxcXLhy5QpdXV289tprVFZWcuLECQCuX7/+b53Jv8TF0NnZSW1tLffu3cPX15dHjx6xevVq9u3bx3vvvce9e/dYu3Yt+fn5XLhwAScnJ3755Re0tLQIDQ1FV1eXGzduUFZWxsOHD2lsbGTp0qXU1NTI3a8AvqalpREbGyt3/aJNUFZWlkaqvv8IfBXtgGgnVFRU0NLSkqEiwkQVGBgo9Qtiei8UgQX/Eeyqra0th47C2yBUlb29vXR3d8v1X29vr+QoCJS8wLmLtaO7uzuGhobU1dXJElhMuC0tLbl8+TKAjEEbOXIkra2t/PTTT4wYMUKuR9PT06W1Oy4ujrS0NGbPnk1RURFBQUFYWVlJmpC6ujrjxo0jIiICW1tbrK2tpVdBTU2NoqIiuX4TF87gwYPR1dWVRKPdu3ejr69PVFQU+fn5ODk54ejoSFZWFs+fP8fU1JQdO3YALw1zQs4uQnbb2tpoaWnB0tJS5jeIx8nJiadPn9LW1kZISAjnz5/n2rVrLF++nKCgIGJjYxk9erQEvwpkvYqKCqdPn/4vl/LIkSOpq6ujtraWGzdusGnTJioqKpg7dy6JiYk8fvwYFxcXrKysmDRpEu3t7fJyFkY50T66ubnR09NDcHAw/fv3Jz4+HmVlZXbt2kV9fT0rV66krq6Oq1evsmXLFuzs7KioqMDd3Z0VK1ZQV1cnwbH5+fnMmDEDAEdHR3p6ehg8eDBjxoyR58nBwUEmiQ0ePFjCYf/s85e4GGxtbfn73/8usVv/+Mc/SEhIoLS0VJqGBIPws88+4+zZszLFt76+nrKyMuzt7bl37x7a2trs2LGD2NhYjh07JsEWCoWCAwcOEBYWJp12gER8qampyXWk8BkIj4N46/T9R6qw2HvX1dVJx2JjYyMeHh4EBgaSk5ODQqGQ7kjxrWAN1NTUUFlZKZ12Ojo6UqMg5hXCOShI1WI7Ilqi8vJyrKysyM7OZty4cVRVVdGvXz+UlJTIycnBw8ODu3fv0tLSwrFjx+SMQEVFhfj4eNra2li3bh0dHR1oaWnxxRdfUFxczGuvvSbnB7q6ulRWVqKjo8P48ePp6OiQrIaenh7GjRuHnp4eo0aN4sKFC7KamT17tvROiIg0NTU1li5dyrZt23B2dub06dO4urpy48YNzp8/T2RkJDdu3MDc3JwrV67g5eXFiRMnSElJYejQody5c4dp06bx8ccfk5eXR0NDA5999hlHjx6VLUVQUBDLly/nwYMHXL16Vb7h9fX1ZUbFuXPnMDQ0JCQkhKqqKsaMGcOePXsYNGgQT58+pbe39w85IyJa7+OPP+bgwYN4eHgAMHfuXBoaGjh+/DjLli3jwYMHlJSUsHHjRvr168evv/7K3r17eeedd9i6dSsODg4cPnyYkSNH4uPjQ3h4OGVlZYwaNYoNGzZw8OBBzp49y/r16wE4cuQI1tbWzJ8/nw8//FBG3JWXl3P79m1aWlr47bffUFJS4tSpU6xcuZIXL15ga2vL0qVLuX37NsuXL2fJkiUyc+TTTz/902fyL7GuVFFR6RO+iM7OTpm2Exsb1GYVxAAAIABJREFUi5mZmVStidAYJycn0tPTZfSakpLSH2zSIl/xVay7IAvr6elhaGjIkSNH6OrqwtLSEnNzc4yNjcnNzZVWWCsrK6ytrfHy8pKleXt7O6WlpTQ3N8vqQjD2xNvip59+4ubNm7KViIqKAl5Kt4cNG0b//v0pLS2loqJC/houLi4SfPqqSrKtrQ0vLy+GDx8uzTqpqanY2tpibm7OnTt3pHKvr68PFxcXysrKKCws5I033uCdd96hp6eHVatWsXz5cuBfQqqenh5OnTpFdHQ0hYWFeHh4UFRUxIQJE3j48KGUl+fm5uLn54eqqiopKSlkZWVJc9mAAQNoa2tj5MiRVFZWUlVVRWdnJ8XFxZJvIUJWxWWro6PDpEmTCA8Pp66uTpqyjI2NpWR6woQJ6OnpERERITUZnZ2dNDU1MX78eCwsLIiJiSEzMxNzc3Ps7e0pLy+nsrKS7u5urKys5GxmxIgRbNq0CUtLSyZPnkxrayunT59GV1dXajL8/Pw4d+4chw4dYvz48ejp6VFaWiqZDz09PcyaNUtG0YlK7dXBs/ha4KXpTaSgmZqa0tDQ8IeLvra2Fnt7e6ZPn46DgwMbN25ETU1Npoo3Njaio6Mjf76IAjQyMqK5uZl9+/axceNG2tvbpcPUzMwMfX19njx5gq2trdyaiRR4VVVVampq/metKw0MDFizZg3q6uqkp6fT2tpKv379cHZ2pra2lrfffhsNDQ0ePXqErq4u6enp1NfXM2PGDKytrXF3d6epqYnBgweTlpZGeHg4xsbG6OjoUFlZiZqaGlpaWlJiKsg/ApwihDWCcNzS0oKKioqMjnNwcJA+d0F3Eqs9d3d39PX1pajH3d1dMisNDQ1RUVGhs7MTFxcXjI2NpV9DtCXCDSqGXwYGBlJubWhoiLOzM/3790dVVZXa2losLCxwcHAgNzeXuXPnSjycqEzOnj3LqlWrJEDX3Nyct99+W8JohPqxqamJsWPHMm3aNN555x3s7OxYtmwZSkpK3Llzh/T0dFasWMHdu3dZtGgR+/fvp7u7m88++0yGz6qoqHDr1i2Cg4OxtrZm9erVHDx4EBMTE2l/v3fvHv7+/rS3t3P48GHu3LlDXV0d27dvJyQkREq833rrLfr160dpaSnvv/8+/fv3Z8+ePRw9epTXX3+dgoICXF1dKSkpITIyUg7T4uPjUVJSoqysTOoVtmzZQnZ2NqNGjUKhUBAaGkpubi7Ozs4UFRWhrKxMVVUVb731ltwWXbhwgdWrV0sWhfg7rqmpYdWqVWzcuJGdO3fKzJCTJ0+SkJAgCdTV1dUMGjQIb29vfHx86O3tJT8/n61bt9LR0cGqVavYt2+fNKdt27aNoUOHEhUVxfz583FwcJCp6ocOHcLf35+srCzMzMxYsmSJzF0Rg82kpCQuXrzIggULZAJ8VFQU4eHhmJqaoqKiwqFDh3j06JHUsvw7z1/iYmhrayM8PBxNTU3y8vJQKBQsWbJEKgt//PFHuru7mTlzJlFRUZSXl2NsbExsbCzBwcG4uroSEBCAvr6+TK/S0dFh1KhRctNQXFws32Dt7e20tbWhqqoqMV0tLS2y6hARec3NzTI/QvSfnZ2dUicgdA1Cui04fyI/UlNTk9dee02+7TIyMqivr8fFxQVDQ0M5ZxC7dEFkFvQmOzs7qd9oamqSSVoqKipSqCVEL+3t7TI1WV1dnV9++YW2tjZ5gOFfK0uxV9+2bRsbNmyQdufW1laePXuGm5sb6enp3L9/nydPnpCZmcncuXNpbW3l9u3bTJo0idraWsleiIyM5PPPPycsLIxjx45x4cIFGhoamD17NsuWLePRo0ekpKQQFxeHt7c3ysrKPH36VOofANzd3fnb3/5GV1cXN2/eZP369dKLUl5eTk5ODikpKWzdupW1a9fy7bff4uDgwNatWwHkgV+9ejXXrl0jKSmJ9vZ2+vfvj7q6Om5ubnR0dJCSksKTJ0+wtraWeRZr165l+vTpXLhwAYVCIT9nysrKfPrpp7i5uXH27Fnu3LlDRUUFR48elQa18ePHs3DhQqqqqnB1dcXV1ZUTJ06grKzM3LlzsbS0pKOjQ8YBCjDMr7/+yv379zE2NmbatGlcuXJFhjmPHDmSmzdvsn37dr755hv27t3LG2+8wf79+9HX12fatGlkZGSQmprK8OHD2b59O2PHjpVft0AdCsx+d3e3vDz+7KP8v/8p//8/IpJr8ODBBAQESJrO2LFjOX/+PGPGjKG7u5vFixdjbW2Nnp4etra2zJ49m/b2doKCgqRxRktLiyVLllBUVMTz589RV1fH2NiY/Px8TExMsLKyknxIFRUVyVIUoFWRbSAgqNXV1X+wUysUCimXFqtIY2PjPyRHibeCsrIyLi4uuLq6Sry8KLnh5WZCCKAEcxJeiqFEPoa9vb1ExVtYWEjQiYmJCT09Pdy7d4/KykqKiorIz8+XK1nhW2hrayM6OlpqM8SlKN6kAg4rEHr9+/fnvffeIywsjKlTp7Jnzx5WrlxJQ0ODNDCJRGkxjBSty7hx4xg3bhwGBga4ublx9+5dTp48SWZmpgxeff78Oc7OzsyZM4eBAwdSXl5OTU0NEydOxMLCQhqiysvLJXPj559/lmG6t27d4rfffuO7777j8ePHWFtbExoayp49e3j+/DmnT5+WWLrbt2+zbds26RkwNjbm3r17WFlZSapzQ0MDlpaWLFmyBCMjIxQKhQzSUVNT48MPPyQ+Pp6vvvqKqqoqLC0t+f777ykoKABeRhnU1NTQ29uLs7Mz8fHxXLt2jd27dxMREUFdXR06OjqcOHECfX19lJSUMDExYfTo0RQXF5OXl8e9e/e4du0a3377LWVlZZw5c4bq6mref/99amtrycvL45NPPuHu3btoampSWFgoI+t6enqws7PjwoULlJaWcvr0abS0tLh06RLx8fG89957WFtb4+Pj82+dyb9ExSAQ2EOHDsXAwIAxY8aQkZHBokWLeO2113Bzc6O1tRUXFxfMzMzQ0dFh7ty5TJs2jbfffptffvmFuXPnoqGhwapVq6RqraKiAgMDA2xsbAgICJCYetEPiotBXBLa2toYGhpKMlFXVxetra20tLTIPl54DlRUVCS/QcwFVFVV5VtZbBTElFskUtXW1lJRUSGpUaJHfbUaEUMvHR0dGYQj+ITDhg2TeRHt7e2Sy+jt7S2DblpbWyVXUqFQEB4eTkhIiKye0tPTsba2ZujQoZSUlJCVlcXf/vY3srKyuHv3rsxMFARiPz8/Tp48iZeXF1OmTMHX1xc/Pz/JaBg4cCD37t2jrKyMa9euyQzFnp4ekpKSUFNTY86cOUybNo3o6GiZzbBr1y50dHRwcnIiNTWV0tJSjhw5IrUV1dXVjBo1ivT0dJ4+fcqJEyfo7u7m2bNn+Pv7ExkZSXNzM1FRUXh4ePDOO+/w5Zdf8sUXX8jPVlZWFs+ePSMmJobs7GzCwsKwtLQkOTlZWqmrq6t59uwZ/fv3p7y8HGVlZfbv38/mzZtxcnKSmaja2tr4+vpy6NAhqqqqCAkJwdTUlGnTptHe3s6DBw+ora3FxMREVhuGhoZSA2FhYSHNa2+++Sb6+vpSNu7i4kJDQwOBgYFSy5KUlMTs2bPR19fnm2++kX6Z7OxsSktLuXTpEkOHDuX69eucPn2aGTNmsHjxYlRVVRk+fDhKSko8ePCAL774gjNnznDz5s0/fSb/EsNHf3//vqioKB4/foyOjg4lJSWkpqbi4+MjnZTNzc0SMZ6fn09QUBDh4eG8++67cnsgFILi7WJkZMTVq1dRKBT4+vry7NkzWlpaGDx4MMHBwRgbG0uBkdhv9/X1kZGRIXf9rq6uslKpqqqShqL6+nrpv7CyssLBwYG+vj7u37/P0qVLGTNmDFZWVmhqalJTUyN32h0dHXLFaGZmJoGqAs5iZGSEoaEhAwYMICAgAIVCwaBBg3jy5Al6enoUFRVhaWlJREQEPT09BAYGkp6ejpeXFy0tLcTExKCiokJCQgL19fV4e3vz3XffYWhoKH0a69evZ/PmzfT09NDY2MidO3dob29HS0tLxq6VlZWxePFi4KX2YcaMGZiamlJfX4+dnR0ff/wxmZmZxMfHExcXR0REBN9//z1JSUkcOHBAshjgpfNTWVmZ1tZWbt68yZYtW3j33XcJCAggOjqampoaTExMGDBgAMHBwSxfvpxvvvmGY8eOERgYyMKFC5k/fz56eno0NzdTUVGBl5cX7u7u8nORk5Mjg3RFlJyuri6lpaV0dXXxxRdfsHXrVhYtWsSpU6doaGhg0qRJXLx4USL8ha1eyNEF7VuwHfX19ZkzZw5XrlzBwcGBmpoaWltbmTx5Mo2NjZKI9dZbb5GYmMjly5f56KOPiI2NpV+/fmhoaLBnzx50dXXx9vamvLycsWPHEhMTQ1NTkxR5OTs7S9y+sFR/8cUXnDp1isLCQkkf19XVxdLSEkNDQ+7duyeZkCNGjCAyMpL29naUlZXR1tbm66+/Zv369f+zho8lJSUUFxczdOhQlJWVZayar6+vnFA7OTmxZ88eWlpaKC4uxtPTEwsLC/bv3091dTVr1qwhOjqakJAQhg4dSk9PDwcPHmTgwIE0NDSQnJxMS0uLFJsIboD4AHR3d8vvW1hYoKmpSXt7O62trbKsVFFRoaOjQw4mBw0aJANZxS0vSMDa2toAEv4i3uLix3t6eqTuQJitRN4DvGyvxDT+u+++o7u7m9dee42UlBSUlJRYtmwZFy5cwNTUlClTptDY2Mjo0aMZPXo033zzDQ0NDaiqqsoLQdCnBdRkyZIlVFdXc+nSJaqqqpg4cSLbtm2jvLycxYsXc/PmTWJjY/nkk09ob29n2rRppKenS65FcXExly5dwsbGhoKCAjIyMsjNzWXnzp309vZy7do1UlJSGDhwIOHh4WzYsIG2tjYyMzMJDQ3l448/Jjk5GV9fX3788UeuX7/OmDFjpGno4MGDLF68mPb2dqKjoyW+T6yX4+PjGT9+PMbGxvLfR7R5SkpKGBoaSuny3bt32b59O0ZGRjx69Ei2aVOnTsXe3p7Ro0dz7949fv75Z6k7UCgUkjFhYmJCUlISUVFRjB8/nuPHj1NWVsa0adNYv349jx49IiEhgc2bN7N161aJo1NRUaGkpIStW7eSmZnJihUrCA0N5fXXX8fV1RWFQkFRURFaWloEBQXx7rvv0tjYSF1dHYMGDeLnn3/m3LlzZGRkkJaWRmFhoaQ+b9u2DS0tLdneidTrZcuW8e6778rPW1NTE1OmTGHz5s3/1pn8S1wMAohSXV2Nnp4e58+f5+OPP6ajo4NvvvkGDQ0NXnvtNe7fv09oaCiOjo7cv38fQ0NDFi1ahI2NDfHx8XR0dHD69Gna2tqwsrIiNTWV4OBg0tLSZKzXixcvsLOzk3OCzs5OqZUX8mjx50lNTaWyspKKigr5DynKfmG1Foe6p6eH6Oho2UIAUqQkbvL29nYpvxWlfn19vcTbC9qzaGtcXV2pra0lNDSUgoICOjo6cHBwQENDg/r6ehwdHWlqapJRaQYGBnz55ZdyMGpkZISlpaUUOQnR1pIlSwgICODx48cUFBTg6OhIbGwsQUFBMpxHhASLQ6Knp8fOnTvZuHEj48eP58MPP8TJyYnnz5+jpKRERUUF/fv3JyEhgZCQENzd3fHx8WHo0KEy57KlpUUq88S6sauri0WLFskBYVNTEwcOHMDf3x8Ab29vli1bxpAhQ2QVV1hYiLGxMX5+fuzfv5979+7x+uuvc+PGDSwtLQGkMzM1NZU5c+bIecLgwYPR09Pj2rVrvP322wQEBKChoSGVixYWFsTHx9Pb20tubi4eHh6ScfHo0SP8/PywsrKScXgiHaytrY3PP/+cnJwckpOTcXNzIyAggPr6eulA1dXV5bfffiM6OpqIiAhaW1t5/fXXsbCwIDY2lo8//pgffviBhoYGYmJiePz4McrKyhKes2PHDqKiovj8889Zu3Ytzc3NFBcX4+fnx9WrVxk2bJicnTQ3N0sJvra2NitWrPi3zuRfIldi375924YPHy45BlOmTGHKlCnyjTh//nwWLlyIqakpiYmJpKWlkZ2djZKSEs+ePeP06dOYm5szevRoHjx4IA+yiYkJUVFR5ObmSnCsECTt3r2bfv36oa2tTUNDA52dnVhYWGBmZoaurq5EeNfU1MjMh66uLsrKyqTrUqyn1NTUqKmpkQcpIiICDw8Pub0QnEgxTFNRUZFkadFaCGCI2A74+/tjY2PD7du30dHRkT27yKEQyd0WFha4u7vj5+eHiYkJgwcPltkOjo6OLFq0SFrL+/r6iI6OxtbWViLzk5KSePLkCe7u7lRWVjJ16lQaGxsJDg4mLi4OY2NjVFVV8fDw4M6dOyxdupQffvhBmozy8/PZvn073333He7u7iQlJfHdd9/x008/Sc9FV1cXGzZsQENDg2vXrsnkscTERKysrIiIiJCBxdeuXUNTU5OjR49y9+5dGRgjDsewYcPYvn07KioqMoV6woQJ3Lx5k+rqambNmsXMmTM5ffo0H374IRMnTqSuro7AwEDGjRtHTEyM5H98/PHHkrhsYGCAg4MDq1atori4mKysLI4dO8b69evR1tYmLy+PkydPkpuby/Dhw6mpqWHatGmcPHkSLS0tGd67YcMGhg0bRmRkJA0NDbz55pv4+PhgZmZGbm4u169f58mTJ4wdO5bRo0dTXl5OdHQ0xsbGlJSUUFpaSnh4OOvWrcPa2pq7d++SlJTEu+++K71CFRUVPHz4kDVr1jB79my0tbUJCwtDXV2diIgINDQ0KCwslJyKTz75BIVCQWxs7J/OlfhLXAyff/75tpKSEq5fv05RUREHDhygoKCAIUOG0NTURHl5ORkZGWhqanLixAkSEhLQ1taWQTM1NTWoq6tLyo/AlD169AhPT08aGxvp6upiwYIF9Pb2YmlpyT/+8Q9MTExk39/a2oqlpaUcbopDXF5ejoqKCtbW1qirq/PixQuJdhs/frws5YyMjKQN+fDhwzg5OUkEvqA2C7tvd3e3DNp9VYItqggxOxABtuKiqq2tpaOjg+LiYry8vHBzc8Pc3FzKmtXV1blw4QIJCQk0NjZia2vLwoULpciru7ubQ4cOoaKiwqNHjygvL+f48eMyBNjNzU0au7755hv69etHR0cH1tbWWFhYkJCQgI+PD8bGxlhaWuLh4cGgQYOoq6vj+++/Z+zYsQQHB8uA4Z7/1d6ZR0Vxpm3/V2wta9gXBQHZxBVBQQMKqLigJFHcJkaNwdFRszjqqFGPSyajxuRNjNEYFzAxJhpE44IL4o5LUEABBQRBQbBpUBZBEAHr+4OuZ8xk5v3MmbyRnMN1Tp+uru62b6yqu57nfq77upqaCA8Pp2PHjqSkpAiRXWdnZ1atWsV7771Hp06dCA4OxtramuvXr+Pj48PYsWMxNjYmJyeHiooKli1bhru7O4aGhkJeTSlKazQaTE1NxejKx8eHYcOGkZ+fT1FRETdu3GDSpEns2bOHr7/+Gg8PD9RqNR4eHpSVlREWFoalpSVffvklJSUl9OrVi9GjR7N161bhhN63b1/at2+PjY0NISEh1NTU4OnpyalTp4QoroWFBffu3aO0tJTExETRuJeSkiIKgQqHZcCAARw7doxVq1Zx8uRJxowZw5gxY9i2bRtVVVVCfOeNN95g7ty5YhVi9+7dXLx4kbKyMoqLi3F2dqZHjx7o6+vj4ODA6NGjefnll8UouLi4mCVLllBeXk6PHj3YsmXLcyeGVrFcqazHGxkZoVarcXd3Z9u2bQQGBlJZWclXX31FdnY2H374IXfu3GHQoEG88847+Pn5ce7cOYKCgpg7dy6WlpZ06dIFW1tb9PT0+Pvf/87169dpamoiNDRUDEH/1UhW4R5UV1dTV1cnJM1cXV0Fi664uFhY1Su8B5VKRUlJibCjU2zmzczMsLa2plOnTri7u2NpaUn37t3x9vYWRi2KjsOz/RGKqpTSlZmdnS0kwxwcHCgvLxe6jgqTLjExkfT0dDQaDT/88INQ/1E0CRVi09OnT2lubqawsJBjx45RVlb2M+/MzZs38/jxY5KTkzl06JCgiBsYGJCWlsbZs2c5deoUSUlJQhTm9u3bqNVqLly4IFSkqqqqyM3NpbS0lAULFrBhwwaOHDki/COysrLIzs5GrVZjbm7Ozp07WbVqFd7e3pSUlGBpaUlZWRkuLi706dOH8ePH4+zszJ07d8jMzEStVrNp0yaamppYt24dX3/9NZcvX+bu3bsMGjQIe3t7vLy8cHZ2FtvV1dWUlpZSXl5OdnY2Li4uTJkyRcRVXV1NVlYWpaWl3L17F0tLS6KiomhubmbNmjVCZOfJkydoNBphwKPQ4E+cOMGJEycICwujqalJDOHNzc0pKSkhOjqapqYmNBoNfn5+2NnZMWbMGAICAujQoQMdOnTgyJEj5OTksHbtWvLy8pg5cyb6+vrcuXOHjh07olarRePZpk2bSEhIYNGiRZiamnLt2jXUajXTpk0T0vpFRUU0NjZSWVnJoUOHhKHy86JV1BgcHR3ZsGEDu3fv5vz588THx9O9e3d+/PFHBg8ezLJly+jcuTOWlpasW7eOAwcOcPr0aXG32rFjB9nZ2Tx8+BBTU1Pmz5/Pxo0bkSSJAQMGUFtby+3btzl37hxr167lgw8+EAzAiooKDA0N0dfXp7CwEGdnZ9G/4OnpiSzLYhlKibW+vh5/f39cXFwEtVaZBlhZWYm/q0uXLjx9+pTS0lKh1aAcLOWhVqupqKgQFXwFqampQktRKbiamZlhZWUlLtTbt29TVFSErq4u1dXVQvSltrYWa2tr3NzcxPKqUk+prq7m7t273Lhxg7q6Ojp37oyDgwM3btygsbGRjz76CFmW6dixIzdv3hSdmUOHDqWgoEC0X69btw4fHx/8/f0ZN24cly5dYuzYscTFxTF37lzOnDlDXFwcN27c4Pr163h7ewPwwQcfsHDhQhwcHCguLmbOnDmYmZmRkJBAQEAAK1asYOvWraIRaf/+/URGRuLg4ICHhwe3b98mMDCQl19+mdTUVCZPnkxiYiIjR45k0KBBhIWFMXjwYBYuXMjrr7+OWq0WEuxr1qyhV69eqFQqtmzZwtWrV4mMjGT69OkUFxcTERGBvr4+M2fOJDc3V7iCzZo1i+3btzN69GgGDBhAfn4++/btY9asWVRXVxMeHo6vry9Xrlzh5MmTdOnShV27dvHll18yY8YMVq5cyeeffy7Ebr777jtiY2P5+OOPWbt2LTNnzqR///7cu3eP6OhoHjx4QEhICHv37kWj0bB+/XrB0zh48KDQktBoNAwYMICzZ89y+PBhgoODuXjxIm+88Qbr16/HxMSEt99+G4Bx48b9qmuyVSSGiooK+vbtKyzbzM3N+fDDD8nIyBD+jM7OznTt2lXIp02aNEnIvjc2NrJ06VJhNfbkyRMxN87IyKC5uRlzc3O8vLw4f/78z9qjFa0EXV1dqqqqKCsrw87ODjMzM2xtbUXhUPGqcHd358GDB3Tt2pWqqio0Gg3W1tbY2dkJ1aaGhgZu3ryJt7c3jY2Nol9AkW1v164ddnZ2P0sSpaWlXLt2TdQRNBoNsiwzbNgwUlNTgRZZeSMjI1566SXS0tIEjVaSJGHS29jYKARa/f39kSRJjBgePXpEfX09I0aMoKCgQHhjpKenc+/ePTp06ECvXr04e/YsU6ZMoby8nC+++AJTU1Pu3r1LU1MTy5cvZ9u2bUydOpXKykr09PT485//TH5+Ptu2bWPChAlCuNfV1ZVXXnmFiRMnMmLECJEkHR0dmT59Oh4eHsTExFBSUsKAAQNQqVRMnTpV1HkGDx7MypUr0dfXZ+XKlaKH4eHDh8ybN4/S0lKGDRuGo6MjmzZtIioqiilTpjB48GAiIiJ49dVXmTFjBqWlpTg4OLBz506KioowNTXlr3/9K1FRURw4cIBhw4ZhYWHBiRMnsLKyorGxkcLCQlELiouLIyYmBgMDA5YuXcrmzZtpaGjg7NmzdO3aVeh8KsuHY8eOJT09nUuXLvH222/j4uLCnj17CAwMJD4+Hnd3d5qampg5cyZ6enrcvXuXvLw8Zs2aRWpqquh+jYiI4OjRo/Ts2ZOnT5+yfPlyBg4cSHBwMAkJCezYsYPIyEg8PT3F9Ds8PJz09HQmTpzIiRMnmDVrlliN+zVoFYmhvLxceCoqMmkTJkzg22+/JSgoSPQunDp1ilWrVrF582bq6ur49NNPOXr0KPr6+kyfPp3/+Z//4dy5c/j7+wOI6r7CPVCpVFy5ckXQnJVeCUWBuby8nLy8PBwcHLC3t+fhw4eYmJhgYWEh1KWfPn2KWq3Gx8dHKCZXV1ej0WgwNDQU06LMzEzCw8PR1dUVeoJK23VjYyO6urqidqG4ITs7Owupe4XD7+TkxL59++jZsyfZ2dnk5eUJQZp+/fpx9OhRysvL6d69O7du3WL79u3Cnbt3797idxobG/nxxx8xMjISXpELFy5kwYIF+Pn5MXLkSF566SWmTZsmKurJycnY2NgwYcIEKioqhLxcXl4e3t7ewhz2k08+ob6+noMHD2JsbIydnR2XL18WOoQDBw7k22+/5eHDh8LO/osvvhBGuKtXrxb+Fhs2bCAnJ4f+/fvz+PFjJk2axKhRo/D29mbz5s1s3bqV4OBgIXEfFxeHs7MzISEhHDhwAGtra4yNjTlz5owghiUkJLBz504KCgr461//SlVVFTExMWg0Gj777DPefPNNUlNTKSoq4i9/+QuWlpakpaUxb9480tPTBaXd3NycIUOGoFarefnll2lubqakpISTJ0/SqVMn+vTpw+TJk1GpVJw5c4Zdu3YRHx9PXl4eISEhvPHGG+jo6LBgwQI++eQTMjIyuHTpEtW/pCQiAAAXnklEQVTV1UJ/UhEoGjlyJJMnT2bUqFG88847ZGZmUllZyerVq/H19WXatGlkZGTg6ekpiHsODg68/fbbxMbG4unpyWuvvYarqyvOzs7Ex8cLPYvnwXMlBkmS7gA1QDPQJMtyb0mSLIEfABfgDjBOluVKqYUG+DkQDtQBb8qynPa//fsNDQ2sXr2ab775Rrg86evrY2pqSkxMDIWFhezYsUP0myuiE25ubjx9+hRHR0c8PDwEfdXIyIhBgwaxdetW4Z2oSKRNmTJFWJsBohtTmTfX1tZSWVkJIOjNRkZGYo557949hg8fLrwuFV767du3xdKQlZUVFy9epL6+HjMzM/T09ASLUnGiNjAwQE9PT7hUSZJE165daWhoID8/XywtFhQUiItbX1+f9u3b4+joKJLU/fv3qa+v5/jx49TW1oqOP0DoKyoKVQqRycnJiYqKCtEfYm1tjb+/vzARVjpbQ0NDqaysxMXFhSdPnrB3714iIiLo3LkzTk5OpKSkYGhoyBdffMGYMWNITEykoaGBxsZG/P39SUpKAuDIkSO4uLiIxNvY2Mj+/fsF41ClUtGpUyeGDx8uJMgU/YBTp06xbds2Vq1aRe/evTl69CgDBw4E4PLlyxw+fBg9PT3y8/MZOHAgOjo6LF68WNDUIyIiWLRoEdXV1Xh5eYkppJ2dHX369GHYsGGkp6dz5swZRo4cKTons7KyCAgIICcnRzhe3bx5EwMDA7p160Z6ejonTpzgH//4B01NTZw7d447Ws/NyZMnc/XqVXx8fHB2dmb8+PF0796dNWvWIEkSgwcPFudKdXU1lZWVDB06lK5du/K3v/0NXV1dsrOziYqKIj8/HzMzM3766Sdqa2vp2rUrf/rTn7h27Rqvvvoqs2bNYseOHWg0GhwdHdm/f7+Q9HvrrbcoLS0lPj5etBc8L37NiCFUluX7z7xeBJyUZXmNJEmLtK8XAsMBD+0jANikff6PaN++PRqNRph5XL9+nVu3buHo6EhoaCgHDhwAWvT+FL08pcnIysqKkJAQ2rVrh5WVFcHBweTl5dGnTx927NjBkCFDyMzMpKSkRFCq6+rqflaUe/TokVBQVg5Uc3MzZmZmwudAUVlWq9ViGqD4UEBLEsnLy8PFxUUo+KalpREUFCTo0yqVSvQCKN/Pzc0VylFK34Ii0wVw5swZAgMDsbGxobi4GEmSRKxJSUmMGDGCW7dukZSUJPgYSiJTphnPui716tWLnj17Ym9vz5MnT/D398fPz4+GhgY2b97M7NmzOX36NLdu3aK8vBxfX18MDQ1xd3cnMzMTe3t7Vq9ezdSpU4mOjsbR0ZHbt28zYMAApk2bxqVLlygoKKCmpoYbN27g7OzMxYsXycjIoKqqChMTE1577TVOnjxJfn4+Tk5O7Nixg/Hjx+Pq6kpqairnzp2jqqpKmP/U19dTXFzMtGnT6NmzJ/X19dja2nLw4EHc3d159OgRSUlJ3Lhxgx49enDkyBH69++Pl5cXlZWVqFQqkpOTMTExYdKkSaSnp6NSqaiqquL8+fMEBQUJD87y8nJGjx4tjIUV/klpaalQ2VJapZXRkqOjI2q1mtdff53s7GxRL0pPTxdeE0+ePMHX15ewsDCRkDUaDb6+vvj5+aFWq4mMjOTixYukpKTw7bffMmbMGMaOHSvk7du3b8/o0aNZu3Yt+/fv58qVK+jo6PDdd9/h5ubGggULKC4uxtLSUqxGQQsXRHGLf178N1OJV4EQ7fY3wBlaEsOrwA65ZSL/kyRJ5pIkOciyrP6PQejp8fXXX2Nvb8/333+Pp6cn06dPZ968eYKAohT5Fi5cSFBQkJBk/+6775g7dy7vvvsub731Fk+fPuXTTz8lMTGR9u3bc/bsWUaMGIGLiwu2trbAP01mFfEO5S5iYmIihofKnd3ExET4UlRUVPDOO+8IX4mqqiru3buHvr6+6C2orKzEysoKHR0d9u/fL9SclH6MhoYGTp48KfQDFOMWQLD2oEWZx8fHh8ePH5OSkiI6PwcMGEBeXh7V1dXk5eURHh4uVKO2bt3K06dPRQu5UnSUJIm6ujqmTp0qJOj09PRQq9XU1dXxwQcfMH/+fAIDA1m3bh3z589n6dKlqNVqjI2NuX79Oi5a2z/lYk5NTRUS9x06dOCdd97B1taWoUOHEhERQWNjI9OmTSM+Pp7XXnuNzMxMunXrxsWLF4mJiWHmzJkYGBiIhqXly5czefJkxo4dK5b76urqmDBhAoGBgezatQuVSsX9+/fp378/0dHReHp6sm3bNvr27YtKpeLs2bNYW1vz5ZdfCg7J6dOnGT16NC4uLhQWFhIfH8/GjRu5cOECq1evpri4mKSkJMzMzLhz5w76+vrs3r2bxMRE+vbtC8Dw4cOpqanBzc2Nu3fvYmVlhYWFhdA6mDBhAn5+fkKtKzU1FXNzc5YtW8bOnTtxdXUVcTk5OVFfX8/9+/fR19enc+fODBo0iH379vHtt9/i7+9PZWUlffr0Yfbs2UyfPp1Ro0axYcMGqqurWbx4MaampsyZM4crV65gYWFBaGgoarWau3fvMnDgQOLi4jh16hSOjo5C9SwlJeVXXdzPmxhk4LgkSTKwWZblLYDdMxd7KWCn3e4APGurW6zd97PEIEnSdGA6tDAEx40bx4YNGzh06BDR0dHk5OTg5eUl9P8V89Lly5czfvx4EhIS8Pb2Zv/+/Tg7O+Pu7s6KFStISUkRUmf5+flERkbi7OxMeXk5rq6uxMTEcP/+fTEPV4bzirArtIiqqNVq7O3tMTQ0FG5QgFBXVhy1a2trRXedtbU1RUVF4t959OgRFy5cQFdXF1NTU0Gz9vDwwMnJSfAWHjx4QE1NjVAQ0tHRwc3Njf79+4vlsYqKCnr06EFaWpqoiQQEBPDkyRO2bNkiKLy6uro0NzczZMgQUXiElruXm5sbqamp2Nrasn37drKystDX16d79+4cP34cT09Ppk2bRmxsLCNGjKCsrAwvLy+6devGrl27MDY2xtjYmMmTJ3PhwgU0Gg1r1qxhwYIFjBw5kqtXr5KZmcnixYuxtbUVd7uNGzeyd+9eIaIaEBBAZmYmgYGB/PDDD8Kg5dChQ8yZM4f169djaWlJx44diYuLE2zQf/zjH/Tt25dp06aRl5cnaL53797lm2++wcLCgs6dO+Pl5cX777+Ps7MzRUVFZGVl0bFjR0JCQoiMjOThw4d0796dv/zlL0JLITw8nNjYWMaMGcOmTZt45ZVX+OSTT0QX6NmzZ0VnqqmpKRs3bmTGjBno6+szZMgQ5s+fj42NDVlZWdTW1vLWW28xbNgwVqxYQbdu3fD19WXQoEEMGTKEhoYGUlNTycjIIDExETs7O8aOHUtFRQUdOnSgtLSUffv2MW/ePKZPny7EYxRBop49e7Ju3ToKCwu5ceMGXl5exMbGYmxsTGNjI2PGjGHy5Mm89957ZGVl0dDQQNeuXSksLHzOy/35E0OQLMslkiTZAomSJOU8+6Ysy7I2aTw3tMllC4BKpZIVVdslS5Zw5MgRwsLCCAsLE/TPqVOnYmZmxuPHj9m3bx8jR44kOTkZlUpFYmKiGMYpJ7osyzx69Ih+/frh5OQk2k5nzJhBdHS0WIlQeAdVVVWCwajoLCrr+EobdnNzs2DFKUN+XV1dZFnGzc2Njh078uDBg58tWZaUlJCTk4Onpyc6OjqCKNPQ0EC7du14/PgxKpWKp0+fCoakjo4OVVVVHDx4kPr6eiZOnMjOnTs5efIkfn5+SJJEUlKSYAIC4vuKjFxkZKSgQsuyzPfff4+enh7BwcGCzKU0KxUWFtKpUyfUajXJycm8++671NXVCev1jIwM+vfvz6effsrBgwfFiG379u1oNBqWLl0KwL1796ioqCA4OBh7e3vWrl3LZ599JiT1ZVkmKCgIe3t7JkyYwKhRoygpKcHOzk4wOoODg0Xx9ciRI5iamhIaGoqBgQF+fn5cvXqVgIAALCwsxOrB559/TufOndmyZQvbt2+na9euxMTE8OOPP5KcnExtbS1mZmZ069aN2bNnExQURF1dHUFBQWK5s0uXLhw9epQHDx6wePFiDA0NKSgooEePHnz00UfCHatXr17cuXMHGxsbunXrhoeHB5WVlbz//vusX78eLy8voqOj0dHR4eLFi0RHR9Pc3MzZs2f54Ycf6N27N2lpaXh7e5OWlsaWLVtYsmSJUKt2cnIS08j09HSSkpIIDg4mKiqK9u3bM3fuXPbs2cOSJUuYO3culy5dorCwkPLyco4fP86uXbvIzMwU4jw+Pj588sknfP75579KQv65EoMsyyXa5zJJkn4E/AGNMkWQJMkBKFOuBcDpma87avf9R1haWhIWFoa+vj6JiYkAbN++nczMTP7+979z6tQp6uvrWbVqFV26dMHMzIwtW7bg4OBAVlaWWM8PCwtj5cqV/PTTTxgYGLB582YxrI+Li6N79+5ixGBiYiKMZZqbm2nfvj35+fm0a9eOmpoaMjMzheya4nmpsAsVbrzStKM0V5WVleHu7i5clwDu379PQUEBxsbGooNS4RYojVw1NTWC7KTc5W1sbLCwsMDOzo7NmzfTrl075s2bx71798jLyxPK0qdOnWL69Ols27aNkpIS0cqtaFXq6OgIP8yIiAgyMzNxcXHBw8ODxMRE2rVrR3l5OdXV1VRXVxMQEEBpaSm7d+/m0aNHvPfee7z00kvEx8cLJSJl7nrs2DGgxVVcpVJx/fp1Pv74Y2xtbZEkid27d9OpUyc2bNjA4cOHhfRYfHy80GYoLy8nOTlZFC3nz5/PsmXLKCkpYcaMGWRlZaGrq8vQoUPJzc3F2NgYf39/zM3Nyc3N5bPPPmPWrFmMGDECKysr3nzzTdLS0oTU+7x581i0aBEVFRVUVFQQERFBQUEB5eXljB8/nrCwMKysrGhqamLx4sWC2nz79m10dHT46aefGDZsmDDsVcSDDA0NWbBgAWVlZejq6uLo6MjMmTORJAlfX1/R+t2nTx8cHBxEe31BQQG1tbWC1BUcHExAQADjxo0TBV/lN4uKinj33XfJzMxk9uzZpKenM2fOHK5evUpcXByRkZGEhITQpUsX4uPjyc3NJSUlBTc3N95//30Rd0NDAyEhIezevft5LnfgORKDJEnGgI4syzXa7SHAB8BBYAqwRvt8QPuVg8DbkiTtpqXoWP2/1RcUWFtbM3HiRM6dO4eDgwMRERHk5ubi7e2Nu7s7sbGxolPS09MTfX19wsPDMTQ0ZNGiRQwcOJA333wTIyMj+vXrh4WFBYGBgdTX11NRUYG7uzt1dXX06tWLr7/+WtxNn4VCdKqvrwcQ0wQ9PT0sLS0xNDREo9Fw584dYcqiyM4rAikajQYTExMxhFfcoGpra8VIxMHBQRQflSmKIvWmfM/Y2JibN2+KJh1omcbs37+fvn378tZbb3HgwAHs7e3Jy8ujrKxMFMzMzMyQZVnUF5ROzqKiItq3b8+FCxdE4tBoNJibm5OQkICRkRHnzp3jq6++ok+fPixfvly4aCtLrQcOHGDv3r0MHTqUnJwc/Pz8WLFiBS+//DKzZ88mPj5e1CaULseqqipBAFNYp1FRUcyfPx8fHx9Gjhwp2IRubm4cO3aMSZMmcfXqVR4+fMiDBw8ICgrCzc2NuLg48vLyyMnJIScnh0mTJmFmZsaCBQswNzfHw8MDR0dHrly5Qk1NDdnZ2cyYMYPGxkZhlqxQwKOjo3F1dUWSJL766iuioqKEBmZYWBg2NjZidKiMrhQrQhsbG0xMTASB6vjx41haWvLxxx+TmZnJ48ePGTp0KOfPn2fIkCFAiytWZWUl3t7emJmZ0bt3b1555RWxjGhvb09BQQHXrl3D3NycUaNG8dFHHxEVFYWvry+TJk3i0KFD2NvbM2LECFxdXdm1axfJyclMnDiRhIQE6uvr8fHxEU7mDQ0Nonfi1+B5KNF2wHlJktKBy8BhWZaP0ZIQwiRJygMGa18DHAEKgFvAVmDW8wRSU1PDuHHjxAllampKZGSk0GtUGkoUItSlS5dobGzE19eXmpoahg8fjpubGyUlJaKgaGpqSmxsLMXFxXh6enLs2DFRaHx2CVBfX19YlinLiPBPF2xAyMzX1dUJ52xlGVI54XNzc7GysqKhoUFc4IrSk0I8qqmpEZwKPT09UeN4VqJeiUtZavXz80NPT0/UT5RkotFoqKysxMjI6Gey58pypVLIVKYZhw8fpqamRqhbKXTwOXPmCEHTmpoakpKSBL23vr4eS0tLAgICMDY2pqysjJqaGm7evIlGo8HZ2Vks3YaGhrJ3714OHToklk+7devG6dOnycjIQKVSkZOTg4GBAZcuXQJaRhvl5eXExsZSWlrKjh07KCwsFEXfw4cPc/XqVYYMGcKHH36IhYWFWOrT09PD2tqamJgYUlJSWLp0KceOHSM0NBQXFxf27dtHbW0tr732mjC7VavVhIaGsmLFCo4fP46NjY3wYDh//jyXL1+moKCAhIQEOnTowL1794QOpTJtTE5OFsf/0aNHuLi4MGjQIPz8/HB1dRXJ74033qChoQFfX1/RQ7Js2TL69evH66+/LnxIT5w4QWZmJocPHyYmJoYzZ86Qm5uLRqPBxcUFMzMzAgICcHV1Zd68eVhYWGBmZsaTJ084c+YM27Zt48qVK4L/MWLECGxtbdmzZw+AEFP+NWgVQi2SJNUAz8++eLGwBu7/fz/14vFHiRP+OLH+UeKEfx+rsyzLNs/z5VbBfARuPq+yzIuGJEkpf4RY/yhxwh8n1j9KnPDfx9oquivb0IY2tC60JYY2tKENv0BrSQzPJR7RSvBHifWPEif8cWL9o8QJ/2WsraL42IY2tKF1obWMGNrQhja0IrzwxCBJ0jBJkm5KknRL26X5ImOJkSSpTJKk68/ss5QkKVGSpDzts4V2vyRJ0npt3BmSJPn+zrE6SZJ0WpKkLEmSbkiS9F5rjFeSpHaSJF2WJCldG+dK7X5XSZKStfH8IEmSgXa/Svv6lvZ9l98jzmfi1ZUk6aokSfGtPM47kiRlSpJ0TZKkFO2+3+7YK27DL+IB6AL5QCfAAEgHurzAeAYAvsD1Z/atBRZptxcBH2m3w4GjgAT0BZJ/51gdAF/ttimQC3RpbfFqf89Eu60PJGt/PxaYoN3/FTBTuz0L+Eq7PQH44Xf+f50LfA/Ea1+31jjvANb/su83O/a/2x/yH/64fkDCM6/fB95/wTG5/EtiuAk4aLcdaOFcAGwG/vTvPveC4j4AhLXmeAEjII0Wqvx9QO9fzwMgAein3dbTfk76neJzBE4CA4F47YXU6uLU/ua/Swy/2bF/0VOJ/9Si3Zrwa9vLf3doh7G9aLkbt7p4tcPza7Q02iXSMkqskmW56d/EIuLUvl8NWPH7YB2wAFC48FatNE74pxRCqlbCAH7DY99amI9/CMjyr28v/7+GJEkmwF5gjizLD5X+CGg98cqy3Az4SJJkDvwIdH7BIf0CkiSNBMpkWU6VJCnkRcfzHPjNpRCexYseMfzqFu0XAI22rZz/tr38t4YkSfq0JIXvZFnep93dauOVZbkKOE3LkNxckiTlxvRsLCJO7fsvAQ9+h/ACgVekFn3T3bRMJz5vhXECP5dCoCXZCikEbUz/1bF/0YnhCuChrfwa0FLEOfiCY/pXKO3l8Mv28snaim9fnrO9/LeC1DI0iAayZVn+tLXGK0mSjXakgCRJhrTUQbJpSRBj/kOcSvxjgFOydmL8fwlZlt+XZdlRlmUXWs7DU7IsT2xtcUKLFIIkSabKNi1SCNf5LY/971Us+V+KKOG0VNTzgSUvOJZdtEjQNdIyD4uiZd54EsgDTgCW2s9KwEZt3JlA79851iBa5pkZwDXtI7y1xQv0AK5q47wOLNPu70RLG/8tYA+g0u5vp319S/t+pxdwHoTwz1WJVhenNqZ07eOGct38lse+jfnYhja04Rd40VOJNrShDa0QbYmhDW1owy/Qlhja0IY2/AJtiaENbWjDL9CWGNrQhjb8Am2JoQ1taMMv0JYY2tCGNvwCbYmhDW1owy/w/wDCUOLEMzF4lgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af848c198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Adaptive Equalization\n",
+    "img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)\n",
+    "plt.imshow(img_adapteq, 'gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4af37482b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Display results\n",
+    "def plot_img_and_hist(img, axes, bins=256):\n",
+    "    \"\"\"Plot an image along with its histogram and cumulative histogram.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    img = img_as_float(img)\n",
+    "    ax_img, ax_hist = axes\n",
+    "    ax_cdf = ax_hist.twinx()\n",
+    "\n",
+    "    # Display image\n",
+    "    ax_img.imshow(img, cmap=plt.cm.gray)\n",
+    "    ax_img.set_axis_off()\n",
+    "    ax_img.set_adjustable('box-forced')\n",
+    "\n",
+    "    # Display histogram\n",
+    "    ax_hist.hist(img.ravel(), bins=bins, histtype='step', color='black')\n",
+    "    ax_hist.ticklabel_format(axis='y', style='scientific', scilimits=(0, 0))\n",
+    "    ax_hist.set_xlabel('Pixel intensity')\n",
+    "    ax_hist.set_xlim(0, 1)\n",
+    "    ax_hist.set_yticks([])\n",
+    "\n",
+    "    # Display cumulative distribution\n",
+    "    img_cdf, bins = exposure.cumulative_distribution(img, bins)\n",
+    "    ax_cdf.plot(bins, img_cdf, 'r')\n",
+    "    ax_cdf.set_yticks([])\n",
+    "\n",
+    "    return ax_img, ax_hist, ax_cdf\n",
+    "\n",
+    "fig = plt.figure(figsize=(16, 8))\n",
+    "axes = np.zeros((2, 4), dtype=np.object)\n",
+    "axes[0, 0] = fig.add_subplot(2, 4, 1)\n",
+    "for i in range(1, 4):\n",
+    "    axes[0, i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])\n",
+    "for i in range(0, 4):\n",
+    "    axes[1, i] = fig.add_subplot(2, 4, 5+i)\n",
+    "\n",
+    "ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])\n",
+    "ax_img.set_title('Low contrast image')\n",
+    "\n",
+    "y_min, y_max = ax_hist.get_ylim()\n",
+    "ax_hist.set_ylabel('Number of pixels')\n",
+    "ax_hist.set_yticks(np.linspace(0, y_max, 5))\n",
+    "\n",
+    "ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_rescale, axes[:, 1])\n",
+    "ax_img.set_title('Contrast stretching')\n",
+    "\n",
+    "ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_eq, axes[:, 2])\n",
+    "ax_img.set_title('Histogram equalization')\n",
+    "\n",
+    "ax_img, ax_hist, ax_cdf = plot_img_and_hist(img_adapteq, axes[:, 3])\n",
+    "ax_img.set_title('Adaptive equalization')\n",
+    "\n",
+    "ax_cdf.set_ylabel('Fraction of total intensity')\n",
+    "ax_cdf.set_yticks(np.linspace(0, 1, 5))\n",
+    "\n",
+    "fig.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lessons/python/image-processing/lesson.md b/lessons/python/image-processing/lesson.md
new file mode 100644
index 000000000..43c5bbb09
--- /dev/null
+++ b/lessons/python/image-processing/lesson.md
@@ -0,0 +1,46 @@
+---
+layout: page
+title: Image Processing in Python
+visible: true
+tags:
+ - python
+ - intermediate
+---
+<!-- change visible to true if you want it on the site -->
+
+
+
+# Image Processing in Python
+
+ - **Authors**: Nil Sahin
+ - **Research field**: Computational Biology and Molecular Genetics
+ - **Lesson topic**: Image Processing in Python
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/image-processing>
+
+
+
+## Image Processing in Python
+
+This is an introductory tutorial on image processing using Python packages. We will understand image data types, manipulate and prepare images for analysis such as image segmentation.
+
+
+### In this tutorial, we will learn:
+
+* to load images and extract basic statistics
+* image data types
+* image preprocessing and manipulation
+* image segmentation
+
+### Useful links for image processing libraries
+
+* [skimage](http://scikit-image.org/) Sklearn's image package
+* [OpenCV](https://opencv.org/) Open Source Computer Vision library
+* [mahotas](http://mahotas.readthedocs.io/en/latest/) Computer Vision in Python
+
+
+### Packages to be installed
+
+* `numpy, matplotlib, sklearn, scipy`
+* `cv2`: pip install opencv-python
+* `PIL`: pip install PILLOW 
+
diff --git a/lessons/python/image-processing/yeast_colony_array.jpg b/lessons/python/image-processing/yeast_colony_array.jpg
new file mode 100644
index 000000000..dff1dbaa3
Binary files /dev/null and b/lessons/python/image-processing/yeast_colony_array.jpg differ
diff --git a/lessons/python/intermediate/lesson.ipynb b/lessons/python/intermediate/lesson.ipynb
new file mode 100644
index 000000000..3484e3c9b
--- /dev/null
+++ b/lessons/python/intermediate/lesson.ipynb
@@ -0,0 +1,701 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Intermediate Topics in Python\n",
+    "\n",
+    "**Author: Ahmed Hasan**\n",
+    "\n",
+    "Made for U of T Coders, to be delivered on 05/04/2017\n",
+    "\n",
+    "### Contents\n",
+    "1. Reading/Writing Files\n",
+    "2. Methods/Attributes\n",
+    "3. List Comprehensions\n",
+    "4. Control Flow Tools - `pass`, `continue`, `break`\n",
+    "5. `assert()` - _if we have time_\n",
+    "6. Try/except - _if we have time_\n",
+    "\n",
+    "\n",
+    "### What I'm assuming you know\n",
+    "Everything in Madeleine's Intro to Python lesson! I tried to have this lesson pick up right where the intro material left off. This includes:\n",
+    "- the interpreter\n",
+    "- variables\n",
+    "- lists\n",
+    "- indexing/slicing\n",
+    "- if statements\n",
+    "- for loops (and loop syntax in general)\n",
+    "- functions\n",
+    "- very basic NumPy, Pandas, matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading (and Writing) Files\n",
+    "\n",
+    "Let's say we have 50 students who wrote a test. This test was out of 40 points, and their results - in percentages - are stored in a text file called `scores.txt`. We want to import these into a Python list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# let's make sure of our working directory\n",
+    "\n",
+    "import os\n",
+    "os.chdir('/Users/Ahmed/Desktop') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "with open('scores.txt', 'r') as file:\n",
+    "    scores = file.read().split(',') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "print(scores) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The eagle-eyed amongst you may notice the quotes around each number - but we'll address that in a bit."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Methods and Attributes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "len(scores) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our txt file missed a student! Let's say she got an 89 on the test. How can we add her score to our list?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# should we do this?\n",
+    "\n",
+    "scoresfixed = scores + [89]  \n",
+    "\n",
+    "# or should we open the file in Notepad/TextEdit and manually put the 89 in?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Python contains built-in methods and attributes for each object type\n",
+    "dir(scores) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "help(scores.append) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks like what we want, but let's check on `insert` and `extend` just to be sure -"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "help(scores.insert) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "help(scores.extend) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# append it is!\n",
+    "\n",
+    "scores.append(89)\n",
+    "\n",
+    "print(scores) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's possible to define a custom set of methods and attributes in order to create new **classes** of objects. We won't go into much depth about these here, though. (See: Lina's lesson on classes in Python)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## List Comprehensions\n",
+    "\n",
+    "Let's say we want to get the original scores, out of 40. Is there a built-in method we could use in order to do the same thing to every item in a list?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "dir(mylist)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apparently not. Perhaps a for loop is the way to go? Let's test that out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "for i in range(len(scores)):\n",
+    "    scores[i] = scores[i] * 0.4\n",
+    "    \n",
+    "print(scores)     "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whoa, that's not right!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(scores) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looks like Python imported the data as a list of strings, save for that one 89% we appended.\n",
+    "\n",
+    "We need to find a way to change those strings to integers instead, and then multiply them by 0.4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# this is one way?\n",
+    "for num in scores:\n",
+    "    if isinstance(num, str):\n",
+    "        num = int(num)\n",
+    "        num = num * 0.4\n",
+    "    else:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# or - we could use a list comprehension!\n",
+    "\n",
+    "scores = [int(num)*0.4 for num in scores] \n",
+    "\n",
+    "# conditionals can also be included in list comprehensions - this is valid syntax\n",
+    "\n",
+    "scores = [int(num)*0.4 for num in scores if isinstance(num, str)] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# else clauses in list comprehensions\n",
+    "\n",
+    "scores = [int(num)*0.4 if isinstance(num, str) else num*0.4 for num in scores] "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For large operations, list comprehensions are also faster than using `list.append`. When the latter is a part of a loop, Python looks up the list in memory at every single iteration, which can really slow things down for large lists. List comprehensions, on the other hand, do not require the look up operation. They're also far more visually compact while conveying the same amount of information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "len(scores) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice how the value we appended is gone. This is because we reassigned the list comprehension to `scores` above. Unlike `append`, list comprehensions do not alter an object in place - they are an object in their own right. It can sometimes be useful to print out a list comprehension to stdout before assigning it.\n",
+    "\n",
+    "For future reference, we could use an assertion to make sure we did things right. We'll talk about that further down."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# putting it all together - the right way, all along!\n",
+    "\n",
+    "with open('scores.txt','r') as f:\n",
+    "     scores = [int(num) for num in f.read().split(',')] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "print(scores) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Control Flow Tools - Pass, Continue, Break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So you'll notice that in that sample loop before the list comprehension, we used a 'pass' statement. Here it is again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "for num in scores:\n",
+    "    if isinstance(num, str):\n",
+    "        num = int(num)\n",
+    "        num = num * 0.4\n",
+    "    else:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`pass` is part of a family of useful operators within Python that allow for more precise control flow operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "mylist = [1,2,3,4,5,6,7] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# pass is a placeholder\n",
+    "for num in mylist:\n",
+    "    if num%2 == 0:\n",
+    "        pass\n",
+    "    print(num)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# break will exit the loop\n",
+    "for num in mylist:\n",
+    "    if num%2 == 0:\n",
+    "        break\n",
+    "    print(num)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# continue will immediately jump to the next iteration\n",
+    "for num in mylist:\n",
+    "    if num%2 == 0:\n",
+    "        continue\n",
+    "    print(num)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "mylist.append('hello')\n",
+    "print(mylist) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# pass is useful to make certain conditions explicit and take care of outside cases\n",
+    "# while it's not 'necessary' here for the loop to function, it makes the operation clearer\n",
+    "\n",
+    "for i in range(len(mylist)):\n",
+    "    if isinstance(mylist[i], int):\n",
+    "        mylist[i] = mylist[i] * 0.5\n",
+    "    elif isinstance(mylist[i], str):\n",
+    "        pass\n",
+    "    \n",
+    "print(mylist)      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assertions ('Sanity Checks')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's say the following function was in the middle of a long script that runs input data through a series of operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def sumdoubles(x,y):\n",
+    "    '''Multiplies inputs by 2, and returns their sum.'''\n",
+    "    out = x*2 + y*2\n",
+    "    return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sumdoubles(2,3) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What happens if something goes wrong, and a pair of strings are somehow given as input?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sumdoubles('hello', 'how goes?') "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That output could be really bad for the downstream parts of our script if they're expecting numerical input!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# assertions are handy to avoid the propagation of errors\n",
+    "\n",
+    "def sumdoubles(x,y):\n",
+    "    assert(not isinstance(x, str) and not isinstance(y, str))\n",
+    "    out = x*2 + y*2\n",
+    "    return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sumdoubles(2,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sumdoubles(4,6.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sumdoubles('hey', 'strings are cool') "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assertions do stop a script in its tracks though. What if we want Python to ignore an error and move forward?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `try`/`except`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "newlist = [1,2,3,'a',4,5] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "for item in newlist:\n",
+    "    print(item * 0.5) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# but let's say we just want to ignore the string, instead of ceasing the operation\n",
+    "\n",
+    "for item in newlist:\n",
+    "    try:\n",
+    "        print(item * 0.5)\n",
+    "    except TypeError:\n",
+    "        print('ignored', item)  "
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lessons/python/intermediate/lesson.md b/lessons/python/intermediate/lesson.md
new file mode 100644
index 000000000..d10c806cc
--- /dev/null
+++ b/lessons/python/intermediate/lesson.md
@@ -0,0 +1,36 @@
+---
+layout: page
+title: Intermediate Topics in Python
+visible: true
+tags:
+  - python
+  - intermediate
+---
+
+ - **Authors**: Ahmed Hasan
+ - **Research field**: Genomics
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/intermediate>
+
+## Topics Covered:
+
+- Reading and Writing Files
+- Methods and Attributes
+- List Comprehensions
+- Control Flow Tools - `pass`, `continue`, `break`
+- `assert()`
+- `try`/`except` Blocks
+
+### What You Will Need
+
+- Python 2.7, or 3.4+ installed
+- Download the files for [this lesson](https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/intermediate)
+  - `scores.txt` is a sample data file for the first part of the lesson
+
+If you'd like to follow along in Jupyter Notebook, do the following:
+
+  - Install [Jupyter Notebook](https://jupyter.readthedocs.io/en/latest/install.html)
+  - Run the notebook by typing `jupyter notebook` into command line.
+  - In the web application that opens, navigate to the folder containing the files you downloaded, and open 'lesson.ipynb' to follow along.
+
+Otherwise, you can follow along in your favourite IDE or command line. 
+
diff --git a/lessons/python/intermediate/scores.txt b/lessons/python/intermediate/scores.txt
new file mode 100644
index 000000000..82ea153d5
--- /dev/null
+++ b/lessons/python/intermediate/scores.txt
@@ -0,0 +1 @@
+61,90,99,68,60,65,92,79,62,77,59,93,96,63,69,67,71,80,83,54,50,85,51,53,70,98,56,55,73,84,58,64,72,52,57,66,74,94,89,82,95,75,76,81,87,91,97,86,100
diff --git a/lessons/python/intro-data-analysis/from-spreadsheets-to-pandas-extended.ipynb b/lessons/python/intro-data-analysis/from-spreadsheets-to-pandas-extended.ipynb
new file mode 100644
index 000000000..bf0a48b0e
--- /dev/null
+++ b/lessons/python/intro-data-analysis/from-spreadsheets-to-pandas-extended.ipynb
@@ -0,0 +1,2186 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**From spreadsheets to pandas**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7R/sysyDRzDRTHCRK7jKzBiM12UX7VKnC0YIEIOxRvPnxfouIuzVr9fXqTo3W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\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ybX0wNiFSzgWjTWAPSgKJyRKt7Ssvv+K9TAfjtdX19bW1p7761KUrl5Q2m1trefqYsLz4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\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3hmhKJQzGBFMWhiFCkFFstK7rSmsFgWvKUohGadk05nzwHuM8DiNCaF1XWmsAgAOeMnY+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krr/qBfZNWtW7f29/cbjQaEMC/yKGQYI+AcwogxCpwTmmuntFUIIWuclIpAHMex0ZoXOUEQIgwQ\ngBAghAhBEAEIEQAGAYghQgBCBxBEwDpg3TtX2ItGaK2g1T4l7UZzebDkMTadhrt724QQC5zne37A\n0nS+eBohDYCcC8Uc1LXQWjPGACRpVhilWo04ChOMMaUUOKy1k9paAxxY5DEYBLHne0ppIYSUinP+\njusehowxIWRRFM694+awmKGAEGqtILSe5wkh6ro2xlDGKKMGOKEkY0xK2Ww2F+IfvGPyD6RUWgFe\nF55PwygEABZFEUVxURQHBzPGKELUWkUwYJTGjWgyOxksNd//gccMcA9fffzO7b1L9717OksxdXk+\nE7rmeV2XEkEwGJxX3NaVqvjs0uX+rZt3CPAIdNgDkednMt/dHbcTSixqNcI8HY3T+vwD6NXXv/QD\nf/a7lc3zYlRLwGgwGVaf/cx/+JN/6o992x/64L/6l79MGcRIr6xvZlmmrSGAEEIQwhgCyqjvs5Oj\n4yj0O52Os/DoaNxs2r39vc2t89NZOlhewYQGQQAJ2d7ZaTQajWZze3f/+HQYxbsnp+Nms3F8ehI1\nEq5lnhf9fn/Q7z//3Iu+F9W1YCyYz3Ip7H0XL7198y2EcBLHly8/cOvmLYzJxfsuHRwcwawQQsjp\n/NadO48//vj66kpR5EGYGCNHw2GvuxQE/sHB4eoqxQiW1RgCPeh1mu2GlLWoivH0FCHXaLQJoUmS\nTCbzdrsFIazqQkppKVVaEoIQ9vI8Rwg4p6ezsbNwNkt9j7YaCTA2iUMEfEYiYfVkPlcG9ptdJ6nm\nCEGmuKKB56SoqxwFPqLUZ0EziUIfp/NJu9PcWF2qRe2HcaPZBGrz9HhIMNrY2IiiqCzLMIxOT0+a\nzVaSNAgCRZUqJVqd7nQ2O51MMUJCiP3Do0WFkTYGCTmbzfK8AAA5B7SFJRd2mgJgGfN9xpSxUgMI\nQdJsWq1935/Nsuk0FVxVVYUQhBB4Pg0oqXlZpDOngU9jbbXIzZ109/j0aDqdCSkI8yCEwuo4jhPf\n47ziRQkgtMZQSqXkyLlFp05Aqda65tV0WvmhhwkURiIIf/YzP/uX/+pH/80v/HzDwXme33f/Ax/8\npg/u7+//0i//UsAYpUQJOZ6MMcJBEAcs9D2fUlpVNfEpgqCuK61VGAZaa62VlFIZBSFAEEPAjNZe\nGHo+CcPw2Wd//du/49ukSnd2t59+4sPtdvvGmzerkoPaKF8mUeT5jACgtOacj0ajIPA8jzkHJOe8\nlhDCo4PDtc11KaTDMAiCbre9v7cPoaOE3Hj9zbdefdNINR2P93Z2V9fXCKX9pUESe3EUI4f6nf58\nMqGItdtdLiWGxAsCrRVl6NU3X93b2T49Pk5/cPrU+z9w/Y3XZ1mZNFsV55JXVZFhZwl0QRBgQpxz\nRVGsOCC4KNK5ljKO4sloHHvB+tpKt91EAMpatNYHcRJ95rM/v7e/0xusQSSBA+1OS9b1ydFBN+me\nqf0zzjjjjDPOOOP3BHUmgFM1lsA6P6AlAoqrTqMbhH6WF5121O520zRbxD4RgjkXEMJer9du96yx\ns9kcAHDt2sMQol9/9ku9Xnt5ZQVY6PuBcc7z/elsdnh02EiS9c2NJ9/7RF2Jt968aYxmgQ8dMNZi\nhgEEEMOKV0IIgjFiJAqDqNnUzr74ykvj8fThhx+5efu2lFJrVRS5NSYMvDrPqqLwwgBTEiVxXlba\nmHmWDZaXyrIqiqLV2TAQHw8ng8FSLeR0OtdKrSythkGwceH84f7ReDzesPahhx5aWl6iPhuPx1lW\nQFqfu3hRKp00mt3uslTKgTGf5cQ65uGldtvzMFciLwpel5zX08kII9tqNgmBLCCUBO1216aQc8lY\n7CwzmkJI0lqKarS2vhV8MLh3597x8REAkCJyfHBqgWMew5gIKSjFy8vLDhhtFIKQUqKUFIIjBAjx\nIFpEyUBtTVFmdc25qAghEALrHAQOIoAgWpggQAAW9vuLtfFFPf9iVR9jDCF0DhCMoXUEQoyRtQZA\nGMUhgDbPC0ppt9d2zpVV5VvAhRKCAwCMAdYApTQhhDGPUiZlyqHD7SalBEIS+v5clNA5j2BFiFLK\nQYcRQQhxLrTWSinGvEajoZTS2ljrlFJCcCmltdY6/VWpjxBK0xRjGIaR53laa9/3gyCoRFXkubXW\nGluV5SK6cVF9EHm+0hZjSimzFpRlHkVxGEaTyQjCRb8ArqqaEBLHkSxLgJHvsTDy3/P41UbTD5PW\nfMb/+Y9/6mQ0+s6PfOuf//N/ipfze3f3v/xr1z3sB16QF9oYcHg4jBuu1WltbK4d71pKWBD7lppp\n6Uaj6uQ421wmdTVByFEEdnZu0vi01Wh+73/9R8Kg83c//r9aOAv95cmJ+rVfvfXhb3vy6W996pd+\n8QtPPP7YZCpnM8MoS8JEa22MqWVtDEqSCNjQGDscDqtarq5t3rx5c2VlEwJsgKtqbkE9mc0ef+xx\nZexoNLz+2mvvunrN90IAwGOPvefo+BQTdHR81Gw20mx+dHy4sbk+Ho+Xl1eVKnzfD4KwKArrXK/b\nT9P0+PgkimJC6WQ6a7abW+fO1RX3Ah9YF4bhjRs37t29e+XyfQhBAGyvt6SkNabudvuzWRaHURh4\nAOAsrbW2Uey3mv1+v5fmk+3texCiIAgwRtPpZH9/jxDcbLbKsjDGBAEbT6ZFUXieX45HBiLGPGt1\ns9loNMD6xtp4NN25s5+n0yCKzm+uGwdWV1cpQmWaOqMYYtBA4vCgPfB9n0BquGuHbUqRj2hV5coi\nxrxO3GHEX11eBQYyhheZ6hQjjBFwWAhBKcVJDKDWLqiEjLVJ07yqSgihA3A8ngghpFIIQq01wgQT\nhhHB1APOaW0xwQCipNkKg1ApNZ/PpTRGOa210TNMoOd5GKM4iRAEENi8KPI8dw4i56Cxke/NJ9N5\nOqsE96kXBqFUShvDGLPKyJpjgBYhpRgip6xPPIed0topg5gLPX+YT8IkDAJ/PJuEcZiX5c17d7b3\nd5784Df9xE/85B/57j/ebDa/9OUv3br1dlGWEMRJHFujESLQAQes1EJb4xAKQp94VHGOCW42G3ma\nW2s4r50DjPnGKEopNLS31Erz0qPe6elpUc9eey38nv/qj/74j//4ua3Nvd390XgWRRGmvlWm5hwC\ngKHzfKq1VEoYIx0ILTAY+UWeY4SGw9PRaBQ3Ey8OEUIrK6tFXjprT0+GVZr3O92tZFMYVRVlkRfS\n6JpzRrxm0kiiuDdYSsJYlEW31z8+PrLOdbvdo+ODqiohcFIJA9S/+8XPPnjt2rVHHvn8v//S/uF+\nzQWvSgw1NJoiIIWQUgZBYB1M03no+RjjlZWV6WjMMMEUKqm00lubm9v3tpeWlkeT0YsvvZQ02wjj\nlbXVRW3azvZ2kiRLg8GZ2j/jjDN+3/D5z3/+6aefXtze29v73Oc+90M/9EMAgI997GM/8AM/cPny\nZfD/8ob4Kr9FF8Crr7766U9/+uMf/zgA4J/8k3/yF/7CX/gax/OfbOz/XebMaOCM3+804nZZ1QjC\n9a31+y5d0Ipbax+6+gDx6O7O3n33X/ED/0tf/nJZFMtLS51OZzKZYEwvXrgQBPFwOOJc1BW/cvkB\nIeRLL74cBo04bBVF4QBeBGU9fPVdRrnheKiUef3Gzafe977jo+H27o7Nc+Z7mNGAhoRiTGjis8g6\nLbWyMohjgMFzL750Op12et1X33w9DMKLFy+ev3Aum8/m81kSBY3z55SUYRhGYbx/cHj3zna32zfG\ndjvdpNUijLU6rddvvpJV5cVGoyo5gNA5sHdweO7c1mQ+H0/G8yy7dfduWdZbW1sPXLly16Pj2bTZ\naff6vbfevhmEUavVWaxg1/5M1BWAjvhBL6BCy9XVFc+nWpvZdEwpDqNYSDnP5sPxSVrMT8YnxsBz\nm/dTzBSXteJOgUayMh6Px+Px9t2d4ehES0UQ9b2o0+7maWmkk7JstVrtdmM6HiopLXCEYK2RMdIB\nZKVy7p3MAp/68/lMa+N5PgCWc4ExBtAihB2AEAGrHXBgUbq/SEDAGMOvlPEvaumttQgCa7QSNefV\ndDoNQyGlhIgCCOIoYX5YFCnzfKkdhAZCjCCxhmttnIWUUIQwhAgASAgL/FBrbbRm1GNECiKBUBgC\nteiApRQhlhWVA2AxGSGEKIoiSRoAgCzLpBRhGNa8tlYvbCAAAL4fVFXt+8z3fd/367rW2jhnKcHW\nwlarNZvNrDFCCMaY7/tFUWSmRA50Or35PO13+15Aa84Bt0EQF0UeRWG30zs8OmTMU0oZaAhEDtlG\nI7p8+fLzLz/71/+7/+HH/s+ffuHFVx977Ik//se+987t3fvvfyj2e/fuHZweT6j1nEJSSnicnb/Y\nrkW+tj64+ebrUW+di1JrGAYNx/zZrNxa9ax2zmlEALBieHKA3w0nk9Haw+f+zv/yI3/zb/yPu/fm\njWj9zs3ptavp+vrqD/y57xkNZ1/8tVdbjRXnYFmUjUaDc+H7EaXI94MkirRRjUbLC8z29t5gsJwk\njbqu5+m80+v7vt9qB0fHJ4cHh41GY211g9dya2vrueeef/ejj3lBdPPmzTiOMSaNZrMsqzdv3KCU\nZVnWancODg7a7bbneXVVaa2bzeZ0Ot3d3fU8j/n07p1tQmgUE8aYqPlCHmdpOp3NG62EYjzPi/X1\nzaqsZpNpHMdhHB8fHsZxFPoUIESYZ3RNGFAaIeJhAOfz+fLy8ng8ZIwJIQ4PD4wxQRAY46XpnDE6\nm02sgd3BkpKq2+2GYVyWVZpl2ojV1f5Sf0U7kxW1dq7TjAQXfrc9n457vWWIYVYW7XbH90MM4GQy\n8X1fCb7UXwFwgDFq97rAYakcB6LRiJutRqfT8XzmtFbSKCXDEERRWJZ8Ok+1UbO80kpJo6WUUqos\nS+dpZoxhjNVcWusoRUJySigEwAGDCYn8wDrHa6mVVVqk2VSqpBl287yAyLX8hBCCCRSCSy4IxlJK\nJS3zfF7NIETA2Fk2dQD4HhNKCq6ssQAB4DTGVAvt+x6AwBpDGYEQOeCEEMaYJElCP8irkjCmnS3y\nHGKICfZ8r+T1Zz77C3/mB/+csm6epZ/93L8DUjPqRSEosoIR1my18zyHCEEEpZbAqSD0jYHtqKko\nK/MSQoQp9SDwgiCdT8uyCAJPK8UwS/PC88PxeBw3fG3Ea9dfxkT/lb/6ly+ef+AX/92/50IiFlGE\nalF6nudTxqsqikPCAq2JqOvZbNZKGu0k0UIJLaVRJwcHW+c3nNHGqk63vbqygiDxg+DK+SvzyfTF\nl5+bZBPmk83NFa3Ui88/K0v93ve+f6faRQgkod9ttwLfz4qsLPOqLufz2draSp5nYRgOlpcajejF\nl1748Ie/I8+rz3z2F5wzEMpmI5F1CQDgik+nozSrFBciyy5duhT7Ua/Xe/HZ5wihSRxZYwTnHmPO\n2UuXLr5x4zWA0Nraeqvdc8ARSrM8C6N4edAnlH2tav9H/87f+z11zXrG/2+P3tl74w/oMfnQ17Ij\nzzzzzK/8yq88/fTTe3t7W1tbX73/4x//+Mc//vHr168/8sgjX+8L//RP//T3f//3f/XP3d3dr2PQ\nv5fE85nRwBl/APjeP/anhBLUw75PkyQeT4Z3793BlDLPH83Gm/bCnZv3do/2u60upp41kGDWTJqi\nVmU+mU9T50AUJQgxofnKxlaWZlHSLUqdzSvf83jFZ6NJv9ut63p8Otnf23vg8uVz5zcn89He4WE6\nKlY31ofFLGkkcZJghaIwioL49ORoNk1fvf4GxuSbv+WZMIp3d3f39vcn8xRRcmHrnB+HAIAkTKaj\n0Wg41S04Hc7PbV6syiqbZ61me3m5PxyNd/bunZ4eLy8tMUy8ZotBCiOHkPMIPtjZHk7ngR++dePt\n48PjeTrbOrfabkeT2cnG6vLJ4cHbN97CmHpeRD0Whr6SEjHseczz/Ihhz2MQAkKZdabV6tZ15QBQ\nWFKPYYLZxLMGra2vdJIGL9OwjRwwSTfJxWg+no1Gk+lk7pOEUAQA1NIgC50CVVliiMLAr6oqKwpl\nbBAEmKC6EopbjABEzlrnIEAAVLKmzEfQSKH8wIsiZrRxzlrtIITOWgSQQwBaQADyPGaNtdoaqxBx\nLPAJRJBgrTWwxmiRF7k/nYpKTuDUAZCmmQNIWTieZlVVKCVqzlutpphIaZV2xmiLEUaUAghns1kU\nRZ7nz7PCpflSf5kR5lGaKo0hYoRa6wDEACDOOYaYElJWlZTcQoAxVIpDCK2zEOOSc+tskiQIoaIo\nEMJS1p5HW61WXdec155HEcLWAWNsFCUQIkZ9SXQU+wAAqQTEKIp8a8x4Pmy3epC4LMuAsYyxMA4e\neOh+a+3t27etg0paQiHzvTAMjk4O3vvex55/7vp3ffefMJq89NLLz3zr03/jb/ytX/jsZ+uy+Nt/\n8xmn8Hd+5MP/+BP/VLmgKpXvoeOT2b2d0yuXNldWo+U1mOdHAR2E4XKDxWl6UlRTqQMjLETayFmz\nvzkaypC1m4Pmyy+8eN/lCz/2T//x3//fPvHLn/s1hLwXnr/3wW95dxC73Vde9QPkHKwrkcStmgME\nvaXlPiYgivwwjk5PT63L251+WhSXLl8+PT31PC8MY0YodGA8HE2n6ZUrV27evBn4kTQgzcsHr70r\nq2qIibJud+/w0cfeo405Gd5eQt79D1y9d/dut92dTqa7u/sXL146Ph0KITY2NvwwkFKGcWQMODg4\nCoKw318qy7rZbHqehxBK87KSKjs8aTVbhNJ7OzsQguXBklTy5t07zrppPg/9YGVlqdYWACt4STBY\nXjpndJ3OR7dv3zJSWaeXl5eNUnGzyZhvrd7aOn96esqob7HLZvNGo8GrfDYZ81p0u/2Vixe1A1KZ\nbDwGxK0uLXe77aqsijxvLXeLIgto0F9ue4zVZRmEISCaBpBL4fuB1mCwunxyOlxd2VCmkEbEjXCw\n3AuCQEvVbveGp6dZNp/NJktLA2VsUYuyrufZ/PT0tKprJSqpFPNImARFXgolKfOdBVppQhgEDhNI\nCAXApGna6wwmk9Q56wcoTjwusoLTsBEaq5WzFAAtDQAOAFTyGkLYaDem4wkkWGnBpaARU1pzIQy0\nEAOEIAIAAmu1poxVXGKCmedb57Q2hFLMPCOkUAYpgwjj82KpsVTVpeR1OsuWV5aHk9F0OsEYnL+w\n8bG//WOhH0Y48DGjFvvNDrRQVAJDrJS1RkZRZJ01Vneiru+FGGlKAq2NVI6yYDIdhUmCCCzLLGC+\ndkYphT3aaEbaCK14GPr37t677/KVXnfjV7/46yxsIEIghIz5WkhlIcZUSlvJutNuF2UFLXDGKmEI\nYqLgSTOo0nkxGwUxBQDFzQgyDCheWd4YT/jzv/baaLq3ut5qtEIfydF4n6kiz6XIqiKv72y/3u+3\n1lZXCKV7B9sXL50LPe/k8NintNlq9Ltda5qNTnM6HN15660La8sPXzn3a89+MU4CrY3SFSEeF8Xu\n7u2y5IN2dzaab1vzoQ99+MZbt0+OZkHoVRXnUlkHOOfvefejjAVHx6dPvO/JdqvLuSSYBYwcHhwv\nL690ugOMva9V7f/3f+uv/a792P/o3/l7Z9esZ1f8Z3rma9f5f2COydeyI9evX3/66ac/97nPAQB+\n6qd+6vu+7/sAAJ///OefeeaZT37yk5/4xCf+ozX8v/gX/+InP/nJ69ev/38+261bt77/+7//Yx/7\n2A/90A9tbm5mWXbjxo2z99UZZ3yjSLOCeMQj1AKY5VkYRYPBkjIGKVVVNXAuiuIH738AIhwnCWV+\nAhHG1AFIKSWMAogRJcoBbR2hHoBYcOH7ns8YwdhaIyWnlHXbnaIqhcTPv/D8h7/1mel0kpd5GIXS\nKMoIAKCuat/zi6KYTSZJHG3f20EYv+c977l5++6FCxevXr0WRtHdO3feePOt1169/i3f/C0XL1yY\nTSYEk9X19fHxaej7ZVmGQVDW1fHJ4ebmxo23h3fv3rpw6ZxHPeCg1U5y0UgSz6edbns2m25tbUVh\nUmZlWRSnJydlNe/1uvfff4VgdPfOHYJIXcuD/RMuOSKIMWaUAQj6lEVJ1GgkEEJCse971KMIIedc\nu9EwxnS7vcFg6YkkGo6GxugoDBGACMLpdDo6GWazOaO02+0QgOtaVEWtpd7Z3oMAOgMpJQjh4XBo\njImiCEKolaaUaW3qusYYL6rZCaUAAGvsouLdWiskJ4R8tdAKAuSAZV/p4HfOQQQpohgjxhghZLHa\nTwgBDlhrjVVZUVRcUuZpY0+HwzTPQutDaIsq00oTQtIsX6xqRmFYc04I4ZwTQhwA8/lc+n4jWWu1\nWq1mS3BljAEOYAiNMVrrOIkcglo7iJCUCgBACDHAOecW6QCUUIKAs85gBBzgnDvnEHoniWDRSau1\ntta1Wg1tjANACqWUyfMCQkTpIribt9ttjGFVl2EURlGgtSGEsJCdHB2urq7GcVxV1blz51544QWK\nCaGMEByE4ZXL91PmUcY2N87/2I/9s/d/4P1/9s/9Nz/3cz//f/3kT6+vr0mlBktLD1976OlvfeYL\nv/ofrEVFpbSpDw8nW2troQ8eunrx2S+/QSiyxmCMNze2du4e19x61HdGEYIRsATDt2+8/eRTz6Rp\ndufONoToR37kr33TB57+h//gxx66+uil+x66/vqvPvDgw9X84PgwDyPs+VArIaQGIKZkEXPAKA20\nAdv39vq9Puei0WgurA0BQPN5WhTFIhaeMT8M41arNZlMVtZW0/k8DENGaavdmU7nFy5cyLPCGjCd\nzO67dPnk5CQMI2NMWRZlWTabTSEEhGg8Hq+srAghs6zY2jovpbx3716/37927Zrv+51OJ03TyWSC\nEDoZDjEmnWZzOB4ZY7zAl0IYi7Min701C8O8aHtRAAAgAElEQVQQE4yA7XWbZVExDwdhUpXFdD5r\nJAnnwjoXBGEQBBiD6WxKMPGDwFk3mcyttc45jClEEAAnlZhluVR6NpteunTZ80gc+VLUYeQLIZhH\nHFBSAUapkLVxSinOORqs9KfTKfWosdr3WVllRVE6YIMoXHwEQj8QnK+srAghlNG379wljM7n6XA8\nsc6UFa+qUmmhhci1FpwrpQCACDroAIQQOIQQwNhBCJyzEOKiKLudvjaiqmfMCx577LEvfOElCEWz\n1UzCSNQV5zUAjhJKiQ+RRRCFoZ8WOcQIOWeAcxASSo0QC3NNZy3FxGdMGAcRNMbUxmCMEULaaiEE\nxhggWPOaEBIEUVVxQkltLMIAAPBN3/TBVqf93AvPPf3Mh5dXBqcnY0aIkCKMwjCIpBScS+Z5znEA\n8CKzAwBgnS2yXBsLACnLKq9Kz/OCKLRKOQeiKHZGK11jRixUXIo4Djvd3sbmwIsYhHj/4CTLSt8P\nMSMEI4UcgM4YpbSGGAZRmFfzOAmNEg6qMPLH42kQUADc6dHh7m4/bDfaUUgpbbc7qrZVXb/y8nVM\n0ebWGkR1r9tuJeHye67mZX7r7UNtqqXlvhfeL1WljQqiADirlCyKIo6j0Wg0mY7f9a531VUZxsFs\nmn3+l3/l+/7kn3jk6oOj4cHOwfa94z3KWBTGpyfHZZ4RzGpehWHIPFrxykHwrkcf5UoYVWtrJvMx\nC/242bDWra2uNZotQimvZRQmRZlzwTnnJa/7q/2vVe2fccYZZ3zD+aqSz7IMALCQ+gCARXn/Qu3/\nxu0/85nPfPKTn3z22Wd/szX/L37xiwCAv/SX/tJgMAAANBqN973vfWfH+YwzvlFM87Q4yaNmOOh1\nJOetZuL7gbWuruvBYLDUG6TzrN/pcWW8MALWAIJrqTCljWbsSeEsZFFoMSh5FcRhB1iphOcxRuji\nUjUIBpzzbqe7srYqtZrPZ0rIRx999PW3btRKAAAIpWVeOOdmSksp11ZWszQDAJzb2tLGNhuNyWRy\nenq6vb3d6/XWV1eHpyeCi5172/u7Ow888MBKfzAfTxrNCGNQlXUcBxaY4+Fxls0vXDi/vDTglSyL\nuh13krgZ+D7nJWO02+m1uv2y4Ix5XhQCABBAw+HpY0889uVnny+yHDnoMa/b7golrTVBGDJMtdZV\nXWGAGkkjy7L5bJ5lqbIqiqJGo3G4t4cQms9mG1vr1g6cBXGUCK7KfO6zwFrYSjrQGM+nWjtRCSGF\nc4aLuq6rOGwsggzSNGOEUoqNMUophFAQBEmS5Hle1zX4Shn8Ip5tof/NVwAAfLXjHQCgtQFOU0op\npYvcvsXthcAGACxcALTWnHMHc0J8JHjFxXg6BsAKbq2zAAAhRV3XdV0vvPTajcZCaVRV5fu+1lpK\n2em0+4N+u9XGkJRFbYwhhHBRG2OajaYFSBsTBIGQ2mLLGAMASKMX/7sIC3AQaKcRws65Rdo8IcQ5\ns6jPr+s6iiJKKSGk5hwiXJZVo9GklFlrAQCcyyRuMo/EcZSmaZIkaZq1Wq1F4F+305NCHR8fJ0m8\ntbV1fHycpdl8lt53+UIU+trInbu7l++/7x/97594+JFHrj387h/9n/7n19+4YRXgZbG3t/3gQ/cz\nGj315Af2dk92d/aSuAEMmk+rk5OZx/y1tdWl1VFZcaC5EPXGxma7+fjh/rMbywwRzyFY13Wz4W/v\n3Lzv8tUgiOazYm/3aDKePf7Ek//wH73r9GTys//206+9+fI3f8v7rj0ScP56llZSuzBMeGkm0+H6\n2moYxr4XSXFCmd9otxbF23meX7x40fP823fu9rq9LMsbjcZ8nlJC8zynlHbaneFw2O/3hRBKKWdt\np9N5+eWXOee9Xu/hh64Nh8PFeaSUVlUFIYyiyBiLMep0OoPBYDabr66uOucuXbp03333ffGLX3zj\njTd8319aWvI8bz6fM8YuX748Go13d/da7dbS0iBNUylkr9sdjU6tc0qp0WgoJT8+wptrK51Ow/eJ\n5/mNpJUkoef5AcYI4SzLMUYEE0K8qqrCMIyicHFmFzMd83QyTxEhtKzqZjNO55PBYHk6GQkh0jRF\nCGPkpJJFmYmaY0SrsjDGVFXZ7jQ5r8uqRIhwzquqRpAQRgXn8crKbDZrxImsuVKq3+9rawile/t7\nNef7+3tc6bIs67qEEFijeF0BiIQQCFLgJIYYAACgWzSzQAgQwgDhIPCPT44oRe1ufPnyxcV8XLPZ\nwAiNx2MlRRj4GGOtFUYIAqescs5poxECi1Yd5+yi3eadBA2MMUIQIWSdBUZrvfg4O+eU1IvvBOec\n1to5B4CTUkDIhBCX7rv4viefuLe786UvfSluNb79O77rO7792//B3/9EZ9Cq8swY7XleFEUAgMlk\nsvDyWHznLPo1AABaS6m4VJIQjBAkGAslMMYAAGWsQ9ILkFT50vLKk08+JUR5597N6a0RZdHm1oPG\nOD8Ila4VUhBZjI3W0kKLMK7EnFCU13ng0W6/p2wtddmgXYxRlqX7+0f3PfiQFBpTb6k/2NnZ29ne\nVrq2iis7X1ntrK6ubW6u/fxn/9VguXPuvqUoWDHaa/e2rNULa49z584jhI0x586d933vpZdfuHnz\n5sbGWlWWSkjP82/fvt1oxH/oD/3hz3/xV+fzVEnd31qy1oVBBCE+OT1ttzsQs5PxaW+pf+7SxbIs\nOc/n+Wz/aPf06Pj49PS1t65zWVZcBEGYZ8X73/8B65znM6FEXmRhEJyp/TPOOOP3DXt7e5ubmwCA\nj370ox/96Ed/4/0AgB/+4R/+jRtnWfaRj3zkh3/4h38zAf/VDv+lpaXFja8WC5xxxhnfEDAjPo5Z\nwIaT8Xw+b2VRI4l7/Y61dtBbYsRzyvpBqDQHECFCoAEVL6q6NgBS5hdl6UFAKMWULq8s10UmqjqM\nEoQQMFZrE4ahQ7CeC6QNIbTRaNy8dfOpDzw1y6b3dnakEjUv03m+t7u7tLT06LveHfr+8ORkub9k\nuJG1xAAfHBx1Oh0jjeKqu94bdPuDXm9n997h0cHK8mD79u08TZtR7HlekyXM8wB0AKF3P/rIZDKN\nvIDn8zzP20knSZKyyAGE7VY7z0utjRACQjgcjZIolLL85me++fT0tNvpAOfd295PszxOGowxjLEo\nOXCAQhJQHyMiazXo94NwgwsxGp3UnPu+rwl2zi2vr0VRNJ3M4igStT44ONbKNOJmVVfW6EabSlk4\nDRmh6yvLuuP2dw/kqhoPp1prgolPWKsTlmVWFAUA4KtW80EQAAAWq5Gc86qq6rqmlC5M+Bhji3T6\nRfLcQsNrp+FvmLddbLZQUAsh4axzxFlr6qrChGJMs7yczKZFkceNRCqVZam2iiCitZFCB35orIaE\nEAeqqiqKAkIopUSEBH5gjNVaK6sX4XkL7cE8DxOspTFGOwchgEEQQAgFF0oprTWEkDHmnNPWIISB\nM845xthiSV9r5xxc7JrneRBCra01rpU0qnwU+iEjrMgLo4wS0mjVoa3ZZIYAPj48YZ4fRYnvRzWX\nzVZrMh6PhhNg0ca6t7W1Jbl4++23d+/tra6tEIoZDXfvHVy6csljwY/+6N+FACOAA4/OJ9PtnbuP\nPfY4Ql6j0Xr/U0/t3Ltd5rXnYVnbnXsnnXY7jpONjaXnX3i702syAkenp90O7nXXiFdyURKMpSyi\nZHA0mgJojo9Ov/iFZy9dPv/NT3/gy89+6drV9zx07f4337rycz/3r19t3/7AB979vvdf+/l/+0sI\nBgD5naXW/8PemzVJcp1Xgnf1PcJjzci9KmvfgMJGEARBUDQK4CqJrWk2xR6TqTU2eps3vdJML/wD\nmjE1Oabp7jFrs6FMLY0k9IjqJglRIEAWUCQLqBW1ZFZW7hkZq+9+93lwEo0GRYjgKqDrewoL97xx\n/fqNSD/fOd/5kEYAQst2pDZFqRACrutwLvO8xJiOJ9Ha6mrg1+/cXut2u7bleq4UQmBM+v3+wsIC\n53w8Hh86dEgIobT2fd8Y43meMaYoijiOG43G8vLy9vYWxrjRaIzHY8dxgiDwPC/LsmrXRVEURVG3\n26WUJkkyHo9d12WMNZtNz/MG+/2lxSUt5MbGhtEmqPm2be3t7fm+DwAsWdlotcssi+PJxtZukiW1\nwGkErmU71HYMRJ5fgwYJZaQStkX29wdhGOzvD7rdbsXtp2nKWE4I4lwQy7KobVMcRdNa4NN6yMrM\naAmghsBoybMkEmXRanWFZBBBIdlwOADAZHnquk5ZMq1Bt9sDCBVcZ3lOKR0MBpLxah9Ok9hxHIBQ\np9ORUk/Gkee5xsDRaIwhcB0LQkywqXJqGCKMCcYUAC2l4FxWxhaBpyDUWpsiL3wv+PrXv+H73bJk\nWZbZBLuuk+e557lSckqxhS2gNeccAmgMMAYYDY1GAAKEiNYGQkgxhRAwJohlaY0QQoQQQogQoigK\nx3GEEBXDzzl3vYDzghB87ty5M2dOXX7t6sVLFwmhGqC//Iu/ePYTn/ibv/r/8klmOQ7BeDKdQoQ8\nx6nyBWVZAm0wxgTh6WQShg1CiFLa8xxiWZwXWmvP88K6H8dTy0ZWYNsuXF480ah1L37v5XvrG35g\n18IgrLcIoQXjtqMN0BhD23eMhVlZIgws31FQtTrh/Fyv5tqHDy3dW90eTYZSlx6tdzpdWcr9vUHY\n7lLHYpC7jgOReeSxM6NRn9D2mdMnH3zggb/96l8RWyf5XpxPVg45nZljRcH29/szM70kSYSQnMuj\nR48XRXZ3bbVeDw8O+o1GuLAwm2dsNBxdfOXi+z/weLd3+LFH3yeE3Nzaevjhx+6tb0BqB45HqVUL\n61nGb9x4vdFoGwAa7cb29maWTqQUk9GEc6kFR1iWJbNtuze/2Gq17ty5c+z4KcexCcU7eztLndn3\nLNp/s1nXW8S9bxz6UeOuH3foR62/fpLW39WW/ckn9tNdBfjVNWN/pxf4Niv/Ntf+z6vV/M/1Tr37\nFuSNa3nLHH7UG+/tJ/njxnnbOHTo0MbGRgX43xx/+qd/CgD47Gc/+6O8/e/93u/9uNHeaNr8rthg\n9+N+/I8QYatZiGI0GmZ5jCBElHAhoygWglNCtNKu42IAfdtzXR9hjBEGUkdRMhlPAEL7/YPZhfmw\n0RyNRr5na62m0bTb7QRB4FqOUgpBCAkGEAAASsYAAEkSM8667fbtO3fiKJomMWO802qdPXmy3Wh+\n5zvftqhb5CUrRXeml8a5USZP8wfOPZjGSTSJBGOtemOm02s8+TQEYBpvWogiTCzLhhACCNM00wgs\nH1phTLq2KxggyNbKKKO11s1maNvu/l5fZYVjB4gSJfRgODx85CGL0ps3b/p+7fSps/Vac3NrV0kj\ntbKpBdwwT7OClUCqvCimk5EBwK+53V631+sppabRNGjU8zybX5jP4sR2SBD449EYAQCV2d3eJYQe\nPrxUb5nhcG8YjSUzjXrbSFwUudEaAOB7HgTQcRxCLACMEEIIodR/0/BXundKacVfVR77FcVXHap+\nWiveXilFKa1s+SuoX9n1GaMrtPCDP9TGGC2lSrOkKFmcZeNJBAAQgkEEGGdSCWDBvCgJwdoAx3aL\ngmklhZRVUQCl1PM8x3Eq1YBQsioTqIoRACJJllNKIMFFzjC1EEaccy65EEIp6Xk+xigvCgAhQlBw\nVTUOAAYCYIwxQnBKbaV0lmWEEAAAQrgsS611FEUIISGlEUIp5Qe1suRFnidZCgCsB2Ge577v85iN\nRmPX9XzfjZN4dXUVIbiyciSOk2g8iaPs1KkTmzubjm3t7wy37u2GXjgYjxu1sCyZVObSpe/9xm98\n5sSJc3/z188tLy+eOXf8+pUrkFkYOgf7yUF/6rjW0WOHb97ZKvIIY6/ITJmbkydmCR5Pp3sBRRhr\nybMPPPHB/b2dmc7MYDDZ3dvza/Sx9z986fKF2d7yxz/5zPzi3L/7v/7k1VcvnTl78qmnP7B1b2jT\nWpqWx44fn0wmEOFoGlNiGQMHB8PebHc6iZRSIz6yqD2dTjnntVp9f78/MzPT7/cBgI1Go9/vd2a6\naZru7OyUZTnT7fb7fd/3K+XFvXv3arXawcFBq9Usy7Lb7dq2PRwOtTbNZj3P852dnTBs3L59e25u\nzvO8KIpc16WUKqUODg5qtVqF+YWQt+/csSxrYWEhiqI0TTzPq9XrMzMzEIK9vb0kiWv1EAAznQzH\nE42gwRD5HvXcWlEUEOCyLISQge9PxqPlpeX1e3d7vbksS8IwhBDmeVaWueu5jBVcsPmFcDwau66f\nZ5FFSRpPIYJKQmMkIch3LaVMHEdVB0qtDWOllFJrJaWklOZ5iTFGlNqut7+3t7y8rJQqWOn7vtQK\nALCzt7u1vd3tdjsz3e3d/aIolRKUWggAADAAEGNSfacMMFr/wP8SIVLV+1CLSik73XYUTZSScRwr\npRljhFDbthzLogRjBMuyhKBqJAEwJVVHCW1M9WhUtdjAGCulDADaaAxRRbxXKb+qe4UQopLtVA+r\nleqHc1FZLWKML1x4JU5j1/YQxWVefP973//Nz3zmE5/42L/70n+o1xr1ej1OkpIxjBDCWAghhXBt\nx/M827aqnwZqWQiTLMs4ZxBCpZXIC4xgUeYzM+1jZ2ePnjq8fW/4rW++mERlqz2jNCuZyLKc2Nbp\n0ydarV6zVfc827PswHMpQUHN81u1WrMGgCiK7HvffWV/uP/o4w+dPn3qhb//1ng8nun1tAKjg1Ga\nZACRdqslS56mkZal71nzCwszvfkXX7rQPzhwawjbMk2Gu/01QkKC/cFwkuVlWZaO68/Mzk6m02vX\nryEEj6wcqcz5d3d3lxZXEBju7+8e9A+ysjh56uSzz37y4ncvYmRNp8ny0qEyy8OwfurM6TQpxtPE\ntvxL3790Z40hoIK69/TTH4LaXLt2YzgcOBbI8oJz8f7HHiuK/ObNW8eOn6QWSfK06Tfe/E/2PYX2\n34xC3wLU33LozQ/6b3Pop4AE/6g3+NtM7Ge5il85vv0ZV/4fHfknPPndtd/e3QsC4X8D529+/Y5w\ne7UIbzPO28Zf/MVfvJnVPzg4+KM/+qMvf/nLX/nKV96w639zCuDMmTP/5AauXtwn9u/H/fiVxzSJ\nkQWFkc1Wy3PtdtjA0FTcb7PRVELKUkBK290eIHgymSRxHNbqgRPknJVlQRHxXd91nDiKtHIdSvb7\n+57v1fygFTYJIYLzzmwPEzIYDiogV7Jic3Oj0+lAADjnjXq4MD+/uLSEEbrw7Zc31+89+ODDBGMI\niZa6UQ+V1hDC0XB0dGXFaJNnyfXrN5SSH/zgB7MsfeCBh/rbO9PJaG+nv7AwbyCwHC/L8/7BCFMb\nI1qrhfVaY3gwkpL7gQsA2t3dTUvmuQGmxA9qqiMtCzzwwAO3b9+ZTCZZXipx6+y5h7rd2e3t7fFk\nDAwihEJttBRZVJa8xD+oGWbbmxsHB3az3Ww1G1yXjWYwGOxhCIu8SOJpWGu12425mXmLuI7jJslk\nkm42m7WFhUXPrtWDdhYxaPB0estxHFaUnDHp2P3+XppO4zgBANi2XavVMMaMMSmlkJJzXoF823E8\n163Y9aqtXUWqAwAwxpZlQW20UhX2rt7UWgGAfogoIELIdizL8pQyaVZwkWklfc/FmBS8VEZxIQjB\nUuk0y8IwLAoW1uvj8TgvMkKxQ20hRBAEFc0YBIHjOJwlAADLsvzA1xowoQjGUhttFKEYE6K14pxh\njBzHFgI5js05Z6x0XJdSwlgJlNYKGA0836kQjtZaa5Wmpe/7hBDbtuMorRr4VVb8jDHbtrM0b7Va\ng3TouTWlVKczM41GZVn6gb+9MZ7tzfq+d+/eelHk8/ML2oBms1WkZcPztUaBGxZFlqU5xjTPC99x\ni7yg1GrU/Oe/+fz/+gf/28mVU2ur986eO/Ivfus3+jubipOykEVe7u6M2t2a75eHDy3cXRsVWeJY\nbpaVd9fz06e62lhSaGppDOX6+h0M007j+EPnH7n4/QsvfefCibPL83Nz23ur165ff/bZT/zJ//ml\n5/7qP0bxZH72EMusa1dvOk4NYex6gV+rT0ZbjmvZlocoiqZRluWTyeTw4cOOY7Isn5+fL0u2tLRc\nFMXdu+sPP/xwURRZls3RecuyoiiSUkKERqNRGIau6+7u7nqOWxRFs9Fcv7e+uLhYVY50Op3JZDKZ\nTH7gB8FYt9sFAFSsfpXTSdNUKVV5BIZh2OvN7O3tLy4uxnE8GAzqtTqhOIqjaTQNggAhpLQZTSfI\nGEytOE5tm2KMR8OBVrIsWVUMUtVueJ4/Hk9mZ+ejaFKv16WUEILhcEApZazkgne73elkFE8j27II\n9rY212u1WpalWhutheM6nJdlIQDitZqf5zkAIAj8g8FYKuk4brvVLgoGAKCE2I57cHCwtro22+vZ\ntp3neZZlfuAvLC7uDw5W1+9Op7FSKk0zQgilBBqDMVVKgR+Q8NoAQwAwBhBS4WVCKCGYGKXTNMIY\nPPDAA7s7+xZ1KpROKeGclYVyHcd1ba11UWRlmdf8wBgDIFRSKq0MhAYYznmlpa+KdDBCtm2XnFuW\nVWUAq/oXaltCiEqEX5YlAMAYbdv2zs7OZDwsy9JybAgQMjBn5fra3RtXr336k5/82lefnwwnOSss\nx7ZsWwjBOa+Sg0IKxpjv+2HYgBAAbTzXFVI62uRZ4jgO0EQqZgAwRlPLunz56r3VfYQt18OuFzRb\n8+cfOnPuwbO+Y588fSRN2MbmvTLNsiSFQLOygJYJ22FvufeRjzw90+seOX7k1Uuv/u9/8n98/KOf\n+L3f/91L33vthRe+1e71okkcjeJGszXbnf3uy69Mx2PcaTgePrRy9OJ3L9249rpfA9jmmOSOb2VZ\ndP3G1cXF08uHDmOMGGP1MC/L4vadO/PzC4tLC0ZybVSSREmSfPWrf/vhD/06pUu7+wdsa2tnb//j\nH//4ubPnJ5Op6wWdVvvKa68BYANtRoPh7Tt3f+s3P9Os155//mtxEh1ZWXjfIw8NB6Nrl69BrafT\nhDF24sSpIysrz/3tV33frddrURwrrYOV4L2J9t+ChSoG743/KD/FoZ96Dm+D7v7Jj/ilTfWXc4E/\n+Zx/9jzLu3G/vSMBxa8e6ldw/R0C9Z8pR/DD+MM//MM30P7BwcEf/MEfPPfccz8K1G/fvv3cc899\n4QtfqNfrP26oKIrCMKxevM1p9+N+3I9fWly/cbUULKh5nVaTFQQp3Qhr1LI453Nzc0mSGAQd38MI\n7e3sK6mQgtPR1PfrRiikUDtsdMOmLHm33XEcC1NkDBgOhuPhaN3cY2XJGHvi/e9vtdsbG/ewRZXi\nrWbt4sWLH/vkJ2dnZ30/cFxnrjc7mU6vXr26dnvNsWyCseu4ne5Mnufb27uOHzTbbc4ZAHA8Hs3P\n9RzbSrPUtoPtrb293V0gZZ7xuYWlIs8dz82yEkKCMZ1M40k+mp+dHw5GcZrMz80CIMNmLc+zPM+6\n3TltcKs9A5U5fuIIVzrJsna7CyE0GKzdXZudnTt+/OjWllXkWTrOfIewEs7OdZI8TvJUG9FqhVxy\nIcX+7nYSDzuzrcowDmkwGO5KIfSMPH36zMrysufUWMnn5lubO8Xa2i3fDUXJ7Kb1+satu6vrknOW\nF6wofd9jZa61qtfqrWYLAFAB+DzPoTFlUbiuG/i+1hojVDIWTae2bVNKtVKC8x/WupsKJAAAHMfR\nWmutK0q2InUBAL7vI4SkFK7rFHlqMO10O1lWUC4NMMPRiDHBBQcAKgCyODYaJnFWq/s7e33JSyml\n0TbGMnTDKh9xaHGxLEslJQQgCAJKhVKGC2U4k0oKaRDFSkkhC8dxEEKO4wDGKr9AIYTruNSyoihS\nUvmeV5Zlo9GoCMyqu56UxnX9PM/n5uYODgYEO9Pp1HVdIYTneZZFx+Nxt9sVQhoDPM+HEGZZmsSZ\n7/ud1ozmWmu9vn6v05mRUgRB/c7t23Ozi3nCEEKU2ktLh65fuwIhVKrkpQQQIAAxhAjR8WR88buX\njq6cOnfu7De+/ty/+f3PPfPMM//x//7zwG8pQKOoyBJeNNnC4uzly3fCmiu1tC17Mp0cjDIv6Eg+\nwFBpxLFR9+6tkiPtMAw/9alPCTg9/9C5K9deC8O2X0Nf+/p//vCHP/w//+vf//ZL37n4ymXXCU+f\nekhrGMcFhDCaTgFGQeBzJgHUeZFnWQYhdBxne3u7amWnlFldXW02mxhjIURlWjkYDAAAcRy7rpul\naZU9qdVqlmX5nr+9vb20tMQ539raarfbcRy3Wi2McSWhdxy7wvzLy8sHBwftdrsoCgjhaDSq1WrV\nmw899BAw0BgkhG612kePHq3q9m3LAdBEcey4bthsTkYjixDLssqiUBrYlmPbdGu332029nb3Xc9p\ndlpBEGip4jhmJaOEFkXRajWGw8HMzMw0GsdxMjc3w1g+Go19PxiODiyLcs7zPI+iKAxrk0kkpciL\nnAtBqJvsxp7vhmEwHB0AoCreO4pjx3E4Z47nJUm6sLBwcHCwu7uLEPI8T2tdFCVEkFJLKTWeTKQU\nVbEMhFhLqZGuHqoq8QrBWAEAIZJSIYQhxEZBrjkCgBAshO50ugcHB7VaoyiFbVtZltmWpaQcDPq+\n7xujLEoti5Ys11ooqTjnjutoAKI4anc6SZIopSCECEBtDKUUIFRB+uo+1mt1ZXRVy1PB9UrCUyUE\nGZMQYsllJfNBkCRx/PLLLz/55AeffOqDf/mf/sqFEAIjtarXayVjnHPf8wjChBDJuBac2Jbj2pZl\nmdgYLW3bZqxEGLmWC4BmUty5c9ev19JcNGu9Iw+efOzRR2wH3bx1eWvzHiHwL//T/+N4dSOJ4iDw\nPaAEQEYyPoon7blud2YWE9Tq9g6vHL3+2utf+8Y31u9ufOY3fssAiCgBlBwM9ofToUWJReny8lLY\nqi8szm9u7d66vV4L2602LcS6FoIQ7KPtYaMAACAASURBVHhONFJXrlzv9eaWDi0tH14ZDAaO4zyz\ndNjz3P7B3mQYY4wbjabnzd5b33z55W8/9vjjNS+8cfP1g8Go1ew+/Mj5me7ceDxWQkJC8rw42B+G\nQR0q8/2XL/7Gb326v7MxHO13G6GDUTQeF1m+tnYXY8B4+dnP/qvtnZ3pNPr4xz8OIBiPx2GjeXd9\n/dGTD/780X6nd+ot7wz7N3+Zjws/dyz0TrH0jzv/HQ3yNif/ytHvz+UC3zPx092p99Ra/RSw/2eO\nN6B+1ZPvLUdv374NAPjIRz7yNiPU6/UvfOELX/ziF7/3ve/96AjvurjfruJ+vAdiMp4oI3Z3N25p\nHdZqdd9dXlw6tLx07MiK47mb21uIIIxxv98v0lxrgyAEEJZpmuY5tSwEoOs4nu1QhAkixpjZ3pwB\nqjfT00qVZTkajcfRZGauN9Pr3Lp9p9VpbO5s7+7tHT916tjx42ura/Nz87s7u/1+P/CDUydPIoja\nnY5UWmtDCCWUJmlclMWpU6cwxu12e3t7u9kMH3jgPCbWeBznWdFpNcJa0Om0lZIH/T4AYK/fP1IL\nHdulBmxsbsRR0pvtKaA8z213u3xfYmoVjLu+F9Qbrm13ujP3tlapZdWbIUKEMx0nUwO057mHVhYG\n+/sEgLIoWqQOIahJJy9qkELGSgCA67l+YM/0ujlLhsO+MWZhbu7ppz/oe35vZrbVbO1u71383stJ\nnGIEs3y4tnZbi6sW8Q4fOrq/MxwOB5NxXOalRQmhyEaYWq7SVYtvXvwwtNa2bbfb7Uq3b1kWREgp\nlaZpxepTSoUQlSldxZeWeRHW6/V6vcL5lZ//G+J/rTUhNI4TTJAUcjAYho2mMjBOEs8PlE4LViKE\ni4JLUaE/lSa5NlIKgSAkhEBgpVmGEGo0G3Ech4FPHEdwads25xJCSClFTAAAMEZKawCBVrqiIhlj\n1YVUkDVJkiKOEUK1Rg0jVFXyGwM4ExgTjHEY1rXWlhVqbYwGAGlKsEVJlmWsLDzPsyjJsywIgk6r\nzUtmgMEE93o9znlFWTebTWPAYHBw7ty5OI7DsMk5+yGxqRAWfi3EGFbNFDHCjuN4ngcxKHX5yoXv\nffa3P3/y5IkvfenO1SvXlxdXHn30sW+/9Eqr1RgMpju7497CTBAEhxYXxsPScwOltVRQSExsr2DS\ntbFREmM9mQ4uXPzOY4/8GrFxqfmN12/0D/oH/fHZ0+chyi9fuTTY7T9w7vF4ii6+fMm2LIRonqdp\nlsz0ujDL8yITXOV5NplMlNJhGColm62GbTl7e3vNZivLMimFZVHLsqbTaRiGSKnBYFBJu7kQzVar\n0uRXj0NVskBKORwOl5aWiqKoHPv29nYbjVBrbUxKKR0Oh57npWlabaFjx47t7++naVqv18fj8frd\nDQiR67pRpAghrVZrPJkMBgPPdwmhZVlSSr0g0FIig3qzs1ka5Yy16oExoGDMq3lSy6JSVuS57/vD\n4bBy+qw81cfjcRQljUYYRUmV4yhL7jpukqRSysrAghAspSzKMkkSjAmAqigzpYWU3HU9CJHWSnBB\nsaWxFkIVRTmZRkEtcB0nmUaMcwghE3xnbxciFDbCm7dvjcYDxRVEGGhICOaC23ZNGwUBAEAjhAmh\n9IelJRAiCBEhFBMAgYzj6OGHH9rf382y0hiIMarENUJwDKHjOIwVnU7bdZw8zwQXEEIAIDAAI1Ky\n8tChw0maYkyEkAAYy3YoocBApVUl768qerTR2ujKrbNK8CGEpBIGAqCrr7jGmBBEIYA2pWmRXr9y\ndXdn91/89m9/+zuvRNNpt9NN4xghCCH0PK8a3KIWQqgsyxqlZZkzxoySvCgLXgIA6g2fl7mQkhBA\nieP7jU996rGjy6fX1zZe+tZLa3dvZPnoE5969tixFdezHZsqRG2vhgHIGO/OtGbmuwyyZz72CTcI\nomiapjmx3KDeRMY+6B/8+3//Hz709NNPPfnk/mTwD99+CVNyyaaCMd9zF+aX4zR74R++efTICZtS\nADJeKKOVZSsMYW928fixBdfzhWbjybRWD13HGQ4Gg+FBf3+/zNMsS2ZmO2EYnjl79vbrd65cufzr\nzzwrhLx3b0ubl/cPDj72zDMPnDv/99/8xmQ8rvs+BrgZtqA2N29cb9T9UyeO3b5VHl5e0kp3W11W\nltAATMivf/jZeti4cu3CiRMnOp3uzddvUsuZTCdzYe8Xwu2/Bdv/KPh/l+oFfnKc9p4HvT/FBb6F\nzH8bYh/8iOL9Pb+eby/xeHcsyJtn+OOm94N2TPAtO+MdfU6lzwcAVFD/woUL/6gJX4X2jx079vaj\n/e7v/u4Xv/jFP/7jP2632+fPn4/j+Fvf+tanP/3p+6DrftyPX0m0G80omxw5fAgAMB2P4ji6fTuP\npsPlxbn9fp9rZXlOqaQBAEGsjVBKQ4SM0QgCCACE0HXdJEvTNMUWFYw1Gs3Nzc12WxOEPM9zPYfa\nNM2i5eXF/vBge3szzTMp5auvvfa5z32uLMu7q3clE4sLi7ZlQQOKsmRSFWWJEHJ917JtaXSj0VBK\ncqVWjhzK0hgh5LpemuVz83OYAFWWRqnxeLC0tMQlE0JbjsM5Wz60dPP65SRNgrpPLcp4ubQ8zwUD\nCPpBjdh22Gw6ts+LdGN7oz8c1n17a3vL9dx22FWKGSAbzdpotLdyZDFqBuPhSCiZZZkxViBtYRSl\nDcdzjDF5njmeF0B3kkxWjqycO3W6XvPjON7rb9++8/q9e5vXrl49fvxEI2zWUPi+Rz8gmIqm2XgU\nj0aR79dYKW2LWIRiDERZTKKswvmc8wokNJtNAEAlYq9Ms8uyLMuSMVZxffSHAX/Y9M6yrJofYIQq\nJ7zKzR5CUIGACiFYlmUAQJg4lEzLaG9vjyutlMaEZkUuhUJIcy4BQABorQ3n0hgFIXJcByGkpE54\nbNu2ASBOEiOFlLIW1I0yxhg/8OM408YAABBCJSuNAULIyoMgyzKEMYSQMVblNTQwtm1DCKrGfnme\nY4ylVJZFK2O5OI7r9bpSGkAAjMYIGq2MlhBBKUrGuFbSsZ2wVk+zlBBiOY7jOJXPmeO6Sqleb46x\nMooi3/eNNpN4wrkIghq1aFGUjuP6vieVIoRJybMsU0opo/JCXbv2+sHBYH9vJ6w1/uI//b//8rf/\npyeeePzqlesYo0ajM53kaVISVKwcPjweXrcplgB351YQ1QBrKRHGVPLMlEmj4d6+uTcY7iwfXdnd\nnX7t67dqNb/V6t68ubqyctgmdHAwuf36f3n00ScmI/byhe86DkrSoizZdBoJKYq8BAANhsOlxcXR\naPTwww9tb2+Px6Oy4J7nYYwwRp1OuyyLqo1FrVar9k+e54wxAEBZllVlRFEU7UbL87zxeNyd6aRZ\n2u/3q40BIVRK/tD00fJ9P45jjHFZlm88HVV6E0ppFEW9Xm99/V4URefOnZtOp1mWEEoOH13Z3tqe\nTofz83OVqaTW2rEwBtD1fGOgMqDRaEjJ8jI30PGNYqxMktiyrCxLbcfivGQME+IfHBwoqQgh0+kU\nY4wgIpgAAKIo9n0/y3JWMgShMVBwKbhEDmFlqZTUBud5TjAVQioDypLZtk8BkkJFUTyZTPoH/V6v\nBwCsDAub7ZbneRubm3bmHT9xfGd3JzeJbdtJkhPq1cOaYLJ6rsKIAGgqkK+1MkYrpRECUioAgUVh\nEHjnz5//6t/+V1ZqjEkYhpWshpU5NIDzwnbs8XhEEPJ837KsLE0t29LaaG2AAWHYZFxkWQ5A5cyP\nMSbGAIywbVVelRIAIKVURgsh3rDh+MEvM4IQIiWl1oZgBKFmTBCKw3p9b2/v4sWLn/vXv/fkB5/8\n2te+VpQFoqTkHADDORcAQAO44BYhSivP81wvyBlnTNRqNcJpnEzLosjzzHGp59pJnC+vhOPB9L/8\n9Z9gTGu+C6sGAgAaqY0Elu/mpRaSaQgpJlKq7a2d2ZW5mW53OBoLwQ3AQmoutVLA82rQ6O3Njcnk\nDDDq/Y8/Mp5GzbD20ouXe52ZMGxeef3W/PzCwsL8aDSMpzEmhCtoFIXAOnT46JmTjyNKBqO9tbXV\n/no/T7PhcEgIcV0LaK2UGhwMO+12nmVnz55+4cUXBwd9z/ODoBZH8fe//9pkNP785z/38MOPKCGy\nOPFcD0Ps2K7yJMuzK5cuScMbYSOOozCss7yYm5sP2+Fjj72v3x/W6mGv1xsOR3lejLZ2nnnmWQPM\nLwTtvwXq/5KJ/Z+dmf8n8e3PSzb/jsb5yTX//2xB7JuV/z9uwr+SqoRfJnT/J90f360L8uYZ/jiq\n/6cT///38eyzzwIA/uzP/uy5554DAHzgAx/4R7+nL7zwAgDgR/383hInTpz4yle+8vnPf74a7X+Q\n7NL9uB//bGN5YX4wQk9+6AnXc69efvXe3bvRZNLv71+5ce38gw9FybTT7WhlAMa256ssz4oUIeRQ\ny3IDIQSXEhG6vbc/TWK/XouiBGMcp8lgMGiGYRB4jWZLA72zszMz35uf723tbAyGo0aj2Z2ZTeLM\nttyw0eQF19IUkjUajXZnJiuLwXhiu1av1wHQZDkLm439/X3bohqYZrsZ+F673Xjt8ovDwQEAigBt\nU0SpU5RJt9u6s7pGqNVotaXkkIC5xZ7neWXBe/MzxMYGgbDVrAOEcWC7vuPWijyxHO/o8RMYS+pZ\nWqu6X59fWkAILc7PRbGTZlF3ph42XWrRLMswxkYbtxbkeT4ajYqiOHF8ZXZ2bm84mu3NLx9aWl27\ndeHCBUqtpz70JCXkkcceDWpeq92Z6cwEtudRd39/ONifbG7seFYomcAGTSYjXmSsEErJ4XhS+c9r\nrV3XXV5ertS5o9EoiqLKD7wSwFcQokIDYRhWPvaVvd8PYL8QlNKqhzbGmBCPcyalrLQAxhhMaVly\npUpKqJKK5UXJuBAySXOEidRaK0MIEUoiACA0ABiMMUSIS6Gksl0LIcQYA1JSCFzXJRiLqhOYAUop\nozUhRIMq6YAZY1XLAIRQ5UlW5R0wIZIzjDFCmHNZpTkqwTnGmFILY1JVd1fe477npWk6ngwxgo1m\nQ2mFMPA8DwDleHatHgRBLc7SamUQQlIoXnJqkePHT+7t7RUFU0LaNuWSG6iTLC6KAgGY54WU+odJ\nE21ZFiaWgPb29u6//bd/0mrUl5eWXnxx/dVLV8+eO/X0h5+4fPlalrO04HfXNh984Gy30/Jda+Pe\n2uzioThLg0arFjosa0iWWJZFLVyrI8ajCy+/ELRq//K3/9Wf/+WfjyfTO7evG6nOnz/zxOOPGWFD\nY/76r//aop5lWf1+v+RlGAaj0VgDmOVF3Q+OrKxE0WRpaQFjsLW1MR4PESKdbmtre8MY02o3tdaj\n0ZAxliSJUNL3/el0KoQwAGSDQRAE9Xo9z/PReOR7fkXXHz58OIoiSmllvFev15MkabVaVUV3VSgO\nIaxa7jHGHMcpy1Iptb297bnB3NzceDy+fPnyww8/3Jufu3792jTqN9stx3P7g0GtVkMEi1ImSUEg\n8H2HMTaNVODbUHEpJWNsb38vmo4d24WICMmxRMaoyiU+z3PLsqRQgkviWcIYo41ru/3BFBFLGZjk\nZcEFJhAhVHBBbFcKyZmkxJZSZig3xmBq/aCiEyCtwTSOjNGjwXDz3sbMzEyz2dzf3x+vrbXa7TiO\nO57jet6RI4d3tjcIset1XwgNNTJaS1F58iEAAEb/3fOkVloALaRgJV9aXlpfvxsnse82pDR5Xigt\nMEYYY2i0EAIiSCmBBqZZDIwhhEZJ6rheVXSztrY6nkw8z6u89zHGEECttQGmkr1UYh+EkJSq6sRR\nffF/oJdBCCOshAZGQwi1BAAYYIBt2UVZfPs7L37sU7/10Wef+fbLF3hRBp7P8sK2rDxJEYQIwCLN\nPNupeYFSuigLIRTjDBigla7X60eOHHYci1oIIrC1c/f69evD/bhI+Ux3LstjzjJWlJJrBB1sXFEA\nbLCSUmtFMBwND6AFH1t8BEIADBBKY0Qcx4vjgigyGk07zXB2toeQeuH55w8dP3L4yJG93X0EUbPZ\nvHHt1tlz5wnlt16/IQVzAotLojkklu/7TWBwf7C3u78fp4kUIsmK4XDM8xxAQHDoWNhxnL293ccf\nf2xjc0Mp2Zvt3bp96+lf+ygm1urd9TOnT0fJ5PVbNx9+6Hyn1bx2+UoaFQWXK8eOrt6+dXP1TqPh\nPfTQg5ZlXbt27cEHHwWEhr5/9NhRDfFwPMmyHCGqlL569frW9s7hw0cfOXP+F47235NU9nsAkf7K\nYfD9BXzL1noXLMhbsPrbkPk/D1T/j8Ybhvxf+cpX3v7MN6P3t4/f+Z3fOX369Je//OUvf/nL4E3a\ngftxP+7HLz8+8IEnxuOD48dWRoPBiaNH5nu9O7dvFWWhlGx12y++8h3kWAgiUeia3UCIGAgBgsJo\nrFWcp9S2AUbj6RRRqiEsSkYwtm13Oolc2y7LPEnjYyeONlvh2uptA8DS8pJXD8+cPtNsNi68/Mr8\n7KxFbS00L3nlibUwv3j4yJH+aIQwCcK6F9SyosiyvNvt1ILAsqkfeI1GWLAiSae3bt+Yn5+Zn+lq\nwaTK9/vx4ZUjx46tbGxvK8Mc2z1z9oxFLc4VAGZhYT7NEgih5wdZzgsGdvf6cbzG8vjUyRVqQaHS\n073TSTJ1LVcIpZXsD/YIQRKw/mgXQJ0mKWPScRyL0p3XrwAIOp3O0tK873v3NtZfvXqrVm/Ytjsa\nRgBgiNGlVy9prT3PyVi28/puGDSXuks841ubu0qAOEq1xJIJA0y9XuMWSNLYANDttjmXCKG5ubmq\nG3aapmVZVqL9yumwwrGu61ZcPQCAc56maUWDE0I450pLi1q1Wq1yVmOM2bb1Rg/zyqifZ1lFKnLO\nldaV219RMAAgAFAKVfUSp9QyWkEIjIbGACmVlAJD4Hh2HMfG6F67bdu267pKqQrGl2VBCAEQEkwK\nxjnnlmVXRGhR5AAApRQmGCGYZSmhxPc9CEGaZZ7nFkVBCKlE/kII27aUEm8wzwhCo5VlEQMchKCU\nMkmSTqfjOBZCxBhjO04QBEJrQnDFQmOMjdaCC06453l5nhOEi6IwUEfRtBKQ244DALBtB0KoTSll\nKYUUEgJjEWr+69e++tSTT+zs7LQa3Z2tPdehCGnbxlq7ZYm3Nndn2h08S44fWZnG17kotYTbe/00\nAZ16qIvCoVQjNb8ws3QoErI202tvbGzNza6EdSnY3dBzppNyMk5qHpaK2Q5eu7Oa55xzBqEZDAaT\naHLo8BHXrQkpkyS6d2/dsqxbt25VuZ5GI4QQCMF939/d3eFMQEhc143jGCBY1XFACJV8g7Envu9X\ndL3neZ7nVp7wnuc1m804joVg1YYJglolmK/67dm2Xf0VAGA6nXqeVxSFRe3BYFCv1z3Pu3DhQnum\nW+n5kzzFGNfr9SzLGo2GkLIouAJARsym1Ka+73vKxkk6HY+H8ws9alFjdFmWQnApRbUNyrK0LJsQ\nIqUihGZZjqlVZKXnBkVRjMfIcRwhZFlyAJRlW1prrTTnPEmSKqmU58C2HQthCHFRMAQotVA0jRrN\nOue8LMvNzc2iKBYXF9fu3d3a2hpNJzdu3/zAU0+df/ThlcMLFy9+FwAoci65QhBblo0Qqp4fMYII\ngUoGX/XeQ1ADhDzXO3Pm5KVLrympiqKs1UKhmNaqLAuKCTBaa81ZYVGbEkoQUVIqKSklGKGyLCHB\neZ4HQQAAqOz3AAAAAi0VspBSP5DqVBUxSCvbtqt2Gxjjqq1ARSxXooDKrR9jC2FQlhwieHdt9dqN\nG+cfevTcuXOrt24DYxDGlFJECAYAa1BlCY0xSkqAsG3ZruOMJ1PHdSAycRyPRixOJlwwYmmCYC1w\nFZNxPGqGdWQ7jOVaQQIdDF0tLKOFa1uOhZVkLFHHj5/48NNPS8njJNZGe543HI+iKG14LYhpURSO\nbU3jaZZNtWSszLozncfc4N7dzd7iEUKtjY07a+u38zT64Acf2d7lBiDOtRB6b3d3Y3OXK+15wf7e\n/ubW5ng0tAnBCDJW1H3fGFEyVuS5ZdF+fxTU6wVnk+nk8Sc+0B8Mjxw9IqUsy3J/f08JcfbBB/NU\nrK/d1UbX68Grr36XifL0uTOTySTOMgUBJARb1srRo3fvrt+6favRaLmu9/zzz1/6/qtLS0u7O7uP\nP/DoLxbtv2eI/bcZ/G2SAu8I+r79OO8BYv8ndOl7tyv5f+o79c9rQX6c2L6C8T/6+h0N+zMo+d/g\n6v/mb/7mHWXo3j7Onz//pS996Utf+tJ9rHU/7sevNhQXyIA0SkbDYZqmjutiRAkWWpua79drtZrn\nX7l+zachqlMmGAAGQpAXmcxUwVm3EQAMtZG+H0ihsiyXggNtdvZ2ijxNkgRCvbW9cfbc6UYj3O/v\nrxw5euaBc8OD4Te+/o2aGzz5xJPDfh8aiBHN87zVane6M6Xgru9LrbZ3drQGlNLBoO95bhSPiiKG\nEC6Fy2lerqwsWxQTAjutehh4lk2E4J7nGwAenj1/d/2ekrzeqsXTWEgthPz7b95UShw9enQ4Hr1y\n8dU0U9OoKHImZb640G01A4RNkkwd247j2CKEMfbIow9JwZJ4alu0Xg/KklFKtVZRNF1ePjw3Nz/b\nm19dW129uz6dxmFz1gfo+s2b+3t7loMvvfb9aTQVZYEQ+tCHPjSdRATQiKa6NJwpKYwSwLGtwHUh\n1FqJyVTFWQQxkqX0PK9er9u2XZbleDyCEEkpGmHDcZ00TTnnruvW63XXdYuiqJjbivOv+nJJKaVS\nrTAUnMdxXKvVADBK6YoSrDreV83zsJRJmpZl6bgeJMRorpUBALuuJZS2bWgMklJUzfCqNmBaK8Gl\n0tJAqJXJs7LTaR9eOZInSVFySuw4SYWQluUArIzWGpiyLFlRWpZlUQwhKMvC8zwglAEQQAOg0UYL\nJjDGQsiaH1TF3kIKi1olZ0KIkuVVksK2bWpRahGpeVWtDaG2bduxHa2U43gQQiWVlNK2HSGYlJIQ\n4jjOII4ppbqqZtZaKA2AzorUcRylpFJSaY0QgggaAIwBlm1bjsMEyKapBqXg6sqVyzOt2WF/oix9\n9erVoycOnTx14uULV13XH48Ho9F0rrdYCt5sN6ntE9c3kEEgw7AzSPcNwJyzdpN86tPP2M7s0RPn\nv/H8i0G93ZvttOsLGBmts+9//0qv0bFtf+3ultaEsdL13DiJ0yxvtjqVE5uF6a07d8KwPhgcWBaF\nCJVlmeeZ1gohaFn2ZDJxHEfw0rIIAKDRaAwGfcuy8jx3PS+PItE/qNfrYRgO9g9mZ2eVUp7nua5b\nFVBYlgUAoJQeOnRoc3NzbnYeEZpEkRCiKHKEcAU1fd/XWqdp6rouY5wxbltWUKtVDRowRq7naaOz\nLK/X667tTKKJAcD3A14UmEA/8BGhe/2DLImarRoi2GgkhNZaRJNJVuRBEFiWNY2i/kHfc1xq20xw\n23aYkAAYrdV+f88YLYRI08QYU5a5ENxTHsZECilYwctceT6llHFOLAtALLQq4lhqECg1Ho8Ixe12\nO8/zNM12dvbygh07fuxb335pPJ5Oo/gf/uFbH/3IU5/5zU806t7q6vrly9e0Vo4XQAOV1lproBFG\nBCGMsAYaaGMglJgiQmjYcKNoHEXjetjMUjGaDIOab4DJ8ty1bNsiQc13bJuXjDEGoEIIYwdTZgCE\nlFKuleM4EKEqZyelpJhAhITRDbfOGDMGUmr/0K3DqnZ0pecH2mBKKoRf5cswRhBSzjkmFGHo+l6W\n5N956cXHHn/fh57+0Na9DVEy13WLLAPAQIiUli5xlVRZlnmeJzhL08xynEOHDo3Go/X11dFk1GzW\niIUR1HOLHQ1MkTLfc/KsQNAgAikmBFOEKAQEGiwl01QJATUUvYX2Mx/76Nz8zPXVG0xIpVWeZzdv\n3iqKwgHs8GIvi4cGqn5/N6z7tZq3ur4221vQEp09e9auNa/efP27l77b3986fmQ5jmJCKBMIAkgp\nTdJ4a3OPK53mxdrqKqW07vuQkCLPo8mo3W55nhV4/u7O7uFDy5TizszscDTZ2NqcWzj0sWc/xjkL\ngmA0OpBSvfLyy4eWDz/44GOY0DSJyzyZmZ+5ees6tpwbt9Zm5xYUxLWw8dSHntJKXbz4ynA0euqp\npw8GB3dW11YOr5w7ey6oBUrp9zi3/4tGwj+jY/9POM57AOq/oyV999L+v4g79atZkLf5uJ9lJr8w\nzv9+3I/78d6Ive2deuhv3FvHwAaK3lvdNhzbJFCFMFx2gsDHxDN4OhgAbmxqUww4L1iZK6CDRk0Z\nTm04ngx7s914GkWTPtBQCoGMQQZ1m61Ot1OvB9eu3phfmj9x4mSzGe7v711/7XKnFkqusNIWpkVW\nKq0tzwtarUSw0XS0s7etJNjd6T9w7sHBeP9bL/49xhwAWTDxwLn3nTzx4LVrV9ZXby0vLhw+ckgr\nFiXTdC/Osng4GqV5lufFdBphDKPxoCzKvGCUus1m66kPP7Wzu/mtF14qShYEzdlOLU709s7g7vre\nzq6zsLB4/Pjx6TRaarTSJA6beP/goCzzPMss5J49/eg0Ghlj2p16UK91Z7o3bt569fmvZQXr9mbP\nPfa+Vmf+hX/45oVvvxQnY14WeVF4tm1hmhb5+GC62F4WTJ4989janQ0/lWVeiFym00mepkkcl6LM\ny8xAaDl+qxVggxVU0+kkTVPLooQQ17OYkIwz13cdz3FttyzLivwEAARBYBOipIziuMgy13V9x/Fs\nJxFMchZPFaWIEBshZJDhJUMI2dRyXRcJXjCGpBYKAKUtyyXU01rnZSFEgSHkSlCCIDBKGQQBIlRr\nDKHxvJBa1EBDbatkenN7v9fuEMvdP5hgjMNmazye5iW3XQdjfHAwqPmu5Mz3HSGEYEUBdb3enE6n\nvChqtZpUUkhOCLYsopUy0ORlpbf8WAAAIABJREFUbjlOVuQ5KzHGjusLqYFBkknHoVkWOY7tWL5S\nCkBi267Wpt5oEkIsy1Zal4I7xBGqMFJpqRIe+74vhGCsnE6ntm0XZYEQwJgURel7HjCGFZkBwCiI\nMV5YWLizukoc13JwGFiTaZIlbDKMu62eE1hpHmml+v1po0GWlw/t7OzUnHB0EF8Ht2fm57uz8+sb\n23N+rduZW1gIm6He2HjdNaTIIi9PO22MqI6iceD7WZbt7uzvbO6UWUYg8B2HWRhpTIC7ub3dbHUo\nsSAk08kEI6SFtCidxmPbthBQCGCLkP39PqXEdZ04jm3b1kBBbAw0xMIWotPpVErmujbGSCl9b31t\neXl5f38/iSZzc/Ort+6EYYgQGg3Hc3NzjLE0TTHGWZYjZCCEQRDsHfQbrbYb1OL9/Xq9XjWZz8uy\n2ekMhkPPdaMopparDBxOokmS1GpBnuf1wLcQNpZDEBJSGgRnOt2Dwb4oJSEkSWLO+Wx3ptFoLy+v\nvH7zmm3btbDd39/Li1RyMZ5EaVbESWY7FsSYSeYCz0CjjMrK1NJWXuRaa6PAdDxsNpuMl1IxDVRW\nJI7jlgxBoBxKlCi1Y4XNZs441brpeeNylA527KWl7d11reX8wrLt+lGcMl5mu7sG4ZMnz+QlG40n\nGJG/+7u/275343/5/X9z4sRhY8pXX3utZMz2AoKsslC24xcFr9s1wwUwyg9swVJKcZpNf+2jn1xd\nXXN9RwgOiQZaC8G0EhgYBNT/z96bBtmWnVViez7znfPeHF/mm6eqeq9GlaRSUaoSEoNA0DSiwd1B\nmMBtO9ph42gIh90R/uOgf/UP46GN2w5DhJtuOqAb0RRCSGUNJZVKqulVvZf15iGnl9PNO55pz9s/\nziuZNoQEQhICcv3IyIxz78l79j55b67v+9ZawGIIgJYSYUAoktI4pwGEkBKppEPOaV1pSbgQlZ9l\nEAQAQedclpWMMa2VlNpaKIQIQqaNghB6XmitoRRprSEAgDiIgDPGOmeBgxhIKT3oWWk9Fly7unrz\n+vUnH3v0xU/9+63xJqN+LWmURcmzzKfMOg2Rw5Roa6IoQVhlWVZkhed7p0+eWt9cK4o08n1ntdEu\nSZKBnTLPR4gIIYKIAmyUKVkAkO+Q1T4FYUjH6aAz2/7Bj3/kAz/49Ls3ru4cbCNMocOvff2Ny29d\npsgH1mTpOAxJGOJ7m3c6vc761kaQNKdFaQ1cWFq59M5bl668rUuzMn+i152jFNuCWkOdg0pl71x6\na2c786NQqHxxvlNL6nEQZ9MUW0MRBMZZrUshKsNOFoTjcVqv1QeD4e3btx994rEszUaj0Xg6eePS\npdNnz/zRi58GGswvzPkh2e2nSa2+ePzczlATvxdG3cmwOHv2fK/TfPXVL+1ubfydn/67t29ef/Ot\nd1qN5sMXLmKMHYTSqO8i2/8rb+x/BznSd5Zu/W2T6/8twV90p/76rdI3IeqHHP4QhzjEdwL1pIaR\nQwArJY22tbhVS+rO6cFw12jlM09L2evMjIZ3tFZREHgMYwQ8nwKMSqMGw4M//PQfFkUWeMHWeF2U\nPA7ChaXFrF4zxiwtLTQaDQfszbs3hOCtZntnd+vSW6/3erMLvYUsL15749WHLzw6no5G6WRpeflg\n0n/lja865KQUGLBzZx5ptzrXVt/J08nZ8ytbW+txGCwvH3EO1GutIAjW1teuXl8dDva7vZnd3R1C\n4Gg4nIzHURTNzS/wPFNSrqwcbTVnlleOzs3NE0rG0/FzH34WI5Zm+bkzFwaj4Zde+eza2t0oTDDG\ngsvuTI9gfPHixSxLb9y8jglbXOrM1Dvj8cjzvJWjRwhBk2z8yldeGU1TIc3s/NLxE6e0AdeuXs2L\n8qELFxQvEIbDg4P+3l631Q3DCAFUZjIMky++/NXpOJd55owBUgPgEHSEoICEzKcQY4QRdlgKUfA8\ny3IphTHK8z0rrHUOY1pVcff294QQlJDKhN86poyp5rEZY57nEUpLnhtjrLUOWAAJY34YhkKISvOv\ntSaEEkIJIRBpCDFCCEAArLPWOguAAxBByigCwBprrXIQVpNiCOFqCpoQWksYglQKvbaxFQaB7/se\n8/vD0XAwjKI4CMMsy6rYP2sMRohrDoDDCAvOKzF51bF3zlYf0cYaCCGAMMszbUyz0XAASCk9z+N5\n6RB0jhijIfCdc0EQUepbV7X4POcchAghyEtusK6k/troytXMWmO5lVI65zgXlCKMEYLQOaeNgQ5A\nAIIwzvJsOBzWmnXgXJpl9SgyOpqmcjJJ763dS5K4KDKMab8/mIy57/tRGDoF0qzEflZvKantcDhq\nNDqj4XhwsPXw+YV6o7PXX2s0mJVqMNhfvXopLUiztci8aHNjmxE21505ffJ0t9Ndv711795GUZTd\n7qyydjJNh8MhxtRaC4HL82xnZ9tndDjM2u12lmWEYONAnudCCN/zR6MRpYRSkmV5rVZ31u3u7kRR\n5Jw1Rjca9SraMMuyosiMVf2DPqUUQTgajyillUJeKamUdM4xz4MQOQvCMPJ9vxLtNxqNoih2728v\nLS5ub29DiNI0TWp1CKFz1hhDEBoOh7BKoZNKSkEwbraatSTM0nQwGNRqNSHEzdu367XoyOJCHNeE\nEOsb29PpSPAcIpgV5TTLm1Y3adNYo4EreFmWJcYIPHCZNJxzLbVRSmtprXHOOGetc1KKEjqPEoQx\nAE4qVXKhtMrLwg8ChLExeppNG43GcDhEmDlrk6Q2nqTTyfSNNy8tHlmM41qv19vr77eb7Vs37/2v\n//x/+7mf/Zmf+eRPr6ws/96nPqUVV1ZBxKzVQRQYq6FTCLpBfxszh5j/j/7Rf/qJn/yJz3zmc29f\nWn3t9bchwggjghHAzGOeR4nVGgAArbXAOucgBABCay1iEFsMKj9M56SUUularWat5ZxrrTHF1hjn\nXKXlAQBijLkoMELUowBYa031dACA066S6lQy/kpsoJS01oZROOgfrK3dfeKxxz720Y/877/+f0YB\nMcbU6jVoNOeF50cQQedcmmbKWIypUopzXuzv9WY7nXZrq8yGg/7c3CymIC0nWTFlJIIOSFUCSaTl\nwhYacoeFdLLViAkxvWb3qQ8++cxzz7595fLe/n6Rluv3bt25da+/OyCIBTSwWhdFThE21tSS2Dlw\n4uTJQhjjSKvbefkrL69v3i2ycbPZnpnpIgggBForShkAdjwZaCuSJEwajVZnuZ7UCWb7u/3xYBRG\nUaNe57ykhNTrSZZPV7pHlbHc14RQ5dLNzc3ZubkgDCCACJLNza0f+ejHrq1c/fznP3v8xInjJ08A\nYP7ojz/bm12cTm4DB6bjtJYEK8vzu7s7eZ5+7Ic+Oh4Mvvzyy8vLx86cO2ctVFppZygl30W2//1J\nvb6JM/y3d+iQ6v/lV/5bdrO//4nxt7dTf80W5E/r9v9/E/5/5qFv2d4/rBQc4hCHeA9lUQhdYAoB\nIAjhICBaKwBhHEdam1arBSFaWlo6GE4QpM5qihkjlIsiF2KSp9QPppNpvdaADvrUX1k5yhCq1+uM\noI2Ne5gsYOqu3bpRayaPPPLItRvXr1694oBiHjPQxI347t21h9AjP/yJj336M5955bUv7x30J+n0\n+eefn5+b7TRnjywupdP8/vb2+vp6mh8AoE+ePD0z05GyCELvqfc9qTS//Pala9eutDrNDz37IaNF\nPs2Gw2G32z116vQXv/iFOA4feuiR+fklq13gx8bpOIyjpZD6wd07a+++++5jTzxxZGll7d5GFCbv\ne+p9Spl+v78wNy+5Dljw2IXHwsiHwE2Hg8XFRcrwzs7OcNinjGKIec4BpLWgMRnlW1s79Xo9CpMy\ny6JGZ2lx4f7W/dmZhYX5haWFJavhxsbGu6vXtUailDzNgdUephgCADFlzEOokJZLKYWgmFlnq+aq\n1p6UPM0yY7TneQgTJZVWFhNMLEEAOegghErKUmuEUPU5VRn1MUriOK7oEUKIMf+BuzchVR47FzKM\nY+sgodRZ4ByEEGotpJSYID/wjdFCK+sAAKByJqssA5xzWhsADCFEG52mU8HLOIq0dv2DQbPZdM5l\nWRbGkYO25IXvUQABRAATrJVCCCGMeMmBdb7v54J7vocRssAgjBkhQklCEKIBLzljdDqdWm1Djxkt\niedbazCilHqcC0pYq90BDmGMWRBmWfbAKaAstZTW2moAvhKuW6elVNY65xzGiDHGeVHlSmilCMKY\nEGMsIdQYAxGUUjLKEMaVeUEYhnOzs81Wc3iwb60zxmUiNcYwSkohEHRFXu7t9yEivh8ODoai9IzO\n7rdYWXBCA+icktIohSAYDvbHY240Wlk5EUfJYnd2NBrv7+wr4bzAl0ZBzFRZplnqnGu325WjQlHk\nnJfAaimFlDLPc8/3KzfHPM9bzRalpMrei+M4z9MqUC1N03q9nmVZq9XiXCCEyrJcW1s3RgshIAQY\n4yxLhRCVh6J8b90IplGSCCGtdX4QcM6dc5PJpNPpjA4GRV50u11C6HA0YowihHw/GI0HsR/EUYQx\nNtaUZSGl9CibTsYO2JmZDkLw7p07EIIg8IUU6+sbhDgAnLVxFEVCFgBChLGQYpLmzPcAQtaYohSj\n0TgI/G9kN2KMAQHQPiC0mBDsnLHWOqO1BtYCAK11igtlHIAEIqaUdg4CAKeTLEkaoWeKophMJlIq\nqUyeZ9qa9XtriEDGmEdplmaBl4yGxa//+m8+9dQTP/QjH+v1Fn7jN37TAhQEflkKShDGyBoVxcEk\n5bWk8au/+j88/tRjrdbM+vr93/vUiyeOnTEGUQIEzyGwjDLnrDYaQQSMtcBWsgjwQGmPlOLV5VT5\nGsaByomTMVYZc0ZB6Pu+MUYIboyy1ljgAASVjgO8J9evanzfeIenhOjKuMEYYwwAUCr1xuuvf/Qj\nH3366ad/+1//tig5ctA5RxgNUSiVss5hhIIwgIRIoRBCURQZq4fDQZpPhSwRcsZK7eykmIzzgyjQ\nYRB6AWMM1MOYhCBIyLMvvG+ms8gY6PU6hNFWp/3mW5cOhqP1e5vbm/e3NnZVqZtxgyLqgFNWGKOY\nH1oDavVWozuTcXnlyo3ewvxbl97UUgOol5bn4qgWhcF0OmzQGqHQAqCtotTVahGGuNNp9eZ66SSb\njEbpNGt3OrVaLQz97e37jNFer9cf7NVrdc8L3r36hU5nplVvZIUoi7LdnhEl39vdn+3Nr65ee/75\nF353Z29vZ6fdaT32xBO3bt3d3t5cWly5d/tu99iJOPKdMWv37h4/fjzPi7X9/aff975jx04MhuPp\neGKs9QLfaP3dYvt/tY39by51/ibO8N/eoe/UC/vzP/j7Vtz+ba/8n8lvv68u7du76m9y6E8+4K/T\ngvxJ3f6fpvF/5qE/k/D/yR//3Kic9r9n+Ja/7jDi/hCH+M4iL7JpOg5i3/diRhkhVCmljWg2O0Ve\nRlEyHo9rSb3b7RY5zybTIh2XRcmlEMY4jC8+cSoviyhKlJQYk1oUA6vS6dg5DZHNi6mFMo6DoydO\nbG5tbWxsfujZ5xqtuN1sOgCsMTt79z/14u9+8mc+mebDvf5WmudZNn333bfbzedb7eZ4Mly9/O7e\n3q4Q5d7O9PiJo1EUA2OdM61WfTTecxAcOXrksfTxosgIIR5l6SSPosRqNzoY72ztLi8uWOmwpffW\n15Ram5+fPX78aFZmOzs7FOGl+XkM4DPPPEuYd/nyarc7t7J89P7WFgKwf7DX63QwRgcH/UYjnp3t\nKpF+7atvam2OHT26vb8fh/UnnngKIG80yK5fuSmk5oWAEA76Q8aYEjoMgwuPXFxYOAIhAQZIBZzz\nx8PpQX+wmxVZVpYgd1qXeQ6cIxgJKxDGzGO8EBhhz2eE4CovjTErpbQWKqEAQFEU5UUKoXPIOmOd\ng0IYQkgYhhBCxUXF1pjPavW61tpZWBWsHQTUYwUvs6JwAAghuDReEFHfM8Yqpd17xmBCcPNedJ81\n1ihNKYOwarrDyjkcIQ9CUBQF5zwMI0zY5uaOMVpppzT3PE/biomhsBZyzn3iQQggAh72oAWVop5S\nijkPKDOUFEUGgUUIeD41RmHrMIZSlsAoq01pFQAWQyc4rzWSTrdbFjKOa73uHMLUAoAA1tKWnGMG\nIABVU1QpVY06E0KkVFJyAIAxAABbOf8bY6qvBBOMMahsz4HLyoJQGoah5iKJY6l4URScCwhhvd7a\n3Nyq1WqVUYK11mMhIRAAMBpOKPN9P/C9aGlxMY6wHwhnMmN1nh9ATD2/PHXy+NNPv5DmVkoUBvV3\n3rry9vZlI1QcJcDhJGlM01RKkaZptY9RFKbpNMuyyWSijZHSRlFUGRkqKSGpihHA9z2oUZqmaTqt\nZvspo5SGVVQeIYTzEkJYlkVRlEopxijGwBjlnNEaf4N5AoeiMKyuSxutlFZSWgsY8zEhPiU8L6y1\n/f5BHMe9Xq/b7QIIlVKN2kzASJqmg+FBmk09z/MYC95zTzBWp2nqe14cx5wX1gCjTSGEsRJCAJAD\nMJpOpxUd9TxPSTWdplEUWWuKosjzEgAYhT7nwkjtnIPWVlkVAAACIaSEWGuAswBIawhmVYiEVAoA\nCyAy2hnjACTD4STPU4JQFNcwQZYbAK212qOeH3qzs7OrV1cbjYbg3Cjj+4F16vc/9cfXb9z+z/+z\nf/grv/zLv/s7/3ZnZwdBzah1VlPflmr6iZ/60V/8xf945djywcHg/v3dq1evz/YWiBdZCTwAndHG\ncqm41cgagxH2CQMAVKU655x1TkullTLWVmwfQhgwv+r/VykA1lbDLwZChxAghBCCtDNGG6UcpRQA\n6xyobob/gAUghJyr6nQPFP7Mv3nt+ttvXXrmmQ99+PnnP/0Hn4YISqGNUgQC55yS0iKoHeRZ6iz0\nPX9/fx8RCDFkjCAcAGjjWtSer7VR88jy0W57vlarhWFgTRHELIx8S6SfsGk54OP8ztaNRr0xv7B8\nZfXq7Rtr16/edBIFxAtpICcCUWiIDmssCmmSxO1OG2DcH4wuX7sBKL5z5w4AjjLUqrUIRcAIwYE2\nEgJjrZSaIwuwVx+NR1vrU6HsNM14WfosbDYai/OL97e25rrdE8eOXr+xOhqNJBej0WhhcVEptbu7\n+/Ajc0mtORwOa7X6zEyPMdbf6TPMluYXHn/i8Xtra7O9mdde/9p/9Uv/xW/+xr886I+kKM+cPZlN\nh1k6XlpcMEamk/TkyZMIUkJIHAW7u7u37tx+/PHH2Xevt/9XO8P/LbnQNzdL+7ZP+y2f8hc6w3f2\nlXxvlvovs/Lft9f4Xd2pv2YL8i1p/HfkPN8f7PpLX/rSIZk/xCG+92+nQRgySiHEEOLKAooyjLCX\n5XmjUc+zvNV0CEBCIEau5LzMcy8IOr2un9Q8j23v7DTqzTIvsmlqhVhcnMMYJLVgbrGd1KKkUcOE\nXb158+3LVy5efKzX7U0n46vb14ejEYRuMBhsbd///OdfWn131VodJ+HcQm9pceXEsWPAuo2N9bt3\nboWB9+QTT8YxWzyycOTISqvdLouMel4ch5N0FCXxww+d39raFEJgiJ754DP9fv/+xqYxZjpNzz70\nUJYWW9vbuzv7lNJWS00mqeczo+wrX/nq6VPnuJSt3sz7n/5QEjW1skoqZ2GaTkMWKq5oGCz2FhAG\neTH0A3Lu7NksK7I8X1440u3OAuy9fXl1MEjDIDyy1IUQSi2TOOh0Omk2CcMwihJe8vE4sw5yaagX\nJDVYpnkYhlbpIs+EkFprKYXWShrh+z5hJE9zhLBLbTW4K5XigkMIKWEwIAihyTTVWnge831fcI4x\n9pivlAIPDLgRRgiCB4VvpbTHfM/zKrroeR5jXhzHxhguhJSaOYBxNQkMrANKKWssRggzz1qlnUYQ\nIkoRQlV7H4D/j0gYo7U2GAMh1cFglHOOEVzfWA9CNj83PxoN4igCwHo+FdJ61FfShkHAmFcUnFGC\nMFZSVVmACEGttOd5EILAZ0LY4Wjk+T4EjlCsldTaBZ7noDHaIEhqSTsOLWU+Zb5zgGAqhAAQAuCs\ncVIpYBTnpda6sig3xthKIwCqUQVYlgVjtOqCQgiBc9ZaCCwEgAvOOU9qNc45sm52tjdJR4zF588/\nNJmOjhw5cv/+tlIaOIeQMUrX41gaCbX1yAPzNmBhmQteKj8QCwsLa2tDRjzJtXO2XovrtfDqtdW9\nnanRmOEAGjA7O2e0sZbc394eT6dBGKVZSqmntJZKGWNLzpWUGELP8wjBeZ6HYSSVpAQXRYEJZsyb\n5tl0OqWUTCbTyq1QCG5t5Stnh8NhFEVlyZVStVpdKclFjiBGCCslk6ROiOec01oB4DsHnANFXkRx\njDEeDodSSihl0KgrpWZnZ9M05ZwPBgMv8OuNBsFoMDhotZqLjflaPR6Px1KIsiwZpVprUXCEwGQy\nqcouvueFvl+WWVkU2gClZJFzQpAxTkqFEAqjQEiZZaXvB1qbohBaG22qMg0ECCkhjNLsQXnCVQb0\nBgBgDXAOQKwdwBB5ni+lccAVRVGWZaXgGI/Hw8EAQtfuaEJYWZYAQoTR8vLST/3dn/rMH/+x57F0\nOkUQEeynGTdG9eZmt+9v/9qv/donPvGjv/wrv/Tqq1/9d7/zO87kR44sPvPB93/4Ix8+c+Y0QvAL\nX3z5xRf/6M7dDUbjWtIaj6ZGY+Z5RkqErFIKU0Ypc9YCCBBExtlKbAIg1NpCADBCVS6GtdbzAo94\n1jptDAAgCALPY9Wd7JwFwCKEGELSOWudMQ/i9xBi732DqrkeKWX1/YOal5LYsOlk+spXXnnooYef\nffZDn/vs55wxXuiNh4VQKoli69zBwUGnMxNHcXWH1Bs1bWW73Zib67Y6dUyAHzLtcQU0cozhqCy4\nNVJI6QqNcDVMhK2hne5iXqRGupc++4VBf5qOi3bS09wi4zBAWiptBKlh4hOlBGFUGXDzxvWb9+62\nZ+dKJaMo7B/s1uLQ9zGAwCHknImiQBklpQQQMI9GUYwh5iV3xsVRvdue7XQ6yEFGSOD7J06cKMrM\nWiuVbLVau9s7Sa3+xKOPfe3112/euHn69Dku+O7eXrPeeP/73v+1r36Vl/zSm2899djD6WSUJOHg\nyt4XXvrsxz/+sf/l1/55EgXOqFu3rudZGgR0YX6u1Wrt3N/GmG1sbJ04eXJpYX5rY51iXO3Ud4Xt\nH+IQhzjEIQ5xiEP8hUApDagvtQIOAoC0toRgxigXPEuzVrOZZSXGVCmJEMAY1eu10A8opbVmi4TR\n8GAAAGg2mrd2bw4Gfe57p04d06YQsqQMjtOJHwdvr66+886V4ydOh1H09Vdf397c5GVZ8JwwBKA9\nd/7s5sZWOplcvHBBW9tqdU6dOuMxbzwa59l0ttddfPzi7GybMjgY9DHCWqpBf1Bvt7wAx3EkhXQq\nWlpa2tjYiILw4OBgrjcb+eHW1hYX3Bg7v7CwubnbbDbb7TZwMMtK3/cD3z9/7hwh7Ma163dfeoky\n8vD5h/Z3dgPPX1leIpDs7+4EHmWUjUZDZKC1IJ2WZSG6M3NhkPm+Nx5P7q2vl6V67NGHx9P0zt17\naZrVG40oCqbjQRCFgue7u8IY4Cza3N4z2vb3B7pUxbRwWgMAtTbOOUyJUxIgSBEFEIwnY8WV53nW\nWqUkxhhhRCnlpcjSaahso9EGiEAgkyROkvr+/r4QPAxChHxrrdYaE+L7PtBGKiGlzLOCNpjv+2VZ\nvjcY7AEAhBAYoSDwCaEQIIIRgkgZq7VGGDnjqsBzQhDFHnCwohAAgKpDWM3GM+YRUlUK7DTNMMZC\nSmBNGIX9/r7nMdNu5nnanZkhhIShn6nSZ14QhHleMEZ9Pyi5qDrDzlnoYD2pT7JJkefM952zlGAI\nnDG6WgrmUcmF5we+FxNEDbAGgLzkxliPBWmaGmMhhFX6ADBKKgkghAAqJbWWVXsTQoQQIgRlWcq5\ncdb6vl+lmjvnnLX1WjJNp5XEXWtdi5M0TXu93mQyjqK42+ts3FtvNBrWWiVdEITWWG0BhBhApJQx\npsTY1mpBvz+AUAeBXlmeE8Ihi5TSRtv7GxtS4jj08VxkNIGOGanLokjiZJoVw+GIKwUgghAKziFG\naZZKXvKydM75HkuSOM8zKUW1jxDCaToBAEgljTGU0kq4QSnVWmujJtNxuz3T7/cZY5zzLEubzWaS\nJCXPpBRxHAEH+/2DKIp93wcAFq7kXFpra0ltt7/nB0HVW67VakWawfey95IkGQwGWsosT6XmrXpr\nNBpMp+MwDFZWVsIwmI4n4/FYSuGAk1IgBKaTLEliPwigNe12azhCEELPI/3+/nQ6BdAY66wDggtM\nqXNQSJHlpZRiOk2lFAASZy2GLvADY0xZcoKJcxATDBAACDrrjDHWIUyQUZYQSD2mTAksLPK839+z\nxmRZnmV5Ne4xGBy0Wm0huIMoCIIwDP7Vb/3W9RvXa426ciKIIiVdGMVFMYUQNBqNyWT02//6X968\nfvnv/ewnH3/0vx+Pxj/28R+JasnW9v0X//D3P/fZl7Z39ybjXGvEmJkMOfFiax3PizimjHoIIYYJ\nBDAvS6cNItgBoI2GCFLiQfggFNNoTSlljFVT/dYYgiljzPd9CFz1J4wx8jyPMVbwnBBcPVFrDSGE\n8MEQ6zcKW0qpMAyrBxBCEIC8KBAhr33t6x954YWzD50/dfrknVu3tdV+6EPraW0ABNba8XjMmNdu\nNbTWcS3SmimlLr391v2djZIX8/O95kKtEFxyR0AAAGrUIoAkAOrYqaMXHjZrt9fHk/zcubOTyfjN\nr78dRg2kaeK3ClliYgB0CBgWIYKAMgIYtrAw353pvfLKq2ubG935BYSJVXJ9fb3RjE4cW2606hub\nW0pqzmWjmRhlqiV1zs10eo89noT+Vm928dTp0/1+v8yKwWAQeN7sXLcosrX1uxcuXjBONxo1IWTJ\n5VNPPdXtzW5u7dQbNa+NRTbgAAAgAElEQVSUk8nklVde+bEf/bh8VNy4ee3tS2+fP7kyHA0YIceP\nrRzs74wOFv7B3/+5jY0NLrJ33rl0/NhKGNbjMO4frFsLdne2wyDcWF9XWn/4w8+5P9VgO2T7hzjE\nIQ5xiEMc4q8MUZRYpxFBRmOEqtBlAAEUpWo0Goz57XZ7Op1mWWasCTyPYsIaDEGikHPODofDdm+G\ni8JYSSjEBJQivb+ztbu7Ja08//D5zbfu37lz78KFixDRa1ev7m0dBMgLCD568uhg3J9b6EZRcPfm\nnXqYvPDc89NpZpRlyBOlMEqNBoPF+YV6El9dfZd55MqVy8+/8IJz7s1Lrz/9gQ8GUR0jTAhhoR+E\nwWg8oYRMsgwREgch9Zkyauv+VqPZWlle2Q/6tVojTSebG5uTyfDIkUWECCUMIuLXa2+/fenll19W\nsnx39fKTTz3++IXHKUGEoi984aVWq0UIubd+b2lxvl5Pbt64TSnp9XqTySgM/MWlRYRVuxM7MB9H\nNaU059zzibEWOgwAxAjmhYh8tr29K4o8H3GKiDEmzzKttTG2yPMg8MOwjjCglI5GQylVNbOtrfEC\nnxCilXEOUN+bm1ucmZnt9/cGQ8GlFf1hOs0dsP3BEGMcBkGRF1EUAQA0cIR6RrswDBFCWZYJIQmh\nWuuyLDHGURQZAyzEFQmEEDHmKS0AqEhCNdftHIIQImAQRBa8p3HDGEGIrDVVZ7JqTkZJrIRUCjQ7\nLQTgZDput5pS8F63G0VhkWVaSIxAp9MSQoS+7yCCEPg+ldIgiAHCAIDJZDyejKIkQQhprRFCQggA\ngO/7vucJLgLfdw7WGk3s+ZEfFHlptNPapNO+UgohaK0tygwCYKwVoqxoTzXvjTFSSvm+n+dp1e4G\nACAItdbWWosNowxApI1BCEGEdnZ2klpte3s3DL2ZbosQevXquz//8z9fT5K3Ll0qS05JkOc5IZRg\n4nnUQk0oNgYIIbV2k8kYQNVssNfeuBz6/mQ6mZmpC66DkNy8eX001tkUaMUoDtvNVhT6Ozs7+/2h\nQ8haS6hnbGER1FqiwBdSCCHmZnuSlxgjrbXv+0KUQkrq+xBCjLF1SknJGFNKIfTAwywMw9Fo5IDW\nRvrYqyW1yrBQaxkEfkUUCaa9Xq9ajcFgMNPpKqUmk4kOwyAI9vf3T5w4AawJPea0Z4zpdDpr9+4u\nLy836rU0TYUSShaKiySJpOT3t/rj0WBxcbHb7cZJuL6xHtKgUUvSfKq1Lstca1mPk6IoOp0ZSomQ\nhR/629v3p9Op1gpCqLXe2twOw9D32d5uH2M8mU4RQs1GkOc5gi7LhXMaE6aMs8B4GDNMIUTWWYip\nVRpYhwgejIaIYGescwA4M+jvVUIGz48IAcYYKZUxmnkUI9Jud+6t3d3d2W23277vGSmQAxiDdqe+\nHM/u7Wxk6bgz0+zNtM6cPjHbbbU7jTJrjSf9f/O7/+rrr7526/atcZp7LCi5jqMWhmim1xoNC6eN\nNhpA5nnewcGegDT0gyAIGCZCSWstBISSB14JnudVt6KSMvR9aRxCSCnFGMMAAuukFhAijDEAIAiC\nasswxtVcD6W0kqVUApMHFn0QIoyrMlBVBfA8z1noIBwNBp/5zGcuXrz42GOPvvvuKoaIEAqtNRZI\nZZutmFIGAChFEcex1ERrbYwCECVJI4z8OEmI9VQuVAEMsASTnfFQ6XxhsZ0PyprfEKm+/MbV6e70\n7/9H/2D/zrh/MPFZCAhW2EHqrORSceMM9WnI/DAMnn766c2N9eFosrS0AhitN9v5Hm+1W48+ej72\n6Y1bN+7v7DuDkqQRBjElUnINsfJ9Xwq9vHS0GNNxOr106c31u3cZY1ubW4vzC+lkNDfbu3nz5n5/\nr9VsaK1mZ2frjfbmxgZj7Lnnnru7tu55ihB0/dr1zc21k6eOJ3G4t7X59pXL73//+2/dvOHHUZpO\nX3/taz/6w58Azrz88pdnZ2eXl4/cvn379Okzg8FoZfnYBz/woZs3bl5ZXW0221EUIYTQfxh7fcj2\nD3GIQxziEIc4xF8ZrHVSaQcdJQwC5AxAGAEA/CCQUlWRZgSzeq1FPEgcklwQiC1EnBfZdIIw6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E+4MDCC3GqCyKagRda/2eaagDAGKMEEKcc61tViqEIAZOG+gxC6HDGDkIGQAOOESIBbDk3BqT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IQAQAQhhBBjAsz+YP4d994D8AyM0VlX/85b/3594mtv33d21LWGCFEANABQa6OVVEphBKWU\nUqqshCGEoNjudjvj4+PNZv2nP/xhqfXa2ur/9cd/ZIyRkisla3t1pWCapsViCexbM6iDVOnb6b3z\nWf1OX4ODsfzsXdn8wdc/Y2SlB2MMRkQrnREfMhsIKdPJybFWq9Fq7c4fmjl2/Nyh+RnBdb3erQwN\nxzF/87WLG+tb8/PHq+VqkiT9Tjfu94MgeOLxJ5rNPde1h4bKWomxsdHBoB9FUakyFMUJxrS2szM6\nWk7SdBDFRScol4eOHj36zDPP3H/f+dnZWQBMPBggCC3HffyJx6VUYamolYrj+Orly5euvN3pdLLZ\n9WgQlfNFx7YXF5fCsAAMbDabQc5DEEVRL01Tz7UwIbm8309iY1ScpoMkMQBgQgih1LIpIYoxwRSE\nIHOhz3glB7SarOJjWZbjOIyxrApg9q9fnJ2rQoj9CxZBTDCECEEkUqaAAEpTiCBEAOgkjmubW7/8\nL/7lH/zhH/V7/X/7b/4NQiAT8DdAKakAAlpnEgyaJdJxfGMM59xxXa210hpDiBAmmEIAEMLEwkpw\no2EQBDeuXfv3f/qnH/3oz8Tx4Pz5c//ps58llAS+22o3pOKUEIjMIBoowUthzhiWjZZorSm1KaUW\npgRhpYFSKkkSJU0cJxRR1/UCEhYKOUxUwgalQq5QLmioSqikoEl4mslaxlE8iAZAi3zgvvDCi9ub\nm9MTPxIn3U6nZ1lusTgkmFYSBLni9tbe8aOnH3zgka989eVDh2Z83z167NCt5ZujE9PlyvDOZq2+\n1z118nin0SoUQsNlaagMNNza2Jyfn2cJq5SrWmoLW0+97/2hm794+dLS0pLlOkGY29nZhNCMjo43\nGs3xsamVxZXVjc1777tw8+aNr3z1q1MTkxceeMBo3Wm20kH04AMPgNvyFt95tD80fOzg58buje/y\n48J3HD98uzjnm73/2/qQ71sU9C4L+7YS9cME897bjn9/trXfw7Hc7c/fjbtxN74jQQkerg4rY9bX\n18uV6vDoiJScWqRQLvbiftLsY2oxpo2SUcSMAUEuhADv7exallPKF3JhjrjUUBDzNEpjCEAURRub\nm0uLK91ux3Vdz/N830/SxHHsM2fvMQYsLiy4jtNpNW8tL5XK4dzcXLVSmZ2eTpKk3WwKxgiya5v1\nubnpv/9jH0iSqNusP/zgufpea2NjYWxkfGlx5crlrywvXp+aODk3N/7EEz+KMei04pWlzc2dxtjY\nVC7vQkIOzZ/t9/u12q37zz2wsrL+5qtvYoSiODHSuJ4HIExSRqlTKpYHUU9rOTw8Uqvt5nKler1p\nU/vawmK33x6wwb0X7hn0eu6QnyRJPsy/9dabSkopeaVaNgBcu37tgQsXzt575sXnXywWhjzXv/TW\nZcf1+/1+3N8DmqSMKaUgAAhm1mhUaa2UpJYFjakMDSGMNzY3pVSUEMu2Pc+tN+qMp/kwHyVxnMSW\nZRGLGggYE14QGGP6gwhDSCgWQgziBEDg2A6AMEliACChlpQik9PP2AGMMWMMglAbDSHJROkJIRAC\nIYRt2YQQzhmEkFpUKT0YDHK5XKVSwZiUy+UkYYyxTrtFLatSqRCMpZC7tV1oAEvjxZs3Hn7wQcem\nucBv7u0FnjtSGSpWy8MjJc/xl24uFvOhVooibLSCQAsWMyHjNNlYW8eY2I577OjRnVqt0+kgBIxR\nOPOuw9i1rTAM8/k8AMZoxTnPBAUIwUJziFCGi6DRB9bu+41QnbWm9/kOmTDenQ0AgrDruIRggsHk\n5NjM9MTjTzwxNzM9MTHmOi4lRBsglQIIel5OyBRijBBFhGBMjIEAQikz2X8EtBFCIJwN2N922Ms8\n9rLIdsIAgDPlPZMx5JVSCGXnBXoHWgYA7Bcq4H4jFwAAMEQIKqMxRFIaJqRRKiN2UEy0ktooBCEA\nwLYsxTUCMOoNfD939sy9CJs0HRCMomgQp0mn0/X94NDxkzcXb2UCjQAAc3v2XmttwNc06m8fhz54\n/HjHg4oBCkFykP/snRCiAwcEiAEwCBiDYEZlRywVK7eWp6fHC8XchYfOT09Prq9vlMvVYqlUq9Uv\nvnmp3egMV8cQwL12d2RkJO711tdXp6am52ZnDs/PtltN13MYTwAEAKJmqzFsjRBCdnZrcb+LkFxb\nXT9z+myhqAkhTz39/r/6/Oe/9KVnf+7nfs52HC+XVzwtlktSinyYl5x3up3Lly/t1etGKaOUBCZl\nqe959XqjMjQEDDBSCyEsSgIv6HTaKYuMlq4XFkvFxYWVrHJELeJoR0MAAEpZqpUqhgWMqAFcSsml\nRBgZbTKDPWMyT02tjQYQWLYlpdQZ3SMjiWBsUWrMvtajlNIAAxGyHEIJVVwghDBESkmZMmJRCxJl\n1NbGxr/85//813/jf93cWP+Lv/iPpWKJEGLAPnnEACCVNlJZOLMe1FpqrZTrOowzo4ESRiuOIUAA\neo6DEaIEpnFkudbq8q2XXnjpH/2jf1IogNlDs1evXuVSCM4IgsRC/X6vWh2ujI7s7mwRAhBCmf+l\nUhJCrbRQRmGMMMEUYwyRRUU8iDlnuVzOojQshACHmOKbCwvj0+Oe7xHbyhnVbDbX1tdbjcZubVfw\n+IEL565fvzY7NYMx1go4jjdz9uigD1qtOI7jIB/4vnf58pXDh2e7/c7O9sZggKemJyglBqh2q33+\n/vPK4L2drZs3rjz68MPNlJXC4q31WxRbeT9v23YURQYYx7YJBI89+sTk9Mxzzz1XqVTSJFFar6+v\neq5XCAs8FSMjo3u79crJ47Nz8y+//FK/Hx89fGRifOLa5Ss/+5GPBH6QJMnfCdofGj52J8J/xz9/\ncOHctw5TvwvI5/sWX717ov7Gae2vHb76gakLvLcVfuuz+t/NhLw3QYpv5QL5Ad3cu3E37sZ3LYJc\nrt/uBcTLB7mdWn2r3kUI5UIyNT/ORaq0VgZ5OVewREDebO15tjN7bH4vDBv1ehpHu7s7QyPVmclZ\nP8wxFscsabc6Z06dHnT7zUa70+lsbm5ayFq6udxs1F9/5fVyuXz+/IMjo6PtdrO2W9vd3d7dq588\nfTof5F/8yst79U6xOLy3u33o0NGf+5l/ADTcWK31ep03Xn9rYnIqFxR6jfbk2MT0xFS3lx4+PEko\n+PTv/x9PPvnk8MjksbMnw8roC8+/wDiMO/H66vVB3H3k0fsQxUIl91449epXX7FRPqwUIACE4KtX\nrp4+fUYa8ebF61PTU48+9uiNGzdWbi25vlOszJw+d/rFF19ySc4YHPdTUza9eODlnfmjs8vLy0CR\nRmPvqSefdCxncWGx10gePPe4mwtqtd1cPn/+3ImFazfTQaqMQRrZxFZIDCSTnFFKJZeOZSnGK5UK\nhPDatWuu60Zx30F2oVzc2d4ZxFG5XJZSxAmj1NZaQ4qSNEUIMc7jODZASQ0c6jGhhTIQGqmBMQZA\npA0EBhBCk4QDYBLGbQCVAQZAhDDQyBioVDYArgkhmNBUSKmNi7DmjGpTKBSSJMEYdzptSqnreYKz\nRqshuCgUwm67ZRHsUNLsdcuF4u5eMjpcaTXrFkFCcMvGg35sgHfy6PG19RWZ8jAId7Y30rg/OlJB\nBERJXxrJ0mR27phFHUzhE08/9epXXr948aIQnGA0Olxt9yJEUZqmGBPX9aI4kgpwoYQUUnCtNYYk\n4goYTIhlDNdSK6kM0BmIMsYYIJXUxkB0e+bZaGWMhgYQgmxKHIq1YMVy9cyZk9Mz03k/mJudmZmd\nBQAganEpMabUtjKdPERtnXXsEUKQIoCzljWEUBkEIYAYQYQBBPBAVC8D6wZoYPYl+RDUOiNhgNti\nbOQOW0QAAEAIaaX3b9lw/w6uv5afmNVutAZKacmZ0RojSCgUQivBldIIAUopglhjADQwShujuWAn\njh75H//FL/7Jv/vj1Vsb95+979ixU2Gp8qz38tVrVxGmjDEAkZTaAGSggQZgiLXWCEBg9sUPs8cI\niKA2Jms7ZwtTSiAMCEW317mfnKyNDyHIOOoQAgyw0hJCW0ptDGs22uVyaXi48torr1+/enV8fNJI\ntLSwdOXS247tEmwt3bx+9PBhy7Ib9d3X3ngdAODngoQlGGPf96tj1Xa7HUdxfxDbnrfXaHU6XQDg\nffedb9W3Ay/X6/ZGxjTEqFAMo7gfp9F/+H//n5/+8IcVV5bt+UHeAHnlytsvvfj8qZPHJ8dHpIgw\nVKPDZaX11ubW9nbNILN0a2ViYmJvd5elLE5iy0ZpEtkOHQwY1BAq4we5KE21UsiYQs7vdgZMsGKh\n5DgOxTRKYiG04Eor7TgOJChNEikktazACVKQxkmCDVZMaykl5wgTx7IkREIISglE8ECrQkpJAZQR\n5yYtFoq7g8RAjRBBFCgDCCYEYGXMzcXr//o3f/3jH//4W2+90mg0gyBgQiKMKbWTJAEGYUIty0kZ\n09pQakumcnYucBVLGTIGA6C1FEJxhIwBxHK0QkBSQpznPv/8SHXswx/5yAc++JPrO+tK8N3NteFi\nOVHatexHHn3k2JFj//q3PpEPfUKoNsZAA4iK017OCTE0FGFlADKIYGSUyOX8brudQLOn5fYOD/Lu\nzPwho5Fle0GQX9tcVUDv7tb63S5jKePJSGWIajA7Pnn/vWd5wny36HluEvPdWr2+V1fAcOF3GgQD\nwtPB9OR0NBh0u50337x0+NhRbHm12uJ995174403lUwefuiCF3iHjx5iUtxYulEoFrlWWnI/LEjO\n6/VW3g+0IkcOnwz84mDQTZJ4fWtdc7Vyc+nEydPQ6HzoS8X26rXACwihq6sbM1Nz0xOHnnziR2dm\n5nb3tirDlb8TtP+D0qt8zxDoO4W0v63P+X6G9+8gq3+zl9596vvOUvcPKwJ8D4f2/ZaQbzZ08K1c\nID/cm3s37sbd+NvH9YXFscrYrZXltd11aPuLK5udQf/EicmHHj61urrSbQvX6QFkThw/TJG3urLW\nVXp7fWN8ZOzE8WN7e7uLt1YQhkapfqddKpcuvvWG6/g8Tn0vKIThUKk8Mjx86tSpfr//8gsvLC8v\nN+vtGwuLM7Mzc3MzszNzExMTu7vbUZQgZH/wAx++dPHy5SuXjx8/9f6nH4UQvPLKV1dXbmGAHrrw\nyNLiSrexlXCRpLEQWgNk2+7P/tzPfuHZv/zsf/7ssWOnSkOjo6NTH/jQB29cX7h25cr66iqEvNts\nfemLz0IMqpVhbOG9Zr1UKUsh9xqNyuio5bqXr11dXl7jQo6Mjmxtrnfae6+93g0Cb2Sk+uTTTwAA\n82HuLZZKISxK4riPMS4U82trq44zu7OzMzo2To21tLBy/v4HIMT1egMRfPnSxWiQamCQgUppzoVS\nCgNoU8xSQSmRUhYKBQBAp9PxfT+Kokqlks/nm81mnMRjY2P9fj+KIkpp9jdcCGFZFuc8E83WxmBI\nAEDKGCllpoPtOI4xKGsRQ4gpNQcs/WxQH0BolHzH7iulMkG2jHBOCM6mbX3fz5j/nXZbShYNulJq\nx7G6nY4WwqLW+Ojo6tpyMQxDPxfmgziOqsNDnXafEDw6Ory+voYgEkIoLqamJgf9bnB4EmKz29i1\nqeN5PgTwwz/1U1t7jdXVtbBULBYKrb363MyMFHoQMyEEhAgizJhwHF8IljLOmIiiJHMQgAApyYVQ\nSmugDYQI430gbYwBAAEkBVea7BOdIUbAQJtaEGiCEKU0H+QmR8eKYb5SKk5PTw9Xh4EBAII0TYGB\nEGJAcGYfR6h7cJ/9+kesLN8amCyD5kCTPuvumwzuf03j/raA3zvH995hX/eOCfk7+fwYI2MwIEQr\nhSBQSkkpFBcQZJrrBmGsQaaNBxACxuAwnzt+4uhPf+hDSsJTp07nc+XtRufG4urCzQWTpkIIBW63\n5fdF+gDQEIB9KwBwW3rg9lI1AEhrZQwwQBmDhBDZ2gghtwn/+8byjDEIoWVZgGBoDvpPsNeLrl69\nure3M1QKR8dHEKQ3+0vrt1anp6c4Sz3bHh8bSeKolbZc1zVAtZqd8YlxgEGhUDDINFpNxmTKRVgq\nGgMb21vl8hBGdG+nNlwtcy67nS5ASCs5NT19//nz0OiVleWLF9944IHzUgkIqQFIKe14br1e39ne\nnJuZHh6qPP/88+vr62fO3EMprjdajmN3u22tJbVw0hys3uqXisVBPxUpxwBBiKUUnU6n0+l4bjDo\n9rSUgetBo0WaQqMzIUYhBYBaAokNQgRgywJGa6CQhVxgaWCESqWSUklktIAAYmhZFO3LN2Q+DwoA\nFcf9fBjGcRzHA4gBJogxhim1LUcqCQCwLEKRefGFL//Zn33mD/7t73/ggz+lpAhz+Va7o6TmTObC\nfNSPMIDQ7GtJCimVVBa2FdJKiYy/gQkByHDGLUkQwEpA7BCCyX/+T8889PBD73vy8Yj3PvnJT42P\nj63eXM6VypWRsQ9/6EO9/sC2baORMEoIobUiFGEClRaCSQYQwUS5wg0ChxKMkUWxMUoIRijudwdX\nLl72w+DG4uKZs6eGx4ajeLCzta2UbLUark2PzBxaWVjwXMuieLfWHB8fa7XarVZ7MBgUSjnH9hqN\nBgIyX6iwOLl161a/NyAWtWz35ZdfyeUKDz74QBrHgvUPzU4WCqWrVy/Pzc/curU6GPSuL1xvtJrl\nciUIgqFipVAoIgB70SBlab4YFofCfrf3pS9/MRfkyDS+8valM2fPMs5K5SLjbBBFGNNBr/7GK6/9\n5E/+ZGVoaK+2UR2uGmj+i0D73/Fu5/cctHzfQqZ3T9R3UBbhbnzPt/ib7ezdzb0bd+NuvOdIBgKN\nWJXqiKbm1Lnzjwuzs1eDsO86eNDrNHZ71arrem6/08fYAAgwpu1O6603L1WqlUq1Ojw8DDCwHIfa\n1vrGZpKkc3PzGBGtTLvd1VpDgOr1RrfT9ny/Uq2mKev2+1sbOyxhN28szM3PuJ6HIGBJtHD96tho\ndWbmJxA0O7u1L33xi4Lxxx55NBcECws3B0m6uLSUpiIMQ8/P1Wp79OrVT3/6d9fXbnElGUsnJ+fe\nfO01x/bHR8cff+z8hfuPr2+sVspDr7726lClPFSqSK4nJ6eHq6O3Vld7g/jI4WNSgWJx6Eceq25v\nb3z5uWdrO1va6NHRiauXLx07fur4sWNb29tbm6sPP3j+lVdf54J5/nC1WpWKa62iKGrARpqKc+fv\nv/TW5ee+/KX7Hrxw7733vPTii+160ygElYUN5JwxxrKp8qw9DADknGOMM1Hu7AfHcTqdTr1eHx4e\nNsZ0Oh0AQBiGSZIAoLQ2EMIM7fu+nxmxmdtQP9vHA1+uA077wST/vjJ2xuaH8KAHnlmmQYgyYJ8N\nbKdpYgwghFQqFcZYo7EnJIfQeF6QpunY2Fi70ZKSC86FkJ7r2I69tbU5Pj7ebrcpsUdGR1zXgRBg\nTOK0XyjmG80dy8KO4/SjruM4nCvfCwZRUiiVXnz19eJQRRke5HMjIyOTU1O3VtaCXC5JmZRSaTAY\nDPwgyOdLADFjYs6FkgZABQAihABgoDIGZK3m/bskxjibWEZIogOFWgO01owxx6KU0kqlMjs9c/z4\nUaVZuTw0NTXj5wIpJYIIgIyHjFFWVEAE3bbQA7eF695FKflOF/oDZbtvdlvPJPpuGwSYd6D9b1j3\nN8Y4jgOA0ZpKgZWURiujlNRaGYMAhMAYKYHWmBCECdQZO8AASAimjz36mOMEcZTGsbx1a2V7e7vX\n6wkpsxmD/acIY6ABRhutpQEGIL0/UIAO+ArmQBDQGINQ1sY3CO1rsN8W8wda60wnglJiWRQAZLTJ\nhrchhAA4CNE04YzL3Vq9UW+ljImEjY6O79R2O7SXz+VtywqCYG1tfbg8ks+FnDGl+M7W1tGjRwth\n3rVsCxPGWBgWCseOd7vdtbU1i+JBv7W0vIIoRdg2WhbC8N577x0droZh2Kjvbm9ujE8ezpQmT506\nHQTereXFRiP5wrNfnhgbf+SRx06e7Lz15sXA9U4eO9Zut9utjk0pQshzXItQjCnGFALcanXa3V6n\n3TZGO46NCRSCEWJhZDL9RGOUAdJACYjWRnOZZlvsWNi23DiJtdEKKSEE18CyiQbYAKORIpQQCqEB\nBgAMkdZaQ6mQcByvH/eDnM85N0gZBKgNMTG2B2XEhZSKAYr9nB/89ic/lfODT//e7/3CL3wsiWIM\nIUGU2JTFaZgLOGcQwWyUgDHWj/q27UKMCKYQAqUkgFgIaVmWlFIplXcdYLTjONXhytLyouWSRx55\n5Pq1a3/4v//+1OxMzMUjP/LY5Mz05575KwOB1lpIJpVCEBJIHNu1sSNFnMkWJEli27ZSCgCUWYRo\nrRXXOd8FGBoud9c3r0gRuA8OjwyHjre1tdHc2p6fn5eCrdxaOXXyZJJGYcFrd/ZW19Z8z7MsOj42\nEYalwA8xIrXt3ShJRsfG5g7NxWkUx3G3271+/frZM2dGR0aPHTsODPjKy1/NF3LAmM31dYJxHEVK\nyq31Ddf1XnzuxVKxNDY26ue9UrHITWxZlu9773v6qfW19V7UNwjeWrt16NBhJrjvBgKL4Wq1XW+8\ndemtxx572LZIWAwdx1lcXqoOzfyQo/2/U9Txt+Ekv4tU+zckwH9/aph/xxfzQ0z2fm+5+r5NyHuA\n9HeZ/HfjbtyNd49CvlTfbWCLUNvyfM/Hdm134+bNt++/d8qhCAKtuJw+Mj0yVq3VNocqwy89/6JF\nKMVWzNKFxZvFYjg0XI3iJGo0L1+52G41gqDgum59t5EmrNVqHz9+XAq5uLhstD569Jht2VGUbm1v\nFIqh61oEwpWlJcbS8YmxXC63snxTSlUaKriee/78g77vp3H8R3/8JzzlJ06eyueL5YrXaDZBKk6c\nPF0I/ctXLsZxVB4qWwRCKEeHiy+++DIGrN3ajKLexPh4Luffc/YspbRUKI+PTQ2iQaPRPnHiTLFY\ntSz61psXt3a2Z0ZHZmYnHXfywrl7Gq3Gbr3e6TQXrl/xPFtrvbm2qiQPwzxCkCeMa44QptS2LGd3\nd3dlefXEkRMTE5NvXbx44/o1x7335MmTX9r8IgIkcAMR7Q8wZxDIaON5bhTFlmV1u92sf16v14eG\nhjjn9Xq9VCpRStfW1ozR+Xz+tkt8pk7Htda2be8LueN90a/sPzOytJSZsvqBSdu+Fl0G7DHG5rYj\nWlYjUEpl1u5ZY5ZSyhiTUgGzr1qfUdZHR0drtW3bpsVCqZgPFRfAgI2NNQJRv9vt5jylNMa42WjP\nzMxQSiybOI6ztb1RrZbXN1byeWdufiblSZKm0phr12+cPHF2EItmq31zaflsWHR9L18o+I7batSj\nOArDUCnQarWgNggRgkmm646xZVENDNVaaMMz8AugMZlaPTQQZkeU2b0ZSnGGoYEBxqCMUW/ZljGm\n1+utb2woI9//9JMjo2Ou72mtXcel1BJCAASJZUGIjTEQHnjpQfB1snkH91lCCPha+5t3QP1vyMe8\ns2P/DR9NwR0KvgfLQAgZowEwGNPMMQ9ChBE2GN/W79f7yvrAaKj2CR5G5nJ5hFC/1xdcLy+vffaz\nf7G0vKyUkkIghLRQEGZr1sAApY3SCn7tgm77BYKDGYTbr0OMMSH7ApC3ef4ok53LTk7GmDEQQYQg\nwpgKwRlDiMBuZ2C0wRh5vj00VBkpDxNMjx4+vnBtYdDdmZgYt6gd5gucsZilGGMlxNHDR8rFUtQf\nBF6+0+trpdrNlm3b169fTxk7d++9u7tbt1ZuTUxNASO0NoEfnDp12necXr/7uc997qsvv/y7v/tp\nP1dI4thxnEq12m40PdedGJ144803/vKZzz30wIOPP/6+iYmJpZsLzz333MTYiGVZtVotnw88P4ij\nZHp6Zmtra6+263hOYaisoOFS1HZrgR8QQjkXtuX9f+y9Z5Rk6Xke9sUbK4eu0KFMzRFwAAAgAElE\nQVQ6znRP3gmLDVgsBiAWwBIAQWPFJMrAIQnC+iGL5JFlSaRt+vhI4rFpnyNKNC0dy1QAbRI0QArA\nchfAYrFxdjbMTg7dPdO5uivnuulL/nF7GrOLBQWSyO73x0xP962q+333Tlc97/sEoFQgPMf3pOJY\nh5wJz3eDICCUKCza/Va/31dKYYw5FxBIHPomKAlA6K6HEEJhJwsAAAEUUnSHfcM0B43+2bNnV1bv\nLC7dMjWNKxFRFoRQAO54nusM0olkLBL5V7/3L/757/zOT33so3/02T/O5wsAoUHfQRCygBECBVAC\ncIgJ53zgDMBuRAVVACikICJASagQ54JQjBDCGvWGg2QiPj4+lkwlFFa/9Euf2ry9+tyzL5x514Mf\n/PCHMcZP/sWTnucDTfmeB5SCALAgEEJCgjVNC28Y3/c9LxBCIaTupgNKnWARMKGEYRmTo+Otdv3P\nPvenCwvzY6VxgyDA2Vghn0mlMMWTMyWqoUazuri4CrEYyY/oukF1rdPtI6Knk+nN9a2JqclCsTgc\nDk3TRIQ89thjmUzmhRdf9DyPYphKpQuFwsGFA41aXUklGLcMs7yxVdmpzs7ORSzrxtVrCsioa95Z\nuYUgmiiNFwr5mdmp2bnpp5/6SqvVFFwgjGem5pQE0WjUMM1SqZROxTkI/MHAc4cDh+bSo/f+L/6u\nof1G9da9Ln0/rlD/bwJU/hL2+3eyim81S/+BoKbvxfb+uI6F/9qL+nHakP2Z/37t13795VUoFNrt\nTn8wwETXde3StWsXL75eqy7q+kcoUXHbjplWq9HIF7LReDyVSLRbbQ3TjdXNRqMxOTWFCRRACim3\ndnYw1lKpdDi+HgwGETuq63oqle71evl8/trVq9vb5Ynx0uT41OEDh7eqWxRpxdF8Mpm4cOG15Vs3\nZw7MWpaJsep3u51Ot9fpptPJTCZz6vT9zWYzkUgzuR4xrXSGur6fzo6YOhZC/tzP/dzUVOlP/vRz\n1Z2tibHJbDr+6qsvf+AD7z944PDm5tbnP/cnR44cOXL8hOMOIFAYI85lPB6/dWuxP+gXS+OJVLJV\nq1+6fDmTTRYKOStqPXzg3b4nOJMbGxu5/MixY8danbYXcCmFYegEk263CyE0DHNkRPc8trmxDZQq\nFot9d/iNb3z9fe89u3BovrxR4R4DACEEMcbhVB1hHKIpSnXf90O8HYvFdF2r1ioIoWQy2Wg0pAy/\nqQ+HQ8YYxjAE3iEgD4P3QlgVovo9jkD4ffDWaPTwsSEAk/cg1fALznmYCR/+U0rJua9pGoSw1+v5\nvm9ZlmmaEMJOp6NTjTFm21a/39V1MoTKts1Op53P5RFCR44ccZxhMpk0Tb3ba2ezqXjcrr1Z/juf\n/I1EMt5uN5hk1LBTqexWeTudyT/00MPfePl8pVqNRCKpbPrqhYvdRqNUKrXagzBvnBCcSKQsy3Jd\nN+AqCIKwnSGlhAgKIYIg4JIToBBCCuxGl0spgZJC8LvGfQBBhBACCEElw5XG4/Gp6amZ6cmTJ09m\nRjIhFAkj6wmhAMF7Uf3b/PPe1kb/1pb6vQj/O5kwhRN+8O3Dld/G+Yeh8z9CkBClJBBSCo4JBUoJ\nKSUPIEQQKQWkEBzC3V4PgMBxHEwIALharX3pS1++fPmyxwSl1PN9CADGKDQRDMn8BBMEEcDf4sx3\n90z2mkG7pwm/6cMfikF03eSce54nhFBKhuwBggljTIjwSVDgiy4b+L5vmhoAcWPUODg/32q22q0W\n8wPbtgeD4cgIKJVKjuOkPD8Wi1Yq2+l0emtrK5VKrtVWDcMYGckNh8NOpzM5OWlZNsLIsu3C2OjQ\nc0NXPB1DjNCbF9989tmv93tdJdWf/PEf/+J/+SnTsgULYpFEcXT8+rUrnAWTkzODwWA49GIxtby8\nPDE+/vjjj29vb29sbiXTqcLYaLVab/f6xNCzhbxEMOABIdTwIqbjCAUFAL1e17BMrhhnzPNYwAJM\nMTZoxNIgRoamR6NROxpBAEopia4ZVAuDCjjnCEEFdjMLgVJScXm3Zxf2qgRXbhD4nnf45JH3ffDs\n17/+DPPdwA8AVBBB1xl4rt+oNIRkUknG0O/+b//r//K7v7u6uvbK+fOZTNa0NM6FkD7VDCAFQghB\npAgCCkEkpYS77UIFsEUpgq7jYIyj0WiY8TE6Onr5ysVaYydfzB0+ujA2NvqBxx4zrWh6JF+amfq/\n//Rz5XLZNE2ogFIQAiwE8H2/3+sjmyilwo6YEMLzvL3+I4TQdVyICVYKE0IAHHY7geergN26foMS\nmEqnTh0/put6LpMtlcZ3KlvJZMy2jWw2ValUIJIIolar22r2gKIUaZFIZH5+HiIYsCB0T+CcJ1Op\nkZERjHG/11lbWT1y+Kht2devX9c16g2dqYlSfzC0SpNIqdHRsTOnzkjAe07bMvV+r7d44+bayko+\nlx8fL5195NHh0ClvbXfqrUGql0hkB/1Bv93xvAGlcGXl9uz0lM9QwFnwVs3Ud3O2/zaXvh91qP/d\nhSh/Q7n+tyL/Hwh8esfX3cdyP5Cu0w98FfvXfb/2a7++Ox8emvVMNu8L0XZ6rucsLd1cW7kjVVcK\nLjmDko+PFQdD99zLLx8/fSII2PT0zM72dmE032mb5fJWabpULI622m0AQDqdhkDaVtR1XBZwGMGa\npgMAFhdvp1LJeCzW7shatSoDCYBSQBRHsm++/sb2zoauabmR7Mnjx9c2VgMoLDtaqTapZvT6Q9f1\nR/KFqZlZ04hUGq12dxBwCZCWzRVWl25F7bjvMt9nczMHbt682Wm37z9zZrSYX7p5PZeJvefdD5XX\ny6+ePzccDCw7omnYsJLJVPL2ynJhPF+5VFm6tDw5UTp6/ChGYHtn3eNBp9LpdPv5/ChEejKVaXd6\nEkiMsOcNyuUtjFA6l/Y9X9eMeq2GED5y6Ig78G9cv9Hv95u9VsD8Z77+tfuOnCgW8ztbdcfxpRRC\n8JAQiyAMAkYpdZyhYZh7RO4wZq9QKHS73Xa7HY68XNcVQlBKEQoxOQzJ/EIIw9TALn0dhqyBML47\nBFp7SDWc6oeQLMRmnDF09yFhCcExxrpOwsH4XVSphsMhQqiQL0glPM9LJpNbW1t+3C9vb4iARaIR\n0zRGRrKuN4QQNOp1gnE8GkslEyPZVKfd6g/aANrdHvvZn/1bBw/ONhp1pYRUwHH9WqOla9bho6P9\nwbA4OrqysYEJGQ6H+WJBBoFuGprjQ6giUdvzAqWE7/sKIKWA2C3FOBfCk1KGfYqAMSkEJhAhqJQK\n/5ISAiCAgmE+GQQIAiCkMgwjlUyeOnXfww8/PD8/n0jGCKY6NQAElGrD4RDrBAF41/4AYowVgPdG\n0IG7yXP3pgDs6SneEcyHD98TULztyF2yBkLv+OHz3m7CXkdG3Y1dR5hgTUpfYkIAgCLwJUAYh+wN\nCZSEGCCEJIJKAt9nMOC+x9c3ti68edF1fE+wULTgBwHAaFfNIWXoNYgpkurtoYB7O7B3qmiXU/CW\n7sZemNxdVr9SQGBMdV0nGHMGhJS+7wMf6BrBCBk6sWwbY7q+vtnv9JzBQHKBEbJMs16t26Y9UZoU\nnA+HgwMH5trtdiQSIYTOzs1Uq1VMoGEYlNJoNMY5u3zl0mDQfexDH06m00JIy7YDFpw7f+6Fbzx/\n5PChRDQ6URx96aWXx8dKH/zw40ABqMDYxER/OHD6fYJws9nSCDFNOxKJXL91I2BBJBp532Pvr9Xq\n1UaDWtbI2BgXQof22Mx0s9m4evV6IpHKj5eoFcUYjeRyiVQqnUnbth2NRg3dMEyD6hqhNFSIUEJC\nkwmEsRACIaRRTSoOAVBKcSEIxpRSKTnjPAgY50wKKaQAMBT0IAhUr9fNpFNHjh0JfI8QLLivESqV\niESid5buvPTii91enxL6+usXPvv/fPY3/4d//Ju/9Vtra2sQEqzjXrsfSBchjDGWEgoONGoQQiDE\nGBHOhZRKCKljmkqlHMdhgTRNgzG+U63YdqRardy4df3m4vW//fM/9/TXvvKhxz82OTl989bNL33p\nSwihEO0DJQjGUkmEsJQqvK9C9gcAKKQpIQQxJppmDIeu67nZZEIogTFutZu+YBOl8cmZKdPSlm8v\nFQu5dCrlus5P/dTHXzn/Yr1esyOGZRuHDx9iDJS3qs36gAWABaBeqyHFKaX94cC2I2HTMJ3NEEJc\nzxOMM8GmpqellJ12ezgYjE9M9LsdIeXh+QXTsJvNpmFYtWp1JJfJpbOUUiF4t9N947U3Ykasjqv9\nZndqcjqfyQ8Gw1a9rTi2TO2+U/dtrN959fyLg2E7X8y4vre+vlXITX6v0P69UP8HYsj/HfqEvU17\n/Nf40fcZ6v+QA7/vyh5+u1D6/99C/R+eDdm/uPu1X/v1PS0u3FarMTE1ReoSANjv9zEFkKt+tx2J\nmuX1FqWwWt2pthu5nZ1CLu/5/vr6esg7HTqDGzdvjo6Pl8vloTOM2BaGIJfLvXLulWq1yrmcm5uD\nELquUygsNBs127aT0bhkEhGSzeZNw9za3Fpdu/PQQw+USqWd7XI2neHcX9ssr66sUU0HQCbi8Rs3\nrrfbnfGJqcOHD2tmpNXsWXbUMAzdsB2XXbt+a35+/uSJU7eXVhSTtmFPjJZmp6czmcS1a9eSiZht\nmZ944mOLS7cvXroaT6USiZhCMGCB6w2oAX0+3NjcSCSiJ07dZ5qaz30AMFAEIIKJoUm1US5zEVia\nkU6lIQKSSYwIhFgz7e3ytlI4EUtubG12O51IwrIszXWd27eXC7kxTdMYlQD4QvBwnAUREEL4vkcp\nMQwjFN4zxtqdTjRqu67baDTCqHbbtkNDvnCq5vuBYRgAAMdxIIJKSca4adIQ54dj/zDZa4+3v4fK\nwu9TShFCVKPhi4YUAAhh2I4RQmIMORcAoBDiSiiDwEcEIkVrtYqSopjLKykcZxiLRCFQ3Xaz2WwC\nJXVNxxgrJctbW+OlsUajpoD0nKGmwWPH5x966MGABY12a2Zm6vrNpXbXSSaTN64tnjhx2gn42MQ4\nJPTipYvVSuWh+89Mj41fvPCmUsK2TcYCaJoAgKEzjEYTXsCkFJRSShH0paLA930ppZJSChE6yIWg\nMrSTQxASTP3ABwhQQoMgIIQYGiEYHT68cPTo0UQioes6RljXdYggpTrEWDOtXf85EVICIBeKkLdM\n7+9l9e/h/HfE8HvHh1+Edon3AuawiXMvzt+D9+E1DQnz974uhFCCkPGtMIYYEQRDDT2GkCkIAEKc\nMRgqGvCehF4BAB3P5T6TEpXLZQihYRhIUdfzhJQQISFl2CTAGEIAFBBCqLusiN3TFkIiDAAEoaQf\n4d3lQggRRPcKE0JB+N5KEQKYYIwoAAoASCmFECogwrUTQjEmvU7fTTmFbM6guh+JWoYRjUYT8fj2\n9rZuaM16jWqa73vC5YyxZDIppaxUKslkstPpKKVS6YRSqt3pjo+Pnjn9sbn5BSYlwrjX78ei8anS\nND6LeBAw35ubm9Wo/oXPf/7okSPFiQke+AjCydL02traTrnc6w0SUXtneycStUpTk5vlrXqn3R4M\n7Eh0cnZmXILbd1Y3NzaobpiGvlAoHj5xOplKWaZlmqZhmYILAGGv1wNKeSzodjqBBP1Wz/d9AJTr\nepxzLjhGmHOOMXJdlzGmFFBQEYI4F0IIAFQQMD9wg8AnhAKgpFRB4CsJEcZB4AnGAJBSSR4EiXhU\no0jX9UQyNjFRyuczj/3kY4RQ24596pd/2Q+CWDr23/32b1VrVSE4BKDX729vVZqNZr8/qFbrLBBC\nyEajq1Pq+66uWYyxfr+PIxFCKKVUKQkh0gzdtM1ev6NRPWrb1fLOndsr0zMz/WE3loj9q//9D5qt\nOuIwaloYIKXpGBOCSej+GTJuGBMQQtM0w19rum4KIYMgiMfj3rDPoMKEmjH7Q+95YHV9LRq3EMFL\nt29vV7aPHj9ca9ZzWv7KtWuF4lg8bjdale3tzeU7K9lMAWPCOPcDiLGBsYrHY5OTkzvVSiwW0yyd\nca6AqlarQIF6rS5k8Mi7Hxl0+uXyZiqZilj21ORkq9VWXFy6eBEjNDc3jxTIJNLbO2XHGWSz2ZgR\nd/suAeTQ3CHbtK5fuxZ4frfbDoRyA37o8KFU+mgul83mRkqlMcZZKp368y9+eaww9egD7/ueoP29\nef4PCuqDb09VupeD9O06l3+lH323TuxvcvCP+va+Iyb8oVr19+GK/5BvyNsUfT+Qi/v8889/P5f8\nn325Rx99dB+e7dd+fReL6LDVbaK6OTU1TQwtk8lsrd3GiHieZ+sG51w3DNPW6BC3Wq1sOkMxlpJb\nptXr9QnB9Xrt5ZdeggT3hgPHtuIxOwgCpSSEYHl5eX5+3vf9qakpSmkul2OMFUZy3tBxXCdgfnl7\nc7SYP3P65LHjR3r9jgTMcYY3b97sDh3bjkWi0Xw+2+u2q9WdwaDXbNZfv3BhfGLyxIkzju9ijEZy\n+Y9NT2EiWSBPnrrv2pVbK7dvey6z7IhhaIYecZyNI0cXksl0t9PSdX1qakIAtLa+MnCd0uR4IhHR\nTHDk8MF0vACBNE1tbXP10pVLzWa7NDGj6ZFez+32+gooZzDAQMVikSDwFw4dMkxTN43l5WUEUSqV\nWry5pGlarpDL5tKGpSshicKu6xCMlVJCcqUkQghBqgDnnGuaputaCAgxxq1WixISIvkQbrmuk0ol\nQ2I5QpCxYK8vIKWkmAIACMF3DwgFtzgcoN1r3RfCyz30qJRCEN3DyL4r5ldq77EIkbBfELYGhBCu\n4/b7vWQ8HgR+IhlPJGLddtt1GKXEskyNUmfouK7b6/YMg+5sbxVH84NhL5mKpTPJuQOzhKJqrVoq\nlVZW1tbXNyen59PJ9Ne+8hwh2vZOpV5rXr9xIxqNeq77xS9+8Wc+/vFDhw+/+MI5LgLLthzX9b2A\nUk0IQSkihHAmw8X6zA/94TDBUJJQtw/upsQDBQECECrT1KUIIwlg4PvZfKpQLKbT6ampqZmZmZC9\njzGGEKm7qofdXIOwYQBRuI1vE+TvAf53fId925z/W9/T31HSv/fR5R7dO/x27+DhI8CuTf6urANA\nRKkmdK6UdJ1u2La4i7eJ77uUUs9x260+5ywSibT6g7vYMuzxkD0aSHiXSCURUvdieEIwgFBIsXdW\nYdsoPK/wfruHj/DNJgVCEADFOYdAIogxAkIITMIlKNf1CCGJeCSRSCYSiUGvzwMWBIHrOL1ut91u\nW5ZFKRk6rmWbnDNKaaPZ8D2/Uqn0er2JUqk0MRGKXHRdP/u+s2PTk4zLm1eujI1PZEZy4QItw5w7\neuyl558TGOdHRmLRyJNPfvnTn/5VousAAEppOp12h8N+v7ddqXTbHcb9keLIzME5omsKQExot9uN\nJVOHjx5OpJKBEJhgDGnAQcBkrb7V7XYazVZ/0G80GkEQtNttCQBCmAf+oNcXQiIEfZ/tAmAulVKY\noOHAYZyF3AjLtoTkvhsgBKlGA98HQEkgFQe6qXueTwmVQCnBqUY1jXDmY4Sk5AhK0zQo1dKpZHIk\nkU4nE4mkYdjxeCIWjba67QcffDBbyK6s3tE0PDc//cjD79Y0s9vtGLoNAMCY/Ps//KPP/tEfZ9Mj\njHu+z0zDcF2HUoIxggj6QSAHIpNJadQAkMdjSYzwlUtXf/FTn5ycnW23G8XR/NN/8RdTE5Oe51qG\nSSnWdYIxFmKX2qR2G3G7NKWQ6xT+8uGcCyV9zgAPFiYP3//ggxyqC5deS2aSVtQskqIZidxcurlw\n9Gi5Unv55fOHDx8sTY2nUyOZdGVlda3bH8biia50MSazs1MzU+ORaLSAURAEjuMsLi32+n3Lsjjn\nSkhCYbvdHvaGGxsb+Xxe13VCSDqV2travnrp8sOPvNsyTYzw0tLytctXJ0olU4vOTE395Ic+dnh+\nvlGvO73+odlZTMjy8q0Ll66YiXh5e22iNmKaWi6XsSyLS+5zduahB0ql0vdqtv8DAfnfOX74Sw74\n6/3oO3zIX+kZvsODv//A73u0vT/wdX0vNue7eBF/SC70d/fi3g1aAgCA97730f/sE3w/0fXzzz+/\nD+b3a7++z6WZpJTOj45OVZqbMZKk1BAKUqwxLg3bZoJHk7ZmaYapIQiVFASjaMTmfoCgHA77v/iL\nf/uNixc3y9uHjh4JAr9YGMkXRmZmp+fm5qqVWq1Wbbc75fLmAw880O12q9XKtcsX4xHr1OnT1Vpt\nbW31xInjs7Mz3W7XjtrdXvv27dsY4YX5hXRm1PO95eXFrc21fD43NjaWyWYUAuWtbcfpjU3M3Lh2\nffHmrVQiev/9Jy9duZwdyf7khx9/+umvQYhj0VQQuN3OYHNjSzBW3q6MjY8LDgbOcHFpuThaHMmm\nb9y4lkrHZlJjX3/mq0jSQiHPhVetlbuD3kg2n84kNM3mTGq6Xq1WC4VCxNC73dbt2xvjE5OWHWVc\nEKLZlu26fqVSMTQdYagZtJDP5UbyzPEr2/VKv8mY7/tBEARCcAV4ELghuVQpRSnFGHe7XaVUIpFQ\nQIZoP5zV7/3eDg3MDcNgjAVBcBcE7qKsXZX1WwfIYYXYNVTg782fEUR7uus9E/h7DwBACsFdV+m6\nxhir1+vdbnc46AvGRscKge8PB/1MNjPodREEStiJRKJSqaTT6cnJyVjMiscjpm3MHpgEQJQmx/P5\nkUa7jQnd2a416u1B3+33+9l0LhKJXbjw5vKd1Wa/b1nW+PhYu9lMJBKXLl06ODc/Nzdz9crVdrud\nSGZsmwaB0HQiJPE83/M8zqQCHN6l2VNAuQw14QIAiBCUUgIIEcAICI2QQAZYKZ0QDWM/YLFYrFgs\nhnmHQghKKCRUAaAgUAoiSMJewV36OoQQKSW+nZHTHg//3i0NZ+lhF+ZtqP7e2T64y4cPN//ulJvs\n6Snu9Q7YY/uHMYEAgl11dNiXwBRAySUTikkFhFCE6koKJaFSCCgJldR1fTDseYGvlNQ0ihASXHAm\nuODhGUu1e0pA8jBaECMAlJJvjRtACO7R+ENqevjoezsd3+IyABBCUgjBFVAIQawwElJgoodift93\nTVOPRGzDMDBGmWw6m067Q4dz5vteoZDzfZfSSDIVTyZTXMh4IhqaSiCCyq9vX7t+bWy82GjVR0dH\nz7zrdDI/Xt1af/bZZ28tLX76M3+XcUYpjUQib25taZpWLBSUlMPB4NiRI+dfPf/SSy+8++z7lFQK\nolQ6c/HNi5qm27lcPBLd2t7s9vvrq+sQ4XQ2K6TwPW9rY3PguFyIeDTm+X5/MDh37vVKtRYEQTaT\nqdUb5fJ2p9sFQHEhLdPWdR0A0Ko3dGpQjQZ+EPooIogJIZigQW8opABAYYJNzUAIKyYVwYZmBkOf\nUMqlEIJDiSikisldnw4mmAcJgh5zQ9sN3xOeX98uV4iJAFAQYko107Q1SqWUp0+e+Pu/9vfiUfuZ\nr39tc3MDMqwRo9/vO0MnEo2OFscff/yjb77xerlciceSSEmCIQIIKMm5DGMdENZtO0I14rpurbrd\n7w8XDi5kM5lWs/6v//W/ue/k6Z//+Z956j/9RdyKQo0IFTbIFMaIUg0ABIAiBIdtR4QIQrutpTCa\n0bIjnueMFLIzB+ZuLN1yfC+eTHDBBGfRWHRtffXW4uJjH/rI/Q+8u9MfXrp66+qNWyPZbHG0ePjI\nScFVpdLod4eJRLo4ko3HIvVGnVD66muv9Ye9WqNu2/axY8fa7bYfBN12s1KtbK1v1Rp1otFisRiL\nRFutdj6Xe+SRRzAmnHPf87Y2y67DDs0fKxTyhxYOljc2a5Wdne0tIfzhoKtp9Oixw2MThVqvd2vx\nVrm8MTlVYoxNz0xfvHy53RtIKASS3yu0v1/7tV/7tV/7tV/79VcqI2JMjI97PltaXpqeOSAFSqdz\n7dbAd7nS0NBzAFbNbh0hlc/lIIStZsMy9Favj4A8vDCvJD8wN7O+uX754sWjxw53Os2XX95SCmRT\n2RMnjt+4eXNysjQxMWbbFoIgEomsLN+YnB5PpGJb25uaRo6dON7r9Z597vl4PHr69Kn7zzwsgdwq\nl1dX13a2twmF8ViMEgyQCgIvnoj0+1YiGUVYlCvluYMzhk76/d61a1cCz/+1X/v148ePvfH6Rd/j\niGrbO5s3bi1dv3H1wNz87OyCIGq0OIGJzjnrdTtKybHR0avXLjn9zpFDR0ayqU63QWhKb4hOa/vC\na4NDCycPzc/3ekOK4IMPPPDyC8+zgI2NjWmaRgj1fN807ANzB13HeeKJJ9ZW11ZX71y9euXy5TfT\nyXREtyfGpizLqrGmDMfmIKScC4wx51wIpes6hHAwGKTTaYRRELhSSsYYY6xQKBiG4XkeACAIgpCf\nH/KiCSEIIQCBVGoP2IcAYG+MH07M9q6vlHJP9h+Cw702AYQwCHyldh3UpVSMsRDCEYJd1w1pxslE\nopDPUw0buk4p2t7cdJyhaRm6TgnFE6WJYr5YKOSF8EdyacY8BJRhWYcOLwydIcEUQvj88y8dOLiA\nsSYYePONC5lUem117erlqx974hND3+sNerqu33fseL/VWrx5Y7w0ed+pE3dur/QGjuBC1zWEUMAD\nAACloTch8LwgdJ4LOeQKKKHCXna4SEgpIQAiDGOxKESQMxGJRHK5/OnTp8+cOROJRBzHSSaTEuzK\n6YFCUioAgEJh2HyI1eE7SuHeNu0Hb83be0dx/tu6A/ci4XtH/bvOBG9N5rv3UsK7iwQYgF2HfCwE\nk1IqACDEECECEQ8YBBBAACESQnIpMQKRiD10Br1BjxrazNz0dq3hMo4AAQQqIQBQ4cgdACClwAgh\ngqRS4euFZ8I5RwDvRQPsdZeUUhB8M4HvbiDiN7tIUkqIwhNHSikumJRSCCIlD5selq1PTIyNjY9q\nlDIWdBqNfreXz+VmpqZ63a4XuAgBU9f6gz4itDcYIkLT0SjjfPbA3AsvPhY1cJYAACAASURBVP/c\niy/86qc/PX94AUi1tbr0B7//B9FY4sSJk57ralTzfM9zvXa7vbG+0W21Di8czKTSly680et1rly9\ndPj4sWQqIznHGJ9939mLFy5cvXxJBH4+l7ty45o7cPtDNxrdWlhYMHQr8LuB4wRBIFwvk0nPz0wf\nmz90587KF77wBaffTsXs7Q1fh6DT7UJIFMaO50mpVMAlCIRSPPAhhEpKhYSGkWIKKEEQVFIRiIBU\nGCGdEqmUDBgCQARM0ylD3HedkL/BfZ8QZOg64zzwgnanSQjhgsdiMV0zPd8LAmZFI1QzvL7brTcI\nQb7vd2qt2k71sQ+9f+HA/M898UTgiCuXr0IAm43Gv/iXv+cMvVql8uu//vf+0T/8x8N+27SjGGJK\ndEopFwIRYhhGPB7XdYtz4QxaGGrJeObWtcWnvvzl6fmZ5557Znlp+X/87/+n8ur6ras3DIo5Z2Eo\nJCDIMHUV4CAINE3DGPs+U0pQSsPfS77vQwB1Q7NtGxOyurq2VVmXQDluv99vRWPR0bHCc9/4eiwa\nAwoePHBoZKT4yrmXr1674nv89tJWNBqdnp4+cfwEIZplmkipRr3e6/UikcjAGdbrdQCBpmnLy8uc\n80wmQzUNY9IdDFKZtO95t2/fjkXtkZGs43gHDlie5xuGATAujo0vLBx/30/8hOe5F69cvnrlElCi\nNJ5Pp0eeeurS1csXNnfuO3byvtLUwftOHn3jjde/8Y1njp+4z3G8VrMNCO45A9dx9tH+fu3Xfu3X\nfu3Xfv1QFJeiP+y+dv5yz21BiGZnD2ysr2KsB1zF4jZARAIZSH8kV0gk4u5gsLW1SSCASCbjiamp\n8VgslkglP/aRj7507pXFxVvvf/9719Y2et0uhjiZSkcidrlcHh0rMsbm5mZqtdrExKPxiHHz1s1C\nYeSRRx42DOMrX/lKr9vDCPsBN01LSUGI3u9WhJCMBYaOKSVCMNs28oX5RCIxOTXx0suv7lQ2CBaP\nPvIezxvMHzpQ26m+9tr5MycfuHljeX19PZqIuK4fjSacQXc49HZ2atlcYXunjBBUCuVGiqWJ0sDv\nX7tyNRq1/EGnzgfdflMpnklaiajVabuV7dV8dmT5xtJEqTTstYvFgh2xEqmU6wW+5/kBcxyn2Wq1\nGo0Tx47n8/kT9x0/9+rztVql1+2YlhkEwfr61mDgMc5CuCilIIQwJjRNoxSHNmaWZSGE+v0+Yz7n\nPAgCSmk8Hg9H+qEnH6XkrqAahzR7BUSYWx5Og0OGv2maYUPh3hFxyOoPKdZ7LnH3OvkBIPew5R6w\nDDPSQwJCIpGI2Fav1yMUyYhlWQbGiGokmUy4QwcCcPjQ4fXV9Wazft+JYxiTWr1jWuTMA6eUUsPh\nMJkZee3V1xzHc90g8EW5XN5a28znR0cyuY9+dPLMmTPPvfRip91u1OpsMBwv5BvV2sbG2tzcgdkD\nMxub5e2tmq4bAEIpGcKKUqKUECLgnHHOQ09xCIFU3yTYh0hUI0QJiTGOxqKEkMBniWTigQfedfbs\n2YmJiXCBAACI8C6UlbtEdKm+aUv3ral796L3e9n19369x2Z/R97+twJ4cA9HY++Lt+bbvdV/R93z\nPLsD+V0XBiGkUkIqqRS8m90mhVAKACGEbduWaSkl4/GokCJgQUiohnivFQHCrdh7WQjRHrDfPQf4\nlj7FnkNEaA0QVniXQoj3ZCYQAowRokRJBe+aIDLmh+Nfy9KLxXy+kI/HY1ABdzjs9/uh2cRw2Bs6\nfdOyqtVKo1FPZbOmHWt3Ool4wg8CPwhSqfQnP/nJY8eOaRr9s89//umnn97aKh+cnX/v2feXqzuE\nUqWUoRvpTNqy7V6/l8vnbcsWzC9NjjdbzY2NzWe++tWP/NTHqaYBADRdz+QymkaTmdSFNy5QTccA\nz01O1ev1dr2ZSCSohG631+v1EomEq2tXNjadwJmanP5H/+DXLl2+8tWvPRM39U69Wsymfd8fDgdC\nCAhwxIoooQTzkRCWZSGMPNcnEEolNQA1TWOcKSWZ5wJNQ0oJzlxHIAUC5usaQgqwwMeEYIwsnQAg\nvcHQss3ZhQMH5+dN06zX6+VyudlsBkHgDDwkKfCxhmyscY1QEPQoMq5euPr6ufO+Pzw4P/cT73/s\nvhMn+/3+wsLCr/3Xf/8P//APv/jFL8Tjsc985ld+//f/D0Mjw6G7yzjBmFKNEIoJRYgEgTANmyBa\nqZQ1itdW15PZ+M984hOf+5Mv/PFn/8MTP/3T//PNJcb8IPABEFKKIPDC8Ig91QznCgC1R3HyPd91\nPd2ghJBBr/faq682OjWphBXRZ+cmJyfHNZ3Mzc4emJut1WqGYU9PZUaLk+95zwdcd1ir1ba2NqTi\nvV6v3+8qIACHQIBUJt3tdjc3Nob+MGJHOt3OnZWViG1fu3b9wIGZRDIJIAAQZnIjTn+wuLg1NTU1\nMTHpeoFS3fDW/8Bjj42XptbW1p988ou5bGa0NCa5m8olNzbuFEsjE9MfdJzO1s5KZ9CfnJoFQC0s\nLECIXnjhJaaE4pAQIqHaR/v7tV8/8vUtQTn7tV/7tV8/moXgxtZGs1VPpKOpVBqowcL84Tde3+l2\n+6OjY4wHvUGfanRyqsQZ63Z7pmFMToxx33eGw2gkEgTuxkq50erMzEy9+mrly09++ex733f8+LFa\npVarVcvlbddxG426rmsUoVq1ls5EW43yxMTY7NyBdDr32msXkunU5NR0caxYrVZfPvdKp9M5ffrU\n+Ng4Lpdffe1cKhM1zAnH7ccTUcPUJ0sTtdrOtWuXXKe/tn5namq0Vq08eOb+Wib7pS99aX524b77\nTrz08rlqrQYgOLiwoBGKMXa84M6dVYiw57FMNj01U2p3mtdvXk3EEp7bazYqBMtozBh6Q9YdapoZ\nixipZHw4aNfr2wTDYqEgOO8NBhBj3TA1XesPnaPHjla2K0qpl195ZWZqql6rSs6L+bxOtUcfenTp\n1h3f9RljGCOMacB8KRUmxMAYAIAxQghLKXVd933f93ypmFIgzLsKQf5gMJBSGoZBKZJShGg/FCcr\nALjgaLd2veJM0wyCIAgCcBd17UHH8HN2OIQFUoK7qmwIIAAIwF1H9XCqH/4peKCU1HXdMAwIgO97\nsUjK0A1d1yePHA6YX6vVstmsRnUFgOM6hfyIMxyYth6N2mfOnMzlcs1Wc2Qkv7a+ubq6QXUbI0qp\nvrNVjsViSopYLFKanhn0+0pIjWqcsaXFWyZBdsT0fH95aVHTzanJSQhwo97hgnHGgVRAASkEBCjk\nPiglFVBSKiW/yRwGSiKENEqxBglGFMF4PG5aVjwen5qaSqUSlGJK9RCRAoVUGGd+FxwDiO6F76Gl\nP7iHDQHAWzsL9/Lt3wn8/+WAfy9db2/oD3e7AArC3VSFt+v8hYIoFBuo0JpfChmeEUJIKSiEoFSX\nnEnJpQj59roSajh0TdPM5/NbWzu9XhcigDHGQkKkQv98LpVSAiOIIdoLfVAA7nWLCCFSSXmP0UNI\nNQAKgXfyBtp7VEjkhhDv+rEDGAodDFOHkJuWVSqVMpkUhNDQaTabTiXiPPAb9frW5mYul0MIaAZt\n1JupbFZCFY/HuGS3by3l84UjB46kUonnnnv+L556slLZmZqayqTThXy+WqtRqpXL26Pj4xgTQ7em\np2c21tYwRL1eT9dIsVjUNO2prz3z0ssvjY1PPPjwI1LJIHCnpqbuLC4FnvPoo+9dXVlvtRpOf9Co\n1rgfIAV0nU6NjfV7va2tLeF7pmW167VXX3pxdGz0+PH7fvWXPnn79uqTTz518cLFwWBgxxMYAAiR\nCDwpFRdCCskYlp5QCgiOgsDnnOs6kYJhTIEQQHAEAcWIECyEBABDKSCQlqkDqRBGnDEAlRJsOOC9\ndru2vZ0vFt/9yMOzM7OMBRCQQcet11rrG5uLi0u3bi3WaxVKaKveTafjGCFKSGWr+n/+m39bHH3q\n1OlTsWhscqr0C7/wC88888znP/+53/iN/+bxxz/85JefiseTUnApGcYYo91byzAMhHCn3Usk4nOz\n85Xt8trqWquz84kn/tbm+uYr58+979Gzo6P5Vq3pep7vB4wzKZXneVgqpVQQMABY6EIqpWCMG7pB\nNQ1jjCBiPNB12u62FecjuczC4YO9QRsCaOpmIhY3TfP28u1Bb6BRA0GaGykqpYrFiZWV1UqlfODg\ntOP2oQLJdEZxACGcnJp64803p+dmbNu6dOWyZduzc7PPP/fcBz5wVirls4Ay/OabF4BUhq61O11C\ndiLRmB2xASanF85opnX+jVcvXbzsMX/u4Jzn9B2H1Zo7Hh9kCwmlAsMDumF6HusPWoQCSsnK6ioE\nBGnUDVxqYKXEd4r2/+k/+93v59v999mL68ep3vve9+5vwn796JT6K/3H3xfS79d+/XiXZHCnUiaG\nZFxFIvGbV2+fvu/k7aU3paC6YSVTCaXYdGnWNpOu9IUEUxNTSvgjmUxNqU6nG4nHheBK+U638eAD\nxy5cvvC1r33xoYceRli/du1WZbs5Whz3XJ/5oF0fRmmiV+8WxjPZ7EhxdPTzn/+zO+trDz/4btO0\n/cBngh89ftwduu12e3FxaW5u9pOf/CXP7UPIG52W73m2EdU0bfn2nXwmCVVQHBvBkK3cXqpsbr/r\nzEPvec/ZF8+df/zxj6xubQ5u9RGGumH6w0E6nW60WuWdSrvbHSkUpg/NNHudW0u3NTM+OX2YIJVL\n2KurS91+E5IIQLLvupw7djLVdZqBdKOJiBmxOFCe5+1Utufn562ohTCwTMvzfHfomyNGp9mq71QS\ndjSTTj508gHf5ZWtWsyOC6/vukEQuBghCRFUIHSt0vVYEARS7pqiSSkRokz4lmUlEgkIQavVCoJA\n13VN0zBGw6ETgnSMCecCQQqgcB0/kUh4bgABVhI6jivlWxTmknMFAcQIQhxmq4VCdIyRUkACgBGU\nQHHJKcIAQSGEVFIBQITQdA0pNRz2AVCxqB2LmZpBUql4LBp1XY+zYLRQHAwG8Vgs8JyxsaLrDVxG\nekN48tSJ/GjBcTwucavjnzv3poKmUIRqFqaabkVMyzhy+OBgMKzXtwqjJcV5u9oa9ga+621vrSVj\nCekzK2b1e907zvDQ4WMQ3r52bTEeTyOFhr2eqRld14cEYYIUAEpKSAAQUrAAYYgAMgwEFDAJjFoR\nXdcVAJlEfHSitLBw6Pjx46YdwVSDmN7lmYu3AfjQ4l5JuWtpj4AUUAophdhNuodQhoZjoUBjtwlB\nvhlqqJQQHCEM7rXLAVDdE8CHEFYKMMZD+cVd8wWlhAyJ86F/HecSAogwgnsoWkElpZIhxgbhcB2A\n0BEAAqAQlBolQIBdAQlWUkIhmFKIBcL3mW1HDEMfGy9ubu/4vCkkF1ISQhTjQkmMCUUQIqCUkoKF\n4B8AwDkL2RAyPAmAMUJKASmFAkAKAKFCCHEuIVQIhfu0e3uHm8AYg5gihCAGSkoAFIBASkEp1gkh\nCBOAoRAYanYsruv69lbZdf1YPJnOjAjBM4lMRI/pmoYRIAhUKpVjRw6NFsZWNlb+w7//t7FY/MSx\no/TUyWw22+8NSuOlcnknYKyQzUkuKNEAUESjO9VKq1Y7NHdw0A0wQbdvr67dWRkdH//yn3/h5IkT\nACNCNYTIux58+NwrrzS7fc64YdiF/OjsgblOp0M1bTDoe56fSKaynDuOowAyrITH0bWbK6+/eV3X\n9SNHjvzyZz7TrHd+71/+3tLSbY3qlhXBCArBIUCUEimUkGEipoxEYoxxKQREREEoAYQQSyn9QEip\nAiY0DXsBYwHXdGoYOgukkFgqQTRTSFku1+qNtn/u/J//2Z/ncoWpqdLo2EQmlztw4OCxd3305+1o\nvdp848IbX3nqqUuXLmw3mrlsGkIQBIEVMXtd96tPf2NsbPypp56ZnBx/4omfWV5efPbZr33qk7/y\n5JNPBUFgWlHPdxFGjiPSpgmk6Ha7nufFkwk/CGr1mlLC99nanUqrWf/Mf/Wp3/md31nfWtR03BsM\nIMBKIMAhBkhwQXBI5fD2PCMpJVKq/sCPx+MQQikF0pEAIpGyi5GUaZmVahki6PmsV64QzWi0OrcW\nbzz91J//8i/93XbXQURzfF/X0ez87BsXXmm2apXt7XQy/dhPTF5fvG4YxszUXDKatInp9YdJMxKJ\n2cJ3splENGru7OzYhl6v1wa9Pmcy8IN+z00m6xKBI8eOnXnwfqjB//fPPre5Xp4/MH9gepK5nj90\ndzbXmOgUR9NmFCFsEhcyGUDlVjorUNmuixG1OJMy4IZl+P5ASfadov3f/Cf/4Pv2Zv9P/9nv7n+m\n/2vXc88992O8e/vGaT/GG7J/cfdrv/ZLKtjqND3fR9gKvCAWi62urJw6eabT2QEAz8/Px2MxIXVK\nzb4MCNE2NzcMggyNjmRGtuv1O3fWIIanT55uNitbW+sx22q1muXy1tzcIcu0+oPV5eUlgkjMjlV0\n8+H7Hyhvr/JAjI6OLy0u71S3z5w53WjXt69fVQpmUtnSxNROubJYvR2xo4u3ljnnH//oRxx3gNfu\nWFFT0zXHGSgpEvFoKhUlRAXeIJtOOkPW6XS6neHCwUNLy7dMU6ea1mjWWq3GiSMLtm1gLXNnbQUb\nxIqZkqjKdmXoO+X17UQ0OlEqLRycVgpfuXGx0apw4QOkFIKaaUbsWCD4ysbq4eMnstkRoJAXeNTU\npOSl0tjy4p3q9o6SwB0Me51Wq9V47OzHB8N+xIw8+/STjZ06hNR1Xc4lxhhDJDgLmE81A0IopWIs\nAACEMv6Q+40QCvXkvV7P9z1CCADA87wwrix8VKip36Nwh/zYULEfWvfvkcwRQiE5G+5KvQGECEJ8\nV/gN0O6TISRRwALmuZqmQ4SAUkIKxhiCUAoRj8dt01CKcR6ECV6RSMTQtX6/l06nx0bHNtbXKYWb\n5ZpukoeOv2tqeopq2mZ5e3xi+qtPP2vYScu0h+7Q8b2AcQVhqTSRTMd9NgzcIQQSclmtVjuNDkEg\nCALBGYGk22xnC7nbt29fk5eOHjuuG8bm+o5tGv3OoF1vCAh8P4AIIKSEZIHvSeFHbMvUqUYxgpAg\nGItYBIBEPBZwgTGKmnYul0ulUqZpYkJ2vewhFFLuhRHeq6IPmQJSShzy5/csxd/Kz9/roQspQk4E\nuGuiB+49IEytv4cDD8JjABBcACAhQhCC3fjAbw74AVBKSIkVDltCYYiDUnLPRfCtk/9dKwcAAIJw\nl6MhhFKKMc58DyEKAB8OO47jxGIRyzB1ghghMhBSCISwEHd1DOqu46CQYTABAGo35lBJKaWSu3sl\nhCCEIrSXaLDrbhjek3v5ggghALCCSCkFIIQYIQgwQghCpKCScNDttuuN4mg+8HwMkeu6mqbZkchw\nOOz1+7qmmbphWSRiWYDAwHePHlrQdf38+ZevXrs6Ozs7MjIyOjrKOV9aWjpx4kR5swwViNkRxxlS\nqjHBKNYy+VyhWGxu7wSMzR+cb3YaQoJ2q5tMJAlEz3zlqZ/8qZ9WCkjG48lMcXTizcrrswcOlLe2\nqvXaydJ9gRAIIUwpbzb7w2E2X2BB8MbFS37ATTtaqdVLpVKlUjn/2uvPfuPFD3zwg//wv/0nL7zw\n8te//vXAC4ASAIpwu+46LECEMIBIAQUxggrtyjEQBgqEUF8pJSWAAAMgBJdAYc6FkEAIpYDCCGFM\ngIIaNRSQtVqtWq1yeY4JPlLMTU3PHlo4fub0/R/6yQ//4if/zmuvvfLv/t3/9eKLLwDFMVAahYwF\nlmXX6s1CPjcYeP/xP37205/+lYceesR1A8/1gaJE87lkrgdN02w0as7Qxpjoui6EMC3b8/0g8GM0\nLQW/duX6gYMzH/nI42t31quNGiaY+YJzYRgaBCokdwghANiN/wBACskhgIQgAITn+USDufgI1tTQ\n6VCKKvVyvzccHx/nXI2Ol1zXuXLlojMcvPHa+U/8Fz+rlN7vDXXbCEQwPjl2cOHAl7/4xfHi+PzB\nIyt31nTdTCSSpmEnIvHXXzk/Nj5+5NCReDJab1bvP33SsnTOA12nm6vrUsp0IptJZ5QCju+fOn1q\nfuHAl770n6KJ2NTUOBDMMkjUNNzhcOnmlemZwur6zubWHYkU0rR0JldvNiQIlNIIIVIYBgYSK4wV\n566QjhBvQfv4t3/7t7/1rffm9QubW9vveeSh79ub/Ysvnvvghz62/6Hnr1fr6+uTk5P7q9u/3Ptr\n2d/b/dqvH7l64bVvbFe2isVivd6enT7Q63bL5c1CMUs1mM/nMMbxeNZ3Rbc3rFcbbr8HpZQ8UFzk\nCoVao4WRZlh2ELD11c1ebyAU8Fyxsb5tmZFMJg+U7PW6QgS2pZ89+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QQlZGJiYmVlZXFp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\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lWsere01ElW4QA4DXVjcYl0EUZwv5WPJu2AWYMAEmDhxYW9947ezZbL6YL5Z13Tl2/E7T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URc3m1uDgoO/7BFlJ1t5qtsfGx0sQEkI63YhzqQ3Keea5lIt4enp6fX3t+sLVWq1UKpXW1lZ7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\n6bTaWZEhgojHpCzCMN7e6dZqNR6EWTIB1jjgJmkGCVNZ0Wi0IMR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gtKae53lelmWMMVkI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\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t1pMzK8d/9yF7tLd/xBFHHHHEEUf8i6GNWHuys7/37MzZc1/4/Mv/+I8//fij90+cOP71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QFMdJ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qdV0TZWdRxAREzEPE3lZLJX1RWPxMbWtnLWB3DOx1lGOCOMGeucMRCI01aKmBN2fnpeLsth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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Graphical abstract"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**ToC**\n",
+    "\n",
+    "- [Preamble](#preamble)\n",
+    "    - [Motivation](#motivation)\n",
+    "    - [Conceptual understanding](#conceptual-understanding)\n",
+    "    \n",
+    "    \n",
+    "- [Programming basics](#programming-basics)\n",
+    "    - [Variable assignment](#variable-assignment)\n",
+    "    - [Assigning multiple values to variables](#assigning-multiple-values-to-variables)\n",
+    "        - [Lists](#lists)\n",
+    "        - [Dictionaries](#dictionaries)\n",
+    "        - [Comparisons](#comparisons)\n",
+    "        \n",
+    "        \n",
+    "- [Using functions](#using-functions)\n",
+    "    - [How to get help](#how-to-get-help)\n",
+    "    - [Packages](#packages)\n",
+    "    - [Data analysis with pandas](#data-analysis-with-pandas)\n",
+    "    - [Subsetting data](#subsetting-data)\n",
+    "    \n",
+    "    \n",
+    "- [Data visualization](#data-visualization)\n",
+    "    - [Plotting with pandas](#plotting-with-pandas)\n",
+    "    - [Plotting with seaborn](#plotting-with-seaborn)\n",
+    "    \n",
+    "    \n",
+    "- [Resources to learn more](#resources-to-learn-more)\n",
+    "    \n",
+    "    \n",
+    "# Preamble <a class=\"anchor\" id=\"preamble\"></a>\n",
+    "\n",
+    "This is a brief tutorial for anyone who is interested in how Python can facilitate their data analyses. The tutorial is aimed at people who currently use a spreadsheet program as their primary data analyses tool, and that have no previous programming experience. If you want to code along, a simple way to install Python [is to follow these instructions](https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/install-python-r.md), but I encourage you to just read through this tutorial on a conceptual level at first.\n",
+    "\n",
+    "\n",
+    "## Motivation <a class=\"anchor\" id=\"motivation\"></a>\n",
+    "\n",
+    "Spreadsheet software is great for viewing and entering small data sets and creating simple visualizations fast. However, it can be tricky to create publication-ready figures, automate reproducible analysis workflows, perform advanced calculations, and clean data sets robustly. Even when using a spreadsheet program to record data, it is often beneficial to pick up some basic programming skills to facilitate the analyses of that data.\n",
+    "\n",
+    "\n",
+    "## Conceptual understanding <a class=\"anchor\" id=\"conceptual-understanding\"></a>\n",
+    "\n",
+    "Spreadsheet programs, such as MS Excel and Libre/OpenOffice, have their functionality sectioned into menus. In programming languages, all the functionality is accessed by typing the name of functions directly instead of finding the functions in the menu hierarchy. Initially this might seem intimidating and non-intuitive for people who are used to the menu-driven approach.\n",
+    "\n",
+    "However, think of it as learning a new natural language. Initially, you will slowly string together sentences by looking up individual words in the dictionary. As you improve, you will only reference the dictionary occasionally since you already know most of the words. Practicing the language whenever you can, and receiving immediate feedback, is often the fastest way to learn. Sitting at home trying to learn every word in the dictionary before engaging in conversation, is destined to kill the joy of learning any language, natural or formal.\n",
+    "\n",
+    "In my experience, learning programming is similar to learning a foreign language, and you will often learn the most from just trying to do something and receiving feedback from the computer! When there is something you can't wrap you head around, or if you are actively trying to find a new way of expressing a thought, then look it up, just as you would with a natural language.\n",
+    "\n",
+    "\n",
+    "# Programming basics <a class=\"anchor\" id=\"programming-basics\"></a>\n",
+    "\n",
+    "Just like in spreadsheet software, the basic installation of Python includes fundamental math operations, e.g. adding numbers together:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4 + 4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Variable assignment <a class=\"anchor\" id=\"variable-assignment\"></a>\n",
+    "\n",
+    "It is possible to assign values to variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 5\n",
+    "a * 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_variable_name = 4\n",
+    "a - my_variable_name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Variables can also hold more data types than just numbers, for example a sequence of characters surrounded by single or double quotation marks (called a *string*). In Python, it is intuitive to append string by adding them together:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hellouniverse'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = 'Hello'\n",
+    "c = 'universe'\n",
+    "b + c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A space can be added to separate the words:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hello universe'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b + ' ' + c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assigning multiple values to variables <a class=\"anchor\" id=\"assigning-multiple-values-to-variables\"></a>\n",
+    "\n",
+    "### Lists <a class=\"anchor\" id=\"lists\"></a>\n",
+    "\n",
+    "Variables can also store more than one value, for example in a list of values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 55, 'Hi']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_of_things = [1, 55, 'Hi']\n",
+    "list_of_things"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I can index the list to access a specific item. Note that numbering in Python starts at 0, so the third item `'Hi'`, has index 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hi'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_of_things[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dictionaries <a class=\"anchor\" id=\"dictionaries\"></a>\n",
+    "\n",
+    "In a dictionary, *values* are paired with names, called *keys*. These are not stored in any specific order, and are therefore accessed by the key name rather than a number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'yellow'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fruit_colors = {'tangerine':'organge', 'banana':'yellow', 'apple':['green', 'red']}\n",
+    "fruit_colors['banana']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['green', 'red']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fruit_colors['apple']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparisons <a class=\"anchor\" id=\"comparisons\"></a>\n",
+    "\n",
+    "Python can compare values and assess whether the expression is true or false. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 == 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 == 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 > 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'hey' == 'Hey'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'hey' == 'hey'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a >= 2 * 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we start working with spreadsheet-like data, we will see that these comparisons are really useful to extract subsets of data, for example observations from a certain time period."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using functions <a class=\"anchor\" id=\"using-functions\"></a>\n",
+    "\n",
+    "To access additional functionality in a spreadsheet program, you need to click the menu and select the tool you want to use. All charts are in one menu, text layout tools in another, data analyses tools in a third, and so on. Programming languages such as Python have so many tools and functions so that they would not fit in a menu. Instead of clicking `File -> Open` and chose the file, you would type something similar to `file.open('<filename>')` in a programming language. Don't worry if you forget the exact expression, it is often enough to just type the few first letters and then hit <kbd>Tab</kbd>, to show the available options, more on that later.\n",
+    "\n",
+    "\n",
+    "## Packages <a class=\"anchor\" id=\"packages\"></a>\n",
+    "\n",
+    "Since there are so many esoteric tools and functions available in Python, it is unnecessary to include all of them with the basics that are loaded by default when you start the programming language (it would be as if your new phone came with every single app preinstalled). Instead, more advanced functionality is grouped into separate packages, which can be accessed by typing `import <package_name>` in Python. You can think of this as that you are telling the program which menu items you want to use (similar to how Excel hides the `Developer menu` by default since most people rarely use it and you need activate it in the settings if you want to access its functionality). Some packages needs to be downloaded before they can be used, just like downloading an addon to a browser or mobile phone.\n",
+    "\n",
+    "Just like in spreadsheet software menus, there are lots of different tools within each Python package. For example, if I want to use numerical Python functions, I can import the **num**erical **py**thon module, `numpy`. I can then access any function by writing `numpy.<function_name>`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "\n",
+    "numpy.mean([1, 2, 3, 4, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How to get help <a class=\"anchor\" id=\"how-to-get-help\"></a>\n",
+    "\n",
+    "Once you start out using Python, you don't know what functions are availble within each package. Luckily, in the Jupyter Notebook, you can type `numpy.`<kbd>Tab</kbd> (that is numpy + period + tab-key) and a small menu will pop up that shows you all the available functions in that module. This is analogous to clicking a 'numpy-menu' and then going through the list of functions. As I mentioned earlier, there are plenty of available functions and it can be helpful to filter the menu by typing the initial letters of the function name.\n",
+    "\n",
+    "To write this tutorial, I am using a software called Jupyter Notebook, and I took a screenshot of how the tab-completion appears in the notebook. Below I use the `Image` function from the `IPython.display` package to display this screenshot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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J03LTOXbsGDw8PMDn8xEREdGkQsmZM2eYa56a2xVVplilra0tVq1ahaFDh8Lc3BwBAQG4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\nzwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image('./tab-completion.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get more info on the function you want to use, you can type out the full name and then press <kbd>Shift + Tab</kbd> once to bring up a help dialogue and again to expand that dialogue. Below I am displaying a screenshot for how this looks in the `numpy.mean` function. We can see that to use this function, we need to supply it with the argument `a`, which should be 'array-like'. An array is essentially just a sequence of numbers. In our previous example, we numbers enclosed in brackets `[]`, which in Python means that these numbers are in a list, which is a sequence of number just like an array.\n",
+    "\n",
+    "We could also have defined `a` as a tuple: `(1, 2, 3, 4, 5)`, or the specific numpy array format: `numpy.array([1, 2, 3, 4, 5])`. For now, it is not important to know all the differences between these formats, and I am mainly including them so you get a better understanding of what it means for something to be 'array-like'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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u7s7HH3+co9zk2HNc2pt7P1CHOh9B2f+1NtauXRs7Ozu90Qaett7zi+1pFRSb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\nRK5CYII=\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image('./shift-tab-help.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you need a more extensive help dialog, you can click  <kbd>Shift + Tab</kbd> four times or just type `?numpy.mean`.\n",
+    "\n",
+    "When you start getting familiar with typing function names, you will notice that this is often faster than looking for functions in menus. It is similar to getting fluent in a language. I know what English words I want to type right now, and it is much easier for me to type them out, than to select each one from a menu. However, sometimes I forget and it is useful to get some hints as described above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is common to give packages nicknames, so that it is faster to type. This is not necessary, but can save some work in long files and make code less verbose so that it is easier to read:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.4142135623730951"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "np.std([1, 2, 3, 4, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data analysis with pandas <a class=\"anchor\" id=\"data-analysis-with-pandas\"></a>\n",
+    "\n",
+    "The Python package that is most commonly used to work with spreadsheet-like data is called `pandas`, the name is derived from \"panel data\", an econometrics term for multidimensional structured data sets. Data are easily loaded into pandas from `.csv` or other spreadsheet formats. The format pandas uses to store this data is called a data frame.\n",
+    "\n",
+    "I do not have any good data set lying around, so I will load a public data set from the web (you can view the data by pasting the url into your browser). I store my data frame in a variable called `iris`, that I can use to access the data from now on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "pd.options.display.max_rows = 10 # Shortens the output from cells\n",
+    "\n",
+    "iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To have a quick peak at the data, I can type `iris.head(<number_of_rows>)`. Notice that I do not have to use the `pd.`-syntax for this. `iris` is now a pandas data frame, and all pandas data frames have a number of built-in functions (called methods) that can be appended directly to the data frame instead of by calling `pandas` separately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal_length  sepal_width  petal_length  petal_width species\n",
+       "0           5.1          3.5           1.4          0.2  setosa\n",
+       "1           4.9          3.0           1.4          0.2  setosa\n",
+       "2           4.7          3.2           1.3          0.2  setosa\n",
+       "3           4.6          3.1           1.5          0.2  setosa\n",
+       "4           5.0          3.6           1.4          0.2  setosa"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.head() # The default is to show 5 rows"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you can see, the data frame row numbering starts at 0, which might be confusing at first, but a standard in many programming languages and you get used to it quickly. I can easily find out how many rows and columns my data set has:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(150, 5)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the name of those five columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
+       "       'species'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To select a column we can *index* the data frame with the column name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      5.1\n",
+       "1      4.9\n",
+       "2      4.7\n",
+       "3      4.6\n",
+       "4      5.0\n",
+       "      ... \n",
+       "145    6.7\n",
+       "146    6.3\n",
+       "147    6.5\n",
+       "148    6.2\n",
+       "149    5.9\n",
+       "Name: sepal_length, Length: 150, dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris['sepal_length']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output is here rendered slightly differently from before, because when we are looking only at one column, it is no longer a data frame, but a *series*. The differences are not important for this lecture, so this is all you need to know about that for now.\n",
+    "\n",
+    "We could now create a new column if we wanted:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      10.2\n",
+       "1       9.8\n",
+       "2       9.4\n",
+       "3       9.2\n",
+       "4      10.0\n",
+       "       ... \n",
+       "145    13.4\n",
+       "146    12.6\n",
+       "147    13.0\n",
+       "148    12.4\n",
+       "149    11.8\n",
+       "Name: sepal_length_x2, Length: 150, dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris['sepal_length_x2'] = iris['sepal_length'] * 2\n",
+    "iris['sepal_length_x2']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And delete that column again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = iris.drop('sepal_length_x2', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are some built-in methods that make it convenient to calculate common operation on data frame columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.843333333333335"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris['sepal_length'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.8"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris['sepal_length'].median()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to use these methods on all columns at the same time without having to type the same thing over and over again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal_length    5.843333\n",
+       "sepal_width     3.057333\n",
+       "petal_length    3.758000\n",
+       "petal_width     1.199333\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, you can get a statistical summary of the data frame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.843333</td>\n",
+       "      <td>3.057333</td>\n",
+       "      <td>3.758000</td>\n",
+       "      <td>1.199333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.828066</td>\n",
+       "      <td>0.435866</td>\n",
+       "      <td>1.765298</td>\n",
+       "      <td>0.762238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.300000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.100000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>2.800000</td>\n",
+       "      <td>1.600000</td>\n",
+       "      <td>0.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.800000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.350000</td>\n",
+       "      <td>1.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>3.300000</td>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>1.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>4.400000</td>\n",
+       "      <td>6.900000</td>\n",
+       "      <td>2.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       sepal_length  sepal_width  petal_length  petal_width\n",
+       "count    150.000000   150.000000    150.000000   150.000000\n",
+       "mean       5.843333     3.057333      3.758000     1.199333\n",
+       "std        0.828066     0.435866      1.765298     0.762238\n",
+       "min        4.300000     2.000000      1.000000     0.100000\n",
+       "25%        5.100000     2.800000      1.600000     0.300000\n",
+       "50%        5.800000     3.000000      4.350000     1.300000\n",
+       "75%        6.400000     3.300000      5.100000     1.800000\n",
+       "max        7.900000     4.400000      6.900000     2.500000"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Subsetting data <a class=\"anchor\" id=\"subsetting-data\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A common task is to subset the data into only those observations that match a criteria. For example, we might be interest in only studying one specific species. First let's find out how many different species there are in our data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris['species'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Let's arbitrarily choose *setosa* as the one to study! To select only observations from this species in the original data frame, we index the data frame with a comparison:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       True\n",
+       "1       True\n",
+       "2       True\n",
+       "3       True\n",
+       "4       True\n",
+       "       ...  \n",
+       "145    False\n",
+       "146    False\n",
+       "147    False\n",
+       "148    False\n",
+       "149    False\n",
+       "Name: species, Length: 150, dtype: bool"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris['species'] == 'setosa'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>4.8</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>5.3</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>50 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    sepal_length  sepal_width  petal_length  petal_width species\n",
+       "0            5.1          3.5           1.4          0.2  setosa\n",
+       "1            4.9          3.0           1.4          0.2  setosa\n",
+       "2            4.7          3.2           1.3          0.2  setosa\n",
+       "3            4.6          3.1           1.5          0.2  setosa\n",
+       "4            5.0          3.6           1.4          0.2  setosa\n",
+       "..           ...          ...           ...          ...     ...\n",
+       "45           4.8          3.0           1.4          0.3  setosa\n",
+       "46           5.1          3.8           1.6          0.2  setosa\n",
+       "47           4.6          3.2           1.4          0.2  setosa\n",
+       "48           5.3          3.7           1.5          0.2  setosa\n",
+       "49           5.0          3.3           1.4          0.2  setosa\n",
+       "\n",
+       "[50 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris[iris['species'] == 'setosa']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can easily perform computation on this subset of the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sepal_length    5.006\n",
+       "sepal_width     3.428\n",
+       "petal_length    1.462\n",
+       "petal_width     0.246\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris[iris['species'] == 'setosa'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The below calculates the mean for each row, but that is not very valuable on this data set\n",
+    "# iris[iris['species'] == 'setosa'].mean(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could also compare all groups within the data against each other, by using the split-apply-combine workflow. This splits data into groups, applies an operation on each group, and then combines the results into a table for display.\n",
+    "\n",
+    "In pandas, we split into groups with the `group_by` command and then we apply an operation to the grouped data frame, e.g. `.mean()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>species</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>setosa</th>\n",
+       "      <td>5.006</td>\n",
+       "      <td>3.428</td>\n",
+       "      <td>1.462</td>\n",
+       "      <td>0.246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>versicolor</th>\n",
+       "      <td>5.936</td>\n",
+       "      <td>2.770</td>\n",
+       "      <td>4.260</td>\n",
+       "      <td>1.326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>virginica</th>\n",
+       "      <td>6.588</td>\n",
+       "      <td>2.974</td>\n",
+       "      <td>5.552</td>\n",
+       "      <td>2.026</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            sepal_length  sepal_width  petal_length  petal_width\n",
+       "species                                                         \n",
+       "setosa             5.006        3.428         1.462        0.246\n",
+       "versicolor         5.936        2.770         4.260        1.326\n",
+       "virginica          6.588        2.974         5.552        2.026"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.groupby('species').mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also easily count the number of observations in each group:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "species\n",
+       "setosa        50\n",
+       "versicolor    50\n",
+       "virginica     50\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.groupby('species').size()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data visualization <a class=\"anchor\" id=\"data-visualization\"></a>\n",
+    "\n",
+    "We can see that there are clear differences between species, but they might be even clearer if we display them graphically in a chart.\n",
+    "\n",
+    "### Plotting with pandas <a class=\"anchor\" id=\"plotting-with-pandas\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas interfaces with one of Python's most powerful data visualization libraries, `matplotlib`, to enable simple visualizations at minimal effort."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6564da3d68>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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d4q6lqdE6bmm2kiPr7iRWAb3l7luTyzqPc/fX4q1OojCzp4H+wELgk6rt7v7d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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6564da32b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Prevent plots from popping up in a new window\n",
+    "%matplotlib inline\n",
+    "\n",
+    "species_comparison = iris.groupby('species').mean() # Assign to a variable\n",
+    "species_comparison.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Depending on what you are interesting in showing, it could be useful to have the species as the different colors and the columns along the x-axis. We can easily achieve this by transposing (`.T`) our data frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f65619dbf98>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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3zcctuUrzcMdDrazU1DYP925ZMR+3glsazN3/HHcNInVx97Fx15BpCu6aNJjW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\nYBTcIiKB+T9V9gIBDhm7JwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6561a142e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "species_comparison.T.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting with seaborn <a class=\"anchor\" id=\"plotting-with-seaborn\"></a>\n",
+    "\n",
+    "Another plotting library is `seaborn`, which also builds upon `matplotlib`, and extends it by adding new styles, additional plot types and some commonly performed statistical measures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f65591a9d30>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Xtjmcc5hJ8yaRlpsGwKCEQbww9AUmfzOZZYeXAZAQmcCMy2cQE+Y962pdV+NT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\n1+/Y9xVVNC0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f65650408d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "sns.swarmplot('species', 'sepal_length', data = iris)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That looks a bit small, so let's change the style we are using for plotting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Kom+8AUO+lqy337nQ3w2TCB02lOKfdjj06d+rZ/NeWCvl8piC7Oxs/va3v3Hk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\nCCGEEAD8Hy4dUEbv1iNYAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6558d11550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(style='ticks', context='talk', rc={'figure.figsize':(8, 5)}) # This applies to all subseque\n",
+    "\n",
+    "sns.swarmplot('species', 'sepal_length', data=iris)\n",
+    "sns.despine() # Remove the right and top axes lines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the same syntax to create many of the common plots in `seaborn`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kyJHy8/OTl5eXxo0bp08//TTP9/Tp6enasmWLxo0bJx8fH3l7e+uVV17RF198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\nfXUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f65618bbf60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot('species', 'sepal_length', data=iris)\n",
+    "sns.despine() # I might set this in sns.set() to not have to type it all the time..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bar charts are a common, but not very useful way of presenting data aggregations (e.g. the mean). A better way is to use the points as we did above, or a plot that capture the distribution of the data, such as a boxplot, or a violin plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2bdoAUCbJnorAzyD4M0kkYSf7F198kcLCQq699lqcTidjx47lpptuorKykhde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\nK/d/pa5KsAt3Nc0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6558b5d048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.violinplot('species', 'sepal_length', data = iris)\n",
+    "sns.despine()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also combine two plots, by simply adding the two line after each other. There is also a more advanced figure interface available in `matplotlib` to explicitly indicate which figure and axes you want the plot to appear in, but this is outside the scope of this tutorial (more info [here](https://matplotlib.org/users/recipes.html#easily-creating-subplots) and [here](https://stackoverflow.com/questions/23969619/plotting-with-seaborn-using-the-matplotlib-object-oriented-interface))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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y5z//mf/93//1ZrxCiF5Yu3YtAJoog1sz8JOmDydxWgoanYb4MYOZeNcMJtwx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\nwHLEHgAAyxF7AAAsR+wBALDcF0wRzMEA1TAKAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f65619bb438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.violinplot('species', 'sepal_length', data=iris, inner=None)\n",
+    "sns.swarmplot('species', 'sepal_length', data=iris, color='black', size=4)\n",
+    "sns.despine()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of plotting one categorical variable vs a numerical variable, we can also plot two numerical values against each other to explore potential correlations between these two variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f65673f7e80>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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/Xxl/9apdspyacpsZ19Yq40shyHHod/vQ6XChtasfPX2enNRXEZu7I0cBNqUq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\nIYQQHiENRwghhEf8D9Yo9mA5XZ1SAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6558ba5208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot('sepal_width', 'sepal_length', data=iris, size=6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is a regression line plotted by default to indicate the trend in the data. Let's turn that off for now and look at only the data points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f6558aef710>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Dbhk5mLhlx5l2f7EDQLWlHlt3nHG6TUnHT2/PiUgtWHDILSMHE0+fL3N5/6lW9yvp+Ont\nORGpBQsOuWTkYKLF5n5d9Q2NsNgaACjr+OntORGpCQsOuWTkYKLZ5H5dQYH+MJuuX3ujpOOnt+dE\npCb6+y1BHmfkYOLQgZEu7x/W6n4lHT+9PScitWDBIbf0GkyU0rBt/p1DEGZu/xlemDkImXcOcbpN\nScdPueTOiUgteFk0SSIiYCmCkjyR3FxN8aWrWPfGcVxssX3fnjdgiYscjlxlVRZs3XEGp1rkcIYN\njEQmczikYiw4JJu3ApbepiRP1HqfxsZG+Pv7u9ynJTkdP5Wy2Br4ng1pgvZ+a5DPabHYAMryRK33\naSo2rvZpydvFBgCLDWmGNn9zECmgJE9k5AwSkaex4JAhKMkTGTmDROQNLDhkCEryREbOIBF5A39S\nyDCU5ImMnEEi8jQWHDIMJXkivWaQiHyBBYd0w12Q02wKwsJ7E5Ga3BdhIdevHgsLCUZqct8OL29u\nvY/dbne7j7fXQaRVQq+nLCkpwerVq3H06FGEhYVh3rx5uP/++9tsl5WVhcOHDyMg4Ieukp9++qnI\nqZJGyA1yKmnYVn7FiveOfN0i+FmDqupaJMZGerSrqBob3BF5krCC43A48Otf/xpjxoxBbm4uvvrq\nK8yePRtDhgzByJEjnbYtKCjAm2++iaFDh4qaHmlQe0HOamsdDh4rRkFRhdtnIFKKTXsN1RodDnxd\ncv12TzRU6+w6iLRCcsG5evUq/vSnP+HcuXOw2Wxt7n/rrbdc7n/y5ElcvnwZixcvRkBAAG655Ra8\n9dZbiIiIcNquvLwcFRUViI2NlTo1MigpQc5pKQM6NYaUhmqbFk/p1Bgi1kGkBpILzrJly3D69GlM\nnToVXbvK/4vu7NmzuOWWW7Bu3Trs3LkTYWFhePjhh/Gzn/3MabuCggKEhoYiKysLhYWFuPnmm/H4\n449jxIgRksaprKxEVVWV020lJQzn6ZGUUGZnf1GLaKgmYh1EaiC54Hz88cd49dVXkZiYqGigK1eu\n4MiRIxg7diwOHjyIM2fOYN68eYiOjkZycnLzdrW1tUhMTMSSJUvQr18/5OfnIzMzE3v27EFUVJTb\ncfLy8pCbm6tojqQdckKZSnMychqqKf0IGxHrIFILyQWne/fuMJlMigcKDg5GeHg4srKyAAAjR47E\nbbfdhv379zsVnPT0dKSnpzd/PWvWLGzfvh1HjhxBRkaG23HmzJnTZruSkhLMnTtX8dxJfZpCma5+\nWXc2lNnUUM1V0elsQzUR6yBSC5eP4rq6uuZ/mZmZyM7OxpdffgmbzeZ0X12d+5a5/fv3h91uh93+\nwyWfdrsdrT+seu/evdi9e7fTbbW1tejSpYukBUVERKB///5O/6KjmZXQIxGhTBEN1RguJaNw+Qxn\n2LBh8Pu/lrlNhWH69Ontbnvu3DmXA02YMAEmkwm5ublYsGABTp06hffffx/btm1z2s5isWD9+vWI\njY1Fv3798Nprr8Fms2HChAmSF0XGkD6qLwqKKjpsN+CJUObS+5LaXKXWxFMN1USsg0gNXPbD+eST\nTyR/o9GjR7vd5uuvv0Z2djZOnz6NsLAwLFiwADNmzMCqVasAANnZ2QCAF198Edu3b0dVVRUGDx6M\n1atXIy4uTvJcWmM/HO2pb7AjKDDA7XYtG8NdrbGha6hJcmM4qWN0pqGasnXUomtoF002uCNyRXID\nttzcXDz00EMICQlxur26uhqbNm3CihUrvDJBT2DB0QYl4ceyKgu27DiD0y06Xw4dGIn5HXS+7GzA\nUsoFAp1fx/Ui5WodRFrksuB88cUXuHz5MgAgMzMT69evb3NJ9Pnz5/H888/js88+8+5MO4EFR/2U\ndOMsq7Jg8aZDqLa0fcM9zByEZxemOP2yVjKGGtdBpFUu38OpqqrCvHnzmr9+9NFH22xjNpvx4IMP\nen5mZChKwo9bdpxp95c0AFRb6rF1xxksn/vDS70iApYi1kGkVS4LzujRo1FYWAgASE1NRX5+Prp3\n7y5kYmQsSsKPp8+XudznVKv7RQQsRayDSKskX9x/4MABFhvyCiWdNS0295026xsaYbE1KB5DLhHr\nINIyycHP1NTU5kukW/Lz80NQUBB69uyJn/70p7jnnns8OkHSPyXhR7Pp+teuflkHBfrDbApUPIZc\nItZBpGWSf7ruv/9+VFRU4Pbbb8eKFSuwYsUKZGRkoLKyEpMnT8b48eOxadOmNrkaIimUhB+HDox0\nuc+wVveLCFiKWAeRVkkuOH/729/w1FNP4bHHHkNaWhrS0tKwaNEirFmzBp988gnmz5+PtWvX4s03\n3/TmfEmnlHTWnH/nEISZ27+qLMwchMw7h3R6DLlErINIqyQXnAsXLmDIkLYP/Pj4eJw/fx4AMGDA\nAJSWlnpudiSbiG6RFpv7jzKSq3VnTYfD4bazZmQ3M55dmIKk+BsRFOgPh8OBoEB/JMXf2O6lxErG\n6Ow6APddRVuvA4DLdbSm5Hywqyj5guQXhhMSEvDyyy/jqaeeQmDg9d3sdjteffVVxMfHAwCOHz+O\n3r17e2em1CER3SLlBiyVcqApFubX6uv2mU1BSIjpAYutAVdr6tA1NBgJMT1crlvuGHIp6Soa2c3c\nfOmzxdbg9j0bJeeDXUXJ1yR/0kBhYSEeeughBAQEYPDgwWhsbMTnn38Ou92OLVu2wGKxYO7cuViz\nZk2bHje+pufgp4gwo4hgopJ1yN1HxLESQcn50MvaSdskv6QWHx+Pf/zjH1iwYAFuuukm9O/fH7/9\n7W+xb98+DB48GL169UJ+fr7qio3eSQkadpaUYGJnKVmH3H1EHCsRlJwPvaydtE3WtZZhYWH4+c9/\n3u59envmoBUiwowigolK1iF3H7101lRyPvSydtI2yQXn4sWLePbZZ3HmzBnU19e36WPz4Ycfenxy\n5JqIbpFygolKsyJK1iF3H7101lRyPvSydtI+yb8hli9fjoqKCjzwwAMICwvz5pxIIhFhRhHBRCXr\nkLuPXjprKjkfelk7aZ/k3xKnT59Gfn4+YmNjvTkfkml0Qi8cPNbx6++eCDMOHRiJE4WXO7zfE8FE\nJeuQu4+IYyWCkvOhl7WTtkn+k6ZPnz6orq725lyoFSlZCRFhRhHBRCXraL1Py1d529tH7vZqpeR8\ntF37D4vX0tpJ2wKefPLJJ6Vs2LNnT6xbtw6RkZGw2+24evUqqqqqmv9FRER4earKXb16Fa+//jp+\n+ctftunnozYWWz32HP4KeXsKseujInx08jtUW+sQ3fOGdjtHBgUGYGTcjQD8UFppRV2DHWEhwZiY\neBNm3hrnsTDjxOF98H1ZDcqqrGhsvB6wHBEbheW/HOWRHI6SdQQFBiC+XwQKv67EhW+voOpaLay1\nDeh/Uzgy7xiC8DBTp7ZXKyXno2ntn39dif98ewVV1+pgrW3AAI2tnbRNcg6nKdzptLOfHxwOB/z8\n/HDu3DmPT85TtJLD8URWQsQbv525QEAqKetofbyaHouAtByOu+21Qsr5aLt2oOmzeLW8dtIWyb81\n9u/f7815EDzTIEzEG78iPrlYyjpaH6+Wn2be3vGSu71WSDkfbdf+w31aXjtpi+TfTjfddBNuuukm\nBAYGori4GD169ECXLl2ab6fOk5KVoB/IPV5GPr5GXjuph+SCY7FY8Mgjj2Dy5Ml48MEHUVpailWr\nVmHWrFmoqKjw5hwNQUSDMD2Re7yMfHyNvHZSF8kFZ926dbh06RL27NmDLl26AAAee+wx1NbW4g9/\n+IPXJmgUTVkJV5iV+IHc42Xk42vktZO6SH6E7d+/H8uXL0f//v2bb4uJicFTTz2FQ4cOeWVyRiOi\nQZieyD1eRj6+Rl47qYfkglNdXd3uJwz4+/ujoYH91j1BRKZGT+QeLyMfXyOvndRDcsGZOHEiXnjh\nBdjtP4QRKysrsW7dOkyYMMErkzMaJc27tMBbzb5aH6/GRtcN1do2YJN/fL3RfE4EvT62SFskX9/6\n+9//Hr/5zW8wbtw42Gw2zJs3D5cuXcKAAQPw9NNPe3OOhqKkeZcaiWr2ZbHV4/R/ynD+myrU1dsR\nHGRFiCkQ44b26nCcHxquOVp93T5Rzee8TS+PLdIuycHPJocPH8aFCxfQ0NCAmJgYTJgwwSnPoEZa\nCX7qhahmX3IbkSmZl4jmc0RG4fJPnLq6ujb/kpKScM8992DmzJkYPXo06uvrUVenzZcZyDtENfuS\n24hMybxENJ8jMgqXL6kNGzbM7bMXLXy0DYklqtmX3EZkSuYlovkckVG4LDivv/66rG/mcDhQUVGB\nHj16dGpSpF2imn3JbUSmZF4ims8RGYnLn5LRo0fL+mbl5eVISUnhsx0DE9XsS24jMiXzEtF8jshI\nPH6ZisxrEEiHRIUMh7pp/Na6EZmSeckdg4g65vGCo/Yr1sj7RIUM5TYiUzIvEc3niIyCF+KrmJFD\nhlLCopHdzHh2YQqS4m9s8Zlp/kiKv7Hdy5XbBj9dB0XbG8PhcLgcQyu8FcYlckXoi88lJSVYvXo1\njh49irCwMMybNw/3339/m+3effddbNiwAeXl5RgzZgxycnIQGWmMly6MHDJUEhaN7GbG8rmj/29/\naW/e/xD09Gv1dcdrSYjpAYutAVdr6tA1NBgJMT00l84XFcYl6oiwZzgOhwO//vWvMWDAABw5cgQv\nv/wycnNzceLECaftCgsLsXr1ajz33HP497//jcjISCxfvlzUNH2qKWR4ovByi4/Vb8SJwstYvOkQ\nyqosPp6hMlKLzaa3P8PBY8Wotl5/ZldtrcPBY8XY9PZnsNhcX2EGuG9E1noMPz/3Y7Tex99f/rzU\nwBPHl6izhBWckydP4vLly1i8eDGCgoJwyy234K233nL69GkA2LlzJ9LS0jB8+HCYTCYsXrwYhw4d\nQlmZ/vMORg4ZigiLKhlDVIjV2/SyDtI2YVepnT17Frfcckvzh33edtttOHnyJCIiIpy2u3DhAgYO\nHNj8dUREBMLDw1FUVCRp/MrKShQVFTn9Ky7Wxg+TkUOGIjpSKhlDL50y9bIO0jaXr0HI+cia4OBg\ndOvWDX/5y1/avf/KlSs4cuQIxo4di4MHD+LMmTOYN28eoqOjkZyc3Lyd1WqFyWRy2jckJARWq1XS\nPPLy8pCbmyt53mph5JChiLCokjFEhVi9TS/rIO3r9EfbNDl37hwCAwMxfPjwdu8PDg5GeHg4srKy\nAAAjR47Ebbfdhv379zsVHJPJBJvN5rSv1WqF2SztDfM5c+YgIyPD6baSkhLMnTtX0v6+YuSQoYiw\nqJIxRIVYvU0v6yDt8+hH27jSv39/2O122O12BAQEAADsdnubl+BiYmKcXj6rqKjAlStXEBMTI2mc\niIiINi/TBQVp4wqcoQMjcaLwcof36zlkODqhFw4e6/ilT0+ERZWMIWJeIuhlHaRtHvloGynvkUyY\nMAEmkwm5ublYsGABTp06hffffx/btm1z2i4jIwNz5szBjBkzMHToUDz33HOYNGlSmyKiR/PvHOLy\no/D1HDJMH9UXBUUVHbYO8ERYVMkYIuYlgl7WQdomuR/Ol19+iaeffhrnz5936vpZV1eHa9euSfr8\ntK+//hrZ2dk4ffo0wsLCsGDBAsyYMQOrVq0CAGRnZwMAdu/ejY0bN6K0tBTJyclYu3Ztpz4QVEv9\ncMqqLNi64wxOtcjhDBsYiUyN5XCUsNjqsf9oMT45W9KcExmd0Atpo6I9lhNRMkbLfa7W1KJraBeP\nz6ul+gY7ggIDPP59RRxfIlckF5xZs2ahsbERM2bMwJo1a/D444/j22+/xZtvvons7Gzccccd3p6r\nYloqOC3p8QIBqUS8gS11DOcw7vVi4OkwruhQJi8QIF+Q/Nvs7Nmz2L59OwYPHox33nkHMTExmD17\nNqKjo5Gfn6/qgqNVRi02gLSwqIgx2nb89HMK43ri423a60TaFMosKKrwWIfUllhsyBckP+r8/f0R\nHh4O4PoFAIWFhQCASZMm4fPPP/fO7Ih8TEQYl6FMMgrJBWfIkCF4++23AQCDBg3CoUOHAFwPavr7\n868l0icRYVyGMskoJL9ms3jxYsyfPx/h4eGYMWMGtm7diltvvRWlpaWYMWOGN+dI5BMiwrgMZZKR\nSP4pGT6Q5P7LAAAgAElEQVR8OA4cOACr1Yrw8HC888472LVrF3r27ImpU6d6c45EPiEijMtQJhmJ\nrJ+U0NBQfPXVV/j4448REBCASZMmSQ5kEmmRiDAuQ5lkFJILzqVLl7BgwQKcOXMG4eHhaGxsRHV1\nNcaPH4/nn38eN9xwgzfnSRqmJFdSVlWDyG7td+f05DjuiAjjMpSpbt7KRRmR5BxOVlYWampqsHbt\nWkRHX/8BKCoqwooVK/CjH/0I69at8+pEO0OrORwtU5Ir+eJiBXK2fYKKq7XNt3Xv2gUrHxiN2L7d\nPTaOXCLCuAxlqgub1XmH5IIzYsQI/OUvf0FsbKzT7efOncOcOXNw/Phxr0zQE1hwxGovV9KkZ4/Q\ndnMlX1yswJJNh9DYzqPR3w9YtzClTdFRMk5niQjj8gIB3/LF48ooJD+qe/fujYsXL7a5vaKiwjDt\nn0kaJbmSnG2ftFtsAKDRAeRsO+qRcTpLRBiXxca3mIvyHsk/PQ8++CBWrVqFoqIiJCUlITAwEAUF\nBdi8eTNmzJiBDz/8sHnbiRMnemWypA1SciXTUgY43dbyZbT2VFy1tblNyThE7vBx5T2SC87vf/97\nAMD69evb3PenP/2p+f9+fn6SPsiT9ElJrqSsqv2/JlurqLKhezeT4nGI3OHjyrskF5ymj7IhckVJ\nrkTq1WhNxUbpOETu8HHlXbKP2okTJ/DOO++guroaX375paw21GQM7nIj7d3fvWsXl/t072pqc5uS\ncYjc4ePKeyQXnIqKCtx77724//778cQTT6CyshLr16/HT3/6U0kN2Mg40kf1Rc8e7T9r6ShXsvKB\n0fDvoJu5vx+w8oFRHhmHyB0+rrxHcsHJyclBZGQkjhw5gi5drv81+swzz6Bv377Iycnx2gRJfeob\n7C7vN5uCsPDeRKQm90VYSDAcjusvQ6Qm9+3wktLYvt2xbmFKm2cy3bua2r0kur1xGhsdbseRsw5P\n7UPe443z0fpxBbh//GqFrx+/kt/D+fjjj/Haa68hNPSHyh8eHo5ly5Zh5syZXpkcqYeSIJwDjub/\nOX/dvti+3fHa6tsAOF8g4G5ep/9ThvPfVKGu3o7gICtCTIEYN7RXu/NSsg6GANVFxPkwm4IwLWUA\npqUM0PwFAmp6/Eo+ina7HY2NbT/E8Nq1awgI4Mc+6FlTEO7gsWJUW6+/Z9fUIGzT25/BYqt3ub2f\nn5/L7dsjpdg0NUc7UXgZ9Q2N8PNzbo5WVmXp1DqU7kPe44vzofVio6bHr+QjmZ6ejnXr1qGiogJ+\nftdfbD9//jzWrFmDtLQ0r02QfE9uEE5UcE5uczQl82IIUF14PuRR2/GSXHBWrFiBsLAwTJgwARaL\nBdOmTcO0adPQp08fLF++3JtzJB+T2yBMVEMxuc3RlMyLzdHUhedDHrUdL8nv4YSFhWH16tX47W9/\ni2+++QYXLlyAxWLB0KFD+UnROiY3CCcqOCe3OZqSeTEEqC48H/Ko8XhJHmXfvn348Y9/jMuXL6N/\n//7Izc3Fnj178Mgjj+CNN97w5hzJh5qCcK60DMLJ3V6ppuZorrRsjqZkXqLWQtLwfMijxuMleaSN\nGzfit7/9LcaPH4/8/Hz07t0bu3btwvr16/Hqq696cYrka3KDcKKCc0PdND9r3RxNybwYAlQXng95\n1Ha8JBecr776ChkZGQCAgwcPNl8oEBcXh7Iy16+lk7bJDcKJCs7Nv3MIwszt/wXXXnM0JfNiCFBd\neD7kUdvxCnjyySeflLLhX//6V8THx8Nms+FPf/oTHn30UfTp0wc7d+7E119/jdmzZ3t5qspdvXoV\nr7/+On75y1+ia9euvp6O5gQFBmBk3I0A/FBaaUVtvR03mIMxMfEmzLw1rs21/K23r2uwIyyk4+3b\nU99gR4C/67+HzKYgTBzeB9+X1aCsygq7vRHBQQEYERuF5b8c1aY5mtx1tLePra4BN5i7yFoLeY4n\nHltGorbjJas9we9+9zsAQGJiIpKSkpCbm4sXXngBf/zjH702QVIPOUFOJcE5JQE1sykICTE9YLE1\n4GpNHbqGBiMhpofLHyS5gVS54VLyLj2FMkVQ0/GS3PETuN7d89tvv8XEiRNhMpnw2WefwWQyIT4+\n3ptz7DR2/OwcER0QlYwhdx8lYzSFS9vL+4SZg/DswhSPtZkm0jtZpW7QoEFIT0+HyXQ9BZ6YmKj6\nYkOdJyI8JiKUqWQMueFSIuoYn4uSWyLCYyJCmUrGkBsuJaKOseCQS3LCYyLHkLuPkjHkhEuJyD0W\nHHJJRHhMRChTyRhyw6VE5BoLDrklIjwmIpSpZAy54VIi6hgLDrklIjwmIpSpZAy54VIi6pisy6K1\nykiXRdc32BEUKL0/kcVWB7MpWMJ29dh/tBifnC3BNUstbjB3weiEXkgbFS2ps6aUObUcoymH424M\nufsoGaOsyoKtO87g1Pmy5hzDsIGRyLxziKEuiZb72CJ5jHB8hRacl19+GRs2bEBQ0A8/2Fu3bkVy\ncrLTdllZWTh8+LBTY7dPP/1U8bh6LzhyA5NlVRZs2XEGp1v8Ah06MBLzXfwCbTnGNUsdbjC7HqOz\nXQaVBNTk7qNkjKZPnzYKNXWL1COjHV+hBeexxx7D4MGD8dBDD7ncLiUlBZs3b8bQoUM9Mq6eC47c\nMKOSIKOIgCWpD8+jdxnx+Ap9D+fcuXMYNGiQy23Ky8tRUVGB2NhYQbPSNrlhRiVBRrV2/CTv4nn0\nLiMeX2EFx2q1oqioCK+//jomTJiAqVOnIj8/v812BQUFCA0NRVZWFsaOHYtf/OIXsl5Oq6ysRFFR\nkdO/4mL9nbgmcsOMSoKMau34Sd7F8+hdRjy+wl6MLisrQ1JSEmbOnIlNmzbh1KlTePjhhxEVFYXJ\nkyc3b1dbW4vExEQsWbIE/fr1Q35+PjIzM7Fnzx5ERUW5HScvLw+5ubneXIpqyO3oJ7dLppIx1Nhl\nkOTjefQuox5fYQUnOjoaeXl5zV8nJyfjjjvuwP79+50KTnp6OtLT05u/njVrFrZv344jR4409+Nx\nZc6cOW22Kykpwdy5czu/CJVpCjO6euC2DDM2BRldFZ3WQUa5Y8jdntSJ59G7jHp8ha3m7Nmz2LJl\ni9NttbW1CA52viR379692L17d5vtunTpImmciIgI9O/f3+lfdLR+mzLJDTMqCTKqteMneRfPo3cZ\n8fgKKzhmsxm5ubnYu3cvGhsbcfjwYezatQs/+9nPnLazWCzIycnB+fPnUV9fj5deegk2mw0TJkwQ\nNVVNkRtmVBJkVGvHT1+ob7D7egoeYbHVud3GF+dRxPGVsnYR9Pxz0hGhl0UfOHAAGzZsQHFxMXr2\n7IlFixbhJz/5CVatWgUAyM7OBgC8+OKL2L59O6qqqjB48GCsXr0acXFxisfV82XRgPwwo5Igo4iA\npVrpJSuhNH/l7fMo4vgqWbsIevo5kYKfNKAzct9kVBJkFBGwVAu9ZCU80UjOG+dRxPHVShM9Lf+c\nSKXv1RmQ3AesktS83DG0/EOkl6yEJxrJeeM8iji+Wmmip+WfE6n0v0KiTtBLVkKtjeREHF+1rt2I\nWHCIOiCi+ZwIam0kJ+L4qnXtRsWCQ9QBEc3nRFBrIzkRx1etazcqdf+kEPmYXrISam0kJ+L4qnXt\nRsSCQ+SCXrISam0kJ+L4qnXtRsTLoonc0EtWQq2N5DrT3E8qta7daPjCJZEbZlMQpqUMwLSUAZrO\nSkR2M2P53NEA1NdIzoGmv3v9Wn3tGWpeu5HwqBPJoNVi05pafuG2Dn76+V2/Mu3gsWIUFFV4JVir\nlrUbkT5+eohIk/QSrCVpWHCIyGf0EqwlaVhwiMgn9BKsJelYcIjIJ/QSrCXpeCaJyGf0EqwlaVhw\niMhn9BKsJWlYcEiV9NJZU62UHF9vnBOzKQgL701EanJfhIVcbzcfFhKM1OS+muk11B4+ftvHC9JJ\nNfTSWVOtlBxfEedEL8FaPn7d40fbkCropbOmWik5vjwn0vFYSaPNPyVIdxgA9C4lx5fnRDoeK2lY\ncEgVGAD0LiXHl+dEOh4raVhwyOcYAPQuJceX50Q6HivpWHDI5xgA9C4lx5fnRDoeK+l4BEgVGAD0\nLiXHl+dEOh4raVhwSBUYAPQuJceX50Q6HitpeFk0yVbfYEdQYIDHv69eOmuKJvV8KOms2XKfqzW1\n6Brq+W6cLcl9bF2ptiI8LMTj81CCj1/3GPwkSRgAVBel50NuZ02LrR6n/1OG899Uoa7ejuAgK0JM\ngRg3tJfHzrvctRRfuoo/vnEcxZeuodHhgL+fH6J73oCl9yUhumdXj8xJCT5+3eMzHHKLoTZ1ERXi\nLKuyYPGmQ6i2tL0CK8wchGcXpiCym1noWoovXcUjG/7V7hVfQYH+eH7RZJ8WHXKNJZjcYqhNXUSF\nOLfsONNusQGAaks9tu44I2PW7ZM7rz++cbzDy4vrGxqx7o3jnZ4TeQ8LDrnFUJu6iApxnj5f5nKf\nU27ul0LuvIovXXO5/UU395NvseCQSwy1qYuoEKfF5v6c1jc0wmJrkDDrjvaXN68r1VY0unkHoNHh\nwNXqOsVzIu9iwSGXGGpTF1EhTrPJ/TkNCvSH2aT8uiO58woPC4G/n5/L7f39/NA1LFjxnMi7+FuC\n3GKoTV1EhTiHDox0uc8wN/dLIXde0T1vcLl9Xzf3k2+x4JBbDLWpi6gQ5/w7hyDM3P4zkDBzEDLv\nHCJj1u2TO6+l9yV1+MwrKNAfS+5L6vScyHt4WbRC3go/dhZDmd4l4rxLGUNUiLOsyoKtO87g1Pky\n1NY1oEtwIIYNjETmnUPcXhKtJJAq5bFVfOkq1r1xHBdb5HD69rwBS3ycwyH3hAY/X375ZWzYsAFB\nQT88iLZu3Yrk5GSn7d59911s2LAB5eXlGDNmDHJychAZ2fmn752l1o5+DGV6l4jjq2QMUSHO5lH8\n/Z2+9tQ65D62ont2xabFUwAAV6vr+J6Nhgh9hvPYY49h8ODBeOihhzrcprCwELNnz8Yrr7yCuLg4\nrFmzBpcvX8bWrVsVj+uJZzhqDT+qdV56IeL4yh1DVIhT7j58LJI7Qv9MPXfuHAYNGuRym507dyIt\nLQ3Dhw+HyWTC4sWLcejQIZSVdf6a/85Qa/hRrfPSCxHHV+4YokKccvfhY5HcEVZwrFYrioqK8Prr\nr2PChAmYOnUq8vPz22x34cIFDBw4sPnriIgIhIeHo6ioSNI4lZWVKCoqcvpXXNz5B7paw49qnZde\niDi+cscQFeKUuw8fi+SOsPdwysrKkJSUhJkzZ2LTpk04deoUHn74YURFRWHy5MnN21mtVphMJqd9\nQ0JCYLVaJY2Tl5eH3Nxcj85dTkBN5Hsbap2XXog4vnLHUDInOSHOplyN3H34WCQphBWc6Oho5OXl\nNX+dnJyMO+64A/v373cqOCaTCTabzWlfq9UKs1nahwTOmTMHGRkZTreVlJRg7ty5iufeFFBz9QPl\ni/CjWuelFyKOr9wxlMypKcTpqoC0DnHK3YePRZJC2Nk/e/YstmzZ4nRbbW0tgoOdrzCJiYlxevms\noqICV65cQUxMjKRxIiIi0L9/f6d/0dGdz4moNfyo1nnphYjjK3cMUSFOufvwsUjuCCs4ZrMZubm5\n2Lt3LxobG3H48GHs2rULP/vZz5y2y8jIwHvvvYdjx46htrYWzz33HCZNmoSIiAhRU22XWsOPnZ1X\nfYNd9phK9tGq1se35TWdUo6vxeb+c73knkNRIU65+7Q9Vj8cLAaECRB8WfSBAwewYcMGFBcXo2fP\nnli0aBF+8pOfYNWqVQCA7OxsAMDu3buxceNGlJaWIjk5GWvXrkWPHj0Uj+up4Kdaw49y56UkK6HW\nDJIIZVUWbNlxBqfPlzW/BzF0YCTmdxB+lLs9oOwcyn0stgxxNs3LXYhT7j5KxiDj4CcNKKTWNz/d\nzUtU8y69aL12h8MBv//7AMn21u6JpmVyH1tKHostLxDw1D5tjxXQ9Fmben+ckDTq+42pEWosNoD7\neYlq3qUXrdfu1+LTittbuyealsl9bCl5LCr5lGd3+7Q9Vj/cp/fHCUmjzt+a5DWimnfphdy1i2ha\nplZGfpyQNCw4BiKqeZdeyF27iKZlamXkxwlJx4JjIKKad+mF3LWLaFqmVkZ+nJB0PPsGI6p5l17I\nXbuIpmVqZeTHCUnDgmMwopp36YXctYtoWqZWRn6ckDS8LNqAlGQ41JpBEsG5cZkNXUNNLtfunEW5\n3oRMbVkUi60OZpPn+8gY+XFC7unvxWRyS0kzNaM3YGtqXFbf0IjLlTaXjcvMpiAkxPSAxdbQ3Fkz\nIaaHz3/hKgmkymXkxwm5x2c4RC7opQmZJwKpRJ3FPz+IXNBLEzJPBFKJOosFh8gFvTQhM3IgldSD\nBYeoA3KDnGoNPxo5kErqwoJD1AG5QU61hh+NHEgldWHBIXJBL03IjBxIJfVgwSFyobNNyFryZfjR\nyIFUUg8WHCIXIruZ8ezCFCTF39j8slRQoD+S4m9s91JisykIC+9NRGpyX4SFXA9WhoUEIzW5r+RL\noqV0CfX2Ooi8gTkcIhnkNi6TGn4UEcpsSUkDNqLO4jMcIhnk/pKWWmwWbzqEE4WXm68mq29oxInC\ny1i86RDKqiyK5uoKiw35AgsOkY8xlElGwYJD5GMMZZJRsOAQ+RBDmWQkLDhEPsRQJhkJCw6RjzGU\nSUbBgkPkYwxlklGw4BB5UX2D3e02rUOZDofD66FMKfPSwhikLXxhmMjDLLZ67Dt6EUfPXmpuszwq\noSfSR/Xt8JMGnLuE1qFraLDHu4QqmZcaxyDt4icNEHmQko6fIrqE6mUM0ja+pEbkQUo6foroEqqX\nMUjbWHCIPEhJx08RXUL1MgZpGwsOkYco6fgpokuoXsYg7WPBIfIQJR0/RXQJ1csYpH08+0QepKTj\np4guoXoZg7SNBUdnmH3wLSUdP0V0CdXLGKRtvCxaB5h9UBeLrR77jxbjk7MlzedjdEIvpI2K7vB8\nKNlHxLzUOAZpl/CCU1ZWhmnTpuEPf/gDpkyZ0ub+rKwsHD58GAEBAc23ffrpp50aU88Fh9kHdZPa\n8bOz+8illzFIW4R/0sDKlStRVVXV4f0FBQV48803MXToUIGz0i4p2YdpKQMEz4qaKPmFK+KXtF7G\nIG0RWnC2b9+OkJAQ9O7du937y8vLUVFRgdjYWMVjVFZWtiloJSX6vf5fSvaBBYeI1EBYwSkqKsK2\nbdvw9ttv46677mp3m4KCAoSGhiIrKwuFhYW4+eab8fjjj2PEiBGSx8nLy0Nubq6npq1qcrIP/GuT\niHxNSMFpaGjA0qVLsXLlSnTr1q3D7Wpra5GYmIglS5agX79+yM/PR2ZmJvbs2YOoqChJY82ZMwcZ\nGRlOt5WUlGDu3LmdWYIqNWUfXBUdZh+ISC2EFJzNmzdj0KBBmDx5ssvt0tPTkZ6e3vz1rFmzsH37\ndhw5cqRNEelIREQEIiIinG4LCtLvm+ajE3rh4LGOP6OK2QciUgshf/ru3r0bu3btQnJyMpKTk/Hd\nd9/h0UcfxZYtW5y227t3L3bv3u10W21tLbp06SJimprE7AMRaYWQZzh79+51+jo1NRVPPPFEm8ui\nLRYL1q9fj9jYWPTr1w+vvfYabDYbJkyYIGKammQ2BWHhvYnMPihQ32BHUGCA+w07uY9cIsYg8gWf\nN2BbtWoVACA7Oxt33XUXSktLMW/ePFRVVWHw4MHYunUrzGbPdzzUE7MpCNNSBmBaygBeIOCGkpAs\nG5cReQY/aYAMw8jN0YjUgH8Kk2EYuTkakRqw4JBhGLk5GpEasOCQIRi5ORqRWrDgkCEYuTkakVrw\nUUyGYeTmaERqwIJDhmHk5mhEasDLoslQOtsc7ZqlDjeY2biMSAmfBz+JRFIaknXA0fw/5699Oy8i\nLWHBIcOS8ku9dSjTz88P1dY6HDxWjIKiCq+EMllsSK/4yCZygaFMIs9hwSFygaFMIs9hwSHqAEOZ\nRJ7FgkPUAYYyiTyLPylELjCUSeQ5LDhELjCUSeQ5LDhELjR1VE1N7ouwkGAA119GS03uyz41RDIx\nh0PkBkOZRJ7BnxwiGVhsiJTjTw8REQnBgkNEREKw4BARkRAsOEREJAQLDhERCcGCQ0REQrDgEBGR\nECw4REQkhCE+acButwMASkrYu4SIyJN69eqFwEBppcQQBae0tBQAMHv2bB/PhIhIX/bv348f/ehH\nkrb1czgcDi/Px+dsNhvOnDmDqKgoBAQEdPr7FRcXY+7cuXj11VcRHW2sTwvm2rl2I63dqOsGpK+d\nz3BaMZlMSE5O9tj3q6+/3gWyV69ekiu7XnDtXLuR1m7UdQPeWTsvGiAiIiFYcIiISAgWHCIiEiLg\nySeffNLXk9Aik8mE0aNHIyQkxNdTEY5r59qNxKjrBjy/dkNcpUZERL7Hl9SIiEgIFhwiIhKCBYeI\niIRgwSEiIiFYcIiISAgWHCIiEoIFh4iIhGDBISIiIVhwOnDs2DHcc889SEpKQnp6Ot566612t3v3\n3XeRlpaGxMREZGVloaysTPBMPU/q2rOysjBs2DCMGDGi+Z/W7d69G1OnTsWIESPw05/+FPv27Wt3\nOz2ed6lr1+N5b1JWVoZx48bh4MGD7d6vx/MOuF+3x865g9qoqqpyjBo1yvH3v//dYbfbHWfOnHGM\nGjXK8dFHHzltd+7cOcfIkSMdn332mcNqtTpWrFjhmDdvno9m7RlS1+5wOBwTJ050nDp1ygez9I4L\nFy44hg8f7jh+/LjD4XA4PvroI0dCQoKjvLzcaTs9nnepa3c49HfeW5o/f74jPj7eceDAgTb36fG8\nN3G1bofDc+ecz3Da8d1332Hy5MmYNm0a/P39kZCQgDFjxuDEiRNO2+3cuRNpaWkYPnw4TCYTFi9e\njEOHDmn6rx6pay8vL0dFRQViY2N9NFPP69+/Pz766COMHDkSDQ0NKCsrQ2hoKIKDg5220+N5l7p2\nPZ73Jtu3b0dISAh69+7d7v16PO+A+3V78pyz4LRj0KBBWLduXfPXV65cwbFjxxAfH++03YULFzBw\n4MDmryMiIhAeHo6ioiJhc/U0qWsvKChAaGgosrKyMHbsWPziF7/Ap59+Knq6HhcaGori4mIMGzYM\nS5cuxaJFixAWFua0jR7POyBt7Xo970VFRdi2bRtcfZaxHs+7lHV78pyz4Lhx7do1PPzww0hISEBq\naqrTfVarFSaTyem2kJAQWK1WkVP0Gldrr62tRWJiIlauXIkPPvgA06dPR2ZmJkpLS300W8/p3bs3\nTp48iW3btuGZZ57B4cOHne7X83l3t3Y9nveGhgYsXboUK1euRLdu3TrcTm/nXeq6PXnOWXBcKC4u\nxi9+8QuEh4cjNzcX/v7Oh8tkMsFmszndZrVaYTabRU7TK9ytPT09HVu2bMEtt9yC4OBgzJo1C717\n98aRI0d8NGPPCQwMRFBQEMaNG4dbb70V+/fvd7pfz+fd3dr1eN43b96MQYMGYfLkyS6309t5l7pu\nT55zFpwOnD17Fvfeey8mTpyIzZs3t/nLBgBiYmKcnk5XVFTgypUriImJETlVj5Oy9r1792L37t1O\nt9XW1qJLly6ipulx//rXvzB37lyn2+rr63HDDTc43abH8y517Xo877t378auXbuQnJyM5ORkfPfd\nd3j00UexZcsWp+30dt6lrtuj57zTlx3oUGlpqWPs2LGOF1980eV2BQUFjpEjRzqOHj3qsNlsjpUr\nVzoyMzMFzdI7pK79nXfecYwfP97x5ZdfOurq6hxbt251pKSkOGpqagTN1PMuX77sSEpKcvy///f/\nHHa73fHPf/7TMXLkSMf58+edttPjeZe6dj2e99amTJnS7tVaejzvLXW0bk+eczZga8cLL7yADRs2\ntHmqfP/996OyshIAkJ2dDeD6XwkbN25EaWkpkpOTsXbtWvTo0UP4nD1FztpffPFFbN++HVVVVRg8\neDBWr16NuLg44XP2pGPHjuEPf/gDvvrqK9x8881YunQpxo4di1WrVgHQ73kHpK9dj+e9pdTUVDzx\nxBOYMmWKIc57E1fr9tQ5Z8EhIiIh+B4OEREJwYJDRERCsOAQEZEQLDhERCQECw4REQnBgkNEREKw\n4BD5wJEjRxAXF4fa2lqPft/U1FRs3769w/vj4uLwwQcfAABqamqQn5/ffN99992HZ5991qPzIWop\n0NcTICLPyc/Pl/zZXtu2bcMHH3yAu+++28uzIrqOBYdIR7p37y55W2a+STS+pEaG9+c//xlpaWkY\nMmQIMjIy8P777wMAqqursWLFCowaNQpjx47FY489hvLy8ub94uLi8L//+7+YOnUqEhMTMX/+fFy6\ndKn5/pMnT+K+++5DYmIihg0bhpkzZ+Lzzz+XNbfGxkaMGTMGu3btar5t7ty5Ts9K/vnPf2L8+PFw\nOBxOL6k1NDTgmWeewZgxYzBu3Di88847zfv89a9/RW5uLk6ePOn0ESXl5eX41a9+hWHDhmHKlCn4\n29/+Jmu+RK6w4JChFRQUICcnB8uWLcM//vEPTJ8+HYsWLUJ5eTlWrlyJkpISvPrqq3j11VdRU1OD\nhx9+2OmZwfr16/HII4/grbfeQk1NDX7961/D4XCguroamZmZSExMxM6dO/HnP/8ZjY2NeOaZZ2TN\nz9/fH+PHj2/+KPi6ujp89tlnOHfuHCwWCwDgww8/REpKCvz8/Jz2/e///m/s2rULzz//PF5++WW8\n/fbbzffdfvvtePDBB5GQkIAPP/yw+fa//e1vmDhxInbt2oVbb70VK1eubP4MPaLOYsEhQ/v2228B\nXG88dtNNNyEzMxMvvPACKioq8I9//APr1q1DQkIC4uPjsX79epw7dw7Hjx9v3v+BBx7Abbfdhvj4\neKxduxZnzpzBmTNnYLVaMX/+fCxatAjR0dEYMmQI7r77bnz55Zey55iSktJccE6ePIno6GjceOON\nOAKWCS4AAAPXSURBVHnyJADgo48+atPTxOFw4O2338ZvfvMbjBs3DoMHD0ZOTk7z/SaTCWazGYGB\ngYiKimq+PTU1FbNnz0Z0dDQWLFiA+vp6nD9/XvacidrD93DI0CZOnIiEhATMmDEDAwcOxJQpU3D3\n3XejqKgIDocD6enpTts3NDSgqKgIycnJAICkpKTm+/r27Ytu3brhP//5D4YOHYq7774bb7zxBgoL\nC1FUVISzZ8+ia9euiua4YsUKXLp0CUeOHMGoUaNQUVGBY8eO4eabb8bFixcxceJEp30qKytRUVHh\n1Bp84MCBbi8oiI6Obv5/01xbNx0jUooFhwwtJCQEf/nLX3D8+HH885//xP79+/Hmm2/iN7/5Dbp0\n6YIdO3a02aflG/OBgc4/Qna7HQEBAbh06RJmzJiB2NhYpKSkYPr06bhw4QI2b94se4433ngj4uLi\n8O9//xtHjhzBzJkzUVlZiffeew+9e/fG8OHDJReygIAA2ffz4gLyFL6kRob26aefIjc3F8nJyVi8\neDF2796N3r17w2q1ora2FrW1tejXrx/69euH8PBwrF27Ft99913z/gUFBc3/LyoqwrVr1xAfH49d\nu3bBZDLhlVdewQMPPIBx48bh22+/VfzLOyUlBR999BFOnz6NUaNGITk5GSdPnsSBAwfabREcERGB\nqKio5pfdgOttw69du9b8dev3fIi8jc9wyNBCQkLwwgsvoHv37pg8eTK++OILfPvtt0hISEBqaiqW\nLl2KJ554Al27dsXTTz/d3JysyebNm9G3b1/06NEDTz31FMaNG4dbbrkFX3zxBUpLS/HBBx9gwIAB\nOHToEPLy8hAcHKxonikpKXjooYdw0003ISoqCpGRkejSpQsOHDiAhQsXttnez88P999/P/7nf/4H\n0dHR6NWrF3JycuDv/8PfmGazGWVlZSguLnZ6KY3IW1hwyNDi4+PxzDPPYPPmzXj66acRFRWFRYsW\nYcqUKUhKSsLatWvxq1/9Cg0NDUhKSsIrr7zi1Mt9xowZyM7ORmlpKVJTU5s7JU6dOhUnTpzAkiVL\nYLfbERcXh+zsbDz++OO4ePGi7HmOHDkSwcHBGDVqFIDrBSU5ORmnT592ep+mpczMTNTW1mL58uWw\n2+2YP38+vvjii+b7b731Vrz11lvIyMjAvn37ZM+JSC52/CRSKC4uDlu3bsWkSZN8PRUiTeAzHCIf\nqq+vR1VVlcttevTo4fRSGJFWseAQ+dDZs2fx85//3OU2H374oVNWhkir+JIaEREJwefpREQkBAsO\nEREJwYJDRERCsOAQEZEQLDhERCTE/wfIG+u/RuDgBAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6558bf2240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot('sepal_width', 'sepal_length', data=iris, fit_reg=False, size=6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There appears to be some structure in this data. At least two clusters of points seem to be present. Let's color according to species and see if that explains what we see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f65586ba0f0>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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KCSRHjrp4loQQX0RFnxAX8/vlpt3LpYVFjNtYW04IIbagok+ICykf3IdfvZvwLPHT6qGS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\n14ZY7bAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6558b0c470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot('sepal_width', 'sepal_length', data=iris, hue='species', fit_reg=False, size=6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can add back the regression line, but this time one for each group."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f65586ba3c8>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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HYE0Jn9wlCRLsJuuoCwBe06ELPARFgxTU4OBMUQn83g9b0X34CPpPnTau/ec4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\nl7LSG/NZMgUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6558697710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot('sepal_width', 'sepal_length', data=iris, hue='species', fit_reg=True, size=6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of creating a plot for each variable against each other, we can easily create a grid of subplots for all variables with a single command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x7f65585cceb8>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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3WGZ9LF843yB3XBghIooQ6VUVkuNpleLj6p3V0scOclDhQr8fEYUnX71Coc30uFtk5jb2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P+1d8YVIqLIxoERCmmept0VS403ZzZ7vc2bY3wxZzYHLcUphQdvrw/789IqViH3/7gLaRWr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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6558674438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(iris, hue=\"species\", size=3.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### More complex visualizations <a class=\"anchor\" id=\"more-complex-visualizations\"></a>\n",
+    "\n",
+    "Many visualizations are easier to create if we first reshape our data frame into the [tidy format](http://vita.had.co.nz/papers/tidy-data.pdf), which is what `seaborn` prefers. This is also referred to as changing the data frame format from wide (many columns) to long (many rows), since it moves information from columns to rows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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V6sTN94EclYC5Lo7xeNxqtbJYLIlEct2cEYZhPp9PLBYT14nTEdPXTEgsRjQa9Xg8PB7v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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image('./molten-data.png') # Image from http://vita.had.co.nz/papers/tidy-data.pdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use pandas built-in `melt`-function to \"melt\" the wide data frame into the long format. The new columns will be given the names `variable` and `value` by default (see the help of `melt` if you would like to change these names)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>species</th>\n",
+       "      <th>variable</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>setosa</td>\n",
+       "      <td>sepal_length</td>\n",
+       "      <td>5.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>setosa</td>\n",
+       "      <td>sepal_length</td>\n",
+       "      <td>4.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>setosa</td>\n",
+       "      <td>sepal_length</td>\n",
+       "      <td>4.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>setosa</td>\n",
+       "      <td>sepal_length</td>\n",
+       "      <td>4.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>setosa</td>\n",
+       "      <td>sepal_length</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>595</th>\n",
+       "      <td>virginica</td>\n",
+       "      <td>petal_width</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>596</th>\n",
+       "      <td>virginica</td>\n",
+       "      <td>petal_width</td>\n",
+       "      <td>1.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>597</th>\n",
+       "      <td>virginica</td>\n",
+       "      <td>petal_width</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>598</th>\n",
+       "      <td>virginica</td>\n",
+       "      <td>petal_width</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>599</th>\n",
+       "      <td>virginica</td>\n",
+       "      <td>petal_width</td>\n",
+       "      <td>1.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>600 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       species      variable  value\n",
+       "0       setosa  sepal_length    5.1\n",
+       "1       setosa  sepal_length    4.9\n",
+       "2       setosa  sepal_length    4.7\n",
+       "3       setosa  sepal_length    4.6\n",
+       "4       setosa  sepal_length    5.0\n",
+       "..         ...           ...    ...\n",
+       "595  virginica   petal_width    2.3\n",
+       "596  virginica   petal_width    1.9\n",
+       "597  virginica   petal_width    2.0\n",
+       "598  virginica   petal_width    2.3\n",
+       "599  virginica   petal_width    1.8\n",
+       "\n",
+       "[600 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris_long = pd.melt(iris, id_vars='species')\n",
+    "iris_long"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We do not need to call `groupby` or `mean` on the long `iris` data frame when plotting with `seaborn`. Instead we control these options from `seaborn` with the plot type we chose (barplot = mean automatically) and the `hue`-parameter, which is analogous to `groupby`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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eccelk8Pi4mJCQ53nmgYHNx65mT9/Punp6axevZpVq1ah0+kYP3488+bNw8ur\n8fSbioqKJvdfrLKykpCQppuph4SEYDabmzxfUVFxyZgMBgN6vZ7q6obfYZd73+Yiidk1RAmKQFU0\nzon/ACGtv0o1yN8LRXHuVQbOHrDcgkrXvmUhgd5oFCgodv7nCAnwJiTQm7wi5x5snnotfr76Vo9b\nCCHaOsUvkMtto6AYWmd1YUpKCtHR0axduxa9Xk9ERAT9+vWjqsrZjqenpxMW1nTLo/j4eMrLyy9Z\nZ0BAAHPnzmXu3LkcPXqUlStXsmTJEhISEvjNb37T6FqDwXDJ/cd27NhBdHQ0/v7+5OTkNHm9uLi4\nybDohfcGKC0tbfR8ZWUlFovlkve0JBkvuoYoHTqiuekh6DYI4nuhmstR68yoDgeOAxtwbP4vallR\ni7x3RXU9W/blk1dUxS+GJ9AtPoj4KH/q6m38YnhnkhJCiIs04qnTMjS1I326hxIXaSQ8xIdO0f4M\n7h1Jp2h/OkX7k3um9VYYCSHar4oqZ7uzL+Nso81mr1XKgPHg9z3Jl18QSv9xl36tBUycOJGNGzey\ndu1axo0bh6Io9O7dG51OR2lpKcnJya6vEydOkJ6e/r11mUwmbrjhBtauXQtA9+7defzxx4mMjKSw\nsLDJ9X369KGyspJ9+/a5nquoqOC+++5j69at9OvXj6ysLLKzs12vWywW1q1bR9++fZvUFx8fT2Bg\nIGvWrGn0/IXVpJe6pyVJj9k1RunUy3kMU+4ROPktjtNHUaK7ou5aBYB6ZCuae15A8Wy+oUKrzcFH\nazJdQ5gp3UKprbeRW1DJyTMVnC6sJDrcwOGsEk4VVHI0p5ShqdHsP+qcZ3b4RCkTbujErsOF2O0q\nR3NM2B0qPRNa5lxPIUT7Z7XZ+feaY1TXOE8NqaiuZ/iAGDdH1bIUX3+UtJtQt33aeK6ZXxBK2k0t\nPvH/YhMnTuSdd94B4Pnnnwec87ymTZvGyy+/TEVFBb169eLYsWPMnz+fkSNH4ufnd8kes6CgIGJj\nY3nhhReoqakhIiKCjRs3UlBQwKhRo5pcP3z4cHr06MGcOXOYM2cOgYGBvPvuu4SGhjJ+/HgURWHp\n0qXcd999zJ49G4PBwAcffEBJSQm//e1vm9Sn1WqZOXMmzz//PP7+/owcOZLMzEzefPNNxo4dS5cu\nrTslSBKza1FhTqPHqvaif+aaSqgohtDmW+ZbXWNxJWUAhcXVlFU2zBsrLDE32pusps7G6YKG46Mc\nqsqp/AqFbuMWAAAgAElEQVTsdvWiOsySmAkhvld1jdWVlIGzzfg50CQNQY1PRt29GrXKhGJw9pS1\nZlIGzlWTXbp0wWKx0KNHD9fzjz76KEFBQXz00UcsXLiQ0NBQ7rrrLmbOnHnZ+t544w1effVVXnvt\nNcrLy4mPj+e1114jLS2tybU6nY7Fixfz6quv8uKLL+JwOEhNTeWDDz7AYHAeXL9s2TJeeeUV5s2b\nh91uJyUlhWXLljWK9WK/+tWv8PLy4v333+c///kPoaGh3HPPPTzwwANX8Sn9NLLz/zXIsWax63Bz\nEvqidOyG+vU/neWgCDRTn0HRNd88LrvDwb9WHeOcybmJ7XV9oiirrCMj2zlenxATQMdwA1/vynOG\n4O/FDf078sn6LByqiqdOy80jE1j5dTa19TYUBW4ekUh8dOs2NEKI9sPucPDPVUcpNjnPDB7SN4oB\nyRFujkqIqyc9Zu2Y6rCjbv4vamEOSkIfNKljcexfj1p2FmJ7onQfBJ6+qHvWQFQXlIS+KD0GN2tS\n5nCobNl7Bo0CXWID6ZkQQnlVPWUVtcRGGukeH4Snpwd7jhQRFepHQmwgESE+bD9YSFiwD7GRRjp1\nDGBvxlmMfnq6xAXSvVMwJ3LL2PltAQkxgaQmhTdbvEKIa4NWo+H2Md3IyC7hRG4ZJ/MrCPL3IiEm\n0N2hCXFVJDFrx9QDX6PudU6WVAuycCga1E3/bng9PB72rgWbc5hR9TGg6dd0vP5qHMg8x96MswAU\nldYQ0cGXTXvyXa+Hh/iyd8dpbHbnSlEfbx2HjxdTen7LDHOdlUqzhcyTzvkSJWW1+HrrXHUWFJsJ\nCfQmLkp6z4QQjXnqtZwtrSH//JY8RSVm7p2cjJ+PrOwW7ZesymzPzN+ZRFl+tnG5stSVlAFQfell\nylcVwkVzPADKL9qTDKCy2uJKysA5H626tuEec42V6pqGGO0OlYrqxnWYaxu/hxBCXHBxG2R3qNTW\n2S5ztRBtnyRm7ZiSPBR8z++vEhINAydB2PldiX0Mzgmh3Qc7y1oPNAPGX7qiq5CcGIKvt84ZQqA3\nA5MjCAt2rvj08fKgf1IY3Ts5N+TTahT6J4UzqFfDPJBBvSPpnxTuOvw8MTaQAckRjepMiGndPWSE\nEO1HalIYHh4N7UdIoLebIxLi6sjk/3ZOtdbj2LoC9m8AHyPKpAfgbC7qlo8BFWXUXShhceDpg+Jj\naJEYrFY7lWYLAQZPtFoNdoeDbzOL2bLvDACj0uLQe2hYtyOXmlobqUlh9O4aiqqqrnPtauts1NXb\nCDB6oihKkzqFEOL7fLf9EKI9k9947V1dDexb59zt31yOuuVj1F2fg7UOrPWo6z5ECQxrsaQMQKfT\nEhzg7UqgtBoNuw4VYbU5sNocrNt+im+PF1NdY8Whqq7XLj5s2NvLg0B/L1ej+t06hRDi+3y3/RCi\nPZPfeu2dRgMXN0YarfPr4rIbXLxvmUajNCoDaKT9FEIIIZqQxKy9UzQofUeBtx8ER6FcPxml32jw\nCwS/QJQxv0YtzEEtP+e6RS3OQz13ukXDGnNdHEZfPX4+OsakxdMtPohgfy+8PT0Y2i8aq83h2vdM\nCCGuhNVq58y5KteCIIdDpbC4mvLKOtc1xaYaaVtEu+b27TK2b9/OG2+8QWZmJsHBwfzyl7/kwQcf\nRKt1T09Pe6KWnMHx75ehvgb8Q1F+8SDqp2+CqRD03ii3PoK683PU7APOBG7svVBWhLrjMwCU1LFo\nht7WIrHFRBiZcWsvVFVl5dfZZOeVoygwdkg8ZZV1/P2zDABSe4YxNLVji8QghLh21FlsLP/iGKaK\nOvQ6LbeOTmTnt0VN2pYdB51nK0rbItort/aY7d27l/vuu4/OnTvzzjvvMHXqVN59910WLVrkzrDa\nDTVjmzMpA6g459zTzHT+wFdLLeq+dZB94PzFDtQD61H3r2u4/6LHLaWiqp7sPOc2HaoKB46dY39G\nQ+/dhfMyhRDicnILKjGd3//QYrWz/+g5aVvENcmtidnrr7/Oddddx8svv8zgwYOZMWMGd911Fzt3\n7nRnWO1HQIdGRaVDR+CiyVvBEaBvWDqu+IeC/0X3+De+vyV4e3mg1zX0fvobPPE3eDYqCyHED/H3\na9xWBPl7SdvSBnXt2pXFixdfVR35+fl07dqVNWvWXPE906ZN4/7777+q920r3LZdhslkIi0tjfT0\ndEaOHPmT6/k5bpehlhag5hyEDh3h7CnU/OPg6Y3SuQ+oKmrmLueFcT1RgqJQD6wHaz1EJ6LE9oT9\n60F1oKT9EiUwrMXjPXOuij2Hz2K12YkONRAbZWT/0XOYa62EBfvQvVMwHYJ8WjwOIUT7djSnlGM5\nJkAlLsqfIH9vDhw716RtUVWVtD5RBBq93B3yz86BAweIjIwkNDT0J9dhsVjIyMggLi6OgIAr28cy\nKysLjUZDp06dfvL7thVuS8y2b9/O3XffzQcffMDSpUvZunUrfn5+TJkyhQcffBCN5so6835uiZla\nUYzj78+Bxdmlr4z/Der2T6HMueu/MmA8qqkIsvY5b4jtidKxK+qWFc6yMRjN9Hko+tZtsHYdKnTt\na2b01TOsf0c+25gNgIdWw9SJ3QkOkI0hhRCXt/LrLLJOO4cwYyONdAw3NGpbpt/Us1FPmhDtjduG\nMsvKygB47LHHiI+P591332XKlCksWrSI9957z11htXlq0UlXUgY4J/aXNRzFpOYehdNHG27IO4aa\nm9FQrixtenRTK8gtqGwIwWwh+3TD8VA2u4OCc9WtHpMQov05XVjlepxXWNWkbSmvrL/UbdcUc62V\nr3ed5rONWXy963SrHlv35JNPMmbMmCbPT548mUcffbTRUOabb77JLbfcwosvvkjfvn25+eabASgv\nL+fRRx+lf//+DBw4kD//+c88+eSTTJs2DWg6lPnEE08wa9Ysli5dyvDhw+nVqxfTpk0jOzvb9f7f\nHcosLy9n7ty5pKWl0bdvX37961+TmZnpej0nJ4dZs2YxaNAgkpKSGDFiBOnp6bSFPffdtirTanX+\nIA0ZMoTHH38cgEGDBlFWVsaiRYu49957ZWXmJSjh8ah6r4Yes069Uc/lNvSYxXRHNQQ29Jh17IoS\n3Q0175izbAiCgJYfvvyumAgjeUXOBtXgq6dzTAAZOaUAaLUKkaF+rR6TEKL9iYkwuHrMOoYbiA43\nNGpbAozX9tyyQyeK2X6ggOqLzgg9kVtGWkokSYktP294woQJrFixgmPHjtGtWzcA8vLyOHz4MLNm\nzWLlypWNrs/MzMTPz4/09HTq6+tRVZXf/va35OfnM3fuXHx9fVm4cCGnTp0iJSXle99327Zt5OXl\nMXfuXOx2Oy+88AJPPPEE//nPf5pca7PZuOeeeyguLubhhx8mLCzMlVesWrUKDw8Ppk+fTqdOnXjl\nlVfw8PDg888/Z+HChXTv3p0RI0Y074f2I7ktMfP19QXg+uuvb/R8Wloay5Yt48yZM8TExLgjtDZN\n8e+A5o4/oOYcRAmNQYlLQo3t4Vyh6WNEVTRQmg/xyc75ZCHRqAc2QEx3lKguKEnXt/owZsG5as6W\nmomJMBAVZiApIQSDr55bR3ehqMRMXJS/DGMKIa7I+KGdOHyiBFVVCQ5wzjG70LbERRpZtyMXVYXr\nUiIJuMbmmJlrrU2SMoDqGivbDhQQHx3gOme4pQwePJiQkBDWrFnjSsxWr15NYGAg1113XZPrbTYb\nTzzxBD169ABg69at7N+/nw8//JCBAwcC0KtXL2688cbLvq/ZbOadd95xzV07e/YsL7zwAmVlZQQG\nBja6duPGjWRkZLBs2TJSU1MB6NmzJ7feeiuHDx/G39+fmJgYFixYQFBQkOv7WrduHbt37/75JmYX\nkq4LPWcX2Gw2ADla4zKUkCiUkKiGso8RJXUsatEp1H/+CXB2xapRibAyHSy1zgt9/VEMgZeoseXU\nW2ysWHcCi9XuDMFbh8FXDzh70WIijK0ajxCiffPQakjpFkq9xca7/z3UqG1Zf7rctblssamGu29O\ncmeozW7XocImSdkF1TVWdh8u4ob+Lbt3m1arZdy4caxZs4bZs2cDzsRszJgxeHhcOqWIi4tzPd61\naxdGo9GVlAGEhYXRp0+fyw4jfndBQXh4OAC1tbVNErP9+/djMBhcSRlAUFAQGzZscJX/+c9/YrVa\nycrK4tSpU2RkZGCz2bBYLFfwKbQst80xS0hIICwsrMly2E2bNhEaGkpUVNT33Cm+j1pxjgtJGQAl\nBQ1JGTTa/b+11NTZXA0nQHnVtT//QwjR8i7VtlzcvlRcg21Ndc3lk4Yqc+t8zxMnTuTkyZNkZmaS\nm5tLRkYGkyZNuuS1Pj4++Pg0rLq/VA8XQEhIyGXf09u78ajKhQWCDoejybUVFRUEBwdftr5FixYx\nePBgJkyYwIsvvkhubi4eHh4/7zlmGo2Ghx9+mMcff5xnn32WsWPHsm3bNv73v//x3HPPXfGqTNFA\niU1CDYo4v/O/F0qfG1EttZBz0Lnzf+/W754NMHjSKdqfnPwKFAV6d/3pS6iFEOKCS7UtZZV17PzW\nucl2Srdrr63x89Ff9nWDb+vMr0tJSSE6Opq1a9ei1+uJiIigX79+V3RvaGgoJpOpyfOXeu6nMhgM\nl6xvx44dREdHs2fPHv7yl7/w7LPPMnHiRAwGA+AczmwL3Hok080334yHhwfvvPMOK1asICIigj/+\n8Y/83//9nzvDuiJ1divvHdvKycpSUkKimZowAI2bhl8d325E3b4SfIzOY5cKslH3rkVdtQhlxFTo\nkYa6fSXqNx/hqK1E06/piprmdrqwkq+25aKqKiMGxtCjczDbDxbwzZ48auusJHfpwBff5FBYbKZz\nTACjBsfK8LUQ4gddrm3pnxTOHeO6snnvGTKyS6m32q+ptmVAcgQncssuOZzp56Ojf1J4q8UyceJE\nNm7cCMC4ceOu+DNOTU3lzTffZPfu3fTv3x9wJmUHDhwgOTm5WWLr06cP77//Pvv27aNv376Asxft\nvvvu46mnniIjI4Pw8HDuvPNO1z1HjhzBZDL9vHvMLpg4cSITJ050dxg/2vozmRwyFQCwpSibnoER\n9A1p/cUKqrkCdf0/nGeSmCtQt64AUxFUOf9aUNcshuiuUOrc50fd9B/Uzn1RAlp29c6XW09RZXZ2\nu6/ZepLoMAOl5c6VpJv25FNVYyUnvwKAwydKiIs00iUuqEVjEkK0fz/UtvTtEcaZ89vvXGtti6+3\njrSUSLZ9ZwGAn4/z+Zae+H+xiRMn8s477wDw/PPPX/F9gwYNIjU1lUceeYRHHnkEX19fFi1aRH19\nfbMl0MOHD6dHjx7MmTOHOXPmEBgYyLvvvktoaCjjx49Hq9WyfPly3nrrLQYMGEB2djbp6ekoikJd\nXd0Pv0ELc3ti1l7Z1cbj2rZLjHO3CofDmZRdYLeDw35R2da4jPqdckuF1RCT3a42Kjufa/x52R3u\n/ytFCNH2/dzblqTEDsRHB7D7cBFV5noMvp70Twpv1aQMIDExkS5dumCxWFwrLq/UwoULef7553nu\nuefQ6/XccccdeHl5NZqLdjV0Oh2LFy/m1Vdf5cUXX8ThcJCamsoHH3yAwWDglltu4dSpUyxfvpz3\n3nuPqKgo7r33XrKzs9m7d2+zxHA13Lbzf3Np7Z3/7aqDktpqPDQa3jm6hdPVJrr6h/Hrrmn4e7b8\nlg9qnRlqqyEgFEVRUK31qFtWOLfE8DGgTHoAzuWibv4YVBVl1F3g6YO6bimYK1FSx6D0Gel8zdBy\nf0WeyC1jzZaTqCqMSovFU69l3fZczLVWUnuGk9oznBXrjnO2tIZO0f5MGt4ZrcwrFEJchsOh8m1m\nMZv35X9v29K9UxBfbD5JSVmttC1tUF5eHocOHWL06NGuVZx2u50RI0YwduxYnnzySTdH6H7SY/Yj\nWOw23ji0npNVpRh1XjzSayRr8jLYfu4kc/es5DfdhtAruOVWk6qnM3B88ibYLChd+qMOuQX1o1eh\nugxColEmz0H9NB2KcsDbgHL7o7DnS9QjW0GrQ/nFA1BRiuNvjwIqypDJaAaMb5FYE2MDSYhxnnG2\nduspjmSXotUq/GJ4Zzp3dK7ImTqxBw6HikZzbcz/EEK0HKvNzn++PE5RiRlvTy23j+3KnsNnG7Ut\nFdUWPlzpPOnkuj6RDOwV6eaoxaU89thjbNu2jQkTJmC1Wvnvf/+LyWTi9ttvd3dobYL8GfEjHC4r\n4GSVc7f6Smsdq88nZQBWh53VeUda9P0du1eDzTm3Qj2+G3XnF86kDKAkH3Z87kzKAGqrUHetcSZl\nAHYr6u41znM1L+xztv3TFo1XURQqqy0cyXZ+Zna7yu5DRY2ukaRMCHElTuZXUFRiBqC23s7u80kZ\nNLQt2w8UuK7fcbDQLXGKy+vYsSNvv/02WVlZPPjgg8yePZuKigr+/ve/07lzZ3eH1yZIj9mPYNQ1\n3kU60NMHnUaL9fycLWML76iv+Pg37FKm0YLxO0OR/t/ZB8YQBFod2M9PEvUxQp0Z6p0bMOLr35Lh\nAqDXa9FqFex2Z+Q+rTwPQghxbfhu2+Hno2/SttRZ7NRbGjacFW3T0KFDGTp0qLvDaLO0zz333HPu\nDuJqVFZW8uGHH3LXXXdhNLbsLvJBXr74euipsVnoHRzNzXG9iTMEUVpfQ5xfMFMS+uOlbcHGIDoR\nqkrBQwedU1AS+6HoPEF1QFQCmrhkCItzJl7BkRDTA6XHIOcKTWMIxCc59zKrKgVvP0hMRTEGo3j5\ntljIOg8NoUE+VJotGH31xEcZCfT3wkMrnbVCiB9msdrJPFWGVqMQFuxLvcVOcIA3MZFGenQKpuqi\ntqV3tw5UVVvx9tKSGBeI0dcTL08PqswWTuSWoaLi56N31WmutV5zxzaJ9k8m/7czanUZjr//EWqr\nnJvG/uJB1B0r4WwuAMqQyVBW5BrCVLqkQnRX1A3LnBUER6IMuwP1k784V2d6+qCZ+kyLb59x4Ng5\nNuw87QzB34spE3ug85DkTAjx/ewOB8u/OMbZUmcv/5C+UZRV1nEkyzmE2SU2kOhwQ6O2ZdiAGD5Z\nfwKHQ8VTr+WXIxP59OssautsKAr8YngCOw4WNKpzQHKEe75BIS5BhjLbGfXMCWdSBqA6UDN3uZIy\nADVrn3MfM1d5v3MV5wWlBajHdjRsmVFfg5p3rMUTsxO5ZQ0hVNRRVllHaFDzLI0WQlybKqstrgQK\nIOt0OaaKukbl2nqbq1xaUcexnFLXFhr1FjtHskuorXNeo6qQedLUpE5JzERbIl0W7YzSoSNoG/Jp\nJbor+DWcO6aEx0NEfMMNYXEoEZ0ayt4GlI7dG8oaLUpYy2+MG9GhYbjU28sDo9/ljxYRQgg/Hz1+\nPg3TQ8JDfIkIaWhLwkJ8mrQtHSMMrrJGUYiL9Ed70SKj6DC/JnUK0ZZIj9lVKjCX83XBcQx6L8ZG\n96DebmN1/hFUVWVsx57465tvbzPVbkM9vgdie4KnN0pcsnOVZmQCOOwoUQkQGA71tRCfjBIaB3E9\nUTO2QVwSBEWgJKbCiT3OsiEYTdcBKKGxzRbj90lLicLXW0eV2UJSYgheevnRE0Jcns5Dw/+N7cbB\n48X4enkQaPSi3mIjPsqf0GAf4iKNZGSXEhdpJMjfi8S4QE6cKiMuyojBV0/XuCBiIozcNqYrJ3LL\nCA32oXunYGIj/V11pnS/9s7UFO2b/Ha8CvV2G28c2kCV1dm1XmGppaimkqzKYgBOVBTzVN9xzfZ+\n6ub/ou77ylnw9EGNSIAN/3CWFQUS+6KuTHcNU6qxPWFlumvoUzEEoW75L5w54bwnNBZl1PRmi+9y\nNBqFPt3DWuW9hBDXDn+DJ0P7RVNsqmHZ50dxnJ8WHRtpZOXGbNcwpcFXz5a9Z1zHMYUG+TBqcBwA\nkaF+RIb6NalTiLZIErOrUGmpdSVlAPnmcs7WVLrKZ2rKm/X91OK8hkJ9TcOeZQCq6px/dvFxS0Un\nG+ajAWpxPpgu2tunJL9Z4xNCiJZiqqhzJWUARSVmV1IGUFxW22j+WUl5bavGJ0RzkTlmVyHYy5cE\nY8Ok+UGhcQwMbZjfdfHj5qB0HwycnysRFoeSNBQ8zs/V8jGi9LqhYb6Z1gMl6XqISmy4v8fg83Vc\nqG9Qs8YnhBAtpWO4wTU3TKtRSEoMIeqiXrAenYPp3qlhb8eLHwvRnsh2Gd/jVFUp/8rajQO4o3M/\ngj19WXpiJ2X1NYyJ7k5qh1iWZe0mp6KYKL8ARkd150hZIbuLc/HXezEyshsWh40v8o5g1HtxV+Ig\ngpphvzD17CmoKoPYHig6T9Tyc1CcB5EJKL7+qDVVqEe2oh7d5kzOrr8NxVrvTNzOLwJQczOce5/F\n9kRRZOd9IUT7UFNn5UhWCUezTWi1Ctf3i6LSbOHQ8WIsFgepSeH4+ehRVZXYSKO0b6JdksTsezy1\neyXFdc65CgF6bxKMHdhT4twrR4PC6I7dWZOX4br+pthefJr7ras8Kqo7GwoysasOAHoFRfFgz2HN\nFt/l2N9/EsrPOQu+/mjvf6NV3lcIIVra+ysOUV5VDzh3948OM5B5ygQ4p9ree0syRj9Pd4YoxFWR\noczvUWOzuh6bbRZq7A1lByrV1vpG11da6hqVq631rqTMWZ+lhSK9hPqaix7LPAshxLXjwpFLFx7X\nWxvmmakqWKyOS90mRLshidn3mByfgkZRUFC4Nb4PE2OS8PFwzm8YEt6ZiTHJhHo55zfEG4IZ37EH\n3QKcqw6DPX2ZGJvEiMiuAHhpPfhFbK9Wi125/jbnWZqKgjL0tlZ7XyGEaGnX94t2ts0KDE2Npl+P\ncDz1WgCSEkPwN+ixWu0/UIsQbZcMZV5Gnc2Kior3+Qn2VoedersVv/OHmdtVB8uz9vJN0Qm8tDoe\n6H49Eb4B+Or0aBVnzlttrUev0aLXtu4CWNVSC6qK4im76wshri0Wqx1VVdlxsJC9GWfRe2gYd308\n9VY7a7floqoqIwfF0qtLy55oIkRLkB6zy/Dy0LmSMgCdRutKygAq6mv5psi5J1id3cqnpw9h1Hu5\nkjIAP51nqydlAIreW5IyIcQ1Sa/TUm+xszfjLAAWm4M9R86ycXc+DoeKqsLG3Xk/UIsQbZMkZldB\np9E2SsK8tbrLXC2EEKK5eHho0Fy06lKv1+Kpa2iPPXVad4QlxFWTxOxHyKsu45DpDBa7c7JpvcPG\niMgudPDyI9EYyi9ie3GwNJ/iizZ1bW1qbTVq9gHnNhpCCHGN8vHSMWZIHIFGL8KCfUiICWD0dfGE\nBvkQ5O9Fn+6h1NRaf7giIdoY2fn/Cm0/m8PS4ztQcU72vy2+LwsOb8DisOOv9+aOzv1Iz9iEqb4G\nnUbL7KThJPi37hlsak0VjmXzoMoEWh2aWx9BuWiDWSGEuJZ07xSMqsKaLSf5alsu4SG+jBgYw8df\nHWfLvjPsP3qOKRO6Y/DV/3BlQrQR0mN2hbafPcmFVRInq0rZXJSF5fzxRxWWWjYWnsB0fpsKq8PO\nruLcVo9RzTvqTMoA7FbUYztbPQYhhGhNGdklrsdFJWa+PV6M1ebcMsNca+VUQYW7QhPiJ5HE7ApF\n+vq7HntrdcQaGo77UIAEYwc0NMx3iPIJaM3wnHEERcBFc94IiWr1GIQQojUFB3i7HnvqtIQHNz5h\nJdjf+7u3CNGmyVDmRRyqyuaiLEz1Zq4L60yot4EDJXnkVJXSOygaHw89p6tM+Oo88dN5MaVzf46W\nFwHODWTv73E9e4tzqbFZqbLWUmOz4KXVfW+dyUGRJPqHcrKyhP2l+XT0C6R/h9ifHL/SoSOam2bi\nOLYT6mudxzPV1aB4yepMIcTVUy21qPs3gOpASRnZrG2Lqqp8m1lMVY2FngkhBBq9yDpdRmGxmU7R\n/kSFGSgsribrdDmhwT50jQuisroeD42GuEgjPt46eiYEU3Cumk7R/ug8NCTGBlJsqiEnv/x76xSi\nrXHrPmZlZWUMGtT0IO0xY8awcOHCK6qjOfcx++TUQVbnHQHAqPNicnwflhzfDoBW0fCbbtfxXuY2\nrOeHMKclDGBV3mHXEOaIyK4U1VaSUVYIOHvREv1DG9V5a3wf3m9U5xDey9zqqvOeLoMZFHZ1h5/b\nP54PuYedhahEtP/3xFXVJ4QQ0LJty5Z9+ew65PxD18fLg6Gp0azZcgoAjUZh4rBOrPomB7vd+Str\nVFosOw4WUmV2nqrSt3sopRV15BZUOsML9SMqzO+ydU6d0J0OQfKHq2hb3NpjduzYMQDef/99fH0b\nup8DAlp/GBAgp7JhrkKltc7VGwbOzWQPlRW4EiiAYxVnXUmZ8/5iii5akZlTWdJoO41Kax0Z36nz\ncNmZRnVmVxZfdWJGYXbD44JsVFWVw3yFEFevBduWwmKz63FNnc2VYAE4HCon8ytcSRlAXmGVKykD\nKCg2Y6poOIKusNiMRtMQW02djdzChvbZ4VApKjVLYibaHLfOMcvMzCQkJITrrruOlJQU11dcXJxb\n4kkJbuhxC/M2MqBDrGufHC+tjsGhnfD1cB6Oq6DQPySWjr6Brnt6B0eTEtwwr6tXcNQP1jkoNL5R\nnb2Cr35emNI5paHQOUWSMiFEs2jJtqVzx4Y/yIP8vejeKcjVVup1Wnp0DsbL09mXoCjQJT6QDoHe\nje6/uI5OHf2b1NktvnGdHWUoU7RBbh3KfPLJJzl79izvv//+T66juYYyj5QVsCYvA1VVSQnuSLwx\nmC9OH8Zss9IjMIxeQdGsP5PJudoq4gxB9A+N40BJPtmV5wj09GVwWDyldWZ2nTuFl4cHqSGx+Ok9\n+Sr/2BXXOSg0nnhjyE/+HgAcBzY4V2PqPVG6DgRvP9Q9X6L4GlGGT0HxMV5V/UKIn6fvti1Kt4Eo\nzXyqyakz/8/efcfHWd2J/v+cqZqRRhr1LlmyLMm94IqNDbaxDXZoIWSXJCSbnkCy97Kp7O8m9+7+\ndhUEvyYAACAASURBVDfsJtnkJhBIsmkkGwgptNgYG2OKcW+4yJLVe++aPs+5fzzyjEZgW8aSR5LP\n+/XK6zVnZp4zXznSw3ee832+p48Bl4+ivERsVhNtXUO0dbnIy3DgjI+hb9BLWVUntU39mM1GVi7I\npK3bRVV9D5rUlzMDAUlZTRc+X5Ci/ERSnLZLzqkok01UlzLLy8uxWq38zd/8DWfOnCExMZEHHniA\nT33qU9f0Ks+Q38tPzr4ZWlJ0WGJ4q62KFpd+m/WA302Xx8WhjloAGod6iDfb2NVUpk8w0EWhI5ln\na46H5pwRl8x/Vx25ojlvz5t3VT+HbDqP3PO78Di9EF79LQR8SEBqGsYPfPGqPkNRlOvP6HMLOaXj\nnpQBzMhOiBinJ8eSPuIuy4Q4K+W1PXT1egDo6feQk+6goXUQgNaOIVYsyKS2SV8Gbe4Y4p6Ns5g/\nK/WicyrKZBO1xCwYDFJVVYXNZuPrX/86WVlZ7N27l+9973t4PB4eeuihaxaLK+CPqPPq9brp87lG\njcO1CwGp0ekdjJij3RM57vQMXfGcg34vCZaruLV7qDdy3N8JgXANBoOjXlcURRmL0eeWwZ7oxAEM\nusLd/IdcfgZH1JkFNUnfoPei71eUqSCqNWZPPPEEzzzzDHfffTcrVqzg61//Ovfddx8///nP8Xq9\nl59gnKTa4lieOgMAkzCwOXcOt+fOC3Ul25o3j1tzSjEb9L3XFifnsCVnTiiJyrYncHvuXPLj9N5m\nDrOVLblzI+bclDP7MnPmkmWP/LZ4xQoWQLr+mdgcGJbfjigdvuvVaMKw7Larm38cRHHlXFGU92vU\nuUUsvCXi5Wv5d71yQWbo8YoFmSydl4HJqP+nrCjPyfL5mcTa9H2Lk502ivKjczOZorxfUa0xey+7\nd+/mwQcf5MUXX6S4uPiy7x/Pdhmtrn7sJgvxFr3uoNszhIYkJSYO0Dv8P1VxkNM9zeTGJfLZ0jW8\n1lzB3uYK4i02Plu6hrqhLp6vPYkEPjprBVaDkd9XHaHP52FzzmxuyiiKmHPA52Eo4CXdFj8uy7cy\nGEC+8wZy358Bibj144jUPIixR7W+rLPHzfN7zjPg8rNsXgarF6vmt4oylbznuSU5G+35/wsDPYhl\nt2FYfffEfb6U7HizhvLabpITYti0uoD6ln72n2gmxmrklhV5COCVfXVoUmPVomwWlaaFkjZFmSqi\n9hvb1tbGM888Q3d3d8TzF66UJSYmvtdhEyrDHh9KygCSYmJDCRRA3UA3p3qakUD9YA87G8t4tbmc\nIJIen4u/1J1gR8NZ3MEAnmCA354/yJutVXR7XQSlxvaGM/i1YMScDksMGfaEcaupE0YT8tBL4HOD\nz4Pc9WtEUkbUi/7fOtZI36APTZMcfKeFnj5PVONRFOXKvNe5RXvrT9DXCVoQefAlZE/r5Sd6n6ob\n+zhX042U0Nnr4fT5Dt461kRQkwy5A5w818Gegw14/UH8AcmBky0qKVOmpKj91vp8Pr71rW/xwgsv\nRDy/c+dOZsyYQWpq6kWOjB7DqOTJKARixDZMBiEitmUyCMO7jhk9nhAjt2USk+PENDrxVB08FGUK\nGn1uGX1+mcDzzehzhhAi4jkhIt9jmBynPkW5YlEr/s/NzWXbtm388Ic/RAjBzJkzefnll3nllVd4\n7LHHohVWhKahXiSSnOFeZRm2eBYn53Cut428uCQ2ZJdgMhjZ31ZNvMXGPTMWUdnfwSuN+t2a9xct\nIyg1Wl39DPg9bM6ZQ6pt4vvmGDZ/Eu2VX4KmYbj1gQn/vLFYuzSH/kEvAy4fy+ZlqNvUFWWKkX4v\nYulm5JGXQYLY+FHQNP0qmasfsew2hDNtwj6/IDuBBcWplNd2kxgfQ8mMJJKdNt4+0YzNamReUQox\nVhO79tehaZJbb3z/29spSjRFtcbM4/Hw+OOP89JLL9HR0cHMmTN58MEHufXWW8c8x3jWmI30Yt0p\nXqo/BcDmnDkUOpL56bl9BKXGHGcGHyxYwvdP7WYo4CMtJo4vz7uFx86+QYurD5vRzP+cv56/Npzh\nZFcjBgR/V7KK5Wkzxi0+RVGUa0V6XWhP/xt0NYPVhvjgV5AHX4SqEyAMiNs+jaF0xTWJZf/JZvaf\naAZg6bwMslJjeen1ajRNkp8Zz90bZ0V0/FeUqSaqfcxiYmJ4+OGHefjhh6MZxnt6telcxOO6hFSC\nUgPgbG8rCU1lDA23omj3DPJKY1moR5k76OfVpnOc7GoEQEOyp7lcJWaKokxJsvaMnpQBeN3IY7v0\npAxAasjju+EaJWbHzrZFPG5Li0XT9OsLdS39dPW61TZLypSmVuEvYmSBfnJMXMTYYjCSOaq1RXas\nk5Hf0TLsCcSMaMCYOuJ4RVGUqUTEJ8PIM1xyBoy4UUokXLua4IQ464jHFpyOcBwmowH7cKsMRZmq\nonrFbDL7/JybeK72JJqU3DljAQlmG0ZhoMfrYn1WCSXOdAIySEVfO7EmK3aThY8Xr+RIRx0gsBkt\nfH72Tbw23ErjnoJFl/1MRVGUaJLdLcjqk4i0PETeHKRrAHnuANjjEZs/iSw/pFfYW+2IDzyIPPEq\n+H3I5EzkYC8izolsLEe21iDy5yJSc8c9xg/cMpO3jjYhpWT14mxi7WYMBsHAkI/Fs9NDPcwUZaqa\ndH3MrtRE1ZiNhV8L8v8f20GrW9/+47bcubS5+znW2QDA3MRMvjzvlktNoSiKMinIvk60p/633g4D\nELd/DnngBehu0ccrtiK7W+H8Uf2AGfMQOSXIt/6kj+NTEDf/DfKFxwAJJguGj34LkZT57g9TFOWi\n1BWzq9DjdYWSMoCynhZa3QMjxq1IKa/pvp+Koijvh2ytDiVlALLqeCgpA5C1Z2Fkn7K6s8hgIDzu\n70RWHgWGv+sHfMim8yoxU5QrpGrMrkKi1U7aiNqxEmcGpc70EeN0lZQpijIliPQCMIfrt0TBAhjR\n/kLklUJuafiA3FJE3uzw2JGEKFxEqBbNaEZkFU1w1Ioy/agrZmPkDQZ4vvYkPV4Xt2SXMCs+lT1N\n5aTaHOTGJTE/KQujMNDi6mN+YhalzgxyYp38tOwtHOYY7pqxEJtJ1T4oijI5CWcqhr99BFl9ElJy\noasJnOmQlo8omI80mqCrBQoW6AlZai68sxfy5yCyZiHmrUE4kpD3PoxsqUHMmIdIzor2j6UoU45K\nzMboj9XHeKO1EoAzPS18IH8+f649EXp9tjODX1ceuHARn6KEVJ4oexN30A+AJ+jj70puvNZhK4qi\njJlIyUGk5KC98zryzT+GX8ibAzt/yYVlSpk9C158DLzDS5/2BIQjSZ8jbw4ib841jlxRpg+1lDlG\nbSNqx7xagKah3ojX6wa7GXkXRdNQbygpG328oijKpNbbHjGU7bUw8gzX2RROygDZ24aiKONDJWZj\ntC5zVmgfzJKEdDZkl2Az6kuTydZYNmaXkj683ZLVaGJ9VgnzEvXL+ALBusxZ0QlcURTlConZq8Bq\n0wfxKYglmyBxuH7WbEUs3gAF84ffLBAL1d3nijJeVLuMK9DuHqDP56bQkYLRYKDP5+aVhjIOd9QS\nb7HxsVnL8QYDpNkcOK12NKlR3d9JnDmGDHv8hMamKIpyJaTfi7b9p9BSjZi5GLHxo8jX/4A8dxBS\n8xC3/C3yndfh3EGIS0Dc+nFoOq93/RcCsf4j4PPqWzO5B/W9MuevveSchq2fQ8SMb1f+vgEvf32j\nmoEhfR/eJXPSL3+QokxiqsbsCqTZHKSN2IRcAK82lyOR9Pk9PFd7kr+fvz70ukEYKEqYuE19FUVR\n3i95bHdoWyV56nWwO/SkC6DuNPLEHji5B6QEVx/yrT/r7TMGuvVjdvwX5BTry5qA3PsMDPREzmmL\ni5zzyA7Emg+O68/x+pEGWjuHANh7uIHCXCdOh/UyRynK5KWWMq9CUErkiLoLv6ZFMRpFUZQrMKIG\nFgC/L3Ic8OtJ2cjxyL5lwVFjJDIQOYcMjPqM0eNxENQiF300dR5WprgrTswOHTrEv//7v/Pwww9T\nUVFBQ0MDL7zwAn7/+P/BTTaugI8O92BoHGuysD6rBANCb4lRsJB29wC+iJOVooR5gn463ANoU7uC\nQJkGxOINkJavDwoWwOq7YdYN+jg5W1+aXLwBhEHfkmnNPYgV2/ReZ0YzYsPH9Nqy2AR9aXPpFn2c\nkh2aU4yec+nmcf851izOJs5uRghYOjedpATbuH+GolxLY64xCwaDfO1rX2P79u2h537xi1/Q09PD\nww8/zJIlS3jyySdxOByXmGX8Xasas3O9rTx+5g28WoBlqfncmb+A773zKj0+F7mxTr489xYeL3uD\nmoEu4s0x/MOCjaquTInQMNjDf57aw1DAS6kznS/NvRmTwRjtsJTrnAwGEEZTxJjBHrRnHoXBHr3e\n7J7/gXzhx9BSrSdp934Vju5EnnlLT9K2fg4GupGv/V6fZPU9GFfcftHPmAhBTcNoUItAytQ35t/i\nJ554gu3bt/O//tf/YteuXVzI5zZu3Mg3vvEN3nnnHR577LEJCzTaXm44i1fTr4Qd7qhjR8MZenwu\nABqGevlrw2lqBroA6Pd72NtSEbVYlcnp1aZzDAW8AJzrbaOir/0yRyjKxBudMAmjCfnOG3pSBtBR\nDwde1JMyAFc/8vAOPSkDCPqRR15Gvv0ceksNCQdeuORnTASVlCnTxZh/k//yl79w7733cv/99xMb\nGxt63mKx8IlPfIIPf/jD7Nq1a0KCnAziLTGhx0ZhINEaeWdRkjU28v3mGBRlJMeI3yEBONTviDJZ\nxY662h+fEjl2JIJxxE4m9nh9STM0vrYrJ4oynYz5a0xbWxvz5s276OvFxcU8++yz4xLUZHRf4RIC\nmka3d4hNObNZmJTDUMBHVX8HqTEO8uKS+FDhEg531JEXm8itObMvP6lyXdmWN59Bv5cWVx83ps8k\nNy4x2iEpynsSC2+B3g5kcyU40xBpebDuw8jyQ2C1I5KzYevnkId3QGwChvUfAc8g2t6nQdMwrPtw\ntH8ERZmyxpyYZWZmUlFx8eW5w4cPk5GRMS5BTUZx5hg+O3tNxHMfKlzCoyde4WhnPUc767lnxiK+\nuWj8i1uV6cFqNPHx4pXRDkNRLksYTXDzh5G//1eoOIxWcRhx072I5GzkmbeQdWcQxcsw/u0j4YPi\nnBjv/Ur0glaUaWLMS5l33303zzzzDC+++CLBYBAAIQRer5fHHnuMv/71r3zgAx+YsEAnoy7PEHWD\n3aHxsa6GKEajKIoyjvo6oa02NJTnjyLPHw2PK48xxfuTK8qkNOYrZp/97GeprKzkq1/9KiaTftjD\nDz9Mf38/gUCAtWvX8vnPf37CAp2MnBYbTouNXp++Z9yMuKQoR6QoijJO4pwQlxi6CUCkFyAtMVBf\npr+eno8QIooBKsr0dMVbMu3fv5/du3fT0NBAMBgkKyuLm2++mQ0bNkxUjJd0Lbdkei8d7gFeb6kk\n3hLDhqwSdWeQ8p66PUO80lSGxWDittw52EyWaIekKJeknX4TWXsGtCAiayYkZyHLD+vbL6XlwowF\ncG6/3j5j2e3g9yAPbQcp9R5oI28GUBRlzK74HuZVq1axatWqiYhlSkq1Obi3cHG0w1Amuf88vYd2\n9wAAza5eHpp7c3QDUpRL0MoOIF/5lT4QAmYtRT7/Y9D0MhaZPxdefAxc/fp7BvuQ3c3QdF5/vakC\n40e+FYXIFWXqG3NiNrKx7KXcfvvtl3+TolxHvMFAKCkDaBzqjWI0ijIG7fXhx1IiG8+FkjIAWqvD\nSRkgOxqguzn8ekfjNQhSUaanMSdmDz/8MEKI9yz2HFlnoBIzRYlkNZpYlJzDiS79P1Yr0mZENyBF\nuQxRvFTfxDzo15cqF6xD1pzS682MJsTcNcj+Lmiu1N8/eyV0NyNP7g2PFUV5X8ZcY3bo0KF3PRcM\nBunu7ubll1/m/Pnz/OQnP6GgoGDcg7yUaNeYKcpYaFLjWGc9rzdXMhjwsjpjJhuzS6MdlqJclOxp\nQ57ci6w9BY5ExJp7of4s8twBMJgRa++Fvk7kqdfB54WlmzHEJugbn8+Yp24MUJT36YqL/y/mC1/4\nAvHx8Tz66KNXfKzP5+POO+9k4cKFfOc737miY1VipkwVz1YfY3fTudD4kUVbyHeoO3mVyUm6B9B+\n+hUI6lvRUbgQuluht00fxyYgckqR5Qf1sTBg+PSjCPU7rShXZdxuIVy/fj179ux5X8f++Mc/prq6\nerxCUZRJaSjgGzX2RikSRRkDvzeclAF4hvT/hcYu5Mix1MDrunbxKco0NW6JWVlZ2fu6dH327Fme\neuopEhPV9jTK9HZz5qzQHqrzk7IodaZHOSJFCZNaEDnckxEARzLMX6c/Nlsx3HgnYtUdIAwgBGLt\nhxBLN8HwvsFi7hpwpiP9vveYXVGUsRpz8f/Pfvaz93ze5/NRXl7Orl27uOOOO67owwOBAI888gif\n+tSnpvUG6Iqyq7GMP9WcwCgEH5+1glXphaoGR5k0ZEs12l9+CJ5BxML1iJXb0J79D+hugawi2PZF\ntFd+AbWnIT4FcedDUHYA+dp/g8WGuOMh8HnQfvwgAGLDRzHMXxvln0pRpqYxJ2bf+973Lj6JycSt\nt97KN7/5zSv68J/97Gf4/X4++9nPqsRMmbb8WpA/1ZxAIglIyfaGM9yYMTPaYSlKiLb/efAMAiBP\n7kHKgJ6UATRXIg5vR9ae1sf9ncjju+H0W/rY50Ye3am//0Kfs9d+DyoxU5T3ZcyJ2auvvvqezxuN\nRpxOJzExMVf0wVVVVTzxxBP86le/wmJRXdCV6csgBBajEe9wvU6M0RzliBQlkrDEELoLTAiwxEa+\nIWbU2GIHgzHc28wSo//PPRgeK4ryvow5McvOzh63D9U0jX/8x3/k3nvvZfFi1TVfmb5qB7pwBXx8\nuuRG/lxzAiEEqzMKcQf82EwqQVMmB7Huw0j3IAx069spla5EDnQjW6ogLQ8xcyEEA8jyQ+BIQuTP\nhrQ85IEXwRKDmLUUElKRe38PmoZhw0ei/SMpypR10cTsYjVllyKE4NOf/vRl3/fUU0/R0tLCT3/6\nUwKB8F0/UkoCgUBok3RFmcp2NZbxx5rjACxIyua+mTfw4zOv83TVUV5rPs83F21WyZkyKQhHEsYP\nfTXyyds+jfzT96DyGFrlMcTGjyGKFiOPvoJsLEfMX4dYdQfy5Z8jX/klZBZiuP//QxjV+VtRrsZF\n/4IuVVN2MWNNzHbv3k1rayvLli2LeP7cuXM899xzqieZMi3sa60KPX6nuwmTwUBQagC0ufup6u9g\nXlJWtMJTlEvr74SGcN89eXof9LSEx2fegr52vaEsQEu1XmeWmnutI1WUaeWiidnFasrGw//5P/+H\noaGhiOe+8pWvUFBQwIMPPkhaWtqEfbaiXCuZsQm0uPX9BJ0WG7mxiRzrbADAJAyk2RzRDE9RLs0e\nDzFxoZsCRHIm0mAIbcNEUiYiOQtZX6aPLTaIc0YpWEWZPi6amI1nTdlohYWF73ouJiYGp9PJ/Pnz\nJ+xzFeVaaXH1kWSNZU5iJokWG6szZnK2u4V5iVnYTRaWp+XzTncT7oCPtZmzSLDYONheQ4urn6Wp\neeTEXl99/epb+qlv6Scn3cGM7AS6et2UVXfhdMQwtyhZtRaJAmGJwfChryCP7dKTtIL5UHEUzFZE\nQgpixTawOfSEbKAbsfAWhPqyoShX7YqKAWpra9m/fz8ulwtN00LPB4NBhoaGOHToEM8888y4B6ko\nU0m/z8N/nNwV6vR/T8Ei/rvyMI1DvQAsTcnjeGcD+9r03S4Od9RzS9Ysnq46CsBrzeX87xu2kTjc\nuHO6a24f5E+7KpASDp1qZdu6QnYfqMPj1e/4c3v9LJuXGeUor08iNRex+ZPInla03/xvfVNzgNs+\nE9p6Say+O3oBKso0NObEbO/evTz00EOhYn0hBBe22RRCYDAYKC19/5syP//88+/7WEWZTFrd/RHb\nL1X2dYSSMoCqgU4shvCfXpu7n/Le9tDYEwzQNNR73SRmLR2DjNyxt7a5P5SUATS3D73HUcq1JNvq\nwkkZ6MuZs1dGLyBFmcbGvCXTT37yE5xOJ//1X//F448/jpSSZ599lmeeeYZt27ZhNBr5p3/6p4mM\nVVGmhNxYJ0kjkqqFyTmUJIS3X1qQlM3C5HCpQIEjmSUp4YLpBIuNGdfRRtB5WfGYjPqpyGgQlBYk\nEh8b7m1YmJMQrdCUYSJ7VriXmRCIAlVyoigTRUg58rvqxS1evJjPfOYzfPGLX0TTNBYvXsyjjz7K\nli1bAPjoRz9KXFwcTzzxxIQGPFpjYyMbNmxQd3Iqk0qfz83+thpOdzdhMhjZkjObds8gxzsb8GtB\nbkwvJMZk4WB7DQM+L6WJ6ZQmpNPqHmB+UtZ1c7Xsgq5eN41tA2SlxpGaZGfI7aeqoRenw0peZny0\nw1MA2deBPLsfWXcGLDYMN92LSFXnXEUZb2NeytQ0jfR0/Vu/wWAgPz+fsrKyUGJ222238eSTT05M\nlIoyxSRYbJT3tnK+vwPQG82uyZzJ2d5WACr7O7i3YDEnuhoBqBroILU4jrWZRVGLOZqSnTaSnbbQ\nONZmZkFxahQjUkYTCaloFUegqwkAracF46cejXJUijL9jHkpMzc3l6qqcF+mwsJCysrKIt4zMDAw\nfpEpyhTX63OHHruDfro9rtBYAm2eyL+XHq8bRZnUBntGPO69+PsURXnfxpyYbdmyhd/+9rc8+eST\n+Hw+Vq9ezf79+9m+fTvnzp3j97//Pfn5+RMZq6JMKZtz52BAb/OwNqOITTmzsQ3vk1kUn8ptOXNJ\njYkDIMlqZ1V6QdRiVZSxECu2hR8v3xrFSBRl+hpzjZnP5+Mb3/gGL7/8MkeOHMFsNnPfffdx7pze\nGdpoNPKDH/yAjRs3TmjAo6kaM2Uyerb6GK81V5BstfO3Rcvo9bl5uvIIZoOBuwsWkRbj4OflbzPk\n97IpezZb8uZiVVvZhLx+uIHj59pxOqzctb4IZ7zaFDuapNSQL/8Xsvyw3lh208cxZLy7H6WiKFdv\nzP8leOaZZ3jkkUf45je/id2uFyY//fTTbN++nb6+PlatWkVJScmEBaooU0XdQDe7m/QvLO2eQfa1\nVnG8q5Gg1PBq8ErjOaxGI33DS50vN55lU+6caIY8qbR1DXH0bBsA3X0e9p1oZutalQREVfU7yLID\n+uPORig/AioxU5QJMebE7F/+5V/4zne+w4oVK9i6dSubNm3C4XBw992quaCijDS6Sb1BXFjQvDAW\niJHPCFB97cNGd/k3qH+c6HvXL/WYq2AURblCY/7r2rFjBw8++CBdXV384z/+IzfeeCNf+MIX2L59\nOx6PZyJjVJRJKahp1Ax00uMNF/U3DPZgNZi4PXcuNqOZLHsCy9Py+UTxShxmK4lWO+uzirmvcAmp\nMXHEmixsyZmDhrzonNebtCQ7KxZkYjUbSU+2s3rxxG0Pp1yalBqyrRaZlIWYv07ffimjAAoXRDs0\nRZm2xlxjNlJNTQ07duxg586dlJeXY7PZWL9+PVu3bmX9+vUTEedFqRozJRoCWpAfnHqN8/3tmA1G\nHpyzjhNdDextOQ/AfYVLsBpN/Pb8YSSSlWkFrM0s4oenXsOrBZgRl8SnS1fz/VOv0u11kWCx8fD8\nDfz2/KGIOWcnZkT5J1WuV1JKtBcfh8pjIAyI2z4Nve3It58DQCy7HcNNH4xylIoy/byvxGykyspK\n/v3f/5033ngDIcS7WmhMNJWYKdFQ2dfBf7yzKzRekJTNO91NoXGi1U6M0UyLqy/03A0puRztbAiN\nb8oo4s3WytB4TfpM3moLt6RZlJzDF+asnagfQVEuSfZ2oP3iG+EnMgqhpxUuXM01GDH8/ZNqg3lF\nGWfv6zYwj8fD3r17efnll3nzzTcZGhqiuLiYbdu2Xf5gRZkGnFYbRmEgKDUAUmPiSLDYQgX9KdY4\nrEZTKDGLNVlIjXGEjjcgyLJHbjWUFZsQMWfyhS1wFCUabLFgiQGfXqoiElKQQT90DCdm8SkqKVOU\nCTDmxMzlcvHaa6+xc+dO3nzzTdxuN3l5eXzsYx9j27ZtFBVdnx3LlelPk5LDHbW4An5Wps3AZrLQ\n5u5nZdoMOjyDZMc6uSE1D4MQ1A1247TYWJ9dwpnuFvxakBijiQ3ZpTQN9TI3MRMBrEwrRENjcXIu\n7qCP2c5MMuzxkXOm5LG76RxF8anMcCTT7R3iWGcD6TYH85PGr+5KttchG8oRWUWIzOjfaVfb1Ed3\nn4eZuU4SHFbau1w0tPaTne4gI0Ulq9eKsNox3PX3aEdeRtjjETfdi/C6kW/9CSk1DKvviXaIijIt\njTkxW7lyJX6/n5SUFD70oQ+xbds2FixQBaDK9Pds9VH2NFcA8HZbNbdkzuLX5w8C4DBbuS13Lt9/\n51UCUsMgBA/NuZknzr4Z6vx//8xlPF/3DlXD2zOtTp9J9UBHaM68uCTizTH839N7Q3Pe/q451/Gb\n8wcj5lyXNeuqfzbZXof2+3+FYABpMGK472uIrOh9yTpb1cnLb9UCcPBUC7ffVMBzr1YS1CQGIbhv\nSwlZaXFRi+96I3KKMeYUh5+wxSG2fT56ASnKdWDMidmdd97J1q1bWbFihbp8rVxXzvS0hh7XD3ZH\n1JIN+L0c6agjMLz8qEnJ0c66iO2YTvU0hZIygLM9LVhGNJN97znrR81ZHzHnmd6W8UnMGsohGNAH\nWhBZXxbVxKy2qT/02O0JUF7bQ1DTy2A1Kalv6VeJmaIo09qY22X88z//MytXrlRJmXLdKUlICz3O\nsicwx5kZGttNZhal5GAY/rsQCBYm5+AwW0PvKXVmkB+XFBoXO9PePeeIuy/tJjOLki89Z/GI46+G\nyJ4FBuPwQCCyiy99wATLyQjX4VktRorynOF/BwE56Y6LHaooijItXPVdmdGm7spUJlpQauxroSJM\nxgAAIABJREFUrcIV8LMmYyZxZivHOhtodfWxJCWXDHsClX0dlPe1Mis+jWJnOu3uAd5uq6Z2oIs4\ns5WbM2dR2ddBxXArjFVphfT73Vc855GOOtJsDpamXv2+tNI9iHz7L8iedkRiOqJ4GSI3Ort3vFPR\nQV1zPznpDuLsZqobehl0+0lLspOfFU9VfQ9dfR4S4qzcuDibWJs5KnFeb2RrDfLoTrDHI268G3xu\nvV2GlIhVdyASUqMdoqJMOyoxU5QJ8vjZNzjZ1QiAwxzDusyZvFR/BtC7/397yVYy7PFRiy/4/I+g\n6oQ+sMdj+Mx/IKKwX2d1Yy/PvRpuG7J+RR6vH24ILWHeMDeditoeBoZ8AORlOrh3k9r+baJJnxvt\nZ18LtccQs1chu1uhrUZ/Q3IWxo//cxQjVJTpSe2roSgTpN0Vrpca8HtoHjHWpKTTMxiNsMJ62sKP\nXf0woobtWurt90aMO7pdoaQMoLvXE0rKAHpGvV+ZIK6BcM8yQPa0QW+43jLi90dRlHGjEjNFmSAj\ni/OXpORyc2Yx5uF6rmy7k6IoLwOJhbeEHxcvRdiiU79VlOcMLU3GWE0snp0WaothMAgWlqYyd2Zy\n6P0LS9Ty2TWRkAIz5uuPhUAsXBf5O7Pw5ujEpSjTnFrKVJSrFJQaT1Uc5ExPCyXOdD5RvJLdTefY\n01xBgtnG7XlzQUqerTmOQRi4NbuU3Dgnv6s8jCvg454Ziyh1ZvDz8n20uPpZkzGTO/InphWNdA2g\nbX8SupoR89bCrMXIt/4M7fWI3FLE5k8ij+1CHt8NzjQMWz+HiEsct8+vrO9h7+EGzEYDm9cUYDQI\ndrxVg8cbYM7MFGYXJvLaoQY6e9zkZ8ezcn4WB0+1UNvUR4rTxpol2dS3DnC8rB1nvJWtawuJs1vG\nLT4lktSC0FwJNgciOUt/rrUWpDYpet4pynR07QtKFGWaOdBWw/52ve7mcEcdKTGx7Gg4C0Cfz82J\nrkaOddbj04IAvN5yHovRSONQLwC/qjjAqvRCzvXqS0N/rT/NvMQsCuNTxj1WeeAFqNe3TZMHXwQ0\nqD2tj8sPIeNT4PB2/c1Dfci3/ozY8qlx+WxNk2x/s4ZAQG8D8srbtZhNBjp79CXUo2dacXn81LcM\nAFBW1Y3DbuFsVRcA9a0DVDb0cuiUvpw25Paz71gTm9cUjEt8yrsJgxFyIuv5RMaM6ASjKNcJlZgp\nylXyDydcF3gu9AUb8XpA08JjGUSEhwSlhu89jpkQAX/k2DeqXiswahwc9f6rIKUkGAz/4IGgxsjm\nO0FNhpK2C/yjxj5/5DgQnNIX/BVFUd5F1ZgpylValV4Y6itW6EjhzvyFrM0oQgCJFjvrMmfx4Zk3\nYBQGrEYTW3Lm8MGCxdhNZgwINufM4ba8uaTG6I1TFyfnkBc7fsuHEeau0WuH0O+yY8kmuLAklVmI\nWHY7lK4ABDiSECvvGLePNhoN3LI8D4NBYDEbuHlZLuuW5WK1GBECls3PYMWCTBIcer+2ojwny+al\nk5Ou/7ukJ9u5YU4aC4r1GjOH3cyC4vG/qqgoihJNUa0x8/l8PPbYY7zwwgv09PSwYMECvv71rzN3\n7twxz6FqzJTJwq8FQ8X9AIfaa/l1xQECUmNjdinLU/P54anXGAr6mO3M4O9mreT7p/fQ6u4nLSaO\nhxds5HfnD3Gqpxm7ycyX595CwTguZ2r7ntOXLw1G2PgxDJYYtB0/1zv/L96IYe5qtD9+DzyDkDcb\nw93/Y0LaZ2iaRAhCzapdHj9/2HGO7n4vToeV+7aUsHt/HdWNfVgtRu7ZOIuq4SVMgxBsWp2PQQh2\n7qslqElumJPOumW54x6noihKNET1itm//du/8dRTT/GZz3yGxx57DJvNxgMPPEBTU9PlD1aUSWZk\nUgawo+FMaFul3U3neKWxjKGg3vahrLeVvzaeodWtt9Bo9wyyvf40p3qaAXAF/OxsLBu32GTAjzz4\nkj7QgnDgJbT9L4S3Yzq+G+3Qdj0pA70OrbF83D5/JINBROwgUl7TTfdwC4zeAS8HTrZQ3dgHgNcX\n5OCpllBdmSYlbx9v5uCp1lBLjaNn23B7AiiKokwHUUvMBgYGePbZZ/nSl77E/fffz+rVq/nhD39I\nIBDg+eefj1ZYijJuHOaY0GOr0USC1RbxepLVHjFOtNoRI6quRm7BdNUMRhj5+XYHjGxua7ZCnHPE\nAQKuUfsMe0xkF3+H3czInd9iY8zEWMNJry3GhD0mfCXPbDJgMqmqDEVRpoeoFf/bbDb+8Ic/kJ2d\nHQ7GZEIIgc/nu8SRijL5dLgHqOrvpMCRTLo9nkG/l/nJWaEi/q15cxn0+yh1puMJ+LkxYybJVjsL\nk3Po9gxR6kxntjODLu8QdQPdZNjjuSmjiANtNaE534t0DyJrTiGcqYisImQwAFXHwWRBFC7U31N7\nGul1IT7wRX07HU2DWTcgckv1sasP8uciipeBZwjZ2QgpuRDQ/w4vOefMxQhTOLGqa+6joq6H4rxE\n8rMTcHsC1DT14Yy3kpUaRzCoUdXQi8lkoDBHTwQtZgMzc50MDPnIzXCQlxVP/5CP1q4hkhJsLChO\nxW4zU9PYh81q4sbFWTS1DxIYvpFg5YJMahoj56xt6sPrC5KfFc+Bd5oxm4ysWpiJwaASuKshpQZV\nJ0FqMHMxQv17Ksq4i1piZjKZmDNnDgCaptHU1MSPfvQjhBDcccf4FRwrykRrcfXxbyd24g0GMBuM\n/I956/l1xX7aPYMI4BMlq3ituYLTPS0AbMwupdfr4r8rDwP6huSFjhQePbkLiSTJamd9VjHfPbU7\nNOfXF24id1Q/Melzo/3+X6G3DYlAbPkksvwQ1JwCQCy5Fcwxw20xgJwSxJJNyL/+BFqrkY4kxNbP\nIf/8n9Bejzz+KuKD/xN2/grO7kM7+/Zl55Q5JRg+9FWEEJyv6+HFvVUAnKroZPOaGRw82ULvgL5M\nuWVNAedquqht0pdvb5iTjslk4OA7+r9LToaDzNRYnt5xDinBEWthcWkaf9hZjj+gYTIa+OCmWex4\nsyZizuPn2i86p8Eg0IaXPCvre/j4nfPG7//465Dc9Rvk6TcBECXLEVs/F+WIFGX6mRTtMh5//HF+\n9KMfAfDlL3+ZwkLVuFCZOk53N+MdrtXya0HebqumfbhWSwJHO+pDSRnA0c56LIbwn15FXzuxJisS\nPYHo9rrY11odMeep7qZ3JWa0N0DvhW1xJLL8cCiBApAVR8ASXk6lsRwZEwcX7vcZ6Eae3gc+jz4O\n+JBn9l3xnLgHwB7P6fMdEeGdKGsPJVAAFbXdoQQKoKKuB5MxfMWlsXWAGIsxHN6Qj9OVnaGWGYGg\nxpnKzog5y2suPac2Ymunrl4PytWRFUfCj88fRUoZUS+oKMrVmxTXoTdu3MhTTz3FQw89xOOPP84P\nfvCDaIekKGOWH5cU0Y+r1JlOjDG8vFfgSCbTFh/x/vy4pNA4JSYuopmsxWCk1JkRMWfeiPeHJKaD\nJVw3JjILYbg7OwDpMxDp+eFxQgpkzQyPTRZE/mwY8Ukid/aVzxmjb5+Umxm53JqfFY/FHK4Ny0yN\nJSkhnNSlJ9tJTwnX2SXEWclKjQ2HZzKQnxlZ55abETlnVtql5xzJZp0U30OntpH/36flq6RMUSbA\npNuS6Tvf+Q6/+93vOHbsGGaz+bLvV+0ylMngna4mynpbKE5IZ3FKLvWD3RxoqyHV5mBd5iwG/B52\nN56j2dVLgtnGsrR8aga6cAX8rM8uJtFi583WKlpdfSxLy6fAkfKuOd+LbK9Hnn0bnGlgj0dWHQev\nW0+eskuQ5w7AYA8kZyGKlyLLDupXxBJSEcVLof4csr0ebLGImQthsA/ZWK7vjZhTctk5DYs3IuLD\n+1juO9ZIdWMfM7ITuOmGHNq7XJyt6sQZH8PCklSG3H6OlbVjNhm4YU46QgiOnW3F4wuyZHY6cXYz\np8530tA6QDCgkZUeh9NhpaqhF5c7QGqSjRlZ8VTW945pzoKsBN463oTRKLj9pkIcsWr7pqshPS7k\n0Z36lkw3bIra/qqKMp1FLTHr6OjgjTfeYPPmzcTFxYWef/755/na177GW2+9RWrq5TcrVomZMlXs\naizjjzXHAb21xreXbCXVFneZo8ZG1peh/fG7obHY8knkq78Dv77sJ1bdgTx3EHqGlykLFyLMVr1+\nDCA+GbHwFuSbf9THJgvi1geQO34ennPzJ5F7Iuc0rLpzXOIfadDl45d/OR1awly5MJPymm56hltq\nFOYkcNeGWZeaQlEUZcqK2lJmf38/jzzyCDt37ox4ft++fSQnJ5OcnHyRIxVlaqof7Ak99mtB2tz9\nl3j3lZEd9ZFPNJ4PJVAAsq0unJQBdDToV8ou6O9CttSExwEfNFREfkbTqDnbG8Yl9tF6B7wRWzG1\ndbpCSRlAe7drQj5XURRlMohaYjZz5kw2b97Mo48+ytNPP82+ffv49re/zfPPP88//MM/qNvalWln\naWoehuF6rpSYWAoc49fVXxQuDPcpM1th3k2QlDn8okDMXgkzF4XfX7pCf+6C/DmIOatADP/dJaTC\ngnURc4rRc5YuH7f4R0pPskfUjc2emczM3HCPtdJC9aVNUZTpK6o1Zm63mx//+Mfs2LGD9vZ2ioqK\n+PznP8+WLVvGPIdaylSmksahHtpcA5Q404kbzwaygOzvRJ56C1l5DGJiEWvvhdZavQZNSsSqO8Hv\nQb7zOniGEPPWQEoO8vhu6GlH5JXC3DX6600VkJSJWPkBOH8sYk4x0KPXqI0sBL+Idyo6OHGuHafD\nyq03zqCj28WbRxsxmQxsXJmPwSDYvb8Ory/I6iXZZKXGsXt/LR29bjJSYllUkkZ5TTc1TX0kxFlZ\nPDuN/iHfFc1ZkJ0wrv/OiqIoE2nSFf9fKZWYKYpOahraT74MXrf+RHo+mGPCWyuZLDD/Jjj+avig\nG++Ct58Lj1fdCftfgOHWHcxbC+cPR8xp/Mi3xhRPd5+bXz13JjSePyuFitoevH696W56sh2TyUBT\nm95axGQyMK8ohRPn2sPhLcri7RPNFx2PZc4vfHgRZrUzgKIoU4S6f1xRpgstGO5JBuAegmAwPA74\nwDUQecxQ33uMR3xXcw+8e84x8niDEWO3NxBKoPTXA5iC4dYXgYCG2+OPDMcdOR50Re4K4hrDnMGg\nphIzRVGmDHW2UpRpQpjMiBvv1gcGI2LNPYjlt8NwTzWx/HbEDZvDe2QWzIcVWyFl+EpzUias3AZF\ni/VxTByGldsQy7dFzDlWmamxzMrXm+LGWE2sWJDJ6sV6TzSjQbB6SQ6rF2dhNOp1dzfMSWfFgqzQ\nPpj5WfGsWphJaqJe55YYH8Py+Rkj5jSydE76JedcPj+DGNW/TFGUKUQtZSrKNCPdA0hhgNef0Tv5\n2xMQWz+r34n5+jNgMCE2fBRiE5Av/US/03LZbYg5N+rbMw10Q+FCuPUBeOExaKmGlBzEnV/GkHDl\nhfeDLh9Wiyl01crt8WMwCKwWU+j15/ZU0t7lIi3Jzt0binjtcAMVtT3E2szcvXEWVfU97D/Zgslo\n4Pa1BfgDGrveriUQlKxenM2C4pSIOX3+IIGg9q4N0hVFUSY79VVSUaYZYXNASzXamX36E64+5Ik9\nUHlc344p6Ece/CuYLeH2F4dfRg706EkZQPVJxMHtyJZqfdzZCBWHYdnYb8y5IM4e2dTVNipZqqzv\npb1Lb4HR3u3iwDstVNTqrUWG3H4OnmymsqEX0Ldlev1wIxazgUBQ/06573gTC0tSQ0kZgMVsjNgh\nQFEUZapQS5mKMh2NvuPTYgPTiITIYo3c89JojBwDxIza2mj06+PEYo48DY1eejSbjRH7X1rMBswj\nki6jQWA0qK2BFEWZHtQVM0WZhkRKNmLdffqVMmcahjX3wKwb0F5/BgwGvSdZRgFyz3+DZxBRsgJK\nlyEHuqCrWd+OqXgZYqgfWXcakVuqt9eYAKUFyTS1D1LX1E9OhoPSgiRcbj91zf0kxsdww9x0EuKs\nnK3uwmo2sunGGRiNgp37avH4AqxZkhORqCmKokxlqsZMUa4jsu4M2nP/F4IBvU/Z1s8h//g9/e5L\nqx3xoa8iX/kltNeDwYhh2+cRRUuuWXy1TX08v6eSoCZJSohh27pCnt1ZgdsbwGox8re3l5KUYLv8\nRIqiKFOUWspUlOuIPPu2npQBdLcgT7ymJ2UAXhfy5B49KQPQgmhn3r6m8Z2t7iKo6d8Vu/s8HC9r\nx+3V4/X6gpyv672m8SiKolxrKjFTlOtJclb4sdGEyCyIeFmkF+iNaC+MkzOvVWQAJI+4GmY0CDJT\nYyNeH7lVk6IoynSkaswU5Toh3YPIYFDvX2ax63tl9rYP9y0TiBnz9AL/WTfoWzZlzIDMIrR9f0bk\nlCDy545bLOequ+jq81A8I5HURDsNrf3UNw+QlRbL6sXZtHQMYjYbEUKwZc0Mqur70KRG/6CXQFCL\nuBlAURRltGBQ42RFB15fkAXFqcTazO8679S39NPQMkBORhz5WZNn6zaVmCnKdUL743ehowEAUbIc\nKo8hT7+pv5iUCbmzkdt/qo8tNph/E/Iv/wlSIg9ux3DvPyDyZl91HCfL23n1gL5cerysjS2rC3jx\n9SouVLtuu7mQo2fb8HgDlNd0c9MNOfQNemnvdlHV0Edr5xBb18286jgURZm+Xj1Yz+nznQCU13Sz\nqCSVPYf089/xsna2rJnBi3v1887BU3DvpmLyMuOjGXKI+tqpKNcB6feGkjIA2XQe2XQ+/IbuFmRD\nWXjsc0P1KULZEhLZXDkusTS1D4Y/xq9R3djLyFuQahr78AzXlQE0tg7Q3u16z+MVRVHey4X9ckGv\nV61vDW9H5/MHqW7sizjvNE+i84pKzBTlOiDMVsgpCY8LFyIKF4TfkFGImHVDeBybALNXhrZzwmga\nt6XMwuzwkoE9xkRpYXJoCyWDQVBakIQjNlznNjPPSW6GI3x8jnNc4lAUZfoqzAmfZzJSYinKC583\nYm1mZhcmhc47RoMgP2tyXC0D1S5DUa4b0u9Dnn0bef4YaAGYuwaD2YJ27iC4BhD5syG7GMoPITub\nECnZMO8maKtFZBUh0vLGLZaG1gG6+9wU5jhxxFro7HHT2DZAVlocaUl2Bl0+qhp6cTqs5Gcl4A9o\nlNd0YzIZKJmRiBCqoayiKBcnpeR8XQ/narpxeQLkZ8aTnR5HeU03nb1uUpw25s1KobK+h8a2QWJt\nZtYtyyUhznr5ySeYqjFTlOuEMFuQA91Qf0Z/oqkCufY+qDwGgGw+D2s/BGf2gRZEtlQhTGYMt9w/\n7rHkZjgiroKlJNpISQzfkRlnt7CwJC00NpsMzJuVMu5xKIoyPQkhEEJQWa+32GluH2Tt0hzOVHah\nSUlLxxBGo6CitgeXRy+dcHkC/M1tpdEMG1BLmYpyfbmwFyboRf09rZGv97SDFgy/ZaDnGgWmKIoy\nvgaGfBHjnn4P2ohFwv4hXygpe6/3R4tKzBTlOiIWrQfr8JWprCLEstshIVUfO5IQy2+DvDn62GzF\nsHhjdAJVFEW5SiUFSaGlSUesheXzMsnL1K/Um00GbpiTwQ1z0gEQApbPy4harCOppUxFuUZ6vC4e\nO/M6La4+VmfM5P6iZdf087Uz+5Cv/TcYzYhbPwGJachnvgOeIVixFTF7FfKFx6CrGYqXY1h/P8Lu\nuOy8iqIo19rrRxo4UdaOM97Knetn0dQ2wGuH6jEZDdy+thCT0cBLr1fh8QZYMT+T2TOTeOG1Srp6\n3RTPSGTt0hz2HKintrmfrNRYbr1xBqcrO/nhU0cvO6fXF2Td0lwWlKROyM+mEjNFuUa215+mYUhf\nGny95TxLU/MpTki7zFHjQwYDyN2/Gd6OyYM8uhPMFhgcXqo8tAM50BtuqVFxCBbdAioxUxRlkmnr\nGuLomTYAuno97DvWSGV9L0FN4vNr7DlYj9lkYNDlB+DQ6RYGXF46etwAVNT24Ii1UN3YB0BzxxCn\nz3dy9OyIOY83Ull38TlfPVhHSUESVotx3H8+tZSpKFES1T8+IQAROR59o6O681FRlElIjDpZCcOo\n8Xu8f/Sd3O86vYnRw7HMOYZg3weVmCnKNbI1bx4zHMnEGE2szyqh6BpdLQMQRhNi099BTBzEORFL\nbkXc/DeQkAJWO2LZbYhlt0FGAVhi9NezZ12z+EYLahptXUMMuf1Ri0FRlMkpLdnO8vkZWMwG0pLs\nrFmczabVM4ixmnDEWtiwKp/1K/JIiLNgtRhZNi+DZXMzyEiJxWI2UFqQxKKSNIrynJhNBrLT45g7\nMzlyziWXnnPDqjws5vG/Wgaqj5miXFdkcyXan74Pfq/eVPb2zyL/+B/Q3wWxTgx/+01EfHTbUgQ1\njT/tOk9j6wAmo4G7NhRNmq1SFEWZOgZdPp7efo7+IR+xNjP3bSlm1/76iHNLZX0vJ861A3DL8lwW\nz06PctTqipmiXFfkyb16UgbQWo08skNPygCGepFlB6MW2wWtnUM0Dm+fEghqoZOmoijKlaio7aF/\nuAXGkNvPkTNtEeeWY2VtEeeXI8N1a9GmEjNFuZ7EJ4cfC4FIyrz461ESZ7NgGFEzEj9ieyZFUZSx\nio+LPHckxcdEnFsSYi3E2szh90+Sc426K1NRriNixTbwe5FdLYi5N2IoXYGmaci604icUgyzV0Y7\nRBIcVratK+RkeQdOh5XVi7OjHZKiKJOc3x+krFrftq20IAkhIBDQKMpz4vUFycuMJyXRxpyZyfT2\ne0h22igpSMZgNNDaOUSczcySOekcK2sj0RFDQU7Cu+Y0GK7NDVFRTcyCwSC/+c1v+MMf/kBLSwtZ\nWVncf//9fOQjH1F74SnKBBAms170P4Jh6WZYujlKEb23orxEivISox2GoihTxJ93n6epfRCAprYB\nzCYDx8r0Zcr0ZDtxdjN/2nUeAHuMiZULMnl2ZzlBTWIwCO7aUMSLe6tC7TA2rsynrLorYs5bb5xx\nTX6WqCZmjz/+OD/96U/54he/yKJFizhy5Aj/+q//itvt5jOf+Uw0Q1MURVEUZQrw+4OhBAqgtrkf\nkzFcqdXW5aKqoTc0dnkCnKvtIajp9z5qmqSitjuUlAFUNfS+a85rJWo1ZsFgkF/+8pd86lOf4gtf\n+AKrVq3iS1/6Eh/+8If5xS9+Ea2wFEVRFEWZQsxmI+nJ9tA4Oz2OnIy40DjZGUN+VvjObqvFSFGu\nM9SHTAgozHFis4avVeVlOt4157UStStmg4OD3HXXXWzatCni+YKCArq7u3G5XNjt9oscrSjKeNFO\nvQl1pyGnBMOi9dEOR1EU5Yp98NZiTpZ30NQ+SDCoUZyfSFqinbqWfqQmkRK2rSukurGPIbef5o5B\nPnhrMZX1vXT3eaht6ufO9UVUN/TS2jVEW7eLdctyaWobDM1Z3dhLYY5zwn+WqCVmCQkJfOtb33rX\n86+99hoZGRkqKVOUa0DWnELu+pU+qDiCtMcjipdGNSZFUZQrFWM1IaWktknfZqmqoY/1y/OorNeX\nMKsa+9iwMo/y2m6CQUldcz+aJqlq6GVgyEd9C/QOeDCbjNS36C016pv7WViSGjHnA3fMJSkhZkJ/\nlknVLuPZZ5/l7bff5tOf/nS0Q1GU64Lsab3kWFEUZaro6feEHmuapK17KOL19i4XwWC4p35Xr4eB\n4T5n+vHeiDlcngCdvZFz9g16JyL0CJMmMXvhhRf49re/zebNm/noRz8a7XAU5bogihaDfbj2IiZW\nXS1TFGXKmluUgnG4pUWy08aS2WnYY/SFwRirkUWz00J1YwaDYGFJKnNmhns3LihOZUFJamg8Kz+R\nhSWpEXNmp018rdmk6GP2y1/+kkcffZT169fz3e9+V7XKUJRrRMSnYPj4P0F7A6RkI2IToh3SdeuP\n1cc53FFLblwinyq5kWOdDbxUf4p4cwyfLLmRdLvalkpRLqaqvpe9hxtwxFpYMiedtGQ729+swWAU\nrFiQQWlBEnsPNTLo8jG7MIkV8zM5fKaV2qY+8jIdrF6UTWP7AEdOt5KWbGfF/AyK8hIRQvCJu+fR\nN+AlMyUW8wTtjzlS1BOz73//+zz55JPcdddd/Mu//AsmU9RDUpTrirA5IH9OtMO4rp3taWFXUxkA\nvd1uXqg7xd7mCjQk3V4XT1cf5e/n3RLlKBVlctI0yV/frCYQ0AB4p6IDs8lAR7cbgCOn2xhyB6hr\n0VtelFV3Ex9r4Uylvh1dfcsAGSm9HDqll3IMuvxUN/YxKz8JgIQ4Kwlx1mv280Q1C/r1r3/Nk08+\nyQMPPMAjjzyirpQpinJd8mnBiLE3GEAjXAvjCwaudUiKMmVoUhIMaqFxIKAxMpsIajKUtF3g8196\nPPr911LUasza29v57ne/S3FxMVu3buXkyZOcOHEi9L9AQJ2IFEW5PsxPymJRcg4AGbZ4PpA/n625\n8xBArMnKPQWLohugorwPMhhA9nUix+GLRTCo0TfopX/Qi9sTns/jDeDxBrhleR4GITCbDCyfn8Ha\npTlYzUaEgOXzM1ixIDN01asoz8my+emherG0JDtLZqczf1YKAHF2c0StmT8QpH/Qi5T6l6WgNjKW\ncFPa8SLkhU+6xv785z/zzW9+8/+1d9/hUVX548ffdyaT3jukEALJEEhCDwRIwKCAgK7LomLBBlgQ\nsaL4W1n3q67uirq4ARXXFhR7QVbKrqBUIXSQFkKAQEhIQnqfZOb+/hgyyRBKgMBM4PN6Hp/Hc2fu\nZ84dzmQ+c86555z18Q0bNuDr63veODk5OQwfPpyVK1cSGhrallUUQogrymBswFHbNJBRbzLioGhk\nNEG0O2pNJaavX4ei4+AdhOb25y56Dmt1bT1fL8+guMx8h6QCjBzSGQcHDcvWHMJoUunbIwh9hC/f\nrzhAbZ2RTh09GTE4gu/+d4Dislq8PZ24baSenzdkczinDCdHLeOujyLrWBmbfs9DoyhWx9+TAAAg\nAElEQVSMGByBosD/1h+xxOwe6ce3Px+gpraBTh09GTUkgm//l0lRqXmYVFHg+sROxEUFnOMKLozN\nhjLHjRvHuHHjbPXyQghhd5onZQA6zeWfaCzE5aDuTzcnZQCl+ah71qEkjLmoWHsPFlmSMgAV+G37\ncXQ6rWVbpa178qmoMlBbZ54WkJ1bzqZdeZbzSsvr2Lgzj8M55jXJ6gxGNv2eR9Yxc9mkqmeMWVll\nsPTQZeeWs3FHniUpA1BV+G17bpsmZnazXIYQQgghrhIupy0r4XLxdxW7OLfsQ3JxdrAshQGgc9Dg\n5qKzeo67m6NV2cNVR/POZ1dnHU6OTT9+zhTT9bSYHqfFPFv9LoXcAimEEDZ2sKyA4rpq4n1DcHbQ\nkVtVxrGqYqK8AvF1cqO0rpqMsnxC3LwJdfOhztjAruLjeOqc0XsH2br6QrTk7AZdekP5SZSwbiix\ngy86VPcufpwsrSHrWCn19SZ8vZwYPjACrUZhZXo2tXVGBvfuSIcAd2rrjBSX1dCjqz899QEYDEay\nc8sJDfagU4gn5dUGThRW4evlTFLfUGK6+LF2aw46Bw0pAzq1KmZdvZGD2SUYGkz4ejlzXUJ4G75x\nNpxj1lZkjpkQoj1bk5fJwoObAQh18+bWzn1I3bOKBtWEm4MTj/UYxjt7V1NeX4tGUZjWfSiLj/7O\nkQrzrf7jO/fmhtAYW16CEFZMvy1C3fgfcyGsG5rxz9h8nmRmdgk/rc5CVcHTzZG7b+qOs5N99k3J\nUKYQQtjQlsKjlv/PqSplQ8FhGlTzrfpVDXWsz8+ivN48T8akqmwoOGxJygC2njyKEPZEzdjcVDi2\nH2oqbVeZUzKzS2jshiqvMpB3surcJ9iQJGZCCGFD4e5Nd5+7OzgR5dk0iViDQjfvYLRK05/qrp4B\neDm6nPF8IeyBEtSpqeDlD86utqvMKY1bMQE4OGjwu8wbkV8K++zHE0KIq4jRZGLF8f0U11WR3CGK\nEDdvNuQf4nBFEbE+HfBydCarvBCtokEFHtAnsrs4jzpjPQU15TzaYyjbTh6ltK6Gk7VVPNRtCFsK\ns8mvrUBVVY5XlVrFjPcNIda3o60vW1xDVFVF3fkrFOVCjyEovh1QTxwGJxfUzG0o+v6oJ46g7lkH\n3oEova9H0Vy5vqE+3YNwcNBw7EQFJpNKZnYJvWOC0GjsbykaScyEEOIy++HITsuWS5sLsxkX0YtP\nD24CYG3eQR7QJ7KrOBejamLLyaNM6NKP/aUnKK+vZWfxcZKDu1JQU8GBsgJ2l+SSWZZPlFcQe0ry\nAPNw5riI3nx6MN0S87leI4jw8DtzhYRoY+q2/6Gu/tpc2LMeZcS98NuPgIq6byMmYz3qLwvBcGrZ\nC0MtSuLNV6x+iqLQJcybtVtzMNSbOHi0FEODicSe9vcDRoYyhRDiMjtaWWz5/6oGA5nlhZayCZWM\nsgKMatMWMIfKCy3zygCOVpVwtLLktLJ1zIPlBVYxc6pK2/w6hDir/GZzHRsMqEczoNm2YmpORlNS\nBqgF2VewcmalFXVWWy8VFFVf8Tq0hiRmQghxmSUENs25CXPzYXBQFxxOzRtzd3AiKbiLZd6YVtEw\nMKgzkR7+lnP6B3QiIaApRj//Ti1iDgqOtIoZ4x18Wa9JiOYUfX9onAvp6Y8SlwyNcyF1TiixSeDb\nofHZKPqEK17HQF9XfJvNLdNH+FzxOrSGDGUKIUQbemHzYgprK3HW6nip7xiyKopYnZdJpIcfA4Mi\n6ejqxQ9HdhLs6kkf/zDifUNYnP07blpH+nQIY1BQZ9acyKLOWE8f/zBSOurJKi8kq7yQbt7BJAd3\nBWB5zp5zxvwyawslddVEewXwW/5hTKrKuM69GNYx2sbvkLBnal0N6opPUYtzUWKTUHqloK79FvXI\nbpQwPcrQCah71qHu+AW8A9HccC8UHsOU/hMEhKHEDISQKNR134OnL0rnnqDvD5uXmxO3HoNR4q+D\nzC0YNy09d8y134KDI0pELGrGJvD0RzPiXhQXjwu+rjqDkRUbs1GAHl38iNcHkJldwqbdJwgL9mBo\nvzC7mW8miZkQQrSRBQc2UlhrXhqg1ljP6zt/ptRQY1n+wsPRhRXH91NQUwFAVb2BvOpydhWbt645\nUVuOi4OOtScOAnC8uoxwd18WHdlpfoHqMjq5+7DyeEarYx6rahoC/SJriyRm4pzUjYtRM8xzFdVf\nP0etq4Ety83lkzng4oF6au4YhccwObvDgc1QZx4WVBUNZO2AnAxzuewk1NfBqZjmvTMDUZvFVF08\nLPPRzhjzVCwKj6GucUUZ+cAFX9fGnblkHDYP/xeV1eLt6cyWPfkAnCypwd/HpU23VboUMpQphBBt\npLSuxqpcY6y3JFAAlfV1VNXXNZUbrMsmVaWs3jpGaZ31PJhyQ+0FxTydwdTQyqsR16TT1xyrKjut\nXErzuWPUVEDzdl9bYR2jvs78nOYqS89Qbhazutw6ZjPqRa6JVlNn3e4rqw3Wj9faz+dCEjMhhGgj\nE6MSUGgaDrmtc2/L0KOTxoFRoTHcEtETzalnjQ2P48bwWJy15v34EgI6MSY0Fj8nNwAi3H0ZEx5L\nlGcgAN6OLowIjbmgmP7Obpb69PDpgKNGBkrE2Sl9R4LrqX0tI+JQEm8C/1O76vgEo/YfDV37mMvO\nbih9R6IM+oO5rNGiDLgJzaBbQHuqnfUZgSZhTFPMTj1QB461iknCaTH7jWqKqWigQxfz/zu5okkY\nfVHX1bdHkGUfzE4dPRkY3wF/H/McOF8vZ2Kj/M91+hUlWzIJIUQbqW0wMHPjImpU869vBZgYNQBv\nRxc+ythAZUMdKR2jGdohinf2riG/poJu3kE8oB/Exxkb2Fd6gkAXDx7rMYyVxzNYlXcAVwdHpsYk\nc7yqhK8PbQNF4a6u/S8oZqP+/p2YHHPxexaKa4PaUG/u9XL3RlEUVJMRU/oS2LAYHHQoox+EhjrU\nnxdAvQFlyB8htBvqkvegohhFn4CaPB4WvwP5RyCoE8ofHkdd/SVkbAI3b5Q/Po6ateOcMZXYJNA4\noDi7olaUgLMris7poq+rwWjiv+sPk3G4BDcXHX8cHmXeuNzFAe0VXFPtfLR//etf/2rrSlyK8vJy\nFixYwL333oun58XvXi+EEJfq28PbyawotDq279R6ZDnV5uGbwxVFmFSVvacSppO1VZhUlU2F5uUD\nqhoMGIwNlnlm9SYjBbUVbC86Rq2pARW1VTGNqsrmQuslCXKry7gxLAaNYj9fQsL+KBotipNL0/6W\nJhPqd28BKpiMcOKIOeGqOLVky9H90FAPuZnmctFxMJkga7u5XFUGxgbYs85crq81J367Vp8zppIw\nGuVUj6/i5IKivbTe3vyiKlZtzjFXocFEraGBuKgANDbex/N08ukUQog24qp1bHHMSeuAY7MvFAXF\nMszYyNnhtLJWZ/Vl4aR1sBqCPGPM02K4nPYajTTyZ19cKEWB5u1L5wjNe660WusygJOLddnxtC2Q\ndE7nj6nRXlq9T6Nz0J5Wts/Pgkw2EEKINnJzRDy/lxznWGUJCgp+Tm7c2bU/FfV1nKytpKrewMjQ\n7gS5epBdWURBbSW9/ULp6xfOiepyDlWcJNzdl6QOXdEoCltPHsXL0YU/dOpJZnk+q/MOokHh9si+\nZ45Z0RTzpk5xNKgm1uVlUm2sR6MojA2PQ2NHQzbCPhl3rzffVTlwLFrvQKitQuk3CnXvb+DojJJy\nFxQdRzXUQH09ypBxoNWZ79qsKjUPQXaKRS04CidzUEL10D0RaipQj+wGnyCUfiPBK+DcMfOzUXWO\nqN5BsGsV+IeiiYy/6Ovy93EhuV8oO/YV4O3pxJA+9jn9SeaYCSHEZWJSVf61+1f2lZ5AAe6OGsCJ\n6nLL9kxDO0TR1TOAjzI2oKLS1TOA8ZG9+efvv1BnbMDH0ZXpscOYt3cNJ2srcdRoeTz2On46uvuc\nMe/s2t92Fy3aNeOyf8O+jU0Hxs+AJe+Z76x0ckW5dQbq/z6GgqOg0aK5aSpqxmbU/eZzlEG3gM4J\ndfVX5vO79kHTcximRf8yD2f6dUQZ8yDqN282i/ks6v8+OmtMNFrzcCeg9BmBZtjtV/AdufKkx0wI\nIS6Tk7WVlsn3KrDuxEHyqsstj687kcWJ6nLUU0sFHCwvZHVuJnVG880DJYZqfsk9wMlTa6MZTEZW\n52WeN6YkZuKiZW6zLq/7tmm5i7pq2PGLOYECMBkx/b4GDu20PF39fY31kOTBbZgUxZyUgXmT8+2/\nWMVUd6y0jrlrDRxuitmYlAGo+36Dqzwxkz5tIYS4TDx1zrg5NM07C3b1ooNr001KwS6eVmVnrY4w\n96ZtYhQg0sPPagmOMHef88YU4qK5nraqfqjeuhzcGZrPX/QLAU+/prJvh2ZbLwFuXij+zUaztA4Q\nHGkVUgmOsI7p39E6ZnMevue/hnZOesyEEOIycXbQ8URcCiuO78dT58LYTrHUNtTz09HdmFSVseGx\nuOuccNbqKK6r5rqO0UR6mtdTyijNR6toaFBNPBgzmC2FRzGYGjCaVB6OSWZd/sGzxhTiot39V/js\n/8yLvHZLQJt8Kya/DnBkD4R1QxM/FNU7CNPvq809aFoHlNEPwe+rUStLwT8EJaoPePigFp8AzwAI\njkQZdAvqicPmmwC0WpSR96Nm7QRVhYZ6lJunwb7fwDsQZcBYiE1C3bQEHJxQPX1h56/g7oNyy+O2\nfocuO5ljJoQQdqbeZOTFLT9RVFcFwPUh3civKef34lwAor0CeTr+eltWUVzjTD+nmYctwdxD1jsF\nVi40lx1dUEZNQv3PO6CaAAXlpkfMa5SdGpZXkm9F3b8JCsxLuij6BDRjHrLBldgf6TETQgg7U1JX\nbUnKAA6WFXCi2bY2B8sKUVW1aZ0pIa4w9XhmU6E4D7L3NZUNNaiHdp5KygBU1MO7LEkZgHosw5KU\ntYh3jZM5ZkIIYWd8nVzp6OplKcf6hhDn27FZuaMkZcKmlM5xTYWgzk1bKgG4eqJ0G9A0b0yjRYlO\nsJofpnTpZTV/Tel88ctgXG2kx0wIIeyISTXx09HduDo40s8/nH4BnejtH4ZRNdHDpwMmVWVAYISt\nqymucUrybSgdIlFrq8G3A+rmpRASjdKpO0T0gK0/m7diCu4M0f1hf7o5MQuLQdMtASUiFjUm0bwk\nhoMjSrcEW1+S3bCbxGzlypU888wzbN++3dZVEUIIm1mdd5Blx/ZYykNObViuVTQkBkWe7TQhrihF\nUcwJl2rCNP9p880CYF765fhByN5tLhflmtch27HSfGJeFvQbaY6hc0SJS7ZJ/e2ZXQxlbtu2jRkz\nZti6GkIIYXPFzeaWgXktMyHsVkODJSkDzHtdVhQ1leuqofxkU1lVobLkytWvHbJpYmYwGPj3v//N\nPffcg4OD3XTeCSGEzQwO6oKHzryvYAcXT3r6yt3mwn4pOkeU3sMbSyj9RqH0HwWKOb1QYpPQ9BnR\ntFdmhy4t10YTVmy6XMaKFSt4/vnneeyxxygtLeXjjz++4KFMWS5DCHG1qWkw8NPR3azJy8TL0YWH\nYpKsFp4V4koz7duI+stCcHBEM/pBcNBh+uldqKtBSb4VvINQV30OpYXmvTJ7D0ddkQa5hyBMj3L9\nPagbFpvnmvkEofnDNNTcrHPG1MQPtfVl24RNu6ni4uJYuXIlnp6epKam2rIqQghhNwwmIyuP70cF\nCmsr+ebQNp6KH37e84S4HFRjg3l/TGMD1FVjWvmpedulimLz4ys/M29SfvK4ubxjpXmF/2MZ5gBH\ndqNuXwl71pnLJ3MwrfseDm47Z0xVn4Di5HLFr9fWbJqYBQUF2fLlhRBCCHFRTl+u5bTyRS3nIkvA\ngJ1M/hdCCNHEy9GF8ZF9cNI6EODszq2Rfc5/khCXiaJ1QBlxPzi5gps3muET0aTcad7P0tEFZfjd\naAbdAkER5qUveg03l7v0Mq9lFhGLZtAfUPqPBgdH8A9FM2TceWNei71lYEdbMqWmpvLRRx/JHDMh\nhBBCXLOkx0wIIYQQwk5IYiaEEEIIYSckMRNCCCGEsBOSmAkhhBBC2Am7Scwee+wx2SdTCCGEENc0\nu0nMhBBCCCGudZKYCSGEEELYCUnMhBBCCCHshCRmQgghhBB2QhIzIYQQQgg7YdNNzIUQQghx8XYW\n5fDtoW04ah24N3og4e6+tq6SuETSYyaEEEK0Q0bVxIf7f6OgtpKcqlLSDmy0dZVEG5DETAghhGiH\nTKpKvcloKRuMDTasjWgrkpgJIYQQ7ZBOo+XWyD5oUHDUODAqrAeqqtq6WuISSWImhBBCtFMpIXpm\n9hqBo0bLgsx0UveswqiabF0tcQkkMRNCCCHasV/zMqlsqANgT0keGaX5Nq6RuBSSmAkhhBDtmLuD\nk3VZ53SWZ4r2QBIzIYQQwk4crSxmc8ERKutrAThZW8mmgiMU1FQAUFVvYHPBEY5UFAFgNJkIc/NG\n7xVImJsPE7r0parewLaTRy03BpwvprAvso6ZEEIIYQd2FOXw3t61qKj4Obkxpdtg5uz+lVpjPU4a\nBx6Pu460AxvJr6lAAR7QD2JzYTa7io8DMCI0hsp6A3OyfgGgm3cQwzpEM3/fOkvMyd0G83azmDN6\n3kCYu48Nr1qcThIzIYQQwg5sLcxGxXxXZVFdFetOZFFrrAegztTA+hOHyD/Vy6WCVVLGqbKjpulr\nfX9pPi5aR6uY60+L+XvxcUnM7IwMZQohhBB2oPmq/TqNFr13EEqzx/XegThrmxKvCA9fglw8rc4P\nb5Zk+Tm50dnD75wxZacA+yM9ZkIIIYQduD6kGzqNlsyyAoyqSkV9LQ/FDGFn0XGqGurIr67g0e5D\n2VyYTVFtFZX1ddwbPYBthUc5UVOBh4Mz/QPDCXLx5GhlMS5aHX5ObtzRpd9ZYx4qP0lXzwCcHXS2\nvnxxiiRmQgghhB1QFIXe/mH8cGQntcZ6thcd46bwOA5XFHGippxdxbnkVJXgpNWxpzSPPaV57Cw6\nzrCO0azIzQAgvfAw90QNYMnR31GB9MIj3B+dyO6SvHPGnNpjqG0vXlhIYiaEEELYiYKaCsscMIAj\nleYEqtHRyhKcmg1nFtVVcaj8pKVcbzKSUZpP8/X/D5QVnDemsB8yx0wIIYSwE2HuPgS5eACgAAkB\nEcT7hlge7xcQTr+ATpay3iuIAYERKKdmjvk5uTEkuAvOWvPQpKNGy+CgyPPGFPZDesyEEEIIG3hx\n80+cqDX3XGlReCtxPAsPbsZB0TAoMJKkDl3ZUZRDcW0VcT4dGdoxinJDLb/mHiDKM5BBQZF4O7my\nOHsn4e4+9A/oRJRnID9m78TP2Y1Yn4709+/E/47vO2/MV7YtI9DFg60nj1rq90LfEYS5+tvq7blm\nSWImhBBC2EBjUgZgROXjfRvYUZoDwPHqMgJdPPhvzl4AcqpL6ezpz3+yd1mGKYNdPdl6OJvqBvMw\npQLsKj7OgbICAE7WVFJvamBT4ZFWxTxWZT2k+crW/zE/6c7Lc/HirCQxE0IIIezASYP1SvwlddVW\n5TJDtdXcscr6WmoamuaOVTbU4XhqtX8wr1NWbqi1jmmwjllaV2MVU9iezDETQggh7MD93QbhcWqf\nyx4+HRjbKZaOrl4ABLl4MCq0B738QgFwc3Dk+pBujAmPA0CraBgdFstNneJwUMxf7dd31HNjeA9L\nzBjvYMaE9WgW05Mx4bGWmM3XNwOI9+54Wa9XnJmiqmq7TpZzcnIYPnw4K1euJDQ01NbVEUIIIVrt\nm6ytnKyp5JFY83IVBmMDlQ11+Di6oigKRpOJZcf28NPR33HUOPCAPhGD0chnBzdhMBm5JaIn0V4B\n/Hv/eorrqkkI6MQtEb14f99ajlQWE+HuyyPdh/Lt4W1sLszGx9GVR3sMxdXBEW9HF7QaDaqqUmKo\nxs3BiYK6Ur7L3MGYkHii/ANt/O5cm2yemH399dd88MEHnDhxgpiYGGbOnEnv3r1bfb4kZkIIIa5W\nDSYjj63/GtOpAccAZ3ectTrLfLDGuyzTT80jA0jpGM0vuQcs5WEdo1nVrNzXP5wHY4ZckfqLC2fT\nocwffviBF198kZtvvpnU1FQ8PDyYNGkSx44ds2W1hBBCCLugKAo6jdZSdtQ44KhtKmsVDY5a6+ni\njUtlWMoa6/Lpzxf2xWb/Oqqqkpqaym233ca0adMAGDRoEKNGjSItLY0XXnjBVlUTQggh7IJW0TCp\n2yC+ObQNR40D90YPxEGj4ZMDG6huMPDHiF508w6mqK6KvOoyBgd1YUx4D8oMNewpySPaK4ibOsXi\nqtPxa+4BAp09+GNET1tfljgHmyVm2dnZHD9+nJSUFMsxnU7HsGHDWLt2ra2qJYQQQtiVnn6h9PSz\nnqrz5943WpUfj73OqnxP9ECr8sjQ7owM7X55KijalM2GMo8cOQJAp06drI6HhYVx9OhRjEbjGc4S\nQgghhLh62Swxq6ysBMDNzc3quJubGyaTiZqaGltUSwghhBDCZmyWmDXeDKoop6+cwjmPCyGEEEJc\nrWyWmHl4mDdUraqqsjpeVVWFVqtt0ZMmhBBCCHG1s1li1ji37PSlMY4dO0ZERIQNaiSEEEIIYVs2\nS8wiIiLo0KEDK1assByrr69n1apVJCYm2qpaQgghhBA2Y7PlMhRFYcqUKbz88st4eXnRp08fPvvs\nM0pKSrjvvvtsVS0hhBBCCJux6fK/d911F3V1dSxYsIBPPvmEmJgYPvzwQ8LCwmxZLSGEEEIIm7D5\nXpmXSvbKFEIIIcTVwqZ7ZQohhBBCiCaSmAkhhBBC2AlJzIQQQggh7IQkZkIIIYQQdsKmd2W2hcbN\nzk+cOGHjmgghhBBCtE5wcDAODi3TsHafmBUWFgLmpTeEEEIIIdqDs60m0e6Xy6itrWX37t0EBASg\n1WptXR0hhBBCiPM6W49Zu0/MhBBCCCGuFjL5XwghhBDCTkhiJoQQQghhJyQxE0IIIYSwE5KYCSGE\nEELYCUnMhBBCCCHshCRmQgghhBB2QhIzIYQQQgg7IYmZEEIIIYSdkMRMXHayhrE4F2kfbe9qf0+v\n9uuzpavlvW3P1yGJWTuTnp6OXq/n999/b/U5M2fOZOzYsZexVmc3d+5cPv/8c0t54sSJPPTQQzap\ny7XuYtrOhcjJyUGv17N8+fKzPuf7779Hr9dTXFwMQGZmJvfee+8Vq+O14PTPXGtc6OeyNf/Wl4u0\nmcvnSrSdC9Wa76+UlBReeuklS7m9f+9IYiYuq9TUVGpra21dDXEFBAYG8tVXXzFw4MBWn7N8+XL5\nQm1jV/tnTtrM5WOPbWfq1Km88cYbF3SOPV7HhWi5e6YQQlwER0dHevXqZetqCCGuIuHh4bauwhUn\nPWYXYOfOndx111307t2bhIQEpk+fzvHjxy2PL1iwgBEjRhAbG8uYMWNYunSp5bHGrv+lS5dy5513\nEh8fz+jRo62eA3Do0CGmT5/OwIEDiY2NJSUlhXnz5rX5eHlr6vrLL78wadIkevbsSVJSEu+++65V\njJycHB555BH69OnDkCFD+PDDD7nvvvuYOXMmAHq9HoDXX3+dlJQUy3kmk4k5c+YwePBgevXqxcMP\nP0xBQUGbXp+9aY9t55NPPiEuLo66ujrLsalTpxIbG2t17OGHH2b69OlnHN5atGgRI0eOJD4+nilT\nplBaWmp5LDU1lblz51JdXY1er+f777+3PLZ//37uuOMO4uLiGD58ON98881FXUN7otfr+eKLL3jo\noYfo2bMnKSkpLFy40Oo5DQ0NvP322wwbNoy4uDjGjRvHhg0brGKA9WdOVVXS0tK46aabiIuLo3fv\n3tx///1kZGS0af2zs7OZOnUqvXv3pl+/fsyYMcMyZA3mIanp06eTlpbGddddR3x8PBMnTiQrK8sq\nzsKFC7nhhhuIj4/nvvvu44cffkCv15OTkyNt5izaS9sxmUwMGDCA1NRUy7F9+/ah1+t5++23Lcd2\n796NXq8nMzOzxVBmYWEh06dPp2/fviQlJbFo0aIW78Xp19H42u3le0cSs1aqqKjgwQcfJCgoiHfe\neYeXX36ZvXv38tRTTwHmMe1//OMfjB49mvfee49Bgwbx1FNPsWzZMqs4f/nLX+jevTtz586lR48e\nPPXUU6xbtw6Aqqoq7rnnHkpLS/nHP/7B/PnzGTBgAP/617/49ddf2+xaWlvX559/np49e/Lee+9x\n3XXXMWfOHFavXg1AbW0t9913H4cPH+a1117j2WefZcGCBWzdutVy/ldffQWYx/fnzp1rOb5u3Tp2\n7NjBa6+9xp///GfS09N5+eWX2+z67E17bTtJSUkYDAa2bdsGmP+wbdmyhfr6enbt2gWAwWAgPT2d\noUOHtjh/2bJlPPfccwwZMoR58+YRGhrKW2+9ZXn81ltvZfz48Tg7O/PVV18xbNgwy2Ovvvoqo0aN\nYv78+ej1embNmsX+/fsv6jrakzfeeAN3d3dSU1O54YYbeOmll/j6668tj8+aNYuPP/6Ye+65h3nz\n5hEZGcmUKVMs/0Zn+sx99NFHvPHGG4wfP54PP/yQWbNmcfDgQZ5//vk2q/fJkye58847yc3N5fXX\nX+f//u//2LFjB5MmTcJgMFie99tvv7Fo0SL+/Oc/M3v2bLKzsy0/5Brr//LLL3P99dczb948wsPD\n+ctf/mJ5XNrM2bWHtqPRaBg0aBAbN260HEtPTwew+u5Yt24dISEhREVFWZ1vNBqZNGkSu3fv5uWX\nX2bmzJn861//Ij8/3/Kcq+F7R4YyWykrK4vS0lImTpxI7969AfDx8WHjxo2Ulpby/vvvM3nyZJ54\n4gkAhgwZQlVVFW+++SY33nijJU5SUhIvvPACAMnJyRw+fJj58+czZMgQDh8+THh4OHPmzMHX1xeA\nxMREVqxYwebNm62y/4tVXl7e6rreeOONTJ8+HYABAwbw3//+lzVr1jB06FAWL1wmCX8AABGLSURB\nVF5Mbm4uy5Yto1OnTgBERkbypz/9yXJ+47BWhw4d6N69u+W4p6cn7777Li4uLgBkZGSwePHiS742\ne9Ve206XLl0ICQkhPT2dxMREMjIyqKmpoWvXrmzZsoX+/fuzfft2ampqSE5OtupFA3j//fdJSkpi\n1qxZlvrn5eVZEsXg4GCCg4PRaDQthkAfeeQRywTv7t27M2DAADZt2kS3bt0u+Draky5duvDmm28C\n5n/jvLw85s+fz2233UZWVhbff/89r7zyCrfeeqvlOYWFhcyZM4cFCxac8TOXl5fH1KlTLe9nQkIC\n5eXlvPbaa1RVVeHm5nbJ9U5LS6Ouro6PPvrI0v7i4+MZOXIkS5cu5ZZbbgHMPyDmz59PYGAgAPn5\n+fztb3+jpKQEHx8f5s2bxy233MJzzz0HmNtMQUGBtJlWaC9tJzk5mVmzZlFbW4uzszObNm2ie/fu\n7Nq1i/r6enQ6HevXrycpKanFuatWrSIjI4OvvvrKUt+IiAjGjRtnec7V8L0jPWat1LVrV7y9vXn4\n4Yd56aWXWL16Nb169WL69Ons2rWLuro6hg0bRkNDg+W/5ORkjh07xrFjxyxxxowZYxU3JSWF7du3\nYzKZiI2N5fPPP8fDw4ODBw+yYsUK5s6dS0NDg9WvzkuxY8eOVte1+R8+jUZDYGAg1dXVgPlXTlRU\nlCUpA4iNjSU0NPS8ddDr9ZYPB0BISAjl5eVtcXl2qT23naSkJMuv2/T0dOLi4khISLD8ul2/fj3d\nu3cnICDA6ryamhr27dtHcnKy1fGRI0e26nUbE1gAb29v3Nzcruo20mj06NFW5eHDh5OTk8OJEyfY\ntGkTYP5ia95Whg4dyrZt28767/zCCy/wyCOPUFxczJYtW/j666/55ZdfANrs70p6ejq9evXC09PT\nUq8OHTrQpUsXq+Gyjh07WpIyMCdaYG4vR44cIT8/n+uvv94q9qhRo1pVh2u1zTRqL21nyJAhNDQ0\nsHXrVksv/KRJk6ipqWHv3r1UV1ezffv2M/bCb9u2DS8vL6vvph49ehASEnLe121P3zvSY9ZK7u7u\nfPbZZ8ybN48ffviBhQsX4unpaRmiApgwYcIZzy0sLLT8MTr9C8zX15f6+nqqq6txd3fn3Xff5cMP\nP6SiooKQkBB69+6Ng4NDm80xa5zj05q6Ojs7Wz2m0Wgs9SgtLbX8Mm7O39//vHVo/uEAUBSlXa85\ncz7tue0kJyfz3XffUV1dzaZNm+jXrx/R0dEsXrwYk8nEunXrWiRfYO6ZVVUVHx8fq+OtaR9w7rZ3\nNWuetACWz1hpaanls3um9xugpKTE0p6ay8rKYtasWWzduhUXFxe6detm6eloy78rO3fupEePHi0e\na95uT//sazTmvgGTyURJSQlAizbj5+fXqjpcq22mUXtpOwEBAcTExJCeno6vry/V1dWkpKQQERHB\nli1bKC4uRlGUM97dXV5e3qJ9NMY8n/b0vSOJ2QWIiopizpw5GAwGtm7dSlpaGm+88QaPPfYYAPPm\nzTtj4+7cubPlg9F88jNAUVERTk5OuLm5sWjRIt5++21efPFFxo4di4eHB2AekmorjTFbU9dzCQwM\nZO/evS2OFxcX07lz50uv6FWmvbadxj+OmzdvZuvWrUyYMAG9Xk9lZSUbNmxg3759luHV5ry8vFAU\nhaKiIqvjrWlb17LG5KRR4/vn6+uLh4cHiqLw5ZdfotVqW5x7pi8sk8nEI488gre3N//5z3/o2rUr\nGo2GhQsXWuYntgV3d3eSk5MtUx+aa+1wV2P7P/09aH4DgTi79tR2kpKSSE9Px9/fn+7du+Pq6kpC\nQgJbtmwhLy+P/v374+rq2uI8b2/vFn9T4Or7uyJDma20Zs0aEhMTKS4uxtHRkcTERMvcmS5duqDT\n6SgqKiIuLs7yX2ZmJvPmzbOKc/pE7JUrV5KQkICiKGzfvp3g4GDuuOMOyxfrnj17KC4ubrPMvmfP\nnq2u67n069ePzMxMq6G2AwcOWJWh6Rfxtaw9tx03Nzf69u3LF198QUVFBX369CEoKIjw8HBSU1Px\n9PQ84xIZzs7O9OrVixUrVlgdb7x5pJG0D2urVq2yKq9cuZLIyEgCAwPp27cvqqpSWVlp1VY2bNjA\nJ598goOD+Xd28/e0uLiY7OxsbrvtNqKjoy2PrV27tk3r3bdvXw4dOoRer7fUKzo6mrlz51pN6j6X\n4OBgQkJCWLlypdXx08vSZs6sPbWd5ORkdu/ezZo1a+jXrx9g/k7ZunUr69evP2vP3oABA6ioqLAa\nHj906BBHjx61el57byPSY9ZK8fHxqKrKtGnTmDJlCjqdjrS0NDw9PRkwYAATJ07k73//O2VlZcTH\nx7N//37++c9/Mnz4cNzd3S0Z/TfffIOvry+9e/dm0aJFZGRk8NlnnwEQFxfHl19+ydy5c0lISCAr\nK4t58+ahKEqbLZbn6+vb6rqey80338x7771nWSrBaDQyZ84cFEVBURTL8zw9Pdm6dSv9+vWjZ8+e\nbXIN7U17bztJSUnMnj2bHj164O7uDpj/iH7//feMHTv2rH8EH3vsMSZPnszzzz/P6NGj2bhxY4tE\nzdPTk5qaGlasWEF8fPwl1fNqsHbtWl566SVSUlJYtWoVP//8M3PmzAEgJiaGkSNHMmPGDKZNm0aX\nLl3YtGkT7777LpMnT7b8OzT/zMXHx9OxY0fS0tLw8/NDq9WyaNEiy5d4TU1Nm9T7/vvv58cff2Ty\n5Mncc8896HQ6PvroI3bs2GG5qeV8NBoNU6dOZdasWfj5+ZGYmMjq1astbab59Umbaak9tZ1evXrh\n4uLC2rVrueuuuwDo378/ZWVllJWVnXF+GcDgwYPp378/M2bM4JlnnsHV1ZU5c+ag0+msntfev3fa\nd1p5BXl7e/PBBx/g5OTEs88+y7Rp06irq+Pjjz/G19eXGTNmMHXqVL755hsmT57MggULuPfee/n7\n3/9uFeeJJ55g3bp1PProo2RnZ/PBBx9YJq2OGzeOKVOm8OWXX/Lggw/y2WefMWnSJMaPH8+OHTva\n7FpaW9dz0el0fPjhhwQHB/Pss8/y6quvcvfdd9OhQweroYtp06aRnp7OlClTaGhoaLNraE/ae9tp\n/PXav39/y7GEhASrx85k8ODBpKamsnv3bqZOncr27duZMWOG1XPGjBlDjx49eOKJJ/jxxx8vqZ5X\ng8mTJ1vWA9u4cSP//Oc/rSa/v/HGG4wbN85yJ++SJUt4+umnLUuvgPVnzmg0kpqaipubG08++ST/\n7//9P2pqavj4448B2uzvSseOHfn8889xcXFhxowZPPnkk5hMJj7++GNiYmJaHWf8+PE899xzLFmy\nhIcffpiDBw/yyCOPAFiGtqTNnFl7ajsODg4MGjQIjUZD3759AXMbCgkJITw8/KzTYRRF4d133yUp\nKYm//e1vvPjii/zxj39scedtu//eUcUVcezYMTU6OlpdtmyZravSJvbv36+uWLHC6lhFRYUaGxur\npqWl2ahWV6erre2IM4uOjlY/+OADW1fDphYvXqwePXrU6tibb76pJiQk2KhG7YO0nauLDGW2Y3v3\n7j3vLcuN6/60tYqKCqZOncrDDz/MoEGDqKys5JNPPsHNza3Fsg7C/tiy7Qj7VVxc3GK+zplcrq23\nvvvuO95//32mTZuGj48Pu3btIi0tjUmTJl2W1xNtx9Zt52oiiVk7Nm3aNKttfc72nMY7/9pSv379\nmD17Nh999BFpaWnodDr69evHwoULW317u7AdW7YdYb9WrVrVqlXd23o7p0azZ89m9uzZvPTSS5SX\nlxMSEsLjjz/O/ffff1leT7QdW7edq4miqna6kIcQQgghxDVGJv8LIYQQQtgJScyEEEIIIeyEJGZC\nCCGEEHZCEjMhxDUtPT0dvV7PkiVLLui81NRU9Ho9hYWF53zezJkziYuLu5QqCiGuIZKYCSGuaV26\ndOH111+X2/iFEHZBlssQQlzT/P39+cMf/mDragghBCA9ZkIIIYQQdkMSMyGEXXvhhReIi4ujsrLS\n6vihQ4fQ6/WkpaVhMBiYO3cuY8aMIT4+nl69enHbbbfx66+/Wp6fk5ODXq/n008/Zfz48cTFxfH0\n00+fcY7ZoUOHePrppxkyZAixsbEkJiby9NNPk5+f36J+GRkZTJgwgbi4OEaMGEFaWtp5r+nYsWM8\n+eSTJCQk0LNnTyZMmMCGDRsu4V0SQlwtJDETQti1MWPGYDAYrJIsgOXLl6PRaLjxxhuZOXMm7733\nHklJScyaNYvJkyeTm5vLo48+yqFDh6zOe+utt4iKiuK5555j5MiRLV6voKCACRMmsHv3bh544AFm\nzZrFsGHDWLp0KU888USL5z/22GMEBgYyc+ZMwsLCePXVV3nnnXfOej15eXncfvvt7Ny5k8mTJ/Pk\nk0/S0NDApEmTWlyjEOLaI3PMhBB2bcCAAQQEBPDf//6Xm266yXJ8+fLlJCQkALB06VKmT5/O1KlT\nLY/36tWLSZMmsWHDBiIjIy3HIyMjee211yzl9PR0q9dbtGgRlZWVfP/994SGhgJw++23YzAY+Omn\nn6iursbV1dXy/FGjRlni3XHHHdx///3Mnz+fiRMn4uHh0eJ63nrrLQC+/fZbfH19AbjrrruYOHEi\nr7zyCkOHDkWjkd/MQlyr5NMvhLBrjb1ia9eupbq6GjAPNWZkZDB27FgCAwPZsmULDzzwgOUco9Fo\n2aS98ZxGffv2PefrPfjgg6xbt86SlAFUVFSg0+kAqKmpsXp+8w22NRoNd999N7W1tS0SPgCTycQv\nv/zCwIEDAfPGz8XFxVRUVJCSkkJOTg4HDx4873sihLh6SY+ZEMLujR07lgULFrBq1SpGjx7N8uXL\n0el0jBgxAgBHR0d+/PFH1q1bx6FDh8jOzqaurg6A07cDbuylOpfa2lrefPNN9uzZw+HDh8nLy7PE\nMZlMlucpikJ4eLjVuWFhYYB5TtvpSkpKqKysZMmSJWddNy0vL4/o6Ojz1lEIcXWSxEwIYfd69uxJ\neHg4y5cvtyRmQ4YMwcvLi9raWu644w4yMzMZOHAgw4YNIyYmhtDQUG699dYWsc43TJiens6UKVPw\n9PRk0KBBJCYmEh8fz/r165k/f36L52u12jPGOdNxo9EImBPNP/3pT2c8r1u3buesnxDi6iaJmRCi\nXRg9ejQLFiwgMzOTjIwMpkyZAsCyZcvYu3cvb775JmPHjrU8f8eOHRf1OnPnzsXDw4MlS5bg5eVl\nOX6mHi5VVcnNzbX0kgEcOXIEoEVPGph765ydnVFVlUGDBlk9duDAAfLy8nBycrqoegshrg4yx0wI\n0S6MHTuW6upqXn/9dVxcXBg+fDgApaWlgHkF/0aqqrJw4UIAGhoaLuh1SktLCQgIsErK8vPz+fnn\nn4GmXq9G3377reX/jUYjn376KR4eHvTv379FbAcHB4YMGcLPP//M4cOHLcfr6+t5/vnneeqpp1AU\n5YLqK4S4ukiPmRCiXYiKiiI6Opo1a9YwevRoy52RiYmJODg48Mwzz3DHHXcA5l60nTt3otFoqKqq\nuqDXSU5O5oMPPmDGjBkkJCSQm5vL119/bYnTPJ6iKHz55ZeUlZURHR3N8uXL2bJlCy+//LLVnZvN\nNa6dNmHCBO6++278/Pz46aef2L17Ny+88MJZzxNCXBukx0wI0W40DlWOGTPGcqxbt27MmTMHrVbL\n7Nmzee+993Bzc+OLL74gJiaGTZs2XdBrTJ8+nXvvvZf09HReeeUVli5dytixY/nkk08A6+U1NBoN\n//73v9m1axevvfYaJ0+eZPbs2dx2221njR8ZGclXX33FgAED+PTTT3n99depra1l9uzZTJw48YLq\nKoS4+ijq6bcsCSGEEEIIm5AeMyGEEEIIOyGJmRBCCCGEnZDETAghhBDCTkhiJoQQQghhJyQxE0II\nIYSwE5KYCSGEEELYCUnMhBBCCCHshCRmQgghhBB2QhIzIYQQQgg78f8BDHbYa7+cIHYAAAAASUVO\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6558551ef0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(context='poster', style='white', rc={'figure.figsize':(10, 6)})\n",
+    "\n",
+    "sns.swarmplot(x='variable', y='value', hue='species', data=iris_long, dodge=True, palette='Set2', size=4)\n",
+    "sns.despine() # removes the right and top black border"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CcXZ2pk6duqhUOaNkMjAmhCgNPD09MZmyraZIlXQeHp6sWRN2z+c4ODgUyd5X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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f6553c1b710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set(context='poster', style='darkgrid', rc={'figure.figsize':(12, 6)})\n",
+    "\n",
+    "sns.boxplot(y='variable', x='value', hue='species', data=iris_long, color='c', )\n",
+    "sns.stripplot(y='variable', x='value', hue='species', data=iris_long, size=2.5, palette=['k']*3, jitter=True, dodge=True)\n",
+    "sns.despine(trim=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to get too fancy and accidentally hide important messages in the data. However, the fact that you now have access to several ways to plot your data forces you to consider what is actually important and how you can best communicate that message, rather than always making the same plot without considering its strengths and weaknesses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Resources to learn more <a class=\"anchor\" id=\"resources-to-learn-more\"></a>\n",
+    "\n",
+    "The documentation for these packages are great resources to learn more.\n",
+    "\n",
+    "- [Introduction to `pandas`](https://pandas.pydata.org/pandas-docs/stable/10min.html)\n",
+    "- [Lessons for new `pandas` users](https://pandas.pydata.org/pandas-docs/stable/tutorials.html#lessons-for-new-pandas-users)\n",
+    "- [The `seaborn` gallery](https://seaborn.pydata.org/examples/index.html)\n",
+    "- [The `seaborn` tutorials](https://seaborn.pydata.org/tutorial.html)\n",
+    "- [The `matplotlib` gallery](https://matplotlib.org/gallery.html) is another great resource. We did not practice using `matplotlib` explicitly in this notebook, but the plotting techniques from `pandas` and `seaborn` are layers on top of the `matplotlib` library, so we have been using it indirectly."
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python [default]",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
+  },
+  "toc": {
+   "colors": {
+    "hover_highlight": "#DAA520",
+    "running_highlight": "#FF0000",
+    "selected_highlight": "#FFD700"
+   },
+   "moveMenuLeft": false,
+   "nav_menu": {
+    "height": "226px",
+    "width": "285px"
+   },
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 4,
+   "toc_cell": false,
+   "toc_section_display": "block",
+   "toc_window_display": false,
+   "widenNotebook": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/lessons/python/intro-data-analysis/lesson.md b/lessons/python/intro-data-analysis/lesson.md
new file mode 100644
index 000000000..33869c86c
--- /dev/null
+++ b/lessons/python/intro-data-analysis/lesson.md
@@ -0,0 +1,19 @@
+---
+layout: page
+title: Introduction to data analysis in Python
+visible: true
+tags:
+  # languages
+  - python
+  # levels
+  - beginner
+---
+
+ - **Authors**: Joel Ostblom
+ - **Research field**: Stem cell bioengineering
+ - **Lesson topic**: Introduction to data analysis in Python
+ - **Lesson content URL**:
+   https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/python/intro-data-analysis/
+
+To follow along, [view the lecture online
+here](https://nbviewer.jupyter.org/github/UofTCoders/studyGroup/blob/gh-pages/lessons/python/intro-data-analysis/from-spreadsheets-to-pandas-extended.ipynb)
diff --git a/lessons/python/intro-nlp/book.txt b/lessons/python/intro-nlp/book.txt
new file mode 100644
index 000000000..fb1b52f19
--- /dev/null
+++ b/lessons/python/intro-nlp/book.txt
@@ -0,0 +1,7728 @@
+Project Gutenberg's The Hound of the Baskervilles, by Arthur Conan Doyle
+
+This eBook is for the use of anyone anywhere at no cost and with
+almost no restrictions whatsoever.  You may copy it, give it away or
+re-use it under the terms of the Project Gutenberg License included
+with this eBook or online at www.gutenberg.org
+
+
+Title: The Hound of the Baskervilles
+
+Author: Arthur Conan Doyle
+
+Posting Date: October 10, 2010
+Release Date: February, 2002 [Etext #3070]
+
+Language: English
+
+
+*** START OF THIS PROJECT GUTENBERG EBOOK THE HOUND OF THE BASKERVILLES ***
+
+
+
+
+Produced by This etext was produced by P. K.Pehtla <ppehtla@nfld.com>
+
+
+
+
+
+
+
+
+The Hound of the Baskervilles
+
+by Sir Arthur Conan Doyle
+
+
+
+
+  CONTENTS
+
+  Chapter 1--Mr. Sherlock Holmes
+  Chapter 2--The Curse of the Baskervilles
+  Chapter 3--The Problem
+  Chapter 4--Sir Henry Baskerville
+  Chapter 5--Three Broken Threads
+  Chapter 6--Baskerville Hall
+  Chapter 7--The Stapletons of Merripit House
+  Chapter 8--First Report of Dr. Watson
+  Chapter 9--The Light Upon The Moor
+  Chapter 10--Extract from the Diary of Dr. Watson
+  Chapter 11--The Man on the Tor
+  Chapter 12--Death on the Moor
+  Chapter 13--Fixing the Nets
+  Chapter 14--The Hound of the Baskervilles
+  Chapter 15--A Retrospection
+
+
+
+
+Chapter 1
+
+Mr. Sherlock Holmes
+
+
+Mr. Sherlock Holmes, who was usually very late in the mornings,
+save upon those not infrequent occasions when he was up all
+night, was seated at the breakfast table. I stood upon the
+hearth-rug and picked up the stick which our visitor had left
+behind him the night before. It was a fine, thick piece of wood,
+bulbous-headed, of the sort which is known as a "Penang lawyer."
+Just under the head was a broad silver band nearly an inch
+across. "To James Mortimer, M.R.C.S., from his friends of the
+C.C.H.," was engraved upon it, with the date "1884." It was just
+such a stick as the old-fashioned family practitioner used to
+carry--dignified, solid, and reassuring.
+
+"Well, Watson, what do you make of it?"
+
+Holmes was sitting with his back to me, and I had given him no
+sign of my occupation.
+
+"How did you know what I was doing? I believe you have eyes in
+the back of your head."
+
+"I have, at least, a well-polished, silver-plated coffee-pot in
+front of me," said he. "But, tell me, Watson, what do you make of
+our visitor's stick? Since we have been so unfortunate as to miss
+him and have no notion of his errand, this accidental souvenir
+becomes of importance. Let me hear you reconstruct the man by an
+examination of it."
+
+"I think," said I, following as far as I could the methods of my
+companion, "that Dr. Mortimer is a successful, elderly medical
+man, well-esteemed since those who know him give him this mark of
+their appreciation."
+
+"Good!" said Holmes. "Excellent!"
+
+"I think also that the probability is in favour of his being a
+country practitioner who does a great deal of his visiting on
+foot."
+
+"Why so?"
+
+"Because this stick, though originally a very handsome one has
+been so knocked about that I can hardly imagine a town
+practitioner carrying it. The thick-iron ferrule is worn down, so
+it is evident that he has done a great amount of walking with
+it."
+
+"Perfectly sound!" said Holmes.
+
+"And then again, there is the 'friends of the C.C.H.' I should
+guess that to be the Something Hunt, the local hunt to whose
+members he has possibly given some surgical assistance, and which
+has made him a small presentation in return."
+
+"Really, Watson, you excel yourself," said Holmes, pushing back
+his chair and lighting a cigarette. "I am bound to say that in
+all the accounts which you have been so good as to give of my own
+small achievements you have habitually underrated your own
+abilities. It may be that you are not yourself luminous, but you
+are a conductor of light. Some people without possessing genius
+have a remarkable power of stimulating it. I confess, my dear
+fellow, that I am very much in your debt."
+
+He had never said as much before, and I must admit that his words
+gave me keen pleasure, for I had often been piqued by his
+indifference to my admiration and to the attempts which I had
+made to give publicity to his methods. I was proud, too, to think
+that I had so far mastered his system as to apply it in a way
+which earned his approval. He now took the stick from my hands
+and examined it for a few minutes with his naked eyes. Then with
+an expression of interest he laid down his cigarette, and
+carrying the cane to the window, he looked over it again with a
+convex lens.
+
+"Interesting, though elementary," said he as he returned to his
+favourite corner of the settee. "There are certainly one or two
+indications upon the stick. It gives us the basis for several
+deductions."
+
+"Has anything escaped me?" I asked with some self-importance. "I
+trust that there is nothing of consequence which I have
+overlooked?"
+
+"I am afraid, my dear Watson, that most of your conclusions were
+erroneous. When I said that you stimulated me I meant, to be
+frank, that in noting your fallacies I was occasionally guided
+towards the truth. Not that you are entirely wrong in this
+instance. The man is certainly a country practitioner. And he
+walks a good deal."
+
+"Then I was right."
+
+"To that extent."
+
+"But that was all."
+
+"No, no, my dear Watson, not all--by no means all. I would
+suggest, for example, that a presentation to a doctor is more
+likely to come from a hospital than from a hunt, and that when
+the initials 'C.C.' are placed before that hospital the words
+'Charing Cross' very naturally suggest themselves."
+
+"You may be right."
+
+"The probability lies in that direction. And if we take this as a
+working hypothesis we have a fresh basis from which to start our
+construction of this unknown visitor."
+
+"Well, then, supposing that 'C.C.H.' does stand for 'Charing
+Cross Hospital,' what further inferences may we draw?"
+
+"Do none suggest themselves? You know my methods. Apply them!"
+
+"I can only think of the obvious conclusion that the man has
+practised in town before going to the country."
+
+"I think that we might venture a little farther than this. Look
+at it in this light. On what occasion would it be most probable
+that such a presentation would be made? When would his friends
+unite to give him a pledge of their good will? Obviously at the
+moment when Dr. Mortimer withdrew from the service of the
+hospital in order to start in practice for himself. We know there
+has been a presentation. We believe there has been a change from
+a town hospital to a country practice. Is it, then, stretching
+our inference too far to say that the presentation was on the
+occasion of the change?"
+
+"It certainly seems probable."
+
+"Now, you will observe that he could not have been on the staff
+of the hospital, since only a man well-established in a London
+practice could hold such a position, and such a one would not
+drift into the country. What was he, then? If he was in the
+hospital and yet not on the staff he could only have been a
+house-surgeon or a house-physician--little more than a senior
+student. And he left five years ago--the date is on the stick. So
+your grave, middle-aged family practitioner vanishes into thin
+air, my dear Watson, and there emerges a young fellow under
+thirty, amiable, unambitious, absent-minded, and the possessor of
+a favourite dog, which I should describe roughly as being larger
+than a terrier and smaller than a mastiff."
+
+I laughed incredulously as Sherlock Holmes leaned back in his
+settee and blew little wavering rings of smoke up to the ceiling.
+
+"As to the latter part, I have no means of checking you," said I,
+"but at least it is not difficult to find out a few particulars
+about the man's age and professional career." From my small
+medical shelf I took down the Medical Directory and turned up the
+name. There were several Mortimers, but only one who could be our
+visitor. I read his record aloud.
+
+"Mortimer, James, M.R.C.S., 1882, Grimpen, Dartmoor,
+Devon. House-surgeon, from 1882 to 1884, at Charing Cross
+Hospital. Winner of the Jackson prize for Comparative Pathology,
+with essay entitled 'Is Disease a Reversion?' Corresponding
+member of the Swedish Pathological Society. Author of 'Some
+Freaks of Atavism' (Lancet 1882). 'Do We Progress?' (Journal of
+Psychology, March, 1883). Medical Officer for the parishes of
+Grimpen, Thorsley, and High Barrow."
+
+"No mention of that local hunt, Watson," said Holmes with a
+mischievous smile, "but a country doctor, as you very astutely
+observed. I think that I am fairly justified in my inferences. As
+to the adjectives, I said, if I remember right, amiable,
+unambitious, and absent-minded. It is my experience that it is
+only an amiable man in this world who receives testimonials, only
+an unambitious one who abandons a London career for the country,
+and only an absent-minded one who leaves his stick and not his
+visiting-card after waiting an hour in your room."
+
+"And the dog?"
+
+"Has been in the habit of carrying this stick behind his master.
+Being a heavy stick the dog has held it tightly by the middle,
+and the marks of his teeth are very plainly visible. The dog's
+jaw, as shown in the space between these marks, is too broad in
+my opinion for a terrier and not broad enough for a mastiff. It
+may have been--yes, by Jove, it is a curly-haired spaniel."
+
+He had risen and paced the room as he spoke. Now he halted in the
+recess of the window. There was such a ring of conviction in his
+voice that I glanced up in surprise.
+
+"My dear fellow, how can you possibly be so sure of that?"
+
+"For the very simple reason that I see the dog himself on our
+very door-step, and there is the ring of its owner. Don't move, I
+beg you, Watson. He is a professional brother of yours, and your
+presence may be of assistance to me. Now is the dramatic moment
+of fate, Watson, when you hear a step upon the stair which is
+walking into your life, and you know not whether for good or ill.
+What does Dr. James Mortimer, the man of science, ask of Sherlock
+Holmes, the specialist in crime? Come in!"
+
+The appearance of our visitor was a surprise to me, since I had
+expected a typical country practitioner. He was a very tall, thin
+man, with a long nose like a beak, which jutted out between two
+keen, gray eyes, set closely together and sparkling brightly from
+behind a pair of gold-rimmed glasses. He was clad in a
+professional but rather slovenly fashion, for his frock-coat was
+dingy and his trousers frayed. Though young, his long back was
+already bowed, and he walked with a forward thrust of his head
+and a general air of peering benevolence. As he entered his eyes
+fell upon the stick in Holmes's hand, and he ran towards it with
+an exclamation of joy. "I am so very glad," said he. "I was not
+sure whether I had left it here or in the Shipping Office. I
+would not lose that stick for the world."
+
+"A presentation, I see," said Holmes.
+
+"Yes, sir."
+
+"From Charing Cross Hospital?"
+
+"From one or two friends there on the occasion of my marriage."
+
+"Dear, dear, that's bad!" said Holmes, shaking his head.
+
+Dr. Mortimer blinked through his glasses in mild astonishment.
+
+"Why was it bad?"
+
+"Only that you have disarranged our little deductions. Your
+marriage, you say?"
+
+"Yes, sir. I married, and so left the hospital, and with it all
+hopes of a consulting practice. It was necessary to make a home
+of my own."
+
+"Come, come, we are not so far wrong, after all," said Holmes.
+"And now, Dr. James Mortimer ------"
+
+"Mister, sir, Mister--a humble M.R.C.S."
+
+"And a man of precise mind, evidently."
+
+"A dabbler in science, Mr. Holmes, a picker up of shells on the
+shores of the great unknown ocean. I presume that it is Mr.
+Sherlock Holmes whom I am addressing and not ------"
+
+"No, this is my friend Dr. Watson."
+
+"Glad to meet you, sir. I have heard your name mentioned in
+connection with that of your friend. You interest me very much,
+Mr. Holmes. I had hardly expected so dolichocephalic a skull or
+such well-marked supra-orbital development. Would you have any
+objection to my running my finger along your parietal fissure? A
+cast of your skull, sir, until the original is available, would
+be an ornament to any anthropological museum. It is not my
+intention to be fulsome, but I confess that I covet your skull."
+
+Sherlock Holmes waved our strange visitor into a chair. "You are
+an enthusiast in your line of thought, I perceive, sir, as I am
+in mine," said he. "I observe from your forefinger that you make
+your own cigarettes. Have no hesitation in lighting one."
+
+The man drew out paper and tobacco and twirled the one up in the
+other with surprising dexterity. He had long, quivering fingers
+as agile and restless as the antennae of an insect.
+
+Holmes was silent, but his little darting glances showed me the
+interest which he took in our curious companion.
+
+"I presume, sir," said he at last, "that it was not merely for
+the purpose of examining my skull that you have done me the
+honour to call here last night and again to-day?"
+
+"No, sir, no; though I am happy to have had the opportunity of
+doing that as well. I came to you, Mr. Holmes, because I
+recognized that I am myself an unpractical man and because I am
+suddenly confronted with a most serious and extraordinary
+problem. Recognizing, as I do, that you are the second highest
+expert in Europe ------"
+
+"Indeed, sir! May I inquire who has the honour to be the first?"
+asked Holmes with some asperity.
+
+"To the man of precisely scientific mind the work of Monsieur
+Bertillon must always appeal strongly."
+
+"Then had you not better consult him?"
+
+"I said, sir, to the precisely scientific mind. But as a
+practical man of affairs it is acknowledged that you stand alone.
+I trust, sir, that I have not inadvertently ------"
+
+"Just a little," said Holmes. "I think, Dr. Mortimer, you would
+do wisely if without more ado you would kindly tell me plainly
+what the exact nature of the problem is in which you demand my
+assistance."
+
+
+
+
+Chapter 2
+
+The Curse of the Baskervilles
+
+
+"I have in my pocket a manuscript," said Dr. James Mortimer.
+
+"I observed it as you entered the room," said Holmes.
+
+"It is an old manuscript."
+
+"Early eighteenth century, unless it is a forgery."
+
+"How can you say that, sir?"
+
+"You have presented an inch or two of it to my examination all
+the time that you have been talking. It would be a poor expert
+who could not give the date of a document within a decade or so.
+You may possibly have read my little monograph upon the subject.
+I put that at 1730."
+
+"The exact date is 1742." Dr. Mortimer drew it from his
+breast-pocket. "This family paper was committed to my care by Sir
+Charles Baskerville, whose sudden and tragic death some three
+months ago created so much excitement in Devonshire. I may say
+that I was his personal friend as well as his medical attendant.
+He was a strong-minded man, sir, shrewd, practical, and as
+unimaginative as I am myself. Yet he took this document very
+seriously, and his mind was prepared for just such an end as did
+eventually overtake him."
+
+Holmes stretched out his hand for the manuscript and flattened it
+upon his knee.
+
+"You will observe, Watson, the alternative use of the long s and
+the short. It is one of several indications which enabled me to
+fix the date."
+
+I looked over his shoulder at the yellow paper and the faded
+script. At the head was written: "Baskerville Hall," and below in
+large, scrawling figures: "1742."
+
+"It appears to be a statement of some sort."
+
+"Yes, it is a statement of a certain legend which runs in the
+Baskerville family."
+
+"But I understand that it is something more modern and practical
+upon which you wish to consult me?"
+
+"Most modern. A most practical, pressing matter, which must be
+decided within twenty-four hours. But the manuscript is short and
+is intimately connected with the affair. With your permission I
+will read it to you."
+
+Holmes leaned back in his chair, placed his finger-tips together,
+and closed his eyes, with an air of resignation. Dr. Mortimer
+turned the manuscript to the light and read in a high, cracking
+voice the following curious, old-world narrative:--
+
+"Of the origin of the Hound of the Baskervilles there have been
+many statements, yet as I come in a direct line from Hugo
+Baskerville, and as I had the story from my father, who also had
+it from his, I have set it down with all belief that it occurred
+even as is here set forth. And I would have you believe, my sons,
+that the same Justice which punishes sin may also most graciously
+forgive it, and that no ban is so heavy but that by prayer and
+repentance it may be removed. Learn then from this story not to
+fear the fruits of the past, but rather to be circumspect in the
+future, that those foul passions whereby our family has suffered
+so grievously may not again be loosed to our undoing.
+
+"Know then that in the time of the Great Rebellion (the history
+of which by the learned Lord Clarendon I most earnestly commend
+to your attention) this Manor of Baskerville was held by Hugo of
+that name, nor can it be gainsaid that he was a most wild,
+profane, and godless man. This, in truth, his neighbours might
+have pardoned, seeing that saints have never flourished in those
+parts, but there was in him a certain wanton and cruel humour
+which made his name a byword through the West. It chanced that
+this Hugo came to love (if, indeed, so dark a passion may be
+known under so bright a name) the daughter of a yeoman who held
+lands near the Baskerville estate. But the young maiden, being
+discreet and of good repute, would ever avoid him, for she
+feared his evil name. So it came to pass that one Michaelmas
+this Hugo, with five or six of his idle and wicked companions,
+stole down upon the farm and carried off the maiden, her father
+and brothers being from home, as he well knew. When they had
+brought her to the Hall the maiden was placed in an upper
+chamber, while Hugo and his friends sat down to a long carouse,
+as was their nightly custom. Now, the poor lass upstairs was like
+to have her wits turned at the singing and shouting and terrible
+oaths which came up to her from below, for they say that the
+words used by Hugo Baskerville, when he was in wine, were such as
+might blast the man who said them. At last in the stress of her
+fear she did that which might have daunted the bravest or most
+active man, for by the aid of the growth of ivy which covered
+(and still covers) the south wall she came down from under the
+eaves, and so homeward across the moor, there being three leagues
+betwixt the Hall and her father's farm.
+
+"It chanced that some little time later Hugo left his guests to
+carry food and drink--with other worse things, perchance--to his
+captive, and so found the cage empty and the bird escaped. Then,
+as it would seem, he became as one that hath a devil, for,
+rushing down the stairs into the dining-hall, he sprang upon the
+great table, flagons and trenchers flying before him, and he
+cried aloud before all the company that he would that very night
+render his body and soul to the Powers of Evil if he might but
+overtake the wench. And while the revellers stood aghast at the
+fury of the man, one more wicked or, it may be, more drunken than
+the rest, cried out that they should put the hounds upon her.
+Whereat Hugo ran from the house, crying to his grooms that they
+should saddle his mare and unkennel the pack, and giving the
+hounds a kerchief of the maid's, he swung them to the line, and
+so off full cry in the moonlight over the moor.
+
+"Now, for some space the revellers stood agape, unable to
+understand all that had been done in such haste. But anon their
+bemused wits awoke to the nature of the deed which was like to be
+done upon the moorlands. Everything was now in an uproar, some
+calling for their pistols, some for their horses, and some for
+another flask of wine. But at length some sense came back to
+their crazed minds, and the whole of them, thirteen in number,
+took horse and started in pursuit. The moon shone clear above
+them, and they rode swiftly abreast, taking that course which the
+maid must needs have taken if she were to reach her own home.
+
+"They had gone a mile or two when they passed one of the night
+shepherds upon the moorlands, and they cried to him to know if he
+had seen the hunt. And the man, as the story goes, was so crazed
+with fear that he could scarce speak, but at last he said that he
+had indeed seen the unhappy maiden, with the hounds upon her
+track. 'But I have seen more than that,' said he, 'for Hugo
+Baskerville passed me upon his black mare, and there ran mute
+behind him such a hound of hell as God forbid should ever be at
+my heels.' So the drunken squires cursed the shepherd and rode
+onward. But soon their skins turned cold, for there came a
+galloping across the moor, and the black mare, dabbled with white
+froth, went past with trailing bridle and empty saddle. Then the
+revellers rode close together, for a great fear was on them, but
+they still followed over the moor, though each, had he been
+alone, would have been right glad to have turned his horse's
+head. Riding slowly in this fashion they came at last upon the
+hounds. These, though known for their valour and their breed,
+were whimpering in a cluster at the head of a deep dip or goyal,
+as we call it, upon the moor, some slinking away and some, with
+starting hackles and staring eyes, gazing down the narrow valley
+before them.
+
+"The company had come to a halt, more sober men, as you may
+guess, than when they started. The most of them would by no means
+advance, but three of them, the boldest, or it may be the most
+drunken, rode forward down the goyal. Now, it opened into a broad
+space in which stood two of those great stones, still to be seen
+there, which were set by certain forgotten peoples in the days of
+old. The moon was shining bright upon the clearing, and there in
+the centre lay the unhappy maid where she had fallen, dead of
+fear and of fatigue. But it was not the sight of her body, nor
+yet was it that of the body of Hugo Baskerville lying near her,
+which raised the hair upon the heads of these three daredevil
+roysterers, but it was that, standing over Hugo, and plucking at
+his throat, there stood a foul thing, a great, black beast,
+shaped like a hound, yet larger than any hound that ever mortal
+eye has rested upon. And even as they looked the thing tore the
+throat out of Hugo Baskerville, on which, as it turned its
+blazing eyes and dripping jaws upon them, the three shrieked with
+fear and rode for dear life, still screaming, across the moor.
+One, it is said, died that very night of what he had seen, and
+the other twain were but broken men for the rest of their days.
+
+"Such is the tale, my sons, of the coming of the hound which is
+said to have plagued the family so sorely ever since. If I have
+set it down it is because that which is clearly known hath less
+terror than that which is but hinted at and guessed. Nor can it
+be denied that many of the family have been unhappy in their
+deaths, which have been sudden, bloody, and mysterious. Yet may
+we shelter ourselves in the infinite goodness of Providence,
+which would not forever punish the innocent beyond that third or
+fourth generation which is threatened in Holy Writ. To that
+Providence, my sons, I hereby commend you, and I counsel you by
+way of caution to forbear from crossing the moor in those dark
+hours when the powers of evil are exalted.
+
+"[This from Hugo Baskerville to his sons Rodger and John, with
+instructions that they say nothing thereof to their sister
+Elizabeth.]"
+
+When Dr. Mortimer had finished reading this singular narrative he
+pushed his spectacles up on his forehead and stared across at Mr.
+Sherlock Holmes. The latter yawned and tossed the end of his
+cigarette into the fire.
+
+"Well?" said he.
+
+"Do you not find it interesting?"
+
+"To a collector of fairy tales."
+
+Dr. Mortimer drew a folded newspaper out of his pocket.
+
+"Now, Mr. Holmes, we will give you something a little more
+recent. This is the Devon County Chronicle of May 14th of this
+year. It is a short account of the facts elicited at the death of
+Sir Charles Baskerville which occurred a few days before that
+date."
+
+My friend leaned a little forward and his expression became
+intent. Our visitor readjusted his glasses and began:--
+
+"The recent sudden death of Sir Charles Baskerville, whose name
+has been mentioned as the probable Liberal candidate for
+Mid-Devon at the next election, has cast a gloom over the county.
+Though Sir Charles had resided at Baskerville Hall for a
+comparatively short period his amiability of character and
+extreme generosity had won the affection and respect of all who
+had been brought into contact with him. In these days of _nouveaux
+riches_ it is refreshing to find a case where the scion of an old
+county family which has fallen upon evil days is able to make his
+own fortune and to bring it back with him to restore the fallen
+grandeur of his line. Sir Charles, as is well known, made large
+sums of money in South African speculation. More wise than those
+who go on until the wheel turns against them, he realized his
+gains and returned to England with them. It is only two years
+since he took up his residence at Baskerville Hall, and it is
+common talk how large were those schemes of reconstruction and
+improvement which have been interrupted by his death. Being
+himself childless, it was his openly expressed desire that the
+whole country-side should, within his own lifetime, profit by his
+good fortune, and many will have personal reasons for bewailing
+his untimely end. His generous donations to local and county
+charities have been frequently chronicled in these columns.
+
+"The circumstances connected with the death of Sir Charles
+cannot be said to have been entirely cleared up by the inquest,
+but at least enough has been done to dispose of those rumours to
+which local superstition has given rise. There is no reason
+whatever to suspect foul play, or to imagine that death could be
+from any but natural causes. Sir Charles was a widower, and a man
+who may be said to have been in some ways of an eccentric habit
+of mind. In spite of his considerable wealth he was simple in his
+personal tastes, and his indoor servants at Baskerville Hall
+consisted of a married couple named Barrymore, the husband acting
+as butler and the wife as housekeeper. Their evidence,
+corroborated by that of several friends, tends to show that Sir
+Charles's health has for some time been impaired, and points
+especially to some affection of the heart, manifesting itself in
+changes of colour, breathlessness, and acute attacks of nervous
+depression. Dr. James Mortimer, the friend and medical attendant
+of the deceased, has given evidence to the same effect.
+
+"The facts of the case are simple. Sir Charles Baskerville was in
+the habit every night before going to bed of walking down the
+famous Yew Alley of Baskerville Hall. The evidence of the
+Barrymores shows that this had been his custom. On the 4th of May
+Sir Charles had declared his intention of starting next day for
+London, and had ordered Barrymore to prepare his luggage. That
+night he went out as usual for his nocturnal walk, in the course
+of which he was in the habit of smoking a cigar. He never
+returned. At twelve o'clock Barrymore, finding the hall door
+still open, became alarmed, and, lighting a lantern, went in
+search of his master. The day had been wet, and Sir Charles's
+footmarks were easily traced down the Alley. Half-way down this
+walk there is a gate which leads out on to the moor. There were
+indications that Sir Charles had stood for some little time here.
+He then proceeded down the Alley, and it was at the far end of it
+that his body was discovered. One fact which has not been
+explained is the statement of Barrymore that his master's
+footprints altered their character from the time that he passed
+the moor-gate, and that he appeared from thence onward to have
+been walking upon his toes. One Murphy, a gipsy horse-dealer, was
+on the moor at no great distance at the time, but he appears by
+his own confession to have been the worse for drink. He declares
+that he heard cries, but is unable to state from what
+direction they came. No signs of violence were to be discovered
+upon Sir Charles's person, and though the doctor's evidence
+pointed to an almost incredible facial distortion--so great that
+Dr. Mortimer refused at first to believe that it was indeed his
+friend and patient who lay before him--it was explained that that
+is a symptom which is not unusual in cases of dyspnoea and death
+from cardiac exhaustion. This explanation was borne out by the
+post-mortem examination, which showed long-standing organic
+disease, and the coroner's jury returned a verdict in accordance
+with the medical evidence. It is well that this is so, for it is
+obviously of the utmost importance that Sir Charles's heir should
+settle at the Hall and continue the good work which has been so
+sadly interrupted. Had the prosaic finding of the coroner not
+finally put an end to the romantic stories which have been
+whispered in connection with the affair, it might have been
+difficult to find a tenant for Baskerville Hall. It is understood
+that the next of kin is Mr. Henry Baskerville, if he be still
+alive, the son of Sir Charles Baskerville's younger brother. The
+young man when last heard of was in America, and inquiries are
+being instituted with a view to informing him of his good
+fortune."
+
+Dr. Mortimer refolded his paper and replaced it in his pocket.
+
+"Those are the public facts, Mr. Holmes, in connection with the
+death of Sir Charles Baskerville."
+
+"I must thank you," said Sherlock Holmes, "for calling my
+attention to a case which certainly presents some features of
+interest. I had observed some newspaper comment at the time, but
+I was exceedingly preoccupied by that little affair of the
+Vatican cameos, and in my anxiety to oblige the Pope I lost touch
+with several interesting English cases. This article, you say,
+contains all the public facts?"
+
+"It does."
+
+"Then let me have the private ones." He leaned back, put his
+finger-tips together, and assumed his most impassive and judicial
+expression.
+
+"In doing so," said Dr. Mortimer, who had begun to show signs of
+some strong emotion, "I am telling that which I have not confided
+to anyone. My motive for withholding it from the coroner's
+inquiry is that a man of science shrinks from placing himself in
+the public position of seeming to indorse a popular superstition.
+I had the further motive that Baskerville Hall, as the paper
+says, would certainly remain untenanted if anything were done to
+increase its already rather grim reputation. For both these
+reasons I thought that I was justified in telling rather less
+than I knew, since no practical good could result from it, but
+with you there is no reason why I should not be perfectly frank.
+
+"The moor is very sparsely inhabited, and those who live near
+each other are thrown very much together. For this reason I saw a
+good deal of Sir Charles Baskerville. With the exception of Mr.
+Frankland, of Lafter Hall, and Mr. Stapleton, the naturalist,
+there are no other men of education within many miles. Sir
+Charles was a retiring man, but the chance of his illness brought
+us together, and a community of interests in science kept us so.
+He had brought back much scientific information from South
+Africa, and many a charming evening we have spent together
+discussing the comparative anatomy of the Bushman and the
+Hottentot.
+
+"Within the last few months it became increasingly plain to me
+that Sir Charles's nervous system was strained to the breaking
+point. He had taken this legend which I have read you exceedingly
+to heart--so much so that, although he would walk in his own
+grounds, nothing would induce him to go out upon the moor at
+night. Incredible as it may appear to you, Mr. Holmes, he was
+honestly convinced that a dreadful fate overhung his family, and
+certainly the records which he was able to give of his ancestors
+were not encouraging. The idea of some ghastly presence
+constantly haunted him, and on more than one occasion he has
+asked me whether I had on my medical journeys at night ever seen
+any strange creature or heard the baying of a hound. The latter
+question he put to me several times, and always with a voice
+which vibrated with excitement.
+
+"I can well remember driving up to his house in the evening some
+three weeks before the fatal event. He chanced to be at his hall
+door. I had descended from my gig and was standing in front of
+him, when I saw his eyes fix themselves over my shoulder, and
+stare past me with an expression of the most dreadful horror. I
+whisked round and had just time to catch a glimpse of something
+which I took to be a large black calf passing at the head of the
+drive. So excited and alarmed was he that I was compelled to go
+down to the spot where the animal had been and look around for
+it. It was gone, however, and the incident appeared to make the
+worst impression upon his mind. I stayed with him all the
+evening, and it was on that occasion, to explain the emotion
+which he had shown, that he confided to my keeping that narrative
+which I read to you when first I came. I mention this small
+episode because it assumes some importance in view of the tragedy
+which followed, but I was convinced at the time that the matter
+was entirely trivial and that his excitement had no
+justification.
+
+"It was at my advice that Sir Charles was about to go to London.
+His heart was, I knew, affected, and the constant anxiety in
+which he lived, however chimerical the cause of it might be, was
+evidently having a serious effect upon his health. I thought that
+a few months among the distractions of town would send him back a
+new man. Mr. Stapleton, a mutual friend who was much concerned at
+his state of health, was of the same opinion. At the last instant
+came this terrible catastrophe.
+
+"On the night of Sir Charles's death Barrymore the butler, who
+made the discovery, sent Perkins the groom on horseback to me,
+and as I was sitting up late I was able to reach Baskerville Hall
+within an hour of the event. I checked and corroborated all the
+facts which were mentioned at the inquest. I followed the
+footsteps down the Yew Alley, I saw the spot at the moor-gate
+where he seemed to have waited, I remarked the change in the
+shape of the prints after that point, I noted that there were no
+other footsteps save those of Barrymore on the soft gravel, and
+finally I carefully examined the body, which had not been touched
+until my arrival. Sir Charles lay on his face, his arms out, his
+fingers dug into the ground, and his features convulsed with some
+strong emotion to such an extent that I could hardly have sworn
+to his identity. There was certainly no physical injury of any
+kind. But one false statement was made by Barrymore at the
+inquest. He said that there were no traces upon the ground round
+the body. He did not observe any. But I did--some little distance
+off, but fresh and clear."
+
+"Footprints?"
+
+"Footprints."
+
+"A man's or a woman's?"
+
+Dr. Mortimer looked strangely at us for an instant, and his voice
+sank almost to a whisper as he answered:--
+
+"Mr. Holmes, they were the footprints of a gigantic hound!"
+
+
+
+
+Chapter 3
+
+The Problem
+
+
+I confess at these words a shudder passed through me. There was a
+thrill in the doctor's voice which showed that he was himself
+deeply moved by that which he told us. Holmes leaned forward in
+his excitement and his eyes had the hard, dry glitter which shot
+from them when he was keenly interested.
+
+"You saw this?"
+
+"As clearly as I see you."
+
+"And you said nothing?"
+
+"What was the use?"
+
+"How was it that no one else saw it?"
+
+"The marks were some twenty yards from the body and no one gave
+them a thought. I don't suppose I should have done so had I not
+known this legend."
+
+"There are many sheep-dogs on the moor?"
+
+"No doubt, but this was no sheep-dog."
+
+"You say it was large?"
+
+"Enormous."
+
+"But it had not approached the body?"
+
+"No."
+
+"What sort of night was it?'
+
+"Damp and raw."
+
+"But not actually raining?"
+
+"No."
+
+"What is the Alley like?"
+
+"There are two lines of old yew hedge, twelve feet high and
+impenetrable. The walk in the centre is about eight feet across."
+
+"Is there anything between the hedges and the walk?"
+
+"Yes, there is a strip of grass about six feet broad on either
+side."
+
+"I understand that the yew hedge is penetrated at one point by a
+gate?"
+
+"Yes, the wicket-gate which leads on to the moor."
+
+"Is there any other opening?"
+
+"None."
+
+"So that to reach the Yew Alley one either has to come down it
+from the house or else to enter it by the moor-gate?"
+
+"There is an exit through a summer-house at the far end."
+
+"Had Sir Charles reached this?"
+
+"No; he lay about fifty yards from it."
+
+"Now, tell me, Dr. Mortimer--and this is important--the
+marks which you saw were on the path and not on the grass?"
+
+"No marks could show on the grass."
+
+"Were they on the same side of the path as the moor-gate?"
+
+"Yes; they were on the edge of the path on the same side as the
+moor-gate."
+
+"You interest me exceedingly. Another point. Was the wicket-gate
+closed?"
+
+"Closed and padlocked."
+
+"How high was it?"
+
+"About four feet high."
+
+"Then anyone could have got over it?"
+
+"Yes."
+
+"And what marks did you see by the wicket-gate?"
+
+"None in particular."
+
+"Good heaven! Did no one examine?"
+
+"Yes, I examined myself."
+
+"And found nothing?"
+
+"It was all very confused. Sir Charles had evidently stood there
+for five or ten minutes."
+
+"How do you know that?"
+
+"Because the ash had twice dropped from his cigar."
+
+"Excellent! This is a colleague, Watson, after our own heart. But
+the marks?"
+
+"He had left his own marks all over that small patch of gravel. I
+could discern no others."
+
+Sherlock Holmes struck his hand against his knee with an
+impatient gesture.
+
+"If I had only been there!" he cried. "It is evidently a case of
+extraordinary interest, and one which presented immense
+opportunities to the scientific expert. That gravel page upon
+which I might have read so much has been long ere this smudged by
+the rain and defaced by the clogs of curious peasants. Oh, Dr.
+Mortimer, Dr. Mortimer, to think that you should not have called
+me in! You have indeed much to answer for."
+
+"I could not call you in, Mr. Holmes, without disclosing these
+facts to the world, and I have already given my reasons for not
+wishing to do so. Besides, besides --"
+
+"Why do you hesitate?"
+
+"There is a realm in which the most acute and most experienced of
+detectives is helpless."
+
+"You mean that the thing is supernatural?"
+
+"I did not positively say so."
+
+"No, but you evidently think it."
+
+"Since the tragedy, Mr. Holmes, there have come to my ears
+several incidents which are hard to reconcile with the settled
+order of Nature."
+
+"For example?"
+
+"I find that before the terrible event occurred several people
+had seen a creature upon the moor which corresponds with this
+Baskerville demon, and which could not possibly be any animal
+known to science. They all agreed that it was a huge creature,
+luminous, ghastly, and spectral. I have cross-examined these men,
+one of them a hard-headed countryman, one a farrier, and one a
+moorland farmer, who all tell the same story of this dreadful
+apparition, exactly corresponding to the hell-hound of the
+legend. I assure you that there is a reign of terror in the
+district, and that it is a hardy man who will cross the moor at
+night."
+
+"And you, a trained man of science, believe it to be
+supernatural?"
+
+"I do not know what to believe."
+
+Holmes shrugged his shoulders.
+
+"I have hitherto confined my investigations to this world," said
+he. "In a modest way I have combated evil, but to take on the
+Father of Evil himself would, perhaps, be too ambitious a task.
+Yet you must admit that the footmark is material."
+
+"The original hound was material enough to tug a man's throat
+out, and yet he was diabolical as well."
+
+"I see that you have quite gone over to the supernaturalists. But
+now, Dr. Mortimer, tell me this. If you hold these views, why
+have you come to consult me at all? You tell me in the same
+breath that it is useless to investigate Sir Charles's death, and
+that you desire me to do it."
+
+"I did not say that I desired you to do it."
+
+"Then, how can I assist you?"
+
+"By advising me as to what I should do with Sir Henry
+Baskerville, who arrives at Waterloo Station"--Dr. Mortimer
+looked at his watch--"in exactly one hour and a quarter."
+
+"He being the heir?"
+
+"Yes. On the death of Sir Charles we inquired for this young
+gentleman and found that he had been farming in Canada. From the
+accounts which have reached us he is an excellent fellow in every
+way. I speak not as a medical man but as a trustee and executor
+of Sir Charles's will."
+
+"There is no other claimant, I presume?"
+
+"None. The only other kinsman whom we have been able to trace was
+Rodger Baskerville, the youngest of three brothers of whom poor
+Sir Charles was the elder. The second brother, who died young, is
+the father of this lad Henry. The third, Rodger, was the black
+sheep of the family. He came of the old masterful Baskerville
+strain, and was the very image, they tell me, of the family
+picture of old Hugo. He made England too hot to hold him, fled to
+Central America, and died there in 1876 of yellow fever. Henry is
+the last of the Baskervilles. In one hour and five minutes I meet
+him at Waterloo Station. I have had a wire that he arrived at
+Southampton this morning. Now, Mr. Holmes, what would you advise
+me to do with him?"
+
+"Why should he not go to the home of his fathers?"
+
+"It seems natural, does it not? And yet, consider that every
+Baskerville who goes there meets with an evil fate. I feel sure
+that if Sir Charles could have spoken with me before his death he
+would have warned me against bringing this, the last of the old
+race, and the heir to great wealth, to that deadly place. And yet
+it cannot be denied that the prosperity of the whole poor, bleak
+country-side depends upon his presence. All the good work which
+has been done by Sir Charles will crash to the ground if there is
+no tenant of the Hall. I fear lest I should be swayed too much by
+my own obvious interest in the matter, and that is why I bring
+the case before you and ask for your advice."
+
+Holmes considered for a little time.
+
+"Put into plain words, the matter is this," said he. "In your
+opinion there is a diabolical agency which makes Dartmoor an
+unsafe abode for a Baskerville--that is your opinion?"
+
+"At least I might go the length of saying that there is some
+evidence that this may be so."
+
+"Exactly. But surely, if your supernatural theory be correct, it
+could work the young man evil in London as easily as in
+Devonshire. A devil with merely local powers like a parish
+vestry would be too inconceivable a thing."
+
+"You put the matter more flippantly, Mr. Holmes, than you would
+probably do if you were brought into personal contact with these
+things. Your advice, then, as I understand it, is that the young
+man will be as safe in Devonshire as in London. He comes in fifty
+minutes. What would you recommend?"
+
+"I recommend, sir, that you take a cab, call off your spaniel who
+is scratching at my front door, and proceed to Waterloo to meet
+Sir Henry Baskerville."
+
+"And then?"
+
+"And then you will say nothing to him at all until I have made up
+my mind about the matter."
+
+"How long will it take you to make up your mind?"
+
+"Twenty-four hours. At ten o'clock to-morrow, Dr. Mortimer, I
+will be much obliged to you if you will call upon me here, and it
+will be of help to me in my plans for the future if you will
+bring Sir Henry Baskerville with you."
+
+"I will do so, Mr. Holmes." He scribbled the appointment on his
+shirtcuff and hurried off in his strange, peering, absent-minded
+fashion. Holmes stopped him at the head of the stair.
+
+"Only one more question, Dr. Mortimer. You say that before Sir
+Charles Baskerville's death several people saw this apparition
+upon the moor?"
+
+"Three people did."
+
+"Did any see it after?"
+
+"I have not heard of any."
+
+"Thank you. Good morning."
+
+Holmes returned to his seat with that quiet look of inward
+satisfaction which meant that he had a congenial task before him.
+
+"Going out, Watson?"
+
+"Unless I can help you."
+
+"No, my dear fellow, it is at the hour of action that I turn to
+you for aid. But this is splendid, really unique from some points
+of view. When you pass Bradley's, would you ask him to send up a
+pound of the strongest shag tobacco? Thank you. It would be as
+well if you could make it convenient not to return before
+evening. Then I should be very glad to compare impressions as to
+this most interesting problem which has been submitted to us this
+morning."
+
+I knew that seclusion and solitude were very necessary for my
+friend in those hours of intense mental concentration during
+which he weighed every particle of evidence, constructed
+alternative theories, balanced one against the other, and made up
+his mind as to which points were essential and which immaterial.
+I therefore spent the day at my club and did not return to Baker
+Street until evening. It was nearly nine o'clock when I found
+myself in the sitting-room once more.
+
+My first impression as I opened the door was that a fire had
+broken out, for the room was so filled with smoke that the light
+of the lamp upon the table was blurred by it. As I entered,
+however, my fears were set at rest, for it was the acrid fumes of
+strong coarse tobacco which took me by the throat and set me
+coughing. Through the haze I had a vague vision of Holmes in his
+dressing-gown coiled up in an armchair with his black clay pipe
+between his lips. Several rolls of paper lay around him.
+
+"Caught cold, Watson?" said he.
+
+"No, it's this poisonous atmosphere."
+
+"I suppose it is pretty thick, now that you mention it."
+
+"Thick! It is intolerable."
+
+"Open the window, then! You have been at your club all day, I
+perceive."
+
+"My dear Holmes!"
+
+"Am I right?"
+
+"Certainly, but how?"
+
+He laughed at my bewildered expression.
+
+"There is a delightful freshness about you, Watson, which makes
+it a pleasure to exercise any small powers which I possess at
+your expense. A gentleman goes forth on a showery and miry day.
+He returns immaculate in the evening with the gloss still on his
+hat and his boots. He has been a fixture therefore all day. He is
+not a man with intimate friends. Where, then, could he have been?
+Is it not obvious?"
+
+"Well, it is rather obvious."
+
+"The world is full of obvious things which nobody by any chance
+ever observes. Where do you think that I have been?"
+
+"A fixture also."
+
+"On the contrary, I have been to Devonshire."
+
+"In spirit?"
+
+"Exactly. My body has remained in this arm-chair and has, I
+regret to observe, consumed in my absence two large pots of
+coffee and an incredible amount of tobacco. After you left I sent
+down to Stamford's for the Ordnance map of this portion of the
+moor, and my spirit has hovered over it all day. I flatter myself
+that I could find my way about."
+
+"A large scale map, I presume?"
+
+"Very large." He unrolled one section and held it over his knee.
+"Here you have the particular district which concerns us. That is
+Baskerville Hall in the middle."
+
+"With a wood round it?"
+
+"Exactly. I fancy the Yew Alley, though not marked under that
+name, must stretch along this line, with the moor, as you
+perceive, upon the right of it. This small clump of buildings
+here is the hamlet of Grimpen, where our friend Dr. Mortimer has
+his headquarters. Within a radius of five miles there are, as you
+see, only a very few scattered dwellings. Here is Lafter Hall,
+which was mentioned in the narrative. There is a house indicated
+here which may be the residence of the naturalist--Stapleton, if
+I remember right, was his name. Here are two moorland
+farm-houses, High Tor and Foulmire. Then fourteen miles away the
+great convict prison of Princetown. Between and around these
+scattered points extends the desolate, lifeless moor. This, then,
+is the stage upon which tragedy has been played, and upon which
+we may help to play it again."
+
+"It must be a wild place."
+
+"Yes, the setting is a worthy one. If the devil did desire to
+have a hand in the affairs of men ----"
+
+"Then you are yourself inclining to the supernatural
+explanation."
+
+"The devil's agents may be of flesh and blood, may they not?
+There are two questions waiting for us at the outset. The one is
+whether any crime has been committed at all; the second is, what
+is the crime and how was it committed? Of course, if Dr.
+Mortimer's surmise should be correct, and we are dealing with
+forces outside the ordinary laws of Nature, there is an end of
+our investigation. But we are bound to exhaust all other
+hypotheses before falling back upon this one. I think we'll shut
+that window again, if you don't mind. It is a singular thing, but
+I find that a concentrated atmosphere helps a concentration of
+thought. I have not pushed it to the length of getting into a box
+to think, but that is the logical outcome of my convictions. Have
+you turned the case over in your mind?"
+
+"Yes, I have thought a good deal of it in the course of the day."
+
+"What do you make of it?"
+
+"It is very bewildering."
+
+"It has certainly a character of its own. There are points of
+distinction about it. That change in the footprints, for example.
+What do you make of that?"
+
+"Mortimer said that the man had walked on tiptoe down that
+portion of the alley."
+
+"He only repeated what some fool had said at the inquest. Why
+should a man walk on tiptoe down the alley?"
+
+"What then?"
+
+"He was running, Watson--running desperately, running for his
+life, running until he burst his heart and fell dead upon his
+face."
+
+"Running from what?"
+
+"There lies our problem. There are indications that the man was
+crazed with fear before ever he began to run."
+
+"How can you say that?"
+
+"I am presuming that the cause of his fears came to him across
+the moor. If that were so, and it seems most probable, only a man
+who had lost his wits would have run from the house instead of
+towards it. If the gipsy's evidence may be taken as true, he ran
+with cries for help in the direction where help was least likely
+to be. Then, again, whom was he waiting for that night, and why
+was he waiting for him in the Yew Alley rather than in his own
+house?"
+
+"You think that he was waiting for someone?"
+
+"The man was elderly and infirm. We can understand his taking an
+evening stroll, but the ground was damp and the night inclement.
+Is it natural that he should stand for five or ten minutes, as
+Dr. Mortimer, with more practical sense than I should have given
+him credit for, deduced from the cigar ash?"
+
+"But he went out every evening."
+
+"I think it unlikely that he waited at the moor-gate every
+evening. On the contrary, the evidence is that he avoided the
+moor. That night he waited there. It was the night before he made
+his departure for London. The thing takes shape, Watson. It
+becomes coherent. Might I ask you to hand me my violin, and we
+will postpone all further thought upon this business until we
+have had the advantage of meeting Dr. Mortimer and Sir Henry
+Baskerville in the morning."
+
+
+
+
+Chapter 4
+
+Sir Henry Baskerville
+
+
+Our breakfast-table was cleared early, and Holmes waited in his
+dressing-gown for the promised interview. Our clients were
+punctual to their appointment, for the clock had just struck ten
+when Dr. Mortimer was shown up, followed by the young baronet.
+The latter was a small, alert, dark-eyed man about thirty years
+of age, very sturdily built, with thick black eyebrows and a
+strong, pugnacious face. He wore a ruddy-tinted tweed suit and
+had the weather-beaten appearance of one who has spent most of
+his time in the open air, and yet there was something in his
+steady eye and the quiet assurance of his bearing which indicated
+the gentleman.
+
+"This is Sir Henry Baskerville," said Dr. Mortimer.
+
+"Why, yes," said he, "and the strange thing is, Mr. Sherlock
+Holmes, that if my friend here had not proposed coming round to
+you this morning I should have come on my own account. I
+understand that you think out little puzzles, and I've had one
+this morning which wants more thinking out than I am able to give
+it."
+
+"Pray take a seat, Sir Henry. Do I understand you to say that you
+have yourself had some remarkable experience since you arrived in
+London?"
+
+"Nothing of much importance, Mr. Holmes. Only a joke, as like as
+not. It was this letter, if you can call it a letter, which
+reached me this morning."
+
+He laid an envelope upon the table, and we all bent over it. It
+was of common quality, grayish in colour. The address, "Sir Henry
+Baskerville, Northumberland Hotel," was printed in rough
+characters; the postmark "Charing Cross," and the date of posting
+the preceding evening.
+
+"Who knew that you were going to the Northumberland Hotel?" asked
+Holmes, glancing keenly across at our visitor.
+
+"No one could have known. We only decided after I met Dr.
+Mortimer."
+
+"But Dr. Mortimer was no doubt already stopping there?"
+
+"No, I had been staying with a friend," said the doctor. "There
+was no possible indication that we intended to go to this hotel."
+
+"Hum! Someone seems to be very deeply interested in your
+movements." Out of the envelope he took a half-sheet of foolscap
+paper folded into four. This he opened and spread flat upon the
+table. Across the middle of it a single sentence had been formed
+by the expedient of pasting printed words upon it. It ran: "As
+you value your life or your reason keep away from the moor." The
+word "moor" only was printed in ink.
+
+"Now," said Sir Henry Baskerville, "perhaps you will tell me, Mr.
+Holmes, what in thunder is the meaning of that, and who it is
+that takes so much interest in my affairs?"
+
+"What do you make of it, Dr. Mortimer? You must allow that there
+is nothing supernatural about this, at any rate?"
+
+"No, sir, but it might very well come from someone who was
+convinced that the business is supernatural."
+
+"What business?" asked Sir Henry sharply. "It seems to me that
+all you gentlemen know a great deal more than I do about my own
+affairs."
+
+"You shall share our knowledge before you leave this room, Sir
+Henry. I promise you that," said Sherlock Holmes. "We will
+confine ourselves for the present with your permission to this
+very interesting document, which must have been put together and
+posted yesterday evening. Have you yesterday's Times, Watson?"
+
+"It is here in the corner."
+
+"Might I trouble you for it--the inside page, please, with the
+leading articles?" He glanced swiftly over it, running his eyes
+up and down the columns. "Capital article this on free trade.
+Permit me to give you an extract from it. 'You may be cajoled
+into imagining that your own special trade or your own industry
+will be encouraged by a protective tariff, but it stands to
+reason that such legislation must in the long run keep away
+wealth from the country, diminish the value of our imports, and
+lower the general conditions of life in this island.' What do you
+think of that, Watson?" cried Holmes in high glee, rubbing his
+hands together with satisfaction. "Don't you think that is an
+admirable sentiment?"
+
+Dr. Mortimer looked at Holmes with an air of professional
+interest, and Sir Henry Baskerville turned a pair of puzzled dark
+eyes upon me.
+
+"I don't know much about the tariff and things of that kind,"
+said he; "but it seems to me we've got a bit off the trail so far
+as that note is concerned."
+
+"On the contrary, I think we are particularly hot upon the trail,
+Sir Henry. Watson here knows more about my methods than you do,
+but I fear that even he has not quite grasped the significance of
+this sentence."
+
+"No, I confess that I see no connection."
+
+"And yet, my dear Watson, there is so very close a connection
+that the one is extracted out of the other. 'You,' 'your,'
+'your,' 'life,' 'reason,' 'value,' 'keep away,' 'from the.' Don't
+you see now whence these words have been taken?"
+
+"By thunder, you're right! Well, if that isn't smart!" cried Sir
+Henry.
+
+"If any possible doubt remained it is settled by the fact that
+'keep away' and 'from the' are cut out in one piece."
+
+"Well, now--so it is!"
+
+"Really, Mr. Holmes, this exceeds anything which I could have
+imagined," said Dr. Mortimer, gazing at my friend in amazement.
+"I could understand anyone saying that the words were from a
+newspaper; but that you should name which, and add that it came
+from the leading article, is really one of the most remarkable
+things which I have ever known. How did you do it?"
+
+"I presume, Doctor, that you could tell the skull of a negro from
+that of an Esquimau?"
+
+"Most certainly."
+
+"But how?"
+
+"Because that is my special hobby. The differences are obvious.
+The supra-orbital crest, the facial angle, the maxillary curve,
+the --"
+
+"But this is my special hobby, and the differences are equally
+obvious. There is as much difference to my eyes between the
+leaded bourgeois type of a Times article and the slovenly print
+of an evening half-penny paper as there could be between your
+negro and your Esquimau. The detection of types is one of the
+most elementary branches of knowledge to the special expert in
+crime, though I confess that once when I was very young I
+confused the Leeds Mercury with the Western Morning News. But a
+Times leader is entirely distinctive, and these words could have
+been taken from nothing else. As it was done yesterday the strong
+probability was that we should find the words in yesterday's
+issue."
+
+"So far as I can follow you, then, Mr. Holmes," said Sir Henry
+Baskerville, "someone cut out this message with a scissors--"
+
+"Nail-scissors," said Holmes. "You can see that it was a very
+short-bladed scissors, since the cutter had to take two snips
+over 'keep away.'"
+
+"That is so. Someone, then, cut out the message with a pair of
+short-bladed scissors, pasted it with paste--"
+
+"Gum," said Holmes.
+
+"With gum on to the paper. But I want to know why the word 'moor'
+should have been written?"
+
+"Because he could not find it in print. The other words were all
+simple and might be found in any issue, but 'moor' would be less
+common."
+
+"Why, of course, that would explain it. Have you read anything
+else in this message, Mr. Holmes?"
+
+"There are one or two indications, and yet the utmost pains have
+been taken to remove all clues. The address, you observe is
+printed in rough characters. But the Times is a paper which is
+seldom found in any hands but those of the highly educated. We
+may take it, therefore, that the letter was composed by an
+educated man who wished to pose as an uneducated one, and his
+effort to conceal his own writing suggests that that writing
+might be known, or come to be known, by you. Again, you will
+observe that the words are not gummed on in an accurate line, but
+that some are much higher than others. 'Life,' for example is
+quite out of its proper place. That may point to carelessness or
+it may point to agitation and hurry upon the part of the cutter.
+On the whole I incline to the latter view, since the matter was
+evidently important, and it is unlikely that the composer of such
+a letter would be careless. If he were in a hurry it opens up the
+interesting question why he should be in a hurry, since any
+letter posted up to early morning would reach Sir Henry before he
+would leave his hotel. Did the composer fear an interruption--and
+from whom?"
+
+"We are coming now rather into the region of guesswork," said Dr.
+Mortimer.
+
+"Say, rather, into the region where we balance probabilities and
+choose the most likely. It is the scientific use of the
+imagination, but we have always some material basis on which to
+start our speculation. Now, you would call it a guess, no doubt,
+but I am almost certain that this address has been written in a
+hotel."
+
+"How in the world can you say that?"
+
+"If you examine it carefully you will see that both the pen and
+the ink have given the writer trouble. The pen has spluttered
+twice in a single word, and has run dry three times in a short
+address, showing that there was very little ink in the bottle.
+Now, a private pen or ink-bottle is seldom allowed to be in such
+a state, and the combination of the two must be quite rare. But
+you know the hotel ink and the hotel pen, where it is rare to get
+anything else. Yes, I have very little hesitation in saying that
+could we examine the waste-paper baskets of the hotels around
+Charing Cross until we found the remains of the mutilated Times
+leader we could lay our hands straight upon the person who sent
+this singular message. Halloa! Halloa! What's this?"
+
+He was carefully examining the foolscap, upon which the words
+were pasted, holding it only an inch or two from his eyes.
+
+"Well?"
+
+"Nothing," said he, throwing it down. "It is a blank half-sheet
+of paper, without even a water-mark upon it. I think we have
+drawn as much as we can from this curious letter; and now, Sir
+Henry, has anything else of interest happened to you since you
+have been in London?"
+
+"Why, no, Mr. Holmes. I think not."
+
+"You have not observed anyone follow or watch you?"
+
+"I seem to have walked right into the thick of a dime novel,"
+said our visitor. "Why in thunder should anyone follow or watch
+me?"
+
+"We are coming to that. You have nothing else to report to us
+before we go into this matter?"
+
+"Well, it depends upon what you think worth reporting."
+
+"I think anything out of the ordinary routine of life well worth
+reporting."
+
+Sir Henry smiled.
+
+"I don't know much of British life yet, for I have spent nearly
+all my time in the States and in Canada. But I hope that to lose
+one of your boots is not part of the ordinary routine of life
+over here."
+
+"You have lost one of your boots?"
+
+"My dear sir," cried Dr. Mortimer, "it is only mislaid. You will
+find it when you return to the hotel. What is the use of
+troubling Mr. Holmes with trifles of this kind?"
+
+"Well, he asked me for anything outside the ordinary routine."
+
+"Exactly," said Holmes, "however foolish the incident may seem.
+You have lost one of your boots, you say?"
+
+"Well, mislaid it, anyhow. I put them both outside my door last
+night, and there was only one in the morning. I could get no
+sense out of the chap who cleans them. The worst of it is that I
+only bought the pair last night in the Strand, and I have never
+had them on."
+
+"If you have never worn them, why did you put them out to be
+cleaned?"
+
+"They were tan boots and had never been varnished. That was why I
+put them out."
+
+"Then I understand that on your arrival in London yesterday you
+went out at once and bought a pair of boots?"
+
+"I did a good deal of shopping. Dr. Mortimer here went round with
+me. You see, if I am to be squire down there I must dress the
+part, and it may be that I have got a little careless in my ways
+out West. Among other things I bought these brown boots--gave six
+dollars for them--and had one stolen before ever I had them on my
+feet."
+
+"It seems a singularly useless thing to steal," said Sherlock
+Holmes. "I confess that I share Dr. Mortimer's belief that it
+will not be long before the missing boot is found."
+
+"And, now, gentlemen," said the baronet with decision, "it seems
+to me that I have spoken quite enough about the little that I
+know. It is time that you kept your promise and gave me a full
+account of what we are all driving at."
+
+"Your request is a very reasonable one," Holmes answered. "Dr.
+Mortimer, I think you could not do better than to tell your story
+as you told it to us."
+
+Thus encouraged, our scientific friend drew his papers from his
+pocket, and presented the whole case as he had done upon the
+morning before. Sir Henry Baskerville listened with the deepest
+attention, and with an occasional exclamation of surprise.
+
+"Well, I seem to have come into an inheritance with a vengeance,"
+said he when the long narrative was finished. "Of course, I've
+heard of the hound ever since I was in the nursery. It's the pet
+story of the family, though I never thought of taking it
+seriously before. But as to my uncle's death--well, it all seems
+boiling up in my head, and I can't get it clear yet. You don't
+seem quite to have made up your mind whether it's a case for a
+policeman or a clergyman."
+
+"Precisely."
+
+"And now there's this affair of the letter to me at the hotel. I
+suppose that fits into its place."
+
+"It seems to show that someone knows more than we do about what
+goes on upon the moor," said Dr. Mortimer.
+
+"And also," said Holmes, "that someone is not ill-disposed
+towards you, since they warn you of danger."
+
+"Or it may be that they wish, for their own purposes, to scare me
+away."
+
+"Well, of course, that is possible also. I am very much indebted
+to you, Dr. Mortimer, for introducing me to a problem which
+presents several interesting alternatives. But the practical
+point which we now have to decide, Sir Henry, is whether it is or
+is not advisable for you to go to Baskerville Hall."
+
+"Why should I not go?"
+
+"There seems to be danger."
+
+"Do you mean danger from this family fiend or do you mean danger
+from human beings?"
+
+"Well, that is what we have to find out."
+
+"Whichever it is, my answer is fixed. There is no devil in hell,
+Mr. Holmes, and there is no man upon earth who can prevent me
+from going to the home of my own people, and you may take that to
+be my final answer." His dark brows knitted and his face flushed
+to a dusky red as he spoke. It was evident that the fiery temper
+of the Baskervilles was not extinct in this their last
+representative. "Meanwhile," said he, "I have hardly had time to
+think over all that you have told me. It's a big thing for a man
+to have to understand and to decide at one sitting. I should like
+to have a quiet hour by myself to make up my mind. Now, look
+here, Mr. Holmes, it's half-past eleven now and I am going back
+right away to my hotel. Suppose you and your friend, Dr. Watson,
+come round and lunch with us at two. I'll be able to tell you
+more clearly then how this thing strikes me."
+
+"Is that convenient to you, Watson?"
+
+"Perfectly."
+
+"Then you may expect us. Shall I have a cab called?"
+
+"I'd prefer to walk, for this affair has flurried me rather."
+
+"I'll join you in a walk, with pleasure," said his companion.
+
+"Then we meet again at two o'clock. Au revoir, and good-morning!"
+
+We heard the steps of our visitors descend the stair and the bang
+of the front door. In an instant Holmes had changed from the
+languid dreamer to the man of action.
+
+"Your hat and boots, Watson, quick! Not a moment to lose!" He
+rushed into his room in his dressing-gown and was back again in a
+few seconds in a frock-coat. We hurried together down the stairs
+and into the street. Dr. Mortimer and Baskerville were still
+visible about two hundred yards ahead of us in the direction of
+Oxford Street.
+
+"Shall I run on and stop them?"
+
+"Not for the world, my dear Watson. I am perfectly satisfied with
+your company if you will tolerate mine. Our friends are wise, for
+it is certainly a very fine morning for a walk."
+
+He quickened his pace until we had decreased the distance which
+divided us by about half. Then, still keeping a hundred yards
+behind, we followed into Oxford Street and so down Regent Street.
+Once our friends stopped and stared into a shop window, upon
+which Holmes did the same. An instant afterwards he gave a little
+cry of satisfaction, and, following the direction of his eager
+eyes, I saw that a hansom cab with a man inside which had halted
+on the other side of the street was now proceeding slowly onward
+again.
+
+"There's our man, Watson! Come along! We'll have a good look at
+him, if we can do no more."
+
+At that instant I was aware of a bushy black beard and a pair of
+piercing eyes turned upon us through the side window of the cab.
+Instantly the trapdoor at the top flew up, something was screamed
+to the driver, and the cab flew madly off down Regent Street.
+Holmes looked eagerly round for another, but no empty one was in
+sight. Then he dashed in wild pursuit amid the stream of the
+traffic, but the start was too great, and already the cab was out
+of sight.
+
+"There now!" said Holmes bitterly as he emerged panting and white
+with vexation from the tide of vehicles. "Was ever such bad luck
+and such bad management, too? Watson, Watson, if you are an
+honest man you will record this also and set it against my
+successes!"
+
+"Who was the man?"
+
+"I have not an idea."
+
+"A spy?"
+
+"Well, it was evident from what we have heard that Baskerville
+has been very closely shadowed by someone since he has been in
+town. How else could it be known so quickly that it was the
+Northumberland Hotel which he had chosen? If they had followed
+him the first day I argued that they would follow him also the
+second. You may have observed that I twice strolled over to the
+window while Dr. Mortimer was reading his legend."
+
+"Yes, I remember."
+
+"I was looking out for loiterers in the street, but I saw none.
+We are dealing with a clever man, Watson. This matter cuts very
+deep, and though I have not finally made up my mind whether it is
+a benevolent or a malevolent agency which is in touch with us, I
+am conscious always of power and design. When our friends left I
+at once followed them in the hopes of marking down their
+invisible attendant. So wily was he that he had not trusted
+himself upon foot, but he had availed himself of a cab so that he
+could loiter behind or dash past them and so escape their notice.
+His method had the additional advantage that if they were to take
+a cab he was all ready to follow them. It has, however, one
+obvious disadvantage."
+
+"It puts him in the power of the cabman."
+
+"Exactly."
+
+"What a pity we did not get the number!"
+
+"My dear Watson, clumsy as I have been, you surely do not
+seriously imagine that I neglected to get the number? No. 2704 is
+our man. But that is no use to us for the moment."
+
+"I fail to see how you could have done more."
+
+"On observing the cab I should have instantly turned and walked
+in the other direction. I should then at my leisure have hired a
+second cab and followed the first at a respectful distance, or,
+better still, have driven to the Northumberland Hotel and waited
+there. When our unknown had followed Baskerville home we should
+have had the opportunity of playing his own game upon himself and
+seeing where he made for. As it is, by an indiscreet eagerness,
+which was taken advantage of with extraordinary quickness and
+energy by our opponent, we have betrayed ourselves and lost our
+man."
+
+We had been sauntering slowly down Regent Street during this
+conversation, and Dr. Mortimer, with his companion, had long
+vanished in front of us.
+
+"There is no object in our following them," said Holmes. "The
+shadow has departed and will not return. We must see what further
+cards we have in our hands and play them with decision. Could you
+swear to that man's face within the cab?"
+
+"I could swear only to the beard."
+
+"And so could I--from which I gather that in all probability it
+was a false one. A clever man upon so delicate an errand has no
+use for a beard save to conceal his features. Come in here,
+Watson!"
+
+He turned into one of the district messenger offices, where he
+was warmly greeted by the manager.
+
+"Ah, Wilson, I see you have not forgotten the little case in
+which I had the good fortune to help you?"
+
+"No, sir, indeed I have not. You saved my good name, and perhaps
+my life."
+
+"My dear fellow, you exaggerate. I have some recollection,
+Wilson, that you had among your boys a lad named Cartwright, who
+showed some ability during the investigation."
+
+"Yes, sir, he is still with us."
+
+"Could you ring him up?--thank you! And I should be glad to have
+change of this five-pound note."
+
+A lad of fourteen, with a bright, keen face, had obeyed the
+summons of the manager. He stood now gazing with great reverence
+at the famous detective.
+
+"Let me have the Hotel Directory," said Holmes. "Thank you! Now,
+Cartwright, there are the names of twenty-three hotels here, all
+in the immediate neighbourhood of Charing Cross. Do you see?"
+
+"Yes, sir."
+
+"You will visit each of these in turn."
+
+"Yes, sir."
+
+"You will begin in each case by giving the outside porter one
+shilling. Here are twenty-three shillings."
+
+"Yes, sir."
+
+"You will tell him that you want to see the waste-paper of
+yesterday. You will say that an important telegram has miscarried
+and that you are looking for it. You understand?"
+
+"Yes, sir."
+
+"But what you are really looking for is the centre page of the
+Times with some holes cut in it with scissors. Here is a copy of
+the Times. It is this page. You could easily recognize it, could
+you not?"
+
+"Yes, sir."
+
+"In each case the outside porter will send for the hall porter,
+to whom also you will give a shilling. Here are twenty-three
+shillings. You will then learn in possibly twenty cases out of
+the twenty-three that the waste of the day before has been burned
+or removed. In the three other cases you will be shown a heap of
+paper and you will look for this page of the Times among it. The
+odds are enormously against your finding it. There are ten
+shillings over in case of emergencies. Let me have a report by
+wire at Baker Street before evening. And now, Watson, it only
+remains for us to find out by wire the identity of the cabman,
+No. 2704, and then we will drop into one of the Bond Street
+picture galleries and fill in the time until we are due at the
+hotel."
+
+
+
+
+Chapter 5
+
+Three Broken Threads
+
+
+Sherlock Holmes had, in a very remarkable degree, the power of
+detaching his mind at will. For two hours the strange business in
+which we had been involved appeared to be forgotten, and he was
+entirely absorbed in the pictures of the modern Belgian masters.
+He would talk of nothing but art, of which he had the crudest
+ideas, from our leaving the gallery until we found ourselves at
+the Northumberland Hotel.
+
+"Sir Henry Baskerville is upstairs expecting you," said the
+clerk. "He asked me to show you up at once when you came."
+
+"Have you any objection to my looking at your register?" said
+Holmes.
+
+"Not in the least."
+
+The book showed that two names had been added after that of
+Baskerville. One was Theophilus Johnson and family, of Newcastle;
+the other Mrs. Oldmore and maid, of High Lodge, Alton.
+
+"Surely that must be the same Johnson whom I used to know," said
+Holmes to the porter. "A lawyer, is he not, gray-headed, and
+walks with a limp?"
+
+"No, sir; this is Mr. Johnson, the coal-owner, a very active
+gentleman, not older than yourself."
+
+"Surely you are mistaken about his trade?"
+
+"No, sir! he has used this hotel for many years, and he is very
+well known to us."
+
+"Ah, that settles it. Mrs. Oldmore, too; I seem to remember the
+name. Excuse my curiosity, but often in calling upon one friend
+one finds another."
+
+"She is an invalid lady, sir. Her husband was once mayor of
+Gloucester. She always comes to us when she is in town."
+
+"Thank you; I am afraid I cannot claim her acquaintance. We have
+established a most important fact by these questions, Watson," he
+continued in a low voice as we went upstairs together. "We know
+now that the people who are so interested in our friend have not
+settled down in his own hotel. That means that while they are, as
+we have seen, very anxious to watch him, they are equally anxious
+that he should not see them. Now, this is a most suggestive
+fact."
+
+"What does it suggest?"
+
+"It suggests--halloa, my dear fellow, what on earth is the
+matter?"
+
+As we came round the top of the stairs we had run up against Sir
+Henry Baskerville himself. His face was flushed with anger, and
+he held an old and dusty boot in one of his hands. So furious was
+he that he was hardly articulate, and when he did speak it was in
+a much broader and more Western dialect than any which we had
+heard from him in the morning.
+
+"Seems to me they are playing me for a sucker in this hotel," he
+cried. "They'll find they've started in to monkey with the wrong
+man unless they are careful. By thunder, if that chap can't find
+my missing boot there will be trouble. I can take a joke with the
+best, Mr. Holmes, but they've got a bit over the mark this time."
+
+"Still looking for your boot?"
+
+"Yes, sir, and mean to find it."
+
+"But, surely, you said that it was a new brown boot?"
+
+"So it was, sir. And now it's an old black one."
+
+"What! you don't mean to say----?"
+
+"That's just what I do mean to say. I only had three pairs in the
+world--the new brown, the old black, and the patent leathers,
+which I am wearing. Last night they took one of my brown ones,
+and to-day they have sneaked one of the black. Well, have you got
+it? Speak out, man, and don't stand staring!"
+
+An agitated German waiter had appeared upon the scene.
+
+"No, sir; I have made inquiry all over the hotel, but I can hear
+no word of it."
+
+"Well, either that boot comes back before sundown or I'll see the
+manager and tell him that I go right straight out of this hotel."
+
+"It shall be found, sir--I promise you that if you will have a
+little patience it will be found."
+
+"Mind it is, for it's the last thing of mine that I'll lose in
+this den of thieves. Well, well, Mr. Holmes, you'll excuse my
+troubling you about such a trifle----"
+
+"I think it's well worth troubling about."
+
+"Why, you look very serious over it."
+
+"How do you explain it?"
+
+"I just don't attempt to explain it. It seems the very maddest,
+queerest thing that ever happened to me."
+
+"The queerest perhaps----" said Holmes, thoughtfully.
+
+"What do you make of it yourself?"
+
+"Well, I don't profess to understand it yet. This case of yours
+is very complex, Sir Henry. When taken in conjunction with your
+uncle's death I am not sure that of all the five hundred cases of
+capital importance which I have handled there is one which cuts
+so deep. But we hold several threads in our hands, and the odds
+are that one or other of them guides us to the truth. We may
+waste time in following the wrong one, but sooner or later we
+must come upon the right."
+
+We had a pleasant luncheon in which little was said of the
+business which had brought us together. It was in the private
+sitting-room to which we afterwards repaired that Holmes asked
+Baskerville what were his intentions.
+
+"To go to Baskerville Hall."
+
+"And when?"
+
+"At the end of the week."
+
+"On the whole," said Holmes, "I think that your decision is a
+wise one. I have ample evidence that you are being dogged in
+London, and amid the millions of this great city it is difficult
+to discover who these people are or what their object can be. If
+their intentions are evil they might do you a mischief, and we
+should be powerless to prevent it. You did not know, Dr. Mortimer,
+that you were followed this morning from my house?"
+
+Dr. Mortimer started violently.
+
+"Followed! By whom?"
+
+"That, unfortunately, is what I cannot tell you. Have you among
+your neighbours or acquaintances on Dartmoor any man with a
+black, full beard?"
+
+"No--or, let me see--why, yes. Barrymore, Sir Charles's butler,
+is a man with a full, black beard."
+
+"Ha! Where is Barrymore?"
+
+"He is in charge of the Hall."
+
+"We had best ascertain if he is really there, or if by any
+possibility he might be in London."
+
+"How can you do that?"
+
+"Give me a telegraph form. 'Is all ready for Sir Henry?' That
+will do. Address to Mr. Barrymore, Baskerville Hall. What is the
+nearest telegraph-office? Grimpen. Very good, we will send a
+second wire to the postmaster, Grimpen: 'Telegram to Mr.
+Barrymore to be delivered into his own hand. If absent, please
+return wire to Sir Henry Baskerville, Northumberland Hotel.' That
+should let us know before evening whether Barrymore is at his
+post in Devonshire or not."
+
+"That's so," said Baskerville. "By the way, Dr. Mortimer, who is
+this Barrymore, anyhow?"
+
+"He is the son of the old caretaker, who is dead. They have
+looked after the Hall for four generations now. So far as I know,
+he and his wife are as respectable a couple as any in the
+county."
+
+"At the same time," said Baskerville, "it's clear enough that so
+long as there are none of the family at the Hall these people
+have a mighty fine home and nothing to do."
+
+"That is true."
+
+"Did Barrymore profit at all by Sir Charles's will?" asked
+Holmes.
+
+"He and his wife had five hundred pounds each."
+
+"Ha! Did they know that they would receive this?"
+
+"Yes; Sir Charles was very fond of talking about the provisions
+of his will."
+
+"That is very interesting."
+
+"I hope," said Dr. Mortimer, "that you do not look with
+suspicious eyes upon everyone who received a legacy from Sir
+Charles, for I also had a thousand pounds left to me."
+
+"Indeed! And anyone else?"
+
+"There were many insignificant sums to individuals, and a large
+number of public charities. The residue all went to Sir Henry."
+
+"And how much was the residue?"
+
+"Seven hundred and forty thousand pounds."
+
+Holmes raised his eyebrows in surprise. "I had no idea that so
+gigantic a sum was involved," said he.
+
+"Sir Charles had the reputation of being rich, but we did not
+know how very rich he was until we came to examine his
+securities. The total value of the estate was close on to a
+million."
+
+"Dear me! It is a stake for which a man might well play a
+desperate game. And one more question, Dr. Mortimer. Supposing
+that anything happened to our young friend here--you will forgive
+the unpleasant hypothesis!--who would inherit the estate?"
+
+"Since Rodger Baskerville, Sir Charles's younger brother died
+unmarried, the estate would descend to the Desmonds, who are
+distant cousins. James Desmond is an elderly clergyman in
+Westmoreland."
+
+"Thank you. These details are all of great interest. Have you met
+Mr. James Desmond?"
+
+"Yes; he once came down to visit Sir Charles. He is a man of
+venerable appearance and of saintly life. I remember that he
+refused to accept any settlement from Sir Charles, though he
+pressed it upon him."
+
+"And this man of simple tastes would be the heir to Sir Charles's
+thousands."
+
+"He would be the heir to the estate because that is entailed. He
+would also be the heir to the money unless it were willed
+otherwise by the present owner, who can, of course, do what he
+likes with it."
+
+"And have you made your will, Sir Henry?"
+
+"No, Mr. Holmes, I have not. I've had no time, for it was only
+yesterday that I learned how matters stood. But in any case I
+feel that the money should go with the title and estate. That was
+my poor uncle's idea. How is the owner going to restore the
+glories of the Baskervilles if he has not money enough to keep up
+the property? House, land, and dollars must go together."
+
+"Quite so. Well, Sir Henry, I am of one mind with you as to the
+advisability of your going down to Devonshire without delay.
+There is only one provision which I must make. You certainly must
+not go alone."
+
+"Dr. Mortimer returns with me."
+
+"But Dr. Mortimer has his practice to attend to, and his house is
+miles away from yours. With all the good will in the world he may
+be unable to help you. No, Sir Henry, you must take with you
+someone, a trusty man, who will be always by your side."
+
+"Is it possible that you could come yourself, Mr. Holmes?"
+
+"If matters came to a crisis I should endeavour to be present in
+person; but you can understand that, with my extensive consulting
+practice and with the constant appeals which reach me from many
+quarters, it is impossible for me to be absent from London for an
+indefinite time. At the present instant one of the most revered
+names in England is being besmirched by a blackmailer, and only I
+can stop a disastrous scandal. You will see how impossible it is
+for me to go to Dartmoor."
+
+"Whom would you recommend, then?"
+
+Holmes laid his hand upon my arm.
+
+"If my friend would undertake it there is no man who is better
+worth having at your side when you are in a tight place. No one
+can say so more confidently than I."
+
+The proposition took me completely by surprise, but before I had
+time to answer, Baskerville seized me by the hand and wrung it
+heartily.
+
+"Well, now, that is real kind of you, Dr. Watson," said he. "You
+see how it is with me, and you know just as much about the matter
+as I do. If you will come down to Baskerville Hall and see me
+through I'll never forget it."
+
+The promise of adventure had always a fascination for me, and I
+was complimented by the words of Holmes and by the eagerness with
+which the baronet hailed me as a companion.
+
+"I will come, with pleasure," said I. "I do not know how I could
+employ my time better."
+
+"And you will report very carefully to me," said Holmes. "When a
+crisis comes, as it will do, I will direct how you shall act. I
+suppose that by Saturday all might be ready?"
+
+"Would that suit Dr. Watson?"
+
+"Perfectly."
+
+"Then on Saturday, unless you hear to the contrary, we shall meet
+at the 10:30 train from Paddington."
+
+We had risen to depart when Baskerville gave a cry, of triumph,
+and diving into one of the corners of the room he drew a brown
+boot from under a cabinet.
+
+"My missing boot!" he cried.
+
+"May all our difficulties vanish as easily!" said Sherlock
+Holmes.
+
+"But it is a very singular thing," Dr. Mortimer remarked. "I
+searched this room carefully before lunch."
+
+"And so did I," said Baskerville. "Every inch of it."
+
+"There was certainly no boot in it then."
+
+"In that case the waiter must have placed it there while we were
+lunching."
+
+The German was sent for but professed to know nothing of the
+matter, nor could any inquiry clear it up. Another item had been
+added to that constant and apparently purposeless series of small
+mysteries which had succeeded each other so rapidly. Setting
+aside the whole grim story of Sir Charles's death, we had a line
+of inexplicable incidents all within the limits of two days,
+which included the receipt of the printed letter, the
+black-bearded spy in the hansom, the loss of the new brown boot,
+the loss of the old black boot, and now the return of the new
+brown boot. Holmes sat in silence in the cab as we drove back to
+Baker Street, and I knew from his drawn brows and keen face that
+his mind, like my own, was busy in endeavouring to frame some
+scheme into which all these strange and apparently disconnected
+episodes could be fitted. All afternoon and late into the evening
+he sat lost in tobacco and thought.
+
+Just before dinner two telegrams were handed in. The first ran:--
+
+"Have just heard that Barrymore is at the Hall.--BASKERVILLE."
+The second:--
+
+"Visited twenty-three hotels as directed, but sorry, to report
+unable to trace cut sheet of Times.--CARTWRIGHT."
+
+"There go two of my threads, Watson. There is nothing more
+stimulating than a case where everything goes against you. We
+must cast round for another scent."
+
+"We have still the cabman who drove the spy."
+
+"Exactly. I have wired to get his name and address from the
+Official Registry. I should not be surprised if this were an
+answer to my question."
+
+The ring at the bell proved to be something even more
+satisfactory than an answer, however, for the door opened and a
+rough-looking fellow entered who was evidently the man himself.
+
+"I got a message from the head office that a gent at this address
+had been inquiring for 2704," said he. "I've driven my cab this
+seven years and never a word of complaint. I came here straight
+from the Yard to ask you to your face what you had against me."
+
+"I have nothing in the world against you, my good man," said
+Holmes. "On the contrary, I have half a sovereign for you if you
+will give me a clear answer to my questions."
+
+"Well, I've had a good day and no mistake," said the cabman, with
+a grin. "What was it you wanted to ask, sir?"
+
+"First of all your name and address, in case I want you again."
+
+"John Clayton, 3 Turpey Street, the Borough. My cab is out of
+Shipley's Yard, near Waterloo Station."
+
+Sherlock Holmes made a note of it.
+
+"Now, Clayton, tell me all about the fare who came and watched
+this house at ten o'clock this morning and afterwards followed
+the two gentlemen down Regent Street."
+
+The man looked surprised and a little embarrassed. "Why, there's
+no good my telling you things, for you seem to know as much as I
+do already," said he. "The truth is that the gentleman told me
+that he was a detective and that I was to say nothing about him
+to anyone."
+
+"My good fellow, this is a very serious business, and you may
+find yourself in a pretty bad position if you try to hide
+anything from me. You say that your fare told you that he was a
+detective?"
+
+"Yes, he did."
+
+"When did he say this?"
+
+"When he left me."
+
+"Did he say anything more?"
+
+"He mentioned his name."
+
+Holmes cast a swift glance of triumph at me. "Oh, he mentioned
+his name, did he? That was imprudent. What was the name that he
+mentioned?"
+
+"His name," said the cabman, "was Mr. Sherlock Holmes."
+
+Never have I seen my friend more completely taken aback than by
+the cabman's reply. For an instant he sat in silent amazement.
+Then he burst into a hearty laugh.
+
+"A touch, Watson--an undeniable touch!" said he. "I feel a foil
+as quick and supple as my own. He got home upon me very prettily
+that time. So his name was Sherlock Holmes, was it?"
+
+"Yes, sir, that was the gentleman's name."
+
+"Excellent! Tell me where you picked him up and all that
+occurred."
+
+"He hailed me at half-past nine in Trafalgar Square. He said that
+he was a detective, and he offered me two guineas if I would do
+exactly what he wanted all day and ask no questions. I was glad
+enough to agree. First we drove down to the Northumberland Hotel
+and waited there until two gentlemen came out and took a cab from
+the rank. We followed their cab until it pulled up somewhere near
+here."
+
+"This very door," said Holmes.
+
+"Well, I couldn't be sure of that, but I dare say my fare knew
+all about it. We pulled up half-way down the street and waited an
+hour and a half. Then the two gentlemen passed us, walking, and
+we followed down Baker Street and along ----"
+
+"I know," said Holmes.
+
+"Until we got three-quarters down Regent Street. Then my
+gentleman threw up the trap, and he cried that I should drive
+right away to Waterloo Station as hard as I could go. I whipped
+up the mare and we were there under the ten minutes. Then he paid
+up his two guineas, like a good one, and away he went into the
+station. Only just as he was leaving he turned round and he said:
+'It might interest you to know that you have been driving Mr.
+Sherlock Holmes.' That's how I come to know the name."
+
+"I see. And you saw no more of him?"
+
+"Not after he went into the station."
+
+"And how would you describe Mr. Sherlock Holmes?"
+
+The cabman scratched his head. "Well, he wasn't altogether such
+an easy gentleman to describe. I'd put him at forty years of age,
+and he was of a middle height, two or three inches shorter than
+you, sir. He was dressed like a toff, and he had a black beard,
+cut square at the end, and a pale face. I don't know as I could
+say more than that."
+
+"Colour of his eyes?"
+
+"No, I can't say that."
+
+"Nothing more that you can remember?"
+
+"No, sir; nothing."
+
+"Well, then, here is your half-sovereign. There's another one
+waiting for you if you can bring any more information. Good
+night!"
+
+"Good night, sir, and thank you!"
+
+John Clayton departed chuckling, and Holmes turned to me with a
+shrug of his shoulders and a rueful smile.
+
+"Snap goes our third thread, and we end where we began," said he.
+"The cunning rascal! He knew our number, knew that Sir Henry
+Baskerville had consulted me, spotted who I was in Regent Street,
+conjectured that I had got the number of the cab and would lay my
+hands on the driver, and so sent back this audacious message. I
+tell you, Watson, this time we have got a foeman who is worthy of
+our steel. I've been checkmated in London. I can only wish you
+better luck in Devonshire. But I'm not easy in my mind about it."
+
+"About what?"
+
+"About sending you. It's an ugly business, Watson, an ugly
+dangerous business, and the more I see of it the less I like it.
+Yes, my dear fellow, you may laugh, but I give you my word that I
+shall be very glad to have you back safe and sound in Baker
+Street once more."
+
+
+
+
+Chapter 6
+
+Baskerville Hall
+
+
+Sir Henry Baskerville and Dr. Mortimer were ready upon the
+appointed day, and we started as arranged for Devonshire. Mr.
+Sherlock Holmes drove with me to the station and gave me his last
+parting injunctions and advice.
+
+"I will not bias your mind by suggesting theories or suspicions,
+Watson," said he; "I wish you simply to report facts in the
+fullest possible manner to me, and you can leave me to do the
+theorizing."
+
+"What sort of facts?" I asked.
+
+"Anything which may seem to have a bearing however indirect upon
+the case, and especially the relations between young Baskerville
+and his neighbours or any fresh particulars concerning the death
+of Sir Charles. I have made some inquiries myself in the last few
+days, but the results have, I fear, been negative. One thing only
+appears to be certain, and that is that Mr. James Desmond, who is
+the next heir, is an elderly gentleman of a very amiable
+disposition, so that this persecution does not arise from him. I
+really think that we may eliminate him entirely from our
+calculations. There remain the people who will actually surround
+Sir Henry Baskerville upon the moor."
+
+"Would it not be well in the first place to get rid of this
+Barrymore couple?"
+
+"By no means. You could not make a greater mistake. If they are
+innocent it would be a cruel injustice, and if they are guilty we
+should be giving up all chance of bringing it home to them. No,
+no, we will preserve them upon our list of suspects. Then there
+is a groom at the Hall, if I remember right. There are two
+moorland farmers. There is our friend Dr. Mortimer, whom I
+believe to be entirely honest, and there is his wife, of whom we
+know nothing. There is this naturalist, Stapleton, and there is
+his sister, who is said to be a young lady of attractions. There
+is Mr. Frankland, of Lafter Hall, who is also an unknown factor,
+and there are one or two other neighbours. These are the folk who
+must be your very special study."
+
+"I will do my best."
+
+"You have arms, I suppose?"
+
+"Yes, I thought it as well to take them."
+
+"Most certainly. Keep your revolver near you night and day, and
+never relax your precautions."
+
+Our friends had already secured a first-class carriage and were
+waiting for us upon the platform.
+
+"No, we have no news of any kind," said Dr. Mortimer in answer to
+my friend's questions. "I can swear to one thing, and that is
+that we have not been shadowed during the last two days. We have
+never gone out without keeping a sharp watch, and no one could
+have escaped our notice."
+
+"You have always kept together, I presume?"
+
+"Except yesterday afternoon. I usually give up one day to pure
+amusement when I come to town, so I spent it at the Museum of the
+College of Surgeons."
+
+"And I went to look at the folk in the park," said Baskerville.
+"But we had no trouble of any kind."
+
+"It was imprudent, all the same," said Holmes, shaking his head
+and looking very grave. "I beg, Sir Henry, that you will not go
+about alone. Some great misfortune will befall you if you do. Did
+you get your other boot?"
+
+"No, sir, it is gone forever."
+
+"Indeed. That is very interesting. Well, good-bye," he added as
+the train began to glide down the platform. "Bear in mind, Sir
+Henry, one of the phrases in that queer old legend which Dr.
+Mortimer has read to us, and avoid the moor in those hours of
+darkness when the powers of evil are exalted."
+
+I looked back at the platform when we had left it far behind, and
+saw the tall, austere figure of Holmes standing motionless and
+gazing after us.
+
+The journey was a swift and pleasant one, and I spent it in
+making the more intimate acquaintance of my two companions and in
+playing with Dr. Mortimer's spaniel. In a very few hours the
+brown earth had become ruddy, the brick had changed to granite,
+and red cows grazed in well-hedged fields where the lush grasses
+and more luxuriant vegetation spoke of a richer, if a damper,
+climate. Young Baskerville stared eagerly out of the window, and
+cried aloud with delight as he recognized the familiar features
+of the Devon scenery.
+
+"I've been over a good part of the world since I left it, Dr.
+Watson," said he; "but I have never seen a place to compare with
+it."
+
+"I never saw a Devonshire man who did not swear by his county," I
+remarked.
+
+"It depends upon the breed of men quite as much as on the
+county," said Dr. Mortimer. "A glance at our friend here reveals
+the rounded head of the Celt, which carries inside it the Celtic
+enthusiasm and power of attachment. Poor Sir Charles's head was
+of a very rare type, half Gaelic, half Ivernian in its
+characteristics. But you were very young when you last saw
+Baskerville Hall, were you not?"
+
+"I was a boy in my 'teens at the time of my father's death, and
+had never seen the Hall, for he lived in a little cottage on the
+South Coast. Thence I went straight to a friend in America. I
+tell you it is all as new to me as it is to Dr. Watson, and I'm
+as keen as possible to see the moor."
+
+"Are you? Then your wish is easily granted, for there is your
+first sight of the moor," said Dr. Mortimer, pointing out of the
+carriage window.
+
+Over the green squares of the fields and the low curve of a wood
+there rose in the distance a gray, melancholy hill, with a
+strange jagged summit, dim and vague in the distance, like some
+fantastic landscape in a dream. Baskerville sat for a long time,
+his eyes fixed upon it, and I read upon his eager face how much
+it meant to him, this first sight of that strange spot where the
+men of his blood had held sway so long and left their mark so
+deep. There he sat, with his tweed suit and his American accent,
+in the corner of a prosaic railway-carriage, and yet as I looked
+at his dark and expressive face I felt more than ever how true a
+descendant he was of that long line of high-blooded, fiery, and
+masterful men. There were pride, valour, and strength in his
+thick brows, his sensitive nostrils, and his large hazel eyes. If
+on that forbidding moor a difficult and dangerous quest should
+lie before us, this was at least a comrade for whom one might
+venture to take a risk with the certainty that he would bravely
+share it.
+
+The train pulled up at a small wayside station and we all
+descended. Outside, beyond the low, white fence, a wagonette with
+a pair of cobs was waiting. Our coming was evidently a great
+event, for station-master and porters clustered round us to carry
+out our luggage. It was a sweet, simple country spot, but I was
+surprised to observe that by the gate there stood two soldierly
+men in dark uniforms, who leaned upon their short rifles and
+glanced keenly at us as we passed. The coachman, a hard-faced,
+gnarled little fellow, saluted Sir Henry Baskerville, and in a
+few minutes we were flying swiftly down the broad, white road.
+Rolling pasture lands curved upward on either side of us, and old
+gabled houses peeped out from amid the thick green foliage, but
+behind the peaceful and sunlit country-side there rose ever, dark
+against the evening sky, the long, gloomy curve of the moor,
+broken by the jagged and sinister hills.
+
+The wagonette swung round into a side road, and we curved upward
+through deep lanes worn by centuries of wheels, high banks on
+either side, heavy with dripping moss and fleshy hart's-tongue
+ferns. Bronzing bracken and mottled bramble gleamed in the light
+of the sinking sun. Still steadily rising, we passed over a
+narrow granite bridge, and skirted a noisy stream which gushed
+swiftly down, foaming and roaring amid the gray boulders. Both
+road and stream wound up through a valley dense with scrub oak
+and fir. At every turn Baskerville gave an exclamation of
+delight, looking eagerly about him and asking countless
+questions. To his eyes all seemed beautiful, but to me a tinge of
+melancholy lay upon the country-side, which bore so clearly the
+mark of the waning year. Yellow leaves carpeted the lanes and
+fluttered down upon us as we passed. The rattle of our wheels
+died away as we drove through drifts of rotting vegetation--sad
+gifts, as it seemed to me, for Nature to throw before the
+carriage of the returning heir of the Baskervilles.
+
+"Halloa!" cried Dr. Mortimer, "what is this?"
+
+A steep curve of heath-clad land, an outlying spur of the moor,
+lay in front of us. On the summit, hard and clear like an
+equestrian statue upon its pedestal, was a mounted soldier, dark
+and stern, his rifle poised ready over his forearm. He was
+watching the road along which we travelled.
+
+"What is this, Perkins?" asked Dr. Mortimer.
+
+Our driver half turned in his seat.
+
+"There's a convict escaped from Princetown, sir. He's been out
+three days now, and the warders watch every road and every
+station, but they've had no sight of him yet. The farmers about
+here don't like it, sir, and that's a fact."
+
+"Well, I understand that they get five pounds if they can give
+information."
+
+"Yes, sir, but the chance of five pounds is but a poor thing
+compared to the chance of having your throat cut. You see, it
+isn't like any ordinary convict. This is a man that would stick
+at nothing."
+
+"Who is he, then?"
+
+"It is Selden, the Notting Hill murderer."
+
+I remembered the case well, for it was one in which Holmes had
+taken an interest on account of the peculiar ferocity of the
+crime and the wanton brutality which had marked all the actions
+of the assassin. The commutation of his death sentence had been
+due to some doubts as to his complete sanity, so atrocious was
+his conduct. Our wagonette had topped a rise and in front of us
+rose the huge expanse of the moor, mottled with gnarled and
+craggy cairns and tors. A cold wind swept down from it and set us
+shivering. Somewhere there, on that desolate plain, was lurking
+this fiendish man, hiding in a burrow like a wild beast, his
+heart full of malignancy against the whole race which had cast
+him out. It needed but this to complete the grim suggestiveness
+of the barren waste, the chilling wind, and the darkling sky.
+Even Baskerville fell silent and pulled his overcoat more closely
+around him.
+
+We had left the fertile country behind and beneath us. We looked
+back on it now, the slanting rays of a low sun turning the
+streams to threads of gold and glowing on the red earth new
+turned by the plough and the broad tangle of the woodlands. The
+road in front of us grew bleaker and wilder over huge russet and
+olive slopes, sprinkled with giant boulders. Now and then we
+passed a moorland cottage, walled and roofed with stone, with no
+creeper to break its harsh outline. Suddenly we looked down into
+a cup-like depression, patched with stunted oaks and firs which
+had been twisted and bent by the fury of years of storm. Two
+high, narrow towers rose over the trees. The driver pointed with
+his whip.
+
+"Baskerville Hall," said he.
+
+Its master had risen and was staring with flushed cheeks and
+shining eyes. A few minutes later we had reached the lodge-gates,
+a maze of fantastic tracery in wrought iron, with weather-bitten
+pillars on either side, blotched with lichens, and surmounted by
+the boars' heads of the Baskervilles. The lodge was a ruin of
+black granite and bared ribs of rafters, but facing it was a new
+building, half constructed, the first fruit of Sir Charles's
+South African gold.
+
+Through the gateway we passed into the avenue, where the wheels
+were again hushed amid the leaves, and the old trees shot their
+branches in a sombre tunnel over our heads. Baskerville shuddered
+as he looked up the long, dark drive to where the house glimmered
+like a ghost at the farther end.
+
+"Was it here?" he asked in a low voice.
+
+"No, no, the Yew Alley is on the other side."
+
+The young heir glanced round with a gloomy face.
+
+"It's no wonder my uncle felt as if trouble were coming on him in
+such a place as this," said he. "It's enough to scare any man.
+I'll have a row of electric lamps up here inside of six months,
+and you won't know it again, with a thousand candle-power Swan
+and Edison right here in front of the hall door."
+
+The avenue opened into a broad expanse of turf, and the house lay
+before us. In the fading light I could see that the centre was a
+heavy block of building from which a porch projected. The whole
+front was draped in ivy, with a patch clipped bare here and there
+where a window or a coat-of-arms broke through the dark veil.
+From this central block rose the twin towers, ancient,
+crenelated, and pierced with many loopholes. To right and left of
+the turrets were more modern wings of black granite. A dull light
+shone through heavy mullioned windows, and from the high chimneys
+which rose from the steep, high-angled roof there sprang a single
+black column of smoke.
+
+"Welcome, Sir Henry! Welcome to Baskerville Hall!"
+
+A tall man had stepped from the shadow of the porch to open the
+door of the wagonette. The figure of a woman was silhouetted
+against the yellow light of the hall. She came out and helped the
+man to hand down our bags.
+
+"You don't mind my driving straight home, Sir Henry?" said Dr.
+Mortimer. "My wife is expecting me."
+
+"Surely you will stay and have some dinner?"
+
+"No, I must go. I shall probably find some work awaiting me. I
+would stay to show you over the house, but Barrymore will be a
+better guide than I. Good-bye, and never hesitate night or day to
+send for me if I can be of service."
+
+The wheels died away down the drive while Sir Henry and I turned
+into the hall, and the door clanged heavily behind us. It was a
+fine apartment in which we found ourselves, large, lofty, and
+heavily raftered with huge balks of age-blackened oak. In the
+great old-fashioned fireplace behind the high iron dogs a
+log-fire crackled and snapped. Sir Henry and I held out our hands
+to it, for we were numb from our long drive. Then we gazed round
+us at the high, thin window of old stained glass, the oak
+panelling, the stags' heads, the coats-of-arms upon the walls,
+all dim and sombre in the subdued light of the central lamp.
+
+"It's just as I imagined it," said Sir Henry. "Is it not the very
+picture of an old family home? To think that this should be the
+same hall in which for five hundred years my people have lived.
+It strikes me solemn to think of it."
+
+I saw his dark face lit up with a boyish enthusiasm as he gazed
+about him. The light beat upon him where he stood, but long
+shadows trailed down the walls and hung like a black canopy above
+him. Barrymore had returned from taking our luggage to our rooms.
+He stood in front of us now with the subdued manner of a
+well-trained servant. He was a remarkable-looking man, tall,
+handsome, with a square black beard and pale, distinguished
+features.
+
+"Would you wish dinner to be served at once, sir?"
+
+"Is it ready?"
+
+"In a very few minutes, sir. You will find hot water in your
+rooms. My wife and I will be happy, Sir Henry, to stay with you
+until you have made your fresh arrangements, but you will
+understand that under the new conditions this house will require
+a considerable staff."
+
+"What new conditions?"
+
+"I only meant, sir, that Sir Charles led a very retired life, and
+we were able to look after his wants. You would, naturally, wish
+to have more company, and so you will need changes in your
+household."
+
+"Do you mean that your wife and you wish to leave?"
+
+"Only when it is quite convenient to you, sir."
+
+"But your family have been with us for several generations, have
+they not? I should be sorry to begin my life here by breaking an
+old family connection."
+
+I seemed to discern some signs of emotion upon the butler's white
+face.
+
+"I feel that also, sir, and so does my wife. But to tell the
+truth, sir, we were both very much attached to Sir Charles, and
+his death gave us a shock and made these surroundings very
+painful to us. I fear that we shall never again be easy in our
+minds at Baskerville Hall."
+
+"But what do you intend to do?"
+
+"I have no doubt, sir, that we shall succeed in establishing
+ourselves in some business. Sir Charles's generosity has given us
+the means to do so. And now, sir, perhaps I had best show you to
+your rooms."
+
+A square balustraded gallery ran round the top of the old hall,
+approached by a double stair. From this central point two long
+corridors extended the whole length of the building, from which
+all the bedrooms opened. My own was in the same wing as
+Baskerville's and almost next door to it. These rooms appeared to
+be much more modern than the central part of the house, and the
+bright paper and numerous candles did something to remove the
+sombre impression which our arrival had left upon my mind.
+
+But the dining-room which opened out of the hall was a place of
+shadow and gloom. It was a long chamber with a step separating
+the dais where the family sat from the lower portion reserved for
+their dependents. At one end a minstrel's gallery overlooked it.
+Black beams shot across above our heads, with a smoke-darkened
+ceiling beyond them. With rows of flaring torches to light it up,
+and the colour and rude hilarity of an old-time banquet, it might
+have softened; but now, when two black-clothed gentlemen sat in
+the little circle of light thrown by a shaded lamp, one's voice
+became hushed and one's spirit subdued. A dim line of ancestors,
+in every variety of dress, from the Elizabethan knight to the
+buck of the Regency, stared down upon us and daunted us by their
+silent company. We talked little, and I for one was glad when the
+meal was over and we were able to retire into the modern
+billiard-room and smoke a cigarette.
+
+"My word, it isn't a very cheerful place," said Sir Henry. "I
+suppose one can tone down to it, but I feel a bit out of the
+picture at present. I don't wonder that my uncle got a little
+jumpy if he lived all alone in such a house as this. However, if
+it suits you, we will retire early to-night, and perhaps things
+may seem more cheerful in the morning."
+
+I drew aside my curtains before I went to bed and looked out from
+my window. It opened upon the grassy space which lay in front of
+the hall door. Beyond, two copses of trees moaned and swung in a
+rising wind. A half moon broke through the rifts of racing
+clouds. In its cold light I saw beyond the trees a broken fringe
+of rocks, and the long, low curve of the melancholy moor. I
+closed the curtain, feeling that my last impression was in
+keeping with the rest.
+
+And yet it was not quite the last. I found myself weary and yet
+wakeful, tossing restlessly from side to side, seeking for the
+sleep which would not come. Far away a chiming clock struck out
+the quarters of the hours, but otherwise a deathly silence lay
+upon the old house. And then suddenly, in the very dead of the
+night, there came a sound to my ears, clear, resonant, and
+unmistakable. It was the sob of a woman, the muffled, strangling
+gasp of one who is torn by an uncontrollable sorrow. I sat up in
+bed and listened intently. The noise could not have been far away
+and was certainly in the house. For half an hour I waited with
+every nerve on the alert, but there came no other sound save the
+chiming clock and the rustle of the ivy on the wall.
+
+
+
+
+Chapter 7
+
+The Stapletons of Merripit House
+
+
+The fresh beauty of the following morning did something to efface
+from our minds the grim and gray impression which had been left
+upon both of us by our first experience of Baskerville Hall. As
+Sir Henry and I sat at breakfast the sunlight flooded in through
+the high mullioned windows, throwing watery patches of colour
+from the coats of arms which covered them. The dark panelling
+glowed like bronze in the golden rays, and it was hard to realize
+that this was indeed the chamber which had struck such a gloom
+into our souls upon the evening before.
+
+"I guess it is ourselves and not the house that we have to
+blame!" said the baronet. "We were tired with our journey and
+chilled by our drive, so we took a gray view of the place. Now we
+are fresh and well, so it is all cheerful once more."
+
+"And yet it was not entirely a question of imagination," I
+answered. "Did you, for example, happen to hear someone, a woman
+I think, sobbing in the night?"
+
+"That is curious, for I did when I was half asleep fancy that I
+heard something of the sort. I waited quite a time, but there was
+no more of it, so I concluded that it was all a dream."
+
+"I heard it distinctly, and I am sure that it was really the sob
+of a woman."
+
+"We must ask about this right away." He rang the bell and asked
+Barrymore whether he could account for our experience. It seemed
+to me that the pallid features of the butler turned a shade paler
+still as he listened to his master's question.
+
+"There are only two women in the house, Sir Henry," he answered.
+"One is the scullery-maid, who sleeps in the other wing. The
+other is my wife, and I can answer for it that the sound could
+not have come from her."
+
+And yet he lied as he said it, for it chanced that after
+breakfast I met Mrs. Barrymore in the long corridor with the sun
+full upon her face. She was a large, impassive, heavy-featured
+woman with a stern set expression of mouth. But her tell-tale
+eyes were red and glanced at me from between swollen lids. It was
+she, then, who wept in the night, and if she did so her husband
+must know it. Yet he had taken the obvious risk of discovery in
+declaring that it was not so. Why had he done this? And why did
+she weep so bitterly? Already round this pale-faced, handsome,
+black-bearded man there was gathering an atmosphere of mystery
+and of gloom. It was he who had been the first to discover the
+body of Sir Charles, and we had only his word for all the
+circumstances which led up to the old man's death. Was it
+possible that it was Barrymore after all whom we had seen in the
+cab in Regent Street? The beard might well have been the same.
+The cabman had described a somewhat shorter man, but such an
+impression might easily have been erroneous. How could I settle
+the point forever? Obviously the first thing to do was to see the
+Grimpen postmaster, and find whether the test telegram had really
+been placed in Barrymore's own hands. Be the answer what it
+might, I should at least have something to report to Sherlock
+Holmes.
+
+Sir Henry had numerous papers to examine after breakfast, so that
+the time was propitious for my excursion. It was a pleasant walk
+of four miles along the edge of the moor, leading me at last to a
+small gray hamlet, in which two larger buildings, which proved to
+be the inn and the house of Dr. Mortimer, stood high above the
+rest. The postmaster, who was also the village grocer, had a
+clear recollection of the telegram.
+
+"Certainly, sir," said he, "I had the telegram delivered to Mr.
+Barrymore exactly as directed."
+
+"Who delivered it?"
+
+"My boy here. James, you delivered that telegram to Mr. Barrymore
+at the Hall last week, did you not?"
+
+"Yes, father, I delivered it."
+
+"Into his own hands?" I asked.
+
+"Well, he was up in the loft at the time, so that I could not put
+it into his own hands, but I gave it into Mrs. Barrymore's hands,
+and she promised to deliver it at once."
+
+"Did you see Mr. Barrymore?"
+
+"No, sir; I tell you he was in the loft."
+
+"If you didn't see him, how do you know he was in the loft?"
+
+"Well, surely his own wife ought to know where he is," said the
+postmaster testily. "Didn't he get the telegram? If there is any
+mistake it is for Mr. Barrymore himself to complain."
+
+It seemed hopeless to pursue the inquiry any farther, but it was
+clear that in spite of Holmes's ruse we had no proof that
+Barrymore had not been in London all the time. Suppose that it
+were so--suppose that the same man had been the last who had seen
+Sir Charles alive, and the first to dog the new heir when he
+returned to England. What then? Was he the agent of others or had
+he some sinister design of his own? What interest could he have
+in persecuting the Baskerville family? I thought of the strange
+warning clipped out of the leading article of the Times. Was that
+his work or was it possibly the doing of someone who was bent
+upon counteracting his schemes? The only conceivable motive was
+that which had been suggested by Sir Henry, that if the family
+could be scared away a comfortable and permanent home would be
+secured for the Barrymores. But surely such an explanation as
+that would be quite inadequate to account for the deep and subtle
+scheming which seemed to be weaving an invisible net round the
+young baronet. Holmes himself had said that no more complex case
+had come to him in all the long series of his sensational
+investigations. I prayed, as I walked back along the gray, lonely
+road, that my friend might soon be freed from his preoccupations
+and able to come down to take this heavy burden of responsibility
+from my shoulders.
+
+Suddenly my thoughts were interrupted by the sound of running
+feet behind me and by a voice which called me by name. I turned,
+expecting to see Dr. Mortimer, but to my surprise it was a
+stranger who was pursuing me. He was a small, slim, clean-shaven,
+prim-faced man, flaxen-haired and lean-jawed, between thirty and
+forty years of age, dressed in a gray suit and wearing a straw
+hat. A tin box for botanical specimens hung over his shoulder and
+he carried a green butterfly-net in one of his hands.
+
+"You will, I am sure, excuse my presumption, Dr. Watson," said
+he, as he came panting up to where I stood. "Here on the moor we
+are homely folk and do not wait for formal introductions. You may
+possibly have heard my name from our mutual friend, Mortimer. I
+am Stapleton, of Merripit House."
+
+"Your net and box would have told me as much," said I, "for I
+knew that Mr. Stapleton was a naturalist. But how did you know
+me?"
+
+"I have been calling on Mortimer, and he pointed you out to me
+from the window of his surgery as you passed. As our road lay the
+same way I thought that I would overtake you and introduce
+myself. I trust that Sir Henry is none the worse for his
+journey?"
+
+"He is very well, thank you."
+
+"We were all rather afraid that after the sad death of Sir
+Charles the new baronet might refuse to live here. It is asking
+much of a wealthy man to come down and bury himself in a place of
+this kind, but I need not tell you that it means a very great
+deal to the country-side. Sir Henry has, I suppose, no
+superstitious fears in the matter?"
+
+"I do not think that it is likely."
+
+"Of course you know the legend of the fiend dog which haunts the
+family?"
+
+"I have heard it."
+
+"It is extraordinary how credulous the peasants are about here!
+Any number of them are ready to swear that they have seen such a
+creature upon the moor." He spoke with a smile, but I seemed to
+read in his eyes that he took the matter more seriously. "The
+story took a great hold upon the imagination of Sir Charles, and
+I have no doubt that it led to his tragic end."
+
+"But how?"
+
+"His nerves were so worked up that the appearance of any dog
+might have had a fatal effect upon his diseased heart. I fancy
+that he really did see something of the kind upon that last night
+in the Yew Alley. I feared that some disaster might occur, for I
+was very fond of the old man, and I knew that his heart was
+weak."
+
+"How did you know that?"
+
+"My friend Mortimer told me."
+
+"You think, then, that some dog pursued Sir Charles, and that he
+died of fright in consequence?"
+
+"Have you any better explanation?"
+
+"I have not come to any conclusion."
+
+"Has Mr. Sherlock Holmes?"
+
+The words took away my breath for an instant, but a glance at the
+placid face and steadfast eyes of my companion showed that no
+surprise was intended.
+
+"It is useless for us to pretend that we do not know you, Dr.
+Watson," said he. "The records of your detective have reached us
+here, and you could not celebrate him without being known
+yourself. When Mortimer told me your name he could not deny your
+identity. If you are here, then it follows that Mr. Sherlock
+Holmes is interesting himself in the matter, and I am naturally
+curious to know what view he may take."
+
+"I am afraid that I cannot answer that question."
+
+"May I ask if he is going to honour us with a visit himself?"
+
+"He cannot leave town at present. He has other cases which engage
+his attention."
+
+"What a pity! He might throw some light on that which is so dark
+to us. But as to your own researches, if there is any possible
+way in which I can be of service to you I trust that you will
+command me. If I had any indication of the nature of your
+suspicions or how you propose to investigate the case, I might
+perhaps even now give you some aid or advice."
+
+"I assure you that I am simply here upon a visit to my friend,
+Sir Henry, and that I need no help of any kind."
+
+"Excellent!" said Stapleton. "You are perfectly right to be wary
+and discreet. I am justly reproved for what I feel was an
+unjustifiable intrusion, and I promise you that I will not
+mention the matter again."
+
+We had come to a point where a narrow grassy path struck off from
+the road and wound away across the moor. A steep,
+boulder-sprinkled hill lay upon the right which had in bygone
+days been cut into a granite quarry. The face which was turned
+towards us formed a dark cliff, with ferns and brambles growing
+in its niches. From over a distant rise there floated a gray
+plume of smoke.
+
+"A moderate walk along this moor-path brings us to Merripit
+House," said he. "Perhaps you will spare an hour that I may have
+the pleasure of introducing you to my sister."
+
+My first thought was that I should be by Sir Henry's side. But
+then I remembered the pile of papers and bills with which his
+study table was littered. It was certain that I could not help
+with those. And Holmes had expressly said that I should study the
+neighbours upon the moor. I accepted Stapleton's invitation, and
+we turned together down the path.
+
+"It is a wonderful place, the moor," said he, looking round over
+the undulating downs, long green rollers, with crests of jagged
+granite foaming up into fantastic surges. "You never tire of the
+moor. You cannot think the wonderful secrets which it contains.
+It is so vast, and so barren, and so mysterious."
+
+"You know it well, then?"
+
+"I have only been here two years. The residents would call me a
+newcomer. We came shortly after Sir Charles settled. But my
+tastes led me to explore every part of the country round, and I
+should think that there are few men who know it better than I
+do."
+
+"Is it hard to know?"
+
+"Very hard. You see, for example, this great plain to the north
+here with the queer hills breaking out of it. Do you observe
+anything remarkable about that?"
+
+"It would be a rare place for a gallop."
+
+"You would naturally think so and the thought has cost several
+their lives before now. You notice those bright green spots
+scattered thickly over it?"
+
+"Yes, they seem more fertile than the rest."
+
+Stapleton laughed.
+
+"That is the great Grimpen Mire," said he. "A false step yonder
+means death to man or beast. Only yesterday I saw one of the moor
+ponies wander into it. He never came out. I saw his head for
+quite a long time craning out of the bog-hole, but it sucked him
+down at last. Even in dry seasons it is a danger to cross it, but
+after these autumn rains it is an awful place. And yet I can find
+my way to the very heart of it and return alive. By George, there
+is another of those miserable ponies!"
+
+Something brown was rolling and tossing among the green sedges.
+Then a long, agonized, writhing neck shot upward and a dreadful
+cry echoed over the moor. It turned me cold with horror, but my
+companion's nerves seemed to be stronger than mine.
+
+"It's gone!" said he. "The mire has him. Two in two days, and
+many more, perhaps, for they get in the way of going there in the
+dry weather, and never know the difference until the mire has
+them in its clutches. It's a bad place, the great Grimpen Mire."
+
+"And you say you can penetrate it?"
+
+"Yes, there are one or two paths which a very active man can
+take. I have found them out."
+
+"But why should you wish to go into so horrible a place?"
+
+"Well, you see the hills beyond? They are really islands cut off
+on all sides by the impassable mire, which has crawled round them
+in the course of years. That is where the rare plants and the
+butterflies are, if you have the wit to reach them."
+
+"I shall try my luck some day."
+
+He looked at me with a surprised face.
+
+"For God's sake put such an idea out of your mind," said he.
+"Your blood would be upon my head. I assure you that there would
+not be the least chance of your coming back alive. It is only by
+remembering certain complex landmarks that I am able to do it."
+
+"Halloa!" I cried. "What is that?"
+
+A long, low moan, indescribably sad, swept over the moor. It
+filled the whole air, and yet it was impossible to say whence it
+came. From a dull murmur it swelled into a deep roar, and then
+sank back into a melancholy, throbbing murmur once again.
+Stapleton looked at me with a curious expression in his face.
+
+"Queer place, the moor!" said he.
+
+"But what is it?"
+
+"The peasants say it is the Hound of the Baskervilles calling for
+its prey. I've heard it once or twice before, but never quite so
+loud."
+
+I looked round, with a chill of fear in my heart, at the huge
+swelling plain, mottled with the green patches of rushes. Nothing
+stirred over the vast expanse save a pair of ravens, which
+croaked loudly from a tor behind us.
+
+"You are an educated man. You don't believe such nonsense as
+that?" said I. "What do you think is the cause of so strange a
+sound?"
+
+"Bogs make queer noises sometimes. It's the mud settling, or the
+water rising, or something."
+
+"No, no, that was a living voice."
+
+"Well, perhaps it was. Did you ever hear a bittern booming?"
+
+"No, I never did."
+
+"It's a very rare bird--practically extinct--in England now, but
+all things are possible upon the moor. Yes, I should not be
+surprised to learn that what we have heard is the cry of the last
+of the bitterns."
+
+"It's the weirdest, strangest thing that ever I heard in my
+life."
+
+"Yes, it's rather an uncanny place altogether. Look at the hill-
+side yonder. What do you make of those?"
+
+The whole steep slope was covered with gray circular rings of
+stone, a score of them at least.
+
+"What are they? Sheep-pens?"
+
+"No, they are the homes of our worthy ancestors. Prehistoric man
+lived thickly on the moor, and as no one in particular has lived
+there since, we find all his little arrangements exactly as he
+left them. These are his wigwams with the roofs off. You can even
+see his hearth and his couch if you have the curiosity to go
+inside.
+
+"But it is quite a town. When was it inhabited?"
+
+"Neolithic man--no date."
+
+"What did he do?"
+
+"He grazed his cattle on these slopes, and he learned to dig for
+tin when the bronze sword began to supersede the stone axe. Look
+at the great trench in the opposite hill. That is his mark. Yes,
+you will find some very singular points about the moor, Dr.
+Watson. Oh, excuse me an instant! It is surely Cyclopides."
+
+A small fly or moth had fluttered across our path, and in an
+instant Stapleton was rushing with extraordinary energy and speed
+in pursuit of it. To my dismay the creature flew straight for the
+great mire, and my acquaintance never paused for an instant,
+bounding from tuft to tuft behind it, his green net waving in the
+air. His gray clothes and jerky, zigzag, irregular progress made
+him not unlike some huge moth himself. I was standing watching
+his pursuit with a mixture of admiration for his extraordinary
+activity and fear lest he should lose his footing in the
+treacherous mire, when I heard the sound of steps, and turning
+round found a woman near me upon the path. She had come from the
+direction in which the plume of smoke indicated the position of
+Merripit House, but the dip of the moor had hid her until she was
+quite close.
+
+I could not doubt that this was the Miss Stapleton of whom I had
+been told, since ladies of any sort must be few upon the moor,
+and I remembered that I had heard someone describe her as being a
+beauty. The woman who approached me was certainly that, and of a
+most uncommon type. There could not have been a greater contrast
+between brother and sister, for Stapleton was neutral tinted,
+with light hair and gray eyes, while she was darker than any
+brunette whom I have seen in England--slim, elegant, and tall.
+She had a proud, finely cut face, so regular that it might have
+seemed impassive were it not for the sensitive mouth and the
+beautiful dark, eager eyes. With her perfect figure and elegant
+dress she was, indeed, a strange apparition upon a lonely
+moorland path. Her eyes were on her brother as I turned, and then
+she quickened her pace towards me. I had raised my hat and was
+about to make some explanatory remark, when her own words turned
+all my thoughts into a new channel.
+
+"Go back!" she said. "Go straight back to London, instantly."
+
+I could only stare at her in stupid surprise. Her eyes blazed at
+me, and she tapped the ground impatiently with her foot.
+
+"Why should I go back?" I asked.
+
+"I cannot explain." She spoke in a low, eager voice, with a
+curious lisp in her utterance. "But for God's sake do what I ask
+you. Go back and never set foot upon the moor again."
+
+"But I have only just come."
+
+"Man, man!" she cried. "Can you not tell when a warning is for
+your own good? Go back to London! Start to-night! Get away from
+this place at all costs! Hush, my brother is coming! Not a word
+of what I have said. Would you mind getting that orchid for me
+among the mares-tails yonder? We are very rich in orchids on the
+moor, though, of course, you are rather late to see the beauties
+of the place."
+
+Stapleton had abandoned the chase and came back to us breathing
+hard and flushed with his exertions.
+
+"Halloa, Beryl!" said he, and it seemed to me that the tone of
+his greeting was not altogether a cordial one.
+
+"Well, Jack, you are very hot."
+
+"Yes, I was chasing a Cyclopides. He is very rare and seldom
+found in the late autumn. What a pity that I should have missed
+him!" He spoke unconcernedly, but his small light eyes glanced
+incessantly from the girl to me.
+
+"You have introduced yourselves, I can see."
+
+"Yes. I was telling Sir Henry that it was rather late for him to
+see the true beauties of the moor."
+
+"Why, who do you think this is?"
+
+"I imagine that it must be Sir Henry Baskerville."
+
+"No, no," said I. "Only a humble commoner, but his friend. My
+name is Dr. Watson."
+
+A flush of vexation passed over her expressive face. "We have
+been talking at cross purposes," said she.
+
+"Why, you had not very much time for talk," her brother remarked
+with the same questioning eyes.
+
+"I talked as if Dr. Watson were a resident instead of being
+merely a visitor," said she. "It cannot much matter to him
+whether it is early or late for the orchids. But you will come
+on, will you not, and see Merripit House?"
+
+A short walk brought us to it, a bleak moorland house, once the
+farm of some grazier in the old prosperous days, but now put into
+repair and turned into a modern dwelling. An orchard surrounded
+it, but the trees, as is usual upon the moor, were stunted and
+nipped, and the effect of the whole place was mean and
+melancholy. We were admitted by a strange, wizened, rusty-coated
+old manservant, who seemed in keeping with the house. Inside,
+however, there were large rooms furnished with an elegance in
+which I seemed to recognize the taste of the lady. As I looked
+from their windows at the interminable granite-flecked moor
+rolling unbroken to the farthest horizon I could not but marvel
+at what could have brought this highly educated man and this
+beautiful woman to live in such a place.
+
+"Queer spot to choose, is it not?" said he as if in answer to my
+thought. "And yet we manage to make ourselves fairly happy, do we
+not, Beryl?"
+
+"Quite happy," said she, but there was no ring of conviction in
+her words.
+
+"I had a school," said Stapleton. "It was in the north country.
+The work to a man of my temperament was mechanical and
+uninteresting, but the privilege of living with youth, of helping
+to mould those young minds, and of impressing them with one's own
+character and ideals, was very dear to me. However, the fates
+were against us. A serious epidemic broke out in the school and
+three of the boys died. It never recovered from the blow, and
+much of my capital was irretrievably swallowed up. And yet, if it
+were not for the loss of the charming companionship of the boys,
+I could rejoice over my own misfortune, for, with my strong
+tastes for botany and zoology, I find an unlimited field of work
+here, and my sister is as devoted to Nature as I am. All this,
+Dr. Watson, has been brought upon your head by your expression as
+you surveyed the moor out of our window."
+
+"It certainly did cross my mind that it might be a little
+dull--less for you, perhaps, than for your sister."
+
+"No, no, I am never dull," said she, quickly.
+
+"We have books, we have our studies, and we have interesting
+neighbours. Dr. Mortimer is a most learned man in his own line.
+Poor Sir Charles was also an admirable companion. We knew him
+well, and miss him more than I can tell. Do you think that I
+should intrude if I were to call this afternoon and make the
+acquaintance of Sir Henry?"
+
+"I am sure that he would be delighted."
+
+"Then perhaps you would mention that I propose to do so. We may
+in our humble way do something to make things more easy for him
+until he becomes accustomed to his new surroundings. Will you
+come upstairs, Dr. Watson, and inspect my collection of
+Lepidoptera? I think it is the most complete one in the
+south-west of England. By the time that you have looked through
+them lunch will be almost ready."
+
+But I was eager to get back to my charge. The melancholy of the
+moor, the death of the unfortunate pony, the weird sound which
+had been associated with the grim legend of the Baskervilles, all
+these things tinged my thoughts with sadness. Then on the top of
+these more or less vague impressions there had come the definite
+and distinct warning of Miss Stapleton, delivered with such
+intense earnestness that I could not doubt that some grave and
+deep reason lay behind it. I resisted all pressure to stay for
+lunch, and I set off at once upon my return journey, taking the
+grass-grown path by which we had come.
+
+It seems, however, that there must have been some short cut for
+those who knew it, for before I had reached the road I was
+astounded to see Miss Stapleton sitting upon a rock by the side
+of the track. Her face was beautifully flushed with her
+exertions, and she held her hand to her side.
+
+"I have run all the way in order to cut you off, Dr. Watson,"
+said she. "I had not even time to put on my hat. I must not stop,
+or my brother may miss me. I wanted to say to you how sorry I am
+about the stupid mistake I made in thinking that you were Sir
+Henry. Please forget the words I said, which have no application
+whatever to you."
+
+"But I can't forget them, Miss Stapleton," said I. "I am Sir
+Henry's friend, and his welfare is a very close concern of mine.
+Tell me why it was that you were so eager that Sir Henry should
+return to London."
+
+"A woman's whim, Dr. Watson. When you know me better you will
+understand that I cannot always give reasons for what I say or
+do."
+
+"No, no. I remember the thrill in your voice. I remember the look
+in your eyes. Please, please, be frank with me, Miss Stapleton,
+for ever since I have been here I have been conscious of shadows
+all round me. Life has become like that great Grimpen Mire, with
+little green patches everywhere into which one may sink and with
+no guide to point the track. Tell me then what it was that you
+meant, and I will promise to convey your warning to Sir Henry."
+
+An expression of irresolution passed for an instant over her
+face, but her eyes had hardened again when she answered me.
+
+"You make too much of it, Dr. Watson," said she. "My brother and
+I were very much shocked by the death of Sir Charles. We knew him
+very intimately, for his favourite walk was over the moor to our
+house. He was deeply impressed with the curse which hung over the
+family, and when this tragedy came I naturally felt that there
+must be some grounds for the fears which he had expressed. I was
+distressed therefore when another member of the family came down
+to live here, and I felt that he should be warned of the danger
+which he will run. That was all which I intended to convey.
+
+"But what is the danger?"
+
+"You know the story of the hound?"
+
+"I do not believe in such nonsense."
+
+"But I do. If you have any influence with Sir Henry, take him
+away from a place which has always been fatal to his family. The
+world is wide. Why should he wish to live at the place of
+danger?"
+
+"Because it is the place of danger. That is Sir Henry's nature. I
+fear that unless you can give me some more definite information
+than this it would be impossible to get him to move."
+
+"I cannot say anything definite, for I do not know anything
+definite."
+
+"I would ask you one more question, Miss Stapleton. If you meant
+no more than this when you first spoke to me, why should you not
+wish your brother to overhear what you said? There is nothing to
+which he, or anyone else, could object."
+
+"My brother is very anxious to have the Hall inhabited, for he
+thinks it is for the good of the poor folk upon the moor. He
+would be very angry if he knew that I have said anything which
+might induce Sir Henry to go away. But I have done my duty now
+and I will say no more. I must get back, or he will miss me and
+suspect that I have seen you. Good-bye!" She turned and had
+disappeared in a few minutes among the scattered boulders, while
+I, with my soul full of vague fears, pursued my way to
+Baskerville Hall.
+
+
+
+
+Chapter 8
+
+First Report of Dr. Watson
+
+
+From this point onward I will follow the course of events by
+transcribing my own letters to Mr. Sherlock Holmes which lie
+before me on the table. One page is missing, but otherwise they
+are exactly as written and show my feelings and suspicions of the
+moment more accurately than my memory, clear as it is upon these
+tragic events, can possibly do.
+
+BASKERVILLE HALL, October 13th.
+
+MY DEAR HOLMES,--My previous letters and telegrams have kept you
+pretty well up to date as to all that has occurred in this most
+God-forsaken corner of the world. The longer one stays here the
+more does the spirit of the moor sink into one's soul, its
+vastness, and also its grim charm. When you are once out upon its
+bosom you have left all traces of modern England behind you, but
+on the other hand you are conscious everywhere of the homes and
+the work of the prehistoric people. On all sides of you as you
+walk are the houses of these forgotten folk, with their graves
+and the huge monoliths which are supposed to have marked their
+temples. As you look at their gray stone huts against the scarred
+hill-sides you leave your own age behind you, and if you were to
+see a skin-clad, hairy man crawl out from the low door fitting a
+flint-tipped arrow on to the string of his bow, you would feel
+that his presence there was more natural than your own. The
+strange thing is that they should have lived so thickly on what
+must always have been most unfruitful soil. I am no antiquarian,
+but I could imagine that they were some unwarlike and harried
+race who were forced to accept that which none other would
+occupy.
+
+All this, however, is foreign to the mission on which you sent me
+and will probably be very uninteresting to your severely
+practical mind. I can still remember your complete indifference
+as to whether the sun moved round the earth or the earth round
+the sun. Let me, therefore, return to the facts concerning Sir
+Henry Baskerville.
+
+If you have not had any report within the last few days it is
+because up to to-day there was nothing of importance to relate.
+Then a very surprising circumstance occurred, which I shall tell
+you in due course. But, first of all, I must keep you in touch
+with some of the other factors in the situation.
+
+One of these, concerning which I have said little, is the escaped
+convict upon the moor. There is strong reason now to believe that
+he has got right away, which is a considerable relief to the
+lonely householders of this district. A fortnight has passed
+since his flight, during which he has not been seen and nothing
+has been heard of him. It is surely inconceivable that he could
+have held out upon the moor during all that time. Of course, so
+far as his concealment goes there is no difficulty at all. Any
+one of these stone huts would give him a hiding-place. But there
+is nothing to eat unless he were to catch and slaughter one of
+the moor sheep. We think, therefore, that he has gone, and the
+outlying farmers sleep the better in consequence.
+
+We are four able-bodied men in this household, so that we could
+take good care of ourselves, but I confess that I have had uneasy
+moments when I have thought of the Stapletons. They live miles
+from any help. There are one maid, an old manservant, the sister,
+and the brother, the latter not a very strong man. They would be
+helpless in the hands of a desperate fellow like this Notting
+Hill criminal, if he could once effect an entrance. Both Sir
+Henry and I were concerned at their situation, and it was
+suggested that Perkins the groom should go over to sleep there,
+but Stapleton would not hear of it.
+
+The fact is that our friend, the baronet, begins to display a
+considerable interest in our fair neighbour. It is not to be
+wondered at, for time hangs heavily in this lonely spot to an
+active man like him, and she is a very fascinating and beautiful
+woman. There is something tropical and exotic about her which
+forms a singular contrast to her cool and unemotional brother.
+Yet he also gives the idea of hidden fires. He has certainly a
+very marked influence over her, for I have seen her continually
+glance at him as she talked as if seeking approbation for what
+she said. I trust that he is kind to her. There is a dry glitter
+in his eyes, and a firm set of his thin lips, which goes with a
+positive and possibly a harsh nature. You would find him an
+interesting study.
+
+He came over to call upon Baskerville on that first day, and the
+very next morning he took us both to show us the spot where the
+legend of the wicked Hugo is supposed to have had its origin. It
+was an excursion of some miles across the moor to a place which
+is so dismal that it might have suggested the story. We found a
+short valley between rugged tors which led to an open, grassy
+space flecked over with the white cotton grass. In the middle of
+it rose two great stones, worn and sharpened at the upper end,
+until they looked like the huge corroding fangs of some monstrous
+beast. In every way it corresponded with the scene of the old
+tragedy. Sir Henry was much interested and asked Stapleton more
+than once whether he did really believe in the possibility of the
+interference of the supernatural in the affairs of men. He spoke
+lightly, but it was evident that he was very much in earnest.
+Stapleton was guarded in his replies, but it was easy to see that
+he said less than he might, and that he would not express his
+whole opinion out of consideration for the feelings of the
+baronet. He told us of similar cases, where families had suffered
+from some evil influence, and he left us with the impression that
+he shared the popular view upon the matter.
+
+On our way back we stayed for lunch at Merripit House, and it was
+there that Sir Henry made the acquaintance of Miss Stapleton.
+From the first moment that he saw her he appeared to be strongly
+attracted by her, and I am much mistaken if the feeling was not
+mutual. He referred to her again and again on our walk home, and
+since then hardly a day has passed that we have not seen
+something of the brother and sister. They dine here to-night, and
+there is some talk of our going to them next week. One would
+imagine that such a match would be very welcome to Stapleton, and
+yet I have more than once caught a look of the strongest
+disapprobation in his face when Sir Henry has been paying some
+attention to his sister. He is much attached to her, no doubt,
+and would lead a lonely life without her, but it would seem the
+height of selfishness if he were to stand in the way of her
+making so brilliant a marriage. Yet I am certain that he does not
+wish their intimacy to ripen into love, and I have several times
+observed that he has taken pains to prevent them from being
+_tête-à-tête_. By the way, your instructions to me never to allow
+Sir Henry to go out alone will become very much more onerous if a
+love affair were to be added to our other difficulties. My
+popularity would soon suffer if I were to carry out your orders
+to the letter.
+
+The other day--Thursday, to be more exact--Dr. Mortimer lunched
+with us. He has been excavating a barrow at Long Down, and has
+got a prehistoric skull which fills him with great joy. Never was
+there such a single-minded enthusiast as he! The Stapletons came
+in afterwards, and the good doctor took us all to the Yew Alley,
+at Sir Henry's request, to show us exactly how everything
+occurred upon that fatal night. It is a long, dismal walk, the
+Yew Alley, between two high walls of clipped hedge, with a narrow
+band of grass upon either side. At the far end is an old
+tumble-down summer-house. Half-way down is the moor-gate, where
+the old gentleman left his cigar-ash. It is a white wooden gate
+with a latch. Beyond it lies the wide moor. I remembered your
+theory of the affair and tried to picture all that had occurred.
+As the old man stood there he saw something coming across the
+moor, something which terrified him so that he lost his wits, and
+ran and ran until he died of sheer horror and exhaustion. There
+was the long, gloomy tunnel down which he fled. And from what? A
+sheep-dog of the moor? Or a spectral hound, black, silent, and
+monstrous? Was there a human agency in the matter? Did the pale,
+watchful Barrymore know more than he cared to say? It was all dim
+and vague, but always there is the dark shadow of crime behind
+it.
+
+One other neighbour I have met since I wrote last. This is Mr.
+Frankland, of Lafter Hall, who lives some four miles to the south
+of us. He is an elderly man, red-faced, white-haired, and
+choleric. His passion is for the British law, and he has spent a
+large fortune in litigation. He fights for the mere pleasure of
+fighting and is equally ready to take up either side of a
+question, so that it is no wonder that he has found it a costly
+amusement. Sometimes he will shut up a right of way and defy the
+parish to make him open it. At others he will with his own hands
+tear down some other man's gate and declare that a path has
+existed there from time immemorial, defying the owner to
+prosecute him for trespass. He is learned in old manorial and
+communal rights, and he applies his knowledge sometimes in favour
+of the villagers of Fernworthy and sometimes against them, so
+that he is periodically either carried in triumph down the
+village street or else burned in effigy, according to his latest
+exploit. He is said to have about seven lawsuits upon his hands
+at present, which will probably swallow up the remainder of his
+fortune and so draw his sting and leave him harmless for the
+future. Apart from the law he seems a kindly, good-natured
+person, and I only mention him because you were particular that I
+should send some description of the people who surround us. He is
+curiously employed at present, for, being an amateur astronomer,
+he has an excellent telescope, with which he lies upon the roof
+of his own house and sweeps the moor all day in the hope of
+catching a glimpse of the escaped convict. If he would confine
+his energies to this all would be well, but there are rumours
+that he intends to prosecute Dr. Mortimer for opening a grave
+without the consent of the next-of-kin, because he dug up the
+Neolithic skull in the barrow on Long Down. He helps to keep our
+lives from being monotonous and gives a little comic relief where
+it is badly needed.
+
+And now, having brought you up to date in the escaped convict,
+the Stapletons, Dr. Mortimer, and Frankland, of Lafter Hall, let
+me end on that which is most important and tell you more about
+the Barrymores, and especially about the surprising development
+of last night.
+
+First of all about the test telegram, which you sent from London
+in order to make sure that Barrymore was really here. I have
+already explained that the testimony of the postmaster shows that
+the test was worthless and that we have no proof one way or the
+other. I told Sir Henry how the matter stood, and he at once, in
+his downright fashion, had Barrymore up and asked him whether he
+had received the telegram himself. Barrymore said that he had.
+
+"Did the boy deliver it into your own hands?" asked Sir Henry.
+
+Barrymore looked surprised, and considered for a little time.
+
+"No," said he, "I was in the box-room at the time, and my wife
+brought it up to me."
+
+"Did you answer it yourself?"
+
+"No; I told my wife what to answer and she went down to write
+it."
+
+In the evening he recurred to the subject of his own accord.
+
+"I could not quite understand the object of your questions this
+morning, Sir Henry," said he. "I trust that they do not mean that
+I have done anything to forfeit your confidence?"
+
+Sir Henry had to assure him that it was not so and pacify him by
+giving him a considerable part of his old wardrobe, the London
+outfit having now all arrived.
+
+Mrs. Barrymore is of interest to me. She is a heavy, solid
+person, very limited, intensely respectable, and inclined to be
+puritanical. You could hardly conceive a less emotional subject.
+Yet I have told you how, on the first night here, I heard her
+sobbing bitterly, and since then I have more than once observed
+traces of tears upon her face. Some deep sorrow gnaws ever at her
+heart. Sometimes I wonder if she has a guilty memory which haunts
+her, and sometimes I suspect Barrymore of being a domestic
+tyrant. I have always felt that there was something singular and
+questionable in this man's character, but the adventure of last
+night brings all my suspicions to a head.
+
+And yet it may seem a small matter in itself. You are aware that
+I am not a very sound sleeper, and since I have been on guard in
+this house my slumbers have been lighter than ever. Last night,
+about two in the morning, I was aroused by a stealthy step
+passing my room. I rose, opened my door, and peeped out. A long
+black shadow was trailing down the corridor. It was thrown by a
+man who walked softly down the passage with a candle held in his
+hand. He was in shirt and trousers, with no covering to his feet.
+I could merely see the outline, but his height told me that it
+was Barrymore. He walked very slowly and circumspectly, and there
+was something indescribably guilty and furtive in his whole
+appearance.
+
+I have told you that the corridor is broken by the balcony which
+runs round the hall, but that it is resumed upon the farther
+side. I waited until he had passed out of sight and then I
+followed him. When I came round the balcony he had reached the
+end of the farther corridor, and I could see from the glimmer of
+light through an open door that he had entered one of the rooms.
+Now, all these rooms are unfurnished and unoccupied, so that his
+expedition became more mysterious than ever. The light shone
+steadily as if he were standing motionless. I crept down the
+passage as noiselessly as I could and peeped round the corner of
+the door.
+
+Barrymore was crouching at the window with the candle held
+against the glass. His profile was half turned towards me, and
+his face seemed to be rigid with expectation as he stared out
+into the blackness of the moor. For some minutes he stood
+watching intently. Then he gave a deep groan and with an
+impatient gesture he put out the light. Instantly I made my way
+back to my room, and very shortly came the stealthy steps passing
+once more upon their return journey. Long afterwards when I had
+fallen into a light sleep I heard a key turn somewhere in a lock,
+but I could not tell whence the sound came. What it all means I
+cannot guess, but there is some secret business going on in this
+house of gloom which sooner or later we shall get to the bottom
+of. I do not trouble you with my theories, for you asked me to
+furnish you only with facts. I have had a long talk with Sir
+Henry this morning, and we have made a plan of campaign founded
+upon my observations of last night. I will not speak about it
+just now, but it should make my next report interesting reading.
+
+
+
+
+Chapter 9
+
+(Second Report of Dr. Watson)
+
+THE LIGHT UPON THE MOOR
+
+BASKERVILLE HALL, Oct. 15th.
+
+
+MY DEAR HOLMES,--If I was compelled to leave you without much
+news during the early days of my mission you must acknowledge
+that I am making up for lost time, and that events are now
+crowding thick and fast upon us. In my last report I ended upon
+my top note with Barrymore at the window, and now I have quite a
+budget already which will, unless I am much mistaken,
+considerably surprise you. Things have taken a turn which I could
+not have anticipated. In some ways they have within the last
+forty-eight hours become much clearer and in some ways they have
+become more complicated. But I will tell you all and you shall
+judge for yourself.
+
+Before breakfast on the morning following my adventure I went
+down the corridor and examined the room in which Barrymore had
+been on the night before. The western window through which he had
+stared so intently has, I noticed, one peculiarity above all
+other windows in the house--it commands the nearest outlook on
+the moor. There is an opening between two trees which enables one
+from this point of view to look right down upon it, while from
+all the other windows it is only a distant glimpse which can be
+obtained. It follows, therefore, that Barrymore, since only this
+window would serve the purpose, must have been looking out for
+something or somebody upon the moor. The night was very dark, so
+that I can hardly imagine how he could have hoped to see anyone.
+It had struck me that it was possible that some love intrigue was
+on foot. That would have accounted for his stealthy movements and
+also for the uneasiness of his wife. The man is a
+striking-looking fellow, very well equipped to steal the heart of
+a country girl, so that this theory seemed to have something to
+support it. That opening of the door which I had heard after I
+had returned to my room might mean that he had gone out to keep
+some clandestine appointment. So I reasoned with myself in the
+morning, and I tell you the direction of my suspicions, however
+much the result may have shown that they were unfounded.
+
+But whatever the true explanation of Barrymore's movements might
+be, I felt that the responsibility of keeping them to myself
+until I could explain them was more than I could bear. I had an
+interview with the baronet in his study after breakfast, and I
+told him all that I had seen. He was less surprised than I had
+expected.
+
+"I knew that Barrymore walked about nights, and I had a mind to
+speak to him about it," said he. "Two or three times I have heard
+his steps in the passage, coming and going, just about the hour
+you name."
+
+"Perhaps then he pays a visit every night to that particular
+window," I suggested.
+
+"Perhaps he does. If so, we should be able to shadow him, and see
+what it is that he is after. I wonder what your friend Holmes
+would do, if he were here."
+
+"I believe that he would do exactly what you now suggest," said
+I. "He would follow Barrymore and see what he did."
+
+"Then we shall do it together."
+
+"But surely he would hear us."
+
+"The man is rather deaf, and in any case we must take our chance
+of that. We'll sit up in my room to-night and wait until he
+passes." Sir Henry rubbed his hands with pleasure, and it was
+evident that he hailed the adventure as a relief to his somewhat
+quiet life upon the moor.
+
+The baronet has been in communication with the architect who
+prepared the plans for Sir Charles, and with a contractor from
+London, so that we may expect great changes to begin here soon.
+There have been decorators and furnishers up from Plymouth, and
+it is evident that our friend has large ideas, and means to spare
+no pains or expense to restore the grandeur of his family. When
+the house is renovated and refurnished, all that he will need
+will be a wife to make it complete. Between ourselves there are
+pretty clear signs that this will not be wanting if the lady is
+willing, for I have seldom seen a man more infatuated with a
+woman than he is with our beautiful neighbour, Miss Stapleton.
+And yet the course of true love does not run quite as smoothly as
+one would under the circumstances expect. To-day, for example,
+its surface was broken by a very unexpected ripple, which has
+caused our friend considerable perplexity and annoyance.
+
+After the conversation which I have quoted about Barrymore, Sir
+Henry put on his hat and prepared to go out. As a matter of
+course I did the same.
+
+"What, are you coming, Watson?" he asked, looking at me in a
+curious way.
+
+"That depends on whether you are going on the moor," said I.
+
+"Yes, I am."
+
+"Well, you know what my instructions are. I am sorry to intrude,
+but you heard how earnestly Holmes insisted that I should not
+leave you, and especially that you should not go alone upon the
+moor."
+
+Sir Henry put his hand upon my shoulder with a pleasant smile.
+
+"My dear fellow," said he, "Holmes, with all his wisdom, did not
+foresee some things which have happened since I have been on the
+moor. You understand me? I am sure that you are the last man in
+the world who would wish to be a spoil-sport. I must go out
+alone."
+
+It put me in a most awkward position. I was at a loss what to say
+or what to do, and before I had made up my mind he picked up his
+cane and was gone.
+
+But when I came to think the matter over my conscience reproached
+me bitterly for having on any pretext allowed him to go out of my
+sight. I imagined what my feelings would be if I had to return to
+you and to confess that some misfortune had occurred through my
+disregard for your instructions. I assure you my cheeks flushed
+at the very thought. It might not even now be too late to
+overtake him, so I set off at once in the direction of Merripit
+House.
+
+I hurried along the road at the top of my speed without seeing
+anything of Sir Henry, until I came to the point where the moor
+path branches off. There, fearing that perhaps I had come in the
+wrong direction after all, I mounted a hill from which I could
+command a view--the same hill which is cut into the dark quarry.
+Thence I saw him at once. He was on the moor path, about a
+quarter of a mile off, and a lady was by his side who could only
+be Miss Stapleton. It was clear that there was already an
+understanding between them and that they had met by appointment.
+They were walking slowly along in deep conversation, and I saw
+her making quick little movements of her hands as if she were
+very earnest in what she was saying, while he listened intently,
+and once or twice shook his head in strong dissent. I stood among
+the rocks watching them, very much puzzled as to what I should do
+next. To follow them and break into their intimate conversation
+seemed to be an outrage, and yet my clear duty was never for an
+instant to let him out of my sight. To act the spy upon a friend
+was a hateful task. Still, I could see no better course than to
+observe him from the hill, and to clear my conscience by
+confessing to him afterwards what I had done. It is true that if
+any sudden danger had threatened him I was too far away to be of
+use, and yet I am sure that you will agree with me that the
+position was very difficult, and that there was nothing more
+which I could do.
+
+Our friend, Sir Henry, and the lady had halted on the path and
+were standing deeply absorbed in their conversation, when I was
+suddenly aware that I was not the only witness of their
+interview. A wisp of green floating in the air caught my eye, and
+another glance showed me that it was carried on a stick by a man
+who was moving among the broken ground. It was Stapleton with his
+butterfly-net. He was very much closer to the pair than I was,
+and he appeared to be moving in their direction. At this instant
+Sir Henry suddenly drew Miss Stapleton to his side. His arm was
+round her, but it seemed to me that she was straining away from
+him with her face averted. He stooped his head to hers, and she
+raised one hand as if in protest. Next moment I saw them spring
+apart and turn hurriedly round. Stapleton was the cause of the
+interruption. He was running wildly towards them, his absurd net
+dangling behind him. He gesticulated and almost danced with
+excitement in front of the lovers. What the scene meant I could
+not imagine, but it seemed to me that Stapleton was abusing Sir
+Henry, who offered explanations, which became more angry as the
+other refused to accept them. The lady stood by in haughty
+silence. Finally Stapleton turned upon his heel and beckoned in a
+peremptory way to his sister, who, after an irresolute glance at
+Sir Henry, walked off by the side of her brother. The
+naturalist's angry gestures showed that the lady was included in
+his displeasure. The baronet stood for a minute looking after
+them, and then he walked slowly back the way that he had come,
+his head hanging, the very picture of dejection.
+
+What all this meant I could not imagine, but I was deeply ashamed
+to have witnessed so intimate a scene without my friend's
+knowledge. I ran down the hill therefore and met the baronet at
+the bottom. His face was flushed with anger and his brows were
+wrinkled, like one who is at his wit's ends what to do.
+
+"Halloa, Watson! Where have you dropped from?" said he. "You don't
+mean to say that you came after me in spite of all?"
+
+I explained everything to him: how I had found it impossible to
+remain behind, how I had followed him, and how I had witnessed
+all that had occurred. For an instant his eyes blazed at me, but
+my frankness disarmed his anger, and he broke at last into a
+rather rueful laugh.
+
+"You would have thought the middle of that prairie a fairly safe
+place for a man to be private," said he, "but, by thunder, the
+whole country-side seems to have been out to see me do my
+wooing--and a mighty poor wooing at that! Where had you engaged a
+seat?"
+
+"I was on that hill."
+
+"Quite in the back row, eh? But her brother was well up to the
+front. Did you see him come out on us?"
+
+"Yes, I did."
+
+"Did he ever strike you as being crazy--this brother of hers?"
+
+"I can't say that he ever did."
+
+"I dare say not. I always thought him sane enough until to-day,
+but you can take it from me that either he or I ought to be in a
+strait-jacket. What's the matter with me, anyhow? You've lived
+near me for some weeks, Watson. Tell me straight, now! Is there
+anything that would prevent me from making a good husband to a
+woman that I loved?"
+
+"I should say not."
+
+"He can't object to my worldly position, so it must be myself
+that he has this down on. What has he against me? I never hurt
+man or woman in my life that I know of. And yet he would not so
+much as let me touch the tips of her fingers."
+
+"Did he say so?"
+
+"That, and a deal more. I tell you, Watson, I've only known her
+these few weeks, but from the first I just felt that she was made
+for me, and she, too--she was happy when she was with me, and
+that I'll swear. There's a light in a woman's eyes that speaks
+louder than words. But he has never let us get together, and it
+was only to-day for the first time that I saw a chance of having
+a few words with her alone. She was glad to meet me, but when she
+did it was not love that she would talk about, and she wouldn't
+have let me talk about it either if she could have stopped it.
+She kept coming back to it that this was a place of danger, and
+that she would never be happy until I had left it. I told her
+that since I had seen her I was in no hurry to leave it, and that
+if she really wanted me to go, the only way to work it was for
+her to arrange to go with me. With that I offered in as many
+words to marry her, but before she could answer, down came this
+brother of hers, running at us with a face on him like a madman.
+He was just white with rage, and those light eyes of his were
+blazing with fury. What was I doing with the lady? How dared I
+offer her attentions which were distasteful to her? Did I think
+that because I was a baronet I could do what I liked? If he had
+not been her brother I should have known better how to answer
+him. As it was I told him that my feelings towards his sister
+were such as I was not ashamed of, and that I hoped that she
+might honour me by becoming my wife. That seemed to make the
+matter no better, so then I lost my temper too, and I answered
+him rather more hotly than I should perhaps, considering that she
+was standing by. So it ended by his going off with her, as you
+saw, and here am I as badly puzzled a man as any in this county.
+Just tell me what it all means, Watson, and I'll owe you more
+than ever I can hope to pay."
+
+I tried one or two explanations, but, indeed, I was completely
+puzzled myself. Our friend's title, his fortune, his age, his
+character, and his appearance are all in his favour, and I know
+nothing against him unless it be this dark fate which runs in his
+family. That his advances should be rejected so brusquely without
+any reference to the lady's own wishes, and that the lady should
+accept the situation without protest, is very amazing. However,
+our conjectures were set at rest by a visit from Stapleton
+himself that very afternoon. He had come to offer apologies for
+his rudeness of the morning, and after a long private interview
+with Sir Henry in his study, the upshot of their conversation was
+that the breach is quite healed, and that we are to dine at
+Merripit House next Friday as a sign of it.
+
+"I don't say now that he isn't a crazy man," said Sir Henry; "I
+can't forget the look in his eyes when he ran at me this morning,
+but I must allow that no man could make a more handsome apology
+than he has done."
+
+"Did he give any explanation of his conduct?"
+
+"His sister is everything in his life, he says. That is natural
+enough, and I am glad that he should understand her value. They
+have always been together, and according to his account he has
+been a very lonely man with only her as a companion, so that the
+thought of losing her was really terrible to him. He had not
+understood, he said, that I was becoming attached to her, but
+when he saw with his own eyes that it was really so, and that she
+might be taken away from him, it gave him such a shock that for a
+time he was not responsible for what he said or did. He was very
+sorry for all that had passed, and he recognized how foolish and
+how selfish it was that he should imagine that he could hold a
+beautiful woman like his sister to himself for her whole life. If
+she had to leave him he had rather it was to a neighbour like
+myself than to anyone else. But in any case it was a blow to him,
+and it would take him some time before he could prepare himself
+to meet it. He would withdraw all opposition upon his part if I
+would promise for three months to let the matter rest and to be
+content with cultivating the lady's friendship during that time
+without claiming her love. This I promised, and so the matter
+rests."
+
+So there is one of our small mysteries cleared up. It is
+something to have touched bottom anywhere in this bog in which we
+are floundering. We know now why Stapleton looked with disfavour
+upon his sister's suitor--even when that suitor was so eligible a
+one as Sir Henry. And now I pass on to another thread which I
+have extricated out of the tangled skein, the mystery of the sobs
+in the night, of the tear-stained face of Mrs. Barrymore, of the
+secret journey of the butler to the western lattice window.
+Congratulate me, my dear Holmes, and tell me that I have not
+disappointed you as an agent--that you do not regret the
+confidence which you showed in me when you sent me down. All
+these things have by one night's work been thoroughly cleared.
+
+I have said "by one night's work," but, in truth, it was by two
+nights' work, for on the first we drew entirely blank. I sat up
+with Sir Henry in his rooms until nearly three o'clock in the
+morning, but no sound of any sort did we hear except the chiming
+clock upon the stairs. It was a most melancholy vigil, and ended
+by each of us falling asleep in our chairs. Fortunately we were
+not discouraged, and we determined to try again. The next night
+we lowered the lamp, and sat smoking cigarettes without making
+the least sound. It was incredible how slowly the hours crawled
+by, and yet we were helped through it by the same sort of patient
+interest which the hunter must feel as he watches the trap into
+which he hopes the game may wander. One struck, and two, and we
+had almost for the second time given it up in despair, when in an
+instant we both sat bolt upright in our chairs, with all our
+weary senses keenly on the alert once more. We had heard the
+creak of a step in the passage.
+
+Very stealthily we heard it pass along until it died away in the
+distance. Then the baronet gently opened his door and we set out
+in pursuit. Already our man had gone round the gallery, and the
+corridor was all in darkness. Softly we stole along until we had
+come into the other wing. We were just in time to catch a glimpse
+of the tall, black-bearded figure, his shoulders rounded, as he
+tip-toed down the passage. Then he passed through the same door
+as before, and the light of the candle framed it in the darkness
+and shot one single yellow beam across the gloom of the corridor.
+We shuffled cautiously towards it, trying every plank before we
+dared to put our whole weight upon it. We had taken the
+precaution of leaving our boots behind us, but, even so, the old
+boards snapped and creaked beneath our tread. Sometimes it seemed
+impossible that he should fail to hear our approach. However, the
+man is fortunately rather deaf, and he was entirely preoccupied
+in that which he was doing. When at last we reached the door and
+peeped through we found him crouching at the window, candle in
+hand, his white, intent face pressed against the pane, exactly as
+I had seen him two nights before.
+
+We had arranged no plan of campaign, but the baronet is a man to
+whom the most direct way is always the most natural. He walked
+into the room, and as he did so Barrymore sprang up from the
+window with a sharp hiss of his breath and stood, livid and
+trembling, before us. His dark eyes, glaring out of the white
+mask of his face, were full of horror and astonishment as he
+gazed from Sir Henry to me.
+
+"What are you doing here, Barrymore?"
+
+"Nothing, sir." His agitation was so great that he could hardly
+speak, and the shadows sprang up and down from the shaking of his
+candle. "It was the window, sir. I go round at night to see that
+they are fastened."
+
+"On the second floor?"
+
+"Yes, sir, all the windows."
+
+"Look here, Barrymore," said Sir Henry, sternly; "we have made up
+our minds to have the truth out of you, so it will save you
+trouble to tell it sooner rather than later. Come, now! No lies!
+What were you doing at that window?"
+
+The fellow looked at us in a helpless way, and he wrung his hands
+together like one who is in the last extremity of doubt and
+misery.
+
+"I was doing no harm, sir. I was holding a candle to the window."
+
+"And why were you holding a candle to the window?"
+
+"Don't ask me, Sir Henry--don't ask me! I give you my word, sir,
+that it is not my secret, and that I cannot tell it. If it
+concerned no one but myself I would not try to keep it from you."
+
+A sudden idea occurred to me, and I took the candle from the
+trembling hand of the butler.
+
+"He must have been holding it as a signal," said I. "Let us see
+if there is any answer." I held it as he had done, and stared out
+into the darkness of the night. Vaguely I could discern the black
+bank of the trees and the lighter expanse of the moor, for the
+moon was behind the clouds. And then I gave a cry of exultation,
+for a tiny pin-point of yellow light had suddenly transfixed the
+dark veil, and glowed steadily in the centre of the black square
+framed by the window.
+
+"There it is!" I cried.
+
+"No, no, sir, it is nothing--nothing at all!" the butler broke
+in; "I assure you, sir ----"
+
+"Move your light across the window, Watson!" cried the baronet.
+"See, the other moves also! Now, you rascal, do you deny that it
+is a signal? Come, speak up! Who is your confederate out yonder,
+and what is this conspiracy that is going on?"
+
+The man's face became openly defiant.
+
+"It is my business, and not yours. I will not tell."
+
+"Then you leave my employment right away."
+
+"Very good, sir. If I must I must."
+
+"And you go in disgrace. By thunder, you may well be ashamed of
+yourself. Your family has lived with mine for over a hundred
+years under this roof, and here I find you deep in some dark plot
+against me."
+
+"No, no, sir; no, not against you!" It was a woman's voice, and
+Mrs. Barrymore, paler and more horror-struck than her husband,
+was standing at the door. Her bulky figure in a shawl and skirt
+might have been comic were it not for the intensity of feeling
+upon her face.
+
+"We have to go, Eliza. This is the end of it. You can pack our
+things," said the butler.
+
+"Oh, John, John, have I brought you to this? It is my doing, Sir
+Henry--all mine. He has done nothing except for my sake and
+because I asked him."
+
+"Speak out, then! What does it mean?"
+
+"My unhappy brother is starving on the moor. We cannot let him
+perish at our very gates. The light is a signal to him that food
+is ready for him, and his light out yonder is to show the spot to
+which to bring it."
+
+"Then your brother is --"
+
+"The escaped convict, sir--Selden, the criminal."
+
+"That's the truth, sir," said Barrymore. "I said that it was not
+my secret and that I could not tell it to you. But now you have
+heard it, and you will see that if there was a plot it was not
+against you."
+
+This, then, was the explanation of the stealthy expeditions at
+night and the light at the window. Sir Henry and I both stared at
+the woman in amazement. Was it possible that this stolidly
+respectable person was of the same blood as one of the most
+notorious criminals in the country?
+
+"Yes, sir, my name was Selden, and he is my younger brother. We
+humoured him too much when he was a lad, and gave him his own way
+in everything until he came to think that the world was made for
+his pleasure, and that he could do what he liked in it. Then as
+he grew older he met wicked companions, and the devil entered
+into him until he broke my mother's heart and dragged our name in
+the dirt. From crime to crime he sank lower and lower, until it
+is only the mercy of God which has snatched him from the
+scaffold; but to me, sir, he was always the little curly-headed
+boy that I had nursed and played with, as an elder sister would.
+That was why he broke prison, sir. He knew that I was here and
+that we could not refuse to help him. When he dragged himself
+here one night, weary and starving, with the warders hard at his
+heels, what could we do? We took him in and fed him and cared for
+him. Then you returned, sir, and my brother thought he would be
+safer on the moor than anywhere else until the hue and cry was
+over, so he lay in hiding there. But every second night we made
+sure if he was still there by putting a light in the window, and
+if there was an answer my husband took out some bread and meat to
+him. Every day we hoped that he was gone, but as long as he was
+there we could not desert him. That is the whole truth, as I am
+an honest Christian woman, and you will see that if there is
+blame in the matter it does not lie with my husband, but with me,
+for whose sake he has done all that he has."
+
+The woman's words came with an intense earnestness which carried
+conviction with them.
+
+"Is this true, Barrymore?"
+
+"Yes, Sir Henry. Every word of it."
+
+"Well, I cannot blame you for standing by your own wife. Forget
+what I have said. Go to your room, you two, and we shall talk
+further about this matter in the morning."
+
+When they were gone we looked out of the window again. Sir Henry
+had flung it open, and the cold night wind beat in upon our
+faces. Far away in the black distance there still glowed that one
+tiny point of yellow light.
+
+"I wonder he dares," said Sir Henry.
+
+"It may be so placed as to be only visible from here."
+
+"Very likely. How far do you think it is?"
+
+"Out by the Cleft Tor, I think."
+
+"Not more than a mile or two off."
+
+"Hardly that."
+
+"Well, it cannot be far if Barrymore had to carry out the food to
+it. And he is waiting, this villain, beside that candle. By
+thunder, Watson, I am going out to take that man!"
+
+The same thought had crossed my own mind. It was not as if the
+Barrymores had taken us into their confidence. Their secret had
+been forced from them. The man was a danger to the community, an
+unmitigated scoundrel for whom there was neither pity nor excuse.
+We were only doing our duty in taking this chance of putting him
+back where he could do no harm. With his brutal and violent
+nature, others would have to pay the price if we held our hands.
+Any night, for example, our neighbours the Stapletons might be
+attacked by him, and it may have been the thought of this which
+made Sir Henry so keen upon the adventure.
+
+"I will come," said I.
+
+"Then get your revolver and put on your boots. The sooner we
+start the better, as the fellow may put out his light and be
+off."
+
+In five minutes we were outside the door, starting upon our
+expedition. We hurried through the dark shrubbery, amid the dull
+moaning of the autumn wind and the rustle of the falling leaves.
+The night air was heavy with the smell of damp and decay. Now and
+again the moon peeped out for an instant, but clouds were driving
+over the face of the sky, and just as we came out on the moor a
+thin rain began to fall. The light still burned steadily in
+front.
+
+"Are you armed?" I asked.
+
+"I have a hunting-crop."
+
+"We must close in on him rapidly, for he is said to be a
+desperate fellow. We shall take him by surprise and have him at
+our mercy before he can resist."
+
+"I say, Watson," said the baronet, "what would Holmes say to
+this? How about that hour of darkness in which the power of evil
+is exalted?"
+
+As if in answer to his words there rose suddenly out of the vast
+gloom of the moor that strange cry which I had already heard upon
+the borders of the great Grimpen Mire. It came with the wind
+through the silence of the night, a long, deep mutter, then a
+rising howl, and then the sad moan in which it died away. Again
+and again it sounded, the whole air throbbing with it, strident,
+wild, and menacing. The baronet caught my sleeve and his face
+glimmered white through the darkness.
+
+"My God, what's that, Watson?"
+
+"I don't know. It's a sound they have on the moor. I heard it
+once before."
+
+It died away, and an absolute silence closed in upon us. We stood
+straining our ears, but nothing came.
+
+"Watson," said the baronet, "it was the cry of a hound."
+
+My blood ran cold in my veins, for there was a break in his voice
+which told of the sudden horror which had seized him.
+
+"What do they call this sound?" he asked.
+
+"Who?"
+
+"The folk on the country-side."
+
+"Oh, they are ignorant people. Why should you mind what they call
+it?"
+
+"Tell me, Watson. What do they say of it?"
+
+I hesitated but could not escape the question.
+
+"They say it is the cry of the Hound of the Baskervilles."
+
+He groaned and was silent for a few moments.
+
+"A hound it was," he said, at last, "but it seemed to come from
+miles away, over yonder, I think."
+
+"It was hard to say whence it came."
+
+"It rose and fell with the wind. Isn't that the direction of the
+great Grimpen Mire?"
+
+"Yes, it is."
+
+"Well, it was up there. Come now, Watson, didn't you think
+yourself that it was the cry of a hound? I am not a child. You
+need not fear to speak the truth."
+
+"Stapleton was with me when I heard it last. He said that it
+might be the calling of a strange bird."
+
+"No, no, it was a hound. My God, can there be some truth in all
+these stories? Is it possible that I am really in danger from so
+dark a cause? You don't believe it, do you, Watson?"
+
+"No, no."
+
+"And yet it was one thing to laugh about it in London, and it is
+another to stand out here in the darkness of the moor and to hear
+such a cry as that. And my uncle! There was the footprint of the
+hound beside him as he lay. It all fits together. I don't think
+that I am a coward, Watson, but that sound seemed to freeze my
+very blood. Feel my hand!"
+
+It was as cold as a block of marble.
+
+"You'll be all right to-morrow."
+
+"I don't think I'll get that cry out of my head. What do you
+advise that we do now?"
+
+"Shall we turn back?"
+
+"No, by thunder; we have come out to get our man, and we will do
+it. We after the convict, and a hell-hound, as likely as not,
+after us. Come on! We'll see it through if all the fiends of the
+pit were loose upon the moor."
+
+We stumbled slowly along in the darkness, with the black loom of
+the craggy hills around us, and the yellow speck of light burning
+steadily in front. There is nothing so deceptive as the distance
+of a light upon a pitch-dark night, and sometimes the glimmer
+seemed to be far away upon the horizon and sometimes it might
+have been within a few yards of us. But at last we could see
+whence it came, and then we knew that we were indeed very close.
+A guttering candle was stuck in a crevice of the rocks which
+flanked it on each side so as to keep the wind from it and also
+to prevent it from being visible, save in the direction of
+Baskerville Hall. A boulder of granite concealed our approach, and
+crouching behind it we gazed over it at the signal light. It was
+strange to see this single candle burning there in the middle of
+the moor, with no sign of life near it--just the one straight
+yellow flame and the gleam of the rock on each side of it.
+
+"What shall we do now?" whispered Sir Henry.
+
+"Wait here. He must be near his light. Let us see if we can get a
+glimpse of him."
+
+The words were hardly out of my mouth when we both saw him. Over
+the rocks, in the crevice of which the candle burned, there was
+thrust out an evil yellow face, a terrible animal face, all
+seamed and scored with vile passions. Foul with mire, with a
+bristling beard, and hung with matted hair, it might well have
+belonged to one of those old savages who dwelt in the burrows on
+the hillsides. The light beneath him was reflected in his small,
+cunning eyes which peered fiercely to right and left through the
+darkness, like a crafty and savage animal who has heard the steps
+of the hunters.
+
+Something had evidently aroused his suspicions. It may have been
+that Barrymore had some private signal which we had neglected to
+give, or the fellow may have had some other reason for thinking
+that all was not well, but I could read his fears upon his wicked
+face. Any instant he might dash out the light and vanish in the
+darkness. I sprang forward therefore, and Sir Henry did the same.
+At the same moment the convict screamed out a curse at us and
+hurled a rock which splintered up against the boulder which had
+sheltered us. I caught one glimpse of his short, squat, strongly-
+built figure as he sprang to his feet and turned to run. At the
+same moment by a lucky chance the moon broke through the clouds.
+We rushed over the brow of the hill, and there was our man
+running with great speed down the other side, springing over the
+stones in his way with the activity of a mountain goat. A lucky
+long shot of my revolver might have crippled him, but I had
+brought it only to defend myself if attacked, and not to shoot an
+unarmed man who was running away.
+
+We were both swift runners and in fairly good training, but we
+soon found that we had no chance of overtaking him. We saw him
+for a long time in the moonlight until he was only a small speck
+moving swiftly among the boulders upon the side of a distant
+hill. We ran and ran until we were completely blown, but the
+space between us grew ever wider. Finally we stopped and sat
+panting on two rocks, while we watched him disappearing in the
+distance.
+
+And it was at this moment that there occurred a most strange and
+unexpected thing. We had risen from our rocks and were turning to
+go home, having abandoned the hopeless chase. The moon was low
+upon the right, and the jagged pinnacle of a granite tor stood up
+against the lower curve of its silver disc. There, outlined as
+black as an ebony statue on that shining back-ground, I saw the
+figure of a man upon the tor. Do not think that it was a
+delusion, Holmes. I assure you that I have never in my life seen
+anything more clearly. As far as I could judge, the figure was
+that of a tall, thin man. He stood with his legs a little
+separated, his arms folded, his head bowed, as if he were
+brooding over that enormous wilderness of peat and granite which
+lay before him. He might have been the very spirit of that
+terrible place. It was not the convict. This man was far from the
+place where the latter had disappeared. Besides, he was a much
+taller man. With a cry of surprise I pointed him out to the
+baronet, but in the instant during which I had turned to grasp
+his arm the man was gone. There was the sharp pinnacle of granite
+still cutting the lower edge of the moon, but its peak bore no
+trace of that silent and motionless figure.
+
+I wished to go in that direction and to search the tor, but it
+was some distance away. The baronet's nerves were still quivering
+from that cry, which recalled the dark story of his family, and
+he was not in the mood for fresh adventures. He had not seen this
+lonely man upon the tor and could not feel the thrill which his
+strange presence and his commanding attitude had given to me. "A
+warder, no doubt," said he. "The moor has been thick with them
+since this fellow escaped." Well, perhaps his explanation may be
+the right one, but I should like to have some further proof of
+it. To-day we mean to communicate to the Princetown people where
+they should look for their missing man, but it is hard lines that
+we have not actually had the triumph of bringing him back as our
+own prisoner. Such are the adventures of last night, and you must
+acknowledge, my dear Holmes, that I have done you very well in
+the matter of a report. Much of what I tell you is no doubt quite
+irrelevant, but still I feel that it is best that I should let
+you have all the facts and leave you to select for yourself those
+which will be of most service to you in helping you to your
+conclusions. We are certainly making some progress. So far as the
+Barrymores go we have found the motive of their actions, and that
+has cleared up the situation very much. But the moor with its
+mysteries and its strange inhabitants remains as inscrutable as
+ever. Perhaps in my next I may be able to throw some light upon
+this also. Best of all would it be if you could come down to us.
+In any case you will hear from me again in the course of the next
+few days.
+
+
+
+
+Chapter 10
+
+Extract from the Diary of Dr. Watson
+
+
+So far I have been able to quote from the reports which I have
+forwarded during these early days to Sherlock Holmes. Now,
+however, I have arrived at a point in my narrative where I am
+compelled to abandon this method and to trust once more to my
+recollections, aided by the diary which I kept at the time. A few
+extracts from the latter will carry me on to those scenes which
+are indelibly fixed in every detail upon my memory. I proceed,
+then, from the morning which followed our abortive chase of the
+convict and our other strange experiences upon the moor.
+
+OCTOBER 16TH.--A dull and foggy day with a drizzle of rain. The
+house is banked in with rolling clouds, which rise now and then
+to show the dreary curves of the moor, with thin, silver veins
+upon the sides of the hills, and the distant boulders gleaming
+where the light strikes upon their wet faces. It is melancholy
+outside and in. The baronet is in a black reaction after the
+excitements of the night. I am conscious myself of a weight at my
+heart and a feeling of impending danger--ever present danger,
+which is the more terrible because I am unable to define it.
+
+And have I not cause for such a feeling? Consider the long
+sequence of incidents which have all pointed to some sinister
+influence which is at work around us. There is the death of the
+last occupant of the Hall, fulfilling so exactly the conditions
+of the family legend, and there are the repeated reports from
+peasants of the appearance of a strange creature upon the moor.
+Twice I have with my own ears heard the sound which resembled the
+distant baying of a hound. It is incredible, impossible, that it
+should really be outside the ordinary laws of nature. A spectral
+hound which leaves material footmarks and fills the air with its
+howling is surely not to be thought of. Stapleton may fall in
+with such a superstition, and Mortimer also; but if I have one
+quality upon earth it is common-sense, and nothing will persuade
+me to believe in such a thing. To do so would be to descend to
+the level of these poor peasants, who are not content with a mere
+fiend dog but must needs describe him with hell-fire shooting
+from his mouth and eyes. Holmes would not listen to such fancies,
+and I am his agent. But facts are facts, and I have twice heard
+this crying upon the moor. Suppose that there were really some
+huge hound loose upon it; that would go far to explain
+everything. But where could such a hound lie concealed, where did
+it get its food, where did it come from, how was it that no one
+saw it by day? It must be confessed that the natural explanation
+offers almost as many difficulties as the other. And always,
+apart from the hound, there is the fact of the human agency in
+London, the man in the cab, and the letter which warned Sir Henry
+against the moor. This at least was real, but it might have been
+the work of a protecting friend as easily as of an enemy. Where
+is that friend or enemy now? Has he remained in London, or has he
+followed us down here? Could he--could he be the stranger whom I
+saw upon the tor?
+
+It is true that I have had only the one glance at him, and yet
+there are some things to which I am ready to swear. He is no one
+whom I have seen down here, and I have now met all the
+neighbours. The figure was far taller than that of Stapleton, far
+thinner than that of Frankland. Barrymore it might possibly have
+been, but we had left him behind us, and I am certain that he
+could not have followed us. A stranger then is still dogging us,
+just as a stranger dogged us in London. We have never shaken him
+off. If I could lay my hands upon that man, then at last we might
+find ourselves at the end of all our difficulties. To this one
+purpose I must now devote all my energies.
+
+My first impulse was to tell Sir Henry all my plans. My second
+and wisest one is to play my own game and speak as little as
+possible to anyone. He is silent and distrait. His nerves have
+been strangely shaken by that sound upon the moor. I will say
+nothing to add to his anxieties, but I will take my own steps to
+attain my own end.
+
+We had a small scene this morning after breakfast. Barrymore
+asked leave to speak with Sir Henry, and they were closeted in
+his study some little time. Sitting in the billiard-room I more
+than once heard the sound of voices raised, and I had a pretty
+good idea what the point was which was under discussion. After a
+time the baronet opened his door and called for me.
+
+"Barrymore considers that he has a grievance," he said. "He
+thinks that it was unfair on our part to hunt his brother-in-law
+down when he, of his own free will, had told us the secret."
+
+The butler was standing very pale but very collected before us.
+
+"I may have spoken too warmly, sir," said he, "and if I have, I
+am sure that I beg your pardon. At the same time, I was very much
+surprised when I heard you two gentlemen come back this morning
+and learned that you had been chasing Selden. The poor fellow has
+enough to fight against without my putting more upon his track."
+
+"If you had told us of your own free will it would have been a
+different thing," said the baronet, "you only told us, or rather
+your wife only told us, when it was forced from you and you could
+not help yourself."
+
+"I didn't think you would have taken advantage of it, Sir
+Henry--indeed I didn't."
+
+"The man is a public danger. There are lonely houses scattered
+over the moor, and he is a fellow who would stick at nothing. You
+only want to get a glimpse of his face to see that. Look at Mr.
+Stapleton's house, for example, with no one but himself to defend
+it. There's no safety for anyone until he is under lock and key."
+
+"He'll break into no house, sir. I give you my solemn word upon
+that. But he will never trouble anyone in this country again. I
+assure you, Sir Henry, that in a very few days the necessary
+arrangements will have been made and he will be on his way to
+South America. For God's sake, sir, I beg of you not to let the
+police know that he is still on the moor. They have given up the
+chase there, and he can lie quiet until the ship is ready for
+him. You can't tell on him without getting my wife and me into
+trouble. I beg you, sir, to say nothing to the police."
+
+"What do you say, Watson?"
+
+I shrugged my shoulders. "If he were safely out of the country it
+would relieve the tax-payer of a burden."
+
+"But how about the chance of his holding someone up before he
+goes?"
+
+"He would not do anything so mad, sir. We have provided him with
+all that he can want. To commit a crime would be to show where he
+was hiding."
+
+"That is true," said Sir Henry. "Well, Barrymore --"
+
+"God bless you, sir, and thank you from my heart! It would have
+killed my poor wife had he been taken again."
+
+"I guess we are aiding and abetting a felony, Watson? But, after
+what we have heard I don't feel as if I could give the man up, so
+there is an end of it. All right, Barrymore, you can go."
+
+With a few broken words of gratitude the man turned, but he
+hesitated and then came back.
+
+"You've been so kind to us, sir, that I should like to do the
+best I can for you in return. I know something, Sir Henry, and
+perhaps I should have said it before, but it was long after the
+inquest that I found it out. I've never breathed a word about it
+yet to mortal man. It's about poor Sir Charles's death."
+
+The baronet and I were both upon our feet. "Do you know how he
+died?"
+
+"No, sir, I don't know that."
+
+"What then?"
+
+"I know why he was at the gate at that hour. It was to meet a
+woman."
+
+"To meet a woman! He?"
+
+"Yes, sir."
+
+"And the woman's name?"
+
+"I can't give you the name, sir, but I can give you the initials.
+Her initials were L. L."
+
+"How do you know this, Barrymore?"
+
+"Well, Sir Henry, your uncle had a letter that morning. He had
+usually a great many letters, for he was a public man and well
+known for his kind heart, so that everyone who was in trouble was
+glad to turn to him. But that morning, as it chanced, there was
+only this one letter, so I took the more notice of it. It was
+from Coombe Tracey, and it was addressed in a woman's hand."
+
+"Well?"
+
+"Well, sir, I thought no more of the matter, and never would have
+done had it not been for my wife. Only a few weeks ago she was
+cleaning out Sir Charles's study--it had never been touched since
+his death--and she found the ashes of a burned letter in the back
+of the grate. The greater part of it was charred to pieces, but
+one little slip, the end of a page, hung together, and the
+writing could still be read, though it was gray on a black
+ground. It seemed to us to be a postscript at the end of the
+letter, and it said: 'Please, please, as you are a gentleman,
+burn this letter, and be at the gate by ten o clock. Beneath it
+were signed the initials L. L."
+
+"Have you got that slip?"
+
+"No, sir, it crumbled all to bits after we moved it."
+
+"Had Sir Charles received any other letters in the same writing?"
+
+"Well, sir, I took no particular notice of his letters. I should
+not have noticed this one, only it happened to come alone."
+
+"And you have no idea who L. L. is?"
+
+"No, sir. No more than you have. But I expect if we could lay our
+hands upon that lady we should know more about Sir Charles's
+death."
+
+"I cannot understand, Barrymore, how you came to conceal this
+important information."
+
+"Well, sir, it was immediately after that our own trouble came to
+us. And then again, sir, we were both of us very fond of Sir
+Charles, as we well might be considering all that he has done for
+us. To rake this up couldn't help our poor master, and it's well
+to go carefully when there's a lady in the case. Even the best of
+us ----"
+
+"You thought it might injure his reputation?"
+
+"Well, sir, I thought no good could come of it. But now you have
+been kind to us, and I feel as if it would be treating you
+unfairly not to tell you all that I know about the matter."
+
+"Very good, Barrymore; you can go." When the butler had left us
+Sir Henry turned to me. "Well, Watson, what do you think of this
+new light?"
+
+"It seems to leave the darkness rather blacker than before."
+
+"So I think. But if we can only trace L. L. it should clear up
+the whole business. We have gained that much. We know that there
+is someone who has the facts if we can only find her. What do you
+think we should do?"
+
+"Let Holmes know all about it at once. It will give him the clue
+for which he has been seeking. I am much mistaken if it does not
+bring him down."
+
+I went at once to my room and drew up my report of the morning's
+conversation for Holmes. It was evident to me that he had been
+very busy of late, for the notes which I had from Baker Street
+were few and short, with no comments upon the information which I
+had supplied and hardly any reference to my mission. No doubt his
+blackmailing case is absorbing all his faculties. And yet this
+new factor must surely arrest his attention and renew his
+interest. I wish that he were here.
+
+OCTOBER 17TH.--All day to-day the rain poured down, rustling on
+the ivy and dripping from the eaves. I thought of the convict out
+upon the bleak, cold, shelterless moor. Poor devil! Whatever his
+crimes, he has suffered something to atone for them. And then I
+thought of that other one--the face in the cab, the figure
+against the moon. Was he also out in that deluged--the unseen
+watcher, the man of darkness? In the evening I put on my
+waterproof and I walked far upon the sodden moor, full of dark
+imaginings, the rain beating upon my face and the wind whistling
+about my ears. God help those who wander into the great mire now,
+for even the firm uplands are becoming a morass. I found the
+black tor upon which I had seen the solitary watcher, and from
+its craggy summit I looked out myself across the melancholy
+downs. Rain squalls drifted across their russet face, and the
+heavy, slate-coloured clouds hung low over the landscape,
+trailing in gray wreaths down the sides of the fantastic hills.
+In the distant hollow on the left, half hidden by the mist, the
+two thin towers of Baskerville Hall rose above the trees. They
+were the only signs of human life which I could see, save only
+those prehistoric huts which lay thickly upon the slopes of the
+hills. Nowhere was there any trace of that lonely man whom I had
+seen on the same spot two nights before.
+
+As I walked back I was overtaken by Dr. Mortimer driving in his
+dog-cart over a rough moorland track which led from the outlying
+farmhouse of Foulmire. He has been very attentive to us, and
+hardly a day has passed that he has not called at the Hall to see
+how we were getting on. He insisted upon my climbing into his
+dog-cart, and he gave me a lift homeward. I found him much
+troubled over the disappearance of his little spaniel. It had
+wandered on to the moor and had never come back. I gave him such
+consolation as I might, but I thought of the pony on the Grimpen
+Mire, and I do not fancy that he will see his little dog again.
+
+"By the way, Mortimer," said I as we jolted along the rough road,
+"I suppose there are few people living within driving distance of
+this whom you do not know?"
+
+"Hardly any, I think."
+
+"Can you, then, tell me the name of any woman whose initials are
+L. L.?"
+
+He thought for a few minutes.
+
+"No," said he. "There are a few gipsies and labouring folk for
+whom I can't answer, but among the farmers or gentry there is no
+one whose initials are those. Wait a bit though," he added after
+a pause. "There is Laura Lyons--her initials are L. L.--but she
+lives in Coombe Tracey."
+
+"Who is she?" I asked.
+
+"She is Frankland's daughter."
+
+"What! Old Frankland the crank?"
+
+"Exactly. She married an artist named Lyons, who came sketching
+on the moor. He proved to be a blackguard and deserted her. The
+fault from what I hear may not have been entirely on one side.
+Her father refused to have anything to do with her because she
+had married without his consent, and perhaps for one or two other
+reasons as well. So, between the old sinner and the young one the
+girl has had a pretty bad time."
+
+"How does she live?"
+
+"I fancy old Frankland allows her a pittance, but it cannot be
+more, for his own affairs are considerably involved. Whatever she
+may have deserved one could not allow her to go hopelessly to the
+bad. Her story got about, and several of the people here did
+something to enable her to earn an honest living. Stapleton did
+for one, and Sir Charles for another. I gave a trifle myself. It
+was to set her up in a typewriting business."
+
+He wanted to know the object of my inquiries, but I managed to
+satisfy his curiosity without telling him too much, for there is
+no reason why we should take anyone into our confidence.
+To-morrow morning I shall find my way to Coombe Tracey, and if I
+can see this Mrs. Laura Lyons, of equivocal reputation, a long
+step will have been made towards clearing one incident in this
+chain of mysteries. I am certainly developing the wisdom of the
+serpent, for when Mortimer pressed his questions to an
+inconvenient extent I asked him casually to what type Frankland's
+skull belonged, and so heard nothing but craniology for the rest
+of our drive. I have not lived for years with Sherlock Holmes for
+nothing.
+
+I have only one other incident to record upon this tempestuous
+and melancholy day. This was my conversation with Barrymore just
+now, which gives me one more strong card which I can play in due
+time.
+
+Mortimer had stayed to dinner, and he and the baronet played
+ecarté afterwards. The butler brought me my coffee into the
+library, and I took the chance to ask him a few questions.
+
+"Well," said I, "has this precious relation of yours departed, or
+is he still lurking out yonder?"
+
+"I don't know, sir. I hope to heaven that he has gone, for he has
+brought nothing but trouble here! I've not heard of him since I
+left out food for him last, and that was three days ago."
+
+"Did you see him then?"
+
+"No, sir, but the food was gone when next I went that way."
+
+"Then he was certainly there?"
+
+"So you would think, sir, unless it was the other man who took
+it."
+
+I sat with my coffee-cup halfway to my lips and stared at
+Barrymore.
+
+"You know that there is another man then?"
+
+"Yes, sir; there is another man upon the moor."
+
+"Have you seen him?"
+
+"No, sir."
+
+"How do you know of him then?"
+
+"Selden told me of him, sir, a week ago or more. He's in hiding,
+too, but he's not a convict as far as I can make out. I don't
+like it, Dr. Watson--I tell you straight, sir, that I don't like
+it." He spoke with a sudden passion of earnestness.
+
+"Now, listen to me, Barrymore! I have no interest in this matter
+but that of your master. I have come here with no object except
+to help him. Tell me, frankly, what it is that you don't like."
+
+Barrymore hesitated for a moment, as if he regretted his
+outburst, or found it difficult to express his own feelings in
+words.
+
+"It's all these goings-on, sir," he cried at last, waving his
+hand towards the rain-lashed window which faced the moor. "There's
+foul play somewhere, and there's black villainy brewing, to that
+I'll swear! Very glad I should be, sir, to see Sir Henry on his
+way back to London again!"
+
+"But what is it that alarms you?"
+
+"Look at Sir Charles's death! That was bad enough, for all that
+the coroner said. Look at the noises on the moor at night.
+There's not a man would cross it after sundown if he was paid for
+it. Look at this stranger hiding out yonder, and watching and
+waiting! What's he waiting for? What does it mean? It means no
+good to anyone of the name of Baskerville, and very glad I shall
+be to be quit of it all on the day that Sir Henry's new servants
+are ready to take over the Hall."
+
+"But about this stranger," said I. "Can you tell me anything
+about him? What did Selden say? Did he find out where he hid, or
+what he was doing?"
+
+"He saw him once or twice, but he is a deep one, and gives
+nothing away. At first he thought that he was the police, but
+soon he found that he had some lay of his own. A kind of
+gentleman he was, as far as he could see, but what he was doing
+he could not make out."
+
+"And where did he say that he lived?"
+
+"Among the old houses on the hillside--the stone huts where the
+old folk used to live."
+
+"But how about his food?"
+
+"Selden found out that he has got a lad who works for him and
+brings him all he needs. I dare say he goes to Coombe Tracey for
+what he wants."
+
+"Very good, Barrymore. We may talk further of this some other
+time." When the butler had gone I walked over to the black
+window, and I looked through a blurred pane at the driving clouds
+and at the tossing outline of the wind-swept trees. It is a wild
+night indoors, and what must it be in a stone hut upon the moor.
+What passion of hatred can it be which leads a man to lurk in
+such a place at such a time! And what deep and earnest purpose
+can he have which calls for such a trial! There, in that hut upon
+the moor, seems to lie the very centre of that problem which has
+vexed me so sorely. I swear that another day shall not have
+passed before I have done all that man can do to reach the heart
+of the mystery.
+
+
+
+
+Chapter 11
+
+The Man on the Tor
+
+
+The extract from my private diary which forms the last chapter
+has brought my narrative up to the 18th of October, a time when
+these strange events began to move swiftly towards their terrible
+conclusion. The incidents of the next few days are indelibly
+graven upon my recollection, and I can tell them without
+reference to the notes made at the time. I start then from the
+day which succeeded that upon which I had established two facts
+of great importance, the one that Mrs. Laura Lyons of Coombe
+Tracey had written to Sir Charles Baskerville and made an
+appointment with him at the very place and hour that he met his
+death, the other that the lurking man upon the moor was to be
+found among the stone huts upon the hill-side. With these two
+facts in my possession I felt that either my intelligence or my
+courage must be deficient if I could not throw some further light
+upon these dark places.
+
+I had no opportunity to tell the baronet what I had learned about
+Mrs. Lyons upon the evening before, for Dr. Mortimer remained
+with him at cards until it was very late. At breakfast, however,
+I informed him about my discovery, and asked him whether he would
+care to accompany me to Coombe Tracey. At first he was very eager
+to come, but on second thoughts it seemed to both of us that if I
+went alone the results might be better. The more formal we made
+the visit the less information we might obtain. I left Sir Henry
+behind, therefore, not without some prickings of conscience, and
+drove off upon my new quest.
+
+When I reached Coombe Tracey I told Perkins to put up the horses,
+and I made inquiries for the lady whom I had come to interrogate.
+I had no difficulty in finding her rooms, which were central and
+well appointed. A maid showed me in without ceremony, and as I
+entered the sitting-room a lady, who was sitting before a
+Remington typewriter, sprang up with a pleasant smile of welcome.
+Her face fell, however, when she saw that I was a stranger, and
+she sat down again and asked me the object of my visit.
+
+The first impression left by Mrs. Lyons was one of extreme
+beauty. Her eyes and hair were of the same rich hazel colour, and
+her cheeks, though considerably freckled, were flushed with the
+exquisite bloom of the brunette, the dainty pink which lurks at
+the heart of the sulphur rose. Admiration was, I repeat, the
+first impression. But the second was criticism. There was
+something subtly wrong with the face, some coarseness of
+expression, some hardness, perhaps, of eye, some looseness of lip
+which marred its perfect beauty. But these, of course, are
+after-thoughts. At the moment I was simply conscious that I was
+in the presence of a very handsome woman, and that she was asking
+me the reasons for my visit. I had not quite understood until
+that instant how delicate my mission was.
+
+"I have the pleasure," said I, "of knowing your father." It was a
+clumsy introduction, and the lady made me feel it.
+
+"There is nothing in common between my father and me," she said.
+"I owe him nothing, and his friends are not mine. If it were not
+for the late Sir Charles Baskerville and some other kind hearts I
+might have starved for all that my father cared."
+
+"It was about the late Sir Charles Baskerville that I have come
+here to see you."
+
+The freckles started out on the lady's face.
+
+"What can I tell you about him?" she asked, and her fingers
+played nervously over the stops of her typewriter.
+
+"You knew him, did you not?"
+
+"I have already said that I owe a great deal to his kindness. If
+I am able to support myself it is largely due to the interest
+which he took in my unhappy situation."
+
+"Did you correspond with him?"
+
+The lady looked quickly up with an angry gleam in her hazel eyes.
+
+"What is the object of these questions?" she asked sharply.
+
+"The object is to avoid a public scandal. It is better that I
+should ask them here than that the matter should pass outside our
+control."
+
+She was silent and her face was still very pale. At last she
+looked up with something reckless and defiant in her manner.
+
+"Well, I'll answer," she said. "What are your questions?"
+
+"Did you correspond with Sir Charles?"
+
+"I certainly wrote to him once or twice to acknowledge his
+delicacy and his generosity."
+
+"Have you the dates of those letters?"
+
+"No."
+
+"Have you ever met him?"
+
+"Yes, once or twice, when he came into Coombe Tracey. He was a
+very retiring man, and he preferred to do good by stealth."
+
+"But if you saw him so seldom and wrote so seldom, how did he
+know enough about your affairs to be able to help you, as you say
+that he has done?"
+
+She met my difficulty with the utmost readiness.
+
+"There were several gentlemen who knew my sad history and united
+to help me. One was Mr. Stapleton, a neighbour and intimate
+friend of Sir Charles's. He was exceedingly kind, and it was
+through him that Sir Charles learned about my affairs."
+
+I knew already that Sir Charles Baskerville had made Stapleton
+his almoner upon several occasions, so the lady's statement bore
+the impress of truth upon it.
+
+"Did you ever write to Sir Charles asking him to meet you?" I
+continued.
+
+Mrs. Lyons flushed with anger again.
+
+"Really, sir, this is a very extraordinary question."
+
+"I am sorry, madam, but I must repeat it."
+
+"Then I answer, certainly not."
+
+"Not on the very day of Sir Charles's death?"
+
+The flush had faded in an instant, and a deathly face was before
+me. Her dry lips could not speak the "No" which I saw rather than
+heard.
+
+"Surely your memory deceives you," said I. "I could even quote a
+passage of your letter. It ran 'Please, please, as you are a
+gentleman, burn this letter, and be at the gate by ten o'clock.'"
+
+I thought that she had fainted, but she recovered herself by a
+supreme effort.
+
+"Is there no such thing as a gentleman?" she gasped.
+
+"You do Sir Charles an injustice. He did burn the letter. But
+sometimes a letter may be legible even when burned. You
+acknowledge now that you wrote it?"
+
+"Yes, I did write it," she cried, pouring out her soul in a
+torrent of words. "I did write it. Why should I deny it? I have
+no reason to be ashamed of it. I wished him to help me. I
+believed that if I had an interview I could gain his help, so I
+asked him to meet me."
+
+"But why at such an hour?"
+
+"Because I had only just learned that he was going to London next
+day and might be away for months. There were reasons why I could
+not get there earlier."
+
+"But why a rendezvous in the garden instead of a visit to the
+house?"
+
+"Do you think a woman could go alone at that hour to a bachelor's
+house?"
+
+"Well, what happened when you did get there?"
+
+"I never went."
+
+"Mrs. Lyons!"
+
+"No, I swear it to you on all I hold sacred. I never went.
+Something intervened to prevent my going."
+
+"What was that?"
+
+"That is a private matter. I cannot tell it."
+
+"You acknowledge then that you made an appointment with Sir
+Charles at the very hour and place at which he met his death, but
+you deny that you kept the appointment."
+
+"That is the truth."
+
+Again and again I cross-questioned her, but I could never get
+past that point.
+
+"Mrs. Lyons," said I, as I rose from this long and inconclusive
+interview, "you are taking a very great responsibility and
+putting yourself in a very false position by not making an
+absolutely clean breast of all that you know. If I have to call
+in the aid of the police you will find how seriously you are
+compromised. If your position is innocent, why did you in the
+first instance deny having written to Sir Charles upon that
+date?"
+
+"Because I feared that some false conclusion might be drawn from
+it and that I might find myself involved in a scandal."
+
+"And why were you so pressing that Sir Charles should destroy
+your letter?"
+
+"If you have read the letter you will know."
+
+"I did not say that I had read all the letter."
+
+"You quoted some of it."
+
+"I quoted the postscript. The letter had, as I said, been burned
+and it was not all legible. I ask you once again why it was that
+you were so pressing that Sir Charles should destroy this letter
+which he received on the day of his death."
+
+"The matter is a very private one."
+
+"The more reason why you should avoid a public investigation."
+
+"I will tell you, then. If you have heard anything of my unhappy
+history you will know that I made a rash marriage and had reason
+to regret it."
+
+"I have heard so much."
+
+"My life has been one incessant persecution from a husband whom I
+abhor. The law is upon his side, and every day I am faced by the
+possibility that he may force me to live with him. At the time
+that I wrote this letter to Sir Charles I had learned that there
+was a prospect of my regaining my freedom if certain expenses
+could be met. It meant everything to me--peace of mind,
+happiness, self-respect--everything. I knew Sir Charles's
+generosity, and I thought that if he heard the story from my own
+lips he would help me."
+
+"Then how is it that you did not go?"
+
+"Because I received help in the interval from another source."
+
+"Why then, did you not write to Sir Charles and explain this?"
+
+"So I should have done had I not seen his death in the paper next
+morning."
+
+The woman's story hung coherently together, and all my questions
+were unable to shake it. I could only check it by finding if she
+had, indeed, instituted divorce proceedings against her husband
+at or about the time of the tragedy.
+
+It was unlikely that she would dare to say that she had not been
+to Baskerville Hall if she really had been, for a trap would be
+necessary to take her there, and could not have returned to
+Coombe Tracey until the early hours of the morning. Such an
+excursion could not be kept secret. The probability was,
+therefore, that she was telling the truth, or, at least, a part
+of the truth. I came away baffled and disheartened. Once again I
+had reached that dead wall which seemed to be built across every
+path by which I tried to get at the object of my mission. And yet
+the more I thought of the lady's face and of her manner the more
+I felt that something was being held back from me. Why should she
+turn so pale? Why should she fight against every admission until
+it was forced from her? Why should she have been so reticent at
+the time of the tragedy? Surely the explanation of all this could
+not be as innocent as she would have me believe. For the moment I
+could proceed no farther in that direction, but must turn back to
+that other clue which was to be sought for among the stone huts
+upon the moor.
+
+And that was a most vague direction. I realized it as I drove
+back and noted how hill after hill showed traces of the ancient
+people. Barrymore's only indication had been that the stranger
+lived in one of these abandoned huts, and many hundreds of them
+are scattered throughout the length and breadth of the moor. But
+I had my own experience for a guide since it had shown me the man
+himself standing upon the summit of the Black Tor. That then
+should be the centre of my search. From there I should explore
+every hut upon the moor until I lighted upon the right one. If
+this man were inside it I should find out from his own lips, at
+the point of my revolver if necessary, who he was and why he had
+dogged us so long. He might slip away from us in the crowd of
+Regent Street, but it would puzzle him to do so upon the lonely
+moor. On the other hand, if I should find the hut and its tenant
+should not be within it I must remain there, however long the
+vigil, until he returned. Holmes had missed him in London. It
+would indeed be a triumph for me if I could run him to earth,
+where my master had failed.
+
+Luck had been against us again and again in this inquiry, but now
+at last it came to my aid. And the messenger of good fortune was
+none other than Mr. Frankland, who was standing, gray-whiskered
+and red-faced, outside the gate of his garden, which opened on to
+the high road along which I travelled.
+
+"Good-day, Dr. Watson," cried he with unwonted good humour, "you
+must really give your horses a rest, and come in to have a glass
+of wine and to congratulate me."
+
+My feelings towards him were very far from being friendly after
+what I had heard of his treatment of his daughter, but I was
+anxious to send Perkins and the wagonette home, and the
+opportunity was a good one. I alighted and sent a message to Sir
+Henry that I should walk over in time for dinner. Then I followed
+Frankland into his dining-room.
+
+"It is a great day for me, sir--one of the red-letter days of my
+life," he cried with many chuckles. "I have brought off a double
+event. I mean to teach them in these parts that law is law, and
+that there is a man here who does not fear to invoke it. I have
+established a right of way through the centre of old Middleton's
+park, slap across it, sir, within a hundred yards of his own
+front door. What do you think of that? We'll teach these magnates
+that they cannot ride roughshod over the rights of the
+commoners, confound them! And I've closed the wood where the
+Fernworthy folk used to picnic. These infernal people seem to
+think that there are no rights of property, and that they can
+swarm where they like with their papers and their bottles. Both
+cases decided, Dr. Watson, and both in my favour. I haven't had
+such a day since I had Sir John Morland for trespass, because he
+shot in his own warren."
+
+"How on earth did you do that?"
+
+"Look it up in the books, sir. It will repay reading--Frankland
+v. Morland, Court of Queen's Bench. It cost me 200 pounds, but I
+got my verdict."
+
+"Did it do you any good?"
+
+"None, sir, none. I am proud to say that I had no interest in the
+matter. I act entirely from a sense of public duty. I have no
+doubt, for example, that the Fernworthy people will burn me in
+effigy to-night. I told the police last time they did it that
+they should stop these disgraceful exhibitions. The County
+Constabulary is in a scandalous state, sir, and it has not
+afforded me the protection to which I am entitled. The case of
+Frankland v. Regina will bring the matter before the attention of
+the public. I told them that they would have occasion to regret
+their treatment of me, and already my words have come true."
+
+"How so?" I asked.
+
+The old man put on a very knowing expression.
+
+"Because I could tell them what they are dying to know; but
+nothing would induce me to help the rascals in any way."
+
+I had been casting round for some excuse by which I could get
+away from his gossip, but now I began to wish to hear more of it.
+I had seen enough of the contrary nature of the old sinner to
+understand that any strong sign of interest would be the surest
+way to stop his confidences.
+
+"Some poaching case, no doubt?" said I, with an indifferent
+manner.
+
+"Ha, ha, my boy, a very much more important matter than that!
+What about the convict on the moor?"
+
+I started. "You don't mean that you know where he is?" said I.
+
+"I may not know exactly where he is, but I am quite sure that I
+could help the police to lay their hands on him. Has it never
+struck you that the way to catch that man was to find out where
+he got his food, and so trace it to him?"
+
+He certainly seemed to be getting uncomfortably near the truth.
+"No doubt," said I; "but how do you know that he is anywhere upon
+the moor?"
+
+"I know it because I have seen with my own eyes the messenger who
+takes him his food."
+
+My heart sank for Barrymore. It was a serious thing to be in the
+power of this spiteful old busybody. But his next remark took a
+weight from my mind.
+
+"You'll be surprised to hear that his food is taken to him by a
+child. I see him every day through my telescope upon the roof. He
+passes along the same path at the same hour, and to whom should
+he be going except to the convict?"
+
+Here was luck indeed! And yet I suppressed all appearance of
+interest. A child! Barrymore had said that our unknown was
+supplied by a boy. It was on his track, and not upon the
+convict's, that Frankland had stumbled. If I could get his
+knowledge it might save me a long and weary hunt. But incredulity
+and indifference were evidently my strongest cards.
+
+"I should say that it was much more likely that it was the son of
+one of the moorland shepherds taking out his father's dinner."
+
+The least appearance of opposition struck fire out of the old
+autocrat. His eyes looked malignantly at me, and his gray
+whiskers bristled like those of an angry cat.
+
+"Indeed, sir!" said he, pointing out over the wide-stretching
+moor. "Do you see that Black Tor over yonder? Well, do you see
+the low hill beyond with the thornbush upon it? It is the
+stoniest part of the whole moor. Is that a place where a shepherd
+would be likely to take his station? Your suggestion, sir, is a
+most absurd one."
+
+I meekly answered that I had spoken without knowing all the
+facts. My submission pleased him and led him to further
+confidences.
+
+"You may be sure, sir, that I have very good grounds before I
+come to an opinion. I have seen the boy again and again with his
+bundle. Every day, and sometimes twice a day, I have been
+able--but wait a moment, Dr. Watson. Do my eyes deceive me, or is
+there at the present moment something moving upon that hill-
+side?"
+
+It was several miles off, but I could distinctly see a small dark
+dot against the dull green and gray.
+
+"Come, sir, come!" cried Frankland, rushing upstairs. "You will
+see with your own eyes and judge for yourself."
+
+The telescope, a formidable instrument mounted upon a tripod,
+stood upon the flat leads of the house. Frankland clapped his eye
+to it and gave a cry of satisfaction.
+
+"Quick, Dr. Watson, quick, before he passes over the hill!"
+
+There he was, sure enough, a small urchin with a little bundle
+upon his shoulder, toiling slowly up the hill. When he reached
+the crest I saw the ragged uncouth figure outlined for an instant
+against the cold blue sky. He looked round him with a furtive and
+stealthy air, as one who dreads pursuit. Then he vanished over
+the hill.
+
+"Well! Am I right?"
+
+"Certainly, there is a boy who seems to have some secret errand."
+
+"And what the errand is even a county constable could guess. But
+not one word shall they have from me, and I bind you to secrecy
+also, Dr. Watson. Not a word! You understand!"
+
+"Just as you wish."
+
+"They have treated me shamefully--shamefully. When the facts come
+out in Frankland v. Regina I venture to think that a thrill of
+indignation will run through the country. Nothing would induce me
+to help the police in any way. For all they cared it might have
+been me, instead of my effigy, which these rascals burned at the
+stake. Surely you are not going! You will help me to empty the
+decanter in honour of this great occasion!"
+
+But I resisted all his solicitations and succeeded in dissuading
+him from his announced intention of walking home with me. I kept
+the road as long as his eye was on me, and then I struck off
+across the moor and made for the stony hill over which the boy
+had disappeared. Everything was working in my favour, and I swore
+that it should not be through lack of energy or perseverance that
+I should miss the chance which fortune had thrown in my way.
+
+The sun was already sinking when I reached the summit of the
+hill, and the long slopes beneath me were all golden-green on one
+side and gray shadow on the other. A haze lay low upon the
+farthest sky-line, out of which jutted the fantastic shapes of
+Belliver and Vixen Tor. Over the wide expanse there was no sound
+and no movement. One great gray bird, a gull or curlew, soared
+aloft in the blue heaven. He and I seemed to be the only living
+things between the huge arch of the sky and the desert beneath
+it. The barren scene, the sense of loneliness, and the mystery
+and urgency of my task all struck a chill into my heart. The boy
+was nowhere to be seen. But down beneath me in a cleft of the
+hills there was a circle of the old stone huts, and in the middle
+of them there was one which retained sufficient roof to act as a
+screen against the weather. My heart leaped within me as I saw
+it. This must be the burrow where the stranger lurked. At last my
+foot was on the threshold of his hiding place--his secret was
+within my grasp.
+
+As I approached the hut, walking as warily as Stapleton would do
+when with poised net he drew near the settled butterfly, I
+satisfied myself that the place had indeed been used as a
+habitation. A vague pathway among the boulders led to the
+dilapidated opening which served as a door. All was silent
+within. The unknown might be lurking there, or he might be
+prowling on the moor. My nerves tingled with the sense of
+adventure. Throwing aside my cigarette, I closed my hand upon the
+butt of my revolver and, walking swiftly up to the door, I looked
+in. The place was empty.
+
+But there were ample signs that I had not come upon a false
+scent. This was certainly where the man lived. Some blankets
+rolled in a waterproof lay upon that very stone slab upon which
+Neolithic man had once slumbered. The ashes of a fire were heaped
+in a rude grate. Beside it lay some cooking utensils and a bucket
+half-full of water. A litter of empty tins showed that the place
+had been occupied for some time, and I saw, as my eyes became
+accustomed to the checkered light, a pannikin and a half-full
+bottle of spirits standing in the corner. In the middle of the
+hut a flat stone served the purpose of a table, and upon this
+stood a small cloth bundle--the same, no doubt, which I had seen
+through the telescope upon the shoulder of the boy. It contained
+a loaf of bread, a tinned tongue, and two tins of preserved
+peaches. As I set it down again, after having examined it, my
+heart leaped to see that beneath it there lay a sheet of paper
+with writing upon it. I raised it, and this was what I read,
+roughly scrawled in pencil:--
+
+Dr. Watson has gone to Coombe Tracey.
+
+For a minute I stood there with the paper in my hands thinking
+out the meaning of this curt message. It was I, then, and not Sir
+Henry, who was being dogged by this secret man. He had not
+followed me himself, but he had set an agent--the boy,
+perhaps--upon my track, and this was his report. Possibly I had
+taken no step since I had been upon the moor which had not been
+observed and reported. Always there was this feeling of an unseen
+force, a fine net drawn round us with infinite skill and
+delicacy, holding us so lightly that it was only at some supreme
+moment that one realized that one was indeed entangled in its
+meshes.
+
+If there was one report there might be others, so I looked round
+the hut in search of them. There was no trace, however, of
+anything of the kind, nor could I discover any sign which might
+indicate the character or intentions of the man who lived in this
+singular place, save that he must be of Spartan habits and cared
+little for the comforts of life. When I thought of the heavy
+rains and looked at the gaping roof I understood how strong and
+immutable must be the purpose which had kept him in that
+inhospitable abode. Was he our malignant enemy, or was he by
+chance our guardian angel? I swore that I would not leave the hut
+until I knew.
+
+Outside the sun was sinking low and the west was blazing with
+scarlet and gold. Its reflection was shot back in ruddy patches
+by the distant pools which lay amid the great Grimpen Mire. There
+were the two towers of Baskerville Hall, and there a distant blur
+of smoke which marked the village of Grimpen. Between the two,
+behind the hill, was the house of the Stapletons. All was sweet
+and mellow and peaceful in the golden evening light, and yet as I
+looked at them my soul shared none of the peace of nature but
+quivered at the vagueness and the terror of that interview which
+every instant was bringing nearer. With tingling nerves, but a
+fixed purpose, I sat in the dark recess of the hut and waited
+with sombre patience for the coming of its tenant.
+
+And then at last I heard him. Far away came the sharp clink of a
+boot striking upon a stone. Then another and yet another, coming
+nearer and nearer. I shrank back into the darkest corner, and
+cocked the pistol in my pocket, determined not to discover myself
+until I had an opportunity of seeing something of the stranger.
+There was a long pause which showed that he had stopped. Then
+once more the footsteps approached and a shadow fell across the
+opening of the hut.
+
+"It is a lovely evening, my dear Watson," said a well-known
+voice. "I really think that you will be more comfortable outside
+than in."
+
+
+
+
+Chapter 12
+
+Death on the Moor
+
+
+For a moment or two I sat breathless, hardly able to believe my
+ears. Then my senses and my voice came back to me, while a
+crushing weight of responsibility seemed in an instant to be
+lifted from my soul. That cold, incisive, ironical voice could
+belong to but one man in all the world.
+
+"Holmes!" I cried--"Holmes!"
+
+"Come out," said he, "and please be careful with the revolver."
+
+I stooped under the rude lintel, and there he sat upon a stone
+outside, his gray eyes dancing with amusement as they fell upon
+my astonished features. He was thin and worn, but clear and
+alert, his keen face bronzed by the sun and roughened by the
+wind. In his tweed suit and cloth cap he looked like any other
+tourist upon the moor, and he had contrived, with that cat-like
+love of personal cleanliness which was one of his
+characteristics, that his chin should be as smooth and his linen
+as perfect as if he were in Baker Street.
+
+"I never was more glad to see anyone in my life," said I, as I
+wrung him by the hand.
+
+"Or more astonished, eh?"
+
+"Well, I must confess to it."
+
+"The surprise was not all on one side, I assure you. I had no
+idea that you had found my occasional retreat, still less that
+you were inside it, until I was within twenty paces of the door."
+
+"My footprint, I presume?"
+
+"No, Watson; I fear that I could not undertake to recognize your
+footprint amid all the footprints of the world. If you seriously
+desire to deceive me you must change your tobacconist; for when I
+see the stub of a cigarette marked Bradley, Oxford Street, I know
+that my friend Watson is in the neighbourhood. You will see it
+there beside the path. You threw it down, no doubt, at that
+supreme moment when you charged into the empty hut."
+
+"Exactly."
+
+"I thought as much--and knowing your admirable tenacity I was
+convinced that you were sitting in ambush, a weapon within reach,
+waiting for the tenant to return. So you actually thought that I
+was the criminal?"
+
+"I did not know who you were, but I was determined to find out."
+
+"Excellent, Watson! And how did you localize me? You saw me,
+perhaps, on the night of the convict hunt, when I was so
+imprudent as to allow the moon to rise behind me?"
+
+"Yes, I saw you then."
+
+"And have no doubt searched all the huts until you came to this
+one?"
+
+"No, your boy had been observed, and that gave me a guide where
+to look."
+
+"The old gentleman with the telescope, no doubt. I could not make
+it out when first I saw the light flashing upon the lens." He
+rose and peeped into the hut. "Ha, I see that Cartwright has
+brought up some supplies. What's this paper? So you have been to
+Coombe Tracey, have you?"
+
+"Yes."
+
+"To see Mrs. Laura Lyons?"
+
+"Exactly."
+
+"Well done! Our researches have evidently been running on
+parallel lines, and when we unite our results I expect we shall
+have a fairly full knowledge of the case."
+
+"Well, I am glad from my heart that you are here, for indeed the
+responsibility and the mystery were both becoming too much for my
+nerves. But how in the name of wonder did you come here, and what
+have you been doing? I thought that you were in Baker Street
+working out that case of blackmailing."
+
+"That was what I wished you to think."
+
+"Then you use me, and yet do not trust me!" I cried with some
+bitterness. "I think that I have deserved better at your hands,
+Holmes."
+
+"My dear fellow, you have been invaluable to me in this as in
+many other cases, and I beg that you will forgive me if I have
+seemed to play a trick upon you. In truth, it was partly for your
+own sake that I did it, and it was my appreciation of the danger
+which you ran which led me to come down and examine the matter
+for myself. Had I been with Sir Henry and you it is confident
+that my point of view would have been the same as yours, and my
+presence would have warned our very formidable opponents to be on
+their guard. As it is, I have been able to get about as I could
+not possibly have done had I been living in the Hall, and I
+remain an unknown factor in the business, ready to throw in all
+my weight at a critical moment."
+
+"But why keep me in the dark?"
+
+"For you to know could not have helped us, and might possibly
+have led to my discovery. You would have wished to tell me
+something, or in your kindness you would have brought me out some
+comfort or other, and so an unnecessary risk would be run. I
+brought Cartwright down with me--you remember the little chap at
+the express office--and he has seen after my simple wants: a loaf
+of bread and a clean collar. What does man want more? He has
+given me an extra pair of eyes upon a very active pair of feet,
+and both have been invaluable."
+
+"Then my reports have all been wasted!"--My voice trembled as I
+recalled the pains and the pride with which I had composed them.
+
+Holmes took a bundle of papers from his pocket.
+
+"Here are your reports, my dear fellow, and very well thumbed, I
+assure you. I made excellent arrangements, and they are only
+delayed one day upon their way. I must compliment you exceedingly
+upon the zeal and the intelligence which you have shown over an
+extraordinarily difficult case."
+
+I was still rather raw over the deception which had been
+practised upon me, but the warmth of Holmes's praise drove my
+anger from my mind. I felt also in my heart that he was right in
+what he said and that it was really best for our purpose that I
+should not have known that he was upon the moor.
+
+"That's better," said he, seeing the shadow rise from my face.
+"And now tell me the result of your visit to Mrs. Laura Lyons--it
+was not difficult for me to guess that it was to see her that you
+had gone, for I am already aware that she is the one person in
+Coombe Tracey who might be of service to us in the matter. In
+fact, if you had not gone to-day it is exceedingly probable that
+I should have gone to-morrow."
+
+The sun had set and dusk was settling over the moor. The air had
+turned chill and we withdrew into the hut for warmth. There,
+sitting together in the twilight, I told Holmes of my
+conversation with the lady. So interested was he that I had to
+repeat some of it twice before he was satisfied.
+
+"This is most important," said he when I had concluded. "It fills
+up a gap which I had been unable to bridge, in this most complex
+affair. You are aware, perhaps, that a close intimacy exists
+between this lady and the man Stapleton?"
+
+"I did not know of a close intimacy."
+
+"There can be no doubt about the matter. They meet, they write,
+there is a complete understanding between them. Now, this puts a
+very powerful weapon into our hands. If I could only use it to
+detach his wife----"
+
+"His wife?"
+
+"I am giving you some information now, in return for all that you
+have given me. The lady who has passed here as Miss Stapleton is
+in reality his wife."
+
+"Good heavens, Holmes! Are you sure of what you say? How could he
+have permitted Sir Henry to fall in love with her?"
+
+"Sir Henry's falling in love could do no harm to anyone except
+Sir Henry. He took particular care that Sir Henry did not make
+love to her, as you have yourself observed. I repeat that the
+lady is his wife and not his sister."
+
+"But why this elaborate deception?"
+
+"Because he foresaw that she would be very much more useful to
+him in the character of a free woman."
+
+All my unspoken instincts, my vague suspicions, suddenly took
+shape and centred upon the naturalist. In that impassive,
+colourless man, with his straw hat and his butterfly-net, I
+seemed to see something terrible--a creature of infinite patience
+and craft, with a smiling face and a murderous heart.
+
+"It is he, then, who is our enemy--it is he who dogged us in
+London?"
+
+"So I read the riddle."
+
+"And the warning--it must have come from her!"
+
+"Exactly."
+
+The shape of some monstrous villainy, half seen, half guessed,
+loomed through the darkness which had girt me so long.
+
+"But are you sure of this, Holmes? How do you know that the woman
+is his wife?"
+
+"Because he so far forgot himself as to tell you a true piece of
+autobiography upon the occasion when he first met you, and I
+dare say he has many a time regretted it since. He was once a
+schoolmaster in the north of England. Now, there is no one more
+easy to trace than a schoolmaster. There are scholastic agencies
+by which one may identify any man who has been in the profession.
+A little investigation showed me that a school had come to grief
+under atrocious circumstances, and that the man who had owned
+it--the name was different--had disappeared with his wife. The
+descriptions agreed. When I learned that the missing man was
+devoted to entomology the identification was complete."
+
+The darkness was rising, but much was still hidden by the
+shadows.
+
+"If this woman is in truth his wife, where does Mrs. Laura Lyons
+come in?" I asked.
+
+"That is one of the points upon which your own researches have
+shed a light. Your interview with the lady has cleared the
+situation very much. I did not know about a projected divorce
+between herself and her husband. In that case, regarding
+Stapleton as an unmarried man, she counted no doubt upon becoming
+his wife."
+
+"And when she is undeceived?"
+
+"Why, then we may find the lady of service. It must be our first
+duty to see her--both of us--to-morrow. Don't you think, Watson,
+that you are away from your charge rather long? Your place should
+be at Baskerville Hall."
+
+The last red streaks had faded away in the west and night had
+settled upon the moor. A few faint stars were gleaming in a
+violet sky.
+
+"One last question, Holmes," I said, as I rose. "Surely there is
+no need of secrecy between you and me. What is the meaning of it
+all? What is he after?"
+
+Holmes's voice sank as he answered:----
+
+"It is murder, Watson--refined, cold-blooded, deliberate murder.
+Do not ask me for particulars. My nets are closing upon him, even
+as his are upon Sir Henry, and with your help he is already
+almost at my mercy. There is but one danger which can threaten
+us. It is that he should strike before we are ready to do so.
+Another day--two at the most--and I have my case complete, but
+until then guard your charge as closely as ever a fond mother
+watched her ailing child. Your mission to-day has justified
+itself, and yet I could almost wish that you had not left his
+side. Hark!"
+
+A terrible scream--a prolonged yell of horror and anguish--burst
+out of the silence of the moor. That frightful cry turned the
+blood to ice in my veins.
+
+"Oh, my God!" I gasped. "What is it? What does it mean?"
+
+Holmes had sprung to his feet, and I saw his dark, athletic
+outline at the door of the hut, his shoulders stooping, his head
+thrust forward, his face peering into the darkness.
+
+"Hush!" he whispered. "Hush!"
+
+The cry had been loud on account of its vehemence, but it had
+pealed out from somewhere far off on the shadowy plain. Now it
+burst upon our ears, nearer, louder, more urgent than before.
+
+"Where is it?" Holmes whispered; and I knew from the thrill of
+his voice that he, the man of iron, was shaken to the soul.
+"Where is it, Watson?"
+
+"There, I think." I pointed into the darkness.
+
+"No, there!"
+
+Again the agonized cry swept through the silent night, louder and
+much nearer than ever. And a new sound mingled with it, a deep,
+muttered rumble, musical and yet menacing, rising and falling
+like the low, constant murmur of the sea.
+
+"The hound!" cried Holmes. "Come, Watson, come! Great heavens, if
+we are too late!"
+
+He had started running swiftly over the moor, and I had followed
+at his heels. But now from somewhere among the broken ground
+immediately in front of us there came one last despairing yell,
+and then a dull, heavy thud. We halted and listened. Not another
+sound broke the heavy silence of the windless night.
+
+I saw Holmes put his hand to his forehead like a man distracted.
+He stamped his feet upon the ground.
+
+"He has beaten us, Watson. We are too late."
+
+"No, no, surely not!"
+
+"Fool that I was to hold my hand. And you, Watson, see what comes
+of abandoning your charge! But, by Heaven, if the worst has
+happened, we'll avenge him!"
+
+Blindly we ran through the gloom, blundering against boulders,
+forcing our way through gorse bushes, panting up hills and
+rushing down slopes, heading always in the direction whence those
+dreadful sounds had come. At every rise Holmes looked eagerly
+round him, but the shadows were thick upon the moor, and nothing
+moved upon its dreary face.
+
+"Can you see anything?"
+
+"Nothing."
+
+"But, hark, what is that?"
+
+A low moan had fallen upon our ears. There it was again upon our
+left! On that side a ridge of rocks ended in a sheer cliff which
+overlooked a stone-strewn slope. On its jagged face was
+spread-eagled some dark, irregular object. As we ran towards it
+the vague outline hardened into a definite shape. It was a
+prostrate man face downward upon the ground, the head doubled
+under him at a horrible angle, the shoulders rounded and the body
+hunched together as if in the act of throwing a somersault. So
+grotesque was the attitude that I could not for the instant
+realize that that moan had been the passing of his soul. Not a
+whisper, not a rustle, rose now from the dark figure over which
+we stooped. Holmes laid his hand upon him, and held it up again,
+with an exclamation of horror. The gleam of the match which he
+struck shone upon his clotted fingers and upon the ghastly pool
+which widened slowly from the crushed skull of the victim. And it
+shone upon something else which turned our hearts sick and faint
+within us--the body of Sir Henry Baskerville!
+
+There was no chance of either of us forgetting that peculiar
+ruddy tweed suit--the very one which he had worn on the first
+morning that we had seen him in Baker Street. We caught the one
+clear glimpse of it, and then the match flickered and went out,
+even as the hope had gone out of our souls. Holmes groaned, and
+his face glimmered white through the darkness.
+
+"The brute! the brute!" I cried with clenched hands. "Oh Holmes,
+I shall never forgive myself for having left him to his fate."
+
+"I am more to blame than you, Watson. In order to have my case
+well rounded and complete, I have thrown away the life of my
+client. It is the greatest blow which has befallen me in my
+career. But how could I know--how could l know--that he would
+risk his life alone upon the moor in the face of all my
+warnings?"
+
+"That we should have heard his screams--my God, those
+screams!--and yet have been unable to save him! Where is this
+brute of a hound which drove him to his death? It may be lurking
+among these rocks at this instant. And Stapleton, where is he? He
+shall answer for this deed."
+
+"He shall. I will see to that. Uncle and nephew have been
+murdered--the one frightened to death by the very sight of a
+beast which he thought to be supernatural, the other driven to
+his end in his wild flight to escape from it. But now we have to
+prove the connection between the man and the beast. Save from
+what we heard, we cannot even swear to the existence of the
+latter, since Sir Henry has evidently died from the fall. But, by
+heavens, cunning as he is, the fellow shall be in my power before
+another day is past!"
+
+We stood with bitter hearts on either side of the mangled body,
+overwhelmed by this sudden and irrevocable disaster which had
+brought all our long and weary labours to so piteous an end.
+Then, as the moon rose we climbed to the top of the rocks over
+which our poor friend had fallen, and from the summit we gazed
+out over the shadowy moor, half silver and half gloom. Far away,
+miles off, in the direction of Grimpen, a single steady yellow
+light was shining. It could only come from the lonely abode of
+the Stapletons. With a bitter curse I shook my fist at it as I
+gazed.
+
+"Why should we not seize him at once?"
+
+"Our case is not complete. The fellow is wary and cunning to the
+last degree. It is not what we know, but what we can prove. If we
+make one false move the villain may escape us yet."
+
+"What can we do?"
+
+"There will be plenty for us to do to-morrow. To-night we can
+only perform the last offices to our poor friend."
+
+Together we made our way down the precipitous slope and
+approached the body, black and clear against the silvered stones.
+The agony of those contorted limbs struck me with a spasm of pain
+and blurred my eyes with tears.
+
+"We must send for help, Holmes! We cannot carry him all the way
+to the Hall. Good heavens, are you mad?"
+
+He had uttered a cry and bent over the body. Now he was dancing
+and laughing and wringing my hand. Could this be my stern,
+self-contained friend? These were hidden fires, indeed!
+
+"A beard! A beard! The man has a beard!"
+
+"A beard?"
+
+"It is not the baronet--it is--why, it is my neighbour, the
+convict!"
+
+With feverish haste we had turned the body over, and that
+dripping beard was pointing up to the cold, clear moon. There
+could be no doubt about the beetling forehead, the sunken animal
+eyes. It was indeed the same face which had glared upon me in the
+light of the candle from over the rock--the face of Selden, the
+criminal.
+
+Then in an instant it was all clear to me. I remembered how the
+baronet had told me that he had handed his old wardrobe to
+Barrymore. Barrymore had passed it on in order to help Selden in
+his escape. Boots, shirt, cap--it was all Sir Henry's. The
+tragedy was still black enough, but this man had at least
+deserved death by the laws of his country. I told Holmes how the
+matter stood, my heart bubbling over with thankfulness and joy.
+
+"Then the clothes have been the poor devil's death," said he. "It
+is clear enough that the hound has been laid on from some article
+of Sir Henry's--the boot which was abstracted in the hotel, in
+all probability--and so ran this man down. There is one very
+singular thing, however: How came Selden, in the darkness, to
+know that the hound was on his trail?"
+
+"He heard him."
+
+"To hear a hound upon the moor would not work a hard man like
+this convict into such a paroxysm of terror that he would risk
+recapture by screaming wildly for help. By his cries he must have
+run a long way after he knew the animal was on his track. How did
+he know?"
+
+"A greater mystery to me is why this hound, presuming that all
+our conjectures are correct --"
+
+"I presume nothing."
+
+"Well, then, why this hound should be loose to-night. I suppose
+that it does not always run loose upon the moor. Stapleton would
+not let it go unless he had reason to think that Sir Henry would
+be there."
+
+"My difficulty is the more formidable of the two, for I think
+that we shall very shortly get an explanation of yours, while
+mine may remain forever a mystery. The question now is, what
+shall we do with this poor wretch's body? We cannot leave it here
+to the foxes and the ravens."
+
+"I suggest that we put it in one of the huts until we can
+communicate with the police."
+
+"Exactly. I have no doubt that you and I could carry it so far.
+Halloa, Watson, what's this? It's the man himself, by all that's
+wonderful and audacious! Not a word to show your suspicions--not a
+word, or my plans crumble to the ground."
+
+A figure was approaching us over the moor, and I saw the dull red
+glow of a cigar. The moon shone upon him, and I could distinguish
+the dapper shape and jaunty walk of the naturalist. He stopped
+when he saw us, and then came on again.
+
+"Why, Dr. Watson, that's not you, is it? You are the last man
+that I should have expected to see out on the moor at this time
+of night. But, dear me, what's this? Somebody hurt? Not--don't
+tell me that it is our friend Sir Henry!" He hurried past me and
+stooped over the dead man. I heard a sharp intake of his breath
+and the cigar fell from his fingers.
+
+"Who--who's this?" he stammered.
+
+"It is Selden, the man who escaped from Princetown."
+
+Stapleton turned a ghastly face upon us, but by a supreme effort
+he had overcome his amazement and his disappointment. He looked
+sharply from Holmes to me.
+
+"Dear me! What a very shocking affair! How did he die?"
+
+"He appears to have broken his neck by falling over these rocks.
+My friend and I were strolling on the moor when we heard a cry."
+
+"I heard a cry also. That was what brought me out. I was uneasy
+about Sir Henry."
+
+"Why about Sir Henry in particular?" I could not help asking.
+
+"Because I had suggested that he should come over. When he did
+not come I was surprised, and I naturally became alarmed for his
+safety when I heard cries upon the moor. By the way"--his eyes
+darted again from my face to Holmes's--"did you hear anything
+else besides a cry?"
+
+"No," said Holmes; "did you?"
+
+"No."
+
+"What do you mean, then?"
+
+"Oh, you know the stories that the peasants tell about a phantom
+hound, and so on. It is said to be heard at night upon the moor.
+I was wondering if there were any evidence of such a sound
+to-night."
+
+"We heard nothing of the kind," said I.
+
+"And what is your theory of this poor fellow's death?"
+
+"I have no doubt that anxiety and exposure have driven him off
+his head. He has rushed about the moor in a crazy state and
+eventually fallen over here and broken his neck."
+
+"That seems the most reasonable theory," said Stapleton, and he
+gave a sigh which I took to indicate his relief. "What do you
+think about it, Mr. Sherlock Holmes?"
+
+My friend bowed his compliments.
+
+"You are quick at identification," said he.
+
+"We have been expecting you in these parts since Dr. Watson came
+down. You are in time to see a tragedy."
+
+"Yes, indeed. I have no doubt that my friend's explanation will
+cover the facts. I will take an unpleasant remembrance back to
+London with me to-morrow."
+
+"Oh, you return to-morrow?"
+
+"That is my intention."
+
+"I hope your visit has cast some light upon those occurrences
+which have puzzled us?"
+
+Holmes shrugged his shoulders.
+
+"One cannot always have the success for which one hopes. An
+investigator needs facts, and not legends or rumours. It has not
+been a satisfactory case."
+
+My friend spoke in his frankest and most unconcerned manner.
+Stapleton still looked hard at him. Then he turned to me.
+
+"I would suggest carrying this poor fellow to my house, but it
+would give my sister such a fright that I do not feel justified
+in doing it. I think that if we put something over his face he
+will be safe until morning."
+
+And so it was arranged. Resisting Stapleton's offer of
+hospitality, Holmes and I set off to Baskerville Hall, leaving
+the naturalist to return alone. Looking back we saw the figure
+moving slowly away over the broad moor, and behind him that one
+black smudge on the silvered slope which showed where the man was
+lying who had come so horribly to his end.
+
+
+
+
+Chapter 13
+
+Fixing the Nets
+
+
+"We're at close grips at last," said Holmes as we walked together
+across the moor. "What a nerve the fellow has! How he pulled
+himself together in the face of what must have been a paralyzing
+shock when he found that the wrong man had fallen a victim to his
+plot. I told you in London, Watson, and I tell you now again,
+that we have never had a foeman more worthy of our steel."
+
+"I am sorry that he has seen you."
+
+"And so was I at first. But there was no getting out of it."
+
+"What effect do you think it will have upon his plans now that he
+knows you are here?"
+
+"It may cause him to be more cautious, or it may drive him to
+desperate measures at once. Like most clever criminals, he may be
+too confident in his own cleverness and imagine that he has
+completely deceived us."
+
+"Why should we not arrest him at once?"
+
+"My dear Watson, you were born to be a man of action. Your
+instinct is always to do something energetic. But supposing, for
+argument's sake, that we had him arrested to-night, what on earth
+the better off should we be for that? We could prove nothing
+against him. There's the devilish cunning of it! If he were
+acting through a human agent we could get some evidence, but if
+we were to drag this great dog to the light of day it would not
+help us in putting a rope round the neck of its master."
+
+"Surely we have a case."
+
+"Not a shadow of one--only surmise and conjecture. We should be
+laughed out of court if we came with such a story and such
+evidence."
+
+"There is Sir Charles's death."
+
+"Found dead without a mark upon him. You and I know that he died
+of sheer fright, and we know also what frightened him; but how
+are we to get twelve stolid jurymen to know it? What signs are
+there of a hound? Where are the marks of its fangs? Of course we
+know that a hound does not bite a dead body and that Sir Charles
+was dead before ever the brute overtook him. But we have to prove
+all this, and we are not in a position to do it."
+
+"Well, then, to-night?"
+
+"We are not much better off to-night. Again, there was no direct
+connection between the hound and the man's death. We never saw
+the hound. We heard it; but we could not prove that it was
+running upon this man's trail. There is a complete absence of
+motive. No, my dear fellow; we must reconcile ourselves to the
+fact that we have no case at present, and that it is worth our
+while to run any risk in order to establish one."
+
+"And how do you propose to do so?"
+
+"I have great hopes of what Mrs. Laura Lyons may do for us when
+the position of affairs is made clear to her. And I have my own
+plan as well. Sufficient for to-morrow is the evil thereof; but I
+hope before the day is past to have the upper hand at last."
+
+I could draw nothing further from him, and he walked, lost in
+thought, as far as the Baskerville gates.
+
+"Are you coming up?"
+
+"Yes; I see no reason for further concealment. But one last word,
+Watson. Say nothing of the hound to Sir Henry. Let him think that
+Selden's death was as Stapleton would have us believe. He will
+have a better nerve for the ordeal which he will have to undergo
+to-morrow, when he is engaged, if I remember your report aright,
+to dine with these people."
+
+"And so am I."
+
+"Then you must excuse yourself and he must go alone. That will be
+easily arranged. And now, if we are too late for dinner, I think
+that we are both ready for our suppers."
+
+Sir Henry was more pleased than surprised to see Sherlock Holmes,
+for he had for some days been expecting that recent events would
+bring him down from London. He did raise his eyebrows, however,
+when he found that my friend had neither any luggage nor any
+explanations for its absence. Between us we soon supplied his
+wants, and then over a belated supper we explained to the baronet
+as much of our experience as it seemed desirable that he should
+know. But first I had the unpleasant duty of breaking the news to
+Barrymore and his wife. To him it may have been an unmitigated
+relief, but she wept bitterly in her apron. To all the world he
+was the man of violence, half animal and half demon; but to her
+he always remained the little wilful boy of her own girlhood, the
+child who had clung to her hand. Evil indeed is the man who has
+not one woman to mourn him.
+
+"I've been moping in the house all day since Watson went off in
+the morning," said the baronet. "I guess I should have some
+credit, for I have kept my promise. If I hadn't sworn not to go
+about alone I might have had a more lively evening, for I had a
+message from Stapleton asking me over there."
+
+"I have no doubt that you would have had a more lively evening,"
+said Holmes drily. "By the way, I don't suppose you appreciate
+that we have been mourning over you as having broken your neck?"
+
+Sir Henry opened his eyes. "How was that?"
+
+"This poor wretch was dressed in your clothes. I fear your
+servant who gave them to him may get into trouble with the
+police."
+
+"That is unlikely. There was no mark on any of them, as far as I
+know."
+
+"That's lucky for him--in fact, it's lucky for all of you, since
+you are all on the wrong side of the law in this matter. I am not
+sure that as a conscientious detective my first duty is not to
+arrest the whole household. Watson's reports are most
+incriminating documents."
+
+"But how about the case?" asked the baronet. "Have you made
+anything out of the tangle? I don't know that Watson and I are
+much the wiser since we came down."
+
+"I think that I shall be in a position to make the situation
+rather more clear to you before long. It has been an exceedingly
+difficult and most complicated business. There are several points
+upon which we still want light--but it is coming all the same."
+
+"We've had one experience, as Watson has no doubt told you. We
+heard the hound on the moor, so I can swear that it is not all
+empty superstition. I had something to do with dogs when I was
+out West, and I know one when I hear one. If you can muzzle that
+one and put him on a chain I'll be ready to swear you are the
+greatest detective of all time."
+
+"I think I will muzzle him and chain him all right if you will
+give me your help."
+
+"Whatever you tell me to do I will do."
+
+"Very good; and I will ask you also to do it blindly, without
+always asking the reason."
+
+"Just as you like."
+
+"If you will do this I think the chances are that our little
+problem will soon be solved. I have no doubt----"
+
+He stopped suddenly and stared fixedly up over my head into the
+air. The lamp beat upon his face, and so intent was it and so
+still that it might have been that of a clear-cut classical
+statue, a personification of alertness and expectation.
+
+"What is it?" we both cried.
+
+I could see as he looked down that he was repressing some
+internal emotion. His features were still composed, but his eyes
+shone with amused exultation.
+
+"Excuse the admiration of a connoisseur," said he as he waved his
+hand towards the line of portraits which covered the opposite
+wall. "Watson won't allow that I know anything of art, but that
+is mere jealousy, because our views upon the subject differ. Now,
+these are a really very fine series of portraits."
+
+"Well, I'm glad to hear you say so," said Sir Henry, glancing
+with some surprise at my friend. "I don't pretend to know much
+about these things, and I'd be a better judge of a horse or a
+steer than of a picture. I didn't know that you found time for
+such things."
+
+"I know what is good when I see it, and I see it now. That's a
+Kneller, I'll swear, that lady in the blue silk over yonder, and
+the stout gentleman with the wig ought to be a Reynolds. They are
+all family portraits, I presume?"
+
+"Every one."
+
+"Do you know the names?"
+
+"Barrymore has been coaching me in them, and I think I can say my
+lessons fairly well."
+
+"Who is the gentleman with the telescope?"
+
+"That is Rear-Admiral Baskerville, who served under Rodney in the
+West Indies. The man with the blue coat and the roll of paper is
+Sir William Baskerville, who was Chairman of Committees of the
+House of Commons under Pitt."
+
+"And this Cavalier opposite to me--the one with the black velvet
+and the lace?"
+
+"Ah, you have a right to know about him. That is the cause of all
+the mischief, the wicked Hugo, who started the Hound of the
+Baskervilles. We're not likely to forget him."
+
+I gazed with interest and some surprise upon the portrait.
+
+"Dear me!" said Holmes, "he seems a quiet, meek-mannered man
+enough, but I dare say that there was a lurking devil in his
+eyes. I had pictured him as a more robust and ruffianly person."
+
+"There's no doubt about the authenticity, for the name and the
+date, 1647, are on the back of the canvas."
+
+Holmes said little more, but the picture of the old roysterer
+seemed to have a fascination for him, and his eyes were
+continually fixed upon it during supper. It was not until later,
+when Sir Henry had gone to his room, that I was able to follow
+the trend of his thoughts. He led me back into the
+banqueting-hall, his bedroom candle in his hand, and he held it
+up against the time-stained portrait on the wall.
+
+"Do you see anything there?"
+
+I looked at the broad plumed hat, the curling love-locks, the
+white lace collar, and the straight, severe face which was framed
+between them. It was not a brutal countenance, but it was prim,
+hard, and stern, with a firm-set, thin-lipped mouth, and a coldly
+intolerant eye.
+
+"Is it like anyone you know?"
+
+"There is something of Sir Henry about the jaw."
+
+"Just a suggestion, perhaps. But wait an instant!" He stood upon
+a chair, and, holding up the light in his left hand, he curved
+his right arm over the broad hat and round the long ringlets.
+
+"Good heavens!" I cried, in amazement.
+
+The face of Stapleton had sprung out of the canvas.
+
+"Ha, you see it now. My eyes have been trained to examine faces
+and not their trimmings. It is the first quality of a criminal
+investigator that he should see through a disguise."
+
+"But this is marvellous. It might be his portrait."
+
+"Yes, it is an interesting instance of a throwback, which appears
+to be both physical and spiritual. A study of family portraits is
+enough to convert a man to the doctrine of reincarnation. The
+fellow is a Baskerville--that is evident."
+
+"With designs upon the succession."
+
+"Exactly. This chance of the picture has supplied us with one of
+our most obvious missing links. We have him, Watson, we have him,
+and I dare swear that before to-morrow night he will be
+fluttering in our net as helpless as one of his own butterflies.
+A pin, a cork, and a card, and we add him to the Baker Street
+collection!" He burst into one of his rare fits of laughter as he
+turned away from the picture. I have not heard him laugh often,
+and it has always boded ill to somebody.
+
+I was up betimes in the morning, but Holmes was afoot earlier
+still, for I saw him as I dressed, coming up the drive.
+
+"Yes, we should have a full day to-day," he remarked, and he
+rubbed his hands with the joy of action. "The nets are all in
+place, and the drag is about to begin. We'll know before the day
+is out whether we have caught our big, lean-jawed pike, or
+whether he has got through the meshes."
+
+"Have you been on the moor already?"
+
+"I have sent a report from Grimpen to Princetown as to the death
+of Selden. I think I can promise that none of you will be
+troubled in the matter. And I have also communicated with my
+faithful Cartwright, who would certainly have pined away at the
+door of my hut, as a dog does at his master's grave, if I had not
+set his mind at rest about my safety."
+
+"What is the next move?"
+
+"To see Sir Henry. Ah, here he is!"
+
+"Good morning, Holmes," said the baronet. "You look like a
+general who is planning a battle with his chief of the staff."
+
+"That is the exact situation. Watson was asking for orders."
+
+"And so do I."
+
+"Very good. You are engaged, as I understand, to dine with our
+friends the Stapletons to-night."
+
+"I hope that you will come also. They are very hospitable people,
+and I am sure that they would be very glad to see you."
+
+"I fear that Watson and I must go to London."
+
+"To London?"
+
+"Yes, I think that we should be more useful there at the present
+juncture."
+
+The baronet's face perceptibly lengthened.
+
+"I hoped that you were going to see me through this business. The
+Hall and the moor are not very pleasant places when one is
+alone."
+
+"My dear fellow, you must trust me implicitly and do exactly what
+I tell you. You can tell your friends that we should have been
+happy to have come with you, but that urgent business required us
+to be in town. We hope very soon to return to Devonshire. Will
+you remember to give them that message?"
+
+"If you insist upon it."
+
+"There is no alternative, I assure you."
+
+I saw by the baronet's clouded brow that he was deeply hurt by
+what he regarded as our desertion.
+
+"When do you desire to go?" he asked coldly.
+
+"Immediately after breakfast. We will drive in to Coombe Tracey,
+but Watson will leave his things as a pledge that he will come
+back to you. Watson, you will send a note to Stapleton to tell
+him that you regret that you cannot come."
+
+"I have a good mind to go to London with you," said the baronet.
+"Why should I stay here alone?"
+
+"Because it is your post of duty. Because you gave me your word
+that you would do as you were told, and I tell you to stay."
+
+"All right, then, I'll stay."
+
+"One more direction! I wish you to drive to Merripit House. Send
+back your trap, however, and let them know that you intend to
+walk home."
+
+"To walk across the moor?"
+
+"Yes."
+
+"But that is the very thing which you have so often cautioned me
+not to do."
+
+"This time you may do it with safety. If I had not every
+confidence in your nerve and courage I would not suggest it, but
+it is essential that you should do it."
+
+"Then I will do it."
+
+"And as you value your life do not go across the moor in any
+direction save along the straight path which leads from Merripit
+House to the Grimpen Road, and is your natural way home."
+
+"I will do just what you say."
+
+"Very good. I should be glad to get away as soon after breakfast
+as possible, so as to reach London in the afternoon."
+
+I was much astounded by this programme, though I remembered that
+Holmes had said to Stapleton on the night before that his visit
+would terminate next day. It had not crossed my mind, however,
+that he would wish me to go with him, nor could I understand how
+we could both be absent at a moment which he himself declared to
+be critical. There was nothing for it, however, but implicit
+obedience; so we bade good-bye to our rueful friend, and a couple
+of hours afterwards we were at the station of Coombe Tracey and
+had dispatched the trap upon its return journey. A small boy was
+waiting upon the platform.
+
+"Any orders, sir?"
+
+"You will take this train to town, Cartwright. The moment you
+arrive you will send a wire to Sir Henry Baskerville, in my name,
+to say that if he finds the pocket-book which I have dropped he
+is to send it by registered post to Baker Street."
+
+"Yes, sir."
+
+"And ask at the station office if there is a message for me."
+
+The boy returned with a telegram, which Holmes handed to me. It
+ran: "Wire received. Coming down with unsigned warrant. Arrive
+five-forty.--LESTRADE."
+
+"That is in answer to mine of this morning. He is the best of the
+professionals, I think, and we may need his assistance. Now,
+Watson, I think that we cannot employ our time better than by
+calling upon your acquaintance, Mrs. Laura Lyons."
+
+His plan of campaign was beginning to be evident. He would use
+the baronet in order to convince the Stapletons that we were
+really gone, while we should actually return at the instant when
+we were likely to be needed. That telegram from London, if
+mentioned by Sir Henry to the Stapletons, must remove the last
+suspicions from their minds. Already I seemed to see our nets
+drawing closer around that lean-jawed pike.
+
+Mrs. Laura Lyons was in her office, and Sherlock Holmes opened
+his interview with a frankness and directness which considerably
+amazed her.
+
+"I am investigating the circumstances which attended the death of
+the late Sir Charles Baskerville," said he. "My friend here, Dr.
+Watson, has informed me of what you have communicated, and also
+of what you have withheld in connection with that matter."
+
+"What have I withheld?" she asked defiantly.
+
+"You have confessed that you asked Sir Charles to be at the gate
+at ten o'clock. We know that that was the place and hour of his
+death. You have withheld what the connection is between these
+events."
+
+"There is no connection."
+
+"In that case the coincidence must indeed be an extraordinary
+one. But I think that we shall succeed in establishing a
+connection after all. I wish to be perfectly frank with you, Mrs.
+Lyons. We regard this case as one of murder, and the evidence may
+implicate not only your friend Mr. Stapleton, but his wife as
+well."
+
+The lady sprang from her chair.
+
+"His wife!" she cried.
+
+"The fact is no longer a secret. The person who has passed for
+his sister is really his wife."
+
+Mrs. Lyons had resumed her seat. Her hands were grasping the arms
+of her chair, and I saw that the pink nails had turned white with
+the pressure of her grip.
+
+"His wife!" she said again. "His wife! He is not a married man."
+
+Sherlock Holmes shrugged his shoulders.
+
+"Prove it to me! Prove it to me! And if you can do so --!" The
+fierce flash of her eyes said more than any words.
+
+"I have come prepared to do so," said Holmes, drawing several
+papers from his pocket. "Here is a photograph of the couple taken
+in York four years ago. It is indorsed 'Mr. and Mrs. Vandeleur,'
+but you will have no difficulty in recognizing him, and her also,
+if you know her by sight. Here are three written descriptions by
+trustworthy witnesses of Mr. and Mrs. Vandeleur, who at that time
+kept St. Oliver's private school. Read them and see if you can
+doubt the identity of these people."
+
+She glanced at them, and then looked up at us with the set, rigid
+face of a desperate woman.
+
+"Mr. Holmes," she said, "this man had offered me marriage on
+condition that I could get a divorce from my husband. He has lied
+to me, the villain, in every conceivable way. Not one word of
+truth has he ever told me. And why--why? I imagined that all was
+for my own sake. But now I see that I was never anything but a
+tool in his hands. Why should I preserve faith with him who never
+kept any with me? Why should I try to shield him from the
+consequences of his own wicked acts? Ask me what you like, and
+there is nothing which I shall hold back. One thing I swear to
+you, and that is that when I wrote the letter I never dreamed of
+any harm to the old gentleman, who had been my kindest friend."
+
+"I entirely believe you, madam," said Sherlock Holmes. "The
+recital of these events must be very painful to you, and perhaps
+it will make it easier if I tell you what occurred, and you can
+check me if I make any material mistake. The sending of this
+letter was suggested to you by Stapleton?"
+
+"He dictated it."
+
+"I presume that the reason he gave was that you would receive
+help from Sir Charles for the legal expenses connected with your
+divorce?"
+
+"Exactly."
+
+"And then after you had sent the letter he dissuaded you from
+keeping the appointment?"
+
+"He told me that it would hurt his self-respect that any other
+man should find the money for such an object, and that though he
+was a poor man himself he would devote his last penny to removing
+the obstacles which divided us."
+
+"He appears to be a very consistent character. And then you heard
+nothing until you read the reports of the death in the paper?"
+
+"No."
+
+"And he made you swear to say nothing about your appointment with
+Sir Charles?"
+
+"He did. He said that the death was a very mysterious one, and
+that I should certainly be suspected if the facts came out. He
+frightened me into remaining silent."
+
+"Quite so. But you had your suspicions?"
+
+She hesitated and looked down.
+
+"I knew him," she said. "But if he had kept faith with me I
+should always have done so with him."
+
+"I think that on the whole you have had a fortunate escape," said
+Sherlock Holmes. "You have had him in your power and he knew it,
+and yet you are alive. You have been walking for some months very
+near to the edge of a precipice. We must wish you good-morning
+now, Mrs. Lyons, and it is probable that you will very shortly
+hear from us again."
+
+"Our case becomes rounded off, and difficulty after difficulty
+thins away in front of us," said Holmes as we stood waiting for
+the arrival of the express from town. "I shall soon be in the
+position of being able to put into a single connected narrative
+one of the most singular and sensational crimes of modern times.
+Students of criminology will remember the analogous incidents in
+Godno, in Little Russia, in the year '66, and of course there are
+the Anderson murders in North Carolina, but this case possesses
+some features which are entirely its own. Even now we have no
+clear case against this very wily man. But I shall be very much
+surprised if it is not clear enough before we go to bed this
+night."
+
+The London express came roaring into the station, and a small,
+wiry bulldog of a man had sprung from a first-class carriage. We
+all three shook hands, and I saw at once from the reverential way
+in which Lestrade gazed at my companion that he had learned a
+good deal since the days when they had first worked together. I
+could well remember the scorn which the theories of the reasoner
+used then to excite in the practical man.
+
+"Anything good?" he asked.
+
+"The biggest thing for years," said Holmes. "We have two hours
+before we need think of starting. I think we might employ it in
+getting some dinner and then, Lestrade, we will take the London
+fog out of your throat by giving you a breath of the pure night
+air of Dartmoor. Never been there? Ah, well, I don't suppose you
+will forget your first visit."
+
+
+
+
+Chapter 14
+
+The Hound of the Baskervilles
+
+
+One of Sherlock Holmes's defects--if, indeed, one may call it a
+defect--was that he was exceedingly loath to communicate his full
+plans to any other person until the instant of their fulfilment.
+Partly it came no doubt from his own masterful nature, which
+loved to dominate and surprise those who were around him. Partly
+also from his professional caution, which urged him never to take
+any chances. The result, however, was very trying for those who
+were acting as his agents and assistants. I had often suffered
+under it, but never more so than during that long drive in the
+darkness. The great ordeal was in front of us; at last we were
+about to make our final effort, and yet Holmes had said nothing,
+and I could only surmise what his course of action would be. My
+nerves thrilled with anticipation when at last the cold wind upon
+our faces and the dark, void spaces on either side of the narrow
+road told me that we were back upon the moor once again. Every
+stride of the horses and every turn of the wheels was taking us
+nearer to our supreme adventure.
+
+Our conversation was hampered by the presence of the driver of
+the hired wagonette, so that we were forced to talk of trivial
+matters when our nerves were tense with emotion and anticipation.
+It was a relief to me, after that unnatural restraint, when we at
+last passed Frankland's house and knew that we were drawing near
+to the Hall and to the scene of action. We did not drive up to
+the door but got down near the gate of the avenue. The wagonette
+was paid off and ordered to return to Coombe Tracey forthwith,
+while we started to walk to Merripit House.
+
+"Are you armed, Lestrade?"
+
+The little detective smiled.
+
+"As long as I have my trousers I have a hip-pocket, and as long
+as I have my hip-pocket I have something in it."
+
+"Good! My friend and I are also ready for emergencies."
+
+"You're mighty close about this affair, Mr. Holmes. What's the
+game now?"
+
+"A waiting game."
+
+"My word, it does not seem a very cheerful place," said the
+detective with a shiver, glancing round him at the gloomy slopes
+of the hill and at the huge lake of fog which lay over the
+Grimpen Mire. "I see the lights of a house ahead of us."
+
+"That is Merripit House and the end of our journey. I must
+request you to walk on tiptoe and not to talk above a whisper."
+
+We moved cautiously along the track as if we were bound for the
+house, but Holmes halted us when we were about two hundred yards
+from it.
+
+"This will do," said he. "These rocks upon the right make an
+admirable screen."
+
+"We are to wait here?"
+
+"Yes, we shall make our little ambush here. Get into this hollow,
+Lestrade. You have been inside the house, have you not, Watson?
+Can you tell the position of the rooms? What are those latticed
+windows at this end?"
+
+"I think they are the kitchen windows."
+
+"And the one beyond, which shines so brightly?"
+
+"That is certainly the dining-room."
+
+"The blinds are up. You know the lie of the land best. Creep
+forward quietly and see what they are doing--but for heaven's
+sake don't let them know that they are watched!"
+
+I tiptoed down the path and stooped behind the low wall which
+surrounded the stunted orchard. Creeping in its shadow I reached
+a point whence I could look straight through the uncurtained
+window.
+
+There were only two men in the room, Sir Henry and Stapleton.
+They sat with their profiles towards me on either side of the
+round table. Both of them were smoking cigars, and coffee and
+wine were in front of them. Stapleton was talking with animation,
+but the baronet looked pale and distrait. Perhaps the thought of
+that lonely walk across the ill-omened moor was weighing heavily
+upon his mind.
+
+As I watched them Stapleton rose and left the room, while Sir
+Henry filled his glass again and leaned back in his chair,
+puffing at his cigar. I heard the creak of a door and the crisp
+sound of boots upon gravel. The steps passed along the path on
+the other side of the wall under which I crouched. Looking over,
+I saw the naturalist pause at the door of an out-house in the
+corner of the orchard. A key turned in a lock, and as he passed
+in there was a curious scuffling noise from within. He was only a
+minute or so inside, and then I heard the key turn once more and
+he passed me and re-entered the house. I saw him rejoin his
+guest, and I crept quietly back to where my companions were
+waiting to tell them what I had seen.
+
+"You say, Watson, that the lady is not there?" Holmes asked, when
+I had finished my report.
+
+"No."
+
+"Where can she be, then, since there is no light in any other
+room except the kitchen?"
+
+"I cannot think where she is."
+
+I have said that over the great Grimpen Mire there hung a dense,
+white fog. It was drifting slowly in our direction, and banked
+itself up like a wall on that side of us, low, but thick and well
+defined. The moon shone on it, and it looked like a great
+shimmering ice-field, with the heads of the distant tors as rocks
+borne upon its surface. Holmes's face was turned towards it, and
+he muttered impatiently as he watched its sluggish drift.
+
+"It's moving towards us, Watson."
+
+"Is that serious?"
+
+"Very serious, indeed--the one thing upon earth which could have
+disarranged my plans. He can't be very long, now. It is already
+ten o'clock. Our success and even his life may depend upon his
+coming out before the fog is over the path."
+
+The night was clear and fine above us. The stars shone cold and
+bright, while a half-moon bathed the whole scene in a soft,
+uncertain light. Before us lay the dark bulk of the house, its
+serrated roof and bristling chimneys hard outlined against the
+silver-spangled sky. Broad bars of golden light from the lower
+windows stretched across the orchard and the moor. One of them
+was suddenly shut off. The servants had left the kitchen. There
+only remained the lamp in the dining-room where the two men, the
+murderous host and the unconscious guest, still chatted over
+their cigars.
+
+Every minute that white woolly plain which covered one half of
+the moor was drifting closer and closer to the house. Already the
+first thin wisps of it were curling across the golden square of
+the lighted window. The farther wall of the orchard was already
+invisible, and the trees were standing out of a swirl of white
+vapour. As we watched it the fog-wreaths came crawling round both
+corners of the house and rolled slowly into one dense bank, on
+which the upper floor and the roof floated like a strange ship
+upon a shadowy sea. Holmes struck his hand passionately upon the
+rock in front of us and stamped his feet in his impatience.
+
+"If he isn't out in a quarter of an hour the path will be
+covered. In half an hour we won't be able to see our hands in
+front of us."
+
+"Shall we move farther back upon higher ground?"
+
+"Yes, I think it would be as well."
+
+So as the fog-bank flowed onward we fell back before it until we
+were half a mile from the house, and still that dense white sea,
+with the moon silvering its upper edge, swept slowly and
+inexorably on.
+
+"We are going too far," said Holmes. "We dare not take the chance
+of his being overtaken before he can reach us. At all costs we
+must hold our ground where we are." He dropped on his knees and
+clapped his ear to the ground. "Thank God, I think that I hear
+him coming."
+
+A sound of quick steps broke the silence of the moor. Crouching
+among the stones we stared intently at the silver-tipped bank in
+front of us. The steps grew louder, and through the fog, as
+through a curtain, there stepped the man whom we were awaiting.
+He looked round him in surprise as he emerged into the clear,
+starlit night. Then he came swiftly along the path, passed close
+to where we lay, and went on up the long slope behind us. As he
+walked he glanced continually over either shoulder, like a man
+who is ill at ease.
+
+"Hist!" cried Holmes, and I heard the sharp click of a cocking
+pistol. "Look out! It's coming!"
+
+There was a thin, crisp, continuous patter from somewhere in the
+heart of that crawling bank. The cloud was within fifty yards of
+where we lay, and we glared at it, all three, uncertain what
+horror was about to break from the heart of it. I was at Holmes's
+elbow, and I glanced for an instant at his face. It was pale and
+exultant, his eyes shining brightly in the moonlight. But
+suddenly they started forward in a rigid, fixed stare, and his
+lips parted in amazement. At the same instant Lestrade gave a
+yell of terror and threw himself face downward upon the ground. I
+sprang to my feet, my inert hand grasping my pistol, my mind
+paralyzed by the dreadful shape which had sprung out upon us from
+the shadows of the fog. A hound it was, an enormous coal-black
+hound, but not such a hound as mortal eyes have ever seen. Fire
+burst from its open mouth, its eyes glowed with a smouldering
+glare, its muzzle and hackles and dewlap were outlined in
+flickering flame. Never in the delirious dream of a disordered
+brain could anything more savage, more appalling, more hellish be
+conceived than that dark form and savage face which broke upon us
+out of the wall of fog.
+
+With long bounds the huge black creature was leaping down the
+track, following hard upon the footsteps of our friend. So
+paralyzed were we by the apparition that we allowed him to pass
+before we had recovered our nerve. Then Holmes and I both fired
+together, and the creature gave a hideous howl, which showed that
+one at least had hit him. He did not pause, however, but bounded
+onward. Far away on the path we saw Sir Henry looking back, his
+face white in the moonlight, his hands raised in horror, glaring
+helplessly at the frightful thing which was hunting him down.
+
+But that cry of pain from the hound had blown all our fears to
+the winds. If he was vulnerable he was mortal, and if we could
+wound him we could kill him. Never have I seen a man run as
+Holmes ran that night. I am reckoned fleet of foot, but he
+outpaced me as much as I outpaced the little professional. In
+front of us as we flew up the track we heard scream after scream
+from Sir Henry and the deep roar of the hound. I was in time to
+see the beast spring upon its victim, hurl him to the ground, and
+worry at his throat. But the next instant Holmes had emptied five
+barrels of his revolver into the creature's flank. With a last
+howl of agony and a vicious snap in the air, it rolled upon its
+back, four feet pawing furiously, and then fell limp upon its
+side. I stooped, panting, and pressed my pistol to the dreadful,
+shimmering head, but it was useless to press the trigger. The
+giant hound was dead.
+
+Sir Henry lay insensible where he had fallen. We tore away his
+collar, and Holmes breathed a prayer of gratitude when we saw
+that there was no sign of a wound and that the rescue had been in
+time. Already our friend's eyelids shivered and he made a feeble
+effort to move. Lestrade thrust his brandy-flask between the
+baronet's teeth, and two frightened eyes were looking up at us.
+
+"My God!" he whispered. "What was it? What, in heaven's name, was
+it?"
+
+"It's dead, whatever it is," said Holmes. "We've laid the family
+ghost once and forever."
+
+In mere size and strength it was a terrible creature which was
+lying stretched before us. It was not a pure bloodhound and it
+was not a pure mastiff; but it appeared to be a combination of
+the two--gaunt, savage, and as large as a small lioness. Even
+now, in the stillness of death, the huge jaws seemed to be
+dripping with a bluish flame and the small, deep-set, cruel eyes
+were ringed with fire. I placed my hand upon the glowing muzzle,
+and as I held them up my own fingers smouldered and gleamed in
+the darkness.
+
+"Phosphorus," I said.
+
+"A cunning preparation of it," said Holmes, sniffing at the dead
+animal. "There is no smell which might have interfered with his
+power of scent. We owe you a deep apology, Sir Henry, for having
+exposed you to this fright. I was prepared for a hound, but not
+for such a creature as this. And the fog gave us little time to
+receive him."
+
+"You have saved my life."
+
+"Having first endangered it. Are you strong enough to stand?"
+
+"Give me another mouthful of that brandy and I shall be ready for
+anything. So! Now, if you will help me up. What do you propose to
+do?"
+
+"To leave you here. You are not fit for further adventures
+to-night. If you will wait, one or other of us will go back with
+you to the Hall."
+
+He tried to stagger to his feet; but he was still ghastly pale
+and trembling in every limb. We helped him to a rock, where he
+sat shivering with his face buried in his hands.
+
+"We must leave you now," said Holmes. "The rest of our work must
+be done, and every moment is of importance. We have our case, and
+now we only want our man.
+
+"It's a thousand to one against our finding him at the house," he
+continued as we retraced our steps swiftly down the path. "Those
+shots must have told him that the game was up."
+
+"We were some distance off, and this fog may have deadened them."
+
+"He followed the hound to call him off--of that you may be
+certain. No, no, he's gone by this time! But we'll search the
+house and make sure."
+
+The front door was open, so we rushed in and hurried from room to
+room to the amazement of a doddering old manservant, who met us
+in the passage. There was no light save in the dining-room, but
+Holmes caught up the lamp and left no corner of the house
+unexplored. No sign could we see of the man whom we were chasing.
+On the upper floor, however, one of the bedroom doors was locked.
+
+"There's someone in here," cried Lestrade. "I can hear a
+movement. Open this door!"
+
+A faint moaning and rustling came from within. Holmes struck the
+door just over the lock with the flat of his foot and it flew
+open. Pistol in hand, we all three rushed into the room.
+
+But there was no sign within it of that desperate and defiant
+villain whom we expected to see. Instead we were faced by an
+object so strange and so unexpected that we stood for a moment
+staring at it in amazement.
+
+The room had been fashioned into a small museum, and the walls
+were lined by a number of glass-topped cases full of that
+collection of butterflies and moths the formation of which had
+been the relaxation of this complex and dangerous man. In the
+centre of this room there was an upright beam, which had been
+placed at some period as a support for the old worm-eaten baulk
+of timber which spanned the roof. To this post a figure was tied,
+so swathed and muffled in the sheets which had been used to
+secure it that one could not for the moment tell whether it was
+that of a man or a woman. One towel passed round the throat and
+was secured at the back of the pillar. Another covered the lower
+part of the face, and over it two dark eyes--eyes full of grief
+and shame and a dreadful questioning--stared back at us. In a
+minute we had torn off the gag, unswathed the bonds, and Mrs.
+Stapleton sank upon the floor in front of us. As her beautiful
+head fell upon her chest I saw the clear red weal of a whiplash
+across her neck.
+
+"The brute!" cried Holmes. "Here, Lestrade, your brandy-bottle!
+Put her in the chair! She has fainted from ill-usage and
+exhaustion."
+
+She opened her eyes again.
+
+"Is he safe?" she asked. "Has he escaped?"
+
+"He cannot escape us, madam."
+
+"No, no, I did not mean my husband. Sir Henry? Is he safe?"
+
+"Yes."
+
+"And the hound?"
+
+"It is dead."
+
+She gave a long sigh of satisfaction.
+
+"Thank God! Thank God! Oh, this villain! See how he has treated
+me!" She shot her arms out from her sleeves, and we saw with
+horror that they were all mottled with bruises. "But this is
+nothing--nothing! It is my mind and soul that he has tortured and
+defiled. I could endure it all, ill-usage, solitude, a life of
+deception, everything, as long as I could still cling to the hope
+that I had his love, but now I know that in this also I have been
+his dupe and his tool." She broke into passionate sobbing as she
+spoke.
+
+"You bear him no good will, madam," said Holmes. "Tell us then
+where we shall find him. If you have ever aided him in evil, help
+us now and so atone."
+
+"There is but one place where he can have fled," she answered.
+"There is an old tin mine on an island in the heart of the mire.
+It was there that he kept his hound and there also he had made
+preparations so that he might have a refuge. That is where he
+would fly."
+
+The fog-bank lay like white wool against the window. Holmes held
+the lamp towards it.
+
+"See," said he. "No one could find his way into the Grimpen Mire
+to-night."
+
+She laughed and clapped her hands. Her eyes and teeth gleamed
+with fierce merriment.
+
+"He may find his way in, but never out," she cried. "How can he
+see the guiding wands to-night? We planted them together, he and
+I, to mark the pathway through the mire. Oh, if I could only have
+plucked them out to-day. Then indeed you would have had him at
+your mercy!"
+
+It was evident to us that all pursuit was in vain until the fog
+had lifted. Meanwhile we left Lestrade in possession of the house
+while Holmes and I went back with the baronet to Baskerville
+Hall. The story of the Stapletons could no longer be withheld
+from him, but he took the blow bravely when he learned the truth
+about the woman whom he had loved. But the shock of the night's
+adventures had shattered his nerves, and before morning he lay
+delirious in a high fever, under the care of Dr. Mortimer. The
+two of them were destined to travel together round the world
+before Sir Henry had become once more the hale, hearty man that
+he had been before he became master of that ill-omened estate.
+
+And now I come rapidly to the conclusion of this singular
+narrative, in which I have tried to make the reader share those
+dark fears and vague surmises which clouded our lives so long and
+ended in so tragic a manner. On the morning after the death of
+the hound the fog had lifted and we were guided by Mrs. Stapleton
+to the point where they had found a pathway through the bog. It
+helped us to realize the horror of this woman's life when we saw
+the eagerness and joy with which she laid us on her husband's
+track. We left her standing upon the thin peninsula of firm,
+peaty soil which tapered out into the widespread bog. From the
+end of it a small wand planted here and there showed where the
+path zigzagged from tuft to tuft of rushes among those
+green-scummed pits and foul quagmires which barred the way to the
+stranger. Rank reeds and lush, slimy water-plants sent an odour
+of decay and a heavy miasmatic vapour onto our faces, while a
+false step plunged us more than once thigh-deep into the dark,
+quivering mire, which shook for yards in soft undulations around
+our feet. Its tenacious grip plucked at our heels as we walked,
+and when we sank into it it was as if some malignant hand was
+tugging us down into those obscene depths, so grim and purposeful
+was the clutch in which it held us. Once only we saw a trace that
+someone had passed that perilous way before us. From amid a tuft
+of cotton grass which bore it up out of the slime some dark thing
+was projecting. Holmes sank to his waist as he stepped from the
+path to seize it, and had we not been there to drag him out he
+could never have set his foot upon firm land again. He held an
+old black boot in the air. "Meyers, Toronto," was printed on the
+leather inside.
+
+"It is worth a mud bath," said he. "It is our friend Sir Henry's
+missing boot."
+
+"Thrown there by Stapleton in his flight."
+
+"Exactly. He retained it in his hand after using it to set the
+hound upon the track. He fled when he knew the game was up, still
+clutching it. And he hurled it away at this point of his flight.
+We know at least that he came so far in safety."
+
+But more than that we were never destined to know, though there
+was much which we might surmise. There was no chance of finding
+footsteps in the mire, for the rising mud oozed swiftly in upon
+them, but as we at last reached firmer ground beyond the morass
+we all looked eagerly for them. But no slightest sign of them
+ever met our eyes. If the earth told a true story, then Stapleton
+never reached that island of refuge towards which he struggled
+through the fog upon that last night. Somewhere in the heart of
+the great Grimpen Mire, down in the foul slime of the huge morass
+which had sucked him in, this cold and cruel-hearted man is
+forever buried.
+
+Many traces we found of him in the bog-girt island where he had
+hid his savage ally. A huge driving-wheel and a shaft half-filled
+with rubbish showed the position of an abandoned mine. Beside it
+were the crumbling remains of the cottages of the miners, driven
+away no doubt by the foul reek of the surrounding swamp. In one
+of these a staple and chain with a quantity of gnawed bones
+showed where the animal had been confined. A skeleton with a
+tangle of brown hair adhering to it lay among the debris.
+
+"A dog!" said Holmes. "By Jove, a curly-haired spaniel. Poor
+Mortimer will never see his pet again. Well, I do not know that
+this place contains any secret which we have not already
+fathomed. He could hide his hound, but he could not hush its
+voice, and hence came those cries which even in daylight were not
+pleasant to hear. On an emergency he could keep the hound in the
+out-house at Merripit, but it was always a risk, and it was only
+on the supreme day, which he regarded as the end of all his
+efforts, that he dared do it. This paste in the tin is no doubt
+the luminous mixture with which the creature was daubed. It was
+suggested, of course, by the story of the family hell-hound, and
+by the desire to frighten old Sir Charles to death. No wonder the
+poor devil of a convict ran and screamed, even as our friend did,
+and as we ourselves might have done, when he saw such a creature
+bounding through the darkness of the moor upon his track. It was
+a cunning device, for, apart from the chance of driving your
+victim to his death, what peasant would venture to inquire too
+closely into such a creature should he get sight of it, as many
+have done, upon the moor? I said it in London, Watson, and I say
+it again now, that never yet have we helped to hunt down a more
+dangerous man than he who is lying yonder"--he swept his long arm
+towards the huge mottled expanse of green-splotched bog which
+stretched away until it merged into the russet slopes of the
+moor.
+
+
+
+
+Chapter 15
+
+A Retrospection
+
+
+It was the end of November and Holmes and I sat, upon a raw and
+foggy night, on either side of a blazing fire in our sitting-room
+in Baker Street. Since the tragic upshot of our visit to
+Devonshire he had been engaged in two affairs of the utmost
+importance, in the first of which he had exposed the atrocious
+conduct of Colonel Upwood in connection with the famous card
+scandal of the Nonpareil Club, while in the second he had
+defended the unfortunate Mme. Montpensier from the charge of
+murder which hung over her in connection with the death of her
+step-daughter, Mlle. Carére, the young lady who, as it will be
+remembered, was found six months later alive and married in New
+York. My friend was in excellent spirits over the success which
+had attended a succession of difficult and important cases, so
+that I was able to induce him to discuss the details of the
+Baskerville mystery. I had waited patiently for the opportunity,
+for I was aware that he would never permit cases to overlap, and
+that his clear and logical mind would not be drawn from its
+present work to dwell upon memories of the past. Sir Henry and
+Dr. Mortimer were, however, in London, on their way to that long
+voyage which had been recommended for the restoration of his
+shattered nerves. They had called upon us that very afternoon, so
+that it was natural that the subject should come up for
+discussion.
+
+"The whole course of events," said Holmes, "from the point of
+view of the man who called himself Stapleton was simple and
+direct, although to us, who had no means in the beginning of
+knowing the motives of his actions and could only learn part of
+the facts, it all appeared exceedingly complex. I have had the
+advantage of two conversations with Mrs. Stapleton, and the case
+has now been so entirely cleared up that I am not aware that
+there is anything which has remained a secret to us. You will
+find a few notes upon the matter under the heading B in my
+indexed list of cases."
+
+"Perhaps you would kindly give me a sketch of the course of
+events from memory."
+
+"Certainly, though I cannot guarantee that I carry all the facts
+in my mind. Intense mental concentration has a curious way of
+blotting out what has passed. The barrister who has his case at
+his fingers' ends, and is able to argue with an expert upon his
+own subject finds that a week or two of the courts will drive it
+all out of his head once more. So each of my cases displaces the
+last, and Mlle. Carére has blurred my recollection of Baskerville
+Hall. To-morrow some other little problem may be submitted to my
+notice which will in turn dispossess the fair French lady and the
+infamous Upwood. So far as the case of the Hound goes, however, I
+will give you the course of events as nearly as I can, and you
+will suggest anything which I may have forgotten.
+
+"My inquiries show beyond all question that the family portrait
+did not lie, and that this fellow was indeed a Baskerville. He
+was a son of that Rodger Baskerville, the younger brother of Sir
+Charles, who fled with a sinister reputation to South America,
+where he was said to have died unmarried. He did, as a matter of
+fact, marry, and had one child, this fellow, whose real name is
+the same as his father's. He married Beryl Garcia, one of the
+beauties of Costa Rica, and, having purloined a considerable sum
+of public money, he changed his name to Vandeleur and fled to
+England, where he established a school in the east of Yorkshire.
+His reason for attempting this special line of business was that
+he had struck up an acquaintance with a consumptive tutor upon
+the voyage home, and that he had used this man's ability to make
+the undertaking a success. Fraser, the tutor, died however, and
+the school which had begun well sank from disrepute into infamy.
+The Vandeleurs found it convenient to change their name to
+Stapleton, and he brought the remains of his fortune, his schemes
+for the future, and his taste for entomology to the south of
+England. I learned at the British Museum that he was a recognized
+authority upon the subject, and that the name of Vandeleur has
+been permanently attached to a certain moth which he had, in his
+Yorkshire days, been the first to describe.
+
+"We now come to that portion of his life which has proved to be
+of such intense interest to us. The fellow had evidently made
+inquiry and found that only two lives intervened between him and
+a valuable estate. When he went to Devonshire his plans were, I
+believe, exceedingly hazy, but that he meant mischief from the
+first is evident from the way in which he took his wife with him
+in the character of his sister. The idea of using her as a decoy
+was clearly already in his mind, though he may not have been
+certain how the details of his plot were to be arranged. He meant
+in the end to have the estate, and he was ready to use any tool
+or run any risk for that end. His first act was to establish
+himself as near to his ancestral home as he could, and his second
+was to cultivate a friendship with Sir Charles Baskerville and
+with the neighbours.
+
+"The baronet himself told him about the family hound, and so
+prepared the way for his own death. Stapleton, as I will continue
+to call him, knew that the old man's heart was weak and that a
+shock would kill him. So much he had learned from Dr. Mortimer.
+He had heard also that Sir Charles was superstitious and had
+taken this grim legend very seriously. His ingenious mind
+instantly suggested a way by which the baronet could be done to
+death, and yet it would be hardly possible to bring home the
+guilt to the real murderer.
+
+"Having conceived the idea he proceeded to carry it out with
+considerable finesse. An ordinary schemer would have been content
+to work with a savage hound. The use of artificial means to make
+the creature diabolical was a flash of genius upon his part. The
+dog he bought in London from Ross and Mangles, the dealers in
+Fulham Road. It was the strongest and most savage in their
+possession. He brought it down by the North Devon line and walked
+a great distance over the moor so as to get it home without
+exciting any remarks. He had already on his insect hunts learned
+to penetrate the Grimpen Mire, and so had found a safe
+hiding-place for the creature. Here he kennelled it and waited
+his chance.
+
+"But it was some time coming. The old gentleman could not be
+decoyed outside of his grounds at night. Several times Stapleton
+lurked about with his hound, but without avail. It was during
+these fruitless quests that he, or rather his ally, was seen by
+peasants, and that the legend of the demon dog received a new
+confirmation. He had hoped that his wife might lure Sir Charles
+to his ruin, but here she proved unexpectedly independent. She
+would not endeavour to entangle the old gentleman in a
+sentimental attachment which might deliver him over to his enemy.
+Threats and even, I am sorry to say, blows refused to move her.
+She would have nothing to do with it, and for a time Stapleton
+was at a deadlock.
+
+"He found a way out of his difficulties through the chance that
+Sir Charles, who had conceived a friendship for him, made him the
+minister of his charity in the case of this unfortunate woman,
+Mrs. Laura Lyons. By representing himself as a single man he
+acquired complete influence over her, and he gave her to
+understand that in the event of her obtaining a divorce from her
+husband he would marry her. His plans were suddenly brought to a
+head by his knowledge that Sir Charles was about to leave the
+Hall on the advice of Dr. Mortimer, with whose opinion he himself
+pretended to coincide. He must act at once, or his victim might
+get beyond his power. He therefore put pressure upon Mrs. Lyons
+to write this letter, imploring the old man to give her an
+interview on the evening before his departure for London. He
+then, by a specious argument, prevented her from going, and so
+had the chance for which he had waited.
+
+"Driving back in the evening from Coombe Tracey he was in time to
+get his hound, to treat it with his infernal paint, and to bring
+the beast round to the gate at which he had reason to expect that
+he would find the old gentleman waiting. The dog, incited by its
+master, sprang over the wicket-gate and pursued the unfortunate
+baronet, who fled screaming down the Yew Alley. In that gloomy
+tunnel it must indeed have been a dreadful sight to see that huge
+black creature, with its flaming jaws and blazing eyes, bounding
+after its victim. He fell dead at the end of the alley from heart
+disease and terror. The hound had kept upon the grassy border
+while the baronet had run down the path, so that no track but the
+man's was visible. On seeing him lying still the creature had
+probably approached to sniff at him, but finding him dead had
+turned away again. It was then that it left the print which was
+actually observed by Dr. Mortimer. The hound was called off and
+hurried away to its lair in the Grimpen Mire, and a mystery was
+left which puzzled the authorities, alarmed the country-side, and
+finally brought the case within the scope of our observation.
+
+"So much for the death of Sir Charles Baskerville. You perceive
+the devilish cunning of it, for really it would be almost
+impossible to make a case against the real murderer. His only
+accomplice was one who could never give him away, and the
+grotesque, inconceivable nature of the device only served to make
+it more effective. Both of the women concerned in the case, Mrs.
+Stapleton and Mrs. Laura Lyons, were left with a strong suspicion
+against Stapleton. Mrs. Stapleton knew that he had designs upon
+the old man, and also of the existence of the hound. Mrs. Lyons
+knew neither of these things, but had been impressed by the death
+occurring at the time of an uncancelled appointment which was
+only known to him. However, both of them were under his
+influence, and he had nothing to fear from them. The first half
+of his task was successfully accomplished but the more difficult
+still remained.
+
+"It is possible that Stapleton did not know of the existence of
+an heir in Canada. In any case he would very soon learn it from
+his friend Dr. Mortimer, and he was told by the latter all
+details about the arrival of Henry Baskerville. Stapleton's first
+idea was that this young stranger from Canada might possibly be
+done to death in London without coming down to Devonshire at all.
+He distrusted his wife ever since she had refused to help him in
+laying a trap for the old man, and he dared not leave her long
+out of his sight for fear he should lose his influence over her.
+It was for this reason that he took her to London with him. They
+lodged, I find, at the Mexborough Private Hotel, in Craven
+Street, which was actually one of those called upon by my agent
+in search of evidence. Here he kept his wife imprisoned in her
+room while he, disguised in a beard, followed Dr. Mortimer to
+Baker Street and afterwards to the station and to the
+Northumberland Hotel. His wife had some inkling of his plans; but
+she had such a fear of her husband--a fear founded upon brutal
+ill-treatment--that she dare not write to warn the man whom she
+knew to be in danger. If the letter should fall into Stapleton's
+hands her own life would not be safe. Eventually, as we know, she
+adopted the expedient of cutting out the words which would form
+the message, and addressing the letter in a disguised hand. It
+reached the baronet, and gave him the first warning of his
+danger.
+
+"It was very essential for Stapleton to get some article of Sir
+Henry's attire so that, in case he was driven to use the dog, he
+might always have the means of setting him upon his track. With
+characteristic promptness and audacity he set about this at once,
+and we cannot doubt that the boots or chamber-maid of the hotel
+was well bribed to help him in his design. By chance, however,
+the first boot which was procured for him was a new one and,
+therefore, useless for his purpose. He then had it returned and
+obtained another--a most instructive incident, since it proved
+conclusively to my mind that we were dealing with a real hound,
+as no other supposition could explain this anxiety to obtain an
+old boot and this indifference to a new one. The more outre and
+grotesque an incident is the more carefully it deserves to be
+examined, and the very point which appears to complicate a case
+is, when duly considered and scientifically handled, the one
+which is most likely to elucidate it.
+
+"Then we had the visit from our friends next morning, shadowed
+always by Stapleton in the cab. From his knowledge of our rooms
+and of my appearance, as well as from his general conduct, I am
+inclined to think that Stapleton's career of crime has been by no
+means limited to this single Baskerville affair. It is suggestive
+that during the last three years there have been four
+considerable burglaries in the West Country, for none of which
+was any criminal ever arrested. The last of these, at Folkestone
+Court, in May, was remarkable for the cold-blooded pistoling of
+the page, who surprised the masked and solitary burglar. I cannot
+doubt that Stapleton recruited his waning resources in this
+fashion, and that for years he has been a desperate and dangerous
+man.
+
+"We had an example of his readiness of resource that morning when
+he got away from us so successfully, and also of his audacity in
+sending back my own name to me through the cabman. From that
+moment he understood that I had taken over the case in London,
+and that therefore there was no chance for him there. He returned
+to Dartmoor and awaited the arrival of the baronet."
+
+"One moment!" said I. "You have, no doubt, described the sequence
+of events correctly, but there is one point which you have left
+unexplained. What became of the hound when its master was in
+London?"
+
+"I have given some attention to this matter and it is undoubtedly
+of importance. There can be no question that Stapleton had a
+confidant, though it is unlikely that he ever placed himself in
+his power by sharing all his plans with him. There was an old
+manservant at Merripit House, whose name was Anthony. His
+connection with the Stapletons can be traced for several years,
+as far back as the schoolmastering days, so that he must have
+been aware that his master and mistress were really husband and
+wife. This man has disappeared and has escaped from the country.
+It is suggestive that Anthony is not a common name in England,
+while Antonio is so in all Spanish or Spanish-American countries.
+The man, like Mrs. Stapleton herself, spoke good English, but
+with a curious lisping accent. I have myself seen this old man
+cross the Grimpen Mire by the path which Stapleton had marked
+out. It is very probable, therefore, that in the absence of his
+master it was he who cared for the hound, though he may never
+have known the purpose for which the beast was used.
+
+"The Stapletons then went down to Devonshire, whither they were
+soon followed by Sir Henry and you. One word now as to how I
+stood myself at that time. It may possibly recur to your memory
+that when I examined the paper upon which the printed words were
+fastened I made a close inspection for the water-mark. In doing
+so I held it within a few inches of my eyes, and was conscious of
+a faint smell of the scent known as white jessamine. There are
+seventy-five perfumes, which it is very necessary that a criminal
+expert should be able to distinguish from each other, and cases
+have more than once within my own experience depended upon their
+prompt recognition. The scent suggested the presence of a lady,
+and already my thoughts began to turn towards the Stapletons.
+Thus I had made certain of the hound, and had guessed at the
+criminal before ever we went to the west country.
+
+"It was my game to watch Stapleton. It was evident, however, that
+I could not do this if I were with you, since he would be keenly
+on his guard. I deceived everybody, therefore, yourself included,
+and I came down secretly when I was supposed to be in London. My
+hardships were not so great as you imagined, though such trifling
+details must never interfere with the investigation of a case. I
+stayed for the most part at Coombe Tracey, and only used the hut
+upon the moor when it was necessary to be near the scene of
+action. Cartwright had come down with me, and in his disguise as
+a country boy he was of great assistance to me. I was dependent
+upon him for food and clean linen. When I was watching Stapleton,
+Cartwright was frequently watching you, so that I was able to
+keep my hand upon all the strings.
+
+"I have already told you that your reports reached me rapidly,
+being forwarded instantly from Baker Street to Coombe Tracey.
+They were of great service to me, and especially that one
+incidentally truthful piece of biography of Stapleton's. I was
+able to establish the identity of the man and the woman and knew
+at last exactly how I stood. The case had been considerably
+complicated through the incident of the escaped convict and the
+relations between him and the Barrymores. This also you cleared
+up in a very effective way, though I had already come to the same
+conclusions from my own observations.
+
+"By the time that you discovered me upon the moor I had a
+complete knowledge of the whole business, but I had not a case
+which could go to a jury. Even Stapleton's attempt upon Sir Henry
+that night which ended in the death of the unfortunate convict
+did not help us much in proving murder against our man. There
+seemed to be no alternative but to catch him red-handed, and to
+do so we had to use Sir Henry, alone and apparently unprotected,
+as a bait. We did so, and at the cost of a severe shock to our
+client we succeeded in completing our case and driving Stapleton
+to his destruction. That Sir Henry should have been exposed to
+this is, I must confess, a reproach to my management of the case,
+but we had no means of foreseeing the terrible and paralyzing
+spectacle which the beast presented, nor could we predict the fog
+which enabled him to burst upon us at such short notice. We
+succeeded in our object at a cost which both the specialist and
+Dr. Mortimer assure me will be a temporary one. A long journey
+may enable our friend to recover not only from his shattered
+nerves but also from his wounded feelings. His love for the lady
+was deep and sincere, and to him the saddest part of all this
+black business was that he should have been deceived by her.
+
+"It only remains to indicate the part which she had played
+throughout. There can be no doubt that Stapleton exercised an
+influence over her which may have been love or may have been
+fear, or very possibly both, since they are by no means
+incompatible emotions. It was, at least, absolutely effective. At
+his command she consented to pass as his sister, though he found
+the limits of his power over her when he endeavoured to make her
+the direct accessory to murder. She was ready to warn Sir Henry
+so far as she could without implicating her husband, and again
+and again she tried to do so. Stapleton himself seems to have
+been capable of jealousy, and when he saw the baronet paying
+court to the lady, even though it was part of his own plan, still
+he could not help interrupting with a passionate outburst which
+revealed the fiery soul which his self-contained manner so
+cleverly concealed. By encouraging the intimacy he made it
+certain that Sir Henry would frequently come to Merripit House
+and that he would sooner or later get the opportunity which he
+desired. On the day of the crisis, however, his wife turned
+suddenly against him. She had learned something of the death of
+the convict, and she knew that the hound was being kept in the
+out-house on the evening that Sir Henry was coming to dinner. She
+taxed her husband with his intended crime, and a furious scene
+followed, in which he showed her for the first time that she had
+a rival in his love. Her fidelity turned in an instant to bitter
+hatred and he saw that she would betray him. He tied her up,
+therefore, that she might have no chance of warning Sir Henry,
+and he hoped, no doubt, that when the whole country-side put down
+the baronet's death to the curse of his family, as they certainly
+would do, he could win his wife back to accept an accomplished
+fact and to keep silent upon what she knew. In this I fancy that
+in any case he made a miscalculation, and that, if we had not
+been there, his doom would none the less have been sealed. A
+woman of Spanish blood does not condone such an injury so
+lightly. And now, my dear Watson, without referring to my notes,
+I cannot give you a more detailed account of this curious case. I
+do not know that anything essential has been left unexplained."
+
+"He could not hope to frighten Sir Henry to death as he had done
+the old uncle with his bogie hound."
+
+"The beast was savage and half-starved. If its appearance did not
+frighten its victim to death, at least it would paralyze the
+resistance which might be offered."
+
+"No doubt. There only remains one difficulty. If Stapleton came
+into the succession, how could he explain the fact that he, the
+heir, had been living unannounced under another name so close to
+the property? How could he claim it without causing suspicion and
+inquiry?"
+
+"It is a formidable difficulty, and I fear that you ask too much
+when you expect me to solve it. The past and the present are
+within the field of my inquiry, but what a man may do in the
+future is a hard question to answer. Mrs. Stapleton has heard her
+husband discuss the problem on several occasions. There were
+three possible courses. He might claim the property from South
+America, establish his identity before the British authorities
+there and so obtain the fortune without ever coming to England at
+all; or he might adopt an elaborate disguise during the short
+time that he need be in London; or, again, he might furnish an
+accomplice with the proofs and papers, putting him in as heir,
+and retaining a claim upon some proportion of his income. We
+cannot doubt from what we know of him that he would have found
+some way out of the difficulty. And now, my dear Watson, we have
+had some weeks of severe work, and for one evening, I think, we
+may turn our thoughts into more pleasant channels. I have a box
+for 'Les Huguenots.' Have you heard the De Reszkes? Might I
+trouble you then to be ready in half an hour, and we can stop at
+Marcini's for a little dinner on the way?"
+
+
+
+
+
+
+
+End of the Project Gutenberg EBook of The Hound of the Baskervilles, by
+Arthur Conan Doyle
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diff --git a/lessons/python/intro-nlp/intro_NLP.ipynb b/lessons/python/intro-nlp/intro_NLP.ipynb
new file mode 100644
index 000000000..bcac03cce
--- /dev/null
+++ b/lessons/python/intro-nlp/intro_NLP.ipynb
@@ -0,0 +1,4309 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import nltk"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get the text we want to process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "with open('book.txt', 'r') as file:\n",
+    "    text = file.readlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a smaller chunk from the text:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# using a list comprehension to simplify iterating over the the text structure\n",
+    "snippet = \" \".join(block.strip() for block in text[175:200])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\"Do none suggest themselves? You know my methods. Apply them!\"  \"I can only think of the obvious conclusion that the man has practised in town before going to the country.\"  \"I think that we might venture a little farther than this. Look at it in this light. On what occasion would it be most probable that such a presentation would be made? When would his friends unite to give him a pledge of their good will? Obviously at the moment when Dr. Mortimer withdrew from the service of the hospital in order to start in practice for himself. We know there has been a presentation. We believe there has been a change from a town hospital to a country practice. Is it, then, stretching our inference too far to say that the presentation was on the occasion of the change?\"  \"It certainly seems probable.\"  \"Now, you will observe that he could not have been on the staff of the hospital, since only a man well-established in a London practice could hold such a position, and such a one would not drift into the country. What was he, then? If he was in the hospital and yet not on the staff he could only have been a house-surgeon or a house-physician--little more than a senior student. And he left five years ago--the date is on the stick. So'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snippet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# alternative with for-loop\n",
+    "other_snippet = []\n",
+    "for block in text[175:200]:\n",
+    "    other_snippet.append(block.strip())\n",
+    "other_snippet = \" \".join(other_snippet)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\"Do none suggest themselves? You know my methods. Apply them!\"  \"I can only think of the obvious conclusion that the man has practised in town before going to the country.\"  \"I think that we might venture a little farther than this. Look at it in this light. On what occasion would it be most probable that such a presentation would be made? When would his friends unite to give him a pledge of their good will? Obviously at the moment when Dr. Mortimer withdrew from the service of the hospital in order to start in practice for himself. We know there has been a presentation. We believe there has been a change from a town hospital to a country practice. Is it, then, stretching our inference too far to say that the presentation was on the occasion of the change?\"  \"It certainly seems probable.\"  \"Now, you will observe that he could not have been on the staff of the hospital, since only a man well-established in a London practice could hold such a position, and such a one would not drift into the country. What was he, then? If he was in the hospital and yet not on the staff he could only have been a house-surgeon or a house-physician--little more than a senior student. And he left five years ago--the date is on the stick. So'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "other_snippet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "whole_text = \" \".join(block.strip() for block in text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' towards the truth. Not that you are entirely wrong in this instance. The man is certainly a country practitioner. And he walks a good deal.\"  \"Then I was right.\"  \"To that extent.\"  \"But that was all.\"  \"No, no, my dear Watson, not all--by no means all. I would suggest, for example, that a presentation to a doctor is more likely to come from a hospital than from a hunt, and that when the initials \\'C.C.\\' are placed before that hospital the words \\'Charing Cross\\' very naturally suggest themselves.\"  \"You may be right.\"  \"The probability lies in that direction. And if we take this as a working hypothesis we have a fresh basis from which to start our construction of this unknown visitor.\"  \"Well, then, supposing that \\'C.C.H.\\' does stand for \\'Charing Cross Hospital,\\' what further inferences may we draw?\"  \"Do none suggest themselves? You know my methods. Apply them!\"  \"I can only think of the obvious conclusion that the man has practised in town before going to the country.\"  \"I think that we might venture a little farther than this. Look at it in this light. On what occasion would it be most probable that such a presentation would be made? When would his friends unite to give him a pledge of their good will? Obviously at the moment when Dr. Mortimer withdrew from the service of the hospital in order to start in practice for himself. We know there has been a presentation. We believe there has been a change from a town hospital to a country practice. Is it, then, stretching our inference too far to say that the presentation was on the occasion of the change?\"  \"It certainly seems probable.\"  \"Now, you will observe that he could not have been on the staff of the hospital, since only a man well-established in a London practice could hold such a position, and such a one would not drift into the country. What was he, then? If he was in the hospital and yet not on the staff he could only have been a house-surgeon or a house-physician--little more than a senior student. And he left five years ago--the date is on the stick. So your grave, middle-aged family practitioner vanishes into thin air, my dear Watson, and there emerges a young fellow under thirty, amiable, unambitious, absent-minded, and the possessor of a favourite dog, which I should describe roughly as being larger than a terrier and smaller than a mastiff.\"  I laughed incredulously as Sherlock Holmes leaned back in his settee and blew little wavering rings of smoke up to the ceiling.  \"As to the latter part'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "whole_text[5000:7500]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tokenizing text\n",
+    "- Tokens are meaningful chunks of text:\n",
+    "    - Sentences\n",
+    "    - words\n",
+    "    - punctuation\n",
+    "    - numbers\n",
+    "    - ?\n",
+    "    \n",
+    "We'll look at some tools in nltk to help break the raw text into sentences and word tokens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nltk.tokenize import sent_tokenize, word_tokenize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "str.split?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['\"Do none suggest themselves? You know my methods',\n",
+       " ' Apply them!\"  \"I can only think of the obvious conclusion that the man has practised in town before going to the country',\n",
+       " '\"  \"I think that we might venture a little farther than this',\n",
+       " ' Look at it in this light',\n",
+       " ' On what occasion would it be most probable that such a presentation would be made? When would his friends unite to give him a pledge of their good will? Obviously at the moment when Dr',\n",
+       " ' Mortimer withdrew from the service of the hospital in order to start in practice for himself',\n",
+       " ' We know there has been a presentation',\n",
+       " ' We believe there has been a change from a town hospital to a country practice',\n",
+       " ' Is it, then, stretching our inference too far to say that the presentation was on the occasion of the change?\"  \"It certainly seems probable',\n",
+       " '\"  \"Now, you will observe that he could not have been on the staff of the hospital, since only a man well-established in a London practice could hold such a position, and such a one would not drift into the country',\n",
+       " ' What was he, then? If he was in the hospital and yet not on the staff he could only have been a house-surgeon or a house-physician--little more than a senior student',\n",
+       " ' And he left five years ago--the date is on the stick',\n",
+       " ' So']"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# you can try to separate sentences by splitting on punctuation\n",
+    "snippet.split('.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['\"Do none suggest themselves?',\n",
+       " 'You know my methods.',\n",
+       " 'Apply them!\"',\n",
+       " '\"I can only think of the obvious conclusion that the man has practised in town before going to the country.\"',\n",
+       " '\"I think that we might venture a little farther than this.',\n",
+       " 'Look at it in this light.',\n",
+       " 'On what occasion would it be most probable that such a presentation would be made?',\n",
+       " 'When would his friends unite to give him a pledge of their good will?',\n",
+       " 'Obviously at the moment when Dr. Mortimer withdrew from the service of the hospital in order to start in practice for himself.',\n",
+       " 'We know there has been a presentation.',\n",
+       " 'We believe there has been a change from a town hospital to a country practice.',\n",
+       " 'Is it, then, stretching our inference too far to say that the presentation was on the occasion of the change?\"',\n",
+       " '\"It certainly seems probable.\"',\n",
+       " '\"Now, you will observe that he could not have been on the staff of the hospital, since only a man well-established in a London practice could hold such a position, and such a one would not drift into the country.',\n",
+       " 'What was he, then?',\n",
+       " 'If he was in the hospital and yet not on the staff he could only have been a house-surgeon or a house-physician--little more than a senior student.',\n",
+       " 'And he left five years ago--the date is on the stick.',\n",
+       " 'So']"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The sentence tokenizer has some clever tricks to do a better job\n",
+    "sent_tokenize(snippet)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['\"Do',\n",
+       " 'none',\n",
+       " 'suggest',\n",
+       " 'themselves?',\n",
+       " 'You',\n",
+       " 'know',\n",
+       " 'my',\n",
+       " 'methods.',\n",
+       " 'Apply',\n",
+       " 'them!\"',\n",
+       " '\"I',\n",
+       " 'can',\n",
+       " 'only',\n",
+       " 'think',\n",
+       " 'of',\n",
+       " 'the',\n",
+       " 'obvious',\n",
+       " 'conclusion',\n",
+       " 'that',\n",
+       " 'the',\n",
+       " 'man',\n",
+       " 'has',\n",
+       " 'practised',\n",
+       " 'in',\n",
+       " 'town',\n",
+       " 'before',\n",
+       " 'going',\n",
+       " 'to',\n",
+       " 'the',\n",
+       " 'country.\"',\n",
+       " '\"I',\n",
+       " 'think',\n",
+       " 'that',\n",
+       " 'we',\n",
+       " 'might',\n",
+       " 'venture',\n",
+       " 'a',\n",
+       " 'little',\n",
+       " 'farther',\n",
+       " 'than',\n",
+       " 'this.',\n",
+       " 'Look',\n",
+       " 'at',\n",
+       " 'it',\n",
+       " 'in',\n",
+       " 'this',\n",
+       " 'light.',\n",
+       " 'On',\n",
+       " 'what',\n",
+       " 'occasion',\n",
+       " 'would',\n",
+       " 'it',\n",
+       " 'be',\n",
+       " 'most',\n",
+       " 'probable',\n",
+       " 'that',\n",
+       " 'such',\n",
+       " 'a',\n",
+       " 'presentation',\n",
+       " 'would',\n",
+       " 'be',\n",
+       " 'made?',\n",
+       " 'When',\n",
+       " 'would',\n",
+       " 'his',\n",
+       " 'friends',\n",
+       " 'unite',\n",
+       " 'to',\n",
+       " 'give',\n",
+       " 'him',\n",
+       " 'a',\n",
+       " 'pledge',\n",
+       " 'of',\n",
+       " 'their',\n",
+       " 'good',\n",
+       " 'will?',\n",
+       " 'Obviously',\n",
+       " 'at',\n",
+       " 'the',\n",
+       " 'moment',\n",
+       " 'when',\n",
+       " 'Dr.',\n",
+       " 'Mortimer',\n",
+       " 'withdrew',\n",
+       " 'from',\n",
+       " 'the',\n",
+       " 'service',\n",
+       " 'of',\n",
+       " 'the',\n",
+       " 'hospital',\n",
+       " 'in',\n",
+       " 'order',\n",
+       " 'to',\n",
+       " 'start',\n",
+       " 'in',\n",
+       " 'practice',\n",
+       " 'for',\n",
+       " 'himself.',\n",
+       " 'We',\n",
+       " 'know',\n",
+       " 'there',\n",
+       " 'has',\n",
+       " 'been',\n",
+       " 'a',\n",
+       " 'presentation.',\n",
+       " 'We',\n",
+       " 'believe',\n",
+       " 'there',\n",
+       " 'has',\n",
+       " 'been',\n",
+       " 'a',\n",
+       " 'change',\n",
+       " 'from',\n",
+       " 'a',\n",
+       " 'town',\n",
+       " 'hospital',\n",
+       " 'to',\n",
+       " 'a',\n",
+       " 'country',\n",
+       " 'practice.',\n",
+       " 'Is',\n",
+       " 'it,',\n",
+       " 'then,',\n",
+       " 'stretching',\n",
+       " 'our',\n",
+       " 'inference',\n",
+       " 'too',\n",
+       " 'far',\n",
+       " 'to',\n",
+       " 'say',\n",
+       " 'that',\n",
+       " 'the',\n",
+       " 'presentation',\n",
+       " 'was',\n",
+       " 'on',\n",
+       " 'the',\n",
+       " 'occasion',\n",
+       " 'of',\n",
+       " 'the',\n",
+       " 'change?\"',\n",
+       " '\"It',\n",
+       " 'certainly',\n",
+       " 'seems',\n",
+       " 'probable.\"',\n",
+       " '\"Now,',\n",
+       " 'you',\n",
+       " 'will',\n",
+       " 'observe',\n",
+       " 'that',\n",
+       " 'he',\n",
+       " 'could',\n",
+       " 'not',\n",
+       " 'have',\n",
+       " 'been',\n",
+       " 'on',\n",
+       " 'the',\n",
+       " 'staff',\n",
+       " 'of',\n",
+       " 'the',\n",
+       " 'hospital,',\n",
+       " 'since',\n",
+       " 'only',\n",
+       " 'a',\n",
+       " 'man',\n",
+       " 'well-established',\n",
+       " 'in',\n",
+       " 'a',\n",
+       " 'London',\n",
+       " 'practice',\n",
+       " 'could',\n",
+       " 'hold',\n",
+       " 'such',\n",
+       " 'a',\n",
+       " 'position,',\n",
+       " 'and',\n",
+       " 'such',\n",
+       " 'a',\n",
+       " 'one',\n",
+       " 'would',\n",
+       " 'not',\n",
+       " 'drift',\n",
+       " 'into',\n",
+       " 'the',\n",
+       " 'country.',\n",
+       " 'What',\n",
+       " 'was',\n",
+       " 'he,',\n",
+       " 'then?',\n",
+       " 'If',\n",
+       " 'he',\n",
+       " 'was',\n",
+       " 'in',\n",
+       " 'the',\n",
+       " 'hospital',\n",
+       " 'and',\n",
+       " 'yet',\n",
+       " 'not',\n",
+       " 'on',\n",
+       " 'the',\n",
+       " 'staff',\n",
+       " 'he',\n",
+       " 'could',\n",
+       " 'only',\n",
+       " 'have',\n",
+       " 'been',\n",
+       " 'a',\n",
+       " 'house-surgeon',\n",
+       " 'or',\n",
+       " 'a',\n",
+       " 'house-physician--little',\n",
+       " 'more',\n",
+       " 'than',\n",
+       " 'a',\n",
+       " 'senior',\n",
+       " 'student.',\n",
+       " 'And',\n",
+       " 'he',\n",
+       " 'left',\n",
+       " 'five',\n",
+       " 'years',\n",
+       " 'ago--the',\n",
+       " 'date',\n",
+       " 'is',\n",
+       " 'on',\n",
+       " 'the',\n",
+       " 'stick.',\n",
+       " 'So']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# splitting a text into tokens based on white space\n",
+    "snippet.split()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "words = word_tokenize(snippet)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['``',\n",
+       " 'Do',\n",
+       " 'none',\n",
+       " 'suggest',\n",
+       " 'themselves',\n",
+       " '?',\n",
+       " 'You',\n",
+       " 'know',\n",
+       " 'my',\n",
+       " 'methods',\n",
+       " '.',\n",
+       " 'Apply',\n",
+       " 'them',\n",
+       " '!',\n",
+       " \"''\",\n",
+       " '``',\n",
+       " 'I',\n",
+       " 'can',\n",
+       " 'only',\n",
+       " 'think',\n",
+       " 'of',\n",
+       " 'the',\n",
+       " 'obvious',\n",
+       " 'conclusion',\n",
+       " 'that',\n",
+       " 'the',\n",
+       " 'man',\n",
+       " 'has',\n",
+       " 'practised',\n",
+       " 'in',\n",
+       " 'town',\n",
+       " 'before',\n",
+       " 'going',\n",
+       " 'to',\n",
+       " 'the',\n",
+       " 'country',\n",
+       " '.',\n",
+       " \"''\",\n",
+       " '``',\n",
+       " 'I',\n",
+       " 'think',\n",
+       " 'that',\n",
+       " 'we',\n",
+       " 'might',\n",
+       " 'venture',\n",
+       " 'a',\n",
+       " 'little',\n",
+       " 'farther',\n",
+       " 'than',\n",
+       " 'this',\n",
+       " '.',\n",
+       " 'Look',\n",
+       " 'at',\n",
+       " 'it',\n",
+       " 'in',\n",
+       " 'this',\n",
+       " 'light',\n",
+       " '.',\n",
+       " 'On',\n",
+       " 'what',\n",
+       " 'occasion',\n",
+       " 'would',\n",
+       " 'it',\n",
+       " 'be',\n",
+       " 'most',\n",
+       " 'probable',\n",
+       " 'that',\n",
+       " 'such',\n",
+       " 'a',\n",
+       " 'presentation',\n",
+       " 'would',\n",
+       " 'be',\n",
+       " 'made',\n",
+       " '?',\n",
+       " 'When',\n",
+       " 'would',\n",
+       " 'his',\n",
+       " 'friends',\n",
+       " 'unite',\n",
+       " 'to',\n",
+       " 'give',\n",
+       " 'him',\n",
+       " 'a',\n",
+       " 'pledge',\n",
+       " 'of',\n",
+       " 'their',\n",
+       " 'good',\n",
+       " 'will',\n",
+       " '?',\n",
+       " 'Obviously',\n",
+       " 'at',\n",
+       " 'the',\n",
+       " 'moment',\n",
+       " 'when',\n",
+       " 'Dr.',\n",
+       " 'Mortimer',\n",
+       " 'withdrew',\n",
+       " 'from',\n",
+       " 'the',\n",
+       " 'service',\n",
+       " 'of',\n",
+       " 'the',\n",
+       " 'hospital',\n",
+       " 'in',\n",
+       " 'order',\n",
+       " 'to',\n",
+       " 'start',\n",
+       " 'in',\n",
+       " 'practice',\n",
+       " 'for',\n",
+       " 'himself',\n",
+       " '.',\n",
+       " 'We',\n",
+       " 'know',\n",
+       " 'there',\n",
+       " 'has',\n",
+       " 'been',\n",
+       " 'a',\n",
+       " 'presentation',\n",
+       " '.',\n",
+       " 'We',\n",
+       " 'believe',\n",
+       " 'there',\n",
+       " 'has',\n",
+       " 'been',\n",
+       " 'a',\n",
+       " 'change',\n",
+       " 'from',\n",
+       " 'a',\n",
+       " 'town',\n",
+       " 'hospital',\n",
+       " 'to',\n",
+       " 'a',\n",
+       " 'country',\n",
+       " 'practice',\n",
+       " '.',\n",
+       " 'Is',\n",
+       " 'it',\n",
+       " ',',\n",
+       " 'then',\n",
+       " ',',\n",
+       " 'stretching',\n",
+       " 'our',\n",
+       " 'inference',\n",
+       " 'too',\n",
+       " 'far',\n",
+       " 'to',\n",
+       " 'say',\n",
+       " 'that',\n",
+       " 'the',\n",
+       " 'presentation',\n",
+       " 'was',\n",
+       " 'on',\n",
+       " 'the',\n",
+       " 'occasion',\n",
+       " 'of',\n",
+       " 'the',\n",
+       " 'change',\n",
+       " '?',\n",
+       " \"''\",\n",
+       " '``',\n",
+       " 'It',\n",
+       " 'certainly',\n",
+       " 'seems',\n",
+       " 'probable',\n",
+       " '.',\n",
+       " \"''\",\n",
+       " '``',\n",
+       " 'Now',\n",
+       " ',',\n",
+       " 'you',\n",
+       " 'will',\n",
+       " 'observe',\n",
+       " 'that',\n",
+       " 'he',\n",
+       " 'could',\n",
+       " 'not',\n",
+       " 'have',\n",
+       " 'been',\n",
+       " 'on',\n",
+       " 'the',\n",
+       " 'staff',\n",
+       " 'of',\n",
+       " 'the',\n",
+       " 'hospital',\n",
+       " ',',\n",
+       " 'since',\n",
+       " 'only',\n",
+       " 'a',\n",
+       " 'man',\n",
+       " 'well-established',\n",
+       " 'in',\n",
+       " 'a',\n",
+       " 'London',\n",
+       " 'practice',\n",
+       " 'could',\n",
+       " 'hold',\n",
+       " 'such',\n",
+       " 'a',\n",
+       " 'position',\n",
+       " ',',\n",
+       " 'and',\n",
+       " 'such',\n",
+       " 'a',\n",
+       " 'one',\n",
+       " 'would',\n",
+       " 'not',\n",
+       " 'drift',\n",
+       " 'into',\n",
+       " 'the',\n",
+       " 'country',\n",
+       " '.',\n",
+       " 'What',\n",
+       " 'was',\n",
+       " 'he',\n",
+       " ',',\n",
+       " 'then',\n",
+       " '?',\n",
+       " 'If',\n",
+       " 'he',\n",
+       " 'was',\n",
+       " 'in',\n",
+       " 'the',\n",
+       " 'hospital',\n",
+       " 'and',\n",
+       " 'yet',\n",
+       " 'not',\n",
+       " 'on',\n",
+       " 'the',\n",
+       " 'staff',\n",
+       " 'he',\n",
+       " 'could',\n",
+       " 'only',\n",
+       " 'have',\n",
+       " 'been',\n",
+       " 'a',\n",
+       " 'house-surgeon',\n",
+       " 'or',\n",
+       " 'a',\n",
+       " 'house-physician',\n",
+       " '--',\n",
+       " 'little',\n",
+       " 'more',\n",
+       " 'than',\n",
+       " 'a',\n",
+       " 'senior',\n",
+       " 'student',\n",
+       " '.',\n",
+       " 'And',\n",
+       " 'he',\n",
+       " 'left',\n",
+       " 'five',\n",
+       " 'years',\n",
+       " 'ago',\n",
+       " '--',\n",
+       " 'the',\n",
+       " 'date',\n",
+       " 'is',\n",
+       " 'on',\n",
+       " 'the',\n",
+       " 'stick',\n",
+       " '.',\n",
+       " 'So']"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# word tokenize treats punctuation as a token\n",
+    "words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# let's plot the frequency of occurrence of different words\n",
+    "nltk.FreqDist?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fdist = nltk.FreqDist(words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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vAZ9y9wuj114AzgT+D3g3KfeGcPf1UboRwAHA74CPJqUbCPzC3Y9IU4YLgR8D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\npMIpEIiIVDgFAhGRCvf/AcKS4dO7RGixAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff8de973908>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fdist.plot(30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# Stop words\n",
+    "Stop words are words that you want to filter out from your text for downstream analysis. They are typically very common words which don't contain much useful information for the task at hand. There is no universal set of stop words and some domain knowledge is helpful for deciding what you want to include when processing your text."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nltk.corpus import stopwords"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "stops = stopwords.words('english')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['i',\n",
+       " 'me',\n",
+       " 'my',\n",
+       " 'myself',\n",
+       " 'we',\n",
+       " 'our',\n",
+       " 'ours',\n",
+       " 'ourselves',\n",
+       " 'you',\n",
+       " 'your',\n",
+       " 'yours',\n",
+       " 'yourself',\n",
+       " 'yourselves',\n",
+       " 'he',\n",
+       " 'him',\n",
+       " 'his',\n",
+       " 'himself',\n",
+       " 'she',\n",
+       " 'her',\n",
+       " 'hers',\n",
+       " 'herself',\n",
+       " 'it',\n",
+       " 'its',\n",
+       " 'itself',\n",
+       " 'they',\n",
+       " 'them',\n",
+       " 'their',\n",
+       " 'theirs',\n",
+       " 'themselves',\n",
+       " 'what',\n",
+       " 'which',\n",
+       " 'who',\n",
+       " 'whom',\n",
+       " 'this',\n",
+       " 'that',\n",
+       " 'these',\n",
+       " 'those',\n",
+       " 'am',\n",
+       " 'is',\n",
+       " 'are',\n",
+       " 'was',\n",
+       " 'were',\n",
+       " 'be',\n",
+       " 'been',\n",
+       " 'being',\n",
+       " 'have',\n",
+       " 'has',\n",
+       " 'had',\n",
+       " 'having',\n",
+       " 'do',\n",
+       " 'does',\n",
+       " 'did',\n",
+       " 'doing',\n",
+       " 'a',\n",
+       " 'an',\n",
+       " 'the',\n",
+       " 'and',\n",
+       " 'but',\n",
+       " 'if',\n",
+       " 'or',\n",
+       " 'because',\n",
+       " 'as',\n",
+       " 'until',\n",
+       " 'while',\n",
+       " 'of',\n",
+       " 'at',\n",
+       " 'by',\n",
+       " 'for',\n",
+       " 'with',\n",
+       " 'about',\n",
+       " 'against',\n",
+       " 'between',\n",
+       " 'into',\n",
+       " 'through',\n",
+       " 'during',\n",
+       " 'before',\n",
+       " 'after',\n",
+       " 'above',\n",
+       " 'below',\n",
+       " 'to',\n",
+       " 'from',\n",
+       " 'up',\n",
+       " 'down',\n",
+       " 'in',\n",
+       " 'out',\n",
+       " 'on',\n",
+       " 'off',\n",
+       " 'over',\n",
+       " 'under',\n",
+       " 'again',\n",
+       " 'further',\n",
+       " 'then',\n",
+       " 'once',\n",
+       " 'here',\n",
+       " 'there',\n",
+       " 'when',\n",
+       " 'where',\n",
+       " 'why',\n",
+       " 'how',\n",
+       " 'all',\n",
+       " 'any',\n",
+       " 'both',\n",
+       " 'each',\n",
+       " 'few',\n",
+       " 'more',\n",
+       " 'most',\n",
+       " 'other',\n",
+       " 'some',\n",
+       " 'such',\n",
+       " 'no',\n",
+       " 'nor',\n",
+       " 'not',\n",
+       " 'only',\n",
+       " 'own',\n",
+       " 'same',\n",
+       " 'so',\n",
+       " 'than',\n",
+       " 'too',\n",
+       " 'very',\n",
+       " 's',\n",
+       " 't',\n",
+       " 'can',\n",
+       " 'will',\n",
+       " 'just',\n",
+       " 'don',\n",
+       " 'should',\n",
+       " 'now',\n",
+       " 'd',\n",
+       " 'll',\n",
+       " 'm',\n",
+       " 'o',\n",
+       " 're',\n",
+       " 've',\n",
+       " 'y',\n",
+       " 'ain',\n",
+       " 'aren',\n",
+       " 'couldn',\n",
+       " 'didn',\n",
+       " 'doesn',\n",
+       " 'hadn',\n",
+       " 'hasn',\n",
+       " 'haven',\n",
+       " 'isn',\n",
+       " 'ma',\n",
+       " 'mightn',\n",
+       " 'mustn',\n",
+       " 'needn',\n",
+       " 'shan',\n",
+       " 'shouldn',\n",
+       " 'wasn',\n",
+       " 'weren',\n",
+       " 'won',\n",
+       " 'wouldn']"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How would you create a list of tokens that doesn't include the stopwords?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "filtered_words = [word.lower() for word in words if word.lower() not in stops]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['``',\n",
+       " 'none',\n",
+       " 'suggest',\n",
+       " '?',\n",
+       " 'know',\n",
+       " 'methods',\n",
+       " '.',\n",
+       " 'apply',\n",
+       " '!',\n",
+       " \"''\",\n",
+       " '``',\n",
+       " 'think',\n",
+       " 'obvious',\n",
+       " 'conclusion',\n",
+       " 'man',\n",
+       " 'practised',\n",
+       " 'town',\n",
+       " 'going',\n",
+       " 'country',\n",
+       " '.',\n",
+       " \"''\",\n",
+       " '``',\n",
+       " 'think',\n",
+       " 'might',\n",
+       " 'venture',\n",
+       " 'little',\n",
+       " 'farther',\n",
+       " '.',\n",
+       " 'look',\n",
+       " 'light',\n",
+       " '.',\n",
+       " 'occasion',\n",
+       " 'would',\n",
+       " 'probable',\n",
+       " 'presentation',\n",
+       " 'would',\n",
+       " 'made',\n",
+       " '?',\n",
+       " 'would',\n",
+       " 'friends',\n",
+       " 'unite',\n",
+       " 'give',\n",
+       " 'pledge',\n",
+       " 'good',\n",
+       " '?',\n",
+       " 'obviously',\n",
+       " 'moment',\n",
+       " 'dr.',\n",
+       " 'mortimer',\n",
+       " 'withdrew',\n",
+       " 'service',\n",
+       " 'hospital',\n",
+       " 'order',\n",
+       " 'start',\n",
+       " 'practice',\n",
+       " '.',\n",
+       " 'know',\n",
+       " 'presentation',\n",
+       " '.',\n",
+       " 'believe',\n",
+       " 'change',\n",
+       " 'town',\n",
+       " 'hospital',\n",
+       " 'country',\n",
+       " 'practice',\n",
+       " '.',\n",
+       " ',',\n",
+       " ',',\n",
+       " 'stretching',\n",
+       " 'inference',\n",
+       " 'far',\n",
+       " 'say',\n",
+       " 'presentation',\n",
+       " 'occasion',\n",
+       " 'change',\n",
+       " '?',\n",
+       " \"''\",\n",
+       " '``',\n",
+       " 'certainly',\n",
+       " 'seems',\n",
+       " 'probable',\n",
+       " '.',\n",
+       " \"''\",\n",
+       " '``',\n",
+       " ',',\n",
+       " 'observe',\n",
+       " 'could',\n",
+       " 'staff',\n",
+       " 'hospital',\n",
+       " ',',\n",
+       " 'since',\n",
+       " 'man',\n",
+       " 'well-established',\n",
+       " 'london',\n",
+       " 'practice',\n",
+       " 'could',\n",
+       " 'hold',\n",
+       " 'position',\n",
+       " ',',\n",
+       " 'one',\n",
+       " 'would',\n",
+       " 'drift',\n",
+       " 'country',\n",
+       " '.',\n",
+       " ',',\n",
+       " '?',\n",
+       " 'hospital',\n",
+       " 'yet',\n",
+       " 'staff',\n",
+       " 'could',\n",
+       " 'house-surgeon',\n",
+       " 'house-physician',\n",
+       " '--',\n",
+       " 'little',\n",
+       " 'senior',\n",
+       " 'student',\n",
+       " '.',\n",
+       " 'left',\n",
+       " 'five',\n",
+       " 'years',\n",
+       " 'ago',\n",
+       " '--',\n",
+       " 'date',\n",
+       " 'stick',\n",
+       " '.']"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "filtered_fdist = nltk.FreqDist(filtered_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9uoPvgrfMbJPovDCzPQl31PnMNbNLgMsj2U8Ccwu0eR5haug4wt3xnWY2092f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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff8da71a940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filtered_fdist.plot(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "string.punctuation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "stops = stopwords.words('english') + list(string.punctuation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['i',\n",
+       " 'me',\n",
+       " 'my',\n",
+       " 'myself',\n",
+       " 'we',\n",
+       " 'our',\n",
+       " 'ours',\n",
+       " 'ourselves',\n",
+       " 'you',\n",
+       " 'your',\n",
+       " 'yours',\n",
+       " 'yourself',\n",
+       " 'yourselves',\n",
+       " 'he',\n",
+       " 'him',\n",
+       " 'his',\n",
+       " 'himself',\n",
+       " 'she',\n",
+       " 'her',\n",
+       " 'hers',\n",
+       " 'herself',\n",
+       " 'it',\n",
+       " 'its',\n",
+       " 'itself',\n",
+       " 'they',\n",
+       " 'them',\n",
+       " 'their',\n",
+       " 'theirs',\n",
+       " 'themselves',\n",
+       " 'what',\n",
+       " 'which',\n",
+       " 'who',\n",
+       " 'whom',\n",
+       " 'this',\n",
+       " 'that',\n",
+       " 'these',\n",
+       " 'those',\n",
+       " 'am',\n",
+       " 'is',\n",
+       " 'are',\n",
+       " 'was',\n",
+       " 'were',\n",
+       " 'be',\n",
+       " 'been',\n",
+       " 'being',\n",
+       " 'have',\n",
+       " 'has',\n",
+       " 'had',\n",
+       " 'having',\n",
+       " 'do',\n",
+       " 'does',\n",
+       " 'did',\n",
+       " 'doing',\n",
+       " 'a',\n",
+       " 'an',\n",
+       " 'the',\n",
+       " 'and',\n",
+       " 'but',\n",
+       " 'if',\n",
+       " 'or',\n",
+       " 'because',\n",
+       " 'as',\n",
+       " 'until',\n",
+       " 'while',\n",
+       " 'of',\n",
+       " 'at',\n",
+       " 'by',\n",
+       " 'for',\n",
+       " 'with',\n",
+       " 'about',\n",
+       " 'against',\n",
+       " 'between',\n",
+       " 'into',\n",
+       " 'through',\n",
+       " 'during',\n",
+       " 'before',\n",
+       " 'after',\n",
+       " 'above',\n",
+       " 'below',\n",
+       " 'to',\n",
+       " 'from',\n",
+       " 'up',\n",
+       " 'down',\n",
+       " 'in',\n",
+       " 'out',\n",
+       " 'on',\n",
+       " 'off',\n",
+       " 'over',\n",
+       " 'under',\n",
+       " 'again',\n",
+       " 'further',\n",
+       " 'then',\n",
+       " 'once',\n",
+       " 'here',\n",
+       " 'there',\n",
+       " 'when',\n",
+       " 'where',\n",
+       " 'why',\n",
+       " 'how',\n",
+       " 'all',\n",
+       " 'any',\n",
+       " 'both',\n",
+       " 'each',\n",
+       " 'few',\n",
+       " 'more',\n",
+       " 'most',\n",
+       " 'other',\n",
+       " 'some',\n",
+       " 'such',\n",
+       " 'no',\n",
+       " 'nor',\n",
+       " 'not',\n",
+       " 'only',\n",
+       " 'own',\n",
+       " 'same',\n",
+       " 'so',\n",
+       " 'than',\n",
+       " 'too',\n",
+       " 'very',\n",
+       " 's',\n",
+       " 't',\n",
+       " 'can',\n",
+       " 'will',\n",
+       " 'just',\n",
+       " 'don',\n",
+       " 'should',\n",
+       " 'now',\n",
+       " 'd',\n",
+       " 'll',\n",
+       " 'm',\n",
+       " 'o',\n",
+       " 're',\n",
+       " 've',\n",
+       " 'y',\n",
+       " 'ain',\n",
+       " 'aren',\n",
+       " 'couldn',\n",
+       " 'didn',\n",
+       " 'doesn',\n",
+       " 'hadn',\n",
+       " 'hasn',\n",
+       " 'haven',\n",
+       " 'isn',\n",
+       " 'ma',\n",
+       " 'mightn',\n",
+       " 'mustn',\n",
+       " 'needn',\n",
+       " 'shan',\n",
+       " 'shouldn',\n",
+       " 'wasn',\n",
+       " 'weren',\n",
+       " 'won',\n",
+       " 'wouldn',\n",
+       " '!',\n",
+       " '\"',\n",
+       " '#',\n",
+       " '$',\n",
+       " '%',\n",
+       " '&',\n",
+       " \"'\",\n",
+       " '(',\n",
+       " ')',\n",
+       " '*',\n",
+       " '+',\n",
+       " ',',\n",
+       " '-',\n",
+       " '.',\n",
+       " '/',\n",
+       " ':',\n",
+       " ';',\n",
+       " '<',\n",
+       " '=',\n",
+       " '>',\n",
+       " '?',\n",
+       " '@',\n",
+       " '[',\n",
+       " '\\\\',\n",
+       " ']',\n",
+       " '^',\n",
+       " '_',\n",
+       " '`',\n",
+       " '{',\n",
+       " '|',\n",
+       " '}',\n",
+       " '~']"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "filtered_words = [word.lower() for word in words if word.lower() not in stops]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "filtered_fdist2 = nltk.FreqDist(filtered_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EjYLjOI7TiRsFp9+QNKLWZXAcp3fcKAwQJP1K0jsl9dt/Iunw3pYix9wr6WuS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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff917f8c400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filtered_fdist2.plot(30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make a function that can combine some of our pre-processing tasks to clean up the raw text:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def process_text(text):\n",
+    "    # break text into word tokens\n",
+    "    tokens = word_tokenize(text)\n",
+    "    # remove stopwords\n",
+    "    filtered_words = [token.lower() for token in tokens if not token.lower() in stops]\n",
+    "    # filter for short punctuation\n",
+    "    filtered_words = [w for w in filtered_words if (len(w) > 2)]\n",
+    "    return filtered_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Project Gutenberg's The Hound of the Baskervilles, by Arthur Conan Doyle  This eBook is for the use of anyone \""
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "whole_text[:110]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "337863"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(whole_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.17 s, sys: 0 ns, total: 1.17 s\n",
+      "Wall time: 1.17 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "clean_text = process_text(whole_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fdist_whole_text = nltk.FreqDist(clean_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tVMQTlw9i2pcbufedeUxetIE/vTMPgKIx73FgpyIO8m59O7Wkb6ciWjXLz3DU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\nBGGMMSau/wd3rWvzukbz1wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff8da640a58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fdist_whole_text.plot(25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### If some of those words don't seem important, we can add them to 'stops' and clean text again"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "boring_words = ['sir', 'upon', 'said', 'one']\n",
+    "stops += boring_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.32 s, sys: 4 ms, total: 1.32 s\n",
+      "Wall time: 1.32 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "cleaned_text = process_text(whole_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "194"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fdist_whole_text['holmes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "116"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fdist_whole_text['watson']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Stemming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nltk.stem import PorterStemmer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on package nltk.stem in nltk:\n",
+      "\n",
+      "NAME\n",
+      "    nltk.stem - NLTK Stemmers\n",
+      "\n",
+      "DESCRIPTION\n",
+      "    Interfaces used to remove morphological affixes from words, leaving\n",
+      "    only the word stem.  Stemming algorithms aim to remove those affixes\n",
+      "    required for eg. grammatical role, tense, derivational morphology\n",
+      "    leaving only the stem of the word.  This is a difficult problem due to\n",
+      "    irregular words (eg. common verbs in English), complicated\n",
+      "    morphological rules, and part-of-speech and sense ambiguities\n",
+      "    (eg. ``ceil-`` is not the stem of ``ceiling``).\n",
+      "    \n",
+      "    StemmerI defines a standard interface for stemmers.\n",
+      "\n",
+      "PACKAGE CONTENTS\n",
+      "    api\n",
+      "    isri\n",
+      "    lancaster\n",
+      "    porter\n",
+      "    regexp\n",
+      "    rslp\n",
+      "    snowball\n",
+      "    util\n",
+      "    wordnet\n",
+      "\n",
+      "FILE\n",
+      "    /home/derek/anaconda3/envs/nlp36/lib/python3.6/site-packages/nltk/stem/__init__.py\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(nltk.stem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "ps = PorterStemmer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "happi\n",
+      "happi\n",
+      "had\n",
+      "fish\n",
+      "fish\n",
+      "fisher\n",
+      "fish\n",
+      "fish\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ps.stem('Happy'))\n",
+    "print(ps.stem('Happiness'))\n",
+    "print(ps.stem('Had'))\n",
+    "\n",
+    "print(ps.stem('Fishing'))\n",
+    "print(ps.stem('Fish'))\n",
+    "print(ps.stem('Fisher'))\n",
+    "print(ps.stem('Fishes'))\n",
+    "print(ps.stem('Fished'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "words = process_text(snippet)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": true,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "stemmed = [ps.stem(word) for word in words]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "none ---> none\n",
+      "suggest ---> suggest\n",
+      "know ---> know\n",
+      "methods ---> method\n",
+      "apply ---> appli\n",
+      "think ---> think\n",
+      "obvious ---> obviou\n",
+      "conclusion ---> conclus\n",
+      "man ---> man\n",
+      "practised ---> practis\n",
+      "town ---> town\n",
+      "going ---> go\n",
+      "country ---> countri\n",
+      "think ---> think\n",
+      "might ---> might\n",
+      "venture ---> ventur\n",
+      "little ---> littl\n",
+      "farther ---> farther\n",
+      "look ---> look\n",
+      "light ---> light\n",
+      "occasion ---> occas\n",
+      "would ---> would\n",
+      "probable ---> probabl\n",
+      "presentation ---> present\n",
+      "would ---> would\n",
+      "made ---> made\n",
+      "would ---> would\n",
+      "friends ---> friend\n",
+      "unite ---> unit\n",
+      "give ---> give\n",
+      "pledge ---> pledg\n",
+      "good ---> good\n",
+      "obviously ---> obvious\n",
+      "moment ---> moment\n",
+      "dr. ---> dr.\n",
+      "mortimer ---> mortim\n",
+      "withdrew ---> withdrew\n",
+      "service ---> servic\n",
+      "hospital ---> hospit\n",
+      "order ---> order\n",
+      "start ---> start\n",
+      "practice ---> practic\n",
+      "know ---> know\n",
+      "presentation ---> present\n",
+      "believe ---> believ\n",
+      "change ---> chang\n",
+      "town ---> town\n",
+      "hospital ---> hospit\n",
+      "country ---> countri\n",
+      "practice ---> practic\n",
+      "stretching ---> stretch\n",
+      "inference ---> infer\n",
+      "far ---> far\n",
+      "say ---> say\n",
+      "presentation ---> present\n",
+      "occasion ---> occas\n",
+      "change ---> chang\n",
+      "certainly ---> certainli\n",
+      "seems ---> seem\n",
+      "probable ---> probabl\n",
+      "observe ---> observ\n",
+      "could ---> could\n",
+      "staff ---> staff\n",
+      "hospital ---> hospit\n",
+      "since ---> sinc\n",
+      "man ---> man\n",
+      "well-established ---> well-establish\n",
+      "london ---> london\n",
+      "practice ---> practic\n",
+      "could ---> could\n",
+      "hold ---> hold\n",
+      "position ---> posit\n",
+      "would ---> would\n",
+      "drift ---> drift\n",
+      "country ---> countri\n",
+      "hospital ---> hospit\n",
+      "yet ---> yet\n",
+      "staff ---> staff\n",
+      "could ---> could\n",
+      "house-surgeon ---> house-surgeon\n",
+      "house-physician ---> house-physician\n",
+      "little ---> littl\n",
+      "senior ---> senior\n",
+      "student ---> student\n",
+      "left ---> left\n",
+      "five ---> five\n",
+      "years ---> year\n",
+      "ago ---> ago\n",
+      "date ---> date\n",
+      "stick ---> stick\n"
+     ]
+    }
+   ],
+   "source": [
+    "for w, stem in zip(words, stemmed):\n",
+    "    print('{} ---> {}'.format(w, stem))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's make another function that stems the word tokens during the processing stage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def stem_process(text):\n",
+    "    # tokenize\n",
+    "    tokens = word_tokenize(text)\n",
+    "    # remove stops\n",
+    "    filtered_words = [token.lower() for token in tokens if not token.lower() in stops]\n",
+    "    filtered_words = [w for w in filtered_words if (len(w) > 2)]\n",
+    "    # stem\n",
+    "    stemmed_words = [ps.stem(w) for w in filtered_words]\n",
+    "    return stemmed_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2.72 s, sys: 16 ms, total: 2.73 s\n",
+      "Wall time: 2.74 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "stemmed = stem_process(whole_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['project',\n",
+       " 'gutenberg',\n",
+       " 'hound',\n",
+       " 'baskervil',\n",
+       " 'arthur',\n",
+       " 'conan',\n",
+       " 'doyl',\n",
+       " 'ebook',\n",
+       " 'use',\n",
+       " 'anyon',\n",
+       " 'anywher',\n",
+       " 'cost',\n",
+       " 'almost',\n",
+       " 'restrict',\n",
+       " 'whatsoev',\n",
+       " 'may',\n",
+       " 'copi',\n",
+       " 'give',\n",
+       " 'away',\n",
+       " 're-us',\n",
+       " 'term',\n",
+       " 'project',\n",
+       " 'gutenberg',\n",
+       " 'licens',\n",
+       " 'includ',\n",
+       " 'ebook',\n",
+       " 'onlin',\n",
+       " 'www.gutenberg.org',\n",
+       " 'titl',\n",
+       " 'hound',\n",
+       " 'baskervil',\n",
+       " 'author',\n",
+       " 'arthur',\n",
+       " 'conan',\n",
+       " 'doyl',\n",
+       " 'post',\n",
+       " 'date',\n",
+       " 'octob',\n",
+       " '2010',\n",
+       " 'releas',\n",
+       " 'date',\n",
+       " 'februari',\n",
+       " '2002',\n",
+       " 'etext',\n",
+       " '3070',\n",
+       " 'languag',\n",
+       " 'english',\n",
+       " '***',\n",
+       " 'start',\n",
+       " 'project',\n",
+       " 'gutenberg',\n",
+       " 'ebook',\n",
+       " 'hound',\n",
+       " 'baskervil',\n",
+       " '***',\n",
+       " 'produc',\n",
+       " 'etext',\n",
+       " 'produc',\n",
+       " 'k.pehtla',\n",
+       " 'ppehtla',\n",
+       " 'nfld.com',\n",
+       " 'hound',\n",
+       " 'baskervil',\n",
+       " 'arthur',\n",
+       " 'conan',\n",
+       " 'doyl',\n",
+       " 'content',\n",
+       " 'chapter',\n",
+       " 'mr.',\n",
+       " 'sherlock',\n",
+       " 'holm',\n",
+       " 'chapter',\n",
+       " 'curs',\n",
+       " 'baskervil',\n",
+       " 'chapter',\n",
+       " 'problem',\n",
+       " 'chapter',\n",
+       " 'henri',\n",
+       " 'baskervil',\n",
+       " 'chapter',\n",
+       " 'three',\n",
+       " 'broken',\n",
+       " 'thread',\n",
+       " 'chapter',\n",
+       " 'baskervil',\n",
+       " 'hall',\n",
+       " 'chapter',\n",
+       " 'stapleton',\n",
+       " 'merripit',\n",
+       " 'hous',\n",
+       " 'chapter',\n",
+       " 'first',\n",
+       " 'report',\n",
+       " 'dr.',\n",
+       " 'watson',\n",
+       " 'chapter',\n",
+       " 'light',\n",
+       " 'moor',\n",
+       " 'chapter',\n",
+       " 'extract',\n",
+       " 'diari',\n",
+       " 'dr.',\n",
+       " 'watson',\n",
+       " 'chapter',\n",
+       " 'man',\n",
+       " 'tor',\n",
+       " 'chapter',\n",
+       " 'death',\n",
+       " 'moor',\n",
+       " 'chapter',\n",
+       " 'fix',\n",
+       " 'net',\n",
+       " 'chapter',\n",
+       " 'hound',\n",
+       " 'baskervil',\n",
+       " 'chapter',\n",
+       " 'retrospect',\n",
+       " 'chapter',\n",
+       " 'mr.',\n",
+       " 'sherlock',\n",
+       " 'holm',\n",
+       " 'mr.',\n",
+       " 'sherlock',\n",
+       " 'holm',\n",
+       " 'usual',\n",
+       " 'late',\n",
+       " 'morn',\n",
+       " 'save',\n",
+       " 'infrequ',\n",
+       " 'occas',\n",
+       " 'night',\n",
+       " 'seat',\n",
+       " 'breakfast',\n",
+       " 'tabl',\n",
+       " 'stood',\n",
+       " 'hearth-rug',\n",
+       " 'pick',\n",
+       " 'stick',\n",
+       " 'visitor',\n",
+       " 'left',\n",
+       " 'behind',\n",
+       " 'night',\n",
+       " 'fine',\n",
+       " 'thick',\n",
+       " 'piec',\n",
+       " 'wood',\n",
+       " 'bulbous-head',\n",
+       " 'sort',\n",
+       " 'known',\n",
+       " 'penang',\n",
+       " 'lawyer',\n",
+       " 'head',\n",
+       " 'broad',\n",
+       " 'silver',\n",
+       " 'band',\n",
+       " 'nearli',\n",
+       " 'inch',\n",
+       " 'across',\n",
+       " 'jame',\n",
+       " 'mortim',\n",
+       " 'm.r.c.s.',\n",
+       " 'friend',\n",
+       " 'c.c.h.',\n",
+       " 'engrav',\n",
+       " 'date',\n",
+       " '1884',\n",
+       " 'stick',\n",
+       " 'old-fashion',\n",
+       " 'famili',\n",
+       " 'practition',\n",
+       " 'use',\n",
+       " 'carri',\n",
+       " 'dignifi',\n",
+       " 'solid',\n",
+       " 'reassur',\n",
+       " 'well',\n",
+       " 'watson',\n",
+       " 'make',\n",
+       " 'holm',\n",
+       " 'sit',\n",
+       " 'back',\n",
+       " 'given',\n",
+       " 'sign',\n",
+       " 'occup',\n",
+       " 'know',\n",
+       " 'believ',\n",
+       " 'eye',\n",
+       " 'back',\n",
+       " 'head',\n",
+       " 'least',\n",
+       " 'well-polish',\n",
+       " 'silver-pl',\n",
+       " 'coffee-pot',\n",
+       " 'front',\n",
+       " 'tell',\n",
+       " 'watson',\n",
+       " 'make',\n",
+       " 'visitor',\n",
+       " 'stick',\n",
+       " 'sinc',\n",
+       " 'unfortun',\n",
+       " 'miss',\n",
+       " 'notion',\n",
+       " 'errand',\n",
+       " 'accident',\n",
+       " 'souvenir',\n",
+       " 'becom',\n",
+       " 'import',\n",
+       " 'let',\n",
+       " 'hear',\n",
+       " 'reconstruct',\n",
+       " 'man',\n",
+       " 'examin',\n",
+       " 'think',\n",
+       " 'follow',\n",
+       " 'far',\n",
+       " 'could',\n",
+       " 'method',\n",
+       " 'companion',\n",
+       " 'dr.',\n",
+       " 'mortim',\n",
+       " 'success',\n",
+       " 'elderli',\n",
+       " 'medic',\n",
+       " 'man',\n",
+       " 'well-esteem',\n",
+       " 'sinc',\n",
+       " 'know',\n",
+       " 'give',\n",
+       " 'mark',\n",
+       " 'appreci',\n",
+       " 'good',\n",
+       " 'holm',\n",
+       " 'excel',\n",
+       " 'think',\n",
+       " 'also',\n",
+       " 'probabl',\n",
+       " 'favour',\n",
+       " 'countri',\n",
+       " 'practition',\n",
+       " 'great',\n",
+       " 'deal',\n",
+       " 'visit',\n",
+       " 'foot',\n",
+       " 'stick',\n",
+       " 'though',\n",
+       " 'origin',\n",
+       " 'handsom',\n",
+       " 'knock',\n",
+       " 'hardli',\n",
+       " 'imagin',\n",
+       " 'town',\n",
+       " 'practition',\n",
+       " 'carri',\n",
+       " 'thick-iron',\n",
+       " 'ferrul',\n",
+       " 'worn',\n",
+       " 'evid',\n",
+       " 'done',\n",
+       " 'great',\n",
+       " 'amount',\n",
+       " 'walk',\n",
+       " 'perfectli',\n",
+       " 'sound',\n",
+       " 'holm',\n",
+       " \"'friend\",\n",
+       " 'c.c.h',\n",
+       " 'guess',\n",
+       " 'someth',\n",
+       " 'hunt',\n",
+       " 'local',\n",
+       " 'hunt',\n",
+       " 'whose',\n",
+       " 'member',\n",
+       " 'possibl',\n",
+       " 'given',\n",
+       " 'surgic',\n",
+       " 'assist',\n",
+       " 'made',\n",
+       " 'small',\n",
+       " 'present',\n",
+       " 'return',\n",
+       " 'realli',\n",
+       " 'watson',\n",
+       " 'excel',\n",
+       " 'holm',\n",
+       " 'push',\n",
+       " 'back',\n",
+       " 'chair',\n",
+       " 'light',\n",
+       " 'cigarett',\n",
+       " 'bound',\n",
+       " 'say',\n",
+       " 'account',\n",
+       " 'good',\n",
+       " 'give',\n",
+       " 'small',\n",
+       " 'achiev',\n",
+       " 'habitu',\n",
+       " 'underr',\n",
+       " 'abil',\n",
+       " 'may',\n",
+       " 'lumin',\n",
+       " 'conductor',\n",
+       " 'light',\n",
+       " 'peopl',\n",
+       " 'without',\n",
+       " 'possess',\n",
+       " 'geniu',\n",
+       " 'remark',\n",
+       " 'power',\n",
+       " 'stimul',\n",
+       " 'confess',\n",
+       " 'dear',\n",
+       " 'fellow',\n",
+       " 'much',\n",
+       " 'debt',\n",
+       " 'never',\n",
+       " 'much',\n",
+       " 'must',\n",
+       " 'admit',\n",
+       " 'word',\n",
+       " 'gave',\n",
+       " 'keen',\n",
+       " 'pleasur',\n",
+       " 'often',\n",
+       " 'piqu',\n",
+       " 'indiffer',\n",
+       " 'admir',\n",
+       " 'attempt',\n",
+       " 'made',\n",
+       " 'give',\n",
+       " 'public',\n",
+       " 'method',\n",
+       " 'proud',\n",
+       " 'think',\n",
+       " 'far',\n",
+       " 'master',\n",
+       " 'system',\n",
+       " 'appli',\n",
+       " 'way',\n",
+       " 'earn',\n",
+       " 'approv',\n",
+       " 'took',\n",
+       " 'stick',\n",
+       " 'hand',\n",
+       " 'examin',\n",
+       " 'minut',\n",
+       " 'nake',\n",
+       " 'eye',\n",
+       " 'express',\n",
+       " 'interest',\n",
+       " 'laid',\n",
+       " 'cigarett',\n",
+       " 'carri',\n",
+       " 'cane',\n",
+       " 'window',\n",
+       " 'look',\n",
+       " 'convex',\n",
+       " 'len',\n",
+       " 'interest',\n",
+       " 'though',\n",
+       " 'elementari',\n",
+       " 'return',\n",
+       " 'favourit',\n",
+       " 'corner',\n",
+       " 'sette',\n",
+       " 'certainli',\n",
+       " 'two',\n",
+       " 'indic',\n",
+       " 'stick',\n",
+       " 'give',\n",
+       " 'basi',\n",
+       " 'sever',\n",
+       " 'deduct',\n",
+       " 'anyth',\n",
+       " 'escap',\n",
+       " 'ask',\n",
+       " 'self-import',\n",
+       " 'trust',\n",
+       " 'noth',\n",
+       " 'consequ',\n",
+       " 'overlook',\n",
+       " 'afraid',\n",
+       " 'dear',\n",
+       " 'watson',\n",
+       " 'conclus',\n",
+       " 'erron',\n",
+       " 'stimul',\n",
+       " 'meant',\n",
+       " 'frank',\n",
+       " 'note',\n",
+       " 'fallaci',\n",
+       " 'occasion',\n",
+       " 'guid',\n",
+       " 'toward',\n",
+       " 'truth',\n",
+       " 'entir',\n",
+       " 'wrong',\n",
+       " 'instanc',\n",
+       " 'man',\n",
+       " 'certainli',\n",
+       " 'countri',\n",
+       " 'practition',\n",
+       " 'walk',\n",
+       " 'good',\n",
+       " 'deal',\n",
+       " 'right',\n",
+       " 'extent',\n",
+       " 'dear',\n",
+       " 'watson',\n",
+       " 'mean',\n",
+       " 'would',\n",
+       " 'suggest',\n",
+       " 'exampl',\n",
+       " 'present',\n",
+       " 'doctor',\n",
+       " 'like',\n",
+       " 'come',\n",
+       " 'hospit',\n",
+       " 'hunt',\n",
+       " 'initi',\n",
+       " \"'c.c\",\n",
+       " 'place',\n",
+       " 'hospit',\n",
+       " 'word',\n",
+       " \"'chare\",\n",
+       " 'cross',\n",
+       " 'natur',\n",
+       " 'suggest',\n",
+       " 'may',\n",
+       " 'right',\n",
+       " 'probabl',\n",
+       " 'lie',\n",
+       " 'direct',\n",
+       " 'take',\n",
+       " 'work',\n",
+       " 'hypothesi',\n",
+       " 'fresh',\n",
+       " 'basi',\n",
+       " 'start',\n",
+       " 'construct',\n",
+       " 'unknown',\n",
+       " 'visitor',\n",
+       " 'well',\n",
+       " 'suppos',\n",
+       " \"'c.c.h\",\n",
+       " 'stand',\n",
+       " \"'chare\",\n",
+       " 'cross',\n",
+       " 'hospit',\n",
+       " 'infer',\n",
+       " 'may',\n",
+       " 'draw',\n",
+       " 'none',\n",
+       " 'suggest',\n",
+       " 'know',\n",
+       " 'method',\n",
+       " 'appli',\n",
+       " 'think',\n",
+       " 'obviou',\n",
+       " 'conclus',\n",
+       " 'man',\n",
+       " 'practis',\n",
+       " 'town',\n",
+       " 'go',\n",
+       " 'countri',\n",
+       " 'think',\n",
+       " 'might',\n",
+       " 'ventur',\n",
+       " 'littl',\n",
+       " 'farther',\n",
+       " 'look',\n",
+       " 'light',\n",
+       " 'occas',\n",
+       " 'would',\n",
+       " 'probabl',\n",
+       " 'present',\n",
+       " 'would',\n",
+       " 'made',\n",
+       " 'would',\n",
+       " 'friend',\n",
+       " 'unit',\n",
+       " 'give',\n",
+       " 'pledg',\n",
+       " 'good',\n",
+       " 'obvious',\n",
+       " 'moment',\n",
+       " 'dr.',\n",
+       " 'mortim',\n",
+       " 'withdrew',\n",
+       " 'servic',\n",
+       " 'hospit',\n",
+       " 'order',\n",
+       " 'start',\n",
+       " 'practic',\n",
+       " 'know',\n",
+       " 'present',\n",
+       " 'believ',\n",
+       " 'chang',\n",
+       " 'town',\n",
+       " 'hospit',\n",
+       " 'countri',\n",
+       " 'practic',\n",
+       " 'stretch',\n",
+       " 'infer',\n",
+       " 'far',\n",
+       " 'say',\n",
+       " 'present',\n",
+       " 'occas',\n",
+       " 'chang',\n",
+       " 'certainli',\n",
+       " 'seem',\n",
+       " 'probabl',\n",
+       " 'observ',\n",
+       " 'could',\n",
+       " 'staff',\n",
+       " 'hospit',\n",
+       " 'sinc',\n",
+       " 'man',\n",
+       " 'well-establish',\n",
+       " 'london',\n",
+       " 'practic',\n",
+       " 'could',\n",
+       " 'hold',\n",
+       " 'posit',\n",
+       " 'would',\n",
+       " 'drift',\n",
+       " 'countri',\n",
+       " 'hospit',\n",
+       " 'yet',\n",
+       " 'staff',\n",
+       " 'could',\n",
+       " 'house-surgeon',\n",
+       " 'house-physician',\n",
+       " 'littl',\n",
+       " 'senior',\n",
+       " 'student',\n",
+       " 'left',\n",
+       " 'five',\n",
+       " 'year',\n",
+       " 'ago',\n",
+       " 'date',\n",
+       " 'stick',\n",
+       " 'grave',\n",
+       " 'middle-ag',\n",
+       " 'famili',\n",
+       " 'practition',\n",
+       " 'vanish',\n",
+       " 'thin',\n",
+       " 'air',\n",
+       " 'dear',\n",
+       " 'watson',\n",
+       " 'emerg',\n",
+       " 'young',\n",
+       " 'fellow',\n",
+       " 'thirti',\n",
+       " 'amiabl',\n",
+       " 'unambiti',\n",
+       " 'absent-mind',\n",
+       " 'possessor',\n",
+       " 'favourit',\n",
+       " 'dog',\n",
+       " 'describ',\n",
+       " 'roughli',\n",
+       " 'larger',\n",
+       " 'terrier',\n",
+       " 'smaller',\n",
+       " 'mastiff',\n",
+       " 'laugh',\n",
+       " 'incredul',\n",
+       " 'sherlock',\n",
+       " 'holm',\n",
+       " 'lean',\n",
+       " 'back',\n",
+       " 'sette',\n",
+       " 'blew',\n",
+       " 'littl',\n",
+       " 'waver',\n",
+       " 'ring',\n",
+       " 'smoke',\n",
+       " 'ceil',\n",
+       " 'latter',\n",
+       " 'part',\n",
+       " 'mean',\n",
+       " 'check',\n",
+       " 'least',\n",
+       " 'difficult',\n",
+       " 'find',\n",
+       " 'particular',\n",
+       " 'man',\n",
+       " 'age',\n",
+       " 'profession',\n",
+       " 'career',\n",
+       " 'small',\n",
+       " 'medic',\n",
+       " 'shelf',\n",
+       " 'took',\n",
+       " 'medic',\n",
+       " 'directori',\n",
+       " 'turn',\n",
+       " 'name',\n",
+       " 'sever',\n",
+       " 'mortim',\n",
+       " 'could',\n",
+       " 'visitor',\n",
+       " 'read',\n",
+       " 'record',\n",
+       " 'aloud',\n",
+       " 'mortim',\n",
+       " 'jame',\n",
+       " 'm.r.c.s.',\n",
+       " '1882',\n",
+       " 'grimpen',\n",
+       " 'dartmoor',\n",
+       " 'devon',\n",
+       " 'house-surgeon',\n",
+       " '1882',\n",
+       " '1884',\n",
+       " 'chare',\n",
+       " 'cross',\n",
+       " 'hospit',\n",
+       " 'winner',\n",
+       " 'jackson',\n",
+       " 'prize',\n",
+       " 'compar',\n",
+       " 'patholog',\n",
+       " 'essay',\n",
+       " 'entitl',\n",
+       " \"'i\",\n",
+       " 'diseas',\n",
+       " 'revers',\n",
+       " 'correspond',\n",
+       " 'member',\n",
+       " 'swedish',\n",
+       " 'patholog',\n",
+       " 'societi',\n",
+       " 'author',\n",
+       " \"'some\",\n",
+       " 'freak',\n",
+       " 'atav',\n",
+       " 'lancet',\n",
+       " '1882',\n",
+       " \"'do\",\n",
+       " 'progress',\n",
+       " 'journal',\n",
+       " 'psycholog',\n",
+       " 'march',\n",
+       " '1883',\n",
+       " 'medic',\n",
+       " 'offic',\n",
+       " 'parish',\n",
+       " 'grimpen',\n",
+       " 'thorsley',\n",
+       " 'high',\n",
+       " 'barrow',\n",
+       " 'mention',\n",
+       " 'local',\n",
+       " 'hunt',\n",
+       " 'watson',\n",
+       " 'holm',\n",
+       " 'mischiev',\n",
+       " 'smile',\n",
+       " 'countri',\n",
+       " 'doctor',\n",
+       " 'astut',\n",
+       " 'observ',\n",
+       " 'think',\n",
+       " 'fairli',\n",
+       " 'justifi',\n",
+       " 'infer',\n",
+       " 'adject',\n",
+       " 'rememb',\n",
+       " 'right',\n",
+       " 'amiabl',\n",
+       " 'unambiti',\n",
+       " 'absent-mind',\n",
+       " 'experi',\n",
+       " 'amiabl',\n",
+       " 'man',\n",
+       " 'world',\n",
+       " 'receiv',\n",
+       " 'testimoni',\n",
+       " 'unambiti',\n",
+       " 'abandon',\n",
+       " 'london',\n",
+       " 'career',\n",
+       " 'countri',\n",
+       " 'absent-mind',\n",
+       " 'leav',\n",
+       " 'stick',\n",
+       " 'visiting-card',\n",
+       " 'wait',\n",
+       " 'hour',\n",
+       " 'room',\n",
+       " 'dog',\n",
+       " 'habit',\n",
+       " 'carri',\n",
+       " 'stick',\n",
+       " 'behind',\n",
+       " 'master',\n",
+       " 'heavi',\n",
+       " 'stick',\n",
+       " 'dog',\n",
+       " 'held',\n",
+       " 'tightli',\n",
+       " 'middl',\n",
+       " 'mark',\n",
+       " 'teeth',\n",
+       " 'plainli',\n",
+       " 'visibl',\n",
+       " 'dog',\n",
+       " 'jaw',\n",
+       " 'shown',\n",
+       " 'space',\n",
+       " 'mark',\n",
+       " 'broad',\n",
+       " 'opinion',\n",
+       " 'terrier',\n",
+       " 'broad',\n",
+       " 'enough',\n",
+       " 'mastiff',\n",
+       " 'may',\n",
+       " 'ye',\n",
+       " 'jove',\n",
+       " 'curly-hair',\n",
+       " 'spaniel',\n",
+       " 'risen',\n",
+       " 'pace',\n",
+       " 'room',\n",
+       " 'spoke',\n",
+       " 'halt',\n",
+       " 'recess',\n",
+       " 'window',\n",
+       " 'ring',\n",
+       " 'convict',\n",
+       " 'voic',\n",
+       " 'glanc',\n",
+       " 'surpris',\n",
+       " 'dear',\n",
+       " 'fellow',\n",
+       " 'possibl',\n",
+       " 'sure',\n",
+       " 'simpl',\n",
+       " 'reason',\n",
+       " 'see',\n",
+       " 'dog',\n",
+       " 'door-step',\n",
+       " 'ring',\n",
+       " 'owner',\n",
+       " \"n't\",\n",
+       " 'move',\n",
+       " 'beg',\n",
+       " 'watson',\n",
+       " 'profession',\n",
+       " 'brother',\n",
+       " 'presenc',\n",
+       " 'may',\n",
+       " 'assist',\n",
+       " 'dramat',\n",
+       " 'moment',\n",
+       " 'fate',\n",
+       " 'watson',\n",
+       " 'hear',\n",
+       " 'step',\n",
+       " 'stair',\n",
+       " 'walk',\n",
+       " 'life',\n",
+       " 'know',\n",
+       " 'whether',\n",
+       " 'good',\n",
+       " 'ill.',\n",
+       " 'dr.',\n",
+       " 'jame',\n",
+       " 'mortim',\n",
+       " 'man',\n",
+       " 'scienc',\n",
+       " 'ask',\n",
+       " 'sherlock',\n",
+       " 'holm',\n",
+       " 'specialist',\n",
+       " 'crime',\n",
+       " 'come',\n",
+       " 'appear',\n",
+       " 'visitor',\n",
+       " 'surpris',\n",
+       " 'sinc',\n",
+       " 'expect',\n",
+       " 'typic',\n",
+       " 'countri',\n",
+       " 'practition',\n",
+       " 'tall',\n",
+       " 'thin',\n",
+       " 'man',\n",
+       " 'long',\n",
+       " 'nose',\n",
+       " 'like',\n",
+       " 'beak',\n",
+       " 'jut',\n",
+       " 'two',\n",
+       " 'keen',\n",
+       " 'gray',\n",
+       " 'eye',\n",
+       " 'set',\n",
+       " 'close',\n",
+       " 'togeth',\n",
+       " 'sparkl',\n",
+       " 'brightli',\n",
+       " 'behind',\n",
+       " 'pair',\n",
+       " 'gold-rim',\n",
+       " 'glass',\n",
+       " 'clad',\n",
+       " 'profession',\n",
+       " 'rather',\n",
+       " 'slovenli',\n",
+       " 'fashion',\n",
+       " 'frock-coat',\n",
+       " 'dingi',\n",
+       " 'trouser',\n",
+       " 'fray',\n",
+       " 'though',\n",
+       " 'young',\n",
+       " 'long',\n",
+       " 'back',\n",
+       " 'alreadi',\n",
+       " 'bow',\n",
+       " 'walk',\n",
+       " 'forward',\n",
+       " 'thrust',\n",
+       " 'head',\n",
+       " 'gener',\n",
+       " 'air',\n",
+       " 'peer',\n",
+       " 'benevol',\n",
+       " 'enter',\n",
+       " 'eye',\n",
+       " 'fell',\n",
+       " 'stick',\n",
+       " 'holm',\n",
+       " 'hand',\n",
+       " 'ran',\n",
+       " 'toward',\n",
+       " 'exclam',\n",
+       " 'joy',\n",
+       " 'glad',\n",
+       " 'sure',\n",
+       " 'whether',\n",
+       " 'left',\n",
+       " 'ship',\n",
+       " 'offic',\n",
+       " 'would',\n",
+       " 'lose',\n",
+       " 'stick',\n",
+       " 'world',\n",
+       " 'present',\n",
+       " 'see',\n",
+       " 'holm',\n",
+       " 'ye',\n",
+       " 'chare',\n",
+       " 'cross',\n",
+       " 'hospit',\n",
+       " 'two',\n",
+       " 'friend',\n",
+       " 'occas',\n",
+       " 'marriag',\n",
+       " 'dear',\n",
+       " 'dear',\n",
+       " 'bad',\n",
+       " 'holm',\n",
+       " 'shake',\n",
+       " 'head',\n",
+       " 'dr.',\n",
+       " 'mortim',\n",
+       " 'blink',\n",
+       " 'glass',\n",
+       " 'mild',\n",
+       " 'astonish',\n",
+       " 'bad',\n",
+       " 'disarrang',\n",
+       " 'littl',\n",
+       " 'deduct',\n",
+       " 'marriag',\n",
+       " 'say',\n",
+       " 'ye',\n",
+       " 'marri',\n",
+       " 'left',\n",
+       " 'hospit',\n",
+       " 'hope',\n",
+       " 'consult',\n",
+       " 'practic',\n",
+       " 'necessari',\n",
+       " 'make',\n",
+       " 'home',\n",
+       " 'come',\n",
+       " 'come',\n",
+       " 'far',\n",
+       " 'wrong',\n",
+       " 'holm',\n",
+       " 'dr.',\n",
+       " 'jame',\n",
+       " 'mortim',\n",
+       " 'mister',\n",
+       " 'mister',\n",
+       " 'humbl',\n",
+       " 'm.r.c.',\n",
+       " 'man',\n",
+       " 'precis',\n",
+       " 'mind',\n",
+       " 'evid',\n",
+       " 'dabbler',\n",
+       " 'scienc',\n",
+       " 'mr.',\n",
+       " 'holm',\n",
+       " 'picker',\n",
+       " 'shell',\n",
+       " 'shore',\n",
+       " 'great',\n",
+       " 'unknown',\n",
+       " 'ocean',\n",
+       " 'presum',\n",
+       " 'mr.',\n",
+       " 'sherlock',\n",
+       " 'holm',\n",
+       " 'address',\n",
+       " 'friend',\n",
+       " 'watson',\n",
+       " 'glad',\n",
+       " 'meet',\n",
+       " 'heard',\n",
+       " 'name',\n",
+       " 'mention',\n",
+       " 'connect',\n",
+       " 'friend',\n",
+       " 'interest',\n",
+       " 'much',\n",
+       " 'mr.',\n",
+       " 'holm',\n",
+       " 'hardli',\n",
+       " 'expect',\n",
+       " 'dolichocephal',\n",
+       " 'skull',\n",
+       " 'well-mark',\n",
+       " 'supra-orbit',\n",
+       " 'develop',\n",
+       " 'would',\n",
+       " 'object',\n",
+       " 'run',\n",
+       " 'finger',\n",
+       " 'along',\n",
+       " 'pariet',\n",
+       " 'fissur',\n",
+       " 'cast',\n",
+       " 'skull',\n",
+       " 'origin',\n",
+       " 'avail',\n",
+       " 'would',\n",
+       " 'ornament',\n",
+       " 'anthropolog',\n",
+       " 'museum',\n",
+       " 'intent',\n",
+       " 'fulsom',\n",
+       " 'confess',\n",
+       " 'covet',\n",
+       " 'skull',\n",
+       " 'sherlock',\n",
+       " 'holm',\n",
+       " 'wave',\n",
+       " 'strang',\n",
+       " 'visitor',\n",
+       " 'chair',\n",
+       " 'enthusiast',\n",
+       " 'line',\n",
+       " 'thought',\n",
+       " 'perceiv',\n",
+       " 'mine',\n",
+       " 'observ',\n",
+       " 'forefing',\n",
+       " 'make',\n",
+       " 'cigarett',\n",
+       " 'hesit',\n",
+       " 'light',\n",
+       " 'man',\n",
+       " 'drew',\n",
+       " 'paper',\n",
+       " 'tobacco',\n",
+       " 'twirl',\n",
+       " 'surpris',\n",
+       " 'dexter',\n",
+       " 'long',\n",
+       " 'quiver',\n",
+       " 'finger',\n",
+       " 'agil',\n",
+       " 'restless',\n",
+       " 'antenna',\n",
+       " 'insect',\n",
+       " 'holm',\n",
+       " 'silent',\n",
+       " 'littl',\n",
+       " 'dart',\n",
+       " ...]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stemmed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fdist_stems = nltk.FreqDist(stemmed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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RoKqbROQHwDOqepeI2AiiCWmVmcHRvTtydO+O/PLMQ1i/dSefLljD+C/X8umCNazevIN/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\nQERERK8EiIiI6PUTwPssJIjpY0IAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff8da4238d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fdist_stems.plot(30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lemmatizing\n",
+    "[In English, for example, run, runs, ran and running are forms of the same lexeme, with run as the lemma. Lexeme, in this context, refers to the set of all the forms that have the same meaning, and lemma refers to the particular form that is chosen by convention to represent the lexeme.](https://en.wikipedia.org/wiki/Lemma_(morphology))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nltk.stem import WordNetLemmatizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "lemmatizer = WordNetLemmatizer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "lemmatizer.lemmatize?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "having\n",
+      "have\n",
+      "had\n",
+      "\n",
+      "fishing\n",
+      "fish\n",
+      "fisher\n",
+      "fish\n",
+      "fished\n",
+      "\n",
+      "am\n",
+      "is\n",
+      "wa\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lemmatizer.lemmatize('having'))\n",
+    "print(lemmatizer.lemmatize('have'))\n",
+    "print(lemmatizer.lemmatize('had'))\n",
+    "print()\n",
+    "print(lemmatizer.lemmatize('fishing'))\n",
+    "print(lemmatizer.lemmatize('fish'))\n",
+    "print(lemmatizer.lemmatize('fisher'))\n",
+    "print(lemmatizer.lemmatize('fishes'))\n",
+    "print(lemmatizer.lemmatize('fished'))\n",
+    "print()\n",
+    "print(lemmatizer.lemmatize('am'))\n",
+    "print(lemmatizer.lemmatize('is'))\n",
+    "print(lemmatizer.lemmatize('was'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "have\n",
+      "have\n",
+      "have\n",
+      "\n",
+      "fish\n",
+      "fish\n",
+      "fisher\n",
+      "fish\n",
+      "fish\n",
+      "\n",
+      "be\n",
+      "be\n",
+      "be\n"
+     ]
+    }
+   ],
+   "source": [
+    "# including POS for the lemmatizer can improve its output\n",
+    "print(lemmatizer.lemmatize('having', pos='v'))\n",
+    "print(lemmatizer.lemmatize('have', pos='v'))\n",
+    "print(lemmatizer.lemmatize('had', pos='v'))\n",
+    "print()\n",
+    "print(lemmatizer.lemmatize('fishing', pos='v'))\n",
+    "print(lemmatizer.lemmatize('fish', pos='v'))\n",
+    "print(lemmatizer.lemmatize('fisher', pos='n'))\n",
+    "print(lemmatizer.lemmatize('fishes', pos='v'))\n",
+    "print(lemmatizer.lemmatize('fished', pos='v'))\n",
+    "print()\n",
+    "print(lemmatizer.lemmatize('am', pos='v'))\n",
+    "print(lemmatizer.lemmatize('is', pos='v'))\n",
+    "print(lemmatizer.lemmatize('was', pos='v'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "lemmatized = [lemmatizer.lemmatize(word) for word in words]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "none ---> none\n",
+      "suggest ---> suggest\n",
+      "know ---> know\n",
+      "methods ---> method\n",
+      "apply ---> apply\n",
+      "think ---> think\n",
+      "obvious ---> obvious\n",
+      "conclusion ---> conclusion\n",
+      "man ---> man\n",
+      "practised ---> practised\n",
+      "town ---> town\n",
+      "going ---> going\n",
+      "country ---> country\n",
+      "think ---> think\n",
+      "might ---> might\n",
+      "venture ---> venture\n",
+      "little ---> little\n",
+      "farther ---> farther\n",
+      "look ---> look\n",
+      "light ---> light\n",
+      "occasion ---> occasion\n",
+      "would ---> would\n",
+      "probable ---> probable\n",
+      "presentation ---> presentation\n",
+      "would ---> would\n",
+      "made ---> made\n",
+      "would ---> would\n",
+      "friends ---> friend\n",
+      "unite ---> unite\n",
+      "give ---> give\n",
+      "pledge ---> pledge\n",
+      "good ---> good\n",
+      "obviously ---> obviously\n",
+      "moment ---> moment\n",
+      "dr. ---> dr.\n",
+      "mortimer ---> mortimer\n",
+      "withdrew ---> withdrew\n",
+      "service ---> service\n",
+      "hospital ---> hospital\n",
+      "order ---> order\n",
+      "start ---> start\n",
+      "practice ---> practice\n",
+      "know ---> know\n",
+      "presentation ---> presentation\n",
+      "believe ---> believe\n",
+      "change ---> change\n",
+      "town ---> town\n",
+      "hospital ---> hospital\n",
+      "country ---> country\n",
+      "practice ---> practice\n",
+      "stretching ---> stretching\n",
+      "inference ---> inference\n",
+      "far ---> far\n",
+      "say ---> say\n",
+      "presentation ---> presentation\n",
+      "occasion ---> occasion\n",
+      "change ---> change\n",
+      "certainly ---> certainly\n",
+      "seems ---> seems\n",
+      "probable ---> probable\n",
+      "observe ---> observe\n",
+      "could ---> could\n",
+      "staff ---> staff\n",
+      "hospital ---> hospital\n",
+      "since ---> since\n",
+      "man ---> man\n",
+      "well-established ---> well-established\n",
+      "london ---> london\n",
+      "practice ---> practice\n",
+      "could ---> could\n",
+      "hold ---> hold\n",
+      "position ---> position\n",
+      "would ---> would\n",
+      "drift ---> drift\n",
+      "country ---> country\n",
+      "hospital ---> hospital\n",
+      "yet ---> yet\n",
+      "staff ---> staff\n",
+      "could ---> could\n",
+      "house-surgeon ---> house-surgeon\n",
+      "house-physician ---> house-physician\n",
+      "little ---> little\n",
+      "senior ---> senior\n",
+      "student ---> student\n",
+      "left ---> left\n",
+      "five ---> five\n",
+      "years ---> year\n",
+      "ago ---> ago\n",
+      "date ---> date\n",
+      "stick ---> stick\n"
+     ]
+    }
+   ],
+   "source": [
+    "for w, lemma in zip(words, lemmatized):\n",
+    "    print('{} ---> {}'.format(w, lemma))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'run'"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lemmatizer.lemmatize('running', pos='v')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def lemma_process(text):\n",
+    "    # tokenize\n",
+    "    tokens = word_tokenize(text)\n",
+    "    # remove stops\n",
+    "    filtered_words = [token.lower() for token in tokens if not token.lower() in stops]\n",
+    "    filtered_words = [w for w in filtered_words if (len(w) > 2)]\n",
+    "    # lemmatize\n",
+    "    lemmatized_words = [lemmatizer.lemmatize(w) for w in filtered_words]\n",
+    "    return lemmatized_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.88 s, sys: 4 ms, total: 1.88 s\n",
+      "Wall time: 1.89 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "lemma_text = lemma_process(whole_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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q/FoReRH4GrBFfSjKMA4kIsJlx/binKFdeWNCCc07dmPZxnKWbNzBsk3lLNmw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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7ff8d5bd9470>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lemma_fdist = nltk.FreqDist(lemma_text)\n",
+    "\n",
+    "lemma_fdist.plot(30)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Part-of-speech (POS) tagging\n",
+    "The process of marking up a word in a text as corresponding to a particular part of speech, based on both its definition and its context.\n",
+    "\n",
+    "POS tagging is tricky because some words can have more than one POS depending on the context.\n",
+    "\n",
+    "\"Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$: dollar\n",
+      "    $ -$ --$ A$ C$ HK$ M$ NZ$ S$ U.S.$ US$\n",
+      "'': closing quotation mark\n",
+      "    ' ''\n",
+      "(: opening parenthesis\n",
+      "    ( [ {\n",
+      "): closing parenthesis\n",
+      "    ) ] }\n",
+      ",: comma\n",
+      "    ,\n",
+      "--: dash\n",
+      "    --\n",
+      ".: sentence terminator\n",
+      "    . ! ?\n",
+      ":: colon or ellipsis\n",
+      "    : ; ...\n",
+      "CC: conjunction, coordinating\n",
+      "    & 'n and both but either et for less minus neither nor or plus so\n",
+      "    therefore times v. versus vs. whether yet\n",
+      "CD: numeral, cardinal\n",
+      "    mid-1890 nine-thirty forty-two one-tenth ten million 0.5 one forty-\n",
+      "    seven 1987 twenty '79 zero two 78-degrees eighty-four IX '60s .025\n",
+      "    fifteen 271,124 dozen quintillion DM2,000 ...\n",
+      "DT: determiner\n",
+      "    all an another any both del each either every half la many much nary\n",
+      "    neither no some such that the them these this those\n",
+      "EX: existential there\n",
+      "    there\n",
+      "FW: foreign word\n",
+      "    gemeinschaft hund ich jeux habeas Haementeria Herr K'ang-si vous\n",
+      "    lutihaw alai je jour objets salutaris fille quibusdam pas trop Monte\n",
+      "    terram fiche oui corporis ...\n",
+      "IN: preposition or conjunction, subordinating\n",
+      "    astride among uppon whether out inside pro despite on by throughout\n",
+      "    below within for towards near behind atop around if like until below\n",
+      "    next into if beside ...\n",
+      "JJ: adjective or numeral, ordinal\n",
+      "    third ill-mannered pre-war regrettable oiled calamitous first separable\n",
+      "    ectoplasmic battery-powered participatory fourth still-to-be-named\n",
+      "    multilingual multi-disciplinary ...\n",
+      "JJR: adjective, comparative\n",
+      "    bleaker braver breezier briefer brighter brisker broader bumper busier\n",
+      "    calmer cheaper choosier cleaner clearer closer colder commoner costlier\n",
+      "    cozier creamier crunchier cuter ...\n",
+      "JJS: adjective, superlative\n",
+      "    calmest cheapest choicest classiest cleanest clearest closest commonest\n",
+      "    corniest costliest crassest creepiest crudest cutest darkest deadliest\n",
+      "    dearest deepest densest dinkiest ...\n",
+      "LS: list item marker\n",
+      "    A A. B B. C C. D E F First G H I J K One SP-44001 SP-44002 SP-44005\n",
+      "    SP-44007 Second Third Three Two * a b c d first five four one six three\n",
+      "    two\n",
+      "MD: modal auxiliary\n",
+      "    can cannot could couldn't dare may might must need ought shall should\n",
+      "    shouldn't will would\n",
+      "NN: noun, common, singular or mass\n",
+      "    common-carrier cabbage knuckle-duster Casino afghan shed thermostat\n",
+      "    investment slide humour falloff slick wind hyena override subhumanity\n",
+      "    machinist ...\n",
+      "NNP: noun, proper, singular\n",
+      "    Motown Venneboerger Czestochwa Ranzer Conchita Trumplane Christos\n",
+      "    Oceanside Escobar Kreisler Sawyer Cougar Yvette Ervin ODI Darryl CTCA\n",
+      "    Shannon A.K.C. Meltex Liverpool ...\n",
+      "NNPS: noun, proper, plural\n",
+      "    Americans Americas Amharas Amityvilles Amusements Anarcho-Syndicalists\n",
+      "    Andalusians Andes Andruses Angels Animals Anthony Antilles Antiques\n",
+      "    Apache Apaches Apocrypha ...\n",
+      "NNS: noun, common, plural\n",
+      "    undergraduates scotches bric-a-brac products bodyguards facets coasts\n",
+      "    divestitures storehouses designs clubs fragrances averages\n",
+      "    subjectivists apprehensions muses factory-jobs ...\n",
+      "PDT: pre-determiner\n",
+      "    all both half many quite such sure this\n",
+      "POS: genitive marker\n",
+      "    ' 's\n",
+      "PRP: pronoun, personal\n",
+      "    hers herself him himself hisself it itself me myself one oneself ours\n",
+      "    ourselves ownself self she thee theirs them themselves they thou thy us\n",
+      "PRP$: pronoun, possessive\n",
+      "    her his mine my our ours their thy your\n",
+      "RB: adverb\n",
+      "    occasionally unabatingly maddeningly adventurously professedly\n",
+      "    stirringly prominently technologically magisterially predominately\n",
+      "    swiftly fiscally pitilessly ...\n",
+      "RBR: adverb, comparative\n",
+      "    further gloomier grander graver greater grimmer harder harsher\n",
+      "    healthier heavier higher however larger later leaner lengthier less-\n",
+      "    perfectly lesser lonelier longer louder lower more ...\n",
+      "RBS: adverb, superlative\n",
+      "    best biggest bluntest earliest farthest first furthest hardest\n",
+      "    heartiest highest largest least less most nearest second tightest worst\n",
+      "RP: particle\n",
+      "    aboard about across along apart around aside at away back before behind\n",
+      "    by crop down ever fast for forth from go high i.e. in into just later\n",
+      "    low more off on open out over per pie raising start teeth that through\n",
+      "    under unto up up-pp upon whole with you\n",
+      "SYM: symbol\n",
+      "    % & ' '' ''. ) ). * + ,. < = > @ A[fj] U.S U.S.S.R * ** ***\n",
+      "TO: \"to\" as preposition or infinitive marker\n",
+      "    to\n",
+      "UH: interjection\n",
+      "    Goodbye Goody Gosh Wow Jeepers Jee-sus Hubba Hey Kee-reist Oops amen\n",
+      "    huh howdy uh dammit whammo shucks heck anyways whodunnit honey golly\n",
+      "    man baby diddle hush sonuvabitch ...\n",
+      "VB: verb, base form\n",
+      "    ask assemble assess assign assume atone attention avoid bake balkanize\n",
+      "    bank begin behold believe bend benefit bevel beware bless boil bomb\n",
+      "    boost brace break bring broil brush build ...\n",
+      "VBD: verb, past tense\n",
+      "    dipped pleaded swiped regummed soaked tidied convened halted registered\n",
+      "    cushioned exacted snubbed strode aimed adopted belied figgered\n",
+      "    speculated wore appreciated contemplated ...\n",
+      "VBG: verb, present participle or gerund\n",
+      "    telegraphing stirring focusing angering judging stalling lactating\n",
+      "    hankerin' alleging veering capping approaching traveling besieging\n",
+      "    encrypting interrupting erasing wincing ...\n",
+      "VBN: verb, past participle\n",
+      "    multihulled dilapidated aerosolized chaired languished panelized used\n",
+      "    experimented flourished imitated reunifed factored condensed sheared\n",
+      "    unsettled primed dubbed desired ...\n",
+      "VBP: verb, present tense, not 3rd person singular\n",
+      "    predominate wrap resort sue twist spill cure lengthen brush terminate\n",
+      "    appear tend stray glisten obtain comprise detest tease attract\n",
+      "    emphasize mold postpone sever return wag ...\n",
+      "VBZ: verb, present tense, 3rd person singular\n",
+      "    bases reconstructs marks mixes displeases seals carps weaves snatches\n",
+      "    slumps stretches authorizes smolders pictures emerges stockpiles\n",
+      "    seduces fizzes uses bolsters slaps speaks pleads ...\n",
+      "WDT: WH-determiner\n",
+      "    that what whatever which whichever\n",
+      "WP: WH-pronoun\n",
+      "    that what whatever whatsoever which who whom whosoever\n",
+      "WP$: WH-pronoun, possessive\n",
+      "    whose\n",
+      "WRB: Wh-adverb\n",
+      "    how however whence whenever where whereby whereever wherein whereof why\n",
+      "``: opening quotation mark\n",
+      "    ` ``\n"
+     ]
+    }
+   ],
+   "source": [
+    "nltk.help.upenn_tagset()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\"Do none suggest themselves? You know my methods. Apply them!\"  \"I can only think of the obvious conclusion that the man has practised in town before going to the country.\"  \"I think that we might venture a little farther than this. Look at it in this light. On what occasion would it be most probable that such a presentation would be made? When would his friends unite to give him a pledge of their good will? Obviously at the moment when Dr. Mortimer withdrew from the service of the hospital in order to start in practice for himself. We know there has been a presentation. We believe there has been a change from a town hospital to a country practice. Is it, then, stretching our inference too far to say that the presentation was on the occasion of the change?\"  \"It certainly seems probable.\"  \"Now, you will observe that he could not have been on the staff of the hospital, since only a man well-established in a London practice could hold such a position, and such a one would not drift into the country. What was he, then? If he was in the hospital and yet not on the staff he could only have been a house-surgeon or a house-physician--little more than a senior student. And he left five years ago--the date is on the stick. So'"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "snippet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('You', 'PRP'),\n",
+       " ('know', 'VBP'),\n",
+       " ('my', 'PRP$'),\n",
+       " ('methods', 'NNS'),\n",
+       " ('.', '.')]"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nltk.pos_tag(word_tokenize(sent_tokenize(snippet)[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def process_POS(text):\n",
+    "    sentences = sent_tokenize(text)\n",
+    "    tagged_words = []\n",
+    "    for sentence in sentences:\n",
+    "        words = word_tokenize(sentence)\n",
+    "        tagged = nltk.pos_tag(words)\n",
+    "        tagged_words.append(tagged)\n",
+    "    return tagged_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "tagged_sentences = process_POS(snippet)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[('``', '``'),\n",
+       "  ('Do', 'VBP'),\n",
+       "  ('none', 'RB'),\n",
+       "  ('suggest', 'VB'),\n",
+       "  ('themselves', 'PRP'),\n",
+       "  ('?', '.')],\n",
+       " [('You', 'PRP'),\n",
+       "  ('know', 'VBP'),\n",
+       "  ('my', 'PRP$'),\n",
+       "  ('methods', 'NNS'),\n",
+       "  ('.', '.')],\n",
+       " [('Apply', 'VB'), ('them', 'PRP'), ('!', '.'), (\"''\", \"''\")],\n",
+       " [('``', '``'),\n",
+       "  ('I', 'PRP'),\n",
+       "  ('can', 'MD'),\n",
+       "  ('only', 'RB'),\n",
+       "  ('think', 'VB'),\n",
+       "  ('of', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('obvious', 'JJ'),\n",
+       "  ('conclusion', 'NN'),\n",
+       "  ('that', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('man', 'NN'),\n",
+       "  ('has', 'VBZ'),\n",
+       "  ('practised', 'VBN'),\n",
+       "  ('in', 'IN'),\n",
+       "  ('town', 'NN'),\n",
+       "  ('before', 'IN'),\n",
+       "  ('going', 'VBG'),\n",
+       "  ('to', 'TO'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('country', 'NN'),\n",
+       "  ('.', '.'),\n",
+       "  (\"''\", \"''\")],\n",
+       " [('``', '``'),\n",
+       "  ('I', 'PRP'),\n",
+       "  ('think', 'VBP'),\n",
+       "  ('that', 'IN'),\n",
+       "  ('we', 'PRP'),\n",
+       "  ('might', 'MD'),\n",
+       "  ('venture', 'NN'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('little', 'RB'),\n",
+       "  ('farther', 'JJR'),\n",
+       "  ('than', 'IN'),\n",
+       "  ('this', 'DT'),\n",
+       "  ('.', '.')],\n",
+       " [('Look', 'NN'),\n",
+       "  ('at', 'IN'),\n",
+       "  ('it', 'PRP'),\n",
+       "  ('in', 'IN'),\n",
+       "  ('this', 'DT'),\n",
+       "  ('light', 'NN'),\n",
+       "  ('.', '.')],\n",
+       " [('On', 'IN'),\n",
+       "  ('what', 'WP'),\n",
+       "  ('occasion', 'NN'),\n",
+       "  ('would', 'MD'),\n",
+       "  ('it', 'PRP'),\n",
+       "  ('be', 'VB'),\n",
+       "  ('most', 'RBS'),\n",
+       "  ('probable', 'JJ'),\n",
+       "  ('that', 'IN'),\n",
+       "  ('such', 'PDT'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('presentation', 'NN'),\n",
+       "  ('would', 'MD'),\n",
+       "  ('be', 'VB'),\n",
+       "  ('made', 'VBN'),\n",
+       "  ('?', '.')],\n",
+       " [('When', 'WRB'),\n",
+       "  ('would', 'MD'),\n",
+       "  ('his', 'PRP$'),\n",
+       "  ('friends', 'NNS'),\n",
+       "  ('unite', 'JJ'),\n",
+       "  ('to', 'TO'),\n",
+       "  ('give', 'VB'),\n",
+       "  ('him', 'PRP'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('pledge', 'NN'),\n",
+       "  ('of', 'IN'),\n",
+       "  ('their', 'PRP$'),\n",
+       "  ('good', 'NN'),\n",
+       "  ('will', 'MD'),\n",
+       "  ('?', '.')],\n",
+       " [('Obviously', 'RB'),\n",
+       "  ('at', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('moment', 'NN'),\n",
+       "  ('when', 'WRB'),\n",
+       "  ('Dr.', 'NNP'),\n",
+       "  ('Mortimer', 'NNP'),\n",
+       "  ('withdrew', 'VBD'),\n",
+       "  ('from', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('service', 'NN'),\n",
+       "  ('of', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('hospital', 'NN'),\n",
+       "  ('in', 'IN'),\n",
+       "  ('order', 'NN'),\n",
+       "  ('to', 'TO'),\n",
+       "  ('start', 'VB'),\n",
+       "  ('in', 'IN'),\n",
+       "  ('practice', 'NN'),\n",
+       "  ('for', 'IN'),\n",
+       "  ('himself', 'PRP'),\n",
+       "  ('.', '.')],\n",
+       " [('We', 'PRP'),\n",
+       "  ('know', 'VBP'),\n",
+       "  ('there', 'EX'),\n",
+       "  ('has', 'VBZ'),\n",
+       "  ('been', 'VBN'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('presentation', 'NN'),\n",
+       "  ('.', '.')],\n",
+       " [('We', 'PRP'),\n",
+       "  ('believe', 'VBP'),\n",
+       "  ('there', 'EX'),\n",
+       "  ('has', 'VBZ'),\n",
+       "  ('been', 'VBN'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('change', 'NN'),\n",
+       "  ('from', 'IN'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('town', 'NN'),\n",
+       "  ('hospital', 'NN'),\n",
+       "  ('to', 'TO'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('country', 'NN'),\n",
+       "  ('practice', 'NN'),\n",
+       "  ('.', '.')],\n",
+       " [('Is', 'VBZ'),\n",
+       "  ('it', 'PRP'),\n",
+       "  (',', ','),\n",
+       "  ('then', 'RB'),\n",
+       "  (',', ','),\n",
+       "  ('stretching', 'VBG'),\n",
+       "  ('our', 'PRP$'),\n",
+       "  ('inference', 'NN'),\n",
+       "  ('too', 'RB'),\n",
+       "  ('far', 'RB'),\n",
+       "  ('to', 'TO'),\n",
+       "  ('say', 'VB'),\n",
+       "  ('that', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('presentation', 'NN'),\n",
+       "  ('was', 'VBD'),\n",
+       "  ('on', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('occasion', 'NN'),\n",
+       "  ('of', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('change', 'NN'),\n",
+       "  ('?', '.'),\n",
+       "  (\"''\", \"''\")],\n",
+       " [('``', '``'),\n",
+       "  ('It', 'PRP'),\n",
+       "  ('certainly', 'RB'),\n",
+       "  ('seems', 'VBZ'),\n",
+       "  ('probable', 'JJ'),\n",
+       "  ('.', '.'),\n",
+       "  (\"''\", \"''\")],\n",
+       " [('``', '``'),\n",
+       "  ('Now', 'RB'),\n",
+       "  (',', ','),\n",
+       "  ('you', 'PRP'),\n",
+       "  ('will', 'MD'),\n",
+       "  ('observe', 'VB'),\n",
+       "  ('that', 'IN'),\n",
+       "  ('he', 'PRP'),\n",
+       "  ('could', 'MD'),\n",
+       "  ('not', 'RB'),\n",
+       "  ('have', 'VB'),\n",
+       "  ('been', 'VBN'),\n",
+       "  ('on', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('staff', 'NN'),\n",
+       "  ('of', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('hospital', 'NN'),\n",
+       "  (',', ','),\n",
+       "  ('since', 'IN'),\n",
+       "  ('only', 'RB'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('man', 'NN'),\n",
+       "  ('well-established', 'JJ'),\n",
+       "  ('in', 'IN'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('London', 'NNP'),\n",
+       "  ('practice', 'NN'),\n",
+       "  ('could', 'MD'),\n",
+       "  ('hold', 'VB'),\n",
+       "  ('such', 'PDT'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('position', 'NN'),\n",
+       "  (',', ','),\n",
+       "  ('and', 'CC'),\n",
+       "  ('such', 'PDT'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('one', 'NN'),\n",
+       "  ('would', 'MD'),\n",
+       "  ('not', 'RB'),\n",
+       "  ('drift', 'VB'),\n",
+       "  ('into', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('country', 'NN'),\n",
+       "  ('.', '.')],\n",
+       " [('What', 'WP'),\n",
+       "  ('was', 'VBD'),\n",
+       "  ('he', 'PRP'),\n",
+       "  (',', ','),\n",
+       "  ('then', 'RB'),\n",
+       "  ('?', '.')],\n",
+       " [('If', 'IN'),\n",
+       "  ('he', 'PRP'),\n",
+       "  ('was', 'VBD'),\n",
+       "  ('in', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('hospital', 'NN'),\n",
+       "  ('and', 'CC'),\n",
+       "  ('yet', 'RB'),\n",
+       "  ('not', 'RB'),\n",
+       "  ('on', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('staff', 'NN'),\n",
+       "  ('he', 'PRP'),\n",
+       "  ('could', 'MD'),\n",
+       "  ('only', 'RB'),\n",
+       "  ('have', 'VB'),\n",
+       "  ('been', 'VBN'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('house-surgeon', 'NN'),\n",
+       "  ('or', 'CC'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('house-physician', 'JJ'),\n",
+       "  ('--', ':'),\n",
+       "  ('little', 'RB'),\n",
+       "  ('more', 'JJR'),\n",
+       "  ('than', 'IN'),\n",
+       "  ('a', 'DT'),\n",
+       "  ('senior', 'JJ'),\n",
+       "  ('student', 'NN'),\n",
+       "  ('.', '.')],\n",
+       " [('And', 'CC'),\n",
+       "  ('he', 'PRP'),\n",
+       "  ('left', 'VBD'),\n",
+       "  ('five', 'CD'),\n",
+       "  ('years', 'NNS'),\n",
+       "  ('ago', 'RB'),\n",
+       "  ('--', ':'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('date', 'NN'),\n",
+       "  ('is', 'VBZ'),\n",
+       "  ('on', 'IN'),\n",
+       "  ('the', 'DT'),\n",
+       "  ('stick', 'NN'),\n",
+       "  ('.', '.')],\n",
+       " [('So', 'RB')]]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tagged_sentences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('``', '``'), ('Do', 'VBP'), ('none', 'RB'), ('suggest', 'VB'), ('themselves', 'PRP'), ('?', '.')]\n",
+      "[('You', 'PRP'), ('know', 'VBP'), ('my', 'PRP$'), ('methods', 'NNS'), ('.', '.')]\n",
+      "[('Apply', 'VB'), ('them', 'PRP'), ('!', '.'), (\"''\", \"''\")]\n",
+      "[('``', '``'), ('I', 'PRP'), ('can', 'MD'), ('only', 'RB'), ('think', 'VB'), ('of', 'IN'), ('the', 'DT'), ('obvious', 'JJ'), ('conclusion', 'NN'), ('that', 'IN'), ('the', 'DT'), ('man', 'NN'), ('has', 'VBZ'), ('practised', 'VBN'), ('in', 'IN'), ('town', 'NN'), ('before', 'IN'), ('going', 'VBG'), ('to', 'TO'), ('the', 'DT'), ('country', 'NN'), ('.', '.'), (\"''\", \"''\")]\n",
+      "[('``', '``'), ('I', 'PRP'), ('think', 'VBP'), ('that', 'IN'), ('we', 'PRP'), ('might', 'MD'), ('venture', 'NN'), ('a', 'DT'), ('little', 'RB'), ('farther', 'JJR'), ('than', 'IN'), ('this', 'DT'), ('.', '.')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "sentences =[]\n",
+    "for sentence in tagged_sentences[:5]:\n",
+    "    print(sentence)\n",
+    "    lemmas = []\n",
+    "    for word, pos in sentence:\n",
+    "        if pos == 'VBP':\n",
+    "            lemmas.append(lemmatizer.lemmatize(word, 'v'))\n",
+    "        elif pos in ['NN', 'NNS']:\n",
+    "            lemmas.append(lemmatizer.lemmatize(word, 'n'))\n",
+    "        else:\n",
+    "            lemmas.append(lemmatizer.lemmatize(word))\n",
+    "    sentences.append(lemmas)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ngrams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from nltk import ngrams\n",
+    "from collections import Counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "bigrams = Counter(ngrams(word_tokenize(whole_text), 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(\"''\", '``')\t859\n",
+      "(',', 'and')\t678\n",
+      "('.', \"''\")\t671\n",
+      "('.', '``')\t475\n",
+      "('of', 'the')\t461\n",
+      "('?', \"''\")\t399\n",
+      "('.', 'I')\t253\n",
+      "('in', 'the')\t251\n",
+      "(',', 'but')\t211\n",
+      "(',', \"''\")\t186\n",
+      "('``', 'I')\t183\n",
+      "(\"''\", 'said')\t175\n",
+      "('.', 'The')\t157\n",
+      "('Sir', 'Henry')\t153\n",
+      "('I', 'have')\t147\n",
+      "('to', 'the')\t144\n",
+      "('.', 'It')\t137\n",
+      "('the', 'moor')\t135\n",
+      "('upon', 'the')\t132\n",
+      "('that', 'he')\t130\n",
+      "(',', 'I')\t129\n",
+      "('that', 'I')\t127\n",
+      "('.', 'He')\t127\n",
+      "('and', 'the')\t120\n",
+      "('at', 'the')\t109\n",
+      "('it', '.')\t109\n",
+      "(',', 'the')\t105\n",
+      "('it', 'was')\t105\n",
+      "('and', 'I')\t104\n",
+      "('I', 'had')\t102\n"
+     ]
+    }
+   ],
+   "source": [
+    "for phrase, freq in bigrams.most_common(30):\n",
+    "    print(\"{}\\t{}\".format(phrase, freq))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "trigrams = Counter(ngrams(word_tokenize(whole_text), 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('.', \"''\", '``')\t508\n",
+      "('?', \"''\", '``')\t316\n",
+      "(',', \"''\", 'said')\t150\n",
+      "(\"''\", '``', 'I')\t107\n",
+      "(',', 'and', 'I')\t77\n",
+      "('.', '``', 'I')\t66\n",
+      "(',', 'and', 'the')\t57\n",
+      "(',', 'sir', ',')\t57\n",
+      "(\"''\", 'said', 'he')\t55\n",
+      "('the', 'moor', '.')\t52\n",
+      "('.', 'It', 'was')\t51\n",
+      "(\"''\", '``', 'No')\t50\n",
+      "(\"''\", '``', 'Yes')\t49\n",
+      "('``', 'No', ',')\t46\n",
+      "(\"''\", '``', 'And')\t44\n",
+      "('upon', 'the', 'moor')\t44\n",
+      "('``', 'Well', ',')\t43\n",
+      "(',', 'and', 'that')\t43\n",
+      "('``', 'Yes', ',')\t43\n",
+      "(',', 'and', 'he')\t41\n",
+      "(\"''\", 'said', 'Holmes')\t39\n",
+      "('it', '.', \"''\")\t38\n",
+      "(',', 'but', 'I')\t36\n",
+      "(\"''\", '``', 'Well')\t34\n",
+      "('.', 'It', 'is')\t34\n",
+      "('.', '``', 'It')\t33\n",
+      "('said', 'he', '.')\t32\n",
+      "(\"''\", '``', 'But')\t32\n",
+      "(',', 'Watson', ',')\t31\n",
+      "('he', '.', '``')\t31\n"
+     ]
+    }
+   ],
+   "source": [
+    "for phrase, freq in trigrams.most_common(30):\n",
+    "    print(\"{}\\t{}\".format(phrase, freq))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Stopwords and punctuation will have an effect on ngrams!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "stemmed = stem_process(whole_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "stemmed_bigrams = Counter(ngrams(stemmed, 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(('dr.', 'mortim'), 73),\n",
+       " (('project', 'gutenberg-tm'), 57),\n",
+       " (('sherlock', 'holm'), 34),\n",
+       " (('baskervil', 'hall'), 31),\n",
+       " (('project', 'gutenberg'), 30),\n",
+       " (('dr.', 'watson'), 28),\n",
+       " (('electron', 'work'), 27),\n",
+       " (('henri', 'baskervil'), 25),\n",
+       " (('mr.', 'holm'), 24),\n",
+       " (('coomb', 'tracey'), 18),\n",
+       " (('gutenberg-tm', 'electron'), 18),\n",
+       " (('merripit', 'hous'), 15),\n",
+       " (('charl', 'baskervil'), 15),\n",
+       " (('mr.', 'sherlock'), 14),\n",
+       " (('baker', 'street'), 14),\n",
+       " (('grimpen', 'mire'), 14),\n",
+       " (('gutenberg', 'literari'), 13),\n",
+       " (('literari', 'archiv'), 13),\n",
+       " (('archiv', 'foundat'), 13),\n",
+       " (('hound', 'baskervil'), 12)]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stemmed_bigrams.most_common(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "stemmed_trigrams = Counter(ngrams(stemmed, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(('project', 'gutenberg-tm', 'electron'), 18),\n",
+       " (('gutenberg-tm', 'electron', 'work'), 18),\n",
+       " (('mr.', 'sherlock', 'holm'), 14),\n",
+       " (('project', 'gutenberg', 'literari'), 13),\n",
+       " (('gutenberg', 'literari', 'archiv'), 13),\n",
+       " (('literari', 'archiv', 'foundat'), 13),\n",
+       " (('mrs.', 'laura', 'lyon'), 10),\n",
+       " (('project', 'gutenberg-tm', 'work'), 10),\n",
+       " (('distribut', 'project', 'gutenberg-tm'), 9),\n",
+       " (('great', 'grimpen', 'mire'), 8),\n",
+       " (('project', 'gutenberg-tm', 'licens'), 8),\n",
+       " (('full', 'project', 'gutenberg-tm'), 6),\n",
+       " (('copi', 'project', 'gutenberg-tm'), 5),\n",
+       " (('arthur', 'conan', 'doyl'), 4),\n",
+       " (('dr.', 'jame', 'mortim'), 4),\n",
+       " (('death', 'charl', 'baskervil'), 4),\n",
+       " (('frankland', 'lafter', 'hall'), 4),\n",
+       " (('dr.', 'mortim', 'look'), 4),\n",
+       " (('phrase', 'project', 'gutenberg'), 4),\n",
+       " (('set', 'forth', 'paragraph'), 4)]"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stemmed_trigrams.most_common(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bag of words (BOW) text representation for machine learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 0 0 1 2 1 2 1 1 1]\n",
+      " [1 1 1 1 1 0 0 1 0 1]]\n",
+      "['also', 'football', 'games', 'john', 'likes', 'mary', 'movies', 'to', 'too', 'watch']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "vectorizer = CountVectorizer()\n",
+    "data_corpus = [\"John likes to watch movies. Mary likes movies too.\", \n",
+    "\"John also likes to watch football games.\"]\n",
+    "X = vectorizer.fit_transform(data_corpus) \n",
+    "print(X.toarray())\n",
+    "print(vectorizer.get_feature_names())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/derek/anaconda3/envs/nlp36/lib/python3.6/site-packages/nltk/twitter/__init__.py:20: UserWarning: The twython library has not been installed. Some functionality from the twitter package will not be available.\n",
+      "  warnings.warn(\"The twython library has not been installed. \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "from nltk.sentiment.vader import SentimentIntensityAnalyzer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "vader = SentimentIntensityAnalyzer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "text = \"I dont hate movies!\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'compound': 0.509, 'neg': 0.0, 'neu': 0.378, 'pos': 0.622}"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vader.polarity_scores(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lessons/python/intro-nlp/lesson.md b/lessons/python/intro-nlp/lesson.md
new file mode 100644
index 000000000..ce014038d
--- /dev/null
+++ b/lessons/python/intro-nlp/lesson.md
@@ -0,0 +1,39 @@
+---
+layout: page
+title: Intro to NLP
+visible: True
+tags:
+  # languages
+  - python
+  # levels
+  - intermediate
+---
+<!-- change visible to true if you want it on the site -->
+<!-- remove any tags listed above that are not relevant -->
+
+ - **Authors**: Derek Howard
+ - **Research field**: Neuroscience
+ - **Lesson topic**: Text processing with NLTK
+ - **Lesson content URL**: <link>
+
+ The lesson was uploaded as a Jupyter notebook.
+
+If you want to follow along, make sure you have the correct packages installed and download the associated data for nltk
+   - pip install nltk
+   - to download only tools/data used in this lesson:
+
+> python -m nltk.downloader stopwords porter_test vader_lexicon tagsets punkt wordnet
+
+   - Alternatively, this will download ~2gb of data, but should ensure all components of nltk work if you're interested in exploring other functionalities of nltk:
+
+> python -m nltk.downloader all
+
+Also save this text as book.txt in a place you can easily access:
+https://www.gutenberg.org/cache/epub/3070/pg3070.txt
+
+If you'd like to follow along in Jupyter Notebook, do the following:
+
+   - Install [Jupyter Notebook](https://jupyter.readthedocs.org/en/latest/install.html)
+   - Run the notebook by typing `jupyter notebook` into command line.
+   - In the web application that opens, navigate to the folder containing the files you downloaded, and open 'intro_NLP.ipynb' to follow along.
+   - A pre-rendered version of the notebook may be [viewed here](https://nbviewer.jupyter.org/github/UofTCoders/studyGroup/blob/gh-pages/lessons/python/intro-nlp/intro_NLP.ipynb).
diff --git a/lessons/python/intro/IntroPython-AH.ipynb b/lessons/python/intro/IntroPython-AH.ipynb
new file mode 100644
index 000000000..59fab4e47
--- /dev/null
+++ b/lessons/python/intro/IntroPython-AH.ipynb
@@ -0,0 +1,781 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Intro to Python\n",
+    "\n",
+    "## UofT Coders, Jan 13 2021\n",
+    "\n",
+    "**Author**: Ahmed Hasan, borrowing heavily from Madeleine Bonsma-Fisher, Lina Tran, Charles Zhu, and Amanda Easson\n",
+    "\n",
+    "---\n",
+    "\n",
+    "Python is a powerful, flexible general purpose programming language. There are many ways to use Python, including but not limited to an interpreter, IDEs (Integrated Development Environments) such as Spyder, and Jupyter Notebooks. We will be using Jupyter for this lesson. \n",
+    "\n",
+    "In the browser-based Jupyter environment, code and text can be written in _cells_, and then executed: code is run on a cell-by-cell basis, while text cells are then rendered based on Markdown styling.\n",
+    "\n",
+    "In this lesson, we will briefly cover different object types in Python to understand how we can use it to encode different kinds of information. We will then briefly look at `for` loops and `if` statements, two essential techniques for automating repetitive tasks intelligently, before wrapping up with a look at different custom Python libraries. \n",
+    "\n",
+    "## The interpreter\n",
+    "\n",
+    "### Math with _integers_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "2 + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "2 * 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "2 ** 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# % is the modulus operator\n",
+    "# x % y -> return the remainder of x / y\n",
+    "2 % 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Math with _floats_\n",
+    "\n",
+    "Floats = floating point numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "2.0 * 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "2.5 * 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "2 * 3;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Variables\n",
+    "\n",
+    "Typically, we will be saving information we care about as _variables_ instead of handling values directly. Python also allows us to work with text information using an object type known as _strings_, which can be created using a pair of quotes. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = 2019\n",
+    "name = 'Ahmed' # this is a string"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can then use _functions_ on our variables to do things with them. The `print` function outputs the value of a variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(year)\n",
+    "\n",
+    "print(name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `type` function returns the object type of a variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(type(name))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(type(2.0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(type(year))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Documentation for a function can be accessed using the `help` function: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# how exactly does print work again?\n",
+    "\n",
+    "help(print)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lists"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_Lists_ are a special object type that let us store multiple values. The elements of a list can be of any object type:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lists - square brackets!\n",
+    "\n",
+    "fruits = ['apple', 'orange', 'mango']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(fruits)\n",
+    "print(type(fruits))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "misc = [42, 'python!', 2.57]\n",
+    "print(misc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# concatenating lists\n",
+    "fruits + fruits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Methods\n",
+    "\n",
+    "In Python, objects have specific *methods* that 'belong' to them. These can be thought of as functions that do object-specific things but only really work on that specific object type. \n",
+    "\n",
+    "Lists have an `append` method that can be used to add another element to the end of a given list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(fruits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fruits.append('lemon')\n",
+    "\n",
+    "print(fruits) # the list has been modified! "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case, `append` is a method that wouldn't really make sense to run on a string or integer, since it specifically adds a new element. Different objects have different methods, and these substantially expand the functionality each object has! "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indexing lists and strings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# indexing in python begins at 0! \n",
+    "\n",
+    "print(fruits[0])\n",
+    "print(type(fruits[0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# slicing multiple things\n",
+    "# start : end (exclusive!)\n",
+    "\n",
+    "# recall that fruits = ['apple', 'orange', 'mango']\n",
+    "\n",
+    "print(fruits[0:2])\n",
+    "print(fruits[0:])\n",
+    "print(fruits[:3])\n",
+    "\n",
+    "print(fruits[-1]) # last item of a list\n",
+    "print(fruits[-2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reassigning item in list\n",
+    "fruits[2] = 'banana'\n",
+    "print(fruits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# slicing and indexing strings\n",
+    "\n",
+    "my_string = 'This is a string.'\n",
+    "\n",
+    "print(my_string[1:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# strings do not support reassignment\n",
+    "# try running this: my_string[0] = 't'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dictionaries allow us to store key value pairs\n",
+    "\n",
+    "fruit_colors = {'banana': 'yellow',\n",
+    "                'apple': 'red',\n",
+    "                'orange': 'orange'}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# keys are 'looked up' using square brackets\n",
+    "\n",
+    "print(fruit_colors['banana'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# additional keys can be added after the fact\n",
+    "fruit_colors['lemon'] = 'yellow'\n",
+    "print(fruit_colors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dicts have their own unique methods too\n",
+    "print(fruit_colors.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## If statements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# python can check whether certain statements are true or false\n",
+    "\n",
+    "2 > 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use == to test for equality\n",
+    "1 == 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = 5\n",
+    "isinstance(x, int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# with these expressions, we can construct if statements\n",
+    "# if statements allow our scripts to encode more complex instructions\n",
+    "\n",
+    "x = 5\n",
+    "if isinstance(x, int):\n",
+    "    print(x, 'is an integer')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if-else\n",
+    "\n",
+    "if isinstance(x, str):\n",
+    "    print(x, 'is a string')\n",
+    "else:\n",
+    "    print(x, 'is not a string')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if-elif-else\n",
+    "# useful if we have multiple conditions to test\n",
+    "\n",
+    "if isinstance(x, str):\n",
+    "    print(x, 'is a string')\n",
+    "elif isinstance(x, int):\n",
+    "    print(x, 'is an integer')\n",
+    "else:\n",
+    "    print(x, 'is neither a string nor an integer')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## For loops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for loops allow us to automate repetitive operations\n",
+    "\n",
+    "# how do we check which values in this list are even?\n",
+    "nums = [1, 2, 3, 4]\n",
+    "\n",
+    "# could check them individually?\n",
+    "print(nums[0] % 2)\n",
+    "print(nums[1] % 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for loops simplify this\n",
+    "# here, 'number' is a placeholder variable for each of the items in the list\n",
+    "\n",
+    "for number in nums:\n",
+    "    if number % 2 == 0:\n",
+    "        print(number, 'is even')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we can also loop over the contents of a string\n",
+    "vowels = 'aeiou'\n",
+    "for letter in vowels:\n",
+    "    print(letter)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# functions allow us to generalize operations\n",
+    "# what is the sum of squares of two numbers?\n",
+    "\n",
+    "x = 5\n",
+    "y = 7\n",
+    "\n",
+    "print((x ** 2) + (y ** 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sum_of_squares(num1, num2):\n",
+    "    ''' (int, int) -> int\n",
+    "    input: two integers\n",
+    "    output: the sum of the squares of the two numbers\n",
+    "    '''\n",
+    "    ss_out = num1 ** 2 + num2 ** 2\n",
+    "    return ss_out\n",
+    "\n",
+    "# def is the keyword to define functions\n",
+    "# each function typically ends with a return statement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = sum_of_squares(x, y)\n",
+    "print(output) # our operation from above\n",
+    "print(sum_of_squares(50, 42)) # works with any values we want!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking on our docstring\n",
+    "help(sum_of_squares)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Some useful packages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The bread and butter of what makes Python so useful and popular in research circles is how it facilitates the creation and dissemination of custom code for specific tasks. This code is bundled in what are referred to as _packages_. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use a package by importing it\n",
+    "# these can be given a shorter alias\n",
+    "\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# packages provide all sorts of useful functionality\n",
+    "# numpy allows for efficient numerical calculations in python\n",
+    "# we can call on a function belonging to a package using the . notation\n",
+    "# packages also can include custom object types - we will have a look at numpy arrays here\n",
+    "# these are essentially vectors/matrices\n",
+    "\n",
+    "np_array = np.arange(15)\n",
+    "list_array = list(range(15))\n",
+    "\n",
+    "print(np_array)\n",
+    "print(type(np_array))\n",
+    "print(list_array)\n",
+    "print(type(list_array))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# numpy arrays also allow for vectorized operations\n",
+    "\n",
+    "print(np_array * 2)\n",
+    "print(list_array * 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# numpy arrays also have helpful methods of their own\n",
+    "\n",
+    "# what's the mean of our array?\n",
+    "print(np_array.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the max value in our array?\n",
+    "print(np_array.max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns # we will use this for plotting\n",
+    "%matplotlib inline\n",
+    "iris = sns.load_dataset('iris')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pull out specific rows with the .loc method\n",
+    "iris.loc[0:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# or rows AND columns\n",
+    "# use a list for multiple columns!\n",
+    "\n",
+    "iris.loc[0:2, 'petal_length']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris.loc[0:2, ['petal_length', 'species']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.relplot(x='petal_length', y='petal_width', data=iris)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.relplot(x='petal_length', y='petal_width', hue='species', data=iris)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/python/intro/IntroPython-MB.ipynb b/lessons/python/intro/IntroPython-MB.ipynb
new file mode 100644
index 000000000..87cb0d709
--- /dev/null
+++ b/lessons/python/intro/IntroPython-MB.ipynb
@@ -0,0 +1,710 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Resources\n",
+    "\n",
+    "For further information and tutorials see:\n",
+    "\n",
+    "* [Software Carpentry Python Programming](https://swcarpentry.github.io/python-novice-inflammation/)\n",
+    "\n",
+    "#### Author: Madeleine Bonsma-Fisher, heavily borrowing from Lina Tran and Charles Zhu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The interpreter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# you can type math directly into Python (IPython, command line)\n",
+    "2+2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "4**2 # this is \"4 to the power of 2\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "1./2 # converts 1 to a float"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "4*3;  # The semicolon suppresses the output in IPython - this is similar to Matlab or Oracle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# the = symbol indicates that what is on the right is assigned to the variable name on the left\n",
+    "name = 'Madeleine'\n",
+    "year = 2017"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# we can then see what our variable is holding using the print() function\n",
+    "print(name)\n",
+    "# we can check the type of our variable using the type(variable_name) function\n",
+    "print(type(year))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# you can pull up help information on any object or function:\n",
+    "help(type)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# you must assign a variable before you call it, otherwise an error will occur\n",
+    "print(age)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lists\n",
+    "Python has two array-like things.  The first is called a \"list\", which can hold any data types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fruits = ['apple', 'banana', 'mango', 'lychee']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(fruits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fruits.append('orange')\n",
+    "print(fruits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# lists don't need to comprise of all the same type \n",
+    "misc = [29, 'dog', fruits]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(misc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(fruits + fruits)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second is called a \"tuple\", which is an immutable list (nothing can be added or subtracted) whose elements also can't be reassigned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "tup1 = (1,2)\n",
+    "print(tup1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "tup1[0] = 2 # this gives an error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indexing and Slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#indexing in Python starts at 0, not 1 (like in Matlab or Oracle)\n",
+    "print(fruits[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(fruits[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# strings are just a particular kind of list\n",
+    "s = 'This is a string.'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(s[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# use -1 to get the last element\n",
+    "print(fruits[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(fruits[-2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# to get a slice of the string use the : symbol\n",
+    "print(s[0:4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(s[:4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(s[4:7])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(s[7:])\n",
+    "print(s[7:len(s)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## If Statements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "s2 = [19034, 23]\n",
+    "\n",
+    "# You will always need to start with an 'if' line\n",
+    "# You do not need the elif or else statements\n",
+    "# You can have as many elif statements as needed\n",
+    "\n",
+    "if type(s2) == str:\n",
+    "    print('s2 is a string')\n",
+    "elif type(s2) == int:\n",
+    "    print('s2 is an integer')\n",
+    "elif type(s2) == float:\n",
+    "    print('s2 is a float')\n",
+    "else:\n",
+    "    print('s2 is not a string or integer')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## For Loops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "nums = [23, 56, 1, 10, 15, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# in this case, 'n' is a dummy variable that will be used by the for loop\n",
+    "# you do not need to assign it ahead of time\n",
+    "\n",
+    "for n in nums:\n",
+    "    if n%2 == 0:\n",
+    "        print('even')\n",
+    "    else:\n",
+    "        print('odd')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# for loops can iterate over strings as well\n",
+    "vowels = 'aeiou'\n",
+    "for vowel in vowels:\n",
+    "    print(vowel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# always use descriptive naming for functions, variables, arguments etc.\n",
+    "def sum_of_squares(num1, num2):\n",
+    "    \"\"\"\n",
+    "    Input: two numbers\n",
+    "    Output: the sum of the squares of the two numbers\n",
+    "    \"\"\"\n",
+    "    ss = num1**2 + num2**2\n",
+    "    return(ss)\n",
+    "\n",
+    "# The stuff inside \"\"\" \"\"\" is called the \"docstring\". It can be accessed by typing help(sum_of_squares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(sum_of_squares(4,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# the return statement in a function allows us to store the output of a function call in a variable for later use\n",
+    "ss1 = sum_of_squares(5,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "print(ss1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Useful Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# use a package by importing it, you can also give it a shorter alias, in this case 'np'\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "array = np.arange(15)\n",
+    "lst = list(range(15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(array)\n",
+    "print(lst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print(type(array))\n",
+    "print(type(lst))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# numpy arrays allow for vectorized calculations\n",
+    "print(array*2)\n",
+    "print(lst*2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "array = array.reshape([5,3])\n",
+    "print(array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# we can get the mean over all rows (using axis=1)\n",
+    "array.mean(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# max value in each column\n",
+    "array.max(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# this will read in a csv file into a pandas DataFrame\n",
+    "# this csv has data of country spending on healthcare\n",
+    "data = pd.read_csv('health.csv', header=0, index_col=0, encoding=\"ISO-8859-1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# the .head() function will allow us to look at first few lines of the dataframe\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# by default, rows are indicated first, followed by the column: [row, column]\n",
+    "data.loc['Canada', '2008']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# you can also slice a dataframe\n",
+    "data.loc['Canada':'Denmark', '1999':'2001']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# the .plot() function will create a simple graph for you to quickly visualize your data\n",
+    "data.loc['Denmark'].plot()\n",
+    "data.loc['Canada'].plot()\n",
+    "data.loc['India'].plot()\n",
+    "plt.legend(loc='best')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/lessons/python/intro/IntroPython.ipynb b/lessons/python/intro/IntroPython.ipynb
new file mode 100644
index 000000000..9027d4238
--- /dev/null
+++ b/lessons/python/intro/IntroPython.ipynb
@@ -0,0 +1,1236 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Resources\n",
+    "\n",
+    "For further information and tutorials see:\n",
+    "\n",
+    "* [Software Carpentry Python Programming](https://swcarpentry.github.io/python-novice-inflammation/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# the = symbol indicates that what is on the right is assigned to the variable name on the left\n",
+    "name = 'Lina'\n",
+    "year = 2016"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lina\n",
+      "<class 'int'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# we can then see what our variable is holding using the print() function\n",
+    "print(name)\n",
+    "# we can check the type of our variable using the type(variable_name) function\n",
+    "print(type(year))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'age' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-27506fe65432>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# you must assign a variable before you call it, otherwise an error will occur\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'age' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# you must assign a variable before you call it, otherwise an error will occur\n",
+    "print(age)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lists"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fruits = ['apple', 'banana', 'mango', 'lychee']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['apple', 'banana', 'mango', 'lychee']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(fruits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "fruits.append('orange')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['apple', 'banana', 'mango', 'lychee', 'orange', 'orange', 'orange']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(fruits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# lists don't need to comprise of all the same type \n",
+    "misc = [29, 'dog', fruits]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[29, 'dog', ['apple', 'banana', 'mango', 'lychee', 'orange', 'orange', 'orange']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(misc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indexing and Slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apple\n"
+     ]
+    }
+   ],
+   "source": [
+    "#indexing in Python starts at 0\n",
+    "print(fruits[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "print(fruits[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "s = 'This is a string.'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "orange\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use -1 to get the last element\n",
+    "print(fruits[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "orange\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(fruits[-2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This\n"
+     ]
+    }
+   ],
+   "source": [
+    "# to get a slice of the string use the : symbol\n",
+    "print(s[0:4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s[:4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " is\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s[4:7])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " a string.\n",
+      " a string.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(s[7:])\n",
+    "print(s[7:len(s)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## If Statements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "s2 is not a string or integer\n"
+     ]
+    }
+   ],
+   "source": [
+    "s2 = [19034, 23]\n",
+    "\n",
+    "# You will always need to start with an 'if' line\n",
+    "# You do not need the elif or else statements\n",
+    "# You can have as many elif statements as needed\n",
+    "\n",
+    "if type(s2) == str:\n",
+    "    print('s2 is a string')\n",
+    "elif type(s2) == int:\n",
+    "    print('s2 is an integer')\n",
+    "elif type(s2) == float:\n",
+    "    print('s2 is a float')\n",
+    "else:\n",
+    "    print('s2 is not a string or integer')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## For Loops"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "nums = [23, 56, 1, 10, 15, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "odd\n",
+      "even\n",
+      "odd\n",
+      "even\n",
+      "odd\n",
+      "even\n"
+     ]
+    }
+   ],
+   "source": [
+    "# in this case, 'n' is a dummy variable that will be used by the for loop\n",
+    "# you do not need to assign it ahead of time\n",
+    "\n",
+    "for n in nums:\n",
+    "    if n%2 == 0:\n",
+    "        print('even')\n",
+    "    else:\n",
+    "        print('odd')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a\n",
+      "e\n",
+      "i\n",
+      "o\n",
+      "u\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for loops can iterate over strings as well\n",
+    "vowels = 'aeiou'\n",
+    "for vowel in vowels:\n",
+    "    print(vowel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# always use descriptive naming for functions, variables, arguments etc.\n",
+    "def sum_of_squares(num1, num2):\n",
+    "    ss = num1**2 + num2**2\n",
+    "    return(ss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sum_of_squares(4,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# the return statement in a function allows us to store the output of a function call in a variable for later use\n",
+    "ss1 = sum_of_squares(5,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(ss1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Useful Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# use a package by importing it, you can also give it a shorter alias, in this case 'np'\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "array = np.arange(15)\n",
+    "lst = list(range(15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n",
+      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(array)\n",
+    "print(lst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'numpy.ndarray'>\n",
+      "<class 'list'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(array))\n",
+    "print(type(lst))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0  2  4  6  8 10 12 14 16 18 20 22 24 26 28]\n",
+      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# numpy arrays allow for vectorized calculations\n",
+    "print(array*2)\n",
+    "print(lst*2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0  1  2]\n",
+      " [ 3  4  5]\n",
+      " [ 6  7  8]\n",
+      " [ 9 10 11]\n",
+      " [12 13 14]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "array = array.reshape([5,3])\n",
+    "print(array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1.,   4.,   7.,  10.,  13.])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# we can get the mean over all rows (using axis=1)\n",
+    "array.mean(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([12, 13, 14])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# max value in each column\n",
+    "array.max(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# this will read in a csv file into a pandas DataFrame\n",
+    "# this csv has data of country spending on healthcare\n",
+    "data = pd.read_csv('health.csv', header=0, index_col=0, encoding=\"ISO-8859-1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1995</th>\n",
+       "      <th>1996</th>\n",
+       "      <th>1997</th>\n",
+       "      <th>1998</th>\n",
+       "      <th>1999</th>\n",
+       "      <th>2000</th>\n",
+       "      <th>2001</th>\n",
+       "      <th>2002</th>\n",
+       "      <th>2003</th>\n",
+       "      <th>2004</th>\n",
+       "      <th>2005</th>\n",
+       "      <th>2006</th>\n",
+       "      <th>2007</th>\n",
+       "      <th>2008</th>\n",
+       "      <th>2009</th>\n",
+       "      <th>2010</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total expenditure on health as percentage of GDP (gross domestic product)</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Abkhazia</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Afghanistan</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>7.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Akrotiri and Dhekelia</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albania</th>\n",
+       "      <td>2.6</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Algeria</th>\n",
+       "      <td>4.2</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>4.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    1995  1996  1997  1998  \\\n",
+       "Total expenditure on health as percentage of GD...                           \n",
+       "Abkhazia                                             NaN   NaN   NaN   NaN   \n",
+       "Afghanistan                                          NaN   NaN   NaN   NaN   \n",
+       "Akrotiri and Dhekelia                                NaN   NaN   NaN   NaN   \n",
+       "Albania                                              2.6   4.0   4.8   5.3   \n",
+       "Algeria                                              4.2   3.8   4.1   4.1   \n",
+       "\n",
+       "                                                    1999  2000  2001  2002  \\\n",
+       "Total expenditure on health as percentage of GD...                           \n",
+       "Abkhazia                                             NaN   NaN   NaN   NaN   \n",
+       "Afghanistan                                          NaN   NaN   NaN   5.7   \n",
+       "Akrotiri and Dhekelia                                NaN   NaN   NaN   NaN   \n",
+       "Albania                                              5.8   6.4   6.0   6.3   \n",
+       "Algeria                                              3.9   3.5   3.8   3.9   \n",
+       "\n",
+       "                                                    2003  2004  2005  2006  \\\n",
+       "Total expenditure on health as percentage of GD...                           \n",
+       "Abkhazia                                             NaN   NaN   NaN   NaN   \n",
+       "Afghanistan                                          6.8   6.4   6.6   6.8   \n",
+       "Akrotiri and Dhekelia                                NaN   NaN   NaN   NaN   \n",
+       "Albania                                              6.2   6.9   6.8   6.7   \n",
+       "Algeria                                              3.7   3.4   3.1   3.1   \n",
+       "\n",
+       "                                                    2007  2008  2009  2010  \n",
+       "Total expenditure on health as percentage of GD...                          \n",
+       "Abkhazia                                             NaN   NaN   NaN   NaN  \n",
+       "Afghanistan                                          7.3   7.0   7.6   7.6  \n",
+       "Akrotiri and Dhekelia                                NaN   NaN   NaN   NaN  \n",
+       "Albania                                              6.9   6.7   6.9   6.5  \n",
+       "Algeria                                              3.5   3.7   4.6   4.2  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# the .head() function will allow us to look at first few lines of the dataframe\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.300000000000001"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# by default, rows are indicated first, followed by the column: [row, column]\n",
+    "data.loc['Canada', '2008']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1999</th>\n",
+       "      <th>2000</th>\n",
+       "      <th>2001</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total expenditure on health as percentage of GDP (gross domestic product)</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>8.9</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>9.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cape Verde</th>\n",
+       "      <td>4.5</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cayman Islands</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Central African Republic</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>3.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chad</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Channel Islands</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chile</th>\n",
+       "      <td>8.2</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>China</th>\n",
+       "      <td>4.5</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>4.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Christmas Island</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cocos Island</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colombia</th>\n",
+       "      <td>9.3</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>7.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Comoros</th>\n",
+       "      <td>3.2</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Congo, Dem. Rep.</th>\n",
+       "      <td>4.3</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Congo, Rep.</th>\n",
+       "      <td>3.1</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>2.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cook Is</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>4.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Costa Rica</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>7.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cote d'Ivoire</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Croatia</th>\n",
+       "      <td>7.3</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>7.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cuba</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>6.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cyprus</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Czech Republic</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Czechoslovakia</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Denmark</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    1999  2000  2001\n",
+       "Total expenditure on health as percentage of GD...                  \n",
+       "Canada                                               8.9   8.8   9.3\n",
+       "Cape Verde                                           4.5   4.6   5.0\n",
+       "Cayman Islands                                       NaN   NaN   NaN\n",
+       "Central African Republic                             3.5   3.8   3.8\n",
+       "Chad                                                 5.9   6.3   6.0\n",
+       "Channel Islands                                      NaN   NaN   NaN\n",
+       "Chile                                                8.2   8.3   8.4\n",
+       "China                                                4.5   4.6   4.6\n",
+       "Christmas Island                                     NaN   NaN   NaN\n",
+       "Cocos Island                                         NaN   NaN   NaN\n",
+       "Colombia                                             9.3   7.3   7.3\n",
+       "Comoros                                              3.2   2.9   2.3\n",
+       "Congo, Dem. Rep.                                     4.3   4.9   4.4\n",
+       "Congo, Rep.                                          3.1   2.1   2.4\n",
+       "Cook Is                                              3.5   3.4   4.6\n",
+       "Costa Rica                                           6.2   6.5   7.1\n",
+       "Cote d'Ivoire                                        5.6   5.1   4.0\n",
+       "Croatia                                              7.3   7.8   7.2\n",
+       "Cuba                                                 6.0   6.1   6.3\n",
+       "Cyprus                                               5.6   5.8   5.8\n",
+       "Czech Republic                                       6.3   6.3   6.4\n",
+       "Czechoslovakia                                       NaN   NaN   NaN\n",
+       "Denmark                                              9.0   8.7   9.1"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# you can also slice a dataframe\n",
+    "data.loc['Canada':'Denmark', '1999':'2001']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fb96f27f4a8>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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vF6/XG8JqRURqHp/Ph8/nK3e+ULpHWgJfWmvPDP7eB3jQWju4xHzqHhERqSTH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\nuMj/B5er+17PIyyXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fb9717a8588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the .plot() function will create a simple graph for you to quickly visualize your data\n",
+    "data.loc['Denmark'].plot()\n",
+    "data.loc['Canada'].plot()\n",
+    "data.loc['India'].plot()\n",
+    "plt.legend(loc='best')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [py3]",
+   "language": "python",
+   "name": "Python [py3]"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/lessons/python/intro/IntroPython_HG_Code_along.ipynb b/lessons/python/intro/IntroPython_HG_Code_along.ipynb
new file mode 100644
index 000000000..d378b2b12
--- /dev/null
+++ b/lessons/python/intro/IntroPython_HG_Code_along.ipynb
@@ -0,0 +1,873 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python [conda root]",
+      "language": "python",
+      "name": "conda-root-py"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.5.2"
+    },
+    "colab": {
+      "name": "IntroPython-HG_Code_along.ipynb",
+      "provenance": []
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9UBQouQox6f-",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Intro to Python\n",
+        "\n",
+        "## UofT Coders, Sep 26 2019\n",
+        "\n",
+        "**Instructor:** Haidy Giratallah \n",
+        "\n",
+        "**Lesson repeat from:** UofT coder lessons (https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/python/intro/IntroPython-AH.ipynb)\n",
+        "\n",
+        "---\n",
+        "\n",
+        "## Before getting started: What is Python? \n",
+        "Python is a general-purpose language with a readable syntax. In summary: \n",
+        "\"Python is powerful... and fast;\n",
+        "plays well with others;\n",
+        "runs everywhere;\n",
+        "is friendly & easy to learn;\n",
+        "is Open.\"\n",
+        "https://www.python.org/\n",
+        "\n",
+        "**Common question: Python vs R?**\n",
+        "\n",
+        "R is mainly used for statistical analysis while Python provides a more general approach to data science. R and Python are state of the art in terms of programming language oriented towards data science. Learning both of them is, of course, the ideal solution. ... (https://www.guru99.com/r-vs-python.html)\n",
+        "\n",
+        "\n",
+        "## Okay let's get started \n",
+        "## The interpreter\n",
+        "\n",
+        "### Math with _integers_"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Kghnx6clx6gA",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QTv7kCA_x6gC",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WnMnw5gGx6gD",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IIaFGj17x6gF",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# % is the modulus operator\n",
+        "# x % y -> return the remainder of x / y\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Y1PoeL9Yx6gK",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Math with _floats_\n",
+        "\n",
+        "Floats = floating point numbers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yWPtJ9iSx6gK",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "14VKsSKWx6gM",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tAWVdB0mx6gO",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "evXY5Bzyx6gP",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Variables"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eANG8ZSgx6gP",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "sReeEHNix6gS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UKbON_2qx6gV",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wfMktdXox6gX",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UQR0tenEx6gZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LQ4z18oax6ga",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cQDNx0tgx6gb",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Lists"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uHz3jBXmx6gb",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Kkg-DZ7Px6gd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cozwu8Nvx6ge",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VKlX-CSax6gg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ppiEJhfUx6gh",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Indexing lists and strings"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "H2Q6ytyTx6gh",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# indexing in python begins at 0! \n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ABr9Z2Rex6gi",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# slicing multiple things\n",
+        "# start : end (exclusive!)\n",
+        "\n",
+        "# recall that fruits = ['apple', 'orange', 'mango']\n",
+        "\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "j1zrqRbkx6gl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# reassigning item in list\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LwyBZwvSx6go",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# slicing and indexing strings\n",
+        "\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "02RPQuIxx6gq",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# strings do not support reassignment\n",
+        "# try running this: my_string[0] = 't'"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0wtHTz64x6gr",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Dictionaries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7f-W8tB1x6gs",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# dictionaries allow us to store key value pairs\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SoazPImHx6gt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# keys are 'looked up' using square brackets\n",
+        "\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8FRLx1pex6gu",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# additional keys can be added after the fact\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TzBRwu5Wx6gw",
+        "colab_type": "text"
+      },
+      "source": [
+        "## If statements"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nb127DO-x6gw",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# python can check whether certain statements are true or false\n",
+        "\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NnQ4n2q5x6gx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# use == to test for equality\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ssy5628Cx6gy",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SGJKG1IZx6g0",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# with these expressions, we can construct if statements\n",
+        "# if statements allow our scripts to encode more complex instructions\n",
+        "\n",
+        "\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "azkCJ4fPx6g1",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# if-else\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7vZOsniqx6g8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# if-elif-else\n",
+        "# useful if we have multiple conditions to test\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S9Rx9ib9x6g-",
+        "colab_type": "text"
+      },
+      "source": [
+        "## For loops"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7_71JnOrx6g_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# for loops allow us to automate repetitive operations\n",
+        "\n",
+        "# how do we check which values in this list are even?\n",
+        "\n",
+        "\n",
+        "# could check them individually?\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "p-PWh6vNx6hA",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# for loops simplify this\n",
+        "# here, 'number' is a placeholder variable for each of the items in the list\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "MrOLrmQQx6hC",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# we can also loop over the contents of a string\n",
+        "vowels = 'aeiou'\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CO_LgnLLx6hD",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WZ2UvBUOx6hE",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# functions allow us to generalize operations\n",
+        "# what is the sum of squares of two numbers?\n",
+        "\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "A2_1mSppx6hF",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "\n",
+        "# def is the keyword to define functions\n",
+        "# each function typically ends with a return statement"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mibTeEX-x6hH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        " # our operation from above\n",
+        " # works with any values we want!"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_Qy5jNFax6hJ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# checking on our docstring\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7f0RDhjqx6hK",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Some useful packages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VoWc-XJSx6hL",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# use a package by importing it\n",
+        "# these can be given a shorter alias\n",
+        "\n",
+        "import numpy as np"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "bsuohLb6x6hL",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# packages provide all sorts of useful functionality\n",
+        "# numpy allows for efficient numerical calculations in python\n",
+        "\n",
+        "np_array = np.arange(15)\n",
+        "list_array = list(range(15))\n",
+        "\n",
+        "print(np_array)\n",
+        "print(type(np_array))\n",
+        "print(list_array)\n",
+        "print(type(list_array))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vF1Vw8Hwx6hN",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# numpy arrays also allow for vectorized operations\n",
+        "\n",
+        "print(np_array * 2)\n",
+        "print(list_array * 2)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aF3oevTax6hO",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# numpy arrays also have helpful 'methods'\n",
+        "# a method is a special function 'attached' to an object, to be used on the object itself\n",
+        "\n",
+        "# what's the mean of our array?\n",
+        "print(np_array.mean())"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "XuAmdGqgx6hS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# the max value in our array?\n",
+        "print(np_array.max())"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xoASM6XIx6hT",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import seaborn as sns # we will use this for plotting\n",
+        "%matplotlib inline\n",
+        "iris = sns.load_dataset('iris')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zy2OHAJpx6hU",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "iris.head()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "srAdNgi4x6hV",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "iris.columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1X8E-AoJx6hW",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# pull out specific rows with the .loc method\n",
+        "iris.loc[0:2]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cjQbSaFLx6hY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# or rows AND columns\n",
+        "# use a list for multiple columns!\n",
+        "\n",
+        "iris.loc[0:2, 'petal_length']"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BAYPDjgzx6hY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "iris.loc[0:2, ['petal_length', 'species']]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "bs-Blj5xx6hZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "sns.relplot(x='petal_length', y='petal_width', data=iris)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qHN6VxnSx6ha",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "sns.relplot(x='petal_length', y='petal_width', hue='species', data=iris)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/lessons/python/intro/health.csv b/lessons/python/intro/health.csv
new file mode 100644
index 000000000..44948fd55
--- /dev/null
+++ b/lessons/python/intro/health.csv
@@ -0,0 +1,263 @@
+Total expenditure on health as percentage of GDP (gross domestic product),1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010
+Abkhazia,,,,,,,,,,,,,,,,
+Afghanistan,,,,,,,,5.7,6.8,6.4,6.6,6.8,7.3,7.0,7.6,7.6
+Akrotiri and Dhekelia,,,,,,,,,,,,,,,,
+Albania,2.6,4.0,4.8,5.3,5.8,6.4,6.0,6.3,6.2,6.9,6.8,6.7,6.9,6.7,6.9,6.5
+Algeria,4.2,3.8,4.1,4.1,3.9,3.5,3.8,3.9,3.7,3.4,3.1,3.1,3.5,3.7,4.6,4.2
+American Samoa,,,,,,,,,,,,,,,,
+Andorra,7.6,8.0,8.0,9.8,7.4,7.6,6.8,7.0,7.1,7.1,7.2,7.4,7.6,7.5,7.5,7.5
+Angola,3.8,2.2,2.3,1.9,1.9,2.4,3.4,2.4,2.6,2.1,2.0,2.4,2.5,3.2,4.9,2.9
+Anguilla,,,,,,,,,,,,,,,,
+Antigua and Barbuda,4.9,4.6,4.6,4.6,4.7,4.8,4.6,4.6,4.7,4.7,4.7,4.5,4.4,4.3,4.8,6.0
+Argentina,8.3,8.0,8.4,8.5,9.4,9.2,9.4,8.3,8.3,8.3,8.5,8.5,8.4,8.4,9.5,8.1
+Armenia,6.4,5.8,5.8,5.8,5.7,6.3,6.0,5.4,5.6,5.5,4.9,4.2,3.9,3.8,4.6,4.4
+Aruba,,,,,,,,,,,,,,,,
+Australia,7.2,7.4,7.5,7.6,7.8,8.0,8.1,8.4,8.3,8.5,8.4,8.5,8.5,8.7,8.7,8.7
+Austria,9.5,9.5,9.8,9.9,10.1,9.9,10.0,10.0,10.2,10.3,10.3,10.2,10.2,10.4,11.0,11.0
+Azerbaijan,5.9,6.2,5.5,5.5,5.4,4.7,4.5,4.5,6.6,7.9,7.8,6.1,5.0,4.3,5.8,5.9
+Bahamas,6.9,6.9,6.5,6.3,6.1,5.9,5.9,6.1,6.5,7.0,7.0,8.0,8.1,8.4,8.3,7.9
+Bahrain,4.6,4.5,4.7,4.7,4.7,3.9,4.3,4.4,4.2,3.9,3.7,3.5,3.6,4.2,4.7,5.0
+Bangladesh,3.5,3.5,2.8,2.7,2.8,2.8,3.0,3.1,3.0,3.1,3.2,3.4,3.5,3.3,3.4,3.5
+Barbados,6.3,6.2,5.9,5.9,6.1,6.3,6.7,7.3,7.0,7.0,7.0,7.1,7.0,7.1,5.9,8.0
+Belarus,6.7,6.2,6.6,6.0,6.2,6.1,6.6,6.5,6.6,6.6,6.9,6.3,6.4,5.9,6.1,5.6
+Belgium,7.6,7.9,7.8,7.9,8.1,8.1,8.3,8.5,10.0,10.1,10.0,9.6,9.6,10.0,10.8,10.7
+Belize,4.2,3.6,3.9,3.8,3.7,4.0,4.5,4.4,4.5,4.4,4.4,4.4,4.8,4.8,5.5,5.2
+Benin,4.7,4.6,4.3,4.4,4.6,4.3,4.7,4.3,4.6,4.6,4.7,4.8,4.6,4.2,4.3,4.1
+Bermuda,,,,,,,,,,,,,,,,
+Bhutan,3.4,4.7,5.4,5.7,7.1,6.9,6.5,8.3,5.1,4.4,4.7,5.2,5.0,5.2,5.1,5.2
+Bolivia,4.5,4.6,4.4,5.1,6.2,6.1,6.3,6.5,5.6,5.3,5.6,4.8,4.7,4.9,5.1,4.8
+Bosnia and Herzegovina,10.6,11.3,7.9,7.8,8.7,7.0,7.2,7.0,7.9,9.1,9.1,9.3,9.8,10.3,10.9,11.1
+Botswana,4.2,4.2,4.4,4.0,3.8,4.7,5.3,6.1,6.0,8.3,7.7,6.7,7.9,7.8,10.0,8.3
+Brazil,6.7,6.8,6.8,6.7,7.1,7.2,7.3,7.2,7.0,7.1,8.2,8.5,8.5,8.3,8.8,9.0
+British Virgin Islands,,,,,,,,,,,,,,,,
+Brunei,2.7,3.6,3.8,4.6,4.5,4.2,4.3,4.3,3.1,3.0,2.6,2.3,2.4,2.3,3.0,2.8
+Bulgaria,5.2,4.7,5.1,5.4,6.0,6.2,7.4,7.6,7.6,7.3,7.3,6.9,6.8,7.0,7.2,6.9
+Burkina Faso,4.3,4.5,4.5,4.7,5.3,5.1,4.9,5.0,5.5,6.1,6.7,6.3,6.3,5.9,6.6,6.7
+Burundi,5.7,5.8,5.3,5.7,5.6,6.3,6.8,7.0,7.2,9.9,12.2,14.3,13.9,12.4,11.4,11.6
+Cambodia,6.8,9.5,8.0,6.9,6.2,6.3,7.1,7.5,8.4,8.0,7.5,5.9,5.0,5.2,5.5,5.7
+Cameroon,3.9,5.2,4.6,4.4,4.4,4.5,4.7,4.9,5.0,4.7,4.7,4.7,4.7,5.1,4.9,5.1
+Canada,9.0,8.8,8.8,9.0,8.9,8.8,9.3,9.6,9.8,9.8,9.8,10.0,10.0,10.3,11.4,11.3
+Cape Verde,5.1,4.6,4.5,5.1,4.5,4.6,5.0,5.0,4.6,5.1,4.6,4.9,4.6,4.4,3.9,4.1
+Cayman Islands,,,,,,,,,,,,,,,,
+Central African Republic,3.6,3.6,3.8,3.5,3.5,3.8,3.8,3.9,4.0,4.0,3.7,3.8,4.1,4.0,4.0,4.0
+Chad,5.8,5.5,5.5,5.4,5.9,6.3,6.0,8.0,5.8,5.1,4.7,4.0,4.2,3.6,4.6,4.5
+Channel Islands,,,,,,,,,,,,,,,,
+Chile,6.7,7.5,7.5,8.0,8.2,8.3,8.4,8.4,7.5,7.1,6.9,6.6,6.9,7.5,8.4,8.0
+China,3.5,3.8,4.0,4.4,4.5,4.6,4.6,4.8,4.8,4.7,4.7,4.6,4.4,4.6,5.1,5.1
+Christmas Island,,,,,,,,,,,,,,,,
+Cocos Island,,,,,,,,,,,,,,,,
+Colombia,7.3,8.8,9.6,9.3,9.3,7.3,7.3,7.3,7.7,7.4,7.4,7.3,7.2,6.9,7.6,7.6
+Comoros,4.0,3.9,3.9,3.4,3.2,2.9,2.3,2.9,3.1,3.4,3.7,4.0,4.3,4.5,3.2,4.5
+"Congo, Dem. Rep.",3.7,3.5,4.1,4.8,4.3,4.9,4.4,4.3,5.2,5.6,5.9,6.2,6.2,7.3,9.6,7.9
+"Congo, Rep.",3.1,2.8,2.9,3.6,3.1,2.1,2.4,2.4,2.6,2.5,2.4,2.4,2.7,2.6,2.8,2.5
+Cook Is,5.2,4.7,4.0,4.2,3.5,3.4,4.6,3.8,4.0,3.9,4.7,4.2,4.4,4.2,4.3,4.4
+Costa Rica,6.5,6.7,6.2,6.3,6.2,6.5,7.1,7.7,7.7,7.7,7.6,7.8,8.4,9.4,10.5,10.9
+Cote d'Ivoire,5.1,5.6,6.2,6.6,5.6,5.1,4.0,3.5,3.8,4.2,4.3,4.7,5.8,5.9,5.2,5.3
+Croatia,6.8,7.1,5.9,5.7,7.3,7.8,7.2,6.3,6.4,6.6,7.0,7.0,7.5,7.8,7.8,7.8
+Cuba,5.2,5.3,5.5,5.8,6.0,6.1,6.3,6.5,6.3,6.1,8.0,7.6,10.4,10.8,12.1,10.6
+Cyprus,4.7,5.2,5.5,5.6,5.6,5.8,5.8,6.1,6.8,6.4,6.4,6.3,6.1,6.0,6.1,6.0
+Czech Republic,6.7,6.4,6.4,6.4,6.3,6.3,6.4,6.8,7.1,6.9,6.9,6.7,6.5,6.8,8.0,7.9
+Czechoslovakia,,,,,,,,,,,,,,,,
+Denmark,8.1,8.2,8.2,8.2,9.0,8.7,9.1,9.3,9.5,9.7,9.8,9.9,9.9,10.2,11.5,11.4
+Djibouti,4.0,4.0,4.5,5.3,6.2,5.8,5.4,5.5,6.3,5.7,7.2,7.0,7.7,7.2,7.8,7.2
+Dominica,5.9,6.0,6.1,5.9,5.9,5.9,6.2,6.5,6.4,6.0,6.0,6.1,6.1,6.0,6.2,7.4
+Dominican Republic,5.5,5.3,5.7,5.8,5.9,6.3,6.2,6.3,6.0,5.3,5.5,5.6,5.4,5.7,5.9,6.2
+East Germany,,,,,,,,,,,,,,,,
+Ecuador,4.1,4.4,4.4,4.3,4.7,4.2,4.6,5.5,6.6,6.6,6.6,6.7,7.0,7.0,8.8,8.1
+Egypt,3.9,4.3,5.0,5.1,5.3,5.4,5.8,6.1,5.8,5.5,5.2,5.3,4.9,4.8,4.8,4.7
+El Salvador,6.4,7.6,8.1,8.2,8.0,8.0,7.8,7.7,7.3,7.2,7.1,6.6,6.3,6.2,6.8,6.9
+Equatorial Guinea,5.9,4.7,4.2,5.2,2.3,1.9,2.0,3.8,2.4,2.1,1.7,1.8,1.9,2.7,4.5,4.5
+Eritrea,4.5,3.7,4.2,4.3,4.7,5.3,5.1,4.4,4.1,3.8,3.0,3.1,3.2,4.3,2.8,2.7
+Eritrea and Ethiopia,,,,,,,,,,,,,,,,
+Estonia,6.3,6.6,6.2,5.6,6.6,6.0,5.5,5.6,5.6,5.8,5.0,5.0,5.1,5.9,6.7,6.0
+Ethiopia,2.9,2.9,3.1,3.6,3.9,4.3,4.7,4.7,4.9,4.3,4.1,4.2,4.8,4.3,4.4,4.9
+Faeroe Islands,,,,,,,,,,,,,,,,
+Falkland Is (Malvinas),,,,,,,,,,,,,,,,
+Fiji,3.1,3.2,3.5,3.2,3.1,3.8,3.3,3.5,3.3,3.6,3.6,3.8,4.4,4.2,5.0,4.9
+Finland,7.8,8.0,7.7,7.4,7.4,7.2,7.4,7.8,8.2,8.2,8.4,8.3,8.0,8.3,9.0,9.0
+France,10.4,10.4,10.3,10.1,10.2,10.1,10.2,10.6,10.9,11.0,11.2,11.1,11.1,11.2,11.9,11.9
+French Guiana,,,,,,,,,,,,,,,,
+French Polynesia,,,,,,,,,,,,,,,,
+Gabon,3.0,2.9,2.5,3.5,3.0,2.5,2.9,3.2,3.3,3.0,2.6,2.8,2.9,2.6,3.6,3.5
+Gambia,6.3,5.6,5.2,6.0,5.8,5.8,5.8,5.9,5.8,6.3,6.2,5.9,5.3,5.2,6.1,5.7
+Georgia,5.1,7.5,8.0,6.5,6.3,6.9,7.8,8.7,8.5,8.5,8.6,8.4,8.2,9.0,10.2,10.1
+Germany,10.1,10.4,10.3,10.3,10.4,10.4,10.5,10.7,10.9,10.7,10.8,10.6,10.5,10.7,11.7,11.6
+Ghana,5.3,5.1,5.3,5.2,5.0,4.7,5.6,4.8,4.8,6.1,7.0,4.4,6.0,5.6,5.0,5.2
+Gibraltar,,,,,,,,,,,,,,,,
+Greece,8.6,8.6,8.4,8.4,8.6,7.9,8.8,9.1,8.9,8.7,9.7,9.7,9.8,10.1,10.6,10.2
+Greenland,,,,,,,,,,,,,,,,
+Grenada,6.9,7.0,7.3,7.3,7.8,6.6,7.6,6.6,6.2,5.7,6.0,6.4,6.1,5.9,6.2,5.9
+Guadeloupe,,,,,,,,,,,,,,,,
+Guam,,,,,,,,,,,,,,,,
+Guatemala,3.7,3.6,3.8,4.1,4.8,5.5,6.4,6.2,6.3,6.4,6.4,7.3,7.2,6.9,7.1,6.9
+Guernsey,,,,,,,,,,,,,,,,
+Guinea,5.5,5.6,5.6,5.6,5.6,5.7,5.9,6.0,5.5,5.3,5.0,5.3,5.4,5.5,5.3,4.9
+Guinea-Bissau,6.4,7.0,6.7,7.1,5.8,6.2,5.9,7.0,5.8,5.6,5.6,5.7,5.7,5.7,8.6,8.5
+Guyana,4.9,4.7,5.0,5.0,4.5,5.6,5.6,5.7,5.6,7.6,9.2,6.0,5.3,6.0,6.2,5.4
+Haiti,6.6,6.4,6.2,6.0,5.7,6.1,5.6,5.2,4.7,4.6,4.4,5.7,5.6,5.4,6.1,6.9
+Holy See,,,,,,,,,,,,,,,,
+Honduras,5.3,5.0,4.9,5.6,5.7,5.4,5.5,6.1,6.4,6.5,5.8,5.7,5.9,6.0,7.0,
+"Hong Kong, China",,,,,,,,,,,,,,,,
+Hungary,7.3,7.1,6.8,7.2,7.3,7.2,7.2,7.6,8.4,8.1,8.3,8.1,7.6,7.3,7.6,7.3
+Iceland,8.4,8.3,9.6,9.1,10.2,9.7,9.6,10.4,10.6,10.0,9.6,9.3,9.3,9.2,9.8,9.4
+India,4.1,4.0,4.3,4.4,4.2,4.4,4.6,4.5,4.4,4.2,4.2,4.0,4.0,4.0,4.2,4.1
+Indonesia,2.0,2.0,1.9,2.1,2.3,2.0,2.2,2.2,2.5,2.4,2.4,2.5,2.7,2.5,2.5,2.6
+Iran,3.8,3.7,4.1,4.5,4.6,4.6,5.1,5.1,4.9,4.9,5.7,5.5,5.5,5.8,5.7,5.6
+Iraq,,1.7,1.7,1.7,1.7,2.8,2.8,1.1,3.6,5.7,4.8,3.6,4.9,4.9,8.4,8.4
+Ireland,6.6,6.4,6.3,6.1,6.0,6.1,6.7,7.0,7.3,7.5,7.6,7.5,7.6,8.8,9.4,9.2
+Isle of Man,,,,,,,,,,,,,,,,
+Israel,7.4,7.5,7.7,7.6,7.3,7.3,7.9,7.8,7.7,7.6,7.7,7.4,7.4,7.5,7.6,7.6
+Italy,7.2,7.3,7.6,7.7,7.7,8.0,8.2,8.3,8.3,8.6,8.9,9.0,8.6,9.0,9.4,9.5
+Jamaica,4.1,4.7,6.0,5.1,4.7,5.5,5.1,4.9,4.6,4.8,4.1,4.2,4.9,5.2,4.9,4.8
+Japan,6.9,7.0,7.0,7.3,7.5,7.7,7.9,8.0,8.1,8.1,8.2,8.2,8.2,8.5,9.5,9.5
+Jersey,,,,,,,,,,,,,,,,
+Jordan,8.3,8.6,9.3,9.3,9.4,9.7,9.9,9.7,9.3,9.3,8.7,8.2,8.4,8.5,9.6,8.0
+Kazakhstan,4.6,4.4,4.7,4.8,4.4,4.2,3.5,3.6,3.7,4.0,4.1,3.9,3.4,3.9,4.5,4.3
+Kenya,4.3,4.1,4.1,4.5,4.2,4.7,4.6,4.5,4.4,4.3,4.4,4.5,4.4,4.2,4.8,4.8
+Kiribati,9.4,9.2,9.4,7.0,8.9,7.9,8.9,8.8,9.6,10.4,10.1,11.0,13.6,12.5,13.0,11.2
+North Korea,,,,,,,,,,,,,,,,
+South Korea,3.8,3.9,3.9,4.1,4.3,4.5,5.1,4.9,5.2,5.3,5.7,6.0,6.3,6.5,6.9,6.9
+United Korea (former),,,,,,,,,,,,,,,,
+Kosovo,,,,,,,,,,,,,,,,
+Kuwait,3.8,3.5,3.7,4.4,3.7,2.5,3.6,3.6,3.2,2.8,2.4,2.3,2.1,1.9,3.8,2.6
+Kyrgyz Republic,6.9,6.3,5.5,6.9,6.0,4.7,4.8,5.4,5.4,5.6,5.8,6.4,6.6,5.7,6.4,6.2
+Lao,4.1,4.6,6.3,6.4,3.7,3.3,4.0,3.9,5.1,4.4,4.0,3.9,4.0,4.5,4.3,4.5
+Latvia,5.8,6.1,6.2,6.3,6.4,6.0,6.1,6.2,6.1,6.5,6.3,6.8,7.0,6.6,6.6,6.7
+Lebanon,10.7,10.7,11.5,11.4,10.4,9.9,9.3,8.6,8.2,8.3,8.1,8.1,8.4,7.7,7.4,7.0
+Lesotho,8.1,7.9,7.1,7.0,6.8,7.0,7.7,7.1,7.1,6.9,6.3,7.0,8.5,8.8,9.4,11.1
+Liberia,,,,5.2,5.2,5.1,5.3,4.8,5.1,5.1,5.2,6.9,11.7,11.9,12.2,11.8
+Libya,3.5,3.3,3.2,4.0,3.6,3.3,4.0,4.9,3.7,3.1,2.5,2.4,2.8,3.0,3.9,3.9
+Liechtenstein,,,,,,,,,,,,,,,,
+Lithuania,5.4,5.6,5.7,6.1,6.2,6.5,6.3,6.4,6.5,5.7,5.8,6.2,6.2,6.6,7.5,7.0
+Luxembourg,5.6,5.7,5.6,5.7,5.8,7.5,7.4,8.3,7.7,8.2,7.9,7.7,7.1,6.8,7.9,7.8
+"Macao, China",,,,,,,,,,,,,,,,
+"Macedonia, FYR",8.5,8.8,9.2,10.1,8.7,8.8,8.5,9.4,9.3,8.7,8.1,7.8,6.9,6.8,6.9,7.1
+Madagascar,2.8,3.2,3.4,3.2,3.2,3.7,3.8,3.8,3.5,3.6,3.7,4.0,4.1,4.5,4.2,3.8
+Malawi,4.9,4.7,5.2,5.5,6.5,6.1,5.1,4.8,6.3,7.8,8.2,9.0,7.0,6.6,6.7,6.6
+Malaysia,2.9,2.9,2.7,3.0,3.1,3.1,3.6,3.6,4.2,3.9,3.6,3.9,3.8,3.8,4.6,4.4
+Maldives,6.0,6.3,6.3,6.3,6.5,7.1,5.4,5.2,6.3,6.5,8.2,7.4,6.8,7.8,6.4,6.3
+Mali,5.2,7.8,7.0,6.2,6.0,6.3,6.4,6.3,6.3,6.3,6.0,5.9,5.8,5.6,5.5,5.0
+Malta,5.8,6.2,6.6,6.6,6.6,6.7,7.2,8.1,8.4,8.9,9.3,9.3,8.7,8.3,8.5,8.6
+Marshall Islands,18.1,21.8,22.8,20.9,20.4,22.6,19.5,18.3,16.2,16.4,15.9,16.6,17.0,16.6,18.9,18.0
+Martinique,,,,,,,,,,,,,,,,
+Mauritania,4.8,3.8,4.0,4.9,6.0,6.0,4.8,6.0,5.4,5.9,5.1,3.5,4.4,4.3,4.2,4.4
+Mauritius,3.6,3.5,3.5,3.6,3.4,3.7,3.8,4.2,3.9,4.2,4.5,4.3,5.2,5.3,5.6,6.0
+Mayotte,,,,,,,,,,,,,,,,
+Mexico,5.1,4.7,4.8,4.9,5.1,5.1,5.5,5.6,5.8,6.0,5.9,5.7,5.8,5.9,6.5,
+"Micronesia, Fed. Sts.",9.2,8.6,7.9,7.2,7.8,8.1,8.8,8.1,9.8,10.4,12.2,12.0,12.0,12.9,13.4,14.2
+Moldova,9.1,10.3,9.5,7.7,5.9,6.7,7.1,8.1,8.3,8.5,9.2,10.6,10.9,11.4,12.5,11.7
+Monaco,3.0,3.0,3.1,3.1,3.4,3.2,3.3,3.3,3.9,4.0,3.9,3.8,3.3,3.6,4.2,4.3
+Mongolia,3.6,4.2,4.2,4.4,4.7,5.5,5.7,6.0,4.7,4.2,3.7,3.6,4.6,5.7,5.7,5.4
+Montenegro,7.7,7.7,7.7,7.7,7.7,7.9,8.9,9.0,9.6,9.1,9.1,8.6,7.8,8.0,9.4,9.1
+Montserrat,,,,,,,,,,,,,,,,
+Morocco,3.9,3.8,4.1,3.9,4.0,4.2,4.4,5.3,5.3,5.2,5.1,5.2,5.2,5.2,5.2,5.2
+Mozambique,5.1,4.3,4.6,4.6,5.2,6.0,5.5,6.0,5.6,5.1,5.8,5.4,4.8,4.1,5.4,5.2
+Myanmar,2.1,2.1,1.9,1.8,1.8,2.1,2.1,2.4,2.3,2.3,2.1,2.1,2.0,2.0,2.1,2.0
+Namibia,6.2,6.6,6.9,6.8,7.0,6.1,6.2,6.2,6.5,6.5,7.3,7.2,6.9,6.8,7.2,6.8
+Nauru,13.0,14.2,14.1,14.7,13.5,12.3,12.4,12.3,11.9,13.0,15.5,16.1,21.7,14.2,11.2,11.2
+Nepal,4.7,4.8,5.1,5.3,5.1,5.2,5.3,5.5,5.4,6.0,6.0,5.8,5.4,5.9,5.8,5.3
+Netherlands,8.3,8.2,7.9,8.1,8.1,8.0,8.3,8.9,9.8,10.0,9.8,9.7,9.7,9.9,12.0,11.9
+Netherlands Antilles,,,,,,,,,,,,,,,,
+New Caledonia,,,,,,,,,,,,,,,,
+New Zealand,7.1,7.0,7.2,7.7,7.5,7.6,7.7,8.0,7.9,8.0,8.4,8.8,8.5,9.3,10.0,10.1
+Ngorno-Karabakh,,,,,,,,,,,,,,,,
+Nicaragua,8.4,8.3,6.8,7.3,6.9,7.0,6.8,7.4,7.9,7.9,7.9,8.2,9.1,9.3,9.6,
+Niger,3.4,3.2,3.6,3.7,3.7,3.4,3.7,3.8,3.6,3.9,6.0,6.0,5.4,5.2,5.4,5.2
+Nigeria,4.4,4.5,4.6,5.6,5.4,4.6,5.2,3.9,6.5,7.0,6.6,5.7,6.0,5.7,6.1,5.1
+Niue,8.0,9.3,9.4,8.2,8.3,7.9,,11.1,12.5,15.1,12.9,15.6,16.1,16.5,17.5,17.4
+Norfolk Island,,,,,,,,,,,,,,,,
+Northern Cyprus,,,,,,,,,,,,,,,,
+Northern Mariana Islands,,,,,,,,,,,,,,,,
+Norway,9.1,8.7,8.4,9.3,9.3,8.4,8.8,9.8,10.0,9.6,9.0,8.6,8.7,8.5,9.7,9.5
+Oman,3.6,3.6,3.4,3.7,3.5,3.1,3.1,3.2,3.2,3.0,2.6,2.3,2.5,2.1,3.0,2.8
+Pakistan,3.3,3.3,3.3,3.6,3.7,3.0,2.8,3.0,2.8,2.8,2.7,2.7,2.6,2.5,2.2,2.2
+Palau,12.4,12.7,12.4,10.9,12.0,12.8,10.4,10.4,10.3,11.5,10.5,10.5,10.8,10.2,10.6,10.3
+Panama,7.7,7.2,7.1,7.1,6.9,7.8,7.7,8.0,7.6,8.1,7.5,7.0,6.7,7.3,8.1,8.1
+Papua New Guinea,3.5,3.7,3.4,3.8,4.1,4.0,4.5,4.5,3.9,4.4,3.9,3.4,3.2,3.3,3.7,3.6
+Paraguay,6.7,6.8,7.0,7.2,7.8,9.4,9.1,8.7,7.2,6.6,6.5,6.6,6.1,6.0,6.6,5.9
+Peru,4.5,4.5,4.3,4.5,4.9,4.7,4.7,4.8,4.5,4.4,4.5,4.5,5.1,5.7,5.3,5.1
+Philippines,3.4,3.5,3.6,3.2,3.2,3.2,3.0,2.8,3.2,3.2,3.5,3.5,3.4,3.3,3.6,3.6
+Pitcairn,,,,,,,,,,,,,,,,
+Poland,5.5,5.9,5.6,5.9,5.7,5.5,5.9,6.3,6.2,6.2,6.2,6.2,6.4,7.0,7.4,7.5
+Portugal,7.5,7.8,7.8,7.7,8.0,9.3,9.3,9.3,9.8,10.1,10.4,10.1,10.0,10.1,10.7,11.0
+Puerto Rico,,,,,,,,,,,,,,,,
+Qatar,3.7,3.6,3.2,3.4,2.9,2.3,2.7,2.8,4.2,3.7,3.3,2.7,2.4,2.1,2.6,1.8
+Reunion,,,,,,,,,,,,,,,,
+Romania,3.5,3.7,4.8,4.6,5.1,5.2,5.4,5.7,5.3,5.5,5.5,5.1,5.2,5.4,5.6,5.6
+Russia,5.4,5.6,7.1,6.6,5.8,5.4,5.7,6.0,5.6,5.2,5.2,5.3,5.4,4.8,5.6,5.1
+Rwanda,4.5,4.6,4.6,5.3,4.7,4.2,4.4,4.2,6.3,6.2,6.5,10.8,9.5,9.4,10.1,10.5
+St. Barth�lemy,,,,,,,,,,,,,,,,
+St. Helena,,,,,,,,,,,,,,,,
+St. Kitts and Nevis,5.4,5.4,5.1,5.1,5.3,5.5,5.1,5.0,4.7,4.4,4.4,5.3,5.4,5.0,5.2,6.7
+St. Lucia,4.7,5.5,5.0,5.0,5.0,5.1,5.7,5.8,5.9,5.8,6.0,7.3,7.2,8.1,8.4,8.7
+St. Martin,,,,,,,,,,,,,,,,
+St. Vincent and the Grenadines,4.45833258,4.44634447,4.7281377,4.1684158,4.1527164,3.72180631,3.63980325,3.92617262,3.90135665,3.71947805,3.65532249,3.88226523,3.9172185,4.6289586,4.60065661,4.47059913
+St.-Pierre-et-Miquelon,,,,,,,,,,,,,,,,
+Samoa,4.6977826,5.22342626,5.45316699,5.58489223,6.28075985,6.01843157,5.75678294,5.52869249,5.14551282,4.9266523,4.95060798,5.31983442,5.86637027,5.49409403,5.44408597,6.46982259
+San Marino,7.83999165,7.55147442,7.53528908,7.40877649,7.44438531,7.51906667,7.36293853,9.28118717,7.32551256,7.57805489,7.48865306,7.20196413,7.108691,7.14165292,7.12611545,7.12611545
+Sao Tome and Principe,8.23324072,7.73768994,8.88817135,7.93407725,8.98203574,9.4870383,9.70914787,9.86959833,10.89366855,10.14680653,9.49611341,7.58308372,7.45394716,7.01813204,7.24194682,7.1688313
+Saudi Arabia,2.96343516,2.96182194,3.05675942,3.34299955,4.12657313,4.25943342,4.5140298,4.29718242,4.02387964,3.69506901,3.54521791,3.8083032,3.82925982,3.19192117,4.37508458,4.29186721
+Senegal,3.93442496,4.08432135,4.08141909,4.10328832,4.36302521,4.33077043,4.52513583,5.06424157,5.40001387,5.70509225,5.40084188,5.82945478,5.7243396,5.81767131,5.66216259,5.6642273
+Serbia,7.31661157,7.31657912,7.22357867,7.40327177,7.40933991,7.4155493,7.57920399,8.87407275,8.81049039,8.6612779,9.05257374,9.4184197,10.35888797,10.36336542,10.51252172,10.36114619
+Serbia and Montenegro,,,,,,,,,,,,,,,,
+Serbia excluding Kosovo,,,,,,,,,,,,,,,,
+Seychelles,5.18969549,5.33529273,5.84536015,5.22847419,5.29394573,4.81819736,4.64011248,4.38333417,4.80140746,5.142936,4.16324926,3.99217625,3.44997619,3.33321428,3.29209398,3.40385947
+Sierra Leone,14.33894344,14.53401106,13.53529751,13.28618861,16.01257445,15.61839028,14.96244618,14.8586967,7.7703857,13.51779005,14.48132071,11.061053,12.83727079,13.77868517,13.86115877,13.06796543
+Singapore,3.0168834,2.95928032,2.89661447,3.196578,2.86201832,2.40991899,2.74603193,2.88423073,3.80888617,3.40109595,3.37029909,3.25737674,3.18562573,3.6145935,4.13247073,3.95961271
+Slovak Republic,6.06387494,6.43714522,5.78420775,5.65522564,5.75379503,5.49890785,5.50461613,5.62930119,5.81850192,7.21098106,7.04485118,7.34768079,7.75529417,8.01005494,9.13728327,8.79029531
+Slovenia,7.45548818,7.63863893,7.75623737,7.81007276,7.79898595,8.26416456,8.56912386,8.62002958,8.60889134,8.33918284,8.40961553,8.27405701,7.78702228,8.35907939,9.28577254,9.4097517
+Solomon Islands,5.15304621,4.70648828,4.98354904,5.52622351,5.59964673,6.92616783,9.46205408,9.1706722,6.12701613,5.66129024,7.62826022,6.34243197,6.23455413,6.03765447,8.73890335,8.55278448
+Somalia,2.68489176,2.59410849,2.63400617,2.69253964,2.80677916,2.44384477,2.251338,,,,,,,,,
+Somaliland,,,,,,,,,,,,,,,,
+South Africa,7.48750209,7.95632223,8.25580965,8.57792681,9.06492593,8.46545768,8.78351744,8.72038817,8.85762592,8.93981546,8.81369876,8.53276693,8.45183383,8.63137082,9.16092737,8.94156391
+South Ossetia,,,,,,,,,,,,,,,,
+Spain,7.44459252,7.46292642,7.33387575,7.3192794,7.31629197,7.21475551,7.24157367,7.25041069,8.17211614,8.20614429,8.28183839,8.34609457,8.48290507,8.96011886,9.56555237,9.53862387
+Sri Lanka,3.42657913,3.44855213,3.33033467,3.68413711,3.60870697,3.70894281,3.7431487,3.81825105,3.8047856,4.10437714,3.94792525,4.03844583,3.72762308,3.40625327,3.19568939,2.94519357
+Sudan,3.36232894,3.95189674,3.74988526,3.99469819,3.79894414,3.36755312,3.5245654,3.58005233,3.84841707,4.0980999,3.98708103,4.85638824,6.02544967,6.90489364,7.32619129,6.31501433
+Suriname,5.19103944,7.41196372,7.75757494,8.60661376,7.62329263,7.95639165,8.07159557,7.1421666,6.62355609,6.87614002,6.77689324,7.65703495,7.27578894,6.78494752,7.47131238,7.0150575
+Svalbard,,,,,,,,,,,,,,,,
+Swaziland,4.95991489,5.40356441,5.11799324,5.55824512,5.3750616,5.37824216,5.30405992,5.31636578,5.93267461,5.48897135,5.84457849,6.33952475,5.98251975,6.5731289,6.71761995,6.60910709
+Sweden,7.96455521,8.20030584,8.02636126,8.11535567,8.19761871,8.17961312,8.85949228,9.22815647,9.31003467,9.08797098,9.06150305,8.94799761,8.91722313,9.22835422,10.00118623,9.63346849
+Switzerland,9.57160447,9.94725716,9.95073528,10.07305601,10.20196005,10.15084343,10.59043662,10.91254262,11.25466327,11.30042365,11.22102376,10.7581161,10.61914519,10.72484272,11.38569524,11.52215329
+Syria,5.51687902,5.5004147,5.29796706,5.12370263,5.24971554,4.92287133,4.87675666,4.94246813,5.11607642,4.47860155,4.10969776,3.82503992,3.79904048,3.37376558,3.54865806,3.40675402
+Taiwan,,,,,,,,,,,,,,,,
+Tajikistan,3.1,3.10909091,3.63590734,3.5595122,3.87722721,4.64036016,4.58542207,4.4802285,4.45875944,4.30071512,4.83678456,4.89156435,5.34676664,5.58479697,5.92834982,5.98259462
+Tanzania,3.58124406,3.3848746,3.64769735,3.39702823,3.29396919,3.37581551,3.53093525,3.4233561,3.74871023,3.4428696,4.01837043,6.5277599,5.69457916,5.45952561,5.52347451,6.01142802
+Thailand,3.53153455,3.84083724,3.99659613,3.73528597,3.49625195,3.39540499,3.31554758,3.70009891,3.57494319,3.50645022,3.54351021,3.49106045,3.56224555,3.96974849,4.17213817,3.88297823
+Timor-Leste,9.51628012,6.50723966,10.54009668,19.32327371,9.94686254,8.37855787,10.84385142,10.55860612,10.95466756,13.61904588,16.07348972,17.93323385,15.43030368,13.80071777,11.87015325,9.12009791
+Togo,4.58781454,4.65959037,4.56163643,4.65890883,4.70460137,4.72656259,5.30968065,5.11494962,5.59719199,6.23449154,6.27367498,6.77493005,6.56043454,6.81448459,7.36705858,7.65250051
+Tokelau,,,,,,,,,,,,,,,,
+Tonga,3.92589383,5.16246796,5.6438144,5.86844418,6.51621693,6.06263908,5.69485378,5.05624055,5.09534293,4.814915,6.52030636,5.52007848,6.76640835,6.24984958,4.67910132,5.07124384
+Transnistria,,,,,,,,,,,,,,,,
+Trinidad and Tobago,4.69113912,4.33058148,4.15924485,3.67985789,4.25257389,3.88317693,4.13126116,4.78450879,4.94320087,4.99625435,5.66268089,4.95998001,4.82359145,4.45252261,5.77562905,5.67534922
+Tunisia,6.14779757,5.69317737,5.63639972,5.71432389,5.76290398,5.95401486,5.82758035,5.84987307,5.91014539,6.19876991,6.1691035,6.23527998,6.15891119,6.1625407,6.35348657,6.2074705
+Turkey,2.50703268,2.90240247,3.13356933,3.59344289,4.7655207,4.94898471,5.16018204,5.3567135,5.33861753,5.37016573,5.44880137,5.81085647,6.0371564,6.07447864,6.74684251,6.74039106
+Turkmenistan,3.08461538,3.24698065,4.8469847,4.80957485,3.45300424,4.03905801,3.70961032,3.47745358,3.7956401,3.57884042,3.1948939,2.66519844,2.49859259,1.92670305,2.48766719,2.49640101
+Turks and Caicos Islands,,,,,,,,,,,,,,,,
+Tuvalu,5.40777108,11.44129594,8.55426756,8.78722649,9.30826295,15.65692267,17.02046051,15.19748727,21.13086633,16.27527748,17.59233629,18.56057291,16.48224647,20.603899,14.5546873,17.48800603
+Uganda,5.83269973,6.06302733,5.54449868,5.32342737,6.26597954,6.58465179,7.32941802,7.53527143,7.41804274,7.46232669,8.1268899,8.89502272,8.39286192,8.62190809,8.47396581,9.00905045
+Ukraine,6.77277764,6.13788048,6.08275574,6.61656924,5.89491009,5.58970764,5.65769003,6.25349386,6.93362184,6.60913962,6.41043647,6.39231981,6.35521436,6.63441822,7.79946242,7.72176681
+United Arab Emirates,2.63612947,2.45902449,2.48706024,3.05542957,2.90524521,2.57443946,2.48427471,2.71758452,2.64640839,2.4621587,2.32022382,2.32803745,2.52218902,3.01042413,4.37449004,3.65963853
+United Kingdom,6.83053626,6.84421396,6.60599665,6.65465141,6.8896048,7.01166415,7.23753109,7.56110606,7.76609952,7.99428337,8.23938397,8.47407122,8.41400829,8.85451868,9.78861961,9.63550688
+United States,13.56009352,13.49748052,13.3530052,13.36772262,13.35061343,13.40859764,14.07449877,14.82363067,15.66720142,15.71394902,15.76469441,15.85246911,16.05489478,16.51372571,17.60704218,17.88732687
+Uruguay,11.50497936,10.79880948,8.40453561,8.57022801,8.66263436,8.48915721,8.40191971,8.24109937,7.23943748,8.4757611,8.27305265,8.28413078,7.85171899,7.7331565,8.4340649,8.35189393
+USSR,,,,,,,,,,,,,,,,
+Uzbekistan,5.17293107,5.43286897,5.20585875,6.42551145,6.00769263,5.70831798,5.68533896,5.52413304,5.24085984,5.42319412,5.26484799,5.76415947,5.53763644,5.3859357,5.92657601,5.805582
+Wallis et Futuna,,,,,,,,,,,,,,,,
+Vanuatu,2.43988654,2.1450182,3.18976347,3.90529258,3.52444118,3.57167858,3.66939152,3.86017801,3.69166068,3.49396156,3.26888842,3.17359994,3.77234657,4.25682159,4.8540586,5.24669199
+Venezuela,4.23301061,3.493338,3.86241177,4.51286643,5.6532436,5.69041934,5.9598229,5.65358913,5.85535475,5.57233226,5.42792196,5.73508838,5.80499325,5.39547873,6.01537872,4.90609951
+West Bank and Gaza,,,,,,,,,,,,,,,,
+West Germany,,,,,,,,,,,,,,,,
+Western Sahara,,,,,,,,,,,,,,,,
+Vietnam,5.09399182,5.00595735,4.85719764,4.94416813,4.88350811,5.27851786,5.58616296,5.14058145,5.22615353,5.53461605,5.85582768,6.44055494,6.99046528,6.61340099,6.89538668,6.84055986
+Virgin Islands (U.S.),,,,,,,,,,,,,,,,
+North Yemen (former),,,,,,,,,,,,,,,,
+South Yemen (former),,,,,,,,,,,,,,,,
+Yemen,4.49440379,4.75017497,4.68246349,5.39944066,4.65668001,4.46250631,4.78454179,4.67447322,5.73694941,5.3319192,4.87997841,5.2569731,5.31281633,5.3774979,5.51728098,5.18468332
+Yugoslavia,,,,,,,,,,,,,,,,
+Zambia,5.60069226,5.88879881,6.20962986,6.52945534,5.79543752,5.6708002,5.91829361,6.62989696,6.581968,6.6123761,6.99993764,6.3352049,5.83397389,6.1373772,6.21220153,5.89118554
+Zimbabwe,8.76312272,7.93644147,9.02558223,10.79536352,7.43640153,9.99351281,8.09644111,,,,,,,,,
+�land,,,,,,,,,,,,,,,,
+South Sudan,,,,,,,,,,,,,,,,
+Christian,,,,,,,,,,,,,,,,
+Coastline,,,,,,,,,,,,,,,,
diff --git a/lessons/python/intro/intro_data_analysis_AE.ipynb b/lessons/python/intro/intro_data_analysis_AE.ipynb
new file mode 100644
index 000000000..448913fc1
--- /dev/null
+++ b/lessons/python/intro/intro_data_analysis_AE.ipynb
@@ -0,0 +1,1757 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data analysis in Python\n",
+    "\n",
+    "\n",
+    "#### Contributors: \n",
+    "\n",
+    "This notebook combines two notebooks (with minor modifications by Amanda Easson) from previous UofT Coders sessions: \n",
+    "\n",
+    "Intro Python (authors: Madeleine Bonsma-Fisher, heavily borrowing from Lina Tran and Charles Zhu): https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/python/intro/IntroPython-MB.ipynb\n",
+    "\n",
+    "Pandas (authors: Joel Ostblom and Luke Johnston): \n",
+    "https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/python/intro-data-analysis/from-spreadsheets-to-pandas-extended.ipynb\n",
+    "\n",
+    "Additional numpy content was added by Amanda Easson, primarily based off of the Python Data Science Handbook by Jake VanderPlas: https://jakevdp.github.io/PythonDataScienceHandbook/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Table of Contents\n",
+    "\n",
+    "- [Preamble](#preamble)\n",
+    "    - [Motivation](#motivation)\n",
+    "    - [Conceptual understanding](#conceptual-understanding)\n",
+    "    \n",
+    "    \n",
+    "- [Programming basics](#programming-basics)\n",
+    "    - [Variable assignment](#variable-assignment)\n",
+    "    - [Assigning multiple values to variables](#assigning-multiple-values-to-variables)\n",
+    "        - [Lists](#lists)\n",
+    "        - [Dictionaries](#dictionaries)\n",
+    "    - [Comparisons](#comparisons)\n",
+    "    - [Indexing and slicing](#index_slice)\n",
+    "    - [If statements](#if_statements)\n",
+    "    - [For loops](#for_loops)\n",
+    "    - [List comprehensions](#list_comprehensions)\n",
+    "    - [Functions](#functions)\n",
+    "        \n",
+    "        \n",
+    "- [Using functions](#using-functions)\n",
+    "    - [Packages](#packages)\n",
+    "    - [How to get help](#how-to-get-help)\n",
+    "    - [Data analysis with pandas](#data-analysis-with-pandas)\n",
+    "    - [Subsetting data](#subsetting-data)\n",
+    "    \n",
+    "    \n",
+    "- [numpy: numerical python](#numpy)\n",
+    "    \n",
+    "    \n",
+    "- [Data visualization](#data-visualization)\n",
+    "    - [Plotting with pandas](#plotting-with-pandas)\n",
+    "    - [Plotting with seaborn](#plotting-with-seaborn)\n",
+    "    \n",
+    "    \n",
+    "- [Resources to learn more](#resources-to-learn-more)\n",
+    "    \n",
+    "    \n",
+    "# Preamble <a class=\"anchor\" id=\"preamble\"></a>\n",
+    "\n",
+    "This is a brief tutorial for anyone who is interested in how Python can facilitate their data analyses. The tutorial is aimed at people who currently use a spreadsheet program as their primary data analyses tool, and that have no previous programming experience. If you want to code along, a simple way to install Python [is to follow these instructions](https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/install-python-r.md), but I encourage you to just read through this tutorial on a conceptual level at first.\n",
+    "\n",
+    "\n",
+    "## Motivation <a class=\"anchor\" id=\"motivation\"></a>\n",
+    "\n",
+    "Spreadsheet software is great for viewing and entering small data sets and creating simple visualizations fast. However, it can be tricky to create publication-ready figures, automate reproducible analysis workflows, perform advanced calculations, and clean data sets robustly. Even when using a spreadsheet program to record data, it is often beneficial to pick up some basic programming skills to facilitate the analyses of that data.\n",
+    "\n",
+    "\n",
+    "## Conceptual understanding <a class=\"anchor\" id=\"conceptual-understanding\"></a>\n",
+    "\n",
+    "Spreadsheet programs, such as MS Excel and Libre/OpenOffice, have their functionality sectioned into menus. In programming languages, all the functionality is accessed by typing the name of functions directly instead of finding the functions in the menu hierarchy. Initially this might seem intimidating and non-intuitive for people who are used to the menu-driven approach.\n",
+    "\n",
+    "However, think of it as learning a new natural language. Initially, you will slowly string together sentences by looking up individual words in the dictionary. As you improve, you will only reference the dictionary occasionally since you already know most of the words. Practicing the language whenever you can, and receiving immediate feedback, is often the fastest way to learn. Sitting at home trying to learn every word in the dictionary before engaging in conversation, is destined to kill the joy of learning any language, natural or formal.\n",
+    "\n",
+    "In my experience, learning programming is similar to learning a foreign language, and you will often learn the most from just trying to do something and receiving feedback from the computer! When there is something you can't wrap you head around, or if you are actively trying to find a new way of expressing a thought, then look it up, just as you would with a natural language.\n",
+    "\n",
+    "\n",
+    "# Programming basics <a class=\"anchor\" id=\"programming-basics\"></a>\n",
+    "\n",
+    "Just like in spreadsheet software, the basic installation of Python includes fundamental math operations, e.g. adding numbers together:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "4 + 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "4**2 # 4 to the power of 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "3*5; # semi-colon suppresses output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Variable assignment <a class=\"anchor\" id=\"variable-assignment\"></a>\n",
+    "\n",
+    "It is possible to assign values to variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 5\n",
+    "a * 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_variable_name = 4\n",
+    "a - my_variable_name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Variables can also hold more data types than just numbers, for example a sequence of characters surrounded by single or double quotation marks (called a *string*). In Python, it is intuitive to append string by adding them together:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = 'Hello'\n",
+    "c = 'universe'\n",
+    "b + c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A space can be added to separate the words:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b + ' ' + c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(b)\n",
+    "print(type(b))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assigning multiple values to variables <a class=\"anchor\" id=\"assigning-multiple-values-to-variables\"></a>\n",
+    "\n",
+    "### Lists <a class=\"anchor\" id=\"lists\"></a>\n",
+    "\n",
+    "Variables can also store more than one value, for example in a list of values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_of_things = [1, 55, 'Hi', ['apple', 'orange', 'banana']]\n",
+    "list_of_things"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_of_things.append('Toronto')\n",
+    "list_of_things"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_of_things.remove(55)\n",
+    "list_of_things"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(list_of_things + list_of_things)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A \"tuple\" is an immutable list (nothing can be added or subtracted) whose elements also can't be reassigned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tup1 = (1,5,2,1)\n",
+    "print(tup1)\n",
+    "tup1[0] = 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dictionaries <a class=\"anchor\" id=\"dictionaries\"></a>\n",
+    "\n",
+    "In a dictionary, *values* are paired with names, called *keys*. These are not stored in any specific order, and are therefore accessed by the key name rather than a number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fruit_colors = {'tangerine':'orange', 'banana':'yellow', 'apple':['green', 'red']}\n",
+    "fruit_colors['banana']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fruit_colors['apple']\n",
+    "list(fruit_colors.keys())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "participant_info = {'ID': 50964, 'age': 15.25, 'sex': 'F', 'IQ': 105, 'medications': None, 'questionnaires': ['Vineland', 'CBCL', 'SRS']}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(participant_info['ID']) \n",
+    "print(participant_info['questionnaires'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparisons <a class=\"anchor\" id=\"comparisons\"></a>\n",
+    "\n",
+    "Python can compare values and assess whether the expression is true or false. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "1 == 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "1 == 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "1 > 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'hey' == 'Hey'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'hey' == 'hey'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a >= 2 * 2 # we defined a = 5 above"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indexing and Slicing <a class=\"anchor\" id=\"index_slice\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#indexing in Python starts at 0, not 1 (like in Matlab or Oracle)\n",
+    "fruits = ['apples', 'oranges', 'bananas']\n",
+    "print(fruits[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(fruits[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# strings are just a particular kind of list\n",
+    "s = 'This_is_a_string.'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(s[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use -1 to get the last element\n",
+    "print(fruits[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(fruits[-2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to get a slice of the string use the : symbol\n",
+    "# s[0:x] will print up to the xth element, or the element with index x-1\n",
+    "print(s[0:4]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(s[:4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = 'This_is_a_string.'\n",
+    "print(s[5:7])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(s[7:])\n",
+    "print(s[7:len(s)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## If Statements <a class=\"anchor\" id=\"if_statements\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s2 = [19034, 23]\n",
+    "\n",
+    "# You will always need to start with an 'if' line\n",
+    "# You do not need the elif or else statements\n",
+    "# You can have as many elif statements as needed\n",
+    "\n",
+    "if type(s2) == str:\n",
+    "    print('s2 is a string')\n",
+    "elif type(s2) == int:\n",
+    "    print('s2 is an integer')\n",
+    "elif type(s2) == float:\n",
+    "    print('s2 is a float')\n",
+    "else:\n",
+    "    print('s2 is not a string, integer or float')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## For Loops <a class=\"anchor\" id=\"for_loops\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums = [23, 56, 1, 10, 15, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# in this case, 'n' is a dummy variable that will be used by the for loop\n",
+    "# you do not need to assign it ahead of time\n",
+    "\n",
+    "for n in nums:\n",
+    "    if n%2 == 0:\n",
+    "        print('even')\n",
+    "    else:\n",
+    "        print('odd')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for loops can iterate over strings as well\n",
+    "vowels = 'aeiou'\n",
+    "for vowel in vowels:\n",
+    "    print(vowel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## List comprehensions <a class=\"anchor\" id=\"list_comprehensions\"></a>\n",
+    "\n",
+    "Format: \n",
+    "    \n",
+    "`mylist = [altered_thing for thing in list_of_things]`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_colours = ['pink', 'purple', 'blue', 'green', 'orange']\n",
+    "\n",
+    "my_light_colours = ['light ' + colour for colour in my_colours]\n",
+    "\n",
+    "print(my_light_colours)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Functions <a class=\"anchor\" id=\"functions\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# always use descriptive naming for functions, variables, arguments etc.\n",
+    "def sum_of_squares(num1, num2):\n",
+    "    \"\"\"\n",
+    "    Input: two numbers\n",
+    "    Output: the sum of the squares of the two numbers\n",
+    "    \"\"\"\n",
+    "    ss = num1**2 + num2**2\n",
+    "    return(ss)\n",
+    "\n",
+    "# The stuff inside \"\"\" \"\"\" is called the \"docstring\". It can be accessed by typing help(sum_of_squares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(sum_of_squares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(sum_of_squares(4,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the return statement in a function allows us to store the output of a function call in a variable for later use\n",
+    "ss1 = sum_of_squares(5,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "print(ss1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we start working with spreadsheet-like data, we will see that these comparisons are really useful to extract subsets of data, for example observations from a certain time period."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Using functions <a class=\"anchor\" id=\"using-functions\"></a>\n",
+    "\n",
+    "To access additional functionality in a spreadsheet program, you need to click the menu and select the tool you want to use. All charts are in one menu, text layout tools in another, data analyses tools in a third, and so on. Programming languages such as Python have so many tools and functions so that they would not fit in a menu. Instead of clicking `File -> Open` and chose the file, you would type something similar to `file.open('<filename>')` in a programming language. Don't worry if you forget the exact expression, it is often enough to just type the few first letters and then hit <kbd>Tab</kbd>, to show the available options, more on that later.\n",
+    "\n",
+    "\n",
+    "## Packages <a class=\"anchor\" id=\"packages\"></a>\n",
+    "\n",
+    "Since there are so many esoteric tools and functions available in Python, it is unnecessary to include all of them with the basics that are loaded by default when you start the programming language (it would be as if your new phone came with every single app preinstalled). Instead, more advanced functionality is grouped into separate packages, which can be accessed by typing `import <package_name>` in Python. You can think of this as telling the program which menu items you want to use (similar to how Excel hides the `Developer menu` by default since most people rarely use it and you need activate it in the settings if you want to access its functionality). Some packages needs to be downloaded before they can be used, just like downloading an addon to a browser or mobile phone.\n",
+    "\n",
+    "Just like in spreadsheet software menus, there are lots of different tools within each Python package. For example, if I want to use numerical Python functions, I can import the **num**erical **py**thon module, `numpy`. I can then access any function by writing `numpy.<function_name>`.\n",
+    "\n",
+    "If the package name is long, it is common to import the package \"as\" another name, like a nickname. For instance, `numpy` is often imported as `np`. All you have to do is type `import numpy as np`. Thiis makes it faster to type and saves a bit of work and also makes the code a bit easier to read.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How to get help <a class=\"anchor\" id=\"how-to-get-help\"></a>\n",
+    "\n",
+    "Once you start out using Python, you don't know what functions are availble within each package. Luckily, in the Jupyter Notebook, you can type `numpy.`<kbd>Tab</kbd> (that is numpy + period + tab-key) and a small menu will pop up that shows you all the available functions in that module. This is analogous to clicking a 'numpy-menu' and then going through the list of functions. As I mentioned earlier, there are plenty of available functions and it can be helpful to filter the menu by typing the initial letters of the function name."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get more info on the function you want to use, you can type out the full name and then press <kbd>Shift + Tab</kbd> once to bring up a help dialogue and again to expand that dialogue."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you need a more extensive help dialog, you can click  <kbd>Shift + Tab</kbd> four times or just type `?` after the function name."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# numpy: numerical python <a class=\"anchor\" id=\"numpy\"></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use a package by importing it, you can also give it a \"nickname\", in this case 'np'\n",
+    "import numpy as np\n",
+    "np.mean?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = np.arange(15)\n",
+    "lst = list(range(15))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(array)\n",
+    "print(lst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(type(array))\n",
+    "print(type(lst))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# numpy arrays allow for vectorized calculations\n",
+    "print(array*2)\n",
+    "print(lst*2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = array.reshape([5,3])\n",
+    "print(array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for each row, take the mean across the 3 columns (using axis=1)\n",
+    "array.mean(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# max value in each column\n",
+    "array.max(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list2array = np.array(lst)\n",
+    "type(list2array)\n",
+    "list2array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array2d = np.array([range(i, i + 3) for i in [2, 4, 6]])\n",
+    "array2d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_zeros = np.zeros((10,1), dtype=int)\n",
+    "my_zeros"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.ones((5,2), dtype=float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.full((3,3), np.pi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create an array filled with a linear sequence: start, stop, step\n",
+    "# up to stop value - 1\n",
+    "# similar to \"range\"\n",
+    "print(np.arange(0, 20, 2))\n",
+    "print(list(range(0, 20, 2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# linspace: evenly spaced values: start, stop, number of values\n",
+    "# up to stop value\n",
+    "print(np.linspace(0, 20.2, 11))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# uniformly distributed random numbers between 0-1\n",
+    "np.random.random((3,3)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# normal distribution\n",
+    "# mean, standard deviation, array size\n",
+    "np.random.seed(0)\n",
+    "my_array = np.random.normal(0, 1, (10000,1))\n",
+    "print(my_array.mean())\n",
+    "print(my_array.std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iq = np.random.normal(100, 15, (30,1))\n",
+    "print(np.mean(iq))\n",
+    "print(np.median(iq))\n",
+    "print(np.max(iq))\n",
+    "print(np.min(iq))\n",
+    "\n",
+    "print('\\n')\n",
+    "\n",
+    "print(iq.mean())\n",
+    "print(iq.max())\n",
+    "print(iq.median())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(100,15,(3,4,5))\n",
+    "\n",
+    "print(\"# dimensions:\", x.ndim)\n",
+    "print(\"Shape:\", x.shape)\n",
+    "print(\"Size:\", x.size)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# indexing\n",
+    "x[0,0,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# change elements\n",
+    "x[0,0,2] = 108\n",
+    "x[0,0,2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.arange(10)\n",
+    "print(x)\n",
+    "print(\"First five elements:\", x[:5]) # first five elements\n",
+    "print(\"elements after index 5:\", x[5:]) # elements after index 5\n",
+    "print(\"every other element:\", x[::4]) # every other element\n",
+    "print(\"every other element, starting at index 3:\", x[3::2]) # every other element, starting at index 3\n",
+    "print(\"all elements, reversed:\",x[::-1]) # all elements, reversed "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# copying arrays\n",
+    "print(x)\n",
+    "x2 = x\n",
+    "x2[0] = 100\n",
+    "print(x2)\n",
+    "print(x)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x3 = np.arange(10)\n",
+    "x4 = x3.copy()\n",
+    "x4[0] = 100\n",
+    "print(x4)\n",
+    "print(x3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# concatenate arrays\n",
+    "\n",
+    "x1 = np.array([1,2,3])\n",
+    "x2 = np.array([4,5,6])\n",
+    "x3 = np.concatenate([x1,x2])\n",
+    "print(x3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# or stack vertically or horizontally\n",
+    "# make sure dimensions agree\n",
+    "x4 = np.vstack([x1,x2])\n",
+    "print(x4)\n",
+    "\n",
+    "x5 = np.hstack([x1,x2])\n",
+    "print(x5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# numpy arithmetic\n",
+    "x = np.arange(5)\n",
+    "print(x)\n",
+    "print(x+5)\n",
+    "print(x*2)\n",
+    "print(x/2)\n",
+    "print(x//2)      # \"floor\" division, i.e. round down to nearest integer\n",
+    "\n",
+    "# alternatively:\n",
+    "print('\\n')\n",
+    "print(np.add(x,5))\n",
+    "print(np.multiply(x,2))\n",
+    "print(np.divide(x,2))\n",
+    "print(np.floor_divide(x,2))\n",
+    "print(np.power(x,3))\n",
+    "print(np.mod(x,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add all elements of x\n",
+    "print(np.add.reduce(x))\n",
+    "print(np.sum(x))\n",
+    "\n",
+    "# add all elements of x cumulatively\n",
+    "print(np.add.accumulate(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# broadcasting\n",
+    "a = np.eye(5)\n",
+    "b = np.ones((5,1))\n",
+    "b = np.ones((5,5))\n",
+    "print(a)\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(a+b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# broadcasting: practical example: mean-centering data\n",
+    "\n",
+    "data = np.random.normal(100, 15, (30, 6))\n",
+    "print(data.shape)\n",
+    "data_mean = data.mean(axis=0)\n",
+    "print(data_mean.shape)\n",
+    "\n",
+    "\n",
+    "data_mean_centered = data - data_mean\n",
+    "\n",
+    "data_mean_centered.mean(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## pandas (\"panel data\"): python data analysis library <a class=\"anchor\" id=\"data-analysis-with-pandas\"></a>\n",
+    "\n",
+    "The Python package that is most commonly used to work with spreadsheet-like data is called `pandas`, the name is derived from \"panel data\", an econometrics term for multidimensional structured data sets. Data are easily loaded into pandas from `.csv` or other spreadsheet formats. The format pandas uses to store this data is called a <b>data frame</b>.\n",
+    "\n",
+    "To have a quick peak at the data, I can type `df.head(<number_of_rows>)` (where \"df\" is the name of the dataframe). Notice that I do not have to use the `pd.`-syntax for this. `iris` is now a pandas data frame, and all pandas data frames have a number of built-in functions (called <b>methods</b>) that can be appended directly to the data frame instead of by calling `pandas` separately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this will read in a csv file into a pandas DataFrame\n",
+    "# this csv has data of country spending on healthcare\n",
+    "#data = pd.read_csv('health.csv', header=0, index_col=0, encoding=\"ISO-8859-1\")\n",
+    "\n",
+    "# load several datasets \n",
+    "data = pd.read_csv('https://tinyurl.com/uoftcode-health', header=0, index_col=0, \n",
+    "                   encoding=\"ISO-8859-1\")\n",
+    "iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the .head() function will allow us to look at first few lines of the dataframe\n",
+    "data.head(10) # default is 5 rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# by default, rows are indicated first, followed by the column: [row, column]\n",
+    "data.loc['Canada', '2008']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# you can also slice a dataframe\n",
+    "data.loc['Canada':'Chile', '1999':'2001']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the .plot() function will create a simple graph for you to quickly visualize your data\n",
+    "data.loc['Denmark'].plot()\n",
+    "data.loc['Canada'].plot()\n",
+    "data.loc['India'].plot()\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris.head() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris.shape # rows, columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris.columns # names of columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To select a column we can *index* the data frame with the column name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris['sepal_length']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output is here rendered slightly differently from before, because when we are looking only at one column, it is no longer a data frame, but a <b>*series*</b>. The differences are not important for this lecture, so this is all you need to know about that for now.\n",
+    "\n",
+    "We could now create a new column if we wanted:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris['sepal_length_x2'] = iris['sepal_length'] * 2\n",
+    "iris['sepal_length_x2']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And delete that column again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = iris.drop('sepal_length_x2', axis=1) # axis 0 = index, axis 1 = columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are some built-in methods that make it convenient to calculate common operation on data frame columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris['sepal_length'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris['sepal_length'].median()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to use these methods on all columns at the same time without having to type the same thing over and over again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, you can get a statistical summary of the data frame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Subsetting data <a class=\"anchor\" id=\"subsetting-data\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A common task is to subset the data into only those observations that match a criteria. For example, we might be interest in only studying one specific species. First let's find out how many different species there are in our data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris['species'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Let's arbitrarily choose *setosa* as the one to study! To select only observations from this species in the original data frame, we index the data frame with a comparison:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "iris['species'] == 'setosa'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "iris[iris['species'] == 'setosa']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can easily perform computation on this subset of the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris[iris['species'] == 'setosa'].mean(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could also compare all groups within the data against each other, by using the <b>split-apply-combine</b> workflow. This splits data into groups, applies an operation on each group, and then combines the results into a table for display.\n",
+    "\n",
+    "In pandas, we split into groups with the `group_by` command and then we apply an operation to the grouped data frame, e.g. `.mean()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris.groupby('species').mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also easily count the number of observations in each group:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris.groupby('species').size()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data visualization <a class=\"anchor\" id=\"data-visualization\"></a>\n",
+    "\n",
+    "We can see that there are clear differences between species, but they might be even clearer if we display them graphically in a chart.\n",
+    "\n",
+    "### Plotting with pandas <a class=\"anchor\" id=\"plotting-with-pandas\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas interfaces with one of Python's most powerful data visualization libraries, `matplotlib`, to enable simple visualizations at minimal effort."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prevent plots from popping up in a new window\n",
+    "%matplotlib inline\n",
+    "\n",
+    "species_comparison = iris.groupby('species').mean() # Assign to a variable\n",
+    "species_comparison.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Depending on what you are interesting in showing, it could be useful to have the species as the different colors and the columns along the x-axis. We can easily achieve this by transposing (`.T`) our data frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "species_comparison.T.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting with seaborn <a class=\"anchor\" id=\"plotting-with-seaborn\"></a>\n",
+    "\n",
+    "Another plotting library is `seaborn`, which also builds upon `matplotlib`, and extends it by adding new styles, additional plot types and some commonly performed statistical measures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "sns.swarmplot('species', 'sepal_length', data = iris)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The labels on this plot look a bit small, so let's change the style we are using for plotting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.set(style='ticks', context='talk', rc={'figure.figsize':(8, 5),'axes.spines.right':False, 'axes.spines.top':False}) # This applies to all subseque\n",
+    "# styles: darkgrid, whitegrid, dark, white, and ticks\n",
+    "# contexts: paper, notebook, talk, poster\n",
+    "#sns.axes_style()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.swarmplot('species', 'sepal_length', data=iris)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the same syntax to create many of the common plots in `seaborn`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.barplot('species', 'sepal_length', data=iris)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bar charts are a common, but not very useful way of presenting data aggregations (e.g. the mean). A better way is to use the points as we did above, or a plot that capture the distribution of the data, such as a boxplot, or a violin plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.violinplot('species', 'sepal_length', data = iris)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also combine two plots, by simply adding the two line after each other. There is also a more advanced figure interface available in `matplotlib` to explicitly indicate which figure and axes you want the plot to appear in, but this is outside the scope of this tutorial (more info [here](https://matplotlib.org/users/recipes.html#easily-creating-subplots) and [here](https://stackoverflow.com/questions/23969619/plotting-with-seaborn-using-the-matplotlib-object-oriented-interface))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.violinplot('species', 'sepal_length', data=iris, inner=None)\n",
+    "sns.swarmplot('species', 'sepal_length', data=iris, color='black', size=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of plotting one categorical variable vs a numerical variable, we can also plot two numerical values against each other to explore potential correlations between these two variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.lmplot('sepal_width', 'sepal_length', data=iris, size=6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is a regression line plotted by default to indicate the trend in the data. Let's turn that off for now and look at only the data points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.lmplot('sepal_width', 'sepal_length', data=iris, fit_reg=False, size=6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There appears to be some structure in this data. At least two clusters of points seem to be present. Let's color according to species and see if that explains what we see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.lmplot('sepal_width', 'sepal_length', data=iris, hue='species', fit_reg=False, size=6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can add back the regression line, but this time one for each group."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.lmplot('sepal_width', 'sepal_length', data=iris, hue='species', fit_reg=True, size=6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of creating a plot for each variable against each other, we can easily create a grid of subplots for all variables with a single command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.pairplot(iris, hue=\"species\", size=3.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### More complex visualizations <a class=\"anchor\" id=\"more-complex-visualizations\"></a>\n",
+    "\n",
+    "Many visualizations are easier to create if we first reshape our data frame into the [tidy format](http://vita.had.co.nz/papers/tidy-data.pdf), which is what `seaborn` prefers. This is also referred to as changing the data frame format from wide (many columns) to long (many rows), since it moves information from columns to rows:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use pandas built-in `melt`-function to \"melt\" the wide data frame into the long format. The new columns will be given the names `variable` and `value` by default (see the help of `melt` if you would like to change these names)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris_long = pd.melt(iris, id_vars = 'species')\n",
+    "iris_long"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We do not need to call `groupby` or `mean` on the long `iris` data frame when plotting with `seaborn`. Instead we control these options from `seaborn` with the plot type we chose (barplot = mean automatically) and the `hue`-parameter, which is analogous to `groupby`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.set(context='poster', style='white', rc={'figure.figsize':(10, 6), 'axes.spines.right':False, 'axes.spines.top':False})\n",
+    "\n",
+    "sns.swarmplot(x='variable', y='value', hue = 'species', data=iris_long, dodge=True, palette='Set2', size=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.set(context='poster', style='darkgrid', rc={'figure.figsize':(12, 6)})\n",
+    "# stripplot: scatterplot where one variable is categorical \n",
+    "\n",
+    "sns.boxplot(y='variable', x='value', hue='species', data=iris_long, color='c', )\n",
+    "sns.stripplot(y='variable', x='value', hue='species', data=iris_long, size=2.5, palette=['k']*3, jitter=True, dodge=True)\n",
+    "plt.xlim([0, 10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to get too fancy and accidentally hide important messages in the data. However, the fact that you now have access to several ways to plot your data forces you to consider what is actually important and how you can best communicate that message, rather than always making the same plot without considering its strengths and weaknesses."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Resources to learn more <a class=\"anchor\" id=\"resources-to-learn-more\"></a>\n",
+    "\n",
+    "The documentation for these packages are great resources to learn more.\n",
+    "\n",
+    "- [Introduction to `pandas`](https://pandas.pydata.org/pandas-docs/stable/10min.html)\n",
+    "- [Lessons for new `pandas` users](https://pandas.pydata.org/pandas-docs/stable/tutorials.html#lessons-for-new-pandas-users)\n",
+    "- [The `seaborn` gallery](https://seaborn.pydata.org/examples/index.html)\n",
+    "- [The `seaborn` tutorials](https://seaborn.pydata.org/tutorial.html)\n",
+    "- [The `matplotlib` gallery](https://matplotlib.org/gallery.html) is another great resource. We did not practice using `matplotlib` explicitly in this notebook, but the plotting techniques from `pandas` and `seaborn` are layers on top of the `matplotlib` library, so we have been using it indirectly."
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  },
+  "toc": {
+   "colors": {
+    "hover_highlight": "#DAA520",
+    "running_highlight": "#FF0000",
+    "selected_highlight": "#FFD700"
+   },
+   "moveMenuLeft": false,
+   "nav_menu": {
+    "height": "226px",
+    "width": "285px"
+   },
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 4,
+   "toc_cell": false,
+   "toc_section_display": "block",
+   "toc_window_display": false,
+   "widenNotebook": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/lessons/python/intro/lesson.md b/lessons/python/intro/lesson.md
new file mode 100644
index 000000000..1e7afb16b
--- /dev/null
+++ b/lessons/python/intro/lesson.md
@@ -0,0 +1,63 @@
+---
+layout: page
+title: 'Intro to Python'
+visible: true
+tags:
+  - python
+  - beginner
+---
+
+ - **Authors**: Charles Zhu, Lina Tran, Madeleine Bonsma
+ - **Research field**: Astronomy & astrophysics
+ - **Lesson topic**: Introduction to Python syntax and functionality
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/intro>
+
+## Preface: ##
+
+This is a very basic introduction to the Python programming language,
+based largely off of the tutorials in the [Beginners' Guide to Python](https://wiki.python.org/moin/BeginnersGuide/Programmers),
+in particular the [Learn Python Tutorials](https://pythonspot.com/).
+
+Only the command line and a basic text editor are required, but quite
+a few integrated development environments exist, however, including [IDLE](https://docs.python.org/3/library/idle.html)
+and [PyCharm](https://www.jetbrains.com/pycharm/).  Madeleine recommends
+[WingIDE101](https://www.wingware.com/downloads/wingide-101/5.1.5-1).
+
+## Lesson Overview: ##
+
+Python implementations generally interpret code (though compilers
+also exist).  The interpreter that comes with python is accessible
+by typing
+
+    python
+
+into the command line (or clicking the executable on your GUI).
+To run scripts and programs, we can instead type
+
+    python practice_code.py
+
+[IPython](https://ipython.org/) is an improved interactive console
+that's both easier to use and supports additional functionality.
+To run IPython, type
+
+    ipython
+
+To run scripts and programs within ipython, type within IPython
+
+    run practice_code.py
+
+The lesson covers:
+
+* Hello World and print statements
+* Number and string manipulation
+* Lists, tuples and dicts
+* Importing libraries
+* Function declaration
+* IPython debugging
+
+The [official Python documentation site](https://docs.python.org) goes into much more detail
+regarding all topics covered in this code-along.  Python is supported
+by a very, very extensive breadth of packages, a list can be found [here](https://pypi.python.org/pypi)
+Like with git, please check out [StackOverflow](https://stackoverflow.com/questions/tagged/python) for
+ any question on it.
+
diff --git a/lessons/python/intro/practice_code.py b/lessons/python/intro/practice_code.py
new file mode 100644
index 000000000..41882b5d4
--- /dev/null
+++ b/lessons/python/intro/practice_code.py
@@ -0,0 +1,295 @@
+# Comments are denoted by "#"
+
+from __future__ import print_function   #Python 2.x compatibility for print function
+
+
+########## HELLO WORLD ##########
+
+# We're working within an interpreter (similar to Matlab),
+# so we type code directly into the interpreter
+
+print("Hello World")
+
+# In Python 2.x, "print" is a statement and the parentheses
+# should be removed.  Alternatively, you can use Python 3.x
+# syntax after importing the __future__ module, as above.
+
+
+########## DATA TYPES: NUMBERS & STRINGS ##########
+
+# Python has five standard data types: numbers, string, lists,
+# tuples, and dictionaries.  Let's deal with numbers and
+# strings first.  Numbers can be ints (and longs in older
+# versions), floats or complex.
+
+# One advantage of the interpreter is we can solve math
+# problems directly in it
+
+2 + 2
+12.*3				# The int gets typecast to float automatically
+(1.+1j)/2.**0.5
+1/2					# Be warned - this returns zero in Python 2.x!
+
+# Note that running this .py as a script will not print those
+# numbers to screen, but running the commands directly on the
+# command line will.
+
+# To figure out the data type of a variable, we can use:
+
+x = 47
+print(type(x))
+x = "spam"
+print(type(x))
+
+# Variables don't have pre-declared types in Python, so you can
+# assign any data type to any variable.
+
+# As an aside, to get documentation on most things within Python:
+
+help(type)
+
+# We can also use the magic function "type?" in IPython.
+
+# Print is intelligent, and we can pass it all sorts of weird
+# things.
+
+x = 42
+print("This is a string, and these are numbers: ", x, 324.13, ", and another string.")
+
+# String are character arrays, and their operations are an art form.
+# Here is a smattering of commands.
+
+s = "This is a test string"
+print(s)
+print(s[0])			# Array syntax in python - to access element i of an array, we do array[i]
+
+# Python is 0-based - the first element of an array is index 0.
+
+print(s[-3])		# Negative indicies access elements backwards from the end of the list
+print(s[2:4])		# Accesses elements 2 - 3
+print(s[6:])		# Accesses elements 6 and above
+print(s + ' ' + s)	# Strings can be concatenated
+print(s*10)			# Or multiplied...
+
+print(s.replace('a test','the bestest')) # print a string with a replaced word
+
+# Above, replace is a method, a function that is a part of an
+# object (in this case, s).  Tab-completion can be used to determine
+# what methods exist for a given object.
+
+# Perform equality tests on strings; the output is a boolean.
+# (Python doesn't care about using ' or " for strings.)
+
+print(s == 'This is a test string')
+
+
+
+########## DATA TYPES: LISTS, TUPLES & DICTS ##########
+
+# Python has two array-like things.  The first is called a "list",
+# which can hold any data types:
+
+mylist = [1, "2", 3, 4.000, "five"]
+print(len(mylist))						# Prints number of elements in list
+print(mylist[2:])						# Indexing is the same as for strings
+print(mylist + mylist)					# Adding lists combines them
+print(mylist.index(4.))					# Return the index of a list element
+
+# The second is called a "tuple", which is an immutable list (nothing
+# can be added or subtracted) whose elements also can't be reassigned.
+# They're easier on memory and processor than lists.
+
+mytuple = (1, "2", 3j, 4.000, "five")
+
+# Python also has dictionaries, which are unordered sets of values accessible
+# by hashable keys (generally strings and numbers, but more exotic things
+# are also possible).
+
+mydict = {"dogs": "DOGS!", "dognumber": 47}			# Can also just write {} for a blank dictionary
+mydict["dognames"] = ["spot", "rover", "agamemnon"]	# Can add dictionary entries after initialization
+print(mydict)										# Print is versatile, and will give you a summary of your dict
+del mydict["dogs"]									# Delete a key and its associated value
+
+# Note that passing an "immutable type" of object, like a float, tuple
+# or int, using "=" will make a copy of the value.
+
+x = 4
+y = x
+print(x, y)
+y = 7
+print(x, y)
+
+# Lists and dicts, however, are "mutable", and
+
+x = ["alpha", "beta"]
+y = x
+print(x, y)
+y[1] = "gamma"
+print(x, y)
+
+# To make a copy of a list, do:
+
+y = list(x)		# x[:] also works
+y[1] = "delta"
+print(x, y)
+
+# See https://en.wikibooks.org/wiki/Python_Programming/Data_Types#Mutable_vs_Immutable_Objects
+
+
+########## IMPORTING LIBRARIES ##########
+
+# If you want Matlab-style arrays, you'll need to install the
+# numpy library.  Here, "try/except/else" means try <try code>, and
+# if Python throws up an exception, do <except code>. If not, do
+# <else code>.
+
+try:
+    import numpy as np						# Imports numpy library
+except:
+    print("You don't have numpy installed!  This section of code won't run.")
+else:
+    myarray = np.array([1,43,32.1,17,216])	# Create numpy array
+    print(myarray)
+    print(myarray*myarray/3.)				# Multiplication is element by element; "3." is typecast to an array of length myarray
+
+
+########## LOOPS & CONDITIONALS ##########
+
+# Like the try/except above, conditional statements and loops in
+# Python are demarcated using indents.
+
+# For loop that cycles over range(starting number, end number,
+# stepsize).
+
+for i in range(3,10,2):
+    print(i)
+
+# For loop; cycles over all elements in mylist from previous section.
+
+for item in mylist:
+    print(item)
+
+# For loop that searches mylist for "five".
+
+for item in mylist:
+    if item == "five":
+        print("'five' found!")
+        break					# Exits loop
+    else:
+        print("Not yet...")
+
+# The standard way of indenting in Python is four spaces.
+
+i = 0
+while i < 10:					# While loop
+    if i > 5 and not i > 8:
+        print(i*i)
+    i += 1
+
+
+########## FUNCTION DECLARATION ##########
+
+# Here's a simple function.  (str() typecasts to string.)
+
+def myf(x,y):
+    print('You called myf(x,y) with the value x = ' + str(x) + ' and y = ' + str(y))
+    print('x * y = ' + str(x*y))
+
+myf(13,17)
+
+# Functions don't have to be declared in a header, and arguments
+# don't need to have a pre-declared data type.
+
+def printtype(x="dummy"):
+    """Functions don't have to be declared in a header, and arguments
+    don't need to have a pre-declared data type.
+
+    Here I've also given argument "x" a default value; if x is not
+    user-defined, its value is set to "dummy".  This function also returns
+    a value (to return multiple values, you can use lists).
+    """
+    print("You've entered a ", type(x), "whose value is", x)
+    return type(x)
+
+rt = printtype(4)
+rt = printtype("eight")
+rt = printtype()
+print(rt)
+
+# The block denoted by """ is called a "docstring", used to document the
+# usage of a function.  It can be accessed by
+
+help(printtype)
+
+# Immutable types passed as an argument into a function can't be changed
+# by the function:
+
+def trytochange(x):
+    x = 10
+
+x = 2
+print(x)
+trytochange(x)
+print(x)
+
+# Mutable types can.
+
+def trytochangelist(x):
+    x[1] = "CHANGED!"
+
+changethis = ["first", "second", "third"]
+print(changethis)
+trytochangelist(changethis)
+print(changethis)
+
+
+########## IPYTHON MAGIC FUNCTIONS ##########
+
+# IPython has a set of "magic functions", including ? above.
+# Another is %paste, which pastes commands onto the command
+# line, properly interpreting indentations (just pasting
+# often doesn't).
+
+# Try to %paste this block of code into IPython.
+
+def badfunction(x):
+    y = x**2
+    z = thisdoesntexist
+    return y*z
+
+badfunction(10)
+
+# It should throw up an error message.  Now type in %debug,
+# which takes you into the interactive debugger.
+
+
+########## IF WE HAVE TIME, WE'LL COVER THESE ##########
+
+########## FUNCTIONS IN FUNCTIONS ##########
+
+## We can also declare functions WITHIN other functions.  This is useful for generating functions with custom values:
+
+#def makepwrlaw(alpha, beta):
+#   def pwrlaw(x):
+#       return alpha*x**beta
+#   return pwrlaw
+
+#mypowerlaw = makepwrlaw(0.5,2)
+#print(mypowerlaw(2))
+
+
+########## CLASS DECLARATIONS ##########
+
+## Lastly, we can define classes, which allow us to create objects that carry both values and associated functions.
+
+#class User:
+
+#   def __init__(self, name, dept):			# class constructor
+#       self.name = name						# store values
+#       self.dept = dept
+#
+#    def sayHello(self):						# class method
+#        print("Hello, my name is " + self.name + " from the " + self.dept)
+
+#jane = User("Jane Doe", "CompSci")
+#jane.sayHello()
diff --git a/lessons/python/large-data/LargeDatasetsPython.ipynb b/lessons/python/large-data/LargeDatasetsPython.ipynb
new file mode 100644
index 000000000..c7d6393cb
--- /dev/null
+++ b/lessons/python/large-data/LargeDatasetsPython.ipynb
@@ -0,0 +1,703 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"This lesson used some personal genome sequencing data as well as packages I wrote myself.\n",
+    "The code in custom packages MotifMatcher and MotifFinder is found \n",
+    "in the Coursera class Biology Meets Progamming, with modifications.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "#Use pandas.DataFrame to store DNA chip results\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         rsid  chromosome   position genotype\n",
+      "0   rs4988235           2  136608646       GG\n",
+      "1     rs53576           3    8804371       GG\n",
+      "2  rs12248560          10   96521657       CC\n",
+      "3  rs28399504          10   96522463       AA\n",
+      "4  rs41291556          10   96535173       TT\n",
+      "5   rs4986893          10   96540410       GG\n",
+      "6   rs4244285          10   96541616       AG\n",
+      "7   rs1815739          11   66328095       CT\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Personal genome sequencing using DNA chip\n",
+    "#Load raw data and print as a table\n",
+    "#Note that a small example of generic gDNA is used for demonstration purpose. \n",
+    "#The usual DNA chip sequencing result is between 300K to 4M rows long.\n",
+    "\n",
+    "generic_gdna = pd.read_table(\"generic_gdna.txt\")\n",
+    "print(generic_gdna)\n",
+    "\n",
+    "#rsid: an ID number for a genetic mutation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>chromosome</th>\n",
+       "      <th>position</th>\n",
+       "      <th>genotype</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>rs53576</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8804371</td>\n",
+       "      <td>GG</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      rsid  chromosome  position genotype\n",
+       "1  rs53576           3   8804371       GG"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Is Generic a compassionate person?\n",
+    "#Find rs53576 in SNPedia\n",
+    "\n",
+    "generic_gdna[generic_gdna[\"rsid\"] == 'rs53576']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>chromosome</th>\n",
+       "      <th>position</th>\n",
+       "      <th>genotype</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>rs1815739</td>\n",
+       "      <td>11</td>\n",
+       "      <td>66328095</td>\n",
+       "      <td>CT</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        rsid  chromosome  position genotype\n",
+       "7  rs1815739          11  66328095       CT"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Should Generic become an athlete?\n",
+    "\n",
+    "generic_gdna[generic_gdna[\"rsid\"] == 'rs1815739']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>chromosome</th>\n",
+       "      <th>position</th>\n",
+       "      <th>genotype</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rs4988235</td>\n",
+       "      <td>2</td>\n",
+       "      <td>136608646</td>\n",
+       "      <td>GG</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        rsid  chromosome   position genotype\n",
+       "0  rs4988235           2  136608646       GG"
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Does Generic have lactose intolerance?\n",
+    "\n",
+    "generic_gdna[generic_gdna[\"rsid\"] == \"rs4988235\"]\n",
+    "\n",
+    "#Genotype not present in SNPedia but it probably means the chip detected the opposite strand"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         rsid identifier consensus mutation      pmid\n",
+      "0   rs4244285         *2        GG        A  23698643\n",
+      "1   rs4986893         *3        GG        A  20979470\n",
+      "2  rs28399504         *4        AA        G  20978260\n",
+      "3  rs41291556         *8        TT        C  19106083\n",
+      "4  rs12248560        *17        CC        T  21716271\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Plavis: an drug to present heart attacks and strokes. Patient response depends on genotype.\n",
+    "#plavix.txt includes a list of mutations that could diminish the efficacy of plavix.\n",
+    "\n",
+    "plavix = pd.read_table(\"plavix.txt\")\n",
+    "print(plavix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['rs4244285', 'rs4986893', 'rs28399504', 'rs41291556', 'rs12248560']\n",
+      "        rsid  chromosome  position genotype\n",
+      "6  rs4244285          10  96541616       AG\n",
+      "        rsid  chromosome  position genotype\n",
+      "5  rs4986893          10  96540410       GG\n",
+      "         rsid  chromosome  position genotype\n",
+      "3  rs28399504          10  96522463       AA\n",
+      "         rsid  chromosome  position genotype\n",
+      "4  rs41291556          10  96535173       TT\n",
+      "         rsid  chromosome  position genotype\n",
+      "2  rs12248560          10  96521657       CC\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Store rsids associated with plavix response in a list\n",
+    "plavix_list = plavix[\"rsid\"].tolist()\n",
+    "print(plavix_list)\n",
+    "\n",
+    "#Loop through the list to find in Generic the genotypes associated with plavix response\n",
+    "for i in plavix_list:\n",
+    "    print(generic_gdna[generic_gdna[\"rsid\"] == i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>rsid</th>\n",
+       "      <th>chromosome</th>\n",
+       "      <th>position</th>\n",
+       "      <th>genotype</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>rs12248560</td>\n",
+       "      <td>10</td>\n",
+       "      <td>96521657</td>\n",
+       "      <td>CC</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>rs28399504</td>\n",
+       "      <td>10</td>\n",
+       "      <td>96522463</td>\n",
+       "      <td>AA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>rs41291556</td>\n",
+       "      <td>10</td>\n",
+       "      <td>96535173</td>\n",
+       "      <td>TT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>rs4986893</td>\n",
+       "      <td>10</td>\n",
+       "      <td>96540410</td>\n",
+       "      <td>GG</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>rs4244285</td>\n",
+       "      <td>10</td>\n",
+       "      <td>96541616</td>\n",
+       "      <td>AG</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         rsid  chromosome  position genotype\n",
+       "2  rs12248560          10  96521657       CC\n",
+       "3  rs28399504          10  96522463       AA\n",
+       "4  rs41291556          10  96535173       TT\n",
+       "5   rs4986893          10  96540410       GG\n",
+       "6   rs4244285          10  96541616       AG"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Assemble into a new DataFrame\n",
+    "generic_plavix = generic_gdna.loc[generic_gdna['rsid'].isin(plavix_list)]\n",
+    "generic_plavix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#Write the output to a new file. \n",
+    "generic_plavix.to_csv(\"generic_plavix.txt\", index=None, sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Position</th>\n",
+       "      <th>Chromosome</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>First author</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>DOI</th>\n",
+       "      <th>Open Access status</th>\n",
+       "      <th>Link</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>rs12662670</td>\n",
+       "      <td>151918856</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>Kristen N Stevens</td>\n",
+       "      <td>Common breast cancer susceptibility loci are a...</td>\n",
+       "      <td>10.1158/0008-5472.CAN-11-1266</td>\n",
+       "      <td>True</td>\n",
+       "      <td>http://www.mendeley.com/research/common-breast...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>rs2046210</td>\n",
+       "      <td>151948366</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>Qiuyin Cai</td>\n",
+       "      <td>Replication and functional genomic analyses of...</td>\n",
+       "      <td>10.1158/0008-5472.CAN-10-2733</td>\n",
+       "      <td>True</td>\n",
+       "      <td>http://www.mendeley.com/research/replication-f...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>rs2046210</td>\n",
+       "      <td>151948366</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2011.0</td>\n",
+       "      <td>Wonshik Han</td>\n",
+       "      <td>Common genetic variants associated with breast...</td>\n",
+       "      <td>10.1158/1055-9965.EPI-10-1282</td>\n",
+       "      <td>False</td>\n",
+       "      <td>http://www.mendeley.com/research/common-geneti...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>rs2046210</td>\n",
+       "      <td>151948366</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2009.0</td>\n",
+       "      <td>Wei Zheng</td>\n",
+       "      <td>Genome-wide association study identifies a new...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>True</td>\n",
+       "      <td>http://www.mendeley.com/research/genomewide-as...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>rs2046210</td>\n",
+       "      <td>151948366</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2010.0</td>\n",
+       "      <td>Simon N Stacey</td>\n",
+       "      <td>Ancestry-Shift Refinement Mapping of the C6orf...</td>\n",
+       "      <td>10.1371/journal.pgen.1001029</td>\n",
+       "      <td>True</td>\n",
+       "      <td>http://www.mendeley.com/research/ancestryshift...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Name   Position Chromosome    Year       First author  \\\n",
+       "0  rs12662670  151918856          6  2011.0  Kristen N Stevens   \n",
+       "1   rs2046210  151948366          6  2011.0         Qiuyin Cai   \n",
+       "2   rs2046210  151948366          6  2011.0        Wonshik Han   \n",
+       "3   rs2046210  151948366          6  2009.0          Wei Zheng   \n",
+       "4   rs2046210  151948366          6  2010.0     Simon N Stacey   \n",
+       "\n",
+       "                                               Title  \\\n",
+       "0  Common breast cancer susceptibility loci are a...   \n",
+       "1  Replication and functional genomic analyses of...   \n",
+       "2  Common genetic variants associated with breast...   \n",
+       "3  Genome-wide association study identifies a new...   \n",
+       "4  Ancestry-Shift Refinement Mapping of the C6orf...   \n",
+       "\n",
+       "                             DOI Open Access status  \\\n",
+       "0  10.1158/0008-5472.CAN-11-1266               True   \n",
+       "1  10.1158/0008-5472.CAN-10-2733               True   \n",
+       "2  10.1158/1055-9965.EPI-10-1282              False   \n",
+       "3                            NaN               True   \n",
+       "4   10.1371/journal.pgen.1001029               True   \n",
+       "\n",
+       "                                                Link  \n",
+       "0  http://www.mendeley.com/research/common-breast...  \n",
+       "1  http://www.mendeley.com/research/replication-f...  \n",
+       "2  http://www.mendeley.com/research/common-geneti...  \n",
+       "3  http://www.mendeley.com/research/genomewide-as...  \n",
+       "4  http://www.mendeley.com/research/ancestryshift...  "
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Many reference lists available online\n",
+    "\n",
+    "mendeley = pd.read_table(\"./annotation/mendeley.csv\", delimiter = \",\")\n",
+    "mendeley.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "\"\"\"Below, two packages I wrote (MotifMatcher and MotifFinder) are used.\n",
+    "MotifMatcher is to find in a list of DNA sequences the strings that matches a given motif.\n",
+    "MotifFinder is to find in a list of DNA sequences the unkown motif.\n",
+    "These packages are easy to write if you go over the Coursera class Biology Meets Progamming.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#Look for common DNA/protein sequences\n",
+    "#The code is for DNA, but for translated protein sequences, the idea is the same.\n",
+    "#This used a list of 10 generic chopped TCR sequences. The goal is to find the shared motif.\n",
+    "\n",
+    "import MotifMatcher as mm\n",
+    "import MotifFinder as mf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['AATAGTTTAGATATAAAAGATTTCTCCCGATTCGCTCCGTTCCCCGTTTACCCGATCCCCGTGTTTCGCTTTTAGCCGAGCCGCAGGCCCAAATCGATTCCGTACCAGCTAGCAGAGGGATAGACTGT', 'AAGATGATAGGGATCGCTCCGATAGCTCGCTAAGAGCTTTCGGCTTTCGCCCTAGGGCTAGTTACTAGCTGCTAGGCTCGATTCGATCGATCGGATATAATAGATTTCTCCCAGAGTTCCGCTCGCTC', 'AAGATATAAAAGATTTCTCTTCTGGGCTGGCTGGCTTTAGGACGCGCCCAGGCGCGCGGGAACGGCAACACAGAGATCGGACCGCGACAGAAAGCTGAGGACGGCGAGAGAGCTGTAGGGAACGGATT', 'TTCTCTATCTCCCTACCTCTTTTCTTTCTCTCTACCACGATATAAAAGATTTGACCCGAGTCGCTCGCCTCCGCTAACTAGTCTACCTAGCTAGCTATGCTAGCTCGAGGATCGATCGATCGGGCTAT', 'TTGCTGGCTGCTACAGCTAGCACCCGGAGGATCGGGATCAAACTGATCGGATATAATCGACTTCTCCCGTTTCTCTTAAATCGTTGGCTTGACCCATAGCCCTAACCCTCGCTAGCTGGCTGGCTCAA', 'TTTCGTTGTCCCCGGCTAAACCATAACGCGCGCAAACAACCCAGGGATATAAAAGATTTCTCCCGGGCCTCGTTTTAAACAACAAAGAAAATGCCCCAAGATGCTAGCTTTAGCTAGCTTAGATCGAA', 'TTCCGCTCGTTGATATCATATACCAAATCATCATTCATCTTTTACAAACATGGGACGGAGGCAGGGGCCCAGTTTCATGATATAAACCATTTCTCCCGGGCTGAACTCGGGACCAGGCAGCGGGACTT', 'CCTCCCGATAGCTCGGCTCGCTTGCTCGTGATATAAAAGATTTCTCCCGTTCTCTCTTCTCCTCTCTCAATACTCTAAATTAACTTAAACTGGCGGGGATGATAGGGGCTGATCGGGCTGGTTTTGCT', 'GATATAAAAGATTTCTCCCGTGGCTCGCTACGATCGATTCGTACTCTAGCTTTAGCTCTATGCTCTCATGCTATACGTCTTCCAATGCTATCGTATCGGTACTAATACAAGCTTACGTCGGCTACTCC', 'TCCATCCCTACAACCCACCAGGGCCGGGATAGCTGGCTTGGGCGGGATCGGGCTTCTTTTCGGGATCTCTCTTCGGATTTCGCTCGGATTGGGCTTTTAGGCTTTCGGGATATAAAAGATTTCTCCCG']\n",
+      "number of elements in tcr: 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Use an example of generic TCR sequences\n",
+    "\n",
+    "tcr = open(\"generic_tcr.txt\").read()\n",
+    "tcr = [x for x in tcr.split('\\n')]\n",
+    "print(tcr)\n",
+    "print(\"number of elements in tcr:\",len(tcr))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
+      "[2, 5, 0, 2, 1, 4, 5, 2, 1, 2]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#If the shared motif is known:\n",
+    "#Find the number of occurrences of a known motif\n",
+    "\n",
+    "my_pat = \"AATAGTTTAGATATAAAAGA\"\n",
+    "\n",
+    "#Find the number of exact matches in each TCR sequence.\n",
+    "exact_match = []\n",
+    "for i in tcr:\n",
+    "    exact_match.append(mm.ApproximatePatternCount(my_pat, i, 0))    \n",
+    "print(exact_match)\n",
+    "\n",
+    "#Find in each TCR sequence the number of strings with a maximum of 10 mismatches.\n",
+    "ten_mismatches = []\n",
+    "for i in tcr:\n",
+    "    ten_mismatches.append(mm.ApproximatePatternCount(my_pat, i, 10))    \n",
+    "print(ten_mismatches)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['AAAAGATTTCTCCCGATTCG', 'AATAGATTTCTCCCAGAGTT', 'AAAAGATTTCTCTTCTGGGC', 'AAAAGATTTGACCCGAGTCG', 'AATCGACTTCTCCCGTTTCT', 'AAAAGATTTCTCCCGGGCCT', 'AAACCATTTCTCCCGGGCTG', 'AAAAGATTTCTCCCGTTCTC', 'AAAAGATTTCTCCCGTGGCT', 'TTCGGATTTCGCTCGGATTG']\n",
+      "the consensus is: AAAAGATTTCTCCCGGGTCG\n",
+      "the number of differences is: 46\n"
+     ]
+    }
+   ],
+   "source": [
+    "#If the shared motif is unknown:\n",
+    "#Find the shared motif first\n",
+    "#This is an extremely time-consuming process. The most common approach is to use randomization.\n",
+    "\n",
+    "#The idea of randomization is, if a motif is shared, \n",
+    "#then it will occur more times in a set of sequences, \n",
+    "#thus picked up more likely by randomization.\n",
+    "\n",
+    "#And then check the goodness of the fit\n",
+    "\n",
+    "#This finds a set of shared motifs by randomization.\n",
+    "best_motifs = GibbsSampler(tcr, 20, 10, 100)\n",
+    "print(best_motifs)\n",
+    "\n",
+    "#This finds the number of differences between the approximate shared motif and the consensus,\n",
+    "#assembled from the shared motif.\n",
+    "print(\"the consensus is:\", Consensus(best_motifs))\n",
+    "print(\"the number of differences is:\", Score(best_motifs))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0:00:02.142653\n",
+      "['GATATAAAAGATTTCTCCCG', 'GATATAATAGATTTCTCCCA', 'GATATAAAAGATTTCTCTTC', 'GATATAAAAGATTTGACCCG', 'GATATAATCGACTTCTCCCG', 'GATATAAAAGATTTCTCCCG', 'GATATAAACCATTTCTCCCG', 'GATATAAAAGATTTCTCCCG', 'GATATAAAAGATTTCTCCCG', 'GATATAAAAGATTTCTCCCG']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 218,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Use the time module to get the runtime of your code\n",
+    "import time\n",
+    "from datetime import timedelta\n",
+    "start_time = time.monotonic()\n",
+    "\n",
+    "#By repeatedly run the randomization, better and better best_motifs is found, with lower scores.\n",
+    "for i in range(30):\n",
+    "    better_motifs = GibbsSampler(tcr, 20, 10, 100)\n",
+    "    if Score(better_motifs) < Score(best_motifs):\n",
+    "        best_motifs = better_motifs\n",
+    "        \n",
+    "end_time = time.monotonic()\n",
+    "print(timedelta(seconds=end_time - start_time))\n",
+    "\n",
+    "print(best_motifs)\n",
+    "\n",
+    "print(\"the consensus is:\", Consensus(best_motifs))\n",
+    "print(\"the number of differences is:\", Score(best_motifs))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/lessons/python/large-data/generic_gdna.txt b/lessons/python/large-data/generic_gdna.txt
new file mode 100644
index 000000000..de6d0f4fa
--- /dev/null
+++ b/lessons/python/large-data/generic_gdna.txt
@@ -0,0 +1,9 @@
+rsid	chromosome	position	genotype
+rs4988235	2	136608646	GG
+rs53576	3	8804371	GG
+rs12248560	10	96521657	CC
+rs28399504	10	96522463	AA
+rs41291556	10	96535173	TT
+rs4986893	10	96540410	GG
+rs4244285	10	96541616	AG
+rs1815739	11	66328095	CT
diff --git a/lessons/python/large-data/generic_tcr.txt b/lessons/python/large-data/generic_tcr.txt
new file mode 100644
index 000000000..dedc2215f
--- /dev/null
+++ b/lessons/python/large-data/generic_tcr.txt
@@ -0,0 +1,10 @@
+AATAGTTTAGATATAAAAGATTTCTCCCGATTCGCTCCGTTCCCCGTTTACCCGATCCCCGTGTTTCGCTTTTAGCCGAGCCGCAGGCCCAAATCGATTCCGTACCAGCTAGCAGAGGGATAGACTGT
+AAGATGATAGGGATCGCTCCGATAGCTCGCTAAGAGCTTTCGGCTTTCGCCCTAGGGCTAGTTACTAGCTGCTAGGCTCGATTCGATCGATCGGATATAATAGATTTCTCCCAGAGTTCCGCTCGCTC
+AAGATATAAAAGATTTCTCTTCTGGGCTGGCTGGCTTTAGGACGCGCCCAGGCGCGCGGGAACGGCAACACAGAGATCGGACCGCGACAGAAAGCTGAGGACGGCGAGAGAGCTGTAGGGAACGGATT
+TTCTCTATCTCCCTACCTCTTTTCTTTCTCTCTACCACGATATAAAAGATTTGACCCGAGTCGCTCGCCTCCGCTAACTAGTCTACCTAGCTAGCTATGCTAGCTCGAGGATCGATCGATCGGGCTAT
+TTGCTGGCTGCTACAGCTAGCACCCGGAGGATCGGGATCAAACTGATCGGATATAATCGACTTCTCCCGTTTCTCTTAAATCGTTGGCTTGACCCATAGCCCTAACCCTCGCTAGCTGGCTGGCTCAA
+TTTCGTTGTCCCCGGCTAAACCATAACGCGCGCAAACAACCCAGGGATATAAAAGATTTCTCCCGGGCCTCGTTTTAAACAACAAAGAAAATGCCCCAAGATGCTAGCTTTAGCTAGCTTAGATCGAA
+TTCCGCTCGTTGATATCATATACCAAATCATCATTCATCTTTTACAAACATGGGACGGAGGCAGGGGCCCAGTTTCATGATATAAACCATTTCTCCCGGGCTGAACTCGGGACCAGGCAGCGGGACTT
+CCTCCCGATAGCTCGGCTCGCTTGCTCGTGATATAAAAGATTTCTCCCGTTCTCTCTTCTCCTCTCTCAATACTCTAAATTAACTTAAACTGGCGGGGATGATAGGGGCTGATCGGGCTGGTTTTGCT
+GATATAAAAGATTTCTCCCGTGGCTCGCTACGATCGATTCGTACTCTAGCTTTAGCTCTATGCTCTCATGCTATACGTCTTCCAATGCTATCGTATCGGTACTAATACAAGCTTACGTCGGCTACTCC
+TCCATCCCTACAACCCACCAGGGCCGGGATAGCTGGCTTGGGCGGGATCGGGCTTCTTTTCGGGATCTCTCTTCGGATTTCGCTCGGATTGGGCTTTTAGGCTTTCGGGATATAAAAGATTTCTCCCG
\ No newline at end of file
diff --git a/lessons/python/large-data/lesson.md b/lessons/python/large-data/lesson.md
new file mode 100644
index 000000000..cb1d3bc8d
--- /dev/null
+++ b/lessons/python/large-data/lesson.md
@@ -0,0 +1,22 @@
+---
+layout: page
+title: Large Datasets in Python
+visible: true
+tags:
+  - python
+  - intermediate
+---
+
+ - **Authors**: Wei Zhan
+ - **Research field**: Immunology
+ - **Lesson topic**: Processing gDNA chip results and single cell PCR results; finding shared motifs.
+ - **Lesson content URL**: <https://github.com/uoftcoders/studyGroup/tree/gh-pages/lessons/python/large-data>
+
+This lesson contains two parts: 1st, accessing and storing data from personal genomic DNA sequencing results using the pandas.DataFrame structure; 2nd, finding shared motifs in single-cell PCR sequencing results.
+
+To follow along, visit [the IPython notebook](https://github.com/uoftcoders/studyGroup/blob/gh-pages/lessons/python/large-data/LargeDatasetsPython.ipynb).
+
+The [generic_gdna.txt](https://github.com/uoftcoders/studyGroup/blob/gh-pages/lessons/python/large-data/generic_gdna.txt) file contains a sample personal gDNA chip sequencing output.
+
+The [generic_tcr.txt](https://github.com/uoftcoders/studyGroup/blob/gh-pages/lessons/python/large-data/generic_tcr.txt) file is a tab-separated plain text file containing 10 sample single-cell PCR sequencing results of the [T cell receptor](https://en.wikipedia.org/wiki/T_cell_receptor).
+
diff --git a/lessons/python/large-data/plavix.txt b/lessons/python/large-data/plavix.txt
new file mode 100644
index 000000000..8d7c3f7ae
--- /dev/null
+++ b/lessons/python/large-data/plavix.txt
@@ -0,0 +1,6 @@
+rsid	identifier	consensus	mutation	pmid
+rs4244285	*2	GG	A	23698643
+rs4986893	*3	GG	A	20979470
+rs28399504	*4	AA	G	20978260
+rs41291556	*8	TT	C	19106083
+rs12248560	*17	CC	T	21716271
\ No newline at end of file
diff --git a/lessons/python/matplotlib/.gitignore b/lessons/python/matplotlib/.gitignore
new file mode 100644
index 000000000..0462707c2
--- /dev/null
+++ b/lessons/python/matplotlib/.gitignore
@@ -0,0 +1,3 @@
+*.mp4
+*.pdf
+*.png
diff --git a/lessons/python/matplotlib/EFN_data.csv b/lessons/python/matplotlib/EFN_data.csv
new file mode 100644
index 000000000..14574f79a
--- /dev/null
+++ b/lessons/python/matplotlib/EFN_data.csv
@@ -0,0 +1,49 @@
+FN,EFN,Fnsugar_per
+No,Yes,2.4009372109
+No,Yes,1.1760569971
+No,Yes,1.6349679609
+No,Yes,1.5510639375
+No,Yes,1.3863539349
+No,Yes,2.6295389648
+No,Yes,1.7608207422
+No,Yes,0
+No,Yes,1.423261627
+No,Yes,2.0883988945
+No,Yes,1.2206025
+No,Yes,0
+Yes,No,1.6280233594
+Yes,No,2.6654755859
+Yes,No,2.4999435677
+Yes,No,1.9207066667
+Yes,No,2.786953125
+Yes,No,2.3198751406
+Yes,No,2.8991691094
+Yes,No,2.5467635273
+Yes,No,0
+Yes,No,3.0253634375
+Yes,No,2.7671667613
+Yes,No,1.7103152813
+Yes,Yes,0
+Yes,Yes,1.423681875
+Yes,Yes,1.4483157188
+Yes,Yes,1.7738578613
+Yes,Yes,1.505169334
+Yes,Yes,2.0278107959
+Yes,Yes,0.7600129141
+Yes,Yes,1.5492884063
+Yes,Yes,1.3983174688
+Yes,Yes,0
+Yes,Yes,2.6685760254
+Yes,Yes,1.3702155762
+No,No,2.3321382127
+No,No,2.3369958413
+No,No,1.9796215938
+No,No,1.7379873291
+No,No,2.3337680469
+No,No,1.5806472773
+No,No,4.5766757813
+No,No,2.1993497936
+No,No,2.3170938516
+No,No,2.8342289063
+No,No,3.0738726217
+No,No,1.2445626445
diff --git a/lessons/python/matplotlib/Seed_Set_ulmifolia.xlsx b/lessons/python/matplotlib/Seed_Set_ulmifolia.xlsx
new file mode 100644
index 000000000..feee5010c
Binary files /dev/null and b/lessons/python/matplotlib/Seed_Set_ulmifolia.xlsx differ
diff --git a/lessons/python/matplotlib/data.csv b/lessons/python/matplotlib/data.csv
new file mode 100644
index 000000000..720808d53
--- /dev/null
+++ b/lessons/python/matplotlib/data.csv
@@ -0,0 +1,48 @@
+2.4009372109,0.4440576159
+1.1760569971,0.2774456412
+1.6349679609,0.5985708184
+1.5510639375,0.3368581276
+1.3863539349,0.3496934638
+2.6295389648,0.3631791867
+1.7608207422,0.3479999219
+0,0.2235631966
+1.423261627,0.138389525
+2.0883988945,0.279712934
+1.2206025,0.1641733747
+0,0.1579980859
+1.6280233594,0.2604635417
+2.6654755859,0.4146782292
+2.4999435677,0.4351966743
+1.9207066667,0.3695751632
+2.786953125,0.0839955516
+2.3198751406,0.4861724953
+2.8991691094,0.4802843862
+2.5467635273,0.275311099
+0,0.5092385221
+3.0253634375,0.310192328
+2.7671667613,0.3308497005
+1.7103152813,0.1650106415
+0,0.2405833648
+1.423681875,0.2705718268
+1.4483157188,0.2826364785
+1.7738578613,0.3981284149
+1.505169334,0.1116874993
+2.0278107959,0.4170833735
+0.7600129141,0.2166015104
+1.5492884063,0.2677585375
+1.3983174688,0.0789038438
+0,0.392558569
+2.6685760254,0.2862090638
+1.3702155762,0.1340227452
+2.3321382127,0.4839778789
+2.3369958413,0.4979863854
+1.9796215938,0.7422585935
+1.7379873291,0.3345406458
+2.3337680469,0.6811817008
+1.5806472773,0.5764762349
+4.5766757813,0.7158074427
+2.1993497936,0.5273031491
+2.3170938516,0.2724633449
+2.8342289063,0.280966923
+3.0738726217,0.2926968993
+1.2445626445,0.4861359799
diff --git a/lessons/python/matplotlib/example.py b/lessons/python/matplotlib/example.py
new file mode 100644
index 000000000..ca3164f86
--- /dev/null
+++ b/lessons/python/matplotlib/example.py
@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+# moviewriter.py
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as an
+
+# Create an FFMPEG writer.
+ffwriter=an.writers['ffmpeg']
+
+# Metadata is good practice.
+metadata=dict(title='Example',artist='Ricardo Harripaul')
+
+# Specify some features.
+writer = ffwriter(fps = 15, metadata = metadata)
+fig = plt.figure()
+
+l,=plt.plot([],[],'ko')
+
+plt.xlim(-3,3); plt.ylim(-3,3)
+
+x0=np.zeros(10.0)
+y0=np.zeros(10.0)
+
+with writer.saving(fig,'example.mp4', 200):
+
+    for i in range(200):
+        print (x0)
+        print (y0)
+        x0 += 0.1 * np.random.randn(10)
+        y0 += 0.1 * np.random.randn(10)
+    
+    #Specify new x and y data for position and velocity
+        l.set_data(x0,y0)
+        writer.grab_frame()  
diff --git a/lessons/python/matplotlib/hwk2.1.py b/lessons/python/matplotlib/hwk2.1.py
new file mode 100644
index 000000000..d11e96b81
--- /dev/null
+++ b/lessons/python/matplotlib/hwk2.1.py
@@ -0,0 +1,23 @@
+#Load in necessary packages
+from scipy.stats import norm
+
+import matplotlib.pyplot as plt
+
+#construct normal distribution
+a=norm.rvs(size=1000000,loc=-2, scale=1.5)
+h=plt.hist(a,bins=100,normed=True)
+
+#construct uniform distribution
+from scipy.stats import uniform
+b=uniform.rvs(size=1000000,loc=-1,scale=2)
+i=plt.hist(b,bins=100,normed=True)
+
+#Calculate x=a*b and then plot histogram.  Save figure
+x=a*b
+
+#Plotting and saving
+h=plt.hist(x,bins=100,normed=True)
+
+plt.savefig('hist1.pdf')
+
+#I eye balled the plots and they are centred at zero.
diff --git a/lessons/python/matplotlib/hwk2.2.py b/lessons/python/matplotlib/hwk2.2.py
new file mode 100644
index 000000000..03e553d11
--- /dev/null
+++ b/lessons/python/matplotlib/hwk2.2.py
@@ -0,0 +1,27 @@
+#import necessary modules for poly1d and arange from numpy
+from numpy import poly1d,arange
+import matplotlib.pyplot as plt
+
+#Generate an empty array for storing the roots
+roots=[]
+
+#Nested loops iterates through all the range of s and zeta passing the values
+#to the poly1d and solving the roots.  The roots are then stored in the roots array
+for s in arange(0.0,0.65,0.01):
+    for z in arange(0.0,0.62,0.01):
+        
+        p=poly1d([1,0,z-2,z,0],variable='s')
+        roots.append(p.r)
+ 
+#Generate empty array for storing non zero values of the roots   
+nonzeros=[]
+#This loop extracts out the non zero values of the roots and appends it to a new array
+for i in range(len(roots)-1):
+    nonzeros.append(roots[i][0:3])
+
+#Plot zeros
+plt.plot(nonzeros)
+
+#save figure
+
+plt.savefig("roots.pdf")
diff --git a/lessons/python/matplotlib/hwk3.1.py b/lessons/python/matplotlib/hwk3.1.py
new file mode 100644
index 000000000..07a363e69
--- /dev/null
+++ b/lessons/python/matplotlib/hwk3.1.py
@@ -0,0 +1,66 @@
+# -*- coding: utf-8 -*-
+from numpy import float32
+from numpy import linspace
+from numpy import polyfit
+from numpy import polyval
+import matplotlib.pyplot as plt
+
+#Read in data from csv
+f=open('data.csv','r')
+line=f.readlines()
+
+#Empty array for data
+FN=[]
+EFN=[]
+#This loop goes through every line, strips new line character and then splits the data on ,.  It will then save data into the arrays
+for l in line:
+    a=l.strip()
+    x,y=a.split(",")
+    FN.append(float32(x))
+    EFN.append(float32(y))
+f.close()
+
+#Generate linear space but this was not used as of yet  
+z=linspace(-1,4)
+
+#Create grid and plot data
+fig = plt.figure(figsize = (4,4), dpi = 600)
+a = fig.add_subplot(1,1,1)
+
+plt.plot(FN,EFN,'ks',markersize=3)
+
+#Created a fitted line for the data
+fit=polyfit(FN,EFN,1)
+plt.plot(z,polyval(fit,z),label=fit,color='k')
+
+#Reset font size
+for t in a.yaxis.get_major_ticks():
+    t.label.set_fontsize(6)
+for t in a.xaxis.get_major_ticks():
+    t.label.set_fontsize(6)
+
+#Set the subplot sizing
+fig.subplots_adjust(top=0.95, right =0.89, left=0.13,bottom=0.25)
+#Set limits and labels
+plt.xlim(-0.2,3.5)
+plt.ylim(0,0.8)
+plt.ylabel(r'Extrafloral Nectar (mg of sugar per extrafloral nectary)',fontsize=6,verticalalignment='center')
+plt.xlabel(r'Floral Nectar (mg of sugar per flower)',fontsize=6,horizontalalignment='center')
+
+#Save as pdf
+fig.savefig('EFNvFN.pdf',dpi=600)
+
+plt.show()
+
+"""In ecology, animals and plants interact with one another in an ecosystem.  
+There are several types of interactions that may occur such as predation, 
+parasitisim and mutualism. Mutualism is where the animals and plants both give 
+one another a survival benefit.  So if a trait is not useful why invest energy 
+into producing it?
+ 
+Different interactions have generally been studied individually even though
+they occur in a community. This plot shows the relationship between EFN and FN 
+production in T. ulmifolia. There is a positive correlation, which suggests that 
+plants that produce more of one also produce more of the other
+This is probably because of overall plant vigour. This was an initial figure
+for a later experiment showing interactions."""
diff --git a/lessons/python/matplotlib/hwk3.2.py b/lessons/python/matplotlib/hwk3.2.py
new file mode 100644
index 000000000..31b284847
--- /dev/null
+++ b/lessons/python/matplotlib/hwk3.2.py
@@ -0,0 +1,44 @@
+#Import necessary modules for movie animation
+from scipy.io.netcdf import *
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as an
+
+
+#Open NETCF file and read in data as file f
+f=netcdf_file('trajectory.nc','r')
+position=f.variables['position'][:]
+time=f.variables['timepoints'][:]
+
+
+
+#Create an FFMPEG writer
+ffwriter=an.writers['ffmpeg']
+
+#Creating Metadata
+metadata=dict(title='Trajectory Animation',artist='Ricardo Harripaul')
+
+#Specify the writer paramters
+
+writer=ffwriter(fps=15, metadata=metadata)
+
+fig=plt.figure()
+
+#Open an empty plot to start plotting
+
+l,=plt.plot([],[],'ko')
+
+plt.xlim(-20,20); plt.ylim(-20,20)
+
+x0=np.zeros(0)
+y0=np.zeros(0)
+
+with writer.saving(fig,'trajectory.mp4', 200):
+    for i in range(202):
+        x0=time[i]
+        y0=position[i]
+    
+    #Specify new x and y data for position and velocity
+        l.set_data(x0,y0)
+        writer.grab_frame()   
+f.close()
diff --git a/lessons/python/matplotlib/lesson.md b/lessons/python/matplotlib/lesson.md
new file mode 100644
index 000000000..61718e4bb
--- /dev/null
+++ b/lessons/python/matplotlib/lesson.md
@@ -0,0 +1,22 @@
+---
+layout: page
+title: 'Plotting in Python'
+visible: true
+tags:
+  - python
+  - beginner
+---
+
+ - **Authors**: Ricardo Harripaul
+ - **Research field**: 
+ - **Lesson topic**: Plotting with Matplotlib
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/matplotlib>
+
+Matplotlib in Python
+====================
+
+This lesson will introduce users to basic plotting in python as well as basic graphs with research quality figures.  There is also a script that allows users to quickly modify a figure for different publications.  For more advanced users, there is a script that creates a particle trajectory movie from matplotlib and ffmpeg.
+
+* hwk2-1.py is basic plotting
+* hwk2-2.py and hwk3.1.py are for reproducible figures using root finding in python
+* example.py and hwk3.2.py are for making movies
diff --git a/lessons/python/matplotlib/plot.ipynb b/lessons/python/matplotlib/plot.ipynb
new file mode 100644
index 000000000..6da0f4a70
--- /dev/null
+++ b/lessons/python/matplotlib/plot.ipynb
@@ -0,0 +1,297 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "a=[1,2,3,4,5]\n",
+    "b=[1,3,2.5,3,5]\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(a,b,color='g',label='income')\n",
+    "plt.ylabel(\"Accounts\")\n",
+    "plt.xlabel(\"Money\")\n",
+    "plt.legend()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(1)\n",
+    "plt.subplot(211)\n",
+    "plt.plot(a,b,'bo',a,b,'g')\n",
+    "plt.title(\"image 1\")\n",
+    "plt.subplot(212)\n",
+    "plt.plot([5,10,15])\n",
+    "\n",
+    "plt.figure(2)\n",
+    "#subplot rows,columns,plot number\n",
+    "plt.subplot(121)\n",
+    "plt.plot([1,2,4,5])\n",
+    "plt.subplot(122)\n",
+    "plt.plot(b,a,'ro',a,b,'k')\n",
+    "plt.title('image 2')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAGxlJREFUeJzt3X+QXGWd7/H3x8kYB2GJkUQnA3FgN5UyCiYwlWVvqBS/NAgURBf3kircILHi3cKV3PWylfgHrNTdAmURsdzCjcIVEQNcDDEbkMgSU2pqRYckECDmit5oZpJLRmECyIDJ8L1/nDM66fTMdJvu0/3MfF5VXdPn9NOnv0lPf/rMc55zHkUEZmaWjjc1ugAzM6uOg9vMLDEObjOzxDi4zcwS4+A2M0uMg9vMLDEObjOzxDi4zcwS4+A2M0vMpHps9IQTTojOzs56bNqq8MQTT/wmIqbVant+X5uD39fxqZr3tS7B3dnZSXd3dz02bVWQ9Ktabs/va3Pw+zo+VfO+uqvEzCwxDm4zs8Q4uM3MEuPgNjNLjIPbzCwxFQW3pP8u6RlJT0taI+kt9S7MzI6UD9ubI2m7pCOGgijzJUnPSXpK0umFF2l1N+ZwQEkdwKeAORExIOl+4HLg63WuzczK+z8RMXeExz4IzMpvfwncnv+0JrBuWy83b9zF3v4BZkxp49pFs1k8r6Pq7VQ6jnsS0CbpIHAMsLfqVzKzIlwKfCOyOQl/LGmKpPaI2Nfowia6ddt6WbV2BwMHBwHo7R9g1dodAFWH95jBHRG9kv4F+DUwAHwvIr5XbdHWGHm31g+AyWTv9wMRcb2kk4F7ganAVuCjEfH70ba1o/cAnSsfqnfJFdl900WNLqEhJAHMkvQE8G8RsbqkSQewZ9hyT77Owd1gN2/c9YfQHjJwcJCbN+6qOrjH7OOW9Dayb/GTgRnAWyVdUabdckndkrr7+vqqKsL+NJ0rHzrsNoLXgXMj4n3AXOACSWcCnwNujYhZwIvAskKKtqOyZcsWgJ1kXSJXS1pY0kRlnnbEjOD+vBZvb/9AVetHU8nByfOB/xsRfRFxEFgL/JfSRhGxOiK6IqJr2rSaXUbBjlJkXskXW/NbAOcCD+Tr7wIWN6A8q9KMGTMAiIj9wIPA/JImPcBJw5ZPpEzXpj+vxZsxpa2q9aOpJLh/DZwp6Rhlf6edR/aNb4mQ1CJpO7AfeBT4BdAfEYfyJkN/TlsT+93vfsfLL78MgKS3Ah8Ani5pth7423x0yZnAAfdvN4drF82mrbXlsHVtrS1cu2h21duqpI/7cUkPkPWDHgK2AaX9atbEImIQmCtpCtle2rvLNSv3XEnLgeUALX/mPbNGev755/nQhz4EMAf4CfCtiHhE0n8DiIivAA8DFwLPAa8CH2tQuVZiqB+7sFElEXE9cH3VW7emEhH9kjYDZwJTJE3K97rL/jmdP2c1+Rf15PZZZcPdinHKKafw5JNPIunZiOgaWp8H9tD9AK5uSIE2psXzOv6koC7lMyfHOUnT8j1tJLWRHbPYCXwfuCxvthT4TmMqNLNq1eV63NZU2oG7JLWQfVHfHxEbJD0L3Cvpf5J1f93RyCLNrHIO7nEuIp4C5pVZ/0uOHJEwqlM7jqd7go6fNmsm7ioxM0uMg9vMLDEObjOzxDi4zcwS4+A2M0uMg9vMLDEObjOzxDi4zcwS4+A2M0uMg9vMLDEObjOzxDi4zcwS4+A2M0uMg9ssIYODgwBzJG0ofUzSlZL6JG3Pbx8vvkIrwpiXdZU0G7hv2KpTgOsi4ot1q8rMyrrtttsARpsW/L6I+GRB5ViDVDLn5C5gLmSTzgK9ZPMWmlmBenp6eOihhwB+0+harLGq7So5D/hFRPyqHsWY2chWrFjB5z//+bGa/bWkpyQ9IOmkkRpJWi6pW1J3X19fbQu1uqs2uC8H1tSjEDMb2YYNG5g+fTpnnHHGaM3+HeiMiNOA/wDuGqlhRKyOiK6I6Jo2bVqNq7V6qzi4Jb0ZuAT43yM87m9wszrZsmUL69evp7OzE7LjTOdK+ubwNhHx24h4PV/8KjBqylu6qtnj/iCwNSKeL/egv8HN6ufGG2+kp6eH3bt3A/wS2BQRVwxvI6l92OIlwM7iKrQiVTNZ8BLcTWLWVCTdAHRHxHrgU5IuAQ4BLwBXNrI2q5+KglvSMcD7gU/Utxwzq8DLEXExQERcN7QyIlYBqxpWlRWmouCOiFeBt9e5FjMzq4DPnDQzS4yD28wsMQ5uM7PEOLjNzBLj4DYzS4yD28wsMQ5uM7PEOLjNzBLj4DYzS4yD28wsMQ5uM7PEOLjNzBLj4DYzS4yD28wsMQ5uswRJ2iZpQ5n1kyXdJ+k5SY9L6iy+Oqs3B7dZet7ByNOSLQNejIi/AG4FPldYVVYYB/c4J+kkSd+XtFPSM5Kuydf/k6ReSdvz24WNrtXG1tPTA3A88LURmlzKH2d3fwA4T5IKKM0KVOnUZVPIflHeCwRwVUT8Zz0Ls5o5BHw6IrZKOg54QtKj+WO3RsS/NLA2q9KKFSsAeoA3RmjSAewBiIhDkg6QzV71m0IKtEJUOlnwbcAjEXGZpDcDx9SxJquhiNgH7MvvvyxpJ9mH2xKzYcMGpk+fDvDqKM3K7V3HEY2k5cBygJkzZ9akPivOmF0lkv4MWAjcARARv4+I/noXZrWXH6iaBzyer/qkpKck3SnpbQ0rzCqyZcsW1q9fD3AqcC9wrqRvljTrAU4CkDSJrFvlhdJtRcTqiOiKiK5p06bVt3CruUr6uE8B+oD/lR/J/pqkt5Y2krRcUrek7r6+vpoXakdH0rHAt4EVEfEScDvw58Bcsj3yW0Z4nt/XJnHjjTcO9XHvAC4HNkXEFSXN1gNL8/uX5W2O2OO2tFUS3JOA04HbI2Ie8DtgZWkjf4M3L0mtZKF9T0SsBYiI5yNiMCLeAL4KzC/3XL+vzU/SDZIuyRfvAN4u6TngHyjzWbX0VdLH3QP0RMTQn9cP4F+GZOQjCu4AdkbEF4atb8/7vwE+BDzdiPrsTxMRm4HN+f3rhq1/DfhIY6qyoowZ3BHx/yTtkTQ7InYB5wHP1r80q5EFwEeBHZK25+s+AyyRNJfswNVu4BONKc/MqlXpqJK/B+7JR5T8EvhY/UqyWoqIH1F+pMHDRddiZrVRUXBHxHagq861mJlZBXzmpJlZYhzcZmaJcXCbmSXGwW1mlhgHt5lZYhzcZmaJcXCbmSXGwW1mlhgHt5lZYhzcZmaJcXCbmSXGwW1mlhgHt5lZYhzcZol47bXXmD9/PsAcSc9I+mxpG0lXSuqTtD2/fbz4Sq3eHNxmiZg8eTKbNm2CbCKTucAFks4s0/S+iJib375WaJFWCAe3WSIkceyxxw4ttuY3TwQ8ATm4zRIyODgIMAfYDzw6bC7Y4f5a0lOSHpB0UqEFWiEqCm5JuyXtyPvMuutdlJmV19LSAllXyYnAfEnvLWny70BnRJwG/AdwV7ntSFouqVtSd19fXz1LtjqoZo/7nLzPzFOYmTVYRPSTzfJ+Qcn630bE6/niV4EzRnj+6ojoioiuadOm1bVWqz13lZgloq+vj/7+fgAktQHnAz8b3kZS+7DFS4CdhRVohal0lvcAvicpgH+LiNWlDSQtB5YDzJw5s3YVFqxz5UOHLe++6aIGVWJ2uH379rF06VLI+rh/CtwfERsk3QB0R8R64FOSLgEOAS8AVzaqXqufSoN7QUTslTQdeFTSzyLiB8Mb5GG+GqCrq8tHus1q7LTTTmPbtm1IenZ4l2VEXDfs/ipgVUMKtMJU1FUSEXvzn/uBB4H59SzKzMxGNmZwS3qrpOOG7gMfAJ6ud2FmZlZeJV0l7wAelDTU/lsR8UhdqzIzsxGNGdwR8UvgfQXUYmZmFfBwQDOzxDi4zcwS4+A2M0uMg9vMLDEObjOzxDi4zcwS4+A2M0uMg9vMLDEObjOzxDi4zcwS4+A2M0tMpdfjtkTlk8V+A3gn8AawOiJukzQVuA/oBHYDfxMRL462rR29B46YaGI8atbJM1577TUWLlwIMEfSM8ADEXH98DaSJpO932cAvwX+a0TsLrpWqy/vcY9/h4BPR8S7gTOBqyXNAVYCj0XELOCxfNma2OTJk9m0aRNkkwXPBS6QdGZJs2XAixHxF8CtwOeKrbK8ddt6WXDTJk5e+RALbtrEum29jS5pVM1er4N7nIuIfRGxNb//MtkchB3ApfxxBvC7gMWNqdAqJYljjz12aLE1v5XONjX8fX0AOE/5NZkbZd22Xlat3UFv/wAB9PYPsGrtjqYLwyEp1OvgnkAkdQLzgMeBd0TEPsjCHZjeuMqsUoODg5DNObkfeDQiHi9p0gHsAYiIQ8AB4O1F1ljq5o27GDg4eNi6gYOD3LxxV4MqGl0K9Tq4JwhJxwLfBlZExEtVPG+5pG5J3YOvHqhfgVaRlpYWyLpKTgTmS3pvSZNye9dHzAE7/H3t6+urfaHD7O0fqGp9o6VQr4N7ApDUShba90TE2nz185La88fbyfbgjhARqyOiKyK6Wo45vpiCbUwR0Q9sBi4oeagHOAlA0iTgeLLZ3kuf/4f3ddq0aXWtdcaUtqrWN1oK9VYc3JJaJG2TtKGeBVlt5f2bdwA7I+ILwx5aDyzN7y8FvlN0bVadvr4++vv7AZDUBpwP/Kyk2fD39TJgU0QcscddpGsXzaatteWwdW2tLVy7aHaDKhpdCvVWs8d9DdmBLUvLAuCjwLmStue3C4GbgPdL+jnw/nzZmti+ffs455xzIOvj/ilZH/cGSTdIuiRvdgfwdknPAf9AE4wWWjyvgxs/fCodU9oQ0DGljRs/fCqL53U0urSyUqi3onHckk4ELgL+meyXwRIRET+ifL8nwHnVbOvUjuPpbtIxzhPBaaedxrZt25D0bER0Da2PiOuG3X8N+EhDChzF4nkdTRV8Y2n2eivd4/4i8I9kJ3CUVeTBDjOziWzM4JZ0MbA/Ip4YrV2RBzvMzCaySva4FwCXSNoN3EvWV/rNulZlZmYjGjO4I2JVRJwYEZ3A5WRHqa+oe2VmZlaWx3GbmSWmqqsDRsRmskH/ZmbWIN7jNjNLjIPbzCwxDm4zs8Q4uM3MEuPgNjNLjIPbzCwxDm4zs8Q4uM3MEuPgNjNLjIPbzCwxDm6zROzZs2doBpz3SHpG0jWlbSSdLenAsNmOrjtyS5a6qq5VYmaNM2nSJG655RbOOOOMZ4BzgCckPRoRz5Y0/WFEXNyAEgu3blsvN2/cxd7+AWZMaePaRbObeuaaWnFwmyWivb2d9vZ2ACLiZUk7gQ6gNLgnhHXbelm1dgcDBwcB6O0fYNXaHQDjPrzdVWKWIEmdwDzg8TIP/5WkJyV9V9J7Ci2sQDdv3PWH0B4ycHCQmzfualBFxfEet1l63gR8G1gRES+VPLYVeFdEvCLpQmAdMKt0A5KWA8sBZs6cWedy62Nv/0BV68cT73GbJeTgwYMAfw7cExFrSx+PiJci4pX8/sNAq6QTyrRLfo7YGVPaqlo/nlQyWfBbJP0k/9PrGUmfLaIwMztcRLBs2TKA1yLiC+XaSHqnJOX355N9xn9bXJXFuXbRbNpaWw5b19bawrWLZjeoouJU0lXyOnBu/qdXK/AjSd+NiB/XuTYzG2bLli3cfffdAMdJ2p6v/gwwEyAivgJcBvydpEPAAHB5REQj6q23oQOQHlVSRv6mv5Ivtua3cfmLYNbMzjrrLCICSc9GRFe5NhHxZeDLBZfWMIvndUyIoC5VUR+3pJb8G34/8GhElDuSbWZmBahoVElEDAJzJU0BHpT03oh4enib0Y5Sd6586A/3d9900VGWbGY2sVU1qiQi+slmeb+gzGPJH6U2M0tBJaNKpuV72khqA84HflbvwszMrLxKukragbsktZAF/f0RsaG+ZZmZ2UgqGVXyFNmptWZm1gR85qSZWWIc3GZmiXFwm5klxsFtZpYYB7eZWWIc3GZmiXFwm5klxsFtZpYYB/c4J+lOSfslPT1s3T9J6pW0Pb9d2Mgazaw6Du7x7+uUuSgYcGtEzM1vDxdck/0J9uzZwznnnAPwnnw2qmtK2yjzJUnPSXpK0unFV2r15uAe5yLiB8ALja7Djt6kSZO45ZZbAJ4BzgSuljSnpNkHySYHnkV2meXbCy3SCuHgnrg+me+R3SnpbY0uxsbW3t7O6adnO9AR8TKwEyid/uVS4BuR+TEwRVJ7sZVavTm4J6bbyWYKnwvsA24ZqaGk5ZK6JXX39fUVVZ+NQVIn2cXfSmej6gD2DFvu4chwt8Q5uCegiHg+IgYj4g3gq8D8Udp6gozm8ybg28CKiHip5DGVaX/EHLH+Qk6bg3sCKvnT+UPA0yO1teZy8OBByP5auici1pZp0gOcNGz5RGBvaSN/IaetojknLV2S1gBnAydI6gGuB86WNJdsT2w38ImGFWgViwiWLVsG8FpEfGGEZuvJjl/cC/wlcCAi9hVVoxXDwT3ORcSSMqvvKLwQO2pbtmzh7rvvBjhO0vZ89WeAmQAR8RXgYeBC4DngVeBjDSjV6mzM4JZ0EvAN4J3AG8DqiLit3oWZ2eHOOussIgJJz0ZEV7k2ERHA1QWXZgWrZI/7EPDpiNgq6TjgCUmPRsSzda7NzMzKGPPgZETsi4it+f2Rxo6amVlBqhpVMsrYUTMzK0jFwS3pWEYeO+pxoWZmBakouCW1koX2SGNHPS7UzKwgYwa3JJENH9s5ythRMzMrSCV73AuAjwLn+vrNZmaNN+ZwwIj4EeWvf2BmZg3ga5WYmSXGwW1mlhgHt5lZYhzcZmaJcXCbmSXGwW1mlhgHt5lZYhzcZmaJcXCbJeKqq65i+vTpAO8p97iksyUdGHaG83XFVmhFcXCbJeLKK6/kkUceGavZDyNibn67oYi6rHgObrNELFy4kKlTpza6DGsCDm6z8eWvJD0p6buSynapWPo8y7vZ+LEVeFdEvJJfwXMdMKtcQ0nLgeUAM2fOLK5CqwnvcZuNExHxUkS8kt9/GGiVdMIIbT3xScIc3GbjhKR35hOfIGk+2ef7t42tyurBXSVmiViyZAmbN28GmCypB7geaAWIiK8AlwF/J+kQMABcHhHRoHKtjhzcZolYs2YNAJK2RkRX6eMR8WXgy0XXZcWrZM7JOyXtl/R0EQWZmdnoKunj/jpwQZ3rMDOzCo0Z3BHxA+CFAmoxM7MKeFSJmVliahbckpZL6pbU3dfXV6vNmplZiZoFtwf0m5kVw10lZmaJqWQ44BrgP4HZknokLat/WWZmNpIxT8CJiCVFFGJmZpVxV4mZWWIc3GZmiXFwm5klxheZGuck3QlcDOyPiPfm66YC9wGdwG7gbyLixbG2taP3AJ0rH6pfsVbW7psuanQJ1mS8xz3+fZ0jrzWzEngsImYBj+XLZpYIB/c4N8K1Zi4F7srv3wUsLrQoMzsqDu6J6R0RsQ8g/zm9wfWYWRUc3Daq4degGXz1QKPLmdCuuuoqpk+fDlB29nZlviTpOUlPSTq92tdYt62XBTdt4uSVD7Hgpk2s29Z7tGVbHTi4J6bnJbUD5D/3j9Rw+DVoWo45vrAC7UhXXnkljzzyyGhNPkg2q/ssshncb69m++u29bJq7Q56+wcIoLd/gFVrdzi8m5CDe2JaDyzN7y8FvtPAWqxCCxcuZOrUqaM1uRT4RmR+DEwZ+oKuxM0bdzFwcPCwdQMHB7l5464/qV6rHwf3ODfCtWZuAt4v6efA+/NlS18HsGfYck++7gjlLsO8t3+g7EZHWm+N43Hc49wo15o5r9ptndpxPN0eU9zMVGZd2VneI2I1sBqgq6srAGZMaaO3TEjPmNJWwxKtFrzHbTZ+9AAnDVs+Edhb6ZOvXTSbttaWw9a1tbZw7aLZtanOasbBbTZ+rAf+Nh9dciZwYGjYZyUWz+vgxg+fSseUNgR0TGnjxg+fyuJ5ZXtbrIHcVWKWiCVLlrB582aAyZJ6gOuBVoCI+ArwMHAh8BzwKvCxal9j8bwOB3UCHNxmiVizZg0AkrZGRFfp4xERwNVF12XFc1eJmVliKgpuSRdI2pWfkeULEpmZNVAlc062AP9KdlbWHGCJpDn1LszMzMqrZI97PvBcRPwyIn4P3Et2hpaZmTVAJcFd8dlYZmZWf8oORI/SQPoIsCgiPp4vfxSYHxF/X9JuOdmFbQBmA+UucHAC8JujLbog46HWd0XEtFq9iKSXKf++jjfN/t7X+n3tA35Vq+2Nodn/b0sVWW/F72slwwErOhtr+Cm0I5HUXW4YUzNyrWXtSuX/5Gik9N7XQi2/BMaS2v9ts9ZbSVfJT4FZkk6W9GbgcrIztMzMrAHG3OOOiEOSPglsBFqAOyPimbpXZmZmZVV05mREPEx2Ou3RGrUrpcm41sa9TqNNlH9nI6T2f9uU9Y55cNLMzJqLT3k3M0tM3YJb0p2S9kt6eti6qZIelfTz/Ofb6vX6lZJ0kqTvS9op6RlJ1zRxrW+R9BNJT+a1fjZff7Kkx/Na78sPItfydSfMJQ8k7Za0Q9J2Sd2Nrme8GOlz1swktUjaJmlDo2spVc897q8DF5SsWwk8FhGzgMfy5UY7BHw6It4NnAlcnZ/S34y1vg6cGxHvA+YCF+TXXf4ccGte64vAslq94AS95ME5ETG3GYeBJWykz1kzuwbY2egiyqlbcEfED4AXSlZfCtyV378LWFyv169UROyLiK35/ZfJ3qgOmrPWiIhX8sXW/BbAucAD+fpa1+pLHthRG+Vz1pQknQhcBHyt0bWUU3Qf9zuGZuTIf04v+PVHJakTmAc8TpPWmv/5th3YDzwK/ALoj4hDeZNaX5Jgol3yIIDvSXoiPxvYaqzkc9asvgj8I/BGowspxwcnc5KOBb4NrIiIlxpdz0giYjAi5pKdwTofeHe5ZjV8yYonoB0nFkTE6WRdQ1dLWtjogsaTFD5nki4G9kfEE42uZSRFB/fzktoB8p/7C379siS1kv0y3RMRa/PVTVnrkIjoBzaT9RdOkTQ0Jr+qCWIrcFQT0KYmIvbmP/cDD5J9OVoNjPA5a0YLgEsk7SbrGjxX0jcbW9Lhig7u9cDS/P5S4DsFv/4RJAm4A9gZEV8Y9lAz1jpN0pT8fhtwPllf4feBy/Jmta51wlzyQNJbJR03dB/4APD06M+ySozyOWs6EbEqIk6MiE6y3/dNEXFFg8s6TN1OwJG0Bjib7Opaz5NNbLoOuB+YCfwa+EhElB7ALJSks4AfAjv4Y3/WZ8j635qt1tPIDj62kH3p3h8RN0g6hWzPYCqwDbgiIl6v4eteSNbnN3TJg3+u1babSf7/+GC+OAn41nj9txZtpM9ZflZ205J0NvA/IuLiRtcynM+cNDNLjA9OmpklxsFtZpYYB7eZWWIc3GZmiXFwm5klxsFtZpYYB7eZWWIc3GZmifn/WV1e7HnZ+qcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig=plt.figure()\n",
+    "#plt.title('nice pictures')\n",
+    "ax1=fig.add_subplot(131)\n",
+    "ax2=fig.add_subplot(132)\n",
+    "ax3=fig.add_subplot(133)\n",
+    "\n",
+    "ax1.bar([10,20,30],[3,5,8])\n",
+    "ax2.barh([10,20,30],[8,5,3])\n",
+    "ax3.scatter(a,b)\n",
+    "#fig.delaxes(ax3)\n",
+    "plt.savefig('hello.jpg')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Acceleration', 'Cylinders', 'Displacement', 'Horsepower',\n",
+       "       'Miles_per_Gallon', 'Name', 'Origin', 'Weight_in_lbs', 'Year'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import altair as alt\n",
+    "\n",
+    "# to use with Jupyter notebook (not JupyterLab) run the following\n",
+    "# alt.renderers.enable('notebook')\n",
+    "\n",
+    "# load a simple dataset as a pandas DataFrame\n",
+    "from vega_datasets import data\n",
+    "cars = data.cars()\n",
+    "cars.columns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"vega-embed\" id=\"1f734b0d-1fb7-48d8-af2c-be6badb05be6\"></div>\n",
+       "\n",
+       "<style>\n",
+       ".vega-embed svg, .vega-embed canvas {\n",
+       "  border: 1px dotted gray;\n",
+       "}\n",
+       "\n",
+       ".vega-embed .vega-actions a {\n",
+       "  margin-right: 6px;\n",
+       "}\n",
+       "</style>\n"
+      ]
+     },
+     "metadata": {
+      "jupyter-vega": "#1f734b0d-1fb7-48d8-af2c-be6badb05be6"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
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{\"Acceleration\": 13.5, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"maxda rx3\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2124, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 18.5, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2310, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 6, \"Displacement\": 155.0, \"Horsepower\": 107.0, \"Miles_per_Gallon\": 21.0, \"Name\": \"mercury capri v6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2472, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"fiat 124 sport coupe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2265, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"chevrolet monte carlo s\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4082, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 9.5, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 230.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"pontiac grand prix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4278, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 19.5, \"Cylinders\": 4, \"Displacement\": 68.0, \"Horsepower\": 49.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"fiat 128\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1867, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"opel manta\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2158, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 114.0, \"Horsepower\": 91.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"audi 100ls\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2582, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 112.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"volvo 144ea\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2868, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"dodge dart custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3399, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"saab 99le\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2660, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 13.5, \"Cylinders\": 6, \"Displacement\": 156.0, \"Horsepower\": 122.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"toyota mark ii\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2807, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, 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\"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"volkswagen dasher\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1963, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"opel manta\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2300, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 76.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"toyota corona\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1649, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 4, \"Displacement\": 83.0, \"Horsepower\": 61.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"datsun 710\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2003, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"dodge colt\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2125, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"fiat 128\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2108, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"fiat 124 tc\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2246, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2489, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 93.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"subaru\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2391, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"fiat x1.9\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2000, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"plymouth valiant custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3264, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"chevrolet nova\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3459, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 21.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"mercury monarch\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3432, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 19.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"ford maverick\", 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\"Displacement\": 305.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet chevelle malibu classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4215, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"dodge coronet brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4190, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.9, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"amc matador\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3962, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 12.8, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 152.0, \"Miles_per_Gallon\": 14.5, \"Name\": \"ford gran torino\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4215, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 22.0, \"Name\": 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22.1, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 24.5, \"Name\": \"chevrolet woody\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2164, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"vw rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1937, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 17.4, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1795, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 17.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"dodge aspen se\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3651, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 21.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"ford 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16.4, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2155, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 26.5, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2565, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 130.0, \"Horsepower\": 102.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"volvo 245\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3150, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"plymouth volare premier v8\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3940, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 21.9, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3270, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 6, \"Displacement\": 156.0, \"Horsepower\": 108.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"toyota mark ii\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2930, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 16.7, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"mercedes-benz 280s\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3820, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 12.1, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"cadillac seville\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4380, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 12.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chevy c10\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4055, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"ford f108\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3870, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"dodge d100\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3755, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 18.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 31.5, \"Name\": \"honda Accelerationord cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2045, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 111.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"buick opel isuzu deluxe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2155, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"renault 5 gtl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1825, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 96.0, \"Miles_per_Gallon\": 25.5, \"Name\": \"plymouth arrow gs\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2300, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 16.8, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"datsun f-10 hatchback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1945, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet caprice classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3880, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"oldsmobile cutlass supreme\", \"Origin\": \"USA\", 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6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"plymouth volare custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3630, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 98.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"ford granada\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3525, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 11.1, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"pontiac grand prix lj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4220, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 11.4, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 170.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"chevrolet monte carlo landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4165, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 12.2, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 190.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"chrysler cordoba\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4325, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 149.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"ford thunderbird\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4335, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"volkswagen rabbit custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1940, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 24.5, \"Name\": \"pontiac sunbird coupe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2740, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"toyota corolla liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2265, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 89.0, \"Miles_per_Gallon\": 25.5, \"Name\": \"ford mustang ii 2+2\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2755, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 30.5, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2051, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 15.9, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 83.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"dodge colt m/m\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2075, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"subaru dl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.1, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 30.5, \"Name\": \"volkswagen dasher\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2190, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"datsun 810\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2815, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 12.8, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"bmw 320i\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2600, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 13.5, \"Cylinders\": 3, \"Displacement\": 80.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"mazda rx-4\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2720, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 21.5, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 43.1, \"Name\": \"volkswagen rabbit custom diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1985, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 66.0, \"Miles_per_Gallon\": 36.1, \"Name\": \"ford fiesta\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1800, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 78.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 32.8, \"Name\": \"mazda glc deluxe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 39.4, \"Name\": \"datsun b210 gx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2070, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 36.1, \"Name\": \"honda civic cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1800, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 19.9, \"Name\": \"oldsmobile cutlass salon brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3365, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 19.4, \"Name\": \"dodge diplomat\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3735, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 12.8, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"mercury monarch ghia\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3570, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 19.2, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"pontiac phoenix lj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3535, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"chevrolet malibu\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3155, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"ford fairmont (auto)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2965, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 25.1, \"Name\": \"ford fairmont (man)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2720, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 17.2, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"plymouth volare\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3430, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 17.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 19.4, \"Name\": \"amc concord\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3210, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 20.6, \"Name\": \"buick century special\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3380, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.7, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 20.8, \"Name\": \"mercury zephyr\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3070, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 18.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 18.6, \"Name\": \"dodge aspen\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3620, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.1, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 18.1, \"Name\": \"amc concord d/l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3410, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"chevrolet monte carlo landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3425, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 165.0, \"Miles_per_Gallon\": 17.7, \"Name\": \"buick regal sport coupe (turbo)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3445, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 11.2, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Miles_per_Gallon\": 18.1, \"Name\": \"ford futura\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3205, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.7, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"dodge magnum xe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4080, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2155, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 27.5, \"Name\": \"toyota corona\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2560, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"datsun 510\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2300, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 30.9, \"Name\": \"dodge omni\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2230, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 21.1, \"Name\": \"toyota celica gt liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2515, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.7, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 23.2, \"Name\": \"plymouth sapporo\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2745, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 17.6, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 23.8, \"Name\": \"oldsmobile starfire sx\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2855, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 23.9, \"Name\": \"datsun 200-sx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2405, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.9, \"Cylinders\": 5, \"Displacement\": 131.0, \"Horsepower\": 103.0, \"Miles_per_Gallon\": 20.3, \"Name\": \"audi 5000\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2830, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.6, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"volvo 264gl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3140, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 21.6, \"Name\": \"saab 99gle\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2795, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 133.0, \"Miles_per_Gallon\": 16.2, \"Name\": \"peugeot 604sl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3410, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 31.5, \"Name\": \"volkswagen scirocco\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1990, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.6, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 29.5, \"Name\": \"honda Accelerationord lx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2135, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"pontiac lemans v6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3245, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 19.8, \"Name\": \"mercury zephyr 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2990, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 22.3, \"Name\": \"ford fairmont 4\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2890, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"amc concord dl 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3265, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 16.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 20.6, \"Name\": \"dodge aspen 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3360, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"chevrolet caprice classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3840, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.4, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 129.0, \"Miles_per_Gallon\": 17.6, \"Name\": \"ford ltd landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3725, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 138.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"mercury grand marquis\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3955, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 135.0, \"Miles_per_Gallon\": 18.2, \"Name\": \"dodge st. regis\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3830, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 155.0, \"Miles_per_Gallon\": 16.9, \"Name\": \"buick estate wagon (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4360, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.3, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 142.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"ford country squire (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4054, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 8, \"Displacement\": 267.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"chevrolet malibu classic (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3605, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"chrysler lebaron town @ country (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3940, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 31.9, \"Name\": \"vw rabbit custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1925, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.2, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 34.1, \"Name\": \"maxda glc deluxe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1975, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 35.7, \"Name\": \"dodge colt hatchback custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1915, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 27.4, \"Name\": \"amc spirit dl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2670, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 20.1, \"Cylinders\": 5, \"Displacement\": 183.0, \"Horsepower\": 77.0, \"Miles_per_Gallon\": 25.4, \"Name\": \"mercedes benz 300d\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3530, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 17.4, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"cadillac eldorado\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3900, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 24.8, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3190, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 22.2, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 23.9, \"Name\": \"oldsmobile cutlass salon brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3420, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.2, \"Name\": \"plymouth horizon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2200, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.5, \"Name\": \"plymouth horizon tc3\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2150, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 19.2, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 31.8, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2020, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 69.0, \"Miles_per_Gallon\": 37.3, \"Name\": \"fiat strada custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2130, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 28.4, \"Name\": \"buick skylark limited\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2670, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 11.3, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 28.8, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2595, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 12.9, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 26.8, \"Name\": \"oldsmobile omega brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2700, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"pontiac phoenix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2556, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 76.0, \"Miles_per_Gallon\": 41.5, \"Name\": \"vw rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2144, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 18.8, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 38.1, \"Name\": \"toyota corolla tercel\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1968, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 32.1, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2120, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 37.2, \"Name\": \"datsun 310\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2019, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2678, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 18.1, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 26.4, \"Name\": \"ford fairmont\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2870, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 20.1, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 24.3, \"Name\": \"amc concord\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3003, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 18.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 19.1, \"Name\": \"dodge aspen\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3381, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 34.3, \"Name\": \"audi 4000\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2188, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 29.8, \"Name\": \"toyota corona liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2711, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 17.5, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 31.3, \"Name\": \"mazda 626\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2542, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"datsun 510 hatchback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2434, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.2, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 32.2, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2265, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 17.9, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 46.6, \"Name\": \"mazda glc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2110, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 27.9, \"Name\": \"dodge colt\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2800, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 19.2, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 40.8, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2110, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 21.7, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 44.3, \"Name\": \"vw rabbit c (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2085, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 23.7, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 43.4, \"Name\": \"vw dasher (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2335, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 19.9, \"Cylinders\": 5, \"Displacement\": 121.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 36.4, \"Name\": \"audi 5000s (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2950, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 21.8, \"Cylinders\": 4, \"Displacement\": 146.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"mercedes-benz 240d\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3250, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 13.8, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 44.6, \"Name\": \"honda civic 1500 gl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1850, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": null, \"Miles_per_Gallon\": 40.9, \"Name\": \"renault lecar deluxe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1835, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 33.8, \"Name\": \"subaru dl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2145, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Miles_per_Gallon\": 29.8, \"Name\": \"vokswagen rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1845, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 11.4, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 132.0, \"Miles_per_Gallon\": 32.7, \"Name\": \"datsun 280-zx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2910, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 12.5, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 23.7, \"Name\": \"mazda rx-7 gs\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2420, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.1, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 35.0, \"Name\": \"triumph tr7 coupe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2500, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 14.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": null, \"Miles_per_Gallon\": 23.6, \"Name\": \"ford mustang cobra\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2905, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 32.4, \"Name\": \"honda Accelerationord\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2290, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"plymouth reliant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2490, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 26.6, \"Name\": \"buick skylark\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2635, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 25.8, \"Name\": \"dodge aries wagon (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2620, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 12.6, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 23.5, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2725, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 12.9, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"plymouth reliant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2385, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.9, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Miles_per_Gallon\": 39.1, \"Name\": \"toyota starlet\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1755, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 64.0, \"Miles_per_Gallon\": 39.0, \"Name\": \"plymouth champ\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1875, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.1, \"Cylinders\": 4, \"Displacement\": 81.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 35.1, \"Name\": \"honda civic 1300\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1760, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.8, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 32.3, \"Name\": \"subaru\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2065, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1975, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Miles_per_Gallon\": 37.7, \"Name\": \"toyota tercel\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2050, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 34.1, \"Name\": \"mazda glc 4\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 34.7, \"Name\": \"plymouth horizon 4\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2215, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 34.4, \"Name\": \"ford escort 4w\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2045, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 20.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 29.9, \"Name\": \"ford escort 2h\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2380, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"volkswagen jetta\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2190, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 100.0, \"Horsepower\": null, \"Miles_per_Gallon\": 34.5, \"Name\": \"renault 18i\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2320, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 33.7, \"Name\": \"honda prelude\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2210, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.8, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 32.4, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2350, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 32.9, \"Name\": \"datsun 200sx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2615, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.3, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 31.6, \"Name\": \"mazda 626\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2635, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 20.4, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 28.1, \"Name\": \"peugeot 505s turbo diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3230, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": null, \"Name\": \"saab 900s\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2800, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.6, \"Cylinders\": 6, \"Displacement\": 145.0, \"Horsepower\": 76.0, \"Miles_per_Gallon\": 30.7, \"Name\": \"volvo diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3160, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 12.6, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 116.0, \"Miles_per_Gallon\": 25.4, \"Name\": \"toyota cressida\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2900, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 13.8, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 24.2, \"Name\": \"datsun 810 maxima\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2930, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 22.4, \"Name\": \"buick century\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3415, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 26.6, \"Name\": \"oldsmobile cutlass ls\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3725, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.1, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"ford granada gl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3060, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 17.6, \"Name\": \"chrysler lebaron salon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3465, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.6, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"chevrolet cavalier\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2605, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"chevrolet cavalier wagon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2640, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 34.0, \"Name\": \"chevrolet cavalier 2-door\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2395, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"pontiac j2000 se hatchback\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2575, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"dodge aries se\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2525, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"pontiac phoenix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2735, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"ford fairmont futura\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2865, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 20.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": null, \"Miles_per_Gallon\": 23.0, \"Name\": \"amc concord dl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3035, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"volkswagen rabbit l\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1980, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"mazda glc custom l\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2025, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.6, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"mazda glc custom\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1970, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"plymouth horizon miser\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2125, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"mercury lynx l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2125, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"nissan stanza xe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2160, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"honda Accelerationord\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2205, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.9, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.0, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2245, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1965, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"honda civic (auto)\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1965, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"datsun 310 gx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1995, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 6, \"Displacement\": 181.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"buick century limited\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2945, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 6, \"Displacement\": 262.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"oldsmobile cutlass ciera (diesel)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3015, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"chrysler lebaron medallion\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2585, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 112.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"ford granada l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2835, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 13.9, \"Cylinders\": 4, \"Displacement\": 144.0, \"Horsepower\": 96.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"toyota celica gt\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2665, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"dodge charger 2.2\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2370, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"chevrolet camaro\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2950, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 86.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"ford mustang gl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2790, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 24.6, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 44.0, \"Name\": \"vw pickup\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2130, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 11.6, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"dodge rampage\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2295, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 79.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"ford ranger\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2625, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 82.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"chevy s-10\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2720, \"Year\": \"1982-01-01\"}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
+       "var selector = \"#1f734b0d-1fb7-48d8-af2c-be6badb05be6\";\n",
+       "var type = \"vega-lite\";\n",
+       "\n",
+       "var output_area = this;\n",
+       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
+       "  vega.render(selector, spec, type, output_area);\n",
+       "}, function (err) {\n",
+       "  if (err.requireType !== 'scripterror') {\n",
+       "    throw(err);\n",
+       "  }\n",
+       "});\n"
+      ]
+     },
+     "metadata": {
+      "jupyter-vega": "#1f734b0d-1fb7-48d8-af2c-be6badb05be6"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {
+      "jupyter-vega": "#1f734b0d-1fb7-48d8-af2c-be6badb05be6"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "alt.Chart(cars).mark_point().encode(\n",
+    "x='Miles_per_Gallon',\n",
+    "    y='Horsepower'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"vega-embed\" id=\"d9d6f01e-02e5-4180-a0ba-4ebe969786a3\"></div>\n",
+       "\n",
+       "<style>\n",
+       ".vega-embed svg, .vega-embed canvas {\n",
+       "  border: 1px dotted gray;\n",
+       "}\n",
+       "\n",
+       ".vega-embed .vega-actions a {\n",
+       "  margin-right: 6px;\n",
+       "}\n",
+       "</style>\n"
+      ]
+     },
+     "metadata": {
+      "jupyter-vega": "#d9d6f01e-02e5-4180-a0ba-4ebe969786a3"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": [
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{\"Acceleration\": 13.5, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"maxda rx3\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2124, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 18.5, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2310, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 6, \"Displacement\": 155.0, \"Horsepower\": 107.0, \"Miles_per_Gallon\": 21.0, \"Name\": \"mercury capri v6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2472, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"fiat 124 sport coupe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2265, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"chevrolet monte carlo s\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4082, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 9.5, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 230.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"pontiac grand prix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4278, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 19.5, \"Cylinders\": 4, \"Displacement\": 68.0, \"Horsepower\": 49.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"fiat 128\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1867, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"opel manta\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2158, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 114.0, \"Horsepower\": 91.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"audi 100ls\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2582, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 112.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"volvo 144ea\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2868, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"dodge dart custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3399, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"saab 99le\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2660, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 13.5, \"Cylinders\": 6, \"Displacement\": 156.0, \"Horsepower\": 122.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"toyota mark ii\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2807, \"Year\": \"1973-01-01\"}, {\"Acceleration\": 11.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, 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\"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"volkswagen dasher\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1963, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"opel manta\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2300, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 76.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"toyota corona\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1649, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 4, \"Displacement\": 83.0, \"Horsepower\": 61.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"datsun 710\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2003, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"dodge colt\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2125, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"fiat 128\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2108, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 116.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"fiat 124 tc\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2246, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2489, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 93.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"subaru\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2391, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"fiat x1.9\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2000, \"Year\": \"1974-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"plymouth valiant custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3264, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"chevrolet nova\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3459, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 21.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"mercury monarch\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3432, \"Year\": \"1975-01-01\"}, {\"Acceleration\": 19.5, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 15.0, \"Name\": \"ford maverick\", 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\"Displacement\": 305.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet chevelle malibu classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4215, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"dodge coronet brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4190, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.9, \"Cylinders\": 8, \"Displacement\": 304.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"amc matador\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3962, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 12.8, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 152.0, \"Miles_per_Gallon\": 14.5, \"Name\": \"ford gran torino\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4215, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 22.0, \"Name\": 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22.1, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 24.5, \"Name\": \"chevrolet woody\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2164, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"vw rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1937, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 17.4, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 53.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1795, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 17.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"dodge aspen se\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3651, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 21.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 18.0, \"Name\": \"ford 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16.4, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2155, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 26.5, \"Name\": \"ford pinto\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2565, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 130.0, \"Horsepower\": 102.0, \"Miles_per_Gallon\": 20.0, \"Name\": \"volvo 245\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3150, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"plymouth volare premier v8\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3940, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 21.9, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3270, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 6, \"Displacement\": 156.0, \"Horsepower\": 108.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"toyota mark ii\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2930, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 16.7, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"mercedes-benz 280s\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3820, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 12.1, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"cadillac seville\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4380, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 12.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"chevy c10\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4055, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"ford f108\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3870, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 13.0, \"Name\": \"dodge d100\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3755, \"Year\": \"1976-01-01\"}, {\"Acceleration\": 18.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 31.5, \"Name\": \"honda Accelerationord cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2045, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 111.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"buick opel isuzu deluxe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2155, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"renault 5 gtl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1825, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 96.0, \"Miles_per_Gallon\": 25.5, \"Name\": \"plymouth arrow gs\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2300, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 16.8, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"datsun f-10 hatchback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1945, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 12.5, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"chevrolet caprice classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3880, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"oldsmobile cutlass supreme\", \"Origin\": \"USA\", 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6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 19.0, \"Name\": \"plymouth volare custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3630, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 6, \"Displacement\": 250.0, \"Horsepower\": 98.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"ford granada\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3525, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 11.1, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 180.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"pontiac grand prix lj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4220, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 11.4, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 170.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"chevrolet monte carlo landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4165, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 12.2, \"Cylinders\": 8, \"Displacement\": 400.0, \"Horsepower\": 190.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"chrysler cordoba\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4325, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 149.0, \"Miles_per_Gallon\": 16.0, \"Name\": \"ford thunderbird\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4335, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"volkswagen rabbit custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1940, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 24.5, \"Name\": \"pontiac sunbird coupe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2740, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"toyota corolla liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2265, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 89.0, \"Miles_per_Gallon\": 25.5, \"Name\": \"ford mustang ii 2+2\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2755, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 30.5, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2051, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 15.9, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 83.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"dodge colt m/m\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2075, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"subaru dl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.1, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 30.5, \"Name\": \"volkswagen dasher\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2190, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"datsun 810\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2815, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 12.8, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"bmw 320i\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2600, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 13.5, \"Cylinders\": 3, \"Displacement\": 80.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"mazda rx-4\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2720, \"Year\": \"1977-01-01\"}, {\"Acceleration\": 21.5, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 43.1, \"Name\": \"volkswagen rabbit custom diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1985, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 66.0, \"Miles_per_Gallon\": 36.1, \"Name\": \"ford fiesta\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1800, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 78.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 32.8, \"Name\": \"mazda glc deluxe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 39.4, \"Name\": \"datsun b210 gx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2070, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 36.1, \"Name\": \"honda civic cvcc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1800, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 19.9, \"Name\": \"oldsmobile cutlass salon brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3365, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 19.4, \"Name\": \"dodge diplomat\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3735, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 12.8, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"mercury monarch ghia\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3570, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 19.2, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"pontiac phoenix lj\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3535, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"chevrolet malibu\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3155, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"ford fairmont (auto)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2965, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 25.1, \"Name\": \"ford fairmont (man)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2720, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 17.2, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 20.5, \"Name\": \"plymouth volare\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3430, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 17.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 19.4, \"Name\": \"amc concord\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3210, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 20.6, \"Name\": \"buick century special\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3380, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.7, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 20.8, \"Name\": \"mercury zephyr\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3070, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 18.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 18.6, \"Name\": \"dodge aspen\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3620, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.1, \"Cylinders\": 6, \"Displacement\": 258.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 18.1, \"Name\": \"amc concord d/l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3410, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 145.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"chevrolet monte carlo landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3425, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 165.0, \"Miles_per_Gallon\": 17.7, \"Name\": \"buick regal sport coupe (turbo)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3445, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 11.2, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 139.0, \"Miles_per_Gallon\": 18.1, \"Name\": \"ford futura\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3205, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.7, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 140.0, \"Miles_per_Gallon\": 17.5, \"Name\": \"dodge magnum xe\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4080, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2155, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 27.5, \"Name\": \"toyota corona\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2560, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"datsun 510\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2300, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 30.9, \"Name\": \"dodge omni\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2230, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 95.0, \"Miles_per_Gallon\": 21.1, \"Name\": \"toyota celica gt liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2515, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.7, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 23.2, \"Name\": \"plymouth sapporo\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2745, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 17.6, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 23.8, \"Name\": \"oldsmobile starfire sx\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2855, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 97.0, \"Miles_per_Gallon\": 23.9, \"Name\": \"datsun 200-sx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2405, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.9, \"Cylinders\": 5, \"Displacement\": 131.0, \"Horsepower\": 103.0, \"Miles_per_Gallon\": 20.3, \"Name\": \"audi 5000\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2830, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 13.6, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"volvo 264gl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3140, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 21.6, \"Name\": \"saab 99gle\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2795, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 163.0, \"Horsepower\": 133.0, \"Miles_per_Gallon\": 16.2, \"Name\": \"peugeot 604sl\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3410, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 31.5, \"Name\": \"volkswagen scirocco\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1990, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 16.6, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 29.5, \"Name\": \"honda Accelerationord lx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2135, \"Year\": \"1978-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 21.5, \"Name\": \"pontiac lemans v6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3245, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 19.8, \"Name\": \"mercury zephyr 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2990, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 22.3, \"Name\": \"ford fairmont 4\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2890, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"amc concord dl 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3265, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 16.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 20.6, \"Name\": \"dodge aspen 6\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3360, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 8, \"Displacement\": 305.0, \"Horsepower\": 130.0, \"Miles_per_Gallon\": 17.0, \"Name\": \"chevrolet caprice classic\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3840, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.4, \"Cylinders\": 8, \"Displacement\": 302.0, \"Horsepower\": 129.0, \"Miles_per_Gallon\": 17.6, \"Name\": \"ford ltd landau\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3725, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 138.0, \"Miles_per_Gallon\": 16.5, \"Name\": \"mercury grand marquis\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3955, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.2, \"Cylinders\": 8, \"Displacement\": 318.0, \"Horsepower\": 135.0, \"Miles_per_Gallon\": 18.2, \"Name\": \"dodge st. regis\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3830, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 155.0, \"Miles_per_Gallon\": 16.9, \"Name\": \"buick estate wagon (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4360, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.3, \"Cylinders\": 8, \"Displacement\": 351.0, \"Horsepower\": 142.0, \"Miles_per_Gallon\": 15.5, \"Name\": \"ford country squire (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 4054, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 8, \"Displacement\": 267.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 19.2, \"Name\": \"chevrolet malibu classic (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3605, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 8, \"Displacement\": 360.0, \"Horsepower\": 150.0, \"Miles_per_Gallon\": 18.5, \"Name\": \"chrysler lebaron town @ country (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3940, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.0, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 31.9, \"Name\": \"vw rabbit custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1925, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.2, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 34.1, \"Name\": \"maxda glc deluxe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1975, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 35.7, \"Name\": \"dodge colt hatchback custom\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1915, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 27.4, \"Name\": \"amc spirit dl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2670, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 20.1, \"Cylinders\": 5, \"Displacement\": 183.0, \"Horsepower\": 77.0, \"Miles_per_Gallon\": 25.4, \"Name\": \"mercedes benz 300d\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3530, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 17.4, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 125.0, \"Miles_per_Gallon\": 23.0, \"Name\": \"cadillac eldorado\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3900, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 24.8, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 71.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"peugeot 504\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3190, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 22.2, \"Cylinders\": 8, \"Displacement\": 260.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 23.9, \"Name\": \"oldsmobile cutlass salon brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3420, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.2, \"Name\": \"plymouth horizon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2200, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.5, \"Name\": \"plymouth horizon tc3\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2150, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 19.2, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 31.8, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2020, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 69.0, \"Miles_per_Gallon\": 37.3, \"Name\": \"fiat strada custom\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2130, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 28.4, \"Name\": \"buick skylark limited\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2670, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 11.3, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 28.8, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2595, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 12.9, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 115.0, \"Miles_per_Gallon\": 26.8, \"Name\": \"oldsmobile omega brougham\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2700, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 13.2, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 33.5, \"Name\": \"pontiac phoenix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2556, \"Year\": \"1979-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 76.0, \"Miles_per_Gallon\": 41.5, \"Name\": \"vw rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2144, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 18.8, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 38.1, \"Name\": \"toyota corolla tercel\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1968, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 32.1, \"Name\": \"chevrolet chevette\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2120, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 37.2, \"Name\": \"datsun 310\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2019, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 16.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2678, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 18.1, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 26.4, \"Name\": \"ford fairmont\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2870, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 20.1, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 24.3, \"Name\": \"amc concord\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3003, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 18.7, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 19.1, \"Name\": \"dodge aspen\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3381, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 78.0, \"Miles_per_Gallon\": 34.3, \"Name\": \"audi 4000\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2188, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.5, \"Cylinders\": 4, \"Displacement\": 134.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 29.8, \"Name\": \"toyota corona liftback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2711, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 17.5, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 31.3, \"Name\": \"mazda 626\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2542, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"datsun 510 hatchback\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2434, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.2, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 32.2, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2265, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 17.9, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 46.6, \"Name\": \"mazda glc\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2110, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 27.9, \"Name\": \"dodge colt\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2800, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 19.2, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 40.8, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2110, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 21.7, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 44.3, \"Name\": \"vw rabbit c (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2085, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 23.7, \"Cylinders\": 4, \"Displacement\": 90.0, \"Horsepower\": 48.0, \"Miles_per_Gallon\": 43.4, \"Name\": \"vw dasher (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2335, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 19.9, \"Cylinders\": 5, \"Displacement\": 121.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 36.4, \"Name\": \"audi 5000s (diesel)\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2950, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 21.8, \"Cylinders\": 4, \"Displacement\": 146.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"mercedes-benz 240d\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3250, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 13.8, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 44.6, \"Name\": \"honda civic 1500 gl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1850, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": null, \"Miles_per_Gallon\": 40.9, \"Name\": \"renault lecar deluxe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1835, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 33.8, \"Name\": \"subaru dl\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2145, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Miles_per_Gallon\": 29.8, \"Name\": \"vokswagen rabbit\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1845, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 11.4, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 132.0, \"Miles_per_Gallon\": 32.7, \"Name\": \"datsun 280-zx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2910, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 12.5, \"Cylinders\": 3, \"Displacement\": 70.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 23.7, \"Name\": \"mazda rx-7 gs\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2420, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.1, \"Cylinders\": 4, \"Displacement\": 122.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 35.0, \"Name\": \"triumph tr7 coupe\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2500, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 14.3, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": null, \"Miles_per_Gallon\": 23.6, \"Name\": \"ford mustang cobra\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2905, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 72.0, \"Miles_per_Gallon\": 32.4, \"Name\": \"honda Accelerationord\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2290, \"Year\": \"1980-01-01\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 27.2, \"Name\": \"plymouth reliant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2490, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 26.6, \"Name\": \"buick skylark\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2635, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 25.8, \"Name\": \"dodge aries wagon (sw)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2620, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 12.6, \"Cylinders\": 6, \"Displacement\": 173.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 23.5, \"Name\": \"chevrolet citation\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2725, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 12.9, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 30.0, \"Name\": \"plymouth reliant\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2385, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.9, \"Cylinders\": 4, \"Displacement\": 79.0, \"Horsepower\": 58.0, \"Miles_per_Gallon\": 39.1, \"Name\": \"toyota starlet\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1755, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 86.0, \"Horsepower\": 64.0, \"Miles_per_Gallon\": 39.0, \"Name\": \"plymouth champ\", \"Origin\": \"USA\", \"Weight_in_lbs\": 1875, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.1, \"Cylinders\": 4, \"Displacement\": 81.0, \"Horsepower\": 60.0, \"Miles_per_Gallon\": 35.1, \"Name\": \"honda civic 1300\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1760, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.8, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 32.3, \"Name\": \"subaru\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2065, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 85.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"datsun 210\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1975, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 89.0, \"Horsepower\": 62.0, \"Miles_per_Gallon\": 37.7, \"Name\": \"toyota tercel\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2050, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 34.1, \"Name\": \"mazda glc 4\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1985, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.9, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 34.7, \"Name\": \"plymouth horizon 4\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2215, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 34.4, \"Name\": \"ford escort 4w\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2045, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 20.7, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 65.0, \"Miles_per_Gallon\": 29.9, \"Name\": \"ford escort 2h\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2380, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.2, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 33.0, \"Name\": \"volkswagen jetta\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2190, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 4, \"Displacement\": 100.0, \"Horsepower\": null, \"Miles_per_Gallon\": 34.5, \"Name\": \"renault 18i\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2320, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.4, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 33.7, \"Name\": \"honda prelude\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2210, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.8, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 32.4, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2350, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.8, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 100.0, \"Miles_per_Gallon\": 32.9, \"Name\": \"datsun 200sx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2615, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.3, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 31.6, \"Name\": \"mazda 626\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2635, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 20.4, \"Cylinders\": 4, \"Displacement\": 141.0, \"Horsepower\": 80.0, \"Miles_per_Gallon\": 28.1, \"Name\": \"peugeot 505s turbo diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3230, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.4, \"Cylinders\": 4, \"Displacement\": 121.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": null, \"Name\": \"saab 900s\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2800, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.6, \"Cylinders\": 6, \"Displacement\": 145.0, \"Horsepower\": 76.0, \"Miles_per_Gallon\": 30.7, \"Name\": \"volvo diesel\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 3160, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 12.6, \"Cylinders\": 6, \"Displacement\": 168.0, \"Horsepower\": 116.0, \"Miles_per_Gallon\": 25.4, \"Name\": \"toyota cressida\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2900, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 13.8, \"Cylinders\": 6, \"Displacement\": 146.0, \"Horsepower\": 120.0, \"Miles_per_Gallon\": 24.2, \"Name\": \"datsun 810 maxima\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2930, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.8, \"Cylinders\": 6, \"Displacement\": 231.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 22.4, \"Name\": \"buick century\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3415, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.0, \"Cylinders\": 8, \"Displacement\": 350.0, \"Horsepower\": 105.0, \"Miles_per_Gallon\": 26.6, \"Name\": \"oldsmobile cutlass ls\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3725, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.1, \"Cylinders\": 6, \"Displacement\": 200.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 20.2, \"Name\": \"ford granada gl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3060, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.6, \"Cylinders\": 6, \"Displacement\": 225.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 17.6, \"Name\": \"chrysler lebaron salon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3465, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.6, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"chevrolet cavalier\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2605, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"chevrolet cavalier wagon\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2640, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 34.0, \"Name\": \"chevrolet cavalier 2-door\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2395, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 112.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"pontiac j2000 se hatchback\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2575, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 29.0, \"Name\": \"dodge aries se\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2525, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.0, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"pontiac phoenix\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2735, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 24.0, \"Name\": \"ford fairmont futura\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2865, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 20.5, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": null, \"Miles_per_Gallon\": 23.0, \"Name\": \"amc concord dl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3035, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.3, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 74.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"volkswagen rabbit l\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 1980, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.2, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 37.0, \"Name\": \"mazda glc custom l\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2025, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.6, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 68.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"mazda glc custom\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1970, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 4, \"Displacement\": 105.0, \"Horsepower\": 63.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"plymouth horizon miser\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2125, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 98.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"mercury lynx l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2125, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 88.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"nissan stanza xe\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2160, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 107.0, \"Horsepower\": 75.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"honda Accelerationord\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2205, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.9, \"Cylinders\": 4, \"Displacement\": 108.0, \"Horsepower\": 70.0, \"Miles_per_Gallon\": 34.0, \"Name\": \"toyota corolla\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2245, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.0, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"honda civic\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1965, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.7, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"honda civic (auto)\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1965, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.2, \"Cylinders\": 4, \"Displacement\": 91.0, \"Horsepower\": 67.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"datsun 310 gx\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 1995, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 16.4, \"Cylinders\": 6, \"Displacement\": 181.0, \"Horsepower\": 110.0, \"Miles_per_Gallon\": 25.0, \"Name\": \"buick century limited\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2945, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.0, \"Cylinders\": 6, \"Displacement\": 262.0, \"Horsepower\": 85.0, \"Miles_per_Gallon\": 38.0, \"Name\": \"oldsmobile cutlass ciera (diesel)\", \"Origin\": \"USA\", \"Weight_in_lbs\": 3015, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.5, \"Cylinders\": 4, \"Displacement\": 156.0, \"Horsepower\": 92.0, \"Miles_per_Gallon\": 26.0, \"Name\": \"chrysler lebaron medallion\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2585, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 14.7, \"Cylinders\": 6, \"Displacement\": 232.0, \"Horsepower\": 112.0, \"Miles_per_Gallon\": 22.0, \"Name\": \"ford granada l\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2835, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 13.9, \"Cylinders\": 4, \"Displacement\": 144.0, \"Horsepower\": 96.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"toyota celica gt\", \"Origin\": \"Japan\", \"Weight_in_lbs\": 2665, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 13.0, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 36.0, \"Name\": \"dodge charger 2.2\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2370, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 17.3, \"Cylinders\": 4, \"Displacement\": 151.0, \"Horsepower\": 90.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"chevrolet camaro\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2950, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 15.6, \"Cylinders\": 4, \"Displacement\": 140.0, \"Horsepower\": 86.0, \"Miles_per_Gallon\": 27.0, \"Name\": \"ford mustang gl\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2790, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 24.6, \"Cylinders\": 4, \"Displacement\": 97.0, \"Horsepower\": 52.0, \"Miles_per_Gallon\": 44.0, \"Name\": \"vw pickup\", \"Origin\": \"Europe\", \"Weight_in_lbs\": 2130, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 11.6, \"Cylinders\": 4, \"Displacement\": 135.0, \"Horsepower\": 84.0, \"Miles_per_Gallon\": 32.0, \"Name\": \"dodge rampage\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2295, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 18.6, \"Cylinders\": 4, \"Displacement\": 120.0, \"Horsepower\": 79.0, \"Miles_per_Gallon\": 28.0, \"Name\": \"ford ranger\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2625, \"Year\": \"1982-01-01\"}, {\"Acceleration\": 19.4, \"Cylinders\": 4, \"Displacement\": 119.0, \"Horsepower\": 82.0, \"Miles_per_Gallon\": 31.0, \"Name\": \"chevy s-10\", \"Origin\": \"USA\", \"Weight_in_lbs\": 2720, \"Year\": \"1982-01-01\"}]}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v1.2.1.json\"};\n",
+       "var selector = \"#d9d6f01e-02e5-4180-a0ba-4ebe969786a3\";\n",
+       "var type = \"vega-lite\";\n",
+       "\n",
+       "var output_area = this;\n",
+       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
+       "  vega.render(selector, spec, type, output_area);\n",
+       "}, function (err) {\n",
+       "  if (err.requireType !== 'scripterror') {\n",
+       "    throw(err);\n",
+       "  }\n",
+       "});\n"
+      ]
+     },
+     "metadata": {
+      "jupyter-vega": "#d9d6f01e-02e5-4180-a0ba-4ebe969786a3"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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"
+     },
+     "metadata": {
+      "jupyter-vega": "#d9d6f01e-02e5-4180-a0ba-4ebe969786a3"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "alt.Chart(cars).mark_bar().encode(\n",
+    " x='Origin',\n",
+    " y='Weight_in_lbs'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lessons/python/matplotlib/trajectory.nc b/lessons/python/matplotlib/trajectory.nc
new file mode 100644
index 000000000..026bb4ace
Binary files /dev/null and b/lessons/python/matplotlib/trajectory.nc differ
diff --git a/lessons/python/matplotlib/turnera_data_v2.xlsx b/lessons/python/matplotlib/turnera_data_v2.xlsx
new file mode 100644
index 000000000..84fffb1ab
Binary files /dev/null and b/lessons/python/matplotlib/turnera_data_v2.xlsx differ
diff --git a/lessons/python/numpy-pandas/Food_Data_HLW.csv b/lessons/python/numpy-pandas/Food_Data_HLW.csv
new file mode 100644
index 000000000..012905ce6
--- /dev/null
+++ b/lessons/python/numpy-pandas/Food_Data_HLW.csv
@@ -0,0 +1,31 @@
+ID,Food_Category,Product_Name,Brand,Price,Container_Size,Container_Size_unit,Serving_Size,Serving_Size_unit,Calories,Fat,Sodium,Carbohydrate,Sugars,Fiber,Protein
+10027,Bakery,PRESIDENT'S CHOICE | Triple Chocolate Cake | 2023-01-19,local,21.29,710,g,118,g,510,31,270,52,37,3,6
+10018,Meat,THE KEG | Prime Rib Beef Burger | 2023-02-16,international,22.99,1020,g,170,g,490,41,660,2,0,0,26
+10012,Snaks,"ORVILLE |  Bowl, Buttery | 2023-02-18",local,5.99,136,g,82,g,430,26,670,41,1,7,6
+10028,Bakery,NO NAME| Raised Chocolate Donuts 6 Pack | 2023-02-08,local,7.99,510,g,85,g,320,16,320,39,13,1,5
+10004,Drinks,STARBUCKS | Frappuccino Vanilla Coffee Drink | 2023-01-07,international,3.99,405,mL,405,mL,290,4.5,150,53,46,0,9
+10008,Snaks,CHEETOS | Crunchy Cheese Flavoured Snacks | 2023-02-18,international,6.49,390,g,50,g,280,18,440,30,1,1,3
+10016,Meat,PRESIDENT'S CHOICE | Cheddar Smokies sausage | 2023-02-16,local,14.29,1000,g,100,g,260,20,770,6,2,0,15
+10011,Snaks,PRINGLES | Mega Can BBQ Flavour Chips | 2023-01-11,international,3.99,202,g,50,g,260,15,240,29,2,1,2
+10019,Meat,"HIGH LINER | Fish Sticks, Family Pack | 2023-02-12",international,10.99,700,g,150,g,250,12,410,20,1,0,14
+10026,Bakery,FARMER'S MARKET | Butter Croissants Mini | 2023-02-17,local,4.99,336,g,56,g,230,12,230,28,5,2,4
+10013,Snaks,NESTLE | KitKat Chunky Milk Chocolate Wafer Bar | 2023-02-03,international,1.79,45,g,45,g,230,12,35,29,22,1,3
+10010,Snaks,PRESIDENT'S CHOICE | Everything Trail Mix Nuts | 2023-01-09,local,4.99,300,g,50,g,220,10,25,31,23,,3
+10003,Drinks,"MONSTER ENERGY | Green Energy Drink, Can | 2023-02-04",local,3.99,473,mL,473,mL,210,,370,55,54,0,0
+10006,Snaks,"M&M'S | Peanut Milk Chocolate Candies, Bowl Size | 2023-02-14",international,9.99,400,g,42,g,210,11,20,25,21,2,4
+10015,Meat,NO NAME | Mild Sugar-Cured Bacon | 2023-01-16,local,7.49,500,g,50,g,200,19,430,2,0,0,4
+10030,Bakery,"WONDER | Bread, Sliced White | 2023-01-18",international,3.99,675,g,75,g,200,2.5,320,38,3,2,7
+10022,Dairy,"ACTIVIA | Yogurt With Probiotics, Cherry Flavour | 2023-01-08",local,3.99,650,g,175,g,160,5,70,22,18,0,7
+10009,Snaks,CHRISTIE | Chips Ahoy! Rainbow Cookies | 2023-01-28,international,3.99,225,g,31,g,150,7,80,22,9,1,1
+10014,Snaks,CHRISTIE | Oreo Snak Pak Cookies | 2023-01-25,international,3.99,180,g,30,g,140,5,140,22,12,1,1
+10029,Bakery,SCHOOL SAFE | Muffin Bars Birthday Cake | 2023-01-30,international,3.99,296,g,37,g,140,6,60,20,11,,2
+10001,Drinks,COCA-COLA | Coca-Cola Bottle | 2023-01-28,international,3.99,2000,mL,355,mL,140,0,25,39,39,0,0
+10020,Dairy,"NEILSON | True Taste, 2% Milk | 2023-01-23",local,5.69,2000,mL,250,mL,130,5,120,12,12,0,9
+10007,Snaks,"GARDEN | Cream Wafers, Chocolate Flavour | 2023-02-19",local,2.39,200,g,25,g,130,7,45,16,6,0,2
+10023,Dairy,NO NAME | Farmer's Marble Cheese | 2023-02-02,local,8.99,700,g,30,g,110,8,220,0,0,0,7
+10002,Drinks,TROPICANA | Orange Juice No Pulp | 2023-02-22,local,6.99,1540,mL,250,mL,110,0,0,27,23,0,2
+10017,Meat,GREENFIELD| Thick Sliced Smoked Ham | 2023-02-04,international,12.99,500,g,50,g,50,1,360,0,0,0,9
+10005,Drinks,GRACE | 100% Pure Coconut Water | 2023-02-11,local,3.39,1000,mL,250,mL,50,0,65,11,10,0,1
+10021,Dairy,PHILADELPHIA | Original Cream Cheese Product | 2023-01-03,international,5.49,227,g,15,g,40,3.5,60,1,1,0,1
+10025,Fruit,DOLE | No Sugar Added Fruit Salad With Cherries | 2023-01-18,international,4.29,540,mL,125,mL,40,0,0,9,6,2,1
+10024,Fruit,SMUCKERS | No Sugar Added Orange Spread | 2023-02-12,international,5.99,310,mL,15,mL,20,0,1,5,5,0,0.1
\ No newline at end of file
diff --git a/lessons/python/numpy-pandas/Python - Data processing with numpy and pandas - 20230301b.ipynb b/lessons/python/numpy-pandas/Python - Data processing with numpy and pandas - 20230301b.ipynb
new file mode 100644
index 000000000..36578ad6f
--- /dev/null
+++ b/lessons/python/numpy-pandas/Python - Data processing with numpy and pandas - 20230301b.ipynb	
@@ -0,0 +1,13358 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "24b5c4d5",
+   "metadata": {},
+   "source": [
+    "# 😆 Python - Data processing with NumPy and Pandas 😆"
+   ]
+  },
+  {
+   "attachments": {
+    "Screen%20Shot%202023-02-18%20at%2011.31.39%20AM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "8dfe3bcd",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Screen%20Shot%202023-02-18%20at%2011.31.39%20AM.png\" width=\"500\">\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b13b14eb",
+   "metadata": {},
+   "source": [
+    "**Author:** HLW (2023-03-01)\n",
+    "\n",
+    "**Topic:** Python, Dataframe, NumPy, and Pandas (using real life data!)\n",
+    "\n",
+    "**Outlines:**\n",
+    "* **1 Introduction** (1.1 import packages, 1.2 import data, 1.3 data types)\n",
+    "* **2 NumPy** (2.1 creating arrays, 2.2 inspecting your arrays, 2.3 array mathematics, 2.4 copying, sorting, subsetting, slicing, and indexing arrays, 2.5 application)\n",
+    "* **3 Pandas** (3.1 creating data, 3.2 inspecting your data, 3.3 selecting and sorting data, 3.4 reshaping and merging data, 3.5 grouping data)\n",
+    "\n",
+    "**References:** \n",
+    "\n",
+    "* Python Data Science Handbook:\n",
+    "https://jakevdp.github.io/PythonDataScienceHandbook/\n",
+    "\n",
+    "* Importing Data - Python Cheat Sheets\n",
+    "https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Cheat+Sheets/Importing_Data_Python_Cheat_Sheet.pdf\n",
+    "\n",
+    "* Numpy - Python Cheat Sheets\n",
+    "https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf\n",
+    "\n",
+    "* Pandas - Python Cheat Sheets\n",
+    "https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf\n",
+    "\n",
+    "* UofT Coders - Joel Ostblom\n",
+    "https://uoftcoders.github.io/studyGroup/lessons/python/intro-data-analysis/lesson/\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13143894",
+   "metadata": {},
+   "source": [
+    "# ➡️ 1 INTRODUCTION"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8914da7",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Screen%20Shot%202023-02-19%20at%2010.03.51%20PM.png\" width=\"400\">\n",
+    "</div> "
+   ]
+  },
+  {
+   "attachments": {
+    "Screen%20Shot%202023-02-19%20at%2010.03.51%20PM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "8731a8e2",
+   "metadata": {},
+   "source": [
+    "### 1.1 Import NumPy and Pandas packages\n",
+    "\n",
+    "* **NumPy** (also called Numerical Python), is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices.\n",
+    "\n",
+    "* **Pandas** (also called Panel data), is a Python package that is most commonly used to work with spreadsheet-like data. Data are easily loaded into pandas from .csv or other spreadsheet formats (e.g., .xlxs). The format pandas uses to store this data is called a Data Frame.\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:Screen%20Shot%202023-02-19%20at%2010.03.51%20PM.png\" width=\"300\">\n",
+    "</div> \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e6bcbd3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.0"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "numpy.mean([10,20,30])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "77387812",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20.0\n",
+      "8.16496580927726\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "print(np.mean([10,20,30]))\n",
+    "print(np.std([10,20,30]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9f8df208",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "attachments": {
+    "Screen%20Shot%202023-02-20%20at%208.54.33%20PM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "a9aecfbe",
+   "metadata": {},
+   "source": [
+    "### 1.2 Import our Food Data\n",
+    "\n",
+    "* The file \".csv\" is a sample data about food and beverage products that are available for purchase in grocery stores. \n",
+    "* This dataset includes food product details (name, brand, etc.) and nutritional information (calories, protein, etc).\n",
+    "\n",
+    "<div>\n",
+    "<img src=\"attachment:Screen%20Shot%202023-02-20%20at%208.54.33%20PM.png\" width=\"600\">\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de1a1267",
+   "metadata": {},
+   "source": [
+    "### Import .csv data with pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "bde64b18",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['ID', 'Food_Category', 'Product_Name', 'Brand', 'Price',\n",
+      "       'Container_Size', 'Container_Size_unit', 'Serving_Size',\n",
+      "       'Serving_Size_unit', 'Calories', 'Fat', 'Sodium', 'Carbohydrate',\n",
+      "       'Sugars', 'Fiber', 'Protein'],\n",
+      "      dtype='object')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>THE KEG | Prime Rib Beef Burger | 2023-02-16</td>\n",
+       "      <td>international</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>ORVILLE |  Bowl, Buttery | 2023-02-18</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>136</td>\n",
+       "      <td>g</td>\n",
+       "      <td>82</td>\n",
+       "      <td>g</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>NO NAME| Raised Chocolate Donuts 6 Pack | 2023...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.99</td>\n",
+       "      <td>510</td>\n",
+       "      <td>g</td>\n",
+       "      <td>85</td>\n",
+       "      <td>g</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>STARBUCKS | Frappuccino Vanilla Coffee Drink |...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>150</td>\n",
+       "      <td>53</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ID Food_Category                                       Product_Name  \\\n",
+       "0  10027        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...   \n",
+       "1  10018          Meat       THE KEG | Prime Rib Beef Burger | 2023-02-16   \n",
+       "2  10012         Snaks              ORVILLE |  Bowl, Buttery | 2023-02-18   \n",
+       "3  10028        Bakery  NO NAME| Raised Chocolate Donuts 6 Pack | 2023...   \n",
+       "4  10004        Drinks  STARBUCKS | Frappuccino Vanilla Coffee Drink |...   \n",
+       "\n",
+       "           Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "0          local  21.29             710                   g           118   \n",
+       "1  international  22.99            1020                   g           170   \n",
+       "2          local   5.99             136                   g            82   \n",
+       "3          local   7.99             510                   g            85   \n",
+       "4  international   3.99             405                  mL           405   \n",
+       "\n",
+       "  Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "0                 g       510  31.0     270            52      37    3.0   \n",
+       "1                 g       490  41.0     660             2       0    0.0   \n",
+       "2                 g       430  26.0     670            41       1    7.0   \n",
+       "3                 g       320  16.0     320            39      13    1.0   \n",
+       "4                mL       290   4.5     150            53      46    0.0   \n",
+       "\n",
+       "   Protein  \n",
+       "0      6.0  \n",
+       "1     26.0  \n",
+       "2      6.0  \n",
+       "3      5.0  \n",
+       "4      9.0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filename = '/Users/hgl/Desktop/Food_Data_HLW.csv'\n",
+    "food = pd.read_csv(filename) # dataframe\n",
+    "print(food.columns)\n",
+    "food.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a565c22d",
+   "metadata": {},
+   "source": [
+    "### Export .csv data with pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d4c8112c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "food.to_csv(\"/Users/hgl/Desktop/Food_Data_HLW_export.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0a89d63",
+   "metadata": {},
+   "source": [
+    "### 1.3 Data Types"
+   ]
+  },
+  {
+   "attachments": {
+    "Screen%20Shot%202023-02-19%20at%2010.31.36%20PM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "285af55c",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Screen%20Shot%202023-02-19%20at%2010.31.36%20PM.png\" width=\"600\">\n",
+    "</div>\n",
+    "\n",
+    "Note: Python is an object-oriented programming language. Everything is in Python treated as an **object**, including variable, function, list, tuple, dictionary, set, etc. \n",
+    "\n",
+    "Ref: https://medium.com/@rishikakapoor29/python-for-data-science-64f0a675b6eb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8c00bef6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 300\n",
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ab0b858c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.6 <class 'float'>\n",
+      "295.4 <class 'float'>\n",
+      "1380.0 <class 'float'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = 4.6\n",
+    "print(b, type(b))\n",
+    "print(a-b, type(a-b))\n",
+    "print(a*b, type(a*b))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "f8e4c496",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sammy <class 'str'>\n",
+      "Hello World ! <class 'str'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = \"Sammy\"\n",
+    "print(c, type(c))\n",
+    "print(\"Hello \" + \"World !\", type(c))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "75fcd3f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'list'>\n",
+      "200.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = [10, \"hello\", 200.3]\n",
+    "print(type(d))\n",
+    "print(d[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ae3d0428",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True <class 'bool'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "e = 1<2\n",
+    "print(e, type(e))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "64ed9427",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "28\n",
+      "<class 'int'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert float to an integer\n",
+    "k1 = 28.9\n",
+    "k2 = int(k1)\n",
+    "print(k2)\n",
+    "print(type(k2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "19314e79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID                       int64\n",
+       "Food_Category           object\n",
+       "Product_Name            object\n",
+       "Brand                   object\n",
+       "Price                  float64\n",
+       "Container_Size           int64\n",
+       "Container_Size_unit     object\n",
+       "Serving_Size             int64\n",
+       "Serving_Size_unit       object\n",
+       "Calories                 int64\n",
+       "Fat                    float64\n",
+       "Sodium                   int64\n",
+       "Carbohydrate             int64\n",
+       "Sugars                   int64\n",
+       "Fiber                  float64\n",
+       "Protein                float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# apply to our food data\n",
+    "food.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed3bba60",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67919c82",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e82bfb6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bd6a934",
+   "metadata": {},
+   "source": [
+    "# ➡️ 2 Numpy"
+   ]
+  },
+  {
+   "attachments": {
+    "Screen%20Shot%202023-02-18%20at%2011.14.56%20AM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "0ef611e1",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Screen%20Shot%202023-02-18%20at%2011.14.56%20AM.png\" width=\"400\">\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "448f07dd",
+   "metadata": {},
+   "source": [
+    "### 2.1 Creating Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e8637e7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a1 = np.array([1,2,3])\n",
+    "a1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "1da9c975",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.5, 2. , 3. ],\n",
+       "       [4. , 5. , 6. ]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a2 = np.array([(1.5,2,3),(4,5,6)], dtype = float)\n",
+    "a2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b1c37124",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[1, 2, 3],\n",
+       "        [4, 5, 6]],\n",
+       "\n",
+       "       [[1, 2, 3],\n",
+       "        [4, 5, 6]]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a3 = np.array([[(1.5,2,3),(4,5,6)],[(1.5,2,3),(4,5,6)]], dtype = int)\n",
+    "a3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c03aecf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an (3x4) array of zeros\n",
+    "np.zeros((3,4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "17c25c42",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[1, 1, 1, 1],\n",
+       "        [1, 1, 1, 1],\n",
+       "        [1, 1, 1, 1]],\n",
+       "\n",
+       "       [[1, 1, 1, 1],\n",
+       "        [1, 1, 1, 1],\n",
+       "        [1, 1, 1, 1]]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an (2x3x4) array of ones\n",
+    "np.ones((2,3,4), dtype = int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "5359404c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[10, 'hello', 200.3]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an (2x3x4) array, range 10-100, each step gap 5\n",
+    "a4 = np.arange(10,100,5)\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d84652dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.  , 0.25, 0.5 , 0.75, 1.  , 1.25, 1.5 , 1.75, 2.  ])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an (2x3x4) array, range 0-2, total numbers\n",
+    "np.linspace(0,2,9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c684c73",
+   "metadata": {},
+   "source": [
+    "### 1.2 Inspecting Your Array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f744fd3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2. , 3. , 4. ],\n",
+       "       [0.2, 0.3, 0.4]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_array = np.array([(2,3,4),(0.2,0.3,0.4)], dtype = float)\n",
+    "my_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "168e7e4a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Array dimensions\n",
+    "my_array.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c2223b5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Length of array\n",
+    "len(my_array) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "4bc348fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Number of array dimensions\n",
+    "my_array.ndim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "41880fdd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Number of array elements\n",
+    "my_array.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "51c76a46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# array type\n",
+    "my_array.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "edbe22ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'float64'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# array type name\n",
+    "my_array.dtype.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "3f5ad797",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2 3 4]\n",
+      " [0 0 0]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'int64'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert an array to a different type (float to int)\n",
+    "my_array2 = my_array.astype(int) \n",
+    "print(my_array2)\n",
+    "my_array2.dtype.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1d8b905",
+   "metadata": {},
+   "source": [
+    "### 2.3 Array Mathematics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "ceca9f4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[8. , 7. , 6. ],\n",
+       "       [9.8, 9.7, 9.6]])"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Subtraction\n",
+    "a1 = np.array([10,10,10])\n",
+    "a2 = np.array([(2,3,4),(0.2,0.3,0.4)], dtype = float)\n",
+    "c = a1-a2\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "28ff56f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[8. , 7. , 6. ],\n",
+       "       [9.8, 9.7, 9.6]])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Subtraction alternative\n",
+    "np.subtract(a1,a2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "77081840",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[12. , 13. , 14. ],\n",
+       "       [10.2, 10.3, 10.4]])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Addition\n",
+    "c = a1 + a2\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "dd415e32",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[12. , 13. , 14. ],\n",
+       "       [10.2, 10.3, 10.4]])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Addition alternative\n",
+    "d = np.add(a1,a2)\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "34a14c99",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[20., 30., 40.],\n",
+       "       [ 2.,  3.,  4.]])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Multiplication\n",
+    "a1*a2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "e9ee2703",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[20., 30., 40.],\n",
+       "       [ 2.,  3.,  4.]])"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Multiplication - alternative\n",
+    "np.multiply(a1, a2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6cca0ec9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 5.        ,  3.33333333,  2.5       ],\n",
+       "       [50.        , 33.33333333, 25.        ]])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Division\n",
+    "a1/a2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "3237d955",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 5.        ,  3.33333333,  2.5       ],\n",
+       "       [50.        , 33.33333333, 25.        ]])"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Division - alternative\n",
+    "np.divide(a1, a2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "8e5851fa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 7.3890561 , 20.08553692, 54.59815003],\n",
+       "       [ 1.22140276,  1.34985881,  1.4918247 ]])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# exponentiation\n",
+    "np.exp(a2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "5ac3bc7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.41421356, 1.73205081, 2.        ],\n",
+       "       [0.4472136 , 0.54772256, 0.63245553]])"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# square root\n",
+    "np.sqrt(a2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "eb27f142",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.69314718,  1.09861229,  1.38629436],\n",
+       "       [-1.60943791, -1.2039728 , -0.91629073]])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# log\n",
+    "np.log(a2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "e70baa8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False],\n",
+       "       [False, False, False]])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# comparason 1\n",
+    "a1==a2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "2d6477ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True,  True, False],\n",
+       "       [False, False,  True]])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# comparason 2\n",
+    "b1 = np.array([1,2,6])\n",
+    "b2 = np.array([(1,2,3),(4,5,6)], dtype = float)\n",
+    "b1==b2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b845b88c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False],\n",
+       "       [ True,  True, False]])"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# comparason 3\n",
+    "b1<b2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "77354ed7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21.0"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Aggregate Functions\n",
+    "c = np.array([(1,2,3),(4,5,6)], dtype = float)\n",
+    "c.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "0500f353",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.5"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "38297468",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.707825127659933"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "3a101833",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "0649285f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "9dd0102e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4., 5., 6.])"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.max(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "b713c189",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3., 6.])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.max(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ab4a0f8",
+   "metadata": {},
+   "source": [
+    "### 2.4 Copying, Sorting, Subsetting, Slicing, Indexing Arrays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "4bbae395",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 10. ,   1. , 100. ],\n",
+       "       [  4.1,   6.1,   0.1]])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Copying Arrays\n",
+    "d = np.array([(10,1,100),(4.1,6.1,0.1)], dtype = float)\n",
+    "d1 = d.copy()\n",
+    "d1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "c6487e51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  1. ,  10. , 100. ],\n",
+       "       [  0.1,   4.1,   6.1]])"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sorting Arrays\n",
+    "d = np.array([(10,1,100),(4.1,6.1,0.1)], dtype = float)\n",
+    "d2 = np.sort(d)\n",
+    "d2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "0314eba8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[  1. ,  10. , 100. ],\n",
+       "       [  0.1,   4.1,   6.1]])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sorting Arrays 2\n",
+    "d = np.array([(10,1,100),(4.1,6.1,0.1)], dtype = float)\n",
+    "d2 = np.sort(d)\n",
+    "d2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "89554aac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['1', '10', '100'],\n",
+       "       ['apple', 'banana', 'cherry']], dtype='<U21')"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sorting Arrays 3\n",
+    "e = np.array([(10,1,100),('banana', 'cherry', 'apple')])\n",
+    "e2 = np.sort(e)\n",
+    "e2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "5dcf20a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "apple\n",
+      "100\n",
+      "100\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Subsetting - Select the element at the 2nd index\n",
+    "e = np.array([(10,1,100),('banana', 'cherry', 'apple')])\n",
+    "print(e[1,2])\n",
+    "print(e[0,2])\n",
+    "print(e[0][2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "9759d978",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "e[1:2] is [['banana' 'cherry' 'apple']]\n",
+      "\n",
+      "e[0,0:2] is ['10' '1']\n",
+      "\n",
+      "e[:2] is [['10' '1' '100']\n",
+      " ['banana' 'cherry' 'apple']]\n",
+      "\n",
+      "e[::2] is [['10' '1' '100']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Slicing - Select items at index 0 and 1\n",
+    "e = np.array([(10,1,100),('banana', 'cherry', 'apple')])\n",
+    "print(\"e[1:2] is\", e[1:2])\n",
+    "print()\n",
+    "print(\"e[0,0:2] is\", e[0,0:2])\n",
+    "print()\n",
+    "print(\"e[:2] is\", e[:2])\n",
+    "print()\n",
+    "print(\"e[::2] is\", e[::2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "f47dd931",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['banana', 'cherry', 'apple'],\n",
+       "       ['10', '1', '100']], dtype='<U21')"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Reversed array\n",
+    "e[ : :-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "9c261677",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10,  1,  2])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Boolean Indexing - select the elements less than 20\n",
+    "f = np.array([(10,1,100),(20,2,200)])\n",
+    "f[f<20]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8cfbf41",
+   "metadata": {},
+   "source": [
+    "### 2.5 Application to our Food Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "b9f76d94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Bakery', 'Meat', 'Snaks', 'Drinks', 'Dairy', 'Fruit'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "food[\"Food_Category\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3c22c18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f06f039a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11e92645",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41a590f9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2cca460",
+   "metadata": {},
+   "source": [
+    "# ➡️  3 Pandas\n",
+    "\n",
+    "The Pandas library is built on NumPy and provides easy-to-use data structures and data analysis tools for Python."
+   ]
+  },
+  {
+   "attachments": {
+    "Screen%20Shot%202023-02-20%20at%209.57.51%20PM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "37dfd185",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Screen%20Shot%202023-02-20%20at%209.57.51%20PM.png\" width=\"600\">\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d415dfa3",
+   "metadata": {},
+   "source": [
+    "### 3.1 Creating/Importing Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "4f279702",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    3\n",
+       "b   -5\n",
+       "c    7\n",
+       "d    4\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Creating Series\n",
+    "s = pd.Series([3,-5,7,4], index=[\"a\",\"b\",\"c\",\"d\"])\n",
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "8e3ed43f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Food</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Calories</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Popcorn</td>\n",
+       "      <td>ORVILLE</td>\n",
+       "      <td>430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Donuts</td>\n",
+       "      <td>No Name</td>\n",
+       "      <td>320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Candies</td>\n",
+       "      <td>M&amp;M'S</td>\n",
+       "      <td>210</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Food    Brand  Calories\n",
+       "0  Popcorn  ORVILLE       430\n",
+       "1   Donuts  No Name       320\n",
+       "2  Candies    M&M'S       210"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Creating DataFrames\n",
+    "df = pd.DataFrame(\n",
+    "    [[\"Popcorn\" ,\"ORVILLE\", 430],\n",
+    "     [\"Donuts\", \"No Name\", 320],\n",
+    "     [\"Candies\", \"M&M'S\", 210]],\n",
+    "     #index = [90001, 90002, 90003],\n",
+    "     columns=['Food', 'Brand', 'Calories'])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "b2df0300",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['ID', 'Food_Category', 'Product_Name', 'Brand', 'Price',\n",
+      "       'Container_Size', 'Container_Size_unit', 'Serving_Size',\n",
+      "       'Serving_Size_unit', 'Calories', 'Fat', 'Sodium', 'Carbohydrate',\n",
+      "       'Sugars', 'Fiber', 'Protein'],\n",
+      "      dtype='object')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>THE KEG | Prime Rib Beef Burger | 2023-02-16</td>\n",
+       "      <td>international</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>ORVILLE |  Bowl, Buttery | 2023-02-18</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>136</td>\n",
+       "      <td>g</td>\n",
+       "      <td>82</td>\n",
+       "      <td>g</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ID Food_Category                                       Product_Name  \\\n",
+       "0  10027        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...   \n",
+       "1  10018          Meat       THE KEG | Prime Rib Beef Burger | 2023-02-16   \n",
+       "2  10012         Snaks              ORVILLE |  Bowl, Buttery | 2023-02-18   \n",
+       "\n",
+       "           Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "0          local  21.29             710                   g           118   \n",
+       "1  international  22.99            1020                   g           170   \n",
+       "2          local   5.99             136                   g            82   \n",
+       "\n",
+       "  Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "0                 g       510  31.0     270            52      37    3.0   \n",
+       "1                 g       490  41.0     660             2       0    0.0   \n",
+       "2                 g       430  26.0     670            41       1    7.0   \n",
+       "\n",
+       "   Protein  \n",
+       "0      6.0  \n",
+       "1     26.0  \n",
+       "2      6.0  "
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# remember import our food data as DataFrames\n",
+    "filename = '/Users/hgl/Desktop/Food_Data_HLW.csv'\n",
+    "food = pd.read_csv(filename) # dataframe\n",
+    "print(food.columns)\n",
+    "food.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e16db496",
+   "metadata": {},
+   "source": [
+    "### 3.2 Inspecting your Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "41140ee6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# food is a DataFrame\n",
+    "type(food)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "481bf6a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get a count of rows\n",
+    "len(food)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "e938d678",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(30, 16)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the size (rows, columns)\n",
+    "food.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "73069b25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=30, step=1)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View index\n",
+    "food.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "ac11a866",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['ID', 'Food_Category', 'Product_Name', 'Brand', 'Price',\n",
+       "       'Container_Size', 'Container_Size_unit', 'Serving_Size',\n",
+       "       'Serving_Size_unit', 'Calories', 'Fat', 'Sodium', 'Carbohydrate',\n",
+       "       'Sugars', 'Fiber', 'Protein'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View variables\n",
+    "food.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "2e6b7bea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID                       int64\n",
+       "Food_Category           object\n",
+       "Product_Name            object\n",
+       "Brand                   object\n",
+       "Price                  float64\n",
+       "Container_Size           int64\n",
+       "Container_Size_unit     object\n",
+       "Serving_Size             int64\n",
+       "Serving_Size_unit       object\n",
+       "Calories                 int64\n",
+       "Fat                    float64\n",
+       "Sodium                   int64\n",
+       "Carbohydrate             int64\n",
+       "Sugars                   int64\n",
+       "Fiber                  float64\n",
+       "Protein                float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View data types for each column/variable\n",
+    "food.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "0506efe9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID                     30\n",
+       "Food_Category          30\n",
+       "Product_Name           30\n",
+       "Brand                  30\n",
+       "Price                  30\n",
+       "Container_Size         30\n",
+       "Container_Size_unit    30\n",
+       "Serving_Size           30\n",
+       "Serving_Size_unit      30\n",
+       "Calories               30\n",
+       "Fat                    29\n",
+       "Sodium                 30\n",
+       "Carbohydrate           30\n",
+       "Sugars                 30\n",
+       "Fiber                  28\n",
+       "Protein                30\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get a count of values in each column/variable (non-NA values)\n",
+    "food.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "38908c59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(27, 16)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Handling Missing Data - Drop\n",
+    "food2 = food.dropna()\n",
+    "food2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "da8b49b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        3.0\n",
+       "1        0.0\n",
+       "2        7.0\n",
+       "3        1.0\n",
+       "4        0.0\n",
+       "5        1.0\n",
+       "6        0.0\n",
+       "7        1.0\n",
+       "8        0.0\n",
+       "9        2.0\n",
+       "10       1.0\n",
+       "11    9999.0\n",
+       "12       0.0\n",
+       "13       2.0\n",
+       "14       0.0\n",
+       "15       2.0\n",
+       "16       0.0\n",
+       "17       1.0\n",
+       "18       1.0\n",
+       "19    9999.0\n",
+       "20       0.0\n",
+       "21       0.0\n",
+       "22       0.0\n",
+       "23       0.0\n",
+       "24       0.0\n",
+       "25       0.0\n",
+       "26       0.0\n",
+       "27       0.0\n",
+       "28       2.0\n",
+       "29       0.0\n",
+       "Name: Fiber, dtype: float64"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Handling Missing Data - Fill with a number\n",
+    "food3 = food.fillna(9999)\n",
+    "food3.shape\n",
+    "food3.count()\n",
+    "food3[\"Fiber\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "3a43c688",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>29.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>10015.500000</td>\n",
+       "      <td>7.046667</td>\n",
+       "      <td>605.666667</td>\n",
+       "      <td>121.300000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>10.258621</td>\n",
+       "      <td>220.200000</td>\n",
+       "      <td>22.866667</td>\n",
+       "      <td>12.766667</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>5.136667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>8.803408</td>\n",
+       "      <td>5.063642</td>\n",
+       "      <td>496.307283</td>\n",
+       "      <td>121.150562</td>\n",
+       "      <td>123.679229</td>\n",
+       "      <td>9.996089</td>\n",
+       "      <td>216.284041</td>\n",
+       "      <td>16.213518</td>\n",
+       "      <td>14.670363</td>\n",
+       "      <td>1.483596</td>\n",
+       "      <td>5.534997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>10001.000000</td>\n",
+       "      <td>1.790000</td>\n",
+       "      <td>45.000000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>10008.250000</td>\n",
+       "      <td>3.990000</td>\n",
+       "      <td>297.000000</td>\n",
+       "      <td>42.750000</td>\n",
+       "      <td>130.000000</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>48.750000</td>\n",
+       "      <td>9.500000</td>\n",
+       "      <td>1.250000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>10015.500000</td>\n",
+       "      <td>5.240000</td>\n",
+       "      <td>486.500000</td>\n",
+       "      <td>65.500000</td>\n",
+       "      <td>200.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>145.000000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>7.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>10022.750000</td>\n",
+       "      <td>7.865000</td>\n",
+       "      <td>700.000000</td>\n",
+       "      <td>165.000000</td>\n",
+       "      <td>257.500000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>350.000000</td>\n",
+       "      <td>30.750000</td>\n",
+       "      <td>20.250000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>10030.000000</td>\n",
+       "      <td>22.990000</td>\n",
+       "      <td>2000.000000</td>\n",
+       "      <td>473.000000</td>\n",
+       "      <td>510.000000</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>770.000000</td>\n",
+       "      <td>55.000000</td>\n",
+       "      <td>54.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>26.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 ID      Price  Container_Size  Serving_Size    Calories  \\\n",
+       "count     30.000000  30.000000       30.000000     30.000000   30.000000   \n",
+       "mean   10015.500000   7.046667      605.666667    121.300000  200.000000   \n",
+       "std        8.803408   5.063642      496.307283    121.150562  123.679229   \n",
+       "min    10001.000000   1.790000       45.000000     15.000000   20.000000   \n",
+       "25%    10008.250000   3.990000      297.000000     42.750000  130.000000   \n",
+       "50%    10015.500000   5.240000      486.500000     65.500000  200.000000   \n",
+       "75%    10022.750000   7.865000      700.000000    165.000000  257.500000   \n",
+       "max    10030.000000  22.990000     2000.000000    473.000000  510.000000   \n",
+       "\n",
+       "             Fat      Sodium  Carbohydrate     Sugars      Fiber    Protein  \n",
+       "count  29.000000   30.000000     30.000000  30.000000  28.000000  30.000000  \n",
+       "mean   10.258621  220.200000     22.866667  12.766667   0.857143   5.136667  \n",
+       "std     9.996089  216.284041     16.213518  14.670363   1.483596   5.534997  \n",
+       "min     0.000000    0.000000      0.000000   0.000000   0.000000   0.000000  \n",
+       "25%     3.500000   48.750000      9.500000   1.250000   0.000000   1.250000  \n",
+       "50%     7.000000  145.000000     22.000000   7.500000   0.000000   3.500000  \n",
+       "75%    15.000000  350.000000     30.750000  20.250000   1.000000   7.000000  \n",
+       "max    41.000000  770.000000     55.000000  54.000000   7.000000  26.000000  "
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Provides various summary statistics\n",
+    "food.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "0b7de320",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.063642094813557"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Statistic summary of the variable: Price\n",
+    "food[\"Price\"].count()\n",
+    "food[\"Price\"].min()\n",
+    "food[\"Price\"].max()\n",
+    "food[\"Price\"].quantile([0.25, 0.75])\n",
+    "food[\"Price\"].sum()\n",
+    "food[\"Price\"].mean()\n",
+    "food[\"Price\"].std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "132d600e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>THE KEG | Prime Rib Beef Burger | 2023-02-16</td>\n",
+       "      <td>international</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>ORVILLE |  Bowl, Buttery | 2023-02-18</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>136</td>\n",
+       "      <td>g</td>\n",
+       "      <td>82</td>\n",
+       "      <td>g</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ID Food_Category                                       Product_Name  \\\n",
+       "0  10027        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...   \n",
+       "1  10018          Meat       THE KEG | Prime Rib Beef Burger | 2023-02-16   \n",
+       "2  10012         Snaks              ORVILLE |  Bowl, Buttery | 2023-02-18   \n",
+       "\n",
+       "           Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "0          local  21.29             710                   g           118   \n",
+       "1  international  22.99            1020                   g           170   \n",
+       "2          local   5.99             136                   g            82   \n",
+       "\n",
+       "  Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "0                 g       510  31.0     270            52      37    3.0   \n",
+       "1                 g       490  41.0     660             2       0    0.0   \n",
+       "2                 g       430  26.0     670            41       1    7.0   \n",
+       "\n",
+       "   Protein  \n",
+       "0      6.0  \n",
+       "1     26.0  \n",
+       "2      6.0  "
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View first 3 rows of data\n",
+    "food.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba0a8930",
+   "metadata": {},
+   "source": [
+    "### 3.2 Selecting and Sorting your Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "918b38cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Food_Category</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Meat</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Food_Category\n",
+       "1          Meat"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select data by position\n",
+    "food.iloc[[0],[0]]\n",
+    "food.iloc[[0],[1]]\n",
+    "food.iloc[[1],[1]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "d02389a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>22.99</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Price\n",
+       "1  22.99"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select data by label\n",
+    "food.loc[[0],[\"Food_Category\"]]\n",
+    "food.loc[[1],[\"Price\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "2348e7ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>THE KEG | Prime Rib Beef Burger | 2023-02-16</td>\n",
+       "      <td>international</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ID Food_Category                                       Product_Name  \\\n",
+       "0  10027        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...   \n",
+       "1  10018          Meat       THE KEG | Prime Rib Beef Burger | 2023-02-16   \n",
+       "\n",
+       "           Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "0          local  21.29             710                   g           118   \n",
+       "1  international  22.99            1020                   g           170   \n",
+       "\n",
+       "  Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "0                 g       510  31.0     270            52      37    3.0   \n",
+       "1                 g       490  41.0     660             2       0    0.0   \n",
+       "\n",
+       "   Protein  \n",
+       "0      6.0  \n",
+       "1     26.0  "
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# select data by rows\n",
+    "food3 = food[food[\"Price\"] > 20]\n",
+    "food3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "114d5970",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10013</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>29</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10010</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Everything Trail Mix Nuts...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>300</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>220</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>31</td>\n",
+       "      <td>23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10003</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>MONSTER ENERGY | Green Energy Drink, Can | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>370</td>\n",
+       "      <td>55</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10006</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>M&amp;M'S | Peanut Milk Chocolate Candies, Bowl Si...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>9.99</td>\n",
+       "      <td>400</td>\n",
+       "      <td>g</td>\n",
+       "      <td>42</td>\n",
+       "      <td>g</td>\n",
+       "      <td>210</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>25</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>10015</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>NO NAME | Mild Sugar-Cured Bacon | 2023-01-16</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.49</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>430</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ID Food_Category                                       Product_Name  \\\n",
+       "10  10013         Snaks  NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...   \n",
+       "11  10010         Snaks  PRESIDENT'S CHOICE | Everything Trail Mix Nuts...   \n",
+       "12  10003        Drinks  MONSTER ENERGY | Green Energy Drink, Can | 202...   \n",
+       "13  10006         Snaks  M&M'S | Peanut Milk Chocolate Candies, Bowl Si...   \n",
+       "14  10015          Meat      NO NAME | Mild Sugar-Cured Bacon | 2023-01-16   \n",
+       "\n",
+       "            Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "10  international   1.79              45                   g            45   \n",
+       "11          local   4.99             300                   g            50   \n",
+       "12          local   3.99             473                  mL           473   \n",
+       "13  international   9.99             400                   g            42   \n",
+       "14          local   7.49             500                   g            50   \n",
+       "\n",
+       "   Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "10                 g       230  12.0      35            29      22    1.0   \n",
+       "11                 g       220  10.0      25            31      23    NaN   \n",
+       "12                mL       210   NaN     370            55      54    0.0   \n",
+       "13                 g       210  11.0      20            25      21    2.0   \n",
+       "14                 g       200  19.0     430             2       0    0.0   \n",
+       "\n",
+       "    Protein  \n",
+       "10      3.0  \n",
+       "11      3.0  \n",
+       "12      0.0  \n",
+       "13      4.0  \n",
+       "14      4.0  "
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# select data by rows\n",
+    "food4 = food.iloc[10:15]\n",
+    "food4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "ba5cda8a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     21.29\n",
+       "1     22.99\n",
+       "2      5.99\n",
+       "3      7.99\n",
+       "4      3.99\n",
+       "5      6.49\n",
+       "6     14.29\n",
+       "7      3.99\n",
+       "8     10.99\n",
+       "9      4.99\n",
+       "10     1.79\n",
+       "11     4.99\n",
+       "12     3.99\n",
+       "13     9.99\n",
+       "14     7.49\n",
+       "15     3.99\n",
+       "16     3.99\n",
+       "17     3.99\n",
+       "18     3.99\n",
+       "19     3.99\n",
+       "20     3.99\n",
+       "21     5.69\n",
+       "22     2.39\n",
+       "23     8.99\n",
+       "24     6.99\n",
+       "25    12.99\n",
+       "26     3.39\n",
+       "27     5.49\n",
+       "28     4.29\n",
+       "29     5.99\n",
+       "Name: Price, dtype: float64"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select data by columns\n",
+    "# Select one variable\n",
+    "food[\"Price\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "062d70b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Bakery', 'Meat', 'Snaks', 'Drinks', 'Dairy', 'Fruit'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# View unique data types for each column/variable\n",
+    "food[\"Food_Category\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "49bc5a2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Snaks     9\n",
+       "Bakery    5\n",
+       "Meat      5\n",
+       "Drinks    5\n",
+       "Dairy     4\n",
+       "Fruit     2\n",
+       "Name: Food_Category, dtype: int64"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select one variable and count different values of this variable\n",
+    "food[\"Food_Category\"]\n",
+    "food[\"Food_Category\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "8c828842",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>150</td>\n",
+       "      <td>53</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10008</td>\n",
+       "      <td>280</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>440</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10016</td>\n",
+       "      <td>260</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>770</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10011</td>\n",
+       "      <td>260</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10019</td>\n",
+       "      <td>250</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>410</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10026</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>230</td>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10013</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>29</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10010</td>\n",
+       "      <td>220</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>31</td>\n",
+       "      <td>23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10003</td>\n",
+       "      <td>210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>370</td>\n",
+       "      <td>55</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10006</td>\n",
+       "      <td>210</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>25</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>10015</td>\n",
+       "      <td>200</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>430</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10030</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>320</td>\n",
+       "      <td>38</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10022</td>\n",
+       "      <td>160</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>22</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>10009</td>\n",
+       "      <td>150</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>80</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10014</td>\n",
+       "      <td>140</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>140</td>\n",
+       "      <td>22</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10029</td>\n",
+       "      <td>140</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10001</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>39</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>10020</td>\n",
+       "      <td>130</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10007</td>\n",
+       "      <td>130</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>10023</td>\n",
+       "      <td>110</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>10002</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>10017</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10005</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>10021</td>\n",
+       "      <td>40</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>10025</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>10024</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ID  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  Protein\n",
+       "0   10027       510  31.0     270            52      37    3.0      6.0\n",
+       "1   10018       490  41.0     660             2       0    0.0     26.0\n",
+       "2   10012       430  26.0     670            41       1    7.0      6.0\n",
+       "3   10028       320  16.0     320            39      13    1.0      5.0\n",
+       "4   10004       290   4.5     150            53      46    0.0      9.0\n",
+       "5   10008       280  18.0     440            30       1    1.0      3.0\n",
+       "6   10016       260  20.0     770             6       2    0.0     15.0\n",
+       "7   10011       260  15.0     240            29       2    1.0      2.0\n",
+       "8   10019       250  12.0     410            20       1    0.0     14.0\n",
+       "9   10026       230  12.0     230            28       5    2.0      4.0\n",
+       "10  10013       230  12.0      35            29      22    1.0      3.0\n",
+       "11  10010       220  10.0      25            31      23    NaN      3.0\n",
+       "12  10003       210   NaN     370            55      54    0.0      0.0\n",
+       "13  10006       210  11.0      20            25      21    2.0      4.0\n",
+       "14  10015       200  19.0     430             2       0    0.0      4.0\n",
+       "15  10030       200   2.5     320            38       3    2.0      7.0\n",
+       "16  10022       160   5.0      70            22      18    0.0      7.0\n",
+       "17  10009       150   7.0      80            22       9    1.0      1.0\n",
+       "18  10014       140   5.0     140            22      12    1.0      1.0\n",
+       "19  10029       140   6.0      60            20      11    NaN      2.0\n",
+       "20  10001       140   0.0      25            39      39    0.0      0.0\n",
+       "21  10020       130   5.0     120            12      12    0.0      9.0\n",
+       "22  10007       130   7.0      45            16       6    0.0      2.0\n",
+       "23  10023       110   8.0     220             0       0    0.0      7.0\n",
+       "24  10002       110   0.0       0            27      23    0.0      2.0\n",
+       "25  10017        50   1.0     360             0       0    0.0      9.0\n",
+       "26  10005        50   0.0      65            11      10    0.0      1.0\n",
+       "27  10021        40   3.5      60             1       1    0.0      1.0\n",
+       "28  10025        40   0.0       0             9       6    2.0      1.0\n",
+       "29  10024        20   0.0       1             5       5    0.0      0.1"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select data by columns\n",
+    "food2 = food.loc[:, ['ID', 'Calories', 'Fat', 'Sodium', 'Carbohydrate','Sugars', 'Fiber', 'Protein']]\n",
+    "food2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "60946c1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>21.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Meat</td>\n",
+       "      <td>THE KEG | Prime Rib Beef Burger | 2023-02-16</td>\n",
+       "      <td>22.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>ORVILLE |  Bowl, Buttery | 2023-02-18</td>\n",
+       "      <td>5.99</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Food_Category                                       Product_Name  Price\n",
+       "0        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...  21.29\n",
+       "1          Meat       THE KEG | Prime Rib Beef Burger | 2023-02-16  22.99\n",
+       "2         Snaks              ORVILLE |  Bowl, Buttery | 2023-02-18   5.99"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select data by columns\n",
+    "# Select 3 variables (food category, product name, and price) of the first 3 food products\n",
+    "selected = [\"Food_Category\", \"Product_Name\",\"Price\"]\n",
+    "food[selected].head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "a1fa937e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>136</td>\n",
+       "      <td>g</td>\n",
+       "      <td>82</td>\n",
+       "      <td>g</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>7.99</td>\n",
+       "      <td>510</td>\n",
+       "      <td>g</td>\n",
+       "      <td>85</td>\n",
+       "      <td>g</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>150</td>\n",
+       "      <td>53</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10008</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>6.49</td>\n",
+       "      <td>390</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>280</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>440</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10016</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>14.29</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>g</td>\n",
+       "      <td>100</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>770</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10011</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>202</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10019</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>10.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>g</td>\n",
+       "      <td>250</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>410</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10026</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>336</td>\n",
+       "      <td>g</td>\n",
+       "      <td>56</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>230</td>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10013</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>29</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10010</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>300</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>220</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>31</td>\n",
+       "      <td>23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10003</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>370</td>\n",
+       "      <td>55</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10006</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>9.99</td>\n",
+       "      <td>400</td>\n",
+       "      <td>g</td>\n",
+       "      <td>42</td>\n",
+       "      <td>g</td>\n",
+       "      <td>210</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>25</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>10015</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>7.49</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>430</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10030</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>675</td>\n",
+       "      <td>g</td>\n",
+       "      <td>75</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>320</td>\n",
+       "      <td>38</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10022</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>650</td>\n",
+       "      <td>g</td>\n",
+       "      <td>175</td>\n",
+       "      <td>g</td>\n",
+       "      <td>160</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>22</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>10009</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>225</td>\n",
+       "      <td>g</td>\n",
+       "      <td>31</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>80</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10014</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>180</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>140</td>\n",
+       "      <td>22</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10029</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>296</td>\n",
+       "      <td>g</td>\n",
+       "      <td>37</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10001</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>355</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>39</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>10020</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>5.69</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>130</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10007</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>2.39</td>\n",
+       "      <td>200</td>\n",
+       "      <td>g</td>\n",
+       "      <td>25</td>\n",
+       "      <td>g</td>\n",
+       "      <td>130</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>10023</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>8.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>110</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>10002</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>6.99</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>10017</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>12.99</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10005</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>10021</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>5.49</td>\n",
+       "      <td>227</td>\n",
+       "      <td>g</td>\n",
+       "      <td>15</td>\n",
+       "      <td>g</td>\n",
+       "      <td>40</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>10025</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>4.29</td>\n",
+       "      <td>540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>125</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>10024</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>310</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>15</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ID Food_Category  Price  Container_Size Container_Size_unit  \\\n",
+       "0   10027        Bakery  21.29             710                   g   \n",
+       "1   10018          Meat  22.99            1020                   g   \n",
+       "2   10012         Snaks   5.99             136                   g   \n",
+       "3   10028        Bakery   7.99             510                   g   \n",
+       "4   10004        Drinks   3.99             405                  mL   \n",
+       "5   10008         Snaks   6.49             390                   g   \n",
+       "6   10016          Meat  14.29            1000                   g   \n",
+       "7   10011         Snaks   3.99             202                   g   \n",
+       "8   10019          Meat  10.99             700                   g   \n",
+       "9   10026        Bakery   4.99             336                   g   \n",
+       "10  10013         Snaks   1.79              45                   g   \n",
+       "11  10010         Snaks   4.99             300                   g   \n",
+       "12  10003        Drinks   3.99             473                  mL   \n",
+       "13  10006         Snaks   9.99             400                   g   \n",
+       "14  10015          Meat   7.49             500                   g   \n",
+       "15  10030        Bakery   3.99             675                   g   \n",
+       "16  10022         Dairy   3.99             650                   g   \n",
+       "17  10009         Snaks   3.99             225                   g   \n",
+       "18  10014         Snaks   3.99             180                   g   \n",
+       "19  10029        Bakery   3.99             296                   g   \n",
+       "20  10001        Drinks   3.99            2000                  mL   \n",
+       "21  10020         Dairy   5.69            2000                  mL   \n",
+       "22  10007         Snaks   2.39             200                   g   \n",
+       "23  10023         Dairy   8.99             700                   g   \n",
+       "24  10002        Drinks   6.99            1540                  mL   \n",
+       "25  10017          Meat  12.99             500                   g   \n",
+       "26  10005        Drinks   3.39            1000                  mL   \n",
+       "27  10021         Dairy   5.49             227                   g   \n",
+       "28  10025         Fruit   4.29             540                  mL   \n",
+       "29  10024         Fruit   5.99             310                  mL   \n",
+       "\n",
+       "    Serving_Size Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  \\\n",
+       "0            118                 g       510  31.0     270            52   \n",
+       "1            170                 g       490  41.0     660             2   \n",
+       "2             82                 g       430  26.0     670            41   \n",
+       "3             85                 g       320  16.0     320            39   \n",
+       "4            405                mL       290   4.5     150            53   \n",
+       "5             50                 g       280  18.0     440            30   \n",
+       "6            100                 g       260  20.0     770             6   \n",
+       "7             50                 g       260  15.0     240            29   \n",
+       "8            150                 g       250  12.0     410            20   \n",
+       "9             56                 g       230  12.0     230            28   \n",
+       "10            45                 g       230  12.0      35            29   \n",
+       "11            50                 g       220  10.0      25            31   \n",
+       "12           473                mL       210   NaN     370            55   \n",
+       "13            42                 g       210  11.0      20            25   \n",
+       "14            50                 g       200  19.0     430             2   \n",
+       "15            75                 g       200   2.5     320            38   \n",
+       "16           175                 g       160   5.0      70            22   \n",
+       "17            31                 g       150   7.0      80            22   \n",
+       "18            30                 g       140   5.0     140            22   \n",
+       "19            37                 g       140   6.0      60            20   \n",
+       "20           355                mL       140   0.0      25            39   \n",
+       "21           250                mL       130   5.0     120            12   \n",
+       "22            25                 g       130   7.0      45            16   \n",
+       "23            30                 g       110   8.0     220             0   \n",
+       "24           250                mL       110   0.0       0            27   \n",
+       "25            50                 g        50   1.0     360             0   \n",
+       "26           250                mL        50   0.0      65            11   \n",
+       "27            15                 g        40   3.5      60             1   \n",
+       "28           125                mL        40   0.0       0             9   \n",
+       "29            15                mL        20   0.0       1             5   \n",
+       "\n",
+       "    Sugars  Fiber  Protein  \n",
+       "0       37    3.0      6.0  \n",
+       "1        0    0.0     26.0  \n",
+       "2        1    7.0      6.0  \n",
+       "3       13    1.0      5.0  \n",
+       "4       46    0.0      9.0  \n",
+       "5        1    1.0      3.0  \n",
+       "6        2    0.0     15.0  \n",
+       "7        2    1.0      2.0  \n",
+       "8        1    0.0     14.0  \n",
+       "9        5    2.0      4.0  \n",
+       "10      22    1.0      3.0  \n",
+       "11      23    NaN      3.0  \n",
+       "12      54    0.0      0.0  \n",
+       "13      21    2.0      4.0  \n",
+       "14       0    0.0      4.0  \n",
+       "15       3    2.0      7.0  \n",
+       "16      18    0.0      7.0  \n",
+       "17       9    1.0      1.0  \n",
+       "18      12    1.0      1.0  \n",
+       "19      11    NaN      2.0  \n",
+       "20      39    0.0      0.0  \n",
+       "21      12    0.0      9.0  \n",
+       "22       6    0.0      2.0  \n",
+       "23       0    0.0      7.0  \n",
+       "24      23    0.0      2.0  \n",
+       "25       0    0.0      9.0  \n",
+       "26      10    0.0      1.0  \n",
+       "27       1    0.0      1.0  \n",
+       "28       6    2.0      1.0  \n",
+       "29       5    0.0      0.1  "
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop columns from DataFrame\n",
+    "food2 = food.drop(columns=[\"Product_Name\",\"Brand\"])\n",
+    "food2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "76cc4921",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Product_ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>136</td>\n",
+       "      <td>g</td>\n",
+       "      <td>82</td>\n",
+       "      <td>g</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>7.99</td>\n",
+       "      <td>510</td>\n",
+       "      <td>g</td>\n",
+       "      <td>85</td>\n",
+       "      <td>g</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>150</td>\n",
+       "      <td>53</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10008</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>6.49</td>\n",
+       "      <td>390</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>280</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>440</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10016</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>14.29</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>g</td>\n",
+       "      <td>100</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>770</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10011</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>202</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10019</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>10.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>g</td>\n",
+       "      <td>250</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>410</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10026</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>336</td>\n",
+       "      <td>g</td>\n",
+       "      <td>56</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>230</td>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10013</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>29</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10010</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>300</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>220</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>31</td>\n",
+       "      <td>23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10003</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>370</td>\n",
+       "      <td>55</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10006</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>9.99</td>\n",
+       "      <td>400</td>\n",
+       "      <td>g</td>\n",
+       "      <td>42</td>\n",
+       "      <td>g</td>\n",
+       "      <td>210</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>25</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>10015</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>7.49</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>430</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10030</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>675</td>\n",
+       "      <td>g</td>\n",
+       "      <td>75</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>320</td>\n",
+       "      <td>38</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10022</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>650</td>\n",
+       "      <td>g</td>\n",
+       "      <td>175</td>\n",
+       "      <td>g</td>\n",
+       "      <td>160</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>22</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>10009</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>225</td>\n",
+       "      <td>g</td>\n",
+       "      <td>31</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>80</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10014</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>180</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>140</td>\n",
+       "      <td>22</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10029</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>296</td>\n",
+       "      <td>g</td>\n",
+       "      <td>37</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10001</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>355</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>39</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>10020</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>5.69</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>130</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10007</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>2.39</td>\n",
+       "      <td>200</td>\n",
+       "      <td>g</td>\n",
+       "      <td>25</td>\n",
+       "      <td>g</td>\n",
+       "      <td>130</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>10023</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>8.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>110</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>10002</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>6.99</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>10017</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>12.99</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10005</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>10021</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>5.49</td>\n",
+       "      <td>227</td>\n",
+       "      <td>g</td>\n",
+       "      <td>15</td>\n",
+       "      <td>g</td>\n",
+       "      <td>40</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>10025</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>4.29</td>\n",
+       "      <td>540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>125</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>10024</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>310</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>15</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Product_ID Food_Category  Price  Container_Size Container_Size_unit  \\\n",
+       "0        10027        Bakery  21.29             710                   g   \n",
+       "1        10018          Meat  22.99            1020                   g   \n",
+       "2        10012         Snaks   5.99             136                   g   \n",
+       "3        10028        Bakery   7.99             510                   g   \n",
+       "4        10004        Drinks   3.99             405                  mL   \n",
+       "5        10008         Snaks   6.49             390                   g   \n",
+       "6        10016          Meat  14.29            1000                   g   \n",
+       "7        10011         Snaks   3.99             202                   g   \n",
+       "8        10019          Meat  10.99             700                   g   \n",
+       "9        10026        Bakery   4.99             336                   g   \n",
+       "10       10013         Snaks   1.79              45                   g   \n",
+       "11       10010         Snaks   4.99             300                   g   \n",
+       "12       10003        Drinks   3.99             473                  mL   \n",
+       "13       10006         Snaks   9.99             400                   g   \n",
+       "14       10015          Meat   7.49             500                   g   \n",
+       "15       10030        Bakery   3.99             675                   g   \n",
+       "16       10022         Dairy   3.99             650                   g   \n",
+       "17       10009         Snaks   3.99             225                   g   \n",
+       "18       10014         Snaks   3.99             180                   g   \n",
+       "19       10029        Bakery   3.99             296                   g   \n",
+       "20       10001        Drinks   3.99            2000                  mL   \n",
+       "21       10020         Dairy   5.69            2000                  mL   \n",
+       "22       10007         Snaks   2.39             200                   g   \n",
+       "23       10023         Dairy   8.99             700                   g   \n",
+       "24       10002        Drinks   6.99            1540                  mL   \n",
+       "25       10017          Meat  12.99             500                   g   \n",
+       "26       10005        Drinks   3.39            1000                  mL   \n",
+       "27       10021         Dairy   5.49             227                   g   \n",
+       "28       10025         Fruit   4.29             540                  mL   \n",
+       "29       10024         Fruit   5.99             310                  mL   \n",
+       "\n",
+       "    Serving_Size Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  \\\n",
+       "0            118                 g       510  31.0     270            52   \n",
+       "1            170                 g       490  41.0     660             2   \n",
+       "2             82                 g       430  26.0     670            41   \n",
+       "3             85                 g       320  16.0     320            39   \n",
+       "4            405                mL       290   4.5     150            53   \n",
+       "5             50                 g       280  18.0     440            30   \n",
+       "6            100                 g       260  20.0     770             6   \n",
+       "7             50                 g       260  15.0     240            29   \n",
+       "8            150                 g       250  12.0     410            20   \n",
+       "9             56                 g       230  12.0     230            28   \n",
+       "10            45                 g       230  12.0      35            29   \n",
+       "11            50                 g       220  10.0      25            31   \n",
+       "12           473                mL       210   NaN     370            55   \n",
+       "13            42                 g       210  11.0      20            25   \n",
+       "14            50                 g       200  19.0     430             2   \n",
+       "15            75                 g       200   2.5     320            38   \n",
+       "16           175                 g       160   5.0      70            22   \n",
+       "17            31                 g       150   7.0      80            22   \n",
+       "18            30                 g       140   5.0     140            22   \n",
+       "19            37                 g       140   6.0      60            20   \n",
+       "20           355                mL       140   0.0      25            39   \n",
+       "21           250                mL       130   5.0     120            12   \n",
+       "22            25                 g       130   7.0      45            16   \n",
+       "23            30                 g       110   8.0     220             0   \n",
+       "24           250                mL       110   0.0       0            27   \n",
+       "25            50                 g        50   1.0     360             0   \n",
+       "26           250                mL        50   0.0      65            11   \n",
+       "27            15                 g        40   3.5      60             1   \n",
+       "28           125                mL        40   0.0       0             9   \n",
+       "29            15                mL        20   0.0       1             5   \n",
+       "\n",
+       "    Sugars  Fiber  Protein  \n",
+       "0       37    3.0      6.0  \n",
+       "1        0    0.0     26.0  \n",
+       "2        1    7.0      6.0  \n",
+       "3       13    1.0      5.0  \n",
+       "4       46    0.0      9.0  \n",
+       "5        1    1.0      3.0  \n",
+       "6        2    0.0     15.0  \n",
+       "7        2    1.0      2.0  \n",
+       "8        1    0.0     14.0  \n",
+       "9        5    2.0      4.0  \n",
+       "10      22    1.0      3.0  \n",
+       "11      23    NaN      3.0  \n",
+       "12      54    0.0      0.0  \n",
+       "13      21    2.0      4.0  \n",
+       "14       0    0.0      4.0  \n",
+       "15       3    2.0      7.0  \n",
+       "16      18    0.0      7.0  \n",
+       "17       9    1.0      1.0  \n",
+       "18      12    1.0      1.0  \n",
+       "19      11    NaN      2.0  \n",
+       "20      39    0.0      0.0  \n",
+       "21      12    0.0      9.0  \n",
+       "22       6    0.0      2.0  \n",
+       "23       0    0.0      7.0  \n",
+       "24      23    0.0      2.0  \n",
+       "25       0    0.0      9.0  \n",
+       "26      10    0.0      1.0  \n",
+       "27       1    0.0      1.0  \n",
+       "28       6    2.0      1.0  \n",
+       "29       5    0.0      0.1  "
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# rename columns\n",
+    "food2.rename(columns = {'ID':'Product_ID'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "f5184c56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Container_Size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Meat</td>\n",
+       "      <td>1020</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Food_Category  Container_Size\n",
+       "0        Bakery             710\n",
+       "1          Meat            1020"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Selecting data by rows condition and specific columns\n",
+    "food.loc[food[\"Price\"]>20,[\"Food_Category\",\"Container_Size\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "bd52fa2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>NO NAME| Raised Chocolate Donuts 6 Pack | 2023...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.99</td>\n",
+       "      <td>510</td>\n",
+       "      <td>g</td>\n",
+       "      <td>85</td>\n",
+       "      <td>g</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10026</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>FARMER'S MARKET | Butter Croissants Mini | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>336</td>\n",
+       "      <td>g</td>\n",
+       "      <td>56</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>230</td>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ID Food_Category                                       Product_Name  \\\n",
+       "0  10027        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...   \n",
+       "3  10028        Bakery  NO NAME| Raised Chocolate Donuts 6 Pack | 2023...   \n",
+       "9  10026        Bakery  FARMER'S MARKET | Butter Croissants Mini | 202...   \n",
+       "\n",
+       "   Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "0  local  21.29             710                   g           118   \n",
+       "3  local   7.99             510                   g            85   \n",
+       "9  local   4.99             336                   g            56   \n",
+       "\n",
+       "  Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "0                 g       510  31.0     270            52      37    3.0   \n",
+       "3                 g       320  16.0     320            39      13    1.0   \n",
+       "9                 g       230  12.0     230            28       5    2.0   \n",
+       "\n",
+       "   Protein  \n",
+       "0      6.0  \n",
+       "3      5.0  \n",
+       "9      4.0  "
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select data by Boolean Indexing (True/False)\n",
+    "# Filter the food data that meets 1 conditions\n",
+    "bakery = food[\"Food_Category\"] == \"Bakery\"\n",
+    "food[bakery].head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "3cd09e08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/js/5n02kbn11px6zj8g4xd6kb_00000gn/T/ipykernel_3874/953696143.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  food[bakery][expensive]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>NO NAME| Raised Chocolate Donuts 6 Pack | 2023...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.99</td>\n",
+       "      <td>510</td>\n",
+       "      <td>g</td>\n",
+       "      <td>85</td>\n",
+       "      <td>g</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10026</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>FARMER'S MARKET | Butter Croissants Mini | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>336</td>\n",
+       "      <td>g</td>\n",
+       "      <td>56</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>230</td>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ID Food_Category                                       Product_Name  \\\n",
+       "0  10027        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...   \n",
+       "3  10028        Bakery  NO NAME| Raised Chocolate Donuts 6 Pack | 2023...   \n",
+       "9  10026        Bakery  FARMER'S MARKET | Butter Croissants Mini | 202...   \n",
+       "\n",
+       "   Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "0  local  21.29             710                   g           118   \n",
+       "3  local   7.99             510                   g            85   \n",
+       "9  local   4.99             336                   g            56   \n",
+       "\n",
+       "  Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "0                 g       510  31.0     270            52      37    3.0   \n",
+       "3                 g       320  16.0     320            39      13    1.0   \n",
+       "9                 g       230  12.0     230            28       5    2.0   \n",
+       "\n",
+       "   Protein  \n",
+       "0      6.0  \n",
+       "3      5.0  \n",
+       "9      4.0  "
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select data by Boolean Indexing (True/False)\n",
+    "# Filter the food data that meets 2 conditions\n",
+    "bakery = food[\"Food_Category\"] == \"Bakery\"\n",
+    "expensive = food[\"Price\"] >= 4.5\n",
+    "food[bakery][expensive]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "bab30ca0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/js/5n02kbn11px6zj8g4xd6kb_00000gn/T/ipykernel_3874/2054920429.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  food[bakery][expensive][\"Price\"].count()\n",
+      "/var/folders/js/5n02kbn11px6zj8g4xd6kb_00000gn/T/ipykernel_3874/2054920429.py:4: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  food[bakery][expensive][\"Price\"].min()\n",
+      "/var/folders/js/5n02kbn11px6zj8g4xd6kb_00000gn/T/ipykernel_3874/2054920429.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  food[bakery][expensive][\"Price\"].max()\n",
+      "/var/folders/js/5n02kbn11px6zj8g4xd6kb_00000gn/T/ipykernel_3874/2054920429.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  food[bakery][expensive][\"Price\"].quantile([0.25, 0.75])\n",
+      "/var/folders/js/5n02kbn11px6zj8g4xd6kb_00000gn/T/ipykernel_3874/2054920429.py:7: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  food[bakery][expensive][\"Price\"].sum()\n",
+      "/var/folders/js/5n02kbn11px6zj8g4xd6kb_00000gn/T/ipykernel_3874/2054920429.py:8: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  food[bakery][expensive][\"Price\"].mean()\n",
+      "/var/folders/js/5n02kbn11px6zj8g4xd6kb_00000gn/T/ipykernel_3874/2054920429.py:9: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  food[bakery][expensive][\"Price\"].std()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "8.675444272965697"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select data by Boolean Indexing (True/False)\n",
+    "# Statistic summary of the Price of filted food data \n",
+    "food[bakery][expensive][\"Price\"].count()\n",
+    "food[bakery][expensive][\"Price\"].min()\n",
+    "food[bakery][expensive][\"Price\"].max()\n",
+    "food[bakery][expensive][\"Price\"].quantile([0.25, 0.75])\n",
+    "food[bakery][expensive][\"Price\"].sum()\n",
+    "food[bakery][expensive][\"Price\"].mean()\n",
+    "food[bakery][expensive][\"Price\"].std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "6c15b340",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    21.29\n",
+      "1    22.99\n",
+      "2     5.99\n",
+      "Name: Price, dtype: float64\n",
+      "0     710\n",
+      "1    1020\n",
+      "2     136\n",
+      "Name: Container_Size, dtype: int64\n",
+      "0    2.998592\n",
+      "1    2.253922\n",
+      "2    4.404412\n",
+      "Name: Price_per_100unit, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Creating newly calculated variables\n",
+    "print(food[\"Price\"].head(3))\n",
+    "print(food[\"Container_Size\"].head(3))\n",
+    "food[\"Price_per_100unit\"] = food[\"Price\"]/food[\"Container_Size\"]*100\n",
+    "print(food[\"Price_per_100unit\"].head(3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "3a8e6875",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "      <th>Brand_Name</th>\n",
+       "      <th>Product_Detail</th>\n",
+       "      <th>Avaliable_Date</th>\n",
+       "      <th>Price_per_100unit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>PRESIDENT'S CHOICE</td>\n",
+       "      <td>Triple Chocolate Cake</td>\n",
+       "      <td>2023-01-19</td>\n",
+       "      <td>2.998592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>THE KEG | Prime Rib Beef Burger | 2023-02-16</td>\n",
+       "      <td>international</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>THE KEG</td>\n",
+       "      <td>Prime Rib Beef Burger</td>\n",
+       "      <td>2023-02-16</td>\n",
+       "      <td>2.253922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>ORVILLE |  Bowl, Buttery | 2023-02-18</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>136</td>\n",
+       "      <td>g</td>\n",
+       "      <td>82</td>\n",
+       "      <td>g</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>ORVILLE</td>\n",
+       "      <td>Bowl, Buttery</td>\n",
+       "      <td>2023-02-18</td>\n",
+       "      <td>4.404412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>NO NAME| Raised Chocolate Donuts 6 Pack | 2023...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.99</td>\n",
+       "      <td>510</td>\n",
+       "      <td>g</td>\n",
+       "      <td>85</td>\n",
+       "      <td>g</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NO NAME</td>\n",
+       "      <td>Raised Chocolate Donuts 6 Pack</td>\n",
+       "      <td>2023-02-08</td>\n",
+       "      <td>1.566667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>STARBUCKS | Frappuccino Vanilla Coffee Drink |...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>150</td>\n",
+       "      <td>53</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>STARBUCKS</td>\n",
+       "      <td>Frappuccino Vanilla Coffee Drink</td>\n",
+       "      <td>2023-01-07</td>\n",
+       "      <td>0.985185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10008</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHEETOS | Crunchy Cheese Flavoured Snacks | 20...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>6.49</td>\n",
+       "      <td>390</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>280</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>440</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>CHEETOS</td>\n",
+       "      <td>Crunchy Cheese Flavoured Snacks</td>\n",
+       "      <td>2023-02-18</td>\n",
+       "      <td>1.664103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10016</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Cheddar Smokies sausage |...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>14.29</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>g</td>\n",
+       "      <td>100</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>770</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>PRESIDENT'S CHOICE</td>\n",
+       "      <td>Cheddar Smokies sausage</td>\n",
+       "      <td>2023-02-16</td>\n",
+       "      <td>1.429000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10011</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>PRINGLES | Mega Can BBQ Flavour Chips | 2023-0...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>202</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>PRINGLES</td>\n",
+       "      <td>Mega Can BBQ Flavour Chips</td>\n",
+       "      <td>2023-01-11</td>\n",
+       "      <td>1.975248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10019</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>HIGH LINER | Fish Sticks, Family Pack | 2023-0...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>10.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>g</td>\n",
+       "      <td>250</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>410</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>HIGH LINER</td>\n",
+       "      <td>Fish Sticks, Family Pack</td>\n",
+       "      <td>2023-02-12</td>\n",
+       "      <td>1.570000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10026</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>FARMER'S MARKET | Butter Croissants Mini | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>336</td>\n",
+       "      <td>g</td>\n",
+       "      <td>56</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>230</td>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>FARMER'S MARKET</td>\n",
+       "      <td>Butter Croissants Mini</td>\n",
+       "      <td>2023-02-17</td>\n",
+       "      <td>1.485119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10013</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>29</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NESTLE</td>\n",
+       "      <td>KitKat Chunky Milk Chocolate Wafer Bar</td>\n",
+       "      <td>2023-02-03</td>\n",
+       "      <td>3.977778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10010</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Everything Trail Mix Nuts...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>300</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>220</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>31</td>\n",
+       "      <td>23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>PRESIDENT'S CHOICE</td>\n",
+       "      <td>Everything Trail Mix Nuts</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>1.663333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10003</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>MONSTER ENERGY | Green Energy Drink, Can | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>370</td>\n",
+       "      <td>55</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>MONSTER ENERGY</td>\n",
+       "      <td>Green Energy Drink, Can</td>\n",
+       "      <td>2023-02-04</td>\n",
+       "      <td>0.843552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10006</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>M&amp;M'S | Peanut Milk Chocolate Candies, Bowl Si...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>9.99</td>\n",
+       "      <td>400</td>\n",
+       "      <td>g</td>\n",
+       "      <td>42</td>\n",
+       "      <td>g</td>\n",
+       "      <td>210</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>25</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>M&amp;M'S</td>\n",
+       "      <td>Peanut Milk Chocolate Candies, Bowl Size</td>\n",
+       "      <td>2023-02-14</td>\n",
+       "      <td>2.497500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>10015</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>NO NAME | Mild Sugar-Cured Bacon | 2023-01-16</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.49</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>430</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NO NAME</td>\n",
+       "      <td>Mild Sugar-Cured Bacon</td>\n",
+       "      <td>2023-01-16</td>\n",
+       "      <td>1.498000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10030</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>WONDER | Bread, Sliced White | 2023-01-18</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>675</td>\n",
+       "      <td>g</td>\n",
+       "      <td>75</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>320</td>\n",
+       "      <td>38</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>WONDER</td>\n",
+       "      <td>Bread, Sliced White</td>\n",
+       "      <td>2023-01-18</td>\n",
+       "      <td>0.591111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10022</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>ACTIVIA | Yogurt With Probiotics, Cherry Flavo...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>650</td>\n",
+       "      <td>g</td>\n",
+       "      <td>175</td>\n",
+       "      <td>g</td>\n",
+       "      <td>160</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>22</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>ACTIVIA</td>\n",
+       "      <td>Yogurt With Probiotics, Cherry Flavour</td>\n",
+       "      <td>2023-01-08</td>\n",
+       "      <td>0.613846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>10009</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHRISTIE | Chips Ahoy! Rainbow Cookies | 2023-...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>225</td>\n",
+       "      <td>g</td>\n",
+       "      <td>31</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>80</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>CHRISTIE</td>\n",
+       "      <td>Chips Ahoy! Rainbow Cookies</td>\n",
+       "      <td>2023-01-28</td>\n",
+       "      <td>1.773333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10014</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHRISTIE | Oreo Snak Pak Cookies | 2023-01-25</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>180</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>140</td>\n",
+       "      <td>22</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>CHRISTIE</td>\n",
+       "      <td>Oreo Snak Pak Cookies</td>\n",
+       "      <td>2023-01-25</td>\n",
+       "      <td>2.216667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10029</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>SCHOOL SAFE | Muffin Bars Birthday Cake | 2023...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>296</td>\n",
+       "      <td>g</td>\n",
+       "      <td>37</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>SCHOOL SAFE</td>\n",
+       "      <td>Muffin Bars Birthday Cake</td>\n",
+       "      <td>2023-01-30</td>\n",
+       "      <td>1.347973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10001</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>COCA-COLA | Coca-Cola Bottle | 2023-01-28</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>355</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>39</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>COCA-COLA</td>\n",
+       "      <td>Coca-Cola Bottle</td>\n",
+       "      <td>2023-01-28</td>\n",
+       "      <td>0.199500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>10020</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>NEILSON | True Taste, 2% Milk | 2023-01-23</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.69</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>130</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NEILSON</td>\n",
+       "      <td>True Taste, 2% Milk</td>\n",
+       "      <td>2023-01-23</td>\n",
+       "      <td>0.284500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10007</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>GARDEN | Cream Wafers, Chocolate Flavour | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>2.39</td>\n",
+       "      <td>200</td>\n",
+       "      <td>g</td>\n",
+       "      <td>25</td>\n",
+       "      <td>g</td>\n",
+       "      <td>130</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>GARDEN</td>\n",
+       "      <td>Cream Wafers, Chocolate Flavour</td>\n",
+       "      <td>2023-02-19</td>\n",
+       "      <td>1.195000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>10023</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>NO NAME | Farmer's Marble Cheese | 2023-02-02</td>\n",
+       "      <td>local</td>\n",
+       "      <td>8.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>110</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>NO NAME</td>\n",
+       "      <td>Farmer's Marble Cheese</td>\n",
+       "      <td>2023-02-02</td>\n",
+       "      <td>1.284286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>10002</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>TROPICANA | Orange Juice No Pulp | 2023-02-22</td>\n",
+       "      <td>local</td>\n",
+       "      <td>6.99</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>TROPICANA</td>\n",
+       "      <td>Orange Juice No Pulp</td>\n",
+       "      <td>2023-02-22</td>\n",
+       "      <td>0.453896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>10017</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>GREENFIELD| Thick Sliced Smoked Ham | 2023-02-04</td>\n",
+       "      <td>international</td>\n",
+       "      <td>12.99</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>GREENFIELD</td>\n",
+       "      <td>Thick Sliced Smoked Ham</td>\n",
+       "      <td>2023-02-04</td>\n",
+       "      <td>2.598000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10005</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>GRACE | 100% Pure Coconut Water | 2023-02-11</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>GRACE</td>\n",
+       "      <td>100% Pure Coconut Water</td>\n",
+       "      <td>2023-02-11</td>\n",
+       "      <td>0.339000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>10021</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>PHILADELPHIA | Original Cream Cheese Product |...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>5.49</td>\n",
+       "      <td>227</td>\n",
+       "      <td>g</td>\n",
+       "      <td>15</td>\n",
+       "      <td>g</td>\n",
+       "      <td>40</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>PHILADELPHIA</td>\n",
+       "      <td>Original Cream Cheese Product</td>\n",
+       "      <td>2023-01-03</td>\n",
+       "      <td>2.418502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>10025</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>DOLE | No Sugar Added Fruit Salad With Cherrie...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>4.29</td>\n",
+       "      <td>540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>125</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>DOLE</td>\n",
+       "      <td>No Sugar Added Fruit Salad With Cherries</td>\n",
+       "      <td>2023-01-18</td>\n",
+       "      <td>0.794444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>10024</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>SMUCKERS | No Sugar Added Orange Spread | 2023...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>310</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>15</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>SMUCKERS</td>\n",
+       "      <td>No Sugar Added Orange Spread</td>\n",
+       "      <td>2023-02-12</td>\n",
+       "      <td>1.932258</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ID Food_Category                                       Product_Name  \\\n",
+       "0   10027        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...   \n",
+       "1   10018          Meat       THE KEG | Prime Rib Beef Burger | 2023-02-16   \n",
+       "2   10012         Snaks              ORVILLE |  Bowl, Buttery | 2023-02-18   \n",
+       "3   10028        Bakery  NO NAME| Raised Chocolate Donuts 6 Pack | 2023...   \n",
+       "4   10004        Drinks  STARBUCKS | Frappuccino Vanilla Coffee Drink |...   \n",
+       "5   10008         Snaks  CHEETOS | Crunchy Cheese Flavoured Snacks | 20...   \n",
+       "6   10016          Meat  PRESIDENT'S CHOICE | Cheddar Smokies sausage |...   \n",
+       "7   10011         Snaks  PRINGLES | Mega Can BBQ Flavour Chips | 2023-0...   \n",
+       "8   10019          Meat  HIGH LINER | Fish Sticks, Family Pack | 2023-0...   \n",
+       "9   10026        Bakery  FARMER'S MARKET | Butter Croissants Mini | 202...   \n",
+       "10  10013         Snaks  NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...   \n",
+       "11  10010         Snaks  PRESIDENT'S CHOICE | Everything Trail Mix Nuts...   \n",
+       "12  10003        Drinks  MONSTER ENERGY | Green Energy Drink, Can | 202...   \n",
+       "13  10006         Snaks  M&M'S | Peanut Milk Chocolate Candies, Bowl Si...   \n",
+       "14  10015          Meat      NO NAME | Mild Sugar-Cured Bacon | 2023-01-16   \n",
+       "15  10030        Bakery          WONDER | Bread, Sliced White | 2023-01-18   \n",
+       "16  10022         Dairy  ACTIVIA | Yogurt With Probiotics, Cherry Flavo...   \n",
+       "17  10009         Snaks  CHRISTIE | Chips Ahoy! Rainbow Cookies | 2023-...   \n",
+       "18  10014         Snaks      CHRISTIE | Oreo Snak Pak Cookies | 2023-01-25   \n",
+       "19  10029        Bakery  SCHOOL SAFE | Muffin Bars Birthday Cake | 2023...   \n",
+       "20  10001        Drinks          COCA-COLA | Coca-Cola Bottle | 2023-01-28   \n",
+       "21  10020         Dairy         NEILSON | True Taste, 2% Milk | 2023-01-23   \n",
+       "22  10007         Snaks  GARDEN | Cream Wafers, Chocolate Flavour | 202...   \n",
+       "23  10023         Dairy      NO NAME | Farmer's Marble Cheese | 2023-02-02   \n",
+       "24  10002        Drinks      TROPICANA | Orange Juice No Pulp | 2023-02-22   \n",
+       "25  10017          Meat   GREENFIELD| Thick Sliced Smoked Ham | 2023-02-04   \n",
+       "26  10005        Drinks       GRACE | 100% Pure Coconut Water | 2023-02-11   \n",
+       "27  10021         Dairy  PHILADELPHIA | Original Cream Cheese Product |...   \n",
+       "28  10025         Fruit  DOLE | No Sugar Added Fruit Salad With Cherrie...   \n",
+       "29  10024         Fruit  SMUCKERS | No Sugar Added Orange Spread | 2023...   \n",
+       "\n",
+       "            Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "0           local  21.29             710                   g           118   \n",
+       "1   international  22.99            1020                   g           170   \n",
+       "2           local   5.99             136                   g            82   \n",
+       "3           local   7.99             510                   g            85   \n",
+       "4   international   3.99             405                  mL           405   \n",
+       "5   international   6.49             390                   g            50   \n",
+       "6           local  14.29            1000                   g           100   \n",
+       "7   international   3.99             202                   g            50   \n",
+       "8   international  10.99             700                   g           150   \n",
+       "9           local   4.99             336                   g            56   \n",
+       "10  international   1.79              45                   g            45   \n",
+       "11          local   4.99             300                   g            50   \n",
+       "12          local   3.99             473                  mL           473   \n",
+       "13  international   9.99             400                   g            42   \n",
+       "14          local   7.49             500                   g            50   \n",
+       "15  international   3.99             675                   g            75   \n",
+       "16          local   3.99             650                   g           175   \n",
+       "17  international   3.99             225                   g            31   \n",
+       "18  international   3.99             180                   g            30   \n",
+       "19  international   3.99             296                   g            37   \n",
+       "20  international   3.99            2000                  mL           355   \n",
+       "21          local   5.69            2000                  mL           250   \n",
+       "22          local   2.39             200                   g            25   \n",
+       "23          local   8.99             700                   g            30   \n",
+       "24          local   6.99            1540                  mL           250   \n",
+       "25  international  12.99             500                   g            50   \n",
+       "26          local   3.39            1000                  mL           250   \n",
+       "27  international   5.49             227                   g            15   \n",
+       "28  international   4.29             540                  mL           125   \n",
+       "29  international   5.99             310                  mL            15   \n",
+       "\n",
+       "   Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "0                  g       510  31.0     270            52      37    3.0   \n",
+       "1                  g       490  41.0     660             2       0    0.0   \n",
+       "2                  g       430  26.0     670            41       1    7.0   \n",
+       "3                  g       320  16.0     320            39      13    1.0   \n",
+       "4                 mL       290   4.5     150            53      46    0.0   \n",
+       "5                  g       280  18.0     440            30       1    1.0   \n",
+       "6                  g       260  20.0     770             6       2    0.0   \n",
+       "7                  g       260  15.0     240            29       2    1.0   \n",
+       "8                  g       250  12.0     410            20       1    0.0   \n",
+       "9                  g       230  12.0     230            28       5    2.0   \n",
+       "10                 g       230  12.0      35            29      22    1.0   \n",
+       "11                 g       220  10.0      25            31      23    NaN   \n",
+       "12                mL       210   NaN     370            55      54    0.0   \n",
+       "13                 g       210  11.0      20            25      21    2.0   \n",
+       "14                 g       200  19.0     430             2       0    0.0   \n",
+       "15                 g       200   2.5     320            38       3    2.0   \n",
+       "16                 g       160   5.0      70            22      18    0.0   \n",
+       "17                 g       150   7.0      80            22       9    1.0   \n",
+       "18                 g       140   5.0     140            22      12    1.0   \n",
+       "19                 g       140   6.0      60            20      11    NaN   \n",
+       "20                mL       140   0.0      25            39      39    0.0   \n",
+       "21                mL       130   5.0     120            12      12    0.0   \n",
+       "22                 g       130   7.0      45            16       6    0.0   \n",
+       "23                 g       110   8.0     220             0       0    0.0   \n",
+       "24                mL       110   0.0       0            27      23    0.0   \n",
+       "25                 g        50   1.0     360             0       0    0.0   \n",
+       "26                mL        50   0.0      65            11      10    0.0   \n",
+       "27                 g        40   3.5      60             1       1    0.0   \n",
+       "28                mL        40   0.0       0             9       6    2.0   \n",
+       "29                mL        20   0.0       1             5       5    0.0   \n",
+       "\n",
+       "    Protein           Brand_Name                              Product_Detail  \\\n",
+       "0       6.0  PRESIDENT'S CHOICE                       Triple Chocolate Cake    \n",
+       "1      26.0             THE KEG                       Prime Rib Beef Burger    \n",
+       "2       6.0             ORVILLE                               Bowl, Buttery    \n",
+       "3       5.0              NO NAME             Raised Chocolate Donuts 6 Pack    \n",
+       "4       9.0           STARBUCKS            Frappuccino Vanilla Coffee Drink    \n",
+       "5       3.0             CHEETOS             Crunchy Cheese Flavoured Snacks    \n",
+       "6      15.0  PRESIDENT'S CHOICE                     Cheddar Smokies sausage    \n",
+       "7       2.0            PRINGLES                  Mega Can BBQ Flavour Chips    \n",
+       "8      14.0          HIGH LINER                    Fish Sticks, Family Pack    \n",
+       "9       4.0     FARMER'S MARKET                      Butter Croissants Mini    \n",
+       "10      3.0              NESTLE      KitKat Chunky Milk Chocolate Wafer Bar    \n",
+       "11      3.0  PRESIDENT'S CHOICE                   Everything Trail Mix Nuts    \n",
+       "12      0.0      MONSTER ENERGY                     Green Energy Drink, Can    \n",
+       "13      4.0               M&M'S    Peanut Milk Chocolate Candies, Bowl Size    \n",
+       "14      4.0             NO NAME                      Mild Sugar-Cured Bacon    \n",
+       "15      7.0              WONDER                         Bread, Sliced White    \n",
+       "16      7.0             ACTIVIA      Yogurt With Probiotics, Cherry Flavour    \n",
+       "17      1.0            CHRISTIE                 Chips Ahoy! Rainbow Cookies    \n",
+       "18      1.0            CHRISTIE                       Oreo Snak Pak Cookies    \n",
+       "19      2.0         SCHOOL SAFE                   Muffin Bars Birthday Cake    \n",
+       "20      0.0           COCA-COLA                            Coca-Cola Bottle    \n",
+       "21      9.0             NEILSON                         True Taste, 2% Milk    \n",
+       "22      2.0              GARDEN             Cream Wafers, Chocolate Flavour    \n",
+       "23      7.0             NO NAME                      Farmer's Marble Cheese    \n",
+       "24      2.0           TROPICANA                        Orange Juice No Pulp    \n",
+       "25      9.0           GREENFIELD                    Thick Sliced Smoked Ham    \n",
+       "26      1.0               GRACE                     100% Pure Coconut Water    \n",
+       "27      1.0        PHILADELPHIA               Original Cream Cheese Product    \n",
+       "28      1.0                DOLE    No Sugar Added Fruit Salad With Cherries    \n",
+       "29      0.1            SMUCKERS                No Sugar Added Orange Spread    \n",
+       "\n",
+       "   Avaliable_Date  Price_per_100unit  \n",
+       "0      2023-01-19           2.998592  \n",
+       "1      2023-02-16           2.253922  \n",
+       "2      2023-02-18           4.404412  \n",
+       "3      2023-02-08           1.566667  \n",
+       "4      2023-01-07           0.985185  \n",
+       "5      2023-02-18           1.664103  \n",
+       "6      2023-02-16           1.429000  \n",
+       "7      2023-01-11           1.975248  \n",
+       "8      2023-02-12           1.570000  \n",
+       "9      2023-02-17           1.485119  \n",
+       "10     2023-02-03           3.977778  \n",
+       "11     2023-01-09           1.663333  \n",
+       "12     2023-02-04           0.843552  \n",
+       "13     2023-02-14           2.497500  \n",
+       "14     2023-01-16           1.498000  \n",
+       "15     2023-01-18           0.591111  \n",
+       "16     2023-01-08           0.613846  \n",
+       "17     2023-01-28           1.773333  \n",
+       "18     2023-01-25           2.216667  \n",
+       "19     2023-01-30           1.347973  \n",
+       "20     2023-01-28           0.199500  \n",
+       "21     2023-01-23           0.284500  \n",
+       "22     2023-02-19           1.195000  \n",
+       "23     2023-02-02           1.284286  \n",
+       "24     2023-02-22           0.453896  \n",
+       "25     2023-02-04           2.598000  \n",
+       "26     2023-02-11           0.339000  \n",
+       "27     2023-01-03           2.418502  \n",
+       "28     2023-01-18           0.794444  \n",
+       "29     2023-02-12           1.932258  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Split single column/variable into two columns/variables use apply()\n",
+    "food[['Brand_Name', 'Product_Detail','Avaliable_Date']] = food[\"Product_Name\"].apply(lambda x: pd.Series(str(x).split(\"|\")))\n",
+    "food"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "452a5798",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "      <th>Price_per_100unit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.998592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>THE KEG | Prime Rib Beef Burger | 2023-02-16</td>\n",
+       "      <td>international</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2.253922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>ORVILLE |  Bowl, Buttery | 2023-02-18</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>136</td>\n",
+       "      <td>g</td>\n",
+       "      <td>82</td>\n",
+       "      <td>g</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4.404412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>NO NAME| Raised Chocolate Donuts 6 Pack | 2023...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.99</td>\n",
+       "      <td>510</td>\n",
+       "      <td>g</td>\n",
+       "      <td>85</td>\n",
+       "      <td>g</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.566667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>STARBUCKS | Frappuccino Vanilla Coffee Drink |...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>150</td>\n",
+       "      <td>53</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.985185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10008</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHEETOS | Crunchy Cheese Flavoured Snacks | 20...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>6.49</td>\n",
+       "      <td>390</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>280</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>440</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.664103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10016</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Cheddar Smokies sausage |...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>14.29</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>g</td>\n",
+       "      <td>100</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>770</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1.429000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10011</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>PRINGLES | Mega Can BBQ Flavour Chips | 2023-0...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>202</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.975248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10019</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>HIGH LINER | Fish Sticks, Family Pack | 2023-0...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>10.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>g</td>\n",
+       "      <td>250</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>410</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1.570000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10026</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>FARMER'S MARKET | Butter Croissants Mini | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>336</td>\n",
+       "      <td>g</td>\n",
+       "      <td>56</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>230</td>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.485119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10013</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>29</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.977778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10010</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Everything Trail Mix Nuts...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>300</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>220</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>31</td>\n",
+       "      <td>23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.663333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10003</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>MONSTER ENERGY | Green Energy Drink, Can | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>370</td>\n",
+       "      <td>55</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.843552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10006</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>M&amp;M'S | Peanut Milk Chocolate Candies, Bowl Si...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>9.99</td>\n",
+       "      <td>400</td>\n",
+       "      <td>g</td>\n",
+       "      <td>42</td>\n",
+       "      <td>g</td>\n",
+       "      <td>210</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>25</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.497500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>10015</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>NO NAME | Mild Sugar-Cured Bacon | 2023-01-16</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.49</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>430</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.498000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10030</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>WONDER | Bread, Sliced White | 2023-01-18</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>675</td>\n",
+       "      <td>g</td>\n",
+       "      <td>75</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>320</td>\n",
+       "      <td>38</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.591111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10022</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>ACTIVIA | Yogurt With Probiotics, Cherry Flavo...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>650</td>\n",
+       "      <td>g</td>\n",
+       "      <td>175</td>\n",
+       "      <td>g</td>\n",
+       "      <td>160</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>22</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.613846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>10009</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHRISTIE | Chips Ahoy! Rainbow Cookies | 2023-...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>225</td>\n",
+       "      <td>g</td>\n",
+       "      <td>31</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>80</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.773333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10014</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHRISTIE | Oreo Snak Pak Cookies | 2023-01-25</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>180</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>140</td>\n",
+       "      <td>22</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.216667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10029</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>SCHOOL SAFE | Muffin Bars Birthday Cake | 2023...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>296</td>\n",
+       "      <td>g</td>\n",
+       "      <td>37</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.347973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10001</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>COCA-COLA | Coca-Cola Bottle | 2023-01-28</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>355</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>39</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.199500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>10020</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>NEILSON | True Taste, 2% Milk | 2023-01-23</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.69</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>130</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.284500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10007</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>GARDEN | Cream Wafers, Chocolate Flavour | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>2.39</td>\n",
+       "      <td>200</td>\n",
+       "      <td>g</td>\n",
+       "      <td>25</td>\n",
+       "      <td>g</td>\n",
+       "      <td>130</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.195000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>10023</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>NO NAME | Farmer's Marble Cheese | 2023-02-02</td>\n",
+       "      <td>local</td>\n",
+       "      <td>8.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>110</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.284286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>10002</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>TROPICANA | Orange Juice No Pulp | 2023-02-22</td>\n",
+       "      <td>local</td>\n",
+       "      <td>6.99</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.453896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>10017</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>GREENFIELD| Thick Sliced Smoked Ham | 2023-02-04</td>\n",
+       "      <td>international</td>\n",
+       "      <td>12.99</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.598000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10005</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>GRACE | 100% Pure Coconut Water | 2023-02-11</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.339000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>10021</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>PHILADELPHIA | Original Cream Cheese Product |...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>5.49</td>\n",
+       "      <td>227</td>\n",
+       "      <td>g</td>\n",
+       "      <td>15</td>\n",
+       "      <td>g</td>\n",
+       "      <td>40</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.418502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>10025</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>DOLE | No Sugar Added Fruit Salad With Cherrie...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>4.29</td>\n",
+       "      <td>540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>125</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.794444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>10024</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>SMUCKERS | No Sugar Added Orange Spread | 2023...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>310</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>15</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>1.932258</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ID Food_Category                                       Product_Name  \\\n",
+       "0   10027        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...   \n",
+       "1   10018          Meat       THE KEG | Prime Rib Beef Burger | 2023-02-16   \n",
+       "2   10012         Snaks              ORVILLE |  Bowl, Buttery | 2023-02-18   \n",
+       "3   10028        Bakery  NO NAME| Raised Chocolate Donuts 6 Pack | 2023...   \n",
+       "4   10004        Drinks  STARBUCKS | Frappuccino Vanilla Coffee Drink |...   \n",
+       "5   10008         Snaks  CHEETOS | Crunchy Cheese Flavoured Snacks | 20...   \n",
+       "6   10016          Meat  PRESIDENT'S CHOICE | Cheddar Smokies sausage |...   \n",
+       "7   10011         Snaks  PRINGLES | Mega Can BBQ Flavour Chips | 2023-0...   \n",
+       "8   10019          Meat  HIGH LINER | Fish Sticks, Family Pack | 2023-0...   \n",
+       "9   10026        Bakery  FARMER'S MARKET | Butter Croissants Mini | 202...   \n",
+       "10  10013         Snaks  NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...   \n",
+       "11  10010         Snaks  PRESIDENT'S CHOICE | Everything Trail Mix Nuts...   \n",
+       "12  10003        Drinks  MONSTER ENERGY | Green Energy Drink, Can | 202...   \n",
+       "13  10006         Snaks  M&M'S | Peanut Milk Chocolate Candies, Bowl Si...   \n",
+       "14  10015          Meat      NO NAME | Mild Sugar-Cured Bacon | 2023-01-16   \n",
+       "15  10030        Bakery          WONDER | Bread, Sliced White | 2023-01-18   \n",
+       "16  10022         Dairy  ACTIVIA | Yogurt With Probiotics, Cherry Flavo...   \n",
+       "17  10009         Snaks  CHRISTIE | Chips Ahoy! Rainbow Cookies | 2023-...   \n",
+       "18  10014         Snaks      CHRISTIE | Oreo Snak Pak Cookies | 2023-01-25   \n",
+       "19  10029        Bakery  SCHOOL SAFE | Muffin Bars Birthday Cake | 2023...   \n",
+       "20  10001        Drinks          COCA-COLA | Coca-Cola Bottle | 2023-01-28   \n",
+       "21  10020         Dairy         NEILSON | True Taste, 2% Milk | 2023-01-23   \n",
+       "22  10007         Snaks  GARDEN | Cream Wafers, Chocolate Flavour | 202...   \n",
+       "23  10023         Dairy      NO NAME | Farmer's Marble Cheese | 2023-02-02   \n",
+       "24  10002        Drinks      TROPICANA | Orange Juice No Pulp | 2023-02-22   \n",
+       "25  10017          Meat   GREENFIELD| Thick Sliced Smoked Ham | 2023-02-04   \n",
+       "26  10005        Drinks       GRACE | 100% Pure Coconut Water | 2023-02-11   \n",
+       "27  10021         Dairy  PHILADELPHIA | Original Cream Cheese Product |...   \n",
+       "28  10025         Fruit  DOLE | No Sugar Added Fruit Salad With Cherrie...   \n",
+       "29  10024         Fruit  SMUCKERS | No Sugar Added Orange Spread | 2023...   \n",
+       "\n",
+       "            Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "0           local  21.29             710                   g           118   \n",
+       "1   international  22.99            1020                   g           170   \n",
+       "2           local   5.99             136                   g            82   \n",
+       "3           local   7.99             510                   g            85   \n",
+       "4   international   3.99             405                  mL           405   \n",
+       "5   international   6.49             390                   g            50   \n",
+       "6           local  14.29            1000                   g           100   \n",
+       "7   international   3.99             202                   g            50   \n",
+       "8   international  10.99             700                   g           150   \n",
+       "9           local   4.99             336                   g            56   \n",
+       "10  international   1.79              45                   g            45   \n",
+       "11          local   4.99             300                   g            50   \n",
+       "12          local   3.99             473                  mL           473   \n",
+       "13  international   9.99             400                   g            42   \n",
+       "14          local   7.49             500                   g            50   \n",
+       "15  international   3.99             675                   g            75   \n",
+       "16          local   3.99             650                   g           175   \n",
+       "17  international   3.99             225                   g            31   \n",
+       "18  international   3.99             180                   g            30   \n",
+       "19  international   3.99             296                   g            37   \n",
+       "20  international   3.99            2000                  mL           355   \n",
+       "21          local   5.69            2000                  mL           250   \n",
+       "22          local   2.39             200                   g            25   \n",
+       "23          local   8.99             700                   g            30   \n",
+       "24          local   6.99            1540                  mL           250   \n",
+       "25  international  12.99             500                   g            50   \n",
+       "26          local   3.39            1000                  mL           250   \n",
+       "27  international   5.49             227                   g            15   \n",
+       "28  international   4.29             540                  mL           125   \n",
+       "29  international   5.99             310                  mL            15   \n",
+       "\n",
+       "   Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "0                  g       510  31.0     270            52      37    3.0   \n",
+       "1                  g       490  41.0     660             2       0    0.0   \n",
+       "2                  g       430  26.0     670            41       1    7.0   \n",
+       "3                  g       320  16.0     320            39      13    1.0   \n",
+       "4                 mL       290   4.5     150            53      46    0.0   \n",
+       "5                  g       280  18.0     440            30       1    1.0   \n",
+       "6                  g       260  20.0     770             6       2    0.0   \n",
+       "7                  g       260  15.0     240            29       2    1.0   \n",
+       "8                  g       250  12.0     410            20       1    0.0   \n",
+       "9                  g       230  12.0     230            28       5    2.0   \n",
+       "10                 g       230  12.0      35            29      22    1.0   \n",
+       "11                 g       220  10.0      25            31      23    NaN   \n",
+       "12                mL       210   NaN     370            55      54    0.0   \n",
+       "13                 g       210  11.0      20            25      21    2.0   \n",
+       "14                 g       200  19.0     430             2       0    0.0   \n",
+       "15                 g       200   2.5     320            38       3    2.0   \n",
+       "16                 g       160   5.0      70            22      18    0.0   \n",
+       "17                 g       150   7.0      80            22       9    1.0   \n",
+       "18                 g       140   5.0     140            22      12    1.0   \n",
+       "19                 g       140   6.0      60            20      11    NaN   \n",
+       "20                mL       140   0.0      25            39      39    0.0   \n",
+       "21                mL       130   5.0     120            12      12    0.0   \n",
+       "22                 g       130   7.0      45            16       6    0.0   \n",
+       "23                 g       110   8.0     220             0       0    0.0   \n",
+       "24                mL       110   0.0       0            27      23    0.0   \n",
+       "25                 g        50   1.0     360             0       0    0.0   \n",
+       "26                mL        50   0.0      65            11      10    0.0   \n",
+       "27                 g        40   3.5      60             1       1    0.0   \n",
+       "28                mL        40   0.0       0             9       6    2.0   \n",
+       "29                mL        20   0.0       1             5       5    0.0   \n",
+       "\n",
+       "    Protein  Price_per_100unit  \n",
+       "0       6.0           2.998592  \n",
+       "1      26.0           2.253922  \n",
+       "2       6.0           4.404412  \n",
+       "3       5.0           1.566667  \n",
+       "4       9.0           0.985185  \n",
+       "5       3.0           1.664103  \n",
+       "6      15.0           1.429000  \n",
+       "7       2.0           1.975248  \n",
+       "8      14.0           1.570000  \n",
+       "9       4.0           1.485119  \n",
+       "10      3.0           3.977778  \n",
+       "11      3.0           1.663333  \n",
+       "12      0.0           0.843552  \n",
+       "13      4.0           2.497500  \n",
+       "14      4.0           1.498000  \n",
+       "15      7.0           0.591111  \n",
+       "16      7.0           0.613846  \n",
+       "17      1.0           1.773333  \n",
+       "18      1.0           2.216667  \n",
+       "19      2.0           1.347973  \n",
+       "20      0.0           0.199500  \n",
+       "21      9.0           0.284500  \n",
+       "22      2.0           1.195000  \n",
+       "23      7.0           1.284286  \n",
+       "24      2.0           0.453896  \n",
+       "25      9.0           2.598000  \n",
+       "26      1.0           0.339000  \n",
+       "27      1.0           2.418502  \n",
+       "28      1.0           0.794444  \n",
+       "29      0.1           1.932258  "
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sort data by labels along an axis\n",
+    "food.sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "4b86176e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "      <th>Price_per_100unit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10013</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>29</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.977778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>ORVILLE |  Bowl, Buttery | 2023-02-18</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>136</td>\n",
+       "      <td>g</td>\n",
+       "      <td>82</td>\n",
+       "      <td>g</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4.404412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10014</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHRISTIE | Oreo Snak Pak Cookies | 2023-01-25</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>180</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>140</td>\n",
+       "      <td>22</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.216667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10007</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>GARDEN | Cream Wafers, Chocolate Flavour | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>2.39</td>\n",
+       "      <td>200</td>\n",
+       "      <td>g</td>\n",
+       "      <td>25</td>\n",
+       "      <td>g</td>\n",
+       "      <td>130</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.195000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10011</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>PRINGLES | Mega Can BBQ Flavour Chips | 2023-0...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>202</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.975248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>10009</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHRISTIE | Chips Ahoy! Rainbow Cookies | 2023-...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>225</td>\n",
+       "      <td>g</td>\n",
+       "      <td>31</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>80</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.773333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>10021</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>PHILADELPHIA | Original Cream Cheese Product |...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>5.49</td>\n",
+       "      <td>227</td>\n",
+       "      <td>g</td>\n",
+       "      <td>15</td>\n",
+       "      <td>g</td>\n",
+       "      <td>40</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.418502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10029</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>SCHOOL SAFE | Muffin Bars Birthday Cake | 2023...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>296</td>\n",
+       "      <td>g</td>\n",
+       "      <td>37</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.347973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10010</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Everything Trail Mix Nuts...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>300</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>220</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>31</td>\n",
+       "      <td>23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.663333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>10024</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>SMUCKERS | No Sugar Added Orange Spread | 2023...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>310</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>15</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>1.932258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10026</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>FARMER'S MARKET | Butter Croissants Mini | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>336</td>\n",
+       "      <td>g</td>\n",
+       "      <td>56</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>230</td>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.485119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10008</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHEETOS | Crunchy Cheese Flavoured Snacks | 20...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>6.49</td>\n",
+       "      <td>390</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>280</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>440</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.664103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10006</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>M&amp;M'S | Peanut Milk Chocolate Candies, Bowl Si...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>9.99</td>\n",
+       "      <td>400</td>\n",
+       "      <td>g</td>\n",
+       "      <td>42</td>\n",
+       "      <td>g</td>\n",
+       "      <td>210</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>25</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.497500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>STARBUCKS | Frappuccino Vanilla Coffee Drink |...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>150</td>\n",
+       "      <td>53</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.985185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10003</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>MONSTER ENERGY | Green Energy Drink, Can | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>370</td>\n",
+       "      <td>55</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.843552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>10017</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>GREENFIELD| Thick Sliced Smoked Ham | 2023-02-04</td>\n",
+       "      <td>international</td>\n",
+       "      <td>12.99</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.598000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>10015</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>NO NAME | Mild Sugar-Cured Bacon | 2023-01-16</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.49</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>430</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.498000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>NO NAME| Raised Chocolate Donuts 6 Pack | 2023...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.99</td>\n",
+       "      <td>510</td>\n",
+       "      <td>g</td>\n",
+       "      <td>85</td>\n",
+       "      <td>g</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.566667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>10025</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>DOLE | No Sugar Added Fruit Salad With Cherrie...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>4.29</td>\n",
+       "      <td>540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>125</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.794444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10022</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>ACTIVIA | Yogurt With Probiotics, Cherry Flavo...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>650</td>\n",
+       "      <td>g</td>\n",
+       "      <td>175</td>\n",
+       "      <td>g</td>\n",
+       "      <td>160</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>22</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.613846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10030</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>WONDER | Bread, Sliced White | 2023-01-18</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>675</td>\n",
+       "      <td>g</td>\n",
+       "      <td>75</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>320</td>\n",
+       "      <td>38</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.591111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10019</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>HIGH LINER | Fish Sticks, Family Pack | 2023-0...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>10.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>g</td>\n",
+       "      <td>250</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>410</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1.570000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>10023</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>NO NAME | Farmer's Marble Cheese | 2023-02-02</td>\n",
+       "      <td>local</td>\n",
+       "      <td>8.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>110</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.284286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.998592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10016</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Cheddar Smokies sausage |...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>14.29</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>g</td>\n",
+       "      <td>100</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>770</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1.429000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10005</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>GRACE | 100% Pure Coconut Water | 2023-02-11</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.339000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>THE KEG | Prime Rib Beef Burger | 2023-02-16</td>\n",
+       "      <td>international</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2.253922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>10002</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>TROPICANA | Orange Juice No Pulp | 2023-02-22</td>\n",
+       "      <td>local</td>\n",
+       "      <td>6.99</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.453896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10001</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>COCA-COLA | Coca-Cola Bottle | 2023-01-28</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>355</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>39</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.199500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>10020</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>NEILSON | True Taste, 2% Milk | 2023-01-23</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.69</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>130</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.284500</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ID Food_Category                                       Product_Name  \\\n",
+       "10  10013         Snaks  NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...   \n",
+       "2   10012         Snaks              ORVILLE |  Bowl, Buttery | 2023-02-18   \n",
+       "18  10014         Snaks      CHRISTIE | Oreo Snak Pak Cookies | 2023-01-25   \n",
+       "22  10007         Snaks  GARDEN | Cream Wafers, Chocolate Flavour | 202...   \n",
+       "7   10011         Snaks  PRINGLES | Mega Can BBQ Flavour Chips | 2023-0...   \n",
+       "17  10009         Snaks  CHRISTIE | Chips Ahoy! Rainbow Cookies | 2023-...   \n",
+       "27  10021         Dairy  PHILADELPHIA | Original Cream Cheese Product |...   \n",
+       "19  10029        Bakery  SCHOOL SAFE | Muffin Bars Birthday Cake | 2023...   \n",
+       "11  10010         Snaks  PRESIDENT'S CHOICE | Everything Trail Mix Nuts...   \n",
+       "29  10024         Fruit  SMUCKERS | No Sugar Added Orange Spread | 2023...   \n",
+       "9   10026        Bakery  FARMER'S MARKET | Butter Croissants Mini | 202...   \n",
+       "5   10008         Snaks  CHEETOS | Crunchy Cheese Flavoured Snacks | 20...   \n",
+       "13  10006         Snaks  M&M'S | Peanut Milk Chocolate Candies, Bowl Si...   \n",
+       "4   10004        Drinks  STARBUCKS | Frappuccino Vanilla Coffee Drink |...   \n",
+       "12  10003        Drinks  MONSTER ENERGY | Green Energy Drink, Can | 202...   \n",
+       "25  10017          Meat   GREENFIELD| Thick Sliced Smoked Ham | 2023-02-04   \n",
+       "14  10015          Meat      NO NAME | Mild Sugar-Cured Bacon | 2023-01-16   \n",
+       "3   10028        Bakery  NO NAME| Raised Chocolate Donuts 6 Pack | 2023...   \n",
+       "28  10025         Fruit  DOLE | No Sugar Added Fruit Salad With Cherrie...   \n",
+       "16  10022         Dairy  ACTIVIA | Yogurt With Probiotics, Cherry Flavo...   \n",
+       "15  10030        Bakery          WONDER | Bread, Sliced White | 2023-01-18   \n",
+       "8   10019          Meat  HIGH LINER | Fish Sticks, Family Pack | 2023-0...   \n",
+       "23  10023         Dairy      NO NAME | Farmer's Marble Cheese | 2023-02-02   \n",
+       "0   10027        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...   \n",
+       "6   10016          Meat  PRESIDENT'S CHOICE | Cheddar Smokies sausage |...   \n",
+       "26  10005        Drinks       GRACE | 100% Pure Coconut Water | 2023-02-11   \n",
+       "1   10018          Meat       THE KEG | Prime Rib Beef Burger | 2023-02-16   \n",
+       "24  10002        Drinks      TROPICANA | Orange Juice No Pulp | 2023-02-22   \n",
+       "20  10001        Drinks          COCA-COLA | Coca-Cola Bottle | 2023-01-28   \n",
+       "21  10020         Dairy         NEILSON | True Taste, 2% Milk | 2023-01-23   \n",
+       "\n",
+       "            Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "10  international   1.79              45                   g            45   \n",
+       "2           local   5.99             136                   g            82   \n",
+       "18  international   3.99             180                   g            30   \n",
+       "22          local   2.39             200                   g            25   \n",
+       "7   international   3.99             202                   g            50   \n",
+       "17  international   3.99             225                   g            31   \n",
+       "27  international   5.49             227                   g            15   \n",
+       "19  international   3.99             296                   g            37   \n",
+       "11          local   4.99             300                   g            50   \n",
+       "29  international   5.99             310                  mL            15   \n",
+       "9           local   4.99             336                   g            56   \n",
+       "5   international   6.49             390                   g            50   \n",
+       "13  international   9.99             400                   g            42   \n",
+       "4   international   3.99             405                  mL           405   \n",
+       "12          local   3.99             473                  mL           473   \n",
+       "25  international  12.99             500                   g            50   \n",
+       "14          local   7.49             500                   g            50   \n",
+       "3           local   7.99             510                   g            85   \n",
+       "28  international   4.29             540                  mL           125   \n",
+       "16          local   3.99             650                   g           175   \n",
+       "15  international   3.99             675                   g            75   \n",
+       "8   international  10.99             700                   g           150   \n",
+       "23          local   8.99             700                   g            30   \n",
+       "0           local  21.29             710                   g           118   \n",
+       "6           local  14.29            1000                   g           100   \n",
+       "26          local   3.39            1000                  mL           250   \n",
+       "1   international  22.99            1020                   g           170   \n",
+       "24          local   6.99            1540                  mL           250   \n",
+       "20  international   3.99            2000                  mL           355   \n",
+       "21          local   5.69            2000                  mL           250   \n",
+       "\n",
+       "   Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "10                 g       230  12.0      35            29      22    1.0   \n",
+       "2                  g       430  26.0     670            41       1    7.0   \n",
+       "18                 g       140   5.0     140            22      12    1.0   \n",
+       "22                 g       130   7.0      45            16       6    0.0   \n",
+       "7                  g       260  15.0     240            29       2    1.0   \n",
+       "17                 g       150   7.0      80            22       9    1.0   \n",
+       "27                 g        40   3.5      60             1       1    0.0   \n",
+       "19                 g       140   6.0      60            20      11    NaN   \n",
+       "11                 g       220  10.0      25            31      23    NaN   \n",
+       "29                mL        20   0.0       1             5       5    0.0   \n",
+       "9                  g       230  12.0     230            28       5    2.0   \n",
+       "5                  g       280  18.0     440            30       1    1.0   \n",
+       "13                 g       210  11.0      20            25      21    2.0   \n",
+       "4                 mL       290   4.5     150            53      46    0.0   \n",
+       "12                mL       210   NaN     370            55      54    0.0   \n",
+       "25                 g        50   1.0     360             0       0    0.0   \n",
+       "14                 g       200  19.0     430             2       0    0.0   \n",
+       "3                  g       320  16.0     320            39      13    1.0   \n",
+       "28                mL        40   0.0       0             9       6    2.0   \n",
+       "16                 g       160   5.0      70            22      18    0.0   \n",
+       "15                 g       200   2.5     320            38       3    2.0   \n",
+       "8                  g       250  12.0     410            20       1    0.0   \n",
+       "23                 g       110   8.0     220             0       0    0.0   \n",
+       "0                  g       510  31.0     270            52      37    3.0   \n",
+       "6                  g       260  20.0     770             6       2    0.0   \n",
+       "26                mL        50   0.0      65            11      10    0.0   \n",
+       "1                  g       490  41.0     660             2       0    0.0   \n",
+       "24                mL       110   0.0       0            27      23    0.0   \n",
+       "20                mL       140   0.0      25            39      39    0.0   \n",
+       "21                mL       130   5.0     120            12      12    0.0   \n",
+       "\n",
+       "    Protein  Price_per_100unit  \n",
+       "10      3.0           3.977778  \n",
+       "2       6.0           4.404412  \n",
+       "18      1.0           2.216667  \n",
+       "22      2.0           1.195000  \n",
+       "7       2.0           1.975248  \n",
+       "17      1.0           1.773333  \n",
+       "27      1.0           2.418502  \n",
+       "19      2.0           1.347973  \n",
+       "11      3.0           1.663333  \n",
+       "29      0.1           1.932258  \n",
+       "9       4.0           1.485119  \n",
+       "5       3.0           1.664103  \n",
+       "13      4.0           2.497500  \n",
+       "4       9.0           0.985185  \n",
+       "12      0.0           0.843552  \n",
+       "25      9.0           2.598000  \n",
+       "14      4.0           1.498000  \n",
+       "3       5.0           1.566667  \n",
+       "28      1.0           0.794444  \n",
+       "16      7.0           0.613846  \n",
+       "15      7.0           0.591111  \n",
+       "8      14.0           1.570000  \n",
+       "23      7.0           1.284286  \n",
+       "0       6.0           2.998592  \n",
+       "6      15.0           1.429000  \n",
+       "26      1.0           0.339000  \n",
+       "1      26.0           2.253922  \n",
+       "24      2.0           0.453896  \n",
+       "20      0.0           0.199500  \n",
+       "21      9.0           0.284500  "
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sort data by values along an axis\n",
+    "food.sort_values(by=\"Price\")\n",
+    "food.sort_values(by=\"ID\")\n",
+    "food.sort_values(by=\"Container_Size\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "97084f35",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "      <th>Price_per_100unit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>THE KEG | Prime Rib Beef Burger | 2023-02-16</td>\n",
+       "      <td>international</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2.253922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.998592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10016</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Cheddar Smokies sausage |...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>14.29</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>g</td>\n",
+       "      <td>100</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>770</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1.429000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>10017</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>GREENFIELD| Thick Sliced Smoked Ham | 2023-02-04</td>\n",
+       "      <td>international</td>\n",
+       "      <td>12.99</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.598000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10019</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>HIGH LINER | Fish Sticks, Family Pack | 2023-0...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>10.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>g</td>\n",
+       "      <td>250</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>410</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1.570000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10006</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>M&amp;M'S | Peanut Milk Chocolate Candies, Bowl Si...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>9.99</td>\n",
+       "      <td>400</td>\n",
+       "      <td>g</td>\n",
+       "      <td>42</td>\n",
+       "      <td>g</td>\n",
+       "      <td>210</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>25</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.497500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>10023</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>NO NAME | Farmer's Marble Cheese | 2023-02-02</td>\n",
+       "      <td>local</td>\n",
+       "      <td>8.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>110</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.284286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>NO NAME| Raised Chocolate Donuts 6 Pack | 2023...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.99</td>\n",
+       "      <td>510</td>\n",
+       "      <td>g</td>\n",
+       "      <td>85</td>\n",
+       "      <td>g</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.566667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>10015</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>NO NAME | Mild Sugar-Cured Bacon | 2023-01-16</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.49</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>430</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.498000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>10002</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>TROPICANA | Orange Juice No Pulp | 2023-02-22</td>\n",
+       "      <td>local</td>\n",
+       "      <td>6.99</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.453896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10008</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHEETOS | Crunchy Cheese Flavoured Snacks | 20...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>6.49</td>\n",
+       "      <td>390</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>280</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>440</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.664103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>10024</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>SMUCKERS | No Sugar Added Orange Spread | 2023...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>310</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>15</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>1.932258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>ORVILLE |  Bowl, Buttery | 2023-02-18</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>136</td>\n",
+       "      <td>g</td>\n",
+       "      <td>82</td>\n",
+       "      <td>g</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4.404412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>10020</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>NEILSON | True Taste, 2% Milk | 2023-01-23</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.69</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>130</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.284500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>10021</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>PHILADELPHIA | Original Cream Cheese Product |...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>5.49</td>\n",
+       "      <td>227</td>\n",
+       "      <td>g</td>\n",
+       "      <td>15</td>\n",
+       "      <td>g</td>\n",
+       "      <td>40</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.418502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10010</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Everything Trail Mix Nuts...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>300</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>220</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>31</td>\n",
+       "      <td>23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.663333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10026</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>FARMER'S MARKET | Butter Croissants Mini | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>336</td>\n",
+       "      <td>g</td>\n",
+       "      <td>56</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>230</td>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.485119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>10025</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>DOLE | No Sugar Added Fruit Salad With Cherrie...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>4.29</td>\n",
+       "      <td>540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>125</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.794444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>STARBUCKS | Frappuccino Vanilla Coffee Drink |...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>150</td>\n",
+       "      <td>53</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.985185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10011</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>PRINGLES | Mega Can BBQ Flavour Chips | 2023-0...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>202</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.975248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10003</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>MONSTER ENERGY | Green Energy Drink, Can | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>370</td>\n",
+       "      <td>55</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.843552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10001</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>COCA-COLA | Coca-Cola Bottle | 2023-01-28</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>355</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>39</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.199500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10029</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>SCHOOL SAFE | Muffin Bars Birthday Cake | 2023...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>296</td>\n",
+       "      <td>g</td>\n",
+       "      <td>37</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.347973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10014</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHRISTIE | Oreo Snak Pak Cookies | 2023-01-25</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>180</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>140</td>\n",
+       "      <td>22</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.216667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>10009</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHRISTIE | Chips Ahoy! Rainbow Cookies | 2023-...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>225</td>\n",
+       "      <td>g</td>\n",
+       "      <td>31</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>80</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.773333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10022</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>ACTIVIA | Yogurt With Probiotics, Cherry Flavo...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>650</td>\n",
+       "      <td>g</td>\n",
+       "      <td>175</td>\n",
+       "      <td>g</td>\n",
+       "      <td>160</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>22</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.613846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10030</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>WONDER | Bread, Sliced White | 2023-01-18</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>675</td>\n",
+       "      <td>g</td>\n",
+       "      <td>75</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>320</td>\n",
+       "      <td>38</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.591111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10005</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>GRACE | 100% Pure Coconut Water | 2023-02-11</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.339000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10007</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>GARDEN | Cream Wafers, Chocolate Flavour | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>2.39</td>\n",
+       "      <td>200</td>\n",
+       "      <td>g</td>\n",
+       "      <td>25</td>\n",
+       "      <td>g</td>\n",
+       "      <td>130</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.195000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10013</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>29</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.977778</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ID Food_Category                                       Product_Name  \\\n",
+       "1   10018          Meat       THE KEG | Prime Rib Beef Burger | 2023-02-16   \n",
+       "0   10027        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...   \n",
+       "6   10016          Meat  PRESIDENT'S CHOICE | Cheddar Smokies sausage |...   \n",
+       "25  10017          Meat   GREENFIELD| Thick Sliced Smoked Ham | 2023-02-04   \n",
+       "8   10019          Meat  HIGH LINER | Fish Sticks, Family Pack | 2023-0...   \n",
+       "13  10006         Snaks  M&M'S | Peanut Milk Chocolate Candies, Bowl Si...   \n",
+       "23  10023         Dairy      NO NAME | Farmer's Marble Cheese | 2023-02-02   \n",
+       "3   10028        Bakery  NO NAME| Raised Chocolate Donuts 6 Pack | 2023...   \n",
+       "14  10015          Meat      NO NAME | Mild Sugar-Cured Bacon | 2023-01-16   \n",
+       "24  10002        Drinks      TROPICANA | Orange Juice No Pulp | 2023-02-22   \n",
+       "5   10008         Snaks  CHEETOS | Crunchy Cheese Flavoured Snacks | 20...   \n",
+       "29  10024         Fruit  SMUCKERS | No Sugar Added Orange Spread | 2023...   \n",
+       "2   10012         Snaks              ORVILLE |  Bowl, Buttery | 2023-02-18   \n",
+       "21  10020         Dairy         NEILSON | True Taste, 2% Milk | 2023-01-23   \n",
+       "27  10021         Dairy  PHILADELPHIA | Original Cream Cheese Product |...   \n",
+       "11  10010         Snaks  PRESIDENT'S CHOICE | Everything Trail Mix Nuts...   \n",
+       "9   10026        Bakery  FARMER'S MARKET | Butter Croissants Mini | 202...   \n",
+       "28  10025         Fruit  DOLE | No Sugar Added Fruit Salad With Cherrie...   \n",
+       "4   10004        Drinks  STARBUCKS | Frappuccino Vanilla Coffee Drink |...   \n",
+       "7   10011         Snaks  PRINGLES | Mega Can BBQ Flavour Chips | 2023-0...   \n",
+       "12  10003        Drinks  MONSTER ENERGY | Green Energy Drink, Can | 202...   \n",
+       "20  10001        Drinks          COCA-COLA | Coca-Cola Bottle | 2023-01-28   \n",
+       "19  10029        Bakery  SCHOOL SAFE | Muffin Bars Birthday Cake | 2023...   \n",
+       "18  10014         Snaks      CHRISTIE | Oreo Snak Pak Cookies | 2023-01-25   \n",
+       "17  10009         Snaks  CHRISTIE | Chips Ahoy! Rainbow Cookies | 2023-...   \n",
+       "16  10022         Dairy  ACTIVIA | Yogurt With Probiotics, Cherry Flavo...   \n",
+       "15  10030        Bakery          WONDER | Bread, Sliced White | 2023-01-18   \n",
+       "26  10005        Drinks       GRACE | 100% Pure Coconut Water | 2023-02-11   \n",
+       "22  10007         Snaks  GARDEN | Cream Wafers, Chocolate Flavour | 202...   \n",
+       "10  10013         Snaks  NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...   \n",
+       "\n",
+       "            Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "1   international  22.99            1020                   g           170   \n",
+       "0           local  21.29             710                   g           118   \n",
+       "6           local  14.29            1000                   g           100   \n",
+       "25  international  12.99             500                   g            50   \n",
+       "8   international  10.99             700                   g           150   \n",
+       "13  international   9.99             400                   g            42   \n",
+       "23          local   8.99             700                   g            30   \n",
+       "3           local   7.99             510                   g            85   \n",
+       "14          local   7.49             500                   g            50   \n",
+       "24          local   6.99            1540                  mL           250   \n",
+       "5   international   6.49             390                   g            50   \n",
+       "29  international   5.99             310                  mL            15   \n",
+       "2           local   5.99             136                   g            82   \n",
+       "21          local   5.69            2000                  mL           250   \n",
+       "27  international   5.49             227                   g            15   \n",
+       "11          local   4.99             300                   g            50   \n",
+       "9           local   4.99             336                   g            56   \n",
+       "28  international   4.29             540                  mL           125   \n",
+       "4   international   3.99             405                  mL           405   \n",
+       "7   international   3.99             202                   g            50   \n",
+       "12          local   3.99             473                  mL           473   \n",
+       "20  international   3.99            2000                  mL           355   \n",
+       "19  international   3.99             296                   g            37   \n",
+       "18  international   3.99             180                   g            30   \n",
+       "17  international   3.99             225                   g            31   \n",
+       "16          local   3.99             650                   g           175   \n",
+       "15  international   3.99             675                   g            75   \n",
+       "26          local   3.39            1000                  mL           250   \n",
+       "22          local   2.39             200                   g            25   \n",
+       "10  international   1.79              45                   g            45   \n",
+       "\n",
+       "   Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "1                  g       490  41.0     660             2       0    0.0   \n",
+       "0                  g       510  31.0     270            52      37    3.0   \n",
+       "6                  g       260  20.0     770             6       2    0.0   \n",
+       "25                 g        50   1.0     360             0       0    0.0   \n",
+       "8                  g       250  12.0     410            20       1    0.0   \n",
+       "13                 g       210  11.0      20            25      21    2.0   \n",
+       "23                 g       110   8.0     220             0       0    0.0   \n",
+       "3                  g       320  16.0     320            39      13    1.0   \n",
+       "14                 g       200  19.0     430             2       0    0.0   \n",
+       "24                mL       110   0.0       0            27      23    0.0   \n",
+       "5                  g       280  18.0     440            30       1    1.0   \n",
+       "29                mL        20   0.0       1             5       5    0.0   \n",
+       "2                  g       430  26.0     670            41       1    7.0   \n",
+       "21                mL       130   5.0     120            12      12    0.0   \n",
+       "27                 g        40   3.5      60             1       1    0.0   \n",
+       "11                 g       220  10.0      25            31      23    NaN   \n",
+       "9                  g       230  12.0     230            28       5    2.0   \n",
+       "28                mL        40   0.0       0             9       6    2.0   \n",
+       "4                 mL       290   4.5     150            53      46    0.0   \n",
+       "7                  g       260  15.0     240            29       2    1.0   \n",
+       "12                mL       210   NaN     370            55      54    0.0   \n",
+       "20                mL       140   0.0      25            39      39    0.0   \n",
+       "19                 g       140   6.0      60            20      11    NaN   \n",
+       "18                 g       140   5.0     140            22      12    1.0   \n",
+       "17                 g       150   7.0      80            22       9    1.0   \n",
+       "16                 g       160   5.0      70            22      18    0.0   \n",
+       "15                 g       200   2.5     320            38       3    2.0   \n",
+       "26                mL        50   0.0      65            11      10    0.0   \n",
+       "22                 g       130   7.0      45            16       6    0.0   \n",
+       "10                 g       230  12.0      35            29      22    1.0   \n",
+       "\n",
+       "    Protein  Price_per_100unit  \n",
+       "1      26.0           2.253922  \n",
+       "0       6.0           2.998592  \n",
+       "6      15.0           1.429000  \n",
+       "25      9.0           2.598000  \n",
+       "8      14.0           1.570000  \n",
+       "13      4.0           2.497500  \n",
+       "23      7.0           1.284286  \n",
+       "3       5.0           1.566667  \n",
+       "14      4.0           1.498000  \n",
+       "24      2.0           0.453896  \n",
+       "5       3.0           1.664103  \n",
+       "29      0.1           1.932258  \n",
+       "2       6.0           4.404412  \n",
+       "21      9.0           0.284500  \n",
+       "27      1.0           2.418502  \n",
+       "11      3.0           1.663333  \n",
+       "9       4.0           1.485119  \n",
+       "28      1.0           0.794444  \n",
+       "4       9.0           0.985185  \n",
+       "7       2.0           1.975248  \n",
+       "12      0.0           0.843552  \n",
+       "20      0.0           0.199500  \n",
+       "19      2.0           1.347973  \n",
+       "18      1.0           2.216667  \n",
+       "17      1.0           1.773333  \n",
+       "16      7.0           0.613846  \n",
+       "15      7.0           0.591111  \n",
+       "26      1.0           0.339000  \n",
+       "22      2.0           1.195000  \n",
+       "10      3.0           3.977778  "
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sort data by values along an axis (reverse)\n",
+    "food.sort_values(by=\"Price\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "eea830ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "      <th>Price_per_100unit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>THE KEG | Prime Rib Beef Burger | 2023-02-16</td>\n",
+       "      <td>international</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2.253922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.998592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "      <td>10016</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Cheddar Smokies sausage |...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>14.29</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>g</td>\n",
+       "      <td>100</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>770</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1.429000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>25</td>\n",
+       "      <td>10017</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>GREENFIELD| Thick Sliced Smoked Ham | 2023-02-04</td>\n",
+       "      <td>international</td>\n",
+       "      <td>12.99</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>360</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.598000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8</td>\n",
+       "      <td>10019</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>HIGH LINER | Fish Sticks, Family Pack | 2023-0...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>10.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>g</td>\n",
+       "      <td>250</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>410</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1.570000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>13</td>\n",
+       "      <td>10006</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>M&amp;M'S | Peanut Milk Chocolate Candies, Bowl Si...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>9.99</td>\n",
+       "      <td>400</td>\n",
+       "      <td>g</td>\n",
+       "      <td>42</td>\n",
+       "      <td>g</td>\n",
+       "      <td>210</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>25</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.497500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>23</td>\n",
+       "      <td>10023</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>NO NAME | Farmer's Marble Cheese | 2023-02-02</td>\n",
+       "      <td>local</td>\n",
+       "      <td>8.99</td>\n",
+       "      <td>700</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>110</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>220</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.284286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3</td>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>NO NAME| Raised Chocolate Donuts 6 Pack | 2023...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.99</td>\n",
+       "      <td>510</td>\n",
+       "      <td>g</td>\n",
+       "      <td>85</td>\n",
+       "      <td>g</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.566667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>14</td>\n",
+       "      <td>10015</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>NO NAME | Mild Sugar-Cured Bacon | 2023-01-16</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.49</td>\n",
+       "      <td>500</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>430</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.498000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>24</td>\n",
+       "      <td>10002</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>TROPICANA | Orange Juice No Pulp | 2023-02-22</td>\n",
+       "      <td>local</td>\n",
+       "      <td>6.99</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.453896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>5</td>\n",
+       "      <td>10008</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHEETOS | Crunchy Cheese Flavoured Snacks | 20...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>6.49</td>\n",
+       "      <td>390</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>280</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>440</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.664103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>29</td>\n",
+       "      <td>10024</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>SMUCKERS | No Sugar Added Orange Spread | 2023...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>310</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>15</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>1.932258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>2</td>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>ORVILLE |  Bowl, Buttery | 2023-02-18</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>136</td>\n",
+       "      <td>g</td>\n",
+       "      <td>82</td>\n",
+       "      <td>g</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4.404412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>21</td>\n",
+       "      <td>10020</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>NEILSON | True Taste, 2% Milk | 2023-01-23</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.69</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>130</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>120</td>\n",
+       "      <td>12</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.284500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>27</td>\n",
+       "      <td>10021</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>PHILADELPHIA | Original Cream Cheese Product |...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>5.49</td>\n",
+       "      <td>227</td>\n",
+       "      <td>g</td>\n",
+       "      <td>15</td>\n",
+       "      <td>g</td>\n",
+       "      <td>40</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.418502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>11</td>\n",
+       "      <td>10010</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Everything Trail Mix Nuts...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>300</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>220</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>31</td>\n",
+       "      <td>23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.663333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>9</td>\n",
+       "      <td>10026</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>FARMER'S MARKET | Butter Croissants Mini | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>4.99</td>\n",
+       "      <td>336</td>\n",
+       "      <td>g</td>\n",
+       "      <td>56</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>230</td>\n",
+       "      <td>28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.485119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>28</td>\n",
+       "      <td>10025</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>DOLE | No Sugar Added Fruit Salad With Cherrie...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>4.29</td>\n",
+       "      <td>540</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>125</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.794444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>4</td>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>STARBUCKS | Frappuccino Vanilla Coffee Drink |...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>150</td>\n",
+       "      <td>53</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.985185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>7</td>\n",
+       "      <td>10011</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>PRINGLES | Mega Can BBQ Flavour Chips | 2023-0...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>202</td>\n",
+       "      <td>g</td>\n",
+       "      <td>50</td>\n",
+       "      <td>g</td>\n",
+       "      <td>260</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>240</td>\n",
+       "      <td>29</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.975248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>12</td>\n",
+       "      <td>10003</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>MONSTER ENERGY | Green Energy Drink, Can | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>473</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>370</td>\n",
+       "      <td>55</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.843552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>20</td>\n",
+       "      <td>10001</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>COCA-COLA | Coca-Cola Bottle | 2023-01-28</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>355</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25</td>\n",
+       "      <td>39</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.199500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>19</td>\n",
+       "      <td>10029</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>SCHOOL SAFE | Muffin Bars Birthday Cake | 2023...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>296</td>\n",
+       "      <td>g</td>\n",
+       "      <td>37</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.347973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>18</td>\n",
+       "      <td>10014</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHRISTIE | Oreo Snak Pak Cookies | 2023-01-25</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>180</td>\n",
+       "      <td>g</td>\n",
+       "      <td>30</td>\n",
+       "      <td>g</td>\n",
+       "      <td>140</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>140</td>\n",
+       "      <td>22</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.216667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>17</td>\n",
+       "      <td>10009</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>CHRISTIE | Chips Ahoy! Rainbow Cookies | 2023-...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>225</td>\n",
+       "      <td>g</td>\n",
+       "      <td>31</td>\n",
+       "      <td>g</td>\n",
+       "      <td>150</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>80</td>\n",
+       "      <td>22</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.773333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>16</td>\n",
+       "      <td>10022</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>ACTIVIA | Yogurt With Probiotics, Cherry Flavo...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>650</td>\n",
+       "      <td>g</td>\n",
+       "      <td>175</td>\n",
+       "      <td>g</td>\n",
+       "      <td>160</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>22</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.613846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>15</td>\n",
+       "      <td>10030</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>WONDER | Bread, Sliced White | 2023-01-18</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>675</td>\n",
+       "      <td>g</td>\n",
+       "      <td>75</td>\n",
+       "      <td>g</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>320</td>\n",
+       "      <td>38</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.591111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>26</td>\n",
+       "      <td>10005</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>GRACE | 100% Pure Coconut Water | 2023-02-11</td>\n",
+       "      <td>local</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>250</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65</td>\n",
+       "      <td>11</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.339000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>22</td>\n",
+       "      <td>10007</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>GARDEN | Cream Wafers, Chocolate Flavour | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>2.39</td>\n",
+       "      <td>200</td>\n",
+       "      <td>g</td>\n",
+       "      <td>25</td>\n",
+       "      <td>g</td>\n",
+       "      <td>130</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.195000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>10</td>\n",
+       "      <td>10013</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>29</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.977778</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    index     ID Food_Category  \\\n",
+       "0       1  10018          Meat   \n",
+       "1       0  10027        Bakery   \n",
+       "2       6  10016          Meat   \n",
+       "3      25  10017          Meat   \n",
+       "4       8  10019          Meat   \n",
+       "5      13  10006         Snaks   \n",
+       "6      23  10023         Dairy   \n",
+       "7       3  10028        Bakery   \n",
+       "8      14  10015          Meat   \n",
+       "9      24  10002        Drinks   \n",
+       "10      5  10008         Snaks   \n",
+       "11     29  10024         Fruit   \n",
+       "12      2  10012         Snaks   \n",
+       "13     21  10020         Dairy   \n",
+       "14     27  10021         Dairy   \n",
+       "15     11  10010         Snaks   \n",
+       "16      9  10026        Bakery   \n",
+       "17     28  10025         Fruit   \n",
+       "18      4  10004        Drinks   \n",
+       "19      7  10011         Snaks   \n",
+       "20     12  10003        Drinks   \n",
+       "21     20  10001        Drinks   \n",
+       "22     19  10029        Bakery   \n",
+       "23     18  10014         Snaks   \n",
+       "24     17  10009         Snaks   \n",
+       "25     16  10022         Dairy   \n",
+       "26     15  10030        Bakery   \n",
+       "27     26  10005        Drinks   \n",
+       "28     22  10007         Snaks   \n",
+       "29     10  10013         Snaks   \n",
+       "\n",
+       "                                         Product_Name          Brand  Price  \\\n",
+       "0        THE KEG | Prime Rib Beef Burger | 2023-02-16  international  22.99   \n",
+       "1   PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...          local  21.29   \n",
+       "2   PRESIDENT'S CHOICE | Cheddar Smokies sausage |...          local  14.29   \n",
+       "3    GREENFIELD| Thick Sliced Smoked Ham | 2023-02-04  international  12.99   \n",
+       "4   HIGH LINER | Fish Sticks, Family Pack | 2023-0...  international  10.99   \n",
+       "5   M&M'S | Peanut Milk Chocolate Candies, Bowl Si...  international   9.99   \n",
+       "6       NO NAME | Farmer's Marble Cheese | 2023-02-02          local   8.99   \n",
+       "7   NO NAME| Raised Chocolate Donuts 6 Pack | 2023...          local   7.99   \n",
+       "8       NO NAME | Mild Sugar-Cured Bacon | 2023-01-16          local   7.49   \n",
+       "9       TROPICANA | Orange Juice No Pulp | 2023-02-22          local   6.99   \n",
+       "10  CHEETOS | Crunchy Cheese Flavoured Snacks | 20...  international   6.49   \n",
+       "11  SMUCKERS | No Sugar Added Orange Spread | 2023...  international   5.99   \n",
+       "12              ORVILLE |  Bowl, Buttery | 2023-02-18          local   5.99   \n",
+       "13         NEILSON | True Taste, 2% Milk | 2023-01-23          local   5.69   \n",
+       "14  PHILADELPHIA | Original Cream Cheese Product |...  international   5.49   \n",
+       "15  PRESIDENT'S CHOICE | Everything Trail Mix Nuts...          local   4.99   \n",
+       "16  FARMER'S MARKET | Butter Croissants Mini | 202...          local   4.99   \n",
+       "17  DOLE | No Sugar Added Fruit Salad With Cherrie...  international   4.29   \n",
+       "18  STARBUCKS | Frappuccino Vanilla Coffee Drink |...  international   3.99   \n",
+       "19  PRINGLES | Mega Can BBQ Flavour Chips | 2023-0...  international   3.99   \n",
+       "20  MONSTER ENERGY | Green Energy Drink, Can | 202...          local   3.99   \n",
+       "21          COCA-COLA | Coca-Cola Bottle | 2023-01-28  international   3.99   \n",
+       "22  SCHOOL SAFE | Muffin Bars Birthday Cake | 2023...  international   3.99   \n",
+       "23      CHRISTIE | Oreo Snak Pak Cookies | 2023-01-25  international   3.99   \n",
+       "24  CHRISTIE | Chips Ahoy! Rainbow Cookies | 2023-...  international   3.99   \n",
+       "25  ACTIVIA | Yogurt With Probiotics, Cherry Flavo...          local   3.99   \n",
+       "26          WONDER | Bread, Sliced White | 2023-01-18  international   3.99   \n",
+       "27       GRACE | 100% Pure Coconut Water | 2023-02-11          local   3.39   \n",
+       "28  GARDEN | Cream Wafers, Chocolate Flavour | 202...          local   2.39   \n",
+       "29  NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...  international   1.79   \n",
+       "\n",
+       "    Container_Size Container_Size_unit  Serving_Size Serving_Size_unit  \\\n",
+       "0             1020                   g           170                 g   \n",
+       "1              710                   g           118                 g   \n",
+       "2             1000                   g           100                 g   \n",
+       "3              500                   g            50                 g   \n",
+       "4              700                   g           150                 g   \n",
+       "5              400                   g            42                 g   \n",
+       "6              700                   g            30                 g   \n",
+       "7              510                   g            85                 g   \n",
+       "8              500                   g            50                 g   \n",
+       "9             1540                  mL           250                mL   \n",
+       "10             390                   g            50                 g   \n",
+       "11             310                  mL            15                mL   \n",
+       "12             136                   g            82                 g   \n",
+       "13            2000                  mL           250                mL   \n",
+       "14             227                   g            15                 g   \n",
+       "15             300                   g            50                 g   \n",
+       "16             336                   g            56                 g   \n",
+       "17             540                  mL           125                mL   \n",
+       "18             405                  mL           405                mL   \n",
+       "19             202                   g            50                 g   \n",
+       "20             473                  mL           473                mL   \n",
+       "21            2000                  mL           355                mL   \n",
+       "22             296                   g            37                 g   \n",
+       "23             180                   g            30                 g   \n",
+       "24             225                   g            31                 g   \n",
+       "25             650                   g           175                 g   \n",
+       "26             675                   g            75                 g   \n",
+       "27            1000                  mL           250                mL   \n",
+       "28             200                   g            25                 g   \n",
+       "29              45                   g            45                 g   \n",
+       "\n",
+       "    Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  Protein  \\\n",
+       "0        490  41.0     660             2       0    0.0     26.0   \n",
+       "1        510  31.0     270            52      37    3.0      6.0   \n",
+       "2        260  20.0     770             6       2    0.0     15.0   \n",
+       "3         50   1.0     360             0       0    0.0      9.0   \n",
+       "4        250  12.0     410            20       1    0.0     14.0   \n",
+       "5        210  11.0      20            25      21    2.0      4.0   \n",
+       "6        110   8.0     220             0       0    0.0      7.0   \n",
+       "7        320  16.0     320            39      13    1.0      5.0   \n",
+       "8        200  19.0     430             2       0    0.0      4.0   \n",
+       "9        110   0.0       0            27      23    0.0      2.0   \n",
+       "10       280  18.0     440            30       1    1.0      3.0   \n",
+       "11        20   0.0       1             5       5    0.0      0.1   \n",
+       "12       430  26.0     670            41       1    7.0      6.0   \n",
+       "13       130   5.0     120            12      12    0.0      9.0   \n",
+       "14        40   3.5      60             1       1    0.0      1.0   \n",
+       "15       220  10.0      25            31      23    NaN      3.0   \n",
+       "16       230  12.0     230            28       5    2.0      4.0   \n",
+       "17        40   0.0       0             9       6    2.0      1.0   \n",
+       "18       290   4.5     150            53      46    0.0      9.0   \n",
+       "19       260  15.0     240            29       2    1.0      2.0   \n",
+       "20       210   NaN     370            55      54    0.0      0.0   \n",
+       "21       140   0.0      25            39      39    0.0      0.0   \n",
+       "22       140   6.0      60            20      11    NaN      2.0   \n",
+       "23       140   5.0     140            22      12    1.0      1.0   \n",
+       "24       150   7.0      80            22       9    1.0      1.0   \n",
+       "25       160   5.0      70            22      18    0.0      7.0   \n",
+       "26       200   2.5     320            38       3    2.0      7.0   \n",
+       "27        50   0.0      65            11      10    0.0      1.0   \n",
+       "28       130   7.0      45            16       6    0.0      2.0   \n",
+       "29       230  12.0      35            29      22    1.0      3.0   \n",
+       "\n",
+       "    Price_per_100unit  \n",
+       "0            2.253922  \n",
+       "1            2.998592  \n",
+       "2            1.429000  \n",
+       "3            2.598000  \n",
+       "4            1.570000  \n",
+       "5            2.497500  \n",
+       "6            1.284286  \n",
+       "7            1.566667  \n",
+       "8            1.498000  \n",
+       "9            0.453896  \n",
+       "10           1.664103  \n",
+       "11           1.932258  \n",
+       "12           4.404412  \n",
+       "13           0.284500  \n",
+       "14           2.418502  \n",
+       "15           1.663333  \n",
+       "16           1.485119  \n",
+       "17           0.794444  \n",
+       "18           0.985185  \n",
+       "19           1.975248  \n",
+       "20           0.843552  \n",
+       "21           0.199500  \n",
+       "22           1.347973  \n",
+       "23           2.216667  \n",
+       "24           1.773333  \n",
+       "25           0.613846  \n",
+       "26           0.591111  \n",
+       "27           0.339000  \n",
+       "28           1.195000  \n",
+       "29           3.977778  "
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# reset index of the sorted data\n",
+    "food_r = food.sort_values(by=\"Price\", ascending=False)\n",
+    "food_r.reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "8069dbda",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "      <th>Price_per_100unit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>25.5</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>25.5</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>22.5</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>26.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>25.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>18.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>21.5</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>23.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>29.5</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>25.5</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>29.5</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>22.5</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>25.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>25.5</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>24.5</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ID  Food_Category  Product_Name  Brand  Price  Container_Size  \\\n",
+       "0   27.0            3.0          23.0   23.5   29.0            24.0   \n",
+       "1   18.0           19.0          28.0    8.5   30.0            27.0   \n",
+       "2   12.0           26.0          19.0   23.5   18.5             2.0   \n",
+       "3   28.0            3.0          18.0   23.5   23.0            18.0   \n",
+       "4    4.0           12.0          27.0    8.5    8.0            14.0   \n",
+       "5    8.0           26.0           2.0    8.5   20.0            12.0   \n",
+       "6   16.0           19.0          21.0   23.5   28.0            25.5   \n",
+       "7   11.0           26.0          24.0    8.5    8.0             5.0   \n",
+       "8   19.0           19.0          11.0    8.5   26.0            22.5   \n",
+       "9   26.0            3.0           7.0   23.5   14.5            11.0   \n",
+       "10  13.0           26.0          15.0    8.5    1.0             1.0   \n",
+       "11  10.0           26.0          22.0   23.5   14.5             9.0   \n",
+       "12   3.0           12.0          13.0   23.5    8.0            15.0   \n",
+       "13   6.0           26.0          12.0    8.5   25.0            13.0   \n",
+       "14  15.0           19.0          17.0   23.5   22.0            16.5   \n",
+       "15  30.0            3.0          30.0    8.5    8.0            21.0   \n",
+       "16  22.0            7.5           1.0   23.5    8.0            20.0   \n",
+       "17   9.0           26.0           3.0    8.5    8.0             6.0   \n",
+       "18  14.0           26.0           4.0    8.5    8.0             3.0   \n",
+       "19  29.0            3.0          25.0    8.5    8.0             8.0   \n",
+       "20   1.0           12.0           5.0    8.5    8.0            29.5   \n",
+       "21  20.0            7.5          14.0   23.5   17.0            29.5   \n",
+       "22   7.0           26.0           8.0   23.5    2.0             4.0   \n",
+       "23  23.0            7.5          16.0   23.5   24.0            22.5   \n",
+       "24   2.0           12.0          29.0   23.5   21.0            28.0   \n",
+       "25  17.0           19.0          10.0    8.5   27.0            16.5   \n",
+       "26   5.0           12.0           9.0   23.5    3.0            25.5   \n",
+       "27  21.0            7.5          20.0    8.5   16.0             7.0   \n",
+       "28  25.0           15.5           6.0    8.5   13.0            19.0   \n",
+       "29  24.0           15.5          26.0    8.5   18.5            10.0   \n",
+       "\n",
+       "    Container_Size_unit  Serving_Size  Serving_Size_unit  Calories   Fat  \\\n",
+       "0                  11.5          20.0               11.5      30.0  28.0   \n",
+       "1                  11.5          23.0               11.5      29.0  29.0   \n",
+       "2                  11.5          17.0               11.5      28.0  27.0   \n",
+       "3                  11.5          18.0               11.5      27.0  23.0   \n",
+       "4                  26.5          29.0               26.5      26.0   9.0   \n",
+       "5                  11.5          12.0               11.5      25.0  24.0   \n",
+       "6                  11.5          19.0               11.5      23.5  26.0   \n",
+       "7                  11.5          12.0               11.5      23.5  22.0   \n",
+       "8                  11.5          22.0               11.5      22.0  20.0   \n",
+       "9                  11.5          15.0               11.5      20.5  20.0   \n",
+       "10                 11.5           9.0               11.5      20.5  20.0   \n",
+       "11                 11.5          12.0               11.5      19.0  17.0   \n",
+       "12                 26.5          30.0               26.5      17.5   NaN   \n",
+       "13                 11.5           8.0               11.5      17.5  18.0   \n",
+       "14                 11.5          12.0               11.5      15.5  25.0   \n",
+       "15                 11.5          16.0               11.5      15.5   7.0   \n",
+       "16                 11.5          24.0               11.5      14.0  11.0   \n",
+       "17                 11.5           6.0               11.5      13.0  14.5   \n",
+       "18                 11.5           4.5               11.5      11.0  11.0   \n",
+       "19                 11.5           7.0               11.5      11.0  13.0   \n",
+       "20                 26.5          28.0               26.5      11.0   3.0   \n",
+       "21                 26.5          26.0               26.5       8.5  11.0   \n",
+       "22                 11.5           3.0               11.5       8.5  14.5   \n",
+       "23                 11.5           4.5               11.5       6.5  16.0   \n",
+       "24                 26.5          26.0               26.5       6.5   3.0   \n",
+       "25                 11.5          12.0               11.5       4.5   6.0   \n",
+       "26                 26.5          26.0               26.5       4.5   3.0   \n",
+       "27                 11.5           1.5               11.5       2.5   8.0   \n",
+       "28                 26.5          21.0               26.5       2.5   3.0   \n",
+       "29                 26.5           1.5               26.5       1.0   3.0   \n",
+       "\n",
+       "    Sodium  Carbohydrate  Sugars  Fiber  Protein  Price_per_100unit  \n",
+       "0     20.0          28.0    27.0   27.0     20.5               28.0  \n",
+       "1     28.0           4.5     2.5    8.5     30.0               24.0  \n",
+       "2     29.0          27.0     6.5   28.0     20.5               30.0  \n",
+       "3     21.5          25.5    21.0   19.5     19.0               16.0  \n",
+       "4     16.0          29.0    29.0    8.5     26.0                9.0  \n",
+       "5     27.0          22.0     6.5   19.5     14.0               19.0  \n",
+       "6     30.0           7.0     9.5    8.5     29.0               13.0  \n",
+       "7     19.0          20.5     9.5   19.5     10.5               22.0  \n",
+       "8     25.0          12.5     6.5    8.5     28.0               17.0  \n",
+       "9     18.0          19.0    12.5   24.5     17.0               14.0  \n",
+       "10     7.0          20.5    24.0   19.5     14.0               29.0  \n",
+       "11     5.5          23.0    25.5    NaN     14.0               18.0  \n",
+       "12    24.0          30.0    30.0    8.5      1.5                8.0  \n",
+       "13     4.0          17.0    23.0   24.5     17.0               26.0  \n",
+       "14    26.0           4.5     2.5    8.5     17.0               15.0  \n",
+       "15    21.5          24.0    11.0   24.5     23.0                5.0  \n",
+       "16    12.0          15.0    22.0    8.5     23.0                6.0  \n",
+       "17    13.0          15.0    16.0   19.5      6.0               20.0  \n",
+       "18    15.0          15.0    19.5   19.5      6.0               23.0  \n",
+       "19     9.5          12.5    18.0    NaN     10.5               12.0  \n",
+       "20     5.5          25.5    28.0    8.5      1.5                1.0  \n",
+       "21    14.0          10.0    19.5    8.5     26.0                2.0  \n",
+       "22     8.0          11.0    14.5    8.5     10.5               10.0  \n",
+       "23    17.0           1.5     2.5    8.5     23.0               11.0  \n",
+       "24     1.5          18.0    25.5    8.5     10.5                4.0  \n",
+       "25    23.0           1.5     2.5    8.5     26.0               27.0  \n",
+       "26    11.0           9.0    17.0    8.5      6.0                3.0  \n",
+       "27     9.5           3.0     6.5    8.5      6.0               25.0  \n",
+       "28     1.5           8.0    14.5   24.5      6.0                7.0  \n",
+       "29     3.0           6.0    12.5    8.5      3.0               21.0  "
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Assign ranks to entries\n",
+    "food.rank()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15251558",
+   "metadata": {},
+   "source": [
+    "### 3.3  Reshaping Data "
+   ]
+  },
+  {
+   "attachments": {
+    "Screen%20Shot%202023-02-20%20at%2011.56.19%20PM.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "29aa5a9e",
+   "metadata": {},
+   "source": [
+    "<div>\n",
+    "<img src=\"attachment:Screen%20Shot%202023-02-20%20at%2011.56.19%20PM.png\" width=\"700\">\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "ba2c7291",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['ID', 'Food_Category', 'Product_Name', 'Brand', 'Price',\n",
+       "       'Container_Size', 'Container_Size_unit', 'Serving_Size',\n",
+       "       'Serving_Size_unit', 'Calories', 'Fat', 'Sodium', 'Carbohydrate',\n",
+       "       'Sugars', 'Fiber', 'Protein', 'Price_per_100unit'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "food.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "34febe23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>150</td>\n",
+       "      <td>53</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ID Food_Category  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "0  10027        Bakery       510  31.0     270            52      37    3.0   \n",
+       "1  10018          Meat       490  41.0     660             2       0    0.0   \n",
+       "2  10012         Snaks       430  26.0     670            41       1    7.0   \n",
+       "3  10028        Bakery       320  16.0     320            39      13    1.0   \n",
+       "4  10004        Drinks       290   4.5     150            53      46    0.0   \n",
+       "\n",
+       "   Protein  \n",
+       "0      6.0  \n",
+       "1     26.0  \n",
+       "2      6.0  \n",
+       "3      5.0  \n",
+       "4      9.0  "
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Gather columns into rows\n",
+    "food5 = food.loc[:, ['ID', 'Food_Category', 'Calories', 'Fat', 'Sodium', 'Carbohydrate','Sugars', 'Fiber', 'Protein']]\n",
+    "food5.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "b5faecbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Nutrients</th>\n",
+       "      <th>Values</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>Calories</td>\n",
+       "      <td>510.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>Calories</td>\n",
+       "      <td>490.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>Calories</td>\n",
+       "      <td>430.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>Calories</td>\n",
+       "      <td>320.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>Calories</td>\n",
+       "      <td>290.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>10017</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>206</th>\n",
+       "      <td>10005</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>207</th>\n",
+       "      <td>10021</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>208</th>\n",
+       "      <td>10025</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>10024</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>Protein</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>210 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        ID Food_Category Nutrients  Values\n",
+       "0    10027        Bakery  Calories   510.0\n",
+       "1    10018          Meat  Calories   490.0\n",
+       "2    10012         Snaks  Calories   430.0\n",
+       "3    10028        Bakery  Calories   320.0\n",
+       "4    10004        Drinks  Calories   290.0\n",
+       "..     ...           ...       ...     ...\n",
+       "205  10017          Meat   Protein     9.0\n",
+       "206  10005        Drinks   Protein     1.0\n",
+       "207  10021         Dairy   Protein     1.0\n",
+       "208  10025         Fruit   Protein     1.0\n",
+       "209  10024         Fruit   Protein     0.1\n",
+       "\n",
+       "[210 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Gather columns into rows\n",
+    "food5s = food5.melt(id_vars=['ID','Food_Category'], \n",
+    "        var_name='Nutrients', \n",
+    "        value_name='Values')\n",
+    "food5s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "f019ff29",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Nutrients</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Sugars</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10001</th>\n",
+       "      <th>Drinks</th>\n",
+       "      <td>140.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10002</th>\n",
+       "      <th>Drinks</th>\n",
+       "      <td>110.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10003</th>\n",
+       "      <th>Drinks</th>\n",
+       "      <td>210.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>370.0</td>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10004</th>\n",
+       "      <th>Drinks</th>\n",
+       "      <td>290.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>46.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10005</th>\n",
+       "      <th>Drinks</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10006</th>\n",
+       "      <th>Snaks</th>\n",
+       "      <td>210.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10007</th>\n",
+       "      <th>Snaks</th>\n",
+       "      <td>130.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10008</th>\n",
+       "      <th>Snaks</th>\n",
+       "      <td>280.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>440.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10009</th>\n",
+       "      <th>Snaks</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10010</th>\n",
+       "      <th>Snaks</th>\n",
+       "      <td>220.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>23.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10011</th>\n",
+       "      <th>Snaks</th>\n",
+       "      <td>260.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>240.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10012</th>\n",
+       "      <th>Snaks</th>\n",
+       "      <td>430.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>670.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10013</th>\n",
+       "      <th>Snaks</th>\n",
+       "      <td>230.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10014</th>\n",
+       "      <th>Snaks</th>\n",
+       "      <td>140.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10015</th>\n",
+       "      <th>Meat</th>\n",
+       "      <td>200.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>430.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10016</th>\n",
+       "      <th>Meat</th>\n",
+       "      <td>260.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>770.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10017</th>\n",
+       "      <th>Meat</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>360.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10018</th>\n",
+       "      <th>Meat</th>\n",
+       "      <td>490.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>660.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10019</th>\n",
+       "      <th>Meat</th>\n",
+       "      <td>250.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>410.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10020</th>\n",
+       "      <th>Dairy</th>\n",
+       "      <td>130.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10021</th>\n",
+       "      <th>Dairy</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10022</th>\n",
+       "      <th>Dairy</th>\n",
+       "      <td>160.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>18.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10023</th>\n",
+       "      <th>Dairy</th>\n",
+       "      <td>110.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>220.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10024</th>\n",
+       "      <th>Fruit</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10025</th>\n",
+       "      <th>Fruit</th>\n",
+       "      <td>40.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10026</th>\n",
+       "      <th>Bakery</th>\n",
+       "      <td>230.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10027</th>\n",
+       "      <th>Bakery</th>\n",
+       "      <td>510.0</td>\n",
+       "      <td>52.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>37.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10028</th>\n",
+       "      <th>Bakery</th>\n",
+       "      <td>320.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>320.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10029</th>\n",
+       "      <th>Bakery</th>\n",
+       "      <td>140.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10030</th>\n",
+       "      <th>Bakery</th>\n",
+       "      <td>200.0</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>320.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Nutrients            Calories  Carbohydrate   Fat  Fiber  Protein  Sodium  \\\n",
+       "ID    Food_Category                                                         \n",
+       "10001 Drinks            140.0          39.0   0.0    0.0      0.0    25.0   \n",
+       "10002 Drinks            110.0          27.0   0.0    0.0      2.0     0.0   \n",
+       "10003 Drinks            210.0          55.0   NaN    0.0      0.0   370.0   \n",
+       "10004 Drinks            290.0          53.0   4.5    0.0      9.0   150.0   \n",
+       "10005 Drinks             50.0          11.0   0.0    0.0      1.0    65.0   \n",
+       "10006 Snaks             210.0          25.0  11.0    2.0      4.0    20.0   \n",
+       "10007 Snaks             130.0          16.0   7.0    0.0      2.0    45.0   \n",
+       "10008 Snaks             280.0          30.0  18.0    1.0      3.0   440.0   \n",
+       "10009 Snaks             150.0          22.0   7.0    1.0      1.0    80.0   \n",
+       "10010 Snaks             220.0          31.0  10.0    NaN      3.0    25.0   \n",
+       "10011 Snaks             260.0          29.0  15.0    1.0      2.0   240.0   \n",
+       "10012 Snaks             430.0          41.0  26.0    7.0      6.0   670.0   \n",
+       "10013 Snaks             230.0          29.0  12.0    1.0      3.0    35.0   \n",
+       "10014 Snaks             140.0          22.0   5.0    1.0      1.0   140.0   \n",
+       "10015 Meat              200.0           2.0  19.0    0.0      4.0   430.0   \n",
+       "10016 Meat              260.0           6.0  20.0    0.0     15.0   770.0   \n",
+       "10017 Meat               50.0           0.0   1.0    0.0      9.0   360.0   \n",
+       "10018 Meat              490.0           2.0  41.0    0.0     26.0   660.0   \n",
+       "10019 Meat              250.0          20.0  12.0    0.0     14.0   410.0   \n",
+       "10020 Dairy             130.0          12.0   5.0    0.0      9.0   120.0   \n",
+       "10021 Dairy              40.0           1.0   3.5    0.0      1.0    60.0   \n",
+       "10022 Dairy             160.0          22.0   5.0    0.0      7.0    70.0   \n",
+       "10023 Dairy             110.0           0.0   8.0    0.0      7.0   220.0   \n",
+       "10024 Fruit              20.0           5.0   0.0    0.0      0.1     1.0   \n",
+       "10025 Fruit              40.0           9.0   0.0    2.0      1.0     0.0   \n",
+       "10026 Bakery            230.0          28.0  12.0    2.0      4.0   230.0   \n",
+       "10027 Bakery            510.0          52.0  31.0    3.0      6.0   270.0   \n",
+       "10028 Bakery            320.0          39.0  16.0    1.0      5.0   320.0   \n",
+       "10029 Bakery            140.0          20.0   6.0    NaN      2.0    60.0   \n",
+       "10030 Bakery            200.0          38.0   2.5    2.0      7.0   320.0   \n",
+       "\n",
+       "Nutrients            Sugars  \n",
+       "ID    Food_Category          \n",
+       "10001 Drinks           39.0  \n",
+       "10002 Drinks           23.0  \n",
+       "10003 Drinks           54.0  \n",
+       "10004 Drinks           46.0  \n",
+       "10005 Drinks           10.0  \n",
+       "10006 Snaks            21.0  \n",
+       "10007 Snaks             6.0  \n",
+       "10008 Snaks             1.0  \n",
+       "10009 Snaks             9.0  \n",
+       "10010 Snaks            23.0  \n",
+       "10011 Snaks             2.0  \n",
+       "10012 Snaks             1.0  \n",
+       "10013 Snaks            22.0  \n",
+       "10014 Snaks            12.0  \n",
+       "10015 Meat              0.0  \n",
+       "10016 Meat              2.0  \n",
+       "10017 Meat              0.0  \n",
+       "10018 Meat              0.0  \n",
+       "10019 Meat              1.0  \n",
+       "10020 Dairy            12.0  \n",
+       "10021 Dairy             1.0  \n",
+       "10022 Dairy            18.0  \n",
+       "10023 Dairy             0.0  \n",
+       "10024 Fruit             5.0  \n",
+       "10025 Fruit             6.0  \n",
+       "10026 Bakery            5.0  \n",
+       "10027 Bakery           37.0  \n",
+       "10028 Bakery           13.0  \n",
+       "10029 Bakery           11.0  \n",
+       "10030 Bakery            3.0  "
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Spread rows into columns\n",
+    "food5ss = food5s.pivot(index=['ID','Food_Category'], columns=\"Nutrients\", values=\"Values\")\n",
+    "food5ss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "980438ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "      <th>Price_per_100unit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.998592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>THE KEG | Prime Rib Beef Burger | 2023-02-16</td>\n",
+       "      <td>international</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2.253922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10013</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>1.79</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>45</td>\n",
+       "      <td>g</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>29</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.977778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10007</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>GARDEN | Cream Wafers, Chocolate Flavour | 202...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>2.39</td>\n",
+       "      <td>200</td>\n",
+       "      <td>g</td>\n",
+       "      <td>25</td>\n",
+       "      <td>g</td>\n",
+       "      <td>130</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.195000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ID Food_Category                                       Product_Name  \\\n",
+       "0   10027        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...   \n",
+       "1   10018          Meat       THE KEG | Prime Rib Beef Burger | 2023-02-16   \n",
+       "10  10013         Snaks  NESTLE | KitKat Chunky Milk Chocolate Wafer Ba...   \n",
+       "22  10007         Snaks  GARDEN | Cream Wafers, Chocolate Flavour | 202...   \n",
+       "\n",
+       "            Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "0           local  21.29             710                   g           118   \n",
+       "1   international  22.99            1020                   g           170   \n",
+       "10  international   1.79              45                   g            45   \n",
+       "22          local   2.39             200                   g            25   \n",
+       "\n",
+       "   Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "0                  g       510  31.0     270            52      37    3.0   \n",
+       "1                  g       490  41.0     660             2       0    0.0   \n",
+       "10                 g       230  12.0      35            29      22    1.0   \n",
+       "22                 g       130   7.0      45            16       6    0.0   \n",
+       "\n",
+       "    Protein  Price_per_100unit  \n",
+       "0       6.0           2.998592  \n",
+       "1      26.0           2.253922  \n",
+       "10      3.0           3.977778  \n",
+       "22      2.0           1.195000  "
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Append rows (observations) of DataFrames\n",
+    "food6a = food.loc[food[\"Price\"]>20]\n",
+    "food6b = food.loc[food[\"Price\"]<3]\n",
+    "food6c = pd.concat([food6a, food6b])\n",
+    "food6c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "5217bf6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10008</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>280</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10016</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>260</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10011</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>260</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10019</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>250</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10026</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10013</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10010</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>220</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10003</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10006</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>210</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>10015</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>200</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10030</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10022</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>160</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>10009</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>150</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10014</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>140</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10029</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>140</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10001</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>10020</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>130</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10007</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>130</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>10023</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>110</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>10002</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>10017</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10005</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>10021</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>40</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>10025</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>10024</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ID Food_Category  Calories   Fat  Sugars  Fiber  Protein\n",
+       "0   10027        Bakery       510  31.0      37    3.0      6.0\n",
+       "1   10018          Meat       490  41.0       0    0.0     26.0\n",
+       "2   10012         Snaks       430  26.0       1    7.0      6.0\n",
+       "3   10028        Bakery       320  16.0      13    1.0      5.0\n",
+       "4   10004        Drinks       290   4.5      46    0.0      9.0\n",
+       "5   10008         Snaks       280  18.0       1    1.0      3.0\n",
+       "6   10016          Meat       260  20.0       2    0.0     15.0\n",
+       "7   10011         Snaks       260  15.0       2    1.0      2.0\n",
+       "8   10019          Meat       250  12.0       1    0.0     14.0\n",
+       "9   10026        Bakery       230  12.0       5    2.0      4.0\n",
+       "10  10013         Snaks       230  12.0      22    1.0      3.0\n",
+       "11  10010         Snaks       220  10.0      23    NaN      3.0\n",
+       "12  10003        Drinks       210   NaN      54    0.0      0.0\n",
+       "13  10006         Snaks       210  11.0      21    2.0      4.0\n",
+       "14  10015          Meat       200  19.0       0    0.0      4.0\n",
+       "15  10030        Bakery       200   2.5       3    2.0      7.0\n",
+       "16  10022         Dairy       160   5.0      18    0.0      7.0\n",
+       "17  10009         Snaks       150   7.0       9    1.0      1.0\n",
+       "18  10014         Snaks       140   5.0      12    1.0      1.0\n",
+       "19  10029        Bakery       140   6.0      11    NaN      2.0\n",
+       "20  10001        Drinks       140   0.0      39    0.0      0.0\n",
+       "21  10020         Dairy       130   5.0      12    0.0      9.0\n",
+       "22  10007         Snaks       130   7.0       6    0.0      2.0\n",
+       "23  10023         Dairy       110   8.0       0    0.0      7.0\n",
+       "24  10002        Drinks       110   0.0      23    0.0      2.0\n",
+       "25  10017          Meat        50   1.0       0    0.0      9.0\n",
+       "26  10005        Drinks        50   0.0      10    0.0      1.0\n",
+       "27  10021         Dairy        40   3.5       1    0.0      1.0\n",
+       "28  10025         Fruit        40   0.0       6    2.0      1.0\n",
+       "29  10024         Fruit        20   0.0       5    0.0      0.1"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Append columns (variables) of DataFrames\n",
+    "food7a = food.loc[:, ['ID', 'Food_Category', 'Calories', 'Fat']]\n",
+    "food7b = food.loc[:, ['ID', 'Food_Category', 'Sugars', 'Fiber', 'Protein']]\n",
+    "food7c = pd.concat([food7a, food7b], axis = 1)\n",
+    "food7c\n",
+    "food7c = food7c.loc[:,~food7c.columns.duplicated()].copy()\n",
+    "food7c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "e71a575b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10008</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>280</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10016</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>260</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10011</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>260</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10019</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>250</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10026</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10013</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>230</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10010</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>220</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10003</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>210</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10006</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>210</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>10015</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>200</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10030</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10022</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>160</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>10009</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>150</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10014</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>140</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10029</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>140</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10001</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>140</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>10020</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>130</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10007</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>130</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>10023</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>110</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>10002</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>110</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>10017</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10005</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>10021</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>40</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>10025</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>10024</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>20</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ID Food_Category  Calories   Fat  Sugars  Fiber  Protein  Score\n",
+       "0   10027        Bakery       510  31.0      37    3.0      6.0     70\n",
+       "1   10018          Meat       490  41.0       0    0.0     26.0     34\n",
+       "2   10012         Snaks       430  26.0       1    7.0      6.0      9\n",
+       "3   10028        Bakery       320  16.0      13    1.0      5.0     12\n",
+       "4   10004        Drinks       290   4.5      46    0.0      9.0     48\n",
+       "5   10008         Snaks       280  18.0       1    1.0      3.0     14\n",
+       "6   10016          Meat       260  20.0       2    0.0     15.0     37\n",
+       "7   10011         Snaks       260  15.0       2    1.0      2.0     42\n",
+       "8   10019          Meat       250  12.0       1    0.0     14.0     10\n",
+       "9   10026        Bakery       230  12.0       5    2.0      4.0     37\n",
+       "10  10013         Snaks       230  12.0      22    1.0      3.0     60\n",
+       "11  10010         Snaks       220  10.0      23    NaN      3.0     20\n",
+       "12  10003        Drinks       210   NaN      54    0.0      0.0     93\n",
+       "13  10006         Snaks       210  11.0      21    2.0      4.0     65\n",
+       "14  10015          Meat       200  19.0       0    0.0      4.0     82\n",
+       "15  10030        Bakery       200   2.5       3    2.0      7.0     74\n",
+       "16  10022         Dairy       160   5.0      18    0.0      7.0     20\n",
+       "17  10009         Snaks       150   7.0       9    1.0      1.0     15\n",
+       "18  10014         Snaks       140   5.0      12    1.0      1.0     79\n",
+       "19  10029        Bakery       140   6.0      11    NaN      2.0     67\n",
+       "20  10001        Drinks       140   0.0      39    0.0      0.0      7\n",
+       "21  10020         Dairy       130   5.0      12    0.0      9.0     24\n",
+       "22  10007         Snaks       130   7.0       6    0.0      2.0     49\n",
+       "23  10023         Dairy       110   8.0       0    0.0      7.0     63\n",
+       "24  10002        Drinks       110   0.0      23    0.0      2.0      2\n",
+       "25  10017          Meat        50   1.0       0    0.0      9.0      7\n",
+       "26  10005        Drinks        50   0.0      10    0.0      1.0     31\n",
+       "27  10021         Dairy        40   3.5       1    0.0      1.0     59\n",
+       "28  10025         Fruit        40   0.0       6    2.0      1.0     25\n",
+       "29  10024         Fruit        20   0.0       5    0.0      0.1     51"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Combine Datasets (merging)\n",
+    "\n",
+    "# importing the random module\n",
+    "import random\n",
+    "score = np.random.choice(100, 30, replace=True)\n",
+    "score\n",
+    "\n",
+    "food8 = food.loc[:, ['ID']]\n",
+    "food8['Score'] = score\n",
+    "food8\n",
+    "\n",
+    "food9 = pd.merge(food7c, food8)\n",
+    "food9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "74a587bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>510.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>490.0</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>430.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>320.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>290.0</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10008</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10016</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10011</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>10019</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10026</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>10013</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>230.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>10010</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>220.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10003</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>210.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>10006</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>210.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>10015</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10030</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10022</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>10009</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10014</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10029</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>10001</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>10020</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10007</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>10023</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>10002</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>10017</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>10005</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>10021</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>10025</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>10024</td>\n",
+       "      <td>Fruit</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>20003</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1058</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>20006</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1059</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ID Food_Category  Calories   Fat  Sugars  Fiber  Protein  Score\n",
+       "0   10027        Bakery     510.0  31.0    37.0    3.0      6.0     70\n",
+       "1   10018          Meat     490.0  41.0     0.0    0.0     26.0     34\n",
+       "2   10012         Snaks     430.0  26.0     1.0    7.0      6.0      9\n",
+       "3   10028        Bakery     320.0  16.0    13.0    1.0      5.0     12\n",
+       "4   10004        Drinks     290.0   4.5    46.0    0.0      9.0     48\n",
+       "5   10008         Snaks     280.0  18.0     1.0    1.0      3.0     14\n",
+       "6   10016          Meat     260.0  20.0     2.0    0.0     15.0     37\n",
+       "7   10011         Snaks     260.0  15.0     2.0    1.0      2.0     42\n",
+       "8   10019          Meat     250.0  12.0     1.0    0.0     14.0     10\n",
+       "9   10026        Bakery     230.0  12.0     5.0    2.0      4.0     37\n",
+       "10  10013         Snaks     230.0  12.0    22.0    1.0      3.0     60\n",
+       "11  10010         Snaks     220.0  10.0    23.0    NaN      3.0     20\n",
+       "12  10003        Drinks     210.0   NaN    54.0    0.0      0.0     93\n",
+       "13  10006         Snaks     210.0  11.0    21.0    2.0      4.0     65\n",
+       "14  10015          Meat     200.0  19.0     0.0    0.0      4.0     82\n",
+       "15  10030        Bakery     200.0   2.5     3.0    2.0      7.0     74\n",
+       "16  10022         Dairy     160.0   5.0    18.0    0.0      7.0     20\n",
+       "17  10009         Snaks     150.0   7.0     9.0    1.0      1.0     15\n",
+       "18  10014         Snaks     140.0   5.0    12.0    1.0      1.0     79\n",
+       "19  10029        Bakery     140.0   6.0    11.0    NaN      2.0     67\n",
+       "20  10001        Drinks     140.0   0.0    39.0    0.0      0.0      7\n",
+       "21  10020         Dairy     130.0   5.0    12.0    0.0      9.0     24\n",
+       "22  10007         Snaks     130.0   7.0     6.0    0.0      2.0     49\n",
+       "23  10023         Dairy     110.0   8.0     0.0    0.0      7.0     63\n",
+       "24  10002        Drinks     110.0   0.0    23.0    0.0      2.0      2\n",
+       "25  10017          Meat      50.0   1.0     0.0    0.0      9.0      7\n",
+       "26  10005        Drinks      50.0   0.0    10.0    0.0      1.0     31\n",
+       "27  10021         Dairy      40.0   3.5     1.0    0.0      1.0     59\n",
+       "28  10025         Fruit      40.0   0.0     6.0    2.0      1.0     25\n",
+       "29  10024         Fruit      20.0   0.0     5.0    0.0      0.1     51\n",
+       "30  20003           NaN       NaN   NaN     NaN    NaN      NaN   1058\n",
+       "31  20006           NaN       NaN   NaN     NaN    NaN      NaN   1059"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Combine Datasets (merging, if more than ID)\n",
+    "food8b = food8\n",
+    "food8.shape\n",
+    "\n",
+    "# adding rows\n",
+    "food8b.loc[\"30\"] = [20003,1058]\n",
+    "food8b.loc[\"31\"] = [20006,1059]\n",
+    "food8b.shape\n",
+    "food8b\n",
+    "\n",
+    "food9b = pd.merge(food7c, food8, how=\"left\")\n",
+    "food9b\n",
+    "\n",
+    "food9c = pd.merge(food7c, food8, how=\"right\")\n",
+    "food9c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c2957b5",
+   "metadata": {},
+   "source": [
+    "### 3.5 Group Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "c5b35592",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bakery</th>\n",
+       "      <td>10,028.00</td>\n",
+       "      <td>280.00</td>\n",
+       "      <td>13.50</td>\n",
+       "      <td>13.80</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>4.80</td>\n",
+       "      <td>52.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dairy</th>\n",
+       "      <td>10,021.50</td>\n",
+       "      <td>110.00</td>\n",
+       "      <td>5.38</td>\n",
+       "      <td>7.75</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>6.00</td>\n",
+       "      <td>41.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Drinks</th>\n",
+       "      <td>10,003.00</td>\n",
+       "      <td>160.00</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>34.40</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>36.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fruit</th>\n",
+       "      <td>10,024.50</td>\n",
+       "      <td>30.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>5.50</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.55</td>\n",
+       "      <td>38.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Meat</th>\n",
+       "      <td>10,017.00</td>\n",
+       "      <td>250.00</td>\n",
+       "      <td>18.60</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>13.60</td>\n",
+       "      <td>34.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snaks</th>\n",
+       "      <td>10,010.00</td>\n",
+       "      <td>227.78</td>\n",
+       "      <td>12.33</td>\n",
+       "      <td>10.78</td>\n",
+       "      <td>1.75</td>\n",
+       "      <td>2.78</td>\n",
+       "      <td>39.22</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     ID  Calories   Fat  Sugars  Fiber  Protein  Score\n",
+       "Food_Category                                                         \n",
+       "Bakery        10,028.00    280.00 13.50   13.80   2.00     4.80  52.00\n",
+       "Dairy         10,021.50    110.00  5.38    7.75   0.00     6.00  41.50\n",
+       "Drinks        10,003.00    160.00  1.12   34.40   0.00     2.40  36.20\n",
+       "Fruit         10,024.50     30.00  0.00    5.50   1.00     0.55  38.00\n",
+       "Meat          10,017.00    250.00 18.60    0.60   0.00    13.60  34.00\n",
+       "Snaks         10,010.00    227.78 12.33   10.78   1.75     2.78  39.22"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.options.display.float_format = '{:,.2f}'.format # change to 2 digits\n",
+    "\n",
+    "food10 = food9c.groupby(by=\"Food_Category\").mean()\n",
+    "food10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "24b98261",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fat</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.00</th>\n",
+       "      <td>50057</td>\n",
+       "      <td>360.00</td>\n",
+       "      <td>83.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>4.10</td>\n",
+       "      <td>116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.00</th>\n",
+       "      <td>10017</td>\n",
+       "      <td>50.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.50</th>\n",
+       "      <td>10030</td>\n",
+       "      <td>200.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3.50</th>\n",
+       "      <td>10021</td>\n",
+       "      <td>40.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.50</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>290.00</td>\n",
+       "      <td>46.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.00</th>\n",
+       "      <td>30056</td>\n",
+       "      <td>430.00</td>\n",
+       "      <td>42.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>17.00</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.00</th>\n",
+       "      <td>10029</td>\n",
+       "      <td>140.00</td>\n",
+       "      <td>11.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7.00</th>\n",
+       "      <td>20016</td>\n",
+       "      <td>280.00</td>\n",
+       "      <td>15.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.00</th>\n",
+       "      <td>10023</td>\n",
+       "      <td>110.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10.00</th>\n",
+       "      <td>10010</td>\n",
+       "      <td>220.00</td>\n",
+       "      <td>23.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11.00</th>\n",
+       "      <td>10006</td>\n",
+       "      <td>210.00</td>\n",
+       "      <td>21.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12.00</th>\n",
+       "      <td>30058</td>\n",
+       "      <td>710.00</td>\n",
+       "      <td>28.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>21.00</td>\n",
+       "      <td>107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15.00</th>\n",
+       "      <td>10011</td>\n",
+       "      <td>260.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16.00</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>320.00</td>\n",
+       "      <td>13.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18.00</th>\n",
+       "      <td>10008</td>\n",
+       "      <td>280.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19.00</th>\n",
+       "      <td>10015</td>\n",
+       "      <td>200.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20.00</th>\n",
+       "      <td>10016</td>\n",
+       "      <td>260.00</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>15.00</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26.00</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>430.00</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>6.00</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31.00</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>510.00</td>\n",
+       "      <td>37.00</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>6.00</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41.00</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>490.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>26.00</td>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NaN</th>\n",
+       "      <td>50012</td>\n",
+       "      <td>210.00</td>\n",
+       "      <td>54.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2210</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          ID  Calories  Sugars  Fiber  Protein  Score\n",
+       "Fat                                                  \n",
+       "0.00   50057    360.00   83.00   2.00     4.10    116\n",
+       "1.00   10017     50.00    0.00   0.00     9.00      7\n",
+       "2.50   10030    200.00    3.00   2.00     7.00     74\n",
+       "3.50   10021     40.00    1.00   0.00     1.00     59\n",
+       "4.50   10004    290.00   46.00   0.00     9.00     48\n",
+       "5.00   30056    430.00   42.00   1.00    17.00    123\n",
+       "6.00   10029    140.00   11.00   0.00     2.00     67\n",
+       "7.00   20016    280.00   15.00   1.00     3.00     64\n",
+       "8.00   10023    110.00    0.00   0.00     7.00     63\n",
+       "10.00  10010    220.00   23.00   0.00     3.00     20\n",
+       "11.00  10006    210.00   21.00   2.00     4.00     65\n",
+       "12.00  30058    710.00   28.00   3.00    21.00    107\n",
+       "15.00  10011    260.00    2.00   1.00     2.00     42\n",
+       "16.00  10028    320.00   13.00   1.00     5.00     12\n",
+       "18.00  10008    280.00    1.00   1.00     3.00     14\n",
+       "19.00  10015    200.00    0.00   0.00     4.00     82\n",
+       "20.00  10016    260.00    2.00   0.00    15.00     37\n",
+       "26.00  10012    430.00    1.00   7.00     6.00      9\n",
+       "31.00  10027    510.00   37.00   3.00     6.00     70\n",
+       "41.00  10018    490.00    0.00   0.00    26.00     34\n",
+       "NaN    50012    210.00   54.00   0.00     0.00   2210"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "food10b = food9c.groupby(by=\"Fat\",dropna=False).sum()\n",
+    "food10b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7b0bd4a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acc8df52",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7255897e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "513ec9cc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "831b4248",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58287f6b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87b03dc9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17db37f9",
+   "metadata": {},
+   "source": [
+    "# Source Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f80db22",
+   "metadata": {},
+   "source": [
+    "### data_txt \n",
+    "* 1 create a new word document\n",
+    "* 2 copy the below data to word document\n",
+    "* 3 save it as txt document with the name: Food_Data_HLW_txt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ceb84f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ID\tFood_Category\tProduct_Name\tBrand\tPrice\tContainer_Size\tContainer_Size_unit\tServing_Size\tServing_Size_unit\tCalories\tFat\tSodium\tCarbohydrate\tSugars\tFiber\tProtein\n",
+    "10027\tBakery\tPRESIDENT'S CHOICE | Triple Chocolate Cake | 2023-01-19\tlocal\t21.29\t710\tg\t118\tg\t510\t31\t270\t52\t37\t3\t6\n",
+    "10018\tMeat\tTHE KEG | Prime Rib Beef Burger | 2023-02-16\tinternational\t22.99\t1020\tg\t170\tg\t490\t41\t660\t2\t0\t0\t26\n",
+    "10012\tSnaks\tORVILLE | Popcorn Bowl, Buttery | 2023-02-18\tlocal\t5.99\t136\tg\t82\tg\t430\t26\t670\t41\t1\t7\t6\n",
+    "10028\tBakery\tNO NAME| Raised Chocolate Donuts 6 Pack | 2023-02-08\tlocal\t7.99\t510\tg\t85\tg\t320\t16\t320\t39\t13\t1\t5\n",
+    "10004\tDrinks\tSTARBUCKS | Frappuccino Vanilla Coffee Drink | 2023-01-07\tinternational\t3.99\t405\tmL\t405\tmL\t290\t4.5\t150\t53\t46\t0\t9\n",
+    "10008\tSnaks\tCHEETOS | Crunchy Cheese Flavoured Snacks | 2023-02-18\tinternational\t6.49\t390\tg\t50\tg\t280\t18\t440\t30\t1\t1\t3\n",
+    "10016\tMeat\tPRESIDENT'S CHOICE | Cheddar Smokies sausage | 2023-02-16\tlocal\t14.29\t1000\tg\t100\tg\t260\t20\t770\t6\t2\t0\t15\n",
+    "10011\tSnaks\tPRINGLES | Mega Can BBQ Flavour Chips | 2023-01-11\tinternational\t3.99\t202\tg\t50\tg\t260\t15\t240\t29\t2\t1\t2\n",
+    "10019\tMeat\tHIGH LINER | Fish Sticks, Family Pack | 2023-02-12\tinternational\t10.99\t700\tg\t150\tg\t250\t12\t410\t20\t1\t0\t14\n",
+    "10026\tBakery\tFARMER'S MARKET | Butter Croissants Mini | 2023-02-17\tlocal\t4.99\t336\tg\t56\tg\t230\t12\t230\t28\t5\t2\t4\n",
+    "10013\tSnaks\tNESTLE | KitKat Chunky Milk Chocolate Wafer Bar | 2023-02-03\tinternational\t1.79\t45\tg\t45\tg\t230\t12\t35\t29\t22\t1\t3\n",
+    "10010\tSnaks\tPRESIDENT'S CHOICE | Everything Trail Mix Nuts | 2023-01-09\tlocal\t4.99\t300\tg\t50\tg\t220\t10\t25\t31\t23\t\t3\n",
+    "10003\tDrinks\tMONSTER ENERGY | Green Energy Drink, Can | 2023-02-04\tlocal\t3.99\t473\tmL\t473\tmL\t210\t\t370\t55\t54\t0\t0\n",
+    "10006\tSnaks\tM&M'S | Peanut Milk Chocolate Candies, Bowl Size | 2023-02-14\tinternational\t9.99\t400\tg\t42\tg\t210\t11\t20\t25\t21\t2\t4\n",
+    "10015\tMeat\tNO NAME | Mild Sugar-Cured Bacon | 2023-01-16\tlocal\t7.49\t500\tg\t50\tg\t200\t19\t430\t2\t0\t0\t4\n",
+    "10030\tBakery\tWONDER | Bread, Sliced White | 2023-01-18\tinternational\t3.99\t675\tg\t75\tg\t200\t2.5\t320\t38\t3\t2\t7\n",
+    "10022\tDairy\tACTIVIA | Yogurt With Probiotics, Cherry Flavour | 2023-01-08\tlocal\t3.99\t650\tg\t175\tg\t160\t5\t70\t22\t18\t0\t7\n",
+    "10009\tSnaks\tCHRISTIE | Chips Ahoy! Rainbow Cookies | 2023-01-28\tinternational\t3.99\t225\tg\t31\tg\t150\t7\t80\t22\t9\t1\t1\n",
+    "10014\tSnaks\tCHRISTIE | Oreo Snak Pak Cookies | 2023-01-25\tinternational\t3.99\t180\tg\t30\tg\t140\t5\t140\t22\t12\t1\t1\n",
+    "10029\tBakery\tSCHOOL SAFE | Muffin Bars Birthday Cake | 2023-01-30\tinternational\t3.99\t296\tg\t37\tg\t140\t6\t60\t20\t11\t\t2\n",
+    "10001\tDrinks\tCOCA-COLA | Coca-Cola Bottle | 2023-01-28\tinternational\t3.99\t2000\tmL\t355\tmL\t140\t0\t25\t39\t39\t0\t0\n",
+    "10020\tDairy\tNEILSON | True Taste, 2% Milk | 2023-01-23\tlocal\t5.69\t2000\tmL\t250\tmL\t130\t5\t120\t12\t12\t0\t9\n",
+    "10007\tSnaks\tGARDEN | Cream Wafers, Chocolate Flavour | 2023-02-19\tlocal\t2.39\t200\tg\t25\tg\t130\t7\t45\t16\t6\t0\t2\n",
+    "10023\tDairy\tNO NAME | Farmer's Marble Cheese | 2023-02-02\tlocal\t8.99\t700\tg\t30\tg\t110\t8\t220\t0\t0\t0\t7\n",
+    "10002\tDrinks\tTROPICANA | Orange Juice No Pulp | 2023-02-22\tlocal\t6.99\t1540\tmL\t250\tmL\t110\t0\t0\t27\t23\t0\t2\n",
+    "10017\tMeat\tGREENFIELD| Thick Sliced Smoked Ham | 2023-02-04\tinternational\t12.99\t500\tg\t50\tg\t50\t1\t360\t0\t0\t0\t9\n",
+    "10005\tDrinks\tGRACE | 100% Pure Coconut Water | 2023-02-11\tlocal\t3.39\t1000\tmL\t250\tmL\t50\t0\t65\t11\t10\t0\t1\n",
+    "10021\tDairy\tPHILADELPHIA | Original Cream Cheese Product | 2023-01-03\tinternational\t5.49\t227\tg\t15\tg\t40\t3.5\t60\t1\t1\t0\t1\n",
+    "10025\tFruit\tDOLE | No Sugar Added Fruit Salad With Cherries | 2023-01-18\tinternational\t4.29\t540\tmL\t125\tmL\t40\t0\t0\t9\t6\t2\t1\n",
+    "10024\tFruit\tSMUCKERS | No Sugar Added Orange Spread | 2023-02-12\tinternational\t5.99\t310\tmL\t15\tmL\t20\t0\t1\t5\t5\t0\t0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 331,
+   "id": "bdd77490",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Food_Category</th>\n",
+       "      <th>Product_Name</th>\n",
+       "      <th>Brand</th>\n",
+       "      <th>Price</th>\n",
+       "      <th>Container_Size</th>\n",
+       "      <th>Container_Size_unit</th>\n",
+       "      <th>Serving_Size</th>\n",
+       "      <th>Serving_Size_unit</th>\n",
+       "      <th>Calories</th>\n",
+       "      <th>Fat</th>\n",
+       "      <th>Sodium</th>\n",
+       "      <th>Carbohydrate</th>\n",
+       "      <th>Sugars</th>\n",
+       "      <th>Fiber</th>\n",
+       "      <th>Protein</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>10027</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>21.29</td>\n",
+       "      <td>710</td>\n",
+       "      <td>g</td>\n",
+       "      <td>118</td>\n",
+       "      <td>g</td>\n",
+       "      <td>510</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>52</td>\n",
+       "      <td>37</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10018</td>\n",
+       "      <td>Meat</td>\n",
+       "      <td>THE KEG | Prime Rib Beef Burger | 2023-02-16</td>\n",
+       "      <td>international</td>\n",
+       "      <td>22.99</td>\n",
+       "      <td>1020</td>\n",
+       "      <td>g</td>\n",
+       "      <td>170</td>\n",
+       "      <td>g</td>\n",
+       "      <td>490</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>660</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10012</td>\n",
+       "      <td>Snaks</td>\n",
+       "      <td>ORVILLE | Popcorn Bowl, Buttery | 2023-02-18</td>\n",
+       "      <td>local</td>\n",
+       "      <td>5.99</td>\n",
+       "      <td>136</td>\n",
+       "      <td>g</td>\n",
+       "      <td>82</td>\n",
+       "      <td>g</td>\n",
+       "      <td>430</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>670</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10028</td>\n",
+       "      <td>Bakery</td>\n",
+       "      <td>NO NAME| Raised Chocolate Donuts 6 Pack | 2023...</td>\n",
+       "      <td>local</td>\n",
+       "      <td>7.99</td>\n",
+       "      <td>510</td>\n",
+       "      <td>g</td>\n",
+       "      <td>85</td>\n",
+       "      <td>g</td>\n",
+       "      <td>320</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>320</td>\n",
+       "      <td>39</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10004</td>\n",
+       "      <td>Drinks</td>\n",
+       "      <td>STARBUCKS | Frappuccino Vanilla Coffee Drink |...</td>\n",
+       "      <td>international</td>\n",
+       "      <td>3.99</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>405</td>\n",
+       "      <td>mL</td>\n",
+       "      <td>290</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>150</td>\n",
+       "      <td>53</td>\n",
+       "      <td>46</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      ID Food_Category                                       Product_Name  \\\n",
+       "0  10027        Bakery  PRESIDENT'S CHOICE | Triple Chocolate Cake | 2...   \n",
+       "1  10018          Meat       THE KEG | Prime Rib Beef Burger | 2023-02-16   \n",
+       "2  10012         Snaks       ORVILLE | Popcorn Bowl, Buttery | 2023-02-18   \n",
+       "3  10028        Bakery  NO NAME| Raised Chocolate Donuts 6 Pack | 2023...   \n",
+       "4  10004        Drinks  STARBUCKS | Frappuccino Vanilla Coffee Drink |...   \n",
+       "\n",
+       "           Brand  Price  Container_Size Container_Size_unit  Serving_Size  \\\n",
+       "0          local  21.29             710                   g           118   \n",
+       "1  international  22.99            1020                   g           170   \n",
+       "2          local   5.99             136                   g            82   \n",
+       "3          local   7.99             510                   g            85   \n",
+       "4  international   3.99             405                  mL           405   \n",
+       "\n",
+       "  Serving_Size_unit  Calories   Fat  Sodium  Carbohydrate  Sugars  Fiber  \\\n",
+       "0                 g       510  31.0     270            52      37    3.0   \n",
+       "1                 g       490  41.0     660             2       0    0.0   \n",
+       "2                 g       430  26.0     670            41       1    7.0   \n",
+       "3                 g       320  16.0     320            39      13    1.0   \n",
+       "4                mL       290   4.5     150            53      46    0.0   \n",
+       "\n",
+       "   Protein  \n",
+       "0      6.0  \n",
+       "1     26.0  \n",
+       "2      6.0  \n",
+       "3      5.0  \n",
+       "4      9.0  "
+      ]
+     },
+     "execution_count": 331,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_txt = pd.read_csv('/Users/hgl/Desktop/Food_Data_HLW_txt.txt', delimiter=\"\\t\")\n",
+    "data_txt.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 333,
+   "id": "cc259515",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# export text data to .csv and ready to use\n",
+    "data_txt.to_csv(\"/Users/hgl/Desktop/Food_Data_HLW_2.csv\", index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lessons/python/numpy-pandas/lesson.md b/lessons/python/numpy-pandas/lesson.md
new file mode 100644
index 000000000..12bc3be3e
--- /dev/null
+++ b/lessons/python/numpy-pandas/lesson.md
@@ -0,0 +1,15 @@
+---
+layout: page
+title: Python: Data processing with numpy and pandas
+visible: true
+tags:
+  - python
+  - Intermediate
+---
+
+ - **Authors**: Guanlan Hu and Wei Lu
+ - **Research field**: Nutritional Sciences
+ - **Lesson topic**: Data processing with numpy and pandas
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/numpy-pandas>
+
+
diff --git a/lessons/python/object_oriented_programming/Object_oriented_programming.ipynb b/lessons/python/object_oriented_programming/Object_oriented_programming.ipynb
new file mode 100644
index 000000000..a8b04d648
--- /dev/null
+++ b/lessons/python/object_oriented_programming/Object_oriented_programming.ipynb
@@ -0,0 +1,1121 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Object Oriented Programming \n",
+    "adapted from  https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/python/classes/lesson.md"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "Object Oriented Programming is an approach in programming in which properties and behaviors are bundled into individual *objects*.\n",
+    "in real world, an object has some properties and functions: a car has color, model, engine type, etc. and can move, speed, brake, etc. or an email has recipient list, subject, body, etc., and behaviors like adding attachments and sending; or a person who has name, height, weight, address and can walk, talk, laugh, etc. \n",
+    "\n",
+    "We can have the same approach in desigining programs based on objects that represent both properties and functions that can be applied to those properties. \n",
+    "\n",
+    "## Classes in Python\n",
+    "Each object is an instance of a class (each person is an instance of *human beings* class!) \n",
+    "Class is a data structure that allows the user to define any needed properties and functions that the object will contain. So the objects are instances of an specific class. \n",
+    "Like when Sara with 170 cm height and dark hair is an instance of human() class. \n",
+    "\n",
+    "so: \n",
+    "\n",
+    "- A class provides the form or structure\n",
+    "- an Object is an instance of a class which has: \n",
+    "    - features, that are called **attributes**\n",
+    "    - functions, that are called **methods**\n",
+    "\n",
+    "## Defining a class in python \n",
+    "Let's assume we want to file the subjects of an ongoing study. \n",
+    "\n",
+    "We want to define a class called *subjet* with attributes such as ID, first name, last name, admission_date and a function that will just print out the full name of the subject. \n",
+    "suppose we wnat to create two objects: \n",
+    "111,  Jane Doe, 2019_01_01\n",
+    "112,  John Smith, 2019_01_09"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first we need to creat a class. \n",
+    "# If you like to create an empty class at first, you use the command pass inside the class\n",
+    "class Subject:  \n",
+    "    pass  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the objects of that class. \n",
+    "# we create 2 and call them S1 and S2: \n",
+    "S1 = Subject()\n",
+    "S1.fname = \"Jane\"\n",
+    "S1.lname = \"Doe\"\n",
+    "S1.ID = 111\n",
+    "S1.date_of_admission = \"2019_01_01\"\n",
+    "\n",
+    "S2 = Subject()\n",
+    "S2.fname = \"John\"\n",
+    "S2.lname = \"Smith\"\n",
+    "S2.ID = 112\n",
+    "S2.date_of_admission = \"2019_01_09\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jane Doe\n"
+     ]
+    }
+   ],
+   "source": [
+    "# you can use these objects in any form. Let's say we want to bring up some info about subject 1\n",
+    "print(S1.fname,S1.lname)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## method\n",
+    "We said in object oriented programming, we can have functions associated with the object. \n",
+    "so, we want this printing of the full name to be a method of the object. \n",
+    "We can rewrite the class and then run the above cell to fill the two objects again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Subject:\n",
+    "    def fullname(self):\n",
+    "        print(self.fname,self.lname)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# since we rewrite the class, we have to fill in the attributes again: \n",
+    "S1 = Subject()\n",
+    "S1.fname = \"Jane\"\n",
+    "S1.lname = \"Doe\"\n",
+    "S1.ID = 111\n",
+    "S1.date_of_admission = \"2019_01_01\"\n",
+    "\n",
+    "S2 = Subject()\n",
+    "S2.fname = \"John\"\n",
+    "S2.lname = \"Smith\"\n",
+    "S2.ID = 112\n",
+    "S2.date_of_admission = \"2019_01_09\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now if we look at the objects it has a method + the attributes: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "John Smith\n"
+     ]
+    }
+   ],
+   "source": [
+    "S2.fullname()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But the above approach is not efficient, we don't want to repeat filling in the attributes individually: both boring and prone to typos. \n",
+    "\n",
+    "## constructor\n",
+    "We actually don't need to. We can use a *constructor* to design the attributes within the class: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Subject: \n",
+    "    def __init__(self, fname,lname,ID,date_of_admission):\n",
+    "        self.fname = fname\n",
+    "        self.lname = lname\n",
+    "        self.ID = ID\n",
+    "        self.date_of_admission = date_of_admission\n",
+    "        \n",
+    "    def fullname(self):\n",
+    "        print(self.fname,self.lname)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this way, to fill in the objects of a class, we can give it the first name, last name, the ID, and the data of admission in order. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's fill in the same subjects' info \n",
+    "S1 = Subject(\"Jane\",\"Doe\",111,\"2019_01_01\")\n",
+    "S2 = Subject(\"John\",\"Smith\",112,\"2019_01_02\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jane Doe\n"
+     ]
+    }
+   ],
+   "source": [
+    "# now the method 'fullname' should give the same info as before:\n",
+    "S1.fullname()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## relationships between objects\n",
+    "\n",
+    "We can have relationships between sets of objects: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a feature that relates the two objects, here we can literally say relative\n",
+    "S1.relatives = S2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "John Smith\n"
+     ]
+    }
+   ],
+   "source": [
+    "S1.relatives.fullname()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example 2\n",
+    "Now let's work with another example: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Programing for a bank: Let's define a class called *Client* in which a new instance stores a client's name, balance, and account level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the Client class below\n",
+    "class Client(object):\n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        #define account level\n",
+    "        if self.balance < 5000:\n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else:\n",
+    "            self.level = \"Advanced\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, lets try creating some new clients named John_Doe, and Jane_Defoe (i.e. two instances of the 'Client' class, or two objects):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C1 = Client(\"John Doe\", 500)\n",
+    "C2 = Client(\"Jane Defoe\", 150000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see the attributes of John_Doe, or Jane_Defoe by calling them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'John Doe'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C1.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Advanced'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C2.level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150100"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C2.balance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also add, remove or modify attributes as we like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C1.email = \"jdoe23@gmail.com\"\n",
+    "C2.email = \"johndoe23@gmail.com\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'jdoe23@gmail.com'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C1.email"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del C1.email"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'johndoe23@gmail.com'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# C1.email\n",
+    "C2.email"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Class Attributes vs. Normal Attributes\n",
+    "\n",
+    "A class attribute is an attribute set at the class-level rather than the instance-level, such that the value of this attribute will be the same across all instances.\n",
+    "\n",
+    "For our *Client* class, we might want to set the name of the bank, and the location, which would not change from instance to instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Client.bank = \"TD\"\n",
+    "Client.location = \"Toronto, ON\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the case of our 'Client' class, we may want to update a person's bank account once they withdraw or deposit money. Let's create these methods below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use the Client class code above to now add methods for withdrawal and depositing of money\n",
+    "\n",
+    "# create the Client class below\n",
+    "class Client(object):\n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        #define account level\n",
+    "        if self.balance < 5000:\n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else:\n",
+    "            self.level = \"Advanced\"\n",
+    "            \n",
+    "    def deposit(self, amount):\n",
+    "        self.balance += amount\n",
+    "        return self.balance\n",
+    "    \n",
+    "    def withdraw(self, amount):\n",
+    "        if amount > self.balance:\n",
+    "            raise RuntimeError(\"Insufficient for withdrawal\")\n",
+    "        else:\n",
+    "            self.balance -= amount\n",
+    "        return self.balance\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C1 = Client(\"John Doe\", 500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Basic'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C1.level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "165600"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C1.deposit(150000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Basic'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C1.level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# yes, we have to repeat the part that defines the account level after each deposit\n",
+    "\n",
+    "class Client():\n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        # define account level \n",
+    "        if self.balance < 5000: \n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else: \n",
+    "            self.level = \"Advanced\"\n",
+    "        \n",
+    "    \n",
+    "    def deposit(self, amount):\n",
+    "            self.balance += amount         \n",
+    "\n",
+    "            if self.balance < 5000: \n",
+    "                self.level = \"Basic\"\n",
+    "            elif self.balance < 15000:\n",
+    "                self.level = \"Intermediate\"\n",
+    "            else: \n",
+    "                self.level = \"Advanced\"\n",
+    "            return self.balance\n",
+    "        \n",
+    "            \n",
+    "        \n",
+    "    def withdraw(self, amount):\n",
+    "            if amount > self.balance: \n",
+    "                raise RuntimeError(\"Insufficient for withdrawal\")\n",
+    "            else: \n",
+    "                self.balance -= amount\n",
+    "            return self.balance\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge: \n",
+    "can we write this Class in a more efficient way to not repeat assigining the balance?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Static Methods \n",
+    "\n",
+    "Static methods are methods that belong to a class but do not have access to *self* and hence don't require an instance to function (i.e. it will work on the class level as well as the instance level). \n",
+    "\n",
+    "We denote these with the line `@staticmethod` before we define our static method.\n",
+    "\n",
+    "Let's create a static method called make_money_sound() that will simply print \"Cha-ching!\" when called."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Add a static method called make_money_sound()\n",
+    "# create the Client class below\n",
+    "class Client(object):\n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        #define account level\n",
+    "        if self.balance < 5000:\n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else:\n",
+    "            self.level = \"Advanced\"\n",
+    "    \n",
+    "    def deposit(self, amount):\n",
+    "        self.balance += amount\n",
+    "        if self.balance < 5000: \n",
+    "               self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "               self.level = \"Intermediate\"\n",
+    "        else: \n",
+    "               self.level = \"Advanced\"\n",
+    "        return self.balance\n",
+    "   \n",
+    "    def withdraw(self, amount):\n",
+    "        if amount > self.balance:\n",
+    "            raise RuntimeError(\"Insufficient for withdrawal\")\n",
+    "        else:\n",
+    "            self.balance -= amount\n",
+    "        return self.balance\n",
+    "            \n",
+    "    @staticmethod\n",
+    "    def make_money_sound():\n",
+    "        print(\"Cha-ching!\")\n",
+    "        \n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cha-ching!\n"
+     ]
+    }
+   ],
+   "source": [
+    "Client.make_money_sound()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cha-ching!\n"
+     ]
+    }
+   ],
+   "source": [
+    "C1.make_money_sound()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Class Methods\n",
+    "\n",
+    "A class method is a type of method that will receive the class rather than the instance as the first parameter. It is also identified similarly to a static method, with `@classmethod`.\n",
+    "\n",
+    "Create a class method called bank_location() that will print both the bank name and location when called upon the class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add a class method called bank_location()\n",
+    "# create the Client class below\n",
+    "class Client(object):\n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        #define account level\n",
+    "        if self.balance < 5000:\n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else:\n",
+    "            self.level = \"Advanced\"\n",
+    "    \n",
+    "    def deposit(self, amount):\n",
+    "        self.balance += amount\n",
+    "        return self.balance\n",
+    "    \n",
+    "    def withdraw(self, amount):\n",
+    "        if amount > self.balance:\n",
+    "            raise RuntimeError(\"Insufficient for withdrawal\")\n",
+    "        else:\n",
+    "            self.balance -= amount\n",
+    "        return self.balance\n",
+    "            \n",
+    "    @staticmethod\n",
+    "    def make_money_sound():\n",
+    "        print(\"Cha-ching!\")\n",
+    "            \n",
+    "    @classmethod\n",
+    "    def bank_location(cls):\n",
+    "        return str(cls.bank + \" \" + cls.location)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'TD Toronto, ON'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Client.bank_location()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The code worked because we have assigned 'bank' and 'location' variables before. You can integrate this information in your class: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Client(object):\n",
+    "    bank = \"TD\"\n",
+    "    location = \"Toronto, ON\"\n",
+    "    \n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        # define account level \n",
+    "        if self.balance < 5000: \n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else: \n",
+    "            self.level = \"Advanced\"\n",
+    "        \n",
+    "    \n",
+    "    def deposit(self, amount):\n",
+    "            self.balance += amount         \n",
+    "            # define account level \n",
+    "            if self.balance < 5000: \n",
+    "                self.level = \"Basic\"\n",
+    "            elif self.balance < 15000:\n",
+    "                self.level = \"Intermediate\"\n",
+    "            else: \n",
+    "                self.level = \"Advanced\"\n",
+    "            return self.balance\n",
+    "        \n",
+    "            \n",
+    "        \n",
+    "    def withdraw(self, amount):\n",
+    "            if amount > self.balance: \n",
+    "                raise RuntimeError(\"Insufficient for withdrawal\")\n",
+    "            else: \n",
+    "                self.balance -= amount\n",
+    "            return self.balance\n",
+    "    \n",
+    "    @classmethod \n",
+    "    def bank_location(cls):\n",
+    "        return str(cls.bank + \" \" + cls.location)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'TD Toronto, ON'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Client.bank_location()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Key Concept: Inheritance\n",
+    "\n",
+    "A 'child' class can be created from a 'parent' class, whereby the child will bring over attributes and methods that its parent has, but where new features can be created as well. \n",
+    "\n",
+    "This would be useful if you want to create multiple classes that would have some features that are kept the same between them. You would simply create a parent class of these children classes that have those maintained features.\n",
+    "\n",
+    "Imagine we want to create different types of clients but still have all the base attributes and methods found in client currently. \n",
+    "\n",
+    "For example, let's create a class called *Savings* that inherits from the *Client* class. In doing so, we do not need to write another `__init__` method as it will inherit this from its parent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the Savings class below\n",
+    "class Savings(Client):\n",
+    "    interest_rate = 0.005\n",
+    "    \n",
+    "    def update_balance(self):\n",
+    "        self.balance += self.balance*self.interest_rate\n",
+    "        return self.balance\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create an instance the same way as a Client but this time by calling Savings instead\n",
+    "C3 = Savings(\"Tom Smith\", 50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tom Smith\n",
+      "150\n",
+      "0.005\n"
+     ]
+    }
+   ],
+   "source": [
+    "# it now has access to the new attributes and methods in Savings...\n",
+    "print(C3.name)\n",
+    "print(C3.balance)\n",
+    "print(C3.interest_rate)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150.75"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# ...as well as access to attributes and methods from the Client class as well\n",
+    "C3.update_balance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#defining a method outside the class definition\n",
+    "def check_balance(self):\n",
+    "    return self.balance\n",
+    "\n",
+    "Client.check_balance = check_balance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150.75"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C3.check_balance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150100"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C2.check_balance()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge:\n",
+    "make a child for Savings Class that doubles the amount of savings and adds 2 more dollars. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class special(Savings):\n",
+    "    def update_special(self):\n",
+    "        self.balance += self.balance + 2\n",
+    "        return self.balance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "201.0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "404.0"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C4 = special(\"Sara\",100)\n",
+    "print(C4.update_balance())\n",
+    "C4.update_special()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/python/object_oriented_programming/livecoding.ipynb b/lessons/python/object_oriented_programming/livecoding.ipynb
new file mode 100644
index 000000000..fdbb29437
--- /dev/null
+++ b/lessons/python/object_oriented_programming/livecoding.ipynb
@@ -0,0 +1,571 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a class\n",
+    "class Subject: \n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "S1 = Subject()\n",
+    "S1.fname = \"Jane\"\n",
+    "S1.lname = \"Doe\"\n",
+    "S1.ID = 111\n",
+    "S1.date_of_admission =\"2019_01_01\"\n",
+    "\n",
+    "S2 = Subject()\n",
+    "S2.fname = \"John\"\n",
+    "S2.lname = \"Smith\"\n",
+    "S2.ID = 112\n",
+    "S2.date_of_admission = \"2019_01_09\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(S1.fname,S1.lname)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Subject: \n",
+    "    def fullname(self):\n",
+    "        print(self.fname,self.lname)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "S2.fullname()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Subject: \n",
+    "    def __init__(self, fname, lname, ID, date_of_admission):\n",
+    "        self.fname = fname\n",
+    "        self.lname = lname \n",
+    "        self.ID = ID\n",
+    "        self.date_of_admission = date_of_admission\n",
+    "    \n",
+    "    def fullname(self):\n",
+    "        print(self.fname,self.lname)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "S1 = Subject(\"Jane\", \"Doe\", 111, \"2019_01_01\")\n",
+    "S2 = Subject(\"John\", \"Smith\", 112, \"2019_01_09\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "S1.fullname()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "S1.reletives = S2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "S1.reletives.fullname()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# example of a bank client database\n",
+    "# create the Client class\n",
+    "class Client(object):\n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        # define account level \n",
+    "        if self.balance < 5000: \n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else: \n",
+    "            self.level = \"Advanced\"\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create two instances of this class (i.e. objects)\n",
+    "C1 = Client(\"John Doe\", 500)\n",
+    "C2 = Client(\"Jane Defoe\", 150000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C2.level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C1.email = \"Jdoe@gmail.com\"\n",
+    "C2.email = \"JaneDd@gmail.com\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "del C1.email"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Client.bank = \"TD\"\n",
+    "Client.location = \"Toronto, ON\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create some methods for this class: update the object upon withdraw or deposit  of money\n",
+    "class Client():\n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        # define account level \n",
+    "        if self.balance < 5000: \n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else: \n",
+    "            self.level = \"Advanced\"\n",
+    "        \n",
+    "    \n",
+    "    def deposit(self, amount):\n",
+    "            self.balance += amount         \n",
+    "            # define account level \n",
+    "            if self.balance < 5000: \n",
+    "                self.level = \"Basic\"\n",
+    "            elif self.balance < 15000:\n",
+    "                self.level = \"Intermediate\"\n",
+    "            else: \n",
+    "                self.level = \"Advanced\"\n",
+    "            return self.balance\n",
+    "        \n",
+    "            \n",
+    "        \n",
+    "    def withdraw(self, amount):\n",
+    "            if amount > self.balance: \n",
+    "                raise RuntimeError(\"Insufficient for withdrawal\")\n",
+    "            else: \n",
+    "                self.balance -= amount\n",
+    "            return self.balance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C1 = Client(\"John Doe\", 500)\n",
+    "C2 = Client(\"Jane Defoe\", 150000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150600"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C1.deposit(150000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Advanced'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C1.level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define static method for Client \n",
+    "\n",
+    "class Client():\n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        # define account level \n",
+    "        if self.balance < 5000: \n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else: \n",
+    "            self.level = \"Advanced\"\n",
+    "        \n",
+    "    \n",
+    "    def deposit(self, amount):\n",
+    "            self.balance += amount         \n",
+    "            # define account level \n",
+    "            if self.balance < 5000: \n",
+    "                self.level = \"Basic\"\n",
+    "            elif self.balance < 15000:\n",
+    "                self.level = \"Intermediate\"\n",
+    "            else: \n",
+    "                self.level = \"Advanced\"\n",
+    "            return self.balance\n",
+    "        \n",
+    "            \n",
+    "        \n",
+    "    def withdraw(self, amount):\n",
+    "            if amount > self.balance: \n",
+    "                raise RuntimeError(\"Insufficient for withdrawal\")\n",
+    "            else: \n",
+    "                self.balance -= amount\n",
+    "            return self.balance\n",
+    "    \n",
+    "    @staticmethod\n",
+    "    def make_money_sound():\n",
+    "        print(\"Cha_Ching!\")\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cha_Ching!\n"
+     ]
+    }
+   ],
+   "source": [
+    "Client.make_money_sound()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C1 = Client(\"John Doe\", 500)\n",
+    "C2 = Client(\"Jane Defoe\", 150000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Client(object):\n",
+    "    bank = \"TD\"\n",
+    "    location = \"Toronto, ON\"\n",
+    "    \n",
+    "    def __init__(self, name, balance):\n",
+    "        self.name = name\n",
+    "        self.balance = balance + 100\n",
+    "        \n",
+    "        # define account level \n",
+    "        if self.balance < 5000: \n",
+    "            self.level = \"Basic\"\n",
+    "        elif self.balance < 15000:\n",
+    "            self.level = \"Intermediate\"\n",
+    "        else: \n",
+    "            self.level = \"Advanced\"\n",
+    "        \n",
+    "    \n",
+    "    def deposit(self, amount):\n",
+    "            self.balance += amount         \n",
+    "            # define account level \n",
+    "            if self.balance < 5000: \n",
+    "                self.level = \"Basic\"\n",
+    "            elif self.balance < 15000:\n",
+    "                self.level = \"Intermediate\"\n",
+    "            else: \n",
+    "                self.level = \"Advanced\"\n",
+    "            return self.balance\n",
+    "        \n",
+    "            \n",
+    "        \n",
+    "    def withdraw(self, amount):\n",
+    "            if amount > self.balance: \n",
+    "                raise RuntimeError(\"Insufficient for withdrawal\")\n",
+    "            else: \n",
+    "                self.balance -= amount\n",
+    "            return self.balance\n",
+    "    \n",
+    "    @classmethod \n",
+    "    def bank_location(cls):\n",
+    "        return str(cls.bank + \" \" + cls.location)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'TD Toronto, ON'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Client.bank_location()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create 'child' for Client class\n",
+    "class Savings(Client):\n",
+    "    interest_rate = 0.005\n",
+    "    \n",
+    "    def update_balance(self):\n",
+    "        self.balance += self.balance*self.interest_rate\n",
+    "        return self.balance "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C3 = Savings(\"Tom Smith\", 50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Basic'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C3.level"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def check_balance(self):\n",
+    "    return self.balance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Client.check_balance = check_balance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "150"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "C3.check_balance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Savings' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-1-2117205206e7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mclass\u001b[0m \u001b[0mblah\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSavings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mupdate_blah\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbalance\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbalance\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbalance\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'Savings' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "class blah(Savings):\n",
+    "    def update_blah(self):\n",
+    "        self.balance += self.balance + 2\n",
+    "        return self.balance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C4 = blah(\"TT\", 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C1."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/python/packages/lesson.md b/lessons/python/packages/lesson.md
new file mode 100644
index 000000000..2ef95e2f4
--- /dev/null
+++ b/lessons/python/packages/lesson.md
@@ -0,0 +1,269 @@
+---
+layout: page
+title: Creating Packages in Python
+visible: true
+tags:
+  - python
+  - advanced
+---
+
+- **Presenter**: John Ladan
+- **Sources**: [*Bill Mills*](https://github.com/BillMills/pythonPackageLesson), Elise Olson, [The Hitchhiker's Guide to Packaging](https://the-hitchhikers-guide-to-packaging.readthedocs.org/en/latest/quickstart.html)
+- **Research Field**: any
+- **Topic**: A quick introduction to creating packages in Python
+- **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/packages>
+
+
+## Why are we doing this?
+
+One of the most powerful things about coding for the sciences is that it costs
+nothing to re-use code we've written in the past, allowing us to build on
+past work rather than starting over every project or paper.
+
+However, one practice we see again and again is copying and pasting code from one project into another. Sometimes it will just be a function, other times (*cough*MATLAB*cough*) it's files.
+
+Assembling
+code in packages makes it really easy to re-use old code: all the scripts and functions end up in a central location and can be called and imported from anywhere on the computer -
+just like the famous packages `numpy` or `matplotlib`.
+
+## Setting up
+
+In this tutorial, we'll walk through the creation of a simple package from some code that's just lying around. You can view and clone the [demo package](https://github.com/jladan/package_demo). For an unpackaged state, checkout the `unpackaged` tag.
+
+## 1. The basics
+
+The most basic directory structure for a Python package looks like this:
+
+```
+project
+|
+|__ setup.py
+|
+|__ myPackage
+     |
+     |_  somePython.py
+     |_  __init__.py
+```
+
+But at the moment, we've just got some flat files.
+
+```
+project
+|
+|__ norms.py
+|__ metrics.py
+```
+
+So, the first step is to move files around. First comes the hardest part: choosing a package name. I'll call mine `measure`. Create a directory with that name, and move the python files in there.
+
+```
+project
+|
+|__ measure
+     |__ norms.py
+     |__ metrics.py
+```
+
+There is one more crucial file: `__init__.py` lets the Python interpreter know that there are importable modules in this directory. This is the script that gets run when you execute `import measure`. For more about what you can do with modules, you can see the (Python docs)[https://docs.python.org/3/tutorial/modules.html]. After adding `__init__.py`, the project directory should be
+
+```
+project
+|
+|__ measure
+     |__ __init__.py
+     |__ norms.py
+     |__ metrics.py
+```
+
+
+## 2. setup.py
+
+At this point, the library can be imported if we're in the same directory, but it isn't a package. To let setuptools and `pip` know how to handle it, we need to add the `setup.py` file.
+
+A very basic version of `setup.py` is
+
+```
+from setuptools import setup
+
+setup(
+    # Whatever arguments you need/want
+)
+```
+
+If you were to run that file, you'd get a whole bunch of warnings, and nothing would actually get packaged. As a bare minimum, here is our `setup.py` for it to work.
+
+```
+from setuptools import setup
+
+setup(
+    # Needed to silence warnings (and to be a worthwhile package)
+    name='Measurements',
+    url='https://github.com/jladan/package_demo',
+    author='John Ladan',
+    author_email='jladan@uwaterloo.ca',
+    # Needed to actually package something
+    packages=['measure'],
+    # Needed for dependencies
+    install_requires=['numpy'],
+    # *strongly* suggested for sharing
+    version='0.1',
+    # The license can be anything you like
+    license='MIT',
+    description='An example of a python package from pre-existing code',
+    # We will also need a readme eventually (there will be a warning)
+    # long_description=open('README.txt').read(),
+)
+```
+
+## 3. Installing?
+
+Technically, you can install the module by running `python setup.py install`, however it's much more convenient to wrap it all up and distribute online.
+
+### Using git
+
+This is my preferred way of sharing/storing/installing packages. Simply create a git repo of the project directory, and put it somewhere. Then, use `pip` to install it from that repo directly
+
+```
+pip install git+https://github.com/jladan/package_demo.git#egg=measurements
+```
+
+An advantage of this is that you can also install to particular branches, tags, and commits. A common practice is add a tag for the version release `git tag v0.1`. Users of the library can then lock into that particular version,
+
+```
+pip install git+https://github.com/jladan/package_demo.git@v0.1#egg=measurements
+```
+
+
+### Distributing via PyPI
+
+In my opinion, distributing on PyPI is more complicated than with git, and not much more useful.
+
+You'll have to maintain the
+package in two places if you intend on collaborating on development on GitHub
+and distributing through PyPI.
+
+So why distribute on PyPI? If you're proud of your software and want others to use it, this is the best place. It's more visible, easier for your users, and more tightly controlled. The stricter version control (not just whatever commit to whatever repo like git) makes the software more robust and easier to support as a developing team.
+
+For those interested, to upload to PyPI, you'll need
+
+```
+python setup.py sdist
+```
+
+This creates a `dist` directory and adds an archive file (a.k.a. a distribution) of your package.
+
+
+To get set up on PyPI for the first time with a
+new package, [make a new account on PyPI](https://pypi.python.org/pypi) (or for trying it out, the [testing PyPI site](https://testpypi.python.org/pypi)), and
+then back in your `package` directory do
+
+```
+python setup.py register
+```
+
+to claim your package name - they must be unique. Then, upload your package; you'll do this every time you want to release a new version of your code:
+
+```
+python setup.py sdist upload
+```
+
+Now, you and anyone else in the world can install your package using the following:
+
+```
+pip install your-package-name
+```
+
+## 4. Document!
+
+We've already begun the documentation by adding a short description, author/maintainer name, and version... but there's a lot more to add. At a minimum, a `README` file should be included, but so should a license text, changes between versions, and actual software documentation.
+
+### README
+
+A readme file summarizes the software. For Python packages, this can named `README`, `README.rst`, or `README.txt`. The recommendation is to use [reST]
+(http://docutils.sourceforge.net/rst.html), as this is the standard on PyPI
+
+```
+===================
+Measurements Module
+===================
+
+**A demo for how to create a python package from existing code**
+
+The actual software provides L1, L2, Supremum, and Hamming metrics and norms for numpy arrays.
+```
+
+### Long Description
+
+Now is a good time to uncomment that `long_description` argument in `setup.py`.
+
+### License
+
+The typical thing is to put the license you choose in `LICENSE.txt`. There are a few choices for licenses, I usually end up using MIT or simple BSD. If you're using github, you can add most standard open source licenses through the website.
+
+**a demo of adding the license file here**
+
+### Changes
+
+Changes between versions are usually tracked in `CHANGES.txt`. This is more of a concern if you're planning to distribute the software.
+
+
+## 5. Other things to think of
+
+A good package should also include full documentation and testing. I won't cover this here, but unit tests can be performed with the [`unittest`](https://docs.python.org/3.5/library/unittest.html) library, with the tests stored in the directory `tests`. Many people use [Sphinx](http://www.sphinx-doc.org/en/stable/) for documentation, and that can be stored in the `doc` directory.
+
+There's a further step required for distributing. If you want to include these files in the distribution (the archive file in `dist`), you'll need a `MANIFEST.in` file.
+
+```
+# MANIFEST.in
+
+include *.txt
+recursive-include docs *.rst
+```
+
+### Scripts
+
+Suppose you want to include a script that can be executed from the command line. Create a directory `scripts`, and put it there. Next, add the `scripts` argument to `setup.py`
+
+```
+setup(
+    #...
+    scripts = ['scripts/hello.py']
+    # ...
+)
+```
+
+## 6. Where to go from here?
+
+So, at this point, we've got a pretty good project skeleton, and most of the basics are covered. Your package should look something like this:
+
+```
+project
+|
+|__ setup.py
+|__ MANIFEST.in
+|
+|__ measure
+|    |__ __init__.py
+|    |__ norms.py
+|    |__ metrics.py
+|
+|__ tests
+|    |__ test_suite.py
+|
+|__ doc
+|    |__ docs.rst
+|
+|__ scripts
+|    |__ hello.py
+|
+|__ README.txt
+|__ CHANGES.txt
+|__ LICENSE.txt
+```
+
+
+If you have software that covers all these points, then congratulations! You're well on your way to a well-maintained software package.
+
+### So where do I actually go?
+
+To get a detailed look at packaging, check out the official [Python docs](https://docs.python.org/3.5/distributing/index.html); they're complete, accessible, and generally more up to date.
diff --git a/lessons/python/pandas/12probe20cm.csv b/lessons/python/pandas/12probe20cm.csv
new file mode 100644
index 000000000..0206a6897
--- /dev/null
+++ b/lessons/python/pandas/12probe20cm.csv
@@ -0,0 +1,9 @@
+Animal,Date,Group,Time to platform,Trial duration,Distance travelled (cm),Average speed,% time near walls,Direction,Average Proximity,Cumulative Proximity,Whishaw's corridor,Platform Quadrant,Zone 1 %,Target Zone %,Zone 3 %,Zone 4 %
+m1,2/1/2015,APP,10.724,59.988,1482.678,24.716,6.918,-117.097,45.391,2064.426,46.541,4,2.935,17.82,31.447,8.805
+m2,2/1/2015,GFP,8.235,59.898,1298.611,21.68,2.516,52.129,47.077,1232.864,37.736,4,4.822,50.943,15.514,0.629
+m3,2/1/2015,GFP,0,59.944,1562.19,26.061,2.096,-0.502,36.535,1799.688,54.717,4,5.451,34.382,10.273,3.774
+m4,2/1/2015,APP,7.102,59.896,1157.948,19.333,0,51.561,41.952,1392.255,39.203,4,11.53,37.945,16.352,0.21
+m5,2/1/2015,GFP,14.931,59.912,1310.405,21.872,0,-21.198,70.274,1550.027,49.476,4,6.709,35.43,18.658,5.451
+m6,2/1/2015,APP,17.235,59.948,1348.853,22.5,0.419,-131.1,29.144,1747.719,61.845,4,6.08,35.22,14.046,8.386
+m7,2/1/2015,GFP,5.086,59.897,1402.622,23.417,0.839,30.67,44.798,1832.923,45.073,4,7.128,35.849,18.658,5.031
+m8,2/1/2015,APP,3.899,59.969,1686.095,28.116,6.289,115.868,49.414,2482.494,62.474,4,3.774,15.094,27.254,13.627
diff --git a/lessons/python/pandas/Pandas Intro.ipynb b/lessons/python/pandas/Pandas Intro.ipynb
new file mode 100644
index 000000000..abe1d8a7d
--- /dev/null
+++ b/lessons/python/pandas/Pandas Intro.ipynb	
@@ -0,0 +1,1330 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Intro to Pandas DataFrames\n",
+    "\n",
+    "DataFrames are a lovely way to store data. They're essentially matrices that can store almost any type of data and are a great option for handling data where you want to keep track of rows and columns with labels. The Pandas library also comes with a handy host of functions that allow you to work with your DataFrames in very smart ways (eg. means, split-apply-combine).\n",
+    "\n",
+    "The Pandas website has a lot of [great documentation](http://pandas.pydata.org/pandas-docs/version/0.15.2/10min.html#min) to help you get started. However, even after reading the official tutorials, I was stumped on some problems using DataFrames. This lesson is based directly off of how I solved those problems, so hopefully some will find it helpful. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creating DataFrames\n",
+    "\n",
+    "You can instantiate DataFrames without any data or with data from any number of sources"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "blank = pd.DataFrame()\n",
+    "\n",
+    "blank"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date Released</th>\n",
+       "      <th>Box Office Gross</th>\n",
+       "      <th>IMDB Score</th>\n",
+       "      <th>Tomatometer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Forrest Gump</th>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scanners</th>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010: Odyssey Two</th>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fern Gully</th>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Date Released  Box Office Gross  IMDB Score  Tomatometer\n",
+       "Forrest Gump                   0                 0           0            0\n",
+       "Scanners                       0                 0           0            0\n",
+       "2010: Odyssey Two              0                 0           0            0\n",
+       "Fern Gully                     0                 0           0            0"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "movies = pd.DataFrame(np.zeros((4,4)), index=['Forrest Gump', 'Scanners', '2010: Odyssey Two', 'Fern Gully'], columns = ['Date Released', 'Box Office Gross', 'IMDB Score', 'Tomatometer'])\n",
+    "\n",
+    "movies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0  1  2  3  4\n",
+       "0  0  0  0  0  0\n",
+       "1  0  0  0  0  0\n",
+       "2  0  0  0  0  0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zeros = pd.DataFrame(np.zeros((3,5)))\n",
+    "\n",
+    "zeros"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Got a csv file you want to read? No problem!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Animal</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Time to platform</th>\n",
+       "      <th>Trial duration</th>\n",
+       "      <th>Distance travelled (cm)</th>\n",
+       "      <th>Average speed</th>\n",
+       "      <th>% time near walls</th>\n",
+       "      <th>Direction</th>\n",
+       "      <th>Average Proximity</th>\n",
+       "      <th>Cumulative Proximity</th>\n",
+       "      <th>Whishaw's corridor</th>\n",
+       "      <th>Platform Quadrant</th>\n",
+       "      <th>Zone 1 %</th>\n",
+       "      <th>Target Zone %</th>\n",
+       "      <th>Zone 3 %</th>\n",
+       "      <th>Zone 4 %</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td> m2</td>\n",
+       "      <td> 2/1/2015</td>\n",
+       "      <td> GFP</td>\n",
+       "      <td> 8.235</td>\n",
+       "      <td> 59.898</td>\n",
+       "      <td> 1298.611</td>\n",
+       "      <td> 21.680</td>\n",
+       "      <td> 2.516</td>\n",
+       "      <td> 52.129</td>\n",
+       "      <td> 47.077</td>\n",
+       "      <td> 1232.864</td>\n",
+       "      <td> 37.736</td>\n",
+       "      <td> 4</td>\n",
+       "      <td>  4.822</td>\n",
+       "      <td> 50.943</td>\n",
+       "      <td> 15.514</td>\n",
+       "      <td> 0.629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td> m3</td>\n",
+       "      <td> 2/1/2015</td>\n",
+       "      <td> GFP</td>\n",
+       "      <td> 0.000</td>\n",
+       "      <td> 59.944</td>\n",
+       "      <td> 1562.190</td>\n",
+       "      <td> 26.061</td>\n",
+       "      <td> 2.096</td>\n",
+       "      <td> -0.502</td>\n",
+       "      <td> 36.535</td>\n",
+       "      <td> 1799.688</td>\n",
+       "      <td> 54.717</td>\n",
+       "      <td> 4</td>\n",
+       "      <td>  5.451</td>\n",
+       "      <td> 34.382</td>\n",
+       "      <td> 10.273</td>\n",
+       "      <td> 3.774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td> m4</td>\n",
+       "      <td> 2/1/2015</td>\n",
+       "      <td> APP</td>\n",
+       "      <td> 7.102</td>\n",
+       "      <td> 59.896</td>\n",
+       "      <td> 1157.948</td>\n",
+       "      <td> 19.333</td>\n",
+       "      <td> 0.000</td>\n",
+       "      <td> 51.561</td>\n",
+       "      <td> 41.952</td>\n",
+       "      <td> 1392.255</td>\n",
+       "      <td> 39.203</td>\n",
+       "      <td> 4</td>\n",
+       "      <td> 11.530</td>\n",
+       "      <td> 37.945</td>\n",
+       "      <td> 16.352</td>\n",
+       "      <td> 0.210</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Animal      Date Group  Time to platform  Trial duration  \\\n",
+       "1     m2  2/1/2015   GFP             8.235          59.898   \n",
+       "2     m3  2/1/2015   GFP             0.000          59.944   \n",
+       "3     m4  2/1/2015   APP             7.102          59.896   \n",
+       "\n",
+       "   Distance travelled (cm)  Average speed  % time near walls  Direction  \\\n",
+       "1                 1298.611         21.680              2.516     52.129   \n",
+       "2                 1562.190         26.061              2.096     -0.502   \n",
+       "3                 1157.948         19.333              0.000     51.561   \n",
+       "\n",
+       "   Average Proximity  Cumulative Proximity  Whishaw's corridor  \\\n",
+       "1             47.077              1232.864              37.736   \n",
+       "2             36.535              1799.688              54.717   \n",
+       "3             41.952              1392.255              39.203   \n",
+       "\n",
+       "   Platform Quadrant  Zone 1 %  Target Zone %  Zone 3 %  Zone 4 %  \n",
+       "1                  4     4.822         50.943    15.514     0.629  \n",
+       "2                  4     5.451         34.382    10.273     3.774  \n",
+       "3                  4    11.530         37.945    16.352     0.210  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probedata = pd.read_csv('12probe20cm.csv')\n",
+    "\n",
+    "probedata[1:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Slicing DataFrames\n",
+    "You can slice DataFrames just like an array, and can also include some more advanced criteria"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date Released</th>\n",
+       "      <th>Box Office Gross</th>\n",
+       "      <th>IMDB Score</th>\n",
+       "      <th>Tomatometer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Scanners</th>\n",
+       "      <td> 1981</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 6.8</td>\n",
+       "      <td> 0.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date Released  Box Office Gross  IMDB Score  Tomatometer\n",
+       "Scanners          1981                12         6.8          0.8"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "movies['Date Released'] = ['1994','1981','1984','1992']\n",
+    "movies['Box Office Gross'] = [50,12,8,16]\n",
+    "movies['IMDB Score'] = [8.8,6.8,6.8,6.4]\n",
+    "movies['Tomatometer'] = [.72,.80,.66,.71]\n",
+    "\n",
+    "movies[1:2]\n",
+    "\n",
+    "#OR\n",
+    "\n",
+    "movies.iloc[1:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>IMDB Score</th>\n",
+       "      <th>Tomatometer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Forrest Gump</th>\n",
+       "      <td> 8.8</td>\n",
+       "      <td> 0.72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scanners</th>\n",
+       "      <td> 6.8</td>\n",
+       "      <td> 0.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010: Odyssey Two</th>\n",
+       "      <td> 6.8</td>\n",
+       "      <td> 0.66</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fern Gully</th>\n",
+       "      <td> 6.4</td>\n",
+       "      <td> 0.71</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   IMDB Score  Tomatometer\n",
+       "Forrest Gump              8.8         0.72\n",
+       "Scanners                  6.8         0.80\n",
+       "2010: Odyssey Two         6.8         0.66\n",
+       "Fern Gully                6.4         0.71"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "movies[['IMDB Score','Tomatometer']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date Released</th>\n",
+       "      <th>Box Office Gross</th>\n",
+       "      <th>IMDB Score</th>\n",
+       "      <th>Tomatometer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Scanners</th>\n",
+       "      <td> 1981</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 6.8</td>\n",
+       "      <td> 0.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fern Gully</th>\n",
+       "      <td> 1992</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 6.4</td>\n",
+       "      <td> 0.71</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Date Released  Box Office Gross  IMDB Score  Tomatometer\n",
+       "Scanners            1981                12         6.8         0.80\n",
+       "Fern Gully          1992                16         6.4         0.71"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "movies[movies['IMDB Score']<(movies['Tomatometer']*10)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Adding new elements\n",
+    "\n",
+    "Columns and rows can be added easily (relatively)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Columns can be added by setting a new column to another DataFrame. Just make sure that the indices are compatible!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date Released</th>\n",
+       "      <th>Box Office Gross</th>\n",
+       "      <th>IMDB Score</th>\n",
+       "      <th>Tomatometer</th>\n",
+       "      <th>Childhood Top 10?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Forrest Gump</th>\n",
+       "      <td> 1994</td>\n",
+       "      <td> 50</td>\n",
+       "      <td> 8.8</td>\n",
+       "      <td> 0.72</td>\n",
+       "      <td> True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scanners</th>\n",
+       "      <td> 1981</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 6.8</td>\n",
+       "      <td> 0.80</td>\n",
+       "      <td> True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010: Odyssey Two</th>\n",
+       "      <td> 1984</td>\n",
+       "      <td>  8</td>\n",
+       "      <td> 6.8</td>\n",
+       "      <td> 0.66</td>\n",
+       "      <td> True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fern Gully</th>\n",
+       "      <td> 1992</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 6.4</td>\n",
+       "      <td> 0.71</td>\n",
+       "      <td> True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Date Released  Box Office Gross  IMDB Score  Tomatometer  \\\n",
+       "Forrest Gump               1994                50         8.8         0.72   \n",
+       "Scanners                   1981                12         6.8         0.80   \n",
+       "2010: Odyssey Two          1984                 8         6.8         0.66   \n",
+       "Fern Gully                 1992                16         6.4         0.71   \n",
+       "\n",
+       "                  Childhood Top 10?  \n",
+       "Forrest Gump                   True  \n",
+       "Scanners                       True  \n",
+       "2010: Odyssey Two              True  \n",
+       "Fern Gully                     True  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "favlist = pd.DataFrame([True,True,True,True],index=movies.index)\n",
+    "\n",
+    "movies['Childhood Top 10?'] = favlist\n",
+    "\n",
+    "movies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rows can be added by using the append function on a DataFrame, taking an appropriately index and columned DataFrame that will be added on the end of the first DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date Released</th>\n",
+       "      <th>Box Office Gross</th>\n",
+       "      <th>IMDB Score</th>\n",
+       "      <th>Tomatometer</th>\n",
+       "      <th>Childhood Top 10?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Forrest Gump</th>\n",
+       "      <td> 1994</td>\n",
+       "      <td> 50</td>\n",
+       "      <td> 8.8</td>\n",
+       "      <td> 0.72</td>\n",
+       "      <td>  True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scanners</th>\n",
+       "      <td> 1981</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 6.8</td>\n",
+       "      <td>  0.8</td>\n",
+       "      <td>  True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010: Odyssey Two</th>\n",
+       "      <td> 1984</td>\n",
+       "      <td>  8</td>\n",
+       "      <td> 6.8</td>\n",
+       "      <td> 0.66</td>\n",
+       "      <td>  True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fern Gully</th>\n",
+       "      <td> 1992</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 6.4</td>\n",
+       "      <td> 0.71</td>\n",
+       "      <td>  True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Wild Wild West</th>\n",
+       "      <td> 1999</td>\n",
+       "      <td>  2</td>\n",
+       "      <td> 4.8</td>\n",
+       "      <td> 0.17</td>\n",
+       "      <td> False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Date Released Box Office Gross IMDB Score Tomatometer  \\\n",
+       "Forrest Gump               1994               50        8.8        0.72   \n",
+       "Scanners                   1981               12        6.8         0.8   \n",
+       "2010: Odyssey Two          1984                8        6.8        0.66   \n",
+       "Fern Gully                 1992               16        6.4        0.71   \n",
+       "Wild Wild West             1999                2        4.8        0.17   \n",
+       "\n",
+       "                  Childhood Top 10?  \n",
+       "Forrest Gump                   True  \n",
+       "Scanners                       True  \n",
+       "2010: Odyssey Two              True  \n",
+       "Fern Gully                     True  \n",
+       "Wild Wild West                False  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wildwildwest = pd.DataFrame(index=['Wild Wild West'], columns=movies.columns)\n",
+    "\n",
+    "wildwildwest.iloc[0] = ['1999', 2, 4.8,.17,False]\n",
+    "\n",
+    "movies.append(wildwildwest)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The concat function is also very useful. It can handle DataFrames with different indices and/or columns. There are multiple ways to joining the indices and columns however you would like"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date Released</th>\n",
+       "      <th>Box Office Gross</th>\n",
+       "      <th>IMDB Score</th>\n",
+       "      <th>Tomatometer</th>\n",
+       "      <th>Childhood Top 10?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Forrest Gump</th>\n",
+       "      <td> 1994</td>\n",
+       "      <td> 50</td>\n",
+       "      <td> 8.8</td>\n",
+       "      <td> 0.72</td>\n",
+       "      <td> True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scanners</th>\n",
+       "      <td> 1981</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 6.8</td>\n",
+       "      <td> 0.80</td>\n",
+       "      <td> True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010: Odyssey Two</th>\n",
+       "      <td> 1984</td>\n",
+       "      <td>  8</td>\n",
+       "      <td> 6.8</td>\n",
+       "      <td> 0.66</td>\n",
+       "      <td> True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fern Gully</th>\n",
+       "      <td> 1992</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 6.4</td>\n",
+       "      <td> 0.71</td>\n",
+       "      <td> True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Forrest Gump</th>\n",
+       "      <td> 1994</td>\n",
+       "      <td> 50</td>\n",
+       "      <td> 8.8</td>\n",
+       "      <td> 0.72</td>\n",
+       "      <td> True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Scanners</th>\n",
+       "      <td> 1981</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 6.8</td>\n",
+       "      <td> 0.80</td>\n",
+       "      <td> True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2010: Odyssey Two</th>\n",
+       "      <td> 1984</td>\n",
+       "      <td>  8</td>\n",
+       "      <td> 6.8</td>\n",
+       "      <td> 0.66</td>\n",
+       "      <td> True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fern Gully</th>\n",
+       "      <td> 1992</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 6.4</td>\n",
+       "      <td> 0.71</td>\n",
+       "      <td> True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Date Released  Box Office Gross  IMDB Score  Tomatometer  \\\n",
+       "Forrest Gump               1994                50         8.8         0.72   \n",
+       "Scanners                   1981                12         6.8         0.80   \n",
+       "2010: Odyssey Two          1984                 8         6.8         0.66   \n",
+       "Fern Gully                 1992                16         6.4         0.71   \n",
+       "Forrest Gump               1994                50         8.8         0.72   \n",
+       "Scanners                   1981                12         6.8         0.80   \n",
+       "2010: Odyssey Two          1984                 8         6.8         0.66   \n",
+       "Fern Gully                 1992                16         6.4         0.71   \n",
+       "\n",
+       "                  Childhood Top 10?  \n",
+       "Forrest Gump                   True  \n",
+       "Scanners                       True  \n",
+       "2010: Odyssey Two              True  \n",
+       "Fern Gully                     True  \n",
+       "Forrest Gump                   True  \n",
+       "Scanners                       True  \n",
+       "2010: Odyssey Two              True  \n",
+       "Fern Gully                     True  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([movies,movies])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Amazing GroupBy\n",
+    "\n",
+    "Pandas.GroupBy is a great function that allows you to process your data in many different ways without having to get fancy or write any loops.\n",
+    "\n",
+    "\n",
+    "Essentially, it carries out three different steps:\n",
+    "\n",
+    "**Splitting** the data into different groups [eg. treatment condition]\n",
+    "\n",
+    "**Applying** some function to your data [eg. mean]\n",
+    "\n",
+    "**Combining** the results back into another DataFrame\n",
+    "\n",
+    "In my work, this function was extremely useful when I wanted to obtain the mean *time spent in target zone* for each of my treatment groups\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Animal</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Time to platform</th>\n",
+       "      <th>Trial duration</th>\n",
+       "      <th>Distance travelled (cm)</th>\n",
+       "      <th>Average speed</th>\n",
+       "      <th>% time near walls</th>\n",
+       "      <th>Direction</th>\n",
+       "      <th>Average Proximity</th>\n",
+       "      <th>Cumulative Proximity</th>\n",
+       "      <th>Whishaw's corridor</th>\n",
+       "      <th>Platform Quadrant</th>\n",
+       "      <th>Zone 1 %</th>\n",
+       "      <th>Target Zone %</th>\n",
+       "      <th>Zone 3 %</th>\n",
+       "      <th>Zone 4 %</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td> m1</td>\n",
+       "      <td> 2/1/2015</td>\n",
+       "      <td> APP</td>\n",
+       "      <td> 10.724</td>\n",
+       "      <td> 59.988</td>\n",
+       "      <td> 1482.678</td>\n",
+       "      <td> 24.716</td>\n",
+       "      <td> 6.918</td>\n",
+       "      <td>-117.097</td>\n",
+       "      <td> 45.391</td>\n",
+       "      <td> 2064.426</td>\n",
+       "      <td> 46.541</td>\n",
+       "      <td> 4</td>\n",
+       "      <td>  2.935</td>\n",
+       "      <td> 17.820</td>\n",
+       "      <td> 31.447</td>\n",
+       "      <td>  8.805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td> m2</td>\n",
+       "      <td> 2/1/2015</td>\n",
+       "      <td> GFP</td>\n",
+       "      <td>  8.235</td>\n",
+       "      <td> 59.898</td>\n",
+       "      <td> 1298.611</td>\n",
+       "      <td> 21.680</td>\n",
+       "      <td> 2.516</td>\n",
+       "      <td>  52.129</td>\n",
+       "      <td> 47.077</td>\n",
+       "      <td> 1232.864</td>\n",
+       "      <td> 37.736</td>\n",
+       "      <td> 4</td>\n",
+       "      <td>  4.822</td>\n",
+       "      <td> 50.943</td>\n",
+       "      <td> 15.514</td>\n",
+       "      <td>  0.629</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td> m3</td>\n",
+       "      <td> 2/1/2015</td>\n",
+       "      <td> GFP</td>\n",
+       "      <td>  0.000</td>\n",
+       "      <td> 59.944</td>\n",
+       "      <td> 1562.190</td>\n",
+       "      <td> 26.061</td>\n",
+       "      <td> 2.096</td>\n",
+       "      <td>  -0.502</td>\n",
+       "      <td> 36.535</td>\n",
+       "      <td> 1799.688</td>\n",
+       "      <td> 54.717</td>\n",
+       "      <td> 4</td>\n",
+       "      <td>  5.451</td>\n",
+       "      <td> 34.382</td>\n",
+       "      <td> 10.273</td>\n",
+       "      <td>  3.774</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td> m4</td>\n",
+       "      <td> 2/1/2015</td>\n",
+       "      <td> APP</td>\n",
+       "      <td>  7.102</td>\n",
+       "      <td> 59.896</td>\n",
+       "      <td> 1157.948</td>\n",
+       "      <td> 19.333</td>\n",
+       "      <td> 0.000</td>\n",
+       "      <td>  51.561</td>\n",
+       "      <td> 41.952</td>\n",
+       "      <td> 1392.255</td>\n",
+       "      <td> 39.203</td>\n",
+       "      <td> 4</td>\n",
+       "      <td> 11.530</td>\n",
+       "      <td> 37.945</td>\n",
+       "      <td> 16.352</td>\n",
+       "      <td>  0.210</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td> m5</td>\n",
+       "      <td> 2/1/2015</td>\n",
+       "      <td> GFP</td>\n",
+       "      <td> 14.931</td>\n",
+       "      <td> 59.912</td>\n",
+       "      <td> 1310.405</td>\n",
+       "      <td> 21.872</td>\n",
+       "      <td> 0.000</td>\n",
+       "      <td> -21.198</td>\n",
+       "      <td> 70.274</td>\n",
+       "      <td> 1550.027</td>\n",
+       "      <td> 49.476</td>\n",
+       "      <td> 4</td>\n",
+       "      <td>  6.709</td>\n",
+       "      <td> 35.430</td>\n",
+       "      <td> 18.658</td>\n",
+       "      <td>  5.451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td> m6</td>\n",
+       "      <td> 2/1/2015</td>\n",
+       "      <td> APP</td>\n",
+       "      <td> 17.235</td>\n",
+       "      <td> 59.948</td>\n",
+       "      <td> 1348.853</td>\n",
+       "      <td> 22.500</td>\n",
+       "      <td> 0.419</td>\n",
+       "      <td>-131.100</td>\n",
+       "      <td> 29.144</td>\n",
+       "      <td> 1747.719</td>\n",
+       "      <td> 61.845</td>\n",
+       "      <td> 4</td>\n",
+       "      <td>  6.080</td>\n",
+       "      <td> 35.220</td>\n",
+       "      <td> 14.046</td>\n",
+       "      <td>  8.386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td> m7</td>\n",
+       "      <td> 2/1/2015</td>\n",
+       "      <td> GFP</td>\n",
+       "      <td>  5.086</td>\n",
+       "      <td> 59.897</td>\n",
+       "      <td> 1402.622</td>\n",
+       "      <td> 23.417</td>\n",
+       "      <td> 0.839</td>\n",
+       "      <td>  30.670</td>\n",
+       "      <td> 44.798</td>\n",
+       "      <td> 1832.923</td>\n",
+       "      <td> 45.073</td>\n",
+       "      <td> 4</td>\n",
+       "      <td>  7.128</td>\n",
+       "      <td> 35.849</td>\n",
+       "      <td> 18.658</td>\n",
+       "      <td>  5.031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td> m8</td>\n",
+       "      <td> 2/1/2015</td>\n",
+       "      <td> APP</td>\n",
+       "      <td>  3.899</td>\n",
+       "      <td> 59.969</td>\n",
+       "      <td> 1686.095</td>\n",
+       "      <td> 28.116</td>\n",
+       "      <td> 6.289</td>\n",
+       "      <td> 115.868</td>\n",
+       "      <td> 49.414</td>\n",
+       "      <td> 2482.494</td>\n",
+       "      <td> 62.474</td>\n",
+       "      <td> 4</td>\n",
+       "      <td>  3.774</td>\n",
+       "      <td> 15.094</td>\n",
+       "      <td> 27.254</td>\n",
+       "      <td> 13.627</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Animal      Date Group  Time to platform  Trial duration  \\\n",
+       "0     m1  2/1/2015   APP            10.724          59.988   \n",
+       "1     m2  2/1/2015   GFP             8.235          59.898   \n",
+       "2     m3  2/1/2015   GFP             0.000          59.944   \n",
+       "3     m4  2/1/2015   APP             7.102          59.896   \n",
+       "4     m5  2/1/2015   GFP            14.931          59.912   \n",
+       "5     m6  2/1/2015   APP            17.235          59.948   \n",
+       "6     m7  2/1/2015   GFP             5.086          59.897   \n",
+       "7     m8  2/1/2015   APP             3.899          59.969   \n",
+       "\n",
+       "   Distance travelled (cm)  Average speed  % time near walls  Direction  \\\n",
+       "0                 1482.678         24.716              6.918   -117.097   \n",
+       "1                 1298.611         21.680              2.516     52.129   \n",
+       "2                 1562.190         26.061              2.096     -0.502   \n",
+       "3                 1157.948         19.333              0.000     51.561   \n",
+       "4                 1310.405         21.872              0.000    -21.198   \n",
+       "5                 1348.853         22.500              0.419   -131.100   \n",
+       "6                 1402.622         23.417              0.839     30.670   \n",
+       "7                 1686.095         28.116              6.289    115.868   \n",
+       "\n",
+       "   Average Proximity  Cumulative Proximity  Whishaw's corridor  \\\n",
+       "0             45.391              2064.426              46.541   \n",
+       "1             47.077              1232.864              37.736   \n",
+       "2             36.535              1799.688              54.717   \n",
+       "3             41.952              1392.255              39.203   \n",
+       "4             70.274              1550.027              49.476   \n",
+       "5             29.144              1747.719              61.845   \n",
+       "6             44.798              1832.923              45.073   \n",
+       "7             49.414              2482.494              62.474   \n",
+       "\n",
+       "   Platform Quadrant  Zone 1 %  Target Zone %  Zone 3 %  Zone 4 %  \n",
+       "0                  4     2.935         17.820    31.447     8.805  \n",
+       "1                  4     4.822         50.943    15.514     0.629  \n",
+       "2                  4     5.451         34.382    10.273     3.774  \n",
+       "3                  4    11.530         37.945    16.352     0.210  \n",
+       "4                  4     6.709         35.430    18.658     5.451  \n",
+       "5                  4     6.080         35.220    14.046     8.386  \n",
+       "6                  4     7.128         35.849    18.658     5.031  \n",
+       "7                  4     3.774         15.094    27.254    13.627  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probedata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Time to platform</th>\n",
+       "      <th>Trial duration</th>\n",
+       "      <th>Distance travelled (cm)</th>\n",
+       "      <th>Average speed</th>\n",
+       "      <th>% time near walls</th>\n",
+       "      <th>Direction</th>\n",
+       "      <th>Average Proximity</th>\n",
+       "      <th>Cumulative Proximity</th>\n",
+       "      <th>Whishaw's corridor</th>\n",
+       "      <th>Platform Quadrant</th>\n",
+       "      <th>Zone 1 %</th>\n",
+       "      <th>Target Zone %</th>\n",
+       "      <th>Zone 3 %</th>\n",
+       "      <th>Zone 4 %</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>APP</th>\n",
+       "      <td> 9.740</td>\n",
+       "      <td> 59.95025</td>\n",
+       "      <td> 1418.8935</td>\n",
+       "      <td> 23.66625</td>\n",
+       "      <td> 3.40650</td>\n",
+       "      <td>-20.19200</td>\n",
+       "      <td> 41.47525</td>\n",
+       "      <td> 1921.7235</td>\n",
+       "      <td> 52.51575</td>\n",
+       "      <td> 4</td>\n",
+       "      <td> 6.07975</td>\n",
+       "      <td> 26.51975</td>\n",
+       "      <td> 22.27475</td>\n",
+       "      <td> 7.75700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>GFP</th>\n",
+       "      <td> 7.063</td>\n",
+       "      <td> 59.91275</td>\n",
+       "      <td> 1393.4570</td>\n",
+       "      <td> 23.25750</td>\n",
+       "      <td> 1.36275</td>\n",
+       "      <td> 15.27475</td>\n",
+       "      <td> 49.67100</td>\n",
+       "      <td> 1603.8755</td>\n",
+       "      <td> 46.75050</td>\n",
+       "      <td> 4</td>\n",
+       "      <td> 6.02750</td>\n",
+       "      <td> 39.15100</td>\n",
+       "      <td> 15.77575</td>\n",
+       "      <td> 3.72125</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Time to platform  Trial duration  Distance travelled (cm)  \\\n",
+       "Group                                                              \n",
+       "APP               9.740        59.95025                1418.8935   \n",
+       "GFP               7.063        59.91275                1393.4570   \n",
+       "\n",
+       "       Average speed  % time near walls  Direction  Average Proximity  \\\n",
+       "Group                                                                   \n",
+       "APP         23.66625            3.40650  -20.19200           41.47525   \n",
+       "GFP         23.25750            1.36275   15.27475           49.67100   \n",
+       "\n",
+       "       Cumulative Proximity  Whishaw's corridor  Platform Quadrant  Zone 1 %  \\\n",
+       "Group                                                                          \n",
+       "APP               1921.7235            52.51575                  4   6.07975   \n",
+       "GFP               1603.8755            46.75050                  4   6.02750   \n",
+       "\n",
+       "       Target Zone %  Zone 3 %  Zone 4 %  \n",
+       "Group                                     \n",
+       "APP         26.51975  22.27475   7.75700  \n",
+       "GFP         39.15100  15.77575   3.72125  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped = probedata.groupby(['Group']).mean()\n",
+    "\n",
+    "grouped"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "    import matplotlib.pylab as plt\n",
+    "    import seaborn as sns\n",
+    "    \n",
+    "    sns.set_context(\"talk\")\n",
+    "    grouped[['Zone 1 %','Target Zone %','Zone 3 %','Zone 4 %']].plot(kind='bar')\n",
+    "    plt.title('Time spent in Zone')\n",
+    "    plt.ylabel('Time (sec)')\n",
+    "    plt.xlabel('Zone')\n",
+    "    locs, labels = plt.xticks()\n",
+    "    plt.setp(labels,rotation=0)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/lessons/python/pandas/lesson.md b/lessons/python/pandas/lesson.md
new file mode 100644
index 000000000..61344e4cd
--- /dev/null
+++ b/lessons/python/pandas/lesson.md
@@ -0,0 +1,142 @@
+---
+layout: page
+title: Intro to Pandas DataFrames
+visible: true
+tags:
+  - python
+  - beginner
+---
+
+ - **Authors**: Dano Morrison
+ - **Research field**: Neuroscience
+ - **Lesson topic**: Usage of Pandas DataFrames for data analysis
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/pandas>
+
+# Introduction
+
+DataFrames are a lovely way to store data. They're essentially matrices that can store almost any type of data and are a great option for handling data where you want to keep track of rows and columns with labels. The Pandas library also comes with a handy host of functions that allow you to work with your DataFrames in very smart ways (eg. means, split-apply-combine).
+
+The Pandas website has a lot of [great documentation](http://pandas.pydata.org/pandas-docs/version/0.15.2/10min.html#min) to help you get started. However, even after reading the official tutorials, I was stumped on some problems using DataFrames. This lesson is based directly off of how I solved those problems, so hopefully some will find it helpful. 
+
+# Creating DataFrames
+
+You can instantiate DataFrames without any data or with data from any number of sources
+
+~~~
+import pandas as pd
+import numpy as np
+
+blank = pd.DataFrame()
+
+blank
+~~~
+
+~~~
+movies = pd.DataFrame(np.zeros((4,4)), index=['Forrest Gump', 'Scanners', '2010: Odyssey Two', 'Fern Gully'], columns = ['Date Released', 'Box Office Gross', 'IMDB Score', 'Tomatometer'])
+
+movies
+~~~
+
+~~~
+zeros = pd.DataFrame(np.zeros((3,5)))
+
+zeros
+~~~
+
+Got a csv file you want to read? No problem!
+
+~~~
+probedata = pd.read_csv('12probe20cm.csv')
+
+probedata[1:4]
+~~~
+
+# Slicing DataFrames
+You can slice DataFrames just like an array, and can also include some more advanced criteria
+
+~~~
+movies['Date Released'] = ['1994','1981','1984','1992']
+movies['Box Office Gross'] = [50,12,8,16]
+movies['IMDB Score'] = [8.8,6.8,6.8,6.4]
+movies['Tomatometer'] = [.72,.80,.66,.71]
+
+movies[1:2]
+
+#OR
+
+movies.iloc[1:2]
+~~~
+
+~~~
+movies[['IMDB Score','Tomatometer']]
+~~~
+
+~~~
+movies[movies['IMDB Score']<(movies['Tomatometer']*10)]
+~~~
+
+# Adding new elements
+
+Columns and rows can be added easily (relatively)
+
+Columns can be added by setting a new column to another DataFrame. Just make sure that the indices are compatible!
+
+~~~
+favlist = pd.DataFrame([True,True,True,True],index=movies.index)
+
+movies['Childhood Top 10?'] = favlist
+
+movies
+~~~
+
+Rows can be added by using the append function on a DataFrame, taking an appropriately index and columned DataFrame that will be added on the end of the first DataFrame.
+
+~~~
+wildwildwest = pd.DataFrame(index=['Wild Wild West'], columns=movies.columns)
+
+wildwildwest.iloc[0] = ['1999', 2, 4.8,.17,False]
+
+movies.append(wildwildwest)
+~~~
+
+The concat function is also very useful. It can handle DataFrames with different indices and/or columns. There are multiple ways to joining the indices and columns however you would like
+
+~~~
+pd.concat([movies,movies])
+~~~
+
+# The Amazing GroupBy
+
+Pandas.GroupBy is a great function that allows you to process your data in many different ways without having to get fancy or write any loops.
+
+Essentially, it carries out three different steps:
+
+- *Splitting*: the data into different groups [eg. treatment condition]
+- *Applying*: some function to your data [eg. mean]
+- *Combining*: the results back into another DataFrame
+
+In my work, this function was extremely useful when I wanted to obtain the mean *time spent in target zone* for each of my treatment groups
+
+~~~
+probedata
+~~~
+
+~~~
+grouped = probedata.groupby(['Group']).mean()
+
+grouped
+~~~
+
+
+    import matplotlib.pylab as plt
+    import seaborn as sns
+    
+    sns.set_context("talk")
+    grouped[['Zone 1 %','Target Zone %','Zone 3 %','Zone 4 %']].plot(kind='bar')
+    plt.title('Time spent in Zone')
+    plt.ylabel('Time (sec)')
+    plt.xlabel('Zone')
+    locs, labels = plt.xticks()
+    plt.setp(labels,rotation=0)
+    plt.show()
+
diff --git a/lessons/python/pandas2/UofT-pandas.ipynb b/lessons/python/pandas2/UofT-pandas.ipynb
new file mode 100644
index 000000000..53af0e3f8
--- /dev/null
+++ b/lessons/python/pandas2/UofT-pandas.ipynb
@@ -0,0 +1,4899 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Objectives\n",
+    "- Read tabular data into an IPython notebook\n",
+    "- Access columns of the data\n",
+    "- Isolate subsets of the data\n",
+    "- Generate plots based on subsetted data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Resources\n",
+    "Pandas has lots of great documentation, tutorials and walkthroughs.\n",
+    "\n",
+    "This tutorial was based largely off of a SWC inspired lesson by Nancy Soontiens found at:\n",
+    "https://nsoontie.github.io/2015-03-05-ubc/novice/python/Pandas-Lesson.html\n",
+    "\n",
+    "I adapted other parts from a great tutorial by Greg Reda:\n",
+    "http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/\n",
+    "\n",
+    "More can be found in the pandas documentation:\n",
+    "http://pandas.pydata.org/pandas-docs/stable/\n",
+    "\n",
+    "A great youtube walkthrough from PyCon 2015:\n",
+    "https://www.youtube.com/watch?v=5JnMutdy6Fw\n",
+    "\n",
+    "Lastly, a set of recent helpful blogposts for intermediate and advanced users can be found at:\n",
+    "https://tomaugspurger.github.io/modern-1.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with dataframes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "pandas introduces two new data structures to Python - **Series** and **DataFrame**, both of which are built on top of NumPy.\n",
+    "\n",
+    "We can load in a tabular data set as a dataframe in a number of different ways."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.read_table('./gapminderDataFiveYear.txt')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>8425333</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>28.801</td>\n",
+       "      <td>779.445314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>9240934</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>30.332</td>\n",
+       "      <td>820.853030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>10267083</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>31.997</td>\n",
+       "      <td>853.100710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>11537966</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>34.020</td>\n",
+       "      <td>836.197138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>13079460</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>36.088</td>\n",
+       "      <td>739.981106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>14880372</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>38.438</td>\n",
+       "      <td>786.113360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1982</td>\n",
+       "      <td>12881816</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>39.854</td>\n",
+       "      <td>978.011439</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>13867957</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>40.822</td>\n",
+       "      <td>852.395945</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>16317921</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>41.674</td>\n",
+       "      <td>649.341395</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>22227415</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>41.763</td>\n",
+       "      <td>635.341351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>25268405</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>42.129</td>\n",
+       "      <td>726.734055</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>31889923</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>43.828</td>\n",
+       "      <td>974.580338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>1282697</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>55.230</td>\n",
+       "      <td>1601.056136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>1476505</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>59.280</td>\n",
+       "      <td>1942.284244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>1728137</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>64.820</td>\n",
+       "      <td>2312.888958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>1984060</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>66.220</td>\n",
+       "      <td>2760.196931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>2263554</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>67.690</td>\n",
+       "      <td>3313.422188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>2509048</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>68.930</td>\n",
+       "      <td>3533.003910</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>1982</td>\n",
+       "      <td>2780097</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>70.420</td>\n",
+       "      <td>3630.880722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>3075321</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>72.000</td>\n",
+       "      <td>3738.932735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>3326498</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>71.581</td>\n",
+       "      <td>2497.437901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>3428038</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>72.950</td>\n",
+       "      <td>3193.054604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>3508512</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>75.651</td>\n",
+       "      <td>4604.211737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>3600523</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>76.423</td>\n",
+       "      <td>5937.029526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>9279525</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>43.077</td>\n",
+       "      <td>2449.008185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>10270856</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>45.685</td>\n",
+       "      <td>3013.976023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>11000948</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>48.303</td>\n",
+       "      <td>2550.816880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>12760499</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>51.407</td>\n",
+       "      <td>3246.991771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>14760787</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>54.518</td>\n",
+       "      <td>4182.663766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>17152804</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>58.014</td>\n",
+       "      <td>4910.416756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1674</th>\n",
+       "      <td>Yemen, Rep.</td>\n",
+       "      <td>1982</td>\n",
+       "      <td>9657618</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>49.113</td>\n",
+       "      <td>1977.557010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1675</th>\n",
+       "      <td>Yemen, Rep.</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>11219340</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>52.922</td>\n",
+       "      <td>1971.741538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1676</th>\n",
+       "      <td>Yemen, Rep.</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>13367997</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>55.599</td>\n",
+       "      <td>1879.496673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1677</th>\n",
+       "      <td>Yemen, Rep.</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>15826497</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>58.020</td>\n",
+       "      <td>2117.484526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1678</th>\n",
+       "      <td>Yemen, Rep.</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>18701257</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>60.308</td>\n",
+       "      <td>2234.820827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1679</th>\n",
+       "      <td>Yemen, Rep.</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>22211743</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>62.698</td>\n",
+       "      <td>2280.769906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1680</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>2672000</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>42.038</td>\n",
+       "      <td>1147.388831</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1681</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>3016000</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>44.077</td>\n",
+       "      <td>1311.956766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1682</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>3421000</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>46.023</td>\n",
+       "      <td>1452.725766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1683</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>3900000</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>47.768</td>\n",
+       "      <td>1777.077318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1684</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>4506497</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>50.107</td>\n",
+       "      <td>1773.498265</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1685</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>5216550</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>51.386</td>\n",
+       "      <td>1588.688299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1686</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>1982</td>\n",
+       "      <td>6100407</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>51.821</td>\n",
+       "      <td>1408.678565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1687</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>7272406</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>50.821</td>\n",
+       "      <td>1213.315116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1688</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>8381163</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>46.100</td>\n",
+       "      <td>1210.884633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1689</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>9417789</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>40.238</td>\n",
+       "      <td>1071.353818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1690</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>10595811</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>39.193</td>\n",
+       "      <td>1071.613938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1691</th>\n",
+       "      <td>Zambia</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>11746035</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>42.384</td>\n",
+       "      <td>1271.211593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1692</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>3080907</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>48.451</td>\n",
+       "      <td>406.884115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1693</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>3646340</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>50.469</td>\n",
+       "      <td>518.764268</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1694</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>4277736</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>52.358</td>\n",
+       "      <td>527.272182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1695</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>4995432</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>53.995</td>\n",
+       "      <td>569.795071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1696</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>5861135</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>55.635</td>\n",
+       "      <td>799.362176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1697</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>6642107</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>57.674</td>\n",
+       "      <td>685.587682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1698</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1982</td>\n",
+       "      <td>7636524</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>60.363</td>\n",
+       "      <td>788.855041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1699</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>9216418</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>62.351</td>\n",
+       "      <td>706.157306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1700</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>10704340</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>60.377</td>\n",
+       "      <td>693.420786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1701</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>11404948</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>46.809</td>\n",
+       "      <td>792.449960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1702</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>11926563</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>39.989</td>\n",
+       "      <td>672.038623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1703</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>12311143</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>43.487</td>\n",
+       "      <td>469.709298</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1704 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          country  year       pop continent  lifeExp    gdpPercap\n",
+       "0     Afghanistan  1952   8425333      Asia   28.801   779.445314\n",
+       "1     Afghanistan  1957   9240934      Asia   30.332   820.853030\n",
+       "2     Afghanistan  1962  10267083      Asia   31.997   853.100710\n",
+       "3     Afghanistan  1967  11537966      Asia   34.020   836.197138\n",
+       "4     Afghanistan  1972  13079460      Asia   36.088   739.981106\n",
+       "5     Afghanistan  1977  14880372      Asia   38.438   786.113360\n",
+       "6     Afghanistan  1982  12881816      Asia   39.854   978.011439\n",
+       "7     Afghanistan  1987  13867957      Asia   40.822   852.395945\n",
+       "8     Afghanistan  1992  16317921      Asia   41.674   649.341395\n",
+       "9     Afghanistan  1997  22227415      Asia   41.763   635.341351\n",
+       "10    Afghanistan  2002  25268405      Asia   42.129   726.734055\n",
+       "11    Afghanistan  2007  31889923      Asia   43.828   974.580338\n",
+       "12        Albania  1952   1282697    Europe   55.230  1601.056136\n",
+       "13        Albania  1957   1476505    Europe   59.280  1942.284244\n",
+       "14        Albania  1962   1728137    Europe   64.820  2312.888958\n",
+       "15        Albania  1967   1984060    Europe   66.220  2760.196931\n",
+       "16        Albania  1972   2263554    Europe   67.690  3313.422188\n",
+       "17        Albania  1977   2509048    Europe   68.930  3533.003910\n",
+       "18        Albania  1982   2780097    Europe   70.420  3630.880722\n",
+       "19        Albania  1987   3075321    Europe   72.000  3738.932735\n",
+       "20        Albania  1992   3326498    Europe   71.581  2497.437901\n",
+       "21        Albania  1997   3428038    Europe   72.950  3193.054604\n",
+       "22        Albania  2002   3508512    Europe   75.651  4604.211737\n",
+       "23        Albania  2007   3600523    Europe   76.423  5937.029526\n",
+       "24        Algeria  1952   9279525    Africa   43.077  2449.008185\n",
+       "25        Algeria  1957  10270856    Africa   45.685  3013.976023\n",
+       "26        Algeria  1962  11000948    Africa   48.303  2550.816880\n",
+       "27        Algeria  1967  12760499    Africa   51.407  3246.991771\n",
+       "28        Algeria  1972  14760787    Africa   54.518  4182.663766\n",
+       "29        Algeria  1977  17152804    Africa   58.014  4910.416756\n",
+       "...           ...   ...       ...       ...      ...          ...\n",
+       "1674  Yemen, Rep.  1982   9657618      Asia   49.113  1977.557010\n",
+       "1675  Yemen, Rep.  1987  11219340      Asia   52.922  1971.741538\n",
+       "1676  Yemen, Rep.  1992  13367997      Asia   55.599  1879.496673\n",
+       "1677  Yemen, Rep.  1997  15826497      Asia   58.020  2117.484526\n",
+       "1678  Yemen, Rep.  2002  18701257      Asia   60.308  2234.820827\n",
+       "1679  Yemen, Rep.  2007  22211743      Asia   62.698  2280.769906\n",
+       "1680       Zambia  1952   2672000    Africa   42.038  1147.388831\n",
+       "1681       Zambia  1957   3016000    Africa   44.077  1311.956766\n",
+       "1682       Zambia  1962   3421000    Africa   46.023  1452.725766\n",
+       "1683       Zambia  1967   3900000    Africa   47.768  1777.077318\n",
+       "1684       Zambia  1972   4506497    Africa   50.107  1773.498265\n",
+       "1685       Zambia  1977   5216550    Africa   51.386  1588.688299\n",
+       "1686       Zambia  1982   6100407    Africa   51.821  1408.678565\n",
+       "1687       Zambia  1987   7272406    Africa   50.821  1213.315116\n",
+       "1688       Zambia  1992   8381163    Africa   46.100  1210.884633\n",
+       "1689       Zambia  1997   9417789    Africa   40.238  1071.353818\n",
+       "1690       Zambia  2002  10595811    Africa   39.193  1071.613938\n",
+       "1691       Zambia  2007  11746035    Africa   42.384  1271.211593\n",
+       "1692     Zimbabwe  1952   3080907    Africa   48.451   406.884115\n",
+       "1693     Zimbabwe  1957   3646340    Africa   50.469   518.764268\n",
+       "1694     Zimbabwe  1962   4277736    Africa   52.358   527.272182\n",
+       "1695     Zimbabwe  1967   4995432    Africa   53.995   569.795071\n",
+       "1696     Zimbabwe  1972   5861135    Africa   55.635   799.362176\n",
+       "1697     Zimbabwe  1977   6642107    Africa   57.674   685.587682\n",
+       "1698     Zimbabwe  1982   7636524    Africa   60.363   788.855041\n",
+       "1699     Zimbabwe  1987   9216418    Africa   62.351   706.157306\n",
+       "1700     Zimbabwe  1992  10704340    Africa   60.377   693.420786\n",
+       "1701     Zimbabwe  1997  11404948    Africa   46.809   792.449960\n",
+       "1702     Zimbabwe  2002  11926563    Africa   39.989   672.038623\n",
+       "1703     Zimbabwe  2007  12311143    Africa   43.487   469.709298\n",
+       "\n",
+       "[1704 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1704, 6)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['country', 'year', 'pop', 'continent', 'lifeExp', 'gdpPercap'], dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>8425333</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>28.801</td>\n",
+       "      <td>779.445314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>9240934</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>30.332</td>\n",
+       "      <td>820.853030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>10267083</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>31.997</td>\n",
+       "      <td>853.100710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>11537966</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>34.020</td>\n",
+       "      <td>836.197138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>13079460</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>36.088</td>\n",
+       "      <td>739.981106</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  year       pop continent  lifeExp   gdpPercap\n",
+       "0  Afghanistan  1952   8425333      Asia   28.801  779.445314\n",
+       "1  Afghanistan  1957   9240934      Asia   30.332  820.853030\n",
+       "2  Afghanistan  1962  10267083      Asia   31.997  853.100710\n",
+       "3  Afghanistan  1967  11537966      Asia   34.020  836.197138\n",
+       "4  Afghanistan  1972  13079460      Asia   36.088  739.981106"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>8425333</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>28.801</td>\n",
+       "      <td>779.445314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>9240934</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>30.332</td>\n",
+       "      <td>820.853030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>10267083</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>31.997</td>\n",
+       "      <td>853.100710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>11537966</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>34.020</td>\n",
+       "      <td>836.197138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>13079460</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>36.088</td>\n",
+       "      <td>739.981106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>14880372</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>38.438</td>\n",
+       "      <td>786.113360</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  year       pop continent  lifeExp   gdpPercap\n",
+       "0  Afghanistan  1952   8425333      Asia   28.801  779.445314\n",
+       "1  Afghanistan  1957   9240934      Asia   30.332  820.853030\n",
+       "2  Afghanistan  1962  10267083      Asia   31.997  853.100710\n",
+       "3  Afghanistan  1967  11537966      Asia   34.020  836.197138\n",
+       "4  Afghanistan  1972  13079460      Asia   36.088  739.981106\n",
+       "5  Afghanistan  1977  14880372      Asia   38.438  786.113360"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1699</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>9216418</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>62.351</td>\n",
+       "      <td>706.157306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1700</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>10704340</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>60.377</td>\n",
+       "      <td>693.420786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1701</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>11404948</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>46.809</td>\n",
+       "      <td>792.449960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1702</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>11926563</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>39.989</td>\n",
+       "      <td>672.038623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1703</th>\n",
+       "      <td>Zimbabwe</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>12311143</td>\n",
+       "      <td>Africa</td>\n",
+       "      <td>43.487</td>\n",
+       "      <td>469.709298</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  year       pop continent  lifeExp   gdpPercap\n",
+       "1699  Zimbabwe  1987   9216418    Africa   62.351  706.157306\n",
+       "1700  Zimbabwe  1992  10704340    Africa   60.377  693.420786\n",
+       "1701  Zimbabwe  1997  11404948    Africa   46.809  792.449960\n",
+       "1702  Zimbabwe  2002  11926563    Africa   39.989  672.038623\n",
+       "1703  Zimbabwe  2007  12311143    Africa   43.487  469.709298"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 1704 entries, 0 to 1703\n",
+      "Data columns (total 6 columns):\n",
+      "country      1704 non-null object\n",
+      "year         1704 non-null int64\n",
+      "pop          1704 non-null float64\n",
+      "continent    1704 non-null object\n",
+      "lifeExp      1704 non-null float64\n",
+      "gdpPercap    1704 non-null float64\n",
+      "dtypes: float64(3), int64(1), object(2)\n",
+      "memory usage: 93.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "country       object\n",
+       "year           int64\n",
+       "pop          float64\n",
+       "continent     object\n",
+       "lifeExp      float64\n",
+       "gdpPercap    float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1704.00000</td>\n",
+       "      <td>1.704000e+03</td>\n",
+       "      <td>1704.000000</td>\n",
+       "      <td>1704.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1979.50000</td>\n",
+       "      <td>2.960121e+07</td>\n",
+       "      <td>59.474439</td>\n",
+       "      <td>7215.327081</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>17.26533</td>\n",
+       "      <td>1.061579e+08</td>\n",
+       "      <td>12.917107</td>\n",
+       "      <td>9857.454543</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1952.00000</td>\n",
+       "      <td>6.001100e+04</td>\n",
+       "      <td>23.599000</td>\n",
+       "      <td>241.165877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1965.75000</td>\n",
+       "      <td>2.793664e+06</td>\n",
+       "      <td>48.198000</td>\n",
+       "      <td>1202.060309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1979.50000</td>\n",
+       "      <td>7.023596e+06</td>\n",
+       "      <td>60.712500</td>\n",
+       "      <td>3531.846989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1993.25000</td>\n",
+       "      <td>1.958522e+07</td>\n",
+       "      <td>70.845500</td>\n",
+       "      <td>9325.462346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2007.00000</td>\n",
+       "      <td>1.318683e+09</td>\n",
+       "      <td>82.603000</td>\n",
+       "      <td>113523.132900</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             year           pop      lifeExp      gdpPercap\n",
+       "count  1704.00000  1.704000e+03  1704.000000    1704.000000\n",
+       "mean   1979.50000  2.960121e+07    59.474439    7215.327081\n",
+       "std      17.26533  1.061579e+08    12.917107    9857.454543\n",
+       "min    1952.00000  6.001100e+04    23.599000     241.165877\n",
+       "25%    1965.75000  2.793664e+06    48.198000    1202.060309\n",
+       "50%    1979.50000  7.023596e+06    60.712500    3531.846989\n",
+       "75%    1993.25000  1.958522e+07    70.845500    9325.462346\n",
+       "max    2007.00000  1.318683e+09    82.603000  113523.132900"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data selection\n",
+    "Sometimes we need to look at only parts of the data. For example, we might want to look at the data for a particular country or in a particular year."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selecting columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>lifeExp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1952</td>\n",
+       "      <td>28.801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1957</td>\n",
+       "      <td>30.332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1962</td>\n",
+       "      <td>31.997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1967</td>\n",
+       "      <td>34.020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1972</td>\n",
+       "      <td>36.088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1977</td>\n",
+       "      <td>38.438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1982</td>\n",
+       "      <td>39.854</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1987</td>\n",
+       "      <td>40.822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>41.674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1997</td>\n",
+       "      <td>41.763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2002</td>\n",
+       "      <td>42.129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>2007</td>\n",
+       "      <td>43.828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1952</td>\n",
+       "      <td>55.230</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1957</td>\n",
+       "      <td>59.280</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1962</td>\n",
+       "      <td>64.820</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1967</td>\n",
+       "      <td>66.220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1972</td>\n",
+       "      <td>67.690</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1977</td>\n",
+       "      <td>68.930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1982</td>\n",
+       "      <td>70.420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1987</td>\n",
+       "      <td>72.000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>71.581</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1997</td>\n",
+       "      <td>72.950</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>2002</td>\n",
+       "      <td>75.651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>2007</td>\n",
+       "      <td>76.423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1952</td>\n",
+       "      <td>43.077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1957</td>\n",
+       "      <td>45.685</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1962</td>\n",
+       "      <td>48.303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1967</td>\n",
+       "      <td>51.407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1972</td>\n",
+       "      <td>54.518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1977</td>\n",
+       "      <td>58.014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1674</th>\n",
+       "      <td>1982</td>\n",
+       "      <td>49.113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1675</th>\n",
+       "      <td>1987</td>\n",
+       "      <td>52.922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1676</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>55.599</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1677</th>\n",
+       "      <td>1997</td>\n",
+       "      <td>58.020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1678</th>\n",
+       "      <td>2002</td>\n",
+       "      <td>60.308</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1679</th>\n",
+       "      <td>2007</td>\n",
+       "      <td>62.698</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1680</th>\n",
+       "      <td>1952</td>\n",
+       "      <td>42.038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1681</th>\n",
+       "      <td>1957</td>\n",
+       "      <td>44.077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1682</th>\n",
+       "      <td>1962</td>\n",
+       "      <td>46.023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1683</th>\n",
+       "      <td>1967</td>\n",
+       "      <td>47.768</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1684</th>\n",
+       "      <td>1972</td>\n",
+       "      <td>50.107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1685</th>\n",
+       "      <td>1977</td>\n",
+       "      <td>51.386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1686</th>\n",
+       "      <td>1982</td>\n",
+       "      <td>51.821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1687</th>\n",
+       "      <td>1987</td>\n",
+       "      <td>50.821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1688</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>46.100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1689</th>\n",
+       "      <td>1997</td>\n",
+       "      <td>40.238</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1690</th>\n",
+       "      <td>2002</td>\n",
+       "      <td>39.193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1691</th>\n",
+       "      <td>2007</td>\n",
+       "      <td>42.384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1692</th>\n",
+       "      <td>1952</td>\n",
+       "      <td>48.451</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1693</th>\n",
+       "      <td>1957</td>\n",
+       "      <td>50.469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1694</th>\n",
+       "      <td>1962</td>\n",
+       "      <td>52.358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1695</th>\n",
+       "      <td>1967</td>\n",
+       "      <td>53.995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1696</th>\n",
+       "      <td>1972</td>\n",
+       "      <td>55.635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1697</th>\n",
+       "      <td>1977</td>\n",
+       "      <td>57.674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1698</th>\n",
+       "      <td>1982</td>\n",
+       "      <td>60.363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1699</th>\n",
+       "      <td>1987</td>\n",
+       "      <td>62.351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1700</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>60.377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1701</th>\n",
+       "      <td>1997</td>\n",
+       "      <td>46.809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1702</th>\n",
+       "      <td>2002</td>\n",
+       "      <td>39.989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1703</th>\n",
+       "      <td>2007</td>\n",
+       "      <td>43.487</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1704 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      year  lifeExp\n",
+       "0     1952   28.801\n",
+       "1     1957   30.332\n",
+       "2     1962   31.997\n",
+       "3     1967   34.020\n",
+       "4     1972   36.088\n",
+       "5     1977   38.438\n",
+       "6     1982   39.854\n",
+       "7     1987   40.822\n",
+       "8     1992   41.674\n",
+       "9     1997   41.763\n",
+       "10    2002   42.129\n",
+       "11    2007   43.828\n",
+       "12    1952   55.230\n",
+       "13    1957   59.280\n",
+       "14    1962   64.820\n",
+       "15    1967   66.220\n",
+       "16    1972   67.690\n",
+       "17    1977   68.930\n",
+       "18    1982   70.420\n",
+       "19    1987   72.000\n",
+       "20    1992   71.581\n",
+       "21    1997   72.950\n",
+       "22    2002   75.651\n",
+       "23    2007   76.423\n",
+       "24    1952   43.077\n",
+       "25    1957   45.685\n",
+       "26    1962   48.303\n",
+       "27    1967   51.407\n",
+       "28    1972   54.518\n",
+       "29    1977   58.014\n",
+       "...    ...      ...\n",
+       "1674  1982   49.113\n",
+       "1675  1987   52.922\n",
+       "1676  1992   55.599\n",
+       "1677  1997   58.020\n",
+       "1678  2002   60.308\n",
+       "1679  2007   62.698\n",
+       "1680  1952   42.038\n",
+       "1681  1957   44.077\n",
+       "1682  1962   46.023\n",
+       "1683  1967   47.768\n",
+       "1684  1972   50.107\n",
+       "1685  1977   51.386\n",
+       "1686  1982   51.821\n",
+       "1687  1987   50.821\n",
+       "1688  1992   46.100\n",
+       "1689  1997   40.238\n",
+       "1690  2002   39.193\n",
+       "1691  2007   42.384\n",
+       "1692  1952   48.451\n",
+       "1693  1957   50.469\n",
+       "1694  1962   52.358\n",
+       "1695  1967   53.995\n",
+       "1696  1972   55.635\n",
+       "1697  1977   57.674\n",
+       "1698  1982   60.363\n",
+       "1699  1987   62.351\n",
+       "1700  1992   60.377\n",
+       "1701  1997   46.809\n",
+       "1702  2002   39.989\n",
+       "1703  2007   43.487\n",
+       "\n",
+       "[1704 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#select multiple columns with a list of column names\n",
+    "df[['year','lifeExp']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       28.801\n",
+       "1       30.332\n",
+       "2       31.997\n",
+       "3       34.020\n",
+       "4       36.088\n",
+       "5       38.438\n",
+       "6       39.854\n",
+       "7       40.822\n",
+       "8       41.674\n",
+       "9       41.763\n",
+       "10      42.129\n",
+       "11      43.828\n",
+       "12      55.230\n",
+       "13      59.280\n",
+       "14      64.820\n",
+       "15      66.220\n",
+       "16      67.690\n",
+       "17      68.930\n",
+       "18      70.420\n",
+       "19      72.000\n",
+       "20      71.581\n",
+       "21      72.950\n",
+       "22      75.651\n",
+       "23      76.423\n",
+       "24      43.077\n",
+       "25      45.685\n",
+       "26      48.303\n",
+       "27      51.407\n",
+       "28      54.518\n",
+       "29      58.014\n",
+       "         ...  \n",
+       "1674    49.113\n",
+       "1675    52.922\n",
+       "1676    55.599\n",
+       "1677    58.020\n",
+       "1678    60.308\n",
+       "1679    62.698\n",
+       "1680    42.038\n",
+       "1681    44.077\n",
+       "1682    46.023\n",
+       "1683    47.768\n",
+       "1684    50.107\n",
+       "1685    51.386\n",
+       "1686    51.821\n",
+       "1687    50.821\n",
+       "1688    46.100\n",
+       "1689    40.238\n",
+       "1690    39.193\n",
+       "1691    42.384\n",
+       "1692    48.451\n",
+       "1693    50.469\n",
+       "1694    52.358\n",
+       "1695    53.995\n",
+       "1696    55.635\n",
+       "1697    57.674\n",
+       "1698    60.363\n",
+       "1699    62.351\n",
+       "1700    60.377\n",
+       "1701    46.809\n",
+       "1702    39.989\n",
+       "1703    43.487\n",
+       "Name: lifeExp, dtype: float64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#alternative selection with dot notation won't work if column names have spaces, uncommon characters or leading numbers \n",
+    "df.lifeExp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selecting rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>8425333</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>28.801</td>\n",
+       "      <td>779.445314</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  year      pop continent  lifeExp   gdpPercap\n",
+       "0  Afghanistan  1952  8425333      Asia   28.801  779.445314"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#by index location\n",
+    "df.iloc[[0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>8425333</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>28.801</td>\n",
+       "      <td>779.445314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>14880372</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>38.438</td>\n",
+       "      <td>786.113360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>25268405</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>42.129</td>\n",
+       "      <td>726.734055</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        country  year       pop continent  lifeExp   gdpPercap\n",
+       "0   Afghanistan  1952   8425333      Asia   28.801  779.445314\n",
+       "5   Afghanistan  1977  14880372      Asia   38.438  786.113360\n",
+       "10  Afghanistan  2002  25268405      Asia   42.129  726.734055"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#you can provide a list of index values to select\n",
+    "df.iloc[[0,5,10]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>8425333</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>28.801</td>\n",
+       "      <td>779.445314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>9240934</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>30.332</td>\n",
+       "      <td>820.853030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>10267083</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>31.997</td>\n",
+       "      <td>853.100710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>11537966</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>34.020</td>\n",
+       "      <td>836.197138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>13079460</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>36.088</td>\n",
+       "      <td>739.981106</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  year       pop continent  lifeExp   gdpPercap\n",
+       "0  Afghanistan  1952   8425333      Asia   28.801  779.445314\n",
+       "1  Afghanistan  1957   9240934      Asia   30.332  820.853030\n",
+       "2  Afghanistan  1962  10267083      Asia   31.997  853.100710\n",
+       "3  Afghanistan  1967  11537966      Asia   34.020  836.197138\n",
+       "4  Afghanistan  1972  13079460      Asia   36.088  739.981106"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#or select with the slice notation\n",
+    "df[0:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#select by index label\n",
+    "#would require named index\n",
+    "country_index = df.set_index('country')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>country</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>1952</td>\n",
+       "      <td>14785584</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>68.750</td>\n",
+       "      <td>11367.16112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>1957</td>\n",
+       "      <td>17010154</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>69.960</td>\n",
+       "      <td>12489.95006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>1962</td>\n",
+       "      <td>18985849</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>71.300</td>\n",
+       "      <td>13462.48555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>1967</td>\n",
+       "      <td>20819767</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>72.130</td>\n",
+       "      <td>16076.58803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>1972</td>\n",
+       "      <td>22284500</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>72.880</td>\n",
+       "      <td>18970.57086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>1977</td>\n",
+       "      <td>23796400</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>74.210</td>\n",
+       "      <td>22090.88306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>1982</td>\n",
+       "      <td>25201900</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>75.760</td>\n",
+       "      <td>22898.79214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>1987</td>\n",
+       "      <td>26549700</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>76.860</td>\n",
+       "      <td>26626.51503</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>1992</td>\n",
+       "      <td>28523502</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>77.950</td>\n",
+       "      <td>26342.88426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>1997</td>\n",
+       "      <td>30305843</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>78.610</td>\n",
+       "      <td>28954.92589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>2002</td>\n",
+       "      <td>31902268</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>79.770</td>\n",
+       "      <td>33328.96507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>2007</td>\n",
+       "      <td>33390141</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>80.653</td>\n",
+       "      <td>36319.23501</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         year       pop continent  lifeExp    gdpPercap\n",
+       "country                                                \n",
+       "Canada   1952  14785584  Americas   68.750  11367.16112\n",
+       "Canada   1957  17010154  Americas   69.960  12489.95006\n",
+       "Canada   1962  18985849  Americas   71.300  13462.48555\n",
+       "Canada   1967  20819767  Americas   72.130  16076.58803\n",
+       "Canada   1972  22284500  Americas   72.880  18970.57086\n",
+       "Canada   1977  23796400  Americas   74.210  22090.88306\n",
+       "Canada   1982  25201900  Americas   75.760  22898.79214\n",
+       "Canada   1987  26549700  Americas   76.860  26626.51503\n",
+       "Canada   1992  28523502  Americas   77.950  26342.88426\n",
+       "Canada   1997  30305843  Americas   78.610  28954.92589\n",
+       "Canada   2002  31902268  Americas   79.770  33328.96507\n",
+       "Canada   2007  33390141  Americas   80.653  36319.23501"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "country_index.loc['Canada']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#boolean indexing\n",
+    "large_pop = df[df['pop'] > 300000000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>China</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>5.562635e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>44.00000</td>\n",
+       "      <td>400.448611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>289</th>\n",
+       "      <td>China</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>6.374080e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>50.54896</td>\n",
+       "      <td>575.987001</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>290</th>\n",
+       "      <td>China</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>6.657700e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>44.50136</td>\n",
+       "      <td>487.674018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>291</th>\n",
+       "      <td>China</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>7.545500e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>58.38112</td>\n",
+       "      <td>612.705693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>292</th>\n",
+       "      <td>China</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>8.620300e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>63.11888</td>\n",
+       "      <td>676.900092</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>China</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>9.434550e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>63.96736</td>\n",
+       "      <td>741.237470</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>China</td>\n",
+       "      <td>1982</td>\n",
+       "      <td>1.000281e+09</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>65.52500</td>\n",
+       "      <td>962.421380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>China</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>1.084035e+09</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>67.27400</td>\n",
+       "      <td>1378.904018</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>China</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>1.164970e+09</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>68.69000</td>\n",
+       "      <td>1655.784158</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>China</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>1.230075e+09</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>70.42600</td>\n",
+       "      <td>2289.234136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>298</th>\n",
+       "      <td>China</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>1.280400e+09</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>72.02800</td>\n",
+       "      <td>3119.280896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>China</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>1.318683e+09</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>72.96100</td>\n",
+       "      <td>4959.114854</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>696</th>\n",
+       "      <td>India</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>3.720000e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>37.37300</td>\n",
+       "      <td>546.565749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>697</th>\n",
+       "      <td>India</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>4.090000e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>40.24900</td>\n",
+       "      <td>590.061996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>698</th>\n",
+       "      <td>India</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>4.540000e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>43.60500</td>\n",
+       "      <td>658.347151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>699</th>\n",
+       "      <td>India</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>5.060000e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>47.19300</td>\n",
+       "      <td>700.770611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>700</th>\n",
+       "      <td>India</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>5.670000e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>50.65100</td>\n",
+       "      <td>724.032527</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>701</th>\n",
+       "      <td>India</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>6.340000e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>54.20800</td>\n",
+       "      <td>813.337323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>702</th>\n",
+       "      <td>India</td>\n",
+       "      <td>1982</td>\n",
+       "      <td>7.080000e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>56.59600</td>\n",
+       "      <td>855.723538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>703</th>\n",
+       "      <td>India</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>7.880000e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>58.55300</td>\n",
+       "      <td>976.512676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>704</th>\n",
+       "      <td>India</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>8.720000e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>60.22300</td>\n",
+       "      <td>1164.406809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>705</th>\n",
+       "      <td>India</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>9.590000e+08</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>61.76500</td>\n",
+       "      <td>1458.817442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>706</th>\n",
+       "      <td>India</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>1.034173e+09</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>62.87900</td>\n",
+       "      <td>1746.769454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>707</th>\n",
+       "      <td>India</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>1.110396e+09</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>64.69800</td>\n",
+       "      <td>2452.210407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1619</th>\n",
+       "      <td>United States</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>3.011399e+08</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>78.24200</td>\n",
+       "      <td>42951.653090</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            country  year           pop continent   lifeExp     gdpPercap\n",
+       "288           China  1952  5.562635e+08      Asia  44.00000    400.448611\n",
+       "289           China  1957  6.374080e+08      Asia  50.54896    575.987001\n",
+       "290           China  1962  6.657700e+08      Asia  44.50136    487.674018\n",
+       "291           China  1967  7.545500e+08      Asia  58.38112    612.705693\n",
+       "292           China  1972  8.620300e+08      Asia  63.11888    676.900092\n",
+       "293           China  1977  9.434550e+08      Asia  63.96736    741.237470\n",
+       "294           China  1982  1.000281e+09      Asia  65.52500    962.421380\n",
+       "295           China  1987  1.084035e+09      Asia  67.27400   1378.904018\n",
+       "296           China  1992  1.164970e+09      Asia  68.69000   1655.784158\n",
+       "297           China  1997  1.230075e+09      Asia  70.42600   2289.234136\n",
+       "298           China  2002  1.280400e+09      Asia  72.02800   3119.280896\n",
+       "299           China  2007  1.318683e+09      Asia  72.96100   4959.114854\n",
+       "696           India  1952  3.720000e+08      Asia  37.37300    546.565749\n",
+       "697           India  1957  4.090000e+08      Asia  40.24900    590.061996\n",
+       "698           India  1962  4.540000e+08      Asia  43.60500    658.347151\n",
+       "699           India  1967  5.060000e+08      Asia  47.19300    700.770611\n",
+       "700           India  1972  5.670000e+08      Asia  50.65100    724.032527\n",
+       "701           India  1977  6.340000e+08      Asia  54.20800    813.337323\n",
+       "702           India  1982  7.080000e+08      Asia  56.59600    855.723538\n",
+       "703           India  1987  7.880000e+08      Asia  58.55300    976.512676\n",
+       "704           India  1992  8.720000e+08      Asia  60.22300   1164.406809\n",
+       "705           India  1997  9.590000e+08      Asia  61.76500   1458.817442\n",
+       "706           India  2002  1.034173e+09      Asia  62.87900   1746.769454\n",
+       "707           India  2007  1.110396e+09      Asia  64.69800   2452.210407\n",
+       "1619  United States  2007  3.011399e+08  Americas  78.24200  42951.653090"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "large_pop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['China', 'India', 'United States'], dtype=object)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "large_pop['country'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also chain together multiple criteria for boolean indexing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "multi_criteria = df[(df['country']=='Canada') & (df['year'] > 1990)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>28523502</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>77.950</td>\n",
+       "      <td>26342.88426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>30305843</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>78.610</td>\n",
+       "      <td>28954.92589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>31902268</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>79.770</td>\n",
+       "      <td>33328.96507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>33390141</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>80.653</td>\n",
+       "      <td>36319.23501</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    country  year       pop continent  lifeExp    gdpPercap\n",
+       "248  Canada  1992  28523502  Americas   77.950  26342.88426\n",
+       "249  Canada  1997  30305843  Americas   78.610  28954.92589\n",
+       "250  Canada  2002  31902268  Americas   79.770  33328.96507\n",
+       "251  Canada  2007  33390141  Americas   80.653  36319.23501"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_criteria"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q:\n",
+    "How many unique countries are there in our dataframe? Years?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise\n",
+    "Write a function 'print_stats()' that will print a given country's life expectancy, population and gdp per capita in a given year. (note data is available only for every 5 years between 1952 and 2007)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def print_stats(df,country,year):\n",
+    "    \"\"\" Prints the life expectancy, gdp per capita and population\n",
+    "    of country in year. \"\"\"\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print_stats(df, 'Canada', 2007)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Groupby\n",
+    "We can use the groupby method to split up the data according to repeated values in each column. For example, group the data by continent. This is helpful if we want to repeat an analysis on each group of data from a continent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.groupby.DataFrameGroupBy object at 0x7f7688f1db38>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "continents = df.groupby('continent')\n",
+    "continents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(continents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>continent</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Africa</th>\n",
+       "      <td>Algeria</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>9279525</td>\n",
+       "      <td>43.077</td>\n",
+       "      <td>2449.008185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Americas</th>\n",
+       "      <td>Argentina</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>17876956</td>\n",
+       "      <td>62.485</td>\n",
+       "      <td>5911.315053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Asia</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>8425333</td>\n",
+       "      <td>28.801</td>\n",
+       "      <td>779.445314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Europe</th>\n",
+       "      <td>Albania</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>1282697</td>\n",
+       "      <td>55.230</td>\n",
+       "      <td>1601.056136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oceania</th>\n",
+       "      <td>Australia</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>8691212</td>\n",
+       "      <td>69.120</td>\n",
+       "      <td>10039.595640</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               country  year       pop  lifeExp     gdpPercap\n",
+       "continent                                                    \n",
+       "Africa         Algeria  1952   9279525   43.077   2449.008185\n",
+       "Americas     Argentina  1952  17876956   62.485   5911.315053\n",
+       "Asia       Afghanistan  1952   8425333   28.801    779.445314\n",
+       "Europe         Albania  1952   1282697   55.230   1601.056136\n",
+       "Oceania      Australia  1952   8691212   69.120  10039.595640"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#helpful way to visualize the groupby object: gives first row of each group\n",
+    "continents.first()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q:\n",
+    "List the names of the continents and the number of data points in each."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q:\n",
+    "How many unique countries are there grouped together in the Americas continent?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can use an aggregate function to get the mean life expectancy in the different continents"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "continent\n",
+       "Africa      48.865330\n",
+       "Americas    64.658737\n",
+       "Asia        60.064903\n",
+       "Europe      71.903686\n",
+       "Oceania     74.326208\n",
+       "Name: lifeExp, dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "continents.lifeExp.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The previous cell showed mean life expectancy values aggregated over all the years.\n",
+    "\n",
+    "Alternatively, we can groupby multiple columns and use an aggregate function to get the mean life expectancy/population/gdpPercap in a specific continent in a specific year of interest:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>pop</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>continent</th>\n",
+       "      <th>year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"12\" valign=\"top\">Africa</th>\n",
+       "      <th>1952</th>\n",
+       "      <td>4.570010e+06</td>\n",
+       "      <td>39.135500</td>\n",
+       "      <td>1252.572466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957</th>\n",
+       "      <td>5.093033e+06</td>\n",
+       "      <td>41.266346</td>\n",
+       "      <td>1385.236062</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1962</th>\n",
+       "      <td>5.702247e+06</td>\n",
+       "      <td>43.319442</td>\n",
+       "      <td>1598.078825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1967</th>\n",
+       "      <td>6.447875e+06</td>\n",
+       "      <td>45.334538</td>\n",
+       "      <td>2050.363801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1972</th>\n",
+       "      <td>7.305376e+06</td>\n",
+       "      <td>47.450942</td>\n",
+       "      <td>2339.615674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1977</th>\n",
+       "      <td>8.328097e+06</td>\n",
+       "      <td>49.580423</td>\n",
+       "      <td>2585.938508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1982</th>\n",
+       "      <td>9.602857e+06</td>\n",
+       "      <td>51.592865</td>\n",
+       "      <td>2481.592960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1987</th>\n",
+       "      <td>1.105450e+07</td>\n",
+       "      <td>53.344788</td>\n",
+       "      <td>2282.668991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1992</th>\n",
+       "      <td>1.267464e+07</td>\n",
+       "      <td>53.629577</td>\n",
+       "      <td>2281.810333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <td>1.430448e+07</td>\n",
+       "      <td>53.598269</td>\n",
+       "      <td>2378.759555</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002</th>\n",
+       "      <td>1.603315e+07</td>\n",
+       "      <td>53.325231</td>\n",
+       "      <td>2599.385159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2007</th>\n",
+       "      <td>1.787576e+07</td>\n",
+       "      <td>54.806038</td>\n",
+       "      <td>3089.032605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"12\" valign=\"top\">Americas</th>\n",
+       "      <th>1952</th>\n",
+       "      <td>1.380610e+07</td>\n",
+       "      <td>53.279840</td>\n",
+       "      <td>4079.062552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957</th>\n",
+       "      <td>1.547816e+07</td>\n",
+       "      <td>55.960280</td>\n",
+       "      <td>4616.043733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1962</th>\n",
+       "      <td>1.733081e+07</td>\n",
+       "      <td>58.398760</td>\n",
+       "      <td>4901.541870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1967</th>\n",
+       "      <td>1.922986e+07</td>\n",
+       "      <td>60.410920</td>\n",
+       "      <td>5668.253496</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1972</th>\n",
+       "      <td>2.117537e+07</td>\n",
+       "      <td>62.394920</td>\n",
+       "      <td>6491.334139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1977</th>\n",
+       "      <td>2.312271e+07</td>\n",
+       "      <td>64.391560</td>\n",
+       "      <td>7352.007126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1982</th>\n",
+       "      <td>2.521164e+07</td>\n",
+       "      <td>66.228840</td>\n",
+       "      <td>7506.737088</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1987</th>\n",
+       "      <td>2.731016e+07</td>\n",
+       "      <td>68.090720</td>\n",
+       "      <td>7793.400261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1992</th>\n",
+       "      <td>2.957096e+07</td>\n",
+       "      <td>69.568360</td>\n",
+       "      <td>8044.934406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <td>3.187602e+07</td>\n",
+       "      <td>71.150480</td>\n",
+       "      <td>8889.300863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002</th>\n",
+       "      <td>3.399091e+07</td>\n",
+       "      <td>72.422040</td>\n",
+       "      <td>9287.677107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2007</th>\n",
+       "      <td>3.595485e+07</td>\n",
+       "      <td>73.608120</td>\n",
+       "      <td>11003.031625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"12\" valign=\"top\">Asia</th>\n",
+       "      <th>1952</th>\n",
+       "      <td>4.228356e+07</td>\n",
+       "      <td>46.314394</td>\n",
+       "      <td>5195.484004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957</th>\n",
+       "      <td>4.735699e+07</td>\n",
+       "      <td>49.318544</td>\n",
+       "      <td>5787.732940</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1962</th>\n",
+       "      <td>5.140476e+07</td>\n",
+       "      <td>51.563223</td>\n",
+       "      <td>5729.369625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1967</th>\n",
+       "      <td>5.774736e+07</td>\n",
+       "      <td>54.663640</td>\n",
+       "      <td>5971.173374</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1972</th>\n",
+       "      <td>6.518098e+07</td>\n",
+       "      <td>57.319269</td>\n",
+       "      <td>8187.468699</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1977</th>\n",
+       "      <td>7.225799e+07</td>\n",
+       "      <td>59.610556</td>\n",
+       "      <td>7791.314020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1982</th>\n",
+       "      <td>7.909502e+07</td>\n",
+       "      <td>62.617939</td>\n",
+       "      <td>7434.135157</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1987</th>\n",
+       "      <td>8.700669e+07</td>\n",
+       "      <td>64.851182</td>\n",
+       "      <td>7608.226508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1992</th>\n",
+       "      <td>9.494825e+07</td>\n",
+       "      <td>66.537212</td>\n",
+       "      <td>8639.690248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <td>1.025238e+08</td>\n",
+       "      <td>68.020515</td>\n",
+       "      <td>9834.093295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002</th>\n",
+       "      <td>1.091455e+08</td>\n",
+       "      <td>69.233879</td>\n",
+       "      <td>10174.090397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2007</th>\n",
+       "      <td>1.155138e+08</td>\n",
+       "      <td>70.728485</td>\n",
+       "      <td>12473.026870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"12\" valign=\"top\">Europe</th>\n",
+       "      <th>1952</th>\n",
+       "      <td>1.393736e+07</td>\n",
+       "      <td>64.408500</td>\n",
+       "      <td>5661.057435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957</th>\n",
+       "      <td>1.459635e+07</td>\n",
+       "      <td>66.703067</td>\n",
+       "      <td>6963.012816</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1962</th>\n",
+       "      <td>1.534517e+07</td>\n",
+       "      <td>68.539233</td>\n",
+       "      <td>8365.486814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1967</th>\n",
+       "      <td>1.603930e+07</td>\n",
+       "      <td>69.737600</td>\n",
+       "      <td>10143.823757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1972</th>\n",
+       "      <td>1.668784e+07</td>\n",
+       "      <td>70.775033</td>\n",
+       "      <td>12479.575246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1977</th>\n",
+       "      <td>1.723882e+07</td>\n",
+       "      <td>71.937767</td>\n",
+       "      <td>14283.979110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1982</th>\n",
+       "      <td>1.770890e+07</td>\n",
+       "      <td>72.806400</td>\n",
+       "      <td>15617.896551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1987</th>\n",
+       "      <td>1.810314e+07</td>\n",
+       "      <td>73.642167</td>\n",
+       "      <td>17214.310727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1992</th>\n",
+       "      <td>1.860476e+07</td>\n",
+       "      <td>74.440100</td>\n",
+       "      <td>17061.568084</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <td>1.896480e+07</td>\n",
+       "      <td>75.505167</td>\n",
+       "      <td>19076.781802</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002</th>\n",
+       "      <td>1.927413e+07</td>\n",
+       "      <td>76.700600</td>\n",
+       "      <td>21711.732422</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2007</th>\n",
+       "      <td>1.953662e+07</td>\n",
+       "      <td>77.648600</td>\n",
+       "      <td>25054.481636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"12\" valign=\"top\">Oceania</th>\n",
+       "      <th>1952</th>\n",
+       "      <td>5.343003e+06</td>\n",
+       "      <td>69.255000</td>\n",
+       "      <td>10298.085650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1957</th>\n",
+       "      <td>5.970988e+06</td>\n",
+       "      <td>70.295000</td>\n",
+       "      <td>11598.522455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1962</th>\n",
+       "      <td>6.641759e+06</td>\n",
+       "      <td>71.085000</td>\n",
+       "      <td>12696.452430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1967</th>\n",
+       "      <td>7.300207e+06</td>\n",
+       "      <td>71.310000</td>\n",
+       "      <td>14495.021790</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1972</th>\n",
+       "      <td>8.053050e+06</td>\n",
+       "      <td>71.910000</td>\n",
+       "      <td>16417.333380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1977</th>\n",
+       "      <td>8.619500e+06</td>\n",
+       "      <td>72.855000</td>\n",
+       "      <td>17283.957605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1982</th>\n",
+       "      <td>9.197425e+06</td>\n",
+       "      <td>74.290000</td>\n",
+       "      <td>18554.709840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1987</th>\n",
+       "      <td>9.787208e+06</td>\n",
+       "      <td>75.320000</td>\n",
+       "      <td>20448.040160</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1992</th>\n",
+       "      <td>1.045983e+07</td>\n",
+       "      <td>76.945000</td>\n",
+       "      <td>20894.045885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1997</th>\n",
+       "      <td>1.112072e+07</td>\n",
+       "      <td>78.190000</td>\n",
+       "      <td>24024.175170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2002</th>\n",
+       "      <td>1.172741e+07</td>\n",
+       "      <td>79.740000</td>\n",
+       "      <td>26938.778040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2007</th>\n",
+       "      <td>1.227497e+07</td>\n",
+       "      <td>80.719500</td>\n",
+       "      <td>29810.188275</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         pop    lifeExp     gdpPercap\n",
+       "continent year                                       \n",
+       "Africa    1952  4.570010e+06  39.135500   1252.572466\n",
+       "          1957  5.093033e+06  41.266346   1385.236062\n",
+       "          1962  5.702247e+06  43.319442   1598.078825\n",
+       "          1967  6.447875e+06  45.334538   2050.363801\n",
+       "          1972  7.305376e+06  47.450942   2339.615674\n",
+       "          1977  8.328097e+06  49.580423   2585.938508\n",
+       "          1982  9.602857e+06  51.592865   2481.592960\n",
+       "          1987  1.105450e+07  53.344788   2282.668991\n",
+       "          1992  1.267464e+07  53.629577   2281.810333\n",
+       "          1997  1.430448e+07  53.598269   2378.759555\n",
+       "          2002  1.603315e+07  53.325231   2599.385159\n",
+       "          2007  1.787576e+07  54.806038   3089.032605\n",
+       "Americas  1952  1.380610e+07  53.279840   4079.062552\n",
+       "          1957  1.547816e+07  55.960280   4616.043733\n",
+       "          1962  1.733081e+07  58.398760   4901.541870\n",
+       "          1967  1.922986e+07  60.410920   5668.253496\n",
+       "          1972  2.117537e+07  62.394920   6491.334139\n",
+       "          1977  2.312271e+07  64.391560   7352.007126\n",
+       "          1982  2.521164e+07  66.228840   7506.737088\n",
+       "          1987  2.731016e+07  68.090720   7793.400261\n",
+       "          1992  2.957096e+07  69.568360   8044.934406\n",
+       "          1997  3.187602e+07  71.150480   8889.300863\n",
+       "          2002  3.399091e+07  72.422040   9287.677107\n",
+       "          2007  3.595485e+07  73.608120  11003.031625\n",
+       "Asia      1952  4.228356e+07  46.314394   5195.484004\n",
+       "          1957  4.735699e+07  49.318544   5787.732940\n",
+       "          1962  5.140476e+07  51.563223   5729.369625\n",
+       "          1967  5.774736e+07  54.663640   5971.173374\n",
+       "          1972  6.518098e+07  57.319269   8187.468699\n",
+       "          1977  7.225799e+07  59.610556   7791.314020\n",
+       "          1982  7.909502e+07  62.617939   7434.135157\n",
+       "          1987  8.700669e+07  64.851182   7608.226508\n",
+       "          1992  9.494825e+07  66.537212   8639.690248\n",
+       "          1997  1.025238e+08  68.020515   9834.093295\n",
+       "          2002  1.091455e+08  69.233879  10174.090397\n",
+       "          2007  1.155138e+08  70.728485  12473.026870\n",
+       "Europe    1952  1.393736e+07  64.408500   5661.057435\n",
+       "          1957  1.459635e+07  66.703067   6963.012816\n",
+       "          1962  1.534517e+07  68.539233   8365.486814\n",
+       "          1967  1.603930e+07  69.737600  10143.823757\n",
+       "          1972  1.668784e+07  70.775033  12479.575246\n",
+       "          1977  1.723882e+07  71.937767  14283.979110\n",
+       "          1982  1.770890e+07  72.806400  15617.896551\n",
+       "          1987  1.810314e+07  73.642167  17214.310727\n",
+       "          1992  1.860476e+07  74.440100  17061.568084\n",
+       "          1997  1.896480e+07  75.505167  19076.781802\n",
+       "          2002  1.927413e+07  76.700600  21711.732422\n",
+       "          2007  1.953662e+07  77.648600  25054.481636\n",
+       "Oceania   1952  5.343003e+06  69.255000  10298.085650\n",
+       "          1957  5.970988e+06  70.295000  11598.522455\n",
+       "          1962  6.641759e+06  71.085000  12696.452430\n",
+       "          1967  7.300207e+06  71.310000  14495.021790\n",
+       "          1972  8.053050e+06  71.910000  16417.333380\n",
+       "          1977  8.619500e+06  72.855000  17283.957605\n",
+       "          1982  9.197425e+06  74.290000  18554.709840\n",
+       "          1987  9.787208e+06  75.320000  20448.040160\n",
+       "          1992  1.045983e+07  76.945000  20894.045885\n",
+       "          1997  1.112072e+07  78.190000  24024.175170\n",
+       "          2002  1.172741e+07  79.740000  26938.778040\n",
+       "          2007  1.227497e+07  80.719500  29810.188275"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(['continent', 'year']).agg(np.mean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also retrieve a particular group with the get_group() command."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>624.00000</td>\n",
+       "      <td>6.240000e+02</td>\n",
+       "      <td>624.00000</td>\n",
+       "      <td>624.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1979.50000</td>\n",
+       "      <td>9.916003e+06</td>\n",
+       "      <td>48.86533</td>\n",
+       "      <td>2193.754578</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>17.27411</td>\n",
+       "      <td>1.549092e+07</td>\n",
+       "      <td>9.15021</td>\n",
+       "      <td>2827.929863</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1952.00000</td>\n",
+       "      <td>6.001100e+04</td>\n",
+       "      <td>23.59900</td>\n",
+       "      <td>241.165877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>1965.75000</td>\n",
+       "      <td>1.342075e+06</td>\n",
+       "      <td>42.37250</td>\n",
+       "      <td>761.247010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1979.50000</td>\n",
+       "      <td>4.579311e+06</td>\n",
+       "      <td>47.79200</td>\n",
+       "      <td>1192.138217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1993.25000</td>\n",
+       "      <td>1.080149e+07</td>\n",
+       "      <td>54.41150</td>\n",
+       "      <td>2377.417422</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2007.00000</td>\n",
+       "      <td>1.350312e+08</td>\n",
+       "      <td>76.44200</td>\n",
+       "      <td>21951.211760</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             year           pop    lifeExp     gdpPercap\n",
+       "count   624.00000  6.240000e+02  624.00000    624.000000\n",
+       "mean   1979.50000  9.916003e+06   48.86533   2193.754578\n",
+       "std      17.27411  1.549092e+07    9.15021   2827.929863\n",
+       "min    1952.00000  6.001100e+04   23.59900    241.165877\n",
+       "25%    1965.75000  1.342075e+06   42.37250    761.247010\n",
+       "50%    1979.50000  4.579311e+06   47.79200   1192.138217\n",
+       "75%    1993.25000  1.080149e+07   54.41150   2377.417422\n",
+       "max    2007.00000  1.350312e+08   76.44200  21951.211760"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "continents.get_group('Africa').describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Q:\n",
+    "What is the maximum life expectancy for a country in Asia?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What country is this? When was the measurement taken? We can figure this out in a few different ways:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "803"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "continents.get_group('Asia').lifeExp.idxmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "803"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#idxmax convenience function will return the index with max value\n",
+    "df[df['continent']=='Asia']['lifeExp'].idxmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "country            Japan\n",
+       "year                2007\n",
+       "pop          1.27468e+08\n",
+       "continent           Asia\n",
+       "lifeExp           82.603\n",
+       "gdpPercap        31656.1\n",
+       "Name: 803, dtype: object"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[803]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How can we rank each country based on their lifeExp?\n",
+    "\n",
+    "Let's create a new column 'lifeExp_rank' that creates an ordered ranking based on the longest life expectancy. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sorted_by_lifeExp = df.sort_values('lifeExp', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sorted_by_lifeExp['lifeExp_rank'] = np.arange(len(sorted_by_lifeExp)) + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "      <th>lifeExp_rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>803</th>\n",
+       "      <td>Japan</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>127467972</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>82.603</td>\n",
+       "      <td>31656.06806</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>671</th>\n",
+       "      <td>Hong Kong, China</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>6980412</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>82.208</td>\n",
+       "      <td>39724.97867</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>802</th>\n",
+       "      <td>Japan</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>127065841</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>82.000</td>\n",
+       "      <td>28604.59190</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>695</th>\n",
+       "      <td>Iceland</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>301931</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>81.757</td>\n",
+       "      <td>36180.78919</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1487</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>7554661</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>81.701</td>\n",
+       "      <td>37506.41907</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               country  year        pop continent  lifeExp    gdpPercap  \\\n",
+       "803              Japan  2007  127467972      Asia   82.603  31656.06806   \n",
+       "671   Hong Kong, China  2007    6980412      Asia   82.208  39724.97867   \n",
+       "802              Japan  2002  127065841      Asia   82.000  28604.59190   \n",
+       "695            Iceland  2007     301931    Europe   81.757  36180.78919   \n",
+       "1487       Switzerland  2007    7554661    Europe   81.701  37506.41907   \n",
+       "\n",
+       "      lifeExp_rank  \n",
+       "803              1  \n",
+       "671              2  \n",
+       "802              3  \n",
+       "695              4  \n",
+       "1487             5  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#lists all rows in order of lifeExp\n",
+    "sorted_by_lifeExp.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### split,apply and combine: the power of groupby\n",
+    "What if we want to rank each country by max life expectancy for each year that data was collected?\n",
+    "\n",
+    "Applying a function on grouped selections can simplify this process:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def ranker(df):\n",
+    "    \"\"\"Assigns a rank to each country based on lifeExp, with 1 having the highest lifeExp.\n",
+    "    Assumes the data is DESC sorted by lifeExp.\"\"\"\n",
+    "    df['lifeExp_rank'] = np.arange(len(df)) + 1\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#apply the ranking function on a per year basis:\n",
+    "sorted_by_lifeExp = sorted_by_lifeExp.groupby('year').apply(ranker)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now subset my new dataframe by year to view the lifeExp ranks for each year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "      <th>lifeExp_rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>802</th>\n",
+       "      <td>Japan</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>127065841</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>82.000</td>\n",
+       "      <td>28604.59190</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>670</th>\n",
+       "      <td>Hong Kong, China</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>6762476</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>81.495</td>\n",
+       "      <td>30209.01516</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1486</th>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>7361757</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>80.620</td>\n",
+       "      <td>34480.95771</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>Iceland</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>288030</td>\n",
+       "      <td>Europe</td>\n",
+       "      <td>80.500</td>\n",
+       "      <td>31163.20196</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>Australia</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>19546792</td>\n",
+       "      <td>Oceania</td>\n",
+       "      <td>80.370</td>\n",
+       "      <td>30687.75473</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               country  year        pop continent  lifeExp    gdpPercap  \\\n",
+       "802              Japan  2002  127065841      Asia   82.000  28604.59190   \n",
+       "670   Hong Kong, China  2002    6762476      Asia   81.495  30209.01516   \n",
+       "1486       Switzerland  2002    7361757    Europe   80.620  34480.95771   \n",
+       "694            Iceland  2002     288030    Europe   80.500  31163.20196   \n",
+       "70           Australia  2002   19546792   Oceania   80.370  30687.75473   \n",
+       "\n",
+       "      lifeExp_rank  \n",
+       "802              1  \n",
+       "670              2  \n",
+       "1486             3  \n",
+       "694              4  \n",
+       "70               5  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_by_lifeExp[sorted_by_lifeExp.year == 2002].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also subset by country=='Canada' to see how Canada's ranking has changed over the years:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "      <th>lifeExp_rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>33390141</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>80.653</td>\n",
+       "      <td>36319.23501</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>2002</td>\n",
+       "      <td>31902268</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>79.770</td>\n",
+       "      <td>33328.96507</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>30305843</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>78.610</td>\n",
+       "      <td>28954.92589</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>1992</td>\n",
+       "      <td>28523502</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>77.950</td>\n",
+       "      <td>26342.88426</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>1987</td>\n",
+       "      <td>26549700</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>76.860</td>\n",
+       "      <td>26626.51503</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>1982</td>\n",
+       "      <td>25201900</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>75.760</td>\n",
+       "      <td>22898.79214</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>23796400</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>74.210</td>\n",
+       "      <td>22090.88306</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>22284500</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>72.880</td>\n",
+       "      <td>18970.57086</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>20819767</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>72.130</td>\n",
+       "      <td>16076.58803</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>18985849</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>71.300</td>\n",
+       "      <td>13462.48555</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>17010154</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>69.960</td>\n",
+       "      <td>12489.95006</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>Canada</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>14785584</td>\n",
+       "      <td>Americas</td>\n",
+       "      <td>68.750</td>\n",
+       "      <td>11367.16112</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    country  year       pop continent  lifeExp    gdpPercap  lifeExp_rank\n",
+       "251  Canada  2007  33390141  Americas   80.653  36319.23501            10\n",
+       "250  Canada  2002  31902268  Americas   79.770  33328.96507             9\n",
+       "249  Canada  1997  30305843  Americas   78.610  28954.92589            10\n",
+       "248  Canada  1992  28523502  Americas   77.950  26342.88426             5\n",
+       "247  Canada  1987  26549700  Americas   76.860  26626.51503             6\n",
+       "246  Canada  1982  25201900  Americas   75.760  22898.79214             8\n",
+       "245  Canada  1977  23796400  Americas   74.210  22090.88306             9\n",
+       "244  Canada  1972  22284500  Americas   72.880  18970.57086             9\n",
+       "243  Canada  1967  20819767  Americas   72.130  16076.58803             7\n",
+       "242  Canada  1962  18985849  Americas   71.300  13462.48555             7\n",
+       "241  Canada  1957  17010154  Americas   69.960  12489.95006            10\n",
+       "240  Canada  1952  14785584  Americas   68.750  11367.16112            10"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_by_lifeExp[(sorted_by_lifeExp['country']=='Canada')]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure you use the following %magic command to allow for inline plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can specify the type of plot with the kind argument. Also, choose the independent and dependent variables with x and y arguments. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Plot year vs life expectancy in a scatter plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7688e79320>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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vDBKXLaolxoMSzG7C3EHLly9X6TytLJEMqKq1LnGpYvJZToM+D63qJillnkR1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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f7688ed3d68>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(x='year',y='lifeExp',kind='scatter')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Plot gdp per capita vs life expectancy in a scatter plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f76867dd128>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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P00yzd68kldrH0ND0rWtRr68yMxPgOGscOZJgfHzsR9ZX6HoMjebHR9eBPAFs\nxT5SqWO0Wn9FqzVMOn2YIOgyN/cm3e4wzeYqUmaQMk0UjSPlW8AksAtlDayhtpSPAUlgjjC8gZro\nQ1Q8YxJlMTSBTv95eZSQvIuyZgqAg5Q5pFwCLmGaE8AgYVhByiuoOMhK//lPAxdIpXqY5gaGcQzb\nnmFz8z0gztDQEKWSw5Ejx0gmLdrtDq1WwJ494HkdPvOZl7hyZZ1Ox7lVzBeGHcLQoFSKcezY0fsS\nAu2O0mg+uWgB2UG2Yh+xmEkqNUq9fp1OZwDXdalUGvR6V/pZVz6GkSAIrqKEYgHlilpH7QT8aVRg\n+yIw0X/1FZQLaz8qlrElKGdQlkUB5aY6gtqGPg+MAd8G3sM0P41ljRJFN4iiUVTH3nf7r1VAWS8R\nhhFRLH4B0xyk3b5GNrufMMwRi23y8z//SwghCcMlPv3pIuvrSxw9alCrSYQQHDmyi4sXl6hWk7iu\nTxRdIZsd21a/odFoHl+0gOwgW8WC3W6PRiNFoVCi2ayxublCEGQIgmHCMEL9Gw70f84DF4C/7b/K\nQZQlofpWgYMSCBvYi8qmkkC6f34FFctooWIZAUIMIeVV4Pv9c34WIQoEwQpCDCKlgWUlCMMBhBjp\nP2cZw6hj28/Q67mUShU6nTSjo0m63TqHDh0iikKy2RzVahrDsBkczDM5mWf/ftVaxHEyTE2laTTW\nMc0qzz9/hKmpKS0eGs0TghaQHSSRiBNFDVzXxHUljUbE5uZVHCeN71cJwzLKLXWgf9tEBc4XUdbH\nXiCFim3sRgnCBZSorKKsk88B11HWyRgwiLIguigR6iLlMCpgfgzIYZoDJBJP4fsbBME54vFBVEX6\nSD9FN04iYWKaJgMDJtlsgaGhFTxvgFIpgZRdkskEnhcAYBgJarUqQ0POrZTZ7am0UxSLz9xTOO4s\nrtzaKdE0zYf+v9BoNA8fLSA7yFZldL0ep17foNNJkEy+QLe7SLv9LeBl4FMosdjkdhptDhUY3+i/\n0gRKSLooV1WEEpC1/v0EyqX1D6iAOUC5/zOFEpQikMMwppHSxPebCGEg5TBBsI5tT2FZNdLpPInE\nOJ4HUeRgmgkqlVk8T2LbWfL5HlNTKTzvfdrtKUzTpVq9zuRkxMmTz21rLfKjYhcfLq6MogbJ5DIn\nT+69KytLo9F88tACsoOYpsmJEzNcvPjXtNtzCDFCu72J45xDyoMoi0KgMqXmUK6pFHAZFRBPomIi\ndaCGimOpklkZAAAgAElEQVQk++e2UJaIi3JjVVGB9C7KpbUCvIBK872OsmD2EkWrQJ0gyKMW+l2i\nKIPvG0CDbhd6vZvEYlOMjz9PLLaJZR1l165hOp0zHDr0BYKgh2W9xXPPlWg260xOhvziL34Oy7r/\nj9OdxZV37iao9x7XaB4ftIDsII7jcPbsHInENLHYAu32LK3WHL6vUmeVKFRRwhDvP+sqyho5gbI8\netyOa/wjyhVlAvtQcZHN/vNywE2UhQJKlKoo62Rv/+cQKtBeJIreJ4ospNzEMGwsK8C2s/h+gijK\nEYs16HbXiCKTQqFIJpNleHgvlcoPSCZz5PNFarVFSqUYJ0+e/LHEA24nGNwpHqD3HtdoHid2VECE\nEAeAP0ctiQXKv/K/AX/aPz6NWk5/TUp5774Vjyl3rrBTqXU2NgJqtQy93s+iJvYIZYFcRF2eaZT1\nUAVeQVkaW+m3PZQ7a6ti/Eso8eih6kUilMXxPMoF1gGOowLu69yuRK+i0n7b/cr2FeAmhjGNEGlS\nqQMEQRnPM0kkkhhGjVgs4siRveRyEZmMzeHDgwwPD7G+vsyRI9x3Ou6H2UowuBd673GN5vFgRwVE\nqtSfEwBCCAPl2P+PwG8D35FS/oEQ4reA3+kfe2LYWmEPDCR59dUPqNWGSKcPEIYFfH8BZVF4qIB5\nHNXgcKvdiOR2JlUX1b9KoEQlD8yiYhrF/uMdVIA8gRKJtf6xrdTeZYQYQ8ozqH95gii6CSwQi32R\neFwFwrPZUUxzuF8oOMzMTIpsNsHISAnDiFOvv8/w8AGKxSJBUGN8PP/AbqatBIN7oVuvazSPB4/S\nyfwlYFZKuQh8FfhG//g3gH/yCMfxSNhaYS8vL7O2Jkmnp/u7/IWoyf06KvNqNypO0UH9OyZRYuHd\ncayKsiQWUK4pA+WySqCyrlIoEfJRBYi7UNlaSyjBKCPl25jmJMqKiZFIpInHT2AYc6TTA0AO1/Vx\nXYcw3MSyKszM7CceD3GcKkJsWUx39qMqPvD1UQkGDo7T3Hb8Ybz240oYhlQqFZaWlqlUKoRh+HEP\nSaP5oTzKGMgvA/+2f39USrkOIKVcE6r44Ilia4XdbrtAHNPME49LpEyg4hhLqMk/hhIKgQp6C5Sb\naRdqsr+GskY2UJbF06gMrDJKRPaiLJU6yiLZhXJtTaKysgQwg+qLVca2XSwrTyIBQdDCMA4QixWI\nxzNkMntoteawrJB8vo5te2QyJp3OGaBIsTjA6uo1pqYSP3E/Kt3rajs6I03zOPJIBEQIYQNfAX6r\nf+jDDa4+suHV17/+9Vv3T506xalTpx7y6HaGrRReIQzicQjDKo6zThgKlLvq89zOkGqgPH1VVEHg\nblTgfCvr6k1UVtZW76txlGAsoayPOqoC/cuowPwSSkzy/Z8xkslxYrHjuO51bPsNDhwYIwwLVKsC\n110iDAWu20EIF9suMDiYBWpMTrb59KdfwTAMVlev8cILQ+zff7uS/Cep49Ct1xU6I03zk3L69GlO\nnz79yN/3kTRTFEJ8BfhNKeWX+79fAk5JKdeFECXgH6SUh+/xvMe6maLjOPz1X7/BH/7haywujlCv\nJ+j1zqJiE0e5PfkL1MQfQ7mqtirTfZTAXEXFOCKUwLyMsmK+h7JeQLmXYv3nTGDbOXz/fWAK0/Qp\nFkvE42NksxGJxAZjYy2iaBQpBWG4Qaslcd0I2x6m05nj+PEpxsYKTE4mePHF43Q6DlLe3DaZ3b1q\nvm1B6FXz/VOpVHj77QYjI3vvemxjY5bnn8/rjDTNj8WT1kzxV4A/u+P3vwR+Dfh94FeBbz2icTxS\nYrEYs7PrzMwcY3PzMtVqGZWaO4HqeDuBSsdtorKrDqNcT4n+sRSqunwI5bYKUdZHDiUYKZTx5qAC\n7WW2MrKCYBYYwbIsJiYGyeXWmJk5zNjYHtbWrjAy8i65nEm3u0GpdIwbN1xiMRvDqJBOT5DPe2Sz\nQzSbDWZnz1EqmdtcS3rV/PDQGWmax5UdFxAhRAoVQP/v7jj8+8BfCCF+HdX86Ws7PY6Pg+vXr3Pl\nSgzLOs7u3ceoVt/ob8UaopocbqKysK6jrBCrf9xCWRKXUPGMNEpYrvWfcxkVDymgXFpplIAksSwL\n06wghCCREBw79hKm6ZDNTtNun6VcrtPp3ODYsSGkrHLgwGdpNrsIYTE0VOqn9M5z/PhRHKfO6mqL\nI0fsu9J1dR3Hw0NnpGkeV3ZcQKSUHVTq0J3HqihReaKZn1+kXgffL1OpeAixh1isjOe1UQHygyhL\nRKBiIJdQ+3MUUS6qBZT1MY+6hEOooPlm//FpVNC8BTQxDItMposQPXbvHiMeNzDNKrt3lxgZyVGr\nWaTTa+RyDX75l0+RSqU4c+Y6S0tNGg0f0+wyMADHjh3AsiwKhSE8r3HPdF29an54bMXLHmT3Rc3j\nwZPa801Xou8QjuNw5coaq6suzeYA5XIdx1nG9yVKBK6ihGMBVV8ZR4nIEkoQkigX1Tso6+MCKptq\nFmWdZFHpvC5C2Nh2k+FhG1glDG3S6d0kEnU6nb8hDD9FqzVFt7vI2Ngy/+JffJVSqYTjOIAgnU4Q\ni9Xo9WIIcX8TmF41Pzx0RtqTzZOcYacFZAfYig8UiydoNL5NrTaD6+bx/SlUxXgDJRgN4DxKKKZR\nab2T3G7f3uZ2i5MKyjoRKIHZaktikk4bzMwcpFDoEkUJoMzP/uxBBgaKCPEzVCr/yORkD9+P8ZWv\n/BKlUunWGE1zL4cO5Zia6nDx4hKNRsCbb77F0aNTpNOdj5zA9Kr54aIz0p5MnvRYoRaQHaBWq+E4\nSVZXN8nlxmi1rhBFg6jgeB3lkkqjAt6fRVkhZZSAbG1HG0NZG3FUnOR4/7xBIIMQFUwzoFDIMTAw\nTSLh0G6X2bOny8mTz1EsDt/aCdD3D1EsGuTzBiMjI7fGeGcMI5VK8eyz+2g2Gywvt9i3L2L//o/+\ncOtV88NH77745PGkxwq1gOwAvZ5LpxNSrQaMjh7G88q0WisoayOBuuwFVKruKsqy2OqDNYJqlvhq\n/6cNzGBZn8EwTAzjPPH4O8RiaZLJGIVCgVRqhcHBVUZGxvnKV36egYFBLl9eoVpNYhgJms0arlvl\n5MkXb03s94phGIZBoTBAEEyRTkdIKalUKh/pt9WrZo3mh/Okxwq1gOwAiUScXq8JWDQabXK5XSQS\nGzhOEWWBGChhqKHSdEv9421UbMRHpeuOoDKurhNFRxGig2V5lEqvMDzcZnp6HNOcJx6H3bsP4ft7\nWVgwWV9f5dChcYLAx3V9mk2DU6ee2eZv/VExjCAw+e53z/9Iv61eNWs0H82THivUS8UdYGBggHy+\nR7u9Sb2+Srlcx3VjqJTcS6itaYdQtRwSZX34qOyrCW5XoPfY6pMVRW8SRZuY5i5ct8rU1C4GB5MI\nMUwqNckzz3yeUilJMplHiHEuX14hl8uTTicolcy7Jvl79aIKw5Dl5XmazeucPXuTKJpiZGQvQ0Nj\njIzsRYjdnDkzSxRFO34NNZongSe955sWkB3ANE1eeukpms13kLKD4zh43gZwGpWB5aEEw0O5qTZR\n6bglVHbVUZSQbG0w9QWgQyzWJpu1GRgYYGnpPO22ycpKnHY7Yn5+nenpUaS8Sbe7zsZGk9nZc0h5\n854xia0YhpQ32diYZWFhln/4h7/n0qULuO4AH3xgce1amU6nc+s5mUyOblf5bTUazY/mw9+zSmWV\njY3Zj/xePm5oF9YO0Ww2GRnZi20v0WjM4bouyl01wu0U3qeBt1GB8RAlGFsZWJ3+/SqwG9ueoFgs\nkMks4LorjI9/gXbbJZPxOXny87TbTc6evcSJE8cQImJjo3HPAsA72YphlMtlXnvtPQ4d2s/4+DTl\n8irNpoEQWS5eXOLZZ/fdsVXt4++31WgeJU9yrFALyA4QhiGvv/4+5XIWx4kIw2FUNtUIt2s4VlEx\njxoqLVf2j6VQrqtBVNZVC7iBZRVoNs8xMJCgWHyKZLLD2FiLQmEv8/M1fD+O4+SIohUGB5MMD8cY\nHy/9yA+pYRiYpkk2u5eREZV6G4/HCcMGqdQY1WqSZrNBoTAAPBl+W43mUfOkxgq1gOwAlUqFq1db\nlMsC05whilKo+MZWVpWDStu1URsy5lAdeDMoQSmgYiA24FMqHcMwutj255DyfYaHB9mzJ8Mrrxzj\n1VfPI0SJfH4YISTpdESvJ1hYeJNC4dB9jffDmSLZrKrx6HabGEYC1/UBXeOhuX+e1MprzXa0gOwA\n6+sb+H6Rbvc6jUaeIMgDLqqN+zRKGOKoeo9xVM1HvH/OMGr/jwqwiW0fxXUzpFI+8fgxDKNFuz3P\nyMhxbNumVJqiXq/RaPRot1dxHMHwcIyhof3U6/X7WvV8OFPENE0OH97LpUuzVKtNRkaGEKKlazw0\n98WTXHmt2Y4WkB2i3W6xuVmnVosIwyJKEKaAm6hYh42KgbRRlkeEcmPN989ZRYhDxGIxguAfcN3n\nEWIBy5KY5vscPvxlfD8gmSwwOjpIpbKI5zV57rmnKRSGqFbX7ztWca+q8lQqw/7904yOnuPEiQyp\nVPKJ8dtqdo4nvfJasx0tIDvA0NAg8/NnWV0tEoY5VIB8qy17FZWmG6GsjT0oy8NDubZsVPzDxDRX\nsaxnkFJiWXUsKySTuco//+efp92+SK1msbmZJgybZDIOhw9/+lb1+Y8Tq/hhVeVf/OJJvWrU3DdP\neuW1ZjtaQHYAtbd1SBhmsCyPIKigYhwd1CUPuO2qCvv3L6PqQYZRsZI2QigBiMezJJMOqVSNPXsE\nhnGITCaD5y0SRWcZHX2RvXvv3Ojp/mMVd/qqjxwZQ0qJ7wdPVKaI5tHxpFdea7ajBWQHOH/+AmE4\nRTyeodk8j9ocahMlDm1UltUoyhK5jhKUcVQn3g7KxWUipYXv1wmCBWKxBSYnDX7u536F8fGDAIyM\nTDA4OMPly98nm41jWZkfqx/V3b7q1idiR0EdgH18edIrrzXb0QLykAnDkPPn5zGMNK57E2V55FDZ\nVmdRwjGE2ro2iXJpZVCWCKjaziEMY5ZM5tOYZp0ommVqKs3ExDgHDx7Z9n7DwyXC8Gn27YtIpyNs\nO4MQWer1Br1e7yMn30+qr1oHYB9vdJfmny60gDxkarVav37iVaQ8Rhi2UG3bbVQG1ibKdTWPaq44\njbI8VlEurHT/8QyuewnLKpNOD2IYkjDcxRtvvM+nPnWEVOq2m8CyMqTTEYVC/r4n30qlwtqaRy7X\nIgg8slklNB+nr/qTKmqa+0d3af7pQgvIQ6bXc/F9E8+TdLtrqKLBDEoYRlDuqU2U9bHevy2jAulT\n/VeZJxbbRRBcwLZ7TE4+zfT0KLadIggG76oOj6IOtp25a/INw5C1tSX+9m//kVdeeZrBwUFM08Rx\nHF5//QOuX89RKBiEoRKaw4f3kkplPjZftQ7APhk8yZXXmu1oAXnI2LbF1auzJBJPEQQGqr/VMEpA\nbnK7DmQTFQtZQVkcDVRdSBzL2o+Uy8RiJTKZZ3BdWFqaZWICbHsfrnu7OnzLNSBEdtvk2+moDaLU\nuTnCcJ5SaZUTJ2Y4e3aOeHw/uZxBoTAGQLfb5NKlWU6ceOpj81XrAOyTw5Naea3ZjhaQHSAMLWq1\nANMcJQwjVKHgIKrKvIKKc0hUw8Rm/7FdWJZNFKWRskwURRjGfqIoRz4/hu9LDOM9kskKm5shq6tJ\nPK96yzVQrzduTb6+7/ODH1wiCEbI5fJks4JcTiBEltOn38WyJiiVdrG6Okun45BKZUgmc2xuZlhZ\nmSef/3h81ToAq9E8Xmib8iHj+0F/y9hNPK+N2kDKRwXJI1Tso4eq+RhCNVWUQLZvsdwginyiaIZu\nN0Gzucjc3FmEaDM5eYJ9+ybZt0/w9NPw/PN5Xn75KTKZTH/y7dDpdPj+99/j2jWDzc0MN250uHZt\nljAMyWRyNBo27XaAaZocObILKVeoVhep18v9jaeufWy+6ie99bVG86ShLZCHTCIRRwiXWMxACA8p\nXZS7ykIFy3ejrBBQFslWllYKuICUAss6TCq1F9/vAD2iaJ1u18XzDrCwMMvMTPyuLrsDAwPE4wu8\n806DIBgkl8uQzQ7Q6zWRMsbSUodSKSKRyPc3u9q+je1HbTz1KNEBWI3m8UILyENGTeR1fN/HMARh\nuMptwXBQjRKvoiyTL6Iq1FdQlejPIOUZTDNCCIlhzGFZcXw/xuqqz5tvXse2C4yNTdPpdLZN9KZp\nsmdPkbffvkgYQrO5SRQ1Mc0ahUKO69dXSSZ98nmDeLx3K81yaxtbx2mSSt298dSjRgdgNQ8bXVe0\nc+y4gAhVTv2vgWMoH86vo2bQP0dFlOeAr0kp7+38fswwTZO9e4cIwzcIwx5KICxUym6EinmEqIys\nfdzO0loE1oA8UfQ+vV6ZVGqGRKKBYZTI5QSlksP09EGSybFtaa1bX5ByucL09DT5/ACGcZ5eT3D9\neo1Ox8TziiwtLTM0dJV/9a9+nuXlm5/YVb4OwGoeFrquaGd5FBbI/w38jZTyvxZCWKh0pP8V+I6U\n8g+EEL8F/A7w249gLDuO53m8++4aAwPPUauZwCQqBvIcytJooURkN3AOeBYlLIPACkIEGEYOIS5g\nWXVcd4xkcgjXXSWfH8VxLKJI4nkqrTWRSNz6grRaCa5erTI6anPs2BH+zb/5D/j+p4jFcnS7yxQK\nNpOTP8df//X7/OZv/hc0m029ytc8sei6op1nRwVECJEDXpJS/hqAlDIAGkKIrwKv9E/7Bmqv1ydC\nQG7evEmnU8L3F1DtSdLc3u/DQQXRh/u3FeANVFpvuv94BcvqkEo9A2SRMkY8niaf34fnFbhxY50j\nR7JYVop2u8P588u3viCDgyH1+nm63Qzvvvs+qdRhXDeD41RJJASZzCGazQDHKXDz5k0OHjz4MVwh\njebRoOuKdp6dlt/dQEUI8SdCiHeFEH8shEgBo1LKdQAp5RrKn/NEUKs1WF7u4DgJVI+rECUQdZQl\nMoba+6OB0m8DZYEEQBUhFgkCQa1WpdczMYwuhYLN0aOHicd3sbTUpdvtEkUder0u3W7mVsuIrX08\nkskyCwvzzM+3iKIcw8MjHDt2kkJhAiGGWV0Nqdebdw9eo3mC0HVFO89Ou7AslI/mf5BSnhFC/F8o\nS0N+6LwP/36Lr3/967funzp1ilOnTj38UT5EwtBncXGjLyCDqPYkTdSOhBlu73eeQomIibJG6kCb\ngYGnmZg4zvz8AsVigWRynd27C6TTGTyvgxAW9XqNoSGTZLKIYWz/F6ZSGU6ceIr19YssLVXYs6dA\nIpG5ZaonEilct4uU21dlnzR04FPzk/LTVFd0+vRpTp8+/cjfd6cFZAlYlFKe6f/+TZSArAshRqWU\n60KIEqoE+57cKSCPA/F4AsdZJIrGUEKRQV2Gjf5tFyoDq4LKvNoDzAJzGMYEUo7Tbq+STufJZEIs\nK06nc5b19TViMZN0uokQNU6e/Fl6vd49vyCGYTAxsYvR0XMEgYNh3BaLZnOFdHqTUumZnb4UD4wO\nfGoeBj9NjR0/vLj+vd/7vUfyvvctIP2J/lMoa+Htvuvph9IXiEUhxAEp5VVU3uqF/u3XgN8HfhX4\n1gOM/ROJaZpYVkgYdoC3UIIRof7kTv9+D+XWmkKJSQDkESJLr1el08kgZYjrGsTjdY4dO0wyadPr\ntfH9Db785V8gk8mQTCY/8gsyOBjxpS89x3vvvcnS0gimOUAY1shmN3j55SNkMulHfGXuDx341Dws\ndF3RznNfAiKE+G+B/x34e1Tp9B8JIf4PKeX/cx9P/5fA/yuEsIEbwD9D+W3+Qgjx66io8tceZPCf\nRGzbwvdtVGxjH0oc0qgsrPMoIdmDEo9nUM0U84BJGPq4bpd0egrflwgxSyqV48KFy0xM7EaIFqXS\nEG+/fQXHaTM4WOz3tro7JffUqWc4e3aO8fHP02iUabVaZLMj5PNHMc3FT2xVtw58ah4mj1td0ePm\nur1fC+R/AU5IKTcBhBCDqPShHykgUsr3gOfv8dCX7neQjxOdTpdGw0E1URxCZWB5qKrz51HurBB4\nr3/cQlkjJpBHSofV1Q84dOgFhocBlkmlJhkaMhgaKmGaPleupLh6dYOjRwXpdIfjx6fY2Nig2Vwm\nl8uyZ88RbNu+tfpKJjOk0xNEUQfTXPyxV1+P8kOtA5+ah83jUlf0OLpu71dANlEFDFu0+sc0H2Jx\ncRHH6aIyrrooEVlDZV4FwAHUJlI94ArKSqn0b/MYxmfxvAbl8hleeeUgmczzWP9/e3ceHdd1H3j+\n+6t9QwFV2BcCICFwXyyK1mpJHFuOPUnaTscZxT3ORI5Pzzkz4zOddvr02O5lovzR00lObznpJGfS\nSSdOujttu9OO1UlnbHlhIsmLxJA0KYmguAEkQOwFoKpQe707f9wHEqRAkQKrABT1+5yDw1ev6tW7\ntfD96m6/60swNFQlnc7j9e4lmYyTSl0lFIpRKBT5gz94ka6uARzHj8+XZnz8DI88srMmv742+kv9\nXur4VGpFozbd3m0AuQD8UES+ju0D+ThwWkR+CcAY86/qVL6GMz8/jzFV7KirI9i3OOn++11soAhg\nU7gXgNPYJq0QXu8M0WgnXm8bzc0VgsEgra29gEMoVGR2Nk5LS5RMJs3SUoGpqRyzszlGRsLk8x5i\nsQ6Wl+c4d+4as7PTfOITz+D3+9f962szvtTvpY5PpVY0atPt3QaQi+7fipVO76baFqfxiXjw+9uw\ni0SNYmseYWwz1XbsnEnBBpBO7ORBA+QwxiCSw+utMjCwnUolwNzcGL29bQQCYcrlMufOjVMuB8lm\ni+TzWc6eHaerazd+f4ipqTJLS1GWlpKcOPEKudwyn/jEh4nFYutqgtqML7V2fKr3okZtur3bAPJr\nxpjC6h0i0maMmbvdAe9V/f3bEPk+cAjbad6GnQ/SgR3GWwYGscHkPLbvowL4cZyDZDIv09zsIRp9\nEijh8ZxnYOAn8HiE0dFLNDe/n2DQIRBIEw634zhB5udnEQkzPx8llargOG0sLe3gL/5inPHxP+XI\nkV2IdL3rJqjN+lI3WsenUveqUZtu7/Z/5Ksi8ujKDRH5BLYTXd2it7eH1tYgtnmqx917EZsscR7Y\nje0HyWJrJ49wYx7lFCKGaFSIRKY4dCjGc899kFBomrm5K+RyU6TToxhzje3b+7DXU0OxmGF8PE8m\nEyQc3kUi8QDx+Daam4/w6qs+TpxI09a2nba2bjo6hhDZzvHjF3Ec5x1fy8oaI2uxX+rgvb1Z72Cl\n47Ovr5e2tjYNHuq+1qhr4dxtDeRTwL8XkWPYq2Ir8MF6FaqRNTXFGRho4vz589gWPi+2yep1bP/H\nXmASOwJrO3ZeSBk71Pc8MEg0GiMWaycWKzIwMMDAALz++hvkcr2k07N4vT0UCksYk0LkDfz+B1hY\nqBIIxAkEIpTLWXy+BWKxHUxPV8lkyqTTKVpabHPT3TZBaX+EUvfmbkcwNmrT7V0FEGPMGRH5Z8Af\nY0dgPWWMGa9ryRqU3+8jk8li+z8E2IcNFlPY4bvT2P6PODfWCdkB/ADYgdfrkE6fJpNJMzz8Yxhj\nWFhYACAWC7JnzwGmp8fJZGbo7IwTDg/zve9NUCh4MaYXuIoxs3R2xvD7m4FFRLwUizc3N91NE1Sj\nfqmV2gre7QjGRmy6vduJhL8PDAEHse0vfy4iv2mM+a16Fq4RzczMMDGxAAxjJwsWsM1WB7DNVlPY\nfpEcdhb6SjNSF17vDMZEMGaQUinIqVMX+dGPRimX4/j9TYyMTPDSS5cZHDxIONzD0lKBctmwa9c8\njuOQyYSJRruIRoW+vgMsL+fweFI0NSUIBm9ubrrbdtVG/FIrtdnWO4KxUeasrLjbJqwzwN81xhjg\nsog8AujQ3TWcP38Rj6cd28rXgh3CO4AdujuODRwp7MisDLCM7VwfoloNEggIwWArbW2GV15ZoFxu\nZ//+QRxnmVSqSrmcYGzsEjt3NmFMiWDQw6OP7mP37kVefPEKwWArLS0PUCxmgXl6etJEImHi8Rtt\nqO+2CarRvtRKbbZGHZb7br1jABGRuDEmbYz5N6v3G2OWRGRjsnU1IMfxYGsXMezoqxy2L6QL2/8h\n7r7L2Oatg9icWU0YM0M4PItIMx7PQzQ3J/H7IzhOlEJhiEAgRXOzl9bWWbq6+ojHtzM3d5knn+yn\ntbWV739/jHw+g88XoLU1z/BwgkjEy9zcZW2CUmqDNOqw3HfrTjWQY9h07IjIt40xH1p135+t3Kdu\n2LNnF8HgC9h5H23Y2kY7tinL4caE/jn3/qr7GAevN0EsFqe7u4rP10Ug0A74yGSWuXp1joUFm2Qx\nm7VJEfv7H8Dj8eDxRPD5HD760cc4cmSOqalpALq6dl//laNNUEptnEYdlvtu3SmAyKrtW8eRCept\nhoaG6OmBixensAPWWrCjrK5i82HFsMEliE1jMoatlbTj8YwQCGynr68dn6+JcjlHLlcilZrH7+90\n1/WIEgwWCAR6OHv2Ig8+eIByOcPysuHatUlCoSD79u1920iPelWXGy35m1Ib4b0ygvFOAcTcZnut\n2wpIp9P09g5jaxoT7t5r2A70CeyorElsYBnHxuU0fv8J2tsP0N4ewe8vEghUuHDhDEtLXny+NmKx\nIpnMHIFAkaGhCm1tAywsXObSpbNMTo4gchCfz7OhCdgaMfmbUhvhvTKC8U4BpMPNdyWrtnFvt9e1\nZA3q2rVJ0ulWbHx1sEGjC/tWtwFXgG3YpqsEkCIY3Etr62EKhVMUCouEQtu4cuU1CoVefL4WMhkv\nxWKFfH4cn+84HR1/h3R6nsXFFKnUSR599Cfp6Oi5XoaNSMDWqMnflNoo74URjHcKIP+OG/muVm8D\n/F5dStTg0uk0c3NZbqw2uAvbXHUWW/PwYOeAZIGLiHTj8UxTLscwZoaennE+8pF9vPxyhdbWbvL5\nGCbuh1IAACAASURBVFeulAkECrS2RkgkDjE8HCASMQQCy8Rih24KHlC7kR4rzVPLy3b99VAoTDQa\nIZFIvGdGmSh1L+73EYzvGECMMTrS6l2KRqOkUhexcz983JhA2IVdA31lLZBeIIYxc5TL4xgzRDxe\nob+/jWq1isg29u9/hOXlFCKXqFYTRKNDzM+fpljM0d7eRSJRIRbrW7Mc9zrSY6V5KpXycuHCIoWC\nIRQq8cAD7SSTE3R3h/F41s6leT+NMlFK3d5d1aVEZKeIfFtEXndvHxSRf1LfojWmYDCAndsxi+0g\nn8fOA4li3+6V+SHnsc1b03g8HYiMkUx2EY8/ybFjM4yPlzh//hoQIBJpY2Ehz9jYPFeuLPLqqz8k\nnT7N0FCS2dlRFhfnqFarN5XjXnJVrTRPOU4/s7MBWlrex44dT5JIvJ+5OYMxA4yMXKNSya55fL3z\nZCmltoa7bYz7d8AXscOJMMacBj5Zr0I1smKxRDjcgV26VoAINg/W17FNWXHsKKwSEEZkiEikm3K5\nn2p1jkOHnqa19QDB4DILC1W+/e2TVCpN7Nmzn23bovT3l9i7dzfnz08yPh5haspw/Pg1Tp16nVzO\nXtDvNQHbSvOUMVAsholEbId4OBwnn4/hOBW83h4qlWsNl/xNKVU7dzsTPWKMeVXkppG7lTqUp+Gl\n02lsXN6BfYsmsZMKn8A2Z11z9w9g+0HAcZYRaaJc7uX06e/T3t5CtTrNlStnmJmJUC6P4/efpaWl\nwPBwlEolSqWyj2i0k8ce6+fNN8dJpUp861svsn17G4lElaNHH1x3Z93KJKhisYzHE7rpPq83QrFY\nxO9vYngYJiffvh77/TTKRCl1e3cbQOZEZAh36K6I/Az2yqjWkM8XsEFjJVBsxw7rrXBjjfRFYAlj\nZgmHDyDShuNMcu1ajpGR4zz00FNEInny+TTFYpZSyYvIDPn8EFNTCwwOdlIslmlpSbB7dw/Hj59j\ndjZApeLF5+vi5MnRdQ+nXZkEFQwmcZybm6mq1RzBYDOl0hKtrUmGh5P39SgTpdTt3W0A+Szwu8Bu\nEZnA5uD4VN1K1cDi8TiFQh6bwmQfNudV3v13DzYb7xS2NfB9wBSZTBK/f4lQaJHz5xcJBHpZXIzj\n83kRGWNg4CBtbf2UywsEg8sUix5ef/0NBgZ2US6XGRm5RlPTLrZv76Gnx6Gzs/eehtOuTIJynCTB\nYJ5cLkskEiOft81THk/r9clQ9/soE6XU7b3jlUVEftHd7DbGPIOd+7HbGPMBY8xY3UvXgEKhoFsD\nWZlxDjbvVRHboe7FJlZ0sBW6JCJTOM4US0uL+P0dtLU9QCTSRbncSqkE7e3DRKNtVKs+RkenmZpa\nYn7ex7lzWV555Uek0x4ikRiVSoZCIc/09AyVSpVsNkwqlXrXr2FlEpTHc4X29hKLi6e4dOklFhZe\no61NEBnTZiql1B1rIL8A/Abwm8BhY8xy/YvU2AqFIo6T58YaICsTCAPYnFdVbODwYwPKEMHgJXw+\nH35/F5FIlEJhhny+DZ9vme3b91IojFIqtXHp0lu0tcWJx+doahoiFmujUFhmYmKRQGCKq1fPAQfx\n+QTHyVIozDA8fHdpTNZKSbIyCerIkWby+RzhcIRIJEwymdTgoZS6YwA5KyLngR4ROb1qvwDGGHPw\nTicQkVHsBAgHKBtjHhaRBPBlbAfBKPCsMWbtzGMNZnFxkXI5D5zCdqQfwTZhncU2WeWBl7DDeqN4\nvfMEAnG83l56eroIh98gEhmjo2MHyeQ23nxzkoGBJOn0EslkhoceOoLfv4/Tp19mebmAxxNmfn6U\nSuUtdu9+mmSy63pZJiYWGBm5xvDwAzdd8G8NFn6/n5MnR9dMSaLNU0qp27nTRMK/IyJdwDeAj63z\nHA5w1BizsGrfF4BvGWN+XUQ+jx0i/IV1Pv+WMj8/R7FYAQ5h42MZm7IkiZ374cFO6LepxIwZp1od\nwOOZYm5uhObmIIcPP8PU1BgzM1fJZnOkUh1EImUOHHiI5uZW8vk0+/d3MzTUS6FQYH4+jdf7wE3B\nI59Pk0gYfL7em2aF35q/qlxOceHCGXbtemzD06EopRrbHTvRjTFT2Kvheglv72v5OPC0u/0lbNr4\n+yKApFILlMst2L6OMDZ+xrABI4p9y0vAa0CUYHAHlYqP5eXvMDT0IJ/4xM8QizXR37+fsbHX8Xr/\nkt7eMsa0Mzk5TaEwh9e7wL59O2lubiWZ9DI83MPExDzz8xfxeiNUq3Y47Z49Q+RymeuzwtfKX7W4\nGKJU2sPVq7O0tXVdDxaakkQpdSd3WlDqK8aYZ0XkDDdn373rJiz3uBdFpAr8v8aY3wM6jTHT2CeZ\nEpGOdZZ/y1lYWMQO1e3GBgw/9m1uwo5BeAOb1t0PJKhUypRKb+LzQVdXjsnJiyST3ZRKGRYXZ4nH\nD9Pc3MXy8iyjoy/S3f04PT37uHSpwOTk62zb1k5np5/u7n4ikWaKxSLBYDPxuB0hlc1OX197YK38\nVcVimUiknXw+TTqdoqXlRrDQlCRKqXdypxrIyiisn7yHczxhjJkUkXbgmyJyjneRGv7555+/vn30\n6FGOHj16D0Wpv+npaWztox2brn0Y6MQuIDWDnY3egQ0yl+jufoxqdY5qNUpz8+NcuzZHIJBFRNi5\n82HK5SzRaJl02mHfvp9jevoSxlTxeuMsLhbJZl/mYx87zIkTl3CcAN3d/dfX47h17YG1VkkLBv04\nTvb6BMHV7qeFb5S6nx07doxjx45t+Hnv1Acy6f677iG7q55jVkT+DHgYmBaRTmPMtNvHMnO741cH\nkEYwOzuPXX1wDNt0dRY796MZO3kQbGf6G0CKbHaG5uZteDxBWloSxOODZLOzTE2N09xcplLJUC4b\n8vkYPT3bCQa99PU5hMOGarWFN95o5vjxBYLBIUZGLjAycpZdu3YSDMrbZoWvtUpaU1OcYHCWxcUC\nweDNfSD308I3St3Pbv1x/Su/sjF5cO/UhJVh7drBShNWfI37Vh8fATzGmKyIRIEfA34FeAH4NPBr\nwHPYRFH3hUQigQ0Yr2InDgaxNZG/webF6sMO750A9pPJFCmV0vT15RDJMTFRwe93WFho4+TJ87S0\njNPbuw+v19YcvN4w4bChra2Vkycv4vUOEY+H6ezspa9vO5OTVykWz/P44wdpa7t5Vvhaq6R5vV4G\nBuLk82coFMLMzZU1JYlS6q7cqQaydr7uu9cJfE1EjHuu/2iM+aaIHAe+IiKfwf5Uf/Yez7NlHDq0\nsuLgQ+6eUWwG3n7sYlIrE/tywACO0wE0UypNMD7+A8rlbbS3J8jlZqlUSrS27mNsbAK/3zZLOU6B\nYDBGJpOmWAwTCJQIBm3mW4/HQ2/vADMzFXet9Jsv/u+0StpnPvNhSqWSpiRRSt21u01lsi7GmMvY\n9ppb96eAZ+p57s3S1dWNrXXMY1cePIANKM3YNdJT2D6QDwHfxOv9cQIBH9nsPIuLe0kmA6TTE6TT\nl4BHmZ8Pk8vl6Ou7CEQJhfLE473Mzs5RLJZpbs4Sj9/czPROnd/vhVXSlFIbo64B5L1ofHxlHfTt\n2NUIL2MXlzoAXMR297RiA4yfSuXb5HKtxOM78XgSJBI7gBai0X5KpUUgTKnUx/z83xAKZWhv30Mq\nNU06PYnjTLFnz1Nvu/jfqfNb81cppWpBA0iNjY9fxc7zaOHG/I8mbD/ILHZeSB5bE2kHRvB6IwQC\nC4RCYbJZOwFwcPAQudwEnZ1Fmpp8dHc/wMc+thufz0ehUMTv7+WNNwTHcW46v3Z+K6U2igaQGrt6\ndRybtn0M21mewM6jrLi3Y9gAMwC8hIgXv7+fUChLU1OFcnmG7u5dBAIRisUY1WqGrq4g27YNU6lU\n6eq6Mds8FApx7NhJxsZChEJxIhEvsVheO7+VUhtCrzI1lsvlsRMIT2HjcwLbYR4EHsUmU2zHNmWd\noFr1EA5P88gjj/Lggw8RCLRy4cLrpNPzLC2N4fen2Lu3DyjctExsNpvl5MlRfL4eQqEohUKKavUa\nhw9vX9caIEop9W5pAKmharWKMUHsMN4YNmhMYlceHMHOCfEA54C/AAaJRrtpaoqztATT0yl27Ggi\nHg8TCk2wa1eZD3zgCI5TuWmZ2NUpSbq6hhkc3ENv7y5KpS6++90TlMvlzXj5Sqn3GG3CqqGFhQUi\nkR5s81UKG0iq2JxYMeyaIOeAt7Ajqv427e0xotEilcoUpVInjjNJuZymUsmyY8ejpFJjb5uTsTol\nSS6X5exZmxzR620mlZoFvseHPvSQ1kSUUnWlAaSGCoUixWIZ24Huw3acP4TtRA9hJxJ6sCOzopRK\naTKZKuFwlebmJg4dGmBu7hLd3WGeemoXTU1hQqHg24bZrqQkqVarnD1rayKtrStzOn2Uy5pJVylV\nf3p1qSG7GuEiNhfWEnYhqSR2XZAebA3EAR4BQjjOORYXZ7h69SzpdIYLF6bweGIkEsKuXbvo6+ul\nra3tbUHApiTJkcnYmkg4fCMhgOMUSCSS5POxda1GqJRSd0trIDWUSCSoVBaxTVcrKd0L2OasPJDB\n1kY87mPA672KMTsYHTV4PEvAD/npn/6Jd1wAKh6PEw5PMDtbxuu9Md8jl8sSDNqJhqlUQTPpKqXq\nSgNIjeXzZWzSxEPYOSBebMBoxs5AnwdOYmsjo8CzNDd34vUu0d6exO8/xPnzs+zd67jp2G9eAGpl\ntcDduztZWhpx+zx8boqTPHv39uHxeDSTrlKq7jSA1NDCwgK5XAW7FkgCO3EwgE1pksHWRmaxKxM6\nQByvt5VsNkM4vEy5nGP//oNkMtdIpVIkEom3LQAFdrLgyMhlPvrRx4AfUC6nSSSSxOO9btDRyYRK\nqfrTAFJDhUKRUqmKDR5F4DA28fAINpniPPA9bPBoBkIYM4pIjkBACIUG8fkMoVCcQqG45gJQcGO1\nwEwmw4c+9JBbQ4FUqqCZdJVSG0YDSA2FQkFKpQx2sagMtolqL7YZK4OdYHgEm8p9EvBQLjsEg53k\n86NMTc2zY4eHaLSLUCi45gJQK1YSJra1tWlyRKXUptAAUkOJRIJSKY0dshvGzgFp5caEwii25nEN\nOzt9EI8nQjgcorv7AywtXSGTmSEcjpFMDpFKpd62ANSK1X0cmhxRKbUZNIDUkNfrxe8XbBNVHzCI\nXfu80719BdsPEgLaEYmRSEzT1uaQSHSSz/soFvPs2JHA4/GQSCQIBK4wPn4Zvz9AMBikqSlBPr+s\nfRxKqU2nAaTGfD4vNxIozgFd2AUcl7BDeT3Y0VktRCIRPJ4BIhEHj8cQCJQJBJooFksA5PN5isUi\nIyMXKJUSiDj4/XMcPJjgySd1kqBSanNpAKmxRKIVO9ejHxswVtZDT2ITKE5jO9hnKBabaW2N4fdH\naWoS2tt3EwhMMTJyjR07tnP8+EWi0X188IMx0uklisUylUonoVCKSGTtvhGllNooGkBqrLOzFVvr\nSGOH8/YAZWxTVgU7R6QElHGcAo6TIRyOuYFkjp6eKD5fjEuXLt00AqulJXH9HDMzFVKplPZ7KKU2\nlbaB1FC1WqVa9QPL2I5zDzZGR7FNWLPYVO5NQB7HuUa1mmF09GWWl1+jv7/Ivn3D+Hwx0unMHUdg\nKaXUZtIAUkN2ImEA21SVAq5im7FmsUHlYeykwmtADyLNBIPjdHXN8/jjnTz22CNEIjEcJ0c83oTj\n5NY8jx2BFVzzPqWU2igaQGqoUChSqRhuDNNd6Tz3YvtBxoAFd/sqXm8/IiX6+x/F7w/dNIt8x44d\nhMNZstn0Tee4Mcs8uaGvTSmlbqV9IDUUCgVZXJzG9oGcAp7GDt+tYANJApvK3S44FY+3Egi0sLh4\ngXJ5PzMzFwmHszz44CBLS0t0d4cZGTlJJtON39+ks8yVUluKBpAaSiQSiCwDQ9h+jlngG0Avdi7I\nDLZJqxcoUiiAx1NhcDDFM8/0E4tFCQTaOXHisps8sQmfT6hUJhge7qG1NamzzJVSW8aGXIlExCMi\nJ0TkBfd2QkS+KSLnROQbInJfpI31er1s396NHXUVxk4oTLj/XsHOTO/AjsLyUqm8RqHwPZ599gMM\nDg6QSCR47bULLC3FMSaEz+envX0HLS2HmZzMk0wmNXgopbaMjboa/SLw5qrbXwC+ZYzZBXwH+OIG\nlaPuOjo6sWlLFrCrEQ5gg0YQWwN5GZuhN4XHM00yGaJSqTI+PsGPfvQjXn11gtFRh0uXPLz++hKn\nTr2Ox+PRBaKUUltO3QOIiPQBPw783qrdHwe+5G5/Cfipepdjo5TLZXdrNzaR4iC2SWsPtsXQh+1M\n78Hj8XP1aoaXXpri9GnDV796kbExL9FoJy0t3bS2DiGynbNnLwIhHbqrlNpSNqIG8q+Bf4jN37Gi\n0xgzDWCMmcL+RL8vTE/PYDvRW7B9IBls01UTsBM7sVCAcUT2Uy6/nytXKuRyZRynn2q1jwsX3sJx\nHADC4Tj5fIylpWs6dFcptaXUtRNdRH4CmDbGnBKRo+/wUHO7O55//vnr20ePHuXo0Xd6ms0XDIZY\nmShom60i2FFYVWwwKWInFuYIBtsIBj2cPbtIa+sEmUwn6XSW+fklOjsnaG/fBkCpZPB4FkgmH9qU\n16SU2tqOHTvGsWPHNvy8Ysxtr933/uQi/w/wc9graBh7Zf0adlGMo8aYaRHpAr5rjNmzxvGmnuWr\nh+eee44/+iMPtsnqCWwTlsF2pH8feBWIEQjkaW7eRTjsYceOnTz+eJxisYVwuI033/wbmppSHD78\nKFAknx/hZ392mMHBwU16VUqpRiIiGGOk3uepaxOWMeYfGWP6jTE7gE8C3zHG/C/AfwM+7T7sOeDr\n9SzHRurs7MH2cazMRL+KzYt1ATv/YxhoxnGyeL2jJJNdtLR0EInECAbzGFNlcLCT3t4kHR1LDA46\nPPxwG/39/Zv2mpRSai2bNQ/kV4GviMhnsFfbZzepHDXX3d2JHaZ7EVvhqmCbrIrAPuwqhfN4PFWS\nyRQ9PW0EAlmamweJRoucPfsa1eo8TU0PUCql6OoKceTIsA7fVUptORsWQIwxfwX8lbudAp7ZqHNv\npK6uTuw8kEFsH8gsNoj0YpuxJoFrdHcP8PDD7czOHicaHeDcuRKlkiEQgGSyl2LxLPv37+J979N1\nP5RSW5PORK+x+flF7KCyMjfSuXuxtZKKux0hFssyONjLrl0R8nkIhxM0NyeJRpMUi1mWlw2Li3Vv\nwlRKqXXTn7Y1ls/ngDbssrUF4DVgHLs64QRQQaSdYnGabdt2k0h4WF6+huNkKZUKLCxcxpjLPPTQ\nQYrFuE4eVEptWVoDqTHHqWIz8Zaw6UsS2EWk/NiEipfxeJYZGvoQ4+MpCoU+UqklEoky+fyb7Nu3\nk64u22yVy2V08qBSasvSAFJj1aqDSBljytg1QA5g+z8WsHNDWgkE/Cwv+8nnW+jvP0QqdZFkchCf\nz8vk5GW6uuxz2XU/7os0YUqp+5A2YdVYIBDA53OwAWMP0IqdQBgAHgMew+vtJ5MZI5nsIhKJEgjA\n4mKKSgUmJ8tcvXqJpaUFXfdDKbWlaQ2kxjo62imXl7GpTBLYjvQgdhhvHhtIIvh852lvH8Lr9dLR\nEeDKlTcYG9tGPl8inT7Ntm2GT37yCR2BpZTasjSA1Nj09DR27Y8HsZ3oy9ihvF5sEJnH77/MgQP7\n8Pl8ZLNpFhamOHjwacBhZuYtDhzYRyLRycjIGB0dHRpElFJbkl6Zamxs7Co2hXsIu7TtODZ5Yg47\nG/1VOjsz9PW1c+3amywvn6erq5+mpmZ8Pi/d3SG2bRsmHm/RFO5KqS1NayA1Vq2WsQEDbHy+is2F\n5cGuBzJGZ2cTH/lILzMz02QyYcbHi8zP2+Vs9+y5sVytxxPRUVhKqS1LA0iN9ff3I/I6xvRj1wEZ\nwA7jLQADeL0VHKfElSs+uroiDA4aRJbo7e0iHr95uVodhaWU2sq0CavGtm/fTjBYxjZXFbD9HitD\neg0tLQN0dBxCJAIMMjq6RDC4zPLyMqszD2ezaR2FpZTa0rQGUmPxeJxIJEihYIApbIyOAzFgiXC4\nTCDQTqGQ5/z5WaanfQwORhkdfYOzZ99i584dhEIQDmc5cmRIO9CVUluWBpAay2QyVKsrfR/bscva\nBrCd6BVKpRNks8LMTAuJRA/JZBNtbQ579hzh2rUxisXzPPHE+2hr267BQym1pWkAqbG5uTmKxQoe\nz8M4zjLwBnY+SB6buT6Ax7NMuRwhEomRz08TDDbj8Xjo69vOzIyDx+PR4KGU2vL0KlVj8/NzOE47\nfv9+vN69eDxlYB6biRf8/hKJRA/GeMnnbT9HPH6jn0NHXimlGoXWQGosEAji9ycolc5jzG48noN4\nPDkgT6VSoFq9gs9XIZsdwxhz07Bd0JFXSqnGoQGkxrZt6yMYPEWhUMRxWoAkIi14PBG83msEgyUe\nfBC6u6O0tAwQicSuH3tj5NX2zXsBSil1lzSA1FC1WsXnS9LammJ5eRgI4TgzGNOEMUuEQpP09rZy\n6NB29u/fx7Fjp7h6NUQoFCcS8RKL5XXklVKqYWgAqaGFhQVCoUEGBlqYmDDAAI6TxnGuIrJEV9c2\nenv9ZDJpTp4cxefrIRQqUiikCAbLHD58mFgsdsfzKKXUVqABpIYKhSK5nEMsNkw0mqFQmMWYGD7f\nDvx+L6XSNFNTF7hypZ1dux6nqyt+/dhsNs2JE5d56ildA10p1Rj0SlVDoVCQVGqSSqWFcnkBkQpe\nbxQIUKksAVmWlyeoVpPEYvGbjo3F4po8USnVULQGUkOJRAKfb55Ll84RiQwhYtcCqVYrVCpVyuV5\nmpoCOE5gzeN1CK9SqpFoAKkhr9fLjh1JHCdLINALZCgUJnCcKtFoAMfJ0dXVic2R9XY6hFcp1Ujq\n2oQlIkER+aGInBSRMyLyy+7+hIh8U0TOicg3ROS+uWo2NTWzb98B/P5xcrkZKhWACKVSFWNaCAQK\ndHX5mJgYY3p6hsXFBarVqiZPVEo1nLrWQIwxRRH5H4wxORHxAq+IyF8CnwC+ZYz5dRH5PPBF4Av1\nLMtGSSSa6euLcurUW8DT+P19iDj4/UV8vjLZ7Ah+v5+zZ9+gXG7DGA+BwAIHDsS0A10p1VDq3oRl\njMm5m0H3fAb4OPC0u/9LwDHukwDS39/P7Ox/oLn5MO3tTeTzOSBCtZrF57tMIDBMOh3ngx98knQ6\nRbFYpFxuJRRaJBKJbHbxlVLqrtU9gIiIB/gb7OpKv2WMeU1EOo0x0wDGmCkR6ah3OTbK8vIyO3fu\n5PLlAoFAGJ+vQqUyQSSSY9u2J8jn56lWbbLElpa268fNzFwklUrR1tb2Ds+ulFJbx0bUQBzgQRGJ\nA18TkX3YWshND7vd8c8///z17aNHj3L06NE6lLJ2CoUivb1D7NtnCIVaSKcX8Hh6aWvrIZ2eIp0e\no6kp+rbjdASWUmq9jh07xrFjxzb8vLJ6Fby6n0zkn2IXxvi7wFFjzLSIdAHfNcbsWePxZiPLVwtz\nc3O88sosL774JrOzwwQCPYgEMKbE9PQrJJPn+dzn/j4+382xe2bmIu9/f7PWQJRS90xEMMZIvc9T\n71FYbSsjrEQkDHwYOAu8AHzafdhzwNfrWY6NlEgkaGrK09nZjDHnWV6+QC43weLiacLhc+ze3Uwu\nl73pGB2BpZRqRHWtgYjIAWwnucf9+7Ix5p+JSBL4CrANu8rSs8aYxTWOb7gaCMDo6Chf/vKbBAJD\npFLzLC/P09Li8OSTT7OwcAWvd4FAoA+PJ4Lj5K4vX6t5sJRStbBRNZANbcJ6txo1gIyPT3DmjF0b\npFgsEgwGiceTeDwe5uYm2bOnQiQSplAoEgoFSSaTOnxXKVUzGxVAdCZ6HYRCQWCJlpbet93nODki\nEe3rUEo1Pv3ZWweJRIJwOEs2m75pv/Z1KKXuJ9qEVSfZbJbjxy+Sz8e0r0MptaG0D4TGDiAAjuOQ\nSqW0r0MptaE0gND4AUQppTbDfTEPRCml1P1LA4hSSql10QCilFJqXTSAKKWUWhcNIEoppdZFA4hS\nSql10VQmdVStVllYWLg+DySRSOD1eje7WEopVRM6D6ROdCa6Umqz6ERCGjeAVKtVXnrpdUS2E4vF\nr+/PZtMYc5mnnjqgM9KVUnWjEwkb2MLCAvl87KbgARCLxcnnY6RSqU0qmVJK1Y4GkDooFIp4PJE1\n79O1z5VS9wsNIHUQCgVxnNya9zlOzl0vRCmlGpsGkDrQ9UCUUu8F2oleJzoKSym1WXQUFo0dQEDX\nA1FKbQ4NIDR+AFFKqc2gw3iVUkptaXUNICLSJyLfEZE3ROSMiPw9d39CRL4pIudE5Bsi0lzPciil\nlKq9etdAKsAvGWP2AY8BnxWR3cAXgG8ZY3YB3wG+WOdybIpjx45tdhHuSSOXv5HLDlr+zdbo5d8o\ndQ0gxpgpY8wpdzsLnAX6gI8DX3If9iXgp+pZjs3S6F/CRi5/I5cdtPybrdHLv1E2rA9ERAaB9wE/\nADqNMdNggwzQsVHlUEopVRsbEkBEJAb8F+AX3ZrIrUOrdKiVUko1mLoP4xURH/DnwF8aY37D3XcW\nOGqMmRaRLuC7xpg9axyrgUUppdZhI4bxbsSCUv8eeHMleLheAD4N/BrwHPD1tQ7ciDdAKaXU+tS1\nBiIiTwB/DZzBNlMZ4B8BrwJfAbYBY8CzxpjFuhVEKaVUzW3pmehKKaW2rg2diS4ivy4iZ0XklIj8\nqYjEV933RRE5797/Y6v2HxaR0yLyloj8m1X7AyLyn91jvi8i/avue859/DkR+fmNe4XXz/9RERlx\ny/D5jT7/qnK864mctfwcavg6PCJyQkReaLTyi0iziHzVLc8bIvJIg5X/cyLyunvu/+ieb8uWX0R+\nX0SmReT0qn0bUt5aXHduU/6te900xmzYH/AM4HG3fxX45+72XuAktk9mELjAjdrRD4H3u9v/HB0u\nogAABqtJREFUHfiIu/2/A7/tbv8s8J/d7QRwEWgGWla2N/A1etzyDwB+4BSweyPf51Vl6QLe527H\ngHPAbmzf0//l7v888Ku1/hxq/Do+B/wH4AX3dsOUH/hD4BfcbZ/7vWyI8gM9wCUg4N7+MrbPcsuW\nH/gAdrrA6VX76l5eanTduU35t+x1c8MvaqvelJ8C/tjd/gLw+VX3/SXwCPYC+Oaq/Z8Efsfd/v+A\nR9xtLzBz62Pc278D/OwGvq5HsSPOVm7f9No28w/4M/fLOIKdi4P7Ho/U8HOYrXGZ+4AXgaPcCCAN\nUX4gDlxcY3+jlL8H20eZwF6kXmiE7w/2x9vqC3A9y1vz686t5b/lvi113dzMZIqfwUZGgF7g6qr7\nJtx9vcD4qv3j7r6bjjHGVIElEUm+w3NtlFvPv7rMm0bubiJnLT6HRfdzqJV/DfxDbp4r1Cjl3w7M\nicgfuE1wvysikUYpvzHmGvAvgStuWZaMMd9qlPKv0lHH8m70dWdLXTdrHkBE5EW37W3l74z7799a\n9Zh/DJSNMX9Sy1PX8LnuK7KxEzlr9jmIyE8A08amw3mn592S5cf+aj8M/JYx5jCwjP3V2Cjvfws2\n7dAAtjYSFZFP0SDlfweNVl57oi143ax5ADHGfNgYc3DV3wH33/8GICKfBn4c+J9XHTaBHdK7os/d\nd7v9Nx0jIl4gboxJufv7b3PMRtjs899E7ETO/4Kt9q7Mt5kWkU73/i5gxt1fy8+hFp4APiYil4A/\nAT4oIn8MTDVI+ceBq8aY4+7tP8UGlEZ5/58BLhljUu6v1a8BjzdQ+VdsRHnr+v9+y14377W98V22\n7X0UeANovWX/SmdQAFvtX90Z9APgYWyk/O/AR939/wc3OoM+ydqdQSvbLRv4Gr3c6EQPYDvR92zk\n+3xLef4I+Fe37Ps13LZT1u5UvOfPoQ6v42lu9IH8eqOUH/grYKe7/cvue98Q7797vjNAyD3vHwKf\n3erlx3Yon9nI7zs1vO6sUf4te93c6IvZeWyn3An377dX3fdF9w04C/zYqv0PuV/i88BvrNofxE5G\nPO++WYOr7vu0u/8t4Oc38jWu+sDPuWX4wkaff1U5ngCq2CB20n3PPwokgW+5Zfzm6i9KLT+HGr+W\n1QGkYcoPHAJecz+D/+r+B22k8v+yW5bT2MzZ/q1cfuA/AdeAIrbv5hewF8S6l5caXHduU/4te93U\niYRKKaXWRZe0VUoptS4aQJRSSq2LBhCllFLrogFEKaXUumgAUUoptS4aQJRSSq2LBhD1niMiAyJy\n5i4eV3VzWJ0RkS+LSGgjyqdUo9AAot6r7mYC1LIx5rAx5gBQBv63u31yEdH/W+q+p19ydd8RkX8q\ndkGvvxaR/yQiv+QusHNKRE5i03GsPPY5EfkzEfmuu5DO/32bp30JeMA95lMi8kO3dvI7IiLu/oyI\n/Av3HI+KyBERecU97w9EJOrWfv5aRI67f4+6xz4tIn8lIn/ulv236/suKXXvNICo+4qIHAH+NnAA\nm3zuiHvXHwCfNcY8uMZh73ePOQT8TyJyeOXp3Of0Af8jcEZEdmMX4nnc2Ay7DvAp9/FR4PvuOV7D\nLsD0fxpj3odNTJgHpoFnjDFHsLmIfvOWcnwW2AM8ICI/fS/vhVL1pgFE3W+eAL5ujCkbm7r+BWwg\naDbGvOI+5o9vOeZFY8yiMaaAzVf1AXd/WEROAK8Co8DvAx/CZtR9za1pfBCbyA5s3rH/6m7vAq4Z\nY04AGGOyxhgHm/ju99wlS7+KDRYrXjXGjBmbX+hPVpVDqS3Jt9kFUKrO7ma9g9utb5Fzaxk3nsw2\nV33JGPOP13ievLk5udxa5/4cMGWMOeim087fRTmU2pK0BqLuN68Af0tEgu5CWj+JvRAvisgT7mN+\n7pZjPiwiLSISxi4Z+rK7f60A8G3gZ0SkHUBEEiKybY3HnwO6ROQh93ExN2A0A5PuY34em/5/xcNu\nH4kH20z2MkptYRpA1H3F2MWbXgB+BPwFNg35EjYt9m+5TVK3/rJ/Fdv0dAr4qjHm5MrTrfH8Z4F/\nAnxTRH6ETQ/efevjjTFlbBD4tyJyyn1cEPht4NNu89dO7CqFK44D/xa79sNFY8zX1vMeKLVRNJ27\nuu+ISNQYs+zWKP4a+F+NXRZ3rcc+BzxkjPl7G1rIt5fjaeAfGGM+tpnlUOrd0D4QdT/6XRHZi/3F\n/4e3Cx5KqXujNRCllFLron0gSiml1kUDiFJKqXXRAKKUUmpdNIAopZRaFw0gSiml1kUDiFJKqXX5\n/wG05oZk44C4xQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f7688efd4e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot(x='gdpPercap',y='lifeExp',kind='scatter', alpha = 0.2, s=50, marker='o')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What's going on with those points on the right? \n",
+    "\n",
+    "High gdp per capita, yet not particularly high lifeExp. We can use boolean selection to rapidly subset and check them out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>852</th>\n",
+       "      <td>Kuwait</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>160000</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>55.565</td>\n",
+       "      <td>108382.35290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>853</th>\n",
+       "      <td>Kuwait</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>212846</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>58.033</td>\n",
+       "      <td>113523.13290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>854</th>\n",
+       "      <td>Kuwait</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>358266</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>60.470</td>\n",
+       "      <td>95458.11176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>855</th>\n",
+       "      <td>Kuwait</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>575003</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>64.624</td>\n",
+       "      <td>80894.88326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>856</th>\n",
+       "      <td>Kuwait</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>841934</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>67.712</td>\n",
+       "      <td>109347.86700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>857</th>\n",
+       "      <td>Kuwait</td>\n",
+       "      <td>1977</td>\n",
+       "      <td>1140357</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>69.343</td>\n",
+       "      <td>59265.47714</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    country  year      pop continent  lifeExp     gdpPercap\n",
+       "852  Kuwait  1952   160000      Asia   55.565  108382.35290\n",
+       "853  Kuwait  1957   212846      Asia   58.033  113523.13290\n",
+       "854  Kuwait  1962   358266      Asia   60.470   95458.11176\n",
+       "855  Kuwait  1967   575003      Asia   64.624   80894.88326\n",
+       "856  Kuwait  1972   841934      Asia   67.712  109347.86700\n",
+       "857  Kuwait  1977  1140357      Asia   69.343   59265.47714"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['gdpPercap'] > 55000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f76866f2940>]], dtype=object)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6uPq4rOO8GyQdknRA0qW9Cm5mZkuzaEcv6VeA54AvRMQ7qn1bgWcj4qZZx24EbgUuANYD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\ngg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f76866fc550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist(column='lifeExp')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7686750978>"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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kmQwkSZgMdrRxh4YevO585TbGt5ltqrW79SGaJ9FGtTY3flbVh/+uPr+O9zNJw/4tzK95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\nwmQgScJkIEnCZCBJwmQgSQL+Pz28Lchm1IZCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f768666b5c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.lifeExp.plot.hist(bins=200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7686611e80>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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slRO6iocbm7Vg6dIkuZxwQt6RWGd0757UWpYsyTsSK1XmiUXSREnLJa2UdGUL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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f768664fcc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['lifeExp'].plot(kind='kde')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercise\n",
+    "Write a function that will take two countries as an argument and plot the life expectancy vs year for each country on the same axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def compare_lifeExp(country1, country2):\n",
+    "    \"\"\"Plot life expectancy vs year for country1 and country2\"\"\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "compare_lifeExp('Canada', 'Mexico')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercises\n",
+    "\n",
+    "Suzy wrote some code to determine which country had the lowest life expectancy in 1982.\n",
+    "\n",
+    "What is wrong with her solution?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "country    Afghanistan\n",
+       "lifeExp         38.445\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spec=['country','lifeExp']\n",
+    "df[df['year']==1982][spec].min()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can do a quick check to look up Afghanistan's life expectancy in 1982."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1982</td>\n",
+       "      <td>12881816</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>39.854</td>\n",
+       "      <td>978.011439</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  year       pop continent  lifeExp   gdpPercap\n",
+       "6  Afghanistan  1982  12881816      Asia   39.854  978.011439"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df['year']==1982) & (df['country']=='Afghanistan')]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This doesnt match with the answer above because the min() function was applied to each column (country and lifeExp).\n",
+    "\n",
+    "She should have done this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### Putting it together:\n",
+    "\n",
+    "We can use all of these ideas to generate a plot that looks at a subset of the data. \n",
+    "\n",
+    "* Plot GDP per capita vs life expectancy in 2007 for each continent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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bgJeBDwE/BtYCXUA/cFlE7Kizb+n7MIZ5cp6ZNUtL7+kt6b8Dtw1fvdMk8JcR0ZNh3y8A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\n+EzxhpdMAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f7680051400>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f76800512b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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k+9D/rqTV+X4jgduAq/LmrOOA7WWnfwb4Ltk9D16KiAf68x6YFcXLm9ugI2l0\nRGzPawyPA/8+Ilb3sO+VwOkR8Y2aBrl3HOcBfxwRF9YzDrPeuA/DBqPbJX2c7Bv9nT0lCzPrG9cw\nzMwsifswzMwsiROGmZklccIwM7MkThhmZpbECcPMzJI4YZiZWZL/Dw2yJzbYnemBAAAAAElFTkSu\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f767fcf9588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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DjhUifqAJ+PUgx2sW/2hZfPCVwPqyx0+mx14JPFF2/In0WL9zImIHsFlJMX2o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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f767fcee4e0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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2AtcBlwOzgP8aES/mGVgz8gORzKyRZW3DWAocC3wGOA64CfiHiPhKhmPHAF8Hfh/YC3wW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\ns0ycMMzMLJP/DzsfR6rOiec9AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f767fbae630>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "continents = df.groupby(['continent'])\n",
+    "for continent in continents.groups:\n",
+    "    group = continents.get_group(continent)\n",
+    "    group[group['year']==2007].plot(kind='scatter', x='gdpPercap', y='lifeExp', title=continent)\n",
+    "    plt.axis([-10000,60000,30,90])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f767fa941d0>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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EHzv/kczcTPx8/IhJiWHMc2MICwuTY0KEEFUmOYwGLDIyktE3jybnqxyuzrqa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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f767fb5f3c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Example \n",
+    "fig,ax = plt.subplots(1,1)\n",
+    "colours = ['m','b','r','g','y']\n",
+    "for continent, colour in zip(continents.groups, colours):\n",
+    "    group = continents.get_group(continent)\n",
+    "    group[group['year']==2007].plot(kind='scatter',x='gdpPercap',y='lifeExp',label=continent,ax=ax,color=colour,alpha=0.5)\n",
+    "ax.set_title(2007)\n",
+    "plt.legend(loc='lower right')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exercise\n",
+    "Write a function the takes a country as an argument and plots the life expectancy against GDP per capita for all years in a scatter plot. Also print the year of the minimum/maximum lifeExp and the year of the miniimim/maximum GDP per capita."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def compare_gdp_lifeExp(df,country):\n",
+    "    \"\"\" plot GDP per capita against life expectancy for a given country.\n",
+    "    print year of min/max gdp per capita and life expectancy\n",
+    "    \"\"\"\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "compare_gdp_lifeExp(df,'Afghanistan')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "compare_gdp_lifeExp(df,'Canada')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Rapid plotting with seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/derek/anaconda2/envs/py3/lib/python3.5/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
+      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>country</th>\n",
+       "      <th>year</th>\n",
+       "      <th>pop</th>\n",
+       "      <th>continent</th>\n",
+       "      <th>lifeExp</th>\n",
+       "      <th>gdpPercap</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1952</td>\n",
+       "      <td>8425333</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>28.801</td>\n",
+       "      <td>779.445314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1957</td>\n",
+       "      <td>9240934</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>30.332</td>\n",
+       "      <td>820.853030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1962</td>\n",
+       "      <td>10267083</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>31.997</td>\n",
+       "      <td>853.100710</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1967</td>\n",
+       "      <td>11537966</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>34.020</td>\n",
+       "      <td>836.197138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>13079460</td>\n",
+       "      <td>Asia</td>\n",
+       "      <td>36.088</td>\n",
+       "      <td>739.981106</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       country  year       pop continent  lifeExp   gdpPercap\n",
+       "0  Afghanistan  1952   8425333      Asia   28.801  779.445314\n",
+       "1  Afghanistan  1957   9240934      Asia   30.332  820.853030\n",
+       "2  Afghanistan  1962  10267083      Asia   31.997  853.100710\n",
+       "3  Afghanistan  1967  11537966      Asia   34.020  836.197138\n",
+       "4  Afghanistan  1972  13079460      Asia   36.088  739.981106"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f767e8a5b00>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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MhMb+yAC8sSHIijz/i+fBae21g0GSiIhyEhs7hZGuX855TXI8ULrXfR6CSoNkrB/JcGZX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5OfEULAlHMBpXv3oPTAPliK635kV6ZWQ9unH4IHDIQ6oSsK/ru2zrF9gR06IGbOA+4q22XqDh2h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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f767e8a5be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_context(\"talk\")\n",
+    "sns.factorplot(data=df, x='year', y='lifeExp', hue='continent', size=8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f767e12b518>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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DpMewbJstu49ScbyRQf0C3DD6ckzDcLpbIiLSw7nmdv+RI0eor6/nhhtuAOCyyy5jxIgR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\njNemTZsW23qqvREjRvDSSy+dW0dFJI7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725PqtvmNANC5wGsLqZ0LnB2qPb9rHnZVHzMD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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f767fcdb320>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.regplot(data=df, x='year', y='gdpPercap', fit_reg=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f767e36ef98>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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xefNmli1bxo4dO7jlllvYvXs3R44cOeu5pz1dVVV9p34RTCaN5OREKfcsGUqx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4kD59+jBlyhR69+7N0qVLcTgcQKSz7K9//Sv/9V//xXXXXcfvfvc7Zs+ezZVXXonF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\nCD+RQBVCCD/5f7btOZTKESCvAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f767e36ea58>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(data=df, x='year', y='gdpPercap', row='continent')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f7686655780>"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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tGy4uLsyePZvExEQWL15MRkYGSUlJbNy4ER8fH7Zv305cXBw7d+7E0dGuNl2I\n247dn7b88ssvfP755zzxxBMAuLm5MWzYMLZu3QrAli1bGDp0KC4ul78iPXr0aHbu3ElxcTHbtm2j\nR48e+Pj4ABAVFYVSirS0tLrZGCHqEbv686uUYvr06fz3v//FxcWFESNGEBgYiKZpegAA+Pv7k5WV\nBUB2djYtWvw6rqivry9KKY4cOUJ2djbBwcEWffj5+ZGZmcnDDz9cOxslRD1lN+FhNBoZNGgQw4cP\np3Xr1uzfv59x48aRnJxcaRAVFxcXTCYTACaTST/qgMunPk5OThQVFWEymXB1dbVY1tXVVV9WCGE7\nuwmPxo0bM3fuXP11x44d6dmzJ2+++SalpaUW85pMJv1X6oxGIyUlv472ZDabKS0txWg0YjQaK/3w\n9tXLWkPTNAwGMBjq7qlCg0HDwaHq/u21Lntkj/vKHmuylt2ER2FhIYWFhfj6/voot9ls1o9CcnNz\n8fPzAyArK4ugoCAAAgICyMnJoVOnTsDl0xhHR0datmxJYGAgOTk5Fv1kZ2fry1rDy8sdTdNo1Mj6\nwLnVGjUy4unZ4JrT6sr16rJH9riv7LEma9lNeHz33XfMnDmTDz74gGbNmnHo0CH+/e9/s3r1as6e\nPcvKlSuZN28eFy5cYP369YwdOxaAAQMGkJKSQmRkJO7u7iQnJxMdHY2zszMxMTHExsaSmZlJYGAg\nqampuLu707lzZ6vrys//BYMBCguLamrTb6iwsIhz5y5dc1pduV5d9sge95U91VTdILGb8OjevTsT\nJkxgzJgxaJqGi4sLc+fOpV27drzyyiskJCQQFhaGg4MDffr0oV+/fgAMGTKEY8eOMXDgQACCg4N5\n5ZVXAGjVqhWJiYnEx8dTVlaGt7c3y5cvx2Cw/iaTUoqKCjCb1a3faCuZzYqKiqr7t9e67JE97it7\nrMladhMeACNGjGDEiBGV2hs2bMiSJUuuudyzzz7Ls88+W+W0qKgooqKiblmNQojL7P45DyGEfZLw\nEELYRMJDCGETCQ8hhE0kPIQQNpHwEELYRMJDCGETCQ8hhE0kPIQQNpHwEELYRMJDCGETCQ8hhE0k\nPIQQNpHwEELYRMJDCGETCQ8hhE0kPITd2L8/jYkTxzBx4hj275ff1rF3Eh7CLiilWLUqmfPnCzh/\nvoBVq5JR6vYZ4vBOZFfDEIr6oaysjPT0g9VaprS0hPz8s/rr/Pyz7N+fhrOzy3WWquy++9pU+p0f\nUTMkPMRynPx1AAASI0lEQVQtl55+kE9mzcSnkYfVy5RXcZSRvvQNHDXrf1fkaOF5mJ1Eu3btrV5G\n2E7CQ9QIn0YeBHh5WT1/qdkMJy1/hqClpyfO1RjpXtQueWeEXXDSNO5ycNBf3+XggFM1jjpE7ZPw\nEHZB0zR6NfLE3WDA3WCgVyNPNAkPuyanLcJutHJzo5Vb87ouQ1hJjjyEEDaR8BBC2ETCQwhhEwkP\nIYRNJDyEEDaR8BBC2ETCQwhhEwkPIYRN7ojwOHDgAEOHDqV3795ER0ezefPmui5JiNtevX/CtLS0\nlLi4OGbMmEFkZCR5eXkMHDiQNm3aEBgYWNflCXHbqvdHHnv27EHTNCIjIwHw9fWle/fufPjhh3Vc\nmRC3t3ofHjk5Ofj5+Vm0+fv7k5WVVUcVCVE/1PvwKCoqwtXV1aLN1dUVk8lURxUJUT/U+2seRqOR\n4uJiizaTyYTRaLRqeU3TMBjAYNC4kH+iJkq8rgv5JzAYNBwcqv56usGgUXjiXC1XBYUnzl2zLoNB\nuzyqVy07WnietjfYV2fOHq/lquDM2ePX3Ve5x87Uek25x87Q6v5r7ytraKqejzL79ddf89JLL/Hl\nl1/qbVOnTqVVq1bExcXVYWVC3N7q/WlLly5dcHBwYNOmTQBkZGSwe/duYmJi6rgyIW5v9f7IAy4H\nRmJiIgUFBbi4uDBlyhRCQ0Pruiwhbmt3RHgIIW69en/aIoSoGRIeQgibSHgIIWwi4SGEsImEhxDC\nJhIet8DChQvp3Lkz+/bts2r+qKgoDh8+bFNfISEhdOvWjaioKP2/yMhIRo4cadP6alpV++bSpUsM\nGjSIkJAQ9u/fX2mZNWvWMHv27FvSV02q7vv4ySefEBsbS3h4OBEREQwYMID169fXYIXXN336dP35\nJ5socVNKS0tVjx491Pvvv6+ee+65Gu+vZ8+e6tNPP63xfm6Fa+2bb7/9VrVr106ZzeYa78tevP32\n26p79+7q22+/1dvS09NVZGSk+tOf/lSHldlOjjxu0meffcb9999Pv3792Lt3LxcuXADg8OHDPP74\n40RHRxMeHs5LL71EaWkpAK1bt+bgwYMAfPvttwwePJjo6GhCQ0P529/+dsM+1TUezdm0aRN9+/bV\nXxcUFNC6dWtOnDjB8ePH+cMf/sDq1auJjIzk559/Jj8/n2eeeYbw8HCioqKYMWMGly5dAuDFF18k\nMTGRiRMn0qtXL8LDw/nuu+/0da9bt46oqCjCwsIYOnSovj032jcZGRm88MILlJWVER0dTVpaGiEh\nISxfvpw+ffqwY8cOli1bxsSJEwEwm83MmzeP0NBQIiIiLPbj1fuue/fu3HXXXZXeh2XLlvHCCy/w\n4osv0qtXL/r27Ut6ejpTpkyhV69eDBo0iNOnTwNQUlJCUlIS4eHhhISEMGXKFAoKCvR9O2rUKOLj\n4/WjvKvfx++//57BgwcTHh7OgAED2LNnDwDl5eUkJCTw5z//mfLyct544w2OHj0KgJ+fH/fccw/v\nvPMOoaGhDBgwgEcffbTSOpRSvPnmm0RERBAaGsqYMWP0dVx5L/r06UNERAR9+/blX//6lz6tdevW\nbN26ldjYWLp168aTTz5JSUkJACNGjGD16tUA/Pzzz0yYMIGoqCh69erFtGnT9P18TXWdXre7ESNG\nqK+//loppdTcuXPVO++8o5RS6plnnlFLly7V55s7d67au3evUkqpoKAg9eOPPyqllOratavavn27\nUkqprKwsdd9996lDhw5ds7+ePXuqHTt2VDlt48aNqk+fPvrrc+fOqdatW6vjx4+rY8eOqaCgIJWc\nnKxPnzx5skpISFBKKVVRUaEmTJigXn31VaWUUjNmzFCdOnVSeXl5SimlVq1apcLCwpRSSn3yySeq\na9eu6uTJk0oppTZv3qxCQkKs3jd79+5VDzzwgMU2TZgwQX+9dOlS/fWqVavU8OHDVXl5uaqoqFBP\nPPGEvl+v3neDBg1SrVu3VocOHbLoa+nSpapTp04qNzdXKaXUY489prp166ZOnz6tv162bJlSSqk5\nc+aoUaNGqZKSEqWUUgkJCWratGn6vm3fvr1KS0vT67zyPpaUlKiHHnpIffXVV0qpy0dWHTt2VL/8\n8otau3atCgkJ0bf32WefVXFxcUoppdasWaNGjRqloqOj1d///nf1wAMPqFmzZlVax9tvv62io6PV\nhQsXlFJKvfnmm+rxxx9XSl3+N9O2bVv1888/K6WUWrdunerSpYtFjVe2obi4WPXo0UNt2rRJKaXU\n8OHD1apVq5RSSk2dOlUlJiYqpZQymUwqLCxMpaSkVHpPryZHHjchJyeHY8eO8dBDDwEwcOBAUlNT\nAWjSpAlfffUVe/fupbS0lJkzZ/Lggw9WWscXX3xBREQEAK1atcLLy4vc3Nzr9puUlGRxvSMqKor3\n3nuvynnVb45SwsLCAKioqOCLL75g9OjRABgMBmJjY/nnP/+pz9u1a1d8fHwAePTRRzl69CinTp1i\nx44dxMTE0KxZM31aUVGRxZHJ9fZNVUJCQqps//TTT+nTpw8ODg4YDAZWrFjBpEmTgF/3XU5ODvn5\n+fq++21fQUFB+Pr6AhAYGMj9999PkyZN9NcnT54EYMeOHYwZMwZnZ2cARo4cyccff6yvp0GDBnTu\n3LlSjfv27cNgMNC1a1cAOnXqxJdffonRaCQ2Npann35a769Lly76++vt7U1WVhaapnHmzBmMRiOJ\niYmV1rFjxw5iY2O566679Lr279/PmTNnaNWqFfv27aNp06b6+gsLC/UjL4B+/foB4OLiQqtWrfTt\nvdqiRYt46aWXgMtDVrRt25YjR45U+Z5cUe+/kl+T1q9fT35+Pl26dAEuf1BNJhP79u3jhRdeYNWq\nVSQlJXH06FEiIiJISEjA3d3dYh0bNmzgH//4B5cuXULTNAoKCjCbzdftNyEhQQ+Bq93o4pemaXh4\neADo/TRu3Fif3rhxY/Lz8y1eX9GoUSMACgsLKSwsZO/evXzxxRf6djs6Olose719U5Urdf1WQUGB\n/qEB9A82/LrvcnNzuXjxIkopXnjhBRwdHS36atCggb6Mg4ODxXvg4OCgH8YXFhYyZ84cXFxcgMun\nTA0aNNBPXa5XY8OGDS3arvRx8uRJNm7cSG5uLpGRkVy8eFHfr6GhoZhMJl5++WXefvttnJ2dOXz4\nMK1atbJYR2FhIcnJyaxZs0bfl15eXpw9e5ZGjRrx+uuv8/XXX1NRUUF5eble+xVX1+bo6EhFRUWl\nbfjmm29YuXIlJ0+exGAwcPbsWR599NEqt1df13WnimsqLS1ly5YtbN++nXvvvVdvf++993j//ffp\n1KkTEydOZOLEiZw4cYIpU6awatUqi2EA0tLSmD9/PqmpqQQFBQHw8MMP37Dv3x5NXGEwGCz+0RQW\nFqJpVY/X4OnpiYODA+fOncPT0xOA/Px8/S8koH9oAM6fvzw+R6NGjWjWrBnBwcHEx8dXue4b7ZvB\ngwffcBuv+N3vfmdRx6VLlzCZTOTk5DB//nzWrFnDuHHj+Pzzzxk8eDCJiYn07t1b7+u3o8hdT7Nm\nzUhMTNSPIKzl5eVlUSPAsWPH8Pb2Ztq0adx99900aNCA559/nlOnTrFu3Tp9vtatW2M2m0lMTGTu\n3LkkJibqR5FX1tGsWTOioqIYMmRIpb6XLl3K3r17ee+992jcuDGHDx+mT58+13zfq1JcXMykSZOY\nOXMmgwYNAi4PW3Ejctpio+3bt+Pr62vx4QAIDw9n586dPPnkk+zduxeAu+++Gx8fn0pv6MWLF7nr\nrrto2bIlcPnDVV5eTlFRkU013X333Zw4cUL/oP/jH/+wmH516BgMBkJCQnj33XeByx/4devW0bt3\nb32evXv36oe4W7dupUWLFjRt2pSIiAg+/PBDzpy5PIjN0aNHeeaZZ/QLbDfaN4WFhdcMwN/q3bs3\nGzdupKSkhIqKCqZNm8a6dev0fffTTz/h6+vLrl27LPbdlb6u7AtrREREkJKSQllZGQCff/45r7/+\n+g2X69ChA46OjvopzoEDB3j00UcpKyvj0qVLtGnThvj4eGbNmsX69ev1Uezmzp3LmDFjePLJJ+nX\nrx/Ozs6cO3eu0joiIiLYsGGDfjH7wIEDzJgxA7gcps2bN6dx48aUlJToRye//PKL1dtdXl5OcXEx\nwcHBAOzfv5/vv//+huuQIw8bpaamEhUVVand29ubNm3a8PDDD7NgwQKKi4tRSvGHP/yBMWPGAOgh\n0q1bN9q3b09YWBienp6MGjWK2NhY5s+fzz333FPlNRJN00hKSmLx4sV6m1IKTdP461//SlhYGH37\n9qVZs2aMGzfOIrB+G16JiYm8+uqrREZGomkaDz74IE8//bQ+vVu3biQlJfHTTz9hMBj0D9IjjzzC\n2LFjGTNmDEopnJycmDRpkn5KcaN9c/LkyevWdbXHH3+c06dPEx4ejqurKx06dGDixIlomkb79u2Z\nNWsWXl5eNGzYsNK+a9OmDVu3bqVjx47XXP/VnnrqKRYuXEhMTAwGgwFPT08SEhKuOf+Vup2dnUlO\nTmbmzJksWrQIo9HIkiVLcHd355lnnuHVV1/F1dWVe+65h5KSEk6cOMEDDzyAn58fjRs3Ztu2bXzy\nySf4+vpSXFxM7969LdYxZMgQzp49y5AhQzAYDBiNRp577jkAhg8fztSpUwkPD8fLy4uZM2fy008/\nMWLECDZt2nTdfXtlWoMGDYiLi2P8+PF4eHjwxz/+kZdffpnp06cTEBDA2LFjq15eWfsnQNxRXnzx\nRdzd3a/74RF3NjltEULYRMJDCGETOW0RQthEjjyEEDaR8BBC2ETCQwhhEwkPIYRNJDyEXYmIiNCf\nkhT2Te62iDqVkZHBmTNneOSRR2q974sXL/LRRx8xbNiwWu+7PpAjD1GnPvjgA/7973/XSd+7d++u\n02EAb3dy5CGscvz4cV599VXS0tJo0KAB/fv3Jz4+nkuXLjF//ny++uorCgsLad26NdOmTeOBBx4A\nLo/T8eSTT7J7926+/vpr3N3defbZZ+nXrx8vv/wyH3zwAQaDgYYNG7Jnzx5CQkIYPXo0I0eO5MUX\nX8TJyQlPT09SU1MpLy+nT58+vPLKK3pdK1euZNOmTZw6dYrmzZsTFxdHeHg4wHWX37JlCy+99JL+\n3Zz169dz33331cm+vW1dd6ggIf6nX79+atasWcpkMqm8vDzVvXt3lZKSouLj49WwYcPU6dOnVUlJ\niXrttdfUgw8+qC5evKiUujxKWK9evdS+fftUeXm5Wrx4sXrggQf00bqGDx9uMYZnz5491d///nel\n1OXRzP7f//t/KiUlRZWVlamvvvpKBQUF6eOAvvfee6pnz54qKytLVVRUqE8//VQFBwerI0eOWLX8\n0qVLVb9+/WptH9Y3ctoibig9PZ2MjAzi4uJwdXXFx8eHN954g7Zt2/Lxxx/z7LPP0qRJE5ydnfWv\n5n/11Vf68o888ggdO3bEwcGBPn36YDKZOHHihFV9e3p68vjjj+Po6EjXrl3x9PQkOzsbuPzt3ZEj\nR9KqVSsMBgNhYWE8+OCDbN682arlxc2Rr+SLG8rLy8PZ2RkvLy+97f777yc9PR1AH/kKwMnJiXvu\nuYe8vDy97eoBeVxdXVFKUVxcbFXfV4YPvHr5K8seOXKE119/nUWLFgGXhyZQSuHt7W3V8uLmSHiI\nGzIYDFUO3nO90bWvHkfCYLD9ANfBweGa01xdXXn55ZctRoyvzvLi5shpi7ghHx8fysrKOH78uN6W\nlpamn3ocOnRIby8qKuL48eO0aNGixuvy8/Pjv//9r0XbyZMnrR6lTNwcCQ9xQ61btyY4OJhFixZx\n6dIljh07xsyZMzlz5gw9evRg6dKl5OfnU1RUxOuvv07Dhg354x//aNW6XV1dOXbsmD6AcXU8/vjj\nfPDBB+zduxez2UxaWhoxMTGkpaVZtbyLiwtnz56loKDgxr9RIiqR8BBWWblyJYWFhTzyyCMMHz6c\nqKgoRo4cybx587j77rvp168fvXr14tixY6SkpODq6gpUPcTg1W0DBw7km2++ITQ0VB9B3pqh8+Dy\nTwqMGzeO6dOn06FDB2bPns3LL7+sj9h+o+XDwsJwcnIiJCTE6sARv5LnPIQQNpEjDyGETSQ8hBA2\nkfAQQthEwkMIYRMJDyGETSQ8hBA2kfAQQthEwkMIYRMJDyGETf4/ou9k07N58/EAAAAASUVORK5C\nYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f767fcb8240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.factorplot(data=df, x='continent', y='gdpPercap', kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f767fc5fd30>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g = sns.FacetGrid(df, col='continent', row='year')\n",
+    "g.map(plt.hist, 'lifeExp')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/lessons/python/pandas2/gapminderDataFiveYear.txt b/lessons/python/pandas2/gapminderDataFiveYear.txt
new file mode 100644
index 000000000..6ffc0de12
--- /dev/null
+++ b/lessons/python/pandas2/gapminderDataFiveYear.txt
@@ -0,0 +1,1705 @@
+country	year	pop	continent	lifeExp	gdpPercap
+Afghanistan	1952	8425333	Asia	28.801	779.4453145
+Afghanistan	1957	9240934	Asia	30.332	820.8530296
+Afghanistan	1962	10267083	Asia	31.997	853.10071
+Afghanistan	1967	11537966	Asia	34.02	836.1971382
+Afghanistan	1972	13079460	Asia	36.088	739.9811058
+Afghanistan	1977	14880372	Asia	38.438	786.11336
+Afghanistan	1982	12881816	Asia	39.854	978.0114388
+Afghanistan	1987	13867957	Asia	40.822	852.3959448
+Afghanistan	1992	16317921	Asia	41.674	649.3413952
+Afghanistan	1997	22227415	Asia	41.763	635.341351
+Afghanistan	2002	25268405	Asia	42.129	726.7340548
+Afghanistan	2007	31889923	Asia	43.828	974.5803384
+Albania	1952	1282697	Europe	55.23	1601.056136
+Albania	1957	1476505	Europe	59.28	1942.284244
+Albania	1962	1728137	Europe	64.82	2312.888958
+Albania	1967	1984060	Europe	66.22	2760.196931
+Albania	1972	2263554	Europe	67.69	3313.422188
+Albania	1977	2509048	Europe	68.93	3533.00391
+Albania	1982	2780097	Europe	70.42	3630.880722
+Albania	1987	3075321	Europe	72	3738.932735
+Albania	1992	3326498	Europe	71.581	2497.437901
+Albania	1997	3428038	Europe	72.95	3193.054604
+Albania	2002	3508512	Europe	75.651	4604.211737
+Albania	2007	3600523	Europe	76.423	5937.029526
+Algeria	1952	9279525	Africa	43.077	2449.008185
+Algeria	1957	10270856	Africa	45.685	3013.976023
+Algeria	1962	11000948	Africa	48.303	2550.81688
+Algeria	1967	12760499	Africa	51.407	3246.991771
+Algeria	1972	14760787	Africa	54.518	4182.663766
+Algeria	1977	17152804	Africa	58.014	4910.416756
+Algeria	1982	20033753	Africa	61.368	5745.160213
+Algeria	1987	23254956	Africa	65.799	5681.358539
+Algeria	1992	26298373	Africa	67.744	5023.216647
+Algeria	1997	29072015	Africa	69.152	4797.295051
+Algeria	2002	31287142	Africa	70.994	5288.040382
+Algeria	2007	33333216	Africa	72.301	6223.367465
+Angola	1952	4232095	Africa	30.015	3520.610273
+Angola	1957	4561361	Africa	31.999	3827.940465
+Angola	1962	4826015	Africa	34	4269.276742
+Angola	1967	5247469	Africa	35.985	5522.776375
+Angola	1972	5894858	Africa	37.928	5473.288005
+Angola	1977	6162675	Africa	39.483	3008.647355
+Angola	1982	7016384	Africa	39.942	2756.953672
+Angola	1987	7874230	Africa	39.906	2430.208311
+Angola	1992	8735988	Africa	40.647	2627.845685
+Angola	1997	9875024	Africa	40.963	2277.140884
+Angola	2002	10866106	Africa	41.003	2773.287312
+Angola	2007	12420476	Africa	42.731	4797.231267
+Argentina	1952	17876956	Americas	62.485	5911.315053
+Argentina	1957	19610538	Americas	64.399	6856.856212
+Argentina	1962	21283783	Americas	65.142	7133.166023
+Argentina	1967	22934225	Americas	65.634	8052.953021
+Argentina	1972	24779799	Americas	67.065	9443.038526
+Argentina	1977	26983828	Americas	68.481	10079.02674
+Argentina	1982	29341374	Americas	69.942	8997.897412
+Argentina	1987	31620918	Americas	70.774	9139.671389
+Argentina	1992	33958947	Americas	71.868	9308.41871
+Argentina	1997	36203463	Americas	73.275	10967.28195
+Argentina	2002	38331121	Americas	74.34	8797.640716
+Argentina	2007	40301927	Americas	75.32	12779.37964
+Australia	1952	8691212	Oceania	69.12	10039.59564
+Australia	1957	9712569	Oceania	70.33	10949.64959
+Australia	1962	10794968	Oceania	70.93	12217.22686
+Australia	1967	11872264	Oceania	71.1	14526.12465
+Australia	1972	13177000	Oceania	71.93	16788.62948
+Australia	1977	14074100	Oceania	73.49	18334.19751
+Australia	1982	15184200	Oceania	74.74	19477.00928
+Australia	1987	16257249	Oceania	76.32	21888.88903
+Australia	1992	17481977	Oceania	77.56	23424.76683
+Australia	1997	18565243	Oceania	78.83	26997.93657
+Australia	2002	19546792	Oceania	80.37	30687.75473
+Australia	2007	20434176	Oceania	81.235	34435.36744
+Austria	1952	6927772	Europe	66.8	6137.076492
+Austria	1957	6965860	Europe	67.48	8842.59803
+Austria	1962	7129864	Europe	69.54	10750.72111
+Austria	1967	7376998	Europe	70.14	12834.6024
+Austria	1972	7544201	Europe	70.63	16661.6256
+Austria	1977	7568430	Europe	72.17	19749.4223
+Austria	1982	7574613	Europe	73.18	21597.08362
+Austria	1987	7578903	Europe	74.94	23687.82607
+Austria	1992	7914969	Europe	76.04	27042.01868
+Austria	1997	8069876	Europe	77.51	29095.92066
+Austria	2002	8148312	Europe	78.98	32417.60769
+Austria	2007	8199783	Europe	79.829	36126.4927
+Bahrain	1952	120447	Asia	50.939	9867.084765
+Bahrain	1957	138655	Asia	53.832	11635.79945
+Bahrain	1962	171863	Asia	56.923	12753.27514
+Bahrain	1967	202182	Asia	59.923	14804.6727
+Bahrain	1972	230800	Asia	63.3	18268.65839
+Bahrain	1977	297410	Asia	65.593	19340.10196
+Bahrain	1982	377967	Asia	69.052	19211.14731
+Bahrain	1987	454612	Asia	70.75	18524.02406
+Bahrain	1992	529491	Asia	72.601	19035.57917
+Bahrain	1997	598561	Asia	73.925	20292.01679
+Bahrain	2002	656397	Asia	74.795	23403.55927
+Bahrain	2007	708573	Asia	75.635	29796.04834
+Bangladesh	1952	46886859	Asia	37.484	684.2441716
+Bangladesh	1957	51365468	Asia	39.348	661.6374577
+Bangladesh	1962	56839289	Asia	41.216	686.3415538
+Bangladesh	1967	62821884	Asia	43.453	721.1860862
+Bangladesh	1972	70759295	Asia	45.252	630.2336265
+Bangladesh	1977	80428306	Asia	46.923	659.8772322
+Bangladesh	1982	93074406	Asia	50.009	676.9818656
+Bangladesh	1987	103764241	Asia	52.819	751.9794035
+Bangladesh	1992	113704579	Asia	56.018	837.8101643
+Bangladesh	1997	123315288	Asia	59.412	972.7700352
+Bangladesh	2002	135656790	Asia	62.013	1136.39043
+Bangladesh	2007	150448339	Asia	64.062	1391.253792
+Belgium	1952	8730405	Europe	68	8343.105127
+Belgium	1957	8989111	Europe	69.24	9714.960623
+Belgium	1962	9218400	Europe	70.25	10991.20676
+Belgium	1967	9556500	Europe	70.94	13149.04119
+Belgium	1972	9709100	Europe	71.44	16672.14356
+Belgium	1977	9821800	Europe	72.8	19117.97448
+Belgium	1982	9856303	Europe	73.93	20979.84589
+Belgium	1987	9870200	Europe	75.35	22525.56308
+Belgium	1992	10045622	Europe	76.46	25575.57069
+Belgium	1997	10199787	Europe	77.53	27561.19663
+Belgium	2002	10311970	Europe	78.32	30485.88375
+Belgium	2007	10392226	Europe	79.441	33692.60508
+Benin	1952	1738315	Africa	38.223	1062.7522
+Benin	1957	1925173	Africa	40.358	959.6010805
+Benin	1962	2151895	Africa	42.618	949.4990641
+Benin	1967	2427334	Africa	44.885	1035.831411
+Benin	1972	2761407	Africa	47.014	1085.796879
+Benin	1977	3168267	Africa	49.19	1029.161251
+Benin	1982	3641603	Africa	50.904	1277.897616
+Benin	1987	4243788	Africa	52.337	1225.85601
+Benin	1992	4981671	Africa	53.919	1191.207681
+Benin	1997	6066080	Africa	54.777	1232.975292
+Benin	2002	7026113	Africa	54.406	1372.877931
+Benin	2007	8078314	Africa	56.728	1441.284873
+Bolivia	1952	2883315	Americas	40.414	2677.326347
+Bolivia	1957	3211738	Americas	41.89	2127.686326
+Bolivia	1962	3593918	Americas	43.428	2180.972546
+Bolivia	1967	4040665	Americas	45.032	2586.886053
+Bolivia	1972	4565872	Americas	46.714	2980.331339
+Bolivia	1977	5079716	Americas	50.023	3548.097832
+Bolivia	1982	5642224	Americas	53.859	3156.510452
+Bolivia	1987	6156369	Americas	57.251	2753.69149
+Bolivia	1992	6893451	Americas	59.957	2961.699694
+Bolivia	1997	7693188	Americas	62.05	3326.143191
+Bolivia	2002	8445134	Americas	63.883	3413.26269
+Bolivia	2007	9119152	Americas	65.554	3822.137084
+Bosnia and Herzegovina	1952	2791000	Europe	53.82	973.5331948
+Bosnia and Herzegovina	1957	3076000	Europe	58.45	1353.989176
+Bosnia and Herzegovina	1962	3349000	Europe	61.93	1709.683679
+Bosnia and Herzegovina	1967	3585000	Europe	64.79	2172.352423
+Bosnia and Herzegovina	1972	3819000	Europe	67.45	2860.16975
+Bosnia and Herzegovina	1977	4086000	Europe	69.86	3528.481305
+Bosnia and Herzegovina	1982	4172693	Europe	70.69	4126.613157
+Bosnia and Herzegovina	1987	4338977	Europe	71.14	4314.114757
+Bosnia and Herzegovina	1992	4256013	Europe	72.178	2546.781445
+Bosnia and Herzegovina	1997	3607000	Europe	73.244	4766.355904
+Bosnia and Herzegovina	2002	4165416	Europe	74.09	6018.975239
+Bosnia and Herzegovina	2007	4552198	Europe	74.852	7446.298803
+Botswana	1952	442308	Africa	47.622	851.2411407
+Botswana	1957	474639	Africa	49.618	918.2325349
+Botswana	1962	512764	Africa	51.52	983.6539764
+Botswana	1967	553541	Africa	53.298	1214.709294
+Botswana	1972	619351	Africa	56.024	2263.611114
+Botswana	1977	781472	Africa	59.319	3214.857818
+Botswana	1982	970347	Africa	61.484	4551.14215
+Botswana	1987	1151184	Africa	63.622	6205.88385
+Botswana	1992	1342614	Africa	62.745	7954.111645
+Botswana	1997	1536536	Africa	52.556	8647.142313
+Botswana	2002	1630347	Africa	46.634	11003.60508
+Botswana	2007	1639131	Africa	50.728	12569.85177
+Brazil	1952	56602560	Americas	50.917	2108.944355
+Brazil	1957	65551171	Americas	53.285	2487.365989
+Brazil	1962	76039390	Americas	55.665	3336.585802
+Brazil	1967	88049823	Americas	57.632	3429.864357
+Brazil	1972	100840058	Americas	59.504	4985.711467
+Brazil	1977	114313951	Americas	61.489	6660.118654
+Brazil	1982	128962939	Americas	63.336	7030.835878
+Brazil	1987	142938076	Americas	65.205	7807.095818
+Brazil	1992	155975974	Americas	67.057	6950.283021
+Brazil	1997	168546719	Americas	69.388	7957.980824
+Brazil	2002	179914212	Americas	71.006	8131.212843
+Brazil	2007	190010647	Americas	72.39	9065.800825
+Bulgaria	1952	7274900	Europe	59.6	2444.286648
+Bulgaria	1957	7651254	Europe	66.61	3008.670727
+Bulgaria	1962	8012946	Europe	69.51	4254.337839
+Bulgaria	1967	8310226	Europe	70.42	5577.0028
+Bulgaria	1972	8576200	Europe	70.9	6597.494398
+Bulgaria	1977	8797022	Europe	70.81	7612.240438
+Bulgaria	1982	8892098	Europe	71.08	8224.191647
+Bulgaria	1987	8971958	Europe	71.34	8239.854824
+Bulgaria	1992	8658506	Europe	71.19	6302.623438
+Bulgaria	1997	8066057	Europe	70.32	5970.38876
+Bulgaria	2002	7661799	Europe	72.14	7696.777725
+Bulgaria	2007	7322858	Europe	73.005	10680.79282
+Burkina Faso	1952	4469979	Africa	31.975	543.2552413
+Burkina Faso	1957	4713416	Africa	34.906	617.1834648
+Burkina Faso	1962	4919632	Africa	37.814	722.5120206
+Burkina Faso	1967	5127935	Africa	40.697	794.8265597
+Burkina Faso	1972	5433886	Africa	43.591	854.7359763
+Burkina Faso	1977	5889574	Africa	46.137	743.3870368
+Burkina Faso	1982	6634596	Africa	48.122	807.1985855
+Burkina Faso	1987	7586551	Africa	49.557	912.0631417
+Burkina Faso	1992	8878303	Africa	50.26	931.7527731
+Burkina Faso	1997	10352843	Africa	50.324	946.2949618
+Burkina Faso	2002	12251209	Africa	50.65	1037.645221
+Burkina Faso	2007	14326203	Africa	52.295	1217.032994
+Burundi	1952	2445618	Africa	39.031	339.2964587
+Burundi	1957	2667518	Africa	40.533	379.5646281
+Burundi	1962	2961915	Africa	42.045	355.2032273
+Burundi	1967	3330989	Africa	43.548	412.9775136
+Burundi	1972	3529983	Africa	44.057	464.0995039
+Burundi	1977	3834415	Africa	45.91	556.1032651
+Burundi	1982	4580410	Africa	47.471	559.603231
+Burundi	1987	5126023	Africa	48.211	621.8188189
+Burundi	1992	5809236	Africa	44.736	631.6998778
+Burundi	1997	6121610	Africa	45.326	463.1151478
+Burundi	2002	7021078	Africa	47.36	446.4035126
+Burundi	2007	8390505	Africa	49.58	430.0706916
+Cambodia	1952	4693836	Asia	39.417	368.4692856
+Cambodia	1957	5322536	Asia	41.366	434.0383364
+Cambodia	1962	6083619	Asia	43.415	496.9136476
+Cambodia	1967	6960067	Asia	45.415	523.4323142
+Cambodia	1972	7450606	Asia	40.317	421.6240257
+Cambodia	1977	6978607	Asia	31.22	524.9721832
+Cambodia	1982	7272485	Asia	50.957	624.4754784
+Cambodia	1987	8371791	Asia	53.914	683.8955732
+Cambodia	1992	10150094	Asia	55.803	682.3031755
+Cambodia	1997	11782962	Asia	56.534	734.28517
+Cambodia	2002	12926707	Asia	56.752	896.2260153
+Cambodia	2007	14131858	Asia	59.723	1713.778686
+Cameroon	1952	5009067	Africa	38.523	1172.667655
+Cameroon	1957	5359923	Africa	40.428	1313.048099
+Cameroon	1962	5793633	Africa	42.643	1399.607441
+Cameroon	1967	6335506	Africa	44.799	1508.453148
+Cameroon	1972	7021028	Africa	47.049	1684.146528
+Cameroon	1977	7959865	Africa	49.355	1783.432873
+Cameroon	1982	9250831	Africa	52.961	2367.983282
+Cameroon	1987	10780667	Africa	54.985	2602.664206
+Cameroon	1992	12467171	Africa	54.314	1793.163278
+Cameroon	1997	14195809	Africa	52.199	1694.337469
+Cameroon	2002	15929988	Africa	49.856	1934.011449
+Cameroon	2007	17696293	Africa	50.43	2042.09524
+Canada	1952	14785584	Americas	68.75	11367.16112
+Canada	1957	17010154	Americas	69.96	12489.95006
+Canada	1962	18985849	Americas	71.3	13462.48555
+Canada	1967	20819767	Americas	72.13	16076.58803
+Canada	1972	22284500	Americas	72.88	18970.57086
+Canada	1977	23796400	Americas	74.21	22090.88306
+Canada	1982	25201900	Americas	75.76	22898.79214
+Canada	1987	26549700	Americas	76.86	26626.51503
+Canada	1992	28523502	Americas	77.95	26342.88426
+Canada	1997	30305843	Americas	78.61	28954.92589
+Canada	2002	31902268	Americas	79.77	33328.96507
+Canada	2007	33390141	Americas	80.653	36319.23501
+Central African Republic	1952	1291695	Africa	35.463	1071.310713
+Central African Republic	1957	1392284	Africa	37.464	1190.844328
+Central African Republic	1962	1523478	Africa	39.475	1193.068753
+Central African Republic	1967	1733638	Africa	41.478	1136.056615
+Central African Republic	1972	1927260	Africa	43.457	1070.013275
+Central African Republic	1977	2167533	Africa	46.775	1109.374338
+Central African Republic	1982	2476971	Africa	48.295	956.7529907
+Central African Republic	1987	2840009	Africa	50.485	844.8763504
+Central African Republic	1992	3265124	Africa	49.396	747.9055252
+Central African Republic	1997	3696513	Africa	46.066	740.5063317
+Central African Republic	2002	4048013	Africa	43.308	738.6906068
+Central African Republic	2007	4369038	Africa	44.741	706.016537
+Chad	1952	2682462	Africa	38.092	1178.665927
+Chad	1957	2894855	Africa	39.881	1308.495577
+Chad	1962	3150417	Africa	41.716	1389.817618
+Chad	1967	3495967	Africa	43.601	1196.810565
+Chad	1972	3899068	Africa	45.569	1104.103987
+Chad	1977	4388260	Africa	47.383	1133.98495
+Chad	1982	4875118	Africa	49.517	797.9081006
+Chad	1987	5498955	Africa	51.051	952.386129
+Chad	1992	6429417	Africa	51.724	1058.0643
+Chad	1997	7562011	Africa	51.573	1004.961353
+Chad	2002	8835739	Africa	50.525	1156.18186
+Chad	2007	10238807	Africa	50.651	1704.063724
+Chile	1952	6377619	Americas	54.745	3939.978789
+Chile	1957	7048426	Americas	56.074	4315.622723
+Chile	1962	7961258	Americas	57.924	4519.094331
+Chile	1967	8858908	Americas	60.523	5106.654313
+Chile	1972	9717524	Americas	63.441	5494.024437
+Chile	1977	10599793	Americas	67.052	4756.763836
+Chile	1982	11487112	Americas	70.565	5095.665738
+Chile	1987	12463354	Americas	72.492	5547.063754
+Chile	1992	13572994	Americas	74.126	7596.125964
+Chile	1997	14599929	Americas	75.816	10118.05318
+Chile	2002	15497046	Americas	77.86	10778.78385
+Chile	2007	16284741	Americas	78.553	13171.63885
+China	1952	556263527.999989	Asia	44	400.448610699994
+China	1957	637408000	Asia	50.54896	575.9870009
+China	1962	665770000	Asia	44.50136	487.6740183
+China	1967	754550000	Asia	58.38112	612.7056934
+China	1972	862030000	Asia	63.11888	676.9000921
+China	1977	943455000	Asia	63.96736	741.2374699
+China	1982	1000281000	Asia	65.525	962.4213805
+China	1987	1084035000	Asia	67.274	1378.904018
+China	1992	1164970000	Asia	68.69	1655.784158
+China	1997	1230075000	Asia	70.426	2289.234136
+China	2002	1280400000	Asia	72.028	3119.280896
+China	2007	1318683096	Asia	72.961	4959.114854
+Colombia	1952	12350771	Americas	50.643	2144.115096
+Colombia	1957	14485993	Americas	55.118	2323.805581
+Colombia	1962	17009885	Americas	57.863	2492.351109
+Colombia	1967	19764027	Americas	59.963	2678.729839
+Colombia	1972	22542890	Americas	61.623	3264.660041
+Colombia	1977	25094412	Americas	63.837	3815.80787
+Colombia	1982	27764644	Americas	66.653	4397.575659
+Colombia	1987	30964245	Americas	67.768	4903.2191
+Colombia	1992	34202721	Americas	68.421	5444.648617
+Colombia	1997	37657830	Americas	70.313	6117.361746
+Colombia	2002	41008227	Americas	71.682	5755.259962
+Colombia	2007	44227550	Americas	72.889	7006.580419
+Comoros	1952	153936	Africa	40.715	1102.990936
+Comoros	1957	170928	Africa	42.46	1211.148548
+Comoros	1962	191689	Africa	44.467	1406.648278
+Comoros	1967	217378	Africa	46.472	1876.029643
+Comoros	1972	250027	Africa	48.944	1937.577675
+Comoros	1977	304739	Africa	50.939	1172.603047
+Comoros	1982	348643	Africa	52.933	1267.100083
+Comoros	1987	395114	Africa	54.926	1315.980812
+Comoros	1992	454429	Africa	57.939	1246.90737
+Comoros	1997	527982	Africa	60.66	1173.618235
+Comoros	2002	614382	Africa	62.974	1075.811558
+Comoros	2007	710960	Africa	65.152	986.1478792
+Congo, Dem. Rep.	1952	14100005	Africa	39.143	780.5423257
+Congo, Dem. Rep.	1957	15577932	Africa	40.652	905.8602303
+Congo, Dem. Rep.	1962	17486434	Africa	42.122	896.3146335
+Congo, Dem. Rep.	1967	19941073	Africa	44.056	861.5932424
+Congo, Dem. Rep.	1972	23007669	Africa	45.989	904.8960685
+Congo, Dem. Rep.	1977	26480870	Africa	47.804	795.757282
+Congo, Dem. Rep.	1982	30646495	Africa	47.784	673.7478181
+Congo, Dem. Rep.	1987	35481645	Africa	47.412	672.774812
+Congo, Dem. Rep.	1992	41672143	Africa	45.548	457.7191807
+Congo, Dem. Rep.	1997	47798986	Africa	42.587	312.188423
+Congo, Dem. Rep.	2002	55379852	Africa	44.966	241.1658765
+Congo, Dem. Rep.	2007	64606759	Africa	46.462	277.5518587
+Congo, Rep.	1952	854885	Africa	42.111	2125.621418
+Congo, Rep.	1957	940458	Africa	45.053	2315.056572
+Congo, Rep.	1962	1047924	Africa	48.435	2464.783157
+Congo, Rep.	1967	1179760	Africa	52.04	2677.939642
+Congo, Rep.	1972	1340458	Africa	54.907	3213.152683
+Congo, Rep.	1977	1536769	Africa	55.625	3259.178978
+Congo, Rep.	1982	1774735	Africa	56.695	4879.507522
+Congo, Rep.	1987	2064095	Africa	57.47	4201.194937
+Congo, Rep.	1992	2409073	Africa	56.433	4016.239529
+Congo, Rep.	1997	2800947	Africa	52.962	3484.164376
+Congo, Rep.	2002	3328795	Africa	52.97	3484.06197
+Congo, Rep.	2007	3800610	Africa	55.322	3632.557798
+Costa Rica	1952	926317	Americas	57.206	2627.009471
+Costa Rica	1957	1112300	Americas	60.026	2990.010802
+Costa Rica	1962	1345187	Americas	62.842	3460.937025
+Costa Rica	1967	1588717	Americas	65.424	4161.727834
+Costa Rica	1972	1834796	Americas	67.849	5118.146939
+Costa Rica	1977	2108457	Americas	70.75	5926.876967
+Costa Rica	1982	2424367	Americas	73.45	5262.734751
+Costa Rica	1987	2799811	Americas	74.752	5629.915318
+Costa Rica	1992	3173216	Americas	75.713	6160.416317
+Costa Rica	1997	3518107	Americas	77.26	6677.045314
+Costa Rica	2002	3834934	Americas	78.123	7723.447195
+Costa Rica	2007	4133884	Americas	78.782	9645.06142
+Cote d'Ivoire	1952	2977019	Africa	40.477	1388.594732
+Cote d'Ivoire	1957	3300000	Africa	42.469	1500.895925
+Cote d'Ivoire	1962	3832408	Africa	44.93	1728.869428
+Cote d'Ivoire	1967	4744870	Africa	47.35	2052.050473
+Cote d'Ivoire	1972	6071696	Africa	49.801	2378.201111
+Cote d'Ivoire	1977	7459574	Africa	52.374	2517.736547
+Cote d'Ivoire	1982	9025951	Africa	53.983	2602.710169
+Cote d'Ivoire	1987	10761098	Africa	54.655	2156.956069
+Cote d'Ivoire	1992	12772596	Africa	52.044	1648.073791
+Cote d'Ivoire	1997	14625967	Africa	47.991	1786.265407
+Cote d'Ivoire	2002	16252726	Africa	46.832	1648.800823
+Cote d'Ivoire	2007	18013409	Africa	48.328	1544.750112
+Croatia	1952	3882229	Europe	61.21	3119.23652
+Croatia	1957	3991242	Europe	64.77	4338.231617
+Croatia	1962	4076557	Europe	67.13	5477.890018
+Croatia	1967	4174366	Europe	68.5	6960.297861
+Croatia	1972	4225310	Europe	69.61	9164.090127
+Croatia	1977	4318673	Europe	70.64	11305.38517
+Croatia	1982	4413368	Europe	70.46	13221.82184
+Croatia	1987	4484310	Europe	71.52	13822.58394
+Croatia	1992	4494013	Europe	72.527	8447.794873
+Croatia	1997	4444595	Europe	73.68	9875.604515
+Croatia	2002	4481020	Europe	74.876	11628.38895
+Croatia	2007	4493312	Europe	75.748	14619.22272
+Cuba	1952	6007797	Americas	59.421	5586.53878
+Cuba	1957	6640752	Americas	62.325	6092.174359
+Cuba	1962	7254373	Americas	65.246	5180.75591
+Cuba	1967	8139332	Americas	68.29	5690.268015
+Cuba	1972	8831348	Americas	70.723	5305.445256
+Cuba	1977	9537988	Americas	72.649	6380.494966
+Cuba	1982	9789224	Americas	73.717	7316.918107
+Cuba	1987	10239839	Americas	74.174	7532.924763
+Cuba	1992	10723260	Americas	74.414	5592.843963
+Cuba	1997	10983007	Americas	76.151	5431.990415
+Cuba	2002	11226999	Americas	77.158	6340.646683
+Cuba	2007	11416987	Americas	78.273	8948.102923
+Czech Republic	1952	9125183	Europe	66.87	6876.14025
+Czech Republic	1957	9513758	Europe	69.03	8256.343918
+Czech Republic	1962	9620282	Europe	69.9	10136.86713
+Czech Republic	1967	9835109	Europe	70.38	11399.44489
+Czech Republic	1972	9862158	Europe	70.29	13108.4536
+Czech Republic	1977	10161915	Europe	70.71	14800.16062
+Czech Republic	1982	10303704	Europe	70.96	15377.22855
+Czech Republic	1987	10311597	Europe	71.58	16310.4434
+Czech Republic	1992	10315702	Europe	72.4	14297.02122
+Czech Republic	1997	10300707	Europe	74.01	16048.51424
+Czech Republic	2002	10256295	Europe	75.51	17596.21022
+Czech Republic	2007	10228744	Europe	76.486	22833.30851
+Denmark	1952	4334000	Europe	70.78	9692.385245
+Denmark	1957	4487831	Europe	71.81	11099.65935
+Denmark	1962	4646899	Europe	72.35	13583.31351
+Denmark	1967	4838800	Europe	72.96	15937.21123
+Denmark	1972	4991596	Europe	73.47	18866.20721
+Denmark	1977	5088419	Europe	74.69	20422.9015
+Denmark	1982	5117810	Europe	74.63	21688.04048
+Denmark	1987	5127024	Europe	74.8	25116.17581
+Denmark	1992	5171393	Europe	75.33	26406.73985
+Denmark	1997	5283663	Europe	76.11	29804.34567
+Denmark	2002	5374693	Europe	77.18	32166.50006
+Denmark	2007	5468120	Europe	78.332	35278.41874
+Djibouti	1952	63149	Africa	34.812	2669.529475
+Djibouti	1957	71851	Africa	37.328	2864.969076
+Djibouti	1962	89898	Africa	39.693	3020.989263
+Djibouti	1967	127617	Africa	42.074	3020.050513
+Djibouti	1972	178848	Africa	44.366	3694.212352
+Djibouti	1977	228694	Africa	46.519	3081.761022
+Djibouti	1982	305991	Africa	48.812	2879.468067
+Djibouti	1987	311025	Africa	50.04	2880.102568
+Djibouti	1992	384156	Africa	51.604	2377.156192
+Djibouti	1997	417908	Africa	53.157	1895.016984
+Djibouti	2002	447416	Africa	53.373	1908.260867
+Djibouti	2007	496374	Africa	54.791	2082.481567
+Dominican Republic	1952	2491346	Americas	45.928	1397.717137
+Dominican Republic	1957	2923186	Americas	49.828	1544.402995
+Dominican Republic	1962	3453434	Americas	53.459	1662.137359
+Dominican Republic	1967	4049146	Americas	56.751	1653.723003
+Dominican Republic	1972	4671329	Americas	59.631	2189.874499
+Dominican Republic	1977	5302800	Americas	61.788	2681.9889
+Dominican Republic	1982	5968349	Americas	63.727	2861.092386
+Dominican Republic	1987	6655297	Americas	66.046	2899.842175
+Dominican Republic	1992	7351181	Americas	68.457	3044.214214
+Dominican Republic	1997	7992357	Americas	69.957	3614.101285
+Dominican Republic	2002	8650322	Americas	70.847	4563.808154
+Dominican Republic	2007	9319622	Americas	72.235	6025.374752
+Ecuador	1952	3548753	Americas	48.357	3522.110717
+Ecuador	1957	4058385	Americas	51.356	3780.546651
+Ecuador	1962	4681707	Americas	54.64	4086.114078
+Ecuador	1967	5432424	Americas	56.678	4579.074215
+Ecuador	1972	6298651	Americas	58.796	5280.99471
+Ecuador	1977	7278866	Americas	61.31	6679.62326
+Ecuador	1982	8365850	Americas	64.342	7213.791267
+Ecuador	1987	9545158	Americas	67.231	6481.776993
+Ecuador	1992	10748394	Americas	69.613	7103.702595
+Ecuador	1997	11911819	Americas	72.312	7429.455877
+Ecuador	2002	12921234	Americas	74.173	5773.044512
+Ecuador	2007	13755680	Americas	74.994	6873.262326
+Egypt	1952	22223309	Africa	41.893	1418.822445
+Egypt	1957	25009741	Africa	44.444	1458.915272
+Egypt	1962	28173309	Africa	46.992	1693.335853
+Egypt	1967	31681188	Africa	49.293	1814.880728
+Egypt	1972	34807417	Africa	51.137	2024.008147
+Egypt	1977	38783863	Africa	53.319	2785.493582
+Egypt	1982	45681811	Africa	56.006	3503.729636
+Egypt	1987	52799062	Africa	59.797	3885.46071
+Egypt	1992	59402198	Africa	63.674	3794.755195
+Egypt	1997	66134291	Africa	67.217	4173.181797
+Egypt	2002	73312559	Africa	69.806	4754.604414
+Egypt	2007	80264543	Africa	71.338	5581.180998
+El Salvador	1952	2042865	Americas	45.262	3048.3029
+El Salvador	1957	2355805	Americas	48.57	3421.523218
+El Salvador	1962	2747687	Americas	52.307	3776.803627
+El Salvador	1967	3232927	Americas	55.855	4358.595393
+El Salvador	1972	3790903	Americas	58.207	4520.246008
+El Salvador	1977	4282586	Americas	56.696	5138.922374
+El Salvador	1982	4474873	Americas	56.604	4098.344175
+El Salvador	1987	4842194	Americas	63.154	4140.442097
+El Salvador	1992	5274649	Americas	66.798	4444.2317
+El Salvador	1997	5783439	Americas	69.535	5154.825496
+El Salvador	2002	6353681	Americas	70.734	5351.568666
+El Salvador	2007	6939688	Americas	71.878	5728.353514
+Equatorial Guinea	1952	216964	Africa	34.482	375.6431231
+Equatorial Guinea	1957	232922	Africa	35.983	426.0964081
+Equatorial Guinea	1962	249220	Africa	37.485	582.8419714
+Equatorial Guinea	1967	259864	Africa	38.987	915.5960025
+Equatorial Guinea	1972	277603	Africa	40.516	672.4122571
+Equatorial Guinea	1977	192675	Africa	42.024	958.5668124
+Equatorial Guinea	1982	285483	Africa	43.662	927.8253427
+Equatorial Guinea	1987	341244	Africa	45.664	966.8968149
+Equatorial Guinea	1992	387838	Africa	47.545	1132.055034
+Equatorial Guinea	1997	439971	Africa	48.245	2814.480755
+Equatorial Guinea	2002	495627	Africa	49.348	7703.4959
+Equatorial Guinea	2007	551201	Africa	51.579	12154.08975
+Eritrea	1952	1438760	Africa	35.928	328.9405571
+Eritrea	1957	1542611	Africa	38.047	344.1618859
+Eritrea	1962	1666618	Africa	40.158	380.9958433
+Eritrea	1967	1820319	Africa	42.189	468.7949699
+Eritrea	1972	2260187	Africa	44.142	514.3242082
+Eritrea	1977	2512642	Africa	44.535	505.7538077
+Eritrea	1982	2637297	Africa	43.89	524.8758493
+Eritrea	1987	2915959	Africa	46.453	521.1341333
+Eritrea	1992	3668440	Africa	49.991	582.8585102
+Eritrea	1997	4058319	Africa	53.378	913.47079
+Eritrea	2002	4414865	Africa	55.24	765.3500015
+Eritrea	2007	4906585	Africa	58.04	641.3695236
+Ethiopia	1952	20860941	Africa	34.078	362.1462796
+Ethiopia	1957	22815614	Africa	36.667	378.9041632
+Ethiopia	1962	25145372	Africa	40.059	419.4564161
+Ethiopia	1967	27860297	Africa	42.115	516.1186438
+Ethiopia	1972	30770372	Africa	43.515	566.2439442
+Ethiopia	1977	34617799	Africa	44.51	556.8083834
+Ethiopia	1982	38111756	Africa	44.916	577.8607471
+Ethiopia	1987	42999530	Africa	46.684	573.7413142
+Ethiopia	1992	52088559	Africa	48.091	421.3534653
+Ethiopia	1997	59861301	Africa	49.402	515.8894013
+Ethiopia	2002	67946797	Africa	50.725	530.0535319
+Ethiopia	2007	76511887	Africa	52.947	690.8055759
+Finland	1952	4090500	Europe	66.55	6424.519071
+Finland	1957	4324000	Europe	67.49	7545.415386
+Finland	1962	4491443	Europe	68.75	9371.842561
+Finland	1967	4605744	Europe	69.83	10921.63626
+Finland	1972	4639657	Europe	70.87	14358.8759
+Finland	1977	4738902	Europe	72.52	15605.42283
+Finland	1982	4826933	Europe	74.55	18533.15761
+Finland	1987	4931729	Europe	74.83	21141.01223
+Finland	1992	5041039	Europe	75.7	20647.16499
+Finland	1997	5134406	Europe	77.13	23723.9502
+Finland	2002	5193039	Europe	78.37	28204.59057
+Finland	2007	5238460	Europe	79.313	33207.0844
+France	1952	42459667	Europe	67.41	7029.809327
+France	1957	44310863	Europe	68.93	8662.834898
+France	1962	47124000	Europe	70.51	10560.48553
+France	1967	49569000	Europe	71.55	12999.91766
+France	1972	51732000	Europe	72.38	16107.19171
+France	1977	53165019	Europe	73.83	18292.63514
+France	1982	54433565	Europe	74.89	20293.89746
+France	1987	55630100	Europe	76.34	22066.44214
+France	1992	57374179	Europe	77.46	24703.79615
+France	1997	58623428	Europe	78.64	25889.78487
+France	2002	59925035	Europe	79.59	28926.03234
+France	2007	61083916	Europe	80.657	30470.0167
+Gabon	1952	420702	Africa	37.003	4293.476475
+Gabon	1957	434904	Africa	38.999	4976.198099
+Gabon	1962	455661	Africa	40.489	6631.459222
+Gabon	1967	489004	Africa	44.598	8358.761987
+Gabon	1972	537977	Africa	48.69	11401.94841
+Gabon	1977	706367	Africa	52.79	21745.57328
+Gabon	1982	753874	Africa	56.564	15113.36194
+Gabon	1987	880397	Africa	60.19	11864.40844
+Gabon	1992	985739	Africa	61.366	13522.15752
+Gabon	1997	1126189	Africa	60.461	14722.84188
+Gabon	2002	1299304	Africa	56.761	12521.71392
+Gabon	2007	1454867	Africa	56.735	13206.48452
+Gambia	1952	284320	Africa	30	485.2306591
+Gambia	1957	323150	Africa	32.065	520.9267111
+Gambia	1962	374020	Africa	33.896	599.650276
+Gambia	1967	439593	Africa	35.857	734.7829124
+Gambia	1972	517101	Africa	38.308	756.0868363
+Gambia	1977	608274	Africa	41.842	884.7552507
+Gambia	1982	715523	Africa	45.58	835.8096108
+Gambia	1987	848406	Africa	49.265	611.6588611
+Gambia	1992	1025384	Africa	52.644	665.6244126
+Gambia	1997	1235767	Africa	55.861	653.7301704
+Gambia	2002	1457766	Africa	58.041	660.5855997
+Gambia	2007	1688359	Africa	59.448	752.7497265
+Germany	1952	69145952	Europe	67.5	7144.114393
+Germany	1957	71019069	Europe	69.1	10187.82665
+Germany	1962	73739117	Europe	70.3	12902.46291
+Germany	1967	76368453	Europe	70.8	14745.62561
+Germany	1972	78717088	Europe	71	18016.18027
+Germany	1977	78160773	Europe	72.5	20512.92123
+Germany	1982	78335266	Europe	73.8	22031.53274
+Germany	1987	77718298	Europe	74.847	24639.18566
+Germany	1992	80597764	Europe	76.07	26505.30317
+Germany	1997	82011073	Europe	77.34	27788.88416
+Germany	2002	82350671	Europe	78.67	30035.80198
+Germany	2007	82400996	Europe	79.406	32170.37442
+Ghana	1952	5581001	Africa	43.149	911.2989371
+Ghana	1957	6391288	Africa	44.779	1043.561537
+Ghana	1962	7355248	Africa	46.452	1190.041118
+Ghana	1967	8490213	Africa	48.072	1125.69716
+Ghana	1972	9354120	Africa	49.875	1178.223708
+Ghana	1977	10538093	Africa	51.756	993.2239571
+Ghana	1982	11400338	Africa	53.744	876.032569
+Ghana	1987	14168101	Africa	55.729	847.0061135
+Ghana	1992	16278738	Africa	57.501	925.060154
+Ghana	1997	18418288	Africa	58.556	1005.245812
+Ghana	2002	20550751	Africa	58.453	1111.984578
+Ghana	2007	22873338	Africa	60.022	1327.60891
+Greece	1952	7733250	Europe	65.86	3530.690067
+Greece	1957	8096218	Europe	67.86	4916.299889
+Greece	1962	8448233	Europe	69.51	6017.190733
+Greece	1967	8716441	Europe	71	8513.097016
+Greece	1972	8888628	Europe	72.34	12724.82957
+Greece	1977	9308479	Europe	73.68	14195.52428
+Greece	1982	9786480	Europe	75.24	15268.42089
+Greece	1987	9974490	Europe	76.67	16120.52839
+Greece	1992	10325429	Europe	77.03	17541.49634
+Greece	1997	10502372	Europe	77.869	18747.69814
+Greece	2002	10603863	Europe	78.256	22514.2548
+Greece	2007	10706290	Europe	79.483	27538.41188
+Guatemala	1952	3146381	Americas	42.023	2428.237769
+Guatemala	1957	3640876	Americas	44.142	2617.155967
+Guatemala	1962	4208858	Americas	46.954	2750.364446
+Guatemala	1967	4690773	Americas	50.016	3242.531147
+Guatemala	1972	5149581	Americas	53.738	4031.408271
+Guatemala	1977	5703430	Americas	56.029	4879.992748
+Guatemala	1982	6395630	Americas	58.137	4820.49479
+Guatemala	1987	7326406	Americas	60.782	4246.485974
+Guatemala	1992	8486949	Americas	63.373	4439.45084
+Guatemala	1997	9803875	Americas	66.322	4684.313807
+Guatemala	2002	11178650	Americas	68.978	4858.347495
+Guatemala	2007	12572928	Americas	70.259	5186.050003
+Guinea	1952	2664249	Africa	33.609	510.1964923
+Guinea	1957	2876726	Africa	34.558	576.2670245
+Guinea	1962	3140003	Africa	35.753	686.3736739
+Guinea	1967	3451418	Africa	37.197	708.7595409
+Guinea	1972	3811387	Africa	38.842	741.6662307
+Guinea	1977	4227026	Africa	40.762	874.6858643
+Guinea	1982	4710497	Africa	42.891	857.2503577
+Guinea	1987	5650262	Africa	45.552	805.5724718
+Guinea	1992	6990574	Africa	48.576	794.3484384
+Guinea	1997	8048834	Africa	51.455	869.4497668
+Guinea	2002	8807818	Africa	53.676	945.5835837
+Guinea	2007	9947814	Africa	56.007	942.6542111
+Guinea-Bissau	1952	580653	Africa	32.5	299.850319
+Guinea-Bissau	1957	601095	Africa	33.489	431.7904566
+Guinea-Bissau	1962	627820	Africa	34.488	522.0343725
+Guinea-Bissau	1967	601287	Africa	35.492	715.5806402
+Guinea-Bissau	1972	625361	Africa	36.486	820.2245876
+Guinea-Bissau	1977	745228	Africa	37.465	764.7259628
+Guinea-Bissau	1982	825987	Africa	39.327	838.1239671
+Guinea-Bissau	1987	927524	Africa	41.245	736.4153921
+Guinea-Bissau	1992	1050938	Africa	43.266	745.5398706
+Guinea-Bissau	1997	1193708	Africa	44.873	796.6644681
+Guinea-Bissau	2002	1332459	Africa	45.504	575.7047176
+Guinea-Bissau	2007	1472041	Africa	46.388	579.231743
+Haiti	1952	3201488	Americas	37.579	1840.366939
+Haiti	1957	3507701	Americas	40.696	1726.887882
+Haiti	1962	3880130	Americas	43.59	1796.589032
+Haiti	1967	4318137	Americas	46.243	1452.057666
+Haiti	1972	4698301	Americas	48.042	1654.456946
+Haiti	1977	4908554	Americas	49.923	1874.298931
+Haiti	1982	5198399	Americas	51.461	2011.159549
+Haiti	1987	5756203	Americas	53.636	1823.015995
+Haiti	1992	6326682	Americas	55.089	1456.309517
+Haiti	1997	6913545	Americas	56.671	1341.726931
+Haiti	2002	7607651	Americas	58.137	1270.364932
+Haiti	2007	8502814	Americas	60.916	1201.637154
+Honduras	1952	1517453	Americas	41.912	2194.926204
+Honduras	1957	1770390	Americas	44.665	2220.487682
+Honduras	1962	2090162	Americas	48.041	2291.156835
+Honduras	1967	2500689	Americas	50.924	2538.269358
+Honduras	1972	2965146	Americas	53.884	2529.842345
+Honduras	1977	3055235	Americas	57.402	3203.208066
+Honduras	1982	3669448	Americas	60.909	3121.760794
+Honduras	1987	4372203	Americas	64.492	3023.096699
+Honduras	1992	5077347	Americas	66.399	3081.694603
+Honduras	1997	5867957	Americas	67.659	3160.454906
+Honduras	2002	6677328	Americas	68.565	3099.72866
+Honduras	2007	7483763	Americas	70.198	3548.330846
+Hong Kong, China	1952	2125900	Asia	60.96	3054.421209
+Hong Kong, China	1957	2736300	Asia	64.75	3629.076457
+Hong Kong, China	1962	3305200	Asia	67.65	4692.648272
+Hong Kong, China	1967	3722800	Asia	70	6197.962814
+Hong Kong, China	1972	4115700	Asia	72	8315.928145
+Hong Kong, China	1977	4583700	Asia	73.6	11186.14125
+Hong Kong, China	1982	5264500	Asia	75.45	14560.53051
+Hong Kong, China	1987	5584510	Asia	76.2	20038.47269
+Hong Kong, China	1992	5829696	Asia	77.601	24757.60301
+Hong Kong, China	1997	6495918	Asia	80	28377.63219
+Hong Kong, China	2002	6762476	Asia	81.495	30209.01516
+Hong Kong, China	2007	6980412	Asia	82.208	39724.97867
+Hungary	1952	9504000	Europe	64.03	5263.673816
+Hungary	1957	9839000	Europe	66.41	6040.180011
+Hungary	1962	10063000	Europe	67.96	7550.359877
+Hungary	1967	10223422	Europe	69.5	9326.64467
+Hungary	1972	10394091	Europe	69.76	10168.65611
+Hungary	1977	10637171	Europe	69.95	11674.83737
+Hungary	1982	10705535	Europe	69.39	12545.99066
+Hungary	1987	10612740	Europe	69.58	12986.47998
+Hungary	1992	10348684	Europe	69.17	10535.62855
+Hungary	1997	10244684	Europe	71.04	11712.7768
+Hungary	2002	10083313	Europe	72.59	14843.93556
+Hungary	2007	9956108	Europe	73.338	18008.94444
+Iceland	1952	147962	Europe	72.49	7267.688428
+Iceland	1957	165110	Europe	73.47	9244.001412
+Iceland	1962	182053	Europe	73.68	10350.15906
+Iceland	1967	198676	Europe	73.73	13319.89568
+Iceland	1972	209275	Europe	74.46	15798.06362
+Iceland	1977	221823	Europe	76.11	19654.96247
+Iceland	1982	233997	Europe	76.99	23269.6075
+Iceland	1987	244676	Europe	77.23	26923.20628
+Iceland	1992	259012	Europe	78.77	25144.39201
+Iceland	1997	271192	Europe	78.95	28061.09966
+Iceland	2002	288030	Europe	80.5	31163.20196
+Iceland	2007	301931	Europe	81.757	36180.78919
+India	1952	3.72e+08	Asia	37.373	546.5657493
+India	1957	4.09e+08	Asia	40.249	590.061996
+India	1962	4.54e+08	Asia	43.605	658.3471509
+India	1967	5.06e+08	Asia	47.193	700.7706107
+India	1972	5.67e+08	Asia	50.651	724.032527
+India	1977	6.34e+08	Asia	54.208	813.337323
+India	1982	7.08e+08	Asia	56.596	855.7235377
+India	1987	7.88e+08	Asia	58.553	976.5126756
+India	1992	8.72e+08	Asia	60.223	1164.406809
+India	1997	9.59e+08	Asia	61.765	1458.817442
+India	2002	1034172547	Asia	62.879	1746.769454
+India	2007	1110396331	Asia	64.698	2452.210407
+Indonesia	1952	82052000	Asia	37.468	749.6816546
+Indonesia	1957	90124000	Asia	39.918	858.9002707
+Indonesia	1962	99028000	Asia	42.518	849.2897701
+Indonesia	1967	109343000	Asia	45.964	762.4317721
+Indonesia	1972	121282000	Asia	49.203	1111.107907
+Indonesia	1977	136725000	Asia	52.702	1382.702056
+Indonesia	1982	153343000	Asia	56.159	1516.872988
+Indonesia	1987	169276000	Asia	60.137	1748.356961
+Indonesia	1992	184816000	Asia	62.681	2383.140898
+Indonesia	1997	199278000	Asia	66.041	3119.335603
+Indonesia	2002	211060000	Asia	68.588	2873.91287
+Indonesia	2007	223547000	Asia	70.65	3540.651564
+Iran	1952	17272000	Asia	44.869	3035.326002
+Iran	1957	19792000	Asia	47.181	3290.257643
+Iran	1962	22874000	Asia	49.325	4187.329802
+Iran	1967	26538000	Asia	52.469	5906.731805
+Iran	1972	30614000	Asia	55.234	9613.818607
+Iran	1977	35480679	Asia	57.702	11888.59508
+Iran	1982	43072751	Asia	59.62	7608.334602
+Iran	1987	51889696	Asia	63.04	6642.881371
+Iran	1992	60397973	Asia	65.742	7235.653188
+Iran	1997	63327987	Asia	68.042	8263.590301
+Iran	2002	66907826	Asia	69.451	9240.761975
+Iran	2007	69453570	Asia	70.964	11605.71449
+Iraq	1952	5441766	Asia	45.32	4129.766056
+Iraq	1957	6248643	Asia	48.437	6229.333562
+Iraq	1962	7240260	Asia	51.457	8341.737815
+Iraq	1967	8519282	Asia	54.459	8931.459811
+Iraq	1972	10061506	Asia	56.95	9576.037596
+Iraq	1977	11882916	Asia	60.413	14688.23507
+Iraq	1982	14173318	Asia	62.038	14517.90711
+Iraq	1987	16543189	Asia	65.044	11643.57268
+Iraq	1992	17861905	Asia	59.461	3745.640687
+Iraq	1997	20775703	Asia	58.811	3076.239795
+Iraq	2002	24001816	Asia	57.046	4390.717312
+Iraq	2007	27499638	Asia	59.545	4471.061906
+Ireland	1952	2952156	Europe	66.91	5210.280328
+Ireland	1957	2878220	Europe	68.9	5599.077872
+Ireland	1962	2830000	Europe	70.29	6631.597314
+Ireland	1967	2900100	Europe	71.08	7655.568963
+Ireland	1972	3024400	Europe	71.28	9530.772896
+Ireland	1977	3271900	Europe	72.03	11150.98113
+Ireland	1982	3480000	Europe	73.1	12618.32141
+Ireland	1987	3539900	Europe	74.36	13872.86652
+Ireland	1992	3557761	Europe	75.467	17558.81555
+Ireland	1997	3667233	Europe	76.122	24521.94713
+Ireland	2002	3879155	Europe	77.783	34077.04939
+Ireland	2007	4109086	Europe	78.885	40675.99635
+Israel	1952	1620914	Asia	65.39	4086.522128
+Israel	1957	1944401	Asia	67.84	5385.278451
+Israel	1962	2310904	Asia	69.39	7105.630706
+Israel	1967	2693585	Asia	70.75	8393.741404
+Israel	1972	3095893	Asia	71.63	12786.93223
+Israel	1977	3495918	Asia	73.06	13306.61921
+Israel	1982	3858421	Asia	74.45	15367.0292
+Israel	1987	4203148	Asia	75.6	17122.47986
+Israel	1992	4936550	Asia	76.93	18051.52254
+Israel	1997	5531387	Asia	78.269	20896.60924
+Israel	2002	6029529	Asia	79.696	21905.59514
+Israel	2007	6426679	Asia	80.745	25523.2771
+Italy	1952	47666000	Europe	65.94	4931.404155
+Italy	1957	49182000	Europe	67.81	6248.656232
+Italy	1962	50843200	Europe	69.24	8243.58234
+Italy	1967	52667100	Europe	71.06	10022.40131
+Italy	1972	54365564	Europe	72.19	12269.27378
+Italy	1977	56059245	Europe	73.48	14255.98475
+Italy	1982	56535636	Europe	74.98	16537.4835
+Italy	1987	56729703	Europe	76.42	19207.23482
+Italy	1992	56840847	Europe	77.44	22013.64486
+Italy	1997	57479469	Europe	78.82	24675.02446
+Italy	2002	57926999	Europe	80.24	27968.09817
+Italy	2007	58147733	Europe	80.546	28569.7197
+Jamaica	1952	1426095	Americas	58.53	2898.530881
+Jamaica	1957	1535090	Americas	62.61	4756.525781
+Jamaica	1962	1665128	Americas	65.61	5246.107524
+Jamaica	1967	1861096	Americas	67.51	6124.703451
+Jamaica	1972	1997616	Americas	69	7433.889293
+Jamaica	1977	2156814	Americas	70.11	6650.195573
+Jamaica	1982	2298309	Americas	71.21	6068.05135
+Jamaica	1987	2326606	Americas	71.77	6351.237495
+Jamaica	1992	2378618	Americas	71.766	7404.923685
+Jamaica	1997	2531311	Americas	72.262	7121.924704
+Jamaica	2002	2664659	Americas	72.047	6994.774861
+Jamaica	2007	2780132	Americas	72.567	7320.880262
+Japan	1952	86459025	Asia	63.03	3216.956347
+Japan	1957	91563009	Asia	65.5	4317.694365
+Japan	1962	95831757	Asia	68.73	6576.649461
+Japan	1967	100825279	Asia	71.43	9847.788607
+Japan	1972	107188273	Asia	73.42	14778.78636
+Japan	1977	113872473	Asia	75.38	16610.37701
+Japan	1982	118454974	Asia	77.11	19384.10571
+Japan	1987	122091325	Asia	78.67	22375.94189
+Japan	1992	124329269	Asia	79.36	26824.89511
+Japan	1997	125956499	Asia	80.69	28816.58499
+Japan	2002	127065841	Asia	82	28604.5919
+Japan	2007	127467972	Asia	82.603	31656.06806
+Jordan	1952	607914	Asia	43.158	1546.907807
+Jordan	1957	746559	Asia	45.669	1886.080591
+Jordan	1962	933559	Asia	48.126	2348.009158
+Jordan	1967	1255058	Asia	51.629	2741.796252
+Jordan	1972	1613551	Asia	56.528	2110.856309
+Jordan	1977	1937652	Asia	61.134	2852.351568
+Jordan	1982	2347031	Asia	63.739	4161.415959
+Jordan	1987	2820042	Asia	65.869	4448.679912
+Jordan	1992	3867409	Asia	68.015	3431.593647
+Jordan	1997	4526235	Asia	69.772	3645.379572
+Jordan	2002	5307470	Asia	71.263	3844.917194
+Jordan	2007	6053193	Asia	72.535	4519.461171
+Kenya	1952	6464046	Africa	42.27	853.540919
+Kenya	1957	7454779	Africa	44.686	944.4383152
+Kenya	1962	8678557	Africa	47.949	896.9663732
+Kenya	1967	10191512	Africa	50.654	1056.736457
+Kenya	1972	12044785	Africa	53.559	1222.359968
+Kenya	1977	14500404	Africa	56.155	1267.613204
+Kenya	1982	17661452	Africa	58.766	1348.225791
+Kenya	1987	21198082	Africa	59.339	1361.936856
+Kenya	1992	25020539	Africa	59.285	1341.921721
+Kenya	1997	28263827	Africa	54.407	1360.485021
+Kenya	2002	31386842	Africa	50.992	1287.514732
+Kenya	2007	35610177	Africa	54.11	1463.249282
+Korea, Dem. Rep.	1952	8865488	Asia	50.056	1088.277758
+Korea, Dem. Rep.	1957	9411381	Asia	54.081	1571.134655
+Korea, Dem. Rep.	1962	10917494	Asia	56.656	1621.693598
+Korea, Dem. Rep.	1967	12617009	Asia	59.942	2143.540609
+Korea, Dem. Rep.	1972	14781241	Asia	63.983	3701.621503
+Korea, Dem. Rep.	1977	16325320	Asia	67.159	4106.301249
+Korea, Dem. Rep.	1982	17647518	Asia	69.1	4106.525293
+Korea, Dem. Rep.	1987	19067554	Asia	70.647	4106.492315
+Korea, Dem. Rep.	1992	20711375	Asia	69.978	3726.063507
+Korea, Dem. Rep.	1997	21585105	Asia	67.727	1690.756814
+Korea, Dem. Rep.	2002	22215365	Asia	66.662	1646.758151
+Korea, Dem. Rep.	2007	23301725	Asia	67.297	1593.06548
+Korea, Rep.	1952	20947571	Asia	47.453	1030.592226
+Korea, Rep.	1957	22611552	Asia	52.681	1487.593537
+Korea, Rep.	1962	26420307	Asia	55.292	1536.344387
+Korea, Rep.	1967	30131000	Asia	57.716	2029.228142
+Korea, Rep.	1972	33505000	Asia	62.612	3030.87665
+Korea, Rep.	1977	36436000	Asia	64.766	4657.22102
+Korea, Rep.	1982	39326000	Asia	67.123	5622.942464
+Korea, Rep.	1987	41622000	Asia	69.81	8533.088805
+Korea, Rep.	1992	43805450	Asia	72.244	12104.27872
+Korea, Rep.	1997	46173816	Asia	74.647	15993.52796
+Korea, Rep.	2002	47969150	Asia	77.045	19233.98818
+Korea, Rep.	2007	49044790	Asia	78.623	23348.13973
+Kuwait	1952	160000	Asia	55.565	108382.3529
+Kuwait	1957	212846	Asia	58.033	113523.1329
+Kuwait	1962	358266	Asia	60.47	95458.11176
+Kuwait	1967	575003	Asia	64.624	80894.88326
+Kuwait	1972	841934	Asia	67.712	109347.867
+Kuwait	1977	1140357	Asia	69.343	59265.47714
+Kuwait	1982	1497494	Asia	71.309	31354.03573
+Kuwait	1987	1891487	Asia	74.174	28118.42998
+Kuwait	1992	1418095	Asia	75.19	34932.91959
+Kuwait	1997	1765345	Asia	76.156	40300.61996
+Kuwait	2002	2111561	Asia	76.904	35110.10566
+Kuwait	2007	2505559	Asia	77.588	47306.98978
+Lebanon	1952	1439529	Asia	55.928	4834.804067
+Lebanon	1957	1647412	Asia	59.489	6089.786934
+Lebanon	1962	1886848	Asia	62.094	5714.560611
+Lebanon	1967	2186894	Asia	63.87	6006.983042
+Lebanon	1972	2680018	Asia	65.421	7486.384341
+Lebanon	1977	3115787	Asia	66.099	8659.696836
+Lebanon	1982	3086876	Asia	66.983	7640.519521
+Lebanon	1987	3089353	Asia	67.926	5377.091329
+Lebanon	1992	3219994	Asia	69.292	6890.806854
+Lebanon	1997	3430388	Asia	70.265	8754.96385
+Lebanon	2002	3677780	Asia	71.028	9313.93883
+Lebanon	2007	3921278	Asia	71.993	10461.05868
+Lesotho	1952	748747	Africa	42.138	298.8462121
+Lesotho	1957	813338	Africa	45.047	335.9971151
+Lesotho	1962	893143	Africa	47.747	411.8006266
+Lesotho	1967	996380	Africa	48.492	498.6390265
+Lesotho	1972	1116779	Africa	49.767	496.5815922
+Lesotho	1977	1251524	Africa	52.208	745.3695408
+Lesotho	1982	1411807	Africa	55.078	797.2631074
+Lesotho	1987	1599200	Africa	57.18	773.9932141
+Lesotho	1992	1803195	Africa	59.685	977.4862725
+Lesotho	1997	1982823	Africa	55.558	1186.147994
+Lesotho	2002	2046772	Africa	44.593	1275.184575
+Lesotho	2007	2012649	Africa	42.592	1569.331442
+Liberia	1952	863308	Africa	38.48	575.5729961
+Liberia	1957	975950	Africa	39.486	620.9699901
+Liberia	1962	1112796	Africa	40.502	634.1951625
+Liberia	1967	1279406	Africa	41.536	713.6036483
+Liberia	1972	1482628	Africa	42.614	803.0054535
+Liberia	1977	1703617	Africa	43.764	640.3224383
+Liberia	1982	1956875	Africa	44.852	572.1995694
+Liberia	1987	2269414	Africa	46.027	506.1138573
+Liberia	1992	1912974	Africa	40.802	636.6229191
+Liberia	1997	2200725	Africa	42.221	609.1739508
+Liberia	2002	2814651	Africa	43.753	531.4823679
+Liberia	2007	3193942	Africa	45.678	414.5073415
+Libya	1952	1019729	Africa	42.723	2387.54806
+Libya	1957	1201578	Africa	45.289	3448.284395
+Libya	1962	1441863	Africa	47.808	6757.030816
+Libya	1967	1759224	Africa	50.227	18772.75169
+Libya	1972	2183877	Africa	52.773	21011.49721
+Libya	1977	2721783	Africa	57.442	21951.21176
+Libya	1982	3344074	Africa	62.155	17364.27538
+Libya	1987	3799845	Africa	66.234	11770.5898
+Libya	1992	4364501	Africa	68.755	9640.138501
+Libya	1997	4759670	Africa	71.555	9467.446056
+Libya	2002	5368585	Africa	72.737	9534.677467
+Libya	2007	6036914	Africa	73.952	12057.49928
+Madagascar	1952	4762912	Africa	36.681	1443.011715
+Madagascar	1957	5181679	Africa	38.865	1589.20275
+Madagascar	1962	5703324	Africa	40.848	1643.38711
+Madagascar	1967	6334556	Africa	42.881	1634.047282
+Madagascar	1972	7082430	Africa	44.851	1748.562982
+Madagascar	1977	8007166	Africa	46.881	1544.228586
+Madagascar	1982	9171477	Africa	48.969	1302.878658
+Madagascar	1987	10568642	Africa	49.35	1155.441948
+Madagascar	1992	12210395	Africa	52.214	1040.67619
+Madagascar	1997	14165114	Africa	54.978	986.2958956
+Madagascar	2002	16473477	Africa	57.286	894.6370822
+Madagascar	2007	19167654	Africa	59.443	1044.770126
+Malawi	1952	2917802	Africa	36.256	369.1650802
+Malawi	1957	3221238	Africa	37.207	416.3698064
+Malawi	1962	3628608	Africa	38.41	427.9010856
+Malawi	1967	4147252	Africa	39.487	495.5147806
+Malawi	1972	4730997	Africa	41.766	584.6219709
+Malawi	1977	5637246	Africa	43.767	663.2236766
+Malawi	1982	6502825	Africa	45.642	632.8039209
+Malawi	1987	7824747	Africa	47.457	635.5173634
+Malawi	1992	10014249	Africa	49.42	563.2000145
+Malawi	1997	10419991	Africa	47.495	692.2758103
+Malawi	2002	11824495	Africa	45.009	665.4231186
+Malawi	2007	13327079	Africa	48.303	759.3499101
+Malaysia	1952	6748378	Asia	48.463	1831.132894
+Malaysia	1957	7739235	Asia	52.102	1810.066992
+Malaysia	1962	8906385	Asia	55.737	2036.884944
+Malaysia	1967	10154878	Asia	59.371	2277.742396
+Malaysia	1972	11441462	Asia	63.01	2849.09478
+Malaysia	1977	12845381	Asia	65.256	3827.921571
+Malaysia	1982	14441916	Asia	68	4920.355951
+Malaysia	1987	16331785	Asia	69.5	5249.802653
+Malaysia	1992	18319502	Asia	70.693	7277.912802
+Malaysia	1997	20476091	Asia	71.938	10132.90964
+Malaysia	2002	22662365	Asia	73.044	10206.97794
+Malaysia	2007	24821286	Asia	74.241	12451.6558
+Mali	1952	3838168	Africa	33.685	452.3369807
+Mali	1957	4241884	Africa	35.307	490.3821867
+Mali	1962	4690372	Africa	36.936	496.1743428
+Mali	1967	5212416	Africa	38.487	545.0098873
+Mali	1972	5828158	Africa	39.977	581.3688761
+Mali	1977	6491649	Africa	41.714	686.3952693
+Mali	1982	6998256	Africa	43.916	618.0140641
+Mali	1987	7634008	Africa	46.364	684.1715576
+Mali	1992	8416215	Africa	48.388	739.014375
+Mali	1997	9384984	Africa	49.903	790.2579846
+Mali	2002	10580176	Africa	51.818	951.4097518
+Mali	2007	12031795	Africa	54.467	1042.581557
+Mauritania	1952	1022556	Africa	40.543	743.1159097
+Mauritania	1957	1076852	Africa	42.338	846.1202613
+Mauritania	1962	1146757	Africa	44.248	1055.896036
+Mauritania	1967	1230542	Africa	46.289	1421.145193
+Mauritania	1972	1332786	Africa	48.437	1586.851781
+Mauritania	1977	1456688	Africa	50.852	1497.492223
+Mauritania	1982	1622136	Africa	53.599	1481.150189
+Mauritania	1987	1841240	Africa	56.145	1421.603576
+Mauritania	1992	2119465	Africa	58.333	1361.369784
+Mauritania	1997	2444741	Africa	60.43	1483.136136
+Mauritania	2002	2828858	Africa	62.247	1579.019543
+Mauritania	2007	3270065	Africa	64.164	1803.151496
+Mauritius	1952	516556	Africa	50.986	1967.955707
+Mauritius	1957	609816	Africa	58.089	2034.037981
+Mauritius	1962	701016	Africa	60.246	2529.067487
+Mauritius	1967	789309	Africa	61.557	2475.387562
+Mauritius	1972	851334	Africa	62.944	2575.484158
+Mauritius	1977	913025	Africa	64.93	3710.982963
+Mauritius	1982	992040	Africa	66.711	3688.037739
+Mauritius	1987	1042663	Africa	68.74	4783.586903
+Mauritius	1992	1096202	Africa	69.745	6058.253846
+Mauritius	1997	1149818	Africa	70.736	7425.705295
+Mauritius	2002	1200206	Africa	71.954	9021.815894
+Mauritius	2007	1250882	Africa	72.801	10956.99112
+Mexico	1952	30144317	Americas	50.789	3478.125529
+Mexico	1957	35015548	Americas	55.19	4131.546641
+Mexico	1962	41121485	Americas	58.299	4581.609385
+Mexico	1967	47995559	Americas	60.11	5754.733883
+Mexico	1972	55984294	Americas	62.361	6809.40669
+Mexico	1977	63759976	Americas	65.032	7674.929108
+Mexico	1982	71640904	Americas	67.405	9611.147541
+Mexico	1987	80122492	Americas	69.498	8688.156003
+Mexico	1992	88111030	Americas	71.455	9472.384295
+Mexico	1997	95895146	Americas	73.67	9767.29753
+Mexico	2002	102479927	Americas	74.902	10742.44053
+Mexico	2007	108700891	Americas	76.195	11977.57496
+Mongolia	1952	800663	Asia	42.244	786.5668575
+Mongolia	1957	882134	Asia	45.248	912.6626085
+Mongolia	1962	1010280	Asia	48.251	1056.353958
+Mongolia	1967	1149500	Asia	51.253	1226.04113
+Mongolia	1972	1320500	Asia	53.754	1421.741975
+Mongolia	1977	1528000	Asia	55.491	1647.511665
+Mongolia	1982	1756032	Asia	57.489	2000.603139
+Mongolia	1987	2015133	Asia	60.222	2338.008304
+Mongolia	1992	2312802	Asia	61.271	1785.402016
+Mongolia	1997	2494803	Asia	63.625	1902.2521
+Mongolia	2002	2674234	Asia	65.033	2140.739323
+Mongolia	2007	2874127	Asia	66.803	3095.772271
+Montenegro	1952	413834	Europe	59.164	2647.585601
+Montenegro	1957	442829	Europe	61.448	3682.259903
+Montenegro	1962	474528	Europe	63.728	4649.593785
+Montenegro	1967	501035	Europe	67.178	5907.850937
+Montenegro	1972	527678	Europe	70.636	7778.414017
+Montenegro	1977	560073	Europe	73.066	9595.929905
+Montenegro	1982	562548	Europe	74.101	11222.58762
+Montenegro	1987	569473	Europe	74.865	11732.51017
+Montenegro	1992	621621	Europe	75.435	7003.339037
+Montenegro	1997	692651	Europe	75.445	6465.613349
+Montenegro	2002	720230	Europe	73.981	6557.194282
+Montenegro	2007	684736	Europe	74.543	9253.896111
+Morocco	1952	9939217	Africa	42.873	1688.20357
+Morocco	1957	11406350	Africa	45.423	1642.002314
+Morocco	1962	13056604	Africa	47.924	1566.353493
+Morocco	1967	14770296	Africa	50.335	1711.04477
+Morocco	1972	16660670	Africa	52.862	1930.194975
+Morocco	1977	18396941	Africa	55.73	2370.619976
+Morocco	1982	20198730	Africa	59.65	2702.620356
+Morocco	1987	22987397	Africa	62.677	2755.046991
+Morocco	1992	25798239	Africa	65.393	2948.047252
+Morocco	1997	28529501	Africa	67.66	2982.101858
+Morocco	2002	31167783	Africa	69.615	3258.495584
+Morocco	2007	33757175	Africa	71.164	3820.17523
+Mozambique	1952	6446316	Africa	31.286	468.5260381
+Mozambique	1957	7038035	Africa	33.779	495.5868333
+Mozambique	1962	7788944	Africa	36.161	556.6863539
+Mozambique	1967	8680909	Africa	38.113	566.6691539
+Mozambique	1972	9809596	Africa	40.328	724.9178037
+Mozambique	1977	11127868	Africa	42.495	502.3197334
+Mozambique	1982	12587223	Africa	42.795	462.2114149
+Mozambique	1987	12891952	Africa	42.861	389.8761846
+Mozambique	1992	13160731	Africa	44.284	410.8968239
+Mozambique	1997	16603334	Africa	46.344	472.3460771
+Mozambique	2002	18473780	Africa	44.026	633.6179466
+Mozambique	2007	19951656	Africa	42.082	823.6856205
+Myanmar	1952	20092996	Asia	36.319	331
+Myanmar	1957	21731844	Asia	41.905	350
+Myanmar	1962	23634436	Asia	45.108	388
+Myanmar	1967	25870271	Asia	49.379	349
+Myanmar	1972	28466390	Asia	53.07	357
+Myanmar	1977	31528087	Asia	56.059	371
+Myanmar	1982	34680442	Asia	58.056	424
+Myanmar	1987	38028578	Asia	58.339	385
+Myanmar	1992	40546538	Asia	59.32	347
+Myanmar	1997	43247867	Asia	60.328	415
+Myanmar	2002	45598081	Asia	59.908	611
+Myanmar	2007	47761980	Asia	62.069	944
+Namibia	1952	485831	Africa	41.725	2423.780443
+Namibia	1957	548080	Africa	45.226	2621.448058
+Namibia	1962	621392	Africa	48.386	3173.215595
+Namibia	1967	706640	Africa	51.159	3793.694753
+Namibia	1972	821782	Africa	53.867	3746.080948
+Namibia	1977	977026	Africa	56.437	3876.485958
+Namibia	1982	1099010	Africa	58.968	4191.100511
+Namibia	1987	1278184	Africa	60.835	3693.731337
+Namibia	1992	1554253	Africa	61.999	3804.537999
+Namibia	1997	1774766	Africa	58.909	3899.52426
+Namibia	2002	1972153	Africa	51.479	4072.324751
+Namibia	2007	2055080	Africa	52.906	4811.060429
+Nepal	1952	9182536	Asia	36.157	545.8657229
+Nepal	1957	9682338	Asia	37.686	597.9363558
+Nepal	1962	10332057	Asia	39.393	652.3968593
+Nepal	1967	11261690	Asia	41.472	676.4422254
+Nepal	1972	12412593	Asia	43.971	674.7881296
+Nepal	1977	13933198	Asia	46.748	694.1124398
+Nepal	1982	15796314	Asia	49.594	718.3730947
+Nepal	1987	17917180	Asia	52.537	775.6324501
+Nepal	1992	20326209	Asia	55.727	897.7403604
+Nepal	1997	23001113	Asia	59.426	1010.892138
+Nepal	2002	25873917	Asia	61.34	1057.206311
+Nepal	2007	28901790	Asia	63.785	1091.359778
+Netherlands	1952	10381988	Europe	72.13	8941.571858
+Netherlands	1957	11026383	Europe	72.99	11276.19344
+Netherlands	1962	11805689	Europe	73.23	12790.84956
+Netherlands	1967	12596822	Europe	73.82	15363.25136
+Netherlands	1972	13329874	Europe	73.75	18794.74567
+Netherlands	1977	13852989	Europe	75.24	21209.0592
+Netherlands	1982	14310401	Europe	76.05	21399.46046
+Netherlands	1987	14665278	Europe	76.83	23651.32361
+Netherlands	1992	15174244	Europe	77.42	26790.94961
+Netherlands	1997	15604464	Europe	78.03	30246.13063
+Netherlands	2002	16122830	Europe	78.53	33724.75778
+Netherlands	2007	16570613	Europe	79.762	36797.93332
+New Zealand	1952	1994794	Oceania	69.39	10556.57566
+New Zealand	1957	2229407	Oceania	70.26	12247.39532
+New Zealand	1962	2488550	Oceania	71.24	13175.678
+New Zealand	1967	2728150	Oceania	71.52	14463.91893
+New Zealand	1972	2929100	Oceania	71.89	16046.03728
+New Zealand	1977	3164900	Oceania	72.22	16233.7177
+New Zealand	1982	3210650	Oceania	73.84	17632.4104
+New Zealand	1987	3317166	Oceania	74.32	19007.19129
+New Zealand	1992	3437674	Oceania	76.33	18363.32494
+New Zealand	1997	3676187	Oceania	77.55	21050.41377
+New Zealand	2002	3908037	Oceania	79.11	23189.80135
+New Zealand	2007	4115771	Oceania	80.204	25185.00911
+Nicaragua	1952	1165790	Americas	42.314	3112.363948
+Nicaragua	1957	1358828	Americas	45.432	3457.415947
+Nicaragua	1962	1590597	Americas	48.632	3634.364406
+Nicaragua	1967	1865490	Americas	51.884	4643.393534
+Nicaragua	1972	2182908	Americas	55.151	4688.593267
+Nicaragua	1977	2554598	Americas	57.47	5486.371089
+Nicaragua	1982	2979423	Americas	59.298	3470.338156
+Nicaragua	1987	3344353	Americas	62.008	2955.984375
+Nicaragua	1992	4017939	Americas	65.843	2170.151724
+Nicaragua	1997	4609572	Americas	68.426	2253.023004
+Nicaragua	2002	5146848	Americas	70.836	2474.548819
+Nicaragua	2007	5675356	Americas	72.899	2749.320965
+Niger	1952	3379468	Africa	37.444	761.879376
+Niger	1957	3692184	Africa	38.598	835.5234025
+Niger	1962	4076008	Africa	39.487	997.7661127
+Niger	1967	4534062	Africa	40.118	1054.384891
+Niger	1972	5060262	Africa	40.546	954.2092363
+Niger	1977	5682086	Africa	41.291	808.8970728
+Niger	1982	6437188	Africa	42.598	909.7221354
+Niger	1987	7332638	Africa	44.555	668.3000228
+Niger	1992	8392818	Africa	47.391	581.182725
+Niger	1997	9666252	Africa	51.313	580.3052092
+Niger	2002	11140655	Africa	54.496	601.0745012
+Niger	2007	12894865	Africa	56.867	619.6768924
+Nigeria	1952	33119096	Africa	36.324	1077.281856
+Nigeria	1957	37173340	Africa	37.802	1100.592563
+Nigeria	1962	41871351	Africa	39.36	1150.927478
+Nigeria	1967	47287752	Africa	41.04	1014.514104
+Nigeria	1972	53740085	Africa	42.821	1698.388838
+Nigeria	1977	62209173	Africa	44.514	1981.951806
+Nigeria	1982	73039376	Africa	45.826	1576.97375
+Nigeria	1987	81551520	Africa	46.886	1385.029563
+Nigeria	1992	93364244	Africa	47.472	1619.848217
+Nigeria	1997	106207839	Africa	47.464	1624.941275
+Nigeria	2002	119901274	Africa	46.608	1615.286395
+Nigeria	2007	135031164	Africa	46.859	2013.977305
+Norway	1952	3327728	Europe	72.67	10095.42172
+Norway	1957	3491938	Europe	73.44	11653.97304
+Norway	1962	3638919	Europe	73.47	13450.40151
+Norway	1967	3786019	Europe	74.08	16361.87647
+Norway	1972	3933004	Europe	74.34	18965.05551
+Norway	1977	4043205	Europe	75.37	23311.34939
+Norway	1982	4114787	Europe	75.97	26298.63531
+Norway	1987	4186147	Europe	75.89	31540.9748
+Norway	1992	4286357	Europe	77.32	33965.66115
+Norway	1997	4405672	Europe	78.32	41283.16433
+Norway	2002	4535591	Europe	79.05	44683.97525
+Norway	2007	4627926	Europe	80.196	49357.19017
+Oman	1952	507833	Asia	37.578	1828.230307
+Oman	1957	561977	Asia	40.08	2242.746551
+Oman	1962	628164	Asia	43.165	2924.638113
+Oman	1967	714775	Asia	46.988	4720.942687
+Oman	1972	829050	Asia	52.143	10618.03855
+Oman	1977	1004533	Asia	57.367	11848.34392
+Oman	1982	1301048	Asia	62.728	12954.79101
+Oman	1987	1593882	Asia	67.734	18115.22313
+Oman	1992	1915208	Asia	71.197	18616.70691
+Oman	1997	2283635	Asia	72.499	19702.05581
+Oman	2002	2713462	Asia	74.193	19774.83687
+Oman	2007	3204897	Asia	75.64	22316.19287
+Pakistan	1952	41346560	Asia	43.436	684.5971438
+Pakistan	1957	46679944	Asia	45.557	747.0835292
+Pakistan	1962	53100671	Asia	47.67	803.3427418
+Pakistan	1967	60641899	Asia	49.8	942.4082588
+Pakistan	1972	69325921	Asia	51.929	1049.938981
+Pakistan	1977	78152686	Asia	54.043	1175.921193
+Pakistan	1982	91462088	Asia	56.158	1443.429832
+Pakistan	1987	105186881	Asia	58.245	1704.686583
+Pakistan	1992	120065004	Asia	60.838	1971.829464
+Pakistan	1997	135564834	Asia	61.818	2049.350521
+Pakistan	2002	153403524	Asia	63.61	2092.712441
+Pakistan	2007	169270617	Asia	65.483	2605.94758
+Panama	1952	940080	Americas	55.191	2480.380334
+Panama	1957	1063506	Americas	59.201	2961.800905
+Panama	1962	1215725	Americas	61.817	3536.540301
+Panama	1967	1405486	Americas	64.071	4421.009084
+Panama	1972	1616384	Americas	66.216	5364.249663
+Panama	1977	1839782	Americas	68.681	5351.912144
+Panama	1982	2036305	Americas	70.472	7009.601598
+Panama	1987	2253639	Americas	71.523	7034.779161
+Panama	1992	2484997	Americas	72.462	6618.74305
+Panama	1997	2734531	Americas	73.738	7113.692252
+Panama	2002	2990875	Americas	74.712	7356.031934
+Panama	2007	3242173	Americas	75.537	9809.185636
+Paraguay	1952	1555876	Americas	62.649	1952.308701
+Paraguay	1957	1770902	Americas	63.196	2046.154706
+Paraguay	1962	2009813	Americas	64.361	2148.027146
+Paraguay	1967	2287985	Americas	64.951	2299.376311
+Paraguay	1972	2614104	Americas	65.815	2523.337977
+Paraguay	1977	2984494	Americas	66.353	3248.373311
+Paraguay	1982	3366439	Americas	66.874	4258.503604
+Paraguay	1987	3886512	Americas	67.378	3998.875695
+Paraguay	1992	4483945	Americas	68.225	4196.411078
+Paraguay	1997	5154123	Americas	69.4	4247.400261
+Paraguay	2002	5884491	Americas	70.755	3783.674243
+Paraguay	2007	6667147	Americas	71.752	4172.838464
+Peru	1952	8025700	Americas	43.902	3758.523437
+Peru	1957	9146100	Americas	46.263	4245.256698
+Peru	1962	10516500	Americas	49.096	4957.037982
+Peru	1967	12132200	Americas	51.445	5788.09333
+Peru	1972	13954700	Americas	55.448	5937.827283
+Peru	1977	15990099	Americas	58.447	6281.290855
+Peru	1982	18125129	Americas	61.406	6434.501797
+Peru	1987	20195924	Americas	64.134	6360.943444
+Peru	1992	22430449	Americas	66.458	4446.380924
+Peru	1997	24748122	Americas	68.386	5838.347657
+Peru	2002	26769436	Americas	69.906	5909.020073
+Peru	2007	28674757	Americas	71.421	7408.905561
+Philippines	1952	22438691	Asia	47.752	1272.880995
+Philippines	1957	26072194	Asia	51.334	1547.944844
+Philippines	1962	30325264	Asia	54.757	1649.552153
+Philippines	1967	35356600	Asia	56.393	1814.12743
+Philippines	1972	40850141	Asia	58.065	1989.37407
+Philippines	1977	46850962	Asia	60.06	2373.204287
+Philippines	1982	53456774	Asia	62.082	2603.273765
+Philippines	1987	60017788	Asia	64.151	2189.634995
+Philippines	1992	67185766	Asia	66.458	2279.324017
+Philippines	1997	75012988	Asia	68.564	2536.534925
+Philippines	2002	82995088	Asia	70.303	2650.921068
+Philippines	2007	91077287	Asia	71.688	3190.481016
+Poland	1952	25730551	Europe	61.31	4029.329699
+Poland	1957	28235346	Europe	65.77	4734.253019
+Poland	1962	30329617	Europe	67.64	5338.752143
+Poland	1967	31785378	Europe	69.61	6557.152776
+Poland	1972	33039545	Europe	70.85	8006.506993
+Poland	1977	34621254	Europe	70.67	9508.141454
+Poland	1982	36227381	Europe	71.32	8451.531004
+Poland	1987	37740710	Europe	70.98	9082.351172
+Poland	1992	38370697	Europe	70.99	7738.881247
+Poland	1997	38654957	Europe	72.75	10159.58368
+Poland	2002	38625976	Europe	74.67	12002.23908
+Poland	2007	38518241	Europe	75.563	15389.92468
+Portugal	1952	8526050	Europe	59.82	3068.319867
+Portugal	1957	8817650	Europe	61.51	3774.571743
+Portugal	1962	9019800	Europe	64.39	4727.954889
+Portugal	1967	9103000	Europe	66.6	6361.517993
+Portugal	1972	8970450	Europe	69.26	9022.247417
+Portugal	1977	9662600	Europe	70.41	10172.48572
+Portugal	1982	9859650	Europe	72.77	11753.84291
+Portugal	1987	9915289	Europe	74.06	13039.30876
+Portugal	1992	9927680	Europe	74.86	16207.26663
+Portugal	1997	10156415	Europe	75.97	17641.03156
+Portugal	2002	10433867	Europe	77.29	19970.90787
+Portugal	2007	10642836	Europe	78.098	20509.64777
+Puerto Rico	1952	2227000	Americas	64.28	3081.959785
+Puerto Rico	1957	2260000	Americas	68.54	3907.156189
+Puerto Rico	1962	2448046	Americas	69.62	5108.34463
+Puerto Rico	1967	2648961	Americas	71.1	6929.277714
+Puerto Rico	1972	2847132	Americas	72.16	9123.041742
+Puerto Rico	1977	3080828	Americas	73.44	9770.524921
+Puerto Rico	1982	3279001	Americas	73.75	10330.98915
+Puerto Rico	1987	3444468	Americas	74.63	12281.34191
+Puerto Rico	1992	3585176	Americas	73.911	14641.58711
+Puerto Rico	1997	3759430	Americas	74.917	16999.4333
+Puerto Rico	2002	3859606	Americas	77.778	18855.60618
+Puerto Rico	2007	3942491	Americas	78.746	19328.70901
+Reunion	1952	257700	Africa	52.724	2718.885295
+Reunion	1957	308700	Africa	55.09	2769.451844
+Reunion	1962	358900	Africa	57.666	3173.72334
+Reunion	1967	414024	Africa	60.542	4021.175739
+Reunion	1972	461633	Africa	64.274	5047.658563
+Reunion	1977	492095	Africa	67.064	4319.804067
+Reunion	1982	517810	Africa	69.885	5267.219353
+Reunion	1987	562035	Africa	71.913	5303.377488
+Reunion	1992	622191	Africa	73.615	6101.255823
+Reunion	1997	684810	Africa	74.772	6071.941411
+Reunion	2002	743981	Africa	75.744	6316.1652
+Reunion	2007	798094	Africa	76.442	7670.122558
+Romania	1952	16630000	Europe	61.05	3144.613186
+Romania	1957	17829327	Europe	64.1	3943.370225
+Romania	1962	18680721	Europe	66.8	4734.997586
+Romania	1967	19284814	Europe	66.8	6470.866545
+Romania	1972	20662648	Europe	69.21	8011.414402
+Romania	1977	21658597	Europe	69.46	9356.39724
+Romania	1982	22356726	Europe	69.66	9605.314053
+Romania	1987	22686371	Europe	69.53	9696.273295
+Romania	1992	22797027	Europe	69.36	6598.409903
+Romania	1997	22562458	Europe	69.72	7346.547557
+Romania	2002	22404337	Europe	71.322	7885.360081
+Romania	2007	22276056	Europe	72.476	10808.47561
+Rwanda	1952	2534927	Africa	40	493.3238752
+Rwanda	1957	2822082	Africa	41.5	540.2893983
+Rwanda	1962	3051242	Africa	43	597.4730727
+Rwanda	1967	3451079	Africa	44.1	510.9637142
+Rwanda	1972	3992121	Africa	44.6	590.5806638
+Rwanda	1977	4657072	Africa	45	670.0806011
+Rwanda	1982	5507565	Africa	46.218	881.5706467
+Rwanda	1987	6349365	Africa	44.02	847.991217
+Rwanda	1992	7290203	Africa	23.599	737.0685949
+Rwanda	1997	7212583	Africa	36.087	589.9445051
+Rwanda	2002	7852401	Africa	43.413	785.6537648
+Rwanda	2007	8860588	Africa	46.242	863.0884639
+Sao Tome and Principe	1952	60011	Africa	46.471	879.5835855
+Sao Tome and Principe	1957	61325	Africa	48.945	860.7369026
+Sao Tome and Principe	1962	65345	Africa	51.893	1071.551119
+Sao Tome and Principe	1967	70787	Africa	54.425	1384.840593
+Sao Tome and Principe	1972	76595	Africa	56.48	1532.985254
+Sao Tome and Principe	1977	86796	Africa	58.55	1737.561657
+Sao Tome and Principe	1982	98593	Africa	60.351	1890.218117
+Sao Tome and Principe	1987	110812	Africa	61.728	1516.525457
+Sao Tome and Principe	1992	125911	Africa	62.742	1428.777814
+Sao Tome and Principe	1997	145608	Africa	63.306	1339.076036
+Sao Tome and Principe	2002	170372	Africa	64.337	1353.09239
+Sao Tome and Principe	2007	199579	Africa	65.528	1598.435089
+Saudi Arabia	1952	4005677	Asia	39.875	6459.554823
+Saudi Arabia	1957	4419650	Asia	42.868	8157.591248
+Saudi Arabia	1962	4943029	Asia	45.914	11626.41975
+Saudi Arabia	1967	5618198	Asia	49.901	16903.04886
+Saudi Arabia	1972	6472756	Asia	53.886	24837.42865
+Saudi Arabia	1977	8128505	Asia	58.69	34167.7626
+Saudi Arabia	1982	11254672	Asia	63.012	33693.17525
+Saudi Arabia	1987	14619745	Asia	66.295	21198.26136
+Saudi Arabia	1992	16945857	Asia	68.768	24841.61777
+Saudi Arabia	1997	21229759	Asia	70.533	20586.69019
+Saudi Arabia	2002	24501530	Asia	71.626	19014.54118
+Saudi Arabia	2007	27601038	Asia	72.777	21654.83194
+Senegal	1952	2755589	Africa	37.278	1450.356983
+Senegal	1957	3054547	Africa	39.329	1567.653006
+Senegal	1962	3430243	Africa	41.454	1654.988723
+Senegal	1967	3965841	Africa	43.563	1612.404632
+Senegal	1972	4588696	Africa	45.815	1597.712056
+Senegal	1977	5260855	Africa	48.879	1561.769116
+Senegal	1982	6147783	Africa	52.379	1518.479984
+Senegal	1987	7171347	Africa	55.769	1441.72072
+Senegal	1992	8307920	Africa	58.196	1367.899369
+Senegal	1997	9535314	Africa	60.187	1392.368347
+Senegal	2002	10870037	Africa	61.6	1519.635262
+Senegal	2007	12267493	Africa	63.062	1712.472136
+Serbia	1952	6860147	Europe	57.996	3581.459448
+Serbia	1957	7271135	Europe	61.685	4981.090891
+Serbia	1962	7616060	Europe	64.531	6289.629157
+Serbia	1967	7971222	Europe	66.914	7991.707066
+Serbia	1972	8313288	Europe	68.7	10522.06749
+Serbia	1977	8686367	Europe	70.3	12980.66956
+Serbia	1982	9032824	Europe	70.162	15181.0927
+Serbia	1987	9230783	Europe	71.218	15870.87851
+Serbia	1992	9826397	Europe	71.659	9325.068238
+Serbia	1997	10336594	Europe	72.232	7914.320304
+Serbia	2002	10111559	Europe	73.213	7236.075251
+Serbia	2007	10150265	Europe	74.002	9786.534714
+Sierra Leone	1952	2143249	Africa	30.331	879.7877358
+Sierra Leone	1957	2295678	Africa	31.57	1004.484437
+Sierra Leone	1962	2467895	Africa	32.767	1116.639877
+Sierra Leone	1967	2662190	Africa	34.113	1206.043465
+Sierra Leone	1972	2879013	Africa	35.4	1353.759762
+Sierra Leone	1977	3140897	Africa	36.788	1348.285159
+Sierra Leone	1982	3464522	Africa	38.445	1465.010784
+Sierra Leone	1987	3868905	Africa	40.006	1294.447788
+Sierra Leone	1992	4260884	Africa	38.333	1068.696278
+Sierra Leone	1997	4578212	Africa	39.897	574.6481576
+Sierra Leone	2002	5359092	Africa	41.012	699.489713
+Sierra Leone	2007	6144562	Africa	42.568	862.5407561
+Singapore	1952	1127000	Asia	60.396	2315.138227
+Singapore	1957	1445929	Asia	63.179	2843.104409
+Singapore	1962	1750200	Asia	65.798	3674.735572
+Singapore	1967	1977600	Asia	67.946	4977.41854
+Singapore	1972	2152400	Asia	69.521	8597.756202
+Singapore	1977	2325300	Asia	70.795	11210.08948
+Singapore	1982	2651869	Asia	71.76	15169.16112
+Singapore	1987	2794552	Asia	73.56	18861.53081
+Singapore	1992	3235865	Asia	75.788	24769.8912
+Singapore	1997	3802309	Asia	77.158	33519.4766
+Singapore	2002	4197776	Asia	78.77	36023.1054
+Singapore	2007	4553009	Asia	79.972	47143.17964
+Slovak Republic	1952	3558137	Europe	64.36	5074.659104
+Slovak Republic	1957	3844277	Europe	67.45	6093.26298
+Slovak Republic	1962	4237384	Europe	70.33	7481.107598
+Slovak Republic	1967	4442238	Europe	70.98	8412.902397
+Slovak Republic	1972	4593433	Europe	70.35	9674.167626
+Slovak Republic	1977	4827803	Europe	70.45	10922.66404
+Slovak Republic	1982	5048043	Europe	70.8	11348.54585
+Slovak Republic	1987	5199318	Europe	71.08	12037.26758
+Slovak Republic	1992	5302888	Europe	71.38	9498.467723
+Slovak Republic	1997	5383010	Europe	72.71	12126.23065
+Slovak Republic	2002	5410052	Europe	73.8	13638.77837
+Slovak Republic	2007	5447502	Europe	74.663	18678.31435
+Slovenia	1952	1489518	Europe	65.57	4215.041741
+Slovenia	1957	1533070	Europe	67.85	5862.276629
+Slovenia	1962	1582962	Europe	69.15	7402.303395
+Slovenia	1967	1646912	Europe	69.18	9405.489397
+Slovenia	1972	1694510	Europe	69.82	12383.4862
+Slovenia	1977	1746919	Europe	70.97	15277.03017
+Slovenia	1982	1861252	Europe	71.063	17866.72175
+Slovenia	1987	1945870	Europe	72.25	18678.53492
+Slovenia	1992	1999210	Europe	73.64	14214.71681
+Slovenia	1997	2011612	Europe	75.13	17161.10735
+Slovenia	2002	2011497	Europe	76.66	20660.01936
+Slovenia	2007	2009245	Europe	77.926	25768.25759
+Somalia	1952	2526994	Africa	32.978	1135.749842
+Somalia	1957	2780415	Africa	34.977	1258.147413
+Somalia	1962	3080153	Africa	36.981	1369.488336
+Somalia	1967	3428839	Africa	38.977	1284.73318
+Somalia	1972	3840161	Africa	40.973	1254.576127
+Somalia	1977	4353666	Africa	41.974	1450.992513
+Somalia	1982	5828892	Africa	42.955	1176.807031
+Somalia	1987	6921858	Africa	44.501	1093.244963
+Somalia	1992	6099799	Africa	39.658	926.9602964
+Somalia	1997	6633514	Africa	43.795	930.5964284
+Somalia	2002	7753310	Africa	45.936	882.0818218
+Somalia	2007	9118773	Africa	48.159	926.1410683
+South Africa	1952	14264935	Africa	45.009	4725.295531
+South Africa	1957	16151549	Africa	47.985	5487.104219
+South Africa	1962	18356657	Africa	49.951	5768.729717
+South Africa	1967	20997321	Africa	51.927	7114.477971
+South Africa	1972	23935810	Africa	53.696	7765.962636
+South Africa	1977	27129932	Africa	55.527	8028.651439
+South Africa	1982	31140029	Africa	58.161	8568.266228
+South Africa	1987	35933379	Africa	60.834	7825.823398
+South Africa	1992	39964159	Africa	61.888	7225.069258
+South Africa	1997	42835005	Africa	60.236	7479.188244
+South Africa	2002	44433622	Africa	53.365	7710.946444
+South Africa	2007	43997828	Africa	49.339	9269.657808
+Spain	1952	28549870	Europe	64.94	3834.034742
+Spain	1957	29841614	Europe	66.66	4564.80241
+Spain	1962	31158061	Europe	69.69	5693.843879
+Spain	1967	32850275	Europe	71.44	7993.512294
+Spain	1972	34513161	Europe	73.06	10638.75131
+Spain	1977	36439000	Europe	74.39	13236.92117
+Spain	1982	37983310	Europe	76.3	13926.16997
+Spain	1987	38880702	Europe	76.9	15764.98313
+Spain	1992	39549438	Europe	77.57	18603.06452
+Spain	1997	39855442	Europe	78.77	20445.29896
+Spain	2002	40152517	Europe	79.78	24835.47166
+Spain	2007	40448191	Europe	80.941	28821.0637
+Sri Lanka	1952	7982342	Asia	57.593	1083.53203
+Sri Lanka	1957	9128546	Asia	61.456	1072.546602
+Sri Lanka	1962	10421936	Asia	62.192	1074.47196
+Sri Lanka	1967	11737396	Asia	64.266	1135.514326
+Sri Lanka	1972	13016733	Asia	65.042	1213.39553
+Sri Lanka	1977	14116836	Asia	65.949	1348.775651
+Sri Lanka	1982	15410151	Asia	68.757	1648.079789
+Sri Lanka	1987	16495304	Asia	69.011	1876.766827
+Sri Lanka	1992	17587060	Asia	70.379	2153.739222
+Sri Lanka	1997	18698655	Asia	70.457	2664.477257
+Sri Lanka	2002	19576783	Asia	70.815	3015.378833
+Sri Lanka	2007	20378239	Asia	72.396	3970.095407
+Sudan	1952	8504667	Africa	38.635	1615.991129
+Sudan	1957	9753392	Africa	39.624	1770.337074
+Sudan	1962	11183227	Africa	40.87	1959.593767
+Sudan	1967	12716129	Africa	42.858	1687.997641
+Sudan	1972	14597019	Africa	45.083	1659.652775
+Sudan	1977	17104986	Africa	47.8	2202.988423
+Sudan	1982	20367053	Africa	50.338	1895.544073
+Sudan	1987	24725960	Africa	51.744	1507.819159
+Sudan	1992	28227588	Africa	53.556	1492.197043
+Sudan	1997	32160729	Africa	55.373	1632.210764
+Sudan	2002	37090298	Africa	56.369	1993.398314
+Sudan	2007	42292929	Africa	58.556	2602.394995
+Swaziland	1952	290243	Africa	41.407	1148.376626
+Swaziland	1957	326741	Africa	43.424	1244.708364
+Swaziland	1962	370006	Africa	44.992	1856.182125
+Swaziland	1967	420690	Africa	46.633	2613.101665
+Swaziland	1972	480105	Africa	49.552	3364.836625
+Swaziland	1977	551425	Africa	52.537	3781.410618
+Swaziland	1982	649901	Africa	55.561	3895.384018
+Swaziland	1987	779348	Africa	57.678	3984.839812
+Swaziland	1992	962344	Africa	58.474	3553.0224
+Swaziland	1997	1054486	Africa	54.289	3876.76846
+Swaziland	2002	1130269	Africa	43.869	4128.116943
+Swaziland	2007	1133066	Africa	39.613	4513.480643
+Sweden	1952	7124673	Europe	71.86	8527.844662
+Sweden	1957	7363802	Europe	72.49	9911.878226
+Sweden	1962	7561588	Europe	73.37	12329.44192
+Sweden	1967	7867931	Europe	74.16	15258.29697
+Sweden	1972	8122293	Europe	74.72	17832.02464
+Sweden	1977	8251648	Europe	75.44	18855.72521
+Sweden	1982	8325260	Europe	76.42	20667.38125
+Sweden	1987	8421403	Europe	77.19	23586.92927
+Sweden	1992	8718867	Europe	78.16	23880.01683
+Sweden	1997	8897619	Europe	79.39	25266.59499
+Sweden	2002	8954175	Europe	80.04	29341.63093
+Sweden	2007	9031088	Europe	80.884	33859.74835
+Switzerland	1952	4815000	Europe	69.62	14734.23275
+Switzerland	1957	5126000	Europe	70.56	17909.48973
+Switzerland	1962	5666000	Europe	71.32	20431.0927
+Switzerland	1967	6063000	Europe	72.77	22966.14432
+Switzerland	1972	6401400	Europe	73.78	27195.11304
+Switzerland	1977	6316424	Europe	75.39	26982.29052
+Switzerland	1982	6468126	Europe	76.21	28397.71512
+Switzerland	1987	6649942	Europe	77.41	30281.70459
+Switzerland	1992	6995447	Europe	78.03	31871.5303
+Switzerland	1997	7193761	Europe	79.37	32135.32301
+Switzerland	2002	7361757	Europe	80.62	34480.95771
+Switzerland	2007	7554661	Europe	81.701	37506.41907
+Syria	1952	3661549	Asia	45.883	1643.485354
+Syria	1957	4149908	Asia	48.284	2117.234893
+Syria	1962	4834621	Asia	50.305	2193.037133
+Syria	1967	5680812	Asia	53.655	1881.923632
+Syria	1972	6701172	Asia	57.296	2571.423014
+Syria	1977	7932503	Asia	61.195	3195.484582
+Syria	1982	9410494	Asia	64.59	3761.837715
+Syria	1987	11242847	Asia	66.974	3116.774285
+Syria	1992	13219062	Asia	69.249	3340.542768
+Syria	1997	15081016	Asia	71.527	4014.238972
+Syria	2002	17155814	Asia	73.053	4090.925331
+Syria	2007	19314747	Asia	74.143	4184.548089
+Taiwan	1952	8550362	Asia	58.5	1206.947913
+Taiwan	1957	10164215	Asia	62.4	1507.86129
+Taiwan	1962	11918938	Asia	65.2	1822.879028
+Taiwan	1967	13648692	Asia	67.5	2643.858681
+Taiwan	1972	15226039	Asia	69.39	4062.523897
+Taiwan	1977	16785196	Asia	70.59	5596.519826
+Taiwan	1982	18501390	Asia	72.16	7426.354774
+Taiwan	1987	19757799	Asia	73.4	11054.56175
+Taiwan	1992	20686918	Asia	74.26	15215.6579
+Taiwan	1997	21628605	Asia	75.25	20206.82098
+Taiwan	2002	22454239	Asia	76.99	23235.42329
+Taiwan	2007	23174294	Asia	78.4	28718.27684
+Tanzania	1952	8322925	Africa	41.215	716.6500721
+Tanzania	1957	9452826	Africa	42.974	698.5356073
+Tanzania	1962	10863958	Africa	44.246	722.0038073
+Tanzania	1967	12607312	Africa	45.757	848.2186575
+Tanzania	1972	14706593	Africa	47.62	915.9850592
+Tanzania	1977	17129565	Africa	49.919	962.4922932
+Tanzania	1982	19844382	Africa	50.608	874.2426069
+Tanzania	1987	23040630	Africa	51.535	831.8220794
+Tanzania	1992	26605473	Africa	50.44	825.682454
+Tanzania	1997	30686889	Africa	48.466	789.1862231
+Tanzania	2002	34593779	Africa	49.651	899.0742111
+Tanzania	2007	38139640	Africa	52.517	1107.482182
+Thailand	1952	21289402	Asia	50.848	757.7974177
+Thailand	1957	25041917	Asia	53.63	793.5774148
+Thailand	1962	29263397	Asia	56.061	1002.199172
+Thailand	1967	34024249	Asia	58.285	1295.46066
+Thailand	1972	39276153	Asia	60.405	1524.358936
+Thailand	1977	44148285	Asia	62.494	1961.224635
+Thailand	1982	48827160	Asia	64.597	2393.219781
+Thailand	1987	52910342	Asia	66.084	2982.653773
+Thailand	1992	56667095	Asia	67.298	4616.896545
+Thailand	1997	60216677	Asia	67.521	5852.625497
+Thailand	2002	62806748	Asia	68.564	5913.187529
+Thailand	2007	65068149	Asia	70.616	7458.396327
+Togo	1952	1219113	Africa	38.596	859.8086567
+Togo	1957	1357445	Africa	41.208	925.9083202
+Togo	1962	1528098	Africa	43.922	1067.53481
+Togo	1967	1735550	Africa	46.769	1477.59676
+Togo	1972	2056351	Africa	49.759	1649.660188
+Togo	1977	2308582	Africa	52.887	1532.776998
+Togo	1982	2644765	Africa	55.471	1344.577953
+Togo	1987	3154264	Africa	56.941	1202.201361
+Togo	1992	3747553	Africa	58.061	1034.298904
+Togo	1997	4320890	Africa	58.39	982.2869243
+Togo	2002	4977378	Africa	57.561	886.2205765
+Togo	2007	5701579	Africa	58.42	882.9699438
+Trinidad and Tobago	1952	662850	Americas	59.1	3023.271928
+Trinidad and Tobago	1957	764900	Americas	61.8	4100.3934
+Trinidad and Tobago	1962	887498	Americas	64.9	4997.523971
+Trinidad and Tobago	1967	960155	Americas	65.4	5621.368472
+Trinidad and Tobago	1972	975199	Americas	65.9	6619.551419
+Trinidad and Tobago	1977	1039009	Americas	68.3	7899.554209
+Trinidad and Tobago	1982	1116479	Americas	68.832	9119.528607
+Trinidad and Tobago	1987	1191336	Americas	69.582	7388.597823
+Trinidad and Tobago	1992	1183669	Americas	69.862	7370.990932
+Trinidad and Tobago	1997	1138101	Americas	69.465	8792.573126
+Trinidad and Tobago	2002	1101832	Americas	68.976	11460.60023
+Trinidad and Tobago	2007	1056608	Americas	69.819	18008.50924
+Tunisia	1952	3647735	Africa	44.6	1468.475631
+Tunisia	1957	3950849	Africa	47.1	1395.232468
+Tunisia	1962	4286552	Africa	49.579	1660.30321
+Tunisia	1967	4786986	Africa	52.053	1932.360167
+Tunisia	1972	5303507	Africa	55.602	2753.285994
+Tunisia	1977	6005061	Africa	59.837	3120.876811
+Tunisia	1982	6734098	Africa	64.048	3560.233174
+Tunisia	1987	7724976	Africa	66.894	3810.419296
+Tunisia	1992	8523077	Africa	70.001	4332.720164
+Tunisia	1997	9231669	Africa	71.973	4876.798614
+Tunisia	2002	9770575	Africa	73.042	5722.895655
+Tunisia	2007	10276158	Africa	73.923	7092.923025
+Turkey	1952	22235677	Europe	43.585	1969.10098
+Turkey	1957	25670939	Europe	48.079	2218.754257
+Turkey	1962	29788695	Europe	52.098	2322.869908
+Turkey	1967	33411317	Europe	54.336	2826.356387
+Turkey	1972	37492953	Europe	57.005	3450.69638
+Turkey	1977	42404033	Europe	59.507	4269.122326
+Turkey	1982	47328791	Europe	61.036	4241.356344
+Turkey	1987	52881328	Europe	63.108	5089.043686
+Turkey	1992	58179144	Europe	66.146	5678.348271
+Turkey	1997	63047647	Europe	68.835	6601.429915
+Turkey	2002	67308928	Europe	70.845	6508.085718
+Turkey	2007	71158647	Europe	71.777	8458.276384
+Uganda	1952	5824797	Africa	39.978	734.753484
+Uganda	1957	6675501	Africa	42.571	774.3710692
+Uganda	1962	7688797	Africa	45.344	767.2717398
+Uganda	1967	8900294	Africa	48.051	908.9185217
+Uganda	1972	10190285	Africa	51.016	950.735869
+Uganda	1977	11457758	Africa	50.35	843.7331372
+Uganda	1982	12939400	Africa	49.849	682.2662268
+Uganda	1987	15283050	Africa	51.509	617.7244065
+Uganda	1992	18252190	Africa	48.825	644.1707969
+Uganda	1997	21210254	Africa	44.578	816.559081
+Uganda	2002	24739869	Africa	47.813	927.7210018
+Uganda	2007	29170398	Africa	51.542	1056.380121
+United Kingdom	1952	50430000	Europe	69.18	9979.508487
+United Kingdom	1957	51430000	Europe	70.42	11283.17795
+United Kingdom	1962	53292000	Europe	70.76	12477.17707
+United Kingdom	1967	54959000	Europe	71.36	14142.85089
+United Kingdom	1972	56079000	Europe	72.01	15895.11641
+United Kingdom	1977	56179000	Europe	72.76	17428.74846
+United Kingdom	1982	56339704	Europe	74.04	18232.42452
+United Kingdom	1987	56981620	Europe	75.007	21664.78767
+United Kingdom	1992	57866349	Europe	76.42	22705.09254
+United Kingdom	1997	58808266	Europe	77.218	26074.53136
+United Kingdom	2002	59912431	Europe	78.471	29478.99919
+United Kingdom	2007	60776238	Europe	79.425	33203.26128
+United States	1952	157553000	Americas	68.44	13990.48208
+United States	1957	171984000	Americas	69.49	14847.12712
+United States	1962	186538000	Americas	70.21	16173.14586
+United States	1967	198712000	Americas	70.76	19530.36557
+United States	1972	209896000	Americas	71.34	21806.03594
+United States	1977	220239000	Americas	73.38	24072.63213
+United States	1982	232187835	Americas	74.65	25009.55914
+United States	1987	242803533	Americas	75.02	29884.35041
+United States	1992	256894189	Americas	76.09	32003.93224
+United States	1997	272911760	Americas	76.81	35767.43303
+United States	2002	287675526	Americas	77.31	39097.09955
+United States	2007	301139947	Americas	78.242	42951.65309
+Uruguay	1952	2252965	Americas	66.071	5716.766744
+Uruguay	1957	2424959	Americas	67.044	6150.772969
+Uruguay	1962	2598466	Americas	68.253	5603.357717
+Uruguay	1967	2748579	Americas	68.468	5444.61962
+Uruguay	1972	2829526	Americas	68.673	5703.408898
+Uruguay	1977	2873520	Americas	69.481	6504.339663
+Uruguay	1982	2953997	Americas	70.805	6920.223051
+Uruguay	1987	3045153	Americas	71.918	7452.398969
+Uruguay	1992	3149262	Americas	72.752	8137.004775
+Uruguay	1997	3262838	Americas	74.223	9230.240708
+Uruguay	2002	3363085	Americas	75.307	7727.002004
+Uruguay	2007	3447496	Americas	76.384	10611.46299
+Venezuela	1952	5439568	Americas	55.088	7689.799761
+Venezuela	1957	6702668	Americas	57.907	9802.466526
+Venezuela	1962	8143375	Americas	60.77	8422.974165
+Venezuela	1967	9709552	Americas	63.479	9541.474188
+Venezuela	1972	11515649	Americas	65.712	10505.25966
+Venezuela	1977	13503563	Americas	67.456	13143.95095
+Venezuela	1982	15620766	Americas	68.557	11152.41011
+Venezuela	1987	17910182	Americas	70.19	9883.584648
+Venezuela	1992	20265563	Americas	71.15	10733.92631
+Venezuela	1997	22374398	Americas	72.146	10165.49518
+Venezuela	2002	24287670	Americas	72.766	8605.047831
+Venezuela	2007	26084662	Americas	73.747	11415.80569
+Vietnam	1952	26246839	Asia	40.412	605.0664917
+Vietnam	1957	28998543	Asia	42.887	676.2854478
+Vietnam	1962	33796140	Asia	45.363	772.0491602
+Vietnam	1967	39463910	Asia	47.838	637.1232887
+Vietnam	1972	44655014	Asia	50.254	699.5016441
+Vietnam	1977	50533506	Asia	55.764	713.5371196
+Vietnam	1982	56142181	Asia	58.816	707.2357863
+Vietnam	1987	62826491	Asia	62.82	820.7994449
+Vietnam	1992	69940728	Asia	67.662	989.0231487
+Vietnam	1997	76048996	Asia	70.672	1385.896769
+Vietnam	2002	80908147	Asia	73.017	1764.456677
+Vietnam	2007	85262356	Asia	74.249	2441.576404
+West Bank and Gaza	1952	1030585	Asia	43.16	1515.592329
+West Bank and Gaza	1957	1070439	Asia	45.671	1827.067742
+West Bank and Gaza	1962	1133134	Asia	48.127	2198.956312
+West Bank and Gaza	1967	1142636	Asia	51.631	2649.715007
+West Bank and Gaza	1972	1089572	Asia	56.532	3133.409277
+West Bank and Gaza	1977	1261091	Asia	60.765	3682.831494
+West Bank and Gaza	1982	1425876	Asia	64.406	4336.032082
+West Bank and Gaza	1987	1691210	Asia	67.046	5107.197384
+West Bank and Gaza	1992	2104779	Asia	69.718	6017.654756
+West Bank and Gaza	1997	2826046	Asia	71.096	7110.667619
+West Bank and Gaza	2002	3389578	Asia	72.37	4515.487575
+West Bank and Gaza	2007	4018332	Asia	73.422	3025.349798
+Yemen, Rep.	1952	4963829	Asia	32.548	781.7175761
+Yemen, Rep.	1957	5498090	Asia	33.97	804.8304547
+Yemen, Rep.	1962	6120081	Asia	35.18	825.6232006
+Yemen, Rep.	1967	6740785	Asia	36.984	862.4421463
+Yemen, Rep.	1972	7407075	Asia	39.848	1265.047031
+Yemen, Rep.	1977	8403990	Asia	44.175	1829.765177
+Yemen, Rep.	1982	9657618	Asia	49.113	1977.55701
+Yemen, Rep.	1987	11219340	Asia	52.922	1971.741538
+Yemen, Rep.	1992	13367997	Asia	55.599	1879.496673
+Yemen, Rep.	1997	15826497	Asia	58.02	2117.484526
+Yemen, Rep.	2002	18701257	Asia	60.308	2234.820827
+Yemen, Rep.	2007	22211743	Asia	62.698	2280.769906
+Zambia	1952	2672000	Africa	42.038	1147.388831
+Zambia	1957	3016000	Africa	44.077	1311.956766
+Zambia	1962	3421000	Africa	46.023	1452.725766
+Zambia	1967	3900000	Africa	47.768	1777.077318
+Zambia	1972	4506497	Africa	50.107	1773.498265
+Zambia	1977	5216550	Africa	51.386	1588.688299
+Zambia	1982	6100407	Africa	51.821	1408.678565
+Zambia	1987	7272406	Africa	50.821	1213.315116
+Zambia	1992	8381163	Africa	46.1	1210.884633
+Zambia	1997	9417789	Africa	40.238	1071.353818
+Zambia	2002	10595811	Africa	39.193	1071.613938
+Zambia	2007	11746035	Africa	42.384	1271.211593
+Zimbabwe	1952	3080907	Africa	48.451	406.8841148
+Zimbabwe	1957	3646340	Africa	50.469	518.7642681
+Zimbabwe	1962	4277736	Africa	52.358	527.2721818
+Zimbabwe	1967	4995432	Africa	53.995	569.7950712
+Zimbabwe	1972	5861135	Africa	55.635	799.3621758
+Zimbabwe	1977	6642107	Africa	57.674	685.5876821
+Zimbabwe	1982	7636524	Africa	60.363	788.8550411
+Zimbabwe	1987	9216418	Africa	62.351	706.1573059
+Zimbabwe	1992	10704340	Africa	60.377	693.4207856
+Zimbabwe	1997	11404948	Africa	46.809	792.4499603
+Zimbabwe	2002	11926563	Africa	39.989	672.0386227
+Zimbabwe	2007	12311143	Africa	43.487	469.7092981
diff --git a/lessons/python/pandas2/lesson.md b/lessons/python/pandas2/lesson.md
new file mode 100644
index 000000000..ff73ac415
--- /dev/null
+++ b/lessons/python/pandas2/lesson.md
@@ -0,0 +1,23 @@
+---
+layout: page
+title: Pandas for data analysis
+visible: true
+tags:
+  - python
+  - beginner
+---
+
+ - **Authors**: Derek Howard
+ - **Research field**: Neuroscience
+ - **Lesson topic**: Data analysis, subsetting data, plotting
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/python/pandas2>
+ - **Lesson video stream**: <https://www.youtube.com/watch?v=9Rg8hh3r_Mk>
+
+The lesson was uploaded as a Jupyter notebook. 
+
+If you'd like to follow along in IPython Notebook, do the following:
+
+  - Install [Jupyter Notebook](https://jupyter.readthedocs.org/en/latest/install.html)
+  - Run the notebook by typing `jupyter notebook` into command line.
+  - In the web application that opens, navigate to the folder containing the files you downloaded, and open 'UofT-pandas.ipynb' to follow along.
+  - A pre-rendered version of the notebook may be [viewed here](https://nbviewer.jupyter.org/github/UofTCoders/studyGroup/blob/gh-pages/lessons/python/pandas2/UofT-pandas.ipynb).
diff --git a/lessons/python/python-for-kids/Python-lesson.ipynb b/lessons/python/python-for-kids/Python-lesson.ipynb
new file mode 100644
index 000000000..448c14e15
--- /dev/null
+++ b/lessons/python/python-for-kids/Python-lesson.ipynb
@@ -0,0 +1,1371 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Python for Youth \n",
+    "- refer to last week's robotic session\n",
+    "- Under the hood, languages like Python program translate human language to something the machine can understand (instructions)\n",
+    "- Python is an open source general purpose language, used for many things from data science, to web development to app development, etc. \n",
+    "- Python emphasizes readability, compared to older langauges many default functions and commands are read like plain English.\n",
+    "- ask who has done programming, who has done Python \n",
+    "- Introduce repl.it, not the only way, easy for first introduction without installation of anything. FORK the repl.it \n",
+    "    and the online python interpreter that we useis here: https://repl.it/@UofTCoders/saturdayprogram\n",
+    "    - explain stickies -> sometimes things go wrong, not a problem! part of the learning experience.\n",
+    "    - we're all human!\n",
+    "- Lesson outline:\n",
+    "    - We learn python through a game, taken from [Code Club Projects](https://codeclubprojects.org/en-GB/python/)\n",
+    "    - A brief intro to general python commands, syntax = the rules/grammar used that Python can always know what you're trying to say.\n",
+    "        - going more in depth with what we did in the game and explaining\n",
+    "     - you'll have an idea of python commands and syntax, and enough for you to go home and find tutorials for what you want to do, (almost anything!)\n",
+    "     \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "s vs p\n",
+      "Player wins!\n",
+      ">8 vs p\n"
+     ]
+    }
+   ],
+   "source": [
+    "# First, let the player choose Rock, Paper or Scissors by typing the letter ‘r’, ‘p’ or ‘s’\n",
+    "# first create a prompt and explain\n",
+    "input('what is your name?')\n",
+    "      \n",
+    "# for python to do anything with the result we need to save it in a variable which we can name anything but this is informative\n",
+    "player = input('rock (r), paper (p) or scissors (s)?')\n",
+    "\n",
+    "# what did we just do? we used a built-in function in Python to prompt the user to input a letter in the console\n",
+    "# and we assigned the input to a variable called 'player'. the = symbol indicates that what is on the right is assigned to the variable name on the left\n",
+    "\n",
+    "# what is a function like input or print? They let us do things with objects that we've made, like the player variable\n",
+    "\n",
+    "# Now print out what the player chose:\n",
+    "#print(player)\n",
+    "#print('you chose', player)\n",
+    "# mention object type as string or integer\n",
+    "#print(player, 'vs')\n",
+    "print(player, 'vs', end=' ')\n",
+    "\n",
+    "\n",
+    "# Second: Computer's Turn\n",
+    "# Use 'randint' function to generate a random number to decide whether the computer has chosen rock, paper or scissors.\n",
+    "# we need to import it from 'random' library\n",
+    "from random import randint\n",
+    "chosen = randint(1,3) # search google for how to use via documentation\n",
+    "#print(chosen)\n",
+    "#print('computer chose',chosen)\n",
+    "\n",
+    "# we like to print the letters not numbers. let's say 1 = rock, 2 = paper, 3=scissors\n",
+    "# we can use if statement to assign letters to whatever number that is selected\n",
+    "if chosen == 1: \n",
+    "  computer = 'r' # 'O'\n",
+    "elif chosen == 2: \n",
+    "  computer = 'p' #'__'\n",
+    "else: \n",
+    "  computer = 's' #'>8'\n",
+    "\n",
+    "print(computer)\n",
+    "# run a few times to show the random nature of the function\n",
+    "# notice that we are not inside the if statement because we don't use indentation\n",
+    "# a nicer output: \n",
+    "#print(player, 'vs', computer)\n",
+    "\n",
+    "# let's add a code for determining the winner \n",
+    "# we need to compare the 'player' and 'computer' variables\n",
+    "# explain briefly booleans to compare\n",
+    "if player == computer:\n",
+    "  print('Draw!')\n",
+    "elif player == 'r' and computer == 's':\n",
+    "  print('player wins!')\n",
+    "elif player == 'r' and computer =='p':\n",
+    "  print('Computer wins!')\n",
+    "elif player == 'p' and computer == 'r':\n",
+    "  print('Player wins!')\n",
+    "elif player == 'p' and computer == 's':\n",
+    "  print('Computer wins!')\n",
+    "elif player == 's' and computer == 'p':\n",
+    "  print('Player wins!')\n",
+    "elif player == 's' and computer == 'r':\n",
+    "  print('Computer wins!')\n",
+    "  \n",
+    "# Challenge: Instead of using the letters r, p and s to represent rock, paper and scissors, can you use ASCII art?\n",
+    "# O for rock, __ for paper, and >8 for scissors\n",
+    "# so now change the lines you print the choices of the player and the computer in ASCII art \n",
+    "\n",
+    "#if player == 'r':\n",
+    "#  print('O', 'vs', end=' ')\n",
+    "#elif player == 'p':\n",
+    "#  print('__', 'vs', end=' ')\n",
+    "#else: \n",
+    "#  print('>8', 'vs', end =' ')\n",
+    "  \n",
+    "if player == 'r':\n",
+    "  print('O', 'vs', computer)\n",
+    "elif player == 'p':\n",
+    "  print('__', 'vs', computer )\n",
+    "else: \n",
+    "  print('>8', 'vs', computer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## summary\n",
+    "What we learned today: \n",
+    "- built-in functions like input prompt and print \n",
+    "- variables\n",
+    "- numbers and strings \n",
+    "- libraries and functions like randint \n",
+    "- if statements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# review: \n",
+    "## Variables "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "name = 'Sara'\n",
+    "year = 2017\n",
+    "# we can check the type of our variable using the type(variable_name) function\n",
+    "print(type(name))\n",
+    "#str is a string: a sequence of characters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Numbers\n",
+    "You can use Python as a calculator: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "7 + 8 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "56"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "7*8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "8%7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2**4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python's order of operations works just like math's:\n",
+    "\n",
+    "- Parentheses\n",
+    "- Exponents\n",
+    "- Multiplication and division\n",
+    "- Addition and subtraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "16 ** 0.5\n",
+    "16 ** (1/2)\n",
+    "16 ** 1/2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Logic\n",
+    "We've talked about how Python handles numbers already. Let's now turn to how it handles inequalities – things that are either true or false."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "6 > 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4 == 6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4 <= 6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4 != 6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "you can continue here \n",
+    "https://docs.trinket.io/getting-started-with-python#/logic/combining-boolean-expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lists\n",
+    "Python has two array-like things. The first is called a \"list\", which can hold any data types.\n",
+    "After the .append() method, explain OOP briefly and why it's useful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['apple', 'banana', 'mango', 'lychee']\n"
+     ]
+    }
+   ],
+   "source": [
+    "fruits = ['apple', 'banana', 'mango', 'lychee']\n",
+    "print(fruits)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['apple', 'banana', 'mango', 'lychee', 'orange', 'orange']\n"
+     ]
+    }
+   ],
+   "source": [
+    "fruits.append('orange')\n",
+    "print(fruits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# lists don't need to comprise of all the same type \n",
+    "misc = [29, 'dog', fruits]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[29, 'dog', ['apple', 'banana', 'mango', 'lychee', 'orange', 'orange']]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(misc)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['apple', 'banana', 'mango', 'lychee', 'orange', 'orange', 'apple', 'banana', 'mango', 'lychee', 'orange', 'orange']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(fruits + fruits)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The second is called a \"tuple\", which is an immutable list (nothing can be added or subtracted) whose elements also can't be reassigned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "tup1 = (1,2)\n",
+    "print(tup1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indexing and Slicing\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#indexing in Python starts at 0, not 1 (like in Matlab or Oracle)\n",
+    "print(fruits[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(fruits[1])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# strings are just a particular kind of list\n",
+    "s = 'This is a string.'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(s[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use -1 to get the last element\n",
+    "print(fruits[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(fruits[-2])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# to get a slice of the string use the : symbol\n",
+    "print(s[0:4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(s[:4])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(s[4:7])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 's' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-16-ca10691ae8a9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 's' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "print(s[7:])\n",
+    "print(s[7:len(s)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## For Loops\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums = [23, 56, 1, 10, 15, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# in this case, 'n' is a dummy variable that will be used by the for loop\n",
+    "# you do not need to assign it ahead of time\n",
+    "\n",
+    "for n in nums:\n",
+    "    if n%2 == 0:\n",
+    "        print('even')\n",
+    "    else:\n",
+    "        print('odd')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for loops can iterate over strings as well\n",
+    "vowels = 'aeiou'\n",
+    "for vowel in vowels:\n",
+    "    print(vowel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# always use descriptive naming for functions, variables, arguments etc.\n",
+    "def sum_of_squares(num1, num2):\n",
+    "    \"\"\"\n",
+    "    Input: two numbers\n",
+    "    Output: the sum of the squares of the two numbers\n",
+    "    \"\"\"\n",
+    "    ss = num1**2 + num2**2\n",
+    "    return(ss)\n",
+    "\n",
+    "# The stuff inside \"\"\" \"\"\" is called the \"docstring\". It can be accessed by typing help(sum_of_squares)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(sum_of_squares(4,2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "50\n"
+     ]
+    }
+   ],
+   "source": [
+    "# the return statement in a function allows us to store the output of a function call in a variable for later use\n",
+    "ss1 = sum_of_squares(5,5)\n",
+    "print(ss1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Useful Packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use a package by importing it, you can also give it a shorter alias, in this case 'np'\n",
+    "import numpy as np\n",
+    "array = np.arange(15)\n",
+    "lst = list(range(15))\n",
+    "print(array)\n",
+    "print(lst)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(type(array))\n",
+    "print(type(lst))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# numpy arrays allow for vectorized calculations\n",
+    "print(array*2)\n",
+    "print(lst*2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array = array.reshape([5,3])\n",
+    "print(array)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we can get the mean over all rows (using axis=1)\n",
+    "array.mean(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# max value in each column\n",
+    "array.max(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# this will read in a csv file into a pandas DataFrame\n",
+    "# this csv has data of country spending on healthcare\n",
+    "data = pd.read_csv('health.csv', header=0, index_col=0, encoding=\"ISO-8859-1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1995</th>\n",
+       "      <th>1996</th>\n",
+       "      <th>1997</th>\n",
+       "      <th>1998</th>\n",
+       "      <th>1999</th>\n",
+       "      <th>2000</th>\n",
+       "      <th>2001</th>\n",
+       "      <th>2002</th>\n",
+       "      <th>2003</th>\n",
+       "      <th>2004</th>\n",
+       "      <th>2005</th>\n",
+       "      <th>2006</th>\n",
+       "      <th>2007</th>\n",
+       "      <th>2008</th>\n",
+       "      <th>2009</th>\n",
+       "      <th>2010</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total expenditure on health as percentage of GDP (gross domestic product)</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Abkhazia</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Afghanistan</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>7.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Akrotiri and Dhekelia</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Albania</th>\n",
+       "      <td>2.6</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>6.4</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Algeria</th>\n",
+       "      <td>4.2</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>4.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    1995  1996  1997  1998  \\\n",
+       "Total expenditure on health as percentage of GD...                           \n",
+       "Abkhazia                                             NaN   NaN   NaN   NaN   \n",
+       "Afghanistan                                          NaN   NaN   NaN   NaN   \n",
+       "Akrotiri and Dhekelia                                NaN   NaN   NaN   NaN   \n",
+       "Albania                                              2.6   4.0   4.8   5.3   \n",
+       "Algeria                                              4.2   3.8   4.1   4.1   \n",
+       "\n",
+       "                                                    1999  2000  2001  2002  \\\n",
+       "Total expenditure on health as percentage of GD...                           \n",
+       "Abkhazia                                             NaN   NaN   NaN   NaN   \n",
+       "Afghanistan                                          NaN   NaN   NaN   5.7   \n",
+       "Akrotiri and Dhekelia                                NaN   NaN   NaN   NaN   \n",
+       "Albania                                              5.8   6.4   6.0   6.3   \n",
+       "Algeria                                              3.9   3.5   3.8   3.9   \n",
+       "\n",
+       "                                                    2003  2004  2005  2006  \\\n",
+       "Total expenditure on health as percentage of GD...                           \n",
+       "Abkhazia                                             NaN   NaN   NaN   NaN   \n",
+       "Afghanistan                                          6.8   6.4   6.6   6.8   \n",
+       "Akrotiri and Dhekelia                                NaN   NaN   NaN   NaN   \n",
+       "Albania                                              6.2   6.9   6.8   6.7   \n",
+       "Algeria                                              3.7   3.4   3.1   3.1   \n",
+       "\n",
+       "                                                    2007  2008  2009  2010  \n",
+       "Total expenditure on health as percentage of GD...                          \n",
+       "Abkhazia                                             NaN   NaN   NaN   NaN  \n",
+       "Afghanistan                                          7.3   7.0   7.6   7.6  \n",
+       "Akrotiri and Dhekelia                                NaN   NaN   NaN   NaN  \n",
+       "Albania                                              6.9   6.7   6.9   6.5  \n",
+       "Algeria                                              3.5   3.7   4.6   4.2  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# the .head() function will allow us to look at first few lines of the dataframe\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.300000000000001"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# by default, rows are indicated first, followed by the column: [row, column]\n",
+    "data.loc['Canada', '2008']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>1999</th>\n",
+       "      <th>2000</th>\n",
+       "      <th>2001</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Total expenditure on health as percentage of GDP (gross domestic product)</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Canada</th>\n",
+       "      <td>8.9</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>9.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cape Verde</th>\n",
+       "      <td>4.5</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cayman Islands</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Central African Republic</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>3.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chad</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Channel Islands</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Chile</th>\n",
+       "      <td>8.2</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>8.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>China</th>\n",
+       "      <td>4.5</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>4.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Christmas Island</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cocos Island</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colombia</th>\n",
+       "      <td>9.3</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>7.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Comoros</th>\n",
+       "      <td>3.2</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Congo, Dem. Rep.</th>\n",
+       "      <td>4.3</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Congo, Rep.</th>\n",
+       "      <td>3.1</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>2.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cook Is</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>4.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Costa Rica</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>7.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cote d'Ivoire</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Croatia</th>\n",
+       "      <td>7.3</td>\n",
+       "      <td>7.8</td>\n",
+       "      <td>7.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cuba</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>6.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Cyprus</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>5.8</td>\n",
+       "      <td>5.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Czech Republic</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Czechoslovakia</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Denmark</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>9.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    1999  2000  2001\n",
+       "Total expenditure on health as percentage of GD...                  \n",
+       "Canada                                               8.9   8.8   9.3\n",
+       "Cape Verde                                           4.5   4.6   5.0\n",
+       "Cayman Islands                                       NaN   NaN   NaN\n",
+       "Central African Republic                             3.5   3.8   3.8\n",
+       "Chad                                                 5.9   6.3   6.0\n",
+       "Channel Islands                                      NaN   NaN   NaN\n",
+       "Chile                                                8.2   8.3   8.4\n",
+       "China                                                4.5   4.6   4.6\n",
+       "Christmas Island                                     NaN   NaN   NaN\n",
+       "Cocos Island                                         NaN   NaN   NaN\n",
+       "Colombia                                             9.3   7.3   7.3\n",
+       "Comoros                                              3.2   2.9   2.3\n",
+       "Congo, Dem. Rep.                                     4.3   4.9   4.4\n",
+       "Congo, Rep.                                          3.1   2.1   2.4\n",
+       "Cook Is                                              3.5   3.4   4.6\n",
+       "Costa Rica                                           6.2   6.5   7.1\n",
+       "Cote d'Ivoire                                        5.6   5.1   4.0\n",
+       "Croatia                                              7.3   7.8   7.2\n",
+       "Cuba                                                 6.0   6.1   6.3\n",
+       "Cyprus                                               5.6   5.8   5.8\n",
+       "Czech Republic                                       6.3   6.3   6.4\n",
+       "Czechoslovakia                                       NaN   NaN   NaN\n",
+       "Denmark                                              9.0   8.7   9.1"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# you can also slice a dataframe\n",
+    "data.loc['Canada':'Denmark', '1999':'2001']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1bdd8cb7160>"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1bdd8b17f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the .plot() function will create a simple graph for you to quickly visualize your data\n",
+    "data.loc['Denmark'].plot()\n",
+    "data.loc['Canada'].plot()\n",
+    "data.loc['India'].plot()\n",
+    "plt.legend(loc='best')\n",
+    "plt.savefig(\"countries_healthexpenditure.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Guessing game\n",
+    "if we have extra time, we can play another game: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello Sara.\n",
+      "I'm thinking of a number between 1 & 10\n",
+      "Guess Higher\n",
+      "Guess Higher\n",
+      "Guess Higher\n",
+      "Guess Higher\n",
+      "Guess Higher\n",
+      "Congrats, you guessed correctly. The number was indeed 10\n",
+      "it had taken you 6 tries\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "\n",
+    "number = random.randint(1, 10)\n",
+    "tries = 0\n",
+    "win = False # setting a win flag to false\n",
+    "\n",
+    "\n",
+    "name = input(\"Hello, What is your username?\")\n",
+    "print(\"Hello \" + name + \".\" )\n",
+    "\n",
+    "question = input(\"Would you like to play a game? [Y/N] \")\n",
+    "if question.lower() == \"n\": #in case of capital letters is entered\n",
+    "    print(\"oh..okay\")\n",
+    "    exit()\n",
+    "elif question.lower() == \"y\":\n",
+    "    print(\"I'm thinking of a number between 1 & 10\")\n",
+    "\n",
+    "while not win:       # while the win is not true, run the while loop. We set win to false at the start therefore this will always run\n",
+    "    guess = int(input(\"Have a guess: \"))\n",
+    "    tries = tries + 1\n",
+    "    if guess == number:\n",
+    "        win = True    # set win to true when the user guesses correctly.\n",
+    "    elif guess < number:\n",
+    "        print(\"Guess Higher\")\n",
+    "    elif guess > number:\n",
+    "        print(\"Guess Lower\")\n",
+    "# if win is true then output message\n",
+    "print(\"Congrats, you guessed correctly. The number was indeed {}\".format(number))\n",
+    "print(\"it had taken you {} tries\".format(tries))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lessons/python/python-for-kids/health.csv b/lessons/python/python-for-kids/health.csv
new file mode 100644
index 000000000..113732ec0
--- /dev/null
+++ b/lessons/python/python-for-kids/health.csv
@@ -0,0 +1,263 @@
+Total expenditure on health as percentage of GDP (gross domestic product),1995,1996,1997,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010
+Abkhazia,,,,,,,,,,,,,,,,
+Afghanistan,,,,,,,,5.7,6.8,6.4,6.6,6.8,7.3,7.0,7.6,7.6
+Akrotiri and Dhekelia,,,,,,,,,,,,,,,,
+Albania,2.6,4.0,4.8,5.3,5.8,6.4,6.0,6.3,6.2,6.9,6.8,6.7,6.9,6.7,6.9,6.5
+Algeria,4.2,3.8,4.1,4.1,3.9,3.5,3.8,3.9,3.7,3.4,3.1,3.1,3.5,3.7,4.6,4.2
+American Samoa,,,,,,,,,,,,,,,,
+Andorra,7.6,8.0,8.0,9.8,7.4,7.6,6.8,7.0,7.1,7.1,7.2,7.4,7.6,7.5,7.5,7.5
+Angola,3.8,2.2,2.3,1.9,1.9,2.4,3.4,2.4,2.6,2.1,2.0,2.4,2.5,3.2,4.9,2.9
+Anguilla,,,,,,,,,,,,,,,,
+Antigua and Barbuda,4.9,4.6,4.6,4.6,4.7,4.8,4.6,4.6,4.7,4.7,4.7,4.5,4.4,4.3,4.8,6.0
+Argentina,8.3,8.0,8.4,8.5,9.4,9.2,9.4,8.3,8.3,8.3,8.5,8.5,8.4,8.4,9.5,8.1
+Armenia,6.4,5.8,5.8,5.8,5.7,6.3,6.0,5.4,5.6,5.5,4.9,4.2,3.9,3.8,4.6,4.4
+Aruba,,,,,,,,,,,,,,,,
+Australia,7.2,7.4,7.5,7.6,7.8,8.0,8.1,8.4,8.3,8.5,8.4,8.5,8.5,8.7,8.7,8.7
+Austria,9.5,9.5,9.8,9.9,10.1,9.9,10.0,10.0,10.2,10.3,10.3,10.2,10.2,10.4,11.0,11.0
+Azerbaijan,5.9,6.2,5.5,5.5,5.4,4.7,4.5,4.5,6.6,7.9,7.8,6.1,5.0,4.3,5.8,5.9
+Bahamas,6.9,6.9,6.5,6.3,6.1,5.9,5.9,6.1,6.5,7.0,7.0,8.0,8.1,8.4,8.3,7.9
+Bahrain,4.6,4.5,4.7,4.7,4.7,3.9,4.3,4.4,4.2,3.9,3.7,3.5,3.6,4.2,4.7,5.0
+Bangladesh,3.5,3.5,2.8,2.7,2.8,2.8,3.0,3.1,3.0,3.1,3.2,3.4,3.5,3.3,3.4,3.5
+Barbados,6.3,6.2,5.9,5.9,6.1,6.3,6.7,7.3,7.0,7.0,7.0,7.1,7.0,7.1,5.9,8.0
+Belarus,6.7,6.2,6.6,6.0,6.2,6.1,6.6,6.5,6.6,6.6,6.9,6.3,6.4,5.9,6.1,5.6
+Belgium,7.6,7.9,7.8,7.9,8.1,8.1,8.3,8.5,10.0,10.1,10.0,9.6,9.6,10.0,10.8,10.7
+Belize,4.2,3.6,3.9,3.8,3.7,4.0,4.5,4.4,4.5,4.4,4.4,4.4,4.8,4.8,5.5,5.2
+Benin,4.7,4.6,4.3,4.4,4.6,4.3,4.7,4.3,4.6,4.6,4.7,4.8,4.6,4.2,4.3,4.1
+Bermuda,,,,,,,,,,,,,,,,
+Bhutan,3.4,4.7,5.4,5.7,7.1,6.9,6.5,8.3,5.1,4.4,4.7,5.2,5.0,5.2,5.1,5.2
+Bolivia,4.5,4.6,4.4,5.1,6.2,6.1,6.3,6.5,5.6,5.3,5.6,4.8,4.7,4.9,5.1,4.8
+Bosnia and Herzegovina,10.6,11.3,7.9,7.8,8.7,7.0,7.2,7.0,7.9,9.1,9.1,9.3,9.8,10.3,10.9,11.1
+Botswana,4.2,4.2,4.4,4.0,3.8,4.7,5.3,6.1,6.0,8.3,7.7,6.7,7.9,7.8,10.0,8.3
+Brazil,6.7,6.8,6.8,6.7,7.1,7.2,7.3,7.2,7.0,7.1,8.2,8.5,8.5,8.3,8.8,9.0
+British Virgin Islands,,,,,,,,,,,,,,,,
+Brunei,2.7,3.6,3.8,4.6,4.5,4.2,4.3,4.3,3.1,3.0,2.6,2.3,2.4,2.3,3.0,2.8
+Bulgaria,5.2,4.7,5.1,5.4,6.0,6.2,7.4,7.6,7.6,7.3,7.3,6.9,6.8,7.0,7.2,6.9
+Burkina Faso,4.3,4.5,4.5,4.7,5.3,5.1,4.9,5.0,5.5,6.1,6.7,6.3,6.3,5.9,6.6,6.7
+Burundi,5.7,5.8,5.3,5.7,5.6,6.3,6.8,7.0,7.2,9.9,12.2,14.3,13.9,12.4,11.4,11.6
+Cambodia,6.8,9.5,8.0,6.9,6.2,6.3,7.1,7.5,8.4,8.0,7.5,5.9,5.0,5.2,5.5,5.7
+Cameroon,3.9,5.2,4.6,4.4,4.4,4.5,4.7,4.9,5.0,4.7,4.7,4.7,4.7,5.1,4.9,5.1
+Canada,9.0,8.8,8.8,9.0,8.9,8.8,9.3,9.6,9.8,9.8,9.8,10.0,10.0,10.3,11.4,11.3
+Cape Verde,5.1,4.6,4.5,5.1,4.5,4.6,5.0,5.0,4.6,5.1,4.6,4.9,4.6,4.4,3.9,4.1
+Cayman Islands,,,,,,,,,,,,,,,,
+Central African Republic,3.6,3.6,3.8,3.5,3.5,3.8,3.8,3.9,4.0,4.0,3.7,3.8,4.1,4.0,4.0,4.0
+Chad,5.8,5.5,5.5,5.4,5.9,6.3,6.0,8.0,5.8,5.1,4.7,4.0,4.2,3.6,4.6,4.5
+Channel Islands,,,,,,,,,,,,,,,,
+Chile,6.7,7.5,7.5,8.0,8.2,8.3,8.4,8.4,7.5,7.1,6.9,6.6,6.9,7.5,8.4,8.0
+China,3.5,3.8,4.0,4.4,4.5,4.6,4.6,4.8,4.8,4.7,4.7,4.6,4.4,4.6,5.1,5.1
+Christmas Island,,,,,,,,,,,,,,,,
+Cocos Island,,,,,,,,,,,,,,,,
+Colombia,7.3,8.8,9.6,9.3,9.3,7.3,7.3,7.3,7.7,7.4,7.4,7.3,7.2,6.9,7.6,7.6
+Comoros,4.0,3.9,3.9,3.4,3.2,2.9,2.3,2.9,3.1,3.4,3.7,4.0,4.3,4.5,3.2,4.5
+"Congo, Dem. Rep.",3.7,3.5,4.1,4.8,4.3,4.9,4.4,4.3,5.2,5.6,5.9,6.2,6.2,7.3,9.6,7.9
+"Congo, Rep.",3.1,2.8,2.9,3.6,3.1,2.1,2.4,2.4,2.6,2.5,2.4,2.4,2.7,2.6,2.8,2.5
+Cook Is,5.2,4.7,4.0,4.2,3.5,3.4,4.6,3.8,4.0,3.9,4.7,4.2,4.4,4.2,4.3,4.4
+Costa Rica,6.5,6.7,6.2,6.3,6.2,6.5,7.1,7.7,7.7,7.7,7.6,7.8,8.4,9.4,10.5,10.9
+Cote d'Ivoire,5.1,5.6,6.2,6.6,5.6,5.1,4.0,3.5,3.8,4.2,4.3,4.7,5.8,5.9,5.2,5.3
+Croatia,6.8,7.1,5.9,5.7,7.3,7.8,7.2,6.3,6.4,6.6,7.0,7.0,7.5,7.8,7.8,7.8
+Cuba,5.2,5.3,5.5,5.8,6.0,6.1,6.3,6.5,6.3,6.1,8.0,7.6,10.4,10.8,12.1,10.6
+Cyprus,4.7,5.2,5.5,5.6,5.6,5.8,5.8,6.1,6.8,6.4,6.4,6.3,6.1,6.0,6.1,6.0
+Czech Republic,6.7,6.4,6.4,6.4,6.3,6.3,6.4,6.8,7.1,6.9,6.9,6.7,6.5,6.8,8.0,7.9
+Czechoslovakia,,,,,,,,,,,,,,,,
+Denmark,8.1,8.2,8.2,8.2,9.0,8.7,9.1,9.3,9.5,9.7,9.8,9.9,9.9,10.2,11.5,11.4
+Djibouti,4.0,4.0,4.5,5.3,6.2,5.8,5.4,5.5,6.3,5.7,7.2,7.0,7.7,7.2,7.8,7.2
+Dominica,5.9,6.0,6.1,5.9,5.9,5.9,6.2,6.5,6.4,6.0,6.0,6.1,6.1,6.0,6.2,7.4
+Dominican Republic,5.5,5.3,5.7,5.8,5.9,6.3,6.2,6.3,6.0,5.3,5.5,5.6,5.4,5.7,5.9,6.2
+East Germany,,,,,,,,,,,,,,,,
+Ecuador,4.1,4.4,4.4,4.3,4.7,4.2,4.6,5.5,6.6,6.6,6.6,6.7,7.0,7.0,8.8,8.1
+Egypt,3.9,4.3,5.0,5.1,5.3,5.4,5.8,6.1,5.8,5.5,5.2,5.3,4.9,4.8,4.8,4.7
+El Salvador,6.4,7.6,8.1,8.2,8.0,8.0,7.8,7.7,7.3,7.2,7.1,6.6,6.3,6.2,6.8,6.9
+Equatorial Guinea,5.9,4.7,4.2,5.2,2.3,1.9,2.0,3.8,2.4,2.1,1.7,1.8,1.9,2.7,4.5,4.5
+Eritrea,4.5,3.7,4.2,4.3,4.7,5.3,5.1,4.4,4.1,3.8,3.0,3.1,3.2,4.3,2.8,2.7
+Eritrea and Ethiopia,,,,,,,,,,,,,,,,
+Estonia,6.3,6.6,6.2,5.6,6.6,6.0,5.5,5.6,5.6,5.8,5.0,5.0,5.1,5.9,6.7,6.0
+Ethiopia,2.9,2.9,3.1,3.6,3.9,4.3,4.7,4.7,4.9,4.3,4.1,4.2,4.8,4.3,4.4,4.9
+Faeroe Islands,,,,,,,,,,,,,,,,
+Falkland Is (Malvinas),,,,,,,,,,,,,,,,
+Fiji,3.1,3.2,3.5,3.2,3.1,3.8,3.3,3.5,3.3,3.6,3.6,3.8,4.4,4.2,5.0,4.9
+Finland,7.8,8.0,7.7,7.4,7.4,7.2,7.4,7.8,8.2,8.2,8.4,8.3,8.0,8.3,9.0,9.0
+France,10.4,10.4,10.3,10.1,10.2,10.1,10.2,10.6,10.9,11.0,11.2,11.1,11.1,11.2,11.9,11.9
+French Guiana,,,,,,,,,,,,,,,,
+French Polynesia,,,,,,,,,,,,,,,,
+Gabon,3.0,2.9,2.5,3.5,3.0,2.5,2.9,3.2,3.3,3.0,2.6,2.8,2.9,2.6,3.6,3.5
+Gambia,6.3,5.6,5.2,6.0,5.8,5.8,5.8,5.9,5.8,6.3,6.2,5.9,5.3,5.2,6.1,5.7
+Georgia,5.1,7.5,8.0,6.5,6.3,6.9,7.8,8.7,8.5,8.5,8.6,8.4,8.2,9.0,10.2,10.1
+Germany,10.1,10.4,10.3,10.3,10.4,10.4,10.5,10.7,10.9,10.7,10.8,10.6,10.5,10.7,11.7,11.6
+Ghana,5.3,5.1,5.3,5.2,5.0,4.7,5.6,4.8,4.8,6.1,7.0,4.4,6.0,5.6,5.0,5.2
+Gibraltar,,,,,,,,,,,,,,,,
+Greece,8.6,8.6,8.4,8.4,8.6,7.9,8.8,9.1,8.9,8.7,9.7,9.7,9.8,10.1,10.6,10.2
+Greenland,,,,,,,,,,,,,,,,
+Grenada,6.9,7.0,7.3,7.3,7.8,6.6,7.6,6.6,6.2,5.7,6.0,6.4,6.1,5.9,6.2,5.9
+Guadeloupe,,,,,,,,,,,,,,,,
+Guam,,,,,,,,,,,,,,,,
+Guatemala,3.7,3.6,3.8,4.1,4.8,5.5,6.4,6.2,6.3,6.4,6.4,7.3,7.2,6.9,7.1,6.9
+Guernsey,,,,,,,,,,,,,,,,
+Guinea,5.5,5.6,5.6,5.6,5.6,5.7,5.9,6.0,5.5,5.3,5.0,5.3,5.4,5.5,5.3,4.9
+Guinea-Bissau,6.4,7.0,6.7,7.1,5.8,6.2,5.9,7.0,5.8,5.6,5.6,5.7,5.7,5.7,8.6,8.5
+Guyana,4.9,4.7,5.0,5.0,4.5,5.6,5.6,5.7,5.6,7.6,9.2,6.0,5.3,6.0,6.2,5.4
+Haiti,6.6,6.4,6.2,6.0,5.7,6.1,5.6,5.2,4.7,4.6,4.4,5.7,5.6,5.4,6.1,6.9
+Holy See,,,,,,,,,,,,,,,,
+Honduras,5.3,5.0,4.9,5.6,5.7,5.4,5.5,6.1,6.4,6.5,5.8,5.7,5.9,6.0,7.0,
+"Hong Kong, China",,,,,,,,,,,,,,,,
+Hungary,7.3,7.1,6.8,7.2,7.3,7.2,7.2,7.6,8.4,8.1,8.3,8.1,7.6,7.3,7.6,7.3
+Iceland,8.4,8.3,9.6,9.1,10.2,9.7,9.6,10.4,10.6,10.0,9.6,9.3,9.3,9.2,9.8,9.4
+India,4.1,4.0,4.3,4.4,4.2,4.4,4.6,4.5,4.4,4.2,4.2,4.0,4.0,4.0,4.2,4.1
+Indonesia,2.0,2.0,1.9,2.1,2.3,2.0,2.2,2.2,2.5,2.4,2.4,2.5,2.7,2.5,2.5,2.6
+Iran,3.8,3.7,4.1,4.5,4.6,4.6,5.1,5.1,4.9,4.9,5.7,5.5,5.5,5.8,5.7,5.6
+Iraq,,1.7,1.7,1.7,1.7,2.8,2.8,1.1,3.6,5.7,4.8,3.6,4.9,4.9,8.4,8.4
+Ireland,6.6,6.4,6.3,6.1,6.0,6.1,6.7,7.0,7.3,7.5,7.6,7.5,7.6,8.8,9.4,9.2
+Isle of Man,,,,,,,,,,,,,,,,
+Israel,7.4,7.5,7.7,7.6,7.3,7.3,7.9,7.8,7.7,7.6,7.7,7.4,7.4,7.5,7.6,7.6
+Italy,7.2,7.3,7.6,7.7,7.7,8.0,8.2,8.3,8.3,8.6,8.9,9.0,8.6,9.0,9.4,9.5
+Jamaica,4.1,4.7,6.0,5.1,4.7,5.5,5.1,4.9,4.6,4.8,4.1,4.2,4.9,5.2,4.9,4.8
+Japan,6.9,7.0,7.0,7.3,7.5,7.7,7.9,8.0,8.1,8.1,8.2,8.2,8.2,8.5,9.5,9.5
+Jersey,,,,,,,,,,,,,,,,
+Jordan,8.3,8.6,9.3,9.3,9.4,9.7,9.9,9.7,9.3,9.3,8.7,8.2,8.4,8.5,9.6,8.0
+Kazakhstan,4.6,4.4,4.7,4.8,4.4,4.2,3.5,3.6,3.7,4.0,4.1,3.9,3.4,3.9,4.5,4.3
+Kenya,4.3,4.1,4.1,4.5,4.2,4.7,4.6,4.5,4.4,4.3,4.4,4.5,4.4,4.2,4.8,4.8
+Kiribati,9.4,9.2,9.4,7.0,8.9,7.9,8.9,8.8,9.6,10.4,10.1,11.0,13.6,12.5,13.0,11.2
+North Korea,,,,,,,,,,,,,,,,
+South Korea,3.8,3.9,3.9,4.1,4.3,4.5,5.1,4.9,5.2,5.3,5.7,6.0,6.3,6.5,6.9,6.9
+United Korea (former),,,,,,,,,,,,,,,,
+Kosovo,,,,,,,,,,,,,,,,
+Kuwait,3.8,3.5,3.7,4.4,3.7,2.5,3.6,3.6,3.2,2.8,2.4,2.3,2.1,1.9,3.8,2.6
+Kyrgyz Republic,6.9,6.3,5.5,6.9,6.0,4.7,4.8,5.4,5.4,5.6,5.8,6.4,6.6,5.7,6.4,6.2
+Lao,4.1,4.6,6.3,6.4,3.7,3.3,4.0,3.9,5.1,4.4,4.0,3.9,4.0,4.5,4.3,4.5
+Latvia,5.8,6.1,6.2,6.3,6.4,6.0,6.1,6.2,6.1,6.5,6.3,6.8,7.0,6.6,6.6,6.7
+Lebanon,10.7,10.7,11.5,11.4,10.4,9.9,9.3,8.6,8.2,8.3,8.1,8.1,8.4,7.7,7.4,7.0
+Lesotho,8.1,7.9,7.1,7.0,6.8,7.0,7.7,7.1,7.1,6.9,6.3,7.0,8.5,8.8,9.4,11.1
+Liberia,,,,5.2,5.2,5.1,5.3,4.8,5.1,5.1,5.2,6.9,11.7,11.9,12.2,11.8
+Libya,3.5,3.3,3.2,4.0,3.6,3.3,4.0,4.9,3.7,3.1,2.5,2.4,2.8,3.0,3.9,3.9
+Liechtenstein,,,,,,,,,,,,,,,,
+Lithuania,5.4,5.6,5.7,6.1,6.2,6.5,6.3,6.4,6.5,5.7,5.8,6.2,6.2,6.6,7.5,7.0
+Luxembourg,5.6,5.7,5.6,5.7,5.8,7.5,7.4,8.3,7.7,8.2,7.9,7.7,7.1,6.8,7.9,7.8
+"Macao, China",,,,,,,,,,,,,,,,
+"Macedonia, FYR",8.5,8.8,9.2,10.1,8.7,8.8,8.5,9.4,9.3,8.7,8.1,7.8,6.9,6.8,6.9,7.1
+Madagascar,2.8,3.2,3.4,3.2,3.2,3.7,3.8,3.8,3.5,3.6,3.7,4.0,4.1,4.5,4.2,3.8
+Malawi,4.9,4.7,5.2,5.5,6.5,6.1,5.1,4.8,6.3,7.8,8.2,9.0,7.0,6.6,6.7,6.6
+Malaysia,2.9,2.9,2.7,3.0,3.1,3.1,3.6,3.6,4.2,3.9,3.6,3.9,3.8,3.8,4.6,4.4
+Maldives,6.0,6.3,6.3,6.3,6.5,7.1,5.4,5.2,6.3,6.5,8.2,7.4,6.8,7.8,6.4,6.3
+Mali,5.2,7.8,7.0,6.2,6.0,6.3,6.4,6.3,6.3,6.3,6.0,5.9,5.8,5.6,5.5,5.0
+Malta,5.8,6.2,6.6,6.6,6.6,6.7,7.2,8.1,8.4,8.9,9.3,9.3,8.7,8.3,8.5,8.6
+Marshall Islands,18.1,21.8,22.8,20.9,20.4,22.6,19.5,18.3,16.2,16.4,15.9,16.6,17.0,16.6,18.9,18.0
+Martinique,,,,,,,,,,,,,,,,
+Mauritania,4.8,3.8,4.0,4.9,6.0,6.0,4.8,6.0,5.4,5.9,5.1,3.5,4.4,4.3,4.2,4.4
+Mauritius,3.6,3.5,3.5,3.6,3.4,3.7,3.8,4.2,3.9,4.2,4.5,4.3,5.2,5.3,5.6,6.0
+Mayotte,,,,,,,,,,,,,,,,
+Mexico,5.1,4.7,4.8,4.9,5.1,5.1,5.5,5.6,5.8,6.0,5.9,5.7,5.8,5.9,6.5,
+"Micronesia, Fed. Sts.",9.2,8.6,7.9,7.2,7.8,8.1,8.8,8.1,9.8,10.4,12.2,12.0,12.0,12.9,13.4,14.2
+Moldova,9.1,10.3,9.5,7.7,5.9,6.7,7.1,8.1,8.3,8.5,9.2,10.6,10.9,11.4,12.5,11.7
+Monaco,3.0,3.0,3.1,3.1,3.4,3.2,3.3,3.3,3.9,4.0,3.9,3.8,3.3,3.6,4.2,4.3
+Mongolia,3.6,4.2,4.2,4.4,4.7,5.5,5.7,6.0,4.7,4.2,3.7,3.6,4.6,5.7,5.7,5.4
+Montenegro,7.7,7.7,7.7,7.7,7.7,7.9,8.9,9.0,9.6,9.1,9.1,8.6,7.8,8.0,9.4,9.1
+Montserrat,,,,,,,,,,,,,,,,
+Morocco,3.9,3.8,4.1,3.9,4.0,4.2,4.4,5.3,5.3,5.2,5.1,5.2,5.2,5.2,5.2,5.2
+Mozambique,5.1,4.3,4.6,4.6,5.2,6.0,5.5,6.0,5.6,5.1,5.8,5.4,4.8,4.1,5.4,5.2
+Myanmar,2.1,2.1,1.9,1.8,1.8,2.1,2.1,2.4,2.3,2.3,2.1,2.1,2.0,2.0,2.1,2.0
+Namibia,6.2,6.6,6.9,6.8,7.0,6.1,6.2,6.2,6.5,6.5,7.3,7.2,6.9,6.8,7.2,6.8
+Nauru,13.0,14.2,14.1,14.7,13.5,12.3,12.4,12.3,11.9,13.0,15.5,16.1,21.7,14.2,11.2,11.2
+Nepal,4.7,4.8,5.1,5.3,5.1,5.2,5.3,5.5,5.4,6.0,6.0,5.8,5.4,5.9,5.8,5.3
+Netherlands,8.3,8.2,7.9,8.1,8.1,8.0,8.3,8.9,9.8,10.0,9.8,9.7,9.7,9.9,12.0,11.9
+Netherlands Antilles,,,,,,,,,,,,,,,,
+New Caledonia,,,,,,,,,,,,,,,,
+New Zealand,7.1,7.0,7.2,7.7,7.5,7.6,7.7,8.0,7.9,8.0,8.4,8.8,8.5,9.3,10.0,10.1
+Ngorno-Karabakh,,,,,,,,,,,,,,,,
+Nicaragua,8.4,8.3,6.8,7.3,6.9,7.0,6.8,7.4,7.9,7.9,7.9,8.2,9.1,9.3,9.6,
+Niger,3.4,3.2,3.6,3.7,3.7,3.4,3.7,3.8,3.6,3.9,6.0,6.0,5.4,5.2,5.4,5.2
+Nigeria,4.4,4.5,4.6,5.6,5.4,4.6,5.2,3.9,6.5,7.0,6.6,5.7,6.0,5.7,6.1,5.1
+Niue,8.0,9.3,9.4,8.2,8.3,7.9,,11.1,12.5,15.1,12.9,15.6,16.1,16.5,17.5,17.4
+Norfolk Island,,,,,,,,,,,,,,,,
+Northern Cyprus,,,,,,,,,,,,,,,,
+Northern Mariana Islands,,,,,,,,,,,,,,,,
+Norway,9.1,8.7,8.4,9.3,9.3,8.4,8.8,9.8,10.0,9.6,9.0,8.6,8.7,8.5,9.7,9.5
+Oman,3.6,3.6,3.4,3.7,3.5,3.1,3.1,3.2,3.2,3.0,2.6,2.3,2.5,2.1,3.0,2.8
+Pakistan,3.3,3.3,3.3,3.6,3.7,3.0,2.8,3.0,2.8,2.8,2.7,2.7,2.6,2.5,2.2,2.2
+Palau,12.4,12.7,12.4,10.9,12.0,12.8,10.4,10.4,10.3,11.5,10.5,10.5,10.8,10.2,10.6,10.3
+Panama,7.7,7.2,7.1,7.1,6.9,7.8,7.7,8.0,7.6,8.1,7.5,7.0,6.7,7.3,8.1,8.1
+Papua New Guinea,3.5,3.7,3.4,3.8,4.1,4.0,4.5,4.5,3.9,4.4,3.9,3.4,3.2,3.3,3.7,3.6
+Paraguay,6.7,6.8,7.0,7.2,7.8,9.4,9.1,8.7,7.2,6.6,6.5,6.6,6.1,6.0,6.6,5.9
+Peru,4.5,4.5,4.3,4.5,4.9,4.7,4.7,4.8,4.5,4.4,4.5,4.5,5.1,5.7,5.3,5.1
+Philippines,3.4,3.5,3.6,3.2,3.2,3.2,3.0,2.8,3.2,3.2,3.5,3.5,3.4,3.3,3.6,3.6
+Pitcairn,,,,,,,,,,,,,,,,
+Poland,5.5,5.9,5.6,5.9,5.7,5.5,5.9,6.3,6.2,6.2,6.2,6.2,6.4,7.0,7.4,7.5
+Portugal,7.5,7.8,7.8,7.7,8.0,9.3,9.3,9.3,9.8,10.1,10.4,10.1,10.0,10.1,10.7,11.0
+Puerto Rico,,,,,,,,,,,,,,,,
+Qatar,3.7,3.6,3.2,3.4,2.9,2.3,2.7,2.8,4.2,3.7,3.3,2.7,2.4,2.1,2.6,1.8
+Reunion,,,,,,,,,,,,,,,,
+Romania,3.5,3.7,4.8,4.6,5.1,5.2,5.4,5.7,5.3,5.5,5.5,5.1,5.2,5.4,5.6,5.6
+Russia,5.4,5.6,7.1,6.6,5.8,5.4,5.7,6.0,5.6,5.2,5.2,5.3,5.4,4.8,5.6,5.1
+Rwanda,4.5,4.6,4.6,5.3,4.7,4.2,4.4,4.2,6.3,6.2,6.5,10.8,9.5,9.4,10.1,10.5
+St. Barth�lemy,,,,,,,,,,,,,,,,
+St. Helena,,,,,,,,,,,,,,,,
+St. Kitts and Nevis,5.4,5.4,5.1,5.1,5.3,5.5,5.1,5.0,4.7,4.4,4.4,5.3,5.4,5.0,5.2,6.7
+St. Lucia,4.7,5.5,5.0,5.0,5.0,5.1,5.7,5.8,5.9,5.8,6.0,7.3,7.2,8.1,8.4,8.7
+St. Martin,,,,,,,,,,,,,,,,
+St. Vincent and the Grenadines,4.45833258,4.44634447,4.7281377,4.1684158,4.1527164,3.72180631,3.63980325,3.92617262,3.90135665,3.71947805,3.65532249,3.88226523,3.9172185,4.6289586,4.60065661,4.47059913
+St.-Pierre-et-Miquelon,,,,,,,,,,,,,,,,
+Samoa,4.6977826,5.22342626,5.45316699,5.58489223,6.28075985,6.01843157,5.75678294,5.52869249,5.14551282,4.9266523,4.95060798,5.31983442,5.86637027,5.49409403,5.44408597,6.46982259
+San Marino,7.83999165,7.55147442,7.53528908,7.40877649,7.44438531,7.51906667,7.36293853,9.28118717,7.32551256,7.57805489,7.48865306,7.20196413,7.108691,7.14165292,7.12611545,7.12611545
+Sao Tome and Principe,8.23324072,7.73768994,8.88817135,7.93407725,8.98203574,9.4870383,9.70914787,9.86959833,10.89366855,10.14680653,9.49611341,7.58308372,7.45394716,7.01813204,7.24194682,7.1688313
+Saudi Arabia,2.96343516,2.96182194,3.05675942,3.34299955,4.12657313,4.25943342,4.5140298,4.29718242,4.02387964,3.69506901,3.54521791,3.8083032,3.82925982,3.19192117,4.37508458,4.29186721
+Senegal,3.93442496,4.08432135,4.08141909,4.10328832,4.36302521,4.33077043,4.52513583,5.06424157,5.40001387,5.70509225,5.40084188,5.82945478,5.7243396,5.81767131,5.66216259,5.6642273
+Serbia,7.31661157,7.31657912,7.22357867,7.40327177,7.40933991,7.4155493,7.57920399,8.87407275,8.81049039,8.6612779,9.05257374,9.4184197,10.35888797,10.36336542,10.51252172,10.36114619
+Serbia and Montenegro,,,,,,,,,,,,,,,,
+Serbia excluding Kosovo,,,,,,,,,,,,,,,,
+Seychelles,5.18969549,5.33529273,5.84536015,5.22847419,5.29394573,4.81819736,4.64011248,4.38333417,4.80140746,5.142936,4.16324926,3.99217625,3.44997619,3.33321428,3.29209398,3.40385947
+Sierra Leone,14.33894344,14.53401106,13.53529751,13.28618861,16.01257445,15.61839028,14.96244618,14.8586967,7.7703857,13.51779005,14.48132071,11.061053,12.83727079,13.77868517,13.86115877,13.06796543
+Singapore,3.0168834,2.95928032,2.89661447,3.196578,2.86201832,2.40991899,2.74603193,2.88423073,3.80888617,3.40109595,3.37029909,3.25737674,3.18562573,3.6145935,4.13247073,3.95961271
+Slovak Republic,6.06387494,6.43714522,5.78420775,5.65522564,5.75379503,5.49890785,5.50461613,5.62930119,5.81850192,7.21098106,7.04485118,7.34768079,7.75529417,8.01005494,9.13728327,8.79029531
+Slovenia,7.45548818,7.63863893,7.75623737,7.81007276,7.79898595,8.26416456,8.56912386,8.62002958,8.60889134,8.33918284,8.40961553,8.27405701,7.78702228,8.35907939,9.28577254,9.4097517
+Solomon Islands,5.15304621,4.70648828,4.98354904,5.52622351,5.59964673,6.92616783,9.46205408,9.1706722,6.12701613,5.66129024,7.62826022,6.34243197,6.23455413,6.03765447,8.73890335,8.55278448
+Somalia,2.68489176,2.59410849,2.63400617,2.69253964,2.80677916,2.44384477,2.251338,,,,,,,,,
+Somaliland,,,,,,,,,,,,,,,,
+South Africa,7.48750209,7.95632223,8.25580965,8.57792681,9.06492593,8.46545768,8.78351744,8.72038817,8.85762592,8.93981546,8.81369876,8.53276693,8.45183383,8.63137082,9.16092737,8.94156391
+South Ossetia,,,,,,,,,,,,,,,,
+Spain,7.44459252,7.46292642,7.33387575,7.3192794,7.31629197,7.21475551,7.24157367,7.25041069,8.17211614,8.20614429,8.28183839,8.34609457,8.48290507,8.96011886,9.56555237,9.53862387
+Sri Lanka,3.42657913,3.44855213,3.33033467,3.68413711,3.60870697,3.70894281,3.7431487,3.81825105,3.8047856,4.10437714,3.94792525,4.03844583,3.72762308,3.40625327,3.19568939,2.94519357
+Sudan,3.36232894,3.95189674,3.74988526,3.99469819,3.79894414,3.36755312,3.5245654,3.58005233,3.84841707,4.0980999,3.98708103,4.85638824,6.02544967,6.90489364,7.32619129,6.31501433
+Suriname,5.19103944,7.41196372,7.75757494,8.60661376,7.62329263,7.95639165,8.07159557,7.1421666,6.62355609,6.87614002,6.77689324,7.65703495,7.27578894,6.78494752,7.47131238,7.0150575
+Svalbard,,,,,,,,,,,,,,,,
+Swaziland,4.95991489,5.40356441,5.11799324,5.55824512,5.3750616,5.37824216,5.30405992,5.31636578,5.93267461,5.48897135,5.84457849,6.33952475,5.98251975,6.5731289,6.71761995,6.60910709
+Sweden,7.96455521,8.20030584,8.02636126,8.11535567,8.19761871,8.17961312,8.85949228,9.22815647,9.31003467,9.08797098,9.06150305,8.94799761,8.91722313,9.22835422,10.00118623,9.63346849
+Switzerland,9.57160447,9.94725716,9.95073528,10.07305601,10.20196005,10.15084343,10.59043662,10.91254262,11.25466327,11.30042365,11.22102376,10.7581161,10.61914519,10.72484272,11.38569524,11.52215329
+Syria,5.51687902,5.5004147,5.29796706,5.12370263,5.24971554,4.92287133,4.87675666,4.94246813,5.11607642,4.47860155,4.10969776,3.82503992,3.79904048,3.37376558,3.54865806,3.40675402
+Taiwan,,,,,,,,,,,,,,,,
+Tajikistan,3.1,3.10909091,3.63590734,3.5595122,3.87722721,4.64036016,4.58542207,4.4802285,4.45875944,4.30071512,4.83678456,4.89156435,5.34676664,5.58479697,5.92834982,5.98259462
+Tanzania,3.58124406,3.3848746,3.64769735,3.39702823,3.29396919,3.37581551,3.53093525,3.4233561,3.74871023,3.4428696,4.01837043,6.5277599,5.69457916,5.45952561,5.52347451,6.01142802
+Thailand,3.53153455,3.84083724,3.99659613,3.73528597,3.49625195,3.39540499,3.31554758,3.70009891,3.57494319,3.50645022,3.54351021,3.49106045,3.56224555,3.96974849,4.17213817,3.88297823
+Timor-Leste,9.51628012,6.50723966,10.54009668,19.32327371,9.94686254,8.37855787,10.84385142,10.55860612,10.95466756,13.61904588,16.07348972,17.93323385,15.43030368,13.80071777,11.87015325,9.12009791
+Togo,4.58781454,4.65959037,4.56163643,4.65890883,4.70460137,4.72656259,5.30968065,5.11494962,5.59719199,6.23449154,6.27367498,6.77493005,6.56043454,6.81448459,7.36705858,7.65250051
+Tokelau,,,,,,,,,,,,,,,,
+Tonga,3.92589383,5.16246796,5.6438144,5.86844418,6.51621693,6.06263908,5.69485378,5.05624055,5.09534293,4.814915,6.52030636,5.52007848,6.76640835,6.24984958,4.67910132,5.07124384
+Transnistria,,,,,,,,,,,,,,,,
+Trinidad and Tobago,4.69113912,4.33058148,4.15924485,3.67985789,4.25257389,3.88317693,4.13126116,4.78450879,4.94320087,4.99625435,5.66268089,4.95998001,4.82359145,4.45252261,5.77562905,5.67534922
+Tunisia,6.14779757,5.69317737,5.63639972,5.71432389,5.76290398,5.95401486,5.82758035,5.84987307,5.91014539,6.19876991,6.1691035,6.23527998,6.15891119,6.1625407,6.35348657,6.2074705
+Turkey,2.50703268,2.90240247,3.13356933,3.59344289,4.7655207,4.94898471,5.16018204,5.3567135,5.33861753,5.37016573,5.44880137,5.81085647,6.0371564,6.07447864,6.74684251,6.74039106
+Turkmenistan,3.08461538,3.24698065,4.8469847,4.80957485,3.45300424,4.03905801,3.70961032,3.47745358,3.7956401,3.57884042,3.1948939,2.66519844,2.49859259,1.92670305,2.48766719,2.49640101
+Turks and Caicos Islands,,,,,,,,,,,,,,,,
+Tuvalu,5.40777108,11.44129594,8.55426756,8.78722649,9.30826295,15.65692267,17.02046051,15.19748727,21.13086633,16.27527748,17.59233629,18.56057291,16.48224647,20.603899,14.5546873,17.48800603
+Uganda,5.83269973,6.06302733,5.54449868,5.32342737,6.26597954,6.58465179,7.32941802,7.53527143,7.41804274,7.46232669,8.1268899,8.89502272,8.39286192,8.62190809,8.47396581,9.00905045
+Ukraine,6.77277764,6.13788048,6.08275574,6.61656924,5.89491009,5.58970764,5.65769003,6.25349386,6.93362184,6.60913962,6.41043647,6.39231981,6.35521436,6.63441822,7.79946242,7.72176681
+United Arab Emirates,2.63612947,2.45902449,2.48706024,3.05542957,2.90524521,2.57443946,2.48427471,2.71758452,2.64640839,2.4621587,2.32022382,2.32803745,2.52218902,3.01042413,4.37449004,3.65963853
+United Kingdom,6.83053626,6.84421396,6.60599665,6.65465141,6.8896048,7.01166415,7.23753109,7.56110606,7.76609952,7.99428337,8.23938397,8.47407122,8.41400829,8.85451868,9.78861961,9.63550688
+United States,13.56009352,13.49748052,13.3530052,13.36772262,13.35061343,13.40859764,14.07449877,14.82363067,15.66720142,15.71394902,15.76469441,15.85246911,16.05489478,16.51372571,17.60704218,17.88732687
+Uruguay,11.50497936,10.79880948,8.40453561,8.57022801,8.66263436,8.48915721,8.40191971,8.24109937,7.23943748,8.4757611,8.27305265,8.28413078,7.85171899,7.7331565,8.4340649,8.35189393
+USSR,,,,,,,,,,,,,,,,
+Uzbekistan,5.17293107,5.43286897,5.20585875,6.42551145,6.00769263,5.70831798,5.68533896,5.52413304,5.24085984,5.42319412,5.26484799,5.76415947,5.53763644,5.3859357,5.92657601,5.805582
+Wallis et Futuna,,,,,,,,,,,,,,,,
+Vanuatu,2.43988654,2.1450182,3.18976347,3.90529258,3.52444118,3.57167858,3.66939152,3.86017801,3.69166068,3.49396156,3.26888842,3.17359994,3.77234657,4.25682159,4.8540586,5.24669199
+Venezuela,4.23301061,3.493338,3.86241177,4.51286643,5.6532436,5.69041934,5.9598229,5.65358913,5.85535475,5.57233226,5.42792196,5.73508838,5.80499325,5.39547873,6.01537872,4.90609951
+West Bank and Gaza,,,,,,,,,,,,,,,,
+West Germany,,,,,,,,,,,,,,,,
+Western Sahara,,,,,,,,,,,,,,,,
+Vietnam,5.09399182,5.00595735,4.85719764,4.94416813,4.88350811,5.27851786,5.58616296,5.14058145,5.22615353,5.53461605,5.85582768,6.44055494,6.99046528,6.61340099,6.89538668,6.84055986
+Virgin Islands (U.S.),,,,,,,,,,,,,,,,
+North Yemen (former),,,,,,,,,,,,,,,,
+South Yemen (former),,,,,,,,,,,,,,,,
+Yemen,4.49440379,4.75017497,4.68246349,5.39944066,4.65668001,4.46250631,4.78454179,4.67447322,5.73694941,5.3319192,4.87997841,5.2569731,5.31281633,5.3774979,5.51728098,5.18468332
+Yugoslavia,,,,,,,,,,,,,,,,
+Zambia,5.60069226,5.88879881,6.20962986,6.52945534,5.79543752,5.6708002,5.91829361,6.62989696,6.581968,6.6123761,6.99993764,6.3352049,5.83397389,6.1373772,6.21220153,5.89118554
+Zimbabwe,8.76312272,7.93644147,9.02558223,10.79536352,7.43640153,9.99351281,8.09644111,,,,,,,,,
+�land,,,,,,,,,,,,,,,,
+South Sudan,,,,,,,,,,,,,,,,
+Christian,,,,,,,,,,,,,,,,
+Coastline,,,,,,,,,,,,,,,,
diff --git a/lessons/python/scikit-learn/UofTCoders_ML.ipynb b/lessons/python/scikit-learn/UofTCoders_ML.ipynb
new file mode 100644
index 000000000..ea9d9a7bb
--- /dev/null
+++ b/lessons/python/scikit-learn/UofTCoders_ML.ipynb
@@ -0,0 +1,913 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to Machine Learning\n",
+    "\n",
+    "\"Machine learning gives computers the ability to learn without explicitly programmed.\" - Arthur Samuel, 1959\n",
+    "\n",
+    "Machine learning originated from pattern recognition and computational learning theory in AI. It is the study and construction of algorithms to learn from and make predictions on data through building a model from sample input.\n",
+    "\n",
+    "### Some uses of learning algorithms\n",
+    "1. Classification: determine which discrete category the example is\n",
+    "2. Recognizing patterns: speech recognition, facial identity ...\n",
+    "3. Recommender Systems: noisy data, commercial pay-off (e.g., Amazon, Netflix)\n",
+    "4. Information retrieval: find documents or images with similar content\n",
+    "5. Computer vision: detection, segmentation, depth estimation, optical flow ...\n",
+    "6. Robotics: perception, planning ...\n",
+    "7. Learning to play games: AlphaGO\n",
+    "8. Recognizing anomalies: Unusual sequences of credit card transactions, panic situation at an airport\n",
+    "9. Spam filtering, fraud detection: The enemy adapts so we must adapt too\n",
+    "\n",
+    "### Types of learning tasks\n",
+    "- Supervised Learning: correct output known for each training example for predicting output when given an input vector\n",
+    "\n",
+    "    1. Classification: 1-of-N output, e.g. object recognition, medical diagnosis\n",
+    "    2. Regression: real-valued-output, e.g.predicting market prices, customer ratings\n",
+    "\n",
+    "\n",
+    "- Unsupervised Learning: for learning an internal representation of the input to capture regularities and structure in the data without any labels\n",
+    "\n",
+    "    1. Clustering: dividing input into groups that are unknown beforehand\n",
+    "    2. Dimensionality reduction: extract informative features, e.g. PCA, t-SNE\n",
+    "\n",
+    "\n",
+    "- Reinforcement Learning: perform an action with the goal to maximize payoff by the feedback of reward and punishments, e.g. playing a game against an opponent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# In this tutorial\n",
+    "\n",
+    "1. Regression (e.g. Linear Regression)\n",
+    "2. Classification (e.g. SVM, Logistic regression)\n",
+    "3. Clustering (e.g. K-means)\n",
+    "4. Dimensionality Reduction (e.g. PCA)\n",
+    "5. Sklearn's TPOT package for optimized machine learning pipelines\n",
+    "\n",
+    "### We won't cover:\n",
+    "1. Other classification and clustering algorithms (e.g. Neural Networks, Hierarchical Clustering)\n",
+    "2. Model selection (such as Bayesian Information Criteria - BIC)\n",
+    "3. Hyper-parameter selection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Useful websites\n",
+    "Sklearn class and function reference page\n",
+    "http://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model\n",
+    "\n",
+    "Machine Learning course from Computer Science Department, UOFT\n",
+    "http://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/CSC411_Fall16.html\n",
+    "\n",
+    "TPOT for optimized machine learning pipelines\n",
+    "https://rhiever.github.io/tpot/\n",
+    "\n",
+    "KERAS package for Neural Networks\n",
+    "https://keras.io/\n",
+    "\n",
+    "Tensorflow Playground for Deep Neural Networks\n",
+    "http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.69754&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pylab inline\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Datasets\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Iris dataset\n",
+    "### Samples of 3 different species of Iris flower with 4 features (sepal and petal lengths and widths)\n",
+    "### We will learn:\n",
+    "- Displaying feature relationships, correlations\n",
+    "- Dimensionality reduction using PCA\n",
+    "- Scaling the data\n",
+    "- Classification and clustering algorithms\n",
+    "- Compare to another image dataset of handwritten digits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = datasets.load_iris()\n",
+    "iris_df = pd.DataFrame(data = np.c_[iris['data'], iris['target']],\n",
+    "                       columns = iris['feature_names'] + ['target'])\n",
+    "iris_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Feature Correlations\n",
+    "iris_df.iloc[:,:4].corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Feature Relationships\n",
+    "X = iris.data\n",
+    "Y = iris.target\n",
+    "key = {0: ('red', 'Iris setosa'),\n",
+    "       1: ('blue', 'Iris versicolor'),\n",
+    "       2: ('green', 'Iris virginica')}\n",
+    "colors = [key[index][0] for index in Y]\n",
+    "# Plot the training points\n",
+    "\n",
+    "# Sepal information\n",
+    "plt.figure(figsize=(12, 12))\n",
+    "plt.subplot(221)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=colors)\n",
+    "plt.xlabel('Sepal length')\n",
+    "plt.ylabel('Sepal width')\n",
+    "plt.title('Sepal Information')\n",
+    "\n",
+    "# Petal information\n",
+    "plt.subplot(222)\n",
+    "plt.scatter(X[:, 2], X[:, 3], c=colors)\n",
+    "plt.xlabel('Petal length')\n",
+    "plt.ylabel('Petal width')\n",
+    "plt.title('Petal Information')\n",
+    "\n",
+    "# Length information\n",
+    "plt.subplot(223)\n",
+    "plt.scatter(X[:, 0], X[:, 2], c=colors)\n",
+    "plt.xlabel('Sepal length')\n",
+    "plt.ylabel('Petal length')\n",
+    "plt.title('Length Information')\n",
+    "\n",
+    "# Width information\n",
+    "plt.subplot(224)\n",
+    "plt.scatter(X[:, 1], X[:, 3], c=colors)\n",
+    "plt.xlabel('Sepal width')\n",
+    "plt.ylabel('Petal width')\n",
+    "plt.title('Width Information')\n",
+    "\n",
+    "# Plot legend\n",
+    "patches = [matplotlib.patches.Patch(color=color, label=label) for color, label in key.values()]\n",
+    "plt.legend(handles=patches, labels=[label for _, label in key.values()],\n",
+    "           bbox_to_anchor = (0.1,-0.1,1,1), bbox_transform = plt.gcf().transFigure)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Applying PCA to iris features\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "pca = PCA(n_components=2)\n",
+    "iris_pca = pca.fit_transform(iris.data)\n",
+    "\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.scatter(iris_pca[:, 0], iris_pca[:, 1], c=colors)\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.title('PCA')\n",
+    "plt.legend(handles=patches, labels=[label for _, label in key.values()],\n",
+    "           bbox_to_anchor = (0.2,-0.1,1,1), bbox_transform = plt.gcf().transFigure)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Interpreting features from PCA\n",
+    "from scipy import stats\n",
+    "\n",
+    "pca_corr = pd.DataFrame(columns = iris['feature_names'])\n",
+    "for i in range(2):\n",
+    "    print('PC%d explained variance = %.2f%s\\n' % (i+1, pca.explained_variance_ratio_[i]*100, '%'))\n",
+    "    corr = []\n",
+    "    for j in range(4):\n",
+    "        corr.append(stats.pearsonr(iris_pca[:, i], iris.data[:, j])[0])\n",
+    "    pca_corr.loc[i, ] = corr\n",
+    "pca_corr = pca_corr.rename(index={0: 'PC1', 1: 'PC2'})\n",
+    "pca_corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scaling data\n",
+    "from sklearn import preprocessing\n",
+    "\n",
+    "X = iris.data\n",
+    "Y = iris.target\n",
+    "\n",
+    "# When the data is scaled to complete data\n",
+    "X_scaled = preprocessing.StandardScaler().fit_transform(X)\n",
+    "\n",
+    "# When the data is scaled to a part of the data\n",
+    "#scaler = preprocessing.StandardScaler().fit(X)\n",
+    "#X_scaled = scaler.transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scaled Feature Relationships\n",
+    "# Plot the training points\n",
+    "\n",
+    "# Sepal information\n",
+    "plt.figure(figsize=(12, 12))\n",
+    "plt.subplot(221)\n",
+    "plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=colors)\n",
+    "plt.xlabel('Sepal length')\n",
+    "plt.ylabel('Sepal width')\n",
+    "plt.title('Sepal Information')\n",
+    "\n",
+    "# Petal information\n",
+    "plt.subplot(222)\n",
+    "plt.scatter(X_scaled[:, 2], X_scaled[:, 3], c=colors)\n",
+    "plt.xlabel('Petal length')\n",
+    "plt.ylabel('Petal width')\n",
+    "plt.title('Petal Information')\n",
+    "\n",
+    "# Length information\n",
+    "plt.subplot(223)\n",
+    "plt.scatter(X_scaled[:, 0], X_scaled[:, 2], c=colors)\n",
+    "plt.xlabel('Sepal length')\n",
+    "plt.ylabel('Petal length')\n",
+    "plt.title('Length Information')\n",
+    "\n",
+    "# Width information\n",
+    "plt.subplot(224)\n",
+    "plt.scatter(X_scaled[:, 1], X_scaled[:, 3], c=colors)\n",
+    "plt.xlabel('Sepal width')\n",
+    "plt.ylabel('Petal width')\n",
+    "plt.title('Width Information')\n",
+    "\n",
+    "# Plot legend\n",
+    "patches = [matplotlib.patches.Patch(color=color, label=label) for color, label in key.values()]\n",
+    "plt.legend(handles=patches, labels=[label for _, label in key.values()],\n",
+    "           bbox_to_anchor = (0.1,-0.1,1,1), bbox_transform = plt.gcf().transFigure)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Applying PCA to iris features\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "pca = PCA(n_components=2)\n",
+    "iris_pca_scaled = pca.fit_transform(X_scaled)\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.subplot(121)\n",
+    "plt.scatter(iris_pca_scaled[:, 0], iris_pca_scaled[:, 1], c=colors)\n",
+    "plt.xlim([-3,3])\n",
+    "plt.ylim([-3,3])\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.title('PCA with Scaled Data')\n",
+    "\n",
+    "# PCA on original data from the previous cell\n",
+    "pca = PCA(n_components=2)\n",
+    "iris_pca = pca.fit_transform(iris.data)\n",
+    "\n",
+    "plt.subplot(122)\n",
+    "plt.scatter(iris_pca[:, 0], iris_pca[:, 1], c=colors)\n",
+    "plt.xlim([-3,3])\n",
+    "plt.ylim([-3,3])\n",
+    "plt.xlabel('PC1')\n",
+    "plt.ylabel('PC2')\n",
+    "plt.title('PCA')\n",
+    "plt.legend(handles=patches, labels=[label for _, label in key.values()],\n",
+    "           bbox_to_anchor = (0.1,-0.1,1,1), bbox_transform = plt.gcf().transFigure)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Interpreting features from PCA\n",
+    "from scipy import stats\n",
+    "\n",
+    "pca_corr = pd.DataFrame(columns = iris['feature_names'])\n",
+    "for i in range(2):\n",
+    "    print('PC%d explained variance = %.2f%s\\n' % (i+1, pca.explained_variance_ratio_[i]*100, '%'))\n",
+    "    corr = []\n",
+    "    for j in range(4):\n",
+    "        corr.append(stats.pearsonr(iris_pca_scaled[:, i], X_scaled[:, j])[0])\n",
+    "    pca_corr.loc[i, ] = corr\n",
+    "pca_corr = pca_corr.rename(index={0: 'PC1', 1: 'PC2'})\n",
+    "pca_corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Iris with 2 classes\n",
+    "iris_df2 = iris_df[(iris_df.target == 0) | (iris_df.target == 1)]\n",
+    "\n",
+    "# Plot 2-Class Iris data\n",
+    "iris2 = np.asarray(iris_df2.iloc[:, :4])\n",
+    "Y2 = list(iris_df2.target)\n",
+    "key = {0: ('red', 'Iris setosa'),\n",
+    "       1: ('blue', 'Iris versicolor')}\n",
+    "colors = [key[index][0] for index in Y2]\n",
+    "# Plot the training points\n",
+    "\n",
+    "# Sepal information\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.subplot(121)\n",
+    "plt.scatter(iris2[:, 0], iris2[:, 1], c=colors)\n",
+    "plt.xlabel('Sepal length')\n",
+    "plt.ylabel('Sepal width')\n",
+    "plt.title('Sepal Information')\n",
+    "# Width information\n",
+    "plt.subplot(122)\n",
+    "plt.scatter(iris2[:, 2], iris2[:, 3], c=colors)\n",
+    "plt.xlabel('Petal length')\n",
+    "plt.ylabel('Petal width')\n",
+    "plt.title('Petal Information')\n",
+    "\n",
+    "# Plot legend\n",
+    "patches = [matplotlib.patches.Patch(color=color, label=label) for color, label in key.values()]\n",
+    "plt.legend(handles=patches, labels=[label for _, label in key.values()],\n",
+    "           bbox_to_anchor = (0.1,-0.1,1,1), bbox_transform = plt.gcf().transFigure)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Binary Classification - Logistic Regression and Linear Regression as a means of Classification\n",
+    "from sklearn import linear_model\n",
+    "\n",
+    "# Model fit\n",
+    "lr = linear_model.LogisticRegression()\n",
+    "#lr = linear_model.LinearRegression()\n",
+    "lr.fit(iris2, Y2)\n",
+    "\n",
+    "# Predict values\n",
+    "Y_pred = lr.predict(iris2)\n",
+    "\n",
+    "# Calculate performance - accuracy\n",
+    "lr.score(iris2, Y2)\n",
+    "\n",
+    "# Using some features\n",
+    "#lr.fit(iris2[:,:2], Y2)\n",
+    "#Y_pred = lr.predict(iris2[:,:2])\n",
+    "#lr.score(iris2[:,:2], Y2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Multiclass Classification - Logistic Regression\n",
+    "from sklearn import linear_model\n",
+    "\n",
+    "# Load data to classify: Complete, partial, scaled, PCA applied\n",
+    "X = iris.data\n",
+    "#X = iris.data[:,:2]\n",
+    "#X = X_scaled\n",
+    "#X = iris_pca\n",
+    "Y = iris.target\n",
+    "\n",
+    "# Model fit\n",
+    "lr_multi = linear_model.LogisticRegression(multi_class = 'multinomial', solver = 'newton-cg')\n",
+    "lr_multi.fit(X, Y)\n",
+    "\n",
+    "# Predict values\n",
+    "Y_pred = lr_multi.predict(X)\n",
+    "\n",
+    "# Calculate performance - accuracy\n",
+    "lr_multi.score(X, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cross-validation\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Split labeled data into training and test set\n",
+    "X = iris.data\n",
+    "#X = iris.data[:,:2]\n",
+    "#X = X_scaled\n",
+    "#X = iris_pca\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(X, iris.target, test_size=0.2, random_state=0)\n",
+    "\n",
+    "print('Training set size')\n",
+    "print(X_train.shape, Y_train.shape)\n",
+    "print('Test set size')\n",
+    "print(X_test.shape, Y_test.shape)\n",
+    "\n",
+    "# Model fit\n",
+    "lr_multi = linear_model.LogisticRegression(multi_class = 'multinomial', solver = 'newton-cg')\n",
+    "lr_multi.fit(X_train, Y_train)\n",
+    "\n",
+    "# Predict values\n",
+    "Y_pred = lr_multi.predict(X_test)\n",
+    "\n",
+    "# Calculate performance - accuracy\n",
+    "lr_multi.score(X_test, Y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Plot confusion matrix\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "import itertools\n",
+    "\n",
+    "# Enter values\n",
+    "model = lr_multi\n",
+    "data = X_test\n",
+    "actual_target = Y_test\n",
+    "predicted_target = Y_pred\n",
+    "classes = ['Iris setosa', 'Iris versicolor', 'Iris virginica']\n",
+    "\n",
+    "# Calculates confusion matrix from actual and predicted target values\n",
+    "cnf = confusion_matrix(actual_target, predicted_target)\n",
+    "# Accuracy results\n",
+    "acc = model.score(data, actual_target)*100\n",
+    "\n",
+    "def plot_confusion_matrix(cm, classes, acc):\n",
+    "    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
+    "    plt.title('Confusion Matrix: Acc %.2f%%' % acc)\n",
+    "    plt.colorbar()\n",
+    "    tick_marks = np.arange(len(classes))\n",
+    "    plt.xticks(tick_marks, classes, rotation=45, ha='right')\n",
+    "    plt.yticks(tick_marks, classes)\n",
+    "    thresh = cm.max() / 2.\n",
+    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+    "        plt.text(j, i, cm[i, j], horizontalalignment='center',color='white' if cm[i, j] > thresh else 'black')\n",
+    "    plt.ylabel('True label')\n",
+    "    plt.xlabel('Predicted label')\n",
+    "    plt.show()\n",
+    "\n",
+    "plot_confusion_matrix(cm=cnf, classes=classes, acc=acc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Classification using Support Vector Machines\n",
+    "from sklearn import svm\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "\n",
+    "# Split labeled data into training and test set\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=0)\n",
+    "\n",
+    "# Model fit - SVM with linear kernel\n",
+    "svm_ = svm.SVC(kernel='linear')\n",
+    "# Model fit - SVM with non-linear kernel (RBF) for data not linearly separable\n",
+    "# svm = svm.SVC(kernel='rbf')\n",
+    "svm_.fit(X_train, Y_train)\n",
+    "\n",
+    "# Predict values\n",
+    "Y_pred = svm_.predict(X_test)\n",
+    "\n",
+    "# Calculate performance - accuracy\n",
+    "print('1-fold CV accuracy: %.2f' % svm_.score(X_test, Y_test))\n",
+    "\n",
+    "scores = cross_val_score(svm_, iris.data, iris.target, cv=5)\n",
+    "print('\\n5-fold CV accuracies:')\n",
+    "print(scores)\n",
+    "print('\\n5-fold CV average accuracy: %.2f' % np.mean(scores))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Working with image dataset of handwritten digits\n",
+    "digits = datasets.load_digits()\n",
+    "X_d = digits.data\n",
+    "Y_d = digits.target\n",
+    "print('Digits image shape: %d x %d pixels'  % (digits.images.shape[1], digits.images.shape[2]))\n",
+    "print('Digits data shape: %d x %d'  % (digits.data.shape[0], digits.data.shape[1]))\n",
+    "print('Digit targets: ', set(Y_d))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Model fit - using cross-validation\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(digits.data, digits.target, test_size=0.4, random_state=0)\n",
+    "lr_multi_d = linear_model.LogisticRegression(multi_class = 'multinomial', solver = 'newton-cg')\n",
+    "# SVM produces higher accuracy (try kernel = 'rbf')\n",
+    "#lr_multi_d = svm.SVC(kernel='linear')\n",
+    "lr_multi_d.fit(X_train, Y_train)\n",
+    "\n",
+    "# Predict values\n",
+    "Y_pred = lr_multi_d.predict(X_test)\n",
+    "\n",
+    "# Enter values for confusion matrix\n",
+    "model = lr_multi_d\n",
+    "data = X_test\n",
+    "actual_target = Y_test\n",
+    "predicted_target = Y_pred\n",
+    "classes = digits.target_names\n",
+    "\n",
+    "# Calculates confusion matrix from actual and predicted target values\n",
+    "cnf = confusion_matrix(actual_target, predicted_target)\n",
+    "# Accuracy results\n",
+    "acc = model.score(data, actual_target)*100\n",
+    "\n",
+    "plot_confusion_matrix(cm=cnf, classes=classes, acc=acc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# K-means Clustering\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "X = iris.data\n",
+    "Y = iris.target\n",
+    "\n",
+    "# Initialization is important in k-means\n",
+    "# k-means++ selects initial cluster centers for k-mean clustering in a smart way to speed up convergence\n",
+    "km = KMeans(n_clusters = 3, init='k-means++')\n",
+    "# Random initialization performs worse\n",
+    "#km = KMeans(n_clusters = 3, init='random')\n",
+    "# Cluster centers learned from a previous successful run\n",
+    "#km = KMeans(n_clusters = 3, init=cc)\n",
+    "\n",
+    "Y_pred = km.fit_predict(X)\n",
+    "cc = km.cluster_centers_\n",
+    "\n",
+    "# Visualize datapoints with targets and cluster centers\n",
+    "# Actual targets\n",
+    "key = {0: ('red', 'Iris setosa'),\n",
+    "       1: ('blue', 'Iris versicolor'),\n",
+    "       2: ('green', 'Iris virginica')}\n",
+    "colors_target = [key[index][0] for index in Y]\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.subplot(121)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=colors_target)\n",
+    "plt.xlabel('Sepal length')\n",
+    "plt.ylabel('Sepal width')\n",
+    "plt.title('Targets')\n",
+    "patches = [matplotlib.patches.Patch(color=color, label=label) for color, label in key.values()]\n",
+    "plt.legend(handles=patches, labels=[label for _, label in key.values()],\n",
+    "           bbox_to_anchor = (0.1,-0.1,1,1), bbox_transform = plt.gcf().transFigure)\n",
+    "\n",
+    "# Cluster Results\n",
+    "key = {0: ('pink', 'Cluster-1'),\n",
+    "       1: ('orange', 'Cluster-2'),\n",
+    "       2: ('purple', 'Cluster-3')}\n",
+    "colors_cluster = [key[index][0] for index in Y_pred]\n",
+    "\n",
+    "plt.subplot(122)\n",
+    "plt.scatter(X[:, 0], X[:, 1], c=colors_cluster)\n",
+    "plt.scatter(cc[:, 0], cc[:, 1], c='black', marker='x', s=60) # Plot cluster centers\n",
+    "plt.xlabel('Sepal length')\n",
+    "plt.ylabel('Sepal width')\n",
+    "plt.title('Clusters')\n",
+    "\n",
+    "# Plot legend\n",
+    "patches = [matplotlib.patches.Patch(color=color, label=label) for color, label in key.values()]\n",
+    "plt.legend(handles=patches, labels=[label for _, label in key.values()],\n",
+    "           bbox_to_anchor = (0.075,-0.3,1,1), bbox_transform = plt.gcf().transFigure)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Enter values for confusion matrix\n",
+    "model = km\n",
+    "data = X\n",
+    "actual_target = Y\n",
+    "predicted_target = Y_pred\n",
+    "classes = iris.target_names\n",
+    "\n",
+    "# Plot cluster results and labels\n",
+    "cnf = confusion_matrix(actual_target, predicted_target)\n",
+    "plt.imshow(cnf, interpolation='nearest', cmap=plt.cm.Blues)\n",
+    "plt.title('Clusters with corresponding targets')\n",
+    "plt.colorbar()\n",
+    "tick_marks = np.arange(len(classes))\n",
+    "plt.xticks(tick_marks, np.array(list(set(Y_pred))))\n",
+    "plt.yticks(tick_marks, classes)\n",
+    "thresh = cnf.max() / 2.\n",
+    "for i, j in itertools.product(range(cnf.shape[0]), range(cnf.shape[1])):\n",
+    "    plt.text(j, i, cnf[i, j], horizontalalignment='center',color='white' if cnf[i, j] > thresh else 'black')\n",
+    "plt.ylabel('True label')\n",
+    "plt.xlabel('Cluster assignment')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Boston housing prices dataset\n",
+    "### 506 samples with 13 features with continuous values\n",
+    "### We will learn:\n",
+    "    - Regression\n",
+    "    - Error as a mean of performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "boston = datasets.load_boston()\n",
+    "boston_df = pd.DataFrame(data = np.c_[boston['data'], boston['target']],\n",
+    "                         columns = list(boston['feature_names']) + ['target'])\n",
+    "\n",
+    "print('Boston Housing Prices: dataframe shape')\n",
+    "print(boston_df.shape)\n",
+    "boston_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Correlation between features and target value\n",
+    "from scipy import stats\n",
+    "\n",
+    "X = boston_df\n",
+    "Y = boston.target\n",
+    "features = boston['feature_names']\n",
+    "\n",
+    "# Plot correlation for each feature\n",
+    "i=1\n",
+    "plt.figure(figsize=(20, 20))\n",
+    "for f in features:\n",
+    "    corr = stats.pearsonr(list(X[f]), Y)[0]\n",
+    "    plt.subplot(4, 4, i)\n",
+    "    plt.scatter(list(X[f]), Y)\n",
+    "    plt.xlabel(f)\n",
+    "    plt.ylabel('Target Housing Price')\n",
+    "    plt.title(('%s and target correlation = %.2f') % (f, corr))\n",
+    "    i+=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Linear Regression\n",
+    "from sklearn import linear_model\n",
+    "\n",
+    "# Data for regression\n",
+    "X = boston.data\n",
+    "Y = boston.target\n",
+    "# Apply PCA\n",
+    "pca = PCA(n_components=3)\n",
+    "#X = pca.fit_transform(X)\n",
+    "# 2 features with highest correlation\n",
+    "#X = np.c_[list(boston_df.RM), list(boston_df.LSTAT)]\n",
+    "\n",
+    "# Model - Linear, Lasso, Ridge, normalized or not\n",
+    "#lr = linear_model.LinearRegression(normalize=False)\n",
+    "lr = linear_model.Lasso(normalize=False)\n",
+    "#lr = linear_model.Ridge(normalize=False)\n",
+    "\n",
+    "# Model fit\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.4, random_state=0)\n",
+    "lr.fit(X_train, Y_train)\n",
+    "# Score: Correlation between actual and predicted values\n",
+    "lr.score(X_test, Y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot Percentage of Variance Explained with additional Principal Components\n",
+    "exp_var = []\n",
+    "for i in range(X.shape[1]):\n",
+    "    pca = PCA(n_components=i)\n",
+    "    pca.fit(X)\n",
+    "    summ = sum(pca.explained_variance_ratio_)\n",
+    "    exp_var.append(summ)\n",
+    "\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.plot(exp_var)\n",
+    "plt.xlabel('Number of PCs')\n",
+    "plt.ylabel('Total % of variance explained')\n",
+    "plt.title('PCA')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## TPOT\n",
+    "#### for optimized machine learning pipelines\n",
+    "#### https://rhiever.github.io/tpot/\n",
+    "#### Examples: https://rhiever.github.io/tpot/examples/\n",
+    "#### TPOT is a Python tool that automatically creates and optimizes machine learning pipelines using genetic programming"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![title](tpot-ml-pipeline.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![title](tpot-pipeline-example.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Classification\n",
+    "from tpot import TPOTClassifier\n",
+    "from sklearn import datasets\n",
+    "# pip install tpot\n",
+    "\n",
+    "data = datasets.load_iris()\n",
+    "#data = datasets.load_digits()\n",
+    "export_file = 'tpot_pipeline_iris.py'\n",
+    "X = data.data\n",
+    "Y = data.target\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.4)\n",
+    "\n",
+    "tpot = TPOTClassifier(generations=5, population_size=50, verbosity=2)\n",
+    "tpot.fit(X_train, y_train)\n",
+    "print(tpot.score(X_test, y_test))\n",
+    "tpot.export(export_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Regression\n",
+    "from tpot import TPOTRegressor\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "data = datasets.load_boston()\n",
+    "export_file = 'tpot_pipeline_boston.py'\n",
+    "X = data.data\n",
+    "Y = data.target\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.4)\n",
+    "\n",
+    "tpot = TPOTRegressor(generations=5, population_size=50, verbosity=2)\n",
+    "tpot.fit(X_train, y_train)\n",
+    "print(tpot.score(X_test, y_test))\n",
+    "tpot.export(export_file)\n",
+    "\n",
+    "y_pred = tpot.predict(X_test)\n",
+    "print('\\nCorrelation between predicted and actual target values: %.2f' % stats.pearsonr(y_pred, y_test)[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/lessons/python/scikit-learn/intro-ml-AH-2023.ipynb b/lessons/python/scikit-learn/intro-ml-AH-2023.ipynb
new file mode 100644
index 000000000..e444882c8
--- /dev/null
+++ b/lessons/python/scikit-learn/intro-ml-AH-2023.ipynb
@@ -0,0 +1,570 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Python: Intro to machine learning\n",
+    "\n",
+    "### Ahmed Hasan, borrowing heavily from Nil Sahin\n",
+    "### UofT Coders, 2023/03/15\n",
+    "\n",
+    "## Intro\n",
+    "\n",
+    "\"Machine learning gives computers the ability to learn without being explicitly programmed.\" - Arthur Samuel, 1959\n",
+    "\n",
+    "Machine learning (ML) originated from pattern recognition and computational learning theory in AI. It is the study and construction of algorithms to learn from and make predictions on data through building a model from sample input.\n",
+    "\n",
+    "At its core, ML is grounded in statistics. That said, in statistics, we are typically choosing either or both of **inference** and **prediction** when approaching a problem. We might want our chosen model to tell us something about the relationship between features of interest, such as how much sales volume has increased given some increase in a marketing budget, and thus **infer** something about said features.\n",
+    "\n",
+    "Machine learning is mostly concerned with the **prediction** side of things, where we use relationships between variables to estimate the output for any arbitrary input. At many levels of ML, we are not as interested in understanding these relationships, and instead tend to treat the models as 'black boxes' -- especially as models increase in complexity. Our models therefore deal with the data as needed, but we're not as interested in peeking 'under the hood' for the purposes of learning more about the relationships within our data. \n",
+    "\n",
+    "## Some uses of ML algorithms\n",
+    "\n",
+    "- Classification: determine which discrete category the example is\n",
+    "- Recognizing patterns: speech recognition, facial identity...\n",
+    "- Recommender Systems: noisy data, commercial pay-off (e.g., Amazon, Netflix)\n",
+    "- Information retrieval: find documents or images with similar content\n",
+    "- Computer vision: detection, segmentation, depth estimation, optical flow...\n",
+    "- Robotics: perception, planning ...\n",
+    "- Learning to play games: AlphaGO\n",
+    "- Recognizing anomalies: Unusual sequences of credit card transactions, panic situation at an airport\n",
+    "- Spam filtering, fraud detection: The enemy adapts so we must adapt too\n",
+    "- Large language models: GPT, LLaMA, PaLM...\n",
+    "\n",
+    "## Some risks to worry about\n",
+    "\n",
+    "- GIGO\n",
+    "    - Garbage in, garbage out - if the data your model is trained on are 'garbage', your model will make equally made predictions (e.g. if you have a model showing a correlation between divorce rates and Honda sales, guess what it'll keep predicting)\n",
+    "- Biased training data\n",
+    "    - If you have an email spam predictor trained on a dataset that only has 1% spam examples, it's going to assume most emails are not spam\n",
+    "- Overfitting\n",
+    "    - Models can be too tightly tied to their data depending on how we fit them, making them not generalizable enough to new data (e.g. a model trying to predict Toronto's next mayor that's just trained on the last four years will likely tell you it'll be John Tory again) \n",
+    "- Lack of explainability\n",
+    "    - The 'black box'-ness of ML models can make their outputs hard to understand or explain\n",
+    "- Extrapolation is not always appropriate\n",
+    "    - Depends on your model! \n",
+    "\n",
+    "Still, ML used well and carefully is an incredibly powerful tool, and is already heavily integrated in the world around us.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Types of learning tasks\n",
+    "\n",
+    "- Supervised Learning: correct output known for each training example for predicting output when given an input vector\n",
+    "    - Classification: 1-of-N output, e.g. object recognition, medical diagnosis\n",
+    "    - Regression: real-valued-output, e.g.predicting market prices, customer ratings\n",
+    "- Unsupervised Learning: for learning an internal representation of the input to capture regularities and structure in the data without any labels\n",
+    "    - Clustering: dividing input into groups that are unknown beforehand\n",
+    "    - Dimensionality reduction: extract informative features, e.g. PCA\n",
+    "- Reinforcement Learning: perform an action with the goal to maximize payoff by the feedback of reward and punishments, e.g. playing a game against an opponent\n",
+    "\n",
+    "In this lesson, we'll cover:\n",
+    "\n",
+    "- Regression (e.g. Linear Regression)\n",
+    "- Simple cross-validation\n",
+    "- Clustering (e.g. K-means)\n",
+    "\n",
+    "We won't cover:\n",
+    "\n",
+    "- Classification (e.g. logistic regression)\n",
+    "- Other classification and clustering algorithms (e.g. Neural Networks, Hierarchical Clustering)\n",
+    "- Model selection (such as Bayesian Information Criteria - BIC)\n",
+    "- Hyper-parameter selection\n",
+    "- Dimensionality Reduction (e.g. PCA)\n",
+    "- Anything else further down the AI world - there have been many developments in the last six months alone! \n",
+    "\n",
+    "We will assume:\n",
+    "\n",
+    "- A working knowledge of basic Python\n",
+    "- Familiarity with the `matplotlib` and (especially) the `pandas` libraries\n",
+    "- Some understanding of intro level undergrad statistics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The `iris` dataset\n",
+    "\n",
+    "- 150 rows with measurements of petal length, petal width, sepal length, and sepal width, split evenly across 3 species\n",
+    "- Included in `sklearn`, the package we'll be using! \n",
+    "\n",
+    "## Linear regression with sklearn\n",
+    "\n",
+    "Linear regression might not be the most exciting thing, and is also not likely to be the first method many people think of when they think about ML. Concepts like 'neural networks' and 'generative AI' might sound very exciting, but it can be hard to get a solid understanding of ML fundamentals if we're to start there, and so they're outside the scope of this lesson. Still, linear models can be surprisingly useful even for fairly complicated-seeming problems, and (more importantly for us) the simplicity of linear regression makes it a useful means of exploring some foundational ML concepts. \n",
+    "\n",
+    "Let's introduce `sklearn` by fitting a linear model to the relationship between petal length (predictor) and petal width (response). From a statistical perspective, we might be interested in the relationship between these two, but in this case we will be fitting this model solely for the purposes of predicting petal width for any given petal length values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import linear_model # for linear regression functions\n",
+    "from sklearn.datasets import load_iris # the iris dataset\n",
+    "\n",
+    "import numpy as np # for general data handling\n",
+    "import pandas as pd # for data frames\n",
+    "import matplotlib # for plotting\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal_length  sepal_width  petal_length  petal_width  species\n",
+       "0           5.1          3.5           1.4          0.2      0.0\n",
+       "1           4.9          3.0           1.4          0.2      0.0\n",
+       "2           4.7          3.2           1.3          0.2      0.0\n",
+       "3           4.6          3.1           1.5          0.2      0.0\n",
+       "4           5.0          3.6           1.4          0.2      0.0"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris_raw = load_iris() # not yet a data frame\n",
+    "iris = pd.DataFrame(\n",
+    "    data=np.c_[iris_raw.data, iris_raw['target']])\n",
+    "iris.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n",
+    "\n",
+    "iris.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.41575542]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# prepping our data for linear regression - need to reshape for sklearn\n",
+    "n = iris.shape[0] # number of rows (150)\n",
+    "x = iris.petal_length.values.reshape(n, 1)\n",
+    "y = iris.petal_width.values.reshape(n, 1)\n",
+    "\n",
+    "# fit model\n",
+    "lm = linear_model.LinearRegression()\n",
+    "lm.fit(x, y) # fit y against x\n",
+    "lm.coef_ # get coefficient - for that stats itch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x12536bac0>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualizing our fit\n",
+    "fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "ax.scatter(x, y)\n",
+    "ax.plot(x, lm.predict(x))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To evaluate our model, we will calculate the **mean squared error** (MSE) and **R^2 scores**. MSE is reflective of the residuals (distance between each point and its prediction on the line) and lower values suggest a better model. R^2, on the other hand, contrasts the residuals with the overall distance (of all points) from the mean y value. While R^2 is probably easier to interpret, MSE is still useful mathematically speaking, as it's often the value we are trying to minimize. There exist many more advanced metrics (RMSE, adjusted R^2, etc.) that cover for some of the imperfections these two have and/or make them more appropriate for testing specific models, but their base versions are still a very useful starting point and important to understand. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.04206730919499318\n",
+      "0.9271098389904927\n"
+     ]
+    }
+   ],
+   "source": [
+    "# evaluating our model\n",
+    "from sklearn.metrics import r2_score, mean_squared_error\n",
+    "\n",
+    "print(mean_squared_error(y, lm.predict(x)))\n",
+    "print(r2_score(y, lm.predict(x)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Train/test splits - the validation set approach\n",
+    "\n",
+    "Look at that, a 0.93 R^2! This model must be amazing at predictions, right?\n",
+    "\n",
+    "Not so fast - this is us 'testing' on the very same data the model was trained on! We're fortunate that our data corresponds well to a linear fit, but this is not a good estimate of how well it generalizes to new data. For that, we need to evaluate it with 'known' values that the model has never seen before. Models may have great training set accuracy but be _terrible_ on new test data! \n",
+    "\n",
+    "One means of evaluating this is called the _validation set approach_, where we withhold a subset of the data from the model fit. We retain a (usually larger) subset of the data for training, and then test the model's predictions on the hold-out set. \n",
+    "\n",
+    "Since we have known expected outputs for the hold-out set (e.g. if we're predicting petal width based on petal length, we have both for these data points) we can compare our model predictions with these 'ground truth' values to assess our model's accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.38182003]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x12546c970>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9LgMAAqvqs00qvP3VeLtg7930j8uO8bEi7AgzW+icK0i2jNuzACDNnH/ffL1ctTbefvKKsTp0UK5/BaFTEdQAkEaanupePnW8LMWJZUhPBDUApAme2Z2d2pxMZmbTzGyNmb3XwvJzzOxdM6s0szfN7LCEZSti/YvMjIvOANBBhHT2SmXW93RJ41pZvlzSCc65EZJuknR3k+UnOecOb+kiOQCgdYR0dmvz1Ldz7lUzG9rK8jcTmnMlDfKgLgCACGl4fx/1RZKeSWg7Sc+Z2UIzm+zxtgAgY9VF6wlpSPJwMpmZnaTtQX1sQvexzrlqMxsgaY6ZfeCce7WFz0+WNFmShgwZ4lVZAJB25iz5XJfc/+20nitPHq5fnbq/jxXBT54EtZkdKuleSac759Y19DvnqmO/rzGzxyWNkpQ0qJ1zdyt2fbugoCB4T2EBgC4w4voybfpmW7y94NpT1H+Xnj5WBL/t8KlvMxsiabaknzjnPkzo721muzT8WdJpkpLOHAcAbL8enRjSK247g5BG20fUZjZT0omS+pnZaknXS8qRJOfcXZL+S9Lukv4Su+l+W2yG9x6SHo/1dZf0sHPu2U74GQAg7XE9Gi1JZdb3pDaWXyzp4iT9yyQd1vwTAIBEhDRaw9uzAMBHhDTaQlADgE8IaaSCoAaALrZpS12jkB538J6ENFrESzkAwGOlFdUqKatSTW1EA3PDKirM14SReZKkZ9/7VJc++HZ8bP+de6ps8Wcae9uLjcZ5vV0/15UO2w0yghoAPFRaUa3i2ZWK1EUlSdW1ERXPrpQk/fXVZXr/043xsb26d9Par75pNq4jwdTadtu7Pi/XlQ7bDTpOfQOAh0rKquJB0yBSF9V/PLKoUUjn5Ya1ZVt9s3ElZVWebrcj6/NyXemw3aAjqAHAQzW1kTbHrLjtjBbHpfL59nyuI+vzuragbzfoCGoA8NDA3HCryxsmjbU0rq3Pt3e7HVmf17UFfbtBR1ADgIeKCvMVzgklXZY4szvZuHBOSEWF+Z5tt6Pr87q2oG836JhMBgAemjAyT845/erRdxr1N739qmFylFcznL1cn9e1BX27QWfOBe9FVQUFBa68vLztgQAQMJ9uiOjoqS/G29eOP1CXHL+PjxUhHZjZwth7MprhiBoAPPLA3E90Xem3Lwmcf83JGrBrLx8rQiYgqAHAAwU3z9EXX22Nt3nSGLxCUAPADuKZ3ehMzPoGgB1ASKOzEdQA0EGENLoCQQ0AHUBIo6sQ1ADQDtui9byiEl2KyWQAkKKFn3ypH9z5Zrz90MWjNXa/fj5WhGxAUANACq6cVaEnFtXE2+/fOE7hHskfFQp4iaAGgDZwPRp+4ho1ALSCkIbfCGoAaAEhjSAgqAEgCUIaQUFQA0AThDSChKAGgJgNm+sahfS5Y4YQ0vAds76BLFNaUa2SsirV1EY0MDesosJ8TRiZ53dZvntiUbWunLUo3n76l8fpoIG7NhqT6r5jH8NLBDWQRUorqlU8u1KRuqgkqbo2ouLZlZKU1UEy6e65emvZunh72a3j1a2bNRqT6r5jH8NrnPoGskhJWVU8QBpE6qIqKavyqSL/Db36qUYhveK2M5qFtJT6vmMfw2sENZBFamoj7erPdO2ZNJbqvmMfw2sENZBFBuaG29Wfydo7szvVfcc+htcIaiCLFBXmK5zT+PnU4ZyQigrzfarIHx25/SrVfcc+hteYTAZkkYbJTNk6I9k5p2HFT8fb+w3YWc9fdUJKn01132X7Pob3zDnndw3NFBQUuPLycr/LAJBBVn+5Wcf+9qV4+/c/OkzfHznIx4qAb5nZQudcQbJlHFEDyHh/e325bvrXkni7fMop6rdzTx8rAlJHUAPIaIfd8Jw2ROribZ40hnRDUAPIWDyzG5mAWd8AMhIhjUxBUAPIOIQ0MglBDSCjENLINAQ1gIxQF61vFNKXHDeMkEZGYDIZgLT3/qcbdfofXou3n7xirA4dlOtfQYCHCGoAae33cz7UH174KN6uunmcenYPtfIJIL0Q1ADSFtejkQ24Rg0gLRHSyBYENYC0Q0gjmxDUANJKYkjv0qs7IY2MxzVqAGmj6e1X155xkI/VAF0jpSNqM5tmZmvM7L0WlpuZ/dHMlprZu2Z2RMKy88zso9iv87wqHED22FIXbRTSD188mpBG1kj1iHq6pD9Lur+F5adLGh77NVrSnZJGm1lfSddLKpDkJC00syedc1/uSNEA0s+U0krNnLdKUecUMtOk0YN184QRbX6u6T3SC6ecot8//6F+8rf57V5XS0orqlVSVqWa2ogG5oZVVJivCSPzOrw+r7frV30IhpSC2jn3qpkNbWXIWZLud845SXPNLNfM9pJ0oqQ5zrn1kmRmcySNkzRzh6oGkFamlFbqwbkr4+2oc/F2awE7/Y3l+u9/fvse6eVTx+u6J97r0LpaUlpRreLZlYrURSVJ1bURFc+ulKRODcNUt+tXfQgOryaT5UlaldBeHetrqR9AFpk5b1W7+iVp4l/eaBTSK247Q2bWoXW1pqSsKh6CDSJ1UZWUVXVofV5v16/6EByBmUxmZpMlTZakIUOG+FwNAC9FnWtXf2u3X7V3XW2pqY20q98rqW7Xr/oQHF4dUVdLGpzQHhTra6m/Gefc3c65AudcQf/+/T0qC0AQhMxS7m/rHun2rCsVA3PD7er3Sqrb9as+BIdXQf2kpJ/GZn+PkbTBOfeppDJJp5nZbma2m6TTYn0Assik0YNT6k8M6cMG9Ul6j3Sq60pVUWG+wjmNnw0ezgmpqDC/Q+vzert+1YfgSOnUt5nN1PaJYf3MbLW2z+TOkSTn3F2SnpY0XtJSSZslXRBbtt7MbpK0ILaqGxsmlgHIHg2TvFqa9e2c07Dip+Pj/+vMg3ThscM6tK72apiQ1dWzqlPdrl/1ITjMdfC6TmcqKChw5eXlfpcBoAt8+fVWjbxpTrz9zJXH6cC9dvWxIqDrmdlC51xBsmWBmUwGIPu89fE6Tbpnbrz9/o3jFO7BKyqBRAQ1AF9MfeZ9/fWVZfE2z+wGkiOoAXS5w298TrWb6+JtQhpoGUENoEvxikqgfXjNJYAuQ0gD7UdQA+gSiSF91uEDCWkgRZz6BtCp6uud9rnm23uk//zjkTrz0IE+VgSkF4IaQKfZtKVOI/77uXj7vRsKtXNP/toB2oP/YwB0imVrv9J3/u+Vb9u3jle3bh17HjeQzQhqAJ57qWqNLrhvQbzN9Wig4whqAJ6646Wl8XclD919J71cdJLPFQHpjaAG4JmLZyzQ8++vkSRNGjVYUyce6nNFQPojqAF4Yv9rn9HWaL0k6baJI3T2qCE+VwRkBoIawA5LvEf6scuO1pF79/WxGiCzENQAOqwuWq/h1z4Tb88tPll79unlY0VA5iGoAXTIuq++0ZE3Px9vf3DTOPXK4RWVgNcIagDt9l71Bp35p9fj7eVTx8uMe6SBzkBQA2iXJxZV68pZi+Jt7pEGOhdBDSBltzy1RPe8tlySdNTQ3fT3S4/xuSIg8xHUAFJy1p9f1zurN0iSfn7ivvrNuAN8rgjIDgQ1gDYl3n71l3OO0PgRe/lYDZBdCGoALXLOaVjxt6+ofObK43TgXrv6WBGQfQhqAElFtkZ14H89G29XXHeqduvdw8eKgOxEUANopro2orG3vRhvL73ldHUPdfOxIiB7EdQAGpm3bJ1+dPfceJvbrwB/8U9kAHEPzP0kHtK79OxOSAMBwBE1AEnSVY8u0uy3qyVJpx+yp+4890ifKwIgEdQAJI259QV9tnGLJOna8QfqkuP38bkiAA0IaiDLJd4j/cBFo3Tc8P4+VgOgKYIayFLReqd9r/n2HulXik7U3rv39rEiAMkQ1EAW2rilTof+93Px9uIbCtW7J38dAEHE/5lAltnUJKSX3Tpe3brxikogqAhqIIt8vnGLLrhvQbzN7VdA8BHUQJZYumaTzpu2QF9u3qoZF47SCfszaQxIBwQ1kAXmL1+vi2csUI/uIT36s6N1SF4fv0sCkCKCGshwT737qX716CIN2i2sGReM0uC+O/ldEoB2IKiBDPa315fr5qeW6Mghu+menxbw9isgDRHUQAaqr3e65en39bfXl2vcwXvq9rMPV6+ckN9lAegAghrIMFvqovr1o+/oqcpPdf4xQ3XdmQcpxO1XQNoiqIEMUrt5qybfv1DzV6zXteMP1MXHDZMZIQ2kM4IayBCrv9ys8+9boJXrNuuPk0bqe4cN9LskAB4gqIEMsLhmgy64b4EidVHNuHCUjt53d79LAuARghpIc699tFaXPrBQfcI5euyyY7T/Hrv4XRIADxHUQBp7bOFq/b/H3tV+A3bW9AtGac8+vfwuCYDHCGogDTnndMdLS/W/z32osfvtrjvPPVK79srxuywAnYCgBtLMtmi9/uvJxXp43kp9f2SefvuDQ9Wjeze/ywLQSQhqII1s3rpNv3i4Qi98sEY/P3FfFRXmc/sVkOEIaiBNfPHVN7poRrkqV9fqpgmH6Cdj9va7JABdgKAG0sCKL77WeffN1+cbt+iuc4/UaQfv6XdJALpISkFtZuMk/UFSSNK9zrnbmiz/vaSTYs2dJA1wzuXGlkUlVcaWrXTOfc+DuoGsUbHyS100o1yS9PAlY3TEkN18rghAV2ozqM0sJOkOSadKWi1pgZk96Zxb0jDGOferhPG/kDQyYRUR59zhnlUMZJE5Sz7XL2a+rT127aXpF4zSsH69/S4JQBdLZaroKElLnXPLnHNbJc2SdFYr4ydJmulFcUA2e3DuJ/rZA+XK32MXPXbZMYQ0kKVSCeo8SasS2qtjfc2Y2d6Shkl6MaG7l5mVm9lcM5vQ0kbMbHJsXPnatWtTKAvITM45lZR9oCml7+nE/AGaOXmM+u3c0++yAPjE68lkZ0v6h3MumtC3t3Ou2sz2kfSimVU65z5u+kHn3N2S7pakgoIC53FdQFrYuq1eVz/2rmZXVGvSqMG66axD1D3EPdJANkslqKslDU5oD4r1JXO2pMsTO5xz1bHfl5nZy9p+/bpZUAPZbtOWOl324Nt6fekX+vWp++uK7+zHPdIAUjr1vUDScDMbZmY9tD2Mn2w6yMwOkLSbpLcS+nYzs56xP/eTNFbSkqafBbLd5xu36N/vektzl61Tyb8dql+cPJyQBiAphSNq59w2M7tCUpm23541zTm32MxulFTunGsI7bMlzXLOJZ62PlDSX82sXtv/UXBb4mxxANKHn2/S+dPma0OkTtPOP0rH79/f75IABIg1ztVgKCgocOXl5X6XAXS6ucvWafL95eqZE9J95x+lQ/L6+F0SAB+Y2ULnXEGyZTyZDPDJv96t0VWPvKPBfcOaceEoDdptJ79LAhBABDXgg3tfW6abn3pfRw3dTff8tEC5O/XwuyQAAUVQA10oWu9081NLdN8bKzR+xJ763Q8PV6+ckN9lAQgwghroIlvqovrVI4v0zHuf6cKxwzTljAPVrRszuwG0jqAGukDt5q265P5yLVjxpaaccaAuPm4fv0sCkCYIaqCTrVq/WeffN1+r1kf05x+P1JmHDvS7JABphKAGOtF71Rt0wfQF+qYuqgcuGqXR++zud0kA0gxBDXSSVz5cq58/uFC5O/XQwxeP1vA9dvG7JABpiKAGOsHfy1epeHalhu+xi6ZfcJT22LWX3yUBSFMENeAh55z+9OJS/W7Ohzp2v36689wjtEuvHL/LApDGCGrAI9ui9bruifc0c/4qTTwiT7dNPFQ9uvOKSgA7hqAGPLB56zZd8XCFXvxgja44aT/9+rT9efsVAE8Q1MAOWrvpG100Y4Heq96gW75/iM4ZvbffJQHIIAQ1sAOWrf1K59+3QGs2bdHdPynQKQft4XdJADIMQQ100Nsrv9RF0xfIzDRr8tE6fHCu3yUByEAENdABzy3+TL+YWaE9+/TSjAtGaWi/3n6XBCBDEdRAOz3w1gpd/+RijRiUq2nnFWj3nXv6XRKADEZQAymqr3f6n7Iq3fXKxzrlwAH646SR2qkH/wsB6Fz8LQOkYOu2ev3mH++odFGNfjx6iG783sHqHuIeaQCdj6AG2rBxS50ufWCh3vx4nYoK8/XzE/flHmkAXYagBlrx6YaILrhvgZau+Uq/++FhmnjEIL9LApBlCGqgBVWfbdL5983Xpi3bdN8FR+m44f39LglAFiKogSTe+nidJj9QrnBOSI/8bIwOHtjH75IAZCmCGmjiyXdq9J+PvqO9d99J0y8cpbzcsN8lAchiBDUQ45zTPa8t061Pf6BRw/rqnp8UqM9OvKISgL8IakBStN7ppn8t0fQ3V+iMQ/fS//37YeqVE/K7LAAgqDNFaUW1SsqqVFMb0cDcsIoK8zVhZJ7fZaWFLXVR/cesRXp28We6+Nhhumb8gerWjduvAAQDQZ0BSiuqVTy7UpG6qCSpujai4tmVkkRYt+HLr7fqkvvLtXDll7ruzIN00bHD/C4JABrh0UoZoKSsKh7SDSJ1UZWUVflUUXpYtX6zfnDXm3q3eoPu+PERhDSAQOKIOgPU1Eba1Q+pcvUGXTB9geqi9XrwotEaNayv3yUBQFIcUWeAgS3cPtRSf7Z7uWqNfnT3W+rZvZseu+xoQhpAoBHUGaCoMF/hJjOUwzkhFRXm+1RRcD26YJUumlGuYf166/GfH6P9Buzid0kA0CpOfWeAhgljzPpumXNOf3jhI93+/Ec6bng/3Xnukdq5J//5Awg+/qbKEBNG5hHMLaiL1mvK4+/pkfJV+rcjB2nqxBHK4RWVANIEQY2M9vU323T5w2/r5aq1+uV39tOvTt2fV1QCSCsENTLW2k3f6MLpC7Tk042aOnGEJo0a4ndJANBuBDUy0sdrv9L5983XF5u26p6fHqnvHLCH3yUBQIcQ1Mg4Cz9Zr4tnlKubmWZNHqPDBuf6XRIAdBhBjYzy7Huf6cpZFRqYG9b0C47S3rv39rskANghBDUyxv1vrdD1Ty7W4YNzde9PC7T7zj39LgkAdhhBjbRXX+/027IP9NdXlunUg/bQH88eqXAPXlEJIDMQ1Ehr32yL6jf/eFdPLKrRuWOG6IbvHaIQr6gEkEEIaqStDZE6XfrAQr21bJ3+37gDdOkJ+3CPNICMQ1AjLdXURnTBfQu07Iuv9PsfHabvjxzkd0kA0CkIaqSdDz7bqPOnLdDX32zT9AtGaex+/fwuCQA6DUGNtPLm0i/0swcWqnfP7nr00qN14F67+l0SAHQqghpp44lF1frPv7+jYf16a/oFo3jfNoCsQFAj8Jxz+uury3TbMx9ozD599defFKhPOMfvsgCgS6T0rj8zG2dmVWa21MyuTrL8fDNba2aLYr8uTlh2npl9FPt1npfFI/NF652uf3KxbnvmA333sIGaceEoQhpAVmnziNrMQpLukHSqpNWSFpjZk865JU2GPuKcu6LJZ/tKul5SgSQnaWHss196Uj3arbSiWiVlVaqpjWhgblhFhfmBfY/1lrqofjmzQs8t+Vw/O34f/b9xB6gb90gDyDKpnPoeJWmpc26ZJJnZLElnSWoa1MkUSprjnFsf++wcSeMkzexYudgRpRXVKp5dqUhdVJJUXRtR8exKSQpcWK//eqsunrFAFatq9d/fPUjnjx3md0kA4ItUTn3nSVqV0F4d62vqB2b2rpn9w8wGt/Oz6AIlZVXxkG4QqYuqpKzKp4qSW7lus/7tzje1uGaj7jznCEIaQFZL6Rp1Cv4paahz7lBJcyTNaO8KzGyymZWbWfnatWs9KguJamoj7er3w7urazXxzje0fvNWPXTxaI07ZC+/SwIAX6US1NWSBie0B8X64pxz65xz38Sa90o6MtXPJqzjbudcgXOuoH///qnUjnZq6XamoNzm9FLVGp1991z1ygnpH5ceo4Khff0uCQB8l0pQL5A03MyGmVkPSWdLejJxgJklHvZ8T9L7sT+XSTrNzHYzs90knRbrgw+KCvMVzmn8VqlwTkhFhfk+VfStRxas1MUzyrVP/96a/fNjtN+Anf0uCQACoc3JZM65bWZ2hbYHbEjSNOfcYjO7UVK5c+5JSb80s+9J2iZpvaTzY59db2Y3aXvYS9KNDRPL0PUaJowFada3c063P/+R/vDCRzp+//76yzlHaOee3N4PAA3MOed3Dc0UFBS48vJyv8tAJ6uL1uua2ZX6+8LV+vcjB+nWiSOUE/Jq2gQApA8zW+icK0i2jEMX+OLrb7bp5w+9rVc+XKsrTx6u/zhlOK+oBIAkCGp0uTWbtujC6Qv0/qeb9NsfjNCPjhrid0kAEFgENbrUx2u/0nnT5mvdV1t1708LdNIBA/wuCQACjaBGlylfsV4X31+u7t1Mj/xsjA4dlOt3SQAQeAQ1usSz732qX85apLzcsGZcMEpDdt/J75IAIC0Q1Oh0099Yrhv+tUQjB+fq3vOOUt/ePfwuCQDSBkGNTlNf73Tbsx/o7leX6bSD9tAfJ41UryYPXAEAtI6gRqf4ZltU//n3d/XPd2r006P31vXfPVghXlEJAO1GUMNzGyJ1mnx/ueYtX6+rTz9APzt+H+6RBoAOIqjhqZraiM6/b76Wf/G1/nD24TrrcN5qCgA7gqCGZ97/dKPOv2++Nn8T1YwLR+mYffv5XRIApD2CGp54Y+kXuvSBherds7v+ftnROmDPXf0uCQAyAkGNHTb06qckSfl77KLpFx6lvfoE4/3WAJAJCGp0mHNOw4qfjrcfvfRo9Qnn+FgRAGQe3imIDtm4pa5RSL/2m5MIaQDoBBxRd4LSimqVlFWppjaigblhFRXma8LIjs1+Pueet/TGx+vj7bH79tVDlxzd4W16Udvimg0644+vx9sD+/TS8f/z0g7/rACA5jii9lhpRbWKZ1equjYiJ6m6NqLi2ZUqrahu97qahrQkvfHxep1zz1sd2qYXtc2cv7JRSIdzQqrZsGWHf1YAQHIEtcdKyqoUqYs26ovURVVSVtXudTUN6Zb6U93mjtZ26QMLVTy7Mt7Oyw179rMCAJIjqD1WUxtpV39XbnNHaht69VN6dvFn8faK287w5WcFgGxDUHtsYG7yW5Na6u/KbXa0tobbryRpzD59teK2M3ZofQCA1BHUHisqzFe4yRuiwjkhFRXmt3tdY/ftm1J/qtvsSG2JIX3t+AM1a/K3E9m8/FkBAMkx69tjDTOevZj1/dAlR6c06zvVbbantm3Reu137TPx9mOXHa0j9278DwQvf1YAQHLmnPO7hmYKCgpceXm532VkrTWbtmjULS/E2wunnKLdd+7pY0UAkNnMbKFzriDZMo6o0chbH6/TpHvmxtvLbh2vbrxHGgB8Q1Aj7k8vfKT/m/NhvN0waQwA4B+CGpKk7/7pdVVWb4i3CWkACAaCGo1mdueETB/dMt7HagAAibg9K8slhvTEkXmENAAEDEfUWSwxpH/3w8M08YhBPlYDAEiGoM5CW+qiOuC6Z+Pt5351vPbfYxcfKwIAtISgzjLPLf5Mkx9YGG8vvqFQvXvynwEABBV/Q2eR86bN1ysfro23l08dLzPukQaAICOos0Ti9WiJ268AIF0w6zsLENIAkL4I6gxHSANAeiOoMxghDQDpj6DOQPX1rlFI/+CIQYQ0AKQpJpNlmBVffK0T//fleHvmJWN09L67+1cQAGCHENSdYEpppWbOW6WocwqZadLowbp5wohGY8655y298fH6eHvsvn310CVHN1tXaUW1SsqqVFMb0cDcsIoK8zVhZF7S7f5vWZX+/NLSeDvZPdLtWR8AwH8EtcemlFbqwbkr4+2oc/F2Q1g3DWlJeuPj9TrnnrcahXVpRbWKZ1cqUheVJFXXRlQ8u1KSmoVrKtej27M+AEAwcI3aYzPnrWqzv2lIt9RfUlYVD9UGkbqoSsqqGvWlOmks1fUBAIKDoPZY1Ll29bempjbSZn97Znansj4AQLAQ1B4LtfBIzpb6WzMwN9xqf3tvv2prfQCA4CGoPTZp9OA2+8fu2zfpmKb9RYX5CueEGvWFc0IqKszv0D3Sra0PABBMBLXHbp4wQueOGRI/gg6Z6dwxQxrN+n7okqObhXKyWd8TRuZp6sQRyssNyyTl5YZ1/XcP0n88sig+5rjh/VK+RzrZ+qZOHMFEMgAIMHMduHba2QoKClx5ebnfZQTOP9+p0S9mVsTbj0weo9H7cI80AKQ7M1vonCtItozbs9LEmFtf0Gcbt8TbH91yunJCnBABgExHUKcBntkNANkrpUMyMxtnZlVmttTMrk6y/CozW2Jm75rZC2a2d8KyqJktiv160sviswEhDQDZrc0jajMLSbpD0qmSVktaYGZPOueWJAyrkFTgnNtsZpdJ+h9JP4otizjnDve27OxASAMAUjmiHiVpqXNumXNuq6RZks5KHOCce8k5tznWnCtpkLdlZh9CGgAgpRbUeZISn4u5OtbXkoskPZPQ7mVm5WY218wmtL/E7ENIAwAaeDqZzMzOlVQg6YSE7r2dc9Vmto+kF82s0jn3cZLPTpY0WZKGDBniZVlp451VtTrrjjfi7f332FnP/eqEVj4BAMh0qQR1taTEx20NivU1YmanSLpW0gnOuW8a+p1z1bHfl5nZy5JGSmoW1M65uyXdLW2/jzr1HyEzjP/Da1ry6cZ4u/TysTp8cK5/BQEAAiGVoF4gabiZDdP2gD5b0o8TB5jZSEl/lTTOObcmoX83SZudc9+YWT9JY7V9ohkScKobANCSNoPaObfNzK6QVCYpJGmac26xmd0oqdw596SkEkk7S/q7bX905krn3PckHSjpr2ZWr+3Xw29rMls86xHSAIDW8AhRHxHSAACp9UeI8gxKnxDSAIBUENQ+IKQBAKkiqLvQtmh9o5AeNawvIQ0AaFVGv5SjtKJaJWVVqqmNaGBuWEWF+Tv07uUppZWaOW+Vos4pZKZJowc3es90g1N/97I+WvN1vD18QG/9/kcjdeafXo/3dZM0f/n6eHB3N2np1OahfcC1T2tL9Nt5BL1Cpg9uGd9s3Ohb5ujzTVvj7T126aF5157abJzX+8Tr9QEAGsvYyWSlFdUqnl2pSF003hfOCWnqxBEdCpIppZV6cO7KZv3njhnSKKybhnQyIUnRJP1Nw7ppSDdoGtZNQ7pB07D2ep94vT4AyFZZOZmspKyqUYBIUqQuqpKyqg6tb+a8VSn1txXSK247I2lIS9K2JpmcLKST9ScL6WT9Xu8Tr9cHAGguY4O6pjbSrv62RFs489BSfzJ+X4/2ep94vT4AQHMZG9QDc8Pt6m9LaPuDXFLuDyKv94nX6wMANJexQV1UmK9wTqhRXzgnpKLC/A6tb9LowSn1Dx/QO+m4xP7uLWR70/5eoeQDm/bvsUuPpOOa9nu9T7xeHwCguYwN6gkj8zR14gjl5YZlkvJywzs0yenmCSN07pgh8SPokFmziWRS8mvUwwf01pyrToy3l049o1koJ5v1/cEt45uFcrJZ3/OuPbVZKCeb9e31PvF6fQCA5jJ21ndX27qtXvtP+fY13LMmj9GYfXb3sSIAQLpobdZ3Rt9H3VU+Wfe1Tih5Od5eOOUU7b5zT/8KAgBkDIJ6B/3r3Rpd8XBFvL3s1vHq1i19JpgBAIKNoN4Bv370HT329up42+/brwAAmYeg7iBerAEA6AoZO+u7MxHSAICuQlC3U2JIn5jfn5AGAHQqTn2nyDmnYcVPx9u3fP8QnTN6bx8rAgBkA4I6BVvqojrgumfj7Rd/fYL26b+zjxUBALIFQd2Gles26/iSl+LtD24ap15NHpsJAEBnIahb8VLVGl1w34J4m+vRAICuRlC34HdzPtQfX/go3iakAQB+IKiT+P5f3lDFylpJUu8eIS2+cZy/BQEAshZB3UTi7VcTDh+o288e6WM1AIBsR1AnSAzpqRNHaNKoIT5WAwAAQS2p+T3ST1w+VocNzvWvIAAAYrI+qJveI73nrr20/IuvOz2op5RWaua8VYo6p5CZJo0erJsnjOjwOABAZsrqoF676Rsddcvzjfo+27hFxbMrJUkTRuZ1ynanlFbqwbkr4+2oc/F2YginOg4AkLmy9lnfH3y2sVlIN4jURVVSVtVp2545b1VK/amOAwBkrqwM6hfe/1zjbn+t1TE1tZFO237UuZT6Ux0HAMhcWRfU9762TBfNKJck3XjWwcrLDScdN7CFfi+EzFLqT3UcACBzZVVQ//rRd3TzU+9Lku6/cJR+evRQFRXmK9zk2d3hnJCKCvM7rY5Jowen1J/qOABA5sqayWSn/O4VLV3zlSTp+auO134DdpH07YSxkrIq1dRGNDA3rKLC/E6bSCZ9OxGsrdncqY4DAGQucwG83llQUODKy8s9WVe03mnfa769R/rt605V3949PFk3AABeMLOFzrmCZMsy/oj68Buei/+56uZx6tmdV1QCANJHxl+jvuDYYTpy7920fOp4QhoAkHYy/tQ3AABB19qp74w/ogYAIJ0R1AAABBhBDQBAgBHUAAAEGEENAECAEdQAAAQYQQ0AQIAR1AAABBhBDQBAgBHUAAAEGEENAECAEdQAAARYSkFtZuPMrMrMlprZ1UmW9zSzR2LL55nZ0IRlxbH+KjMr9LB2AAAyXptBbWYhSXdIOl3SQZImmdlBTYZdJOlL59x+kn4v6bexzx4k6WxJB0saJ+kvsfUBAIAUpHJEPUrSUufcMufcVkmzJJ3VZMxZkmbE/vwPSSebmcX6ZznnvnHOLZe0NLY+AACQglSCOk/SqoT26lhf0jHOuW2SNkjaPcXPSpLMbLKZlZtZ+dq1a1OrHgCADBeYyWTOubudcwXOuYL+/fv7XQ4AAIGQSlBXSxqc0B4U60s6xsy6S+ojaV2KnwUAAC0w51zrA7YH74eSTtb2kF0g6cfOucUJYy6XNMI5d6mZnS1ponPuh2Z2sKSHtf269EBJL0ga7pyLtrHNtZI+6fiP1Uw/SV94uD50HN9FcPBdBAffRXD49V3s7ZxLejq5e1ufdM5tM7MrJJVJCkma5pxbbGY3Sip3zj0p6W+SHjCzpZLWa/tMb8XGPSppiaRtki5vK6Rjn/P03LeZlTvnCrxcJzqG7yI4+C6Cg+8iOIL4XbR5RJ0JgrjjsxXfRXDwXQQH30VwBPG7CMxkMgAA0Fy2BPXdfheAOL6L4OC7CA6+i+AI3HeRFae+AQBIV9lyRA0AQFrK6KA2s2lmtsbM3vO7lmxmZoPN7CUzW2Jmi83sSr9rylZm1svM5pvZO7Hv4ga/a8p2ZhYyswoz+5fftWQzM1thZpVmtsjMyv2uJ1FGn/o2s+MlfSXpfufcIX7Xk63MbC9Jeznn3jazXSQtlDTBObfE59KyTuwZ/L2dc1+ZWY6k1yVd6Zyb63NpWcvMrpJUIGlX59yZfteTrcxshaQC51zg7mfP6CNq59yr2n5fN3zknPvUOfd27M+bJL2vFp75js7ltvsq1syJ/crcf60HnJkNknSGpHv9rgXBldFBjeCJvat8pKR5PpeStWKnWhdJWiNpjnOO78I/t0v6jaR6n+vA9n+wPmdmC81sst/FJCKo0WXMbGdJj0n6D+fcRr/ryVbOuahz7nBtf/b+KDPjspAPzOxMSWuccwv9rgWSpGOdc0dIOl3S5bFLp4FAUKNLxK6HPibpIefcbL/rgeScq5X0kqRxPpeSrcZK+l7s2ugsSd8xswf9LSl7OeeqY7+vkfS4tr+jIhAIanS62ASmv0l63zn3O7/ryWZm1t/McmN/Dks6VdIHvhaVpZxzxc65Qc65odr+foQXnXPn+lxWVjKz3rGJrjKz3pJOkxSYu4UyOqjNbKaktyTlm9lqM7vI75qy1FhJP9H2I4ZFsV/j/S4qS+0l6SUze1fb34Q3xznHbUHIdntIet3M3pE0X9JTzrlnfa4pLqNvzwIAIN1l9BE1AADpjqAGACDACGoAAAKMoAYAIMAIagAAAoygBgAgwAhqAAACjKAGACDA/j8Z+ZPaZ822fgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 576x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# let's withhold 50 observations for the test set, and use the remaining 50 for training the model:\n",
+    "\n",
+    "iris_train = iris.iloc[:-50] # exclude last 50 rows\n",
+    "iris_test = iris.iloc[-50:] # only keep last 50 rows\n",
+    "\n",
+    "# get training x and y data\n",
+    "train_x = iris_train.petal_length.values.reshape(iris_train.shape[0], 1)\n",
+    "train_y = iris_train.petal_width.values.reshape(iris_train.shape[0], 1)\n",
+    "\n",
+    "# get test x and y data\n",
+    "test_x = iris_test.petal_length.values.reshape(iris_test.shape[0], 1)\n",
+    "test_y = iris_test.petal_width.values.reshape(iris_test.shape[0], 1)\n",
+    "\n",
+    "# fit model with training data\n",
+    "lm = linear_model.LinearRegression() \n",
+    "lm.fit(train_x, train_y)\n",
+    "print(lm.coef_)\n",
+    "\n",
+    "# we can also plot what's going on here\n",
+    "fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "ax.scatter(train_x, train_y)\n",
+    "ax.plot(train_x, lm.predict(train_x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.1260677797201568\n",
+      "-0.7053701060569884\n"
+     ]
+    }
+   ],
+   "source": [
+    "# testing the model on the test data\n",
+    "pred_y = lm.predict(test_x)\n",
+    "\n",
+    "# comparing predicted y values against actual y values\n",
+    "print(mean_squared_error(test_y, pred_y))\n",
+    "print(r2_score(test_y, pred_y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## K-fold cross-validation\n",
+    "\n",
+    "Another approach to validating our model performance that goes a step beyond validation set is _K-fold cross validation_. This is very similar to the approach above, but instead of us using a single hold-out set (which in the example above is a bit biased too, since the last few rows are all from one out of the three species!) we instead repeat the train/test split K times, with the different chunk of the data used as the hold-out set each time. K-fold allows for more data points to have the opportunity to be in both the train and test sets at some point. \n",
+    "\n",
+    "We'll do 5-fold CV below. The choice of K is a bit arbitrary, but a good starting point is to pick values where the dataset is nicely divisible by K, and 1/K of the dataset corresponds to a reasonable subsection (i.e. not like, half the dataset, nor a very tiny fraction). \n",
+    "\n",
+    "Fortunately, we don't need to do the train/test splitting K times ourselves - `sklearn` provides some functions to simplify this for us. The `KFold` function won't do the actual model fitting, but it'll return indices corresponding to train/test splits. That way, we can just re-fit the model each time in a loop. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.010950502617330904, 0.01435887741431815, 0.02917583830584813, 0.06226445107434637, 0.10967123374012987]\n",
+      "0.04528418063039469\n",
+      "[-0.1148701759726034, 0.9526300734104822, 0.38011674987574795, 0.6875668712817142, -0.45925651043933957]\n",
+      "0.28923740163120026\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "\n",
+    "k = 5\n",
+    "k_splits = KFold(n_splits=k, random_state=None) # think of random_state like a seed value in R\n",
+    "lm = linear_model.LinearRegression()\n",
+    "\n",
+    "# we'll store our metrics in lists\n",
+    "mse_values = []\n",
+    "r2_values = []\n",
+    "\n",
+    "for train_idx, test_idx in k_splits.split(iris.petal_length):\n",
+    "    # training data\n",
+    "    n = len(train_idx)\n",
+    "    train_x = iris.iloc[train_idx].petal_length.values.reshape(n, 1)\n",
+    "    train_y = iris.iloc[train_idx].petal_width.values.reshape(n, 1)\n",
+    "    \n",
+    "    # test data\n",
+    "    n_test = len(test_idx)\n",
+    "    test_x = iris.iloc[test_idx].petal_length.values.reshape(n_test, 1)\n",
+    "    test_y = iris.iloc[test_idx].petal_width.values.reshape(n_test, 1)\n",
+    "    \n",
+    "    lm.fit(train_x, train_y)\n",
+    "    pred_y = lm.predict(test_x)\n",
+    "    mse_values.append(mean_squared_error(test_y, pred_y))\n",
+    "    r2_values.append(r2_score(test_y, pred_y))\n",
+    "    \n",
+    "print(mse_values)\n",
+    "print(sum(mse_values) / k)\n",
+    "print(r2_values)\n",
+    "print(sum(r2_values) / k)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## K-means clustering\n",
+    "\n",
+    "We will wrap up by switching gears to clustering. So far, with regression, we've been looking at a _supervised_ approach, where our expected output is pre-defined (petal width values in our case). Clustering, on the other hand, is an example of an _unsupervised_ approach, in that we are using it to learn more about our data (in this case, the structure of it) without necessarily telling the algorithm what the expected output is. \n",
+    "\n",
+    "Let's see what kind of structure k-means finds within the petal width by petal length data. We'll leave the species labels out - this is an unsupervised algorithm after all! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "# kmeans expects data as numpy arrays in the form [[x1, y1], [x2, y2], ..., [xn, yn]]\n",
+    "iris_petal_paired = np.c_[iris.petal_length, iris.petal_width]\n",
+    "\n",
+    "# we'll give it 3 clusters to begin with\n",
+    "n_clusters = 3\n",
+    "kmeans = KMeans(n_clusters)\n",
+    "kmeans.fit(iris_petal_paired)\n",
+    "clusters = kmeans.predict(iris_petal_paired) # predicted cluster centers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x127bc6850>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting the data - we'll do clusters, and then shade by species out of curiosity\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n",
+    "ax1.scatter(iris_petal_paired[:, 0], iris_petal_paired[:, 1], c=clusters)\n",
+    "ax2.scatter(iris_petal_paired[:, 0], iris_petal_paired[:, 1], c=iris.species)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Further reading\n",
+    "\n",
+    "- N\n",
+    "- `sklearn` class and function reference page http://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model\n",
+    "- Machine Learning course from Computer Science Department, UofT http://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/CSC411_Fall16.html \n",
+    "- TPOT for optimized machine learning pipelines https://rhiever.github.io/tpot/\n",
+    "- KERAS package for Neural Networks https://keras.io/"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/python/scikit-learn/lesson.md b/lessons/python/scikit-learn/lesson.md
new file mode 100644
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--- /dev/null
+++ b/lessons/python/scikit-learn/lesson.md
@@ -0,0 +1,86 @@
+---
+layout: page
+title: Machine Learning with scikit-learn
+visible: true
+tags:
+ - python
+ - intermediate
+---
+<!-- change visible to true if you want it on the site -->
+
+
+
+# Machine learning in Python
+
+ - **Authors**: Nil Sahin
+ - **Research field**: Computational Biology and Molecular Genetics
+ - **Lesson topic**: Machine Learning using Python's Sklearn packages
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/scikit-learn>
+
+
+
+## Introduction to Machine Learning
+
+"Machine learning gives computers the ability to learn without explicitly programmed." - Arthur Samuel, 1959
+
+Machine learning originated from pattern recognition and computational learning theory in AI. It is the study and construction of algorithms to learn from and make predictions on data through building a model from sample input.
+
+
+
+### Some uses of learning algorithms
+
+* Classification: determine which discrete category the example is
+* Recognizing patterns: speech recognition, facial identity ...
+* Recommender Systems: noisy data, commercial pay-off (e.g., Amazon, Netflix)
+* Information retrieval: find documents or images with similar content
+* Computer vision: detection, segmentation, depth estimation, optical flow ...
+* Robotics: perception, planning ...
+* Learning to play games: AlphaGO
+* Recognizing anomalies: Unusual sequences of credit card transactions, panic situation at an airport
+* Spam filtering, fraud detection: The enemy adapts so we must adapt too
+
+
+
+### Types of learning tasks
+
+* **Supervised Learning**: correct output known for each training example for predicting output when given an input vector
+	* Classification: 1-of-N output, e.g. object recognition, medical diagnosis
+	* Regression: real-valued-output, e.g.predicting market prices, customer ratings
+
+* **Unsupervised Learning**: for learning an internal representation of the input to capture regularities and structure in the data without any labels
+	* Clustering: dividing input into groups that are unknown beforehand
+	* Dimensionality reduction: extract informative features, e.g. PCA, t-SNE
+
+* **Reinforcement Learning**: perform an action with the goal to maximize payoff by the feedback of reward and punishments, e.g. playing a game against an opponent
+
+
+
+### Packages to be installed
+
+* `numpy, pandas, matplotlib, sklearn, scipy, itertools`
+* `tpot`: For installation, please refer to [TPOT.](https://rhiever.github.io/tpot/installing/)
+
+
+
+### In this tutorial, we will cover:
+
+* Regression (e.g. Linear Regression)
+* Classification (e.g. SVM, Logistic regression)
+* Clustering (e.g. K-means)
+* Dimensionality Reduction (e.g. PCA)
+* Sklearn's TPOT package for optimized machine learning pipelines
+##### We won't cover:
+* Other classification and clustering algorithms (e.g. Neural Networks, Hierarchical Clustering)
+* Model selection (such as Bayesian Information Criteria - BIC)
+* Hyper-parameter selection
+
+
+
+### Useful websites
+
+* [Sklearn](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model) class and function reference page
+* [Machine Learning course](http://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/CSC411_Fall16.html) from Computer Science Department, UOFT
+* [TPOT](https://rhiever.github.io/tpot/) for optimized machine learning pipelines
+* [KERAS](https://keras.io/) package for Neural Networks
+* [Tensorflow Playground](http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.69754&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false) for Deep Neural Networks
+
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diff --git a/lessons/python/snakemake_elephant_demo/Readme.rst b/lessons/python/snakemake_elephant_demo/Readme.rst
new file mode 100644
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+++ b/lessons/python/snakemake_elephant_demo/Readme.rst
@@ -0,0 +1,9 @@
+The main part of the tutorial is present in form of a jupyter notebook.
+You can run it locally or online using binder_ by following (|notebook1|).
+
+.. _binder: https://mybinder.org/
+.. _jupyter notebooks: https://jupyter.org/
+
+.. |notebook1|  image:: https://mybinder.org/badge.svg
+  :target: https://mybinder.org/v2/gh/UofTCoders/snakemake_elephant_demo/master?filepath=Snakemake_Elephant_Tutorial.ipynb
+  :alt: Binder Link
diff --git a/lessons/python/snakemake_elephant_demo/Snakemake_Elephant_Tutorial.ipynb b/lessons/python/snakemake_elephant_demo/Snakemake_Elephant_Tutorial.ipynb
new file mode 100644
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--- /dev/null
+++ b/lessons/python/snakemake_elephant_demo/Snakemake_Elephant_Tutorial.ipynb
@@ -0,0 +1,9149 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Snakemake Introduction to Elephant\n",
+    "## Workflow management based on a electrophysiology example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table  bgcolor=\"#000000\"><tr>\n",
+    "<td><img src=logos/snakemake_logo.svg alt=\"Drawing\" style=\"width: 300px;\"/>\n",
+    "<td><img src=http://neo.readthedocs.org/en/latest/_images/neologo.png alt=\"Drawing\" style=\"width: 400px;\"/>\n",
+    "<td><img src=http://elephant.readthedocs.org/en/latest/_static/elephant_logo_sidebar.png alt=\"Drawing\" style=\"width: 400px;\"/>\n",
+    "</tr></table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial I will first introduce the workflow management system [snakemake](https://snakemake.readthedocs.io) and show how this can be used for analysis of electrophysiology data using the electrophysiology analysis toolkit [Elephant](http://neuralensemble.org/elephant). For snakemake as well as Elephant tutorials exist already as part of the documentation and were partially reused in the tutorial presented here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*NOTE: When running this tutorial locally, please first install the [requirements](environment.yml) to ensure the examples are working.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Workflow management - Do I need this?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are a number of reasons for managing your workflows besides the classical 'I always run first script a and then script b':\n",
+    "* **Growing Complexity** When starting a seemingly small and simple project everything is still pretty easy, but typically projects tend to grow beyond the initially expected size and complexity. This affects two different aspects 1) the dependency between different steps in workflow as well as 2) the dependency of your analysis results on versions of intermediate steps in the pipeline (data as well as code).\n",
+    "* **Collaboration & Sharing** Some day a colleague of yours wants to use your workflow or you are required to publish the analysis together with the manuscript presenting the results of your work to the scientific community. Instead of writing a book about which versions of what program you installed in which order, wouldn't it be nice to have a structured and self explanatory compilation of your analysis steps already at hand?\n",
+    "* **Software Evolution** Luckily, software develops and bugs are fixed from time to time. Unfortunately this also implies that your workflow might break unexpectedly upon updating your system. Having a well defined environment to run your analysis in is essential for reproducibility of your results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Snakemake  <img src=logos/snakemake_logo.svg alt=\"Drawing\" style=\"width: 100px;\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Snakemake helps you to structure your workflow by providing a framework to specify the dependencies between individual steps in your analysis workflow. By doing so it enforces a modular structure in the project and allows to specify the (Python) software versions used in each step of the process."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The workflow definition according to snakemake is file based, i.e. each step in the workflow is specified via the required input files and the generated output files, as you might know from common [`Makefiles`](https://en.wikipedia.org/wiki/Make_(software)). Each step is defined in a `rule`, specifying input and output files as well as instructions on how to get from the input to the output files. Here the instructions to convert `fileA.txt` into `fileB.txt` are a simple copy performed in a shell:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing Snakefile\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile Snakefile\n",
+    "rule:\n",
+    "    input: 'fileA.txt'\n",
+    "    output: 'fileB.txt'\n",
+    "    shell: 'cp fileA.txt fileB.txt'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For snakemake the workflow definition needs to be specified in a `Snakefile` and can be executed by calling `snakemake` in a terminal in the same location as the `Snakefile`. Here the example rule above has been exported into a [`Snakefile`](Snakefile) using the `%%writefile` jupyter magic command."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can ask snakemake to generate `fileB.txt` for us:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "MissingInputException in line 1 of snakemake_elephant_demo/Snakefile:\n",
+      "Missing input files for rule 1:\n",
+      "fileA.txt\n"
+     ]
+    },
+    {
+     "ename": "CalledProcessError",
+     "evalue": "Command 'b'snakemake fileB.txt\\n'' returned non-zero exit status 1.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-48e59f7e46c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sh'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'snakemake fileB.txt\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/var/container/conda/envs/snakemake/lib/python3.6/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2350\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2351\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2352\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2353\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2354\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/var/container/conda/envs/snakemake/lib/python3.6/site-packages/IPython/core/magics/script.py\u001b[0m in \u001b[0;36mnamed_script_magic\u001b[0;34m(line, cell)\u001b[0m\n\u001b[1;32m    140\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m                 \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscript\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshebang\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0;31m# write a basic docstring:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m</var/container/conda/envs/snakemake/lib/python3.6/site-packages/decorator.py:decorator-gen-110>\u001b[0m in \u001b[0;36mshebang\u001b[0;34m(self, line, cell)\u001b[0m\n",
+      "\u001b[0;32m/var/container/conda/envs/snakemake/lib/python3.6/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/var/container/conda/envs/snakemake/lib/python3.6/site-packages/IPython/core/magics/script.py\u001b[0m in \u001b[0;36mshebang\u001b[0;34m(self, line, cell)\u001b[0m\n\u001b[1;32m    243\u001b[0m             \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_error\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mCalledProcessError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturncode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_script\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_close\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mCalledProcessError\u001b[0m: Command 'b'snakemake fileB.txt\\n'' returned non-zero exit status 1."
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake fileB.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This fails with snakemake complaining about\n",
+    "```\n",
+    "Missing input files for rule 1:\n",
+    "fileA.txt\n",
+    "```\n",
+    "Which is correct, since there is no `fileA.txt` present to generate `fileB.txt` from. So let's add second rule which is capable of generating `fileA.txt` without required inputfiles.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Appending to Snakefile\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile -a Snakefile\n",
+    "rule:\n",
+    "    output: 'fileA.txt'\n",
+    "    shell: 'touch fileA.txt'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And ask snakemake again to generate the `fileB.txt` for us"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "Using shell: /usr/bin/bash\n",
+      "Provided cores: 1\n",
+      "Rules claiming more threads will be scaled down.\n",
+      "Job counts:\n",
+      "\tcount\tjobs\n",
+      "\t1\t1\n",
+      "\t1\t2\n",
+      "\t2\n",
+      "\n",
+      "[Thu Jun 20 16:19:48 2019]\n",
+      "rule 2:\n",
+      "    output: fileA.txt\n",
+      "    jobid: 1\n",
+      "\n",
+      "[Thu Jun 20 16:19:48 2019]\n",
+      "Finished job 1.\n",
+      "1 of 2 steps (50%) done\n",
+      "\n",
+      "[Thu Jun 20 16:19:48 2019]\n",
+      "rule 1:\n",
+      "    input: fileA.txt\n",
+      "    output: fileB.txt\n",
+      "    jobid: 0\n",
+      "\n",
+      "[Thu Jun 20 16:19:48 2019]\n",
+      "Finished job 0.\n",
+      "2 of 2 steps (100%) done\n",
+      "Complete log: snakemake_elephant_demo/.snakemake/log/2019-06-20T161948.544955.snakemake.log\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Internally snakemake is first resolving the set of rules into a directed acyclic graph (dag) to determine in which order the rules need to be executed. We can generate a visualization of the workflow using the `--dag` flag in combination with `dot` and `display` (for local notebook instances) or save the graph as svg (e.g. for remote instances)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "Building DAG of jobs...\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake --dag | dot | display\n",
+    "snakemake --dag | dot -Tsvg > dag0.svg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resulting graph shows the dependencies between the two rules, which were automatically enumerated. The line style (continuous/dashed) indicated whether the rules were already executed or not."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![DAG](dag0.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also provide explicit names for rules to make the graph more human readable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting Snakefile\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile Snakefile\n",
+    "rule copy_A_to_B:\n",
+    "    input: 'fileA.txt'\n",
+    "    output: 'fileB.txt'\n",
+    "    shell: 'cp {input} {output}'\n",
+    "rule create_A:\n",
+    "    output: 'fileA.txt'\n",
+    "    shell: 'touch fileA.txt'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "Building DAG of jobs...\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake --dag | dot | display\n",
+    "snakemake --dag | dot -Tsvg > dag1.svg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![DAG](dag1.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we already used a different notation to specify in the shell command `cp {input} {output}` instead of explicitly repeating the input and output filenames. These placeholders will be substituted by snakemake during execution by the filenames defined as `input` / `output`. We can use the same notation to generalize the required input of the rule depending on the output, e.g., we permit the copy rule to work for arbitrary files having a certain naming scheme. Here a new folder `new_folder` is automatically generated for the copied files."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting Snakefile\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile Snakefile\n",
+    "\n",
+    "rule copy_to_new_folder:\n",
+    "    input: 'original_data/file{id}.txt'\n",
+    "    output: 'new_data/file{id}.txt'\n",
+    "    shell: 'cp {input} {output}'\n",
+    "        \n",
+    "rule create_file:\n",
+    "    output: 'original_data/file{id}.txt'\n",
+    "    shell: 'touch {output}'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For running the workflow now, we need to specify which file we actually need as a final result and snakemake takes care of the individual steps to generate that file. We specify the desired output file as a snakemake argument:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "Building DAG of jobs...\n",
+      "Building DAG of jobs...\n",
+      "Using shell: /usr/bin/bash\n",
+      "Provided cores: 1\n",
+      "Rules claiming more threads will be scaled down.\n",
+      "Job counts:\n",
+      "\tcount\tjobs\n",
+      "\t1\tcopy_to_new_folder\n",
+      "\t1\tcreate_file\n",
+      "\t2\n",
+      "\n",
+      "[Thu Jun 20 16:22:24 2019]\n",
+      "rule create_file:\n",
+      "    output: original_data/fileZ.txt\n",
+      "    jobid: 1\n",
+      "    wildcards: id=Z\n",
+      "\n",
+      "[Thu Jun 20 16:22:24 2019]\n",
+      "Finished job 1.\n",
+      "1 of 2 steps (50%) done\n",
+      "\n",
+      "[Thu Jun 20 16:22:24 2019]\n",
+      "rule copy_to_new_folder:\n",
+      "    input: original_data/fileZ.txt\n",
+      "    output: new_data/fileZ.txt\n",
+      "    jobid: 0\n",
+      "    wildcards: id=Z\n",
+      "\n",
+      "[Thu Jun 20 16:22:24 2019]\n",
+      "Finished job 0.\n",
+      "2 of 2 steps (100%) done\n",
+      "Complete log: snakemake_elephant_demo/.snakemake/log/2019-06-20T162224.836068.snakemake.log\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake new_data/fileZ.txt --dag | dot | display\n",
+    "snakemake new_data/fileZ.txt --dag | dot -Tsvg > dag2.svg\n",
+    "snakemake new_data/fileZ.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To generate a set of output files, we can either request these individually when running snakemake, e.g. using `snakemake -np new_folder/file{0,1,2,3,4,5,6,7,8,9}.txt`. In case the workflow output is not being changed frequently, it is also possible to add a final rule (conventionally named 'all'), which requests all desired output files of the workflow:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting Snakefile\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile Snakefile\n",
+    "rule all:\n",
+    "    input: expand('new_data/file{id}.txt', id=range(10))\n",
+    "rule copy_to_new_folder:\n",
+    "    input: 'original_data/file{id}.txt'\n",
+    "    output: 'new_data/file{id}.txt'\n",
+    "    shell: 'cp {input} {output}'\n",
+    "rule create_file:\n",
+    "    output: 'original_data/file{id}.txt'\n",
+    "    shell: 'touch {output}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "Building DAG of jobs...\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake --dag | dot | display\n",
+    "snakemake --dag | dot -Tsvg > dag3.svg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here I used the snakemake function `expand`, which extends a given statement (here `new_folder_file{id}.txt`) for all combinations of parameters provided (here `id` values from 0 to 10). This permits to easily applied a set of rules to a number of different files."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![DAG](dag3.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Typically, analysis is a bit more complicated than creating empty files and copying them from A to B using shell commands. Snakemake also support a number of different execution methods\n",
+    "* in a shell (as used above)\n",
+    "* in Python (using run:)\n",
+    "* run Python/R/Markdown scripts directly (using script:)\n",
+    "As an example we can use a small Python script to generate our initial data files and store a (randomly generated) value. The Python script would look like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing generate_data.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile generate_data.py\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "\n",
+    "def generate_random_data(output_filename):\n",
+    "    # write a random number in an output file\n",
+    "    with open(output_filename, \"w\") as f:\n",
+    "        f.write(np.random.random())\n",
+    "\n",
+    "# extracting the output filename from the command line parameters provided\n",
+    "output_filename = sys.argv[1]\n",
+    "generate_random_data(output_filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The corresponding snakemake rule now needs to provide the argument to the `generate_data.py` script:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting Snakefile\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile Snakefile\n",
+    "\n",
+    "rule all:\n",
+    "    input: expand('new_data/file{id}.txt', id=range(10))\n",
+    "        \n",
+    "rule copy_to_new_folder:\n",
+    "    input: 'original_data/file{id}.txt'\n",
+    "    output: 'new_data/file{id}.txt'\n",
+    "    shell: 'cp {input} {output}'\n",
+    "        \n",
+    "rule generate_data:\n",
+    "    output: 'original_data/file{id}.txt'\n",
+    "    run: 'generate_data.py {output}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "Building DAG of jobs...\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake --dag | dot | display\n",
+    "snakemake --dag | dot -Tsvg > dag4.svg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![DAG](dag4.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Additional features worth having a look at\n",
+    "* conda integration (--use-conda flag)\n",
+    "* test runs (--dryrun)\n",
+    "* print shell commands (-p)\n",
+    "* includes of other Snakefiles\n",
+    "* configuration files\n",
+    "* rulegraph (--rulegraph)\n",
+    "* remote files (e.g. Amazon Simple Storage Service, Google Cloud Storage, SSH, HTTP, FTP, Dopbox, WebDAV, ...)\n",
+    "* [snakemake documentation](https://snakemake.readthedocs.io) and [FAQ](https://snakemake.readthedocs.io/en/stable/project_info/faq.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Utilizing Snakemake for Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The electrophysiology analysis toolkit\n",
+    "<img src=https://elephant.readthedocs.io/en/latest/_static/elephant_logo_sidebar.png alt=\"ElephantLogo\" style=\"width: 300px;\"/>  \n",
+    "We will present a simple data analysis workflow based on a published dataset of complex electrophysiology data using the electrophysiology analysis toolkit [Elephant](http://neuralensemble.org/elephant). Elephant provides a number of methods for spiketrain and LFP analysis. Elephant contains a number of methods focussing on analysis of higher order correlation in spiking activity and the statistical evaluation of these. Elephant is based on the [Neo](https://neo.readthedocs.io) data framework, which we will first introduce based on artificially generated spiking data with a known correlation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Neo - the data framework <img src=http://neo.readthedocs.org/en/latest/_images/neologo.png alt=\"NeoLogo\" style=\"width: 400px;\"/>    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "![NeoDataModel](https://neo.readthedocs.io/en/0.7.0/_images/base_schematic.png)\n",
+    "*Neo* provides a set of classes for representing time series data (`SpikeTrain`, `AnalogSignalArray`, etc.), for representing the hierarchical arrangement of data in an experiment (`Segment`, `Block`) and for representing the relationship between spike trains and recording channels (`ChannelIndex`, `Unit`). *Neo* is used by a number of data visualization tools ([OpenElectrophy](http://neuralensemble.org/OpenElectrophy), [SpykeViewer](https://spyke-viewer.readthedocs.org/)) and by the [PyNN](http://neuralensemble.org/PyNN) metasimulator. *Neo* offers reading capabilities for a large number of proprietary electrophysiology data formats and conversion capability to generic open source data formats/models.\n",
+    "<img src=images/IODiagram.svg alt=\"IODiagram\" style=\"width: 600px;\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The *Neo* data model consists of container (`Block`, `Segment`, `ChannelIndex`, `Unit`) and data objects (`AnalogSignals`, `SpikeTrain`, `Epoch`, `Events`). Container objects provide the relation between the data stored in the *Neo* structure. Here, we consider only `Block` and `Segment` objects as containers. `Block`s are designed to contain everything related to a whole recording session and link to `Segment`s, which hold data belonging to a common time frame. For data objects we will use `SpikeTrain`s, designed to capture the time series describing the occurrence of spikes together with the corresponding waveforms. All *Neo* data objects are derivatives of NumPy arrays enhanced with the handling of physical quantities, minimal metadata as well as a generic mechanism to add custom metadata.\n",
+    "<img src=https://neo.readthedocs.io/en/0.7.1/_downloads/simple_generated_diagram.svg alt=\"ClassDiagram\" style=\"width: 800px;\"/>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Performing the analysis requires minimum 3 steps:\n",
+    "* retrieving / generating the data\n",
+    "* running the main analysis\n",
+    "* generating result plots"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generating ground-truth data\n",
+    "Before running the analysis on experimental data, let's generate some data with known ground truth. Here Elephant provides a number of methods to generate spiketrain activity with defined statistical properties. The simplest would be to have independent spike time (Poisson process). Since we later want to detect higher order correlations, we will use a Poisson process as background activity and add correlated spikes to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports for data handling and visualization\n",
+    "from quantities import Hz, ms\n",
+    "from elephant.spike_train_generation import homogeneous_poisson_process, compound_poisson_process\n",
+    "import neo\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Neo and Elephant are handling physical units consistently during the analysis by using the Python module [quantities](https://python-quantities.readthedocs.io). This also requires parameters to be supplied in the correct dimension, such that the physical units can be matched during analysis. In general Neo objects capture all minimal information relevant for the interpretation of the data. In case of the spiketrain, this encompasses the start and stop times of recording/spike extraction as well the sampling rate and potential custom annotations in form of a dictionary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The spiketrain [ 78.66631048 128.95310733 232.22989392 279.17234478 282.41831547\n",
+      " 283.06639741 421.20410277 439.47305479 487.09765106 514.43881461\n",
+      " 541.28850204 652.37100276 695.09105462 726.38803919 842.85698826\n",
+      " 876.63682793 880.60917324 884.43428556 916.81849043 938.03769936] ms\n",
+      "Spiketrain attributes and physical units\n",
+      "['t_start: 0.0', 't_stop: 1000.0', 'sampling_rate: 1.0', 'annotations: {}']\n",
+      "['t_start: ms', 't_stop: ms', 'sampling_rate: Hz']\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/quantities/quantity.py:321: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return np.true_divide(other, self)\n"
+     ]
+    }
+   ],
+   "source": [
+    "spiketrain = homogeneous_poisson_process(20*Hz, 0*ms, 1000*ms)\n",
+    "print('The spiketrain', spiketrain)\n",
+    "print('Spiketrain attributes and physical units')\n",
+    "print(['{}: {}'.format(att, getattr(spiketrain, att))\n",
+    "       for att in ['t_start', 't_stop', 'sampling_rate', 'annotations']])\n",
+    "print(['{}: {}'.format(att, getattr(spiketrain, att).units.dimensionality)\n",
+    "       for att in ['t_start', 't_stop', 'sampling_rate']])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An a first rule in our new workflow, let's generate multiple datasets with spiking activity and save them for future analysis steps. From the variety of file formats supported by Neo NIX has an hdf5 backend. Let's implement a virtual expiment, generating 100 Poisson spiketrains stored in the NIX framework:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import neo\n",
+    "import quantities as pq\n",
+    "from elephant.spike_train_generation import homogeneous_poisson_process, compound_poisson_process\n",
+    "def generate_data(output_file, n=10):\n",
+    "    with neo.io.NixIO(output_file, 'ow') as io:\n",
+    "        # generate neo structure\n",
+    "        block = neo.Block(experiment='poisson')\n",
+    "        block.segments.append(neo.Segment(name='trial 1'))\n",
+    "        # generate correlated spike trains\n",
+    "        sts = compound_poisson_process(rate=5*pq.Hz, A=[0]+[0.90]+[0]*9+[0.1], t_stop=10*pq.s)\n",
+    "        # add background poisson spike trains\n",
+    "        for i in range(89):\n",
+    "            sts.append(homogeneous_poisson_process(rate=5*pq.Hz, t_stop=10*pq.s))\n",
+    "        block.segments[0].spiketrains.extend(sts)\n",
+    "        block.create_relationship()\n",
+    "        io.write_block(block)\n",
+    "    \n",
+    "generate_data('original_data/example_dataset.nix')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We export this piece of code into a standalone script so we can use it in the snakemake workflow:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing generate_poisson.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile generate_poisson.py\n",
+    "import sys\n",
+    "import neo\n",
+    "import quantities as pq\n",
+    "from quantities import Hz, ms\n",
+    "from elephant.spike_train_generation import homogeneous_poisson_process, compound_poisson_process\n",
+    "def generate_data(output_file, n=10):\n",
+    "    with neo.io.NixIO(output_file, 'ow') as io:\n",
+    "        # generate neo structure\n",
+    "        block = neo.Block(experiment='poisson')\n",
+    "        block.segments.append(neo.Segment(name='trial 1'))\n",
+    "        # generate correlated spike trains\n",
+    "        sts = compound_poisson_process(rate=5*pq.Hz, A=[0]+[0.90]+[0]*9+[0.1], t_stop=10*pq.s)\n",
+    "        # add background poisson spike trains\n",
+    "        for i in range(89):\n",
+    "            sts.append(homogeneous_poisson_process(rate=5*pq.Hz, t_stop=10*pq.s))\n",
+    "        block.segments[0].spiketrains.extend(sts)\n",
+    "        block.create_relationship()\n",
+    "        io.write_block(block)\n",
+    "    \n",
+    "if __name__=='__main__':\n",
+    "    generate_data(*sys.argv[1:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/quantities/quantity.py:321: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return np.true_divide(other, self)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "python generate_poisson.py original_data/example_dataset.nix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first rule in the workflow knows how to utilize the script to generate 10 new datasets. We limit the ids of the generated datasets from 0 to 9 to be able to use higher ids for different datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting Snakefile\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile Snakefile\n",
+    "rule all:\n",
+    "    input: expand('original_data/dataset{id}.nix', id=range(10))\n",
+    "        \n",
+    "rule generate_data:\n",
+    "    output: 'original_data/dataset{id,[0-9]}.nix'\n",
+    "    shell: 'python generate_poisson.py {output}'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And running the workflow. The output files should appear [here](original_data). Have a look at the shell output of snakemake. It should list the number and type of jobs which are going to be run and the wildcards and status of each job."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "Using shell: /usr/bin/bash\n",
+      "Provided cores: 1\n",
+      "Rules claiming more threads will be scaled down.\n",
+      "Job counts:\n",
+      "\tcount\tjobs\n",
+      "\t1\tall\n",
+      "\t10\tgenerate_data\n",
+      "\t11\n",
+      "\n",
+      "[Thu Jun 20 16:32:27 2019]\n",
+      "rule generate_data:\n",
+      "    output: original_data/dataset6.nix\n",
+      "    jobid: 7\n",
+      "    reason: Missing output files: original_data/dataset6.nix\n",
+      "    wildcards: id=6\n",
+      "\n",
+      "python generate_poisson.py original_data/dataset6.nix\n",
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/quantities/quantity.py:321: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return np.true_divide(other, self)\n",
+      "[Thu Jun 20 16:32:29 2019]\n",
+      "Finished job 7.\n",
+      "1 of 11 steps (9%) done\n",
+      "\n",
+      "[Thu Jun 20 16:32:29 2019]\n",
+      "rule generate_data:\n",
+      "    output: original_data/dataset5.nix\n",
+      "    jobid: 6\n",
+      "    reason: Missing output files: original_data/dataset5.nix\n",
+      "    wildcards: id=5\n",
+      "\n",
+      "python generate_poisson.py original_data/dataset5.nix\n",
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/quantities/quantity.py:321: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return np.true_divide(other, self)\n",
+      "[Thu Jun 20 16:32:31 2019]\n",
+      "Finished job 6.\n",
+      "2 of 11 steps (18%) done\n",
+      "\n",
+      "[Thu Jun 20 16:32:31 2019]\n",
+      "rule generate_data:\n",
+      "    output: original_data/dataset4.nix\n",
+      "    jobid: 5\n",
+      "    reason: Missing output files: original_data/dataset4.nix\n",
+      "    wildcards: id=4\n",
+      "\n",
+      "python generate_poisson.py original_data/dataset4.nix\n",
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/quantities/quantity.py:321: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return np.true_divide(other, self)\n",
+      "[Thu Jun 20 16:32:33 2019]\n",
+      "Finished job 5.\n",
+      "3 of 11 steps (27%) done\n",
+      "\n",
+      "[Thu Jun 20 16:32:33 2019]\n",
+      "rule generate_data:\n",
+      "    output: original_data/dataset3.nix\n",
+      "    jobid: 4\n",
+      "    reason: Missing output files: original_data/dataset3.nix\n",
+      "    wildcards: id=3\n",
+      "\n",
+      "python generate_poisson.py original_data/dataset3.nix\n",
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/quantities/quantity.py:321: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return np.true_divide(other, self)\n",
+      "[Thu Jun 20 16:32:36 2019]\n",
+      "Finished job 4.\n",
+      "4 of 11 steps (36%) done\n",
+      "\n",
+      "[Thu Jun 20 16:32:36 2019]\n",
+      "rule generate_data:\n",
+      "    output: original_data/dataset7.nix\n",
+      "    jobid: 8\n",
+      "    reason: Missing output files: original_data/dataset7.nix\n",
+      "    wildcards: id=7\n",
+      "\n",
+      "python generate_poisson.py original_data/dataset7.nix\n",
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/quantities/quantity.py:321: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return np.true_divide(other, self)\n",
+      "[Thu Jun 20 16:32:38 2019]\n",
+      "Finished job 8.\n",
+      "5 of 11 steps (45%) done\n",
+      "\n",
+      "[Thu Jun 20 16:32:38 2019]\n",
+      "rule generate_data:\n",
+      "    output: original_data/dataset8.nix\n",
+      "    jobid: 9\n",
+      "    reason: Missing output files: original_data/dataset8.nix\n",
+      "    wildcards: id=8\n",
+      "\n",
+      "python generate_poisson.py original_data/dataset8.nix\n",
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/quantities/quantity.py:321: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return np.true_divide(other, self)\n",
+      "[Thu Jun 20 16:32:40 2019]\n",
+      "Finished job 9.\n",
+      "6 of 11 steps (55%) done\n",
+      "\n",
+      "[Thu Jun 20 16:32:40 2019]\n",
+      "rule generate_data:\n",
+      "    output: original_data/dataset0.nix\n",
+      "    jobid: 1\n",
+      "    reason: Missing output files: original_data/dataset0.nix\n",
+      "    wildcards: id=0\n",
+      "\n",
+      "python generate_poisson.py original_data/dataset0.nix\n",
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/quantities/quantity.py:321: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return np.true_divide(other, self)\n",
+      "[Thu Jun 20 16:32:42 2019]\n",
+      "Finished job 1.\n",
+      "7 of 11 steps (64%) done\n",
+      "\n",
+      "[Thu Jun 20 16:32:42 2019]\n",
+      "rule generate_data:\n",
+      "    output: original_data/dataset1.nix\n",
+      "    jobid: 2\n",
+      "    reason: Missing output files: original_data/dataset1.nix\n",
+      "    wildcards: id=1\n",
+      "\n",
+      "python generate_poisson.py original_data/dataset1.nix\n",
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/quantities/quantity.py:321: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return np.true_divide(other, self)\n",
+      "[Thu Jun 20 16:32:44 2019]\n",
+      "Finished job 2.\n",
+      "8 of 11 steps (73%) done\n",
+      "\n",
+      "[Thu Jun 20 16:32:44 2019]\n",
+      "rule generate_data:\n",
+      "    output: original_data/dataset9.nix\n",
+      "    jobid: 10\n",
+      "    reason: Missing output files: original_data/dataset9.nix\n",
+      "    wildcards: id=9\n",
+      "\n",
+      "python generate_poisson.py original_data/dataset9.nix\n",
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/quantities/quantity.py:321: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return np.true_divide(other, self)\n",
+      "[Thu Jun 20 16:32:46 2019]\n",
+      "Finished job 10.\n",
+      "9 of 11 steps (82%) done\n",
+      "\n",
+      "[Thu Jun 20 16:32:46 2019]\n",
+      "rule generate_data:\n",
+      "    output: original_data/dataset2.nix\n",
+      "    jobid: 3\n",
+      "    reason: Missing output files: original_data/dataset2.nix\n",
+      "    wildcards: id=2\n",
+      "\n",
+      "python generate_poisson.py original_data/dataset2.nix\n",
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/quantities/quantity.py:321: RuntimeWarning: divide by zero encountered in true_divide\n",
+      "  return np.true_divide(other, self)\n",
+      "[Thu Jun 20 16:32:48 2019]\n",
+      "Finished job 3.\n",
+      "10 of 11 steps (91%) done\n",
+      "\n",
+      "[Thu Jun 20 16:32:48 2019]\n",
+      "localrule all:\n",
+      "    input: original_data/dataset0.nix, original_data/dataset1.nix, original_data/dataset2.nix, original_data/dataset3.nix, original_data/dataset4.nix, original_data/dataset5.nix, original_data/dataset6.nix, original_data/dataset7.nix, original_data/dataset8.nix, original_data/dataset9.nix\n",
+      "    jobid: 0\n",
+      "    reason: Input files updated by another job: original_data/dataset5.nix, original_data/dataset1.nix, original_data/dataset0.nix, original_data/dataset8.nix, original_data/dataset2.nix, original_data/dataset9.nix, original_data/dataset7.nix, original_data/dataset6.nix, original_data/dataset4.nix, original_data/dataset3.nix\n",
+      "\n",
+      "[Thu Jun 20 16:32:48 2019]\n",
+      "Finished job 0.\n",
+      "11 of 11 steps (100%) done\n",
+      "Complete log: snakemake_elephant_demo/.snakemake/log/2019-06-20T163227.522982.snakemake.log\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake -pr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "Building DAG of jobs...\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake --dag | dot | display\n",
+    "snakemake --dag | dot -Tsvg > dag5.svg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![DAG](dag5.svg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To visualize the generated data, we implement a utility script to load the data and a second script plot the data using Matplotlib."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting plot_data.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile plot_data.py\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import neo\n",
+    "import matplotlib.pyplot as plt\n",
+    "def plot_data(data_filename, plot_filename):\n",
+    "    # opening the data file\n",
+    "    with neo.io.NixIO(data_filename, 'ro') as io:\n",
+    "        block = io.read_block()\n",
+    "    # plot spiketrains\n",
+    "    for i, spiketrain in enumerate(block.segments[0].spiketrains):\n",
+    "        plt.plot(spiketrain, [i]*len(spiketrain), 'C0.')\n",
+    "        # this is for later steps in the analysis: plot patterns if already detected\n",
+    "        if 'pattern' in spiketrain.annotations:\n",
+    "            pattern_spikes = spiketrain[np.where(spiketrain.annotations['pattern'])]\n",
+    "            plt.plot(pattern_spikes, [i]*len(pattern_spikes), 'r.')\n",
+    "    plt.xlabel('Time [{}]'.format(spiketrain[0].units.dimensionality.latex))\n",
+    "    plt.ylabel('Spiketrains')\n",
+    "    plt.savefig(plot_filename)\n",
+    "    \n",
+    "if __name__=='__main__':\n",
+    "    plot_data(*sys.argv[1:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can visualize the previously generated data. The introduced correlation should be visible already by eye in this arrangement of the spiketrains. In case of random reordering however this looks different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from plot_data import plot_data\n",
+    "plot_data('original_data/dataset2.nix', 'original_data/dataset2.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the context of this analysis we teach snakemake how to go from arbitraty datasets in the nix format to a plot of the data. Now the exact data location of the nix file is not relevant any more since we introduced an additional `folder` wildcard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Appending to Snakefile\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile -a Snakefile\n",
+    "\n",
+    "rule plot_data:\n",
+    "    input: '{folder}/dataset{id}.nix'\n",
+    "    output: '{folder}/dataset{id}.png'\n",
+    "    shell: 'python plot_data.py {input} {output}'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "Using shell: /usr/bin/bash\n",
+      "Provided cores: 1\n",
+      "Rules claiming more threads will be scaled down.\n",
+      "Job counts:\n",
+      "\tcount\tjobs\n",
+      "\t1\tplot_data\n",
+      "\t1\n",
+      "\n",
+      "[Thu Jun 20 16:33:55 2019]\n",
+      "rule plot_data:\n",
+      "    input: original_data/dataset0.nix\n",
+      "    output: original_data/dataset0.png\n",
+      "    jobid: 0\n",
+      "    wildcards: folder=original_data, id=0\n",
+      "\n",
+      "[Thu Jun 20 16:33:58 2019]\n",
+      "Finished job 0.\n",
+      "1 of 1 steps (100%) done\n",
+      "Complete log: snakemake_elephant_demo/.snakemake/log/2019-06-20T163355.928946.snakemake.log\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake original_data/dataset0.png "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Running SPADE\n",
+    "In the next step we want to run an analysis on the data, which is detecting the injected higher order correlation in the data. One of the methods suited for the detection of spatio-temporal patterns is `SPADE` [(Quaglio et al, 2017)](https://doi.org/10.3389/fncom.2017.00041)\n",
+    "."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing run_spade.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile run_spade.py\n",
+    "import sys\n",
+    "import numpy as np\n",
+    "import neo\n",
+    "import quantities as pq\n",
+    "import elephant.spade\n",
+    "\n",
+    "\n",
+    "def clean_nix_annotations(block):\n",
+    "    '''helper function needed for neo 0.7.1'''\n",
+    "    # removing unnessesary annotations to permit block to be saved again via nix\n",
+    "    objs = [block] + block.filter(data=True, container=True)\n",
+    "    for obj in objs:\n",
+    "        if 'nix_name' in obj.annotations:\n",
+    "            obj.annotations.pop('nix_name')\n",
+    "        if 'neo_name' in obj.annotations:\n",
+    "            obj.annotations.pop('neo_name')\n",
+    "    \n",
+    "\n",
+    "def run_spade(data_filename, output_filename):\n",
+    "    # loading dataset\n",
+    "    with neo.io.get_io(data_filename, 'rw') as io:\n",
+    "        block = io.read_block()\n",
+    "    spiketrains = block.segments[0].spiketrains\n",
+    "    \n",
+    "    # run spade analysis\n",
+    "    patterns = elephant.spade.spade(\n",
+    "        data=spiketrains, binsize=1*pq.ms, winlen=1, dither=5*pq.ms,\n",
+    "        min_spikes=3, n_surr=10, psr_param=[0,0,3],\n",
+    "        output_format='patterns')['patterns']\n",
+    "    \n",
+    "    # convert first pattern into annotations of spiketrains\n",
+    "    # default: no spike belongs to any pattern\n",
+    "    for spiketrain in spiketrains:\n",
+    "        spiketrain.annotate(pattern=np.array([False]*len(spiketrain)))\n",
+    "        \n",
+    "    # convert first detected pattern to annotations of spiketrains\n",
+    "    for pattern_id, pattern in enumerate(patterns):\n",
+    "        neurons = pattern['neurons']\n",
+    "        times = pattern['times']\n",
+    "        lags = pattern['lags']\n",
+    "        for n, neuron in enumerate(neurons):\n",
+    "            if n == 0:\n",
+    "                pattern_times = times\n",
+    "            else:\n",
+    "                pattern_times = times + lags[n-1]\n",
+    "            for pattern_time in pattern_times:\n",
+    "                spike_idx = np.abs(spiketrains[neuron]-pattern_time).argmin()\n",
+    "                spiketrains[neuron].annotations['pattern'][spike_idx] = True\n",
+    "\n",
+    "    # overwrite original neo file\n",
+    "    clean_nix_annotations(block)\n",
+    "    with neo.io.NixIO(output_filename, 'ow') as io2: \n",
+    "        io2.write_block(block)\n",
+    "        \n",
+    "if __name__=='__main__':\n",
+    "    run_spade(*sys.argv[1:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We add a rule to the snakemake workflow for runnig spade:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Appending to Snakefile\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile -a Snakefile\n",
+    "\n",
+    "rule run_spade:\n",
+    "    input: 'original_data/dataset{id}.nix'\n",
+    "    output: 'results/dataset{id}.nix'\n",
+    "    shell: 'python run_spade.py {input} {output}'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can run spade by simply requesting the analysis result from snakemake"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time for pvalue spectrum computation: 3.103538751602173\n",
+      "Time for data mining: 0.19341516494750977\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "Using shell: /usr/bin/bash\n",
+      "Provided cores: 1\n",
+      "Rules claiming more threads will be scaled down.\n",
+      "Job counts:\n",
+      "\tcount\tjobs\n",
+      "\t1\trun_spade\n",
+      "\t1\n",
+      "\n",
+      "[Thu Jun 20 16:35:23 2019]\n",
+      "rule run_spade:\n",
+      "    input: original_data/dataset0.nix\n",
+      "    output: results/dataset0.nix\n",
+      "    jobid: 0\n",
+      "    wildcards: id=0\n",
+      "\n",
+      "[Thu Jun 20 16:35:29 2019]\n",
+      "Finished job 0.\n",
+      "1 of 1 steps (100%) done\n",
+      "Complete log: snakemake_elephant_demo/.snakemake/log/2019-06-20T163523.313306.snakemake.log\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake results/dataset0.nix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Apply analysis to experimental data <img src=https://web.gin.g-node.org/img/favicon.png alt=\"GIN\" style=\"width: 100px;\"/>\n",
+    "In the next step we want to apply the same workflow to a set of experimental data. The data are publicly available at the data hosting platform [GIN](https://web.gin.g-node.org/) and are described in detail in [Brochier (2018)](https://doi.org/10.1038/sdata.2018.55). We are going to download two specific files of the dataset, which are saved in BlackRock nev/ns2 format. Since Neo also supports BlackRock formats, we use it to extract the relevant data and save them in the nix format to be consistent with the simulated data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing convert_to_nix.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile convert_to_nix.py\n",
+    "import sys\n",
+    "import neo\n",
+    "import quantities as pq\n",
+    "\n",
+    "def convert_data_to_nix(input_filename, nix_filename):\n",
+    "    print(input_filename)\n",
+    "    '''extracting first seconds of first spiketrains into separate nix file''' \n",
+    "    io = neo.io.BlackrockIO(input_filename)\n",
+    "    original_block = io.read_block()\n",
+    "    \n",
+    "    # Extracting spiketrain and append to new block\n",
+    "    block = neo.Block(experiment='monkey')\n",
+    "    block.segments.append(neo.Segment(name='first seconds of recording'))\n",
+    "    new_spiketrains = [st.time_slice(0*pq.s,10*pq.s) for st in original_block.segments[-1].spiketrains[:100]]\n",
+    "    block.segments[0].spiketrains = new_spiketrains\n",
+    "    \n",
+    "    # save new block\n",
+    "    with neo.io.NixIO(nix_filename, 'ow') as io2:\n",
+    "        io2.write_block(block)\n",
+    "        \n",
+    "if __name__=='__main__':\n",
+    "    convert_data_to_nix(*sys.argv[1:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For fetching the data we directly download the data using `wget`. A more elegant version would involve installation of `git annex` or the `gin` client to be consistent with the version management handled by gin. We also add a generic rule which permits the conversion of BlackRock files to nix files while cropping only a selection of the data. The final rule `run_complete` requests visualizations of all analysis runs and therefore triggers the whole workflow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Appending to Snakefile\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile -a Snakefile\n",
+    "\n",
+    "rule download_data:\n",
+    "    output:\n",
+    "        nev = 'original_data/dataset10.nev',\n",
+    "        ns2 = 'original_data/dataset10.ns2'\n",
+    "    shell: '''\n",
+    "    wget -O {output.nev} https://web.gin.g-node.org/INT/multielectrode_grasp/raw/24cd5caee3ae79066ca37844cab931d04dcad977/datasets/i140703-001-03.nev\n",
+    "    wget -O {output.ns2} https://web.gin.g-node.org/INT/multielectrode_grasp/raw/24cd5caee3ae79066ca37844cab931d04dcad977/datasets/i140703-001.ns2\n",
+    "    '''\n",
+    "        \n",
+    "rule convert_to_nix:\n",
+    "    input: '{filename}.nev',\n",
+    "           '{filename}.ns2'\n",
+    "    output: '{filename}.nix'\n",
+    "    shell: 'python convert_to_nix.py {wildcards.filename} {output}'\n",
+    "        \n",
+    "rule run_complete:\n",
+    "    input: expand('results/dataset{id}.png', id=range(11))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time for pvalue spectrum computation: 3.1645641326904297\n",
+      "Time for data mining: 0.1927504539489746\n",
+      "Time for pvalue spectrum computation: 3.0785861015319824\n",
+      "Time for data mining: 0.19078874588012695\n",
+      "Time for pvalue spectrum computation: 3.1839020252227783\n",
+      "Time for data mining: 0.1887214183807373\n",
+      "Time for pvalue spectrum computation: 3.316995143890381\n",
+      "Time for data mining: 0.20294404029846191\n",
+      "Time for pvalue spectrum computation: 3.085864305496216\n",
+      "Time for data mining: 0.18788671493530273\n",
+      "Time for pvalue spectrum computation: 3.155500650405884\n",
+      "Time for data mining: 0.1973717212677002\n",
+      "Time for pvalue spectrum computation: 3.171705961227417\n",
+      "Time for data mining: 0.19454050064086914\n",
+      "Time for pvalue spectrum computation: 3.122002124786377\n",
+      "Time for data mining: 0.19073867797851562\n",
+      "Time for pvalue spectrum computation: 3.098390579223633\n",
+      "Time for data mining: 0.1885700225830078\n",
+      "original_data/dataset10\n",
+      "Time for pvalue spectrum computation: 3.2113535404205322\n",
+      "Time for data mining: 0.20441222190856934\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "Using shell: /usr/bin/bash\n",
+      "Provided cores: 1\n",
+      "Rules claiming more threads will be scaled down.\n",
+      "Job counts:\n",
+      "\tcount\tjobs\n",
+      "\t1\tconvert_to_nix\n",
+      "\t1\tdownload_data\n",
+      "\t11\tplot_data\n",
+      "\t1\trun_complete\n",
+      "\t10\trun_spade\n",
+      "\t24\n",
+      "\n",
+      "[Thu Jun 20 16:35:50 2019]\n",
+      "rule plot_data:\n",
+      "    input: results/dataset0.nix\n",
+      "    output: results/dataset0.png\n",
+      "    jobid: 1\n",
+      "    wildcards: folder=results, id=0\n",
+      "\n",
+      "[Thu Jun 20 16:35:53 2019]\n",
+      "Finished job 1.\n",
+      "1 of 24 steps (4%) done\n",
+      "\n",
+      "[Thu Jun 20 16:35:53 2019]\n",
+      "rule run_spade:\n",
+      "    input: original_data/dataset6.nix\n",
+      "    output: results/dataset6.nix\n",
+      "    jobid: 18\n",
+      "    wildcards: id=6\n",
+      "\n",
+      "[Thu Jun 20 16:35:59 2019]\n",
+      "Finished job 18.\n",
+      "2 of 24 steps (8%) done\n",
+      "\n",
+      "[Thu Jun 20 16:35:59 2019]\n",
+      "rule plot_data:\n",
+      "    input: results/dataset6.nix\n",
+      "    output: results/dataset6.png\n",
+      "    jobid: 7\n",
+      "    wildcards: folder=results, id=6\n",
+      "\n",
+      "[Thu Jun 20 16:36:02 2019]\n",
+      "Finished job 7.\n",
+      "3 of 24 steps (12%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:02 2019]\n",
+      "rule run_spade:\n",
+      "    input: original_data/dataset8.nix\n",
+      "    output: results/dataset8.nix\n",
+      "    jobid: 20\n",
+      "    wildcards: id=8\n",
+      "\n",
+      "[Thu Jun 20 16:36:08 2019]\n",
+      "Finished job 20.\n",
+      "4 of 24 steps (17%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:08 2019]\n",
+      "rule plot_data:\n",
+      "    input: results/dataset8.nix\n",
+      "    output: results/dataset8.png\n",
+      "    jobid: 9\n",
+      "    wildcards: folder=results, id=8\n",
+      "\n",
+      "[Thu Jun 20 16:36:11 2019]\n",
+      "Finished job 9.\n",
+      "5 of 24 steps (21%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:11 2019]\n",
+      "rule run_spade:\n",
+      "    input: original_data/dataset5.nix\n",
+      "    output: results/dataset5.nix\n",
+      "    jobid: 17\n",
+      "    wildcards: id=5\n",
+      "\n",
+      "[Thu Jun 20 16:36:17 2019]\n",
+      "Finished job 17.\n",
+      "6 of 24 steps (25%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:17 2019]\n",
+      "rule plot_data:\n",
+      "    input: results/dataset5.nix\n",
+      "    output: results/dataset5.png\n",
+      "    jobid: 6\n",
+      "    wildcards: folder=results, id=5\n",
+      "\n",
+      "[Thu Jun 20 16:36:20 2019]\n",
+      "Finished job 6.\n",
+      "7 of 24 steps (29%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:20 2019]\n",
+      "rule run_spade:\n",
+      "    input: original_data/dataset4.nix\n",
+      "    output: results/dataset4.nix\n",
+      "    jobid: 16\n",
+      "    wildcards: id=4\n",
+      "\n",
+      "[Thu Jun 20 16:36:27 2019]\n",
+      "Finished job 16.\n",
+      "8 of 24 steps (33%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:27 2019]\n",
+      "rule plot_data:\n",
+      "    input: results/dataset4.nix\n",
+      "    output: results/dataset4.png\n",
+      "    jobid: 5\n",
+      "    wildcards: folder=results, id=4\n",
+      "\n",
+      "[Thu Jun 20 16:36:29 2019]\n",
+      "Finished job 5.\n",
+      "9 of 24 steps (38%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:29 2019]\n",
+      "rule run_spade:\n",
+      "    input: original_data/dataset1.nix\n",
+      "    output: results/dataset1.nix\n",
+      "    jobid: 13\n",
+      "    wildcards: id=1\n",
+      "\n",
+      "[Thu Jun 20 16:36:36 2019]\n",
+      "Finished job 13.\n",
+      "10 of 24 steps (42%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:36 2019]\n",
+      "rule plot_data:\n",
+      "    input: results/dataset1.nix\n",
+      "    output: results/dataset1.png\n",
+      "    jobid: 2\n",
+      "    wildcards: folder=results, id=1\n",
+      "\n",
+      "[Thu Jun 20 16:36:38 2019]\n",
+      "Finished job 2.\n",
+      "11 of 24 steps (46%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:38 2019]\n",
+      "rule run_spade:\n",
+      "    input: original_data/dataset3.nix\n",
+      "    output: results/dataset3.nix\n",
+      "    jobid: 15\n",
+      "    wildcards: id=3\n",
+      "\n",
+      "[Thu Jun 20 16:36:45 2019]\n",
+      "Finished job 15.\n",
+      "12 of 24 steps (50%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:45 2019]\n",
+      "rule plot_data:\n",
+      "    input: results/dataset3.nix\n",
+      "    output: results/dataset3.png\n",
+      "    jobid: 4\n",
+      "    wildcards: folder=results, id=3\n",
+      "\n",
+      "[Thu Jun 20 16:36:47 2019]\n",
+      "Finished job 4.\n",
+      "13 of 24 steps (54%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:47 2019]\n",
+      "rule run_spade:\n",
+      "    input: original_data/dataset9.nix\n",
+      "    output: results/dataset9.nix\n",
+      "    jobid: 21\n",
+      "    wildcards: id=9\n",
+      "\n",
+      "[Thu Jun 20 16:36:54 2019]\n",
+      "Finished job 21.\n",
+      "14 of 24 steps (58%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:54 2019]\n",
+      "rule plot_data:\n",
+      "    input: results/dataset9.nix\n",
+      "    output: results/dataset9.png\n",
+      "    jobid: 10\n",
+      "    wildcards: folder=results, id=9\n",
+      "\n",
+      "[Thu Jun 20 16:36:56 2019]\n",
+      "Finished job 10.\n",
+      "15 of 24 steps (62%) done\n",
+      "\n",
+      "[Thu Jun 20 16:36:56 2019]\n",
+      "rule run_spade:\n",
+      "    input: original_data/dataset7.nix\n",
+      "    output: results/dataset7.nix\n",
+      "    jobid: 19\n",
+      "    wildcards: id=7\n",
+      "\n",
+      "[Thu Jun 20 16:37:03 2019]\n",
+      "Finished job 19.\n",
+      "16 of 24 steps (67%) done\n",
+      "\n",
+      "[Thu Jun 20 16:37:03 2019]\n",
+      "rule plot_data:\n",
+      "    input: results/dataset7.nix\n",
+      "    output: results/dataset7.png\n",
+      "    jobid: 8\n",
+      "    wildcards: folder=results, id=7\n",
+      "\n",
+      "[Thu Jun 20 16:37:05 2019]\n",
+      "Finished job 8.\n",
+      "17 of 24 steps (71%) done\n",
+      "\n",
+      "[Thu Jun 20 16:37:05 2019]\n",
+      "rule run_spade:\n",
+      "    input: original_data/dataset2.nix\n",
+      "    output: results/dataset2.nix\n",
+      "    jobid: 14\n",
+      "    wildcards: id=2\n",
+      "\n",
+      "[Thu Jun 20 16:37:12 2019]\n",
+      "Finished job 14.\n",
+      "18 of 24 steps (75%) done\n",
+      "\n",
+      "[Thu Jun 20 16:37:12 2019]\n",
+      "rule plot_data:\n",
+      "    input: results/dataset2.nix\n",
+      "    output: results/dataset2.png\n",
+      "    jobid: 3\n",
+      "    wildcards: folder=results, id=2\n",
+      "\n",
+      "[Thu Jun 20 16:37:14 2019]\n",
+      "Finished job 3.\n",
+      "19 of 24 steps (79%) done\n",
+      "\n",
+      "[Thu Jun 20 16:37:14 2019]\n",
+      "rule download_data:\n",
+      "    output: original_data/dataset10.nev, original_data/dataset10.ns2\n",
+      "    jobid: 34\n",
+      "\n",
+      "--2019-06-20 16:37:14--  https://web.gin.g-node.org/INT/multielectrode_grasp/raw/24cd5caee3ae79066ca37844cab931d04dcad977/datasets/i140703-001-03.nev\n",
+      "Resolving web.gin.g-node.org (web.gin.g-node.org)... 141.84.41.216\n",
+      "Connecting to web.gin.g-node.org (web.gin.g-node.org)|141.84.41.216|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: unspecified [application/octet-stream]\n",
+      "Saving to: ‘original_data/dataset10.nev’\n",
+      "\n",
+      "     0K .......... .......... .......... .......... ..........  198K\n",
+      "    50K .......... .......... .......... .......... ..........  412K\n",
+      "   100K .......... .......... .......... .......... .......... 2.68M\n",
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+      "164350K .......... .......... ........                         2.54M=1m46s\n",
+      "\n",
+      "2019-06-20 16:39:01 (1.52 MB/s) - ‘original_data/dataset10.nev’ saved [168323228]\n",
+      "\n",
+      "--2019-06-20 16:39:01--  https://web.gin.g-node.org/INT/multielectrode_grasp/raw/24cd5caee3ae79066ca37844cab931d04dcad977/datasets/i140703-001.ns2\n",
+      "Resolving web.gin.g-node.org (web.gin.g-node.org)... 141.84.41.216\n",
+      "Connecting to web.gin.g-node.org (web.gin.g-node.org)|141.84.41.216|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
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+      "Saving to: ‘original_data/dataset10.ns2’\n",
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+      "199800K .......... .......... .......... .......... .......... 2.54M\n",
+      "199850K .......... .....                                       3.18M=1m44s\n",
+      "\n",
+      "2019-06-20 16:40:46 (1.88 MB/s) - ‘original_data/dataset10.ns2’ saved [204661895]\n",
+      "\n",
+      "[Thu Jun 20 16:40:46 2019]\n",
+      "Finished job 34.\n",
+      "20 of 24 steps (83%) done\n",
+      "\n",
+      "[Thu Jun 20 16:40:46 2019]\n",
+      "rule convert_to_nix:\n",
+      "    input: original_data/dataset10.nev, original_data/dataset10.ns2\n",
+      "    output: original_data/dataset10.nix\n",
+      "    jobid: 33\n",
+      "    wildcards: filename=original_data/dataset10\n",
+      "\n",
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/neo/rawio/blackrockrawio.py:1211: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  if len(data[mask_outside]) > 0:\n",
+      "/var/container/conda/envs/snakemake/lib/python3.6/site-packages/neo/rawio/blackrockrawio.py:1222: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  if len(data[mask_after_seg]) > 0:\n",
+      "[Thu Jun 20 16:41:01 2019]\n",
+      "Finished job 33.\n",
+      "21 of 24 steps (88%) done\n",
+      "\n",
+      "[Thu Jun 20 16:41:01 2019]\n",
+      "rule run_spade:\n",
+      "    input: original_data/dataset10.nix\n",
+      "    output: results/dataset10.nix\n",
+      "    jobid: 22\n",
+      "    wildcards: id=10\n",
+      "\n",
+      "[Thu Jun 20 16:41:08 2019]\n",
+      "Finished job 22.\n",
+      "22 of 24 steps (92%) done\n",
+      "\n",
+      "[Thu Jun 20 16:41:08 2019]\n",
+      "rule plot_data:\n",
+      "    input: results/dataset10.nix\n",
+      "    output: results/dataset10.png\n",
+      "    jobid: 11\n",
+      "    wildcards: folder=results, id=10\n",
+      "\n",
+      "[Thu Jun 20 16:41:11 2019]\n",
+      "Finished job 11.\n",
+      "23 of 24 steps (96%) done\n",
+      "\n",
+      "[Thu Jun 20 16:41:11 2019]\n",
+      "localrule run_complete:\n",
+      "    input: results/dataset0.png, results/dataset1.png, results/dataset2.png, results/dataset3.png, results/dataset4.png, results/dataset5.png, results/dataset6.png, results/dataset7.png, results/dataset8.png, results/dataset9.png, results/dataset10.png\n",
+      "    jobid: 0\n",
+      "\n",
+      "[Thu Jun 20 16:41:11 2019]\n",
+      "Finished job 0.\n",
+      "24 of 24 steps (100%) done\n",
+      "Complete log: snakemake_elephant_demo/.snakemake/log/2019-06-20T163550.395765.snakemake.log\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake run_complete"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The whole workflow encompasses now artificial data generation as well as retrieval of experimental data as well as a common analysis and visualization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Building DAG of jobs...\n",
+      "Building DAG of jobs...\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%sh\n",
+    "snakemake run_complete --dag | dot | display\n",
+    "snakemake run_complete --dag | dot -Tsvg > run_complete.svg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![DAG](run_complete.svg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lessons/python/snakemake_elephant_demo/environment.yml b/lessons/python/snakemake_elephant_demo/environment.yml
new file mode 100644
index 000000000..924ed8d5d
--- /dev/null
+++ b/lessons/python/snakemake_elephant_demo/environment.yml
@@ -0,0 +1,14 @@
+name: demo
+
+channels:
+  - conda-forge
+
+dependencies:
+  - python>=3
+  - jupyter
+  - bioconda::snakemake
+  - matplotlib
+  - pip
+  - pip:
+    - nixio==1.5.0b3
+    - elephant
diff --git a/lessons/python/snakemake_elephant_demo/images/IODiagram.svg b/lessons/python/snakemake_elephant_demo/images/IODiagram.svg
new file mode 100644
index 000000000..5033d0e34
--- /dev/null
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diff --git a/lessons/python/snakemake_elephant_demo/lesson.md b/lessons/python/snakemake_elephant_demo/lesson.md
new file mode 100644
index 000000000..f562e80b1
--- /dev/null
+++ b/lessons/python/snakemake_elephant_demo/lesson.md
@@ -0,0 +1,17 @@
+---
+layout: page
+title: 'Designing data analysis workflows'
+visible: true
+tags:
+  - python
+  - intermediate
+---
+
+ - **Authors**: Julia Sprenger
+ - **Lesson topic**: Designing data analysis workflows
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/snakemake_elephant_demo>
+
+The main part of the tutorial is present in form of a Jupyter notebook. You can
+run it locally or online using [binder](https://mybinder.org/) by following
+[![Binder
+Link](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/UofTCoders/snakemake_elephant_demo/master?filepath=Snakemake_Elephant_Tutorial.ipynb).
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diff --git a/lessons/python/statistics/2018-12-06.ipynb b/lessons/python/statistics/2018-12-06.ipynb
new file mode 100755
index 000000000..c4e1dc5d0
--- /dev/null
+++ b/lessons/python/statistics/2018-12-06.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to Statistics in Python\n",
+    "https://www.scipy-lectures.org/packages/statistics/index.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing and Exploring our Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import pandas and use magic function for plotting\n",
+    "import pandas as pd\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import our data using pandas read_csv() function where \n",
+    "# delimiter = ';', index_col = 0, na_values = '.'\n",
+    "data = pd.read_csv('https://www.scipy-lectures.org/_downloads/brain_size.csv',\n",
+    "                  delimiter=';', index_col=0, na_values='.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>133</td>\n",
+       "      <td>132</td>\n",
+       "      <td>124</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>64.5</td>\n",
+       "      <td>816932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>140</td>\n",
+       "      <td>150</td>\n",
+       "      <td>124</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.5</td>\n",
+       "      <td>1001121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>139</td>\n",
+       "      <td>123</td>\n",
+       "      <td>150</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>73.3</td>\n",
+       "      <td>1038437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>133</td>\n",
+       "      <td>129</td>\n",
+       "      <td>128</td>\n",
+       "      <td>172.0</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>965353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>137</td>\n",
+       "      <td>132</td>\n",
+       "      <td>134</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>951545</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender  FSIQ  VIQ  PIQ  Weight  Height  MRI_Count\n",
+       "1  Female   133  132  124   118.0    64.5     816932\n",
+       "2    Male   140  150  124     NaN    72.5    1001121\n",
+       "3    Male   139  123  150   143.0    73.3    1038437\n",
+       "4    Male   133  129  128   172.0    68.8     965353\n",
+       "5  Female   137  132  134   147.0    65.0     951545"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check out our data using pandas df.head() function\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>40.00000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>4.000000e+01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>113.450000</td>\n",
+       "      <td>112.350000</td>\n",
+       "      <td>111.02500</td>\n",
+       "      <td>151.052632</td>\n",
+       "      <td>68.525641</td>\n",
+       "      <td>9.087550e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>24.082071</td>\n",
+       "      <td>23.616107</td>\n",
+       "      <td>22.47105</td>\n",
+       "      <td>23.478509</td>\n",
+       "      <td>3.994649</td>\n",
+       "      <td>7.228205e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>71.000000</td>\n",
+       "      <td>72.00000</td>\n",
+       "      <td>106.000000</td>\n",
+       "      <td>62.000000</td>\n",
+       "      <td>7.906190e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>89.750000</td>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>88.25000</td>\n",
+       "      <td>135.250000</td>\n",
+       "      <td>66.000000</td>\n",
+       "      <td>8.559185e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>116.500000</td>\n",
+       "      <td>113.000000</td>\n",
+       "      <td>115.00000</td>\n",
+       "      <td>146.500000</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>9.053990e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>135.500000</td>\n",
+       "      <td>129.750000</td>\n",
+       "      <td>128.00000</td>\n",
+       "      <td>172.000000</td>\n",
+       "      <td>70.500000</td>\n",
+       "      <td>9.500780e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>144.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.00000</td>\n",
+       "      <td>192.000000</td>\n",
+       "      <td>77.000000</td>\n",
+       "      <td>1.079549e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             FSIQ         VIQ        PIQ      Weight     Height     MRI_Count\n",
+       "count   40.000000   40.000000   40.00000   38.000000  39.000000  4.000000e+01\n",
+       "mean   113.450000  112.350000  111.02500  151.052632  68.525641  9.087550e+05\n",
+       "std     24.082071   23.616107   22.47105   23.478509   3.994649  7.228205e+04\n",
+       "min     77.000000   71.000000   72.00000  106.000000  62.000000  7.906190e+05\n",
+       "25%     89.750000   90.000000   88.25000  135.250000  66.000000  8.559185e+05\n",
+       "50%    116.500000  113.000000  115.00000  146.500000  68.000000  9.053990e+05\n",
+       "75%    135.500000  129.750000  128.00000  172.000000  70.500000  9.500780e+05\n",
+       "max    144.000000  150.000000  150.00000  192.000000  77.000000  1.079549e+06"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(40, 7)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# how many observations do we have? use pandas df.shape attribute\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1     Female\n",
+       "2       Male\n",
+       "3       Male\n",
+       "4       Male\n",
+       "5     Female\n",
+       "6     Female\n",
+       "7     Female\n",
+       "8     Female\n",
+       "9       Male\n",
+       "10      Male\n",
+       "11    Female\n",
+       "12      Male\n",
+       "13      Male\n",
+       "14    Female\n",
+       "15    Female\n",
+       "16    Female\n",
+       "17    Female\n",
+       "18      Male\n",
+       "19    Female\n",
+       "20      Male\n",
+       "21      Male\n",
+       "22      Male\n",
+       "23    Female\n",
+       "24      Male\n",
+       "25    Female\n",
+       "26      Male\n",
+       "27    Female\n",
+       "28      Male\n",
+       "29    Female\n",
+       "30    Female\n",
+       "31    Female\n",
+       "32      Male\n",
+       "33      Male\n",
+       "34      Male\n",
+       "35    Female\n",
+       "36    Female\n",
+       "37      Male\n",
+       "38    Female\n",
+       "39      Male\n",
+       "40      Male\n",
+       "Name: Gender, dtype: object"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check out one column with df['column name'] or df.column_name\n",
+    "#data['Gender']\n",
+    "data.Gender"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make a groupby object on the dataframe\n",
+    "data_grouped = data.groupby(\"Gender\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Female    62.0\n",
+       "Male      66.3\n",
+       "Name: Height, dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# look at mean weight per group in data_grouped\n",
+    "data_grouped['Height'].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QG4rjjyZpr7WtqrU0xV6MeWfm/2XN9chMRb7vhpu/Mc9x3wK+tZzHUBTQp6UutJr+vVubebt/mnaPDafVyPePDjIejPH+bfP3w2djV2ctybTEaBBsXWJ664vnJvBHkwxPR9nWXrPiIo/PvTvG2dEAB9Y2cGBtZX34BWJJfnB0kJQmeXh3B03u3LovjQbBw7s7uDgR4pb2lU0rvmtjE3aLkTqH5aaSNZFEitcuTuKxm7GbjdQ583BlkqNoIs2TxwYJx1M8tKud9iVmxOVLyVYECSHuBDZKKf+nEKIRcEspr5QqHkXJVo3NzHs2NQEw4o8ykCm4eGrIv+wkYzIauHVdQ1bHttfa8UeTNLmtWFY4KSGV1jg1pBf4PDEwXXFJ5vJEmKmIPiH1wlgw5yQDes25fNSds1uM3LWxad6fWU1GGl0WvKFE0T7cb9TvizARjANwZiRQ3UlGCPEnQC+wGfif6B0O/4Q+rqIoFaPRZaWjzs54IHatq2QhiZTG0atTOK3GFY113H9LC709dXjsZsQKV/ybjAa2tddwbjRY8orEybTGkb4p7BZj1rPo1jY6OWY3k9I0NrS4Chzh8hkNgkcPdOOPJmkoQSsGoKveToPLQjieLuo6q1K1ZH4N2AMcA5BSDt8wiF/WKnV1er7k8vtXimRa45dnx4mnNO7d0px12Q+z0cAneruyOvbNKz7e6tPrk7ltZtYus7ilEILGPM5qu/+WVu6/pTVv51uut/p8HL6s/31cVhMbmpdOGh67mV+/M/8lEWPJNM+fGUcIeO+W5rwU0zQbDXl93nLlsJj43G09RX/cUiWZhJRSCiEkQGZ9jKKUzImBaf75zX5SmsQg4EM72/P+GFaz3rUlBFhXWFCzGs0eWyr13+fUkJ/zY/o07laPLee1Ifl2xRvmZ++M0uC08JE9HSVfu5WLUiWZfxVC/C1QK4T4DeDXgf9WolgUhWgiTSypoUlJMJbdtGQpJZe9YdxWE801S+9N0rum7trAb6n65cvZ3u5aamwmbGYjXfUO/NEkY4EYPQ3OFVe5zlWL24ZBCISA5mWM8+Tb6SE/0USawUSUUX+sovYuKvYU5q8Br6FPK74HfWX+ZuD/llL+opixKMpsu7prObSpkVhS457NzVnd5/AVH7+6NIlBCD59a/eSXSHJtF5FwGU1kV0H2+oihGBji95rnkxr/POb/UQSadY3u/jwrvy0LEf8US6Oh9jaVrPo89Xd4OALd/QgBCvaJyhftrbVcHUyTL3TSnNN6ZNeLordkukEvg1sAU4Cr6MnnaNFjkNR5qixmfnK3Rtyus94IMa50QBWs5FwLLVkknmrz8ebV66POagNxxaWSstrNbLC8fwseJVS8tTbQ8STesWGL96x+FiOx349ucSSaV44q4/R3LOlecXTxnO1odnFb9+zYcUTPUqh2Otk/gBACGFBn112O5muMiHEtJRyWzHjUZSVEEJgNRuxmYxkU67CbDSQSGkYDBS9+6fS2C1GHtzZxtXJcF7rtVmMBuJJLee//+kh/7VSO801Kxuj0TRJMJ6ixmbKKWlUYoKB0o3J2NHrinky/4aBUyWKRVGWZU2D49p4QZ1j6WmptXYToXgSp9WE07J6Nq1arg3NrqxmmGVLCMHHe7von4ywLse9ZZozYzSgb++wEt8/NsjQlL6Qthxm9RVascdkngBuAYLom429DvyZlHKqmHEoSj7s7KylvdaO3WzMasrz4HSUeqf+ATUWjFVcJd5q4LGbl7UeqLvBwRdu7wExtxstV6m0xtCUXresfzKy7PNUkmJfTnUDVuACMAQMkltpf0UpK7mse9jTVYc3lMBpMS17jYxSOvm4KDAZDRza1MjZ0WBRK2uXUrHHZB4QesfiLejjMb8PbBdC+IBfSSn/pJjxKNUpmkhjMFD0wdml1DktcxZuapoklEiVxeylahWMJXFYTEQSKRwWU1lsV71vTX3Vb58wW9E7hjMl/U8LIabRNxXzAx9C39Fy0SQjhGgHngG2AS5gH/DnQBo4IqX83cxxfuDtzN0ekVL6somtECvZq3F1fDm7PBHixydGMJsEj+7vpr5EJTyWIqW81je/o8PDfduy385Wyc7L5yc4enWKUDyF02KkqcbGY/u7ctqfXlm5ov61hRBfFUL8sxBiAHgZPbmcAx4BskntPuBeru8RcxV4r5TyLqBZCLEjc/spKeXdmX9ZJRilOgxMRdGkJJ7UGPXHSh3OglKavNY33zcZLnE01elq5u/a5w2T0iTeYJxwXG1fXWzFbsn0AN8HfldKOZLrnaWUMSA2M5VPSjk668cp9BYNwFYhxCvoa3D+eDkboimVaVenhzF/DKvZwPrm8h33MBsN3LGhkXNjQfb3rI6++WK7bX0jb1ye5IM72ogm03TXO9RkixIo9pjM7xXivEKInUCjlPLdzE0bgSngb4CHgKdvOP5x4HGA7u7uQoSkFJGmSaLJNE6riVqHhU/sr4z19AfW1ldcaf1CiCXTmXpu+R1Dy/cUaGV5Kr5zUghRD/wX4Eszt0kpfZnWyw+B7TfeR0r5hJSyV0rZ29Q0//4PSuX4lyMDPPHyZV694C11KEqOBqci/LeXL/PfX7nCeLB8uzeV5avoJCOEMKHvQ/OHM11nQginEGLmkugO4FKp4lMKT5Py2tjLFW+oxNEouRrwRUlpkkTq+voRpbpU1LJjIYQZ+CmwC/gZ+uSB/cC3MuM0fwxEgb8TQoSByywxY02pbAYh6O2po88b5rb12e0sqZSPWzpqGJiKYDIItiyx7bRSmSoqyUgpk8B9N9z8jXkO3VuEcJQycdfGpgW3vc2WL5Tg//23d4mnNL5270Y2tFTMHnoVrcZmzmrTtzMjAV676KWnwTlnuvd4IMZPT4/isBh5aFd7XjYXU/KrorvLFCVfXr4wweBUlIlgnJ+9O1bqcJQbHLk6RTCW4tSQn2Asee32U0N+fOEEg1NR+n2ro0xLpVFJRlGAfd11OK1GLCYDB9WMr7KzOdOy7Kyzzykuur7JhckgcNtMtHmW3jhOKb6K6i5TCqsaqxOcHwtybjTIrs5auhsW3k2wq8HBE5/pRUOV4S9HB9bWs6e7ds62w5om6fdF2NDs4q6NjbhUeZ6ypJKMUrU0TfLs6VHSmmQsEOPLd61b9HiTSi5l7cZ97S+Mhzh6VS/g7rSaOLRJLUcoR+pdpVQtg0FQl1nhXa41zJTlq3OYrxW8bHCp57dcqZaMUtU+sb+L8UCcVtVfX3Waa2x89tY1JNIaLTXq+S1XKskoVc1qMtJVv/BYjFLZ6lQLteyp7jJFURSlYFSSUapaLJlmwBchmdZKHYqyCG8ormqXVSnVXaZUte8dGcAbSrCmwcEjeztLHY4yjwFfhCePDSIlPLSrjQ3NqtpCNVEtGaVqaZpkKqKvDveFEyWORlnIdCTJzI5PkyH1PFUb1ZJRqpbBIPjA9lbOjQXZ2VFb6nCUBWxtc+MNx0mnJbu71fNUbcRq3zSysbFR9vT0lDoMZZaUJglGkxgMghq7GbHIsX19fajnrzKp5678aFLijyYRCDx2M2KRN9/Ro0ellHLJ3rBV35Lp6enhyJEjpQ5DmeX5M2OcHPQD8KGdbWxcpCJyb2+vev4qlHruys+vLk3yxuVJAO7Z0szuroVblkKIY9mcU43JKGUlEEviCycIxZPYLUaa3WqRnVIehqajvH7Jiz+aXPrgCtVZZ8dkEFhMBtrztIB51bdklPLy7KlRhqajOMwmPnWgmxp79RU9zKUQad83HyxgJEq2EimNp44NkkxL+icjPHqgu9QhFURXvYMv37UOIcjb3jwqyShlZaYWlc1ixGpWDW2lPAihTyQhLa+9RquV3ZLfjd9UklHKygd3tHFuLEhHrR2rSe1yqJQHs9HAJ3q7GJyKsqnFVepwKopKMkpZsVuMiw42KkqpNLqsNLqspQ6j4qgkoxSNP5Lk6RNDCCH48O52anLcZCoUT/H08WFSmsaHdrar8v1K2frl2TEujoe4bV0jOzo9eTtvKq3xzMkRvKE479/WuuhGfOWioju9hRAPCCFezPwbEUJ8RAjhn3Wb2ke3jJwdDeANJZgIxrkwFsr5/pfGQ4wFYkyGEpwdCRQgQkVZuVgyzYkBP+F4miNXfXk992ggxhVvmGAsxfHB6byeu1AqOslIKZ+VUt4tpbwb6AeeA07N3CalzO8zrKzI2kYnNrMRu8VIzzKuwLrrHTitRiwmA+uaVL+4Up6sJgNrG50AbGmtyeu5m9xWGl0WDEJUzNhQVXSXCSHWAWNSypAQYqsQ4hXgNeCP5WovaVBGmmts/OYhfQtkwzJm6NQ5LfzGXeuQcnn3V5RiEELwkT0dpNIaJmN+r+OtJiOfuXUNmqRiZrlVdEtmlkeApzJfbwQOAXXAQ/MdLIR4XAhxRAhxZGJiokghKqAnh5UkCCFWdn9FKZZ8J5gZQoiKSTBQPUnmIeBpACmlL9N6+SGwfb6DpZRPSCl7pZS9TU1NRQxTURRldan4JCOEaAUSUspJIYRTCDGzuOIO4FIJQysp1UuoKKWh3ntzVcOYzMPAjzJfbwT+TggRBi4Df1KyqEro+MA0L52boKvezkd2d6juJUUpgkRK48ljg0wE49x/SyubW9Xma1AFSUZK+bezvj4O7C1hOGXh3eEAmpRcnYwQjKXwOKqv/peilBtvKM6oX99C+sxIQCWZjIrvLlNutrurFrvFyKYWNzX28riOmAzFefrEMIczZcQVpdo0u62saXDgsBjZOWsB5nQkwY9PDPP6JW8Joyud8vgEUpbl9JCf00N+dnR6uKX9+ot6W3sN29rzOz9/pV696OXyRJhL4yHWNjpprlEl/JXqYjIaeGRv5023v3ZxkovjIS6Ow5oGJx219hJEp1cLeO7MGOF4mvu2thSth0O1ZCrYC2fHGfHHePFc+U/DbsrUfLJbjLhs6tpGWT2a3Ppr32o2UFPC1/5lb5gzI0H6fRGO9hdvnbp6t1ewzno7fd4InXWluTLKxe0bGlnb5KTGZsZhUS87ZfU4sLae7noHLpsJl7V0r/1mtxWr2UAipdFRW7yaZ+rdXsEe3tXBdDRJbYVs7NXmKf9kqCiF0JqnXSZnaJokkkznlLRqHRZ+/Y61JNJazsVpV0IlmQpmMAhViVhRVhkpJd8/NsjQVJQ93bXcvbk56/vazMa87XiZrbIakxFCPJ/NbYqiKKtVPKUxNBUF4Io3XOJollYWLRkhhA1wAI1CiDpgZvVgDdBessCUrCRSGj9/d5RYUuN921rwVEj3naIUgpSSF89NMBaI8Z7NTXnvJraZjRxcV8/F8RAH1pb/biZlkWSA3wS+hp5QjnI9yQSA/1qqoJTsXBwPXdsf5vjANO/ZpOrBKavXeDDO8QF9r5c3Lk/ya3tunta8Urevb+T29Y15P28hlEWSkVJ+G/i2EOL/kFJ+p9TxKLlp9diwmAyk0rIiZropSiF57GZq7GYC0STd9eW/c2WhlUWSmSGl/I4Q4nagh1mxSSn/oWRBKUuqd1r40p1rSWsSZwmnaCpKObCZjXzutjVEk+mizuIqV2X1iSCE+EdgPXAcSGduloBKMmWu2DNWFKWcmY0GzAXaT6bSlFWSAXqBbWo3S0VRlOpQbqn2NNBa6iAURVGU/CiLlowQ4sfo3WJu4F0hxJtAfObnUsoPlyq2cnd5IsQvz47T5rHzge2tWe0dI6Xk5++OMeCLcGhTE5taVElypTol0xrPnBxmKpzk/u2tJStOCfD8mTGueMPctr5hTkHbalcWSQb4z8u9oxDiAeDrmW83A19B37zsYeAq8AUpZXLFEZapY/3TBGMpgrEg+3vqsqpuPB1J8u5wAICjV6dUklGq1tBUlD5vBIATA9MlSzLheIqTg35Af8+pJFNkUsqXVnDfZ4FnAYQQh9HX2TwupbxTCPFHwEeA7+Ul0DK0ucXN4FSEJreVugVKzFyaCNHnDbOrq5ZGl5Uau5n2Whsj/lhJEsypQT/ecJwDPfVqNppSUC01NuocZgKxFBubXVnd5/jANNORBAfW1uetmKvDYqS73kG/L8LmCr2oOz3kZyIYp7enDncOs+bK6h0uhAiid5vN5geOAL8vpby8yH3XAWPATuDFzM05mjZvAAAgAElEQVTPAZ+iipPMjk4PW9vcGA0CIW7uKosl0zxzYgRNSsYCcT51sBujQfDJ/d2k0hqmIs+AGQvEeO7MGADxZJoHtrcV9fGV1cVuMfL523tIazKr1/rgVIQXzo4DkExL3retJS9xCCH46L5OkmmtImedeUNxfvGu/r4NJ1J8aGf2hVjKKskAfwYMA99FX/X/KPpEgHPA3wF3L3LfR4CngFr0SgGgJ6i6Gw8UQjwOPA7Q3d2dn8hLaLE3j9EgsFsMhONpnNa504yLnWAAbCYjJoMgVSFranq+/pOsjuv75oMFjkRZLiEEJuPSY5UAdrMRo0Fk1nzlf1p+JSYYAKvJgNkoSKZzf9+W27v8ASnlwVnfPyGEeENK+adCiH+/xH0fQk80twIdmdtqgOkbD5RSPgE8AdDb21vV06XNRgOf3N/NWCBGT4Oz1OHgcZh57GA305Ek6xpLH4+izNbgsvLogS6CsZR6fc7itpl57EA3U5EE6xqz63acUW5pVRNCfEIIYcj8+8Ssny2YDIQQrUBCSjkJvAW8J/Oj+4A3Chdu+ZlviZHHbmZTixuLybDkscXQ6LKyodnFPL17ilISs98LzW4b65tc83Y/53KeatPgsrKh2Z3VDNbZyq0l82ng28BfoSeVN4DPCCHswO8scr+HgR8BSCnHhRAvCyFeBfqBvyhsyOUhlkzz/aODTEcSfGBHG+ubFr7a0DTJD48PMTgV5e7NTezsrM1LDKmURiiRotax9B43P39nlDMjQfatqePOjZVR6E+pTlcnwzxzcoQam4mP93blXL3CH0nyvaMDjAViGA2CrnoHH9vXidW08HliyTRWkwEhBJomSaS1qq2aUVZJJjOw/9ACP351kfv97Q3ffwv4Vh5DK3vjgTgTQX1p0dmR4KJJxh9NcnVSn9b5znAgL0kmkdL4oydPMjwd5YM72vj87T0LHqtpkncyU6hPD/tVklFK6uxokERKwxtKMDQdXfS9M5++yTDBWIrBqSh2sxGrych4IE7XAsUxX7kwwZG+KbrqHTy4o5V/PTLIVCTBfVtb2N5RfVOby6K7TAjxf2X+/44Q4i9v/Ffq+PLFG4rz4xPDHL3qy/u5Wz02OursOCxGtnfULHqsx25mY4sLq9nArjy1Ysb8MYan9Y2U3u6/aRhsDoNBsKe7FqvZwJ6u/Dy+otxoeDrK0yeGOZVZn7KQW9prcFqNtHlsy1pHs7bJSYPLwvomJ511djrr7LQssl7t4ri+LcaAL8KoP4YvnEBKfalBNSqXlsyZzP9HShpFgb18foKrkxEujofoaXDS4LLm7dwWk4FP9HZldazBIHKagpiNjjobB9fWc2E8xId3L33uuzc357RtrKLk6pdnx5kIxrk8EWJ9s3PBNS+ddQ4eP7R+2Y9TYzPzudt6sj7+4NoGDl+ZZEOzizUNTja3upkIxtnbfdNE2KpQFklGSvnjzP9/DyCEcEopy39f0Rw1uKxcnYzgsBjnTAOUUnJhPITdbKSr3kEsmebieIiOWvucBZZpTXJuNEi900KrZ+mV/cVkMBj4vfdvLnUYSgWbeR/YTEa6G1a+D0ujy8JEME6NzYwlM3XYH00y4IuwttFZsin0HbV29vfUs67JicEg+OCO6l4rVhZJZoYQ4jbgfwAuoFsIsQv4TSnlb5U2svw4tLGRjc0uPHbznEG+Y/1TvHzeC8DHezt547KPAV8Eu8XIl+9ce209yysXJni7fxqDEHzutjULrvBfjbJdz1Jpj18p63Ry+f0XivVY/zQvn58A4GP7Ohcc08jW+7e1sr3DQ6PLisloQErJ944MEIylaK6x8umDa1Z0/uXQNMm/HOknHE/T5rHx6IHKX6e3lLIYk5nlL4D7gUkAKeUJ4FBJI8ojIQTttfabrqDiKW3O1/GUvpVOKq2hyZuP06QkmdZQlGqSuOF9sFIGg6CzzjHngi6Red/Ek6V5/0j0SgKQn9+xEpRVSwZASjlww/z09ELHVov9PfUYhMBuNrK+yUW9w8LpYT89Dc45a1ves6kJl9VEg8uSVSFMRakkvT11CKGvut+QZZ2xXAgh+LU9HVwcD7GldfHJMYViNAg+sqeDyxMhtraVJoZiK7ckM5DZflkKISzAV7k+KaBqmY0Gbl3XcO37OqeFuzY23XSczWzkjg1quq9SnW58HxRCm8dOm6d05f5BH5Mp5ZYDxVZu3WX/Dvht9LIwg8DuzPfKKucNxfnXIwP84t0xNK16V1Ur1evt/in+95v9nBsNljqUoiqrloyU0ou+6l9R5njrio+hqShDU1E2tehTPxWlUqQ1yUvnJ5BSX8qwubUyy/0vR1kkGSHEd1ikNpmU8qtFDKdieENxLo2H2NDsymrNTTKtcXLQj9tmmncfmauTYcaDcXZ0eMquxEVXvYOzo0EcFmNe1xcple/0kJ+UJtnZ4VmyrpamSU4O+TEbRVE3DpOZyTrTkSRb21ZPgoEySTLMXYT5DeBPShVIqV3xhrk6GWZLq5uL42Fq7KYFy748dWyIUDzF6eEAX7pz7ZLnfuPyJEf6pgBw7jfN6Rf2R5L88O1hNCmZCMbLbu7+9g4P3Q0OrCbDojWhlKVVyrTomffCrs7aBafrnx8LXtvnRJNyyQWNbw9cXy5gMRrYmOUGYmlNcqTPhxCC3jV1OReJfLPPh1EIXFYTW1ZRKwbKJMnMLMIEEEJ8bfb3q0ksmebHJ4ZJa5IXz43jsetvrDqHZd41AzOT8GZe79FEmmgyTf0Cb0jDrFl7N71HROZ8cu5xKxGMJdGkXsYmH2py2I1PqWyz3wuj/tiC60lmv46N87xuEykNfzRJo8uCEHM39ptJFMm0xlQkQaPTumDyODk4zeuXJgGwmQ051/szZB7bZjZiNJTbUHhhlUWSucGqHdU1CIHFZCCaSOPKrKURAqzm+V+UH93byWVvmA1NLoKxJP/0Rj+xZJq7NzexZ54rulvXNeC2mXDbzDfNsPHYzTyyt4OJYJxt7SufWjnij/K9I4NICR/e3c5atTeHkgODEJiNBtJaGrtl4ZbrhmY3D+7UE8W2G6YEpzXJP7/Vz2QowY4OD/dta2FPVy0WowGLyXCtEOb3jgwyFoixscW1YLml2THYl9GVvL+nHqfFhMNqXPEi00pTjklm1bKYDDy6v4sRf4y1jQ4GfFFcNhPN7vnXxNQ5LezLtFoGfBFiSX1J0Yg/xp55jjcaxKJXYJ11Djrr8vMGGA/ESWdmgY0FYirJKDmxmAw8dkB/L6xrWvy1M9/4IkA8lWYylAD0ix7Q18rMrnSc1iTjwRgAo/7Ygo+xpbUGm8mIECxr0onRINjRWX0VlrNRFklGCBHkegvGIYSY2T5ZAFJKuTpWLQG1Dsu1/Viy7S8G6Kyzs6e7lqlIouBrDbKxpc3NiD+qD8iu0jeXsjKz3wvL4bCYOLSpiSveMAd66uc9xmgQ3LulhbOjAfauWXw8p0ddKC1LWSQZKeWyR8KEEJ8DPg8Ygf8A/AB9AWdCSvn+/ERY/oQQZVXV2Goy8sD28po8oKw++9bUsW+J5LGj07NqWxnFUBZJZrmEEB3Ae6SU92a+7wF+IaX8TCnjKqX+yQgXJ4Jsb/eo0jOKUmaOD0wTiCY5sLa+7JYJFEqlT3O4HzAKIZ7PrLUxAvcIIV4RQvxuiWMrurQmefrEECcG/PzbqZFSh6MoyiwDvggvnB3n6NUpfpWZqbYaVHqSaQEsmZZMBOgFNgH3APcJIXbOdychxONCiCNCiCMTExMrCiCSSPHqhQm8oYUHDVcqGEtycTzI5YnQtcH9+RgE166OFtqgSVEqUVqT9HnDhOOpm342OBVhMhTP+2OOBWKMB/L3vraZjdeWByw2Y24+UkoGfBH8kWTe4imWSv8k8gMvZb7+JdA7s9mZEOIZYDtw8sY7SSmfAJ4A6O3tXdGU6f/0b2c5PxbEYzfzV5/ai8mU37wdS6b5/w73c7RvCpvZwP619QvuwieE4JP7uxiajtKjyq4oVeTn74xydjSIy2riC3f0YM7ssXR8YJoXzo5jNAgePdC14EzMXF0cD/HjE8MIAR/Z3ZGXQf8mt5VHD3QRjKVYv8SMuRv96vIkhy/7sJgMfObWNXlbe1YMld6SeR2Yaa3sRi+qOeMO4FKhA5gM61dQwViKWAH2h4inNKKJNLFUmlgyjT+SXLRApNtm1qdbrpL+XmV1mMpcwYcTqTl7KU1H9CnKaU0SjN3cylkuf1Q/r5QwHc1f66GlxsaGZhcixwXPMy2YREqbtzVXziq6JSOlPC6EiAohXgS8wD8IIY4CceBVKeXhQsfwm4fW8czJEfb31OOy5f/P6bGbuW9rC01uCxajgX099TmXtFCUSnfftmaO9k2xpsE5pyv44NoGUmmJw2pkXR6nGO/srCUQS2EQgu15WJy8UndsbMRgEDQ4LbRX2DYBFZ1kAKSUf3DDTU8X8/F3ddWxq2vxKZIrpaZYKqtds9vGB+app2e3GLlvW0veH89sNHBPGS0JqLGZuf+W1lKHsSyV3l2mKIqilDGVZBRFUZSCUUkGfdDwzEhgyemK8VSad4b9+MKJIkWmKKtDnzfMxfFQzvcb8EW4MBZEylVbV7fsVfyYTD68fH6C4wPTGA2Cz9/Wg8cx//TAZ0+PcnkijNVs4Et3rlX7mihlI9s9Ykp9zvlcmgjx9PFhAN63rWVOAcvFDE5F+P5RfULpoU1NS5aPUUpDtWTg2gLHtCZJagtPQ45npiin0pJFDlvysdQe9YpyXTx5/c0UTy282PhGsdn3W2SRMuh7Lan3XWmolgzwns1NuG1mGt0WGhfZ2vf+ba2cGJymq96R84pdgGP9U7x0boJGl4VHD3RfW1CmKKvZ1jY30WSaVFpjdw4zNTc0u7hnSzPRRJrenoXvd/jyJK9fmqSlxsYn93dhVEsAikolGfQSLHdubFzyOI/DzKFNTct+nEuZPmdvKMF0JEmTW+1VryhCiGV3de3uWnqHyksTYUAvExOKpRbsDlcKQyWZAjg7GuB4/zTb2mvmbBJ2YG09kcQEbR4bja7l75OhKNUiHE/x3JkxDELwvm0tBalUcWBtPa9d9NJVb1cJpgRUkimAF89NEE2kGQvE2dHhuVZCYk2Dk8/frmqKKcqMU0N+LmdaGl31jqxaJrna0OxiQ7Mr7+dVsqMGBQqgs04v+9Bea8u5RpGirCbtHjtGg8BsFLSq/Y+qkljt88sbGxtlT09PqcMoqLSUpFISi9lAtaW8vr4+qv35A0imNaQES56rfJfSannuqtXRo0ellHLJF+Sq7y7r6enhyJEjpQ6jYFJpjb977QrheJp1TU4e3t1R6pDyqre3t6qfP4Crk2F+cGwIgPtvaWVbGRRszIfV8NxVMyHEsWyOq57LImVeKU0STejrCUIVViJc0c1+3tRzqFSaVd+SqXY2s5EHd7bR5w2zuzv/g6pK4W1trSEQTZHSNPao51CpMCrJrAKVOLtmLBDDZjKqKaeAwSC4bX1DqcNYMU2TDPujNDity1rMXGlyKcvT980HCxhJaakko5SdU4N+njszhskgeOxg96JVGJTK8YszY7w7HMBtM/H523tKHY5SJGpMRik73syW1ilNMh3J39a3SmlNhvTq5aF4ikQBtipXypNqyShlZ39PPbFEGqfVlNctdZXSeu+WZt7s89HT4MBpVR89q4V6ppWy47Ka5t1qV6lsrR4bH97VXuowlCJT3WWKoihKwagkU8EmgnFev+TFG4qXOhSlgo36Y7x+ycuU2vFVKQCVZCrYU28Pcviyjx++PVTqUJQKpWmSJ4/pr6NnTg6XOhylCmWdZIQQ38rmNqV4jAb96TOpTZiUFZh5/cy8nhQln3IZ+H8f8Ec33PaBeW5TiuRjezu57A2xrqmyFloq5cNgEHy8t4urk2E2trhLHY5ShZZMMkKIrwC/BawTQpyc9SM38FqhAlOW5nGY2dO9vB0FFWVGvdNCvVNtoqcURjYtme8CPwX+E/D1WbcHpZS+gkSlKIqiVIUlk4yU0g/4gceEEEagJXM/lxDCJaXsL3CMiqIoSoXKekxGCPE7wP8DjAEzNSEksDP/YSmKoijVIJfpJF8DNkspb5FS7sj8WzTBCCHahRDHhBAxIYQpc9ufCyFeEUJ8e9ZxBb9NURRFKb5cZpcNoHeb5cIH3As8BSCE2As4pZR3CSH+WgixH0gX+jYp5Vs5xq0oVScUT/FWn48ml5XtHZ5Sh1NWsi3LX80l+QsllyRzGXhRCPET4NoScynlny10ByllDIgJcW0dx23Ac5mvnwNuRe96K/RtKslUgVgyzU9Pj5BMSx7Y3kqNTe01k4uXz09wbjQIQHONlWa3bcXnjCRS/PTUKJqUfGBHGy5V+FK5QS7dZf3ALwAL+vTlmX+5qAUCma/9QF2RbptDCPG4EOKIEOLIxMREjr9C8UUSKYamo0gpSx1KSZ0bDdLnjTA0FeX0YK6N6qVNhROMB2N5P2+5cGQ2CjMZBDZzfjYNOzMSpN8XYXAqyjtD+X9OlMqX9WWHlPIbeXi8aaAm83VN5vt0EW678Xd5AngCoLe3t6w/ueOpNP/0xlXC8TS7ujy8d0tLqUMqmfZaOxaTAU2TdNU78nruUX+Mf3lrIHNF3sqW1pql71RhDm1sorPOTq3DkrdWYGed/pxImf/nRKkOucwuewF9NtkcUsr35vB4vwJ+E/hX4D7gfwGpItxWsWIJjXA8DYA3tLoLGDa5rXz5rrVISd6uxGf4wgm0TEtxskr/zgaDYENzflf1t9TY+NKda4H8PydKdcilA/UPZn1tAz6K/oG+ICGEGX0h5y7gZ8C/Rx+jeQU4IaV8M3NcwW+rVB6Hmbs3NzE4FeXguvpSh1NyVlNhPsg2t7oZC8aIJzX2qioKOVHJRVlMLt1lR2+46TUhxEtL3CeJ3pqY7fA8x/2fhb6tku3prlPlYwrMaBDcs7m51GEoStXJpbts9mW0AdgHtOY9IkVRFKVq5NJddhR9TEagd5NdAb5UiKCUyiSlJKVJzEZVMj5XiZSGxaT+bvmU7doXpbBy6S5bW8hAlMqW1iRPHh1kaDrKoU2N7Fujxo+y9cLZcY4PTLOxxcWHdraXOhxFyatcNi0zCyG+KoT4fubf72QG9hWFUExfywNwbjRU4mgqy7kxfYHkxfEQmlbWM+oVJWe5tM//Gn0c5q8y//ZlblNWKJHSeO2il2P9UxW74LLGbsJpMdLvi7C2Ua2XmE88lea1i17e7p+ac3t3vYN+X4R2jw2D2uVUqTK5jMnsl1LumvX9L4UQJ/Id0Gr0Vp+PN6/oW/PU2MxsaM5+p0spJaOBGB67GYfFxKlBP+fGguzprmV9EXfMDMZThBPpax+Yt63P/2MM+CK4bSZqHYXfYKt/MsKbfT7WNTlzmtIspWRwKkqNzYzHMbeh/+YVH0f69ATjsZuv7Wg6NBWlu97BaCCOpsklE01a05/zBqdFTR9Wyl4uSSYthFgvpbwEIIRYh766Xlkhe6bchxDXv87Wqxe9HOmbwmEx8qkD3Tx/dgwpYTqSuCnJjPij/NupUdw2Ew/vbs/rmhObyYjbZiIYS9HgtM57zHgwhttqzvl3BP0D+rWLXsxGwWduXVPwRPPi+XEmQwkGfBG2ttYsGHNakzxzcpjxQJz7trUw6o/xxuVJLCYDn7l1DR779URjN8//PDe4LITiKeqclqxaMj89PcKFsRD1TgufvXVN3lo/k6E4NrMRp6o/puRRLq+mPwReEEJcRp9htgb4YkGiWmX2dNVSY9M/fDtq7Tnd1xvSa5VGEmniaY1mt42xQIw2z83nOTXoJxBNEogmGfBFc2oxLcViMvCpg934wgna53nsX12a5I3LkzitRj57a0/OiWYqoq/CT6YlwViq4EmmzWNnMpSg0WVZdNbXeDDG5YkwAMcHprBlEncipRGKp+YkmX1r6qh1WHBYjHOen4d2tTPqj9FcM39yvpE3qD/nU5EEKU1iyUOSOT3k5xfvjmExGfj0we6itBaV1SGX2WXPCyE2ApvRk8xZKWV8ibspWRBCLPsD/66NTRgNk7R5bDS6rHy8t5OpSILGeVoTm1rcnB8L4rSaaK9deQXeGzksJhyW+V9SM4UnQ7EUwVgy5yRz+/oGAOoclqLUyLpvazO7ujzU2i0YF/kQb3Baaa6x4g0m2NxSQ2e9HSEEDS7LTRcMCz3PZqMhp9/p3q0tHOufYkOzK2/Tnkf9+vOTSGn4womck4yUklnV1hXlmiWTjBDiM4CQUv5jJqmczNz+G0KIsJTyu4UOUllYo8vKh3ddn/ZqNhoWLOHe0+jkt+7ekPfB5WgizY+ODxFNpnlwZ9u8j3/7+kYC0STvjgT40fFhPrqvk3pn9h9kbpuZ+28p3tpfIURWpfD1K/81c8ZSHthe2Di76h1ZJaVUWuOZkyN4Q3Hev62V7oaF77N/bT3hRIoau5meBmfWsfjCCZ48OogmJY/s7aTJnV1r7Ea5rGlRe7pUlmwug34f+OE8t/9L5mdKmZgMxenzhvGF4jx/ZozzmamxsxVi9tJlb4gRf4zpSJJ3hwPzHtPktrKpxU2z20YonuKKN5z3OEopH3/XwanItRZFPoz4Y1zxhgnGUhwfvKkY+Rweu5mHd3dwz+ZmDAbBpYkQz707xkRw8c6KK94woXiKSCLN5Qk1dV25WTbdZUYp5U2fVlLKgFonUz6mIwm+e7iflCaJpdLYTEZODflpr7XPu5FUPJUmGEvR6FreledsXfUO3DYT8ZS2aLffphY3Z0YCmWrAxZv5VgnOjQZ56u1BBIJHD3SxJofWxEKaa6w0uq1MhRNsbsm++nI8leYnJ0euzWL7zK1rFjx2Q7OLd4b9aJpkUw6Poawe2SQZsxDCKaWcc+kphHCjb2CmlIFwIk1qZiFf5j+ryYhpnivsZFrju4f7mY4k2bemjkObmlb02DU2M1+6Uy/Bv9gVfZ3TwhfuUIUj5nNuLMDx/mmEEBxcV5+XJGM1GfnsrWtIa3LRcaUbGYXAbjYSiqeW3OnSYzfzudt6VhipUs2ySTL/A/i+EOIrUso+ACFED/BfMz9TykBHrZ33bG5iKpxg35o6vKE4TS7bvOsoIvE005EkoE9rzgchBGrcd/nqnRZaPTYMQuR9W+lcEgyAyWjg0QNdjPpjeUl2yuq2ZJKRUv5nIUQIeEkI4UK/Tg4D35RSqhX/ZWT2osHFZgd5HGZuX99Avy/C7RsaixGasoS93XVMhZOYDIJbOkq/K6fbZsad52SnrE5ZTWGWUv4N8DeZJCPmG6MRQnxeSvn3+Q5QKYyD6xo4uK6h1GEoGQ6LiYd2qeKYSvXJaZK9lDI0X4LJqKqNwkrl4niIX54dYzK0OpcgnR8L8vev9/HS+YlSh7KkaCLNi+fGb6pFpujiqTRPvT3Idw/3r9rXs5JjklmC6pFfoVhSn9VzYsDPz98dK3U4JfHG5Ul84QTHrk4Rji+6u3fJvX7Jy9v907x4boIBX6TU4ZSdyxNh+rwRxgIxTg75Sx2OUiL5TDKVWT64jBgNAptZf0oc86yIr9QKzbmYqbfWXmu7VuurmHL5G8/U+DIIsax6bNWu3WPHYTFiNIicFngq1SWflfBUS2aFzEYDjx7oZiwQu+lN+eoFL0eu+tjWVsP7i7jyvdju2NDI3u46rCZDUcveB2JJvndkkERK4yN72uet/Xajg2vraXJbcVtNeVlvVG08Dn1qe0qTqlr0KpbPJPNaHs+1anns5jlFFWecHvYjJbwzHOC+rS0Vu++Ipkl+/u4YvnCC925pptVzc+mWUrQK+icjBKL6tO4LY6GskowQoijbKcSSaX56eoRkWvLA9ta8T3EuJJPRQB6LfSsVKJvaZb+32M+llH+W+f938hWUcrM9XbUc7Z9ia1tNxSYYgGF/lDMjeumZt/p8ZTOjqqfRSaPbSjyZZktbea1cPzcapM+rj/mcHvSraedKRcmmJVNe77hVKpspx8PTUfzRJJta3DkvwCuWBqcVt81Evy/C0FSUAV+kKFWVl+KymvjsIuVTZkukNM6PBWmusWZVRDPbc7520YvRILh9fQMm4/Xh0vZaOxaTAU2TZfG3UpRcZLMY8xsL/UwIoUbzysRkKM73jujVcCeC8RWXiikUu0UvdfJfXrhINJnmZ++M8uW71pU6rJz88uwYZ0aCmI2CL96xNi+bfB0fmOb4gF7Ess5hYUen59rPmtxWvnyXXrZHjW0olSard4cQogNoA05KKRNCiGbga8AXgPLo71jlkmmJlpkZFU9pJY5mcVazkSa3lclQYt7xp3I38/dNafJ6vbgVqsts1SwE8/5N8rmLaaXLZVuAanrsSpXNmMzXgP8AXASsQohvA38G/AOwr7DhrU5SSqYiSWpspjndJotp9dh4YHsrU+EEe9dkvyf9bAO+COPBGLe0ewp+xfyJXr02VnuOO4GWg3u3ttDgnKbVY8NjN9M/GWEiFGN7h2fJZBCIJbEYDTf9fTe2uHnsgBmDgbx1wSlKOcimJfM4sFlK6RNCdKMnm0NSyjcKG9rq9dyZcU4P+WmpsfHo/q6sB/q3ti2/5pU/kuQHx4bQpGQ8EOcDO9qWfa5s2MxGehors7fVZTVx50Z98H06kuCpt4cy3ZSJRTcsOzca5KenR7CYDDy2v5u6GzZtm2+mnaJUumwuk2NSSh+AlLIfOK8SzPKkNcn5sSC+cGLR44am9JlEY4EYiXThur5SBTx3KV3xhhmezk916Xwano4iJcSTGhMrLLNSrc+dUn2yacl0CiH+ctb3zbO/l1J+Nf9hVadfnh3n1OA0VrORL9zes+CA8V2bmjjS52NDs6sg3VZpTfLk0UGG/VEObWpib3cdHoeZR/Z2XOsuWykpJaeHAqQ0jV2dtUWbdn18YJoXzo4jBHy8t4uOAnTHRRIpTg76afPYWNPg5Nf2dFzrLlvM3u46fOEhA1MAABLjSURBVOEETquJdZlWXK57vQC8ftHL4Ss+1jU5eXh3x7J/D0UphmySzB/e8P3RQgSyGpwdCXD4ig+3zcQnezsXTDLrm1wFXeQXiCYZylzpnxsNXtsiINu947NxdjTIc2eu11/b0728caJcRTL1zqS8/nW+PXdmnEvjIQxC8MU7e+hucNDdsPTfzeMw89F9nde+//GJYS6Ohziwtp47clj7cmZUr1F7eSJMPJVWkwKUspbNFGZVvj9PPA4zLTU2XFYTsoRVeGodZra21TA4FWHfMicJLGX2jpzFXLPT21OPJsFmNhRsi+eZ380g9Lply5FIaVwcDwFwZiSQU5LZ31PH4cs+NrS4VIJRyl42s8ueXuznUsoP5y+c6nb7+kbiSY22WhsNztLtXC2EWHSAOh82trj50E59mu+W1uKt57WYDNcG5Qvl3q3NtNfaaamxLrk98UIsJgO7u2s5PxrMOdHv7KxlZ2ftsh5XUYotm3fIbcAA8L+Bw6hCmMu2odlVsKvrcrSxpTqLRVhNRnZ3rfxD/p7NzdyzuTkPESmVrhDrb/q++WDeHz+Xc87IZnZZK/Dvge3At4H3AV4p5UtSypdyfsQK548m58zsGfXHePb0KBfHF9rLrXDOjAT4/tFBzo0W/7ErhaZJ/JHkkiX8NU3y+kUvL5wbJ55K5/QYr17w8oNjg4wHYysJVVGqUjZjMmngWeBZIYQVeAx4UQjxp1LK7xQ6wHLyyoUJjvRN0eS28tiBbowGwc/fHWUylODcaJCv3O3EYsrnFj2Le/7MGMm0ZCwQY3MRu6QqyQ/eHmLAF2Frm5sHti+89uf8eJDDV3wAWE0Gbl+fXZfbWCDGW336/YyGSTXbS1FukNUnohDCKoR4BPgn4LeBvwR+UMjAytHVSX39ykQwTiShz1yaKbvutBrnDHYXQ2umHH17rVrEN5+0JhnMrDmaee4W4raZmRnDz6WUfo3NjNOqD7631qjnQVFulM3A/9+jd5X9FPiGlPJ0waMqU3dsaORXlybpaXDgznwQPbizjX5fhNYaW9FL8H9kdzu+SIIGp9owaz5Gg+CujU2cHQ1cm6a9kI5aO5860E0irdFZl/00brvFyOdu6yEUT6mNyxRlHtkM/H8WCAObgK+K61M2BSCllMuvZVJh1jY6WXtDKZTpSJKrk2GMQuSlGm8uTEaDqnO1hH1r6rKavSWl5KovQiKl0ey25dTtaTMbVXVkRVlANmMyxRtkqEDP/v/t3XuQlNWZx/Hvj5lhuDs4KCArsKKrRkAEZANGxVs0FRPXJBV1K2pMZV03FSMhWmVltyqX3VrdJJhlc5FQW0RjgmvprpEY70ZdBeQmKrAkEghGwQuDMjBynZln/zhvQ9sM0w3T723m+VRNTb9vd7/nvOed6afPOe85Z/VbNLXsZc2m7dwwfQx1FU5o6bLltXdaeGFdExDGvkwd0/naPc65yiT71bsb6te7FthLn7qaIx6Y15HWtnZWb97OoD61nJDAEr89Xb+iJZ8LfSzl+DVyWZaVZQkSDzKS+gH3A/2BZuDzwO3AZOAlM7spet0Pq7kvLpeePpyNTTsZ3tCnqiPbF2/YyvKN7wNw5ZTjK1pz3h2544/ux+fPPJ69re0HNYkeyqL1W1nxerhGV00Z6bMoO9eBNNp2LgGWmNl0YClwK9DfzM4Geks6U9LEau6L82Tqa2s4edjAw7ojqRLFwzqKH7+7Y/f+O9tcdY1o6FtxgAGwDz2ubPGyrS172LF732HmzLn8SqO5bD0HFjtrAFqAp6Ltp4CPAu1V3resXKbMDFWxuaurpo5ppH99LUf1rd2/sNfyje/x/Lom+tTVcPXUUUc8pUlPE9e1nTamkQH1tRzVt66imuaazc08seYd6mrEVVNG0uh3o7keII2azDrgryWtITRptQLbo+eagcGE4FPNfR8i6XpJyyUt37JlC6+9s4Mf/+6PzF/yZ/ZWYeniPa1tbNjSwq69hzdyvFhdTS8mjRrMicceGGT5zvawBsnufW007/JvwwUte1rZsKWlwzVWHlv9NrOfXre/U7+aDlyjyvpj3t0Rrt++Niu7ppBz3UUaX4WvBR43s+9LupnQN1O4DXoQsA1oq/K+DzGzucBcgMmTJ9vat7bT2h5Gzje17OnyksAPrdzMpm27aBzQm2umju7SsYpNHdNIa3s7jf3rOc7b/4HQZHXvkj/TsqeVvxo6kE+OPzCqv63dWPtW+L6xZnNz7BNnljN51GBadrfSv77GbxRwPUYaNRkB70WPC18vL4h+Xwi8CCyu8r5OjRtxFP161zCqsR/HDux6E0ahlrF9V/k5sw7H0f17c9mEEXzspCGZatpLkxl8EPVRldbuanqJM0Y2UF/XK7H1bDozsE8dnzr9OM4/ZWiiyx84l6Y0ajLzgfskXQ3sA64AviXpeeAVM1sKIGl3Nfd15oRjBvD351bvm+Unxg1j9abtnDxsoAeDmPUSXDJ2GBubdjJx1MEzI08/+Vim+0zHzqVG1fymnUeStgCvJ5DUEA7U3JLWndOeCLwU4/HTLLtS3S0vh7p2WTrPLEu7nEaZ2THlXtTjg0xSJC03s8medr5kKf89JS9ZOs8sy0s5+RwozjnnYuNBxjnnXGw8yCRnrqedS1nKf0/JS5bOM8tyUU7eJ+Occy42XpNxzjkXGw8yzjnnYuNBxjnnXGx8Gt+YSGows23R40uBsYQZqB+wGDvCJNUAf0OYfXowYe62F4Ffm1msawSkdc5xkDSWKP9mVnYW7xjST+06HiI/sV1bSZMoOU8zW97FLHc7eS0n7/iPiaTfmdn5km4jzAz9EHAW8Bdmdl2M6d4DvAo8TZiFehBhDrfTzewLcaUbpZ3KOVeLpMfM7BJJMwjz3/2WkP9NZnZrwnlJ7ToeIj+xXNtogcF6wrIcxefZZmZf63rOu4c8l5PXZOI3zczOjR4/Jum5mNMbbWZXl+xbGc3llpSkz7laeke/LwfON7M2YI6kF1LISxauY0eqfW0nmdk5JfselPS/XTxud5PbcvIgE5+J0QfCqYWmBkm9gLjneH9I0sPAs4R1dY4CzgF+E3O6kN45V8tHJP0CGEMIOLui/Wmsq1B6HQcB55LMdexIXNd2uaQ5hG/ohfO8gHjno8uj3JaTN5clSFI/4CQzeyXmdIYAUwjNGtuAZWa2Jc40O8lLIudcDZKmAZuAGsLqrScCbwLvm9nDCefl08AK4HSycR0vA540s51F+6pybSWdAUwlfCEq9DWs7Moxu6O8lpPfXRYjSadJOqWwHf2Dxv6t2MyazOwRQpv+IGB03GlC+GCMPniK87IzDwEm8i9m9jpwEyHIrACGA9ekkJc5wAPA9UAdsDitABO5E3ha0q8lXStpcBWvba/op5YQ4GuqcMzuKJfl5DWZmEiaBQwlLC/dCHzJzLYUOlBjTDe1zmtJmwnLJrwDPAgsMLP340yzmiQ9ZWYXFn4X7X/GzM5LOC/PmNl5kv4S+AzwKWAP8JCZ/TTJvMSZn6hDuzcH3+CQ+Q7tJOW5nLxPJj6TCx2kksYD90u6JYF0izuvzzOzdpLrvP5DyQfRg5JS+2A8AndL+k/gDUm/BJ4DxgOp3SZqZn8CZgGzJA0FLksrLzHlJ7cd2gnLbTl5TSYmkhYSPuT3RtuDgV8Sgs/QGNN9G3gCOJ/QXr4r2h/72hMdfeMvfBCZWT4m85OOAy4m1EKbgUVpNPdJutjMHk863UOJKz+S7gD6cXCH9h4zm1Ht9PIqz+XkQSYmkqYAG83s3aJ9tcA3zey7MaY7qmjzLTPbK2kAMDPOdKO0O/wgknRmGgMaXT7ktUM7aXktJ28ui89ygOg2z4J24OyY032jeCNKf1cC6QI8WXK+AAL+FbgogfRdPuWyQzsFuSwnr8nERNJOwjQgAgqFLGC8mTV2t3TTTtvlU547tJOU53Lymkx81gKXm1lz8U5JT3bTdNNOO5MktZjZgKLtLxL65b7ayXs+DXzEzG7v5DXTgZvN7NIOnpsBzC0e05Jhue3QTlhuy8mDTHwu5cCI8WKf6Kbppp12t2FmC4AFXTjEDMJNJnkIMrkdyZ6w3JaTN5c5F6POajKSjiEMuhwZPT3DzBaWvGYM8CtC+/ujhBs4BkQ1mW8DTYQZkVcAXwBuBH4A/AFoSnp8z5HIa4d20vJaTl6TcS5efSW9XLR9NAdqKbOBH5rZC5JGAo8Dp5a8fzYw28zulXRDyXNnAKcBm4GFwFlm9h+SZhJun2+q9snEJJcd2inIZTl5kHEuXrvMbEJho1BLiTYvJEzKWXh6kKSBJe+fSlhXBmA+oZZSsNTM3oyO+zJh+qA0Zow+YiUd2msJzUDXSbom6x3aScpzOXmQyajoj+p1M/v3aPtx4A0z+3K0PYswVcwdh3j/IjObViaNjYRmmaaS/dOBvWa2qMsn4jrTC5haGDBbUBR0ytlT9LiNfP4/57ZDO2G5LSefIDO7FgHTYP9YlyGEppGCaYQmkg6VCzBlTC+k7WL1BLD/LjNJEzp4zYvAZ6PHV1Z43B1AaY0oq5ZLmiPpc5I+Hv2+kxx0aCcst+XkQSa7FnLgg/40YDWwQ9JgSfWEtvuVkm6RtEzSq5K+U3izpJbody9JP5W0RtLDkh6R9LmidG6U9JKkVZJOkTQauAH4uqSXJSUxiLOn+howObp2/0co91IzgJmSlhJmhG7u4DWl5gKPSnqmelmNh5nNBH4GHEuY+foYwu3XmZ4qJWl5Lqc8Vq97BDPbLKk16hCeBiwGRhDa6JsJ0/hPB04irB0jYIGkc8ysuAr9GUJb/TjCH+haYF7R801mNlHSVwjjLr4c3SrZYmbF7f/uCBTfWRZt3wXcFT1uAq7o4D37X0NY3+ajZmaSriSaScLMniUsaFZ4z1eLHv8I+FG1ziEBuezQTkEuy8mDTLYVajPTgDsIQWYa0cSNwMejn8JtjAMIQac4yHwMuD+ajfntDr7d/k/0ewUhILlsmQT8WKGjZhvwpZTzU1V57tBOUp7LyYNMthX6ZcYRmsveAL5BGIw1j1CTuc3MftbJMcr1Ihc6j/PacdytmdnzhNUxu6vcdmgnLLfl5B8q2baQEFQ2mFkb8J6kBkIfzd8RAsQ/S/qVmbVIGgHsK575mXBL67WS7ia0404n3ArbmR2Eb0rOxS23I9kTltty8iCTbasId5XNL9k3IGrPf0LSqcDi6LbXFsKo7+Ig89+EP8bVwGvAEsp3Hv8GeEBhXfcbo2/TzsXh1ujnVMIqsnWEwaV56lOKnZnNLBrxfxKh6XRuHkb8+7QyPYCkAVFNpxFYShgZ/nba+XJO0oPAMqCB0P/0CGGqnL81s4vTzFvWSJpECDKDgfcJ08qktmprpTzI9ACSniX8E/cGvhfdveRc6lS0mqqkVWY2rnS/86n+XcaZ2fS08+DcIXwg6Z+AesLdj98A3uPDsxm4HHf8e03GOZcaSX2BS4D1wDrgWsIdkfNL1yXqySTdAfTj4I7/PVkfkOlBxjnncqCo47+B0PG/GKg1s2WpZqwMDzLOOZdx0fyFB+0GHjOzi5LOz+HwPhnnnMu+FsJkqcUEjE8hL4fFg4xzzmXfWuDy0n4qSU+mlJ+KeXOZc85lnKThwFYz21uyv9bMWlPKVkU8yDjnnIuNryfjnHMuNh5knHPOxcaDjHPOudh4kHHO9TiSTNI9Rdu1krZIejja/mK0/bKk30v6etFrvy3p5jLHvzl632pJr0i6psr5b4hWs808DzLOuZ7oA2BsNK0NwEWEpa6L3WdmE4CzgH+UdHwlB5Z0Q3S8KWY2FjiH8osHHq4GwIOMc85l2KPAJ6PHVwH3dvQiM9sK/BEYXuFxvwl8xcy2R+9vNrO7ASRdIGmlpFWS5kmqj/ZvlDQkejw5mjm9UGuaJ+lZSRskFWZcvh0YE9W0vn+4J54kDzLOuZ7qv4ArJfUhjJxf0tGLJI0E+gCvljugpIHAQDNb38FzfYC7gCuiJQ1qgX+oIJ+nABcDU4BvSaojLPS23swmmNktFRwjNR5knHM9kpm9Cowm1GIe6eAlV0haA2wAZpvZ7goOK+BQgw9PBv5kZq9F23cTmtLK+a2Z7YlWw30XGFrBezLDg4xzridbAPyAjpvK7jOz04CzgVmShpU7WNRE9oGkEzp4urN+mVYOfB73KXmueG2dNnI2HZgHGedcTzYP+K6ZrTrUC8xsMXAPcFOFx7wN+ImkQQCSBkm6Hvg9MFrSidHrrgaeix5vJCw/DfDZCtLYAQysMD+p8iDjnOuxzOxNM5tdwUv/Dbgu6nMp507gGWCZpNWEQLIzam67Drhf0iqgHZgTvec7wGxJzxNqK+XyvRVYGN0inemOf5+7zDnnXGy8JuOccy42uepAcs65rJD0E8JAzWKzzeznaeQnq7y5zDnnXGy8ucw551xsPMg455yLjQcZ55xzsfEg45xzLjYeZJxzzsXm/wHMMFNIyRW8PQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# import plotting from pandas\n",
+    "from pandas import plotting\n",
+    "\n",
+    "# take a look at our data distributions and pair-wise correlations \n",
+    "# using plotting.scatter_matrix() on Weight, Height, and MRI_Count\n",
+    "plotting.scatter_matrix(data[['Weight', 'Height', 'MRI_Count']]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# scatter_matrix on PIQ VIQ FSIQ\n",
+    "plotting.scatter_matrix(data[['PIQ', 'VIQ', 'FSIQ']]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting and Comparing Distributions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# import seaborn plotting package\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# use the kdeplot() function on FSIQ, PIQ and VIQ\n",
+    "sns.kdeplot(data['FSIQ']);\n",
+    "sns.kdeplot(data['PIQ']);\n",
+    "sns.kdeplot(data['VIQ']);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.kdeplot(data['FSIQ'], cumulative=True);\n",
+    "sns.kdeplot(data['PIQ'], cumulative=True);\n",
+    "sns.kdeplot(data['VIQ'], cumulative=True);\n",
+    "\n",
+    "# cumulative = True\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hypothesis Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import stats from scipy \n",
+    "from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1-sample t-test: Testing the value of a population mean. \n",
+    "\n",
+    "`scipy.stats.ttest_1samp()` tests if the population mean of data is likely to be equal to a given value (technically if observations are drawn from a Gaussian distributions of given population mean). It returns the T statistic, and the p-value (see the function’s help)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_1sampResult(statistic=30.088099970849328, pvalue=1.3289196468728067e-28)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# run a 1 sample t-test on VIQ against 0\n",
+    "stats.ttest_1samp(data['VIQ'], 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2-sample t-test: testing for difference across populations\n",
+    "We have seen above that the mean VIQ in the male and female populations were different.  \n",
+    "To test if this is significant, we do a 2-sample t-test with scipy.stats.ttest_ind()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up two series for female and male VIQ\n",
+    "female = data[data.Gender == 'Female']['VIQ']\n",
+    "male = data[data.Gender == 'Male']['VIQ']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_indResult(statistic=-0.7726161723275011, pvalue=0.44452876778583217)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use stats.ttest_ind() on the two series\n",
+    "stats.ttest_ind(female, male)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Paired tests: repeated measurements on the same indivuals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The PIQ, VIQ and FSIQ are three different measures of IQ in the same individual.   \n",
+    "We can first look if FSIQ and PIQ are different using the 2-sample t-test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_indResult(statistic=0.465637596380964, pvalue=0.6427725009414841)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# run a 2-sample t-test on FSIQ and PIQ\n",
+    "stats.ttest_ind(data['FSIQ'], data['PIQ'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However this doesn't account for individual differences contributing to variance in data.  \n",
+    "We can use a **paired t-test** or **repeated measures test** to account for these individual differences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_relResult(statistic=1.7842019405859857, pvalue=0.08217263818364236)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# use the ttest_rel() to do a paired t-test on FSIQ and PIQ\n",
+    "stats.ttest_rel(data['FSIQ'], data['PIQ'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is actually equivalent to doing a 1-sample t-test on the difference of the two measures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_1sampResult(statistic=1.7842019405859857, pvalue=0.08217263818364236)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 1-sample ttest on the difference\n",
+    "stats.ttest_1samp(data['FSIQ'] - data['PIQ'], 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These tests assume normality in the data. A non-parametric alternative is the Wilcoxian signed rank test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WilcoxonResult(statistic=274.5, pvalue=0.10659492713506856)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# for a non-parametric version of this test, use stats.wilcoxon()\n",
+    "stats.wilcoxon(data['FSIQ'], data['PIQ'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: \n",
+    "The corresponding test in the non paired case is the Mann–Whitney U test, `scipy.stats.mannwhitneyu()`. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear models, multiple factors, and analysis of variance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given two set of observations, x and y, we want to test the hypothesis that y is a linear function of x.\n",
+    "We will use the statsmodels module to:\n",
+    "\n",
+    "1. Fit a linear model. We will use the simplest strategy, ordinary least squares (OLS).\n",
+    "2. Test that coef is non zero.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's simulate some data according to the model\n",
+    "# import numpy\n",
+    "import numpy as np\n",
+    "# create range of 20 x values over -5 to 5 using np.linspace()\n",
+    "x = np.linspace(-5,5,20)\n",
+    "# set random seed to 1\n",
+    "np.random.seed(1)\n",
+    "# normal distributed noise of y = -5 + 3x + 4 * np.random.normal(size=x.shape)\n",
+    "y = -5 + 3*x + 4 * np.random.normal(size=x.shape)\n",
+    "# Create a DataFrame named sim_data containing all the relevant variables\n",
+    "fake_data = pd.DataFrame({\"x\": x, \"y\": y})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import ols from statsmodels.formmula.api\n",
+    "from statsmodels.formula.api import ols\n",
+    "\n",
+    "# Specify an OLS model (y ~ x) and fit it using the .fit() method \n",
+    "model = ols('y ~ x', fake_data).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Intercept   -5.533459\n",
+      "x            2.936888\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inspect the results of the model using model.summary()\n",
+    "model.summary()\n",
+    "print(model.params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve the model params, note tab completion\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Categorical variables: comparing groups or multiple categories"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>133</td>\n",
+       "      <td>132</td>\n",
+       "      <td>124</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>64.5</td>\n",
+       "      <td>816932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>140</td>\n",
+       "      <td>150</td>\n",
+       "      <td>124</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.5</td>\n",
+       "      <td>1001121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>139</td>\n",
+       "      <td>123</td>\n",
+       "      <td>150</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>73.3</td>\n",
+       "      <td>1038437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>133</td>\n",
+       "      <td>129</td>\n",
+       "      <td>128</td>\n",
+       "      <td>172.0</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>965353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>137</td>\n",
+       "      <td>132</td>\n",
+       "      <td>134</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>951545</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender  FSIQ  VIQ  PIQ  Weight  Height  MRI_Count\n",
+       "1  Female   133  132  124   118.0    64.5     816932\n",
+       "2    Male   140  150  124     NaN    72.5    1001121\n",
+       "3    Male   139  123  150   143.0    73.3    1038437\n",
+       "4    Male   133  129  128   172.0    68.8     965353\n",
+       "5  Female   137  132  134   147.0    65.0     951545"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# remind ourselves of our data using head()\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>           <td>VIQ</td>       <th>  R-squared:         </th> <td>   0.015</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.010</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>  0.5969</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 06 Dec 2018</td> <th>  Prob (F-statistic):</th>  <td> 0.445</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>18:51:58</td>     <th>  Log-Likelihood:    </th> <td> -182.42</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>    40</td>      <th>  AIC:               </th> <td>   368.8</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>    38</td>      <th>  BIC:               </th> <td>   372.2</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th>      <td>  109.4500</td> <td>    5.308</td> <td>   20.619</td> <td> 0.000</td> <td>   98.704</td> <td>  120.196</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Gender[T.Male]</th> <td>    5.8000</td> <td>    7.507</td> <td>    0.773</td> <td> 0.445</td> <td>   -9.397</td> <td>   20.997</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>26.188</td> <th>  Durbin-Watson:     </th> <td>   1.709</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>   3.703</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td> 0.010</td> <th>  Prob(JB):          </th> <td>   0.157</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 1.510</td> <th>  Cond. No.          </th> <td>    2.62</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                    VIQ   R-squared:                       0.015\n",
+       "Model:                            OLS   Adj. R-squared:                 -0.010\n",
+       "Method:                 Least Squares   F-statistic:                    0.5969\n",
+       "Date:                Thu, 06 Dec 2018   Prob (F-statistic):              0.445\n",
+       "Time:                        18:51:58   Log-Likelihood:                -182.42\n",
+       "No. Observations:                  40   AIC:                             368.8\n",
+       "Df Residuals:                      38   BIC:                             372.2\n",
+       "Df Model:                           1                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==================================================================================\n",
+       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "----------------------------------------------------------------------------------\n",
+       "Intercept        109.4500      5.308     20.619      0.000      98.704     120.196\n",
+       "Gender[T.Male]     5.8000      7.507      0.773      0.445      -9.397      20.997\n",
+       "==============================================================================\n",
+       "Omnibus:                       26.188   Durbin-Watson:                   1.709\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                3.703\n",
+       "Skew:                           0.010   Prob(JB):                        0.157\n",
+       "Kurtosis:                       1.510   Cond. No.                         2.62\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can write a comparison between VIQ of male and female \n",
+    "# (VIQ ~ Gender) using a linear model:\n",
+    "model = ols(\"VIQ ~ Gender\", data).fit()\n",
+    "\n",
+    "# ols automatically detects Gender as categorical\n",
+    "# model = ols('VIQ ~ C(Gender)', data).fit()\n",
+    "\n",
+    "# view results of the model using .summary()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multiple Regression: including multiple factors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read in our iris data https://www.scipy-lectures.org/_downloads/iris.csv\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use seaborns pairplot() function and hue='name', use ; to remove output\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sepal and petal size tend to be related: bigger flowers are bigger!  \n",
+    "But is there in addition a systematic effect of species?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's make a model that takes into account this potential interaction of name (sepal_width ~ name + petal_length)\n",
+    "\n",
+    "\n",
+    "# view the results of the model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis of Variance (ANOVA)\n",
+    "#### 1-Way Anova"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remind ourselves of our iris data\n",
+    "iris = pd.read_csv(\"https://www.scipy-lectures.org/_downloads/iris.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal_length  sepal_width  petal_length  petal_width    name\n",
+       "0           5.1          3.5           1.4          0.2  setosa\n",
+       "1           4.9          3.0           1.4          0.2  setosa\n",
+       "2           4.7          3.2           1.3          0.2  setosa\n",
+       "3           4.6          3.1           1.5          0.2  setosa\n",
+       "4           5.0          3.6           1.4          0.2  setosa"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iris.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# let's visualize the difference species and their sepal_length in \n",
+    "# a box plot using seaborns box_plot()\n",
+    "sns.boxplot(x=\"name\", y=\"sepal_length\", data=iris);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we need to split our data to run an anova using stats.f_oneway()\n",
+    "# make a variable for setosa, versicolor and virginica on sepal_length\n",
+    "setosa = iris[iris['name'] == 'setosa']['sepal_length']\n",
+    "versicolor = iris[iris['name'] == 'versicolor']['sepal_length']\n",
+    "virginica = iris[iris['name'] == 'virginica']['sepal_length']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "119.26450218450468 1.6696691907693826e-31\n"
+     ]
+    }
+   ],
+   "source": [
+    "# use stats.f_oneway() on the series you made for each species and \n",
+    "# get an f_value and p_value\n",
+    "f_value, p_value = stats.f_oneway(setosa, versicolor, virginica)\n",
+    "\n",
+    "# print the results\n",
+    "print(f_value, p_value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             sum_sq     df           F        PR(>F)\n",
+      "name      63.212133    2.0  119.264502  1.669669e-31\n",
+      "Residual  38.956200  147.0         NaN           NaN\n"
+     ]
+    }
+   ],
+   "source": [
+    "# another way to do it that prevents having to split your data\n",
+    "# import sm from statsmodels.api and import ols from statsmodels.formula.api\n",
+    "import statsmodels.api as sm\n",
+    "from statsmodels.formula.api import ols\n",
+    "\n",
+    "# specify the model (sepal_length ~ name) and fit\n",
+    "model = ols('sepal_length ~ name', iris).fit()\n",
+    "\n",
+    "# make an aov table using sm.stats.anova_lm() using type=2 since we don't have an interaction term\n",
+    "aov_table = sm.stats.anova_lm(model, typ=2)\n",
+    "\n",
+    "# print the aov_table\n",
+    "print(aov_table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ols('FSIQ ~ Gender + MRI_Count + Gender:MRI_Count', data).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aov_table = sm.stats.anova_lm(model, typ=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                    df        sum_sq      mean_sq         F    PR(>F)\n",
+      "Gender             1.0     96.100000    96.100000  0.187565  0.667535\n",
+      "MRI_Count          1.0   3864.000366  3864.000366  7.541622  0.009353\n",
+      "Gender:MRI_Count   1.0    212.959469   212.959469  0.415647  0.523205\n",
+      "Residual          36.0  18444.840165   512.356671       NaN       NaN\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(aov_table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lessons/python/statistics/intro-stats-python.ipynb b/lessons/python/statistics/intro-stats-python.ipynb
new file mode 100755
index 000000000..24d1cad9b
--- /dev/null
+++ b/lessons/python/statistics/intro-stats-python.ipynb
@@ -0,0 +1,1553 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to Statistics in Python\n",
+    "https://www.scipy-lectures.org/packages/statistics/index.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing and Exploring our Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import pandas and use magic function\n",
+    "import pandas as pd\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import our data using pandas read_csv() function where delimiter = ';', index_col = 0, na_values = '.'\n",
+    "data = pd.read_csv('https://www.scipy-lectures.org/_downloads/brain_size.csv', \n",
+    "                   delimiter=';', index_col=0, na_values='.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>133</td>\n",
+       "      <td>132</td>\n",
+       "      <td>124</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>64.5</td>\n",
+       "      <td>816932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>140</td>\n",
+       "      <td>150</td>\n",
+       "      <td>124</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.5</td>\n",
+       "      <td>1001121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>139</td>\n",
+       "      <td>123</td>\n",
+       "      <td>150</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>73.3</td>\n",
+       "      <td>1038437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>133</td>\n",
+       "      <td>129</td>\n",
+       "      <td>128</td>\n",
+       "      <td>172.0</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>965353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>137</td>\n",
+       "      <td>132</td>\n",
+       "      <td>134</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>951545</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender  FSIQ  VIQ  PIQ  Weight  Height  MRI_Count\n",
+       "1  Female   133  132  124   118.0    64.5     816932\n",
+       "2    Male   140  150  124     NaN    72.5    1001121\n",
+       "3    Male   139  123  150   143.0    73.3    1038437\n",
+       "4    Male   133  129  128   172.0    68.8     965353\n",
+       "5  Female   137  132  134   147.0    65.0     951545"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check out our data using pandas df.head() function\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(40, 7)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# how many observations do we have? use pandas df.shape attribute\n",
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    Female\n",
+       "2      Male\n",
+       "3      Male\n",
+       "4      Male\n",
+       "5    Female\n",
+       "Name: Gender, dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check out one column with df['column name'] or df.column_nae\n",
+    "data.Gender.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make a groupby object on the dataframe\n",
+    "groupby_gender = data.groupby('Gender')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>111.9</td>\n",
+       "      <td>109.45</td>\n",
+       "      <td>110.45</td>\n",
+       "      <td>137.200000</td>\n",
+       "      <td>65.765000</td>\n",
+       "      <td>862654.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>115.0</td>\n",
+       "      <td>115.25</td>\n",
+       "      <td>111.60</td>\n",
+       "      <td>166.444444</td>\n",
+       "      <td>71.431579</td>\n",
+       "      <td>954855.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         FSIQ     VIQ     PIQ      Weight     Height  MRI_Count\n",
+       "Gender                                                         \n",
+       "Female  111.9  109.45  110.45  137.200000  65.765000   862654.6\n",
+       "Male    115.0  115.25  111.60  166.444444  71.431579   954855.4"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# take the mean of the groupby object across all measures using the .mean() method\n",
+    "groupby_gender.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# take a look at our data distributions and pair-wise correlations\n",
+    "from pandas import plotting\n",
+    "plotting.scatter_matrix(data[['Weight', 'Height', 'MRI_Count']]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plotting.scatter_matrix(data[['PIQ', 'VIQ', 'FSIQ']]);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Distributions and "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45e76efac8>"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.kdeplot(data['FSIQ'])\n",
+    "sns.kdeplot(data['PIQ'])\n",
+    "sns.kdeplot(data['VIQ'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45e7491668>"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.kdeplot(data['FSIQ'],cumulative=True)\n",
+    "sns.kdeplot(data['PIQ'],cumulative=True)\n",
+    "sns.kdeplot(data['VIQ'],cumulative=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hypothesis Testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1-sample t-test: Testing the value of a population mean. \n",
+    "\n",
+    "`scipy.stats.ttest_1samp()` tests if the population mean of data is likely to   \n",
+    "be equal to a given value (technically if observations are drawn from a Gaussian  \n",
+    "distributions of given population mean). It returns the T statistic, and the   \n",
+    "p-value (see the function’s help)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_1sampResult(statistic=30.088099970849328, pvalue=1.3289196468728067e-28)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# runa 1 sample t-test\n",
+    "stats.ttest_1samp(data['VIQ'], 0)   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2-sample t-test: testing for difference across populations\n",
+    "We have seen above that the mean VIQ in the male and female populations were different.  \n",
+    "To test if this is significant, we do a 2-sample t-test with scipy.stats.ttest_ind()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_indResult(statistic=-0.7726161723275011, pvalue=0.44452876778583217)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "female_viq = data[data['Gender'] == 'Female']['VIQ']\n",
+    "male_viq = data[data['Gender'] == 'Male']['VIQ']\n",
+    "stats.ttest_ind(female_viq, male_viq)   \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Paired tests: repeated measurements on the same indivuals"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The PIQ, VIQ and FSIQ are three different measures of IQ in the same individual.   \n",
+    "We can first look if FSIQ and PIQ are different using the 2-sample t-test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_indResult(statistic=0.465637596380964, pvalue=0.6427725009414841)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.ttest_ind(data['FSIQ'], data['PIQ'])  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However this doesn't account for individual differences contributing to variance in data.  \n",
+    "We can use a **paired t-test** or **repeated measures test** to account for these individual differences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_relResult(statistic=1.7842019405859857, pvalue=0.08217263818364236)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.ttest_rel(data['FSIQ'], data['PIQ'])   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is actually equivalent to doing a 1-sample t-test on the difference of the two measures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_1sampResult(statistic=1.7842019405859857, pvalue=0.08217263818364236)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.ttest_1samp(data['FSIQ'] - data['PIQ'], 0)   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These tests assume normality in the data. A non-parametric alternative is the Wilcoxian signed rank test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WilcoxonResult(statistic=274.5, pvalue=0.10659492713506856)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.wilcoxon(data['FSIQ'], data['PIQ'])   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note: \n",
+    "The corresponding test in the non paired case is the Mann–Whitney U test, `scipy.stats.mannwhitneyu()`. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear models, multiple factors, and analysis of variance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given two set of observations, x and y, we want to test the hypothesis that y is a linear function of x.\n",
+    "We will use the statsmodels module to:\n",
+    "\n",
+    "1. Fit a linear model. We will use the simplest strategy, ordinary least squares (OLS).\n",
+    "2. Test that coef is non zero.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's simulate some data according to the model\n",
+    "import numpy as np\n",
+    "\n",
+    "x = np.linspace(-5, 5, 20)\n",
+    "np.random.seed(1)\n",
+    "\n",
+    "# normal distributed noise\n",
+    "y = -5 + 3*x + 4 * np.random.normal(size=x.shape)\n",
+    "\n",
+    "# Create a data frame containing all the relevant variables\n",
+    "sim_data = pd.DataFrame({'x': x, 'y': y})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify an OLS model and fit it\n",
+    "from statsmodels.formula.api import ols\n",
+    "model = ols(\"y ~ x\", sim_data).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                      y   R-squared:                       0.804\n",
+      "Model:                            OLS   Adj. R-squared:                  0.794\n",
+      "Method:                 Least Squares   F-statistic:                     74.03\n",
+      "Date:                Thu, 06 Dec 2018   Prob (F-statistic):           8.56e-08\n",
+      "Time:                        11:51:32   Log-Likelihood:                -57.988\n",
+      "No. Observations:                  20   AIC:                             120.0\n",
+      "Df Residuals:                      18   BIC:                             122.0\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "Intercept     -5.5335      1.036     -5.342      0.000      -7.710      -3.357\n",
+      "x              2.9369      0.341      8.604      0.000       2.220       3.654\n",
+      "==============================================================================\n",
+      "Omnibus:                        0.100   Durbin-Watson:                   2.956\n",
+      "Prob(Omnibus):                  0.951   Jarque-Bera (JB):                0.322\n",
+      "Skew:                          -0.058   Prob(JB):                        0.851\n",
+      "Kurtosis:                       2.390   Cond. No.                         3.03\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inspect the results of the model fit\n",
+    "print(model.summary()) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intercept   -5.533459\n",
+       "x            2.936888\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Retrieve the model params, note tab completion\n",
+    "model.params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Categorical variables: comparing groups or multiple categories"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>133</td>\n",
+       "      <td>132</td>\n",
+       "      <td>124</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>64.5</td>\n",
+       "      <td>816932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>140</td>\n",
+       "      <td>150</td>\n",
+       "      <td>124</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.5</td>\n",
+       "      <td>1001121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>139</td>\n",
+       "      <td>123</td>\n",
+       "      <td>150</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>73.3</td>\n",
+       "      <td>1038437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>133</td>\n",
+       "      <td>129</td>\n",
+       "      <td>128</td>\n",
+       "      <td>172.0</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>965353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>137</td>\n",
+       "      <td>132</td>\n",
+       "      <td>134</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>951545</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender  FSIQ  VIQ  PIQ  Weight  Height  MRI_Count\n",
+       "1  Female   133  132  124   118.0    64.5     816932\n",
+       "2    Male   140  150  124     NaN    72.5    1001121\n",
+       "3    Male   139  123  150   143.0    73.3    1038437\n",
+       "4    Male   133  129  128   172.0    68.8     965353\n",
+       "5  Female   137  132  134   147.0    65.0     951545"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                    VIQ   R-squared:                       0.015\n",
+      "Model:                            OLS   Adj. R-squared:                 -0.010\n",
+      "Method:                 Least Squares   F-statistic:                    0.5969\n",
+      "Date:                Thu, 06 Dec 2018   Prob (F-statistic):              0.445\n",
+      "Time:                        11:56:48   Log-Likelihood:                -182.42\n",
+      "No. Observations:                  40   AIC:                             368.8\n",
+      "Df Residuals:                      38   BIC:                             372.2\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "==================================================================================\n",
+      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "----------------------------------------------------------------------------------\n",
+      "Intercept        109.4500      5.308     20.619      0.000      98.704     120.196\n",
+      "Gender[T.Male]     5.8000      7.507      0.773      0.445      -9.397      20.997\n",
+      "==============================================================================\n",
+      "Omnibus:                       26.188   Durbin-Watson:                   1.709\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                3.703\n",
+      "Skew:                           0.010   Prob(JB):                        0.157\n",
+      "Kurtosis:                       1.510   Cond. No.                         2.62\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# We can write a comparison between IQ of male and female using a linear model:\n",
+    "\n",
+    "model = ols(\"VIQ ~ Gender\", data).fit()\n",
+    "\n",
+    "# ols automatically detects Gender as categorical\n",
+    "# model = ols('VIQ ~ C(Gender)', data).fit()\n",
+    "\n",
+    "print(model.summary())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Link to t-tests between different FSIQ and PIQ\n",
+    "\n",
+    "To compare different types of IQ, we need to create a “long-form” table,   \n",
+    "listing IQs, where the type of IQ is indicated by a categorical variable:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iq_melt = pd.melt(data, value_vars=['FSIQ', 'PIQ'], value_name='iq', var_name=\"type\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>type</th>\n",
+       "      <th>iq</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>FSIQ</td>\n",
+       "      <td>133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>FSIQ</td>\n",
+       "      <td>140</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>FSIQ</td>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>FSIQ</td>\n",
+       "      <td>133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>FSIQ</td>\n",
+       "      <td>137</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   type   iq\n",
+       "0  FSIQ  133\n",
+       "1  FSIQ  140\n",
+       "2  FSIQ  139\n",
+       "3  FSIQ  133\n",
+       "4  FSIQ  137"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iq_melt.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:                     iq   R-squared:                       0.003\n",
+      "Model:                            OLS   Adj. R-squared:                 -0.010\n",
+      "Method:                 Least Squares   F-statistic:                    0.2168\n",
+      "Date:                Thu, 06 Dec 2018   Prob (F-statistic):              0.643\n",
+      "Time:                        12:05:30   Log-Likelihood:                -364.35\n",
+      "No. Observations:                  80   AIC:                             732.7\n",
+      "Df Residuals:                      78   BIC:                             737.5\n",
+      "Df Model:                           1                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "===============================================================================\n",
+      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "-------------------------------------------------------------------------------\n",
+      "Intercept     113.4500      3.683     30.807      0.000     106.119     120.781\n",
+      "type[T.PIQ]    -2.4250      5.208     -0.466      0.643     -12.793       7.943\n",
+      "==============================================================================\n",
+      "Omnibus:                      164.598   Durbin-Watson:                   1.531\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):                8.062\n",
+      "Skew:                          -0.110   Prob(JB):                       0.0178\n",
+      "Kurtosis:                       1.461   Cond. No.                         2.62\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = ols(\"iq ~ type\", iq_melt).fit()\n",
+    "print(model.summary())  \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that we can get the same results now of doing the independant t-tests for each pariing. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ttest_indResult(statistic=0.2570673685607642, pvalue=0.7978036781233253)"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats.ttest_ind(data['VIQ'], data['PIQ']) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Multiple Regression: including multiple factors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "iris = pd.read_csv('https://www.scipy-lectures.org/_downloads/iris.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 804.75x720 with 20 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.pairplot(iris, hue='name');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sepal and petal size tend to be related: bigger flowers are bigger!  \n",
+    "But is there in addition a systematic effect of species?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                            OLS Regression Results                            \n",
+      "==============================================================================\n",
+      "Dep. Variable:            sepal_width   R-squared:                       0.478\n",
+      "Model:                            OLS   Adj. R-squared:                  0.468\n",
+      "Method:                 Least Squares   F-statistic:                     44.63\n",
+      "Date:                Thu, 06 Dec 2018   Prob (F-statistic):           1.58e-20\n",
+      "Time:                        12:10:56   Log-Likelihood:                -38.185\n",
+      "No. Observations:                 150   AIC:                             84.37\n",
+      "Df Residuals:                     146   BIC:                             96.41\n",
+      "Df Model:                           3                                         \n",
+      "Covariance Type:            nonrobust                                         \n",
+      "======================================================================================\n",
+      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
+      "--------------------------------------------------------------------------------------\n",
+      "Intercept              2.9813      0.099     29.989      0.000       2.785       3.178\n",
+      "name[T.versicolor]    -1.4821      0.181     -8.190      0.000      -1.840      -1.124\n",
+      "name[T.virginica]     -1.6635      0.256     -6.502      0.000      -2.169      -1.158\n",
+      "petal_length           0.2983      0.061      4.920      0.000       0.178       0.418\n",
+      "==============================================================================\n",
+      "Omnibus:                        2.868   Durbin-Watson:                   1.753\n",
+      "Prob(Omnibus):                  0.238   Jarque-Bera (JB):                2.885\n",
+      "Skew:                          -0.082   Prob(JB):                        0.236\n",
+      "Kurtosis:                       3.659   Cond. No.                         54.0\n",
+      "==============================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = ols('sepal_width ~ name + petal_length', iris).fit()\n",
+    "print(model.summary())  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Analysis of Variance (ANOVA)\n",
+    "#### 1-Way Anova"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>133</td>\n",
+       "      <td>132</td>\n",
+       "      <td>124</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>64.5</td>\n",
+       "      <td>816932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>140</td>\n",
+       "      <td>150</td>\n",
+       "      <td>124</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.5</td>\n",
+       "      <td>1001121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>139</td>\n",
+       "      <td>123</td>\n",
+       "      <td>150</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>73.3</td>\n",
+       "      <td>1038437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>133</td>\n",
+       "      <td>129</td>\n",
+       "      <td>128</td>\n",
+       "      <td>172.0</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>965353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>137</td>\n",
+       "      <td>132</td>\n",
+       "      <td>134</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>951545</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender  FSIQ  VIQ  PIQ  Weight  Height  MRI_Count\n",
+       "1  Female   133  132  124   118.0    64.5     816932\n",
+       "2    Male   140  150  124     NaN    72.5    1001121\n",
+       "3    Male   139  123  150   143.0    73.3    1038437\n",
+       "4    Male   133  129  128   172.0    68.8     965353\n",
+       "5  Female   137  132  134   147.0    65.0     951545"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45e6c9fba8>"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot('name', \"sepal_length\", data=iris)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "setosa = iris[iris['name'] == 'setosa']['sepal_length']\n",
+    "versicolor = iris[iris['name'] == 'versicolor']['sepal_length']\n",
+    "virginica = iris[iris['name'] == 'virginica']['sepal_length']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_value, p_value = stats.f_oneway(setosa, versicolor, virginica)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "119.26450218450468 1.6696691907693826e-31\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f_value, p_value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>FSIQ</th>\n",
+       "      <th>VIQ</th>\n",
+       "      <th>PIQ</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>MRI_Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>133</td>\n",
+       "      <td>132</td>\n",
+       "      <td>124</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>64.5</td>\n",
+       "      <td>816932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>140</td>\n",
+       "      <td>150</td>\n",
+       "      <td>124</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>72.5</td>\n",
+       "      <td>1001121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>139</td>\n",
+       "      <td>123</td>\n",
+       "      <td>150</td>\n",
+       "      <td>143.0</td>\n",
+       "      <td>73.3</td>\n",
+       "      <td>1038437</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>133</td>\n",
+       "      <td>129</td>\n",
+       "      <td>128</td>\n",
+       "      <td>172.0</td>\n",
+       "      <td>68.8</td>\n",
+       "      <td>965353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>137</td>\n",
+       "      <td>132</td>\n",
+       "      <td>134</td>\n",
+       "      <td>147.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>951545</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender  FSIQ  VIQ  PIQ  Weight  Height  MRI_Count\n",
+       "1  Female   133  132  124   118.0    64.5     816932\n",
+       "2    Male   140  150  124     NaN    72.5    1001121\n",
+       "3    Male   139  123  150   143.0    73.3    1038437\n",
+       "4    Male   133  129  128   172.0    68.8     965353\n",
+       "5  Female   137  132  134   147.0    65.0     951545"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = data.dropna(axis=0, how='any')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender       0\n",
+       "FSIQ         0\n",
+       "VIQ          0\n",
+       "PIQ          0\n",
+       "Weight       0\n",
+       "Height       0\n",
+       "MRI_Count    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             sum_sq     df           F        PR(>F)\n",
+      "name      63.212133    2.0  119.264502  1.669669e-31\n",
+      "Residual  38.956200  147.0         NaN           NaN\n"
+     ]
+    }
+   ],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "from statsmodels.formula.api import ols\n",
+    " \n",
+    "mod = ols('sepal_length ~ name',\n",
+    "                data=iris).fit();\n",
+    "                \n",
+    "aov_table = sm.stats.anova_lm(mod, typ=2);\n",
+    "print(aov_table);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/lessons/python/statistics/lesson.md b/lessons/python/statistics/lesson.md
new file mode 100755
index 000000000..ab1ee8076
--- /dev/null
+++ b/lessons/python/statistics/lesson.md
@@ -0,0 +1,32 @@
+---
+layout: page
+title:
+visible: true
+tags:
+  # languages
+  - python
+  # levels
+  - intermediate
+---
+<!-- change visible to true if you want it on the site -->
+<!-- remove any tags listed above that are not relevant -->
+
+ - **Authors**: Lina Tran
+ - **Research field**: Physiology
+ - **Lesson topic**: Statistics in Python
+ - **Lesson content URL**: https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/python/statistics/intro-stats-python.ipynb
+
+Source: https://www.scipy-lectures.org/packages/statistics/index.html
+
+## Topics Covered
+- Hypothesis testing: 1-sample, 2-sample, paired t-tests.
+- Linear models, multiple factors, and analysis of variance
+- Multiple Regression (including multiple factors), ANOVA
+- Inspecting our data with seaborn/pandas
+
+## Packages Used
+- pandas
+- numpy
+- seaborn
+- scipy.stats
+- statsmodels
diff --git a/lessons/python/unittests/inclass/module.py b/lessons/python/unittests/inclass/module.py
new file mode 100644
index 000000000..3b7a99e87
--- /dev/null
+++ b/lessons/python/unittests/inclass/module.py
@@ -0,0 +1,20 @@
+''' 
+  A class we can use to test unit testing
+'''
+
+import numpy as np
+
+class Var(object):
+  ''' A container class for numpy arrays and some meta data '''
+
+  def __init__(self, name, data, axes=None, units=None):
+    ''' construct variable object with meta data '''
+    self.name = name
+    self.units = units
+    self.axes = axes
+    self.data = data
+
+  def __iadd__(self, data):
+    ''' overload in-place addition function: add number or arrays '''
+    self.data += data
+    return self
\ No newline at end of file
diff --git a/lessons/python/unittests/inclass/tests.py b/lessons/python/unittests/inclass/tests.py
new file mode 100644
index 000000000..52692d23e
--- /dev/null
+++ b/lessons/python/unittests/inclass/tests.py
@@ -0,0 +1,37 @@
+# import unittest stuff etc.
+import unittest, gc
+
+# import module we want to test
+from module import Var
+import numpy as np
+
+class TestCase(unittest.TestCase):
+  ''' a suite of unit tests '''
+  
+  def setUp(self):
+    ''' setup for all tests '''
+    self.var1 = Var(name='test', data=np.random.rand(5,5))
+    
+  def tearDown(self):
+    ''' cleanup for all tests '''
+    gc.collect() # for example, call garbage collector...
+  
+  # test cases come here
+  
+  def testCreate(self):
+    ''' test creation of variable objects '''
+    #assert isinstance(self.var1,Var)
+    self.assertIsInstance(self.var1,Var)
+  
+  def testAdd(self):
+    ''' test addition functions '''
+    ref_data = self.var1.data.copy()*1e6
+    self.var1 += 1
+    assert np.allclose( self.var1.data ,(ref_data+1e6)/1.e6 )
+    
+    
+  
+if __name__ == '__main__':
+  
+    # run all tests
+    unittest.main()
\ No newline at end of file
diff --git a/lessons/python/unittests/lesson.md b/lessons/python/unittests/lesson.md
new file mode 100644
index 000000000..b536fbcc2
--- /dev/null
+++ b/lessons/python/unittests/lesson.md
@@ -0,0 +1,26 @@
+---
+layout: page
+title: 'Unit Testing in Python'
+visible: true
+tags:
+  - python
+  - advanced
+---
+
+# A Brief Introduction to the Python unittest Module
+
+ - **Authors**: Andre R. Erler
+ - **Research field**: Atmospheric Physics
+ - **Lesson topic**: Unit Testing (software development)
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/unittests>
+
+The [unittest](https://docs.python.org/3/library/unittest.html) module primarily provides a class TestCase to facilitate the writing of tests for "units" of code inside a larger project.
+Larger software projects benefit from unit tests, because smaller units of the software can be tested individually and checked for errors before they are added to the main project. After a project has grown over time, a suite of unit tests can also be very helpful in debugging, because the source of an error can quickly be located.
+
+Typically test cases are implemented as child classes of the `unittest.TestCase` class. The methods `TestCase.setUp()` can be overloaded to provide common setup procedures for all test methods; the method `TestCase.tearDown()` can be used for cleanup; both methods are executed before (after) each test. All methods of the form `TestCase.test*()` are considered test cases and will be executed by `unittest`, if `unittest.main()` is called. Alternatively, tests can be run using the `python -m unittest` command with the module name.
+
+To run several tests suites at once, the command line utility `nosetests` (available in the separate `nose` package) can be used: it scans all Python scrips in the current directory and executes all available test cases.
+
+The code used during the lesson is available in the [inclass](https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/unittests/inclass) folder, the somewhat longer original material is available in the [original](https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/python/unittests/original) folder.
+
+An example of a more advanced use case, where individual test can be selected and inheritance between TestCase's is used, can be found [here](https://github.com/aerler/GeoPy/tree/master/src/geodata_test.py).
diff --git a/lessons/python/unittests/original/module.py b/lessons/python/unittests/original/module.py
new file mode 100644
index 000000000..ea6a7250c
--- /dev/null
+++ b/lessons/python/unittests/original/module.py
@@ -0,0 +1,38 @@
+''' 
+  A class we can use to test unit testing
+'''
+
+import numpy as np
+
+
+class Var(object):
+  ''' A container class for numpy arrays and some meta data '''
+
+  def __init__(self, name, data, axes=None, units=None):
+    ''' construct variable object with meta data '''
+    self.name = name
+    self.units = units
+    self.axes = axes
+    self.data = data
+    
+  def __iadd__(self, data):
+    ''' overload in-place addition function: add number or arrays '''
+    self.data += data
+    return self
+  
+  def __add__(self, other):
+    ''' overload addition function: add arrays and create new variable '''
+    # check input
+    if not isinstance(other, Var): return NotImplemented
+    # handle more specific errors and print useful info
+    if self.axes != other.axes: 
+      raise ValueError("Axes mismatch: {:s} != {:s}".format(self.axes,other.axes))
+    if self.units != other.units: 
+      raise ValueError("Units mismatch: {:s} != {:s}".format(self.units,other.units))
+    # add arrays
+    data = self.data + other.data
+    # create new variable object
+    name = self.name + '+' + other.name
+    new = Var(name, data, axes=self.axes, units=self.units)
+    # return result
+    return new
diff --git a/lessons/python/unittests/original/tests.py b/lessons/python/unittests/original/tests.py
new file mode 100644
index 000000000..d3d948dbd
--- /dev/null
+++ b/lessons/python/unittests/original/tests.py
@@ -0,0 +1,40 @@
+# import unittest stuff etc.
+import unittest, gc
+import numpy as np
+
+# import module we want to test
+from module import Var
+
+class TestCase(unittest.TestCase):
+  ''' a suite of unit tests '''
+  
+  def setUp(self):
+    ''' setup for all tests '''
+    self.var = Var('test',np.random.randn(5,5))
+  
+  def tearDown(self):
+    ''' cleanup for all tests '''
+    gc.collect() # for example, call garbage collector...
+  
+  
+  # test cases come here
+  
+  def testCreate(self):
+    ''' test creation of variable objects '''
+    self.assertIsInstance(self.var,Var)
+  
+  def testAdd(self):
+    # test in-place addition
+    ref_data = self.var.data.copy() + 2
+    self.var += 2
+    assert np.all( self.var.data == ref_data )
+    # test regular addition
+    new = self.var + self.var
+    self.assertIsInstance(new,Var)
+    assert np.all( new.data == self.var.data*2 )
+  
+  
+if __name__ == '__main__':
+  
+    # run all tests
+    unittest.main()
\ No newline at end of file
diff --git a/lessons/r/blogdown-intro/lesson.Rmd b/lessons/r/blogdown-intro/lesson.Rmd
new file mode 100644
index 000000000..f92a898bf
--- /dev/null
+++ b/lessons/r/blogdown-intro/lesson.Rmd
@@ -0,0 +1,378 @@
+---
+title: Creating websites with RStudio and blogdown
+author: Vicki M. Zhang, Ahmed Hasan
+date: '2021-12-01'
+---
+
+-   **Authors**: Vicki M. Zhang, Ahmed Hasan
+-   **Lesson Topic**: Creating websites with RStudio and blogdown
+
+```{r include = FALSE}
+knitr::opts_chunk$set(eval = FALSE, echo = FALSE, collapse = TRUE, comment = NA, tidy = FALSE)
+```
+
+# Introduction
+
+## Why create a static site?
+
+With the multitude of website-building platforms touting a "website in less than
+5 minutes", there are plenty of ways to put up a personal site to showcase your
+research. However, there are several reasons to switch from something like Wix,
+WordPress or SquareSpace to your own static site.
+
+Firstly, **no ads**. Usually, website generations require you to upgrade your
+subscription to remove ads from your site. Secondly, **you can fix your own bugs**. 
+In contrast, the bugs that might plague your generated site - such as misbehaving
+widgets or inconsistent behaviours across platforms - might be
+subject to the developers to fix. But thirdly, and I think most importantly,
+**learning a new thing is fun**. Making your own static site using RStudio and
+`blogdown` is a very rewarding challenge.
+
+## What is blogdown?
+
+The package that we are using is `blogdown`, developed by [Yihui
+Xie](https://yihui.org/en/), [Amber Thomas](https://amber.rbind.io/), [Alison
+Presmanes Hill](https://alison.rbind.io/). It allows users to generate a static
+website using R markdown files. Note that today, we won't have time to push
+everything to GitHub, which serves our website on the web. We will only be
+creating our site locally - please refer to the [official blogdown
+guide](https://bookdown.org/yihui/blogdown/deployment.html) under 'Deployment'
+for more on getting a blogdown website live for the world to see.
+
+## What is Hugo?
+
+Hugo is a open-source static site generator. It comes with multiple themes, and
+it is fully customizable via **widgets**, which are content blocks on a webpage.
+The theme that we are using today is the [Hugo
+Lithium](https://github.com/yihui/hugo-lithium) theme, which is a relatively simple
+but flexible template to create a first website with. More themes can be found on
+the [Hugo themes gallery](https://themes.gohugo.io/). 
+
+# Installing 
+
+If you don't already have blogdown installed, make sure to do so:
+
+```{r}
+install.packages('blogdown')
+```
+
+You also need to create a project in R that will store all of your website's files. To do this, click:
+
+Click File > New Project > New Directory > New Project.
+
+Then, locate the folder and name the folder that will contain your files (make sure it's empty!).
+
+Click Create Project.
+
+```{r}
+library(blogdown)
+new_site(theme = "yihui/hugo-lithium") # an optional dir arg can be provided too
+```
+
+
+# Modifying your site
+
+## What do you want your site to look like?
+
+Let's assume we are making a simple personal academic website. From this follows a structure
+when we consider the things we might want on there: for instance, a CV page, a blog posts page,
+and perhaps also a contact page. 
+
+### The `config.toml` file
+
+No matter how we want our website to look, modifying any blogdown theme starts
+with the `config.toml` file. Every blogdown theme will have one of these (note
+that in rare instances, the file may be called `config.yaml` instead).
+
+In the Hugo Lithium theme, this file will be found in the root of the project (it may be
+in a `content` folder or something similar in other themes) and should look like a series
+of key-value pairs - something like this:
+
+```
+title = "A Hugo website"
+theme = "hugo-lithium"
+googleAnalytics = ""
+disqusShortname = ""
+```
+
+Don't worry if some of the keys/values there don't make a lot of sense right now -
+turns out a lot of these are set to sensible defaults and likely won't need to
+be modified. For now, let's modify the title and input our socials in the designated
+parts of the file:
+
+```
+title = "My Hugo website"
+theme = "hugo-lithium"
+googleAnalytics = ""
+disqusShortname = ""
+ignoreFiles = ["\\.Rmd$", "\\.Rmarkdown$", "_cache$", "\\.knit\\.md$", "\\.utf8\\.md$"]
+
+[permalinks]
+    post = "/:year/:month/:day/:slug/"
+
+[[menu.main]]
+    name = "About"
+    url = "/about/"
+[[menu.main]]
+    name = "GitHub"
+    url = "https://github.com/UofTCoders"
+[[menu.main]]
+    name = "Twitter"
+    url = "https://twitter.com/UofTCoders"
+```
+
+We'll be revisiting this file over the course of the lesson as we modify our website further.
+For now, however, let's save the `.toml` file and then get a live look at these changes.
+For this purpose, blogdown includes a function called `serve_site` that'll render a local
+instance of your website right here in RStudio. Let's give it a go:
+
+```{r}
+blogdown::serve_site()
+```
+
+This local instance of your website will show up in the Viewer pane in the
+bottom right of your RStudio instance. Note that **this is live** - that means
+if you make any changes to your website, they will be immediately reflected in
+the viewer! This also means `serve_site()` only has to be run once, which
+simplifies things a good bit. 
+
+## Working with pages
+
+### Updating an existing page
+
+We've now updated the GitHub and Twitter links seen on the top right of our website,
+but if we click on About, we see a brief blurb about the theme itself. Let's change that up!
+The About page (and all other pages for our websites) is rendered from the `content` folder -
+in this case, specifically in a file called `content/about.md`. Let's open that up and modify
+the text to something like:
+
+```
+This is my new About page. Interests of mine include R and blogdown.
+```
+
+After the file is saved, the About page should be updated live in the Viewer
+pane. Note that Markdown styling is fully supported here and blogdown will
+faithfully render text however it's been styled:
+
+```
+This is my new About page. Interests of mine include **R** and **blogdown**. They're _really_ neat.
+```
+
+### Creating a new page
+
+Of course, we might want to create pages beyond just the defaults that a theme provides us. Let's
+say we want to add a page containing a CV summary. 
+
+First off, we revisit `config.toml`. Remember the following section?
+
+```
+[[menu.main]]
+    name = "About"
+    url = "/about/"
+[[menu.main]]
+    name = "GitHub"
+    url = "https://github.com/UofTCoders"
+[[menu.main]]
+    name = "Twitter"
+    url = "https://twitter.com/UofTCoders"
+```
+
+In the Lithium theme, this is where the page structure of our website has been
+defined. You might notice that the About page's url is simply `about`. It follows
+that if we want to create a new page, say one called 'CV':
+
+1. This part of the config file will need to be modified, and
+2. We'll need to make an `.md` file, much like `about.md`, containing the contents of the file.
+
+Fortunately, blogdown simplifies the second step in this process for us with a
+function called `new_content`. Let's try it out:
+
+```{r}
+new_content('content/cv.md')
+```
+
+This should immediately open a new Markdown file in RStudio for us to work with.
+
+Before we add content, you might notice that the top of the file looks something like this:
+
+```
+---
+title: ''
+date: ''
+---
+```
+
+This is referred to as a **YAML header** and is where we can store some metadata about the file
+(or in this case, the page). Let's update it:
+
+```
+---
+title: 'Curriculum Vitae'
+date: '2021-12-01'
+author: 'me!'
+---
+```
+
+We might notice that despite adding a date and author field, neither actually
+shows in the Viewer pane! This is because the parts of the YAML header that show
+on the page are governed by the theme. Modifying themes is beyond the scope of
+this lesson, but definitely something worth learning if you'd like to take your
+blogdown journey further and customize your site in detail.
+
+In either case, let's add some content underneath the header:
+
+```
+## Research Publications
+
+Me, My cat. (2021) The efficacy of giving cats pats on the head. _Nature_. 
+Me, My cat. (2020) Why treats for your cat are always a great idea. _Science_.
+
+## Other projects in progress
+
+Me. (2021) Earl Grey tea provides significant boost to mood. _Cell_.
+```
+
+The Viewer pane should continue to update live as soon as we save the file. As
+great as this page now looks, we still do need to update the header to add a
+link! Here's where things get a little theme-dependent - different themes may
+have different approaches to `config.toml` files, and may require a bit of
+detective work to modify correctly. Usually, understanding how the existing
+template pages are organized is enough to understand how to add our own pages
+as needed (or even potentially remove some that are included in the theme).
+
+Let's head back to `config.toml` and add a link to the CV page:
+
+```
+[[menu.main]]
+    name = "About"
+    url = "/about/"
+[[menu.main]]
+    name = "GitHub"
+    url = "https://github.com/UofTCoders"
+[[menu.main]]
+    name = "Twitter"
+    url = "https://twitter.com/rstudio"
+[[menu.main]]
+    name = "CV"
+    url = "/cv/"
+```
+
+Here, we have to make sure the url field specifically points to our Markdown file,
+which was named `cv.md` (see how About is formatted at the top, for instance).
+Once `config.toml` is saved, there should now be a CV tab in the Viewer pane! 
+
+## Making new blog posts
+
+To round out the lesson, we'll have a look at how to make blog posts for your website.
+Most blogdown themes will have blogging functionality built in (it's in the name after all)
+which means that making new blog posts can be done entirely through a blogdown function
+once more.
+
+We make a new post by running:
+
+```{r}
+blogdown::new_post(title = "My first post", 
+                   ext = '.Rmd', 
+                   subdir = "post")
+```
+
+
+This creates a new .Rmd file in a new folder, all in `content/post`, and
+automatically navigates to it. Then, in your directory, you will be able to see
+the folder with the **slug**, or the unique part of the URL that identifies your
+post. In your folder, all of the files needed for this blog post will be
+bundled, including the main .Rmd post, as well as any images.
+
+As before, the Viewer should immediately update with our first post, and RStudio
+will be primed for editing it. Something that should immediately stick out is the
+increased number of fields in the YAML header for a blog post:
+
+```
+---
+title: My first post
+author: ''
+date: '2021-12-01'
+slug: my-first-post
+categories: []
+tags: []
+---
+```
+
+Categories and tags are user-defined and can be used to sort blog posts by topic
+and more. For now, however, we'll leave those blank and just modify the author
+field at most before we get writing.
+
+Let's add some content. Note that **this is still a legitimate R Markdown
+file**, and that means that features like code chunks and inline output are all
+supported!
+
+Let's try that out and see for ourselves:
+
+````
+---
+title: My first post
+author: 'Me'
+date: '2021-12-01'
+slug: my-first-post
+categories: []
+tags: []
+---
+
+This is my first blog post in blogdown. It essentially allows me to
+write an R Markdown file and have it rendered on a website.
+
+Here's some _styling_ using Markdown syntax.
+
+Here's a code chunk:
+
+```{r}`r ''`
+2 + 2
+```
+
+And a plot:
+
+```{r}`r ''`
+plot(iris$Sepal.Length, iris$Sepal.Width)
+```
+
+````
+
+Once this file is saved, the website will render the full contents of the Rmd
+file and _also run each code chunk_, placing the output of each just below
+the corresponding lines of code. That includes plots! This right here is
+an especially powerful feature of blogdown - it's very amenable to writing
+programming tutorials and such because of inline output rendering.
+
+If rendering this on a machine that has Python installed, Python code can
+be run here as well:
+
+````
+```{python}
+l = [1, 2, 3]
+for i in l:
+    print(i, i * 2)
+```
+````
+
+# Conclusion
+
+In today's lesson, we've covered:
+
+- Creating a blogdown site using a theme
+- Understanding the role of the `config.toml` file
+- Creating a new page and modifying existing pages
+- Creating a new blog post
+
+One important thing we did _not_ cover is how to push your website to a hosting
+service (e.g. GitHub, Netlify). This is a bit more involved - but not
+necessarily difficult - and a full guide on how to do so can be found in the
+[official blogdown guide](https://bookdown.org/yihui/blogdown/deployment.html)
+under 'Deployment'.
+
+## Further reading
+
+-   [What is hard about blogdown?](https://community.rstudio.com/t/what-is-hard-about-blogdown/8108)
+-   [Up and Running with blogdown](https://alison.rbind.io/post/2017-06-12-up-and-running-with-blogdown/)
+-   [Creating a website with the academic theme in blogdown](https://www.kevinzolea.com/post/blogdown/creating-a-website-with-the-academic-theme-in-blogdown/)
+-   [Overwhelmed by Hugo academic theme: a beginner's guide](https://andreaczhang.rbind.io/post/my-1st-blogpost/)
+-   [Fun blogdown in R to design a personal website](https://annielyu.com/2020/01/12/blogdown-website/)
diff --git a/lessons/r/blogdown-intro/lesson.md b/lessons/r/blogdown-intro/lesson.md
new file mode 100644
index 000000000..756730cb5
--- /dev/null
+++ b/lessons/r/blogdown-intro/lesson.md
@@ -0,0 +1,12 @@
+---
+title: Creating websites with RStudio and blogdown
+author: Vicki M. Zhang, Ahmed Hasan
+date: '2021-12-01'
+---
+
+-   **Authors**: Vicki M. Zhang, Ahmed Hasan
+-   **Lesson Topic**: Creating websites with RStudio and blogdown
+
+Lesson material can be found
+[here](https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/blogdown-intro)
+under `lesson.Rmd`.
diff --git a/lessons/r/clustering/Clustering.Rmd b/lessons/r/clustering/Clustering.Rmd
new file mode 100644
index 000000000..ef13494e8
--- /dev/null
+++ b/lessons/r/clustering/Clustering.Rmd
@@ -0,0 +1,201 @@
+---
+title: "ClusteringLesson"
+author: "Tara"
+date: "2/4/2022"
+output:
+  pdf_document: default
+  html_document: default
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+#Introduction
+
+Today we will learn two techniques that we can use to explore our data and find patterns in our dataset. Clustering analysis is a type of unsupervised machine learning that we use to find patterns or groupings in the data.
+
+The two methods we are learning are hierarchical agglomerative clustering and k-means clustering. The main difference between these two methods is that with hierarchical clustering, we choose the number of clusters we want a posteriori (or after we conduct the analysis), for k-means clustering, we determine the number of clusters we want a priori (prior to the anlalysis). In addition to the choice of the two clustering methods, there are a few methodological decisions we can make that will affect our results, we will dicuss some of these today. 
+
+##Setting up the data and preprocessing
+```{r}
+#Load in a dataset that is already in R
+data("mtcars")
+#Use the help function to learn about the dataset
+?mtcars
+#What types of variables do you see? Which ones do you find interesting? we can conduct clustering analysis on a subset of the variables or on the entire dataset because we have numeric (including two binary) variables
+```
+
+```{r}
+#preprocessing the data
+#1 check for missing values
+summary(mtcars)
+#this data set doesn't have any missing values so we can proceed with our analysis
+#Normalization and Scaling, chose either: 
+  #Normalize function from library(BBmisc)
+  #install.packages("BBmisc")
+  library("BBmisc")
+  dfNorm<- normalize(mtcars)
+  #scale the data using scale()
+  dfScaled<- scale(mtcars)
+```
+
+
+##Hierarchical Agglomerative Clustering
+For hierarchical agglomerative clustering begins with each observation in a distinct (singleton) cluster, and successively merges clusters together until a stopping criterion is satisfied. This approach can be visualized as a dendroagram, a tree-like diagram, showing how pairs of observations are fused together. 
+
+There are three different method choices that will affect the way our tree looks: 
+  (1) distance metric. this is the calculation that determines how similar the pairs are to eachother. 
+  (2) different algorithms for how to etermine how close pairs of observations are to eachother. This will give us different shapes of dendrogrmas. 
+
+```{r}
+#Step 1. Create the distance matrix
+  #using Euclidean distance, this is most common for data that has continuous numerical values
+dist_Euc <- dist(dfScaled, method = "euclidean")
+  #what other methods are there?
+?dist
+  #we can also look at the maximum distance, manhattan distance, canberra, binari, and minkowski. Let's also try the maximum distsance, create another distance matrix adn change the method agreemeent to "maximum"
+dist_Max <- dist(dfScaled, method = "maximum")
+```
+
+```{r}
+#Step 2. Plot the clustering algorithms
+#Run the code for how many clusters does it look like there are in each dendrogram?
+#complete method
+plot(hclust(dist_Euc, method = "complete"))
+#average method
+plot(hclust(dist_Euc, method = "average"))
+#single method
+plot(hclust(dist_Euc, method = "single"))
+#ward.D2
+plot(hclust(dist_Euc, method = "ward.D2"))
+
+#which method gives the most balanced?
+
+#try the exercise with the dist_Max matrix
+```
+
+```{r}
+#Step 3. Choosing the number of clusters Storing the Cluster ID for each observation
+
+#using the call bellow, we are taking our clustering and "cutting" the tree into four clusters. The call will return a vector where each observation is assigned to a cluster.
+cutree(hclust(dist_Euc, method = "complete"), k=4)
+
+#we can append this vector to a copy of the original dataframe to interpret the clusters
+df_hclust<- mtcars
+df_hclust$cluster<-cutree(hclust(dist_Euc, method = "complete"), k=4)
+```
+
+```{r}
+#Step. 4 Interpretation
+#how many cars are in each cluster?
+  summary(as.factor(df_hclust$cluster))
+#let's summarize by the groups of clusters
+  #need dplyr package
+  library(dplyr)
+
+#compare the mean mpg between clusters
+df_hclust_summary <- df_hclust %>% 
+  group_by(cluster) %>% 
+  summarize(mpg_mean = mean(mpg)) 
+
+#on your own compare another variable mean between clusters
+
+#visualize the clusters 
+library(ggplot2)
+df_hclust %>% 
+ggplot(aes(x =hp, y=mpg, col = as.factor(cluster)))+  geom_point()
+
+```
+
+
+##Kmeans Clustering
+K= the number of centres (the number of clusters that we specify apriori).
+K-means aims to divide data into K groups (clusters). This involves finding K centroids. Centroids are invented or existing points that represent the centers of the clusters. In K-means clustering the aim is to indetify K number of centroids and allocate every object to the nearest cluster.
+
+The algorithm for K-means is as follows:
+
+1. Place K points into the object data space. These points represent the initial group of centroids,
+
+2. Assign each data point to the closest centroid, based on Euclidean distance.
+
+3. Recalculate the positions of the K centroids. This is done by taking the mean of all data points assigned to that centroid’s cluster.
+
+4. Repeats steps 2 and 3 until the posistions of the centroids no longer change and the sum of distances of individual units from centroids is as small as possible.
+
+Here is a pipeline for conducting K-Means analysis. 
+
+Step 1: Preprocessing (scaling/normalization)
+Step 2: Figure out the optimal K-value
+Step 3: Run KMeans algorithm
+Step 4: Interpret
+
+
+###Step 2
+```{r}
+##Step 2: Figure out the optimal K-value. 
+#Different methodological approach: trial and error, choose a priori based on theoretical knowledge, choose based on the hierarchical agglomerative clustering result, or use an numerical approach: elbow plot, gap statistic, silohuette.
+#we can use these packages to do all three of these numerical approaches
+#install.packages("factoextra")
+library(factoextra)
+#install.packages("NbClust")
+library(NbClust)
+
+#elbow method
+fviz_nbclust(dfScaled, kmeans, method = "wss") +
+  labs(subtitle = "Elbow method")
+
+#silohuette
+fviz_nbclust(dfScaled, kmeans, method = "silhouette")+
+  labs(subtitle = "Silhouette method")
+
+#gap statistic, 
+set.seed(123)
+fviz_nbclust(dfScaled, kmeans, nstart = 10,  method = "gap_stat", nboot = 50)+
+  labs(subtitle = "Gap statistic method")
+
+```
+
+##Step 3: Run KMeans algorithm
+```{r}
+#set the seed for the analysis, for reproducibility
+set.seed(123)
+#run the algorithm to start the clusters
+kclust<- kmeans(dfScaled, nstart=1, centers=2, iter.max = 10)
+#try to look at what the results are in the kclust object
+kclust
+```
+
+###Step 4: Interpret
+
+```{r}
+#save the scaled data as a dataframe
+dfKmeans <- as.data.frame(dfScaled)
+#create a new column as of the clustering labels 
+dfKmeans$cluster <- as.factor(kclust$cluster)
+#now you group by the clusters, run stats or visualize 
+```
+
+
+##Heatmaps and other visualization methods (time permitting)
+
+```{r}
+#the factoextra package has many helpful functions for computing cluster analyses and visualizing them
+#another way to visualize clusters, helpful package 
+#note that the dimensions are created using Principal components analysis first
+fviz_cluster(kmeans(dfScaled, centers = 2), geom = "text", data = dfScaled)
+```
+
+##Heatmaps
+
+##Notes and other helpful resources: 
+PAM (Partitioning around mediods) works similar to kmeans but, uses a mediod (median) instead of the mean to create clusters. Kmeans uses the centroid, which is an artificial point, while PAM uses the mediod (a real point). 
+
+Here is a helpful textbook to learn more about the theory of these clustering techniques and applying unsupervised and supervised machine learning techniques in R:
+
+James, Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R (Vol. 103). Springer. https://doi.org/10.1007/978-1-4614-7138-7
+
+This book is available for free from the UofT library at
+https://librarysearch.library.utoronto.ca/permalink/01UTORONTO_INST/fedca1/cdi_askewsholts_vlebooks_9781461471387
+
+
diff --git a/lessons/r/clustering/ClusteringLesson.Rproj b/lessons/r/clustering/ClusteringLesson.Rproj
new file mode 100644
index 000000000..8e3c2ebc9
--- /dev/null
+++ b/lessons/r/clustering/ClusteringLesson.Rproj
@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
diff --git a/lessons/r/clustering/Clustering_student.Rmd b/lessons/r/clustering/Clustering_student.Rmd
new file mode 100644
index 000000000..6bfcc7f5b
--- /dev/null
+++ b/lessons/r/clustering/Clustering_student.Rmd
@@ -0,0 +1,177 @@
+---
+title: "Clustering_student"
+author: "Tara"
+date: "2/9/2022"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+#Introduction
+
+Today we will learn two techniques that we can use to explore our data and find patterns in our dataset. Clustering analysis is a type of unsupervised machine learning that we use to find patterns or groupings in the data.
+
+The two methods we are learning are hierarchical agglomerative clustering and k-means clustering. The main difference between these two methods is that with hierarchical clustering, we choose the number of clusters we want a posteriori (or after we conduct the analysis), for k-means clustering, we determine the number of clusters we want a priori (prior to the anlalysis). In addition to the choice of the two clustering methods, there are a few methodological decisions we can make that will affect our results, we will dicuss some of these today. 
+
+##Setting up the data and preprocessing
+```{r}
+#Load in a dataset that is already in R
+
+#Use the help function to learn about the dataset
+
+#What types of variables do you see? Which ones do you find interesting? we can conduct clustering analysis on a subset of the variables or on the entire dataset because we have numeric (including two binary) variables
+```
+
+```{r}
+#preprocessing the data
+#1 check for missing values
+
+#this data set doesn't have any missing values so we can proceed with our analysis
+#Normalization and Scaling, chose either: 
+  #Normalize function from library(BBmisc)
+  #install.packages("BBmisc")
+ 
+  #scale the data using scale()
+
+```
+
+
+##Hierarchical Agglomerative Clustering
+For hierarchical agglomerative clustering begins with each observation in a distinct (singleton) cluster, and successively merges clusters together until a stopping criterion is satisfied. This approach can be visualized as a dendroagram, a tree-like diagram, showing how pairs of observations are fused together. 
+
+There are three different method choices that will affect the way our tree looks: 
+  (1) distance metric. this is the calculation that determines how similar the pairs are to eachother. 
+  (2) different algorithms for how to etermine how close pairs of observations are to eachother. This will give us different shapes of dendrogrmas. 
+
+```{r}
+#Step 1. Create the distance matrix
+  #using Euclidean distance, this is most common for data that has continuous numerical values
+  #what other methods are there?
+
+  #we can also look at the maximum distance, manhattan distance, canberra, binari, and minkowski. Let's also try the maximum distsance, create another distance matrix adn change the method agreemeent to "maximum"
+```
+
+```{r}
+#Step 2. Plot the clustering algorithms
+#Run the code for how many clusters does it look like there are in each dendrogram?
+#complete method
+#average method
+#single method
+#ward.D2
+
+#which method gives the most balanced?
+
+#try the exercise with the dist_Max matrix
+```
+
+```{r}
+#Step 3. Choosing the number of clusters Storing the Cluster ID for each observation
+
+#using the call bellow, we are taking our clustering and "cutting" the tree into four clusters. The call will return a vector where each observation is assigned to a cluster.
+
+
+#we can append this vector to a copy of the original dataframe to interpret the clusters
+
+```
+
+```{r}
+#Step. 4 Interpretation
+#how many cars are in each cluster?
+
+#let's summarize by the groups of clusters
+  #need dplyr package
+
+
+#compare the mean mpg between clusters
+
+
+#on your own compare another variable mean between clusters
+
+#visualize the clusters 
+
+
+```
+
+
+##Kmeans Clustering
+K= the number of centres (the number of clusters that we specify apriori).
+K-means aims to divide data into K groups (clusters). This involves finding K centroids. Centroids are invented or existing points that represent the centers of the clusters. In K-means clustering the aim is to indetify K number of centroids and allocate every object to the nearest cluster.
+
+The algorithm for K-means is as follows:
+
+1. Place K points into the object data space. These points represent the initial group of centroids,
+
+2. Assign each data point to the closest centroid, based on Euclidean distance.
+
+3. Recalculate the positions of the K centroids. This is done by taking the mean of all data points assigned to that centroid’s cluster.
+
+4. Repeats steps 2 and 3 until the posistions of the centroids no longer change and the sum of distances of individual units from centroids is as small as possible.
+
+Here is a pipeline for conducting K-Means analysis. 
+
+Step 1: Preprocessing (scaling/normalization)
+Step 2: Figure out the optimal K-value
+Step 3: Run KMeans algorithm
+Step 4: Interpret
+
+
+###Step 2
+```{r}
+##Step 2: Figure out the optimal K-value. 
+#Different methodological approach: trial and error, choose a priori based on theoretical knowledge, choose based on the hierarchical agglomerative clustering result, or use an numerical approach: elbow plot, gap statistic, silohuette.
+#we can use these packages to do all three of these numerical approaches
+#install.packages("factoextra")
+#install.packages("NbClust")
+
+#elbow method
+
+
+#silohuette
+
+
+#gap statistic, 
+
+
+```
+
+##Step 3: Run KMeans algorithm
+```{r}
+#set the seed for the analysis, for reproducibility
+#run the algorithm to start the clusters
+#try to look at what the results are in the kclust object
+
+```
+
+###Step 4: Interpret
+
+```{r}
+#save the scaled data as a dataframe
+#create a new column as of the clustering labels 
+#now you group by the clusters, run stats or visualize 
+```
+
+
+##Heatmaps and other visualization methods (time permitting)
+
+```{r}
+#the factoextra package has many helpful functions for computing cluster analyses and visualizing them
+#another way to visualize clusters, helpful package 
+#note that the dimensions are created using Principal components analysis first
+fviz_cluster(kmeans(dfScaled, centers = 2), geom = "text", data = dfScaled)
+```
+
+##Heatmaps
+
+##Notes and other helpful resources: 
+PAM (Partitioning around mediods) works similar to kmeans but, uses a mediod (median) instead of the mean to create clusters. Kmeans uses the centroid, which is an artificial point, while PAM uses the mediod (a real point). 
+
+Here is a helpful textbook to learn more about the theory of these clustering techniques and applying unsupervised and supervised machine learning techniques in R:
+
+James, Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With applications in R (Vol. 103). Springer. https://doi.org/10.1007/978-1-4614-7138-7
+
+This book is available for free from the UofT library at
+https://librarysearch.library.utoronto.ca/permalink/01UTORONTO_INST/fedca1/cdi_askewsholts_vlebooks_9781461471387
+
+
diff --git a/lessons/r/clustering/README.md b/lessons/r/clustering/README.md
new file mode 100644
index 000000000..8c1229943
--- /dev/null
+++ b/lessons/r/clustering/README.md
@@ -0,0 +1,5 @@
+# Clustering Lesson for UofT Coders
+
+This is a lesson on how to use R for clustering. We will learn how to cluster two types of data: (1) hierarchical clustering and (2) kmeans clustering. This lesson was created for U of T coders. 
+
+
diff --git a/lessons/r/debugging/crazify.R b/lessons/r/debugging/crazify.R
new file mode 100644
index 000000000..c35ccc168
--- /dev/null
+++ b/lessons/r/debugging/crazify.R
@@ -0,0 +1,48 @@
+
+reverseLetters <- function(word){
+  vec <- strsplit(word, "")
+  vec <- rev(unlist(vec))
+  revStr <- paste(vec, collapse = "")
+  return(revStr)
+}
+
+reverseWords <- function(phrase){
+  vec <- strsplit(phrase, " ")
+  vec <- rev(unlist(vec))
+  revStr <- paste(vec, collapse = " ")
+}
+
+crazify <- function(phrase){
+  revPhrase <- reverseWords(phrase)
+  vec <- strsplit(revPhrase, " ")
+  for(i in 1:length(vec)){
+      vec[i] <- reverseLetters(vec[i]) #vec[i]
+  }
+  return(paste(vec, collapse = " "))
+}
+
+crazify("here i am")
+
+# Challenge: try calling confuseTheCustomers() on the myMenu object below. 
+# It fails! Try using the debugger to fix it
+
+# The output should look like:
+# Titles                                   Descriptions
+# 1 Sandwich  ## hcihwdnas otamot dna ecuttel ,nocaB ##
+# 2     Soup      ## puos keel dna moorhsum fo maerC ## 
+
+#' print a crazified version of the descriptions in a menu 
+#' stored as a dataframe, but not the titles of the entries in the menu
+#' @param dataframe where column 1 is the name of the food and column 2 is the description
+confuseTheCustomers<-function(menu){
+  colnames(menu)<-c("Titles","Descriptions")
+  menu$Descriptions <- crazify(menu$Descriptions) 
+  print(menu)
+}
+myMenu <- data.frame(
+  titles = c("Sandwich","Soup"),
+  descriptions = c("## Bacon, lettuce and tomato sandwhich ##",
+                   "## Cream of mushroom and leek soup ##")
+  )
+#print(myMenu)
+#confuseTheCustomers(myMenu)
diff --git a/lessons/r/debugging/lesson.md b/lessons/r/debugging/lesson.md
new file mode 100644
index 000000000..a5bf86fe7
--- /dev/null
+++ b/lessons/r/debugging/lesson.md
@@ -0,0 +1,143 @@
+---
+layout: page
+title: RStudio Debugging
+visible: true
+tags: 
+  - r
+  - intermediate
+---
+
+- **Author**: Thea Van Rossum
+- **Lesson Topic**: RStudio Debugging
+
+## Set-up
+1. Open a new project in RStudio, save it somewhere
+2. Download these two files: [printTriangle.R](https://raw.githubusercontent.com/theavanrossum/debuggerRMarkdownLesson/master/printTriangle.R), [crazify.R](https://raw.githubusercontent.com/theavanrossum/debuggerRMarkdownLesson/master/crazify.R)
+3. Add these files to your project
+
+
+## Pre-test 
+Let's get a feel for the room. Who here:
+* knows what an IDE is?
+* has used RStudio before?
+* has used a debugger before?
+* has written an R function before?
+* who is here for debugging? (this will be the focus today!)
+* who is more interested in Rmarkdown or making packages? - maybe for a future lesson or quickly at the end
+
+
+## Motivation
+RStudio is a kind of software that programmers call an "IDE": integrated development environment. IDEs can (and do) exist for hundreds[1] of languages. The reason they're so ubiquitous is that they make your life SO MUCH EASIER. That's what they were designed to do: make coding easier. They do so with, typically, three main components: source code editor, build automation tools and a debugger. We'll look at some of these features today. 
+
+[1] Totally guessing here. But there are lots, at least dozens.
+
+## Getting Oriented
+Say hello to RStudio.
+Four main panels:
+* Top left: Script viewer - this is the best place to write your code
+* Bottom left: Console - this is where R is actually running, it's a dangerous place to write your code
+* Top right: Variable/history displayer
+* Bottom right: Files/plots/packages/help displayer 
+
+Note that you can move these windows around, if you like.
+
+## The Debugger
+Let's not waste any time and push right into the good stuff.
+
+### Dealing with unexpected results
+
+1. open a the printTriangle.R script
+2. click "source" in the top right
+
+Read the description of what this method should do and look at the output. Does the output look right? Nope.
+
+(Side note: yes this uses loops, and yes loops are a no-no in R. But they're great for watching a debugger work so just relax go with it, ok? ok.)
+
+There aren't any errors... so we just did something wrong. We could put in a bunch of print statements but let's try something less painful: the debugger!
+
+#### Breakpoints
+The first thing we need to do is add a "breakpoint". A breakpoint tells R to pause ("break") while it's running and open up a hatch for us to crawl inside the machine amongst the cogs to look around.
+
+We add a breakpoint by clicking next to the left of the line number. Then let's press "source" again.
+
+(Thea will now describe the changes in the IDE that happen when you enter debugger mode. Including: describe control buttons, click through next to see what's going on)
+
+Aha - we can see what's going wrong here: stars is growing, not being reset with every loop. An easy fix.
+
+### Dealing with errors
+
+1. open a the crazify.R script
+2. click "source" in the top right
+
+Oh no, a wild error has appeared!
+Luckily, R gives us a great description of what went wrong and why:
+
+```
+Error in strsplit(word, "") : non-character argument 
+```
+
+Hm. Or not.
+Let's see what "Show Traceback" gives us.
+
+### Stack
+The "traceback" shows us the series of commands (aka "stack") that led to the error.
+
+```
+ Error in strsplit(word, "") : non-character argument 
+7 strsplit(word, "") at crazify.R#3
+6 reverseLetters(vec[i]) at crazify.R#19
+5 crazify("here i am") at crazify.R#24
+4 eval(expr, envir, enclos) 
+3 eval(ei, envir) 
+2 withVisible(eval(ei, envir)) 
+1 source("C:/Users/tvanrossum/Dropbox/ScientificProgrammingStudyGroupSFU/debuggerRMarkdownLesson/crazify.R", 
+    echo = TRUE) 
+```
+
+Ok, so now we know where the script when wrong but the cause is still not clear. Let's see what RStudio's debugger can do for us. 
+
+### Breakpoints
+Let's add a "breakpoint" where the error occurred, at line 3. Or we can click "Rerun with Debug", which will rerun the last command and break when it encounters an error.
+
+Now we're in debug mode again. Check out the "Traceback" window on the right -- this shows us where we are and allows us to jump to different parts of the code. Let's jump up a level and look around.
+
+Ok, everything looks fine there, let's get back down to where the error occured.
+
+The "word" entry in the "values" pane isn't very useful. But we can use the console to get a better look. (Fun note - we can also use the console to change values mid-execution!)
+
+Let's type in "word" in the console to see what's going on.
+
+Aha. word is a list here, not a vector. I forgot to call unlist on the results of strsplit (line 17). Easy to fix.
+
+And let's not forget to remove our breakpoint.
+
+# Challenge 
+
+Try calling confuseTheCustomers() on the myMenu object in crazify.R:
+```
+confuseTheCustomers(myMenu) 
+```
+
+It fails! Try using the debugger to fix it.
+
+
+##Other ways to debug
+You can also call the debug() function on a function (like: debug(crazify)) and you can stick the browser() command into a function -- like a breakpoint. 
+
+Check out https://vimeo.com/99375765 for more
+
+# If time remains...
+
+Vote: who wants to:
+
+1. Work on some of your own code and force yourself to use the debugger when you hit any issues.
+2. Look at how to create a package in RStudio
+3. Get a breif intro to using RMarkdown
+
+
+
+Answers:
+> Triangle: stars<-"" should be in the i loop || Crazify:
+(1) missing unlist from strsplit in crazify function
+(2) missing stringsasfactors = F in definition of myMenu
+(3) need to use sapply instead of trying to call: crazify(menu$Descriptions) 
diff --git a/lessons/r/debugging/notes.md b/lessons/r/debugging/notes.md
new file mode 100644
index 000000000..b49acf147
--- /dev/null
+++ b/lessons/r/debugging/notes.md
@@ -0,0 +1,136 @@
+# RStudio - beyond the editor
+
+- **Author**: Thea Van Rossum
+- **Lesson Topic**: RStudio Debugging
+
+## Set-up
+1. Open a new project in RStudio, save it somewhere
+2. Download these two files: [printTriangle.R](https://raw.githubusercontent.com/theavanrossum/debuggerRMarkdownLesson/master/printTriangle.R), [crazify.R](https://raw.githubusercontent.com/theavanrossum/debuggerRMarkdownLesson/master/crazify.R)
+3. Add these files to your project
+
+
+## Pre-test 
+Let's get a feel for the room. Who here:
+* knows what an IDE is?
+* has used RStudio before?
+* has used a debugger before?
+* has written an R function before?
+* who is here for debugging? (this will be the focus today!)
+* who is more interested in Rmarkdown or making packages? - maybe for a future lesson or quickly at the end
+
+
+## Motivation
+RStudio is a kind of software that programmers call an "IDE": integrated development environment. IDEs can (and do) exist for hundreds[1] of languages. The reason they're so ubiquitous is that they make your life SO MUCH EASIER. That's what they were designed to do: make coding easier. They do so with, typically, three main components: source code editor, build automation tools and a debugger. We'll look at some of these features today. 
+
+[1] Totally guessing here. But there are lots, at least dozens.
+
+## Getting Oriented
+Say hello to RStudio.
+Four main panels:
+* Top left: Script viewer - this is the best place to write your code
+* Bottom left: Console - this is where R is actually running, it's a dangerous place to write your code
+* Top right: Variable/history displayer
+* Bottom right: Files/plots/packages/help displayer 
+
+Note that you can move these windows around, if you like.
+
+## The Debugger
+Let's not waste any time and push right into the good stuff.
+
+### Dealing with unexpected results
+
+1. open a the printTriangle.R script
+2. click "source" in the top right
+
+Read the description of what this method should do and look at the output. Does the output look right? Nope.
+
+(Side note: yes this uses loops, and yes loops are a no-no in R. But they're great for watching a debugger work so just relax go with it, ok? ok.)
+
+There aren't any errors... so we just did something wrong. We could put in a bunch of print statements but let's try something less painful: the debugger!
+
+#### Breakpoints
+The first thing we need to do is add a "breakpoint". A breakpoint tells R to pause ("break") while it's running and open up a hatch for us to crawl inside the machine amongst the cogs to look around.
+
+We add a breakpoint by clicking next to the left of the line number. Then let's press "source" again.
+
+(Thea will now describe the changes in the IDE that happen when you enter debugger mode. Including: describe control buttons, click through next to see what's going on)
+
+Aha - we can see what's going wrong here: stars is growing, not being reset with every loop. An easy fix.
+
+### Dealing with errors
+
+1. open a the crazify.R script
+2. click "source" in the top right
+
+Oh no, a wild error has appeared!
+Luckily, R gives us a great description of what went wrong and why:
+
+```
+Error in strsplit(word, "") : non-character argument 
+```
+
+Hm. Or not.
+Let's see what "Show Traceback" gives us.
+
+### Stack
+The "traceback" shows us the series of commands (aka "stack") that led to the error.
+
+```
+ Error in strsplit(word, "") : non-character argument 
+7 strsplit(word, "") at crazify.R#3
+6 reverseLetters(vec[i]) at crazify.R#19
+5 crazify("here i am") at crazify.R#24
+4 eval(expr, envir, enclos) 
+3 eval(ei, envir) 
+2 withVisible(eval(ei, envir)) 
+1 source("C:/Users/tvanrossum/Dropbox/ScientificProgrammingStudyGroupSFU/debuggerRMarkdownLesson/crazify.R", 
+    echo = TRUE) 
+```
+
+Ok, so now we know where the script when wrong but the cause is still not clear. Let's see what RStudio's debugger can do for us. 
+
+### Breakpoints
+Let's add a "breakpoint" where the error occurred, at line 3. Or we can click "Rerun with Debug", which will rerun the last command and break when it encounters an error.
+
+Now we're in debug mode again. Check out the "Traceback" window on the right -- this shows us where we are and allows us to jump to different parts of the code. Let's jump up a level and look around.
+
+Ok, everything looks fine there, let's get back down to where the error occured.
+
+The "word" entry in the "values" pane isn't very useful. But we can use the console to get a better look. (Fun note - we can also use the console to change values mid-execution!)
+
+Let's type in "word" in the console to see what's going on.
+
+Aha. word is a list here, not a vector. I forgot to call unlist on the results of strsplit (line 17). Easy to fix.
+
+And let's not forget to remove our breakpoint.
+
+# Challenge 
+
+Try calling confuseTheCustomers() on the myMenu object in crazify.R:
+```
+confuseTheCustomers(myMenu) 
+```
+
+It fails! Try using the debugger to fix it.
+
+
+##Other ways to debug
+You can also call the debug() function on a function (like: debug(crazify)) and you can stick the browser() command into a function -- like a breakpoint. 
+
+Check out https://vimeo.com/99375765 for more
+
+# If time remains...
+
+Vote: who wants to:
+
+1. Work on some of your own code and force yourself to use the debugger when you hit any issues.
+2. Look at how to create a package in RStudio
+3. Get a breif intro to using RMarkdown
+
+
+
+Answers:
+> Triangle: stars<-"" should be in the i loop || Crazify:
+(1) missing unlist from strsplit in crazify function
+(2) missing stringsasfactors = F in definition of myMenu
+(3) need to use sapply instead of trying to call: crazify(menu$Descriptions) 
diff --git a/lessons/r/debugging/printTriangle.R b/lessons/r/debugging/printTriangle.R
new file mode 100644
index 000000000..37c09c47e
--- /dev/null
+++ b/lessons/r/debugging/printTriangle.R
@@ -0,0 +1,17 @@
+
+#' Print a right-angle triangle of height triangleHeight, 
+#' with the right angle at the top left 
+#' @param triangleHeight integer height of the triangle
+printTriangle <- function(triangleHeight){
+  
+  stars<-""
+  for(i in 1:triangleHeight){
+    for(j in i:triangleHeight){
+      stars <- paste(stars,"*")
+    }
+    print(stars)
+  }
+  
+}
+
+printTriangle(3)
\ No newline at end of file
diff --git a/lessons/r/dplyrmagrittr/lesson.Rmd b/lessons/r/dplyrmagrittr/lesson.Rmd
new file mode 100644
index 000000000..e669f2909
--- /dev/null
+++ b/lessons/r/dplyrmagrittr/lesson.Rmd
@@ -0,0 +1,351 @@
+---
+layout: page
+title: Data wrangling with dplyr and magrittr
+visible: false
+tags:
+  - r
+  - intermediate
+---
+
+```{r setup, include=FALSE, echo = FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+options(tibble.print_max = 5)
+```
+
+ - **Authors**: Ahmed Hasan
+ - **Research field**: Genomics
+ - **Lesson topic**: Data wrangling with dplyr and magrittr
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/dplyrmagrittr>
+ - **Lesson video stream**: coming soon!
+
+## Why `dplyr` and `magrittr`?
+
+`dplyr` is a package for data wrangling and manipulation developed primarily by Hadley Wickham as part of his 'tidyverse' group of packages. It provides a powerful suite of functions that operate specifically on data frame objects, allowing for easy subsetting, filtering, sampling, summarising, and more.
+
+The overall philosophy binding `dplyr` together is rather simple - save for a select few exceptions, nearly every function in the package follows the convention of taking in a data frame as input and returning a modified data frame as output. Coupled with the pipe operator from the `magrittr` package, chaining `dplyr` functions together makes data frame manipulation an absolute breeze.
+
+## Meet the pipe
+
+The R pipe, or `%>%` (Ctrl/Cmd + Shift + M in RStudio) initially began life outside of `dplyr`, finding its R beginnings in the `magrittr` package instead. Its creator, Stefan Milton Bache, was inspired by the pipe operator in F# (`|>`) in addition to its Unix shell equivalent (`|`).
+
+Much like in those examples, the idea behind the `magrittr` pipe is also relatively simple: it essentially stands for 'evaluate the left hand side, and feed the result as input to the function on the right hand side'. For functions that take multiple arguments, the pipe will feed it to the first one by default. Like so:
+
+```{r}
+library(magrittr)
+
+x <- c(1,2)
+
+sum(x, 3)
+x %>% sum(3)
+sum(x, 3) == x %>% sum(3)
+```
+
+Of course, sometimes we want our LHS to be fed into a different argument that just so happens to not be the first one. In these cases, we use `.` to tell R where to send the LHS instead:
+
+```{r}
+
+seq(1, 10, 2)
+
+1 %>% seq(10, 2)
+
+10 %>% seq(1, ., 2)
+
+```
+
+Given the `dplyr` concept of each function taking in a data frame and returning a modified version, it made a lot of sense to integrate the pipe into the `dplyr` workflow. This way, a given data frame would be 'piped' through a series of functions one after the other in order to obtain a specific desired output.
+
+To summarise:
+
+```{r, eval = FALSE}
+
+f(x) == x %>% f()
+
+f(x, y) == x %>% f(y)
+
+f(x, y) == y %>% f(x, .)
+
+```
+
+
+## Tidy data
+
+One final thing to note before we delve into `dplyr` itself is the concept of _tidy data_. This basically describes a data set where each column is a variable or feature of the data, and every row is a single observation. Fisher and Anderson's `iris`, which we'll be using in today's lesson, is a good example of a tidy dataset.
+
+
+```{r}
+head(iris)
+```
+
+The `iris` dataset gives measurements of sepal length/width and petal length/width for 50 flower samples from each of _Iris setosa_, _Iris versicolor_, and _Iris virginica_. Notice how each row corresponds to measurements from a single flower sample, and each column represents a specific feature of that flower. 
+
+`dplyr` is optimized for use with tidy data. However, real world data are rarely in a tidy format from the get-go. For this reason, Hadley and co. have also developed the `tidyr` package, which provides useful functions for reshaping data. We won't be covering `tidyr` in today's lesson, but a wealth of tutorials are available online.
+
+Alright, let's dive right into `dplyr`!
+
+## `filter` - subsetting rows
+
+`filter` is the first `dplyr` verb we'll be looking at. At its core, and much like all `dplyr` functions, `filter` will take an input data frame as its first argument. Following that, we can define a set of conditions that we want to filter the rows of our data frame by.
+
+To begin with, let's look at all the flowers that have a sepal length greater than or equal to 7.0 cm.
+
+```{r, warning = FALSE, message = FALSE}
+library(dplyr)
+
+filter(iris, Sepal.Length >= 7.0)
+```
+
+We can simply tack on any further conditions we might want to filter by as separate arguments:
+
+```{r}
+filter(iris, Sepal.Length >= 7.0, Species == 'versicolor')
+```
+
+## `select` - subsetting columns
+
+`select` operates in a similar fashion to `filter`, but allows for subsetting columns instead. Since `select` is primarily given column names (which can be thought of as text strings) for input, it also allows for easy globbing of multiple columns at once.
+
+A quick aside - we are also going to convert `iris` to a tibble from this point onwards. Tibbles are another feature of `dplyr`; they're essentially data frame objects with a number of under-the-hood improvements, but those are somewhat beyond the scope of this lesson. What's relevant to us here is that they prevent R from printing out the entire data frame when it's called, and are also previewed in an informative format that tells us about the object types stored within each data frame column and more. ([More details here if you're interested.](https://cran.r-project.org/web/packages/tibble/vignettes/tibble.html))
+
+```{r}
+ir <- as_tibble(iris)
+
+select(ir, Species)
+```
+
+We can select multiple columns by simply providing their names as further arguments. `dplyr` also allows use of the `:` operator for consecutive columns.
+
+```{r}
+select(ir, Sepal.Length:Petal.Length, Species)
+```
+
+The `-` operator can be used to exclude a column:
+
+```{r}
+select(ir, -Species)
+```
+
+Finally, `select` features the helper functions `contains`, `starts_with`, and `ends_with` for globbing column names. Note that while we generally treat column names as objects in the body of `dplyr` functions, these helper functions _require strings as input_.
+
+```{r}
+select(ir, contains('Sepal'))
+
+select(ir, starts_with('Petal'))
+
+select(ir, ends_with('Length'))
+```
+
+### Here comes the pipe
+
+But what if we want to select certain columns, _and then_ filter the resultant rows by a certain condition? (Or vice versa)
+
+We could certainly nest functions, like so:
+
+```{r}
+filter(select(ir, contains('Sepal')), Sepal.Length >= 7.0)
+```
+
+But this is messy enough as is, and that's just with a single instance of nesting. Imagine we had three or four nested functions! We'd fast approach having more parentheses than actual code.
+
+An alternative is using intermediate objects:
+
+```{r}
+sepals <- select(ir, contains('Sepal'))
+filter(sepals, Sepal.Length >= 7.0)
+```
+
+But that can be a drain on memory if we're working with large amounts of data; not to mention that the number of intermediates would only grow as our analysis goes on, increasingly cluttering our workspace.
+
+However, recall that every `dplyr` function both takes in a data frame object as its first argument, and returns one as output. With this being the case, the pipe allows us to easily chain `dplyr` functions together.
+
+```{r}
+ir %>% 
+  select(contains('Sepal')) %>% 
+  filter(Sepal.Length >= 7.0)
+```
+
+Notice how that code is both easy to conceptualize and understand after the fact, and that neither of the `dplyr` functions have the data frame actually listed as part of their arguments. The overall code block effectively reads like:
+
+```{r}
+# Take ir, then -
+#   select all columns with 'Sepal' in the name, then -
+#   filter for rows where sepal length is greater than or equal to 7.0.
+```
+
+In using the pipe with `dplyr` functions, we can write up simple 'recipes' that manipulate data frame objects in a quick and straightforward manner.
+
+Of course, there's more to `dplyr` than just subsetting -
+
+## `arrange` - sort your data
+
+Much like the name implies, `arrange` allows us to sort data. Not much to it:
+
+```{r}
+ir %>% 
+  select(starts_with('Sepal')) %>% 
+  filter(Sepal.Length >= 7.0) %>% 
+  arrange(Sepal.Length)
+```
+
+Sorting in descending order is not much harder:
+
+```{r}
+ir %>% 
+  select(starts_with('Sepal')) %>% 
+  filter(Sepal.Length >= 7.0) %>% 
+  arrange(desc(Sepal.Length))
+```
+
+Finally, feeding two (or more) arguments to `arrange` will be interpreted as 'sort by first column and then by second column'.
+
+```{r}
+ir %>% 
+  select(starts_with('Sepal')) %>% 
+  filter(Sepal.Length >= 7.0) %>% 
+  arrange(Sepal.Length, Sepal.Width)
+```
+
+## `mutate` - make new variables
+
+`mutate` is arguably where `dplyr` really comes to life. So far, we've just been playing around with existing data, subsetting it and arranging it how we please. Which is certainly very useful! But `mutate` allows us to create entirely new variables (i.e. columns) in our data frame object out of operations performed on the data frame itself.
+
+`mutate` employs _window functions_ in order to do this. Unlike an _aggregation function_ (such as `mean`) which takes in $n$ values and returns one value, a window function will take in _n_ elements as input and return _n_ elements as output. In the case of `mutate`, this means performing an operation on every single row of a data frame and returning the resultant values in a new column.
+
+Let's use `mutate` to calculate the approximate area of sepals and petals in our dataset.
+
+```{r}
+ir %>% 
+  select(contains('Sepal')) %>% 
+  mutate(Sepal.Area = Sepal.Length * Sepal.Width)
+```
+
+We could also get the differences between sepal length and petal length for each flower:
+
+```{r}
+ir %>% 
+  select(ends_with('Length')) %>% 
+  mutate(Length.Diff = Sepal.Length - Petal.Length)
+```
+
+Gone are the days of clumsy loops over data frame rows!
+
+Of course, if we are performing some kind of complex operation that `mutate` is behaving strangely with, we can use `rowwise` to evaluate the operation one row at a time.
+
+```{r}
+ir %>% 
+  select(ends_with('Length'), Species) %>% 
+  rowwise() %>% 
+  mutate(Length.Diff = Sepal.Length - Petal.Length)
+```
+
+Finally, it is sometimes useful to drop all other columns once we've performed a `mutate` operation. Instead of having to use `select` after the fact to keep the columns we need, `transmute` is a version of `mutate` that only keeps the variables you create.
+
+```{r}
+ir %>% 
+  select(contains('Sepal'), Species) %>% 
+  transmute(Sepal.Area = Sepal.Length * Sepal.Width)
+```
+
+Notice how the Species column is also gone in the example above.
+
+## `summarise` - summarise your data
+
+Where `mutate` is all about window functions, `summarise` returns us to the familiar world of aggregate functions, outputting a single value for however many values are fed as input. These should generally be quite familiar:
+
+```{r}
+ir %>% 
+  select(contains('Sepal')) %>% 
+  summarise(Mean.S.Length = mean(Sepal.Length), Mean.S.Width = mean(Sepal.Width))
+```
+
+The important thing to note is that in using `summarise`, we are obtaining our summary values within a data frame structure still. Using something like `mean` outside of `summarise` would simply return a float.
+
+## `group_by` - group your data
+
+`group_by` is primarily a helper function itself, but it provides an immense amount of added utility to `mutate` and `summarise`. It does so by adding grouping information in an under-the-hood fashion. While this does not necessarily do anything explicit to the data, both `summarise` and `mutate` behave quite differently when presented with a grouped data frame.
+
+For instance, let's redo the `summarise` function we did above, but now in a grouped manner:
+
+```{r}
+ir %>% 
+  select(contains('Sepal'), Species) %>% 
+  group_by(Species) %>% 
+  summarise(Mean.S.Length = mean(Sepal.Length), Mean.S.Width = mean(Sepal.Width))
+```
+
+We can also quickly obtain the total number of records for each species:
+
+```{r}
+ir %>% 
+  group_by(Species) %>% 
+  summarise(count = n())
+```
+
+Or the number of distinct values in a given column:
+
+```{r}
+ir %>% 
+  group_by(Species) %>% 
+  summarise(distinct = n_distinct(Sepal.Length))
+```
+
+`group_by` also works with `mutate` for grouped operations. For instance, if we want to examine the ratio of each flower's sepal length from the mean for that species, we could try something like this:
+
+```{r}
+ir %>% 
+  select(Sepal.Length, Species) %>% 
+  group_by(Species) %>% 
+  mutate(Grouped.SL.Ratio = Sepal.Length / mean(Sepal.Length))
+```
+
+In this example, `mean(Sepal.Length)` is computed for each species, instead of from the entire dataset.
+
+Finally, grouping information can be removed with the `ungroup` function. This is useful when performing one grouped operation and then performing a second in the same `dplyr` chain but off of a different column grouping.
+
+## To `summarise`
+
+- `select` allows you to subset by column
+- `filter` allows you to subset rows that meet one or more conditions
+- `arrange` allows you to sort your data
+- `group_by` allows you to group your data for grouped downstream operations
+- `mutate` allows you to compute new variables from your data
+- `summarise` allows you to apply summary functions on your data
+
+The official [Data Wrangling with R cheat sheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) is a stellar reference for working with all these functions and more. Chain them together however you please using the pipe, and watch your adventures wrangling data frames become substantially more straightforward!
+
+## Some `magrittr` tricks
+
+While we may have covered the basics of `dplyr` and how the pipe synergizes with it, it's still worth going over some of the unique tricks `magrittr` itself offers.
+
+### The tee operator - `%T>%`
+
+Another operator that derives its inspiration from the Unix shell equivalent, the tee operator will still pass on LHS to RHS, but _returns LHS to stdout_ instead of RHS.
+
+In the context of a `dplyr` chain of commands, this is especially useful when we are saving the final result of said commands back to a data frame, but also have some kind of ancillary function tacked on at the very end (i.e. `plot()`) that we don't want saved to the object.
+
+```{r, eval = FALSE}
+sepalsonly <- ir %>% 
+  select(Sepal.Length, Sepal.Width) %T>% 
+  plot()
+```
+
+Here, the tee operator allows us to view a plot of our data, but without the output of `plot` ending up saved to the `sepalsonly` object. `plot` is therefore still part of our command chain, but not in an obtrusive way.
+
+### The compound assignment operator - `%<>%`
+
+The compound assignment operator also comes in handy when we're performing a series of `dplyr` commands on a data frame and saving the results back to it.
+
+Where we might do:
+
+```{r}
+ir <- ir %>% 
+  select(contains('Sepal'), Species)
+```
+
+The compound assignment operator makes for a useful bit of shorthand:
+
+```{r}
+ir %<>%
+  select(contains('Sepal'), Species)
+```
+
+
diff --git a/lessons/r/dplyrmagrittr/lesson.md b/lessons/r/dplyrmagrittr/lesson.md
new file mode 100644
index 000000000..143d5febc
--- /dev/null
+++ b/lessons/r/dplyrmagrittr/lesson.md
@@ -0,0 +1,770 @@
+---
+layout: page
+title: Data wrangling with dplyr and magrittr
+visible: true
+tags:
+  - r
+  - intermediate
+---
+
+
+
+ - **Authors**: Ahmed Hasan
+ - **Research field**: Genomics
+ - **Lesson topic**: Data wrangling with dplyr and magrittr
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/dplyrmagrittr>
+ - **Lesson video stream**: coming soon!
+
+## Why `dplyr` and `magrittr`?
+
+`dplyr` is a package for data wrangling and manipulation developed primarily by Hadley Wickham as part of his 'tidyverse' group of packages. It provides a powerful suite of functions that operate specifically on data frame objects, allowing for easy subsetting, filtering, sampling, summarising, and more.
+
+The overall philosophy binding `dplyr` together is rather simple - save for a select few exceptions, nearly every function in the package follows the convention of taking in a data frame as input and returning a modified data frame as output. Coupled with the pipe operator from the `magrittr` package, chaining `dplyr` functions together makes data frame manipulation an absolute breeze.
+
+## Meet the pipe
+
+The R pipe, or `%>%` (Ctrl/Cmd + Shift + M in RStudio) initially began life outside of `dplyr`, finding its R beginnings in the `magrittr` package instead. Its creator, Stefan Milton Bache, was inspired by the pipe operator in F# (`|>`) in addition to its Unix shell equivalent (`|`).
+
+Much like in those examples, the idea behind the `magrittr` pipe is also relatively simple: it essentially stands for 'evaluate the left hand side, and feed the result as input to the function on the right hand side'. For functions that take multiple arguments, the pipe will feed it to the first one by default. Like so:
+
+
+```r
+library(magrittr)
+
+x <- c(1,2)
+
+sum(x, 3)
+```
+
+```
+## [1] 6
+```
+
+```r
+x %>% sum(3)
+```
+
+```
+## [1] 6
+```
+
+```r
+sum(x, 3) == x %>% sum(3)
+```
+
+```
+## [1] TRUE
+```
+
+Of course, sometimes we want our LHS to be fed into a different argument that just so happens to not be the first one. In these cases, we use `.` to tell R where to send the LHS instead:
+
+
+```r
+seq(1, 10, 2)
+```
+
+```
+## [1] 1 3 5 7 9
+```
+
+```r
+1 %>% seq(10, 2)
+```
+
+```
+## [1] 1 3 5 7 9
+```
+
+```r
+10 %>% seq(1, ., 2)
+```
+
+```
+## [1] 1 3 5 7 9
+```
+
+Given the `dplyr` concept of each function taking in a data frame and returning a modified version, it made a lot of sense to integrate the pipe into the `dplyr` workflow. This way, a given data frame would be 'piped' through a series of functions one after the other in order to obtain a specific desired output.
+
+To summarise:
+
+
+```r
+f(x) == x %>% f()
+
+f(x, y) == x %>% f(y)
+
+f(x, y) == y %>% f(x, .)
+```
+
+
+## Tidy data
+
+One final thing to note before we delve into `dplyr` itself is the concept of _tidy data_. This basically describes a data set where each column is a variable or feature of the data, and every row is a single observation. Fisher and Anderson's `iris`, which we'll be using in today's lesson, is a good example of a tidy dataset.
+
+
+
+```r
+head(iris)
+```
+
+```
+##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
+## 1          5.1         3.5          1.4         0.2  setosa
+## 2          4.9         3.0          1.4         0.2  setosa
+## 3          4.7         3.2          1.3         0.2  setosa
+## 4          4.6         3.1          1.5         0.2  setosa
+## 5          5.0         3.6          1.4         0.2  setosa
+## 6          5.4         3.9          1.7         0.4  setosa
+```
+
+The `iris` dataset gives measurements of sepal length/width and petal length/width for 50 flower samples from each of _Iris setosa_, _Iris versicolor_, and _Iris virginica_. Notice how each row corresponds to measurements from a single flower sample, and each column represents a specific feature of that flower. 
+
+`dplyr` is optimized for use with tidy data. However, real world data are rarely in a tidy format from the get-go. For this reason, Hadley and co. have also developed the `tidyr` package, which provides useful functions for reshaping data. We won't be covering `tidyr` in today's lesson, but a wealth of tutorials are available online.
+
+Alright, let's dive right into `dplyr`!
+
+## `filter` - subsetting rows
+
+`filter` is the first `dplyr` verb we'll be looking at. At its core, and much like all `dplyr` functions, `filter` will take an input data frame as its first argument. Following that, we can define a set of conditions that we want to filter the rows of our data frame by.
+
+To begin with, let's look at all the flowers that have a sepal length greater than or equal to 7.0 cm.
+
+
+```r
+library(dplyr)
+
+filter(iris, Sepal.Length >= 7.0)
+```
+
+```
+##    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
+## 1           7.0         3.2          4.7         1.4 versicolor
+## 2           7.1         3.0          5.9         2.1  virginica
+## 3           7.6         3.0          6.6         2.1  virginica
+## 4           7.3         2.9          6.3         1.8  virginica
+## 5           7.2         3.6          6.1         2.5  virginica
+## 6           7.7         3.8          6.7         2.2  virginica
+## 7           7.7         2.6          6.9         2.3  virginica
+## 8           7.7         2.8          6.7         2.0  virginica
+## 9           7.2         3.2          6.0         1.8  virginica
+## 10          7.2         3.0          5.8         1.6  virginica
+## 11          7.4         2.8          6.1         1.9  virginica
+## 12          7.9         3.8          6.4         2.0  virginica
+## 13          7.7         3.0          6.1         2.3  virginica
+```
+
+We can simply tack on any further conditions we might want to filter by as separate arguments:
+
+
+```r
+filter(iris, Sepal.Length >= 7.0, Species == 'versicolor')
+```
+
+```
+##   Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
+## 1            7         3.2          4.7         1.4 versicolor
+```
+
+## `select` - subsetting columns
+
+`select` operates in a similar fashion to `filter`, but allows for subsetting columns instead. Since `select` is primarily given column names (which can be thought of as text strings) for input, it also allows for easy globbing of multiple columns at once.
+
+A quick aside - we are also going to convert `iris` to a tibble from this point onwards. Tibbles are another feature of `dplyr`; they're essentially data frame objects with a number of under-the-hood improvements, but those are somewhat beyond the scope of this lesson. What's relevant to us here is that they prevent R from printing out the entire data frame when it's called, and are also previewed in an informative format that tells us about the object types stored within each data frame column and more. ([More details here if you're interested.](https://cran.r-project.org/web/packages/tibble/vignettes/tibble.html))
+
+
+```r
+ir <- as_tibble(iris)
+
+select(ir, Species)
+```
+
+```
+## # A tibble: 150 x 1
+##    Species
+##     <fctr>
+##  1  setosa
+##  2  setosa
+##  3  setosa
+##  4  setosa
+##  5  setosa
+##  6  setosa
+##  7  setosa
+##  8  setosa
+##  9  setosa
+## 10  setosa
+## # ... with 140 more rows
+```
+
+We can select multiple columns by simply providing their names as further arguments. `dplyr` also allows use of the `:` operator for consecutive columns.
+
+
+```r
+select(ir, Sepal.Length:Petal.Length, Species)
+```
+
+```
+## # A tibble: 150 x 4
+##    Sepal.Length Sepal.Width Petal.Length Species
+##           <dbl>       <dbl>        <dbl>  <fctr>
+##  1          5.1         3.5          1.4  setosa
+##  2          4.9         3.0          1.4  setosa
+##  3          4.7         3.2          1.3  setosa
+##  4          4.6         3.1          1.5  setosa
+##  5          5.0         3.6          1.4  setosa
+##  6          5.4         3.9          1.7  setosa
+##  7          4.6         3.4          1.4  setosa
+##  8          5.0         3.4          1.5  setosa
+##  9          4.4         2.9          1.4  setosa
+## 10          4.9         3.1          1.5  setosa
+## # ... with 140 more rows
+```
+
+The `-` operator can be used to exclude a column:
+
+
+```r
+select(ir, -Species)
+```
+
+```
+## # A tibble: 150 x 4
+##    Sepal.Length Sepal.Width Petal.Length Petal.Width
+##           <dbl>       <dbl>        <dbl>       <dbl>
+##  1          5.1         3.5          1.4         0.2
+##  2          4.9         3.0          1.4         0.2
+##  3          4.7         3.2          1.3         0.2
+##  4          4.6         3.1          1.5         0.2
+##  5          5.0         3.6          1.4         0.2
+##  6          5.4         3.9          1.7         0.4
+##  7          4.6         3.4          1.4         0.3
+##  8          5.0         3.4          1.5         0.2
+##  9          4.4         2.9          1.4         0.2
+## 10          4.9         3.1          1.5         0.1
+## # ... with 140 more rows
+```
+
+Finally, `select` features the helper functions `contains`, `starts_with`, and `ends_with` for globbing column names. Note that while we generally treat column names as objects in the body of `dplyr` functions, these helper functions _require strings as input_.
+
+
+```r
+select(ir, contains('Sepal'))
+```
+
+```
+## # A tibble: 150 x 2
+##    Sepal.Length Sepal.Width
+##           <dbl>       <dbl>
+##  1          5.1         3.5
+##  2          4.9         3.0
+##  3          4.7         3.2
+##  4          4.6         3.1
+##  5          5.0         3.6
+##  6          5.4         3.9
+##  7          4.6         3.4
+##  8          5.0         3.4
+##  9          4.4         2.9
+## 10          4.9         3.1
+## # ... with 140 more rows
+```
+
+```r
+select(ir, starts_with('Petal'))
+```
+
+```
+## # A tibble: 150 x 2
+##    Petal.Length Petal.Width
+##           <dbl>       <dbl>
+##  1          1.4         0.2
+##  2          1.4         0.2
+##  3          1.3         0.2
+##  4          1.5         0.2
+##  5          1.4         0.2
+##  6          1.7         0.4
+##  7          1.4         0.3
+##  8          1.5         0.2
+##  9          1.4         0.2
+## 10          1.5         0.1
+## # ... with 140 more rows
+```
+
+```r
+select(ir, ends_with('Length'))
+```
+
+```
+## # A tibble: 150 x 2
+##    Sepal.Length Petal.Length
+##           <dbl>        <dbl>
+##  1          5.1          1.4
+##  2          4.9          1.4
+##  3          4.7          1.3
+##  4          4.6          1.5
+##  5          5.0          1.4
+##  6          5.4          1.7
+##  7          4.6          1.4
+##  8          5.0          1.5
+##  9          4.4          1.4
+## 10          4.9          1.5
+## # ... with 140 more rows
+```
+
+### Here comes the pipe
+
+But what if we want to select certain columns, _and then_ filter the resultant rows by a certain condition? (Or vice versa)
+
+We could certainly nest functions, like so:
+
+
+```r
+filter(select(ir, contains('Sepal')), Sepal.Length >= 7.0)
+```
+
+```
+## # A tibble: 13 x 2
+##    Sepal.Length Sepal.Width
+##           <dbl>       <dbl>
+##  1          7.0         3.2
+##  2          7.1         3.0
+##  3          7.6         3.0
+##  4          7.3         2.9
+##  5          7.2         3.6
+##  6          7.7         3.8
+##  7          7.7         2.6
+##  8          7.7         2.8
+##  9          7.2         3.2
+## 10          7.2         3.0
+## # ... with 3 more rows
+```
+
+But this is messy enough as is, and that's just with a single instance of nesting. Imagine we had three or four nested functions! We'd fast approach having more parentheses than actual code.
+
+An alternative is using intermediate objects:
+
+
+```r
+sepals <- select(ir, contains('Sepal'))
+filter(sepals, Sepal.Length >= 7.0)
+```
+
+```
+## # A tibble: 13 x 2
+##    Sepal.Length Sepal.Width
+##           <dbl>       <dbl>
+##  1          7.0         3.2
+##  2          7.1         3.0
+##  3          7.6         3.0
+##  4          7.3         2.9
+##  5          7.2         3.6
+##  6          7.7         3.8
+##  7          7.7         2.6
+##  8          7.7         2.8
+##  9          7.2         3.2
+## 10          7.2         3.0
+## # ... with 3 more rows
+```
+
+But that can be a drain on memory if we're working with large amounts of data; not to mention that the number of intermediates would only grow as our analysis goes on, increasingly cluttering our workspace.
+
+However, recall that every `dplyr` function both takes in a data frame object as its first argument, and returns one as output. With this being the case, the pipe allows us to easily chain `dplyr` functions together.
+
+
+```r
+ir %>% 
+  select(contains('Sepal')) %>% 
+  filter(Sepal.Length >= 7.0)
+```
+
+```
+## # A tibble: 13 x 2
+##    Sepal.Length Sepal.Width
+##           <dbl>       <dbl>
+##  1          7.0         3.2
+##  2          7.1         3.0
+##  3          7.6         3.0
+##  4          7.3         2.9
+##  5          7.2         3.6
+##  6          7.7         3.8
+##  7          7.7         2.6
+##  8          7.7         2.8
+##  9          7.2         3.2
+## 10          7.2         3.0
+## # ... with 3 more rows
+```
+
+Notice how that code is both easy to conceptualize and understand after the fact, and that neither of the `dplyr` functions have the data frame actually listed as part of their arguments. The overall code block effectively reads like:
+
+
+```r
+# Take ir, then -
+#   select all columns with 'Sepal' in the name, then -
+#   filter for rows where sepal length is greater than or equal to 7.0.
+```
+
+In using the pipe with `dplyr` functions, we can write up simple 'recipes' that manipulate data frame objects in a quick and straightforward manner.
+
+Of course, there's more to `dplyr` than just subsetting -
+
+## `arrange` - sort your data
+
+Much like the name implies, `arrange` allows us to sort data. Not much to it:
+
+
+```r
+ir %>% 
+  select(starts_with('Sepal')) %>% 
+  filter(Sepal.Length >= 7.0) %>% 
+  arrange(Sepal.Length)
+```
+
+```
+## # A tibble: 13 x 2
+##    Sepal.Length Sepal.Width
+##           <dbl>       <dbl>
+##  1          7.0         3.2
+##  2          7.1         3.0
+##  3          7.2         3.6
+##  4          7.2         3.2
+##  5          7.2         3.0
+##  6          7.3         2.9
+##  7          7.4         2.8
+##  8          7.6         3.0
+##  9          7.7         3.8
+## 10          7.7         2.6
+## # ... with 3 more rows
+```
+
+Sorting in descending order is not much harder:
+
+
+```r
+ir %>% 
+  select(starts_with('Sepal')) %>% 
+  filter(Sepal.Length >= 7.0) %>% 
+  arrange(desc(Sepal.Length))
+```
+
+```
+## # A tibble: 13 x 2
+##    Sepal.Length Sepal.Width
+##           <dbl>       <dbl>
+##  1          7.9         3.8
+##  2          7.7         3.8
+##  3          7.7         2.6
+##  4          7.7         2.8
+##  5          7.7         3.0
+##  6          7.6         3.0
+##  7          7.4         2.8
+##  8          7.3         2.9
+##  9          7.2         3.6
+## 10          7.2         3.2
+## # ... with 3 more rows
+```
+
+Finally, feeding two (or more) arguments to `arrange` will be interpreted as 'sort by first column and then by second column'.
+
+
+```r
+ir %>% 
+  select(starts_with('Sepal')) %>% 
+  filter(Sepal.Length >= 7.0) %>% 
+  arrange(Sepal.Length, Sepal.Width)
+```
+
+```
+## # A tibble: 13 x 2
+##    Sepal.Length Sepal.Width
+##           <dbl>       <dbl>
+##  1          7.0         3.2
+##  2          7.1         3.0
+##  3          7.2         3.0
+##  4          7.2         3.2
+##  5          7.2         3.6
+##  6          7.3         2.9
+##  7          7.4         2.8
+##  8          7.6         3.0
+##  9          7.7         2.6
+## 10          7.7         2.8
+## # ... with 3 more rows
+```
+
+## `mutate` - make new variables
+
+`mutate` is arguably where `dplyr` really comes to life. So far, we've just been playing around with existing data, subsetting it and arranging it how we please. Which is certainly very useful! But `mutate` allows us to create entirely new variables (i.e. columns) in our data frame object out of operations performed on the data frame itself.
+
+`mutate` employs _window functions_ in order to do this. Unlike an _aggregation function_ (such as `mean`) which takes in $n$ values and returns one value, a window function will take in $n$ elements as input and return $n$ elements as output. In the case of `mutate`, this means performing an operation on every single row of a data frame and returning the resultant values in a new column.
+
+Let's use `mutate` to calculate the approximate area of sepals and petals in our dataset.
+
+
+```r
+ir %>% 
+  select(contains('Sepal')) %>% 
+  mutate(Sepal.Area = Sepal.Length * Sepal.Width)
+```
+
+```
+## # A tibble: 150 x 3
+##    Sepal.Length Sepal.Width Sepal.Area
+##           <dbl>       <dbl>      <dbl>
+##  1          5.1         3.5      17.85
+##  2          4.9         3.0      14.70
+##  3          4.7         3.2      15.04
+##  4          4.6         3.1      14.26
+##  5          5.0         3.6      18.00
+##  6          5.4         3.9      21.06
+##  7          4.6         3.4      15.64
+##  8          5.0         3.4      17.00
+##  9          4.4         2.9      12.76
+## 10          4.9         3.1      15.19
+## # ... with 140 more rows
+```
+
+We could also get the differences between sepal length and petal length for each flower:
+
+
+```r
+ir %>% 
+  select(ends_with('Length')) %>% 
+  mutate(Length.Diff = Sepal.Length - Petal.Length)
+```
+
+```
+## # A tibble: 150 x 3
+##    Sepal.Length Petal.Length Length.Diff
+##           <dbl>        <dbl>       <dbl>
+##  1          5.1          1.4         3.7
+##  2          4.9          1.4         3.5
+##  3          4.7          1.3         3.4
+##  4          4.6          1.5         3.1
+##  5          5.0          1.4         3.6
+##  6          5.4          1.7         3.7
+##  7          4.6          1.4         3.2
+##  8          5.0          1.5         3.5
+##  9          4.4          1.4         3.0
+## 10          4.9          1.5         3.4
+## # ... with 140 more rows
+```
+
+Gone are the days of clumsy loops over data frame rows!
+
+Of course, if we are performing some kind of complex operation that `mutate` is behaving strangely with, we can use `rowwise` to evaluate the operation one row at a time.
+
+
+```r
+ir %>% 
+  select(ends_with('Length'), Species) %>% 
+  rowwise() %>% 
+  mutate(Length.Diff = Sepal.Length - Petal.Length)
+```
+
+```
+## Source: local data frame [150 x 4]
+## Groups: <by row>
+## 
+## # A tibble: 150 x 4
+##    Sepal.Length Petal.Length Species Length.Diff
+##           <dbl>        <dbl>  <fctr>       <dbl>
+##  1          5.1          1.4  setosa         3.7
+##  2          4.9          1.4  setosa         3.5
+##  3          4.7          1.3  setosa         3.4
+##  4          4.6          1.5  setosa         3.1
+##  5          5.0          1.4  setosa         3.6
+##  6          5.4          1.7  setosa         3.7
+##  7          4.6          1.4  setosa         3.2
+##  8          5.0          1.5  setosa         3.5
+##  9          4.4          1.4  setosa         3.0
+## 10          4.9          1.5  setosa         3.4
+## # ... with 140 more rows
+```
+
+Finally, it is sometimes useful to drop all other columns once we've performed a `mutate` operation. Instead of having to use `select` after the fact to keep the columns we need, `transmute` is a version of `mutate` that only keeps the variables you create.
+
+
+```r
+ir %>% 
+  select(contains('Sepal'), Species) %>% 
+  transmute(Sepal.Area = Sepal.Length * Sepal.Width)
+```
+
+```
+## # A tibble: 150 x 1
+##    Sepal.Area
+##         <dbl>
+##  1      17.85
+##  2      14.70
+##  3      15.04
+##  4      14.26
+##  5      18.00
+##  6      21.06
+##  7      15.64
+##  8      17.00
+##  9      12.76
+## 10      15.19
+## # ... with 140 more rows
+```
+
+Notice how the Species column is also gone in the example above.
+
+## `summarise` - summarise your data
+
+Where `mutate` is all about window functions, `summarise` returns us to the familiar world of aggregate functions, outputting a single value for however many values are fed as input. These should generally be quite familiar:
+
+
+```r
+ir %>% 
+  select(contains('Sepal')) %>% 
+  summarise(Mean.S.Length = mean(Sepal.Length), Mean.S.Width = mean(Sepal.Width))
+```
+
+```
+## # A tibble: 1 x 2
+##   Mean.S.Length Mean.S.Width
+##           <dbl>        <dbl>
+## 1      5.843333     3.057333
+```
+
+The important thing to note is that in using `summarise`, we are obtaining our summary values within a data frame structure still. Using something like `mean` outside of `summarise` would simply return a float.
+
+## `group_by` - group your data
+
+`group_by` is primarily a helper function itself, but it provides an immense amount of added utility to `mutate` and `summarise`. It does so by adding grouping information in an under the hood fashion. While this does not necessarily do anything explicit to the data, both `summarise` and `mutate` behave quite differently when presented with a grouped data frame.
+
+For instance, let's redo the `summarise` function we did above, but now in a grouped manner:
+
+
+```r
+ir %>% 
+  select(contains('Sepal'), Species) %>% 
+  group_by(Species) %>% 
+  summarise(Mean.S.Length = mean(Sepal.Length), Mean.S.Width = mean(Sepal.Width))
+```
+
+```
+## # A tibble: 3 x 3
+##      Species Mean.S.Length Mean.S.Width
+##       <fctr>         <dbl>        <dbl>
+## 1     setosa         5.006        3.428
+## 2 versicolor         5.936        2.770
+## 3  virginica         6.588        2.974
+```
+
+We can also quickly obtain the total number of records for each species:
+
+
+```r
+ir %>% 
+  group_by(Species) %>% 
+  summarise(count = n())
+```
+
+```
+## # A tibble: 3 x 2
+##      Species count
+##       <fctr> <int>
+## 1     setosa    50
+## 2 versicolor    50
+## 3  virginica    50
+```
+
+Or the number of distinct values in a given column:
+
+
+```r
+ir %>% 
+  group_by(Species) %>% 
+  summarise(distinct = n_distinct(Sepal.Length))
+```
+
+```
+## # A tibble: 3 x 2
+##      Species distinct
+##       <fctr>    <int>
+## 1     setosa       15
+## 2 versicolor       21
+## 3  virginica       21
+```
+
+`group_by` also works with `mutate` for grouped operations. For instance, if we want to examine the ratio of each flower's sepal length from the mean for that species, we could try something like this:
+
+
+```r
+ir %>% 
+  select(Sepal.Length, Species) %>% 
+  group_by(Species) %>% 
+  mutate(Grouped.SL.Ratio = Sepal.Length / mean(Sepal.Length))
+```
+
+```
+## # A tibble: 150 x 3
+## # Groups:   Species [3]
+##    Sepal.Length Species Grouped.SL.Ratio
+##           <dbl>  <fctr>            <dbl>
+##  1          5.1  setosa        1.0187775
+##  2          4.9  setosa        0.9788254
+##  3          4.7  setosa        0.9388734
+##  4          4.6  setosa        0.9188973
+##  5          5.0  setosa        0.9988014
+##  6          5.4  setosa        1.0787056
+##  7          4.6  setosa        0.9188973
+##  8          5.0  setosa        0.9988014
+##  9          4.4  setosa        0.8789453
+## 10          4.9  setosa        0.9788254
+## # ... with 140 more rows
+```
+
+In this example, `mean(Sepal.Length)` is computed for each species, instead of from the entire dataset.
+
+Finally, grouping information can be removed with the `ungroup` function. This is useful when performing one grouped operation and then performing a second in the same `dplyr` chain but off of a different column grouping.
+
+## To `summarise`
+
+- `select` allows you to subset by column
+- `filter` allows you to subset rows that meet one or more conditions
+- `arrange` allows you to sort your data
+- `group_by` allows you to group your data for grouped downstream operations
+- `mutate` allows you to compute new variables from your data
+- `summarise` allows you to apply summary functions on your data
+
+The official [Data Wrangling with R cheat sheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) is a stellar reference for working with all these functions and more. Chain them together however you please using the pipe, and watch your adventures wrangling data frames become substantially more straightforward!
+
+## Some `magrittr` tricks
+
+While we may have covered the basics of `dplyr` and how the pipe synergizes with it, it's still worth going over some of the unique tricks `magrittr` itself offers.
+
+### The tee operator - `%T>%`
+
+Another operator that derives its inspiration from the Unix shell equivalent, the tee operator will still pass on LHS to RHS, but _returns LHS to stdout_ instead of RHS.
+
+In the context of a `dplyr` chain of commands, this is especially useful when we are saving the final result of said commands back to a data frame, but also have some kind of ancillary function tacked on at the very end (i.e. `plot()`) that we don't want saved to the object.
+
+
+```r
+sepalsonly <- ir %>% 
+  select(Sepal.Length, Sepal.Width) %T>% 
+  plot()
+```
+
+Here, the tee operator allows us to view a plot of our data, but without the output of `plot` ending up saved to the `sepalsonly` object. `plot` is therefore still part of our command chain, but not in an obtrusive way.
+
+### The compound assignment operator - `%<>%`
+
+The compound assignment operator also comes in handy when we're performing a series of `dplyr` commands on a data frame and saving the results back to it.
+
+Where we might do:
+
+
+```r
+ir <- ir %>% 
+  select(contains('Sepal'), Species)
+```
+
+The compound assignment operator makes for a useful bit of shorthand:
+
+
+```r
+ir %<>%
+  select(contains('Sepal'), Species)
+```
+
+
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+---
+title: Data visualization in R with ggplot2
+author: Vicki Zhang, borrowed from Ahmed Hasan, James Santangelo, and Joel Ostblom
+output: pdf_document
+---
+  
+## Lesson preamble
+  
+> ### Learning Objectives
+>
+> - Produce scatter plots, histograms, bar plots,
+> and boxplots using ggplot.
+> - Plot model fits with `geom_smooth`.
+> - Set universal plot settings.
+> - Understand and apply faceting in ggplot.
+> - Customize plots with `theme`.
+> 
+> ### Setup/Required packages
+>
+> - `install.packages('dplyr')`
+> - `install.packages('ggplot2')`
+>
+
+
+```{r, echo=FALSE}
+knitr::opts_chunk$set(eval=FALSE)
+```
+-----
+  
+# Plotting with ggplot2
+
+## Introduction
+
+`ggplot2` is a plotting package that makes it simple to create complex plots
+from data frames. The name `ggplot2` comes from its inspiration, the book [_A
+Grammar of Graphics_](https://www.springer.com/gp/book/9780387245447), and the
+main goal is to allow coders to distill complex data structure and express their
+desired graphical outcome in a concise manner instead of telling the computer
+every detail about what should happen. For example, you would say "colour my data
+by species" instead of "go through this data frame and plot any observations of
+species1 in blue, any observations of species2 in red, and so on". Thanks to
+this functional way of interfacing with data, only minimal changes are required
+if the underlying data change. This helps in thinking about the data and creating
+publication quality plots with minimal amounts of adjustments and tweaking.
+
+`ggplot2` graphics are built step by step by adding new elements, or layers.
+Adding layers in this fashion allows for extensive flexibility and customization
+of plots.
+
+```{r}
+library(ggplot2)
+```
+
+## The `diamonds` dataset
+
+We will be working with the `diamonds` dataset in this lesson. This is a large
+(~54,000 x 10) dataset containing measurements and prices of various diamonds.
+If you have an older computer and would like to just work with a subset of the
+data, you can create that subsetted data frame as follows:
+  
+```{r}
+d <- head(diamonds, n = 1000)
+```
+
+This will create a new data frame `d` containing just the first 1000 rows
+of the dataset.
+
+## Plotting with `ggplot2` - the geoms
+
+### Making our first scatter plot
+
+To build a ggplot, we need to:
+  
+1. Use the `ggplot()` function and bind the plot to a specific data frame using
+the `data` argument
+
+```{r}
+ggplot(data = diamonds)
+```
+
+If the arguments are provided in the right order then the
+names of the arguments can be omitted (as with `dplyr`)
+
+```{r}
+ggplot(diamonds)
+```
+
+2. Define aesthetics (`aes`), by selecting the variables to be plotted and the
+variables to define the presentation such as plotting size, shape colour, etc.
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price))
+```
+
+3. Add `geoms` -- geometrical objects as a graphical representation of the data
+in the plot (points, lines, bars). `ggplot2` offers many different geoms; we
+will use a few common ones today, including:
+* `geom_point()` for scatter plots, dot plots, etc.
+* `geom_line()` for trend lines, time-series, etc.
+* `geom_histogram()` for histograms
+
+To add a geom to the plot, use the `+` operator. Because we have two continuous
+variables, let's use `geom_point()` first:
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price)) +
+geom_point()
+```
+
+The `+` in the `ggplot2` package is particularly useful because it allows you
+to modify existing `ggplot` objects. This means you can easily set up plot
+"templates" and conveniently explore different types of plots, so the above
+plot can also be generated with code like this:
+
+```{r, first-ggplot-with-plus}
+# assign plot to a variable
+diamonds_plot <- ggplot(diamonds, aes(x = carat, y = price))
+
+# draw the plot
+diamonds_plot + geom_point()
+```
+
+Notes:
+
+- Anything you put in the `ggplot()` function can be seen by any geom layers
+  that you add (i.e., these are universal plot settings). This includes the x
+and y axis you set up in `aes()`.
+- You can also specify aesthetics for a given geom independently of the
+  aesthetics defined globally in the `ggplot()` function.
+- The `+` sign used to add layers must be placed at the end of each line
+  containing a layer. If, instead, the `+` sign is added in the line before the
+other layer, `ggplot2` will not add the new layer and R will return an error
+message.
+
+```{r eval = FALSE}
+# this will raise an error
+ggplot(diamonds, aes(x = carat, y = price))
++ geom_point()
+```
+
+
+### Building plots iteratively + scatter plots
+
+Building plots with `ggplot2` is typically an iterative process. We start by
+defining the dataset we'll use, lay the axes, and choose a geom:
+  
+```{r}
+ggplot(diamonds, aes(x = carat, y = price)) +
+  geom_point()
+```
+
+Then, we start modifying this plot to extract more information from it. For
+instance, we can add transparency (`alpha`) to reduce overplotting:
+  
+  
+```{r}
+ggplot(diamonds, aes(x = carat, y = price)) +
+  geom_point(alpha = 0.5)
+```
+
+From the eye test, we can see some evidence of clusters in this scatter plot.
+It's possible that one of the categorical variables we have in the data could
+explain this pattern. Colouring the data points according to a categorical
+variable is an easy way to assess if there seems to be correlation. Let's try
+this with `cut`.
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price, colour = cut)) +
+  geom_point(alpha = 0.5)
+```
+
+While some clustering is visible here, the pattern is not especially clear.
+Perhaps something else in our dataset might be a better predictor. Let's try
+`clarity` instead:
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price, colour = clarity)) +
+  geom_point(alpha = 0.5)
+```
+
+Turns out clarity appears to be a strong predictor here after all.  Given that
+IF1 is the best possible clarity measurement, we see that even 1-2 carat IF1
+diamonds fetch equivalent prices to 4-5 carat I1 (lowest clarity) diamonds.
+
+### Histograms
+
+Similarly to scatter plots above, histograms in `ggplot2` can simply be tacked
+on as another geom. The difference is that histograms do not take in a y
+aesthetic, since that'll be 'count' by definition:
+
+```{r}
+ggplot(diamonds, aes(x = carat)) +
+  geom_histogram()
+```
+
+The binning of a given histogram can be modified by either specifying a
+binwidth or a fixed number of bins:
+
+```{r}
+ggplot(diamonds, aes(x = carat)) +
+  geom_histogram(binwidth = 0.05)
+```
+
+### Boxplots
+
+Let's go back to the pattern we saw earlier, where `clarity` seemed to be related to `carat`.
+We might ask: what is the distribution of `carat` for each value of `clarity`? This is where
+a boxplot might be handy:
+
+```{r}
+ggplot(diamonds, aes(x = clarity, y = carat)) +
+  geom_boxplot()
+```
+
+## Model fits with `geom_smooth`
+
+Fitting models to data is a common means of investigating whether certain
+relationships are at play in a given dataset. `ggplot2` makes visualizing
+these easy by largely consolidating line-fitting into a single geom -
+`geom_smooth`.
+
+By default, `geom_smooth` will automatically select what it thinks is the best
+model for your dataset. However, rarely will it select a linear fit. To draw a
+linear fit (or any other sort of fit, i.e. LOESS, quadratic, etc.) we have to
+specify it:
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price)) +
+  geom_point(alpha = 0.5) +
+  geom_smooth(method = 'lm')
+```
+
+Adding the colour aesthetic we had previously will cause lines to be fit to each individual group:
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price, colour = clarity)) +
+  geom_point(alpha = 0.5) +
+  geom_smooth(method = 'lm')
+```
+
+To still fit a line across the entire dataset, specify the colour aesthetic _within_ `geom_point`, and not globally.
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price)) +
+  geom_point(aes(colour = clarity), alpha = 0.5) +
+  geom_smooth(method = 'lm')
+```
+
+## Intermediate `ggplot` 
+
+### Customizing plot appearance
+
+Let's create a graph object with all of our variables plotted nicely, and then we can customize the non-data components of the plot.
+```{r}
+# create graph object
+p <- ggplot(diamonds, aes(x = carat, y = price, colour = clarity)) +
+  geom_point(alpha = 0.5)
+```
+
+We can change the axis titles by scaling them.
+
+```{r}
+p +
+  labs(x = "Carat", y = "Price (USD)", colour = "Clarity", # capitalize axis labels, indicate currency
+  title = "Price of diamonds for different carats and clarity") # add title
+```
+
+We can customize the appearance more by passing some _theme_ statements[^theme] to `ggplot`.
+```{r}
+p + 
+  labs(x = "Carat", y = "Price (USD)", colour = "Clarity", title = "Price of diamonds for different carats and clarity") +
+  theme(plot.title = element_text(family = "Helvetica",
+                                  face = "bold",
+                                  hjust = 0.5, # [0,]
+                                  ),
+        axis.text.x = element_text(colour = "red"),
+        axis.line.y = element_line(size = 2),
+        axis.title.x = element_blank(), # remove the x-axis
+        axis.title.y = element_text(size = 7),
+        panel.background = element_rect(fill = "lightblue"), # only plot area
+        plot.background = element_rect(fill = "grey", # plot is for the entire plot (very confusing)
+                                       colour = "black"))
+```
+
+[^theme]: The amount of control over various plot elements in `ggplot` is truly astonishing. Check out the complete list of themes [here](https://ggplot2.tidyverse.org/reference/theme.html). Have fun!
+
+The above plot is probably the most atrocious figure I have ever made. Thankfully, `ggplot2` also comes preloaded with a host of pre-made themes - try them out below. I like to use the classic `theme_classic` because it also gets rid of grid lines - it looks so clean!
+
+```{r}
+p + theme_bw()
+p + theme_classic()
+```
+
+There are also ways to create your own custom themes using the `theme()`
+function, but that is a bit outside of the scope of this lesson. There are
+plenty of excellent resources available online though, some of which are linked
+at the end of this lesson! 
+
+### Faceting
+
+Finally, `ggplot` has a special technique called *faceting* that allows the
+user to split one plot into multiple subplots based on a variable included in
+the dataset. We will use `facet_wrap` to make the same scatter plot as above
+(price by carat) but split it by clarity:
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price, colour = clarity)) +
+  geom_point(alpha = 0.5) +
+  facet_wrap(~ clarity) +
+  theme_classic()
+```
+
+Alternatively, it may be useful to facet by two categorical variables. This is
+where `facet_grid` comes in:
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price, colour = clarity)) +
+  geom_point(alpha = 0.5) +
+  facet_grid(color ~ cut) + # y2 ~ x2ß
+  theme_classic()
+```
+
+# Wrapping up
+
+We've covered many of the core elements of `ggplot2` today:
+
+* Basic structure of a ggplot call
+* The `geom` family
+    * `geom_point`
+    * `geom_histogram`
+    * `geom_boxplot`
+* Model fits with `geom_smooth`
+* Customizing plots with `theme`
+* Faceting with `facet_wrap` and `facet_grid`
+
+
+More fun stuff you can do on  `ggplot`
+
+* using `patchwork` to combine plots
+* changing all aspects of the graph by using `scale_`, such as the x-axis, the
+y-axis, the colour, the legend, etc.
+* changing non-graphical components of the plot using `theme` and `element_`
+arguments
+* using arguments such as `jitter` and `position_dodge` to increase readability
+of plots (e.g., avoid overlaps, etc.)
+
+## Further resources
+
+* EEB313 - [Introduction to `dplyr` and `ggplot2`][eeb313]
+* Data Carpentry's [Data visualization with `ggplot2`][dc-r]
+* The [data visualization][data-vis] chapter from Hadley Wickham and Garrett Grolemund's _R for Data Science_
+* Hadley Wickham's [_ggplot2_ book][ggplot-book]
+* Winston Chang's [_R Graphics Cookbook_][cookbook]
+
+[cookbook]: http://www.cookbook-r.com/Graphs/
+[data-vis]: https://r4ds.had.co.nz/data-visualisation.html
+[dc-r]: https://datacarpentry.org/R-ecology-lesson/04-visualization-ggplot2.html
+[eeb313]: https://uoftcoders.github.io/rcourse/lec04-dplyr.html
+[ggplot-book]: https://www.amazon.com/dp/0387981403/ref=cm_sw_su_dp?tag=ggplot2-20
+
+
diff --git a/lessons/r/ggplot2/ggplot2-AH.Rmd b/lessons/r/ggplot2/ggplot2-AH.Rmd
new file mode 100644
index 000000000..c48440b5b
--- /dev/null
+++ b/lessons/r/ggplot2/ggplot2-AH.Rmd
@@ -0,0 +1,315 @@
+---
+title: Data visualization in R with ggplot2
+author: Ahmed Hasan and James Santangelo, borrowing heavily from Joel Ostblom
+output: pdf_document
+---
+  
+## Lesson preamble
+  
+> ### Learning Objectives
+>
+> - Produce scatter plots, histograms, bar plots,
+> and boxplots using ggplot.
+> - Plot model fits with `geom_smooth`.
+> - Set universal plot settings.
+> - Understand and apply faceting in ggplot.
+> - Customize plots with `theme`.
+> 
+> ### Setup/Required packages
+>
+> - `install.packages('dplyr')`
+> - `install.packages('ggplot2')`
+>
+
+
+```{r, echo=FALSE}
+knitr::opts_chunk$set(eval=FALSE)
+```
+-----
+  
+# Plotting with ggplot2
+
+## Introduction
+
+`ggplot2` is a plotting package that makes it simple to create complex plots
+from data frames. The name `ggplot2` comes from its inspiration, the book "A
+grammar of graphics", and the main goal is to allow coders to express their
+desired outcome on a high level instead of telling the computer every detail
+about what will happen. For example, you would say "color my data by species"
+instead of "go through this data frame and plot any observations of species1 in
+blue, any observations of species2 in red, and so on". Thanks to this
+functional way of interfacing with data, only minimal changes are required if
+the underlying data change. This helps in thinking about the data and creating
+publication quality plots with minimal amounts of adjustments and tweaking.
+
+`ggplot2` graphics are built step by step by adding new elements, or layers.
+Adding layers in this fashion allows for extensive flexibility and
+customization of plots. 
+
+```{r}
+library(ggplot2)
+```
+
+## The `diamonds` dataset
+
+We will be working with the `diamonds` dataset in this lesson. This is a large
+(~54,000 x 10) dataset containing measurements and prices of various diamonds.
+If you have an older computer and would like to just work with a subset of the
+data, you can create that subsetted data frame as follows:
+  
+```{r}
+d <- head(diamonds, n = 1000)
+```
+
+This will create a new data frame `d` containing just the first 1000 rows
+of the dataset.
+
+## Plotting with `ggplot2` - the geoms
+
+### Making our first scatter plot
+
+To build a ggplot, we need to:
+  
+1. Use the `ggplot()` function and bind the plot to a specific data frame using
+the `data` argument
+
+```{r}
+ggplot(data = diamonds)
+```
+
+If the arguments are provided in the right order then the
+names of the arguments can be omitted (as with `dplyr`)
+
+```{r}
+ggplot(diamonds)
+```
+
+2. Define aesthetics (`aes`), by selecting the variables to be plotted and the
+variables to define the presentation such as plotting size, shape color, etc.
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price))
+```
+
+3. Add `geoms` -- geometrical objects as a graphical representation of the data
+in the plot (points, lines, bars). `ggplot2` offers many different geoms; we
+will use a few common ones today, including:
+* `geom_point()` for scatter plots, dot plots, etc.
+* `geom_line()` for trend lines, time-series, etc.
+* `geom_histogram()` for histograms
+
+To add a geom to the plot, use the `+` operator. Because we have two continuous
+variables, let's use `geom_point()` first:
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price)) +
+geom_point()
+```
+
+The `+` in the `ggplot2` package is particularly useful because it allows you
+to modify existing `ggplot` objects. This means you can easily set up plot
+"templates" and conveniently explore different types of plots, so the above
+plot can also be generated with code like this:
+
+```{r, first-ggplot-with-plus}
+# assign plot to a variable
+diamonds_plot <- ggplot(diamonds, aes(x = carat, y = price))
+
+# draw the plot
+diamonds_plot + geom_point()
+```
+
+Notes:
+
+- Anything you put in the `ggplot()` function can be seen by any geom layers
+  that you add (i.e., these are universal plot settings). This includes the x
+and y axis you set up in `aes()`.
+- You can also specify aesthetics for a given geom independently of the
+  aesthetics defined globally in the `ggplot()` function.
+- The `+` sign used to add layers must be placed at the end of each line
+  containing a layer. If, instead, the `+` sign is added in the line before the
+other layer, `ggplot2` will not add the new layer and R will return an error
+message.
+
+```{r}
+# this will raise an error
+ggplot(diamonds, aes(x = carat, y = price)) 
++ geom_point()
+```
+
+
+### Building plots iteratively + scatter plots
+
+Building plots with `ggplot2` is typically an iterative process. We start by
+defining the dataset we'll use, lay the axes, and choose a geom:
+  
+```{r}
+ggplot(diamonds, aes(x = carat, y = price)) +
+  geom_point()
+```
+
+Then, we start modifying this plot to extract more information from it. For
+instance, we can add transparency (`alpha`) to reduce overplotting:
+  
+  
+```{r}
+ggplot(diamonds, aes(x = carat, y = price)) +
+  geom_point(alpha = 0.5)
+```
+
+From the eye test, we can see some evidence of clusters in this scatter plot.
+It's possible that one of the categorical variables we have in the data could
+explain this pattern. Coloring the data points according to a categorical
+variable is an easy way to assess if there seems to be correlation. Let's try
+this with `cut`.
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price, color = cut)) +
+  geom_point(alpha = 0.5)
+```
+
+While some clustering is visible here, the pattern is not especially clear.
+Perhaps something else in our dataset might be a better predictor. Let's try
+`clarity` instead:
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price, color = clarity)) +
+geom_point(alpha = 0.5)
+```
+
+Turns out clarity appears to be a strong predictor here after all.  Given that
+IF1 is the best possible clarity measurement, we see that even 1-2 carat IF1
+diamonds fetch equivalent prices to 4-5 carat I1 (lowest clarity) diamonds.
+
+### Histograms
+
+Similarly to scatter plots above, histograms in `ggplot2` can simply be tacked
+on as another geom. The difference is that histograms do not take in a y
+aesthetic, since that'll be 'count' by definition:
+
+```{r}
+ggplot(diamonds, aes(x = carat)) +
+  geom_histogram()
+```
+
+The binning of a given histogram can be modified by either specifying a
+binwidth or a fixed number of bins:
+
+```{r}
+ggplot(diamonds, aes(x = carat)) +
+  geom_histogram(binwidth = 0.05)
+```
+
+### Boxplots
+
+Let's go back to the pattern we saw earlier, where `clarity` seemed to be related to `carat`.
+We might ask: what is the distribution of `carat` for each value of `clarity`? This is where
+a boxplot might be handy:
+
+```{r}
+ggplot(diamonds, aes(x = clarity, y = carat)) +
+  geom_boxplot()
+```
+
+## Model fits with `geom_smooth`
+
+Fitting models to data is a common means of investigating whether certain
+relationships are at play in a given dataset. `ggplot2` makes visualizing
+these easy by largely consolidating line-fitting into a single geom -
+`geom_smooth`.
+
+By default, `geom_smooth` will automatically select what it thinks is the best
+model for your dataset. However, rarely will it select a linear fit. To draw a
+linear fit (or any other sort of fit, i.e. LOESS, quadratic, etc.) we have to
+specify it:
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price)) +
+  geom_point(alpha = 0.5) +
+  geom_smooth(method = 'lm')
+```
+
+Adding the color aesthetic we had previously will cause lines to be fit to each individual group:
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price, color = clarity)) +
+  geom_point(alpha = 0.5) +
+  geom_smooth(method = 'lm')
+```
+
+To still fit a line across the entire dataset, specify the color aesthetic _within_ `geom_point`, and not globally.
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price)) +
+  geom_point(aes(color = clarity), alpha = 0.5) +
+  geom_smooth(method = 'lm')
+```
+
+## Intermediate `ggplot` 
+
+### Customizing plot appearance
+
+`ggplot2` also comes preloaded with a host of pre-made themes - try them out:
+
+```{r}
+p + theme_bw()
+p + theme_classic()
+```
+
+There are also ways to create your own custom themes using the `theme()`
+function, but that is a bit outside of the scope of this lesson. There are
+plenty of excellent resources available online though, some of which are linked
+at the end of this lesson! 
+
+### Faceting
+
+Finally, `ggplot` has a special technique called *faceting* that allows the
+user to split one plot into multiple subplots based on a variable included in
+the dataset. We will use `facet_wrap` to make the same scatter plot as above
+(price by carat) but split it by clarity:
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price, color = clarity)) +
+  geom_point(alpha = 0.5) +
+  facet_wrap(~ clarity) +
+  theme_classic()
+```
+
+Alternatively, it may be useful to facet by two categorical variables. This is
+where `facet_grid` comes in:
+
+```{r}
+ggplot(diamonds, aes(x = carat, y = price, color = clarity)) +
+  geom_point(alpha = 0.5) +
+  facet_grid(cut ~ clarity) +
+  theme_classic()
+```
+
+# Wrapping up
+
+We've covered many of the core elements of `ggplot2` today:
+
+* Basic structure of a ggplot call
+* The `geom` family
+    * `geom_point`
+    * `geom_histogram`
+    * `geom_boxplot`
+* Model fits with `geom_smooth`
+* Customizing plots with `theme`
+* Faceting with `facet_wrap` and `facet_grid`
+
+## Further resources
+
+* EEB313 - [Introduction to `dplyr` and `ggplot2`][eeb313]
+* Data Carpentry's [Data visualization with `ggplot2`][dc-r]
+* The [data visualization][data-vis] chapter from Hadley Wickham and Garrett Grolemund's _R for Data Science_
+* Hadley Wickham's [_ggplot2_ book][ggplot-book]
+* Winston Chang's [_R Graphics Cookbook_][cookbook]
+
+[cookbook]: http://www.cookbook-r.com/Graphs/
+[data-vis]: https://r4ds.had.co.nz/data-visualisation.html
+[dc-r]: https://datacarpentry.org/R-ecology-lesson/04-visualization-ggplot2.html
+[eeb313]: https://uoftcoders.github.io/rcourse/lec04-dplyr.html
+[ggplot-book]: https://www.amazon.com/dp/0387981403/ref=cm_sw_su_dp?tag=ggplot2-20
+
+
diff --git a/lessons/r/ggplot2/ggplot2lesson.R b/lessons/r/ggplot2/ggplot2lesson.R
new file mode 100644
index 000000000..9020fa195
--- /dev/null
+++ b/lessons/r/ggplot2/ggplot2lesson.R
@@ -0,0 +1,53 @@
+#BASE PLOTTING FUNCTIONS
+data()
+plot(cars)
+hist(cars$speed)
+
+#We'll investigate the dataset we're going to use to make some graphs....
+library(ggplot2)
+str(diamonds)
+
+#Our first graph?
+ggplot(diamonds, aes(x=cut, y=carat))
+
+#We've defined the structure of our graph, but we need to add a "layer" to it in order to define what type of graph it is. 
+#Let's make a scatterplot:
+ggplot(diamonds, aes(x=carat, y=price)) + 
+  geom_point()
+
+#You can also save the structure of the graph to a variable and then add layers and other attributes to it. Let's do that:
+g <- ggplot(diamonds, aes(x=carat, y=price))
+g + geom_point()
+
+#We can add other "layers" of graphs to our first graph. Let's add a trend line to our scatterplot:
+g + geom_point() + geom_smooth() 
+
+#We can also "facet" our graphs, which means dividing a plot into subplots based on the values of one or more discrete variables.
+g + geom_point() + geom_smooth() + facet_grid(cut~.)
+
+#Let's say we don't want to facet, but we want to colour the dots depending on the cut of the diamonds:
+ggplot(diamonds, aes(x=carat, y=price, color=cut)) + geom_point()
+
+#We can also change some aesthetic features of the graph. 
+#Let's get rid of the color aspect of our structure and change the colour and size of the points:
+ggplot(diamonds, aes(x=carat, y=price)) + geom_point(color="steelblue", size=4)
+
+#Let's look at how to change the theme and labels:
+g + geom_point() +
+  ylab("Diamond Price") +
+  xlab("Carat") +
+  ggtitle("Diamond Price by Weight") +
+  theme_bw()
+
+#What about a bar plot? Let's find out how many diamonds of each cut are in the dataset.
+ggplot(diamonds, aes(cut)) + geom_bar()
+
+#What about making interactive graphs in R?
+#There is a package called plotly that lets us make our ggplot2 graphs interactive:
+library(plotly)
+g <- ggplot(diamonds, aes(cut)) + geom_bar()
+p=ggplotly(g)
+p
+
+#Finally, here is qplot, a way of making super quick scatterplots in R.
+qplot(carat, price, data = diamonds)
diff --git a/lessons/r/ggplot2/interactivegraph.html b/lessons/r/ggplot2/interactivegraph.html
new file mode 100644
index 000000000..c74713486
--- /dev/null
+++ b/lessons/r/ggplot2/interactivegraph.html
@@ -0,0 +1,18 @@
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    }
    // TODO: Verify that .name is a valid CSS classname

    // Support new-style instance-bound definitions. Old-style class-bound
    // definitions have one widget "object" per widget per type/class of
    // widget; the renderValue and resize methods on such widget objects
    // take el and instance arguments, because the widget object can't
    // store them. New-style instance-bound definitions have one widget
    // object per widget instance; the definition that's passed in doesn't
    // provide renderValue or resize methods at all, just the single method
    //   factory(el, width, height)
    // which returns an object that has renderValue(x) and resize(w, h).
    // This enables a far more natural programming style for the widget
    // author, who can store per-instance state using either OO-style
    // instance fields or functional-style closure variables (I guess this
    // is in contrast to what can only be called C-style pseudo-OO which is
    // what we required before).
    if (definition.factory) {
      definition = createLegacyDefinitionAdapter(definition);
    }

    if (!definition.renderValue) {
      throw new Error("Widget must have a renderValue function");
    }

    // For static rendering (non-Shiny), use a simple widget registration
    // scheme. We also use this scheme for Shiny apps/documents that also
    // contain static widgets.
    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
    // Merge defaults into the definition; don't mutate the original definition.
    var staticBinding = extend({}, defaults, definition);
    overrideMethod(staticBinding, "find", function(superfunc) {
      return function(scope) {
        var results = superfunc(scope);
        // Filter out Shiny outputs, we only want the static kind
        return filterByClass(results, "html-widget-output", false);
      };
    });
    window.HTMLWidgets.widgets.push(staticBinding);

    if (shinyMode) {
      // Shiny is running. Register the definition with an output binding.
      // The definition itself will not be the output binding, instead
      // we will make an output binding object that delegates to the
      // definition. This is because we foolishly used the same method
      // name (renderValue) for htmlwidgets definition and Shiny bindings
      // but they actually have quite different semantics (the Shiny
      // bindings receive data that includes lots of metadata that it
      // strips off before calling htmlwidgets renderValue). We can't
      // just ignore the difference because in some widgets it's helpful
      // to call this.renderValue() from inside of resize(), and if
      // we're not delegating, then that call will go to the Shiny
      // version instead of the htmlwidgets version.

      // Merge defaults with definition, without mutating either.
      var bindingDef = extend({}, defaults, definition);

      // This object will be our actual Shiny binding.
      var shinyBinding = new Shiny.OutputBinding();

      // With a few exceptions, we'll want to simply use the bindingDef's
      // version of methods if they are available, otherwise fall back to
      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
      // methods in the future, and we want them to be overrideable by
      // HTMLWidget binding definitions, then we'll need to add them to this
      // list.
      delegateMethod(shinyBinding, bindingDef, "getId");
      delegateMethod(shinyBinding, bindingDef, "onValueChange");
      delegateMethod(shinyBinding, bindingDef, "onValueError");
      delegateMethod(shinyBinding, bindingDef, "renderError");
      delegateMethod(shinyBinding, bindingDef, "clearError");
      delegateMethod(shinyBinding, bindingDef, "showProgress");

      // The find, renderValue, and resize are handled differently, because we
      // want to actually decorate the behavior of the bindingDef methods.

      shinyBinding.find = function(scope) {
        var results = bindingDef.find(scope);

        // Only return elements that are Shiny outputs, not static ones
        var dynamicResults = results.filter(".html-widget-output");

        // It's possible that whatever caused Shiny to think there might be
        // new dynamic outputs, also caused there to be new static outputs.
        // Since there might be lots of different htmlwidgets bindings, we
        // schedule execution for later--no need to staticRender multiple
        // times.
        if (results.length !== dynamicResults.length)
          scheduleStaticRender();

        return dynamicResults;
      };

      // Wrap renderValue to handle initialization, which unfortunately isn't
      // supported natively by Shiny at the time of this writing.

      shinyBinding.renderValue = function(el, data) {
        // Resolve strings marked as javascript literals to objects
        if (!(data.evals instanceof Array)) data.evals = [data.evals];
        for (var i = 0; data.evals && i < data.evals.length; i++) {
          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
        }
        if (!bindingDef.renderOnNullValue) {
          if (data.x === null) {
            el.style.visibility = "hidden";
            return;
          } else {
            el.style.visibility = "inherit";
          }
        }
        if (!elementData(el, "initialized")) {
          initSizing(el);

          elementData(el, "initialized", true);
          if (bindingDef.initialize) {
            var result = bindingDef.initialize(el, el.offsetWidth,
              el.offsetHeight);
            elementData(el, "init_result", result);
          }
        }
        Shiny.renderDependencies(data.deps);
        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
      };

      // Only override resize if bindingDef implements it
      if (bindingDef.resize) {
        shinyBinding.resize = function(el, width, height) {
          // Shiny can call resize before initialize/renderValue have been
          // called, which doesn't make sense for widgets.
          if (elementData(el, "initialized")) {
            bindingDef.resize(el, width, height, elementData(el, "init_result"));
          }
        };
      }

      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
    }
  };

  var scheduleStaticRenderTimerId = null;
  function scheduleStaticRender() {
    if (!scheduleStaticRenderTimerId) {
      scheduleStaticRenderTimerId = setTimeout(function() {
        scheduleStaticRenderTimerId = null;
        window.HTMLWidgets.staticRender();
      }, 1);
    }
  }

  // Render static widgets after the document finishes loading
  // Statically render all elements that are of this widget's class
  window.HTMLWidgets.staticRender = function() {
    var bindings = window.HTMLWidgets.widgets || [];
    forEach(bindings, function(binding) {
      var matches = binding.find(document.documentElement);
      forEach(matches, function(el) {
        var sizeObj = initSizing(el, binding);

        if (hasClass(el, "html-widget-static-bound"))
          return;
        el.className = el.className + " html-widget-static-bound";

        var initResult;
        if (binding.initialize) {
          initResult = binding.initialize(el,
            sizeObj ? sizeObj.getWidth() : el.offsetWidth,
            sizeObj ? sizeObj.getHeight() : el.offsetHeight
          );
          elementData(el, "init_result", initResult);
        }

        if (binding.resize) {
          var lastSize = {};
          var resizeHandler = function(e) {
            var size = {
              w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
              h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
            };
            if (size.w === 0 && size.h === 0)
              return;
            if (size.w === lastSize.w && size.h === lastSize.h)
              return;
            lastSize = size;
            binding.resize(el, size.w, size.h, initResult);
          };

          on(window, "resize", resizeHandler);

          // This is needed for cases where we're running in a Shiny
          // app, but the widget itself is not a Shiny output, but
          // rather a simple static widget. One example of this is
          // an rmarkdown document that has runtime:shiny and widget
          // that isn't in a render function. Shiny only knows to
          // call resize handlers for Shiny outputs, not for static
          // widgets, so we do it ourselves.
          if (window.jQuery) {
            window.jQuery(document).on(
              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
              resizeHandler
            );
            window.jQuery(document).on(
              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
              resizeHandler
            );
          }

          // This is needed for the specific case of ioslides, which
          // flips slides between display:none and display:block.
          // Ideally we would not have to have ioslide-specific code
          // here, but rather have ioslides raise a generic event,
          // but the rmarkdown package just went to CRAN so the
          // window to getting that fixed may be long.
          if (window.addEventListener) {
            // It's OK to limit this to window.addEventListener
            // browsers because ioslides itself only supports
            // such browsers.
            on(document, "slideenter", resizeHandler);
            on(document, "slideleave", resizeHandler);
          }
        }

        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
        if (scriptData) {
          var data = JSON.parse(scriptData.textContent || scriptData.text);
          // Resolve strings marked as javascript literals to objects
          if (!(data.evals instanceof Array)) data.evals = [data.evals];
          for (var k = 0; data.evals && k < data.evals.length; k++) {
            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
          }
          binding.renderValue(el, data.x, initResult);
          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
        }
      });
    });

    invokePostRenderHandlers();
  }

  // Wait until after the document has loaded to render the widgets.
  if (document.addEventListener) {
    document.addEventListener("DOMContentLoaded", function() {
      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
      window.HTMLWidgets.staticRender();
    }, false);
  } else if (document.attachEvent) {
    document.attachEvent("onreadystatechange", function() {
      if (document.readyState === "complete") {
        document.detachEvent("onreadystatechange", arguments.callee);
        window.HTMLWidgets.staticRender();
      }
    });
  }


  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
    // If no key, default to the first item
    if (typeof(key) === "undefined")
      key = 1;

    var link = document.getElementById(depname + "-" + key + "-attachment");
    if (!link) {
      throw new Error("Attachment " + depname + "/" + key + " not found in document");
    }
    return link.getAttribute("href");
  };

  window.HTMLWidgets.dataframeToD3 = function(df) {
    var names = [];
    var length;
    for (var name in df) {
        if (df.hasOwnProperty(name))
            names.push(name);
        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
            throw new Error("All fields must be arrays");
        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
            throw new Error("All fields must be arrays of the same length");
        }
        length = df[name].length;
    }
    var results = [];
    var item;
    for (var row = 0; row < length; row++) {
        item = {};
        for (var col = 0; col < names.length; col++) {
            item[names[col]] = df[names[col]][row];
        }
        results.push(item);
    }
    return results;
  };

  window.HTMLWidgets.transposeArray2D = function(array) {
      if (array.length === 0) return array;
      var newArray = array[0].map(function(col, i) {
          return array.map(function(row) {
              return row[i]
          })
      });
      return newArray;
  };
  // Split value at splitChar, but allow splitChar to be escaped
  // using escapeChar. Any other characters escaped by escapeChar
  // will be included as usual (including escapeChar itself).
  function splitWithEscape(value, splitChar, escapeChar) {
    var results = [];
    var escapeMode = false;
    var currentResult = "";
    for (var pos = 0; pos < value.length; pos++) {
      if (!escapeMode) {
        if (value[pos] === splitChar) {
          results.push(currentResult);
          currentResult = "";
        } else if (value[pos] === escapeChar) {
          escapeMode = true;
        } else {
          currentResult += value[pos];
        }
      } else {
        currentResult += value[pos];
        escapeMode = false;
      }
    }
    if (currentResult !== "") {
      results.push(currentResult);
    }
    return results;
  }
  // Function authored by Yihui/JJ Allaire
  window.HTMLWidgets.evaluateStringMember = function(o, member) {
    var parts = splitWithEscape(member, '.', '\\');
    for (var i = 0, l = parts.length; i < l; i++) {
      var part = parts[i];
      // part may be a character or 'numeric' member name
      if (o !== null && typeof o === "object" && part in o) {
        if (i == (l - 1)) { // if we are at the end of the line then evalulate
          if (typeof o[part] === "string")
            o[part] = eval("(" + o[part] + ")");
        } else { // otherwise continue to next embedded object
          o = o[part];
        }
      }
    }
  };

  // Retrieve the HTMLWidget instance (i.e. the return value of an
  // HTMLWidget binding's initialize() or factory() function)
  // associated with an element, or null if none.
  window.HTMLWidgets.getInstance = function(el) {
    return elementData(el, "init_result");
  };

  // Finds the first element in the scope that matches the selector,
  // and returns the HTMLWidget instance (i.e. the return value of
  // an HTMLWidget binding's initialize() or factory() function)
  // associated with that element, if any. If no element matches the
  // selector, or the first matching element has no HTMLWidget
  // instance associated with it, then null is returned.
  //
  // The scope argument is optional, and defaults to window.document.
  window.HTMLWidgets.find = function(scope, selector) {
    if (arguments.length == 1) {
      selector = scope;
      scope = document;
    }

    var el = scope.querySelector(selector);
    if (el === null) {
      return null;
    } else {
      return window.HTMLWidgets.getInstance(el);
    }
  };

  // Finds all elements in the scope that match the selector, and
  // returns the HTMLWidget instances (i.e. the return values of
  // an HTMLWidget binding's initialize() or factory() function)
  // associated with the elements, in an array. If elements that
  // match the selector don't have an associated HTMLWidget
  // instance, the returned array will contain nulls.
  //
  // The scope argument is optional, and defaults to window.document.
  window.HTMLWidgets.findAll = function(scope, selector) {
    if (arguments.length == 1) {
      selector = scope;
      scope = document;
    }

    var nodes = scope.querySelectorAll(selector);
    var results = [];
    for (var i = 0; i < nodes.length; i++) {
      results.push(window.HTMLWidgets.getInstance(nodes[i]));
    }
    return results;
  };

  var postRenderHandlers = [];
  function invokePostRenderHandlers() {
    while (postRenderHandlers.length) {
      var handler = postRenderHandlers.shift();
      if (handler) {
        handler();
      }
    }
  }

  // Register the given callback function to be invoked after the
  // next time static widgets are rendered.
  window.HTMLWidgets.addPostRenderHandler = function(callback) {
    postRenderHandlers.push(callback);
  };

  // Takes a new-style instance-bound definition, and returns an
  // old-style class-bound definition. This saves us from having
  // to rewrite all the logic in this file to accomodate both
  // types of definitions.
  function createLegacyDefinitionAdapter(defn) {
    var result = {
      name: defn.name,
      type: defn.type,
      initialize: function(el, width, height) {
        return defn.factory(el, width, height);
      },
      renderValue: function(el, x, instance) {
        return instance.renderValue(x);
      },
      resize: function(el, width, height, instance) {
        return instance.resize(width, height);
      }
    };

    if (defn.find)
      result.find = defn.find;
    if (defn.renderError)
      result.renderError = defn.renderError;
    if (defn.clearError)
      result.clearError = defn.clearError;

    return result;
  }
})();

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f=Math.max(e,o,u),h=Math.sqrt(2*f-c+1);e>=f?(t[0]=.5*h,t[1]=.5*(i+r)/h,t[2]=.5*(s+n)/h,t[3]=.5*(a-l)/h):o>=f?(t[0]=.5*(r+i)/h,t[1]=.5*h,t[2]=.5*(l+a)/h,t[3]=.5*(s-n)/h):(t[0]=.5*(n+s)/h,t[1]=.5*(a+l)/h,t[2]=.5*h,t[3]=.5*(r-i)/h)}return t}e.exports=n},{}],38:[function(t,e,r){"use strict";function n(t,e,r){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2))}function i(t,e,r,n){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2)+Math.pow(n,2))}function o(t,e){var r=e[0],n=e[1],o=e[2],a=e[3],s=i(r,n,o,a);s>1e-6?(t[0]=r/s,t[1]=n/s,t[2]=o/s,t[3]=a/s):(t[0]=t[1]=t[2]=0,t[3]=1)}function a(t,e,r){this.radius=l([r]),this.center=l(e),this.rotation=l(t),this.computedRadius=this.radius.curve(0),this.computedCenter=this.center.curve(0),this.computedRotation=this.rotation.curve(0),this.computedUp=[.1,0,0],this.computedEye=[.1,0,0],this.computedMatrix=[.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],this.recalcMatrix(0)}function s(t){t=t||{};var 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s=0,l=0;3>l;++l)s+=Math.pow(r[l]-e[l],2);this.radius.set(t,.5*Math.log(Math.max(s,1e-6))),this.center.set(t,r[0],r[1],r[2])},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e){var r=this.computedRotation;f(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),o(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;h(n,e);var i=n[15];if(Math.abs(i)>1e-6){var a=n[12]/i,s=n[13]/i,l=n[14]/i;this.recalcMatrix(t);var u=Math.exp(this.computedRadius[0]);this.center.set(t,a-n[2]*u,s-n[6]*u,l-n[10]*u),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-(1/0),
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e=t.center||[0,0,0],r=t.up||[0,1,0],i=t.right||o(r),s=t.radius||1,l=t.theta||0,u=t.phi||0;if(e=[].slice.call(e,0,3),r=[].slice.call(r,0,3),f(r,r),i=[].slice.call(i,0,3),f(i,i),"eye"in t){var c=t.eye,d=[c[0]-e[0],c[1]-e[1],c[2]-e[2]];h(i,d,r),n(i[0],i[1],i[2])<1e-6?i=o(r):f(i,i),s=n(d[0],d[1],d[2]);var m=p(r,d)/s,g=p(i,d)/s;u=Math.acos(m),l=Math.acos(g)}return s=Math.log(s),new a(t.zoomMin,t.zoomMax,e,r,i,s,l,u)}e.exports=s;var l=t("filtered-vector"),u=t("gl-mat4/invert"),c=t("gl-mat4/rotate"),h=t("gl-vec3/cross"),f=t("gl-vec3/normalize"),p=t("gl-vec3/dot"),d=a.prototype;d.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-(1/0),e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},d.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},d.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,i=0,o=0,a=0;3>a;++a)o+=e[a]*r[a],i+=e[a]*e[a];for(var s=Math.sqrt(i),l=0,a=0;3>a;++a)r[a]-=e[a]*o/i,l+=r[a]*r[a],e[a]/=s;for(var u=Math.sqrt(l),a=0;3>a;++a)r[a]/=u;var c=this.computedToward;h(c,e,r),f(c,c);for(var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),v=Math.sin(d),y=Math.cos(m),b=Math.sin(m),x=this.computedCenter,_=g*y,w=v*y,M=b,k=-g*b,A=-v*b,T=y,E=this.computedEye,S=this.computedMatrix,a=0;3>a;++a){var L=_*r[a]+w*c[a]+M*e[a];S[4*a+1]=k*r[a]+A*c[a]+T*e[a],S[4*a+2]=L,S[4*a+3]=0}var z=S[1],I=S[5],P=S[9],C=S[2],R=S[6],j=S[10],D=I*j-P*R,O=P*C-z*j,F=z*R-I*C,N=n(D,O,F);D/=N,O/=N,F/=N,S[0]=D,S[4]=O,S[8]=F;for(var a=0;3>a;++a)E[a]=x[a]+S[2+4*a]*p;for(var a=0;3>a;++a){for(var l=0,B=0;3>B;++B)l+=S[a+4*B]*E[B];S[12+a]=-l}S[15]=1},d.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;16>n;++n)e[n]=r[n];return e}return r};var m=[0,0,0];d.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;m[0]=i[2],m[1]=i[6],m[2]=i[10];for(var o=this.computedUp,a=this.computedRight,s=this.computedToward,l=0;3>l;++l)i[4*l]=o[l],i[4*l+1]=a[l],i[4*l+2]=s[l];c(i,i,n,m);for(var l=0;3>l;++l)o[l]=i[4*l],a[l]=i[4*l+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,a[0],a[1],a[2])}},d.pan=function(t,e,r,i){e=e||0,r=r||0,i=i||0,this.recalcMatrix(t);var o=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),o[1]),s=o[5],l=o[9],u=n(a,s,l);a/=u,s/=u,l/=u;var c=o[0],h=o[4],f=o[8],p=c*a+h*s+f*l;c-=a*p,h-=s*p,f-=l*p;var d=n(c,h,f);c/=d,h/=d,f/=d;var m=c*e+a*r,g=h*e+s*r,v=f*e+l*r;this.center.move(t,m,g,v);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+i),this.radius.set(t,Math.log(y))},d.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},d.setMatrix=function(t,e,r,o){var a=1;"number"==typeof r&&(a=0|r),(0>a||a>3)&&(a=1);var s=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var l=e[a],c=e[a+4],h=e[a+8];if(o){var f=Math.abs(l),p=Math.abs(c),d=Math.abs(h),m=Math.max(f,p,d);f===m?(l=0>l?-1:1,c=h=0):d===m?(h=0>h?-1:1,l=c=0):(c=0>c?-1:1,l=h=0)}else{var g=n(l,c,h);l/=g,c/=g,h/=g}var v=e[s],y=e[s+4],b=e[s+8],x=v*l+y*c+b*h;v-=l*x,y-=c*x,b-=h*x;var _=n(v,y,b);v/=_,y/=_,b/=_;var w=c*b-h*y,M=h*v-l*b,k=l*y-c*v,A=n(w,M,k);w/=A,M/=A,k/=A,this.center.jump(t,G,H,X),this.radius.idle(t),this.up.jump(t,l,c,h),this.right.jump(t,v,y,b);var T,E;if(2===a){var S=e[1],L=e[5],z=e[9],I=S*v+L*y+z*b,P=S*w+L*M+z*k;T=0>D?-Math.PI/2:Math.PI/2,E=Math.atan2(P,I)}else{var C=e[2],R=e[6],j=e[10],D=C*l+R*c+j*h,O=C*v+R*y+j*b,F=C*w+R*M+j*k;T=Math.asin(i(D)),E=Math.atan2(F,O)}this.angle.jump(t,E,T),this.recalcMatrix(t);var N=e[2],B=e[6],U=e[10],V=this.computedMatrix;u(V,e);var q=V[15],G=V[12]/q,H=V[13]/q,X=V[14]/q,Y=Math.exp(this.computedRadius[0]);this.center.jump(t,G-N*Y,H-B*Y,X-U*Y)},d.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},d.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},d.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},d.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},d.lookAt=function(t,e,r,o){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter,o=o||this.computedUp;var a=o[0],s=o[1],l=o[2],u=n(a,s,l);if(!(1e-6>u)){a/=u,s/=u,l/=u;var c=e[0]-r[0],h=e[1]-r[1],f=e[2]-r[2],p=n(c,h,f);if(!(1e-6>p)){c/=p,h/=p,f/=p;var d=this.computedRight,m=d[0],g=d[1],v=d[2],y=a*m+s*g+l*v;m-=y*a,g-=y*s,v-=y*l;var b=n(m,g,v);if(!(.01>b&&(m=s*f-l*h,g=l*c-a*f,v=a*h-s*c,b=n(m,g,v),1e-6>b))){m/=b,g/=b,v/=b,this.up.set(t,a,s,l),this.right.set(t,m,g,v),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var x=s*v-l*g,_=l*m-a*v,w=a*g-s*m,M=n(x,_,w);x/=M,_/=M,w/=M;var k=a*c+s*h+l*f,A=m*c+g*h+v*f,T=x*c+_*h+w*f,E=Math.asin(i(k)),S=Math.atan2(T,A),L=this.angle._state,z=L[L.length-1],I=L[L.length-2];z%=2*Math.PI;var P=Math.abs(z+2*Math.PI-S),C=Math.abs(z-S),R=Math.abs(z-2*Math.PI-S);C>P&&(z+=2*Math.PI),C>R&&(z-=2*Math.PI),this.angle.jump(this.angle.lastT(),z,I),this.angle.set(t,S,E)}}}}},{"filtered-vector":21,"gl-mat4/invert":148,"gl-mat4/rotate":152,"gl-vec3/cross":24,"gl-vec3/dot":25,"gl-vec3/normalize":28}],40:[function(t,e,r){"use strict";function n(t,e){this._controllerNames=Object.keys(t),this._controllerList=this._controllerNames.map(function(e){return t[e]}),this._mode=e,this._active=t[e],this._active||(this._mode="turntable",this._active=t.turntable),this.modes=this._controllerNames,this.computedMatrix=this._active.computedMatrix,this.computedEye=this._active.computedEye,this.computedUp=this._active.computedUp,this.computedCenter=this._active.computedCenter,this.computedRadius=this._active.computedRadius}function i(t){t=t||{};var e=t.eye||[0,0,1],r=t.center||[0,0,0],i=t.up||[0,1,0],l=t.distanceLimits||[0,1/0],u=t.mode||"turntable",c=o(),h=a(),f=s();return c.setDistanceLimits(l[0],l[1]),c.lookAt(0,e,r,i),h.setDistanceLimits(l[0],l[1]),h.lookAt(0,e,r,i),f.setDistanceLimits(l[0],l[1]),f.lookAt(0,e,r,i),new n({turntable:c,orbit:h,matrix:f},u)}e.exports=i;var o=t("turntable-camera-controller"),a=t("orbit-camera-controller"),s=t("matrix-camera-controller"),l=n.prototype,u=[["flush",1],["idle",1],["lookAt",4],["rotate",4],["pan",4],["translate",4],["setMatrix",2],["setDistanceLimits",2],["setDistance",2]];u.forEach(function(t){for(var e=t[0],r=[],n=0;n<t[1];++n)r.push("a"+n);var i="var cc=this._controllerList;for(var i=0;i<cc.length;++i){cc[i]."+t[0]+"("+r.join()+")}";l[e]=Function.apply(null,r.concat(i))}),l.recalcMatrix=function(t){this._active.recalcMatrix(t)},l.getDistance=function(t){return this._active.getDistance(t)},l.getDistanceLimits=function(t){return this._active.getDistanceLimits(t)},l.lastT=function(){return this._active.lastT()},l.setMode=function(t){if(t!==this._mode){var e=this._controllerNames.indexOf(t);if(!(0>e)){var r=this._active,n=this._controllerList[e],i=Math.max(r.lastT(),n.lastT());r.recalcMatrix(i),n.setMatrix(i,r.computedMatrix),this._active=n,this._mode=t,this.computedMatrix=this._active.computedMatrix,this.computedEye=this._active.computedEye,this.computedUp=this._active.computedUp,this.computedCenter=this._active.computedCenter,this.computedRadius=this._active.computedRadius}}},l.getMode=function(){return this._mode}},{"matrix-camera-controller":29,"orbit-camera-controller":38,"turntable-camera-controller":39}],41:[function(t,e,r){function n(t,e){return o(i(t,e))}e.exports=n;var i=t("alpha-complex"),o=t("simplicial-complex-boundary")},{"alpha-complex":42,"simplicial-complex-boundary":45}],42:[function(t,e,r){"use strict";function n(t,e){return i(e).filter(function(r){for(var n=new Array(r.length),i=0;i<r.length;++i)n[i]=e[r[i]];return o(n)*t<1})}e.exports=n;var i=t("delaunay-triangulate"),o=t("circumradius")},{circumradius:43,"delaunay-triangulate":125}],43:[function(t,e,r){function n(t){for(var e=i(t),r=0,n=0;n<t.length;++n)for(var o=t[n],a=0;a<e.length;++a)r+=Math.pow(o[a]-e[a],2);return Math.sqrt(r/t.length)}e.exports=n;var i=t("circumcenter")},{circumcenter:44}],44:[function(t,e,r){"use strict";function n(t,e){for(var r=0,n=t.length,i=0;n>i;++i)r+=t[i]*e[i];return r}function i(t){var e=t.length;if(0===e)return[];var r=(t[0].length,a([t.length+1,t.length+1],1)),i=a([t.length+1],1);r[e][e]=0;for(var o=0;e>o;++o){for(var l=0;o>=l;++l)r[l][o]=r[o][l]=2*n(t[o],t[l]);i[o]=n(t[o],t[o])}for(var u=s(r,i),c=0,h=u[e+1],o=0;o<h.length;++o)c+=h[o];for(var f=new Array(e),o=0;e>o;++o){for(var h=u[o],p=0,l=0;l<h.length;++l)p+=h[l];f[o]=p/c}return f}function o(t){if(0===t.length)return[];for(var e=t[0].length,r=a([e]),n=i(t),o=0;o<t.length;++o)for(var s=0;e>s;++s)r[s]+=t[o][s]*n[o];return r}var a=t("dup"),s=t("robust-linear-solve");o.barycenetric=i,e.exports=o},{dup:126,"robust-linear-solve":459}],45:[function(t,e,r){"use strict";function n(t){return o(i(t))}e.exports=n;var 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e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},i.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0===(8191&e)&&(r+=13,e>>>=13),0===(127&e)&&(r+=7,e>>>=7),0===(15&e)&&(r+=4,e>>>=4),0===(3&e)&&(r+=2,e>>>=2),0===(1&e)&&r++,r},i.prototype.bitLength=function(){var t=0,e=this.words[this.length-1],t=this._countBits(e);return 26*(this.length-1)+t},i.prototype.zeroBits=function(){if(0===this.cmpn(0))return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},i.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},i.prototype.neg=function(){if(0===this.cmpn(0))return this.clone();var t=this.clone();return t.sign=!this.sign,t},i.prototype.ior=function(t){for(this.sign=this.sign||t.sign;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]|t.words[e];return this.strip()},i.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},i.prototype.iand=function(t){this.sign=this.sign&&t.sign;var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},i.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},i.prototype.ixor=function(t){this.sign=this.sign||t.sign;var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},i.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},i.prototype.setn=function(t,e){r("number"==typeof t&&t>=0);for(var n=t/26|0,i=t%26;this.length<=n;)this.words[this.length++]=0;return e?this.words[n]=this.words[n]|1<<i:this.words[n]=this.words[n]&~(1<<i),this.strip()},i.prototype.iadd=function(t){if(this.sign&&!t.sign){this.sign=!1;var e=this.isub(t);return this.sign=!this.sign,this._normSign()}if(!this.sign&&t.sign){t.sign=!1;var e=this.isub(t);return t.sign=!0,e._normSign()}var r,n;this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,o=0;o<n.length;o++){var e=r.words[o]+n.words[o]+i;this.words[o]=67108863&e,i=e>>>26}for(;0!==i&&o<r.length;o++){var e=r.words[o]+i;this.words[o]=67108863&e,i=e>>>26}if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this},i.prototype.add=function(t){if(t.sign&&!this.sign){t.sign=!1;var e=this.sub(t);return t.sign=!0,e}if(!t.sign&&this.sign){this.sign=!1;var e=t.sub(this);return this.sign=!0,e}return this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},i.prototype.isub=function(t){if(t.sign){t.sign=!1;var e=this.iadd(t);return t.sign=!0,e._normSign()}if(this.sign)return this.sign=!1,this.iadd(t),this.sign=!0,this._normSign();var r=this.cmp(t);if(0===r)return this.sign=!1,this.length=1,this.words[0]=0,this;var n,i;r>0?(n=this,i=t):(n=t,i=this);for(var o=0,a=0;a<i.length;a++){var e=n.words[a]-i.words[a]+o;o=e>>26,this.words[a]=67108863&e}for(;0!==o&&a<n.length;a++){var e=n.words[a]+o;o=e>>26,this.words[a]=67108863&e}if(0===o&&a<n.length&&n!==this)for(;a<n.length;a++)this.words[a]=n.words[a];return this.length=Math.max(this.length,a),n!==this&&(this.sign=!0),this.strip()},i.prototype.sub=function(t){return this.clone().isub(t)},i.prototype._smallMulTo=function(t,e){e.sign=t.sign!==this.sign,e.length=this.length+t.length;for(var r=0,n=0;n<e.length-1;n++){for(var i=r>>>26,o=67108863&r,a=Math.min(n,t.length-1),s=Math.max(0,n-this.length+1);a>=s;s++){var l=n-s,u=0|this.words[l],c=0|t.words[s],h=u*c,f=67108863&h;i=i+(h/67108864|0)|0,f=f+o|0,o=67108863&f,i=i+(f>>>26)|0}e.words[n]=o,r=i}return 0!==r?e.words[n]=r:e.length--,e.strip()},i.prototype._bigMulTo=function(t,e){e.sign=t.sign!==this.sign,e.length=this.length+t.length;for(var r=0,n=0,i=0;i<e.length-1;i++){var o=n;n=0;for(var a=67108863&r,s=Math.min(i,t.length-1),l=Math.max(0,i-this.length+1);s>=l;l++){var u=i-l,c=0|this.words[u],h=0|t.words[l],f=c*h,p=67108863&f;o=o+(f/67108864|0)|0,p=p+a|0,a=67108863&p,o=o+(p>>>26)|0,n+=o>>>26,o&=67108863}e.words[i]=a,r=o,o=n}return 0!==r?e.words[i]=r:e.length--,e.strip()},i.prototype.mulTo=function(t,e){var r;return r=this.length+t.length<63?this._smallMulTo(t,e):this._bigMulTo(t,e)},i.prototype.mul=function(t){var e=new i(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},i.prototype.imul=function(t){if(0===this.cmpn(0)||0===t.cmpn(0))return this.words[0]=0,this.length=1,this;var e=this.length,r=t.length;this.sign=t.sign!==this.sign,this.length=this.length+t.length,this.words[this.length-1]=0;for(var n=this.length-2;n>=0;n--){for(var i=0,o=0,a=Math.min(n,r-1),s=Math.max(0,n-e+1);a>=s;s++){var l=n-s,u=this.words[l],c=t.words[s],h=u*c,f=67108863&h;i+=h/67108864|0,f+=o,o=67108863&f,i+=f>>>26}this.words[n]=o,this.words[n+1]+=i,i=0}for(var i=0,l=1;l<this.length;l++){var p=this.words[l]+i;this.words[l]=67108863&p,i=p>>>26}return this.strip()},i.prototype.imuln=function(t){r("number"==typeof t);for(var e=0,n=0;n<this.length;n++){var i=this.words[n]*t,o=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=o>>>26,this.words[n]=67108863&o}return 0!==e&&(this.words[n]=e,this.length++),this},i.prototype.muln=function(t){return this.clone().imuln(t)},i.prototype.sqr=function(){return this.mul(this)},i.prototype.isqr=function(){return this.mul(this)},i.prototype.ishln=function(t){r("number"==typeof t&&t>=0);var e=t%26,n=(t-e)/26,i=67108863>>>26-e<<26-e;if(0!==e){for(var o=0,a=0;a<this.length;a++){var s=this.words[a]&i,l=this.words[a]-s<<e;this.words[a]=l|o,o=s>>>26-e}o&&(this.words[a]=o,this.length++)}if(0!==n){for(var a=this.length-1;a>=0;a--)this.words[a+n]=this.words[a];for(var a=0;n>a;a++)this.words[a]=0;this.length+=n}return this.strip()},i.prototype.ishrn=function(t,e,n){r("number"==typeof t&&t>=0);var i;i=e?(e-e%26)/26:0;var o=t%26,a=Math.min((t-o)/26,this.length),s=67108863^67108863>>>o<<o,l=n;if(i-=a,i=Math.max(0,i),l){for(var u=0;a>u;u++)l.words[u]=this.words[u];l.length=a}if(0===a);else if(this.length>a){this.length-=a;for(var u=0;u<this.length;u++)this.words[u]=this.words[u+a]}else this.words[0]=0,this.length=1;for(var c=0,u=this.length-1;u>=0&&(0!==c||u>=i);u--){var h=this.words[u];this.words[u]=c<<26-o|h>>>o,c=h&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip(),this},i.prototype.shln=function(t){return this.clone().ishln(t)},i.prototype.shrn=function(t){return this.clone().ishrn(t)},i.prototype.testn=function(t){r("number"==typeof t&&t>=0);var e=t%26,n=(t-e)/26,i=1<<e;if(this.length<=n)return!1;var o=this.words[n];return!!(o&i)},i.prototype.imaskn=function(t){r("number"==typeof t&&t>=0);var e=t%26,n=(t-e)/26;if(r(!this.sign,"imaskn works only with positive numbers"),0!==e&&n++,this.length=Math.min(n,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},i.prototype.maskn=function(t){return this.clone().imaskn(t)},i.prototype.iaddn=function(t){return r("number"==typeof t),0>t?this.isubn(-t):this.sign?1===this.length&&this.words[0]<t?(this.words[0]=t-this.words[0],this.sign=!1,this):(this.sign=!1,this.isubn(t),this.sign=!0,this):this._iaddn(t)},i.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},i.prototype.isubn=function(t){if(r("number"==typeof t),0>t)return this.iaddn(-t);if(this.sign)return this.sign=!1,this.iaddn(t),this.sign=!0,this;this.words[0]-=t;for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},i.prototype.addn=function(t){return this.clone().iaddn(t)},i.prototype.subn=function(t){return this.clone().isubn(t)},i.prototype.iabs=function(){return this.sign=!1,this},i.prototype.abs=function(){return this.clone().iabs()},i.prototype._ishlnsubmul=function(t,e,n){var i,o=t.length+n;if(this.words.length<o){for(var a=new Array(o),i=0;i<this.length;i++)a[i]=this.words[i];this.words=a}else i=this.length;for(this.length=Math.max(this.length,o);i<this.length;i++)this.words[i]=0;for(var s=0,i=0;i<t.length;i++){var l=this.words[i+n]+s,u=t.words[i]*e;l-=67108863&u,s=(l>>26)-(u/67108864|0),this.words[i+n]=67108863&l}for(;i<this.length-n;i++){var l=this.words[i+n]+s;s=l>>26,this.words[i+n]=67108863&l}if(0===s)return this.strip();r(-1===s),s=0;for(var i=0;i<this.length;i++){var l=-this.words[i]+s;s=l>>26,this.words[i]=67108863&l}return this.sign=!0,this.strip()},i.prototype._wordDiv=function(t,e){var r=this.length-t.length,n=this.clone(),o=t,a=o.words[o.length-1],s=this._countBits(a);r=26-s,0!==r&&(o=o.shln(r),n.ishln(r),a=o.words[o.length-1]);var l,u=n.length-o.length;if("mod"!==e){l=new i(null),l.length=u+1,l.words=new Array(l.length);for(var c=0;c<l.length;c++)l.words[c]=0}var h=n.clone()._ishlnsubmul(o,1,u);h.sign||(n=h,l&&(l.words[u]=1));for(var f=u-1;f>=0;f--){var p=67108864*n.words[o.length+f]+n.words[o.length+f-1];for(p=Math.min(p/a|0,67108863),n._ishlnsubmul(o,p,f);n.sign;)p--,n.sign=!1,n._ishlnsubmul(o,1,f),0!==n.cmpn(0)&&(n.sign=!n.sign);l&&(l.words[f]=p)}return l&&l.strip(),n.strip(),"div"!==e&&0!==r&&n.ishrn(r),{div:l?l:null,mod:n}},i.prototype.divmod=function(t,e){if(r(0!==t.cmpn(0)),this.sign&&!t.sign){var n,o,a=this.neg().divmod(t,e);return"mod"!==e&&(n=a.div.neg()),"div"!==e&&(o=0===a.mod.cmpn(0)?a.mod:t.sub(a.mod)),{div:n,mod:o}}if(!this.sign&&t.sign){var n,a=this.divmod(t.neg(),e);return"mod"!==e&&(n=a.div.neg()),{div:n,mod:a.mod}}return this.sign&&t.sign?this.neg().divmod(t.neg(),e):t.length>this.length||this.cmp(t)<0?{div:new i(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new i(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new i(this.modn(t.words[0]))}:this._wordDiv(t,e)},i.prototype.div=function(t){return this.divmod(t,"div").div},i.prototype.mod=function(t){return this.divmod(t,"mod").mod},i.prototype.divRound=function(t){var e=this.divmod(t);if(0===e.mod.cmpn(0))return e.div;var r=e.div.sign?e.mod.isub(t):e.mod,n=t.shrn(1),i=t.andln(1),o=r.cmp(n);return 0>o||1===i&&0===o?e.div:e.div.sign?e.div.isubn(1):e.div.iaddn(1)},i.prototype.modn=function(t){r(67108863>=t);for(var e=(1<<26)%t,n=0,i=this.length-1;i>=0;i--)n=(e*n+this.words[i])%t;return n},i.prototype.idivn=function(t){r(67108863>=t);for(var e=0,n=this.length-1;n>=0;n--){var i=this.words[n]+67108864*e;this.words[n]=i/t|0,e=i%t}return this.strip()},i.prototype.divn=function(t){return this.clone().idivn(t)},i.prototype.egcd=function(t){r(!t.sign),r(0!==t.cmpn(0));var e=this,n=t.clone();e=e.sign?e.mod(t):e.clone();for(var o=new i(1),a=new i(0),s=new i(0),l=new i(1),u=0;e.isEven()&&n.isEven();)e.ishrn(1),n.ishrn(1),++u;for(var c=n.clone(),h=e.clone();0!==e.cmpn(0);){for(;e.isEven();)e.ishrn(1),o.isEven()&&a.isEven()?(o.ishrn(1),a.ishrn(1)):(o.iadd(c).ishrn(1),a.isub(h).ishrn(1));for(;n.isEven();)n.ishrn(1),s.isEven()&&l.isEven()?(s.ishrn(1),l.ishrn(1)):(s.iadd(c).ishrn(1),l.isub(h).ishrn(1));e.cmp(n)>=0?(e.isub(n),o.isub(s),a.isub(l)):(n.isub(e),s.isub(o),l.isub(a))}return{a:s,b:l,gcd:n.ishln(u)}},i.prototype._invmp=function(t){r(!t.sign),r(0!==t.cmpn(0));var e=this,n=t.clone();e=e.sign?e.mod(t):e.clone();for(var o=new i(1),a=new i(0),s=n.clone();e.cmpn(1)>0&&n.cmpn(1)>0;){for(;e.isEven();)e.ishrn(1),o.isEven()?o.ishrn(1):o.iadd(s).ishrn(1);for(;n.isEven();)n.ishrn(1),a.isEven()?a.ishrn(1):a.iadd(s).ishrn(1);e.cmp(n)>=0?(e.isub(n),o.isub(a)):(n.isub(e),a.isub(o))}return 0===e.cmpn(1)?o:a},i.prototype.gcd=function(t){if(0===this.cmpn(0))return t.clone();if(0===t.cmpn(0))return this.clone();var e=this.clone(),r=t.clone();e.sign=!1,r.sign=!1;for(var n=0;e.isEven()&&r.isEven();n++)e.ishrn(1),r.ishrn(1);for(;;){for(;e.isEven();)e.ishrn(1);for(;r.isEven();)r.ishrn(1);var i=e.cmp(r);if(0>i){var o=e;e=r,r=o}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.ishln(n)},i.prototype.invm=function(t){return this.egcd(t).a.mod(t)},i.prototype.isEven=function(){return 0===(1&this.words[0])},i.prototype.isOdd=function(){return 1===(1&this.words[0])},i.prototype.andln=function(t){return this.words[0]&t},i.prototype.bincn=function(t){r("number"==typeof t);var e=t%26,n=(t-e)/26,i=1<<e;if(this.length<=n){for(var o=this.length;n+1>o;o++)this.words[o]=0;return this.words[n]|=i,this.length=n+1,this}for(var a=i,o=n;0!==a&&o<this.length;o++){var s=this.words[o];s+=a,a=s>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},i.prototype.cmpn=function(t){var e=0>t;if(e&&(t=-t),this.sign&&!e)return-1;if(!this.sign&&e)return 1;t&=67108863,this.strip();
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g=0;h>g;++g)t.color[g]=s(r,u,c,m,r.RGBA,r.COLOR_ATTACHMENT0+g);0===h?(t._color_rb=l(r,u,c,r.RGBA4,r.COLOR_ATTACHMENT0),f&&f.drawBuffersWEBGL(y[0])):h>1&&f.drawBuffersWEBGL(y[h]);var v=r.getExtension("WEBGL_depth_texture");v?p?t.depth=s(r,u,c,v.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):d&&(t.depth=s(r,u,c,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):d&&p?t._depth_rb=l(r,u,c,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):d?t._depth_rb=l(r,u,c,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):p&&(t._depth_rb=l(r,u,c,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var b=r.checkFramebufferStatus(r.FRAMEBUFFER);if(b!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(var g=0;g<t.color.length;++g)t.color[g].dispose(),t.color[g]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),i(r,e),a(b)}i(r,e)}function c(t,e,r,n,i,o,a,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var l=0;i>l;++l)this.color[l]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=o,this._useStencil=a;var c=this,h=[0|e,0|r];Object.defineProperties(h,{0:{get:function(){return c._shape[0]},set:function(t){return c.width=t}},1:{get:function(){return c._shape[1]},set:function(t){return c.height=t}}}),this._shapeVector=h,u(this)}function h(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var o=t.gl,s=o.getParameter(o.MAX_RENDERBUFFER_SIZE);if(0>e||e>s||0>r||r>s)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var l=n(o),u=0;u<t.color.length;++u)t.color[u].shape=t._shape;t._color_rb&&(o.bindRenderbuffer(o.RENDERBUFFER,t._color_rb),o.renderbufferStorage(o.RENDERBUFFER,o.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(o.bindRenderbuffer(o.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?o.renderbufferStorage(o.RENDERBUFFER,o.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?o.renderbufferStorage(o.RENDERBUFFER,o.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&o.renderbufferStorage(o.RENDERBUFFER,o.STENCIL_INDEX,t._shape[0],t._shape[1])),o.bindFramebuffer(o.FRAMEBUFFER,t.handle);var c=o.checkFramebufferStatus(o.FRAMEBUFFER);c!==o.FRAMEBUFFER_COMPLETE&&(t.dispose(),i(o,l),a(c)),i(o,l)}}function f(t,e,r,n){d||(d=t.FRAMEBUFFER_UNSUPPORTED,m=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,g=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,v=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var i=t.getExtension("WEBGL_draw_buffers");if(!y&&i&&o(t,i),Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]),"number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var a=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(0>e||e>a||0>r||r>a)throw new Error("gl-fbo: Parameters are too large for FBO");n=n||{};var s=1;if("color"in n){if(s=Math.max(0|n.color,0),0>s)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(s>1){if(!i)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(s>t.getParameter(i.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+s+" draw buffers");
}}var l=t.UNSIGNED_BYTE,u=t.getExtension("OES_texture_float");if(n.float&&s>0){if(!u)throw new Error("gl-fbo: Context does not support floating point textures");l=t.FLOAT}else n.preferFloat&&s>0&&u&&(l=t.FLOAT);var h=!0;"depth"in n&&(h=!!n.depth);var f=!1;return"stencil"in n&&(f=!!n.stencil),new c(t,e,r,l,s,h,f,i)}var p=t("gl-texture2d");e.exports=f;var d,m,g,v,y=null,b=c.prototype;Object.defineProperties(b,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return h(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return t=0|t,h(this,t,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t=0|t,h(this,this._shape[0],t),t},enumerable:!1}}),b.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},b.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":233}],135:[function(t,e,r){r.lineVertex="precision mediump float;\n#define GLSLIFY 1\n\nfloat inverse_1_0(float m) {\n  return 1.0 / m;\n}\n\nmat2 inverse_1_0(mat2 m) {\n  return mat2(m[1][1],-m[0][1],\n             -m[1][0], m[0][0]) / (m[0][0]*m[1][1] - m[0][1]*m[1][0]);\n}\n\nmat3 inverse_1_0(mat3 m) {\n  float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\n  float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\n  float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\n\n  float b01 = a22 * a11 - a12 * a21;\n  float b11 = -a22 * a10 + a12 * a20;\n  float b21 = a21 * a10 - a11 * a20;\n\n  float det = a00 * b01 + a01 * b11 + a02 * b21;\n\n  return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\n              b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\n              b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\n}\n\nmat4 inverse_1_0(mat4 m) {\n  float\n      a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\n      a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\n      a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\n      a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\n\n      b00 = a00 * a11 - a01 * a10,\n      b01 = a00 * a12 - a02 * a10,\n      b02 = a00 * a13 - a03 * a10,\n      b03 = a01 * a12 - a02 * a11,\n      b04 = a01 * a13 - a03 * a11,\n      b05 = a02 * a13 - a03 * a12,\n      b06 = a20 * a31 - a21 * a30,\n      b07 = a20 * a32 - a22 * a30,\n      b08 = a20 * a33 - a23 * a30,\n      b09 = a21 * a32 - a22 * a31,\n      b10 = a21 * a33 - a23 * a31,\n      b11 = a22 * a33 - a23 * a32,\n\n      det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\n\n  return mat4(\n      a11 * b11 - a12 * b10 + a13 * b09,\n      a02 * b10 - a01 * b11 - a03 * b09,\n      a31 * b05 - a32 * b04 + a33 * b03,\n      a22 * b04 - a21 * b05 - a23 * b03,\n      a12 * b08 - a10 * b11 - a13 * b07,\n      a00 * b11 - a02 * b08 + a03 * b07,\n      a32 * b02 - a30 * b05 - a33 * b01,\n      a20 * b05 - a22 * b02 + a23 * b01,\n      a10 * b10 - a11 * b08 + a13 * b06,\n      a01 * b08 - a00 * b10 - a03 * b06,\n      a30 * b04 - a31 * b02 + a33 * b00,\n      a21 * b02 - a20 * b04 - a23 * b00,\n      a11 * b07 - a10 * b09 - a12 * b06,\n      a00 * b09 - a01 * b07 + a02 * b06,\n      a31 * b01 - a30 * b03 - a32 * b00,\n      a20 * b03 - a21 * b01 + a22 * b00) / det;\n}\n\n\n\nattribute vec2 a, d;\n\nuniform mat3 matrix;\nuniform vec2 screenShape;\nuniform float width;\n\nvarying vec2 direction;\n\nvoid main() {\n  vec2 dir = (matrix * vec3(d, 0)).xy;\n  vec3 base = matrix * vec3(a, 1);\n  vec2 n = 0.5 * width *\n    normalize(screenShape.yx * vec2(dir.y, -dir.x)) / screenShape.xy;\n  vec2 tangent = normalize(screenShape.xy * dir);\n  if(dir.x < 0.0 || (dir.x == 0.0 && dir.y < 0.0)) {\n    direction = -tangent;\n  } else {\n    direction = tangent;\n  }\n  gl_Position = vec4(base.xy/base.z + n, 0, 1);\n}\n",r.lineFragment="precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nuniform vec2 screenShape;\nuniform sampler2D dashPattern;\nuniform float dashLength;\n\nvarying vec2 direction;\n\nvoid main() {\n  float t = fract(dot(direction, gl_FragCoord.xy) / dashLength);\n  vec4 pcolor = color * texture2D(dashPattern, vec2(t, 0.0)).r;\n  gl_FragColor = vec4(pcolor.rgb * pcolor.a, pcolor.a);\n}\n",r.mitreVertex="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 p;\n\nuniform mat3  matrix;\nuniform vec2 screenShape;\nuniform float radius;\n\nvoid main() {\n  vec3 pp = matrix * vec3(p, 1);\n  gl_Position  = vec4(pp.xy, 0, pp.z);\n  gl_PointSize = radius;\n}\n",r.mitreFragment="precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\n\nvoid main() {\n  if(length(gl_PointCoord.xy - 0.5) > 0.25) {\n    discard;\n  }\n  gl_FragColor = vec4(color.rgb, color.a);\n}\n",r.pickVertex="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 a, d;\nattribute vec4 pick0, pick1;\n\nuniform mat3 matrix;\nuniform vec2 screenShape;\nuniform float width;\n\nvarying vec4 pickA, pickB;\n\nfloat inverse_1_0(float m) {\n  return 1.0 / m;\n}\n\nmat2 inverse_1_0(mat2 m) {\n  return mat2(m[1][1],-m[0][1],\n             -m[1][0], m[0][0]) / (m[0][0]*m[1][1] - m[0][1]*m[1][0]);\n}\n\nmat3 inverse_1_0(mat3 m) {\n  float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\n  float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\n  float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\n\n  float b01 = a22 * a11 - a12 * a21;\n  float b11 = -a22 * a10 + a12 * a20;\n  float b21 = a21 * a10 - a11 * a20;\n\n  float det = a00 * b01 + a01 * b11 + a02 * b21;\n\n  return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\n              b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\n              b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\n}\n\nmat4 inverse_1_0(mat4 m) {\n  float\n      a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\n      a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\n      a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\n      a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\n\n      b00 = a00 * a11 - a01 * a10,\n      b01 = a00 * a12 - a02 * a10,\n      b02 = a00 * a13 - a03 * a10,\n      b03 = a01 * a12 - a02 * a11,\n      b04 = a01 * a13 - a03 * a11,\n      b05 = a02 * a13 - a03 * a12,\n      b06 = a20 * a31 - a21 * a30,\n      b07 = a20 * a32 - a22 * a30,\n      b08 = a20 * a33 - a23 * a30,\n      b09 = a21 * a32 - a22 * a31,\n      b10 = a21 * a33 - a23 * a31,\n      b11 = a22 * a33 - a23 * a32,\n\n      det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\n\n  return mat4(\n      a11 * b11 - a12 * b10 + a13 * b09,\n      a02 * b10 - a01 * b11 - a03 * b09,\n      a31 * b05 - a32 * b04 + a33 * b03,\n      a22 * b04 - a21 * b05 - a23 * b03,\n      a12 * b08 - a10 * b11 - a13 * b07,\n      a00 * b11 - a02 * b08 + a03 * b07,\n      a32 * b02 - a30 * b05 - a33 * b01,\n      a20 * b05 - a22 * b02 + a23 * b01,\n      a10 * b10 - a11 * b08 + a13 * b06,\n      a01 * b08 - a00 * b10 - a03 * b06,\n      a30 * b04 - a31 * b02 + a33 * b00,\n      a21 * b02 - a20 * b04 - a23 * b00,\n      a11 * b07 - a10 * b09 - a12 * b06,\n      a00 * b09 - a01 * b07 + a02 * b06,\n      a31 * b01 - a30 * b03 - a32 * b00,\n      a20 * b03 - a21 * b01 + a22 * b00) / det;\n}\n\n\n\nvoid main() {\n  vec3 base = matrix * vec3(a, 1);\n  vec2 n = width *\n    normalize(screenShape.yx * vec2(d.y, -d.x)) / screenShape.xy;\n  gl_Position = vec4(base.xy/base.z + n, 0, 1);\n  pickA = pick0;\n  pickB = pick1;\n}\n",r.pickFragment="precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 pickOffset;\n\nvarying vec4 pickA, pickB;\n\nvoid main() {\n  vec4 fragId = vec4(pickA.xyz, 0.0);\n  if(pickB.w > pickA.w) {\n    fragId.xyz = pickB.xyz;\n  }\n\n  fragId += pickOffset;\n\n  fragId.y += floor(fragId.x / 256.0);\n  fragId.x -= floor(fragId.x / 256.0) * 256.0;\n\n  fragId.z += floor(fragId.y / 256.0);\n  fragId.y -= floor(fragId.y / 256.0) * 256.0;\n\n  fragId.w += floor(fragId.z / 256.0);\n  fragId.z -= floor(fragId.z / 256.0) * 256.0;\n\n  gl_FragColor = fragId / 255.0;\n}\n",r.fillVertex="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 a, d;\n\nuniform mat3 matrix;\nuniform vec2 projectAxis;\nuniform float projectValue;\nuniform float depth;\n\nvoid main() {\n  vec3 base = matrix * vec3(a, 1);\n  vec2 p = base.xy / base.z;\n  if(d.y < 0.0 || (d.y == 0.0 && d.x < 0.0)) {\n    if(dot(p, projectAxis) < projectValue) {\n      p = p * (1.0 - abs(projectAxis)) + projectAxis * projectValue;\n    }\n  }\n  gl_Position = vec4(p, depth, 1);\n}\n",r.fillFragment="precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\n\nvoid main() {\n  gl_FragColor = vec4(color.rgb * color.a, color.a);\n}\n"},{}],136:[function(t,e,r){"use strict";function n(t,e,r,n,i,o,a,s){this.plot=t,this.dashPattern=e,this.lineBuffer=r,this.pickBuffer=n,this.lineShader=i,this.mitreShader=o,this.fillShader=a,this.pickShader=s,this.usingDashes=!1,this.bounds=[1/0,1/0,-(1/0),-(1/0)],this.width=1,this.color=[0,0,1,1],this.fill=[!1,!1,!1,!1],this.fillColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.data=null,this.numPoints=0,this.vertCount=0,this.pickOffset=0,this.lodBuffer=[]}function i(t){return t.map(function(t){return t.slice()})}function o(t,e){var r=t.gl,i=s(r),o=s(r),u=l(r,[1,1]),c=a(r,h.lineVertex,h.lineFragment),f=a(r,h.mitreVertex,h.mitreFragment),p=a(r,h.fillVertex,h.fillFragment),d=a(r,h.pickVertex,h.pickFragment),m=new n(t,u,i,o,c,f,p,d);return t.addObject(m),m.update(e),m}e.exports=o;var a=t("gl-shader"),s=t("gl-buffer"),l=t("gl-texture2d"),u=t("ndarray"),c=t("typedarray-pool"),h=t("./lib/shaders"),f=n.prototype;f.draw=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0],r=[1,0],n=[-1,0],i=[0,1],o=[0,-1];return function(){var a=this.plot,s=this.color,l=this.width,u=(this.numPoints,this.bounds),c=this.vertCount;if(c){var h=a.gl,f=a.viewBox,p=a.dataBox,d=a.pixelRatio,m=u[2]-u[0],g=u[3]-u[1],v=p[2]-p[0],y=p[3]-p[1],b=f[2]-f[0],x=f[3]-f[1];t[0]=2*m/v,t[4]=2*g/y,t[6]=2*(u[0]-p[0])/v-1,t[7]=2*(u[1]-p[1])/y-1,e[0]=b,e[1]=x;var _=this.lineBuffer;_.bind();var w=this.fill;if(w[0]||w[1]||w[2]||w[3]){var M=this.fillShader;M.bind();var k=M.uniforms;k.matrix=t,k.depth=a.nextDepthValue();var A=M.attributes;A.a.pointer(h.FLOAT,!1,16,0),A.d.pointer(h.FLOAT,!1,16,8),h.depthMask(!0),h.enable(h.DEPTH_TEST);var T=this.fillColor;w[0]&&(k.color=T[0],k.projectAxis=n,k.projectValue=1,h.drawArrays(h.TRIANGLES,0,c)),w[1]&&(k.color=T[1],k.projectAxis=o,k.projectValue=1,h.drawArrays(h.TRIANGLES,0,c)),w[2]&&(k.color=T[2],k.projectAxis=r,k.projectValue=1,h.drawArrays(h.TRIANGLES,0,c)),w[3]&&(k.color=T[3],k.projectAxis=i,k.projectValue=1,h.drawArrays(h.TRIANGLES,0,c)),h.depthMask(!1),h.disable(h.DEPTH_TEST)}var E=this.lineShader;E.bind();var S=E.uniforms;S.matrix=t,S.color=s,S.width=l*d,S.screenShape=e,S.dashPattern=this.dashPattern.bind(),S.dashLength=this.dashLength*d;var L=E.attributes;if(L.a.pointer(h.FLOAT,!1,16,0),L.d.pointer(h.FLOAT,!1,16,8),h.drawArrays(h.TRIANGLES,0,c),l>2&&!this.usingDashes){var z=this.mitreShader;z.bind();var I=z.uniforms;I.matrix=t,I.color=s,I.screenShape=e,I.radius=l*d,z.attributes.p.pointer(h.FLOAT,!1,48,0),h.drawArrays(h.POINTS,0,c/3|0)}}}}(),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0],r=[0,0,0,0];return function(n){var i=this.plot,o=this.pickShader,a=this.lineBuffer,s=this.pickBuffer,l=this.width,u=this.numPoints,c=this.bounds,h=this.vertCount,f=i.gl,p=i.viewBox,d=i.dataBox,m=i.pickPixelRatio,g=c[2]-c[0],v=c[3]-c[1],y=d[2]-d[0],b=d[3]-d[1],x=p[2]-p[0],_=p[3]-p[1];if(this.pickOffset=n,!h)return n+u;t[0]=2*g/y,t[4]=2*v/b,t[6]=2*(c[0]-d[0])/y-1,t[7]=2*(c[1]-d[1])/b-1,e[0]=x,e[1]=_,r[0]=255&n,r[1]=n>>>8&255,r[2]=n>>>16&255,r[3]=n>>>24,o.bind();var w=o.uniforms;w.matrix=t,w.width=l*m,w.pickOffset=r,w.screenShape=e;var M=o.attributes;return a.bind(),M.a.pointer(f.FLOAT,!1,16,0),M.d.pointer(f.FLOAT,!1,16,8),s.bind(),M.pick0.pointer(f.UNSIGNED_BYTE,!1,8,0),M.pick1.pointer(f.UNSIGNED_BYTE,!1,8,4),f.drawArrays(f.TRIANGLES,0,h),n+u}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.numPoints;if(n>r||r>=n+i)return null;var o=r-n,a=this.data;return{object:this,pointId:o,dataCoord:[a[2*o],a[2*o+1]]}},f.update=function(t){t=t||{};var e=this.plot.gl;!!t.connectGaps;this.color=(t.color||[0,0,1,1]).slice(),this.width=+(t.width||1),this.fill=(t.fill||[!1,!1,!1,!1]).slice(),this.fillColor=i(t.fillColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);for(var r=t.dashes||[1],n=0,o=0;o<r.length;++o)n+=r[o];for(var a=c.mallocUint8(n),s=0,h=255,o=0;o<r.length;++o){for(var f=0;f<r[o];++f)a[s++]=h;h^=255}this.dashPattern.dispose(),this.usingDashes=r.length>1,this.dashPattern=l(e,u(a,[n,1,4],[1,0,0])),this.dashPattern.minFilter=e.NEAREST,this.dashPattern.magFilter=e.NEAREST,this.dashLength=n,c.free(a);var p=t.positions;this.data=p;var d=this.bounds;d[0]=d[1]=1/0,d[2]=d[3]=-(1/0);var m=this.numPoints=p.length>>>1;if(0!==m){for(var o=0;m>o;++o){var g=p[2*o],v=p[2*o+1];isNaN(g)||isNaN(v)||(d[0]=Math.min(d[0],g),d[1]=Math.min(d[1],v),d[2]=Math.max(d[2],g),d[3]=Math.max(d[3],v))}d[0]===d[2]&&(d[2]+=1),d[3]===d[1]&&(d[3]+=1);for(var y=c.mallocFloat32(24*(m-1)),b=c.mallocUint32(12*(m-1)),x=y.length,_=b.length,s=m,w=0;s>1;){var M=--s,g=p[2*s],v=p[2*s+1],k=M-1,A=p[2*k],T=p[2*k+1];if(!(isNaN(g)||isNaN(v)||isNaN(A)||isNaN(T))){w+=1,g=(g-d[0])/(d[2]-d[0]),v=(v-d[1])/(d[3]-d[1]),A=(A-d[0])/(d[2]-d[0]),T=(T-d[1])/(d[3]-d[1]);var E=A-g,S=T-v,L=M|1<<24,z=M-1,I=M,P=M-1|1<<24;y[--x]=-S,y[--x]=-E,y[--x]=v,y[--x]=g,b[--_]=L,b[--_]=z,y[--x]=S,y[--x]=E,y[--x]=T,y[--x]=A,b[--_]=I,b[--_]=P,y[--x]=-S,y[--x]=-E,y[--x]=T,y[--x]=A,b[--_]=I,b[--_]=P,y[--x]=S,y[--x]=E,y[--x]=T,y[--x]=A,b[--_]=I,b[--_]=P,y[--x]=-S,y[--x]=-E,y[--x]=v,y[--x]=g,b[--_]=L,b[--_]=z,y[--x]=S,y[--x]=E,y[--x]=v,y[--x]=g,b[--_]=L,b[--_]=z}}this.vertCount=6*w,this.lineBuffer.update(y.subarray(x)),this.pickBuffer.update(b.subarray(_)),c.free(y),c.free(b)}},f.dispose=function(){this.plot.removeObject(this),this.lineBuffer.dispose(),this.pickBuffer.dispose(),this.lineShader.dispose(),this.mitreShader.dispose(),this.fillShader.dispose(),this.pickShader.dispose(),this.dashPattern.dispose()}},{"./lib/shaders":135,"gl-buffer":129,"gl-shader":208,"gl-texture2d":233,ndarray:456,"typedarray-pool":481}],137:[function(t,e,r){var n=t("gl-shader"),i="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvoid main() {\n  vec4 projected = projection * view * model * vec4(position, 1.0);\n  vec4 tangentClip = projection * view * model * vec4(nextPosition - position, 0.0);\n  vec2 tangent = normalize(screenShape * tangentClip.xy);\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(tangent.y, -tangent.x) / screenShape;\n\n  gl_Position = vec4(projected.xy + projected.w * offset, projected.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n",o="precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\n    discard;\n  }\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n",a="precision mediump float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\nlowp vec4 encode_float_1_0(highp float v) {\n  highp float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  highp vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  highp float e = floor(log2(av));\n  highp float m = av * pow(2.0, -e) - 1.0;\n  \n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n  \n  //Unpack exponent\n  highp float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0; \n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\n\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = vec4(pickId/255.0, encode_float_1_0(pixelArcLength).xyz);\n}",s=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];r.createShader=function(t){return n(t,i,o,null,s)},r.createPickShader=function(t){return n(t,i,a,null,s)}},{"gl-shader":208}],138:[function(t,e,r){"use strict";function n(t,e){for(var r=0,n=0;3>n;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function i(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;3>r;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function o(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function a(t,e,r,n,i,o){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=o,this.dashScale=1,this.opacity=1,this.dirty=!0,this.pixelRatio=1}function s(t){var e=t.gl||t.scene&&t.scene.gl,r=m(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var n=g(e);n.attributes.position.location=0,n.attributes.nextPosition.location=1,n.attributes.arcLength.location=2,n.attributes.lineWidth.location=3,n.attributes.color.location=4;for(var i=l(e),o=u(e,[{buffer:i,size:3,offset:0,stride:48},{buffer:i,size:3,offset:12,stride:48},{buffer:i,size:1,offset:24,stride:48},{buffer:i,size:1,offset:28,stride:48},{buffer:i,size:4,offset:32,stride:48}]),s=p(new Array(1024),[256,1,4]),h=0;1024>h;++h)s.data[h]=255;var f=c(e,s);f.wrap=e.REPEAT;var d=new a(e,r,n,i,o,f);return d.update(t),d}e.exports=s;var l=t("gl-buffer"),u=t("gl-vao"),c=t("gl-texture2d"),h=t("glsl-read-float"),f=t("binary-search-bounds"),p=t("ndarray"),d=t("./lib/shaders"),m=d.createShader,g=d.createPickShader,v=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],y=a.prototype;y.isTransparent=function(){return this.opacity<1},y.isOpaque=function(){return this.opacity>=1},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||v,view:t.view||v,projection:t.projection||v,clipBounds:i(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.drawPick=function(t){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||v,view:t.view||v,projection:t.projection||v,pickId:this.pickId,clipBounds:i(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.update=function(t){var e,r;this.dirty=!0;var i=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),"opacity"in t&&(this.opacity=+t.opacity);var o=t.position||t.positions;if(o){var a=t.color||t.colors||[0,0,0,1],s=t.lineWidth||1,l=[],u=[],c=[],h=0,d=0,m=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],g=!1;t:for(e=1;e<o.length;++e){var v=o[e-1],y=o[e];for(u.push(h),c.push(v.slice()),r=0;3>r;++r){if(isNaN(v[r])||isNaN(y[r])||!isFinite(v[r])||!isFinite(y[r])){if(!i&&l.length>0){for(var b=0;24>b;++b)l.push(l[l.length-12]);d+=2,g=!0}continue t}m[0][r]=Math.min(m[0][r],v[r],y[r]),m[1][r]=Math.max(m[1][r],v[r],y[r])}var x,_;Array.isArray(a[0])?(x=a[e-1],_=a[e]):x=_=a,3===x.length&&(x=[x[0],x[1],x[2],1]),3===_.length&&(_=[_[0],_[1],_[2],1]);var w;w=Array.isArray(s)?s[e-1]:s;var M=h;if(h+=n(v,y),g){for(r=0;2>r;++r)l.push(v[0],v[1],v[2],y[0],y[1],y[2],M,w,x[0],x[1],x[2],x[3]);d+=2,g=!1}l.push(v[0],v[1],v[2],y[0],y[1],y[2],M,w,x[0],x[1],x[2],x[3],v[0],v[1],v[2],y[0],y[1],y[2],M,-w,x[0],x[1],x[2],x[3],y[0],y[1],y[2],v[0],v[1],v[2],h,-w,_[0],_[1],_[2],_[3],y[0],y[1],y[2],v[0],v[1],v[2],h,w,_[0],_[1],_[2],_[3]),d+=4}if(this.buffer.update(l),u.push(h),c.push(o[o.length-1].slice()),this.bounds=m,this.vertexCount=d,this.points=c,this.arcLength=u,"dashes"in t){var k=t.dashes,A=k.slice();for(A.unshift(0),e=1;e<A.length;++e)A[e]=A[e-1]+A[e];var T=p(new Array(1024),[256,1,4]);for(e=0;256>e;++e){for(r=0;4>r;++r)T.set(e,0,r,0);1&f.le(A,A[A.length-1]*e/255)?T.set(e,0,0,0):T.set(e,0,0,255)}this.texture.setPixels(T)}}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=h(t.value[0],t.value[1],t.value[2],0),r=f.le(this.arcLength,e);if(0>r)return null;if(r===this.arcLength.length-1)return new o(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),s=1-a,l=[0,0,0],u=0;3>u;++u)l[u]=s*n[u]+a*i[u];var c=Math.min(.5>a?r:r+1,this.points.length-1);return new o(e,l,c,this.points[c])}},{"./lib/shaders":137,"binary-search-bounds":139,"gl-buffer":129,"gl-texture2d":233,"gl-vao":237,"glsl-read-float":140,ndarray:456}],139:[function(t,e,r){arguments[4][22][0].apply(r,arguments)},{dup:22}],140:[function(t,e,r){function n(t,e,r,n){return i[0]=n,i[1]=r,i[2]=e,i[3]=t,o[0]}e.exports=n;var i=new Uint8Array(4),o=new Float32Array(i.buffer)},{}],141:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],o=e[3],a=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=c*a-s*u,f=-c*o+s*l,p=u*o-a*l,d=r*h+n*f+i*p;return d?(d=1/d,t[0]=h*d,t[1]=(-c*n+i*u)*d,t[2]=(s*n-i*a)*d,t[3]=f*d,t[4]=(c*r-i*l)*d,t[5]=(-s*r+i*o)*d,t[6]=p*d,t[7]=(-u*r+n*l)*d,t[8]=(a*r-n*o)*d,t):null}e.exports=n},{}],142:[function(t,e,r){function n(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}e.exports=n},{}],143:[function(t,e,r){function n(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],144:[function(t,e,r){function n(t){var e=t[0],r=t[1],n=t[2],i=t[3],o=t[4],a=t[5],s=t[6],l=t[7],u=t[8],c=t[9],h=t[10],f=t[11],p=t[12],d=t[13],m=t[14],g=t[15],v=e*a-r*o,y=e*s-n*o,b=e*l-i*o,x=r*s-n*a,_=r*l-i*a,w=n*l-i*s,M=u*d-c*p,k=u*m-h*p,A=u*g-f*p,T=c*m-h*d,E=c*g-f*d,S=h*g-f*m;return v*S-y*E+b*T+x*A-_*k+w*M}e.exports=n},{}],145:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],o=e[3],a=r+r,s=n+n,l=i+i,u=r*a,c=n*a,h=n*s,f=i*a,p=i*s,d=i*l,m=o*a,g=o*s,v=o*l;return t[0]=1-h-d,t[1]=c+v,t[2]=f-g,t[3]=0,t[4]=c-v,t[5]=1-u-d,t[6]=p+m,t[7]=0,t[8]=f+g,t[9]=p-m,t[10]=1-u-h,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],146:[function(t,e,r){function n(t,e,r){var n=e[0],i=e[1],o=e[2],a=e[3],s=n+n,l=i+i,u=o+o,c=n*s,h=n*l,f=n*u,p=i*l,d=i*u,m=o*u,g=a*s,v=a*l,y=a*u;return t[0]=1-(p+m),t[1]=h+y,t[2]=f-v,t[3]=0,t[4]=h-y,t[5]=1-(c+m),t[6]=d+g,t[7]=0,t[8]=f+v,t[9]=d-g,t[10]=1-(c+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}e.exports=n},{}],147:[function(t,e,r){function n(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],148:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],o=e[3],a=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*a,b=r*l-i*a,x=r*u-o*a,_=n*l-i*s,w=n*u-o*s,M=i*u-o*l,k=c*m-h*d,A=c*g-f*d,T=c*v-p*d,E=h*g-f*m,S=h*v-p*m,L=f*v-p*g,z=y*L-b*S+x*E+_*T-w*A+M*k;return z?(z=1/z,t[0]=(s*L-l*S+u*E)*z,t[1]=(i*S-n*L-o*E)*z,t[2]=(m*M-g*w+v*_)*z,t[3]=(f*w-h*M-p*_)*z,t[4]=(l*T-a*L-u*A)*z,t[5]=(r*L-i*T+o*A)*z,t[6]=(g*x-d*M-v*b)*z,t[7]=(c*M-f*x+p*b)*z,t[8]=(a*S-s*T+u*k)*z,t[9]=(n*T-r*S-o*k)*z,t[10]=(d*w-m*x+v*y)*z,t[11]=(h*x-c*w-p*y)*z,t[12]=(s*A-a*E-l*k)*z,t[13]=(r*E-n*A+i*k)*z,t[14]=(m*b-d*_-g*y)*z,t[15]=(c*_-h*b+f*y)*z,t):null}e.exports=n},{}],149:[function(t,e,r){function n(t,e,r,n){var o,a,s,l,u,c,h,f,p,d,m=e[0],g=e[1],v=e[2],y=n[0],b=n[1],x=n[2],_=r[0],w=r[1],M=r[2];return Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-M)<1e-6?i(t):(h=m-_,f=g-w,p=v-M,d=1/Math.sqrt(h*h+f*f+p*p),h*=d,f*=d,p*=d,o=b*p-x*f,a=x*h-y*p,s=y*f-b*h,d=Math.sqrt(o*o+a*a+s*s),d?(d=1/d,o*=d,a*=d,s*=d):(o=0,a=0,s=0),l=f*s-p*a,u=p*o-h*s,c=h*a-f*o,d=Math.sqrt(l*l+u*u+c*c),d?(d=1/d,l*=d,u*=d,c*=d):(l=0,u=0,c=0),t[0]=o,t[1]=l,t[2]=h,t[3]=0,t[4]=a,t[5]=u,t[6]=f,t[7]=0,t[8]=s,t[9]=c,t[10]=p,t[11]=0,t[12]=-(o*m+a*g+s*v),t[13]=-(l*m+u*g+c*v),t[14]=-(h*m+f*g+p*v),t[15]=1,t)}var i=t("./identity");e.exports=n},{"./identity":147}],150:[function(t,e,r){function n(t,e,r){var n=e[0],i=e[1],o=e[2],a=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],b=r[0],x=r[1],_=r[2],w=r[3];return t[0]=b*n+x*s+_*h+w*m,t[1]=b*i+x*l+_*f+w*g,t[2]=b*o+x*u+_*p+w*v,t[3]=b*a+x*c+_*d+w*y,b=r[4],x=r[5],_=r[6],w=r[7],t[4]=b*n+x*s+_*h+w*m,t[5]=b*i+x*l+_*f+w*g,t[6]=b*o+x*u+_*p+w*v,t[7]=b*a+x*c+_*d+w*y,b=r[8],x=r[9],_=r[10],w=r[11],t[8]=b*n+x*s+_*h+w*m,t[9]=b*i+x*l+_*f+w*g,t[10]=b*o+x*u+_*p+w*v,t[11]=b*a+x*c+_*d+w*y,b=r[12],x=r[13],_=r[14],w=r[15],t[12]=b*n+x*s+_*h+w*m,t[13]=b*i+x*l+_*f+w*g,t[14]=b*o+x*u+_*p+w*v,t[15]=b*a+x*c+_*d+w*y,t}e.exports=n},{}],151:[function(t,e,r){function n(t,e,r,n,i){var o=1/Math.tan(e/2),a=1/(n-i);return t[0]=o/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=o,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*a,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*a,t[15]=0,t}e.exports=n},{}],152:[function(t,e,r){function n(t,e,r,n){var i,o,a,s,l,u,c,h,f,p,d,m,g,v,y,b,x,_,w,M,k,A,T,E,S=n[0],L=n[1],z=n[2],I=Math.sqrt(S*S+L*L+z*z);return Math.abs(I)<1e-6?null:(I=1/I,S*=I,L*=I,z*=I,i=Math.sin(r),o=Math.cos(r),a=1-o,s=e[0],l=e[1],u=e[2],c=e[3],h=e[4],f=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],b=S*S*a+o,x=L*S*a+z*i,_=z*S*a-L*i,w=S*L*a-z*i,M=L*L*a+o,k=z*L*a+S*i,A=S*z*a+L*i,T=L*z*a-S*i,E=z*z*a+o,t[0]=s*b+h*x+m*_,t[1]=l*b+f*x+g*_,t[2]=u*b+p*x+v*_,t[3]=c*b+d*x+y*_,t[4]=s*w+h*M+m*k,t[5]=l*w+f*M+g*k,t[6]=u*w+p*M+v*k,t[7]=c*w+d*M+y*k,t[8]=s*A+h*T+m*E,t[9]=l*A+f*T+g*E,t[10]=u*A+p*T+v*E,t[11]=c*A+d*T+y*E,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)}e.exports=n},{}],153:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),o=e[4],a=e[5],s=e[6],l=e[7],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[4]=o*i+u*n,t[5]=a*i+c*n,t[6]=s*i+h*n,t[7]=l*i+f*n,t[8]=u*i-o*n,t[9]=c*i-a*n,t[10]=h*i-s*n,t[11]=f*i-l*n,t}e.exports=n},{}],154:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),o=e[0],a=e[1],s=e[2],l=e[3],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=o*i-u*n,t[1]=a*i-c*n,t[2]=s*i-h*n,t[3]=l*i-f*n,t[8]=o*n+u*i,t[9]=a*n+c*i,t[10]=s*n+h*i,t[11]=l*n+f*i,t}e.exports=n},{}],155:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),o=e[0],a=e[1],s=e[2],l=e[3],u=e[4],c=e[5],h=e[6],f=e[7];return e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=o*i+u*n,t[1]=a*i+c*n,t[2]=s*i+h*n,t[3]=l*i+f*n,t[4]=u*i-o*n,t[5]=c*i-a*n,t[6]=h*i-s*n,t[7]=f*i-l*n,t}e.exports=n},{}],156:[function(t,e,r){function n(t,e,r){var n=r[0],i=r[1],o=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*o,t[9]=e[9]*o,t[10]=e[10]*o,t[11]=e[11]*o,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}e.exports=n},{}],157:[function(t,e,r){function n(t,e,r){var n,i,o,a,s,l,u,c,h,f,p,d,m=r[0],g=r[1],v=r[2];return e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],o=e[2],a=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=o,t[3]=a,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=h,t[9]=f,t[10]=p,t[11]=d,t[12]=n*m+s*g+h*v+e[12],t[13]=i*m+l*g+f*v+e[13],t[14]=o*m+u*g+p*v+e[14],t[15]=a*m+c*g+d*v+e[15]),t}e.exports=n},{}],158:[function(t,e,r){function n(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],o=e[6],a=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=o,t[11]=e[14],t[12]=i,t[13]=a,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}e.exports=n},{}],159:[function(t,e,r){"use strict";function n(t,e){for(var r=[0,0,0,0],n=0;4>n;++n)for(var i=0;4>i;++i)r[i]+=t[4*n+i]*e[n];return r}function i(t,e,r,i,o){for(var a=n(i,n(r,n(e,[t[0],t[1],t[2],1]))),s=0;3>s;++s)a[s]/=a[3];return[.5*o[0]*(1+a[0]),.5*o[1]*(1-a[1])]}function o(t,e){if(2===t.length){for(var r=0,n=0,i=0;2>i;++i)r+=Math.pow(e[i]-t[0][i],2),n+=Math.pow(e[i]-t[1][i],2);return r=Math.sqrt(r),n=Math.sqrt(n),1e-6>r+n?[1,0]:[n/(r+n),r/(n+r)]}if(3===t.length){var o=[0,0];return u(t[0],t[1],t[2],e,o),l(t,o)}return[]}function a(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],o=e[n],a=0;3>a;++a)r[a]+=o*i[a];return r}function s(t,e,r,n,s,l){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),c=0;c<t.length;++c)u[c]=i(t[c],r,n,s,l);for(var h=0,f=1/0,c=0;c<u.length;++c){for(var p=0,d=0;2>d;++d)p+=Math.pow(u[c][d]-e[d],2);f>p&&(f=p,h=c)}for(var m=o(u,e),g=0,c=0;3>c;++c){if(m[c]<-.001||m[c]>1.0001)return null;g+=m[c]}return Math.abs(g-1)>.001?null:[h,a(t,m),m]}var l=t("barycentric"),u=t("polytope-closest-point/lib/closest_point_2d.js");e.exports=s},{barycentric:162,"polytope-closest-point/lib/closest_point_2d.js":164}],160:[function(t,e,r){var n="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec4 m_position  = model * vec4(position, 1.0);\n  vec4 t_position  = view * m_position;\n  gl_Position      = projection * t_position;\n  f_color          = color;\n  f_normal         = normal;\n  f_data           = position;\n  f_eyeDirection   = eyePosition   - position;\n  f_lightDirection = lightPosition - position;\n  f_uv             = uv;\n}",i="precision mediump float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution_2_0(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\n\n\nfloat cookTorranceSpecular_1_1(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution_2_0(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular\n            , opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if(any(lessThan(f_data, clipBounds[0])) || \n     any(greaterThan(f_data, clipBounds[1]))) {\n    discard;\n  }\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n  \n  if(!gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = cookTorranceSpecular_1_1(L, V, N, roughness, fresnel);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}",o="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}",a="precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if(any(lessThan(f_data, clipBounds[0])) || \n     any(greaterThan(f_data, clipBounds[1]))) {\n    discard;\n  }\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}",s="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0])) || \n     any(greaterThan(position, clipBounds[1]))) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}",l="precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5,0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}",u="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}",c="precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if(any(lessThan(f_position, clipBounds[0])) || \n     any(greaterThan(f_position, clipBounds[1]))) {\n    discard;\n  }\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}",h="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0])) || \n     any(greaterThan(position, clipBounds[1]))) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}",f="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}",p="precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor,1);\n}\n";
r.meshShader={vertex:n,fragment:i,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.wireShader={vertex:o,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.pointShader={vertex:s,fragment:l,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},r.pickShader={vertex:u,fragment:c,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},r.pointPickShader={vertex:h,fragment:c,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},r.contourShader={vertex:f,fragment:p,attributes:[{name:"position",type:"vec3"}]}},{}],161:[function(t,e,r){"use strict";function n(t,e,r,n,i,o,a,s,l,u,c,h,f,p,d,m,g,v,y,b,x,_,w,M,k,A,T){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=o,this.pointPickShader=a,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=f,this.triangleUVs=h,this.triangleIds=u,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=b,this.pointColors=_,this.pointUVs=w,this.pointSizes=M,this.pointIds=x,this.pointVAO=k,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=A,this.contourVAO=T,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],this.clipBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this._model=j,this._view=j,this._projection=j,this._resolution=[1,1]}function i(t){for(var e=k({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;256>n;++n){for(var i=e[n],o=0;3>o;++o)r[4*n+o]=i[o];r[4*n+3]=255*i[3]}return M(r,[256,256,4],[4,0,1])}function o(t,e,r){for(var n=new Array(e),i=0;e>i;++i)n[i]=0;for(var o=t.length,i=0;o>i;++i)for(var a=t[i],s=0;s<a.length;++s)n[a[s]]=r[i];return n}function a(t){for(var e=t.length,r=new Array(e),n=0;e>n;++n)r[n]=t[n][2];return r}function s(t){var e=g(t,L);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function l(t){var e=g(t,z);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function u(t){var e=g(t,I);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function c(t){var e=g(t,P);return e.attributes.position.location=0,e.attributes.id.location=1,e}function h(t){var e=g(t,C);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function f(t){var e=g(t,R);return e.attributes.position.location=0,e}function p(t){var e=t.gl,r=s(e),i=l(e),o=u(e),a=c(e),p=h(e),d=f(e),m=b(e,M(new Uint8Array([255,255,255,255]),[1,1,4]));m.generateMipmap(),m.minFilter=e.LINEAR_MIPMAP_LINEAR,m.magFilter=e.LINEAR;var g=v(e),x=v(e),_=v(e),w=v(e),k=v(e),A=y(e,[{buffer:g,type:e.FLOAT,size:3},{buffer:k,type:e.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:x,type:e.FLOAT,size:4},{buffer:_,type:e.FLOAT,size:2},{buffer:w,type:e.FLOAT,size:3}]),T=v(e),E=v(e),S=v(e),L=v(e),z=y(e,[{buffer:T,type:e.FLOAT,size:3},{buffer:L,type:e.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:E,type:e.FLOAT,size:4},{buffer:S,type:e.FLOAT,size:2}]),I=v(e),P=v(e),C=v(e),R=v(e),j=v(e),D=y(e,[{buffer:I,type:e.FLOAT,size:3},{buffer:j,type:e.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:P,type:e.FLOAT,size:4},{buffer:C,type:e.FLOAT,size:2},{buffer:R,type:e.FLOAT,size:1}]),O=v(e),F=y(e,[{buffer:O,type:e.FLOAT,size:3}]),N=new n(e,m,r,i,o,a,p,d,g,k,x,_,w,A,T,L,E,S,z,I,j,P,C,R,D,O,F);return N.update(t),N}var d=1e-6,m=1e-6,g=t("gl-shader"),v=t("gl-buffer"),y=t("gl-vao"),b=t("gl-texture2d"),x=t("normals"),_=t("gl-mat4/multiply"),w=t("gl-mat4/invert"),M=t("ndarray"),k=t("colormap"),A=t("simplicial-complex-contour"),T=t("typedarray-pool"),E=t("./lib/shaders"),S=t("./lib/closest-point"),L=E.meshShader,z=E.wireShader,I=E.pointShader,P=E.pickShader,C=E.pointPickShader,R=E.contourShader,j=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],D=n.prototype;D.isOpaque=function(){return this.opacity>=1},D.isTransparent=function(){return this.opacity<1},D.pickSlots=1,D.setPickBase=function(t){this.pickId=t},D.highlight=function(t){if(!t||!this.contourEnable)return void(this.contourCount=0);for(var e=A(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,o=r.length,a=T.mallocFloat32(6*o),s=0,l=0;o>l;++l)for(var u=r[l],c=0;2>c;++c){var h=u[0];2===u.length&&(h=u[c]);for(var f=n[h][0],p=n[h][1],d=i[h],m=1-d,g=this.positions[f],v=this.positions[p],y=0;3>y;++y)a[s++]=d*g[y]+m*v[y]}this.contourCount=s/3|0,this.contourPositions.update(a.subarray(0,s)),T.free(a)},D.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=b(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(i(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var s=[],l=[],u=[],c=[],h=[],f=[],p=[],g=[],v=[],y=[],_=[],w=[],M=[],k=[];this.cells=r,this.positions=n;var A=t.vertexNormals,T=t.cellNormals,E=void 0===t.vertexNormalsEpsilon?d:t.vertexNormalsEpsilon,S=void 0===t.faceNormalsEpsilon?m:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=x.faceNormals(r,n,S)),T||A||(A=x.vertexNormals(r,n,E));var L=t.vertexColors,z=t.cellColors,I=t.meshColor||[1,1,1,1],P=t.vertexUVs,C=t.vertexIntensity,R=t.cellUVs,j=t.cellIntensity,D=1/0,O=-(1/0);if(!P&&!R)if(C)for(var F=0;F<C.length;++F){var N=C[F];D=Math.min(D,N),O=Math.max(O,N)}else if(j)for(var F=0;F<j.length;++F){var N=j[F];D=Math.min(D,N),O=Math.max(O,N)}else for(var F=0;F<n.length;++F){var N=n[F][2];D=Math.min(D,N),O=Math.max(O,N)}C?this.intensity=C:j?this.intensity=o(r,n.length,j):this.intensity=a(n);var B=t.pointSizes,U=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]];for(var F=0;F<n.length;++F)for(var V=n[F],q=0;3>q;++q)!isNaN(V[q])&&isFinite(V[q])&&(this.bounds[0][q]=Math.min(this.bounds[0][q],V[q]),this.bounds[1][q]=Math.max(this.bounds[1][q],V[q]));var G=0,H=0,X=0;t:for(var F=0;F<r.length;++F){var Y=r[F];switch(Y.length){case 1:for(var W=Y[0],V=n[W],q=0;3>q;++q)if(isNaN(V[q])||!isFinite(V[q]))continue t;y.push(V[0],V[1],V[2]);var Z;Z=L?L[W]:z?z[F]:I,3===Z.length?_.push(Z[0],Z[1],Z[2],1):_.push(Z[0],Z[1],Z[2],Z[3]);var Q;Q=P?P[W]:C?[(C[W]-D)/(O-D),0]:R?R[F]:j?[(j[F]-D)/(O-D),0]:[(V[2]-D)/(O-D),0],w.push(Q[0],Q[1]),B?M.push(B[W]):M.push(U),k.push(F),X+=1;break;case 2:for(var q=0;2>q;++q)for(var W=Y[q],V=n[W],K=0;3>K;++K)if(isNaN(V[K])||!isFinite(V[K]))continue t;for(var q=0;2>q;++q){var W=Y[q],V=n[W];f.push(V[0],V[1],V[2]);var Z;Z=L?L[W]:z?z[F]:I,3===Z.length?p.push(Z[0],Z[1],Z[2],1):p.push(Z[0],Z[1],Z[2],Z[3]);var Q;Q=P?P[W]:C?[(C[W]-D)/(O-D),0]:R?R[F]:j?[(j[F]-D)/(O-D),0]:[(V[2]-D)/(O-D),0],g.push(Q[0],Q[1]),v.push(F)}H+=1;break;case 3:for(var q=0;3>q;++q)for(var W=Y[q],V=n[W],K=0;3>K;++K)if(isNaN(V[K])||!isFinite(V[K]))continue t;for(var q=0;3>q;++q){var W=Y[q],V=n[W];s.push(V[0],V[1],V[2]);var Z;Z=L?L[W]:z?z[F]:I,3===Z.length?l.push(Z[0],Z[1],Z[2],1):l.push(Z[0],Z[1],Z[2],Z[3]);var Q;Q=P?P[W]:C?[(C[W]-D)/(O-D),0]:R?R[F]:j?[(j[F]-D)/(O-D),0]:[(V[2]-D)/(O-D),0],c.push(Q[0],Q[1]);var J;J=A?A[W]:T[F],u.push(J[0],J[1],J[2]),h.push(F)}G+=1}}this.pointCount=X,this.edgeCount=H,this.triangleCount=G,this.pointPositions.update(y),this.pointColors.update(_),this.pointUVs.update(w),this.pointSizes.update(M),this.pointIds.update(new Uint32Array(k)),this.edgePositions.update(f),this.edgeColors.update(p),this.edgeUVs.update(g),this.edgeIds.update(new Uint32Array(v)),this.trianglePositions.update(s),this.triangleColors.update(l),this.triangleUVs.update(c),this.triangleNormals.update(u),this.triangleIds.update(new Uint32Array(h))}},D.drawTransparent=D.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||j,n=t.view||j,i=t.projection||j,o=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],a=0;3>a;++a)o[0][a]=Math.max(o[0][a],this.clipBounds[0][a]),o[1][a]=Math.min(o[1][a],this.clipBounds[1][a]);var s={model:r,view:n,projection:i,clipBounds:o,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,contourColor:this.contourColor,texture:0};this.texture.bind(0);var l=new Array(16);_(l,s.view,s.model),_(l,s.projection,l),w(l,l);for(var a=0;3>a;++a)s.eyePosition[a]=l[12+a]/l[15];for(var u=l[15],a=0;3>a;++a)u+=this.lightPosition[a]*l[4*a+3];for(var a=0;3>a;++a){for(var c=l[12+a],h=0;3>h;++h)c+=l[4*h+a]*this.lightPosition[h];s.lightPosition[a]=c/u}if(this.triangleCount>0){var f=this.triShader;f.bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}if(this.edgeCount>0&&this.lineWidth>0){var f=this.lineShader;f.bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()}if(this.pointCount>0){var f=this.pointShader;f.bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}if(this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0){var f=this.contourShader;f.bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind()}},D.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||j,n=t.view||j,i=t.projection||j,o=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],a=0;3>a;++a)o[0][a]=Math.max(o[0][a],this.clipBounds[0][a]),o[1][a]=Math.min(o[1][a],this.clipBounds[1][a]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:o,pickId:this.pickId/255},l=this.pickShader;if(l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0){var l=this.pointPickShader;l.bind(),l.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}},D.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),o=0;o<r.length;++o)i[o]=n[r[o]];var a=S(i,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!a)return null;for(var s=a[2],l=0,o=0;o<r.length;++o)l+=s[o]*this.intensity[r[o]];return{position:a[1],index:r[a[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[a[0]]]}},D.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=p},{"./lib/closest-point":159,"./lib/shaders":160,colormap:111,"gl-buffer":129,"gl-mat4/invert":148,"gl-mat4/multiply":150,"gl-shader":208,"gl-texture2d":233,"gl-vao":237,ndarray:456,normals:163,"simplicial-complex-contour":165,"typedarray-pool":481}],162:[function(t,e,r){"use 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t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,o=this.grid,a=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var u=this.borderColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT),t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var c=this.backgroundColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT),o.draw();var h=this.zeroLineEnable,f=this.zeroLineColor,p=this.zeroLineWidth;if(h[0]||h[1]){a.bind();for(var d=0;2>d;++d)if(h[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?a.drawLine(m,e[1],m,e[3],p[d],f[d]):a.drawLine(e[0],m,e[2],m,p[d],f[d])}}for(var d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),a.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;g[1]&&a.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&a.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&a.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&a.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind();for(var d=0;2>d;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();for(var b=this.overlays,d=0;d<b.length;++d)b[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}}}(),f.drawPick=function(){return function(){var t=this.pickBuffer;this.gl;this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}}(),f.pick=function(){return function(t,e){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,o=0|Math.round((t-i[0]/r)*n),a=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(o,a,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),u=this.objects,c=0;c<u.length;++c){var h=u[c].pick(o,a,l);if(h)return h}return null}}(),f.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},f.setDataBox=function(t){var e=this.dataBox,r=e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3];r&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},f.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},f.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,a=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/a,10,10/a]),this.borderColor=(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=i(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=i(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=i(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t&&!t.titleEnable),this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=i(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=i(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=i(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var s=t.ticks||[[],[]],l=this._tickBounds;l[0]=l[1]=1/0,l[2]=l[3]=-(1/0);for(var u=0;2>u;++u){var 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i(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=c(t)}function o(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}function a(t,e,r,n,i){for(var o=t.primalOffset,a=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,u=n[e],c=0;3>c;++c)if(e!==c){var h=o,f=s,p=a,d=l;u&1<<c&&(h=s,f=o,p=l,d=a),h[c]=r[0][c],f[c]=r[1][c],i[c]>0?(p[c]=-1,d[c]=0):(p[c]=0,d[c]=1)}}function s(t,e){var r=new i(t);return r.update(e),r}e.exports=s;var l=t("./lib/text.js"),u=t("./lib/lines.js"),c=t("./lib/background.js"),h=t("./lib/cube.js"),f=t("./lib/ticks.js"),p=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),d=i.prototype;d.update=function(t){function e(e,r,n){if(n in t){var i,o=t[n],a=this[n];(e?Array.isArray(o)&&Array.isArray(o[0]):Array.isArray(o))?this[n]=i=[r(o[0]),r(o[1]),r(o[2])]:this[n]=i=[r(o),r(o),r(o)];for(var s=0;3>s;++s)if(i[s]!==a[s])return!0}return!1}t=t||{};var r,n=e.bind(this,!1,Number),i=e.bind(this,!1,Boolean),o=e.bind(this,!1,String),a=e.bind(this,!0,function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]}),s=!1,c=!1;if("bounds"in t)for(var h=t.bounds,p=0;2>p;++p)for(var d=0;3>d;++d)h[p][d]!==this.bounds[p][d]&&(c=!0),this.bounds[p][d]=h[p][d];if("ticks"in t){r=t.ticks,s=!0,this.autoTicks=!1;for(var p=0;3>p;++p)this.tickSpacing[p]=0}else n("tickSpacing")&&(this.autoTicks=!0,c=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),c=!0,s=!0,this._firstInit=!1),c&&this.autoTicks&&(r=f.create(this.bounds,this.tickSpacing),s=!0),s){for(var p=0;3>p;++p)r[p].sort(function(t,e){return t.x-e.x});f.equal(r,this.ticks)?s=!1:this.ticks=r}i("tickEnable"),o("tickFont")&&(s=!0),n("tickSize"),n("tickAngle"),n("tickPad"),a("tickColor");var m=o("labels");o("labelFont")&&(m=!0),i("labelEnable"),n("labelSize"),n("labelPad"),a("labelColor"),i("lineEnable"),i("lineMirror"),n("lineWidth"),a("lineColor"),i("lineTickEnable"),i("lineTickMirror"),n("lineTickLength"),n("lineTickWidth"),a("lineTickColor"),i("gridEnable"),n("gridWidth"),a("gridColor"),i("zeroEnable"),a("zeroLineColor"),n("zeroLineWidth"),i("backgroundEnable"),a("backgroundColor"),this._text?this._text&&(m||s)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=l(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&s&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=u(this.gl,this.bounds,this.ticks))};var m=[new o,new o,new o],g=[0,0,0],v={model:p,view:p,projection:p};d.isOpaque=function(){return!0},d.isTransparent=function(){return!1},d.drawTransparent=function(t){};var y=[0,0,0],b=[0,0,0],x=[0,0,0];d.draw=function(t){t=t||v;for(var e=this.gl,r=t.model||p,i=t.view||p,o=t.projection||p,s=this.bounds,l=h(r,i,o,s),u=l.cubeEdges,c=l.axis,f=i[12],d=i[13],_=i[14],w=i[15],M=this.pixelRatio*(o[3]*f+o[7]*d+o[11]*_+o[15]*w)/e.drawingBufferHeight,k=0;3>k;++k)this.lastCubeProps.cubeEdges[k]=u[k],this.lastCubeProps.axis[k]=c[k];for(var A=m,k=0;3>k;++k)a(m[k],k,this.bounds,u,c);for(var e=this.gl,T=g,k=0;3>k;++k)this.backgroundEnable[k]?T[k]=c[k]:T[k]=0;this._background.draw(r,i,o,s,T,this.backgroundColor),this._lines.bind(r,i,o,this);for(var k=0;3>k;++k){var E=[0,0,0];c[k]>0?E[k]=s[1][k]:E[k]=s[0][k];for(var S=0;2>S;++S){var L=(k+1+S)%3,z=(k+1+(1^S))%3;this.gridEnable[L]&&this._lines.drawGrid(L,z,this.bounds,E,this.gridColor[L],this.gridWidth[L]*this.pixelRatio)}for(var S=0;2>S;++S){var L=(k+1+S)%3,z=(k+1+(1^S))%3;this.zeroEnable[z]&&s[0][z]<=0&&s[1][z]>=0&&this._lines.drawZero(L,z,this.bounds,E,this.zeroLineColor[z],this.zeroLineWidth[z]*this.pixelRatio)}}for(var k=0;3>k;++k){this.lineEnable[k]&&this._lines.drawAxisLine(k,this.bounds,A[k].primalOffset,this.lineColor[k],this.lineWidth[k]*this.pixelRatio),this.lineMirror[k]&&this._lines.drawAxisLine(k,this.bounds,A[k].mirrorOffset,this.lineColor[k],this.lineWidth[k]*this.pixelRatio);for(var I=n(y,A[k].primalMinor),P=n(b,A[k].mirrorMinor),C=this.lineTickLength,S=0;3>S;++S){var R=M/r[5*S];I[S]*=C[S]*R,P[S]*=C[S]*R}this.lineTickEnable[k]&&this._lines.drawAxisTicks(k,A[k].primalOffset,I,this.lineTickColor[k],this.lineTickWidth[k]*this.pixelRatio),this.lineTickMirror[k]&&this._lines.drawAxisTicks(k,A[k].mirrorOffset,P,this.lineTickColor[k],this.lineTickWidth[k]*this.pixelRatio)}this._text.bind(r,i,o,this.pixelRatio);for(var k=0;3>k;++k){for(var j=A[k].primalMinor,D=n(x,A[k].primalOffset),S=0;3>S;++S)this.lineTickEnable[k]&&(D[S]+=M*j[S]*Math.max(this.lineTickLength[S],0)/r[5*S]);if(this.tickEnable[k]){for(var S=0;3>S;++S)D[S]+=M*j[S]*this.tickPad[S]/r[5*S];this._text.drawTicks(k,this.tickSize[k],this.tickAngle[k],D,this.tickColor[k])}if(this.labelEnable[k]){for(var S=0;3>S;++S)D[S]+=M*j[S]*this.labelPad[S]/r[5*S];D[k]+=.5*(s[0][k]+s[1][k]),this._text.drawLabel(k,this.labelSize[k],this.labelAngle[k],D,this.labelColor[k])}}},d.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":182,"./lib/cube.js":183,"./lib/lines.js":184,"./lib/text.js":186,"./lib/ticks.js":187}],182:[function(t,e,r){"use strict";function n(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}function i(t){for(var e=[],r=[],i=0,l=0;3>l;++l)for(var u=(l+1)%3,c=(l+2)%3,h=[0,0,0],f=[0,0,0],p=-1;1>=p;p+=2){r.push(i,i+2,i+1,i+1,i+2,i+3),h[l]=p,f[l]=p;for(var d=-1;1>=d;d+=2){h[u]=d;for(var m=-1;1>=m;m+=2)h[c]=m,e.push(h[0],h[1],h[2],f[0],f[1],f[2]),i+=1}var g=u;u=c,c=g}var v=o(t,new Float32Array(e)),y=o(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),b=a(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),x=s(t);return x.attributes.position.location=0,x.attributes.normal.location=1,new n(t,v,b,x)}e.exports=i;var o=t("gl-buffer"),a=t("gl-vao"),s=t("./shaders").bg,l=n.prototype;l.draw=function(t,e,r,n,i,o){for(var a=!1,s=0;3>s;++s)a=a||i[s];if(a){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),
this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:o},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),l.disable(l.POLYGON_OFFSET_FILL)}},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":185,"gl-buffer":129,"gl-vao":237}],183:[function(t,e,r){"use strict";function n(t,e,r){for(var n=0;4>n;++n){t[n]=r[12+n];for(var i=0;3>i;++i)t[n]+=e[i]*r[4*i+n]}}function i(t){for(var e=0;e<m.length;++e)if(t=l.positive(t,m[e]),t.length<3)return 0;for(var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0,e=1;e+1<t.length;++e){var a=t[e],s=t[e+1],u=a[0]/a[3],c=a[1]/a[3],h=s[0]/s[3],f=s[1]/s[3],p=u-n,d=c-i,g=h-n,v=f-i;o+=Math.abs(p*v-d*g)}return o}function o(t,e,r,o){s(c,e,t),s(c,r,c);for(var l=0,m=0;2>m;++m){p[2]=o[m][2];for(var b=0;2>b;++b){p[1]=o[b][1];for(var x=0;2>x;++x)p[0]=o[x][0],n(h[l],p,c),l+=1}}for(var _=-1,m=0;8>m;++m){for(var w=h[m][3],M=0;3>M;++M)f[m][M]=h[m][M]/w;0>w&&(0>_?_=m:f[m][2]<f[_][2]&&(_=m))}if(0>_){_=0;for(var k=0;3>k;++k){for(var A=(k+2)%3,T=(k+1)%3,E=-1,S=-1,L=0;2>L;++L){var z=L<<k,I=z+(L<<A)+(1-L<<T),P=z+(1-L<<A)+(L<<T);u(f[z],f[I],f[P],d)<0||(L?E=1:S=1)}if(0>E||0>S)S>E&&(_|=1<<k);else{for(var L=0;2>L;++L){var z=L<<k,I=z+(L<<A)+(1-L<<T),P=z+(1-L<<A)+(L<<T),C=i([h[z],h[I],h[P],h[z+(1<<A)+(1<<T)]]);L?E=C:S=C}S>E&&(_|=1<<k)}}}for(var R=7^_,j=-1,m=0;8>m;++m)m!==_&&m!==R&&(0>j?j=m:f[j][1]>f[m][1]&&(j=m));for(var D=-1,m=0;3>m;++m){var O=j^1<<m;if(O!==_&&O!==R){0>D&&(D=O);var T=f[O];T[0]<f[D][0]&&(D=O)}}for(var F=-1,m=0;3>m;++m){var O=j^1<<m;if(O!==_&&O!==R&&O!==D){0>F&&(F=O);var T=f[O];T[0]>f[F][0]&&(F=O)}}var N=g;N[0]=N[1]=N[2]=0,N[a.log2(D^j)]=j&D,N[a.log2(j^F)]=j&F;var B=7^F;B===_||B===R?(B=7^D,N[a.log2(F^B)]=B&F):N[a.log2(D^B)]=B&D;for(var U=v,V=_,k=0;3>k;++k)V&1<<k?U[k]=-1:U[k]=1;return y}e.exports=o;var a=t("bit-twiddle"),s=t("gl-mat4/multiply"),l=(t("gl-mat4/invert"),t("split-polygon")),u=t("robust-orientation"),c=new Array(16),h=(new Array(16),new Array(8)),f=new Array(8),p=new Array(3),d=[0,0,0];!function(){for(var t=0;8>t;++t)h[t]=[1,1,1,1],f[t]=[1,1,1]}();var m=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]],g=[1,1,1],v=[0,0,0],y={cubeEdges:g,axis:v}},{"bit-twiddle":51,"gl-mat4/invert":148,"gl-mat4/multiply":150,"robust-orientation":462,"split-polygon":189}],184:[function(t,e,r){"use strict";function n(t){return t[0]=t[1]=t[2]=0,t}function i(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function o(t,e,r,n,i,o,a,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=o,this.gridCount=a,this.gridOffset=s}function a(t,e,r){var n=[],i=[0,0,0],a=[0,0,0],c=[0,0,0],h=[0,0,0];n.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;3>f;++f){for(var p=n.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;n.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=n.length/3|0;i[f]=p,a[f]=g-p;for(var p=n.length/3|0,v=0;v<r[f].length;++v){var m=+r[f][v].x;n.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=n.length/3|0;c[f]=p,h[f]=g-p}var y=s(t,new Float32Array(n)),b=l(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),x=u(t);return x.attributes.position.location=0,new o(t,y,b,x,a,i,h,c)}e.exports=a;var s=t("gl-buffer"),l=t("gl-vao"),u=t("./shaders").line,c=[0,0,0],h=[0,0,0],f=[0,0,0],p=[0,0,0],d=[1,1],m=o.prototype;m.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,d[0]=this.gl.drawingBufferWidth,d[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=d,this.vao.bind()},m.drawAxisLine=function(t,e,r,o,a){var s=n(h);this.shader.uniforms.majorAxis=h,s[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=s;var l=i(p,r);l[t]+=e[0][t],this.shader.uniforms.offset=l,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=o;var u=n(f);u[(t+2)%3]=1,this.shader.uniforms.screenAxis=u,this.vao.draw(this.gl.TRIANGLES,6);var u=n(f);u[(t+1)%3]=1,this.shader.uniforms.screenAxis=u,this.vao.draw(this.gl.TRIANGLES,6)},m.drawAxisTicks=function(t,e,r,i,o){if(this.tickCount[t]){var a=n(c);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=i,this.shader.uniforms.lineWidth=o;var s=n(f);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},m.drawGrid=function(t,e,r,o,a,s){if(this.gridCount[t]){var l=n(h);l[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=l;var u=i(p,o);u[e]+=r[0][e],this.shader.uniforms.offset=u;var d=n(c);d[t]=1,this.shader.uniforms.majorAxis=d;var m=n(f);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=s,this.shader.uniforms.color=a,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},m.drawZero=function(t,e,r,o,a,s){var l=n(h);this.shader.uniforms.majorAxis=l,l[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=l;var u=i(p,o);u[t]+=r[0][t],this.shader.uniforms.offset=u;var c=n(f);c[e]=1,this.shader.uniforms.screenAxis=c,this.shader.uniforms.lineWidth=s,this.shader.uniforms.color=a,this.vao.draw(this.gl.TRIANGLES,6)},m.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{"./shaders":185,"gl-buffer":129,"gl-vao":237}],185:[function(t,e,r){"use strict";var n=t("gl-shader"),i="#define GLSLIFY 1\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n",o="precision mediump float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}";r.line=function(t){return n(t,i,o,null,[{name:"position",type:"vec3"}])};var a="#define GLSLIFY 1\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvoid main() {  \n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n  mat2 planeXform = scale * mat2(cos(angle), sin(angle),\n                                -sin(angle), cos(angle));\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute world offset\n  float axisDistance = position.z;\n  vec3 dataPosition = axisDistance * axis + offset;\n  vec4 worldPosition = model * vec4(dataPosition, 1);\n  \n  //Compute clip position\n  vec4 viewPosition = view * worldPosition;\n  vec4 clipPosition = projection * viewPosition;\n  clipPosition /= clipPosition.w;\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec4(viewOffset, 0, 0);\n\n  //Done\n  gl_Position = clipPosition;\n}",s="precision mediump float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}";r.text=function(t){return n(t,a,s,null,[{name:"position",type:"vec3"}])};var l="#define GLSLIFY 1\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  if(dot(normal, enable) > 0.0) {\n    vec3 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n=O[r];n.shape=H,n.begin();for(var i=0;t>i;++i)if(j[i]===r){var o=R[i];o.drawPick&&(o.pixelRatio=1,o.drawPick(V))}n.end()}}}function b(){if(!s()){e();var t=G.camera.tick();V.view=G.camera.matrix,F=F||t,N=N||t,I.pixelRatio=G.pixelRatio,C.pixelRatio=G.pixelRatio;var r=R.length,n=W[0],i=W[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-(1/0);for(var a=0;r>a;++a){var l=R[a];l.pixelRatio=G.pixelRatio,l.axes=G.axes,F=F||!!l.dirty,N=N||!!l.dirty;var u=l.bounds;if(u)for(var h=u[0],f=u[1],p=0;3>p;++p)n[p]=Math.min(n[p],h[p]),i[p]=Math.max(i[p],f[p])}var m=G.bounds;if(G.autoBounds)for(var p=0;3>p;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var v=.05*(i[p]-n[p]);n[p]=n[p]-v,i[p]=i[p]+v}m[0][p]=n[p],m[1][p]=i[p]}for(var b=!1,p=0;3>p;++p)b=b||Z[0][p]!==m[0][p]||Z[1][p]!==m[1][p],Z[0][p]=m[0][p],Z[1][p]=m[1][p];if(b){for(var x=[0,0,0],a=0;3>a;++a)x[a]=o((m[1][a]-m[0][a])/10);I.autoTicks?I.update({bounds:m,tickSpacing:x}):I.update({bounds:m})}N=N||b,F=F||b;var 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L=!1;I.enable&&(L=L||I.isTransparent(),I.draw(V)),C.axes=I,T.object&&C.draw(V),k.disable(k.CULL_FACE);for(var a=0;r>a;++a){var l=R[a];l.axes=I,l.pixelRatio=G.pixelRatio,l.isOpaque&&l.isOpaque()&&l.draw(V),l.isTransparent&&l.isTransparent()&&(L=!0)}if(L){E.shape=q,E.bind(),k.clear(k.DEPTH_BUFFER_BIT),k.colorMask(!1,!1,!1,!1),k.depthMask(!0),k.depthFunc(k.LESS),I.enable&&I.isTransparent()&&I.drawTransparent(V);for(var a=0;r>a;++a){var l=R[a];l.isOpaque&&l.isOpaque()&&l.draw(V)}k.enable(k.BLEND),k.blendEquation(k.FUNC_ADD),k.blendFunc(k.ONE,k.ONE_MINUS_SRC_ALPHA),k.colorMask(!0,!0,!0,!0),k.depthMask(!1),k.clearColor(0,0,0,0),k.clear(k.COLOR_BUFFER_BIT),I.isTransparent()&&I.drawTransparent(V);for(var a=0;r>a;++a){var l=R[a];l.isTransparent&&l.isTransparent()&&l.drawTransparent(V)}k.bindFramebuffer(k.FRAMEBUFFER,null),k.blendFunc(k.ONE,k.ONE_MINUS_SRC_ALPHA),k.disable(k.DEPTH_TEST),S.bind(),E.color[0].bind(0),S.uniforms.accumBuffer=0,d(k),k.disable(k.BLEND)}F=!1;for(var a=0;r>a;++a)R[a].dirty=!1}}}function x(){_||G.contextLost||(requestAnimationFrame(x),b())}t=t||{};var _=!1,w=(t.pixelRatio||parseFloat(window.devicePixelRatio),t.canvas);if(!w)if(w=document.createElement("canvas"),t.container){var M=t.container;M.appendChild(w)}else document.body.appendChild(w);var k=t.gl;if(k||(k=i(w,t.glOptions||{premultipliedAlpha:!0,antialias:!0})),!k)throw new Error("webgl not supported");var A=t.bounds||[[-10,-10,-10],[10,10,10]],T=new n,E=p(k,[k.drawingBufferWidth,k.drawingBufferHeight],{preferFloat:!0}),S=v(k),L=t.camera||{eye:[2,0,0],center:[0,0,0],up:[0,1,0],zoomMin:.1,zoomMax:100,mode:"turntable"},z=t.axes||{},I=u(k,z);I.enable=!z.disable;var P=t.spikes||{},C=h(k,P),R=[],j=[],D=[],O=[],F=!0,N=!0,B=new Array(16),U=new Array(16),V={view:null,projection:B,model:U},N=!0,q=[k.drawingBufferWidth,k.drawingBufferHeight],G={gl:k,contextLost:!1,pixelRatio:t.pixelRatio||parseFloat(window.devicePixelRatio),canvas:w,selection:T,camera:l(w,L),axes:I,axesPixels:null,spikes:C,bounds:A,objects:R,shape:q,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:a(t.autoResize),autoBounds:a(t.autoBounds),autoScale:!!t.autoScale,autoCenter:a(t.autoCenter),clipToBounds:a(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:V,oncontextloss:null,mouseListener:null},H=[k.drawingBufferWidth/G.pixelRatio|0,k.drawingBufferHeight/G.pixelRatio|0];G.autoResize&&e(),window.addEventListener("resize",e),G.update=function(t){_||(t=t||{},F=!0,N=!0)},G.add=function(t){_||(t.axes=I,R.push(t),j.push(-1),F=!0,N=!0,r())},G.remove=function(t){if(!_){var e=R.indexOf(t);0>e||(R.splice(e,1),j.pop(),F=!0,N=!0,r())}},G.dispose=function(){if(!_&&(_=!0,window.removeEventListener("resize",e),w.removeEventListener("webglcontextlost",s),G.mouseListener.enabled=!1,!G.contextLost)){I.dispose(),C.dispose();for(var t=0;t<R.length;++t)R[t].dispose();E.dispose();for(var t=0;t<O.length;++t)O[t].dispose();S.dispose(),k=null,I=null,C=null,R=[]}};var X=!1,Y=0;G.mouseListener=m(w,function(t,e,r){if(!_){var n=O.length,i=R.length,o=T.object;T.distance=1/0,T.mouse[0]=e,T.mouse[1]=r,T.object=null,T.screen=null,T.dataCoordinate=T.dataPosition=null;var a=!1;if(t&&Y)X=!0;else{X&&(N=!0),X=!1;for(var s=0;n>s;++s){var l=O[s].query(e,H[1]-r-1,G.pickRadius);if(l){if(l.distance>T.distance)continue;for(var u=0;i>u;++u){var c=R[u];if(j[u]===s){var h=c.pick(l);h&&(T.buttons=t,T.screen=l.coord,T.distance=l.distance,T.object=c,T.index=h.distance,T.dataPosition=h.position,T.dataCoordinate=h.dataCoordinate,T.data=h,a=!0)}}}}}o&&o!==T.object&&(o.highlight&&o.highlight(null),F=!0),T.object&&(T.object.highlight&&T.object.highlight(T.data),F=!0),a=a||T.object!==o,a&&G.onselect&&G.onselect(T),1&t&&!(1&Y)&&G.onclick&&G.onclick(T),Y=t}}),w.addEventListener("webglcontextlost",s);var W=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],Z=[W[0].slice(),W[1].slice()];return x(),G.redraw=function(){_||(F=!0,b())},G}e.exports=s;var l=t("3d-view-controls"),u=t("gl-axes3d"),c=t("gl-axes3d/properties"),h=t("gl-spikes3d"),f=t("gl-select-static"),p=t("gl-fbo"),d=t("a-big-triangle"),m=t("mouse-change"),g=t("gl-mat4/perspective"),v=t("./lib/shader")},{"./lib/shader":177,"3d-view-controls":178,"a-big-triangle":180,"gl-axes3d":181,"gl-axes3d/properties":191,"gl-fbo":134,"gl-mat4/perspective":151,"gl-select-static":207,"gl-spikes3d":193,"mouse-change":444}],195:[function(t,e,r){"use strict";e.exports={vertex:"precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec2 offset;\nattribute vec4 color;\n\nuniform mat3 viewTransform;\nuniform vec2 pixelScale;\n\nvarying vec4 fragColor;\n\nvec4 computePosition_1_0(vec2 position, vec2 offset, mat3 view, vec2 scale) {\n  vec3 xposition = view * vec3(position, 1.0);\n  return vec4(\n    xposition.xy + scale * offset * xposition.z,\n    0,\n    xposition.z);\n}\n\n\n\n\nvoid main() {\n  fragColor = color;\n\n  gl_Position = computePosition_1_0(\n    position,\n    offset,\n    viewTransform,\n    pixelScale);\n}\n",fragment:"precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n",pickVertex:"precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec2 offset;\nattribute vec4 id;\n\nuniform mat3 viewTransform;\nuniform vec2 pixelScale;\nuniform vec4 pickOffset;\n\nvarying vec4 fragColor;\n\nvec4 computePosition_1_0(vec2 position, vec2 offset, mat3 view, vec2 scale) {\n  vec3 xposition = view * vec3(position, 1.0);\n  return vec4(\n    xposition.xy + scale * offset * xposition.z,\n    0,\n    xposition.z);\n}\n\n\n\n\nvoid main() {\n  vec4 fragId = id + pickOffset;\n\n  fragId.y += floor(fragId.x / 256.0);\n  fragId.x -= floor(fragId.x / 256.0) * 256.0;\n\n  fragId.z += floor(fragId.y / 256.0);\n  fragId.y -= floor(fragId.y / 256.0) * 256.0;\n\n  fragId.w += floor(fragId.z / 256.0);\n  fragId.z -= floor(fragId.z / 256.0) * 256.0;\n\n  fragColor = fragId / 255.0;\n\n  gl_Position = computePosition_1_0(\n    position,\n    offset,\n    viewTransform,\n    pixelScale);\n}\n",pickFragment:"precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = fragColor;\n}\n"}},{}],196:[function(t,e,r){"use strict";function n(t){if(t in f)return f[t];var e=c(t,{polygons:!0,font:"sans-serif",textAlign:"left",textBaseline:"alphabetic"}),r=[],n=[];e.forEach(function(t){t.forEach(function(t){for(var e=0;e<t.length;++e){var i=t[(e+t.length-1)%t.length],o=t[e],a=t[(e+1)%t.length],s=t[(e+2)%t.length],l=o[0]-i[0],u=o[1]-i[1],c=Math.sqrt(l*l+u*u);l/=c,u/=c,r.push(i[0],i[1]+1.4),n.push(u,-l),r.push(i[0],i[1]+1.4),n.push(-u,l),r.push(o[0],o[1]+1.4),n.push(-u,l),r.push(o[0],o[1]+1.4),n.push(-u,l),r.push(i[0],i[1]+1.4),n.push(u,-l),r.push(o[0],o[1]+1.4),n.push(u,-l);var h=s[0]-a[0],f=s[1]-a[1],p=Math.sqrt(h*h+f*f);h/=p,f/=p,r.push(o[0],o[1]+1.4),n.push(u,-l),r.push(o[0],o[1]+1.4),n.push(-u,l),r.push(a[0],a[1]+1.4),n.push(-f,h),r.push(a[0],a[1]+1.4),n.push(-f,h),r.push(o[0],o[1]+1.4),n.push(f,-h),r.push(a[0],a[1]+1.4),n.push(f,-h)}})});for(var i=[1/0,1/0,-(1/0),-(1/0)],o=0;o<r.length;o+=2)for(var a=0;2>a;++a)i[a]=Math.min(i[a],r[o+a]),i[2+a]=Math.max(i[2+a],r[o+a]);return f[t]={coords:r,normals:n,bounds:i}}function i(t,e,r,n,i,o,a){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.offsetBuffer=i,this.colorBuffer=o,this.idBuffer=a,this.bounds=[1/0,1/0,-(1/0),-(1/0)],this.numPoints=0,this.numVertices=0,this.pickOffset=0,this.points=null}function o(t,e){var r=t.gl,n=a(r,h.vertex,h.fragment),o=a(r,h.pickVertex,h.pickFragment),l=s(r),u=s(r),c=s(r),f=s(r),p=new i(t,n,o,l,u,c,f);return p.update(e),t.addObject(p),p}e.exports=o;var a=t("gl-shader"),s=t("gl-buffer"),l=t("text-cache"),u=t("typedarray-pool"),c=t("vectorize-text"),h=t("./lib/shaders"),f={},p=i.prototype;!function(){function t(){var t=this.plot,n=this.bounds,i=t.viewBox,o=t.dataBox,a=t.pixelRatio,s=n[2]-n[0],l=n[3]-n[1],u=o[2]-o[0],c=o[3]-o[1];e[0]=2*s/u,e[4]=2*l/c,e[6]=2*(n[0]-o[0])/u-1,e[7]=2*(n[1]-o[1])/c-1;var h=i[2]-i[0],f=i[3]-i[1];r[0]=2*a/h,r[1]=2*a/f}var e=[1,0,0,0,1,0,0,0,1],r=[1,1];p.draw=function(){var n=this.plot,i=this.shader,o=this.numVertices;if(o){var a=n.gl;t.call(this),i.bind(),i.uniforms.pixelScale=r,i.uniforms.viewTransform=e,this.positionBuffer.bind(),i.attributes.position.pointer(),this.offsetBuffer.bind(),i.attributes.offset.pointer(),this.colorBuffer.bind(),i.attributes.color.pointer(a.UNSIGNED_BYTE,!0),a.drawArrays(a.TRIANGLES,0,o)}};var n=[0,0,0,0];p.drawPick=function(i){var o=this.plot,a=this.pickShader,s=this.numVertices,l=o.gl;if(this.pickOffset=i,!s)return i;for(var u=0;4>u;++u)n[u]=i>>8*u&255;return t.call(this),a.bind(),a.uniforms.pixelScale=r,a.uniforms.viewTransform=e,a.uniforms.pickOffset=n,this.positionBuffer.bind(),a.attributes.position.pointer(),this.offsetBuffer.bind(),a.attributes.offset.pointer(),this.idBuffer.bind(),a.attributes.id.pointer(l.UNSIGNED_BYTE,!1),l.drawArrays(l.TRIANGLES,0,s),i+this.numPoints}}(),p.pick=function(t,e,r){var n=this.pickOffset,i=this.numPoints;if(n>r||r>=n+i)return null;var o=r-n,a=this.points;return{object:this,pointId:o,dataCoord:[a[2*o],a[2*o+1]]}},p.update=function(t){t=t||{};var e=t.positions||[],r=t.colors||[],i=t.glyphs||[],o=t.sizes||[],a=t.borderWidths||[],s=t.borderColors||[];this.points=e;for(var c=this.bounds=[1/0,1/0,-(1/0),-(1/0)],h=0,f=0;f<i.length;++f){h+=l("sans-serif",i[f]).data.length+n(i[f]).coords.length>>1;for(var p=0;2>p;++p)c[p]=Math.min(c[p],e[2*f+p]),c[2+p]=Math.max(c[2+p],e[2*f+p])}c[0]===c[2]&&(c[2]+=1),c[3]===c[1]&&(c[3]+=1);for(var d=1/(c[2]-c[0]),m=1/(c[3]-c[1]),g=c[0],v=c[1],y=u.mallocFloat32(2*h),b=u.mallocFloat32(2*h),x=u.mallocUint8(4*h),_=u.mallocUint32(h),w=0,f=0;f<i.length;++f){for(var M=l("sans-serif",i[f]),k=n(i[f]),A=d*(e[2*f]-g),T=m*(e[2*f+1]-v),E=o[f],S=255*r[4*f],L=255*r[4*f+1],z=255*r[4*f+2],I=255*r[4*f+3],P=.5*(k.bounds[0]+k.bounds[2]),C=.5*(k.bounds[1]+k.bounds[3]),p=0;p<M.data.length;p+=2)y[2*w]=A,y[2*w+1]=T,b[2*w]=-E*(M.data[p]-P),b[2*w+1]=-E*(M.data[p+1]-C),x[4*w]=S,x[4*w+1]=L,x[4*w+2]=z,x[4*w+3]=I,_[w]=f,w+=1;var R=a[f];S=255*s[4*f],L=255*s[4*f+1],z=255*s[4*f+2],I=255*s[4*f+3];
for(var p=0;p<k.coords.length;p+=2)y[2*w]=A,y[2*w+1]=T,b[2*w]=-(E*(k.coords[p]-P)+R*k.normals[p]),b[2*w+1]=-(E*(k.coords[p+1]-C)+R*k.normals[p+1]),x[4*w]=S,x[4*w+1]=L,x[4*w+2]=z,x[4*w+3]=I,_[w]=f,w+=1}this.numPoints=i.length,this.numVertices=h,this.positionBuffer.update(y),this.offsetBuffer.update(b),this.colorBuffer.update(x),this.idBuffer.update(_),u.free(y),u.free(b),u.free(x),u.free(_)},p.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.offsetBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{"./lib/shaders":195,"gl-buffer":129,"gl-shader":208,"text-cache":476,"typedarray-pool":481,"vectorize-text":483}],197:[function(t,e,r){r.pointVertex="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute float weight;\n\nuniform mat3 matrix;\nuniform float pointSize, useWeight;\n\nvarying float fragWeight;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n  fragWeight = mix(1.0, weight, useWeight);\n}\n",r.pointFragment="precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\n\nvarying float fragWeight;\n\nfloat smoothStep(float x, float y) {\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\n}\n\nvoid main() {\n  float radius = length(2.0*gl_PointCoord.xy-1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  vec4 baseColor = mix(borderColor, color, smoothStep(radius, centerFraction));\n  float alpha = 1.0 - pow(1.0 - baseColor.a, fragWeight);\n  gl_FragColor = vec4(baseColor.rgb * alpha, alpha);\n}\n",r.pickVertex="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n",r.pickFragment="precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0*gl_PointCoord.xy-1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"},{}],198:[function(t,e,r){arguments[4][73][0].apply(r,arguments)},{dup:73}],199:[function(t,e,r){"use strict";function n(t,e,r,n,o){4*f>=o?i(0,o-1,t,e,r,n):h(0,o-1,t,e,r,n)}function i(t,e,r,n,i,o){for(var a=t+1;e>=a;++a){for(var s=r[a],l=n[2*a],u=n[2*a+1],c=i[a],h=o[a],f=a;f>t;){var p=r[f-1],d=n[2*(f-1)];if((p-s||l-d)>=0)break;r[f]=p,n[2*f]=d,n[2*f+1]=n[2*f-1],i[f]=i[f-1],o[f]=o[f-1],f-=1}r[f]=s,n[2*f]=l,n[2*f+1]=u,i[f]=c,o[f]=h}}function o(t,e,r,n,i,o){var a=r[t],s=n[2*t],l=n[2*t+1],u=i[t],c=o[t];r[t]=r[e],n[2*t]=n[2*e],n[2*t+1]=n[2*e+1],i[t]=i[e],o[t]=o[e],r[e]=a,n[2*e]=s,n[2*e+1]=l,i[e]=u,o[e]=c}function a(t,e,r,n,i,o){r[t]=r[e],n[2*t]=n[2*e],n[2*t+1]=n[2*e+1],i[t]=i[e],o[t]=o[e]}function s(t,e,r,n,i,o,a){var s=n[t],l=i[2*t],u=i[2*t+1],c=o[t],h=a[t];n[t]=n[e],i[2*t]=i[2*e],i[2*t+1]=i[2*e+1],o[t]=o[e],a[t]=a[e],n[e]=n[r],i[2*e]=i[2*r],i[2*e+1]=i[2*r+1],o[e]=o[r],a[e]=a[r],n[r]=s,i[2*r]=l,i[2*r+1]=u,o[r]=c,a[r]=h}function l(t,e,r,n,i,o,a,s,l,u,c){s[t]=s[e],l[2*t]=l[2*e],l[2*t+1]=l[2*e+1],u[t]=u[e],c[t]=c[e],s[e]=r,l[2*e]=n,l[2*e+1]=i,u[e]=o,c[e]=a}function u(t,e,r,n,i){return(r[t]-r[e]||n[2*e]-n[2*t]||i[t]-i[e])<0}function c(t,e,r,n,i,o,a,s){return(e-o[t]||a[2*t]-r||i-s[t])<0}function h(t,e,r,n,p,d){var 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if(!c(it,R,j,D,O,r,n,p))for(;;){if(c(E,R,j,D,O,r,n,p)){c(E,L,z,I,P,r,n,p)?(s(it,T,E,r,n,p,d),++T,--E):(o(it,E,r,n,p,d),--E);break}if(--E<it)break}l(t,T-1,L,z,I,P,C,r,n,p,d),l(e,E+1,R,j,D,O,F,r,n,p,d),f>=T-2-t?i(t,T-2,r,n,p,d):h(t,T-2,r,n,p,d),f>=e-(E+2)?i(E+2,e,r,n,p,d):h(E+2,e,r,n,p,d),f>=E-T?i(T,E,r,n,p,d):h(T,E,r,n,p,d)}e.exports=n;var f=32},{}],200:[function(t,e,r){"use strict";function n(t,e,r,n,i,o,a,s){for(var l=r,u=r;n>u;++u){var c=t[2*u],h=t[2*u+1],f=e[u];c>=i&&a>=c&&h>=o&&s>=h&&(u===l?l+=1:(t[2*u]=t[2*l],t[2*u+1]=t[2*l+1],e[u]=e[l],t[2*l]=c,t[2*l+1]=h,e[l]=f,l+=1))}return l}function i(t,e,r){this.pixelSize=t,this.offset=e,this.count=r}function o(t,e,r,o){function l(i,o,a,s,u,c){var h=.5*a,f=s+1,p=u-s;r[_]=p,x[_++]=c;for(var d=0;2>d;++d)for(var m=0;2>m;++m){var g=i+d*h,v=o+m*h,y=n(t,e,f,u,g,v,g+h,v+h);if(y!==f){if(y-f>=Math.max(.9*p,32)){var b=u+s>>>1;l(g,v,h,f,b,c+1),f=b}l(g,v,h,f,y,c+1),f=y}}}var u=t.length>>>1;if(1>u)return[];for(var c=1/0,h=1/0,f=-(1/0),p=-(1/0),d=0;u>d;++d){var m=t[2*d],g=t[2*d+1];c=Math.min(c,m),f=Math.max(f,m),h=Math.min(h,g),p=Math.max(p,g),e[d]=d}c===f&&(f+=1+Math.abs(f)),h===p&&(p+=1+Math.abs(f));var v=1/(f-c),y=1/(p-h),b=Math.max(f-c,p-h);o=o||[0,0,0,0],o[0]=c,o[1]=h,o[2]=f,o[3]=p;var x=a.mallocInt32(u),_=0;l(c,h,b,0,u,0),s(x,t,e,r,u);for(var w=[],M=0,k=u,_=u-1;_>=0;--_){t[2*_]=(t[2*_]-c)*v,t[2*_+1]=(t[2*_+1]-h)*y;var A=x[_];A!==M&&(w.push(new i(b*Math.pow(.5,A),_+1,k-(_+1))),k=_+1,M=A)}return w.push(new i(b*Math.pow(.5,A+1),0,k)),a.free(x),w}var a=t("typedarray-pool"),s=t("./lib/sort");e.exports=o},{"./lib/sort":199,"typedarray-pool":481}],201:[function(t,e,r){"use strict";function n(t,e,r,n,i,o){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.weightBuffer=n,this.shader=i,this.pickShader=o,this.scales=[],this.size=12,this.borderSize=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.bounds=[1/0,1/0,-(1/0),-(1/0)],this.pickOffset=0,this.points=null,this.xCoords=null}function i(t,e){var r=t.gl,i=a(r),s=a(r),l=a(r),u=o(r,c.pointVertex,c.pointFragment),h=o(r,c.pickVertex,c.pickFragment),f=new n(t,i,s,l,u,h);return f.update(e),t.addObject(f),f}var o=t("gl-shader"),a=t("gl-buffer"),s=t("binary-search-bounds"),l=t("snap-points-2d"),u=t("typedarray-pool"),c=t("./lib/shader");e.exports=i;var h=n.prototype;h.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.xCoords&&u.free(this.xCoords),this.plot.removeObject(this)},h.update=function(t){function e(e,r){return e in t?t[e]:r}t=t||{},this.size=e("size",12),this.color=e("color",[1,0,0,1]).slice(),this.borderSize=e("borderSize",1),this.borderColor=e("borderColor",[0,0,0,1]).slice(),this.xCoords&&u.free(this.xCoords);var r=t.positions,n=u.mallocFloat32(r.length),i=u.mallocInt32(r.length>>>1);n.set(r);var o=u.mallocFloat32(r.length);this.points=r,this.scales=l(n,i,o,this.bounds),this.offsetBuffer.update(n),this.pickBuffer.update(i),this.weightBuffer.update(o);for(var a=u.mallocFloat32(r.length>>>1),s=0,c=0;s<r.length;s+=2,++c)a[c]=n[s];u.free(i),u.free(n),u.free(o),this.xCoords=a,this.pointCount=r.length>>>1,this.pickOffset=0},h.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,o=this.scales,a=this.offsetBuffer,l=this.pickBuffer,u=this.bounds,c=this.size,h=this.borderSize,f=n.gl,p=n.pickPixelRatio,d=n.viewBox,m=n.dataBox;if(0===this.pointCount)return r;var g=u[2]-u[0],v=u[3]-u[1],y=m[2]-m[0],b=m[3]-m[1],x=(d[2]-d[0])*p/n.pixelRatio,_=(d[3]-d[1])*p/n.pixelRatio,w=Math.min(y/x,b/_);t[0]=2*g/y,t[4]=2*v/b,t[6]=2*(u[0]-m[0])/y-1,t[7]=2*(u[1]-m[1])/b-1,this.pickOffset=r,e[0]=255&r,e[1]=r>>8&255,e[2]=r>>16&255,e[3]=r>>24&255,i.bind(),i.uniforms.matrix=t,i.uniforms.color=this.color,i.uniforms.borderColor=this.borderColor,i.uniforms.pointSize=p*(c+h),i.uniforms.pickOffset=e,0===this.borderSize?i.uniforms.centerFraction=2:i.uniforms.centerFraction=c/(c+h+1.25),a.bind(),i.attributes.position.pointer(),l.bind(),i.attributes.pickId.pointer(f.UNSIGNED_BYTE);for(var M=this.xCoords,k=(m[0]-u[0]-w*c*p)/g,A=(m[2]-u[0]+w*c*p)/g,T=o.length-1;T>=0;--T){var E=o[T];if(!(E.pixelSize<w&&T>1)){var S=E.offset,L=E.count+S,z=s.ge(M,k,S,L-1),I=s.lt(M,A,z,L-1)+1;I>z&&f.drawArrays(f.POINTS,z,I-z)}}return r+this.pointCount}}(),h.draw=function(){var t=[1,0,0,0,1,0,0,0,1];return function(){var e=this.plot,r=this.shader,n=this.scales,i=this.offsetBuffer,o=this.bounds,a=this.size,l=this.borderSize,u=e.gl,c=e.pixelRatio,h=e.viewBox,f=e.dataBox;if(0!==this.pointCount){var p=o[2]-o[0],d=o[3]-o[1],m=f[2]-f[0],g=f[3]-f[1],v=h[2]-h[0],y=h[3]-h[1],b=Math.min(m/v,g/y);t[0]=2*p/m,t[4]=2*d/g,t[6]=2*(o[0]-f[0])/m-1,t[7]=2*(o[1]-f[1])/g-1,r.bind(),r.uniforms.matrix=t,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointSize=c*(a+l),r.uniforms.useWeight=1,0===this.borderSize?r.uniforms.centerFraction=2:r.uniforms.centerFraction=a/(a+l+1.25),i.bind(),r.attributes.position.pointer(),this.weightBuffer.bind(),r.attributes.weight.pointer();for(var x=this.xCoords,_=(f[0]-o[0]-b*a*c)/p,w=(f[2]-o[0]+b*a*c)/p,M=!0,k=n.length-1;k>=0;--k){var A=n[k];if(!(A.pixelSize<b&&k>1)){var T=A.offset,E=A.count+T,S=s.ge(x,_,T,E-1),L=s.lt(x,w,S,E-1)+1;L>S&&u.drawArrays(u.POINTS,S,L-S),M&&(M=!1,r.uniforms.useWeight=0)}}}}}(),h.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(n>r||r>=n+i)return null;var o=r-n,a=this.points;return{object:this,pointId:o,dataCoord:[a[2*o],a[2*o+1]]}}},{"./lib/shader":197,"binary-search-bounds":198,"gl-buffer":129,"gl-shader":208,"snap-points-2d":200,"typedarray-pool":481}],202:[function(t,e,r){"use strict";function n(t,e){var r=o[e];if(r||(r=o[e]={}),t in r)return r[t];for(var n=i(t,{textAlign:"center",textBaseline:"middle",lineHeight:1,font:e}),a=i(t,{triangles:!0,textAlign:"center",textBaseline:"middle",lineHeight:1,font:e}),s=[[1/0,1/0],[-(1/0),-(1/0)]],l=0;l<n.positions.length;++l)for(var u=n.positions[l],c=0;2>c;++c)s[0][c]=Math.min(s[0][c],u[c]),s[1][c]=Math.max(s[1][c],u[c]);return r[t]=[a,n,s]}var i=t("vectorize-text");e.exports=n;var o={}},{"vectorize-text":483}],203:[function(t,e,r){function n(t,e){var r=i(t,e),n=r.attributes;return n.position.location=0,n.color.location=1,n.glyph.location=2,n.id.location=3,r}var i=t("gl-shader"),o="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0]))   || \n     any(greaterThan(position, clipBounds[1])) ) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n    \n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}",a="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0])) || any(greaterThan(position, clipBounds[1]))) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n    \n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}",s="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(position, clipBounds[0]))   ||\n     any(greaterThan(position, clipBounds[1])) ) {\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n",l="precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(dataCoordinate, fragClipBounds[0]))   ||\n     any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\n    discard;\n  } else {\n    gl_FragColor = interpColor * opacity;\n  }\n}\n",u="precision mediump float;\n#define GLSLIFY 1\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if(any(lessThan(dataCoordinate, fragClipBounds[0]))   || \n     any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\n    discard;\n  } else {\n    gl_FragColor = vec4(pickGroup, pickId.bgr);\n  }\n}",c=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],h={vertex:o,fragment:l,attributes:c},f={vertex:a,fragment:l,attributes:c},p={vertex:s,fragment:l,attributes:c},d={vertex:o,fragment:u,attributes:c},m={vertex:a,fragment:u,attributes:c},g={vertex:s,fragment:u,attributes:c};r.createPerspective=function(t){return n(t,h)},r.createOrtho=function(t){return n(t,f)},r.createProject=function(t){return n(t,p)},r.createPickPerspective=function(t){return n(t,d)},r.createPickOrtho=function(t){return n(t,m)},r.createPickProject=function(t){return n(t,g)}},{"gl-shader":208}],204:[function(t,e,r){"use strict";function n(t,e){var r=t[0],n=t[1],i=t[2],o=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*o,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*o,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*o,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*o,t}function i(t,e,r,i){return n(i,i,r),n(i,i,e),n(i,i,t)}function o(t,e){this.index=t,this.dataCoordinate=this.position=e}function a(t,e,r,n,i,a,s,l,u,c,h,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=s,this.idBuffer=l,this.vao=u,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=h,this.pickProjectShader=f,this.points=[],this._selectResult=new o(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.dirty=!0}function s(t){return t[0]=t[1]=t[2]=0,t}function l(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function u(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function c(t){for(var e=L,r=0;2>r;++r)for(var n=0;3>n;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}function h(t,e,r,n,o){var a,h=e.axesProject,f=e.gl,p=t.uniforms,d=r.model||x,m=r.view||x,g=r.projection||x,y=e.axesBounds,b=c(e.clipBounds);a=e.axes?e.axes.lastCubeProps.axis:[1,1,1],w[0]=2/f.drawingBufferWidth,w[1]=2/f.drawingBufferHeight,t.bind(),p.view=m,p.projection=g,p.screenSize=w,p.highlightId=e.highlightId,p.highlightScale=e.highlightScale,p.clipBounds=b,p.pickGroup=e.pickId/255,p.pixelRatio=e.pixelRatio;for(var _=0;3>_;++_)if(h[_]&&e.projectOpacity[_]<1===n){p.scale=e.projectScale[_],p.opacity=e.projectOpacity[_];for(var L=E,z=0;16>z;++z)L[z]=0;for(var z=0;4>z;++z)L[5*z]=1;L[5*_]=0,a[_]<0?L[12+_]=y[0][_]:L[12+_]=y[1][_],v(L,d,L),p.model=L;var I=(_+1)%3,P=(_+2)%3,C=s(M),R=s(k);C[I]=1,R[P]=1;var j=i(g,m,d,l(A,C)),D=i(g,m,d,l(T,R));if(Math.abs(j[1])>Math.abs(D[1])){var O=j;j=D,D=O,O=C,C=R,R=O;var F=I;I=P,P=F}j[0]<0&&(C[I]=-1),D[1]>0&&(R[P]=-1);for(var N=0,B=0,z=0;4>z;++z)N+=Math.pow(d[4*I+z],2),B+=Math.pow(d[4*P+z],2);C[I]/=Math.sqrt(N),R[P]/=Math.sqrt(B),p.axes[0]=C,p.axes[1]=R,p.fragClipBounds[0]=u(S,b[0],_,-1e8),p.fragClipBounds[1]=u(S,b[1],_,1e8),e.vao.draw(f.TRIANGLES,e.vertexCount),e.lineWidth>0&&(f.lineWidth(e.lineWidth),e.vao.draw(f.LINES,e.lineVertexCount,e.vertexCount))}}function f(t,e,r,n,i,o){var a=r.gl;if(r.vao.bind(),i===r.opacity<1||o){t.bind();var s=t.uniforms;s.model=n.model||x,s.view=n.view||x,s.projection=n.projection||x,w[0]=2/a.drawingBufferWidth,w[1]=2/a.drawingBufferHeight,s.screenSize=w,s.highlightId=r.highlightId,s.highlightScale=r.highlightScale,s.fragClipBounds=P,s.clipBounds=r.axes.bounds,s.opacity=r.opacity,s.pickGroup=r.pickId/255,s.pixelRatio=r.pixelRatio,r.vao.draw(a.TRIANGLES,r.vertexCount),r.lineWidth>0&&(a.lineWidth(r.lineWidth),r.vao.draw(a.LINES,r.lineVertexCount,r.vertexCount))}h(e,r,n,i,o),r.vao.unbind()}function p(t){var e=t.gl,r=y.createPerspective(e),n=y.createOrtho(e),i=y.createProject(e),o=y.createPickPerspective(e),s=y.createPickOrtho(e),l=y.createPickProject(e),u=d(e),c=d(e),h=d(e),f=d(e),p=m(e,[{buffer:u,size:3,type:e.FLOAT},{buffer:c,size:4,type:e.FLOAT},{buffer:h,size:2,type:e.FLOAT},{buffer:f,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),g=new a(e,r,n,i,u,c,h,f,p,o,s,l);return g.update(t),g}var d=t("gl-buffer"),m=t("gl-vao"),g=t("typedarray-pool"),v=t("gl-mat4/multiply"),y=t("./lib/shaders"),b=t("./lib/glyphs"),x=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];e.exports=p;var _=a.prototype;_.pickSlots=1,_.setPickBase=function(t){this.pickId=t},_.isTransparent=function(){if(this.opacity<1)return!0;for(var t=0;3>t;++t)if(this.axesProject[t]&&this.projectOpacity[t]<1)return!0;return!1},_.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;3>t;++t)if(this.axesProject[t]&&this.projectOpacity[t]>=1)return!0;return!1};var w=[0,0],M=[0,0,0],k=[0,0,0],A=[0,0,0,1],T=[0,0,0,1],E=x.slice(),S=[0,0,0],L=[[0,0,0],[0,0,0]],z=[-1e8,-1e8,-1e8],I=[1e8,1e8,1e8],P=[z,I];_.draw=function(t){var e=this.useOrtho?this.orthoShader:this.shader;f(e,this.projectShader,this,t,!1,!1)},_.drawTransparent=function(t){var e=this.useOrtho?this.orthoShader:this.shader;f(e,this.projectShader,this,t,!0,!1)},_.drawPick=function(t){var e=this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader;f(e,this.pickProjectShader,this,t,!1,!0)},_.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||0>e)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;3>i;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},_.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},_.update=function(t){if(t=t||{},"perspective"in t&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if("projectOpacity"in t)if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{var r=+t.projectOpacity;this.projectOpacity=[r,r,r]}"opacity"in t&&(this.opacity=t.opacity),this.dirty=!0;var n=t.position;if(n){var i=t.font||"normal",o=t.alignment||[0,0],a=[1/0,1/0,1/0],s=[-(1/0),-(1/0),-(1/0)],l=t.glyph,u=t.color,c=t.size,h=t.angle,f=t.lineColor,p=0,d=0,m=0,v=n.length;t:for(var y=0;v>y;++y){for(var x=n[y],_=0;3>_;++_)if(isNaN(x[_])||!isFinite(x[_]))continue t;var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b("\u25cf",i);var M=w[0],k=w[1],A=w[2];d+=3*M.cells.length,m+=2*k.edges.length}var T=d+m,E=g.mallocFloat(3*T),S=g.mallocFloat(4*T),L=g.mallocFloat(2*T),z=g.mallocUint32(T),I=[0,o[1]],P=0,C=d,R=[0,0,0,1],j=[0,0,0,1],D=Array.isArray(u)&&Array.isArray(u[0]),O=Array.isArray(f)&&Array.isArray(f[0]);t:for(var y=0;v>y;++y){for(var x=n[y],_=0;3>_;++_){if(isNaN(x[_])||!isFinite(x[_])){p+=1;continue t}s[_]=Math.max(s[_],x[_]),a[_]=Math.min(a[_],x[_])}var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b("\u25cf",i);var M=w[0],k=w[1],A=w[2];if(Array.isArray(u)){var F;if(F=D?u[y]:u,3===F.length){for(var _=0;3>_;++_)R[_]=F[_];R[3]=1}else if(4===F.length)for(var _=0;4>_;++_)R[_]=F[_]}else R[0]=R[1]=R[2]=0,R[3]=1;if(Array.isArray(f)){var F;if(F=O?f[y]:f,3===F.length){for(var _=0;3>_;++_)j[_]=F[_];j[_]=1}else if(4===F.length)for(var _=0;4>_;++_)j[_]=F[_]}else j[0]=j[1]=j[2]=0,j[3]=1;var N=.5;Array.isArray(c)?N=+c[y]:c?N=+c:this.useOrtho&&(N=12);var B=0;Array.isArray(h)?B=+h[y]:h&&(B=+h);for(var U=Math.cos(B),V=Math.sin(B),x=n[y],_=0;3>_;++_)s[_]=Math.max(s[_],x[_]),a[_]=Math.min(a[_],x[_]);o[0]<0?I[0]=o[0]*(1+A[1][0]):o[0]>0&&(I[0]=-o[0]*(1+A[0][0]));for(var q=M.cells,G=M.positions,_=0;_<q.length;++_)for(var H=q[_],X=0;3>X;++X){for(var Y=0;3>Y;++Y)E[3*P+Y]=x[Y];for(var Y=0;4>Y;++Y)S[4*P+Y]=R[Y];z[P]=p;var W=G[H[X]];L[2*P]=N*(U*W[0]-V*W[1]+I[0]),L[2*P+1]=N*(V*W[0]+U*W[1]+I[1]),P+=1}for(var q=k.edges,G=k.positions,_=0;_<q.length;++_)for(var H=q[_],X=0;2>X;++X){for(var Y=0;3>Y;++Y)E[3*C+Y]=x[Y];for(var Y=0;4>Y;++Y)S[4*C+Y]=j[Y];z[C]=p;var W=G[H[X]];L[2*C]=N*(U*W[0]-V*W[1]+I[0]),L[2*C+1]=N*(V*W[0]+U*W[1]+I[1]),C+=1}p+=1}this.vertexCount=d,this.lineVertexCount=m,this.pointBuffer.update(E),this.colorBuffer.update(S),this.glyphBuffer.update(L),this.idBuffer.update(new Uint32Array(z)),g.free(E),g.free(S),g.free(L),g.free(z),this.bounds=[a,s],this.points=n,this.pointCount=n.length}},_.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{"./lib/glyphs":202,"./lib/shaders":203,"gl-buffer":129,"gl-mat4/multiply":150,"gl-vao":237,"typedarray-pool":481}],205:[function(t,e,r){"use strict";r.boxVertex="precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 vertex;\n\nuniform vec2 cornerA, cornerB;\n\nvoid main() {\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\n}\n",r.boxFragment="precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\n\nvoid main() {\n  gl_FragColor = color;\n}\n"},{}],206:[function(t,e,r){"use strict";function n(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-(1/0),-(1/0)],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}function i(t,e){var r=t.gl,i=a(r,[0,0,0,1,1,0,1,1]),l=o(r,s.boxVertex,s.boxFragment),u=new n(t,i,l);return u.update(e),t.addOverlay(u),u}var o=t("gl-shader"),a=t("gl-buffer"),s=t("./lib/shaders");e.exports=i;var l=n.prototype;l.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),o=this.borderColor,a=t.box,s=t.screenBox,l=t.dataBox,u=t.viewBox,c=t.pixelRatio,h=(e[0]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],f=(e[1]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1],p=(e[2]-l[0])*(u[2]-u[0])/(l[2]-l[0])+u[0],d=(e[3]-l[1])*(u[3]-u[1])/(l[3]-l[1])+u[1];if(h=Math.max(h,u[0]),f=Math.max(f,u[1]),p=Math.min(p,u[2]),d=Math.min(d,u[3]),!(h>p||f>d)){a.bind();var m=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(a.drawBox(0,0,m,f,i),a.drawBox(0,f,h,d,i),a.drawBox(0,d,m,g,i),a.drawBox(p,f,m,d,i)),this.innerFill&&a.drawBox(h,f,p,d,n),r>0){var v=r*c;a.drawBox(h-v,f-v,p+v,f+v,o),a.drawBox(h-v,d-v,p+v,d+v,o),a.drawBox(h-v,f-v,h+v,d+v,o),a.drawBox(p-v,f-v,p+v,d+v,o)}}}},l.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},l.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{"./lib/shaders":205,"gl-buffer":129,"gl-shader":208}],207:[function(t,e,r){"use strict";function n(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function i(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}function o(t,e){var r=a(t,e),n=s.mallocUint8(e[0]*e[1]*4);return new i(t,r,n)}e.exports=o;var a=t("gl-fbo"),s=t("typedarray-pool"),l=t("ndarray"),u=t("bit-twiddle").nextPow2,c=t("cwise/lib/wrapper")({args:["array",{offset:[0,0,1],array:0},{offset:[0,0,2],array:0},{offset:[0,0,3],array:0},"scalar","scalar","index"],pre:{body:"{this_closestD2=1e8,this_closestX=-1,this_closestY=-1}",args:[],thisVars:["this_closestD2","this_closestX","this_closestY"],localVars:[]},body:{body:"{if(255>_inline_46_arg0_||255>_inline_46_arg1_||255>_inline_46_arg2_||255>_inline_46_arg3_){var _inline_46_l=_inline_46_arg4_-_inline_46_arg6_[0],_inline_46_a=_inline_46_arg5_-_inline_46_arg6_[1],_inline_46_f=_inline_46_l*_inline_46_l+_inline_46_a*_inline_46_a;_inline_46_f<this_closestD2&&(this_closestD2=_inline_46_f,this_closestX=_inline_46_arg6_[0],this_closestY=_inline_46_arg6_[1])}}",args:[{name:"_inline_46_arg0_",lvalue:!1,rvalue:!0,count:1},{name:"_inline_46_arg1_",lvalue:!1,rvalue:!0,count:1},{name:"_inline_46_arg2_",lvalue:!1,rvalue:!0,count:1},{name:"_inline_46_arg3_",lvalue:!1,rvalue:!0,count:1},{name:"_inline_46_arg4_",lvalue:!1,rvalue:!0,count:1},{name:"_inline_46_arg5_",lvalue:!1,rvalue:!0,count:1},{name:"_inline_46_arg6_",lvalue:!1,rvalue:!0,count:4}],thisVars:["this_closestD2","this_closestX","this_closestY"],localVars:["_inline_46_a","_inline_46_f","_inline_46_l"]},post:{body:"{return[this_closestX,this_closestY,this_closestD2]}",args:[],thisVars:["this_closestD2","this_closestX","this_closestY"],localVars:[]},debug:!1,funcName:"cwise",blockSize:64}),h=i.prototype;Object.defineProperty(h,"shape",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){s.free(this.buffer);for(var n=this.buffer=s.mallocUint8(u(r*e*4)),i=0;r*e*4>i;++i)n[i]=255}return t}}}),h.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},h.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},h.query=function(t,e,r){if(!this.gl)return null;var i=this.fbo.shape.slice();t=0|t,e=0|e,"number"!=typeof r&&(r=1);var o=0|Math.min(Math.max(t-r,0),i[0]),a=0|Math.min(Math.max(t+r,0),i[0]),s=0|Math.min(Math.max(e-r,0),i[1]),u=0|Math.min(Math.max(e+r,0),i[1]);if(o>=a||s>=u)return null;var h=[a-o,u-s],f=l(this.buffer,[h[0],h[1],4],[4,4*i[0],1],4*(o+i[0]*s)),p=c(f.hi(h[0],h[1],1),r,r),d=p[0],m=p[1];if(0>d||Math.pow(this.radius,2)<p[2])return null;var g=f.get(d,m,0),v=f.get(d,m,1),y=f.get(d,m,2),b=f.get(d,m,3);return new n(d+o|0,m+s|0,g,[v,y,b],Math.sqrt(p[2]))},h.dispose=function(){this.gl&&(this.fbo.dispose(),s.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{"bit-twiddle":51,"cwise/lib/wrapper":123,"gl-fbo":134,ndarray:456,"typedarray-pool":481}],208:[function(t,e,r){"use strict";function n(t){this.gl=t,this._vref=this._fref=this._relink=this.vertShader=this.fragShader=this.program=this.attributes=this.uniforms=this.types=null}function i(t,e){return t.name<e.name?-1:1}function o(t,e,r,i,o){var a=new n(t);return a.update(e,r,i,o),a}var a=t("./lib/create-uniforms"),s=t("./lib/create-attributes"),l=t("./lib/reflect"),u=t("./lib/shader-cache"),c=t("./lib/runtime-reflect"),h=t("./lib/GLError"),f=n.prototype;f.bind=function(){this.program||this._relink(),this.gl.useProgram(this.program)},f.dispose=function(){this._fref&&this._fref.dispose(),this._vref&&this._vref.dispose(),this.attributes=this.types=this.vertShader=this.fragShader=this.program=this._relink=this._fref=this._vref=null},f.update=function(t,e,r,n){function o(){p.program=u.program(d,p._vref,p._fref,x,_);for(var t=0;t<r.length;++t)S[t]=d.getUniformLocation(p.program,r[t].name)}if(!e||1===arguments.length){var f=t;t=f.vertex,e=f.fragment,r=f.uniforms,n=f.attributes}var p=this,d=p.gl,m=p._vref;p._vref=u.shader(d,d.VERTEX_SHADER,t),m&&m.dispose(),p.vertShader=p._vref.shader;var g=this._fref;if(p._fref=u.shader(d,d.FRAGMENT_SHADER,e),g&&g.dispose(),p.fragShader=p._fref.shader,!r||!n){var 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a=t("dup"),s=t("cwise-compiler"),l={},u={},c={body:"",args:[],thisVars:[],localVars:[]},h=s({args:["array","array","array"],pre:c,post:c,body:{args:[{name:"out",lvalue:!0,rvalue:!1,count:1},{name:"left",lvalue:!1,rvalue:!0,count:1},{name:"right",lvalue:!1,rvalue:!0,count:1}],body:"out=0.5*(left-right)",thisVars:[],localVars:[]},funcName:"cdiff"}),f=s({args:["array"],pre:c,post:c,body:{args:[{name:"out",lvalue:!0,rvalue:!1,count:1}],body:"out=0",thisVars:[],localVars:[]},funcName:"zero"})},{"cwise-compiler":120,dup:126}],230:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("./doConvert.js");e.exports=function(t,e){for(var r=[],o=t,a=1;o instanceof Array;)r.push(o.length),a*=o.length,o=o[0];return 0===r.length?n():(e||(e=n(new Float64Array(a),r)),i(e,t),e)}},{"./doConvert.js":231,ndarray:456}],231:[function(t,e,r){e.exports=t("cwise-compiler")({args:["array","scalar","index"],pre:{body:"{}",args:[],thisVars:[],localVars:[]},body:{body:"{\nvar 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}],232:[function(t,e,r){"use strict";function n(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}function i(t){var e=x([y({colormap:t,nshades:D,format:"rgba"}).map(function(t){return[t[0],t[1],t[2],255*t[3]]})]);return b.divseq(e,255),e}function o(t,e,r,i,o,a,s,l,u,c,h,f,p,d){this.gl=t,this.shape=e,this.bounds=r,this.intensityBounds=[],this._shader=i,this._pickShader=o,this._coordinateBuffer=a,this._vao=s,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=f,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new n([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=p,this._dynamicVAO=d,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[_(v.mallocFloat(1024),[0,0]),_(v.mallocFloat(1024),[0,0]),_(v.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.snapToData=!1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.dirty=!0}function a(t,e){var r,n,i,o=e.axes&&e.axes.lastCubeProps.axis||F,a=e.showSurface,s=e.showContour;for(r=0;3>r;++r)for(a=a||e.surfaceProject[r],n=0;3>n;++n)s=s||e.contourProject[r][n];for(r=0;3>r;++r){var l=N.projections[r];for(n=0;16>n;++n)l[n]=0;for(n=0;4>n;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(o[r]>0)][r],M(l,t.model,l);var u=N.clipBounds[r];for(i=0;2>i;++i)for(n=0;3>n;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return N.showSurface=a,N.showContour=s,N}function s(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=B;n.model=t.model||C,n.view=t.view||C,n.projection=t.projection||C,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.contourColor=this.contourColor[0],n.inverseModel=k(n.inverseModel,n.model);for(var i=0;2>i;++i)for(var o=n.clipBounds[i],s=0;3>s;++s)o[s]=Math.min(Math.max(this.clipBounds[i][s],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=V;var l=U;for(M(l,n.view,n.model),M(l,n.projection,l),k(l,l),i=0;3>i;++i)n.eyePosition[i]=l[12+i]/l[15];var u=l[15];for(i=0;3>i;++i)u+=this.lightPosition[i]*l[4*i+3];for(i=0;3>i;++i){var c=l[12+i];for(s=0;3>s;++s)c+=l[4*s+i]*this.lightPosition[s];n.lightPosition[i]=c/u}var h=a(n,this);if(h.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;3>i;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=h.projections[i],this._shader.uniforms.clipBounds=h.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(h.showContour&&!e){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var p=this._contourVAO;for(p.bind(),i=0;3>i;++i)for(f.uniforms.permutation=j[i],r.lineWidth(this.contourWidth[i]),s=0;s<this.contourLevels[i].length;++s)this._contourCounts[i][s]&&(s===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==s&&s-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),f.uniforms.height=this.contourLevels[i][s],p.draw(r.LINES,this._contourCounts[i][s],this._contourOffsets[i][s]));for(i=0;3>i;++i)for(f.uniforms.model=h.projections[i],f.uniforms.clipBounds=h.clipBounds[i],s=0;3>s;++s)if(this.contourProject[i][s]){f.uniforms.permutation=j[s],r.lineWidth(this.contourWidth[s]);for(var d=0;d<this.contourLevels[s].length;++d)d===this.highlightLevel[s]?(f.uniforms.contourColor=this.highlightColor[s],f.uniforms.contourTint=this.highlightTint[s]):0!==d&&d-1!==this.highlightLevel[s]||(f.uniforms.contourColor=this.contourColor[s],f.uniforms.contourTint=this.contourTint[s]),f.uniforms.height=this.contourLevels[s][d],p.draw(r.LINES,this._contourCounts[s][d],this._contourOffsets[s][d])}for(p=this._dynamicVAO,p.bind(),i=0;3>i;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=j[i],r.lineWidth(this.dynamicWidth[i]),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],p.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),s=0;3>s;++s)this.contourProject[s][i]&&(f.uniforms.model=h.projections[s],f.uniforms.clipBounds=h.clipBounds[s],p.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));p.unbind()}}function l(t,e){var r=e.shape.slice(),n=t.shape.slice();b.assign(t.lo(1,1).hi(r[0],r[1]),e),b.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),b.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),b.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),b.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))}function u(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function c(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function h(t){if(Array.isArray(t)){if(Array.isArray(t))return[c(t[0]),c(t[1]),c(t[2])];var e=c(t);return[e.slice(),e.slice(),e.slice()]}}function f(t){var e=t.gl,r=S(e),n=z(e),i=L(e),a=I(e),s=d(e),l=m(e,[{buffer:s,size:4,stride:P,offset:0},{buffer:s,size:3,stride:P,offset:16},{buffer:s,size:3,stride:P,offset:28}]),u=d(e),c=m(e,[{buffer:u,size:4,stride:20,offset:0},{buffer:u,size:1,stride:20,offset:16}]),h=d(e),f=m(e,[{buffer:h,size:2,type:e.FLOAT}]),p=g(e,1,D,e.RGBA,e.UNSIGNED_BYTE);p.minFilter=e.LINEAR,p.magFilter=e.LINEAR;var v=new o(e,[0,0],[[0,0,0],[0,0,0]],r,n,s,l,p,i,a,u,c,h,f),y={levels:[[],[],[]]};for(var b in t)y[b]=t[b];return y.colormap=y.colormap||"jet",v.update(y),v}e.exports=f;var p=t("bit-twiddle"),d=t("gl-buffer"),m=t("gl-vao"),g=t("gl-texture2d"),v=t("typedarray-pool"),y=t("colormap"),b=t("ndarray-ops"),x=t("ndarray-pack"),_=t("ndarray"),w=t("surface-nets"),M=t("gl-mat4/multiply"),k=t("gl-mat4/invert"),A=t("binary-search-bounds"),T=t("ndarray-gradient"),E=t("./lib/shaders"),S=E.createShader,L=E.createContourShader,z=E.createPickShader,I=E.createPickContourShader,P=40,C=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],R=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],j=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];!function(){for(var t=0;3>t;++t){var e=j[t],r=(t+1)%3,n=(t+2)%3;e[r+0]=1,e[n+3]=1,e[t+6]=1}}();var D=265,O=o.prototype;O.isTransparent=function(){return this.opacity<1},O.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;3>t;++t)if(this._contourCounts[t].length>0||this._dynamicCounts[t]>0)return!0;return!1},O.pickSlots=1,O.setPickBase=function(t){this.pickId=t};var F=[0,0,0],N={showSurface:!1,showContour:!1,projections:[C.slice(),C.slice(),C.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]},B={model:C,view:C,projection:C,inverseModel:C.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1},U=C.slice(),V=[1,0,0,0,1,0,0,0,1];O.draw=function(t){return s.call(this,t,!1)},O.drawTransparent=function(t){return s.call(this,t,!0)};var q={model:C,view:C,projection:C,inverseModel:C,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};O.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=q;r.model=t.model||C,r.view=t.view||C,r.projection=t.projection||C,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.permutation=V;for(var n=0;2>n;++n)for(var i=r.clipBounds[n],o=0;3>o;++o)i[o]=Math.min(Math.max(this.clipBounds[n][o],-1e8),1e8);var s=a(r,this);if(s.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;3>n;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=s.projections[n],this._pickShader.uniforms.clipBounds=s.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(s.showContour){var l=this._contourPickShader;l.bind(),l.uniforms=r;var u=this._contourVAO;for(u.bind(),o=0;3>o;++o)for(e.lineWidth(this.contourWidth[o]),l.uniforms.permutation=j[o],n=0;n<this.contourLevels[o].length;++n)this._contourCounts[o][n]&&(l.uniforms.height=this.contourLevels[o][n],u.draw(e.LINES,this._contourCounts[o][n],this._contourOffsets[o][n]));for(n=0;3>n;++n)for(l.uniforms.model=s.projections[n],l.uniforms.clipBounds=s.clipBounds[n],o=0;3>o;++o)if(this.contourProject[n][o]){l.uniforms.permutation=j[o],e.lineWidth(this.contourWidth[o]);for(var c=0;c<this.contourLevels[o].length;++c)this._contourCounts[o][c]&&(l.uniforms.height=this.contourLevels[o][c],u.draw(e.LINES,this._contourCounts[o][c],this._contourOffsets[o][c]))}u.unbind()}},O.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),o=n-i,a=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(a),l=a-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;2>c;++c)for(var h=c?o:1-o,f=0;2>f;++f)for(var p=f?l:1-l,d=i+c,m=s+f,g=h*p,v=0;3>v;++v)u[v]+=this._field[v].get(d,m)*g;for(var y=this._pickResult.level,b=0;3>b;++b)if(y[b]=A.le(this.contourLevels[b],u[b]),y[b]<0)this.contourLevels[b].length>0&&(y[b]=0);else if(y[b]<this.contourLevels[b].length-1){var x=this.contourLevels[b][y[b]],_=this.contourLevels[b][y[b]+1];Math.abs(x-u[b])>Math.abs(_-u[b])&&(y[b]+=1)}for(r.index[0]=.5>o?i:i+1,r.index[1]=.5>l?s:s+1,r.uv[0]=n/e[0],r.uv[1]=a/e[1],v=0;3>v;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},O.update=function(t){t=t||{},this.dirty=!0,"contourWidth"in t&&(this.contourWidth=u(t.contourWidth,Number)),"showContour"in t&&(this.showContour=u(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=u(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=h(t.contourColor)),"contourProject"in t&&(this.contourProject=u(t.contourProject,function(t){return u(t,Boolean)})),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=h(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=u(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=u(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"colorBounds"in t&&(this.colorBounds=t.colorBounds);var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var n=(e.shape[0]+2)*(e.shape[1]+2);n>this._field[2].data.length&&(v.freeFloat(this._field[2].data),this._field[2].data=v.mallocFloat(p.nextPow2(n))),this._field[2]=_(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),l(this._field[2],e),this.shape=e.shape.slice();for(var o=this.shape,a=0;2>a;++a)this._field[2].size>this._field[a].data.length&&(v.freeFloat(this._field[a].data),this._field[a].data=v.mallocFloat(this._field[2].size)),this._field[a]=_(this._field[a].data,[o[0]+2,o[1]+2]);if(t.coords){var s=t.coords;if(!Array.isArray(s)||3!==s.length)throw new Error("gl-surface: invalid coordinates for x/y");for(a=0;2>a;++a){var c=s[a];for(y=0;2>y;++y)if(c.shape[y]!==o[y])throw new Error("gl-surface: coords have incorrect shape");l(this._field[a],c)}}else if(t.ticks){var f=t.ticks;if(!Array.isArray(f)||2!==f.length)throw new Error("gl-surface: invalid ticks");for(a=0;2>a;++a){var d=f[a];if((Array.isArray(d)||d.length)&&(d=_(d)),d.shape[0]!==o[a])throw new Error("gl-surface: invalid tick length");var m=_(d.data,o);m.stride[a]=d.stride[0],m.stride[1^a]=0,l(this._field[a],m)}}else{for(a=0;2>a;++a){var g=[0,0];g[a]=1,this._field[a]=_(this._field[a].data,[o[0]+2,o[1]+2],g,0)}this._field[0].set(0,0,0);for(var y=0;y<o[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(o[0]+1,0,o[0]-1),this._field[1].set(0,0,0),y=0;y<o[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,o[1]+1,o[1]-1)}var b=this._field,x=_(v.mallocFloat(3*b[2].size*2),[3,o[0]+2,o[1]+2,2]);for(a=0;3>a;++a)T(x.pick(a),b[a],"mirror");var M=_(v.mallocFloat(3*b[2].size),[o[0]+2,o[1]+2,3]);for(a=0;a<o[0]+2;++a)for(y=0;y<o[1]+2;++y){var k=x.get(0,a,y,0),A=x.get(0,a,y,1),E=x.get(1,a,y,0),S=x.get(1,a,y,1),L=x.get(2,a,y,0),z=x.get(2,a,y,1),I=E*z-S*L,P=L*A-z*k,C=k*S-A*E,j=Math.sqrt(I*I+P*P+C*C);1e-8>j?(j=Math.max(Math.abs(I),Math.abs(P),Math.abs(C)),1e-8>j?(C=1,P=I=0,j=1):j=1/j):j=1/Math.sqrt(j),M.set(a,y,0,I*j),M.set(a,y,1,P*j),M.set(a,y,2,C*j)}v.free(x.data);var D=[1/0,1/0,1/0],O=[-(1/0),-(1/0),-(1/0)],F=1/0,N=-(1/0),B=(o[0]-1)*(o[1]-1)*6,U=v.mallocFloat(p.nextPow2(10*B)),V=0,q=0;for(a=0;a<o[0]-1;++a)t:for(y=0;y<o[1]-1;++y){for(var G=0;2>G;++G)for(var H=0;2>H;++H)for(var X=0;3>X;++X){var Y=this._field[X].get(1+a+G,1+y+H);if(isNaN(Y)||!isFinite(Y))continue t}for(X=0;6>X;++X){var W=a+R[X][0],Z=y+R[X][1],Q=this._field[0].get(W+1,Z+1),K=this._field[1].get(W+1,Z+1);Y=this._field[2].get(W+1,Z+1);var J=Y;I=M.get(W+1,Z+1,0),P=M.get(W+1,Z+1,1),C=M.get(W+1,Z+1,2),t.intensity&&(J=t.intensity.get(W,Z)),U[V++]=W,U[V++]=Z,U[V++]=Q,U[V++]=K,U[V++]=Y,U[V++]=0,U[V++]=J,U[V++]=I,U[V++]=P,U[V++]=C,D[0]=Math.min(D[0],Q),D[1]=Math.min(D[1],K),D[2]=Math.min(D[2],Y),F=Math.min(F,J),O[0]=Math.max(O[0],Q),O[1]=Math.max(O[1],K),O[2]=Math.max(O[2],Y),N=Math.max(N,J),q+=1}}for(t.intensityBounds&&(F=+t.intensityBounds[0],N=+t.intensityBounds[1]),a=6;V>a;a+=10)U[a]=(U[a]-F)/(N-F);this._vertexCount=q,this._coordinateBuffer.update(U.subarray(0,V)),v.freeFloat(U),v.free(M.data),this.bounds=[D,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===F&&this.intensityBounds[1]===N||(r=!0),this.intensityBounds=[F,N]}if("levels"in t){var $=t.levels;for($=Array.isArray($[0])?$.slice():[[],[],$],a=0;3>a;++a)$[a]=$[a].slice(),$.sort(function(t,e){return t-e});t:for(a=0;3>a;++a){if($[a].length!==this.contourLevels[a].length){r=!0;break}for(y=0;y<$[a].length;++y)if($[a][y]!==this.contourLevels[a][y]){r=!0;break t}}this.contourLevels=$}if(r){b=this._field,o=this.shape;for(var tt=[],et=0;3>et;++et){$=this.contourLevels[et];var rt=[],nt=[],it=[0,0,0];for(a=0;a<$.length;++a){var ot=w(this._field[et],$[a]);rt.push(tt.length/5|0),q=0;t:for(y=0;y<ot.cells.length;++y){var at=ot.cells[y];for(X=0;2>X;++X){var st=ot.positions[at[X]],lt=st[0],ut=0|Math.floor(lt),ct=lt-ut,ht=st[1],ft=0|Math.floor(ht),pt=ht-ft,dt=!1;e:for(var mt=0;3>mt;++mt){it[mt]=0;var gt=(et+mt+1)%3;for(G=0;2>G;++G){var vt=G?ct:1-ct;for(W=0|Math.min(Math.max(ut+G,0),o[0]),H=0;2>H;++H){var yt=H?pt:1-pt;if(Z=0|Math.min(Math.max(ft+H,0),o[1]),Y=2>mt?this._field[gt].get(W,Z):(this.intensity.get(W,Z)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(Y)||isNaN(Y)){dt=!0;break e}var bt=vt*yt;it[mt]+=bt*Y}}}if(dt){if(X>0){for(var xt=0;5>xt;++xt)tt.pop();q-=1}continue t}tt.push(it[0],it[1],st[0],st[1],it[2]),q+=1}}nt.push(q)}this._contourOffsets[et]=rt,this._contourCounts[et]=nt}var _t=v.mallocFloat(tt.length);for(a=0;a<tt.length;++a)_t[a]=tt[a];this._contourBuffer.update(_t),v.freeFloat(_t)}t.colormap&&this._colorMap.setPixels(i(t.colormap))},O.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;3>t;++t)v.freeFloat(this._field[t].data)},O.highlight=function(t){if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(var e=0;3>e;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;var r;if(r=this.snapToData?t.dataCoordinate:t.position,this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,o=v.mallocFloat(12*i[0]*i[1]),a=0;3>a;++a)if(this.enableDynamic[a]){this.dynamicLevel[a]=r[a];var s=(a+1)%3,l=(a+2)%3,u=this._field[a],c=this._field[s],h=this._field[l],f=(this.intensity,w(u,r[a])),p=f.cells,d=f.positions;for(this._dynamicOffsets[a]=n,e=0;e<p.length;++e)for(var m=p[e],g=0;2>g;++g){var y=d[m[g]],b=+y[0],x=0|b,_=0|Math.min(x+1,i[0]),M=b-x,k=1-M,A=+y[1],T=0|A,E=0|Math.min(T+1,i[1]),S=A-T,L=1-S,z=k*L,I=k*S,P=M*L,C=M*S,R=z*c.get(x,T)+I*c.get(x,E)+P*c.get(_,T)+C*c.get(_,E),j=z*h.get(x,T)+I*h.get(x,E)+P*h.get(_,T)+C*h.get(_,E);if(isNaN(R)||isNaN(j)){g&&(n-=1);break}o[2*n+0]=R,o[2*n+1]=j,n+=1}this._dynamicCounts[a]=n-this._dynamicOffsets[a]}else this.dynamicLevel[a]=NaN,this._dynamicCounts[a]=0;this._dynamicBuffer.update(o.subarray(0,2*n)),v.freeFloat(o)}}},{"./lib/shaders":227,"binary-search-bounds":228,"bit-twiddle":51,colormap:111,"gl-buffer":129,"gl-mat4/invert":148,"gl-mat4/multiply":150,"gl-texture2d":233,"gl-vao":237,ndarray:456,"ndarray-gradient":229,"ndarray-ops":455,"ndarray-pack":230,"surface-nets":475,"typedarray-pool":481}],233:[function(t,e,r){"use strict";function n(t){g=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],v=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],y=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function i(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(0>e||e>i||0>r||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function o(t,e,r,n,i,o){this.gl=t,this.handle=e,this.format=i,this.type=o,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var a=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return a._wrapS},set:function(t){return a.wrapS=t}},{get:function(){return a._wrapT},set:function(t){return a.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return a._shape[0]},set:function(t){return a.width=t}},{get:function(){return a._shape[1]},set:function(t){return a.height=t}}]),this._shapeVector=l}function a(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function s(t,e,r,n,i,o,s,l){var u=l.dtype,c=l.shape.slice();if(c.length<2||c.length>3)throw new Error("gl-texture2d: Invalid ndarray, must be 2d or 3d");var h=0,f=0,g=a(c,l.stride.slice());"float32"===u?h=t.FLOAT:"float64"===u?(h=t.FLOAT,g=!1,u="float32"):"uint8"===u?h=t.UNSIGNED_BYTE:(h=t.UNSIGNED_BYTE,g=!1,u="uint8");var v=1;if(2===c.length)f=t.LUMINANCE,c=[c[0],c[1],1],l=p(l.data,c,[l.stride[0],l.stride[1],1],l.offset);else{if(3!==c.length)throw new Error("gl-texture2d: Invalid shape for texture");if(1===c[2])f=t.ALPHA;else if(2===c[2])f=t.LUMINANCE_ALPHA;else if(3===c[2])f=t.RGB;else{if(4!==c[2])throw new Error("gl-texture2d: Invalid shape for pixel coords");f=t.RGBA}v=c[2]}if(f!==t.LUMINANCE&&f!==t.ALPHA||i!==t.LUMINANCE&&i!==t.ALPHA||(f=i),f!==i)throw new Error("gl-texture2d: Incompatible texture format for setPixels");var y=l.size,x=s.indexOf(n)<0;if(x&&s.push(n),h===o&&g)0===l.offset&&l.data.length===y?x?t.texImage2D(t.TEXTURE_2D,n,i,c[0],c[1],0,i,o,l.data):t.texSubImage2D(t.TEXTURE_2D,n,e,r,c[0],c[1],i,o,l.data):x?t.texImage2D(t.TEXTURE_2D,n,i,c[0],c[1],0,i,o,l.data.subarray(l.offset,l.offset+y)):t.texSubImage2D(t.TEXTURE_2D,n,e,r,c[0],c[1],i,o,l.data.subarray(l.offset,l.offset+y));else{var _;_=o===t.FLOAT?m.mallocFloat32(y):m.mallocUint8(y);var w=p(_,c,[c[2],c[2]*c[0],1]);h===t.FLOAT&&o===t.UNSIGNED_BYTE?b(w,l):d.assign(w,l),x?t.texImage2D(t.TEXTURE_2D,n,i,c[0],c[1],0,i,o,_.subarray(0,y)):t.texSubImage2D(t.TEXTURE_2D,n,e,r,c[0],c[1],i,o,_.subarray(0,y)),o===t.FLOAT?m.freeFloat32(_):m.freeUint8(_)}}function l(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function u(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(0>e||e>a||0>r||r>a)throw new Error("gl-texture2d: Invalid texture shape");if(i===t.FLOAT&&!t.getExtension("OES_texture_float"))throw new Error("gl-texture2d: Floating point textures not supported on this platform");var s=l(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new o(t,s,e,r,n,i)}function c(t,e,r,n){var i=l(t);return t.texImage2D(t.TEXTURE_2D,0,r,r,n,e),new o(t,i,0|e.width,0|e.height,r,n)}function h(t,e){var r=e.dtype,n=e.shape.slice(),i=t.getParameter(t.MAX_TEXTURE_SIZE);if(n[0]<0||n[0]>i||n[1]<0||n[1]>i)throw new Error("gl-texture2d: Invalid texture size");var s=a(n,e.stride.slice()),u=0;"float32"===r?u=t.FLOAT:"float64"===r?(u=t.FLOAT,s=!1,r="float32"):"uint8"===r?u=t.UNSIGNED_BYTE:(u=t.UNSIGNED_BYTE,s=!1,r="uint8");var c=0;if(2===n.length)c=t.LUMINANCE,n=[n[0],n[1],1],e=p(e.data,n,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==n.length)throw new Error("gl-texture2d: Invalid shape for texture");if(1===n[2])c=t.ALPHA;else if(2===n[2])c=t.LUMINANCE_ALPHA;else if(3===n[2])c=t.RGB;else{if(4!==n[2])throw new Error("gl-texture2d: Invalid shape for pixel coords");c=t.RGBA}}u!==t.FLOAT||t.getExtension("OES_texture_float")||(u=t.UNSIGNED_BYTE,s=!1);var h,f,g=e.size;if(s)h=0===e.offset&&e.data.length===g?e.data:e.data.subarray(e.offset,e.offset+g);else{var v=[n[2],n[2]*n[0],1];f=m.malloc(g,r);var y=p(f,n,v,0);"float32"!==r&&"float64"!==r||u!==t.UNSIGNED_BYTE?d.assign(y,e):b(y,e),h=f.subarray(0,g)}var x=l(t);return t.texImage2D(t.TEXTURE_2D,0,c,n[0],n[1],0,c,u,h),s||m.free(f),new o(t,x,n[0],n[1],c,u)}function f(t){if(arguments.length<=1)throw new 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t.serialize()})}),childLayerIds:this.childLayers.map(function(t){return t.id})}},n.prototype.createFilter=function(){this.filter||(this.filter=s(this.layer.filter))};var f={lastIntegerZoom:1/0,lastIntegerZoomTime:0,lastZoom:0};n.prototype.recalculateStyleLayers=function(){for(var t=0;t<this.childLayers.length;t++)this.childLayers[t].recalculate(this.zoom,f)},n.prototype.populatePaintArrays=function(t,e,r,n,i){for(var o=0;o<this.childLayers.length;o++)for(var a=this.childLayers[o],s=this.arrayGroups[t],l=n.index;l<s.length;l++){var u=s[l],c=u.layoutVertexArray.length,h=u.paintVertexArrays[a.id];h.resize(c);for(var f=this.paintAttributes[t][a.id].attributes,p=0;p<f.length;p++)for(var d=f[p],m=d.getValue(a,e,r),g=d.multiplier||1,v=d.components||1,y=l===n.index?i:0,b=y;c>b;b++)for(var x=h.get(b),_=0;v>_;_++){var w=v>1?d.name+_:d.name;x[w]=m[_]*g}}},n.VertexArrayType=function(t){return new h({members:t,alignment:4})},n.ElementArrayType=function(t){return new h({members:[{type:"Uint16",name:"vertices",components:t||3}]})}},{"../util/struct_array":364,"../util/util":366,"./array_group":252,"./bucket/circle_bucket":254,"./bucket/fill_bucket":255,"./bucket/line_bucket":256,"./bucket/symbol_bucket":257,"./buffer_group":259,"feature-filter":370}],254:[function(t,e,r){"use strict";function n(){i.apply(this,arguments)}var i=t("../bucket"),o=t("../../util/util"),a=t("../load_geometry"),s=i.EXTENT;e.exports=n,n.prototype=o.inherit(i,{}),n.prototype.addCircleVertex=function(t,e,r,n,i){return t.emplaceBack(2*e+(n+1)/2,2*r+(i+1)/2)},n.prototype.programInterfaces={circle:{layoutVertexArrayType:new i.VertexArrayType([{name:"a_pos",components:2,type:"Int16"}]),elementArrayType:new i.ElementArrayType,paintAttributes:[{name:"a_color",components:4,type:"Uint8",getValue:function(t,e,r){return t.getPaintValue("circle-color",e,r)},multiplier:255,paintProperty:"circle-color"},{name:"a_radius",components:1,type:"Uint16",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue("circle-radius",e,r)]},multiplier:10,paintProperty:"circle-radius"},{name:"a_blur",components:1,type:"Uint16",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue("circle-blur",e,r)]},multiplier:10,paintProperty:"circle-blur"},{name:"a_opacity",components:1,type:"Uint16",isLayerConstant:!1,getValue:function(t,e,r){return[t.getPaintValue("circle-opacity",e,r)]},multiplier:255,paintProperty:"circle-opacity"}]}},n.prototype.addFeature=function(t){for(var e={zoom:this.zoom},r=a(t),n=this.prepareArrayGroup("circle",0),i=n.layoutVertexArray.length,o=0;o<r.length;o++)for(var l=0;l<r[o].length;l++){var u=r[o][l].x,c=r[o][l].y;if(!(0>u||u>=s||0>c||c>=s)){var h=this.prepareArrayGroup("circle",4),f=h.layoutVertexArray,p=this.addCircleVertex(f,u,c,-1,-1);this.addCircleVertex(f,u,c,1,-1),this.addCircleVertex(f,u,c,1,1),this.addCircleVertex(f,u,c,-1,1),h.elementArray.emplaceBack(p,p+1,p+2),h.elementArray.emplaceBack(p,p+3,p+2)}}this.populatePaintArrays("circle",e,t.properties,n,i)}},{"../../util/util":366,"../bucket":253,"../load_geometry":261}],255:[function(t,e,r){"use strict";function n(){i.apply(this,arguments)}var i=t("../bucket"),o=t("../../util/util"),a=t("../load_geometry"),s=t("earcut"),l=t("../../util/classify_rings"),u=500;e.exports=n,n.prototype=o.inherit(i,{}),n.prototype.programInterfaces={fill:{layoutVertexArrayType:new i.VertexArrayType([{name:"a_pos",components:2,type:"Int16"}]),elementArrayType:new i.ElementArrayType(1),elementArrayType2:new i.ElementArrayType(2),paintAttributes:[{name:"a_color",components:4,type:"Uint8",getValue:function(t,e,r){return t.getPaintValue("fill-color",e,r)},multiplier:255,paintProperty:"fill-color"},{name:"a_outline_color",components:4,type:"Uint8",getValue:function(t,e,r){return t.getPaintValue("fill-outline-color",e,r)},multiplier:255,paintProperty:"fill-outline-color"},{name:"a_opacity",components:1,type:"Uint8",getValue:function(t,e,r){return[t.getPaintValue("fill-opacity",e,r)]},multiplier:255,paintProperty:"fill-opacity"}]}},n.prototype.addFeature=function(t){for(var e=a(t),r=l(e,u),n=this.prepareArrayGroup("fill",0),i=n.layoutVertexArray.length,o=0;o<r.length;o++)this.addPolygon(r[o]);this.populatePaintArrays("fill",{zoom:this.zoom},t.properties,n,i)},n.prototype.addPolygon=function(t){for(var e=0,r=0;r<t.length;r++)e+=t[r].length;for(var n=this.prepareArrayGroup("fill",e),i=[],o=[],a=n.layoutVertexArray.length,l=0;l<t.length;l++){var u=t[l];l>0&&o.push(i.length/2);for(var c=0;c<u.length;c++){var h=u[c],f=n.layoutVertexArray.emplaceBack(h.x,h.y);c>=1&&n.elementArray2.emplaceBack(f-1,f),i.push(h.x),i.push(h.y)}}for(var p=s(i,o),d=0;d<p.length;d++)n.elementArray.emplaceBack(p[d]+a)}},{"../../util/classify_rings":354,"../../util/util":366,"../bucket":253,"../load_geometry":261,earcut:369}],256:[function(t,e,r){"use strict";function n(){i.apply(this,arguments)}var i=t("../bucket"),o=t("../../util/util"),a=t("../load_geometry"),s=i.EXTENT,l=63,u=Math.cos(37.5*(Math.PI/180)),c=15,h=15,f=.5,p=Math.pow(2,h-1)/f;e.exports=n,n.prototype=o.inherit(i,{}),n.prototype.addLineVertex=function(t,e,r,n,i,o,a){return t.emplaceBack(e.x<<1|n,e.y<<1|i,Math.round(l*r.x)+128,Math.round(l*r.y)+128,(0===o?0:0>o?-1:1)+1|(a*f&63)<<2,a*f>>6)},n.prototype.programInterfaces={line:{layoutVertexArrayType:new i.VertexArrayType([{name:"a_pos",components:2,type:"Int16"},{name:"a_data",components:4,type:"Uint8"}]),elementArrayType:new i.ElementArrayType}},n.prototype.addFeature=function(t){for(var e=a(t,h),r=0;r<e.length;r++)this.addLine(e[r],this.layer.layout["line-join"],this.layer.layout["line-cap"],this.layer.layout["line-miter-limit"],this.layer.layout["line-round-limit"])},n.prototype.addLine=function(t,e,r,n,i){for(var o=t.length;o>2&&t[o-1].equals(t[o-2]);)o--;if(!(t.length<2)){"bevel"===e&&(n=1.05);var a=c*(s/(512*this.overscaling)),l=t[0],h=t[o-1],f=l.equals(h);if(this.prepareArrayGroup("line",10*o),2!==o||!f){this.distance=0;var p,d,m,g,v,y,b,x=r,_=f?"butt":r,w=!0;this.e1=this.e2=this.e3=-1,f&&(p=t[o-2],v=l.sub(p)._unit()._perp());for(var M=0;o>M;M++)if(m=f&&M===o-1?t[1]:t[M+1],!m||!t[M].equals(m)){v&&(g=v),p&&(d=p),p=t[M],v=m?m.sub(p)._unit()._perp():g,g=g||v;var k=g.add(v)._unit(),A=k.x*v.x+k.y*v.y,T=1/A,E=u>A&&d&&m;if(E&&M>0){var S=p.dist(d);if(S>2*a){var L=p.sub(p.sub(d)._mult(a/S)._round());this.distance+=L.dist(d),this.addCurrentVertex(L,this.distance,g.mult(1),0,0,!1),d=L}}var z=d&&m,I=z?e:m?x:_;if(z&&"round"===I&&(i>T?I="miter":2>=T&&(I="fakeround")),"miter"===I&&T>n&&(I="bevel"),"bevel"===I&&(T>2&&(I="flipbevel"),n>T&&(I="miter")),d&&(this.distance+=p.dist(d)),"miter"===I)k._mult(T),this.addCurrentVertex(p,this.distance,k,0,0,!1);else if("flipbevel"===I){if(T>100)k=v.clone();else{var P=g.x*v.y-g.y*v.x>0?-1:1,C=T*g.add(v).mag()/g.sub(v).mag();k._perp()._mult(C*P)}this.addCurrentVertex(p,this.distance,k,0,0,!1),this.addCurrentVertex(p,this.distance,k.mult(-1),0,0,!1)}else if("bevel"===I||"fakeround"===I){var R=g.x*v.y-g.y*v.x>0,j=-Math.sqrt(T*T-1);if(R?(b=0,y=j):(y=0,b=j),w||this.addCurrentVertex(p,this.distance,g,y,b,!1),"fakeround"===I){for(var D,O=Math.floor(8*(.5-(A-.5))),F=0;O>F;F++)D=v.mult((F+1)/(O+1))._add(g)._unit(),this.addPieSliceVertex(p,this.distance,D,R);this.addPieSliceVertex(p,this.distance,k,R);for(var N=O-1;N>=0;N--)D=g.mult((N+1)/(O+1))._add(v)._unit(),this.addPieSliceVertex(p,this.distance,D,R)}m&&this.addCurrentVertex(p,this.distance,v,-y,-b,!1)}else"butt"===I?(w||this.addCurrentVertex(p,this.distance,g,0,0,!1),m&&this.addCurrentVertex(p,this.distance,v,0,0,!1)):"square"===I?(w||(this.addCurrentVertex(p,this.distance,g,1,1,!1),this.e1=this.e2=-1),m&&this.addCurrentVertex(p,this.distance,v,-1,-1,!1)):"round"===I&&(w||(this.addCurrentVertex(p,this.distance,g,0,0,!1),this.addCurrentVertex(p,this.distance,g,1,1,!0),this.e1=this.e2=-1),m&&(this.addCurrentVertex(p,this.distance,v,-1,-1,!0),this.addCurrentVertex(p,this.distance,v,0,0,!1)));if(E&&o-1>M){var B=p.dist(m);if(B>2*a){var U=p.add(m.sub(p)._mult(a/B)._round());this.distance+=U.dist(p),this.addCurrentVertex(U,this.distance,v.mult(1),0,0,!1),p=U}}w=!1}}}},n.prototype.addCurrentVertex=function(t,e,r,n,i,o){var a,s=o?1:0,l=this.arrayGroups.line[this.arrayGroups.line.length-1],u=l.layoutVertexArray,c=l.elementArray;a=r.clone(),n&&a._sub(r.perp()._mult(n)),this.e3=this.addLineVertex(u,t,a,s,0,n,e),this.e1>=0&&this.e2>=0&&c.emplaceBack(this.e1,this.e2,this.e3),this.e1=this.e2,this.e2=this.e3,a=r.mult(-1),i&&a._sub(r.perp()._mult(i)),this.e3=this.addLineVertex(u,t,a,s,1,-i,e),this.e1>=0&&this.e2>=0&&c.emplaceBack(this.e1,this.e2,this.e3),this.e1=this.e2,this.e2=this.e3,e>p/2&&(this.distance=0,this.addCurrentVertex(t,this.distance,r,n,i,o))},n.prototype.addPieSliceVertex=function(t,e,r,n){var i=n?1:0;r=r.mult(n?-1:1);var o=this.arrayGroups.line[this.arrayGroups.line.length-1],a=o.layoutVertexArray,s=o.elementArray;
this.e3=this.addLineVertex(a,t,r,0,i,0,e),this.e1>=0&&this.e2>=0&&s.emplaceBack(this.e1,this.e2,this.e3),n?this.e2=this.e3:this.e1=this.e3}},{"../../util/util":366,"../bucket":253,"../load_geometry":261}],257:[function(t,e,r){"use strict";function n(t){a.apply(this,arguments),this.showCollisionBoxes=t.showCollisionBoxes,this.overscaling=t.overscaling,this.collisionBoxArray=t.collisionBoxArray,this.symbolQuadsArray=t.symbolQuadsArray,this.symbolInstancesArray=t.symbolInstancesArray,this.sdfIcons=t.sdfIcons,this.iconsNeedLinear=t.iconsNeedLinear,this.adjustedTextSize=t.adjustedTextSize,this.adjustedIconSize=t.adjustedIconSize,this.fontstack=t.fontstack}function i(t,e,r,n,i,o,a,s,l,u,c){return t.emplaceBack(e,r,Math.round(64*n),Math.round(64*i),o/4,a/4,10*(u||0),c,10*(s||0),10*Math.min(l||25,25))}var o=t("point-geometry"),a=t("../bucket"),s=t("../../symbol/anchor"),l=t("../../symbol/get_anchors"),u=t("../../util/token"),c=t("../../symbol/quads"),h=t("../../symbol/shaping"),f=t("../../symbol/resolve_text"),p=t("../../symbol/mergelines"),d=t("../../symbol/clip_line"),m=t("../../util/util"),g=t("../load_geometry"),v=t("../../symbol/collision_feature"),y=h.shapeText,b=h.shapeIcon,x=c.getGlyphQuads,_=c.getIconQuads,w=a.EXTENT;e.exports=n,n.prototype=m.inherit(a,{}),n.prototype.serialize=function(){var t=a.prototype.serialize.apply(this);return t.sdfIcons=this.sdfIcons,t.iconsNeedLinear=this.iconsNeedLinear,t.adjustedTextSize=this.adjustedTextSize,t.adjustedIconSize=this.adjustedIconSize,t.fontstack=this.fontstack,t};var M=new a.VertexArrayType([{name:"a_pos",components:2,type:"Int16"},{name:"a_offset",components:2,type:"Int16"},{name:"a_texture_pos",components:2,type:"Uint16"},{name:"a_data",components:4,type:"Uint8"}]),k=new a.ElementArrayType;n.prototype.addCollisionBoxVertex=function(t,e,r,n,i){return t.emplaceBack(e.x,e.y,Math.round(r.x),Math.round(r.y),10*n,10*i)},n.prototype.programInterfaces={glyph:{layoutVertexArrayType:M,elementArrayType:k},icon:{layoutVertexArrayType:M,elementArrayType:k},collisionBox:{layoutVertexArrayType:new a.VertexArrayType([{name:"a_pos",components:2,type:"Int16"},{name:"a_extrude",components:2,type:"Int16"},{name:"a_data",components:2,type:"Uint8"}])}},n.prototype.populateArrays=function(t,e,r){var n={lastIntegerZoom:1/0,lastIntegerZoomTime:0,lastZoom:0};this.adjustedTextMaxSize=this.layer.getLayoutValue("text-size",{zoom:18,zoomHistory:n}),this.adjustedTextSize=this.layer.getLayoutValue("text-size",{zoom:this.zoom+1,zoomHistory:n}),this.adjustedIconMaxSize=this.layer.getLayoutValue("icon-size",{zoom:18,zoomHistory:n}),this.adjustedIconSize=this.layer.getLayoutValue("icon-size",{zoom:this.zoom+1,zoomHistory:n});var i=512*this.overscaling;this.tilePixelRatio=w/i,this.compareText={},this.iconsNeedLinear=!1,this.symbolInstancesStartIndex=this.symbolInstancesArray.length;var o=this.layer.layout,a=this.features,s=this.textFeatures,l=.5,c=.5;switch(o["text-anchor"]){case"right":case"top-right":case"bottom-right":l=1;break;case"left":case"top-left":case"bottom-left":l=0}switch(o["text-anchor"]){case"bottom":case"bottom-right":case"bottom-left":c=1;break;case"top":case"top-right":case"top-left":c=0}for(var h="right"===o["text-justify"]?1:"left"===o["text-justify"]?0:.5,f=24,d=o["text-line-height"]*f,v="line"!==o["symbol-placement"]?o["text-max-width"]*f:0,x=o["text-letter-spacing"]*f,_=[o["text-offset"][0]*f,o["text-offset"][1]*f],M=this.fontstack=o["text-font"].join(","),k=[],A=0;A<a.length;A++)k.push(g(a[A]));if("line"===o["symbol-placement"]){var T=p(a,s,k);k=T.geometries,a=T.features,s=T.textFeatures}for(var E,S,L=0;L<a.length;L++)if(k[L]){if(E=s[L]?y(s[L],e[M],v,d,l,c,h,x,_):null,o["icon-image"]){var z=u(a[L].properties,o["icon-image"]),I=r[z];S=b(I,o),I&&(void 0===this.sdfIcons?this.sdfIcons=I.sdf:this.sdfIcons!==I.sdf&&m.warnOnce("Style sheet warning: Cannot mix SDF and non-SDF icons in one buffer"),1!==I.pixelRatio?this.iconsNeedLinear=!0:0===o["icon-rotate"]&&this.layer.isLayoutValueFeatureConstant("icon-rotate")||(this.iconsNeedLinear=!0))}else S=null;(E||S)&&this.addFeature(k[L],E,S,a[L])}this.symbolInstancesEndIndex=this.symbolInstancesArray.length,this.placeFeatures(t,this.showCollisionBoxes),this.trimArrays()},n.prototype.addFeature=function(t,e,r,n){var i=this.layer.layout,o=24,a=this.adjustedTextSize/o,u=void 0!==this.adjustedTextMaxSize?this.adjustedTextMaxSize:this.adjustedTextSize,c=this.tilePixelRatio*a,h=this.tilePixelRatio*u/o,f=this.tilePixelRatio*this.adjustedIconSize,p=this.tilePixelRatio*i["symbol-spacing"],m=i["symbol-avoid-edges"],g=i["text-padding"]*this.tilePixelRatio,v=i["icon-padding"]*this.tilePixelRatio,y=i["text-max-angle"]/180*Math.PI,b="map"===i["text-rotation-alignment"]&&"line"===i["symbol-placement"],x="map"===i["icon-rotation-alignment"]&&"line"===i["symbol-placement"],_=i["text-allow-overlap"]||i["icon-allow-overlap"]||i["text-ignore-placement"]||i["icon-ignore-placement"],M="line"===i["symbol-placement"],k=p/2;M&&(t=d(t,0,0,w,w));for(var A=0;A<t.length;A++){var T,E=t[A];T=M?l(E,p,y,e,r,o,h,this.overscaling,w):[new s(E[0].x,E[0].y,0)];for(var S=0,L=T.length;L>S;S++){var z=T[S];if(!(e&&M&&this.anchorIsTooClose(e.text,k,z))){var I=!(z.x<0||z.x>w||z.y<0||z.y>w);if(!m||I){var P=I||_;this.addSymbolInstance(z,E,e,r,this.layer,P,this.symbolInstancesArray.length,this.collisionBoxArray,n.index,this.sourceLayerIndex,this.index,c,g,b,f,v,x,{zoom:this.zoom},n.properties)}}}}},n.prototype.anchorIsTooClose=function(t,e,r){var n=this.compareText;if(t in n){for(var i=n[t],o=i.length-1;o>=0;o--)if(r.dist(i[o])<e)return!0}else n[t]=[];return n[t].push(r),!1},n.prototype.placeFeatures=function(t,e){this.recalculateStyleLayers(),this.createArrays();var r=this.layer.layout,n=t.maxScale,i="map"===r["text-rotation-alignment"]&&"line"===r["symbol-placement"],o="map"===r["icon-rotation-alignment"]&&"line"===r["symbol-placement"],a=r["text-allow-overlap"]||r["icon-allow-overlap"]||r["text-ignore-placement"]||r["icon-ignore-placement"];if(a){var s=this.symbolInstancesArray.toArray(this.symbolInstancesStartIndex,this.symbolInstancesEndIndex),l=t.angle,u=Math.sin(l),c=Math.cos(l);this.sortedSymbolInstances=s.sort(function(t,e){var r=u*t.anchorPointX+c*t.anchorPointY|0,n=u*e.anchorPointX+c*e.anchorPointY|0;return r-n||e.index-t.index})}for(var h=this.symbolInstancesStartIndex;h<this.symbolInstancesEndIndex;h++){var f=this.sortedSymbolInstances?this.sortedSymbolInstances[h-this.symbolInstancesStartIndex]:this.symbolInstancesArray.get(h),p={boxStartIndex:f.textBoxStartIndex,boxEndIndex:f.textBoxEndIndex},d={boxStartIndex:f.iconBoxStartIndex,boxEndIndex:f.iconBoxEndIndex},m=!(f.textBoxStartIndex===f.textBoxEndIndex),g=!(f.iconBoxStartIndex===f.iconBoxEndIndex),v=r["text-optional"]||!m,y=r["icon-optional"]||!g,b=m?t.placeCollisionFeature(p,r["text-allow-overlap"],r["symbol-avoid-edges"]):t.minScale,x=g?t.placeCollisionFeature(d,r["icon-allow-overlap"],r["symbol-avoid-edges"]):t.minScale;v||y?!y&&b?b=Math.max(x,b):!v&&x&&(x=Math.max(x,b)):x=b=Math.max(x,b),m&&(t.insertCollisionFeature(p,b,r["text-ignore-placement"]),n>=b&&this.addSymbols("glyph",f.glyphQuadStartIndex,f.glyphQuadEndIndex,b,r["text-keep-upright"],i,t.angle)),g&&(t.insertCollisionFeature(d,x,r["icon-ignore-placement"]),n>=x&&this.addSymbols("icon",f.iconQuadStartIndex,f.iconQuadEndIndex,x,r["icon-keep-upright"],o,t.angle))}e&&this.addToDebugBuffers(t)},n.prototype.addSymbols=function(t,e,r,n,o,a,s){for(var l=this.prepareArrayGroup(t,4*(r-e)),u=l.elementArray,c=l.layoutVertexArray,h=this.zoom,f=Math.max(Math.log(n)/Math.LN2+h,0),p=e;r>p;p++){var d=this.symbolQuadsArray.get(p).SymbolQuad,m=(d.anchorAngle+s+Math.PI)%(2*Math.PI);if(!(o&&a&&(m<=Math.PI/2||m>3*Math.PI/2))){var g=d.tl,v=d.tr,y=d.bl,b=d.br,x=d.tex,_=d.anchorPoint,w=Math.max(h+Math.log(d.minScale)/Math.LN2,f),M=Math.min(h+Math.log(d.maxScale)/Math.LN2,25);if(!(w>=M)){w===f&&(w=0);var k=Math.round(d.glyphAngle/(2*Math.PI)*256),A=i(c,_.x,_.y,g.x,g.y,x.x,x.y,w,M,f,k);i(c,_.x,_.y,v.x,v.y,x.x+x.w,x.y,w,M,f,k),i(c,_.x,_.y,y.x,y.y,x.x,x.y+x.h,w,M,f,k),i(c,_.x,_.y,b.x,b.y,x.x+x.w,x.y+x.h,w,M,f,k),u.emplaceBack(A,A+1,A+2),u.emplaceBack(A+1,A+2,A+3)}}}},n.prototype.updateIcons=function(t){this.recalculateStyleLayers();var e=this.layer.layout["icon-image"];if(e)for(var r=0;r<this.features.length;r++){var n=u(this.features[r].properties,e);n&&(t[n]=!0)}},n.prototype.updateFont=function(t){this.recalculateStyleLayers();var e=this.layer.layout["text-font"],r=t[e]=t[e]||{};this.textFeatures=f(this.features,this.layer.layout,r)},n.prototype.addToDebugBuffers=function(t){for(var e=this.prepareArrayGroup("collisionBox",0),r=e.layoutVertexArray,n=-t.angle,i=t.yStretch,a=this.symbolInstancesStartIndex;a<this.symbolInstancesEndIndex;a++){var s=this.symbolInstancesArray.get(a);s.textCollisionFeature={boxStartIndex:s.textBoxStartIndex,boxEndIndex:s.textBoxEndIndex},s.iconCollisionFeature={boxStartIndex:s.iconBoxStartIndex,boxEndIndex:s.iconBoxEndIndex};for(var l=0;2>l;l++){var u=s[0===l?"textCollisionFeature":"iconCollisionFeature"];if(u)for(var c=u.boxStartIndex;c<u.boxEndIndex;c++){var h=this.collisionBoxArray.get(c),f=h.anchorPoint,p=new o(h.x1,h.y1*i)._rotate(n),d=new o(h.x2,h.y1*i)._rotate(n),m=new o(h.x1,h.y2*i)._rotate(n),g=new o(h.x2,h.y2*i)._rotate(n),v=Math.max(0,Math.min(25,this.zoom+Math.log(h.maxScale)/Math.LN2)),y=Math.max(0,Math.min(25,this.zoom+Math.log(h.placementScale)/Math.LN2));this.addCollisionBoxVertex(r,f,p,v,y),this.addCollisionBoxVertex(r,f,d,v,y),this.addCollisionBoxVertex(r,f,d,v,y),this.addCollisionBoxVertex(r,f,g,v,y),this.addCollisionBoxVertex(r,f,g,v,y),this.addCollisionBoxVertex(r,f,m,v,y),this.addCollisionBoxVertex(r,f,m,v,y),this.addCollisionBoxVertex(r,f,p,v,y)}}}},n.prototype.addSymbolInstance=function(t,e,r,n,i,o,a,s,l,u,c,h,f,p,d,m,g,y,b){var w,M,k,A,T,E,S,L;if(r&&(S=o?x(t,r,h,e,i,p):[],T=new v(s,e,t,l,u,c,r,h,f,p,!1)),w=this.symbolQuadsArray.length,S&&S.length)for(var z=0;z<S.length;z++)this.addSymbolQuad(S[z]);M=this.symbolQuadsArray.length;var I=T?T.boxStartIndex:this.collisionBoxArray.length,P=T?T.boxEndIndex:this.collisionBoxArray.length;n&&(L=o?_(t,n,d,e,i,g,r,y,b):[],E=new v(s,e,t,l,u,c,n,d,m,g,!0)),k=this.symbolQuadsArray.length,L&&1===L.length&&this.addSymbolQuad(L[0]),A=this.symbolQuadsArray.length;var C=E?E.boxStartIndex:this.collisionBoxArray.length,R=E?E.boxEndIndex:this.collisionBoxArray.length;return this.symbolInstancesArray.emplaceBack(I,P,C,R,w,M,k,A,t.x,t.y,a)},n.prototype.addSymbolQuad=function(t){return this.symbolQuadsArray.emplaceBack(t.anchorPoint.x,t.anchorPoint.y,t.tl.x,t.tl.y,t.tr.x,t.tr.y,t.bl.x,t.bl.y,t.br.x,t.br.y,t.tex.h,t.tex.w,t.tex.x,t.tex.y,t.anchorAngle,t.glyphAngle,t.maxScale,t.minScale)}},{"../../symbol/anchor":315,"../../symbol/clip_line":317,"../../symbol/collision_feature":319,"../../symbol/get_anchors":321,"../../symbol/mergelines":324,"../../symbol/quads":325,"../../symbol/resolve_text":326,"../../symbol/shaping":327,"../../util/token":365,"../../util/util":366,"../bucket":253,"../load_geometry":261,"point-geometry":424}],258:[function(t,e,r){"use strict";function n(t,e,r){this.arrayBuffer=t.arrayBuffer,this.length=t.length,this.attributes=e.members,this.itemSize=e.bytesPerElement,this.type=r,this.arrayType=e}e.exports=n,n.prototype.bind=function(t){var e=t[this.type];this.buffer?t.bindBuffer(e,this.buffer):(this.buffer=t.createBuffer(),t.bindBuffer(e,this.buffer),t.bufferData(e,this.arrayBuffer,t.STATIC_DRAW),this.arrayBuffer=null)};var i={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT"};n.prototype.setVertexAttribPointers=function(t,e){for(var r=0;r<this.attributes.length;r++){var n=this.attributes[r],o=e[n.name];void 0!==o&&t.vertexAttribPointer(o,n.components,t[i[n.type]],!1,this.arrayType.bytesPerElement,n.offset)}},n.prototype.destroy=function(t){this.buffer&&t.deleteBuffer(this.buffer)},n.BufferType={VERTEX:"ARRAY_BUFFER",ELEMENT:"ELEMENT_ARRAY_BUFFER"}},{}],259:[function(t,e,r){"use strict";function n(t,e){this.layoutVertexBuffer=new o(t.layoutVertexArray,e.layoutVertexArrayType,o.BufferType.VERTEX),t.elementArray&&(this.elementBuffer=new o(t.elementArray,e.elementArrayType,o.BufferType.ELEMENT));var r,n=this.vaos={};t.elementArray2&&(this.elementBuffer2=new o(t.elementArray2,e.elementArrayType2,o.BufferType.ELEMENT),r=this.secondVaos={}),this.paintVertexBuffers=i.mapObject(t.paintVertexArrays,function(i,s){return n[s]=new a,t.elementArray2&&(r[s]=new a),new o(i,e.paintVertexArrayTypes[s],o.BufferType.VERTEX)})}var i=t("../util/util"),o=t("./buffer"),a=t("../render/vertex_array_object");e.exports=n,n.prototype.destroy=function(t){this.layoutVertexBuffer.destroy(t),this.elementBuffer&&this.elementBuffer.destroy(t),this.elementBuffer2&&this.elementBuffer2.destroy(t);for(var e in this.paintVertexBuffers)this.paintVertexBuffers[e].destroy(t);for(var r in this.vaos)this.vaos[r].destroy(t);for(var n in this.secondVaos)this.secondVaos[n].destroy(t)}},{"../render/vertex_array_object":281,"../util/util":366,"./buffer":258}],260:[function(t,e,r){"use strict";function n(t,e,r){if(t.grid){var n=t,i=e;t=n.coord,e=n.overscaling,this.grid=new d(n.grid),this.featureIndexArray=new k(n.featureIndexArray),this.rawTileData=i,this.bucketLayerIDs=n.bucketLayerIDs}else this.grid=new d(h,16,0),this.featureIndexArray=new k;this.coord=t,this.overscaling=e,this.x=t.x,this.y=t.y,this.z=t.z-Math.log(e)/Math.LN2,this.setCollisionTile(r)}function i(t){return Math.sqrt(t[0]*t[0]+t[1]*t[1])}function o(t,e){return e-t}function a(t){return t["line-gap-width"]>0?t["line-gap-width"]+2*t["line-width"]:t["line-width"]}function s(t,e,r,n,i){if(!e[0]&&!e[1])return t;e=u.convert(e),"viewport"===r&&e._rotate(-n);for(var o=[],a=0;a<t.length;a++){for(var s=t[a],l=[],c=0;c<s.length;c++)l.push(s[c].sub(e._mult(i)));o.push(l)}return o}function l(t,e){for(var r=[],n=new u(0,0),i=0;i<t.length;i++){for(var o=t[i],a=[],s=0;s<o.length;s++){var l=o[s-1],c=o[s],h=o[s+1],f=0===s?n:c.sub(l)._unit()._perp(),p=s===o.length-1?n:h.sub(c)._unit()._perp(),d=f._add(p)._unit(),m=d.x*p.x+d.y*p.y;d._mult(1/m),a.push(d._mult(e)._add(c))}r.push(a)}return r}var u=t("point-geometry"),c=t("./load_geometry"),h=t("./bucket").EXTENT,f=t("feature-filter"),p=t("../util/struct_array"),d=t("grid-index"),m=t("../util/dictionary_coder"),g=t("vector-tile"),v=t("pbf"),y=t("../util/vectortile_to_geojson"),b=t("../util/util").arraysIntersect,x=t("../util/intersection_tests"),_=x.multiPolygonIntersectsBufferedMultiPoint,w=x.multiPolygonIntersectsMultiPolygon,M=x.multiPolygonIntersectsBufferedMultiLine,k=new p({members:[{type:"Uint32",name:"featureIndex"},{type:"Uint16",name:"sourceLayerIndex"},{type:"Uint16",name:"bucketIndex"}]});e.exports=n,n.prototype.insert=function(t,e,r,n){var i=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(e,r,n);for(var o=c(t),a=0;a<o.length;a++){for(var s=o[a],l=[1/0,1/0,-(1/0),-(1/0)],u=0;u<s.length;u++){var h=s[u];l[0]=Math.min(l[0],h.x),l[1]=Math.min(l[1],h.y),l[2]=Math.max(l[2],h.x),l[3]=Math.max(l[3],h.y)}this.grid.insert(i,l[0],l[1],l[2],l[3])}},n.prototype.setCollisionTile=function(t){this.collisionTile=t},n.prototype.serialize=function(){var t={coord:this.coord,overscaling:this.overscaling,grid:this.grid.toArrayBuffer(),featureIndexArray:this.featureIndexArray.serialize(),bucketLayerIDs:this.bucketLayerIDs};return{data:t,transferables:[t.grid,t.featureIndexArray.arrayBuffer]}},n.prototype.query=function(t,e){this.vtLayers||(this.vtLayers=new g.VectorTile(new v(new Uint8Array(this.rawTileData))).layers,this.sourceLayerCoder=new m(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"]));var r={},n=t.params||{},s=h/t.tileSize/t.scale,l=f(n.filter),c=0;for(var p in e){var d=e[p],y=d.paint,b=0;"line"===d.type?b=a(y)/2+Math.abs(y["line-offset"])+i(y["line-translate"]):"fill"===d.type?b=i(y["fill-translate"]):"circle"===d.type&&(b=y["circle-radius"]+i(y["circle-translate"])),c=Math.max(c,b*s)}for(var x=t.queryGeometry.map(function(t){return t.map(function(t){return new u(t.x,t.y)})}),_=1/0,w=1/0,M=-(1/0),k=-(1/0),A=0;A<x.length;A++)for(var T=x[A],E=0;E<T.length;E++){var S=T[E];_=Math.min(_,S.x),w=Math.min(w,S.y),M=Math.max(M,S.x),k=Math.max(k,S.y)}var L=this.grid.query(_-c,w-c,M+c,k+c);L.sort(o),this.filterMatching(r,L,this.featureIndexArray,x,l,n.layers,e,t.bearing,s);var z=this.collisionTile.queryRenderedSymbols(_,w,M,k,t.scale);return z.sort(),this.filterMatching(r,z,this.collisionTile.collisionBoxArray,x,l,n.layers,e,t.bearing,s),r},n.prototype.filterMatching=function(t,e,r,n,i,o,u,h,f){for(var p,d=0;d<e.length;d++){var m=e[d];if(m!==p){p=m;var g=r.get(m),v=this.bucketLayerIDs[g.bucketIndex];if(!o||b(o,v)){var x=this.sourceLayerCoder.decode(g.sourceLayerIndex),k=this.vtLayers[x],A=k.feature(g.featureIndex);if(i(A))for(var T=null,E=0;E<v.length;E++){var S=v[E];if(!(o&&o.indexOf(S)<0)){var L=u[S];if(L){var z;if("symbol"!==L.type){T||(T=c(A));var I=L.paint;if("line"===L.type){z=s(n,I["line-translate"],I["line-translate-anchor"],h,f);var P=a(I)/2*f;if(I["line-offset"]&&(T=l(T,I["line-offset"]*f)),!M(z,T,P))continue}else if("fill"===L.type){if(z=s(n,I["fill-translate"],I["fill-translate-anchor"],h,f),!w(z,T))continue}else if("circle"===L.type){z=s(n,I["circle-translate"],I["circle-translate-anchor"],h,f);var C=I["circle-radius"]*f;if(!_(z,T,C))continue}}var R=new y(A,this.z,this.x,this.y);R.layer=L.serialize({includeRefProperties:!0});var j=t[S];void 0===j&&(j=t[S]=[]),j.push(R)}}}}}}}},{"../util/dictionary_coder":356,"../util/intersection_tests":361,"../util/struct_array":364,"../util/util":366,"../util/vectortile_to_geojson":367,"./bucket":253,"./load_geometry":261,"feature-filter":370,"grid-index":392,pbf:422,"point-geometry":424,"vector-tile":434}],261:[function(t,e,r){"use strict";function n(t){return{min:-1*Math.pow(2,t-1),max:Math.pow(2,t-1)-1}}var i=t("../util/util"),o=t("./bucket").EXTENT,a={15:n(15),16:n(16)};e.exports=function(t,e){for(var r=a[e||16],n=o/t.extent,s=t.loadGeometry(),l=0;l<s.length;l++)for(var u=s[l],c=0;c<u.length;c++){var h=u[c];h.x=Math.round(h.x*n),h.y=Math.round(h.y*n),(h.x<r.min||h.x>r.max||h.y<r.min||h.y>r.max)&&i.warnOnce("Geometry exceeds allowed extent, reduce your vector tile buffer size")}return s}},{"../util/util":366,"./bucket":253}],262:[function(t,e,r){"use strict";function n(t,e,r){this.column=t,this.row=e,this.zoom=r}e.exports=n,n.prototype={clone:function(){return new n(this.column,this.row,this.zoom)},zoomTo:function(t){return this.clone()._zoomTo(t)},sub:function(t){return this.clone()._sub(t)},_zoomTo:function(t){var e=Math.pow(2,t-this.zoom);return this.column*=e,this.row*=e,this.zoom=t,this},_sub:function(t){return t=t.zoomTo(this.zoom),this.column-=t.column,this.row-=t.row,this}}},{}],263:[function(t,e,r){"use strict";function n(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")}e.exports=n;var i=t("../util/util").wrap;n.prototype.wrap=function(){return new n(i(this.lng,-180,180),this.lat)},n.prototype.toArray=function(){return[this.lng,this.lat]},n.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},n.convert=function(t){return t instanceof n?t:Array.isArray(t)?new n(t[0],t[1]):t}},{"../util/util":366}],264:[function(t,e,r){"use strict";function n(t,e){t&&(e?this.extend(t).extend(e):4===t.length?this.extend([t[0],t[1]]).extend([t[2],t[3]]):this.extend(t[0]).extend(t[1]))}e.exports=n;var i=t("./lng_lat");n.prototype={extend:function(t){var e,r,o=this._sw,a=this._ne;if(t instanceof i)e=t,r=t;else{if(!(t instanceof n))return t?this.extend(i.convert(t)||n.convert(t)):this;if(e=t._sw,r=t._ne,!e||!r)return this}return o||a?(o.lng=Math.min(e.lng,o.lng),o.lat=Math.min(e.lat,o.lat),a.lng=Math.max(r.lng,a.lng),a.lat=Math.max(r.lat,a.lat)):(this._sw=new i(e.lng,e.lat),this._ne=new i(r.lng,r.lat)),this},getCenter:function(){return new i((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},getSouthWest:function(){return this._sw},getNorthEast:function(){return this._ne},getNorthWest:function(){return new i(this.getWest(),this.getNorth())},getSouthEast:function(){return new i(this.getEast(),this.getSouth())},getWest:function(){return this._sw.lng},getSouth:function(){return this._sw.lat},getEast:function(){return this._ne.lng},getNorth:function(){return this._ne.lat},toArray:function(){return[this._sw.toArray(),this._ne.toArray()]},toString:function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"}},n.convert=function(t){return!t||t instanceof n?t:new n(t)}},{"./lng_lat":263}],265:[function(t,e,r){"use strict";function n(t,e){this.tileSize=512,this._minZoom=t||0,this._maxZoom=e||22,this.latRange=[-85.05113,85.05113],this.width=0,this.height=0,this._center=new i(0,0),this.zoom=0,this.angle=0,this._altitude=1.5,this._pitch=0,this._unmodified=!0}var i=t("./lng_lat"),o=t("point-geometry"),a=t("./coordinate"),s=t("../util/util").wrap,l=t("../util/interpolate"),u=t("../source/tile_coord"),c=t("../data/bucket").EXTENT,h=t("gl-matrix"),f=h.vec4,p=h.mat4,d=h.mat2;e.exports=n,n.prototype={get minZoom(){return this._minZoom},set minZoom(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},get maxZoom(){return this._maxZoom},set maxZoom(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},get worldSize(){return this.tileSize*this.scale},get centerPoint(){return this.size._div(2)},get size(){return new o(this.width,this.height)},get bearing(){return-this.angle/Math.PI*180},set bearing(t){var e=-s(t,-180,180)*Math.PI/180;this.angle!==e&&(this._unmodified=!1,this.angle=e,this._calcMatrices(),this.rotationMatrix=d.create(),d.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},get pitch(){return this._pitch/Math.PI*180},set pitch(t){var e=Math.min(60,t)/180*Math.PI;this._pitch!==e&&(this._unmodified=!1,this._pitch=e,this._calcMatrices())},get altitude(){return this._altitude},set altitude(t){var e=Math.max(.75,t);this._altitude!==e&&(this._unmodified=!1,this._altitude=e,this._calcMatrices())},get zoom(){return this._zoom},set zoom(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._calcMatrices(),this._constrain())},get center(){return this._center},set center(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._calcMatrices(),this._constrain())},coveringZoomLevel:function(t){return(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize))},coveringTiles:function(t){var e=this.coveringZoomLevel(t),r=e;if(e<t.minzoom)return[];e>t.maxzoom&&(e=t.maxzoom);var n=this,i=n.locationCoordinate(n.center)._zoomTo(e),a=new o(i.column-.5,i.row-.5);return u.cover(e,[n.pointCoordinate(new o(0,0))._zoomTo(e),n.pointCoordinate(new o(n.width,0))._zoomTo(e),n.pointCoordinate(new o(n.width,n.height))._zoomTo(e),n.pointCoordinate(new o(0,n.height))._zoomTo(e)],t.reparseOverscaled?r:e).sort(function(t,e){return a.dist(t)-a.dist(e)})},resize:function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._calcMatrices(),this._constrain()},get unmodified(){return this._unmodified},zoomScale:function(t){return Math.pow(2,t)},scaleZoom:function(t){return Math.log(t)/Math.LN2},project:function(t,e){return new o(this.lngX(t.lng,e),this.latY(t.lat,e))},unproject:function(t,e){return new i(this.xLng(t.x,e),this.yLat(t.y,e))},get x(){return this.lngX(this.center.lng)},get y(){return this.latY(this.center.lat)},get point(){return new o(this.x,this.y)},lngX:function(t,e){return(180+t)*(e||this.worldSize)/360},latY:function(t,e){var r=180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360));return(180-r)*(e||this.worldSize)/360},xLng:function(t,e){return 360*t/(e||this.worldSize)-180},yLat:function(t,e){var r=180-360*t/(e||this.worldSize);return 360/Math.PI*Math.atan(Math.exp(r*Math.PI/180))-90},panBy:function(t){var e=this.centerPoint._add(t);this.center=this.pointLocation(e)},setLocationAtPoint:function(t,e){var r=this.locationCoordinate(t),n=this.pointCoordinate(e),i=this.pointCoordinate(this.centerPoint),o=n._sub(r);this._unmodified=!1,this.center=this.coordinateLocation(i._sub(o))},locationPoint:function(t){return this.coordinatePoint(this.locationCoordinate(t))},pointLocation:function(t){return this.coordinateLocation(this.pointCoordinate(t))},locationCoordinate:function(t){var e=this.zoomScale(this.tileZoom)/this.worldSize,r=i.convert(t);return new a(this.lngX(r.lng)*e,this.latY(r.lat)*e,this.tileZoom)},coordinateLocation:function(t){var e=this.zoomScale(t.zoom);return new i(this.xLng(t.column,e),this.yLat(t.row,e))},pointCoordinate:function(t){var e=0,r=[t.x,t.y,0,1],n=[t.x,t.y,1,1];f.transformMat4(r,r,this.pixelMatrixInverse),f.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],o=n[3],s=r[0]/i,u=n[0]/o,c=r[1]/i,h=n[1]/o,p=r[2]/i,d=n[2]/o,m=p===d?0:(e-p)/(d-p),g=this.worldSize/this.zoomScale(this.tileZoom);return new a(l(s,u,m)/g,l(c,h,m)/g,this.tileZoom)},coordinatePoint:function(t){var e=this.worldSize/this.zoomScale(t.zoom),r=[t.column*e,t.row*e,0,1];return f.transformMat4(r,r,this.pixelMatrix),new o(r[0]/r[3],r[1]/r[3])},calculatePosMatrix:function(t,e){void 0===e&&(e=1/0),t instanceof u&&(t=t.toCoordinate(e));var r=Math.min(t.zoom,e),n=this.worldSize/Math.pow(2,r),i=new Float64Array(16);return p.identity(i),p.translate(i,i,[t.column*n,t.row*n,0]),p.scale(i,i,[n/c,n/c,1]),p.multiply(i,this.projMatrix,i),new Float32Array(i)},_constrain:function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var 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o,a,s,l,u,c,h,f,p=[];for(o=0,a=t.length;a>o;o++)if(u=n[t[o]]){for(f=null,s=0,l=u[1].length;l>s;s+=2)-1===u[1][s]&&-1===u[1][s+1]?f=null:(c=e+u[1][s]*i,h=r-u[1][s+1]*i,f&&p.push(f.x,f.y,c,h),f={x:c,y:h});e+=u[0]*i}return p}},{}],267:[function(t,e,r){"use strict";var n=e.exports={};n.version=t("../package.json").version,n.Map=t("./ui/map"),n.Control=t("./ui/control/control"),n.Navigation=t("./ui/control/navigation"),n.Geolocate=t("./ui/control/geolocate"),n.Attribution=t("./ui/control/attribution"),n.Popup=t("./ui/popup"),n.Marker=t("./ui/marker"),n.Style=t("./style/style"),n.LngLat=t("./geo/lng_lat"),n.LngLatBounds=t("./geo/lng_lat_bounds"),n.Point=t("point-geometry"),n.Evented=t("./util/evented"),n.util=t("./util/util"),n.supported=t("./util/browser").supported;var i=t("./util/ajax");n.util.getJSON=i.getJSON,n.util.getArrayBuffer=i.getArrayBuffer;var o=t("./util/config");n.config=o,Object.defineProperty(n,"accessToken",{get:function(){return o.ACCESS_TOKEN},set:function(t){o.ACCESS_TOKEN=t}})},{"../package.json":443,"./geo/lng_lat":263,"./geo/lng_lat_bounds":264,"./style/style":302,"./ui/control/attribution":333,"./ui/control/control":334,"./ui/control/geolocate":335,"./ui/control/navigation":336,"./ui/map":345,"./ui/marker":346,"./ui/popup":347,"./util/ajax":349,"./util/browser":350,"./util/config":355,"./util/evented":358,"./util/util":366,"point-geometry":424}],268:[function(t,e,r){"use strict";e.exports=function(t){for(var e={define:{},initialize:{}},r=0;r<t.length;r++){var n=t[r],i="{precision} "+(1===n.components?"float":"vec"+n.components);e.define[n.name.slice(2)]="uniform "+i+" "+n.name+";\n",e.initialize[n.name.slice(2)]=i+" "+n.name.slice(2)+" = "+n.name+";\n"}return e}},{}],269:[function(t,e,r){"use strict";function n(t,e,r){var n,s=t.gl,l=t.transform,u=r.paint["background-color"],c=r.paint["background-pattern"],h=r.paint["background-opacity"],f=c?t.spriteAtlas.getPosition(c.from,!0):null,p=c?t.spriteAtlas.getPosition(c.to,!0):null;if(t.setDepthSublayer(0),f&&p){if(t.isOpaquePass)return;n=t.useProgram("pattern"),s.uniform1i(n.u_image,0),s.uniform2fv(n.u_pattern_tl_a,f.tl),s.uniform2fv(n.u_pattern_br_a,f.br),s.uniform2fv(n.u_pattern_tl_b,p.tl),s.uniform2fv(n.u_pattern_br_b,p.br),s.uniform1f(n.u_opacity,h),s.uniform1f(n.u_mix,c.t),s.uniform2fv(n.u_pattern_size_a,f.size),s.uniform2fv(n.u_pattern_size_b,p.size),s.uniform1f(n.u_scale_a,c.fromScale),s.uniform1f(n.u_scale_b,c.toScale),s.activeTexture(s.TEXTURE0),t.spriteAtlas.bind(s,!0),t.tileExtentPatternVAO.bind(s,n,t.tileExtentBuffer)}else{if(t.isOpaquePass!==(1===u[3]))return;var d=o([{name:"u_color",components:4},{name:"u_opacity",components:1}]);n=t.useProgram("fill",[],d,d),s.uniform4fv(n.u_color,u),s.uniform1f(n.u_opacity,h),t.tileExtentVAO.bind(s,n,t.tileExtentBuffer)}s.disable(s.STENCIL_TEST);for(var m=l.coveringTiles({tileSize:a}),g=0;g<m.length;g++){var v=m[g];if(f&&p){var y={coord:v,tileSize:a};s.uniform1f(n.u_tile_units_to_pixels,1/i(y,1,t.transform.tileZoom));var b=y.tileSize*Math.pow(2,t.transform.tileZoom-y.coord.z),x=b*(y.coord.x+v.w*Math.pow(2,y.coord.z)),_=b*y.coord.y;s.uniform2f(n.u_pixel_coord_upper,x>>16,_>>16),s.uniform2f(n.u_pixel_coord_lower,65535&x,65535&_)}s.uniformMatrix4fv(n.u_matrix,!1,t.transform.calculatePosMatrix(v)),s.drawArrays(s.TRIANGLE_STRIP,0,t.tileExtentBuffer.length)}s.stencilMask(0),s.stencilFunc(s.EQUAL,128,128)}var i=t("../source/pixels_to_tile_units"),o=t("./create_uniform_pragmas"),a=512;e.exports=n},{"../source/pixels_to_tile_units":287,"./create_uniform_pragmas":268}],270:[function(t,e,r){"use strict";function n(t,e,r,n){if(!t.isOpaquePass){var o=t.gl;t.setDepthSublayer(0),t.depthMask(!1),o.disable(o.STENCIL_TEST);for(var a=0;a<n.length;a++){var s=n[a],l=e.getTile(s),u=l.getBucket(r);if(u){var c=u.bufferGroups.circle;if(c){var h=u.paintAttributes.circle[r.id],f=t.useProgram("circle",h.defines,h.vertexPragmas,h.fragmentPragmas);"map"===r.paint["circle-pitch-scale"]?(o.uniform1i(f.u_scale_with_map,!0),o.uniform2f(f.u_extrude_scale,t.transform.pixelsToGLUnits[0]*t.transform.altitude,t.transform.pixelsToGLUnits[1]*t.transform.altitude)):(o.uniform1i(f.u_scale_with_map,!1),o.uniform2fv(f.u_extrude_scale,t.transform.pixelsToGLUnits)),o.uniform1f(f.u_devicepixelratio,i.devicePixelRatio),o.uniformMatrix4fv(f.u_matrix,!1,t.translatePosMatrix(s.posMatrix,l,r.paint["circle-translate"],r.paint["circle-translate-anchor"])),u.setUniforms(o,"circle",f,r,{zoom:t.transform.zoom});for(var p=0;p<c.length;p++){var d=c[p];d.vaos[r.id].bind(o,f,d.layoutVertexBuffer,d.elementBuffer,d.paintVertexBuffers[r.id]),o.drawElements(o.TRIANGLES,3*d.elementBuffer.length,o.UNSIGNED_SHORT,0)}}}}}}var i=t("../util/browser");e.exports=n},{"../util/browser":350}],271:[function(t,e,r){"use strict";function n(t,e,r,n){var i=t.gl;i.enable(i.STENCIL_TEST);for(var o=t.useProgram("collisionbox"),a=0;a<n.length;a++){var s=n[a],l=e.getTile(s),u=l.getBucket(r);if(u){var c=u.bufferGroups.collisionBox;if(c&&c.length){var h=c[0];0!==h.layoutVertexBuffer.length&&(i.uniformMatrix4fv(o.u_matrix,!1,s.posMatrix),t.enableTileClippingMask(s),t.lineWidth(1),i.uniform1f(o.u_scale,Math.pow(2,t.transform.zoom-l.coord.z)),i.uniform1f(o.u_zoom,10*t.transform.zoom),i.uniform1f(o.u_maxzoom,10*(l.coord.z+1)),h.vaos[r.id].bind(i,o,h.layoutVertexBuffer),i.drawArrays(i.LINES,0,h.layoutVertexBuffer.length))}}}}e.exports=n},{}],272:[function(t,e,r){"use strict";function n(t,e,r){if(!t.isOpaquePass&&t.options.debug)for(var n=0;n<r.length;n++)i(t,e,r[n])}function i(t,e,r){var n=t.gl;n.disable(n.STENCIL_TEST),t.lineWidth(1*a.devicePixelRatio);var i=r.posMatrix,h=t.useProgram("debug");n.uniformMatrix4fv(h.u_matrix,!1,i),n.uniform4f(h.u_color,1,0,0,1),t.debugVAO.bind(n,h,t.debugBuffer),n.drawArrays(n.LINE_STRIP,0,t.debugBuffer.length);for(var f=o(r.toString(),50,200,5),p=new t.PosArray,d=0;d<f.length;d+=2)p.emplaceBack(f[d],f[d+1]);var m=new u(p.serialize(),t.PosArray.serialize(),u.BufferType.VERTEX),g=new c;g.bind(n,h,m),n.uniform4f(h.u_color,1,1,1,1);for(var v=e.getTile(r).tileSize,y=l/(Math.pow(2,t.transform.zoom-r.z)*v),b=[[-1,-1],[-1,1],[1,-1],[1,1]],x=0;x<b.length;x++){var _=b[x];n.uniformMatrix4fv(h.u_matrix,!1,s.translate([],i,[y*_[0],y*_[1],0])),n.drawArrays(n.LINES,0,m.length)}n.uniform4f(h.u_color,0,0,0,1),n.uniformMatrix4fv(h.u_matrix,!1,i),n.drawArrays(n.LINES,0,m.length)}var o=t("../lib/debugtext"),a=t("../util/browser"),s=t("gl-matrix").mat4,l=t("../data/bucket").EXTENT,u=t("../data/buffer"),c=t("./vertex_array_object");e.exports=n},{"../data/bucket":253,"../data/buffer":258,"../lib/debugtext":266,"../util/browser":350,"./vertex_array_object":281,"gl-matrix":382}],273:[function(t,e,r){"use strict";function n(t,e,r,n){var a=t.gl;a.enable(a.STENCIL_TEST);var s;if(s=r.paint["fill-pattern"]?!1:r.isPaintValueFeatureConstant("fill-color")&&r.isPaintValueFeatureConstant("fill-opacity")&&1===r.paint["fill-color"][3]&&1===r.paint["fill-opacity"],t.isOpaquePass===s){t.setDepthSublayer(1);for(var l=0;l<n.length;l++)i(t,e,r,n[l])}if(!t.isOpaquePass&&r.paint["fill-antialias"]){t.lineWidth(2),t.depthMask(!1);var u=r.getPaintProperty("fill-outline-color");(u||!r.paint["fill-pattern"])&&u?t.setDepthSublayer(2):t.setDepthSublayer(0);for(var c=0;c<n.length;c++)o(t,e,r,n[c])}}function i(t,e,r,n){var i=e.getTile(n),o=i.getBucket(r);if(o){var s=o.bufferGroups.fill;if(s){var l,u=t.gl,c=r.paint["fill-pattern"];if(c)l=t.useProgram("pattern"),a(c,r.paint["fill-opacity"],i,n,t,l),u.activeTexture(u.TEXTURE0),t.spriteAtlas.bind(u,!0);else{var h=o.paintAttributes.fill[r.id];l=t.useProgram("fill",h.defines,h.vertexPragmas,h.fragmentPragmas),o.setUniforms(u,"fill",l,r,{zoom:t.transform.zoom})}u.uniformMatrix4fv(l.u_matrix,!1,t.translatePosMatrix(n.posMatrix,i,r.paint["fill-translate"],r.paint["fill-translate-anchor"])),t.enableTileClippingMask(n);for(var f=0;f<s.length;f++){var p=s[f];p.vaos[r.id].bind(u,l,p.layoutVertexBuffer,p.elementBuffer,p.paintVertexBuffers[r.id]),u.drawElements(u.TRIANGLES,p.elementBuffer.length,u.UNSIGNED_SHORT,0)}}}}function o(t,e,r,n){var i=e.getTile(n),o=i.getBucket(r);if(o){var s,l=t.gl,u=o.bufferGroups.fill,c=r.paint["fill-pattern"],h=r.paint["fill-opacity"],f=r.getPaintProperty("fill-outline-color");if(c&&!f)s=t.useProgram("outlinepattern"),l.uniform2f(s.u_world,l.drawingBufferWidth,l.drawingBufferHeight);else{var p=o.paintAttributes.fill[r.id];s=t.useProgram("outline",p.defines,p.vertexPragmas,p.fragmentPragmas),l.uniform2f(s.u_world,l.drawingBufferWidth,l.drawingBufferHeight),l.uniform1f(s.u_opacity,h),o.setUniforms(l,"fill",s,r,{zoom:t.transform.zoom})}l.uniformMatrix4fv(s.u_matrix,!1,t.translatePosMatrix(n.posMatrix,i,r.paint["fill-translate"],r.paint["fill-translate-anchor"])),c&&a(c,h,i,n,t,s),t.enableTileClippingMask(n);for(var d=0;d<u.length;d++){var m=u[d];m.secondVaos[r.id].bind(l,s,m.layoutVertexBuffer,m.elementBuffer2,m.paintVertexBuffers[r.id]),l.drawElements(l.LINES,2*m.elementBuffer2.length,l.UNSIGNED_SHORT,0)}}}function a(t,e,r,n,i,o){var a=i.gl,l=i.spriteAtlas.getPosition(t.from,!0),u=i.spriteAtlas.getPosition(t.to,!0);if(l&&u){a.uniform1i(o.u_image,0),a.uniform2fv(o.u_pattern_tl_a,l.tl),a.uniform2fv(o.u_pattern_br_a,l.br),a.uniform2fv(o.u_pattern_tl_b,u.tl),a.uniform2fv(o.u_pattern_br_b,u.br),a.uniform1f(o.u_opacity,e),a.uniform1f(o.u_mix,t.t),a.uniform1f(o.u_tile_units_to_pixels,1/s(r,1,i.transform.tileZoom)),a.uniform2fv(o.u_pattern_size_a,l.size),a.uniform2fv(o.u_pattern_size_b,u.size),a.uniform1f(o.u_scale_a,t.fromScale),a.uniform1f(o.u_scale_b,t.toScale);var c=r.tileSize*Math.pow(2,i.transform.tileZoom-r.coord.z),h=c*(r.coord.x+n.w*Math.pow(2,r.coord.z)),f=c*r.coord.y;a.uniform2f(o.u_pixel_coord_upper,h>>16,f>>16),a.uniform2f(o.u_pixel_coord_lower,65535&h,65535&f),a.activeTexture(a.TEXTURE0),i.spriteAtlas.bind(a,!0)}}var s=t("../source/pixels_to_tile_units");e.exports=n},{"../source/pixels_to_tile_units":287}],274:[function(t,e,r){"use strict";var n=t("../util/browser"),i=t("gl-matrix").mat2,o=t("../source/pixels_to_tile_units");e.exports=function(t,e,r,a){if(!t.isOpaquePass){t.setDepthSublayer(0),t.depthMask(!1);var s=t.gl;if(s.enable(s.STENCIL_TEST),!(r.paint["line-width"]<=0)){var l=1/n.devicePixelRatio,u=r.paint["line-blur"]+l,c=r.paint["line-color"],h=t.transform,f=i.create();i.scale(f,f,[1,Math.cos(h._pitch)]),i.rotate(f,f,t.transform.angle);var p,d,m,g,v,y=Math.sqrt(h.height*h.height/4*(1+h.altitude*h.altitude)),b=h.height/2*Math.tan(h._pitch),x=(y+b)/y-1,_=r.paint["line-dasharray"],w=r.paint["line-pattern"];if(_)p=t.useProgram("linesdfpattern"),s.uniform1f(p.u_linewidth,r.paint["line-width"]/2),s.uniform1f(p.u_gapwidth,r.paint["line-gap-width"]/2),s.uniform1f(p.u_antialiasing,l/2),s.uniform1f(p.u_blur,u),s.uniform4fv(p.u_color,c),s.uniform1f(p.u_opacity,r.paint["line-opacity"]),d=t.lineAtlas.getDash(_.from,"round"===r.layout["line-cap"]),m=t.lineAtlas.getDash(_.to,"round"===r.layout["line-cap"]),s.uniform1i(p.u_image,0),s.activeTexture(s.TEXTURE0),t.lineAtlas.bind(s),s.uniform1f(p.u_tex_y_a,d.y),s.uniform1f(p.u_tex_y_b,m.y),s.uniform1f(p.u_mix,_.t),s.uniform1f(p.u_extra,x),s.uniform1f(p.u_offset,-r.paint["line-offset"]),s.uniformMatrix2fv(p.u_antialiasingmatrix,!1,f);else if(w){if(g=t.spriteAtlas.getPosition(w.from,!0),v=t.spriteAtlas.getPosition(w.to,!0),!g||!v)return;p=t.useProgram("linepattern"),s.uniform1i(p.u_image,0),s.activeTexture(s.TEXTURE0),t.spriteAtlas.bind(s,!0),s.uniform1f(p.u_linewidth,r.paint["line-width"]/2),s.uniform1f(p.u_gapwidth,r.paint["line-gap-width"]/2),s.uniform1f(p.u_antialiasing,l/2),s.uniform1f(p.u_blur,u),s.uniform2fv(p.u_pattern_tl_a,g.tl),s.uniform2fv(p.u_pattern_br_a,g.br),s.uniform2fv(p.u_pattern_tl_b,v.tl),s.uniform2fv(p.u_pattern_br_b,v.br),s.uniform1f(p.u_fade,w.t),s.uniform1f(p.u_opacity,r.paint["line-opacity"]),s.uniform1f(p.u_extra,x),s.uniform1f(p.u_offset,-r.paint["line-offset"]),s.uniformMatrix2fv(p.u_antialiasingmatrix,!1,f)}else p=t.useProgram("line"),s.uniform1f(p.u_linewidth,r.paint["line-width"]/2),s.uniform1f(p.u_gapwidth,r.paint["line-gap-width"]/2),s.uniform1f(p.u_antialiasing,l/2),s.uniform1f(p.u_blur,u),s.uniform1f(p.u_extra,x),s.uniform1f(p.u_offset,-r.paint["line-offset"]),s.uniformMatrix2fv(p.u_antialiasingmatrix,!1,f),s.uniform4fv(p.u_color,c),s.uniform1f(p.u_opacity,r.paint["line-opacity"]);for(var M=0;M<a.length;M++){var k=a[M],A=e.getTile(k),T=A.getBucket(r);if(T){var E=T.bufferGroups.line;if(E){t.enableTileClippingMask(k);var S=t.translatePosMatrix(k.posMatrix,A,r.paint["line-translate"],r.paint["line-translate-anchor"]);s.uniformMatrix4fv(p.u_matrix,!1,S);var L=1/o(A,1,t.transform.zoom);if(_){var z=d.width*_.fromScale,I=m.width*_.toScale,P=[1/o(A,z,t.transform.tileZoom),-d.height/2],C=[1/o(A,I,t.transform.tileZoom),-m.height/2],R=t.lineAtlas.width/(256*Math.min(z,I)*n.devicePixelRatio)/2;s.uniform1f(p.u_ratio,L),s.uniform2fv(p.u_patternscale_a,P),s.uniform2fv(p.u_patternscale_b,C),s.uniform1f(p.u_sdfgamma,R)}else w?(s.uniform1f(p.u_ratio,L),s.uniform2fv(p.u_pattern_size_a,[o(A,g.size[0]*w.fromScale,t.transform.tileZoom),v.size[1]]),s.uniform2fv(p.u_pattern_size_b,[o(A,v.size[0]*w.toScale,t.transform.tileZoom),v.size[1]])):s.uniform1f(p.u_ratio,L);for(var j=0;j<E.length;j++){var D=E[j];D.vaos[r.id].bind(s,p,D.layoutVertexBuffer,D.elementBuffer),s.drawElements(s.TRIANGLES,3*D.elementBuffer.length,s.UNSIGNED_SHORT,0)}}}}}}}},{"../source/pixels_to_tile_units":287,"../util/browser":350,"gl-matrix":382}],275:[function(t,e,r){"use strict";function n(t,e,r,n){if(!t.isOpaquePass){var o=t.gl;o.enable(o.DEPTH_TEST),t.depthMask(!0),o.depthFunc(o.LESS);for(var a=n.length&&n[0].z,s=0;s<n.length;s++){var l=n[s];t.setDepthSublayer(l.z-a),i(t,e,r,l)}o.depthFunc(o.LEQUAL)}}function i(t,e,r,n){var i=t.gl;i.disable(i.STENCIL_TEST);var u=e.getTile(n),c=t.transform.calculatePosMatrix(n,e.maxzoom),h=t.useProgram("raster");i.uniformMatrix4fv(h.u_matrix,!1,c),i.uniform1f(h.u_brightness_low,r.paint["raster-brightness-min"]),i.uniform1f(h.u_brightness_high,r.paint["raster-brightness-max"]),i.uniform1f(h.u_saturation_factor,s(r.paint["raster-saturation"])),i.uniform1f(h.u_contrast_factor,a(r.paint["raster-contrast"])),i.uniform3fv(h.u_spin_weights,o(r.paint["raster-hue-rotate"]));var f,p,d=u.source&&u.source.findLoadedParent(n,0,{}),m=l(u,d,r,t.transform);i.activeTexture(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,u.texture),i.activeTexture(i.TEXTURE1),d?(i.bindTexture(i.TEXTURE_2D,d.texture),f=Math.pow(2,d.coord.z-u.coord.z),p=[u.coord.x*f%1,u.coord.y*f%1]):(i.bindTexture(i.TEXTURE_2D,u.texture),m[1]=0),i.uniform2fv(h.u_tl_parent,p||[0,0]),i.uniform1f(h.u_scale_parent,f||1),i.uniform1f(h.u_buffer_scale,1),i.uniform1f(h.u_opacity0,m[0]),i.uniform1f(h.u_opacity1,m[1]),i.uniform1i(h.u_image0,0),i.uniform1i(h.u_image1,1);var g=u.boundsBuffer||t.rasterBoundsBuffer,v=u.boundsVAO||t.rasterBoundsVAO;v.bind(i,h,g),i.drawArrays(i.TRIANGLE_STRIP,0,g.length)}function o(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}function a(t){return t>0?1/(1-t):1+t}function s(t){return t>0?1-1/(1.001-t):-t}function l(t,e,r,n){var i=[1,0],o=r.paint["raster-fade-duration"];if(t.source&&o>0){var a=(new Date).getTime(),s=(a-t.timeAdded)/o,l=e?(a-e.timeAdded)/o:-1,c=n.coveringZoomLevel(t.source),h=e?Math.abs(e.coord.z-c)>Math.abs(t.coord.z-c):!1;!e||h?(i[0]=u.clamp(s,0,1),i[1]=1-i[0]):(i[0]=u.clamp(1-l,0,1),i[1]=1-i[0])}var f=r.paint["raster-opacity"];return i[0]*=f,i[1]*=f,i}var u=t("../util/util"),c=t("../util/struct_array");e.exports=n,n.RasterBoundsArray=new c({members:[{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]})},{"../util/struct_array":364,"../util/util":366}],276:[function(t,e,r){"use strict";function n(t,e,r,n){if(!t.isOpaquePass){var o=!(r.layout["text-allow-overlap"]||r.layout["icon-allow-overlap"]||r.layout["text-ignore-placement"]||r.layout["icon-ignore-placement"]),a=t.gl;o?a.disable(a.STENCIL_TEST):a.enable(a.STENCIL_TEST),t.setDepthSublayer(0),t.depthMask(!1),a.disable(a.DEPTH_TEST),i(t,e,r,n,!1,r.paint["icon-translate"],r.paint["icon-translate-anchor"],r.layout["icon-rotation-alignment"],r.layout["icon-rotation-alignment"],r.layout["icon-size"],r.paint["icon-halo-width"],r.paint["icon-halo-color"],r.paint["icon-halo-blur"],r.paint["icon-opacity"],r.paint["icon-color"]),i(t,e,r,n,!0,r.paint["text-translate"],r.paint["text-translate-anchor"],r.layout["text-rotation-alignment"],r.layout["text-pitch-alignment"],r.layout["text-size"],r.paint["text-halo-width"],r.paint["text-halo-color"],r.paint["text-halo-blur"],r.paint["text-opacity"],r.paint["text-color"]),a.enable(a.DEPTH_TEST),e.map.showCollisionBoxes&&s(t,e,r,n)}}function i(t,e,r,n,i,a,s,l,u,c,h,f,p,d,m){for(var g=0;g<n.length;g++){var v=e.getTile(n[g]),y=v.getBucket(r);if(y){var b=y.bufferGroups,x=i?b.glyph:b.icon;x.length&&(t.enableTileClippingMask(n[g]),o(t,r,n[g].posMatrix,v,y,x,i,i||y.sdfIcons,!i&&y.iconsNeedLinear,i?y.adjustedTextSize:y.adjustedIconSize,y.fontstack,a,s,l,u,c,h,f,p,d,m))}}}function o(t,e,r,n,i,o,s,u,c,h,f,p,d,m,g,v,y,b,x,_,w){var M,k,A,T=t.gl,E=t.transform,S="map"===m,L="map"===g,z=s?24:1,I=v/z;if(L?(k=l(n,1,t.transform.zoom)*I,A=1/Math.cos(E._pitch),M=[k,k]):(k=t.transform.altitude*I,A=1,M=[E.pixelsToGLUnits[0]*k,E.pixelsToGLUnits[1]*k]),s||t.style.sprite.loaded()){var P=t.useProgram(u?"sdf":"icon");if(T.uniformMatrix4fv(P.u_matrix,!1,t.translatePosMatrix(r,n,p,d)),T.uniform1i(P.u_rotate_with_map,S),T.uniform1i(P.u_pitch_with_map,L),T.uniform2fv(P.u_extrude_scale,M),T.activeTexture(T.TEXTURE0),T.uniform1i(P.u_texture,0),s){var C=f&&t.glyphSource.getGlyphAtlas(f);if(!C)return;C.updateTexture(T),T.uniform2f(P.u_texsize,C.width/4,C.height/4)}else{var R=t.options.rotating||t.options.zooming,j=1!==I||a.devicePixelRatio!==t.spriteAtlas.pixelRatio||c,D=L||t.transform.pitch;t.spriteAtlas.bind(T,u||R||j||D),T.uniform2f(P.u_texsize,t.spriteAtlas.width/4,t.spriteAtlas.height/4)}var O=Math.log(v/h)/Math.LN2||0;T.uniform1f(P.u_zoom,10*(t.transform.zoom-O)),T.activeTexture(T.TEXTURE1),t.frameHistory.bind(T),T.uniform1i(P.u_fadetexture,1);var F;if(u){var N=8,B=1.19,U=6,V=.105*z/v/a.devicePixelRatio;if(y){T.uniform1f(P.u_gamma,(x*B/I/N+V)*A),T.uniform4fv(P.u_color,b),T.uniform1f(P.u_opacity,_),T.uniform1f(P.u_buffer,(U-y/I)/N);for(var q=0;q<o.length;q++)F=o[q],F.vaos[e.id].bind(T,P,F.layoutVertexBuffer,F.elementBuffer),T.drawElements(T.TRIANGLES,3*F.elementBuffer.length,T.UNSIGNED_SHORT,0)}T.uniform1f(P.u_gamma,V*A),T.uniform4fv(P.u_color,w),T.uniform1f(P.u_opacity,_),T.uniform1f(P.u_buffer,.75),T.uniform1f(P.u_pitch,E.pitch/360*2*Math.PI),T.uniform1f(P.u_bearing,E.bearing/360*2*Math.PI),T.uniform1f(P.u_aspect_ratio,E.width/E.height);for(var G=0;G<o.length;G++)F=o[G],F.vaos[e.id].bind(T,P,F.layoutVertexBuffer,F.elementBuffer),T.drawElements(T.TRIANGLES,3*F.elementBuffer.length,T.UNSIGNED_SHORT,0)}else{T.uniform1f(P.u_opacity,_);for(var H=0;H<o.length;H++)F=o[H],F.vaos[e.id].bind(T,P,F.layoutVertexBuffer,F.elementBuffer),T.drawElements(T.TRIANGLES,3*F.elementBuffer.length,T.UNSIGNED_SHORT,0)}}}var a=t("../util/browser"),s=t("./draw_collision_debug"),l=t("../source/pixels_to_tile_units");e.exports=n},{"../source/pixels_to_tile_units":287,"../util/browser":350,"./draw_collision_debug":271}],277:[function(t,e,r){"use strict";function n(){this.changeTimes=new Float64Array(256),this.changeOpacities=new Uint8Array(256),this.opacities=new Uint8ClampedArray(256),this.array=new Uint8Array(this.opacities.buffer),this.fadeDuration=300,this.previousZoom=0,this.firstFrame=!0}e.exports=n,n.prototype.record=function(t){var e=Date.now();this.firstFrame&&(e=0,this.firstFrame=!1),t=Math.floor(10*t);var r;if(t<this.previousZoom)for(r=t+1;r<=this.previousZoom;r++)this.changeTimes[r]=e,this.changeOpacities[r]=this.opacities[r];else for(r=t;r>this.previousZoom;r--)this.changeTimes[r]=e,this.changeOpacities[r]=this.opacities[r];for(r=0;256>r;r++){var n=e-this.changeTimes[r],i=n/this.fadeDuration*255;t>=r?this.opacities[r]=this.changeOpacities[r]+i:this.opacities[r]=this.changeOpacities[r]-i}this.changed=!0,this.previousZoom=t},n.prototype.bind=function(t){this.texture?(t.bindTexture(t.TEXTURE_2D,this.texture),this.changed&&(t.texSubImage2D(t.TEXTURE_2D,0,0,0,256,1,t.ALPHA,t.UNSIGNED_BYTE,this.array),this.changed=!1)):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texImage2D(t.TEXTURE_2D,0,t.ALPHA,256,1,0,t.ALPHA,t.UNSIGNED_BYTE,this.array))}},{}],278:[function(t,e,r){"use strict";function n(t,e){this.width=t,this.height=e,this.nextRow=0,this.bytes=4,this.data=new Uint8Array(this.width*this.height*this.bytes),this.positions={}}var i=t("../util/util");e.exports=n,n.prototype.setSprite=function(t){this.sprite=t},n.prototype.getDash=function(t,e){var r=t.join(",")+e;return this.positions[r]||(this.positions[r]=this.addDash(t,e)),this.positions[r]},n.prototype.addDash=function(t,e){var r=e?7:0,n=2*r+1,o=128;if(this.nextRow+n>this.height)return i.warnOnce("LineAtlas out of space"),null;for(var a=0,s=0;s<t.length;s++)a+=t[s];for(var l=this.width/a,u=l/2,c=t.length%2===1,h=-r;r>=h;h++)for(var f=this.nextRow+r+h,p=this.width*f,d=c?-t[t.length-1]:0,m=t[0],g=1,v=0;v<this.width;v++){for(;v/l>m;)d=m,m+=t[g],c&&g===t.length-1&&(m+=t[0]),g++;var y,b=Math.abs(v-d*l),x=Math.abs(v-m*l),_=Math.min(b,x),w=g%2===1;if(e){var M=r?h/r*(u+1):0;if(w){var k=u-Math.abs(M);y=Math.sqrt(_*_+k*k)}else y=u-Math.sqrt(_*_+M*M)}else y=(w?1:-1)*_;this.data[3+4*(p+v)]=Math.max(0,Math.min(255,y+o))}var A={y:(this.nextRow+r+.5)/this.height,height:2*r/this.height,width:a};return this.nextRow+=n,this.dirty=!0,A},n.prototype.bind=function(t){this.texture?(t.bindTexture(t.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width,this.height,t.RGBA,t.UNSIGNED_BYTE,this.data))):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.REPEAT),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.REPEAT),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texImage2D(t.TEXTURE_2D,0,t.RGBA,this.width,this.height,0,t.RGBA,t.UNSIGNED_BYTE,this.data))}},{"../util/util":366}],279:[function(t,e,r){"use strict";function n(t,e){this.gl=t,this.transform=e,this.reusableTextures={},this.preFbos={},this.frameHistory=new a,this.setup(),this.numSublayers=s.maxUnderzooming+s.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.lineWidthRange=t.getParameter(t.ALIASED_LINE_WIDTH_RANGE)}var i=t("../util/browser"),o=t("gl-matrix").mat4,a=t("./frame_history"),s=t("../source/source_cache"),l=t("../data/bucket").EXTENT,u=t("../source/pixels_to_tile_units"),c=t("../util/util"),h=t("../util/struct_array"),f=t("../data/buffer"),p=t("./vertex_array_object"),d=t("./draw_raster").RasterBoundsArray,m=t("./create_uniform_pragmas");e.exports=n,c.extend(n.prototype,t("./painter/use_program")),n.prototype.resize=function(t,e){var r=this.gl;this.width=t*i.devicePixelRatio,this.height=e*i.devicePixelRatio,r.viewport(0,0,this.width,this.height)},n.prototype.setup=function(){var t=this.gl;t.verbose=!0,t.enable(t.BLEND),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),t.enable(t.STENCIL_TEST),t.enable(t.DEPTH_TEST),t.depthFunc(t.LEQUAL),this._depthMask=!1,t.depthMask(!1);var e=this.PosArray=new h({members:[{name:"a_pos",type:"Int16",components:2}]}),r=new e;r.emplaceBack(0,0),r.emplaceBack(l,0),r.emplaceBack(0,l),r.emplaceBack(l,l),this.tileExtentBuffer=new f(r.serialize(),e.serialize(),f.BufferType.VERTEX),this.tileExtentVAO=new p,this.tileExtentPatternVAO=new p;var n=new e;n.emplaceBack(0,0),n.emplaceBack(l,0),n.emplaceBack(l,l),n.emplaceBack(0,l),n.emplaceBack(0,0),this.debugBuffer=new f(n.serialize(),e.serialize(),f.BufferType.VERTEX),this.debugVAO=new p;var i=new d;i.emplaceBack(0,0,0,0),i.emplaceBack(l,0,32767,0),i.emplaceBack(0,l,0,32767),i.emplaceBack(l,l,32767,32767),this.rasterBoundsBuffer=new f(i.serialize(),d.serialize(),f.BufferType.VERTEX),this.rasterBoundsVAO=new p},n.prototype.clearColor=function(){var t=this.gl;t.clearColor(0,0,0,0),t.clear(t.COLOR_BUFFER_BIT)},n.prototype.clearStencil=function(){var t=this.gl;t.clearStencil(0),t.stencilMask(255),t.clear(t.STENCIL_BUFFER_BIT)},n.prototype.clearDepth=function(){var t=this.gl;t.clearDepth(1),this.depthMask(!0),t.clear(t.DEPTH_BUFFER_BIT)},n.prototype._renderTileClippingMasks=function(t){var e=this.gl;e.colorMask(!1,!1,!1,!1),this.depthMask(!1),e.disable(e.DEPTH_TEST),e.enable(e.STENCIL_TEST),e.stencilMask(248),e.stencilOp(e.KEEP,e.KEEP,e.REPLACE);var r=1;this._tileClippingMaskIDs={};for(var n=0;n<t.length;n++){var i=t[n],o=this._tileClippingMaskIDs[i.id]=r++<<3;e.stencilFunc(e.ALWAYS,o,248);var a=m([{name:"u_color",components:4},{name:"u_opacity",components:1}]),s=this.useProgram("fill",[],a,a);e.uniformMatrix4fv(s.u_matrix,!1,i.posMatrix),this.tileExtentVAO.bind(e,s,this.tileExtentBuffer),e.drawArrays(e.TRIANGLE_STRIP,0,this.tileExtentBuffer.length)}e.stencilMask(0),e.colorMask(!0,!0,!0,!0),this.depthMask(!0),e.enable(e.DEPTH_TEST)},n.prototype.enableTileClippingMask=function(t){var e=this.gl;e.stencilFunc(e.EQUAL,this._tileClippingMaskIDs[t.id],248)},n.prototype.prepareBuffers=function(){},n.prototype.bindDefaultFramebuffer=function(){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,null)};var g={symbol:t("./draw_symbol"),circle:t("./draw_circle"),line:t("./draw_line"),fill:t("./draw_fill"),raster:t("./draw_raster"),background:t("./draw_background"),debug:t("./draw_debug")};n.prototype.render=function(t,e){this.style=t,this.options=e,this.lineAtlas=t.lineAtlas,this.spriteAtlas=t.spriteAtlas,this.spriteAtlas.setSprite(t.sprite),this.glyphSource=t.glyphSource,this.frameHistory.record(this.transform.zoom),this.prepareBuffers(),this.clearColor(),this.clearDepth(),this.showOverdrawInspector(e.showOverdrawInspector),this.depthRange=(t._order.length+2)*this.numSublayers*this.depthEpsilon,this.renderPass({isOpaquePass:!0}),this.renderPass({isOpaquePass:!1})},n.prototype.renderPass=function(t){var e=this.style._groups,r=t.isOpaquePass;this.currentLayer=r?this.style._order.length:-1;for(var n=0;n<e.length;n++){var i,o=e[r?e.length-1-n:n],a=this.style.sources[o.source],s=[];if(a){for(s=a.getVisibleCoordinates(),i=0;i<s.length;i++)s[i].posMatrix=this.transform.calculatePosMatrix(s[i],a.maxzoom);this.clearStencil(),a.prepare&&a.prepare(),a.isTileClipped&&this._renderTileClippingMasks(s)}for(r?(this._showOverdrawInspector||this.gl.disable(this.gl.BLEND),this.isOpaquePass=!0):(this.gl.enable(this.gl.BLEND),this.isOpaquePass=!1,s.reverse()),i=0;i<o.length;i++){var l=o[r?o.length-1-i:i];this.currentLayer+=r?-1:1,this.renderLayer(this,a,l,s)}a&&g.debug(this,a,s)}},n.prototype.depthMask=function(t){t!==this._depthMask&&(this._depthMask=t,this.gl.depthMask(t))},n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||n.length)&&(this.id=r.id,g[r.type](t,e,r,n))},n.prototype.setDepthSublayer=function(t){var e=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon,r=e-1+this.depthRange;this.gl.depthRange(r,e)},n.prototype.translatePosMatrix=function(t,e,r,n){if(!r[0]&&!r[1])return t;if("viewport"===n){var i=Math.sin(-this.transform.angle),a=Math.cos(-this.transform.angle);r=[r[0]*a-r[1]*i,r[0]*i+r[1]*a]}var s=[u(e,r[0],this.transform.zoom),u(e,r[1],this.transform.zoom),0],l=new Float32Array(16);return o.translate(l,t,s),l},n.prototype.saveTexture=function(t){var e=this.reusableTextures[t.size];e?e.push(t):this.reusableTextures[t.size]=[t]},n.prototype.getTexture=function(t){var e=this.reusableTextures[t];return e&&e.length>0?e.pop():null},n.prototype.lineWidth=function(t){this.gl.lineWidth(c.clamp(t,this.lineWidthRange[0],this.lineWidthRange[1]))},n.prototype.showOverdrawInspector=function(t){if(t||this._showOverdrawInspector){this._showOverdrawInspector=t;var e=this.gl;if(t){e.blendFunc(e.CONSTANT_COLOR,e.ONE);var r=8,n=1/r;e.blendColor(n,n,n,0),e.clearColor(0,0,0,1),e.clear(e.COLOR_BUFFER_BIT)}else e.blendFunc(e.ONE,e.ONE_MINUS_SRC_ALPHA)}}},{"../data/bucket":253,"../data/buffer":258,"../source/pixels_to_tile_units":287,"../source/source_cache":291,"../util/browser":350,"../util/struct_array":364,"../util/util":366,"./create_uniform_pragmas":268,"./draw_background":269,"./draw_circle":270,"./draw_debug":272,"./draw_fill":273,"./draw_line":274,"./draw_raster":275,"./draw_symbol":276,"./frame_history":277,"./painter/use_program":280,"./vertex_array_object":281,"gl-matrix":382}],280:[function(t,e,r){"use strict";function n(t,e){return t.replace(/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,function(t,r,n,i,o){return e[r][o].replace(/{type}/g,i).replace(/{precision}/g,n)})}var i=t("../../util/util"),o=t("mapbox-gl-shaders"),a=o.util;e.exports._createProgram=function(t,e,r,s){for(var l=this.gl,u=l.createProgram(),c=o[t],h="#define MAPBOX_GL_JS;\n",f=0;f<e.length;f++)h+="#define "+e[f]+";\n";var p=l.createShader(l.FRAGMENT_SHADER);l.shaderSource(p,n(h+c.fragmentSource,s)),l.compileShader(p),l.attachShader(u,p);var d=l.createShader(l.VERTEX_SHADER);l.shaderSource(d,n(h+a+c.vertexSource,r)),l.compileShader(d),l.attachShader(u,d),l.linkProgram(u);for(var m={},g=l.getProgramParameter(u,l.ACTIVE_ATTRIBUTES),v=0;g>v;v++){var y=l.getActiveAttrib(u,v);m[y.name]=l.getAttribLocation(u,y.name)}for(var b={},x=l.getProgramParameter(u,l.ACTIVE_UNIFORMS),_=0;x>_;_++){
var w=l.getActiveUniform(u,_);b[w.name]=l.getUniformLocation(u,w.name)}return i.extend({program:u,definition:c,attributes:m,numAttributes:g},m,b)},e.exports._createProgramCached=function(t,e,r,n){this.cache=this.cache||{};var i=JSON.stringify({name:t,defines:e,vertexPragmas:r,fragmentPragmas:n});return this.cache[i]||(this.cache[i]=this._createProgram(t,e,r,n)),this.cache[i]},e.exports.useProgram=function(t,e,r,n){var i=this.gl;e=e||[],this._showOverdrawInspector&&(e=e.concat("OVERDRAW_INSPECTOR"));var o=this._createProgramCached(t,e,r,n),a=this.currentProgram;return a!==o&&(i.useProgram(o.program),this.currentProgram=o),o}},{"../../util/util":366,"mapbox-gl-shaders":394}],281:[function(t,e,r){"use strict";function n(){this.boundProgram=null,this.boundVertexBuffer=null,this.boundVertexBuffer2=null,this.boundElementBuffer=null,this.vao=null}e.exports=n,n.prototype.bind=function(t,e,r,n,i){void 0===t.extVertexArrayObject&&(t.extVertexArrayObject=t.getExtension("OES_vertex_array_object"));var o=!this.vao||this.boundProgram!==e||this.boundVertexBuffer!==r||this.boundVertexBuffer2!==i||this.boundElementBuffer!==n;!t.extVertexArrayObject||o?this.freshBind(t,e,r,n,i):t.extVertexArrayObject.bindVertexArrayOES(this.vao)},n.prototype.freshBind=function(t,e,r,n,i){var o,a=e.numAttributes;if(t.extVertexArrayObject)this.vao&&this.destroy(t),this.vao=t.extVertexArrayObject.createVertexArrayOES(),t.extVertexArrayObject.bindVertexArrayOES(this.vao),o=0,this.boundProgram=e,this.boundVertexBuffer=r,this.boundVertexBuffer2=i,this.boundElementBuffer=n;else{o=t.currentNumAttributes||0;for(var s=a;o>s;s++)t.disableVertexAttribArray(s)}for(var l=o;a>l;l++)t.enableVertexAttribArray(l);r.bind(t),r.setVertexAttribPointers(t,e),i&&(i.bind(t),i.setVertexAttribPointers(t,e)),n&&n.bind(t),t.currentNumAttributes=a},n.prototype.unbind=function(t){var e=t.extVertexArrayObject;e&&e.bindVertexArrayOES(null)},n.prototype.destroy=function(t){var e=t.extVertexArrayObject;e&&this.vao&&(e.deleteVertexArrayOES(this.vao),this.vao=null)}},{}],282:[function(t,e,r){"use strict";function n(t,e,r){e=e||{},this.id=t,this.dispatcher=r,this._data=e.data,void 0!==e.maxzoom&&(this.maxzoom=e.maxzoom),e.type&&(this.type=e.type);var n=s/this.tileSize;this.workerOptions=o.extend({source:this.id,cluster:e.cluster||!1,geojsonVtOptions:{buffer:(void 0!==e.buffer?e.buffer:128)*n,tolerance:(void 0!==e.tolerance?e.tolerance:.375)*n,extent:s,maxZoom:this.maxzoom},superclusterOptions:{maxZoom:Math.min(e.clusterMaxZoom,this.maxzoom-1)||this.maxzoom-1,extent:s,radius:(e.clusterRadius||50)*n,log:!1}},e.workerOptions),this._updateWorkerData(function(t){return t?void this.fire("error",{error:t}):void this.fire("load")}.bind(this))}var i=t("../util/evented"),o=t("../util/util"),a=t("resolve-url"),s=t("../data/bucket").EXTENT;e.exports=n,n.prototype=o.inherit(i,{type:"geojson",minzoom:0,maxzoom:18,tileSize:512,isTileClipped:!0,reparseOverscaled:!0,onAdd:function(t){this.map=t},setData:function(t){return this._data=t,this._updateWorkerData(function(t){return t?this.fire("error",{error:t}):void this.fire("change")}.bind(this)),this},_updateWorkerData:function(t){var e=o.extend({},this.workerOptions),r=this._data;"string"==typeof r?e.url="undefined"!=typeof window?a(window.location.href,r):r:e.data=JSON.stringify(r),this.workerID=this.dispatcher.send(this.type+".loadData",e,function(e){this._loaded=!0,t(e)}.bind(this))},loadTile:function(t,e){var r=t.coord.z>this.maxzoom?Math.pow(2,t.coord.z-this.maxzoom):1,n={type:this.type,uid:t.uid,coord:t.coord,zoom:t.coord.z,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,overscaling:r,angle:this.map.transform.angle,pitch:this.map.transform.pitch,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID=this.dispatcher.send("load tile",n,function(r,n){return t.unloadVectorData(this.map.painter),t.aborted?void 0:r?e(r):(t.loadVectorData(n,this.map.style),t.redoWhenDone&&(t.redoWhenDone=!1,t.redoPlacement(this)),e(null))}.bind(this),this.workerID)},abortTile:function(t){t.aborted=!0},unloadTile:function(t){t.unloadVectorData(this.map.painter),this.dispatcher.send("remove tile",{uid:t.uid,source:this.id},function(){},t.workerID)},serialize:function(){return{type:this.type,data:this._data}}})},{"../data/bucket":253,"../util/evented":358,"../util/util":366,"resolve-url":426}],283:[function(t,e,r){"use strict";function n(t,e,r){r&&(this.loadGeoJSON=r),h.call(this,t,e)}var i=t("../util/util"),o=t("../util/ajax"),a=t("geojson-rewind"),s=t("./geojson_wrapper"),l=t("vt-pbf"),u=t("supercluster"),c=t("geojson-vt"),h=t("./vector_tile_worker_source");e.exports=n,n.prototype=i.inherit(h,{_geoJSONIndexes:{},loadVectorData:function(t,e){var r=t.source,n=t.coord;if(!this._geoJSONIndexes[r])return e(null,null);var i=this._geoJSONIndexes[r].getTile(Math.min(n.z,t.maxZoom),n.x,n.y);if(!i)return e(null,null);var o=new s(i.features);o.name="_geojsonTileLayer";var a=l({layers:{_geojsonTileLayer:o}});0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{tile:o,rawTileData:a.buffer})},loadData:function(t,e){var r=function(r,n){return r?e(r):"object"!=typeof n?e(new Error("Input data is not a valid GeoJSON object.")):(a(n,!0),void this._indexData(n,t,function(r,n){return r?e(r):(this._geoJSONIndexes[t.source]=n,void e(null))}.bind(this)))}.bind(this);this.loadGeoJSON(t,r)},loadGeoJSON:function(t,e){if(t.url)o.getJSON(t.url,e);else{if("string"!=typeof t.data)return e(new Error("Input data is not a valid GeoJSON object."));try{return e(null,JSON.parse(t.data))}catch(r){return e(new Error("Input data is not a valid GeoJSON object."))}}},_indexData:function(t,e,r){try{e.cluster?r(null,u(e.superclusterOptions).load(t.features)):r(null,c(t,e.geojsonVtOptions))}catch(n){return r(n)}}})},{"../util/ajax":349,"../util/util":366,"./geojson_wrapper":284,"./vector_tile_worker_source":295,"geojson-rewind":371,"geojson-vt":377,supercluster:428,"vt-pbf":438}],284:[function(t,e,r){"use strict";function n(t){this.features=t,this.length=t.length,this.extent=s}function i(t){if(this.type=t.type,1===t.type){this.rawGeometry=[];for(var e=0;e<t.geometry.length;e++)this.rawGeometry.push([t.geometry[e]])}else this.rawGeometry=t.geometry;this.properties=t.tags,this.extent=s}var o=t("point-geometry"),a=t("vector-tile").VectorTileFeature,s=t("../data/bucket").EXTENT;e.exports=n,n.prototype.feature=function(t){return new i(this.features[t])},i.prototype.loadGeometry=function(){var t=this.rawGeometry;this.geometry=[];for(var e=0;e<t.length;e++){for(var r=t[e],n=[],i=0;i<r.length;i++)n.push(new o(r[i][0],r[i][1]));this.geometry.push(n)}return this.geometry},i.prototype.bbox=function(){this.geometry||this.loadGeometry();for(var t=this.geometry,e=1/0,r=-(1/0),n=1/0,i=-(1/0),o=0;o<t.length;o++)for(var a=t[o],s=0;s<a.length;s++){var l=a[s];e=Math.min(e,l.x),r=Math.max(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.y)}return[e,n,r,i]},i.prototype.toGeoJSON=a.prototype.toGeoJSON},{"../data/bucket":253,"point-geometry":424,"vector-tile":434}],285:[function(t,e,r){"use strict";function n(t,e,r){this.id=t,this.dispatcher=r,this.url=e.url,this.coordinates=e.coordinates,u.getImage(e.url,function(t,r){return t?this.fire("error",{error:t}):(this.image=r,this.image.addEventListener("load",function(){this.map._rerender()}.bind(this)),this._loaded=!0,this.fire("load"),void(this.map&&this.setCoordinates(e.coordinates)))}.bind(this))}var i=t("../util/util"),o=t("./tile_coord"),a=t("../geo/lng_lat"),s=t("point-geometry"),l=t("../util/evented"),u=t("../util/ajax"),c=t("../data/bucket").EXTENT,h=t("../render/draw_raster").RasterBoundsArray,f=t("../data/buffer"),p=t("../render/vertex_array_object");e.exports=n,n.prototype=i.inherit(l,{minzoom:0,maxzoom:22,tileSize:512,onAdd:function(t){this.map=t,this.image&&this.setCoordinates(this.coordinates)},setCoordinates:function(t){this.coordinates=t;var e=this.map,r=t.map(function(t){return e.transform.locationCoordinate(a.convert(t)).zoomTo(0)}),n=this.centerCoord=i.getCoordinatesCenter(r);return n.column=Math.round(n.column),n.row=Math.round(n.row),this.minzoom=this.maxzoom=n.zoom,this._coord=new o(n.zoom,n.column,n.row),this._tileCoords=r.map(function(t){var e=t.zoomTo(n.zoom);return new s(Math.round((e.column-n.column)*c),Math.round((e.row-n.row)*c))}),this.fire("change"),this},_setTile:function(t){this._prepared=!1,this.tile=t;var e=32767,r=new h;r.emplaceBack(this._tileCoords[0].x,this._tileCoords[0].y,0,0),r.emplaceBack(this._tileCoords[1].x,this._tileCoords[1].y,e,0),r.emplaceBack(this._tileCoords[3].x,this._tileCoords[3].y,0,e),r.emplaceBack(this._tileCoords[2].x,this._tileCoords[2].y,e,e),this.tile.buckets={},this.tile.boundsBuffer=new f(r.serialize(),h.serialize(),f.BufferType.VERTEX),this.tile.boundsVAO=new p,this.tile.state="loaded"},prepare:function(){if(this._loaded&&this.image&&this.image.complete&&this.tile){var t=this.map.painter,e=t.gl;this._prepared?(e.bindTexture(e.TEXTURE_2D,this.tile.texture),e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,this.image)):(this.tile.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.tile.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,this.image))}},loadTile:function(t,e){this._coord&&this._coord.toString()===t.coord.toString()?(this._setTile(t),e(null)):(t.state="errored",e(null))},serialize:function(){return{type:"image",urls:this.url,coordinates:this.coordinates}}})},{"../data/bucket":253,"../data/buffer":258,"../geo/lng_lat":263,"../render/draw_raster":275,"../render/vertex_array_object":281,"../util/ajax":349,"../util/evented":358,"../util/util":366,"./tile_coord":293,"point-geometry":424}],286:[function(t,e,r){"use strict";var n=t("../util/util"),i=t("../util/ajax"),o=t("../util/browser"),a=t("../util/mapbox").normalizeSourceURL;e.exports=function(t,e){var r=function(t,r){if(t)return e(t);var i=n.pick(r,["tiles","minzoom","maxzoom","attribution"]);r.vector_layers&&(i.vectorLayers=r.vector_layers,i.vectorLayerIds=i.vectorLayers.map(function(t){return t.id})),e(null,i)};t.url?i.getJSON(a(t.url),r):o.frame(r.bind(null,null,t))}},{"../util/ajax":349,"../util/browser":350,"../util/mapbox":363,"../util/util":366}],287:[function(t,e,r){"use strict";var n=t("../data/bucket");e.exports=function(t,e,r){return e*(n.EXTENT/(t.tileSize*Math.pow(2,r-t.coord.z)))}},{"../data/bucket":253}],288:[function(t,e,r){"use strict";function n(t,e){var r=t.coord,n=e.coord;return r.z-n.z||r.y-n.y||r.w-n.w||r.x-n.x}function i(t){for(var e=t[0]||{},r=1;r<t.length;r++){var n=t[r];for(var i in n){var o=n[i],a=e[i];if(void 0===a)a=e[i]=o;else for(var s=0;s<o.length;s++)a.push(o[s])}}return e}var o=t("./tile_coord");r.rendered=function(t,e,r,o,a,s){var l=t.tilesIn(r);l.sort(n);for(var u=[],c=0;c<l.length;c++){var h=l[c];h.tile.featureIndex&&u.push(h.tile.featureIndex.query({queryGeometry:h.queryGeometry,scale:h.scale,tileSize:h.tile.tileSize,bearing:s,params:o},e))}return i(u)},r.source=function(t,e){for(var r=t.renderedIDs().map(function(e){return t.getTileByID(e)}),n=[],i={},a=0;a<r.length;a++){var s=r[a],l=new o(Math.min(s.sourceMaxZoom,s.coord.z),s.coord.x,s.coord.y,0).id;i[l]||(i[l]=!0,s.querySourceFeatures(n,e))}return n}},{"./tile_coord":293}],289:[function(t,e,r){"use strict";function n(t,e,r){this.id=t,this.dispatcher=r,i.extend(this,i.pick(e,["url","scheme","tileSize"])),s(e,function(t,e){return t?this.fire("error",t):(i.extend(this,e),void this.fire("load"))}.bind(this))}var i=t("../util/util"),o=t("../util/ajax"),a=t("../util/evented"),s=t("./load_tilejson"),l=t("../util/mapbox").normalizeTileURL;e.exports=n,n.prototype=i.inherit(a,{minzoom:0,maxzoom:22,roundZoom:!0,scheme:"xyz",tileSize:512,_loaded:!1,onAdd:function(t){this.map=t},serialize:function(){return{type:"raster",url:this.url,tileSize:this.tileSize}},loadTile:function(t,e){function r(r,n){if(delete t.request,!t.aborted){if(r)return e(r);var i=this.map.painter.gl;t.texture=this.map.painter.getTexture(n.width),t.texture?(i.bindTexture(i.TEXTURE_2D,t.texture),i.texSubImage2D(i.TEXTURE_2D,0,0,0,i.RGBA,i.UNSIGNED_BYTE,n)):(t.texture=i.createTexture(),i.bindTexture(i.TEXTURE_2D,t.texture),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,i.LINEAR_MIPMAP_NEAREST),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,i.LINEAR),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,i.CLAMP_TO_EDGE),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,i.CLAMP_TO_EDGE),i.pixelStorei(i.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0),i.texImage2D(i.TEXTURE_2D,0,i.RGBA,i.RGBA,i.UNSIGNED_BYTE,n),t.texture.size=n.width),i.generateMipmap(i.TEXTURE_2D),this.map.animationLoop.set(this.map.style.rasterFadeDuration),t.state="loaded",e(null)}}var n=l(t.coord.url(this.tiles,null,this.scheme),this.url,this.tileSize);t.request=o.getImage(n,r.bind(this))},abortTile:function(t){t.request&&(t.request.abort(),delete t.request)},unloadTile:function(t){t.texture&&this.map.painter.saveTexture(t.texture)}})},{"../util/ajax":349,"../util/evented":358,"../util/mapbox":363,"../util/util":366,"./load_tilejson":286}],290:[function(t,e,r){"use strict";var n=t("../util/util"),i={vector:t("../source/vector_tile_source"),raster:t("../source/raster_tile_source"),geojson:t("../source/geojson_source"),video:t("../source/video_source"),image:t("../source/image_source")};r.create=function(t,e,r){if(e=new i[e.type](t,e,r),e.id!==t)throw new Error("Expected Source id to be "+t+" instead of "+e.id);return n.bindAll(["load","abort","unload","serialize","prepare"],e),e},r.getType=function(t){return i[t]},r.setType=function(t,e){i[t]=e}},{"../source/geojson_source":282,"../source/image_source":285,"../source/raster_tile_source":289,"../source/vector_tile_source":294,"../source/video_source":296,"../util/util":366}],291:[function(t,e,r){"use strict";function n(t,e,r){this.id=t,this.dispatcher=r;var n=this._source=a.create(t,e,r).on("load",function(){this.map&&this._source.onAdd&&this._source.onAdd(this.map),this._sourceLoaded=!0,this.tileSize=n.tileSize,this.minzoom=n.minzoom,this.maxzoom=n.maxzoom,this.roundZoom=n.roundZoom,this.reparseOverscaled=n.reparseOverscaled,this.isTileClipped=n.isTileClipped,this.attribution=n.attribution,this.vectorLayerIds=n.vectorLayerIds,this.fire("load")}.bind(this)).on("error",function(t){this._sourceErrored=!0,this.fire("error",t)}.bind(this)).on("change",function(){this.reload(),this.transform&&this.update(this.transform,this.map&&this.map.style.rasterFadeDuration),this.fire("change")}.bind(this));this._tiles={},this._cache=new c(0,this.unloadTile.bind(this)),this._isIdRenderable=this._isIdRenderable.bind(this)}function i(t,e,r){var n=r.zoomTo(Math.min(t.z,e));return{x:(n.column-(t.x+t.w*Math.pow(2,t.z)))*p,y:(n.row-t.y)*p}}function o(t,e){return t%32-e%32}var a=t("./source"),s=t("./tile"),l=t("../util/evented"),u=t("./tile_coord"),c=t("../util/lru_cache"),h=t("../geo/coordinate"),f=t("../util/util"),p=t("../data/bucket").EXTENT;e.exports=n,n.maxOverzooming=10,n.maxUnderzooming=3,n.prototype=f.inherit(l,{onAdd:function(t){this.map=t,this._source&&this._source.onAdd&&this._source.onAdd(t)},loaded:function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},getSource:function(){return this._source},loadTile:function(t,e){return this._source.loadTile(t,e)},unloadTile:function(t){return this._source.unloadTile?this._source.unloadTile(t):void 0},abortTile:function(t){return this._source.abortTile?this._source.abortTile(t):void 0},serialize:function(){return this._source.serialize()},prepare:function(){return this._sourceLoaded&&this._source.prepare?this._source.prepare():void 0},getIds:function(){return Object.keys(this._tiles).map(Number).sort(o)},getRenderableIds:function(){return this.getIds().filter(this._isIdRenderable)},_isIdRenderable:function(t){return this._tiles[t].isRenderable()&&!this._coveredTiles[t]},reload:function(){this._cache.reset();for(var t in this._tiles){var e=this._tiles[t];"loading"!==e.state&&(e.state="reloading"),this.loadTile(this._tiles[t],this._tileLoaded.bind(this,this._tiles[t]))}},_tileLoaded:function(t,e){return e?(t.state="errored",this.fire("tile.error",{tile:t,error:e}),void this._source.fire("tile.error",{tile:t,error:e})):(t.source=this,t.timeAdded=(new Date).getTime(),this.fire("tile.load",{tile:t}),void this._source.fire("tile.load",{tile:t}))},getTile:function(t){return this.getTileByID(t.id)},getTileByID:function(t){return this._tiles[t]},getZoom:function(t){return t.zoom+t.scaleZoom(t.tileSize/this.tileSize)},findLoadedChildren:function(t,e,r){var n=!1;for(var i in this._tiles){var o=this._tiles[i];if(!(r[i]||!o.isRenderable()||o.coord.z<=t.z||o.coord.z>e)){var a=Math.pow(2,Math.min(o.coord.z,this.maxzoom)-Math.min(t.z,this.maxzoom));if(Math.floor(o.coord.x/a)===t.x&&Math.floor(o.coord.y/a)===t.y)for(r[i]=!0,n=!0;o&&o.coord.z-1>t.z;){var s=o.coord.parent(this.maxzoom).id;o=this._tiles[s],o&&o.isRenderable()&&(delete r[i],r[s]=!0)}}}return n},findLoadedParent:function(t,e,r){for(var n=t.z-1;n>=e;n--){t=t.parent(this.maxzoom);var i=this._tiles[t.id];if(i&&i.isRenderable())return r[t.id]=!0,i;if(this._cache.has(t.id))return this.addTile(t),r[t.id]=!0,this._tiles[t.id]}},updateCacheSize:function(t){var e=Math.ceil(t.width/t.tileSize)+1,r=Math.ceil(t.height/t.tileSize)+1,n=e*r,i=5;this._cache.setMaxSize(Math.floor(n*i))},update:function(t,e){if(this._sourceLoaded){var r,i,o;this.updateCacheSize(t);var a=(this.roundZoom?Math.round:Math.floor)(this.getZoom(t)),s=Math.max(a-n.maxOverzooming,this.minzoom),l=Math.max(a+n.maxUnderzooming,this.minzoom),c={},h=(new Date).getTime();this._coveredTiles={};var p=this.used?t.coveringTiles(this._source):[];for(r=0;r<p.length;r++)i=p[r],o=this.addTile(i),c[i.id]=!0,o.isRenderable()||this.findLoadedChildren(i,l,c)||this.findLoadedParent(i,s,c);for(var d={},m=Object.keys(c),g=0;g<m.length;g++){var v=m[g];i=u.fromID(v),o=this._tiles[v],o&&o.timeAdded>h-(e||0)&&(this.findLoadedChildren(i,l,c)&&(c[v]=!0),this.findLoadedParent(i,s,d))}var y;for(y in d)c[y]||(this._coveredTiles[y]=!0);for(y in d)c[y]=!0;var b=f.keysDifference(this._tiles,c);for(r=0;r<b.length;r++)this.removeTile(+b[r]);this.transform=t}},addTile:function(t){var e=this._tiles[t.id];if(e)return e;var r=t.wrapped();if(e=this._tiles[r.id],e||(e=this._cache.get(r.id),e&&this._redoPlacement&&this._redoPlacement(e)),!e){var n=t.z,i=n>this.maxzoom?Math.pow(2,n-this.maxzoom):1;e=new s(r,this.tileSize*i,this.maxzoom),this.loadTile(e,this._tileLoaded.bind(this,e))}return e.uses++,this._tiles[t.id]=e,this.fire("tile.add",{tile:e}),this._source.fire("tile.add",{tile:e}),e},removeTile:function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this.fire("tile.remove",{tile:e}),this._source.fire("tile.remove",{tile:e}),e.uses>0||(e.isRenderable()?this._cache.add(e.coord.wrapped().id,e):(e.aborted=!0,this.abortTile(e),this.unloadTile(e))))},clearTiles:function(){for(var t in this._tiles)this.removeTile(t);this._cache.reset()},tilesIn:function(t){for(var e={},r=this.getIds(),n=1/0,o=1/0,a=-(1/0),s=-(1/0),l=t[0].zoom,c=0;c<t.length;c++){var f=t[c];n=Math.min(n,f.column),o=Math.min(o,f.row),a=Math.max(a,f.column),s=Math.max(s,f.row)}for(var d=0;d<r.length;d++){var m=this._tiles[r[d]],g=u.fromID(r[d]),v=[i(g,m.sourceMaxZoom,new h(n,o,l)),i(g,m.sourceMaxZoom,new h(a,s,l))];if(v[0].x<p&&v[0].y<p&&v[1].x>=0&&v[1].y>=0){for(var y=[],b=0;b<t.length;b++)y.push(i(g,m.sourceMaxZoom,t[b]));var x=e[m.coord.id];void 0===x&&(x=e[m.coord.id]={tile:m,coord:g,queryGeometry:[],scale:Math.pow(2,this.transform.zoom-m.coord.z)}),x.queryGeometry.push(y)}}var _=[];for(var w in e)_.push(e[w]);return _},redoPlacement:function(){for(var t=this.getIds(),e=0;e<t.length;e++){var r=this.getTileByID(t[e]);r.redoPlacement(this)}},getVisibleCoordinates:function(){return this.getRenderableIds().map(u.fromID)}})},{"../data/bucket":253,"../geo/coordinate":262,"../util/evented":358,"../util/lru_cache":362,"../util/util":366,"./source":290,"./tile":292,"./tile_coord":293}],292:[function(t,e,r){"use strict";function n(t,e,r){this.coord=t,this.uid=o.uniqueId(),this.uses=0,this.tileSize=e,this.sourceMaxZoom=r,this.buckets={},this.state="loading"}function i(t,e){if(e){for(var r={},n=0;n<t.length;n++){var i=e.getLayer(t[n].layerId);if(i){var s=a.create(o.extend({layer:i,childLayers:t[n].childLayerIds.map(e.getLayer.bind(e)).filter(function(t){return t})},t[n]));r[s.id]=s}}return r}}var o=t("../util/util"),a=t("../data/bucket"),s=t("../data/feature_index"),l=t("vector-tile"),u=t("pbf"),c=t("../util/vectortile_to_geojson"),h=t("feature-filter"),f=t("../symbol/collision_tile"),p=t("../symbol/collision_box"),d=t("../symbol/symbol_instances"),m=t("../symbol/symbol_quads");e.exports=n,n.prototype={loadVectorData:function(t,e){this.state="loaded",t&&(this.collisionBoxArray=new p(t.collisionBoxArray),this.collisionTile=new f(t.collisionTile,this.collisionBoxArray),this.symbolInstancesArray=new d(t.symbolInstancesArray),this.symbolQuadsArray=new m(t.symbolQuadsArray),this.featureIndex=new s(t.featureIndex,t.rawTileData,this.collisionTile),this.rawTileData=t.rawTileData,this.buckets=i(t.buckets,e))},reloadSymbolData:function(t,e,r){if("unloaded"!==this.state){this.collisionTile=new f(t.collisionTile,this.collisionBoxArray),this.featureIndex.setCollisionTile(this.collisionTile);for(var n in this.buckets){var a=this.buckets[n];"symbol"===a.type&&(a.destroy(e.gl),delete this.buckets[n])}o.extend(this.buckets,i(t.buckets,r))}},unloadVectorData:function(t){for(var e in this.buckets){var r=this.buckets[e];r.destroy(t.gl)}this.collisionBoxArray=null,this.symbolQuadsArray=null,this.symbolInstancesArray=null,this.collisionTile=null,this.featureIndex=null,this.rawTileData=null,this.buckets=null,this.state="unloaded"},redoPlacement:function(t){function e(e,r){this.reloadSymbolData(r,t.map.painter,t.map.style),t.fire("tile.load",{tile:this}),this.state="loaded",this.redoWhenDone&&(this.redoPlacement(t),this.redoWhenDone=!1)}return"loaded"!==this.state||"reloading"===this.state?void(this.redoWhenDone=!0):(this.state="reloading",void t.dispatcher.send("redo placement",{uid:this.uid,source:t.id,angle:t.map.transform.angle,pitch:t.map.transform.pitch,showCollisionBoxes:t.map.showCollisionBoxes},e.bind(this),this.workerID))},getBucket:function(t){return this.buckets&&this.buckets[t.ref||t.id]},querySourceFeatures:function(t,e){if(this.rawTileData){this.vtLayers||(this.vtLayers=new l.VectorTile(new u(new Uint8Array(this.rawTileData))).layers);var r=this.vtLayers._geojsonTileLayer||this.vtLayers[e.sourceLayer];if(r)for(var n=h(e.filter),i={z:this.coord.z,x:this.coord.x,y:this.coord.y},o=0;o<r.length;o++){var a=r.feature(o);if(n(a)){var s=new c(a,this.coord.z,this.coord.x,this.coord.y);s.tile=i,t.push(s)}}}},isRenderable:function(){return"loaded"===this.state||"reloading"===this.state}}},{"../data/bucket":253,"../data/feature_index":260,"../symbol/collision_box":318,"../symbol/collision_tile":320,"../symbol/symbol_instances":329,"../symbol/symbol_quads":330,"../util/util":366,"../util/vectortile_to_geojson":367,"feature-filter":370,pbf:422,"vector-tile":434}],293:[function(t,e,r){"use strict";function n(t,e,r,n){isNaN(n)&&(n=0),this.z=+t,this.x=+e,this.y=+r,this.w=+n,n*=2,0>n&&(n=-1*n-1);var i=1<<this.z;this.id=32*(i*i*n+i*this.y+this.x)+this.z,this.posMatrix=null}function i(t,e,r){for(var n,i="",o=t;o>0;o--)n=1<<o-1,i+=(e&n?1:0)+(r&n?2:0);return i}function o(t,e){if(t.row>e.row){var r=t;t=e,e=r}return{x0:t.column,y0:t.row,x1:e.column,y1:e.row,dx:e.column-t.column,dy:e.row-t.row}}function a(t,e,r,n,i){var o=Math.max(r,Math.floor(e.y0)),a=Math.min(n,Math.ceil(e.y1));if(t.x0===e.x0&&t.y0===e.y0?t.x0+e.dy/t.dy*t.dx<e.x1:t.x1-e.dy/t.dy*t.dx<e.x0){var s=t;t=e,e=s}for(var l=t.dx/t.dy,u=e.dx/e.dy,c=t.dx>0,h=e.dx<0,f=o;a>f;f++){var p=l*Math.max(0,Math.min(t.dy,f+c-t.y0))+t.x0,d=u*Math.max(0,Math.min(e.dy,f+h-e.y0))+e.x0;i(Math.floor(d),Math.ceil(p),f)}}function s(t,e,r,n,i,s){var l,u=o(t,e),c=o(e,r),h=o(r,t);u.dy>c.dy&&(l=u,u=c,c=l),u.dy>h.dy&&(l=u,u=h,h=l),c.dy>h.dy&&(l=c,c=h,h=l),u.dy&&a(h,u,n,i,s),c.dy&&a(h,c,n,i,s)}var l=t("whoots-js"),u=t("../geo/coordinate");e.exports=n,n.prototype.toString=function(){return this.z+"/"+this.x+"/"+this.y},n.prototype.toCoordinate=function(t){var e=Math.min(this.z,t),r=Math.pow(2,e),n=this.y,i=this.x+r*this.w;return new u(i,n,e)},n.fromID=function(t){var e=t%32,r=1<<e,i=(t-e)/32,o=i%r,a=(i-o)/r%r,s=Math.floor(i/(r*r));return s%2!==0&&(s=-1*s-1),s/=2,new n(e,o,a,s)},n.prototype.url=function(t,e,r){var n=l.getTileBBox(this.x,this.y,this.z),o=i(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",Math.min(this.z,e||this.z)).replace("{x}",this.x).replace("{y}","tms"===r?Math.pow(2,this.z)-this.y-1:this.y).replace("{quadkey}",o).replace("{bbox-epsg-3857}",n)},n.prototype.parent=function(t){return 0===this.z?null:this.z>t?new n(this.z-1,this.x,this.y,this.w):new n(this.z-1,Math.floor(this.x/2),Math.floor(this.y/2),this.w)},n.prototype.wrapped=function(){return new n(this.z,this.x,this.y,0)},n.prototype.children=function(t){if(this.z>=t)return[new n(this.z+1,this.x,this.y,this.w)];var e=this.z+1,r=2*this.x,i=2*this.y;return[new n(e,r,i,this.w),new n(e,r+1,i,this.w),new n(e,r,i+1,this.w),new n(e,r+1,i+1,this.w)]},n.cover=function(t,e,r){function i(t,e,i){var s,l,u;if(i>=0&&o>=i)for(s=t;e>s;s++)l=(s%o+o)%o,u=new n(r,l,i,Math.floor(s/o)),a[u.id]=u}var o=1<<t,a={};return s(e[0],e[1],e[2],0,o,i),s(e[2],e[3],e[0],0,o,i),Object.keys(a).map(function(t){return a[t]})}},{"../geo/coordinate":262,"whoots-js":442}],294:[function(t,e,r){"use strict";function n(t,e,r){if(this.id=t,this.dispatcher=r,o.extend(this,o.pick(e,["url","scheme","tileSize"])),this._options=o.extend({type:"vector"},e),512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");a(e,function(t,e){return t?void this.fire("error",t):(o.extend(this,e),void this.fire("load"))}.bind(this))}var i=t("../util/evented"),o=t("../util/util"),a=t("./load_tilejson"),s=t("../util/mapbox").normalizeTileURL;e.exports=n,n.prototype=o.inherit(i,{minzoom:0,maxzoom:22,scheme:"xyz",tileSize:512,reparseOverscaled:!0,isTileClipped:!0,onAdd:function(t){this.map=t},serialize:function(){return o.extend({},this._options)},loadTile:function(t,e){function r(r,n){if(!t.aborted){if(r)return e(r);t.loadVectorData(n,this.map.style),t.redoWhenDone&&(t.redoWhenDone=!1,t.redoPlacement(this)),e(null),t.reloadCallback&&(this.loadTile(t,t.reloadCallback),t.reloadCallback=null)}}var n=t.coord.z>this.maxzoom?Math.pow(2,t.coord.z-this.maxzoom):1,i={url:s(t.coord.url(this.tiles,this.maxzoom,this.scheme),this.url),uid:t.uid,coord:t.coord,zoom:t.coord.z,tileSize:this.tileSize*n,source:this.id,overscaling:n,angle:this.map.transform.angle,pitch:this.map.transform.pitch,showCollisionBoxes:this.map.showCollisionBoxes};t.workerID?"loading"===t.state?t.reloadCallback=e:(i.rawTileData=t.rawTileData,this.dispatcher.send("reload tile",i,r.bind(this),t.workerID)):t.workerID=this.dispatcher.send("load tile",i,r.bind(this))},abortTile:function(t){this.dispatcher.send("abort tile",{uid:t.uid,source:this.id},null,t.workerID)},unloadTile:function(t){t.unloadVectorData(this.map.painter),this.dispatcher.send("remove tile",{uid:t.uid,source:this.id},null,t.workerID)}})},{"../util/evented":358,"../util/mapbox":363,"../util/util":366,"./load_tilejson":286}],295:[function(t,e,r){"use strict";function n(t,e,r){this.actor=t,this.styleLayers=e,r&&(this.loadVectorData=r),this.loading={},this.loaded={}}var i=t("../util/ajax"),o=t("vector-tile"),a=t("pbf"),s=t("./worker_tile");e.exports=n,n.prototype={loadTile:function(t,e){function r(t,r){return delete this.loading[n][i],t?e(t):r?(o.data=r.tile,o.parse(o.data,this.styleLayers.getLayerFamilies(),this.actor,r.rawTileData,e),this.loaded[n]=this.loaded[n]||{},void(this.loaded[n][i]=o)):e(null,null)}var n=t.source,i=t.uid;this.loading[n]||(this.loading[n]={});var o=this.loading[n][i]=new s(t);o.abort=this.loadVectorData(t,r.bind(this))},reloadTile:function(t,e){var r=this.loaded[t.source],n=t.uid;if(r&&r[n]){var i=r[n];i.parse(i.data,this.styleLayers.getLayerFamilies(),this.actor,t.rawTileData,e)}},abortTile:function(t){var e=this.loading[t.source],r=t.uid;e&&e[r]&&e[r].abort&&(e[r].abort(),delete e[r])},removeTile:function(t){var e=this.loaded[t.source],r=t.uid;e&&e[r]&&delete e[r]},loadVectorData:function(t,e){function r(t,r){if(t)return e(t);var n=new o.VectorTile(new a(new Uint8Array(r)));e(t,{tile:n,rawTileData:r})}var n=i.getArrayBuffer(t.url,r.bind(this));return function(){n.abort()}},redoPlacement:function(t,e){var r=this.loaded[t.source],n=this.loading[t.source],i=t.uid;if(r&&r[i]){var o=r[i],a=o.redoPlacement(t.angle,t.pitch,t.showCollisionBoxes);a.result&&e(null,a.result,a.transferables)}else n&&n[i]&&(n[i].angle=t.angle)}}},{"../util/ajax":349,"./worker_tile":298,pbf:422,"vector-tile":434}],296:[function(t,e,r){"use strict";function n(t,e){this.id=t,this.urls=e.urls,this.coordinates=e.coordinates,u.getVideo(e.urls,function(t,r){if(t)return this.fire("error",{error:t});this.video=r,this.video.loop=!0;var n;this.video.addEventListener("playing",function(){n=this.map.style.animationLoop.set(1/0),this.map._rerender()}.bind(this)),this.video.addEventListener("pause",function(){this.map.style.animationLoop.cancel(n)}.bind(this)),this.map&&(this.video.play(),this.setCoordinates(e.coordinates)),this.fire("load")}.bind(this))}var i=t("../util/util"),o=t("./tile_coord"),a=t("../geo/lng_lat"),s=t("point-geometry"),l=t("../util/evented"),u=t("../util/ajax"),c=t("../data/bucket").EXTENT,h=t("../render/draw_raster").RasterBoundsArray,f=t("../data/buffer"),p=t("../render/vertex_array_object");e.exports=n,n.prototype=i.inherit(l,{minzoom:0,maxzoom:22,tileSize:512,roundZoom:!0,getVideo:function(){return this.video},onAdd:function(t){this.map||(this.map=t,this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},setCoordinates:function(t){this.coordinates=t;var e=this.map,r=t.map(function(t){return e.transform.locationCoordinate(a.convert(t)).zoomTo(0)}),n=this.centerCoord=i.getCoordinatesCenter(r);return n.column=Math.round(n.column),n.row=Math.round(n.row),this.minzoom=this.maxzoom=n.zoom,this._coord=new o(n.zoom,n.column,n.row),this._tileCoords=r.map(function(t){var e=t.zoomTo(n.zoom);return new s(Math.round((e.column-n.column)*c),Math.round((e.row-n.row)*c))}),this.fire("change"),this},_setTile:function(t){this._prepared=!1,this.tile=t;var e=32767,r=new h;r.emplaceBack(this._tileCoords[0].x,this._tileCoords[0].y,0,0),r.emplaceBack(this._tileCoords[1].x,this._tileCoords[1].y,e,0),r.emplaceBack(this._tileCoords[3].x,this._tileCoords[3].y,0,e),r.emplaceBack(this._tileCoords[2].x,this._tileCoords[2].y,e,e),this.tile.buckets={},this.tile.boundsBuffer=new f(r.serialize(),h.serialize(),f.BufferType.VERTEX),this.tile.boundsVAO=new p,this.tile.state="loaded"},prepare:function(){if(!(this.video.readyState<2)&&this.tile){var t=this.map.painter.gl;this._prepared?(t.bindTexture(t.TEXTURE_2D,this.tile.texture),t.texSubImage2D(t.TEXTURE_2D,0,0,0,t.RGBA,t.UNSIGNED_BYTE,this.video)):(this._prepared=!0,this.tile.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.tile.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texImage2D(t.TEXTURE_2D,0,t.RGBA,t.RGBA,t.UNSIGNED_BYTE,this.video)),this._currentTime=this.video.currentTime}},loadTile:function(t,e){this._coord&&this._coord.toString()===t.coord.toString()?(this._setTile(t),e(null)):(t.state="errored",e(null));
},serialize:function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}}})},{"../data/bucket":253,"../data/buffer":258,"../geo/lng_lat":263,"../render/draw_raster":275,"../render/vertex_array_object":281,"../util/ajax":349,"../util/evented":358,"../util/util":366,"./tile_coord":293,"point-geometry":424}],297:[function(t,e,r){"use strict";function n(t){this.self=t,this.actor=new o(t,this);var e={getLayers:function(){return this.layers}.bind(this),getLayerFamilies:function(){return this.layerFamilies}.bind(this)};this.workerSources={vector:new l(this.actor,e),geojson:new u(this.actor,e)},this.self.registerWorkerSource=function(t,r){if(this.workerSources[t])throw new Error('Worker source with name "'+t+'" already registered.');this.workerSources[t]=new r(this.actor,e)}.bind(this)}function i(t){var e={};for(var r in t){var n=t[r],i=n.ref||n.id,o=t[i];o.layout&&"none"===o.layout.visibility||(e[i]=e[i]||[],r===i?e[i].unshift(n):e[i].push(n))}return e}var o=t("../util/actor"),a=t("../style/style_layer"),s=t("../util/util"),l=t("./vector_tile_worker_source"),u=t("./geojson_worker_source");e.exports=function(t){return new n(t)},s.extend(n.prototype,{"set layers":function(t){function e(t){var e=a.create(t,t.ref&&r.layers[t.ref]);e.updatePaintTransitions({},{transition:!1}),r.layers[e.id]=e}this.layers={};for(var r=this,n=[],o=0;o<t.length;o++){var s=t[o];"fill"!==s.type&&"line"!==s.type&&"circle"!==s.type&&"symbol"!==s.type||(s.ref?n.push(o):e(s))}for(var l=0;l<n.length;l++)e(t[n[l]]);this.layerFamilies=i(this.layers)},"update layers":function(t){function e(t){var e=o.layers[t.ref];o.layers[t.id]?o.layers[t.id].set(t,e):o.layers[t.id]=a.create(t,e),o.layers[t.id].updatePaintTransitions({},{transition:!1})}var r,n,o=this;for(r in t)n=t[r],n.ref&&e(n);for(r in t)n=t[r],n.ref||e(n);this.layerFamilies=i(this.layers)},"load tile":function(t,e){var r=t.type||"vector";this.workerSources[r].loadTile(t,e)},"reload tile":function(t,e){var r=t.type||"vector";this.workerSources[r].reloadTile(t,e)},"abort tile":function(t){var e=t.type||"vector";this.workerSources[e].abortTile(t)},"remove tile":function(t){var e=t.type||"vector";this.workerSources[e].removeTile(t)},"redo placement":function(t,e){var r=t.type||"vector";this.workerSources[r].redoPlacement(t,e)},"load worker source":function(t,e){try{this.self.importScripts(t.url),e()}catch(r){e(r)}}})},{"../style/style_layer":305,"../util/actor":348,"../util/util":366,"./geojson_worker_source":283,"./vector_tile_worker_source":295}],298:[function(t,e,r){"use strict";function n(t){this.coord=t.coord,this.uid=t.uid,this.zoom=t.zoom,this.tileSize=t.tileSize,this.source=t.source,this.overscaling=t.overscaling,this.angle=t.angle,this.pitch=t.pitch,this.showCollisionBoxes=t.showCollisionBoxes}function i(t){return!t.isEmpty()}function o(t){return t.serialize()}function a(t){var e=[];for(var r in t)t[r].getTransferables(e);return e}function s(t){return t.id}var l=t("../data/feature_index"),u=t("../symbol/collision_tile"),c=t("../data/bucket"),h=t("../symbol/collision_box"),f=t("../util/dictionary_coder"),p=t("../util/util"),d=t("../symbol/symbol_instances"),m=t("../symbol/symbol_quads");e.exports=n,n.prototype.parse=function(t,e,r,n,g){function v(t,e){for(var r=0;r<t.length;r++){var n=t.feature(r);n.index=r;for(var i in e)e[i].filter(n)&&e[i].features.push(n)}}function y(t){if(t)return g(t);if(N++,2===N){for(var e=R.length-1;e>=0;e--)b(S,R[e]);x()}}function b(t,e){if(e.populateArrays(A,F,O),"symbol"!==e.type)for(var r=0;r<e.features.length;r++){var n=e.features[r];T.insert(n,n.index,e.sourceLayerIndex,e.index)}e.features=null}function x(){S.status="done",S.redoPlacementAfterDone&&(S.redoPlacement(S.angle,S.pitch,null),S.redoPlacementAfterDone=!1);var t=T.serialize(),e=A.serialize(),r=S.collisionBoxArray.serialize(),s=S.symbolInstancesArray.serialize(),l=S.symbolQuadsArray.serialize(),u=[n].concat(t.transferables).concat(e.transferables),c=C.filter(i);g(null,{buckets:c.map(o),featureIndex:t.data,collisionTile:e.data,collisionBoxArray:r,symbolInstancesArray:s,symbolQuadsArray:l,rawTileData:n},a(c).concat(u))}this.status="parsing",this.data=t,this.collisionBoxArray=new h,this.symbolInstancesArray=new d,this.symbolQuadsArray=new m;var _,w,M,k,A=new u(this.angle,this.pitch,this.collisionBoxArray),T=new l(this.coord,this.overscaling,A,t.layers),E=new f(t.layers?Object.keys(t.layers).sort():["_geojsonTileLayer"]),S=this,L={},z={},I=0;for(var P in e)w=e[P][0],w.source===this.source&&(w.ref||w.minzoom&&this.zoom<w.minzoom||w.maxzoom&&this.zoom>=w.maxzoom||w.layout&&"none"===w.layout.visibility||t.layers&&!t.layers[w.sourceLayer]||(k=c.create({layer:w,index:I++,childLayers:e[P],zoom:this.zoom,overscaling:this.overscaling,showCollisionBoxes:this.showCollisionBoxes,collisionBoxArray:this.collisionBoxArray,symbolQuadsArray:this.symbolQuadsArray,symbolInstancesArray:this.symbolInstancesArray,sourceLayerIndex:E.encode(w.sourceLayer||"_geojsonTileLayer")}),k.createFilter(),L[w.id]=k,t.layers&&(M=w.sourceLayer,z[M]=z[M]||{},z[M][w.id]=k)));if(t.layers)for(M in z)1===w.version&&p.warnOnce('Vector tile source "'+this.source+'" layer "'+M+'" does not use vector tile spec v2 and therefore may have some rendering errors.'),w=t.layers[M],w&&v(w,z[M]);else v(t,L);var C=[],R=this.symbolBuckets=[],j=[];T.bucketLayerIDs={};for(var D in L)k=L[D],0!==k.features.length&&(T.bucketLayerIDs[k.index]=k.childLayers.map(s),C.push(k),"symbol"===k.type?R.push(k):j.push(k));var O={},F={},N=0;if(R.length>0){for(_=R.length-1;_>=0;_--)R[_].updateIcons(O),R[_].updateFont(F);for(var B in F)F[B]=Object.keys(F[B]).map(Number);O=Object.keys(O),r.send("get glyphs",{uid:this.uid,stacks:F},function(t,e){F=e,y(t)}),O.length?r.send("get icons",{icons:O},function(t,e){O=e,y(t)}):y()}for(_=j.length-1;_>=0;_--)b(this,j[_]);return 0===R.length?x():void 0},n.prototype.redoPlacement=function(t,e,r){if("done"!==this.status)return this.redoPlacementAfterDone=!0,this.angle=t,{};for(var n=new u(t,e,this.collisionBoxArray),s=this.symbolBuckets,l=s.length-1;l>=0;l--)s[l].placeFeatures(n,r);var c=n.serialize(),h=s.filter(i);return{result:{buckets:h.map(o),collisionTile:c.data},transferables:a(h).concat(c.transferables)}}},{"../data/bucket":253,"../data/feature_index":260,"../symbol/collision_box":318,"../symbol/collision_tile":320,"../symbol/symbol_instances":329,"../symbol/symbol_quads":330,"../util/dictionary_coder":356,"../util/util":366}],299:[function(t,e,r){"use strict";function n(){this.n=0,this.times=[]}e.exports=n,n.prototype.stopped=function(){return this.times=this.times.filter(function(t){return t.time>=(new Date).getTime()}),!this.times.length},n.prototype.set=function(t){return this.times.push({id:this.n,time:t+(new Date).getTime()}),this.n++},n.prototype.cancel=function(t){this.times=this.times.filter(function(e){return e.id!==t})}},{}],300:[function(t,e,r){"use strict";function n(t){this.base=t,this.retina=s.devicePixelRatio>1;var e=this.retina?"@2x":"";a.getJSON(l(t,e,".json"),function(t,e){return t?void this.fire("error",{error:t}):(this.data=e,void(this.img&&this.fire("load")))}.bind(this)),a.getImage(l(t,e,".png"),function(t,e){if(t)return void this.fire("error",{error:t});for(var r=e.getData(),n=e.data=new Uint8Array(r.length),i=0;i<r.length;i+=4){var o=r[i+3]/255;n[i+0]=r[i+0]*o,n[i+1]=r[i+1]*o,n[i+2]=r[i+2]*o,n[i+3]=r[i+3]}this.img=e,this.data&&this.fire("load")}.bind(this))}function i(){}var o=t("../util/evented"),a=t("../util/ajax"),s=t("../util/browser"),l=t("../util/mapbox").normalizeSpriteURL;e.exports=n,n.prototype=Object.create(o),n.prototype.toJSON=function(){return this.base},n.prototype.loaded=function(){return!(!this.data||!this.img)},n.prototype.resize=function(){if(s.devicePixelRatio>1!==this.retina){var t=new n(this.base);t.on("load",function(){this.img=t.img,this.data=t.data,this.retina=t.retina}.bind(this))}},i.prototype={x:0,y:0,width:0,height:0,pixelRatio:1,sdf:!1},n.prototype.getSpritePosition=function(t){if(!this.loaded())return new i;var e=this.data&&this.data[t];return e&&this.img?e:new i}},{"../util/ajax":349,"../util/browser":350,"../util/evented":358,"../util/mapbox":363}],301:[function(t,e,r){"use strict";var n=t("csscolorparser").parseCSSColor,i=t("../util/util"),o=t("./style_function"),a={};e.exports=function s(t){if(o.isFunctionDefinition(t))return i.extend({},t,{stops:t.stops.map(function(t){return[t[0],s(t[1])]})});if("string"==typeof t){if(!a[t]){var e=n(t);if(!e)throw new Error("Invalid color "+t);a[t]=[e[0]/255*e[3],e[1]/255*e[3],e[2]/255*e[3],e[3]]}return a[t]}throw new Error("Invalid color "+t)}},{"../util/util":366,"./style_function":304,csscolorparser:368}],302:[function(t,e,r){"use strict";function n(t,e,r){this.animationLoop=e||new m,this.dispatcher=new d(r||1,this),this.spriteAtlas=new l(1024,1024),this.lineAtlas=new u(256,512),this._layers={},this._order=[],this._groups=[],this.sources={},this.zoomHistory={},c.bindAll(["_forwardSourceEvent","_forwardTileEvent","_forwardLayerEvent","_redoPlacement"],this),this._resetUpdates();var n=function(t,e){if(t)return void this.fire("error",{error:t});if(!g.emitErrors(this,g(e))){this._loaded=!0,this.stylesheet=e,this.updateClasses();var r=e.sources;for(var n in r)this.addSource(n,r[n]);e.sprite&&(this.sprite=new a(e.sprite),this.sprite.on("load",this.fire.bind(this,"change"))),this.glyphSource=new s(e.glyphs),this._resolve(),this.fire("load")}}.bind(this);"string"==typeof t?h.getJSON(f(t),n):p.frame(n.bind(this,null,t)),this.on("source.load",function(t){var e=t.source;if(e&&e.vectorLayerIds)for(var r in this._layers){var n=this._layers[r];n.source===e.id&&this._validateLayer(n)}})}var i=t("../util/evented"),o=t("./style_layer"),a=t("./image_sprite"),s=t("../symbol/glyph_source"),l=t("../symbol/sprite_atlas"),u=t("../render/line_atlas"),c=t("../util/util"),h=t("../util/ajax"),f=t("../util/mapbox").normalizeStyleURL,p=t("../util/browser"),d=t("../util/dispatcher"),m=t("./animation_loop"),g=t("./validate_style"),v=t("../source/source"),y=t("../source/query_features"),b=t("../source/source_cache"),x=t("./style_spec"),_=t("./style_function");e.exports=n,n.prototype=c.inherit(i,{_loaded:!1,_validateLayer:function(t){var e=this.sources[t.source];t.sourceLayer&&e&&e.vectorLayerIds&&-1===e.vectorLayerIds.indexOf(t.sourceLayer)&&this.fire("error",{error:new Error('Source layer "'+t.sourceLayer+'" does not exist on source "'+e.id+'" as specified by style layer "'+t.id+'"')})},loaded:function(){if(!this._loaded)return!1;if(Object.keys(this._updates.sources).length)return!1;for(var t in this.sources)if(!this.sources[t].loaded())return!1;return!this.sprite||this.sprite.loaded()},_resolve:function(){var t,e;this._layers={},this._order=this.stylesheet.layers.map(function(t){return t.id});for(var r=0;r<this.stylesheet.layers.length;r++)e=this.stylesheet.layers[r],e.ref||(t=o.create(e),this._layers[t.id]=t,t.on("error",this._forwardLayerEvent));for(var n=0;n<this.stylesheet.layers.length;n++)if(e=this.stylesheet.layers[n],e.ref){var i=this.getLayer(e.ref);t=o.create(e,i),this._layers[t.id]=t,t.on("error",this._forwardLayerEvent)}this._groupLayers(),this._updateWorkerLayers()},_groupLayers:function(){var t;this._groups=[];for(var e=0;e<this._order.length;++e){var r=this._layers[this._order[e]];t&&r.source===t.source||(t=[],t.source=r.source,this._groups.push(t)),t.push(r)}},_updateWorkerLayers:function(t){this.dispatcher.broadcast(t?"update layers":"set layers",this._serializeLayers(t))},_serializeLayers:function(t){t=t||this._order;for(var e=[],r={includeRefProperties:!0},n=0;n<t.length;n++)e.push(this._layers[t[n]].serialize(r));return e},_applyClasses:function(t,e){if(this._loaded){t=t||[],e=e||{transition:!0};var r=this.stylesheet.transition||{},n=this._updates.allPaintProps?this._layers:this._updates.paintProps;for(var i in n){var o=this._layers[i],a=this._updates.paintProps[i];if(this._updates.allPaintProps||a.all)o.updatePaintTransitions(t,e,r,this.animationLoop);else for(var s in a)this._layers[i].updatePaintTransition(s,t,e,r,this.animationLoop)}}},_recalculate:function(t){for(var e in this.sources)this.sources[e].used=!1;this._updateZoomHistory(t),this.rasterFadeDuration=300;for(var r in this._layers){var n=this._layers[r];n.recalculate(t,this.zoomHistory),!n.isHidden(t)&&n.source&&(this.sources[n.source].used=!0)}var i=300;Math.floor(this.z)!==Math.floor(t)&&this.animationLoop.set(i),this.z=t,this.fire("zoom")},_updateZoomHistory:function(t){var e=this.zoomHistory;void 0===e.lastIntegerZoom&&(e.lastIntegerZoom=Math.floor(t),e.lastIntegerZoomTime=0,e.lastZoom=t),Math.floor(e.lastZoom)<Math.floor(t)?(e.lastIntegerZoom=Math.floor(t),e.lastIntegerZoomTime=Date.now()):Math.floor(e.lastZoom)>Math.floor(t)&&(e.lastIntegerZoom=Math.floor(t+1),e.lastIntegerZoomTime=Date.now()),e.lastZoom=t},_checkLoaded:function(){if(!this._loaded)throw new Error("Style is not done loading")},update:function(t,e){if(!this._updates.changed)return this;if(this._updates.allLayers)this._groupLayers(),this._updateWorkerLayers();else{var r=Object.keys(this._updates.layers);r.length&&this._updateWorkerLayers(r)}var n,i=Object.keys(this._updates.sources);for(n=0;n<i.length;n++)this._reloadSource(i[n]);for(n=0;n<this._updates.events.length;n++){var o=this._updates.events[n];this.fire(o[0],o[1])}return this._applyClasses(t,e),this._updates.changed&&this.fire("change"),this._resetUpdates(),this},_resetUpdates:function(){this._updates={events:[],layers:{},sources:{},paintProps:{}}},addSource:function(t,e){if(this._checkLoaded(),void 0!==this.sources[t])throw new Error("There is already a source with this ID");if(!e.type)throw new Error("The type property must be defined, but the only the following properties were given: "+Object.keys(e)+".");var r=["vector","raster","geojson","video","image"],n=r.indexOf(e.type)>=0;return n&&this._handleErrors(g.source,"sources."+t,e)?this:(e=new b(t,e,this.dispatcher),this.sources[t]=e,e.style=this,e.on("load",this._forwardSourceEvent).on("error",this._forwardSourceEvent).on("change",this._forwardSourceEvent).on("tile.add",this._forwardTileEvent).on("tile.load",this._forwardTileEvent).on("tile.error",this._forwardTileEvent).on("tile.remove",this._forwardTileEvent).on("tile.stats",this._forwardTileEvent),this._updates.events.push(["source.add",{source:e}]),this._updates.changed=!0,this)},removeSource:function(t){if(this._checkLoaded(),void 0===this.sources[t])throw new Error("There is no source with this ID");var e=this.sources[t];return delete this.sources[t],delete this._updates.sources[t],e.off("load",this._forwardSourceEvent).off("error",this._forwardSourceEvent).off("change",this._forwardSourceEvent).off("tile.add",this._forwardTileEvent).off("tile.load",this._forwardTileEvent).off("tile.error",this._forwardTileEvent).off("tile.remove",this._forwardTileEvent).off("tile.stats",this._forwardTileEvent),this._updates.events.push(["source.remove",{source:e}]),this._updates.changed=!0,this},getSource:function(t){return this.sources[t]&&this.sources[t].getSource()},addLayer:function(t,e){if(this._checkLoaded(),!(t instanceof o)){if(this._handleErrors(g.layer,"layers."+t.id,t,!1,{arrayIndex:-1}))return this;var r=t.ref&&this.getLayer(t.ref);t=o.create(t,r)}return this._validateLayer(t),t.on("error",this._forwardLayerEvent),this._layers[t.id]=t,this._order.splice(e?this._order.indexOf(e):1/0,0,t.id),this._updates.allLayers=!0,t.source&&(this._updates.sources[t.source]=!0),this._updates.events.push(["layer.add",{layer:t}]),this.updateClasses(t.id)},removeLayer:function(t){this._checkLoaded();var e=this._layers[t];if(void 0===e)throw new Error("There is no layer with this ID");for(var r in this._layers)this._layers[r].ref===t&&this.removeLayer(r);return e.off("error",this._forwardLayerEvent),delete this._layers[t],delete this._updates.layers[t],delete this._updates.paintProps[t],this._order.splice(this._order.indexOf(t),1),this._updates.allLayers=!0,this._updates.events.push(["layer.remove",{layer:e}]),this._updates.changed=!0,this},getLayer:function(t){return this._layers[t]},getReferentLayer:function(t){var e=this.getLayer(t);return e.ref&&(e=this.getLayer(e.ref)),e},setLayerZoomRange:function(t,e,r){this._checkLoaded();var n=this.getReferentLayer(t);return n.minzoom===e&&n.maxzoom===r?this:(null!=e&&(n.minzoom=e),null!=r&&(n.maxzoom=r),this._updateLayer(n))},setFilter:function(t,e){this._checkLoaded();var r=this.getReferentLayer(t);return null!==e&&this._handleErrors(g.filter,"layers."+r.id+".filter",e)?this:c.deepEqual(r.filter,e)?this:(r.filter=c.clone(e),this._updateLayer(r))},getFilter:function(t){return this.getReferentLayer(t).filter},setLayoutProperty:function(t,e,r){this._checkLoaded();var n=this.getReferentLayer(t);return c.deepEqual(n.getLayoutProperty(e),r)?this:(n.setLayoutProperty(e,r),this._updateLayer(n))},getLayoutProperty:function(t,e){return this.getReferentLayer(t).getLayoutProperty(e)},setPaintProperty:function(t,e,r,n){this._checkLoaded();var i=this.getLayer(t);if(c.deepEqual(i.getPaintProperty(e,n),r))return this;var o=i.isPaintValueFeatureConstant(e);i.setPaintProperty(e,r,n);var a=!(r&&_.isFunctionDefinition(r)&&"$zoom"!==r.property&&void 0!==r.property);return a&&o||(this._updates.layers[t]=!0,i.source&&(this._updates.sources[i.source]=!0)),this.updateClasses(t,e)},getPaintProperty:function(t,e,r){return this.getLayer(t).getPaintProperty(e,r)},updateClasses:function(t,e){if(this._updates.changed=!0,t){var r=this._updates.paintProps;r[t]||(r[t]={}),r[t][e||"all"]=!0}else this._updates.allPaintProps=!0;return this},serialize:function(){return c.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:c.mapObject(this.sources,function(t){return t.serialize()}),layers:this._order.map(function(t){return this._layers[t].serialize()},this)},function(t){return void 0!==t})},_updateLayer:function(t){return this._updates.layers[t.id]=!0,t.source&&(this._updates.sources[t.source]=!0),this._updates.changed=!0,this},_flattenRenderedFeatures:function(t){for(var e=[],r=this._order.length-1;r>=0;r--)for(var n=this._order[r],i=0;i<t.length;i++){var o=t[i][n];if(o)for(var a=0;a<o.length;a++)e.push(o[a])}return e},queryRenderedFeatures:function(t,e,r,n){e&&e.filter&&this._handleErrors(g.filter,"queryRenderedFeatures.filter",e.filter,!0);var i={};if(e&&e.layers)for(var o=0;o<e.layers.length;o++){var a=e.layers[o];i[this._layers[a].source]=!0}var s=[];for(var l in this.sources)if(!e.layers||i[l]){var u=this.sources[l],c=y.rendered(u,this._layers,t,e,r,n);s.push(c)}return this._flattenRenderedFeatures(s)},querySourceFeatures:function(t,e){e&&e.filter&&this._handleErrors(g.filter,"querySourceFeatures.filter",e.filter,!0);var r=this.sources[t];return r?y.source(r,e):[]},addSourceType:function(t,e,r){return v.getType(t)?r(new Error('A source type called "'+t+'" already 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r=this.sprite;r.loaded()?e(null,{sprite:r.data,retina:r.retina}):r.on("load",function(){e(null,{sprite:r.data,retina:r.retina})})},"get icons":function(t,e){var r=this.sprite,n=this.spriteAtlas;r.loaded()?(n.setSprite(r),n.addIcons(t.icons,e)):r.on("load",function(){n.setSprite(r),n.addIcons(t.icons,e)})},"get glyphs":function(t,e){function r(t,r,n){t&&console.error(t),o[n]=r,i--,0===i&&e(null,o)}var n=t.stacks,i=Object.keys(n).length,o={};for(var a in n)this.glyphSource.getSimpleGlyphs(a,n[a],t.uid,r)}})},{"../render/line_atlas":278,"../source/query_features":288,"../source/source":290,"../source/source_cache":291,"../symbol/glyph_source":323,"../symbol/sprite_atlas":328,"../util/ajax":349,"../util/browser":350,"../util/dispatcher":357,"../util/evented":358,"../util/mapbox":363,"../util/util":366,"./animation_loop":299,"./image_sprite":300,"./style_function":304,"./style_layer":305,"./style_spec":312,"./validate_style":314}],303:[function(t,e,r){"use strict";function n(t,e){this.value=s.clone(e),this.isFunction=o.isFunctionDefinition(e),this.json=JSON.stringify(this.value);var r="color"===t.type&&this.value?a(this.value):e;if(this.calculate=o[t.function||"piecewise-constant"](r),this.isFeatureConstant=this.calculate.isFeatureConstant,this.isZoomConstant=this.calculate.isZoomConstant,"piecewise-constant"===t.function&&t.transition&&(this.calculate=i(this.calculate)),!this.isFeatureConstant&&!this.isZoomConstant){this.stopZoomLevels=[];for(var n=[],l=this.value.stops,u=0;u<this.value.stops.length;u++){var c=l[u][0].zoom;this.stopZoomLevels.indexOf(c)<0&&(this.stopZoomLevels.push(c),n.push([c,n.length]))}this.calculateInterpolationT=o.interpolated({stops:n,base:e.base})}}function i(t){return function(e,r){var n,i,o,a=e.zoom,s=e.zoomHistory,l=e.duration,u=a%1,c=Math.min((Date.now()-s.lastIntegerZoomTime)/l,1),h=1,f=1;return a>s.lastIntegerZoom?(n=u+(1-u)*c,h*=2,i=t({zoom:a-1},r),o=t({zoom:a},r)):(n=1-(1-c)*u,o=t({zoom:a},r),i=t({zoom:a+1},r),h/=2),void 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f="-transition";n.create=function(e,r){var n={background:t("./style_layer/background_style_layer"),circle:t("./style_layer/circle_style_layer"),fill:t("./style_layer/fill_style_layer"),line:t("./style_layer/line_style_layer"),raster:t("./style_layer/raster_style_layer"),symbol:t("./style_layer/symbol_style_layer")};return new n[(r||e).type](e,r)},n.prototype=o.inherit(h,{set:function(t,e){this.id=t.id,this.ref=t.ref,this.metadata=t.metadata,this.type=(e||t).type,this.source=(e||t).source,this.sourceLayer=(e||t)["source-layer"],this.minzoom=(e||t).minzoom,this.maxzoom=(e||t).maxzoom,this.filter=(e||t).filter,this.paint={},this.layout={},this._paintSpecifications=l["paint_"+this.type],this._layoutSpecifications=l["layout_"+this.type],this._paintTransitions={},this._paintTransitionOptions={},this._paintDeclarations={},this._layoutDeclarations={},this._layoutFunctions={};var r,n;for(var i in t){var o=i.match(/^paint(?:\.(.*))?$/);if(o){var a=o[1]||"";for(r in 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n="layers."+this.id+(r?'["paint.'+r+'"].':".paint.")+t;if(o.endsWith(t,f))if(this._paintTransitionOptions[r||""]||(this._paintTransitionOptions[r||""]={}),null===e||void 0===e)delete this._paintTransitionOptions[r||""][t];else{if(this._handleErrors(u.paintProperty,n,t,e))return;this._paintTransitionOptions[r||""][t]=e}else if(this._paintDeclarations[r||""]||(this._paintDeclarations[r||""]={}),null===e||void 0===e)delete this._paintDeclarations[r||""][t];else{if(this._handleErrors(u.paintProperty,n,t,e))return;this._paintDeclarations[r||""][t]=new s(this._paintSpecifications[t],e)}},getPaintProperty:function(t,e){return e=e||"",o.endsWith(t,f)?this._paintTransitionOptions[e]&&this._paintTransitionOptions[e][t]:this._paintDeclarations[e]&&this._paintDeclarations[e][t]&&this._paintDeclarations[e][t].value},getPaintValue:function(t,e,r){var n=this._paintSpecifications[t],i=this._paintTransitions[t];return 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n=""===r?"paint":"paint."+r;e[n]=o.mapObject(this._paintDeclarations[r],i)}return(!this.ref||t&&t.includeRefProperties)&&o.extend(e,{type:this.type,source:this.source,"source-layer":this.sourceLayer,filter:this.filter,layout:o.mapObject(this._layoutDeclarations,i)}),o.filterObject(e,function(t,e){return void 0!==t&&!("layout"===e&&!Object.keys(t).length)})},_applyPaintDeclaration:function(t,e,r,n,i){var l=r.transition?this._paintTransitions[t]:void 0,u=this._paintSpecifications[t];if(null!==e&&void 0!==e||(e=new s(u,u.default)),!l||l.declaration.json!==e.json){var c=o.extend({duration:300,delay:0},n,this.getPaintProperty(t+f)),h=this._paintTransitions[t]=new a(u,e,l,c);h.instant()||(h.loopID=i.set(h.endTime-Date.now())),l&&i.cancel(l.loopID)}},_updateLayoutValue:function(t){var e=this._layoutDeclarations[t];e&&e.isFunction?this._layoutFunctions[t]=!0:(delete this._layoutFunctions[t],this.layout[t]=this.getLayoutValue(t))},_handleErrors:function(t,e,r,n){return u.emitErrors(this,t.call(u,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:l,style:{glyphs:!0,sprite:!0}}))}})},{"../util/evented":358,"../util/util":366,"./parse_color":301,"./style_declaration":303,"./style_layer/background_style_layer":306,"./style_layer/circle_style_layer":307,"./style_layer/fill_style_layer":308,"./style_layer/line_style_layer":309,"./style_layer/raster_style_layer":310,"./style_layer/symbol_style_layer":311,"./style_spec":312,"./style_transition":313,"./validate_style":314}],306:[function(t,e,r){"use strict";function n(){o.apply(this,arguments)}var i=t("../../util/util"),o=t("../style_layer");e.exports=n,n.prototype=i.inherit(o,{})},{"../../util/util":366,"../style_layer":305}],307:[function(t,e,r){"use strict";function n(){o.apply(this,arguments)}var i=t("../../util/util"),o=t("../style_layer");e.exports=n,n.prototype=i.inherit(o,{})},{"../../util/util":366,"../style_layer":305}],308:[function(t,e,r){"use strict";function n(){o.apply(this,arguments)}var i=t("../../util/util"),o=t("../style_layer");n.prototype=i.inherit(o,{getPaintValue:function(t,e,r){return"fill-outline-color"===t&&void 0===this.getPaintProperty("fill-outline-color")?o.prototype.getPaintValue.call(this,"fill-color",e,r):o.prototype.getPaintValue.call(this,t,e,r)},getPaintValueStopZoomLevels:function(t){return"fill-outline-color"===t&&void 0===this.getPaintProperty("fill-outline-color")?o.prototype.getPaintValueStopZoomLevels.call(this,"fill-color"):o.prototype.getPaintValueStopZoomLevels.call(this,arguments)},getPaintInterpolationT:function(t,e){return"fill-outline-color"===t&&void 0===this.getPaintProperty("fill-outline-color")?o.prototype.getPaintInterpolationT.call(this,"fill-color",e):o.prototype.getPaintInterpolationT.call(this,t,e)},isPaintValueFeatureConstant:function(t){return"fill-outline-color"===t&&void 0===this.getPaintProperty("fill-outline-color")?o.prototype.isPaintValueFeatureConstant.call(this,"fill-color"):o.prototype.isPaintValueFeatureConstant.call(this,t)},isPaintValueZoomConstant:function(t){return"fill-outline-color"===t&&void 0===this.getPaintProperty("fill-outline-color")?o.prototype.isPaintValueZoomConstant.call(this,"fill-color"):o.prototype.isPaintValueZoomConstant.call(this,t)}}),e.exports=n},{"../../util/util":366,"../style_layer":305}],309:[function(t,e,r){"use strict";function n(){o.apply(this,arguments)}var i=t("../../util/util"),o=t("../style_layer");e.exports=n,n.prototype=i.inherit(o,{getPaintValue:function(t,e,r){var n=o.prototype.getPaintValue.apply(this,arguments);if(n&&"line-dasharray"===t){var i=Math.floor(e.zoom);this._flooredZoom!==i&&(this._flooredZoom=i,this._flooredLineWidth=this.getPaintValue("line-width",e,r)),n.fromScale*=this._flooredLineWidth,n.toScale*=this._flooredLineWidth}return n}})},{"../../util/util":366,"../style_layer":305}],310:[function(t,e,r){"use strict";function n(){o.apply(this,arguments)}var i=t("../../util/util"),o=t("../style_layer");e.exports=n,n.prototype=i.inherit(o,{})},{"../../util/util":366,"../style_layer":305}],311:[function(t,e,r){"use strict";function n(){o.apply(this,arguments)}var i=t("../../util/util"),o=t("../style_layer");e.exports=n,n.prototype=i.inherit(o,{isHidden:function(){if(o.prototype.isHidden.apply(this,arguments))return!0;var t=0===this.paint["text-opacity"]||!this.layout["text-field"],e=0===this.paint["icon-opacity"]||!this.layout["icon-image"];return!(!t||!e)},getLayoutValue:function(t,e,r){return("text-rotation-alignment"!==t||"line"!==this.getLayoutValue("symbol-placement",e,r)||this.getLayoutProperty("text-rotation-alignment"))&&("icon-rotation-alignment"!==t||"line"!==this.getLayoutValue("symbol-placement",e,r)||this.getLayoutProperty("icon-rotation-alignment"))?"text-pitch-alignment"!==t||this.getLayoutProperty("text-pitch-alignment")?o.prototype.getLayoutValue.apply(this,arguments):this.getLayoutValue("text-rotation-alignment"):"map";
}})},{"../../util/util":366,"../style_layer":305}],312:[function(t,e,r){"use strict";e.exports=t("mapbox-gl-style-spec/reference/latest")},{"mapbox-gl-style-spec/reference/latest":416}],313:[function(t,e,r){"use strict";function n(t,e,r,n){this.declaration=e,this.startTime=this.endTime=(new Date).getTime(),"piecewise-constant"===t.function&&t.transition?this.interp=i:this.interp=a[t.type],this.oldTransition=r,this.duration=n.duration||0,this.delay=n.delay||0,this.instant()||(this.endTime=this.startTime+this.duration+this.delay,this.ease=o.easeCubicInOut),r&&r.endTime<=this.startTime&&delete r.oldTransition}function i(t,e,r){return void 0===(t&&t.to)||void 0===(e&&e.to)?void 0:{from:t.to,fromScale:t.toScale,to:e.to,toScale:e.toScale,t:r}}var o=t("../util/util"),a=t("../util/interpolate");e.exports=n,n.prototype.instant=function(){return!this.oldTransition||!this.interp||0===this.duration&&0===this.delay},n.prototype.calculate=function(t,e){var r=this.declaration.calculate(o.extend({},t,{duration:this.duration}),e);if(this.instant())return r;var n=t.time||Date.now();if(n<this.endTime){var i=this.oldTransition.calculate(o.extend({},t,{time:this.startTime}),e),a=this.ease((n-this.startTime-this.delay)/this.duration);r=this.interp(i,r,a)}return r}},{"../util/interpolate":360,"../util/util":366}],314:[function(t,e,r){"use strict";e.exports=t("mapbox-gl-style-spec/lib/validate_style.min"),e.exports.emitErrors=function(t,e){if(e&&e.length){for(var r=0;r<e.length;r++)t.fire("error",{error:new Error(e[r].message)});return!0}return!1},e.exports.throwErrors=function(t,e){if(e)for(var r=0;r<e.length;r++)throw new Error(e[r].message)}},{"mapbox-gl-style-spec/lib/validate_style.min":415}],315:[function(t,e,r){"use strict";function n(t,e,r,n){this.x=t,this.y=e,this.angle=r,void 0!==n&&(this.segment=n)}var i=t("point-geometry");e.exports=n,n.prototype=Object.create(i.prototype),n.prototype.clone=function(){return new n(this.x,this.y,this.angle,this.segment)}},{"point-geometry":424}],316:[function(t,e,r){"use strict";function n(t,e,r,n,i){if(void 0===e.segment)return!0;for(var o=e,a=e.segment+1,s=0;s>-r/2;){if(a--,0>a)return!1;s-=t[a].dist(o),o=t[a]}s+=t[a].dist(t[a+1]),a++;for(var l=[],u=0;r/2>s;){var c=t[a-1],h=t[a],f=t[a+1];if(!f)return!1;var p=c.angleTo(h)-h.angleTo(f);for(p=Math.abs((p+3*Math.PI)%(2*Math.PI)-Math.PI),l.push({distance:s,angleDelta:p}),u+=p;s-l[0].distance>n;)u-=l.shift().angleDelta;if(u>i)return!1;a++,s+=h.dist(f)}return!0}e.exports=n},{}],317:[function(t,e,r){"use strict";function n(t,e,r,n,o){for(var a=[],s=0;s<t.length;s++)for(var l,u=t[s],c=0;c<u.length-1;c++){var h=u[c],f=u[c+1];h.x<e&&f.x<e||(h.x<e?h=new i(e,h.y+(f.y-h.y)*((e-h.x)/(f.x-h.x)))._round():f.x<e&&(f=new i(e,h.y+(f.y-h.y)*((e-h.x)/(f.x-h.x)))._round()),h.y<r&&f.y<r||(h.y<r?h=new i(h.x+(f.x-h.x)*((r-h.y)/(f.y-h.y)),r)._round():f.y<r&&(f=new i(h.x+(f.x-h.x)*((r-h.y)/(f.y-h.y)),r)._round()),h.x>=n&&f.x>=n||(h.x>=n?h=new i(n,h.y+(f.y-h.y)*((n-h.x)/(f.x-h.x)))._round():f.x>=n&&(f=new i(n,h.y+(f.y-h.y)*((n-h.x)/(f.x-h.x)))._round()),h.y>=o&&f.y>=o||(h.y>=o?h=new i(h.x+(f.x-h.x)*((o-h.y)/(f.y-h.y)),o)._round():f.y>=o&&(f=new i(h.x+(f.x-h.x)*((o-h.y)/(f.y-h.y)),o)._round()),l&&h.equals(l[l.length-1])||(l=[h],a.push(l)),l.push(f)))))}return a}var i=t("point-geometry");e.exports=n},{"point-geometry":424}],318:[function(t,e,r){"use strict";var n=t("../util/struct_array"),i=t("../util/util"),o=t("point-geometry"),a=e.exports=new n({members:[{type:"Int16",name:"anchorPointX"},{type:"Int16",name:"anchorPointY"},{type:"Int16",name:"x1"},{type:"Int16",name:"y1"},{type:"Int16",name:"x2"},{type:"Int16",name:"y2"},{type:"Float32",name:"maxScale"},{type:"Uint32",name:"featureIndex"},{type:"Uint16",name:"sourceLayerIndex"},{type:"Uint16",name:"bucketIndex"},{type:"Int16",name:"bbox0"},{type:"Int16",name:"bbox1"},{type:"Int16",name:"bbox2"},{type:"Int16",name:"bbox3"},{type:"Float32",name:"placementScale"}]});i.extendAll(a.prototype.StructType.prototype,{get anchorPoint(){return new o(this.anchorPointX,this.anchorPointY)}})},{"../util/struct_array":364,"../util/util":366,"point-geometry":424}],319:[function(t,e,r){"use strict";function n(t,e,r,n,i,o,a,s,l,u,c){var h=a.top*s-l,f=a.bottom*s+l,p=a.left*s-l,d=a.right*s+l;if(this.boxStartIndex=t.length,u){var m=f-h,g=d-p;if(m>0)if(m=Math.max(10*s,m),c){var v=e[r.segment+1].sub(e[r.segment])._unit()._mult(g),y=[r.sub(v),r.add(v)];this._addLineCollisionBoxes(t,y,r,0,g,m,n,i,o)}else this._addLineCollisionBoxes(t,e,r,r.segment,g,m,n,i,o)}else t.emplaceBack(r.x,r.y,p,h,d,f,1/0,n,i,o,0,0,0,0,0);this.boxEndIndex=t.length}e.exports=n,n.prototype._addLineCollisionBoxes=function(t,e,r,n,i,o,a,s,l){var u=o/2,c=Math.floor(i/u),h=-o/2,f=this.boxes,p=r,d=n+1,m=h;do{if(d--,0>d)return f;m-=e[d].dist(p),p=e[d]}while(m>-i/2);for(var g=e[d].dist(e[d+1]),v=0;c>v;v++){for(var y=-i/2+v*u;y>m+g;){if(m+=g,d++,d+1>=e.length)return f;g=e[d].dist(e[d+1])}var b=y-m,x=e[d],_=e[d+1],w=_.sub(x)._unit()._mult(b)._add(x)._round(),M=Math.max(Math.abs(y-h)-u/2,0),k=i/2/M;t.emplaceBack(w.x,w.y,-o/2,-o/2,o/2,o/2,k,a,s,l,0,0,0,0,0)}return f}},{}],320:[function(t,e,r){"use strict";function n(t,e,r){if("object"==typeof t){var n=t;r=e,t=n.angle,e=n.pitch,this.grid=new a(n.grid),this.ignoredGrid=new a(n.ignoredGrid)}else this.grid=new a(o,12,6),this.ignoredGrid=new a(o,12,0);this.angle=t,this.pitch=e;var i=Math.sin(t),s=Math.cos(t);if(this.rotationMatrix=[s,-i,i,s],this.reverseRotationMatrix=[s,i,-i,s],this.yStretch=1/Math.cos(e/180*Math.PI),this.yStretch=Math.pow(this.yStretch,1.3),this.collisionBoxArray=r,0===r.length){r.emplaceBack();var l=32767;r.emplaceBack(0,0,0,-l,0,l,l,0,0,0,0,0,0,0,0,0),r.emplaceBack(o,0,0,-l,0,l,l,0,0,0,0,0,0,0,0,0),r.emplaceBack(0,0,-l,0,l,0,l,0,0,0,0,0,0,0,0,0),r.emplaceBack(0,o,-l,0,l,0,l,0,0,0,0,0,0,0,0,0)}this.tempCollisionBox=r.get(0),this.edges=[r.get(1),r.get(2),r.get(3),r.get(4)]}var i=t("point-geometry"),o=t("../data/bucket").EXTENT,a=t("grid-index");e.exports=n,n.prototype.serialize=function(){var t={angle:this.angle,pitch:this.pitch,grid:this.grid.toArrayBuffer(),ignoredGrid:this.ignoredGrid.toArrayBuffer()};return{data:t,transferables:[t.grid,t.ignoredGrid]}},n.prototype.minScale=.25,n.prototype.maxScale=2,n.prototype.placeCollisionFeature=function(t,e,r){for(var n=this.collisionBoxArray,o=this.minScale,a=this.rotationMatrix,s=this.yStretch,l=t.boxStartIndex;l<t.boxEndIndex;l++){var u=n.get(l),c=u.anchorPoint._matMult(a),h=c.x,f=c.y,p=h+u.x1,d=f+u.y1*s,m=h+u.x2,g=f+u.y2*s;if(u.bbox0=p,u.bbox1=d,u.bbox2=m,u.bbox3=g,!e)for(var v=this.grid.query(p,d,m,g),y=0;y<v.length;y++){var b=n.get(v[y]),x=b.anchorPoint._matMult(a);if(o=this.getPlacementScale(o,c,u,x,b),o>=this.maxScale)return o}if(r){var _;if(this.angle){var w=this.reverseRotationMatrix,M=new i(u.x1,u.y1).matMult(w),k=new i(u.x2,u.y1).matMult(w),A=new i(u.x1,u.y2).matMult(w),T=new i(u.x2,u.y2).matMult(w);_=this.tempCollisionBox,_.anchorPointX=u.anchorPoint.x,_.anchorPointY=u.anchorPoint.y,_.x1=Math.min(M.x,k.x,A.x,T.x),_.y1=Math.min(M.y,k.x,A.x,T.x),_.x2=Math.max(M.x,k.x,A.x,T.x),_.y2=Math.max(M.y,k.x,A.x,T.x),_.maxScale=u.maxScale}else _=u;for(var E=0;E<this.edges.length;E++){var S=this.edges[E];if(o=this.getPlacementScale(o,u.anchorPoint,_,S.anchorPoint,S),o>=this.maxScale)return o}}}return o},n.prototype.queryRenderedSymbols=function(t,e,r,n,o){var a={},s=[],l=this.collisionBoxArray,u=this.rotationMatrix,c=new i(t,e)._matMult(u),h=this.tempCollisionBox;h.anchorX=c.x,h.anchorY=c.y,h.x1=0,h.y1=0,h.x2=r-t,h.y2=n-e,h.maxScale=o,o=h.maxScale;for(var f=[c.x+h.x1/o,c.y+h.y1/o*this.yStretch,c.x+h.x2/o,c.y+h.y2/o*this.yStretch],p=this.grid.query(f[0],f[1],f[2],f[3]),d=this.ignoredGrid.query(f[0],f[1],f[2],f[3]),m=0;m<d.length;m++)p.push(d[m]);for(var g=0;g<p.length;g++){var v=l.get(p[g]),y=v.sourceLayerIndex,b=v.featureIndex;if(void 0===a[y]&&(a[y]={}),!a[y][b]){var x=v.anchorPoint.matMult(u),_=this.getPlacementScale(this.minScale,c,h,x,v);_>=o&&(a[y][b]=!0,s.push(p[g]))}}return s},n.prototype.getPlacementScale=function(t,e,r,n,i){var o=e.x-n.x,a=e.y-n.y,s=(i.x1-r.x2)/o,l=(i.x2-r.x1)/o,u=(i.y1-r.y2)*this.yStretch/a,c=(i.y2-r.y1)*this.yStretch/a;(isNaN(s)||isNaN(l))&&(s=l=1),(isNaN(u)||isNaN(c))&&(u=c=1);var h=Math.min(Math.max(s,l),Math.max(u,c)),f=i.maxScale,p=r.maxScale;return h>f&&(h=f),h>p&&(h=p),h>t&&h>=i.placementScale&&(t=h),t},n.prototype.insertCollisionFeature=function(t,e,r){for(var n=r?this.ignoredGrid:this.grid,i=this.collisionBoxArray,o=t.boxStartIndex;o<t.boxEndIndex;o++){var a=i.get(o);a.placementScale=e,e<this.maxScale&&n.insert(o,a.bbox0,a.bbox1,a.bbox2,a.bbox3)}}},{"../data/bucket":253,"grid-index":392,"point-geometry":424}],321:[function(t,e,r){"use strict";function n(t,e,r,n,o,a,s,l,u){var c=n?.6*a*s:0,h=Math.max(n?n.right-n.left:0,o?o.right-o.left:0),f=0===t[0].x||t[0].x===u||0===t[0].y||t[0].y===u;e/4>e-h*s&&(e=h*s+e/4);var p=2*a,d=f?e/2*l%e:(h/2+p)*s*l%e;return i(t,d,e,c,r,h*s,f,!1,u)}function i(t,e,r,n,l,u,c,h,f){for(var p=u/2,d=0,m=0;m<t.length-1;m++)d+=t[m].dist(t[m+1]);for(var g=0,v=e-r,y=[],b=0;b<t.length-1;b++){for(var x=t[b],_=t[b+1],w=x.dist(_),M=_.angleTo(x);g+w>v+r;){v+=r;var k=(v-g)/w,A=o(x.x,_.x,k),T=o(x.y,_.y,k);if(A>=0&&f>A&&T>=0&&f>T&&v-p>=0&&d>=v+p){var E=new a(A,T,M,b)._round();n&&!s(t,E,u,n,l)||y.push(E)}}g+=w}return h||y.length||c||(y=i(t,g/2,r,n,l,u,c,!0,f)),y}var o=t("../util/interpolate"),a=t("../symbol/anchor"),s=t("./check_max_angle");e.exports=n},{"../symbol/anchor":315,"../util/interpolate":360,"./check_max_angle":316}],322:[function(t,e,r){"use strict";function n(){this.width=s,this.height=s,this.bin=new i(this.width,this.height),this.index={},this.ids={},this.data=new Uint8Array(this.width*this.height)}var i=t("shelf-pack"),o=t("../util/util"),a=4,s=128,l=2048;e.exports=n,n.prototype.getGlyphs=function(){var t,e,r,n={};for(var i in this.ids)t=i.split("#"),e=t[0],r=t[1],n[e]||(n[e]=[]),n[e].push(r);return n},n.prototype.getRects=function(){var t,e,r,n={};for(var i in this.ids)t=i.split("#"),e=t[0],r=t[1],n[e]||(n[e]={}),n[e][r]=this.index[i];return n},n.prototype.addGlyph=function(t,e,r,n){if(!r)return null;var i=e+"#"+r.id;if(this.index[i])return this.ids[i].indexOf(t)<0&&this.ids[i].push(t),this.index[i];if(!r.bitmap)return null;var a=r.width+2*n,s=r.height+2*n,l=1,u=a+2*l,c=s+2*l;u+=4-u%4,c+=4-c%4;var h=this.bin.packOne(u,c);if(h||(this.resize(),h=this.bin.packOne(u,c)),!h)return o.warnOnce("glyph bitmap overflow"),null;this.index[i]=h,this.ids[i]=[t];for(var f=this.data,p=r.bitmap,d=0;s>d;d++)for(var m=this.width*(h.y+d+l)+h.x+l,g=a*d,v=0;a>v;v++)f[m+v]=p[g+v];return this.dirty=!0,h},n.prototype.resize=function(){var t=this.width,e=this.height;if(!(t>=l||e>=l)){this.texture&&(this.gl&&this.gl.deleteTexture(this.texture),this.texture=null),this.width*=a,this.height*=a,this.bin.resize(this.width,this.height);for(var r=new ArrayBuffer(this.width*this.height),n=0;e>n;n++){var i=new Uint8Array(this.data.buffer,e*n,t),o=new Uint8Array(r,e*n*a,t);o.set(i)}this.data=new Uint8Array(r)}},n.prototype.bind=function(t){this.gl=t,this.texture?t.bindTexture(t.TEXTURE_2D,this.texture):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.LINEAR),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),t.texImage2D(t.TEXTURE_2D,0,t.ALPHA,this.width,this.height,0,t.ALPHA,t.UNSIGNED_BYTE,null))},n.prototype.updateTexture=function(t){this.bind(t),this.dirty&&(t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width,this.height,t.ALPHA,t.UNSIGNED_BYTE,this.data),this.dirty=!1)}},{"../util/util":366,"shelf-pack":427}],323:[function(t,e,r){"use strict";function n(t){this.url=t&&a(t),this.atlases={},this.stacks={},this.loading={}}function i(t,e,r){var n=1;this.advance=t.advance,this.left=t.left-r-n,this.top=t.top+r+n,this.rect=e}function o(t,e,r,n){return n=n||"abc",r.replace("{s}",n[t.length%n.length]).replace("{fontstack}",t).replace("{range}",e)}var a=t("../util/mapbox").normalizeGlyphsURL,s=t("../util/ajax").getArrayBuffer,l=t("../util/glyphs"),u=t("../symbol/glyph_atlas"),c=t("pbf");e.exports=n,n.prototype.getSimpleGlyphs=function(t,e,r,n){void 0===this.stacks[t]&&(this.stacks[t]={}),void 0===this.atlases[t]&&(this.atlases[t]=new u);for(var o,a={},s=this.stacks[t],l=this.atlases[t],c=3,h={},f=0,p=0;p<e.length;p++){var d=e[p];if(o=Math.floor(d/256),s[o]){var m=s[o].glyphs[d],g=l.addGlyph(r,t,m,c);m&&(a[d]=new i(m,g,c))}else void 0===h[o]&&(h[o]=[],f++),h[o].push(d)}f||n(void 0,a,t);var v=function(e,o,s){if(!e)for(var u=this.stacks[t][o]=s.stacks[0],p=0;p<h[o].length;p++){var d=h[o][p],m=u.glyphs[d],g=l.addGlyph(r,t,m,c);m&&(a[d]=new i(m,g,c))}f--,f||n(void 0,a,t)}.bind(this);for(var y in h)this.loadRange(t,y,v)},n.prototype.loadRange=function(t,e,r){if(256*e>65535)return r("glyphs > 65535 not supported");void 0===this.loading[t]&&(this.loading[t]={});var n=this.loading[t];if(n[e])n[e].push(r);else{n[e]=[r];var i=256*e+"-"+(256*e+255),a=o(t,i,this.url);s(a,function(t,r){for(var i=!t&&new l(new c(new Uint8Array(r))),o=0;o<n[e].length;o++)n[e][o](t,e,i);delete n[e]})}},n.prototype.getGlyphAtlas=function(t){return this.atlases[t]}},{"../symbol/glyph_atlas":322,"../util/ajax":349,"../util/glyphs":359,"../util/mapbox":363,pbf:422}],324:[function(t,e,r){"use strict";e.exports=function(t,e,r){function n(n){c.push(t[n]),h.push(r[n]),f.push(e[n]),p++}function i(t,e,r){var n=u[t];return delete u[t],u[e]=n,h[n][0].pop(),h[n][0]=h[n][0].concat(r[0]),n}function o(t,e,r){var n=l[e];return delete l[e],l[t]=n,h[n][0].shift(),h[n][0]=r[0].concat(h[n][0]),n}function a(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}var s,l={},u={},c=[],h=[],f=[],p=0;for(s=0;s<t.length;s++){var d=r[s],m=e[s];if(m){var g=a(m,d),v=a(m,d,!0);if(g in u&&v in l&&u[g]!==l[v]){var y=o(g,v,d),b=i(g,v,h[y]);delete l[g],delete u[v],u[a(m,h[b],!0)]=b,h[y]=null}else g in u?i(g,v,d):v in l?o(g,v,d):(n(s),l[g]=p-1,u[v]=p-1)}else n(s)}return{features:c,textFeatures:f,geometries:h}}},{}],325:[function(t,e,r){"use strict";function n(t,e,r,n,i,o,a,s,l,u){this.anchorPoint=t,this.tl=e,this.tr=r,this.bl=n,this.br=i,this.tex=o,this.anchorAngle=a,this.glyphAngle=s,this.minScale=l,this.maxScale=u}function i(t,e,r,i,o,a,u,c,h){var f,p,d,m,g=e.image.rect,v=o.layout,y=1,b=e.left-y,x=b+g.w/e.image.pixelRatio,_=e.top-y,w=_+g.h/e.image.pixelRatio;if("none"!==v["icon-text-fit"]&&u){var M=x-b,k=w-_,A=v["text-size"]/24,T=u.left*A,E=u.right*A,S=u.top*A,L=u.bottom*A,z=E-T,I=L-S,P=v["icon-text-fit-padding"][0],C=v["icon-text-fit-padding"][1],R=v["icon-text-fit-padding"][2],j=v["icon-text-fit-padding"][3],D="width"===v["icon-text-fit"]?.5*(I-k):0,O="height"===v["icon-text-fit"]?.5*(z-M):0,F="width"===v["icon-text-fit"]||"both"===v["icon-text-fit"]?z:M,N="height"===v["icon-text-fit"]||"both"===v["icon-text-fit"]?I:k;f=new s(T+O-j,S+D-P),p=new s(T+O+C+F,S+D-P),d=new s(T+O+C+F,S+D+R+N),m=new s(T+O-j,S+D+R+N)}else f=new s(b,_),p=new s(x,_),d=new s(x,w),m=new s(b,w);var B=o.getLayoutValue("icon-rotate",c,h)*Math.PI/180;if(a){var U=i[t.segment];if(t.y===U.y&&t.x===U.x&&t.segment+1<i.length){var V=i[t.segment+1];B+=Math.atan2(t.y-V.y,t.x-V.x)+Math.PI}else B+=Math.atan2(t.y-U.y,t.x-U.x)}if(B){var q=Math.sin(B),G=Math.cos(B),H=[G,-q,q,G];f=f.matMult(H),p=p.matMult(H),m=m.matMult(H),d=d.matMult(H)}return[new n(new s(t.x,t.y),f,p,m,d,e.image.rect,0,0,l,1/0)]}function o(t,e,r,i,o,u){for(var c=o.layout["text-rotate"]*Math.PI/180,h=o.layout["text-keep-upright"],f=e.positionedGlyphs,p=[],d=0;d<f.length;d++){var m=f[d],g=m.glyph,v=g.rect;if(v){var y,b=(m.x+g.advance/2)*r,x=l;u?(y=[],x=a(y,t,b,i,t.segment,!0),h&&(x=Math.min(x,a(y,t,b,i,t.segment,!1)))):y=[{anchorPoint:new s(t.x,t.y),offset:0,angle:0,maxScale:1/0,minScale:l}];for(var _=m.x+g.left,w=m.y-g.top,M=_+v.w,k=w+v.h,A=new s(_,w),T=new s(M,w),E=new s(_,k),S=new s(M,k),L=0;L<y.length;L++){var z=y[L],I=A,P=T,C=E,R=S;if(c){var j=Math.sin(c),D=Math.cos(c),O=[D,-j,j,D];I=I.matMult(O),P=P.matMult(O),C=C.matMult(O),R=R.matMult(O)}var F=Math.max(z.minScale,x),N=(t.angle+z.offset+2*Math.PI)%(2*Math.PI),B=(z.angle+z.offset+2*Math.PI)%(2*Math.PI);p.push(new n(z.anchorPoint,I,P,C,R,v,N,B,F,z.maxScale))}}}return p}function a(t,e,r,n,i,o){var a=!o;0>r&&(o=!o),o&&i++;var u=new s(e.x,e.y),c=n[i],h=1/0;r=Math.abs(r);for(var f=l;;){var p=u.dist(c),d=r/p,m=Math.atan2(c.y-u.y,c.x-u.x);if(o||(m+=Math.PI),t.push({anchorPoint:u,offset:a?Math.PI:0,minScale:d,maxScale:h,angle:(m+2*Math.PI)%(2*Math.PI)}),f>=d)break;for(u=c;u.equals(c);)if(i+=o?1:-1,c=n[i],!c)return d;var g=c.sub(u)._unit();u=u.sub(g._mult(p)),h=d}return f}var s=t("point-geometry");e.exports={getIconQuads:i,getGlyphQuads:o,SymbolQuad:n};var l=.5},{"point-geometry":424}],326:[function(t,e,r){"use strict";function n(t,e,r){for(var n=[],o=0,a=t.length;a>o;o++){var s=i(t[o].properties,e["text-field"]);if(s){s=s.toString();var l=e["text-transform"];"uppercase"===l?s=s.toLocaleUpperCase():"lowercase"===l&&(s=s.toLocaleLowerCase());for(var u=0;u<s.length;u++)r[s.charCodeAt(u)]=!0;n[o]=s}else n[o]=null}return n}var i=t("../util/token");e.exports=n},{"../util/token":365}],327:[function(t,e,r){"use strict";function n(t,e,r,n){this.codePoint=t,this.x=e,this.y=r,this.glyph=n}function i(t,e,r,n,i,o){this.positionedGlyphs=t,this.text=e,this.top=r,this.bottom=n,this.left=i,this.right=o}function o(t,e,r,o,s,l,u,c,h){for(var f=[],p=new i(f,t,h[1],h[1],h[0],h[0]),d=-17,m=0,g=d,v=0;v<t.length;v++){var y=t.charCodeAt(v),b=e[y];b&&(f.push(new n(y,m,g,b)),m+=b.advance+c)}return f.length?(a(p,e,o,r,s,l,u,h),p):!1}function a(t,e,r,n,i,o,a,u){var c=null,p=0,d=0,m=0,g=0,v=t.positionedGlyphs;if(n)for(var y=0;y<v.length;y++){var b=v[y];if(b.x-=p,b.y+=r*m,b.x>n&&null!==c){var x=v[c+1].x;g=Math.max(x,g);for(var _=c+1;y>=_;_++)v[_].y+=r,v[_].x-=x;if(a){var w=c;h[v[c].codePoint]&&w--,s(v,e,d,w,a)}d=c+1,c=null,p+=x,m++}f[b.codePoint]&&(c=y)}var M=v[v.length-1],k=M.x+e[M.codePoint].advance;g=Math.max(g,k);var A=(m+1)*r;s(v,e,d,v.length-1,a),l(v,a,i,o,g,r,m,u),t.top+=-o*A,t.bottom=t.top+A,t.left+=-i*g,t.right=t.left+g}function s(t,e,r,n,i){for(var o=e[t[n].codePoint].advance,a=(t[n].x+o)*i,s=r;n>=s;s++)t[s].x-=a}function l(t,e,r,n,i,o,a,s){for(var l=(e-r)*i+s[0],u=(-n*(a+1)+.5)*o+s[1],c=0;c<t.length;c++)t[c].x+=l,t[c].y+=u}function u(t,e){if(!t||!t.rect)return null;var r=e["icon-offset"][0],n=e["icon-offset"][1],i=r-t.width/2,o=i+t.width,a=n-t.height/2,s=a+t.height;return new c(t,a,s,i,o)}function c(t,e,r,n,i){this.image=t,this.top=e,this.bottom=r,this.left=n,this.right=i}e.exports={shapeText:o,shapeIcon:u};var h={32:!0,8203:!0},f={32:!0,38:!0,43:!0,45:!0,47:!0,173:!0,183:!0,8203:!0,8208:!0,8211:!0}},{}],328:[function(t,e,r){"use strict";function n(t,e){this.width=t,this.height=e,this.bin=new a(t,e),this.images={},this.data=!1,this.texture=0,this.filter=0,this.pixelRatio=1,this.dirty=!0}function i(t,e,r,n,i,o,a,s,l,u,c){var h,f,p=n*e+r,d=s*o+a;if(c)for(d-=o,f=-1;u>=f;f++,p=((f+u)%u+n)*e+r,d+=o)for(h=-1;l>=h;h++)i[d+h]=t[p+(h+l)%l];else for(f=0;u>f;f++,p+=e,d+=o)for(h=0;l>h;h++)i[d+h]=t[p+h]}function o(t,e,r,n,i){this.rect=t,this.width=e,this.height=r,this.sdf=n,this.pixelRatio=i}var a=t("shelf-pack"),s=t("../util/browser"),l=t("../util/util");e.exports=n,n.prototype.allocateImage=function(t,e){t/=this.pixelRatio,e/=this.pixelRatio;var r=2,n=t+r+(4-(t+r)%4),i=e+r+(4-(e+r)%4),o=this.bin.packOne(n,i);return o?o:(l.warnOnce("SpriteAtlas out of space."),null)},n.prototype.getImage=function(t,e){if(this.images[t])return this.images[t];if(!this.sprite)return null;var r=this.sprite.getSpritePosition(t);if(!r.width||!r.height)return null;var n=this.allocateImage(r.width,r.height);if(!n)return null;var i=new o(n,r.width/r.pixelRatio,r.height/r.pixelRatio,r.sdf,r.pixelRatio/this.pixelRatio);return this.images[t]=i,this.copy(n,r,e),i},n.prototype.getPosition=function(t,e){var r=this.getImage(t,e),n=r&&r.rect;if(!n)return null;var i=r.width*r.pixelRatio,o=r.height*r.pixelRatio,a=1;return{size:[r.width,r.height],tl:[(n.x+a)/this.width,(n.y+a)/this.height],br:[(n.x+a+i)/this.width,(n.y+a+o)/this.height]}},n.prototype.allocate=function(){if(!this.data){var t=Math.floor(this.width*this.pixelRatio),e=Math.floor(this.height*this.pixelRatio);this.data=new Uint32Array(t*e);for(var r=0;r<this.data.length;r++)this.data[r]=0}},n.prototype.copy=function(t,e,r){if(this.sprite.img.data){var n=new Uint32Array(this.sprite.img.data.buffer);this.allocate();var o=this.data,a=1;i(n,this.sprite.img.width,e.x,e.y,o,this.width*this.pixelRatio,(t.x+a)*this.pixelRatio,(t.y+a)*this.pixelRatio,e.width,e.height,r),this.dirty=!0}},n.prototype.setSprite=function(t){t&&(this.pixelRatio=s.devicePixelRatio>1?2:1,this.canvas&&(this.canvas.width=this.width*this.pixelRatio,this.canvas.height=this.height*this.pixelRatio)),this.sprite=t},n.prototype.addIcons=function(t,e){for(var r=0;r<t.length;r++)this.getImage(t[r]);e(null,this.images)},n.prototype.bind=function(t,e){var r=!1;this.texture?t.bindTexture(t.TEXTURE_2D,this.texture):(this.texture=t.createTexture(),t.bindTexture(t.TEXTURE_2D,this.texture),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),r=!0);var n=e?t.LINEAR:t.NEAREST;n!==this.filter&&(t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,n),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,n),this.filter=n),this.dirty&&(this.allocate(),r?t.texImage2D(t.TEXTURE_2D,0,t.RGBA,this.width*this.pixelRatio,this.height*this.pixelRatio,0,t.RGBA,t.UNSIGNED_BYTE,new Uint8Array(this.data.buffer)):t.texSubImage2D(t.TEXTURE_2D,0,0,0,this.width*this.pixelRatio,this.height*this.pixelRatio,t.RGBA,t.UNSIGNED_BYTE,new Uint8Array(this.data.buffer)),this.dirty=!1)}},{"../util/browser":350,"../util/util":366,"shelf-pack":427}],329:[function(t,e,r){"use strict";var n=t("../util/struct_array"),i=t("../util/util"),o=t("point-geometry"),a=e.exports=new n({members:[{type:"Uint16",name:"textBoxStartIndex"},{type:"Uint16",name:"textBoxEndIndex"},{type:"Uint16",name:"iconBoxStartIndex"},{type:"Uint16",name:"iconBoxEndIndex"},{type:"Uint16",name:"glyphQuadStartIndex"},{type:"Uint16",name:"glyphQuadEndIndex"},{type:"Uint16",name:"iconQuadStartIndex"},{type:"Uint16",name:"iconQuadEndIndex"},{type:"Int16",name:"anchorPointX"},{type:"Int16",name:"anchorPointY"},{type:"Int8",name:"index"}]});i.extendAll(a.prototype.StructType.prototype,{get anchorPoint(){return new o(this.anchorPointX,this.anchorPointY)}})},{"../util/struct_array":364,"../util/util":366,"point-geometry":424}],330:[function(t,e,r){"use strict";var n=t("../util/struct_array"),i=t("../util/util"),o=t("point-geometry"),a=t("./quads").SymbolQuad,s=e.exports=new n({members:[{type:"Int16",name:"anchorPointX"},{type:"Int16",name:"anchorPointY"},{type:"Float32",name:"tlX"},{type:"Float32",name:"tlY"},{type:"Float32",name:"trX"},{type:"Float32",name:"trY"},{type:"Float32",name:"blX"},{type:"Float32",name:"blY"},{type:"Float32",name:"brX"},{type:"Float32",name:"brY"},{type:"Int16",name:"texH"},{type:"Int16",name:"texW"},{type:"Int16",name:"texX"},{type:"Int16",name:"texY"},{type:"Float32",name:"anchorAngle"},{type:"Float32",name:"glyphAngle"},{type:"Float32",name:"maxScale"},{type:"Float32",name:"minScale"}]});i.extendAll(s.prototype.StructType.prototype,{get anchorPoint(){return new o(this.anchorPointX,this.anchorPointY)},get SymbolQuad(){return new a(this.anchorPoint,new o(this.tlX,this.tlY),new o(this.trX,this.trY),new o(this.blX,this.blY),new o(this.brX,this.brY),{x:this.texX,y:this.texY,h:this.texH,w:this.texW,height:this.texH,width:this.texW},this.anchorAngle,this.glyphAngle,this.minScale,this.maxScale)}})},{"../util/struct_array":364,"../util/util":366,"./quads":325,"point-geometry":424}],331:[function(t,e,r){"use strict";var n=t("../util/dom"),i=t("point-geometry"),o={scrollZoom:t("./handler/scroll_zoom"),boxZoom:t("./handler/box_zoom"),dragRotate:t("./handler/drag_rotate"),dragPan:t("./handler/drag_pan"),keyboard:t("./handler/keyboard"),doubleClickZoom:t("./handler/dblclick_zoom"),touchZoomRotate:t("./handler/touch_zoom_rotate")};e.exports=function(t,e){function r(t){v("mouseout",t)}function a(e){t.stop(),_=n.mousePos(b,e),v("mousedown",e)}function s(e){var r=t.dragRotate&&t.dragRotate.isActive();x&&!r&&v("contextmenu",x),x=null,v("mouseup",e)}function l(e){if(!(t.dragPan&&t.dragPan.isActive()||t.dragRotate&&t.dragRotate.isActive())){for(var r=e.toElement||e.target;r&&r!==b;)r=r.parentNode;r===b&&v("mousemove",e)}}function u(e){t.stop(),y("touchstart",e),!e.touches||e.touches.length>1||(w?(clearTimeout(w),w=null,v("dblclick",e)):w=setTimeout(p,300))}function c(t){y("touchmove",t)}function h(t){y("touchend",t)}function f(t){y("touchcancel",t)}function p(){w=null}function d(t){var e=n.mousePos(b,t);e.equals(_)&&v("click",t)}function m(t){v("dblclick",t),t.preventDefault()}function g(t){x=t,t.preventDefault()}function v(e,r){var i=n.mousePos(b,r);return t.fire(e,{lngLat:t.unproject(i),point:i,originalEvent:r})}function y(e,r){var o=n.touchPos(b,r),a=o.reduce(function(t,e,r,n){return t.add(e.div(n.length))},new i(0,0));return t.fire(e,{lngLat:t.unproject(a),point:a,lngLats:o.map(function(e){return t.unproject(e)},this),points:o,originalEvent:r})}var b=t.getCanvasContainer(),x=null,_=null,w=null;for(var M in o)t[M]=new o[M](t,e),e.interactive&&e[M]&&t[M].enable();b.addEventListener("mouseout",r,!1),b.addEventListener("mousedown",a,!1),b.addEventListener("mouseup",s,!1),b.addEventListener("mousemove",l,!1),b.addEventListener("touchstart",u,!1),b.addEventListener("touchend",h,!1),b.addEventListener("touchmove",c,!1),b.addEventListener("touchcancel",f,!1),b.addEventListener("click",d,!1),b.addEventListener("dblclick",m,!1),b.addEventListener("contextmenu",g,!1)}},{"../util/dom":352,"./handler/box_zoom":337,"./handler/dblclick_zoom":338,"./handler/drag_pan":339,"./handler/drag_rotate":340,"./handler/keyboard":341,"./handler/scroll_zoom":342,"./handler/touch_zoom_rotate":343,"point-geometry":424}],332:[function(t,e,r){"use strict";var n=t("../util/util"),i=t("../util/interpolate"),o=t("../util/browser"),a=t("../geo/lng_lat"),s=t("../geo/lng_lat_bounds"),l=t("point-geometry"),u=e.exports=function(){};n.extend(u.prototype,{getCenter:function(){return this.transform.center},setCenter:function(t,e){return this.jumpTo({center:t},e),this},panBy:function(t,e,r){return this.panTo(this.transform.center,n.extend({offset:l.convert(t).mult(-1)},e),r),this},panTo:function(t,e,r){return this.easeTo(n.extend({center:t},e),r)},getZoom:function(){return this.transform.zoom},setZoom:function(t,e){return this.jumpTo({zoom:t},e),this},zoomTo:function(t,e,r){return this.easeTo(n.extend({zoom:t},e),r)},zoomIn:function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},zoomOut:function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},getBearing:function(){return this.transform.bearing},setBearing:function(t,e){return this.jumpTo({bearing:t},e),this},rotateTo:function(t,e,r){return this.easeTo(n.extend({bearing:t},e),r)},resetNorth:function(t,e){return this.rotateTo(0,n.extend({duration:1e3},t),e),this},snapToNorth:function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},getPitch:function(){return this.transform.pitch},setPitch:function(t,e){return this.jumpTo({pitch:t},e),this},fitBounds:function(t,e,r){e=n.extend({padding:0,offset:[0,0],maxZoom:1/0},e),t=s.convert(t);var i=l.convert(e.offset),o=this.transform,a=o.project(t.getNorthWest()),u=o.project(t.getSouthEast()),c=u.sub(a),h=(o.width-2*e.padding-2*Math.abs(i.x))/c.x,f=(o.height-2*e.padding-2*Math.abs(i.y))/c.y;return e.center=o.unproject(a.add(u).div(2)),e.zoom=Math.min(o.scaleZoom(o.scale*Math.min(h,f)),e.maxZoom),e.bearing=0,e.linear?this.easeTo(e,r):this.flyTo(e,r)},jumpTo:function(t,e){this.stop();var r=this.transform,n=!1,i=!1,o=!1;return"zoom"in t&&r.zoom!==+t.zoom&&(n=!0,r.zoom=+t.zoom),"center"in t&&(r.center=a.convert(t.center)),"bearing"in t&&r.bearing!==+t.bearing&&(i=!0,r.bearing=+t.bearing),"pitch"in t&&r.pitch!==+t.pitch&&(o=!0,r.pitch=+t.pitch),this.fire("movestart",e).fire("move",e),n&&this.fire("zoomstart",e).fire("zoom",e).fire("zoomend",e),i&&this.fire("rotate",e),o&&this.fire("pitch",e),this.fire("moveend",e)},easeTo:function(t,e){this.stop(),t=n.extend({offset:[0,0],duration:500,easing:n.ease},t);var r,o,s=this.transform,u=l.convert(t.offset),c=this.getZoom(),h=this.getBearing(),f=this.getPitch(),p="zoom"in t?+t.zoom:c,d="bearing"in t?this._normalizeBearing(t.bearing,h):h,m="pitch"in t?+t.pitch:f;"center"in t?(r=a.convert(t.center),o=s.centerPoint.add(u)):"around"in t?(r=a.convert(t.around),o=s.locationPoint(r)):(o=s.centerPoint.add(u),r=s.pointLocation(o));var g=s.locationPoint(r);return t.animate===!1&&(t.duration=0),this.zooming=p!==c,this.rotating=h!==d,this.pitching=m!==f,t.noMoveStart||this.fire("movestart",e),this.zooming&&this.fire("zoomstart",e),clearTimeout(this._onEaseEnd),this._ease(function(t){this.zooming&&(s.zoom=i(c,p,t)),this.rotating&&(s.bearing=i(h,d,t)),this.pitching&&(s.pitch=i(f,m,t)),s.setLocationAtPoint(r,g.add(o.sub(g)._mult(t))),this.fire("move",e),this.zooming&&this.fire("zoom",e),this.rotating&&this.fire("rotate",e),this.pitching&&this.fire("pitch",e)},function(){t.delayEndEvents?this._onEaseEnd=setTimeout(this._easeToEnd.bind(this,e),t.delayEndEvents):this._easeToEnd(e)}.bind(this),t),this},_easeToEnd:function(t){var e=this.zooming;this.zooming=!1,this.rotating=!1,this.pitching=!1,e&&this.fire("zoomend",t),this.fire("moveend",t)},flyTo:function(t,e){function r(t){var e=(A*A-k*k+(t?-1:1)*L*L*T*T)/(2*(t?A:k)*L*T);return Math.log(Math.sqrt(e*e+1)-e)}function o(t){return(Math.exp(t)-Math.exp(-t))/2}function s(t){return(Math.exp(t)+Math.exp(-t))/2}function u(t){return o(t)/s(t)}this.stop(),t=n.extend({offset:[0,0],speed:1.2,curve:1.42,easing:n.ease},t);var c=this.transform,h=l.convert(t.offset),f=this.getZoom(),p=this.getBearing(),d=this.getPitch(),m="center"in t?a.convert(t.center):this.getCenter(),g="zoom"in t?+t.zoom:f,v="bearing"in t?this._normalizeBearing(t.bearing,p):p,y="pitch"in t?+t.pitch:d;Math.abs(c.center.lng)+Math.abs(m.lng)>180&&(c.center.lng>0&&m.lng<0?m.lng+=360:c.center.lng<0&&m.lng>0&&(m.lng-=360));var b=c.zoomScale(g-f),x=c.point,_="center"in t?c.project(m).sub(h.div(b)):x,w=c.worldSize,M=t.curve,k=Math.max(c.width,c.height),A=k/b,T=_.sub(x).mag();if("minZoom"in t){var E=n.clamp(Math.min(t.minZoom,f,g),c.minZoom,c.maxZoom),S=k/c.zoomScale(E-f);M=Math.sqrt(S/T*2)}var L=M*M,z=r(0),I=function(t){return s(z)/s(z+M*t)},P=function(t){return k*((s(z)*u(z+M*t)-o(z))/L)/T},C=(r(1)-z)/M;if(Math.abs(T)<1e-6){if(Math.abs(k-A)<1e-6)return this.easeTo(t);var R=k>A?-1:1;C=Math.abs(Math.log(A/k))/M,P=function(){return 0},I=function(t){return Math.exp(R*M*t)}}if("duration"in t)t.duration=+t.duration;else{var j="screenSpeed"in t?+t.screenSpeed/M:+t.speed;t.duration=1e3*C/j}return this.zooming=!0,p!==v&&(this.rotating=!0),d!==y&&(this.pitching=!0),this.fire("movestart",e),this.fire("zoomstart",e),this._ease(function(t){var r=t*C,n=P(r);c.zoom=f+c.scaleZoom(1/I(r)),c.center=c.unproject(x.add(_.sub(x).mult(n)),w),this.rotating&&(c.bearing=i(p,v,t)),this.pitching&&(c.pitch=i(d,y,t)),this.fire("move",e),this.fire("zoom",e),this.rotating&&this.fire("rotate",e),this.pitching&&this.fire("pitch",e)},function(){this.zooming=!1,this.rotating=!1,this.pitching=!1,this.fire("zoomend",e),this.fire("moveend",e)},t),this},isEasing:function(){return!!this._abortFn},stop:function(){return this._abortFn&&(this._abortFn(),this._finishEase()),this},_ease:function(t,e,r){this._finishFn=e,this._abortFn=o.timed(function(e){t.call(this,r.easing(e)),1===e&&this._finishEase()},r.animate===!1?0:r.duration,this)},_finishEase:function(){delete this._abortFn;var t=this._finishFn;delete this._finishFn,t.call(this)},_normalizeBearing:function(t,e){t=n.wrap(t,-180,180);var r=Math.abs(t-e);return Math.abs(t-360-e)<r&&(t-=360),Math.abs(t+360-e)<r&&(t+=360),t},_updateEasing:function(t,e,r){var i;
if(this.ease){var o=this.ease,a=(Date.now()-o.start)/o.duration,s=o.easing(a+.01)-o.easing(a),l=.27/Math.sqrt(s*s+1e-4)*.01,u=Math.sqrt(.0729-l*l);i=n.bezier(l,u,.25,1)}else i=r?n.bezier.apply(n,r):n.ease;return this.ease={start:(new Date).getTime(),to:Math.pow(2,e),duration:t,easing:i},i}})},{"../geo/lng_lat":263,"../geo/lng_lat_bounds":264,"../util/browser":350,"../util/interpolate":360,"../util/util":366,"point-geometry":424}],333:[function(t,e,r){"use strict";function n(t){a.setOptions(this,t)}var i=t("./control"),o=t("../../util/dom"),a=t("../../util/util");e.exports=n,n.createAttributionString=function(t){var e=[];for(var r in t){var n=t[r];n.attribution&&e.indexOf(n.attribution)<0&&e.push(n.attribution)}return e.sort(function(t,e){return t.length-e.length}),e=e.filter(function(t,r){for(var n=r+1;n<e.length;n++)if(e[n].indexOf(t)>=0)return!1;return!0}),e.join(" | ")},n.prototype=a.inherit(i,{options:{position:"bottom-right"},onAdd:function(t){var e="mapboxgl-ctrl-attrib",r=this._container=o.create("div",e,t.getContainer());return this._update(),t.on("source.load",this._update.bind(this)),t.on("source.change",this._update.bind(this)),t.on("source.remove",this._update.bind(this)),t.on("moveend",this._updateEditLink.bind(this)),r},_update:function(){this._map.style&&(this._container.innerHTML=n.createAttributionString(this._map.style.sources)),this._editLink=this._container.getElementsByClassName("mapbox-improve-map")[0],this._updateEditLink()},_updateEditLink:function(){if(this._editLink){var t=this._map.getCenter();this._editLink.href="https://www.mapbox.com/map-feedback/#/"+t.lng+"/"+t.lat+"/"+Math.round(this._map.getZoom()+1)}}})},{"../../util/dom":352,"../../util/util":366,"./control":334}],334:[function(t,e,r){"use strict";function n(){}var i=t("../../util/util"),o=t("../../util/evented");e.exports=n,n.prototype={addTo:function(t){this._map=t;var e=this._container=this.onAdd(t);if(this.options&&this.options.position){var r=this.options.position,n=t._controlCorners[r];e.className+=" mapboxgl-ctrl",-1!==r.indexOf("bottom")?n.insertBefore(e,n.firstChild):n.appendChild(e)}return this},remove:function(){return this._container.parentNode.removeChild(this._container),this.onRemove&&this.onRemove(this._map),this._map=null,this}},i.extend(n.prototype,o)},{"../../util/evented":358,"../../util/util":366}],335:[function(t,e,r){"use strict";function n(t){s.setOptions(this,t)}var i=t("./control"),o=t("../../util/browser"),a=t("../../util/dom"),s=t("../../util/util");e.exports=n;var l={enableHighAccuracy:!1,timeout:6e3};n.prototype=s.inherit(i,{options:{position:"top-right"},onAdd:function(t){var e="mapboxgl-ctrl",r=this._container=a.create("div",e+"-group",t.getContainer());return o.supportsGeolocation?(this._container.addEventListener("contextmenu",this._onContextMenu.bind(this)),this._geolocateButton=a.create("button",e+"-icon "+e+"-geolocate",this._container),this._geolocateButton.type="button",this._geolocateButton.addEventListener("click",this._onClickGeolocate.bind(this)),r):r},_onContextMenu:function(t){t.preventDefault()},_onClickGeolocate:function(){navigator.geolocation.getCurrentPosition(this._success.bind(this),this._error.bind(this),l),this._timeoutId=setTimeout(this._finish.bind(this),1e4)},_success:function(t){this._map.jumpTo({center:[t.coords.longitude,t.coords.latitude],zoom:17,bearing:0,pitch:0}),this.fire("geolocate",t),this._finish()},_error:function(t){this.fire("error",t),this._finish()},_finish:function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0}})},{"../../util/browser":350,"../../util/dom":352,"../../util/util":366,"./control":334}],336:[function(t,e,r){"use strict";function n(t){s.setOptions(this,t)}function i(t){return new MouseEvent(t.type,{button:2,buttons:2,bubbles:!0,cancelable:!0,detail:t.detail,view:t.view,screenX:t.screenX,screenY:t.screenY,clientX:t.clientX,clientY:t.clientY,movementX:t.movementX,movementY:t.movementY,ctrlKey:t.ctrlKey,shiftKey:t.shiftKey,altKey:t.altKey,metaKey:t.metaKey})}var o=t("./control"),a=t("../../util/dom"),s=t("../../util/util");e.exports=n,n.prototype=s.inherit(o,{options:{position:"top-right"},onAdd:function(t){var e="mapboxgl-ctrl",r=this._container=a.create("div",e+"-group",t.getContainer());return this._container.addEventListener("contextmenu",this._onContextMenu.bind(this)),this._zoomInButton=this._createButton(e+"-icon "+e+"-zoom-in",t.zoomIn.bind(t)),this._zoomOutButton=this._createButton(e+"-icon "+e+"-zoom-out",t.zoomOut.bind(t)),this._compass=this._createButton(e+"-icon "+e+"-compass",t.resetNorth.bind(t)),this._compassArrow=a.create("div","arrow",this._compass),this._compass.addEventListener("mousedown",this._onCompassDown.bind(this)),this._onCompassMove=this._onCompassMove.bind(this),this._onCompassUp=this._onCompassUp.bind(this),t.on("rotate",this._rotateCompassArrow.bind(this)),this._rotateCompassArrow(),this._el=t.getCanvasContainer(),r},_onContextMenu:function(t){t.preventDefault()},_onCompassDown:function(t){0===t.button&&(a.disableDrag(),document.addEventListener("mousemove",this._onCompassMove),document.addEventListener("mouseup",this._onCompassUp),this._el.dispatchEvent(i(t)),t.stopPropagation())},_onCompassMove:function(t){0===t.button&&(this._el.dispatchEvent(i(t)),t.stopPropagation())},_onCompassUp:function(t){0===t.button&&(document.removeEventListener("mousemove",this._onCompassMove),document.removeEventListener("mouseup",this._onCompassUp),a.enableDrag(),this._el.dispatchEvent(i(t)),t.stopPropagation())},_createButton:function(t,e){var r=a.create("button",t,this._container);return r.type="button",r.addEventListener("click",function(){e()}),r},_rotateCompassArrow:function(){var t="rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassArrow.style.transform=t}})},{"../../util/dom":352,"../../util/util":366,"./control":334}],337:[function(t,e,r){"use strict";function n(t){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),a.bindHandlers(this)}var i=t("../../util/dom"),o=t("../../geo/lng_lat_bounds"),a=t("../../util/util");e.exports=n,n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()||(this._el.addEventListener("mousedown",this._onMouseDown,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener("mousedown",this._onMouseDown),this._enabled=!1)},_onMouseDown:function(t){t.shiftKey&&0===t.button&&(document.addEventListener("mousemove",this._onMouseMove,!1),document.addEventListener("keydown",this._onKeyDown,!1),document.addEventListener("mouseup",this._onMouseUp,!1),i.disableDrag(),this._startPos=i.mousePos(this._el,t),this._active=!0)},_onMouseMove:function(t){var e=this._startPos,r=i.mousePos(this._el,t);this._box||(this._box=i.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var n=Math.min(e.x,r.x),o=Math.max(e.x,r.x),a=Math.min(e.y,r.y),s=Math.max(e.y,r.y);i.setTransform(this._box,"translate("+n+"px,"+a+"px)"),this._box.style.width=o-n+"px",this._box.style.height=s-a+"px"},_onMouseUp:function(t){if(0===t.button){var e=this._startPos,r=i.mousePos(this._el,t),n=new o(this._map.unproject(e),this._map.unproject(r));this._finish(),e.x===r.x&&e.y===r.y?this._fireEvent("boxzoomcancel",t):this._map.fitBounds(n,{linear:!0}).fire("boxzoomend",{originalEvent:t,boxZoomBounds:n})}},_onKeyDown:function(t){27===t.keyCode&&(this._finish(),this._fireEvent("boxzoomcancel",t))},_finish:function(){this._active=!1,document.removeEventListener("mousemove",this._onMouseMove,!1),document.removeEventListener("keydown",this._onKeyDown,!1),document.removeEventListener("mouseup",this._onMouseUp,!1),this._container.classList.remove("mapboxgl-crosshair"),this._box&&(this._box.parentNode.removeChild(this._box),this._box=null),i.enableDrag()},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})}}},{"../../geo/lng_lat_bounds":264,"../../util/dom":352,"../../util/util":366}],338:[function(t,e,r){"use strict";function n(t){this._map=t,this._onDblClick=this._onDblClick.bind(this)}e.exports=n,n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()||(this._map.on("dblclick",this._onDblClick),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._map.off("dblclick",this._onDblClick),this._enabled=!1)},_onDblClick:function(t){this._map.zoomTo(this._map.getZoom()+(t.originalEvent.shiftKey?-1:1),{around:t.lngLat},t)}}},{}],339:[function(t,e,r){"use strict";function n(t){this._map=t,this._el=t.getCanvasContainer(),o.bindHandlers(this)}var i=t("../../util/dom"),o=t("../../util/util");e.exports=n;var a=.3,s=o.bezier(0,0,a,1),l=1400,u=2500;n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()||(this._el.addEventListener("mousedown",this._onDown),this._el.addEventListener("touchstart",this._onDown),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener("mousedown",this._onDown),this._el.removeEventListener("touchstart",this._onDown),this._enabled=!1)},_onDown:function(t){this._ignoreEvent(t)||this.isActive()||(t.touches?(document.addEventListener("touchmove",this._onMove),document.addEventListener("touchend",this._onTouchEnd)):(document.addEventListener("mousemove",this._onMove),document.addEventListener("mouseup",this._onMouseUp)),this._active=!1,this._startPos=this._pos=i.mousePos(this._el,t),this._inertia=[[Date.now(),this._pos]])},_onMove:function(t){if(!this._ignoreEvent(t)){this.isActive()||(this._active=!0,this._fireEvent("dragstart",t),this._fireEvent("movestart",t));var e=i.mousePos(this._el,t),r=this._map;r.stop(),this._drainInertiaBuffer(),this._inertia.push([Date.now(),e]),r.transform.setLocationAtPoint(r.transform.pointLocation(this._pos),e),this._fireEvent("drag",t),this._fireEvent("move",t),this._pos=e,t.preventDefault()}},_onUp:function(t){if(this.isActive()){this._active=!1,this._fireEvent("dragend",t),this._drainInertiaBuffer();var e=function(){this._fireEvent("moveend",t)}.bind(this),r=this._inertia;if(r.length<2)return void e();var n=r[r.length-1],i=r[0],o=n[1].sub(i[1]),c=(n[0]-i[0])/1e3;if(0===c||n[1].equals(i[1]))return void e();var h=o.mult(a/c),f=h.mag();f>l&&(f=l,h._unit()._mult(f));var p=f/(u*a),d=h.mult(-p/2);this._map.panBy(d,{duration:1e3*p,easing:s,noMoveStart:!0},{originalEvent:t})}},_onMouseUp:function(t){this._ignoreEvent(t)||(this._onUp(t),document.removeEventListener("mousemove",this._onMove),document.removeEventListener("mouseup",this._onMouseUp))},_onTouchEnd:function(t){this._ignoreEvent(t)||(this._onUp(t),document.removeEventListener("touchmove",this._onMove),document.removeEventListener("touchend",this._onTouchEnd))},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})},_ignoreEvent:function(t){var e=this._map;if(e.boxZoom&&e.boxZoom.isActive())return!0;if(e.dragRotate&&e.dragRotate.isActive())return!0;if(t.touches)return t.touches.length>1;if(t.ctrlKey)return!0;var r=1,n=0;return"mousemove"===t.type?t.buttons&0===r:t.button!==n},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now(),r=160;t.length>0&&e-t[0][0]>r;)t.shift()}}},{"../../util/dom":352,"../../util/util":366}],340:[function(t,e,r){"use strict";function n(t,e){this._map=t,this._el=t.getCanvasContainer(),this._bearingSnap=e.bearingSnap,a.bindHandlers(this)}var i=t("../../util/dom"),o=t("point-geometry"),a=t("../../util/util");e.exports=n;var s=.25,l=a.bezier(0,0,s,1),u=180,c=720;n.prototype={_enabled:!1,_active:!1,isEnabled:function(){return this._enabled},isActive:function(){return this._active},enable:function(){this.isEnabled()||(this._el.addEventListener("mousedown",this._onDown),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener("mousedown",this._onDown),this._enabled=!1)},_onDown:function(t){if(!this._ignoreEvent(t)&&!this.isActive()){document.addEventListener("mousemove",this._onMove),document.addEventListener("mouseup",this._onUp),this._active=!1,this._inertia=[[Date.now(),this._map.getBearing()]],this._startPos=this._pos=i.mousePos(this._el,t),this._center=this._map.transform.centerPoint;var e=this._startPos.sub(this._center),r=e.mag();200>r&&(this._center=this._startPos.add(new o(-200,0)._rotate(e.angle()))),t.preventDefault()}},_onMove:function(t){if(!this._ignoreEvent(t)){this.isActive()||(this._active=!0,this._fireEvent("rotatestart",t),this._fireEvent("movestart",t));var e=this._map;e.stop();var r=this._pos,n=i.mousePos(this._el,t),o=this._center,a=r.sub(o).angleWith(n.sub(o))/Math.PI*180,s=e.getBearing()-a,l=this._inertia,u=l[l.length-1];this._drainInertiaBuffer(),l.push([Date.now(),e._normalizeBearing(s,u[1])]),e.transform.bearing=s,this._fireEvent("rotate",t),this._fireEvent("move",t),this._pos=n}},_onUp:function(t){if(!this._ignoreEvent(t)&&(document.removeEventListener("mousemove",this._onMove),document.removeEventListener("mouseup",this._onUp),this.isActive())){this._active=!1,this._fireEvent("rotateend",t),this._drainInertiaBuffer();var e=this._map,r=e.getBearing(),n=this._inertia,i=function(){Math.abs(r)<this._bearingSnap?e.resetNorth({noMoveStart:!0},{originalEvent:t}):this._fireEvent("moveend",t)}.bind(this);if(n.length<2)return void i();var o=n[0],a=n[n.length-1],h=n[n.length-2],f=e._normalizeBearing(r,h[1]),p=a[1]-o[1],d=0>p?-1:1,m=(a[0]-o[0])/1e3;if(0===p||0===m)return void i();var g=Math.abs(p*(s/m));g>u&&(g=u);var v=g/(c*s),y=d*g*(v/2);f+=y,Math.abs(e._normalizeBearing(f,0))<this._bearingSnap&&(f=e._normalizeBearing(0,f)),e.rotateTo(f,{duration:1e3*v,easing:l,noMoveStart:!0},{originalEvent:t})}},_fireEvent:function(t,e){return this._map.fire(t,{originalEvent:e})},_ignoreEvent:function(t){var e=this._map;if(e.boxZoom&&e.boxZoom.isActive())return!0;if(e.dragPan&&e.dragPan.isActive())return!0;if(t.touches)return t.touches.length>1;var r=t.ctrlKey?1:2,n=t.ctrlKey?0:2;return"mousemove"===t.type?t.buttons&0===r:t.button!==n},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now(),r=160;t.length>0&&e-t[0][0]>r;)t.shift()}}},{"../../util/dom":352,"../../util/util":366,"point-geometry":424}],341:[function(t,e,r){"use strict";function n(t){this._map=t,this._el=t.getCanvasContainer(),this._onKeyDown=this._onKeyDown.bind(this)}e.exports=n;var i=80,o=2,a=5;n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()||(this._el.addEventListener("keydown",this._onKeyDown,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener("keydown",this._onKeyDown),this._enabled=!1)},_onKeyDown:function(t){if(!(t.altKey||t.ctrlKey||t.metaKey)){var e=this._map,r={originalEvent:t};if(!e.isEasing())switch(t.keyCode){case 61:case 107:case 171:case 187:e.zoomTo(Math.round(e.getZoom())+(t.shiftKey?2:1),r);break;case 189:case 109:case 173:e.zoomTo(Math.round(e.getZoom())-(t.shiftKey?2:1),r);break;case 37:t.shiftKey?e.easeTo({bearing:e.getBearing()-o},r):(t.preventDefault(),e.panBy([-i,0],r));break;case 39:t.shiftKey?e.easeTo({bearing:e.getBearing()+o},r):(t.preventDefault(),e.panBy([i,0],r));break;case 38:t.shiftKey?e.easeTo({pitch:e.getPitch()+a},r):(t.preventDefault(),e.panBy([0,-i],r));break;case 40:t.shiftKey?e.easeTo({pitch:Math.max(e.getPitch()-a,0)},r):(t.preventDefault(),e.panBy([0,i],r))}}}}},{}],342:[function(t,e,r){"use strict";function n(t){this._map=t,this._el=t.getCanvasContainer(),a.bindHandlers(this)}var i=t("../../util/dom"),o=t("../../util/browser"),a=t("../../util/util");e.exports=n;var s="undefined"!=typeof navigator?navigator.userAgent.toLowerCase():"",l=-1!==s.indexOf("firefox"),u=-1!==s.indexOf("safari")&&-1===s.indexOf("chrom");n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()||(this._el.addEventListener("wheel",this._onWheel,!1),this._el.addEventListener("mousewheel",this._onWheel,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener("wheel",this._onWheel),this._el.removeEventListener("mousewheel",this._onWheel),this._enabled=!1)},_onWheel:function(t){var e;"wheel"===t.type?(e=t.deltaY,l&&t.deltaMode===window.WheelEvent.DOM_DELTA_PIXEL&&(e/=o.devicePixelRatio),t.deltaMode===window.WheelEvent.DOM_DELTA_LINE&&(e*=40)):"mousewheel"===t.type&&(e=-t.wheelDeltaY,u&&(e/=3));var r=o.now(),n=r-(this._time||0);this._pos=i.mousePos(this._el,t),this._time=r,0!==e&&e%4.000244140625===0?(this._type="wheel",e=Math.floor(e/4)):0!==e&&Math.abs(e)<4?this._type="trackpad":n>400?(this._type=null,this._lastValue=e,this._timeout=setTimeout(this._onTimeout,40)):this._type||(this._type=Math.abs(n*e)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,e+=this._lastValue)),t.shiftKey&&e&&(e/=4),this._type&&this._zoom(-e,t),t.preventDefault()},_onTimeout:function(){this._type="wheel",this._zoom(-this._lastValue)},_zoom:function(t,e){if(0!==t){var r=this._map,n=2/(1+Math.exp(-Math.abs(t/100)));0>t&&0!==n&&(n=1/n);var i=r.ease?r.ease.to:r.transform.scale,o=r.transform.scaleZoom(i*n);r.zoomTo(o,{duration:0,around:r.unproject(this._pos),delayEndEvents:200},{originalEvent:e})}}}},{"../../util/browser":350,"../../util/dom":352,"../../util/util":366}],343:[function(t,e,r){"use strict";function n(t){this._map=t,this._el=t.getCanvasContainer(),o.bindHandlers(this)}var i=t("../../util/dom"),o=t("../../util/util");e.exports=n;var a=.15,s=o.bezier(0,0,a,1),l=12,u=2.5,c=.15,h=4;n.prototype={_enabled:!1,isEnabled:function(){return this._enabled},enable:function(){this.isEnabled()||(this._el.addEventListener("touchstart",this._onStart,!1),this._enabled=!0)},disable:function(){this.isEnabled()&&(this._el.removeEventListener("touchstart",this._onStart),this._enabled=!1)},disableRotation:function(){this._rotationDisabled=!0},enableRotation:function(){this._rotationDisabled=!1},_onStart:function(t){if(2===t.touches.length){var e=i.mousePos(this._el,t.touches[0]),r=i.mousePos(this._el,t.touches[1]);this._startVec=e.sub(r),this._startScale=this._map.transform.scale,this._startBearing=this._map.transform.bearing,this._gestureIntent=void 0,this._inertia=[],document.addEventListener("touchmove",this._onMove,!1),document.addEventListener("touchend",this._onEnd,!1)}},_onMove:function(t){if(2===t.touches.length){var e=i.mousePos(this._el,t.touches[0]),r=i.mousePos(this._el,t.touches[1]),n=e.add(r).div(2),o=e.sub(r),a=o.mag()/this._startVec.mag(),s=this._rotationDisabled?0:180*o.angleWith(this._startVec)/Math.PI,l=this._map;if(this._gestureIntent){var u={duration:0,around:l.unproject(n)};"rotate"===this._gestureIntent&&(u.bearing=this._startBearing+s),"zoom"!==this._gestureIntent&&"rotate"!==this._gestureIntent||(u.zoom=l.transform.scaleZoom(this._startScale*a)),l.stop(),this._drainInertiaBuffer(),this._inertia.push([Date.now(),a,n]),l.easeTo(u,{originalEvent:t})}else{var f=Math.abs(1-a)>c,p=Math.abs(s)>h;p?this._gestureIntent="rotate":f&&(this._gestureIntent="zoom"),this._gestureIntent&&(this._startVec=o,this._startScale=l.transform.scale,this._startBearing=l.transform.bearing)}t.preventDefault()}},_onEnd:function(t){document.removeEventListener("touchmove",this._onMove),document.removeEventListener("touchend",this._onEnd),this._drainInertiaBuffer();var e=this._inertia,r=this._map;if(e.length<2)return void r.snapToNorth({},{originalEvent:t});var n=e[e.length-1],i=e[0],o=r.transform.scaleZoom(this._startScale*n[1]),c=r.transform.scaleZoom(this._startScale*i[1]),h=o-c,f=(n[0]-i[0])/1e3,p=n[2];if(0===f||o===c)return void r.snapToNorth({},{originalEvent:t});var d=h*a/f;Math.abs(d)>u&&(d=d>0?u:-u);var m=1e3*Math.abs(d/(l*a)),g=o+d*m/2e3;0>g&&(g=0),r.easeTo({zoom:g,duration:m,easing:s,around:r.unproject(p)},{originalEvent:t})},_drainInertiaBuffer:function(){for(var t=this._inertia,e=Date.now(),r=160;t.length>2&&e-t[0][0]>r;)t.shift()}}},{"../../util/dom":352,"../../util/util":366}],344:[function(t,e,r){"use strict";function n(){i.bindAll(["_onHashChange","_updateHash"],this)}e.exports=n;var i=t("../util/util");n.prototype={addTo:function(t){return this._map=t,window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},remove:function(){return window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),delete this._map,this},_onHashChange:function(){var t=location.hash.replace("#","").split("/");return t.length>=3?(this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:+(t[3]||0)}),!0):!1},_updateHash:function(){var t=this._map.getCenter(),e=this._map.getZoom(),r=this._map.getBearing(),n=Math.max(0,Math.ceil(Math.log(e)/Math.LN2)),i="#"+Math.round(100*e)/100+"/"+t.lat.toFixed(n)+"/"+t.lng.toFixed(n)+(r?"/"+Math.round(10*r)/10:"");window.history.replaceState("","",i)}}},{"../util/util":366}],345:[function(t,e,r){"use strict";function n(t){t.parentNode&&t.parentNode.removeChild(t)}var i=t("../util/canvas"),o=t("../util/util"),a=t("../util/browser"),s=t("../util/browser").window,l=t("../util/evented"),u=t("../util/dom"),c=t("../style/style"),h=t("../style/animation_loop"),f=t("../render/painter"),p=t("../geo/transform"),d=t("./hash"),m=t("./bind_handlers"),g=t("./camera"),v=t("../geo/lng_lat"),y=t("../geo/lng_lat_bounds"),b=t("point-geometry"),x=t("./control/attribution"),_=0,w=20,M={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:_,maxZoom:w,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,bearingSnap:7,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,workerCount:Math.max(a.hardwareConcurrency-1,1)},k=e.exports=function(t){if(t=o.extend({},M,t),t.workerCount<1)throw new Error("workerCount must an integer greater than or equal to 1.");this._interactive=t.interactive,this._failIfMajorPerformanceCaveat=t.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=t.preserveDrawingBuffer,this._trackResize=t.trackResize,this._workerCount=t.workerCount,this._bearingSnap=t.bearingSnap,"string"==typeof t.container?this._container=document.getElementById(t.container):this._container=t.container,this.animationLoop=new h,this.transform=new p(t.minZoom,t.maxZoom),t.maxBounds&&this.setMaxBounds(t.maxBounds),o.bindAll(["_forwardStyleEvent","_forwardSourceEvent","_forwardLayerEvent","_forwardTileEvent","_onStyleLoad","_onStyleChange","_onSourceAdd","_onSourceRemove","_onSourceUpdate","_onWindowOnline","_onWindowResize","_update","_render"],this),this._setupContainer(),this._setupPainter(),this.on("move",this._update.bind(this,!1)),this.on("zoom",this._update.bind(this,!0)),this.on("moveend",function(){this.animationLoop.set(300),this._rerender()}.bind(this)),"undefined"!=typeof s&&(s.addEventListener("online",this._onWindowOnline,!1),s.addEventListener("resize",this._onWindowResize,!1)),m(this,t),this._hash=t.hash&&(new d).addTo(this),this._hash&&this._hash._onHashChange()||this.jumpTo({center:t.center,zoom:t.zoom,bearing:t.bearing,pitch:t.pitch}),this.stacks={},this._classes=[],this.resize(),t.classes&&this.setClasses(t.classes),t.style&&this.setStyle(t.style),t.attributionControl&&this.addControl(new x(t.attributionControl));var e=this.fire.bind(this,"error");this.on("style.error",e),this.on("source.error",e),this.on("tile.error",e),this.on("layer.error",e)};o.extend(k.prototype,l),o.extend(k.prototype,g.prototype),o.extend(k.prototype,{addControl:function(t){return t.addTo(this),this},addClass:function(t,e){return this._classes.indexOf(t)>=0||""===t?this:(this._classes.push(t),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0))},removeClass:function(t,e){var r=this._classes.indexOf(t);return 0>r||""===t?this:(this._classes.splice(r,1),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0))},setClasses:function(t,e){for(var r={},n=0;n<t.length;n++)""!==t[n]&&(r[t[n]]=!0);return this._classes=Object.keys(r),this._classOptions=e,this.style&&this.style.updateClasses(),this._update(!0)},hasClass:function(t){return this._classes.indexOf(t)>=0},getClasses:function(){return this._classes},resize:function(){var t=0,e=0;return this._container&&(t=this._container.offsetWidth||400,e=this._container.offsetHeight||300),this._canvas.resize(t,e),this.transform.resize(t,e),this.painter.resize(t,e),this.fire("movestart").fire("move").fire("resize").fire("moveend")},getBounds:function(){var t=new y(this.transform.pointLocation(new b(0,0)),this.transform.pointLocation(this.transform.size));return(this.transform.angle||this.transform.pitch)&&(t.extend(this.transform.pointLocation(new b(this.transform.size.x,0))),t.extend(this.transform.pointLocation(new b(0,this.transform.size.y)))),t},setMaxBounds:function(t){if(t){var e=y.convert(t);this.transform.lngRange=[e.getWest(),e.getEast()],this.transform.latRange=[e.getSouth(),e.getNorth()],this.transform._constrain(),this._update()}else null!==t&&void 0!==t||(this.transform.lngRange=[],this.transform.latRange=[],this._update());return this},setMinZoom:function(t){if(t=null===t||void 0===t?_:t,t>=_&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between "+_+" and the current maxZoom, inclusive")},setMaxZoom:function(t){if(t=null===t||void 0===t?w:t,t>=this.transform.minZoom&&w>=t)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be between the current minZoom and "+w+", inclusive")},project:function(t){return this.transform.locationPoint(v.convert(t))},unproject:function(t){return this.transform.pointLocation(b.convert(t))},queryRenderedFeatures:function(){function t(t){return t instanceof b||Array.isArray(t)}var e,r={};return 2===arguments.length?(e=arguments[0],r=arguments[1]):1===arguments.length&&t(arguments[0])?e=arguments[0]:1===arguments.length&&(r=arguments[0]),this.style.queryRenderedFeatures(this._makeQueryGeometry(e),r,this.transform.zoom,this.transform.angle)},_makeQueryGeometry:function(t){void 0===t&&(t=[b.convert([0,0]),b.convert([this.transform.width,this.transform.height])]);var e,r=t instanceof b||"number"==typeof t[0];if(r){var n=b.convert(t);e=[n]}else{var i=[b.convert(t[0]),b.convert(t[1])];e=[i[0],new b(i[1].x,i[0].y),i[1],new b(i[0].x,i[1].y),i[0]]}return e=e.map(function(t){return this.transform.pointCoordinate(t)}.bind(this))},querySourceFeatures:function(t,e){return this.style.querySourceFeatures(t,e)},setStyle:function(t){return this.style&&(this.style.off("load",this._onStyleLoad).off("error",this._forwardStyleEvent).off("change",this._onStyleChange).off("source.add",this._onSourceAdd).off("source.remove",this._onSourceRemove).off("source.load",this._onSourceUpdate).off("source.error",this._forwardSourceEvent).off("source.change",this._onSourceUpdate).off("layer.add",this._forwardLayerEvent).off("layer.remove",this._forwardLayerEvent).off("layer.error",this._forwardLayerEvent).off("tile.add",this._forwardTileEvent).off("tile.remove",this._forwardTileEvent).off("tile.load",this._update).off("tile.error",this._forwardTileEvent).off("tile.stats",this._forwardTileEvent)._remove(),this.off("rotate",this.style._redoPlacement),this.off("pitch",this.style._redoPlacement)),t?(t instanceof c?this.style=t:this.style=new c(t,this.animationLoop,this._workerCount),this.style.on("load",this._onStyleLoad).on("error",this._forwardStyleEvent).on("change",this._onStyleChange).on("source.add",this._onSourceAdd).on("source.remove",this._onSourceRemove).on("source.load",this._onSourceUpdate).on("source.error",this._forwardSourceEvent).on("source.change",this._onSourceUpdate).on("layer.add",this._forwardLayerEvent).on("layer.remove",this._forwardLayerEvent).on("layer.error",this._forwardLayerEvent).on("tile.add",this._forwardTileEvent).on("tile.remove",this._forwardTileEvent).on("tile.load",this._update).on("tile.error",this._forwardTileEvent).on("tile.stats",this._forwardTileEvent),this.on("rotate",this.style._redoPlacement),this.on("pitch",this.style._redoPlacement),this):(this.style=null,this)},getStyle:function(){return this.style?this.style.serialize():void 0},addSource:function(t,e){return this.style.addSource(t,e),this._update(!0),this},addSourceType:function(t,e,r){return this.style.addSourceType(t,e,r)},removeSource:function(t){return this.style.removeSource(t),this._update(!0),this},getSource:function(t){return this.style.getSource(t)},addLayer:function(t,e){return this.style.addLayer(t,e),this._update(!0),this},removeLayer:function(t){return this.style.removeLayer(t),this._update(!0),this},getLayer:function(t){return this.style.getLayer(t)},setFilter:function(t,e){return this.style.setFilter(t,e),this._update(!0),this},setLayerZoomRange:function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0),this},getFilter:function(t){return this.style.getFilter(t)},setPaintProperty:function(t,e,r,n){return this.style.setPaintProperty(t,e,r,n),this._update(!0),this},getPaintProperty:function(t,e,r){return this.style.getPaintProperty(t,e,r)},setLayoutProperty:function(t,e,r){return this.style.setLayoutProperty(t,e,r),this._update(!0),this},getLayoutProperty:function(t,e){return this.style.getLayoutProperty(t,e)},getContainer:function(){return 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this._vertices},set vertices(t){this._vertices=t,this._update()}})},{"../geo/lng_lat":263,"../geo/lng_lat_bounds":264,"../geo/transform":265,"../render/painter":279,"../style/animation_loop":299,"../style/style":302,"../util/browser":350,"../util/canvas":351,"../util/dom":352,"../util/evented":358,"../util/util":366,"./bind_handlers":331,"./camera":332,"./control/attribution":333,"./hash":344,"point-geometry":424}],346:[function(t,e,r){"use strict";function n(t,e){t||(t=i.create("div")),t.classList.add("mapboxgl-marker"),this._el=t,this._offset=a.convert(e&&e.offset||[0,0]),this._update=this._update.bind(this)}e.exports=n;var i=t("../util/dom"),o=t("../geo/lng_lat"),a=t("point-geometry");n.prototype={addTo:function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._el),t.on("move",this._update),this._update(),this},remove:function(){this._map&&(this._map.off("move",this._update),this._map=null);var t=this._el.parentNode;return 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this._content&&this._content.parentNode&&this._content.parentNode.removeChild(this._content),this._container&&(this._container.parentNode.removeChild(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("click",this._onClickClose),delete this._map),this.fire("close"),this},getLngLat:function(){return this._lngLat},setLngLat:function(t){return this._lngLat=s.convert(t),this._update(),this},setText:function(t){return this.setDOMContent(document.createTextNode(t))},setHTML:function(t){var e,r=document.createDocumentFragment(),n=document.createElement("body");for(n.innerHTML=t;;){if(e=n.firstChild,!e)break;r.appendChild(e)}return this.setDOMContent(r)},setDOMContent:function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},_createContent:function(){this._content&&this._content.parentNode&&this._content.parentNode.removeChild(this._content),this._content=a.create("div","mapboxgl-popup-content",this._container),this.options.closeButton&&(this._closeButton=a.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClickClose))},_update:function(){if(this._map&&this._lngLat&&this._content){this._container||(this._container=a.create("div","mapboxgl-popup",this._map.getContainer()),this._tip=a.create("div","mapboxgl-popup-tip",this._container),this._container.appendChild(this._content));var t=this._map.project(this._lngLat).round(),e=this.options.anchor;if(!e){var r=this._container.offsetWidth,n=this._container.offsetHeight;e=t.y<n?["top"]:t.y>this._map.transform.height-n?["bottom"]:[],t.x<r/2?e.push("left"):t.x>this._map.transform.width-r/2&&e.push("right"),e=0===e.length?"bottom":e.join("-")}var i={top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"},o=this._container.classList;for(var s in i)o.remove("mapboxgl-popup-anchor-"+s);o.add("mapboxgl-popup-anchor-"+e),a.setTransform(this._container,i[e]+" translate("+t.x+"px,"+t.y+"px)")}},_onClickClose:function(){this.remove()}})},{"../geo/lng_lat":263,"../util/dom":352,"../util/evented":358,"../util/util":366}],348:[function(t,e,r){"use strict";function n(t,e){this.target=t,this.parent=e,this.callbacks={},this.callbackID=0,this.receive=this.receive.bind(this),this.target.addEventListener("message",this.receive,!1)}e.exports=n,n.prototype.receive=function(t){function e(t,e,r){this.postMessage({type:"<response>",id:String(i),error:t?String(t):null,data:e},r)}var r,n=t.data,i=n.id;if("<response>"===n.type)r=this.callbacks[n.id],delete this.callbacks[n.id],r&&r(n.error||null,n.data);else if("undefined"!=typeof n.id&&this.parent[n.type])this.parent[n.type](n.data,e.bind(this));else if("undefined"!=typeof n.id&&this.parent.workerSources){var o=n.type.split(".");this.parent.workerSources[o[0]][o[1]](n.data,e.bind(this))}else this.parent[n.type](n.data)},n.prototype.send=function(t,e,r,n){var i=null;r&&(this.callbacks[i=this.callbackID++]=r),this.postMessage({type:t,id:String(i),data:e},n)},n.prototype.postMessage=function(t,e){this.target.postMessage(t,e)}},{}],349:[function(t,e,r){"use strict";function n(t){var e=document.createElement("a");return e.href=t,e.protocol===document.location.protocol&&e.host===document.location.host}r.getJSON=function(t,e){var r=new XMLHttpRequest;return r.open("GET",t,!0),r.setRequestHeader("Accept","application/json"),r.onerror=function(t){e(t)},r.onload=function(){if(r.status>=200&&r.status<300&&r.response){var t;try{t=JSON.parse(r.response)}catch(n){return e(n)}e(null,t)}else e(new Error(r.statusText))},r.send(),r},r.getArrayBuffer=function(t,e){var r=new XMLHttpRequest;return r.open("GET",t,!0),r.responseType="arraybuffer",r.onerror=function(t){e(t)},r.onload=function(){r.status>=200&&r.status<300&&r.response?e(null,r.response):e(new Error(r.statusText))},r.send(),r},r.getImage=function(t,e){return r.getArrayBuffer(t,function(t,r){if(t)return e(t);var n=new Image;n.onload=function(){e(null,n),(window.URL||window.webkitURL).revokeObjectURL(n.src)};var i=new Blob([new Uint8Array(r)],{type:"image/png"});return n.src=(window.URL||window.webkitURL).createObjectURL(i),n.getData=function(){var t=document.createElement("canvas"),e=t.getContext("2d");return t.width=n.width,t.height=n.height,e.drawImage(n,0,0),e.getImageData(0,0,n.width,n.height).data},n})},r.getVideo=function(t,e){var r=document.createElement("video");r.onloadstart=function(){e(null,r)};for(var i=0;i<t.length;i++){var o=document.createElement("source");n(t[i])||(r.crossOrigin="Anonymous"),o.src=t[i],r.appendChild(o)}return r.getData=function(){return r},r}},{}],350:[function(t,e,r){"use strict";r.window=window,e.exports.now=function(){return window.performance&&window.performance.now?window.performance.now.bind(window.performance):Date.now.bind(Date)}();var n=window.requestAnimationFrame||window.mozRequestAnimationFrame||window.webkitRequestAnimationFrame||window.msRequestAnimationFrame;r.frame=function(t){return n(t)};var i=window.cancelAnimationFrame||window.mozCancelAnimationFrame||window.webkitCancelAnimationFrame||window.msCancelAnimationFrame;r.cancelFrame=function(t){i(t)},r.timed=function(t,n,i){function o(l){a||(l=e.exports.now(),l>=s+n?t.call(i,1):(t.call(i,(l-s)/n),r.frame(o)))}if(!n)return t.call(i,1),null;var a=!1,s=e.exports.now();return r.frame(o),function(){a=!0}},r.supported=t("mapbox-gl-supported"),r.hardwareConcurrency=navigator.hardwareConcurrency||4,Object.defineProperty(r,"devicePixelRatio",{get:function(){return window.devicePixelRatio}}),r.supportsWebp=!1;var o=document.createElement("img");o.onload=function(){r.supportsWebp=!0},o.src="data:image/webp;base64,UklGRh4AAABXRUJQVlA4TBEAAAAvAQAAAAfQ//73v/+BiOh/AAA=",r.supportsGeolocation=!!navigator.geolocation},{"mapbox-gl-supported":420}],351:[function(t,e,r){"use strict";function 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t[0]=e[0]+r[0]*n,t[1]=e[1]+r[1]*n,t[2]=e[2]+r[2]*n,t[3]=e[3]+r[3]*n,t},e.exports=i},{"./common.js":383}],385:[function(t,e,r){var n=t("./common.js"),i={};i.create=function(){var t=new n.ARRAY_TYPE(6);return t[0]=1,t[1]=0,t[2]=0,t[3]=1,t[4]=0,t[5]=0,t},i.clone=function(t){var e=new n.ARRAY_TYPE(6);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e},i.copy=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t},i.identity=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=1,t[4]=0,t[5]=0,t},i.fromValues=function(t,e,r,i,o,a){var s=new n.ARRAY_TYPE(6);return s[0]=t,s[1]=e,s[2]=r,s[3]=i,s[4]=o,s[5]=a,s},i.set=function(t,e,r,n,i,o,a){return t[0]=e,t[1]=r,t[2]=n,t[3]=i,t[4]=o,t[5]=a,t},i.invert=function(t,e){var r=e[0],n=e[1],i=e[2],o=e[3],a=e[4],s=e[5],l=r*o-n*i;return l?(l=1/l,t[0]=o*l,t[1]=-n*l,t[2]=-i*l,t[3]=r*l,t[4]=(i*s-o*a)*l,t[5]=(n*a-r*s)*l,t):null},i.determinant=function(t){return t[0]*t[3]-t[1]*t[2]},i.multiply=function(t,e,r){var n=e[0],i=e[1],o=e[2],a=e[3],s=e[4],l=e[5],u=r[0],c=r[1],h=r[2],f=r[3],p=r[4],d=r[5];return t[0]=n*u+o*c,t[1]=i*u+a*c,t[2]=n*h+o*f,t[3]=i*h+a*f,t[4]=n*p+o*d+s,t[5]=i*p+a*d+l,t},i.mul=i.multiply,i.rotate=function(t,e,r){var n=e[0],i=e[1],o=e[2],a=e[3],s=e[4],l=e[5],u=Math.sin(r),c=Math.cos(r);return t[0]=n*c+o*u,t[1]=i*c+a*u,t[2]=n*-u+o*c,t[3]=i*-u+a*c,t[4]=s,t[5]=l,t},i.scale=function(t,e,r){var n=e[0],i=e[1],o=e[2],a=e[3],s=e[4],l=e[5],u=r[0],c=r[1];return t[0]=n*u,t[1]=i*u,t[2]=o*c,t[3]=a*c,t[4]=s,t[5]=l,t},i.translate=function(t,e,r){var n=e[0],i=e[1],o=e[2],a=e[3],s=e[4],l=e[5],u=r[0],c=r[1];return t[0]=n,t[1]=i,t[2]=o,t[3]=a,t[4]=n*u+o*c+s,t[5]=i*u+a*c+l,t},i.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=-r,t[3]=n,t[4]=0,t[5]=0,t},i.fromScaling=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=e[1],t[4]=0,t[5]=0,t},i.fromTranslation=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=1,t[4]=e[0],t[5]=e[1],t},i.str=function(t){return"mat2d("+t[0]+", 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Math.abs(r-u)<=n.EPSILON*Math.max(1,Math.abs(r),Math.abs(u))&&Math.abs(i-c)<=n.EPSILON*Math.max(1,Math.abs(i),Math.abs(c))&&Math.abs(o-h)<=n.EPSILON*Math.max(1,Math.abs(o),Math.abs(h))&&Math.abs(a-f)<=n.EPSILON*Math.max(1,Math.abs(a),Math.abs(f))&&Math.abs(s-p)<=n.EPSILON*Math.max(1,Math.abs(s),Math.abs(p))&&Math.abs(l-d)<=n.EPSILON*Math.max(1,Math.abs(l),Math.abs(d))},e.exports=i},{"./common.js":383}],386:[function(t,e,r){var n=t("./common.js"),i={};i.create=function(){var t=new n.ARRAY_TYPE(9);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=1,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},i.fromMat4=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[4],t[4]=e[5],t[5]=e[6],t[6]=e[8],t[7]=e[9],t[8]=e[10],t},i.clone=function(t){var e=new n.ARRAY_TYPE(9);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e},i.copy=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t},i.fromValues=function(t,e,r,i,o,a,s,l,u){var c=new n.ARRAY_TYPE(9);return c[0]=t,c[1]=e,c[2]=r,c[3]=i,c[4]=o,c[5]=a,c[6]=s,c[7]=l,c[8]=u,c},i.set=function(t,e,r,n,i,o,a,s,l,u){return t[0]=e,t[1]=r,t[2]=n,t[3]=i,t[4]=o,t[5]=a,t[6]=s,t[7]=l,t[8]=u,t},i.identity=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=1,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},i.transpose=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[5];t[1]=e[3],t[2]=e[6],t[3]=r,t[5]=e[7],t[6]=n,t[7]=i}else t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8];return t},i.invert=function(t,e){var r=e[0],n=e[1],i=e[2],o=e[3],a=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=c*a-s*u,f=-c*o+s*l,p=u*o-a*l,d=r*h+n*f+i*p;return d?(d=1/d,t[0]=h*d,t[1]=(-c*n+i*u)*d,t[2]=(s*n-i*a)*d,t[3]=f*d,t[4]=(c*r-i*l)*d,t[5]=(-s*r+i*o)*d,t[6]=p*d,t[7]=(-u*r+n*l)*d,t[8]=(a*r-n*o)*d,t):null},i.adjoint=function(t,e){var r=e[0],n=e[1],i=e[2],o=e[3],a=e[4],s=e[5],l=e[6],u=e[7],c=e[8];return t[0]=a*c-s*u,t[1]=i*u-n*c,t[2]=n*s-i*a,t[3]=s*l-o*c,t[4]=r*c-i*l,t[5]=i*o-r*s,t[6]=o*u-a*l,t[7]=n*l-r*u,t[8]=r*a-n*o,t},i.determinant=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],o=t[4],a=t[5],s=t[6],l=t[7],u=t[8];return e*(u*o-a*l)+r*(-u*i+a*s)+n*(l*i-o*s)},i.multiply=function(t,e,r){var n=e[0],i=e[1],o=e[2],a=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=r[0],p=r[1],d=r[2],m=r[3],g=r[4],v=r[5],y=r[6],b=r[7],x=r[8];return t[0]=f*n+p*a+d*u,t[1]=f*i+p*s+d*c,t[2]=f*o+p*l+d*h,t[3]=m*n+g*a+v*u,t[4]=m*i+g*s+v*c,t[5]=m*o+g*l+v*h,t[6]=y*n+b*a+x*u,t[7]=y*i+b*s+x*c,t[8]=y*o+b*l+x*h,t},i.mul=i.multiply,i.translate=function(t,e,r){var n=e[0],i=e[1],o=e[2],a=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=r[0],p=r[1];return t[0]=n,t[1]=i,t[2]=o,t[3]=a,t[4]=s,t[5]=l,t[6]=f*n+p*a+u,t[7]=f*i+p*s+c,t[8]=f*o+p*l+h,t},i.rotate=function(t,e,r){var n=e[0],i=e[1],o=e[2],a=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=Math.sin(r),p=Math.cos(r);return t[0]=p*n+f*a,t[1]=p*i+f*s,t[2]=p*o+f*l,t[3]=p*a-f*n,t[4]=p*s-f*i,t[5]=p*l-f*o,t[6]=u,t[7]=c,t[8]=h,t},i.scale=function(t,e,r){var n=r[0],i=r[1];return t[0]=n*e[0],t[1]=n*e[1],t[2]=n*e[2],t[3]=i*e[3],t[4]=i*e[4],t[5]=i*e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t},i.fromTranslation=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=1,t[5]=0,t[6]=e[0],t[7]=e[1],t[8]=1,t},i.fromRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},i.fromScaling=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=e[1],t[5]=0,t[6]=0,t[7]=0,t[8]=1,t},i.fromMat2d=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=0,t[3]=e[2],t[4]=e[3],t[5]=0,t[6]=e[4],t[7]=e[5],t[8]=1,t},i.fromQuat=function(t,e){var r=e[0],n=e[1],i=e[2],o=e[3],a=r+r,s=n+n,l=i+i,u=r*a,c=n*a,h=n*s,f=i*a,p=i*s,d=i*l,m=o*a,g=o*s,v=o*l;return t[0]=1-h-d,t[3]=c-v,t[6]=f+g,t[1]=c+v,t[4]=1-u-d,t[7]=p-m,t[2]=f-g,t[5]=p+m,t[8]=1-u-h,t},i.normalFromMat4=function(t,e){var r=e[0],n=e[1],i=e[2],o=e[3],a=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*a,b=r*l-i*a,x=r*u-o*a,_=n*l-i*s,w=n*u-o*s,M=i*u-o*l,k=c*m-h*d,A=c*g-f*d,T=c*v-p*d,E=h*g-f*m,S=h*v-p*m,L=f*v-p*g,z=y*L-b*S+x*E+_*T-w*A+M*k;return z?(z=1/z,t[0]=(s*L-l*S+u*E)*z,t[1]=(l*T-a*L-u*A)*z,t[2]=(a*S-s*T+u*k)*z,t[3]=(i*S-n*L-o*E)*z,t[4]=(r*L-i*T+o*A)*z,t[5]=(n*T-r*S-o*k)*z,t[6]=(m*M-g*w+v*_)*z,t[7]=(g*x-d*M-v*b)*z,t[8]=(d*w-m*x+v*y)*z,t):null},i.str=function(t){return"mat3("+t[0]+", "+t[1]+", "+t[2]+", "+t[3]+", "+t[4]+", "+t[5]+", "+t[6]+", "+t[7]+", "+t[8]+")"},i.frob=function(t){return Math.sqrt(Math.pow(t[0],2)+Math.pow(t[1],2)+Math.pow(t[2],2)+Math.pow(t[3],2)+Math.pow(t[4],2)+Math.pow(t[5],2)+Math.pow(t[6],2)+Math.pow(t[7],2)+Math.pow(t[8],2))},i.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t[3]=e[3]+r[3],t[4]=e[4]+r[4],t[5]=e[5]+r[5],t[6]=e[6]+r[6],t[7]=e[7]+r[7],t[8]=e[8]+r[8],t},i.subtract=function(t,e,r){return t[0]=e[0]-r[0],t[1]=e[1]-r[1],t[2]=e[2]-r[2],t[3]=e[3]-r[3],t[4]=e[4]-r[4],t[5]=e[5]-r[5],t[6]=e[6]-r[6],t[7]=e[7]-r[7],t[8]=e[8]-r[8],t},i.sub=i.subtract,i.multiplyScalar=function(t,e,r){return t[0]=e[0]*r,t[1]=e[1]*r,t[2]=e[2]*r,t[3]=e[3]*r,t[4]=e[4]*r,t[5]=e[5]*r,t[6]=e[6]*r,t[7]=e[7]*r,t[8]=e[8]*r,t},i.multiplyScalarAndAdd=function(t,e,r,n){return t[0]=e[0]+r[0]*n,t[1]=e[1]+r[1]*n,t[2]=e[2]+r[2]*n,t[3]=e[3]+r[3]*n,t[4]=e[4]+r[4]*n,t[5]=e[5]+r[5]*n,t[6]=e[6]+r[6]*n,t[7]=e[7]+r[7]*n,t[8]=e[8]+r[8]*n,t},i.exactEquals=function(t,e){return t[0]===e[0]&&t[1]===e[1]&&t[2]===e[2]&&t[3]===e[3]&&t[4]===e[4]&&t[5]===e[5]&&t[6]===e[6]&&t[7]===e[7]&&t[8]===e[8]},i.equals=function(t,e){var r=t[0],i=t[1],o=t[2],a=t[3],s=t[4],l=t[5],u=t[6],c=t[7],h=t[8],f=e[0],p=e[1],d=e[2],m=e[3],g=e[4],v=e[5],y=t[6],b=e[7],x=e[8];return Math.abs(r-f)<=n.EPSILON*Math.max(1,Math.abs(r),Math.abs(f))&&Math.abs(i-p)<=n.EPSILON*Math.max(1,Math.abs(i),Math.abs(p))&&Math.abs(o-d)<=n.EPSILON*Math.max(1,Math.abs(o),Math.abs(d))&&Math.abs(a-m)<=n.EPSILON*Math.max(1,Math.abs(a),Math.abs(m))&&Math.abs(s-g)<=n.EPSILON*Math.max(1,Math.abs(s),Math.abs(g))&&Math.abs(l-v)<=n.EPSILON*Math.max(1,Math.abs(l),Math.abs(v))&&Math.abs(u-y)<=n.EPSILON*Math.max(1,Math.abs(u),Math.abs(y))&&Math.abs(c-b)<=n.EPSILON*Math.max(1,Math.abs(c),Math.abs(b))&&Math.abs(h-x)<=n.EPSILON*Math.max(1,Math.abs(h),Math.abs(x))},e.exports=i},{"./common.js":383}],387:[function(t,e,r){var n=t("./common.js"),i={scalar:{},SIMD:{}};i.create=function(){var t=new n.ARRAY_TYPE(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.clone=function(t){var e=new n.ARRAY_TYPE(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},i.copy=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.fromValues=function(t,e,r,i,o,a,s,l,u,c,h,f,p,d,m,g){var v=new n.ARRAY_TYPE(16);return v[0]=t,v[1]=e,v[2]=r,v[3]=i,v[4]=o,v[5]=a,v[6]=s,v[7]=l,v[8]=u,v[9]=c,v[10]=h,v[11]=f,v[12]=p,v[13]=d,v[14]=m,v[15]=g,v},i.set=function(t,e,r,n,i,o,a,s,l,u,c,h,f,p,d,m,g){return t[0]=e,t[1]=r,t[2]=n,t[3]=i,t[4]=o,t[5]=a,t[6]=s,t[7]=l,t[8]=u,t[9]=c,t[10]=h,t[11]=f,t[12]=p,t[13]=d,t[14]=m,t[15]=g,t},i.identity=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.scalar.transpose=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],o=e[6],a=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=o,t[11]=e[14],t[12]=i,t[13]=a,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t},i.SIMD.transpose=function(t,e){var r,n,i,o,a,s,l,u,c,h;
return r=SIMD.Float32x4.load(e,0),n=SIMD.Float32x4.load(e,4),i=SIMD.Float32x4.load(e,8),o=SIMD.Float32x4.load(e,12),a=SIMD.Float32x4.shuffle(r,n,0,1,4,5),s=SIMD.Float32x4.shuffle(i,o,0,1,4,5),l=SIMD.Float32x4.shuffle(a,s,0,2,4,6),u=SIMD.Float32x4.shuffle(a,s,1,3,5,7),SIMD.Float32x4.store(t,0,l),SIMD.Float32x4.store(t,4,u),a=SIMD.Float32x4.shuffle(r,n,2,3,6,7),s=SIMD.Float32x4.shuffle(i,o,2,3,6,7),c=SIMD.Float32x4.shuffle(a,s,0,2,4,6),h=SIMD.Float32x4.shuffle(a,s,1,3,5,7),SIMD.Float32x4.store(t,8,c),SIMD.Float32x4.store(t,12,h),t},i.transpose=n.USE_SIMD?i.SIMD.transpose:i.scalar.transpose,i.scalar.invert=function(t,e){var r=e[0],n=e[1],i=e[2],o=e[3],a=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*a,b=r*l-i*a,x=r*u-o*a,_=n*l-i*s,w=n*u-o*s,M=i*u-o*l,k=c*m-h*d,A=c*g-f*d,T=c*v-p*d,E=h*g-f*m,S=h*v-p*m,L=f*v-p*g,z=y*L-b*S+x*E+_*T-w*A+M*k;return z?(z=1/z,t[0]=(s*L-l*S+u*E)*z,t[1]=(i*S-n*L-o*E)*z,t[2]=(m*M-g*w+v*_)*z,t[3]=(f*w-h*M-p*_)*z,t[4]=(l*T-a*L-u*A)*z,t[5]=(r*L-i*T+o*A)*z,t[6]=(g*x-d*M-v*b)*z,t[7]=(c*M-f*x+p*b)*z,t[8]=(a*S-s*T+u*k)*z,t[9]=(n*T-r*S-o*k)*z,t[10]=(d*w-m*x+v*y)*z,t[11]=(h*x-c*w-p*y)*z,t[12]=(s*A-a*E-l*k)*z,t[13]=(r*E-n*A+i*k)*z,t[14]=(m*b-d*_-g*y)*z,t[15]=(c*_-h*b+f*y)*z,t):null},i.SIMD.invert=function(t,e){var r,n,i,o,a,s,l,u,c,h,f=SIMD.Float32x4.load(e,0),p=SIMD.Float32x4.load(e,4),d=SIMD.Float32x4.load(e,8),m=SIMD.Float32x4.load(e,12);return a=SIMD.Float32x4.shuffle(f,p,0,1,4,5),n=SIMD.Float32x4.shuffle(d,m,0,1,4,5),r=SIMD.Float32x4.shuffle(a,n,0,2,4,6),n=SIMD.Float32x4.shuffle(n,a,1,3,5,7),a=SIMD.Float32x4.shuffle(f,p,2,3,6,7),o=SIMD.Float32x4.shuffle(d,m,2,3,6,7),i=SIMD.Float32x4.shuffle(a,o,0,2,4,6),o=SIMD.Float32x4.shuffle(o,a,1,3,5,7),a=SIMD.Float32x4.mul(i,o),a=SIMD.Float32x4.swizzle(a,1,0,3,2),s=SIMD.Float32x4.mul(n,a),l=SIMD.Float32x4.mul(r,a),a=SIMD.Float32x4.swizzle(a,2,3,0,1),s=SIMD.Float32x4.sub(SIMD.Float32x4.mul(n,a),s),l=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,a),l),l=SIMD.Float32x4.swizzle(l,2,3,0,1),a=SIMD.Float32x4.mul(n,i),a=SIMD.Float32x4.swizzle(a,1,0,3,2),s=SIMD.Float32x4.add(SIMD.Float32x4.mul(o,a),s),c=SIMD.Float32x4.mul(r,a),a=SIMD.Float32x4.swizzle(a,2,3,0,1),s=SIMD.Float32x4.sub(s,SIMD.Float32x4.mul(o,a)),c=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,a),c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),a=SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(n,2,3,0,1),o),a=SIMD.Float32x4.swizzle(a,1,0,3,2),i=SIMD.Float32x4.swizzle(i,2,3,0,1),s=SIMD.Float32x4.add(SIMD.Float32x4.mul(i,a),s),u=SIMD.Float32x4.mul(r,a),a=SIMD.Float32x4.swizzle(a,2,3,0,1),s=SIMD.Float32x4.sub(s,SIMD.Float32x4.mul(i,a)),u=SIMD.Float32x4.sub(SIMD.Float32x4.mul(r,a),u),u=SIMD.Float32x4.swizzle(u,2,3,0,1),a=SIMD.Float32x4.mul(r,n),a=SIMD.Float32x4.swizzle(a,1,0,3,2),u=SIMD.Float32x4.add(SIMD.Float32x4.mul(o,a),u),c=SIMD.Float32x4.sub(SIMD.Float32x4.mul(i,a),c),a=SIMD.Float32x4.swizzle(a,2,3,0,1),u=SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,a),u),c=SIMD.Float32x4.sub(c,SIMD.Float32x4.mul(i,a)),a=SIMD.Float32x4.mul(r,o),a=SIMD.Float32x4.swizzle(a,1,0,3,2),l=SIMD.Float32x4.sub(l,SIMD.Float32x4.mul(i,a)),u=SIMD.Float32x4.add(SIMD.Float32x4.mul(n,a),u),a=SIMD.Float32x4.swizzle(a,2,3,0,1),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(i,a),l),u=SIMD.Float32x4.sub(u,SIMD.Float32x4.mul(n,a)),a=SIMD.Float32x4.mul(r,i),a=SIMD.Float32x4.swizzle(a,1,0,3,2),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(o,a),l),c=SIMD.Float32x4.sub(c,SIMD.Float32x4.mul(n,a)),a=SIMD.Float32x4.swizzle(a,2,3,0,1),l=SIMD.Float32x4.sub(l,SIMD.Float32x4.mul(o,a)),c=SIMD.Float32x4.add(SIMD.Float32x4.mul(n,a),c),h=SIMD.Float32x4.mul(r,s),h=SIMD.Float32x4.add(SIMD.Float32x4.swizzle(h,2,3,0,1),h),h=SIMD.Float32x4.add(SIMD.Float32x4.swizzle(h,1,0,3,2),h),a=SIMD.Float32x4.reciprocalApproximation(h),h=SIMD.Float32x4.sub(SIMD.Float32x4.add(a,a),SIMD.Float32x4.mul(h,SIMD.Float32x4.mul(a,a))),(h=SIMD.Float32x4.swizzle(h,0,0,0,0))?(SIMD.Float32x4.store(t,0,SIMD.Float32x4.mul(h,s)),SIMD.Float32x4.store(t,4,SIMD.Float32x4.mul(h,l)),SIMD.Float32x4.store(t,8,SIMD.Float32x4.mul(h,u)),SIMD.Float32x4.store(t,12,SIMD.Float32x4.mul(h,c)),t):null},i.invert=n.USE_SIMD?i.SIMD.invert:i.scalar.invert,i.scalar.adjoint=function(t,e){var r=e[0],n=e[1],i=e[2],o=e[3],a=e[4],s=e[5],l=e[6],u=e[7],c=e[8],h=e[9],f=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15];return t[0]=s*(f*v-p*g)-h*(l*v-u*g)+m*(l*p-u*f),t[1]=-(n*(f*v-p*g)-h*(i*v-o*g)+m*(i*p-o*f)),t[2]=n*(l*v-u*g)-s*(i*v-o*g)+m*(i*u-o*l),t[3]=-(n*(l*p-u*f)-s*(i*p-o*f)+h*(i*u-o*l)),t[4]=-(a*(f*v-p*g)-c*(l*v-u*g)+d*(l*p-u*f)),t[5]=r*(f*v-p*g)-c*(i*v-o*g)+d*(i*p-o*f),t[6]=-(r*(l*v-u*g)-a*(i*v-o*g)+d*(i*u-o*l)),t[7]=r*(l*p-u*f)-a*(i*p-o*f)+c*(i*u-o*l),t[8]=a*(h*v-p*m)-c*(s*v-u*m)+d*(s*p-u*h),t[9]=-(r*(h*v-p*m)-c*(n*v-o*m)+d*(n*p-o*h)),t[10]=r*(s*v-u*m)-a*(n*v-o*m)+d*(n*u-o*s),t[11]=-(r*(s*p-u*h)-a*(n*p-o*h)+c*(n*u-o*s)),t[12]=-(a*(h*g-f*m)-c*(s*g-l*m)+d*(s*f-l*h)),t[13]=r*(h*g-f*m)-c*(n*g-i*m)+d*(n*f-i*h),t[14]=-(r*(s*g-l*m)-a*(n*g-i*m)+d*(n*l-i*s)),t[15]=r*(s*f-l*h)-a*(n*f-i*h)+c*(n*l-i*s),t},i.SIMD.adjoint=function(t,e){var r,n,i,o,a,s,l,u,c,h,f,p,d,r=SIMD.Float32x4.load(e,0),n=SIMD.Float32x4.load(e,4),i=SIMD.Float32x4.load(e,8),o=SIMD.Float32x4.load(e,12);return c=SIMD.Float32x4.shuffle(r,n,0,1,4,5),s=SIMD.Float32x4.shuffle(i,o,0,1,4,5),a=SIMD.Float32x4.shuffle(c,s,0,2,4,6),s=SIMD.Float32x4.shuffle(s,c,1,3,5,7),c=SIMD.Float32x4.shuffle(r,n,2,3,6,7),u=SIMD.Float32x4.shuffle(i,o,2,3,6,7),l=SIMD.Float32x4.shuffle(c,u,0,2,4,6),u=SIMD.Float32x4.shuffle(u,c,1,3,5,7),c=SIMD.Float32x4.mul(l,u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),h=SIMD.Float32x4.mul(s,c),f=SIMD.Float32x4.mul(a,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(SIMD.Float32x4.mul(s,c),h),f=SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,c),f),f=SIMD.Float32x4.swizzle(f,2,3,0,1),c=SIMD.Float32x4.mul(s,l),c=SIMD.Float32x4.swizzle(c,1,0,3,2),h=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),h),d=SIMD.Float32x4.mul(a,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(h,SIMD.Float32x4.mul(u,c)),d=SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,c),d),d=SIMD.Float32x4.swizzle(d,2,3,0,1),c=SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,2,3,0,1),u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),l=SIMD.Float32x4.swizzle(l,2,3,0,1),h=SIMD.Float32x4.add(SIMD.Float32x4.mul(l,c),h),p=SIMD.Float32x4.mul(a,c),c=SIMD.Float32x4.swizzle(c,2,3,0,1),h=SIMD.Float32x4.sub(h,SIMD.Float32x4.mul(l,c)),p=SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,c),p),p=SIMD.Float32x4.swizzle(p,2,3,0,1),c=SIMD.Float32x4.mul(a,s),c=SIMD.Float32x4.swizzle(c,1,0,3,2),p=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),p),d=SIMD.Float32x4.sub(SIMD.Float32x4.mul(l,c),d),c=SIMD.Float32x4.swizzle(c,2,3,0,1),p=SIMD.Float32x4.sub(SIMD.Float32x4.mul(u,c),p),d=SIMD.Float32x4.sub(d,SIMD.Float32x4.mul(l,c)),c=SIMD.Float32x4.mul(a,u),c=SIMD.Float32x4.swizzle(c,1,0,3,2),f=SIMD.Float32x4.sub(f,SIMD.Float32x4.mul(l,c)),p=SIMD.Float32x4.add(SIMD.Float32x4.mul(s,c),p),c=SIMD.Float32x4.swizzle(c,2,3,0,1),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(l,c),f),p=SIMD.Float32x4.sub(p,SIMD.Float32x4.mul(s,c)),c=SIMD.Float32x4.mul(a,l),c=SIMD.Float32x4.swizzle(c,1,0,3,2),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(u,c),f),d=SIMD.Float32x4.sub(d,SIMD.Float32x4.mul(s,c)),c=SIMD.Float32x4.swizzle(c,2,3,0,1),f=SIMD.Float32x4.sub(f,SIMD.Float32x4.mul(u,c)),d=SIMD.Float32x4.add(SIMD.Float32x4.mul(s,c),d),SIMD.Float32x4.store(t,0,h),SIMD.Float32x4.store(t,4,f),SIMD.Float32x4.store(t,8,p),SIMD.Float32x4.store(t,12,d),t},i.adjoint=n.USE_SIMD?i.SIMD.adjoint:i.scalar.adjoint,i.determinant=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],o=t[4],a=t[5],s=t[6],l=t[7],u=t[8],c=t[9],h=t[10],f=t[11],p=t[12],d=t[13],m=t[14],g=t[15],v=e*a-r*o,y=e*s-n*o,b=e*l-i*o,x=r*s-n*a,_=r*l-i*a,w=n*l-i*s,M=u*d-c*p,k=u*m-h*p,A=u*g-f*p,T=c*m-h*d,E=c*g-f*d,S=h*g-f*m;return v*S-y*E+b*T+x*A-_*k+w*M},i.SIMD.multiply=function(t,e,r){var n=SIMD.Float32x4.load(e,0),i=SIMD.Float32x4.load(e,4),o=SIMD.Float32x4.load(e,8),a=SIMD.Float32x4.load(e,12),s=SIMD.Float32x4.load(r,0),l=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,2,2,2,2),o),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(s,3,3,3,3),a))));SIMD.Float32x4.store(t,0,l);var u=SIMD.Float32x4.load(r,4),c=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,2,2,2,2),o),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(u,3,3,3,3),a))));SIMD.Float32x4.store(t,4,c);var h=SIMD.Float32x4.load(r,8),f=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,2,2,2,2),o),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(h,3,3,3,3),a))));SIMD.Float32x4.store(t,8,f);var p=SIMD.Float32x4.load(r,12),d=SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(p,0,0,0,0),n),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(p,1,1,1,1),i),SIMD.Float32x4.add(SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(p,2,2,2,2),o),SIMD.Float32x4.mul(SIMD.Float32x4.swizzle(p,3,3,3,3),a))));return SIMD.Float32x4.store(t,12,d),t},i.scalar.multiply=function(t,e,r){var n=e[0],i=e[1],o=e[2],a=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],b=r[0],x=r[1],_=r[2],w=r[3];return t[0]=b*n+x*s+_*h+w*m,t[1]=b*i+x*l+_*f+w*g,t[2]=b*o+x*u+_*p+w*v,t[3]=b*a+x*c+_*d+w*y,b=r[4],x=r[5],_=r[6],w=r[7],t[4]=b*n+x*s+_*h+w*m,t[5]=b*i+x*l+_*f+w*g,t[6]=b*o+x*u+_*p+w*v,t[7]=b*a+x*c+_*d+w*y,b=r[8],x=r[9],_=r[10],w=r[11],t[8]=b*n+x*s+_*h+w*m,t[9]=b*i+x*l+_*f+w*g,t[10]=b*o+x*u+_*p+w*v,t[11]=b*a+x*c+_*d+w*y,b=r[12],x=r[13],_=r[14],w=r[15],t[12]=b*n+x*s+_*h+w*m,t[13]=b*i+x*l+_*f+w*g,t[14]=b*o+x*u+_*p+w*v,t[15]=b*a+x*c+_*d+w*y,t},i.multiply=n.USE_SIMD?i.SIMD.multiply:i.scalar.multiply,i.mul=i.multiply,i.scalar.translate=function(t,e,r){var n,i,o,a,s,l,u,c,h,f,p,d,m=r[0],g=r[1],v=r[2];return e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],o=e[2],a=e[3],s=e[4],l=e[5],u=e[6],c=e[7],h=e[8],f=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=o,t[3]=a,t[4]=s,t[5]=l,t[6]=u,t[7]=c,t[8]=h,t[9]=f,t[10]=p,t[11]=d,t[12]=n*m+s*g+h*v+e[12],t[13]=i*m+l*g+f*v+e[13],t[14]=o*m+u*g+p*v+e[14],t[15]=a*m+c*g+d*v+e[15]),t},i.SIMD.translate=function(t,e,r){var n=SIMD.Float32x4.load(e,0),i=SIMD.Float32x4.load(e,4),o=SIMD.Float32x4.load(e,8),a=SIMD.Float32x4.load(e,12),s=SIMD.Float32x4(r[0],r[1],r[2],0);e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11]),n=SIMD.Float32x4.mul(n,SIMD.Float32x4.swizzle(s,0,0,0,0)),i=SIMD.Float32x4.mul(i,SIMD.Float32x4.swizzle(s,1,1,1,1)),o=SIMD.Float32x4.mul(o,SIMD.Float32x4.swizzle(s,2,2,2,2));var l=SIMD.Float32x4.add(n,SIMD.Float32x4.add(i,SIMD.Float32x4.add(o,a)));return SIMD.Float32x4.store(t,12,l),t},i.translate=n.USE_SIMD?i.SIMD.translate:i.scalar.translate,i.scalar.scale=function(t,e,r){var n=r[0],i=r[1],o=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*o,t[9]=e[9]*o,t[10]=e[10]*o,t[11]=e[11]*o,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.SIMD.scale=function(t,e,r){var n,i,o,a=SIMD.Float32x4(r[0],r[1],r[2],0);return n=SIMD.Float32x4.load(e,0),SIMD.Float32x4.store(t,0,SIMD.Float32x4.mul(n,SIMD.Float32x4.swizzle(a,0,0,0,0))),i=SIMD.Float32x4.load(e,4),SIMD.Float32x4.store(t,4,SIMD.Float32x4.mul(i,SIMD.Float32x4.swizzle(a,1,1,1,1))),o=SIMD.Float32x4.load(e,8),SIMD.Float32x4.store(t,8,SIMD.Float32x4.mul(o,SIMD.Float32x4.swizzle(a,2,2,2,2))),t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t},i.scale=n.USE_SIMD?i.SIMD.scale:i.scalar.scale,i.rotate=function(t,e,r,i){var o,a,s,l,u,c,h,f,p,d,m,g,v,y,b,x,_,w,M,k,A,T,E,S,L=i[0],z=i[1],I=i[2],P=Math.sqrt(L*L+z*z+I*I);return Math.abs(P)<n.EPSILON?null:(P=1/P,L*=P,z*=P,I*=P,o=Math.sin(r),a=Math.cos(r),s=1-a,l=e[0],u=e[1],c=e[2],h=e[3],f=e[4],p=e[5],d=e[6],m=e[7],g=e[8],v=e[9],y=e[10],b=e[11],x=L*L*s+a,_=z*L*s+I*o,w=I*L*s-z*o,M=L*z*s-I*o,k=z*z*s+a,A=I*z*s+L*o,T=L*I*s+z*o,E=z*I*s-L*o,S=I*I*s+a,t[0]=l*x+f*_+g*w,t[1]=u*x+p*_+v*w,t[2]=c*x+d*_+y*w,t[3]=h*x+m*_+b*w,t[4]=l*M+f*k+g*A,t[5]=u*M+p*k+v*A,t[6]=c*M+d*k+y*A,t[7]=h*M+m*k+b*A,t[8]=l*T+f*E+g*S,t[9]=u*T+p*E+v*S,t[10]=c*T+d*E+y*S,t[11]=h*T+m*E+b*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)},i.scalar.rotateX=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),o=e[4],a=e[5],s=e[6],l=e[7],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[4]=o*i+u*n,t[5]=a*i+c*n,t[6]=s*i+h*n,t[7]=l*i+f*n,t[8]=u*i-o*n,t[9]=c*i-a*n,t[10]=h*i-s*n,t[11]=f*i-l*n,t},i.SIMD.rotateX=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var o=SIMD.Float32x4.load(e,4),a=SIMD.Float32x4.load(e,8);return SIMD.Float32x4.store(t,4,SIMD.Float32x4.add(SIMD.Float32x4.mul(o,i),SIMD.Float32x4.mul(a,n))),SIMD.Float32x4.store(t,8,SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),t},i.rotateX=n.USE_SIMD?i.SIMD.rotateX:i.scalar.rotateX,i.scalar.rotateY=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),o=e[0],a=e[1],s=e[2],l=e[3],u=e[8],c=e[9],h=e[10],f=e[11];return e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=o*i-u*n,t[1]=a*i-c*n,t[2]=s*i-h*n,t[3]=l*i-f*n,t[8]=o*n+u*i,t[9]=a*n+c*i,t[10]=s*n+h*i,t[11]=l*n+f*i,t},i.SIMD.rotateY=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var o=SIMD.Float32x4.load(e,0),a=SIMD.Float32x4.load(e,8);return SIMD.Float32x4.store(t,0,SIMD.Float32x4.sub(SIMD.Float32x4.mul(o,i),SIMD.Float32x4.mul(a,n))),SIMD.Float32x4.store(t,8,SIMD.Float32x4.add(SIMD.Float32x4.mul(o,n),SIMD.Float32x4.mul(a,i))),t},i.rotateY=n.USE_SIMD?i.SIMD.rotateY:i.scalar.rotateY,i.scalar.rotateZ=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),o=e[0],a=e[1],s=e[2],l=e[3],u=e[4],c=e[5],h=e[6],f=e[7];return e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=o*i+u*n,t[1]=a*i+c*n,t[2]=s*i+h*n,t[3]=l*i+f*n,t[4]=u*i-o*n,t[5]=c*i-a*n,t[6]=h*i-s*n,t[7]=f*i-l*n,t},i.SIMD.rotateZ=function(t,e,r){var n=SIMD.Float32x4.splat(Math.sin(r)),i=SIMD.Float32x4.splat(Math.cos(r));e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);var o=SIMD.Float32x4.load(e,0),a=SIMD.Float32x4.load(e,4);return SIMD.Float32x4.store(t,0,SIMD.Float32x4.add(SIMD.Float32x4.mul(o,i),SIMD.Float32x4.mul(a,n))),SIMD.Float32x4.store(t,4,SIMD.Float32x4.sub(SIMD.Float32x4.mul(a,i),SIMD.Float32x4.mul(o,n))),t},i.rotateZ=n.USE_SIMD?i.SIMD.rotateZ:i.scalar.rotateZ,i.fromTranslation=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t},i.fromScaling=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromRotation=function(t,e,r){var i,o,a,s=r[0],l=r[1],u=r[2],c=Math.sqrt(s*s+l*l+u*u);return Math.abs(c)<n.EPSILON?null:(c=1/c,s*=c,l*=c,u*=c,i=Math.sin(e),o=Math.cos(e),a=1-o,t[0]=s*s*a+o,t[1]=l*s*a+u*i,t[2]=u*s*a-l*i,t[3]=0,t[4]=s*l*a-u*i,t[5]=l*l*a+o,t[6]=u*l*a+s*i,t[7]=0,t[8]=s*u*a+l*i,t[9]=l*u*a-s*i,t[10]=u*u*a+o,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t)},i.fromXRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromYRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=0,t[2]=-r,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=r,t[9]=0,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromZRotation=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=0,t[4]=-r,t[5]=n,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.fromRotationTranslation=function(t,e,r){var n=e[0],i=e[1],o=e[2],a=e[3],s=n+n,l=i+i,u=o+o,c=n*s,h=n*l,f=n*u,p=i*l,d=i*u,m=o*u,g=a*s,v=a*l,y=a*u;return t[0]=1-(p+m),t[1]=h+y,t[2]=f-v,t[3]=0,t[4]=h-y,t[5]=1-(c+m),t[6]=d+g,t[7]=0,t[8]=f+v,t[9]=d-g,t[10]=1-(c+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t},i.getTranslation=function(t,e){return t[0]=e[12],t[1]=e[13],t[2]=e[14],t},i.getRotation=function(t,e){var r=e[0]+e[5]+e[10],n=0;return r>0?(n=2*Math.sqrt(r+1),t[3]=.25*n,t[0]=(e[6]-e[9])/n,t[1]=(e[8]-e[2])/n,t[2]=(e[1]-e[4])/n):e[0]>e[5]&e[0]>e[10]?(n=2*Math.sqrt(1+e[0]-e[5]-e[10]),t[3]=(e[6]-e[9])/n,t[0]=.25*n,t[1]=(e[1]+e[4])/n,t[2]=(e[8]+e[2])/n):e[5]>e[10]?(n=2*Math.sqrt(1+e[5]-e[0]-e[10]),t[3]=(e[8]-e[2])/n,t[0]=(e[1]+e[4])/n,t[1]=.25*n,t[2]=(e[6]+e[9])/n):(n=2*Math.sqrt(1+e[10]-e[0]-e[5]),t[3]=(e[1]-e[4])/n,t[0]=(e[8]+e[2])/n,t[1]=(e[6]+e[9])/n,t[2]=.25*n),t},i.fromRotationTranslationScale=function(t,e,r,n){var i=e[0],o=e[1],a=e[2],s=e[3],l=i+i,u=o+o,c=a+a,h=i*l,f=i*u,p=i*c,d=o*u,m=o*c,g=a*c,v=s*l,y=s*u,b=s*c,x=n[0],_=n[1],w=n[2];return t[0]=(1-(d+g))*x,t[1]=(f+b)*x,t[2]=(p-y)*x,t[3]=0,t[4]=(f-b)*_,t[5]=(1-(h+g))*_,t[6]=(m+v)*_,t[7]=0,t[8]=(p+y)*w,t[9]=(m-v)*w,t[10]=(1-(h+d))*w,t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t},i.fromRotationTranslationScaleOrigin=function(t,e,r,n,i){var o=e[0],a=e[1],s=e[2],l=e[3],u=o+o,c=a+a,h=s+s,f=o*u,p=o*c,d=o*h,m=a*c,g=a*h,v=s*h,y=l*u,b=l*c,x=l*h,_=n[0],w=n[1],M=n[2],k=i[0],A=i[1],T=i[2];return t[0]=(1-(m+v))*_,t[1]=(p+x)*_,t[2]=(d-b)*_,t[3]=0,t[4]=(p-x)*w,t[5]=(1-(f+v))*w,t[6]=(g+y)*w,t[7]=0,t[8]=(d+b)*M,t[9]=(g-y)*M,t[10]=(1-(f+m))*M,t[11]=0,t[12]=r[0]+k-(t[0]*k+t[4]*A+t[8]*T),t[13]=r[1]+A-(t[1]*k+t[5]*A+t[9]*T),t[14]=r[2]+T-(t[2]*k+t[6]*A+t[10]*T),t[15]=1,t},i.fromQuat=function(t,e){var r=e[0],n=e[1],i=e[2],o=e[3],a=r+r,s=n+n,l=i+i,u=r*a,c=n*a,h=n*s,f=i*a,p=i*s,d=i*l,m=o*a,g=o*s,v=o*l;return t[0]=1-h-d,t[1]=c+v,t[2]=f-g,t[3]=0,t[4]=c-v,t[5]=1-u-d,t[6]=p+m,t[7]=0,t[8]=f+g,t[9]=p-m,t[10]=1-u-h,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t},i.frustum=function(t,e,r,n,i,o,a){var 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r=t[0],i=t[1],o=t[2],a=t[3],s=t[4],l=t[5],u=t[6],c=t[7],h=t[8],f=t[9],p=t[10],d=t[11],m=t[12],g=t[13],v=t[14],y=t[15],b=e[0],x=e[1],_=e[2],w=e[3],M=e[4],k=e[5],A=e[6],T=e[7],E=e[8],S=e[9],L=e[10],z=e[11],I=e[12],P=e[13],C=e[14],R=e[15];return 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t},r.isFeatureConstant=!0,r.isZoomConstant=!0;return r}function i(t,e){for(var r=0;r<t.stops.length;r++)if(e===t.stops[r][0])return t.stops[r][1];return t.stops[0][1]}function o(t,e){for(var r=0;r<t.stops.length&&!(e<t.stops[r][0]);r++);return t.stops[Math.max(r-1,0)][1]}function a(t,e){for(var r=void 0!==t.base?t.base:1,n=0;;){if(n>=t.stops.length)break;if(e<=t.stops[n][0])break;n++}return 0===n?t.stops[n][1]:n===t.stops.length?t.stops[n-1][1]:s(e,r,t.stops[n-1][0],t.stops[n][0],t.stops[n-1][1],t.stops[n][1])}function s(t,e,r,n,i,o){return"function"==typeof i?function(){var a=i.apply(void 0,arguments),l=o.apply(void 0,arguments);return s(t,e,r,n,a,l)}:i.length?u(t,e,r,n,i,o):l(t,e,r,n,i,o)}function l(t,e,r,n,i,o){var a,s=n-r,l=t-r;return a=1===e?l/s:(Math.pow(e,l)-1)/(Math.pow(e,s)-1),i*(1-a)+o*a}function u(t,e,r,n,i,o){for(var a=[],s=0;s<i.length;s++)a[s]=l(t,e,r,n,i[s],o[s]);return a}function c(t){return"object"==typeof 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opacity\n\n    gl_FragColor = color * opacity;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nattribute vec2 a_pos;\n\nuniform mat4 u_matrix;\n\n#pragma mapbox: define lowp vec4 color\n#pragma mapbox: define lowp float opacity\n\nvoid main() {\n    #pragma mapbox: initialize lowp vec4 color\n    #pragma mapbox: initialize lowp float opacity\n\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n}\n"},circle:{fragmentSource:"#ifdef GL_ES\nprecision mediump float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\n#pragma mapbox: define lowp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n\nvarying vec2 v_extrude;\nvarying lowp float v_antialiasblur;\n\nvoid main() {\n    #pragma mapbox: initialize lowp vec4 color\n    #pragma mapbox: initialize lowp float blur\n    #pragma mapbox: initialize lowp float opacity\n\n    float t = smoothstep(1.0 - max(blur, v_antialiasblur), 1.0, length(v_extrude));\n    gl_FragColor = color * (1.0 - t) * opacity;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform mat4 u_matrix;\nuniform bool u_scale_with_map;\nuniform vec2 u_extrude_scale;\nuniform float u_devicepixelratio;\n\nattribute vec2 a_pos;\n\n#pragma mapbox: define lowp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n\nvarying vec2 v_extrude;\nvarying lowp float v_antialiasblur;\n\nvoid main(void) {\n    #pragma mapbox: initialize lowp vec4 color\n    #pragma mapbox: initialize mediump float radius\n    #pragma mapbox: initialize lowp float blur\n    #pragma mapbox: initialize lowp float opacity\n\n    // unencode the extrusion vector that we snuck into the a_pos vector\n    v_extrude = vec2(mod(a_pos, 2.0) * 2.0 - 1.0);\n\n    vec2 extrude = v_extrude * radius * u_extrude_scale;\n    // multiply a_pos by 0.5, since we had it * 2 in order to sneak\n    // in extrusion data\n    gl_Position = u_matrix * vec4(floor(a_pos * 0.5), 0, 1);\n\n    if (u_scale_with_map) {\n        gl_Position.xy += extrude;\n    } else {\n        gl_Position.xy += extrude * gl_Position.w;\n    }\n\n    // This is a minimum blur distance that serves as a faux-antialiasing for\n    // the circle. since blur is a ratio of the circle's size and the intent is\n    // to keep the blur at roughly 1px, the two are inversely related.\n    v_antialiasblur = 1.0 / u_devicepixelratio / radius;\n}\n"},line:{fragmentSource:"#ifdef GL_ES\nprecision mediump float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform lowp vec4 u_color;\nuniform lowp float u_opacity;\nuniform float u_blur;\n\nvarying vec2 v_linewidth;\nvarying vec2 v_normal;\nvarying float v_gamma_scale;\n\nvoid main() {\n    // Calculate the distance of the pixel from the line in pixels.\n    float dist = length(v_normal) * v_linewidth.s;\n\n    // Calculate the antialiasing fade factor. This is either when fading in\n    // the line in case of an offset line (v_linewidth.t) or when fading out\n    // (v_linewidth.s)\n    float blur = u_blur * v_gamma_scale;\n    float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\n\n    gl_FragColor = u_color * (alpha * u_opacity);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\n// floor(127 / 2) == 63.0\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\n// there are also \"special\" normals that have a bigger length (of up to 126 in\n// this case).\n// #define scale 63.0\n#define scale 0.015873016\n\nattribute vec2 a_pos;\nattribute vec4 a_data;\n\nuniform mat4 u_matrix;\nuniform mediump float u_ratio;\nuniform mediump float u_linewidth;\nuniform mediump float u_gapwidth;\nuniform mediump float u_antialiasing;\nuniform mediump float u_extra;\nuniform mat2 u_antialiasingmatrix;\nuniform mediump float u_offset;\nuniform mediump float u_blur;\n\nvarying vec2 v_normal;\nvarying vec2 v_linewidth;\nvarying float v_gamma_scale;\n\nvoid main() {\n    vec2 a_extrude = a_data.xy - 128.0;\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\n\n    // We store the texture normals in the most insignificant bit\n    // transform y so that 0 => -1 and 1 => 1\n    // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\n    // y is 1 if the normal points up, and -1 if it points down\n    mediump vec2 normal = mod(a_pos, 2.0);\n    normal.y = sign(normal.y - 0.5);\n    v_normal = normal;\n\n    float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\n    float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\n\n    // Scale the extrusion vector down to a normal and then up by the line width\n    // of this vertex.\n    mediump vec2 dist = outset * a_extrude * scale;\n\n    // Calculate the offset when drawing a line that is to the side of the actual line.\n    // We do this by creating a vector that points towards the extrude, but rotate\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\n    // extrude vector points in another direction.\n    mediump float u = 0.5 * a_direction;\n    mediump float t = 1.0 - abs(u);\n    mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\n\n    // Remove the texture normal bit of the position before scaling it with the\n    // model/view matrix.\n    gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\n\n    // position of y on the screen\n    float y = gl_Position.y / gl_Position.w;\n\n    // how much features are squished in the y direction by the tilt\n    float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\n\n    // how much features are squished in all directions by the perspectiveness\n    float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\n\n    v_linewidth = vec2(outset, inset);\n    v_gamma_scale = perspective_scale * squish_scale;\n}\n"},linepattern:{fragmentSource:"#ifdef GL_ES\nprecision mediump float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform float u_blur;\n\nuniform vec2 u_pattern_size_a;\nuniform vec2 u_pattern_size_b;\nuniform vec2 u_pattern_tl_a;\nuniform vec2 u_pattern_br_a;\nuniform vec2 u_pattern_tl_b;\nuniform vec2 u_pattern_br_b;\nuniform float u_fade;\nuniform float u_opacity;\n\nuniform sampler2D u_image;\n\nvarying vec2 v_normal;\nvarying vec2 v_linewidth;\nvarying float v_linesofar;\nvarying float v_gamma_scale;\n\nvoid main() {\n    // Calculate the distance of the pixel from the line in pixels.\n    float dist = length(v_normal) * v_linewidth.s;\n\n    // Calculate the antialiasing fade factor. This is either when fading in\n    // the line in case of an offset line (v_linewidth.t) or when fading out\n    // (v_linewidth.s)\n    float blur = u_blur * v_gamma_scale;\n    float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\n\n    float x_a = mod(v_linesofar / u_pattern_size_a.x, 1.0);\n    float x_b = mod(v_linesofar / u_pattern_size_b.x, 1.0);\n    float y_a = 0.5 + (v_normal.y * v_linewidth.s / u_pattern_size_a.y);\n    float y_b = 0.5 + (v_normal.y * v_linewidth.s / u_pattern_size_b.y);\n    vec2 pos_a = mix(u_pattern_tl_a, u_pattern_br_a, vec2(x_a, y_a));\n    vec2 pos_b = mix(u_pattern_tl_b, u_pattern_br_b, vec2(x_b, y_b));\n\n    vec4 color = mix(texture2D(u_image, pos_a), texture2D(u_image, pos_b), u_fade);\n\n    alpha *= u_opacity;\n\n    gl_FragColor = color * alpha;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\n// floor(127 / 2) == 63.0\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\n// there are also \"special\" normals that have a bigger length (of up to 126 in\n// this case).\n// #define scale 63.0\n#define scale 0.015873016\n\n// We scale the distance before adding it to the buffers so that we can store\n// long distances for long segments. Use this value to unscale the distance.\n#define LINE_DISTANCE_SCALE 2.0\n\nattribute vec2 a_pos;\nattribute vec4 a_data;\n\nuniform mat4 u_matrix;\nuniform mediump float u_ratio;\nuniform mediump float u_linewidth;\nuniform mediump float u_gapwidth;\nuniform mediump float u_antialiasing;\nuniform mediump float u_extra;\nuniform mat2 u_antialiasingmatrix;\nuniform mediump float u_offset;\n\nvarying vec2 v_normal;\nvarying vec2 v_linewidth;\nvarying float v_linesofar;\nvarying float v_gamma_scale;\n\nvoid main() {\n    vec2 a_extrude = a_data.xy - 128.0;\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\n    float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\n\n    // We store the texture normals in the most insignificant bit\n    // transform y so that 0 => -1 and 1 => 1\n    // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\n    // y is 1 if the normal points up, and -1 if it points down\n    mediump vec2 normal = mod(a_pos, 2.0);\n    normal.y = sign(normal.y - 0.5);\n    v_normal = normal;\n\n    float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\n    float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\n\n    // Scale the extrusion vector down to a normal and then up by the line width\n    // of this vertex.\n    mediump vec2 dist = outset * a_extrude * scale;\n\n    // Calculate the offset when drawing a line that is to the side of the actual line.\n    // We do this by creating a vector that points towards the extrude, but rotate\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\n    // extrude vector points in another direction.\n    mediump float u = 0.5 * a_direction;\n    mediump float t = 1.0 - abs(u);\n    mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\n\n    // Remove the texture normal bit of the position before scaling it with the\n    // model/view matrix.\n    gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\n    v_linesofar = a_linesofar;\n\n    // position of y on the screen\n    float y = gl_Position.y / gl_Position.w;\n\n    // how much features are squished in the y direction by the tilt\n    float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\n\n    // how much features are squished in all directions by the perspectiveness\n    float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\n\n    v_linewidth = vec2(outset, inset);\n    v_gamma_scale = perspective_scale * squish_scale;\n}\n"},linesdfpattern:{fragmentSource:"#ifdef GL_ES\nprecision mediump float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform lowp vec4 u_color;\nuniform lowp float u_opacity;\n\nuniform float u_blur;\nuniform sampler2D u_image;\nuniform float u_sdfgamma;\nuniform float u_mix;\n\nvarying vec2 v_normal;\nvarying vec2 v_linewidth;\nvarying vec2 v_tex_a;\nvarying vec2 v_tex_b;\nvarying float v_gamma_scale;\n\nvoid main() {\n    // Calculate the distance of the pixel from the line in pixels.\n    float dist = length(v_normal) * v_linewidth.s;\n\n    // Calculate the antialiasing fade factor. This is either when fading in\n    // the line in case of an offset line (v_linewidth.t) or when fading out\n    // (v_linewidth.s)\n    float blur = u_blur * v_gamma_scale;\n    float alpha = clamp(min(dist - (v_linewidth.t - blur), v_linewidth.s - dist) / blur, 0.0, 1.0);\n\n    float sdfdist_a = texture2D(u_image, v_tex_a).a;\n    float sdfdist_b = texture2D(u_image, v_tex_b).a;\n    float sdfdist = mix(sdfdist_a, sdfdist_b, u_mix);\n    alpha *= smoothstep(0.5 - u_sdfgamma, 0.5 + u_sdfgamma, sdfdist);\n\n    gl_FragColor = u_color * (alpha * u_opacity);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\n// floor(127 / 2) == 63.0\n// the maximum allowed miter limit is 2.0 at the moment. the extrude normal is\n// stored in a byte (-128..127). we scale regular normals up to length 63, but\n// there are also \"special\" normals that have a bigger length (of up to 126 in\n// this case).\n// #define scale 63.0\n#define scale 0.015873016\n\n// We scale the distance before adding it to the buffers so that we can store\n// long distances for long segments. Use this value to unscale the distance.\n#define LINE_DISTANCE_SCALE 2.0\n\nattribute vec2 a_pos;\nattribute vec4 a_data;\n\nuniform mat4 u_matrix;\nuniform mediump float u_ratio;\nuniform mediump float u_linewidth;\nuniform mediump float u_gapwidth;\nuniform mediump float u_antialiasing;\nuniform vec2 u_patternscale_a;\nuniform float u_tex_y_a;\nuniform vec2 u_patternscale_b;\nuniform float u_tex_y_b;\nuniform float u_extra;\nuniform mat2 u_antialiasingmatrix;\nuniform mediump float u_offset;\n\nvarying vec2 v_normal;\nvarying vec2 v_linewidth;\nvarying vec2 v_tex_a;\nvarying vec2 v_tex_b;\nvarying float v_gamma_scale;\n\nvoid main() {\n    vec2 a_extrude = a_data.xy - 128.0;\n    float a_direction = mod(a_data.z, 4.0) - 1.0;\n    float a_linesofar = (floor(a_data.z / 4.0) + a_data.w * 64.0) * LINE_DISTANCE_SCALE;\n\n    // We store the texture normals in the most insignificant bit\n    // transform y so that 0 => -1 and 1 => 1\n    // In the texture normal, x is 0 if the normal points straight up/down and 1 if it's a round cap\n    // y is 1 if the normal points up, and -1 if it points down\n    mediump vec2 normal = mod(a_pos, 2.0);\n    normal.y = sign(normal.y - 0.5);\n    v_normal = normal;\n\n    float inset = u_gapwidth + (u_gapwidth > 0.0 ? u_antialiasing : 0.0);\n    float outset = u_gapwidth + u_linewidth * (u_gapwidth > 0.0 ? 2.0 : 1.0) + u_antialiasing;\n\n    // Scale the extrusion vector down to a normal and then up by the line width\n    // of this vertex.\n    mediump vec2 dist = outset * a_extrude * scale;\n\n    // Calculate the offset when drawing a line that is to the side of the actual line.\n    // We do this by creating a vector that points towards the extrude, but rotate\n    // it when we're drawing round end points (a_direction = -1 or 1) since their\n    // extrude vector points in another direction.\n    mediump float u = 0.5 * a_direction;\n    mediump float t = 1.0 - abs(u);\n    mediump vec2 offset = u_offset * a_extrude * scale * normal.y * mat2(t, -u, u, t);\n\n    // Remove the texture normal bit of the position before scaling it with the\n    // model/view matrix.\n    gl_Position = u_matrix * vec4(floor(a_pos * 0.5) + (offset + dist) / u_ratio, 0.0, 1.0);\n\n    v_tex_a = vec2(a_linesofar * u_patternscale_a.x, normal.y * u_patternscale_a.y + u_tex_y_a);\n    v_tex_b = vec2(a_linesofar * u_patternscale_b.x, normal.y * u_patternscale_b.y + u_tex_y_b);\n\n    // position of y on the screen\n    float y = gl_Position.y / gl_Position.w;\n\n    // how much features are squished in the y direction by the tilt\n    float squish_scale = length(a_extrude) / length(u_antialiasingmatrix * a_extrude);\n\n    // how much features are squished in all directions by the perspectiveness\n    float perspective_scale = 1.0 / (1.0 - min(y * u_extra, 0.9));\n\n    v_linewidth = vec2(outset, inset);\n    v_gamma_scale = perspective_scale * squish_scale;\n}\n"},outline:{fragmentSource:"#ifdef GL_ES\nprecision mediump float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\n#pragma mapbox: define lowp vec4 outline_color\n#pragma mapbox: define lowp float opacity\n\nvarying vec2 v_pos;\n\nvoid main() {\n    #pragma mapbox: initialize lowp vec4 outline_color\n    #pragma mapbox: initialize lowp float opacity\n\n    float dist = length(v_pos - gl_FragCoord.xy);\n    float alpha = smoothstep(1.0, 0.0, dist);\n    gl_FragColor = outline_color * (alpha * opacity);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nattribute vec2 a_pos;\n\nuniform mat4 u_matrix;\nuniform vec2 u_world;\n\nvarying vec2 v_pos;\n\n#pragma mapbox: define lowp vec4 outline_color\n#pragma mapbox: define lowp float opacity\n\nvoid main() {\n    #pragma mapbox: initialize lowp vec4 outline_color\n    #pragma mapbox: initialize lowp float opacity\n\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n    v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\n}\n"},outlinepattern:{fragmentSource:"#ifdef GL_ES\nprecision mediump float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform float u_opacity;\nuniform vec2 u_pattern_tl_a;\nuniform vec2 u_pattern_br_a;\nuniform vec2 u_pattern_tl_b;\nuniform vec2 u_pattern_br_b;\nuniform float u_mix;\n\nuniform sampler2D u_image;\n\nvarying vec2 v_pos_a;\nvarying vec2 v_pos_b;\nvarying vec2 v_pos;\n\nvoid main() {\n    vec2 imagecoord = mod(v_pos_a, 1.0);\n    vec2 pos = mix(u_pattern_tl_a, u_pattern_br_a, imagecoord);\n    vec4 color1 = texture2D(u_image, pos);\n\n    vec2 imagecoord_b = mod(v_pos_b, 1.0);\n    vec2 pos2 = mix(u_pattern_tl_b, u_pattern_br_b, imagecoord_b);\n    vec4 color2 = texture2D(u_image, pos2);\n\n    // find distance to outline for alpha interpolation\n\n    float dist = length(v_pos - gl_FragCoord.xy);\n    float alpha = smoothstep(1.0, 0.0, dist);\n    \n\n    gl_FragColor = mix(color1, color2, u_mix) * alpha * u_opacity;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform vec2 u_pattern_size_a;\nuniform vec2 u_pattern_size_b;\nuniform vec2 u_pixel_coord_upper;\nuniform vec2 u_pixel_coord_lower;\nuniform float u_scale_a;\nuniform float u_scale_b;\nuniform float u_tile_units_to_pixels;\n\nattribute vec2 a_pos;\n\nuniform mat4 u_matrix;\nuniform vec2 u_world;\n\nvarying vec2 v_pos_a;\nvarying vec2 v_pos_b;\nvarying vec2 v_pos;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n    vec2 scaled_size_a = u_scale_a * u_pattern_size_a;\n    vec2 scaled_size_b = u_scale_b * u_pattern_size_b;\n\n    // the correct offset needs to be calculated.\n    //\n    // The offset depends on how many pixels are between the world origin and\n    // the edge of the tile:\n    // vec2 offset = mod(pixel_coord, size)\n    //\n    // At high zoom levels there are a ton of pixels between the world origin\n    // and the edge of the tile. The glsl spec only guarantees 16 bits of\n    // precision for highp floats. We need more than that.\n    //\n    // The pixel_coord is passed in as two 16 bit values:\n    // pixel_coord_upper = floor(pixel_coord / 2^16)\n    // pixel_coord_lower = mod(pixel_coord, 2^16)\n    //\n    // The offset is calculated in a series of steps that should preserve this precision:\n    vec2 offset_a = mod(mod(mod(u_pixel_coord_upper, scaled_size_a) * 256.0, scaled_size_a) * 256.0 + u_pixel_coord_lower, scaled_size_a);\n    vec2 offset_b = mod(mod(mod(u_pixel_coord_upper, scaled_size_b) * 256.0, scaled_size_b) * 256.0 + u_pixel_coord_lower, scaled_size_b);\n\n    v_pos_a = (u_tile_units_to_pixels * a_pos + offset_a) / scaled_size_a;\n    v_pos_b = (u_tile_units_to_pixels * a_pos + offset_b) / scaled_size_b;\n\n    v_pos = (gl_Position.xy / gl_Position.w + 1.0) / 2.0 * u_world;\n}\n"
},pattern:{fragmentSource:"#ifdef GL_ES\nprecision mediump float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform float u_opacity;\nuniform vec2 u_pattern_tl_a;\nuniform vec2 u_pattern_br_a;\nuniform vec2 u_pattern_tl_b;\nuniform vec2 u_pattern_br_b;\nuniform float u_mix;\n\nuniform sampler2D u_image;\n\nvarying vec2 v_pos_a;\nvarying vec2 v_pos_b;\n\nvoid main() {\n\n    vec2 imagecoord = mod(v_pos_a, 1.0);\n    vec2 pos = mix(u_pattern_tl_a, u_pattern_br_a, imagecoord);\n    vec4 color1 = texture2D(u_image, pos);\n\n    vec2 imagecoord_b = mod(v_pos_b, 1.0);\n    vec2 pos2 = mix(u_pattern_tl_b, u_pattern_br_b, imagecoord_b);\n    vec4 color2 = texture2D(u_image, pos2);\n\n    gl_FragColor = mix(color1, color2, u_mix) * u_opacity;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform mat4 u_matrix;\nuniform vec2 u_pattern_size_a;\nuniform vec2 u_pattern_size_b;\nuniform vec2 u_pixel_coord_upper;\nuniform vec2 u_pixel_coord_lower;\nuniform float u_scale_a;\nuniform float u_scale_b;\nuniform float u_tile_units_to_pixels;\n\nattribute vec2 a_pos;\n\nvarying vec2 v_pos_a;\nvarying vec2 v_pos_b;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n    vec2 scaled_size_a = u_scale_a * u_pattern_size_a;\n    vec2 scaled_size_b = u_scale_b * u_pattern_size_b;\n\n    // the correct offset needs to be calculated.\n    //\n    // The offset depends on how many pixels are between the world origin and\n    // the edge of the tile:\n    // vec2 offset = mod(pixel_coord, size)\n    //\n    // At high zoom levels there are a ton of pixels between the world origin\n    // and the edge of the tile. The glsl spec only guarantees 16 bits of\n    // precision for highp floats. We need more than that.\n    //\n    // The pixel_coord is passed in as two 16 bit values:\n    // pixel_coord_upper = floor(pixel_coord / 2^16)\n    // pixel_coord_lower = mod(pixel_coord, 2^16)\n    //\n    // The offset is calculated in a series of steps that should preserve this precision:\n    vec2 offset_a = mod(mod(mod(u_pixel_coord_upper, scaled_size_a) * 256.0, scaled_size_a) * 256.0 + u_pixel_coord_lower, scaled_size_a);\n    vec2 offset_b = mod(mod(mod(u_pixel_coord_upper, scaled_size_b) * 256.0, scaled_size_b) * 256.0 + u_pixel_coord_lower, scaled_size_b);\n\n    v_pos_a = (u_tile_units_to_pixels * a_pos + offset_a) / scaled_size_a;\n    v_pos_b = (u_tile_units_to_pixels * a_pos + offset_b) / scaled_size_b;\n}\n"},raster:{fragmentSource:"#ifdef GL_ES\nprecision mediump float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform float u_opacity0;\nuniform float u_opacity1;\nuniform sampler2D u_image0;\nuniform sampler2D u_image1;\nvarying vec2 v_pos0;\nvarying vec2 v_pos1;\n\nuniform float u_brightness_low;\nuniform float u_brightness_high;\n\nuniform float u_saturation_factor;\nuniform float u_contrast_factor;\nuniform vec3 u_spin_weights;\n\nvoid main() {\n\n    // read and cross-fade colors from the main and parent tiles\n    vec4 color0 = texture2D(u_image0, v_pos0);\n    vec4 color1 = texture2D(u_image1, v_pos1);\n    vec4 color = color0 * u_opacity0 + color1 * u_opacity1;\n    vec3 rgb = color.rgb;\n\n    // spin\n    rgb = vec3(\n        dot(rgb, u_spin_weights.xyz),\n        dot(rgb, u_spin_weights.zxy),\n        dot(rgb, u_spin_weights.yzx));\n\n    // saturation\n    float average = (color.r + color.g + color.b) / 3.0;\n    rgb += (average - rgb) * u_saturation_factor;\n\n    // contrast\n    rgb = (rgb - 0.5) * u_contrast_factor + 0.5;\n\n    // brightness\n    vec3 u_high_vec = vec3(u_brightness_low, u_brightness_low, u_brightness_low);\n    vec3 u_low_vec = vec3(u_brightness_high, u_brightness_high, u_brightness_high);\n\n    gl_FragColor = vec4(mix(u_high_vec, u_low_vec, rgb), color.a);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform mat4 u_matrix;\nuniform vec2 u_tl_parent;\nuniform float u_scale_parent;\nuniform float u_buffer_scale;\n\nattribute vec2 a_pos;\nattribute vec2 a_texture_pos;\n\nvarying vec2 v_pos0;\nvarying vec2 v_pos1;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos, 0, 1);\n    v_pos0 = (((a_texture_pos / 32767.0) - 0.5) / u_buffer_scale ) + 0.5;\n    v_pos1 = (v_pos0 * u_scale_parent) + u_tl_parent;\n}\n"},icon:{fragmentSource:"#ifdef GL_ES\nprecision mediump float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform sampler2D u_texture;\nuniform sampler2D u_fadetexture;\nuniform lowp float u_opacity;\n\nvarying vec2 v_tex;\nvarying vec2 v_fade_tex;\n\nvoid main() {\n    lowp float alpha = texture2D(u_fadetexture, v_fade_tex).a * u_opacity;\n    gl_FragColor = texture2D(u_texture, v_tex) * alpha;\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nattribute vec2 a_pos;\nattribute vec2 a_offset;\nattribute vec2 a_texture_pos;\nattribute vec4 a_data;\n\n\n// matrix is for the vertex position.\nuniform mat4 u_matrix;\n\nuniform mediump float u_zoom;\nuniform bool u_rotate_with_map;\nuniform vec2 u_extrude_scale;\n\nuniform vec2 u_texsize;\n\nvarying vec2 v_tex;\nvarying vec2 v_fade_tex;\n\nvoid main() {\n    vec2 a_tex = a_texture_pos.xy;\n    mediump float a_labelminzoom = a_data[0];\n    mediump vec2 a_zoom = a_data.pq;\n    mediump float a_minzoom = a_zoom[0];\n    mediump float a_maxzoom = a_zoom[1];\n\n    // u_zoom is the current zoom level adjusted for the change in font size\n    mediump float z = 2.0 - step(a_minzoom, u_zoom) - (1.0 - step(a_maxzoom, u_zoom));\n\n    vec2 extrude = u_extrude_scale * (a_offset / 64.0);\n    if (u_rotate_with_map) {\n        gl_Position = u_matrix * vec4(a_pos + extrude, 0, 1);\n        gl_Position.z += z * gl_Position.w;\n    } else {\n        gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\n    }\n\n    v_tex = a_tex / u_texsize;\n    v_fade_tex = vec2(a_labelminzoom / 255.0, 0.0);\n}\n"},sdf:{fragmentSource:"#ifdef GL_ES\nprecision mediump float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform sampler2D u_texture;\nuniform sampler2D u_fadetexture;\nuniform lowp vec4 u_color;\nuniform lowp float u_opacity;\nuniform lowp float u_buffer;\nuniform lowp float u_gamma;\n\nvarying vec2 v_tex;\nvarying vec2 v_fade_tex;\nvarying float v_gamma_scale;\n\nvoid main() {\n    lowp float dist = texture2D(u_texture, v_tex).a;\n    lowp float fade_alpha = texture2D(u_fadetexture, v_fade_tex).a;\n    lowp float gamma = u_gamma * v_gamma_scale;\n    lowp float alpha = smoothstep(u_buffer - gamma, u_buffer + gamma, dist) * fade_alpha;\n\n    gl_FragColor = u_color * (alpha * u_opacity);\n\n#ifdef OVERDRAW_INSPECTOR\n    gl_FragColor = vec4(1.0);\n#endif\n}\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nconst float PI = 3.141592653589793;\n\nattribute vec2 a_pos;\nattribute vec2 a_offset;\nattribute vec2 a_texture_pos;\nattribute vec4 a_data;\n\n\n// matrix is for the vertex position.\nuniform mat4 u_matrix;\n\nuniform mediump float u_zoom;\nuniform bool u_rotate_with_map;\nuniform bool u_pitch_with_map;\nuniform mediump float u_pitch;\nuniform mediump float u_bearing;\nuniform mediump float u_aspect_ratio;\nuniform vec2 u_extrude_scale;\n\nuniform vec2 u_texsize;\n\nvarying vec2 v_tex;\nvarying vec2 v_fade_tex;\nvarying float v_gamma_scale;\n\nvoid main() {\n    vec2 a_tex = a_texture_pos.xy;\n    mediump float a_labelminzoom = a_data[0];\n    mediump vec2 a_zoom = a_data.pq;\n    mediump float a_minzoom = a_zoom[0];\n    mediump float a_maxzoom = a_zoom[1];\n\n    // u_zoom is the current zoom level adjusted for the change in font size\n    mediump float z = 2.0 - step(a_minzoom, u_zoom) - (1.0 - step(a_maxzoom, u_zoom));\n\n    // pitch-alignment: map\n    // rotation-alignment: map | viewport\n    if (u_pitch_with_map) {\n        lowp float angle = u_rotate_with_map ? (a_data[1] / 256.0 * 2.0 * PI) : u_bearing;\n        lowp float asin = sin(angle);\n        lowp float acos = cos(angle);\n        mat2 RotationMatrix = mat2(acos, asin, -1.0 * asin, acos);\n        vec2 offset = RotationMatrix * a_offset;\n        vec2 extrude = u_extrude_scale * (offset / 64.0);\n        gl_Position = u_matrix * vec4(a_pos + extrude, 0, 1);\n        gl_Position.z += z * gl_Position.w;\n    // pitch-alignment: viewport\n    // rotation-alignment: map\n    } else if (u_rotate_with_map) {\n        // foreshortening factor to apply on pitched maps\n        // as a label goes from horizontal <=> vertical in angle\n        // it goes from 0% foreshortening to up to around 70% foreshortening\n        lowp float pitchfactor = 1.0 - cos(u_pitch * sin(u_pitch * 0.75));\n\n        lowp float lineangle = a_data[1] / 256.0 * 2.0 * PI;\n\n        // use the lineangle to position points a,b along the line\n        // project the points and calculate the label angle in projected space\n        // this calculation allows labels to be rendered unskewed on pitched maps\n        vec4 a = u_matrix * vec4(a_pos, 0, 1);\n        vec4 b = u_matrix * vec4(a_pos + vec2(cos(lineangle),sin(lineangle)), 0, 1);\n        lowp float angle = atan((b[1]/b[3] - a[1]/a[3])/u_aspect_ratio, b[0]/b[3] - a[0]/a[3]);\n        lowp float asin = sin(angle);\n        lowp float acos = cos(angle);\n        mat2 RotationMatrix = mat2(acos, -1.0 * asin, asin, acos);\n\n        vec2 offset = RotationMatrix * (vec2((1.0-pitchfactor)+(pitchfactor*cos(angle*2.0)), 1.0) * a_offset);\n        vec2 extrude = u_extrude_scale * (offset / 64.0);\n        gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\n        gl_Position.z += z * gl_Position.w;\n    // pitch-alignment: viewport\n    // rotation-alignment: viewport\n    } else {\n        vec2 extrude = u_extrude_scale * (a_offset / 64.0);\n        gl_Position = u_matrix * vec4(a_pos, 0, 1) + vec4(extrude, 0, 0);\n    }\n\n    v_gamma_scale = (gl_Position.w - 0.5);\n\n    v_tex = a_tex / u_texsize;\n    v_fade_tex = vec2(a_labelminzoom / 255.0, 0.0);\n}\n"},collisionbox:{fragmentSource:"#ifdef GL_ES\nprecision mediump float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nuniform float u_zoom;\nuniform float u_maxzoom;\n\nvarying float v_max_zoom;\nvarying float v_placement_zoom;\n\nvoid main() {\n\n    float alpha = 0.5;\n\n    gl_FragColor = vec4(0.0, 1.0, 0.0, 1.0) * alpha;\n\n    if (v_placement_zoom > u_zoom) {\n        gl_FragColor = vec4(1.0, 0.0, 0.0, 1.0) * alpha;\n    }\n\n    if (u_zoom >= v_max_zoom) {\n        gl_FragColor = vec4(0.0, 0.0, 0.0, 1.0) * alpha * 0.25;\n    }\n\n    if (v_placement_zoom >= u_maxzoom) {\n        gl_FragColor = vec4(0.0, 0.0, 1.0, 1.0) * alpha * 0.2;\n    }\n}\n",vertexSource:"#ifdef GL_ES\nprecision highp float;\n#else\n#define lowp\n#define mediump\n#define highp\n#endif\n\nattribute vec2 a_pos;\nattribute vec2 a_extrude;\nattribute vec2 a_data;\n\nuniform mat4 u_matrix;\nuniform float u_scale;\n\nvarying float v_max_zoom;\nvarying float v_placement_zoom;\n\nvoid main() {\n    gl_Position = u_matrix * vec4(a_pos + a_extrude / u_scale, 0.0, 1.0);\n\n    v_max_zoom = a_data.x;\n    v_placement_zoom = a_data.y;\n}\n"}},e.exports.util="float evaluate_zoom_function_1(const vec4 values, const float t) {\n    if (t < 1.0) {\n        return mix(values[0], values[1], t);\n    } else if (t < 2.0) {\n        return mix(values[1], values[2], t - 1.0);\n    } else {\n        return mix(values[2], values[3], t - 2.0);\n    }\n}\nvec4 evaluate_zoom_function_4(const vec4 value0, const vec4 value1, const vec4 value2, const vec4 value3, const float t) {\n    if (t < 1.0) {\n        return mix(value0, value1, t);\n    } else if (t < 2.0) {\n        return mix(value1, value2, t - 1.0);\n    } else {\n        return mix(value2, value3, t - 2.0);\n    }\n}\n"},{path:58}],395:[function(t,e,r){"use strict";function n(t,e){this.message=(t?t+": 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r=t("./validate_function"),a=t("./validate_object"),s={"*":function(){return[]},array:t("./validate_array"),"boolean":t("./validate_boolean"),number:t("./validate_number"),color:t("./validate_color"),constants:t("./validate_constants"),"enum":t("./validate_enum"),filter:t("./validate_filter"),"function":t("./validate_function"),layer:t("./validate_layer"),object:t("./validate_object"),source:t("./validate_source"),string:t("./validate_string")},l=e.value,u=e.valueSpec,c=e.key,h=e.styleSpec,f=e.style;if("string"===i(l)&&"@"===l[0]){if(h.$version>7)return[new n(c,l,"constants have been deprecated as of v8")];if(!(l in f.constants))return[new n(c,l,'constant "%s" not found',l)];e=o({},e,{value:f.constants[l]})}return 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n=t("../error/validation_error"),i=t("./validate_enum"),o=t("../util/get_type"),a=t("../util/unbundle_jsonlint");e.exports=function s(t){var e,r=t.value,l=t.key,u=t.styleSpec,c=[];if("array"!==o(r))return[new n(l,r,"array expected, %s found",o(r))];if(r.length<1)return[new n(l,r,"filter array must have at least 1 element")];switch(c=c.concat(i({key:l+"[0]",value:r[0],valueSpec:u.filter_operator,style:t.style,styleSpec:t.styleSpec})),a(r[0])){case"<":case"<=":case">":case">=":r.length>=2&&"$type"==r[1]&&c.push(new n(l,r,'"$type" cannot be use with operator "%s"',r[0]));case"==":case"!=":3!=r.length&&c.push(new n(l,r,'filter array for operator "%s" must have 3 elements',r[0]));case"in":case"!in":r.length>=2&&(e=o(r[1]),"string"!==e?c.push(new n(l+"[1]",r[1],"string expected, %s found",e)):"@"===r[1][0]&&c.push(new n(l+"[1]",r[1],"filter key cannot be a constant")));for(var h=2;h<r.length;h++)e=o(r[h]),"$type"==r[1]?c=c.concat(i({key:l+"["+h+"]",value:r[h],valueSpec:u.geometry_type,style:t.style,styleSpec:t.styleSpec})):"string"===e&&"@"===r[h][0]?c.push(new n(l+"["+h+"]",r[h],"filter value cannot be a constant")):"string"!==e&&"number"!==e&&"boolean"!==e&&c.push(new n(l+"["+h+"]",r[h],"string, number, or boolean expected, %s found",e));break;case"any":case"all":case"none":for(h=1;h<r.length;h++)c=c.concat(s({key:l+"["+h+"]",value:r[h],style:t.style,styleSpec:t.styleSpec}));break;case"has":case"!has":e=o(r[1]),2!==r.length?c.push(new n(l,r,'filter array for "%s" operator must have 2 elements',r[0])):"string"!==e?c.push(new n(l+"[1]",r[1],"string expected, %s found",e)):"@"===r[1][0]&&c.push(new n(l+"[1]",r[1],"filter key cannot be a constant"))}return c}},{"../error/validation_error":395,"../util/get_type":397,"../util/unbundle_jsonlint":398,"./validate_enum":404}],406:[function(t,e,r){"use strict";var n=t("../error/validation_error"),i=t("../util/get_type"),o=t("./validate"),a=t("./validate_object"),s=t("./validate_array"),l=t("./validate_number");e.exports=function(t){function e(t){var e=[],o=t.value;return e=e.concat(s({key:t.key,value:o,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:r})),"array"===i(o)&&0===o.length&&e.push(new n(t.key,o,"array must have at least one stop")),e}function r(t){var e=[],r=t.value,s=t.key;if("array"!==i(r))return[new n(s,r,"array expected, %s found",i(r))];if(2!==r.length)return[new n(s,r,"array length %d expected, length %d found",2,r.length)];var f=i(r[0]);if(c||(c=f),f!==c)return[new n(s,r,"%s stop key type must match previous stop key type %s",f,c)];if("object"===f){if(void 0===r[0].zoom)return[new n(s,r,"object stop key must have zoom")];if(void 0===r[0].value)return[new n(s,r,"object stop key must have 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0===t.value.property||"object"===c,d=a({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:e}});return t.styleSpec.$version>=8&&(f&&!t.valueSpec["property-function"]?d.push(new n(t.key,t.value,"property functions not supported")):p&&!t.valueSpec["zoom-function"]&&d.push(new n(t.key,t.value,"zoom functions not supported"))),d}},{"../error/validation_error":395,"../util/get_type":397,"./validate":399,"./validate_array":400,"./validate_number":410,"./validate_object":411}],407:[function(t,e,r){"use strict";var n=t("../error/validation_error"),i=t("./validate_string");e.exports=function(t){var e=t.value,r=t.key,o=i(t);return o.length?o:(-1===e.indexOf("{fontstack}")&&o.push(new n(r,e,'"glyphs" url must include a "{fontstack}" token')),-1===e.indexOf("{range}")&&o.push(new n(r,e,'"glyphs" url must include a "{range}" token')),o)}},{"../error/validation_error":395,"./validate_string":414}],408:[function(t,e,r){"use strict";var n=t("../error/validation_error"),i=t("../util/unbundle_jsonlint"),o=t("./validate_object"),a=t("./validate_filter"),s=t("./validate_paint_property"),l=t("./validate_layout_property"),u=t("../util/extend");e.exports=function(t){var e=[],r=t.value,c=t.key,h=t.style,f=t.styleSpec;r.type||r.ref||e.push(new n(c,r,'either "type" or "ref" is required'));var p=i(r.type),d=i(r.ref);if(r.id)for(var m=0;m<t.arrayIndex;m++){var g=h.layers[m];i(g.id)===i(r.id)&&e.push(new n(c,r.id,'duplicate layer id "%s", previously used at line %d',r.id,g.id.__line__))}if("ref"in r){["type","source","source-layer","filter","layout"].forEach(function(t){t in r&&e.push(new n(c,r[t],'"%s" is prohibited for ref layers',t))});var v;h.layers.forEach(function(t){t.id==d&&(v=t)}),v?v.ref?e.push(new n(c,r.ref,"ref cannot reference another ref layer")):p=i(v.type):e.push(new n(c,r.ref,'ref layer "%s" not found',d))}else if("background"!==p)if(r.source){var y=h.sources&&h.sources[r.source];y?"vector"==y.type&&"raster"==p?e.push(new n(c,r.source,'layer "%s" requires a raster source',r.id)):"raster"==y.type&&"raster"!=p?e.push(new n(c,r.source,'layer "%s" requires a vector source',r.id)):"vector"!=y.type||r["source-layer"]||e.push(new n(c,r,'layer "%s" must specify a "source-layer"',r.id)):e.push(new n(c,r.source,'source "%s" not found',r.source))}else e.push(new n(c,r,'missing required property "source"'));return e=e.concat(o({key:c,value:r,valueSpec:f.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{filter:a,layout:function(t){return o({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return l(u({layerType:p},t))}}})},paint:function(t){return o({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return s(u({layerType:p},t))}}})}}}))}},{"../error/validation_error":395,"../util/extend":396,"../util/unbundle_jsonlint":398,"./validate_filter":405,"./validate_layout_property":409,"./validate_object":411,"./validate_paint_property":412}],409:[function(t,e,r){"use strict";var n=t("./validate"),i=t("../error/validation_error");e.exports=function(t){var e=t.key,r=t.style,o=t.styleSpec,a=t.value,s=t.objectKey,l=o["layout_"+t.layerType];if(t.valueSpec||l[s]){var u=[];return"symbol"===t.layerType&&("icon-image"===s&&r&&!r.sprite?u.push(new i(e,a,'use of "icon-image" requires a style "sprite" property')):"text-field"===s&&r&&!r.glyphs&&u.push(new i(e,a,'use of "text-field" requires a style "glyphs" property'))),u.concat(n({key:t.key,value:a,valueSpec:t.valueSpec||l[s],style:r,styleSpec:o}))}return[new i(e,a,'unknown property "%s"',s)]}},{"../error/validation_error":395,"./validate":399}],410:[function(t,e,r){"use strict";var n=t("../util/get_type"),i=t("../error/validation_error");e.exports=function(t){var e=t.key,r=t.value,o=t.valueSpec,a=n(r);return"number"!==a?[new i(e,r,"number expected, %s found",a)]:"minimum"in o&&r<o.minimum?[new i(e,r,"%s is less than the minimum value %s",r,o.minimum)]:"maximum"in o&&r>o.maximum?[new i(e,r,"%s is greater than the maximum value %s",r,o.maximum)]:[]}},{"../error/validation_error":395,"../util/get_type":397}],411:[function(t,e,r){"use strict";var n=t("../error/validation_error"),i=t("../util/get_type"),o=t("./validate");e.exports=function(t){var e=t.key,r=t.value,a=t.valueSpec,s=t.objectElementValidators||{},l=t.style,u=t.styleSpec,c=[],h=i(r);if("object"!==h)return[new n(e,r,"object expected, %s found",h)];for(var f in r){var p=f.split(".")[0],d=a&&(a[p]||a["*"]),m=s[p]||s["*"];d||m?c=c.concat((m||o)({key:(e?e+".":e)+f,value:r[f],valueSpec:d,style:l,styleSpec:u,object:r,objectKey:f})):""!==e&&1!==e.split(".").length&&c.push(new n(e,r[f],'unknown property "%s"',f))}for(p in a)a[p].required&&void 0===a[p].default&&void 0===r[p]&&c.push(new n(e,r,'missing required property "%s"',p));return c}},{"../error/validation_error":395,"../util/get_type":397,"./validate":399}],412:[function(t,e,r){"use strict";var n=t("./validate"),i=t("../error/validation_error");e.exports=function(t){var e=t.key,r=t.style,o=t.styleSpec,a=t.value,s=t.objectKey,l=o["paint_"+t.layerType],u=s.match(/^(.*)-transition$/);return u&&l[u[1]]&&l[u[1]].transition?n({key:e,value:a,valueSpec:o.transition,style:r,styleSpec:o}):t.valueSpec||l[s]?n({key:t.key,value:a,valueSpec:t.valueSpec||l[s],style:r,styleSpec:o}):[new i(e,a,'unknown property "%s"',s)]}},{"../error/validation_error":395,"./validate":399}],413:[function(t,e,r){"use strict";var n=t("../error/validation_error"),i=t("../util/unbundle_jsonlint"),o=t("./validate_object"),a=t("./validate_enum");e.exports=function(t){var e=t.value,r=t.key,s=t.styleSpec,l=t.style;if(!e.type)return[new n(r,e,'"type" is required')];var u=i(e.type);switch(u){case"vector":case"raster":var c=[];if(c=c.concat(o({key:r,value:e,valueSpec:s.source_tile,style:t.style,styleSpec:s})),"url"in e)for(var h in e)["type","url","tileSize"].indexOf(h)<0&&c.push(new n(r+"."+h,e[h],'a source with a "url" property may not include a "%s" property',h));return c;case"geojson":return o({key:r,value:e,valueSpec:s.source_geojson,style:l,styleSpec:s});case"video":return o({key:r,value:e,valueSpec:s.source_video,style:l,styleSpec:s});case"image":return o({key:r,value:e,valueSpec:s.source_image,style:l,styleSpec:s});default:return a({key:r+".type",value:e.type,valueSpec:{values:["vector","raster","geojson","video","image"]},style:l,styleSpec:s})}}},{"../error/validation_error":395,"../util/unbundle_jsonlint":398,"./validate_enum":404,"./validate_object":411}],414:[function(t,e,r){"use strict";var n=t("../util/get_type"),i=t("../error/validation_error");e.exports=function(t){var e=t.value,r=t.key,o=n(e);return"string"!==o?[new i(r,e,"string expected, %s found",o)]:[]}},{"../error/validation_error":395,"../util/get_type":397}],415:[function(t,e,r){"use strict";function n(t,e){e=e||l;var r=[];return r=r.concat(s({key:"",value:t,valueSpec:e.$root,styleSpec:e,style:t,objectElementValidators:{glyphs:u}})),e.$version>7&&t.constants&&(r=r.concat(a({key:"constants",value:t.constants,style:t,styleSpec:e}))),i(r)}function i(t){return[].concat(t).sort(function(t,e){return t.line-e.line})}function o(t){return function(){return i(t.apply(this,arguments))}}var a=t("./validate/validate_constants"),s=t("./validate/validate"),l=t("../reference/latest.min"),u=t("./validate/validate_glyphs_url");n.source=o(t("./validate/validate_source")),n.layer=o(t("./validate/validate_layer")),n.filter=o(t("./validate/validate_filter")),n.paintProperty=o(t("./validate/validate_paint_property")),n.layoutProperty=o(t("./validate/validate_layout_property")),e.exports=n},{"../reference/latest.min":417,"./validate/validate":399,"./validate/validate_constants":403,"./validate/validate_filter":405,"./validate/validate_glyphs_url":407,"./validate/validate_layer":408,"./validate/validate_layout_property":409,"./validate/validate_paint_property":412,"./validate/validate_source":413}],416:[function(t,e,r){e.exports=t("./v8.json")},{"./v8.json":418}],417:[function(t,e,r){e.exports=t("./v8.min.json")},{"./v8.min.json":419}],418:[function(t,e,r){e.exports={$version:8,$root:{version:{required:!0,type:"enum",values:[8],doc:"Style specification version number. Must be 8.",example:8},name:{type:"string",doc:"A human-readable name for the style.",example:"Bright"},metadata:{type:"*",doc:"Arbitrary properties useful to track with the stylesheet, but do not influence rendering. Properties should be prefixed to avoid collisions, like 'mapbox:'."},center:{type:"array",value:"number",doc:"Default map center in longitude and latitude.  The style center will be used only if the map has not been positioned by other means (e.g. map options or user interaction).",example:[-73.9749,40.7736]},zoom:{type:"number",doc:"Default zoom level.  The style zoom will be used only if the map has not been positioned by other means (e.g. map options or user interaction).",example:12.5},bearing:{type:"number","default":0,period:360,units:"degrees",doc:"Default bearing, in degrees.  The style bearing will be used only if the map has not been positioned by other means (e.g. map options or user interaction).",example:29},pitch:{type:"number","default":0,units:"degrees",doc:"Default pitch, in degrees. Zero is perpendicular to the surface.  The style pitch will be used only if the map has not been positioned by other means (e.g. map options or user interaction).",example:50},sources:{required:!0,type:"sources",doc:"Data source specifications.",example:{"mapbox-streets":{type:"vector",url:"mapbox://mapbox.mapbox-streets-v6"}}},sprite:{type:"string",doc:"A base URL for retrieving the sprite image and metadata. The extensions `.png`, `.json` and scale factor `@2x.png` will be automatically appended. This property is required if any layer uses the 'sprite-image' layout property.",example:"mapbox://sprites/mapbox/bright-v8"},glyphs:{type:"string",doc:"A URL template for loading signed-distance-field glyph sets in PBF format. The URL must include `{fontstack}` and `{range}` tokens. This property is required if any layer uses the 'text-field' layout property.",example:"mapbox://fonts/mapbox/{fontstack}/{range}.pbf"},transition:{type:"transition",doc:"A global transition definition to use as a default across properties.",example:{duration:300,delay:0}},layers:{required:!0,type:"array",value:"layer",doc:"Layers will be drawn in the order of this array.",example:[{id:"water",source:"mapbox-streets","source-layer":"water",type:"fill",paint:{"fill-color":"#00ffff"}}]}},sources:{"*":{type:"source",doc:"Specification of a data source. For vector and raster sources, either TileJSON or a URL to a TileJSON must be provided. For GeoJSON and video sources, a URL must be provided."}},source:["source_tile","source_geojson","source_video","source_image"],source_tile:{type:{required:!0,type:"enum",values:["vector","raster"],doc:"The data type of the tile source."},url:{type:"string",doc:"A URL to a TileJSON resource. Supported protocols are `http:`, `https:`, and `mapbox://<mapid>`."},tiles:{type:"array",value:"string",doc:"An array of one or more tile source URLs, as in the TileJSON spec."},minzoom:{type:"number","default":0,doc:"Minimum zoom level for which tiles are available, as in the TileJSON spec."},maxzoom:{type:"number","default":22,doc:"Maximum zoom level for which tiles are available, as in the TileJSON spec. Data from tiles at the maxzoom are used when displaying the map at higher zoom levels."},tileSize:{type:"number","default":512,units:"pixels",doc:"The minimum visual size to display tiles for this layer. Only configurable for raster layers."},"*":{type:"*",doc:"Other keys to configure the data source."}},source_geojson:{type:{required:!0,type:"enum",values:["geojson"],doc:"The data type of the GeoJSON source."
},data:{type:"*",doc:"A URL to a GeoJSON file, or inline GeoJSON."},maxzoom:{type:"number","default":14,doc:"Maximum zoom level at which to create vector tiles (higher means greater detail at high zoom levels)."},buffer:{type:"number","default":64,doc:"Tile buffer size on each side (higher means fewer rendering artifacts near tile edges but slower performance)."},tolerance:{type:"number","default":3,doc:"Douglas-Peucker simplification tolerance (higher means simpler geometries and faster performance)."},cluster:{type:"boolean","default":!1,doc:"If the data is a collection of point features, setting this to true clusters the points by radius into groups."},clusterRadius:{type:"number","default":400,doc:"Radius of each cluster when clustering points, relative to 4096 tile."},clusterMaxZoom:{type:"number",doc:"Max zoom to cluster points on. Defaults to one zoom less than maxzoom (so that last zoom features are not clustered)."}},source_video:{type:{required:!0,type:"enum",values:["video"],doc:"The data type of the video source."},urls:{required:!0,type:"array",value:"string",doc:"URLs to video content in order of preferred format."},coordinates:{required:!0,doc:"Corners of video specified in longitude, latitude pairs.",type:"array",length:4,value:{type:"array",length:2,value:"number",doc:"A single longitude, latitude pair."}}},source_image:{type:{required:!0,type:"enum",values:["image"],doc:"The data type of the image source."},url:{required:!0,type:"string",doc:"URL that points to an image"},coordinates:{required:!0,doc:"Corners of image specified in longitude, latitude pairs.",type:"array",length:4,value:{type:"array",length:2,value:"number",doc:"A single longitude, latitude pair."}}},layer:{id:{type:"string",doc:"Unique layer name.",required:!0},type:{type:"enum",values:["fill","line","symbol","circle","raster","background"],doc:"Rendering type of this layer."},metadata:{type:"*",doc:"Arbitrary properties useful to track with the layer, but do not influence rendering. Properties should be prefixed to avoid collisions, like 'mapbox:'."},ref:{type:"string",doc:"References another layer to copy `type`, `source`, `source-layer`, `minzoom`, `maxzoom`, `filter`, and `layout` properties from. This allows the layers to share processing and be more efficient."},source:{type:"string",doc:"Name of a source description to be used for this layer."},"source-layer":{type:"string",doc:"Layer to use from a vector tile source. Required if the source supports multiple layers."},minzoom:{type:"number",minimum:0,maximum:22,doc:"The minimum zoom level on which the layer gets parsed and appears on."},maxzoom:{type:"number",minimum:0,maximum:22,doc:"The maximum zoom level on which the layer gets parsed and appears on."},interactive:{type:"boolean",doc:"Enable querying of feature data from this layer for interactivity.","default":!1},filter:{type:"filter",doc:"A expression specifying conditions on source features. Only features that match the filter are displayed."},layout:{type:"layout",doc:"Layout properties for the layer."},paint:{type:"paint",doc:"Default paint properties for this layer."},"paint.*":{type:"paint",doc:"Class-specific paint properties for this layer. The class name is the part after the first dot."}},layout:["layout_fill","layout_line","layout_circle","layout_symbol","layout_raster","layout_background"],layout_background:{visibility:{type:"enum","function":"piecewise-constant","zoom-function":!0,values:["visible","none"],"default":"visible",doc:"The display of this layer. `none` hides this layer.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}}},layout_fill:{visibility:{type:"enum","function":"piecewise-constant","zoom-function":!0,values:["visible","none"],"default":"visible",doc:"The display of this layer. `none` hides this layer.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}}},layout_circle:{visibility:{type:"enum","function":"piecewise-constant","zoom-function":!0,values:["visible","none"],"default":"visible",doc:"The display of this layer. `none` hides this layer.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}}},layout_line:{"line-cap":{type:"enum","function":"piecewise-constant","zoom-function":!0,"property-function":!0,values:["butt","round","square"],"default":"butt",doc:"The display of line endings.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"line-join":{type:"enum","function":"piecewise-constant","zoom-function":!0,"property-function":!0,values:["bevel","round","miter"],"default":"miter",doc:"The display of lines when joining.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"line-miter-limit":{type:"number","default":2,"function":"interpolated","zoom-function":!0,"property-function":!0,doc:"Used to automatically convert miter joins to bevel joins for sharp angles.",requires:[{"line-join":"miter"}],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"line-round-limit":{type:"number","default":1.05,"function":"interpolated","zoom-function":!0,"property-function":!0,doc:"Used to automatically convert round joins to miter joins for shallow angles.",requires:[{"line-join":"round"}],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},visibility:{type:"enum","function":"piecewise-constant","zoom-function":!0,values:["visible","none"],"default":"visible",doc:"The display of this layer. `none` hides this layer.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}}},layout_symbol:{"symbol-placement":{type:"enum","function":"piecewise-constant","zoom-function":!0,"property-function":!0,values:["point","line"],"default":"point",doc:"Label placement relative to its geometry. `line` can only be used on LineStrings and Polygons.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"symbol-spacing":{type:"number","default":250,minimum:1,"function":"interpolated","zoom-function":!0,"property-function":!0,units:"pixels",doc:"Distance between two symbol anchors.",requires:[{"symbol-placement":"line"}],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"symbol-avoid-edges":{type:"boolean","function":"piecewise-constant","zoom-function":!0,"property-function":!0,"default":!1,doc:"If true, the symbols will not cross tile edges to avoid mutual collisions. Recommended in layers that don't have enough padding in the vector tile to prevent collisions, or if it is a point symbol layer placed after a line symbol layer.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"icon-allow-overlap":{type:"boolean","function":"piecewise-constant","zoom-function":!0,"property-function":!0,"default":!1,doc:"If true, the icon will be visible even if it collides with other previously drawn symbols.",requires:["icon-image"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"icon-ignore-placement":{type:"boolean","function":"piecewise-constant","zoom-function":!0,"property-function":!0,"default":!1,doc:"If true, other symbols can be visible even if they collide with the icon.",requires:["icon-image"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"icon-optional":{type:"boolean","function":"piecewise-constant","zoom-function":!0,"property-function":!0,"default":!1,doc:"If true, text will display without their corresponding icons when the icon collides with other symbols and the text does not.",requires:["icon-image","text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"icon-rotation-alignment":{type:"enum","function":"piecewise-constant","zoom-function":!0,"property-function":!0,values:["map","viewport"],"default":"viewport",doc:"Orientation of icon when map is rotated.",requires:["icon-image"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"icon-size":{type:"number","default":1,minimum:0,"function":"interpolated","zoom-function":!0,"property-function":!0,doc:"Scale factor for icon. 1 is original size, 3 triples the size.",requires:["icon-image"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"icon-text-fit":{type:"enum","function":"piecewise-constant","zoom-function":!0,"property-function":!1,values:["none","both","width","height"],"default":"none",doc:"Position and scale an icon by the its corresponding text.",requires:["icon-image","text-field"],"sdk-support":{basic:{}}},"icon-text-fit-padding":{type:"array",value:"number",length:4,"default":[0,0,0,0],units:"pixels","function":"interpolated","zoom-function":!0,"property-function":!0,doc:"Size of padding area around the text-fit size in clockwise order: top, right, bottom, left.",requires:["icon-image","icon-text-fit","text-field"],"sdk-support":{basic:{}}},"icon-image":{type:"string","function":"piecewise-constant","zoom-function":!0,"property-function":!0,doc:"A string with {tokens} replaced, referencing the data property to pull from.",tokens:!0,"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"icon-rotate":{type:"number","default":0,period:360,"function":"interpolated","zoom-function":!0,"property-function":!0,units:"degrees",doc:"Rotates the icon clockwise.",requires:["icon-image"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"icon-padding":{type:"number","default":2,minimum:0,"function":"interpolated","zoom-function":!0,"property-function":!0,units:"pixels",doc:"Size of the additional area around the icon bounding box used for detecting symbol collisions.",requires:["icon-image"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"icon-keep-upright":{type:"boolean","function":"piecewise-constant","zoom-function":!0,"property-function":!0,"default":!1,doc:"If true, the icon may be flipped to prevent it from being rendered upside-down.",requires:["icon-image",{"icon-rotation-alignment":"map"},{"symbol-placement":"line"}],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"icon-offset":{type:"array",value:"number",length:2,"default":[0,0],"function":"interpolated","zoom-function":!0,"property-function":!0,doc:"Offset distance of icon from its anchor. Positive values indicate right and down, while negative values indicate left and up.",requires:["icon-image"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-pitch-alignment":{type:"enum","function":"piecewise-constant","zoom-function":!0,"property-function":!0,values:["map","viewport"],doc:"Aligns text to the plane of the `viewport` or the `map` when the map is pitched. Matches `text-rotation-alignment` if unspecified.",requires:["text-field"],"sdk-support":{basic:{}}},"text-rotation-alignment":{type:"enum","function":"piecewise-constant","zoom-function":!0,"property-function":!0,values:["map","viewport"],"default":"viewport",doc:"Orientation of text when map is rotated.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-field":{type:"string","function":"piecewise-constant","zoom-function":!0,"property-function":!0,"default":"",tokens:!0,doc:"Value to use for a text label. Feature properties are specified using tokens like {field_name}.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-font":{type:"array",value:"string","function":"piecewise-constant","zoom-function":!0,"property-function":!0,"default":["Open Sans Regular","Arial Unicode MS Regular"],doc:"Font stack to use for displaying text.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-size":{type:"number","default":16,minimum:0,units:"pixels","function":"interpolated","zoom-function":!0,"property-function":!0,doc:"Font size.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-max-width":{type:"number","default":10,minimum:0,units:"em","function":"interpolated","zoom-function":!0,"property-function":!0,doc:"The maximum line width for text wrapping.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-line-height":{type:"number","default":1.2,units:"em","function":"interpolated","zoom-function":!0,"property-function":!0,doc:"Text leading value for multi-line text.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-letter-spacing":{type:"number","default":0,units:"em","function":"interpolated","zoom-function":!0,"property-function":!0,doc:"Text tracking amount.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-justify":{type:"enum","function":"piecewise-constant","zoom-function":!0,"property-function":!0,values:["left","center","right"],"default":"center",doc:"Text justification options.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-anchor":{type:"enum","function":"piecewise-constant","zoom-function":!0,"property-function":!0,values:["center","left","right","top","bottom","top-left","top-right","bottom-left","bottom-right"],"default":"center",doc:"Part of the text placed closest to the anchor.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-max-angle":{type:"number","default":45,units:"degrees","function":"interpolated","zoom-function":!0,"property-function":!0,doc:"Maximum angle change between adjacent characters.",requires:["text-field",{"symbol-placement":"line"}],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-rotate":{type:"number","default":0,period:360,units:"degrees","function":"interpolated","zoom-function":!0,"property-function":!0,doc:"Rotates the text clockwise.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-padding":{type:"number","default":2,minimum:0,units:"pixels","function":"interpolated","zoom-function":!0,"property-function":!0,doc:"Size of the additional area around the text bounding box used for detecting symbol collisions.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-keep-upright":{type:"boolean","function":"piecewise-constant","zoom-function":!0,"property-function":!0,"default":!0,doc:"If true, the text may be flipped vertically to prevent it from being rendered upside-down.",requires:["text-field",{"text-rotation-alignment":"map"},{"symbol-placement":"line"}],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-transform":{type:"enum","function":"piecewise-constant","zoom-function":!0,"property-function":!0,values:["none","uppercase","lowercase"],"default":"none",doc:"Specifies how to capitalize text, similar to the CSS `text-transform` property.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-offset":{type:"array",doc:"Offset distance of text from its anchor. Positive values indicate right and down, while negative values indicate left and up.",value:"number",units:"ems","function":"interpolated","zoom-function":!0,"property-function":!0,length:2,"default":[0,0],requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-allow-overlap":{type:"boolean","function":"piecewise-constant","zoom-function":!0,"property-function":!0,"default":!1,doc:"If true, the text will be visible even if it collides with other previously drawn symbols.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-ignore-placement":{type:"boolean","function":"piecewise-constant","zoom-function":!0,"property-function":!0,"default":!1,doc:"If true, other symbols can be visible even if they collide with the text.",requires:["text-field"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"text-optional":{type:"boolean","function":"piecewise-constant","zoom-function":!0,"property-function":!0,"default":!1,doc:"If true, icons will display without their corresponding text when the text collides with other symbols and the icon does not.",requires:["text-field","icon-image"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},visibility:{type:"enum","function":"piecewise-constant","zoom-function":!0,values:["visible","none"],"default":"visible",doc:"The display of this layer. `none` hides this layer.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}}},layout_raster:{visibility:{type:"enum","function":"piecewise-constant","zoom-function":!0,values:["visible","none"],"default":"visible",doc:"The display of this layer. `none` hides this layer.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}}},filter:{type:"array",value:"*",doc:"A filter selects specific features from a layer."},filter_operator:{type:"enum",values:["==","!=",">",">=","<","<=","in","!in","all","any","none","has","!has"],doc:"The filter operator."},geometry_type:{type:"enum",values:["Point","LineString","Polygon"],doc:"The geometry type for the filter to select."},color_operation:{type:"enum",values:["lighten","saturate","spin","fade","mix"],doc:"A color operation to apply."},"function":{stops:{type:"array",required:!0,doc:"An array of stops.",value:"function_stop"},base:{type:"number","default":1,minimum:0,doc:"The exponential base of the interpolation curve. It controls the rate at which the result increases. Higher values make the result increase more towards the high end of the range. With `1` the stops are interpolated linearly."},property:{type:"string",doc:"The name of a global property or feature property to use as the function input.","default":"$zoom"},type:{type:"enum",values:["exponential","interval","categorical"],doc:"The interpolation strategy to use in function evaluation.","default":"exponential"}},function_stop:{type:"array",minimum:0,maximum:22,value:["number","color"],length:2,doc:"Zoom level and value pair."},paint:["paint_fill","paint_line","paint_circle","paint_symbol","paint_raster","paint_background"],paint_fill:{"fill-antialias":{type:"boolean","function":"piecewise-constant","zoom-function":!0,"property-function":!0,"default":!0,doc:"Whether or not the fill should be antialiased.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"fill-opacity":{type:"number","function":"interpolated","zoom-function":!0,"property-function":!0,"default":1,minimum:0,maximum:1,doc:"The opacity of the entire fill layer. In contrast to the fill-color, this value will also affect the 1px stroke around the fill, if the stroke is used.",transition:!0,"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"fill-color":{type:"color","default":"#000000",doc:"The color of the filled part of this layer. This color can be specified as rgba with an alpha component and the color's opacity will not affect the opacity of the 1px stroke, if it is used.","function":"interpolated","zoom-function":!0,"property-function":!0,transition:!0,requires:[{"!":"fill-pattern"}],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"},"property-function":{js:"0.19.0"}}},"fill-outline-color":{type:"color",doc:"The outline color of the fill. Matches the value of `fill-color` if unspecified.","function":"interpolated","zoom-function":!0,"property-function":!0,transition:!0,requires:[{"!":"fill-pattern"},{"fill-antialias":!0}],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"},"property-function":{js:"0.19.0"}}},"fill-translate":{type:"array",value:"number",length:2,"default":[0,0],"function":"interpolated","zoom-function":!0,"property-function":!0,transition:!0,units:"pixels",doc:"The geometry's offset. Values are [x, y] where negatives indicate left and up, respectively.","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"fill-translate-anchor":{type:"enum","function":"piecewise-constant","zoom-function":!0,"property-function":!0,values:["map","viewport"],doc:"Control whether the translation is relative to the map (north) or viewport (screen)","default":"map",requires:["fill-translate"],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"fill-pattern":{type:"string","function":"piecewise-constant","zoom-function":!0,"property-function":!0,transition:!0,doc:"Name of image in sprite to use for drawing image fills. For seamless patterns, image width and height must be a factor of two (2, 4, 8, ..., 512).","sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}}},paint_line:{"line-opacity":{type:"number",doc:"The opacity at which the line will be drawn.","function":"interpolated","zoom-function":!0,"property-function":!0,"default":1,minimum:0,maximum:1,transition:!0,"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"line-color":{type:"color",doc:"The color with which the line will be drawn.","default":"#000000","function":"interpolated","zoom-function":!0,"property-function":!0,transition:!0,requires:[{"!":"line-pattern"}],"sdk-support":{basic:{js:"0.10.0",ios:"2.0.0",android:"2.0.1"}}},"line-translate":{type:"array",value:"number",length:2,"default":[0,0],"function":"interpolated","zoom-function":!0,"property-function":!0,transition:!0,units:"pixels",doc:"The geometry's offset. 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t.yaxis1);var n=C.Axes.list({_fullLayout:t});for(e=0;e<n.length;e++){var i=n[e];i.anchor&&"free"!==i.anchor&&(i.anchor=C.Axes.cleanId(i.anchor)),i.overlaying&&(i.overlaying=C.Axes.cleanId(i.overlaying)),i.type||(i.isdate?i.type="date":i.islog?i.type="log":i.isdate===!1&&i.islog===!1&&(i.type="linear")),"withzero"!==i.autorange&&"tozero"!==i.autorange||(i.autorange=!0,i.rangemode="tozero"),delete i.islog,delete i.isdate,delete i.categories,f(i,"domain")&&delete i.domain,void 0!==i.autotick&&(void 0===i.tickmode&&(i.tickmode=i.autotick?"auto":"linear"),delete i.autotick)}void 0===t.annotations||Array.isArray(t.annotations)||(R.warn("Annotations must be an array."),delete t.annotations);var o=(t.annotations||[]).length;for(e=0;o>e;e++){var a=t.annotations[e];a.ref&&("paper"===a.ref?(a.xref="paper",a.yref="paper"):"data"===a.ref&&(a.xref="x",a.yref="y"),delete a.ref),u(a,"xref"),u(a,"yref")}void 0===t.shapes||Array.isArray(t.shapes)||(R.warn("Shapes must be an array."),delete t.shapes);var s=(t.shapes||[]).length;for(e=0;s>e;e++){var l=t.shapes[e];u(l,"xref"),u(l,"yref")}var c=t.legend;c&&(c.x>3?(c.x=1.02,c.xanchor="left"):c.x<-2&&(c.x=-.02,c.xanchor="right"),c.y>3?(c.y=1.02,c.yanchor="bottom"):c.y<-2&&(c.y=-.02,c.yanchor="top")),"rotate"===t.dragmode&&(t.dragmode="orbit"),t.scene1&&(t.scene||(t.scene=t.scene1),delete t.scene1);var h=O.getSubplotIds(t,"gl3d");for(e=0;e<h.length;e++){var p=t[h[e]],d=p.cameraposition;if(Array.isArray(d)&&4===d[0].length){var m=d[0],g=d[1],v=d[2],y=I([],m),b=[];for(r=0;3>r;++r)b[r]=g[e]+v*y[2+4*r];p.camera={eye:{x:b[0],y:b[1],z:b[2]},center:{x:g[0],y:g[1],z:g[2]},up:{x:y[1],y:y[5],z:y[9]}},delete p.cameraposition}}return N.clean(t),t}function u(t,e){var r=t[e],n=e.charAt(0);r&&"paper"!==r&&(t[e]=C.Axes.cleanId(r,n))}function c(t,e){for(var r=[],n=(t.concat(Array.isArray(e)?e:[]).filter(function(t){return"uid"in t}).map(function(t){return t.uid})),i=0;i<t.length;i++){var o,a=t[i];if(!("uid"in a)||-1!==r.indexOf(a.uid)){var s;for(o=0;100>o&&(s=R.randstr(n),-1!==r.indexOf(s));o++);a.uid=R.randstr(n),n.push(a.uid)}if(r.push(a.uid),"histogramy"===a.type&&"xbins"in a&&!("ybins"in a)&&(a.ybins=a.xbins,delete a.xbins),a.error_y&&"opacity"in a.error_y){var l=N.defaults,u=a.error_y.color||(O.traceIs(a,"bar")?N.defaultLine:l[i%l.length]);a.error_y.color=N.addOpacity(N.rgb(u),N.opacity(u)*a.error_y.opacity),delete a.error_y.opacity}if("bardir"in a&&("h"!==a.bardir||!O.traceIs(a,"bar")&&"histogram"!==a.type.substr(0,9)||(a.orientation="h",_(a)),delete a.bardir),"histogramy"===a.type&&_(a),"histogramx"!==a.type&&"histogramy"!==a.type||(a.type="histogram"),"scl"in a&&(a.colorscale=a.scl,delete a.scl),"reversescl"in a&&(a.reversescale=a.reversescl,delete a.reversescl),a.xaxis&&(a.xaxis=C.Axes.cleanId(a.xaxis,"x")),a.yaxis&&(a.yaxis=C.Axes.cleanId(a.yaxis,"y")),O.traceIs(a,"gl3d")&&a.scene&&(a.scene=O.subplotsRegistry.gl3d.cleanId(a.scene)),O.traceIs(a,"pie")||(Array.isArray(a.textposition)?a.textposition=a.textposition.map(h):a.textposition&&(a.textposition=h(a.textposition))),O.traceIs(a,"2dMap")&&("YIGnBu"===a.colorscale&&(a.colorscale="YlGnBu"),"YIOrRd"===a.colorscale&&(a.colorscale="YlOrRd")),O.traceIs(a,"markerColorscale")&&a.marker){var c=a.marker;"YIGnBu"===c.colorscale&&(c.colorscale="YlGnBu"),"YIOrRd"===c.colorscale&&(c.colorscale="YlOrRd")}if("surface"===a.type&&R.isPlainObject(a.contours)){var p=["x","y","z"];for(o=0;o<p.length;o++){var d=a.contours[p[o]];R.isPlainObject(d)&&(d.highlightColor&&(d.highlightcolor=d.highlightColor,delete d.highlightColor),d.highlightWidth&&(d.highlightwidth=d.highlightWidth,delete d.highlightWidth))}}f(a,"line")&&delete a.line,"marker"in a&&(f(a.marker,"line")&&delete a.marker.line,f(a,"marker")&&delete a.marker),N.clean(a)}}function h(t){var e="middle",r="center";return-1!==t.indexOf("top")?e="top":-1!==t.indexOf("bottom")&&(e="bottom"),-1!==t.indexOf("left")?r="left":-1!==t.indexOf("right")&&(r="right"),e+" "+r}function f(t,e){return e in t&&"object"==typeof t[e]&&0===Object.keys(t[e]).length}function p(t){var e,r=C.Axes.list(t),n=t._fullData,i=t._fullLayout,o=t.calcdata=new Array(n.length);for(t.firstscatter=!0,t.numboxes=0,t._hmpixcount=0,t._hmlumcount=0,i._piecolormap={},i._piedefaultcolorcount=0,e=0;e<r.length;e++)r[e]._categories=r[e]._initialCategories.slice();for(e=0;e<n.length;e++){var a=n[e],s=a._module,l=[];s&&a.visible===!0&&s.calc&&(l=s.calc(t,a)),Array.isArray(l)&&l[0]||(l=[{x:!1,y:!1}]),l[0].t||(l[0].t={}),l[0].trace=a,o[e]=l}}function d(t,e){var r,n,i=e+1,o=[];for(r=0;r<t.length;r++)n=t[r],0>n?o.push(i+n):o.push(n);return o}function m(t,e,r){var n,i;for(n=0;n<e.length;n++){if(i=e[n],i!==parseInt(i,10))throw new Error("all values in "+r+" must be integers");if(i>=t.data.length||i<-t.data.length)throw new Error(r+" must be valid indices for gd.data.");if(e.indexOf(i,n+1)>-1||i>=0&&e.indexOf(-t.data.length+i)>-1||0>i&&e.indexOf(t.data.length+i)>-1)throw new Error("each index in "+r+" must be unique.")}}function g(t,e,r){if(!Array.isArray(t.data))throw new Error("gd.data must be an array.");if("undefined"==typeof e)throw new Error("currentIndices is a required argument.");if(Array.isArray(e)||(e=[e]),m(t,e,"currentIndices"),"undefined"==typeof r||Array.isArray(r)||(r=[r]),"undefined"!=typeof r&&m(t,r,"newIndices"),"undefined"!=typeof r&&e.length!==r.length)throw new Error("current and new indices must be of equal length.")}function v(t,e,r){var n,i;if(!Array.isArray(t.data))throw new Error("gd.data must be an array.");if("undefined"==typeof e)throw new Error("traces must be defined.");for(Array.isArray(e)||(e=[e]),n=0;n<e.length;n++)if(i=e[n],"object"!=typeof i||Array.isArray(i)||null===i)throw new Error("all values in traces array must be non-array objects");if("undefined"==typeof r||Array.isArray(r)||(r=[r]),"undefined"!=typeof r&&r.length!==e.length)throw new Error("if indices is specified, traces.length must equal indices.length")}function y(t,e,r,n){var i=R.isPlainObject(n);if(!Array.isArray(t.data))throw new Error("gd.data must be an array");if(!R.isPlainObject(e))throw new Error("update must be a key:value object");if("undefined"==typeof r)throw new Error("indices must be an integer or array of integers");m(t,r,"indices");for(var o in e){if(!Array.isArray(e[o])||e[o].length!==r.length)throw new Error("attribute "+o+" must be an array of length equal to indices array length");if(i&&(!(o in n)||!Array.isArray(n[o])||n[o].length!==e[o].length))throw new Error("when maxPoints is set as a key:value object it must contain a 1:1 corrispondence with the keys and number of traces in the update object")}}function b(t,e,r,n){var i,o,a,s,l,u=R.isPlainObject(n),c=[];Array.isArray(r)||(r=[r]),r=d(r,t.data.length-1);for(var h in e)for(var f=0;f<r.length;f++){if(i=t.data[r[f]],a=R.nestedProperty(i,h),o=a.get(),s=e[h][f],!Array.isArray(s))throw new Error("attribute: "+h+" index: "+f+" must be an array");if(!Array.isArray(o))throw new Error("cannot extend missing or non-array attribute: "+h);l=u?n[h][f]:n,P(l)||(l=-1),c.push({prop:a,target:o,insert:s,maxp:Math.floor(l)})}return c}function x(t,e,r,n,i,o){y(t,e,r,n);for(var a,s,l,u=b(t,e,r,n),c=[],h={},f={},p=0;p<u.length;p++)s=u[p].prop,l=u[p].maxp,a=i(u[p].target,u[p].insert),l>=0&&l<a.length&&(c=o(a,l)),l=u[p].target.length,s.set(a),Array.isArray(h[s.astr])||(h[s.astr]=[]),Array.isArray(f[s.astr])||(f[s.astr]=[]),h[s.astr].push(c),f[s.astr].push(l);return{update:h,maxPoints:f}}function _(t){var e;if(R.swapAttrs(t,["?","?0","d?","?bins","nbins?","autobin?","?src","error_?"]),Array.isArray(t.z)&&Array.isArray(t.z[0])&&(t.transpose?delete t.transpose:t.transpose=!0),t.error_x&&t.error_y){var r=t.error_y,n="copy_ystyle"in r?r.copy_ystyle:!(r.color||r.thickness||r.width);R.swapAttrs(t,["error_?.copy_ystyle"]),n&&R.swapAttrs(t,["error_?.color","error_?.thickness","error_?.width"])}if(t.hoverinfo){var i=t.hoverinfo.split("+");for(e=0;e<i.length;e++)"x"===i[e]?i[e]="y":"y"===i[e]&&(i[e]="x");t.hoverinfo=i.join("+")}}function w(t){var e,r={left:0,right:0,bottom:0,top:0};if(t)for(e in t)t.hasOwnProperty(e)&&(r.left+=t[e].left||0,r.right+=t[e].right||0,r.bottom+=t[e].bottom||0,r.top+=t[e].top||0);return r}function M(t,e){var r,n,i,o=t._fullLayout,a=t._context;if(t.emit("plotly_autosize"),t._context.fillFrame)i=window.innerWidth,n=window.innerHeight,document.body.style.overflow="hidden";else if(P(a.frameMargins)&&a.frameMargins>0){var s=w(t._boundingBoxMargins),l=s.left+s.right,u=s.bottom+s.top,c=o._container.node().getBoundingClientRect(),h=1-2*a.frameMargins;i=Math.round(h*(c.width-l)),n=Math.round(h*(c.height-u))}else r=window.getComputedStyle(t),n=parseFloat(r.height)||o.height,i=parseFloat(r.width)||o.width;return Math.abs(o.width-i)>1||Math.abs(o.height-n)>1?(o.height=t.layout.height=n,o.width=t.layout.width=i):"initial"!==o.autosize&&(delete e.autosize,o.autosize=t.layout.autosize=!0),O.sanitizeMargins(o),e}function k(t){var e=z.select(t),r=t._fullLayout;if(r._container=e.selectAll(".plot-container").data([0]),r._container.enter().insert("div",":first-child").classed("plot-container",!0).classed("plotly",!0),r._paperdiv=r._container.selectAll(".svg-container").data([0]),r._paperdiv.enter().append("div").classed("svg-container",!0).style("position","relative"),"initial"===r.autosize&&(M(t,{}),r.autosize=!0,t.layout.autosize=!0),r._glcontainer=r._paperdiv.selectAll(".gl-container").data([0]),r._glcontainer.enter().append("div").classed("gl-container",!0),r._geocontainer=r._paperdiv.selectAll(".geo-container").data([0]),r._geocontainer.enter().append("div").classed("geo-container",!0),r._paperdiv.selectAll(".main-svg").remove(),r._paper=r._paperdiv.insert("svg",":first-child").classed("main-svg",!0),r._toppaper=r._paperdiv.append("svg").classed("main-svg",!0),!r._uid){var n=[];z.selectAll("defs").each(function(){this.id&&n.push(this.id.split("-")[1])}),r._uid=R.randstr(n)}r._paperdiv.selectAll(".main-svg").attr(Q.svgAttrs),r._defs=r._paper.append("defs").attr("id","defs-"+r._uid),r._topdefs=r._toppaper.append("defs").attr("id","topdefs-"+r._uid),r._draggers=r._paper.append("g").classed("draglayer",!0);var i=r._paper.append("g").classed("layer-below",!0);r._imageLowerLayer=i.append("g").classed("imagelayer",!0),r._shapeLowerLayer=i.append("g").classed("shapelayer",!0);var o=C.Axes.getSubplots(t);o.join("")!==Object.keys(t._fullLayout._plots||{}).join("")&&A(t,o),r._has("cartesian")&&T(t,o),r._ternarylayer=r._paper.append("g").classed("ternarylayer",!0);var a=r._paper.selectAll(".layer-subplot");r._imageSubplotLayer=a.selectAll(".imagelayer"),r._shapeSubplotLayer=a.selectAll(".shapelayer");var s=r._paper.append("g").classed("layer-above",!0);r._imageUpperLayer=s.append("g").classed("imagelayer",!0),r._shapeUpperLayer=s.append("g").classed("shapelayer",!0),r._pielayer=r._paper.append("g").classed("pielayer",!0),r._glimages=r._paper.append("g").classed("glimages",!0),r._geoimages=r._paper.append("g").classed("geoimages",!0),r._infolayer=r._toppaper.append("g").classed("infolayer",!0),r._zoomlayer=r._toppaper.append("g").classed("zoomlayer",!0),r._hoverlayer=r._toppaper.append("g").classed("hoverlayer",!0),t.emit("plotly_framework");var l=R.syncOrAsync([E,function(){return C.Axes.doTicks(t,"redraw")},F.init],t);return l&&l.then&&t._promises.push(l),l}function A(t,e){function r(e,r){return function(){return C.Axes.getFromId(t,e,r)}}for(var n,i,o=t._fullLayout._plots={},a=0;a<e.length;a++)n=e[a],i=o[n]={},i.id=n,i.x=r(n,"x"),i.y=r(n,"y"),i.xaxis=i.x(),i.yaxis=i.y()}function T(t,e){function r(t){t.append("g").classed("imagelayer",!0),t.append("g").classed("maplayer",!0),t.append("g").classed("barlayer",!0),t.append("g").classed("boxlayer",!0),t.append("g").classed("scatterlayer",!0)}var n=t._fullLayout,i=[];n._paper.selectAll("g.subplot").data(e).enter().append("g").classed("subplot",!0).each(function(o){var a=n._plots[o],s=a.plotgroup=z.select(this).classed(o,!0),l=a.xaxis,u=a.yaxis;a.overlays=[];var c=C.Axes.getFromId(t,l.overlaying)||l;c!==l&&c.overlaying&&(c=l,l.overlaying=!1);var h=C.Axes.getFromId(t,u.overlaying)||u;h!==u&&h.overlaying&&(h=u,u.overlaying=!1);var f=c._id+h._id;if(f!==o&&-1!==e.indexOf(f))a.mainplot=f,i.push(a),l.domain=c.domain.slice(),u.domain=h.domain.slice();else{a.bg=s.append("rect").style("stroke-width",0);var p=s.append("g").classed("layer-subplot",!0);a.shapelayer=p.append("g").classed("shapelayer",!0),a.imagelayer=p.append("g").classed("imagelayer",!0),a.gridlayer=s.append("g"),a.overgrid=s.append("g"),a.zerolinelayer=s.append("g"),a.overzero=s.append("g"),a.plot=s.append("g").call(r),a.overplot=s.append("g"),a.xlines=s.append("path"),a.ylines=s.append("path"),a.overlines=s.append("g"),a.xaxislayer=s.append("g"),a.yaxislayer=s.append("g"),a.overaxes=s.append("g")}a.draglayer=n._draggers.append("g")}),i.forEach(function(t){var e=n._plots[t.mainplot];e.overlays.push(t),t.gridlayer=e.overgrid.append("g"),t.zerolinelayer=e.overzero.append("g"),t.plot=e.overplot.append("g").call(r),t.xlines=e.overlines.append("path"),t.ylines=e.overlines.append("path"),t.xaxislayer=e.overaxes.append("g"),t.yaxislayer=e.overaxes.append("g")}),e.forEach(function(t){var e=n._plots[t];e.xlines.style("fill","none").classed("crisp",!0),e.ylines.style("fill","none").classed("crisp",!0)})}function E(t){return R.syncOrAsync([O.doAutoMargin,S],t)}function S(t){var e,r=t._fullLayout,n=r._size,i=C.Axes.list(t);for(e=0;e<i.length;e++)i[e]._linepositions={};r._paperdiv.style({width:r.width+"px",height:r.height+"px"}).selectAll(".main-svg").call(B.setSize,r.width,r.height),t._context.setBackground(t,r.paper_bgcolor);var o=[];return r._paper.selectAll("g.subplot").each(function(e){var i=r._plots[e],a=C.Axes.getFromId(t,e,"x"),s=C.Axes.getFromId(t,e,"y");a.setScale(),s.setScale(),i.bg&&i.bg.call(B.setRect,a._offset-n.p,s._offset-n.p,a._length+2*n.p,s._length+2*n.p).call(N.fill,r.plot_bgcolor),i.clipId="clip"+r._uid+e+"plot";var l=r._defs.selectAll("g.clips").selectAll("#"+i.clipId).data([0]);l.enter().append("clipPath").attr({"class":"plotclip",id:i.clipId}).append("rect"),l.selectAll("rect").attr({width:a._length,height:s._length}),i.plot.call(R.setTranslate,a._offset,s._offset),i.plot.call(B.setClipUrl,i.clipId);var 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title/.test(r.title)?"":r.title,d=0;2>=d;d+=2)this.labelEnable[p+d]=!1,this.labels[p+d]=a(n),this.labelColor[p+d]=s(r.titlefont.color),this.labelFont[p+d]=r.titlefont.family,this.labelSize[p+d]=r.titlefont.size,this.labelPad[p+d]=this.getLabelPad(e,r),this.tickEnable[p+d]=!1,this.tickColor[p+d]=s((r.tickfont||{}).color),this.tickAngle[p+d]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[p+d]=this.getTickPad(r),this.tickMarkLength[p+d]=0,this.tickMarkWidth[p+d]=r.tickwidth||0,this.tickMarkColor[p+d]=s(r.tickcolor),this.borderLineEnable[p+d]=!1,this.borderLineColor[p+d]=s(r.linecolor),this.borderLineWidth[p+d]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,i=r.mirror||!1,h=c?-1!==String(i).indexOf("all"):!!i,f=c?"allticks"===i:-1!==String(i).indexOf("ticks"),o?this.labelEnable[p]=!0:l&&(this.labelEnable[p+2]=!0),o?this.tickEnable[p]=r.showticklabels:l&&(this.tickEnable[p+2]=r.showticklabels),(o||h)&&(this.borderLineEnable[p]=r.showline),(l||h)&&(this.borderLineEnable[p+2]=r.showline),(o||f)&&(this.tickMarkLength[p]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[p+2]=this.getTickMarkLength(r)),this.gridLineEnable[p]=r.showgrid,this.gridLineColor[p]=s(r.gridcolor),this.gridLineWidth[p]=r.gridwidth,this.zeroLineEnable[p]=r.zeroline,this.zeroLineColor[p]=s(r.zerolinecolor),this.zeroLineWidth[p]=r.zerolinewidth}},l.hasSharedAxis=function(t){var e=this.scene,r=o.Plots.getSubplotIds(e.fullLayout,"gl2d"),n=o.Axes.findSubplotsWithAxis(r,t);return 0!==n.indexOf(e.id)},l.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},l.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},l.getLabelPad=function(t,e){var r=1.5,n=e.titlefont.size,i=e.showticklabels;return"xaxis"===t?"top"===e.side?-10+n*(r+(i?1:0)):-10+n*(r+(i?.5:0)):"yaxis"===t?"right"===e.side?10+n*(r+(i?1:.5)):10+n*(r+(i?.5:0)):void 0},l.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},l.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},e.exports=i},{"../../lib/html2unicode":590,"../../lib/str2rgbarray":603,"../../plotly":612}],648:[function(t,e,r){"use strict";var n=t("./scene2d"),i=t("../plots"),o=t("../../constants/xmlns_namespaces");r.name="gl2d",r.attr=["xaxis","yaxis"],r.idRoot=["x","y"],r.idRegex={x:/^x([2-9]|[1-9][0-9]+)?$/,y:/^y([2-9]|[1-9][0-9]+)?$/},r.attrRegex={x:/^xaxis([2-9]|[1-9][0-9]+)?$/,y:/^yaxis([2-9]|[1-9][0-9]+)?$/},r.attributes=t("../cartesian/attributes"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,o=i.getSubplotIds(e,"gl2d"),a=0;a<o.length;a++){var s=o[a],l=e._plots[s],u=i.getSubplotData(r,"gl2d",s),c=l._scene2d;void 0===c&&(c=new n({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),l._scene2d=c),c.plot(u,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var o=i.getSubplotIds(n,"gl2d"),a=0;a<o.length;a++){var s=o[a],l=n._plots[s];if(l._scene2d){var u=i.getSubplotData(t,"gl2d",s);0===u.length&&(l._scene2d.destroy(),delete n._plots[s])}}},r.toSVG=function(t){for(var e=t._fullLayout,r=i.getSubplotIds(e,"gl2d"),n=0;n<r.length;n++){var a=e._plots[r[n]],s=a._scene2d,l=s.toImage("png"),u=e._glimages.append("svg:image");u.attr({xmlns:o.svg,"xlink:href":l,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),s.destroy()}}},{"../../constants/xmlns_namespaces":579,"../cartesian/attributes":614,"../plots":671,"./scene2d":649}],649:[function(t,e,r){"use strict";function n(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.fullLayout=e,this.updateAxes(e),this.makeFramework(),this.glplotOptions=f(this),this.glplotOptions.merge(e),this.glplot=u(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=h(this.glplot,{innerFill:!1,outerFill:!0}),this.pickResult=null,this.bounds=[1/0,1/0,-(1/0),-(1/0)],this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw()}function i(t,e){for(var r=0;2>r;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var o=0;o<n.length;++o)if(n[o].x!==i[o].x)return!0}return!1}var o,a,s=t("../../plots/cartesian/axes"),l=t("../../plots/cartesian/graph_interact"),u=t("gl-plot2d"),c=t("gl-spikes2d"),h=t("gl-select-box"),f=t("./convert"),p=t("./camera"),d=t("../../lib/html2unicode"),m=t("../../lib/show_no_webgl_msg"),g=["xaxis","yaxis"];e.exports=n;var v=n.prototype;v.makeFramework=function(){if(this.staticPlot){if(!a){o=document.createElement("canvas");try{a=o.getContext("webgl",{preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0})}catch(t){throw new Error(["Error creating static canvas/context for image server"].join(" "))}}this.canvas=o,this.gl=a}else{var e,r=document.createElement("canvas"),n={premultipliedAlpha:!0};try{e=r.getContext("webgl",n)}catch(t){}if(!e)try{e=r.getContext("experimental-webgl",n)}catch(t){}e||m(this),this.canvas=r,this.gl=e}var i=this.canvas,s=this.pixelRatio,l=this.fullLayout;i.width=0|Math.ceil(s*l.width),i.height=0|Math.ceil(s*l.height),i.style.width="100%",i.style.height="100%",i.style.position="absolute",i.style.top="0px",i.style.left="0px",i.style["pointer-events"]="none";var u=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");u.style.position="absolute",u.style.top=u.style.left="0px",u.style.width=u.style.height="100%",u.style["z-index"]=20,u.style["pointer-events"]="none";var c=this.mouseContainer=document.createElement("div");c.style.position="absolute";var h=this.container;h.appendChild(i),h.appendChild(u),h.appendChild(c)},v.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(o),this.glplot.setDirty(),this.glplot.draw();var e=this.glplot.gl,r=e.drawingBufferWidth,n=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var i=new Uint8Array(r*n*4);e.readPixels(0,0,r,n,e.RGBA,e.UNSIGNED_BYTE,i);for(var a=0,s=n-1;s>a;++a,--s)for(var l=0;r>l;++l)for(var u=0;4>u;++u){var c=i[4*(r*a+l)+u];i[4*(r*a+l)+u]=i[4*(r*s+l)+u],i[4*(r*s+l)+u]=c}var h=document.createElement("canvas");h.width=r,h.height=n;var f=h.getContext("2d"),p=f.createImageData(r,n);p.data.set(i),f.putImageData(p,0,0);var d;switch(t){case"jpeg":d=h.toDataURL("image/jpeg");break;case"webp":d=h.toDataURL("image/webp");break;default:d=h.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(o),d},v.computeTickMarks=function(){this.xaxis._length=this.glplot.viewBox[2]-this.glplot.viewBox[0],this.yaxis._length=this.glplot.viewBox[3]-this.glplot.viewBox[1];for(var t=[s.calcTicks(this.xaxis),s.calcTicks(this.yaxis)],e=0;2>e;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=d(t[e][r].text+"").replace(/\n/g," ");return t},v.updateAxes=function(t){var e=s.subplotMatch,r="xaxis"+this.id.match(e)[1],n="yaxis"+this.id.match(e)[2];this.xaxis=t[r],this.yaxis=t[n]},v.updateFx=function(t){var e=this.fullLayout;e.dragmode=t.dragmode,e.hovermode=t.hovermode};var y=function(t){var e=t.xaxis.range,r=t.yaxis.range;t.graphDiv.layout.xaxis.autorange=t.xaxis.autorange,t.graphDiv.layout.xaxis.range=e.slice(0),t.graphDiv.layout.yaxis.autorange=t.yaxis.autorange,t.graphDiv.layout.yaxis.range=r.slice(0);var n={lastInputTime:t.camera.lastInputTime};n[t.xaxis._name]=e.slice(),n[t.yaxis._name]=r.slice(),t.graphDiv.emit("plotly_relayout",n)};v.cameraChanged=function(){var t=this.camera,e=this.xaxis.range,r=this.yaxis.range;this.glplot.setDataBox([e[0],r[0],e[1],r[1]]);var n=this.computeTickMarks(),o=this.glplotOptions.ticks;i(n,o)&&(this.glplotOptions.ticks=n,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),y(this))},v.destroy=function(){var t=this.traces;t&&Object.keys(t).map(function(e){t[e].dispose(),delete t[e]}),this.glplot.dispose(),this.staticPlot||this.container.removeChild(this.canvas),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.glplot=null,this.stopped=!0},v.plot=function(t,e,r){var n=this.glplot,i=this.pixelRatio;this.fullLayout=r,this.updateAxes(r),this.updateTraces(t,e);var o=r.width,a=r.height,l=0|Math.ceil(i*o),u=0|Math.ceil(i*a),c=this.canvas;c.width===l&&c.height===u||(c.width=l,c.height=u);var h=this.glplotOptions;h.merge(r),h.screenBox=[0,0,o,a];var f=r._size,p=this.xaxis.domain,d=this.yaxis.domain;
h.viewBox=[f.l+p[0]*f.w,f.b+d[0]*f.h,o-f.r-(1-p[1])*f.w,a-f.t-(1-d[1])*f.h],this.mouseContainer.style.width=f.w*(p[1]-p[0])+"px",this.mouseContainer.style.height=f.h*(d[1]-d[0])+"px",this.mouseContainer.height=f.h*(d[1]-d[0]),this.mouseContainer.style.left=f.l+p[0]*f.w+"px",this.mouseContainer.style.top=f.t+(1-d[1])*f.h+"px";var m=this.bounds;m[0]=m[1]=1/0,m[2]=m[3]=-(1/0);var v,y,b=Object.keys(this.traces);for(y=0;y<b.length;++y)for(var x=this.traces[b[y]],_=0;2>_;++_)m[_]=Math.min(m[_],x.bounds[_]),m[_+2]=Math.max(m[_+2],x.bounds[_+2]);for(y=0;2>y;++y)m[y]>m[y+2]&&(m[y]=-1,m[y+2]=1),v=this[g[y]],v._length=h.viewBox[y+2]-h.viewBox[y],s.doAutoRange(v);h.ticks=this.computeTickMarks();var w=this.xaxis.range,M=this.yaxis.range;h.dataBox=[w[0],M[0],w[1],M[1]],h.merge(r),n.update(h),this.glplot.draw()},v.updateTraces=function(t,e){var r,n,i,o=Object.keys(this.traces);t:for(r=0;r<o.length;r++){var a=o[r],s=this.traces[a];for(n=0;n<t.length;n++)if(i=t[n],i.uid===a&&i.type===s.type)continue t;s.dispose(),delete this.traces[a]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],u=this.traces[i.uid];u?u.update(i,l):(u=i._module.plot(this,i,l),this.traces[i.uid]=u)}},v.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=this.fullLayout;this.cameraChanged();var i=r.x*t.pixelRatio,o=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===n.dragmode)this.selectBox.enabled=!0,this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],t.setDirty();else{this.selectBox.enabled=!1;var a=n._size,s=this.xaxis.domain,u=this.yaxis.domain,c=t.pick(i/t.pixelRatio+a.l+s[0]*a.w,o/t.pixelRatio-(a.t+(1-u[1])*a.h));if(c&&n.hovermode){var h=c.object._trace.handlePick(c);if(h&&(!this.lastPickResult||this.lastPickResult.traceUid!==h.trace.uid||this.lastPickResult.dataCoord[0]!==h.dataCoord[0]||this.lastPickResult.dataCoord[1]!==h.dataCoord[1])){var f=h;this.lastPickResult={traceUid:h.trace?h.trace.uid:null,dataCoord:h.dataCoord.slice()},this.spikes.update({center:c.dataCoord}),f.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(c.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(c.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio];var p=f.hoverinfo;if("all"!==p){var d=p.split("+");-1===d.indexOf("x")&&(f.traceCoord[0]=void 0),-1===d.indexOf("y")&&(f.traceCoord[1]=void 0),-1===d.indexOf("z")&&(f.traceCoord[2]=void 0),-1===d.indexOf("text")&&(f.textLabel=void 0),-1===d.indexOf("name")&&(f.name=void 0)}l.loneHover({x:f.screenCoord[0],y:f.screenCoord[1],xLabel:this.hoverFormatter("xaxis",f.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",f.traceCoord[1]),zLabel:f.traceCoord[2],text:f.textLabel,name:f.name,color:f.color},{container:this.svgContainer})}}else!c&&this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,l.loneUnhover(this.svgContainer))}t.draw()}},v.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return s.tickText(r,r.c2l(e),"hover").text}}},{"../../lib/html2unicode":590,"../../lib/show_no_webgl_msg":601,"../../plots/cartesian/axes":615,"../../plots/cartesian/graph_interact":622,"./camera":646,"./convert":647,"gl-plot2d":176,"gl-select-box":206,"gl-spikes2d":226}],650:[function(t,e,r){"use strict";function n(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];"distanceLimits"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]),"zoomMin"in e&&(r[0]=e.zoomMin),"zoomMax"in e&&(r[1]=e.zoomMax);var n=o({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),l=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],u=0,c=t.clientWidth,h=t.clientHeight,f={keyBindingMode:"rotate",view:n,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:n.modes,tick:function(){var e=i(),r=this.delay,o=e-2*r;n.idle(e-r),n.recalcMatrix(o),n.flush(e-(100+2*r));for(var a=!0,s=n.computedMatrix,f=0;16>f;++f)a=a&&l[f]===s[f],l[f]=s[f];var p=t.clientWidth===c&&t.clientHeight===h;return c=t.clientWidth,h=t.clientHeight,a?!p:(u=Math.exp(n.computedRadius[0]),!0)},lookAt:function(t,e,r){n.lookAt(n.lastT(),t,e,r)},rotate:function(t,e,r){n.rotate(n.lastT(),t,e,r)},pan:function(t,e,r){n.pan(n.lastT(),t,e,r)},translate:function(t,e,r){n.translate(n.lastT(),t,e,r)}};Object.defineProperties(f,{matrix:{get:function(){return n.computedMatrix},set:function(t){return n.setMatrix(n.lastT(),t),n.computedMatrix},enumerable:!0},mode:{get:function(){return n.getMode()},set:function(t){var e=n.computedUp.slice(),r=n.computedEye.slice(),o=n.computedCenter.slice();if(n.setMode(t),"turntable"===t){var a=i();n._active.lookAt(a,r,o,e),n._active.lookAt(a+500,r,o,[0,0,1]),n._active.flush(a)}return n.getMode()},enumerable:!0},center:{get:function(){return n.computedCenter},set:function(t){return n.lookAt(n.lastT(),null,t),n.computedCenter},enumerable:!0},eye:{get:function(){return n.computedEye},set:function(t){return n.lookAt(n.lastT(),t),n.computedEye},enumerable:!0},up:{get:function(){return n.computedUp},set:function(t){return n.lookAt(n.lastT(),null,null,t),n.computedUp},enumerable:!0},distance:{get:function(){return u},set:function(t){return n.setDistance(n.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return n.getDistanceLimits(r)},set:function(t){return n.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",function(t){return t.preventDefault(),!1});var p=0,d=0;return a(t,function(e,r,o,a){var s="rotate"===f.keyBindingMode,l="pan"===f.keyBindingMode,c="zoom"===f.keyBindingMode,h=!!a.control,m=!!a.alt,g=!!a.shift,v=!!(1&e),y=!!(2&e),b=!!(4&e),x=1/t.clientHeight,_=x*(r-p),w=x*(o-d),M=f.flipX?1:-1,k=f.flipY?1:-1,A=i(),T=Math.PI*f.rotateSpeed;if((s&&v&&!h&&!m&&!g||v&&!h&&!m&&g)&&n.rotate(A,M*T*_,-k*T*w,0),(l&&v&&!h&&!m&&!g||y||v&&h&&!m&&!g)&&n.pan(A,-f.translateSpeed*_*u,f.translateSpeed*w*u,0),c&&v&&!h&&!m&&!g||b||v&&!h&&m&&!g){var E=-f.zoomSpeed*w/window.innerHeight*(A-n.lastT())*100;n.pan(A,0,0,u*(Math.exp(E)-1))}return p=r,d=o,!0}),s(t,function(t,e){var r=f.flipX?1:-1,o=f.flipY?1:-1,a=i();if(Math.abs(t)>Math.abs(e))n.rotate(a,0,0,-t*r*Math.PI*f.rotateSpeed/window.innerWidth);else{var s=-f.zoomSpeed*o*e/window.innerHeight*(a-n.lastT())/100;n.pan(a,0,0,u*(Math.exp(s)-1))}},!0),f}e.exports=n;var i=t("right-now"),o=t("3d-view"),a=t("mouse-change"),s=t("mouse-wheel")},{"3d-view":40,"mouse-change":444,"mouse-wheel":448,"right-now":458}],651:[function(t,e,r){"use strict";function n(t,e){for(var r=0;3>r;++r){var n=s[r];e[n]._gd=t}}var i=t("./scene"),o=t("../plots"),a=t("../../constants/xmlns_namespaces"),s=["xaxis","yaxis","zaxis"];r.name="gl3d",r.attr="scene",r.idRoot="scene",r.idRegex=/^scene([2-9]|[1-9][0-9]+)?$/,r.attrRegex=/^scene([2-9]|[1-9][0-9]+)?$/,r.attributes=t("./layout/attributes"),r.layoutAttributes=t("./layout/layout_attributes"),r.supplyLayoutDefaults=t("./layout/defaults"),r.plot=function(t){var e=t._fullLayout,r=t._fullData,a=o.getSubplotIds(e,"gl3d");e._paperdiv.style({width:e.width+"px",height:e.height+"px"}),t._context.setBackground(t,e.paper_bgcolor);for(var s=0;s<a.length;s++){var l=a[s],u=o.getSubplotData(r,"gl3d",l),c=e[l],h=c._scene;void 0===h&&(n(t,c),h=new 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e=t.substr(5);return"1"===e&&(e=""),"scene"+e}},r.setConvert=t("./set_convert")},{"../../constants/xmlns_namespaces":579,"../plots":671,"./layout/attributes":652,"./layout/defaults":656,"./layout/layout_attributes":657,"./scene":661,"./set_convert":662}],652:[function(t,e,r){"use strict";e.exports={scene:{valType:"subplotid",dflt:"scene"}}},{}],653:[function(t,e,r){"use strict";var n=t("../../../components/color"),i=t("../../cartesian/layout_attributes"),o=t("../../../lib/extend").extendFlat;e.exports={showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 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204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth}},{"../../../components/color":506,"../../../lib/extend":586,"../../cartesian/layout_attributes":624}],654:[function(t,e,r){"use strict";var n=t("tinycolor2").mix,i=t("../../../lib"),o=t("./axis_attributes"),a=t("../../cartesian/axis_defaults"),s=["xaxis","yaxis","zaxis"],l=function(){},u=13600/187;e.exports=function(t,e,r){function c(t,e){return i.coerce(h,f,o,t,e)}for(var h,f,p=0;p<s.length;p++){var d=s[p];h=t[d]||{},f={_id:d[0]+r.scene,_name:d},e[d]=f=a(h,f,c,{font:r.font,letter:d[0],data:r.data,showGrid:!0,bgColor:r.bgColor}),c("gridcolor",n(f.color,r.bgColor,u).toRgbString()),c("title",d[0]),f.setScale=l,c("showspikes")&&(c("spikesides"),c("spikethickness"),c("spikecolor",f.color)),c("showaxeslabels"),c("showbackground")&&c("backgroundcolor")}}},{"../../../lib":591,"../../cartesian/axis_defaults":616,"./axis_attributes":653,tinycolor2:477}],655:[function(t,e,r){"use strict";function n(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=l(this.tickPad),this._defaultLabelPad=l(this.labelPad),this._defaultLineTickLength=l(this.lineTickLength)}function i(t){var e=new n;return e.merge(t),e}var o=t("arraytools"),a=t("../../../lib/html2unicode"),s=t("../../../lib/str2rgbarray"),l=o.copy1D,u=["xaxis","yaxis","zaxis"],c=n.prototype;c.merge=function(t){for(var e=this,r=0;3>r;++r){var n=t[u[r]];e.labels[r]=a(n.title),"titlefont"in n&&(n.titlefont.color&&(e.labelColor[r]=s(n.titlefont.color)),n.titlefont.family&&(e.labelFont[r]=n.titlefont.family),n.titlefont.size&&(e.labelSize[r]=n.titlefont.size)),"showline"in n&&(e.lineEnable[r]=n.showline),"linecolor"in n&&(e.lineColor[r]=s(n.linecolor)),"linewidth"in n&&(e.lineWidth[r]=n.linewidth),"showgrid"in n&&(e.gridEnable[r]=n.showgrid),"gridcolor"in n&&(e.gridColor[r]=s(n.gridcolor)),"gridwidth"in n&&(e.gridWidth[r]=n.gridwidth),"log"===n.type?e.zeroEnable[r]=!1:"zeroline"in n&&(e.zeroEnable[r]=n.zeroline),"zerolinecolor"in n&&(e.zeroLineColor[r]=s(n.zerolinecolor)),"zerolinewidth"in n&&(e.zeroLineWidth[r]=n.zerolinewidth),"ticks"in n&&n.ticks?e.lineTickEnable[r]=!0:e.lineTickEnable[r]=!1,"ticklen"in n&&(e.lineTickLength[r]=e._defaultLineTickLength[r]=n.ticklen),"tickcolor"in n&&(e.lineTickColor[r]=s(n.tickcolor)),"tickwidth"in n&&(e.lineTickWidth[r]=n.tickwidth),"tickangle"in n&&(e.tickAngle[r]="auto"===n.tickangle?0:Math.PI*-n.tickangle/180),"showticklabels"in n&&(e.tickEnable[r]=n.showticklabels),"tickfont"in n&&(n.tickfont.color&&(e.tickColor[r]=s(n.tickfont.color)),n.tickfont.family&&(e.tickFont[r]=n.tickfont.family),n.tickfont.size&&(e.tickSize[r]=n.tickfont.size)),"mirror"in n?-1!==["ticks","all","allticks"].indexOf(n.mirror)?(e.lineTickMirror[r]=!0,e.lineMirror[r]=!0):n.mirror===!0?(e.lineTickMirror[r]=!1,e.lineMirror[r]=!0):(e.lineTickMirror[r]=!1,e.lineMirror[r]=!1):e.lineMirror[r]=!1,"showbackground"in n&&n.showbackground!==!1?(e.backgroundEnable[r]=!0,e.backgroundColor[r]=s(n.backgroundcolor)):e.backgroundEnable[r]=!1}},e.exports=i},{"../../../lib/html2unicode":590,"../../../lib/str2rgbarray":603,arraytools:50}],656:[function(t,e,r){"use strict";function n(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),u=Object.keys(a.camera),c=0;c<u.length;c++)r("camera."+u[c]+".x"),r("camera."+u[c]+".y"),r("camera."+u[c]+".z");var h=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),f=h?"manual":"auto",p=r("aspectmode",f);h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===p&&(e.aspectmode="auto")),s(t,e,{font:n.font,scene:n.id,data:n.fullData,bgColor:l}),r("dragmode",n.getDfltFromLayout("dragmode")),r("hovermode",n.getDfltFromLayout("hovermode"))}var i=t("../../../components/color"),o=t("../../subplot_defaults"),a=t("./layout_attributes"),s=t("./axis_defaults");e.exports=function(t,e,r){function i(e){if(!s){var r=-1!==a[e].values.indexOf(t[e]);return r?t[e]:void 0}}var s=e._has("cartesian")||e._has("geo")||e._has("gl2d")||e._has("pie")||e._has("ternary");o(t,e,r,{type:"gl3d",attributes:a,handleDefaults:n,font:e.font,fullData:r,getDfltFromLayout:i,paper_bgcolor:e.paper_bgcolor})}},{"../../../components/color":506,"../../subplot_defaults":677,"./axis_defaults":654,"./layout_attributes":657}],657:[function(t,e,r){"use strict";function n(t,e,r){return{x:{valType:"number",dflt:t},y:{valType:"number",dflt:e},z:{valType:"number",dflt:r}}}var i=t("./axis_attributes"),o=t("../../../lib/extend").extendFlat;e.exports={bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)"},camera:{up:o(n(0,0,1),{}),center:o(n(0,0,0),{}),eye:o(n(1.25,1.25,1.25),{})},domain:{x:{valType:"info_array",items:[{valType:"number",min:0,max:1},{valType:"number",min:0,max:1}],dflt:[0,1]},y:{valType:"info_array",items:[{valType:"number",min:0,max:1},{valType:"number",min:0,max:1}],dflt:[0,1]}},aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto"},aspectratio:{x:{valType:"number",min:0},y:{valType:"number",min:0},z:{valType:"number",min:0}},xaxis:i,yaxis:i,zaxis:i,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan"],dflt:"turntable"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest"},_deprecated:{cameraposition:{valType:"info_array"}}}},{"../../../lib/extend":586,"./axis_attributes":653}],658:[function(t,e,r){"use strict";function n(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}function i(t){var e=new n;return e.merge(t),e}var o=t("../../../lib/str2rgbarray"),a=["xaxis","yaxis","zaxis"],s=n.prototype;s.merge=function(t){for(var e=0;3>e;++e){var r=t[a[e]];this.enabled[e]=r.showspikes,this.colors[e]=o(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness}},e.exports=i},{"../../../lib/str2rgbarray":603}],659:[function(t,e,r){"use strict";function n(t){for(var e=new Array(3),r=0;3>r;++r){for(var n=t[r],i=new Array(n.length),o=0;o<n.length;++o)i[o]=n[o].x;e[r]=i}return e}function i(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,i=t.fullSceneLayout,u=[[],[],[]],c=0;3>c;++c){var h=i[s[c]];if(h._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(h._length)===1/0)u[c]=[];else{h.range[0]=r[c].lo/t.dataScale[c],h.range[1]=r[c].hi/t.dataScale[c],h._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),h.range[0]===h.range[1]&&(h.range[0]-=1,h.range[1]+=1);var f=h.tickmode;if("auto"===h.tickmode){h.tickmode="linear";var p=h.nticks||o.Lib.constrain(h._length/40,4,9);o.Axes.autoTicks(h,Math.abs(h.range[1]-h.range[0])/p)}for(var d=o.Axes.calcTicks(h),m=0;m<d.length;++m)d[m].x=d[m].x*t.dataScale[c],d[m].text=a(d[m].text);u[c]=d,h.tickmode=f}}e.ticks=u;for(var c=0;3>c;++c){l[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(var m=0;2>m;++m)e.bounds[m][c]=t.glplot.bounds[m][c]}t.contourLevels=n(u)}e.exports=i;var o=t("../../../plotly"),a=t("../../../lib/html2unicode"),s=["xaxis","yaxis","zaxis"],l=[0,0,0]},{"../../../lib/html2unicode":590,"../../../plotly":612}],660:[function(t,e,r){"use strict";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;4>r;++r)for(n=0;4>n;++n)i[n]+=t[4*r+n]*e[r];return i}function i(t,e){var r=n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])));return r}e.exports=i},{}],661:[function(t,e,r){"use strict";function n(t){function e(e,r){if("string"==typeof r)return r;var n=t.fullSceneLayout[e];return m.tickText(n,n.c2l(r),"hover").text}var r,n=t.svgContainer,i=t.container.getBoundingClientRect(),o=i.width,a=i.height;n.setAttributeNS(null,"viewBox","0 0 "+o+" "+a),n.setAttributeNS(null,"width",o),n.setAttributeNS(null,"height",a),k(t),t.glplot.axes.update(t.axesOptions);for(var s=Object.keys(t.traces),l=null,u=t.glplot.selection,c=0;c<s.length;++c)r=t.traces[s[c]],r.handlePick(u)&&(l=r),r.setContourLevels&&r.setContourLevels();var h;if(null!==l){var f=x(t.glplot.cameraParams,u.dataCoordinate);r=l.data;var p=r.hoverinfo,d=e("xaxis",u.traceCoordinate[0]),v=e("yaxis",u.traceCoordinate[1]),y=e("zaxis",u.traceCoordinate[2]);if("all"!==p){var b=p.split("+");-1===b.indexOf("x")&&(d=void 0),-1===b.indexOf("y")&&(v=void 0),-1===b.indexOf("z")&&(y=void 0),-1===b.indexOf("text")&&(u.textLabel=void 0),-1===b.indexOf("name")&&(l.name=void 0)}t.fullSceneLayout.hovermode&&g.loneHover({x:(.5+.5*f[0]/f[3])*o,y:(.5-.5*f[1]/f[3])*a,xLabel:d,yLabel:v,zLabel:y,text:u.textLabel,name:l.name,color:l.color},{container:n});var _={points:[{x:d,y:v,z:y,data:r._input,fullData:r,curveNumber:r.index,pointNumber:u.data.index}]};u.buttons&&u.distance<5?t.graphDiv.emit("plotly_click",_):t.graphDiv.emit("plotly_hover",_),h=_}else g.loneUnhover(n),t.graphDiv.emit("plotly_unhover",h)}function i(t,e,r,i){var o={canvas:r,gl:i,container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1};if(t.staticMode){if(!h){c=document.createElement("canvas");try{h=c.getContext("webgl",{preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0})}catch(a){throw new Error("error creating static canvas/context for image server")}}o.pixelRatio=t.pixelRatio,o.gl=h,o.canvas=c}try{t.glplot=f(o)}catch(a){y(t)}var s=function(t){var e={};e[t.id]=u(t.camera),t.saveCamera(t.graphDiv.layout),t.graphDiv.emit("plotly_relayout",e)};if(t.glplot.canvas.addEventListener("mouseup",s.bind(null,t)),t.glplot.canvas.addEventListener("wheel",s.bind(null,t)),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",function(t){p.warn("Lost WebGL context."),t.preventDefault()}),!t.camera){var l=t.fullSceneLayout.camera;t.camera=b(t.container,{center:[l.center.x,l.center.y,l.center.z],eye:[l.eye.x,l.eye.y,l.eye.z],up:[l.up.x,l.up.y,l.up.z],zoomMin:.1,zoomMax:100,mode:"orbit"})}return t.glplot.mouseListener.enabled=!1,t.glplot.camera=t.camera,t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=n.bind(null,t),t.traces={},!0}function o(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var 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s(t,e,r){var n=t.fullSceneLayout;a(n.xaxis,e.x,0,r),a(n.yaxis,e.y,1,r),a(n.zaxis,e.z,2,r)}function l(t){return[[t.eye.x,t.eye.y,t.eye.z],[t.center.x,t.center.y,t.center.z],[t.up.x,t.up.y,t.up.z]]}function u(t){return{up:{x:t.up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]}}}var c,h,f=t("gl-plot3d"),p=t("../../lib"),d=t("../../plots/plots"),m=t("../../plots/cartesian/axes"),g=t("../../plots/cartesian/graph_interact"),v=t("../../lib/str2rgbarray"),y=t("../../lib/show_no_webgl_msg"),b=t("./camera"),x=t("./project"),_=t("./set_convert"),w=t("./layout/convert"),M=t("./layout/spikes"),k=t("./layout/tick_marks"),A=o.prototype;A.recoverContext=function(){function t(){return r.isContextLost()?void requestAnimationFrame(t):i(e,e.fullLayout,n,r)?void e.plot.apply(e,e.plotArgs):void p.error("Catastrophic and unrecoverable WebGL error. 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i=M+a.pxmid[0]*e.rCenter+(e.x||0),s=k+a.pxmid[1]*e.rCenter+(e.y||0);e.outside&&(a.yLabelMin=s-r.height/2,a.yLabelMid=s,a.yLabelMax=s+r.height/2,a.labelExtraX=0,a.labelExtraY=0,v=!0),t.attr("transform","translate("+i+","+s+")"+(e.scale<1?"scale("+e.scale+")":"")+(e.rotate?"rotate("+e.rotate+")":"")+"translate("+-(r.left+r.right)/2+","+-(r.top+r.bottom)/2+")")})}),v&&a(l,g),s.each(function(t){if(t.labelExtraX||t.labelExtraY){var e=c.select(this),r=e.select("g.slicetext text");r.attr("transform","translate("+t.labelExtraX+","+t.labelExtraY+")"+r.attr("transform"));var n=t.cxFinal+t.pxmid[0],i=t.cyFinal+t.pxmid[1],o="M"+n+","+i,a=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var s=t.labelExtraX*t.pxmid[1]/t.pxmid[0],l=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);o+=Math.abs(s)>Math.abs(l)?"l"+l*t.pxmid[0]/t.pxmid[1]+","+l+"H"+(n+t.labelExtraX+a):"l"+t.labelExtraX+","+s+"v"+(l-s)+"h"+a}else 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o=r.marker.line.width||0;Array.isArray(o)&&(o=o[e.i]||0),t.style({"stroke-width":o,fill:e.color}).call(n.stroke,i)}},{"../../components/color":506}],772:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t){var e=t[0].trace,r=e.marker;if(n.mergeArray(e.text,t,"tx"),n.mergeArray(e.textposition,t,"tp"),e.textfont&&(n.mergeArray(e.textfont.size,t,"ts"),n.mergeArray(e.textfont.color,t,"tc"),n.mergeArray(e.textfont.family,t,"tf")),r&&r.line){var i=r.line;n.mergeArray(r.opacity,t,"mo"),n.mergeArray(r.symbol,t,"mx"),n.mergeArray(r.color,t,"mc"),n.mergeArray(i.color,t,"mlc"),n.mergeArray(i.width,t,"mlw")}}},{"../../lib":591}],773:[function(t,e,r){"use strict";var n=t("../../components/colorscale/color_attributes"),i=t("../../components/drawing"),o=(t("./constants"),t("../../lib/extend").extendFlat);e.exports={x:{valType:"data_array"},x0:{valType:"any",dflt:0},dx:{valType:"number",dflt:1},y:{valType:"data_array"},y0:{valType:"any",dflt:0},dy:{valType:"number",
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y="projection."+g[v];f(y+".show")&&(f(y+".opacity"),f(y+".scale"))}u(t,e,r,{axis:"z"}),u(t,e,r,{axis:"y",inherit:"z"}),u(t,e,r,{axis:"x",inherit:"z"})}},{"../../components/errorbars/defaults":534,"../../lib":591,"../scatter/line_defaults":784,"../scatter/marker_defaults":788,"../scatter/subtypes":792,"../scatter/text_defaults":793,"./attributes":795}],800:[function(t,e,r){"use strict";var n={};n.plot=t("./convert"),n.attributes=t("./attributes"),n.markerSymbols=t("../../constants/gl_markers"),n.supplyDefaults=t("./defaults"),n.colorbar=t("../scatter/colorbar"),n.calc=t("./calc"),n.moduleType="trace",n.name="scatter3d",n.basePlotModule=t("../../plots/gl3d"),n.categories=["gl3d","symbols","markerColorscale","showLegend"],n.meta={},e.exports=n},{"../../constants/gl_markers":577,"../../plots/gl3d":651,"../scatter/colorbar":776,"./attributes":795,"./calc":796,"./convert":798,"./defaults":799}],801:[function(t,e,r){"use strict";var n=t("../scatter/attributes"),i=t("../../plots/attributes"),o=t("../../components/colorscale/color_attributes"),a=t("../../lib/extend").extendFlat,s=n.marker,l=n.line,u=s.line;e.exports={lon:{valType:"data_array"},lat:{valType:"data_array"},locations:{valType:"data_array"},locationmode:{valType:"enumerated",values:["ISO-3","USA-states","country names"],dflt:"ISO-3"},mode:a({},n.mode,{dflt:"markers"}),text:a({},n.text,{}),line:{color:l.color,width:l.width,dash:l.dash},marker:a({},{symbol:s.symbol,opacity:s.opacity,size:s.size,sizeref:s.sizeref,sizemin:s.sizemin,sizemode:s.sizemode,showscale:s.showscale,line:a({},{width:u.width},o("marker.line"))},o("marker")),textfont:n.textfont,textposition:n.textposition,hoverinfo:a({},i.hoverinfo,{flags:["lon","lat","location","text","name"]}),_nestedModules:{"marker.colorbar":"Colorbar"}}},{"../../components/colorscale/color_attributes":514,"../../lib/extend":586,"../../plots/attributes":613,"../scatter/attributes":773}],802:[function(t,e,r){"use 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o=i.line;n(i.opacity,"mo"),n(i.symbol,"mx"),n(i.color,"mc"),n(i.size,"ms"),n(o.color,"mlc"),n(o.width,"mlw")}}function i(t){for(var e=t.lon.length,r=new Array(e),n=0;e>n;n++)r[n]=[t.lon[n],t.lat[n]];return{type:"LineString",coordinates:r,trace:t}}function o(t,e){function r(e){var r=t.mockAxis;return u.tickText(r,r.c2l(e),"hover").text+"\xb0"}var n=e.hoverinfo;if("none"===n)return function(t){delete t.textLabel};var i="all"===n?g.hoverinfo.flags:n.split("+"),o=-1!==i.indexOf("location")&&Array.isArray(e.locations),a=-1!==i.indexOf("lon"),s=-1!==i.indexOf("lat"),l=-1!==i.indexOf("text");
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n=t("../scatter/attributes"),i=t("../../components/colorscale/color_attributes"),o=t("../../constants/gl2d_dashes"),a=t("../../constants/gl_markers"),s=t("../../lib/extend").extendFlat,l=t("../../lib/extend").extendDeep,u=n.line,c=n.marker,h=c.line;e.exports={x:n.x,x0:n.x0,dx:n.dx,y:n.y,y0:n.y0,dy:n.dy,text:s({},n.text,{}),mode:{valType:"flaglist",flags:["lines","markers"],extras:["none"]},line:{color:u.color,width:u.width,dash:{valType:"enumerated",values:Object.keys(o),dflt:"solid"}},marker:l({},i("marker"),{symbol:{valType:"enumerated",values:Object.keys(a),dflt:"circle",arrayOk:!0},size:c.size,sizeref:c.sizeref,sizemin:c.sizemin,sizemode:c.sizemode,opacity:c.opacity,showscale:c.showscale,line:l({},i("marker.line"),{width:h.width})}),connectgaps:n.connectgaps,fill:s({},n.fill,{values:["none","tozeroy","tozerox"]}),fillcolor:n.fillcolor,_nestedModules:{error_x:"ErrorBars",error_y:"ErrorBars","marker.colorbar":"Colorbar"}}},{"../../components/colorscale/color_attributes":514,"../../constants/gl2d_dashes":575,"../../constants/gl_markers":577,"../../lib/extend":586,"../scatter/attributes":773}],807:[function(t,e,r){"use 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e=this.scene,r=e.xaxis,n=e.yaxis,o=this.bounds,a=this.pickXData=r.makeCalcdata(t,"x").slice(),l=this.pickYData=n.makeCalcdata(t,"y").slice();this.xData=a.slice(),this.yData=l.slice();var c,h,f,p,d,m,g,v,b=y.calcFromTrace(t,e.fullLayout),x=a.length,_=new Array(x),M=new Float32Array(2*x),k=new Float32Array(4*x),A=new Float32Array(4*x),T=0,E=0,L=0,I=0,P="log"===r.type?function(t){return r.d2l(t)}:function(t){return t},C="log"===n.type?function(t){return n.d2l(t)}:function(t){return t};for(c=0;x>c;++c)this.xData[c]=f=P(a[c]),this.yData[c]=p=C(l[c]),isNaN(f)||isNaN(p)||(_[T++]=c,M[E++]=f,M[E++]=p,d=k[L++]=f-b[c].xs||0,m=k[L++]=b[c].xh-f||0,k[L++]=0,k[L++]=0,A[I++]=0,A[I++]=0,g=A[I++]=p-b[c].ys||0,v=A[I++]=b[c].yh-p||0,o[0]=Math.min(o[0],f-d),o[1]=Math.min(o[1],p-g),o[2]=Math.max(o[2],f+m),o[3]=Math.max(o[3],p+v));M=u(M,E),this.idToIndex=_,this.updateLines(t,M),this.updateError("X",t,M,k),this.updateError("Y",t,M,A);var 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i=this["error"+t],o=e["error_"+t.toLowerCase()],s=this["error"+t+"Options"];"x"===t.toLowerCase()&&o.copy_ystyle&&(o=e.error_y),this["hasError"+t]?(s.positions=r,s.errors=n,s.capSize=o.width,s.lineWidth=o.thickness/2,s.color=a(o.color,1,1)):s.positions=new Float32Array(0),i.update(s)},E.expandAxesFast=function(t,e){for(var r,n,i,o=e||10,a=0;2>a;a++)r=this.scene[T[a]],n=r._min,n||(n=[]),n.push({val:t[a],pad:o}),i=r._max,i||(i=[]),i.push({val:t[a+2],pad:o})},E.expandAxesFancy=function(t,e,r){var n=this.scene,i={padded:!0,ppad:r};v.expand(n.xaxis,t,i),v.expand(n.yaxis,e,i)},E.dispose=function(){this.line.dispose(),this.errorX.dispose(),this.errorY.dispose(),this.scatter.dispose(),this.fancyScatter.dispose()},e.exports=c},{"../../components/errorbars":535,"../../constants/gl2d_dashes":575,"../../constants/gl_markers":577,"../../lib":591,"../../lib/gl_format_color":589,"../../lib/str2rgbarray":603,"../../plots/cartesian/axes":615,"../scatter/get_trace_color":781,"../scatter/make_bubble_size_func":787,"../scatter/subtypes":792,"fast-isnumeric":128,"gl-error2d":130,"gl-line2d":136,"gl-scatter2d":201,"gl-scatter2d-fancy":196}],808:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../scatter/constants"),o=t("../scatter/subtypes"),a=t("../scatter/xy_defaults"),s=t("../scatter/marker_defaults"),l=t("../scatter/line_defaults"),u=t("../scatter/fillcolor_defaults"),c=t("../../components/errorbars/defaults"),h=t("./attributes");e.exports=function(t,e,r,f){function p(r,i){return n.coerce(t,e,h,r,i)}var d=a(t,e,p);return d?(p("text"),p("mode",d<i.PTS_LINESONLY?"lines+markers":"lines"),o.hasLines(e)&&(p("connectgaps"),l(t,e,r,f,p)),o.hasMarkers(e)&&s(t,e,r,f,p),p("fill"),"none"!==e.fill&&u(t,e,r,p),c(t,e,r,{axis:"y"}),void c(t,e,r,{axis:"x",inherit:"y"})):void(e.visible=!1)}},{"../../components/errorbars/defaults":534,"../../lib":591,"../scatter/constants":778,"../scatter/fillcolor_defaults":780,"../scatter/line_defaults":784,"../scatter/marker_defaults":788,"../scatter/subtypes":792,"../scatter/xy_defaults":794,"./attributes":806}],809:[function(t,e,r){"use strict";var n={};n.attributes=t("./attributes"),n.supplyDefaults=t("./defaults"),n.colorbar=t("../scatter/colorbar"),n.calc=t("../scatter3d/calc"),n.plot=t("./convert"),n.moduleType="trace",n.name="scattergl",n.basePlotModule=t("../../plots/gl2d"),n.categories=["gl2d","symbols","errorBarsOK","markerColorscale","showLegend"],n.meta={},e.exports=n},{"../../plots/gl2d":648,"../scatter/colorbar":776,"../scatter3d/calc":796,"./attributes":806,"./convert":807,"./defaults":808}],810:[function(t,e,r){"use strict";var n=t("../scattergeo/attributes"),i=t("../scatter/attributes"),o=t("../../plots/mapbox/layout_attributes"),a=t("../../plots/attributes"),s=t("../../lib/extend").extendFlat,l=n.line,u=n.marker;e.exports={lon:n.lon,lat:n.lat,mode:{valType:"flaglist",flags:["lines","markers","text"],dflt:"markers",extras:["none"]},text:s({},i.text,{}),line:{color:l.color,width:l.width,dash:l.dash},connectgaps:i.connectgaps,marker:{symbol:{valType:"string",dflt:"circle",arrayOk:!0},opacity:s({},u.opacity,{arrayOk:!1}),size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,color:u.color,colorscale:u.colorscale,cauto:u.cauto,cmax:u.cmax,cmin:u.cmin,autocolorscale:u.autocolorscale,reversescale:u.reversescale,showscale:u.showscale},fill:{valType:"enumerated",values:["none","toself"],dflt:"none"},fillcolor:i.fillcolor,textfont:o.layers.symbol.textfont,textposition:o.layers.symbol.textposition,hoverinfo:s({},a.hoverinfo,{flags:["lon","lat","text","name"]}),_nestedModules:{"marker.colorbar":"Colorbar"}}},{"../../lib/extend":586,"../../plots/attributes":613,"../../plots/mapbox/layout_attributes":668,"../scatter/attributes":773,"../scattergeo/attributes":801}],811:[function(t,e,r){"use 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e=[Math.min(0|Math.round(t.data.index[0]/this.dataScale-1),this.data.z[0].length-1),Math.min(0|Math.round(t.data.index[1]/this.dataScale-1),this.data.z.length-1)],r=[0,0,0];Array.isArray(this.data.x[0])?r[0]=this.data.x[e[1]][e[0]]:r[0]=this.data.x[e[0]],Array.isArray(this.data.y[0])?r[1]=this.data.y[e[1]][e[0]]:r[1]=this.data.y[e[1]],r[2]=this.data.z[e[1]][e[0]],t.traceCoordinate=r;var n=this.scene.fullSceneLayout;t.dataCoordinate=[n.xaxis.d2l(r[0])*this.scene.dataScale[0],n.yaxis.d2l(r[1])*this.scene.dataScale[1],n.zaxis.d2l(r[2])*this.scene.dataScale[2]];var i=this.data.text;return i&&i[e[1]]&&void 0!==i[e[1]][e[0]]?t.textLabel=i[e[1]][e[0]]:t.textLabel="",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}},g.setContourLevels=function(){for(var t=[[],[],[]],e=!1,r=0;3>r;++r)this.showContour[r]&&(e=!0,t[r]=this.scene.contourLevels[r]);e&&this.surface.update({levels:t})},g.update=function(t){var e,r=this.scene,n=r.fullSceneLayout,o=this.surface,s=t.opacity,l=i(t.colorscale,s),c=t.z,f=t.x,p=t.y,m=n.xaxis,g=n.yaxis,v=n.zaxis,y=r.dataScale,b=c[0].length,x=c.length,_=[u(new Float32Array(b*x),[b,x]),u(new Float32Array(b*x),[b,x]),u(new Float32Array(b*x),[b,x])],w=_[0],M=_[1],k=r.contourLevels;this.data=t,h(_[2],function(t,e){return v.d2l(c[e][t])*y[2]}),Array.isArray(f[0])?h(w,function(t,e){return m.d2l(f[e][t])*y[0]}):h(w,function(t){return m.d2l(f[t])*y[0]}),Array.isArray(p[0])?h(M,function(t,e){return g.d2l(p[e][t])*y[1]}):h(M,function(t,e){return g.d2l(p[e])*y[1]});var A={colormap:l,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacity:1};if(A.intensityBounds=[t.cmin,t.cmax],t.surfacecolor){var T=u(new Float32Array(b*x),[b,x]);h(T,function(e,r){return t.surfacecolor[r][e]}),_.push(T)}else A.intensityBounds[0]*=y[2],A.intensityBounds[1]*=y[2];this.dataScale=a(_),t.surfacecolor&&(A.intensity=_.pop()),"opacity"in t&&t.opacity<1&&(A.opacity=.25*t.opacity);var E=[!0,!0,!0],S=["x","y","z"];for(e=0;3>e;++e){var L=t.contours[S[e]];E[e]=L.highlight,A.showContour[e]=L.show||L.highlight,A.showContour[e]&&(A.contourProject[e]=[L.project.x,L.project.y,L.project.z],L.show?(this.showContour[e]=!0,A.levels[e]=k[e],o.highlightColor[e]=A.contourColor[e]=d(L.color),L.usecolormap?o.highlightTint[e]=A.contourTint[e]=0:o.highlightTint[e]=A.contourTint[e]=1,A.contourWidth[e]=L.width):this.showContour[e]=!1,L.highlight&&(A.dynamicColor[e]=d(L.highlightcolor),A.dynamicWidth[e]=L.highlightwidth))}A.coords=_,o.update(A),o.visible=t.visible,o.enableDynamic=E,o.snapToData=!0,"lighting"in 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diff --git a/lessons/r/ggplot2/lesson.Rmd b/lessons/r/ggplot2/lesson.Rmd
new file mode 100644
index 000000000..356b5b400
--- /dev/null
+++ b/lessons/r/ggplot2/lesson.Rmd
@@ -0,0 +1,150 @@
+---
+always_allow_html: yes
+layout: page
+title: 'Introduction to ggplot2'
+visible: false
+tags:
+  - r
+  - intermediate
+---
+
+* Author: Lindsay Coome
+* Lesson Topic: Plotting in R using ggplot2
+* [Study Materials](https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/ggplot2)
+* Lesson video stream: <https://www.youtube.com/watch?v=EKkpflv-O_k>
+
+## WHAT IS GGPLOT?
+ggplot2 is meant to be an implementation of the Grammar of Graphics, hence gg-plot. The basic notion is that there is a grammar to the composition of graphical components in statistical graphics, and by directly controlling that grammar, you can generate a large set of carefully constructed graphics tailored to your particular needs. Each component is added to the plot as a layer.
+
+## COMPONENTS OF A GGPLOT2 PLOT
+Plots convey information through various aspects of their aesthetics. Some aesthetics that plots use are:
+
+* x position
+* y position
+* size of elements
+* shape of elements
+* color of elements
+
+The elements in a plot are geometric shapes, like
+
+* points
+* lines
+* line segments
+* bars
+* text
+
+Some of these geometries have their own particular aesthetics. For instance:
+
+**points**
+
+* point shape
+* point size
+
+**lines**
+
+* line type
+* line weight
+
+**bars**
+
+* y minimum
+* y maximum
+* fill color
+* outline color
+
+**text**
+
+* label value
+
+##LET'S GET STARTED!
+
+Here is an example of the base plotting functions in R:
+```{r cars}
+plot(cars)
+hist(cars$speed)
+```
+
+[But ggplot2 plots are prettier...](https://www.google.ca/search?q=ggplot2&client=safari&rls=en&source=lnms&tbm=isch&sa=X&ved=0ahUKEwisuv6V6IvSAhVk0oMKHTNkARkQ_AUICCgB&biw=1279&bih=621#tbm=isch&q=ggplot2+examples)
+
+We'll investigate the dataset we're going to use to make some graphs....
+```{r}
+library(ggplot2)
+str(diamonds)
+```
+
+Our first graph?
+```{r}
+ggplot(diamonds, aes(x=cut, y=carat))
+```
+
+We've defined the structure of our graph, but we need to add a "layer" to it in order to define what type of graph it is. Let's make a scatterplot:
+```{r}
+ggplot(diamonds, aes(x=carat, y=price)) + 
+  geom_point()
+```
+
+You can also save the structure of the graph to a variable and then add layers and other attributes to it. Let's do that:
+```{r}
+g <- ggplot(diamonds, aes(x=carat, y=price))
+g + geom_point()
+```
+
+We can add other "layers" of graphs to our first graph. Let's add a trend line to our scatterplot:
+```{r}
+g + geom_point() + geom_smooth() 
+```
+
+We can also "facet" our graphs, which means dividing a plot into subplots based on the values of one or more discrete variables.
+```{r}
+g + geom_point() + geom_smooth() + facet_grid(cut~.)
+```
+
+There are lots of ways to organize your facet. See "faceting" at the bottom of [ggplot cheetsheet](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf).
+
+Let's say we don't want to facet, but we want to colour the dots depending on the cut of the diamonds:
+```{r}
+ggplot(diamonds, aes(x=carat, y=price, color=cut)) + geom_point()
+```
+
+We can also change some aesthetic features of the graph. Let's get rid of the color aspect of our structure and change the colour and size of the points:
+```{r}
+ggplot(diamonds, aes(x=carat, y=price)) + geom_point(color="steelblue", size=4)
+```
+
+Let's look at how to change the theme and labels:
+
+```{r}
+g + geom_point() +
+  ylab("Diamond Price") +
+  xlab("Carat") +
+  ggtitle("Diamond Price by Weight") +
+  theme_bw()
+```
+
+What about a bar plot? Let's find out how many diamonds of each cut are in the dataset.
+```{r}
+ggplot(diamonds, aes(cut)) + geom_bar()
+```
+
+What about making interactive graphs in R?
+There is a package called plotly that lets us make our ggplot2 graphs interactive:
+```{r}
+library(plotly)
+g <- ggplot(diamonds, aes(cut)) + geom_bar()
+p=ggplotly(g)
+p
+```
+
+Finally, here is qplot, a way of making super quick scatterplots in R.
+```{r}
+qplot(carat, price, data = diamonds)
+```
+
+## MORE RESOURCES ON GGPLOT2
+[ggplot2 documentation](http://had.co.nz/ggplot2/)
+
+[Book by Hadley Wickham](https://www.amazon.com/ggplot2-Elegant-Graphics-Data-Analysis/dp/0387981403)
+
+[ggplot2 cheat sheet](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)
+
+[r graph gallery for inspiration (not just limited to ggplot2 graphs)](https://www.r-graph-gallery.com/all-graphs/)
diff --git a/lessons/r/ggplot2/lesson.md b/lessons/r/ggplot2/lesson.md
new file mode 100644
index 000000000..39271e27e
--- /dev/null
+++ b/lessons/r/ggplot2/lesson.md
@@ -0,0 +1,230 @@
+---
+always_allow_html: yes
+layout: page
+title: 'Introduction to ggplot2'
+visible: true
+tags:
+  - r
+  - intermediate
+---
+
+* Author: Lindsay Coome
+* Lesson Topic: Plotting in R using ggplot2
+* [Study Materials](https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/ggplot2)
+* Lesson video stream: <https://www.youtube.com/watch?v=EKkpflv-O_k>
+
+## WHAT IS GGPLOT?
+ggplot2 is meant to be an implementation of the Grammar of Graphics, hence gg-plot. The basic notion is that there is a grammar to the composition of graphical components in statistical graphics, and by directly controlling that grammar, you can generate a large set of carefully constructed graphics tailored to your particular needs. Each component is added to the plot as a layer.
+
+## COMPONENTS OF A GGPLOT2 PLOT
+Plots convey information through various aspects of their aesthetics. Some aesthetics that plots use are:
+
+* x position
+* y position
+* size of elements
+* shape of elements
+* color of elements
+
+The elements in a plot are geometric shapes, like
+
+* points
+* lines
+* line segments
+* bars
+* text
+
+Some of these geometries have their own particular aesthetics. For instance:
+
+**points**
+
+* point shape
+* point size
+
+**lines**
+
+* line type
+* line weight
+
+**bars**
+
+* y minimum
+* y maximum
+* fill color
+* outline color
+
+**text**
+
+* label value
+
+##LET'S GET STARTED!
+
+Here is an example of the base plotting functions in R:
+
+```r
+plot(cars)
+```
+
+![center](../figure/cars-1.png)
+
+```r
+hist(cars$speed)
+```
+
+![center](../figure/cars-2.png)
+
+[But ggplot2 plots are prettier...](https://www.google.ca/search?q=ggplot2&client=safari&rls=en&source=lnms&tbm=isch&sa=X&ved=0ahUKEwisuv6V6IvSAhVk0oMKHTNkARkQ_AUICCgB&biw=1279&bih=621#tbm=isch&q=ggplot2+examples)
+
+We'll investigate the dataset we're going to use to make some graphs....
+
+```r
+library(ggplot2)
+str(diamonds)
+```
+
+```
+## Classes 'tbl_df', 'tbl' and 'data.frame':	53940 obs. of  10 variables:
+##  $ carat  : num  0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
+##  $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
+##  $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
+##  $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
+##  $ depth  : num  61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
+##  $ table  : num  55 61 65 58 58 57 57 55 61 61 ...
+##  $ price  : int  326 326 327 334 335 336 336 337 337 338 ...
+##  $ x      : num  3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
+##  $ y      : num  3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
+##  $ z      : num  2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
+```
+
+Our first graph?
+
+```r
+ggplot(diamonds, aes(x=cut, y=carat))
+```
+
+![center](../figure/unnamed-chunk-2-1.png)
+
+We've defined the structure of our graph, but we need to add a "layer" to it in order to define what type of graph it is. Let's make a scatterplot:
+
+```r
+ggplot(diamonds, aes(x=carat, y=price)) + 
+  geom_point()
+```
+
+![center](../figure/unnamed-chunk-3-1.png)
+
+You can also save the structure of the graph to a variable and then add layers and other attributes to it. Let's do that:
+
+```r
+g <- ggplot(diamonds, aes(x=carat, y=price))
+g + geom_point()
+```
+
+![center](../figure/unnamed-chunk-4-1.png)
+
+We can add other "layers" of graphs to our first graph. Let's add a trend line to our scatterplot:
+
+```r
+g + geom_point() + geom_smooth() 
+```
+
+```
+## `geom_smooth()` using method = 'gam'
+```
+
+![center](../figure/unnamed-chunk-5-1.png)
+
+We can also "facet" our graphs, which means dividing a plot into subplots based on the values of one or more discrete variables.
+
+```r
+g + geom_point() + geom_smooth() + facet_grid(cut~.)
+```
+
+```
+## `geom_smooth()` using method = 'gam'
+```
+
+![center](../figure/unnamed-chunk-6-1.png)
+
+There are lots of ways to organize your facet. See "faceting" at the bottom of [ggplot cheetsheet](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf).
+
+Let's say we don't want to facet, but we want to colour the dots depending on the cut of the diamonds:
+
+```r
+ggplot(diamonds, aes(x=carat, y=price, color=cut)) + geom_point()
+```
+
+![center](../figure/unnamed-chunk-7-1.png)
+
+We can also change some aesthetic features of the graph. Let's get rid of the color aspect of our structure and change the colour and size of the points:
+
+```r
+ggplot(diamonds, aes(x=carat, y=price)) + geom_point(color="steelblue", size=4)
+```
+
+![center](../figure/unnamed-chunk-8-1.png)
+
+Let's look at how to change the theme and labels:
+
+
+```r
+g + geom_point() +
+  ylab("Diamond Price") +
+  xlab("Carat") +
+  ggtitle("Diamond Price by Weight") +
+  theme_bw()
+```
+
+![center](../figure/unnamed-chunk-9-1.png)
+
+What about a bar plot? Let's find out how many diamonds of each cut are in the dataset.
+
+```r
+ggplot(diamonds, aes(cut)) + geom_bar()
+```
+
+![center](../figure/unnamed-chunk-10-1.png)
+
+What about making interactive graphs in R?
+There is a package called plotly that lets us make our ggplot2 graphs interactive:
+
+```r
+library(plotly)
+```
+
+```
+## Error in library(plotly): there is no package called 'plotly'
+```
+
+```r
+g <- ggplot(diamonds, aes(cut)) + geom_bar()
+p=ggplotly(g)
+```
+
+```
+## Error in eval(expr, envir, enclos): could not find function "ggplotly"
+```
+
+```r
+p
+```
+
+```
+## Error in eval(expr, envir, enclos): object 'p' not found
+```
+
+Finally, here is qplot, a way of making super quick scatterplots in R.
+
+```r
+qplot(carat, price, data = diamonds)
+```
+
+![center](../figure/unnamed-chunk-12-1.png)
+
+## MORE RESOURCES ON GGPLOT2
+[ggplot2 documentation](http://had.co.nz/ggplot2/)
+
+[Book by Hadley Wickham](https://www.amazon.com/ggplot2-Elegant-Graphics-Data-Analysis/dp/0387981403)
+
+[ggplot2 cheat sheet](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)
+
+[r graph gallery for inspiration (not just limited to ggplot2 graphs)](https://www.r-graph-gallery.com/all-graphs/)
diff --git a/lessons/r/intermediate/lesson.Rmd b/lessons/r/intermediate/lesson.Rmd
new file mode 100644
index 000000000..c3b864bfc
--- /dev/null
+++ b/lessons/r/intermediate/lesson.Rmd
@@ -0,0 +1,206 @@
+---
+layout: page
+title: "Intermediate topics in R: Strings and factors"
+visible: false
+tags: 
+  # languages
+  - r
+  # levels
+  - intermediate
+---
+
+```{r, echo=FALSE}
+knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
+```
+
+# Strings, factors, and regular expressions
+
+ - **Authors**: Luke Johnston (@lwjohnst86)
+ - **Research field**: Nutritional Sciences
+ - **Lesson topic**: Working with character/string data
+ - **Lesson video link**: <https://youtu.be/jdMOmg9SMEc>
+ 
+Install these packages:
+
+```{r, eval=FALSE}
+install.packages(c("stringr", "forcats"))
+```
+
+Base R has some pretty decent string manipulation and searching functionality.
+The primary way of working with strings is through 
+[regular expressions](http://www.regular-expressions.info/). Regular
+expressions, or regex in R, are a group of characters that have special meaning
+and function. For instance, the `.` means wildcard and it will search for any
+character (letter, number, symbol, etc). Or `$` means end of the string or line.
+If you want to see more details on regex in R, run `?regex` to get help on it.
+
+In general, the most commonly used functions (at least that I use and that I've
+seen) are `grep`, `grepl`, `sub`, and `gsub` commands. Each of these take regex
+characters (`$ ^ * . + ? [] () |` are a few; see `?regex` for more). Let's
+begin with the base R functions!
+
+```{r}
+fruits <- c("apple", "banana", "pear")
+
+# replace `a` at beginning of string with nothing. `gsub` = global substitution
+gsub("^a", "", fruits)
+# replace letters `an` or `ar` with `bb`
+gsub("(an|ar)", "bb", fruits)
+# same as above but only first search and replace. `sub` = substitution
+sub("(an|ar)", "bb", fruits)
+# search for fruits starting with a. Returns the number position of the search
+# result.
+grep("^a", fruits)
+# same as above, but returns the actual value rather than the number
+grep("^a", fruits, value = TRUE)
+# same as above, but returns a vector of logical values
+grepl("^a", fruits)
+```
+
+While base R is pretty powerful, it isn't always easy to work with and there are
+some major bits of functionality missing. That's where the `stringr` package
+(which is a subset of common functions from the `stringi` package) comes into
+play. `stringr` greatly expands on string manipulation in R. Let's go through
+some.
+
+```{r}
+library(stringr)
+
+# month.name is a default variable in R. I'm putting it in a new variable to
+# make it shorter to type out.
+mth <- month.name
+mth
+
+# Almost every stringr function begins with `str_`.
+# Convert cases (lower case, UPPER CASE)
+str_to_lower(mth)
+str_to_upper(mth)
+
+# Find out the number of characters in each string of the vector
+str_length(mth)
+mth
+
+# Combine (concatenate) the strings together. Similar to `paste`
+str_c(mth, collapse = ", ")
+
+# Take a subset of the months based on a regex.
+# for months starting with `J`
+str_subset(mth, "^J")
+# for months ending in y
+str_subset(mth, "y$")
+# for months that contain either s, n, or d (only lower case)
+str_subset(mth, "[snd]")
+# same as above, but all letters since months have been converted to lower case
+str_subset(str_to_lower(mth), "[snd]")
+
+# Because stringr is part of the tidyverse packages, you can also use the pipe
+# `%>%` command with the commands, to make it easier to read.
+mth %>%
+    str_to_lower() %>%
+    str_subset("[snd]")
+
+# Using the fruit variable in the stringr package... different from the `fruits`
+# we created above!
+# str_detect converts to logical, for use in filtering, subsetting, etc.
+# searches for all fruit beginning with `be`
+str_detect(fruit, "^be")
+
+# Find out the proportion (mean of values that are either 0 or 1) of fruit with
+# at least one vowel at the end of the string.
+str_detect(fruit, "[aeiou]$") %>%
+    mean()
+
+# search for any fruit that does not end in an `a`
+str_detect(fruit, "^.*[^a]$")
+# this is the same as above, but subsets instead
+str_subset(fruit, "[^a]$")
+
+# similar to sub
+# search any month that starts with J and ends with e and replace the middle
+# letters with `HIII`.
+str_replace(mth, "^(J).*(e)$", "\\1HIII\\2")
+
+# vector of sentences
+head(sentences)
+
+# you can split strings up by a specific character. `str_split` creates a list,
+# so I use `[[1]]` or `[1]` to show only the first one.
+str_split(sentences, " ")[[1]] # this outputs a vector
+str_split(sentences, " ")[1] # this outputs a list
+# or split by the letter `e`
+str_split(sentences, "e")[[1]]
+# if you want to split by the word, use the `boundary` function.
+str_split(sentences, boundary("word"))[[1]]
+# or by a sentence
+str_split(sentences, boundary("sentence"))[[1]]
+
+# include in a pipe
+str_split(sentences, boundary("sentence"))[[1]] %>%
+    # convert to Title Case
+    str_to_title()
+```
+
+String manipulation is great, but sometimes you need to convert to factor
+variables for use in statistical models or when plotting (for instance when
+using `ggplot2`). The `forcats` package makes it easier to work with factors.
+Base R really only has one function for working with factors... the `factor`
+command.
+
+```{r}
+library(forcats)
+
+str(mth)
+# This is base function. Compare this...
+as.factor(mth)
+# with this...
+as_factor(mth)
+# base R as.factor orders alphabetically, while forcats as_factor orders in
+# appearance.
+
+mth2 <- c(mth[10:12], mth[6:12], mth)
+mth2
+
+# you can also order by frequency:
+fct_infreq(mth2)
+# this more explicitly orders by appearance.
+fct_inorder(mth2)
+
+# this counts the number of levels within the factors. It is a simple summary
+fct_count(mth2, sort = TRUE)
+
+# if you want to convert levels that contains only a few (in this case, less the
+# 10% of the total proportion), they will be converted to `Other`. This is a
+# nice, quick function to group together uncommon values.
+fct_lump(mth2, prop = 0.1)
+# or any level with only an n=1 in the category
+fct_lump(mth2, n = 1)
+# or if you want them renamed to `None`
+fct_lump(mth2, prop = 0.1, other_level = 'None')
+
+# this is also a really nice function to rename levels within a factor.
+fct_recode(mth2,
+           Jan = "January",
+           Feb = "February",
+           "Dec ember" = "December"
+           )
+
+```
+
+
+# Resources
+
+Very good book on R and data (<https://r4ds.had.co.nz>). Also the
+[tidyverse](https://tidyverse.org) resource is good.
+
+Factors (`forcats`):
+
+- <https://forcats.tidyverse.org/>
+- <https://r4ds.had.co.nz/factors.html>
+
+Strings (`stringr`):
+
+- <https://stringr.tidyverse.org/>
+- <https://r4ds.had.co.nz/strings.html>
+- For more complex string work, check out the
+[stringi package](https://cran.r-project.org/web/packages/stringi/index.html)
+
diff --git a/lessons/r/intermediate/lesson.md b/lessons/r/intermediate/lesson.md
new file mode 100644
index 000000000..b54ad1beb
--- /dev/null
+++ b/lessons/r/intermediate/lesson.md
@@ -0,0 +1,349 @@
+---
+layout: page
+title: "Intermediate topics in R: Strings and factors"
+visible: true
+tags: 
+  # languages
+  - r
+  # levels
+  - intermediate
+---
+
+
+
+# Strings, factors, and regular expressions
+
+ - **Authors**: Luke Johnston (@lwjohnst86)
+ - **Research field**: Nutritional Sciences
+ - **Lesson topic**: Working with character/string data
+ - **Lesson video link**: <https://youtu.be/jdMOmg9SMEc>
+ 
+Install these packages:
+
+
+```r
+install.packages(c("stringr", "forcats"))
+```
+
+Base R has some pretty decent string manipulation and searching functionality.
+The primary way of working with strings is through 
+[regular expressions](http://www.regular-expressions.info/). Regular
+expressions, or regex in R, are a group of characters that have special meaning
+and function. For instance, the `.` means wildcard and it will search for any
+character (letter, number, symbol, etc). Or `$` means end of the string or line.
+If you want to see more details on regex in R, run `?regex` to get help on it.
+
+In general, the most commonly used functions (at least that I use and that I've
+seen) are `grep`, `grepl`, `sub`, and `gsub` commands. Each of these take regex
+characters (`$ ^ * . + ? [] () |` are a few; see `?regex` for more). Let's
+begin with the base R functions!
+
+
+```r
+fruits <- c("apple", "banana", "pear")
+
+# replace `a` at beginning of string with nothing. `gsub` = global substitution
+gsub("^a", "", fruits)
+#> [1] "pple"   "banana" "pear"
+# replace letters `an` or `ar` with `bb`
+gsub("(an|ar)", "bb", fruits)
+#> [1] "apple"  "bbbbba" "pebb"
+# same as above but only first search and replace. `sub` = substitution
+sub("(an|ar)", "bb", fruits)
+#> [1] "apple"  "bbbana" "pebb"
+# search for fruits starting with a. Returns the number position of the search
+# result.
+grep("^a", fruits)
+#> [1] 1
+# same as above, but returns the actual value rather than the number
+grep("^a", fruits, value = TRUE)
+#> [1] "apple"
+# same as above, but returns a vector of logical values
+grepl("^a", fruits)
+#> [1]  TRUE FALSE FALSE
+```
+
+While base R is pretty powerful, it isn't always easy to work with and there are
+some major bits of functionality missing. That's where the `stringr` package
+(which is a subset of common functions from the `stringi` package) comes into
+play. `stringr` greatly expands on string manipulation in R. Let's go through
+some.
+
+
+```r
+library(stringr)
+
+# month.name is a default variable in R. I'm putting it in a new variable to
+# make it shorter to type out.
+mth <- month.name
+mth
+#>  [1] "January"   "February"  "March"     "April"     "May"      
+#>  [6] "June"      "July"      "August"    "September" "October"  
+#> [11] "November"  "December"
+
+# Almost every stringr function begins with `str_`.
+# Convert cases (lower case, UPPER CASE)
+str_to_lower(mth)
+#>  [1] "january"   "february"  "march"     "april"     "may"      
+#>  [6] "june"      "july"      "august"    "september" "october"  
+#> [11] "november"  "december"
+str_to_upper(mth)
+#>  [1] "JANUARY"   "FEBRUARY"  "MARCH"     "APRIL"     "MAY"      
+#>  [6] "JUNE"      "JULY"      "AUGUST"    "SEPTEMBER" "OCTOBER"  
+#> [11] "NOVEMBER"  "DECEMBER"
+
+# Find out the number of characters in each string of the vector
+str_length(mth)
+#>  [1] 7 8 5 5 3 4 4 6 9 7 8 8
+mth
+#>  [1] "January"   "February"  "March"     "April"     "May"      
+#>  [6] "June"      "July"      "August"    "September" "October"  
+#> [11] "November"  "December"
+
+# Combine (concatenate) the strings together. Similar to `paste`
+str_c(mth, collapse = ", ")
+#> [1] "January, February, March, April, May, June, July, August, September, October, November, December"
+
+# Take a subset of the months based on a regex.
+# for months starting with `J`
+str_subset(mth, "^J")
+#> [1] "January" "June"    "July"
+# for months ending in y
+str_subset(mth, "y$")
+#> [1] "January"  "February" "May"      "July"
+# for months that contain either s, n, or d (only lower case)
+str_subset(mth, "[snd]")
+#> [1] "January" "June"    "August"
+# same as above, but all letters since months have been converted to lower case
+str_subset(str_to_lower(mth), "[snd]")
+#> [1] "january"   "june"      "august"    "september" "november"  "december"
+
+# Because stringr is part of the tidyverse packages, you can also use the pipe
+# `%>%` command with the commands, to make it easier to read.
+mth %>%
+    str_to_lower() %>%
+    str_subset("[snd]")
+#> [1] "january"   "june"      "august"    "september" "november"  "december"
+
+# Using the fruit variable in the stringr package... different from the `fruits`
+# we created above!
+# str_detect converts to logical, for use in filtering, subsetting, etc.
+# searches for all fruit beginning with `be`
+str_detect(fruit, "^be")
+#>  [1] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
+#> [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+#> [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+#> [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+#> [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+#> [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+#> [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
+#> [78] FALSE FALSE FALSE
+
+# Find out the proportion (mean of values that are either 0 or 1) of fruit with
+# at least one vowel at the end of the string.
+str_detect(fruit, "[aeiou]$") %>%
+    mean()
+#> [1] 0.35
+
+# search for any fruit that does not end in an `a`
+str_detect(fruit, "^.*[^a]$")
+#>  [1]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
+#> [12]  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
+#> [23]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
+#> [34]  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
+#> [45]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
+#> [56] FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
+#> [67]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE
+#> [78]  TRUE  TRUE  TRUE
+# this is the same as above, but subsets instead
+str_subset(fruit, "[^a]$")
+#>  [1] "apple"             "apricot"           "avocado"          
+#>  [4] "bell pepper"       "bilberry"          "blackberry"       
+#>  [7] "blackcurrant"      "blood orange"      "blueberry"        
+#> [10] "boysenberry"       "breadfruit"        "canary melon"     
+#> [13] "cantaloupe"        "cherry"            "chili pepper"     
+#> [16] "clementine"        "cloudberry"        "coconut"          
+#> [19] "cranberry"         "cucumber"          "currant"          
+#> [22] "damson"            "date"              "dragonfruit"      
+#> [25] "durian"            "eggplant"          "elderberry"       
+#> [28] "fig"               "goji berry"        "gooseberry"       
+#> [31] "grape"             "grapefruit"        "honeydew"         
+#> [34] "huckleberry"       "jackfruit"         "jambul"           
+#> [37] "jujube"            "kiwi fruit"        "kumquat"          
+#> [40] "lemon"             "lime"              "loquat"           
+#> [43] "lychee"            "mandarine"         "mango"            
+#> [46] "mulberry"          "nectarine"         "nut"              
+#> [49] "olive"             "orange"            "pamelo"           
+#> [52] "passionfruit"      "peach"             "pear"             
+#> [55] "persimmon"         "physalis"          "pineapple"        
+#> [58] "plum"              "pomegranate"       "pomelo"           
+#> [61] "purple mangosteen" "quince"            "raisin"           
+#> [64] "rambutan"          "raspberry"         "redcurrant"       
+#> [67] "rock melon"        "salal berry"       "star fruit"       
+#> [70] "strawberry"        "tamarillo"         "tangerine"        
+#> [73] "ugli fruit"        "watermelon"
+
+# similar to sub
+# search any month that starts with J and ends with e and replace the middle
+# letters with `HIII`.
+str_replace(mth, "^(J).*(e)$", "\\1HIII\\2")
+#>  [1] "January"   "February"  "March"     "April"     "May"      
+#>  [6] "JHIIIe"    "July"      "August"    "September" "October"  
+#> [11] "November"  "December"
+
+# vector of sentences
+head(sentences)
+#> [1] "The birch canoe slid on the smooth planks." 
+#> [2] "Glue the sheet to the dark blue background."
+#> [3] "It's easy to tell the depth of a well."     
+#> [4] "These days a chicken leg is a rare dish."   
+#> [5] "Rice is often served in round bowls."       
+#> [6] "The juice of lemons makes fine punch."
+
+# you can split strings up by a specific character. `str_split` creates a list,
+# so I use `[[1]]` or `[1]` to show only the first one.
+str_split(sentences, " ")[[1]] # this outputs a vector
+#> [1] "The"     "birch"   "canoe"   "slid"    "on"      "the"     "smooth" 
+#> [8] "planks."
+str_split(sentences, " ")[1] # this outputs a list
+#> [[1]]
+#> [1] "The"     "birch"   "canoe"   "slid"    "on"      "the"     "smooth" 
+#> [8] "planks."
+# or split by the letter `e`
+str_split(sentences, "e")[[1]]
+#> [1] "Th"              " birch cano"     " slid on th"     " smooth planks."
+# if you want to split by the word, use the `boundary` function.
+str_split(sentences, boundary("word"))[[1]]
+#> [1] "The"    "birch"  "canoe"  "slid"   "on"     "the"    "smooth" "planks"
+# or by a sentence
+str_split(sentences, boundary("sentence"))[[1]]
+#> [1] "The birch canoe slid on the smooth planks."
+
+# include in a pipe
+str_split(sentences, boundary("sentence"))[[1]] %>%
+    # convert to Title Case
+    str_to_title()
+#> [1] "The Birch Canoe Slid On The Smooth Planks."
+```
+
+String manipulation is great, but sometimes you need to convert to factor
+variables for use in statistical models or when plotting (for instance when
+using `ggplot2`). The `forcats` package makes it easier to work with factors.
+Base R really only has one function for working with factors... the `factor`
+command.
+
+
+```r
+library(forcats)
+
+str(mth)
+#>  chr [1:12] "January" "February" "March" "April" "May" ...
+# This is base function. Compare this...
+as.factor(mth)
+#>  [1] January   February  March     April     May       June      July     
+#>  [8] August    September October   November  December 
+#> 12 Levels: April August December February January July June March ... September
+# with this...
+as_factor(mth)
+#>  [1] January   February  March     April     May       June      July     
+#>  [8] August    September October   November  December 
+#> 12 Levels: January February March April May June July August ... December
+# base R as.factor orders alphabetically, while forcats as_factor orders in
+# appearance.
+
+mth2 <- c(mth[10:12], mth[6:12], mth)
+mth2
+#>  [1] "October"   "November"  "December"  "June"      "July"     
+#>  [6] "August"    "September" "October"   "November"  "December" 
+#> [11] "January"   "February"  "March"     "April"     "May"      
+#> [16] "June"      "July"      "August"    "September" "October"  
+#> [21] "November"  "December"
+
+# you can also order by frequency:
+fct_infreq(mth2)
+#>  [1] October   November  December  June      July      August    September
+#>  [8] October   November  December  January   February  March     April    
+#> [15] May       June      July      August    September October   November 
+#> [22] December 
+#> 12 Levels: December November October August July June September ... May
+# this more explicitly orders by appearance.
+fct_inorder(mth2)
+#>  [1] October   November  December  June      July      August    September
+#>  [8] October   November  December  January   February  March     April    
+#> [15] May       June      July      August    September October   November 
+#> [22] December 
+#> 12 Levels: October November December June July August ... May
+
+# this counts the number of levels within the factors. It is a simple summary
+fct_count(mth2, sort = TRUE)
+#> # A tibble: 12 × 2
+#>            f     n
+#>       <fctr> <int>
+#> 1   December     3
+#> 2   November     3
+#> 3    October     3
+#> 4     August     2
+#> 5       July     2
+#> 6       June     2
+#> 7  September     2
+#> 8      April     1
+#> 9   February     1
+#> 10   January     1
+#> 11     March     1
+#> 12       May     1
+
+# if you want to convert levels that contains only a few (in this case, less the
+# 10% of the total proportion), they will be converted to `Other`. This is a
+# nice, quick function to group together uncommon values.
+fct_lump(mth2, prop = 0.1)
+#>  [1] October  November December Other    Other    Other    Other   
+#>  [8] October  November December Other    Other    Other    Other   
+#> [15] Other    Other    Other    Other    Other    October  November
+#> [22] December
+#> Levels: December November October Other
+# or any level with only an n=1 in the category
+fct_lump(mth2, n = 1)
+#>  [1] October  November December Other    Other    Other    Other   
+#>  [8] October  November December Other    Other    Other    Other   
+#> [15] Other    Other    Other    Other    Other    October  November
+#> [22] December
+#> Levels: December November October Other
+# or if you want them renamed to `None`
+fct_lump(mth2, prop = 0.1, other_level = 'None')
+#>  [1] October  November December None     None     None     None    
+#>  [8] October  November December None     None     None     None    
+#> [15] None     None     None     None     None     October  November
+#> [22] December
+#> Levels: December November October None
+
+# this is also a really nice function to rename levels within a factor.
+fct_recode(mth2,
+           Jan = "January",
+           Feb = "February",
+           "Dec ember" = "December"
+           )
+#>  [1] October   November  Dec ember June      July      August    September
+#>  [8] October   November  Dec ember Jan       Feb       March     April    
+#> [15] May       June      July      August    September October   November 
+#> [22] Dec ember
+#> 12 Levels: April August Dec ember Feb Jan July June March May ... September
+```
+
+
+# Resources
+
+Very good book on R and data (<https://r4ds.had.co.nz>). Also the
+[tidyverse](https://tidyverse.org) resource is good.
+
+Factors (`forcats`):
+
+- <https://forcats.tidyverse.org/>
+- <https://r4ds.had.co.nz/factors.html>
+
+Strings (`stringr`):
+
+- <https://stringr.tidyverse.org/>
+- <https://r4ds.had.co.nz/strings.html>
+- For more complex string work, check out the
+[stringi package](https://cran.r-project.org/web/packages/stringi/index.html)
+
diff --git a/lessons/r/intro/Intro_R_Sept21.R b/lessons/r/intro/Intro_R_Sept21.R
new file mode 100644
index 000000000..ab13fa0e2
--- /dev/null
+++ b/lessons/r/intro/Intro_R_Sept21.R
@@ -0,0 +1,122 @@
+
+### DATA LOADING ###
+
+# Set the file location (working directory).  This is where R will look for files and where R will put any files that it writes. 
+setwd()
+
+# Taking existing file (that was built into R) called "swiss" and writing it as a file that now exists on your computer, in your working directory
+write.csv(swiss, file="SwissData.csv")
+
+# Reading/uploading a file from your computer and putting it into R.  We are assigning it a name, df1
+df1 <- read.csv("SwissData.csv")
+
+# Hey look, we can see our datafile in RStudio! 
+View(df1)
+
+# Here are four other different ways of seeing your data
+summary(df1)
+dim(df1)
+head(df1)
+names(df1)
+
+# This is one way that we can select and refer to a single column
+df1$Fertility
+
+### DATA CLEANING ###
+
+# How to reassign a value (sort of like "find and replace" for a single column).  We are replacing every "Fertility" value that is under 65 with a NA. 
+df1$Fertility[df1$Fertility<=65] <- NA
+
+
+# Here, we are creating a new data file (df2) that only includes the rows that had full and complete data. It's excluding rows that had a NA value anywhere in it. Now any county (row) with any missing value is absent.  
+df2 <- na.omit(df1) 
+
+dim(df1)
+dim(df2)
+
+
+# Here we are creating a new variable, based on a condition.  "BinaryEd" is now a new variable, unconnected to anything else, floating in space
+BinaryEd <- ifelse(df2$Education < 8, 'Low', 'High')
+
+# Here we are a creating a new variable (column) that is in the dataset.  We have changed the df2 object.  
+df2$BinaryEd <- ifelse(df2$Education < 8, 'Low', 'High')
+
+
+
+# Here, we've create a new variable separately (floating in space), but then attach it to a dataframe using column bind/cbind.
+# You might use cbind (or rbind, to bind rows) to paste together datasets or something. 
+AgRain <- df2$Agriculture / 30
+df3 < - cbind(AgRain, df2)
+
+
+
+### STATISTICS USING A DATASET ###
+
+# Here is the t-test function, comparing infant mortality for the low and high education counties.  
+t.test(df2$Infant.Mortality[df2$BinaryEd == 'Low'], df2$Infant.Mortality[df2$BinaryEd == 'High']))
+
+# If we don't want to repeat df2$  all of the time, since it can be repetitive, we can specify our dataset by doing our t-test within a "with" statement. 
+with(df2, t.test(Infant.Mortality[BinaryEd == 'Low'], Infant.Mortality[BinaryEd == 'High']))
+
+# Correlation with three different syntaxes
+with(df2, cor.test(Infant.Mortality, Catholic))
+
+cor.test(df2$Infant.Mortality, df2$Catholic)
+
+attach(df2)  
+cor.test(Infant.Mortality, Catholic)
+detach(df2)
+## USE WITH CAUTION:  Attaching and Detaching are not smart functions, and can result in over-writing, not remembering what data-set you are using, and general disaster.  Basically, whenever you "attach", "detach" right away.  Or use other better options.  
+
+
+# Some functions are smart enough to know that you'll want to indicate a dataset.  Here we run a linear regression, and specify data = df2
+summary(lm(Infant.Mortality ~ Catholic, data = df2))
+
+
+### INTRO TO PLOTS ###
+
+plot(df2$Infant.Mortality, df2$Catholic)  # initially ugly
+plot(df2$Education, df2$Catholic, xlab="Education (%)", ylab = "% Catholic", pch= 20, cex = 1.2)  
+abline( lm( df2$Catholic ~ df2$Education))  # add a line based on the coefficients from a linear regression
+# There are infinite specifications that you can change on your plots.  To have a prettier starting plot, learn/use ggplot2 (requires ggplot2 library)
+
+
+
+
+## MORE COOL STUFF THAT REQUIRED THE UNAVAILABLE PACKAGES ## 
+
+# Check out the youtube video from last year:  https://www.youtube.com/watch?v=pbG_3ZuNyx8
+# And the code-along information:  https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/r/intro/lesson.md
+# No really, watch the video, it will explain what the heck this following code does and how it's doing it and how you can do it. 
+
+install.packages("dplyr") 
+library(dplyr)
+
+
+df2 %>% 
+    gather(ColumnName, Value, -X, -BinaryEd) %>% 
+    group_by(ColumnName)  %>% 
+    summarize(MeanSD = paste0(round(mean(Value),2), 
+                              " (", 
+                              round(sd(Value), 2), 
+                              ")"
+                              ))
+                              
+                            
+ 
+ # If you install the package knitr ....
+ install.packages("knitr")
+ library(knitr)
+ 
+ 
+ ##Then try this!!
+ df2 %>% 
+    gather(ColumnName, Value, -X, -BinaryEd, - Education) %>% 
+    group_by(ColumnName, BinaryEd)  %>% 
+    summarize(MeanSD = paste0(round(mean(Value),2), 
+                              " (", 
+                              round(sd(Value), 2), 
+                              ")"
+                              ))  %>% 
+    spread(ColumnName, MeanSD)  %>% 
+    kable()                                                    
diff --git a/lessons/r/intro/Intro_to_R_May23.Rmd b/lessons/r/intro/Intro_to_R_May23.Rmd
new file mode 100644
index 000000000..d5f85997e
--- /dev/null
+++ b/lessons/r/intro/Intro_to_R_May23.Rmd
@@ -0,0 +1,303 @@
+---
+title: "Introduction to R"
+author: "Margot Lautens"
+date: "May 23, 2019"
+output: html_document
+---
+
+ - **Authors**: Margot Lautens, adapted from original by Luke Johnston <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/intro>
+ - **Research field**: Molecular Genetics (Alternative metabolism)
+ - **Lesson topic**: Intro to R and data wrangling
+ - **Lesson content URL**: <>
+ - **Lesson video stream**: <>
+
+This is a brief introduction to R, focussing on data wrangling using
+`dplyr` and `tidyr` packages.
+
+You **don't need to read all of this** for the session.  It's more of
+a **resource and reference**.
+
+R is a statistical computing environment to analyze data and write
+programs.  The strength of R comes from:
+
+* Being developed by statisticians to do statistical analysis.
+* Publication-quality graphic capabilities.
+* Ability to make reproducible documents.
+* Its extensive and active community of users for doing statistical
+  work:
+    - R is the top tag (x20005) in the
+      [StackExchange CrossValidated site](https://stats.stackexchange.com/tags)
+      for statistical questions
+    - There are more than 14000 packages on
+      [CRAN](https://cran.r-project.org/web/packages/) that stores all
+      R packages (and installing these packages is straight-forward),
+      from the obscure and cutting-edge statistical techniques to
+      plotting to data wrangling.
+
+I should mention a quick caveat.  While R is a general-purpose
+programming language, it works a bit differently from other languages
+such as Python (it was developed by statisticians after all!).  As
+such, programming in R may not be as intuitive, powerful, or easy as
+it may be in Python (though it can be done), especially if you come
+from a computer science background.  If your work involves a lot of
+programming, I would recommend Python as your main tool.  *However*,
+it never hurts to learn more than one language, especially as R is
+great for data analysis and plotting.
+
+One of the most important differences is that R is base 1 (as opposed to 0). 
+This means that the first item in any object is noted by 1.
+
+Ok, firstly, I've made this session with some assumptions (see the
+[slides_20190523.html](../slides_20190523.html)) file.  Briefly I'm assuming you want to
+use R for statistical analysis, plotting, and/or writing up reports.
+I'm using R Markdown to show how to write up documents with R code and
+since getting the data into an analyzable form is the hardest part of
+an analysis, I'm using packages specific to that task.
+
+While you can create functions in R, I won't be going over them in this session.  A
+great resource for R functions is
+[this page from Hadley Wickham's 'Advanced R' book](http://adv-r.had.co.nz/Functions.html)
+If you'd be interested in writing functions in R, please let me know and we'll
+try and offer a session on them.
+
+# R Markdown #
+
+An `.Rmd` or [R Markdown](http://rmarkdown.rstudio.com/) file is a
+[markdown](https://en.wikipedia.org/wiki/Markdown) file that contains
+R code chunks that can be processed to output the results of the R
+code into a generated `.md` file.  This is an incredible (and recent)
+strength of using R, as this then allows you to create html, pdf, or
+Word doc files from the `.md` file using the `rmarkdown` package
+(which relies on [pandoc](https://pandoc.org)).
+
+On the top of each `.Rmd` file is the
+[YAML](https://en.wikipedia.org/wiki/YAML) front matter, which looks
+like:
+
+```
+---
+title: "Introduction to R"
+author: "Margot Lautens"
+date: "May 23, 2019"
+output: 
+  html_document
+
+    
+---
+```
+
+Note the starting and ending `---` 'tags'.  This starts the YAML
+block.
+
+Markdown syntax for formatting is used in `.Rmd`.  Check out the
+[R Markdown documentation](http://rmarkdown.rstudio.com/) for a quick
+tutorial on the syntax.
+
+# Import/export your data #
+
+You'll need to import your data into R to analyze it. If at any time
+you need help with a command, use the `?` command, appended with the
+command of interest (eg. `?write.csv`).  R comes with many internal
+datasets that you can practice on.  The one I'm going to use is the
+`CO2` dataset. 
+
+You can also import datasets in RStudio with Tools/Import Dataset.
+
+```{r importData}
+?write.csv
+
+write.csv(CO2, file = 'CO2.csv') # Export
+ds <- read.csv('CO2.csv') # Import
+```
+
+# Viewing your data #
+
+R has several very useful and easy tools for quickly viewing your
+data as does Rstudio. 
+
+To view the data in full use `View()` and a new window will open in RStudio.
+
+`head()` shows the first few rows of a data.frame/tibble (both structures 
+for storing data that can include numbers, integers, factors, strings, etc).  
+`names()` shows the column names.
+`summary()` shows a quick description of the summary statistics (means, 
+median, frequency)for each of your columns.    
+`str()` shows the structure,such as what the object is, and its contents.  
+
+`glimpse()` is a `dplyr` only function that ressembles `str` but unlike `str`
+it allows you to see all your columns.
+
+
+
+```{r viewData}
+head(ds)
+names(ds)
+summary(ds)
+str(ds)
+
+library(dplyr)
+glimpse(ds)
+```
+
+# Wrangling your data #
+
+Data wrangling is a bit tedious in base R. There are two great packages which we loaded in at the beginning of the session, `dplyr`and `tidyverse`.
+
+designed to make this easier.  Load packages by using the `library()`
+function.  `dplyr` allows for easy manipulation of datasets.
+
+`select` is a `dplyr` function that lets us select columns from the dataset. 
+
+Columns can be selected
+individually with `,`, in a series with `:` or removed with `-`.
+
+```{r wrangleSelect}
+library(dplyr)
+#The first two  lines of code are the same. 
+select(df,id,pluralty,outcome,date)
+select(df,id:date)
+
+#This line of code selects all columns except ht and wt.
+select(df,-ht, -wt)
+```
+
+`dplyr` also comes with a `%>%` pipe function (via the
+`magrittr` package), which works similar to how the Bash shell `|`
+pipe works.  The command on the right-hand side takes the output from
+the command on the left-hand side, just like how a plumbing pipe works
+for water. The `.` object represents the output from the pipe, but it 
+doesn't have to be used as using `%>%` implies also using `.`.
+
+```{r wranglePipe}
+
+select(ds,Plant,Type,Treatment)
+
+ds %>% select(Plant,Type,Treatment)
+ds %>% select(.,Plant,Type,Treatment)
+ds %>% select(Plant,Type,Treatment,conc) %>% select(Plant,Type,Treatment)
+```
+
+You can rename columns either using `rename` or `select` (the new name
+is on the left hand side, so `newname = oldname`).  However, with the
+`select` command, only that column gets selected, while `rename`
+selects all columns.
+
+```{r wrangleRename}
+
+ds %>% rename(Concentration = conc)
+ds %>% select(Concentration = conc)
+```
+
+You can subset the dataset using `filter`.  Note the double equal sign
+`==` for testing if 'Examination' is equal to 15.  A single `=` is
+used for something else (assigning things to objects). Use `&` or `,`
+to apply multiple filters.
+
+```{r wrangleFilter}
+
+filter(ds,conc==95)
+ds %>% filter(uptake>20)
+ds %>% filter(Type=="Quebec")
+
+## These are all the same:
+ds %>% filter(conc==95) %>% filter(Type=="Quebec")
+ds %>% filter(conc==95 , Type=="Quebec")
+ds %>% filter(conc==95 & Type=="Quebec")
+```
+
+You can use filter so long as the test generates a logical output
+(ie TRUE, FALSE). This means you can `grepl` inside of `filter` to 
+search and find partial string matches.
+
+You input into `grepl` your desired pattern *first*.
+
+```{r wrangleSearch}
+
+grepl("Qn",ds$Plant) 
+
+ds %>% filter(grepl("Qn",Plant))
+
+ds %>% filter(grepl("chill",Treatment))
+```
+
+We can start chaining these commands together using the `%>%` command.
+There is no limit to how long a chain can be.  `arrange`
+sorts/orders/re-arranges the column uptake in ascending
+order. `mutate` creates a new column. `ifelse` performs a logical test 
+(just like `filter`), then fills the cell depending on the output of that test.
+
+```{r wrangleChain}
+ds %>%
+  filter(Treatment=="chilled") %>%
+  select(Plant, conc, uptake) 
+  
+ds %>%
+  filter(Treatment=="chilled") %>%
+  select(Plant, conc, uptake) %>%
+  arrange(uptake) %>%
+  mutate(Qn = ifelse(grepl("Qc",Plant),"Yes","No"),
+         testing = 'Yes' ## Create a testing column to show how mutate works.
+         )
+```
+
+R is very powerful at analyzing data but is biased. It prefers long datasets like `CO2`. 
+Compare `CO2` with how some of your datasets may be configured (ie wide). Check out 
+[Hadley Wickham's 2014 paper](http://vita.had.co.nz/papers/tidy-data.html) on analyzing 
+long vs wide data. In R talk we call these datasets 'tidy'.
+
+To get your data into a nicer and more analyable format, you can use the `tidyr` package.  
+See what `gather` does in the code below.  Then see what `spread` does.  
+
+```{r reorg}
+library(tidyr)
+## Compare this:
+ds
+## With this:
+ds %>%
+  spread(conc,uptake)
+## And back again:
+ds %>%
+  spread(conc,uptake) %>%
+  gather(conc, uptake,-Plant,-Type,-Treatment)
+```
+
+Combined with `dplyr`'s `group_by` and `summarise` you can quickly
+summarise data or do further, more complicated analyses. `group_by`
+makes it so further analyses or operations work on the groups.
+`summarise` transforms the data to only contain the new variable(s)
+created, in this case the mean. You can try out `max()`,`min()`,`median()` and `sd()`.
+
+```{r reorgChain}
+ds %>%
+  group_by(Treatment,conc) %>%
+  summarise(mean = mean(uptake))
+```
+
+# Generate this document #
+
+Check out the documentation on
+[`knitr`](https://yihui.name/knitr/options/) or
+[R Markdown](http://rmarkdown.rstudio.com/authoring_rcodechunks.html)
+for R code chunk options.  If you look at the raw `.Rmd` file for this
+[document](../main.Rmd), you can see that the below code chunk uses
+`eval = FALSE`, which tells knitr to not run this code chunk.
+
+These two commands generate either a html or a md file.
+
+```{r render, eval = FALSE}
+## into html
+library(rmarkdown)
+render('lesson.Rmd') ## or can use rmarkdown::render('main.Rmd')
+## into md
+library(knitr)
+knit('lesson.Rmd') ## or can use knitr::knit('main.Rmd')
+```
+
+# Challenge: Try this out for yourself! #
+
+Using the `swiss` make a tibble with the means + sd of Agriculture, Examination, Education,
+and Infant.Mortality for fertility above 50 vs below 50 when Catholic is less than 60.
+
+Investigate `distinct`, `drop_na`, `separate`/`unite` and the `join` family using `?` for more ways to manipulate your data!
+
+Check out stackechange if you have questions or bring them to our weekly coworking sessions.
\ No newline at end of file
diff --git a/lessons/r/intro/Intro_to_R_slides_May23.Rmd b/lessons/r/intro/Intro_to_R_slides_May23.Rmd
new file mode 100644
index 000000000..3d893df55
--- /dev/null
+++ b/lessons/r/intro/Intro_to_R_slides_May23.Rmd
@@ -0,0 +1,29 @@
+---
+title: "Introduction to R: Initial comments"
+author: "Margot Lautens"
+date: "May 23, 2019"
+output: 
+  slidy_presentation
+
+---
+
+## Learning goals/expectations
+
+- How to interact with R in RStudio
+- How to import/export your data
+- How to view the structure of your data
+- How to work with the data
+- How to organize data into analyzable format (long vs wide)
+
+## 4-5 main concepts:
+
+- **Getting the data**: read.csv, write.csv
+- **View the data**: View, head, names, str, glimpse, summary
+- **Working the data (dplyr):** select, filter, grepl, mutate,
+  summarise, arrange, rename, group\_by, `%>%` pipe
+- **(Re)Organize the data (tidyr):** gather, spread
+
+## Generate these slides using: ##
+
+```{r render, eval = FALSE}
+rmarkdown::render('20190523_Intro_to_R_slides.Rmd')
diff --git a/lessons/r/intro/StudentCopy_Oct13.Rmd b/lessons/r/intro/StudentCopy_Oct13.Rmd
new file mode 100644
index 000000000..486452ceb
--- /dev/null
+++ b/lessons/r/intro/StudentCopy_Oct13.Rmd
@@ -0,0 +1,348 @@
+---
+title: "StudentCopy_Oct13"
+author: "Tara"
+date: "10/8/2021"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+#Welcome
+
+ - **Authors**: Tara Henechowicz adapted from plans by Margot Lautens and Luke Johnston
+ - **Research field**: Neuroscience (Music and Health)
+ - **Lesson topic**: Intro to R, some data wrangling, statistics, and some visualization
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/intro>
+
+#Why should we use R? 
+- reproducible science 
+- wonderful packages to help you for working with your data (even datasets that are very big)!
+- packages specific to disciplines: biology, genetics, even neuroimaging
+- can customize and create reports with RMarkdown. 
+
+#What can we do with R? 
+- Organize: Tidy data
+- Analyze: statistics, machine learning, field-specific techniques
+- Visualize
+
+#Starting with R - the command line
+
+##Command Line
+
+The command line executes code like a calculator but does not keep a record of your work. 
+Try entering 2+2 in the commandline, what is the output? 
+
+Now lets save 4 as a variable a. The way we do this in R is typing `a <- 4` in the command line. Here the `<-` is acting as an = sign. 
+
+In summary, you can execute code in the command line, but you will need to scroll back to see the previous code you executed. To reproduce your code, you will need to create a script. 
+
+##Scripts
+The type of script we are working in is called and .Rmd file it is an R Markdown script. THis script is helpful for building reports which can later be rendered as a pdf or html file. 
+
+Below is a `code chunk`, where you keep a record of your code and you can execute that code. Anything outside of the code can be used as text to supplement your report or write notes about your process. You can open a code chunk using the `Insert` button or using the short cut for your device. 
+
+Try running the code chunk below by pressing the green play button. 
+```{r}
+#this is a comment, this will not run as code. 
+#Assign the value of 4 to variable "a"
+
+#return the value of a
+
+#Ask r if 2+2 is equal to "a"
+
+```
+
+##Other windows in R
+- Environment: all the variables we have saved, our functions, and libraries we have loaded
+- History
+- Files
+- Help 
+
+#Starting your script
+1. Set our "working directory", this means we are telling R where to take files from and where to write them. 
+2. Load in the packages we need to use for our project. 
+If we don't have the package installed already, you will use the `install.packages` function.
+Whether you have the package or not, you will load the package using the `library` function.
+```{r}
+#Load in the tidyverse amd Using R libraries
+
+```
+
+3. Load in our data.
+We will be using a dataset that is already in R, so lets take a look at `babies` and try to store as variable `df` in our environment.
+```{r}
+#call the babies dataset
+
+#we still need to store this dataset in our R environment
+
+```
+
+Now if we call df, we can see we have the babies dataset 
+```{r}
+#viewing df
+
+#OR
+
+```
+
+Lets use the function write.csv to take that data and save it as a .csv file in our current directory
+
+```{r}
+
+```
+
+A few ways to lad data are to:
+- manually use the import dataset button in Rstudio (and copy the code from the output)
+- use `read_csv()` function from readr package
+```{r}
+#copy the code generated by the import dataset button
+
+```
+
+#Viewing the data and its structure
+
+1. Instead of viewing the whole dataset we can use the `head` function to view the first 6 rows of the data. 
+2. If you want to view more rows of data, use the argument n= to change the size of the table returned. 
+```{r}
+#view the whole dataset
+
+#use the head function to view the first 6 rows of the data
+
+#Look at the first 10 rows of the data
+
+```
+
+3. Another important function is `str` it returns the structure of the object. For a `data.frame` that we have here, it will also give you the column names and the structure of those columns. 
+`dim` is also a useful function that will retrun the dimesnions of your data.frame.
+`names()` shows the column names.
+`glimpse()` is a `dplyr` only function that ressembles `str` but unlike `str`
+it allows you to see all your columns.
+`row.names()` or `rownames()` will return a vector of the rownames of the dataset. However, only `rownames()`can be changed, we will return to the uses of this function later. 
+
+```{r}
+#use the str() function on df
+
+#use the dim() function to return the size of df
+
+#use names() to return the column names
+
+#use row.names() or rownames() to return a vector of the rownames
+
+```
+
+#Summary statistics and exploring patterns
+
+1. `summary()` shows a quick description of the summary statistics (means, 
+median, frequency)for each of your columns.  
+
+This function is especially useful for finding issues in the data coding and in the data structure.
+For example, look at the variable sex - is there is something strange here?
+Are there any other variables that might have issues?
+These are all issues we can fix when we tidy the data, we will return to data tidying in the next section. 
+```{r}
+#give a summary of df
+
+```
+
+2. Plotting to explore the data
+
+Before we plot any of the columns, we need to know how to select columns from the dataset. 
+To call a column of the data we can use the $ operator: df$sex will give us the sex column. 
+With dataframes, we can call specific rows and collumns using the index. For example with df[1,6], will give us the 1st row and the 6th column. We can also select a range of columns or rows using `:` operator. 
+
+```{r}
+#Try calling the wt column using the `$`
+
+#Try another way to get the wt column
+
+#Try to get the first ten rows of the weight column
+
+```
+
+
+2. There are some base functions that allow us to visualize our data:
+`hist()` will provide a historgram of a variable.
+`plot()` can allow you to compare two variables, x and y, in many different types of plots. 
+
+```{r}
+#Try plotting wt as a histogram
+
+#try plotting ht as the x variable and wt as the y variable
+
+#Now lets use the help function ? to view the plot function
+
+#Lets try changing the type argument
+
+```
+
+#Introduction to data tidying and tidyverse
+
+1. When we looked at the `summary()` of our data, we found that some variables were coded as numeric, when they should be binary or categorical (factors).
+`as.logical()` will transform the variable into a logical `TRUE` or `FALSE`
+`as.numeric()` will transform the variable into a numeric variable
+`as.character()` will transform the variable into character strings
+`as.factor()` will transform the variable into a factor. However, the levels will be created automatically and might not be the correct order. You might want to use `factor()` to change the variable. 
+
+```{r}
+#lets take a look at the summary again
+
+#let's look at what the coding should be for the variables in babies by looking at the data key
+
+#Sex is a factor with 3 levels, 1,2,9
+
+#only one level is showing, let's use factor() to and set levels = c("1","0")
+
+#now we have to save our new variable 
+
+```
+
+Try it yourself:
+1. check the data key on the babies data set using ?babies and look at the variable marital
+2. change the variable marital to a character 
+3. look at the summary of marital as a character variable
+4. now change marital to a factor
+5. look at the summary of marital as a factor, what is the difference between the summary if marital is a character or a factor?
+```{r}
+
+```
+
+2. There are many helpful functions to deal with missing data in R.
+You might want to check how many missing values there are using `is.na()` or remove missing all rows of data using `na.omit()`.
+
+Sometimes you will receive datasets where NAs are missing and othertimes the coders have used key to notate NAs. In the babies dataset, NAs in the wt column are coded as 999. We saw this when we visualized wt and ht.
+
+```{r}
+
+```
+
+
+For this dataset, in the ht column, NAs are coded as 99.
+We want to create a statement telling R that if df$ht is equal to 99, we want to rewrite that code as NA, if that is not TRUE, then keep the original output. 
+This is called an ifelse statement. An easy way to do this in R is using the `ifelse()` function. The first value is the statement of what we are looking for, `df$ht==99`, the next value will be what we should do if that statement is `TRUE`, the third value will be what to do if the statement is `FALSE`. 
+Once we have the 99s coded as NA, we can count the number of NAs in the vector using `is.na()`. 
+Lastly, we can remove rows that have NAs using `na.omit()`.
+```{r}
+#Use the ifelse() function to tell R to replace df$ht values that are 99 with NA. 
+
+#Store the new variable back into df$ht
+
+#To count the number of NAs 
+
+#To remove the rows that have NA in them from the dataset, we can use the na.omit() function
+
+#Let's store the  df with NAs omited into a new data frame, name it df2
+
+
+```
+
+#Introduction to Tidyverse
+
+Data wrangling is a bit tedious in base R. At the beginning of the session we loaded in a great collection of packages, `tidyverse` designed to make this easier.  Load packages by using the `library()`
+function.  `dplyr` allows for easy manipulation of datasets.
+
+`select` is a `dplyr` function that lets us select columns from the dataset. 
+
+Columns can be selected
+individually with `,`, in a series with `:` or removed with `-`.
+```{r wrangleSelect}
+#The first two  lines of code are the same. 
+dplyr::select(df,id,pluralty,outcome,date)
+dplyr::select(df, id:date)
+
+#This line of code selects all columns except ht and wt.
+dplyr::select(df,-ht, -wt)
+```
+
+`dplyr` also comes with a `%>%` pipe function (via the
+`magrittr` package), which works similar to how the Bash shell `|`
+pipe works. The command on the right-hand side takes the output from
+the command on the left-hand side, just like how a plumbing pipe works
+for water. The `.` object represents the output from the pipe, but it 
+doesn't have to be used as using `%>%` implies also using `.`.
+
+```{r wranglePipe}
+
+#The outputs of each of these two lines of code are the same. 
+df %>% 
+  dplyr::select(id,pluralty,outcome,date)
+df %>%
+  dplyr::select(id:date)
+
+#This line of code selects all columns except ht and wt.
+df %>%
+  dplyr::select(-ht, -wt)
+```
+
+You can rename columns either using `rename` or `select` (the new name
+is on the left hand side, so `newname = oldname`).  However, with the
+`select` command, only that column gets selected, while `rename`
+selects all columns.
+
+```{r wrangleRename}
+
+df %>% rename(weight = wt) #rename wt column as weight
+df %>% dplyr::select(weight = wt) #rename wt column as weight and only select weight column
+```
+
+You can subset the dataset using `filter`.  Note the double equal sign
+`==` for testing if 'Examination' is equal to 15.  A single `=` is
+used for something else (assigning things to objects). Use `&` or `,`
+to apply multiple filters.
+
+```{r wrangleFilter}
+#find the babies that have a weight that equal to 100 grams
+filter(df,wt==100)
+#find the babies that have a weight that are greater than 100 grams
+df %>% filter(wt>100)
+#filter for marital status does not equal 1
+df %>% filter(marital!=1)
+
+## These are all the same:
+df %>% filter(wt>100)  %>% filter(marital!=1)
+df %>% filter(wt>100 , marital!=1)
+df %>% filter(wt>100 & marital!=1)
+```
+
+We can start chaining these commands together using the `%>%` command.
+There is no limit to how long a chain can be. `mutate` creates a new column. 
+
+```{r wrangleChain}
+df %>%
+  filter(wt>100) %>% #filter wt>100
+  mutate(hwratio = wt/ht) %>% #creates a new varaiable that is the weight to height ratio
+  dplyr::select(-ht, -wt) #do not select the original ht and wt columns
+```
+
+R is very powerful at analyzing data but is biased. To get your data into a nicer and more analyable format, you can use the `tidyverse` package.  
+See what `gather` does in the code below.  Then see what `spread` does.  
+
+```{r reorg}
+## Compare this:
+df
+## With this:
+df %>%
+  gather(ht,wt, key ="variable", value = "grams")
+
+## And back again:
+df %>%
+  gather(ht,wt, key ="variable", value = "grams") %>% 
+  spread(variable, grams) 
+
+```
+
+Combined with `dplyr`'s `group_by` and `summarise` you can quickly
+summarise data or do further, more complicated analyses. `group_by`
+makes it so further analyses or operations work on the groups.
+`summarise` transforms the data to only contain the new variable(s)
+created, in this case the mean. You can try out `max()`,`min()`,`median()` and `sd()`.
+
+```{r reorgChain}
+df %>%
+  group_by(smoke) %>% #group by smoking status
+  summarise(mean = mean(wt)) #summarize the mean
+
+```
+
+#show how to knit using knit button
diff --git a/lessons/r/intro/TeachCopy_Oct13.Rmd b/lessons/r/intro/TeachCopy_Oct13.Rmd
new file mode 100644
index 000000000..18a1cc902
--- /dev/null
+++ b/lessons/r/intro/TeachCopy_Oct13.Rmd
@@ -0,0 +1,353 @@
+---
+title: "Intro_R_Oct8_2021"
+author: "Tara"
+date: "10/8/2021"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+#Welcome
+
+ - **Authors**: Tara Henechowicz adapted from plans by Margot Lautens and Luke Johnston
+ - **Research field**: Neuroscience (Music and Health)
+ - **Lesson topic**: Intro to R, some data wrangling, statistics, and some visualization
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/intro>
+
+#Why should we use R? 
+- reproducible science 
+- wonderful packages to help you for working with your data (even datasets that are very big)!
+- packages specific to disciplines: biology, genetics, even neuroimaging
+- can customize and create reports with RMarkdown. 
+
+#What can we do with R? 
+- Organize: Tidy data
+- Analyze: statistics, machine learning, field-specific techniques
+- Visualize
+
+#Starting with R - the command line
+
+##Command Line
+
+The command line executes code like a calculator but does not keep a record of your work. 
+Try entering 2+2 in the commandline, what is the output? 
+
+Now lets save 4 as a variable a. The way we do this in R is typing `a <- 4` in the command line. Here the `<-` is acting as an = sign. 
+
+In summary, you can execute code in the command line, but you will need to scroll back to see the previous code you executed. To reproduce your code, you will need to create a script. 
+
+##Scripts
+The type of script we are working in is called and .Rmd file it is an R Markdown script. THis script is helpful for building reports which can later be rendered as a pdf or html file. 
+
+Below is a `code chunk`, where you keep a record of your code and you can execute that code. Anything outside of the code can be used as text to supplement your report or write notes about your process. You can open a code chunk using the `Insert` button or using the short cut for your device. 
+
+Try running the code chunk below by pressing the green play button. 
+```{r}
+#this is a comment, this will not run as code. 
+#Assign the value of 4 to variable "a"
+a<-4
+#return the value of a
+print(a)
+#Ask r if 2+2 is equal to "a"
+2+2==a
+```
+
+##Other windows in R
+- Environment: all the variables we have saved, our functions, and libraries we have loaded
+- History
+- Files
+- Help 
+
+#Starting your script
+1. Set our "working directory", this means we are telling R where to take files from and where to write them. 
+2. Load in the packages we need to use for our project. 
+If we don't have the package installed already, you will use the `install.packages` function.
+Whether you have the package or not, you will load the package using the `library` function.
+```{r}
+#Load in the tidyverse amd Using R libraries
+library(tidyverse)
+library(UsingR)
+```
+
+3. Load in our data.
+We will be using a dataset that is already in R, so lets take a look at `babies` and try to store as variable `df` in our environment.
+```{r}
+#call the babies dataset
+babies
+#we still need to store this dataset in our R environment
+df <- babies
+```
+
+Now if we call df, we can see we have the babies dataset 
+```{r}
+#viewing df
+view(df)
+#OR
+df
+```
+
+Lets use the function write.csv to take that data and save it as a .csv file in our current directory
+
+```{r}
+write.csv(babies, "babies.csv")
+```
+
+A few ways to lad data are to:
+- manually use the import dataset button in Rstudio (and copy the code from the output)
+- use `read_csv()` function from readr package
+```{r}
+#copy the code generated by the import dataset button
+library(readr)
+babies2 <- read_csv("babies.csv")
+View(babies)
+```
+
+#Viewing the data and its structure
+
+1. Instead of viewing the whole dataset we can use the `head` function to view the first 6 rows of the data. 
+2. If you want to view more rows of data, use the argument n= to change the size of the table returned. 
+```{r}
+#view the whole dataset
+view(df)
+#use the head function to view the first 6 rows of the data
+head(df)
+#Look at the first 10 rows of the data
+head(df, n=10)
+```
+
+3. Another important function is `str` it returns the structure of the object. For a `data.frame` that we have here, it will also give you the column names and the structure of those columns. 
+`dim` is also a useful function that will retrun the dimesnions of your data.frame.
+`names()` shows the column names.
+`glimpse()` is a `dplyr` only function that ressembles `str` but unlike `str`
+it allows you to see all your columns.
+`row.names()` or `rownames()` will return a vector of the rownames of the dataset. However, only `rownames()`can be changed, we will return to the uses of this function later. 
+
+```{r}
+#use the str() function on df
+str(df)
+#use the dim() function to return the size of df
+dim(df)
+#use names() to return the column names
+names(df)
+#use row.names() or rownames() to return a vector of the rownames
+row.names(df)
+```
+
+#Summary statistics and exploring patterns
+
+1. `summary()` shows a quick description of the summary statistics (means, 
+median, frequency)for each of your columns.  
+
+This function is especially useful for finding issues in the data coding and in the data structure.
+For example, look at the variable sex - is there is something strange here?
+Are there any other variables that might have issues?
+These are all issues we can fix when we tidy the data, we will return to data tidying in the next section. 
+```{r}
+#give a summary of df
+summary(df)
+```
+
+2. Plotting to explore the data
+
+Before we plot any of the columns, we need to know how to select columns from the dataset. 
+To call a column of the data we can use the $ operator: df$sex will give us the sex column. 
+With dataframes, we can call specific rows and collumns using the index. For example with df[1,6], will give us the 1st row and the 6th column. We can also select a range of columns or rows using `:` operator. 
+
+```{r}
+#Try calling the wt column using the `$`
+df$wt
+#Try another way to get the wt column
+df[,7]
+#Try to get the first ten rows of the weight column
+df[1:10,7]
+```
+
+
+2. There are some base functions that allow us to visualize our data:
+`hist()` will provide a historgram of a variable.
+`plot()` can allow you to compare two variables, x and y, in many different types of plots. 
+
+```{r}
+#Try plotting wt as a histogram
+hist(df$wt)
+#try plotting ht as the x variable and wt as the y variable
+plot(x=df$ht, y=df$wt)
+#Now lets use the help function ? to view the plot function
+?plot
+#Lets try changing the type argument
+plot(x=df$ht, y=df$wt, type = "l")
+```
+
+#Introduction to data tidying and tidyverse
+
+1. When we looked at the `summary()` of our data, we found that some variables were coded as numeric, when they should be binary or categorical (factors).
+`as.logical()` will transform the variable into a logical `TRUE` or `FALSE`
+`as.numeric()` will transform the variable into a numeric variable
+`as.character()` will transform the variable into character strings
+`as.factor()` will transform the variable into a factor. However, the levels will be created automatically and might not be the correct order. You might want to use `factor()` to change the variable. 
+
+```{r}
+#lets take a look at the summary again
+summary(df)
+#let's look at what the coding should be for the variables in babies by looking at the data key
+?babies
+#Sex is a factor with 3 levels, 1,2,9
+as.factor(df$sex)
+#only one level is showing, let's use factor() to and set levels = c("1","0")
+factor(df$sex, levels = c("1", "2", "9"))
+#now we have to save our new variable 
+df$sex <- factor(df$sex, levels = c("1", "2", "9"))
+
+```
+
+Try it yourself:
+1. check the data key on the babies data set using ?babies and look at the variable marital
+2. change the variable marital to a character 
+3. look at the summary of marital as a character variable
+4. now change marital to a factor
+5. look at the summary of marital as a factor, what is the difference between the summary if marital is a character or a factor?
+```{r}
+
+```
+
+2. There are many helpful functions to deal with missing data in R.
+You might want to check how many missing values there are using `is.na()` or remove missing all rows of data using `na.omit()`.
+
+Sometimes you will receive datasets where NAs are missing and othertimes the coders have used key to notate NAs. In the babies dataset, NAs in the wt column are coded as 999. We saw this when we visualized wt and ht.
+
+```{r}
+plot(df$wt, df$ht)
+```
+
+
+For this dataset, in the ht column, NAs are coded as 99.
+We want to create a statement telling R that if df$ht is equal to 99, we want to rewrite that code as NA, if that is not TRUE, then keep the original output. 
+This is called an ifelse statement. An easy way to do this in R is using the `ifelse()` function. The first value is the statement of what we are looking for, `df$ht==99`, the next value will be what we should do if that statement is `TRUE`, the third value will be what to do if the statement is `FALSE`. 
+Once we have the 99s coded as NA, we can count the number of NAs in the vector using `is.na()`. 
+Lastly, we can remove rows that have NAs using `na.omit()`.
+```{r}
+#Use the ifelse() function to tell R to replace df$ht values that are 99 with NA. 
+ifelse(df$ht == 99, NA, df$ht)
+#Store the new variable back into df$ht
+df$ht <- ifelse(df$ht == 99, NA, df$ht)
+#To count the number of NAs 
+summary(is.na(df$ht))
+#To remove the rows that have NA in them from the dataset, we can use the na.omit() function
+na.omit(df)
+#Let's store the  df with NAs omited into a new data frame, name it df2
+df2 <- na.omit(df)
+
+```
+
+#Introduction to Tidyverse
+
+Data wrangling is a bit tedious in base R. At the beginning of the session we loaded in a great collection of packages, `tidyverse` designed to make this easier.  Load packages by using the `library()`
+function.  `dplyr` allows for easy manipulation of datasets.
+
+`select` is a `dplyr` function that lets us select columns from the dataset. 
+
+Columns can be selected
+individually with `,`, in a series with `:` or removed with `-`.
+```{r wrangleSelect}
+#The first two  lines of code are the same. 
+dplyr::select(df,id,pluralty,outcome,date)
+dplyr::select(df, id:date)
+
+#This line of code selects all columns except ht and wt.
+dplyr::select(df,-ht, -wt)
+```
+
+`dplyr` also comes with a `%>%` pipe function (via the
+`magrittr` package), which works similar to how the Bash shell `|`
+pipe works. The command on the right-hand side takes the output from
+the command on the left-hand side, just like how a plumbing pipe works
+for water. The `.` object represents the output from the pipe, but it 
+doesn't have to be used as using `%>%` implies also using `.`.
+
+```{r wranglePipe}
+
+#The outputs of each of these two lines of code are the same. 
+df %>% 
+  dplyr::select(id,pluralty,outcome,date)
+df %>%
+  dplyr::select(id:date)
+
+#This line of code selects all columns except ht and wt.
+df %>%
+  dplyr::select(-ht, -wt)
+```
+
+You can rename columns either using `rename` or `select` (the new name
+is on the left hand side, so `newname = oldname`).  However, with the
+`select` command, only that column gets selected, while `rename`
+selects all columns.
+
+```{r wrangleRename}
+
+df %>% rename(weight = wt)
+df %>% dplyr::select(weight = wt)
+```
+
+You can subset the dataset using `filter`.  Note the double equal sign
+`==` for testing if 'Examination' is equal to 15.  A single `=` is
+used for something else (assigning things to objects). Use `&` or `,`
+to apply multiple filters.
+
+```{r wrangleFilter}
+#find the babies that have a weight that equal to 100 grams
+filter(df,wt==100)
+#find the babies that have a weight that are greater than 100 grams
+df %>% filter(wt>100)
+#filter for marital status does not equal 1
+df %>% filter(marital!=1)
+
+## These are all the same:
+df %>% filter(wt>100)  %>% filter(marital!=1)
+df %>% filter(wt>100 , marital!=1)
+df %>% filter(wt>100 & marital!=1)
+```
+
+We can start chaining these commands together using the `%>%` command.
+There is no limit to how long a chain can be. `mutate` creates a new column. 
+
+```{r wrangleChain}
+df %>%
+  filter(wt>100) %>% 
+  mutate(hwratio = wt/ht) %>% 
+  dplyr::select(-ht, -wt) 
+```
+
+R is very powerful at analyzing data but is biased. To get your data into a nicer and more analyable format, you can use the `tidyverse` package.  
+See what `gather` does in the code below.  Then see what `spread` does.  
+
+```{r reorg}
+## Compare this:
+df
+## With this:
+df %>%
+  gather(ht,wt, key ="variable", value = "grams")
+
+## And back again:
+df %>%
+  gather(ht,wt, key ="variable", value = "grams") %>% 
+  spread(variable, grams) 
+
+```
+
+Combined with `dplyr`'s `group_by` and `summarise` you can quickly
+summarise data or do further, more complicated analyses. `group_by`
+makes it so further analyses or operations work on the groups.
+`summarise` transforms the data to only contain the new variable(s)
+created, in this case the mean. You can try out `max()`,`min()`,`median()` and `sd()`.
+
+```{r reorgChain}
+df %>%
+  group_by(smoke) %>% #group by smoking status
+  summarise(mean = mean(wt)) #summarize the mean
+
+```
+
+#show how to knit using knit button
+
diff --git a/lessons/r/intro/lesson.Rmd b/lessons/r/intro/lesson.Rmd
new file mode 100644
index 000000000..03832de4a
--- /dev/null
+++ b/lessons/r/intro/lesson.Rmd
@@ -0,0 +1,308 @@
+---
+title: "Introduction to R"
+author: "Luke Johnston"
+date: "May 26, 2016"
+output: md_document
+---
+
+ - **Authors**: Luke W. Johnston
+ - **Research field**: Nutritional Sciences (epidemiology)
+ - **Lesson topic**: Intro to R and data wrangling
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/intro>
+ - **Lesson video stream**: <https://www.youtube.com/watch?v=pbG_3ZuNyx8>
+
+This is a brief introduction to R, focussing on data wrangling using
+`dplyr` and `tidyr` packages and generating reproducible documents
+using `knitr` and `rmarkdown` packages.
+
+You **don't need to read all of this** for the session.  It's more of
+a **resource and reference**.
+
+R is a statistical computing environment to analyze data and write
+programs.  The strength of R comes from:
+
+* It being developed by statisticians to do statistical analysis
+* Its graphic capacities are top-quality
+* It has excellent resources for creating reproducible documents.
+* Its extensive and active community of users for doing statistical
+  work:
+    - R is the top tag in the
+      [StackExchange CrossValidated site](https://stats.stackexchange.com/tags)
+      for statistical questions
+    - There are more than 6000 packages on
+      [CRAN](https://cran.r-project.org/web/packages/) that stores all
+      R packages (and installing these packages is straight-forward),
+      from the obscure and cutting-edge statistical techniques to
+      plotting to data wrangling.
+
+I should mention a quick caveat.  While R is a general-purpose
+programming language, it works a bit differently from other languages
+such as Python (it was developed by statisticians after all!).  As
+such, programming in R may not be as intuitive, powerful, or easy as
+it may be in Python (though it can be done), especially if you come
+from a computer science background.  If your work involves a lot of
+programming, I would recommend Python as your main tool.  *However*,
+it never hurts to learn more than one language, especially as R is
+great for data analysis and plotting.
+
+Ok, firstly, I've made this session with some assumptions (see the
+[slides.html](../slides.html)) file.  Briefly I'm assuming you want to
+use R for statistical analysis, plotting, and/or writing up reports.
+I'm using R Markdown to show how to write up documents with R code and
+since getting the data into an analyzable form is the hardest part of
+an analysis, I'm using packages specific to that task.
+
+While you can create functions in R, I won't be going over them.  A
+great resource for R functions is
+[this page from Hadley Wickham's 'Advanced R' book](http://adv-r.had.co.nz/Functions.html)
+
+# R Markdown #
+
+An `.Rmd` or [R Markdown](http://rmarkdown.rstudio.com/) file is a
+[markdown](https://en.wikipedia.org/wiki/Markdown) file that contains
+R code chunks that can be processed to output the results of the R
+code into a generated `.md` file.  This is an incredible (and recent)
+strength of using R, as this then allows you to create html, pdf, or
+Word doc files from the `.md` file using the `rmarkdown` package
+(which relies on [pandoc](https://pandoc.org)).
+
+On the top of each `.Rmd` file is the
+[YAML](https://en.wikipedia.org/wiki/YAML) front matter, which looks
+like:
+
+```
+---
+title: "Introduction to R"
+author: "Luke Johnston"
+date: "July 23, 2015"
+output: 
+  html_document: 
+    highlight: tango
+    number_sections: yes
+    theme: readable
+    toc: yes
+    
+---
+```
+
+Note the starting and ending `---` 'tags'.  This starts the YAML
+block.
+
+Markdown syntax for formatting is used in `.Rmd`.  Check out the
+[R Markdown documentation](http://rmarkdown.rstudio.com/) for a quick
+tutorial on the syntax.
+
+# Import/export your data #
+
+You'll need to import your data into R to analyze it.  I'm assuming
+the data is already cleaned and ready for analysis.  If at any time
+you need help with a command, use the `?` command, appended with the
+command of interest (eg. `?write.csv`).  R comes with many internal
+datasets that you can practice on.  The one I'm going to use is the
+`swiss` dataset.
+
+```{r importData}
+
+write.csv(swiss, file = 'swiss.csv') # Export
+ds <- read.csv('swiss.csv') # Import
+
+```
+
+# Viewing your data #
+
+R has several very useful and easy tools for quickly viewing your
+data.  `head()` shows the first few rows of a data.frame (a structure
+for storing data that can include numbers, integers, factors, strings,
+etc).  `names()` shows the column names.  `str()` shows the structure,
+such as what the object is, and its contents.  `summary()` shows a
+quick description of the summary statistics (means, median, frequency)
+for each of your columns.  `class()` is like `str()` but only shows
+the top level name of the object, so eg. while a data.frame contains
+multiple columns that `str()` would show, `class()` would only show
+that the object is a "data.frame".
+
+```{r viewData}
+head(ds)
+names(ds)
+str(ds)
+summary(ds)
+class(ds)
+```
+
+# Wrangling your data #
+
+Data wrangling is a bit tedious in base R.  So I'm using two packages
+designed to make this easier.  Load packages by using the `library()`
+function.  `dplyr` comes with a `%>%` pipe function (via the
+`magrittr` package), which works similar to how the Bash shell `|`
+pipe works.  The command on the right-hand side takes the output from
+the command on the left-hand side, just like how a plumbing pipe works
+for water.
+
+The four lines of code below using `tbl_df` are all the same.  The `.`
+object represents the output from the pipe, but it doesn't have to be
+used as using `%>%` implies also using `.`.  `tbl_df` makes the object
+into a `tbl` class, making printing of the output nicer.
+
+```{r wrangle}
+
+library(dplyr)
+tbl_df(ds)
+ds %>% tbl_df()
+ds %>% tbl_df
+ds %>% tbl_df(.)
+
+## Let's put it into a new object
+ds2 <- tbl_df(ds)
+
+```
+
+Again, these next lines are the same. `select` does as it says: select
+the column from the dataset.
+
+```{r wrangleSelect}
+
+select(ds2, Education, Catholic, Fertility)
+ds2 %>% select(Education, Catholic, Fertility)
+ds2 %>% select(., Education, Catholic, Fertility)
+
+```
+
+You can rename columns either using `rename` or `select` (the new name
+is on the left hand side, so `newname = oldname`).  However, with the
+`select` command, only that column gets selected, while `rename`
+selects all columns.
+
+```{r wrangleRename}
+ds2 %>% rename(County = X)
+ds2 %>% select(County = X)
+```
+
+You can subset the dataset using `filter`.  Note the double equal sign
+`==` for testing if 'Examination' is equal to 15.  A single `=` is
+used for something else (assigning things to objects).
+
+```{r wrangleFilter}
+
+filter(ds2, Catholic < 20, Examination == 15)
+ds2 %>% filter(Catholic < 20, Examination == 15)
+ds2 %>% filter(., Catholic < 20, Examination == 15)
+## For string/factor variables
+ds2 %>% filter(X == 'Aigle')
+
+```
+
+We can start chaining these commands together using the `%>%` command.
+There is no limit to how long a chain can be.  `arrange`
+sorts/orders/re-arranges the column Education in ascending
+order. `mutate` creates a new column.
+
+```{r wrangleChain}
+
+ds2 %>%
+  filter(Catholic > 20) %>%
+  select(Education, Fertility) 
+  
+ds2 %>%
+  filter(Catholic > 20) %>%
+  select(County = X, Education, Fertility, Agriculture) %>%
+  arrange(Education) %>%
+  mutate(infertile = ifelse(Fertility < 50, 'yes', 'no'),
+         testing = 'Yes' ## Create a testing column to show how mutate works.
+         )
+
+```
+
+To get the data into a nicer and more analyable format, you can use
+the `tidyr` package.  See what `gather` does in the code below.  Then
+see what `spread` does.  Note that you can remove a column by having a
+minus `-` sign in front of a variable when you use `select`.
+
+```{r reorg}
+
+library(tidyr)
+## Compare this:
+ds2 %>%
+  select(-Infant.Mortality) %>%
+  rename(County = X)
+
+## With this:
+ds2 %>%
+  select(-Infant.Mortality) %>%
+  rename(County = X) %>%
+  gather(Measure, Value, -County)
+
+## And back again:
+ds2 %>%
+  select(-Infant.Mortality) %>%
+  rename(County = X) %>%
+  gather(Measure, Value, -County) %>%
+  spread(Measure, Value)
+
+```
+
+Combined with `dplyr`'s `group_by` and `summarise` you can quickly
+summarise data or do further, more complicated analyses. `group_by`
+makes it so further analyses or operations work on the groups.
+`summarise` transforms the data to only contain the new variable(s)
+created, in this case the mean.
+
+```{r reorgChain}
+
+ds2 %>%
+  select(-X) %>%
+  gather(Measure, Value) %>%
+  group_by(Measure) %>%
+  summarise(mean = mean(Value))
+
+```
+
+You can extend this to be created as a table in the generated `.md` or
+`.html` file using the `kable` command (short for 'knitr table').
+
+```{r table}
+
+library(knitr)
+ds2 %>%
+  select(-X) %>%
+  gather(Measure, Value) %>%
+  group_by(Measure) %>%
+  summarise(mean = mean(Value)) %>%
+  kable()
+
+```
+
+# Generate this document #
+
+Check out the documentation on
+[`knitr`](https://yihui.name/knitr/options/) or
+[R Markdown](http://rmarkdown.rstudio.com/authoring_rcodechunks.html)
+for R code chunk options.  If you look at the raw `.Rmd` file for this
+[document](../main.Rmd), you can see that the below code chunk uses
+`eval = FALSE`, which tells knitr to not run this code chunk.
+
+These two commands generate either a html or a md file.
+
+```{r render, eval = FALSE}
+
+## into html
+library(rmarkdown)
+render('lesson.Rmd') ## or can use rmarkdown::render('main.Rmd')
+
+## into md
+library(knitr)
+knit('lesson.Rmd') ## or can use knitr::knit('main.Rmd')
+
+```
+
+# Challenge: Try this out for yourself! #
+
+Make a table with the means of Agriculture, Examination, Education,
+and Infant.Mortality for each category of Fertility (hint: convert it
+into a factor by values >50 vs <50), when Catholic is less than 60
+(hint, use `dplyr` commands + `gather`).  Have the Fertility groups as
+two columns in the new table (hint, use `spread` + `kable`).
+
+# Python-ized version (courtesy of [@QuLogic](https://github.com/QuLogic))
+
+<script src="https://gist.github.com/QuLogic/c65772479f6101393fc3.js"></script>
diff --git a/lessons/r/intro/lesson.md b/lessons/r/intro/lesson.md
new file mode 100644
index 000000000..b0ceca12f
--- /dev/null
+++ b/lessons/r/intro/lesson.md
@@ -0,0 +1,729 @@
+---
+title: "Introduction to R"
+author: "Luke Johnston"
+date: "May 26, 2016"
+layout: page
+visible: true
+output: md_document
+tags:
+  - r
+  - beginner
+---
+
+ - **Authors**: Luke W. Johnston
+ - **Research field**: Nutritional Sciences (epidemiology)
+ - **Lesson topic**: Intro to R and data wrangling
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/intro>
+ - **Lesson video stream**: <https://www.youtube.com/watch?v=pbG_3ZuNyx8>
+
+This is a brief introduction to R, focussing on data wrangling using
+`dplyr` and `tidyr` packages and generating reproducible documents
+using `knitr` and `rmarkdown` packages.
+
+You **don't need to read all of this** for the session.  It's more of
+a **resource and reference**.
+
+R is a statistical computing environment to analyze data and write
+programs.  The strength of R comes from:
+
+* It being developed by statisticians to do statistical analysis
+* Its graphic capacities are top-quality
+* It has excellent resources for creating reproducible documents.
+* Its extensive and active community of users for doing statistical
+  work:
+    - R is the top tag in the
+      [StackExchange CrossValidated site](https://stats.stackexchange.com/tags)
+      for statistical questions
+    - There are more than 6000 packages on
+      [CRAN](https://cran.r-project.org/web/packages/) that stores all
+      R packages (and installing these packages is straight-forward),
+      from the obscure and cutting-edge statistical techniques to
+      plotting to data wrangling.
+
+I should mention a quick caveat.  While R is a general-purpose
+programming language, it works a bit differently from other languages
+such as Python (it was developed by statisticians after all!).  As
+such, programming in R may not be as intuitive, powerful, or easy as
+it may be in Python (though it can be done), especially if you come
+from a computer science background.  If your work involves a lot of
+programming, I would recommend Python as your main tool.  *However*,
+it never hurts to learn more than one language, especially as R is
+great for data analysis and plotting.
+
+Ok, firstly, I've made this session with some assumptions (see the
+[slides.html](../slides.html)) file.  Briefly I'm assuming you want to
+use R for statistical analysis, plotting, and/or writing up reports.
+I'm using R Markdown to show how to write up documents with R code and
+since getting the data into an analyzable form is the hardest part of
+an analysis, I'm using packages specific to that task.
+
+While you can create functions in R, I won't be going over them.  A
+great resource for R functions is
+[this page from Hadley Wickham's 'Advanced R' book](http://adv-r.had.co.nz/Functions.html)
+
+# R Markdown #
+
+An `.Rmd` or [R Markdown](http://rmarkdown.rstudio.com/) file is a
+[markdown](https://en.wikipedia.org/wiki/Markdown) file that contains
+R code chunks that can be processed to output the results of the R
+code into a generated `.md` file.  This is an incredible (and recent)
+strength of using R, as this then allows you to create html, pdf, or
+Word doc files from the `.md` file using the `rmarkdown` package
+(which relies on [pandoc](https://pandoc.org)).
+
+On the top of each `.Rmd` file is the
+[YAML](https://en.wikipedia.org/wiki/YAML) front matter, which looks
+like:
+
+```
+---
+title: "Introduction to R"
+author: "Luke Johnston"
+date: "July 23, 2015"
+output: 
+  html_document: 
+    highlight: tango
+    number_sections: yes
+    theme: readable
+    toc: yes
+    
+---
+```
+
+Note the starting and ending `---` 'tags'.  This starts the YAML
+block.
+
+Markdown syntax for formatting is used in `.Rmd`.  Check out the
+[R Markdown documentation](http://rmarkdown.rstudio.com/) for a quick
+tutorial on the syntax.
+
+# Import/export your data #
+
+You'll need to import your data into R to analyze it.  I'm assuming
+the data is already cleaned and ready for analysis.  If at any time
+you need help with a command, use the `?` command, appended with the
+command of interest (eg. `?write.csv`).  R comes with many internal
+datasets that you can practice on.  The one I'm going to use is the
+`swiss` dataset.
+
+
+```r
+write.csv(swiss, file = 'swiss.csv') # Export
+ds <- read.csv('swiss.csv') # Import
+```
+
+# Viewing your data #
+
+R has several very useful and easy tools for quickly viewing your
+data.  `head()` shows the first few rows of a data.frame (a structure
+for storing data that can include numbers, integers, factors, strings,
+etc).  `names()` shows the column names.  `str()` shows the structure,
+such as what the object is, and its contents.  `summary()` shows a
+quick description of the summary statistics (means, median, frequency)
+for each of your columns.  `class()` is like `str()` but only shows
+the top level name of the object, so eg. while a data.frame contains
+multiple columns that `str()` would show, `class()` would only show
+that the object is a "data.frame".
+
+
+```r
+head(ds)
+```
+
+```
+##              X Fertility Agriculture Examination Education Catholic
+## 1   Courtelary      80.2        17.0          15        12     9.96
+## 2     Delemont      83.1        45.1           6         9    84.84
+## 3 Franches-Mnt      92.5        39.7           5         5    93.40
+## 4      Moutier      85.8        36.5          12         7    33.77
+## 5   Neuveville      76.9        43.5          17        15     5.16
+## 6   Porrentruy      76.1        35.3           9         7    90.57
+##   Infant.Mortality
+## 1             22.2
+## 2             22.2
+## 3             20.2
+## 4             20.3
+## 5             20.6
+## 6             26.6
+```
+
+```r
+names(ds)
+```
+
+```
+## [1] "X"                "Fertility"        "Agriculture"     
+## [4] "Examination"      "Education"        "Catholic"        
+## [7] "Infant.Mortality"
+```
+
+```r
+str(ds)
+```
+
+```
+## 'data.frame':	47 obs. of  7 variables:
+##  $ X               : Factor w/ 47 levels "Aigle","Aubonne",..: 8 9 12 26 28 34 5 13 15 38 ...
+##  $ Fertility       : num  80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 ...
+##  $ Agriculture     : num  17 45.1 39.7 36.5 43.5 35.3 70.2 67.8 53.3 45.2 ...
+##  $ Examination     : int  15 6 5 12 17 9 16 14 12 16 ...
+##  $ Education       : int  12 9 5 7 15 7 7 8 7 13 ...
+##  $ Catholic        : num  9.96 84.84 93.4 33.77 5.16 ...
+##  $ Infant.Mortality: num  22.2 22.2 20.2 20.3 20.6 26.6 23.6 24.9 21 24.4 ...
+```
+
+```r
+summary(ds)
+```
+
+```
+##         X        Fertility      Agriculture     Examination   
+##  Aigle   : 1   Min.   :35.00   Min.   : 1.20   Min.   : 3.00  
+##  Aubonne : 1   1st Qu.:64.70   1st Qu.:35.90   1st Qu.:12.00  
+##  Avenches: 1   Median :70.40   Median :54.10   Median :16.00  
+##  Boudry  : 1   Mean   :70.14   Mean   :50.66   Mean   :16.49  
+##  Broye   : 1   3rd Qu.:78.45   3rd Qu.:67.65   3rd Qu.:22.00  
+##  Conthey : 1   Max.   :92.50   Max.   :89.70   Max.   :37.00  
+##  (Other) :41                                                  
+##    Education        Catholic       Infant.Mortality
+##  Min.   : 1.00   Min.   :  2.150   Min.   :10.80   
+##  1st Qu.: 6.00   1st Qu.:  5.195   1st Qu.:18.15   
+##  Median : 8.00   Median : 15.140   Median :20.00   
+##  Mean   :10.98   Mean   : 41.144   Mean   :19.94   
+##  3rd Qu.:12.00   3rd Qu.: 93.125   3rd Qu.:21.70   
+##  Max.   :53.00   Max.   :100.000   Max.   :26.60   
+## 
+```
+
+```r
+class(ds)
+```
+
+```
+## [1] "data.frame"
+```
+
+# Wrangling your data #
+
+Data wrangling is a bit tedious in base R.  So I'm using two packages
+designed to make this easier.  Load packages by using the `library()`
+function.  `dplyr` comes with a `%>%` pipe function (via the
+`magrittr` package), which works similar to how the Bash shell `|`
+pipe works.  The command on the right-hand side takes the output from
+the command on the left-hand side, just like how a plumbing pipe works
+for water.
+
+The four lines of code below using `tbl_df` are all the same.  The `.`
+object represents the output from the pipe, but it doesn't have to be
+used as using `%>%` implies also using `.`.  `tbl_df` makes the object
+into a `tbl` class, making printing of the output nicer.
+
+
+```r
+library(dplyr)
+```
+
+```
+## 
+## Attaching package: 'dplyr'
+```
+
+```
+## The following objects are masked from 'package:stats':
+## 
+##     filter, lag
+```
+
+```
+## The following objects are masked from 'package:base':
+## 
+##     intersect, setdiff, setequal, union
+```
+
+```r
+tbl_df(ds)
+```
+
+```
+## # A tibble: 47 x 7
+##               X Fertility Agriculture Examination Education Catholic
+##          <fctr>     <dbl>       <dbl>       <int>     <int>    <dbl>
+## 1    Courtelary      80.2        17.0          15        12     9.96
+## 2      Delemont      83.1        45.1           6         9    84.84
+## 3  Franches-Mnt      92.5        39.7           5         5    93.40
+## 4       Moutier      85.8        36.5          12         7    33.77
+## 5    Neuveville      76.9        43.5          17        15     5.16
+## 6    Porrentruy      76.1        35.3           9         7    90.57
+## 7         Broye      83.8        70.2          16         7    92.85
+## 8         Glane      92.4        67.8          14         8    97.16
+## 9       Gruyere      82.4        53.3          12         7    97.67
+## 10       Sarine      82.9        45.2          16        13    91.38
+## # ... with 37 more rows, and 1 more variables: Infant.Mortality <dbl>
+```
+
+```r
+ds %>% tbl_df()
+```
+
+```
+## # A tibble: 47 x 7
+##               X Fertility Agriculture Examination Education Catholic
+##          <fctr>     <dbl>       <dbl>       <int>     <int>    <dbl>
+## 1    Courtelary      80.2        17.0          15        12     9.96
+## 2      Delemont      83.1        45.1           6         9    84.84
+## 3  Franches-Mnt      92.5        39.7           5         5    93.40
+## 4       Moutier      85.8        36.5          12         7    33.77
+## 5    Neuveville      76.9        43.5          17        15     5.16
+## 6    Porrentruy      76.1        35.3           9         7    90.57
+## 7         Broye      83.8        70.2          16         7    92.85
+## 8         Glane      92.4        67.8          14         8    97.16
+## 9       Gruyere      82.4        53.3          12         7    97.67
+## 10       Sarine      82.9        45.2          16        13    91.38
+## # ... with 37 more rows, and 1 more variables: Infant.Mortality <dbl>
+```
+
+```r
+ds %>% tbl_df
+```
+
+```
+## # A tibble: 47 x 7
+##               X Fertility Agriculture Examination Education Catholic
+##          <fctr>     <dbl>       <dbl>       <int>     <int>    <dbl>
+## 1    Courtelary      80.2        17.0          15        12     9.96
+## 2      Delemont      83.1        45.1           6         9    84.84
+## 3  Franches-Mnt      92.5        39.7           5         5    93.40
+## 4       Moutier      85.8        36.5          12         7    33.77
+## 5    Neuveville      76.9        43.5          17        15     5.16
+## 6    Porrentruy      76.1        35.3           9         7    90.57
+## 7         Broye      83.8        70.2          16         7    92.85
+## 8         Glane      92.4        67.8          14         8    97.16
+## 9       Gruyere      82.4        53.3          12         7    97.67
+## 10       Sarine      82.9        45.2          16        13    91.38
+## # ... with 37 more rows, and 1 more variables: Infant.Mortality <dbl>
+```
+
+```r
+ds %>% tbl_df(.)
+```
+
+```
+## # A tibble: 47 x 7
+##               X Fertility Agriculture Examination Education Catholic
+##          <fctr>     <dbl>       <dbl>       <int>     <int>    <dbl>
+## 1    Courtelary      80.2        17.0          15        12     9.96
+## 2      Delemont      83.1        45.1           6         9    84.84
+## 3  Franches-Mnt      92.5        39.7           5         5    93.40
+## 4       Moutier      85.8        36.5          12         7    33.77
+## 5    Neuveville      76.9        43.5          17        15     5.16
+## 6    Porrentruy      76.1        35.3           9         7    90.57
+## 7         Broye      83.8        70.2          16         7    92.85
+## 8         Glane      92.4        67.8          14         8    97.16
+## 9       Gruyere      82.4        53.3          12         7    97.67
+## 10       Sarine      82.9        45.2          16        13    91.38
+## # ... with 37 more rows, and 1 more variables: Infant.Mortality <dbl>
+```
+
+```r
+## Let's put it into a new object
+ds2 <- tbl_df(ds)
+```
+
+Again, these next lines are the same. `select` does as it says: select
+the column from the dataset.
+
+
+```r
+select(ds2, Education, Catholic, Fertility)
+```
+
+```
+## # A tibble: 47 x 3
+##    Education Catholic Fertility
+##        <int>    <dbl>     <dbl>
+## 1         12     9.96      80.2
+## 2          9    84.84      83.1
+## 3          5    93.40      92.5
+## 4          7    33.77      85.8
+## 5         15     5.16      76.9
+## 6          7    90.57      76.1
+## 7          7    92.85      83.8
+## 8          8    97.16      92.4
+## 9          7    97.67      82.4
+## 10        13    91.38      82.9
+## # ... with 37 more rows
+```
+
+```r
+ds2 %>% select(Education, Catholic, Fertility)
+```
+
+```
+## # A tibble: 47 x 3
+##    Education Catholic Fertility
+##        <int>    <dbl>     <dbl>
+## 1         12     9.96      80.2
+## 2          9    84.84      83.1
+## 3          5    93.40      92.5
+## 4          7    33.77      85.8
+## 5         15     5.16      76.9
+## 6          7    90.57      76.1
+## 7          7    92.85      83.8
+## 8          8    97.16      92.4
+## 9          7    97.67      82.4
+## 10        13    91.38      82.9
+## # ... with 37 more rows
+```
+
+```r
+ds2 %>% select(., Education, Catholic, Fertility)
+```
+
+```
+## # A tibble: 47 x 3
+##    Education Catholic Fertility
+##        <int>    <dbl>     <dbl>
+## 1         12     9.96      80.2
+## 2          9    84.84      83.1
+## 3          5    93.40      92.5
+## 4          7    33.77      85.8
+## 5         15     5.16      76.9
+## 6          7    90.57      76.1
+## 7          7    92.85      83.8
+## 8          8    97.16      92.4
+## 9          7    97.67      82.4
+## 10        13    91.38      82.9
+## # ... with 37 more rows
+```
+
+You can rename columns either using `rename` or `select` (the new name
+is on the left hand side, so `newname = oldname`).  However, with the
+`select` command, only that column gets selected, while `rename`
+selects all columns.
+
+
+```r
+ds2 %>% rename(County = X)
+```
+
+```
+## # A tibble: 47 x 7
+##          County Fertility Agriculture Examination Education Catholic
+##          <fctr>     <dbl>       <dbl>       <int>     <int>    <dbl>
+## 1    Courtelary      80.2        17.0          15        12     9.96
+## 2      Delemont      83.1        45.1           6         9    84.84
+## 3  Franches-Mnt      92.5        39.7           5         5    93.40
+## 4       Moutier      85.8        36.5          12         7    33.77
+## 5    Neuveville      76.9        43.5          17        15     5.16
+## 6    Porrentruy      76.1        35.3           9         7    90.57
+## 7         Broye      83.8        70.2          16         7    92.85
+## 8         Glane      92.4        67.8          14         8    97.16
+## 9       Gruyere      82.4        53.3          12         7    97.67
+## 10       Sarine      82.9        45.2          16        13    91.38
+## # ... with 37 more rows, and 1 more variables: Infant.Mortality <dbl>
+```
+
+```r
+ds2 %>% select(County = X)
+```
+
+```
+## # A tibble: 47 x 1
+##          County
+##          <fctr>
+## 1    Courtelary
+## 2      Delemont
+## 3  Franches-Mnt
+## 4       Moutier
+## 5    Neuveville
+## 6    Porrentruy
+## 7         Broye
+## 8         Glane
+## 9       Gruyere
+## 10       Sarine
+## # ... with 37 more rows
+```
+
+You can subset the dataset using `filter`.  Note the double equal sign
+`==` for testing if 'Examination' is equal to 15.  A single `=` is
+used for something else (assigning things to objects).
+
+
+```r
+filter(ds2, Catholic < 20, Examination == 15)
+```
+
+```
+## # A tibble: 3 x 7
+##            X Fertility Agriculture Examination Education Catholic
+##       <fctr>     <dbl>       <dbl>       <int>     <int>    <dbl>
+## 1 Courtelary      80.2        17.0          15        12     9.96
+## 2    Yverdon      65.4        49.5          15         8     6.10
+## 3 Val de Ruz      77.6        37.6          15         7     4.97
+## # ... with 1 more variables: Infant.Mortality <dbl>
+```
+
+```r
+ds2 %>% filter(Catholic < 20, Examination == 15)
+```
+
+```
+## # A tibble: 3 x 7
+##            X Fertility Agriculture Examination Education Catholic
+##       <fctr>     <dbl>       <dbl>       <int>     <int>    <dbl>
+## 1 Courtelary      80.2        17.0          15        12     9.96
+## 2    Yverdon      65.4        49.5          15         8     6.10
+## 3 Val de Ruz      77.6        37.6          15         7     4.97
+## # ... with 1 more variables: Infant.Mortality <dbl>
+```
+
+```r
+ds2 %>% filter(., Catholic < 20, Examination == 15)
+```
+
+```
+## # A tibble: 3 x 7
+##            X Fertility Agriculture Examination Education Catholic
+##       <fctr>     <dbl>       <dbl>       <int>     <int>    <dbl>
+## 1 Courtelary      80.2        17.0          15        12     9.96
+## 2    Yverdon      65.4        49.5          15         8     6.10
+## 3 Val de Ruz      77.6        37.6          15         7     4.97
+## # ... with 1 more variables: Infant.Mortality <dbl>
+```
+
+```r
+## For string/factor variables
+ds2 %>% filter(X == 'Aigle')
+```
+
+```
+## # A tibble: 1 x 7
+##        X Fertility Agriculture Examination Education Catholic
+##   <fctr>     <dbl>       <dbl>       <int>     <int>    <dbl>
+## 1  Aigle      64.1          62          21        12     8.52
+## # ... with 1 more variables: Infant.Mortality <dbl>
+```
+
+We can start chaining these commands together using the `%>%` command.
+There is no limit to how long a chain can be.  `arrange`
+sorts/orders/re-arranges the column Education in ascending
+order. `mutate` creates a new column.
+
+
+```r
+ds2 %>%
+  filter(Catholic > 20) %>%
+  select(Education, Fertility) 
+```
+
+```
+## # A tibble: 21 x 2
+##    Education Fertility
+##        <int>     <dbl>
+## 1          9      83.1
+## 2          5      92.5
+## 3          7      85.8
+## 4          7      76.1
+## 5          7      83.8
+## 6          8      92.4
+## 7          7      82.4
+## 8         13      82.9
+## 9          6      87.1
+## 10         2      68.3
+## # ... with 11 more rows
+```
+
+```r
+ds2 %>%
+  filter(Catholic > 20) %>%
+  select(County = X, Education, Fertility, Agriculture) %>%
+  arrange(Education) %>%
+  mutate(infertile = ifelse(Fertility < 50, 'yes', 'no'),
+         testing = 'Yes' ## Create a testing column to show how mutate works.
+         )
+```
+
+```
+## # A tibble: 21 x 6
+##          County Education Fertility Agriculture infertile testing
+##          <fctr>     <int>     <dbl>       <dbl>     <chr>   <chr>
+## 1     Echallens         2      68.3        72.6        no     Yes
+## 2       Conthey         2      75.5        85.9        no     Yes
+## 3        Herens         2      77.3        89.7        no     Yes
+## 4       Monthey         3      79.4        64.9        no     Yes
+## 5        Sierre         3      92.2        84.6        no     Yes
+## 6  Franches-Mnt         5      92.5        39.7        no     Yes
+## 7       Veveyse         6      87.1        64.5        no     Yes
+## 8     Entremont         6      69.3        84.9        no     Yes
+## 9      Martigwy         6      70.5        78.2        no     Yes
+## 10      Moutier         7      85.8        36.5        no     Yes
+## # ... with 11 more rows
+```
+
+To get the data into a nicer and more analyable format, you can use
+the `tidyr` package.  See what `gather` does in the code below.  Then
+see what `spread` does.  Note that you can remove a column by having a
+minus `-` sign in front of a variable when you use `select`.
+
+
+```r
+library(tidyr)
+## Compare this:
+ds2 %>%
+  select(-Infant.Mortality) %>%
+  rename(County = X)
+```
+
+```
+## # A tibble: 47 x 6
+##          County Fertility Agriculture Examination Education Catholic
+##          <fctr>     <dbl>       <dbl>       <int>     <int>    <dbl>
+## 1    Courtelary      80.2        17.0          15        12     9.96
+## 2      Delemont      83.1        45.1           6         9    84.84
+## 3  Franches-Mnt      92.5        39.7           5         5    93.40
+## 4       Moutier      85.8        36.5          12         7    33.77
+## 5    Neuveville      76.9        43.5          17        15     5.16
+## 6    Porrentruy      76.1        35.3           9         7    90.57
+## 7         Broye      83.8        70.2          16         7    92.85
+## 8         Glane      92.4        67.8          14         8    97.16
+## 9       Gruyere      82.4        53.3          12         7    97.67
+## 10       Sarine      82.9        45.2          16        13    91.38
+## # ... with 37 more rows
+```
+
+```r
+## With this:
+ds2 %>%
+  select(-Infant.Mortality) %>%
+  rename(County = X) %>%
+  gather(Measure, Value, -County)
+```
+
+```
+## # A tibble: 235 x 3
+##          County   Measure Value
+##          <fctr>     <chr> <dbl>
+## 1    Courtelary Fertility  80.2
+## 2      Delemont Fertility  83.1
+## 3  Franches-Mnt Fertility  92.5
+## 4       Moutier Fertility  85.8
+## 5    Neuveville Fertility  76.9
+## 6    Porrentruy Fertility  76.1
+## 7         Broye Fertility  83.8
+## 8         Glane Fertility  92.4
+## 9       Gruyere Fertility  82.4
+## 10       Sarine Fertility  82.9
+## # ... with 225 more rows
+```
+
+```r
+## And back again:
+ds2 %>%
+  select(-Infant.Mortality) %>%
+  rename(County = X) %>%
+  gather(Measure, Value, -County) %>%
+  spread(Measure, Value)
+```
+
+```
+## # A tibble: 47 x 6
+##        County Agriculture Catholic Education Examination Fertility
+## *      <fctr>       <dbl>    <dbl>     <dbl>       <dbl>     <dbl>
+## 1       Aigle        62.0     8.52        12          21      64.1
+## 2     Aubonne        67.5     2.27         7          14      66.9
+## 3    Avenches        60.7     4.43        12          19      68.9
+## 4      Boudry        38.4     5.62        12          26      70.4
+## 5       Broye        70.2    92.85         7          16      83.8
+## 6     Conthey        85.9    99.71         2           3      75.5
+## 7    Cossonay        69.3     2.82         5          22      61.7
+## 8  Courtelary        17.0     9.96        12          15      80.2
+## 9    Delemont        45.1    84.84         9           6      83.1
+## 10  Echallens        72.6    24.20         2          18      68.3
+## # ... with 37 more rows
+```
+
+Combined with `dplyr`'s `group_by` and `summarise` you can quickly
+summarise data or do further, more complicated analyses. `group_by`
+makes it so further analyses or operations work on the groups.
+`summarise` transforms the data to only contain the new variable(s)
+created, in this case the mean.
+
+
+```r
+ds2 %>%
+  select(-X) %>%
+  gather(Measure, Value) %>%
+  group_by(Measure) %>%
+  summarise(mean = mean(Value))
+```
+
+```
+## # A tibble: 6 x 2
+##            Measure     mean
+##              <chr>    <dbl>
+## 1      Agriculture 50.65957
+## 2         Catholic 41.14383
+## 3        Education 10.97872
+## 4      Examination 16.48936
+## 5        Fertility 70.14255
+## 6 Infant.Mortality 19.94255
+```
+
+You can extend this to be created as a table in the generated `.md` or
+`.html` file using the `kable` command (short for 'knitr table').
+
+
+```r
+library(knitr)
+ds2 %>%
+  select(-X) %>%
+  gather(Measure, Value) %>%
+  group_by(Measure) %>%
+  summarise(mean = mean(Value)) %>%
+  kable()
+```
+
+
+
+|Measure          |     mean|
+|:----------------|--------:|
+|Agriculture      | 50.65957|
+|Catholic         | 41.14383|
+|Education        | 10.97872|
+|Examination      | 16.48936|
+|Fertility        | 70.14255|
+|Infant.Mortality | 19.94255|
+
+# Generate this document #
+
+Check out the documentation on
+[`knitr`](https://yihui.name/knitr/options/) or
+[R Markdown](http://rmarkdown.rstudio.com/authoring_rcodechunks.html)
+for R code chunk options.  If you look at the raw `.Rmd` file for this
+[document](../main.Rmd), you can see that the below code chunk uses
+`eval = FALSE`, which tells knitr to not run this code chunk.
+
+These two commands generate either a html or a md file.
+
+
+```r
+## into html
+library(rmarkdown)
+render('lesson.Rmd') ## or can use rmarkdown::render('main.Rmd')
+
+## into md
+library(knitr)
+knit('lesson.Rmd') ## or can use knitr::knit('main.Rmd')
+```
+
+# Challenge: Try this out for yourself! #
+
+Make a table with the means of Agriculture, Examination, Education,
+and Infant.Mortality for each category of Fertility (hint: convert it
+into a factor by values >50 vs <50), when Catholic is less than 60
+(hint, use `dplyr` commands + `gather`).  Have the Fertility groups as
+two columns in the new table (hint, use `spread` + `kable`).
+
+# Python-ized version (courtesy of [@QuLogic](https://github.com/QuLogic))
+
+<script src="https://gist.github.com/QuLogic/c65772479f6101393fc3.js"></script>
diff --git a/lessons/r/intro/session-2018-01-11.Rmd b/lessons/r/intro/session-2018-01-11.Rmd
new file mode 100644
index 000000000..410c0a001
--- /dev/null
+++ b/lessons/r/intro/session-2018-01-11.Rmd
@@ -0,0 +1,88 @@
+---
+title: "R Notebook"
+output: html_notebook
+---
+
+```{r}
+library(dplyr)
+library(tidyr)
+```
+
+With `name()` is a function compared to `name` is a variable/object
+
+```{r}
+iris
+```
+
+- select
+- mutate
+- arrange
+- group_by
+- filter
+- summarize
+
+```{r}
+select(iris, Sepal.Length, Sepal.Width)
+```
+
+```{r}
+select(iris, -Sepal.Length)
+```
+
+```{r}
+mutate(iris, NewColumn = "test", 
+       WidthTimes100 = Petal.Width * 100,
+       Petal.Length = log(Petal.Length))
+```
+
+```{r}
+arrange(iris, Sepal.Width)
+arrange(iris, desc(Sepal.Width))
+arrange(iris, Species, Sepal.Width)
+# colnames(iris)
+```
+
+```{r}
+summary(iris)
+filter(.data = iris, Sepal.Length < 5.5 & # AND
+       Petal.Length == 1.4)
+filter(.data = iris, Sepal.Length < 5.5 | # OR
+       Petal.Length == 1.4)
+```
+
+```{r}
+summarize(iris, MeanPL = mean(Petal.Length),
+          MeanSL = mean(Sepal.Length))
+```
+
+```{r}
+iris %>% 
+    select(Sepal.Length) %>% 
+    filter(Sepal.Length < 5)
+# iris2 <- select(iris, Sepal.Length)
+# iris2 <- filter(iris2, Sepal.Length < 5)
+```
+
+```{r}
+iris %>% 
+    group_by(Species) %>% 
+    summarize(MeanSL = mean(Sepal.Length),
+              MedianSL = median(Sepal.Length))
+```
+
+- gather
+- spread
+
+```{r}
+iris %>% 
+    gather(ColumnName, ColumnValue, -Species) %>% 
+    group_by(Species, ColumnName) %>% 
+    summarize(MeanValue =
+                  paste0(
+                      round(mean(ColumnValue), 2),
+                      " (",
+                      round(sd(ColumnValue), 2),
+                      ")")) %>% 
+    spread(Species, MeanValue)
+```
+
diff --git a/lessons/r/intro/slides.Rmd b/lessons/r/intro/slides.Rmd
new file mode 100644
index 000000000..443308630
--- /dev/null
+++ b/lessons/r/intro/slides.Rmd
@@ -0,0 +1,32 @@
+---
+title: "Introduction to R: Initial comments"
+author: "Luke Johnston"
+date: "May 26, 2016"
+output: 
+  slidy_presentation
+
+---
+
+## Learning goals/expectations
+
+- How to interact with R in RStudio
+- How to import/export your data
+- How to view the structure of your data
+- How to wrangle data into analyzable format
+- How to write reproducible reports (maybe)
+
+## 4-5 main concepts:
+
+- **Getting the data**: read.table, write.table
+- **View the data**: str, summary, names, class
+- **Working the data (dplyr):** (tbl\_df), select, filter, mutate,
+  summarise, arrange, rename, group\_by, `%>%` pipe
+- **(Re)Organize the data (tidyr):** gather, spread
+- (Depending on time) **Generate a reproducible document (knitr/rmarkdown):** YAML, code
+  chunks, inline code, kable (pander too), render (Knit HTML button)
+
+## Generate these slides using: ##
+
+```{r render, eval = FALSE}
+rmarkdown::render('slides.Rmd')
+```
diff --git a/lessons/r/loops/HighPerformanceLoops.Rmd b/lessons/r/loops/HighPerformanceLoops.Rmd
new file mode 100644
index 000000000..e5ab21fec
--- /dev/null
+++ b/lessons/r/loops/HighPerformanceLoops.Rmd
@@ -0,0 +1,304 @@
+---
+title: "High Performance Loops in R"
+author: "Adapted from Roz Dakin's https://github.com/rozdakin/R_loops_demo"
+date: "February 11, 2016"
+output: html_document
+---
+## Getting Started
+
+Install and load the following packages:
+
+* gapminder
+* dplyr
+* microbenchmark
+
+```{r load_packages, include=FALSE}
+library(gapminder)
+library(dplyr)
+library(microbenchmark)
+```
+
+
+The for loop is used in the following form:
+
+> for(counter) {#action steps}
+
+### Loops Two Ways
+
+1. Loop over indices e.g.:
+```{r eval=FALSE}
+for(i in 1:10){
+	print(i)
+}
+```
+2. Loop over elements of a data structure e.g.:
+```{r eval=FALSE}
+for(i in head(gapminder$country)) {
+  print(i)
+}
+```
++ Related: Loop over names/levels of a factor
+```{r eval=FALSE}
+for(i in levels(gapminder$country)){
+  print(i)
+}
+```
+
+## Why Loop?
+
+### 1. Resampling Statistics or Simulation (e.g. permutation tests, bootstrapping)
+
+In the following example, we will simulate the central limit theorem by randomly sampling and taking the mean (over 10000 iterations) from a poisson distribution.
+
+```{r}
+results <- rep(NA, 10000)
+for(i in 1:10000){
+	a <- rpois(50, lambda=10)
+	results[i] <-mean(a)
+}
+hist(results)
+```
+
+Bootstrapping: Resampling with replacement to determine confidence intervals on a statistic, e.g. the mean. Let's get CIs on the mean of the gdpPercap factor of gapminder. The basic steps involve:
+
+1. Sampling **with replacement**.
+2. Calculating the statistic you wish from this sample you've obtained.
+3. Repeat over x iterations (generally >1000)
+4. Estimate sample distribution to determine confidence intervals for statistic of interest.
+```{r}
+gdpresults <- rep(NA, 10000)
+for(i in 1:10000){
+	resample <- sample(gapminder$gdpPercap, replace=T)
+	gdpresults[i] <- mean(resample)
+}
+quantile(gdpresults, c(0.025, 0.975)) # get 95% CI
+```
+
+### 2. Processing/Analyzing/Visualizing large and/or distributed datasets
+
+You may want to perform the same analysis on different subsets/columns of a dataframe/list. This is particularly useful if the analysis has multiple components. 
+
+```{r}
+#loop over subsets
+countries <- levels(gapminder$country)
+results <- rep(NA, length(countries))
+for(i in 1:length(countries)){
+	mod <- lm(lifeExp ~ gdpPercap, subset(gapminder, country==countries[i]))
+	results[i]<-summary(mod)$r.squared
+}
+
+#loop over certain columns (lm on column ~ year)
+responses <- names(gapminder)[4:6]
+results <- rep(NA, length(responses))
+for(i in 1:length(responses)){
+	myformula <- paste(responses[i],'~year')
+	mod <- lm(as.formula(myformula), data=gapminder)
+	results[i]<-summary(mod)$r.squared
+}
+results[1:3]
+```
+
+We can make and save lots of separate plots. First, create an empty folder and set it as your working directory. The loop below will generate many plots in that folder.
+
+```{r eval=FALSE}
+for(i in 1:length(countries)){
+	mypath <- file.path(getwd(), paste(countries[i], '_', paste('00',i,sep=''), ".jpg", sep = ""))
+	jpeg(file=mypath, width=4, height=3.5, units='in', res=150)
+	par(las=1, bty='n', family='Times', tck=0.02)
+	plot(gdpPercap~year,subset(gapminder, country==countries[i]), xlab='year', ylab='gdp per capita (units)', type='l', ylim=c(1,10000))
+	dev.off()
+} 
+```
+
+### Instead of For Loops...
+
+While the concept of for looping is very powerful, it is often not the best choice available. 
+
+Here you are attempting to create a new column with rounded values of a column using a for loop. (Don't do this!)
+```{r}
+for(i in 1:length(gapminder$pop)){
+	gapminder$popround[i] <- round(gapminder$pop[i], -3)
+}
+head(gapminder)
+```
+
+Do this instead (vectorized!)
+
+```{r}
+gapminder$popround <- round(gapminder$pop, -3)
+```
+
+**Recoding Values**
+
+Here we want to change all values of iris' Group Value to 1 if Sepal Length is between 5 and 6, and set to 2 if greater than or equal to 6 (and set to 0 otherwise).
+Don't do this:
+```{r}
+iris$sep.group <- 0
+for (i in 1:length(iris$Sepal.Length)){
+	if(iris$Sepal.Length[i] > 5 & iris$Sepal.Length[i] < 6){
+		iris$sep.group[i] <- 1
+	}
+	if(iris$Sepal.Length[i] >= 6){
+		iris$sep.group[i] <- 2
+	}
+}
+head(iris)
+```
+It is more efficient to use `ifelse(<condition>, <yes>, <no>)` which can also be nested:
+```{r}
+iris$sep.group <- ifelse(iris$Sepal.Length > 6, 2, ifelse(iris$Sepal.Length > 5, 1, 0))
+head(iris)
+```
+
+Do not use for loops to apply summary functions (mean, max, min  etc.) to subsets of your data.
+```{r}
+countries <- levels(gapminder$country)
+cmeans <- rep(NA, length(countries))
+for(i in 1:length(countries)){
+	data <- subset(gapminder, country==countries[i])
+	cmeans[i] <- mean(data$pop)
+}
+head(cmeans)
+```
+
+dplyr has a summarise() function for this purpose
+
+```{r}
+by.country <- group_by(gapminder, country)
+summ.country <- summarise(by.country, meanpop = round(mean(pop)/1000,-3)) 
+head(summ.country)
+```
+
+#### The Apply Functions
+
+In base R, the apply family is very useful in place of writing your own loops:
+```{r eval=FALSE}
+help("apply")
+apply(gapminder[,4:6], 2, 'mean') # applied to columns
+head(apply(iris[,1:4], 1, 'sum')) #applied to rows
+
+help(tapply) # good for dataframes or separate vectors which can be ragged (different sizes)
+tapply(gapminder$lifeExp, gapminder$country, FUN='mean')
+# tapply(vector, factor, function)
+tapply(gapminder$lifeExp, gapminder$country, FUN=function(x) round(mean(x),-1))
+# can write your own functions to bue used with apply & co. can be done inside or outside the loop
+```
+Note that you can write your own function to be used with the apply family, either inside or outside the loop.
+
+In this example, we will use tapply() to normalize all the values of gapminder$pop based on country. Then we will use lapply() which works on lists to apply our functions.
+
+```{r eval=FALSE}
+poppercent <- tapply(gapminder$pop, gapminder$country, function(x) x/max(x))
+#this will give a list of lists, ordered by country
+head(poppercent)
+#now we can use lapply on this list
+lapply(poppercent, mean) #to find the mean
+lapply(poppercent, function(x) sd(x)/sqrt(length(x))) #to find the SEM
+
+```
+
+### Speed Up Your Loops
+> #### How to Time Your Code
+> 1. Enclose in system.time()
+> ``` {r} 
+> system.time(mean(1:1000000))
+> ```
+> 2. For big chunks, determine the processing time of code with proc.time()
+> ```{r}
+> pmt <- proc.time()
+> mean(1:1000000)
+> proc.time() - pmt
+> ```
+>
+> 3. Using microbenchmark, it will be a more accurate estimate as it samples the action many times (default = 100, but can change as desired). This will take longer but can be useful if choosing between two methods that you will use extensively.
+> ```{r}
+> microbenchmark(mean(1:1000000))
+> ```
+> You can also pass it multiple things to compare speeds:
+> ```{r}
+> rnormtime <- microbenchmark(rnorm(1000), rnorm(10000), rnorm(100000), times=50)
+> boxplot(rnormtime)
+> ```
+
+Here are some basic principles to follow when looping:
+
+**1.Initialize your data structures rather than growing them as you loop (avoid appending to a given structure; pre-allocate space instead and then access!)**
+
+Let's look at an example here of growing vs. pre-allocating/accessing:
+```{r}
+hit <- NA
+system.time (for(i in 1:100000){if(runif(1) < 0.3) hit[i] <- T})
+
+hit2 <- rep(NA, 100000)
+system.time (for(i in 1:100000){if(runif(1) < 0.3) hit2[i] <- T})
+```
+
+**2.Take advantage of vectorized operations when possible and take the work outside of loops. **
+
+Note, sometimes more code is not always a bad thing, especially if your primary goal is time saving in the computations, especially as you begin to work with increasingly large datasets.  
+
+Here we will try to denote all years in which lifeExp of a country decreased as a "bad" year.   
+
+First with a for loop...
+```{r eval=FALSE}
+pmt <- proc.time()
+gapminder$badyear <- NA
+rows <- length(gapminder[,1])
+for(i in 2:rows){
+  if((gapminder$lifeExp[i] < gapminder$lifeExp[i-1]) & (gapminder$country[i]==gapminder$country[i-1])){
+    gapminder$badyear[i] <- 'bad'
+  }
+}
+proc.time() - pmt
+summary(factor(gapminder$badyear))
+```
+
+And without:
+```{r}
+start <- proc.time()
+gapminder$lifeExpnext <- NA
+rows <- length(gapminder[,1])
+# create a column where, for each year, the next year's lifeExp is indicated
+gapminder$lifeExpnext[1:rows-1] <- gapminder$lifeExp[2:rows]
+gapminder$countrynext <- NA
+# in order to keep track of country (don't want to compare two different countries)
+gapminder$countrynext[1:rows-1] <- as.character(gapminder$country[2:rows])
+# the vectorized component
+gapminder$badyear <- ifelse(gapminder$countrynext==gapminder$country & gapminder$lifeExpnext < gapminder$lifeExp, "bad", NA)
+proc.time() - start
+```
+
+**3.Indexing vectors is faster than indexing dataframes.**
+```{r}
+#in a dataframe
+results <- data.frame(t1=rep(NA,1000), pval=NA)
+
+microbenchmark(
+for(i in 1:1000){
+  a <- rnorm(50, 10)
+  grp <- rep(1:2, each = length(a)/2)
+  stat <- t.test(a~grp)
+  results$t1[i] <- stat$statistic
+  results$pval[i] <- stat$p.value
+}, times = 1
+)
+#as two vectors
+t1 <- rep(NA, 1000)
+pval <- rep(NA, 1000)
+
+microbenchmark(
+  for(i in 1:1000){
+    a <- rnorm(50, 10)
+    grp <- rep(1:2, each = length(a)/2)  
+    stat <- t.test(a ~ grp)
+    t1[i] <- stat$statistic
+    pval[i] <- stat$p.value
+  }, times = 1
+)
+#Small difference, but can save time on larger datasets
+```
+
+### Resources (free online!)
+Patrick Burns' R Inferno: http://www.burns-stat.com/pages/Tutor/R_inferno.pdf  
+Hadley Wickham's Advanced R (esp. Functionals and Profiling): http://adv-r.had.co.nz/ 
+
diff --git a/lessons/r/loops/HighPerformanceLoops.html b/lessons/r/loops/HighPerformanceLoops.html
new file mode 100644
index 000000000..9e15dc1d3
--- /dev/null
+++ b/lessons/r/loops/HighPerformanceLoops.html
@@ -0,0 +1,394 @@
+<!DOCTYPE html>
+
+<html xmlns="http://www.w3.org/1999/xhtml">
+
+<head>
+
+<meta charset="utf-8">
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="pandoc" />
+
+<meta name="author" content="Adapted from Roz Dakin’s https://github.com/rozdakin/R_loops_demo" />
+
+<meta name="date" content="2016-02-11" />
+
+<title>High Performance Loops in R</title>
+
+<script 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oPEio8DsNzZQqgUMoq0%2BNMALrygrOlErJohXvIJ%2F0IqziZok6OuApG2eoGAEFsTc6yWf9eRcklYZRO3PkA%2BW8aN5ME8aLNC3Kdwtm64WyqytOEW1WQIg6bOFHAs1ovo3OfCvvZASxBcxg2SRCRTTJDBKajFH0LL1kSBroe6L9xvVxMCIFS7Zo6y4ZOEIkAI9QtGVIKKBlCvUUEq2S8FiR7VkJGbvBFaS6UzQV%2BayV6NKw62tbJbEjkJ7AMC17DK399DZr%2FtE5sUtaAPwaHNj9VCoVB4VAG0iNI9IjDo0mr97FWjYeDRSr3pstZTVZ6nj5Vp3IbssG3g0r9E2eSmHY97LYobIHPnEGkKDJP15WPWtwMgS8HeLd6NEnP4mIFlRJB4zLeDeoBElpUNRIQMzLbRQsGLj3DZhQytDQPLAUE9JWHiLKAJxVTOJNZsObEiutLqwox%2BM51t7vJQhBeQm2i5bTa4D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlKNcrJfmvyKlbaqps0Oa%2FBSPXk4FJjtWF2wOHfpFY63qA49US3T180HXmbqEjYsSpRFkkOVMCBH1C9JAJTRgjCWNAjl2MSA%2BvZjADldAJGTIoWBOcOEUI4qwvBSI4hMj8m7WHoFosA0M4Q4yS8XtN%2B5emVGJDGqX8D5SVbE%2F0bWETJRyRFYKQ8umFTkY2ZnV9Lz%2BVW9hgHq%2BHYhsrghzcANy%2B02yEIiJuDFngZnEHSFnTnqDAicaKOgtLZkRXBqGS3bgcqBK6B%2BmV5G4%2BxzA1IlYTTtL30wAPCkFZuhlgsViFEnA5Rg4UXmBbXBAW7JuNvZlXTVsYDMV305DdUPX6QyUFMwndSexwLkpBDsIQ4lUnD9QoEF8QmpcQk8FJckIIkVwvETqBSrIvHRim18B7iqFwNCcFoUKmSj9cSfX37WL%2BCT4mAKH%2BS76AYlpBE3eYErTSL68qrzEKiOZo1mbN1ujQapDrmTWb1s3iBTeQMCYcYfVeS5ks0rTlCPWr9AjV%2FbgZ8To6s7qOr3%2BkRUCKT5GAAUzFlHOkmV3fJEImmhCZ4S5ivYOcC3SUx70Is%2BgfKTQfQBsES9NEX4x0WTcQkWV5JKEJNl0UV8pDmBNnC%2F4qRYIkjLTUknCCTbfLBLJzy4rnSGTntJHjdpYIWlqzxK%2BG%2BRBy22IJ8gMQIibkjgh8Y9Q2H5aenHLwEJg2iDmhQybR8G3gfKxBAZwppQkDi9rFMfLSe8Fgalu2z9mKNwe%2BF24HWYNjNI3zoQfXsIck6YiAwCMhAsEDMvxfp9PxAAxF94h42eFp8V8BZxS3g%2FoQhPG05yIyOc5GxA27VgPq%2FN9uxGcdZCP9PAWYlmmiIlMHE%2FRj3IzE8YKgGEh8OmOkN4KuO6GYyNnRhoWNSgmY9%2BVfMfhBm7xIwZPp7ggg%3D%3D%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cb08AABFOAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAjPL%2FpQAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAAxADsAUABTACAAMAAwADEALgAwADAAMQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAHa4ADV2ADV8AC1cEs3pishg2FfJaEtxSn94IlU6ciwvljRcm9wVDgxsadLb0%2FGIypoPBp1Fx0xGTbQdxoAU51YoZ9RXGB0bXNPyK3JLMApRwa%2FIMy1PPoJDx39kimekZX1c%2BDSW41tEZBuFiwdwx1dRoPVA2vWPSlsSDqhNkqYfhrqlVUGD0J3HEAgZavmtLnDC5WBriSpD8Uk02KsUkJ9vCFz2CZXAd5viwGZ2xcVYRPa1bEIai51nMYlbL2ERuB2TCzLAbPWWRZ3%2FsZ%2FKBjLk8%2FgAZzK1OaxNw4oGBbsNhx6Reg5HRFVCrwa15kGmJEy5kX1YypVm%2FHo7TjKP3l%2B%2FnuCTOyiOa6S8QEJbuiGYNlCnM7tHChCHRRQHXQh7yPXASlcvc5KrNKol6orb35kbo%2BiEwl8d230cfWPwTy00bFDRYURYGchbwsoDcaR2AJsFGCrbWCzBdQ0qCobWwLfueqAzSkzaHX3yCjDlGYPV0VZqVXlqbr4poRaG5NNPMDv5MCHkLSFyABMRBhMiGSZiDgABYmwsLWDjUZCUnHwvXt0VAUy4%2FqjwVgjfUqp1evzUqutJB7DW99Cq5aEhGePsa4omKUp7LnSQUz%2BZq51pU72ApMxkaVZE9jF3IGKVnF0GlK1E6iqLJXFzy1BEv6Kr0ngCzIgVANRwCkVHYJj86Sv2dzqgVrjYKlg8W94FbPGQghEbTakZ4AoS9h0u39DOoRJTA2iirs9xa5m9Ir0KwkogoqFNVDbTIGO0%2FWuJYfp61Kq0bhgbcbnvbTDj4HBESsXMdvrvpp7Owf%2FS2EbndoEoglO%2FjigGAuLUTUmG07ZGlRCZ4QZLu81tPEgTtAvF99BxfnxNslgVYH5Jc4AfHwsiktDBO4wjgjizFMsIeBTGmIYrhtrK3tvtBu5tYB0PDMXILUkFYPM%2B5yLLGOU9DtoDLYWLQig%2B8wNRZbJZZWeK5JyKNwumLgP7X6pqgiZMFUvfCTl6Urx1gUOjzhcICumUUsiNESZCmTANkgHoh%2BKU0t1fgQYOBkNfp4MRiJysezS1Vz%2BahE7ohM0iJBn4I%2BT9RJN%2FuQk6SjQGwC%2Bom%2FSD7Zq2RxvcSjtdxHfo7NtktA46mEg5gBkTtMKu7oFb1KgqwqBLzQqZI%2Fs4QSRhYF3ani1URTcMD38kDDqlc3DChmYJTCR9WpaWrYFiXeNi7jYa1Oe%2BSP30gxVyeC10DYOakGpWaU1qkQ9OXlujEbM4A6A%2BQ243TA2QnI8mKzNXNo1HPN1CFawST6uC9dkyQTqq5bOKkkuGjz0nkj35Sr%2BQ%2FkLGg2iQMrTKImSGshnX7%2F0Jyh0JziDbTzxp3IrbosXgpchLcBJpLLUBrFbt%2FU6qXqAaNFJoeBItBoqgQlPZkpTq31s5VZsgIkFupAno5G9oKwCGRqv5294xFqOrEpewcjx%2Be06mxDkN85c0ckmWmMzbkFX9YHAf83p0EsnBywjZbcPxZw91kqg7voxasp3VfDhEDAufUx0MTddLO5kCyKkUBwOm%2FMurQH4bJQgK%2Bp14dkBBhgxpBUkTlSbTE8DDu9qKiDSCtvmPY7FRMn0wuS6XR7J%2BnpjjeaDNwiOgIKUWCP9aLb%2BXpc4toQCryp6gjCVq1Cl1Di%2BtEx4WnBp2hmGZpEksZwTSggXuqAWD4B7WAMcir2wmA%2BI43ECxUSeTDT%2BxgR3O4XFPOGxHh09lxpScampLahUbICao0db7%2F%2F%2B4CBMcCokQ7aOQ02MOzh0NEaJGGOvPR1t4sY1I55lNfQ3IkF04onnAomXxiNg2EbBbPTMBBlv%2BggPSmkb4GNFW6Ehd6nDzJlnIXFfzr1bVWQlfylDodFBbhCDf%2FVB13j441dc2rx3h4hfAWIJuZqbH%2Fwbuafg%2FnTQJ9bnMfR3U6TwYgDQJIOPOtmZW4ZHsR2VifZGaC6Pzv4ZzC2nSAaqSe09F9MxuI4UmUVA2RAUqBUIETEIAhB%2BtBhsxhVMXUmv3RPe1grhYWLM%2BPHwwO5W069qIgDXtETXZQ3741DSk%2BvO6y8k%2F7Mg6GFC5fSK02yXZ1XV0NZe8iknoIbSFnDm%2BbMP%2BBCQhoLKOeU3FVNQ%2BgPEntwkHT950cQYEYQc%2FXuL4nxwcRW2bIN%2ByskPaD5Rz6lN8LW5IDLYfuvhxlLcjWXSADeTshWsJk%2FHCuRbwjqLcx66fc6UlF33HipliQ1cvfKlgRpVcsmIsiYnJjzJIovUyqi%2F2OTQcIwt%2B3WW44BAQh%2F2GymOIscSpIt0YUePiwOCJN%2BQ60lqkSRbWi10lotzgWZsqzEI7OGFGeIoFyqli41%2FNn7LfLQ0qtxkaAwbsKZQ0D9s%2FwhBqoxhAlxCVNVF%2FJ6rsUBQ49MLtR3ICtKPLKZ%2FQPEeIRgWUNLbNR3v3tTaL8uiXO%2BOj3tSJc56KJHol9nGPuclbrQpCn4keT05ETSOQ%2FDwwKjCRLgIzBi7x%2BU7ByTZGUWcGLPY0VtXWC7kQ38C5thIGJx%2FxLM3KsoGyRwC5AFUNWPzU0QBCaVXo6z1NCclLuPVA8%2FwWuNhiAG9CPPSSlIOCuh2AQM1d6MyIEIQArIa9YenQ5ug37h9gPNDHVLImcjE91ewI7pz7HgOEVqTD%2FjREYRtEoDIfgxzDePkVvHWJAtcpCJ%2Bg2o4akZpphRUQ0U92lJcKZGJI9mnpl8UVzpMi8SBGXUAPpgcS2cDAusSE6q0hNibhCgZHLReLsA%2BSv8wF6LiDL3TogB1DsO%2FHeMg0ETucRO2hIOvbp1NVkFMclluNmBQbTy%2Bosr2bJ7oWdUtSHi1s%2B2vWxq8njY1eW4BCVr2EqSyIUuFKS9iwVF5FBGeg0hRcHwCKMtqYLBfvVSYXHdGZK3Ug95C2d%2B3t9ZWEYqq6F4TocgcDS4tI8cvEEXK1%2ByB9RofdKpHoA3eP9%2FB%2FEBG%2BIgvmwZ4Iir1SOYFEtUG%2FoVkECQqoPTdHoNpQKpA9K9k7TisAUBsKPtWgBriyeOxs1o%2B4KCIFumN744dH3Yf7X8F426LGtEhFLULhlKFgXF8Wq2D6kFSBkViLad9D9U%2FP6A%2Fr8SE7jsr9s%2Bsb5ODWI5uS6BkZJI5RQ5QDVZ5%2FKOG6Dzc1RLQKAdGkgwLiZLQdEYQ9yhQLtD5kWr4wGHTv4GqloWePyHYyuy3Ek3iQVQkdkAz0MxcsHzccdMLO8KFh7ywo4jXywraIhIicAI4dWdD5wEXBhsMklzbWc8zi8opC3IlPsLXHCWOqOgq4iPU6xyDS2tdSLnlhSFjsCCWvMRt5TAI30EHR4f16NYyzoG%2FoKgCUsW4182Wvj5gpb0bGGLe8YRIYRGVgF2hwIlEfm7eUwwLZWW%2BmmBZPggPnIKI0b6G%2BlHCncx0wkaVPRQpZKWIIrzh4yC4Ar2vTKzhVKSutNi1dSzmM73AGUXSBJrQH5J4Dfb%2BRU25tYrIVdLr4ogiwTE2i2hl39ffJWHNTSWq3PvVJplNqDTJgJcvCHYoQAuHeOew0gZGfmX3p4D0aSr1BhXbXRA1qipfS6LiggtiT1eDg4FTCWwCdAFC9FCOZ52EdrFetMBxUdC0dfnqpr0mELJE2m5FVMvwplyf0XIKclHa6%2FQrw%2FFOVkVKK71Ab5iAVWm4GA3HnkCJhYkW06XJ3TDGB9YhVW9RuC39AFUcFWwU5eTNzuck7IQty7ZC9IjAGNHuZhQ8yDWZhGUoNyhDcKYQiUDqAC782makzJ1o3XJFIEDXgObv%2BTlS%2FdAr5rn2C%2FCDmkI0WO3cSTJwSLrS5dBdkyfpxAu4XghB3jgGShG8UCorOibUp7NciZgEDLmT6eOxDegpLRk7HomganEaTmRZAAMrhobApDjFWtOxht%2BNQngtKVgGL2yy6Qnsmju6DAg4d%2FRvxHa5mBaEMcBekCMtwaPPGeKFPMsgAJTidusMLUbTAUBJMOmXRwhONkoLPqBhpTuLSYN%2Bf8aeX1NKSxF2OUST9ezObkHETTsr04iWClpoFDE8OdIiDPQJqkHhnUPTcE43OsKBGS%2Fzk6F57sqVmhZKjPxle2S%2BiIE%2BMXOiTOoRKF0Q7mbNRYGPZDp5eIdPOZHboa4CTvwnlZb0zCOW6kZJh6klDDvYghmQ2FCDKB5DD%2B0nImiNcfSzaGT8aSPzEZjMQ2GPpA1Ws4Xeysyv0zSSMVVsiOoiowo2SfYFdapUBsWIoRXMAKYdguyVcwqX%2BzVWYLiNddi8p%2BxMV4dQIoUs6SQ4C1%2FBjtBw0ocOzLmMkEv5D8N4%2FZu4gYA4ryDb5Y3gkIxdb37qMCWYmDGgsyEE00y3BZ4FgyT%2FdJLHxMQwx0gViQjWL0QyBfcufCgRYYGiCHrxez874MrcqldLCEQLkCCK6pWCnULCh%2BVK6FW%2B1nm%2BN668e16QTKWprvRw3mkwitjPefHt5QKrVumkXsvsVrHdAJrodfWckxhhzknXZ8cMVIvKphJVfhvWbhoZbbgo3fygOr8PlKcVEYLTmUbjp8rTFlPRtPrdA8tljZ0REfnhwApCZ65Igtn1gBROJdPCCb9ZIqlQUq3W89ZjSgSnUz8nEBW03JCcQ%2FSxouQtYBE7ichYHbASPB5J1%2FzVfPINn38CqTMws1KKo%2FyvNXpDgBvCI08SBLSwX5Szp6WlpzoPEio8DsNzZQqgUMoq0%2BNMALrygrOlErJohXvIJ%2F0IqziZok6OuApG2eoGAEFsTc6yWf9eRcklYZRO3PkA%2BW8aN5ME8aLNC3Kdwtm64WyqytOEW1WQIg6bOFHAs1ovo3OfCvvZASxBcxg2SRCRTTJDBKajFH0LL1kSBroe6L9xvVxMCIFS7Zo6y4ZOEIkAI9QtGVIKKBlCvUUEq2S8FiR7VkJGbvBFaS6UzQV%2BayV6NKw62tbJbEjkJ7AMC17DK399DZr%2FtE5sUtaAPwaHNj9VCoVB4VAG0iNI9IjDo0mr97FWjYeDRSr3pstZTVZ6nj5Vp3IbssG3g0r9E2eSmHY97LYobIHPnEGkKDJP15WPWtwMgS8HeLd6NEnP4mIFlRJB4zLeDeoBElpUNRIQMzLbRQsGLj3DZhQytDQPLAUE9JWHiLKAJxVTOJNZsObEiutLqwox%2BM51t7vJQhBeQm2i5bTa4D8qIUjqI1sL9G6Vha6U6IPUabeCUMIufG%2F%2F6USyxymXNlfnVinkNvKZGUjz0EWKIgZEC4WkUEr%2BgP%2BFpkIPUVM%2BQEJPxFxKDlpBEeiUSaTgCm4sEfRnBxu0eF%2FLgLp0IFHMEt8aZrsQzTF3wRw8TdpzHNGMZrHHa6nU8KCQppWBVpJ8b2Xjq278mtoTfrgfAMSAm3cYEcDw%2BzcSsXTaUyd2PH6DCU0DdBiauxpsyoKlAh9IUAzS32X%2F0d3x7h4gL36Q5sC4YzkbttmSwhenP5pxxrBEEcoAohUGB1jHGtB2%2BI0FEcCM2y8EY1e2NPXJS7wp7APp46lC7gSit7FEFQCIDnNX%2F196lJs%2BgXTyJrXAze%2FQ%2B9GEFC7p5W7W5FEooh5QlO01B1OcYDH4VTmRSO8fHPjRo4FkT9GrnUZw9pXnDplu0YFyOaAsLOkxdsu2gfYJ%2BDzBNihSUutbDbDy7VKS3KwwUTChYQkWCmldyS%2Fy05EvDGbLbpkDfbbHNGEhTSiaAEDmoKTERX3pEuKYJT%2FlNS4IawkdDACC5Jk4hgNa2EGV8yblsXoSHDrqHDJs4JDXLLA67oCyia7LABDzBMInY5gnZCpFuyyuaWXjKHRgy409j2wNaI1bGlosUnhaB5iqHHZf2%2BbDPpsIwoahsWRL69BhhyPHQBUNCyBR%2BUJzMOBRMxTVhNsS0%2B1n5kboqmNZcC%2Bt6HYVscRaBeox%2Fvv3%2FxwdoUoRvs6CamykRGihd8HTNaL%2BxRuoo3QVZvFT7xS9opWLiNbQRti9PXbNg0J5WDsxaNesdmkYOcwpiqsURuor%2BS8hhgWniGGll%2BtD55enFSsyATy9MonNXUJV5Q7eAouzR%2FP4HlA5YWBYYjm6NDAEk%2Bx9On0MGm65%2FEm1GKndnRp4cv%2FjFAdM7hnXsaXnGKAeK4aE%2F6BhOq1%2FAWmgCMQ4zC8G0wBzhuMSeeVhgAl0jpwicoR64K%2BBYlYbWz0gTGAGtI3puhx3F5%2F5QKXradYFuhJPYWq3ITCUUNLpugdVBegedngCO%2FDN5mJBWzGEL932jL1YntvCgzzLeR0WczKl0J4g8Gcbo6p5LOslpYBw4kE3ZDMiHPjBGyAUjkrWXqYTrl7gRUwex0zKVekIhoI0dsFRDtQxsLctjOlRs5Y2gYpQeBORQFR4C3hXh8EnZUzuloqFLFATXzpzv6k5XoHB3fZLNUiPV5DvAfroLwH8tYhkcOxWnHqePfQulM0DCPTadXa%2FTpoHYkh9BeeAwpmi9Kbo%2Fm88ob17pFzKP2lJpfWxbyD3PccS041A0MQWUqNDHOPOFt57Q6TiHQEKJQsI4v1JqcD7Q3orOdsLvExRQwS0nlUtV%2FpQbx0wSlrRSneWAQ%2FJhPaT6rMvIcid93ahrnKe34YxGA3D30BI2lEiLGh5iAUkBLBoXRshvpjGJGLzGcvonIqzVubgEJv4mbEl7JggQUWb6AYaQMmGUXEwIjylByVLbgcEbUQL5RFiRkX%2FbOhzb9XavzmFpR0cOdx3etgoaCPbiL5gR8o0wJNdPMCYcmyP0SDmmsW8o0O%2Fy5HNRxqz3IhmaxvIT%2FxpN8Pj%2Bx4i%2FGawfTOod%2BpvHN9BMNQUKG555l7yAKsma4r8JwVm6gxqBECXHA3e%2BdCsONR41WrcokynYzYqB0xlCdg9Nu%2FVL4rgwOiTVy8K70TghEjAOHeFBYvGwmkrDIzlwQsyBsr3K%2FEBFKO8UQ6J1hU%2FYGFA8fGgTnBoNnAmZc%2B9S%2BXnpbpJ4QKgdip6TZnI6woSQaYgxGpdaxxSuQkxu0avCBOSwchx0SS0x5HgR6iz9AkbeoDFTnGInFfbnMUP%2B8p3uSQu2CVkHxeVeEMICXKlVDsj89UY7AfyMvxTyws7ilT3y4zYSLVToLb1IwDJHJ804mZKVSKgSOa0mHCQrjWQLaz9LrCiISicIHspx5moJ%2F3Ng4rjU8aMqTDl5gjJAFgAMupkEoWEalYEtHh8CvvMqUP0Ra4v6BRr%2BdgNXFRLo%2BjKWRhaP4x63qqfsnw%2B%2FlDAo2doOxQP7GwahE%2BiZ2OX2l5DodFT8bGiL0YglcRmgDoYIEaRBLqdbj7HFD8xfZz45H2Tg6upN9XjuGCxtdxuVuRjssowtewrBo9gBM3T7d77hNjF9rd8QTMjwGLx3oWax6aa17t0I2gcG6kcQgw%2FphwfsB4gsCGgMGZU8P%2BORAg4iQDmTP9XqByg%2BjtRqWtpaHjiHSL8CNah084UP6Rfjx8GU2eaRw4mH%2BA9Ad42DummqhAj0%2FeMNS3V8lEHUSc1ntW779KspgJUBtJQEpS59ODiBRghAyioIrf%2FKoPh0eAJWqxh%2BSurG%2F5xVdzZBLgKEiYy4izBuKhCRIhfoADEt%2FB%2BirTWSOZVkcfzWBbA%2FAO817phMFhWQnvTwzjirEna6JgrjgHyk%2B8nWk0k06KkkSOOryl2OrOta3U1Vai9SLWxcE3cY161P64wA5VUgBExRGc8qtWCkEHIJfWtybVbTm%2BhrXnbVSsP4ucJTC9onJ8N4r9lRaV%2Fc4a2SZKmPNCgEUW7LDBXhvFxUvZYtnIuGD61WQtIrDXUc13hksUbImFeHrIBgvnG4ogmKBk5CClzY5DWqCCMzdrHBGSSOTyO%2BE8inLMGSApQqKJB3XKm%2F%2FMFoO17KuwfwqcaqjAyaJ5JOWW1Myr%2Fs7ZQiw%2FPqVAMH%2BlK6DYqjGUMcAXnVxy6z6CDl6j5mDa5EG4j8C6pr0hKyRNnoiR1vl9DWOfC2%2BM4v44bO3onXro%2BNlz00ZlGV2ICJ5CcCazVCJHIETBqVFhGT7KSBdNDv5ILpSacSTL0S6fX%2FP1CEIT5NZgcwpI0rkV7LqiHRoLUAPjXEqCZ5mLntX5JkkUEelCsU2tXCMZ6cZivQI6hYQNbXl4nc1sSNtBUTkQ8Y442KxRe7Uy5yOzB%2BUlA3MhvFmXweJJj%2F%2BG%2BqwJFWFLNYp0JhzD%2B4sl4JYn6a%2FvShCCo8hchMfrswjZgUqiToKfoOGaHdIAnKE%2BiEedBkyLwMSoNVX83TJERFy8PLMpBjsXtBITuS9lpPCTVl%2Fvs2Aqzlzwnj7lpl6VX%2FQ74tWqUSzaKziwue1phr9BpcZjNc%2FGXuJUq15TMDnCsJS0X2wAtW5JQA5MaTyUH4cDRif3MDxZx3Se89P4WSIvEEeLjToJbXf%2FrRhBMtRevJxuf%2Fs8EK4woZOFli3yTq3xqjBjrlierqgmUpFA15HFkeUOuLDjuhzLDjAGKK6WThTynzd%2FtCQWU3Nzd8%2Fa0q%2FxwZYn%2FUPEHU6lsdG%2Fn8gN%2BYACURCU%2BW%2FldrXpdUK7WxhgWmNd%2BLDoQFCU0sFyHn3KqFz%2BIg9e2C5oe9EOi7KdTBxPE1k4k5tkX%2FoZzSih8DJDFd%2BrITKNSsAAGtscSW9VDasqkEamMzeUOhRqRRAj9lc%2B7An2YOdL46GdcOLAsgYaFsR9hs0ToUzZ9rFRLG1%2F7eYLz1l5ijumbe1druwaHNFzZjtwt%2Bn7paZaMZu2EIx%2BlxX%2F2SBrWJQQM0wmE19eWrGPIVMeAzVWWRp76m1skeqzaBBZ6So4eD8Gpv31bn%2BnO4WG%2FOg%2FB7xQu9kpA3eZL3w9cwzrZfMmbUSk5OiF3Qx7bQYhL0VoUQQP3Hmps1bBJDQU0qAJpWwnb4iFOMBthzwP81ilVnh1bIuk0C9Xe%2B1kSK%2FK%2FMYOh7vIa7ghqGEKzCiHrehs3cA1pzgec5NpLDtCqMPp1GVECA4QZKiZX0hQ%2FGqjOhhObmpH0vwx0WHctTrmGC84Ub6BOQVdXIqoxqMVNhQjd%2B0CYMRk1zp6%2FPnIOsW7k%2BhQEruSB7%2BDEGv1lziYEEwur9gsigQBG8EJ3zeigdA74WvD0h2IbdNzPw3okAFsdmi3qKH41lxijDPDtvZcEzXwUnRNn4WpCP3vPNHhO8czUiYjRn6GRIQ4SymW9MhjUu%2FjbWOhOqNeh9OSRNh1EaFZcalxYnJMLCpTlEDttzYxhNCcZ86PUpbEkFmkU5HRiAF9AahZ8jQ769NUzJGkq9YLj9HWZLEK%2BIryXyPLZHAAHsBSe5moWq2RRQT655EACRYKQ1AR2mNQU6hNdrqLyfpSeJ5PrccWf3dXVxmIidWyyIkCm1v1oFYgyGcXwrCRPXPMLYHRFvGVuYyAUhzgYntJmDhE4VCNKoSxWlSFiFuMGumZHJnCJyAdT%2BHcgUhIEJZgWOjmy%2FZWisKRYo4JjuPntQGSxlJg3XJ5DBsFM3kGGQjqECyjqYFW6ccOkCDy3EhUt6taBIaHclyblt3Xxs353KjciddJC7gI4cZaPj4zBxOVNfei9MW9cLUHspngSFFni3AL7sLsbqo3totPWMf0yD2bdWKWuK2a4eF5jKcYCZyvORgcHeETtqeqhAYtIyiFGAYJQMOX4VJzPjyDKiavWJRtjWCnaTUdPR6FRkTfFAARc%2B3Ln3oFDETvxxiaDT3roIpaWwEEjRlhjvs9ZKiU2o1HJOMuVRFAA5fWzMgAqSKDaSLNQcQZbaFzsgKxXWIvjaOqsoiS0kA1bx2TN4NrJBVMJcmRWNIqrjepyobiDa9yXYKmJvgQWZhEBvF8wEx0pMQqCzcf9JemmcVMROHpvzARvqDrI5WOXmah0Zkh%2B6PIozOYm78ieRAJObTPxSCLuwQiRlWaLSxJ5hgIIrjsEUKPWMqRFuMgh1FsNDFw5jPGKPTWzyiSEIS6IPweoS55G9SZZ6wcpW%2BHYMSkoSETssJ3QQM9bQRo1JdCQdS1np6F5WR9wujL48y6xB4M6Y7FMnX7f22J6F5gyYVK0QlgEQxVXFUuC964aXqqhT5cX8XaZv5hVzeTU7bSoy40QuTOUjLVShZVi5iWJMAbFhku4I5QyO1x0Pm%2BcTesk5XsjnzwoAIsmXCFbWyIZQuawuVuXuR8DK92lWJDrNrbFtNbCj5sTTCx7e2Wb0UydtbZ%2F0CSIT7OnVIY0fRntwN3jNOdh7nGpmmq9CbBm1uLafMptryjPHHp7tKZEuAL7OjXHySRjUkXydXE%2BSrNxA2Um5Al8Dh3EZC5V4SFynxsMTKCAIMqEqNKzuJCQM3tvVBjRfT7xHSuCO72a6pSo2wn8%2FaXBmKI9sW2BccnPF8aML8%2Bohhb7ciWy%2Fxch7McUey2CPGCyVKaKDX8%2BSBNgqZqMLbCgZ4NPkIF6NfeH7gtElIrOqbudNqU8yyyewVZneKXuzObCUxU5b2tecTa7Jn7SE6UlSYMTkMfCs3Q7fVCaqK5k6pf%2F2wW%2F%2FosIJEgW3skUNgQHyb%2BsAi4ECMNcyjicIITuXGyXkkRIXyN50Jg3r1FpMmaQSUl9pGxnNMp8jf24vS5eX%2FyJYlMUISy9e6JooaPn7fiuG4QEscaDgCZpra85ygZUcJInLV6C314YPJdG2UL6qljb1QV1ZhrNcFecs0j2qb5gvkKHOFxHphwdBgEJc3y8qihwdZCQpJ62h1voTUccffiDJEzkU8jbXRvLFdPbcmVBUhArEuTKQFe570IglefLJ30dWmVAtx23vIGmF7N6auVNpZUQVHNmAlGgjNet51laJgLQSpfZyxBEqLwoCEGlF6ISp0NUmhUHT5a12%2FZElPE5UGO9AVnKpae3D5llVvXAHK0FIH3uE8AXFPF%2FSwx3OdCN6aViMIxEVcqv1UsLJo03R0grVOHRogBCiYVrK%2FqFBPmGrDyXLgr6DxBMSKnf1uSBgiRjCiwL1QoM981XHZnusKIVJ405xXwx6aBUuM2YXS0jaCPwaC2Zdp07J8gxM1KnpWMqNkRCfPaYTelLbBmd97YeNAOFaFOlV29YQn0qIInmIeMFxikX4U1pEusNYtOX6tKZKkgVONLhIF9ZPpxNWGA5fslgv82Lx49LF6FltT3Qp7q4Rcu4hKczn2m1GTA4unFBAkDCqY%2BD5s2dyVIfDeGeBgwDnPbGYXQqwM0E%2FBCl69QtVYbUwkQZAtSB618VkAzCPAs93Ip9qWnBMX%2BRUX0PFxCACZQwpfvNvAMGrD69RE6JSyNNOvRD8v3gzw46n8oB8mFQiYiwgidAyJgrfY1SaNrL5QDEjIIgYLECALRSK49wPmSO8jZLnesJ8oFZjWJyTrfBrNRCeOMoaVxOojhX6WVAhhJ6dvBPZ4AbMXC37YPHBFKUgA7t8mCk8U07mFgWg3wbOZDgsEZBOC%2FApoLJL8CzpXhZuoEh0ntzsvrUPhE40%2BUUkCAm26DxGMecbxuJ%2BpPJct9BZL6lVcWLmHps6SSRwqmRJeypwrd3vNnIawZkqBn%2BumMrwB%2Fc%2F%2BIAx3X3Ehjr%2FzJIVUK8Luxlv5IeNoBQPnIhvJDZfGo8KIpLh8O4iRusiuQOtytJ61yCvQUGFmCxEEliRlQmkN5wrVLkx3CUrlDG3ABe14lLsoysj9hXY%2FTJpUb3tCvpw8Ez7Wb20lfIScq%2F6wGFIrBkgzjtNLBoYqAgJoKvDHcdkTRNbmMKP7Fha6v0z1qPmfqQzKW%2Bi9g0kSWn1vQuqaJyaTAX7hrJwJgfaPjJvb6wXroHZRAD5NH2E2KaWtBw9TGd9mIea9%2BPrxlBpyjnQhg6rLogT7414uB8V19PPcBZUhnNx%2Fhr1%2BtX%2F1jKG4nnHv2EtR3VOjo9oWEibOHX8508Tra1pFg4lnAwoQ2u2JcsDi7diOc%2Fuhvd8qexCvYgoI6m9VYAg7Vl0PRXiuCdgHdKDIIHG0j73u07ULaOJJ60Y178Lsgtd7zYdOUGlMCYhR2Uhyglt3KUjPCpM8MKjVS6OgTHgqD1SSQ%2BqhzInvSk8VzFP1cI8vLxtdDvH35uPzMi4mQtJCrVUqhBWbQRke9Sjt9laOS7Y9YqUkXldpaAoofKmUNsx0D7eYLc%2BspsKbO71qdmmaGglAJjo4kj6XlnIWS1hQZORC%2Fh4i%2FCdcbaFFuxb0AYMJ7EdQExeIHEdPRtCxVECvtxHzbEyldDvie9cGKwImUuI0xEL9W%2B7FhefRJ05ePV427NJDYmhOCwBDNBP5LrIiblD6aw7pSeWFA%2FlDoWYMm%2BZEcF16VDdA3ALq7hDBlLUzNTL9AoVyczpCwb7xHucfK6T64W0AnDzYLg0HYWadOMPdoP%2FlliqiAlAWP4rio6stY02elA%2FLOPBnEr6rsAqtMRsS2g6HJtNANGwpxTdZEP5Xt5mE2gPs7dE0gyQv%2BFdd8QTBEqmAeMHtNnjBtKvg7KmIZCj9303GT9mWQXZxolBupTHIhJT%2B0WN7uu1FbRp6NnSRDIj1aXRJD25s%2FAD5%2FIc2OQ0iDp7%2F3Dbv3Oa8McFBhXrPpAKzhrFANFpcnLmEVTXjq0MO5sJAPVxFbMh93VfD%2FDppG4mBzXSgVfXJeWNNxoKF8QTwcQFXwQPB7QLE2PENdF%2F4vDL9zn0ZgQ1i6Nyr35KAwetsESlUtEENQbCFOeUFb9lg4Q%2FW5lUNo2fsscGbWY4T6pynpP0Krl%2BJFEfY%2BGGHI3aj8aBO0K%2FndAEPa5FX51Niq7CEOhoB2hSpGAgBT5%2BuzxAMzSuVEeQmZIbYew4tM4UsHYdZtT%2F9ibeR1u6rlX6lBQlSBT6LQ9YUEAgmAxLkAKQL9PSm%2F3NQF4w7ZvdqRqMJ5DYZh3j3I1rDmyXyti0llyZjkLnoPtHHwZd%2BfwWVz9ztMTMCFjpKBVWAdcYhRV6wY0kRv47nqddzEhQvXsqNstE6GNiLTd8iliAfI5vfm2KjfTQwbxz2uFzaUL0A9VKWzB7VrWS0QvlqVw9VCXOB6RDSgbNtW05PB7pEz%2FHZkv%2BXQTUD8mSQ3mJk%2FEUe3%2F2ixiuDVAOBfyIscXixo%2Frh7BHtl5KSBigokAOClzsk5pj5X5S9GLKgu5AbQNotZBWU5zoIzScHQKjeFNr0MiX0Mp%2BlHmHKuaDcfU2H%2F%2FhJmeXl1nPE8E%2Fk8ULZ%2FCLYtwJWG4CHI8h5ySfOAFbkRVFJyi0KYkQ%2FfhDAjciw08ETminGEMzG%2FwOERVW0AEslZEl4sfr5AYRKGVSMzkDSQ%2FnhkipQ8y8EF9ZRGKCOxRzfpNMubhc%2Bs9lZBHqNegMaRLozjP%2Bg8CUsaVzp5Wd0ZI5SaeSdNAD1HIc0vTPw7SLi4Jpa1gGgtEY865KzsFCn6I1RvKT3r6BVdYGsBPWdVzeLfbxKi8BVQVzd9f83goEpezGYiAXT8S%2FpW37TxnZ6f%2Bog248Vvj9otMMAmzAfvCpPYmXIx6ZAKAwBH737j2KJpWyuPMnFIBfqRrEBYiTiWVjvdx5T7k0dwFjgjLrLVm28m%2FXLNfjkSOzkoh3hangxcOjvxzVDIGSxYBxI9ak%2BDsrVTPAlMWZD7OThbuxdkWjCtGwtH4QzQdpmaBsHV8Yrg1iAK64TC2WMbQJAFFciwJFPSRQ86OtUrnPSODgRE%2FhD4qWAHDlWBDy1KPUCEbpodyyU4TnaIFzU%2BtJJAes10UhzVTNE6fGDGUN33wCgAZYoIz44wHk6fDO7wGbAEwmcVQg1HGGByFjYGdwJD1joA5AM8olCJ1LlTUGB%2FkhlrmbJbCbD4Uj0GZTy06zCTjOeVkJKZzqk0nE4%2B3RmlgDHpJwCwwZKOl8SEEH%2B8TQK7R6TiIPAy%2F5M08zWqjpXg4KWF%2FMNQpvipZJ4KZfGsivxCXyinAY16NjMD%2Bkgtfc8pGpAgSyN27l%2BuW3OjjOCnldBAJLpzjAYdBoh3b0yuYkLj7c6iwxR9NHW9WBeGVYjvaTEHVq2t96HA16vtKz3ZgtNuJgpjnNZRGEjbmQuiQEBJ2BdgHxFDqf0F1dQJcS4sIvDHlCASh65anDjgr07Qa98i8Jb1rAbSdOOz0cM9wlKXm7ugzOMTmi8EbeTBIoHrudZ9RSwEtADDsgyFwnxyfzXecDzLGFMmkRrGWCdnWVnSMMcc4cl7NaF%2Bt%2BgTHgy9s1SV05LLaw7YYgNxNKt2kpiEgpahsRp%2F0tarSWdfS3AdWYIAnrT68WTwX1vtPgL7EMcIzmOpOxL7oZh%2BcoizveUkJVmjedZZlkwkno1E7%2BjVJIoz8APlgweTgHUL%2FGciPnYgtfrq4TtxE4adiNQ2ZgyJHWYWmrMlW6ebFkPaAUwzKsAhoGXxXGRGKFObHKQOqFOnGgkyLajFNwhUa4fqva2yI2DlREZ6p0CNOGwR4XCrS4K3SH8pDQGvZIaDcMPtJUoLRM0owJ0iT9QcdRK5IRIIpAOzsUotf3TbAVvbQH8n5voA6kUzw6LqVnwPkmnYxgJLYiQ7hQYlEsYL4k5GCZvd58ypvZfwO5t1fXMTj11hE8GuAZhX78JMPsK%2FJ0yQqDPk4Z%2BJbbXS1tia0EBwCjgWUJsUI18clQ%2FL%2FH39pOY3jRcZFFfW7bC5v98f%2FwQjY4osg1CYvJfYmKVWbiPLEpDvlTEGLjUbCmh74cUqKtsVVr9hspTEdTKrKSRjqUHkhwgyUQeNizUsUp3cLmGnOoxPIuzYDGHFJxwclXasrQR5Pp4%2FX9adXL0zyaNRcp5govE%2FKIiLMyYKgb3rKpuxbm7t8U7GiR8qcIvzarYOuIr0z8D1QgHRWmwQj1jMXJlqpuDBiVUQD80Fnr7quJElyEAs47dXRoms8yEtA2VxNaYbJmUQ%2BlsKhyYLlk2q0QMXQSsMctgTGOka0GYae3AxbsfNKwpf8YnyAYZzDx20P8xvD5aQ4OkVtbj%2FxeO9gPGmBGT3BiXsWroQWXXm7Mw8XnUAttSlNyNEsqWLOLy5MJLmMa9m7Abm0SuOJF9RWA8VDp5xMP%2FI6LaAKUmkqt6%2Fxs1XyWrgcDo5hjmvshCSHhWpwCludJPk5i4bpUhqFw%2BRmK4VAI0O11JGtbQ64l%2By0LUdW7GarSUUwP47kW2CYDEdvsA%2FTXw0I4OPf2SvJ0FKjn7aZxxwVp2xjhpWMTDxaZMkiOxaFFLydXq16fefH2qVJybgXVpioGcKFhS4AjNTEa5T37zLo22Qv1apFD5qUjB9w8yA0vSc2RMiA7xIsRIGBTWOjHH9SZlK6QJGru7w%2Bq2GoC22fzSVxkr%2BWlT9rAABqi3RiLzDxns68PSEUIxqrqFw8oMUhIeL%2BSdTHAvw8M5C0b29DIIfScMQS0TKwvPEq3kAQvxMTC0D%2B7tnB7QNRliGiWl0NuVnIUY7QMwRkP%2FgVT2WDBX08%2BWLDc%2BiW0mz2CZ3IAvWqesMkIe3lWqMvUDL5Q33McSklm9U0P9SHgPGGVlX40diyUBp%2BpfWi%2Fh9sKTOvCRCj%2BeFIWvUXobOyAxrHlhKFGhOiw1vyKUiNMgN3k9No1jfVEpQEU35laY1OWX0u%2BF%2F3AcQq9opTw0RwEyxVosGLDgIkSdn7hs%2BIHrv2jR%2BJUh2kADlTtyX8HxtYxLUhMLj3%2FFpJAhfxzoRspqTIQicEOQtI1iv4n0xi9GiED3BLS%2BriXyEyU1v6KfcGAWCaq%2FgQ5kImR6wna1UoP6KtpT9XLI4EebxCRQriHsHkCOgDlZM8hJEHcCfGIERpbdeZFFHtE3lBjH2cIQ5p7TNVfRTwBkflxGhmddY2YfZbMF0bFv76YdpbH3dxSKmVVsblZW2xdvOxqSIQqbw536IFhJKhELagVlMG3LQqHU43e%2BRZjx4BJExsLcLX1oShyJFvhFevBTFOUF3Exw2yNBWwMCW5QA%2B7UmPhwM7AMOXRddPg6OxuaNAmiDYRKlcHo4FwUeta%2FLSOUyYddHQi3OFkb4z35KYG%2BxJAOZ8LTMyJ%2FCgARNXgZ%2FC%2BfKy8W7VwKPzhRpDnIMS0ClN6sPMpa421GfuLwApsG0C4FpdCsaLM0NtsctgKVSkeql94%2BXUmITqdGBajY4m4NmDFEk2HiFjYtNLmZSxNY1QM4KakEvIQIFts%2BOJ0QCRKvhCmQcfRAjN4VmYgb58CBUMiSUpcyuEfRFZS0SThDQPO4aFcJiNJUvlFdO0s8RaeSdpOSHscV%2BJAIchHCniOYTsEqhdLZtWVSiAw6hEDO7LI4or%2BkXTE6zB0CA9o4NUeZIpS0Fn7Q8lU4X9sZNCO0uPfjoWqOaqDVcbxBJSAs8RV9IcKmOPY%2FLDUnXDLSYfFT1xyNTWRLKRW0kFxctTIuvuU6R%2F%2F9ApGo0EHrqGH7dEmsI%2BgA0UXpfNwWFxUHz9CYK9toXP6IlTFRhuGh7z3bhwL5qzzoRAF8dkwboKslbtuftJDrshFMoBNaIvpSXyAFRCCKFOKEkzCkB3NDQB2qtV%2FANwHNea%2BAUDr6WWScJazRx4glReLp8vQ8sFtz%2BhqehlJO6MM5NMIc8%2FkWnwbE768NqCDKBi%2BpY288WynJ1CT2Kq9%2Bo6rhlUpRatsVIsacJhA%2Bq8M9X%2FUQMRZhshDAvsXwlZfxDn%2BWCfyJyLFA2s54on9a54PSC3WgchQWZLZgEl8xGrFUqCofhkvqYtXjA9XyewEKlnAtD26XlSvqtdhXOT%2Bi5BrX6S6hHVcqbZ8qbh%2BEzffUpCfTG5HULg9DQQaQ6ENkkq3DvUSAQwcxh24qkmtdRoIMDxOVDdy0T2w3afIBABsQGxnqJaAOY1gWawTUuhslaSivzrXaPcRyGhUkH0R541UbNvLKiY070WOGYY4R%2Fg1rfKhHcjoV4p45o21tldJXmpG0XsOekNlYucZlkAWkVUHtjkSeUbdfBMKrGATodKL9arS5JFVovaKRlOWbNJIS6GHxuLt8CX%2BBYQUYDfxlQrQQhL7z6vChINNDAVVN0rXxaKjegwIPOJf7rXfHBBHGpBNpaeLGFXCBrWAdpIwF6mcE7pE2z4HdIxcWkU0QA3TKjZQ%2FPNc4VwWA3OqmXuoleHBmMRLsU3ENaVgIpVnIWvBLVDYNLzVRdIefms6mo9VoRQZ6cCkQSOdK6KDkWEMZkQyIEM6QUltTfQ1xFpxUL%2FlFho06RSy0MhFQCIEh5FrTToDEgpZU4QcT%2FbYWc9XTPTTM6ZiDUk6d8MHSyFOgi3cE0Oh5I2Tv%2FThHNjxg7A0zwweFEVnLpE0Ai8Q9OWYcdB1zNKwLEBtxjc45oHjshf4AMMWhwwRhRdBnjEU3iL3TIBscW%2BvDMwGJnW4omSkqIJxsnzyWwZBg9lQCsSZ2CK25fZnAEtrlT3WZ1xUoqsQBEiEMEmCZAjQU8EhBFQSUEVhPwf6Ekh%2FQtYOKDqg%2BIfAJ8CuQ0oaANSG8CrQwYJAhV0I0D4CXAOIBYAcYMUAO4A0AuQNYAwAWgJAFzA9A0AgoIOA4ARIJoBoDTBVATUAVA%2FfmHjl6j%2B5Pa79V8K%2Fh%2FQfNvjz3P7ler%2FHHh7%2FPw68DnObsHvPy3cPPA5wScuOB7qBzX7b8SuTXQrw88fvOjUnszbV9wOhztB3%2FrTR53FdNbVFq3a82jxmuwOcluBDDjLjOjALxD3%2FQiP39Y7a5Rt0oNm7G8V06nAGXPxV3oiByNCcCfXAkpuOrai62YwdiJaYXnQsst7b8q9lbH1AhQ4fOtIhMRO4kqFfJyARyYkd8VFgh1WJGVCPlA1ZMLjSDAUNwDDwkxBcqWcEmILYipcSYqtCIqlSAqaSAqUCAqkjRU7GCpiLFSkWKlAgVIxAqRiBUbDiowGFRIMKhYMKgwKK%2FYQV6wYrughXTBCtmAFaz8VpvxWa%2FFYZ4KujsVY%2FYqr%2BxVOdCp85FSnwKkjcVGeoqJdRUiaio4xFRNgKhy0VBtoqArRXysFeyurkV1bquqMqqonq%2Bh2r6EqfoBp%2B%2BNH3jn%2B5M325m%2BzqX6%2F4%2FrnjqtKKqtoqqPhqmWGqVIapJgqjWCqxYKqxeqp52qenapVdqkp2qOnaopdqh52qD3Krsbqs1uquGqqZaqmdqqVmapLZqjdgLkVYtPvgiXswWobLWxLWyK0qjACE6uL48VDS74vVFa9UPr1QitVAa1XzWq9S1XKUq4ilUQUqiClULTqhKdUDSqgSNX2hV9IVfB9XkdViTasMbVeTarwZVcjKrSZVYS6q6XVVS6qiXVCBdUEF1fgqrzlVcsqrdlVbQqrWk%2FZ8n7Vkfagj7JEfYwf7Cj%2FXof68j%2FW8f62DVWIarWDVaUarJi1YsWrEC1XsWq9C1XQWq1iVWMSqMBKoqEqhgSqBxKvuJV7g6ucHVwg6tqHVrg6vuFV6QquaFVwQatsDVrwatGDVnAascDVhwaoyAqiv9UO%2FqhL9UBfq%2Bn6vB%2F7kf%2B2%2Fx%2BtVskpnZEuCUxHC%2B%2FYuIK6NNKiB%2FEzIk5DuCcPq3Ew4GQBYmlqw4gUEVMiRKFQwiMCQcTgAAAAEDAWAAF5ItM%2B6bFophhd3N1nA4zIKh4NARWfqCooqII1UQ4kBYJ2AQsGyO2lAp%2B6Xiqm%2FCkfz5urtugisAuGzxeNHQh1A73Vh4dG0%2FNDkMQKtSO%2FBTm3DojsrbF3jq6kKdaxNxwpKvTKteAnM7mCQ85oHLN5AxXtO3FxElkoEAFo%2FY7SdVN5daSO3q5Y%2BGBpQAYrusw%2BEY9%2FXMmtYT6y1gbXXfGgzHr8ic6AN9hcvHwEYELAoOjr69A1yaVwaL10euR3YUZUZvQl5xZXoT8YeJLqVKFrjMLT83%2BN7REmekLvRn0TLh%2BZMzVhvjjOcuh7%2FDGoSIzhcgAEyu2q%2BgmErl0SoAyvHf0gOOHkuRFZQSSkFEDSG4HCQ838BiFt5mdNnEIYrPlCai6RoN3%2FEdkyVeA7o%2BHQvKrwD5CYAAo2RJZIeP1xRy%2BlJaWuskPYYJ6GKPdNLzxcio1M6LXHJDQbx0X1i8B9YnLsQYerRiZjBe%2B2alDqaPIASEoxCeRAUMVuivCDeCNo43Zi%2BmFz4viBZJrJMbwxhXGCv8CUG%2BGpgMJzaVH06hQdq5woKyZyKYDByVHQl05OpUqPrSuURMAxrqnfhkQENbMcnlzk3MLnnmnljBLSdHj488rWP0ithH7Mq2sETRkNmgYc3AZSu6gL%2BCk4IDhPtHR057XmxZs5enMzU7IR5rdjAD5bhqPdP0mGax4%2FBC5rzvd%2FNmFSTuivxBTftCPQY%2BTO9CX4l81r5FFfrkZ1yrTcaX%2FE35vu8hUNdTlgS2iD0q93mIVdiG%2Bzz6ZoWvKaG4axR1VGYKVxYs%2FhBQR7uUeb1mU%2F3VlT93WVa55WteFObdmuaGauUaHua6G9Lh9q5rpXj%2Fwgi3iPylvEGKzMA9lXBUjbXHUnsEBSjM4dcmrgF7f0G9lwAQt4RLmuXHbpvBr9TrIO6tAdGiB79MRHozMKD8uVjof8zfR8GjzHzhs033A2Py%2BejgTAGqXxpL%2Fw2WVk1lFTJqz%2B0kBeRuJCR5GRySPRQSmUbztwmWP9kLIWUhlXSVdCicr0AP0QVDW1JhE6NsgqFunp2ST1zYgQv6Qnhu0JlL3GiFmbc6pMG8mzBJsoRi1z3Z5NTM0f1ndDFjciaUQqsNg6uq3fi8dFUAWOQGs1VX8XhLFmvpemPhnFsJ4EJp5fMYZskQQsqfYfD9NJDx6cnQ0sTSuR2L40hG49L%2Bmrs3RoC4ghACgxYOijJAtdpXHsZI0rFXEhZYgy3algwWFOreuLxQmZTNtXroCxgYhVHmAFChJHArQSkYK5FLPlzPCQ38AzsClDlC4ke6RHiVatgqkBMcH5a6mGXLmcLkgxPCTi3IYssNhBMcXoBJkK7iAw%2Fq1cJO27YZZ1dgwK6TjpcOKZhOClzaJK94ByvnLhalwYwwHNGbpKKrMoiCUVvMU4ZDGftbGmdYDSWM4M18njvCm2QeRAFzCJCDrWq4fFsh0jgTZAy3lAyEhSb5p3l%2B7zqhPPDOxcQ5kAj9XEi83gCXQDB330FUKUN8JTMzokg%2BMHLm0D3oGAQxNjGVUC4qv3mNqY80W8Wma%2FG%2FRJg0Pokw2Jil%2BUKRbOEH5QZhYg2DwSICOBEYPtpPgwWBiCfyJ0YCZEGKrTfFYsGyw21JEW52PoQdw%2BSszylGkFpTMhd5kWLnyTIQai8z1ij55qLRN75GxKwkR9E1MhMUCbxmmw63GyiBjM6OORqQNMpJtSUqMssFsiIRB3bOWC1CBqx4cDrtymvUnfKZrNpclIQX0iC%2B0SWyKIyxEbrSIkB4ly9nnkYJu6fFFI5UkewzCvGzpCmkfDtvWO%2FvOQBSKG%2Bw4WeOGpxQqahRKjqs7pmglalwleZKZnV2WNi1CBepIZxSes8CtZlRI0GWmUOVcYlpvK0hUKRhWaEyXRViM53DzUU8eSDYlUJQqDMmUYc2RuqTtfeLBUIWJzpxmeJiOCoAXmCaSqYM441iWisAz0U0EQHSkfuH3o9uyZk0g2C%2B1Huy1lYiCySpvTSdjMKwTiKdQVCax08qRNA1Vs2wiL2IyamfCpg%2BlZwXvOo2sB1QB9xIdX6Bj6VzRgd4UoHI9PenyASnBA8k3EjsE1S2ipMclWB3LPJeWzSHOikaNhJsFX5zoP45XxaGhDK%2FpP%2FDIYztiS6vqMPACWcgP2kpZWjOfJBHYRQNI4tp3WUQGWpE8ZqoZEFvYJaFCTkUJPdTqeYpTEWodgDyHYOTQomy%2BoKxLC8BtejPZfK3UMphCswZ56qASoLK60kVHNgZzT8%2FUWcqkls5xsQ57uoPAv%2BCrnsSCROispXTpjPKe4ND10sCf%2FElOsPgR%2B1K2fX5hXhJfNGzBWCpiE4EjGUBgUzovLqcdL1yqDbTutsHLe0YqwBcpKRqcP65%2Fvot41Eg4cbCrFOObHzAyIO3Np2zmgIymMqrmdZ5wgWiOKMA%2BV8kKigbnyOAnZzQK1uMozbw7SBWI%2BtpCLJTN4yFsnUOPsR6JqbOmnG2mYUYJV0I%2FFDRgDb2YBKeBAzYRv4d82IRIjKNJ6kthxBIf4f6tg5Rewqogb%2BBL89Xa%2B8Y7mPwJBnr%2BqyHL3Me2KqwE3nnFG4NiIrI%2BvziW0DG1jcQIHeE7iAwtWHXwFYTmEfOmdkgt4Pr1n2vJwnSQymN0R0NLRTgAHjDVSIfITmkvlvfz7MU64jgUChw5hCXsYgG5gXyQ1osKsxCRhvPgsghFG5zI1CXCBIAwKkjyKLeqBc%2BfEUFiCvfGOta3gR%2FU26my2RNT0gywIO1yxoMTM8Mlfo35lpDQ4jfU18jrE6QiUumMhcIndvm8iDoRH8amzyNFXNUKcSoCnh5AQYr61lElsUE1avUS%2FnDanpv%2FZFa3VH2JIq5OrC2wDxYOhlkZf5BIr5I2JAQY6MewSa45Ob%2Fz%2BEjpgzBQTlG3AdVyHpu2GkjfOxGCSQ%2F4KBsYV%2B4WC4tHrRIEC3vi8DWBGimk2R3cQNWDhpiR%2F9KQZE618tdreVPJR485XjWwoxbnLTn6IZ4EjKrhRraDtVPs4zmDldmpWjWM5L49vWKkkh21cJ0GELX5Hje5CUKJRNkIrvqKWk7bw8b7gYD5fEhk7VgXTHJI0UsIqSimKH3B62CsLrQWXjB%2BoWwKWamDL6RlAYnwhMdNFIyfJuJIyo2hI%2BRLmmREVRHaJdaBYDQo4bDl91nK3Zy7hcqlXpT%2F%2Fg9jl5m4DVYTnaJOAoq7zjeA2QzPBWRnDjFKG0AJybS5gxwd7oVkVIAgAqWCrwA2TuCFHn%2BlQSxm2EjeZemixAGSIByWTtmzQII9iyI6QxlnEfiedIUpRSDRKzAojtJx830mEfqcSqHRLRGSUmpIkhYAfBXhL3h5wRAsiPnzp5CmVoG70nYHSlNBsQEOYNSYjVHmDMCMyyhMYHx%2BhksBoo78rYc0vB6ClzXpCsSQL0xuJj0lRG0q3NgS%2FgqHn0%2FdBeijOqfmwAAwNmamjdRSr62goEAhLCK0y0mqVtUz8B3F3pE3rUMN9wQoCtpvRAK3sFTK3tcPZnt6Z27rdUEcCb1qxd8b7ARVibBsC%2BRyonUCCcPwOsBKtzEuPpnx3V%2BQo3FTRLkQsTzT%2Ft5GIpAhUffp2BuiRh%2FhQEK3waIhUoJczWFQtyb9PP5ODdKmoRpQHa5EoivjH117VzUEyb9h3I8XFhYGY2GgobkujLXrm%2BbJ7HN7wo3WOTojHaQLy5ZQH60wuIar4h1s2FLyTWktIBtEtP6PSDSPebTewSizvHczvHVzmh8LyM8TZbIzBlYhsTDRW2MtA4tMbKoUyaOwZNB0EcTEoAHayrWzlDMhazQx8GCH8%2BXZI4EskKhE1hnN1De89ZjtBMDWCxLN%2B4bn5AnyOhxpTflbO1kA%2F0EGhUliSyMtWT0UMb%2BqEPinhEFYyHEbAUyp8tYuqYDRipLscjyGJA3zbmKdMTMI8V72v2ZQ8R1C6WY4qjndJBCREkDNEWaBovAOsU5JvqCCECn5%2Fq30Wfs27Q1NX3PB1CxWQYK4rW6gl2DEhCJLvzF%2BBEKd6LLjbQMVuJRpueMSQJN40n5WAdyXvuYQeXUQsoQDysgsemARNTywlJKFiZeuo5AiRfoTmTq6hkwijNVgqADIQ0SrFqBN9hK5RC2ZzmikwVm0CCZzso1HPlzzN0IP%2BQCIQZ%2FzA%2FJ0cIiWLfbbDGvgdRmzRiOWxhWyl0iXpQfpiIVEQuNo2d7rj0OZeiVOeay6x5oKlhwZCcg8KUij44xrtH4IX6UM37YQMPXh6QvlpkcPG94IVYA3iwMJ0L8LuuaogTlCTJsenAjL0PQ7L1Pi8hmGJdOqOJeVo7ECLPOdRHN4hjBEITrTPlmX622a1Nkllc%2BXvRIMYsWFbQasKlkKf0TuoImAklJXaN12zLmMOiREi6fUAhVGrkOVhqjWR2eenOmgUY7GLcmbnAqWpftrThQTmBEC5zJQBDcPyN4AoXnInBM2UJew3uo3Osd%2FVotG4JFRB1dhhN21R2ig74o0Jw8ZN7VnA0lWB0lDFZ5PDtyXLlhvcGuF0S6Wr3U2CqpnkDjwB2mBQdPGRuL5x1IFo03LEOsHEh4MKRvE9edrMh0Snaf0cShzOIJdiyBYWbPeMzTRGYsBHAfX1laNIl1ZVxzggRI5m51PG59wVXQ7HQGRYRRnGy89qI5okgmTpsdUtZgAxPvJ%2BWcqIbSWx6tf5yN%2BuJkKOjROUqEcP4y6WDOTFIXxQkr7hHBS7hmbyT8AljD77OJLW4CQGYAb2Pei8bNTgMttEECV0uBXZksyIJd39JlDyojumSt0T2TM5i3S2cviYGBEzQtmTKzAFB8QEcXkZq4YmJ%2FTDWdYLS0Kazft%2Ba%2BC7HwNZAJaVEY3kfqPHY2SBxchsqiGqJLn5a%2FmjEa8QgmABHka5AWJ%2BG18I9w3WjonkFGEwKhtgp0j8YPUeYEncSOLNhhwVJnEiplRLfp6lN7z5sAMYVx%2BA71pVUbhIzIaaNzUbAarADBLMSSoXqKXumbEiOijClhzIbSA5gGB2qtutd0MjQyWBaAzJmfqki7MMaEQAOCXbwaXixbEGNFnHCqmWg8qjxIZ0oeAxEPKD1A7yRyuS4AIRwOrQJ6DxVHVPpAc7aCkBHpHC9GMxBqe9Qt62zjQGJy9sjB5efNIj3%2Fyv2r6z%2BSM%2FzuFE0Za9wOJ37u2rcQy7P6SvWZqCuEJKtvjAUTarXAcyMQRrVOlrD0HTsN2KDiUivtwmgj5U9inAK5%2BC4IqL6iNHhU7OH98NmQILOWxNwJiPwwlnf1QAEMLagJuGLoYUdlqBRDn8QcRAg9AuLhe4myJn%2FEoxUfepXHhZ8Dk0BKaH6znSNmNJJ2IRdEvxoTfFkKRNn0cEvSio6ek7OG7AZljFvqNgRejEgmOwp7toPkAA5CIjKCsWmYPIlxUdVDDj%2F6UQEQZwMPCocey9jKiCj3ryLWjtGlYDdewTAsT%2FytgOZKoG6jDf%2FUzE%2BDBwpk%2FPx891KJ7wcBDtmnc4Zr8PjCwfnWIE7Tz5Wh4XZOloMKHqBYibF9JCb589kpcmC6MqgAH60Nwcj%2BnBhrvYWI7IfIiYD0IThyXLu%2BBjtuDKdFNmVqTy8ELHUIcco0LUngtn%2BupQtGnWSsHRGOZxzUOEULCoCKWugvY7ZqEN0MDmNHqeKtmwQSOPoR%2FZ0KGKGB2wdDYj1CrZsodEQx2TukU2%2FMDvJyPuRgDSC80jH6IO5DbRYrS0rIXfANDM017avY1V29khrSTX8G%2FWm5HQ3j02j1MGoCeHm%2B25tIebrUOsJUhfKhD0EIEeXj3%2B%2BQ43UQYWPGKRd5tI6RgOxzkuNv3Ji8YcsIU0cpJNn1rQt1eBb5t%2Fe6vSIMsxyrbVRxAHaSs8%2Fijk5AW1W3ajQf6DP51FkKbjEEaQp7ThQOL8pUajXNZgjP%2BAGWp2PntYtYtaMRzL3lOTlAMAU2jKG8l3wMD%2BbxPb8RDFLiiPnwnwm42DzJDXbmbGIZ0jLkON0Ub4bgHg3f5pBR%2FoVAu0YDFztE%2Bzps%2BP3bAAWSgSxlY2S3LazAF0KZAmzACvKJ%2FMz0beXxl%2BeIEFgZCLRMTD0eI7mFnojAK95ZgCTCfQOybhXCo6576fGouBIpcPFCU9ALkeU0pQaG70p3LmSrRNvUUN3vI9R8lCXjgQBKD9twFivCVAT47aBVWQPv7zSwNj8Un%2FSFG3FqLOS5c%2BWZHKZKVGCPSmIgHEwoN5C6HgtxiaB5%2BUvBrgQmTbO5rMJ%2BCuldZzfzxgdXp9XgD508hFWCeR8zw0tDDW2xoicZACrgqgnQ48NRFvjGjGQLa0bZ%2FYIPRHYC9plKdMERvlMGhAQeqDBMhf6LZGmyElMCwEIYOo1BZ6PH7%2BWHUp1YWZa7wqK1XoOCvECZSwoJH1Mkaogtg1cnvzmDI1q5S6K8zNTnhUkglWVoNqdHdJkfg0Fxp37eM%2FnNEjkRlbYvQhIWSCHT%2FqEBqUlbxYgGY%2BMSFSRxt4bg1PVnRhKNZIXooJOn0HVNY%2FcfiwOh4kjntxHmybS7MJkM71QCMhZGbYSMrJ68qAj1YN82a3v5utE6PJoWbuOva8RkRMPNZOJG3h4NcQ9LTUNP7WRG34U%2B6pZFDjhFpbJyEq7ddJp5LrxHhOcWeKH5m0I1Q2anhxKm%2BO1yIfm7BGh7B%2FjqYiPKEBJ6cFrT2ZB6TmSyLIpnTVkU%2FYqVla2WfZcp7SSoTzv6vLYtG8AgtXlxfWZxN6Gzmta1EwxIU6yE9s0x%2FZlSkSYE8X5a7%2FVPKA%2Flig684F00nmacRk4WooPYSFBnHTa1ebr6zgD%2BMcOV4BeWlJJLrL0qnYZSAVtBA5UxpOASkxyTpYRBUMiUqxjxU%2Bce5iSPwywJQRsno2JDcLSzoQnpUALmNJE%2BWSFPdsteXHMocr%2FC0IqI26humQ1wKQoHcGmUNYBVwIFZqI1azUTzdFmIOugxHeiO7FGrlsIVmnE3L1JrCuYeQCJCpQc%2F0JCGgQIe79NvrOoYeDQJ2REZcyhK7ihxb2NeHkVj0aI0LIicFJjQk21YaBO4G64TBu2IKBTzYheH2CASyWnlbZNFDbFlJqA1Q4kIljGpwnmhmXbOSQEgWsMmsHkBZYKRUWBv9HZZ1N1JSN64WPlCBOt7zqAJMKpczyNBpzQk5o%2F%2FxDjQg86ozQyN4waltVoDaLJOfWjYqewlULt9QO8N4AqlP%2Fo1ZIqJMaZ%2FORyILlDFoGoeY2%2FswI7MBMVXSMJXCUeRsJjHIU6kNEsAqZoBDoHo8Y8pZtM%2BGL8OLfcPdBbsbvRZuDQcyyrZ42OoTsMJ2GH21od%2FRCfgHrkeCyZfMOkWTSYgXtqYxGIGLcI5gKg%2FZR2xnb1upteR3rcNGITv8oKHpUWdLK9kIZlFfEtPUNAgnOY66o0dWYJq2a0UT3usCxxDZm0UIViE7Q13vLoyXH6VgLZoJt476o2dZ1DSsnihertfmTnTdREBRHm5ASIGxMbMSITKCmV5fMqgFiewxFtxqh5yfCPkmlKNcrJfmvyKlbaqps0Oa%2FBSPXk4FJjtWF2wOHfpFY63qA49US3T180HXmbqEjYsSpRFkkOVMCBH1C9JAJTRgjCWNAjl2MSA%2BvZjADldAJGTIoWBOcOEUI4qwvBSI4hMj8m7WHoFosA0M4Q4yS8XtN%2B5emVGJDGqX8D5SVbE%2F0bWETJRyRFYKQ8umFTkY2ZnV9Lz%2BVW9hgHq%2BHYhsrghzcANy%2B02yEIiJuDFngZnEHSFnTnqDAicaKOgtLZkRXBqGS3bgcqBK6B%2BmV5G4%2BxzA1IlYTTtL30wAPCkFZuhlgsViFEnA5Rg4UXmBbXBAW7JuNvZlXTVsYDMV305DdUPX6QyUFMwndSexwLkpBDsIQ4lUnD9QoEF8QmpcQk8FJckIIkVwvETqBSrIvHRim18B7iqFwNCcFoUKmSj9cSfX37WL%2BCT4mAKH%2BS76AYlpBE3eYErTSL68qrzEKiOZo1mbN1ujQapDrmTWb1s3iBTeQMCYcYfVeS5ks0rTlCPWr9AjV%2FbgZ8To6s7qOr3%2BkRUCKT5GAAUzFlHOkmV3fJEImmhCZ4S5ivYOcC3SUx70Is%2BgfKTQfQBsES9NEX4x0WTcQkWV5JKEJNl0UV8pDmBNnC%2F4qRYIkjLTUknCCTbfLBLJzy4rnSGTntJHjdpYIWlqzxK%2BG%2BRBy22IJ8gMQIibkjgh8Y9Q2H5aenHLwEJg2iDmhQybR8G3gfKxBAZwppQkDi9rFMfLSe8Fgalu2z9mKNwe%2BF24HWYNjNI3zoQfXsIck6YiAwCMhAsEDMvxfp9PxAAxF94h42eFp8V8BZxS3g%2FoQhPG05yIyOc5GxA27VgPq%2FN9uxGcdZCP9PAWYlmmiIlMHE%2FRj3IzE8YKgGEh8OmOkN4KuO6GYyNnRhoWNSgmY9%2BVfMfhBm7xIwZPp7ggg%3D%3D%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFsYABEAAAAAoUAAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABgAAAABwAAAAcalXC8EdERUYAAAGcAAAAHgAAACABCAAET1MvMgAAAbwAAABDAAAAYGenS4RjbWFwAAACAAAAARsAAAJySvAJmmN2dCAAAAMcAAAACAAAAAgAKAOHZnBnbQAAAyQAAAGxAAACZVO0L6dnYXNwAAAE2AAAAAgAAAAIAAAAEGdseWYAAATgAABODAAAiTweHjMhaGVhZAAAUuwAAAA0AAAANgJiWP5oaGVhAABTIAAAABwAAAAkCjIED2htdHgAAFM8AAABFAAAAvTBwRGObG9jYQAAVFAAAAGrAAABuDSPVk5tYXhwAABV%2FAAAACAAAAAgAgQBoG5hbWUAAFYcAAABggAAA3zUr5ntcG9zdAAAV6AAAANAAAAIhLlGpmlwcmVwAABa4AAAAC4AAAAusPIrFHdlYmYAAFsQAAAABgAAAAZYr1LmAAAAAQAAAADMPaLPAAAAAM8MFvIAAAAAzwwJLnjaY2BkYGDgA2IJBhBgYmAEwltAzALmMQAADagBDQAAeNpjYGZpZJzAwMrAwszDdIGBgSEKQjMuYTBi2gHkA6Wwg1DvcD8GBwbeRwzMB%2F4LANVJMNQAhRmRlCgwMAIAC2EJ1gB42s2RP0vDYBDG723aSIrSUESsiHcIWqqDXbvFRe0gBJw6tTgUCx2Kk926dusixc0P4OiXaQZzjx2cnNRFhPiagENdHBx84P693P0O7iUihzLbJGM9mb6tTFrnTWhjSAEVyLfZCgnt060U5UDacrdd3vnYNVWvWlJHPa1oTRva1JZ2tKdDHesUHiqooYEjNNFCD0OMcY2bR0qSr10pcc8S6QfRaEF9Fa1roKElnutARzqBgQ9BHQFOEKKDAUaYYJoSTfKWzJMo6epSPI%2Fv44sHJ9qI1malWVEqsi5lWRZXiN%2F5lV%2F4mZ8YfMWX3Ocud7jNLT7jUz7mQw62ouwafyvj0jfW5KzLLTZkX5EpX6B%2FLXfxYfU3U5%2BPg2iWAAAAAI8AKAL4eNpdUbtOW0EQ3Q0PA4HE2CA52hSzmZDGe6EFCcTVjWJkO4XlCGk3cpGLcQEfQIFEDdqvGaChpEibBiEXSHxCPiESM2uIojQ7O7NzzpkzS8qRqnfpa89T5ySQwt0GzTb9Tki1swD3pOvrjYy0gwdabGb0ynX7%2FgsGm9GUO2oA5T1vKQ8ZTTuBWrSn%2FtH8Cob7%2FB%2FzOxi0NNP01DoJ6SEE5ptxS4PvGc26yw%2F6gtXhYjAwpJim4i4%2FplL%2BtzTnasuwtZHRvIMzEfnJNEBTa20Emv7UIdXzcRRLkMumsTaYmLL%2BJBPBhcl0VVO1zPjawV2ys%2BhggyrNgQfYw1Z5DB4ODyYU0rckyiwNEfZiq8QIEZMcCjnl3Mn%2BpED5SBLGvElKO%2BOGtQbGkdfAoDZPs%2F88m01tbx3C%2BFkcwXe%2FGUs6%2BMiG2hgRYjtiKYAJREJGVfmGGs%2B9LAbkUvvPQJSA5fGPf50ItO7YRDyXtXUOMVYIen7b3PLLirtWuc6LQndvqmqo0inN%2B17OvscDnh4Lw0FjwZvP%2B%2F5Kgfo8LK40aA4EQ3o3ev%2BiteqIq7wXPrIn07%2BxWgAAAAABAAH%2F%2FwAPeNq9vQlgG%2BWVADzfzEijWxpJo5FkS7IkS%2FIpxZJlxfGR4Nx3yM2dgIBCOMIVrhAghXK0CYZCKA0tKSyQco6UUNpu2G3pwoq26hnSQrcsvVhajm0hu90m8fC%2F981Ilu0Eyu7%2F%2F3EkzSXNe%2B973%2FvePQzLtDEM%2BQzXwnCMwKRLhMkMlgWefS9bMhr%2BbbDMsbDJlDg8bMDDZcHIHRssEzyeE6NiIifG28gs9e0%2F%2F5lrOfZmG%2FsThjBFpsgv4ZcwMtPKKExGsecUUlWsWaL4M4rnkGLIKu6qImRLAdLJTOvx5KOpgizmxIIsRKWoLKTEuCikCkXCPb%2Fj%2BQq8CKeO1TYPTzqgjtHLAA0G%2FtH7WplFTNnCMJ14c4He3JAtE8bSuW8W4cydRLFlFMshhc0q5qrCZ8tmC54yC%2BbOssWMmxbG3FmyU%2BgCJCrW%2Fsgo6SKj6mb14PiWupmMUpwN%2FBf4Z5gCs4VRshmlrVpuy%2BJPtaXNFI4ohSOSVQwZpTmnGKtKMKtIGcVaLUtWvFByIWTTM0qBQharlkKRLHy6St2kU3FllXRVcWZL%2FaSzFCuIbkXoV7rFsrUl29%2Ffj1Qs5HoL8UKur9CXy8o%2BOd6bZuMxBytEhahRgrcwn8sOs%2FmcUTDGY6k0SRWLlxleL96WWHzV%2Fg82Dxqfyy1ZFPb3z5npIdcX1YNGsgvezdP6Z%2Bek0KIluWciGzc9Xr26dcRGDhcz%2BeLjp%2B544fwLC%2BunBbxdpw4X033FRVeOdDqD09bln7rwqq%2Bmn7yGwTGpkFF%2BCXsA%2BMtNeYGrEoXPlAzayANRK9wtx7YiORlGH8Pavx7Y%2F%2BgD0sW%2FyJ%2FESEyEUbiM4qjCmBHFlynJ8BMlMye6S1axv39aD%2Bf15aLZvt5kPCakSTxmlLyygzguW86%2Bf80TT1yT7up67pIv%2FZIdWUPeX3H50x885dh49S8eCNgcm5Bt4FXkFeAdHuaBhbEzTJ7IBZIQzQYY9QpgcXQF6VIPsuvZ9TD6XUU8qG6u0L3DY4%2Byp6k20jW2F36H%2B%2BjDjz7kX%2BBfYFjGyLgYRkgAexN4Ffp6MyQZE%2Bxk6b%2Bfeog979DaQ%2BfY7Q84W532zf9%2BsnbgdHvK8YDdXqMFwKQwZibHlI3Iz0JV4WAaWTKK6RCQssyZkHM4AzCuicNNkxEY10qJK0aJCNMpHxV5BYE79iaQUhl7fez1YpFN4u87GBPMl2eZFoZJhklhmOTFhJgUHETW93qTgsFBJDhm5FtvX34SID9r0fJ1bvfty%2BetcljGXBYH7H%2F%2BkvagtKGDPXP32F9dcvDyvkJ7QD65wF1msXLf5TyWseWiP8AANeSP3uX%2FhX%2BA8TBNzClM2YEYuTOKXFWaNMZozijkkCJVFcmFskExwBwAdg9Ionsfx7rcrXK%2FYhCB85mS2wFTwNyvyKLi6lea3PsIIxjg%2FLQetwsQkrwC8XmdxBhLEWB31uVr6XMlW2TSDKzWvIx0CcLFJq9JPXj1zspnXyTu735XfZ%2B8h%2BfU31d2Xq0ehJMXCwLpWsZepP75xe%2FCFRp%2FFpn1%2FLP8HCYAHEmUYEZhDiFPOqqlJo2le4fZMJHhjZW8Dk5I88Xpp1599bVd066%2F5qr1fbOvvWXv8PDjt1w7m3PN2rKqm184Z%2B4CvnvVlln91950Y3nNmvKNN10LtProm8x8%2Fj4YewtjA5aKenKeKPGYiYcrLCD%2Ffg97D%2Fmluv1u9SZ1%2Bz33slwLFUV%2FUmcSj%2Foe%2BWf4pHzY%2BBs%2Bxs8odhC%2FVAzxwERypuSnIE%2F4dY8sFORUIZ4Spt5n9r8s%2FM73Fn33naXLjnNH9sqdv%2F3Cjj984ec%2Fn8C7HpjHeZKPJYdIb1%2FWFyJeY1wiEhlNzFT%2BR5mZIKN3EVK8L1fZqihbK7n7iupHdzE4%2F%2Bj3l9B1yco44Xdk4JoIE2dSTCeTgRmhMGUGOYivlvwtICiFjBLLKaaq0ppVrBklmVNsVaUtqzgzSkdOcVWVrqziySjpnOKtKtOyiH84m0NyEKWXjiNf3WeyubytclbhXSii9pntooS7%2FqrSDO8aV4aySkt1X2tb1zQ81eIqReHKRHt3D%2B5y1VIeBZPNCoIp2NTfrzjFUnMIBVQPXS%2Fz8fykF0pBmKVRcpxzvHJ0f7H%2BD8TN2F76OjB%2BkF%2FSeAnKUhBOx7bWjzC6fNPWRifQMMPMZEaYsh2p15UDtJFkoYwySEnTC7wxi9LDRddHP7y7Si2AOBCtvapMz5ZOonwD8qEPJK5PEmFMo7FkgEzcJ59wPgRcIIVCkroZ38e32fUnOpNtOMxe3LAz9sKJzjAgPSfjvpw5i7mUuZG5C9ZqSoXS4EU5pEOpt5hFSpSWbM0hLUpzroT97kzp9C%2FAfmu1tPqWLFBnFKlTciHTmYH52rKUQtNAUs1aAdv91dKC9fjpKm2AY5tvgu0LqqVrbs9mS3dTyuFcGCDabOgksaSY%2F%2Fh9kv%2F%2F9vqQVEGCneCNjP7fzmfre%2BTR420e%2B6%2F%2F6wWwztbkReMYr9V5fJDyeC%2Bd%2FktySnNVmYOjqpyOY6qshhHdMGFE5ROMaGnj8UfP64PdPjichF2jNPk8jgbyPZV9UY36H7f%2FydRmD%2BDO2Bx8P%2F52I83Hv%2F0pKIoyPMTE%2BA%2F4DsbAMLA4pEgqRB7hsgfHvvYj8rJ6OtcLWz%2FG6y5kLuTn8fNATuN1BTORzUQwkwtJUH3rIAmS4EH1LfoGH8MT9w%2FiNbBWTdANUszzE7QDUAviOSVUVaLZcnMI1ZzmJGg8oWbcDEVAv9b1h7YG%2FSEJAiucVRJVpSVbTiTx0kQcvpVM4GayGb6VqGsZ7TDSSdAyyqwPRHa%2FkhAVd78SAK1DdgUbtA7ZDVqHq78UAu1jH2OQ%2FHiuSSx7A6S%2F%2FxN0Dw7keU7KSXEpnv9YPWResQKi%2FhO0EfUUvAile412X6a0izBnHE%2BzapmqWUV1zeo51KyaQx%2BjWz2HulVz%2BBO1Kw6WsY%2FHDLUG8r6O2I5r1VcFH1xlqiH2InG9CIhRXZGR%2BQdAf2iC1X4ug4ajBdDoouuSUC0L1JgTwJhTBFfJBhh5qmWbBw%2FawI5Ck6lkE2CQZH9zHACnmpm7IIE26wVg873JVN7nBg2NjaVZok1g1HfpBJZ%2F9vj5W4%2BQs49sPf%2Fxn522%2B9V3X919Gvl1SCri7CjiAvU8aRt%2BrFC5pnTkSOmaSuGxYfWXz2%2BBq%2BBiYhtfs2DdLTIE5FKprsdMY8o8rjgmWy5HFFdG4REfRMIESFjBTNbQcVZLIpU2BbCwCRjHtb8iGCNdFRj6CkGDhBxWbWh5g6KwAukWZJr4XfwuZj5zMnM2g0ywoqoszih9QL2VlHoLqsoCV2kp3AMWtFVApwUM2lBDwPZLxX1OKdePrNDqLgUyYFiWVgDLlwRQYJTF4j4umpqLZ%2Fvcpc6TkOE93gjrixDBJxdkH2zmsjPZvplE7iukCn2wme%2FNsMkMKSRTQioJm%2FGYkzU6ScooyIIRNg2oFEpeYywZNN7POvzciu7BWw3dPYZkW6wpkzSmM4bPTZ%2B2gvM7yZcMhi8Rl8yt6Br8nKF7mlG7wpDtMtw6kF7BBezs%2FUZy%2FrbyNvjPnhJtTxp7ugyfG0ifzAXhlMFwP2sPcid3DX3O0NWDX27uThpzHYbPzciczAUc2s87AtzJmRmfM2TSxuS0wPpt29afsm0byEKBKX70Ea8YvKD3j2ugBeYOphzFudYK1v603iwsFalMuSPblwN1oakKKiYqFY4uOJ7JULsVzPvgIVx1Wl2lBM7DrNKGmmk50UYlFAMM3eZCeaRMAy3VVeqBre6s0ltV7Nlybw9e1OuBi3pdqGeCUESHAHWi4CJSX0kalhMPqJ3aFrKSB16obnr07aLTQjwD7aSrfYB4LE6H9ehbVkeRu2WgfWxO%2B0CRyhjkM03a8CRrcRbbBwba4XtZq8Nx7BJkvvbBwXb2wNgc9gBYxkf3a5uavQR%2FBon%2FNdggXUzZTPV14HvQz82HUFc3UT0bqQRMXzKYQPAQOlnNJF5zurCj7C0V9SD%2Bsaewt4xtHduLcoRdj%2FyOAvBtkBMORmRiTBmMpU6CXA%2FSzlgtGwkSzGgCieDRZhPVSesUISIpOqzkV2TUYT32jNXBriddAX6b1aHaxs4A9Dg34o4ylmd4%2Fin%2BKZjDHuCB7Qza4M6q4kUhW5a9eBtZgnHx4JBTwxAgsMOouUpeQHD8ogBcJLtwcsO8R6Ox5LWDnLJwDg%2FMrJIow46Zd6Kty5S8TtgTGLsLT3nwlJGINl0Qu1tbeLeL5Vta3boA9qCEkPg9xE3mEPeePer76gH1fd%2BHZO2HH6pPzAeR8Y3GE3v2sGepT3yIp8dUIOlBaiuwHz3IMAYv0BTlVVq3soQcYgwWlTNDHQ9O6nggZmRhlGO6tIqTHAd%2FJMrFOU%2BOixfJsz%2BVHvb%2BhDw79lb7B209bzY9wSvoQDm6gq4FhzW%2FD6vr6No9FzFlK95TuxuYICQ7LirLvIC35hlN9NuBjhYqK0sCD5KKNYOksosgX6lXDL0g6KcEiKjtRf6kvoHyU30Dtn7z7LPUjwciFP14lSKMM%2FAr2PfPAhQyE2bO0TVID53LBhjYCB1YsaqI2kIKymSzqyTBFiiOLbikiqJ7v513y1Q3aBaVcL8iuffbDB5fiI6p7IE1l5jMTFBfUXuH2WyYpUsRqY0kR4U9R87a88bhN%2FacpX2c%2FgFZ%2FcEH6lMrdlV2HSENJ%2BCDZdWnPsDzKp2uwLQTeTbIbKzxrM6iTQ0sipiAjeuv82UzYgJ8uR%2F50ouY%2BEVF6lfgCDKnj2LicSImRoGhetDxWZJrgokcF0%2FEk8uPHNl1Yq5Uz9ZwoUzC1n0KiE%2B7Lk2AR1xUsWcBJW8GRwKMbzOocjxxUJ0sL3qiTTVZmJOjhRwXrXAt%2F0pA1m23OiqVLOnKVjaOPRwgv0LJpSZg4pO3YJk9PIGGEuNn5jXMe6RhgNIQYLDVKRcEyok2IAxvtnA%2Bf30aU0L55CmE8uj6SAvMbMOJyPQhmUHaJ5OJ7IcRf%2BpJOH8nubsCnFvTmZqZVQy6mWFsxYwSADhDFE6A2%2BkqWTReDQOcFoTLwHu8cgCH2CyWJB%2Bu%2BSKOPGHMXgkPB0SFAu0Ns%2BhAjnuNLUkXOpmFqCjAiu4gsq4k7TqifhU0otsvuYeM3v2Vl0EjYn%2F0rqYWbQFVCTSqk%2FHMdaftZjQdj6Hwupgocx1TFnE8YX75qmVfBOe4Lwhz3I7zrmw34AE7gy7ymOacgIHXGDdYLcUBGReqMaK7HwF%2BXjBYLaCbRBH%2BoLtkNiFavggsL1EqHsx4nQGd6JrLDrApiNFCEt3GnBgVQJHpy%2BdgTsZjKcRuy8tfuVvd%2FMXNt5WOLCSj9HOXdphdj4rfdXh2xZESLk34SY8xsOoVdZ49nh9rBaOYMoo%2FhyIumMWgRHMOBy2URWnXkkMxE80ir7XmUHdPZKmJYzpUtrm8qGxYq2WH2wdbqDCAtMOVDZf7BBgaqYaXBxb8IRKVEnntpfmMuFuOvVlzEqEMHH%2Bh1wnd8tryr%2F0f21us7zbIa9SHQF7LOHYtmo7TCiN2CEalbAjioBlQUgdduNQpDgC4CQ86QIdBBYgpteCqxjsMQToz0Kc0MG4w5xq8SaDcaOo5KB9bVpKulVuoInJsK2guIMWp3Vpk17cPVFZu2bKyAgrMXjjP%2FmEX1cR1XYH6gTiYy7K2rmEkAbmJq9J4ApAQ5K5nmITZYVIQHSTNGQVAe%2FC2a7ZefmGxve36W0Zvu%2FJUL9KPjA5Os0ebDMtPJodPnmtpa7PMPRnJwuly6n7g6h7mJOZ8ppxB6gznlGRV6aPjHAIyjei2DK5iMaBNZ1XpdJWysDVQVQZcJRdVG0qzgbWznUAjmzXUTKVJMgOzU3B5vBxytw3WOifKObFuxQyTljCRxvfTbMzBSh5Rc0sgQdEtkZi077CCQrfj98T4%2Bx1085xH3nj7jUfOqdhMe0w2%2BsauH98mHpCSVtJ1408vv%2FynN6oHtb0r4QvwvSvHXiU%2FxAvVXnxv2NbleIVfz73NGEBnY8SoQTRESQHjIzJYBSkwczWpzyo7dsyv%2FSejILMr6pMNh2q%2FxW2mv%2BVk3AxVExyHUL6JehAEfz1RABtDSIGYrf%2F2gbmXXjh7rfar%2Ffnbnv36rX2X3HlPbfy%2BzZ%2FN%2FZX%2BZogZpHEr9BKH9d9G35BHEzwRGB0PrDb7XD5ZW94Jgwqcw6Pxc4BEE2KC1O8PVpIEhrNcQMuIaNCQeygsZwrXXy2cbNw5aiR3A1zH3qyQVwCwJ24tXHLn3VXT%2Fd99YZdpxFT%2B3VtlU90%2FroBUEYGbAwBnlNokM5nZYPstZpYTonF4adocMEbc1XJ7z1yUGcFMOdW5RDNQyonupXgslimxRjgSB4U8C2iWHAtgL1ot9S5Ef%2BcKNFpKhjD6RqslDqOSIc07ZkfTJldV8vDuKqWtnUoHdbnMypbT1M%2BSjpk7y8TqxLskXKUCfGfOAtgeqJaSi%2FDTVVoClBzJKsur5f6heSjLToaLpoVgZfJK%2FkDvTFR9CwlY0Nvap2OosxRrAgp7vNOpnRoXy%2FyMAfTZhN2zzAZfoK8wODRrNiV%2BT1SM59HdkstH0ZmuCUcCLw6EIofqYB5UQ%2BqRwYu0oyAAorirXc2BuCQoQuFFDqPdTUaLRdVGtSw0xFF4dmkWueajh%2B3K2F78wEuobNVlbBcIDYwhHsB3lKfsAfwlelGxAltF7m1kzgqK4814EQpo7hYQvnM0P765Pu5T15PNmvVT5r3NOLr2akkKZbP0GNVcW2nQUPMwoGyxZVGtRX0QTBQryhq0S%2FywBZZrk%2Bbxj1dRSJdcGMwQjP39pSaQ1jScgf5%2FNCGBPCmgmIwrDXxK8JnXP3Gfol2pryKVCvf20RWwxb19zF857bTjfk7Q9QRN1xN0jxZPtTxUZmCKU13PLQBrMEYr1fVED64cZsJ4ZV%2B20NebSsYEAhbu6%2BwBsOh%2BGQqvCofwDYbgVxYn6Ho27nerQ%2BFwCN%2Fo3Cdg%2B%2Fxav28A5Ld2Zzmn3VzxZmu2nVmL%2BNH774f7%2BwKU55rIVAgSOfSeElA4dUB%2Bod0Q3lhrEY9kyYsIktUxCSQF9%2BFkLfao0cQCs1y3yjS910qXEktVsUwwpy0MejQFE501Au4Q3kDplBMLoLuBnhMVi89yXymOuYvs%2Bzx59pgfWLBIhwBj93S9LMH9GMBA7E3COmyUKuRF8mJIOvamFCIH1Ln8Nm847G1Y91A%2Fl0G7OYUpJxBGsJAs1Ga0cLXUCkXIljnLBAuyjaIA3MlmkUFNmm9E0FyzJiuuf85UTPPkAYOhGg%2BTOyp5YItLE4RM4ArDBPWGCPFRlaEC6gCw3LE3823siuEzWZdFHba42JEk67GQwxYPm2StlrG9FhwDmLNzKhX2ta1byam4lh29%2BUsWh8OCbxPyA0JMgukGvriUZiOAqGvPKN2gpmVgMhFM%2FAAsItVyhPonIxnALuIqpejyXsoBKhEYBiXQX%2BpMAUrR1kQLXTS622EvEoujRahExVK6BwSax73fGghmpmmKkZbdoPko0zws2A7WSbMcGHSheY0x1uvD7IcYKn8D7dzbqBzt4p25wqqOSvvKGWmrbRdoSsXRyuhoxTi8fnh4PQHq4FWoNJma5uY7YUfyDrSDKQzXjJIWvGj4TKrv5enYPgs2cQxeGvfZc0q4WmZtKN2JEs%2BUWnUtNA%2BW7EwSlWEYpGg%2BmUoTWOviYAA5iJMQj%2BzJk4vbh1s9p5PbVro68%2BRrsTZ32GhUbzxDvczfbOtwOsmmcvrKEV9f15%2Fe6F43MkI6PGmHnXvrmHtaky0oCORfyStfUL8HPIf%2Bn%2B8Bz7WAlOhhzmXKLQhZtKp9Ejo%2BHVWlJ6NZusBhdJT4Q3ANLlZRWLYwvQa065ZDIjArHaVEGkbJBwPTHoWN5n6lQ4RdpQed%2F4BgniZHpLlU3oDeSxyJMA%2BfJJ5CuwGHKK9txEFHZc2%2B1gj5xTXflqMxqw0I3du55bHlla%2BdctMNZz70%2BSUX7d19ipBr4waa5ZDdKcwnyhcKZxTaTAJny520Zd7qu5dWNq4%2B9ebi9UtXbqzPS%2B5Sqnu36CNBaJgWxIADo94oovR0BAS14OnDIUjlo7KDcxLO6JOlytIrTE9b2uYbzQbydTY6IxYwGG6yTJvfL8zOcCdPb%2FUQjvT3m%2BKphM127F97B439DPvRc7pfyAxzYBtTbtLstTJr8OGCYwOut1GudwHXW4AjklRQahouWp0wW0AHCFFfQ9lPAzn%2BIAZy%2FDSQ0wRf82trk7OKc6ZkAdlaMlDbLdKkxSRsosLDEKRg9hfkuJgTPGIuCjswGp1ElIHdCiKYpJy0ePHiG26A15Fd7IFdWZeUjsYqRXVzsRKLZrwiqH2PP37szce5c3CZDaUkM3vsmWyxmOVWsmYpFaJrUPSjb%2FB3gfxDfO9gQO0CfNkqNVEb0bUA3E4NXcuhT49kCHSbfazJTFDRcfnRGgJRRsUCawL8GRDZSkQE2R2ksnsmASRlAWQfdwLUo49r2BU11L2ZcdTTkit3g0YZ7sd11Me2IOrsnTXU9XyBxngqowsgjHLSZeDT7ockfglaYkf34zvXgu%2B4hsD223TbTyOY62kEcy%2BoLris0IhL5WO3%2Fh5YxUn7nk84PxHW8e3KCcOtdXDI4fqmuvl4R48Db1L3MoJ%2B0Z3B4EAog4kYQxmlt4q5GHq%2BxRCZGCf25D%2FdfiMmxUayVxrHozJhELSQFwDPXlTfVN8%2B3tGPx0vDSMPuk%2FASP3W8%2FPijdaLtcaTIrE%2FYRJRMx80twPyRlTp%2BS2h%2BwRyaQ3M6zaFZTfMLLqL5BcUsYrw1hzGcK0FM3IQ6QtnVMi1Lsw3K%2FrbpaPXc%2FL%2FKDDle5sf%2FjZa1IOfHv6G%2FZcIc%2FsTtT0H1Y6OfZoSMH%2B3Wx8cImmqQ6WBmMItgZUSrPUmz3dJA9cUZNDQ185yuIBiOBylK42CfXrRNSPg495XHHkPzBmcOznOcOTDnu3BbPTg%2B%2F1VbDWoUBWAWohxWbfySo2%2F9vcIO%2FhmpjSI18ONswFdmyrOoP2txRpl1SJldpehO6yn8H3HD0LNHpulo5NPhx7UUo49XKn%2BvHD%2F6FjrV2OQNi4uLdR%2BRwr1NvTrdNHMRphhafx6qYoP15YW108zQfAymxNt15y%2B6XCammYHl0hVh3490dUXG3JEu7hYw1z3snXR%2FC7yTopa4yzXMcxkoe4o%2Bu2G5r4VyaR7FBN9zqIquED38gxkVoQBAJPYrkljyuKniApCVXR4JXRRNouLtn5o4N5MYUsQjRt26Jh908%2Bs9gYDn6F53sPhX0rGIJN495r9o2eZlyzZ3cYfdwaD7mA3e3%2FvGZx8kw%2Bqb5LD6Izy3DOh270cf8s38C4wXtPQhHYOQBnuEwq7lgKAz1qQFrJokIJ2dR1hDCKvA%2BKme4ekFayobJl6qtaf5VBJ5iZoYxnuv%2Fel11%2F%2Fk2oUL%2F7G%2F3xI754wrOme%2BeP%2BFm%2B6%2F%2F9Au9o9bf3Hztlf%2F%2B77L%2F3tkxBzbdOmexZ%2FfRc%2FcjzrVeHxNYhaOR9e8WnTN1xBd82hEldHDJuogYojCgJG9ktcDihJG0Y4bQ8uJsYmhs6VHFWVyxMysfvUItxZguorhuX83OBkf6HkMoM0VwkbU6oU0WwgTuZBmU8m%2BmcRBrppz8RVXhKWFy9ctnZlYtu3Lyy%2F%2F3k3bHKed5hJ8aYuTNZtPL5BdZ37za195acP8u7ZcfcVVn5t7ygPFAd545je3rbwoeLbRt6ht4Y7e3CW76vbyS%2FxnmGYmCmvlOqYcRGo4qxgVwAFL0QHToktIDV%2B11IbUsGEcOBRG01HxiaVIDAliwlCxFNJCHry4ryUWT%2BguSOA2XhYmKDoFSWO9RIGkzIBihrzzKLkk8m31FTQIyeH2gco7j6oHH32H7P8aefBydRP522WXSaejo%2F3Rd4ymb4ORaMMrb6j85tF33rkwSx68DK75n8suW9M8Hs%2B9D%2BZSM5NmFuiYwbSJZZRUVTFrs9mlGAHLDMXSXkWHIlOKuYAFpVArsGDJHIS5lOgv8Ub4TNantmwU4uO45GuokGEuQiQzkWIOAyBjYG9dc%2FdkZB4Pn3%2F%2BWeEQ%2BYz6gOBfMHvt7H4NoxUt62sYPZAHhEg02esxE%2FJTMocM%2F5J1yLnZF4%2FjtZf6AeK4%2FjfpeJm1EWvVcRkP%2B1P%2FGY3wC%2BEI%2Bi8wqyoK42UGRPf5wObXxqvUEm0QXicYLTObIinArWtopKw%2BMhm5taSJLdyhrrxHw0ku1VCaW%2Fk64rRD%2FT2eP%2FmLWn62lpckM3OZshux8FHrEewbs1ao4z2kcFnEzUQLdUouL0xCI5VpPswJssswH1mxxNio541IsEaYSRw%2BzCQPH2bMZ5bQGwoCN%2FU96g2tfE99Dd4r7Gmk7WXtyMvqL9WDL1eoM%2FXlcfv2FqCxB6isRQWtOer202gL8Ej1ZA4tgkHgT1OAKpp6cli10Y2spuWqi8l%2FHN1P3lYX1XIba7IeJX1ZQj%2B53YkZWX56F7GGNVMLSdXuMn4n%2FUY0e2DKzdSAfkN%2F%2FaaEub2up3j1eg7MhREy6O3CnBft129ne%2FE32SSbHP%2BVBn%2FcEuRCoC4tnMGrKF9WmCJ3C10vIwxTmJTQNjnBrXL85N1dx9fe9HsX%2BSX095nCuCtKcwg2fmkXLugN30M6t9Vwpr4KQyPOwDciLNDkMNs79kN%2BCSKMFSrk7SljRGMZINlzNebksyg0AvWfIZPiWXk8BsMzrrSiqoV3gNdz4yYn%2BQYOEtwPswy4t2t8R2F1aHznphVZ1qrOd9Hj3aur8U70PhPuocUfQafh26hO49Z1GlRoUFQgO0%2FWzaNkgpnGLxl7%2FUhdCydOthdo9C1G4P7Gf53m6GLqbepb7Io%2Fqw%2BRjX9m140pfyYbYQtxc330M34lfyVaMwkzKRBZkOAg2ag%2B9Bd2UCJjyov00rGXfISMlTRZxzEc%2FzT%2FNPXnjtDMB0cVS9YQav%2FUzIeAnvmwDzMfqP9CwjRZzHuYmvWQAzmBSmVcinN7iAfkrGfPHvU99YD63h0V%2Bo%2BmO9QP7tlDTqaHJ8DlBB1iPC8J4XI1wGXX4MLcIPsEuGwWjGcwsMTwsGqWiLF%2FMnjRfHQKWFh4cRygptIqS2HS9VV%2F3QmmUUggcEebC8Uoj2DYpSk3lwtyIVVICSlBngzEqld37Hh15054P7hjCixttTPwPhEmcSJM7gaYPHWYHOMwOafABJwlpCZDs4l4ye5n3z1vCiS3qO%2BQ3c%2B8e54WTx2HYzozyFxEIemvKoNUF4hS9bpXG70hOtfCVSWsuWUBvmGAL4zrTbQX1pu0uN%2FMt80YoMOYGtSHUYmKSgdmKpdtzjQq2r1iyUim4JDLood82DCD5hV4jYKDl3PDfL43zWGlpAf4cTKGr9zFOwUja2DNvIP3cB6fSTY4UyE%2FqRjc3bHmeM%2BcnqbsyXOTt0%2FlVzbAGm0WwcCxxOvy2VyElyNDSc7Snl2fzS1p8xv9%2BQ1qujiBPhjJHahxtEQXZDdN4vFVMdeIH3ftltxSjYd9qB1PRbcQxYgnJpwfD7H3yKgWeJwK%2BB1gEBUrcL4xDucHS%2FMCpuzV43ARWhDFZMqOQDRHi6jKtng3jQNMQ%2BlcNjS3o3%2FDUy0L4RT6N3pQcwUVYl8sns7Q4XPDniL1lxhMywn1K0Qsh9vatThePjdMCvmcFGZlQMBBBCmeT5NUDtRxONpXiOdzsAsH4VRO%2Bl7kts9X%2BrZscv%2FgB4HnT6l84fbwBv%2Bliyv39b5SCZy7qLL4Ev9PyeHKDfcFKxXvis%2FOrZy3N%2FrSS03337DwYv%2BPfjTtq5VFF%2Fp%2F%2BAP5ogUVjV8b8%2BYi4zlfnroJWM%2Bb0yUfGn6YM0VzvrzBiclxNC%2FgBMlxDVkPJ0r9WnjbbRfX%2Fp84ffNXDVcxU%2FNVJ%2BMQnIpD0wQc%2FJNwCHwMDoABORH0m3as%2B%2BFO9bETZ%2FjFd6yr7lQfnwJzkw6zj7I%2FXy8O1SR7QIOZFofWcu2cXgqz6NNhdjQdJ9dOJpji5aHpXb2pKVAPs9OLVz75YXXj5ic%2FHJiab3dFkS0Mfzj2wcCHT27eWKtXPof6KYxMkObcYThW125KrAEYm6HKMc1kxuLhsb1snLxEs5l%2FPuX7ep5J7ft1XxXWoIOWpH2fpqx2qQPqLNKtwfBDmKOLYI4aUU8yUC%2BQQHOw%2BCr%2BUIk3NKRVIyA%2FJC%2BSA%2BohrZx5bC%2FWFOLvfAYG4a3a7xhrv0PGfweWCcXYrxdug3ryGdIGsHSoM2u%2FU6PJv1K9DauowZhG0B2ccd4d%2F%2FEgXnWW8g%2FbK7e8%2BvT5JuLUsGBb51zVKGuM8E2xlp%2FlyJScuo4nE%2FjzwEuMyjeT5E719Zvf2UmSN6ubbyaj%2BgHOjEdgF34vxsT5nfxO%2BnsMGLpREWxd7VdiJIUXqa%2BRvTeT1E71tZsVOKJu3q6%2Bxubxx1I71Ne2Y20Nzal8gOrVcWY51dpaqkqc%2BrNDmVpmBWl0Cpk1qy%2FgwuxKm52PxrUs0DjIaTMuVyER6Nk%2F2W9BPGGCajrG61BNR7Fdy6zUPsjaGXzc2%2BeN8zN4v%2FfHkn8aSO%2Ffjp%2BHD3ae%2BjNvKOQlaXgfewlkuKFO1yTTwUxjemGF6WXKKZTkPTkMjqLUhsW4h3Zm6KWdGYYA%2Ft4e0b3favPkZ2hhdzEnRWlyDMDV15vMEKyyd8JUg02cVU7iIF6JlrN6YlgEQrNtCmHMAh%2FF9eRSwrEx61CqJfSfPrdHej8aSg1Z4ix7aRFzwYtczON%2B9Vewedf3DY4WN8u1aBZikVg9pqdS08RRu89vG3WnkyWT10Jo14DDG6Zl%2Fhm31M3cLS%2FZ2tf1Ix999Cf%2BWVo7lB3P88uBtoEpsKVeyk1SvpDGHL9hQ8HLOvg0Gxf6fIW%2BxDAmEaSSMS1WzQu%2FNJs4aXo6c8G5X77j7c90LfrMZZ%2B95vrTVznPcadmF8j0eRs3ndbu402CNyrGvzI0pN51iu%2Bu7%2BYHbzp7%2B8Dgmlx3uD%2F8gvrDn%2BxekzMbPc7CjfZVI3uTM8%2B6aXXWazaEArkrWlpe%2BiHOmUsZGz%2BH%2Fw9mAXAZURZSjppbVea6SoMwFIvgNTgX2GdGH029cXtlWAl9GrBhArCCLhMmiA9f6GNTPtmXSqbSbAE0nDDn4AUjbpNLDeZ58yIjM6a3OVsc3uVrRUPEZzE7Oc7kCLT6%2B%2Bdsmj3LmXz0H3xSar7TvXyVIWfvOH9R2snaeBMhVlfQnZwhC47emeRbI%2BHpN3ZLlsTwUHjkvdSKvcXowi6Pt8Upm6yEN3qbh2ZfMvspsurirjUPGVgh%2BrnX7jNFzrv48VW2gabepjY54OIt6aVrW9KXYJ3695gb%2BPf4HBNlZjLzmdkMLk%2FtVWWGlk66IKMMHlIK1dJCIEIBVL5ZFnMgaGpvyw%2FTPC%2BmZJ4hup9nxNZ0fnhkPhX6QBdjNmyIAFmMsbQhVQgbskCZNJ%2FSio3yvQWj7JMLmJgwFDQ2z1t%2BxZYvjn5xyxXL5zUbg5MP7BZmd5%2B2%2Brqbrl55StcCKymEZvSHe5T%2FUdLn3dpzwQWJJR4vu77N0rZu9Yb5mcz8DavXtWE26MR9y4ahM6d3tuVOH%2FiMgyxMzJ0RWHQKlryvWbjuiq2zL%2FBvaG3FaQq0YIAWCuOjGQ0nMV9jys6aJTqSwWxQpS%2F47aH%2F%2FM9fM1KnRXGmHYrtO4aSg%2FzNodi%2Fozhd%2B6xOm6dzn4u%2BB%2Bh7kL630vcEvpfhbMudLXfGjWCg9SuBfiXYr7T2K4l%2BxdrPPG%2B12V2BYGsirf8jsyxwyOGccDCdVmYFCaPRGtOncySOBC64kaw6gWcSmvoBVxSSMAgsDghfJ%2FiCocHEverL9yZWPnb5BSnL7p89n9h0FyVqRg7PcDpIW9LXnYlN82TIxs7U7JvT9mBQSJx67hfHyS3uPLriLunc86%2Bd7mxT3%2Brb4X78Eo2imXXNK5uCY4%2Ff7UxmkzP9s9gbp93Td9csb1ubrXfT2efR%2FghfBeHopvqGG3PRk9StkQKQBbSkQX2WxCaSj1%2FalDA4Ek3S2DbvdO%2FYtv8iL5AX1Mcx4DNnzvwWviVmaDl6qMj%2BfKy7iIIMu4PU8rUkKnfbYVZj8ihyc0dGSVA568OOOGUfzZL0%2Bc2d5YSvVqNa6gRO9yVguofCaJOZw7DpS1CnfL63r4DeB%2BqPChP0FaQSIvUZZEhcMGKfhYJoMCaLPifZtH7rerLJ6fOL6gPFoOfMN870BIvqA6KfGNu9PQ4yl6waXr9%2BWH1a%2FUdHj7fda7f9Tf3bab5ZXtG8bJlZ9M7ynUaEv9lwNa7le5sndWEowGpSzzllVjHr6tH3PJaZabkwmAUEhj5G3huz0j%2FltuYLObqfXxKSdmHi5C4pVPzYfzT2xL5fuzAkHVtUS2GH9ebv2tL89JzhGv4ZxsK4QJc%2BieppfhpZ9NCkGxNtL8Flx40zQmsRfLDUW8V9gt1N6yhcYKU5cBCHCPZ6QC8pcBmY%2BlFSkLV9WCDX%2FwUXvYfG9qpPEu66Hc%2BpT7KP7bjuITz4l0qFXf8yelBfQpXtup37x%2B7beb12oNFf5gZdUqsBdoEhmZvoZZoYHiNiPJasZYuNPaX%2BpIjZ9JgQpuXPIzcPMFqeFR3%2FBHBzN7OBKUfw983VspmWapgxj0%2FKYg5MRw7N%2Bk6gRpr6UpPYFAMbcXCukpvQDGEZSx5LGeByDlVckDxusRSOALlkTI7TekJJXhAgoKgPG1O9WLzgdRhAlNfaMYGNahTXpPKJgfaIaAUbG%2Fb9qaXnfuU7Xzl3acpvxPZMpItdXzlyWcvpAavV2xxv60r7RdORSsfKbRdetiKXW3HZhdtWkrVaIi9W%2BWG%2Fod8y3%2BQ%2Fw72BWhMno6Ms%2FDuygWz87dg%2Fkd2%2FQ3fZ79jZSOt5zOv8D3mmVhtvJvPYOezIb9WH1D1caOwFdvbv0Amn1dPNYIqGLaDjm2AetTGoU%2BtOGIGq1uiB4QVNRdc2HLiE0QpIGTQoeKFjXXyZdJKuH5Cbf3HsTXLvL8gNuN9ZYQ%2BQJvUPtBUR1rVtVv9AmlCdBxjdMGZPwJhlAdpyDMcrkFO6gPYuJallB2Pwha8im6BsAhWpFbMXMWWxWYtegLJUkpsBpG46KskC8A7oHOj7RM8n52A7CV1R6QjRzzgur3hZNJZMuQ1OR4C7KWloEs%2FEydj3CmsKuyOhjpB6EN6yNI1GnjZ2W0u3gXvS7%2FY8LEpw%2FNhM3u6jszervYe9UqjS4WMacoOxjsPZmAHHozau2LMUl3pFInZdIfmczvRRCQulK3rB62EsXuVu0Qpatd%2FW8n%2Fxt10wVvXfdtL8QYP222IGGbnBPsuRKBgW4%2FeIEmy1pB5suNMxP9aW1aYWOhoISQOPbacyNapVs9HOTtQzhz2cNLccb6YaH8lpTJCDr738MukgHS%2B%2FrB5CDteSyXX%2Bhf8ZsCe3w2820%2BoitEctGdoSajydjIZroihxotL4T8An%2FVH1Fy%2B91JADpgCVtfqChVqGuRKsYmeFEC1RMlDu8VZpWVZTzXcXoVZQOUKbqUWwmRpWaJkjoj69J6f8NBEtc1f2YNAIhGG8JpOO%2BdsHIk8ZWr3sHk%2FC8JR6V1FLtKdDBm9kDfs7X6pbHmul3iy6ru%2FWc3WtIKsKYPXQsmsGC4IVo6vUAbBladl0qcOIfs8gJTAHTDuTSGlezg9yw4YBkhATcY%2FPAfyMHv4US9vZ6HIzljw3GwjwjljQ9wphWd4g8BX2kbEzkhnWYnMauFXeEF%2Fg3g5JyLzYMCDrz9rtR1hB8kXXHAF92sILnIElR449U1HftVlJs0ROVn%2FLraS%2B%2FmdoXgvLLAL6nwf074R1Nq9XEeVoRmskhzLVny2305zq9gTWxfVmtNZD7TnAqqtHi%2BH3DZCCB%2ByCVJIGLASjFEYbuu4ABVTQkDOE0fA0xhY1ed1XzXRet8ptdHvP9ML7quucM7eI3qag2xQuXvjQ8hv%2BMmOGO0get02fN9067SJyphR6msxfus3b4glKnlbiuXGp%2Bq2nAfM2gbOGJHe71yu0%2F1vP0FCPEflTAp3oQf5B1Ldoqyn8S6G3uyCgwzslC7L0x2Vvdu%2Fe3f3msj%2Ft3%2F%2Bn2vYf95EX6Mc%2BevrBrt8s%2B%2BP%2B%2FX9c9puuB7U1uqjXRcSYNKx%2FNK7cdghj5lio4msD1vPWqhaId4BgRAwsbS3xpZ7v4mRBRxFnkpwkRCUunsLgZXFoq%2FNIOvF%2BaCilfrN5KJVtmubYcLe3uKsIhvOLRw7djm2ZPlxc7CfrI53FrpO3CLu6Tm5rfvlfggn1DnL1qwef%2Fc0N6h3FxtqwJTqcm5lyM45qgnp7mRw2A4BVU8CSZ2x2YanHx2G4Yf3kaU2GEtfam2gVu9grQNSwTMZFd9nU1oUucK1et92tdMKqkkYR3kE9STmxIPYOEOQG2aNPwHr6WIZNFiIwBVPRPBcX4jDTPgn3Ioq0sb03kFEggLp3MgEOvgrIk6vpvIT1r2iQ%2BDUU95OYaxl0pbbSFiXtNEPbi2iXe2idfA8t9Rw5Lu7dWnJtN82o7R4E6YJFct2A%2FH6b2OSdSY3CplZAOQ4UEBV%2Ff8nbA5SRB0%2Bi3uWCSIkA0z0BMzpCaElWtLaiEdoqQY566DJHk7ijHSzmv%2BTB8k0ZTJVis8NBLnRYRdvFDusakl6z6eLVm7YLTU71SeExsAESnzU2OX%2FgcrtdaoFYeTMn8DxrsHxxrfo07eC1mLWLCYv5LaNts8Prdly7ZHSZ%2BrQv9XDTKWSV1BaWvCFCWM7A2UwO%2B%2FOr3tVl2rnAN2uZMI2hXEs7omiZorBeT8f6CmVGtjydioPp%2FUi7IbqUYC0CbYIQ6dJqEZBxgNBeF0pAZVALsCQjWFJoa%2BbbKe1CrTThQomICtNf4qZj5Uiqi9KOBSFo0EiBJcCGMFvLYkpx8VgqhzI8WhATAha6CQ5DB0ElMyeeexESyuq42O6yOsjFgjf6lEl90tVk3L5pteUNZ5PxsySxSf36urstBmIAgpl5K1FbuT9W2NmjS8i1Lpdkv8xmfIu3p8SxD%2Bxfa5PIqmK7V336FPLf7636ptVpNXMYaSGqDSmszbWNqLMaHGCx9DEzwEIpF3CuTYc5NZBRpENKAfuZldslKkJnAHlycMCFein280O%2FSzsGWdx9%2Ff0lR4bmdTFY4RNma%2FHvVCKP6VCSmEoancSITVAKw4ZBrA7ow0nlkw2iEMUaso0UdNZuf7hNUp9eNrrkWmfCsdl32qrnfQEN8mwWkTpFNZvY9eyP1fuMnshjAlmLpLlw9SWb1qg%2FW20j9kt81rX3uO11GpEKutK%2Bj4RUXwe7paLHMFjGwr1n8MO63QscM5dR8loPmH6apJ7PKp3VcictwemcDphHs1h8mtKy0mcA5p0YB7SBSl5ypgBzO1X%2FjBEwOWeCrJCw1mSIJLQmkZgF4CScJyqLBgc1SZN5cRBWlVTSSVCTZ12%2Bzfak81qydHQZWeVtf9g%2B9iHMAMuX3yV%2FPUV9ytuWzXKC0e%2F75j2A%2BsKI7xI7sa7JVdOrL7pk9YU4mcha4bFmh1PdDqOuvr7d2Oz8vs%2Ft9pGKVXCZOLv7HvYyijiL%2BjjMkycZB6w0M3R9xUX1Nz2%2FjKbvac1DXTZ0yLq1Hn2Mh1aEl0ze%2FnpNuB42YEEFF8xEMpy159fXPVwPlb27hyRYUJ%2FIPeol7Jrrfr3nrD3qu3oA1EMsr7xIkqTjJV1ffRJgcsGqN123zGAK%2BjSYtGwGi5bKUPJi7yU35nWVLYzYr1Wpo0tLpBBhV0kKErqzC9E8QHRt450r5B4EST2oAUQB1gB6VH39xVdemkKjYZ1GYp06Wvadezz7TkRj1ubsp1aaQcIsPHfJ6tW6zk6gU4J2rSBT6QTraKUGUwORHkXLi4xOhmmpDpO3cdC0vMUGsKQJYLl0sESaHGjDdZBx9U8dyoSZ9qM4Doi0irNyHBg3oyWp6dd8HU4XczvzJebLzFZ9PDuqyvKM8oUMUXZTcDURu17rTvIg5oAx2sCuF2c5HAZPNN46uGjNNbfcese96BuwuGdZ7LHktIFTz9%2FywJepEF7eIbqf903rG5y3YM06vOYL4iyzhfGuv%2BjWHTvvpW5Oz0Se8Bh9gtcnZNE1LIfZgg9dcF4HQaHEFmA6wn%2FsCGwEZRCjqX1pgtuFvkKYlcMkAko5nCkMc4VkAUPlhTSX6kvhjIcrU8aUAyYptocV4D4OnPwFXxbevD4pzRVylKTXUuJpJP2bqckYIzaf3%2BRf7RvuD5nSXEe3kRg%2Bc1U4Fuccabtjnmgeakm7si6eGNt51hQIyh6P1egytjUZbW0Oh4fnE7zBIvh9Rpcp4pbNlvb4TJvVHOmzWYXMSqfH7ewMDpucQ05pmOM8hOshHBfkLKLVLcTMXc2tD5Mp84CcEr262TwvYPXwtrApE%2Bbdi7zzzUaPxcZdEWodjpiJILishLVa4zKbYe0mzpd0hwKh5rDLSIhg8STMJm6hJHdaHB3egNnt4cxWOSW1CCmDnTPwrXGfjeNsbqOFALWElNMqC7HLLrO1ChabyPvXENbI286qxclY%2Fln%2BVsbLMIOkQID0fbJBLoCSniLGWIY42fXRZRv%2BYac6dvdR%2Bz9cv33sKWen89K7Ol3sunNf6Tv93Dvf3vbs6fMyY0%2B5XJcwtAfiBuDNp%2FS8cM03t4LBfgO9VaVArePmDPaVAkMfphPol9OruBQmqjT%2Bkz9U6ukf72lZzlPrLY%2FWG3ZGxJjQ%2F6Jag2CzKdpwaqJRd6JtsEIPqzatdrpS%2BZdxQ%2B9bx9tUH0BHIHugON7fAW0ArNjS6rX0kKrWMVmzq5kSY9INd1ycDKCfCGKRe%2FvYIu4W9eEKv6SIBd7qQWwAwzTaFminJ5gN1INDaFVrKIeagj%2BG1e1aBWgyozgPKTHaddAZQwKCagJg0Ebeca0nIRaGxWlhGHYhZLO47jIlB6y4SkRzD0qNHVPrJR7Ua0fd0rQzbI12pGugvdI%2BgH4GIBz9Rw1jMto%2BoBEKSzppHTozwa7HuKgXtKNaZp47h1F2jKpr4VbMTCA0Fwk9lNgfhStgEFW4ubKzglFX7YN9Td%2BrYEi1gi84wLXQj2MPaCfZt7RrGL1X515%2BL813iYCNPsIsYX6md2x3VtFTviinBKrK3CyWBffTvgS92XIbpVnbSebOWvudpQ0pAbAk6IXrUWsndtpIaqb%2BcFUZdtG4yYLqvsKC2aZOpYdaGwXaB0%2FPfliGSjD2FbA4aFuN0jBI31L3IOg%2FBTAdnjM7PXxGyzoKaz25bKLWG2dfR%2FfgMG62aUk4SlwsZQZw4fGT43X58ekd7%2BKxPupPwC0ZbBGWes20%2FZSghakSWkEA2Ogn6gt0cSTd0iL7lsKrpcUnr2yPXEfM2o7DCXbOghwJt6QjcKqFlE%2FQPug27fzvu6It6ZZvt%2FQYthGHw4c7lrt9odZcbqWvBW9D%2FaO%2FZAb4bwPvLAftCvMdUlW04kCa9MGoVbFXBPZvYEopzDmY2a%2B0iPutzmBTD1JIcpeyOWpEGGG5yWVxVeorEIy3pjCALBDgN71%2FvYCNJeGwfgk26AnDwgNqZoTgN1C5jMdAwpKm4LT4lnkzp0dntafaRHJn3O9sOtVrTp8UV68X5pJrvR65Kel0tY79undh4VSzwTmtNen3ktN7hzb1BeSztpiE046NCXNZw%2Bzpouu85XNXbTq1bb7KkEP%2FtKx%2FVrOrp6OzC3%2F17Mw6kY3G1SuF2eQqn0tuTeEvjsyMD89qbZXw9wp5Xtx99rmn%2Fe0jRhgkH83ef8a6z7VGC374Mc23uILp47%2FHr4cZl2eGGJTC3dSPI2QJkg%2B0Bq2bWIemNWCzjI5mbH0uOLV8HC4HRDVofh0gCHVHYZ0CdlRMsylYhb24jPtkMG2HWbRcY05KJ%2BOKl2656%2Bzz77r5xfi62fNf2iC6Oi%2BfNzJ%2F9rr4E0PDs3xnbFx5mW3W7OEN%2FYtmbLk8t2DwHM712Zduvvmlz6ZPvWj%2BvH%2F%2BrCzPuHneSfPmX3RqumntScO%2BUy89%2FVLb8Lz1zfM3rnrymRXnaPj1f%2FQBfzX%2FT4wf1h7G4wVAYIwJbXqfErADPoyzYKQnQKgQkC7wCSZEL80fgBHvH9kx57QzRkjTyMgOk%2FWCg%2Br1fz3LnQ4VRg6O7BBsFxwkt8J%2BJhyKF0Z2jJxx1iz1DyNk41mzdo7gV749ssNswu%2F8z1nu7umFEcKfAd%2By0G9tEHtyofiFnXBkZET9wyxNrlf03oHYQSWEefA00hqgVrWbemSMWtcXGBnYd2tVGLSDBhU1Gdr%2BpckNyqbR4NMew1AQtZSOmlVciIpRt16ZXxk878EnvlycoYVWimio%2FsvQ2qGhtUXuW1I2HQ6ns9Kx%2BSCx%2F8IeOPYanhia0EvdP54VE8hgozEU0wTjR6lkb6EvKxOfV0DFgYCMJpkf%2FwxUWmvc6XR0OEgT%2FWhV%2F3jwJ2TrTw4SuRV2nU71LSd%2BxNX%2FUg%2F%2B7McMR1KMG%2BzK3zA9wKGzmfsZrGPsoPWNw9lyN089LuhJGKBOSHe17B7AY24Puhzm0AY5sNZlq7hAZl2lLtJZ5t0zMBUQDE5vZJhuuUqzgJAgOpqx5r6pWpqLTY4wu4jtV7pExYyBkG7Ybe9XBsRvMI6m1Awa61fcmDGI3nDQccU0iaf5fK%2BeKcjLcRAKooyV%2BEbMF8Sid483zAGXiQ7ioU1fSerzhnWLjB42PbIsE12%2FfXkxOXflYCf3kKlv0azY4IpCW3l38Utr2gJ7XWKHt1nghxb%2B6Yn1K0lp6TlOspwYHYFM%2F%2FrC6bfNFpYt5z1dMy8YWbDYrlYdgqdr8Nzhzz9hXbpMXNe2iQ2HuvySUQCz3m0aGOt03zprfkCPCZ%2FH3cs%2FDbTdweAqBHYDqChhGk1spo3srNWyhz4QxOPQaSocUvppD%2FrWbNlPux%2F6w%2BbOskD9XwKjVZQ7Na8FUtKPjW%2FZ6SOUYk7xOWukpaMnhzsOt9IFtO3owcUNjUtGLHUNaoHlHNILaEmlSryXPkJDAEsfKJ2T4j7YFoZJrg%2FntIx90ZI0QyY1bMAclOJjfq%2FFzGW6Nz528%2FcfmLOiNbHa2x6Vw395ySRJ7TPiZ0vRL0SXDGbbl6c65J9lU2tlf8EoWUSbaJ5ma2XXF3sKwYG1Z7atL1%2Fdu7g5JHX1N6%2FqyBWz20MDfVYHiYW%2FGJC5EZ4P2qx38w6j3SpaCnfNB5kD1jX%2FjK7LRGr9Px0ZxYbNRJAoJSsYg4qp3lQ0LppZfGdpDAXsZf2TRgFpSQUNhxzbyt2C0T1WlxFG%2BgwF7H7xmN4TxpgDIa1tmmhipbdaYnhgbzvNk6eeuXKIRkdDMrZAwz5LOIosbZah5dXhgwYwtc4uwxct%2BiNfLLSisNSE7chdoLIGstl9bpdkopVyHpRN2Gmjlo6nmERYE8DoNdFaSMUuKiIOqGaLR2nPaQz0aI2QOFFrilQAFZJ9uYIxpNEirb45p3gOOruLBFv2ICFo01c8q7U4OowNiiqVCTRx0DrDGLNHyxcsCRbsPER9koCrifpdQHK6qJO3mYpU31QCCICMUwJkgX9FJIDDReNfoRaNKB4gzr6gxWrSNLwMNt5qJICgESAWwWoD3oDVBqVmF5zzYx2mEmigho49N5kqDdRAGhTrJDlnnBoaBdgD41Q5thVLcigPLtF5xA1csqnWvxRNAnRfACEoU5TtDtrGUMSJLWcUEX1R%2Bzwu0UQf7SLQRyV5aB4hdmSyUuzxIR20A85%2BhuWMtEuEA6tdDTydt7o%2FgyLGYXBDREtL527kZRgzbUxrDA4aOiDCvQ1GDj4oosJ8HA4Chd6NmJTdtL%2B2G9t7YLFYleJgRd%2FVPg8dHTvFU6A4WDQcRMrCFAfPRBx8k3FIUOg1Bk014kBbEBfPacShSIHHP8qVuERSvtTtGgtwJmKxktERsNZqRFz6VKWwOw%2BVBasHlyRgUmw7Sbva%2BixmfAfpq8FtNaE45YxaFUuN4LQvWO2Vk2rihNaD0da2mLrYQHTatrv%2B%2Bt%2FBawV4nRReEDAgfCx0nbC4EV6L7xPgzUlcA8x1eCsakHoz3hODW%2B%2F5qNWdd4zX7jZPLY2W6wXRzNSC7Mn1Y1jQ%2BPD77z2CRYwVraRRq1zU6hi1E%2B%2BcVCt3HGivxdFqsMTQizkJmvhUaFBkyCAWaKklU2r24GbseCXjkyH0dJP3H0ZYsNLykfcmAFkhS9V9COkj72l1mI2QqrYjR8bjfv9reGPj8Eb%2FHnjlJiznrUM0Ad7ikSMatO8%2FPBnaIiCy9P932kY5hLU2zhNhJYePaEyAiACPzGwg7WaAlfKkwUvrC4PMIFNfjqnbBysH3FqnY1AHZNrVGESpQI2oWvaGIooKp%2BXYY0KEJ18HLl%2BDWnvUW6UGFxlFeBCuivqw%2BjA%2BBmxCIS7V814DQXeI%2FyoTxYwfh%2BYbgiWxmUakMRnC16wlQ5Q86PF34kwFwoAtF0HLuI%2FJ9YHhZuhzF%2FoyoImBrsUIxtdMMknvNhjMZrPDZmWdxGE2yerPyZdZVjCbnRb%2BgPrhYhcreNRFXslqctrZCzyiSHjyrM1pM7uMP1d%2FutZTn8uUbn4Y45lMmdPpZsKabCw1CtK8DhCL3vrUtmoTuhTk9MxExSUqzVrnURjSQj6KhZDUY4VZn7R5BKUpjm8mr9Uik8OOyhlnPPoOrkxU6AAxnd%2FS6PctXmDZd2hQQKNsQ01AiGllVutwOumiGgTAEg22mVXz5CRR5QOT7Dl%2FIBxpaUWtF%2FSDOFYtYzWLyRyOxenRoEi9bfUi65Sge920gt9CCvMVE7RqQJ9Ny66rVVQ%2FuGVR8dF3yJ%2FJxvvI4fvUJzSWOHvh9ffWGPS6Ze88aiZr71Nt96kPTaltObVWj9NEH9Lhaaxw0apysKUEWKKBuoeq9iC053mzxeF0e7RKRrkJjhCjYLO7ROaENTqs3ljiRFU68uV7Lr98z4mLdNx4%2BvIGOYa%2BUBfofmm9C7eod3CgD3mUaBGfCfQF%2BnQ6NzqQfbpPsSYLzCxt7Q%2BrvMPK%2B62uY37yAnkRMxmxWyY2l3Va1Pe4FuxGjGwytiXLHOf%2BnZPu%2F%2FfdXEshNE%2B4excWqauzyZnjd2cP0PDU4aNvTbj3jXBv7LtZw11boQ1aY0rQ6GxZvL1Iby%2Fi7aUpuIMZOuHmPezqL6vzdrG3Ntz8u%2BqP2dW71Xn3j83I1p%2FfhXijz9Zfx1yqYgDRoPUi91HMffTWPrx1cDLmciHFJTxYWdoIwMaDGzez3%2BKO3HfwPvKjBvL%2F29mvbjz2JmH%2F8T%2FhjGo%2FHhzpE8Dhyf4doHhkQS7I1gZQth48%2B1UO7%2FY3ck4DMYoHNx4k7wB8977HfuvoH7OaDJPxmRz8s6BJmbDOg6e2n5lWPhmr2HFOK6rHmh8zkfkH1IT6%2FeXsnrEN5CHOdfQV4lKvJnuq3BNj02sysUhxwyrMpQyuHkYYSCMdSHzmhiNbNor0gRI87Jmytef%2FgfwxUhHkpO4hWkrnNIrustkna14hTaTgcwhkWjGOOm4siXiHpCPA8ZWwd9fYnF2YKVYs7uLe9oapEDz2DBboq5tD3PkVyn%2FDtP%2Beh%2BbjzWJQPwQInRRCJxgVZaNzAnjh2kJtpJ2KmZIRRGDZKvv1xvh1oCSQfhSkRAHLo7hxwNR3r9mmfg4hm3v9NWTj9WriN40Qvq6%2Bd82NAGAJTm64Xm3lnqjU5orR4KCwykycPiuxtQGYRENnBY0qQorUaeJpKIvUwTj%2FCfWQ%2BrxOoOITT9xY%2Bz8OivohvYgS6%2Bir9ORNX8f3Cb1J3ZR2F9E1GWjnoLRzoEljdKHKHcRy5BJvytbJ5wQKM%2BOBHLpAOl3Iz9j9x5alTsAgtijxYGk5rOYlJLB2yDKRzvhATkpmTSnnaFa7RufNqo2ip4WhKw00BjsCsHqSCiPaPrhSYY6D02nHwel42EyAvxF4JSRinkcJi0LKVlsA%2FUOwSE5GQR8oLdZvJuMoVID6l1AcaLRfPdiIwyUVOjY6Fvj01BoOFZoPZ6acMrl2QNMQwVzR%2BrcdXcErev86MnqE5g4ni7RrxZib%2FiDGls6D33tGt6m0ztg3Y6Si5AvmsL1JSW6mD4ssWZ05fFxkyebSqSPUWuhaqSsNvRA%2BugpTIlktaFUZNOPVh40NeM7p0mNAbq8kah0btZJPUDlKxAv0MyFhtXRb%2BKNuB%2FqiehHuDmDWv6Zf0h7Ro3rTaLoJBtrL7LdpRA%2Fzcg%2B9TON79V72tbrsGVpdNi29ZmpOFdmKS9M%2Bs0s2Yf9njNWYqcZusVJHYY8mHkX9ycvam1DvZ5HwYrXOeA957UmwxWxW%2F08bhtBGgVYL3W4Nqu8GEuwB2h4ba2Bq75t%2FoJ5Ee2D8czGUj%2FqNBvUK2o9vZcQjXinOjTT2Nl7C5DBDhkYjbTklVy2zVgfau22Zsr85gZXmKXxWwniyaxsmuxpbaFPjaB4hp8%2F2kbAHLZenSX7APjRwiksPboc51OqdBCi8OEAOBxaTUZrJmw0E1M2LA6otsFjdXDsy3ZYkd6pbkrbp04nBLDm4a3wBwk6Hw%2BoWcmf98LHb8bAe29RsJz%2FTzLSAnZxmskzZr2sKvgz1mdNSH7cX3UCZjNb4yO2iLBVoplqcOMVKKuQS2K%2B5bsNHZSkB6KSiJJ%2BQtCxz0E4PotE0WrkAdJl1RfWOYq24AQswUxfQY6hQaVdrBs3YnMrZ6qFi3dwfrZx9dqU4Nu%2BRWx%2BBcXExLv4h%2FiF8RrA7zMlhLjeT9GHBJxAwTVKFYYLpw07iIpHhe847%2F6f3Xx%2BJPCIKnu94el1bdh47d4todHze0e1%2BmPt95x13HTzv%2FHuGouRhd9pxu4MXt5x7bOcWscf7oscoPjwxtx%2FXt4zeXwmf45qheXsnZUoj9AEvTWhfZmCCtYn7ku3ZWZRomJpQIPneYS6LzCvp9VE0K8Hrm0mGyRDpa5ElL3aIBo0%2FzcdjDt5JJAe5mPUlV25emfQRQi52SChkvOGQ9%2Fve0A4yvINuGIPFra9vO%2F2xq4ozu8zm7EFvzkEyWd7eEpC8Tc0WS1b9qSPnxXZ47JNSgI%2FyMekev%2F8eKQabAaliCpzU0y5F29qiVkt9%2FmIecKAefycTO%2FdGpUQ0hT1dJa2LPX2JMn16x0fcN26gj%2BPQus5Xdh2hT6TGf%2Brmou5zqN3DTDvatOoRdm%2BOSjvLIepN1vpUaQuB9gSReuO%2FWoubXH2riKkGtEKIfhT1Hc0cr6VwsAc0rqImJj5LSu%2Bph70UsGpsge5PcNXtnq4Gf4Ko%2BRPw%2BX2iiz4DoCXe1qF1I6D7wZZkW7tW5DmpjR6G2jzoPMxpXf0NOXQogjqBfeiVrewBrLccm7NVoZ2TKMNjLyqgm%2F6UM3IYr1Bt8I7VGns1IUa930XNoff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AxNRE0NjMhMhYVERQGIyEiJhkBNDYzITIWFREUBiMhIiYBETQ2MyEyFhURFAYjISImGQE0NjMhMhYVERQGIyEiJh0VAZAVHR0V%2FnAVHR0VAZAVHR0V%2FnAVHQJYHRUBkBUdHRX%2BcBUdHRUBkBUdHRX%2BcBUdMgGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHf29AZAVHR0V%2FnAVHR0CbQGQFR0dFf5wFR0dAAAJAAAAAARMBEwADwAfAC8APwBPAF8AbwB%2FAI8AdgCyDQAAK7E8bDMzsATNsTRkMjKwHS%2BxTHwzM7AUzbFEdDIysC0vsVyMMzOwJM2xVIQyMgGwkC%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%2BwTDOwHM2wRDKwLS%2BwXDOwJM2wVDIBsGAvsADWsRAgMjKwCc2xFygyMrAJELEwASuxQFAyMrA5zbFIWDIysWEBKwAwMT0BNDY7ATIWHQEUBisBIiYRFBY7ATI2PQE0JisBIgYVPQE0NjsBMhYdARQGKwEiJgE1NDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJh0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHQGQHRUCvBUdHRX9RBUdHRUCvBUdHRX9RBUdHRUCvBUdHRX9RBUdMsgVHR0VyBUdHQGlFR0dFcgVHR0VyMgVHR0VyBUdHfz1yBUdHRXIFR0dAaXIFR0dFcgVHR0BpcgVHR0VyBUdHQAAAAEAHQAiBPIEKgAFAAATCQEnAScdAaMDMtT9oc4Bxv5cAzTU%2FaHPAAABAGoAagRGBEYACwAAEwkBNwkBFwkBBwkBagEa%2FubUARoBGtT%2B5gEa1P7m%2FuYBPgEaARrU%2FuYBGtT%2B5v7m1AEa%2FuYAAAMAF%2F%2FsBMQEmQATABsAJwC3ALIOAAArsBIvsBfNsBwvsCMzsB3NsCEyshwdCiuzQBwmCSuyHRwKK7NAHR8JK7AbL7ADzQGwKC%2BwAdawFc2wFRCxJgErsB4ysCXNsCAysiUmCiuzQCUjCSuyJiUKK7NAJhwJK7AlELEZASuwBc2xKQErsSYVERKyAhYbOTk5sCURsBI5sBkSswMXEBokFzmwBRGwBzkAsRcSERKwEDmxHQ4RErMABRUYJBc5sBsRshQZATk5OTAxEhAAIAAVFAcBFhQPAQYiJwEGIyICEBYgNhAmIAM1MzUzFTMVIxUjNRcBHAGQARxOASwHB20IFAj%2B1HeOyIPCARLBwf7uRmTIZGTIAe0BkAEc%2FuTIjnf%2B1AgUCG0HBwEsTgJs%2FvDCwQESwf5ZyGRkyGRkAAADABf%2F7ATEBJoAEwAbAB8AXQCyDgAAK7ASL7AXzbAbL7ADzQGwIC%2BwAdawFc2wFRCxGQErsAXNsSEBK7EZFREStQMCEBIcHSQXObAFEbAHOQCxEg4RErAJObAXEbAQObAbErUBAAcFHB4kFzkwMRIQACAAFRQHARYUDwEGIicBBiMiAhAWIDYQJiADITUhFwEcAZABHE4BLAcHbQgUCP7Ud47Ig8IBEsHB%2Fu5GAZD%2BcAHuAZABHP7kyI15%2FtUHFgdtCAgBLE4CbP7wwsIBEML%2BWcgAAAIAFwAXBJkEsAAbACsARQCwGC%2BwCs0BsCwvsADWsAfNsgcACiuzQAcDCSuwBxCxDAErsBPNsgwTCiuzQAwQCSuxLQErsQwHERKzFxgcIyQXOQAwMRM0EjcVDgEVFBYgNjU0Jic1FhIVFA4CIi4CARQWOwEyNjURNCYrASIGFRfSp2d8%2BgFi%2Bnxnp9Jbm9Xs1ZtbAd0dFWQVHR0VZBUdAli3ASg%2BpjfIeLH6%2BrF4yDemPv7Yt3bVm1tbm9UBDBUdHRUBkBUdHRUABABkAAEEsASxAAMABwALAA8AMACyBAAAK7EIDDMzAbAQL7AE1rAHzbAHELEIASuwC82wCxCxDAErsA%2FNsREBKwAwMTczESMBETMRMxEzETMRMxFkyMgBLMhkyGTIAQEs%2FtQB9P4MAyD84ASw%2B1AAAAIAGgAbBJYElgBHAFEAYgCwEi%2BwUM2wSy%2BwNs0BsFIvsADWsEjNsEgQsU0BK7AkzbFTASuxSAARErELPTk5sE0Rsw8VMzkkFzmwJBKxGS85OQCxUBIRErEHHTk5sEsRswMhJ0UkFzmwNhKxK0E5OTAxExQfAhYfAQcWFzcXFh8CFjMyPwI2PwEXNjcnNzY%2FAjY1NC8CJi8BNyYnBycmLwImIyIPAgYPAScGBxcHBg8CBgU0NjIWFRQGIiYaBpcCDhgDUC08hQUtMQUmKCIbLyYGLi8FhjgwUAMYDwGYBQWYARAXA1AsPIYFLTAGJigiGy8mBTIsBYU7LlADGQ0ClwYBZ36yfn6yfgJZISkmBjEsBYY7LlEDGg0ClwUFlwINGgNRLD2GBSwxBiYoIhwtJgYzKgWGOi9RAxkOAZgFBZgBDhkDUS86hgUvLgYmMBlYfn5YWX5%2BAAAABwBk%2F%2F8EsAUUABkAIwAnACsALwAzADcAiQCyIQAAK7AkzbIoMDQyMjKwJy%2ByKjI2MzMzsBvNsBcvsATNsQ4sMjKwLy%2BwCc0BsDgvsBrWsCTNsCQQsSUBK7AFMrAozbAsMrIlKAors0AlAAkrsCgQsSkBK7AwzbAwELExASuwLTKwNM2wDTKyNDEKK7NANBMJK7A0ELE1ASuwHc2xOQErADAxEzU0NjMhNTQ2MyEyFh0BITIWHQEUBiMhIiYTESERFAYjISImNzMRIxMzESMRITUhEzMRIxMzESNkDwoBEzspASwpOwETCg8OC%2FvmCw5kA4Q7Kf1EKTtkZGTIZGQBLP7UyGRkyGRkBAEyCg9kKTs7KWQPCjILDg78bgMg%2FOApPDwpArz9RAK8ASxk%2B7QCvP1EArwAAAAAAQABAAEFFQTdAAoALACyCQAAK7AEMwGwCy%2BwCdawCM2wCBCxBQErsATNsQwBK7EFCBESsAE5ADAxEwkBIxEhESERIREBApAChMj%2B1P7U%2FtQCWQKE%2FXz9qAGQ%2FnACWAAAAgBkAAAD6ASwAA4AEQAiALIMAAArAbASL7AB1rAGzbIGAQors0AGCAkrsRMBKwAwMTcRNDYzIREhERQGIyEiJgERAWQOCwHbAZAOC%2FyuCw4CWAEsGQR%2BCw7%2BDP1dCw4OAxIBLP7UAAADAAQABASsBKwACwATABkAggCwCi%2BwD82wFC%2BwF82yFxQKK7NAFxUJK7ATL7AEzQGwGi%2BwAdawDc2wDRCxFAErsBfNshcUCiuzQBcZCSuwFxCxEQErsAfNsRsBK7EUDRESswoDDhMkFzmxERcRErMJBA8SJBc5ALEUDxESswcADRAkFzmxExcRErMGAREMJBc5MDESEBIkIAQSEAIEICQSEBYgNhAmIBMRMxEzFQSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2BqkdkyAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP9%2FgGQ%2FtRkAAAAAAL%2FnAAABRQEsAALAA8ALgCyAAAAK7AHM7AKL7AMzbAPL7ADzbIDDwors0ADAQkrsAUyAbAQL7ERASsAMDEjATMDMwMzASEDIwMTMwMjZAGv0RWiFNABr%2F3mKfIoMeAbqgSw%2FtQBLPtQAZD%2BcAH0ASwAAAAAAgAAAAAETASwAAsADwBKALILAAArsAzNsA8vsAnNsAEyAbAQL7AE1rAHzbIEBwors0AEAAkrsAcQsQ0BK7AKzbERASuxBwQRErECCTk5sA0RsQgMOTkAMDExESEBMxEhETMBIRElMzUjAer%2B3sgBLMj%2B3gHq%2FuGvrwGQASwB9P4M%2FtT%2BcMhkAAMAAQABBK8ErwAPABcAHgBjALINAAArsBPNsBcvsAXNAbAfL7AB1rARzbARELEZASuwHM2wHBCxFQErsAnNsSABK7EZEREStA0EEhcYJBc5sBwRsB45sBUStAwFExYdJBc5ALEXExEStQEICQAaHiQXOTAxEjQ%2BAjIeAhQOAiIuARIQFiA2ECYgAzMRMxEzAwFfoN703qBfX6De9N6gXPIBVPLy%2FqxQlsiW%2BgHe9N6gX1%2Bg3vTeoF9foAIC%2Fqzy8gFU8v5kASz%2B1P7UAAMABAAEBKwErAALABMAGgBhALAKL7APzbATL7AEzQGwGy%2BwAdawDc2wDRCxGQErsBjNsBgQsREBK7AHzbEcASuxGQ0RErQKAw4TFCQXObAYEbAVObARErQJBA8SFiQXOQCxEw8RErUBBgcAFRgkFzkwMRIQEiQgBBIQAgQgJBIQFiA2ECYgAxsBIxEjEQSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2Bqk%2F6%2BpbIAbYBRAESoKD%2B7v68%2Fu6goAJf%2Fqrz8wFW8%2F5iASz%2B1P7UASwAAAACAAAAAASwBLAACwATACkAsgkAACuwDM2wEDKwEy%2BwAs0BsBQvsRUBKwCxDAkRErIEAQ45OTkwMTUREyEbARQGIyEiJhMzFyE3MwMhyAMgxwEOC%2FuCCw7IyDIBLDLIYf2iGQHbArz9RP4lCw4OAebIyAH0AAMABAAEBKwErAALABUAGABGALAKL7APzbAUL7AEzQGwGS%2BwAdawDM2wDBCxEQErsAfNsRoBK7ERDBEStQQJCgMWGCQXOQCxFA8RErUBBgcAFhckFzkwMRIQEiQgBBIQAgQgJBMUFiA2NTQmIAYBEQUEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqrzAToBKQG2AUQBEqCg%2Fu7%2BvP7uoKABtKzy8qyr8%2FP%2BigGRyAABABcAFwSZBLAAHABTALAFL7ANzbARL7AZzQGwHS%2BwAdawD82wDxCxCgErsAnNsR4BK7EKDxEStQUEExQXGSQXObAJEbEVFjk5ALERDREStAEACRQVJBc5sBkRsBc5MDESFB4CMj4CNSMUBiAmEDYzMhcHIREHJiMiDgEXW5vV7NWbW5b6%2Fp76%2BrGIbpIBkJGdxnbVmwLO7NWbW1ub1Xax%2BvoBYvpRkgGQkXpbmwAAAAIAFwAABJkEsAAQACEAegCyEQAAK7AfL7AWzbIWHwors0AWGgkrsA0vsAXNsg0FCiuzQA0ACSsBsCIvsADWsBDNsBAQsRkBK7AazbEjASuxEAARErEREjk5sBkRtwcFCwoTFB8hJBc5sBoSsQkIOTkAsRYfERKwITmwDRGxCRI5ObAFErAHOTAxEzQ%2BAjMyFzcRITcmIyIGFQMRIQcWMzI2NTMUDgIjIicXW5vVdsadkf5wk3CHsfpJAZCTcIex%2BpZbm9V2xp0CWHbVm1t6kf5wk1D6sf2oAZCTUPqxdtWbW3oAAAoAZAAABLAEsAADAAcACwAPABMAFwAbAB8AIwAnAFAAsAgvsBgzsAnNsBoysAwvsBwzsA3NsB0ysBAvsCAzsBHNsCEysBQvsCQzsBXNsCUyAbAoL7AI1rIMEBQyMjKwC82yDhIWMjIysSkBKwAwMTMhESETESERJTUzFSc1MxUnNTMVJzUzFRMhNSE9ASEVJTUhFSU1IRVkBEz7tGQDhPzgZGRkZGRkZGQB9P4MAfT%2BDAH0%2FgwB9ASw%2B7QDhPx8ZGRkyGRkyGRkyGRk%2FahkZGRkyGRkyGRkAAIAAAAABEwEsAAZACMASgCyFwAAK7AgL7AJzQGwJC%2BwBdawGs2yBRoKK7NABQAJK7AaELEbASuwDs2yDhsKK7NADhMJK7ElASsAsSAXERKzBA4FGiQXOTAxNRE0NjsBNTQ2MyEyFh0BMzIWFREUBiMhIiYBITU0JisBIgYVOylkdlIBLFJ2ZCk7Oyn8fCk7AZABLB0VyBUdZAJYKTvIUnZ2Usg7Kf2oKTs7AuWWFR0dFQAAAAIAZAAABEwETAADABUAFwCyAAAAKwGwFi%2BwANawA82xFwErADAxMxEzERM%2BAR4CPgE3EQ4BLgMGB2RkZDyHeHxyamQpKHuEkId0WhQETPu0AZA8MA0hGwVPUQH0UUUKKCgKRVEAAAAAAwAAAAAEsASXACEAMQBBAGcAsi8AACuwPjOwJs2wNjKwDC%2BwHc0BsEIvsADWsAfNsAcQsSIBK7ArzbArELEyASuwO82wOxCxEAErsBfNsUMBK7EyKxESswwLHRwkFzkAsSYvERK0BxATFAMkFzmwDBGxCA85OTAxERQWOwEyNjURND4BIB4BFREUFjsBMjY1ETQuAiIOAhUTETQ2OwEyFhURFAYrASImJRE0NjsBMhYVERQGKwEiJg4LMgsOjeQBBuSNDgsyCw5jo97o3qNjyAwIoAgMDAigCAwCWAwIoAgMDAigCAwBEwsODgsBLH%2FRcnLRf%2F7UCw4OCwEsdN6jY2Oj3nT91QHMCAwMCP40CAwMCAHMCAwMCP40CAwMAAAAAgAAAMgEWAPoAAUAEQAAESEFEQUhATcnNxc3FwcXBycHASwBLP7U%2FtQCsI2NR42NR42NR42NAZDIAyDI%2FquNjUeNjUeNjUeNjQAAAAIAAADIA3AD6AAFAA8AEgABsBAvsA7WsAnNsREBKwAwMREhBREFISU3FhUUByc2NTQBLAEs%2FtT%2B1AK8RW9qQ1YBkMgDIMg5NYevqYU2boqSAAAAAAMAAAC6BGID9wAFAA8AHQA8ALAAL7ABzQGwHi%2BwDtawCc2wCRCxEwErsBrNsR8BK7ETCRESsxAWFx0kFzkAsQEAERKzCQ4TGiQXOTAxGQEhJRElATcWFRQHJzY1NDcXFhUUDwEXNzY1NC8BASwBLP7UAZJFb2pDVl4He3cHUQaOkAYBkQGQyPzgyAHJNYevqoU3boqRzQiXwb2WCEIIsuPmsggADQAAAAAEsASwAAcAEQAVABkAHQAhAC8AMwA%2FAEMARwBLAE8BAQCyAAAAK7EwRDMzsBLNsikxRTIyMrAaL7InK0wzMzOwG82yJS1NMjIysCIvsQIGMzOwI82wCDKwHi%2BxDkgzM7AhzbE0STIyAbBQL7Aa1rEFHjIysB3NsQMfMjKwHRCxMAErsQ0sMjKwM82wNTKwMxCxLgsrsCoysCXNsEAysi4lCiuzQC4iCSuyAQsPMjIysCUQsTcBK7FESDIysDvNsSZKMjKwOxCxTAErsEIysE%2FNsjk9RjIyMrFRASuxMB0RErUUFRgZND8kFzmxNyURErIoKTg5OTkAsSIbERKzExQ4OSQXObAjEbIEOjs5OTmwHhJACQUWGTY3PD1AQyQXOTAxMSERIzUjFSM1MzUhNTM1IxEhExEhEQERIREDNTMVAzM1IwE1IREzFSMVIzUjNTM1AzUzFQMzETMRITUjNTMRIRMRIREDNSEVATUzFRM1MxUB9MhkyGQBkGRk%2FgxkASz%2B1AEsyGRkZGQBLAEsyGTIZGRkZGRkyAEsyMj9qMgBLMgBLP7UZGRkAfRkZGRkZGQBLPu0ASz%2B1AK8ASz%2B1P2oZGQCvGT%2BDGT%2B1GRkZGTI%2FgxkZAPo%2FtT%2B1MhkAfT%2BcAEs%2FtT84GRkA4RkZP1EZGQAAAAACQAAAAAEsASwAAMABwALAA8AEwAXABsAHwAjAHAAsgwAACuyBBQcMzMzsA3NsRUdMjKyDAAAK7AFzQGwJC%2BwCNawC82wCxCxEAErsAwysBPNsA%2FNsBMQsRQLK7AXzbAXELEYCyuwG82wGxCxIAErsCPNsSUBK7EQCxESsQcGOTmxGxcRErEcHTk5ADAxNTMRIxM1IRUnETMRFzUzFScRMxEVNTMVNREzERU1MxUnETMRZGRkASzIZMhkZMhkZMhkyMgD6PtQZGTIA%2Bj8GMhbW8gD6PwYyFtbyAPo%2FBjIW1vIA%2Bj8GAAAAAIAAAAABLAEsAAHABMAKQCyBwAAK7ASL7AEzQGwFC%2BwAdawCc2xFQErALESBxESsgAGCzk5OTAxERM0NjMhCQEAFBcWMjc2NCcmIgcBDwoB2gK8%2Fgz92B0eUx4dHR5THgK8AdsKD%2F1E%2FgwD41QdHh4dVB0eHgAAAAMAAQAABd0EsAAHABMAGQAxALIHAAArsBczsBIvsATNsBQyAbAaL7AB1rAJzbEbASsAsRIHERK0AAYLFhkkFzkwMRsBNDYzIQkBABQXFjI3NjQnJiIHJTMJAScBAQEOCwHaArz%2BDP3XHh1UHR4eHVQdAgtkArz%2BDDIBwgK8AdsLDv1E%2FgwD41QdHh4dVB0eHrD9RP4MMgHCAAAAAAEAZAAABLAEsAAKAD8AsgAAACuwBy%2BwAs0BsAsvsADWsArNsAoQsQUBK7AEzbEMASuxCgARErICBwg5OTkAsQcAERKyAQQFOTk5MDEzETchEQcRIQchEWSvA51k%2FRJkAu4EAa%2F8GGQD6GT8GAAAAAABAMgAAARMBLEACgAAMwkBETQmIyEiBhXIAcIBwh0V%2FOAVHQG8%2FkUEfhQeHhQAAAADAAAAAASwBLAACwAXACcAWQCyJQAAK7AczbAKL7ADzbIKAwors0AKAAkrsAcysgMKCiuzQAMBCSuwBTIBsCgvsADWsAvNsAIysAsQsQgBK7AFMrAHzbEpASuxCAsRErMMDyciJBc5ADAxNREzFyE3MxEjNSEVExchNwMuASMhIgYHAzc%2BATMhMhYfARYGIyEiJshkAlhkyMj84DUoAlA%2BXgIQCv4%2BChACQiYCEwoB9AoTAiYCCwr9qAoLZAK8yMj9RMjIAtl8fAFaCw4OC%2FuBmAoODgqYCg4OAAAABAAAAGQEsARMAB0AJQAtADEAbwCwAy%2BwJc2wKS%2BwLc2wIS%2BwE80BsDIvsADWsB%2FNsh8ACiuzQB8vCSuwHxCxJwErsCvNsTMBK7EfABESsBk5sSsnERKzISQlICQXOQCxLSkRErMfIiMeJBc5sCERsS4xOTmwExK0CxkaLzAkFzkwMTUUFjMhMjY1ETQmKwEuBCsBIg4CDwEjIgYVADQ2MhYUBiICFBYyNjQmIiU1MxU7KQPoKTs7KZYEDzM3UyrIKVI6LgsMlik7AWSQyJCQyAY%2BWD4%2BWAFYZMgpOzspAlgpOwgbRTUrKTs7FRQ7Kf5wyJCQyJABIFg%2BPlg%2BXmRkAAIANQAABLAErwAeACIAHgCyAAAAK7ANM7AezbICDA8yMjIBsCMvsSQBKwAwMTMhNSIuAT8BIRcWBiMVITUmJy4BLwEBIwEGBw4BDwEBExcTNQFtKT4kE1wBh1IQKzUBoSIoEh4GBv5%2FXf5xGBwMKg8PAWuyLnRCFjYs6t4tV0JCASoTLg4NA%2Bb8EjAbDBoHBwHHAcmM%2FsMAAwBkAAADwwSwACAAKQAxAGUAsiAAACuwIc2yIAAAK7ABzbApL7AqzbAxL7ANzbANELALzQGwMi%2BwBNawIc2wKjKwIRCxLgErsBDNsCUg1hGwHM2xMwErsSUuERKwFjkAsSkhERKwHDmwKhGwFjmwMRKwEDkwMTM1PgE1ETQuAyc1BTIWFRQOAg8BHgQVFA4BIyczMjY1NCYrATUzMjY1NCYjZCk7AgkWJB8B13i6FyEiCwwIG0U0K3amT8ihWYB9Xp%2BLTGyom1kHMygDOxwXHQ0PB0cBsIw3XTcoCAcDDDNBdkZUkU3IYVRagWR7TVJhAAAAAAEAyAAAA28EsAAZACAAsgAAACuwAc2wGDKwCy%2BwDjOwDM0BsBovsRsBKwAwMTM1PgE3EzYmJy4BJzUhFw4DDwEDBhYXFchNcwitCihHBgkFAakCIToiGQUFgAowRzkHQy8DUTgkEwEDATk5CCMnJQwM%2FMc0PAY5AAL%2FtQAABRQEsAAJACUAfgCyGwAAK7AfL7ICBRYzMzOwDM2yHwwKK7NAHxAJK7AKMgGwJi%2BwAdawB82wBxCxCgErsCXNsCUQsR0BK7AYzbIYHQors0AYGgkrsh0YCiuzQB0bCSuwGBCxEAErsA%2FNsScBK7EKBxESsQUIOTkAsR8bERKwCTmwDBGwBDkwMSczESM3FyMRMwcTETMhMxEjNC4DKwERFxUhNTcRIyIOAxVLS0t9fUtLffqWAryWMhAVLiEiyGT%2BcGTIIiEvFBHIAyCnp%2FzgpwNjASz%2B1B0nFQkC%2FK4yZGQyA1ICCRUnHQAAAAIAIf%2B2BI8EsQAJACUAiQCyCAAAK7ACzbAfL7AWM7AMzbIfDAors0AfCgkrsA8yAbAmL7AK1rAlzbAlELEdASuwGM2yGB0KK7NAGBoJK7IdGAors0AdGwkrsBgQsRABK7APzbEnASuxHSURErMCCAkBJBc5sRAYERKzBAYHAyQXOQCxAggRErEABTk5sB8RsgEEGjk5OTAxPwEVITUXBzUhFQMRMyEzESM0LgMrAREXFSE1NxEjIg4DFSGnAyCnp%2FzgZJYCvJYyEBQvISLIZP5wZMgiIS4VEDN9S0t9fUtLA88BLP7UHScVCQL9djJkZDICigIJFScdAAAAAAQAAAAABLAETAAPAB8ALwA%2FAAA1FBYzITI2PQE0JiMhIgYVNRQWMyEyNj0BNCYjISIGFTUUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVHRUETBUdHRX7tBUdHRUDIBUdHRX84BUdHRUD6BUdHRX8GBUdHRUCWBUdHRX9qBUdMhQeHhRkFR0dFcgUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFQAEAAAAAASwBEwADwAfAC8APwAANRQWMyEyNj0BNCYjISIGFREUFjMhMjY9ATQmIyEiBhUTFBYzITI2PQE0JiMhIgYVERQWMyEyNj0BNCYjISIGFR0VBEwVHR0V%2B7QVHR0VBEwVHR0V%2B7QVHcgdFQK8FR0dFf1EFR0dFQK8FR0dFf1EFR0yFB4eFGQVHR0VAfQUHh4UZBUdHRX%2BcBQeHhRkFR0dFQH0FB4eFGQVHR0VAAQAAAAABLAETAAPAB8ALwA%2FACYAsg0AACuwBM2wLS%2BwJM2wHS%2BwFM2wPS%2BwNM0BsEAvsUEBKwAwMT0BNDYzITIWHQEUBiMhIiYTNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJhM1NDYzITIWHQEUBiMhIiYdFQRMFR0dFfu0FR1kHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHcgdFQJYFR0dFf2oFR0yZBUdHRVkFB4eAmxkFR0dFWQUHh7%2B6GQVHR0VZBQeHgJsZBUdHRVkFB4eAAQAAAAABLAETAAPAB8ALwA%2FACYAsg0AACuwBM2wHS%2BwFM2wLS%2BwJM2wPS%2BwNM0BsEAvsUEBKwAwMT0BNDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYdFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0yZBUdHRVkFB4eAUBkFR0dFWQUHh4BQGQVHR0VZBQeHgFAZBUdHRVkFB4eAAAAAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AUgCyDQAAK7BMM7AEzbBEMrAdL7BcM7AUzbBUMrAtL7BsM7AkzbBkMrA9L7B8M7A0zbB0MgGwgC%2BwANayECAwMjIysAnNshgoODIyMrGBASsAMDE9ATQ2OwEyFh0BFAYrASImETU0NjsBMhYdARQGKwEiJhE1NDY7ATIWHQEUBisBIiYRNTQ2OwEyFh0BFAYrASImATU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYRNTQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJh0VZBUdHRVkFR0dFWQVHR0VZBUdHRVkFR0dFWQVHR0VZBUdHRVkFR0BLB0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHR0VAyAVHR0V%2FOAVHTJkFR0dFWQUHh4BQGQVHR0VZBQeHgFAZBUdHRVkFB4eAUBkFR0dFWQUHh78kGQVHR0VZBQeHgFAZBUdHRVkFB4eAUBkFR0dFWQUHh4BQGQVHR0VZBQeHgAABv%2BbAAAEsARMAAYACgAaACoAOgBKACAAsAAvsCYzsAHNsC4yAbBLL7FMASsAsQEAERKwBDkwMQM1MzUXBzUTMxEjExQWMyEyNj0BNCYjISIGFTUUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVNRQWOwEyNj0BNCYrASIGFWXJpqbIZGTIHRUCWBQeHhT9qBUdHRUBLBQeHhT%2B1BUdHRUB9BQeHhT%2BDBUdHRVkFB4eFGQVHQH0ZEt9fUv%2BDARM%2B%2BYUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFcgUHh4UZBUdHRUAAAAABgABAAAFFQRMAA8AHwAvAD8AQwBKABcAskAAACsBsEsvsEDWsEPNsUwBKwAwMTcUFjMhMjY9ATQmIyEiBhU1FBYzITI2PQE0JiMhIgYVNRQWMyEyNj0BNCYjISIGFTUUFjsBMjY9ATQmKwEiBhUBETMRExc1MzUjNQEdFQJYFB4eFP2oFR0dFQEsFB4eFP7UFR0dFQH0FB4eFP4MFR0dFWQUHh4UZBUdAyBkIafIyDIUHh4UZBUdHRXIFB4eFGQVHR0VyBQeHhRkFR0dFcgUHh4UZBUdHRX75gRM%2B7QCJn1LZEsAAgAAAMgEsAPoAA8AEgAtALANL7AEzbAEzQGwEy%2BwANawCc2xFAErsQkAERKwEDkAsQQNERKxERI5OTAxGQE0NjMhMhYVERQGIyEiJgkBESwfAu4fLCwf%2FRIfLAOEASwBEwKKHywsH%2F12HywsAWQBLP2oAAADAAAAAASwBEwADwAXAB8AWQCyDQAAK7AfL7AbzbAXL7AEzQGwIC%2BwANawEM2wEBCxGQErsB3NsB0QsRUBK7AJzbEhASuxHRkRErARObAVEbETEjk5ALEfDRESshATFTk5ObAbEbAUOTAxNRE0NjMhMhYVERQGIyEiJj8BBScBExEhEjQ2MhYUBiIaEgRYExkZE%2FuoEhpk9wEqSgEl7PwYbE5wTk5wLAP0EhoaEvwMEhoa7baDnAE%2B%2FuAB9P7OcE5OcE4AAgCU%2F%2FMEHAS9ABQAHgA9ALINAAArsB0vsATNAbAfL7AA1rAVzbAVELEbASuwCM2xIAErsRsVERKxDQQ5OQCxHQ0RErIHABg5OTkwMRM0PgEzMh4BFAcOAQ8BLgQnJjcUFjMyNjQmIgaUedF6e9B5SUm7OTkKImNdcys%2Fwpdqa5eX1pYC6XzXgX7V9pVy9kJCCSJrb6BLi5Zrl5fWlpcAAAIAAQABBK8ErwAPABUASQCyDQAAK7ATzbAUL7AFzQGwFi%2BwAdawEc2wERCxEwErsAnNsRcBK7ETERESsQ0EOTmwCRGxBQw5OQCxFBMRErMBCAkAJBc5MDESND4CMh4CFA4CIi4BEhAWMxEiAV%2Bg3vTeoF9foN703qBN%2B7CwAd703qBfX6De9N6gX1%2BgAgn%2BnvoDVgACAHUABAPfBQ8AFgAlAAATND4DNx4GFRQOAgcuAjceARc3LgInJjY%2FAQ4BdURtc3MeFUlPV00%2FJU5%2Bmk9yw4B%2BDltbEAcWLgoPAgkJXDcBll64oZ3FYEePdndzdYZFWZlkOwQGXrh%2BUmwaYgYWSihJjTQzbpYAAAADAAAAAATFBGgAHAAhACYAVwCyGgAAK7APzbAIL7AEzQGwJy%2BwANawDM2wDBCxEwErsBbNsSgBK7ETDBESswYdHiAkFzmwFhGxHyI5OQCxCA8RErMVHR8hJBc5sAQRsyAiIyUkFzkwMRkBNDYzBBcHISIGFREUFjMhMjY9ATcVFAYjISImJTcBJwkBFzcvAeulAW4fuv7JKTs7KQH0KTvI66X%2B1KXrAbShAZxy%2FmsB%2BXFxFVwBkAEspesGCLo7Kf4MKTs7KX3I4aXr62oyAZxx%2FmsB%2BHFxVRwAAAAAAgAAAAAElQRMABwALgBIALIaAAArsBDNsCIvsCfNsAkvsATNsAQQsAbNAbAvL7AA1rANzbEwASsAsSIQERKyFR0kOTk5sQkaERKwJTmxBAYRErAmOTAxGQE0NjMhFwYHIyIGFREUFjMhMjY1NxUUBiMhIiYBPgMfARUJARUiDgXrpQEFAoVVkSk7OykB9Ck7yOul%2FtSl6wGnHmdnXx4dAWj%2BmQcYSENWQzkBkAEspetQIFg7Kf4MKTs7KZk1pevrASEmNBMJAQHRAUQBPtgCDhczQ20AAAAAAgAAAAAEqARMAB0AIwBSALIbAAArsBDNsAkvsATNAbAkL7AA1rANzbANELEUASuwF82xJQErsRQNERKzBx4fIiQXObAXEbAhOQCxCRARErMWHyIjJBc5sAQRsSAhOTkwMRkBNDYzITIXByEiBhURFBYzITI2PQE3FRQGIyEiJgkCJwEn66UBLD1Csv6jKTs7KQH0KTvI66X%2B1KXrAVYBGwI3if5SkgGQASyl6xexOyn%2BDCk7OylFyKml6%2BsBjf7kAjeJ%2FlGTAAABAAAAAQSwBLEAFwBFALISAAArsBYvsA4zsALNsAkyAbAYL7AU1rADMrAQzbAIMrEZASuxEBQRErEGEjk5ALEWEhESsQ0XOTmwAhGxAAw5OTAxEQEVMzUjCQEjFTM1CQE1IxUzCQEzNSMVASzIyAEsASfDyAEs%2FtTIw%2F7Z%2FtTIyAJbASjGyAEs%2FtTIxv7Y%2FtTGyP7UASzIxgAAAAABAMgAAAOEBEwAEwAdALIRAAArsAszAbAUL7AA1rANzbAIMrEVASsAMDE3ETQ2OwEyFhURAREBERQGKwEiJsgdFWQVHQH0%2FgwdFWQVHTID6BUdHRX%2BSwHn%2B7QB6P5KFR0dAAAAAQAAAAAEsARMABcAHwCyFQAAK7ENDzMzAbAYL7AA1rARzbAIMrEZASsAMDE1ETQ2OwEyFhURAREBEQERAREUBisBIiYdFWQVHQH0AfT%2BDP4MHRVkFR0yA%2BgVHR0V%2FksB5%2F4ZAef7tAHo%2FhgB6P5KFR0dAAABAIgAAASwBEwABgAUALIGAAArsAQzAbAHL7EIASsAMDETAREBEQERiAI0AfT%2BDAImAib%2BGQHn%2B7QB6P4YAAAAAQDIAAAETARMAAIAADMJAcgDhPx8AiYCJgAAAAIAyABkA4QD6AAPAB8AADcUFjsBMjY1ETQmKwEiBhUBFBY7ATI2NRE0JisBIgYVyB0VyBUdHRXIFR0BkB0VyBUdHRXIFR2WFR0dFQMgFR0dFfzgFR0dFQMgFR0dFQAAAAEAyABkBEwD6AAPAAA3FBYzITI2NRE0JiMhIgYVyB0VAyAVHR0V%2FOAVHZYUHh4UAyAVHR0VAAAAAQAAAAAEKARMAAYAFACyAAAAK7AFMwGwBy%2BxCAErADAxMREBEQkBEQH0AjT9zARM%2FhkB5%2F3a%2FdoB6AAAAQAAAAAEsARMABcAHwCyAAAAK7EQFjMzAbAYL7AU1rAEMrANzbEZASsAMDExEQERARE0NjsBMhYVERQGKwEiJjURAREB9AH0HRVkFR0dFWQVHf4MBEz%2BGQHn%2FhkBtRUdHRX8GBUdHRUBtv4YAegAAAEBLAAAA%2BgETAATAB0AsgAAACuwDjMBsBQvsBLWsAIysAvNsRUBKwAwMSERARE0NjsBMhYVERQGKwEiJjURASwB9B0VZBUdHRVkFR0ETP4ZAbUVHR0V%2FBgVHR0VAbYAAAIAZADIBLAEKAAPABIAEgCwDS%2BwBM0BsBMvsRQBKwAwMTc1NDYzITIWHQEUBiMhIiYRIQFkHRUD6BUdHRX8GBUdBEz92vpkFR0dFWQVHR0BDwI0AAEAuQAHA%2FkEqQAFAAATATcJASe5AlDw%2Fp8BYfACV%2F2w8AFhAWHwAAABARD%2F0gRSBHQACAAAJQkBNwEXBxUBARABYf6f8QI8FQH9sMIBYQFh8P3FFgEB%2FbEAAAAAAgADAAMErQStAAsAFwBCALAKL7AOzbAVL7AEzQGwGC%2BwAdawDM2wDBCxEQErsAfNsRkBK7ERDBESswQJCgMkFzkAsRUOERKzAQYHACQXOTAxEhASJCAEEhACBCAkEzMVMzUzNSM1IxUjA6ABEwFEAROgoP7t%2Frz%2B7YnIyMjIyMgBtgFEAROgoP7t%2Frz%2B7aCgAVHIyMjIyAACAAMAAwStBK0ACwAPAEkAsAovsAzNsA8vsATNAbAQL7AB1rAMzbAMELENASuwB82xEQErsQ0MERKzBAkKAyQXOQCxDAoRErEHADk5sQQPERKxAQY5OTAxEhASJCAEEhACBCAkEyE1IQOgARMBRAEToKD%2B7f68%2Fu2JAlj9qAG2AUQBE6Cg%2Fu3%2BvP7toKABUcgAAAAAAgADAAMErQStAAsAFwAyALAKL7AEzbAEzQGwGC%2BwAdawB82wB82xGQErsQcBERKxDBA5OQCxBAoRErENFTk5MDESEBIkIAQSEAIEICQTFzcXNyc3JwcnBxcDoAETAUQBE6Cg%2Fu3%2BvP7tU9WNjdWOjtWNjdSNAbYBRAEToKD%2B7f68%2Fu2goAEo1Y6O1Y2N1I2O1Y0AAAIAAwADBK0ErQALABEAMgCwCi%2BwBM2wBM0BsBIvsAHWsAfNsAfNsRMBK7EHARESsQwOOTkAsQQKERKxDQ85OTAxEhASJCAEEhACBCAkEwkBJwcnA6ABEwFEAROgoP7t%2Frz%2B7WsBFAGbr%2BxmAbYBRAEToKD%2B7f68%2Fu2goAGE%2FusBm67sZgAAAAADAAMAAwStBK0ACwA4ADwAbACwCi%2BwOc2wPC%2BwJ82wIS%2BwG82wNC%2BwBM0BsD0vsBLWsB7NsB4QsS4BK7AHzbE%2BASuxHhIRErEhNDk5sC4RtAkEKDo7JBc5ALEnPBESsQcAOTmwIRGwKjmwGxKyDA8uOTk5sDQRsQYBOTkwMRIQEiQgBBIQAgQgJBMzMhYyNjQ%2BBToBMzIWFRQGBw4EFzM%2BBDU0LgMjIg4CEzM1IwOgARMBRAEToKD%2B7f68%2Fu3JjwQPBwYCBQIJBA4EEwMTFggXBQ8nHRgByAUSLSIcIzFEMRsyUUUmiMjIAbYBRAEToKD%2B7f68%2Fu2goAIZAgYMCgcFAwIBFBAWDBABBBcfPSYDCikyWDIzTCgYBhg1YP4uZAADAAMAAwStBK0ACwAVABkAOwCwCi%2BwDM2wFS%2BwDjOwEs2wES%2BwFs2wGS%2BwBM0BsBovsRsBKwCxEhURErEHADk5sRYRERKxBgE5OTAxEhASJCAEEhACBCAkNyE1IxEhFTMVIxMzNSMDoAETAUQBE6Cg%2Fu3%2BvP7t7QGQZP7UZGRkyMgBtgFEAROgoP7t%2Frz%2B7aCgiWQBLGTIAZBkAAACAAAAAASwBLAAGAAvAGkAshQAACuwEi%2BwH82wHDKwAC%2ByDhkhMzMzsAHNsgwjLTIyMgGwMC%2BwFNayBRwpMjIysBPNsgceJzIyMrExASuxExQRErMiIy4vJBc5ALESFBESsBU5sQAfERKwHTmwARGyHigpOTk5MDERNTM%2BATc1MxUeAhczFSMOAQcVIzUuASczHgEXNTMVNjcjNTMuAScVIzUOAQczFcMfh4vINnZrEsvLGbdZyIyIHmAYb0vIlTTJyBllSshLbhjRAfTIfZUayMgUUINFyGaoIcXFG5d9SW0Yzs4wnshKahjMyxdsSMgAAAAAAwAEAAQErASsAAsAEwAfAEYAsAovsA%2FNsBMvsATNAbAgL7AB1rANzbANELERASuwB82xIQErsRENERK1BAkKAxQaJBc5ALETDxEStQEGBwAXHSQXOTAxEhASJCAEEhACBCAkEhAWIDYQJiADNyc3FzcXBxcHJwcEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqpJh4dth4dth4dth4cBtgFEARKgoP7u%2Frz%2B7qCgAl%2F%2BqvPzAVbz%2FduHh22Hh22Hh22HhwAAAAMABAAEBKwErAALABMAGQBGALAKL7APzbATL7AEzQGwGi%2BwAdawDc2wDRCxEQErsAfNsRsBK7ERDREStQQJCgMUGCQXOQCxEw8RErUBBgcAFxkkFzkwMRIQEiQgBBIQAgQgJBIQFiA2ECYgAzcXNxcBBKABEgFEARKgoP7u%2Frz%2B7hbzAVbz8%2F6qa41XzI7%2BpgG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP%2BI41XzY7%2BpwAAAAMABAAEBKwErAALABMAGwBGALAKL7AWzbARL7AEzQGwHC%2BwAdawDM2wDBCxGQErsAfNsR0BK7EZDBEStQQJCgMPFCQXOQCxERYRErUBBgcADhskFzkwMRIQEiQgBBIQAgQgJBMUFwEmIyIGExYzMjY1NCcEoAESAUQBEqCg%2Fu7%2BvP7uFj4COGR0q%2FPNYXCr8zsBtgFEARKgoP7u%2Frz%2B7qCgAbRzZAI3PvP98jvzq3BhAAAAAAEAAABjBLAD6AAGABoAsAUvsALNAbAHL7EIASsAsQIFERKwADkwMREBESERIRECWAJY%2FagCIwHF%2FtT%2B1P7TAAABAAAAYwSwA%2BgABgAaALAAL7ABzQGwBy%2BxCAErALEBABESsAQ5MDEZASERCQERAlgCWP2oAZABLAEs%2Fjv%2BQAEtAAAAAAEAzAAABEoEsAAGAB8AsgUAACsBsAcvsAXWsATNsQgBK7EEBRESsAE5ADAxEwkBIREhEcwBwgG8%2Ftb%2B1AJYAlj9qP2oAlgAAAEAaAAAA%2BYEsAAGAB8AsgYAACsBsAcvsAHWsATNsQgBK7EEARESsAY5ADAxEyERIREhAWgBKAEsASr%2BPwJYAlj9qP2oAAAAAAEAAADHBLAETAANAAA1PgM3EQkBEQ4DBkaJ55wCWP2oX7CkgsiE1a1nCAEP%2Fjv%2BQAEtAiREdQAAAgAAAAAEsASwAAYADQARALIAAAArAbAOL7EPASsAMDExERcBFwEXExcBFxEhF4EBJo7%2B2oHrjgEmgf5wgQGQgQEmjv7agQMJjgEmgQGQgQACACIAIwSOBI4ABgANAAA3ASchEScJAREXARcBFyIBJ4EBkIH%2B2QGogQEnjv7ZgbABJ4H%2BcIL%2B2QI1AZCBASeN%2FtmCAAMAFwAXBJkEmQAPAB8AIwBPALANL7AgzbAjL7AUzbAdL7AFzQGwJC%2BwAdawEM2wEBCxGQErsAnNsSUBK7EZEBEStQUMDQQhIyQXOQCxFCMRErEJADk5sB0RsQgBOTkwMRI0PgIyHgIUDgIiLgEBEx4BOwEyNjcTNiYrASIGEzM1Ixdbm9Xs1ZtbW5vV7NWbAVY6BCMUNhQjBDoEGBTPFRgwyMgB4uzVm1tbm9Xs1ZtbW5sCRv7SFB0dFAEuFB0d%2FcVkAAAFAAAAAASwBLAAJgAqADAANAA7ADQAsicAACuwMTOwKs2wMjIBsDwvsDHWsAUysDTNsAcysT0BK7E0MREStAsMEzU6JBc5ADAxETMVIREzESE1MzUjNjQmLwEuASMiDwEGByYvASYjIgYPAQ4BFBUjEyERIRMiNj8BFxMRIREBNx4DI2QBkMgBkGRvAQMCIgs9JyAd7xYSExXuIR0nPQojAgJvZAGQ%2FnBkAyITEtXbAZD%2Bj8oFDiASAgMgyAEs%2FtTIZAELEwisJzARkA0WGAyQEi4msQgUCgH8fAGQAfRgMC%2B%2F%2FHwBkP5wA4TFDClXOQACAAD%2F6gSvBLAAGwAyABcAsgAAACsBsDMvsCfWsA%2FNsTQBKwAwMRU1Ny4CPgE3PgU3FAIOBC4CIwc2Fjc2JT4DNz4BJyYiBgcOAQ8BBAfYCQgDFTguL2llmonoaCxKaHGDeHtcUw9jEidDNwE4RmFrWykWBAgHFCERI509Pf6PWRaPwTU8gGKCOzxVMy0eOR69%2FszQm1UzCQYTDzd%2FDVNCqCY%2FX4BUMhQJBR0ZM3MgIMXMAAABAG8ADAREBOcASAAjAAGwSS%2BwAdawRc2wRRCxPAErsUoBK7E8RRESsTo2OTkAMDESFBceARcWPgM3PgEnHgEHDgEHDgQeAT4BNz4ENzYCJxYXFicmJy4CNw4EFx4DDgQHBi4CNw4BbwUJRkYfQjo4KA8gDhRPVhEFHxYKCQ8DAwgOGSQYOURrQ0APJqWkFhUnRw8ST1MFMw0qZ0ouDwIMBAgBAQsQGhImOhcHDjQ%2FAblCHjh%2FLRUKJT49HkLtJ1CoZCFJLBMUIA8XCAsBBAYUHD1DbkOsAVNtLFWfBQIHIYbZlQgfZm2nUww7GzQbKBcZEAQKLk1WIC5uAAAD%2F8MAfQTtBDMAIQA%2FAEcAQwCwGi%2BwKc2wOi%2BwCc0BsEgvsDzWsDfNsUkBK7E3PBESQAoJGRoIKSg1PkBDJBc5ALEJOhEStwARJC41PkJHJBc5MDEDNz4GMh4FHwEHDgYiLgUnNx4FMj4ENy4EJxYVFAYiJjU0NwYXFhc3LgEvAT0aBhxGT3N2k5CTdnNPRhwGGhoGHEZPc3aTkJN2c09GHAabB0MtW1R6gHdSWSxICwE3HTo5HjGw%2BLAuZoUxaWklTBMUAlgoCihXVGBHLy9HYFRXKAooKAooV1RgRy8vR2BUVygKKApgPV44KygzXDtoDgFJJUU6GUpZfLCwfFVJV3N8Q2kYYCQkAAAABP%2FDAAAE7QSwABYAIAApAEEAoQCyDwAAK7AOMwGwQi%2BxQwErsDYauj3v790AFSsKsA8uDrAMwAWxDgH5DrANwLAPELMLDwwTK7MQDwwTK7MZDwwTK7MaDwwTK7MkDwwTK7MlDwwTK7IQDwwgiiCKIwYOERI5sBk5sBo5sCQ5sCU5sAs5ALcLDA0QGRokJS4uLi4uLi4uAUAKCwwNDg8QGRokJS4uLi4uLi4uLi6wQBoBADAxAzc%2BBjMyFzczASM3LgQnNxIXNy4BNTQ3BhcWFz8BLgEvAQE3PgY3Jic3HgIfAQcOBD0aBhxGT3N2k0g9PCWU%2FsaUJVKmcmknCpvStyVrjy5mhTFpLxceOg8OASgmFi0vIjATLwFhKydDgS4NGhoHJVplkwJYKAooV1RgRy8RjvtQjxVlZ3k4Dyj%2B5jaNEqduVUlXc3xDL1ccUhsa%2FaeRDyYyJj8YQAJ%2FMJI2j0AUKCgMNGtiZgAAAAP%2FngAABRIErAALABIAFwAAJhYzITI2JwEuAQcBNwkBITUjFREbATUjbxslBQ4lGxX9fhQ4FP1%2B9QG9Ab3%2Bp8hkZMhEREcgBCAiBSD71mQC0%2F0tZGQBkP7UASxkAAAAAAEAZAAVBLAEsAApAEgAsB4vsAnNAbAqL7Al1rAFMrAWzbALMrIWJQors0AWGAkrsiUWCiuzQCUjCSuxKwErsRYlERKxHR45OQCxCR4RErEWJTk5MDETNTQ2NwERNDYyFhURAR4BHQEUBiclERYdARQGLwEjBwYmPQE0NxEFBiZkFg8Ba1h8WAFrDxYYEf6ZZBoTXt5eExpk%2FpkRGAEGKRQxDgFFAVM%2BWFg%2B%2Fq3%2Buw4xFCkUDQz5%2FvlbFkAUEQlOTgkRFEAWWwEH%2BQwNABEAAAAABEwEsAAJABsAHwAjACcAKwAvADMANwA7AD8AQwBHAEsATwBTAFcAADUUFjMhMjY1ESE1ITU0JisBNSMVITUjFSMiBhUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUTNTMVJzUzFSc1MxUdFQPoFR37tARMHRWWZP4MZJYVHWRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZGRkZDIUHh4UAu5klhUdZGRkZB0V%2FEpkZMhkZMhkZP5wZGTIZGTIZGT%2BcGRkyGRkyGRk%2FnBkZMhkZMhkZP5wZGTIZGTIZGQAAAMAAAADBXgErgAKABAAGQBBALAAL7AYM7ABzbATMrALL7AIM7AQzbADMgGwGi%2BxGwErALEBABESsREWOTmwCxGzBw0SFSQXObAQErEGDjk5MDE9ASEBMzUJATUjCQEhFzcnIQE3FzM1CQE1IwEDAljxASz%2B1J%2F9qP6rAQN6jbX%2BqwKmjXqfASz%2B1PHIyAJYxv7Z%2FtTF%2FagCWHqOtP2VjnvG%2Ftn%2B1MUAAAEAAAAABLAETAASABoAsg4AACuwEC%2BwDDOwBM0BsBMvsRQBKwAwMRkBNDYzITIWFREUBiMhAREjIiY7KQPoKTs7Kf2s%2FtBkKTsBkAJYKTs7Kf2oKTv%2B1AEsOwAAAwBkAAAETASwACUAKQAtAGAAsh8AACuwCc2yCR8KK7NACQEJK7AVMrAmL7AqM7AnzbArMgGwLi%2BwANawJjKwA82wKDKwAxCxEgErsCoysBfNsCwysS8BK7EDABESsCQ5sBIRsR4fOTmwFxKwGTkAMDETNSEVFBcWFxYzMj4GJzQ9ASEVFA4FIi4FGQEhESERIRFkASwGEVUnNSU7KR8RCwMCAQEsBhgnTWWdwJ1lTScYBgEsAZABLAJYyPpxIFwZCwsUHCMoLC4YEQj6yCpSfmpxUDMzUHFqflIBVgEs%2FtQBLP7UAAAAAAH%2F4gC4BGgD3gAFAAADFwkBNwEe4wFgAWHi%2Fb4Bm%2BMBYf6f4wJDAAABAEYA2gTMBAAABQAAEwkBJwkBRgJEAkLi%2Fp%2F%2BoAMd%2Fb0CQ%2BP%2BnwFhAAAAAAL%2FOgBkBXYD6AAIABEAKACwBy%2BwBM0BsBIvsAfWsATNsRMBK7EEBxESsAE5ALEEBxESsA45MDEDCQEjESEXIREBFyERIwkBIxHGASsBLMsBgdf84AGU1wF9xgErASvIArwBG%2F7l%2FnDIAlgBLMj%2BcP7lARsCWAAAAAEAEgAABKoEsAAyAEYAsiIAACuwGTOwLM2wLBCwJs2xFR0yMrAvL7AEzbAQL7AJzQGwMy%2BwJNawH82wHxCxHAErsBfNsTQBK7EXHBESsC05ADAxEyY3NjMhNz4BOwEyFhQGKwEDDgIrARUUBiImPQEhFRQGIiY9ASMiJjU0NjMhNyEiJicSBQ8OGQOAJgUbEV4UHh4UNskCCB4SHx0qHf7UHSodMhUdHRUCFzD9hyAtBQOrGBITohEVHSod%2FD8EDRYyFB4eFDIyFB4eFDIeFBUdyCoWAAAAAAIAAAAABLAETAADAA8AIACyAAAAK7ABzbAEL7AFzbANMrAJzQGwEC%2BxEQErADAxMREhEQE1MzQ2MyEyFhUhFQSw%2B1DIOykBLCk7AfQDIPzgA4RkKTs7KWQAAAAAAgABAAAF3QRMAAMAEAAoALIAAAArsAHNsA8vsA3NsAUysAnNAbARL7ESASsAsQEAERKwBDkwMTMBIQkBETM0NjMhMhYVIRUhAQEsBLD%2B1PtQyDspASwpOwH0%2FBgCvP1EAZACWCk7OynIAAAAAQEuAAADggSwAAkAIQCyCQAAKwGwCi%2BwAdawB82xCwErsQcBERKxBAk5OQAwMQEzESMJASMRMwEBLsbGASoBKsbG%2FtYBLAJYASz%2B1P2o%2FtQAAAAAAQAAAS8EsAOCAAkAHACwCC%2BwAs0BsAovsQsBKwCxAggRErEABTk5MDERARUhNQkBNSEVASwCWAEs%2FtT9qAJYASrGxv7W%2FtfFxQAAAAQAAAAABLAEsAAPABkAHQAhAEkAsgwAACuwGs2wHjKwHS%2BwIDOwBc2wEC%2BwFM0BsCIvsBvWsB7NsB4QsR8BK7AJzbIfCQors0AfAAkrsSMBK7EJHxESsBk5ADAxPQE0NjMhMhYdARQGIyEiJhsBPgEzITIWFxMBMzUjFzM1IzspA%2BgpOzsp%2FBgpOx%2BsBSQUAqATJQWs%2Fo9kZMhkZGRkKTs7KWQpOzsBVQLjFictF%2F0k%2FtRkZGQAAAAD%2F5sAZASwBEwACwApADcAJgABsDgvsADWsAbNsAYQsSoBK7AyzbE5ASuxKgYRErEMGjk5ADAxAzU0Nj8BFS4EFzU8Az4FOwElESUjExYOASMiKwEiJicCARE0NjMyFhURFAYjIiZlMhkZBA4iGhbJAQICBAUHBMgCo%2F1dJi8CCgwPBQNTFB0ENwPoHRUUHh4UFR0CWDIYMg0N%2BgIHFRYhVfoCDAQKBAcDBQIB%2BvyuyP7sDAsBHBUBUf7iA1IVHR0V%2FK4UHh4AAAIASgAABGYEsAArADMANQCyLwAAK7AzzbApL7AfM7ADzbAYMrIpAwors0ApJQkrAbA0L7E1ASsAsSkzERKxLDE5OTAxEzQ2OwE3Ez4BNycmNjsBMhYHBhUeARcTFzMyFhUUBgcOBCMiJi8BLgEFHgEyNjcGIkobFBJ1Pw96UxIGEhReFBIGElN6Dz92ERQbGhIIHmRqn0998To6EhoBpww4RjgLMGwBXhUdrQFHTX4UIBMaFRMkARN%2FTf65rR0VFCgHAwsdFRIpFBQHKdwxPT0xBgAAAQAVABUEnAScABcAABMXBzcXNxc3Fyc3JzcnNwcnBycHJxcHFxXpTuAtm5st4E7qtLTqTuAtm5st4E7pswG9LeBO6bOz6U7gLZucLOFO6bS06U7hLJwAAAMAAABkBLAEsAADACIALgAaAAGwLy%2BwKNawFs2xMAErsRYoERKwFDkAMDE1MxEjARQ7ARY7ATI3EzY9ATQmIyE2PQE0JisBIgYPAgYVExE%2FATMVByEVAyMnyMgBLGQ9fA%2F6LiXuHT0n%2FrgcPScyGzAOYJEUZJZkMjIBwvrWiMgCWP3zS2Q5AVgfK2QsUXYHlixRKBzGxBol%2FokBd9XUr%2BF9%2FolkAAAAAAMAAAAABLAETAADACIALgBwALIcAAArsCXNsBUvsAAzsCjNsC4vsAfNsAEysCwvsArNAbAvL7AA1rADzbADELEEASuwI82wIxCxJgErsBjNsBgQsSkBK7ARzbEwASuxJiMRErIIKCw5OTmwGBGwFTmwKRKwKzkAsS4cERKwKjkwMRkBMxE3ETQ7ATY7ATIXExYdARQGIyEWHQEUBisBIiYvAiY3HwEzNSchNQMjByPIZGQ9fA%2F6LiXuHT0n%2FrgcPScyGzAOYJEUZJZkMjIBwvrWiGQBkAJY%2Fah9AZBLZDn%2BqB8rZCxRdgeWLFEoHMbEGiXU1a%2FhfQF3ZAAAAAMACABkBRUEVQADACIAQQB5ALAgL7AkzbAbL7ApzbAxL7AUzbABMrIxFAors0AxAAkrAbBCL7AA1rADzbADELEEASuwI82wIxCxLQErsBjNsi0YCiuzQC08CSuxQwErsS0jERK0DBEbFD8kFzkAsRskERKwIzmwKRGwGDmxFDERErIXPEE5OTkwMTcRMxE3ETQ2PwElNjMyHwEWFRQPASEyFhQGKwEDDgEjISImNxchEz4BOwEyNjU0JiMhKgIuBCcmNTQ%2FAScFCMhkHA4OAWoOCxEMbQ4LVQEuVWttVGuCBxsP%2FqsHpmRkASWDBhsPyxASEhD%2BNwELBAkDBwQEAgUKk1b%2BrcgCWP2oSwINESUKCeYGDHAOFBIOeUyQTv6tFieiG1kBUxUoHhUUHQEBAgMFAwwIDg23U%2BwAAAAD%2F5sAZQSwBFYAHgA4ADwAeQCwGC%2BwJM2wHS%2BwH82wOC%2BwA82wOjKyOAMKK7NAODkJKwGwPS%2BwAdawH82yHwEKK7NAHywJK7AfELEmASuwFM2wFBCxOQErsDzNsT4BK7EmHxEStAcMHAQpJBc5ALEdJBESsCY5sB8RsAA5sQM4ERKyAScsOTk5MDECNDYzBScmNTQ%2FATYzMhcFHgIVERQGIyEiJicDIyInMzIWFxMhNxElBxcWFRQHDgUqASMhAREzEWVsVQEuVQsObQ0QCw4BbQcUIawI%2FqsQGwaCa1QK3g8bBoMBJWv%2BqVeRCgUBBQMHBAkECwH%2BJAPpyAJDkEwBeRAQFQ1xDAbmBA0nEf3yDaEoFQFTZCkU%2Fq1ZAfbtU7gLDwsJAwUDAgEB%2FgwCWP2oAAAAAAMAYQAABEwFDgAbADYAOgBHALI3AAArsDjNAbA7L7AV1rA3MrApzbIpFQors0ApOgkrsDMysCkQsS8BK7AOzbE8ASuxKRURErESNjk5sQ4vERKwETkAMDEbAR4CMyEyNjURNCYnJTU0JiIGFREnJgYPAQYXNxcWNz4FPAE1ETQ2Fh0BFBYXBREHIQM1IRVh5gQNJxECDQ2iKBX%2BrU6QTHkPJQ5wFltTtxYZAwUDAgEBMjIoFQFTWf4JCAJYAs%2F%2BlQYTH6YHAVYPGwaDalRua1X%2B0lQMAQ1uFgtWkhINAQUDBwQJBAsBAcgWEhMVyhAbBoL%2B2mT%2BcMjIAAAAAAMAAgAKA%2B0FGAAdADQAOABFALA1L7A2zQGwOS%2BwCtawNTKwK82yKwoKK7NAKzgJK7AhMrArELEnASuwD82xOgErsSsKERKxDB85ObEPJxESsA05ADAxEwYfAR4BPwEUBhUUFjI2PQElPgE1ETQmIyEiBg8BAxMhFxEFDgEdARQGJjURPAEuAScmDwETNSEVAhAWcA0mD3kBTZBOAVMUKaIN%2FfMRJQoKmuwB91n%2BrBQoMjIDBwYYFriSAlgCSR8Wbg0BC1UzzS5UbG5UaoMGGw8BVgemHA4P%2FoIBU2T%2B2oIGHA%2FKFhISFgHICwcQCAMNEpICccjIAAAAAgAFAAAEsASrAA4AFQA6ALIMAAArsBDNsA8vsAXNAbAWL7AA1rAQzbEXASsAsRAMERKxCRI5ObAPEbEAEzk5sAUSsQgUOTkwMRM0PgIzMgQSEAIEICQCARchBwkBFQVfoN16ogEToKD%2B7f68%2FuygASUCASwCAZL%2BbgJVet2gX6D%2B7P68%2Fu2goAETAQrJwgEmASrFAAIAAAAABKsEqwAQABcAOACyDgAAK7AUzbAWL7AFzQGwGC%2BwFNawCs2xGQErALEUDhESsBI5sBYRsgoAETk5ObAFErAXOTAxETQ%2BAjMyHgIVFAIEICQCNwEnITchNV%2Bg3Xl63aBfoP7s%2Frz%2B7aDIAZICASwC%2FtQCVXrdoF9foN16ov7toKABE6X%2B2sLJxQAAAAIABQAABLAEqwAOABUAPgCyDAAAK7ARzQGwFi%2BwANawEc2wERCxEgErsAnNsRcBK7ERABESsQwPOTmwEhGxBRU5ObAJErELFDk5ADAxEzQ%2BAjMyBBIQAgQgJAIlMxEzETMBBV%2Bg3XqiAROgoP7t%2Frz%2B7KABJ8jIyP7UAlV63aBfoP7s%2Frz%2B7aCgAROl%2FtQBLAGQAAACAAUAAASwBKsADgAVAE0AsgwAACuwFC%2BwBc0BsBYvsADWsBXNsBUQsRIBK7AJzbEXASuxFQARErEMDzk5sBIRsQUQOTmwCRKxCxE5OQCxFAwRErIIABA5OTkwMRM0PgIzMgQSEAIEICQCJQkBIxEjEQVfoN16ogEToKD%2B7f68%2FuygAScBLAEsyMgCVXrdoF%2Bg%2Fuz%2BvP7toKABE6X%2BcAGQASz%2B1AAAAAAEAAUAAASwBKsAEACIAJgAmgB8ALIOAAArsCrNsE8vsIwvAbCbL7AA1rAUzbAUELFYASuwCs2xnAErsRQAERKwEjmwWBFADg4FEyEjJDxKVXiEhYmUJBc5sAoSQAoNIiYwO1pndpmaJBc5ALFPKhEStxYwNjxGSFVXJBc5sIwRQAkKABRYbIWNjpQkFzkwMRM0PgIzMh4CFRQCBCAkAhMGFgcUFgcyHgEXFhceAjcWBhcWFxQOARcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQcGJyY3NjceAhceAR8BNDYnJjY3PgM3JjcyFjI2Ny4DJzYnHgE%2FATYuAScGJw4DBwYmBw4BBwYWBw4BJT4BNxYyPgE3FBYVLgM3MwVfoN16ed2gX6D%2B7f68%2Fuyg%2BQgbBiIBDBYYCBhUFj45HQguAyotBgEFaHUeIiMDDi4NDkYRCT0gLhAyEAQBBikEAggZGhcTEwsGEAYoGwYMKA4OEwQEJQQFCgcYFgYQCB8SFwkKKSM%2FDAsJHzYMCwcvUg8TEg8rGj4IDz0PFT4DAxMBAzEBAwMaAwoRCxIHIgksHCSiAQJVet2gX1%2Bg3Xqi%2Fu2goAETAVkhdxwJRhkLEwQMHggvHgQSShRHCQYTCgwDcx0kPh8JAQcHEAsBAgsLIxcCLwINCAMWJhIdGR0cHhAGAQEHChMlCQgDSRUXKwoOKhQZCRITAwkLFycVIAcnBQ0DBQQkIxYMAwMMEgYKAQMHBgcnDwsXByJxcQwkBwoMEQQYVQECBgQMXwAAAAABAAAAAgSvBIUAFAAAPAE3ASY2NzYXBRc3FgcGJwEGIi8BDwJYIU5gpI7%2B%2FZH7DaR7gv2sDysPb48rEAJXZck2XGWK6H6vXEYv%2FawQEG4AAAYAAABgBLAErAAPAB8ALwAzADcAOwBQALAML7A0zbA3L7AFzbAcL7AwzbAzL7AVzbAsL7A4zbA7L7AlzQGwPC%2BwNdaxMTkyMrAJzbEYKDIysjUJCiuzQDUACSuxECAyMrE9ASsAMDE9ATQ2MyEyFh0BFAYjISImETU0NjMhMhYdARQGIyEiJhE1NDYzITIWHQEUBiMhIiYBITUhEyE1IRMzNSM7KQPoKTs7KfwYKTs7KQPoKTs7KfwYKTs7KQPoKTs7KfwYKTsCWAH0%2FgzIASz%2B1GTIyMRkKTs7KWQpOzsBuWQpOzspZCk7OwG5ZCk7OylkKTs7%2Fplk%2FgxkArxkAAACAGQAAARMBLAAAwAJACUAsggAACuwAC%2BwAc0BsAovsAjWsAfNsQsBKwCxAAgRErAEOTAxEzUhFQUhAREHEWQD6PxKA4T%2BosgETGRkZP4M%2FtTIAfQAAAAAAwAAAGQEsASwAAkAIQAlAGAAsAcvsAHNsAovsB0zsA7NsRgiMjKwJS%2BwE80BsCYvsA%2FWsCLNsCAysg8iCiuzQA8LCSuwADKwIhCxIwErsB4ysBjNshgjCiuzQBgcCSuwAjKxJwErALEOChESsB85MDE9ASEVFAYjISImGQE0NjMhNTQ2OwEyFh0BITIWFREhNSMVETM1IwSwOyn8GCk7OykBLDspyCk7ASwpO%2F4MyMjIyMjIKTs7AVUBkCk7ZCk7OylkOyn%2BcGRkAfRkAAAAAAQAAAAABLAEsAAGAA0AFAAbABQAsgAAACuwEjMBsBwvsR0BKwAwMTERFzcXBxcBNxc3JzchATcXNxEhNwM3JyERJweByI7Igf5wgciOyIH%2BcALZjsiB%2FnCByMiBAZCByAGQgciOyIEDIIHIjsiB%2FJmOyIH%2BcIEC5siB%2FnCByAAABgAAAAAEqASoAAsAFQAfACkAQgBMANIAsgoAACuwD82wHi%2BwSjOwGc2wRTKwKC%2BwOTOwI82wNDKyKCMKK7NAKEEJK7AUL7AEzQGwTS%2BwAdawDM2wDBCxFwErsBvNsBsQsSELK7AmzbAmELEqASuwPs2wPhCxQwErsEjNszdIQwgrsDHNsDEvsDfNsEgQsREBK7AHzbFOASuxJhsRErMKDhQDJBc5sT4qERKxLTw5ObE3MREStQkPEwQvOyQXOQCxHg8RErMHAAwRJBc5sBkRsyotPD4kFzmwKBKxBgE5ObAjEbEvOzk5MDEYARIkIAQSEAIEICQTFBYgNjU0JiAGFjQ2MhYVFAYjIjY0NjMyFhQGIyIXNDY%2FAiY1NDYzMhYUBiMiJwcWFRQGIiYlNDYyFhQGIyImoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqrzbR8uICAXFk0gFxYgIBYXUikfegEJIBcWICAWDg83ETNIMwEeIC4fIBYXIAGyAUQBEqCg%2Fu7%2BvP7uoKABtKzy8qyr8%2FOHLh8gFhcg5CwhIC4guiAxBX4BDg4WISAuIAqRFh0kMzNSFiAfLiAgAAAAAf%2FYADsEugSwAE8AOgCwBS%2BwJ82wIC%2BwFc2wNi%2BwSs0BsFAvsVEBKwCxJwURErA%2FObAgEbQLDxobMSQXObAVErEyMzk5MDECBhceATMyNz4CNzY3AT4BJyYnJiMiBgcBBxcBNjc2MzIXFgcBBiMiJicmPgI3NjcBPgIzMhceAQcGDwEDHwEBPgEnLgEnJiMiBwYHARsaMCN2Rj84IUApJygRAYojGA8bWhQJLkEj%2FnsHRQF5FBMXGyYPECT93TRJN1oJBQ8wJCYYFAFcND1rNhkXX3YIB1v8%2FQdFAgVDOBEQZk9FU2taKEf%2BAAHWvk45QBwQMSorLBEBiiNiL1cRAiEj%2FnQHQwF1FhAXJCck%2Fd00Qj8jPkAkJBUUAVw0NzUEEZtiZVv5%2FwAHPAH%2FQ7RdV4YkITcYR%2F4AAAAAAAIAUAA2BMMEWAAbADUAPQCwMy%2BwLTOwA82wBzIBsDYvsADWsBzNsBwQsSoBK7AKzbE3ASuxKhwRErMDBw8YJBc5ALEDMxESsAU5MDETNDYzMhc2MzIWFRQOAgcGDwEnLgInLgM3FB4BHwEWFzY%2FAT4CNTQmIyIPAScmIyIGUMWEj2Jnj4HCI1dDR8VgERArckZCR0NXI6o9PkAWXWFScQxAQz5gOUo6dnIzSDxjAxCDxYGBxYMuWmxHRr%2BDFxc6gUZBRkdsWi4bVkE%2BFlpvXG8MPkZYHEdhU6uuUGMAAAAAAgA5%2F%2FIEdwS%2BABgAMwAAExQfARYzMjcBNjQvASYnBxcBJzcnJicHBhMUHwEWFzcnARcHFxYXNz4BNTQvASYjIgcBBjlCjURbXUIBG0JCjQgLadT%2Be%2FdfEi4dN0LUQo0HDGnUAYX3XxIvHh0jN0KNQl1fQP7lQgFhX0COQkIBG0K6Qo0JCGnU%2Fnv4XxItODdCAQRdQo0HCmnUAYX3YBExMx0jaitdQo1CQv7lQAAAAAADAMgAAAPoBLAAEQAVAB0ARQCyDwAAK7AZzbAdL7ASzbAVL7AGzQGwHi%2BwANawEs2wEhCxEwErsAvNsAsQsBvNsBsvsR8BK7EbEhESsgYFFjk5OQAwMTcRND4CMh4CFREUBiMhIiY3IREhEhQWMjY0JiLIPGacqppkOjsp%2FagpO2QCWP2oxD1WPT1WZAO5FTIuHh4uMhX8Ryk7O%2FECvPzZVj09Vj0AAAABAAAAAASwBLAAGAARALIAAAArAbAZL7EaASsAMDExATcnIQEnJjQ3NjIXARYUBwYiLwEBEScHAS%2FP0gEsAQsjDw8OKg4BGw8PDioOJP7p1NABfNDUARckDioODw%2F%2B5g8qDg8PI%2F71%2FtTSzwADAScAEgQJBOEAMQA9AEMAlwCwLS%2BwKjOwBM2wPjKyLQQKK7NALSwJK7A7L7AfM7ASzbISOwors0ASEwkrAbBEL7AO1rAAMrAyzbABzbAyELEsASuyBBI6MjIysCvNshQfPjIyMrArELFAASuwJ82wHCDWEbAbzbFFASuxMgERErACObAsEbAJOQCxBC0RErApObA7EbYADhsnOkBDJBc5sBISsBU5MDEBMx4BFxEuASMuBDU0PgE3NTMVHgQXIy4BJxEXHgQVFAYHFSM1JicuARMUHgMXFhcRDgETNjU0JicBJ4sFV0oGEwIuQk4vIViCT2QmRVI8KwOfCDZKQCI8UDcosptkmFUoGagQESoUHAcEPUnqqlhSAbFNYw8BTwEGDhkvOVg3XIdDB05PBBMsP2lCSEsN%2Fs0OBxMsPGU%2Bi6oLTU4RVyhrAh4dLBgVBgcCAQESCDv9KxKFQEcZAAAAAQBkAGYDlAStAEMAjQCwMS%2BwKs2wAC%2BwHjOwAc2wHDKwEy%2BwC82yEwsKK7NAEw4JKwGwRC%2BwB9awOTKwGM2wJDKyGAcKK7NAGB4JK7IHGAors0AHAAkrsBgQsQ8BK7AOzbFFASuxGAcRErMCOEJDJBc5sA8RtQsfICoxMyQXObAOErAsOQCxKjERErEtOTk5sAARsSw8OTkwMRM1MyYnLgE%2BATc2MzIWFSM0LgEjIgYHBhUUHgEXMxUjFgYHBgc%2BATM2FjMyNxcOAiMiJgcOAQ8BJz4FNz4BJ2SmGBQKCQMvLWGmgcqZRFAkJVQUKSEXHvHFCBUVKTojYhUhjCFMPDIpTycqF9IyJ1YXGDcGFQoRDBEJMAwkAlhkMTcaO1ZeKFiydzRLHB0VLDkcUyozZDKCHTs2Cw4BIh6TGRcDQgQEGgwLkQQOBg0LEQo3j0cAAgACAAAErgSwAAYADQAfALIMAAArAbAOL7AM1rALzbEPASuxCwwRErAIOQAwMRMJASMRIxEJAiMRIxECASoBKsbIAZIBKgEqxsgBLP7UASwDhPx8AlgBLP7U%2FHwDhAAABQACAAAD6ASwAAYADAAWAB4AIgCmALIHAAArsAYzsArNsgcAACuwCM2wEy%2BwFM2xAAQyMrANL7AOzbAdL7AfzbIdHwors0AdFwkrsBoysCIvsBjNsAIyAbAjL7AB1rAEzbAEELEIASuxDRcyMrAKzbEdHzIysAoQsRUBK7EbIDIysBDNsQsZMjKzEhAVCCuwE82wEy%2BwEs2xJAErsQQBERKwBjmwCBGwBTkAsRQIERKwEDmwDRGwETkwMRMzETMRMwEhNTMVMxUBNSEVIxUjNTM1AxEhESM1IxU3MzUjAsbIxv7WAZBkyP7UASxjZGPIASxkZAFkZAEsA4T8fP7UyGRkAZBkyGRkZAEsAfT%2BDGRkyMgABQACAAAD6ASwAAYADgAUAB4AIgCgALIGAAArsQcKMzOwDS%2BwH82wIi%2BwCM2wDy%2BwEs2wEM2wGy%2BwHM2wFS%2BwFs2wAjIBsCMvsAHWsATNsAQQsQcBK7EPFTIysA7NsREfMjKwDhCxCwErsR0gMjKwCs2xExcyMrMaCgsIK7AbzbAbL7AazbEkASuxBAERErAGObAHEbAFOQCxIh8RErMBBAUAJBc5sRwQERKwGDmwFRGwGTkwMRMzETMRMwEhESERIzUjFQM1MxUzFQE1IRUjFSM1MzUDMzUjAsbIxv7WAZABLGRkZGTI%2FtQBLGNkY2NkZAEsA4T8fP7UAfT%2BDGRkArzIZGQBkGTIZGRk%2FHzIAAAEAAIAAARMBLAABgAMABIAFgBrALILAAArsAwvsBPNsBYvsAjNsA0vsA7Nsg0OCiuzQA0RCSsBsBcvsBHWsBDNsxMQEQgrsAfNsAcvsA0zsBPNsBAQsRQLK7ALMrAKzbEYASsAsQgLERK0AAIDBgEkFzmxDg0RErEFBDk5MDETCQEjESMRBREhESM1AzUzESMREzM1IwIBKgEqxsgCWAEsZMjIZAFkZAEs%2FtQBLAOE%2FHzIAZD%2BDGQD6GT%2BDAGQ%2FHzIAAAABAACAAAETASwAAYADAASABYAawCyCwAAK7AHL7AIzbASL7ATzbISEwors0ASEAkrsBYvsA7NAbAXL7AL1rAKzbMTCgsIK7ANzbANL7AHM7ATzbAKELEUCyuwETKwEM2xGAErALETCxEStAACAwYBJBc5sQ4WERKxBQQ5OTAxEwkBIxEjESU1MxEjEQMRIREjNSczNSMCASoBKsbIAljIZGQBLGRjZGQBLP7UASwDhPx8ZGT%2BDAGQAZABkP4MZGTIAAAAAAUAAgAABLAEsAAGAAoADgASABYAUgCwBy%2BwCM2wCy%2BwDM2wDy%2BwEM2wEy%2BwFM0BsBcvsA%2FWsgcLEzIyMrASzbAWzbIWDwors0AWCgkrs0AWDgkrsRgBKwCxCwgRErMCAwYAJBc5MDETCQEjESMRBTUhFQE1IRUBNSEVATUzFQIBKgEqxsgB9AH0%2FgwBkP5wASz%2B1MgBLP7UASwDhPx8yMjIASzIyAEsyMgBLMjIAAAABQACAAAEsASwAAYACgAOABIAFgBSALAHL7AIzbALL7AMzbAPL7AQzbATL7AUzQGwFy%2BwC9ayBw8TMjIysA7NsArNsgoLCiuzQAoSCSuzQAoWCSuxGAErALELCBESswIDBgAkFzkwMRMJASMRIxEFNTMVAzUhFQE1IRUBNSEVAgEqASrGyAH0yMgBLP7UAZD%2BcAH0ASz%2B1AEsA4T8fMjIyAEsyMgBLMjIASzIyAAAAAACAAAAAARMBEwADwAfACoAsg0AACuwE82wHC%2BwBM0BsCAvsADWsBDNsBAQsRcBK7AJzbEhASsAMDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFeulASyi7u2j%2FtSl68g7KQH0KTs7Kf4MKTsBkAEspevto%2F7UpevrQSk7OykB9Ck7OykAAAMAAAAABEwETAAPAB8AIgA%2BALINAAArsBPNsBwvsATNAbAjL7AA1rAQzbAQELEXASuwCc2xJAErsRcQERKxICE5OQCxHBMRErEgIjk5MDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFRMtAe6iASyl6%2Bul%2FtSj7cg7KQH0KTs7Kf4MKTvIAU3%2BswGQASyj7eul%2FtSl6%2BtBKTs7KQH0KTs7Kf4M%2BvoAAAAAAwAAAAAETARMAA8AHwAiAD4Asg0AACuwE82wHC%2BwBM0BsCMvsADWsBDNsBAQsRcBK7AJzbEkASuxFxARErEgIjk5ALEcExESsSAhOTkwMRkBNDYzITIWFREUBiMhIiY3FBYzITI2NRE0JiMhIgYVFxsB66UBLKPt66X%2B1KXryDspAfQpOzsp%2FgwpO2T6%2BgGQASyj7e6i%2FtSl6%2BtBKTs7KQH0KTs7KWT%2BswFNAAMAAAAABEwETAAPAB8AIgA%2BALINAAArsBPNsBwvsATNAbAjL7AA1rAQzbAQELEXASuwCc2xJAErsRcQERKxICE5OQCxHBMRErEgIjk5MDEZATQ2MyEyFhURFAYjISImNxQWMyEyNjURNCYjISIGFRMhA%2BulASyl6%2B2j%2FtSl68g7KQH0KTs7Kf4MKTtkAfT6AZABLKXr66X%2B1KLu7T8pOzspAfQpOzsp%2FnABTQACAAAAAAUUBEwABgAaADwAsgcAACuwCM2wAC%2BwAc2wES%2BwEs0BsBsvsAzWsBfNsRwBKwCxCAcRErAFObEBABESsAQ5sBERsAM5MDEZASE1CQE1EzUhMjY1ETQmIyE1ITIWFREUBiMBLAGQ%2FnDIAfQpOzsp%2FgwBkKXr66UBkAEsyP6i%2FqLI%2FnDIOykB9Ck7yOul%2FtSl6wAAAAEA2QACA9YEngAhACgAsA4vsBPNAbAiL7Ac1rAWzbEjASuxFhwRErAZOQCxEw4RErAPOTAxExYzIQIHBh8BMzI3NgAzNicmIwU2Ejc2LwEjIgcOAQAHBtkIFwEumwUFCQkJDgwLAg8BDgkIF%2F7TAZoCAgcJCQ8KBLz%2B80sQAgcT%2FkoUFQsIDw0CaRMREQEEAa8MDwwJEAbT%2FtFWEwAAAAACAAAAAAUUBEwAGAAfAD8AsgMAACuwCM2wCBCwBs2wGS%2BwGs2wEC%2BwEs2wFM0BsCAvsADWsAvNsSEBKwCxGgMRErEdHjk5sBARsBw5MDERFBYzITI3NSEiJjURNDYzITUuAS8BIgYVAREhNQkBNeulASwvNf4MKTs7KQH0DshdXaXrAlgBLAGQ%2FnABkKXrD7k7KQH0KTu5BAcCAuul%2FtQBLMj%2Bov6iyAACAAAAAASwBLAAHQAkAFQAsgMAACuwD82wFi%2BwGs0BsCUvsADWsBLNsBIQsQsBK7AHzbEmASuxCxIRErUYGR4fICQkFzmwBxGwIzkAsRYPERK1CAkeIiMkJBc5sBoRsB85MDERFBYzITI2PQEnBxUUBiMhIiY1ETQ2OwE3JyMiBhUlASchEScB66UBLKPtTno7Kf4MKTs7KZx2SmSl6wHwAWGVAfSV%2FqoBkKXr66ViSXuUKTs7KQH0KTt6TuulCQFWlf4Mlf6fAAMABAAEBKwErAALABMAGwBaALAKL7APzbAbL7AXzbATL7AEzQGwHC%2BwAdawDc2wDRCxFQErsBnNsBkQsREBK7AHzbEdASuxGRURErcECQoODxITAyQXOQCxFxsRErcBBgcMDRARACQXOTAxEhASJCAEEhACBCAkEhAWIDYQJiACNDYyFhQGIgSgARIBRAESoKD%2B7v68%2Fu4W8wFW8%2FP%2BqhdyoHJyoAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP%2BEqBycqByAAAAAwAAAAAETASwAAkAEAAUAC4AsgkAACuwEc2wFC%2BwBc2wCzIBsBUvsBLWsAjNshIICiuzQBIACSuxFgErADAxMRE0NjMhMhYVEQkCIREhEQEzNSMOCwQYCxD8GAG9AcL%2B2f7UAfRkZAETCw4PCv7tAyD%2BDAH0AZD%2BcP12MgAAAAADAAAAAARMBLAACQAQABQAKwCyCQAAK7ARzbAUL7AFzQGwFS%2BwEtawCM2yEggKK7NAEgAJK7EWASsAMDExETQ2MyEyFhURASERIREhCQEzNSMOCwQYCxD8GAEsASwBJ%2F5DAV5kZAETCw4PCv7tArz%2B1AEsAfT75jIAAAAAAwAAAAAETAR%2FAAkADwATAC4AsgkAACuwEM2wEy%2BwBc0BsBQvsBHWsAwysAjNshEICiuzQBEACSuxFQErADAxMRE0NjMhMhYVEQkCJwEnATM1Iw4LBBgLEPwYATECVJr%2BRpYChWRkARMLDg8K%2Fu0Cwf7PAlSb%2FkaX%2FToyAAQAAAAABEwEsAAJAA0AFAAYACsAsgkAACuwFc2wGC%2BwBc0BsBkvsBbWsAjNshYICiuzQBYACSuxGgErADAxMRE0NjMhMhYVEQEXNycDJRMHJwcXATM1Iw4LBBgLEPwYYdRhcAK5A%2FqV1JUBzmRkARMLDg8K%2Fu0D3GLVYfzgAQK775XUlf4NMgAEAAAAAARMBLAACQANABQAGAAuALIJAAArsBXNsBgvsAXNAbAZL7AW1rATMrAIzbIWCAors0AWAAkrsRoBKwAwMTERNDYzITIWFREBFzcnExcHFzcXCwEzNSMOCwQYCxD8fNRi1QPvldSV%2BQFjZGQBEwsODwr%2B7QJk1GHUAev6ldSU7QK5%2B%2BkyAAAAAAIAF%2F%2F%2FBLAErwAFAAgAFwCyBAAAKwGwCS%2BwBdawBs2xCgErADAxEwERCQERFwkBFwSZ%2FiX%2Byk8CoP1gAZ8DEPvJARD%2BdwGgzQOq%2FTgAAAAAAgAAAGQETASwABUAGQBNALARL7AGzbIRBgors0AREwkrsA4ysgYRCiuzQAYECSuwCDIBsBovsADWsBLNsAbNsBIQsQ8BK7ALzbEbASuxDwYRErIJFhc5OTkAMDE1ETQ2OwERIREzFxEUBisBESERIyImATM1Ix0V%2BgH0ZMgeFJb9RJYVHQJYZGSWA%2BgUHv7UASzI%2FK4VHQGQ%2FnAdA2fIAAMAAAA%2BBRQEsAATABkAHQBAALAPL7AGzbIPBgors0APEQkrsgYPCiuzQAYECSuwCDIBsB4vsADWsBDNsAbNsR8BKwCxBg8RErILFxg5OTkwMTURNDY7AREhETMXFQEnByERIyImJTcXARcBAzM1Ix0V%2BgH0ZMj%2B7Hh%2B%2FoaWFR0CRXt4AWF7%2FiXhZGSWA%2BgUHv7UASzI2v7teH%2F%2BcB2xe3gBYHv%2BJAOqyAAAAAADAAAABgUOBLAAEwAXACMAFQABsCQvsADWsBDNsAbNsSUBKwAwMTURNDY7AREhETMXEQcnASERIyImATM1IxM3JzcXNxcHFwcnBx0V%2BgH0ZMhnqv7W%2FreWFR0CWGRkZKqqf6qqf6qqf6qqlgPoFB7%2B1AEsyP7zZ6r%2B1v5wHQNnyPvVqqp%2FqqqAqap%2FqqoAAAADAAAAAASwBLAAEgAZAB0AbACwDi%2BwBs2yDgYKK7NADhAJK7AGELAMzbAaL7AbzbEECDIyAbAeL7AA1rAPzbIPAAors0APCwkrsAAQsAbNsA8QsRoBK7AdzbAMMrEfASuxGgYRErATOQCxDA4RErEXGDk5sRoGERKwCjkwMTURNDY7AREhETMXESEVIREjIiYlCQEjESMRAzUzFR0V%2BgH0ZMj%2BcP4MlhUdAlgBLAEsyMjIZJYD6BQe%2FtQBLMj%2B1Mj%2BcB2r%2FtQBLAEs%2FtQCvMjIAAAAAAMAAAAABLAEsAASABkAHQBbALAOL7AGzbIOBgors0AOEAkrsBovsBvNsQQIMjIBsB4vsADWsA%2FNsAbNsA8QsRoBK7AdzbEfASuxGgYRErATObAdEbANOQCxBg4RErILDBk5OTmwGhGwCjkwMTURNDY7AREhETMXEScBIREjIiYlMxEzETMJATUzFR0V%2BgH0ZMjI%2Ftb%2BbpYVHQJYyMjI%2FtT%2B1GSWA%2BgUHv7UASzI%2Fm7I%2Ftb%2BcB2r%2FtQBLAEsAZDIyAAAAAADAAAAyASwBEwACQATABcAADUUFjMhMjY1ESE1ITU0JiMhIgYVEzUhFR0VBEwVHftQBLAdFfu0FR1kAZD6FR0dFQImZJYVHR0V%2FRLIyAAAAAYAAABmBLAErgAGAAoADgAVABkAHQCBALAFL7EWGjMzsALNsRccMjKwBy%2BxCw8zM7AIzbEMEDIyAbAeL7AH1rAKzbAKELELASuwDs2wDhCxFgErsBnNsR8BK7ELChESswIFBgEkFzmxFg4RErMEAw8QJBc5sBkRsxIUFREkFzkAsQIFERKwADmwBxGxARQ5ObAIErATOTAxEQEVIRUhFQM1MxUzNTMVMzUhNQkBNQM1MxU7ATUjASwBkP5wyGRkZGQBkAEs%2FtRkZGRkZAGQASrGyMYCusjIyMjIxv7W%2FtbG%2FgzIyMgAAAACAGQAAASwBLAAGAAvADoAshQAACsBsDAvsADWsATNsAQQsRcLK7AQzbMJEBcIK7AFzbAFL7AJzbAQELEKCyuwDs2xMQErADAxExE3FxEzETcXETMRNxcRBxEUBisBIiY1ESUUHgIfAREUFjsBMjY1ETQmBwUOARVkMjJkMjJkMjJkHRXIFR0CWBUdHQsKHRXIFR0kGv7sGSUCvAGQZGT%2B1AEsZGT%2B1AEsZGT%2BcMv%2BQRUdHRUBv2QdNSEYBgX%2BcxUdHRUEUh8TEXQRRR8AAAAAAQBkAAAEsARMADMAOACyAAAAK7AMM7AzzbICCw4yMjKwKC%2ByGBwlMzMzsCfNsBoyAbA0L7E1ASsAsSgzERKxBiA5OTAxMyE1IiY1ESERFAYjFSE1Ii4DNRE0Nj8BNSEVMhYVESERNDYzNSEVMh4DFREUBg8BZAGQSxkB9BlLAZAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGTgMJgGK%2FnYmDDg4AQUJFQ4DeBYZAQI4OAwm%2FnYBiiYMODgBBQkVDvyIFhkBAgAGAAAAAARMBEwADwAYABwAIAAqAC4AMgCyGAAAK7AQzbAWL7ASzbISFgors0ASFAkrAbAvL7EwASsAsRIQERKzBAMZHCQXOTAxERQWMyEyNjURNCYjISIGFRMhNzMlESEHIQM1IRUBNSEVASEXFSUzNSEnIQE1JRU7KQEsKTs7Kf7UKTtkAZDIaQEn%2Fldk%2FolkASz%2B1AEs%2FtQBkMgBJ2n%2BV2T%2BiQH0AZABLCk7OykB9Ck7Oyn9RMhi%2FtZkASzIyAEsyMgBkMhiYshk%2FUajhaMAAQAQABAEnwSfACAAABIeAxceAzM%2FATYmLwEmBg8BLgEnNz4BLwEuAQ8BEAEfPpJmZ9GXex8foxEHE8ATNBB2jvxldhEGDosOLRKiA%2BQriY%2FUZmeSPSEBohIuDogOBBF2ZfyOdhExFMITBhGiAAAAAAIAAAAABLAETAAdAEAALwCyGwAAK7AMzbAoL7A4zQGwQS%2BxQgErALEMGxESsSAvOTmwKBGzJikyQCQXOTAxPQE0NjcBNTQ%2BAzIeAh8BFQEeAR0BFAYjISImERQWPwE%2BAT0BNiAXFRQWHwEWNj0BLgQjIg4EDwEVDgFtAhYmUnBSJhYBAQFtDhUdFfu0FB4dFMoUHY0BPo0dFMoUHQYaZHzaflymdWQ%2FLAkJMtQUMw4BLzIEDSAZFRQbHAoKMv7RDjMU1BUdHQKrFRkEIQQiFZIYGJIVIgQhBBkVyAgZQTEpFSEoKCELCgACAGQAAASwBEwAAwAZABQAsgAAACuwAc0BsBovsRsBKwAwMTM1IRUlISc1NxEjFSM1IxUjNSMVIzUjERcVZARM%2B%2F8Dtn1kZGTIZMhkZGRkZMiW%2BmQBkMjIyMjIyP5wZPoAAAAAAwBkAAAEsARMAAkAEwAdACQAsgoAACuwFDMBsB4vsArWsBPNsBMQsRQBK7AdzbEfASsAMDEzIRE0JisBIgYVARE0NjsBMhYVETMRNDY7ATIWFRFkASw7KWQpOwGQOylkKTtkOylkKTsBkCk7Oyn%2BcAPoKTs7KfwYArwpOzsp%2FUQAAAAABf%2BcAAAEsARMAA8AEwAfACcAKwBIALINAAArsBDNsBMvsATNAbAsL7AA1rAQzbAQELERASuwCc2xLQErsREQERK1FBUgIygqJBc5ALETEBEStRQaICYoKSQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNTM1IREzFSMFMzUzESM1IxMRMxFksHwCvHywsHz9RHywyAOE%2FHxkASzIyP7UyMgBkMhkZMhkZAEsAfR8sLB8%2Fgx8sLAYArz9qAEsZGT%2B1GRkZAEsZP5wASz%2B1AAAAAAF%2F5wAAASwBEwADwATAB8AJwArAEgAsg0AACuwEM2wEy%2BwBM0BsCwvsADWsBDNsBAQsREBK7AJzbEtASuxERARErUUGSAjKCokFzkAsRMQERK1FBogJigpJBc5MDEDETQ2MyEyFhURFAYjISImNyERIRMzNTMVMxEjFSM1IwEzNTMRIzUjExEzEWSwfAK8fLCwfP1EfLDIA4T8fGRkZGRkZGQBkMhkZMhkZAEsAfR8sLB8%2Fgx8sLAYArz9qMjIAfTIyP4MZAEsZP5wASz%2B1AAABP%2BcAAAEsARMAA8AEwAbACMARACyDQAAK7AQzbATL7AEzQGwJC%2BwANawEM2wEBCxEQErsAnNsSUBK7EREBESsxQVHB0kFzkAsRMQERKzFBocIiQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETITUjETM1IQEhNSMRMzUhZLB8Arx8sLB8%2FUR8sMgDhPx8ZAEsyMj%2B1AGQASzIyP7UASwB9HywsHz%2BDHywsBgCvP2oZAEsZP4MZAEsZAAAAAAE%2F5wAAASwBEwADwATABYAGQBEALINAAArsBDNsBMvsATNAbAaL7AA1rAQzbAQELERASuwCc2xGwErsREQERKzFBUXGCQXOQCxExARErMVFhcZJBc5MDEDETQ2MyEyFhURFAYjISImNyERIRMFERMtAWSwfAK8fLCwfP1EfLDIA4T8fGQBLGQBLP7UASwB9HywsHz%2BDHywsBgCvP6ilgEs%2FtSWlgAAAAAF%2F5wAAASwBEwADwATABcAHwAnAFoAsg0AACuwEM2wFC%2BwGM2wIzKwHy%2BwJTOwFc2wEy%2BwBM0BsCgvsADWsBDNsBAQsRQBK7AYzbAYELEcASuwIc2wIRCxJAErsBfNsBcQsREBK7AJzbEpASsAMDEDETQ2MyEyFhURFAYjISImNyERIRMRIRElMzI2NCYrAQQUFjsBESMiZLB8Arx8sLB8%2FUR8sMgDhPx8ZAK8%2FaiCKTY5JoIBEzYpgoImASwB9HywsHz%2BDHywsBgCvP2oAfT%2BDGRUglZWglQBLAAABf%2BcAAAEsARMAA8AEwAfACMAKQBIALINAAArsBDNsBMvsATNAbAqL7AA1rAQzbAQELERASuwCc2xKwErsREQERK1FBUgISQnJBc5ALETEBEStRQaICImKCQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNTM1IREzFSMFMzUjEzMRMxEjZLB8Arx8sLB8%2FUR8sMgDhPx8ZAEsyMj%2B1MjIAZFkZGNkZMgBLAH0fLCwfP4MfLCwGAK8%2FagBLGRk%2FtRkZGQBLP5wAfQABv%2BcAAAEsARMAA8AEwAZAB0AIQAnAEwAsg0AACuwEM2wEy%2BwBM0BsCgvsADWsBDNsBAQsREBK7AJzbEpASuxERARErcUFRocHh8iJSQXOQCxExARErcUGBobHiAkJiQXOTAxAxE0NjMhMhYVERQGIyEiJjchESETIREjNSMTNTMVFzM1IxMzETMRI2SwfAK8fLCwfP1EfLDIA4T8fGQBLMhkZWTIZGRjZGTIASwB9HywsHz%2BDHywsBgCvP2oAZBk%2FnDIyGRkASz%2BcAH0AAAAAAb%2FnAAABLAETAAPABMAHQAhACUAKwCbALINAAArsBDNsB4vsSIpMzOwH82wIzKwGi%2BwG82wFC%2BwJjOwFc2wJzKwEy%2BwBM0BsCwvsADWsBDNsBAQsR4BK7AUMrAhzbAhELEcASuwF82zGRccCCuwGs2wGi%2BwGc2wFxCxIgErsCXNsCUQsSoBK7ApzbApELAmzbAmL7ApELERASuwCc2xLQErALEbHxESsBc5sBQRsBg5MDEDETQ2MyEyFhURFAYjISImNyERIRc1IREjFSM1MzUDNTMVITUzFQM1MxEjEWSwfAK8fLCwfP1EfLDIA4T8fGQBLGNkY8dkASxkAchkASwB9HywsHz%2BDHywsBgCvMhk%2FtRkZMj%2BcGRkZGQBkGT%2BDAGQAAADAAQABASsBKwACwATAB0AeQCwCi%2BwD82wHS%2BwGs2wGS%2BwFs2wEy%2BwBM0BsB4vsAHWsA3NsA0QsRQBK7AazbAaELERASuwB82xHwErsRoUERK1Cg4TAxYdJBc5sBERtQkPBBIXGyQXOQCxGh0RErQHDRAAFCQXObAZEbAVObAWErMGDBEBJBc5MDESEBIkIAQSEAIEICQSEBYgNhAmIAM1NyEVIRUhFSEEoAESAUQBEqCg%2Fu7%2BvP7uFvMBVvPz%2FqodZAEs%2FtQBLP7UAbYBRAESoKD%2B7v68%2Fu6goAJf%2Fqrz8wFW8%2F3%2ByGRkyGQAAAQAAAAEBKgErAALABMAIAAkAKAAsAovsA%2FNsCEvsBQzsCLNsBsvsBXNsBMvsATNAbAlL7AB1rANzbANELEUASuwIM2wGzKyIBQKK7NAIB4JK7AgELEhASuwGTKwJM2wFzKwJBCxEQErsAfNsSYBK7EgFBESswoOEwMkFzmwIRGwFjmwJBKzCQ8SBCQXOQCxIiERErQHDRAAHiQXObAbEbIXGB85OTmwFRKzBgwRASQXOTAxGAESJCAEEhACBCAkEhAWIDYQJiADESEXFSM1IxUzFSMVMzUzFaABEgFEARKgoP7u%2Frz%2B7hbzAVbz8%2F6qGQEsZGTIyMjIZAG2AUQBEqCg%2Fu7%2BvP7uoKACX%2F6q8%2FMBVvP9mgGQZGRkZGRkZGQA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%2F4WwAY0AS7AIUFixAQGOWbFGBitYIbAQWUuwFFJYIbCAWR2wBitcWFmwFCsAAAABUuZYrgAA%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIC8%2BCjxnbHlwaCAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZDsiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDQiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzNCIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI2IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTciIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYzIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMjEyOyIgZD0iTTIwMCA0MDBoOTAwdjMwMGgtOTAwdi0zMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjVmYzsiIGhvcml6LWFkdi14PSI1MDAiIGQ9Ik0wIDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjYwMTsiIGQ9Ik0tMTQgNDk0cTAgLTgwIDU2LjUgLTEzN3QxMzUuNSAtNTdoNzUwcTEyMCAwIDIwNSA4Ni41dDg1IDIwNy41dC04NSAyMDd0LTIwNSA4NnEtNDYgMCAtOTAgLTE0cS00NCA5NyAtMTM0LjUgMTU2LjV0LTIwMC41IDU5LjVxLTE1MiAwIC0yNjAgLTEwNy41dC0xMDggLTI2MC41cTAgLTI1IDIgLTM3cS02NiAtMTQgLTEwOC41IC02Ny41dC00Mi41IC0xMjIuNXoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDEzOyIgZD0iTTI5IDQ1NGw0MTkgLTQyMGw4MTggODIwbC0yMTIgMjEybC02MDcgLTYwN2wtMjA2IDIwN3oiIC8%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI3OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzEgMTIxLjUgLTI5Mi41dDI5Mi41IC0xMjEuNXQyOTIuNSAxMjEuNXQxMjEuNSAyOTIuNXQtMTIxLjUgMjkyLjV0LTI5Mi41IDEyMS41dC0yOTIuNSAtMTIxLjV0LTEyMS41IC0yOTIuNXpNMzUwIDYwMGwyNTAgMzAwIGwyNTAgLTMwMGgtMTUwdi0zMDBoLTIwMHYzMDBoLTE1MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI4OyIgZD0iTTAgMjV2NDc1bDIwMCA3MDBoODAwbDE5OSAtNzAwbDEgLTQ3NXEwIC0xMSAtNyAtMTh0LTE4IC03aC0xMTUwcS0xMSAwIC0xOCA3dC03IDE4ek0yMDAgNTAwaDIwMGw1MCAtMjAwaDMwMGw1MCAyMDBoMjAwbC05NyA1MDBoLTYwNnoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI5OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzIgMTIxLjUgLTI5M3QyOTIuNSAtMTIxdDI5Mi41IDEyMXQxMjEuNSAyOTNxMCAxNzEgLTEyMS41IDI5Mi41dC0yOTIuNSAxMjEuNXQtMjkyLjUgLTEyMS41dC0xMjEuNSAtMjkyLjV6TTUwMCAzOTd2NDAxIGwyOTcgLTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMwOyIgZD0iTTIzIDYwMHEwIC0xMTggNDUuNSAtMjI0LjV0MTIzIC0xODR0MTg0IC0xMjN0MjI0LjUgLTQ1LjV0MjI0LjUgNDUuNXQxODQgMTIzdDEyMyAxODR0NDUuNSAyMjQuNWgtMTUwcTAgLTE3NyAtMTI1IC0zMDJ0LTMwMiAtMTI1dC0zMDIgMTI1dC0xMjUgMzAydDEyNSAzMDJ0MzAyIDEyNXExMzYgMCAyNDYgLTgxbC0xNDYgLTE0Nmg0MDB2NDAwbC0xNDUgLTE0NXEtMTU3IDEyMiAtMzU1IDEyMnEtMTE4IDAgLTIyNC41IC00NS41dC0xODQgLTEyMyB0LTEyMyAtMTg0dC00NS41IC0yMjQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMxOyIgZD0iTTIzIDYwMHEwIDExOCA0NS41IDIyNC41dDEyMyAxODR0MTg0IDEyM3QyMjQuNSA0NS41cTE5OCAwIDM1NSAtMTIybDE0NSAxNDV2LTQwMGgtNDAwbDE0NyAxNDdxLTExMiA4MCAtMjQ3IDgwcS0xNzcgMCAtMzAyIC0xMjV0LTEyNSAtMzAyaC0xNTB6TTEwMCAwdjQwMGg0MDBsLTE0NyAtMTQ3cTExMiAtODAgMjQ3IC04MHExNzcgMCAzMDIgMTI1dDEyNSAzMDJoMTUwcTAgLTExOCAtNDUuNSAtMjI0LjV0LTEyMyAtMTg0dC0xODQgLTEyMyB0LTIyNC41IC00NS41cS0xOTggMCAtMzU1IDEyMnoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDM0OyIgZD0iTTEwMCAwdjExMDBoMTAwdi0xMTAwaC0xMDB6TTMwMCA0MDBxNjAgNjAgMTI3LjUgODR0MTI3LjUgMTcuNXQxMjIgLTIzdDExOSAtMzB0MTEwIC0xMXQxMDMgNDJ0OTEgMTIwLjV2NTAwcS00MCAtODEgLTEwMS41IC0xMTUuNXQtMTI3LjUgLTI5LjV0LTEzOCAyNXQtMTM5LjUgNDB0LTEyNS41IDI1dC0xMDMgLTI5LjV0LTY1IC0xMTUuNXYtNTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMzU7IiBkPSJNMCAyNzVxMCAtMTEgNyAtMTh0MTggLTdoNTBxMTEgMCAxOCA3dDcgMTh2MzAwcTAgMTI3IDcwLjUgMjMxLjV0MTg0LjUgMTYxLjV0MjQ1IDU3dDI0NSAtNTd0MTg0LjUgLTE2MS41dDcwLjUgLTIzMS41di0zMDBxMCAtMTEgNyAtMTh0MTggLTdoNTBxMTEgMCAxOCA3dDcgMTh2MzAwcTAgMTE2IC00OS41IDIyN3QtMTMxIDE5Mi41dC0xOTIuNSAxMzF0LTIyNyA0OS41dC0yMjcgLTQ5LjV0LTE5Mi41IC0xMzF0LTEzMSAtMTkyLjUgdC00OS41IC0yMjd2LTMwMHpNMjAwIDIwdjQ2MHEwIDggNiAxNHQxNCA2aDE2MHE4IDAgMTQgLTZ0NiAtMTR2LTQ2MHEwIC04IC02IC0xNHQtMTQgLTZoLTE2MHEtOCAwIC0xNCA2dC02IDE0ek04MDAgMjB2NDYwcTAgOCA2IDE0dDE0IDZoMTYwcTggMCAxNCAtNnQ2IC0xNHYtNDYwcTAgLTggLTYgLTE0dC0xNCAtNmgtMTYwcS04IDAgLTE0IDZ0LTYgMTR6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzNjsiIGQ9Ik0wIDQwMGgzMDBsMzAwIC0yMDB2ODAwbC0zMDAgLTIwMGgtMzAwdi00MDB6TTY4OCA0NTlsMTQxIDE0MWwtMTQxIDE0MWw3MSA3MWwxNDEgLTE0MWwxNDEgMTQxbDcxIC03MWwtMTQxIC0xNDFsMTQxIC0xNDFsLTcxIC03MWwtMTQxIDE0MWwtMTQxIC0xNDF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzNzsiIGQ9Ik0wIDQwMGgzMDBsMzAwIC0yMDB2ODAwbC0zMDAgLTIwMGgtMzAwdi00MDB6TTcwMCA4NTdsNjkgNTNxMTExIC0xMzUgMTExIC0zMTBxMCAtMTY5IC0xMDYgLTMwMmwtNjcgNTRxODYgMTEwIDg2IDI0OHEwIDE0NiAtOTMgMjU3eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwMzg7IiBkPSJNMCA0MDF2NDAwaDMwMGwzMDAgMjAwdi04MDBsLTMwMCAyMDBoLTMwMHpNNzAyIDg1OGw2OSA1M3ExMTEgLTEzNSAxMTEgLTMxMHEwIC0xNzAgLTEwNiAtMzAzbC02NyA1NXE4NiAxMTAgODYgMjQ4cTAgMTQ1IC05MyAyNTd6TTg4OSA5NTFsNyAtOHExMjMgLTE1MSAxMjMgLTM0NHEwIC0xODkgLTExOSAtMzM5bC03IC04bDgxIC02Nmw2IDhxMTQyIDE3OCAxNDIgNDA1cTAgMjMwIC0xNDQgNDA4bC02IDh6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTAzOTsiIGQ9Ik0wIDBoNTAwdjUwMGgtMjAwdjEwMGgtMTAwdi0xMDBoLTIwMHYtNTAwek0wIDYwMGgxMDB2MTAwaDQwMHYxMDBoMTAwdjEwMGgtMTAwdjMwMGgtNTAwdi02MDB6TTEwMCAxMDB2MzAwaDMwMHYtMzAwaC0zMDB6TTEwMCA4MDB2MzAwaDMwMHYtMzAwaC0zMDB6TTIwMCAyMDB2MTAwaDEwMHYtMTAwaC0xMDB6TTIwMCA5MDBoMTAwdjEwMGgtMTAwdi0xMDB6TTUwMCA1MDB2MTAwaDMwMHYtMzAwaDIwMHYtMTAwaC0xMDB2LTEwMGgtMjAwdjEwMCBoLTEwMHYxMDBoMTAwdjIwMGgtMjAwek02MDAgMHYxMDBoMTAwdi0xMDBoLTEwMHpNNjAwIDEwMDBoMTAwdi0zMDBoMjAwdi0zMDBoMzAwdjIwMGgtMjAwdjEwMGgyMDB2NTAwaC02MDB2LTIwMHpNODAwIDgwMHYzMDBoMzAwdi0zMDBoLTMwMHpNOTAwIDB2MTAwaDMwMHYtMTAwaC0zMDB6TTkwMCA5MDB2MTAwaDEwMHYtMTAwaC0xMDB6TTExMDAgMjAwdjEwMGgxMDB2LTEwMGgtMTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwNDA7IiBkPSJNMCAyMDBoMTAwdjEwMDBoLTEwMHYtMTAwMHpNMTAwIDB2MTAwaDMwMHYtMTAwaC0zMDB6TTIwMCAyMDB2MTAwMGgxMDB2LTEwMDBoLTEwMHpNNTAwIDB2OTFoMTAwdi05MWgtMTAwek01MDAgMjAwdjEwMDBoMjAwdi0xMDAwaC0yMDB6TTcwMCAwdjkxaDEwMHYtOTFoLTEwMHpNODAwIDIwMHYxMDAwaDEwMHYtMTAwMGgtMTAwek05MDAgMHY5MWgyMDB2LTkxaC0yMDB6TTEwMDAgMjAwdjEwMDBoMjAwdi0xMDAwaC0yMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA0MTsiIGQ9Ik0wIDcwMGwxIDQ3NXEwIDEwIDcuNSAxNy41dDE3LjUgNy41aDQ3NGw3MDAgLTcwMGwtNTAwIC01MDB6TTE0OCA5NTNxMCAtNDIgMjkgLTcxcTMwIC0zMCA3MS41IC0zMHQ3MS41IDMwcTI5IDI5IDI5IDcxdC0yOSA3MXEtMzAgMzAgLTcxLjUgMzB0LTcxLjUgLTMwcS0yOSAtMjkgLTI5IC03MXoiIC8%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDU5OyIgZD0iTTAgMjc1djY1MHEwIDMxIDIyIDUzdDUzIDIyaDc1MHEzMSAwIDUzIC0yMnQyMiAtNTN2LTY1MHEwIC0zMSAtMjIgLTUzdC01MyAtMjJoLTc1MHEtMzEgMCAtNTMgMjJ0LTIyIDUzek05MDAgNjAwbDMwMCAzMDB2LTYwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDYwOyIgZD0iTTAgNDR2MTAxMnEwIDE4IDEzIDMxdDMxIDEzaDExMTJxMTkgMCAzMS41IC0xM3QxMi41IC0zMXYtMTAxMnEwIC0xOCAtMTIuNSAtMzF0LTMxLjUgLTEzaC0xMTEycS0xOCAwIC0zMSAxM3QtMTMgMzF6TTEwMCAyNjNsMjQ3IDE4MmwyOTggLTEzMWwtNzQgMTU2bDI5MyAzMThsMjM2IC0yODh2NTAwaC0xMDAwdi03Mzd6TTIwOCA3NTBxMCA1NiAzOSA5NXQ5NSAzOXQ5NSAtMzl0MzkgLTk1dC0zOSAtOTV0LTk1IC0zOXQtOTUgMzl0LTM5IDk1eiAiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEzNiA1NTBsNTY0IDU1MHYtNDg3bDUwMCA0ODd2LTExMDBsLTUwMCA0ODh2LTQ4OHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc4OyIgZD0iTTEwMCAyNTB2MTAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWgxMDAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di0xMDBxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTEwMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41ek0xMDAgNTAwaDExMDBsLTU1MCA1NjR6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3OTsiIGQ9Ik0xODUgNTk5bDU5MiAtNTkybDI0MCAyNDBsLTM1MyAzNTNsMzUzIDM1M2wtMjQwIDI0MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDgwOyIgZD0iTTI3MiAxOTRsMzUzIDM1M2wtMzUzIDM1M2wyNDEgMjQwbDU3MiAtNTcxbDIxIC0yMmwtMSAtMXYtMWwtNTkyIC01OTF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA4MTsiIGQ9Ik0zIDYwMHEwIDE2MiA4MCAyOTkuNXQyMTcuNSAyMTcuNXQyOTkuNSA4MHQyOTkuNSAtODB0MjE3LjUgLTIxNy41dDgwIC0yOTkuNXQtODAgLTI5OS41dC0yMTcuNSAtMjE3LjV0LTI5OS41IC04MHQtMjk5LjUgODB0LTIxNy41IDIxNy41dC04MCAyOTkuNXpNMzAwIDUwMGgyMDB2LTIwMGgyMDB2MjAwaDIwMHYyMDBoLTIwMHYyMDBoLTIwMHYtMjAwaC0yMDB2LTIwMHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDg5OyIgZD0iTTQgNjAwcTAgMTYyIDgwIDI5OXQyMTcgMjE3dDI5OSA4MHQyOTkgLTgwdDIxNyAtMjE3dDgwIC0yOTl0LTgwIC0yOTl0LTIxNyAtMjE3dC0yOTkgLTgwdC0yOTkgODB0LTIxNyAyMTd0LTgwIDI5OXpNMTg2IDYwMHEwIC0xNzEgMTIxLjUgLTI5Mi41dDI5Mi41IC0xMjEuNXQyOTIuNSAxMjEuNXQxMjEuNSAyOTIuNXQtMTIxLjUgMjkyLjV0LTI5Mi41IDEyMS41dC0yOTIuNSAtMTIxLjV0LTEyMS41IC0yOTIuNXpNMzIyIDUzN2wxNDEgMTQxIGw4NyAtODdsMjA0IDIwNWwxNDIgLTE0MmwtMzQ2IC0zNDV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MDsiIGQ9Ik00IDYwMHEwIDE2MiA4MCAyOTl0MjE3IDIxN3QyOTkgODB0Mjk5IC04MHQyMTcgLTIxN3Q4MCAtMjk5dC04MCAtMjk5dC0yMTcgLTIxN3QtMjk5IC04MHQtMjk5IDgwdC0yMTcgMjE3dC04MCAyOTl6TTE4NiA2MDBxMCAtMTE1IDYyIC0yMTVsNTY4IDU2N3EtMTAwIDYyIC0yMTYgNjJxLTE3MSAwIC0yOTIuNSAtMTIxLjV0LTEyMS41IC0yOTIuNXpNMzkxIDI0NXE5NyAtNTkgMjA5IC01OXExNzEgMCAyOTIuNSAxMjEuNXQxMjEuNSAyOTIuNSBxMCAxMTIgLTU5IDIwOXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDkxOyIgZD0iTTAgNTQ3bDYwMCA0NTN2LTMwMGg2MDB2LTMwMGgtNjAwdi0zMDF6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MjsiIGQ9Ik0wIDQwMHYzMDBoNjAwdjMwMGw2MDAgLTQ1M2wtNjAwIC00NDh2MzAxaC02MDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5MzsiIGQ9Ik0yMDQgNjAwbDQ1MCA2MDBsNDQ0IC02MDBoLTI5OHYtNjAwaC0zMDB2NjAwaC0yOTZ6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5NDsiIGQ9Ik0xMDQgNjAwaDI5NnY2MDBoMzAwdi02MDBoMjk4bC00NDkgLTYwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk1OyIgZD0iTTAgMjAwcTYgMTMyIDQxIDIzOC41dDEwMy41IDE5M3QxODQgMTM4dDI3MS41IDU5LjV2MjcxbDYwMCAtNDUzbC02MDAgLTQ0OHYzMDFxLTk1IC0yIC0xODMgLTIwdC0xNzAgLTUydC0xNDcgLTkyLjV0LTEwMCAtMTM1LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA5NjsiIGQ9Ik0wIDB2NDAwbDEyOSAtMTI5bDI5NCAyOTRsMTQyIC0xNDJsLTI5NCAtMjk0bDEyOSAtMTI5aC00MDB6TTYzNSA3NzdsMTQyIC0xNDJsMjk0IDI5NGwxMjkgLTEyOXY0MDBoLTQwMGwxMjkgLTEyOXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk3OyIgZD0iTTM0IDE3NmwyOTUgMjk1bC0xMjkgMTI5aDQwMHYtNDAwbC0xMjkgMTMwbC0yOTUgLTI5NXpNNjAwIDYwMHY0MDBsMTI5IC0xMjlsMjk1IDI5NWwxNDIgLTE0MWwtMjk1IC0yOTVsMTI5IC0xMzBoLTQwMHoiIC8%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTQ0OyIgZD0iTTU3IDM1M3EwIC05NSA2NiAtMTU5bDE0MSAtMTQycTY4IC02NiAxNTkgLTY2cTkzIDAgMTU5IDY2bDI4MyAyODNxNjYgNjYgNjYgMTU5dC02NiAxNTlsLTE0MSAxNDFxLTggOSAtMTkgMTdsLTEwNSAtMTA1bDIxMiAtMjEybC0zODkgLTM4OWwtMjQ3IDI0OGw5NSA5NWwtMTggMThxLTQ2IDQ1IC03NSAxMDFsLTU1IC01NXEtNjYgLTY2IC02NiAtMTU5ek0yNjkgNzA2cTAgLTkzIDY2IC0xNTlsMTQxIC0xNDFxNyAtNyAxOSAtMTdsMTA1IDEwNSBsLTIxMiAyMTJsMzg5IDM4OWwyNDcgLTI0N2wtOTUgLTk2bDE4IC0xN3E0NyAtNDkgNzcgLTEwMGwyOSAyOXEzNSAzNSA2Mi41IDg4dDI3LjUgOTZxMCA5MyAtNjYgMTU5bC0xNDEgMTQxcS02NiA2NiAtMTU5IDY2cS05NSAwIC0xNTkgLTY2bC0yODMgLTI4M3EtNjYgLTY0IC02NiAtMTU5eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNDU7IiBkPSJNMjAwIDEwMHY5NTNxMCAyMSAzMCA0NnQ4MSA0OHQxMjkgMzh0MTYzIDE1dDE2MiAtMTV0MTI3IC0zOHQ3OSAtNDh0MjkgLTQ2di05NTNxMCAtNDEgLTI5LjUgLTcwLjV0LTcwLjUgLTI5LjVoLTYwMHEtNDEgMCAtNzAuNSAyOS41dC0yOS41IDcwLjV6TTMwMCAzMDBoNjAwdjcwMGgtNjAwdi03MDB6TTQ5NiAxNTBxMCAtNDMgMzAuNSAtNzMuNXQ3My41IC0zMC41dDczLjUgMzAuNXQzMC41IDczLjV0LTMwLjUgNzMuNXQtNzMuNSAzMC41IHQtNzMuNSAtMzAuNXQtMzAuNSAtNzMuNXoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUwOyIgZD0iTTIgMzAwbDI5OCAtMzAwbDI5OCAzMDBoLTE5OHY5MDBoLTIwMHYtOTAwaC0xOTh6TTYwMiA5MDBsMjk4IDMwMGwyOTggLTMwMGgtMTk4di05MDBoLTIwMHY5MDBoLTE5OHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUxOyIgZD0iTTIgMzAwaDE5OHY5MDBoMjAwdi05MDBoMTk4bC0yOTggLTMwMHpNNzAwIDB2MjAwaDEwMHYtMTAwaDIwMHYtMTAwaC0zMDB6TTcwMCA0MDB2MTAwaDMwMHYtMjAwaC05OXYtMTAwaC0xMDB2MTAwaDk5djEwMGgtMjAwek03MDAgNzAwdjUwMGgzMDB2LTUwMGgtMTAwdjEwMGgtMTAwdi0xMDBoLTEwMHpNODAxIDkwMGgxMDB2MjAwaC0xMDB2LTIwMHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTU0OyIgZD0iTTIgMzAwbDI5OCAtMzAwbDI5OCAzMDBoLTE5OHY5MDBoLTIwMHYtOTAwaC0xOTh6TTgwMCA0MDB2MTAwaDIwMHYtNTAwaC0xMDB2NDAwaC0xMDB6TTgwMCA4MDB2NDAwaDMwMHYtNTAwaC0xMDB2MTAwaC0yMDB6TTkwMSA5MDBoMTAwdjIwMGgtMTAwdi0yMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE1NTsiIGQ9Ik0yIDMwMGwyOTggLTMwMGwyOTggMzAwaC0xOTh2OTAwaC0yMDB2LTkwMGgtMTk4ek03MDAgMTAwdjIwMGg1MDB2LTIwMGgtNTAwek03MDAgNDAwdjIwMGg0MDB2LTIwMGgtNDAwek03MDAgNzAwdjIwMGgzMDB2LTIwMGgtMzAwek03MDAgMTAwMHYyMDBoMjAwdi0yMDBoLTIwMHoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY3OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgNzAwaDMwMHYtMzAwaDMwMHYzMDBoMjk1bC00NDUgNTAwek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY4OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgNzA1bDMwNSAtMzA1bDU5NiA1OTZsLTE1NCAxNTVsLTQ0MiAtNDQybC0xNTAgMTUxek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTY5OyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0xMDAgOTg4bDk3IC05OGwyMTIgMjEzbC05NyA5N3pNMjAwIDQwMGw2OTcgMWwzIDY5OWwtMjUwIC0yMzlsLTE0OSAxNDlsLTIxMiAtMjEybDE0OSAtMTQ5ek05MDAgMTUwaDEwMHY1MGgtMTAwdi01MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTcwOyIgZD0iTTAgMHYyNzVxMCAxMSA3IDE4dDE4IDdoMTA0OHExMSAwIDE5IC03LjV0OCAtMTcuNXYtMjc1aC0xMTAwek0yMDAgNjEybDIxMiAtMjEybDk4IDk3bC0yMTMgMjEyek0zMDAgMTIwMGwyMzkgLTI1MGwtMTQ5IC0xNDlsMjEyIC0yMTJsMTQ5IDE0OGwyNDkgLTIzN2wtMSA2OTd6TTkwMCAxNTBoMTAwdjUwaC0xMDB2LTUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNzE7IiBkPSJNMjMgNDE1bDExNzcgNzg0di0xMDc5bC00NzUgMjcybC0zMTAgLTM5M3Y0MTZoLTM5MnpNNDk0IDIxMGw2NzIgOTM4bC02NzIgLTcxMnYtMjI2eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNzI7IiBkPSJNMCAxNTB2MTAwMHEwIDIwIDE0LjUgMzV0MzUuNSAxNWgyNTB2LTMwMGg1MDB2MzAwaDEwMGwyMDAgLTIwMHYtODUwcTAgLTIxIC0xNSAtMzUuNXQtMzUgLTE0LjVoLTE1MHY0MDBoLTcwMHYtNDAwaC0xNTBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41ek02MDAgMTAwMGgxMDB2MjAwaC0xMDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTczOyIgZD0iTTAgMTUwdjEwMDBxMCAyMCAxNC41IDM1dDM1LjUgMTVoMjUwdi0zMDBoNTAwdjMwMGgxMDBsMjAwIC0yMDB2LTIxOGwtMjc2IC0yNzVsLTEyMCAxMjBsLTEyNiAtMTI3aC0zNzh2LTQwMGgtMTUwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXpNNTgxIDMwNmwxMjMgMTIzbDEyMCAtMTIwbDM1MyAzNTJsMTIzIC0xMjNsLTQ3NSAtNDc2ek02MDAgMTAwMGgxMDB2MjAwaC0xMDB2LTIwMHoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTg0OyIgZD0iTTEwMCAwdjEwMGgxMTAwdi0xMDBoLTExMDB6TTE3NSAyMDBoOTUwbC0xMjUgMTUwdjI1MGwxMDAgMTAwdjQwMGgtMTAwdi0yMDBoLTEwMHYyMDBoLTIwMHYtMjAwaC0xMDB2MjAwaC0yMDB2LTIwMGgtMTAwdjIwMGgtMTAwdi00MDBsMTAwIC0xMDB2LTI1MHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTg1OyIgZD0iTTEwMCAwaDMwMHY0MDBxMCA0MSAtMjkuNSA3MC41dC03MC41IDI5LjVoLTEwMHEtNDEgMCAtNzAuNSAtMjkuNXQtMjkuNSAtNzAuNXYtNDAwek01MDAgMHYxMDAwcTAgNDEgMjkuNSA3MC41dDcwLjUgMjkuNWgxMDBxNDEgMCA3MC41IC0yOS41dDI5LjUgLTcwLjV2LTEwMDBoLTMwMHpNOTAwIDB2NzAwcTAgNDEgMjkuNSA3MC41dDcwLjUgMjkuNWgxMDBxNDEgMCA3MC41IC0yOS41dDI5LjUgLTcwLjV2LTcwMGgtMzAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxODY7IiBkPSJNLTEwMCAzMDB2NTAwcTAgMTI0IDg4IDIxMnQyMTIgODhoNzAwcTEyNCAwIDIxMiAtODh0ODggLTIxMnYtNTAwcTAgLTEyNCAtODggLTIxMnQtMjEyIC04OGgtNzAwcS0xMjQgMCAtMjEyIDg4dC04OCAyMTJ6TTEwMCAyMDBoOTAwdjcwMGgtOTAwdi03MDB6TTIwMCAzMDBoMzAwdjMwMGgtMjAwdjEwMGgyMDB2MTAwaC0zMDB2LTMwMGgyMDB2LTEwMGgtMjAwdi0xMDB6TTYwMCAzMDBoMjAwdjEwMGgxMDB2MzAwaC0xMDB2MTAwaC0yMDB2LTUwMCB6TTcwMCA0MDB2MzAwaDEwMHYtMzAwaC0xMDB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE4NzsiIGQ9Ik0tMTAwIDMwMHY1MDBxMCAxMjQgODggMjEydDIxMiA4OGg3MDBxMTI0IDAgMjEyIC04OHQ4OCAtMjEydi01MDBxMCAtMTI0IC04OCAtMjEydC0yMTIgLTg4aC03MDBxLTEyNCAwIC0yMTIgODh0LTg4IDIxMnpNMTAwIDIwMGg5MDB2NzAwaC05MDB2LTcwMHpNMjAwIDMwMGgxMDB2MjAwaDEwMHYtMjAwaDEwMHY1MDBoLTEwMHYtMjAwaC0xMDB2MjAwaC0xMDB2LTUwMHpNNjAwIDMwMGgyMDB2MTAwaDEwMHYzMDBoLTEwMHYxMDBoLTIwMHYtNTAwIHpNNzAwIDQwMHYzMDBoMTAwdi0zMDBoLTEwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTg4OyIgZD0iTS0xMDAgMzAwdjUwMHEwIDEyNCA4OCAyMTJ0MjEyIDg4aDcwMHExMjQgMCAyMTIgLTg4dDg4IC0yMTJ2LTUwMHEwIC0xMjQgLTg4IC0yMTJ0LTIxMiAtODhoLTcwMHEtMTI0IDAgLTIxMiA4OHQtODggMjEyek0xMDAgMjAwaDkwMHY3MDBoLTkwMHYtNzAwek0yMDAgMzAwaDMwMHYxMDBoLTIwMHYzMDBoMjAwdjEwMGgtMzAwdi01MDB6TTYwMCAzMDBoMzAwdjEwMGgtMjAwdjMwMGgyMDB2MTAwaC0zMDB2LTUwMHoiIC8%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%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+</head>
+
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+
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+.main-container {
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+  margin-right: auto;
+}
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+  color: inherit;
+  background-color: rgba(0, 0, 0, 0.04);
+}
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+  height: auto; 
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+</style>
+<div class="container-fluid main-container">
+
+
+<div id="header">
+<h1 class="title">High Performance Loops in R</h1>
+<h4 class="author"><em>Adapted from Roz Dakin’s <a href="https://github.com/rozdakin/R_loops_demo" class="uri">https://github.com/rozdakin/R_loops_demo</a></em></h4>
+<h4 class="date"><em>February 11, 2016</em></h4>
+</div>
+
+
+<div id="getting-started" class="section level2">
+<h2>Getting Started</h2>
+<p>Install and load the following packages:</p>
+<ul>
+<li>gapminder</li>
+<li>dplyr</li>
+<li>microbenchmark</li>
+</ul>
+<p>The for loop is used in the following form:</p>
+<blockquote>
+<p>for(counter) {#action steps}</p>
+</blockquote>
+<div id="loops-two-ways" class="section level3">
+<h3>Loops Two Ways</h3>
+<ol style="list-style-type: decimal">
+<li>Loop over indices e.g.:</li>
+</ol>
+<pre class="r"><code>for(i in 1:10){
+    print(i)
+}</code></pre>
+<ol start="2" style="list-style-type: decimal">
+<li>Loop over elements of a data structure e.g.:</li>
+</ol>
+<pre class="r"><code>for(i in head(gapminder$country)) {
+  print(i)
+}</code></pre>
+<ul>
+<li>Related: Loop over names/levels of a factor</li>
+</ul>
+<pre class="r"><code>for(i in levels(gapminder$country)){
+  print(i)
+}</code></pre>
+</div>
+</div>
+<div id="why-loop" class="section level2">
+<h2>Why Loop?</h2>
+<div id="resampling-statistics-or-simulation-e.g.permutation-tests-bootstrapping" class="section level3">
+<h3>1. Resampling Statistics or Simulation (e.g. permutation tests, bootstrapping)</h3>
+<p>In the following example, we will simulate the central limit theorem by randomly sampling and taking the mean (over 10000 iterations) from a poisson distribution.</p>
+<pre class="r"><code>results &lt;- rep(NA, 10000)
+for(i in 1:10000){
+    a &lt;- rpois(50, lambda=10)
+    results[i] &lt;-mean(a)
+}
+hist(results)</code></pre>
+<p><img 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" title alt width="672" /></p>
+<p>Bootstrapping: Resampling with replacement to determine confidence intervals on a statistic, e.g. the mean. Let’s get CIs on the mean of the gdpPercap factor of gapminder. The basic steps involve:</p>
+<ol style="list-style-type: decimal">
+<li>Sampling <strong>with replacement</strong>.</li>
+<li>Calculating the statistic you wish from this sample you’ve obtained.</li>
+<li>Repeat over x iterations (generally &gt;1000)</li>
+<li>Estimate sample distribution to determine confidence intervals for statistic of interest.</li>
+</ol>
+<pre class="r"><code>gdpresults &lt;- rep(NA, 10000)
+for(i in 1:10000){
+    resample &lt;- sample(gapminder$gdpPercap, replace=T)
+    gdpresults[i] &lt;- mean(resample)
+}
+quantile(gdpresults, c(0.025, 0.975)) # get 95% CI</code></pre>
+<pre><code>##     2.5%    97.5% 
+## 6758.579 7687.294</code></pre>
+</div>
+<div id="processinganalyzingvisualizing-large-andor-distributed-datasets" class="section level3">
+<h3>2. Processing/Analyzing/Visualizing large and/or distributed datasets</h3>
+<p>You may want to perform the same analysis on different subsets/columns of a dataframe/list. This is particularly useful if the analysis has multiple components.</p>
+<pre class="r"><code>#loop over subsets
+countries &lt;- levels(gapminder$country)
+results &lt;- rep(NA, length(countries))
+for(i in 1:length(countries)){
+    mod &lt;- lm(lifeExp ~ gdpPercap, subset(gapminder, country==countries[i]))
+    results[i]&lt;-summary(mod)$r.squared
+}
+
+#loop over certain columns (lm on column ~ year)
+responses &lt;- names(gapminder)[4:6]
+results &lt;- rep(NA, length(responses))
+for(i in 1:length(responses)){
+    myformula &lt;- paste(responses[i],'~year')
+    mod &lt;- lm(as.formula(myformula), data=gapminder)
+    results[i]&lt;-summary(mod)$r.squared
+}
+results[1:3]</code></pre>
+<pre><code>## [1] 0.18975714 0.00677462 0.05167350</code></pre>
+<p>We can make and save lots of separate plots. First, create an empty folder and set it as your working directory. The loop below will generate many plots in that folder.</p>
+<pre class="r"><code>for(i in 1:length(countries)){
+    mypath &lt;- file.path(getwd(), paste(countries[i], '_', paste('00',i,sep=''), &quot;.jpg&quot;, sep = &quot;&quot;))
+    jpeg(file=mypath, width=4, height=3.5, units='in', res=150)
+    par(las=1, bty='n', family='Times', tck=0.02)
+    plot(gdpPercap~year,subset(gapminder, country==countries[i]), xlab='year', ylab='gdp per capita (units)', type='l', ylim=c(1,10000))
+    dev.off()
+} </code></pre>
+</div>
+<div id="instead-of-for-loops" class="section level3">
+<h3>Instead of For Loops…</h3>
+<p>While the concept of for looping is very powerful, it is often not the best choice available.</p>
+<p>Here you are attempting to create a new column with rounded values of a column using a for loop. (Don’t do this!)</p>
+<pre class="r"><code>for(i in 1:length(gapminder$pop)){
+    gapminder$popround[i] &lt;- round(gapminder$pop[i], -3)
+}
+head(gapminder)</code></pre>
+<pre><code>## Source: local data frame [6 x 7]
+## 
+##       country continent  year lifeExp      pop gdpPercap popround
+##        (fctr)    (fctr) (int)   (dbl)    (int)     (dbl)    (dbl)
+## 1 Afghanistan      Asia  1952  28.801  8425333  779.4453  8425000
+## 2 Afghanistan      Asia  1957  30.332  9240934  820.8530  9241000
+## 3 Afghanistan      Asia  1962  31.997 10267083  853.1007 10267000
+## 4 Afghanistan      Asia  1967  34.020 11537966  836.1971 11538000
+## 5 Afghanistan      Asia  1972  36.088 13079460  739.9811 13079000
+## 6 Afghanistan      Asia  1977  38.438 14880372  786.1134 14880000</code></pre>
+<p>Do this instead (vectorized!)</p>
+<pre class="r"><code>gapminder$popround &lt;- round(gapminder$pop, -3)</code></pre>
+<p><strong>Recoding Values</strong></p>
+<p>Here we want to change all values of iris’ Group Value to 1 if Sepal Length is between 5 and 6, and set to 2 if greater than or equal to 6 (and set to 0 otherwise). Don’t do this:</p>
+<pre class="r"><code>iris$sep.group &lt;- 0
+for (i in 1:length(iris$Sepal.Length)){
+    if(iris$Sepal.Length[i] &gt; 5 &amp; iris$Sepal.Length[i] &lt; 6){
+        iris$sep.group[i] &lt;- 1
+    }
+    if(iris$Sepal.Length[i] &gt;= 6){
+        iris$sep.group[i] &lt;- 2
+    }
+}
+head(iris)</code></pre>
+<pre><code>##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species sep.group
+## 1          5.1         3.5          1.4         0.2  setosa         1
+## 2          4.9         3.0          1.4         0.2  setosa         0
+## 3          4.7         3.2          1.3         0.2  setosa         0
+## 4          4.6         3.1          1.5         0.2  setosa         0
+## 5          5.0         3.6          1.4         0.2  setosa         0
+## 6          5.4         3.9          1.7         0.4  setosa         1</code></pre>
+<p>It is more efficient to use <code>ifelse(&lt;condition&gt;, &lt;yes&gt;, &lt;no&gt;)</code> which can also be nested:</p>
+<pre class="r"><code>iris$sep.group &lt;- ifelse(iris$Sepal.Length &gt; 6, 2, ifelse(iris$Sepal.Length &gt; 5, 1, 0))
+head(iris)</code></pre>
+<pre><code>##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species sep.group
+## 1          5.1         3.5          1.4         0.2  setosa         1
+## 2          4.9         3.0          1.4         0.2  setosa         0
+## 3          4.7         3.2          1.3         0.2  setosa         0
+## 4          4.6         3.1          1.5         0.2  setosa         0
+## 5          5.0         3.6          1.4         0.2  setosa         0
+## 6          5.4         3.9          1.7         0.4  setosa         1</code></pre>
+<p>Do not use for loops to apply summary functions (mean, max, min etc.) to subsets of your data.</p>
+<pre class="r"><code>countries &lt;- levels(gapminder$country)
+cmeans &lt;- rep(NA, length(countries))
+for(i in 1:length(countries)){
+    data &lt;- subset(gapminder, country==countries[i])
+    cmeans[i] &lt;- mean(data$pop)
+}
+head(cmeans)</code></pre>
+<pre><code>## [1] 15823715  2580249 19875406  7309390 28602240 14649313</code></pre>
+<p>dplyr has a summarise() function for this purpose</p>
+<pre class="r"><code>by.country &lt;- group_by(gapminder, country)
+summ.country &lt;- summarise(by.country, meanpop = round(mean(pop)/1000,-3)) 
+head(summ.country)</code></pre>
+<pre><code>## Source: local data frame [6 x 2]
+## 
+##       country meanpop
+##        (fctr)   (dbl)
+## 1 Afghanistan   16000
+## 2     Albania    3000
+## 3     Algeria   20000
+## 4      Angola    7000
+## 5   Argentina   29000
+## 6   Australia   15000</code></pre>
+<div id="the-apply-functions" class="section level4">
+<h4>The Apply Functions</h4>
+<p>In base R, the apply family is very useful in place of writing your own loops:</p>
+<pre class="r"><code>help(&quot;apply&quot;)
+apply(gapminder[,4:6], 2, 'mean') # applied to columns
+head(apply(iris[,1:4], 1, 'sum')) #applied to rows
+
+help(tapply) # good for dataframes or separate vectors which can be ragged (different sizes)
+tapply(gapminder$lifeExp, gapminder$country, FUN='mean')
+# tapply(vector, factor, function)
+tapply(gapminder$lifeExp, gapminder$country, FUN=function(x) round(mean(x),-1))
+# can write your own functions to bue used with apply &amp; co. can be done inside or outside the loop</code></pre>
+<p>Note that you can write your own function to be used with the apply family, either inside or outside the loop.</p>
+<p>In this example, we will use tapply() to normalize all the values of gapminder$pop based on country. Then we will use lapply() which works on lists to apply our functions.</p>
+<pre class="r"><code>poppercent &lt;- tapply(gapminder$pop, gapminder$country, function(x) x/max(x))
+#this will give a list of lists, ordered by country
+head(poppercent)
+#now we can use lapply on this list
+lapply(poppercent, mean) #to find the mean
+lapply(poppercent, function(x) sd(x)/sqrt(length(x))) #to find the SEM</code></pre>
+</div>
+</div>
+<div id="speed-up-your-loops" class="section level3">
+<h3>Speed Up Your Loops</h3>
+<blockquote>
+<h4>How to Time Your Code</h4>
+<ol style="list-style-type: decimal">
+<li>Enclose in system.time()</li>
+</ol>
+<pre class="r"><code>system.time(mean(1:1000000))</code></pre>
+<pre><code>##    user  system elapsed 
+##    0.02    0.00    0.01</code></pre>
+<ol start="2" style="list-style-type: decimal">
+<li>For big chunks, determine the processing time of code with proc.time()</li>
+</ol>
+<pre class="r"><code>pmt &lt;- proc.time()
+mean(1:1000000)</code></pre>
+<pre><code>## [1] 500000.5</code></pre>
+<pre class="r"><code>proc.time() - pmt</code></pre>
+<pre><code>##    user  system elapsed 
+##       0       0       0</code></pre>
+<ol start="3" style="list-style-type: decimal">
+<li>Using microbenchmark, it will be a more accurate estimate as it samples the action many times (default = 100, but can change as desired). This will take longer but can be useful if choosing between two methods that you will use extensively.</li>
+</ol>
+<pre class="r"><code>microbenchmark(mean(1:1000000))</code></pre>
+<pre><code>## Unit: milliseconds
+##           expr      min     lq     mean   median       uq      max neval
+##  mean(1:1e+06) 2.144687 2.5918 11.58111 3.735134 4.797427 47.87291   100</code></pre>
+<p>You can also pass it multiple things to compare speeds:</p>
+<pre class="r"><code>rnormtime &lt;- microbenchmark(rnorm(1000), rnorm(10000), rnorm(100000), times=50)
+boxplot(rnormtime)</code></pre>
+<p><img src="data:image/png;base64,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" title alt width="672" /></p>
+</blockquote>
+<p>Here are some basic principles to follow when looping:</p>
+<p><strong>1.Initialize your data structures rather than growing them as you loop (avoid appending to a given structure; pre-allocate space instead and then access!)</strong></p>
+<p>Let’s look at an example here of growing vs. pre-allocating/accessing:</p>
+<pre class="r"><code>hit &lt;- NA
+system.time (for(i in 1:100000){if(runif(1) &lt; 0.3) hit[i] &lt;- T})</code></pre>
+<pre><code>##    user  system elapsed 
+##    1.97    0.04    2.00</code></pre>
+<pre class="r"><code>hit2 &lt;- rep(NA, 100000)
+system.time (for(i in 1:100000){if(runif(1) &lt; 0.3) hit2[i] &lt;- T})</code></pre>
+<pre><code>##    user  system elapsed 
+##    0.22    0.00    0.22</code></pre>
+<p><strong>2.Take advantage of vectorized operations when possible and take the work outside of loops. </strong></p>
+<p>Note, sometimes more code is not always a bad thing, especially if your primary goal is time saving in the computations, especially as you begin to work with increasingly large datasets.</p>
+<p>Here we will try to denote all years in which lifeExp of a country decreased as a “bad” year.</p>
+<p>First with a for loop…</p>
+<pre class="r"><code>pmt &lt;- proc.time()
+gapminder$badyear &lt;- NA
+rows &lt;- length(gapminder[,1])
+for(i in 2:rows){
+  if((gapminder$lifeExp[i] &lt; gapminder$lifeExp[i-1]) &amp; (gapminder$country[i]==gapminder$country[i-1])){
+    gapminder$badyear[i] &lt;- 'bad'
+  }
+}
+proc.time() - pmt
+summary(factor(gapminder$badyear))</code></pre>
+<p>And without:</p>
+<pre class="r"><code>start &lt;- proc.time()
+gapminder$lifeExpnext &lt;- NA
+rows &lt;- length(gapminder[,1])
+# create a column where, for each year, the next year's lifeExp is indicated
+gapminder$lifeExpnext[1:rows-1] &lt;- gapminder$lifeExp[2:rows]
+gapminder$countrynext &lt;- NA
+# in order to keep track of country (don't want to compare two different countries)
+gapminder$countrynext[1:rows-1] &lt;- as.character(gapminder$country[2:rows])
+# the vectorized component
+gapminder$badyear &lt;- ifelse(gapminder$countrynext==gapminder$country &amp; gapminder$lifeExpnext &lt; gapminder$lifeExp, &quot;bad&quot;, NA)
+proc.time() - start</code></pre>
+<pre><code>##    user  system elapsed 
+##       0       0       0</code></pre>
+<p><strong>3.Indexing vectors is faster than indexing dataframes.</strong></p>
+<pre class="r"><code>#in a dataframe
+results &lt;- data.frame(t1=rep(NA,1000), pval=NA)
+
+microbenchmark(
+for(i in 1:1000){
+  a &lt;- rnorm(50, 10)
+  grp &lt;- rep(1:2, each = length(a)/2)
+  stat &lt;- t.test(a~grp)
+  results$t1[i] &lt;- stat$statistic
+  results$pval[i] &lt;- stat$p.value
+}, times = 1
+)</code></pre>
+<pre><code>## Unit: milliseconds
+##                                                                                                                                                                                      expr
+##  for (i in 1:1000) {     a &lt;- rnorm(50, 10)     grp &lt;- rep(1:2, each = length(a)/2)     stat &lt;- t.test(a ~ grp)     results$t1[i] &lt;- stat$statistic     results$pval[i] &lt;- stat$p.value }
+##       min       lq     mean   median       uq      max neval
+##  931.6818 931.6818 931.6818 931.6818 931.6818 931.6818     1</code></pre>
+<pre class="r"><code>#as two vectors
+t1 &lt;- rep(NA, 1000)
+pval &lt;- rep(NA, 1000)
+
+microbenchmark(
+  for(i in 1:1000){
+    a &lt;- rnorm(50, 10)
+    grp &lt;- rep(1:2, each = length(a)/2)  
+    stat &lt;- t.test(a ~ grp)
+    t1[i] &lt;- stat$statistic
+    pval[i] &lt;- stat$p.value
+  }, times = 1
+)</code></pre>
+<pre><code>## Unit: milliseconds
+##                                                                                                                                                                      expr
+##  for (i in 1:1000) {     a &lt;- rnorm(50, 10)     grp &lt;- rep(1:2, each = length(a)/2)     stat &lt;- t.test(a ~ grp)     t1[i] &lt;- stat$statistic     pval[i] &lt;- stat$p.value }
+##       min       lq     mean   median       uq      max neval
+##  903.2607 903.2607 903.2607 903.2607 903.2607 903.2607     1</code></pre>
+<pre class="r"><code>#Small difference, but can save time on larger datasets</code></pre>
+</div>
+<div id="resources-free-online" class="section level3">
+<h3>Resources (free online!)</h3>
+<p>Patrick Burns’ R Inferno: <a href="http://www.burns-stat.com/pages/Tutor/R_inferno.pdf" class="uri">http://www.burns-stat.com/pages/Tutor/R_inferno.pdf</a><br />Hadley Wickham’s Advanced R (esp. Functionals and Profiling): <a href="http://adv-r.had.co.nz/" class="uri">http://adv-r.had.co.nz/</a></p>
+</div>
+</div>
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+$(document).ready(function () {
+  $('tr.header').parent('thead').parent('table').addClass('table table-condensed');
+});
+
+</script>
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
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+</html>
diff --git a/lessons/r/loops/lesson.md b/lessons/r/loops/lesson.md
new file mode 100644
index 000000000..1ae9c6684
--- /dev/null
+++ b/lessons/r/loops/lesson.md
@@ -0,0 +1,38 @@
+---
+layout: page
+title: High Performance Loops in R
+visible: true
+tags:
+  - r
+  - beginner
+---
+
+ - **Authors**: Lina Tran adapted from Roz Dakin
+ - **Research field**: Neuroscience
+ - **Lesson topic**: For Loops 
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/loops>
+
+To For Loop or not to For Loop? Learn different ways to make use of for loops as well as when it might be more efficient to use other methods in favour of looping. We will also learn how to use the Microbenchmark package to time how long it takes to execute code when deciding to use one method over another to maximize efficiency.
+
+## Topics Covered:
+
+- When loops are useful
+- When other alternatives might work better
+- The Apply family
+- How to time your code
+- Speeding up your loops
+
+##What You Will Need:
+
+- [R](https://cran.rstudio.com/)
+- [RStudio](https://www.rstudio.com/products/rstudio/#Desktop)
+
+Once you have these set up, make sure to install the following packages (gapminder, dplyr, & microbenchmark) either through Tools > Install Packages in RStudio, or in the console:
+
+```
+install.packages('gapminder')
+install.packages('dplyr')
+install.packages('microbenchmark')
+```
+
+[**Here is the lesson material with the R output**](../HighPerformanceLoops.html)
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new file mode 100644
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@@ -0,0 +1,120 @@
+---
+layout: page
+title: "Introduction to Machine Learning in R"
+visible: false
+tags:
+  # languages
+  - r
+  # levels
+  - intermediate
+---
+
+ - **Authors**: Raul Samayoa ([@ralle123](https://github.com/ralle123))
+ - **Lesson topic**: Machine learning
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/ml-intro>
+
+Slides are available [at this link](../intro-machine-learning.pdf)
+
+## Demo examples:
+
+### Linear regression
+
+```{r}
+#install.packages("lattice")
+library(lattice)
+alligator <- data.frame(
+  lnLength = c(3.87, 3.61, 4.33, 3.43, 3.81, 3.83, 3.46, 3.76,
+    3.50, 3.58, 4.19, 3.78, 3.71, 3.73, 3.78),
+  lnWeight = c(4.87, 3.93, 6.46, 3.33, 4.38, 4.70, 3.50, 4.50,
+    3.58, 3.64, 5.90, 4.43, 4.38, 4.42, 4.25)
+  )
+
+# Plot our information
+plot(
+    alligator$lnWeight,
+    alligator$lnLength,
+    col = "blue",
+    xlab = "Snout vent length (inches) on log scale",
+    ylab = "Weight (pounds) on log scale",
+    main = "Alligators in Central Florida"
+    )
+
+```
+
+[lm](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/lm.html) function
+fits a linear model. A typical model has the form response ~ terms
+
+```{r}
+myModel <- lm(lnLength ~ lnWeight, data = alligator)
+```
+
+Visualize the regression
+
+```{r}
+# Plot the chart with lm.
+plot(
+    alligator$lnWeight,
+    alligator$lnLength,
+    col = "blue",
+    abline(myModel, col = 'red'),
+    xlab = "Snout vent length (inches) on log scale",
+    ylab = "Weight (pounds) on log scale",
+    main = "Alligators in Central Florida"
+    )
+
+```
+
+```{r}
+# Find weight of a alligator with weight of 5.5.
+find_value <- data.frame(lnWeight = 5.5)
+result <- predict(myModel, find_value)
+result
+```
+
+If we trace the line with the value we are looking for we can see that the value
+seems correct
+
+```{r}
+library(ggplot2)
+end <- ggplot(alligator, aes(x = lnWeight, y = lnLength)) + 
+    geom_point()
+end + geom_smooth(method = 'lm', se = FALSE) + 
+    geom_vline(xintercept = 5.5) + 
+    geom_hline(yintercept = 4.06312, color = 'red')
+```
+
+### k-means
+
+```{r}
+library(datasets)
+head(iris)
+ggplot(iris, aes(Petal.Width, Petal.Length)) + 
+    geom_point()
+```
+
+After resetting the seed I do a
+[kmeans](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html)
+just selecting the 3-4 columns of iris, collect 3 iterations, selecting 20
+random sets
+
+```{r}
+set.seed(20)
+irisCluster <- kmeans(iris[, 3:4], 3, nstart = 20)
+#Comparing cluster with species
+table(irisCluster$cluster, iris$Species)
+```
+
+We can graph our result
+
+```{r}
+irisCluster$cluster <- as.factor(irisCluster$cluster)
+ggplot(iris, aes(Petal.Length, Petal.Width, color = irisCluster$cluster)) + 
+    geom_point()
+```
+
+Why did we do 3 clusters and not 4? dataset only has 3 species
+
+```{r}
+ggplot(iris, aes(Petal.Length, Petal.Width, color = Species)) + 
+    geom_point()
+```
diff --git a/lessons/r/ml-intro/lesson.md b/lessons/r/ml-intro/lesson.md
new file mode 100644
index 000000000..47302ecab
--- /dev/null
+++ b/lessons/r/ml-intro/lesson.md
@@ -0,0 +1,165 @@
+---
+layout: page
+title: "Introduction to Machine Learning in R"
+visible: true
+tags:
+  # languages
+  - r
+  # levels
+  - intermediate
+---
+
+ - **Authors**: Raul Samayoa ([@ralle123](https://github.com/ralle123))
+ - **Lesson topic**: Machine learning
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/ml-intro>
+
+Slides are available [at this link](../intro-machine-learning.pdf)
+
+## Demo examples:
+
+### Linear regression
+
+
+```r
+#install.packages("lattice")
+library(lattice)
+alligator <- data.frame(
+  lnLength = c(3.87, 3.61, 4.33, 3.43, 3.81, 3.83, 3.46, 3.76,
+    3.50, 3.58, 4.19, 3.78, 3.71, 3.73, 3.78),
+  lnWeight = c(4.87, 3.93, 6.46, 3.33, 4.38, 4.70, 3.50, 4.50,
+    3.58, 3.64, 5.90, 4.43, 4.38, 4.42, 4.25)
+  )
+
+# Plot our information
+plot(
+    alligator$lnWeight,
+    alligator$lnLength,
+    col = "blue",
+    xlab = "Snout vent length (inches) on log scale",
+    ylab = "Weight (pounds) on log scale",
+    main = "Alligators in Central Florida"
+    )
+```
+
+![center](../figure/unnamed-chunk-1-1.png)
+
+[lm](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/lm.html) function
+fits a linear model. A typical model has the form response ~ terms
+
+
+```r
+myModel <- lm(lnLength ~ lnWeight, data = alligator)
+```
+
+Visualize the regression
+
+
+```r
+# Plot the chart with lm.
+plot(
+    alligator$lnWeight,
+    alligator$lnLength,
+    col = "blue",
+    abline(myModel, col = 'red'),
+    xlab = "Snout vent length (inches) on log scale",
+    ylab = "Weight (pounds) on log scale",
+    main = "Alligators in Central Florida"
+    )
+```
+
+![center](../figure/unnamed-chunk-3-1.png)
+
+
+```r
+# Find weight of a alligator with weight of 5.5.
+find_value <- data.frame(lnWeight = 5.5)
+result <- predict(myModel, find_value)
+result
+```
+
+```
+##        1 
+## 4.067312
+```
+
+If we trace the line with the value we are looking for we can see that the value
+seems correct
+
+
+```r
+library(ggplot2)
+end <- ggplot(alligator, aes(x = lnWeight, y = lnLength)) + 
+    geom_point()
+end + geom_smooth(method = 'lm', se = FALSE) + 
+    geom_vline(xintercept = 5.5) + 
+    geom_hline(yintercept = 4.06312, color = 'red')
+```
+
+![center](../figure/unnamed-chunk-5-1.png)
+
+### k-means
+
+
+```r
+library(datasets)
+head(iris)
+```
+
+```
+##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
+## 1          5.1         3.5          1.4         0.2  setosa
+## 2          4.9         3.0          1.4         0.2  setosa
+## 3          4.7         3.2          1.3         0.2  setosa
+## 4          4.6         3.1          1.5         0.2  setosa
+## 5          5.0         3.6          1.4         0.2  setosa
+## 6          5.4         3.9          1.7         0.4  setosa
+```
+
+```r
+ggplot(iris, aes(Petal.Width, Petal.Length)) + 
+    geom_point()
+```
+
+![center](../figure/unnamed-chunk-6-1.png)
+
+After resetting the seed I do a
+[kmeans](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html)
+just selecting the 3-4 columns of iris, collect 3 iterations, selecting 20
+random sets
+
+
+```r
+set.seed(20)
+irisCluster <- kmeans(iris[, 3:4], 3, nstart = 20)
+#Comparing cluster with species
+table(irisCluster$cluster, iris$Species)
+```
+
+```
+##    
+##     setosa versicolor virginica
+##   1     50          0         0
+##   2      0         48         4
+##   3      0          2        46
+```
+
+We can graph our result
+
+
+```r
+irisCluster$cluster <- as.factor(irisCluster$cluster)
+ggplot(iris, aes(Petal.Length, Petal.Width, color = irisCluster$cluster)) + 
+    geom_point()
+```
+
+![center](../figure/unnamed-chunk-8-1.png)
+
+Why did we do 3 clusters and not 4? dataset only has 3 species
+
+
+```r
+ggplot(iris, aes(Petal.Length, Petal.Width, color = Species)) + 
+    geom_point()
+```
+
+![center](../figure/unnamed-chunk-9-1.png)
diff --git a/lessons/r/packages/lesson.md b/lessons/r/packages/lesson.md
new file mode 100644
index 000000000..1e8c0292e
--- /dev/null
+++ b/lessons/r/packages/lesson.md
@@ -0,0 +1,154 @@
+---
+layout: page
+title: Making packages in R using devtools
+visible: true
+tags:
+  - r
+  - intermediate
+---
+
+ - **Authors**: Luke Johnston
+ - **Research field**: PhD in Nutritional Sciences
+ - **Lesson topic**: R package development
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/packages>
+ 
+-----
+
+Making your own package in R of functions that you commonly use can help you
+save an incredible amount of time in your data analysis. This lesson will
+briefly go over the basics of package development in R. For more information on
+this, I highly highly recommend the book (online and free!) 
+[**R Packages**](http://r-pkgs.had.co.nz/) written by Hadley Wickham (a prolific R
+package developer and creator of `devtools`). This book is written in a way that
+most novice R users (i.e. those slightly familiar with R) would be able to
+understand what is being explained. Check it out!
+
+So, briefly, developing R packages is done by running:
+
+```r
+devtools::create('pkg_name')
+```
+
+... Or by using the menu options `File -> New Project -> New Directory -> R Package` in RStudio.
+
+This creates a new directory called `pkg_name` (or whatever other name) along with:
+
+- An `R/` subfolder: contains all of the R code that the new R package will have.
+- A `DESCRIPTION` file: Contains the information about the package (like version
+number, title, author, etc)
+- A `NAMESPACE` file: Contains the R functions or code that R will look for when
+the package is 'officially' installed.
+- An `.Rproj` file: Tells RStudio what the folder structure is used for (package
+development) and sets RStudio options.
+
+## Making functions
+
+Make new R functions in the `R/` folder. A simple one can be created:
+
+```r
+sum_nums <- function(arg1, arg2) {
+    arg1 + arg2
+}
+```
+
+You can now access this function in the R console when developing the package by
+running `devtools::load_all()` or `Ctrl-Shift-L` in RStudio. Now, when you start
+typing `sum_` you'll see the autocompletion list the `sum_nums`. All new
+functions can be created this way.
+
+Sometimes you need to use functions from other packages, such as `dplyr` or
+`ggplot2`. R packages access these differently then when running interactively.
+You can't include a `library()` statement in your R functions. So first you do:
+
+```r
+devtools::use_package('dplyr')
+```
+
+This will output a message to the R console, telling you how to use the `dplyr`
+package functions. It also adds `dplyr` to the Import section of the DESCRIPTION
+file. You also need to prefix `dplyr::` to each `dplyr` function. So, if we
+create a new function:
+
+```r
+filter_down <- function(cutoff = 30) {
+    dplyr::filter(swiss, Agriculture < cutoff)
+}
+```
+
+Hit `Ctrl-Shift-L` to run `devtools::load_all()` to access the new function. Now
+type in the console `filter_down(40)` and it will list off a filtered version of
+the `swiss` dataset.
+
+## Documenting your functions
+
+It's often a good idea to document your functions. Put your cursor inside the
+function and hit `Ctrl-Shift-Alt-R`. Something like this should be added to the
+function:
+
+```r
+#' Title
+#'
+#' @param cutoff 
+#'
+#' @return
+#' @export
+#'
+#' @examples
+filter_down <- function(cutoff = 30) {
+    dplyr::filter(swiss, Agriculture < cutoff)
+}
+```
+
+These tags are called `roxygen` document tags. 
+
+- `@param`: Parameter/argument name and description.
+- `@return`: What is output from the function. In this case, a data frame.
+- `@export`: Whether to send this function to the NAMESPACE. This dictates
+whether the function will be external (accessible to the end user) or internal
+(only accessible to the functions within the R package, but not the user).
+- `@examples`: Usually good to give an example of how your function is used.
+
+These tags are used whenever you type `?filter_down`, as they are used for the
+help documentation. To generate the documentation files, hit `Ctrl-Shift-D` to
+run `devtools::document()`.
+
+## Documenting your package
+
+Depending on how complicated your package is (and even if it isn't), it helps to
+write up a more descriptive documentation on how to use your package. These
+documentations are called vignettes. Even if no one but you will use the
+package, I would still recommend writing up at least an introduction since
+you'll be guaranteed to forget what your package does later in the future (it's
+happened to me many many times). So, to start writing up a vignette, type in the
+console:
+
+```r
+devtools::use_vignette('introduction')
+```
+
+This will create a `vignette/` folder and an 
+[R Markdown](http://rmarkdown.rstudio.com/) file, where you can mix text, code, and
+code output for a polished tutorial on using your package. You can access this
+vignette when the package is installed by using:
+
+```r
+vignette('introduction', package = 'your_package_name')
+```
+
+## Installing your package and other bits
+
+You can install your package by running:
+
+```r
+devtools::install()
+```
+
+This installs the package and lets you access the functions by using `library()`
+command.
+
+There are several other bits of information for building an R package which may
+not get covered. These include unit testing and integration with Git/GitHub.
+Unit testing (via the `testthat` package and `devtools::use_testthat()`) allows
+you to include tests to confirm that your functions work as intended (run 
+`Ctrl-Shift-T` to run `devtools::test()` and run the unit tests). R packages and
+RStudio integrate very well with Git and GitHub, so you can make use of them!
diff --git a/lessons/r/programming-tidyverse/Programming with Tidyverse.Rmd b/lessons/r/programming-tidyverse/Programming with Tidyverse.Rmd
new file mode 100644
index 000000000..8f0eca1cc
--- /dev/null
+++ b/lessons/r/programming-tidyverse/Programming with Tidyverse.Rmd	
@@ -0,0 +1,129 @@
+---
+title: "Programming with Tidyverse"
+author: "Margot Lautens"
+date: "15/03/2022"
+output: html_document
+---
+
+The goal of this workshop is to learn how to write functions which include Tidyverse packages.
+
+Functions are a way to keep a useful workflow that you use a lot separate so that you can change variables and not accidentally miss one. In this example, the function is a simple addition. Yours can be a subtraction and you can also choose whichever numbers you'd like
+```{r}
+littleAddition <- function(number1,number2){
+  finalNumber=number1+number2
+  return(finalNumber)
+}
+
+littleAddition(1,2)
+```
+
+It's important for this workshop that you have the most recent version of tidyverse since until recently, programming in Tidyverse worked differently.
+
+```{r setup, include=FALSE}
+install.packages("tidyverse")
+library(tidyverse)
+```
+
+Tidyverse is a useful and easy way to manipulate and plot data in R. The normal workflow is import -> tidy -> transform -> visualize -> model and tidyverse has the tools to do all of that. Using the diamonds dataset, we'll go through a basic version of this workflow.
+
+```{r}
+#IMPORT#
+diamonds <- diamonds
+glimpse(diamonds)
+
+```
+
+As you can see, the diamonds dataset has both quantitative and qualitative data. Based on what you saw in glimpse, pick a color of diamond that you're interested in learning more about. I've chosen 'E'. Also for both group_by and colour in the ggplot call, choose between "cut", "clarity", "depth" or "table". I've chosen "cut".
+
+```{r}
+#BASIC WORKFLOW#
+normalizedDiamonds <- diamonds%>%
+    filter(color=="E")%>%
+    group_by(cut)%>%
+    mutate(normalizedPrice=(price-mean(price))/sd(price))
+
+ggplot(normalizedDiamonds,
+       aes(x=carat,y=normalizedPrice,colour=cut))+
+    geom_point()
+```
+
+But what if you wanted to make a series of graphs using this same simple workflow many times over? What if you wanted to easily switch between these options? The best option would be to make a function which contained your simple workflow.
+
+```{r}
+#TIDYING IN A FUNCTION, ATTEMPT 1#
+diamondsFunction <- function(diamondColor, diamondVar1, diamondVar2){
+  normalizedDiamonds <- diamonds%>%
+    filter(color==diamondColor)%>%
+    group_by(diamondVar1)%>%
+    mutate(normalizedPrice=(price-mean(price))/sd(price))
+
+  diamondPlot <- ggplot(normalizedDiamonds,
+                        aes(x=carat,y=normalizedPrice,colour=diamondVar2))+
+    geom_point()
+print(diamondPlot)
+return(normalizedDiamonds)
+}
+
+```
+
+Now repeat the variables you picked before when calling the function.
+```{r}
+#TIDYING IN A FUNCTION, ATTEMPT 1, RESULTS#
+diamondsFunction("E",cut,cut)
+```
+An error will pull up saying that 'diamondVar1' is not found in your group_by. That's because tidyverse scarifices ease of normal workflow for an extra step in programming contexts. What we need to do is *embrace* the variable for diamondVar1 as well as diamondVar2 in the function. The diamondColor variable is fine because in the context of filter, it's part of an expression which produces a logical value, not a variable (the variable is "color" itself).
+```{r}
+#TIDYING IN A FUNCTION, ATTEMPT 2#
+diamondsFunction <- function(diamondColor, diamondVar1,diamondVar2){
+  normalizedDiamonds <- diamonds%>%
+    filter(color==diamondColor)%>%
+    group_by({{diamondVar1}})%>%
+    mutate(normalizedPrice=(price-mean(price))/sd(price))
+
+  diamondPlot <- ggplot(normalizedDiamonds,
+                        aes(x=carat,y=normalizedPrice,colour={{diamondVar2}}))+
+    geom_point()
+print(diamondPlot)
+return(normalizedDiamonds)
+}
+
+diamondsFunction("E",cut,cut)
+```
+Now you should have a working function! If you want to allow for the possibility of several varaibles to be used, as is the case with diamondsVar1, instead of embracing we'll use `all_of`.
+
+```{r}
+#TIDYING IN A FUNCTION, ATTEMPT 3#
+diamondsFunction <- function(diamondColor, diamondVar1,diamondVar2){
+  normalizedDiamonds <- diamonds%>%
+    filter(color==diamondColor)%>%
+    group_by(all_of(diamondVar1))%>%
+    mutate(normalizedPrice=(price-mean(price))/sd(price))
+
+  diamondPlot <- ggplot(normalizedDiamonds,
+                        aes(x=carat,y=normalizedPrice,colour={{diamondVar2}}))+
+    geom_point()
+print(diamondPlot)
+return(normalizedDiamonds)
+}
+vars <- c("cut","clarity")
+diamondsFunction("E",vars,cut)
+```
+You'll have gotten an error. The problem is that `group_by` is an action verb and not a selection verb so it needs a bit of help understanding inputs like all_of. Use `across` in addition to `all_of` instead to fix the problem.
+```{r}
+#TIDYING IN A FUNCTION, ATTEMPT 4#
+diamondsFunction <- function(diamondColor, diamondVar1,diamondVar2){
+  normalizedDiamonds <- diamonds%>%
+    filter(color==diamondColor)%>%
+    group_by(across(all_of(diamondVar1)))%>%
+    mutate(normalizedPrice=(price-mean(price))/sd(price))
+
+  diamondPlot <- ggplot(normalizedDiamonds,
+                        aes(x=carat,y=normalizedPrice,colour={{diamondVar2}}))+
+    geom_point()
+print(diamondPlot)
+return(normalizedDiamonds)
+}
+vars <- c("cut","clarity")
+diamondsFunction("E",vars,cut)
+```
+Hope this is helpful to you and your programming adventures! For further reading see: [https://dplyr.tidyverse.org/articles/programming.html]. If you're interested in replacing for-loops, look into purrr and this tutorial [https://emoriebeck.github.io/R-tutorials/purrr/].
diff --git a/lessons/r/regression-models/20190321-regression-models.ipynb b/lessons/r/regression-models/20190321-regression-models.ipynb
new file mode 100644
index 000000000..81b4f44a0
--- /dev/null
+++ b/lessons/r/regression-models/20190321-regression-models.ipynb
@@ -0,0 +1,1948 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Load functions**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loads libraries and automatically installs them if needed.\n",
+    "# Source code: https://stackoverflow.com/questions/19596359/install-package-library-if-not-installed\n",
+    "usePackage <- function(p) {\n",
+    "    if (!is.element(p, installed.packages()[,1]))\n",
+    "        install.packages(p, dependencies = TRUE)\n",
+    "    require(p, character.only = TRUE)\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading required package: GGally\n",
+      "Loading required package: ggplot2\n",
+      "Loading required package: ppcor\n",
+      "Loading required package: MASS\n",
+      "Loading required package: openintro\n",
+      "Please visit openintro.org for free statistics materials\n",
+      "\n",
+      "Attaching package: 'openintro'\n",
+      "\n",
+      "The following objects are masked from 'package:MASS':\n",
+      "\n",
+      "    housing, mammals\n",
+      "\n",
+      "The following object is masked from 'package:ggplot2':\n",
+      "\n",
+      "    diamonds\n",
+      "\n",
+      "The following objects are masked from 'package:datasets':\n",
+      "\n",
+      "    cars, trees\n",
+      "\n",
+      "Loading required package: stargazer\n",
+      "\n",
+      "Please cite as: \n",
+      "\n",
+      " Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.\n",
+      " R package version 5.2.2. https://CRAN.R-project.org/package=stargazer \n",
+      "\n",
+      "Loading required package: mctest\n",
+      "Loading required package: yhat\n"
+     ]
+    }
+   ],
+   "source": [
+    "usePackage('GGally')\n",
+    "usePackage('ggplot2')\n",
+    "usePackage('ppcor')\n",
+    "usePackage('openintro')\n",
+    "usePackage('stargazer')\n",
+    "usePackage('mctest')\n",
+    "usePackage('yhat')\n",
+    "# usePackage('lmtest') # This package is for the bp test further down. It did not work in Jupyter notebook for Mac but it does work in Windows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<table width=\"100%\" summary=\"page for ncbirths {openintro}\"><tr><td>ncbirths {openintro}</td><td style=\"text-align: right;\">R Documentation</td></tr></table>\n",
+       "\n",
+       "<h2>\n",
+       "North Carolina births\n",
+       "</h2>\n",
+       "\n",
+       "<h3>Description</h3>\n",
+       "\n",
+       "<p>In 2004, the state of North Carolina released to the public a large data set containing information on births recorded in this state.  This data set has been of interest to medical researchers who are studying the relation between habits and practices of expectant mothers and the birth of their children. This is a random sample of 1,000 cases from this data set.\n",
+       "</p>\n",
+       "\n",
+       "\n",
+       "<h3>Usage</h3>\n",
+       "\n",
+       "<pre>data(ncbirths)</pre>\n",
+       "\n",
+       "\n",
+       "<h3>Format</h3>\n",
+       "\n",
+       "<p>A data frame with 1000 observations on the following 13 variables.\n",
+       "</p>\n",
+       "\n",
+       "<dl>\n",
+       "<dt><code>fage</code></dt><dd><p>Father's age in years. </p>\n",
+       "</dd>\n",
+       "<dt><code>mage</code></dt><dd><p>Mother's age in years. </p>\n",
+       "</dd>\n",
+       "<dt><code>mature</code></dt><dd><p>Maturity status of mother. </p>\n",
+       "</dd>\n",
+       "<dt><code>weeks</code></dt><dd><p>Length of pregnancy in weeks. </p>\n",
+       "</dd>\n",
+       "<dt><code>premie</code></dt><dd><p>Whether the birth was classified as premature (premie) or \tfull-term. </p>\n",
+       "</dd>\n",
+       "<dt><code>visits</code></dt><dd><p>Number of hospital visits during pregnancy. </p>\n",
+       "</dd>\n",
+       "<dt><code>gained</code></dt><dd><p>Weight gained by mother during pregnancy in pounds. </p>\n",
+       "</dd>\n",
+       "<dt><code>weight</code></dt><dd><p>Weight of the baby at birth in pounds. </p>\n",
+       "</dd>\n",
+       "<dt><code>lowbirthweight</code></dt><dd><p>Whether baby was classified as low birthweight (<code>low</code>) or not (<code>not low</code>). </p>\n",
+       "</dd>\n",
+       "<dt><code>gender</code></dt><dd><p>Gender of the baby, <code>female</code> or <code>male</code>. </p>\n",
+       "</dd>\n",
+       "<dt><code>habit</code></dt><dd><p>Status of the mother as a <code>nonsmoker</code> or a \t<code>smoker</code>. </p>\n",
+       "</dd>\n",
+       "<dt><code>marital</code></dt><dd><p>Whether mother is <code>married</code> or <code>not married</code> at birth. </p>\n",
+       "</dd>\n",
+       "<dt><code>whitemom</code></dt><dd><p>Whether mom is <code>white</code> or <code>not white</code>. </p>\n",
+       "</dd>\n",
+       "</dl>\n",
+       "\n",
+       "\n",
+       "\n",
+       "<h3>Examples</h3>\n",
+       "\n",
+       "<pre>\n",
+       "data(ncbirths)\n",
+       "boxPlot(ncbirths$weight, fact = ncbirths$habit)\n",
+       "boxPlot(ncbirths$visits, fact = ncbirths$whitemom)\n",
+       "boxPlot(ncbirths$gained, fact = ncbirths$mature)\n",
+       "</pre>\n",
+       "\n",
+       "<hr /><div style=\"text-align: center;\">[Package <em>openintro</em> version 1.7.1 ]</div>"
+      ],
+      "text/latex": [
+       "\\inputencoding{utf8}\n",
+       "\\HeaderA{ncbirths}{North Carolina births}{ncbirths}\n",
+       "\\keyword{datasets}{ncbirths}\n",
+       "%\n",
+       "\\begin{Description}\\relax\n",
+       "In 2004, the state of North Carolina released to the public a large data set containing information on births recorded in this state.  This data set has been of interest to medical researchers who are studying the relation between habits and practices of expectant mothers and the birth of their children. This is a random sample of 1,000 cases from this data set.\n",
+       "\\end{Description}\n",
+       "%\n",
+       "\\begin{Usage}\n",
+       "\\begin{verbatim}\n",
+       "data(ncbirths)\n",
+       "\\end{verbatim}\n",
+       "\\end{Usage}\n",
+       "%\n",
+       "\\begin{Format}\n",
+       "A data frame with 1000 observations on the following 13 variables.\n",
+       "\\begin{description}\n",
+       "\n",
+       "\\item[\\code{fage}] Father's age in years. \n",
+       "\\item[\\code{mage}] Mother's age in years. \n",
+       "\\item[\\code{mature}] Maturity status of mother. \n",
+       "\\item[\\code{weeks}] Length of pregnancy in weeks. \n",
+       "\\item[\\code{premie}] Whether the birth was classified as premature (premie) or \tfull-term. \n",
+       "\\item[\\code{visits}] Number of hospital visits during pregnancy. \n",
+       "\\item[\\code{gained}] Weight gained by mother during pregnancy in pounds. \n",
+       "\\item[\\code{weight}] Weight of the baby at birth in pounds. \n",
+       "\\item[\\code{lowbirthweight}] Whether baby was classified as low birthweight (\\code{low}) or not (\\code{not low}). \n",
+       "\\item[\\code{gender}] Gender of the baby, \\code{female} or \\code{male}. \n",
+       "\\item[\\code{habit}] Status of the mother as a \\code{nonsmoker} or a \t\\code{smoker}. \n",
+       "\\item[\\code{marital}] Whether mother is \\code{married} or \\code{not married} at birth. \n",
+       "\\item[\\code{whitemom}] Whether mom is \\code{white} or \\code{not white}. \n",
+       "\n",
+       "\\end{description}\n",
+       "\n",
+       "\\end{Format}\n",
+       "%\n",
+       "\\begin{Examples}\n",
+       "\\begin{ExampleCode}\n",
+       "data(ncbirths)\n",
+       "boxPlot(ncbirths$weight, fact = ncbirths$habit)\n",
+       "boxPlot(ncbirths$visits, fact = ncbirths$whitemom)\n",
+       "boxPlot(ncbirths$gained, fact = ncbirths$mature)\n",
+       "\\end{ExampleCode}\n",
+       "\\end{Examples}"
+      ],
+      "text/plain": [
+       "ncbirths               package:openintro               R Documentation\n",
+       "\n",
+       "_\bN_\bo_\br_\bt_\bh _\bC_\ba_\br_\bo_\bl_\bi_\bn_\ba _\bb_\bi_\br_\bt_\bh_\bs\n",
+       "\n",
+       "_\bD_\be_\bs_\bc_\br_\bi_\bp_\bt_\bi_\bo_\bn:\n",
+       "\n",
+       "     In 2004, the state of North Carolina released to the public a\n",
+       "     large data set containing information on births recorded in this\n",
+       "     state.  This data set has been of interest to medical researchers\n",
+       "     who are studying the relation between habits and practices of\n",
+       "     expectant mothers and the birth of their children. This is a\n",
+       "     random sample of 1,000 cases from this data set.\n",
+       "\n",
+       "_\bU_\bs_\ba_\bg_\be:\n",
+       "\n",
+       "     data(ncbirths)\n",
+       "     \n",
+       "_\bF_\bo_\br_\bm_\ba_\bt:\n",
+       "\n",
+       "     A data frame with 1000 observations on the following 13 variables.\n",
+       "\n",
+       "     'fage' Father's age in years.\n",
+       "\n",
+       "     'mage' Mother's age in years.\n",
+       "\n",
+       "     'mature' Maturity status of mother.\n",
+       "\n",
+       "     'weeks' Length of pregnancy in weeks.\n",
+       "\n",
+       "     'premie' Whether the birth was classified as premature (premie) or\n",
+       "          full-term.\n",
+       "\n",
+       "     'visits' Number of hospital visits during pregnancy.\n",
+       "\n",
+       "     'gained' Weight gained by mother during pregnancy in pounds.\n",
+       "\n",
+       "     'weight' Weight of the baby at birth in pounds.\n",
+       "\n",
+       "     'lowbirthweight' Whether baby was classified as low birthweight\n",
+       "          ('low') or not ('not low').\n",
+       "\n",
+       "     'gender' Gender of the baby, 'female' or 'male'.\n",
+       "\n",
+       "     'habit' Status of the mother as a 'nonsmoker' or a 'smoker'.\n",
+       "\n",
+       "     'marital' Whether mother is 'married' or 'not married' at birth.\n",
+       "\n",
+       "     'whitemom' Whether mom is 'white' or 'not white'.\n",
+       "\n",
+       "_\bE_\bx_\ba_\bm_\bp_\bl_\be_\bs:\n",
+       "\n",
+       "     data(ncbirths)\n",
+       "     boxPlot(ncbirths$weight, fact = ncbirths$habit)\n",
+       "     boxPlot(ncbirths$visits, fact = ncbirths$whitemom)\n",
+       "     boxPlot(ncbirths$gained, fact = ncbirths$mature)\n",
+       "     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Look at the data dictionary\n",
+    "help(ncbirths)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# From here onwards we will assign the data set to a new variable so the original dataframe remains untouched and in its originial condition.\n",
+    "df <- ncbirths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'data.frame':\t1000 obs. of  13 variables:\n",
+      " $ fage          : int  NA NA 19 21 NA NA 18 17 NA 20 ...\n",
+      " $ mage          : int  13 14 15 15 15 15 15 15 16 16 ...\n",
+      " $ mature        : Factor w/ 2 levels \"mature mom\",\"younger mom\": 2 2 2 2 2 2 2 2 2 2 ...\n",
+      " $ weeks         : int  39 42 37 41 39 38 37 35 38 37 ...\n",
+      " $ premie        : Factor w/ 2 levels \"full term\",\"premie\": 1 1 1 1 1 1 1 2 1 1 ...\n",
+      " $ visits        : int  10 15 11 6 9 19 12 5 9 13 ...\n",
+      " $ marital       : Factor w/ 2 levels \"married\",\"not married\": 1 1 1 1 1 1 1 1 1 1 ...\n",
+      " $ gained        : int  38 20 38 34 27 22 76 15 NA 52 ...\n",
+      " $ weight        : num  7.63 7.88 6.63 8 6.38 5.38 8.44 4.69 8.81 6.94 ...\n",
+      " $ lowbirthweight: Factor w/ 2 levels \"low\",\"not low\": 2 2 2 2 2 1 2 1 2 2 ...\n",
+      " $ gender        : Factor w/ 2 levels \"female\",\"male\": 2 2 1 2 1 2 2 2 2 1 ...\n",
+      " $ habit         : Factor w/ 2 levels \"nonsmoker\",\"smoker\": 1 1 1 1 1 1 1 1 1 1 ...\n",
+      " $ whitemom      : Factor w/ 2 levels \"not white\",\"white\": 1 1 2 2 1 1 1 1 2 2 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Look at the structure of the data\n",
+    "# Notice how there are continous and categorical variables\n",
+    "# Categorical variables are known as factor type data in R. The sample below has factors with different levels.\n",
+    "# These factors can be given an order. This would make them ordinal variables.\n",
+    "str(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>fage</th><th scope=col>mage</th><th scope=col>mature</th><th scope=col>weeks</th><th scope=col>premie</th><th scope=col>visits</th><th scope=col>marital</th><th scope=col>gained</th><th scope=col>weight</th><th scope=col>lowbirthweight</th><th scope=col>gender</th><th scope=col>habit</th><th scope=col>whitemom</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>NA         </td><td>13         </td><td>younger mom</td><td>39         </td><td>full term  </td><td>10         </td><td>married    </td><td>38         </td><td>7.63       </td><td>not low    </td><td>male       </td><td>nonsmoker  </td><td>not white  </td></tr>\n",
+       "\t<tr><td>NA         </td><td>14         </td><td>younger mom</td><td>42         </td><td>full term  </td><td>15         </td><td>married    </td><td>20         </td><td>7.88       </td><td>not low    </td><td>male       </td><td>nonsmoker  </td><td>not white  </td></tr>\n",
+       "\t<tr><td>19         </td><td>15         </td><td>younger mom</td><td>37         </td><td>full term  </td><td>11         </td><td>married    </td><td>38         </td><td>6.63       </td><td>not low    </td><td>female     </td><td>nonsmoker  </td><td>white      </td></tr>\n",
+       "\t<tr><td>21         </td><td>15         </td><td>younger mom</td><td>41         </td><td>full term  </td><td> 6         </td><td>married    </td><td>34         </td><td>8.00       </td><td>not low    </td><td>male       </td><td>nonsmoker  </td><td>white      </td></tr>\n",
+       "\t<tr><td>NA         </td><td>15         </td><td>younger mom</td><td>39         </td><td>full term  </td><td> 9         </td><td>married    </td><td>27         </td><td>6.38       </td><td>not low    </td><td>female     </td><td>nonsmoker  </td><td>not white  </td></tr>\n",
+       "\t<tr><td>NA         </td><td>15         </td><td>younger mom</td><td>38         </td><td>full term  </td><td>19         </td><td>married    </td><td>22         </td><td>5.38       </td><td>low        </td><td>male       </td><td>nonsmoker  </td><td>not white  </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllllllllll}\n",
+       " fage & mage & mature & weeks & premie & visits & marital & gained & weight & lowbirthweight & gender & habit & whitemom\\\\\n",
+       "\\hline\n",
+       "\t NA          & 13          & younger mom & 39          & full term   & 10          & married     & 38          & 7.63        & not low     & male        & nonsmoker   & not white  \\\\\n",
+       "\t NA          & 14          & younger mom & 42          & full term   & 15          & married     & 20          & 7.88        & not low     & male        & nonsmoker   & not white  \\\\\n",
+       "\t 19          & 15          & younger mom & 37          & full term   & 11          & married     & 38          & 6.63        & not low     & female      & nonsmoker   & white      \\\\\n",
+       "\t 21          & 15          & younger mom & 41          & full term   &  6          & married     & 34          & 8.00        & not low     & male        & nonsmoker   & white      \\\\\n",
+       "\t NA          & 15          & younger mom & 39          & full term   &  9          & married     & 27          & 6.38        & not low     & female      & nonsmoker   & not white  \\\\\n",
+       "\t NA          & 15          & younger mom & 38          & full term   & 19          & married     & 22          & 5.38        & low         & male        & nonsmoker   & not white  \\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "fage | mage | mature | weeks | premie | visits | marital | gained | weight | lowbirthweight | gender | habit | whitemom | \n",
+       "|---|---|---|---|---|---|\n",
+       "| NA          | 13          | younger mom | 39          | full term   | 10          | married     | 38          | 7.63        | not low     | male        | nonsmoker   | not white   | \n",
+       "| NA          | 14          | younger mom | 42          | full term   | 15          | married     | 20          | 7.88        | not low     | male        | nonsmoker   | not white   | \n",
+       "| 19          | 15          | younger mom | 37          | full term   | 11          | married     | 38          | 6.63        | not low     | female      | nonsmoker   | white       | \n",
+       "| 21          | 15          | younger mom | 41          | full term   |  6          | married     | 34          | 8.00        | not low     | male        | nonsmoker   | white       | \n",
+       "| NA          | 15          | younger mom | 39          | full term   |  9          | married     | 27          | 6.38        | not low     | female      | nonsmoker   | not white   | \n",
+       "| NA          | 15          | younger mom | 38          | full term   | 19          | married     | 22          | 5.38        | low         | male        | nonsmoker   | not white   | \n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  fage mage mature      weeks premie    visits marital gained weight\n",
+       "1 NA   13   younger mom 39    full term 10     married 38     7.63  \n",
+       "2 NA   14   younger mom 42    full term 15     married 20     7.88  \n",
+       "3 19   15   younger mom 37    full term 11     married 38     6.63  \n",
+       "4 21   15   younger mom 41    full term  6     married 34     8.00  \n",
+       "5 NA   15   younger mom 39    full term  9     married 27     6.38  \n",
+       "6 NA   15   younger mom 38    full term 19     married 22     5.38  \n",
+       "  lowbirthweight gender habit     whitemom \n",
+       "1 not low        male   nonsmoker not white\n",
+       "2 not low        male   nonsmoker not white\n",
+       "3 not low        female nonsmoker white    \n",
+       "4 not low        male   nonsmoker white    \n",
+       "5 not low        female nonsmoker not white\n",
+       "6 low            male   nonsmoker not white"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Look at a sample of the data\n",
+    "head(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "      fage            mage            mature        weeks             premie   \n",
+       " Min.   :14.00   Min.   :13   mature mom :133   Min.   :20.00   full term:846  \n",
+       " 1st Qu.:25.00   1st Qu.:22   younger mom:867   1st Qu.:37.00   premie   :152  \n",
+       " Median :30.00   Median :27                     Median :39.00   NA's     :  2  \n",
+       " Mean   :30.26   Mean   :27                     Mean   :38.33                  \n",
+       " 3rd Qu.:35.00   3rd Qu.:32                     3rd Qu.:40.00                  \n",
+       " Max.   :55.00   Max.   :50                     Max.   :45.00                  \n",
+       " NA's   :171                                    NA's   :2                      \n",
+       "     visits            marital        gained          weight      \n",
+       " Min.   : 0.0   married    :386   Min.   : 0.00   Min.   : 1.000  \n",
+       " 1st Qu.:10.0   not married:613   1st Qu.:20.00   1st Qu.: 6.380  \n",
+       " Median :12.0   NA's       :  1   Median :30.00   Median : 7.310  \n",
+       " Mean   :12.1                     Mean   :30.33   Mean   : 7.101  \n",
+       " 3rd Qu.:15.0                     3rd Qu.:38.00   3rd Qu.: 8.060  \n",
+       " Max.   :30.0                     Max.   :85.00   Max.   :11.750  \n",
+       " NA's   :9                        NA's   :27                      \n",
+       " lowbirthweight    gender          habit          whitemom  \n",
+       " low    :111    female:503   nonsmoker:873   not white:284  \n",
+       " not low:889    male  :497   smoker   :126   white    :714  \n",
+       "                             NA's     :  1   NA's     :  2  \n",
+       "                                                            \n",
+       "                                                            \n",
+       "                                                            \n",
+       "                                                            "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Summary statistics\n",
+    "summary(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Creating ordinal variables**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creating levels for factor type data\n",
+    "\n",
+    "df$lowbirthweight <- factor(df$lowbirthweight,\n",
+    "                                                  levels = c('low', 'not low'),\n",
+    "                                                  labels = c('low', 'not low'),\n",
+    "                            ordered=TRUE) # If ordered is left as FALSE then there won't be any order beyong the alphabetic order.\n",
+    "\n",
+    "df$habit <- factor(df$habit,\n",
+    "                                                  levels = c('nonsmoker', 'smoker'),\n",
+    "                                                  labels = c('nonsmoker', 'smoker'),\n",
+    "                            ordered=TRUE) # If ordered is left as FALSE then there won't be any order beyong the alphabetic order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'data.frame':\t1000 obs. of  13 variables:\n",
+      " $ fage          : int  NA NA 19 21 NA NA 18 17 NA 20 ...\n",
+      " $ mage          : int  13 14 15 15 15 15 15 15 16 16 ...\n",
+      " $ mature        : Factor w/ 2 levels \"mature mom\",\"younger mom\": 2 2 2 2 2 2 2 2 2 2 ...\n",
+      " $ weeks         : int  39 42 37 41 39 38 37 35 38 37 ...\n",
+      " $ premie        : Factor w/ 2 levels \"full term\",\"premie\": 1 1 1 1 1 1 1 2 1 1 ...\n",
+      " $ visits        : int  10 15 11 6 9 19 12 5 9 13 ...\n",
+      " $ marital       : Factor w/ 2 levels \"married\",\"not married\": 1 1 1 1 1 1 1 1 1 1 ...\n",
+      " $ gained        : int  38 20 38 34 27 22 76 15 NA 52 ...\n",
+      " $ weight        : num  7.63 7.88 6.63 8 6.38 5.38 8.44 4.69 8.81 6.94 ...\n",
+      " $ lowbirthweight: Ord.factor w/ 2 levels \"low\"<\"not low\": 2 2 2 2 2 1 2 1 2 2 ...\n",
+      " $ gender        : Factor w/ 2 levels \"female\",\"male\": 2 2 1 2 1 2 2 2 2 1 ...\n",
+      " $ habit         : Ord.factor w/ 2 levels \"nonsmoker\"<\"smoker\": 1 1 1 1 1 1 1 1 1 1 ...\n",
+      " $ whitemom      : Factor w/ 2 levels \"not white\",\"white\": 1 1 2 2 1 1 1 1 2 2 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Notice how the 'habit' column is has an order to the values of the data column.\n",
+    "# This is useful for understanding regression. We'll use the variable 'habit' later on.\n",
+    "str(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Finally, don't be afraid of looking at the data in Excel.\n",
+    "# Notice the ordinal nature of the variables are not apparent.\n",
+    "write.csv(df, \"df.csv\", row.names=FALSE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Cleaning up the dataset**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop the missing data from the dataframe for the purpose of this exercise\n",
+    "# How to report and treat missing data is important in your analysis. We won't disucss this in detail this session.\n",
+    "# To keep things simple we will drop all rows with missing data.\n",
+    "# Source code: https://stackoverflow.com/questions/4862178/remove-rows-with-all-or-some-nas-missing-values-in-data-frame#4862502\n",
+    "df <- na.omit(df) # We are overwriting the df."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "'data.frame':\t800 obs. of  13 variables:\n",
+      " $ fage          : int  19 21 18 17 20 30 21 14 16 20 ...\n",
+      " $ mage          : int  15 15 15 15 16 16 16 16 16 17 ...\n",
+      " $ mature        : Factor w/ 2 levels \"mature mom\",\"younger mom\": 2 2 2 2 2 2 2 2 2 2 ...\n",
+      " $ weeks         : int  37 41 37 35 37 45 38 40 24 40 ...\n",
+      " $ premie        : Factor w/ 2 levels \"full term\",\"premie\": 1 1 1 2 1 1 1 1 2 1 ...\n",
+      " $ visits        : int  11 6 12 5 13 9 15 12 5 8 ...\n",
+      " $ marital       : Factor w/ 2 levels \"married\",\"not married\": 1 1 1 1 1 1 1 1 1 1 ...\n",
+      " $ gained        : int  38 34 76 15 52 28 75 9 12 20 ...\n",
+      " $ weight        : num  6.63 8 8.44 4.69 6.94 7.44 7.56 5.81 1.5 8.25 ...\n",
+      " $ lowbirthweight: Ord.factor w/ 2 levels \"low\"<\"not low\": 2 2 2 1 2 2 2 2 1 2 ...\n",
+      " $ gender        : Factor w/ 2 levels \"female\",\"male\": 1 2 2 2 1 2 1 1 2 1 ...\n",
+      " $ habit         : Ord.factor w/ 2 levels \"nonsmoker\"<\"smoker\": 1 1 1 1 1 1 2 2 1 1 ...\n",
+      " $ whitemom      : Factor w/ 2 levels \"not white\",\"white\": 2 2 1 1 2 2 2 2 1 2 ...\n",
+      " - attr(*, \"na.action\")= 'omit' Named int  1 2 5 6 9 12 13 14 16 21 ...\n",
+      "  ..- attr(*, \"names\")= chr  \"1\" \"2\" \"5\" \"6\" ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Notice how we have 200 rows removed from the total of 1000.\n",
+    "str(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Plot your data**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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K8fH9HRBj2QSn5TKashdwvNN/oWnjnW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ulFTLEh/S9WRSwOza4WEr0rk1IK23BdGu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\nOQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "source": "R display func"
+    }
+   ],
+   "source": [
+    "# The ggpairs package allows for seeing the relationship of all continous variables in two by two comparisons and the distribution of each variable.\n",
+    "# Drop column from data frame with a non-continous variable.\n",
+    "drops <- c('mature', 'premie', 'marital', 'lowbirthweight', 'gender', 'habit', 'whitemom')\n",
+    "df_cor <- df[ , !(names(df) %in% drops)]\n",
+    "# Print correlations\n",
+    "print(ggpairs(df_cor))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {},
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Saving 6.67 x 6.67 in image\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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v2SiNTcaUKkophUYd7SWDyqTSTbj5N0pGAoIskodYU5D2OTzKt1ofMgWwXPkRRh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\n/9fXAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "plot without title"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "source": "R display func"
+    }
+   ],
+   "source": [
+    "# First plot the relationship you want to use in your regression\n",
+    "p1 <- ggplot(df,aes(y = weight,x = weeks)) +\n",
+    "        geom_point() +\n",
+    "        geom_smooth(method=\"lm\")\n",
+    "\n",
+    "p1\n",
+    "\n",
+    "ggsave('p1.pdf', plot = p1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1 <- lm(weight ~ weeks, data = df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "Call:\n",
+       "lm(formula = weight ~ weeks, data = df)\n",
+       "\n",
+       "Residuals:\n",
+       "    Min      1Q  Median      3Q     Max \n",
+       "-3.3371 -0.6961 -0.0534  0.7211  3.6594 \n",
+       "\n",
+       "Coefficients:\n",
+       "            Estimate Std. Error t value Pr(>|t|)    \n",
+       "(Intercept)  -5.5908     0.5513  -10.14   <2e-16 ***\n",
+       "weeks         0.3327     0.0143   23.27   <2e-16 ***\n",
+       "---\n",
+       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+       "\n",
+       "Residual standard error: 1.113 on 798 degrees of freedom\n",
+       "Multiple R-squared:  0.4044,\tAdjusted R-squared:  0.4036 \n",
+       "F-statistic: 541.7 on 1 and 798 DF,  p-value: < 2.2e-16\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "summary(m1)\n",
+    "# Adjusted R squared is the proportion of the variance in the data that's explained by the model. \n",
+    "# From 0 to 100. Usually above 85 is good, this high number not achieved in a lot of clinicla work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Startgazer is a useful method to create regression tables for presentation.\n",
+    "# You can costumize it using this link: https://www.jakeruss.com/cheatsheets/stargazer/\n",
+    "# Simply copy/paste the output of the HTML file into Word. Or you can use R markdown.\n",
+    "invisible(capture.output(stargazer(m1, out = 'result1.html', model.names = FALSE))) # Change to 'TRUE' to display OLS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Model quality**\n",
+    "\n",
+    "Akaike Information Criterion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "2445.331012977"
+      ],
+      "text/latex": [
+       "2445.331012977"
+      ],
+      "text/markdown": [
+       "2445.331012977"
+      ],
+      "text/plain": [
+       "[1] 2445.331"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A measure of model quality and simplicity. The lower the value the better.\n",
+    "# You want to select a model with the least complexity that explains the most variance.\n",
+    "AIC(m1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Assumptions of the linear regression**\n",
+    "\n",
+    "1. The relationship is linear (already checked in the first plotting)\n",
+    "2. The distrubtion of the residuals are normal (the plot below)\n",
+    "3. The variance (standard deviation) of the outcome y is constant over the predictor x (the test below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Bu6z0eCxlz7+TN6GvcWUgtZuwgxty0k8z5M32t5ayXdXEjnE+P7NS2k6UJgICT9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\nCQAF/gcnjf5m6O2fzQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "Plot with title \"\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "source": "R display func"
+    }
+   ],
+   "source": [
+    "par(mfrow=c(2,2)) # to put the plots 2 by 2.\n",
+    "\n",
+    "# The first plot is used to identify heteroscedasticity with residual plots. You want it to be random.\n",
+    "# The second plot shows the normal distribution of the residuals.\n",
+    "# You can plot these in ggplot for a better visualization as well.\n",
+    "\n",
+    "plot(m1)\n",
+    "\n",
+    "# Formal checking of Heteroscedasticity with the Breusch-Pagan test. The p value must be non-significant.\n",
+    "# bptest(m1) # Currently doesn't work in Jupyter on Mac, but worked in Windows version of Jupyter Lab's Anaconda\n",
+    "\n",
+    "# Cook's distance measures the influence of a given data point. \n",
+    "# These influential points can be omitted if beyond a certain threshold of influence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can also include categorical variables such as smoking habit (yes/no).\n",
+    "# We made 'habit' an ordinal variable for this purpose. Non-smokers became the reference.\n",
+    "# If more than one category you'd need dummy variables.\n",
+    "m2 <- lm(weight ~ weeks + habit, data = df)\n",
+    "invisible(capture.output(stargazer(m2, out = 'result2.html')))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "2441.60896643476"
+      ],
+      "text/latex": [
+       "2441.60896643476"
+      ],
+      "text/markdown": [
+       "2441.60896643476"
+      ],
+      "text/plain": [
+       "[1] 2441.609"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "AIC(m2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Tools to assess existence of multicollinearity**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Notice how mother's age and father's age are highly correlated in the graph above. This is called collinearity, which can lead to distorted estimates.\n",
+    "# We will discuss this again further down.\n",
+    "m3 <- lm(weight ~ weeks + mage + fage, data = df)\n",
+    "invisible(capture.output(stargazer(m3, out = 'result3.html')))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop column from data frame with a non-continous variable.\n",
+    "# We are about to assess all predictor variables that are continous so we will remove categorical ones and \n",
+    "# 'weight' which is the outcome.\n",
+    "drops <- c('mature', 'premie', 'marital', 'lowbirthweight', 'gender', 'habit', 'whitemom', 'weight')\n",
+    "df_cor <- df[ , !(names(df) %in% drops)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Call:\n",
+      "omcdiag(x = df_cor, y = df$weight)\n",
+      "\n",
+      "\n",
+      "Overall Multicollinearity Diagnostics\n",
+      "\n",
+      "                       MC Results detection\n",
+      "Determinant |X'X|:         0.3633         0\n",
+      "Farrar Chi-Square:       805.9693         1\n",
+      "Red Indicator:             0.2616         0\n",
+      "Sum of Lambda Inverse:     8.3075         0\n",
+      "Theil's Method:           -0.3511         0\n",
+      "Condition Number:         48.5278         1\n",
+      "\n",
+      "1 --> COLLINEARITY is detected by the test \n",
+      "0 --> COLLINEARITY is not detected by the test\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(omcdiag(x = df_cor, y = df$weight))\n",
+    "\n",
+    "# Error is most likely because the outcome variable is still in the dataframe.\n",
+    "# As such the outcome variable is also listed as a predictor.\n",
+    "# The error tells us one of the variables has exactly the same characteristic as the outcome variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Call:\n",
+      "imcdiag(x = df_cor, y = df$weight, vif = 2.5, tol = 0.4, all = TRUE)\n",
+      "\n",
+      "\n",
+      "All Individual Multicollinearity Diagnostics in 0 or 1 \n",
+      "\n",
+      "       VIF TOL Wi Fi Leamer CVIF Klein\n",
+      "fage     1   1  1  1      0    0     1\n",
+      "mage     1   1  1  1      0    0     1\n",
+      "weeks    0   0  1  1      0    0     0\n",
+      "visits   0   0  1  1      0    0     0\n",
+      "gained   0   0  0  1      0    0     0\n",
+      "\n",
+      "1 --> COLLINEARITY is detected by the test \n",
+      "0 --> COLLINEARITY is not detected by the test\n",
+      "\n",
+      "fage , mage , visits , coefficient(s) are non-significant may be due to multicollinearity\n",
+      "\n",
+      "R-square of y on all x: 0.4229 \n",
+      "\n",
+      "* use method argument to check which regressors may be the reason of collinearity\n",
+      "===================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# VIF and TOl are the same thing but inversed. You set the threshold or leave the default values.\n",
+    "print(imcdiag(x = df_cor, y = df$weight, vif = 2.5, tol = 0.40, all = TRUE))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NULL\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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WKN2gdD4LSKdNvDmNM+0ib/AiB9JM9c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\nfusPAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "Plot with title \"Eigenvalues Plot\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "source": "R display func"
+    }
+   ],
+   "source": [
+    "print(mc.plot(x = df_cor, y = df$weight, vif = 2.5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Tools to support interpreting multiple regression in the face of multicollinearity.**\n",
+    "\n",
+    "Review: https://www.ncbi.nlm.nih.gov/pubmed/22457655"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<table width=\"100%\" summary=\"page for yhat-package {yhat}\"><tr><td>yhat-package {yhat}</td><td style=\"text-align: right;\">R Documentation</td></tr></table>\n",
+       "\n",
+       "<h2>Interpreting Regression Effects</h2>\n",
+       "\n",
+       "<h3>Description</h3>\n",
+       "\n",
+       "<p>The purpose of this package is to provide methods to interpret multiple linear regression and canonical correlation results including beta weights, structure coefficients, validity coefficients, product measures, relative weights, all-possible-subsets regression, dominance analysis, commonality analysis, and adjusted effect sizes.\n",
+       "</p>\n",
+       "\n",
+       "\n",
+       "<h3>Details</h3>\n",
+       "\n",
+       "\n",
+       "<table summary=\"Rd table\">\n",
+       "<tr>\n",
+       " <td style=\"text-align: left;\">\n",
+       "Package: </td><td style=\"text-align: left;\"> yhat</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       " <td style=\"text-align: left;\">\n",
+       "Type: </td><td style=\"text-align: left;\"> Package</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       " <td style=\"text-align: left;\">\n",
+       "Version: </td><td style=\"text-align: left;\"> 2.0-0</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       " <td style=\"text-align: left;\">\n",
+       "Date: </td><td style=\"text-align: left;\"> 2013-09-10</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       " <td style=\"text-align: left;\">\n",
+       "License: </td><td style=\"text-align: left;\"> GPL (&gt;= 2)</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       " <td style=\"text-align: left;\">\n",
+       "LazyLoad: </td><td style=\"text-align: left;\"> yes</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       " <td style=\"text-align: left;\">\n",
+       "</td>\n",
+       "</tr>\n",
+       "\n",
+       "</table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "<h3>Author(s)</h3>\n",
+       "\n",
+       "<p>Kim Nimon &lt;kim.nimon@gmail.com&gt;, Fred L. Oswald, J. Kyle Roberts</p>\n",
+       "\n",
+       "\n",
+       "<h3>References</h3>\n",
+       "\n",
+       "<p>Beaton, A. E. (1973) <em>Commonality</em>. (ERIC Document Reproduction\n",
+       "Service No. ED111829)\n",
+       "</p>\n",
+       "<p>Butts, C. T. (2009). <em>yacca: Yet Another Canonical Correlation\n",
+       "Analysis Package</em>. R package version 1.1.\n",
+       "</p>\n",
+       "<p>Mood, A. M. (1969) Macro-analysis of the American educational \n",
+       "system. <em>Operations Research</em>, <b>17</b>, 770-784.\n",
+       "</p>\n",
+       "<p>Nimon, K., Lewis, M., Kane, R. &amp; Haynes, R. M. (2008) An R package\n",
+       "to compute commonality coefficients in the multiple regression\n",
+       "case: An introduction to the package and a practical example. \n",
+       "<em>Behavior Research Methods</em>, <b>40</b>(2), 457-466.\n",
+       "</p>\n",
+       "<p>Nimon, K., &amp; Oswald, F. L. (2013). Understanding the results of multiple linear regression: Beyond standardized regression coefficients. <em>Organizational Research Methods</em>, <em>16</em>, \n",
+       "650-674.\n",
+       "</p>\n",
+       "\n",
+       "\n",
+       "<h3>See Also</h3>\n",
+       "\n",
+       "<p><code>regr</code>\n",
+       "<code>commonalityCoefficients</code>\n",
+       "<code>canonCommonality</code>\n",
+       "<code>calc.yhat</code>\n",
+       "<code>boot.yhat</code>\n",
+       "<code>booteval.yhat</code>\n",
+       "<code>plotCI.yhat</code>\n",
+       "<code>aps</code>\n",
+       "<code>commonality</code>\n",
+       "<code>dominance</code>\n",
+       "<code>dombin</code>\n",
+       "<code>rlw</code>\n",
+       "</p>\n",
+       "\n",
+       "<hr /><div style=\"text-align: center;\">[Package <em>yhat</em> version 2.0-0 ]</div>"
+      ],
+      "text/latex": [
+       "\\inputencoding{utf8}\n",
+       "\\HeaderA{yhat-package}{Interpreting Regression Effects}{yhat.Rdash.package}\n",
+       "\\aliasA{yhat}{yhat-package}{yhat}\n",
+       "\\keyword{package}{yhat-package}\n",
+       "%\n",
+       "\\begin{Description}\\relax\n",
+       "The purpose of this package is to provide methods to interpret multiple linear regression and canonical correlation results including beta weights, structure coefficients, validity coefficients, product measures, relative weights, all-possible-subsets regression, dominance analysis, commonality analysis, and adjusted effect sizes.\n",
+       "\\end{Description}\n",
+       "%\n",
+       "\\begin{Details}\\relax\n",
+       "\n",
+       "\\Tabular{ll}{\n",
+       "Package: & yhat\\\\{}\n",
+       "Type: & Package\\\\{}\n",
+       "Version: & 2.0-0\\\\{}\n",
+       "Date: & 2013-09-10\\\\{}\n",
+       "License: & GPL (>= 2)\\\\{}\n",
+       "LazyLoad: & yes\\\\{}\n",
+       "}\n",
+       "\n",
+       "\\end{Details}\n",
+       "%\n",
+       "\\begin{Author}\\relax\n",
+       "Kim Nimon <kim.nimon@gmail.com>, Fred L. Oswald, J. Kyle Roberts\n",
+       "\\end{Author}\n",
+       "%\n",
+       "\\begin{References}\\relax\n",
+       "Beaton, A. E. (1973) \\emph{Commonality}. (ERIC Document Reproduction\n",
+       "Service No. ED111829)\n",
+       "\n",
+       "Butts, C. T. (2009). \\emph{yacca: Yet Another Canonical Correlation\n",
+       "Analysis Package}. R package version 1.1.\n",
+       "\n",
+       "Mood, A. M. (1969) Macro-analysis of the American educational \n",
+       "system. \\emph{Operations Research}, \\bold{17}, 770-784.\n",
+       "\n",
+       "Nimon, K., Lewis, M., Kane, R. \\& Haynes, R. M. (2008) An R package\n",
+       "to compute commonality coefficients in the multiple regression\n",
+       "case: An introduction to the package and a practical example. \n",
+       "\\emph{Behavior Research Methods}, \\bold{40}(2), 457-466.\n",
+       "\n",
+       "Nimon, K., \\& Oswald, F. L. (2013). Understanding the results of multiple linear regression: Beyond standardized regression coefficients. \\emph{Organizational Research Methods}, \\emph{16}, \n",
+       "650-674.\n",
+       "\n",
+       "\\end{References}\n",
+       "%\n",
+       "\\begin{SeeAlso}\\relax\n",
+       "\\code{\\LinkA{regr}{regr}}\n",
+       "\\code{\\LinkA{commonalityCoefficients}{commonalityCoefficients}}\n",
+       "\\code{\\LinkA{canonCommonality}{canonCommonality}}\n",
+       "\\code{\\LinkA{calc.yhat}{calc.yhat}}\n",
+       "\\code{\\LinkA{boot.yhat}{boot.yhat}}\n",
+       "\\code{\\LinkA{booteval.yhat}{booteval.yhat}}\n",
+       "\\code{\\LinkA{plotCI.yhat}{plotCI.yhat}}\n",
+       "\\code{\\LinkA{aps}{aps}}\n",
+       "\\code{\\LinkA{commonality}{commonality}}\n",
+       "\\code{\\LinkA{dominance}{dominance}}\n",
+       "\\code{\\LinkA{dombin}{dombin}}\n",
+       "\\code{\\LinkA{rlw}{rlw}}\n",
+       "\\end{SeeAlso}"
+      ],
+      "text/plain": [
+       "yhat-package               package:yhat                R Documentation\n",
+       "\n",
+       "_\bI_\bn_\bt_\be_\br_\bp_\br_\be_\bt_\bi_\bn_\bg _\bR_\be_\bg_\br_\be_\bs_\bs_\bi_\bo_\bn _\bE_\bf_\bf_\be_\bc_\bt_\bs\n",
+       "\n",
+       "_\bD_\be_\bs_\bc_\br_\bi_\bp_\bt_\bi_\bo_\bn:\n",
+       "\n",
+       "     The purpose of this package is to provide methods to interpret\n",
+       "     multiple linear regression and canonical correlation results\n",
+       "     including beta weights, structure coefficients, validity\n",
+       "     coefficients, product measures, relative weights,\n",
+       "     all-possible-subsets regression, dominance analysis, commonality\n",
+       "     analysis, and adjusted effect sizes.\n",
+       "\n",
+       "_\bD_\be_\bt_\ba_\bi_\bl_\bs:\n",
+       "\n",
+       "       Package:   yhat       \n",
+       "       Type:      Package    \n",
+       "       Version:   2.0-0      \n",
+       "       Date:      2013-09-10 \n",
+       "       License:   GPL (>= 2) \n",
+       "       LazyLoad:  yes        \n",
+       "      \n",
+       "_\bA_\bu_\bt_\bh_\bo_\br(_\bs):\n",
+       "\n",
+       "     Kim Nimon <kim.nimon@gmail.com>, Fred L. Oswald, J. Kyle Roberts\n",
+       "\n",
+       "_\bR_\be_\bf_\be_\br_\be_\bn_\bc_\be_\bs:\n",
+       "\n",
+       "     Beaton, A. E. (1973) _Commonality_. (ERIC Document Reproduction\n",
+       "     Service No. ED111829)\n",
+       "\n",
+       "     Butts, C. T. (2009). _yacca: Yet Another Canonical Correlation\n",
+       "     Analysis Package_. R package version 1.1.\n",
+       "\n",
+       "     Mood, A. M. (1969) Macro-analysis of the American educational\n",
+       "     system. _Operations Research_, *17*, 770-784.\n",
+       "\n",
+       "     Nimon, K., Lewis, M., Kane, R. & Haynes, R. M. (2008) An R package\n",
+       "     to compute commonality coefficients in the multiple regression\n",
+       "     case: An introduction to the package and a practical example.\n",
+       "     _Behavior Research Methods_, *40*(2), 457-466.\n",
+       "\n",
+       "     Nimon, K., & Oswald, F. L. (2013). Understanding the results of\n",
+       "     multiple linear regression: Beyond standardized regression\n",
+       "     coefficients. _Organizational Research Methods_, _16_, 650-674.\n",
+       "\n",
+       "_\bS_\be_\be _\bA_\bl_\bs_\bo:\n",
+       "\n",
+       "     'regr' 'commonalityCoefficients' 'canonCommonality' 'calc.yhat'\n",
+       "     'boot.yhat' 'booteval.yhat' 'plotCI.yhat' 'aps' 'commonality'\n",
+       "     'dominance' 'dombin' 'rlw'\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Understand package\n",
+    "help('yhat')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<table width=\"100%\" summary=\"page for plotCI.yhat {yhat}\"><tr><td>plotCI.yhat {yhat}</td><td style=\"text-align: right;\">R Documentation</td></tr></table>\n",
+       "\n",
+       "<h2>Plot CIs from yhat</h2>\n",
+       "\n",
+       "<h3>Description</h3>\n",
+       "\n",
+       "<p>This function plots CIs that have been produced from /codebooteval.yhat.\n",
+       "</p>\n",
+       "\n",
+       "\n",
+       "<h3>Usage</h3>\n",
+       "\n",
+       "<pre>\n",
+       "  plotCI.yhat(sampStat, upperCI, lowerCI, pid=1:ncol(sampStat), nr=2, nc=2)\n",
+       "</pre>\n",
+       "\n",
+       "\n",
+       "<h3>Arguments</h3>\n",
+       "\n",
+       "<table summary=\"R argblock\">\n",
+       "<tr valign=\"top\"><td><code>sampStat</code></td>\n",
+       "<td>\n",
+       "\n",
+       "<p>Set of sample statistics\n",
+       "</p>\n",
+       "</td></tr>\n",
+       "<tr valign=\"top\"><td><code>upperCI</code></td>\n",
+       "<td>\n",
+       "\n",
+       "<p>Set of upper CIs\n",
+       "</p>\n",
+       "</td></tr>\n",
+       "<tr valign=\"top\"><td><code>lowerCI</code></td>\n",
+       "<td>\n",
+       "\n",
+       "<p>Set of lower CIs\n",
+       "</p>\n",
+       "</td></tr>\n",
+       "<tr valign=\"top\"><td><code>pid</code></td>\n",
+       "<td>\n",
+       "\n",
+       "<p>Which set of Metrics to plot (default to all)\n",
+       "</p>\n",
+       "</td></tr>\n",
+       "<tr valign=\"top\"><td><code>nr</code></td>\n",
+       "<td>\n",
+       "\n",
+       "<p>Number of rows (default = 2)\n",
+       "</p>\n",
+       "</td></tr>\n",
+       "<tr valign=\"top\"><td><code>nc</code></td>\n",
+       "<td>\n",
+       "\n",
+       "<p>Number of columns(default = 2)\n",
+       "</p>\n",
+       "</td></tr>\n",
+       "</table>\n",
+       "\n",
+       "\n",
+       "<h3>Details</h3>\n",
+       "\n",
+       "<p>This function plots CIs that have been produced from /codebooteval.yhat.   \n",
+       "</p>\n",
+       "\n",
+       "\n",
+       "<h3>Value</h3>\n",
+       "\n",
+       "<p>This returns a plot of CIs that have been produced from /codebooteval.yhat.\n",
+       "</p>\n",
+       "\n",
+       "\n",
+       "<h3>Author(s)</h3>\n",
+       "\n",
+       "<p> Kim Nimon &lt;kim.nimon@gmail.com&gt;</p>\n",
+       "\n",
+       "\n",
+       "<h3>References</h3>\n",
+       "\n",
+       "<p>Nimon, K., &amp; Oswald, F. L. (2013). Understanding the results of multiple linear regression: Beyond standardized regression coefficients. <em>Organizational Research Methods</em>, <em>16</em>, \n",
+       "650-674.\n",
+       "</p>\n",
+       "\n",
+       "\n",
+       "<h3>See Also</h3>\n",
+       "\n",
+       "<p><code>lm</code>\n",
+       "<code>calc.yhat</code>\n",
+       "<code>boot</code>\n",
+       "<code>booteval.yhat</code>\n",
+       "</p>\n",
+       "\n",
+       "\n",
+       "<h3>Examples</h3>\n",
+       "\n",
+       "<pre>\n",
+       "  ## Bootstrap regression results predicting paragraph     \n",
+       "  ## comprehension based on three verbal tests: general info, \n",
+       "  ## sentence comprehension, &amp; word classification \n",
+       " \n",
+       "  ## Use HS dataset in MBESS \n",
+       "     require (\"MBESS\")\n",
+       "     data(HS.data)\n",
+       "\n",
+       "  ## Regression\n",
+       "     lm.out&lt;-lm(paragrap~general+sentence+wordc,data=HS.data)\n",
+       "\n",
+       "  ## Calculate regression metrics\n",
+       "     regrOut&lt;-calc.yhat(lm.out)\n",
+       "\n",
+       "  ## Bootstrap results\n",
+       "     require (\"boot\")\n",
+       "     boot.out&lt;-boot(HS.data,boot.yhat,100,lmOut=lm.out,regrout0=regrOut)\n",
+       "\n",
+       "  ## Evaluate bootstrap results\n",
+       "     result&lt;-booteval.yhat(regrOut,boot.out,bty=\"perc\")\n",
+       "\n",
+       "  ## Plot results\n",
+       "  ## plotCI.yhat(regrOut$PredictorMetrics[-nrow(regrOut$PredictorMetrics),],\n",
+       "  ## result$upperCIpm,result$lowerCIpm, pid=which(colnames(regrOut$PredictorMetrics) \n",
+       "  ## %in% c(\"Beta\",\"rs\",\"CD:0\",\"CD:1\",\"CD:2\",\"GenDom\",\"Pratt\",\"RLW\") == TRUE),nr=3,nc=3)\n",
+       "</pre>\n",
+       "\n",
+       "<hr /><div style=\"text-align: center;\">[Package <em>yhat</em> version 2.0-0 ]</div>"
+      ],
+      "text/latex": [
+       "\\inputencoding{utf8}\n",
+       "\\HeaderA{plotCI.yhat}{Plot CIs from yhat}{plotCI.yhat}\n",
+       "\\keyword{models}{plotCI.yhat}\n",
+       "\\keyword{regression}{plotCI.yhat}\n",
+       "%\n",
+       "\\begin{Description}\\relax\n",
+       "This function plots CIs that have been produced from /codebooteval.yhat.\n",
+       "\\end{Description}\n",
+       "%\n",
+       "\\begin{Usage}\n",
+       "\\begin{verbatim}\n",
+       "  plotCI.yhat(sampStat, upperCI, lowerCI, pid=1:ncol(sampStat), nr=2, nc=2)\n",
+       "\\end{verbatim}\n",
+       "\\end{Usage}\n",
+       "%\n",
+       "\\begin{Arguments}\n",
+       "\\begin{ldescription}\n",
+       "\\item[\\code{sampStat}] \n",
+       "Set of sample statistics\n",
+       "\n",
+       "\\item[\\code{upperCI}] \n",
+       "Set of upper CIs\n",
+       "\n",
+       "\\item[\\code{lowerCI}] \n",
+       "Set of lower CIs\n",
+       "\n",
+       "\\item[\\code{pid}] \n",
+       "Which set of Metrics to plot (default to all)\n",
+       "\n",
+       "\\item[\\code{nr}] \n",
+       "Number of rows (default = 2)\n",
+       "\n",
+       "\\item[\\code{nc}] \n",
+       "Number of columns(default = 2)\n",
+       "\n",
+       "\\end{ldescription}\n",
+       "\\end{Arguments}\n",
+       "%\n",
+       "\\begin{Details}\\relax\n",
+       "This function plots CIs that have been produced from /codebooteval.yhat.   \n",
+       "\\end{Details}\n",
+       "%\n",
+       "\\begin{Value}\n",
+       "This returns a plot of CIs that have been produced from /codebooteval.yhat.\n",
+       "\\end{Value}\n",
+       "%\n",
+       "\\begin{Author}\\relax\n",
+       " Kim Nimon <kim.nimon@gmail.com>\n",
+       "\\end{Author}\n",
+       "%\n",
+       "\\begin{References}\\relax\n",
+       "Nimon, K., \\& Oswald, F. L. (2013). Understanding the results of multiple linear regression: Beyond standardized regression coefficients. \\emph{Organizational Research Methods}, \\emph{16}, \n",
+       "650-674.\n",
+       "\\end{References}\n",
+       "%\n",
+       "\\begin{SeeAlso}\\relax\n",
+       "\\code{\\LinkA{lm}{lm}}\n",
+       "\\code{\\LinkA{calc.yhat}{calc.yhat}}\n",
+       "\\code{\\LinkA{boot}{boot}}\n",
+       "\\code{\\LinkA{booteval.yhat}{booteval.yhat}}\n",
+       "\\end{SeeAlso}\n",
+       "%\n",
+       "\\begin{Examples}\n",
+       "\\begin{ExampleCode}\n",
+       "  ## Bootstrap regression results predicting paragraph     \n",
+       "  ## comprehension based on three verbal tests: general info, \n",
+       "  ## sentence comprehension, & word classification \n",
+       " \n",
+       "  ## Use HS dataset in MBESS \n",
+       "     require (\"MBESS\")\n",
+       "     data(HS.data)\n",
+       "\n",
+       "  ## Regression\n",
+       "     lm.out<-lm(paragrap~general+sentence+wordc,data=HS.data)\n",
+       "\n",
+       "  ## Calculate regression metrics\n",
+       "     regrOut<-calc.yhat(lm.out)\n",
+       "\n",
+       "  ## Bootstrap results\n",
+       "     require (\"boot\")\n",
+       "     boot.out<-boot(HS.data,boot.yhat,100,lmOut=lm.out,regrout0=regrOut)\n",
+       "\n",
+       "  ## Evaluate bootstrap results\n",
+       "     result<-booteval.yhat(regrOut,boot.out,bty=\"perc\")\n",
+       "\n",
+       "  ## Plot results\n",
+       "  ## plotCI.yhat(regrOut$PredictorMetrics[-nrow(regrOut$PredictorMetrics),],\n",
+       "  ## result$upperCIpm,result$lowerCIpm, pid=which(colnames(regrOut$PredictorMetrics) \n",
+       "  ## %in% c(\"Beta\",\"rs\",\"CD:0\",\"CD:1\",\"CD:2\",\"GenDom\",\"Pratt\",\"RLW\") == TRUE),nr=3,nc=3)\n",
+       "\\end{ExampleCode}\n",
+       "\\end{Examples}"
+      ],
+      "text/plain": [
+       "plotCI.yhat                package:yhat                R Documentation\n",
+       "\n",
+       "_\bP_\bl_\bo_\bt _\bC_\bI_\bs _\bf_\br_\bo_\bm _\by_\bh_\ba_\bt\n",
+       "\n",
+       "_\bD_\be_\bs_\bc_\br_\bi_\bp_\bt_\bi_\bo_\bn:\n",
+       "\n",
+       "     This function plots CIs that have been produced from\n",
+       "     /codebooteval.yhat.\n",
+       "\n",
+       "_\bU_\bs_\ba_\bg_\be:\n",
+       "\n",
+       "       plotCI.yhat(sampStat, upperCI, lowerCI, pid=1:ncol(sampStat), nr=2, nc=2)\n",
+       "     \n",
+       "_\bA_\br_\bg_\bu_\bm_\be_\bn_\bt_\bs:\n",
+       "\n",
+       "sampStat: Set of sample statistics\n",
+       "\n",
+       " upperCI: Set of upper CIs\n",
+       "\n",
+       " lowerCI: Set of lower CIs\n",
+       "\n",
+       "     pid: Which set of Metrics to plot (default to all)\n",
+       "\n",
+       "      nr: Number of rows (default = 2)\n",
+       "\n",
+       "      nc: Number of columns(default = 2)\n",
+       "\n",
+       "_\bD_\be_\bt_\ba_\bi_\bl_\bs:\n",
+       "\n",
+       "     This function plots CIs that have been produced from\n",
+       "     /codebooteval.yhat.\n",
+       "\n",
+       "_\bV_\ba_\bl_\bu_\be:\n",
+       "\n",
+       "     This returns a plot of CIs that have been produced from\n",
+       "     /codebooteval.yhat.\n",
+       "\n",
+       "_\bA_\bu_\bt_\bh_\bo_\br(_\bs):\n",
+       "\n",
+       "     Kim Nimon <kim.nimon@gmail.com>\n",
+       "\n",
+       "_\bR_\be_\bf_\be_\br_\be_\bn_\bc_\be_\bs:\n",
+       "\n",
+       "     Nimon, K., & Oswald, F. L. (2013). Understanding the results of\n",
+       "     multiple linear regression: Beyond standardized regression\n",
+       "     coefficients. _Organizational Research Methods_, _16_, 650-674.\n",
+       "\n",
+       "_\bS_\be_\be _\bA_\bl_\bs_\bo:\n",
+       "\n",
+       "     'lm' 'calc.yhat' 'boot' 'booteval.yhat'\n",
+       "\n",
+       "_\bE_\bx_\ba_\bm_\bp_\bl_\be_\bs:\n",
+       "\n",
+       "       ## Bootstrap regression results predicting paragraph     \n",
+       "       ## comprehension based on three verbal tests: general info, \n",
+       "       ## sentence comprehension, & word classification \n",
+       "      \n",
+       "       ## Use HS dataset in MBESS \n",
+       "          require (\"MBESS\")\n",
+       "          data(HS.data)\n",
+       "     \n",
+       "       ## Regression\n",
+       "          lm.out<-lm(paragrap~general+sentence+wordc,data=HS.data)\n",
+       "     \n",
+       "       ## Calculate regression metrics\n",
+       "          regrOut<-calc.yhat(lm.out)\n",
+       "     \n",
+       "       ## Bootstrap results\n",
+       "          require (\"boot\")\n",
+       "          boot.out<-boot(HS.data,boot.yhat,100,lmOut=lm.out,regrout0=regrOut)\n",
+       "     \n",
+       "       ## Evaluate bootstrap results\n",
+       "          result<-booteval.yhat(regrOut,boot.out,bty=\"perc\")\n",
+       "     \n",
+       "       ## Plot results\n",
+       "       ## plotCI.yhat(regrOut$PredictorMetrics[-nrow(regrOut$PredictorMetrics),],\n",
+       "       ## result$upperCIpm,result$lowerCIpm, pid=which(colnames(regrOut$PredictorMetrics) \n",
+       "       ## %in% c(\"Beta\",\"rs\",\"CD:0\",\"CD:1\",\"CD:2\",\"GenDom\",\"Pratt\",\"RLW\") == TRUE),nr=3,nc=3)\n",
+       "     "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Use sample code of the package in our data set\n",
+    "help('plotCI.yhat')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading required package: boot\n",
+      "Warning message in cor(x, y, method = \"kendall\"):\n",
+      "\"the standard deviation is zero\"Warning message in cor(x, y, method = \"kendall\"):\n",
+      "\"the standard deviation is zero\"Warning message in cor(x, y, method = \"kendall\"):\n",
+      "\"the standard deviation is zero\""
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "NULL"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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tL+NxaMNQq5YFGRdtY7esSM3rVI5ILamw3lrWNG73WzoTxc6lwS4vZ32BlaiLnX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\nBxQJcPA/vA0DNEr8iVsAAAAASUVORK5CYII=",
+      "text/plain": [
+       "Plot with title \"RLW\""
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data",
+     "source": "R display func"
+    }
+   ],
+   "source": [
+    "## Regression\n",
+    "lm.out <- m3 # you can also just type in the model.\n",
+    "\n",
+    "## Calculate regression metrics\n",
+    " regrOut<-calc.yhat(lm.out)\n",
+    "\n",
+    "## Bootstrap results\n",
+    " usePackage (\"boot\")\n",
+    " boot.out<-boot(df,boot.yhat,100,lmOut=lm.out,regrout0=regrOut)\n",
+    "\n",
+    "## Evaluate bootstrap results\n",
+    " result<-booteval.yhat(regrOut,boot.out,bty=\"perc\")\n",
+    "\n",
+    "## Plot results\n",
+    " plotCI.yhat(regrOut$PredictorMetrics[-nrow(regrOut$PredictorMetrics),],\n",
+    " result$upperCIpm,result$lowerCIpm, pid=which(colnames(regrOut$PredictorMetrics) \n",
+    " %in% c(\"Beta\",\"rs\",\"CD:0\",\"CD:1\",\"CD:2\",\"GenDom\",\"Pratt\",\"RLW\") == TRUE),nr=3,nc=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<dl>\n",
+       "\t<dt>$PredictorMetrics</dt>\n",
+       "\t\t<dd><table>\n",
+       "<thead><tr><th></th><th scope=col>b</th><th scope=col>Beta</th><th scope=col>r</th><th scope=col>rs</th><th scope=col>rs2</th><th scope=col>Unique</th><th scope=col>Common</th><th scope=col>CD:0</th><th scope=col>CD:1</th><th scope=col>CD:2</th><th scope=col>GenDom</th><th scope=col>Pratt</th><th scope=col>RLW</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>weeks</th><td>0.334 </td><td>0.638 </td><td>0.636 </td><td>0.992 </td><td>0.983 </td><td>0.406 </td><td>-0.001</td><td>0.404 </td><td>0.406 </td><td>0.406 </td><td>0.405 </td><td>0.406 </td><td>0.405 </td></tr>\n",
+       "\t<tr><th scope=row>mage</th><td>0.010 </td><td>0.041 </td><td>0.052 </td><td>0.081 </td><td>0.007 </td><td>0.001 </td><td> 0.002</td><td>0.003 </td><td>0.003 </td><td>0.001 </td><td>0.002 </td><td>0.002 </td><td>0.002 </td></tr>\n",
+       "\t<tr><th scope=row>fage</th><td>0.010 </td><td>0.046 </td><td>0.073 </td><td>0.114 </td><td>0.013 </td><td>0.001 </td><td> 0.005</td><td>0.005 </td><td>0.004 </td><td>0.001 </td><td>0.004 </td><td>0.003 </td><td>0.003 </td></tr>\n",
+       "\t<tr><th scope=row>Total</th><td>   NA </td><td>   NA </td><td>   NA </td><td>   NA </td><td>1.003 </td><td>0.408 </td><td> 0.006</td><td>0.412 </td><td>0.413 </td><td>0.408 </td><td>0.411 </td><td>0.411 </td><td>0.410 </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n",
+       "</dd>\n",
+       "\t<dt>$OrderedPredictorMetrics</dt>\n",
+       "\t\t<dd><table>\n",
+       "<thead><tr><th></th><th scope=col>b</th><th scope=col>Beta</th><th scope=col>r</th><th scope=col>rs</th><th scope=col>rs2</th><th scope=col>Unique</th><th scope=col>Common</th><th scope=col>CD:0</th><th scope=col>CD:1</th><th scope=col>CD:2</th><th scope=col>GenDom</th><th scope=col>Pratt</th><th scope=col>RLW</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>weeks</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>3</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr>\n",
+       "\t<tr><th scope=row>mage</th><td>2</td><td>3</td><td>3</td><td>3</td><td>3</td><td>2</td><td>2</td><td>3</td><td>3</td><td>2</td><td>3</td><td>3</td><td>3</td></tr>\n",
+       "\t<tr><th scope=row>fage</th><td>3</td><td>2</td><td>2</td><td>2</td><td>2</td><td>3</td><td>1</td><td>2</td><td>2</td><td>3</td><td>2</td><td>2</td><td>2</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n",
+       "</dd>\n",
+       "\t<dt>$PairedDominanceMetrics</dt>\n",
+       "\t\t<dd><table>\n",
+       "<thead><tr><th></th><th scope=col>Comp</th><th scope=col>Cond</th><th scope=col>Gen</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>weeks&gt;mage</th><td>1</td><td>1</td><td>1</td></tr>\n",
+       "\t<tr><th scope=row>weeks&gt;fage</th><td>1</td><td>1</td><td>1</td></tr>\n",
+       "\t<tr><th scope=row>mage&gt;fage</th><td>0</td><td>0</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n",
+       "</dd>\n",
+       "\t<dt>$APSRelatedMetrics</dt>\n",
+       "\t\t<dd><table>\n",
+       "<thead><tr><th></th><th scope=col>Commonality</th><th scope=col> % Total</th><th scope=col>R2</th><th scope=col>weeks.Inc</th><th scope=col>mage.Inc</th><th scope=col>fage.Inc</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><th scope=row>weeks</th><td> 0.406</td><td> 0.987</td><td>0.404 </td><td>   NA </td><td>0.006 </td><td>0.006 </td></tr>\n",
+       "\t<tr><th scope=row>mage</th><td> 0.001</td><td> 0.002</td><td>0.003 </td><td>0.408 </td><td>   NA </td><td>0.003 </td></tr>\n",
+       "\t<tr><th scope=row>fage</th><td> 0.001</td><td> 0.002</td><td>0.005 </td><td>0.405 </td><td>0.000 </td><td>   NA </td></tr>\n",
+       "\t<tr><th scope=row>weeks,mage</th><td>-0.001</td><td>-0.001</td><td>0.410 </td><td>   NA </td><td>   NA </td><td>0.001 </td></tr>\n",
+       "\t<tr><th scope=row>weeks,fage</th><td> 0.002</td><td> 0.005</td><td>0.411 </td><td>   NA </td><td>0.001 </td><td>   NA </td></tr>\n",
+       "\t<tr><th scope=row>mage,fage</th><td> 0.005</td><td> 0.013</td><td>0.005 </td><td>0.406 </td><td>   NA </td><td>   NA </td></tr>\n",
+       "\t<tr><th scope=row>weeks,mage,fage</th><td>-0.003</td><td>-0.007</td><td>0.411 </td><td>   NA </td><td>   NA </td><td>   NA </td></tr>\n",
+       "\t<tr><th scope=row>Total</th><td> 0.411</td><td> 1.000</td><td>   NA </td><td>   NA </td><td>   NA </td><td>   NA </td></tr>\n",
+       "</tbody>\n",
+       "</table>\n",
+       "</dd>\n",
+       "</dl>\n"
+      ],
+      "text/latex": [
+       "\\begin{description}\n",
+       "\\item[\\$PredictorMetrics] \\begin{tabular}{r|lllllllllllll}\n",
+       "  & b & Beta & r & rs & rs2 & Unique & Common & CD:0 & CD:1 & CD:2 & GenDom & Pratt & RLW\\\\\n",
+       "\\hline\n",
+       "\tweeks & 0.334  & 0.638  & 0.636  & 0.992  & 0.983  & 0.406  & -0.001 & 0.404  & 0.406  & 0.406  & 0.405  & 0.406  & 0.405 \\\\\n",
+       "\tmage & 0.010  & 0.041  & 0.052  & 0.081  & 0.007  & 0.001  &  0.002 & 0.003  & 0.003  & 0.001  & 0.002  & 0.002  & 0.002 \\\\\n",
+       "\tfage & 0.010  & 0.046  & 0.073  & 0.114  & 0.013  & 0.001  &  0.005 & 0.005  & 0.004  & 0.001  & 0.004  & 0.003  & 0.003 \\\\\n",
+       "\tTotal &    NA  &    NA  &    NA  &    NA  & 1.003  & 0.408  &  0.006 & 0.412  & 0.413  & 0.408  & 0.411  & 0.411  & 0.410 \\\\\n",
+       "\\end{tabular}\n",
+       "\n",
+       "\\item[\\$OrderedPredictorMetrics] \\begin{tabular}{r|lllllllllllll}\n",
+       "  & b & Beta & r & rs & rs2 & Unique & Common & CD:0 & CD:1 & CD:2 & GenDom & Pratt & RLW\\\\\n",
+       "\\hline\n",
+       "\tweeks & 1 & 1 & 1 & 1 & 1 & 1 & 3 & 1 & 1 & 1 & 1 & 1 & 1\\\\\n",
+       "\tmage & 2 & 3 & 3 & 3 & 3 & 2 & 2 & 3 & 3 & 2 & 3 & 3 & 3\\\\\n",
+       "\tfage & 3 & 2 & 2 & 2 & 2 & 3 & 1 & 2 & 2 & 3 & 2 & 2 & 2\\\\\n",
+       "\\end{tabular}\n",
+       "\n",
+       "\\item[\\$PairedDominanceMetrics] \\begin{tabular}{r|lll}\n",
+       "  & Comp & Cond & Gen\\\\\n",
+       "\\hline\n",
+       "\tweeks>mage & 1 & 1 & 1\\\\\n",
+       "\tweeks>fage & 1 & 1 & 1\\\\\n",
+       "\tmage>fage & 0 & 0 & 0\\\\\n",
+       "\\end{tabular}\n",
+       "\n",
+       "\\item[\\$APSRelatedMetrics] \\begin{tabular}{r|llllll}\n",
+       "  & Commonality &  \\% Total & R2 & weeks.Inc & mage.Inc & fage.Inc\\\\\n",
+       "\\hline\n",
+       "\tweeks &  0.406 &  0.987 & 0.404  &    NA  & 0.006  & 0.006 \\\\\n",
+       "\tmage &  0.001 &  0.002 & 0.003  & 0.408  &    NA  & 0.003 \\\\\n",
+       "\tfage &  0.001 &  0.002 & 0.005  & 0.405  & 0.000  &    NA \\\\\n",
+       "\tweeks,mage & -0.001 & -0.001 & 0.410  &    NA  &    NA  & 0.001 \\\\\n",
+       "\tweeks,fage &  0.002 &  0.005 & 0.411  &    NA  & 0.001  &    NA \\\\\n",
+       "\tmage,fage &  0.005 &  0.013 & 0.005  & 0.406  &    NA  &    NA \\\\\n",
+       "\tweeks,mage,fage & -0.003 & -0.007 & 0.411  &    NA  &    NA  &    NA \\\\\n",
+       "\tTotal &  0.411 &  1.000 &    NA  &    NA  &    NA  &    NA \\\\\n",
+       "\\end{tabular}\n",
+       "\n",
+       "\\end{description}\n"
+      ],
+      "text/markdown": [
+       "$PredictorMetrics\n",
+       ":   \n",
+       "| <!--/--> | b | Beta | r | rs | rs2 | Unique | Common | CD:0 | CD:1 | CD:2 | GenDom | Pratt | RLW | \n",
+       "|---|---|---|---|\n",
+       "| weeks | 0.334  | 0.638  | 0.636  | 0.992  | 0.983  | 0.406  | -0.001 | 0.404  | 0.406  | 0.406  | 0.405  | 0.406  | 0.405  | \n",
+       "| mage | 0.010  | 0.041  | 0.052  | 0.081  | 0.007  | 0.001  |  0.002 | 0.003  | 0.003  | 0.001  | 0.002  | 0.002  | 0.002  | \n",
+       "| fage | 0.010  | 0.046  | 0.073  | 0.114  | 0.013  | 0.001  |  0.005 | 0.005  | 0.004  | 0.001  | 0.004  | 0.003  | 0.003  | \n",
+       "| Total |    NA  |    NA  |    NA  |    NA  | 1.003  | 0.408  |  0.006 | 0.412  | 0.413  | 0.408  | 0.411  | 0.411  | 0.410  | \n",
+       "\n",
+       "\n",
+       "\n",
+       "$OrderedPredictorMetrics\n",
+       ":   \n",
+       "| <!--/--> | b | Beta | r | rs | rs2 | Unique | Common | CD:0 | CD:1 | CD:2 | GenDom | Pratt | RLW | \n",
+       "|---|---|---|\n",
+       "| weeks | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | \n",
+       "| mage | 2 | 3 | 3 | 3 | 3 | 2 | 2 | 3 | 3 | 2 | 3 | 3 | 3 | \n",
+       "| fage | 3 | 2 | 2 | 2 | 2 | 3 | 1 | 2 | 2 | 3 | 2 | 2 | 2 | \n",
+       "\n",
+       "\n",
+       "\n",
+       "$PairedDominanceMetrics\n",
+       ":   \n",
+       "| <!--/--> | Comp | Cond | Gen | \n",
+       "|---|---|---|\n",
+       "| weeks>mage | 1 | 1 | 1 | \n",
+       "| weeks>fage | 1 | 1 | 1 | \n",
+       "| mage>fage | 0 | 0 | 0 | \n",
+       "\n",
+       "\n",
+       "\n",
+       "$APSRelatedMetrics\n",
+       ":   \n",
+       "| <!--/--> | Commonality |  % Total | R2 | weeks.Inc | mage.Inc | fage.Inc | \n",
+       "|---|---|---|---|---|---|---|---|\n",
+       "| weeks |  0.406 |  0.987 | 0.404  |    NA  | 0.006  | 0.006  | \n",
+       "| mage |  0.001 |  0.002 | 0.003  | 0.408  |    NA  | 0.003  | \n",
+       "| fage |  0.001 |  0.002 | 0.005  | 0.405  | 0.000  |    NA  | \n",
+       "| weeks,mage | -0.001 | -0.001 | 0.410  |    NA  |    NA  | 0.001  | \n",
+       "| weeks,fage |  0.002 |  0.005 | 0.411  |    NA  | 0.001  |    NA  | \n",
+       "| mage,fage |  0.005 |  0.013 | 0.005  | 0.406  |    NA  |    NA  | \n",
+       "| weeks,mage,fage | -0.003 | -0.007 | 0.411  |    NA  |    NA  |    NA  | \n",
+       "| Total |  0.411 |  1.000 |    NA  |    NA  |    NA  |    NA  | \n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "$PredictorMetrics\n",
+       "          b  Beta     r    rs   rs2 Unique Common  CD:0  CD:1  CD:2 GenDom\n",
+       "weeks 0.334 0.638 0.636 0.992 0.983  0.406 -0.001 0.404 0.406 0.406  0.405\n",
+       "mage  0.010 0.041 0.052 0.081 0.007  0.001  0.002 0.003 0.003 0.001  0.002\n",
+       "fage  0.010 0.046 0.073 0.114 0.013  0.001  0.005 0.005 0.004 0.001  0.004\n",
+       "Total    NA    NA    NA    NA 1.003  0.408  0.006 0.412 0.413 0.408  0.411\n",
+       "      Pratt   RLW\n",
+       "weeks 0.406 0.405\n",
+       "mage  0.002 0.002\n",
+       "fage  0.003 0.003\n",
+       "Total 0.411 0.410\n",
+       "\n",
+       "$OrderedPredictorMetrics\n",
+       "      b Beta r rs rs2 Unique Common CD:0 CD:1 CD:2 GenDom Pratt RLW\n",
+       "weeks 1    1 1  1   1      1      3    1    1    1      1     1   1\n",
+       "mage  2    3 3  3   3      2      2    3    3    2      3     3   3\n",
+       "fage  3    2 2  2   2      3      1    2    2    3      2     2   2\n",
+       "\n",
+       "$PairedDominanceMetrics\n",
+       "           Comp Cond Gen\n",
+       "weeks>mage    1    1   1\n",
+       "weeks>fage    1    1   1\n",
+       "mage>fage     0    0   0\n",
+       "\n",
+       "$APSRelatedMetrics\n",
+       "                Commonality  % Total    R2 weeks.Inc mage.Inc fage.Inc\n",
+       "weeks                 0.406    0.987 0.404        NA    0.006    0.006\n",
+       "mage                  0.001    0.002 0.003     0.408       NA    0.003\n",
+       "fage                  0.001    0.002 0.005     0.405    0.000       NA\n",
+       "weeks,mage           -0.001   -0.001 0.410        NA       NA    0.001\n",
+       "weeks,fage            0.002    0.005 0.411        NA    0.001       NA\n",
+       "mage,fage             0.005    0.013 0.005     0.406       NA       NA\n",
+       "weeks,mage,fage      -0.003   -0.007 0.411        NA       NA       NA\n",
+       "Total                 0.411    1.000    NA        NA       NA       NA\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "regrOut"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**k-fold validation of models**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading required package: caret\n",
+      "Loading required package: lattice\n",
+      "\n",
+      "Attaching package: 'lattice'\n",
+      "\n",
+      "The following object is masked from 'package:boot':\n",
+      "\n",
+      "    melanoma\n",
+      "\n",
+      "The following object is masked from 'package:openintro':\n",
+      "\n",
+      "    lsegments\n",
+      "\n",
+      "\n",
+      "Attaching package: 'caret'\n",
+      "\n",
+      "The following object is masked from 'package:openintro':\n",
+      "\n",
+      "    dotPlot\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Choosing the best model with the lowest AIC. This AIC is a measure of the simplicity of the model, the lower the better.\n",
+    "# Source code: http://www.sthda.com/english/articles/37-model-selection-essentials-in-r/154-stepwise-regression-essentials-in-r/\n",
+    "\n",
+    "model_kfold <- function(df){\n",
+    "    \n",
+    "    usePackage(\"caret\")\n",
+    "\n",
+    "    \n",
+    "    # Set up repeated k-fold cross-validation\n",
+    "    train.control <- trainControl(method = \"repeatedcv\", number=10, repeats=3)\n",
+    "    # Train the model\n",
+    "    model <- train(weight ~ weeks, data = df, # instead of 'weight ~.', replace the '.' with specific predictors or interest.\n",
+    "                    method = \"lm\", # replace lm with lmStepAIC and see what happens\n",
+    "                    trControl = train.control)\n",
+    "    \n",
+    "    detach(package:caret)\n",
+    "    \n",
+    "    return(model)\n",
+    "    \n",
+    "}\n",
+    "\n",
+    "# When you replace the 'lm' method with the 'lmStepAIC' in the above function the model will \n",
+    "\n",
+    "# We won't be discussing how to review the different models. Our focus is on the final chosen mdoel.\n",
+    "\n",
+    "# Capture modeling\n",
+    "m4 <- model_kfold(df)\n",
+    "# Capture final model\n",
+    "fit4 <- m4$finalModel\n",
+    "# Capture RMSE\n",
+    "RMSE4 <- m4$results[, 2]\n",
+    "# Capture MAE\n",
+    "MAE4 <- m4$results[, 4]\n",
+    "\n",
+    "# You can just click the blue bar on the left of the output to close the output of the modeling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "invisible(capture.output(stargazer(fit4, out = 'result4.html')))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table>\n",
+       "<thead><tr><th scope=col>intercept</th><th scope=col>RMSE</th><th scope=col>Rsquared</th><th scope=col>MAE</th><th scope=col>RMSESD</th><th scope=col>RsquaredSD</th><th scope=col>MAESD</th></tr></thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>TRUE      </td><td>1.111576  </td><td>0.3963065 </td><td>0.8811238 </td><td>0.08105668</td><td>0.1390833 </td><td>0.07053324</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ],
+      "text/latex": [
+       "\\begin{tabular}{r|lllllll}\n",
+       " intercept & RMSE & Rsquared & MAE & RMSESD & RsquaredSD & MAESD\\\\\n",
+       "\\hline\n",
+       "\t TRUE       & 1.111576   & 0.3963065  & 0.8811238  & 0.08105668 & 0.1390833  & 0.07053324\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/markdown": [
+       "\n",
+       "intercept | RMSE | Rsquared | MAE | RMSESD | RsquaredSD | MAESD | \n",
+       "|---|\n",
+       "| TRUE       | 1.111576   | 0.3963065  | 0.8811238  | 0.08105668 | 0.1390833  | 0.07053324 | \n",
+       "\n",
+       "\n"
+      ],
+      "text/plain": [
+       "  intercept RMSE     Rsquared  MAE       RMSESD     RsquaredSD MAESD     \n",
+       "1 TRUE      1.111576 0.3963065 0.8811238 0.08105668 0.1390833  0.07053324"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "Call:\n",
+       "lm(formula = .outcome ~ ., data = dat)\n",
+       "\n",
+       "Coefficients:\n",
+       "(Intercept)        weeks  \n",
+       "    -5.5908       0.3327  \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "Call:\n",
+       "lm(formula = .outcome ~ ., data = dat)\n",
+       "\n",
+       "Residuals:\n",
+       "    Min      1Q  Median      3Q     Max \n",
+       "-3.3371 -0.6961 -0.0534  0.7211  3.6594 \n",
+       "\n",
+       "Coefficients:\n",
+       "            Estimate Std. Error t value Pr(>|t|)    \n",
+       "(Intercept)  -5.5908     0.5513  -10.14   <2e-16 ***\n",
+       "weeks         0.3327     0.0143   23.27   <2e-16 ***\n",
+       "---\n",
+       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
+       "\n",
+       "Residual standard error: 1.113 on 798 degrees of freedom\n",
+       "Multiple R-squared:  0.4044,\tAdjusted R-squared:  0.4036 \n",
+       "F-statistic: 541.7 on 1 and 798 DF,  p-value: < 2.2e-16\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Model accuracy\n",
+    "m4$results\n",
+    "# Final model coefficients\n",
+    "m4$finalModel\n",
+    "# Summary of the model\n",
+    "summary(m4$finalModel)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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+---
+title: "Reproducible Science with RStudio"
+author: "Ahmed Hasan"
+output:
+  pdf_document: default
+  html_document: default
+always_allow_html: true
+---
+
+# Introduction
+
+This is an introduction to R and RStudio with an emphasis on project management
+and the various features RStudio has to make reproducible science easier. We'll
+be working through a simple project workflow using the various project management
+features RStudio provides, and pick up some basic R along the way. Although no
+prior experience is required, it may be helpful to read across the material from
+the UofT Coders [Intro to R][intro-r] lesson.
+
+Note: material for an extended version of this lesson, delivered in the Spring
+2020 semester, can be found [here][lesson-old]. The version in this file is
+truncated and intended to be delivered in 40-50 minutes online, thus
+de-emphasizing the code-along sections.
+
+## What does it mean to work reproducibly in RStudio?
+
+Reproducible computational science endeavours to make both the data used in
+analyses as well as the code used to process this data available and reusable.
+However, ensuring reproducibility is difficult unless certain principles are
+adhered to early on; projects may branch off into various unplanned subtasks,
+certain scripts may undergo several major changes, leading to cluttered file
+directories, and files/scripts may not be named in a consistent manner, making
+retracing steps difficult. While these are important considerations when
+working with other scientists, it's worth noting that possibly your most
+frequent collaborator may be future you, going back to your older work to
+figure out how you proceeded with an analysis. It's therefore important to make
+sure projects are well managed and documented, and RStudio offers a suite of
+features to help facilitate this.
+
+## What is the difference between R and RStudio?
+
+**R** is a statistical programming language, designed primarily with data
+analysis and visualization in mind, while **RStudio** is an interface for
+working in R. Although it is possible to use R independently of RStudio,
+RStudio is widely considered the default platform for working in R due to
+its powerful features and ongoing community support. 
+
+## An overview of RStudio
+
+At its core, RStudio consists of four panes (one of which is often hidden upon
+startup):
+
+1. Source (text editor)
+2. Console (an R interpreter)
+3. Environment and history
+4. Miscellaneous (files, plots, packages, help docs, etc.)
+
+Most of the time, working in RStudio involves working in the Source pane. This
+pane can be opened up by opening either a new or an existing file, and consists
+of a text editor. Code in the source pane can be 'sent' to the console to be
+run, allowing a user to stay in Source for the most part while they work, but
+the Source pane can also be used to edit other text files; this very text is
+being written in the Source panel of RStudio! The console is the real engine 
+which carries out the commands. All your code is *"run"* through the console
+but a copy of the code is kept and organized in the script. 
+
+# R Projects
+
+In this lesson, we'll be making extensive use of yet another feature of RStudio:
+**RStudio Projects**. As the name implies, these are designed to keep all files
+and scripts associated with a given project, in addition to maintaining
+project-specific command history and other useful things.
+
+## Creating a project
+
+Projects can be created using the dropdown in the top right of RStudio. This
+menu should currently say `Project: (None)`, but will otherwise reflect the name
+of whatever project is open at a given time. Clicking on the dropdown and
+selecting `New Project` will open a dialog with three options:
+
+- New Directory
+- Existing Directory
+- Version Control
+
+We'll be selecting `New Directory` for now. This will create a new folder on your
+device that will act as the headquarters for the project. RStudio will ask us to name
+the project, and also offer to enable version control with Git for the project.
+Although we won't be using Git much in this lesson given time constraints, we
+will still check off this checkbox, as it enables a few extra features that are
+helpful to know about.
+
+Once the project has been created, RStudio will in fact appear to 'restart' --
+the project will now open in a new instance of RStudio. RStudio creates a new 
+folder where you specified and also quietly performs a few other operations in the
+background:
+
+1. In the newly made project directory, RStudio creates an `.Rproj` file, which
+contains various project settings. This can be seen in the Files pane in the
+bottom right.
+  - This file is also used by certain packages to detect where the project root
+  is in the file tree.
+2. RStudio also creates a hidden directory in the same location called
+`.Rproj.user` containing various project-related temporary files.
+3. Since we enabled Git earlier, RStudio should also initialize this folder as a
+Git repository and create a `.gitignore` file. If you aren't familiar with Git, don't
+worry about this step -- what's essentially happening is that RStudio has primed this folder
+for use with Git.
+  - There should also be a new 'Git' tab added to the Environment & History pane. 
+  
+## Setting up our directory structure
+
+One of the most important steps when getting started with a project is deciding upon
+and setting up a coherent, understandable directory structure. It's very easy to make
+a series of 'temporary' directories that eventually lead to all manner of important
+files being scattered every which way -- we've all been there! To avoid this, let's start
+with a simple directory structure. 
+
+In the Files pane, use the `New Folder` button to create three directories:
+`data`, `analysis`, and `plots`. This directory structure may differ from how
+you prefer to do things; some scientists like to have `plots` as a subdirectory
+of `analysis`, while others split `data` into `data-raw` and `data-clean`. In
+your own work, be sure to experiment with what works for you, but above all: try
+to be consistent!
+
+The idea behind this structure is:
+
+- `data` will contain data, as the name implies; however, an important thing to
+keep in mind is that every file in this folder should be considered
+**read-only**. If data is modified in any way, say after you've filtered out a
+few columns in R, these modified datasets should be saved as new files within
+this folder.
+- `analysis` will contain scripts, notes to oneself (such as digital 'lab
+notebooks') and so on. We'll primarily be working from within this folder.
+- `plots` will contain any and all data visualizations.
+
+(Again, this is _just an example structure_; you don't have to adhere to this in your own work!)
+
+# Getting started with a sample project
+
+## R Scripts and R Notebooks
+
+Although RStudio is primarily designed to work with R code and scripts, it
+also supports a variety of other file formats. If we navigate to the `File`
+menu and look at the options under `New`, we can see file formats ranging
+from R Notebooks to Markdown files and Python scripts. Any of these file formats
+can be used and edited within RStudio (and more importantly for our purposes,
+within an R Project).
+
+You may notice that many of these seem to be variants of R scripts, such as
+R Markdown, R Notebooks, R Documentation, and more. Of these, perhaps the two
+to make note of are R Scripts and R Notebooks.
+
+An **R Script** is simply a plain text file with R code in it and nothing but.
+R scripts are very lightweight and perfectly usable for all sorts of R-related
+purposes.  They can also be easily run from the command line (e.g. run in their
+entirety outside of RStudio) although this is a more advanced technique. 
+
+An **R Notebook**, on the other hand, is a special RStudio-only format that
+allows users to easily document and organize code as they go along. R Notebooks
+are incredibly powerful and highly recommended for any sorts of analyses in
+RStudio, since they make it very easy to ensure that code is well annotated and
+presented in a logical manner.
+
+## Using R Notebooks
+
+How do R Notebooks do this? At their core, all R Notebooks are actually
+Markdown files. Markdown is a means of styling plain text files using simple
+text characters. This lesson material you are reading has actually been written
+in Markdown! Since R Notebooks are Markdown files, anything typed into an R
+Notebook will be interpreted as Markdown-styled text by RStudio instead of code
+(which would be the case with an R Script). 
+
+So that's all well and good, but where does the code come in? Well, R Notebooks
+feature the ability to intersperse _code chunks_, which are blocks within
+these files that RStudio knows to interpret as code. We can insert a code chunk
+in an R Notebook using either Shift + Option/Alt + I or the `Insert` dialog at
+the top of the screen.
+
+Let's add in a code chunk and write our first bit of R code -- some simple math:
+
+```{r}
+2 + 2
+```
+We can run the contents of a code chunk by either clicking on the `Run` button
+at the top of the screen or by pressing Ctrl/Cmd + Shift + Enter. RStudio will
+then execute the code in the console, but will *also* show the output directly
+underneath the code chunk. Since each code chunk's output is right next to the
+code itself, this means we can work entirely within the source panel if we so
+choose! 
+
+(Side note: the **R Markdown** format is effectively identical to R Notebooks,
+save for the fact that chunks of code in R Markdown files are run all at once,
+while R Notebooks allow for line-by-line running of code. This is why R Notebooks
+still have the `Rmd` extension.)
+
+## A sample workflow
+
+R features various object types to hold different kinds of information, such as
+numbers and text. We won't go in detail about these object types -- if you're
+interested in digging deeper into these topics, we recommend checking out
+our [Intro R][intro-r] material. 
+
+For this demo, we'll be working with a dataset on air quality in New York City.
+This is a dataset already built into R. The specifics of the code don't matter
+as much here; the point of this lesson is to better understand RStudio's
+features for reproducible science over working through R itself, so we'll gloss
+over some of the details for now.
+
+Opening a new R Notebook, the first thing we notice is the header at the top of
+the screen. This is referred to as a **YAML header**, and stores information
+about the file itself as well as other related metadata related to knitting. We
+will not worry too much about this today, but it's worth knowing what it is for
+more advanced R Notebook uses.
+
+The first thing to do in any notebook is *import the packages we are using*.
+Packages are extensions of R that provide new functionality. Packages only need
+to be installed once, and then loaded in every time they are used. They can
+almost be thought of as apps on your phone: you install an app once, but need
+to open it every time you use it. 
+
+The package we will be working with today is called `here`. `here` provides a
+single added feature that makes it very easy to navigate through the folders in
+our project.
+
+```{r}
+library(here)
+```
+
+We will revisit `here` shortly, but are importing it now because it is good
+practice to import packages at the start of a notebook or script. Instead,
+let's now look at the `airquality` dataset. This is already loaded into R,
+and can be viewed by simply typing in `airquality`:
+
+```{r}
+airquality
+```
+
+In this example workflow, we are interested in whether recorded wind speed
+correlates with the temperature on any given day. Let's plot the two variables
+against one another:
+
+```{r}
+plot(airquality$Wind, airquality$Temp)
+```
+
+We see that there seems to roughly be an inverse correlation. Let's add
+a regression line using a linear model:
+
+```{r}
+abline(lm(airquality$Temp ~ airquality$Wind))
+```
+
+Normally, we would of course do much more in depth statistical work -- for
+instance, creating a linear model and checking whether the assumptions of
+simple linear regression are met, among other things. For the sake of this
+lesson, however, we'll stop here.
+
+Let's say this is our final plot and we want to save it for publication.
+Let's first save our notebook to our `analysis` folder.
+
+Now, if we want to save this plot to the `plots` folder, we might be in a bit
+of trouble, since we'd need R to go back out one folder (from `analysis` to the
+project root) and then into `plots`. This is where the `here` package comes in.
+`here` lets us write file paths as if we were always in the project root.
+Let's save this plot:
+
+```{r}
+png('airquality_plot.png')
+plot(airquality$Wind, airquality$Temp)
+abline(lm(airquality$Temp ~ airquality$Wind))
+dev.off()
+```
+
+Checking our folders, it seems that this file has been saved into `analysis` and
+not `plots`. Changing the above to `plots/airquality_plot.png` raises an error.
+How do we fix this with `here`? 
+
+```{r}
+png(here('plots/airquality_plot.png'))
+plot(airquality$Wind, airquality$Temp)
+abline(lm(airquality$Temp ~ airquality$Wind))
+dev.off()
+```
+
+There we go -- the plot has now been saved to the `plots` folder! It's good
+practice to always use `here` when working with any kind of file paths in R,
+whether that involves reading in or saving files. 
+
+# Other, more advanced features
+
+That about does it for what we'll be covering in this lesson, but here's a quick rundown
+of a few other features/integrations RStudio offers to help with reproducibility.
+
+## Git integration
+
+RStudio projects are fully compatible with Git, and feature various GUI features to help
+facilitate Git usage. Git is a version control system that allows you to track changes in
+your files as well as preserve file history. Using Git with your projects means that past
+versions of files can be retrieved, and changes can be easily documented. Git alone
+could take up an entire lesson itself, however, which is why we haven't covered it today,
+but if you're interested in learning how to use it feel free to have a look at our
+[Intro Git][intro-git] lesson or check out [Happy Git with R][happy-git-r].
+
+## Packrat
+
+Packrat is a package management system for R that allows users to associate
+specific versions of packages with projects. This means that a project can
+contain its own self contained packages forever, which is especially useful if
+these packages eventually get updated in ways that break compatibility (since
+Packrat will specifically allow for the maintenance of whichever version of a
+given package was used). More can be found [here][packrat].
+
+
+
+[happy-git-r]: https://happygitwithr.com/
+[intro-git]: https://uoftcoders.github.io/studyGroup/lessons/git/intro/lesson/
+[intro-r]: https://uoftcoders.github.io/studyGroup/lessons/r/intro/lesson/
+[lesson-old]: https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/r/reproducible-rstudio/lesson.Rmd
+[packrat]: https://rstudio.github.io/packrat/
+
+
diff --git a/lessons/r/reproducible-rstudio/lesson.Rmd b/lessons/r/reproducible-rstudio/lesson.Rmd
new file mode 100644
index 000000000..fa12ec351
--- /dev/null
+++ b/lessons/r/reproducible-rstudio/lesson.Rmd
@@ -0,0 +1,386 @@
+---
+title: "Reproducible Science with RStudio"
+author: "Frances Wong and Ahmed Hasan"
+output:
+  pdf_document: default
+  html_document: default
+always_allow_html: true
+---
+
+# Introduction
+
+This is an introduction to R and RStudio with an emphasis on project management
+and the various features RStudio has to make reproducible science easier. We'll
+be working through a simple project workflow using the various project management
+features RStudio provides, and pick up some basic R along the way. Although no
+prior experience is required, it may be helpful to read across the material from
+the UofT Coders [Intro to R][intro-r] lesson.
+
+## What does it mean to work reproducibly in RStudio?
+
+Reproducible computational science endeavours to make both the data used in
+analyses as well as the code used to process this data both available and
+reusable. However, ensuring reproducibility is difficult unless certain
+principles are adhered to early on; projects may branch off into various
+unplanned subtasks, certain scripts may undergo several major changes, leading
+to cluttered file directories, and files/scripts may not be named in a
+consistent manner, making retracing steps difficult. While these are important
+considerations when working with other scientists, it's worth noting that
+possibly your most frequent collaborator may be future you, going back to your
+older work to figure out how you proceeded with an analysis. It's therefore
+important to make sure projects are well managed and documented, and RStudio
+offers a suite of features to help facilitate this.
+
+## What is the difference between R and RStudio?
+
+**R** is a statistical programming language, designed primarily with data
+analysis and visualization in mind, while **RStudio** is an interface for
+working in R. Although it is possible to use R independently of RStudio,
+RStudio is widely considered the default platform for working in R due to
+its powerful features and ongoing community support. 
+
+## An overview of RStudio
+
+At its core, RStudio consists of four panes (one of which is often hidden upon
+startup):
+
+1. Source (text editor)
+2. Console (an R interpreter)
+3. Environment and history
+4. Miscellaneous (files, plots, packages, help docs, etc.)
+
+Most of the time, working in RStudio involves working in the Source pane. This
+pane can be opened up by opening either a new or an existing file, and consists
+of a text editor. Code in the source pane can be 'sent' to the console to be
+run, allowing a user to stay in Source for the most part while they work, but
+the Source pane can also be used to edit other text files; this very text is
+being written in the Source panel of RStudio! The console is the real engine 
+which carries out the commands. All your code is *"run"* through the console
+but a copy of the code is kept and organized in the script. 
+
+# R Projects
+
+In this lesson, we'll be making extensive use of yet another feature of RStudio:
+**RStudio Projects**. As the name implies, these are designed to keep all files
+and scripts associated with a given project, in addition to maintaining
+project-specific command history and other useful things.
+
+## Creating a project
+
+Projects can be created using the dropdown in the top right of RStudio. This
+menu should currently say `Project: (None)`, but will otherwise reflect the name
+of whatever project is open at a given time. Clicking on the dropdown and
+selecting `New Project` will open a dialog with three options:
+
+- New Directory
+- Existing Directory
+- Version Control
+
+We'll be selecting `New Directory` for now. This will create a new folder on your
+device that will act as the headquarters for the project. RStudio will ask us to name
+the project, and also offer to enable version control with Git for the project.
+Although we won't be using Git much in this lesson given time constraints, we
+will still check off this checkbox, as it enables a few extra features that are
+helpful to know about.
+
+Once the project has been created, RStudio will in fact appear to 'restart' --
+the project will now open in a new instance of RStudio. RStudio created a new 
+folder where you specified and also quietly performs a few other operations in the
+background:
+
+1. In the newly made project directory, RStudio creates an `.Rproj` file, which
+contains various project settings. This can be seen in the Files pane in the
+bottom right.
+  - This file is also used by certain packages to detect where the project root
+  is in the file tree.
+2. RStudio also creates a hidden directory in the same location called
+`.Rproj.user` containing various project-related temporary files.
+3. Since we enabled Git earlier, RStudio should also initialize this folder as a
+Git repository and create a `.gitignore` file. If you aren't familiar with Git, don't
+worry about this step -- what's essentially happening is that RStudio has primed this folder
+for use with Git.
+  - There should also be a new 'Git' tab added to the Environment & History pane. 
+  
+## Setting up our directory structure
+
+One of the most important steps when getting started with a project is deciding upon
+and setting up a coherent, understandable directory structure. It's very easy to make
+a series of 'temporary' directories that eventually lead to all manner of important
+files being scattered every which way -- we've all been there! To avoid this, let's start
+with a simple directory structure. 
+
+In the Files pane, use the `New Folder` button to create three directories:
+`data`, `analysis`, and `plots`. This directory structure may differ from how
+you prefer to do things; some scientists like to have `plots` as a subdirectory
+of `analysis`, while others split `data` into `data-raw` and `data-clean`. In
+your own work, be sure to experiment with what works for you, but above all: try
+to be consistent!
+
+The idea behind this structure is:
+
+- `data` will contain data, as the name implies; however, an important thing to
+keep in mind is that every file in this folder should be considered
+**read-only**. If data is modified in any way, say after you've filtered out a
+few columns in R, these modified datasets should be saved as new files within
+this folder.
+- `analysis` will contain scripts, notes to oneself (such as digital 'lab
+notebooks') and so on. We'll primarily be working from within this folder.
+- `plots` will contain any and all data visualizations.
+
+(Again, this is _just an example structure_; you don't have to adhere to this in your own work!)
+
+# Getting started with a sample project
+
+R Markdown is great for keeping your code and outputs organized together. This
+is done through code chunks. Text outside of the code chunks, such as this, is
+interpreted as plain text. Code needs to be within a code chunk, and the
+language said code is written in is specified in the header. They can be
+inserted with the green button on the top of this panel or with a keyboard
+shortcut (Ctrl/Cmd + Alt + i). Plain text can be added with code chunks by
+starting a line with a #.
+
+First step of any project: load in your libraries. It's a good habit to keep
+them all together at the start of your document for consistency. Think of
+libraries as apps for your phone: you need to download them once the first time
+you want to use it (in R, install new packages with `install.packages()` for
+base R) but you still need to open it (in R, `library()`) every time you want to
+use it. You'll only need to install a package once, but you'll need to load it
+in with every new session.
+
+```{r loadingPackages}
+# Packages may need to be installed with install.packages() the first time 
+library(tidyverse)
+library(ggplot2)
+library(here)
+library(knitr)
+library(plotly)
+```
+
+Now that we have our first code chunk, the whole segment can be executed using
+the green play button at the top right corner of each chunk or by pressing Shift
++ Ctrl/Cmd + Enter. To run a single line within the code chunk, either
+highlight it before pressing the green play button or place your cursor on the
+line and execute with Ctrl/Cmd + Enter.
+
+Let's start with some basics. R at its core can be used as a giant calculator to
+which we can feed variables and equations.
+
+```{r}
+2+2
+```
+
+Values can also be stored into objects. Notice how the object names and quick
+information are populated into the 'Environment' tab when created.
+
+```{r}
+bigNumber <- 239
+smlNumber <- 3
+
+bigNumber/smlNumber
+```
+
+We can start to explore the strength of R by working with more than one value at
+once. When an object holds a string of values, they are called vectors. There
+are multiple types depending on what kind of information they are holding.
+Notice how RStudio has a feature called "syntax highlighting" which will
+indicate when different data types are recognized. The actual colors depend on
+the theme selected in your settings.
+
+```{r}
+numericVec <- c(3, 1, 4, 1, 5)
+characterVec <- c("is", "it", "pi", "or", "pie") # character strings must be enclosed in quotations or else they will be interpreted as objects in the environment. 
+logicalVec <- c(FALSE, FALSE, TRUE, FALSE, F)
+```
+
+Note: logical booleans must be written with all caps (FALSE or TRUE) or by a single capital letter (F or T). 
+
+What happens if there are multiple data types within a vector? 
+
+```{r}
+mixedVec <- c("UofT", "Coders", 2020)
+mixedVec
+class(mixedVec)
+```
+
+All values will be coersed into a single data type. 
+
+Everything we've been working with so far has been one dimensional, a vector.
+Let's get a little more power with two dimensional data structures. Matrices can
+only hold one data type.
+
+```{r}
+matrix <- matrix(1:25, nrow = 5) # colon asks for values 1 "through" 25
+
+matrix[5, 3] # Row 5, column 3
+```
+
+Matrices can be acted on fully or column-wise:
+
+```{r}
+matrix[, 2]*2
+
+matrix[, 2] <- matrix[, 2]*2
+```
+
+A second complex data structure is the **data frame**. In contrast to matrices, which can 
+only hold one type of data, each column of a dataframe does not need to be the same data type. 
+
+```{r}
+df <- data.frame(numericVec, characterVec, logicalVec)
+str(df)
+```
+
+This allows more flexability in its usage. 
+
+Next, let's bring in a fun dataset to work with: 
+
+```{r readingDataset}
+nobel <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-14/nobel_winners.csv")
+```
+
+This is a collection of metadata for almost all Nobel laureates in physics,
+chemistry, and physiology or medicine from 1900 to 2016.
+
+This data is brought in as a modified data.frame specific to tidyverse called a
+tibble.
+
+Let's first take a look at the data we have:
+
+```{r exploringDataset}
+dim(nobel)
+colnames(nobel)
+summary(nobel) # not the best for handling characters ... 
+```
+
+The most comprehensive one that is really useful for data frames, because
+they can hold multiple data types, is `str`, or structure:
+
+```{r}
+str(nobel)
+```
+
+Let's pretend we just finished data entry and we'd like to save our dataset.
+We'll keep a copy in our data folder:
+
+```{r saveSnapshot}
+# Using full path 
+#write_csv(nobel, "../../nobel_complete.csv")
+
+# or alternatively, using here to navigate from the root of the project folder 
+#write_csv(nobel, here("data/nobel_complete.csv"))
+```
+
+Using the `here()` function brings the path back to the root directory for the
+project. This is great to ensure reproducibility because relative paths are
+easily confused when moving between scripts.
+
+Another common practice is commenting out the `write_csv()` line after executing
+to prevent accidentally writing over your dataset when re-running code in future
+analysis. It is completely up to you if you'd like to adapt this into your
+practice!
+
+Looking at one column, for example `category`, we can see that there are a few
+possible discrete values. We can take a look at the values and distribution:
+
+```{r exploringCategory}
+head(nobel$category)
+table(nobel$category)
+```
+
+Factors are great, especially for graphing, because they turn values into
+discrete categorical data. The different categories are called levels. The
+`forcats` package is handy when manipulating factors and level, but that's for
+another day!
+
+```{r}
+head(nobel$category)
+
+nobel$category <- as.factor(nobel$category)
+head(nobel$category)
+levels(nobel$category)
+```
+
+Let's look at how the category of prize won varies by a laureate's date of birth:
+
+```{r startPlotting}
+ggplot(data = nobel, aes(x = birth_date, y = category)) + 
+  geom_point()
+```
+
+Add a color as a dimension:
+
+```{r}
+ggplot(data = nobel, aes(x = birth_date, y = category, color = gender)) + 
+  geom_point()
+```
+
+Let's look at a different comparison. How does the prize year relate to the
+awardee's birth date? We can use the same point plot but add a layer with a fit
+on top.
+
+```{r}
+ggplot(data = nobel, aes(x = prize_year, y = birth_date, color = category)) +
+  geom_point() + geom_smooth()
+```
+
+There's too much going on in this plot; let's just look at the data from one
+country. Most data in the dataset are from the United States, so let's focus on
+that. We'll also save the filtered dataset in case we need to reference it in
+the future.
+
+```{r}
+nobel_US <- filter(nobel, birth_country == "United States of America")
+
+#write_csv(nobel_US, here("data/nobel_USonly.csv"))
+
+ggplot(data = nobel_US, aes(x = prize_year, y = birth_date, color = category)) +
+  geom_point() + geom_smooth()
+```
+
+Now we can start to see some trends! 
+
+Let's compare the distribution between the five largest countries in North
+America by population.
+
+```{r}
+nobel_NA <- filter(nobel, birth_country %in% c("United States of America", "Mexico", "Canada","Guatemala", "Cuba"))
+
+ggplot(data = nobel_NA, aes(x = prize_year, y = birth_date, color = birth_country, shape = category)) +
+  geom_point() + 
+  theme_classic()
+```
+
+The last plot generated can be saved using `ggsave`. 
+
+```{r}
+ggsave(here("data/nobel_USonly.png"), width = 6, height = 4)
+```
+
+This complex plot can benefit from being intereactive. `ggplot` and `plotly` makes this very 
+simple: save the graph into an object and bring it into plotly! Voila! 
+
+```{r}
+plot <- ggplot(data = nobel_NA, aes(x = prize_year, y = birth_date, color = birth_country, shape = category)) +
+  geom_point() + 
+  theme_classic()
+
+ggplotly(plot)
+```
+
+Lastly, let's knit this document to have a static snapshot of what we did. The
+file (.Rmd) needs to be saved before getting started with the knitting process.
+The code also needs to be able to execute 100% perfectly without user input;
+errors will break the knitting process. Start knitting by clicking the cleverly
+labeld yarn ball at the top of the script console. You'll notice the console
+pane where the code was running switched to the R Markdown tab. Keep your eye on
+this: any errors and their associated line numbers will be displayed here.
+
+Note: Some devices require additional set up for compiling pdf documents. This may involve 
+downloading LaTeX outside of R. 
+
+[intro-r]: https://uoftcoders.github.io/studyGroup/lessons/r/intro/lesson/
+
+
+
+
+
diff --git a/lessons/r/reproducible-rstudio/lesson.md b/lessons/r/reproducible-rstudio/lesson.md
new file mode 100644
index 000000000..11b7f757f
--- /dev/null
+++ b/lessons/r/reproducible-rstudio/lesson.md
@@ -0,0 +1,16 @@
+---
+title: "Reproducible Science in RStudio"
+author: "Ahmed Hasan and Frances Wong"
+layout: page
+visible: true
+output: md_document
+tags:
+  - r
+  - beginner
+---
+
+ - **Authors**: Ahmed Hasan, Frances Wong
+ - **Lesson topic**: Reproducible Science in RStudio
+ - **Lesson content URL (long version)**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/reproducible-rstudio/lesson.Rmd>
+ - **Lesson content URL (short version)**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/reproducible-rstudio/lesson-2020.Rmd>
+
diff --git a/lessons/r/resampling/lesson.Rmd b/lessons/r/resampling/lesson.Rmd
new file mode 100644
index 000000000..759a18271
--- /dev/null
+++ b/lessons/r/resampling/lesson.Rmd
@@ -0,0 +1,172 @@
+---
+layout: page
+title: "Resampling techniques in R: bootstrapping and permutation testing"
+visible: false
+tags:
+  # languages
+  - r
+  # levels
+  - intermediate
+---
+
+ - **Authors**: Luke Johnston 
+ - **Research field**: Nutritional Sciences
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/resampling>
+
+-----
+
+In statistics, we use samples to infer about the population given some set of
+conditions. We generally do not know or can ever know (without substantial cost
+and time) the entire characteristics of the population, so we use the sample to
+make guesses. Because the sample is not the entire population, the sample does
+not completely represent the population. That's why there are standard
+deviations, standard errors, and confidence intervals with all statistics. If we
+took another sample, it would be slightly different from the original sample and
+would have slightly different statistics. Since standard errors of the
+statistics are calculated based on the sample, these estimates can be biased to
+the sample and have certain mathematical assumptions about the distribution.
+What resampling does is to take randomly drawn (sub)samples of the sample and
+calculate the statistic from that (sub)sample. Do this enough times and you can
+get a distribution of statistic values that can provide an empirical measure of
+the accuracy/precision of the test statistic, with less rigid assumptions. In
+this lesson, I'll cover bootstrapping and permutation testing. The differences
+come down to essentially: 
+
+- Bootstrapping is best used to estimate confidence intervals of test statistics.
+- Permutation testing is best used for testing hypotheses.
+
+## Bootstrapping
+
+Bootstrapping is the process of resampling *with replacement* (*all* values in
+the sample have an *equal* probability of being selected, including multiple
+times, so a value could have a duplicate). Resample, calculate a statistic (e.g.
+the mean), repeat this hundreds or thousands of times and you are able to
+estimate a precise/accurate uncertainty of the mean (confidence interval) of the
+data's distribution. There are less assumptions about the underlying
+distribution using bootstrap compared to calculating the standard error
+directly.
+
+```{r}
+x <- runif(10)
+hist(x)
+mean(x)
+
+resample <- sample(x, replace = TRUE)
+mean(resample)
+
+library(dplyr)
+library(ggplot2)
+
+# Resample 100 times, and find the mean of each
+data_frame(num = 1:100) %>% 
+    group_by(num) %>% 
+    mutate(means = mean(sample(x, replace = TRUE))) %>% 
+    ggplot(aes(x = means)) +
+    geom_freqpoly()
+    
+# Resample a 5000 times, and find the mean of each
+data_frame(num = 1:100) %>% 
+    group_by(num) %>% 
+    mutate(means = mean(sample(x, replace = TRUE))) %>% 
+    ggplot(aes(x = means)) +
+    geom_freqpoly()
+```
+
+There is a R package that does boostrapping, called `boot`. The `boot` function
+needs a function that calculates the mean based on the resample of the data. It
+takes two arguments, the values (`x`) and the resample vector of the values
+(`i`).
+
+```{r}
+library(boot)
+
+# Create a function to take a resample of the values, 
+# and then calculate the mean
+boot_mean <- function(original_vector, resample_vector) {
+    mean(original_vector[resample_vector])
+}
+
+# R is number of replications
+mean_results <- boot(x, boot_mean, R = 2000)
+
+# Load broom to get a tidy dataframe as output.
+library(broom)
+tidy(mean_results)
+
+# Calculate the confidence intervals
+boot.ci(mean_results)
+```
+
+If you bootstrap for a statistic with two values, the individual rows are
+resampled. However, the rows are still paired together (no reshuffling of the
+order occurs).
+
+```{r}
+boot_corr <- function(data, resample_vector) {
+    cor(data$x[resample_vector], data$y[resample_vector])
+}
+
+ds <- data.frame(y = runif(10), x = runif(10))
+
+# Original correlation
+cor(ds$x, ds$y)
+
+# Bootstrapped correlation
+cor_results <- boot(example_data, boot_corr, R = 100)
+tidy(cor_results)
+
+# Plus confidence interval
+boot.ci(cor_results)
+```
+
+## Permutation testing
+
+Similar to bootstrapping, except permutation testing resamples *without
+replacement* (meaning when a value is selected, it can not be selected again, so
+no value can be a duplicate). This simply shuffles the values. In the case of a
+univariate statistic (e.g. mean), this will not change anything. However, when
+there are two or more variables, reshuffling one variable will change the test
+statistic (e.g. correlation or regression). Usually this is done on the
+response/outcome/y variable, and usually for tests that use discrete or
+categorical ("yes", "no") variables.
+
+```{r}
+ds <- data.frame(y = runif(10), x = runif(10))
+ds
+# Using spearman correlation to be consistent with the next example
+cor(ds$y, ds$x, method = "spearman") 
+
+# Resampled the y only (reshuffled the order)
+ds$resample_y <- sample(ds$y)
+ds
+
+# Correlation again, with the resampled y
+cor(ds$resample_y, ds$x, method = "spearman")
+
+# 1000 resamples of the correlation of each.
+data_frame(num = 1:1000) %>% 
+    group_by(num) %>% 
+    mutate(corr = cor(sample(y), x, method = "spearman")) %>% 
+    ggplot(aes(x = corr)) +
+    geom_freqpoly()
+```
+
+There is a `coin` package that does permutation testing for several tests.
+
+```{r}
+library(coin)
+ds <- data.frame(y, x)
+spearman_test(y ~ x, data = ds)
+```
+
+There are many more tests found within the `coin` package. Check them out in the vignette:
+
+```{r, eval=FALSE}
+vignette("coin", package = "coin")
+```
+
+## Resources:
+
+- [CrossValidated Q&A on resampling](https://stats.stackexchange.com/questions/104040/resampling-simulation-methods-monte-carlo-bootstrapping-jackknifing-cross)
+- [Wikipedia on Bootstrap](https://en.wikipedia.org/wiki/Bootstrapping_(statistics))
+- [Documentation on the `coin` package](https://cran.r-project.org/web/packages/coin/vignettes/coin.pdf)
diff --git a/lessons/r/resampling/lesson.md b/lessons/r/resampling/lesson.md
new file mode 100644
index 000000000..9982b97ad
--- /dev/null
+++ b/lessons/r/resampling/lesson.md
@@ -0,0 +1,366 @@
+---
+layout: page
+title: "Resampling techniques in R: bootstrapping and permutation testing"
+visible: true
+tags:
+  # languages
+  - r
+  # levels
+  - intermediate
+---
+
+ - **Authors**: Luke Johnston 
+ - **Research field**: Nutritional Sciences
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/resampling>
+
+-----
+
+In statistics, we use samples to infer about the population given some set of
+conditions. We generally do not know or can ever know (without substantial cost
+and time) the entire characteristics of the population, so we use the sample to
+make guesses. Because the sample is not the entire population, the sample does
+not completely represent the population. That's why there are standard
+deviations, standard errors, and confidence intervals with all statistics. If we
+took another sample, it would be slightly different from the original sample and
+would have slightly different statistics. Since standard errors of the
+statistics are calculated based on the sample, these estimates can be biased to
+the sample and have certain mathematical assumptions about the distribution.
+What resampling does is to take randomly drawn (sub)samples of the sample and
+calculate the statistic from that (sub)sample. Do this enough times and you can
+get a distribution of statistic values that can provide an empirical measure of
+the accuracy/precision of the test statistic, with less rigid assumptions. In
+this lesson, I'll cover bootstrapping and permutation testing. The differences
+come down to essentially: 
+
+- Bootstrapping is best used to estimate confidence intervals of test statistics.
+- Permutation testing is best used for testing hypotheses.
+
+## Bootstrapping
+
+Bootstrapping is the process of resampling *with replacement* (*all* values in
+the sample have an *equal* probability of being selected, including multiple
+times, so a value could have a duplicate). Resample, calculate a statistic (e.g.
+the mean), repeat this hundreds or thousands of times and you are able to
+estimate a precise/accurate uncertainty of the mean (confidence interval) of the
+data's distribution. There are less assumptions about the underlying
+distribution using bootstrap compared to calculating the standard error
+directly.
+
+
+```r
+x <- runif(10)
+hist(x)
+mean(x)
+```
+
+```
+## [1] 0.5701154
+```
+
+```r
+resample <- sample(x, replace = TRUE)
+mean(resample)
+```
+
+```
+## [1] 0.6443582
+```
+
+```r
+library(dplyr)
+```
+
+```
+## 
+## Attaching package: 'dplyr'
+```
+
+```
+## The following objects are masked from 'package:stats':
+## 
+##     filter, lag
+```
+
+```
+## The following objects are masked from 'package:base':
+## 
+##     intersect, setdiff, setequal, union
+```
+
+```r
+library(ggplot2)
+```
+
+![center](../figure/unnamed-chunk-1-1.png)
+
+```r
+# Resample 100 times, and find the mean of each
+data_frame(num = 1:100) %>% 
+    group_by(num) %>% 
+    mutate(means = mean(sample(x, replace = TRUE))) %>% 
+    ggplot(aes(x = means)) +
+    geom_freqpoly()
+```
+
+```
+## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+```
+
+![center](../figure/unnamed-chunk-1-2.png)
+
+```r
+# Resample a 5000 times, and find the mean of each
+data_frame(num = 1:100) %>% 
+    group_by(num) %>% 
+    mutate(means = mean(sample(x, replace = TRUE))) %>% 
+    ggplot(aes(x = means)) +
+    geom_freqpoly()
+```
+
+```
+## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+```
+
+![center](../figure/unnamed-chunk-1-3.png)
+
+There is a R package that does boostrapping, called `boot`. The `boot` function
+needs a function that calculates the mean based on the resample of the data. It
+takes two arguments, the values (`x`) and the resample vector of the values
+(`i`).
+
+
+```r
+library(boot)
+
+# Create a function to take a resample of the values, 
+# and then calculate the mean
+boot_mean <- function(original_vector, resample_vector) {
+    mean(original_vector[resample_vector])
+}
+
+# R is number of replications
+mean_results <- boot(x, boot_mean, R = 2000)
+
+# Load broom to get a tidy dataframe as output.
+library(broom)
+tidy(mean_results)
+```
+
+```
+##   statistic         bias  std.error
+## 1 0.5701154 -0.004093434 0.08803179
+```
+
+```r
+# Calculate the confidence intervals
+boot.ci(mean_results)
+```
+
+```
+## Warning in boot.ci(mean_results): bootstrap variances needed for
+## studentized intervals
+```
+
+```
+## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
+## Based on 2000 bootstrap replicates
+## 
+## CALL : 
+## boot.ci(boot.out = mean_results)
+## 
+## Intervals : 
+## Level      Normal              Basic         
+## 95%   ( 0.4017,  0.7467 )   ( 0.4046,  0.7517 )  
+## 
+## Level     Percentile            BCa          
+## 95%   ( 0.3885,  0.7356 )   ( 0.4044,  0.7445 )  
+## Calculations and Intervals on Original Scale
+```
+
+If you bootstrap for a statistic with two values, the individual rows are
+resampled. However, the rows are still paired together (no reshuffling of the
+order occurs).
+
+
+```r
+boot_corr <- function(data, resample_vector) {
+    cor(data$x[resample_vector], data$y[resample_vector])
+}
+
+ds <- data.frame(y = runif(10), x = runif(10))
+
+# Original correlation
+cor(ds$x, ds$y)
+```
+
+```
+## [1] -0.2155279
+```
+
+```r
+# Bootstrapped correlation
+cor_results <- boot(example_data, boot_corr, R = 100)
+```
+
+```
+## Error in NROW(data): object 'example_data' not found
+```
+
+```r
+tidy(cor_results)
+```
+
+```
+## Error in tidy(cor_results): object 'cor_results' not found
+```
+
+```r
+# Plus confidence interval
+boot.ci(cor_results)
+```
+
+```
+## Error in boot.ci(cor_results): object 'cor_results' not found
+```
+
+## Permutation testing
+
+Similar to bootstrapping, except permutation testing resamples *without
+replacement* (meaning when a value is selected, it can not be selected again, so
+no value can be a duplicate). This simply shuffles the values. In the case of a
+univariate statistic (e.g. mean), this will not change anything. However, when
+there are two or more variables, reshuffling one variable will change the test
+statistic (e.g. correlation or regression). Usually this is done on the
+response/outcome/y variable, and usually for tests that use discrete or
+categorical ("yes", "no") variables.
+
+
+```r
+ds <- data.frame(y = runif(10), x = runif(10))
+ds
+```
+
+```
+##             y          x
+## 1  0.26248353 0.60789022
+## 2  0.27608331 0.81612978
+## 3  0.35928985 0.47067467
+## 4  0.34308694 0.86670014
+## 5  0.61019344 0.23596764
+## 6  0.31907683 0.53597891
+## 7  0.51808777 0.77629172
+## 8  0.39792031 0.09387856
+## 9  0.04529551 0.75595895
+## 10 0.56524394 0.44996910
+```
+
+```r
+# Using spearman correlation to be consistent with the next example
+cor(ds$y, ds$x, method = "spearman") 
+```
+
+```
+## [1] -0.5393939
+```
+
+```r
+# Resampled the y only (reshuffled the order)
+ds$resample_y <- sample(ds$y)
+ds
+```
+
+```
+##             y          x resample_y
+## 1  0.26248353 0.60789022 0.31907683
+## 2  0.27608331 0.81612978 0.04529551
+## 3  0.35928985 0.47067467 0.56524394
+## 4  0.34308694 0.86670014 0.39792031
+## 5  0.61019344 0.23596764 0.34308694
+## 6  0.31907683 0.53597891 0.35928985
+## 7  0.51808777 0.77629172 0.51808777
+## 8  0.39792031 0.09387856 0.26248353
+## 9  0.04529551 0.75595895 0.27608331
+## 10 0.56524394 0.44996910 0.61019344
+```
+
+```r
+# Correlation again, with the resampled y
+cor(ds$resample_y, ds$x, method = "spearman")
+```
+
+```
+## [1] -0.07878788
+```
+
+```r
+# 1000 resamples of the correlation of each.
+data_frame(num = 1:1000) %>% 
+    group_by(num) %>% 
+    mutate(corr = cor(sample(y), x, method = "spearman")) %>% 
+    ggplot(aes(x = corr)) +
+    geom_freqpoly()
+```
+
+```
+## Error in mutate_impl(.data, dots): Evaluation error: object 'y' not found.
+```
+
+There is a `coin` package that does permutation testing for several tests.
+
+
+```r
+library(coin)
+```
+
+```
+## Loading required package: methods
+```
+
+```
+## Loading required package: survival
+```
+
+```
+## 
+## Attaching package: 'survival'
+```
+
+```
+## The following object is masked from 'package:boot':
+## 
+##     aml
+```
+
+```r
+ds <- data.frame(y, x)
+```
+
+```
+## Error in data.frame(y, x): object 'y' not found
+```
+
+```r
+spearman_test(y ~ x, data = ds)
+```
+
+```
+## 
+## 	Asymptotic Spearman Correlation Test
+## 
+## data:  y by x
+## Z = -1.6182, p-value = 0.1056
+## alternative hypothesis: true rho is not equal to 0
+```
+
+There are many more tests found within the `coin` package. Check them out in the vignette:
+
+
+```r
+vignette("coin", package = "coin")
+```
+
+## Resources:
+
+- [CrossValidated Q&A on resampling](https://stats.stackexchange.com/questions/104040/resampling-simulation-methods-monte-carlo-bootstrapping-jackknifing-cross)
+- [Wikipedia on Bootstrap](https://en.wikipedia.org/wiki/Bootstrapping_(statistics))
+- [Documentation on the `coin` package](https://cran.r-project.org/web/packages/coin/vignettes/coin.pdf)
diff --git a/lessons/r/shiny/1/server.r b/lessons/r/shiny/1/server.r
new file mode 100644
index 000000000..f2fec5e1a
--- /dev/null
+++ b/lessons/r/shiny/1/server.r
@@ -0,0 +1,35 @@
+#Here we first familiarize ourselves with the basic layout of the Shiny user interface. It is important to note, that while we set-up interactivity, user interactivity is not included within this first shiny App activity.
+
+#ui.R
+#	1. After initially loading the shiny library and relevant data, the overall UI is wrapped within the shinyUI function
+#	2. Next, we define a page that is fluid (fluidPage)--that can change based on the user's input. For this page, we name it with a title (titlePanel)
+#	3. The set-up of the page is one based on a template (sidebarLayout): a sidebar (sidebarPanel; on the left, from which the user will make their selections); and a main Panel (mainPanel) which is changes based on the user's input in the left side-bar panel
+#	4. The mainPanel will display the PCA of the metrics, which is identified as "pcaplot". This object is created/assigned within the server.R script.
+
+#server.R
+#	1. The shiny library and relevant data is first loaded
+#	2. We define the server for the Shiny app as something with both objects that are input (from the ui.R) and output (from the server.R)
+#	3. We create a reactive Shiny plot that is output from server.R to ui.R with the function renderPlot. renderPlot is an reactive function that can take input data from the ui.R script and feed it into the server.R script. It then actively updates the information within its function. We name the object that has the renderPlot function to plot the PCAs as "pcaplot". "pcaplot" is then output and displayed through the ui.R within the plotOutput function.
+
+library(shiny)	#First load shiny library
+load("../pcas.RDATA")	#Load data
+
+#Define a server for the Shiny app
+shinyServer(function(input, output) {
+
+	#Create a reactive Shiny plot to send to the ui.r called "pcaplot"
+	output$pcaplot <- renderPlot({
+		#Render the scatterplot of PCA
+		plot(PC2~PC1, data=foo)	
+  })
+})
+#TASKS:
+#1. Change the plot's x- and y-axis label names to "PC1" and "PC2". 
+#2. Instead of plotting points, plot the metrics as text points instead 
+
+#HINTS:
+#1. use "xlab" and "ylab" within the function "plot" to change the axes labels
+#2. For the function "plot", enter the parameter "type="n"" to create a blank plot that maintains the axes labels and plotting parameters:
+#	plot(PC2 ~ PC1, data=foo, type="n", xlab="PC1", ylab="PC2")
+#Next use the function "text" to plot the text values. Use "foo$metric" as the label values
+#	text(PC2 ~ PC1, data=foo, labels=foo$metric)
diff --git a/lessons/r/shiny/1/ui.r b/lessons/r/shiny/1/ui.r
new file mode 100644
index 000000000..7a6a122a5
--- /dev/null
+++ b/lessons/r/shiny/1/ui.r
@@ -0,0 +1,47 @@
+#Here we first familiarize ourselves with the basic layout of the Shiny user interface. It is important to note, that while we set-up interactivity, user interactivity is not included within this first shiny App activity.
+
+#ui.R
+#	1. After initially loading the shiny library and relevant data, the overall UI is wrapped within the shinyUI function
+#	2. Next, we define a page that is fluid (fluidPage)--that can change based on the user's input. For this page, we name it with a title (titlePanel)
+#	3. The set-up of the page is one based on a template (sidebarLayout): a sidebar (sidebarPanel; on the left, from which the user will make their selections); and a main Panel (mainPanel) which is changes based on the user's input in the left side-bar panel
+#	4. The mainPanel will display the PCA of the metrics, which is identified as "pcaplot". This object is created/assigned within the server.R script.
+
+#server.R
+#	1. The shiny library and relevant data is first loaded
+#	2. We define the server for the Shiny app as something with both objects that are input (from the ui.R) and output (from the server.R)
+#	3. We create a reactive Shiny plot that is output from server.R to ui.R with the function renderPlot. renderPlot is an reactive function that can take input data from the ui.R script and feed it into the server.R script. It then actively updates the information within its function. We name the object that has the renderPlot function to plot the PCAs as "pcaplot". "pcaplot" is then output and displayed through the ui.R within the plotOutput function.
+
+library(shiny)	#First load shiny library
+load("../pcas.RDATA")	#Load data
+ 
+#Define the overall UI
+shinyUI(
+	#Use a fluid Bootstrap layout
+	fluidPage(
+		#Give the page a title
+		titlePanel("Title"),
+		#Define page with a sidebar panel and main panel
+		sidebarLayout(
+			#Sidebar Panel
+			sidebarPanel(
+				#We will fill this out later
+			),		
+			#Create a spot for the plot
+			mainPanel(
+				plotOutput("pcaplot")
+			)
+		)
+
+    )
+ )
+ 
+ #TASK:
+ #1. Change the title to something more relevant, e.g., "PCA of Metrics"
+ 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
\ No newline at end of file
diff --git a/lessons/r/shiny/2/server.r b/lessons/r/shiny/2/server.r
new file mode 100644
index 000000000..8ea21241b
--- /dev/null
+++ b/lessons/r/shiny/2/server.r
@@ -0,0 +1,35 @@
+#Here, we add interactivity to the plot. In the sidebarPanel in the ui.R, we add a sliderInput widget, which allows the user to (a) select a value from the ui interface, which is then (b) fed to the server script. The server script then (c) readjusts the plot with the new input value and (d) feeds the output plot to the ui interface.
+
+library(shiny)	#First load shiny library
+load("../pcas.RDATA")	#Load data
+
+#Define a server for the Shiny app
+shinyServer(function(input, output) {
+
+	#Create a reactive Shiny plot to send to the ui.r called "pcaplot"
+	output$pcaplot <- renderPlot({
+		#Render the scatterplot of PCA
+		plot(PC2~PC1, data=foo, type="n", xlab="PC1", ylab="PC2")
+		text(PC2~PC1, data=foo, labels=foo$metric, cex=input$cexSlider)
+  })
+})
+
+#TASKS:
+#1. The PCA plot (pcaplot) currently is showing all the values, disregarding the different variables (Var: colless, lambdaE, lambdaR, Landscape, Numsp, Repulsion, Spatial). We however would like to show the PCA plot for each variable scenario. 
+#To do so:
+#	a. Complete Task 3 in ui.r to create the selectInput drop-down menu, which we will call 'var'
+#	b. subset the foo dataframe for only instances when foo$Var==input$var
+
+
+#HINTS
+#1. output$pcaplot <- renderPlot({
+#		#Subset for input$var and assign this subset to new object, "fbar"
+#		fbar<-droplevels(foo[which(foo$Var==input$var),])
+#		
+#		#Change "foo" to "fbar" for the plot and text
+#		plot(PC2 ~ PC1, data=fbar, type="n", xlab="PC1", ylab="PC2")
+#		text(PC2 ~ PC1, data=fbar, labels=fbar$metric, cex=input$cexSlider)
+#  })
+
+
+
diff --git a/lessons/r/shiny/2/ui.r b/lessons/r/shiny/2/ui.r
new file mode 100644
index 000000000..719ba36fb
--- /dev/null
+++ b/lessons/r/shiny/2/ui.r
@@ -0,0 +1,57 @@
+#Here, we add interactivity to the plot. In the sidebarPanel in the ui.R, we add a sliderInput widget, which allows the user to (a) select a value from the ui interface, which is then (b) fed to the server script. The server script then (c) readjusts the plot with the new input value and (d) feeds the output plot to the ui interface.
+
+library(shiny)	#First load shiny library
+load("../pcas.RDATA")	#Load data
+
+#Define the overall UI
+shinyUI(
+	#Use a fluid Bootstrap layout
+	fluidPage(
+		#Give the page a title
+		titlePanel("PCA of Metrics"),
+		#Define page with a sidebar panel and main panel
+		sidebarLayout(
+			#Sidebar Panel
+			sidebarPanel(
+				#3.a. Add selectInput function here
+				#Create a slider that will adjust the cex value of the text displayed called "cexSlider".
+				sliderInput(inputId = "cexSlider", label="Adjust cex",
+				min = 0, max=1, value=1)
+			),		
+			#Create a spot for the plot
+			mainPanel(
+				plotOutput("pcaplot")
+			)
+		)
+
+    )
+)
+
+#TASKS:
+#1. Change the size of label of the cexSlider with the wrapper, "h4"
+#2. Adjust the maximum cexSlider cex size to 5
+#3. The PCA plot (pcaplot) currently is showing all the values, disregarding the different variables (Var: colless, numsp, Spatial). We however would like to show the PCA plot for each variable scenario. 
+#To do so:
+#	a. Create a drop-down list of the variables with the function "selectInput" within the "sidebarPanel" function. 
+#	b. Assign the selectInput object as 'var', and the label as "h3('Variable')"
+#	c. for the choices, use:
+		# choices = c(
+			# "Colless" = "colless",
+			# "Species Pool Size" = "numsp",
+			# "Spatially Contiguous" = "spatial"
+		# )
+#	d. Link 'var' as input$var in server.r (refer to server.r)
+
+ 
+#HINTS:
+#1. sliderInput(..., label = h4("Adjust cex"))
+#2. sliderInput(..., max = 5)
+#3. selectInput(inputId='var', label = h3('Variable'),
+		# choices = c(
+			# "Colless" = "colless",
+			# "Species Pool Size" = "numsp",
+			# "Spatially Contiguous" = "spatial"
+		# )
+#	)
+
+ 
\ No newline at end of file
diff --git a/lessons/r/shiny/3/server.r b/lessons/r/shiny/3/server.r
new file mode 100644
index 000000000..b5acb0471
--- /dev/null
+++ b/lessons/r/shiny/3/server.r
@@ -0,0 +1,117 @@
+#We've now added interactivity to the plot. The user can (a) specify the Variable (Var) scenario they wish to plot the PCA of, and (b) adjust the text's cex values. The server.r script now includes code to add colors that correspond to each selected variable's parameter values (param), as well as a legend.
+
+library(shiny)	#First load shiny library
+load("../pcas.RDATA")	#Load data
+
+#Define a server for the Shiny app
+shinyServer(function(input, output) {
+
+	#Create a reactive Shiny plot to send to the ui.r called "pcaplot"
+	output$pcaplot <- renderPlot({
+		#Match user's variable input selection by subsetting for when foo$Var==input$var
+		fbar<-droplevels(foo[which(foo$Var==input$var),])
+		
+		#Generate colors to correspond to the different Variables' parameters. E.g., for the variable "colless", different colors for "loIc","midIc", and "hiIc"
+		cols<-c("#1f77b4","#ff7f0e","#2ca02c")[1:length(levels(fbar$param))]
+		
+		#Render the scatterplot of PCA
+		plot(PC2 ~ PC1, data=fbar,  type="n", xlab="PC1", ylab="PC2")
+
+		#For each of the different parameter values (different colors), plot the text names of the metrics
+		for(i in levels(fbar$param)){
+			foo2<-subset(fbar,param==i)
+			text(PC2 ~ PC1, data=foo2, labels=foo2$metric, col=cols[which(levels(fbar$param)==i)],  cex=input$cexSlider)
+		}
+		#Add a legend for the different parameter values (with corresponding colors) of the selected Variable
+		legend("topright", fill=cols, legend=levels(fbar$param))
+  })
+})
+
+#TASKS:
+#1. Depending on the variable that people choose (input$var), we now want to display the chosen variable's parameter (param) values as a check box group. Even though logically we would begin with the checkboxGroupInput function on the ui.R script to display check box options, first remember what the capabilities of the user side (ui.R) are. ui.R cannot evaluate/process information, but only displays output values calculated on the server side (server.R). In other words, the ui.R only takes in the values a user specifies and then sends this information to be evaluated/processed on the server side. 
+#	So to create a checkboxgroup of param values that will change based on what the user selects for a variable (input$var), our first step is to evaluate what the user selects as a variable. This evaluation can only take place on the server side. We therefore need to create an object on the server side (output$paramchkbxgrp) that will evaluate the user's Variable selection (input$var), which will then decide which check box options to display.  This decision of what to display is then fed back to the ui.R with the object uiOutput. 
+
+#	1.a. Depending on what Variable is selected by the user (input$var), generate a different set of check box options to display (with the function checkboxGroupInput). For instance, if "colless" is selected as the variable, display the parameters values that correspond to "colless", e.g., loIc, midIc and hiIc. This switch function takes the given value (input$var) and then evaluates it. When input$var=="colless", then it evaluates the checkboxGroupInput function with the choices of  c("Low" = "loIc", "Mid" = "midIc", "High" = "hiIc"). 
+#Use the following script as a framework for the paramchkbxgrp object:
+
+	#	output$paramchkbxgrp <- renderUI({
+			# if (is.null(input$var))
+			  # return()
+		## Depending on input$var, we'll generate a different
+		## UI component and send it to the client.
+			# switch(input$var,
+			# "colless" = checkboxGroupInput(inputId = "PARAMS", label = "",
+				# choices = c(
+					# "Low" = "loIc",
+					# "Mid" = "midIc",
+					# "High" = "hiIc"
+					# ),
+				# selected = c("loIc","midIc","hiIc")),	
+			##Fill in the ellipses with the corresponding parameter values 
+			# "numsp" = checkboxGroupInput(...),
+			# "spatial" = checkboxGroupInput(...),
+			# )
+		  # })
+			
+	# })
+#2. When certain checkboxes are unselected, display the text as grey with some transparency. To do this, within the renderPlot function, adjust the cols object such that the unselected are black with high transparency (display as grey). To do this for the cols object, subset for when input$PARAMS is not in levels(fbar$PARAMS):
+#	cols[which(!(levels(fbar$param) %in% input$PARAMS))]
+#Then assign all of these instances as black rgb(0,0,0, maxColorValue=255) with very high transparency rgb(0,0,0, alpha =25, maxColorValue=255):
+#	cols[which(!(levels(fbar$param) %in% input$PARAMS))]<- rgb(0,0,0, alpha=25,maxColorValue=255)
+
+# #HINTS
+# 1.a 	Drop-down menu to select Factor	
+	# output$paramchkbxgrp <- renderUI({
+		# if (is.null(input$var))
+		  # return()
+
+		# # Depending on input$var, we'll generate a different
+		# # UI component and send it to the client.
+		# switch(input$var,
+		  # "colless" = checkboxGroupInput("PARAMS", "",
+			# choices = c("Low" = "loIc",
+						# "Mid" = "midIc",
+						# "High" = "hiIc"
+						# ),
+			# selected = c("loIc","midIc","hiIc")),
+		  # "numsp" = checkboxGroupInput("PARAMS", "",
+			# choices = c("16 Species" = 16,
+						# "64 Species" = 64,
+						# "256 Species" = 256),
+			# selected = c(16,64,256)),
+		  # "spatial" = checkboxGroupInput("PARAMS", "",
+			# choices = c("True" = "TRUE",
+						# "False" = "FALSE"),
+			# selected = c("TRUE","FALSE")),
+		# )
+	  # })
+
+
+# 2. 
+# output$pcaplot <- renderPlot({
+		# #Match user's variable input selection by subsetting for when foo$Var==input$var
+		# fbar<-droplevels(foo[which(foo$Var==input$var),])
+		
+		# #Generate colors to correspond to the different Variables' parameters. E.g., for the variable "colless", different colors for "loIc","midIc", and "hiIc"
+		# cols<-c("#1f77b4","#ff7f0e","#2ca02c")[1:length(levels(fbar$param))]
+		
+		# #***********************ADD THIS TO EXISTING SCRIPT *************************
+		# #If the param is not selected, set the color value to grey with transparency
+		# cols[which(!(levels(fbar$param) %in% input$PARAMS))]<- rgb(0,0,0, alpha=25,maxColorValue=255)
+		# #****************************************************************************
+		
+		# #Render the scatterplot of PCA
+		# plot(PC2 ~ PC1, data=fbar, type="n", xlab="PC1", ylab="PC2")
+
+		# #For each of the different parameter values (different colors), plot the text names of the metrics
+		# for(i in levels(fbar$param)){
+			# foo2<-subset(fbar,param==i)
+			# text(PC2 ~ PC1, data=foo2, labels=foo2$metric, col=cols[which(levels(fbar$param)==i)],  cex=input$cexSlider)
+		# }
+		
+		# #Add a legend for the different parameter values (with corresponding colors) of the selected Variable
+		# legend("topright", fill=cols, legend=levels(fbar$param))
+		
+  # })
+
+
diff --git a/lessons/r/shiny/3/ui.r b/lessons/r/shiny/3/ui.r
new file mode 100644
index 000000000..1def9da26
--- /dev/null
+++ b/lessons/r/shiny/3/ui.r
@@ -0,0 +1,46 @@
+#We've now added interactivity to the plot. The user can (a) specify the Variable (Var) scenario they wish to plot the PCA of, and (b) adjust the text's cex values. The server.r script now includes code to add colors that correspond to each selected variable's parameter values (param), as well as a legend.
+
+library(shiny)	#First load shiny library
+load("../pcas.RDATA")	#Load data
+
+#Define the overall UI
+shinyUI(
+	#Use a fluid Bootstrap layout
+	fluidPage(
+		#Give the page a title
+		titlePanel("PCA of Metrics"),
+		#Define page with a sidebar panel and main panel
+		sidebarLayout(
+			#Sidebar Panel
+			sidebarPanel(
+				#Create drop-down menu to select Variable to plot PCA for
+				selectInput(inputId='var', label = h3('Variable'),
+					choices = c(
+						"Colless" = "colless",
+						"Species Pool Size" = "numsp",
+						"Spatially Contiguous" = "spatial"
+						)
+					),
+				#Create a slider that will adjust the cex value of the text displayed called "cexSlider".
+				sliderInput(inputId = "cexSlider", label=h4("Adjust cex"),
+				min = 0, max=5, value=1)
+			),		
+			#Create a spot for the plot
+			mainPanel(
+				plotOutput("pcaplot")
+			)
+		)
+
+    )
+)
+
+#TASKS:
+#1. Depending on the variable that people choose (input$var), we now want to display the chosen variable's parameter (foo$param) values as a check box group. Even though logically we would begin with the checkboxGroupInput function on the ui.R script to display check box options, first remember what the capabilities of the user side (ui.R) are. ui.R cannot evaluate/process information, but only displays output values calculated on the server side (server.R). In other words, the ui.R only takes in the values a user specifies and then sends this information to be evaluated/processed on the server side. 
+#	So to create a checkboxgroup of param values that will change based on what the user selects for a variable (input$var), our first step is to evaluate what the user selects as a variable. This evaluation can only take place on the server side. We therefore need to create an object on the server side (output$paramchkbxgrp) that will evaluate the user's Variable selection (input$var), which will then decide which check box options to display. This decision of what to display is then fed back to the ui.R with the object uiOutput. 
+#	1.a. With the function uiOutput, display the computation result of the output function paramchkbxgrp
+ 
+#HINTS:
+#1.a. Place the following snippet of code in ui.R within the sidebarPanel, after the selectInput for the variables:
+#	uiOutput(outputId="paramchkbxgrp")
+
+ 
\ No newline at end of file
diff --git a/lessons/r/shiny/4/server.r b/lessons/r/shiny/4/server.r
new file mode 100644
index 000000000..27f33ce93
--- /dev/null
+++ b/lessons/r/shiny/4/server.r
@@ -0,0 +1,142 @@
+# We've now learned some of the basic tools for building a shiny app. Now complete the tasks to add additional bells and whistles to the shiny app.
+# After you have finished with your app, you can deploy it to the world to see. There are a number of options for this, which I won't go into detail because it depends on your personal preference for how you want the data shared (See https://shiny.rstudio.com/tutorial/lesson7/)
+
+library(shiny)	#First load shiny library
+load("../pcas.RDATA")	#Load data
+
+#Define a server for the Shiny app
+shinyServer(function(input, output) {
+	output$paramchkbxgrp <- renderUI({
+		if (is.null(input$var))
+		return()
+
+		#Check box group
+		#Depending on input$var, we'll generate a different UI component and send it to the client.
+		switch(input$var,
+			"colless" = checkboxGroupInput("PARAMS", "",
+				choices = c("Low" = "loIc",
+					"Mid" = "midIc",
+					"High" = "hiIc"),
+				selected = c("loIc","midIc","hiIc")
+			),
+			"numsp" = checkboxGroupInput("PARAMS", "",
+				choices = c("16 Species" = 16,
+						"64 Species" = 64,
+						"256 Species" = 256),
+				selected = c(16,64,256)
+			),
+			"spatial" = checkboxGroupInput("PARAMS", "",
+				choices = c("True" = "TRUE",
+						"False" = "FALSE"),
+				selected = c("TRUE","FALSE")
+			)
+		)
+	})
+	  
+	#Create a reactive Shiny plot to send to the ui.r called "pcaplot"
+	output$pcaplot <- renderPlot({
+		#Match user's variable input selection by subsetting for when foo$Var==input$var
+		fbar<-droplevels(foo[which(foo$Var==input$var),])
+		
+		#Generate colors to correspond to the different Variables' parameters. E.g., for the variable "colless", different colors for "loIc","midIc", and "hiIc"
+		cols<-c("#1f77b4","#ff7f0e","#2ca02c")[1:length(levels(fbar$param))]
+		
+		#If the param is not selected, set the color value to grey with transparency
+		cols[which(!(levels(fbar$param) %in% input$PARAMS))]<- rgb(0,0,0, alpha=25,maxColorValue=255)
+		
+		#Render the scatterplot of PCA
+		plot(PC2 ~ PC1, data=fbar, type="n", xlab="PC1", ylab="PC2")
+
+		#For each of the different parameter values (different colors), plot the text names of the metrics
+		for(i in levels(fbar$param)){
+			foo2<-subset(fbar,param==i)
+			text(PC2 ~ PC1, data=foo2, labels=foo2$metric, col=cols[which(levels(fbar$param)==i)],  cex=input$cexSlider)
+		}
+		
+		#Add a legend for the different parameter values (with corresponding colors) of the selected Variable
+		legend("topright", fill=cols, legend=levels(fbar$param))
+		
+  })
+})
+
+#TASKS:
+#1. Create two sliderInput objects to adjust the x- and y-axes of the plot depending on the Variable selected. To do so:
+#	a. Within the sidebarPanel of the ui.R script, add two uiOutput objects, containing the outputId names of "sliderX" and "sliderY" (see ui.R)
+#	b. In script.R, create the output$sliderX and output$sliderY objects with the function renderUI. The renderUI function allows the function within to be reactive.
+#		i. within the renderUI wrapper, use the sliderInput function:
+#			sliderInput(inputId, label, min, max, value)
+#			>inputId: "SLIDERX" for the x-axis; "SLIDERY" for the y-axis
+#			>label: "h5("x-axes")" for the x-axis; "h5("y-axes")" for the y-axis
+#			>min: subset the object foo (with the function "subset") for the minimum (use function "min") value of PC1 (for x-axis) or PC2 (for y-axis) when Var==input$var
+#			>max: subset the object foo (with the function "subset") for the maximum (use function "max") value of PC1 (for x-axis) or PC2 (for y-axis) when Var==input$var
+#			>value: the concatenated values of the two aforementioned min and max values: subset the object foo for the minimum/maximum value of PC1/PC2 when Var==input$var
+#	c. For the scatterplot (plot) of PCA within the output$pcaplot function, change the plot parameters "xlim" and "ylim" to change with the input$SLIDERX and input$SLIDERY values.
+#2. After evaluating the plot function, add red (col = "red") dotted lines (lty = 2) at x=0 (h=0) & y=0 (v=0) with the function "abline". 
+#3. Implement the percent variation explained into the plot axes. 
+#	a. First, within the output$pcaplot's renderPlot function, create an object "pcaper" to subset pca_percent for when pca_percent$Var==input$var
+#	b. Within the plot function, adjust the xlab and ylab parameters. With the "paste" function, for xlab, add "pcaper[2]" as the x-axis percent variation explained: xlab=paste("PCA Axis 1 (",pcaper[2],"%)")
+#		Do the same for ylab, where instead use pcaper[3]
+#4. Use the R expressions instead to display labels. Paste the following code into your script:
+	# output$pcaplot <- renderPlot({
+		# ...
+		# for(i in levels(fbar$param)){
+			# foo2<-subset(fbar,param==i)
+			# labs<-paste("c(",paste("expression(",foo2$Rexpression,")",sep="",collapse=","),")",sep="")
+			# text(PC2 ~ PC1, data=foo2, labels=eval(parse(text=labs)), col=cols[which(levels(fbar$param)==i)],  cex=input$cexSlider)
+		# }
+		# ...
+	# })
+
+#HINTS
+# 1.b 
+# shinyServer(function(input, output) {
+	# output$paramchkbxgrp <- renderUI({...})
+	
+	#********ADD THIS SCRIPT********
+	# #X-axis
+	  	# output$sliderX <-renderUI({
+			# sliderInput(inputId="SLIDERX", 
+				# label = h5("x-axes"), 
+				# min = min(subset(foo,Var==input$var)[,"PC1"]), 
+				# max = max(subset(foo,Var==input$var)[,"PC1"]), 
+				# value = c(min(subset(foo,Var==input$var)[,"PC1"]),max(subset(foo,Var==input$var)[,"PC1"])))
+		# })
+	# #Y-axis
+	  	# output$sliderY <-renderUI({
+			# sliderInput(inputId="SLIDERY", 
+				# label = h5("y-axes"), 
+				# min = min(subset(foo,Var==input$var)[,"PC2"]), 
+				# max = max(subset(foo,Var==input$var)[,"PC2"]), 
+				# value = c(min(subset(foo,Var==input$var)[,"PC2"]),max(subset(foo,Var==input$var)[,"PC2"])))
+		# })
+	#*******************************
+	
+	# output$pcaplot <- renderPlot({...})
+# })
+
+#1.c.	
+#	output$pcaplot <- renderPlot({
+#		...
+#	plot(PC2 ~ PC1, data=fbar, type="n", xlab="PC1", ylab="PC2", xlim=input$SLIDERX, ylim=input$SLIDERY)
+#		...
+# })
+#2.	
+#	output$pcaplot <- renderPlot({
+#		...
+#	plot(PC2 ~ PC1, data=fbar, type="n", xlab="PC1", ylab="PC2", xlim=input$SLIDERX, ylim=input$SLIDERY)
+#	abline(h=0,v=0,col="red",lty=2)
+#		...
+# })
+#
+#3.	
+#	output$pcaplot <- renderPlot({
+#		fbar<-droplevels(...)
+
+#		pcaper<-pca_percent[which(pca_percent$Var==input$var),]
+#		
+#		plot(PC2 ~ PC1, data=fbar, type="n", xlim=input$SLIDERX, ylim=input$SLIDERY,
+#			xlab=paste("PCA Axis 1 (",pcaper[2],"%)"),
+#			ylab=paste("PCA Axis 2 (",pcaper[3],"%)"))
+#		
+#		...
+#	})
diff --git a/lessons/r/shiny/4/ui.r b/lessons/r/shiny/4/ui.r
new file mode 100644
index 000000000..c744ba818
--- /dev/null
+++ b/lessons/r/shiny/4/ui.r
@@ -0,0 +1,58 @@
+# We've now learned some of the basic tools for building a shiny app. Now complete the tasks to add additional bells and whistles to the shiny app.
+# After you have finished with your app, you can deploy it to the world to see. There are a number of options for this, which I won't go into detail because it depends on your personal preference for how you want the data shared (See https://shiny.rstudio.com/tutorial/lesson7/)
+
+library(shiny)	#First load shiny library
+load("../pcas.RDATA")	#Load data
+
+#Define the overall UI
+shinyUI(
+	#Use a fluid Bootstrap layout
+	fluidPage(
+		#Give the page a title
+		titlePanel("PCA of Metrics"),
+		#Define page with a sidebar panel and main panel
+		sidebarLayout(
+			#Sidebar Panel
+			sidebarPanel(
+				#Create drop-down menu to select Variable to plot PCA for
+				selectInput(inputId='var', label = h3('Variable'),
+					choices = c(
+						"Colless" = "colless",
+						"Species Pool Size" = "numsp",
+						"Spatially Contiguous" = "spatial"
+						)
+					),
+				#Check box group, contingent upon previous drop-down menu selection
+				uiOutput(outputId="paramchkbxgrp"),
+				#Create a slider that will adjust the cex value of the text displayed called "cexSlider".
+				sliderInput(inputId = "cexSlider", label=h4("Adjust cex"),
+				min = 0, max=5, value=1)
+			),		
+			#Create a spot for the plot
+			mainPanel(
+				plotOutput("pcaplot")
+			)
+		)
+
+    )
+)
+
+#TASKS:
+#1. Create the ability to zoom in or out of the plots. Create two sliderInput objects to adjust the x- and y-axes of the plot depending on the Variable selected. To do so:
+#	a. Within the sidebarPanel of the ui.R script, add two uiOutput objects, containing the outputId names of "sliderX" and "sliderY"
+#	b. In script.R, create the output$sliderX and output$sliderY objects with the function renderUI. (see script.R)
+#2. Change the plot width. For plotOutput(outputId="pcaplot"), add width (width = "750px") and height (height = "650px") parameters
+ 
+#HINTS:
+#1.a. 
+# sidebarPanel(
+	# selectInput(inputId='var', ...),
+	# uiOutput(outputId="paramchkbxgrp"),
+	# sliderInput(inputId = "cexSlider", ...),
+	# #********ADD THIS SCRIPT********
+	# uiOutput(outputId="sliderX"),
+	# uiOutput(outputId="sliderY")
+	# #*******************************
+# )
+#2. plotOutput(outputId="pcaplot",  width="750px",height="750px")
+ 
diff --git a/lessons/r/shiny/5/server.r b/lessons/r/shiny/5/server.r
new file mode 100644
index 000000000..66b40d04f
--- /dev/null
+++ b/lessons/r/shiny/5/server.r
@@ -0,0 +1,81 @@
+library(shiny)	#First load shiny library
+load("../pcas.RDATA")	#Load data
+
+#Define a server for the Shiny app
+shinyServer(function(input, output) {
+	output$paramchkbxgrp <- renderUI({
+		if (is.null(input$var))
+		return()
+
+		#Check box group
+		#Depending on input$var, we'll generate a different UI component and send it to the client.
+		switch(input$var,
+			"colless" = checkboxGroupInput("PARAMS", "",
+				choices = c("Low" = "loIc",
+					"Mid" = "midIc",
+					"High" = "hiIc"),
+				selected = c("loIc","midIc","hiIc")
+			),
+			"numsp" = checkboxGroupInput("PARAMS", "",
+				choices = c("16 Species" = 16,
+						"64 Species" = 64,
+						"256 Species" = 256),
+				selected = c(16,64,256)
+			),
+			"spatial" = checkboxGroupInput("PARAMS", "",
+				choices = c("True" = "TRUE",
+						"False" = "FALSE"),
+				selected = c("TRUE","FALSE")
+			)
+		)
+	})
+	
+	#X-axis
+	output$sliderX <-renderUI({
+		sliderInput(inputId="SLIDERX", 
+			label = h5("x-axes"), 
+			min = min(subset(foo,Var==input$var)[,"PC1"]), 
+			max = max(subset(foo,Var==input$var)[,"PC1"]), 
+			value = c(min(subset(foo,Var==input$var)[,"PC1"]),max(subset(foo,Var==input$var)[,"PC1"])))
+	})
+	#Y-axis
+	output$sliderY <-renderUI({
+		sliderInput(inputId="SLIDERY", 
+			label = h5("y-axes"), 
+			min = min(subset(foo,Var==input$var)[,"PC2"]), 
+			max = max(subset(foo,Var==input$var)[,"PC2"]), 
+			value = c(min(subset(foo,Var==input$var)[,"PC2"]),max(subset(foo,Var==input$var)[,"PC2"])))
+	})
+		
+	#Create a reactive Shiny plot to send to the ui.r called "pcaplot"
+	output$pcaplot <- renderPlot({
+		#Match user's variable input selection by subsetting for when foo$Var==input$var
+		fbar<-droplevels(foo[which(foo$Var==input$var),])
+		
+		#Generate colors to correspond to the different Variables' parameters. E.g., for the variable "colless", different colors for "loIc","midIc", and "hiIc"
+		cols<-c("#1f77b4","#ff7f0e","#2ca02c")[1:length(levels(fbar$param))]
+		
+		#If the param is not selected, set the color value to grey with transparency
+		cols[which(!(levels(fbar$param) %in% input$PARAMS))]<- rgb(0,0,0, alpha=25,maxColorValue=255)
+		
+		#Subset the pca_percent function for when Var==input$var
+		pcaper<-pca_percent[which(pca_percent$Var==input$var),]
+		#Render the scatterplot of PCA
+		plot(PC2 ~ PC1, data=fbar, type="n", xlim=input$SLIDERX, ylim=input$SLIDERY,
+			xlab=paste("PCA Axis 1 (",pcaper[2],"%)"),
+			ylab=paste("PCA Axis 2 (",pcaper[3],"%)"))
+		abline(h=0,v=0,col="red",lty=2)
+
+		#For each of the different parameter values (different colors), plot the text names of the metrics
+		for(i in levels(fbar$param)){
+			foo2<-subset(fbar,param==i)
+			labs<-paste("c(",paste("expression(",foo2$Rexpression,")",sep="",collapse=","),")",sep="")
+			text(PC2 ~ PC1, data=foo2, labels=eval(parse(text=labs)), col=cols[which(levels(fbar$param)==i)],  cex=input$cexSlider)
+		}
+		
+		#Add a legend for the different parameter values (with corresponding colors) of the selected Variable
+		legend("topright", fill=cols, legend=levels(fbar$param))
+		
+  })
+})
+
diff --git a/lessons/r/shiny/5/ui.r b/lessons/r/shiny/5/ui.r
new file mode 100644
index 000000000..f202ed7f3
--- /dev/null
+++ b/lessons/r/shiny/5/ui.r
@@ -0,0 +1,40 @@
+library(shiny)	#First load shiny library
+load("../pcas.RDATA")	#Load data
+
+#Define the overall UI
+shinyUI(
+	#Use a fluid Bootstrap layout
+	fluidPage(
+		#Give the page a title
+		titlePanel("PCA of Metrics"),
+		#Define page with a sidebar panel and main panel
+		sidebarLayout(
+			#Sidebar Panel
+			sidebarPanel(
+				#Create drop-down menu to select Variable to plot PCA for
+				selectInput(inputId='var', label = h3('Variable'),
+					choices = c(
+						"Colless" = "colless",
+						"Species Pool Size" = "numsp",
+						"Spatially Contiguous" = "spatial"
+						)
+					),
+				#Check box group, contingent upon previous drop-down menu selection
+				uiOutput(outputId="paramchkbxgrp"),
+				#Create a slider that will adjust the cex value of the text displayed called "cexSlider".
+				sliderInput(inputId = "cexSlider", label=h4("Adjust cex"),
+				min = 0, max=5, value=1),
+				uiOutput(outputId="sliderX"),
+				uiOutput(outputId="sliderY")
+			),		
+			#Create a spot for the plot
+			mainPanel(
+				 plotOutput(outputId="pcaplot",  width="500px",height="500px")
+			)
+		)
+
+    )
+)
+
+
+ 
\ No newline at end of file
diff --git a/lessons/r/shiny/Shiny.pptx b/lessons/r/shiny/Shiny.pptx
new file mode 100644
index 000000000..4d7b8754b
Binary files /dev/null and b/lessons/r/shiny/Shiny.pptx differ
diff --git a/lessons/r/shiny/lesson.md b/lessons/r/shiny/lesson.md
new file mode 100644
index 000000000..a48dbb7a2
--- /dev/null
+++ b/lessons/r/shiny/lesson.md
@@ -0,0 +1,375 @@
+---
+layout: page
+title: 'Making interactive plots using Shiny in R'
+visible: true
+tags:
+  - r
+  - advanced
+---
+
+ - **Authors**: Lanna Jin
+ - **Research field**: Ecology & Evolutionary Biology
+ - **Lesson topic**: Interactive Visualizations with the Shiny library in R
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/shiny>
+
+<h1>Introduction</h1>
+Introduction to Data Set
+
+<h1>Module 1</h1>
+First familiarize ourselves with the basic layout of the Shiny user interface. It is important to note, that while we set-up interactivity, user interactivity is not included within this first shiny App activity.
+
+<h2>ui.R</h2>
+1. After initially loading the shiny library and relevant data, the overall UI is wrapped within the `shinyUI` function
+2. Next, we define a page that is fluid (`fluidPage`)--that can change based on the user's input. For this page, we name it with a title (`titlePanel`)
+3. The set-up of the page is one based on a template (`sidebarLayout`): a sidebar (`sidebarPanel`; on the left, from which the user will make their selections); and a main Panel (`mainPanel`) which changes based on the user's input in the left side-bar panel
+4. The `mainPanel` will display the PCA of the metrics, which is identified as `pcaplot`. This object is created/assigned within the `server.R` script.
+
+<h3>TASK:</h3>
+ 1. Change the `title` to something more relevant, e.g., `PCA of Metrics`
+
+<h2>server.R</h2>
+1. The shiny library and relevant data is first loaded
+2. We define the server for the Shiny app as something with both objects that are input (from the `ui.R`) and output (from the `server.R`)
+3. We create a reactive Shiny plot that is output from `server.R` to `ui.R` with the function `renderPlot`. `renderPlot` is an reactive function that can take input data from the `ui.R` script and feed it into the `server.R` script. It then actively updates the information within its function. We name the object that has the `renderPlot` function to plot the PCAs as `pcaplot`. `pcaplot` is then output and displayed through the `ui.R` within the `plotOutput` function.
+
+<h3>TASKS:</h3>
+1. Change the plot's x- and y-axis label names to `PC1` and `PC2`.
+2. Instead of plotting points, plot the metrics as text points instead
+
+<h4>HINTS:</h4>
+1. use `xlab` and `ylab` within the function `plot` to change the axes labels
+2. For the function `plot`, enter the parameter `type="n"` to create a blank plot that maintains the axes labels and plotting parameters:
+
+   ~~~
+   plot(PC2 ~ PC1, data=foo, type="n", xlab="PC1", ylab="PC2")
+   ~~~
+
+   Next use the function `text` to plot the text values. Use `foo$metric` as the label values
+
+   ~~~
+   text(PC2 ~ PC1, data=foo, labels=foo$metric)
+   ~~~
+
+<h1>Module 2</h1>
+Add interactivity to the plot. In the `sidebarPanel` in the `ui.R`, we add a `sliderInput` widget, which allows the user to (a) select a value from the ui interface, which is then (b) fed to the server script. The server script then (c) readjusts the plot with the new input value and (d) feeds the output plot to the ui interface.
+
+<h2>ui.R</h2>
+<h3>TASK:</h3>
+1. Change the size of label of the cexSlider with the wrapper, `h4`
+2. Adjust the maximum `cexSlider` `cex` size to 5
+3. The PCA plot (`pcaplot`) currently is showing all the values, disregarding the different variables (Var: colless, numsp, Spatial). We however would like to show the PCA plot for each variable scenario.
+
+
+To do so:
+
+1. Create a drop-down list of the variables with the function `selectInput` within the `sidebarPanel` function.
+
+2. Assign the `selectInput` object as `var`, and the `label` as `h3('Variable')`
+
+3. for the choices, use:
+
+   ~~~
+   choices = c(
+               "Colless" = "colless",
+               "Species Pool Size" = "numsp",
+               "Spatially Contiguous" = "spatial"
+           )
+   ~~~
+
+4. Link `var` as `input$var` in `server.r` (refer to `server.r`)
+
+<h4>HINTS:</h4>
+
+1. `sliderInput(..., label = h4("Adjust cex"))`
+2. `sliderInput(..., max = 5)`
+3. ~~~
+   selectInput(inputId='var', label = h3('Variable'),
+               choices = c(
+               "Colless" = "colless",
+               "Species Pool Size" = "numsp",
+               "Spatially Contiguous" = "spatial"
+              )
+   )
+   ~~~
+
+<h2>server.R</h2>
+<h3>TASKS:</h3>
+1. The PCA plot (`pcaplot`) currently is showing all the values, disregarding the different variables (Var: colless, lambdaE, lambdaR, Landscape, Numsp, Repulsion, Spatial). We however would like to show the PCA plot for each variable scenario.
+   To do so:
+
+   1. Complete Task 3 in `ui.r` to create the `selectInput` drop-down menu, which we will call `var`
+   2. subset the `foo` dataframe for only instances when `foo$Var==input$var`
+
+<h4>HINTS:</h4>
+1. ~~~
+   output$pcaplot <- renderPlot({
+       #Subset for input$var and assign this subset to new object, "fbar"
+       fbar<-droplevels(foo[which(foo$Var==input$var),])
+   #Change "foo" to "fbar" for the plot and text
+   plot(PC2 ~ PC1, data=fbar, type="n", xlab="PC1", ylab="PC2")
+   text(PC2 ~ PC1, data=fbar, labels=fbar$metric, cex=input$cexSlider)
+   })
+   ~~~
+
+<h1>Module 3</h1>
+We've now added interactivity to the plot. The user can (a) specify the Variable (`Var`) scenario they wish to plot the PCA of, and (b) adjust the text's `cex` values. The `server.r` script now includes code to add colors that correspond to each selected variable's parameter values (`param`), as well as a legend.
+
+<h2>ui.R</h2>
+<h3>TASK:</h3>
+1. Depending on the variable that people choose (`input$var`), we now want to display the chosen variable's parameter (`foo$param`) values as a check box group. Even though logically we would begin with the `checkboxGroupInput` function on the `ui.R` script to display check box options, first remember what the capabilities of the user side (`ui.R`) are. `ui.R` cannot evaluate/process information, but only displays output values calculated on the server side (`server.R`). In other words, the `ui.R` only takes in the values a user specifies and then sends this information to be evaluated/processed on the server side.
+   So to create a `checkboxgroup` of `param` values that will change based on what the user selects for a variable (`input$var`), our first step is to evaluate what the user selects as a variable. This evaluation can only take place on the server side. We therefore need to create an object on the server side (`output$paramchkbxgrp`) that will evaluate the user's Variable selection (`input$var`), which will then decide which check box options to display. This decision of what to display is then fed back to the `ui.R` with the object `uiOutput`.
+
+   1. With the function `uiOutput`, display the computation result of the output function `paramchkbxgrp`
+
+<h4>HINTS:</h4>
+
+1. Place the following snippet of code in `ui.R` within the `sidebarPanel`, after the `selectInput` for the variables:
+
+   ~~~
+   uiOutput(outputId="paramchkbxgrp")
+   ~~~
+
+<h2>server.R</h2>
+<h3>TASKS:</h3>
+1. Depending on the variable that people choose (`input$var`), we now want to display the chosen variable's parameter (`param`) values as a check box group. Even though logically we would begin with the `checkboxGroupInput` function on the `ui.R` script to display check box options, first remember what the capabilities of the user side (`ui.R`) are. `ui.R` cannot evaluate/process information, but only displays output values calculated on the server side (`server.R`). In other words, the `ui.R` only takes in the values a user specifies and then sends this information to be evaluated/processed on the server side.
+
+   So to create a `checkboxgroup` of `param` values that will change based on what the user selects for a variable (`input$var`), our first step is to evaluate what the user selects as a variable. This evaluation can only take place on the server side. We therefore need to create an object on the server side (`output$paramchkbxgrp`) that will evaluate the user's Variable selection (`input$var`), which will then decide which check box options to display.  This decision of what to display is then fed back to the `ui.R` with the object `uiOutput`.
+
+   1. Depending on what Variable is selected by the user (`input$var`), generate a different set of check box options to display (with the function `checkboxGroupInput`). For instance, if `colless` is selected as the variable, display the parameters values that correspond to `colless`, e.g., `loIc`, `midIc` and `hiIc`. This `switch` function takes the given value (`input$var`) and then evaluates it. When `input$var=="colless"`, then it evaluates the `checkboxGroupInput` function with the choices of  `c("Low" = "loIc", "Mid" = "midIc", "High" = "hiIc")`.
+
+   Use the following script as a framework for the `paramchkbxgrp` object:
+
+   ~~~
+   output$paramchkbxgrp <- renderUI({
+                if (is.null(input$var))
+                 return()
+           # Depending on input$var, we'll generate a different
+           # UI component and send it to the client.
+               switch(input$var,
+               "colless" = checkboxGroupInput(inputId = "PARAMS", label = "",
+                   choices = c(
+                       "Low" = "loIc",
+                       "Mid" = "midIc",
+                       "High" = "hiIc"
+                       ),
+                    selected = c("loIc","midIc","hiIc")),
+               #Fill in the ellipses with the corresponding parameter values
+                "numsp" = checkboxGroupInput(...),
+                "spatial" = checkboxGroupInput(...),
+                )
+             })
+           })
+   ~~~
+
+2. When certain checkboxes are unselected, display the text as grey with some transparency. To do this, within the `renderPlot` function, adjust the `cols` object such that the unselected are black with high transparency (display as grey). To do this for the `cols` object, subset for when `input$PARAMS` is not in `levels(fbar$PARAMS)`:
+
+   ~~~
+   cols[which(!(levels(fbar$param) %in% input$PARAMS))]
+   ~~~
+
+   Then assign all of these instances as black `rgb(0,0,0, maxColorValue=255)` with very high transparency `rgb(0,0,0, alpha =25, maxColorValue=255)`:
+
+   ~~~
+   cols[which(!(levels(fbar$param) %in% input$PARAMS))]<- rgb(0,0,0, alpha=25,maxColorValue=255)
+   ~~~
+
+<h4>HINTS:</h4>
+
+1. ~~~
+   ## Drop-down menu to select Factor
+   output$paramchkbxgrp <- renderUI({
+        if (is.null(input$var))
+          return()
+
+     # Depending on input$var, we'll generate a different
+     # UI component and send it to the client.
+      switch(input$var,
+        "colless" = checkboxGroupInput("PARAMS", "",
+          choices = c("Low" = "loIc",
+                      "Mid" = "midIc",
+                      "High" = "hiIc"
+                      ),
+          selected = c("loIc","midIc","hiIc")),
+        "numsp" = checkboxGroupInput("PARAMS", "",
+          choices = c("16 Species" = 16,
+                      "64 Species" = 64,
+                      "256 Species" = 256),
+          selected = c(16,64,256)),
+        "spatial" = checkboxGroupInput("PARAMS", "",
+          choices = c("True" = "TRUE",
+                      "False" = "FALSE"),
+          selected = c("TRUE","FALSE")),
+      )
+      })
+   ~~~
+
+2. ~~~
+   output$pcaplot <- renderPlot({
+            #Match user's variable input selection by subsetting for when foo$Var==input$var
+            fbar<-droplevels(foo[which(foo$Var==input$var),])
+
+           # Generate colors to correspond to the different Variables' parameters. E.g., for the variable "colless", different colors for "loIc","midIc", and "hiIc"
+            cols<-c("#1f77b4","#ff7f0e","#2ca02c")[1:length(levels(fbar$param))]
+
+            #***********************ADD THIS TO EXISTING SCRIPT *************************
+            #If the param is not selected, set the color value to grey with transparency
+            cols[which(!(levels(fbar$param) %in% input$PARAMS))]<- rgb(0,0,0, alpha=25,maxColorValue=255)
+            #****************************************************************************
+
+            #Render the scatterplot of PCA
+            plot(PC2 ~ PC1, data=fbar, type="n", xlab="PC1", ylab="PC2")
+
+            #For each of the different parameter values (different colors), plot the text names of the metrics
+            for(i in levels(fbar$param)){
+                foo2<-subset(fbar,param==i)
+                text(PC2 ~ PC1, data=foo2, labels=foo2$metric, col=cols[which(levels(fbar$param)==i)],  cex=input$cexSlider)
+            }
+
+            #Add a legend for the different parameter values (with corresponding colors) of the selected Variable
+            legend("topright", fill=cols, legend=levels(fbar$param))
+
+      })
+   ~~~
+
+<h1>Module 4</h1>
+
+We've now learned some of the basic tools for building a shiny app. Now complete the tasks to add additional bells and whistles to the shiny app. After you have finished with your app, you can deploy it to the world to see. There are a number of options for this, which I won't go into detail because it depends on your personal preference for how you want the data shared (See https://shiny.rstudio.com/tutorial/lesson7/)
+
+<h2>ui.R</h2>
+
+<h3>TASK:</h3>
+
+1. Create the ability to zoom in or out of the plots. Create two `sliderInput` objects to adjust the x- and y-axes of the plot depending on the Variable selected. To do so:
+   1. Within the `sidebarPanel` of the `ui.R` script, add two `uiOutput` objects, containing the `outputId` names of `sliderX` and `sliderY`
+   2. In `script.R`, create the `output$sliderX` and `output$sliderY` objects with the function `renderUI`. (see `script.R`)
+2. Change the plot `width`. For `plotOutput(outputId="pcaplot")`, add width `(width = "750px")` and height `(height = "650px")` parameters
+
+<h4>HINTS:</h4>
+
+1. ~~~
+   sidebarPanel(
+        selectInput(inputId='var', ...),
+        uiOutput(outputId="paramchkbxgrp"),
+        sliderInput(inputId = "cexSlider", ...),
+        #********ADD THIS SCRIPT********
+        uiOutput(outputId="sliderX"),
+        uiOutput(outputId="sliderY")
+        #*******************************
+   )
+   ~~~
+
+2. ~~~
+   plotOutput(outputId="pcaplot",  width="750px",height="750px")
+   ~~~
+
+<h2>server.R</h2>
+<h3>TASKS:</h3>
+1. Create two `sliderInput` objects to adjust the x- and y-axes of the plot depending on the Variable selected. To do so:
+   1. Within the `sidebarPanel` of the `ui.R` script, add two `uiOutput` objects, containing the `outputId` names of `sliderX` and `sliderY` (see `ui.R`)
+   2. In `server.R`, create the `output$sliderX` and `output$sliderY` objects with the function `renderUI`. The `renderUI` function allows the function within to be reactive.
+      1. within the `renderUI` wrapper, use the `sliderInput` function:
+
+         ~~~
+         sliderInput(inputId, label, min, max, value)
+         ~~~
+
+         `inputId`
+         : `SLIDERX` for the x-axis; `SLIDERY` for the y-axis
+
+         `label`
+         : `h5("x-axes")` for the x-axis; `h5("y-axes")` for the y-axis
+
+         `min`
+         : subset the object `foo` (with the function `subset`) for the minimum (use function `min`) value of PC1 (for x-axis) or PC2 (for y-axis) when `Var==input$var`
+
+         `max`
+         : subset the object `foo` (with the function `subset`) for the maximum (use function `max`) value of PC1 (for x-axis) or PC2 (for y-axis) when `Var==input$var`
+
+         `value`
+         : the concatenated values of the two aforementioned `min` and `max` values: subset the object `foo` for the minimum/maximum value of PC1/PC2 when `Var==input$var`
+
+   3. For the scatterplot (`plot`) of PCA within the `output$pcaplot` function, change the plot parameters `xlim` and `ylim` to change with the `input$SLIDERX` and `input$SLIDERY` values.
+
+2. After evaluating the `plot` function, add red (`col = "red"`) dotted lines (`lty = 2`) at x=0 (`h=0`) & y=0 (`v=0`) with the function `abline`.
+
+3. Implement the percent variation explained into the plot axes.
+   1. First, within the `output$pcaplot`'s `renderPlot` function, create an object `pcaper` to subset `pca_percent` for when `pca_percent$Var==input$var`
+   2. Within the `plot` function, adjust the `xlab` and `ylab` parameters. With the `paste` function, for `xlab`, add `pcaper[2]` as the x-axis percent variation explained: `xlab=paste("PCA Axis 1 (",pcaper[2],"%)")`
+
+      Do the same for `ylab`, where instead use `pcaper[3]`
+
+4. Use the R `expressions` instead to display labels. Paste the following code into your script:
+
+   ~~~
+   output$pcaplot <- renderPlot({
+            ...
+            for(i in levels(fbar$param)){
+                foo2<-subset(fbar,param==i)
+                labs<-paste("c(",paste("expression(",foo2$Rexpression,")",sep="",collapse=","),")",sep="")
+                text(PC2 ~ PC1, data=foo2, labels=eval(parse(text=labs)), col=cols[which(levels(fbar$param)==i)],  cex=input$cexSlider)
+            }
+            #...
+        })
+   ~~~
+
+<h4>HINTS:</h4>
+1. ~~~
+   shinyServer(function(input, output) {
+        output$paramchkbxgrp <- renderUI({...})
+
+       ********ADD THIS SCRIPT********
+        #X-axis
+              output$sliderX <-renderUI({
+                sliderInput(inputId="SLIDERX",
+                    label = h5("x-axes"),
+                    min = min(subset(foo,Var==input$var)[,"PC1"]),
+                    max = max(subset(foo,Var==input$var)[,"PC1"]),
+                    value = c(min(subset(foo,Var==input$var)[,"PC1"]),max(subset(foo,Var==input$var)[,"PC1"])))
+            })
+       # Y-axis
+              output$sliderY <-renderUI({
+                sliderInput(inputId="SLIDERY",
+                    label = h5("y-axes"),
+                    min = min(subset(foo,Var==input$var)[,"PC2"]),
+                    max = max(subset(foo,Var==input$var)[,"PC2"]),
+                    value = c(min(subset(foo,Var==input$var)[,"PC2"]),max(subset(foo,Var==input$var)[,"PC2"])))
+            })
+       *******************************
+
+        output$pcaplot <- renderPlot({...})
+    })
+   ~~~
+
+2. ~~~
+   output$pcaplot <- renderPlot({
+       #...
+       plot(PC2 ~ PC1, data=fbar, type="n", xlab="PC1", ylab="PC2", xlim=input$SLIDERX, ylim=input$SLIDERY)
+       #...
+   })
+   ~~~
+
+3. ~~~
+   output$pcaplot <- renderPlot({
+        #...
+        plot(PC2 ~ PC1, data=fbar, type="n", xlab="PC1", ylab="PC2", xlim=input$SLIDERX, ylim=input$SLIDERY)
+        abline(h=0,v=0,col="red",lty=2)
+        #...
+   })
+   ~~~
+
+4. ~~~
+   output$pcaplot <- renderPlot({
+        fbar<-droplevels(...)
+
+        pcaper<-pca_percent[which(pca_percent$Var==input$var),]
+
+        plot(PC2 ~ PC1, data=fbar, type="n", xlim=input$SLIDERX, ylim=input$SLIDERY,
+            xlab=paste("PCA Axis 1 (",pcaper[2],"%)"),
+            ylab=paste("PCA Axis 2 (",pcaper[3],"%)"))
+        #    ...
+   })
+   ~~~
+
+<h1>Module 5</h1>
+
+Running this module should create the final output acheived when completing all the tasks.
diff --git a/lessons/r/shiny/pcas.RDATA b/lessons/r/shiny/pcas.RDATA
new file mode 100644
index 000000000..8f09688da
Binary files /dev/null and b/lessons/r/shiny/pcas.RDATA differ
diff --git a/lessons/r/shiny_2019/app.R b/lessons/r/shiny_2019/app.R
new file mode 100644
index 000000000..0cb54364f
--- /dev/null
+++ b/lessons/r/shiny_2019/app.R
@@ -0,0 +1,93 @@
+library(shiny)
+library(tidyverse)
+library(gapminder)
+library(shinydashboard)
+
+
+dat <- gapminder
+
+cont <- levels(dat$continent)
+
+year <- levels(factor(dat$year))
+
+
+
+#user interface
+ui <- dashboardPage(skin = "green",
+          dashboardHeader(title = "Shiny Apps with U of T Coders", titleWidth=350),
+          dashboardSidebar(
+            sidebarMenu(id = "menu1", 
+                        menuItem("Life Expectancy Over Time", tabName = "life", icon = icon("th")),
+                        menuItem("Population Over Time", tabName = "pop",icon = icon("area-chart")),
+                        menuItem("GDP Per Capita Over Time", tabName = "gdp", icon = icon("cogs"))),
+            conditionalPanel(condition = "input.menu1 == 'life'", 
+                             selectInput("select", label = "Select Continent", 
+                                         choices = c("Africa", "Americas",
+                                                     "Asia", "Europe", "Oceania"), selected = "Europe")), 
+            conditionalPanel(condition = "input.menu1 == 'pop'", 
+                checkboxGroupInput("sample", "Check for Year Selection:", choices = year),
+                checkboxInput("bulk", "Select All", value = TRUE))),
+            
+            dashboardBody(
+              tabItems(
+                tabItem(tabName = "life",
+                        fluidRow(
+                          box(title = h3("Shiny App with Gapminder"), width = 12, htmlOutput("text1"), br()),
+                          box(title = "Gapminder: Life Expectancy Over Time",plotOutput("plot1"), width = 8))
+              ),
+              tabItem(tabName = "pop",
+                      fluidRow(box(title = "Gapminder: Population Over Time",plotOutput("plot2"), width = 8))),
+              tabItem(tabName = "gdp",
+                      fluidRow(
+                        titlePanel("Mean GDP per Capita 1952:2007"),
+                        
+                        box(dataTableOutput("view"), width = 8))))))
+             
+                         
+            
+
+
+#server
+server <- function(input, output, session) {
+  
+  
+  output$text1 <- renderText({paste0("The ","<b>Shiny App Lesson</b>"," for ", "<font color=\"#008000\"><b>UofTCoders</b></font>",".")})
+  
+  output$plot1 <- renderPlot({
+    sub1 <- dat %>% filter(continent==input$select)
+    
+    ggplot(sub1, aes(year, lifeExp, color = country)) + geom_point() +
+      stat_smooth(method = "lm") +
+      theme_bw() +
+      ggtitle(paste0("Continent == ", input$select))
+  })  
+  
+  #Reactive update of check boxes for mutants, including the bulk options 'all' or 'none'  
+  observe({
+    updateCheckboxGroupInput(session, "sample",
+                             choices = year,
+                             selected = if (input$bulk) year)
+  })
+  
+  output$plot2 <- renderPlot({
+      if (any(input$sample %in% year)){
+
+      sub1 <- dat %>% filter(year %in% input$sample)
+    
+      ggplot(sub1, aes(continent, pop, fill = factor(year))) + geom_bar(stat="identity", position = "dodge") +
+        theme_bw() + coord_flip() 
+      } else {
+        ggplot(dat, aes(continent, pop, fill = factor(year))) + geom_bar(stat="identity", position = "dodge") +
+          theme_bw() + coord_flip()
+      }
+  })  
+  
+  
+  output$view <- renderDataTable({
+    dat %>% group_by(continent, country) %>% summarize(mean = round(mean(gdpPercap),2))})
+ 
+}
+
+
+
+shinyApp(ui, server)
diff --git a/lessons/r/shiny_2019/dashboard.png b/lessons/r/shiny_2019/dashboard.png
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+---
+layout: page
+title: 'Interactive Plotting with Shiny Apps'
+visible: true
+tags:
+  - r
+  - advanced
+---
+
+ - **Authors**: Erica Acton
+ - **Lesson topic**: Interactive Plotting with Shiny Apps
+ - **Lesson content URL**: <https://github.com/UofTCoders/studyGroup/tree/gh-pages/lessons/r/shiny_2019>
+ - **Lesson video stream**: <https://www.youtube.com/watch?v=Pp3zMKq_G-0>
+
+# Introduction
+
+[Shiny](https://shiny.rstudio.com/) is an R package to make interactive web
+applications for exploring your data.
+
+For this lesson, please install `shiny`, `tidyverse`, `gapminder`,
+`shinydashboard`:
+
+```r
+install.packages(c("shiny", "tidyverse", "gapminder", "shinydashboard"))
+```
+
+You should be able to open the provided `app.R` in RStudio and tell it to start
+the application using the "Run App" button. See the video stream for a
+graphical explanation of this procedure.
+
+You may also run the application from the command-line:
+
+```
+# Use the path to the directory containing app.R (do not include app.R below):
+$ R -e "shiny::runApp('/path/to/app.R')"
+```
+
+# Overview
+
+Our Shiny app will have a layout that will look approximately like this:
+
+![Dashboard layout](../dashboard.png)
+
+There will be three different "views" with different plots and options to
+change how they look:
+
+![Tab 1 layout](../tab1.png)
+
+![Tab 2 layout](../tab2.png)
+
+![Tab 3 layout](../tab3.png)
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diff --git a/lessons/r/statistics/Intermediate Statistics in R - Repeated Measures and Messy Data.R b/lessons/r/statistics/Intermediate Statistics in R - Repeated Measures and Messy Data.R
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+### Read in the Data and Packages ####
+
+data <- read.table(file = "http://personality-project.org/r/datasets/R.appendix4.data", header = TRUE)
+
+# If you've never used these packages before, install them first (one time only)
+install.packages("car") 
+install.packages("lme4") 
+install.packages("lmerTest") 
+
+library(car)  # load the packages before using 
+library(lme4)
+library(lmerTest)
+
+View(data)  # See what our data looks like in R Studio 
+
+
+# Review: linear regression (y ~ x)
+basicmodel <- lm(Recall ~ Valence, data= data)
+basicmodel
+summary(basicmodel)
+Anova(basicmodel)
+
+### Paired T-test ####
+
+# Create a partial dataset with only the Positive and Negative valences (remove the Neutral)
+partialdata <- subset(data, data$Valence != "Neu")
+
+t.test(partialdata$Recall ~ partialdata$Valence, paired = TRUE) # paired t-test for repeated measures
+
+with(partialdata, t.test(Recall ~ Valence, paired = TRUE))  # another way to write the exact same t-test
+
+### Linear Mixed Model (Hierarchical/Multilevel Model) ####
+rmmodel <- lmer(Recall ~ Valence + (1|Subject), data= data)  
+# the (1|X) is how to indicate the "nesting" variable (e.g. person, school, field) that the data is repeated within
+
+summary(rmmodel)
+anova(rmmodel)
+
+data$ValenceN <- relevel(data$Valence, ref = "Neu") # relevel the factor to see other contrasts 
+
+rmmodel2 <- lmer(Recall ~ ValenceN + (1|Subject), 
+                data= data)
+summary(rmmodel2)
+
+data$Imagine <- rep(c(1,2,3,4,5), 6) # create a fake continuous variable, for demonstration purposes 
+
+rmmodel3 <- lmer(Recall ~ ValenceN*Imagine + (1|Subject), data= data)  
+#this model is using "0 imagine" as its default, which doesn't exist (it's outside the range of "Imagine") and so doesn't make sense
+Anova(rmmodel3)
+summary(rmmodel3)
+
+data$ImagineC <- data$Imagine - mean(data$Imagine, na.rm=T) 
+# so, we center the Imagine variable, so now the model will 
+#test the effect of Valence when "Imagine" is average 
+#(still using "0 imagine" as its default, but now 0 represents the mean of 3)
+
+rmmodel3 <- lmer(Recall ~ ValenceN * ImagineC + (1|Subject), 
+                 data= data)
+rmmodel3 <- lmer(Recall ~ ValenceN + ImagineC + (1|Subject),data= data) 
+#if you aren't testing interaction terms, then centering your variables doesn't matter
+Anova(rmmodel3) 
+summary(rmmodel3)
+
+### Some Messy data things ####
+View(airquality)
+
+cor(airquality$Ozone, airquality$Solar.R) # returns NA 
+cor(airquality$Ozone, airquality$Solar.R, use = "pairwise.complete.obs") # look at help (?cor) to find the options
+cor.test(airquality$Ozone, airquality$Solar.R)
+
+mean(airquality$Ozone)  # returns NA
+mean(airquality$Ozone, na.rm=T)  # returns the mean 
+
+completeair <- na.omit(airquality) # remove all rows with ANY missing value
+
+dim(airquality)
+dim(completeair) # we can see it has fewer rows 
+
+allozone <- airquality[!is.na(airquality$Ozone),]
+dim(allozone)
+
+# Dealing with a messy variable that might have letters/numbers that we want to remove: one example (use google!)
+airquality$WindPostDec <- gsub(".*\\.", "", airquality$Wind)
+airquality$WindPreDec <- as.numeric(gsub("\\..*", "", airquality$Wind))  
+
+summary(airquality$WindPostDec) #since we didn't turn it back into numeric, this variable is still a string 
+summary(airquality$WindPreDec) # since we turned this one back into numeric, we get the actual means/descriptives
diff --git a/lessons/r/statistics/rstats.R b/lessons/r/statistics/rstats.R
new file mode 100644
index 000000000..94ad0b04c
--- /dev/null
+++ b/lessons/r/statistics/rstats.R
@@ -0,0 +1,100 @@
+install.packages("car")
+install.packages("psych")
+install.packages("multcomp")
+library(car)
+library(psych)
+library(multcomp)
+View(iris)
+View(Wong)
+View(mtcars)
+
+#frequencies with table()
+#The table() function displays counts of identical observations 
+#for either a single data vector or a dataframe
+
+#table
+table(mtcars$gear)
+#cross tabulate
+table(mtcars$gear, mtcars$carb)
+#note: first variable as the rows
+
+#mean
+#without the na.rm=T argument, mean() will return a 
+#null value if there is any missing data in the object
+mean(Wong$viq, na.rm=T)
+
+#median
+#The median() function finds the median (50th percentile) value
+median(Wong$viq, na.rm=T)
+
+#variance and sd
+var(Wong$viq, na.rm=T)
+sd(Wong$viq, na.rm=T)
+
+#range
+range(Wong$viq, na.rm=T)
+
+#describe() from the psych package
+#item name ,item number, nvalid, mean, sd, 
+#median, mad (median absolute deviation from the median), min, max, skew, kurtosis, se
+describe(Wong$age)
+
+#describeBy() from the psych package
+#A simple way of generating summary statistics by grouping variable
+describeBy(Wong$age, Wong$sex)
+
+#inferential statistics
+#one-sample t test
+t.test(Wong$age, mu=30)
+
+#two sample t-test
+#t.test( object1, object1)
+t.test(Wong$age~Wong$sex)
+
+#paired samples t-test
+#t.test(object1, object1, paired=TRUE)
+
+#correlations
+#use cor.test
+cor.test(iris$Petal.Length, iris$Petal.Width)
+
+#general linear model (i.e., with a normally-distributed dependent variable)
+#dependent variables are predicted by a tilde “~” 
+#the formula to regress “y” on “x” is y ~ x
+result <- lm(viq~duration, data=Wong)
+summary(result)
+
+result <- lm(viq~duration*days*sex, data=Wong)
+summary(result)
+
+result <- lm(viq~duration*days + sex, data=Wong)
+summary(result)
+
+#ANOVA is simply a different way of evaluating explained variance in linear modelling
+#you must always wrap the anova() function around a lm() function
+
+anova(lm(mpg ~ am*gear, data=mtcars))
+
+#HOWEVER, R uses type II sums of squares by default
+#Anova from the car package can give you “Type III Sums of Squares” 
+#this matters when you have more than one predictor (i.e. )
+#this will match what you get from SPSS or SAS
+
+result <- lm(mpg ~ am*gear, data=mtcars)
+Anova(result, type=3)
+
+#R comes with a default pairwise t-test (pairwise.t.test())
+#multcomp offers more posthoc options than base R
+result <- lm(Petal.Length ~ Species, data=iris)
+postHocs<-glht(result, linfct = mcp(Species = "Tukey"))
+summary(postHocs)
+confint(postHocs)
+
+#logistic regression
+#must use the glm() function rather than lm()
+summary(glm(Wong$sex ~ Wong$piq*Wong$viq, family=binomial))
+
+#scales and reliability - Cronbach's Alpha from the psych package
+newiris <- iris[c(1:4)]
+rowMeans(newiris, na.rm=TRUE)
+alpha(newiris)
diff --git a/lessons/r/statistics/rstats.Rmd b/lessons/r/statistics/rstats.Rmd
new file mode 100644
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+---
+always_allow_html: yes
+layout: page
+title: 'Introduction to Statistics in R'
+visible: false
+tags:
+  - r
+  - intermediate
+  - statistics
+---
+
+* Author: Lindsay Coome
+* Lesson Topic: Introduction to Statistics in R
+* Lesson video stream: <https://www.youtube.com/watch?v=A7AD40fpLK8>
+
+
+```{r}
+# Have the following packages installed and loaded:
+# install.packages("car")
+# install.packages("psych")
+# install.packages("multcomp")
+library(car)
+library(psych)
+library(multcomp)
+```
+
+## Descriptive statistics
+### Frequencies with table():
+The table() function displays counts of identical observations for either a single data vector or a dataframe
+
+```{r}
+#table
+table(mtcars$gear)
+#cross tabulate
+#note: first variable is set as the rows
+table(mtcars$gear, mtcars$carb)
+```
+
+
+### Mean:
+Without the na.rm=T argument, mean() will return a null value if there is any missing data in the object
+
+```{r}
+mean(Wong$viq, na.rm=T)
+```
+
+### Median:
+
+```{r}
+median(Wong$viq, na.rm=T)
+```
+
+### Variance and SD:
+
+```{r}
+var(Wong$viq, na.rm=T)
+sd(Wong$viq, na.rm=T)
+```
+
+### Range: 
+
+```{r}
+range(Wong$viq, na.rm=T)
+```
+
+### describe() from the psych package:
+Provides item name, item number, nvalid, mean, sd, median, mad (median absolute deviation from the median), min, max, skew, kurtosis, se
+
+```{r}
+describe(Wong$age)
+```
+
+
+### describeBy() from the psych package:
+A simple way of generating summary statistics by grouping variable
+
+```{r}
+describeBy(Wong$age, Wong$sex)
+```
+
+## Inferential statistics
+### One-sample t test:
+
+```{r}
+t.test(Wong$age, mu=30)
+```
+
+### Two sample t-test:
+
+```{r}
+#one way to compare groups:
+#t.test( object1, object1)
+#or use sex as the grouping variable:
+t.test(Wong$age~Wong$sex)
+```
+
+### Paired samples t-test:
+
+```{r}
+#t.test(object1, object1, paired=TRUE)
+```
+
+### Correlations:
+cor.test gives the correlation and significance test on the correlation
+
+```{r}
+cor.test(iris$Petal.Length, iris$Petal.Width)
+```
+
+### General linear model (i.e., with a normally-distributed dependent variable):
+Dependent variables are predicted by a tilde “~” 
+The formula to regress “y” on “x” is y ~ x
+
+```{r}
+result <- lm(viq~duration, data=Wong)
+summary(result)
+
+#multiple predictors with interaction terms
+result <- lm(viq~duration*days*sex, data=Wong)
+summary(result)
+
+#use + for main effect of predictor only
+result <- lm(viq~duration*days + sex, data=Wong)
+summary(result)
+```
+
+### Analysis of Variance:
+ANOVA is simply a different way of evaluating explained variance in linear modelling
+You must always wrap the anova() function around a lm() function
+
+```{r}
+anova(lm(mpg ~ am*gear, data=mtcars))
+```
+
+However, R uses type II sums of squares by default
+Anova() from the car package can give you “Type III Sums of Squares” 
+This matters when you have more than one predictor (e.g. treatment x sex)
+Asking for type III sums of squares will match what you get from SPSS or SAS
+
+```{r}
+result <- lm(mpg ~ am*gear, data=mtcars)
+Anova(result, type=3)
+```
+
+### Post hoc tests:
+R comes with a default pairwise t-test (pairwise.t.test())
+multcomp offers more posthoc options than base R
+
+```{r}
+result <- lm(Petal.Length ~ Species, data=iris)
+postHocs<-glht(result, linfct = mcp(Species = "Tukey"))
+#summary function gives results of multiple comparisons
+summary(postHocs)
+#you can also ask for confidence intervals
+confint(postHocs)
+```
+
+### Logistic regression:
+Normally-distributed dependent variable assumption violated
+So, you must use the glm() function rather than lm()
+
+```{r}
+summary(glm(Wong$sex ~ Wong$piq*Wong$viq, family=binomial))
+```
+
+### Scales and reliability:
+Learn how to sum across rows (useful for getting a summary score from a scale)
+Can get Cronbach's Alpha from the ```psych``` package
+
+```{r}
+#Subset iris data so only numerical columns remain
+newiris <- iris[c(1:4)]
+#Calculate row means
+rowMeans(newiris, na.rm=TRUE)
+#Use the psych package for Cronbach's Alpha
+alpha(newiris)
+```
+
+
+
+
+
+
+
+
+
diff --git a/lessons/r/statistics/rstats.html b/lessons/r/statistics/rstats.html
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+<!DOCTYPE html>
+
+<html xmlns="http://www.w3.org/1999/xhtml">
+
+<head>
+
+<meta charset="utf-8" />
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+<meta name="generator" content="pandoc" />
+
+
+
+
+<title>Introduction to Statistics in R</title>
+
+<script src="data:application/x-javascript;base64,/*! jQuery v1.11.3 | (c) 2005, 2015 jQuery Foundation, Inc. | jquery.org/license */
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e0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%3Bsrc%3Aurl%28data%3Aapplication%2Fvnd%2Ems%2Dfontobject%3Bbase64%2Cn04AAEFNAAACAAIABAAAAAAABQAAAAAAAAABAJABAAAEAExQAAAAAAAAAAIAAAAAAAAAAAEAAAAAAAAAJxJ%2FLAAAAAAAAAAAAAAAAAAAAAAAACgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzAAAADgBSAGUAZwB1AGwAYQByAAAAeABWAGUAcgBzAGkAbwBuACAAMQAuADAAMAA5ADsAUABTACAAMAAwADEALgAwADAAOQA7AGgAbwB0AGMAbwBuAHYAIAAxAC4AMAAuADcAMAA7AG0AYQBrAGUAbwB0AGYALgBsAGkAYgAyAC4ANQAuADUAOAAzADIAOQAAADgARwBMAFkAUABIAEkAQwBPAE4AUwAgAEgAYQBsAGYAbABpAG4AZwBzACAAUgBlAGcAdQBsAGEAcgAAAAAAQlNHUAAAAAAAAAAAAAAAAAAAAAADAKncAE0TAE0ZAEbuFM3pjM%2FSEdmjKHUbyow8ATBE40IvWA3vTu8LiABDQ%2BpexwUMcm1SMnNryctQSiI1K5ZnbOlXKmnVV5YvRe6RnNMFNCOs1KNVpn6yZhCJkRtVRNzEufeIq7HgSrcx4S8h%2Fv4vnrrKc6oCNxmSk2uKlZQHBii6iKFoH0746ThvkO1kJHlxjrkxs%2BLWORaDQBEtiYJIR5IB9Bi1UyL4Rmr0BNigNkMzlKQmnofBHviqVzUxwdMb3NdCn69hy%2BpRYVKGVS%2F1tnsqv4LL7wCCPZZAZPT4aCShHjHJVNuXbmMrY5LeQaGnvAkXlVrJgKRAUdFjrWEah9XebPeQMj7KS7DIBAFt8ycgC5PLGUOHSE3ErGZCiViNLL5ZARfywnCoZaKQCu6NuFX42AEeKtKUGnr%2FCm2Cy8tpFhBPMW5Fxi4Qm4TkDWh4IWFDClhU2hRWosUWqcKLlgyXB%2BlSHaWaHiWlBAR8SeSgSPCQxdVQgzUixWKSTrIQEbU94viDctkvX%2BVSjJuUmV8L4CXShI11esnp0pjWNZIyxKHS4wVQ2ime1P4RnhvGw0aDN1OLAXGERsB7buFpFGGBAre4QEQR0HOIO5oYH305G%2BKspT%2FFupEGGafCCwxSe6ZUa%2B073rXHnNdVXE6eWvibUS27XtRzkH838mYLMBmYysZTM0EM3A1fbpCBYFccN1B%2FEnCYu%2FTgCGmr7bMh8GfYL%2BBfcLvB0gRagC09w9elfldaIy%2FhNCBLRgBgtCC7jAF63wLSMAfbfAlEggYU0bUA7ACCJmTDpEmJtI78w4%2FBO7dN7JR7J7ZvbYaUbaILSQsRBiF3HGk5fEg6p9unwLvn98r%2BvnsV%2B372uf1xBLq4qU%2F45fTuqaAP%2BpssmCCCTF0mhEow8ZXZOS8D7Q85JsxZ%2BAzok7B7O%2Ff6J8AzYBySZQB%2FQHYUSA%2BEeQhEWiS6AIQzgcsDiER4MjgMBAWDV4AgQ3g1eBgIdweCQmCjJEMkJ%2BPKRWyFHHmg1Wi%2F6xzUgA0LREoKJChwnQa9B%2B5RQZRB3IlBlkAnxyQNaANwHMowzlYSMCBgnbpzvqpl0iTJNCQidDI9ZrSYNIRBhHtUa5YHMHxyGEik9hDE0AKj72AbTCaxtHPUaKZdAZSnQTyjGqGLsmBStCejApUhg4uBMU6mATujEl%2BKdDPbI6Ag4vLr%2BhjY6lbjBeoLKnZl0UZgRX8gTySOeynZVz1wOq7e1hFGYIq%2BMhrGxDLak0PrwYzSXtcuyhXEhwOYofiW%2BEcI%2Fjw8P6IY6ed%2BetAbuqKp5QIapT77LnAe505lMuqL79a0ut4rWexzFttsOsLDy7zvtQzcq3U1qabe7tB0wHWVXji%2BzDbo8x8HyIRUbXnwUcklFv51fvTymiV%2BMXLSmGH9d9%2BaXpD5X6lao41anWGig7IwIdnoBY2ht%2FpO9mClLo4NdXHAsefqWUKlXJkbqPOFhMoR4aiA1BXqhRNbB2Xwi%2B7u%2FjpAoOpKJ0UX24EsrzMfHXViakCNcKjBxuQX8BO0ZqjJ3xXzf%2B61t2VXOSgJ8xu65QKgtN6FibPmPYsXbJRHHqbgATcSZxBqGiDiU4NNNsYBsKD0MIP%2FOfKnlk%2FLkaid%2FO2NbKeuQrwOB2Gq3YHyr6ALgzym5wIBnsdC1ZkoBFZSQXChZvlesPqvK2c5oHHT3Q65jYpNxnQcGF0EHbvYqoFw60WNlXIHQF2HQB7zD6lWjZ9rVqUKBXUT6hrkZOle0RFYII0V5ZYGl1JAP0Ud1fZZMvSomBzJ710j4Me8mjQDwEre5Uv2wQfk1ifDwb5ksuJQQ3xt423lbuQjvoIQByQrNDh1JxGFkOdlJvu%2FgFtuW0wR4cgd%2BZKesSV7QkNE2kw6AV4hoIuC02LGmTomyf8PiO6CZzOTLTPQ%2BHW06H%2Btx%2BbQ8LmDYg1pTFrp2oJXgkZTyeRJZM0C8aE2LpFrNVDuhARsN543%2FFV6klQ6Tv1OoZGXLv0igKrl%2FCmJxRmX7JJbJ998VSIPQRyDBICzl4JJlYHbdql30NvYcOuZ7a10uWRrgoieOdgIm4rlq6vNOQBuqESLbXG5lzdJGHw2m0sDYmODXbYGTfSTGRKpssTO95fothJCjUGQgEL4yKoGAF%2F0SrpUDNn8CBgBcSDQByAeNkCXp4S4Ro2Xh4OeaGRgR66PVOsU8bc6TR5%2FxTcn4IVMLOkXSWiXxkZQCbvKfmoAvQaKjO3EDKwkwqHChCDEM5loQRPd5ACBki1TjF772oaQhQbQ5C0lcWXPFOzrfsDGUXGrpxasbG4iab6eByaQkQfm0VFlP0ZsDkvvqCL6QXMUwCjdMx1ZOyKhTJ7a1GWAdOUcJ8RSejxNVyGs31OKMyRyBVoZFjqIkmKlLQ5eHMeEL4MkUf23cQ%2F1SgRCJ1dk4UdBT7OoyuNgLs0oCd8RnrEIb6QdMxT2QjD4zMrJkfgx5aDMcA4orsTtKCqWb%2FVeyceqa5OGSmB28YwH4rFbkQaLoUN8OQQYnD3w2eXpI4ScQfbCUZiJ4yMOIKLyyTc7BQ4uXUw6Ee6%2FxM%2B4Y67ngNBknxIPwuppgIhFcwJyr6EIj%2BLzNj%2FmfR2vhhRlx0BILZoAYruF0caWQ7YxO66UmeguDREAFHYuC7HJviRgVO6ruJH59h%2FC%2FPkgSle8xNzZJULLWq9JMDTE2fjGE146a1Us6PZDGYle6ldWRqn%2FpdpgHKNGrGIdkRK%2BKPETT9nKT6kLyDI8xd9A1FgWmXWRAIHwZ37WyZHOVyCadJEmMVz0MadMjDrPho%2BEIochkVC2xgGiwwsQ6DMv2P7UXqT4x7CdcYGId2BJQQa85EQKmCmwcRejQ9Bm4oATENFPkxPXILHpMPUyWTI5rjNOsIlmEeMbcOCEqInpXACYQ9DDxmFo9vcmsDblcMtg4tqBerNngkIKaFJmrQAPnq1dEzsMXcwjcHdfdCibcAxxA%2Bq%2Fj9m3LM%2FO7WJka4tSidVCjsvo2lQ%2F2ewyoYyXwAYyr2PlRoR5MpgVmSUIrM3PQxXPbgjBOaDQFIyFMJvx3Pc5RSYj12ySVF9fwFPQu2e2KWVoL9q3Ayv3IzpGHUdvdPdrNUdicjsTQ2ISy7QU3DrEytIjvbzJnAkmANXjAFERA0MUoPF3%2F5KFmW14bBNOhwircYgMqoDpUMcDtCmBE82QM2YtdjVLB4kBuKho%2FbcwQdeboqfQartuU3CsCf%2BcXkgYAqp%2F0Ee3RorAZt0AvvOCSI4JICIlGlsV0bsSid%2FNIEALAAzb6HAgyWHBps6xAOwkJIGcB82CxRQq4sJf3FzA70A%2BTRqcqjEMETCoez3mkPcpnoALs0ugJY8kQwrC%2BJE5ik3w9rzrvDRjAQnqgEVvdGrNwlanR0SOKWzxOJOvLJhcd8Cl4AshACUkv9czdMkJCVQSQhp6kp7StAlpVRpK0t0SW6LHeBJnE2QchB5Ccu8kxRghZXGIgZIiSj7gEKMJDClcnX6hgoqJMwiQDigIXg3ioFLCgDgjPtYHYpsF5EiA4kcnN18MZtOrY866dEQAb0FB34OGKHGZQjwW%2FWDHA60cYFaI%2FPjpzquUqdaYGcIq%2BmLez3WLFFCtNBN2QJcrlcoELgiPku5R5dSlJFaCEqEZle1AQzAKC%2B1SotMcBNyQUFuRHRF6OlimSBgjZeTBCwLyc6A%2BP%2FoFRchXTz5ADknYJHxzrJ5pGuIKRQISU6WyKTBBjD8WozmVYWIsto1AS5rxzKlvJu4E%2FvwOiKxRtCWsDM%2BeTHUrmwrCK5BIfMzGkD%2B0Fk5LzBs0jMYXktNDblB06LMNJ09U8pzSLmo14MS0OMjcdrZ31pyQqxJJpRImlSvfYAK8inkYU52QY2FPEVsjoWewpwhRp5yAuNpkqhdb7ku9Seefl2D0B8SMTFD90xi4CSOwwZy9IKkpMtI3FmFUg3%2FkFutpQGNc3pCR7gvC4sgwbupDu3DyEN%2BW6YGLNM21jpB49irxy9BSlHrVDlnihGKHwPrbVFtc%2Bh1rVQKZduxIyojccZIIcOCmhEnC7UkY68WXKQgLi2JCDQkQWJRQuk60hZp0D3rtCTINSeY9Ej2kIKYfGxwOs4j9qMM7fYZiipzgcf7TamnehqdhsiMiCawXnz4xAbyCkLAx5EGbo3Ax1u3dUIKnTxIaxwQTHehPl3V491H0%2BbC5zgpGz7Io%2BmjdhKlPJ01EeMpM7UsRJMi1nGjmJg35i6bQBAAxjO%2FENJubU2mg3ONySEoWklCwdABETcs7ck3jgiuU9pcKKpbgn%2B3YlzV1FzIkB6pmEDOSSyDfPPlQskznctFji0kpgZjW5RZe6x9kYT4KJcXg0bNiCyif%2BpZACCyRMmYsfiKmN9tSO65F0R2OO6ytlEhY5Sj6uRKfFxw0ijJaAx%2Fk3QgnAFSq27%2F2i4GEBA%2BUvTJKK%2F9eISNvG46Em5RZfjTYLdeD8kdXHyrwId%2FDQZUaMCY4gGbke2C8vfjgV%2FY9kkRQOJIn%2FxM9INZSpiBnqX0Q9GlQPpPKAyO5y%2BW5NMPSRdBCUlmuxl40ZfMCnf2Cp044uI9WLFtCi4YVxKjuRCOBWIb4XbIsGdbo4qtMQnNOQz4XDSui7W%2FN6l54qOynCqD3DpWQ%2BmpD7C40D8BZEWGJX3tlAaZBMj1yjvDYKwCJBa201u6nBKE5UE%2B7QSEhCwrXfbRZylAaAkplhBWX50dumrElePyNMRYUrC99UmcSSNgImhFhDI4BXjMtiqkgizUGCrZ8iwFxU6fQ8GEHCFdLewwxYWxgScAYMdMLmcZR6b7rZl95eQVDGVoUKcRMM1ixXQtXNkBETZkVVPg8LoSrdetHzkuM7DjZRHP02tCxA1fmkXKF3VzfN1pc1cv%2F8lbTIkkYpqKM9VOhp65ktYk%2BQ46myFWBapDfyWUCnsnI00QTBQmuFjMZTcd0V2NQ768Fhpby04k2IzNR1wKabuGJqYWwSly6ocMFGTeeI%2BejsWDYgEvr66QgqdcIbFYDNgsm0x9UHY6SCd5%2B7tpsLpKdvhahIDyYmEJQCqMqtCF6UlrE5GXRmbu%2Bvtm3BFSxI6ND6UxIE7GsGMgWqghXxSnaRJuGFveTcK5ZVSPJyjUxe1dKgI6kNF7EZhIZs8y8FVqwEfbM0Xk2ltORVDKZZM40SD3qQoQe0orJEKwPfZwm3YPqwixhUMOndis6MhbmfvLBKjC8sKKIZKbJk8L11oNkCQzCgvjhyyEiQSuJcgCQSG4Mocfgc0Hkwcjal1UNgP0CBPikYqBIk9tONv4kLtBswH07vUCjEaHiFGlLf8MgXKzSgjp2HolRRccAOh0ILHz9qlGgIFkwAnzHJRjWFhlA7ROwINyB5HFj59PRZHFor6voq7l23EPNRwdWhgawqbivLSjRA4htEYUFkjESu67icTg5S0aW1sOkCiIysfJ9UnIWevOOLGpepcBxy1wEhd2WI3AZg7sr9WBmHWyasxMcvY%2FiOmsLtHSWNUWEGk9hScMPShasUA1AcHOtRZlqMeQ0OzYS9vQvYUjOLrzP07BUAFikcJNMi7gIxEw4pL1G54TcmmmoAQ5s7TGWErJZ2Io4yQ0ljRYhL8H5e62oDtLF8aDpnIvZ5R3GWJyAugdiiJW9hQAVTsnCBHhwu7rkBlBX6r3b7ejEY0k5GGeyKv66v%2B6dg7mcJTrWHbtMywbedYqCQ0FPwoytmSWsL8WTtChZCKKzEF7vP6De4x2BJkkniMgSdWhbeBSLtJZR9CTHetK1xb34AYIJ37OegYIoPVbXgJ%2FqDQK%2BbfCtxQRVKQu77WzOoM6SGL7MaZwCGJVk46aImai9fmam%2BWpHG%2B0BtQPWUgZ7RIAlPq6lkECUhZQ2gqWkMYKcYMYaIc4gYCDFHYa2d1nzp3%2BJ1eCBay8IYZ0wQRKGAqvCuZ%2FUgbQPyllosq%2BXtfKIZOzmeJqRazpmmoP%2F76YfkjzV2NlXTDSBYB04SVlNQsFTbGPk1t%2FI4Jktu0XSgifO2ozFOiwd%2F0SssJDn0dn4xqk4GDTTKX73%2FwQyBLdqgJ%2BWx6AQaba3BA9CKEzjtQYIfAsiYamapq80LAamYjinlKXUkxdpIDk0puXUEYzSalfRibAeDAKpNiqQ0FTwoxuGYzRnisyTotdVTclis1LHRQCy%2FqqL8oUaQzWRxilq5Mi0IJGtMY02cGLD69vGjkj3p6pGePKI8bkBv5evq8SjjyU04vJR2cQXQwSJyoinDsUJHCQ50jrFTT7yRdbdYQMB3MYCb6uBzJ9ewhXYPAIZSXfeEQBZZ3GPN3Nbhh%2FwkvAJLXnQMdi5NYYZ5GHE400GS5rXkOZSQsdZgIbzRnF9ueLnsfQ47wHAsirITnTlkCcuWWIUhJSbpM3wWhXNHvt2xUsKKMpdBSbJnBMcihkoDqAd1Zml%2FR4yrzow1Q2A5G%2Bkzo%2FRhRxQS2lCSDRV8LlYLBOOoo1bF4jwJAwKMK1tWLHlu9i0j4Ig8qVm6wE1DxXwAwQwsaBWUg2pOOol2dHxyt6npwJEdLDDVYyRc2D0HbcbLUJQj8gPevQBUBOUHXPrsAPBERICpnYESeu2OHotpXQxRGlCCtLdIsu23MhZVEoJg8Qumj%2FUMMc34IBqTKLDTp76WzL%2FdMjCxK7MjhiGjeYAC%2Fkj%2FjY%2FRde7hpSM1xChrog6yZ7OWTuD56xBJnGFE%2BpT2ElSyCnJcwVzCjkqeNLfMEJqKW0G7OFIp0G%2B9mh50I9o8k1tpCY0xYqFNIALgIfc2me4n1bmJnRZ89oepgLPT0NTMLNZsvSCZAc3TXaNB07vail36%2FdBySis4m9%2FDR8izaLJW6bWCkVgm5T%2Bius3ZXq4xI%2BGnbveLbdRwF2mNtsrE0JjYc1AXknCOrLSu7Te%2Fr4dPYMCl5qtiHNTn%2BTPbh1jCBHH%2BdMJNhwNgs3nT%2BOhQoQ0vYif56BMG6WowAcHR3DjQolxLzyVekHj00PBAaW7IIAF1EF%2BuRIWyXjQMAs2chdpaKPNaB%2BkSezYt0%2BCA04sOg5vx8Fr7Ofa9sUv87h7SLAUFSzbetCCZ9pmyLt6l6%2FTzoA1%2FZBG9bIUVHLAbi%2FkdBFgYGyGwRQGBpkqCEg2ah9UD6EedEcEL3j4y0BQQCiExEnocA3SZboh%2Bepgd3YsOkHskZwPuQ5OoyA0fTA5AXrHcUOQF%2BzkJHIA7PwCDk1gGVmGUZSSoPhNf%2BTklauz98QofOlCIQ%2FtCD4dosHYPqtPCXB3agggQQIqQJsSkB%2Bqn0rkQ1toJjON%2FOtCIB9RYv3PqRA4C4U68ZMlZn6BdgEvi2ziU%2BTQ6NIw3ej%2BAtDwMGEZk7e2IjxUWKdAxyaw9OCwSmeADTPPleyk6UhGDNXQb%2B%2BW6Uk4q6F7%2Frg6WVTo82IoCxSIsFDrav4EPHphD3u4hR53WKVvYZUwNCCeM4PMBWzK%2BEfIthZOkuAwPo5C5jgoZgn6dUdvx5rIDmd58cXXdKNfw3l%2BwM2UjgrDJeQHhbD7HW2QDoZMCujgIUkk5Fg8VCsdyjOtnGRx8wgKRPZN5dR0zPUyfGZFVihbFRniXZFOZGKPnEQzU3AnD1KfR6weHW2XS6KbPJxUkOTZsAB9vTVp3Le1F8q5l%2BDMcLiIq78jxAImD2pGFw0VHfRatScGlK6SMu8leTmhUSMy8Uhdd6xBiH3Gdman4tjQGLboJfqz6fL2WKHTmrfsKZRYX6BTDjDldKMosaSTLdQS7oDisJNqAUhw1PfTlnacCO8vl8706Km1FROgLDmudzxg%2BEWTiArtHgLsRrAXYWdB0NmToNCJdKm0KWycZQqb%2BMw76Qy29iQ5up%2FX7oyw8QZ75kP5F6iJAJz6KCmqxz8fEa%2FxnsMYcIO%2FvEkGRuMckhr4rIeLrKaXnmIzlNLxbFspOphkcnJdnz%2FChp%2FVlpj2P7jJQmQRwGnltkTV5dbF9fE3%2FfxoSqTROgq9wFUlbuYzYcasE0ouzBo%2BdDCDzxKAfhbAZYxQiHrLzV2iVexnDX%2FQnT1fsT%2Fxuhu1ui5qIytgbGmRoQkeQooO8eJNNZsf0iALur8QxZFH0nCMnjerYQqG1pIfjyVZWxhVRznmmfLG00BcBWJE6hzQWRyFknuJnXuk8A5FRDCulwrWASSNoBtR%2BCtGdkPwYN2o7DOw%2FVGlCZPusRBFXODQdUM5zeHDIVuAJBLqbO%2Ff9Qua%2BpDqEPk230Sob9lEZ8BHiCorjVghuI0lI4JDgHGRDD%2FprQ84B1pVGkIpVUAHCG%2Biz3Bn3qm2AVrYcYWhock4jso5%2BJ7HfHVj4WMIQdGctq3psBCVVzupQOEioBGA2Bk%2BUILT7%2BVoX5mdxxA5fS42gISQVi%2FHTzrgMxu0fY6hE1ocUwwbsbWcezrY2n6S8%2F6cxXkOH4prpmPuFoikTzY7T85C4T2XYlbxLglSv2uLCgFv8Quk%2FwdesUdWPeHYIH0R729JIisN9Apdd4eB10aqwXrPt%2BSu9mA8k8n1sjMwnfsfF2j3jMUzXepSHmZ%2FBfqXvzgUNQQWOXO8YEuFBh4QTYCkOAPxywpYu1VxiDyJmKVcmJPGWk%2Fgc3Pov02StyYDahwmzw3E1gYC9wkupyWfDqDSUMpCTH5e5N8B%2F%2FlHiMuIkTNw4USHrJU67bjXGqNav6PBuQSoqTxc8avHoGmvqNtXzIaoyMIQIiiUHIM64cXieouplhNYln7qgc4wBVAYR104kO%2BCvKqsg4yIUlFNThVUAKZxZt1XA34h3TCUUiXVkZ0w8Hh2R0Z5L0b4LZvPd%2Fp1gi%2F07h8qfwHrByuSxglc9cI4QIg2oqvC%2Fqm0i7tjPLTgDhoWTAKDO2ONW5oe%2B%2FeKB9vZB8K6C25yCZ9RFVMnb6NRdRjyVK57CHHSkJBfnM2%2Fj4ODUwRkqrtBBCrDsDpt8jhZdXoy%2F1BCqw3sSGhgGGy0a5Jw6BP%2FTExoCmNFYjZl248A0osgPyGEmRA%2BfAsqPVaNAfytu0vuQJ7rk3J4kTDTR2AlCHJ5cls26opZM4w3jMULh2YXKpcqGBtuleAlOZnaZGbD6DHzMd6i2oFeJ8z9XYmalg1Szd%2FocZDc1C7Y6vcALJz2lYnTXiWEr2wawtoR4g3jvWUU2Ngjd1cewtFzEvM1NiHZPeLlIXFbBPawxNgMwwAlyNSuGF3zizVeOoC9bag1qRAQKQE%2FEZBWC2J8mnXAN2aTBboZ7HewnObE8CwROudZHmUM5oZ%2FUgd%2FJZQK8lvAm43uDRAbyW8gZ%2BZGq0EVerVGUKUSm%2FIdn8AQHdR4m7bue88WBwft9mSCeMOt1ncBwziOmJYI2ZR7ewNMPiCugmSsE4EyQ%2BQATJG6qORMGd4snEzc6B4shPIo4G1T7PgSm8PY5eUkPdF8JZ0VBtadbHXoJgnEhZQaODPj2gpODKJY5Yp4DOsLBFxWbvXN755KWylJm%2BoOd4zEL9Hpubuy2gyyfxh8oEfFutnYWdfB8PdESLWYvSqbElP9qo3u6KTmkhoacDauMNNjj0oy40DFV7Ql0aZj77xfGl7TJNHnIwgqOkenruYYNo6h724%2BzUQ7%2BvkCpZB%2BpGA562hYQiDxHVWOq0oDQl%2FQsoiY%2BcuI7iWq%2FZIBtHcXJ7kks%2Bh2fCNUPA82BzjnqktNts%2BRLdk1VSu%2BtqEn7QZCCsvEqk6FkfiOYkrsw092J8jsfIuEKypNjLxrKA9kiA19mxBD2suxQKCzwXGws7kEJvlhUiV9tArLIdZW0IORcxEzdzKmjtFhsjKy%2F44XYXdI5noQoRcvjZ1RMPACRqYg2V1%2BOwOepcOknRLLFdYgTkT5UApt%2FJhLM3jeFYprZV%2BZow2g8fP%2BU68hkKFWJj2yBbKqsrp25xkZX1DAjUw52IMYWaOhab8Kp05VrdNftqwRrymWF4OQSjbdfzmRZirK8FMJELEgER2PHjEAN9pGfLhCUiTJFbd5LBkOBMaxLr%2FA1SY9dXFz4RjzoU9ExfJCmx%2FI9FKEGT3n2cmzl2X42L3Jh%2BAbQq6sA%2BSs1kitoa4TAYgKHaoybHUDJ51oETdeI%2F9ThSmjWGkyLi5QAGWhL0BG1UsTyRGRJOldKBrYJeB8ljLJHfATWTEQBXBDnQexOHTB%2BUn44zExFE4vLytcu5NwpWrUxO%2F0ZICUGM7hGABXym0V6ZvDST0E370St9MIWQOTWngeoQHUTdCJUP04spMBMS8LSker9cReVQkULFDIZDFPrhTzBl6sed9wcZQTbL%2BBDqMyaN3RJPh%2Fanbx%2BIv%2BqgQdAa3M9Z5JmvYlh4qop%2BHo1F1W5gbOE9YKLgAnWytXElU4G8GtW47lhgFE6gaSs%2Bgs37sFvi0PPVvA5dnCBgILTwoKd%2F%2BDoL9F6inlM7H4rOTzD79KJgKlZO%2FZgt22UsKhrAaXU5ZcLrAglTVKJEmNJvORGN1vqrcfSMizfpsgbIe9zno%2BgBoKVXgIL%2FVI8dB1O5o%2FR3Suez%2FgD7M781ShjKpIIORM%2FnxG%2BjjhhgPwsn2IoXsPGPqYHXA63zJ07M2GPEykQwJBYLK808qYxuIew4frk52nhCsnCYmXiR6CuapvE1IwRB4%2FQftDbEn%2BAucIr1oxrLabRj9q4ae0%2BfXkHnteAJwXRbVkR0mctVSwEbqhJiMSZUp9DNbEDMmjX22m3ABpkrPQQTP3S1sib5pD2VRKRd%2BeNAjLYyT0hGrdjWJZy24OYXRoWQAIhGBZRxuBFMjjZQhpgrWo8SiFYbojcHO8V5DyscJpLTHyx9Fimassyo5U6WNtquUMYgccaHY5amgR3PQzq3ToNM5ABnoB9kuxsebqmYZm0R9qxJbFXCQ1UPyFIbxoUraTJFDpCk0Wk9GaYJKz%2F6oHwEP0Q14lMtlddQsOAU9zlYdMVHiT7RQP3XCmWYDcHCGbVRHGnHuwzScA0BaSBOGkz3lM8CArjrBsyEoV6Ys4qgDK3ykQQPZ3hCRGNXQTNNXbEb6tDiTDLKOyMzRhCFT%2BmAUmiYbV3YQVqFVp9dorv%2BTsLeCykS2b5yyu8AV7IS9cxcL8z4Kfwp%2BxJyYLv1OsxQCZwTB4a8BZ%2F5EdxTBJthApqyfd9u3ifr%2FWILTqq5VqgwMT9SOxbSGWLQJUUWCVi4k9tho9nEsbUh7U6NUsLmkYFXOhZ0kmamaJLRNJzSj%2Fqn4Mso6zb6iLLBXoaZ6AqeWCjHQm2lztnejYYM2eubnpBdKVLORZhudH3JF1waBJKA9%2BW8EhMj3Kzf0L4vi4k6RoHh3Z5YgmSZmk6ns4fjScjAoL8GoOECgqgYEBYUGFVO4FUv4%2FYtowhEmTs0vrvlD%2FCrisnoBNDAcUi%2FteY7OctFlmARQzjOItrrlKuPO6E2Ox93L4O%2F4DcgV%2FdZ7qR3VBwVQxP1GCieA4RIpweYJ5FoYrHxqRBdJjnqbsikA2Ictbb8vE1GYIo9dacK0REgDX4smy6GAkxlH1yCGGsk%2BtgiDhNKuKu3yNrMdxafmKTF632F8Vx4BNK57GvlFisrkjN9WDAtjsWA0ENT2e2nETUb%2Fn7qwhvGnrHuf5bX6Vh%2Fn3xffU3PeHdR%2BFA92i6ufT3AlyAREoNDh6chiMWTvjKjHDeRhOa9YkOQRq1vQXEMppAQVwHCuIcV2g5rBn6GmZZpTR7vnSD6ZmhdSl176gqKTXu5E%2BYbfL0adwNtHP7dT7t7b46DVZIkzaRJOM%2BS6KcrzYVg%2BT3wSRFRQashjfU18NutrKa%2F7PXbtuJvpIjbgPeqd%2BpjmRw6YKpnANFSQcpzTZgpSNJ6J7uiagAbir%2F8tNXJ%2FOsOnRh6iuIexxrmkIneAgz8QoLmiaJ8sLQrELVK2yn3wOHp57BAZJhDZjTBzyoRAuuZ4eoxHruY1pSb7qq79cIeAdOwin4GdgMeIMHeG%2BFZWYaiUQQyC5b50zKjYw97dFjAeY2I4Bnl105Iku1y0lMA1ZHolLx19uZnRdILcXKlZGQx%2FGdEqSsMRU1BIrFqRcV1qQOOHyxOLXEGcbRtAEsuAC2V4K3p5mFJ22IDWaEkk9ttf5Izb2LkD1MnrSwztXmmD%2FQi%2FEmVEFBfiKGmftsPwVaIoZanlKndMZsIBOskFYpDOq3QUs9aSbAAtL5Dbokus2G4%2FasthNMK5UQKCOhU97oaOYNGsTah%2BjfCKsZnTRn5TbhFX8ghg8CBYt%2FBjeYYYUrtUZ5jVij%2Fop7V5SsbA4mYTOwZ46hqdpbB6Qvq3AS2HHNkC15pTDIcDNGsMPXaBidXYPHc6PJAkRh29Vx8KcgX46LoUQBhRM%2B3SW6Opll%2FwgxxsPgKJKzr5QCmwkUxNbeg6Wj34SUnEzOemSuvS2OetRCO8Tyy%2BQbSKVJcqkia%2BGvDefFwMOmgnD7h81TUtMn%2BmRpyJJ349HhAnoWFTejhpYTL9G8N2nVg1qkXBeoS9Nw2fB27t7trm7d%2FQK7Cr4uoCeOQ7%2F8JfKT77KiDzLImESHw%2F0wf73QeHu74hxv7uihi4fTX%2BXEwAyQG3264dwv17aJ5N335Vt9sdrAXhPOAv8JFvzqyYXwfx8WYJaef1gMl98JRFyl5Mv5Uo%2FoVH5ww5OzLFsiTPDns7fS6EURSSWd%2F92BxMYQ8sBaH%2Bj%2BwthQPdVgDGpTfi%2BJQIWMD8xKqULliRH01rTeyF8x8q%2FGBEEEBrAJMPf25UQwi0b8tmqRXY7kIvNkzrkvRWLnxoGYEJsz8u4oOyMp8cHyaybb1HdMCaLApUE%2B%2F7xLIZGP6H9xuSEXp1zLIdjk5nBaMuV%2FyTDRRP8Y2ww5RO6d2D94o%2B6ucWIqUAvgHIHXhZsmDhjVLczmZ3ca0Cb3PpKwt2UtHVQ0BgFJsqqTsnzZPlKahRUkEu4qmkJt%2Bkqdae76ViWe3STan69yaF9%2BfESD2lcQshLHWVu4ovItXxO69bqC5p1nZLvI8NdQB9s9UNaJGlQ5mG947ipdDA0eTIw%2FA1zEdjWquIsQXXGIVEH0thC5M%2BW9pZe7IhAVnPJkYCCXN5a32HjN6nsvokEqRS44tGIs7s2LVTvcrHAF%2BRVmI8L4HUYk4x%2B67AxSMJKqCg8zrGOgvK9kNMdDrNiUtSWuHFpC8%2Fp5qIQrEo%2FH%2B1l%2F0cAwQ2nKmpWxKcMIuHY44Y6DlkpO48tRuUGBWT0FyHwSKO72Ud%2BtJUfdaZ4CWNijzZtlRa8%2BCkmO%2FEwHYfPZFU%2FhzjFWH7vnzHRMo%2BaF9u8qHSAiEkA2HjoNQPEwHsDKOt6hOoK3Ce%2F%2B%2F9boMWDa44I6FrQhdgS7OnNaSzwxWKZMcyHi6LN4WC6sSj0qm2PSOGBTvDs%2FGWJS6SwEN%2FULwpb4LQo9fYjUfSXRwZkynUazlSpvX9e%2BG2zor8l%2BYaMxSEomDdLHGcD6YVQPegTaA74H8%2BV4WvJkFUrjMLGLlvSZQWvi8%2FQA7yzQ8GPno%2F%2F5SJHRP%2FOqKObPCo81s%2F%2B6WgLqykYpGAgQZhVDEBPXWgU%2FWzFZjKUhSFInufPRiMAUULC6T11yL45ZrRoB4DzOyJShKXaAJIBS9wzLYIoCEcJKQW8GVCx4fihqJ6mshBUXSw3wWVj3grrHQlGNGhIDNNzsxQ3M%2BGWn6ASobIWC%2BLbYOC6UpahVO13Zs2zOzZC8z7FmA05JhUGyBsF4tsG0drcggIFzgg%2Fkpf3%2BCnAXKiMgIE8Jk%2FMhpkc8DUJEUzDSnWlQFme3d0sHZDrg7LavtsEX3cHwjCYA17pMTfx8Ajw9hHscN67hyo%2BRJQ4458RmPywXykkVcW688oVUrQhahpPRvTWPnuI0B%2BSkQu7dCyvLRyFYlC1LG1gRCIvn3rwQeINzZQC2KXq31FaR9UmVV2QeGVqBHjmE%2BVMd3b1fhCynD0pQNhCG6%2FWCDbKPyE7NRQzL3BzQAJ0g09aUzcQA6mUp9iZFK6Sbp%2FYbHjo%2B%2B7%2FWj8S4YNa%2BZdqAw1hDrKWFXv9%2BzaXpf8ZTDSbiqsxnwN%2FCzK5tPkOr4tRh2kY3Bn9JtalbIOI4b3F7F1vPQMfoDcdxMS8CW9m%2FNCW%2FHILTUVWQIPiD0j1A6bo8vsv6P1hCESl2abrSJWDrq5sSzUpwoxaCU9FtJyYH4QFMxDBpkkBR6kn0LMPO%2B5EJ7Z6bCiRoPedRZ%2FP0SSdii7ZnPAtVwwHUidcdyspwncz5uq6vvm4IEDbJVLUFCn%2FLvIHfooUBTkFO130FC7CmmcrKdgDJcid9mvVzsDSibOoXtIf9k6ABle3PmIxejodc4aob0QKS432srrCMndbfD454q52V01G4q913mC5HOsTzWF4h2No1av1VbcUgWAqyoZl%2B11PoFYnNv2HwAODeNRkHj%2B8SF1fcvVBu6MrehHAZK1Gm69ICcTKizykHgGFx7QdowTVAsYEF2tVc0Z6wLryz2FI1sc5By2znJAAmINndoJiB4sfPdPrTC8RnkW7KRCwxC6YvXg5ahMlQuMpoCSXjOlBy0Kij%2BbsCYPbGp8BdCBiLmLSAkEQRaieWo1SYvZIKJGj9Ur%2FeWHjiB7SOVdqMAVmpBvfRiebsFjger7DC%2B8kRFGtNrTrnnGD2GAJb8rQCWkUPYHhwXsjNBSkE6lGWUj5QNhK0DMNM2l%2BkXRZ0KLZaGsFSIdQz%2FHXDxf3%2FTE30%2BDgBKWGWdxElyLccJfEpjsnszECNoDGZpdwdRgCixeg9L4EPhH%2BRptvRMVRaahu4cySjS3P5wxAUCPkmn%2BrhyASpmiTaiDeggaIxYBmtLZDDhiWIJaBgzfCsAGUF1Q1SFZYyXDt9skCaxJsxK2Ms65dmdp5WAZyxik%2FzbrTQk5KmgxCg%2Ff45L0jywebOWUYFJQAJia7XzCV0x89rpp%2Ff3AVWhSPyTanqmik2SkD8A3Ml4NhIGLAjBXtPShwKYfi2eXtrDuKLk4QlSyTw1ftXgwqA2jUuopDl%2B5tfUWZNwBpEPXghzbBggYCw%2Fdhy0ntds2yeHCDKkF%2FYxQjNIL%2FF%2F37jLPHCKBO9ibwYCmuxImIo0ijV2Wbg3kSN2psoe8IsABv3RNFaF9uMyCtCYtqcD%2BqNOhwMlfARQUdJ2tUX%2BMNJqOwIciWalZsmEjt07tfa8ma4cji9sqz%2BQ9hWfmMoKEbIHPOQORbhQRHIsrTYlnVTNvcq1imqmmPDdVDkJgRcTgB8Sb6epCQVmFZe%2BjGDiNJQLWnfx%2BdrTKYjm0G8yH0ZAGMWzEJhUEQ4Maimgf%2Fbkvo8PLVBsZl152y5S8%2BHRDfZIMCbYZ1WDp4yrdchOJw8k6R%2B%2F2pHmydK4NIK2PHdFPHtoLmHxRDwLFb7eB%2BM4zNZcB9NrAgjVyzLM7xyYSY13ykWfIEEd2n5%2FiYp3ZdrCf7fL%2Ben%2BsIJu2W7E30MrAgZBD1rAAbZHPgeAMtKCg3NpSpYQUDWJu9bT3V7tOKv%2BNRiJc8JAKqqgCA%2FPNRBR7ChpiEulyQApMK1AyqcWnpSOmYh6yLiWkGJ2mklCSPIqN7UypWj3dGi5MvsHQ87MrB4VFgypJaFriaHivwcHIpmyi5LhNqtem4q0n8awM19Qk8BOS0EsqGscuuydYsIGsbT5GHnERUiMpKJl4ON7qjB4fEqlGN%2FhCky89232UQCiaeWpDYCJINXjT6xl4Gc7DxRCtgV0i1ma4RgWLsNtnEBRQFqZggCLiuyEydmFd7WlogpkCw5G1x4ft2psm3KAREwVwr1Gzl6RT7FDAqpVal34ewVm3VH4qn5mjGj%2BbYL1NgfLNeXDwtmYSpwzbruDKpTjOdgiIHDVQSb5%2FzBgSMbHLkxWWgghIh9QTFSDILixVwg0Eg1puooBiHAt7DzwJ7m8i8%2Fi%2BjHvKf0QDnnHVkVTIqMvIQImOrzCJwhSR7qYB5gSwL6aWL9hERHCZc4G2%2BJrpgHNB8eCCmcIWIQ6rSdyPCyftXkDlErUkHafHRlkOIjxGbAktz75bnh50dU7YHk%2BMz7wwstg6RFZb%2BTZuSOx1qqP5C66c0mptQmzIC2dlpte7vZrauAMm%2F7RfBYkGtXWGiaWTtwvAQiq2oD4YixPLXE2khB2FRaNRDTk%2B9sZ6K74Ia9VntCpN4BhJGJMT4Z5c5FhSepRCRWmBXqx%2BwhVZC4me4saDs2iNqXMuCl6iAZflH8fscC1sTsy4PHeC%2BXYuqMBMUun5YezKbRKmEPwuK%2BCLzijPEQgfhahQswBBLfg%2FGBgBiI4QwAqzJkkyYAWtjzSg2ILgMAgqxYfwERRo3zruBL9WOryUArSD8sQOcD7fvIODJxKFS615KFPsb68USBEPPj1orNzFY2xoTtNBVTyzBhPbhFH0PI5AtlJBl2aSgNPYzxYLw7XTDBDinmVoENwiGzmngrMo8OmnRP0Z0i0Zrln9DDFcnmOoBZjABaQIbPOJYZGqX%2BRCMlDDbElcjaROLDoualmUIQ88Kekk3iM4OQrADcxi3rJguS4MOIBIgKgXrjd1WkbCdqxJk%2F4efRIFsavZA7KvvJQqp3Iid5Z0NFc5aiMRzGN3vrpBzaMy4JYde3wr96PjN90AYOIbyp6T4zj8LoE66OGcX1Ef4Z3KoWLAUF4BTg7ug%2FAbkG5UNQXAMkQezujSHeir2uTThgd3gpyzDrbnEdDRH2W7U6PeRvBX1ZFMP5RM%2BZu6UUZZD8hDPHldVWntTCNk7To8IeOW9yn2wx0gmurwqC60AOde4r3ETi5pVMSDK8wxhoGAoEX9NLWHIR33VbrbMveii2jAJlrxwytTHbWNu8Y4N8vCCyZjAX%2FpcsfwXbLze2%2BD%2Bu33OGBoJyAAL3jn3RuEcdp5If8O%2Ba4NKWvxOTyDltG0IWoHhwVGe7dKkCWFT%2B%2Btm%2BhaBCikRUUMrMhYKZJKYoVuv%2FbsJzO8DwfVIInQq3g3BYypiz8baogH3r3GwqCwFtZnz4xMjAVOYnyOi5HWbFA8n0qz1OjSpHWFzpQOpvkNETZBGpxN8ybhtqV%2FDMUxd9uFZmBfKXMCn%2FSqkWJyKPnT6lq%2B4zBZni6fYRByJn6OK%2BOgPBGRAJluwGSk4wxjOOzyce%2FPKODwRlsgrVkdcsEiYrqYdXo0Er2GXi2GQZd0tNJT6c9pK1EEJG1zgDJBoTVuCXGAU8BKTvCO%2FcEQ1Wjk3Zzuy90JX4m3O5IlxVFhYkSUwuQB2up7jhvkm%2BbddRQu5F9s0XftGEJ9JSuSk%2BZachCbdU45fEqbugzTIUokwoAKvpUQF%2FCvLbWW5BNQFqFkJg2f30E%2F48StNe5QwBg8zz3YAJ82FZoXBxXSv4QDooDo79NixyglO9AembuBcx5Re3CwOKTHebOPhkmFC7wNaWtoBhFuV4AkEuJ0J%2B1pT0tLkvFVZaNzfhs%2FKd3%2BA9YsImlO4XK4vpCo%2FelHQi%2F9gkFg07xxnuXLt21unCIpDV%2BbbRxb7FC6nWYTsMFF8%2B1LUg4JFjVt3vqbuhHmDKbgQ4e%2BRGizRiO8ky05LQGMdL2IKLSNar0kNG7lHJMaXr5mLdG3nykgj6vB%2FKVijd1ARWkFEf3yiUw1v%2FWaQivVUpIDdSNrrKbjO5NPnxz6qTTGgYg03HgPhDrCFyYZTi3XQw3HXCva39mpLNFtz8AiEhxAJHpWX13gCTAwgm9YTvMeiqetdNQv6IU0hH0G%2BZManTqDLPjyrOse7WiiwOJCG%2BJ0pZYULhN8NILulmYYvmVcV2MjAfA39sGKqGdjpiPo86fecg65UPyXDIAOyOkCx5NQsLeD4gGVjTVDwOHWkbbBW0GeNjDkcSOn2Nq4cEssP54t9D749A7M1AIOBl0Fi0sSO5v3P7LCBrM6ZwFY6kp2FX6AcbGUdybnfChHPyu6WlRZ2Fwv9YM0RMI7kISRgR8HpQSJJOyTfXj%2F6gQKuihPtiUtlCQVPohUgzfezTg8o1b3n9pNZeco1QucaoXe40Fa5JYhqdTspFmxGtW9h5ezLFZs3j%2FN46f%2BS2rjYNC2JySXrnSAFhvAkz9a5L3pza8eYKHNoPrvBRESpxYPJdKVUxBE39nJ1chrAFpy4MMkf0qKgYALctGg1DQI1kIymyeS2AJNT4X240d3IFQb%2F0jQbaHJ2YRK8A%2Bls6WMhWmpCXYG5jqapGs5%2FeOJErxi2%2F2KWVHiPellTgh%2FfNl%2F2KYPKb7DUcAg%2BmCOPQFCiU9Mq%2FWLcU1xxC8aLePFZZlE%2BPCLzf7ey46INWRw2kcXySR9FDgByXzfxiNKwDFbUSMMhALPFSedyjEVM5442GZ4hTrsAEvZxIieSHGSgkwFh%2FnFNdrrFD4tBH4Il7fW6ur4J8Xaz7RW9jgtuPEXQsYk7gcMs2neu3zJwTyUerHKSh1iTBkj2YJh1SSOZL5pLuQbFFAvyO4k1Hxg2h99MTC6cTUkbONQIAnEfGsGkNFWRbuRyyaEZInM5pij73EA9rPIUfU4XoqQpHT9THZkW%2BoKFLvpyvTBMM69tN1Ydwv1LIEhHsC%2BueVG%2Bw%2BkyCPsvV3erRikcscHjZCkccx6VrBkBRusTDDd8847GA7p2Ucy0y0HdSRN6YIBciYa4vuXcAZbQAuSEmzw%2BH%2FAuOx%2BaH%2BtBL88H57D0MsqyiZxhOEQkF%2F8DR1d2hSPMj%2FsNOa5rxcUnBgH8ictv2J%2Bcb4BA4v3MCShdZ2vtK30vAwkobnEWh7rsSyhmos3WC93Gn9C4nnAd%2FPjMMtQfyDNZsOPd6XcAsnBE%2FmRHtHEyJMzJfZFLE9OvQa0i9kUmToJ0ZxknTgdl%2FXPV8xoh0K7wNHHsnBdvFH3sv52lU7UFteseLG%2FVanIvcwycVA7%2BBE1Ulyb20BvwUWZcMTKhaCcmY3ROpvonVMV4N7yBXTL7IDtHzQ4CCcqF66LjF3xUqgErKzolLyCG6Kb7irP%2FMVTCCwGRxfrPGpMMGvPLgJ881PHMNMIO09T5ig7AzZTX%2F5PLlwnJLDAPfuHynSGhV4tPqR3gJ4kg4c06c%2FF1AcjGytKm2Yb5jwMotF7vro4YDLWlnMIpmPg36NgAZsGA0W1spfLSue4xxat0Gdwd0lqDBOgIaMANykwwDKejt5YaNtJYIkrSgu0KjIg0pznY0SCd1qlC6R19g97UrWDoYJGlrvCE05J%2F5wkjpkre727p5PTRX5FGrSBIfJqhJE%2FIS876PaHFkx9pGTH3oaY3jJRvLX9Iy3Edoar7cFvJqyUlOhAEiOSAyYgVEGkzHdug%2BoRHIEOXAExMiTSKU9A6nmRC8mp8iYhwWdP2U%2F5EkFAdPrZw03YA3gSyNUtMZeh7dDCu8pF5x0VORCTgKp07ehy7NZqKTpIC4UJJ89lnboyAfy5OyXzXtuDRbtAFjZRSyGFTpFrXwkpjSLIQIG3N0Vj4BtzK3wdlkBJrO18MNsgseR4BysJilI0wI6ZahLhBFA0XBmV8d4LUzEcNVb0xbLjLTETYN8OEVqNxkt10W614dd1FlFFVTIgB7%2FBQQp1sWlNolpIu4ekxUTBV7NmxOFKEBmmN%2BnA7pvF78%2FRII5ZHA09OAiE%2F66MF6HQ%2BqVEJCHxwymukkNvzqHEh52dULPbVasfQMgTDyBZzx4007YiKdBuUauQOt27Gmy8ISclPmEUCIcuLbkb1mzQSqIa3iE0PJh7UMYQbkpe%2BhXjTJKdldyt2mVPwywoODGJtBV1lJTgMsuSQBlDMwhEKIfrvsxGQjHPCEfNfMAY2oxvyKcKPUbQySkKG6tj9AQyEW3Q5rpaDJ5Sns9ScLKeizPRbvWYAw4bXkrZdmB7CQopCH8NAmqbuciZChHN8lVGaDbCnmddnqO1PQ4ieMYfcSiBE5zzMz%2BJV%2F4eyzrzTEShvqSGzgWimkNxLvUj86iAwcZuIkqdB0VaIB7wncLRmzHkiUQpPBIXbDDLHBlq7vp9xwuC9AiNkIptAYlG7Biyuk8ILdynuUM1cHWJgeB%2BK3wBP%2FineogxkvBNNQ4AkW0hvpBOQGFfeptF2YTR75MexYDUy7Q%2F9uocGsx41O4IZhViw%2F2FvAEuGO5g2kyXBUijAggWM08bRhXg5ijgMwDJy40QeY%2FcQpUDZiIzmvskQpO5G1zyGZA8WByjIQU4jRoFJt56behxtHUUE%2Fom7Rj2psYXGmq3llVOCgGYKNMo4pzwntITtapDqjvQtqpjaJwjHmDzSVGLxMt12gEXAdLi%2FcaHSM3FPRGRf7dB7YC%2BcD2ho6oL2zGDCkjlf%2FDFoQVl8GS%2F56wur3rdV6ggtzZW60MRB3g%2BU1W8o8cvqIpMkctiGVMzXUFI7FacFLrgtdz4mTEr4aRAaQ2AFQaNeG7GX0yOJgMRYFziXdJf24kg%2FgBQIZMG%2FYcPEllRTVNoDYR6oSJ8wQNLuihfw81UpiKPm714bZX1KYjcXJdfclCUOOpvTxr9AAJevTY4HK%2FG7F3mUc3GOAKqh60zM0v34v%2BELyhJZqhkaMA8UMMOU90f8RKEJFj7EqepBVwsRiLbwMo1J2zrE2UYJnsgIAscDmjPjnzI8a719Wxp757wqmSJBjXowhc46QN4RwKIxqEE6E5218OeK7RfcpGjWG1jD7qND%2B%2FGTk6M56Ig4yMsU6LUW1EWE%2BfIYycVV1thldSlbP6ltdC01y3KUfkobkt2q01YYMmxpKRvh1Z48uNKzP%2FIoRIZ%2FF6buOymSnW8gICitpJjKWBscSb9JJKaWkvEkqinAJ2kowKoqkqZftRqfRQlLtKoqvTRDi2vg%2FRrPD%2Fd3a09J8JhGZlEkOM6znTsoMCsuvTmywxTCDhw5dd0GJOHCMPbsj3QLkTE3MInsZsimDQ3HkvthT7U9VA4s6G07sID0FW4SHJmRGwCl%2BMu4xf0ezqeXD2PtPDnwMPo86sbwDV%2B9PWcgFcARUVYm3hrFQrHcgMElFGbSM2A1zUYA3baWfheJp2AINmTJLuoyYD%2FOwA4a6V0ChBN97E8YtDBerUECv0u0TlxR5yhJCXvJxgyM73Bb6pyq0jTFJDZ4p1Am1SA6sh8nADd1hAcGBMfq4d%2FUfwnmBqe0Jun1n1LzrgKuZMAnxA3NtCN7Klf4BH%2B14B7ibBmgt0TGUafVzI4uKlpF7v8NmgNjg90D6QE3tbx8AjSAC%2BOA1YJvclyPKgT27QpIEgVYpbPYGBsnyCNrGz9XUsCHkW1QAHgL2STZk12QGqmvAB0NFteERkvBIH7INDsNW9KKaAYyDMdBEMzJiWaJHZALqDxQDWRntumSDPcplyFiI1oDpT8wbwe01AHhW6%2BvAUUBoGhY3CT2tgwehdPqU%2F4Q7ZLYvhRl%2FogOvR9O2%2BwkkPKW5vCTjD2fHRYXONCoIl4Jh1bZY0ZE1O94mMGn%2FdFSWBWzQ%2FVYk%2BGezi46RgiDv3EshoTmMSlioUK6MQEN8qeyK6FRninyX8ZPeUWjjbMJChn0n%2FyJvrq5bh5UcCAcBYSafTFg7p0jDgrXo2QWLb3WpSOET%2FHh4oSadBTvyDo10IufLzxiMLAnbZ1vcUmj3w7BQuIXjEZXifwukVxrGa9j%2BDXfpi12m1RbzYLg9J2wFergEwOxFyD0%2FJstNK06ZN2XdZSGWxcJODpQHOq4iKqjqkJUmPu1VczL5xTGUfCgLEYyNBCCbMBFT%2FcUP6pE%2FmujnHsSDeWxMbhrNilS5MyYR0nJyzanWXBeVcEQrRIhQeJA6Xt4f2eQESNeLwmC10WJVHqwx8SSyrtAAjpGjidcj1E2FYN0LObUcFQhafUKTiGmHWRHGsFCB%2BHEXgrzJEB5bp0QiF8ZHh11nFX8AboTD0PS4O1LqF8XBks2MpjsQnwKHF6HgaKCVLJtcr0XjqFMRGfKv8tmmykhLRzu%2BvqQ02%2BKpJBjaLt9ye1Ab%2BBbEBhy4EVdIJDrL2naV0o4wU8YZ2Lq04FG1mWCKC%2BUwkXOoAjneU%2FxHplMQo2cXUlrVNqJYczgYlaOEczVCs%2FOCgkyvLmTmdaBJc1iBLuKwmr6qtRnhowngsDxhzKFAi02tf8bmET8BO27ovJKF1plJwm3b0JpMh38%2BxsrXXg7U74QUM8ZCIMOpXujHntKdaRtsgyEZl5MClMVMMMZkZLNxH9%2Bb8fH6%2Bb8Lev30A9TuEVj9CqAdmwAAHBPbfOBFEATAPZ2CS0OH1Pj%2F0Q7PFUcC8hDrxESWdfgFRm%2B7vvWbkEppHB4T%2F1ApWnlTIqQwjcPl0VgS1yHSmD0OdsCVST8CQVwuiew1Y%2Bg3QGFjNMzwRB2DSsAk26cmA8lp2wIU4p93AUBiUHFGOxOajAqD7Gm6NezNDjYzwLOaSXRBYcWipTSONHjUDXCY4mMI8XoVCR%2FRrs%2FJLKXgEx%2BqkmeDlFOD1%2FyTQNDClRuiUyKYCllfMiQiyFkmuTz2vLsBNyRW%2Bxz%2B5FElFxWB28VjYIGZ0Yd%2B5wIjkcoMaggxswbT0pCmckRAErbRlIlcOGdBo4djTNO8FAgQ%2BlT6vPS60BwTRSUAM3ddkEAZiwtEyArrkiDRnS7LJ%2B2hwbzd2YDQagSgACpsovmjil5wfPuXq3GuH0CyE7FK3M4FgRaFoIkaodORrPx1%2BJpI9psyNYIFuJogZa0%2F1AhOWdlHQxdAgbwacsHqPZo8u%2FngAH2GmaTdhYnBfSDbBfh8CHq6Bx5bttP2%2BRdM%2BMAaYaZ0Y%2FADkbNCZuAyAVQa2OcXOeICmDn9Q%2FeFkDeFQg5MgHEDXq%2FtVjj%2Bjtd26nhaaolWxs1ixSUgOBwrDhRIGOLyOVk2%2FBc0UxvseQCO2pQ2i%2BKrfhu%2FWeBovNb5dJxQtJRUDv2mCwYVpNl2efQM9xQHnK0JwLYt%2FU0Wf%2BphiA4uw8G91slC832pmOTCAoZXohg1fewCZqLBhkOUBofBWpMPsqg7XEXgPfAlDo2U5WXjtFdS87PIqClCK5nW6adCeXPkUiTGx0emOIDQqw1yFYGHEVx20xKjJVYe0O8iLmnQr3FA9nSIQilUKtJ4ZAdcTm7%2BExseJauyqo30hs%2B1qSW211A1SFAOUgDlCGq7eTIcMAeyZkV1SQJ4j%2Fe1Smbq4HcjqgFbLAGLyKxlMDMgZavK5NAYH19Olz3la%2FQCTiVelFnU6O%2FGCvykqS%2FwZJDhKN9gBtSOp%2F1SP5VRgJcoVj%2Bkmf2wBgv4gjrgARBWiURYx8xENV3bEVUAAWWD3dYDKAIWk5opaCFCMR5ZjJExiCAw7gYiSZ2rkyTce4eNMY3lfGn%2B8p6%2BvBckGlKEXnA6Eota69OxDO9oOsJoy28BXOR0UoXNRaJD5ceKdlWMJlOFzDdZNpc05tkMGQtqeNF2lttZqNco1VtwXgRstLSQ6tSPChgqtGV5h2DcDReIQadaNRR6AsAYKL5gSFsCJMgfsaZ7DpKh8mg8Wz8V7H%2BgDnLuMxaWEIUPevIbClgap4dqmVWSrPgVYCzAoZHIa5z2Ocx1D%2FGvDOEqMOKLrMefWIbSWHZ6jbgA8qVBhYNHpx0P%2BjAgN5TB3haSifDcApp6yymEi6Ij%2FGsEpDYUgcHATJUYDUAmC1SCkJ4cuZXSAP2DEpQsGUjQmKJfJOvlC2x%2FpChkOyLW7KEoMYc5FDC4v2FGqSoRWiLsbPCiyg1U5yiHZVm1XLkHMMZL11%2Fyxyw0UnGig3MFdZklN5FI%2FqiT65T%2BjOXOdO7XbgWurOAZR6Cv9uu1cm5LjkXX4xi6mWn5r5NjBS0gTliHhMZI2WNqSiSphEtiCAwnafS11JhseDGHYQ5%2BbqWiAYiAv6Jsf79%2FVUs4cIl%2Bn6%2BWOjcgB%2F2l5TreoAV2717JzZbQIR0W1cl%2FdEqCy5kJ3ZSIHuU0vBoHooEpiHeQWVkkkOqRX27eD1FWw4BfO9CJDdKoSogQi3hAAwsPRFrN5RbX7bqLdBJ9JYMohWrgJKHSjVl1sy2xAG0E3sNyO0oCbSGOxCNBRRXTXenYKuwAoDLfnDcQaCwehUOIDiHAu5m5hMpKeKM4sIo3vxACakIxKoH2YWF2QM84e6F5C5hJU4g8uxuFOlAYnqtwxmHyNEawLW%2FPhoawJDrGAP0JYWHgAVUByo%2FbGdiv2T2EMg8gsS14%2FrAdzlOYazFE7w4OzxeKiWdm3nSOnQRRKXSlVo8HEAbBfyJMKqoq%2BSCcTSx5NDtbFwNlh8VhjGGDu7JG5%2FTAGAvniQSSUog0pNzTim8Owc6QTuSKSTXlQqwV3eiEnklS3LeSXYPXGK2VgeZBqNcHG6tZHvA3vTINhV0ELuQdp3t1y9%2BogD8Kk%2FW7QoRN1UWPqM4%2BxdygkFDPLoTaumKReKiLWoPHOfY54m3qPx4c%2B4pgY3MRKKbljG8w4wvz8pxk3AqKsy4GMAkAtmRjRMsCxbb4Q2Ds0Ia9ci8cMT6DmsJG00XaHCIS%2Bo3F8YVVeikw13w%2BOEDaCYYhC0ZE54kA4jpjruBr5STWeqQG6M74HHL6TZ3lXrd99ZX%2B%2B7LhNatQaZosuxEf5yRA15S9gPeHskBIq3Gcw81AGb9%2FO53DYi%2F5CsQ51EmEh8Rkg4vOciClpy4d04eYsfr6fyQkBmtD%2BP8sNh6e%2BXYHJXT%2FlkXxT4KXU5F2sGxYyzfniMMQkb9OjDN2C8tRRgTyL7GwozH14PrEUZc6oz05Emne3Ts5EG7WolDmU8OB1LDG3VrpQxp%2BpT0KYV5dGtknU64JhabdqcVQbGZiAxQAnvN1u70y1AnmvOSPgLI6uB4AuDGhmAu3ATkJSw7OtS%2F2ToPjqkaq62%2F7WFG8advGlRRqxB9diP07JrXowKR9tpRa%2BjGJ91zxNTT1h8I2PcSfoUPtd7NejVoH03EUcqSBuFZPkMZhegHyo2ZAITovmm3zAIdGFWxoNNORiMRShgwdYwFzkPw5PA4a5MIIQpmq%2Bnsp3YMuXt%2FGkXxLx%2FP6%2BZJS0lFyz4MunC3eWSGE8xlCQrKvhKUPXr0hjpAN9ZK4PfEDrPMfMbGNWcHDzjA7ngMxTPnT7GMHar%2BgMQQ3NwHCv4zH4BIMYvzsdiERi6gebRmerTsVwZJTRsL8dkZgxgRxmpbgRcud%2BYlCIRpPwHShlUSwuipZnx9QCsEWziVazdDeKSYU5CF7UVPAhLer3CgJOQXl%2Fzh575R5rsrmRnKAzq4POFdgbYBuEviM4%2BLVC15ssLNFghbTtHWerS1hDt5s4qkLUha%2FqpZXhWh1C6lTQAqCNQnaDjS7UGFBC6wTu8yFnKJnExCnAs3Ok9yj5KpfZESQ4lTy5pTGTnkAUpxI%2ByjEldJfSo4y0QhG4i4IwkRFGcjWY8%2BEzgYYJUK7BXQksLxAww%2FYYWBMhJILB9e8ePEJ4OP7z%2B4%2FwOQDl64iOYDp26DaONPxpKtBxq%2FaTzRGarm3VkPYTLJKx6Z%2FMw2YbBGseJhPMwhhNswrIkyvV2BYzrvZbxLpKwcWJhYmFtVZ%2BlPEq91FzVp1HlQY1bZVLqeNR9SAUn6n0E28k%2FUuGkNpP1DBI5ch%2FEehZfjUQ9aE41NhETExoPT2gGQz0IhWJbEOvTQ4wgcXCHHFBhewYUiFHuhRSAUVmEHeCRQHQkXGFwkAgyzREJCVN7TRnTon36Zw3tPhx4EALwNdwDv%2BJ41YSP4B2CQqz0EFgARZ4ESgBHQgROwAVn9GTI%2BHYexTUevLUeta4%2FDqKrbMVS%2BYqb8hUwYCrlgKtmAq1YCrFgKrd4qpXiqZcKn1oqdWipjYKpWwVPVYqW6xUpVipKqFR3QKjagVEtAqHpxUMTitsnFaJOKx2cVhswq35RVpyiq9lFVNIKnOQVMkgqtYxVNxiqQjFS7GKlSIVIsQqPIhUWwioigFQ%2B%2BKkN8VHr49HDw9Ebo9EDo9DTo9Crg9BDg9%2FWx7gWx7YWwlobYrOGxWPNisAaAHEyALpkAVDIAeWAArsABVXACYuAD5cAF6wAKFQAQqgAbVAAsoAAlQAUaYAfkwAvogBWQACOgAD9AAHSAAKT4GUdMiOvFngBTwCn2AZ7Dv6B6k%2F90B8%2ByRnkV144AIBoAMTQATGgAjNAA4YABgwABZgB%2FmQCwyAVlwCguASlwCEuAQFwB4uAMlwBYuAJlQAUVAAhUD2KgdpUDaJgaRMDFJgX5MC1JgWJEAokQCWRAHxEAWkQBMRADpEAMkQAYROAEecC484DRpwBDTnwNOdw05tjTmiNOYwtswhYFwLA7BYG4LA2BYGOLAwRYFuLAsxYFQJAohIEyJAMwkAwiQC0JAJgkAeiQBkJAFokAPCQA0JABwcD4Dgc4cDdDgaYcDIDgYgUC6CgWgUClCgUYUAVBQBOFAEYMALgwAgDA9QYAdIn8AZzeBB2L5EcWrenUT1KXienEsuJJ7x5U8XlTjc1NVzUyXFTGb1LlpUtWlTDIjqwE4LsagowoCi2gJLKAkpoBgJQNpAIhNqaEoneI6kiiqQ6Go%2Fn6j0cS%2Ba2gEU8gIHJ%2BBwfgZX4GL%2BBd%2FgW34FZ%2BBS%2FgUH4FN6BTegTvoEv6BJegRnYEF2A79gOvYDl2BdEjCkqkGtwXp0LNToIskOTXzh%2FF062yJ7AAAAEDAWAAABWhJ%2BKPEIJgBFxMVP7w2QJBGHASQnOBKXKFIdUK4igKA9IEaYJg%29%20format%28%27embedded%2Dopentype%27%29%2Curl%28data%3Aapplication%2Fx%2Dfont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFuAAA8AAAAAsVwAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABGRlRNAAABWAAAABwAAAAcbSqX3EdERUYAAAF0AAAAHwAAACABRAAET1MvMgAAAZQAAABFAAAAYGe5a4ljbWFwAAAB3AAAAsAAAAZy2q3jgWN2dCAAAAScAAAABAAAAAQAKAL4Z2FzcAAABKAAAAAIAAAACP%2F%2FAANnbHlmAAAEqAAATRcAAJSkfV3Cb2hlYWQAAFHAAAAANAAAADYFTS%2FYaGhlYQAAUfQAAAAcAAAAJApEBBFobXR4AABSEAAAAU8AAAN00scgYGxvY2EAAFNgAAACJwAAAjBv%2B5XObWF4cAAAVYgAAAAgAAAAIAFqANhuYW1lAABVqAAAAZ4AAAOisyygm3Bvc3QAAFdIAAAELQAACtG6o%2BU1d2ViZgAAW3gAAAAGAAAABsMYVFAAAAABAAAAAMw9os8AAAAA0HaBdQAAAADQdnOXeNpjYGRgYOADYgkGEGBiYGRgZBQDkixgHgMABUgASgB42mNgZulmnMDAysDCzMN0gYGBIQpCMy5hMGLaAeQDpRCACYkd6h3ux%2BDAoPD%2FP%2FOB%2FwJAdSIM1UBhRiQlCgyMADGWCwwAAAB42u2UP2hTQRzHf5ekaVPExv6JjW3fvTQ0sa3QLA5xylBLgyBx0gzSWEUaXbIoBBQyCQGHLqXUqYNdtIIgIg5FHJxEtwqtpbnfaV1E1KFaSvX5vVwGEbW6OPngk8%2FvvXfv7pt3v4SImojIDw6BViKxRgIVBaZwVdSv%2BxvXA%2BIuzqcog2cOkkvDNE8Lbqs74k64i%2B5Sf3u8Z2AnIRLbyVCyTflVSEXVoEqrrMqrgiqqsqqqWQ5xlAc5zWOc5TwXucxVnuE5HdQhHdFRHdNJndZZndeFLc%2FzsKJLQ%2FWV6BcrCdWkwspVKZVROaw0qUqqoqZZcJhdTnGGxznHBS5xhad5VhNWCuturBTXKZ3RObuS98pb9c57k6ql9rp2v1as5deb1r6s9q1GV2IrHSt73T631424YXzjgPwqt%2BRn%2BVG%2BlRvyirwsS%2FKCPCfPytPypDwhj8mjctRZd9acF86y89x55jxxHjkPnXstXfbt%2FpNjj%2FnwXW%2BcHa6%2FSYvZ7yEwbDYazDcIgoUGzY3h2HtqgUcs1AFPWKgTXrRQF7xkoQhRf7uF9hPFeyzUTTSwY6EoUUJY6AC8bSGMS4Ys1Au3WaiPSGGsMtkdGH2rzJgYHAaYjxIwQqtB1CnYkEZ9BM6ALOpROAfyqI%2FDBQudgidBETXuqRIooz4DV0AV9UV4GsyivkTEyMMmw1UYGdhkuAYjA5sMGMvIwCbDDRgZeAz1TXgcmDy3YeRhk%2BcOjCxsMjyAkYFNhscwMrDJ8BQ2886gXoaRhedQvyTSkDZ7uA6HLLQBI5vGntAbGHugTc53cMxC7%2BE4SKL%2BACOzNpk3YWTWJid%2BiRo5NXIKM3fBItAPW55FdJLY3FeHBDr90606JCIU9Jk%2BMs3%2FY%2F8L8jUq3y79bJ%2F0%2F%2BROoP4v9v%2F4%2Fmj%2Bi7HBXUd0%2FelU6IHfHt8Aj9EPGAAoAvgAAAAB%2F%2F8AAnjaxb0JfBvVtTA%2BdxaN1hltI1m2ZVuSJVneLVlSHCdy9oTEWchqtrBEJRAgCYEsQNhC2EsbWmpI2dqkQBoSYgKlpaQthVL0yusrpW77aEubfq%2Fly%2BujvJampSTW5Dvnzmi1E%2Bjr%2F%2F3%2BXmbu3Llz77nnbuece865DMu0MAy5jGtiOEZkOp8lTNeUwyLP%2FDH%2BrEH41ZTDHAtB5lkOowWMPiwayNiUwwTjE46AI5xwhFrINPXYn%2F7ENY0dbWHfZAiTZbL8ID%2FInAd5xz2NpIH4STpDGonHIJNE3OP1KG4ISaSNeBuITAyRLgIxoiEUhFAnmUpEiXSRSGqAQEw0kuyFUIb0k2gnGSApyBFi0il2SI5YLGb5MdFjXCey4mNHzQ7WwLGEdZiPPgYR64we8THZHAt%2BwnT84D%2Fx8YTpGPgheKH4CMEDVF9xBOIeP3EbQgGH29BGgpGkIxCMTCW9qUTA0Zsir%2BQUP1mt%2BP2KusevwIO6Bx%2FIaj8%2FOD5O0VNrZW2EsqZBWbO1skRiEKE0DdlKKaSVO5VAuRpqk8VQJAqY7ydxaK44YJvrO2EWjOoDBoFYzQbDNkON%2BUbiKoRkywMWWf1j4bEY2iIY1AeMgvmEz%2FkVo9v4FSc%2FaMZMrFbjl4zWLL0%2BY5FlyzNlEVYDudJohg8gPUP7kcB%2Fmn%2BG6cd%2B5PV4Q72dXCgocWJADBgUuDTwiXiGSyZo14HOEQ2lE6k0XDIEusexDzZOMXwt1Dutz%2BtqmxTvlskNWXXUQIbhaurum9GrePqm9Yaeabjkiqf%2BbUvzDOvb2Y1E%2BEX2DnemcTP%2FzLcuu7xjQXdAtjR0Lo5n4%2FHs%2FGtntMlysHt%2B29NXbH6se%2F%2FWbFcyu%2Br28H0MwzI30DYeYTLMXIA2EG8QlHpAsyS0EfEToR0a3utIxFPJ3kiIHCCrZ66b0e2xEmL1dM9YN%2FMwS5p01N5jMX%2FBLKt%2F1R83l0LyC29M6%2BiYxo%2FUNg%2FEF7c2WyyW5tYl8WnhWg2%2FhyySbD5UhnDyS7OcU0dnrFw%2BDfGdI7v4QfYIIzOMq9hFtY55gmvC7jZ2FK7sEdrn6IXBuucYhjsGdQ8z0yEbWkkczjjsE5hNAIZrPx2zOLZDmKNXcXtg7EMqidAEEWg%2BSJCBBNwxvxJfc%2FbZa%2BKKf%2BxoKZybnq5vaqpPTye7CiF%2BZFjxZ8%2F7Qij0hfOG%2FcowPA1rT1l4ymWnrKmxxqfErTVrpgwPlz1kC%2BOy8NMDz6c%2BIO38K%2Fx0xkPnLW8Kx6qGAoQdL%2BTD9V9rb%2B%2Fctn%2F%2Ftrxz8dUrZrD%2Fzk%2FferF0cNt1BzctmX2FZPXt%2FjnFCQNz4Ah%2FiKllGiCMs1w5Lkg0kiEwj6VTXCDKsX9rMpnvIj9pcDecXAIXMnqn2dTUbN6w0XQ9ue6FV%2FnnXCH7S3lPWGltVcLsH75ub3ab7A8M28caNrIeOr3o5Q0yFsYL80xaa0EY%2FUEczV7icUMY5pnelAkmUAXmHYjvFWFGxuqlSaow3OM%2B%2FiYY7%2Fl%2FhVELF4EjRqNR%2FbvRbOY%2BDUGzGR%2FOh3EqmE%2FugIQQguGt%2FeMYz%2F%2BL0cimjeZfQDI3phXMbMQsqH%2BCjwVz%2Fhf4idHovgVmB8gLvjbicDcC%2FNypP536E%2F9N%2FpuMibExdohBmNwyiaZdJGoigos7GpF222xrfnZhML%2F7Z%2BylaqP63Hr%2Bm7bdUkQ6%2F2cXqdfmvwixY%2Bs2ksXFeXcE%2BiX0Z%2BIow76DBNgjJ7TOdUK18iPsPflfQD%2BDPsZG2Aj9VmKMMJ4fYRrhIaxhTDR0Elh2vA6h%2FAE6xUb29mj3sjmL72petXjejPy%2Boel60M99tFduCI59N3221xe7apOvxs6aHs7vab1IqY2tv7q2xsHeHGml%2FcV06u%2F8S%2FxTjJ%2BJYc0bWEX0ukW6YmIbGkJRMdjJ9mYIH5QIdJF4hvRGyK7cC7ctImQRcUET99fGXOoft35GYLMQu%2Bg2smnkgZUrH8AL%2F9Si217IssJ916nv14ZrJrvdxLkQvrvtBcjgPC0NXOicO8Qf4mcxPqh3hgUw3DDfdvLJXngg7N3dN2zbPJSaed3OfZnMU7dvmznp3C3bruO%2BNmue0LFsy7S%2B6265%2BfCKFYdvvuW6vmlblnUI8xCXp37CrOZv4B9gauDBlYp7adcUXB5DNCwYImlXOJJKkAdvExXxVvKEYnCo%2B3eIskP9qrrfIYs71CccBjfXRC52udTHHdaP1A1ui%2FVvH1otbrLrpNXBsGX5B89QghDyimlvNB2KfkxZ5C9%2Fem3%2Bd1%2Bd%2F%2FIfFp2%2B2Oxn%2Fs%2B9n%2F79p39S3s8idN6g0yZObwJOgKUpNB3GyU0Ls0PbRzIRq4lcarLKOJBkLRzJQD4j2090XrbA7DW8K3jNF5hlGS5e4V2D17zgss4T20egOJte5iD0bReM9yjTxnQxCRj3c5kFzGJmGbNKmwGw39IJDJcXJZGMkaAB4jyJAKw0jt5IAuIE%2BA%2BU3cVAZZrq9zhDyBrU8oosuxcGNTzCKJfla7JjNVmuSb%2F%2BtuzN2H%2BX4vlB%2BPpdfMXXmuVsNiub1T34SFbjYw5itEvVi0K0Nt9pNJUMI7SLGRhf2xipfCYf8z5OdlGKayOucFeVPeS%2Fdbo3lBrbSMmwUiQN5%2Fed7g0Ds1s17IuZC5kNzM3MZ6EWCa0DtekdJfAxz%2BR%2FOX28sND7yRMTBcf%2B%2Bs8mQCQWHya4qBv%2FufeMoWyslPA9DtMxUknxkH%2FyfTnm2CMYzs%2BCq3r7PxY%2FMXomrvTEsRpfEGHa%2BWN8E1AHjElb7d06ddA7oK%2F%2B5Mdsv9EtPms0jv0Z5kf1FqPxWdFtfFr0kHfgDX0Y%2B5PRSG7RUj0tQr7rmfX8DH4G5W28kKeJLtmQsQkuwMP1pk16EV4sl7vrMJATfyUWo%2FGwEco4rh4XFQgaiUX9qxZHrMQqKnz%2Fc2d8b9TysYrAuXpP%2FRf%2FGr8b1qwwc5a%2BeuLa6S6sneNXToG2XrEJi4R5SGs8Sq2S3d97bsfCRaTdaLwKClRHt37mkudvXbjwVrLhuYeGhh56bvfQkHpk2CwvwClqgWwuBfndC3c8dwmstj81KkagcUgbfPY8Zje0W%2F82VPWJHmSq6pP8hPWpotc%2FEexDOK3qU%2BwngPhOCiO9MJRm8TJefjelrzoKnG2Bn%2B1NCUmPE4gHFmBN9jrTigRIpsACrc9Gstg58ULkp9467%2BGf%2FeFnD5%2F31lNrt2967dhrm7bzI%2BVT5m%2BfzKhvf2MzpICEm79Bopkn07lt1762adNr127LwVqQLdJ5%2BlpQDcvHPQtVY5knhYrK6q8%2FJsiP6EuhGZdFdaNszjvpqvc%2BPI0CdjN0AXsFOC3ZfALDJwr4q2Xq%2BGF%2BGNbsxUg5NLLIEXi8otcDQcUts0D8eQ1iVDRAMBTsYiNdRIxE09EIBJO9A2xqgERTaW86BUFn0OD2xFO97FAgFhF6OoQ7prYt4XwSeUgQHiJyDbeke9IdQntciLQ1FlJMaYcUNvZBg%2BFB1ubjlnRNvl3o6IEU2w7fdNPhm%2Fhh%2BFLysUu6%2B%2BDLHkOkrSHYEjH0tEPe7WdD3uyDgvAgK%2Fm4szFFR7ch0toUgBTdWHr7EpaWru6%2B6dmbbnqWEbV2EtxAsXiZAPTtGPSbHsotI2leoM8TePEqgSQprs7AGFf8kuOkPdZPXGb55POAW1d%2FjLST9v5YflasP6v%2FCO7%2BGNAPC2BMZWmsOjp2NNbfHwMCJD%2BLPVL%2BD%2FOYlWEEI%2F9jpPddOFkB5d1GSuKZYggmCCd7JUxD7EXAzxyirYnNDLdDZoFdx14kivkvGc3579Jm36reTTvDgBnaO6vzyQ6chQmlsMoIkIQ2%2BbBDWBud1Va4pcCn8CPqxlh%2FfgtG8IPaPH8C5wk6%2FnZDv69jurV5QhtwE0x2iqOsj9Mx8B9%2F0EaUdiPfOYYDCi%2Fq9jhWRuupMDEU0%2BCtX0sDFxv07T%2FK5niBPqN9%2BtQjgEc31NGCXFeMcCEuQBIc%2FBK4CO78u7EPYvl3yaEfK3vcb6qP1R2tI7vUjVDDUdKubsSrNjYKY1qBEa2P50SJoaXiksIoLiCwnxS6EBuBde87botNfdEWwYvF%2FR0%2Fu5yCqhGeEOR2ynSeyXjt6ka7neyye8kryBSWE52y%2BRBgogrXPZ8E1yIHoHIFUM%2BAbJhE7lbMtt8ApL%2BxmZW7PwbjAO0fAVoXQOuiSP%2FksIVdFZ0aulsamKUzwPZ%2FNYDMJRBPCxsBqLzqHyneXF6Ej9HlIFo7%2Bpg%2BjUb3unRmGpstGkm6etOuDBGA5wCMefp1gTHcdZlvPBXlOslvYTp1cd8UjYLVd%2FJ5awNrIOKLnIt9MD9qdrKrWCvA6ALm3QV9VrsPm60Q7%2BRHJHP%2B2hqfugo%2FMvI2H%2Fmqr4b9tFnKSRY1Y5Ek80Nm%2FWIhr1ikKnxGz9TWXrokf9xwujfvcOTtNTWnxd0F37Y2W79tteBqZ4G5qLCuomw%2BnSr28QESCRVLTyYKILGJOPfcnaIFOsewhRdvv%2BrWa%2FWih0vlbX6Zb75T5C0qNKVFvH1QL%2FvazSWgC2s6oWXXIuUxQelKiJbowuJDQViatLmLijg9CQBMg8WiPgiw3LEeYRmm5f%2BXdnvkDnxLLjMLxtvX74C3OlwPQqx4xwIdpPx38LrlDphiyWUWHWKAzzxurS%2FxTo%2BP5wGFak62ap1PVFFN4v%2Fy%2BxuR39WnIO7lsWfwgVsK17wxrs9K8ltIKuhkw7f%2F6dhK6gQokFKhWX3urrjk%2FrnI0pgfpGMeuQIUaEM7%2BGF5q2iMkCaMQwxxOzcvU0eXbsnS9XknXvP7Gtw5dwPXlFu2ecvSHEZgNDsU6x%2FGdXBYXyOQjzZReSedeEPY6nEv9gJR4oBQJtFO6Kd0fwC6BO4LNHDeBujB6dSNcUQC9zIv2LnAzGk99bUDrdFY%2B9yGFQtEo0GQPNv6vS2drj4%2B1jHbv3aJSMUWP%2BQTZrmbNTjU8wyG%2FiXNNpskybLcJ3CiTF5Ir%2BJYzmJwE0mSVhlxbtbmvweB3ulB6Til5UuUZydpgiFVeobhU0WaBqpJ198d%2B%2FXeNRTZ9%2F1OPfG7%2B2hwzd5W3D%2BhmyjsRcUg%2F%2BCavb%2B%2BVh2ls3L7zT%2FetOnHNxeerv313vzLVqPai4nJv%2BK1FC6040%2F4udw7sAb3laSg0XCkAAs0npBO6VJabS4Elk%2FU%2BD4gTXW%2Bj0wnrMlqNamq4tMIYB87tE10i0FR3LZNhJsb7%2FR561btmes8YBCRkhYNByRtKd55mqTas9FYhJnbRGHuOh3M4QTdgQSqmgRxuzGdSvZGcbMxNQGk5C3ebLjoXIOFM4l%2BWKHmLTJwRv9E8GWJ6dYvf%2FFmEyEGr%2Bgyrr1p5zrgkz0Cw2j94Hv8Jdx7dIVegBSNtgsqGsRQEYiIBoXwD0LNvQ5d7s5Z00QzwNhqZA0b%2BtMG1tQq5nd84uq8R0zPvX35G8uRaze4jcOHzz0w1%2BQ2BIRvf6J6Kgatnrbiem%2BCFvAxfkrndzD9MFPP1GWTUHclpASUkCNAQkpCCcCgDSUDAhDZ%2BCuEkgn8J7i9nMA7pA4lISappxILKfAeSAbIcSDuN2bJcfZILqeO5rLs0MnngSHYRdrHjmaz7JEsEPw51ZqDJDmUIOZIe34WaQeegNsJn1qz8AIpT3yCjyEih%2FxELkuJ0lEMYTLVCiWpo5oYMleMH6USyYJcD%2BuOe%2BkWKpn1Qns34iyYDjkSLvgnZXcgVQNeqINXr48m3iS7cjm8tedyY0f1QvTnHHdsrKby%2F%2BSSbPY8%2FNH6vpl%2FEsq3Ae4ZU1HC44KFiI9o7CEgab%2FRqHbj7s5KAg06s39ZP%2FzxI%2FmVuF%2FTbTSy%2B3Fb8If9%2Fcv7%2Bwt91yy8RfP1QXtW5RzQn7qIiZyuFM5QfJ5E9uVnqT85TanFx0lkP3ukBAMprvsRyi%2FC8NAJL1xbIIirSvnSj4O5netb4JxmNANHPssHAcHMHsFRgEug816gDBeMbdfiuRcghqYcm0%2BXxx%2F5IAEtN3fqFF3LzAXqwoT0PN0OVTNqxo8sxMkd5Ig6k79Zk7VxxX6gMLOZFQgvpW2RrMW1D0BDihaXQ9wVRoBxPLfpknmkeMtoB%2FqM9cRc9IqmMD2XUmdZ7GSRKPUZvChf8BoykriM2MnKYbOHX8R7cLdNCxSFFVQqoYswnlWtlFS2mNkhswVpZiQW1J%2FUKFfipHGlUkM6UKBhMz1istELIHJLMSctu3ugzfaVSOjKvUgc%2FTHK4Sdg2Wscz69leKIkkrwuuWiOe9yGYKQXRumkC3qbRcMwrvhjNXgdZk3RxAUEhuSPvn3nnd%2B%2BU%2F3vlVOmrJzCD8JLxV1OHRjrZifbcFDOuRNTGqdgQm1tSNJ2OcQ04YiEXuxtII1ECSQRoQGYioEsgCfchB4ghAtw7FfJre4WZ9hkVi9MtjuWqtdNDlpMrfEG9fOT6q21okg%2Be4As38MfGquNt7oUws6Ysarj1%2FefE%2Byst86YUVNvDdts3Pv5c8m%2FaP0C%2Bf8%2FQb%2BIMnGq09BgwN01oIOAnAdagI8mBSrqk1gxTDUBOtk2ousEtBH2z4Ir2d3f6k8PXXVlt2qN9RODxRuoJT%2Fv27wm09jRYVc%2Fe%2B%2Biyx2tyzJb%2Fn3J0htXP87eSsQaf2Ly0s6Zmxela88REy1cf4273mI3iXNJ7KxrZibOm9xm6rl4fqy%2Ft27smU8tOfdW2ucBzg2UfmOIVyLIl3kpYlwphDISTXJXsctmiDtN7fNV6zelgxwnWxsVr83Aj%2FS5ki1jL%2Fa0GC6%2B2L6Um%2BaoddlNFuj%2BbJ8mH%2FiaLh8I0%2FU51NspIEfq0dohwyFXKgm4NggwQ4rRhCOUFtxxo8XnitT4cnGfT93IS8FaT85XE3H5LMY4zIEPL1hw443wz%2B1UmhTJyJGxZzw%2BwsKkKZgUiVtKOKMEb2AKHTv61FNc01PQFwKnvsZ%2F9pPA4RKTASWahmh%2B8MxwzHxKy74IRn5LGRjsPUUwTu64UYNY38caqd7HKucZ%2FtHnODtENw%2F2UfHRMaq1UUPDJQ0OKkWCeet5fYOhII1VRz8%2B%2FElg5j4Gxur3J8o2PJ4rg%2B2d08T%2FfwEzSVbyZ9XPro95T477lRKqUSRXQnauHNsISAl27oWi6Fv9z48JMv8r%2FaMMj8onCP%2FDuDZOuN%2BGPPr%2F%2Bp7bx%2B7JlbYdppcNhzKU%2F1Px5aiaGDn%2Fs1iGMaBcleKUo%2Fv9rcxkZj7DBEKOfrayytXNLYiUdBY%2BpleQXdnscKlQcpzuWluxsieeyuXIK6SdxozitWyGOV3vOHHjguyCQ6fpIYy2JwvrQEF%2FQa9Pdf%2FQqOSqCiE%2FEE1%2FXIVKTc2tzWbHnimrEd%2BVyz311Ml3P0GVTj7PD5aDnsvCvH36alEaPMePcMegXs7x8igTu4B9v7G9vTHvhCu%2FkzIdx%2BBxC0ay9zRSvoS0F2lIxI%2BX7klU63I40gLQ3w5ep5na%2BSFnba3z5D64zv%2BQtM4n4ffG3tq4aNHGRfxgrXPMim%2B5487abL7xhdseIRn1KDl%2B7aINixdv0OD%2BJSPwKf5%2BxoP6aiTeQIDVlIhMcL1H5R9PYXvprs3fv2bO7MOplCmweuiq2JRZ1zz%2B9a%2Fv2PH1Hfz9236w%2BZrPXvWfAxlj4NLLHpq3c%2FPQ3uvmvbrjG7fe%2Bo2y%2FcLdtE6VUlXi0ASb1VLUBVSUWSU4HdvAraTyS8xzM8NxvxFkXV6pUVRiJwcgC5zEeht4rwcp7ki0k41G0qlQhG1Vzlq8alEmnFi58caB5Q9vn988MLhqyVlHvLEWjtQFeupdiocF%2FtkkOGPW2ibWaBTkeZ%2FdvPWazXfOnnvL6jkRXpi85sFzZt%2B55ZptW3bl1cCCHZPD06MhySha7UFzjcjbp8fOecFCirzAG%2FyVjBX6OFIaadSjQq1nNhyIe8tVbaaSdHlXIWKacMeuZA1uxS95zILhyrxAdsXTL6m7kNQlx2P9uZf2qhufePFFbpI6%2FOU0WcP99RrCsrwseVot5mtytpf6Y0gm9sdeyKnPQ7onyK4nXlR%2Frg7H95M1upzu89DH6pgUcikoiihJ6NJKmRxV1x%2BMJiOA3YwhDRQrWU0u%2F0rvq0VYXnyCwsLeTJYBq3dAtJDavuzyoVpzZ99Z0%2Ba0uoiFH%2FxcqgDR7rUFeOrUn6Cywb8ZeNMbhLV5ugP9l0zv9UN5b5mFkjzxUcpPJCn3V402pRxtJd2GrnLdhtVk9ZSZh9W91fCSH5B7ofxPiWL%2Bj3D%2FuwhBRdyAyozeZwvQzs79soi%2BBKSnafLviZCcfrpBpLyimfLfTyJtbyruIQKD01tUwJyKEo%2FybaxkSNFUMdMkhQoJyRBQFhnUkDQSXhTM%2B3NmY0EDM7ffLIjqWEGt8lCO6mLia3PukFnghosJD5p5SIho%2FVDkzQfLE%2BIrYoJXkD19pdP7OwG%2FvoIUtagiWiZ4PAFTHHlTVhRZ7dYmPar%2BNJ%2B8JhmR6DFK5DV1foHoLNO%2FpHrvZfmWZ15RQlwvoVDKhCWNK3CCch9lfFBuAqUgpFSShmNaPj%2Bi5%2B%2BWZfKeViJfW5HnUakVL4UCNVkA4%2BETfIqx4B5xSaP2L1yn0zn2ltPn4%2BOqZGmwwEVCaCSqG53ldtL1oLGAhdMLd09MpCCF6tD6ZnAZBY9hDaYsP0jzZ0j5ZjKsF4i1UmLuhbJMCnYJPt5VwFNvmZawXjEvLJqIH8STonZjq7BZ8gKgR20C9MDFqJAX1H64QW2NEup6qgzLP8cvppL%2FNNTOBTCJABOHeWoXzLhw4Wuy7gaBtjKr9kgKq8ZlRYBS32Lpxc8vIhpNDTfyNXWybMJbn2RyQ5EmWc2QF9wmSZ0KYCE%2BcPuYO6b15Uotj2Kd4MItLS7gtFbkTdrFND6pvEZqv5Yv7jXAus7Pg7avo7KDot50NX3CPkP%2BKps8J9%2F3mGQIteY%2FLGPC%2BL7872SPR2br5fy8MtKBMHedGuM28%2FMZmPJMrGgi3Gb1S%2BSi1%2FL%2FzrZwO9XH1ce%2Fz7ZQ1WSoY%2F%2BpMb5FT4ua0Wm%2BJf%2F298nFmChEQ%2BTi71est4mq9VYI6RsymoRJKYidElT2FGnDTZvqtfhGAFTbeqEw68GqtfmbVa%2F1IFO1%2FjdWr%2F8BDRRtQh9XNjubEm4aWVpVonpTGR7PVGc%2BKJNoBIWF7kYi4gUV3r1U6723i6TxUl3n3%2FtM27aZfKb7THiHW9VzFSwHJ05VfK6Ar7kaB0XgPPE0BSkSFKsBUpaLihEWoA9wBt8qirh2VSOkZwXEwyrxZ5jyt2rJmSo9gX7cg6jsEUGJU9z9xJPOEM3uQQxKgkh35DNATnVyrmJ3mbCNyIB%2Fyox4wH1bg2DwN7q9kov4pFqny8oSm3RQbGgJ1QQTs6ZMLilOVYJ9v6Wha3HcJ9jddsXp9YhGUXLXt%2FqMDnvLpPNTXfNa60z5%2FyjXQOMq%2BlNmwh5egpYrdfZQZV9rI47xlRkuyTjpzsmCBSWNkAXVoK8sgYWqQJWbo1RLo6QH0YW6pxqfCnRgkd%2BRiFjUQUQ7poIaYoakgXxwFd9BuuI38H1xBxXSFb%2FpBDIKQFn7YB3dB36l7sG1FLaKiBdp1KxLvfswap%2F30lnVESgNnvjbUoT6w9N%2BXoio0qcYOIM%2Bheg940YimsucQVvli9NEcft2UZwGQwLuilj1fFr1i3NP94X%2BPE7Hpvtj6lBJfJ4R6NvWiaL6MgzWHxiN66DExa%2BdAdAbMYX6HVF8A%2B7rjEZIXAVbDe7PVI9rmN69JOLV1DOSvRPxWNPZBZf%2FNf%2BNy65BhYxxxV%2B77XJ2wfQ389%2FIQPgajXbwMsuAz%2F0IaQcXJavKbRqR2IqyZruXjVC2%2Bhdee%2F5vdnYOedpmVtR3NGXldxSzDSIiBVpkGb9by89UpEPKrSLZmyFDzMab%2FwXl2CNe7s%2FqCtTvWgG5kpBmCBlSzDS%2Fr8N4uwBwohRW63JTS1y32f0TQsPfXVGEHQrV8%2FNCfiOUVirYcBbIeA2%2BiF68rQIo3B%2FS628vYESr79ehzS7Q9LEL9UXmik9XVHb1yBO3Ngvt5935%2Bk1efkV51mzzrM0LL3%2F20avnwMeKuWyOUZg2TasSqZ%2BKcZQiOn1Iu2Vh497ALUVZiCKt%2Fgh6IvTIj1ZLRjWAkpHKOKovNwp00eqPROiAbiNEKieXwMLcXhVJ1%2FuzmLP4tfxaHR59cBdJVG1kTAgl9ze9QKUEQ946Hkb%2BokJ5JRDyf54Axur1D%2BWS49cLr0tTPEu7UmXrxcSr3XNvumv4yXzInXKH4F7Tc7p17Zt%2Bt%2FqW2%2B93k063X7VW6lALxTY7i1nBXMxcxmzQbabxz%2BtJo%2BwijYaIGMNS8AoSMgAPt84DdHOoMPfjXhF%2BkuH1tZvuFQrRCN07xGcXRX9MYxYchDe5BcHj%2BZ4i%2B42WyPc8Xofi7bbZJN5nJLJ5qr6IqRtzqNlM17SpFsnkEyTWoABEjz4JXOQvzWYuwdnV5LNGOwTM5v9r4RpQ8ZXsYodks3o31JBlzbYtNotisnm22MxiwGFXam5oN1n0TA%2FhRvshvTSDwHff4nNzRo9Dum6PaJbMXzDz%2Bx%2BFkj4L4bFNBb1asqsgH7Dyh4DvbkPtf5yMDKzEwyoaESMSNS9P9gJVA3%2FRTlwoMwZvxECFWxIPNw9gi01nOHjP32esZTtmXHnxvZd8ZtakqQ7ekajbXetpNa6ocTVxJtY%2BuSe69OLz77zh5bDR3xjZMzUz6fxrz1nqrZGcHQHfPVefN%2BfiK86LeXj%2BSc5lPKy%2Bk%2FvCUI%2FDaLFYCWHr6nbXuILTIsb5imNKY%2FrCm28fSMxPhkN1XbNMNZGuqwOBhtTSxWuTk6bw0ZaG86b1hKddePOKuBvmiguYBn4T%2FyOqOyGRBt7bKUI1GjioBC8aUKwF7Q319UgcmtFGIzCJGBqwQij0ynDsfdFGc3TS3BlNfJ25xmzniMkpXXTPvCaD3ZaZvyzjmZdudBostmhb0ORZNN2sJBeed1HXkrUsywueQH%2BL0eCPxmsa5ZpgRJSDZ11yDv%2Bjmbd86vxZfc1WcZJ3UkMq1BOOOVtvu%2F%2BpB%2Ben186d3GTwWAw2jheaJs09%2F%2BLNfZft37DALyrNj1wABMuUKbODyTVnT%2FKYbJ3Tpq8IrNh92dkxOj5P%2FYpZx4%2FycyiVcDYdn4JbEoKdQi9054iBKsygLW46FRGxAb0NPNCm8BSNCPjoKcj6EAus4SuP3rB%2BcV99%2FeTF6294dA8%2BTK6v74MHVpYNRt%2FI30e8QGTOOdfGWzzxcy%2B87a7bLjw37rHw1nPzp0KyyRSeZO%2BQQhInt3dYgvycjrPOv%2BT8s1rptaP84VeywdWX2T4ysr0%2F7TLIs6%2Bx9zib56ye1dM9e%2FXsZmePY3NDs9zlnNVt4%2BWgHJbbz3Livg4P9WWgviOMm4kCRT6I8vw0NbUUEnFvOuFKoxQW1gTsvFirsF5pb7qTUCx4i7VmtToveaDxvK9uOaedVvPRpVOnNz0Q6bry7uiSdQ8t7Vy4JQKVS%2BXPplV2ts4bvCwZu%2BKzgITtxepaPRzWdpv74muvv6RO0SorX6cu%2FdqKn%2FXWnrtp%2FZragz13DUCl5myiFW2Ycvb0PtsXnU%2Btx8pvLFbUspLX68mdegwmOif%2FNPDONajTGoUh6tU56HBJCTBASVvNUB5VIiKpc9kd7kludodSFz7xQbiOmMk5dOYk56gzL6uaf7N8a6MQOHm0ae6snZpFDfuT3%2FjdYzjzwkXXIVHoXNuCfQslQZqBZjTsoHMqrkE4jaYdgkGz2ATOgB3cPkSukD01DnV3ttb1wx%2B6arPqbkcNAHoFPzKUUQ%2BqL0k97pjbZv1I%2FegC9zTFbrrlFpNdmea%2BgIgfWW3wqkcis8ky5FAcRd1If5nNZrl2FFpungc8wpoCl1BpQV%2FScS%2BzjlASyUTVv%2FAJ46gkJI4bHX4lTnloctxPZE1ckS3%2BjG2fKIjkQFyzuo8jvYQG1OrGvJPSTu%2FnSp9PHNTl4z5hK%2F8gtXVKF6gEKiglgcKiRlCESsQCV5QIlKWKpr34lt%2FwkSx%2FJCmP5%2FcBKQfl%2F5gd%2BrOS%2F%2Bp91%2F%2BYCg5CXK2W4M9fu%2B%2F6xxX%2BvnelVuldIDCG0VQTpU9Dw4pRfei%2B6zWx0MLie0gPbyrkmRU7OwT16JGeyXLHqOLqAfVN1GPlBzWtFNzj0TRTCjogtP1NjIvu5habN5Aoa1k66wGpqriVetJgiGdwDZtKhnN0y4n9sXYnsqGmZfDSR15%2B5NLBlhoDaedEm7sxmpqRija6ZEEg2EAnTiAC8IrmFbGz1q08P9PSkjl%2F5bqzYqT9hMmptEXDgTqP3Wiye%2BsD4Wir4jCeoHbbp5hRfpB7BakUIppIlPCD30dR1GtslDz8OsqbXmejFC%2Fv8wu5X2myq7SJ8Avzv9DFUJySf5uNvq4%2BTi7W9D%2FOZrLChdwxmPNiBRqVjnpK%2FaGxRCDspVYKAW9AN1JANoo8wP4BJUlGqdgw6m1qPQ2QW3%2BOfU5%2FieLS%2FNuKpDU3uf8bcAXyBal5jMR2NEAbPAZt0K3hvxHBEDlUxfIGcD%2BN2gNSNx36nfqlAYow0puatNpRz0e4W2oahKzQHsjf2c16ad%2F3t2KTtPobnX6D8C8pd0MDP%2BKx7wnXqGGlLQcvikMErm6TmfsuxJXbSAxqNjOogJLQBLiKEHAE%2BJGTS3JoEhTrz8%2FCB%2B5YlupJ58aOat8Kv4JvregxwcU5Cp8GFAFm1FyOfto6GS2m1NGTS6CPNKkbsTdCBlnN9onMho55BX8IJZtEQ35lk%2BhtwN5A0V3RCPoD%2FyXAcv6pAtbZczRUA64JmcUf4q7Q89ZHLeJVZ5D1Ps%2Ft%2B0iCT3AHVtZC7JDCXfR7OSb%2FXja5H3zQbZL1B%2BULX1BMTEk3AseSpmnKEK4T9ekMIidUCRQFfcbj7z8gNLvzF7mbhQN8h6ZbRset%2BnQWdS%2FZX3k7WpS8P9sfo0iGS64wV516pOhjI6TZ2dApgI5%2BLhxywYoWxKUrykKJsIoDsR4mSrCTg0egMPnLW%2F3Q5Nn8BZEuzqEI7HK3n0%2BzFmuO3TtWQ5WJoG9YqCD6Gc32SxnbnVPfsxvrFXK2dILl7bLthDp6glhcsfp4bYvbSmj%2FmQ94uBTw0E73x2jbNRCvC6VL6GCFDwU7eWQDcC5FY5s0slieRDwtAbRsbLXbaXAuu14e2OJw1dc6jQ3ZdY8v7rv2%2FBWZLqvFWVvvcmwZkK9f5jS4muO9yR5res4kfkRxhV03L1RfPOiPtYi8pd7jNEsOpyTwxpaY%2FyCZu%2FAmd5Or9uS3DYaeqVOhH7gZN%2F8I%2Fwi1fEuLXvyNivibjuKvN%2B1Nc01HF%2F3h%2Bef%2FsOhox8MPd5SFucPjorQwXT%2BytA8EmA5mamHNFDVhBI5pjZbQpugBNkO8MvRub8KVDKST1Wag7D3xlin1ZF7LFP%2F79nbvCXFOY%2BPUjrT7%2FotsPXXZ4exdPzuhZuL5LUXVAn7k7PbhG89uz3b41X01gbjP1xwlu5rrvvf9%2Bpbs6E%2FVu7Nk642%2FPYRaAiUBdrmO6CDTBLPQFA1ur0uXoBR1INDMkypKpoTqnSMx5GiEdTEaSHLs0Alvu%2F19%2F5QW9Rv1U1ridT22i%2B53pzumbs%2BXFFXYC%2B%2BCGsTj5JUT%2FGCgRt3n78i2n71FHG4%2Fu6X%2B%2B9%2Braya7os3ZbDmgWfXun44e%2Bu2NZKuGZ0HiF8M4TlMPR%2BEU6rPKRJ8wOU2RFUFLex3egEsz3YqEAq0cqhAAW19dBZIlVzR61tuIdTnpXH7l%2BuXrbjPUyep%2B8cl6aXKWhPHpDcXl9KiTWDNr4mBQc8Tq%2BNzK%2FOKSbsfl79o9G20R%2BbrBXYvUg0rLHhtrc4TN81TTOWSZ0gL1ZVlOYH2ery%2F7XVUjFMbzYpg7UswcqJPQwBd0LKLabJ8IaCr2otcjSkIrGwootKECaUd4XH1%2BSdazRrfddkBU98t1htvWrbjqSqjaCguxrffM%2F5zDCpBALUycmajhd%2BR6ww4SWafuZ5eU%2BtPid4lgd3gt%2Bb%2FY9rQoZNmiXYPXyRHbRs8zX%2Ff4WIFjWZJtUdSD55AP3xtXH%2BZipC0EqdBGDA4CoYEU6gRLGPU11QhkLTBiEYPiqOeQgwTCl9aok1Qr5pFf71qEeNxjy%2F8F0GoqYPv75Yh9j3x4DuJ%2BuEzHRpAq2lMqb%2BqfTdiq6kGtzfOWsv0c7lSeMXDHBDe1MT%2BLUgx0Pg%2Fp87u2UicdIvqQi8DkxhcUwUXCedMpb4NQjwY3npTmgsURJavLwCRyEcN2HfWsDVGfv%2Fu9ZUWUx%2BPYFueUKwaNvbtu%2BXps3eVWbN1GcgVrdMnWJ7WmJz9SD66EBidag0NF1Ukep0t5A7sFCWdhzvYwHv6L%2FBehXuHqfaBwBEU7hfVLcXvS4VQv%2BT%2FvaSIl7cbeMc7ekv9i8S3e1L5xxpvMGcu1EYPbKyCiijjGXcDKckm43PqU2qNWlXusZMiqF82cuVzolUHN9NNR0HZPxFPV9V0wLtvq%2Bk4DqOwVWDlzuQLVdqFiP08cRX7aRlBVfR8cb55bWe5LExnlcsDp1vAP8Q9BucPMk1Ulh4GnN0SAdxcNHv3q9ohx1Ati4S%2FtkWjIDe3hQdkUGrGRaFBiUdiTSkI41UkMuuQHP%2BEaSQYlPQTFWJF03BNPpTu5KFAdkWgDukzsZKMG0Q1TAQQglScOaP%2FdsZ8%2BfP75D%2F9Uu5Gs3FY%2F2SxPld0DHOciXI9gqjcEidXjE%2B3BLosy0OcX3T7O5g65ROGyzQ2BZs7WbZVnO5ydLe32hMwTQ4wnnKXW6XW5LAa7oaXOIHoUl0FgLQLH2by8wSTWeAx2Y5PDazK3BqZbeJZwXGPaYhX87ZNszoDdaRxotXO1nNlpdvAPFWHDm8PqEE0sZxDEqGzxisFNnuCWetPcGrObN0p23tTZwMuRVodSV8%2BLTrOV3eRvzjQZiSjaLYS1WEJe0kNsJlZu9LFun7%2B%2BwW4gRDRbaxw2nrOGm%2BxOj9cmtbp9ZqeTM1m8UXfQQCSTVSQox6pvtjot%2FFpHvIUjJovFEoYvHYV9C5Y%2FxN9OfcalvII37UEhTbTg%2FAQIaPb4Vz6j5u8%2FaViycMod%2FfkDcpu8QZbZoeBi%2FvbzP3XPsZvOubMtaPHkD9jt6%2BU2O7vqU%2F9C9SMvgrXpQNG%2FE0oJxun%2BCiElUa0IKQSUwERxOntKSV7ekcuh9VBZBBo3VUcB58ofKBHCwLyf9qFosz9Ibf8dGqwaBMjRig4SGOZ2UkWI7UiO9OfUPdxOYFApUZyfpY7mgEc5rtNGGk2H1lPhAk1Hp%2FVAMqQEHEUfEYkkUQq1JMdzsX7kklRrTrUi1wMcDjmu1YYfATj7Y%2BpGpPEBXuoQIj8rR9mgCl4C9yqmF7xnVWxGVniNqtpVmXBvQ6iwni5YQ8a1jYrXtc2J13HvgkvqWxuva1sbr%2BP2S5ceKGyBwDv2DbrToe1u6BkAJV7xnVLUaq0sJB8pFqcUIPi3yuwxi4JuLr%2BP30f3OkPQ72aO0xYo3%2FEsmO3QO5qEF8S0qQH0UsKXv0brnl9%2B8M7jF174%2BDsfvPOl1au%2FRL5%2F9DsbNnwHL2pHR1NTRxMZhJtHktOOxLxErPF6YlLvpC9YP73x%2B4ofw%2B3xVdrHcDE0dQQCmCRgvt9b35xINDf1CDcRSfJ%2BpYl%2BSf8YcurfmXP5F%2Fkj6J82jNsrkWiEuhVlgFfyNkB3S5MUzLhoNiwSCYcxQ7Ui4J0Xh7fmqRbaPa1tzujxkBRlsEHy0%2FOM4pYLPb7g9O6BQJN6l9zQ0OGyCaZz0vMTbHOzXfQ7a2tsterTcqxeInODoemdktw%2B1SbVhKwtW9ffe8VKadK0OVuC3bWzyKm5LeddsWTeorWyY9IMtUFutdu5g%2BRn533qkocdvLs2HmhU75br%2FMmWtD8zA3OP2t1ea636jEzqYxJZGAwFiDEd61oTsrRuW3%2F3pYNi3bS%2BRd%2BGjOfVpAPNd6y64Gsz1GaZleWIPoYL%2Fv9mTeQBENVEguiF1aC4YeXxFETw6QyPfn0m9g8IrMFAvKM1EI11DARnbqibHk%2FIojy5rSdgCyZi06y8sS024PeuO4MfwQ5Y9yKRZCqyYaF30vzeHlmUprR21tR0t0yz8KZY66zWuGvxVQB%2F36kP%2BK38t2Hu6NQ9SFJfw0AdpqPEK2qTMpf2VCqJwqPoJezTL824b8akoL%2Bx03nhh%2BoNo5e77psxg9Q5LzebIKD%2BfsY34f2MtB9fk9v5b8PT6tYrgv4kRPwd0q9z3gdJSJ0653KjCYPwCaR5aUY63eW48O%2Fkdo33yxX9wCiMv2QTrk8eGSI6Ag6moG9t2P%2FF7GRNlDjl0gw7pJ5aOXXqyqn8SENnXBmbSwUYLyqJjv3UmY1nKr4t80no0faXsaIEiF%2FBRaIBnItSce4OUif7W6Vm9T9H1X9Vj71BEm%2BRdmIJQST%2FZfVdudUvh9S%2FqqNvqT98g9SQ3lHibZY0mRVHooyDN%2FFHmTgzjdozKw28NwQ0hwN6BCoPKaEk3YtKwNhwRLXuk076CGoZNXDQcRwZvreTZY9EZi%2Bd0s4%2Bztv8iei04JQl6ZbDD2eHV7X4uHuFVfPrOmcs6m6Kr7hssr%2B1VZFcEZ%2FPdJkn1hOs8SXS%2FNFFgqt94PIZzZ3tdaL6Q5vo6piSzdy737pwsX1VyxUrF15iJ4uNkq%2Brbyg1Z%2BO8VsNC1UmcvORPRfxtPrfRwL2p%2FoA1eZp6Z%2FaGffoewaXcA%2FxBlKlQLfhQL%2FoPgBGP3qsA7IQS8qDVNswHKRSheDUvA3Q7MZoRcJMxlEygujn1QdyzfPfq3dEp%2FbXh5e5YXW2Ngfvza0ZF6UgFL%2FE0fTq4LBlvTE2qb%2FKuuzYSXVnjTfM1osvqMHVbm9950quIZlbqaL6YP7jk3kUtA0GnX2nvq53f3WoSsvEdDRnULgo2fN7lNZJgI8%2FVWi33c3bBZnGY05%2Bdm%2B3qc7fNmj4YGKLj2nfqFP%2Bg7jdDlxEV5XsJQZP6hYrS1l0VQr4c69Xueixp90gnZPmE5OF22j%2BSYEWHlZ0K%2FHgsh%2FZtsbh6h2DNRlvv6jJh9XaJaHCZDiUDKNTMkvb8vsqCyf3ZNdSmO0fa0Y4baJTtpbKzuVzeeSI7fCKr2Z0WypapnXJ4gnoWy3PoUIlIQ1TXdqhQJIXp9Wx5fYdpeWh2TY5D%2BYVyKd0jw3iumwi%2FBC3cEy4o83QlZnW79MrCgCjbhWXBlRZVVZZv4rIKpXC01HFlHdHLoeWVl6UVc%2FJ5uGm6CViW5mulYMk%2BHqNYr0AyUPivLg2oMs2MPqtuhHyRyiwvNJej1Br%2BfcLyoAyu8D9B7bgmzUqfFobF5nKnK4%2Bt8MPJkI%2FxHUNWk117jugWF%2BxazTAALQn6%2BUE9lhoI5ApGA%2FiuJOsrlNP28SVVuBVajXmircLel46w2bJS1Q0Ft0KDuikDFL%2F3pYrid1Q4FvofwRIo4R9h2ftSwc6jHAMqLcCql8YPHtlzGoByNXYN6v8hXnRaOhUvx0sVLCexwupGDR4NOYC7PePa5keIPACnuAdD7dEadRuTIiS6Lb7uskb381My5yjzF8lGCjBRqdwrWJCagfB3yCy7XT1i92hbcZ5Ci1FJkgYMDf6n%2BjspIsHFjJrTOdzSMuOa9DbDcj%2FnH9N9bIoGVgzHPWIQuFuYtaMRaq8eCKI0gEF6lPOZjBz3EEvaaxwSUT9U%2F8JbJZPJJLBLolH1La%2FRbF9AbC8JJjv%2FmMnssKjLRBJyqj9QXxNko0Ux%2FX79epfiXkm6fmKwF%2Fen1HLc6LxloXWKvGa5rVCVL83VuiPcDEX%2FK5pTXOxHfx6HHB0t2FI0qI2rCZFTrvPWU67zVuS%2FkTsLnc7IKhFg30e4FOkqNSfH5PtkmUy6Cpiv%2F36k2sbqCeCFNa%2BURpoY0sZoYmCgCr3qgZz6s8I0gP1bYiR%2BD79H56NOz0EVWCTy2%2FfffvSCCx59W7uRV9995eqrX8GLesOXNm360iZ%2BT%2FEl3uZqL%2BFyzSZ8XxpTiI%2FG0nkT4zznFZ0t4ipMz5v4q9ssqbdKUZt6u82knPCrt6PZwsnn0XySVnyPR1ZXAn72yx48bWJsu7apnI3Hy8bygUK5Js32qcytapqgmn95uexccj205vGgJ%2BeuOeG2SORmKZr%2FqKzcx9SFctMJdwMUFZDJITs7dnOp1EKZCxg304Cevyfya%2BvlKqv6aXK1qIj3imL%2BL6hL%2ByvUlFfE0VKZ7E8gBY3M%2F8VoJCFgizH1W6VyC76nH6b7jiibYVxUmVIEspry%2FLgZIlCeP11Z4zs%2FAwvVwtGFEut5S1JY4lfyT0N%2FevOLo%2BrUEgjcqc9IkGpQbv3iW7Co5b%2BKgjvpzYdH85PLcc4X21ouwEGl%2FS4qnUAvoSlXUUhR1eKr2VWFTB%2BGMl6FsiQsVD1R3urlAAIoSn7JQkmiVVCHSpCwDH%2FqPepXQ0Db77CJOAImohB%2BRPWr31ev5g%2FkE%2BzTa4lbvZo8xdWPffQu9yJTPCNB66s%2BzXoJt%2F0L6hSoCuBIoK8fnBGG87OoRckJpLqyWe4YbpGi50g0%2B3I3UD85Oa0fzubfoXxPLbW3FDWzigmyJeM0tQkax7PqTy80%2BUxfUHPlBZIRVNQ%2Bv0xRm8REKPoLmNr0%2BUo48v9GFbXPKylqQ2IKm00QddgyWGMROCTxdLB9nCY8P7j2DjlsV%2F%2Bmfr0C0r%2FNkeXbbpPlOTBBwT0mVz1zx9S%2FwJecBF9Wgv3p032iP2v4VSgfgW2G%2BHUEdEXU6iq4CtpLJfIN9XQG8dwa1VoO8XC2SrPDDyCOQptXgbcPvlAgBfxBoGwftQKeKFrNTASPt3pGGqDt%2FQRasn2kri%2BH6L80MJRsmVYJrAKyDItpJUy3%2F15WYIJqcJ9Q5N%2FLFJ4c3dc1URpWl9hW6mu50MUIelg4ucTPf15zs5DFo1c0VSp1tKB9jkwIyuM45kb%2BIP8gHed%2B6jO3v0KbIknzLy636E8KPTdCuUpB0wLo9JKnAO6pv0vS31EtBha%2FfJemkgLVVnd8KCk4qBTpQ5m7FbifBKrPJcq0pZAFVG%2FXbOFz%2BTcq2MLrcmV28Nmi%2FOHskh82bau0k8eWCaPijQPWQ5lUvslwVCfHkXBMIehqUgtDNLeauH1huvZTbYmw%2BluPjyWoNGEuxRLR7LK5fSyXFUyK7PURQv2v8D3XOt2NJ6liBbmPGOsakw1kbeOs%2B31Wm5qpH%2BiJWSzqdPr2O7zc2TmtnrzCig6bBd%2FvgQmzOlz0STWIlmZEQfupogOZFHUZ7EkUnMn0RrpIMqAgHRJAOjIJ3yGw1I%2FMAp9q9S3Q%2FclADNm1wEeO%2Bxbwg5OIYHZLY3ehG5lJk2xhco%2B6JWybpEVz2wrR6hZyD0QXZbeDVB%2BonmlimpkWprdAs4WEZDSQppsDlcdCBJJESIYFuAtUnC4GIF2C3Uu2Kv7L1bdz6FxtqxpG4TqQOqOUNAJ2HLvPWA2GgDy4O4vaDrtyl6P%2B1fAll%2BSyFcQ28GHqh7fvvf37udylf0fNwhzgz87Y%2Bcf5x9GnF6ygHu18sAbipWeF0YPBgp2GaKeQduxxdEr3SgbH1kvH7tvqSLhedomOvZyts2dw8acu3dY%2Ff%2BucuMtCuP%2Fe4zC4XnH3OLZ8ZuxTWxy8dJfU5dhDeKPSlJy5pn%2F%2B7u3XrJhmr9C5CuleGflGQocKnlAUaRKp0BAHV0ZwUt9VCqk6zYOgRIuMfePJzdmBdpPJ7%2F6B23%2Bf%2Bsp9NMDZevovvfYHG5dGPISQq1DojqNckchVrCcCYz%2FQ0hI0m3NKDRfkgsrnamo%2Bp0CAq1FyvC3a3Nak%2Fs5VX282x9Ufy3E39VAx6o7LpCvO2wK%2Bch9jNqpJCutcIOooKnYWtDK8gTRVYygRQfwgzKM5%2BjP2jOZdx3r32Py7rQUPOzAnoRs95NvRAR0qLGU11Taqu1bUYSzMcWjMEir067JQQHfIrLBHsrgv00%2FWavd8HRLMEEYFSW3HCSNQehnrHztKqHcDyo4VfZ6gPKCR%2BgufwA8GegxUEo4A%2Bgd0BASHiH6jYMLIsUdQJTs%2FC641KN4oCHWolCMLlMfIdtWKScjx7SM5LD9HnfmhrGI0S139UWfUnxgOXdJFW%2BAMcGjKr6eHAttHF5sUoeArYKDcxMSYcKA%2FxUDhPiEOEAPafSIUFArN0r24ynI91EPARDXvIDYyvqZaWeroBOUABQA%2FE%2BDXC7PWafDLQY2oiwpUEyj4RQtVlUp1GrM7In2p2A7VuiOW6otMiGOo5Mrp05ejVuTy6dNX%2Fk%2F7mybZQ0nUmfrbx3U4KueDnlHm5wdh8FFeKnoaKKh%2FTK18StOPhwG9Xo5mqXAxvw%2F79YQwwDR%2BnAKQQ4izVXioB84qcppWB7IqjU45z4CE17OvF1Dw%2BoTFqxtz8dxwtogBnF9MjIl%2Fin%2BK8s3hM9laIn0TiCbTAXL0T798bPXqx36p3chrv0O%2BGC9Xaj48Ecv8U8UEeBvUEsDlTepiU5OvlpeNGvpnKF0RvUooWhIjnx6GeBapXCQYTw9DNg6%2FOC3gZjp76oNTj9Kz6Jqobxb9NDqc08vcKReOpcsQV2K8InXFaXW3aI6Ofr1k48rp7CX7rx%2Bv1UKPsfvzQU0Kc83i2VdILmd2%2FyX55zT9luN2%2BCu4nKfwPcK%2FCvDVU%2BpHh8%2BLaldIf1fA5h3ndT6Fln9%2FW%2F9Ce1vndfvJtnPVO2xhm3qbafHVCN1X363UXHq9xuVD8OSD29Z8pZ5cZrern9cAdGW%2Fuib%2Fud%2BVK0L9a42r6C90kL8KzxwLQw9NkIQJL0ASU8M%2BVG0KsUdgdvpgP%2F6NqqP0%2FgHZFUfGEijZLHpiIgvV5%2FBltrj8Qd7XQd5p4P%2B7tJo30NMO6VGBwahSPMYiaaBYoLY6uEnciyhhh1Z%2FvvacG%2Frjpsvnpzs0B1Id6fmX8119l88XnOxe%2FuGrzzHcdu7UtY3%2B2vmXN5zUyj3ZcPl8p1sZSs6%2FnGXtwrV7Ka0XZdz83fwjjINpZWYw85lL8BRK4nGyIir2RiOsEyipuEcIakpGjWgBjLiHWOgj0Yi34gW1kKPxHt2Na5q%2Blwg1RdRSpFDNzosb44YJXnAfoEOpZW%2F%2F6u1lhYA6leevezbI26zNHO811M2dc5HFxpk4i1jPC0s21%2FBWW5DnPQbn2X1WK43%2FaM2n18DfSoybbNHijFpamzXI31eRibGUOxSu%2FlT96YZlq1Yt20DaSBuG6knw2eusHs5EPBfNmVvHKdaQzcDfz9ZsXmLDWGXy2U5OsYSsIn8CS12jQIyD12KKqZrLPy7mSPdICmd6WGHG8NDZkkHuE4h9TU8FpmUO%2FVjC%2FEinToFyoNDz2p9XD6g78WgQdPG7Z3R0T%2FZ5dTM9lsL8Ktek7szl2L%2BgQwGgwkZHc2g5Su7NvVqwGy2Ua4KSXUwt1X4PaM5paaEu6jQ5zVFyNabxvUksVt2T%2F4VeamYPlLtffdQsk%2B2sUTY%2FzDXl%2F05W53%2FBz9UK3p7LjapZ2ZxOm%2BUlZXrL3HHGqO8%2BwVroDaCTTnTxitMxmiAAYQzVJQH%2Bnj3oIHnPaN6Zq6sNSLjBl8tKgVr2mj%2F9CWi9dnKca8rBQBsd5R1tzVlgrl5pbnPw6kZclCr2CHxMnHohLz%2B3KRQokzALyeIKFU1TNCiayJdoHvDYe7K6mZLm8S3uJ9dojuaJ62%2FqN%2FtjQxnSnhnKPw%2BLNrLi8ZKyJ3x1YhiI1aNAtP6NzCGzYv3DmaGh%2FLvQZnt0evgIhTFV0kE%2FPYxAnOHhCQUZdCWY5JWJwMzlAGl1mpNbDU7yyGnhRMILsYhH3VRAijrPcBU8%2FCj1Y9NY6cnGVW0CjTLaz7E3epvaT%2FLtTV72Rs%2B0WVVmd0dz%2FMGTI5F0OsIviaqDlbbO5X6xT3PeXbXHRtf%2Fz%2Bfdka%2BeKPr8KF7IF4vBsT9MFPuPJMBTBMq9hQxXelQ%2Bbewnf18ap4Ib%2BmSMrtDU5zqlD8QANa5MBGh%2FOwOvSDfcV2d66mfEWsbGWmIz6nsyZDWQSmqmxDneYyvjHPmRXHZxeueyRGLZzvRioKnGto9nIPkibAJA16adcOZRQr1iAP3bUyBR7T4RgAWTKxhkCYFwshq%2B7iV9r0whk50cmRcTg4fy5x4OmmNkHndIA2%2BYuMbmE9dwGYB4KFTsvnDE6Ah47r%2FfE3AYI%2BoXADpkdlENcZ8OZEEf8FFGZNxMs6ZLpG3SUFLL7Q2kcFU%2FA%2FJsw%2BvWDa%2F7emewLaoeibaF1B9qUNnuqWK3%2BUfXYVL1v%2FomD15xxeDkPnXTOKSVcCbDGtOu0YQNpGAP7U1HU58UrqGu8xIbHtkQ3LVhb7Dx46ET3Ffcm1q0YcOizNmf3bC3VjWfAcpSv3MyTlgJ23FHQgmgvk%2Bgk8pL0mcCDOn08MDAQlf%2B%2FSlTZ1z12fnqntOhbOTL9%2FZdevbAPN%2Byby1f%2FuUtC%2Fixm8ZBo59LTXEW060hGrTDplNprWd58fwB%2Fb%2FE27BdS%2Fs7U%2BrGVCeQ46nzaw9QccnmZerGZZs3Yw9aVHt%2BKh6HN4ti6lxIhT%2FwahnZtWwzlY9QHQ2c79C%2BdxzvVDKy8GqKWQERO9YAKbpsDUTLdWV5dE8PVPjvj9pqw7ah%2FPFVtkit7aj6G5xY9mfJrCz1j1e0BcnPol4UjtrCdbahIVtd2HaURujnFJR8CuOuUUfhrGhgKKgjCYNSvCc1WKlEp8wHUaAYynFNyzZn%2B2MnYv36dbMDBTonl%2FT%2Fma5IKAyEGz%2B4eRnVtaX6tss2o34u8mWorFtuFgm4A6qK%2Fyp%2FgLEBVat5WnPDdKA574ubuFJ%2FIUfZ%2FY2Nt6mN%2BZNNTSTaeI56gKwkXerTe9DDHUw8%2FH35FY3nNN7GGuBKWhrV9ep%2B0k1WjNWVaHkW1yA%2BQHWNu8rtBw2a5YXuE40rs7%2FGA%2Bj09V3hA98yRnFPOGr8ltGlsFdD%2F7tRce3LH6Trcneuiy7K7J3khKu%2B3qUaXPWaX7T6%2FKfj9BX2eZq2XAcZT79u1ClJzUtHUqfqSMWBcZS43Ena0cUGLgpkKxB1QM%2B0Fxz10wgg6r5rltnFpH05pepUq3Y2HfYqeKRntmUFNz%2BXmcOs1H31U6cC6RTVLfCg7RNBF1UF2%2FwBgu0fFQtPEU1sSg3VcNsR7dWq3af87tUFn1l3ltXpaJxpNvtcZkH2WmMst3JqRpxUH%2BWC0E1qOGtP66s1MYv%2BVLu8%2FXFXvV%2FZbunYYBeVN64ls0ur6NzpV9xzlmQwB5qC4Tq70WC0tk8dWJXeHvkD0h9zJOM0vD86%2F1NJMaIAolctvlByferCsqOKDKceOfUu1PsmoFCamV5mCrMUOCi6V6FJosMF22AcrKJgQDVhfYh6tepp%2FlYgvnCEAbJQ1L0rOpajEmRcasMiPfxhgGoVo4rwreQpV6fUJHH2e8fa1s2c13Apl1b89a58ozdoap2sjgLN9uISl7P1DrulyeIkt0zr6JjWocoPOZsaXPb6jtqBblsgsaRre2xHi4nELm0MhG1%2Bx1SXwLpFi53b%2BaHRYo%2FIrbZtuWAKu5cSEXfybnnmUCaXGTpQr0xK2O2WWY76f%2BnAjNVf7nCZHU5XqIkTnpt6VtvsFlPXg1031g%2FVRdpkkyVpD7jnmax88QwDvg%2F66NnMRdRXTcGTmQc3cuINwN5IQqi0yzb%2BYFVHuVqI5s4ADfg5oE4ybDLd28mFSFmYvRoomsWXEdLU2Wl3GJy93ZNb%2Fd5gqmNaqJZSO1l6PVRy0nZIj%2F45EetjLguh1rLqR%2BSK0hO6NrsqcNX8zoUdjQYDJ7tb4os6%2Bi%2BY0qpY2AWlnLRDWdGFTfGY1gV0zNAtJ7pdo24se0D88AwLY%2FgZmE9iuP4V5v7CSR%2FRThaHLh%2BUeBkXwU6BC7lGOevK65udTv%2BtS%2FPfW7qj3ljTcj3b9OkbV85t8xsMj7Ddj7DGpthZKwKPvso%2Fc%2F1K9aLE12fMWLV1y1D9ua8lyJdWXr%2FbG%2BnoCFutf%2FmLILe39ITUV4igr3876fpX5g2zeB52sWnIL4fXHlgeUzOx5QfIvJQyrKQE9wHUqVq%2BPEaOrz0wVvNbJZVSfsuMzxN4l9PkedFzw9V5Dj%2BnzpgoT4ZxCxJfC5RWLc74YVHxKlExCYt0JAOMatREhHBSCAtSfod6x6Ls8HCWECLwXZ9nd5Dz1T24JUdWs6fU3%2B%2BfcnT49Qe%2BkBs%2BwdsMZgPXMp3U5S958snPP%2FEE7bvkOPCuTUDTUQ%2FUzirLhML9yPahoe1D5Fj5jWsaoveyP00PehdUAHk%2FseDVWsvDWXXXsyn%2F4wfpXc2V3%2FQxli3jl%2F5hj%2F83avSCfpTNxOEKLmTjxOEKuxgNlsQn0xgct724mhynupNW1Ph6o3RYS3%2F%2B2TJrzLlkFz%2Bip3qCHKf6eqW02QJLjBYuuj4sobhCWqa%2FYHGEHpcnumuWSOhxeaL7sOakNR6vvmo%2BYcfFA8UFXEPZf9UjyudIOyNwx%2Fi90DdsujS%2FFX2UAwvWSVK4NxaMhAGw3oowp%2Fuc8CTi7D2rBgZWwb%2F60faR7SPsEbjkXy4G0XaqhXPwe2cePjxjxuHD6ssQuR1fq6PF0E%2Bo2t1nePTn8TUmxz%2FA3crMoCc7egESuoTHYc7mYdg6etORoOhR7BBGD%2BqJopELrl4S6cJNRtEAsLP%2FOdvnJq0Wo0GolY2Et9VFB2Kf%2B4bZvVyxfOMz3WdFfSIryj6DwWghre7aQbdiDrkTL3A3vNDuDpk93HqXwam%2BbWmUJZfNn5ozKV5Pmmq8PF%2FjVY%2B2Tlk2M2RzSXKjmbQ4RZcQavEYrN%2F9rlXwtIQqzxQNMzPPfHYLvuPoO9TbT8bpGw5CQPGd%2BSyX%2FCyf0Vxjd2R9NmsunnXYa8xGHzn%2BsSfM5J0y0DZEXWWxkXjcR75KBLNLHi7XvX2G8VOrf4Ykg0AMdBESIpo7MgAfyakA6rkqpI6UjNs0px7cMV%2BD5BF49Tez1VGnYmq0WIijp985m4Sn2gJR9b07riPPFo97OYbUZbxJCpot7H%2FlpZBicglCPN7WOfJkcHqc3ElWqvvz%2F1E6bIQrG%2Btz6WkM1SM9FBTR7FSs8KyBBytSmNEoquJNFN5EQyTiCrnKDx1h58yxCepPHU5nxGoxEQeeOZi2m80DxNxncVhr6BmEfUarxejw%2BWSiHhWk19bSY7aKR5MsteblJpfTLtjimBouXsm3d3djjYM%2BwEW0El9dM%2FueVRWIsXwe43R7SgbVZqrnqoJ1X%2FkuF7pcgf8duv4q6vayV5U9zMV91GxO59UUjW8rHV6u799WzKMT7umRCXbYUKM%2BfoaCcwgaoqZUtmodV3p%2BX7akb4dnU9B9La38RPFUG2SCC90tVA4XwEFhyOpZZrUCsgWYHsczLFBBVGNtstoN1bw0Z%2BO4fYIbvZVt4EUcJEKOhHeincWqONw%2Bq6w5Go%2BWGOSR7LhKV%2BKBqbBPpfUvOf9QqkpDyVhBeyyZQGMsdA5FBUqvFMtUyGq9vjnsAJU4UcrxldP1CCaofyDkSAifoP5QwWx%2BSyUGxp75BzGAvtG7uQ38LehlyEQMeh0TeE6Bm7tYdXqdkt0uOb3kfYlNwmOdDyacOq%2FqlFo1v%2BPTmTi3E%2FglC9W11b34A22zmLzvb231Q0L2Bgg60OTW4YdstO%2BYOJnO38TtpH7zy9ymokWyA79qlVSn38HtpFlImFnhu3b4boNWXklOXV0Iwo7lQ1hrZyPFcwtjwFP7iEKSHSSJw509kh8kj6pr%2BH1jR7km9vcvqN9657vffefkv%2BfKxge1X%2B7RdjYUPIESN7gTvRkB%2FRMYtEkaVkdHApmdBPpnKmz0n1xSWFOyVIuLrinZwpoCRe6kyiVZoHX088F%2BUX4%2BWKS4iBTP0IWxGtZgOdMaV4KTayqHQF%2FVihBwTbgDXTCmKoOBJeNhwJMzEVjtjIFLuU38fPR7hqNG1JS7g%2FqRCuy3vmQ3W9Vu8qbVbP%2BSzazGRJH83MzP90Ck2m31mMjP8TiLn5uwD2Ugr2PFvPQjB5BnSJvQxGQZZEB%2BLopqzGzDbMmbkAPkZVJjeO5FzOSBKCgJze2ZS4Gemc9twrwY6u9H61iUQTcRvtdT9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%2FTAF4EewSUABgAAAETNjIXEyEyFgcFExYGJyUFBiY3EyUmNjMBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAyABYRMT%2Fp8RDPn%2BbxQLDPb3DAsUAZD7DBEAAv%2FTAF4EewSUABgAIgAAARM2MhcTITIWBwUTFgYnJQUGJjcTJSY2MwUjFwc3Fyc3IycBl4MHFQeBAaUVBhH%2BqoIHDxH%2Bqf6qEQ8Hgv6lEQYUAfPwxUrBw0rA6k4DIAFhExP%2BnxEM%2Bf5vFAsM9vcMCxQBkPsMEWSO4ouM5YzTAAABAAAAAASwBLAAJgAAATIWHQEUBiMVFBYXBR4BHQEUBiMhIiY9ATQ2NyU%2BAT0BIiY9ATQ2Alh8sD4mDAkBZgkMDwr7ggoPDAkBZgkMJj6wBLCwfPouaEsKFwbmBRcKXQoPDwpdChcF5gYXCktoLvp8sAAAAA0AAAAABLAETAAPABMAIwAnACsALwAzADcARwBLAE8AUwBXAAATITIWFREUBiMhIiY1ETQ2FxUzNSkBIgYVERQWMyEyNjURNCYzFTM1BRUzNSEVMzUFFTM1IRUzNQchIgYVERQWMyEyNjURNCYFFTM1IRUzNQUVMzUhFTM1GQR%2BCg8PCvuCCg8PVWQCo%2F3aCg8PCgImCg8Pc2T8GGQDIGT8GGQDIGTh%2FdoKDw8KAiYKDw%2F872QDIGT8GGQDIGQETA8K%2B%2BYKDw8KBBoKD2RkZA8K%2FqIKDw8KAV4KD2RkyGRkZGTIZGRkZGQPCv6iCg8PCgFeCg9kZGRkZMhkZGRkAAAEAAAAAARMBEwADwAfAC8APwAAEyEyFhURFAYjISImNRE0NikBMhYVERQGIyEiJjURNDYBITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NjIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR39vQGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHQRMHRX%2BcBUdHRUBkBUdHRX%2BcBUdHRUBkBUd%2FagdFf5wFR0dFQGQFR0dFf5wFR0dFQGQFR0AAAkAAAAABEwETAAPAB8ALwA%2FAE8AXwBvAH8AjwAAEzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2ATMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYhMzIWHQEUBisBIiY9ATQ2MsgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR389cgVHR0VyBUdHQGlyBUdHRXIFR0dAaXIFR0dFcgVHR0ETB0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHf5wHRXIFR0dFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHR0VyBUdHRXIFR0ABgAAAAAEsARMAA8AHwAvAD8ATwBfAAATMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYyyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHf6FyBUdHRXIFR0dAaUCvBUdHRX9RBUdHQRMHRXIFR0dFcgVHR0VyBUdHRXIFR3%2BcB0VyBUdHRXIFR0dFcgVHR0VyBUd%2FnAdFcgVHR0VyBUdHRXIFR0dFcgVHQAAAAABACYALAToBCAAFwAACQE2Mh8BFhQHAQYiJwEmND8BNjIfARYyAdECOwgUB7EICPzxBxUH%2FoAICLEHFAirBxYB3QI7CAixBxQI%2FPAICAGACBQHsQgIqwcAAQBuAG4EQgRCACMAAAEXFhQHCQEWFA8BBiInCQEGIi8BJjQ3CQEmND8BNjIXCQE2MgOIsggI%2FvUBCwgIsggVB%2F70%2FvQHFQiyCAgBC%2F71CAiyCBUHAQwBDAcVBDuzCBUH%2FvT%2B9AcVCLIICAEL%2FvUICLIIFQcBDAEMBxUIsggI%2FvUBDAcAAwAX%2F%2BsExQSZABkAJQBJAAAAMh4CFRQHARYUDwEGIicBBiMiLgI0PgEEIg4BFB4BMj4BNCYFMzIWHQEzMhYdARQGKwEVFAYrASImPQEjIiY9ATQ2OwE1NDYBmcSzgk1OASwICG0HFQj%2B1HeOYrSBTU2BAW%2BzmFhYmLOZWFj%2BvJYKD0sKDw8KSw8KlgoPSwoPDwpLDwSZTYKzYo15%2FtUIFQhsCAgBK01NgbTEs4JNWJmzmFhYmLOZIw8KSw8KlgoPSwoPDwpLDwqWCg9LCg8AAAMAF%2F%2FrBMUEmQAZACUANQAAADIeAhUUBwEWFA8BBiInAQYjIi4CND4BBCIOARQeATI%2BATQmBSEyFh0BFAYjISImPQE0NgGZxLOCTU4BLAgIbQcVCP7Ud45itIFNTYEBb7OYWFiYs5lYWP5YAV4KDw8K%2FqIKDw8EmU2Cs2KNef7VCBUIbAgIAStNTYG0xLOCTViZs5hYWJizmYcPCpYKDw8KlgoPAAAAAAIAFwAXBJkEsAAPAC0AAAEzMhYVERQGKwEiJjURNDYFNRYSFRQOAiIuAjU0EjcVDgEVFB4BMj4BNTQmAiZkFR0dFWQVHR0BD6fSW5vW6tabW9KnZ3xyxejFcnwEsB0V%2FnAVHR0VAZAVHeGmPv7ZuHXWm1tbm9Z1uAEnPqY3yHh0xXJyxXR4yAAEAGQAAASwBLAADwAfAC8APwAAATMyFhURFAYrASImNRE0NgEzMhYVERQGKwEiJjURNDYBMzIWFREUBisBIiY1ETQ2BTMyFh0BFAYrASImPQE0NgQBlgoPDwqWCg8P%2Ft6WCg8PCpYKDw%2F%2B3pYKDw8KlgoPD%2F7elgoPDwqWCg8PBLAPCvuCCg8PCgR%2BCg%2F%2BcA8K%2FRIKDw8KAu4KD%2F7UDwr%2BPgoPDwoBwgoPyA8K%2BgoPDwr6Cg8AAAAAAgAaABsElgSWAEcATwAAATIfAhYfATcWFwcXFh8CFhUUDwIGDwEXBgcnBwYPAgYjIi8CJi8BByYnNycmLwImNTQ%2FAjY%2FASc2Nxc3Nj8CNhIiBhQWMjY0AlghKSYFMS0Fhj0rUAMZDgGYBQWYAQ8YA1AwOIYFLDIFJisfISkmBTEtBYY8LFADGQ0ClwYGlwINGQNQLzqFBS0xBSYreLJ%2BfrJ%2BBJYFmAEOGQJQMDmGBSwxBiYrHiIoJgYxLAWGPSxRAxkOApcFBZcCDhkDUTA5hgUtMAYmKiAhKCYGMC0Fhj0sUAIZDgGYBf6ZfrF%2BfrEABwBkAAAEsAUUABMAFwAhACUAKQAtADEAAAEhMhYdASEyFh0BITU0NjMhNTQ2FxUhNQERFAYjISImNREXETMRMxEzETMRMxEzETMRAfQBLCk7ARMKD%2Fu0DwoBEzspASwBLDsp%2FUQpO2RkZGRkZGRkBRQ7KWQPCktLCg9kKTtkZGT%2B1PzgKTs7KQMgZP1EArz9RAK8%2FUQCvP1EArwAAQAMAAAFCATRAB8AABMBNjIXARYGKwERFAYrASImNREhERQGKwEiJjURIyImEgJsCBUHAmAIBQqvDwr6Cg%2F%2B1A8K%2BgoPrwoFAmoCYAcH%2FaAICv3BCg8PCgF3%2FokKDw8KAj8KAAIAZAAAA%2BgEsAARABcAAAERFBYzIREUBiMhIiY1ETQ2MwEjIiY9AQJYOykBLB0V%2FOAVHR0VA1L6FR0EsP5wKTv9dhUdHRUETBUd%2FnAdFfoAAwAXABcEmQSZAA8AGwAwAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBTMyFhURMzIWHQEUBisBIiY1ETQ2AePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnL%2BfDIKD68KDw8K%2BgoPDwSZW5vW6tabW1ub1urWmztyxejFcnLF6MUNDwr%2B7Q8KMgoPDwoBXgoPAAAAAAL%2FnAAABRQEsAALAA8AACkBAyMDIQEzAzMDMwEDMwMFFP3mKfIp%2FeYBr9EVohTQ%2Fp4b4BsBkP5wBLD%2B1AEs%2FnD%2B1AEsAAAAAAIAZAAABLAEsAAVAC8AAAEzMhYVETMyFgcBBiInASY2OwERNDYBMzIWFREUBiMhIiY1ETQ2OwEyFh0BITU0NgImyBUdvxQLDf65DSYN%2FrkNCxS%2FHQJUMgoPDwr75goPDwoyCg8DhA8EsB0V%2Fj4XEP5wEBABkBAXAcIVHfzgDwr%2BogoPDwoBXgoPDwqvrwoPAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUzMhYVETMyFgcDBiInAyY2OwERNDYB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv58lgoPiRUKDd8NJg3fDQoViQ8EmVub1urWm1tbm9bq1ps7csXoxXJyxejFDQ8K%2Fu0XEP7tEBABExAXARMKDwAAAAMAFwAXBJkEmQAPABsAMQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiUTFgYrAREUBisBIiY1ESMiJjcTNjIB4%2BrWm1tbm9bq1ptbW5sBv%2BjFcnLF6MVycv7n3w0KFYkPCpYKD4kVCg3fDSYEmVub1urWm1tbm9bq1ps7csXoxXJyxejFAf7tEBf%2B7QoPDwoBExcQARMQAAAAAAIAAAAABLAEsAAZADkAABMhMhYXExYVERQGBwYjISImJyY1EzQ3Ez4BBSEiBgcDBhY7ATIWHwEeATsBMjY%2FAT4BOwEyNicDLgHhAu4KEwO6BwgFDBn7tAweAgYBB7kDEwKX%2FdQKEgJXAgwKlgoTAiYCEwr6ChMCJgITCpYKDAJXAhIEsA4K%2FXQYGf5XDB4CBggEDRkBqRkYAowKDsgOC%2F4%2BCw4OCpgKDg4KmAoODgsBwgsOAAMAFwAXBJkEmQAPABsAJwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgUXFhQPAQYmNRE0NgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJy%2Fov9ERH9EBgYBJlbm9bq1ptbW5vW6tabO3LF6MVycsXoxV2%2BDCQMvgwLFQGQFQsAAQAXABcEmQSwACgAAAE3NhYVERQGIyEiJj8BJiMiDgEUHgEyPgE1MxQOAiIuAjQ%2BAjMyA7OHBwsPCv6WCwQHhW2BdMVycsXoxXKWW5vW6tabW1ub1nXABCSHBwQL%2FpYKDwsHhUxyxejFcnLFdHXWm1tbm9bq1ptbAAAAAAIAFwABBJkEsAAaADUAAAE3NhYVERQGIyEiJj8BJiMiDgEVIzQ%2BAjMyEzMUDgIjIicHBiY1ETQ2MyEyFg8BFjMyPgEDs4cHCw8L%2FpcLBAeGboF0xXKWW5vWdcDrllub1nXAnIYHCw8LAWgKBQiFboJ0xXIEJIcHBAv%2BlwsPCweGS3LFdHXWm1v9v3XWm1t2hggFCgFoCw8LB4VMcsUAAAAKAGQAAASwBLAADwAfAC8APwBPAF8AbwB%2FAI8AnwAAEyEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0NgczMhYdARQGKwEiJj0BNDYzITIWHQEUBiMhIiY9ATQ2BzMyFh0BFAYrASImPQE0NjMhMhYdARQGIyEiJj0BNDYHMzIWHQEUBisBIiY9ATQ2MyEyFh0BFAYjISImPQE0Nn0EGgoPDwr75goPDwPA%2FK4KDw8KA1IKDw%2F9CDIKDw8KMgoPD9IBwgoPDwr%2BPgoPD74yCg8PCjIKDw%2FSAcIKDw8K%2Fj4KDw%2B%2BMgoPDwoyCg8P0gHCCg8PCv4%2BCg8PvjIKDw8KMgoPD9IBwgoPDwr%2BPgoPDwSwDwr7ggoPDwoEfgoPyA8K%2FK4KDw8KA1IKD2QPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKD8gPCjIKDw8KMgoPDwoyCg8PCjIKDwAAAAACAAAAAARMBLAAGQAjAAABNTQmIyEiBh0BIyIGFREUFjMhMjY1ETQmIyE1NDY7ATIWHQEDhHVT%2FtRSdmQpOzspA4QpOzsp%2FageFMgUHgMgyFN1dlLIOyn9qCk7OykCWCk7lhUdHRWWAAIAZAAABEwETAAJADcAABMzMhYVESMRNDYFMhcWFREUBw4DIyIuAScuAiMiBwYjIicmNRE%2BATc2HgMXHgIzMjc2fTIKD2QPA8AEBRADIUNAMRwaPyonKSxHHlVLBwgGBQ4WeDsXKC4TOQQpLUUdZ1AHBEwPCvvNBDMKDzACBhH%2BWwYGO1AkDQ0ODg8PDzkFAwcPAbY3VwMCAwsGFAEODg5XCAAAAwAAAAAEsASXACEAMQBBAAAAMh4CFREUBisBIiY1ETQuASAOARURFAYrASImNRE0PgEDMzIWFREUBisBIiY1ETQ2ITMyFhURFAYrASImNRE0NgHk6N6jYw8KMgoPjeT%2B%2BuSNDwoyCg9joyqgCAwMCKAIDAwCYKAIDAwIoAgMDASXY6PedP7UCg8PCgEsf9FyctF%2F%2FtQKDw8KASx03qP9wAwI%2FjQIDAwIAcwIDAwI%2FjQIDAwIAcwIDAAAAAACAAAA0wRHA90AFQA5AAABJTYWFREUBiclJisBIiY1ETQ2OwEyBTc2Mh8BFhQPARcWFA8BBiIvAQcGIi8BJjQ%2FAScmND8BNjIXAUEBAgkMDAn%2B%2FhUZ%2BgoPDwr6GQJYeAcUByIHB3h4BwciBxQHeHgHFAciBwd3dwcHIgcUBwMurAYHCv0SCgcGrA4PCgFeCg%2BEeAcHIgcUB3h4BxQHIgcHd3cHByIHFAd4eAcUByIICAAAAAACAAAA0wNyA90AFQAvAAABJTYWFREUBiclJisBIiY1ETQ2OwEyJTMWFxYVFAcGDwEiLwEuATc2NTQnJjY%2FATYBQQECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcDLqwGBwr9EgoHBqwODwoBXgoPZAEJgaGafwkBAQYXBxMIZ36EaggUBxYFAAAAAAMAAADEBGID7AAbADEASwAAATMWFxYVFAYHBgcjIi8BLgE3NjU0JicmNj8BNgUlNhYVERQGJyUmKwEiJjURNDY7ATIlMxYXFhUUBwYPASIvAS4BNzY1NCcmNj8BNgPHAwsGh0RABwoDCQcqCAIGbzs3BgIJKgf9ggECCQwMCf7%2BFRn6Cg8PCvoZAdIECgZgWgYLAwkHHQcDBkhOBgMIHQcD7AEJs9lpy1QJAQYiBhQIlrJarEcJFAYhBb6sBgcK%2FRIKBwasDg8KAV4KD2QBCYGhmn8JAQEGFwcTCGd%2BhGoIFQYWBQAAAAANAAAAAASwBLAACQAVABkAHQAhACUALQA7AD8AQwBHAEsATwAAATMVIxUhFSMRIQEjFTMVIREjESM1IQURIREhESERBSM1MwUjNTMBMxEhETM1MwEzFSMVIzUjNTM1IzUhBREhEQcjNTMFIzUzASM1MwUhNSEB9GRk%2FnBkAfQCvMjI%2FtTIZAJY%2B7QBLAGQASz84GRkArxkZP1EyP4MyGQB9MhkyGRkyAEs%2FUQBLGRkZAOEZGT%2BDGRkAfT%2B1AEsA4RkZGQCWP4MZMgBLAEsyGT%2B1AEs%2FtQBLMhkZGT%2BDP4MAfRk%2FtRkZGRkyGTI%2FtQBLMhkZGT%2B1GRkZAAAAAAJAAAAAASwBLAAAwAHAAsADwATABcAGwAfACMAADcjETMTIxEzASMRMxMjETMBIxEzASE1IRcjNTMXIzUzBSM1M2RkZMhkZAGQyMjIZGQBLMjI%2FOD%2B1AEsyGRkyGRkASzIyMgD6PwYA%2Bj8GAPo%2FBgD6PwYA%2Bj7UGRkW1tbW1sAAAIAAAAKBKYEsAANABUAAAkBFhQHAQYiJwETNDYzBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAUM7Uzs7UzsEsP1WCBQI%2FjAICAKqAdsKD807O1Q7OwAAAAADAAAACgXSBLAADQAZACEAAAkBFhQHAQYiJwETNDYzIQEWFAcBBiIvAQkBBCYiBhQWMjYB9AKqCAj%2BMAgUCP1WAQ8KAwYCqggI%2FjAIFAg4Aaj9RP7TO1M7O1M7BLD9VggUCP4wCAgCqgHbCg%2F9VggUCP4wCAg4AaoCvM07O1Q7OwAAAAABAGQAAASwBLAAJgAAASEyFREUDwEGJjURNCYjISIPAQYWMyEyFhURFAYjISImNRE0PwE2ASwDOUsSQAgKDwr9RBkSQAgFCgK8Cg8PCvyuCg8SixIEsEv8fBkSQAgFCgO2Cg8SQAgKDwr8SgoPDwoDzxkSixIAAAABAMj%2F%2FwRMBLAACgAAEyEyFhURCQERNDb6AyAVHf4%2B%2Fj4dBLAdFfuCAbz%2BQwR%2FFR0AAAAAAwAAAAAEsASwABUARQBVAAABISIGBwMGHwEeATMhMjY%2FATYnAy4BASMiBg8BDgEjISImLwEuASsBIgYVERQWOwEyNj0BNDYzITIWHQEUFjsBMjY1ETQmASEiBg8BBhYzITI2LwEuAQM2%2FkQLEAFOBw45BhcKAcIKFwY%2BDgdTARABVpYKFgROBBYK%2FdoKFgROBBYKlgoPDwqWCg8PCgLuCg8PCpYKDw%2F%2Bsf4MChMCJgILCgJYCgsCJgITBLAPCv7TGBVsCQwMCWwVGAEtCg%2F%2BcA0JnAkNDQmcCQ0PCv12Cg8PCpYKDw8KlgoPDwoCigoP%2FagOCpgKDg4KmAoOAAAAAAQAAABkBLAETAAdACEAKQAxAAABMzIeAh8BMzIWFREUBiMhIiY1ETQ2OwE%2BBAEVMzUEIgYUFjI2NCQyFhQGIiY0AfTIOF00JAcGlik7Oyn8GCk7OymWAgknM10ByGT%2Bz76Hh76H%2Fu9WPDxWPARMKTs7FRQ7Kf2oKTs7KQJYKTsIG0U1K%2F7UZGRGh76Hh74IPFY8PFYAAAAAAgA1AAAEsASvACAAIwAACQEWFx4BHwEVITUyNi8BIQYHBh4CMxUhNTY3PgE%2FAQEDIQMCqQGBFCgSJQkK%2Fl81LBFS%2Fnk6IgsJKjIe%2FpM4HAwaBwcBj6wBVKIEr%2FwaMioTFQECQkJXLd6RWSIuHAxCQhgcDCUNDQPu%2FVoByQAAAAADAGQAAAPwBLAAJwAyADsAAAEeBhUUDgMjITU%2BATURNC4EJzUFMh4CFRQOAgclMzI2NTQuAisBETMyNjU0JisBAvEFEzUwOyodN1htbDD%2BDCk7AQYLFyEaAdc5dWM%2BHy0tEP6Pi05pESpTPnbYUFJ9Xp8CgQEHGB0zOlIuQ3VONxpZBzMoAzsYFBwLEAkHRwEpSXNDM1s6KwkxYUopOzQb%2FK5lUFqBAAABAMgAAANvBLAAGQAAARcOAQcDBhYXFSE1NjcTNjQuBCcmJzUDbQJTQgeECSxK%2Fgy6Dq0DAw8MHxUXDQYEsDkTNSj8uTEoBmFhEFIDQBEaExAJCwYHAwI5AAAAAAL%2FtQAABRQEsAAlAC8AAAEjNC4FKwERFBYfARUhNTI%2BAzURIyIOBRUjESEFIxEzByczESM3BRQyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj7m0tLfX1LS30DhBUgFQ4IAwH8rhYZAQJkZAEFCRUOA1IBAwgOFSAVASzI%2FOCnpwMgpwACACH%2FtQSPBLAAJQAvAAABIzQuBSsBERQWHwEVITUyPgM1ESMiDgUVIxEhEwc1IRUnNxUhNQRMMggLGRMmGBnIMhkZ%2FnAEDiIaFsgZGCYTGQsIMgPoQ6f84KenAyADhBUgFQ4IAwH9dhYZAQJkZAEFCRUOAooBAwgOFSAVASz7gn1LS319S0sABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyAlgVHR0V%2FagVHR0VA%2BgVHR0V%2FBgVHR0VAyAVHR0V%2FOAVHR0VBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AABMhMhYdARQGIyEiJj0BNDYDITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NgMhMhYdARQGIyEiJj0BNDb6ArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR3dArwVHR0V%2FUQVHR2zBEwVHR0V%2B7QVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0ABAAAAAAEsARMAA8AHwAvAD8AAAE1NDYzITIWHQEUBiMhIiYBNTQ2MyEyFh0BFAYjISImEzU0NjMhMhYdARQGIyEiJgE1NDYzITIWHQEUBiMhIiYB9B0VAlgVHR0V%2FagVHf5wHRUD6BUdHRX8GBUdyB0VAyAVHR0V%2FOAVHf7UHRUETBUdHRX7tBUdA7ZkFR0dFWQVHR3%2B6WQVHR0VZBUdHf7pZBUdHRVkFR0d%2FulkFR0dFWQVHR0AAAQAAAAABLAETAAPAB8ALwA%2FAAATITIWHQEUBiMhIiY9ATQ2EyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2MgRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dFQRMFR0dFfu0FR0dBEwdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAgAAAAABLAETAAPAB8ALwA%2FAE8AXwBvAH8AABMzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2ATMyFh0BFAYrASImPQE0NikBMhYdARQGIyEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2KQEyFh0BFAYjISImPQE0NgEzMhYdARQGKwEiJj0BNDYpATIWHQEUBiMhIiY9ATQ2MmQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR3%2B6WQVHR0VZBUdHQFBAyAVHR0V%2FOAVHR0ETB0VZBUdHRVkFR0dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdHRVkFR0dFWQVHf7UHRVkFR0dFWQVHR0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0dFWQVHR0VZBUdAAAG%2F5wAAASwBEwAAwATACMAKgA6AEoAACEjETsCMhYdARQGKwEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2BQc1IzUzNQUhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2AZBkZJZkFR0dFWQVHR0VAfQVHR0V%2FgwVHR3%2B%2BqfIyAHCASwVHR0V%2FtQVHR0VAlgVHR0V%2FagVHR0ETB0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR36fUtkS68dFWQVHR0VZBUd%2FtQdFWQVHR0VZBUdAAAABgAAAAAFFARMAA8AEwAjACoAOgBKAAATMzIWHQEUBisBIiY9ATQ2ASMRMwEhMhYdARQGIyEiJj0BNDYFMxUjFSc3BSEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYyZBUdHRVkFR0dA2dkZPyuAfQVHR0V%2FgwVHR0EL8jIp6f75gEsFR0dFf7UFR0dFQJYFR0dFf2oFR0dBEwdFWQVHR0VZBUd%2B7QETP7UHRVkFR0dFWQVHchkS319rx0VZBUdHRVkFR3%2B1B0VZBUdHRVkFR0AAAAAAgAAAMgEsAPoAA8AEgAAEyEyFhURFAYjISImNRE0NgkCSwLuHywsH%2F0SHywsBIT%2B1AEsA%2BgsH%2F12HywsHwKKHyz9RAEsASwAAwAAAAAEsARMAA8AFwAfAAATITIWFREUBiMhIiY1ETQ2FxE3BScBExEEMhYUBiImNCwEWBIaGhL7qBIaGkr3ASpKASXs%2FNJwTk5wTgRMGhL8DBIaGhID9BIaZP0ftoOcAT7%2B4AH0dE5vT09vAAAAAAIA2wAFBDYEkQAWAB4AAAEyHgEVFAcOAQ8BLgQnJjU0PgIWIgYUFjI2NAKIdcZzRkWyNjYJIV5YbSk8RHOft7eCgreCBJF4ynVzj23pPz4IIWZomEiEdVijeUjDgriBgbgAAAACABcAFwSZBJkADwAXAAAAMh4CFA4CIi4CND4BAREiDgEUHgEB4%2BrWm1tbm9bq1ptbW5sBS3TFcnLFBJlbm9bq1ptbW5vW6tab%2FG8DVnLF6MVyAAACAHUAAwPfBQ8AGgA1AAABHgYVFA4DBy4DNTQ%2BBQMOAhceBBcWNj8BNiYnLgInJjc2IyYCKhVJT1dOPiUzVnB9P1SbfEokP0xXUEm8FykoAwEbITEcExUWAgYCCQkFEikMGiACCAgFD0iPdXdzdYdFR4BeRiYEBTpjl1lFh3ZzeHaQ%2Ff4hS4I6JUEnIw4IBwwQIgoYBwQQQSlZtgsBAAAAAwAAAAAEywRsAAwAKgAvAAABNz4CHgEXHgEPAiUhMhcHISIGFREUFjMhMjY9ATcRFAYjISImNRE0NgkBBzcBA%2BhsAgYUFR0OFgoFBmz9BQGQMje7%2FpApOzspAfQpO8i7o%2F5wpbm5Azj%2BlqE3AWMD9XMBAgIEDw4WKgsKc8gNuzsp%2FgwpOzsptsj%2BtKW5uaUBkKW5%2Ftf%2BljKqAWMAAgAAAAAEkwRMABsANgAAASEGByMiBhURFBYzITI2NTcVFAYjISImNRE0NgUBFhQHAQYmJzUmDgMHPgY3NT4BAV4BaaQ0wyk7OykB9Ck7yLml%2FnClubkCfwFTCAj%2BrAcLARo5ZFRYGgouOUlARioTAQsETJI2Oyn%2BDCk7OymZZ6W5uaUBkKW5G%2F7TBxUH%2Fs4GBAnLAQINFjAhO2JBNB0UBwHSCgUAAAAAAgAAAAAEnQRMAB0ANQAAASEyFwchIgYVERQWMyEyNj0BNxUUBiMhIiY1ETQ2CQE2Mh8BFhQHAQYiLwEmND8BNjIfARYyAV4BXjxDsv6jKTs7KQH0KTvIuaX%2BcKW5uQHKAYsHFQdlBwf97QcVB%2FgHB2UHFQdvCBQETBexOyn%2BDCk7OylFyNulubmlAZCluf4zAYsHB2UHFQf97AcH%2BAcVB2UHB28HAAAAAQAKAAoEpgSmADsAAAkBNjIXARYGKwEVMzU0NhcBFhQHAQYmPQEjFTMyFgcBBiInASY2OwE1IxUUBicBJjQ3ATYWHQEzNSMiJgE%2BAQgIFAgBBAcFCqrICggBCAgI%2FvgICsiqCgUH%2FvwIFAj%2B%2BAgFCq%2FICgj%2B%2BAgIAQgICsivCgUDlgEICAj%2B%2BAgKyK0KBAf%2B%2FAcVB%2F73BwQKrcgKCP74CAgBCAgKyK0KBAcBCQcVBwEEBwQKrcgKAAEAyAAAA4QETAAZAAATMzIWFREBNhYVERQGJwERFAYrASImNRE0NvpkFR0B0A8VFQ%2F%2BMB0VZBUdHQRMHRX%2BSgHFDggV%2FBgVCA4Bxf5KFR0dFQPoFR0AAAABAAAAAASwBEwAIwAAEzMyFhURATYWFREBNhYVERQGJwERFAYnAREUBisBIiY1ETQ2MmQVHQHQDxUB0A8VFQ%2F%2BMBUP%2FjAdFWQVHR0ETB0V%2FkoBxQ4IFf5KAcUOCBX8GBUIDgHF%2FkoVCA4Bxf5KFR0dFQPoFR0AAAABAJ0AGQSwBDMAFQAAAREUBicBERQGJwEmNDcBNhYVEQE2FgSwFQ%2F%2BMBUP%2FhQPDwHsDxUB0A8VBBr8GBUIDgHF%2FkoVCA4B4A4qDgHgDggV%2FkoBxQ4IAAAAAQDIABYEMwQ2AAsAABMBFhQHAQYmNRE0NvMDLhIS%2FNISGRkEMv4OCx4L%2Fg4LDhUD6BUOAAIAyABkA4QD6AAPAB8AABMzMhYVERQGKwEiJjURNDYhMzIWFREUBisBIiY1ETQ2%2BsgVHR0VyBUdHQGlyBUdHRXIFR0dA%2BgdFfzgFR0dFQMgFR0dFfzgFR0dFQMgFR0AAAEAyABkBEwD6AAPAAABERQGIyEiJjURNDYzITIWBEwdFfzgFR0dFQMgFR0DtvzgFR0dFQMgFR0dAAAAAAEAAAAZBBMEMwAVAAABETQ2FwEWFAcBBiY1EQEGJjURNDYXAfQVDwHsDw%2F%2BFA8V%2FjAPFRUPAmQBthUIDv4gDioO%2FiAOCBUBtv47DggVA%2BgVCA4AAAH%2F%2FgACBLMETwAjAAABNzIWFRMUBiMHIiY1AwEGJjUDAQYmNQM0NhcBAzQ2FwEDNDYEGGQUHgUdFWQVHQL%2BMQ4VAv4yDxUFFQ8B0gIVDwHSAh0ETgEdFfwYFR0BHRUBtf46DwkVAbX%2BOQ4JFAPoFQkP%2Fj4BthQJDv49AbYVHQAAAQEsAAAD6ARMABkAAAEzMhYVERQGKwEiJjURAQYmNRE0NhcBETQ2A1JkFR0dFWQVHf4wDxUVDwHQHQRMHRX8GBUdHRUBtv47DggVA%2BgVCA7%2BOwG2FR0AAAIAZADIBLAESAALABsAAAkBFgYjISImNwE2MgEhMhYdARQGIyEiJj0BNDYCrgH1DwkW%2B%2B4WCQ8B9Q8q%2FfcD6BUdHRX8GBUdHQQ5%2FeQPFhYPAhwP%2FUgdFWQVHR0VZBUdAAEAiP%2F8A3UESgAFAAAJAgcJAQN1%2FqABYMX92AIoA4T%2Bn%2F6fxgIoAiYAAAAAAQE7%2F%2FwEKARKAAUAAAkBJwkBNwQo%2FdnGAWH%2Bn8YCI%2F3ZxgFhAWHGAAIAFwAXBJkEmQAPADMAAAAyHgIUDgIiLgI0PgEFIyIGHQEjIgYdARQWOwEVFBY7ATI2PQEzMjY9ATQmKwE1NCYB4%2BrWm1tbm9bq1ptbW5sBfWQVHZYVHR0Vlh0VZBUdlhUdHRWWHQSZW5vW6tabW1ub1urWm7odFZYdFWQVHZYVHR0Vlh0VZBUdlhUdAAAAAAIAFwAXBJkEmQAPAB8AAAAyHgIUDgIiLgI0PgEBISIGHQEUFjMhMjY9ATQmAePq1ptbW5vW6tabW1ubAkX%2BDBUdHRUB9BUdHQSZW5vW6tabW1ub1urWm%2F5%2BHRVkFR0dFWQVHQACABcAFwSZBJkADwAzAAAAMh4CFA4CIi4CND4BBCIPAScmIg8BBhQfAQcGFB8BFjI%2FARcWMj8BNjQvATc2NC8BAePq1ptbW5vW6tabW1ubAeUZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0JCXh4CQmNBJlbm9bq1ptbW5vW6tabrQl4eAkJjQkZCXh4CRkJjQkJeHgJCY0JGQl4eAkZCY0AAgAXABcEmQSZAA8AJAAAADIeAhQOAiIuAjQ%2BAQEnJiIPAQYUHwEWMjcBNjQvASYiBwHj6tabW1ub1urWm1tbmwEVVAcVCIsHB%2FIHFQcBdwcHiwcVBwSZW5vW6tabW1ub1urWm%2F4xVQcHiwgUCPEICAF3BxUIiwcHAAAAAAMAFwAXBJkEmQAPADsASwAAADIeAhQOAiIuAjQ%2BAQUiDgMVFDsBFjc%2BATMyFhUUBgciDgUHBhY7ATI%2BAzU0LgMTIyIGHQEUFjsBMjY9ATQmAePq1ptbW5vW6tabW1ubAT8dPEIyIRSDHgUGHR8UFw4TARkOGhITDAIBDQ6tBx4oIxgiM0Q8OpYKDw8KlgoPDwSZW5vW6tabW1ub1urWm5ELHi9PMhkFEBQQFRIXFgcIBw4UHCoZCBEQKDhcNi9IKhsJ%2FeMPCpYKDw8KlgoPAAADABcAFwSZBJkADwAfAD4AAAAyHgIUDgIiLgI0PgEFIyIGHQEUFjsBMjY9ATQmAyMiBh0BFBY7ARUjIgYdARQWMyEyNj0BNCYrARE0JgHj6tabW1ub1urWm1tbmwGWlgoPDwqWCg8PCvoKDw8KS0sKDw8KAV4KDw8KSw8EmVub1urWm1tbm9bq1ptWDwqWCg8PCpYKD%2F7UDwoyCg%2FIDwoyCg8PCjIKDwETCg8AAgAAAAAEsASwAC8AXwAAATMyFh0BHgEXMzIWHQEUBisBDgEHFRQGKwEiJj0BLgEnIyImPQE0NjsBPgE3NTQ2ExUUBisBIiY9AQ4BBzMyFh0BFAYrAR4BFzU0NjsBMhYdAT4BNyMiJj0BNDY7AS4BAg2WCg9nlxvCCg8PCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw%2B5DwqWCg9EZheoCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmBLAPCsIbl2cPCpYKD2eXG8IKDw8KwhuXZw8KlgoPZ5cbwgoP%2Fs2oCg8PCqgXZkQPCpYKD0RmF6gKDw8KqBdmRA8KlgoPRGYAAwAXABcEmQSZAA8AGwA%2FAAAAMh4CFA4CIi4CND4BBCIOARQeATI%2BATQmBxcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyAePq1ptbW5vW6tabW1ubAb%2FoxXJyxejFcnKaQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwdABxUHfHwHFQSZW5vW6tabW1ub1urWmztyxejFcnLF6MVaQAcVB3x8BxUHQAcHfHwHB0AHFQd8fAcVB0AHB3x8BwAAAAMAFwAXBJkEmQAPABsAMAAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcXFhQHAQYiLwEmND8BNjIfATc2MgHj6tabW1ub1urWm1tbmwG%2F6MVycsXoxXJyg2oHB%2F7ACBQIyggIagcVB0%2FFBxUEmVub1urWm1tbm9bq1ps7csXoxXJyxejFfWoHFQf%2BvwcHywcVB2oICE%2FFBwAAAAMAFwAXBJkEmQAPABgAIQAAADIeAhQOAiIuAjQ%2BAQUiDgEVFBcBJhcBFjMyPgE1NAHj6tabW1ub1urWm1tbmwFLdMVyQQJLafX9uGhzdMVyBJlbm9bq1ptbW5vW6tabO3LFdHhpAktB0P24PnLFdHMAAAAAAQAXAFMEsAP5ABUAABMBNhYVESEyFh0BFAYjIREUBicBJjQnAgoQFwImFR0dFf3aFxD99hACRgGrDQoV%2Ft0dFcgVHf7dFQoNAasNJgAAAAABAAAAUwSZA%2FkAFQAACQEWFAcBBiY1ESEiJj0BNDYzIRE0NgJ%2FAgoQEP32EBf92hUdHRUCJhcD8f5VDSYN%2FlUNChUBIx0VyBUdASMVCgAAAAEAtwAABF0EmQAVAAAJARYGIyERFAYrASImNREhIiY3ATYyAqoBqw0KFf7dHRXIFR3%2B3RUKDQGrDSYEif32EBf92hUdHRUCJhcQAgoQAAAAAQC3ABcEXQSwABUAAAEzMhYVESEyFgcBBiInASY2MyERNDYCJsgVHQEjFQoN%2FlUNJg3%2BVQ0KFQEjHQSwHRX92hcQ%2FfYQEAIKEBcCJhUdAAABAAAAtwSZBF0AFwAACQEWFAcBBiY1EQ4DBz4ENxE0NgJ%2FAgoQEP32EBdesKWBJAUsW4fHfhcEVf5VDSYN%2FlUNChUBIwIkRHVNabGdcUYHAQYVCgACAAAAAASwBLAAFQArAAABITIWFREUBi8BBwYiLwEmND8BJyY2ASEiJjURNDYfATc2Mh8BFhQPARcWBgNSASwVHRUOXvkIFAhqBwf5Xg4I%2FiH%2B1BUdFQ5e%2BQgUCGoHB%2FleDggEsB0V%2FtQVCA5e%2BQcHaggUCPleDhX7UB0VASwVCA5e%2BQcHaggUCPleDhUAAAACAEkASQRnBGcAFQArAAABFxYUDwEXFgYjISImNRE0Nh8BNzYyASEyFhURFAYvAQcGIi8BJjQ%2FAScmNgP2agcH%2BV4OCBX%2B1BUdFQ5e%2BQgU%2FQwBLBUdFQ5e%2BQgUCGoHB%2FleDggEYGoIFAj5Xg4VHRUBLBUIDl75B%2F3xHRX%2B1BUIDl75BwdqCBQI%2BV4OFQAAAAADABcAFwSZBJkADwAfAC8AAAAyHgIUDgIiLgI0PgEFIyIGFxMeATsBMjY3EzYmAyMiBh0BFBY7ATI2PQE0JgHj6tabW1ub1urWm1tbmwGz0BQYBDoEIxQ2FCMEOgQYMZYKDw8KlgoPDwSZW5vW6tabW1ub1urWm7odFP7SFB0dFAEuFB3%2BDA8KlgoPDwqWCg8AAAAABQAAAAAEsASwAEkAVQBhAGgAbwAAATIWHwEWHwEWFxY3Nj8BNjc2MzIWHwEWHwIeATsBMhYdARQGKwEiBh0BIREjESE1NCYrASImPQE0NjsBMjY1ND8BNjc%2BBAUHBhY7ATI2LwEuAQUnJgYPAQYWOwEyNhMhIiY1ESkBERQGIyERAQQJFAUFFhbEFQ8dCAsmxBYXERUXMA0NDgQZCAEPCj0KDw8KMgoP%2FnDI%2FnAPCjIKDw8KPQsOCRkFDgIGFRYfAp2mBwQK2woKAzMDEP41sQgQAzMDCgrnCwMe%2FokKDwGQAlgPCv6JBLAEAgIKDXYNCxUJDRZ2DQoHIREQFRh7LAkLDwoyCg8PCq8BLP7UrwoPDwoyCg8GBQQwgBkUAwgWEQ55ogcKDgqVCgSqnQcECo8KDgr8cg8KAXf%2BiQoPAZAAAAAAAgAAAAwErwSmACsASQAAATYWFQYCDgQuAScmByYOAQ8BBiY1NDc%2BATc%2BAScuAT4BNz4GFyYGBw4BDwEOBAcOARY2Nz4CNz4DNz4BBI0IGgItQmxhi2KORDg9EQQRMxuZGhYqCFUYEyADCQIQOjEnUmFch3vAJQgdHyaiPT44XHRZUhcYDhItIRmKcVtGYWtbKRYEBKYDEwiy%2Ft3IlVgxEQgLCwwBAQIbG5kYEyJAJghKFRE8Hzdff4U%2FM0o1JSMbL0QJGCYvcSEhHjZST2c1ODwEJygeW0AxJUBff1UyFAABAF0AHgRyBM8ATwAAAQ4BHgQXLgc%2BATceAwYHDgQHBicmNzY3PgQuAScWDgMmJy4BJyY%2BBDcGHgM3PgEuAicmPgMCjScfCic4R0IgBBsKGAoQAwEJEg5gikggBhANPkpTPhZINx8SBgsNJysiCRZOQQoVNU1bYC9QZwICBAUWITsoCAYdJzIYHw8YIiYHDyJJYlkEz0OAZVxEOSQMBzgXOB42IzElKRIqg5Gnl0o3Z0c6IAYWCwYNAwQFIDhHXGF1OWiqb0sdBxUknF0XNTQ8PEUiNWNROBYJDS5AQVUhVZloUSkAAAAAA%2F%2FcAGoE1ARGABsAPwBRAAAAMh4FFA4FIi4FND4EBSYGFxYVFAYiJjU0NzYmBwYHDgEXHgQyPgM3NiYnJgUHDgEXFhcWNj8BNiYnJicuAQIGpJ17bk85HBw6T257naKde25POhwcOU9uewIPDwYIGbD4sBcIBw5GWg0ECxYyWl%2BDiINfWjIWCwQMWv3%2FIw8JCSU4EC0OIw4DDywtCyIERi1JXGJcSSpJXGJcSS0tSVxiXEkqSVxiXEncDwYTOT58sLB8OzcTBg9FcxAxEiRGXkQxMEVeRSQSMRF1HiQPLxJEMA0EDyIPJQ8sSRIEAAAABP%2FcAAAE1ASwABQAJwA7AEwAACEjNy4ENTQ%2BBTMyFzczEzceARUUDgMHNz4BNzYmJyYlBgcOARceBBc3LgE1NDc2JhcHDgEXFhcWNj8CJyYnLgECUJQfW6l2WSwcOU9ue51SPUEglCYvbIknUGqYUi5NdiYLBAw2%2FVFGWg0ECxIqSExoNSlrjxcIB3wjDwkJJTgQLQ4MFgMsLQsieBRhdHpiGxVJXGJcSS0Pef5StVXWNBpacm5jGq0xiD8SMRFGckVzEDESHjxRQTkNmhKnbjs3EwZwJA8vEkQwDQQPC1YELEkSBAAAAAP%2FngAABRIEqwALABgAKAAAJwE2FhcBFgYjISImJSE1NDY7ATIWHQEhAQczMhYPAQ4BKwEiJi8BJjZaAoIUOBQCghUbJfryJRsBCgFZDwqWCg8BWf5DaNAUGAQ6BCMUNhQjBDoEGGQEKh8FIfvgIEdEhEsKDw8KSwLT3x0U%2FBQdHRT8FB0AAAABAGQAFQSwBLAAKAAAADIWFREBHgEdARQGJyURFh0BFAYvAQcGJj0BNDcRBQYmPQE0NjcBETQCTHxYAWsPFhgR%2FplkGhPNzRMaZP6ZERgWDwFrBLBYPv6t%2FrsOMRQpFA0M%2Bf75XRRAFRAJgIAJEBVAFF0BB%2FkMDRQpFDEOAUUBUz4AAAARAAAAAARMBLAAHQAnACsALwAzADcAOwA%2FAEMARwBLAE8AUwBXAFsAXwBjAAABMzIWHQEzMhYdASE1NDY7ATU0NjsBMhYdASE1NDYBERQGIyEiJjURFxUzNTMVMzUzFTM1MxUzNTMVMzUFFTM1MxUzNTMVMzUzFTM1MxUzNQUVMzUzFTM1MxUzNTMVMzUzFTM1A1JkFR0yFR37tB0VMh0VZBUdAfQdAQ8dFfwYFR1kZGRkZGRkZGRk%2FHxkZGRkZGRkZGT8fGRkZGRkZGRkZASwHRUyHRWWlhUdMhUdHRUyMhUd%2FnD9EhUdHRUC7shkZGRkZGRkZGRkyGRkZGRkZGRkZGTIZGRkZGRkZGRkZAAAAAMAAAAZBXcElwAZACUANwAAARcWFA8BBiY9ASMBISImPQE0NjsBATM1NDYBBycjIiY9ATQ2MyEBFxYUDwEGJj0BIyc3FzM1NDYEb%2FkPD%2FkOFZ%2F9qP7dFR0dFdECWPEV%2FamNetEVHR0VASMDGvkPD%2FkOFfG1jXqfFQSN5g4qDuYOCBWW%2FagdFWQVHQJYlhUI%2FpiNeh0VZBUd%2Fk3mDioO5g4IFZa1jXqWFQgAAAABAAAAAASwBEwAEgAAEyEyFhURFAYjIQERIyImNRE0NmQD6Ck7Oyn9rP7QZCk7OwRMOyn9qCk7%2FtQBLDspAlgpOwAAAAMAZAAABEwEsAAJABMAPwAAEzMyFh0BITU0NiEzMhYdASE1NDYBERQOBSIuBTURIRUUFRwBHgYyPgYmNTQ9AZbIFR3%2B1B0C0cgVHf7UHQEPBhgoTGacwJxmTCgYBgEsAwcNFB8nNkI2Jx8TDwUFAQSwHRX6%2BhUdHRX6%2BhUd%2FnD%2B1ClJalZcPigoPlxWakkpASz6CRIVKyclIRsWEAgJEBccISUnKhURCPoAAAAB%2F%2F8A1ARMA8IABQAAAQcJAScBBEzG%2Fp%2F%2Bn8UCJwGbxwFh%2Fp%2FHAicAAQAAAO4ETQPcAAUAAAkCNwkBBE392v3ZxgFhAWEDFf3ZAifH%2Fp8BYQAAAAAC%2F1EAZAVfA%2BgAFAApAAABITIWFREzMhYPAQYiLwEmNjsBESElFxYGKwERIRchIiY1ESMiJj8BNjIBlALqFR2WFQgO5g4qDuYOCBWW%2FoP%2BHOYOCBWWAYHX%2FRIVHZYVCA7mDioD6B0V%2FdkVDvkPD%2FkOFQGRuPkOFf5wyB0VAiYVDvkPAAABAAYAAASeBLAAMAAAEzMyFh8BITIWBwMOASMhFyEyFhQGKwEVFAYiJj0BIRUUBiImPQEjIiYvAQMjIiY0NjheERwEJgOAGB4FZAUsIf2HMAIXFR0dFTIdKh3%2B1B0qHR8SHQYFyTYUHh4EsBYQoiUY%2FiUVK8gdKh0yFR0dFTIyFR0dFTIUCQoDwR0qHQAAAAACAAAAAASwBEwACwAPAAABFSE1MzQ2MyEyFhUFIREhBLD7UMg7KQEsKTv9RASw%2B1AD6GRkKTs7Kcj84AACAAAAAAXcBEwADAAQAAATAxEzNDYzITIWFSEVBQEhAcjIyDspASwqOgH0ASz%2B1PtQASwDIP5wAlgpOzspyGT9RAK8AAEBRQAAA2sErwAbAAABFxYGKwERMzIWDwEGIi8BJjY7AREjIiY%2FATYyAnvmDggVlpYVCA7mDioO5g4IFZaWFQgO5g4qBKD5DhX9pxUO%2BQ8P%2BQ4VAlkVDvkPAAAAAQABAUQErwNrABsAAAEXFhQPAQYmPQEhFRQGLwEmND8BNhYdASE1NDYDqPkODvkPFf2oFQ%2F5Dg75DxUCWBUDYOUPKQ%2FlDwkUl5cUCQ%2FlDykP5Q8JFZWVFQkAAAAEAAAAAASwBLAACQAZAB0AIQAAAQMuASMhIgYHAwUhIgYdARQWMyEyNj0BNCYFNTMVMzUzFQSRrAUkFP1gFCQFrAQt%2FBgpOzspA%2BgpOzv%2Bq2RkZAGQAtwXLSgV%2FR1kOylkKTs7KWQpO8hkZGRkAAAAA%2F%2BcAGQEsARMAAsAIwAxAAAAMhYVERQGIiY1ETQDJSMTFgYjIisBIiYnAj0BNDU0PgE7ASUBFSIuAz0BND4CNwRpKh0dKh1k%2FV0mLwMRFQUCVBQdBDcCCwzIAqP8GAQOIhoWFR0dCwRMHRX8rhUdHRUDUhX8mcj%2B7BAIHBUBUQ76AgQQDw36%2FtT6AQsTKRwyGigUDAEAAAACAEoAAARmBLAALAA1AAABMzIWDwEeARcTFzMyFhQGBw4EIyIuBC8BLgE0NjsBNxM%2BATcnJjYDFjMyNw4BIiYCKV4UEgYSU3oPP3YRExwaEggeZGqfTzl0XFU%2BLwwLEhocExF2Pw96UxIGEyQyNDUxDDdGOASwFRMlE39N%2FrmtHSkoBwQLHBYSCg4REg4FBAgoKR2tAUdNfhQgExr7vgYGMT09AAEAFAAUBJwEnAAXAAABNwcXBxcHFycHJwcnBzcnNyc3Jxc3FzcDIOBO6rS06k7gLZubLeBO6rS06k7gLZubA7JO4C2bmy3gTuq0tOpO4C2bmy3gTuq0tAADAAAAZASwBLAAIQAtAD0AAAEzMhYdAQchMhYdARQHAw4BKwEiJi8BIyImNRE0PwI%2BARcPAREzFzMTNSE3NQEzMhYVERQGKwEiJjURNDYCijIoPBwBSCg8He4QLBf6B0YfHz0tNxSRYA0xG2SWZIjW%2Bv4%2BMv12ZBUdHRVkFR0dBLBRLJZ9USxkLR3%2BqBghMhkZJCcBkCQbxMYcKGTU1f6JZAF3feGv%2FtQdFf4MFR0dFQH0FR0AAAAAAwAAAAAEsARMACAAMAA8AAABMzIWFxMWHQEUBiMhFh0BFAYrASImLwImNRE0NjsBNgUzMhYVERQGKwEiJjURNDYhByMRHwEzNSchNQMCWPoXLBDuHTwo%2FrgcPCgyGzENYJEUNy09fP3pZBUdHRVkFR0dAl%2BIZJZkMjIBwvoETCEY%2FqgdLWQsUXYHlixRKBzGxBskAZAnJGRkHRX%2BDBUdHRUB9BUdZP6J1dSv4X0BdwADAAAAZAUOBE8AGwA3AEcAAAElNh8BHgEPASEyFhQGKwEDDgEjISImNRE0NjcXERchEz4BOwEyNiYjISoDLgQnJj8BJwUzMhYVERQGKwEiJjURNDYBZAFrHxZuDQEMVAEuVGxuVGqDBhsP%2FqoHphwOOmQBJYMGGw%2FLFRMSFv44AgoCCQMHAwUDAQwRklb9T2QVHR0VZBUdHQNp5hAWcA0mD3lMkE7%2BrRUoog0CDRElCkj%2BCVkBUxUoMjIBAgIDBQIZFrdT5B0V%2FgwVHR0VAfQVHQAAAAP%2FnABkBLAETwAdADYARgAAAQUeBBURFAYjISImJwMjIiY0NjMhJyY2PwE2BxcWBw4FKgIjIRUzMhYXEyE3ESUFMzIWFREUBisBIiY1ETQ2AdsBbgIIFBANrAf%2Bqg8bBoNqVW1sVAEuVQsBDW4WSpIRDAIDBQMHAwkDCgH%2BJd0PHAaCASZq%2FqoCUGQVHR0VZBUdHQRP5gEFEBEXC%2F3zDaIoFQFTTpBMeQ8mDXAWrrcWGQIFAwICAWQoFf6tWQH37OQdFf4MFR0dFQH0FR0AAAADAGEAAARMBQ4AGwA3AEcAAAAyFh0BBR4BFREUBiMhIiYvAQMmPwE%2BAR8BETQXNTQmBhURHAMOBAcGLwEHEyE3ESUuAQMhMhYdARQGIyEiJj0BNDYB3pBOAVMVKKIN%2FfMRJQoJ5hAWcA0mD3nGMjIBAgIDBQIZFrdT7AH3Wf6tFSiWAfQVHR0V%2FgwVHR0FDm5UaoMGGw%2F%2BqgemHA4OAWsfFm4NAQxUAS5U1ssVExIW%2FjgCCgIJAwcDBQMBDBGSVv6tZAElgwYb%2FQsdFWQVHR0VZBUdAAP%2F%2FQAGA%2BgFFAAPAC0ASQAAASEyNj0BNCYjISIGHQEUFgEVFAYiJjURBwYmLwEmNxM%2BBDMhMhYVERQGBwEDFzc2Fx4FHAIVERQWNj0BNDY3JREnAV4B9BUdHRX%2BDBUdHQEPTpBMeQ8mDXAWEOYBBRARFwsCDQ2iKBX9iexTtxYZAgUDAgIBMjIoFQFTWQRMHRVkFR0dFWQVHfzmalRubFQBLlQMAQ1uFh8BawIIEw8Mpgf%2Bqg8bBgHP%2Fq1WkhEMAQMFAwcDCQIKAv44FhITFcsPGwaDASVkAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBJSYGHQEhIgYdARQWMyEVFBY3JTY0AeLs1ptbW5vW7NabW1ubAob%2B7RAX%2Fu0KDw8KARMXEAETEASaW5vW7NabW1ub1uzWm%2F453w0KFYkPCpYKD4kVCg3fDSYAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgENAQYUFwUWNj0BITI2PQE0JiMhNTQmAeLs1ptbW5vW7NabW1ubASX%2B7RAQARMQFwETCg8PCv7tFwSaW5vW7NabW1ub1uzWm%2BjfDSYN3w0KFYkPCpYKD4kVCgAAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEBAyYiBwMGFjsBERQWOwEyNjURMzI2AeLs1ptbW5vW7NabW1ubAkvfDSYN3w0KFYkPCpYKD4kVCgSaW5vW7NabW1ub1uzWm%2F5AARMQEP7tEBf%2B7QoPDwoBExcAAAIAFgAWBJoEmgAPACUAAAAyHgIUDgIiLgI0PgEFIyIGFREjIgYXExYyNxM2JisBETQmAeLs1ptbW5vW7NabW1ubAZeWCg%2BJFQoN3w0mDd8NChWJDwSaW5vW7NabW1ub1uzWm7sPCv7tFxD%2B7RAQARMQFwETCg8AAAMAGAAYBJgEmAAPAJYApgAAADIeAhQOAiIuAjQ%2BASUOAwcGJgcOAQcGFgcOAQcGFgcUFgcyHgEXHgIXHgI3Fg4BFx4CFxQGFBcWNz4CNy4BJy4BJyIOAgcGJyY2NS4BJzYuAQYHBicmNzY3HgIXHgMfAT4CJyY%2BATc%2BAzcmNzIWMjY3LgMnND4CJiceAT8BNi4CJwYHFB4BFS4CJz4BNxYyPgEB5OjVm1xcm9Xo1ZtcXJsBZA8rHDoKDz0PFD8DAxMBAzEFCRwGIgEMFhkHECIvCxU%2FOR0HFBkDDRQjEwcFaHUeISQDDTAMD0UREi4oLBAzDwQBBikEAQMLGhIXExMLBhAGKBsGBxYVEwYFAgsFAwMNFwQGCQcYFgYQCCARFwkKKiFBCwQCAQMDHzcLDAUdLDgNEiEQEgg%2FKhADGgMKEgoRBJhcm9Xo1ZtcXJvV6NWbEQwRBwkCAwYFBycPCxcHInIWInYcCUcYChQECA4QBAkuHgQPJioRFRscBAcSCgwCch0kPiAIAQcHEAsBAgsLIxcBMQENCQIPHxkCFBkdHB4QBgEBBwoMGBENBAMMJSAQEhYXDQ4qFBkKEhIDCQsXJxQiBgEOCQwHAQ0DBAUcJAwSCwRnETIoAwEJCwsLJQcKDBEAAAAAAQAAAAIErwSFABYAAAE2FwUXNxYGBw4BJwEGIi8BJjQ3ASY2AvSkjv79kfsGUE08hjv9rA8rD28PDwJYIk8EhVxliuh%2BWYcrIgsW%2FawQEG4PKxACV2XJAAYAAABgBLAErAAPABMAIwAnADcAOwAAEyEyFh0BFAYjISImPQE0NgUjFTMFITIWHQEUBiMhIiY9ATQ2BSEVIQUhMhYdARQGIyEiJj0BNDYFIRUhZAPoKTs7KfwYKTs7BBHIyPwYA%2BgpOzsp%2FBgpOzsEEf4MAfT8GAPoKTs7KfwYKTs7BBH%2B1AEsBKw7KWQpOzspZCk7ZGTIOylkKTs7KWQpO2RkyDspZCk7OylkKTtkZAAAAAIAZAAABEwEsAALABEAABMhMhYUBiMhIiY0NgERBxEBIZYDhBUdHRX8fBUdHQI7yP6iA4QEsB0qHR0qHf1E%2FtTIAfQB9AAAAAMAAABkBLAEsAAXABsAJQAAATMyFh0BITIWFREhNSMVIRE0NjMhNTQ2FxUzNQEVFAYjISImPQEB9MgpOwEsKTv%2BDMj%2BDDspASw7KcgB9Dsp%2FBgpOwSwOylkOyn%2BcGRkAZApO2QpO2RkZP1EyCk7OynIAAAABAAAAAAEsASwABUAKwBBAFcAABMhMhYPARcWFA8BBiIvAQcGJjURNDYpATIWFREUBi8BBwYiLwEmND8BJyY2ARcWFA8BFxYGIyEiJjURNDYfATc2MgU3NhYVERQGIyEiJj8BJyY0PwE2MhcyASwVCA5exwcHaggUCMdeDhUdAzUBLBUdFQ5exwgUCGoHB8deDgj%2BL2oHB8deDggV%2FtQVHRUOXscIFALLXg4VHRX%2B1BUIDl7HBwdqCBQIBLAVDl7HCBQIagcHx14OCBUBLBUdHRX%2B1BUIDl7HBwdqCBQIx14OFf0maggUCMdeDhUdFQEsFQgOXscHzl4OCBX%2B1BUdFQ5exwgUCGoHBwAAAAYAAAAABKgEqAAPABsAIwA7AEMASwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JiQyFhQGIiY0JDIWFAYjIicHFhUUBiImNTQ2PwImNTQEMhYUBiImNCQyFhQGIiY0Advy3Z9fX5%2Fd8t2gXl6gAcbgv29vv%2BC%2Fb2%2F%2BLS0gIC0gAUwtICAWDg83ETNIMykfegEJ%2FoctICAtIAIdLSAgLSAEqF%2Bf3fLdoF5eoN3y3Z9Xb7%2Fgv29vv%2BC%2FBiAtISEtICAtIQqRFxwkMzMkIDEFfgEODhekIC0gIC0gIC0gIC0AAf%2FYAFoEuQS8AFsAACUBNjc2JicmIyIOAwcABw4EFx4BMzI3ATYnLgEjIgcGBwEOASY0NwA3PgEzMhceARcWBgcOBgcGIyImJyY2NwE2NzYzMhceARcWBgcBDgEnLgECIgHVWwgHdl8WGSJBMD8hIP6IDx4eLRMNBQlZN0ozAiQkEAcdEhoYDRr%2Bqw8pHA4BRyIjQS4ODyw9DQ4YIwwod26La1YOOEBGdiIwGkQB%2F0coW2tQSE5nDxE4Qv4eDyoQEAOtAdZbZWKbEQQUGjIhH%2F6JDxsdNSg3HT5CMwIkJCcQFBcMGv6uDwEcKQ4BTSIjIQEINykvYyMLKnhuiWZMBxtAOU6%2BRAH%2FSBg3ISSGV121Qv4kDwIPDyYAAAACAGQAWASvBEQAGQBEAAABPgIeAhUUDgMHLgQ1ND4CHgEFIg4DIi4DIyIGFRQeAhcWFx4EMj4DNzY3PgQ1NCYCiTB7eHVYNkN5hKg%2BPqeFeEM4WnZ4eQEjIT8yLSohJyktPyJDbxtBMjMPBw86KzEhDSIzKUAMBAgrKT8dF2oDtURIBS1TdkA5eYB%2FslVVsn%2BAeTlAdlMtBUgtJjY1JiY1NiZvTRc4SjQxDwcOPCouGBgwKEALBAkpKkQqMhNPbQACADn%2F8gR3BL4AFwAuAAAAMh8BFhUUBg8BJi8BNycBFwcvASY0NwEDNxYfARYUBwEGIi8BJjQ%2FARYfAQcXAQKru0KNQjgiHR8uEl%2F3%2FnvUaRONQkIBGxJpCgmNQkL%2B5UK6Qo1CQjcdLhJf9wGFBL5CjUJeKmsiHTUuEl%2F4%2FnvUahKNQrpCARv%2BRmkICY1CukL%2B5UJCjUK7Qjc3LxFf%2BAGFAAAAAAMAyAAAA%2BgEsAARABUAHQAAADIeAhURFAYjISImNRE0PgEHESERACIGFBYyNjQCBqqaZDo7Kf2oKTs8Zj4CWP7%2FVj09Vj0EsB4uMhX8Ryk7OykDuRUzLar9RAK8%2FRY9Vj09VgABAAAAAASwBLAAFgAACQEWFAYiLwEBEScBBRMBJyEBJyY0NjIDhgEbDx0qDiT%2B6dT%2BzP7oywEz0gEsAQsjDx0qBKH%2B5g8qHQ8j%2FvX%2B1NL%2BzcsBGAE01AEXJA4qHQAAAAADAScAEQQJBOAAMgBAAEsAAAEVHgQXIy4DJxEXHgQVFAYHFSM1JicuASczHgEXEScuBDU0PgI3NRkBDgMVFB4DFxYXET4ENC4CArwmRVI8LAKfBA0dMydAIjxQNyiym2SWVygZA4sFV0obLkJOMCAyVWg6HSoqFQ4TJhkZCWgWKTEiGBkzNwTgTgUTLD9pQiQuLBsH%2Fs0NBxMtPGQ%2Bi6oMTU8QVyhrVk1iEAFPCA4ZLzlYNkZwSCoGTf4SARIEDh02Jh0rGRQIBgPQ%2FsoCCRYgNEM0JRkAAAABAGQAZgOUBK0ASgAAATIeARUjNC4CIyIGBwYVFB4BFxYXMxUjFgYHBgc%2BATM2FjMyNxcOAyMiLgEHDgEPASc%2BBTc%2BAScjNTMmJy4CPgE3NgIxVJlemSc8OxolVBQpGxoYBgPxxQgVFS02ImIWIIwiUzUyHzY4HCAXanQmJ1YYFzcEGAcTDBEJMAwk3aYXFQcKAg4tJGEErVCLTig%2FIhIdFSw5GkowKgkFZDKCHj4yCg8BIh6TExcIASIfBAMaDAuRAxAFDQsRCjePR2QvORQrREFMIVgAAAACABn%2F%2FwSXBLAADwAfAAABMzIWDwEGIi8BJjY7AREzBRcWBisBESMRIyImPwE2MgGQlhUIDuYOKg7mDggVlsgCF%2BYOCBWWyJYVCA7mDioBLBYO%2Bg8P%2Bg4WA4QQ%2BQ4V%2FHwDhBUO%2BQ8AAAQAGf%2F%2FA%2BgEsAAHABcAGwAlAAABIzUjFSMRIQEzMhYPAQYiLwEmNjsBETMFFTM1EwczFSE1NyM1IQPoZGRkASz9qJYVCA7mDioO5g4IFZbIAZFkY8jI%2FtTIyAEsArxkZAH0%2FHwWDvoPD%2FoOFgOEZMjI%2FRL6ZJb6ZAAAAAAEABn%2F%2FwPoBLAADwAZACEAJQAAATMyFg8BBiIvASY2OwERMwUHMxUhNTcjNSERIzUjFSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAljIyP7UyMgBLGRkZAEsx2QBLBYO%2Bg8P%2Bg4WA4SW%2BmSW%2BmT7UGRkAfRkyMgAAAAEABn%2F%2FwRMBLAADwAVABsAHwAAATMyFg8BBiIvASY2OwERMwEjESM1MxMjNSMRIQcVMzUBkJYVCA7mDioO5g4IFZbIAlhkZMhkZMgBLMdkASwWDvoPD%2FoOFgOE%2FgwBkGT7UGQBkGTIyAAAAAAEABn%2F%2FwRMBLAADwAVABkAHwAAATMyFg8BBiIvASY2OwERMwEjNSMRIQcVMzUDIxEjNTMBkJYVCA7mDioO5g4IFZbIArxkyAEsx2QBZGTIASwWDvoPD%2FoOFgOE%2FgxkAZBkyMj7tAGQZAAAAAAFABn%2F%2FwSwBLAADwATABcAGwAfAAABMzIWDwEGIi8BJjY7AREzBSM1MxMhNSETITUhEyE1IQGQlhUIDuYOKg7mDggVlsgB9MjIZP7UASxk%2FnABkGT%2BDAH0ASwWDvoPD%2FoOFgOEyMj%2BDMj%2BDMj%2BDMgABQAZ%2F%2F8EsASwAA8AEwAXABsAHwAAATMyFg8BBiIvASY2OwERMwUhNSEDITUhAyE1IQMjNTMBkJYVCA7mDioO5g4IFZbIAyD%2BDAH0ZP5wAZBk%2FtQBLGTIyAEsFg76Dw%2F6DhYDhMjI%2FgzI%2FgzI%2FgzIAAIAAAAABEwETAAPAB8AAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmAV4BkKK8u6P%2BcKW5uQJn%2FgwpOzspAfQpOzsETLuj%2FnClubmlAZClucg7Kf4MKTs7KQH0KTsAAAAAAwAAAAAETARMAA8AHwArAAABITIWFREUBiMhIiY1ETQ2BSEiBhURFBYzITI2NRE0JgUXFhQPAQYmNRE0NgFeAZClubml%2FnCju7wCZP4MKTs7KQH0KTs7%2Fm%2F9ERH9EBgYBEy5pf5wpbm5pQGQo7vIOyn%2BDCk7OykB9Ck7gr4MJAy%2BDAsVAZAVCwAAAAADAAAAAARMBEwADwAfACsAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFg8BBiIvASY2AV4BkKO7uaX%2BcKW5uQJn%2FgwpOzspAfQpOzv%2BFQGQFQsMvgwkDL4MCwRMvKL%2BcKW5uaUBkKO7yDsp%2FgwpOzspAfQpO8gYEP0REf0QGAAAAAMAAAAABEwETAAPAB8AKwAAASEyFhURFAYjISImNRE0NgUhIgYVERQWMyEyNjURNCYFFxYGIyEiJj8BNjIBXgGQpbm5pf5wo7u5Amf%2BDCk7OykB9Ck7O%2F77vgwLFf5wFQsMvgwkBEy5pf5wo7u8ogGQpbnIOyn%2BDCk7OykB9Ck7z%2F0QGBgQ%2FREAAAAAAgAAAAAFFARMAB8ANQAAASEyFhURFAYjISImPQE0NjMhMjY1ETQmIyEiJj0BNDYHARYUBwEGJj0BIyImPQE0NjsBNTQ2AiYBkKW5uaX%2BcBUdHRUBwik7Oyn%2BPhUdHb8BRBAQ%2FrwQFvoVHR0V%2BhYETLml%2FnCluR0VZBUdOykB9Ck7HRVkFR3p%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgAAAQDZAAID1wSeACMAAAEXFgcGAgclMhYHIggBBwYrAScmNz4BPwEhIicmNzYANjc2MwMZCQgDA5gCASwYEQ4B%2Fvf%2B8wQMDgkJCQUCUCcn%2FtIXCAoQSwENuwUJEASeCQoRC%2F5TBwEjEv7K%2FsUFDwgLFQnlbm4TFRRWAS%2FTBhAAAAACAAAAAAT%2BBEwAHwA1AAABITIWHQEUBiMhIgYVERQWMyEyFh0BFAYjISImNRE0NgUBFhQHAQYmPQEjIiY9ATQ2OwE1NDYBXgGQFR0dFf4%2BKTs7KQHCFR0dFf5wpbm5AvEBRBAQ%2FrwQFvoVHR0V%2BhYETB0VZBUdOyn%2BDCk7HRVkFR25pQGQpbnp%2FuQOJg7%2B5A4KFZYdFcgVHZYVCgACAAAAAASwBLAAFQAxAAABITIWFREUBi8BAQYiLwEmNDcBJyY2ASMiBhURFBYzITI2PQE3ERQGIyEiJjURNDYzIQLuAZAVHRUObf7IDykPjQ8PAThtDgj%2B75wpOzspAfQpO8i7o%2F5wpbm5pQEsBLAdFf5wFQgObf7IDw%2BNDykPAThtDhX%2B1Dsp%2FgwpOzsplMj%2B1qW5uaUBkKW5AAADAA4ADgSiBKIADwAbACMAAAAyHgIUDgIiLgI0PgEEIg4BFB4BMj4BNCYEMhYUBiImNAHh7tmdXV2d2e7ZnV1dnQHD5sJxccLmwnFx%2FnugcnKgcgSiXZ3Z7tmdXV2d2e7ZnUdxwubCcXHC5sJzcqBycqAAAAMAAAAABEwEsAAVAB8AIwAAATMyFhURMzIWBwEGIicBJjY7ARE0NgEhMhYdASE1NDYFFTM1AcLIFR31FAoO%2FoEOJw3%2BhQ0JFfod%2FoUD6BUd%2B7QdA2dkBLAdFf6iFg%2F%2BVg8PAaoPFgFeFR38fB0V%2BvoVHWQyMgAAAAMAAAAABEwErAAVAB8AIwAACQEWBisBFRQGKwEiJj0BIyImNwE%2BAQEhMhYdASE1NDYFFTM1AkcBeg4KFfQiFsgUGPoUCw4Bfw4n%2FfkD6BUd%2B7QdA2dkBJ7%2BTQ8g%2BhQeHRX6IQ8BrxAC%2FH8dFfr6FR1kMjIAAwAAAAAETARLABQAHgAiAAAJATYyHwEWFAcBBiInASY0PwE2MhcDITIWHQEhNTQ2BRUzNQGMAXEHFQeLBwf98wcVB%2F7cBweLCBUH1APoFR37tB0DZ2QC0wFxBweLCBUH%2FfMICAEjCBQIiwcH%2FdIdFfr6FR1kMjIABAAAAAAETASbAAkAGQAjACcAABM3NjIfAQcnJjQFNzYWFQMOASMFIiY%2FASc3ASEyFh0BITU0NgUVMzWHjg4qDk3UTQ4CFtIOFQIBHRX9qxUIDtCa1P49A%2BgVHfu0HQNnZAP%2Fjg4OTdRMDyqa0g4IFf2pFB4BFQ7Qm9T9Oh0V%2BvoVHWQyMgAAAAQAAAAABEwEsAAPABkAIwAnAAABBR4BFRMUBi8BByc3JyY2EwcGIi8BJjQ%2FAQEhMhYdASE1NDYFFTM1AV4CVxQeARUO0JvUm9IOCMNMDyoOjg4OTf76A%2BgVHfu0HQNnZASwAgEdFf2rFQgO0JrUmtIOFf1QTQ4Ojg4qDk3%2BWB0V%2BvoVHWQyMgACAAT%2F7ASwBK8ABQAIAAAlCQERIQkBFQEEsP4d%2Fsb%2BcQSs%2FTMCq2cBFP5xAacDHPz55gO5AAAAAAIAAABkBEwEsAAVABkAAAERFAYrAREhESMiJjURNDY7AREhETMHIzUzBEwdFZb9RJYVHR0V%2BgH0ZMhkZAPo%2FK4VHQGQ%2FnAdFQPoFB7%2B1AEsyMgAAAMAAABFBN0EsAAWABoALwAAAQcBJyYiDwEhESMiJjURNDY7AREhETMHIzUzARcWFAcBBiIvASY0PwE2Mh8BATYyBEwC%2FtVfCRkJlf7IlhUdHRX6AfRkyGRkAbBqBwf%2BXAgUCMoICGoHFQdPASkHFQPolf7VXwkJk%2F5wHRUD6BQe%2FtQBLMjI%2Fc5qBxUH%2FlsHB8sHFQdqCAhPASkHAAMAAAANBQcEsAAWABoAPgAAAREHJy4BBwEhESMiJjURNDY7AREhETMHIzUzARcWFA8BFxYUDwEGIi8BBwYiLwEmND8BJyY0PwE2Mh8BNzYyBExnhg8lEP72%2FreWFR0dFfoB9GTIZGQB9kYPD4ODDw9GDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykD6P7zZ4YPAw7%2B9v5wHRUD6BQe%2FtQBLMjI%2FYxGDykPg4MPKQ9GDw%2BDgw8PRg8pD4ODDykPRg8Pg4MPAAADAAAAFQSXBLAAFQAZAC8AAAERISIGHQEhESMiJjURNDY7AREhETMHIzUzEzMyFh0BMzIWDwEGIi8BJjY7ATU0NgRM%2FqIVHf4MlhUdHRX6AfRkyGRklmQVHZYVCA7mDioO5g4IFZYdA%2Bj%2B1B0Vlv5wHRUD6BQe%2FtQBLMjI%2FagdFfoVDuYODuYOFfoVHQAAAAADAAAAAASXBLAAFQAZAC8AAAERJyYiBwEhESMiJjURNDY7AREhETMHIzUzExcWBisBFRQGKwEiJj0BIyImPwE2MgRMpQ4qDv75%2Fm6WFR0dFfoB9GTIZGTr5g4IFZYdFWQVHZYVCA7mDioD6P5wpQ8P%2Fvf%2BcB0VA%2BgUHv7UASzIyP2F5Q8V%2BhQeHhT6FQ%2FlDwADAAAAyASwBEwACQATABcAABMhMhYdASE1NDYBERQGIyEiJjURExUhNTIETBUd%2B1AdBJMdFfu0FR1kAZAETB0VlpYVHf7U%2FdoVHR0VAib%2B1MjIAAAGAAMAfQStBJcADwAZAB0ALQAxADsAAAEXFhQPAQYmPQEhNSE1NDYBIyImPQE0NjsBFyM1MwE3NhYdASEVIRUUBi8BJjQFIzU7AjIWHQEUBisBA6f4Dg74DhX%2BcAGQFf0vMhUdHRUyyGRk%2FoL3DhUBkP5wFQ73DwOBZGRkMxQdHRQzBI3mDioO5g4IFZbIlhUI%2FoUdFWQVHcjI%2FcvmDggVlsiWFQgO5g4qecgdFWQVHQAAAAACAGQAAASwBLAAFgBRAAABJTYWFREUBisBIiY1ES4ENRE0NiUyFh8BERQOAg8BERQGKwEiJjURLgQ1ETQ%2BAzMyFh8BETMRPAE%2BAjMyFh8BETMRND4DA14BFBklHRXIFR0EDiIaFiX%2B4RYZAgEVHR0LCh0VyBUdBA4iGhYBBwoTDRQZAgNkBQkVDxcZAQFkAQUJFQQxdBIUH%2FuuFR0dFQGNAQgbHzUeAWcfRJEZDA3%2BPhw%2FMSkLC%2F5BFR0dFQG%2FBA8uLkAcAcICBxENCxkMDf6iAV4CBxENCxkMDf6iAV4CBxENCwABAGQAAASwBEwAMwAAARUiDgMVERQWHwEVITUyNjURIREUFjMVITUyPgM1ETQmLwE1IRUiBhURIRE0JiM1BLAEDiIaFjIZGf5wSxn%2BDBlL%2FnAEDiIaFjIZGQGQSxkB9BlLBEw4AQUKFA78iBYZAQI4OA0lAYr%2BdiUNODgBBQoUDgN4FhkBAjg4DSX%2BdgGKJQ04AAAABgAAAAAETARMAAwAHAAgACQAKAA0AAABITIWHQEjBTUnITchBSEyFhURFAYjISImNRE0NhcVITUBBTUlBRUhNQUVFAYjIQchJyE3MwKjAXcVHWn%2B2cj%2BcGQBd%2F4lASwpOzsp%2FtQpOzspASwCvP5wAZD8GAEsArwdFf6JZP6JZAGQyGkD6B0VlmJiyGTIOyn%2BDCk7OykB9Ck7ZMjI%2FveFo4XGyMhm%2BBUdZGTIAAEAEAAQBJ8EnwAmAAATNzYWHwEWBg8BHgEXNz4BHwEeAQ8BBiIuBicuBTcRohEuDosOBhF3ZvyNdxEzE8ATBxGjAw0uMUxPZWZ4O0p3RjITCwED76IRBhPCFDERdo78ZXYRBA6IDi8RogEECBUgNUNjO0qZfHNVQBAAAAACAAAAAASwBEwAIwBBAAAAMh4EHwEVFAYvAS4BPQEmIAcVFAYPAQYmPQE%2BBRIyHgIfARUBHgEdARQGIyEiJj0BNDY3ATU0PgIB%2FLimdWQ%2FLAkJHRTKFB2N%2FsKNHRTKFB0DDTE7ZnTKcFImFgEBAW0OFR0V%2B7QVHRUOAW0CFiYETBUhKCgiCgrIFRgDIgMiFZIYGJIVIgMiAxgVyAQNJyQrIP7kExwcCgoy%2FtEPMhTUFR0dFdQUMg8BLzIEDSEZAAADAAAAAASwBLAADQAdACcAAAEHIScRMxUzNTMVMzUzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYETMj9qMjIyMjIyPyuArwVHR0VDIn8SokMFR0dswRMFR37UB0CvMjIAfTIyMjI%2FOAdKh1kZB0qHf7UHRUyMhUdAAAAAwBkAAAEsARMAAkAEwAdAAABIyIGFREhETQmASMiBhURIRE0JgEhETQ2OwEyFhUCvGQpOwEsOwFnZCk7ASw7%2FRv%2B1DspZCk7BEw7KfwYA%2BgpO%2F7UOyn9RAK8KTv84AGQKTs7KQAAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQURByMRMwcRMxHIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkyMhkZARMsHz%2BDHywsHwB9HywyP1EArzIZP7UZGQBLGT%2B1GQB9GT%2B1AEsAAAABf%2BcAAAEsARMAA8AEwAfACUAKQAAEyEyFhURFAYjISImNRE0NhcRIREBIzUjFSMRMxUzNTMFEQcjETMHETMRyAK8fLCwfP1EfLCwGAOE%2FgxkZGRkZGQBkGTIyGRkBEywfP4MfLCwfAH0fLDI%2FUQCvP2oyMgB9MjIZP7UZAH0ZP7UASwABP%2BcAAAEsARMAA8AEwAbACMAABMhMhYVERQGIyEiJjURNDYXESERBSMRMxUhESEFIxEzFSERIcgCvHywsHz9RHywsBgDhP4MyMj%2B1AEsAZDIyP7UASwETLB8%2Fgx8sLB8AfR8sMj9RAK8yP7UZAH0ZP7UZAH0AAAABP%2BcAAAEsARMAA8AEwAWABkAABMhMhYVERQGIyEiJjURNDYXESERAS0BDQERyAK8fLCwfP1EfLCwGAOE%2Fgz%2B1AEsAZD%2B1ARMsHz%2BDHywsHwB9HywyP1EArz%2BDJaWlpYBLAAAAAX%2FnAAABLAETAAPABMAFwAgACkAABMhMhYVERQGIyEiJjURNDYXESERAyERIQcjIgYVFBY7AQERMzI2NTQmI8gCvHywsHz9RHywsBgDhGT9RAK8ZIImOTYpgv4Mgik2OSYETLB8%2Fgx8sLB8AfR8sMj9RAK8%2FagB9GRWQUFUASz%2B1FRBQVYAAAAF%2F5wAAASwBEwADwATAB8AJQApAAATITIWFREUBiMhIiY1ETQ2FxEhEQUjFTMRITUzNSMRIQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMjI%2FtTIyAEsAZBkZMjIZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yGT%2B1GRkASz%2BDAGQZP4MZAAG%2F5wAAASwBEwADwATABkAHwAjACcAABMhMhYVERQGIyEiJjURNDYXESERBTMRIREzASMRIzUzBRUzNQEjNTPIArx8sLB8%2FUR8sLAYA4T9RMj%2B1GQCWGRkyP2oZAEsZGQETLB8%2Fgx8sLB8AfR8sMj9RAK8yP5wAfT%2BDAGQZMjIyP7UZAAF%2F5wAAASwBEwADwATABwAIgAmAAATITIWFREUBiMhIiY1ETQ2FxEhEQEHIzU3NSM1IQEjESM1MwMjNTPIArx8sLB8%2FUR8sLAYA4T%2BDMdkx8gBLAGQZGTIx2RkBEywfP4MfLCwfAH0fLDI%2FUQCvP5wyDLIlmT%2BDAGQZP4MZAAAAAMACQAJBKcEpwAPABsAJQAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgchFSEVISc1NyEB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWz%2B1AEs%2FtRkZAEsBKdentvw255eXp7b8NueTHHC5MJxccLkwtDIZGTIZAAAAAAEAAkACQSnBKcADwAbACcAKwAAADIeAhQOAiIuAjQ%2BAQQiDgEUHgEyPgE0JgcVBxcVIycjFSMRIQcVMzUB4PDbnl5entvw255eXp4BxeTCcXHC5MJxcWwyZGRklmQBLMjIBKdentvw255eXp7b8NueTHHC5MJxccLkwtBkMmQyZGQBkGRkZAAAAv%2Fy%2F50EwgRBACAANgAAATIWFzYzMhYUBisBNTQmIyEiBh0BIyImNTQ2NyY1ND4BEzMyFhURMzIWDwEGIi8BJjY7ARE0NgH3brUsLC54qqp4gB0V%2FtQVHd5QcFZBAmKqepYKD4kVCg3fDSYN3w0KFYkPBEF3YQ6t8a36FR0dFfpzT0VrDhMSZKpi%2FbMPCv7tFxD0EBD0EBcBEwoPAAAAAAL%2F8v%2BcBMMEQQAcADMAAAEyFhc2MzIWFxQGBwEmIgcBIyImNTQ2NyY1ND4BExcWBisBERQGKwEiJjURIyImNzY3NjIB9m62LCsueaoBeFr%2Bhg0lDf6DCU9xVkECYqnm3w0KFYkPCpYKD4kVCg3HGBMZBEF3YQ%2BteGOkHAFoEBD%2Bk3NPRWsOExNkqWP9kuQQF%2F7tCg8PCgETFxDMGBMAAAABAGQAAARMBG0AGAAAJTUhATMBMwkBMwEzASEVIyIGHQEhNTQmIwK8AZD%2B8qr%2B8qr%2B1P7Uqv7yqv7yAZAyFR0BkB0VZGQBLAEsAU3%2Bs%2F7U%2FtRkHRUyMhUdAAAAAAEAeQAABDcEmwAvAAABMhYXHgEVFAYHFhUUBiMiJxUyFh0BITU0NjM1BiMiJjU0Ny4BNTQ2MzIXNCY1NDYCWF6TGll7OzIJaUo3LRUd%2FtQdFS03SmkELzlpSgUSAqMEm3FZBoNaPWcfHRpKaR77HRUyMhUd%2Bx5pShIUFVg1SmkCAhAFdKMAAAAGACcAFASJBJwAEQAqAEIASgBiAHsAAAEWEgIHDgEiJicmAhI3PgEyFgUiBw4BBwYWHwEWMzI3Njc2Nz4BLwEmJyYXIgcOAQcGFh8BFjMyNz4BNz4BLwEmJyYWJiIGFBYyNjciBw4BBw4BHwEWFxYzMjc%2BATc2Ji8BJhciBwYHBgcOAR8BFhcWMzI3PgE3NiYvASYD8m9PT29T2dzZU29PT29T2dzZ%2Fj0EBHmxIgQNDCQDBBcGG0dGYAsNAwkDCwccBAVQdRgEDA0iBAQWBhJROQwMAwkDCwf5Y4xjY4xjVhYGElE6CwwDCQMLBwgEBVB1GAQNDCIEjRcGG0dGYAsNAwkDCwcIBAR5sSIEDQwkAwPyb%2F7V%2FtVvU1dXU28BKwErb1NXVxwBIrF5DBYDCQEWYEZHGwMVDCMNBgSRAhh1UA0WAwkBFTpREgMVCyMMBwT6Y2OMY2MVFTpREQQVCyMMBwQCGHVQDRYDCQEkFmBGRxsDFQwjDQYEASKxeQwWAwkBAAAABQBkAAAD6ASwAAwADwAWABwAIgAAASERIzUhFSERNDYzIQEjNQMzByczNTMDISImNREFFRQGKwECvAEstP6s%2FoQPCgI%2FASzIZKLU1KJktP51Cg8DhA8KwwMg%2FoTIyALzCg%2F%2B1Mj84NTUyP4MDwoBi8jDCg8AAAAABQBkAAAD6ASwAAkADAATABoAIQAAASERCQERNDYzIQEjNRMjFSM1IzcDISImPQEpARUUBisBNQK8ASz%2Bov3aDwoCPwEsyD6iZKLUqv6dCg8BfAIIDwqbAyD9%2BAFe%2FdoERwoP%2FtTI%2FHzIyNT%2BZA8KNzcKD1AAAAAAAwAAAAAEsAP0AAgAGQAfAAABIxUzFyERIzcFMzIeAhUhFSEDETM0PgIBMwMhASEEiqJkZP7UotT9EsgbGiEOASz9qMhkDiEaAnPw8PzgASwB9AMgyGQBLNTUBBErJGT%2BogHCJCsRBP5w%2FnAB9AAAAAMAAAAABEwETAAZADIAOQAAATMyFh0BMzIWHQEUBiMhIiY9ATQ2OwE1NDYFNTIWFREUBiMhIic3ARE0NjMVFBYzITI2AQc1IzUzNQKKZBUdMhUdHRX%2B1BUdHRUyHQFzKTs7Kf2oARP2%2Fro7KVg%2BASw%2BWP201MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7KfzgKTsE9gFGAUQpO5Y%2BWFj95tSiZKIAAwBkAAAEvARMABkANgA9AAABMzIWHQEzMhYdARQGIyEiJj0BNDY7ATU0NgU1MhYVESMRMxQOAiMhIiY1ETQ2MxUUFjMhMjYBBzUjNTM1AcJkFR0yFR0dFf7UFR0dFTIdAXMpO8jIDiEaG%2F2oKTs7KVg%2BASw%2BWAGc1MjIBEwdFTIdFWQVHR0VZBUdMhUd%2BpY7Kf4M%2FtQkKxEEOykDICk7lj5YWP3m1KJkogAAAAP%2FogAABRYE1AALABsAHwAACQEWBiMhIiY3ATYyEyMiBhcTHgE7ATI2NxM2JgMVMzUCkgJ9FyAs%2BwQsIBcCfRZARNAUGAQ6BCMUNhQjBDoEGODIBK37sCY3NyYEUCf%2BTB0U%2FtIUHR0UAS4UHf4MZGQAAAAACQAAAAAETARMAA8AHwAvAD8ATwBfAG8AfwCPAAABMzIWHQEUBisBIiY9ATQ2EzMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBMzIWHQEUBisBIiY9ATQ2ITMyFh0BFAYrASImPQE0NiEzMhYdARQGKwEiJj0BNDYBqfoKDw8K%2BgoPDwr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPD%2Fzq%2BgoPDwr6Cg8PAZr6Cg8PCvoKDw8BmvoKDw8K%2BgoPDwRMDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg%2F%2B1A8KlgoPDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKD%2F7UDwqWCg8PCpYKDw8KlgoPDwqWCg8PCpYKDw8KlgoPAAAAAwAAAAAEsAUUABkAKQAzAAABMxUjFSEyFg8BBgchJi8BJjYzITUjNTM1MwEhMhYUBisBFyE3IyImNDYDITIWHQEhNTQ2ArxkZAFePjEcQiko%2FPwoKUIcMT4BXmRkyP4%2BArwVHR0VDIn8SooNFR0dswRMFR37UB0EsMhkTzeEUzMzU4Q3T2TIZPx8HSodZGQdKh3%2B1B0VMjIVHQAABAAAAAAEsAUUAAUAGQArADUAAAAyFhUjNAchFhUUByEyFg8BIScmNjMhJjU0AyEyFhQGKwEVBSElNSMiJjQ2AyEyFh0BITU0NgIwUDnCPAE6EgMBSCkHIq%2F9WrIiCikBSAOvArwVHR0VlgET%2FEoBE5YVHR2zBEwVHftQHQUUOykpjSUmCBEhFpGRFiERCCb%2BlR0qHcjIyMgdKh39qB0VMjIVHQAEAAAAAASwBJ0ABwAUACQALgAAADIWFAYiJjQTMzIWFRQXITY1NDYzASEyFhQGKwEXITcjIiY0NgMhMhYdASE1NDYCDZZqapZqty4iKyf%2BvCcrI%2F7NArwVHR0VDYr8SokMFR0dswRMFR37UB0EnWqWamqW%2Fus5Okxra0w6Of5yHSodZGQdKh3%2B1B0VMjIVHQAEAAAAAASwBRQADwAcACwANgAAATIeARUUBiImNTQ3FzcnNhMzMhYVFBchNjU0NjMBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYL1szb5xvIpBvoyIfLiIrJ%2F68Jysj%2Fs0CvBUdHRUNivxKiQwVHR2zBEwVHftQHQUUa4s2Tm9vTj5Rj2%2BjGv4KOTpMa2tMOjn%2Bch0qHWRkHSod%2FtQdFTIyFR0AAAADAAAAAASwBRIAEgAiACwAAAEFFSEUHgMXIS4BNTQ%2BAjcBITIWFAYrARchNyMiJjQ2AyEyFh0BITU0NgJYASz%2B1CU%2FP00T%2Fe48PUJtj0r%2BogK8FR0dFQ2K%2FEqJDBUdHbMETBUd%2B1AdBLChizlmUT9IGVO9VFShdksE%2FH4dKh1kZB0qHf7UHRUyMhUdAAIAyAAAA%2BgFFAAPACkAAAAyFh0BHgEdASE1NDY3NTQDITIWFyMVMxUjFTMVIxUzFAYjISImNRE0NgIvUjsuNv5wNi5kAZA2XBqsyMjIyMh1U%2F5wU3V1BRQ7KU4aXDYyMjZcGk4p%2Fkc2LmRkZGRkU3V1UwGQU3UAAAMAZP%2F%2FBEwETAAPAC8AMwAAEyEyFhURFAYjISImNRE0NgMhMhYdARQGIyEXFhQGIi8BIQcGIiY0PwEhIiY9ATQ2BQchJ5YDhBUdHRX8fBUdHQQDtgoPDwr%2B5eANGiUNWP30Vw0mGg3g%2Ft8KDw8BqmQBRGQETB0V%2FgwVHR0VAfQVHf1EDwoyCg%2FgDSUbDVhYDRslDeAPCjIKD2RkZAAAAAAEAAAAAASwBEwAGQAjAC0ANwAAEyEyFh0BIzQmKwEiBhUjNCYrASIGFSM1NDYDITIWFREhETQ2ExUUBisBIiY9ASEVFAYrASImPQHIAyBTdWQ7KfopO2Q7KfopO2R1EQPoKTv7UDvxHRVkFR0D6B0VZBUdBEx1U8gpOzspKTs7KchTdf4MOyn%2B1AEsKTv%2BDDIVHR0VMjIVHR0VMgADAAEAAASpBKwADQARABsAAAkBFhQPASEBJjQ3ATYyCQMDITIWHQEhNTQ2AeACqh8fg%2F4f%2FfsgIAEnH1n%2BrAFWAS%2F%2Bq6IDIBUd%2FHwdBI39VR9ZH4MCBh9ZHwEoH%2F5u%2FqoBMAFV%2FBsdFTIyFR0AAAAAAgCPAAAEIQSwABcALwAAAQMuASMhIgYHAwYWMyEVFBYyNj0BMzI2AyE1NDY7ATU0NjsBETMRMzIWHQEzMhYVBCG9CCcV%2FnAVJwi9CBMVAnEdKh19FROo%2Fa0dFTIdFTDILxUdMhUdAocB%2BhMcHBP%2BBhMclhUdHRWWHP2MMhUdMhUdASz%2B1B0VMh0VAAAEAAAAAASwBLAADQAQAB8AIgAAASERFAYjIREBNTQ2MyEBIzUBIREUBiMhIiY1ETQ2MyEBIzUDhAEsDwr%2Bif7UDwoBdwEsyP2oASwPCv12Cg8PCgF3ASzIAyD9wQoPAk8BLFQKD%2F7UyP4M%2FcEKDw8KA7YKD%2F7UyAAC%2F5wAZAUUBEcARgBWAAABMzIeAhcWFxY2NzYnJjc%2BARYXFgcOASsBDgEPAQ4BKwEiJj8BBisBIicHDgErASImPwEmLwEuAT0BNDY7ATY3JyY2OwE2BSMiBh0BFBY7ATI2PQE0JgHkw0uOakkMEhEfQwoKGRMKBQ8XDCkCA1Y9Pgc4HCcDIhVkFRgDDDEqwxgpCwMiFWQVGAMaVCyfExwdFXwLLW8QBxXLdAFF%2BgoPDwr6Cg8PBEdBa4pJDgYKISAiJRsQCAYIDCw9P1c3fCbqFB0dFEYOCEAUHR0UnUplNQcmFTIVHVdPXw4TZV8PCjIKDw8KMgoPAAb%2FnP%2FmBRQEfgAJACQANAA8AFIAYgAAASU2Fh8BFgYPASUzMhYfASEyFh0BFAYHBQYmJyYjISImPQE0NhcjIgYdARQ7ATI2NTQmJyYEIgYUFjI2NAE3PgEeARceAT8BFxYGDwEGJi8BJjYlBwYfAR4BPwE2Jy4BJy4BAoEBpxMuDiAOAxCL%2FCtqQ0geZgM3FR0cE%2F0fFyIJKjr%2B1D5YWLlQExIqhhALIAsSAYBALS1ALf4PmBIgHhMQHC0aPzANITNQL3wpgigJASlmHyElDR0RPRMFAhQHCxADhPcICxAmDyoNeMgiNtQdFTIVJgeEBBQPQ1g%2ByD5YrBwVODMQEAtEERzJLUAtLUD%2B24ITChESEyMgAwWzPUkrRSgJL5cvfRxYGyYrDwkLNRAhFEgJDAQAAAAAAwBkAAAEOQSwAFEAYABvAAABMzIWHQEeARcWDgIPATIeBRUUDgUjFRQGKwEiJj0BIxUUBisBIiY9ASMiJj0BNDY7AREjIiY9ATQ2OwE1NDY7ATIWHQEzNTQ2AxUhMj4CNTc0LgMjARUhMj4CNTc0LgMjAnGWCg9PaAEBIC4uEBEGEjQwOiodFyI2LUAjGg8KlgoPZA8KlgoPrwoPDwpLSwoPDwqvDwqWCg9kD9cBBxwpEwsBAQsTKRz%2B%2BQFrHCkTCwEBCxMpHASwDwptIW1KLk0tHwYGAw8UKDJOLTtdPCoVCwJLCg8PCktLCg8PCksPCpYKDwJYDwqWCg9LCg8PCktLCg%2F%2B1MgVHR0LCgQOIhoW%2FnDIFR0dCwoEDiIaFgAAAwAEAAIEsASuABcAKQAsAAATITIWFREUBg8BDgEjISImJy4CNRE0NgQiDgQPARchNy4FAyMT1AMMVnokEhIdgVL9xFKCHAgYKHoCIIx9VkcrHQYGnAIwnAIIIClJVSGdwwSuelb%2BYDO3QkJXd3ZYHFrFMwGgVnqZFyYtLSUMDPPzBQ8sKDEj%2FsIBBQACAMgAAAOEBRQADwAZAAABMzIWFREUBiMhIiY1ETQ2ARUUBisBIiY9AQHblmesVCn%2BPilUrAFINhWWFTYFFKxn%2FgwpVFQpAfRnrPwY4RU2NhXhAAACAMgAAAOEBRQADwAZAAABMxQWMxEUBiMhIiY1ETQ2ARUUBisBIiY9AQHbYLOWVCn%2BPilUrAFINhWWFTYFFJaz%2FkIpVFQpAfRnrPwY4RU2NhXhAAACAAAAFAUOBBoAFAAaAAAJASUHFRcVJwc1NzU0Jj4CPwEnCQEFJTUFJQUO%2FYL%2Bhk5klpZkAQEBBQQvkwKCAVz%2Bov6iAV4BXgL%2F%2FuWqPOCWx5SVyJb6BA0GCgYDKEEBG%2F1ipqaTpaUAAAMAZAH0BLADIAAHAA8AFwAAEjIWFAYiJjQkMhYUBiImNCQyFhQGIiY0vHxYWHxYAeh8WFh8WAHofFhYfFgDIFh8WFh8WFh8WFh8WFh8WFh8AAAAAAMBkAAAArwETAAHAA8AFwAAADIWFAYiJjQSMhYUBiImNBIyFhQGIiY0Aeh8WFh8WFh8WFh8WFh8WFh8WARMWHxYWHz%2ByFh8WFh8%2FshYfFhYfAAAAAMAZABkBEwETAAPAB8ALwAAEyEyFh0BFAYjISImPQE0NhMhMhYdARQGIyEiJj0BNDYTITIWHQEUBiMhIiY9ATQ2fQO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PCgO2Cg8PCvxKCg8PBEwPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoP%2FnAPCpYKDw8KlgoPAAAABAAAAAAEsASwAA8AHwAvADMAAAEhMhYVERQGIyEiJjURNDYFISIGFREUFjMhMjY1ETQmBSEyFhURFAYjISImNRE0NhcVITUBXgH0ory7o%2F4Mpbm5Asv9qCk7OykCWCk7O%2F2xAfQVHR0V%2FgwVHR1HAZAEsLuj%2FgylubmlAfSlucg7Kf2oKTs7KQJYKTtkHRX%2B1BUdHRUBLBUdZMjIAAAAAAEAZABkBLAETAA7AAATITIWFAYrARUzMhYUBisBFTMyFhQGKwEVMzIWFAYjISImNDY7ATUjIiY0NjsBNSMiJjQ2OwE1IyImNDaWA%2BgVHR0VMjIVHR0VMjIVHR0VMjIVHR0V%2FBgVHR0VMjIVHR0VMjIVHR0VMjIVHR0ETB0qHcgdKh3IHSodyB0qHR0qHcgdKh3IHSodyB0qHQAAAAYBLAAFA%2BgEowAHAA0AEwAZAB8AKgAAAR4BBgcuATYBMhYVIiYlFAYjNDYBMhYVIiYlFAYjNDYDFRQGIiY9ARYzMgKKVz8%2FV1c%2FP%2F75fLB8sAK8sHyw%2FcB8sHywArywfLCwHSodKAMRBKNDsrJCQrKy%2FsCwfLB8fLB8sP7UsHywfHywfLD%2B05AVHR0VjgQAAAH%2FtQDIBJQDgQBCAAABNzYXAR4BBw4BKwEyFRQOBCsBIhE0NyYiBxYVECsBIi4DNTQzIyImJyY2NwE2HwEeAQ4BLwEHIScHBi4BNgLpRRkUASoLCAYFGg8IAQQNGyc%2FKZK4ChRUFQu4jjBJJxkHAgcPGQYGCAsBKhQaTBQVCiMUM7YDe7YsFCMKFgNuEwYS%2FtkLHw8OEw0dNkY4MhwBIBgXBAQYF%2F7gKjxTQyMNEw4PHwoBKBIHEwUjKBYGDMHBDAUWKCMAAAAAAgAAAAAEsASwACUAQwAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjNC4DKwERFBYXMxUjNTI1ESMiDgMVIzUhBLAyCAsZEyYYGcgyGRn%2BcAQOIhoWyBkYJhMZCwgyA%2Bj9RBkIChgQEWQZDQzIMmQREBgKCBkB9AOEFSAVDggDAfyuFhkBAmRkAQUJFQ4DUgEDCA4VIBUBLP0SDxMKBQH%2BVwsNATIyGQGpAQUKEw%2BWAAAAAAMAAAAABEwErgAdACAAMAAAATUiJy4BLwEBIwEGBw4BDwEVITUiJj8BIRcWBiMVARsBARUUBiMhIiY9ATQ2MyEyFgPoGR4OFgUE%2Ft9F%2FtQSFQkfCwsBETE7EkUBJT0NISf%2B7IZ5AbEdFfwYFR0dFQPoFR0BLDIgDiIKCwLr%2FQ4jFQkTBQUyMisusKYiQTIBhwFW%2Fqr942QVHR0VZBUdHQADAAAAAASwBLAADwBHAEoAABMhMhYVERQGIyEiJjURNDYFIyIHAQYHBgcGHQEUFjMhMjY9ATQmIyInJj8BIRcWBwYjIgYdARQWMyEyNj0BNCYnIicmJyMBJhMjEzIETBUdHRX7tBUdHQJGRg0F%2FtUREhImDAsJAREIDAwINxAKCj8BCjkLEQwYCAwMCAE5CAwLCBEZGQ8B%2FuAFDsVnBLAdFfu0FR0dFQRMFR1SDP0PIBMSEAUNMggMDAgyCAwXDhmjmR8YEQwIMggMDAgyBwwBGRskAuwM%2FgUBCAAABAAAAAAEsASwAAMAEwAjACcAAAEhNSEFITIWFREUBiMhIiY1ETQ2KQEyFhURFAYjISImNRE0NhcRIREEsPtQBLD7ggGQFR0dFf5wFR0dAm0BkBUdHRX%2BcBUdHUcBLARMZMgdFfx8FR0dFQOEFR0dFf5wFR0dFQGQFR1k%2FtQBLAAEAAAAAASwBLAADwAfACMAJwAAEyEyFhURFAYjISImNRE0NgEhMhYVERQGIyEiJjURNDYXESEREyE1ITIBkBUdHRX%2BcBUdHQJtAZAVHR0V%2FnAVHR1HASzI%2B1AEsASwHRX8fBUdHRUDhBUd%2FgwdFf5wFR0dFQGQFR1k%2FtQBLP2oZAAAAAACAAAAZASwA%2BgAJwArAAATITIWFREzNTQ2MyEyFh0BMxUjFRQGIyEiJj0BIxEUBiMhIiY1ETQ2AREhETIBkBUdZB0VAZAVHWRkHRX%2BcBUdZB0V%2FnAVHR0CnwEsA%2BgdFf6ilhUdHRWWZJYVHR0Vlv6iFR0dFQMgFR3%2B1P7UASwAAAQAAAAABLAEsAADABMAFwAnAAAzIxEzFyEyFhURFAYjISImNRE0NhcRIREBITIWFREUBiMhIiY1ETQ2ZGRklgGQFR0dFf5wFR0dRwEs%2FqIDhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAAAAgBkAAAETASwACcAKwAAATMyFhURFAYrARUhMhYVERQGIyEiJjURNDYzITUjIiY1ETQ2OwE1MwcRIRECWJYVHR0VlgHCFR0dFfx8FR0dFQFelhUdHRWWZMgBLARMHRX%2BcBUdZB0V%2FnAVHR0VAZAVHWQdFQGQFR1kyP7UASwAAAAEAAAAAASwBLAAAwATABcAJwAAISMRMwUhMhYVERQGIyEiJjURNDYXESERASEyFhURFAYjISImNRE0NgSwZGT9dgGQFR0dFf5wFR0dRwEs%2FK4DhBUdHRX8fBUdHQSwZB0V%2FnAVHR0VAZAVHWT%2B1AEs%2FgwdFf5wFR0dFQGQFR0AAAEBLAAwA28EgAAPAAAJAQYjIiY1ETQ2MzIXARYUA2H%2BEhcSDhAQDhIXAe4OAjX%2BEhcbGQPoGRsX%2FhIOKgAAAAABAUEAMgOEBH4ACwAACQE2FhURFAYnASY0AU8B7h0qKh3%2BEg4CewHuHREp%2FBgpER0B7g4qAAAAAAEAMgFBBH4DhAALAAATITIWBwEGIicBJjZkA%2BgpER3%2BEg4qDv4SHREDhCod%2FhIODgHuHSoAAAAAAQAyASwEfgNvAAsAAAkBFgYjISImNwE2MgJ7Ae4dESn8GCkRHQHuDioDYf4SHSoqHQHuDgAAAAACAAgAAASwBCgABgAKAAABFQE1LQE1ASE1IQK8%2FUwBnf5jBKj84AMgAuW2%2Fr3dwcHd%2B9jIAAAAAAIAAABkBLAEsAALADEAAAEjFTMVIREzNSM1IQEzND4FOwERFAYPARUhNSIuAzURMzIeBRUzESEEsMjI%2FtTIyAEs%2B1AyCAsZEyYYGWQyGRkBkAQOIhoWZBkYJhMZCwgy%2FOADhGRkASxkZP4MFSAVDggDAf3aFhkBAmRkAQUJFQ4CJgEDCA4VIBUBLAAAAgAAAAAETAPoACUAMQAAASM0LgUrAREUFh8BFSE1Mj4DNREjIg4FFSMRIQEjFTMVIREzNSM1IQMgMggLGRMmGBlkMhkZ%2FnAEDiIaFmQZGCYTGQsIMgMgASzIyP7UyMgBLAK8FSAVDggDAf3aFhkCAWRkAQUJFQ4CJgEDCA4VIBUBLPzgZGQBLGRkAAABAMgAZgNyBEoAEgAAATMyFgcJARYGKwEiJwEmNDcBNgK9oBAKDP4wAdAMChCgDQr%2BKQcHAdcKBEoWDP4w%2FjAMFgkB1wgUCAHXCQAAAQE%2BAGYD6ARKABIAAAEzMhcBFhQHAQYrASImNwkBJjYBU6ANCgHXBwf%2BKQoNoBAKDAHQ%2FjAMCgRKCf4pCBQI%2FikJFgwB0AHQDBYAAAEAZgDIBEoDcgASAAAAFh0BFAcBBiInASY9ATQ2FwkBBDQWCf4pCBQI%2FikJFgwB0AHQA3cKEKANCv4pBwcB1woNoBAKDP4wAdAAAAABAGYBPgRKA%2BgAEgAACQEWHQEUBicJAQYmPQE0NwE2MgJqAdcJFgz%2BMP4wDBYJAdcIFAPh%2FikKDaAQCgwB0P4wDAoQoA0KAdcHAAAAAgDZ%2F%2FkEPQSwAAUAOgAAARQGIzQ2BTMyFh8BNjc%2BAh4EBgcOBgcGIiYjIgYiJy4DLwEuAT4EHgEXJyY2A%2BiwfLD%2BVmQVJgdPBQsiKFAzRyorDwURAQQSFyozTSwNOkkLDkc3EDlfNyYHBw8GDyUqPjdGMR%2BTDA0EsHywfLDIHBPCAQIGBwcFDx81S21DBxlLR1xKQhEFBQcHGWt0bCQjP2hJNyATBwMGBcASGAAAAAACAMgAFQOEBLAAFgAaAAATITIWFREUBisBEQcGJjURIyImNRE0NhcVITX6AlgVHR0Vlv8TGpYVHR2rASwEsB0V%2FnAVHf4MsgkQFQKKHRUBkBUdZGRkAAAAAgDIABkETASwAA4AEgAAEyEyFhURBRElIREjETQ2ARU3NfoC7ic9%2FUQCWP1EZB8BDWQEsFEs%2FFt1A7Z9%2FBgEARc0%2FV1kFGQAAQAAAAECTW%2FDBF9fDzz1AB8EsAAAAADQdnOXAAAAANB2c5f%2FUf%2BcBdwFFAAAAA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%3D%29%20format%28%27truetype%27%29%2Curl%28data%3Aimage%2Fsvg%2Bxml%3Bbase64%2CPD94bWwgdmVyc2lvbj0iMS4wIiBzdGFuZGFsb25lPSJubyI%2FPgo8IURPQ1RZUEUgc3ZnIFBVQkxJQyAiLS8vVzNDLy9EVEQgU1ZHIDEuMS8vRU4iICJodHRwOi8vd3d3LnczLm9yZy9HcmFwaGljcy9TVkcvMS4xL0RURC9zdmcxMS5kdGQiID4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgo8bWV0YWRhdGE%2BPC9tZXRhZGF0YT4KPGRlZnM%2BCjxmb250IGlkPSJnbHlwaGljb25zX2hhbGZsaW5nc3JlZ3VsYXIiIGhvcml6LWFkdi14PSIxMjAwIiA%2BCjxmb250LWZhY2UgdW5pdHMtcGVyLWVtPSIxMjAwIiBhc2NlbnQ9Ijk2MCIgZGVzY2VudD0iLTI0MCIgLz4KPG1pc3NpbmctZ2x5cGggaG9yaXotYWR2LXg9IjUwMCIgLz4KPGdseXBoIGhvcml6LWFkdi14PSIwIiAvPgo8Z2x5cGggaG9yaXotYWR2LXg9IjQwMCIgLz4KPGdseXBoIHVuaWNvZGU9IiAiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDAzOyIgaG9yaXotYWR2LXg9IjEzMDAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA0OyIgaG9yaXotYWR2LXg9IjQzMyIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDU7IiBob3Jpei1hZHYteD0iMzI1IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwNjsiIGhvcml6LWFkdi14PSIyMTYiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDA3OyIgaG9yaXotYWR2LXg9IjIxNiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeDIwMDg7IiBob3Jpei1hZHYteD0iMTYyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAwOTsiIGhvcml6LWFkdi14PSIyNjAiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3gyMDBhOyIgaG9yaXotYWR2LXg9IjcyIiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4MjAyZjsiIGhvcml6LWFkdi14PSIyNjAiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3gyNmZhOyIgZD0iTTc3NCAxMTkzLjVxMTYgLTkuNSAyMC41IC0yN3QtNS41IC0zMy41bC0xMzYgLTE4N2w0NjcgLTc0NmgzMHEyMCAwIDM1IC0xOC41dDE1IC0zOS41di00MmgtMTIwMHY0MnEwIDIxIDE1IDM5LjV0MzUgMTguNWgzMGw0NjggNzQ2bC0xMzUgMTgzcS0xMCAxNiAtNS41IDM0dDIwLjUgMjh0MzQgNS41dDI4IC0yMC41bDExMSAtMTQ4bDExMiAxNTBxOSAxNiAyNyAyMC41dDM0IC01ek02MDAgMjAwaDM3N2wtMTgyIDExMmwtMTk1IDUzNHYtNjQ2eiAiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDI0OyIgZD0iTTEzMDAgMGgtNTM4bC00MSA0MDBoLTI0MmwtNDEgLTQwMGgtNTM4bDQzMSAxMjAwaDIwOWwtMjEgLTMwMGgxNjJsLTIwIDMwMGgyMDh6TTUxNSA4MDBsLTI3IC0zMDBoMjI0bC0yNyAzMDBoLTE3MHoiIC8%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDMxOyIgZD0iTTk0NyAxMDYwbDEzNSAxMzVxNyA3IDEyLjUgNXQ1LjUgLTEzdi0zNjFxMCAtMTEgLTcuNSAtMTguNXQtMTguNSAtNy41aC0zNjFxLTExIDAgLTEzIDUuNXQ1IDEyLjVsMTM0IDEzNHEtMTEwIDc1IC0yMzkgNzVxLTExNiAwIC0yMTQuNSAtNTd0LTE1NS41IC0xNTUuNXQtNTcgLTIxNC41aC0xNTBxMCAxMTcgNDUuNSAyMjR0MTIzIDE4NC41dDE4NC41IDEyM3QyMjQgNDUuNXExOTIgMCAzNDcgLTExN3pNMTAyNyA2MDBoMTUwIHEwIC0xMTcgLTQ1LjUgLTIyNHQtMTIzIC0xODQuNXQtMTg0LjUgLTEyM3QtMjI0IC00NS41cS0xOTIgMCAtMzQ4IDExOGwtMTM0IC0xMzRxLTcgLTggLTEyLjUgLTUuNXQtNS41IDEyLjV2MzYwcTAgMTEgNy41IDE4LjV0MTguNSA3LjVoMzYwcTEwIDAgMTIuNSAtNS41dC01LjUgLTEyLjVsLTEzMyAtMTMzcTExMCAtNzYgMjQwIC03NnExMTYgMCAyMTQuNSA1N3QxNTUuNSAxNTUuNXQ1NyAyMTQuNXoiIC8%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQyOyIgZD0iTTUwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTY4MiA2ODJsMSA0NzVxMCAxMCA3LjUgMTcuNXQxNy41IDcuNWg0NzR6TTgwMCAxMjAwbDY4MiAtNjgycTggLTggOCAtMTh0LTggLTE4bC00NjQgLTQ2NHEtOCAtOCAtMTggLTh0LTE4IDhsLTU2IDU2bDQyNCA0MjZsLTcwMCA3MDBoMTUwek0zMTkuNSAxMDI0LjVxLTI5LjUgMjkuNSAtNzEgMjkuNXQtNzEgLTI5LjUgdC0yOS41IC03MS41dDI5LjUgLTcxLjV0NzEgLTI5LjV0NzEgMjkuNXQyOS41IDcxLjV0LTI5LjUgNzEuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQzOyIgZD0iTTMwMCAxMjAwaDgyNXE3NSAwIDc1IC03NXYtOTAwcTAgLTI1IC0xOCAtNDNsLTY0IC02NHEtOCAtOCAtMTMgLTUuNXQtNSAxMi41djk1MHEwIDEwIC03LjUgMTcuNXQtMTcuNSA3LjVoLTcwMHEtMjUgMCAtNDMgLTE4bC02NCAtNjRxLTggLTggLTUuNSAtMTN0MTIuNSAtNWg3MDBxMTAgMCAxNy41IC03LjV0Ny41IC0xNy41di05NTBxMCAtMTAgLTcuNSAtMTcuNXQtMTcuNSAtNy41aC04NTBxLTEwIDAgLTE3LjUgNy41dC03LjUgMTcuNXY5NzUgcTAgMjUgMTggNDNsMTM5IDEzOXExOCAxOCA0MyAxOHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ0OyIgZD0iTTI1MCAxMjAwaDgwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMTE1MGwtNDUwIDQ0NGwtNDUwIC00NDV2MTE1MXEwIDIxIDE0LjUgMzUuNXQzNS41IDE0LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA0NTsiIGQ9Ik04MjIgMTIwMGgtNDQ0cS0xMSAwIC0xOSAtNy41dC05IC0xNy41bC03OCAtMzAxcS03IC0yNCA3IC00NWw1NyAtMTA4cTYgLTkgMTcuNSAtMTV0MjEuNSAtNmg0NTBxMTAgMCAyMS41IDZ0MTcuNSAxNWw2MiAxMDhxMTQgMjEgNyA0NWwtODMgMzAxcS0xIDEwIC05IDE3LjV0LTE5IDcuNXpNMTE3NSA4MDBoLTE1MHEtMTAgMCAtMjEgLTYuNXQtMTUgLTE1LjVsLTc4IC0xNTZxLTQgLTkgLTE1IC0xNS41dC0yMSAtNi41aC01NTAgcS0xMCAwIC0yMSA2LjV0LTE1IDE1LjVsLTc4IDE1NnEtNCA5IC0xNSAxNS41dC0yMSA2LjVoLTE1MHEtMTAgMCAtMTcuNSAtNy41dC03LjUgLTE3LjV2LTY1MHEwIC0xMCA3LjUgLTE3LjV0MTcuNSAtNy41aDE1MHExMCAwIDE3LjUgNy41dDcuNSAxNy41djE1MHEwIDEwIDcuNSAxNy41dDE3LjUgNy41aDc1MHExMCAwIDE3LjUgLTcuNXQ3LjUgLTE3LjV2LTE1MHEwIC0xMCA3LjUgLTE3LjV0MTcuNSAtNy41aDE1MHExMCAwIDE3LjUgNy41IHQ3LjUgMTcuNXY2NTBxMCAxMCAtNy41IDE3LjV0LTE3LjUgNy41ek04NTAgMjAwaC01MDBxLTEwIDAgLTE5LjUgLTd0LTExLjUgLTE3bC0zOCAtMTUycS0yIC0xMCAzLjUgLTE3dDE1LjUgLTdoNjAwcTEwIDAgMTUuNSA3dDMuNSAxN2wtMzggMTUycS0yIDEwIC0xMS41IDE3dC0xOS41IDd6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA0NjsiIGQ9Ik01MDAgMTEwMGgyMDBxNTYgMCAxMDIuNSAtMjAuNXQ3Mi41IC01MHQ0NCAtNTl0MjUgLTUwLjVsNiAtMjBoMTUwcTQxIDAgNzAuNSAtMjkuNXQyOS41IC03MC41di02MDBxMCAtNDEgLTI5LjUgLTcwLjV0LTcwLjUgLTI5LjVoLTEwMDBxLTQxIDAgLTcwLjUgMjkuNXQtMjkuNSA3MC41djYwMHEwIDQxIDI5LjUgNzAuNXQ3MC41IDI5LjVoMTUwcTIgOCA2LjUgMjEuNXQyNCA0OHQ0NSA2MXQ3MiA0OHQxMDIuNSAyMS41ek05MDAgODAwdi0xMDAgaDEwMHYxMDBoLTEwMHpNNjAwIDczMHEtOTUgMCAtMTYyLjUgLTY3LjV0LTY3LjUgLTE2Mi41dDY3LjUgLTE2Mi41dDE2Mi41IC02Ny41dDE2Mi41IDY3LjV0NjcuNSAxNjIuNXQtNjcuNSAxNjIuNXQtMTYyLjUgNjcuNXpNNjAwIDYwM3E0MyAwIDczIC0zMHQzMCAtNzN0LTMwIC03M3QtNzMgLTMwdC03MyAzMHQtMzAgNzN0MzAgNzN0NzMgMzB6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA0NzsiIGQ9Ik02ODEgMTE5OWwzODUgLTk5OHEyMCAtNTAgNjAgLTkycTE4IC0xOSAzNi41IC0yOS41dDI3LjUgLTExLjVsMTAgLTJ2LTY2aC00MTd2NjZxNTMgMCA3NSA0My41dDUgODguNWwtODIgMjIyaC0zOTFxLTU4IC0xNDUgLTkyIC0yMzRxLTExIC0zNCAtNi41IC01N3QyNS41IC0zN3Q0NiAtMjB0NTUgLTZ2LTY2aC0zNjV2NjZxNTYgMjQgODQgNTJxMTIgMTIgMjUgMzAuNXQyMCAzMS41bDcgMTNsMzk5IDEwMDZoOTN6TTQxNiA1MjFoMzQwIGwtMTYyIDQ1N3oiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDQ5OyIgZD0iTTg3NyAxMjAwbDIgLTU3cS04MyAtMTkgLTExNiAtNDUuNXQtNDAgLTY2LjVsLTEzMiAtODM5cS05IC00OSAxMyAtNjl0OTYgLTI2di05N2gtNTAwdjk3cTE4NiAxNiAyMDAgOThsMTczIDgzMnEzIDE3IDMgMzB0LTEuNSAyMi41dC05IDE3LjV0LTEzLjUgMTIuNXQtMjEuNSAxMHQtMjYgOC41dC0zMy41IDEwcS0xMyAzIC0xOSA1djU3aDQyNXoiIC8%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDY5OyIgZD0iTTI1MCAxMTAwaDEwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtNDM4bDQ2NCA0NTNxMTUgMTQgMjUuNSAxMHQxMC41IC0yNXYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTQuNSAtMzUuNXQtMzUuNSAtMTQuNWgtMTAwcS0yMSAwIC0zNS41IDE0LjV0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcwOyIgZD0iTTUwIDExMDBoMTAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di00MzhsNDY0IDQ1M3ExNSAxNCAyNS41IDEwdDEwLjUgLTI1di0xMDAwcTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xMC41IC0yNXQtMjUuNSAxMGwtNDY0IDQ1M3YtNDM4cTAgLTIxIC0xNC41IC0zNS41dC0zNS41IC0xNC41aC0xMDBxLTIxIDAgLTM1LjUgMTQuNSB0LTE0LjUgMzUuNXYxMDAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDcxOyIgZD0iTTEyMDAgMTA1MHYtMTAwMHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ2NCA0NTN2LTQzOHEwIC0yMSAtMTAuNSAtMjV0LTI1LjUgMTBsLTQ5MiA0ODBxLTE1IDE0IC0xNSAzNXQxNSAzNWw0OTIgNDgwcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV2LTQzOGw0NjQgNDUzcTE1IDE0IDI1LjUgMTB0MTAuNSAtMjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTA3MjsiIGQ9Ik0yNDMgMTA3NGw4MTQgLTQ5OHExOCAtMTEgMTggLTI2dC0xOCAtMjZsLTgxNCAtNDk4cS0xOCAtMTEgLTMwLjUgLTR0LTEyLjUgMjh2MTAwMHEwIDIxIDEyLjUgMjh0MzAuNSAtNHoiIC8%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDc5OyIgZD0iTTg4NSA5MDBsLTM1MiAtMzUzbDM1MiAtMzUzbC0xOTcgLTE5OGwtNTUyIDU1Mmw1NTIgNTUweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUwODA7IiBkPSJNMTA2NCA1NDdsLTU1MSAtNTUxbC0xOTggMTk4bDM1MyAzNTNsLTM1MyAzNTNsMTk4IDE5OHoiIC8%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMDk2OyIgZD0iTTg1MCAxMjAwaDMwMHEyMSAwIDM1LjUgLTE0LjV0MTQuNSAtMzUuNXYtMzAwcTAgLTIxIC0xMC41IC0yNXQtMjQuNSAxMGwtOTQgOTRsLTI0OSAtMjQ5cS04IC03IC0xOCAtN3QtMTggN2wtMTA2IDEwNnEtNyA4IC03IDE4dDcgMThsMjQ5IDI0OWwtOTQgOTRxLTE0IDE0IC0xMCAyNC41dDI1IDEwLjV6TTM1MCAwaC0zMDBxLTIxIDAgLTM1LjUgMTQuNXQtMTQuNSAzNS41djMwMHEwIDIxIDEwLjUgMjV0MjQuNSAtMTBsOTQgLTk0bDI0OSAyNDkgcTggNyAxOCA3dDE4IC03bDEwNiAtMTA2cTcgLTggNyAtMTh0LTcgLTE4bC0yNDkgLTI0OWw5NCAtOTRxMTQgLTE0IDEwIC0yNC41dC0yNSAtMTAuNXoiIC8%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTUzOyIgZD0iTTQwMCAzMDBoMTUwcTIxIDAgMjUgLTExdC0xMCAtMjVsLTIzMCAtMjUwcS0xNCAtMTUgLTM1IC0xNXQtMzUgMTVsLTIzMCAyNTBxLTE0IDE0IC0xMCAyNXQyNSAxMWgxNTB2OTAwaDIwMHYtOTAwek0xMDAwIDcwMGgtMTAwdjQwMGgtMTAwdjEwMGgyMDB2LTUwMHpNMTEwMCAwaC0xMDB2MTAwaC0yMDB2NDAwaDMwMHYtNTAwek05MDEgNDAwdi0yMDBoMTAwdjIwMGgtMTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNTQ7IiBkPSJNNDAwIDMwMGgxNTBxMjEgMCAyNSAtMTF0LTEwIC0yNWwtMjMwIC0yNTBxLTE0IC0xNSAtMzUgLTE1dC0zNSAxNWwtMjMwIDI1MHEtMTQgMTQgLTEwIDI1dDI1IDExaDE1MHY5MDBoMjAwdi05MDB6TTExMDAgNzAwaC0xMDB2MTAwaC0yMDB2NDAwaDMwMHYtNTAwek05MDEgMTEwMHYtMjAwaDEwMHYyMDBoLTEwMHpNMTAwMCAwaC0xMDB2NDAwaC0xMDB2MTAwaDIwMHYtNTAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNTU7IiBkPSJNNDAwIDMwMGgxNTBxMjEgMCAyNSAtMTF0LTEwIC0yNWwtMjMwIC0yNTBxLTE0IC0xNSAtMzUgLTE1dC0zNSAxNWwtMjMwIDI1MHEtMTQgMTQgLTEwIDI1dDI1IDExaDE1MHY5MDBoMjAwdi05MDB6TTkwMCAxMDAwaC0yMDB2MjAwaDIwMHYtMjAwek0xMDAwIDcwMGgtMzAwdjIwMGgzMDB2LTIwMHpNMTEwMCA0MDBoLTQwMHYyMDBoNDAwdi0yMDB6TTEyMDAgMTAwaC01MDB2MjAwaDUwMHYtMjAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNTY7IiBkPSJNNDAwIDMwMGgxNTBxMjEgMCAyNSAtMTF0LTEwIC0yNWwtMjMwIC0yNTBxLTE0IC0xNSAtMzUgLTE1dC0zNSAxNWwtMjMwIDI1MHEtMTQgMTQgLTEwIDI1dDI1IDExaDE1MHY5MDBoMjAwdi05MDB6TTEyMDAgMTAwMGgtNTAwdjIwMGg1MDB2LTIwMHpNMTEwMCA3MDBoLTQwMHYyMDBoNDAwdi0yMDB6TTEwMDAgNDAwaC0zMDB2MjAwaDMwMHYtMjAwek05MDAgMTAwaC0yMDB2MjAwaDIwMHYtMjAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNTc7IiBkPSJNMzUwIDExMDBoNDAwcTE2MiAwIDI1NiAtOTMuNXQ5NCAtMjU2LjV2LTQwMHEwIC0xNjUgLTkzLjUgLTI1Ny41dC0yNTYuNSAtOTIuNWgtNDAwcS0xNjUgMCAtMjU3LjUgOTIuNXQtOTIuNSAyNTcuNXY0MDBxMCAxNjUgOTIuNSAyNTcuNXQyNTcuNSA5Mi41ek04MDAgOTAwaC01MDBxLTQxIDAgLTcwLjUgLTI5LjV0LTI5LjUgLTcwLjV2LTUwMHEwIC00MSAyOS41IC03MC41dDcwLjUgLTI5LjVoNTAwcTQxIDAgNzAuNSAyOS41dDI5LjUgNzAuNSB2NTAwcTAgNDEgLTI5LjUgNzAuNXQtNzAuNSAyOS41eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNTg7IiBkPSJNMzUwIDExMDBoNDAwcTE2NSAwIDI1Ny41IC05Mi41dDkyLjUgLTI1Ny41di00MDBxMCAtMTY1IC05Mi41IC0yNTcuNXQtMjU3LjUgLTkyLjVoLTQwMHEtMTYzIDAgLTI1Ni41IDkyLjV0LTkzLjUgMjU3LjV2NDAwcTAgMTYzIDk0IDI1Ni41dDI1NiA5My41ek04MDAgOTAwaC01MDBxLTQxIDAgLTcwLjUgLTI5LjV0LTI5LjUgLTcwLjV2LTUwMHEwIC00MSAyOS41IC03MC41dDcwLjUgLTI5LjVoNTAwcTQxIDAgNzAuNSAyOS41dDI5LjUgNzAuNSB2NTAwcTAgNDEgLTI5LjUgNzAuNXQtNzAuNSAyOS41ek00NDAgNzcwbDI1MyAtMTkwcTE3IC0xMiAxNyAtMzB0LTE3IC0zMGwtMjUzIC0xOTBxLTE2IC0xMiAtMjggLTYuNXQtMTIgMjYuNXY0MDBxMCAyMSAxMiAyNi41dDI4IC02LjV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTE1OTsiIGQ9Ik0zNTAgMTEwMGg0MDBxMTYzIDAgMjU2LjUgLTk0dDkzLjUgLTI1NnYtNDAwcTAgLTE2NSAtOTIuNSAtMjU3LjV0LTI1Ny41IC05Mi41aC00MDBxLTE2NSAwIC0yNTcuNSA5Mi41dC05Mi41IDI1Ny41djQwMHEwIDE2MyA5Mi41IDI1Ni41dDI1Ny41IDkzLjV6TTgwMCA5MDBoLTUwMHEtNDEgMCAtNzAuNSAtMjkuNXQtMjkuNSAtNzAuNXYtNTAwcTAgLTQxIDI5LjUgLTcwLjV0NzAuNSAtMjkuNWg1MDBxNDEgMCA3MC41IDI5LjV0MjkuNSA3MC41IHY1MDBxMCA0MSAtMjkuNSA3MC41dC03MC41IDI5LjV6TTM1MCA3MDBoNDAwcTIxIDAgMjYuNSAtMTJ0LTYuNSAtMjhsLTE5MCAtMjUzcS0xMiAtMTcgLTMwIC0xN3QtMzAgMTdsLTE5MCAyNTNxLTEyIDE2IC02LjUgMjh0MjYuNSAxMnoiIC8%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTYyOyIgZD0iTTc5MyAxMTgybDkgLTlxOCAtMTAgNSAtMjdxLTMgLTExIC03OSAtMjI1LjV0LTc4IC0yMjEuNWwzMDAgMXEyNCAwIDMyLjUgLTE3LjV0LTUuNSAtMzUuNXEtMSAwIC0xMzMuNSAtMTU1dC0yNjcgLTMxMi41dC0xMzguNSAtMTYyLjVxLTEyIC0xNSAtMjYgLTE1aC05bC05IDhxLTkgMTEgLTQgMzJxMiA5IDQyIDEyMy41dDc5IDIyNC41bDM5IDExMGgtMzAycS0yMyAwIC0zMSAxOXEtMTAgMjEgNiA0MXE3NSA4NiAyMDkuNSAyMzcuNSB0MjI4IDI1N3Q5OC41IDExMS41cTkgMTYgMjUgMTZoOXoiIC8%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMTc4OyIgZD0iTTkzNSAxMTY1bDI0OCAtMjMwcTE0IC0xNCAxNCAtMzV0LTE0IC0zNWwtMjQ4IC0yMzBxLTE0IC0xNCAtMjQuNSAtMTB0LTEwLjUgMjV2MTUwaC00MDB2MjAwaDQwMHYxNTBxMCAyMSAxMC41IDI1dDI0LjUgLTEwek0yMDAgODAwaC01MHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2MTAwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNWg1MHYtMjAwek00MDAgODAwaC0xMDB2MjAwaDEwMHYtMjAwek0xOCA0MzVsMjQ3IDIzMCBxMTQgMTQgMjQuNSAxMHQxMC41IC0yNXYtMTUwaDQwMHYtMjAwaC00MDB2LTE1MHEwIC0yMSAtMTAuNSAtMjV0LTI0LjUgMTBsLTI0NyAyMzBxLTE1IDE0IC0xNSAzNXQxNSAzNXpNOTAwIDMwMGgtMTAwdjIwMGgxMDB2LTIwMHpNMTAwMCA1MDBoNTFxMjAgMCAzNC41IC0xNC41dDE0LjUgLTM1LjV2LTEwMHEwIC0yMSAtMTQuNSAtMzUuNXQtMzQuNSAtMTQuNWgtNTF2MjAweiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUxNzk7IiBkPSJNODYyIDEwNzNsMjc2IDExNnEyNSAxOCA0My41IDh0MTguNSAtNDF2LTExMDZxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2Mzk3cS00IDEgLTExIDV0LTI0IDE3LjV0LTMwIDI5dC0yNCA0MnQtMTEgNTYuNXYzNTlxMCAzMSAxOC41IDY1dDQzLjUgNTJ6TTU1MCAxMjAwcTIyIDAgMzQuNSAtMTIuNXQxNC41IC0yNC41bDEgLTEzdi00NTBxMCAtMjggLTEwLjUgLTU5LjUgdC0yNSAtNTZ0LTI5IC00NXQtMjUuNSAtMzEuNWwtMTAgLTExdi00NDdxMCAtMjEgLTE0LjUgLTM1LjV0LTM1LjUgLTE0LjVoLTIwMHEtMjEgMCAtMzUuNSAxNC41dC0xNC41IDM1LjV2NDQ3cS00IDQgLTExIDExLjV0LTI0IDMwLjV0LTMwIDQ2dC0yNCA1NXQtMTEgNjB2NDUwcTAgMiAwLjUgNS41dDQgMTJ0OC41IDE1dDE0LjUgMTJ0MjIuNSA1LjVxMjAgMCAzMi41IC0xMi41dDE0LjUgLTI0LjVsMyAtMTN2LTM1MGgxMDB2MzUwdjUuNXQyLjUgMTIgdDcgMTV0MTUgMTJ0MjUuNSA1LjVxMjMgMCAzNS41IC0xMi41dDEzLjUgLTI0LjVsMSAtMTN2LTM1MGgxMDB2MzUwcTAgMiAwLjUgNS41dDMgMTJ0NyAxNXQxNSAxMnQyNC41IDUuNXoiIC8%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%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjIxOyIgZD0iTTQ4MCAxMTY1bDY4MiAtNjgzcTMxIC0zMSAzMSAtNzUuNXQtMzEgLTc1LjVsLTEzMSAtMTMxaC00ODFsLTUxNyA1MThxLTMyIDMxIC0zMiA3NS41dDMyIDc1LjVsMjk1IDI5NnEzMSAzMSA3NS41IDMxdDc2LjUgLTMxek0xMDggNzk0bDM0MiAtMzQybDMwMyAzMDRsLTM0MSAzNDF6TTI1MCAxMDBoODAwcTIxIDAgMzUuNSAtMTQuNXQxNC41IC0zNS41di01MGgtOTAwdjUwcTAgMjEgMTQuNSAzNS41dDM1LjUgMTQuNXoiIC8%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%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%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%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%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%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%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%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%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUwOyIgZD0iTTg2NSA1NjVsLTQ5NCAtNDk0cS0yMyAtMjMgLTQxIC0yM3EtMTQgMCAtMjIgMTMuNXQtOCAzOC41djEwMDBxMCAyNSA4IDM4LjV0MjIgMTMuNXExOCAwIDQxIC0yM2w0OTQgLTQ5NHExNCAtMTQgMTQgLTM1dC0xNCAtMzV6IiAvPgo8Z2x5cGggdW5pY29kZT0iJiN4ZTI1MTsiIGQ9Ik0zMzUgNjM1bDQ5NCA0OTRxMjkgMjkgNTAgMjAuNXQyMSAtNDkuNXYtMTAwMHEwIC00MSAtMjEgLTQ5LjV0LTUwIDIwLjVsLTQ5NCA0OTRxLTE0IDE0IC0xNCAzNXQxNCAzNXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUyOyIgZD0iTTEwMCA5MDBoMTAwMHE0MSAwIDQ5LjUgLTIxdC0yMC41IC01MGwtNDk0IC00OTRxLTE0IC0xNCAtMzUgLTE0dC0zNSAxNGwtNDk0IDQ5NHEtMjkgMjkgLTIwLjUgNTB0NDkuNSAyMXoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjUzOyIgZD0iTTYzNSA4NjVsNDk0IC00OTRxMjkgLTI5IDIwLjUgLTUwdC00OS41IC0yMWgtMTAwMHEtNDEgMCAtNDkuNSAyMXQyMC41IDUwbDQ5NCA0OTRxMTQgMTQgMzUgMTR0MzUgLTE0eiIgLz4KPGdseXBoIHVuaWNvZGU9IiYjeGUyNTQ7IiBkPSJNNzAwIDc0MXYtMTgybC02OTIgLTMyM3YyMjFsNDEzIDE5M2wtNDEzIDE5M3YyMjF6TTEyMDAgMGgtODAwdjIwMGg4MDB2LTIwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU1OyIgZD0iTTEyMDAgOTAwaC0yMDB2LTEwMGgyMDB2LTEwMGgtMzAwdjMwMGgyMDB2MTAwaC0yMDB2MTAwaDMwMHYtMzAwek0wIDcwMGg1MHEwIDIxIDQgMzd0OS41IDI2LjV0MTggMTcuNXQyMiAxMXQyOC41IDUuNXQzMSAydDM3IDAuNWgxMDB2LTU1MHEwIC0yMiAtMjUgLTM0LjV0LTUwIC0xMy41bC0yNSAtMnYtMTAwaDQwMHYxMDBxLTQgMCAtMTEgMC41dC0yNCAzdC0zMCA3dC0yNCAxNXQtMTEgMjQuNXY1NTBoMTAwcTI1IDAgMzcgLTAuNXQzMSAtMiB0MjguNSAtNS41dDIyIC0xMXQxOCAtMTcuNXQ5LjUgLTI2LjV0NCAtMzdoNTB2MzAwaC04MDB2LTMwMHoiIC8%2BCjxnbHlwaCB1bmljb2RlPSImI3hlMjU2OyIgZD0iTTgwMCA3MDBoLTUwcTAgMjEgLTQgMzd0LTkuNSAyNi41dC0xOCAxNy41dC0yMiAxMXQtMjguNSA1LjV0LTMxIDJ0LTM3IDAuNWgtMTAwdi01NTBxMCAtMjIgMjUgLTM0LjV0NTAgLTE0LjVsMjUgLTF2LTEwMGgtNDAwdjEwMHE0IDAgMTEgMC41dDI0IDN0MzAgN3QyNCAxNXQxMSAyNC41djU1MGgtMTAwcS0yNSAwIC0zNyAtMC41dC0zMSAtMnQtMjguNSAtNS41dC0yM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+
+</div>
+
+
+<ul>
+<li>Author: Lindsay Coome</li>
+<li>Lesson Topic: Introduction to Statistics in R</li>
+<li>Lesson video stream: <a href="https://www.youtube.com/watch?v=A7AD40fpLK8" class="uri">https://www.youtube.com/watch?v=A7AD40fpLK8</a></li>
+</ul>
+<pre class="r"><code># Have the following packages installed and loaded:
+# install.packages(&quot;car&quot;)
+# install.packages(&quot;psych&quot;)
+# install.packages(&quot;multcomp&quot;)
+library(car)</code></pre>
+<pre><code>## Loading required package: carData</code></pre>
+<pre class="r"><code>library(psych)</code></pre>
+<pre><code>## 
+## Attaching package: 'psych'</code></pre>
+<pre><code>## The following object is masked from 'package:car':
+## 
+##     logit</code></pre>
+<pre class="r"><code>library(multcomp)</code></pre>
+<pre><code>## Loading required package: mvtnorm</code></pre>
+<pre><code>## Loading required package: survival</code></pre>
+<pre><code>## Loading required package: TH.data</code></pre>
+<pre><code>## Loading required package: MASS</code></pre>
+<pre><code>## 
+## Attaching package: 'TH.data'</code></pre>
+<pre><code>## The following object is masked from 'package:MASS':
+## 
+##     geyser</code></pre>
+<div id="descriptive-statistics" class="section level2">
+<h2>Descriptive statistics</h2>
+<div id="frequencies-with-table" class="section level3">
+<h3>Frequencies with table():</h3>
+<p>The table() function displays counts of identical observations for either a single data vector or a dataframe</p>
+<pre class="r"><code>#table
+table(mtcars$gear)</code></pre>
+<pre><code>## 
+##  3  4  5 
+## 15 12  5</code></pre>
+<pre class="r"><code>#cross tabulate
+#note: first variable is set as the rows
+table(mtcars$gear, mtcars$carb)</code></pre>
+<pre><code>##    
+##     1 2 3 4 6 8
+##   3 3 4 3 5 0 0
+##   4 4 4 0 4 0 0
+##   5 0 2 0 1 1 1</code></pre>
+</div>
+<div id="mean" class="section level3">
+<h3>Mean:</h3>
+<p>Without the na.rm=T argument, mean() will return a null value if there is any missing data in the object</p>
+<pre class="r"><code>mean(Wong$viq, na.rm=T)</code></pre>
+<pre><code>## [1] 94.96375</code></pre>
+</div>
+<div id="median" class="section level3">
+<h3>Median:</h3>
+<pre class="r"><code>median(Wong$viq, na.rm=T)</code></pre>
+<pre><code>## [1] 94</code></pre>
+</div>
+<div id="variance-and-sd" class="section level3">
+<h3>Variance and SD:</h3>
+<pre class="r"><code>var(Wong$viq, na.rm=T)</code></pre>
+<pre><code>## [1] 197.4896</code></pre>
+<pre class="r"><code>sd(Wong$viq, na.rm=T)</code></pre>
+<pre><code>## [1] 14.0531</code></pre>
+</div>
+<div id="range" class="section level3">
+<h3>Range:</h3>
+<pre class="r"><code>range(Wong$viq, na.rm=T)</code></pre>
+<pre><code>## [1]  64 132</code></pre>
+</div>
+<div id="describe-from-the-psych-package" class="section level3">
+<h3>describe() from the psych package:</h3>
+<p>Provides item name, item number, nvalid, mean, sd, median, mad (median absolute deviation from the median), min, max, skew, kurtosis, se</p>
+<pre class="r"><code>describe(Wong$age)</code></pre>
+<pre><code>##    vars   n  mean    sd median trimmed   mad  min   max range skew
+## X1    1 331 31.85 13.87  26.88   30.05 10.03 6.51 80.03 73.52 1.05
+##    kurtosis   se
+## X1     0.19 0.76</code></pre>
+</div>
+<div id="describeby-from-the-psych-package" class="section level3">
+<h3>describeBy() from the psych package:</h3>
+<p>A simple way of generating summary statistics by grouping variable</p>
+<pre class="r"><code>describeBy(Wong$age, Wong$sex)</code></pre>
+<pre><code>## 
+##  Descriptive statistics by group 
+## group: Female
+##    vars  n  mean    sd median trimmed  mad   min   max range skew kurtosis
+## X1    1 71 30.87 12.22  27.28   28.94 7.96 16.98 76.66 59.68 1.51     1.95
+##      se
+## X1 1.45
+## -------------------------------------------------------- 
+## group: Male
+##    vars   n  mean    sd median trimmed   mad  min   max range skew
+## X1    1 260 32.12 14.29  26.31   30.39 10.12 6.51 80.03 73.52 0.94
+##    kurtosis   se
+## X1    -0.13 0.89</code></pre>
+</div>
+</div>
+<div id="inferential-statistics" class="section level2">
+<h2>Inferential statistics</h2>
+<div id="one-sample-t-test" class="section level3">
+<h3>One-sample t test:</h3>
+<pre class="r"><code>t.test(Wong$age, mu=30)</code></pre>
+<pre><code>## 
+##  One Sample t-test
+## 
+## data:  Wong$age
+## t = 2.4312, df = 330, p-value = 0.01558
+## alternative hypothesis: true mean is not equal to 30
+## 95 percent confidence interval:
+##  30.35370 33.35268
+## sample estimates:
+## mean of x 
+##  31.85319</code></pre>
+</div>
+<div id="two-sample-t-test" class="section level3">
+<h3>Two sample t-test:</h3>
+<pre class="r"><code>#one way to compare groups:
+#t.test( object1, object1)
+#or use sex as the grouping variable:
+t.test(Wong$age~Wong$sex)</code></pre>
+<pre><code>## 
+##  Welch Two Sample t-test
+## 
+## data:  Wong$age by Wong$sex
+## t = -0.74009, df = 127.28, p-value = 0.4606
+## alternative hypothesis: true difference in means is not equal to 0
+## 95 percent confidence interval:
+##  -4.621540  2.105522
+## sample estimates:
+## mean in group Female   mean in group Male 
+##             30.86503             32.12303</code></pre>
+</div>
+<div id="paired-samples-t-test" class="section level3">
+<h3>Paired samples t-test:</h3>
+<pre class="r"><code>#t.test(object1, object1, paired=TRUE)</code></pre>
+</div>
+<div id="correlations" class="section level3">
+<h3>Correlations:</h3>
+<p>cor.test gives the correlation and significance test on the correlation</p>
+<pre class="r"><code>cor.test(iris$Petal.Length, iris$Petal.Width)</code></pre>
+<pre><code>## 
+##  Pearson's product-moment correlation
+## 
+## data:  iris$Petal.Length and iris$Petal.Width
+## t = 43.387, df = 148, p-value &lt; 2.2e-16
+## alternative hypothesis: true correlation is not equal to 0
+## 95 percent confidence interval:
+##  0.9490525 0.9729853
+## sample estimates:
+##       cor 
+## 0.9628654</code></pre>
+</div>
+<div id="general-linear-model-i.e.-with-a-normally-distributed-dependent-variable" class="section level3">
+<h3>General linear model (i.e., with a normally-distributed dependent variable):</h3>
+<p>Dependent variables are predicted by a tilde “~” The formula to regress “y” on “x” is y ~ x</p>
+<pre class="r"><code>result &lt;- lm(viq~duration, data=Wong)
+summary(result)</code></pre>
+<pre><code>## 
+## Call:
+## lm(formula = viq ~ duration, data = Wong)
+## 
+## Residuals:
+##     Min      1Q  Median      3Q     Max 
+## -30.973  -9.969  -0.964  10.041  37.039 
+## 
+## Coefficients:
+##              Estimate Std. Error t value Pr(&gt;|t|)    
+## (Intercept) 9.496e+01  8.828e-01 107.562   &lt;2e-16 ***
+## duration    3.198e-04  2.975e-02   0.011    0.991    
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Residual standard error: 14.07 on 329 degrees of freedom
+## Multiple R-squared:  3.511e-07,  Adjusted R-squared:  -0.003039 
+## F-statistic: 0.0001155 on 1 and 329 DF,  p-value: 0.9914</code></pre>
+<pre class="r"><code>#multiple predictors with interaction terms
+result &lt;- lm(viq~duration*days*sex, data=Wong)
+summary(result)</code></pre>
+<pre><code>## 
+## Call:
+## lm(formula = viq ~ duration * days * sex, data = Wong)
+## 
+## Residuals:
+##     Min      1Q  Median      3Q     Max 
+## -31.095  -9.819  -0.641   9.970  36.919 
+## 
+## Coefficients:
+##                         Estimate Std. Error t value Pr(&gt;|t|)    
+## (Intercept)           94.3524593  2.6451725  35.670   &lt;2e-16 ***
+## duration              -0.1728963  0.1729387  -1.000   0.3182    
+## days                   0.0096278  0.0061044   1.577   0.1157    
+## sexMale                1.3174223  2.8691046   0.459   0.6464    
+## duration:days         -0.0003005  0.0002095  -1.434   0.1524    
+## duration:sexMale       0.1424415  0.1779604   0.800   0.4241    
+## days:sexMale          -0.0109230  0.0061812  -1.767   0.0781 .  
+## duration:days:sexMale  0.0003399  0.0002107   1.613   0.1077    
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Residual standard error: 13.96 on 323 degrees of freedom
+## Multiple R-squared:  0.03453,    Adjusted R-squared:  0.01361 
+## F-statistic:  1.65 on 7 and 323 DF,  p-value: 0.1206</code></pre>
+<pre class="r"><code>#use + for main effect of predictor only
+result &lt;- lm(viq~duration*days + sex, data=Wong)
+summary(result)</code></pre>
+<pre><code>## 
+## Call:
+## lm(formula = viq ~ duration * days + sex, data = Wong)
+## 
+## Residuals:
+##     Min      1Q  Median      3Q     Max 
+## -31.353  -9.810  -1.098  10.154  37.173 
+## 
+## Coefficients:
+##                 Estimate Std. Error t value Pr(&gt;|t|)    
+## (Intercept)    9.501e+01  1.735e+00  54.765   &lt;2e-16 ***
+## duration      -4.900e-02  4.076e-02  -1.202   0.2302    
+## days          -1.260e-03  9.595e-04  -1.313   0.1902    
+## sexMale        9.062e-01  1.897e+00   0.478   0.6332    
+## duration:days  4.137e-05  2.287e-05   1.809   0.0714 .  
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## 
+## Residual standard error: 14.06 on 326 degrees of freedom
+## Multiple R-squared:  0.01049,    Adjusted R-squared:  -0.001656 
+## F-statistic: 0.8636 on 4 and 326 DF,  p-value: 0.486</code></pre>
+</div>
+<div id="analysis-of-variance" class="section level3">
+<h3>Analysis of Variance:</h3>
+<p>ANOVA is simply a different way of evaluating explained variance in linear modelling You must always wrap the anova() function around a lm() function</p>
+<pre class="r"><code>anova(lm(mpg ~ am*gear, data=mtcars))</code></pre>
+<pre><code>## Analysis of Variance Table
+## 
+## Response: mpg
+##           Df Sum Sq Mean Sq F value    Pr(&gt;F)    
+## am         1 405.15  405.15 19.9021 0.0001208 ***
+## gear       1   0.05    0.05  0.0024 0.9614808    
+## am:gear    1 150.85  150.85  7.4099 0.0110327 *  
+## Residuals 28 570.00   20.36                      
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+<p>However, R uses type II sums of squares by default Anova() from the car package can give you “Type III Sums of Squares” This matters when you have more than one predictor (e.g. treatment x sex) Asking for type III sums of squares will match what you get from SPSS or SAS</p>
+<pre class="r"><code>result &lt;- lm(mpg ~ am*gear, data=mtcars)
+Anova(result, type=3)</code></pre>
+<pre><code>## Anova Table (Type III tests)
+## 
+## Response: mpg
+##             Sum Sq Df F value   Pr(&gt;F)   
+## (Intercept)   0.49  1  0.0241 0.877644   
+## am          206.11  1 10.1246 0.003564 **
+## gear         77.17  1  3.7907 0.061633 . 
+## am:gear     150.85  1  7.4099 0.011033 * 
+## Residuals   570.00 28                    
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1</code></pre>
+</div>
+<div id="post-hoc-tests" class="section level3">
+<h3>Post hoc tests:</h3>
+<p>R comes with a default pairwise t-test (pairwise.t.test()) multcomp offers more posthoc options than base R</p>
+<pre class="r"><code>result &lt;- lm(Petal.Length ~ Species, data=iris)
+postHocs&lt;-glht(result, linfct = mcp(Species = &quot;Tukey&quot;))
+#summary function gives results of multiple comparisons
+summary(postHocs)</code></pre>
+<pre><code>## 
+##   Simultaneous Tests for General Linear Hypotheses
+## 
+## Multiple Comparisons of Means: Tukey Contrasts
+## 
+## 
+## Fit: lm(formula = Petal.Length ~ Species, data = iris)
+## 
+## Linear Hypotheses:
+##                             Estimate Std. Error t value Pr(&gt;|t|)    
+## versicolor - setosa == 0     2.79800    0.08607   32.51   &lt;2e-16 ***
+## virginica - setosa == 0      4.09000    0.08607   47.52   &lt;2e-16 ***
+## virginica - versicolor == 0  1.29200    0.08607   15.01   &lt;2e-16 ***
+## ---
+## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+## (Adjusted p values reported -- single-step method)</code></pre>
+<pre class="r"><code>#you can also ask for confidence intervals
+confint(postHocs)</code></pre>
+<pre><code>## 
+##   Simultaneous Confidence Intervals
+## 
+## Multiple Comparisons of Means: Tukey Contrasts
+## 
+## 
+## Fit: lm(formula = Petal.Length ~ Species, data = iris)
+## 
+## Quantile = 2.3673
+## 95% family-wise confidence level
+##  
+## 
+## Linear Hypotheses:
+##                             Estimate lwr    upr   
+## versicolor - setosa == 0    2.7980   2.5943 3.0017
+## virginica - setosa == 0     4.0900   3.8863 4.2937
+## virginica - versicolor == 0 1.2920   1.0883 1.4957</code></pre>
+</div>
+<div id="logistic-regression" class="section level3">
+<h3>Logistic regression:</h3>
+<p>Normally-distributed dependent variable assumption violated So, you must use the glm() function rather than lm()</p>
+<pre class="r"><code>summary(glm(Wong$sex ~ Wong$piq*Wong$viq, family=binomial))</code></pre>
+<pre><code>## 
+## Call:
+## glm(formula = Wong$sex ~ Wong$piq * Wong$viq, family = binomial)
+## 
+## Deviance Residuals: 
+##     Min       1Q   Median       3Q      Max  
+## -2.0308   0.5555   0.6621   0.7220   0.9876  
+## 
+## Coefficients:
+##                     Estimate Std. Error z value Pr(&gt;|z|)
+## (Intercept)       -3.9800716  4.9899172  -0.798    0.425
+## Wong$piq           0.0403298  0.0572426   0.705    0.481
+## Wong$viq           0.0737994  0.0527622   1.399    0.162
+## Wong$piq:Wong$viq -0.0006197  0.0005721  -1.083    0.279
+## 
+## (Dispersion parameter for binomial family taken to be 1)
+## 
+##     Null deviance: 344.15  on 330  degrees of freedom
+## Residual deviance: 339.88  on 327  degrees of freedom
+## AIC: 347.88
+## 
+## Number of Fisher Scoring iterations: 4</code></pre>
+</div>
+<div id="scales-and-reliability" class="section level3">
+<h3>Scales and reliability:</h3>
+<p>Learn how to sum across rows (useful for getting a summary score from a scale) Can get Cronbach’s Alpha from the <code>psych</code> package</p>
+<pre class="r"><code>#Subset iris data so only numerical columns remain
+newiris &lt;- iris[c(1:4)]
+#Calculate row means
+rowMeans(newiris, na.rm=TRUE)</code></pre>
+<pre><code>##   [1] 2.550 2.375 2.350 2.350 2.550 2.850 2.425 2.525 2.225 2.400 2.700
+##  [12] 2.500 2.325 2.125 2.800 3.000 2.750 2.575 2.875 2.675 2.675 2.675
+##  [23] 2.350 2.650 2.575 2.450 2.600 2.600 2.550 2.425 2.425 2.675 2.725
+##  [34] 2.825 2.425 2.400 2.625 2.500 2.225 2.550 2.525 2.100 2.275 2.675
+##  [45] 2.800 2.375 2.675 2.350 2.675 2.475 4.075 3.900 4.100 3.275 3.850
+##  [56] 3.575 3.975 2.900 3.850 3.300 2.875 3.650 3.300 3.775 3.350 3.900
+##  [67] 3.650 3.400 3.600 3.275 3.925 3.550 3.800 3.700 3.725 3.850 3.950
+##  [78] 4.100 3.725 3.200 3.200 3.150 3.400 3.850 3.600 3.875 4.000 3.575
+##  [89] 3.500 3.325 3.425 3.775 3.400 2.900 3.450 3.525 3.525 3.675 2.925
+## [100] 3.475 4.525 3.875 4.525 4.150 4.375 4.825 3.400 4.575 4.200 4.850
+## [111] 4.200 4.075 4.350 3.800 4.025 4.300 4.200 5.100 4.875 3.675 4.525
+## [122] 3.825 4.800 3.925 4.450 4.550 3.900 3.950 4.225 4.400 4.550 5.025
+## [133] 4.250 3.925 3.925 4.775 4.425 4.200 3.900 4.375 4.450 4.350 3.875
+## [144] 4.550 4.550 4.300 3.925 4.175 4.325 3.950</code></pre>
+<pre class="r"><code>#Use the psych package for Cronbach's Alpha
+alpha(newiris)</code></pre>
+<pre><code>## Warning in alpha(newiris): Some items were negatively correlated with the total scale and probably 
+## should be reversed.  
+## To do this, run the function again with the 'check.keys=TRUE' option</code></pre>
+<pre><code>## Some items ( Sepal.Width ) were negatively correlated with the total scale and 
+## probably should be reversed.  
+## To do this, run the function again with the 'check.keys=TRUE' option</code></pre>
+<pre><code>## 
+## Reliability analysis   
+## Call: alpha(x = newiris)
+## 
+##   raw_alpha std.alpha G6(smc) average_r S/N   ase mean   sd median_r
+##       0.71      0.62     0.9      0.29 1.6 0.019  3.5 0.78     0.35
+## 
+##  lower alpha upper     95% confidence boundaries
+## 0.67 0.71 0.75 
+## 
+##  Reliability if an item is dropped:
+##              raw_alpha std.alpha G6(smc) average_r   S/N alpha se  var.r
+## Sepal.Length      0.45      0.15    0.72     0.056  0.18   0.0337 0.6176
+## Sepal.Width       0.88      0.96    0.96     0.884 22.90   0.0067 0.0054
+## Petal.Length      0.49      0.27    0.64     0.111  0.38   0.0529 0.3898
+## Petal.Width       0.47      0.27    0.77     0.109  0.37   0.0401 0.4610
+##              med.r
+## Sepal.Length -0.37
+## Sepal.Width   0.87
+## Petal.Length -0.12
+## Petal.Width  -0.12
+## 
+##  Item statistics 
+##                n raw.r std.r r.cor r.drop mean   sd
+## Sepal.Length 150  0.94 0.940  0.93   0.89  5.8 0.83
+## Sepal.Width  150 -0.22 0.032 -0.15  -0.35  3.1 0.44
+## Petal.Length 150  0.97 0.880  0.91   0.86  3.8 1.77
+## Petal.Width  150  0.95 0.883  0.90   0.92  1.2 0.76</code></pre>
+</div>
+</div>
+
+
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.header').parent('thead').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
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diff --git a/lessons/template/lesson.md b/lessons/template/lesson.md
index 6155b23c2..c15e91b51 100644
--- a/lessons/template/lesson.md
+++ b/lessons/template/lesson.md
@@ -1,8 +1,25 @@
-#My Lesson Title
+---
+layout: page
+title:
+visible: false
+tags:
+  # languages
+  - python
+  - r
+  - git
+  - misc
+  # levels
+  - beginner
+  - intermediate
+  - advanced
+---
+<!-- change visible to true if you want it on the site -->
+<!-- remove any tags listed above that are not relevant -->
 
  - **Authors**: My Name
  - **Research field**: My field of study
- - **Lesson Topic**: What computing topics does this lesson cover?
+ - **Lesson topic**: What computing topics does this lesson cover?
+ - **Lesson content URL**: <link>
 
 Write up your lesson here! If you're unsure of markdown, feel free to cut and paste your notes verbatim, and we'll help you format them.
 
@@ -10,4 +27,28 @@ Be sure to include any data or examples you need in this same directory.
 
 If you'd like to share your lesson with the wider world, consider sending a pull request or opening an issue at Mozilla Science's collection of lessons: https://github.com/mozillascience/studyGroupLessons
 
+# Use the markdown syntax:
 
+If you want to include code blocks, please with indent with **four
+spaces** (not a tab) like so:
+
+    Code here
+
+Or you can use three tilde's:
+
+~~~
+Code here
+~~~
+
+
+# Header 1
+
+## Header 2
+
+### Header 3
+
+**bolding** and *italizing*
+
+Inline `code`
+
+[links](url)
diff --git a/mail/contact_me.php b/mail/contact_me.php
deleted file mode 100644
index 51611bba1..000000000
--- a/mail/contact_me.php
+++ /dev/null
@@ -1,26 +0,0 @@
-<?php
-// Check for empty fields
-if(empty($_POST['name'])  		||
-   empty($_POST['email']) 		||
-   empty($_POST['phone']) 		||
-   empty($_POST['message'])	||
-   !filter_var($_POST['email'],FILTER_VALIDATE_EMAIL))
-   {
-	echo "No arguments Provided!";
-	return false;
-   }
-
-$name = $_POST['name'];
-$email_address = $_POST['email'];
-$phone = $_POST['phone'];
-$message = $_POST['message'];
-
-// Create the email and send the message
-$to = 'jerome.lachaud@gmail.com'; // Add your email address inbetween the '' replacing yourname@yourdomain.com - This is where the form will send a message to.
-$email_subject = "Website Contact Form:  $name";
-$email_body = "You have received a new message from your website contact form.\n\n"."Here are the details:\n\nName: $name\n\nEmail: $email_address\n\nPhone: $phone\n\nMessage:\n$message";
-$headers = "From: noreply@yourdomain.com\n"; // This is the email address the generated message will be from. We recommend using something like noreply@yourdomain.com.
-$headers .= "Reply-To: $email_address";
-mail($to,$email_subject,$email_body,$headers);
-return true;
-?>
\ No newline at end of file
diff --git a/past-events.html b/past-events.html
new file mode 100644
index 000000000..3e00bbf3e
--- /dev/null
+++ b/past-events.html
@@ -0,0 +1,6 @@
+---
+layout: page
+title: "Past events and sessions"
+---
+
+{% include past-events.html %}
diff --git a/pull-request.md b/pull-request.md
new file mode 100644
index 000000000..ac8ee529f
--- /dev/null
+++ b/pull-request.md
@@ -0,0 +1,102 @@
+---
+layout: page
+title: Submitting a Pull Request for Lessons
+---
+
+Lessons should be submitted as a Pull Request (PR) at least *one full day* before the
+session. Submitting the lessons as a Pull Request is also a great way to find
+out whether your lesson matches what is advertised in the Events repo. New lessons
+should be created from a copy of the `lessons/template/lesson.md` file. Follow the
+details and requests in the template lesson file and write up your lesson!
+
+Please follow the instructions below before submitting your PR:
+
+## Brief step-by-step
+
+1. Fork the `studyGroup` repo
+2. (Optional) `git clone` your forked version onto your computer
+3. Use the lesson template and make your lesson
+4. Follow the file/folder naming rules (see below)
+5. `git add` and `git commit` that lesson
+6. `git push` your lesson to your forked `studyGroup` repo
+7. Submit a PR using Github's Pull Request button on your forked version
+
+## Detailed step-by-step
+
+1. Fork the `studyGroup` into your GitHub account. See
+[this GitHub help for info on forking](https://help.github.com/articles/fork-a-repo/).
+2. After it is forked, `git clone` from your terminal or Git Bash of your new
+forked version of `studyGroup` onto your computer *from your account*. If you
+want it on your Desktop do:
+  ```
+  cd Desktop
+  git clone https://github.com/YOUR-USERNAME/studyGroup.git
+  ```
+
+  ...replacing YOUR-USERNAME with your own user name on GitHub (eg: mine would
+  look like `git clone https://github.com/lwjohnst86/studyGroup.git`).
+3. Type `ls` to confirm that the `studyGroup` folder was created. Then
+`cd studyGroup` and `git status` or `git log` to confirm that you are now in the
+new repo.
+4. Add the original Study Group repo using this command:
+  ```
+  git remote add upstream https://github.com/UofTCoders/studyGroup.git
+  git fetch upstream
+  ```
+
+5. Create a new folder in the `lessons` folder of `studyGroup` on your computer,
+naming it appropriately (without spaces) to the lesson you are planning on
+teaching. Files and folders should be named as such:
+    - Create the lesson template under whichever programming language you are
+    teaching (e.g. under `lesson/r` or `lesson/python`.). Use the `misc/` folder
+    if you aren't sure.
+    - Name the new lesson folder, all small caps, so that it simply explains
+    what the topic is (e.g. `python/intro/`, `r/loops/`).
+    - If you need more than one word, keep all small caps and use a dash (`-`)
+    for a space (e.g. `misc/bash-intro/`, `misc/jekyll-ghpages/`).
+6. Copy the `lessons/template/lesson.md` into the new folder and write up your
+lesson in there, filling out requested information. This is a
+[Markdown](https://help.github.com/articles/markdown-basics/) file (`.md`).
+The reason it should be Markdown or plain text is because GitHub
+renders the Markdown file into HTML so it's nicer to read on the site and for
+others.
+
+7. Save the new file in the git history:
+  ```
+  git add ./lessons/yourlesson/lesson.md
+  git commit -m "Added file on lesson"
+  ```
+
+8. Push up to your GitHub repo:
+  ```
+  git push origin gh-pages
+  ```
+9. Submit a Pull Request *from* your GitHub account *into* the UofTCoders.
+Make sure the **base fork** is set to `UofTCoders/studyGroup` (and not `mozillascience/studyGroup`).
+Check [the GitHub help on Pull Requests](https://help.github.com/articles/using-pull-requests/).
+
+**Note**: For those wanting to contribute regularly or who edit their
+repositories often, it's best to create a new branch for each PR you make.  For
+example, if you want to clean up some bits of the repo, you can follow a
+workflow such as this:
+
+```
+## Good to name the branch to reflect what you are doing.
+git checkout -b cleaningUp
+## Make edits/changes/cleaning up
+git add files-changed
+git commit
+git push origin cleaningUp
+```
+
+You can now make a pull request **of the `cleaningUp` branch**.  Once the pull
+request has been completed, you can delete the now old branch via:
+
+```
+git checkout gh-pages ## Move back to main branch
+git branch --delete cleaningUp
+## If you want to delete the remote branch too do:
+git push origin --delete cleaningUp
+## Update your main branch from the new upstream branch
+git pull upstream gh-pages
+```
diff --git a/scripts/Pipfile b/scripts/Pipfile
new file mode 100644
index 000000000..58fd0758a
--- /dev/null
+++ b/scripts/Pipfile
@@ -0,0 +1,18 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+pygithub = "*"
+dateparser = "*"
+python-frontmatter = "*"
+cmarkgfm = "*"
+
+[dev-packages]
+
+[requires]
+python_version = "3.12"
+
+[scripts]
+publish_events = "python publish_events.py ../_posts/ failures.yml"
diff --git a/scripts/Pipfile.lock b/scripts/Pipfile.lock
new file mode 100644
index 000000000..c4897f40e
--- /dev/null
+++ b/scripts/Pipfile.lock
@@ -0,0 +1,682 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "b69b61cea0b60b7450c834a91cc4f75a38b20ccd4e10cbcea9258c830657349b"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.12"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
+        "certifi": {
+            "hashes": [
+                "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651",
+                "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"
+            ],
+            "markers": "python_version >= '3.6'",
+            "version": "==2025.1.31"
+        },
+        "cffi": {
+            "hashes": [
+                "sha256:045d61c734659cc045141be4bae381a41d89b741f795af1dd018bfb532fd0df8",
+                "sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2",
+                "sha256:0e2b1fac190ae3ebfe37b979cc1ce69c81f4e4fe5746bb401dca63a9062cdaf1",
+                "sha256:0f048dcf80db46f0098ccac01132761580d28e28bc0f78ae0d58048063317e15",
+                "sha256:1257bdabf294dceb59f5e70c64a3e2f462c30c7ad68092d01bbbfb1c16b1ba36",
+                "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824",
+                "sha256:1d599671f396c4723d016dbddb72fe8e0397082b0a77a4fab8028923bec050e8",
+                "sha256:28b16024becceed8c6dfbc75629e27788d8a3f9030691a1dbf9821a128b22c36",
+                "sha256:2bb1a08b8008b281856e5971307cc386a8e9c5b625ac297e853d36da6efe9c17",
+                "sha256:30c5e0cb5ae493c04c8b42916e52ca38079f1b235c2f8ae5f4527b963c401caf",
+                "sha256:31000ec67d4221a71bd3f67df918b1f88f676f1c3b535a7eb473255fdc0b83fc",
+                "sha256:386c8bf53c502fff58903061338ce4f4950cbdcb23e2902d86c0f722b786bbe3",
+                "sha256:3edc8d958eb099c634dace3c7e16560ae474aa3803a5df240542b305d14e14ed",
+                "sha256:45398b671ac6d70e67da8e4224a065cec6a93541bb7aebe1b198a61b58c7b702",
+                "sha256:46bf43160c1a35f7ec506d254e5c890f3c03648a4dbac12d624e4490a7046cd1",
+                "sha256:4ceb10419a9adf4460ea14cfd6bc43d08701f0835e979bf821052f1805850fe8",
+                "sha256:51392eae71afec0d0c8fb1a53b204dbb3bcabcb3c9b807eedf3e1e6ccf2de903",
+                "sha256:5da5719280082ac6bd9aa7becb3938dc9f9cbd57fac7d2871717b1feb0902ab6",
+                "sha256:610faea79c43e44c71e1ec53a554553fa22321b65fae24889706c0a84d4ad86d",
+                "sha256:636062ea65bd0195bc012fea9321aca499c0504409f413dc88af450b57ffd03b",
+                "sha256:6883e737d7d9e4899a8a695e00ec36bd4e5e4f18fabe0aca0efe0a4b44cdb13e",
+                "sha256:6b8b4a92e1c65048ff98cfe1f735ef8f1ceb72e3d5f0c25fdb12087a23da22be",
+                "sha256:6f17be4345073b0a7b8ea599688f692ac3ef23ce28e5df79c04de519dbc4912c",
+                "sha256:706510fe141c86a69c8ddc029c7910003a17353970cff3b904ff0686a5927683",
+                "sha256:72e72408cad3d5419375fc87d289076ee319835bdfa2caad331e377589aebba9",
+                "sha256:733e99bc2df47476e3848417c5a4540522f234dfd4ef3ab7fafdf555b082ec0c",
+                "sha256:7596d6620d3fa590f677e9ee430df2958d2d6d6de2feeae5b20e82c00b76fbf8",
+                "sha256:78122be759c3f8a014ce010908ae03364d00a1f81ab5c7f4a7a5120607ea56e1",
+                "sha256:805b4371bf7197c329fcb3ead37e710d1bca9da5d583f5073b799d5c5bd1eee4",
+                "sha256:85a950a4ac9c359340d5963966e3e0a94a676bd6245a4b55bc43949eee26a655",
+                "sha256:8f2cdc858323644ab277e9bb925ad72ae0e67f69e804f4898c070998d50b1a67",
+                "sha256:9755e4345d1ec879e3849e62222a18c7174d65a6a92d5b346b1863912168b595",
+                "sha256:98e3969bcff97cae1b2def8ba499ea3d6f31ddfdb7635374834cf89a1a08ecf0",
+                "sha256:a08d7e755f8ed21095a310a693525137cfe756ce62d066e53f502a83dc550f65",
+                "sha256:a1ed2dd2972641495a3ec98445e09766f077aee98a1c896dcb4ad0d303628e41",
+                "sha256:a24ed04c8ffd54b0729c07cee15a81d964e6fee0e3d4d342a27b020d22959dc6",
+                "sha256:a45e3c6913c5b87b3ff120dcdc03f6131fa0065027d0ed7ee6190736a74cd401",
+                "sha256:a9b15d491f3ad5d692e11f6b71f7857e7835eb677955c00cc0aefcd0669adaf6",
+                "sha256:ad9413ccdeda48c5afdae7e4fa2192157e991ff761e7ab8fdd8926f40b160cc3",
+                "sha256:b2ab587605f4ba0bf81dc0cb08a41bd1c0a5906bd59243d56bad7668a6fc6c16",
+                "sha256:b62ce867176a75d03a665bad002af8e6d54644fad99a3c70905c543130e39d93",
+                "sha256:c03e868a0b3bc35839ba98e74211ed2b05d2119be4e8a0f224fba9384f1fe02e",
+                "sha256:c59d6e989d07460165cc5ad3c61f9fd8f1b4796eacbd81cee78957842b834af4",
+                "sha256:c7eac2ef9b63c79431bc4b25f1cd649d7f061a28808cbc6c47b534bd789ef964",
+                "sha256:c9c3d058ebabb74db66e431095118094d06abf53284d9c81f27300d0e0d8bc7c",
+                "sha256:ca74b8dbe6e8e8263c0ffd60277de77dcee6c837a3d0881d8c1ead7268c9e576",
+                "sha256:caaf0640ef5f5517f49bc275eca1406b0ffa6aa184892812030f04c2abf589a0",
+                "sha256:cdf5ce3acdfd1661132f2a9c19cac174758dc2352bfe37d98aa7512c6b7178b3",
+                "sha256:d016c76bdd850f3c626af19b0542c9677ba156e4ee4fccfdd7848803533ef662",
+                "sha256:d01b12eeeb4427d3110de311e1774046ad344f5b1a7403101878976ecd7a10f3",
+                "sha256:d63afe322132c194cf832bfec0dc69a99fb9bb6bbd550f161a49e9e855cc78ff",
+                "sha256:da95af8214998d77a98cc14e3a3bd00aa191526343078b530ceb0bd710fb48a5",
+                "sha256:dd398dbc6773384a17fe0d3e7eeb8d1a21c2200473ee6806bb5e6a8e62bb73dd",
+                "sha256:de2ea4b5833625383e464549fec1bc395c1bdeeb5f25c4a3a82b5a8c756ec22f",
+                "sha256:de55b766c7aa2e2a3092c51e0483d700341182f08e67c63630d5b6f200bb28e5",
+                "sha256:df8b1c11f177bc2313ec4b2d46baec87a5f3e71fc8b45dab2ee7cae86d9aba14",
+                "sha256:e03eab0a8677fa80d646b5ddece1cbeaf556c313dcfac435ba11f107ba117b5d",
+                "sha256:e221cf152cff04059d011ee126477f0d9588303eb57e88923578ace7baad17f9",
+                "sha256:e31ae45bc2e29f6b2abd0de1cc3b9d5205aa847cafaecb8af1476a609a2f6eb7",
+                "sha256:edae79245293e15384b51f88b00613ba9f7198016a5948b5dddf4917d4d26382",
+                "sha256:f1e22e8c4419538cb197e4dd60acc919d7696e5ef98ee4da4e01d3f8cfa4cc5a",
+                "sha256:f3a2b4222ce6b60e2e8b337bb9596923045681d71e5a082783484d845390938e",
+                "sha256:f6a16c31041f09ead72d69f583767292f750d24913dadacf5756b966aacb3f1a",
+                "sha256:f75c7ab1f9e4aca5414ed4d8e5c0e303a34f4421f8a0d47a4d019ceff0ab6af4",
+                "sha256:f79fc4fc25f1c8698ff97788206bb3c2598949bfe0fef03d299eb1b5356ada99",
+                "sha256:f7f5baafcc48261359e14bcd6d9bff6d4b28d9103847c9e136694cb0501aef87",
+                "sha256:fc48c783f9c87e60831201f2cce7f3b2e4846bf4d8728eabe54d60700b318a0b"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==1.17.1"
+        },
+        "charset-normalizer": {
+            "hashes": [
+                "sha256:0167ddc8ab6508fe81860a57dd472b2ef4060e8d378f0cc555707126830f2537",
+                "sha256:01732659ba9b5b873fc117534143e4feefecf3b2078b0a6a2e925271bb6f4cfa",
+                "sha256:01ad647cdd609225c5350561d084b42ddf732f4eeefe6e678765636791e78b9a",
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+                "sha256:2369eea1ee4a7610a860d88f268eb39b95cb588acd7235e02fd5a5601773d4fa",
+                "sha256:237bdbe6159cff53b4f24f397d43c6336c6b0b42affbe857970cefbb620911c8",
+                "sha256:28bf57629c75e810b6ae989f03c0828d64d6b26a5e205535585f96093e405ed1",
+                "sha256:2967f74ad52c3b98de4c3b32e1a44e32975e008a9cd2a8cc8966d6a5218c5cb2",
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+                "sha256:2d074908e1aecee37a7635990b2c6d504cd4766c7bc9fc86d63f9c09af3fa11b",
+                "sha256:2fb9bd477fdea8684f78791a6de97a953c51831ee2981f8e4f583ff3b9d9687e",
+                "sha256:311f30128d7d333eebd7896965bfcfbd0065f1716ec92bd5638d7748eb6f936a",
+                "sha256:329ce159e82018d646c7ac45b01a430369d526569ec08516081727a20e9e4af4",
+                "sha256:345b0426edd4e18138d6528aed636de7a9ed169b4aaf9d61a8c19e39d26838ca",
+                "sha256:363e2f92b0f0174b2f8238240a1a30142e3db7b957a5dd5689b0e75fb717cc78",
+                "sha256:3a3bd0dcd373514dcec91c411ddb9632c0d7d92aed7093b8c3bbb6d69ca74408",
+                "sha256:3bed14e9c89dcb10e8f3a29f9ccac4955aebe93c71ae803af79265c9ca5644c5",
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+                "sha256:4532bff1b8421fd0a320463030c7520f56a79c9024a4e88f01c537316019005a",
+                "sha256:49402233c892a461407c512a19435d1ce275543138294f7ef013f0b63d5d3765",
+                "sha256:4c0907b1928a36d5a998d72d64d8eaa7244989f7aaaf947500d3a800c83a3fd6",
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+                "sha256:5df196eb874dae23dcfb968c83d4f8fdccb333330fe1fc278ac5ceeb101003a9",
+                "sha256:619a609aa74ae43d90ed2e89bdd784765de0a25ca761b93e196d938b8fd1dbbd",
+                "sha256:6e27f48bcd0957c6d4cb9d6fa6b61d192d0b13d5ef563e5f2ae35feafc0d179c",
+                "sha256:6ff8a4a60c227ad87030d76e99cd1698345d4491638dfa6673027c48b3cd395f",
+                "sha256:73d94b58ec7fecbc7366247d3b0b10a21681004153238750bb67bd9012414545",
+                "sha256:7461baadb4dc00fd9e0acbe254e3d7d2112e7f92ced2adc96e54ef6501c5f176",
+                "sha256:75832c08354f595c760a804588b9357d34ec00ba1c940c15e31e96d902093770",
+                "sha256:7709f51f5f7c853f0fb938bcd3bc59cdfdc5203635ffd18bf354f6967ea0f824",
+                "sha256:78baa6d91634dfb69ec52a463534bc0df05dbd546209b79a3880a34487f4b84f",
+                "sha256:7974a0b5ecd505609e3b19742b60cee7aa2aa2fb3151bc917e6e2646d7667dcf",
+                "sha256:7a4f97a081603d2050bfaffdefa5b02a9ec823f8348a572e39032caa8404a487",
+                "sha256:7b1bef6280950ee6c177b326508f86cad7ad4dff12454483b51d8b7d673a2c5d",
+                "sha256:7d053096f67cd1241601111b698f5cad775f97ab25d81567d3f59219b5f1adbd",
+                "sha256:804a4d582ba6e5b747c625bf1255e6b1507465494a40a2130978bda7b932c90b",
+                "sha256:807f52c1f798eef6cf26beb819eeb8819b1622ddfeef9d0977a8502d4db6d534",
+                "sha256:80ed5e856eb7f30115aaf94e4a08114ccc8813e6ed1b5efa74f9f82e8509858f",
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+                "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df",
+                "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"
+            ],
+            "markers": "python_version >= '3.9'",
+            "version": "==2.3.0"
+        },
+        "wrapt": {
+            "hashes": [
+                "sha256:08e7ce672e35efa54c5024936e559469436f8b8096253404faeb54d2a878416f",
+                "sha256:0a6e821770cf99cc586d33833b2ff32faebdbe886bd6322395606cf55153246c",
+                "sha256:0b929ac182f5ace000d459c59c2c9c33047e20e935f8e39371fa6e3b85d56f4a",
+                "sha256:129a150f5c445165ff941fc02ee27df65940fcb8a22a61828b1853c98763a64b",
+                "sha256:13e6afb7fe71fe7485a4550a8844cc9ffbe263c0f1a1eea569bc7091d4898555",
+                "sha256:1473400e5b2733e58b396a04eb7f35f541e1fb976d0c0724d0223dd607e0f74c",
+                "sha256:18983c537e04d11cf027fbb60a1e8dfd5190e2b60cc27bc0808e653e7b218d1b",
+                "sha256:1a7ed2d9d039bd41e889f6fb9364554052ca21ce823580f6a07c4ec245c1f5d6",
+                "sha256:1e1fe0e6ab7775fd842bc39e86f6dcfc4507ab0ffe206093e76d61cde37225c8",
+                "sha256:1fb5699e4464afe5c7e65fa51d4f99e0b2eadcc176e4aa33600a3df7801d6662",
+                "sha256:2696993ee1eebd20b8e4ee4356483c4cb696066ddc24bd70bcbb80fa56ff9061",
+                "sha256:35621ae4c00e056adb0009f8e86e28eb4a41a4bfa8f9bfa9fca7d343fe94f998",
+                "sha256:36ccae62f64235cf8ddb682073a60519426fdd4725524ae38874adf72b5f2aeb",
+                "sha256:3cedbfa9c940fdad3e6e941db7138e26ce8aad38ab5fe9dcfadfed9db7a54e62",
+                "sha256:3d57c572081fed831ad2d26fd430d565b76aa277ed1d30ff4d40670b1c0dd984",
+                "sha256:3fc7cb4c1c744f8c05cd5f9438a3caa6ab94ce8344e952d7c45a8ed59dd88392",
+                "sha256:4011d137b9955791f9084749cba9a367c68d50ab8d11d64c50ba1688c9b457f2",
+                "sha256:40d615e4fe22f4ad3528448c193b218e077656ca9ccb22ce2cb20db730f8d306",
+                "sha256:410a92fefd2e0e10d26210e1dfb4a876ddaf8439ef60d6434f21ef8d87efc5b7",
+                "sha256:41388e9d4d1522446fe79d3213196bd9e3b301a336965b9e27ca2788ebd122f3",
+                "sha256:468090021f391fe0056ad3e807e3d9034e0fd01adcd3bdfba977b6fdf4213ea9",
+                "sha256:49703ce2ddc220df165bd2962f8e03b84c89fee2d65e1c24a7defff6f988f4d6",
+                "sha256:4a721d3c943dae44f8e243b380cb645a709ba5bd35d3ad27bc2ed947e9c68192",
+                "sha256:4afd5814270fdf6380616b321fd31435a462019d834f83c8611a0ce7484c7317",
+                "sha256:4c82b8785d98cdd9fed4cac84d765d234ed3251bd6afe34cb7ac523cb93e8b4f",
+                "sha256:4db983e7bca53819efdbd64590ee96c9213894272c776966ca6306b73e4affda",
+                "sha256:582530701bff1dec6779efa00c516496968edd851fba224fbd86e46cc6b73563",
+                "sha256:58455b79ec2661c3600e65c0a716955adc2410f7383755d537584b0de41b1d8a",
+                "sha256:58705da316756681ad3c9c73fd15499aa4d8c69f9fd38dc8a35e06c12468582f",
+                "sha256:5bb1d0dbf99411f3d871deb6faa9aabb9d4e744d67dcaaa05399af89d847a91d",
+                "sha256:5c803c401ea1c1c18de70a06a6f79fcc9c5acfc79133e9869e730ad7f8ad8ef9",
+                "sha256:5cbabee4f083b6b4cd282f5b817a867cf0b1028c54d445b7ec7cfe6505057cf8",
+                "sha256:612dff5db80beef9e649c6d803a8d50c409082f1fedc9dbcdfde2983b2025b82",
+                "sha256:62c2caa1585c82b3f7a7ab56afef7b3602021d6da34fbc1cf234ff139fed3cd9",
+                "sha256:69606d7bb691b50a4240ce6b22ebb319c1cfb164e5f6569835058196e0f3a845",
+                "sha256:6d9187b01bebc3875bac9b087948a2bccefe464a7d8f627cf6e48b1bbae30f82",
+                "sha256:6ed6ffac43aecfe6d86ec5b74b06a5be33d5bb9243d055141e8cabb12aa08125",
+                "sha256:703919b1633412ab54bcf920ab388735832fdcb9f9a00ae49387f0fe67dad504",
+                "sha256:766d8bbefcb9e00c3ac3b000d9acc51f1b399513f44d77dfe0eb026ad7c9a19b",
+                "sha256:80dd7db6a7cb57ffbc279c4394246414ec99537ae81ffd702443335a61dbf3a7",
+                "sha256:8112e52c5822fc4253f3901b676c55ddf288614dc7011634e2719718eaa187dc",
+                "sha256:8c8b293cd65ad716d13d8dd3624e42e5a19cc2a2f1acc74b30c2c13f15cb61a6",
+                "sha256:8fdbdb757d5390f7c675e558fd3186d590973244fab0c5fe63d373ade3e99d40",
+                "sha256:91bd7d1773e64019f9288b7a5101f3ae50d3d8e6b1de7edee9c2ccc1d32f0c0a",
+                "sha256:95c658736ec15602da0ed73f312d410117723914a5c91a14ee4cdd72f1d790b3",
+                "sha256:99039fa9e6306880572915728d7f6c24a86ec57b0a83f6b2491e1d8ab0235b9a",
+                "sha256:9a2bce789a5ea90e51a02dfcc39e31b7f1e662bc3317979aa7e5538e3a034f72",
+                "sha256:9a7d15bbd2bc99e92e39f49a04653062ee6085c0e18b3b7512a4f2fe91f2d681",
+                "sha256:9abc77a4ce4c6f2a3168ff34b1da9b0f311a8f1cfd694ec96b0603dff1c79438",
+                "sha256:9e8659775f1adf02eb1e6f109751268e493c73716ca5761f8acb695e52a756ae",
+                "sha256:9fee687dce376205d9a494e9c121e27183b2a3df18037f89d69bd7b35bcf59e2",
+                "sha256:a5aaeff38654462bc4b09023918b7f21790efb807f54c000a39d41d69cf552cb",
+                "sha256:a604bf7a053f8362d27eb9fefd2097f82600b856d5abe996d623babd067b1ab5",
+                "sha256:abbb9e76177c35d4e8568e58650aa6926040d6a9f6f03435b7a522bf1c487f9a",
+                "sha256:acc130bc0375999da18e3d19e5a86403667ac0c4042a094fefb7eec8ebac7cf3",
+                "sha256:b18f2d1533a71f069c7f82d524a52599053d4c7166e9dd374ae2136b7f40f7c8",
+                "sha256:b4e42a40a5e164cbfdb7b386c966a588b1047558a990981ace551ed7e12ca9c2",
+                "sha256:b5e251054542ae57ac7f3fba5d10bfff615b6c2fb09abeb37d2f1463f841ae22",
+                "sha256:b60fb58b90c6d63779cb0c0c54eeb38941bae3ecf7a73c764c52c88c2dcb9d72",
+                "sha256:b870b5df5b71d8c3359d21be8f0d6c485fa0ebdb6477dda51a1ea54a9b558061",
+                "sha256:ba0f0eb61ef00ea10e00eb53a9129501f52385c44853dbd6c4ad3f403603083f",
+                "sha256:bb87745b2e6dc56361bfde481d5a378dc314b252a98d7dd19a651a3fa58f24a9",
+                "sha256:bb90fb8bda722a1b9d48ac1e6c38f923ea757b3baf8ebd0c82e09c5c1a0e7a04",
+                "sha256:bc570b5f14a79734437cb7b0500376b6b791153314986074486e0b0fa8d71d98",
+                "sha256:c86563182421896d73858e08e1db93afdd2b947a70064b813d515d66549e15f9",
+                "sha256:c958bcfd59bacc2d0249dcfe575e71da54f9dcf4a8bdf89c4cb9a68a1170d73f",
+                "sha256:d18a4865f46b8579d44e4fe1e2bcbc6472ad83d98e22a26c963d46e4c125ef0b",
+                "sha256:d5e2439eecc762cd85e7bd37161d4714aa03a33c5ba884e26c81559817ca0925",
+                "sha256:e3890b508a23299083e065f435a492b5435eba6e304a7114d2f919d400888cc6",
+                "sha256:e496a8ce2c256da1eb98bd15803a79bee00fc351f5dfb9ea82594a3f058309e0",
+                "sha256:e8b2816ebef96d83657b56306152a93909a83f23994f4b30ad4573b00bd11bb9",
+                "sha256:eaf675418ed6b3b31c7a989fd007fa7c3be66ce14e5c3b27336383604c9da85c",
+                "sha256:ec89ed91f2fa8e3f52ae53cd3cf640d6feff92ba90d62236a81e4e563ac0e991",
+                "sha256:ecc840861360ba9d176d413a5489b9a0aff6d6303d7e733e2c4623cfa26904a6",
+                "sha256:f09b286faeff3c750a879d336fb6d8713206fc97af3adc14def0cdd349df6000",
+                "sha256:f393cda562f79828f38a819f4788641ac7c4085f30f1ce1a68672baa686482bb",
+                "sha256:f917c1180fdb8623c2b75a99192f4025e412597c50b2ac870f156de8fb101119",
+                "sha256:fc78a84e2dfbc27afe4b2bd7c80c8db9bca75cc5b85df52bfe634596a1da846b",
+                "sha256:ff04ef6eec3eee8a5efef2401495967a916feaa353643defcc03fc74fe213b58"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==1.17.2"
+        }
+    },
+    "develop": {}
+}
diff --git a/scripts/announcements.R b/scripts/announcements.R
new file mode 100644
index 000000000..04e58eada
--- /dev/null
+++ b/scripts/announcements.R
@@ -0,0 +1,247 @@
+# Setup and Usage ---------------------------------------------------------
+#
+# Will need to get a GitHub PAT to use some of these commands.
+# See [here](https://help.github.com/articles/creating-a-personal-access-token-for-the-command-line/). Once you have the GitHub PAT, you will need to save this PAT in your
+# home directory ("~/") in a file called ".Renviron". Inside the file, type out:
+#
+#   GITHUB_PAT= # paste the PAT number after the equal sign
+#
+# NOTE: Keep this PAT **ONLY ON YOUR COMPUTER**. Don't save it to GitHub.
+#
+# This script can be used in a few ways. Either:
+# 1.    Open the main.Rproj, open the scripts/announcements.R, and "Source" the file
+#       using the button or the shortcut Ctrl-Shift-S.
+# 2.    Open R console and run the command "source('announcements.R')" when in the
+#       script folder.
+# 3.    Open the main.Rproj, run "source('scripts/announcements.R')" in the console.
+
+# Installation and Loading Packages ---------------------------------------
+# Running these lines will install the necessary packages.
+
+if (!require(devtools)) install.packages("devtools")
+devtools::install_dev_deps()
+library(stringr)
+library(dplyr)
+library(purrr)
+library(lubridate)
+library(glue)
+library(assertr)
+library(yaml)
+library(readr)
+
+# Importing and Filtering the Event Data ----------------------------------
+
+session_details <-
+    yaml.load_file(here::here("_data", "events.yml")) %>%
+    map_dfr(as_tibble) %>%
+    arrange(date) %>%
+    # drop sessions that are not set (NA in date)
+    filter(!is.na(date)) %>%
+    mutate_at(vars(start_time, end_time), funs(
+        as.POSIXct(., format = "%H:%M", tz = "GMT") %>%
+            strftime(format = "%H:%M", tz = "GMT"))) %>%
+    mutate(
+        location_url = na_if(location_url, ""),
+        location_string = case_when(
+            # if both location and url are included!is.na(location) &
+            !is.na(location_url) ~ glue("[{location}]({location_url})"),
+            # if only location is included!is.na(location) &
+            is.na(location_url) ~ location,
+            # if neither location nor url are included
+            TRUE ~ "TBD"
+        ))
+
+coffee_code_details <-
+    yaml.load_file(here::here("_data", "coffee-code.yml")) %>%
+    map_dfr(as_tibble) %>%
+    arrange(date) %>%
+    # drop sessions that are not set (NA in date)
+    filter(!is.na(date)) %>%
+    mutate_at(vars(start_time, end_time), funs(
+        as.POSIXct(., format = "%H:%M", tz = "GMT") %>%
+            strftime(format = "%H:%M", tz = "GMT"))) %>%
+    mutate(
+        location_url = na_if(location_url, ""),
+        location_string = case_when(
+            # if both location and url are included!is.na(location) &
+            !is.na(location_url) ~ glue::glue("[{location}]({location_url})"),
+            # if only location is included!is.na(location) &
+            is.na(location_url) ~ location,
+            # if neither location nor url are included
+            TRUE ~ "TBD"
+        ),
+        title = "Coffee and Code",
+        description = "A casual co-working session - bring your laptop and whatever you're working on!",
+        key = "coffee-and-code")
+
+# Find any existing posts, take the date, and filter out those sessions from the
+# session_details dataframe.
+keep_only_new <- function(events) {
+    existing_post_dates <- fs::dir_ls(here::here("_posts"), regexp = ".md$|.markdown$") %>%
+        str_extract("[0-9]{4}-[0-9]{2}-[0-9]{2}")
+
+    events %>%
+        filter(!as.character(date) %in% existing_post_dates)
+}
+
+new_sessions <- keep_only_new(session_details)
+new_coffee_code <- keep_only_new(coffee_code_details)
+
+# Create a GitHub Issue of the session ------------------------------------
+
+# Format as eg August 23
+day_month <- function(date_var, add_name = TRUE) {
+    date_format <- "%B %e" # as August 23
+    if (add_name) date_format <- "%A, %B %e" # as Monday, August 23
+    trimws(format(as.Date(date_var), format = date_format))
+}
+
+
+post_gh_issue <- function(title, body, labels) {
+    # Will need to set up a GitHub PAT via (I think) the function
+    # usethis::browse_github_pat() in the console
+    cat("\n\nPosting `", title, "`\n\n")
+    if (ui_yeah("Are you sure you want to post this event as an Issue?")) {
+        gh::gh(
+            "POST /repos/:owner/:repo/issues",
+            owner = "uoftcoders",
+            repo = "Events",
+            title = title,
+            body = body,
+
+            labels = array(c(labels))
+        )
+        usethis::ui_done("Event posted as an Issue to UofTCoders/Events.")
+        return(invisible())
+    } else {
+        message("Event not posted to Issue.")
+    }
+}
+
+gh_issue_info_event <- function(events) {
+    content <- events %>%
+        mutate(needs_packages = ifelse(
+            !is.na(packages),
+            str_c(
+                "Please also install these packages: ",
+                str_replace_all(packages, " ", ", "), "."
+            ),
+            ""
+        )) %>%
+        glue_data(
+            "
+            {description}
+
+            - **Where**: {location_string}
+            - **When**: {day_month(date)}, from {start_time}-{end_time} pm
+            - **Instructor**: TBA
+            - **Skill level**: {skill_level}
+
+            *Installation instructions*: You will need to install the appropriate programs. See the {program_language} section of the [installation instructions page](https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/install-git-python-r.md). {needs_packages}
+
+            *Watch*: This event will be [streamed live]({youtube_link}). If you have questions during the live stream (or just want to chat with us), please ask in our [Gitter lobby](https://gitter.im/UofTCoders/Lobby) and we will forward your questions to the instructor! (Although we aim to live stream each event, there are sometimes technical difficulties so it's best to attend in person if you can.)
+
+            **Directions:**  MADLab is located in [Gerstein Science Information Centre](https://goo.gl/maps/2916Y54jQkx), Room B112 at the south end of the first lower level. Once you go through the main entrance of Gerstein, take a right turn down a corridor (across from the admin desk or just past the reading room), then take the stairs down and follow the signs to MADLab, the door should be open 10-15 minutes before the lesson.
+            "
+        )
+
+    events %>%
+        mutate(content = content, title = str_c(title, " - ", day_month(date, add_name = FALSE))) %>%
+        select(title, content, skill_level, gh_labels)
+}
+
+gh_issue_info_coffee_code <- function(events) {
+    content <- events %>%
+        glue_data(
+            "
+            Our bi-weekly 'Coffee and Code' meet-up:
+
+            Show up anytime between the allotted hours for as long or short as you'd like (see below for details about when and where Coffee and Code will take place). You can even show up earlier or stay later if you're in a coding groove! Look for the UofT Coders posters to find where we're sitting.
+
+            Bring something to work on whether it be data analysis, a Kaggle competition, making figures, setting up software or anything else you'd like to work on.
+
+            Ask for help and discuss. This is friendly, welcoming environment for coders of all levels from absolute beginner to advanced, and of course we know coding is great, but it can be a lot more fun when you're not alone.
+
+            - **Where**: {location_string}
+            - **When**: {day_month(date)}, from {start_time}-{end_time}
+            - **What to bring**: A laptop with something to work on
+            "
+        )
+
+    events %>%
+        mutate(content = content, title = str_c(title, " - ", day_month(date, add_name = FALSE))) %>%
+        select(title, content, gh_labels)
+}
+
+create_gh_issues_coffee_code <- function(events) {
+    events %>%
+        gh_issue_info_coffee_code() %>%
+        pmap( ~ post_gh_issue(..1, ..2, ..3))
+}
+
+create_gh_issues_events <- function(events) {
+    events %>%
+        gh_issue_info_event() %>%
+        pmap( ~ post_gh_issue(..1, ..2, c(..3, ..4)))
+}
+
+create_gh_issues_coffee_code(new_coffee_code)
+create_gh_issues_events(new_sessions)
+
+# Create files in _posts/ -------------------------------------------------
+# Adds the new sessions/events to the _posts folder.
+
+create_new_posts_with_content <- function(events) {
+    new_post_filenames <-
+        glue_data(events, "{here::here('_posts')}/{date}-{key}.md")
+
+    # Get the GitHub Issue URL for the event.
+    gh_issue_number <- gh::gh("GET /repos/:owner/:repo/issues",
+                              owner = "uoftcoders",
+                              repo = "Events") %>%
+        map_dfr(~ tibble(by_title = .x$title, url = .x$html_url))
+
+    new_post_content <- events %>%
+        mutate(by_title = str_c(title, " - ", day_month(date, add_name = FALSE))) %>%
+        left_join(gh_issue_number, by = "by_title") %>%
+        glue_data(
+            '
+            ---
+            title: "{title}"
+            text: "{description}"
+            location: "{location}"
+            link: "{location_url}"
+            date: "{as.Date(date)}"
+            startTime: "{start_time}"
+            endTime: "{end_time}"
+            ---
+            '
+        )
+
+    # Save post content to file
+    fs::dir_create(here::here("_posts"))
+    map2(new_post_content, new_post_filenames, ~ readr::write_lines(x = .x, path = .y))
+    usethis::ui_done("Markdown posts created in _posts/ folder.")
+    return(invisible())
+}
+
+new_sessions <- new_sessions %>%
+    add_row(
+        key = 'co-working',
+        title = 'Weekly co-working',
+        description = 'An informal co-working session - come hang out with us while
+        working on code or whatever else you want to get done.',
+        date = 'Every Thursday from Jan 14 to Mar 18',
+        start_time = '13:00',
+        end_time = '14:00',
+        location = 'Zoom',
+        location_url = 'https://github.com/UofTCoders/Events/issues/302',
+        packages = '',
+        youtube_link = '',
+        skill_level = '',
+        program_language = 'Any',
+        gh_labels = 'co-working',
+        location_string = 'Zoom')
+
+create_new_posts_with_content(new_sessions)
+create_new_posts_with_content(new_coffee_code)
diff --git a/scripts/publish_events.py b/scripts/publish_events.py
new file mode 100644
index 000000000..77f11c493
--- /dev/null
+++ b/scripts/publish_events.py
@@ -0,0 +1,157 @@
+import hashlib
+import json
+import os
+import pathlib
+import re
+import sys
+
+import cmarkgfm
+import dateparser
+import frontmatter
+import github
+
+
+# The milestone used to determine which event issues are to be published.
+MILESTONE = 'Publishable'
+
+
+def main(event_dir: pathlib.Path, failure_path: pathlib.Path) -> None:
+    """
+    Convert publishable event issues into entries on the website.
+
+    Publishable event issues are those that are in the UofTCoders/Events repo on the
+    ``MILESTONE`` milestone. Published events are saved to *event_dir*. Parsing errors
+    are saved in *failure_path*.
+    """
+    try:
+        auth = github.Auth.Token(os.environ['GITHUB_TOKEN'])
+    except KeyError:
+        auth = None
+        print('Connecting to GitHub without authentication')
+    else:
+        print('Connecting to GitHub with $GITHUB_TOKEN')
+    gh = github.Github(auth=auth)
+
+    repo = gh.get_repo('UofTCoders/Events')
+
+    milestone = None
+    for ms in repo.get_milestones():
+        if ms.title == MILESTONE:
+            milestone = ms
+            break
+    else:
+        raise ValueError(f'Unable to find the {MILESTONE!r} milestone')
+    print(f'Found {MILESTONE!r} milestone {milestone.number}')
+
+    failures = {}
+    event_summary = []
+    for issue in repo.get_issues(milestone=milestone):
+        try:
+            event = parse_issue(issue)
+        except Exception as e:
+            print(f'::error title=Failed to parse event {issue.title}::{e}')
+            failures[issue.number] = str(e)
+        else:
+            event_summary.append(
+                f'- {event["title"]} on {event["date"]} at {event["startTime"]}')
+            print(f'Parsed event: {issue.title}')
+            with (event_dir / post_to_filename(event)).open('wb') as f:
+                frontmatter.dump(event, f)
+
+    summary = f'Parsed **{len(event_summary)}** issue(s) into event posts:'
+    if 'GITHUB_STEP_SUMMARY' in os.environ:
+        with open(os.environ['GITHUB_STEP_SUMMARY'], 'w+') as f:
+            f.write('# Event publishing\n')
+            f.write(f'{summary}\n')
+            f.write('\n'.join(event_summary))
+    else:
+        print(summary)
+
+    with open(failure_path, 'w') as f:
+        json.dump(failures, f)
+
+
+def parse_issue(issue: github.Issue) -> frontmatter.Post:
+    """
+    Parse an event issue into information required to make a website entry.
+
+    The issue title is used for the entry. If the title ends with something that looks
+    like a date in parentheses, then that will be stripped.
+
+    The issue body is parsed for description, date, time, and location. The issue body
+    is expected to have the format::
+
+        Some description of the event.
+
+        The description may be multiple lines, but is terminated by the first "metadata"
+        tag, which is anything bolded like below (i.e., something that looks like
+        ``**key:**``).
+
+        **Where:** Some location.
+        **When:** A date and time acceptable to dateparser.
+        **Instructor:** Unused.
+        **Materials:** Unused.
+    """
+    body = issue.body
+    end_of_description = None
+    metadata = {}
+    # Search for the boldened text + colon on a line by itself.
+    for match in re.finditer(r'^\*\*(.+)\*\*: (.+)$', body, re.MULTILINE):
+        if end_of_description is None:
+            # Description ends at first match.
+            end_of_description = match.start()
+        key = match.group(1).lower()
+        value = match.group(2).strip()
+        metadata[key] = value
+
+    title = issue.title.strip()
+    if (ending := re.search(r' *\((.+)\)$', title)) is not None:
+        if dateparser.parse(ending.group(1)) is not None:
+            # Looks like a date, so strip it.
+            title = title[:ending.start()]
+    if end_of_description is None:
+        raise ValueError(f'Could not find any metadata in {body!r}')
+    description = markdownify(body[:end_of_description].strip())
+    dt = dateparser.parse(metadata['when'], locales=['en-CA'])
+    if dt is None:
+        raise ValueError(f'Failed to parse date from {metadata["when"]!r}')
+    if dt.hour == dt.minute == 0:
+        print(
+            f'::warning title={issue.title} may have no start time::'
+            f'Trying to parse date and start time from {metadata["when"]!r}')
+    location = markdownify(metadata['where'], inline=True)
+
+    return frontmatter.Post(
+        content='',
+        title=title,
+        text=description,
+        location=location,
+        link=issue.html_url,
+        date=dt.date().isoformat(),
+        startTime=dt.time().isoformat(timespec='minutes'),
+    )
+
+
+def post_to_filename(post: frontmatter.Post) -> str:
+    """Convert a post from date and title to a safe filename."""
+    safe_title = hashlib.sha256(post['title'].encode()).hexdigest()[:10]
+    return f'{post["date"]}-{safe_title}.md'
+
+
+def markdownify(content: str, inline: bool = False) -> str:
+    markdown = cmarkgfm.github_flavored_markdown_to_html(content).strip()
+    if inline:
+        if markdown.startswith('<p>') and markdown.endswith('</p>'):
+            markdown = markdown[len('<p>'):-len('</p>')]
+    return markdown
+
+
+if __name__ == '__main__':
+    if len(sys.argv) != 3:
+        raise SystemExit(f'usage: {__file__} EVENT_DIR FAILURE_FILE')
+    event_dir = pathlib.Path(sys.argv[1])
+    if not event_dir.is_dir():
+        raise SystemExit(f'{event_dir} is not a directory')
+
+    failure_path = pathlib.Path(sys.argv[2])
+    main(event_dir, failure_path)
diff --git a/scripts/updateCalendar.py b/scripts/updateCalendar.py
index 87026416d..ba8d1f7d6 100644
--- a/scripts/updateCalendar.py
+++ b/scripts/updateCalendar.py
@@ -41,12 +41,12 @@
 
 # Modify these variables in step 2 above -------------------
 # APPLICATION_NAME: app name you created in step one above:
-APPLICATION_NAME = 'test'
+APPLICATION_NAME = 'UofTCoders Calendar'
 # CALENDAR_ID: google account name you created for your calendar:
-CALENDAR_ID = 'USER@gmail.com'
+CALENDAR_ID = 'utorontostudygroup@gmail.com'
 # TIME_ZONE_STR: check here:
 # https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
-TIME_ZONE_STR = 'America/Vancouver'
+TIME_ZONE_STR = 'America/Toronto'
 # -----------------------------------------------------------
 
 SCOPES = 'https://www.googleapis.com/auth/calendar'
diff --git a/studyGroup.Rproj b/studyGroup.Rproj
new file mode 100644
index 000000000..4626a85a5
--- /dev/null
+++ b/studyGroup.Rproj
@@ -0,0 +1,19 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 4
+Encoding: UTF-8
+
+RnwWeave: knitr
+LaTeX: pdfLaTeX
+
+AutoAppendNewline: Yes
+StripTrailingWhitespace: Yes
+
+BuildType: Custom
+CustomScriptPath: scripts/announcements.R